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 WaterFacilityDamage(BaseAnalysis): """Computes water facility damage for an earthquake tsunami, tornado, or hurricane exposure. """ DEFAULT_EQ_FRAGILITY_KEY = "pga" DEFAULT_TSU_FRAGILITY_KEY = "Non-Retrofit inundationDepth Fragility ID Code" DEFAULT_LIQ_FRAGILITY_KEY = "pgd" def __init__(self, incore_client): # Create Hazard and Fragility service self.hazardsvc = HazardService(incore_client) self.fragilitysvc = FragilityService(incore_client) super(WaterFacilityDamage, self).__init__(incore_client) def get_spec(self): return { 'name': 'water-facility-damage', 'description': 'water facility damage analysis', 'input_parameters': [ { 'id': 'result_name', 'required': False, '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_geology_dataset_id', 'required': False, 'description': 'Liquefaction geology/susceptibility dataset id. ' 'If not provided, liquefaction will be ignored', 'type': str }, { 'id': 'liquefaction_fragility_key', 'required': False, 'description': 'Fragility key to use in liquefaction mapping dataset', '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': 'water_facilities', 'required': True, 'description': 'Water Facility Inventory', 'type': ['ergo:waterFacilityTopo'], }, { 'id': 'dfr3_mapping_set', 'required': True, 'description': 'DFR3 Mapping Set Object', 'type': ['incore:dfr3MappingSet'], } ], 'output_datasets': [ { 'id': 'result', 'parent_type': 'water_facilities', 'description': 'A csv file with limit state probabilities and damage states ' 'for each water facility', 'type': 'ergo:waterFacilityDamageVer4' } ] } def run(self): """Performs Water facility damage analysis by using the parameters from the spec and creates an output dataset in csv format Returns: bool: True if successful, False otherwise """ # Facility dataset inventory_set = self.get_input_dataset( "water_facilities").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") 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( inventory_set), user_defined_cpu) avg_bulk_input_size = int(len(inventory_set) / num_workers) inventory_args = [] count = 0 inventory_list = list(inventory_set) while count < len(inventory_list): inventory_args.append( inventory_list[count:count + avg_bulk_input_size]) count += avg_bulk_input_size results = self.waterfacility_damage_concurrent_execution( self.waterfacilityset_damage_analysis, 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 waterfacility_damage_concurrent_execution(self, function_name, parallel_processes, *args): """Utilizes concurrent.future module. Args: function_name (function): The function to be parallelized. parallel_processes (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 damage results and other data/metadata. """ output = [] with concurrent.futures.ProcessPoolExecutor( max_workers=parallel_processes) as executor: for ret in executor.map(function_name, *args): output.extend(ret) return output def waterfacilityset_damage_analysis(self, facilities, hazard_type, hazard_dataset_id): """Gets applicable fragilities and calculates damage Args: facilities (list): Multiple water facilities from input inventory set. hazard_type (str): A hazard type of the hazard exposure (earthquake, tsunami, tornado, or hurricane). hazard_dataset_id (str): An id of the hazard exposure. Returns: list: A list of ordered dictionaries with water facility damage values and metadata. """ result = [] liq_fragility = None use_liquefaction = self.get_parameter("use_liquefaction") liq_geology_dataset_id = self.get_parameter( "liquefaction_geology_dataset_id") uncertainty = self.get_parameter("use_hazard_uncertainty") fragility_key = self.get_parameter("fragility_key") if hazard_type == 'earthquake': if fragility_key is None: fragility_key = self.DEFAULT_EQ_FRAGILITY_KEY pga_fragility_set = self.fragilitysvc.match_inventory(self.get_input_dataset("dfr3_mapping_set"), facilities, fragility_key) liq_fragility_set = [] if 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"), facilities, liq_fragility_key) for facility in facilities: fragility = pga_fragility_set[facility["id"]] if facility["id"] in liq_fragility_set: liq_fragility = liq_fragility_set[facility["id"]] result.append( self.waterfacility_damage_analysis(facility, fragility, liq_fragility, hazard_type, hazard_dataset_id, liq_geology_dataset_id, uncertainty)) elif hazard_type == 'tsunami': if fragility_key is None: fragility_key = self.DEFAULT_TSU_FRAGILITY_KEY inundation_fragility_set = self.fragilitysvc.match_inventory( self.get_input_dataset("dfr3_mapping_set"), facilities, fragility_key) for facility in facilities: fragility = inundation_fragility_set[facility["id"]] result.append( self.waterfacility_damage_analysis(facility, fragility, [], hazard_type, hazard_dataset_id, "", False)) else: raise ValueError( "Hazard type other than Earthquake and Tsunami are not currently supported.") return result def waterfacility_damage_analysis(self, facility, fragility, liq_fragility, hazard_type, hazard_dataset_id, liq_geology_dataset_id, uncertainty): """Computes damage analysis for a single facility Args: facility (obj): A JSON mapping of a facility based on mapping attributes fragility (obj): A JSON description of fragility mapped to the building. liq_fragility (obj): A JSON description of liquefaction fragility mapped to the building. hazard_type (str): A string that indicates the hazard type hazard_dataset_id (str): Hazard id from the hazard service liq_geology_dataset_id (str): Geology dataset id from data service to use for liquefaction calculation, if applicable uncertainty (bool): Whether to use hazard standard deviation values for uncertainty Returns: OrderedDict: A dictionary with water facility damage values and other data/metadata. """ std_dev = 0 if uncertainty: std_dev = random.random() hazard_demand_type = fragility.demand_type demand_units = fragility.demand_units liq_hazard_type = "" liq_hazard_val = 0.0 liquefaction_prob = 0.0 location = GeoUtil.get_location(facility) point = str(location.y) + "," + str(location.x) if hazard_type == "earthquake": hazard_val_set = self.hazardsvc.get_earthquake_hazard_values( hazard_dataset_id, hazard_demand_type, demand_units, [point]) elif hazard_type == "tsunami": hazard_val_set = self.hazardsvc.get_tsunami_hazard_values( hazard_dataset_id, hazard_demand_type, demand_units, [point]) else: raise ValueError( "Hazard type other than Earthquake and Tsunami are not currently supported.") hazard_val = hazard_val_set[0]['hazardValue'] if hazard_val < 0: hazard_val = 0 limit_states = fragility.calculate_limit_state(hazard_val, std_dev) if liq_fragility is not None and liq_geology_dataset_id: liq_hazard_type = liq_fragility.demand_type pgd_demand_units = liq_fragility.demand_units point = str(location.y) + "," + str(location.x) liquefaction = self.hazardsvc.get_liquefaction_values( hazard_dataset_id, liq_geology_dataset_id, pgd_demand_units, [point]) liq_hazard_val = liquefaction[0][liq_hazard_type] liquefaction_prob = liquefaction[0]['liqProbability'] pgd_limit_states = liq_fragility.calculate_limit_state(liq_hazard_val, std_dev) limit_states = AnalysisUtil.adjust_limit_states_for_pgd( limit_states, pgd_limit_states) dmg_intervals = AnalysisUtil.calculate_damage_interval(limit_states) result = collections.OrderedDict() result = {**limit_states, **dmg_intervals} # Needs py 3.5+ metadata = collections.OrderedDict() metadata['guid'] = facility['properties']['guid'] metadata['hazardtype'] = hazard_type metadata['demandtype'] = hazard_demand_type metadata['hazardval'] = hazard_val metadata['liqhaztype'] = liq_hazard_type metadata['liqhazval'] = liq_hazard_val metadata['liqprobability'] = liquefaction_prob result = {**metadata, **result} return result
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' }] }
class NonStructBuildingDamage(BaseAnalysis): """Computes non-structural structural building damage for an earthquake hazard. Args: incore_client (IncoreClient): Service authentication. """ def __init__(self, incore_client): self.hazardsvc = HazardService(incore_client) self.fragilitysvc = FragilityService(incore_client) super(NonStructBuildingDamage, self).__init__(incore_client) def run(self): """Executes building damage analysis.""" # Building dataset building_set = self.get_input_dataset("buildings").get_inventory_reader() # set Default Fragility key fragility_key_as = self.get_parameter("fragility_key_as") if fragility_key_as is None: self.set_parameter("fragility_key_as", NonStructBuildingUtil.DEFAULT_FRAGILITY_KEY_AS) fragility_key_ds = self.get_parameter("fragility_key_ds") if fragility_key_ds is None: self.set_parameter("fragility_key_ds", NonStructBuildingUtil.DEFAULT_FRAGILITY_KEY_DS) # Set Default Hazard Uncertainty use_hazard_uncertainty = self.get_parameter("use_hazard_uncertainty") if use_hazard_uncertainty is None: self.set_parameter("use_hazard_uncertainty", False) # Set Default Liquefaction use_liquefaction = self.get_parameter("use_liquefaction") if use_liquefaction is None: self.set_parameter("use_liquefaction", False) results = [] 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(building_set), user_defined_cpu) avg_bulk_input_size = int(len(building_set) / num_workers) inventory_args = [] count = 0 inventory_list = list(building_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) self.set_result_csv_data("result", results, name=self.get_parameter("result_name")) return True def building_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 building_damage_analysis_bulk_input(self, buildings): """Run analysis for multiple buildings. Args: buildings (list): Multiple buildings from input inventory set. Returns: list: A list of ordered dictionaries with building damage values and other data/metadata. """ result = [] fragility_sets_as = self.fragilitysvc.match_inventory(self.get_input_dataset("dfr3_mapping_set"), buildings, self.get_parameter("fragility_key_as")) fragility_sets_ds = self.fragilitysvc.match_inventory(self.get_input_dataset("dfr3_mapping_set"), buildings, self.get_parameter("fragility_key_ds")) for building in buildings: fragility_set_as = None fragility_set_ds = None if building["id"] in fragility_sets_as \ and building["id"] in fragility_sets_ds: fragility_set_as = fragility_sets_as[building["id"]] fragility_set_ds = fragility_sets_ds[building["id"]] result.append(self.building_damage_analysis(building, fragility_set_as, fragility_set_ds)) return result def building_damage_analysis(self, building, fragility_set_as, fragility_set_ds): """Calculates bridge damage results for a single building. Args: building (obj): A JSON-mapping of a geometric object from the inventory: current building. fragility_set_as (obj): A JSON description of acceleration-sensitive (AS) fragility assigned to the building. fragility_set_ds (obj): A JSON description of drift-sensitive (DS) fragility assigned to the building. Returns: OrderedDict: A dictionary with building damage values and other data/metadata. """ building_results = collections.OrderedDict() dmg_probability_as = collections.OrderedDict() dmg_probability_ds = collections.OrderedDict() hazard_demand_type_as = None hazard_demand_type_ds = None hazard_val_as = 0.0 hazard_val_ds = 0.0 # read static parameters from object self hazard_dataset_id = self.get_parameter("hazard_id") liq_geology_dataset_id = self.get_parameter("liq_geology_dataset_id") use_liquefaction = self.get_parameter("use_liquefaction") use_hazard_uncertainty = self.get_parameter("use_hazard_uncertainty") # Acceleration-Sensitive Fragility ID Code if fragility_set_as is not None: hazard_demand_type_as = AnalysisUtil.get_hazard_demand_type(building, fragility_set_as, 'earthquake') demand_units_as = fragility_set_as.demand_units location = GeoUtil.get_location(building) point = str(location.y) + "," + str(location.x) hazard_val_as = self.hazardsvc.get_earthquake_hazard_values( hazard_dataset_id, hazard_demand_type_as, demand_units_as, points=[point])[0]['hazardValue'] dmg_probability_as = fragility_set_as.calculate_limit_state(hazard_val_as) # adjust dmg probability for liquefaction if use_liquefaction: if liq_geology_dataset_id is not None: liqufaction_dmg = self.hazardsvc.get_liquefaction_values( hazard_dataset_id, liq_geology_dataset_id, 'in', points=[point])[0][ 'groundFailureProb'] else: raise ValueError('Hazard does not support liquefaction! \ Check to make sure you defined the liquefaction\ portion of your scenario earthquake.') dmg_probability_as = NonStructBuildingUtil.adjust_damage_for_liquefaction(dmg_probability_as, liqufaction_dmg) # TODO this value needs to come from the hazard service # adjust dmg probability for hazard uncertainty if use_hazard_uncertainty: raise ValueError('Uncertainty has not yet been implemented!') else: dmg_probability_as['immocc'] = 0.0 dmg_probability_as['lifesfty'] = 0.0 dmg_probability_as['collprev'] = 0.0 dmg_interval_as = AnalysisUtil.calculate_damage_interval(dmg_probability_as) # Drift-Sensitive Fragility ID Code if fragility_set_ds is not None: hazard_demand_type_ds = AnalysisUtil.get_hazard_demand_type(building, fragility_set_ds, 'earthquake') demand_units_ds = fragility_set_ds.demand_units location = GeoUtil.get_location(building) point = str(location.y) + "," + str(location.x) hazard_val_ds = self.hazardsvc.get_earthquake_hazard_values( hazard_dataset_id, hazard_demand_type_ds, demand_units_ds, points=[point])[0]['hazardValue'] dmg_probability_ds = fragility_set_ds.calculate_limit_state(hazard_val_ds) # adjust hazard value for liquefaction if use_liquefaction: if liq_geology_dataset_id is not None: liqufaction_dmg = self.hazardsvc.get_liquefaction_values( hazard_dataset_id, liq_geology_dataset_id, 'in', points=[point])[0][ 'groundFailureProb'] else: raise ValueError('Hazard does not support liquefaction! \ Check to make sure you defined the liquefaction\ portion of your scenario earthquake.') dmg_probability_ds = NonStructBuildingUtil.adjust_damage_for_liquefaction(dmg_probability_ds, liqufaction_dmg) # TODO this value needs to come from the hazard service # adjust dmg probability for hazard uncertainty if use_hazard_uncertainty: raise ValueError('Uncertainty has not yet been implemented!') else: dmg_probability_ds['immocc'] = 0.0 dmg_probability_ds['lifesfty'] = 0.0 dmg_probability_ds['collprev'] = 0.0 dmg_interval_ds = AnalysisUtil.calculate_damage_interval(dmg_probability_ds) # put results in dictionary building_results['guid'] = building['properties']['guid'] building_results['immocc_as'] = dmg_probability_as['immocc'] building_results['lifsfty_as'] = dmg_probability_as['lifesfty'] building_results['collpre_as'] = dmg_probability_as['collprev'] building_results['insig_as'] = dmg_interval_as['insignific'] building_results['mod_as'] = dmg_interval_as['moderate'] building_results['heavy_as'] = dmg_interval_as['heavy'] building_results['comp_as'] = dmg_interval_as['complete'] building_results['immocc_ds'] = dmg_probability_ds['immocc'] building_results['lifsfty_ds'] = dmg_probability_ds['lifesfty'] building_results['collpre_ds'] = dmg_probability_ds['collprev'] building_results['insig_ds'] = dmg_interval_ds['insignific'] building_results['mod_ds'] = dmg_interval_ds['moderate'] building_results['heavy_ds'] = dmg_interval_ds['heavy'] building_results['comp_ds'] = dmg_interval_ds['complete'] building_results["hzrdtyp_as"] = hazard_demand_type_as building_results["hzrdval_as"] = hazard_val_as building_results["hzrdtyp_ds"] = hazard_demand_type_ds building_results["hzrdval_ds"] = hazard_val_ds return building_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_as', 'required': False, 'description': 'AS Fragility key to use in mapping dataset', 'type': str }, { 'id': 'fragility_key_ds', 'required': False, 'description': 'DS Fragility key to use in mapping dataset', 'type': str }, { 'id': 'use_liquefaction', 'required': False, 'description': 'Use liquefaction', 'type': bool }, { 'id': 'liq_geology_dataset_id', 'required': False, 'description': 'liquefaction geology dataset id, \ if use liquefaction, you have to provide this id', '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': 'buildings', 'required': True, 'description': 'building Inventory', 'type': ['ergo:buildingInventoryVer4'], }, { '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 non-structural damage', 'type': 'ergo:nsBuildingInventoryDamage' } ] }
class RoadDamage(BaseAnalysis): """Road Damage Analysis calculates the probability of road damage based on an earthquake or tsunami hazard. Args: incore_client (IncoreClient): Service authentication. """ DEFAULT_FRAGILITY_KEY = "Non-Retrofit Fragility ID Code" def __init__(self, incore_client): self.hazardsvc = HazardService(incore_client) self.fragilitysvc = FragilityService(incore_client) super(RoadDamage, self).__init__(incore_client) def run(self): """Executes road damage analysis.""" # Road dataset road_set = self.get_input_dataset("roads").get_inventory_reader() # Get Fragility key fragility_key = self.get_parameter("fragility_key") if fragility_key is None: fragility_key = self.DEFAULT_FRAGILITY_KEY # Get hazard input hazard_dataset_id = self.get_parameter("hazard_id") # Get hazard type hazard_type = self.get_parameter("hazard_type") # Liquefaction use_liquefaction = False if self.get_parameter("use_liquefaction") is not None: use_liquefaction = self.get_parameter("use_liquefaction") # Get geology dataset for liquefaction geology_dataset_id = None if self.get_parameter("liquefaction_geology_dataset_id") is not None: geology_dataset_id = self.get_parameter( "liquefaction_geology_dataset_id") # 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") user_defined_cpu = 1 if self.get_parameter( "num_cpu") is not None and self.get_parameter("num_cpu") > 0: user_defined_cpu = self.get_parameter("num_cpu") num_workers = AnalysisUtil.determine_parallelism_locally( self, len(road_set), user_defined_cpu) avg_bulk_input_size = int(len(road_set) / num_workers) inventory_args = [] count = 0 inventory_list = list(road_set) while count < len(inventory_list): inventory_args.append(inventory_list[count:count + avg_bulk_input_size]) count += avg_bulk_input_size results = self.road_damage_concurrent_future( self.road_damage_analysis_bulk_input, num_workers, inventory_args, repeat(hazard_type), repeat(hazard_dataset_id), repeat(use_hazard_uncertainty), repeat(geology_dataset_id), repeat(fragility_key), repeat(use_liquefaction)) self.set_result_csv_data("result", results, name=self.get_parameter("result_name")) return True def road_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 road 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 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 def get_spec(self): """Get specifications of the road damage analysis. Returns: obj: A JSON object of specifications of the road damage analysis. """ return { 'name': 'road-damage', 'description': 'road 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_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': 'roads', 'required': True, 'description': 'Road Inventory', 'type': ['ergo:roadLinkTopo', 'incore:roads'] }, { 'id': 'dfr3_mapping_set', 'required': True, 'description': 'DFR3 Mapping Set Object', 'type': ['incore:dfr3MappingSet'], }], 'output_datasets': [{ 'id': 'result', 'parent_type': 'roads', 'description': 'CSV file of road structural damage', 'type': 'ergo:roadDamage' }] }