def test_files(self):
print('\n########################################')
print('Test File vs File')
print('\n########################################')
print(' Loading data...')
nw_1 = load_nd_from_pickle(filename='ding0_tests_grids_1.pkl')
nw_2 = load_nd_from_pickle(filename='ding0_tests_grids_2.pkl')
#test equality
print('\n########################################')
print(' Testing equality...')
equals_e, msg = dataframe_equal(nw_1, nw_1)
print(' ...' + msg)
#test difference
print('\n########################################')
print(' Testing difference...')
equals_d, msg = dataframe_equal(nw_1, nw_2)
print(' ...' + msg)
#compare results
if equals_e and not equals_d:
msg = 'No failure'
passed = True
elif equals_e and equals_d:
msg = 'Only difference failed'
passed = False
elif not equals_e and not equals_d:
msg = 'Only equality failed'
passed = False
elif not equals_e and equals_d:
msg = 'Both failed'
passed = False
print(' ' + msg)
self.assertTrue(passed, msg=msg)
def example_stats(filename):
"""
Obtain statistics from create grid topology
Prints some statistical numbers and produces exemplary figures
"""
nd = results.load_nd_from_pickle(filename=filename)
nodes_df, edges_df = nd.to_dataframe()
# get statistical numbers about grid
stats = results.calculate_mvgd_stats(nd)
# plot distribution of load/generation of subjacent LV grids
stations = nodes_df[nodes_df['type'] == 'LV Station']
f, axarr = plt.subplots(2, sharex=True)
f.suptitle("Peak load (top) / peak generation capacity (bottom) at LV "
"substations in kW")
stations['peak_load'].hist(bins=20, alpha=0.5, ax=axarr[0])
axarr[0].set_title("Peak load in kW")
stations['generation_capacity'].hist(bins=20, alpha=0.5, ax=axarr[1])
axarr[1].set_title("Peak generation capacity in kW")
plt.show()
# Introduction of report
print("You are analyzing MV grid district {mvgd}\n".format(
mvgd=int(stats.index.values)))
# print all the calculated stats
# this isn't a particularly beautiful format but it is
# information rich
with option_context('display.max_rows', None, 'display.max_columns', None,
'display.max_colwidth', -1):
print(stats.T)
def manual_ding0_test(mv_grid_districts=[3545],
filename='ding0_tests_grids_1.pkl'):
''' Compares a new run of ding0 over districts and an old one saved in
filename.
Parameters
----------
mv_grid_districts: :obj:`list` of :obj:`int`
Districts IDs: Defaults to [3545]
filename: :obj:`str`
Defaults to 'ding0_tests_grids_1.pkl'
'''
print('\n########################################')
print('Test ding0 vs File')
print('\n########################################')
print(' Loading file', filename, '...')
nw_1 = load_nd_from_pickle(filename=filename)
print('\n########################################')
print(' Running ding0 for district', mv_grid_districts, '...')
# database connection/ session
engine = db.connection(readonly=True)
session = sessionmaker(bind=engine)()
nw_2 = NetworkDing0(name='network')
nw_2.run_ding0(session=session, mv_grid_districts_no=mv_grid_districts)
# test equality
print('\n########################################')
print(' Testing equality...')
passed, msg = dataframe_equal(nw_1, nw_2)
print(' ...' + msg)
def nd_load_and_stats(filenames, base_path=BASEPATH):
"""
Load multiple files from disk and generate stats
Passes the list of files assuming the ding0 data structure as default in
:code:`~/.ding0`.
Data will concatenated and key indicators for each grid district are
returned in table and graphic format
Parameters
----------
filenames : list of str
Provide list of files you want to analyze
base_path : str
Root directory of Ding0 data structure, i.e. '~/.ding0' (which is
default).
Returns
-------
stats : pandas.DataFrame
Statistics of each MV grid districts
"""
# load Ding0 data
nds = []
for filename in filenames:
try:
nd_load = results.load_nd_from_pickle(filename=
os.path.join(base_path,
'results',
filename))
nds.append(nd_load)
except:
print("File {mvgd} not found. It was maybe excluded by Ding0 or "
"just forgotten to generate by you...".format(mvgd=filename))
nd = nds[0]
for n in nds[1:]:
nd.add_mv_grid_district(n._mv_grid_districts[0])
nodes_df, edges_df = nd.to_dataframe()
# get statistical numbers about grid
stats = results.calculate_mvgd_stats(nodes_df, edges_df)
# TODO: correct LV peak load/ generation capacity. Same in all LV GD
return stats
def test_ding0_file(self):
print('\n########################################')
print('Test ding0 vs File')
print('\n########################################')
print(' Loading data...')
nw_1 = load_nd_from_pickle(filename='ding0_tests_grids_1.pkl')
print('\n########################################')
print(' Running ding0 for the same configuration...')
# database connection/ session
engine = db.connection(readonly=True)
session = sessionmaker(bind=engine)()
mv_grid_districts = [3545]
nw_2 = NetworkDing0(name='network')
nw_2.run_ding0(session=session, mv_grid_districts_no=mv_grid_districts)
#test equality
print(' Testing equality...')
passed, msg = dataframe_equal(nw_1, nw_2)
print(' ...' + msg)
self.assertTrue(passed, msg=msg)
from ding0.tools.results import export_network, load_nd_from_pickle, export_data_tocsv
import json
grid_id = '76'
base_path = "/home/local/RL-INSTITUT/inga.loeser/ding0/20170922152523/ding0_grids__"
path = ''.join([base_path, grid_id])
# Load network and run export function
nw = load_nd_from_pickle(filename=''.join([path, '.pkl']))
run_id, lv_grid, lv_gen, lv_cd, lv_stations, lv_trafos, lv_loads, mv_grid, mv_gen, mv_cb, mv_cd, mv_stations, mv_trafos, mv_loads, edges, mapping = export_network(
nw)
# Create directory for exported data
import os
if not os.path.exists(os.path.join(path, run_id)):
os.makedirs(os.path.join(path, run_id))
export_data_tocsv(path, run_id, lv_grid, lv_gen, lv_cd, lv_stations, lv_trafos,
lv_loads, mv_grid, mv_gen, mv_cb, mv_cd, mv_stations,
mv_trafos, mv_loads, edges, mapping)
# Save metadata
metadata = nw.metadata #keys: version, mv_grid_districts, database_tables, data_version, assumptions, run_id
with open(os.path.join(path, run_id, 'Ding0_{}.meta'.format(run_id)),
'w') as f: #'Ding0.meta'),'w') as f: #
json.dump(metadata, f)
print(mapping)
def example_stats(filename, plotpath=''):
"""
Obtain statistics from create grid topology
Prints some statistical numbers and produces exemplary figures
"""
nd = results.load_nd_from_pickle(filename=filename)
nodes_df, edges_df = nd.to_dataframe()
# get statistical numbers about grid
stats = results.calculate_mvgd_stats(nodes_df, edges_df)
# plot distribution of load/generation of subjacent LV grids
stations = nodes_df[nodes_df['type'] == 'LV Station']
f, axarr = plt.subplots(2, sharex=True)
f.suptitle("Peak load (top)/ peak generation capacity (bottom) at LV "
"substation in kW")
stations.hist(column=['peak_load'], bins=20, alpha=0.5, ax=axarr[0])
axarr[0].set_title("Peak load in kW")
stations.hist(column=['generation_capacity'], bins=20, alpha=0.5, ax=axarr[1])
axarr[1].set_title("Peak generation capacity in kW")
plt.show()
# Introduction of report
print("You are analyzing MV grid district {mvgd}\n".format(
mvgd=int(stats.index.values)))
# Print peak load
print("Total peak load: {load:.0f} kW".format(load=float(stats['peak_load'])))
print("\t thereof MV: {load:.0f} kW".format(load=0))
print("\t thereof LV: {load:.0f} kW".format(load=float(stats['LV peak load'])))
# Print generation capacity
print("\nTotal generation capacity: {gen:.0f} kW".format(
gen=float(stats['generation_capacity'])))
print("\t thereof MV: {gen:.0f} kW".format(
gen=float(stats['MV generation capacity'])))
print("\t thereof LV: {gen:.0f} kW".format(
gen=float(stats['LV generation capacity'])))
# print total length of cables/overhead lines
print("\nTotal cable length: {length:.1f} km".format(length=float(stats['km_cable'])))
print("Total line length: {length:.1f} km".format(length=float(stats['km_line'])))
# Other predefined functions bring extra information for example the number
# of generators directly connected to the bus bar of LV station
stations_generators = results.lv_grid_generators_bus_bar(nd)
print('\nGenerators directly connected to the substation')
for k, v in stations_generators.items():
if v:
print("{station}: {gens}".format(station=k, gens=len(v)))
# Number of line/cable equipment type
print("\n")
for t, cnt in dict(edges_df.groupby('type_name').size()).items():
print("Line/cable of type {type} occurs {cnt} times".format(type=t,
cnt=cnt))
# Access results directly from nd-object if they are not available in stats/
# nodes_df or edges_df
# One example: print length of each half ring in this MVGD
print("\n")
root = nd._mv_grid_districts[0].mv_grid.station()
for circ_breaker in nd._mv_grid_districts[0].mv_grid.circuit_breakers():
for half_ring in [0, 1]:
half_ring_length = nd._mv_grid_districts[
0].mv_grid.graph_path_length(
circ_breaker.branch_nodes[half_ring], root) / 1000
print('Length to circuit breaker', repr(circ_breaker),
', half-ring', str(half_ring), ':', str(half_ring_length),
'km')
== load_effective_lv_load
],
axis=1)
compare_by_load.columns = ['table', 'ding0', 'equal?']
compare_by_load.index.names = ['sector']
return compare_by_district, compare_by_load
########################################################
if __name__ == "__main__":
# database connection/ session
engine = db.connection(section='oedb')
session = sessionmaker(bind=engine)()
nw = load_nd_from_pickle(filename='ding0_tests_grids_1.pkl')
compare_by_level, compare_by_type = validate_generation(session, nw)
print('\nCompare Generation by Level')
print(compare_by_level)
print('\nCompare Generation by Type/Subtype')
print(compare_by_type)
compare_by_la, compare_la_ids = validate_load_areas(session, nw)
print('\nCompare Load by Load Areas')
print(compare_by_la)
#print(compare_la_ids)
compare_by_district, compare_by_load = validate_lv_districts(session, nw)
print('\nCompare Load by LV Districts')
print(compare_by_district)
# run DING0 on selected MV Grid District
nd.run_ding0(session=session, mv_grid_districts_no=mv_grid_districts)
# export grid to file (pickle)
save_nd_to_pickle(nd,
path=test_path,
filename='ding0_grids_example{}.pkl'.format(i))
#endregion
#region EXTRACT DATAFRAMES
components_for_comparison = {}
lines_for_comparison = {}
for i in range(nr_test_runs):
try:
if not is_load_from_csv:
nw = load_nd_from_pickle(
path=test_path, filename='ding0_grids_example{}.pkl'.format(i))
if not is_alternative_lv_line_naming:
# extract component dataframes from network
components = create_grid_component_dataframes_from_network(nw)
if is_save_to_csv:
# save dataframes as csv
dir = test_path + "/" + str(i)
if not os.path.exists(dir):
os.makedirs(dir)
components['mv_grids'].to_csv(os.path.join(
dir, '{}_mv_grids.csv'.format(i)),
sep=';')
components['lv_grids'].to_csv(os.path.join(
dir, '{}_lv_grids.csv'.format(i)),
sep=';')