def test_economic_loss_comparison_for_system_sanity(self):
root_dir = os.path.dirname(os.path.abspath(__file__))
conf_file_path = os.path.join(root_dir, "simulation_setup",
"test_scenario_pscoal_600MW.json")
config = Configuration(conf_file_path)
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
response_list = calculate_response(hazards, scenario, infrastructure)
economic_loss_array = response_list[5]
input_pickle_filename \
= os.path.join(root_dir, "historical_data",
"economic_loss_for_system_sanity_testing.p")
historical_economic_loss_array \
= pickle.load(open(input_pickle_filename, 'rb'))
self.assertTrue(
np.array_equal(economic_loss_array,
historical_economic_loss_array),
str(len(economic_loss_array)) + '\n' +
str(len(historical_economic_loss_array)))
def test_run_scenario_upper_limit(self):
root_dir = os.path.dirname(os.path.abspath(__file__))
conf_file_path = os.path.join(root_dir, "simulation_setup",
"test_scenario_upper_limit.json")
config = Configuration(conf_file_path)
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
response_list = calculate_response(hazards, scenario, infrastructure)
self.assertTrue(int(response_list[4][0][0]) == int(0))
def test_run_scenario_lower_limit(self):
root_dir = os.path.dirname(os.path.abspath(__file__))
conf_file_path = os.path.join(root_dir, "simulation_setup",
"test_scenario_lower_limit.json")
config = Configuration(conf_file_path)
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
response_list = calculate_response(hazards, scenario, infrastructure)
output_node_capacity = 0
with open(config.SYS_CONF_FILE, 'r') as f:
json_infra_model = json.load(f)
output_node_capacity \
= json_infra_model\
["sysout_setup"]["output_node"]["output_node_capacity"]
self.assertTrue(
int(response_list[4][0][0]) == int(output_node_capacity))
def test_compare_economic_loss_for_existing_models(self):
print("\n\n>> Initiating comparison with existing " "models...\n")
conf_file_paths = []
root_dir = os.path.dirname(os.path.abspath(__file__))
for root, dir_names, file_names in os.walk(root_dir):
for file_name in file_names:
if "tests" in root:
if "simulation_setup" in root:
# adjusted to keep track of old values in old branches
if 'test_scenario_pscoal_600MW' not in file_name and \
'test_setup' not in file_name:
conf_file_paths.append(
os.path.join(root, file_name))
for conf_file_path in conf_file_paths:
if os.path.isfile(conf_file_path):
print("\nMatching results for : " +
os.path.basename(conf_file_path))
config = Configuration(conf_file_path)
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
response_list = calculate_response(hazards, scenario,
infrastructure)
economic_loss_of_model = response_list[5]
pickle_flename = os.path.join(
root_dir, 'historical_data',
"economic_loss_for_" + config.SCENARIO_NAME + '.p')
history_economic_loss_for_model \
= pickle.load(open(pickle_flename, 'rb'))
self.assertTrue(
np.array_equal(economic_loss_of_model,
history_economic_loss_for_model),
conf_file_path)
print("OK")
def main():
# --------------------------------------------------------------------------
# *** BEGIN : SETUP ***
# Read in SETUP data
parser = argparse.ArgumentParser()
parser.add_argument("-s",
"--setup",
type=str,
help="Setup file for simulation scenario, and \n"
"locations of inputs, outputs, and system model.")
parser.add_argument("-v",
"--verbose",
type=str,
help="Choose option for logging level from: \n"
"DEBUG, INFO, WARNING, ERROR, CRITICAL.")
parser.add_argument("-d",
"--dirfile",
type=str,
help="JSON file with location of input/output files "
"from past simulation that is to be analysed\n.")
args = parser.parse_args()
if args.setup is None:
raise ValueError("Must provide a correct setup argument: "
"`-s` or `--setup`,\n"
"Setup file for simulation scenario, and \n"
"locations of inputs, outputs, and system model.\n")
if not os.path.exists(args.dirfile):
raise ValueError("Could not locate file with directory locations"
"with results from pre-run simulations.\n")
# Define input files, output location, scenario inputs
with open(args.dirfile, 'r') as dat:
dir_dict = json.load(dat)
# Configure simulation model.
# Read data and control parameters and construct objects.
config = Configuration(args.setup,
run_mode='analysis',
output_path=dir_dict["OUTPUT_PATH"])
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
if not config.SYS_CONF_FILE_NAME == dir_dict["SYS_CONF_FILE_NAME"]:
raise NameError("Names for supplied system model names did not match."
"Aborting.\n")
SYS_CONFIG_FILE = config.SYS_CONF_FILE
OUTPUT_PATH = dir_dict["OUTPUT_PATH"]
RAW_OUTPUT_DIR = dir_dict["RAW_OUTPUT_DIR"]
RESTORATION_STREAMS = scenario.restoration_streams
FOCAL_HAZARD_SCENARIOS = hazards.focal_hazard_scenarios
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# READ in raw output files from prior analysis of system fragility
economic_loss_array = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'economic_loss_array.npy'))
calculated_output_array = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'calculated_output_array.npy'))
exp_damage_ratio = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'exp_damage_ratio.npy'))
sys_frag = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'sys_frag.npy'))
# TODO: NEED TO IMPLEMENT.
# output_array_given_recovery = \
# np.load(os.path.join(
# RAW_OUTPUT_DIR, 'output_array_given_recovery.npy'))
#
# required_time = \
# np.load(os.path.join(RAW_OUTPUT_DIR, 'required_time.npy'))
if infrastructure.system_class.lower() == 'powerstation':
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_econloss.npy'))
elif infrastructure.system_class.lower() == 'substation':
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_cpfailrate.npy'))
elif infrastructure.system_class.lower() == \
'PotableWaterTreatmentPlant'.lower():
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_econloss.npy'))
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Read in SIMULATED HAZARD RESPONSE for <COMPONENT TYPES>
comptype_resp_df = \
pd.read_csv(os.path.join(OUTPUT_PATH, 'comptype_response.csv'),
index_col=['component_type', 'response'],
skipinitialspace=True)
comptype_resp_df.columns = hazards.hazard_scenario_name
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Nodes not considered in the loss calculations
# NEED TO MOVE THESE TO A MORE LOGICAL PLACE
uncosted_comptypes = [
'CONN_NODE', 'SYSTEM_INPUT', 'SYSTEM_OUTPUT', 'JUNCTION_NODE',
'JUNCTION', 'Generation Source', 'Grounding'
]
cp_types_in_system = infrastructure.get_component_types()
cp_types_costed = [
x for x in cp_types_in_system if x not in uncosted_comptypes
]
comptype_resp_df = comptype_resp_df.drop(uncosted_comptypes,
level='component_type',
axis=0)
# Get list of only those components that are included in cost calculations:
cpmap = {
c: sorted(list(infrastructure.get_components_for_type(c)))
for c in cp_types_in_system
}
comps_costed = [v for x in cp_types_costed for v in cpmap[x]]
nodes_all = infrastructure.components.keys()
nodes_all.sort()
comps_uncosted = list(set(nodes_all).difference(comps_costed))
ctype_failure_mean = comptype_resp_df.xs('num_failures', level='response')
ctype_failure_mean.columns.names = ['Scenario Name']
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Value of component types relative to system value
comptype_value_dict = {}
for ct in sorted(cp_types_costed):
comp_val = [
infrastructure.components[comp_id].cost_fraction
for comp_id in cpmap[ct]
]
comptype_value_dict[ct] = sum(comp_val)
comptype_value_list = [
comptype_value_dict[ct] for ct in sorted(comptype_value_dict.keys())
]
component_response = \
pd.read_csv(os.path.join(OUTPUT_PATH, 'component_response.csv'),
index_col=['component_id', 'response'],
skiprows=0, skipinitialspace=True)
# component_response = component_response.drop(
# comps_uncosted, level='component_id', axis=0)
component_meanloss = \
component_response.query('response == "loss_mean"').\
reset_index('response').drop('response', axis=1)
comptype_resp_df = comptype_resp_df.drop(uncosted_comptypes,
level='component_type',
axis=0)
# TODO : ADD THIS BACK IN!!
# # Read in the <SYSTEM FRAGILITY MODEL> fitted to simulated data
# system_fragility_mdl = \
# pd.read_csv(os.path.join(OUTPUT_PATH, 'system_model_fragility.csv'),
# index_col=0)
# system_fragility_mdl.index.name = "Damage States"
# Define the system as a network, with components as nodes
# Network setup with igraph
G = infrastructure._component_graph
# --------------------------------------------------------------------------
# *** END : SETUP ***
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Set weighting criteria for edges:
# This influences the path chosen for restoration
# Options are:
# [1] None
# [2] 'MIN_COST'
# [3] 'MIN_TIME'
weight_criteria = 'MIN_COST'
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
col_tp = []
for h in FOCAL_HAZARD_SCENARIOS:
col_tp.extend(zip([h] * len(RESTORATION_STREAMS), RESTORATION_STREAMS))
mcols = pd.MultiIndex.from_tuples(col_tp,
names=['Hazard', 'Restoration Streams'])
time_to_full_restoration_for_lines_df = \
pd.DataFrame(index=infrastructure.output_nodes.keys(), columns=mcols)
time_to_full_restoration_for_lines_df.index.name = 'Output Lines'
# --------------------------------------------------------------------------
# *** BEGIN : FOCAL_HAZARD_SCENARIOS FOR LOOP ***
for sc_haz_str in FOCAL_HAZARD_SCENARIOS:
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Differentiated setup based on hazard input type - scenario vs array
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
sc_haz_str = "{:.3f}".format(float(sc_haz_str))
if config.HAZARD_INPUT_METHOD == "hazard_array":
scenario_header = hazards.hazard_scenario_name[
hazards.hazard_scenario_list.index(sc_haz_str)]
elif config.HAZARD_INPUT_METHOD == "scenario_file":
scenario_header = sc_haz_str
scenario_tag = str(sc_haz_str) \
+ " " + hazards.intensity_measure_unit \
+ " " + hazards.intensity_measure_param
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Extract scenario-specific values from the 'hazard response' dataframe
# Scenario response: by component type
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ctype_resp_scenario \
= comptype_resp_df[scenario_header].unstack(level=-1)
ctype_resp_scenario = ctype_resp_scenario.sort_index()
ctype_resp_scenario['loss_per_type']\
= ctype_resp_scenario['loss_mean']/comptype_value_list
ctype_resp_scenario['loss_per_type_std']\
= ctype_resp_scenario['loss_std'] \
* [len(list(infrastructure.get_components_for_type(ct)))
for ct in ctype_resp_scenario.index.values.tolist()]
ctype_resp_sorted = ctype_resp_scenario.sort_values(by='loss_tot',
ascending=False)
ctype_loss_vals_tot \
= ctype_resp_sorted['loss_tot'].values * 100
ctype_loss_by_type \
= ctype_resp_sorted['loss_per_type'].values * 100
ctype_lossbytype_rank = \
len(ctype_loss_by_type) - \
stats.rankdata(ctype_loss_by_type, method='dense').astype(int)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Economic loss percentage for component types
# fig_name = 'fig_SC_' + sc_haz_str + '_loss_sys_vs_comptype_s0.png'
# draw_component_loss_barchart_bidir(ctype_loss_vals_tot,
# ctype_loss_by_type,
# ctype_lossbytype_rank,
# ctype_resp_sorted,
# scenario_tag,
# hazards.hazard_type,
# OUTPUT_PATH,
# fig_name)
fig_name = 'fig_SC_' + sc_haz_str + '_loss_sys_vs_comptype_s1.png'
draw_component_loss_barchart_s1(ctype_resp_sorted, scenario_tag,
hazards.hazard_type, OUTPUT_PATH,
fig_name)
fig_name = 'fig_SC_' + sc_haz_str + '_loss_sys_vs_comptype_s2.png'
draw_component_loss_barchart_s2(ctype_resp_sorted, scenario_tag,
hazards.hazard_type, OUTPUT_PATH,
fig_name)
fig_name = 'fig_SC_' + sc_haz_str + '_loss_sys_vs_comptype_s3.png'
draw_component_loss_barchart_s3(ctype_resp_sorted, scenario_tag,
hazards.hazard_type, OUTPUT_PATH,
fig_name)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# FAILURE RATE -- PERCENTAGE of component types
fig_name = 'fig_SC_' + sc_haz_str + '_comptype_failures.png'
draw_component_failure_barchart(uncosted_comptypes, ctype_failure_mean,
scenario_header, scenario_tag,
hazards.hazard_type, OUTPUT_PATH,
fig_name)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# RESTORATION PROGNOSIS for specified scenarios
component_fullrst_time, ctype_scenario_outcomes = \
calc_comptype_damage_scenario_given_hazard(infrastructure,
scenario,
hazards,
ctype_resp_sorted,
component_response,
cp_types_costed,
scenario_header)
# All the nodes that need to be fixed for each output node:
repair_list_combined = prep_repair_list(
infrastructure, component_meanloss, component_fullrst_time,
comps_uncosted, weight_criteria, scenario_header)
repair_path = copy.deepcopy(repair_list_combined)
output_node_list = infrastructure.output_nodes.keys()
for num_rst_steams in RESTORATION_STREAMS:
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# SYSTEM RESTORATION for given scenario & restoration setup
rst_setup_filename = 'restoration_setup_' + sc_haz_str + '_' \
+ hazards.intensity_measure_unit + '_' \
+ hazards.intensity_measure_param + '.csv'
rst_setup_df = calc_restoration_setup(
component_meanloss, output_node_list, comps_uncosted,
repair_list_combined, num_rst_steams, RESTORATION_OFFSET,
component_fullrst_time, scenario.output_path, scenario_header,
rst_setup_filename)
# Check if nothing to repair, i.e. if repair list is empty:
comps_to_repair = rst_setup_df.index.values.tolist()
if not comps_to_repair:
print("\n*** Scenario: " + scenario_tag)
print("Nothing to repair. Time to repair is zero.")
print("Skipping repair visualisation for this scenario. \n")
break
fig_rst_gantt_name = \
'fig_SC_' + sc_haz_str + '_str' + str(num_rst_steams) + \
'_restoration.png'
restoration_timeline_array, time_to_full_restoration_for_lines = \
vis_restoration_process(
scenario,
infrastructure,
rst_setup_df,
num_rst_steams,
repair_path,
fig_rst_gantt_name,
scenario_tag,
hazards.hazard_type
)
time_to_full_restoration_for_lines_df[(sc_haz_str, num_rst_steams)]\
= [time_to_full_restoration_for_lines[x]
for x in output_node_list]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# COMPONENT CRITICALITY for given scenario & restoration setup
fig_name = 'fig_SC_' + sc_haz_str + '_str' +\
str(num_rst_steams) + '_component_criticality.png'
component_criticality(infrastructure, scenario,
ctype_scenario_outcomes, hazards.hazard_type,
scenario_tag, fig_name)
# --------------------------------------------------------------------------
# *** END : FOCAL_HAZARD_SCENARIOS FOR LOOP ***
time_to_full_restoration_for_lines_csv = \
os.path.join(OUTPUT_PATH, 'line_restoration_prognosis.csv')
time_to_full_restoration_for_lines_df.to_csv(
time_to_full_restoration_for_lines_csv, sep=',')
print("--- --- --- --- --- --- --- --- ---")
print(Fore.YELLOW + "Scenario loss analysis complete." + Fore.RESET + "\n")
def main():
# ------------------------------------------------------------------------
# Read in SETUP data
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--setup", type=str,
help="Setup file for simulation scenario, and \n"
"locations of inputs, outputs, and system model.")
parser.add_argument("-v", "--verbose", type=str,
help="Choose option for logging level from: \n"
"DEBUG, INFO, WARNING, ERROR, CRITICAL.")
parser.add_argument("-d", "--dirfile", type=str,
help="JSON file with location of input/output files "
"from past simulation that is to be analysed\n.")
args = parser.parse_args()
if args.setup is None:
raise ValueError("Must provide a correct setup argument: "
"`-s` or `--setup`,\n"
"Setup file for simulation scenario, and \n"
"locations of inputs, outputs, and system model.\n")
if not os.path.exists(args.dirfile):
raise ValueError("Could not locate file with directory locations"
"with results from pre-run simulations.\n")
# Define input files, output location, scenario inputs
with open(args.dirfile, 'r') as dat:
dir_dict = json.load(dat)
# Configure simulation model.
# Read data and control parameters and construct objects.
config = Configuration(args.setup,
run_mode='analysis',
output_path=dir_dict["OUTPUT_PATH"])
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
if not config.SYS_CONF_FILE_NAME == dir_dict["SYS_CONF_FILE_NAME"]:
raise NameError("Names for supplied system model names did not match."
"Aborting.\n")
# --------------------------------------------------------------------------
OUTPUT_PATH = dir_dict["OUTPUT_PATH"]
RAW_OUTPUT_DIR = dir_dict["RAW_OUTPUT_DIR"]
hazard_scenarios = hazards.hazard_scenario_list
sys_limit_states = infrastructure.get_system_damage_states()
# one_comp = infrastructure.components.values()[0]
# sys_limit_states = [one_comp.damage_states[ds].damage_state_name
# for ds in one_comp.damage_states]
# Test switches
FIT_PE_DATA = scenario.fit_pe_data
# SWITCH_FIT_RESTORATION_DATA = scenario.SWITCH_FIT_RESTORATION_DATA
# RESTORATION_TIME_RANGE = scenario.restoration_time_range
# ------------------------------------------------------------------------
# READ in raw output files from prior analysis of system fragility
economic_loss_array = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'economic_loss_array.npy'))
calculated_output_array = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'calculated_output_array.npy'))
exp_damage_ratio = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'exp_damage_ratio.npy'))
sys_frag = \
np.load(os.path.join(
RAW_OUTPUT_DIR, 'sys_frag.npy'))
# output_array_given_recovery = \
# np.load(os.path.join(
# RAW_OUTPUT_DIR, 'output_array_given_recovery.npy'))
# required_time = \
# np.load(os.path.join(RAW_OUTPUT_DIR, 'required_time.npy'))
# --------------------------------------------------------------------------
if infrastructure.system_class.lower() == 'powerstation':
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_econloss.npy'))
elif infrastructure.system_class.lower() == 'substation':
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_cpfailrate.npy'))
# elif infrastructure.system_class.lower() == 'substation':
# pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_econloss.npy'))
elif infrastructure.system_class.lower() in [
"potablewatertreatmentplant", "pwtp",
"wastewatertreatmentplant", "wwtp",
"watertreatmentplant", "wtp"]:
pe_sys = np.load(os.path.join(RAW_OUTPUT_DIR, 'pe_sys_econloss.npy'))
# --------------------------------------------------------------------------
# Calculate & Plot Fitted Models
if FIT_PE_DATA:
fit_prob_exceed_model(hazard_scenarios,
pe_sys,
sys_limit_states,
OUTPUT_PATH,
config)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--setup", type=str,
help="Setup file for simulation scenario, and \n"
"locations of inputs, outputs, and system model.")
parser.add_argument("-v", "--verbose", type=str,
help="Choose option for logging level from: \n"
"DEBUG, INFO, WARNING, ERROR, CRITICAL.")
args = parser.parse_args()
level = logging.DEBUG
if args.verbose is not None:
if args.verbose.upper() == "DEBUG":
level = logging.DEBUG
elif args.verbose.upper() == "INFO":
level = logging.INFO
elif args.verbose.upper() == "WARNING":
level = logging.WARNING
elif args.verbose.upper() == "ERROR":
level = logging.ERROR
elif args.verbose.upper() == "CRITICAL":
level = logging.CRITICAL
rootLogger.set_log_level(level)
if args.setup is not None:
rootLogger.info('Simulation initiated...')
# ---------------------------------------------------------------------
# Configure simulation model.
# Read data and control parameters and construct objects.
config = Configuration(args.setup)
scenario = Scenario(config)
hazards = HazardsContainer(config)
infrastructure = ingest_model(config)
# ---------------------------------------------------------------------
# Run simulation.
# Get the results of running a simulation
#
# response_list = [
# {}, # hazard level vs component damage state index
# {}, # hazard level vs infrastructure output
# {}, # hazard level vs component response
# {}, # hazard level vs component type response
# [], # array of infrastructure output for each sample
# []] # array infrastructure econ loss for each sample
response_list = calculate_response(hazards, scenario, infrastructure)
# ---------------------------------------------------------------------
# Post simulation processing.
# After the simulation has run the results are aggregated, saved
# and the system fragility is calculated.
write_system_response(response_list, infrastructure, scenario, hazards)
economic_loss_array = response_list[5]
plot_mean_econ_loss(scenario, economic_loss_array, hazards)
if config.HAZARD_INPUT_METHOD == "hazard_array":
pe_by_component_class(response_list, infrastructure,
scenario, hazards)
# ---------------------------------------------------------------------
# Visualizations
# Construct visualization for system topology
sys_topology_view = SystemTopology(infrastructure, scenario)
sys_topology_view.draw_sys_topology(viewcontext="as-built")
# ---------------------------------------------------------------------
else:
print("Input file not found: " + str(args.setup))
rootLogger.info('End')
