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 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 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 road_damage_analysis_bulk_input(self, roads, hazard_type,
hazard_dataset_id,
use_hazard_uncertainty,
geology_dataset_id, fragility_key,
use_liquefaction):
"""Run analysis for multiple roads.
Args:
roads (list): Multiple roads from input inventory set.
hazard_type (str): A hazard type of the hazard exposure (earthquake or tsunami).
hazard_dataset_id (str): An id of the hazard exposure.
use_hazard_uncertainty(bool): Flag to indicate use uncertainty or not
geology_dataset_id (str): An id of the geology for use in liquefaction.
fragility_key (str): Fragility key describing the type of fragility.
use_liquefaction (bool): Liquefaction. True for using liquefaction information to modify the damage,
False otherwise.
Returns:
list: A list of ordered dictionaries with road damage values and other data/metadata.
"""
road_results = []
fragility_sets = self.fragilitysvc.match_inventory(
self.get_input_dataset("dfr3_mapping_set"), roads, fragility_key)
list_roads = roads
# Converting list of roads into a dictionary for ease of reference
roads = dict()
for rd in list_roads:
roads[rd["id"]] = rd
del list_roads
processed_roads = []
grouped_roads = AnalysisUtil.group_by_demand_type(
roads, fragility_sets)
for demand, grouped_road_items in grouped_roads.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
road_chunks = list(AnalysisUtil.chunks(grouped_road_items, 50))
for road_chunk in road_chunks:
points = []
for road_id in road_chunk:
location = GeoUtil.get_location(roads[road_id])
points.append(str(location.y) + "," + str(location.x))
liquefaction = []
if hazard_type == 'earthquake':
hazard_vals = self.hazardsvc.get_earthquake_hazard_values(
hazard_dataset_id, input_demand_type,
input_demand_units, points)
if input_demand_type.lower(
) == 'pgd' and use_liquefaction and geology_dataset_id is not None:
liquefaction = self.hazardsvc.get_liquefaction_values(
hazard_dataset_id, geology_dataset_id,
input_demand_units, points)
elif hazard_type == 'tornado':
raise ValueError(
'Earthquake and tsunamis are the only hazards supported for road damage'
)
elif hazard_type == 'hurricane':
raise ValueError(
'Earthquake and tsunamis are the only hazards supported for road damage'
)
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 road_id in road_chunk:
road_result = collections.OrderedDict()
road = roads[road_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 Yet.")
selected_fragility_set = fragility_sets[road_id]
dmg_probability = selected_fragility_set.calculate_limit_state(
hazard_val, std_dev=std_dev)
dmg_interval = AnalysisUtil.calculate_damage_interval(
dmg_probability)
road_result['guid'] = road['properties']['guid']
road_result.update(dmg_probability)
road_result.update(dmg_interval)
road_result['demandtype'] = input_demand_type
road_result['demandunits'] = input_demand_units
road_result['hazardtype'] = hazard_type
road_result['hazardval'] = hazard_val
# if there is liquefaction, overwrite the hazardval with liquefaction value
# recalculate dmg_probability and dmg_interval
if len(liquefaction) > 0:
if input_demand_type in liquefaction[i]:
liquefaction_val = liquefaction[i][
input_demand_type]
elif input_demand_type.lower() in liquefaction[i]:
liquefaction_val = liquefaction[i][
input_demand_type.lower()]
elif input_demand_type.upper() in liquefaction[i]:
liquefaction_val = liquefaction[i][
input_demand_type.upper]
else:
liquefaction_val = 0.0
dmg_probability = selected_fragility_set.calculate_limit_state(
liquefaction_val, std_dev=std_dev)
dmg_interval = AnalysisUtil.calculate_damage_interval(
dmg_probability)
road_result['hazardval'] = liquefaction_val
road_result.update(dmg_probability)
road_result.update(dmg_interval)
road_results.append(road_result)
processed_roads.append(road_id)
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 road_id, rd in roads.items():
if road_id not in processed_roads:
unmapped_rd_result = collections.OrderedDict()
unmapped_rd_result['guid'] = rd['properties']['guid']
unmapped_rd_result.update(unmapped_dmg_probability)
unmapped_rd_result.update(unmapped_dmg_intervals)
unmapped_rd_result['demandtype'] = "None"
unmapped_rd_result['demandunits'] = "None"
unmapped_rd_result['hazardtype'] = "None"
unmapped_rd_result['hazardval'] = 0.0
road_results.append(unmapped_rd_result)
return road_results