class EpfDamage(BaseAnalysis):
"""Computes electric power facility structural damage for an earthquake, tsunami, tornado, and hurricane hazards.
Args:
incore_client (IncoreClient): Service authentication.
"""
DEFAULT_LIQ_FRAGILITY_KEY = "pgd"
DEFAULT_FRAGILITY_KEY = "pga"
def __init__(self, incore_client):
self.hazardsvc = HazardService(incore_client)
self.fragilitysvc = FragilityService(incore_client)
super(EpfDamage, self).__init__(incore_client)
def run(self):
"""Executes electric power facility damage analysis."""
epf_set = self.get_input_dataset("epfs").get_inventory_reader()
# Get Fragility key
fragility_key = self.get_parameter("fragility_key")
if fragility_key is None:
fragility_key = self.DEFAULT_FRAGILITY_KEY
self.set_parameter("fragility_key", fragility_key)
# Get hazard input
hazard_dataset_id = self.get_parameter("hazard_id")
# Hazard type, note this is here for future use if additional hazards are supported by this analysis
hazard_type = self.get_parameter("hazard_type")
# Hazard Uncertainty
use_hazard_uncertainty = False
if self.get_parameter("use_hazard_uncertainty") is not None:
use_hazard_uncertainty = self.get_parameter(
"use_hazard_uncertainty")
# Liquefaction
use_liquefaction = False
if self.get_parameter("use_liquefaction") is not None:
use_liquefaction = self.get_parameter("use_liquefaction")
liq_geology_dataset_id = self.get_parameter(
"liquefaction_geology_dataset_id")
user_defined_cpu = 1
if not self.get_parameter("num_cpu") is None and self.get_parameter(
"num_cpu") > 0:
user_defined_cpu = self.get_parameter("num_cpu")
num_workers = AnalysisUtil.determine_parallelism_locally(
self, len(epf_set), user_defined_cpu)
avg_bulk_input_size = int(len(epf_set) / num_workers)
inventory_args = []
count = 0
inventory_list = list(epf_set)
while count < len(inventory_list):
inventory_args.append(inventory_list[count:count +
avg_bulk_input_size])
count += avg_bulk_input_size
results = self.epf_damage_concurrent_future(
self.epf_damage_analysis_bulk_input, num_workers, inventory_args,
repeat(hazard_type), repeat(hazard_dataset_id),
repeat(use_hazard_uncertainty), repeat(use_liquefaction),
repeat(liq_geology_dataset_id))
self.set_result_csv_data("result",
results,
name=self.get_parameter("result_name"))
return True
def epf_damage_concurrent_future(self, function_name, num_workers, *args):
"""Utilizes concurrent.future module.
Args:
function_name (function): The function to be parallelized.
num_workers (int): Maximum number workers in parallelization.
*args: All the arguments in order to pass into parameter function_name.
Returns:
list: A list of ordered dictionaries with epf damage values and other data/metadata.
"""
output = []
with concurrent.futures.ProcessPoolExecutor(
max_workers=num_workers) as executor:
for ret in executor.map(function_name, *args):
output.extend(ret)
return output
def epf_damage_analysis_bulk_input(self, epfs, hazard_type,
hazard_dataset_id,
use_hazard_uncertainty,
use_liquefaction,
liq_geology_dataset_id):
"""Run analysis for multiple epfs.
Args:
epfs (list): Multiple epfs from input inventory set.
hazard_type (str): A type of hazard exposure (earthquake, tsunami, tornado, or hurricane).
hazard_dataset_id (str): An id of the hazard exposure.
use_hazard_uncertainty (bool): Hazard uncertainty. True for using uncertainty when computing damage,
False otherwise.
use_liquefaction (bool): Liquefaction. True for using liquefaction information to modify the damage,
False otherwise.
liq_geology_dataset_id (str): geology_dataset_id (str): A dataset id for geology dataset for liquefaction.
Returns:
list: A list of ordered dictionaries with epf damage values and other data/metadata.
"""
result = []
fragility_key = self.get_parameter("fragility_key")
fragility_set = dict()
fragility_set = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), epfs, fragility_key)
epf_results = []
# Converting list of epfs into a dictionary for ease of reference
list_epfs = epfs
epfs = dict()
for epf in list_epfs:
epfs[epf["id"]] = epf
del list_epfs # Clear as it's not needed anymore
processed_epf = []
grouped_epfs = AnalysisUtil.group_by_demand_type(epfs, fragility_set)
for demand, grouped_epf_items in grouped_epfs.items():
input_demand_type = demand[0]
input_demand_units = demand[1]
# For every group of unique demand and demand unit, call the end-point once
epf_chunks = list(AnalysisUtil.chunks(grouped_epf_items, 50))
for epf_chunk in epf_chunks:
points = []
for epf_id in epf_chunk:
location = GeoUtil.get_location(epfs[epf_id])
points.append(str(location.y) + "," + str(location.x))
if hazard_type == 'earthquake':
hazard_vals = self.hazardsvc.get_earthquake_hazard_values(
hazard_dataset_id, input_demand_type,
input_demand_units, points)
elif hazard_type == 'tornado':
hazard_vals = self.hazardsvc.get_tornado_hazard_values(
hazard_dataset_id, input_demand_units, points)
elif hazard_type == 'hurricane':
# TODO: implement hurricane
raise ValueError(
'Hurricane hazard has not yet been implemented!')
elif hazard_type == 'tsunami':
hazard_vals = self.hazardsvc.get_tsunami_hazard_values(
hazard_dataset_id, input_demand_type,
input_demand_units, points)
else:
raise ValueError("Missing hazard type.")
# Parse the batch hazard value results and map them back to the building and fragility.
# This is a potential pitfall as we are relying on the order of the returned results
i = 0
for epf_id in epf_chunk:
epf_result = collections.OrderedDict()
epf = epfs[epf_id]
hazard_val = hazard_vals[i]['hazardValue']
# Sometimes the geotiffs give large negative values for out of bounds instead of 0
if hazard_val <= 0.0:
hazard_val = 0.0
std_dev = 0.0
if use_hazard_uncertainty:
raise ValueError("Uncertainty Not Implemented!")
selected_fragility_set = fragility_set[epf_id]
limit_states = selected_fragility_set.calculate_limit_state(
hazard_val, std_dev=std_dev)
dmg_interval = AnalysisUtil.calculate_damage_interval(
limit_states)
epf_result['guid'] = epf['properties']['guid']
epf_result.update(limit_states)
epf_result.update(dmg_interval)
epf_result['demandtype'] = input_demand_type
epf_result['demandunits'] = input_demand_units
epf_result['hazardtype'] = hazard_type
epf_result['hazardval'] = hazard_val
epf_results.append(epf_result)
processed_epf.append(epf_id)
i = i + 1
# when there is liquefaction, limit state need to be modified
if hazard_type == 'earthquake' and use_liquefaction and liq_geology_dataset_id is not None:
liq_fragility_key = self.get_parameter(
"liquefaction_fragility_key")
if liq_fragility_key is None:
liq_fragility_key = self.DEFAULT_LIQ_FRAGILITY_KEY
liq_fragility_set = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), epfs,
liq_fragility_key)
grouped_liq_epfs = AnalysisUtil.group_by_demand_type(
epfs, liq_fragility_set)
for liq_demand, grouped_liq_epf_items in grouped_liq_epfs.items():
liq_input_demand_type = liq_demand[0]
liq_input_demand_units = liq_demand[1]
# For every group of unique demand and demand unit, call the end-point once
liq_epf_chunks = list(
AnalysisUtil.chunks(grouped_liq_epf_items, 50))
for liq_epf_chunk in liq_epf_chunks:
points = []
for liq_epf_id in liq_epf_chunk:
location = GeoUtil.get_location(epfs[liq_epf_id])
points.append(str(location.y) + "," + str(location.x))
liquefaction_vals = self.hazardsvc.get_liquefaction_values(
hazard_dataset_id, liq_geology_dataset_id,
liq_input_demand_units, points)
# Parse the batch hazard value results and map them back to the building and fragility.
# This is a potential pitfall as we are relying on the order of the returned results
i = 0
for liq_epf_id in liq_epf_chunk:
liq_hazard_val = liquefaction_vals[i][
liq_input_demand_type]
std_dev = 0.0
if use_hazard_uncertainty:
raise ValueError("Uncertainty Not Implemented!")
liquefaction_prob = liquefaction_vals[i][
'liqProbability']
selected_liq_fragility = liq_fragility_set[liq_epf_id]
pgd_limit_states = selected_liq_fragility.calculate_limit_state(
liq_hazard_val, std_dev=std_dev)
# match id and add liqhaztype, liqhazval, liqprobability field as well as rewrite limit
# states and dmg_interval
for epf_result in epf_results:
if epf_result['guid'] == epfs[liq_epf_id]['guid']:
limit_states = {
"ls-slight": epf_result['ls-slight'],
"ls-moderat": epf_result['ls-moderat'],
"ls-extensi": epf_result['ls-extensi'],
"ls-complet": epf_result['ls-complet']
}
liq_limit_states = AnalysisUtil.adjust_limit_states_for_pgd(
limit_states, pgd_limit_states)
liq_dmg_interval = AnalysisUtil.calculate_damage_interval(
liq_limit_states)
epf_result.update(liq_limit_states)
epf_result.update(liq_dmg_interval)
epf_result[
'liqhaztype'] = liq_input_demand_type
epf_result['liqhazval'] = liq_hazard_val
epf_result[
'liqprobability'] = liquefaction_prob
i = i + 1
unmapped_limit_states = {
"ls-slight": 0.0,
"ls-moderat": 0.0,
"ls-extensi": 0.0,
"ls-complet": 0.0
}
unmapped_dmg_intervals = AnalysisUtil.calculate_damage_interval(
unmapped_limit_states)
for epf_id, epf in epfs.items():
if epf_id not in processed_epf:
unmapped_epf_result = collections.OrderedDict()
unmapped_epf_result['guid'] = epf['properties']['guid']
unmapped_epf_result.update(unmapped_limit_states)
unmapped_epf_result.update(unmapped_dmg_intervals)
unmapped_epf_result["demandtype"] = "None"
unmapped_epf_result['demandunits'] = "None"
unmapped_epf_result["hazardtype"] = "None"
unmapped_epf_result['hazardval'] = 0.0
unmapped_epf_result['liqhaztype'] = "NA"
unmapped_epf_result['liqhazval'] = "NA"
unmapped_epf_result['liqprobability'] = "NA"
epf_results.append(unmapped_epf_result)
return epf_results
def get_spec(self):
"""Get specifications of the epf damage analysis.
Returns:
obj: A JSON object of specifications of the epf damage analysis.
"""
return {
'name':
'epf-damage',
'description':
'Electric Power Facility damage analysis.',
'input_parameters': [
{
'id': 'result_name',
'required': True,
'description': 'A name of the resulting dataset',
'type': str
},
{
'id': 'hazard_type',
'required': True,
'description': 'Hazard type (e.g. earthquake).',
'type': str
},
{
'id':
'hazard_id',
'required':
True,
'description':
'Hazard ID which defines the particular hazard (e.g. New madrid earthquake '
'using Atkinson Boore 1995).',
'type':
str
},
{
'id': 'fragility_key',
'required': False,
'description':
'Fragility key to use in mapping dataset ()',
'type': str
},
{
'id': 'use_liquefaction',
'required': False,
'description':
'Use a ground liquifacition to modify damage interval.',
'type': bool
},
{
'id':
'liquefaction_geology_dataset_id',
'required':
False,
'description':
'Liquefaction geology/susceptibility dataset id. '
'If not provided, liquefaction will be ignored',
'type':
str
},
{
'id': 'use_hazard_uncertainty',
'required': False,
'description': 'Use hazard uncertainty',
'type': bool
},
{
'id': 'num_cpu',
'required': False,
'description':
'If using parallel execution, the number of cpus to request.',
'type': int
},
],
'input_datasets': [{
'id': 'epfs',
'required': True,
'description': 'Electric Power Facility Inventory',
'type': ['incore:epf', 'ergo:epf'],
}, {
'id': 'dfr3_mapping_set',
'required': True,
'description': 'DFR3 Mapping Set Object',
'type': ['incore:dfr3MappingSet'],
}],
'output_datasets': [{
'id': 'result',
'parent_type': 'epfs',
'type': 'incore:epfDamage'
}]
}
class BuildingDamage(BaseAnalysis):
"""Building Damage Analysis calculates the probability of building damage based on
different hazard type such as earthquake, tsunami, and tornado.
Args:
incore_client (IncoreClient): Service authentication.
"""
def __init__(self, incore_client):
self.hazardsvc = HazardService(incore_client)
self.fragilitysvc = FragilityService(incore_client)
super(BuildingDamage, self).__init__(incore_client)
def run(self):
"""Executes building damage analysis."""
# Building dataset
bldg_set = self.get_input_dataset("buildings").get_inventory_reader()
# Get hazard input
hazard_dataset_id = self.get_parameter("hazard_id")
# Hazard type of the exposure
hazard_type = self.get_parameter("hazard_type")
# Get Fragility key
fragility_key = self.get_parameter("fragility_key")
if fragility_key is None:
fragility_key = BuildingUtil.DEFAULT_TSUNAMI_MMAX_FRAGILITY_KEY if hazard_type == 'tsunami' else \
BuildingUtil.DEFAULT_FRAGILITY_KEY
self.set_parameter("fragility_key", fragility_key)
user_defined_cpu = 1
if not self.get_parameter("num_cpu") is None and self.get_parameter(
"num_cpu") > 0:
user_defined_cpu = self.get_parameter("num_cpu")
num_workers = AnalysisUtil.determine_parallelism_locally(
self, len(bldg_set), user_defined_cpu)
avg_bulk_input_size = int(len(bldg_set) / num_workers)
inventory_args = []
count = 0
inventory_list = list(bldg_set)
while count < len(inventory_list):
inventory_args.append(inventory_list[count:count +
avg_bulk_input_size])
count += avg_bulk_input_size
results = self.building_damage_concurrent_future(
self.building_damage_analysis_bulk_input, num_workers,
inventory_args, repeat(hazard_type), repeat(hazard_dataset_id))
self.set_result_csv_data("result",
results,
name=self.get_parameter("result_name"))
return True
def building_damage_concurrent_future(self, function_name, parallelism,
*args):
"""Utilizes concurrent.future module.
Args:
function_name (function): The function to be parallelized.
parallelism (int): Number of workers in parallelization.
*args: All the arguments in order to pass into parameter function_name.
Returns:
list: A list of ordered dictionaries with building damage values and other data/metadata.
"""
output = []
with concurrent.futures.ProcessPoolExecutor(
max_workers=parallelism) as executor:
for ret in executor.map(function_name, *args):
output.extend(ret)
return output
def building_damage_analysis_bulk_input(self, buildings, hazard_type,
hazard_dataset_id):
"""Run analysis for multiple buildings.
Args:
buildings (list): Multiple buildings from input inventory set.
hazard_type (str): Hazard type, either earthquake, tornado, or tsunami.
hazard_dataset_id (str): An id of the hazard exposure.
Returns:
list: A list of ordered dictionaries with building damage values and other data/metadata.
"""
fragility_key = self.get_parameter("fragility_key")
fragility_sets = dict()
fragility_sets = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), buildings,
fragility_key)
bldg_results = []
list_buildings = buildings
buildings = dict()
# Converting list of buildings into a dictionary for ease of reference
for b in list_buildings:
buildings[b["id"]] = b
list_buildings = None # Clear as it's not needed anymore
grouped_buildings = AnalysisUtil.group_by_demand_type(buildings,
fragility_sets,
hazard_type,
is_building=True)
for demand, grouped_bldgs in grouped_buildings.items():
input_demand_type = demand[0]
input_demand_units = demand[1]
# For every group of unique demand and demand unit, call the end-point once
bldg_chunks = list(AnalysisUtil.chunks(
grouped_bldgs, 50)) # TODO: Move to globals?
for bldgs in bldg_chunks:
points = []
for bldg_id in bldgs:
location = GeoUtil.get_location(buildings[bldg_id])
points.append(str(location.y) + "," + str(location.x))
if hazard_type == 'earthquake':
hazard_vals = self.hazardsvc.get_earthquake_hazard_values(
hazard_dataset_id, input_demand_type,
input_demand_units, points)
elif hazard_type == 'tornado':
hazard_vals = self.hazardsvc.get_tornado_hazard_values(
hazard_dataset_id, input_demand_units, points)
elif hazard_type == 'tsunami':
hazard_vals = self.hazardsvc.get_tsunami_hazard_values(
hazard_dataset_id, input_demand_type,
input_demand_units, points)
elif hazard_type == 'hurricane':
# TODO implement hurricane
print("hurricane not yet implemented")
# Parse the batch hazard value results and map them back to the building and fragility.
# This is a potential pitfall as we are relying on the order of the returned results
i = 0
for bldg_id in bldgs:
bldg_result = collections.OrderedDict()
building = buildings[bldg_id]
hazard_val = hazard_vals[i]['hazardValue']
output_demand_type = hazard_vals[i]['demand']
if hazard_type == 'earthquake':
period = float(hazard_vals[i]['period'])
if period > 0:
output_demand_type = str(
hazard_vals[i]
['period']) + " " + output_demand_type
num_stories = building['properties']['no_stories']
selected_fragility_set = fragility_sets[bldg_id]
building_period = selected_fragility_set.fragility_curves[
0].get_building_period(num_stories)
dmg_probability = selected_fragility_set.calculate_limit_state(
hazard_val, building_period)
dmg_interval = AnalysisUtil.calculate_damage_interval(
dmg_probability)
bldg_result['guid'] = building['properties']['guid']
bldg_result.update(dmg_probability)
bldg_result.update(dmg_interval)
bldg_result['demandtype'] = output_demand_type
bldg_result['demandunits'] = input_demand_units
bldg_result['hazardval'] = hazard_val
bldg_results.append(bldg_result)
del buildings[bldg_id]
i = i + 1
unmapped_hazard_val = 0.0
unmapped_output_demand_type = "None"
unmapped_output_demand_unit = "None"
for unmapped_bldg_id, unmapped_bldg in buildings.items():
unmapped_bldg_result = collections.OrderedDict()
unmapped_bldg_result['guid'] = unmapped_bldg['properties']['guid']
unmapped_bldg_result['demandtype'] = unmapped_output_demand_type
unmapped_bldg_result['demandunits'] = unmapped_output_demand_unit
unmapped_bldg_result['hazardval'] = unmapped_hazard_val
bldg_results.append(unmapped_bldg_result)
return bldg_results
def get_spec(self):
"""Get specifications of the building damage analysis.
Returns:
obj: A JSON object of specifications of the building damage analysis.
"""
return {
'name':
'building-damage',
'description':
'building damage analysis',
'input_parameters': [
{
'id': 'result_name',
'required': True,
'description': 'result dataset name',
'type': str
},
{
'id': 'hazard_type',
'required': True,
'description': 'Hazard Type (e.g. earthquake)',
'type': str
},
{
'id': 'hazard_id',
'required': True,
'description': 'Hazard ID',
'type': str
},
{
'id': 'fragility_key',
'required': False,
'description': 'Fragility key to use in mapping dataset',
'type': str
},
{
'id': 'use_liquefaction',
'required': False,
'description': 'Use liquefaction',
'type': bool
},
{
'id': 'use_hazard_uncertainty',
'required': False,
'description': 'Use hazard uncertainty',
'type': bool
},
{
'id': 'num_cpu',
'required': False,
'description':
'If using parallel execution, the number of cpus to request',
'type': int
},
],
'input_datasets': [{
'id':
'buildings',
'required':
True,
'description':
'Building Inventory',
'type': [
'ergo:buildingInventoryVer4', 'ergo:buildingInventoryVer5',
'ergo:buildingInventoryVer6'
],
}, {
'id': 'dfr3_mapping_set',
'required': True,
'description': 'DFR3 Mapping Set Object',
'type': ['incore:dfr3MappingSet'],
}],
'output_datasets': [{
'id': 'result',
'parent_type': 'buildings',
'description': 'CSV file of building structural damage',
'type': 'ergo:buildingDamageVer4'
}]
}
class BridgeDamage(BaseAnalysis):
"""Computes bridge structural damage for earthquake, tsunami, tornado, and hurricane hazards.
Args:
incore_client (IncoreClient): Service authentication.
"""
def __init__(self, incore_client):
self.hazardsvc = HazardService(incore_client)
self.fragilitysvc = FragilityService(incore_client)
super(BridgeDamage, self).__init__(incore_client)
def run(self):
"""Executes bridge damage analysis."""
# Bridge dataset
bridge_set = self.get_input_dataset("bridges").get_inventory_reader()
# Get hazard input
hazard_type = self.get_parameter("hazard_type")
hazard_dataset_id = self.get_parameter("hazard_id")
user_defined_cpu = 1
if not self.get_parameter("num_cpu") is None and self.get_parameter(
"num_cpu") > 0:
user_defined_cpu = self.get_parameter("num_cpu")
num_workers = AnalysisUtil.determine_parallelism_locally(self, len(
bridge_set), user_defined_cpu)
avg_bulk_input_size = int(len(bridge_set) / num_workers)
inventory_args = []
count = 0
inventory_list = list(bridge_set)
while count < len(inventory_list):
inventory_args.append(
inventory_list[count:count + avg_bulk_input_size])
count += avg_bulk_input_size
results = self.bridge_damage_concurrent_future(
self.bridge_damage_analysis_bulk_input, num_workers,
inventory_args, repeat(hazard_type),
repeat(hazard_dataset_id))
self.set_result_csv_data("result", results,
name=self.get_parameter("result_name"))
return True
def bridge_damage_concurrent_future(self, function_name, num_workers,
*args):
"""Utilizes concurrent.future module.
Args:
function_name (function): The function to be parallelized.
num_workers (int): Maximum number workers in parallelization.
*args: All the arguments in order to pass into parameter function_name.
Returns:
list: A list of ordered dictionaries with bridge damage values and other data/metadata.
"""
output = []
with concurrent.futures.ProcessPoolExecutor(
max_workers=num_workers) as executor:
for ret in executor.map(function_name, *args):
output.extend(ret)
return output
def bridge_damage_analysis_bulk_input(self, bridges, hazard_type,
hazard_dataset_id):
"""Run analysis for multiple bridges.
Args:
bridges (list): Multiple bridges from input inventory set.
hazard_type (str): Hazard type, either earthquake, tornado, tsunami, or hurricane.
hazard_dataset_id (str): An id of the hazard exposure.
Returns:
list: A list of ordered dictionaries with bridge damage values and other data/metadata.
"""
# Get Fragility key
fragility_key = self.get_parameter("fragility_key")
if fragility_key is None:
fragility_key = BridgeUtil.DEFAULT_TSUNAMI_HMAX_FRAGILITY_KEY if hazard_type == 'tsunami' else \
BridgeUtil.DEFAULT_FRAGILITY_KEY
self.set_parameter("fragility_key", fragility_key)
# Hazard Uncertainty
use_hazard_uncertainty = False
if hazard_type == "earthquake" and self.get_parameter(
"use_hazard_uncertainty") is not None:
use_hazard_uncertainty = self.get_parameter(
"use_hazard_uncertainty")
# Liquefaction
use_liquefaction = False
if hazard_type == "earthquake" and self.get_parameter(
"use_liquefaction") is not None:
use_liquefaction = self.get_parameter("use_liquefaction")
fragility_set = dict()
fragility_set = self.fragilitysvc.match_inventory(self.get_input_dataset("dfr3_mapping_set"), bridges,
fragility_key)
bridge_results = []
list_bridges = bridges
# Converting list of bridges into a dictionary for ease of reference
bridges = dict()
for br in list_bridges:
bridges[br["id"]] = br
list_bridges = None # Clear as it's not needed anymore
processed_bridges = []
grouped_bridges = AnalysisUtil.group_by_demand_type(bridges, fragility_set)
for demand, grouped_brs in grouped_bridges.items():
input_demand_type = demand[0]
input_demand_units = demand[1]
# For every group of unique demand and demand unit, call the end-point once
br_chunks = list(AnalysisUtil.chunks(grouped_brs, 50)) # TODO: Move to globals?
for brs in br_chunks:
points = []
for br_id in brs:
location = GeoUtil.get_location(bridges[br_id])
points.append(str(location.y) + "," + str(location.x))
if hazard_type == "earthquake":
hazard_vals = \
self.hazardsvc.get_earthquake_hazard_values(
hazard_dataset_id,
input_demand_type,
input_demand_units,
points)
elif hazard_type == "tsunami":
hazard_vals = self.hazardsvc.get_tsunami_hazard_values(
hazard_dataset_id, input_demand_type, input_demand_units, points)
elif hazard_type == "tornado":
hazard_vals = self.hazardsvc.get_tornado_hazard_values(
hazard_dataset_id, input_demand_units, points)
elif hazard_type == "hurricane":
hazard_vals = self.hazardsvc.get_hurricanewf_values(
hazard_dataset_id, input_demand_type, input_demand_units, points)
else:
raise ValueError("We only support Earthquake, Tornado, Tsunami, and Hurricane at the moment!")
# Parse the batch hazard value results and map them back to the building and fragility.
# This is a potential pitfall as we are relying on the order of the returned results
i = 0
for br_id in brs:
bridge_result = collections.OrderedDict()
bridge = bridges[br_id]
selected_fragility_set = fragility_set[br_id]
hazard_val = hazard_vals[i]['hazardValue']
hazard_std_dev = 0.0
if use_hazard_uncertainty:
# TODO Get this from API once implemented
raise ValueError("Uncertainty Not Implemented!")
adjusted_fragility_set = copy.deepcopy(selected_fragility_set)
if use_liquefaction and 'liq' in bridge['properties']:
for fragility in adjusted_fragility_set.fragility_curves:
fragility.adjust_fragility_for_liquefaction(bridge['properties']['liq'])
dmg_probability = adjusted_fragility_set.calculate_limit_state(hazard_val, std_dev=hazard_std_dev)
retrofit_cost = BridgeUtil.get_retrofit_cost(fragility_key)
retrofit_type = BridgeUtil.get_retrofit_type(fragility_key)
dmg_intervals = AnalysisUtil.calculate_damage_interval(dmg_probability)
bridge_result['guid'] = bridge['properties']['guid']
bridge_result.update(dmg_probability)
bridge_result.update(dmg_intervals)
bridge_result["retrofit"] = retrofit_type
bridge_result["retrocost"] = retrofit_cost
bridge_result["demandtype"] = input_demand_type
bridge_result["demandunits"] = input_demand_units
bridge_result["hazardtype"] = hazard_type
bridge_result["hazardval"] = hazard_val
# add spans to bridge output so mean damage calculation can use that info
if "spans" in bridge["properties"] and bridge["properties"]["spans"] \
is not None and bridge["properties"]["spans"].isdigit():
bridge_result['spans'] = int(bridge["properties"]["spans"])
elif "SPANS" in bridge["properties"] and bridge["properties"]["SPANS"] \
is not None and bridge["properties"]["SPANS"].isdigit():
bridge_result['spans'] = int(bridge["properties"]["SPANS"])
else:
bridge_result['spans'] = 1
bridge_results.append(bridge_result)
processed_bridges.append(br_id) # remove processed bridges
i = i + 1
unmapped_dmg_probability = {"ls-slight": 0.0, "ls-moderat": 0.0,
"ls-extensi": 0.0, "ls-complet": 0.0}
unmapped_dmg_intervals = AnalysisUtil.calculate_damage_interval(unmapped_dmg_probability)
for br_id, br in bridges.items():
if br_id not in processed_bridges:
unmapped_br_result = collections.OrderedDict()
unmapped_br_result['guid'] = br['properties']['guid']
unmapped_br_result.update(unmapped_dmg_probability)
unmapped_br_result.update(unmapped_dmg_intervals)
unmapped_br_result["retrofit"] = "Non-Retrofit"
unmapped_br_result["retrocost"] = 0.0
unmapped_br_result["demandtype"] = "None"
unmapped_br_result['demandunits'] = "None"
unmapped_br_result["hazardtype"] = "None"
unmapped_br_result['hazardval'] = 0.0
bridge_results.append(unmapped_br_result)
return bridge_results
def get_spec(self):
"""Get specifications of the bridge damage analysis.
Returns:
obj: A JSON object of specifications of the bridge damage analysis.
"""
return {
'name': 'bridge-damage',
'description': 'bridge damage analysis',
'input_parameters': [
{
'id': 'result_name',
'required': True,
'description': 'result dataset name',
'type': str
},
{
'id': 'hazard_type',
'required': True,
'description': 'Hazard Type (e.g. earthquake)',
'type': str
},
{
'id': 'hazard_id',
'required': True,
'description': 'Hazard ID',
'type': str
},
{
'id': 'fragility_key',
'required': False,
'description': 'Fragility key to use in mapping dataset',
'type': str
},
{
'id': 'use_liquefaction',
'required': False,
'description': 'Use liquefaction',
'type': bool
},
{
'id': 'use_hazard_uncertainty',
'required': False,
'description': 'Use hazard uncertainty',
'type': bool
},
{
'id': 'num_cpu',
'required': False,
'description': 'If using parallel execution, the number of cpus to request',
'type': int
},
],
'input_datasets': [
{
'id': 'bridges',
'required': True,
'description': 'Bridge Inventory',
'type': ['ergo:bridges'],
},
{
'id': 'dfr3_mapping_set',
'required': True,
'description': 'DFR3 Mapping Set Object',
'type': ['incore:dfr3MappingSet'],
}
],
'output_datasets': [
{
'id': 'result',
'parent_type': 'bridges',
'description': 'CSV file of bridge structural damage',
'type': 'ergo:bridgeDamage'
}
]
}
class PipelineDamageRepairRate(BaseAnalysis):
"""Computes pipeline damage for a hazard.
Args:
incore_client: Service client with authentication info
"""
def __init__(self, incore_client):
self.hazardsvc = HazardService(incore_client)
self.fragilitysvc = FragilityService(incore_client)
super(PipelineDamageRepairRate, self).__init__(incore_client)
def run(self):
"""Execute pipeline damage analysis """
# Pipeline dataset
pipeline_dataset = self.get_input_dataset(
"pipeline").get_inventory_reader()
# Get hazard type
hazard_type = self.get_parameter("hazard_type")
# Get hazard input
hazard_dataset_id = self.get_parameter("hazard_id")
user_defined_cpu = 1
if not self.get_parameter("num_cpu") is None and self.get_parameter(
"num_cpu") > 0:
user_defined_cpu = self.get_parameter("num_cpu")
dataset_size = len(pipeline_dataset)
num_workers = AnalysisUtil.determine_parallelism_locally(
self, dataset_size, user_defined_cpu)
avg_bulk_input_size = int(dataset_size / num_workers)
inventory_args = []
count = 0
inventory_list = list(pipeline_dataset)
while count < len(inventory_list):
inventory_args.append(inventory_list[count:count +
avg_bulk_input_size])
count += avg_bulk_input_size
results = self.pipeline_damage_concurrent_future(
self.pipeline_damage_analysis_bulk_input, num_workers,
inventory_args, repeat(hazard_type), repeat(hazard_dataset_id))
self.set_result_csv_data("result",
results,
name=self.get_parameter("result_name"))
return True
def pipeline_damage_concurrent_future(self, function_name, num_workers,
*args):
"""Utilizes concurrent.future module.
Args:
function_name (function): The function to be parallelized.
num_workers (int): Maximum number workers in parallelization.
*args: All the arguments in order to pass into parameter function_name.
Returns:
list: A list of ordered dictionaries with building damage values and other data/metadata.
"""
output = []
with concurrent.futures.ProcessPoolExecutor(
max_workers=num_workers) as executor:
for ret in executor.map(function_name, *args):
output.extend(ret)
return output
def pipeline_damage_analysis_bulk_input(self, pipelines, hazard_type,
hazard_dataset_id):
"""Run pipeline damage analysis for multiple pipelines.
Args:
pipelines (list): multiple pipelines from pieline dataset.
hazard_type (str): Hazard type
hazard_dataset_id (str): An id of the hazard exposure.
Returns:
list: A list of ordered dictionaries with pipeline damage values and other data/metadata.
"""
result = []
# Get Fragility key
fragility_key = self.get_parameter("fragility_key")
if fragility_key is None:
fragility_key = PipelineUtil.DEFAULT_TSU_FRAGILITY_KEY if hazard_type == 'tsunami' else \
PipelineUtil.DEFAULT_EQ_FRAGILITY_KEY
self.set_parameter("fragility_key", fragility_key)
# get fragility set
fragility_sets = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), pipelines,
fragility_key)
# Get Liquefaction Fragility Key
liquefaction_fragility_key = self.get_parameter(
"liquefaction_fragility_key")
if hazard_type == "earthquake" and liquefaction_fragility_key is None:
liquefaction_fragility_key = PipelineUtil.LIQ_FRAGILITY_KEY
# Liquefaction
use_liquefaction = False
if hazard_type == "earthquake" and self.get_parameter(
"use_liquefaction") is not None:
use_liquefaction = self.get_parameter("use_liquefaction")
# Get geology dataset id
geology_dataset_id = self.get_parameter(
"liquefaction_geology_dataset_id")
if geology_dataset_id is not None:
fragility_sets_liq = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), pipelines,
liquefaction_fragility_key)
for pipeline in pipelines:
if pipeline["id"] in fragility_sets.keys():
liq_fragility_set = None
# Check if mapping contains liquefaction fragility
if geology_dataset_id is not None and \
fragility_sets_liq is not None and \
pipeline["id"] in fragility_sets_liq:
liq_fragility_set = fragility_sets_liq[pipeline["id"]]
result.append(
self.pipeline_damage_analysis(
pipeline, hazard_type, fragility_sets[pipeline["id"]],
liq_fragility_set, hazard_dataset_id,
geology_dataset_id, use_liquefaction))
return result
def pipeline_damage_analysis(self, pipeline, hazard_type, fragility_set,
fragility_set_liq, hazard_dataset_id,
geology_dataset_id, use_liquefaction):
"""Run pipeline damage for a single pipeline.
Args:
pipeline (obj): a single pipeline.
hazard_type (str): hazard type.
fragility_set (obj): A JSON description of fragility assigned to the building.
fragility_set_liq (obj): A JSON description of fragility assigned to the building with liqufaction.
hazard_dataset_id (str): A hazard dataset to use.
geology_dataset_id (str): A dataset id for geology dataset for liqufaction.
use_liquefaction (bool): Liquefaction. True for using liquefaction information to modify the damage,
False otherwise.
Returns:
OrderedDict: A dictionary with pipeline damage values and other data/metadata.
"""
pipeline_results = collections.OrderedDict()
pgv_repairs = 0.0
pgd_repairs = 0.0
liq_hazard_type = ""
liq_hazard_val = 0.0
liquefaction_prob = 0.0
if fragility_set is not None:
demand_type = fragility_set.demand_type.lower()
demand_units = fragility_set.demand_units
location = GeoUtil.get_location(pipeline)
point = str(location.y) + "," + str(location.x)
if hazard_type == 'earthquake':
hazard_resp = self.hazardsvc.get_earthquake_hazard_values(
hazard_dataset_id, demand_type, demand_units, [point])
elif hazard_type == 'tsunami':
hazard_resp = self.hazardsvc.get_tsunami_hazard_values(
hazard_dataset_id, demand_type, demand_units, [point])
elif hazard_type == 'tornado':
hazard_resp = self.hazardsvc.get_tornado_hazard_values(
hazard_dataset_id, demand_units, [point])
elif hazard_type == 'hurricane':
hazard_resp = self.hazardsvc.get_hurricanewf_values(
hazard_dataset_id, demand_type, demand_units, [point])
else:
raise ValueError("Hazard type are not currently supported.")
hazard_val = hazard_resp[0]['hazardValue']
if hazard_val <= 0.0:
hazard_val = 0.0
diameter = PipelineUtil.get_pipe_diameter(pipeline)
fragility_vars = {'x': hazard_val, 'y': diameter}
fragility_curve = fragility_set.fragility_curves[0]
# TODO: here assume that custom fragility set only has one limit state
pgv_repairs = fragility_set.calculate_custom_limit_state(
fragility_vars)['failure']
# Convert PGV repairs to SI units
pgv_repairs = PipelineUtil.convert_result_unit(
fragility_curve.description, pgv_repairs)
if use_liquefaction is True and fragility_set_liq is not None and geology_dataset_id is not None:
liq_fragility_curve = fragility_set_liq.fragility_curves[0]
liq_hazard_type = fragility_set_liq.demand_type
pgd_demand_units = fragility_set_liq.demand_units
# Get PGD hazard value from hazard service
location_str = str(location.y) + "," + str(location.x)
liquefaction = self.hazardsvc.get_liquefaction_values(
hazard_dataset_id, geology_dataset_id, pgd_demand_units,
[location_str])
liq_hazard_val = liquefaction[0]['pgd']
liquefaction_prob = liquefaction[0]['liqProbability']
liq_fragility_vars = {
'x': liq_hazard_val,
'y': liquefaction_prob
}
pgd_repairs = liq_fragility_curve.compute_custom_limit_state_probability(
liq_fragility_vars)
# Convert PGD repairs to SI units
pgd_repairs = PipelineUtil.convert_result_unit(
liq_fragility_curve.description, pgd_repairs)
total_repair_rate = pgd_repairs + pgv_repairs
break_rate = 0.2 * pgv_repairs + 0.8 * pgd_repairs
leak_rate = 0.8 * pgv_repairs + 0.2 * pgd_repairs
length = PipelineUtil.get_pipe_length(pipeline)
failure_probability = 1 - math.exp(-1.0 * break_rate * length)
num_pgd_repairs = pgd_repairs * length
num_pgv_repairs = pgv_repairs * length
num_repairs = num_pgd_repairs + num_pgv_repairs
pipeline_results['guid'] = pipeline['properties']['guid']
if 'pipetype' in pipeline['properties']:
pipeline_results['pipeclass'] = pipeline['properties'][
'pipetype']
elif 'pipelinesc' in pipeline['properties']:
pipeline_results['pipeclass'] = pipeline['properties'][
'pipelinesc']
else:
pipeline_results['pipeclass'] = ""
pipeline_results['pgvrepairs'] = pgv_repairs
pipeline_results['pgdrepairs'] = pgd_repairs
pipeline_results['repairspkm'] = total_repair_rate
pipeline_results['breakrate'] = break_rate
pipeline_results['leakrate'] = leak_rate
pipeline_results['failprob'] = failure_probability
pipeline_results['demandtype'] = demand_type
pipeline_results['hazardtype'] = hazard_type
pipeline_results['hazardval'] = hazard_val
pipeline_results['liqhaztype'] = liq_hazard_type
pipeline_results['liqhazval'] = liq_hazard_val
pipeline_results['liqprobability'] = liquefaction_prob
pipeline_results['numpgvrpr'] = num_pgv_repairs
pipeline_results['numpgdrpr'] = num_pgd_repairs
pipeline_results['numrepairs'] = num_repairs
return pipeline_results
def get_spec(self):
"""Get specifications of the pipeline damage analysis.
Returns:
obj: A JSON object of specifications of the pipeline damage analysis.
"""
return {
'name':
'pipeline-damage',
'description':
'buried pipeline damage analysis',
'input_parameters': [{
'id': 'result_name',
'required': True,
'description': 'result dataset name',
'type': str
}, {
'id': 'hazard_type',
'required': True,
'description': 'Hazard Type (e.g. earthquake)',
'type': str
}, {
'id': 'hazard_id',
'required': True,
'description': 'Hazard ID',
'type': str
}, {
'id': 'fragility_key',
'required': False,
'description': 'Fragility key to use in mapping dataset',
'type': str
}, {
'id': 'use_liquefaction',
'required': False,
'description': 'Use liquefaction',
'type': bool
}, {
'id': 'liquefaction_fragility_key',
'required': False,
'description':
'Fragility key to use in liquefaction mapping dataset',
'type': str
}, {
'id': 'num_cpu',
'required': False,
'description':
'If using parallel execution, the number of cpus to request',
'type': int
}, {
'id': 'liquefaction_geology_dataset_id',
'required': False,
'description': 'Geology dataset id',
'type': str,
}],
'input_datasets': [{
'id':
'pipeline',
'required':
True,
'description':
'Pipeline Inventory',
'type': ['ergo:buriedPipelineTopology', 'ergo:pipeline'],
}, {
'id': 'dfr3_mapping_set',
'required': True,
'description': 'DFR3 Mapping Set Object',
'type': ['incore:dfr3MappingSet'],
}],
'output_datasets': [{
'id': 'result',
'parent_type': 'pipeline',
'type': 'ergo:pipelineDamage'
}]
}