Merge branch 'develop' into fix-requirementes

CHANGELOG.md

@@ -7,8 +7,38 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 
 ## [Unreleased]
 
+### Fixed
+- Refactoring tornadoepndamage for hazardDatasets [#495](https://github.com/IN-CORE/pyincore/issues/495)
+
+## [1.16.0] - 2024-02-07
+
+### Added
+- Create GUID field in geopackage file [#478](https://github.com/IN-CORE/pyincore/issues/478)
+
+### Changed
+- Tornado and Earthquake model [#474](https://github.com/IN-CORE/pyincore/issues/474)
+- Disable methods interact with services if in offline mode [#458](https://github.com/IN-CORE/pyincore/issues/458)
+
+### Fixed
+- Fix semantics search pytest by switching to an existing search term 
+
+
+## [1.15.1] - 2023-12-20 
+
+### Fixed
+- Fix NCI Functionality [#463](https://github.com/IN-CORE/pyincore/issues/463)
+
+
+## [1.15.0] - 2023-12-13
+
+### Added
+- Add hazard models to documentation [#448](https://github.com/IN-CORE/pyincore/issues/448)
+
 ### Changed
 - Upgrade python version from 3.6 to 3.9 [#447](https://github.com/IN-CORE/pyincore/issues/447)
+- Enable offline mode for pyincore [#455](https://github.com/IN-CORE/pyincore/issues/455)
+- Update MCS analysis to output only required columns for `failure_probability` [#401](https://github.com/IN-CORE/pyincore/issues/401)
+- Update CommercialBuildingRecovery to input damage results as a required dataset [#460](https://github.com/IN-CORE/pyincore/issues/460)
 
 
 ## [1.14.0] - 2023-11-08

docs/source/conf.py

@@ -33,9 +33,9 @@
 author = ''
 
 # The short X.Y version
-version = '1.14'
+version = '1.16'
 # The full version, including alpha/beta/rc tags
-release = '1.14.0'
+release = '1.16.0'
 
 # -- General configuration ---------------------------------------------------
 

docs/source/modules.rst

@@ -346,6 +346,51 @@ analyses/wfnfunctionality
 models
 ^^^^^^
 
+models/hazard/earthquake
+===================
+..  autoclass:: models.earthquake.Earthquake
+    :members:
+
+models/hazard/flood
+===================
+..  autoclass:: models.flood.Flood
+    :members:
+
+models/hazard/hazard
+===================
+..  autoclass:: models.hazard.Hazard
+    :members:
+
+models/hazard/hazarddataset
+===========================
+..  autoclass:: models.hazarddataset.HazardDataset
+    :members:
+..  autoclass:: models.hazarddataset.HurricaneDataset
+    :members:
+..  autoclass:: models.hazarddataset.EarthquakeDataset
+    :members:
+..  autoclass:: models.hazarddataset.TsunamiDataset
+    :members:
+..  autoclass:: models.hazarddataset.TornadoDataset
+    :members:
+..  autoclass:: models.hazarddataset.FloodDataset
+    :members:
+
+models/hazard/hurricane
+=======================
+..  autoclass:: models.hurricane.Hurricane
+    :members:
+
+models/hazard/tornado
+=====================
+..  autoclass:: models.tornado.Tornado
+    :members:
+
+models/hazard/tsunami
+=====================
+..  autoclass:: models.tsunami.Tsunami
+    :members:
+
 models/dfr3curve.py
 ===================
 ..  autoclass:: models.dfr3curve.DFR3Curve
@@ -366,6 +411,11 @@ models/mappingset
 ..  autoclass:: models.mappingset.MappingSet
     :members:
 
+models/networkdataset
+=================
+..  autoclass:: models.networkdataset.NetworkDataset
+    :members:
+
 models/repaircurveset
 =====================
 ..  autoclass:: models.repaircurveset.RepairCurveSet
@@ -376,6 +426,11 @@ models/restorationcurveset
 ..  autoclass:: models.restorationcurveset.RestorationCurveSet
     :members:
 
+models/units
+==========================
+..  autoclass:: models.units.Units
+    :members:
+
 utilities
 ^^^^^^^^^
 

environment.yml

@@ -21,3 +21,4 @@ dependencies:
   - rtree>=1.1.0
   - scipy>=1.11.3
   - shapely>=2.0.2
+  - openssl<=3.2.0

pyincore/analyses/commercialbuildingrecovery/commercialbuildingrecovery.py

@@ -67,19 +67,20 @@ def run(self):
         sample_damage_states = self.get_input_dataset("sample_damage_states").get_dataframe_from_csv(low_memory=False)
         mcs_failure = self.get_input_dataset("mcs_failure").get_dataframe_from_csv(low_memory=False)
         redi_delay_factors = self.get_input_dataset("delay_factors").get_dataframe_from_csv(low_memory=False)
+        building_dmg = self.get_input_dataset("building_dmg").get_dataframe_from_csv(low_memory=False)
 
         # Returns dataframe
         total_delay, recovery, time_stepping_recovery = self.commercial_recovery(buildings, sample_damage_states,
                                                                                  mcs_failure, redi_delay_factors,
-                                                                                 num_samples)
+                                                                                 building_dmg, num_samples)
         self.set_result_csv_data("total_delay", total_delay, result_name + "_delay", "dataframe")
         self.set_result_csv_data("recovery", recovery, result_name + "_recovery", "dataframe")
         self.set_result_csv_data("time_stepping_recovery", time_stepping_recovery,
                                  result_name + "_time_stepping_recovery", "dataframe")
 
         return True
 
-    def commercial_recovery(self, buildings, sample_damage_states, mcs_failure, redi_delay_factors, num_samples):
+    def commercial_recovery(self, buildings, sample_damage_states, mcs_failure, redi_delay_factors, building_dmg, num_samples):
         """
         Calculates commercial building recovery for buildings
 
@@ -88,6 +89,7 @@ def commercial_recovery(self, buildings, sample_damage_states, mcs_failure, redi
             sample_damage_states (pd.DataFrame): Sample damage states
             redi_delay_factors (pd.DataFrame): Delay factors based on REDi framework
             mcs_failure (pd.DataFrame): Building inventory failure probabilities
+            building_dmg (pd.DataFrame): Building damage states
             num_samples (int): number of sample scenarios to use
 
         Returns:
@@ -97,7 +99,7 @@ def commercial_recovery(self, buildings, sample_damage_states, mcs_failure, redi
 
         start_total_delay = time.process_time()
         total_delay = CommercialBuildingRecovery.total_delay(buildings, sample_damage_states, mcs_failure,
-                                                             redi_delay_factors, num_samples)
+                                                             redi_delay_factors, building_dmg, num_samples)
         end_total_delay = time.process_time()
         print("Finished executing total_delay() in " + str(end_total_delay - start_total_delay) + " secs")
 
@@ -116,7 +118,7 @@ def commercial_recovery(self, buildings, sample_damage_states, mcs_failure, redi
         return total_delay, recovery, time_stepping_recovery
 
     @staticmethod
-    def total_delay(buildings, sample_damage_states, mcs_failure, redi_delay_factors, num_samples):
+    def total_delay(buildings, sample_damage_states, mcs_failure, redi_delay_factors, damage, num_samples):
         """ Calculates total delay by combining financial delay and other factors from REDi framework
 
         Args:
@@ -125,14 +127,15 @@ def total_delay(buildings, sample_damage_states, mcs_failure, redi_delay_factors
             mcs_failure (pd.DataFrame): Building inventory failure probabilities
             redi_delay_factors (pd.DataFrame): Delay impeding factors such as post-disaster inspection, insurance claim,
                 financing, and government permit based on building's damage state.
+            damage (pd.DataFrame): Damage states for building structural damage
             num_samples (int): number of sample scenarios to use
 
         Returns:
             pd.DataFrame: Total delay time of all impeding factors from REDi framework.
         """
 
         # Obtain the commercial buildings in damage
-        damage = mcs_failure[mcs_failure['haz_expose'] == 'yes']
+        damage = mcs_failure[damage['haz_expose'] == 'yes']
         commercial = []
         commercial_archetypes = [6, 7, 8, 15, 16, 18, 19]
         for i, b in enumerate(buildings):
@@ -398,6 +401,12 @@ def get_spec(self):
                     'description': 'Delay impeding factors such as post-disaster inspection, insurance claim, '
                                    'and government permit based on building\'s damage state. Provided by REDi framework',
                     'type': ['incore:buildingRecoveryFactors']
+                },
+                {
+                    'id': 'building_dmg',
+                    'required': True,
+                    'description': 'damage result that has damage intervals',
+                    'type': ['ergo:buildingDamageVer6']
                 }
             ],
             'output_datasets': [

pyincore/analyses/montecarlofailureprobability/montecarlofailureprobability.py

@@ -265,7 +265,7 @@ def monte_carlo_failure_probability(self, dmg, damage_interval_keys,
 
         # failure probability
         fp_result = collections.OrderedDict()
-        fp_result.update(dmg)
+        fp_result['guid'] = dmg['guid']
 
         ds_sample = self.sample_damage_interval(dmg, damage_interval_keys,
                                                 num_samples, seed)

pyincore/analyses/ncifunctionality/ncifunctionality.py

@@ -5,7 +5,6 @@
 # and is available at https://www.mozilla.org/en-US/MPL/2.0/
 from pyincore import BaseAnalysis, NetworkDataset
 from pyincore.utils.networkutil import NetworkUtil
-from numpy.linalg import inv
 from typing import List
 from scipy import stats
 import networkx as nx
@@ -75,12 +74,15 @@ def run(self):
         wds_dmg_results = self.get_input_dataset('wds_dmg_results').get_dataframe_from_csv()
         wds_inventory_rest_map = self.get_input_dataset('wds_inventory_rest_map').get_dataframe_from_csv()
 
+        # Load limit state probabilities and damage states for each electric power facility
+        epf_damage = self.get_input_dataset('epf_damage').get_dataframe_from_csv()
+
         epf_cascading_functionality = self.nci_functionality(discretized_days, epf_network_nodes, epf_network_links,
                                                              wds_network_nodes, wds_network_links,
                                                              epf_wds_intdp_table, wds_epf_intdp_table,
                                                              epf_subst_failure_results, epf_inventory_rest_map,
                                                              epf_time_results, wds_dmg_results,  wds_inventory_rest_map,
-                                                             wds_time_results)
+                                                             wds_time_results, epf_damage)
 
         result_name = self.get_parameter("result_name")
         self.set_result_csv_data("epf_cascading_functionality", epf_cascading_functionality, name=result_name,
@@ -91,7 +93,7 @@ def run(self):
     def nci_functionality(self, discretized_days, epf_network_nodes, epf_network_links, wds_network_nodes,
                           wds_network_links, epf_wds_intdp_table, wds_epf_intdp_table, epf_subst_failure_results,
                           epf_inventory_rest_map, epf_time_results, wds_dmg_results, wds_inventory_rest_map,
-                          wds_time_results):
+                          wds_time_results, epf_damage):
         """Compute EPF and WDS cascading functionality outcomes
 
         Args:
@@ -108,14 +110,15 @@ def nci_functionality(self, discretized_days, epf_network_nodes, epf_network_lin
             wds_dmg_results (pd.DataFrame): damage results for WDS network
             wds_inventory_rest_map (pd.DataFrame): inventory restoration map for WDS network
             wds_time_results (pd.DataFrame): time results for WDS network
+            epf_damage (pd.DataFrame): limit state probabilities and damage states for each guid
 
         Returns:
             (pd.DataFrame, pd.DataFrame): results for EPF and WDS networks
         """
 
         # Compute updated EPF and WDS node information
         efp_nodes_updated = self.update_epf_discretized_func(epf_network_nodes, epf_subst_failure_results,
-                                                             epf_inventory_rest_map, epf_time_results)
+                                                             epf_inventory_rest_map, epf_time_results, epf_damage)
 
         wds_nodes_updated = self.update_wds_discretized_func(wds_network_nodes, wds_dmg_results,
                                                              wds_inventory_rest_map, wds_time_results)
@@ -125,7 +128,6 @@ def nci_functionality(self, discretized_days, epf_network_nodes, epf_network_lin
 
         # Generate the functionality data
         df_functionality_nodes = pd.concat([efp_nodes_updated, wds_nodes_updated], ignore_index=True)
-        df_functionality_nodes = pd.concat([efp_nodes_updated, wds_nodes_updated], ignore_index=True)
 
         # Create each individual graph
         g_epf = NetworkUtil.create_network_graph_from_dataframes(epf_network_nodes, epf_network_links)
@@ -156,13 +158,15 @@ def nci_functionality(self, discretized_days, epf_network_nodes, epf_network_lin
 
     @staticmethod
     def update_epf_discretized_func(epf_nodes, epf_subst_failure_results, epf_inventory_restoration_map,
-                                    epf_time_results):
+                                    epf_time_results, epf_damage):
         epf_time_results = epf_time_results.loc[
             (epf_time_results['time'] == 1) | (epf_time_results['time'] == 3) | (epf_time_results['time'] == 7) | (
                         epf_time_results['time'] == 30) | (epf_time_results['time'] == 90)]
         epf_time_results.insert(2, 'PF_00', list(
             np.ones(len(epf_time_results))))  # PF_00, PF_0, PF_1, PF_2, PF_3  ---> DS_0, DS_1, DS_2, DS_3, DS_4
 
+        epf_subst_failure_results = pd.merge(epf_damage, epf_subst_failure_results, on='guid', how='outer')
+
         epf_nodes_updated = pd.merge(epf_nodes[['nodenwid', 'utilfcltyc', 'guid']], epf_subst_failure_results[
             ['guid', 'DS_0', 'DS_1', 'DS_2', 'DS_3', 'DS_4', 'failure_probability']], on='guid', how='outer')
 
@@ -387,6 +391,12 @@ def get_spec(self):
                     'required': True,
                     'description': 'A csv file recording repair time for WDS per class and limit state',
                     'type': ['incore:waterFacilityRestorationTime']
+                },
+                {
+                    'id': 'epf_damage',
+                    'required': True,
+                    'description': 'A csv file with limit state probabilities and damage states for each electric power facility',
+                    'type': ['incore:epfDamageVer3']
                 }
             ],
             'output_datasets': [

pyincore/analyses/tornadoepndamage/tornadoepndamage.py

@@ -97,7 +97,7 @@ def run(self):
             tornado_id = tornado.id
 
         tornado_metadata = self.hazardsvc.get_tornado_hazard_metadata(tornado_id)
-        self.load_remote_input_dataset("tornado", tornado_metadata["datasetId"])
+        self.load_remote_input_dataset("tornado", tornado_metadata["hazardDatasets"][0].get("datasetId"))
         tornado_dataset = self.get_input_dataset("tornado").get_inventory_reader()
         ds_results, damage_results = self.get_damage(network_dataset, tornado_dataset, tornado_id)