Merge pull request #430 from IN-CORE/release-1.14.0

Release 1.14.0

.github/workflows/conda.yaml

@@ -53,7 +53,7 @@ jobs:
         uses: mamba-org/setup-micromamba@v1
         with:
           create-args: >-
-            python=3.8
+            python=3.9
             conda
           environment-file: environment.yml
           generate-run-shell: true

.github/workflows/pypi.yaml

@@ -36,10 +36,10 @@ jobs:
             echo "TOKEN=${{ secrets.TEST_PYPI_API_TOKEN }}" >> $GITHUB_ENV
           fi
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.9
 
       - name: Install dependencies
         run: python -m pip install --upgrade pip setuptools wheel

.github/workflows/pytests.yml

@@ -14,8 +14,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        vars: [ {python-version: '3.8', sleep: '0s'}, {python-version: '3.9', sleep: '60s'},
-                {python-version: '3.10', sleep: '120s'}, {python-version: '3.11', sleep: '180s'} ]
+        vars: [ {python-version: '3.9', sleep: '0s'}, {python-version: '3.10', sleep: '60s'},
+                {python-version: '3.11', sleep: '120s'}, {python-version: '3.12', sleep: '180s'} ]
     name: Python ${{ matrix.vars.python-version }} Test
     steps:
       - name: Checkout source code

CHANGELOG.md

@@ -5,6 +5,23 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
+
+## [1.14.0] - 2023-11-02
+
+### Changed
+- Properly set the output dataset in Building Portfolio Recovery Analysis [#423](https://github.com/IN-CORE/pyincore/issues/423)
+- Dependency clean up [#431](https://github.com/IN-CORE/pyincore/issues/431)
+
+### Added
+- Add support for hazard object input from local and remote for building damage analysis [#427](https://github.com/IN-CORE/pyincore/issues/427)
+
+### Fixed
+- CGE warning that using series is deprecated and will raise a type error [#357](https://github.com/IN-CORE/pyincore/issues/357)
+- Pytest fix in workflow [#425](https://github.com/IN-CORE/pyincore/issues/425)
+- Mapping rule to match local repair curve [#438](https://github.com/IN-CORE/pyincore/issues/438)
+- Local tornado x and y axis reversed [#439](https://github.com/IN-CORE/pyincore/issues/439)
+
+
 ## [1.13.0] - 2023-10-11
 
 ### Changed
@@ -15,7 +32,6 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 - Capability to support for local hazard [#404](https://github.com/IN-CORE/pyincore/issues/404)
 - Add support for local hazard with backward compatibility to analyses [#415](https://github.com/IN-CORE/pyincore/issues/415)
 
-
 ### Fixed
 - Aggregate hazard exposure column for non-structural building damage analysis to avoid column name cutoff and chaining issue with mean damage [#393](https://github.com/IN-CORE/pyincore/issues/393)
 

docs/source/conf.py

@@ -33,9 +33,9 @@
 author = ''
 
 # The short X.Y version
-version = '1.13'
+version = '1.14'
 # The full version, including alpha/beta/rc tags
-release = '1.13.0'
+release = '1.14.0'
 
 # -- General configuration ---------------------------------------------------
 

environment.yml

@@ -4,19 +4,19 @@ channels:
   - defaults
 dependencies:
   - ipopt>=3.11
-  - fiona>=1.8.4
-  - geopandas>=0.6.1
-  - matplotlib>=2.1.0
-  - networkx>=2.2
-  - numpy>=1.16.6,<2.0a0
-  - pandas>=1.1.0
+  - fiona>=1.9.5
+  - geopandas>=0.14.0
+  - matplotlib>=3.8.0
+  - networkx>=3.2.1
+  - numpy>=1.26.0,<2.0a0
+  - pandas>=2.1.2
   - pycodestyle>=2.6.0
-  - pyomo>=5.6
-  - pyproj>=1.9.6
+  - pyomo>=6.6.2
+  - pyproj>=3.6.1
   - pytest>=3.9.0
   - python-jose>=3.0
-  - rasterio>=1.0.18
-  - requests>=2.20.0
-  - rtree>=0.8.3
-  - scipy>=1.2.0
-  - shapely>=1.6.4.post1
+  - rasterio>=1.3.9
+  - requests>=2.31.0
+  - rtree>=1.1.0
+  - scipy>=1.11.3
+  - shapely>=2.0.2

pyincore/analyses/buildingdamage/buildingdamage.py

@@ -41,12 +41,24 @@ def run(self):
         else:
             retrofit_strategy = None
 
-        # Accomodating to multihazard
-        # Get hazard input
-        hazard_dataset_ids = self.get_parameter("hazard_id").split("+")
-
-        # Hazard type of the exposure
-        hazard_types = self.get_parameter("hazard_type").split("+")
+        # Accommodating to multi-hazard
+        hazards = []  # hazard objects
+        hazard_object = self.get_input_hazard("hazard")
+
+        # To use local hazard
+        if hazard_object is not None:
+            # Right now only supports single hazard for local hazard object
+            hazard_types = [hazard_object.hazard_type]
+            hazard_dataset_ids = [hazard_object.id]
+            hazards = [hazard_object]
+        # To use remote hazard
+        elif self.get_parameter("hazard_id") is not None and self.get_parameter("hazard_type") is not None:
+            hazard_dataset_ids = self.get_parameter("hazard_id").split("+")
+            hazard_types = self.get_parameter("hazard_type").split("+")
+            for hazard_type, hazard_dataset_id in zip(hazard_types, hazard_dataset_ids):
+                hazards.append(BaseAnalysis._create_hazard_object(hazard_type, hazard_dataset_id, self.hazardsvc))
+        else:
+            raise ValueError("Either hazard object or hazard id + hazard type must be provided")
 
         # Get Fragility key
         fragility_key = self.get_parameter("fragility_key")
@@ -74,6 +86,7 @@ def run(self):
                                                                               num_workers,
                                                                               inventory_args,
                                                                               repeat(retrofit_strategy),
+                                                                              repeat(hazards),
                                                                               repeat(hazard_types),
                                                                               repeat(hazard_dataset_ids))
 
@@ -105,12 +118,14 @@ def building_damage_concurrent_future(self, function_name, parallelism, *args):
 
         return output_ds, output_dmg
 
-    def building_damage_analysis_bulk_input(self, buildings, retrofit_strategy, hazard_types, hazard_dataset_ids):
+    def building_damage_analysis_bulk_input(self, buildings, retrofit_strategy, hazards, hazard_types,
+                                            hazard_dataset_ids):
         """Run analysis for multiple buildings.
 
         Args:
             buildings (list): Multiple buildings from input inventory set.
             retrofit_strategy (list): building guid and its retrofit level 0, 1, 2, etc. This is Optional
+            hazards (list): List of hazard objects.
             hazard_types (list): List of Hazard type, either earthquake, tornado, or tsunami.
             hazard_dataset_ids (list): List of id of the hazard exposure.
 
@@ -130,7 +145,7 @@ def building_damage_analysis_bulk_input(self, buildings, retrofit_strategy, haza
         multihazard_vals = {}
         adjust_demand_types_mapping = {}
 
-        for hazard_type, hazard_dataset_id in zip(hazard_types, hazard_dataset_ids):
+        for hazard, hazard_type, hazard_dataset_id in zip(hazards, hazard_types, hazard_dataset_ids):
             # get allowed demand types for the hazard type
             allowed_demand_types = [item["demand_type"].lower() for item in self.hazardsvc.get_allowed_demands(
                 hazard_type)]
@@ -172,19 +187,7 @@ def building_damage_analysis_bulk_input(self, buildings, retrofit_strategy, haza
                 else:
                     unmapped_buildings.append(b)
 
-            if hazard_type == 'earthquake':
-                hazard_vals = self.hazardsvc.post_earthquake_hazard_values(hazard_dataset_id, values_payload)
-            elif hazard_type == 'tornado':
-                hazard_vals = self.hazardsvc.post_tornado_hazard_values(hazard_dataset_id, values_payload,
-                                                                        self.get_parameter('seed'))
-            elif hazard_type == 'tsunami':
-                hazard_vals = self.hazardsvc.post_tsunami_hazard_values(hazard_dataset_id, values_payload)
-            elif hazard_type == 'hurricane':
-                hazard_vals = self.hazardsvc.post_hurricane_hazard_values(hazard_dataset_id, values_payload)
-            elif hazard_type == 'flood':
-                hazard_vals = self.hazardsvc.post_flood_hazard_values(hazard_dataset_id, values_payload)
-            else:
-                raise ValueError("The provided hazard type is not supported yet by this analysis")
+            hazard_vals = hazard.read_hazard_values(values_payload, self.hazardsvc)
 
             # map demand type from payload to response
             # worst code I have ever written
@@ -261,7 +264,7 @@ def building_damage_analysis_bulk_input(self, buildings, retrofit_strategy, haza
                             AnalysisUtil.adjust_damage_for_liquefaction(dmg_probability, ground_failure_prob))
 
                     dmg_interval = selected_fragility_set.calculate_damage_interval(
-                        dmg_probability, hazard_type=self.get_parameter("hazard_type"), inventory_type="building")
+                        dmg_probability, hazard_type="+".join(hazard_types), inventory_type="building")
             else:
                 raise ValueError("One of the fragilities is in deprecated format. This should not happen. If you are "
                                  "seeing this please report the issue.")
@@ -325,13 +328,13 @@ def get_spec(self):
                 },
                 {
                     'id': 'hazard_type',
-                    'required': True,
+                    'required': False,
                     'description': 'Hazard Type (e.g. earthquake)',
                     'type': str
                 },
                 {
                     'id': 'hazard_id',
-                    'required': True,
+                    'required': False,
                     'description': 'Hazard ID',
                     'type': str
                 },
@@ -372,6 +375,14 @@ def get_spec(self):
                     'type': str,
                 }
             ],
+            'input_hazards': [
+                {
+                    'id': 'hazard',
+                    'required': False,
+                    'description': 'Hazard object',
+                    'type': ["earthquake", "tornado", "hurricane", "flood", "tsunami"]
+                },
+            ],
             'input_datasets': [
                 {
                     'id': 'buildings',

pyincore/analyses/buildingportfolio/recovery/BuildingPortfolioRecoveryAnalysis.py → pyincore/analyses/buildingportfolio/BuildingPortfolioRecoveryAnalysis.py

@@ -10,8 +10,8 @@
 import scipy.stats
 import concurrent.futures
 
-from pyincore.analyses.buildingportfolio.recovery.BuildingData import BuildingData
-from pyincore import BaseAnalysis
+from pyincore.analyses.buildingportfolio.BuildingData import BuildingData
+from pyincore import BaseAnalysis, Dataset
 
 
 class BuildingPortfolioRecoveryAnalysis(BaseAnalysis):
@@ -117,6 +117,7 @@ def get_spec(self):
                 {
                     'id': 'result',
                     'parent_type': 'buildingPortfolio',
+                    'description': 'Building portfolio recovery result.',
                     'type': 'incore:portfolioRecovery'
                 }
             ]
@@ -230,8 +231,7 @@ def run(self):
         # Trajectory for Best Line Functionality and Full Functionality
         mean_recovery_output = sum(recovery_fp) / sample_size
 
-        output_file = output_base_name + 'building-recovery' + '.csv'
-        with open(output_file, 'w+', newline='') as output_file:
+        with open(output_base_name + '_building-recovery.csv', 'w+', newline='') as output_file:
             spam_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
             spam_writer.writerow(['Building_ID', 'Building_Lon', 'Building_Lat']
                                  + list(range(1, time_steps + 1)))
@@ -343,9 +343,7 @@ def run(self):
                         upper_bound95[t] = 1
 
             # END: Additional Code for uncertainty Analysis
-
-            output_file = output_base_name + 'portfolio-recovery' + '.csv'
-            with open(output_file, 'w+', newline='') as output_file:
+            with open(output_base_name + '_portfolio-recovery.csv', 'w+', newline='') as output_file:
                 spam_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
                 spam_writer.writerow(['Week', 'Recovery_Percent_Func_Probability', '75P_Upper_Bound',
                                       '75P_Lower_Bound', '95P_Upper_Bound', '95P_Lower_Bound',
@@ -356,28 +354,15 @@ def run(self):
                                           lower_bound95[i], upper_bound95[i]] + list(mean_recovery[i]) +
                                          [pdf[i]])
         else:
-            output_file = output_base_name + 'portfolio-recovery' + '.csv'
-            with open(output_file, 'w+', newline='') as output_file:
+            with open(output_base_name + '_portfolio-recovery.csv', 'w+', newline='') as output_file:
                 spam_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
                 spam_writer.writerow(['Week', 'Recovery_Percent_Func_Probability', 'RecPercent_RE',
                                       'RecPercent_RU', 'RecPercent_RO', 'RecPercent_BF', 'RecPercent_FF'])
                 for i in range(time_steps):
                     spam_writer.writerow([i + 1, mean_recovery_output[i]] + list(mean_recovery[i]))
 
-        # Not needed as graphs are generated in jupyter notebook
-        # fig2 = plt.figure(2)
-        # plt.plot(range(time_steps), mean_recovery_output, color="black")
-        # if uncertainty:
-        #     plt.plot(range(time_steps), lower_bound75, color="red")
-        #     plt.plot(range(time_steps), upper_bound75, color="red")
-        #     plt.plot(range(time_steps), lower_bound95, color="blue")
-        #     plt.plot(range(time_steps), upper_bound95, color="blue")
-        # plt.xlabel('Expected recovery time (weeks)')
-        # plt.ylabel('Percentage of Buildings Recovered')
-        # plt.title('Building Portfolio Recovery Analysis with uncertainty')
-        #
-        # output_image2 = output_base_name + 'portfolio-recovery' + '.png'
-        # fig2.savefig(output_image2)
+        self.set_output_dataset("result", Dataset.from_file(output_base_name + '_portfolio-recovery.csv',
+                                                            data_type=self.output_datasets["result"]["spec"]["type"]))
 
         print("INFO: Finished executing Building Portfolio Recovery Analysis")
 

pyincore/analyses/buildingportfolio/__init__.py

@@ -0,0 +1,3 @@
+from pyincore.analyses.buildingportfolio.BuildingPortfolioRecoveryAnalysis import BuildingPortfolioRecoveryAnalysis
+from pyincore.analyses.buildingportfolio.BuildingDamage import BuildingDamage
+from pyincore.analyses.buildingportfolio.BuildingData import BuildingData

pyincore/analyses/commercialbuildingrecovery/commercialbuildingrecovery.py

@@ -224,7 +224,11 @@ def recovery_rate(self, buildings, sample_damage_states, total_delay):
 
         # This is sort of a workaround until we define Repair Curve models and abstract this out there
         for i, b in enumerate(buildings):
-            repair_sets_by_guid[b["properties"]['guid']] = repair_sets[str(i)]
+            # if building id has a matched repair curve set
+            if b['id'] in repair_sets.keys():
+                repair_sets_by_guid[b["properties"]['guid']] = repair_sets[b['id']]
+            else:
+                repair_sets_by_guid[b["properties"]['guid']] = None
 
         # Obtain the column names
         colnames = list(total_delay.columns)[1:]
@@ -267,10 +271,13 @@ def idx(x, y):
                 percent_func = np.random.random(num_samples)
                 # NOTE: Even though the kwarg name is "repair_time", it actually takes  percent of functionality. DFR3
                 # system currently doesn't have a way to represent the name correctly when calculating the inverse.
-                repair_time = mapped_repair.repair_curves[state].solve_curve_for_inverse(
-                    hazard_values={}, curve_parameters=mapped_repair.curve_parameters, **{"repair_time": percent_func}
-                ) / 7
-
+                if mapped_repair is not None:
+                    repair_time = mapped_repair.repair_curves[state].solve_curve_for_inverse(
+                        hazard_values={}, curve_parameters=mapped_repair.curve_parameters, **{"repair_time": percent_func}
+                    ) / 7
+                else:
+                    repair_time = np.full(num_samples, np.nan)
+
                 for j in range(0, num_samples):
                     samples_n1_n2[build, idx(i, j)] = round(samples_np[build, i] + repair_time[j], 1)
 

pyincore/analyses/galvestoncge/galvestoncge.py

@@ -749,7 +749,7 @@ def _(x):
 
         CG0T.loc[I, GN] = SAM.loc[I, GN].div(P0.loc[I], axis='index')
 
-        DEPR = float((SAM.loc[IG, ["INVES"]].sum(0)) / (KS0.loc[K, IG].sum(1).sum(0)))
+        DEPR = float(((SAM.loc[IG, ["INVES"]].sum(0)) / (KS0.loc[K, IG].sum(1).sum(0))).iloc[0])
 
         N0.loc[K, IG] = KS0.loc[K, IG] * DEPR