From e11289e718e71d8965cd1dfb7fb6ca990b473ff3 Mon Sep 17 00:00:00 2001 From: Chen Wang Date: Wed, 11 Oct 2023 10:01:23 -0500 Subject: [PATCH] Release 4.6.0 (#277) * 260 update notebook using restoration utils (#268) * update restoration util construction * changelog entry * 274 update indp documentation (#275) * add parameter and also citation * changelog * update the markdown link * 272 local hazard tutorial (#276) * Added multiple hazards from local * added description * removed unused packages * added zip * updated zip * Update CHANGELOG.md * added tutorials.md * added files * Update _toc.yml * Update _toc.yml * Commercial buildings markdown and notebook added (#273) * Commercial buildings added * Adding changelog and references * Corrections * Update commercial_building_recovery.md * Update commercial_building_recovery.md * changelog --------- Co-authored-by: YONG WOOK KIM Co-authored-by: Ya-Lan Yang <63822845+ylyangtw@users.noreply.github.com> Co-authored-by: Vismayak Mohanarajan --- CHANGELOG.md | 14 + manual_jb/content/_config.yml | 4 +- manual_jb/content/_toc.yml | 1 + manual_jb/content/analyses.md | 71 +-- .../analyses/commercial_building_recovery.md | 88 +++ manual_jb/content/analyses/indp.md | 39 +- .../create_local_hazard.ipynb | 541 ++++++++++++++++++ .../files/Inundation_Raster.tif | Bin 0 -> 526505 bytes .../files/Surge_Raster.tif | Bin 0 -> 526507 bytes .../files/Tsu_100yr_Hmax.tif | Bin 0 -> 6891 bytes .../files/Tsu_100yr_Mmax.tif | Bin 0 -> 2071 bytes .../files/Tsu_100yr_Vmax.tif | Bin 0 -> 6891 bytes .../create_local_hazard/files/Wave_Raster.tif | Bin 0 -> 526505 bytes .../files/eq-dataset-PGA.tif | Bin 0 -> 7784 bytes .../files/eq-dataset-SA.tif | Bin 0 -> 7784 bytes .../create_local_hazard/files/eq-dataset.json | 31 + .../files/flood-WSE-50ft.tif | Bin 0 -> 6226332 bytes .../files/flood-dataset.json | 23 + .../files/flood-inundationDepth-50ft.tif | Bin 0 -> 6226332 bytes .../files/hurricane-dataset.json | 31 + 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mode 100644 manual_jb/content/notebooks/create_local_hazard/files/Surge_Raster.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/Tsu_100yr_Hmax.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/Tsu_100yr_Mmax.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/Tsu_100yr_Vmax.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/Wave_Raster.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/eq-dataset-PGA.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/eq-dataset-SA.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/eq-dataset.json create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/flood-WSE-50ft.tif create mode 100644 manual_jb/content/notebooks/create_local_hazard/files/flood-dataset.json create mode 100644 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notebooks/create_local_hazard.zip diff --git a/CHANGELOG.md b/CHANGELOG.md index 035def73..e3a908fe 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,20 @@ All notable changes to the INCORE documents generated by Sphinx package will be The format is based on [Keep a Changelog](http://keepachangelog.com/) and this project adheres to [Semantic Versioning](http://semver.org/). + +## [4.6.0] - 2023-10-11 + +### Added +- Added commercial buildings markdown and notebook [#271](https://github.com/IN-CORE/incore-docs/issues/271) +- Local hazard tutorial [#272](https://github.com/IN-CORE/incore-docs/issues/272) + +### Fixed +- Broken notebook using restoration util class [#260](https://github.com/IN-CORE/incore-docs/issues/260) + +### Changed +- Add additional input dataset and citation to INDP [#274](https://github.com/IN-CORE/incore-docs/issues/274) + + ## [4.5.0] - 2023-08-16 ### Added diff --git a/manual_jb/content/_config.yml b/manual_jb/content/_config.yml index 43bc383d..19f76afb 100644 --- a/manual_jb/content/_config.yml +++ b/manual_jb/content/_config.yml @@ -1,9 +1,9 @@ -title: "IN-CORE Manual
4.5.0" +title: "IN-CORE Manual
4.6.0" project: "IN-CORE Manual" author: IN-CORE Community copyright: "2023" # Copyright year to be placed in the footer logo: images/resilience-logo.png -version: "4.5.0" # Version is not part of JB config, we use it for autobuild of incore-docs +version: "4.6.0" # Version is not part of JB config, we use it for autobuild of incore-docs exclude_patterns: [_build, .DS_Store, "**.ipynb_checkpoints"] repository: diff --git a/manual_jb/content/_toc.yml b/manual_jb/content/_toc.yml index 7349c981..1b234108 100644 --- a/manual_jb/content/_toc.yml +++ b/manual_jb/content/_toc.yml @@ -61,6 +61,7 @@ chapters: - file: notebooks/create_tornado/create_tornado.ipynb - file: notebooks/retrofit_plan_Joplin_2021_12_01/retrofit_plan_Joplin_2021_12_01.ipynb - file: notebooks/create_network_dataset/create_network_dataset.ipynb + - file: notebooks/create_local_hazard/create_local_hazard.ipynb - file: faq - file: workshops sections: diff --git a/manual_jb/content/analyses.md b/manual_jb/content/analyses.md index f1da1bc9..56c5f4f6 100644 --- a/manual_jb/content/analyses.md +++ b/manual_jb/content/analyses.md @@ -7,38 +7,39 @@ 5. [Capital shocks](analyses/capital_shocks) 6. [Combined wind wave surge building damage](analyses/combined_wind_wave_surge_building_dmg) 7. [Combined wind wave surge building loss](analyses/combined_wind_wave_surge_building_loss) -8. [Cumulative building damage](analyses/cumulative_building_dmg) -9. [Electric power facility damage](analyses/epf_dmg) -10. [Electric power facility repair cost](analyses/epf_repair_cost) -11. [Electric power facility restoration](analyses/epf_restoration) -12. [Electric power network functionality](analyses/epn_functionality) -13. [Household-level housing sequential recovery](analyses/housing_household_recovery) -14. [Galveston Computable General Equilibrium (CGE)](analyses/galveston_cge.md) -15. [Housing recovery](analyses/housing_recovery) -16. [Housing unit allocation](analyses/housingunitallocation) -17. [Interdependent Network Design Problem](analyses/indp) -18. [Joplin Computable General Equilibrium (CGE)](analyses/joplin_cge) -19. [Joplin empirical restoration](analyses/joplin_empirical_restoration) -20. [Mean damage](analyses/mean_dmg) -21. [Multi-objective retrofit optimization](analyses/multi_retrofit_optimization) -22. [Monte Carlo failure probability](analyses/mc_failure_prob) -23. [Network cascading interdependency functionality](analyses/nci_functionality) -24. [Nonstructural building damage](analyses/non_structural_building_dmg) -25. [Pipeline damage](analyses/pipeline_dmg) -26. [Pipeline damage with repair rate](analyses/pipeline_dmg_w_repair_rate) -27. [Pipeline functionality](analyses/pipeline_functionality) -28. [Pipeline repair cost](analyses/pipeline_repair_cost) -29. [Pipeline restoration](analyses/pipeline_restoration) -30. [Population dislocation](analyses/populationdislocation) -31. [Portfolio recovery](analyses/portfolio_recovery) -32. [Residential building recovery](analyses/residential_building_recovery) -33. [Road damage](analyses/road_dmg) -34. [Salt Lake City Computable General Equilibrium (CGE)](analyses/slc_cge.md) -35. [Seaside Computable General Equilibrium (CGE)](analyses/seaside_cge) -36. [Social Vulnerability](analyses/social_vulnerability) -37. [Tornado electric power network (EPN) damage](analyses/tornadoepn_dmg) -38. [Transportation recovery](analyses/transportation_recovery) -39. [Water facility damage](analyses/waterfacility_dmg) -40. [Water network functionality](analyses/wfn_functionality) -41. [Water facility repair cost](analyses/water_facility_repair_cost) -42. [Water facility restoration](analyses/water_facility_restoration) +8. [Commercial building recovery](analyses/commercial_building_recovery) +9. [Cumulative building damage](analyses/cumulative_building_dmg) +10. [Electric power facility damage](analyses/epf_dmg) +11. [Electric power facility repair cost](analyses/epf_repair_cost) +12. [Electric power facility restoration](analyses/epf_restoration) +13. [Electric power network functionality](analyses/epn_functionality) +14. [Household-level housing sequential recovery](analyses/housing_household_recovery) +15. [Galveston Computable General Equilibrium (CGE)](analyses/galveston_cge.md) +16. [Housing recovery](analyses/housing_recovery) +17. [Housing unit allocation](analyses/housingunitallocation) +18. [Interdependent Network Design Problem](analyses/indp) +19. [Joplin Computable General Equilibrium (CGE)](analyses/joplin_cge) +20. [Joplin empirical restoration](analyses/joplin_empirical_restoration) +21. [Mean damage](analyses/mean_dmg) +22. [Multi-objective retrofit optimization](analyses/multi_retrofit_optimization) +23. [Monte Carlo failure probability](analyses/mc_failure_prob) +24. [Network cascading interdependency functionality](analyses/nci_functionality) +25. [Nonstructural building damage](analyses/non_structural_building_dmg) +26. [Pipeline damage](analyses/pipeline_dmg) +27. [Pipeline damage with repair rate](analyses/pipeline_dmg_w_repair_rate) +28. [Pipeline functionality](analyses/pipeline_functionality) +29. [Pipeline repair cost](analyses/pipeline_repair_cost) +30. [Pipeline restoration](analyses/pipeline_restoration) +31. [Population dislocation](analyses/populationdislocation) +32. [Portfolio recovery](analyses/portfolio_recovery) +33. [Residential building recovery](analyses/residential_building_recovery) +34. [Road damage](analyses/road_dmg) +35. [Salt Lake City Computable General Equilibrium (CGE)](analyses/slc_cge.md) +36. [Seaside Computable General Equilibrium (CGE)](analyses/seaside_cge) +37. [Social Vulnerability](analyses/social_vulnerability) +38. [Tornado electric power network (EPN) damage](analyses/tornadoepn_dmg) +39. [Transportation recovery](analyses/transportation_recovery) +40. [Water facility damage](analyses/waterfacility_dmg) +41. [Water network functionality](analyses/wfn_functionality) +42. [Water facility repair cost](analyses/water_facility_repair_cost) +43. [Water facility restoration](analyses/water_facility_restoration) diff --git a/manual_jb/content/analyses/commercial_building_recovery.md b/manual_jb/content/analyses/commercial_building_recovery.md new file mode 100644 index 00000000..dd66b359 --- /dev/null +++ b/manual_jb/content/analyses/commercial_building_recovery.md @@ -0,0 +1,88 @@ +# Commercial building recovery + +**Description** + +This analysis computes the recovery time needed for each commercial building from any damage states to receive the +full restoration. Currently, supported hazards are tornadoes. + +The methodology incorporates the multi-layer Monte Carlo simulation approach and determines the two-step recovery +time that includes delay and repair. The delay model was modified based on the REDi framework and calculated the +end-result outcomes resulted from delay impeding factors such as post-disaster inspection, insurance claim, +financing and government permit. The repair model followed the FEMA P-58 approach and was controlled by fragility +functions. + +The outputs of this analysis is a CSV file with time-stepping recovery probabilities at the building level. + +**Contributors** + +- Science: Wanting Lisa Wang, John W. van de Lindt +- Implementation: Wanting Lisa Wang and NCSA IN-CORE Dev Team + +**Related publications** + +- Wang, W.L., Watson, M., van de Lindt, J.W. and Xiao, Y., 2023. Commercial Building Recovery Methodology for Use + in Community Resilience Modeling. Natural Hazards Review, 24(4), p.04023031. + +**Input parameters** + +key name | type | name | description +--- | --- | --- | --- +`result_name` * | `str` | Result name | Name of the result dataset. +`num_samples` * | `int` | Samples number | Number of sample scenarios. +`seed` | `int` | Seed | Initial seed for the probabilistic model. +`repair_key` | `str` | Repair key | A repair key to use in mapping dataset. + +**Input datasets** + +key name | type | name | description +--- | --- | --- | --- +`buildings` * | `ergo:buildingInventoryVer4`
`ergo:buildingInventoryVer5`
`ergo:buildingInventoryVer6`
`ergo:buildingInventoryVer7` | Building dataset | A building dataset. +`dfr3_mapping_set` * | `incore:dfr3MappingSet` | DFR3 Mapping Set | DFR3 Mapping Set. +`sample_damage_states` * | `incore:sampleDamageState` | Damage states | Sample damage states. +`mcs_failure` * | `incore:failureProbability` | MCS failure | mcs_failure. +`delay_factors` * | `incore:buildingRecoveryFactors` | Delay factors | Delay impeding factors such as post-disaster inspection, insurance claim,
and government permit based on building's damage state. Provided by REDi framework. + +**Output datasets** + +key name | type | parent key | name | description +--- | --- |-------------------------| --- | --- +`time_stepping_recovery` * | `incore:buildingRecovery` | `buildings` | Results | A dataset containing results (format: CSV)
with percentages of commercial building recovery. | A dataset containing results (format: CSV)
with percentages of commercial building recovery. +`recovery` * | `incore:buildingRecoveryTime` | `buildings` | Building Recovery Time | A dataset containing results (format: CSV)
with commercial building recovery time. +`total_delay` * | `incore:buildingRecoveryDelay` | `buildings` | Building Recovery Delay | A dataset containing results (format: CSV)
with commercial building delay time. + +(* required) + +**Execution** + +code snippet: + +``` + # Create Commercial building recovery instance + comm_recovery = CommercialBuildingRecovery(client) + + # Load input building infrastructure dataset + comm_recovery.load_remote_input_dataset("buildings", buildings) + + # Load repair mapping + repair_service = RepairService(client) + mapping_set = MappingSet(repair_service.get_mapping(mapping_id)) + comm_recovery.set_input_dataset('dfr3_mapping_set', mapping_set) + + # Load input datasets + com_recovery.load_remote_input_dataset("sample_damage_states", sample_damage_states) + com_recovery.load_remote_input_dataset("mcs_failure", mcs_failure) + com_recovery.load_remote_input_dataset("delay_factors", delay_factors) + + # Specify the result name + result_name = "joplin_recovery" + + # Set analysis parameters + comm_recovery.set_parameter("result_name", result_name) + comm_recovery.set_parameter("seed", seed) + comm_recovery.set_parameter("num_samples", 10) + + # Run commercial recovery analysis + comm_recovery.run_analysis() +``` + +full analysis: [commercial_building_recovery.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/commercial_building_recovery.ipynb) \ No newline at end of file diff --git a/manual_jb/content/analyses/indp.md b/manual_jb/content/analyses/indp.md index 4163b9a0..00dc589d 100644 --- a/manual_jb/content/analyses/indp.md +++ b/manual_jb/content/analyses/indp.md @@ -10,26 +10,30 @@ systems subject to budget and operational constraints. - Implementation: Hesam Talebiyan, Chen Wang, and NCSA IN-CORE Dev Team +**Related publications** +- Talebiyan, Hesam. "Interdependent Restoration of Infrastructure Networks with Humans in the Loop: decentralized and strategic decision processes." (2021) Diss., Rice University. [https://hdl.handle.net/1911/111232](https://hdl.handle.net/1911/111232) + + **Input parameters** - key name | type | name | description ---------------------------------|---------|-----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - `network_type` * | `str` | Network Type | Type of the network, which is set to `from_csv` for Seaside networks. e.g. *from_csv*, *incore*. - `MAGS` * | `list` | MAGS | The earthquake return period. - `sample_range` * | `range` | Sample Range | The range of sample scenarios to be analyzed. - `dislocation_data_type` | `str` | Dislocation Data Type | Dislocation Data Type. - `return_model` | `str` | Return Model | Type of the model for the return of the dislocated population. Options: *step_function* and *linear*. - `testbed_name` | `str` | Testbed Name | Sets the name of the testbed in analysis. - `extra_commodity` * | `dict` | Extra Commodity | Multi-commodity parameters dictionary. + key name | type | name | description +--------------------------------|---------|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + `network_type` * | `str` | Network Type | Type of the network, which is set to `from_csv` for Seaside networks. e.g. *from_csv*, *incore*. + `MAGS` * | `list` | MAGS | The earthquake return period. + `sample_range` * | `range` | Sample Range | The range of sample scenarios to be analyzed. + `dislocation_data_type` | `str` | Dislocation Data Type | Dislocation Data Type. + `return_model` | `str` | Return Model | Type of the model for the return of the dislocated population. Options: *step_function* and *linear*. + `testbed_name` | `str` | Testbed Name | Sets the name of the testbed in analysis. + `extra_commodity` * | `dict` | Extra Commodity | Multi-commodity parameters dictionary. `RC` * | `list` | Resource Caps | List of resource caps or the number of available resources in each step of the analysis. Each item of the list is a dictionary whose items show the type of resource and the available number of that type of resource. For example: * If `network_type`=*from_csv*, you have two options:* if, for example, `R_c`= [{"budget": 3}, {"budget": 6}], then the analysis is done for the cases when there are 3 and 6 resources available of type "budget" (total resource assignment).* if, for example, `R_c`= [{"budget": {1:1, 2:1}}, {"budget": {1:1, 2:2}}, {"budget": {1:3, 2:3}}] and given there are 2 layers, then the analysis is done for the case where each layer gets 1 resource of type "budget", AND the case where layer 1 gets 1 and layer 2 gets 2 resources of type "budget", AND the case where each layer gets 3 resources of type "budget" (Prescribed resource for each layer). - `layers` * | `list` | Layers | List of layers in the analysis. - `method` * | `str` | Method | There are two choices of method: 1. `INDP`: runs Interdependent Network Restoration Problem (INDP). 2. `TDINDP`: runs time-dependent INDP (td-INDP). In both cases, if "time_resource" is True, then the repair time for each element is considered in devising the restoration plans. - `t_steps` | `int` | Time steps | Number of time steps of the analysis. - `time_resource` | `bool` | Time Resource | If time resource is True, then the repair time for each element is considered in devising the restoration plans. - `save_model` | `bool` | Save Model | If the optimization model should be saved to file. The default is False. - `solver_engine` | `str` | Solver Engine | Solver to use for optimization model. Default to use SCIP solver. - `solver_path` | `str` | Solver Engine Path | Executable Path to the Solver to use for optimization model. Default to SCIP solver. - `solver_time_limit` | `int` | Solve Time Limit | Solving time limit in seconds. + `layers` * | `list` | Layers | List of layers in the analysis. + `method` * | `str` | Method | There are two choices of method: 1. `INDP`: runs Interdependent Network Restoration Problem (INDP). 2. `TDINDP`: runs time-dependent INDP (td-INDP). In both cases, if "time_resource" is True, then the repair time for each element is considered in devising the restoration plans. + `t_steps` | `int` | Time steps | Number of time steps of the analysis. + `time_resource` | `bool` | Time Resource | If time resource is True, then the repair time for each element is considered in devising the restoration plans. + `save_model` | `bool` | Save Model | If the optimization model should be saved to file. The default is False. + `solver_engine` | `str` | Solver Engine | Solver to use for optimization model. Default to use SCIP solver. + `solver_path` | `str` | Solver Engine Path | Executable Path to the Solver to use for optimization model. Default to SCIP solver. + `solver_time_limit` | `int` | Solve Time Limit | Solving time limit in seconds. **Input datasets** @@ -54,6 +58,7 @@ systems subject to budget and operational constraints. `epf_failure_state` * | `incore:sampleFailureState` | Electric Power Facility Failure State | MCS failure state of electric power facilities. `epf_damage_state` * | `incore:sampleDamageState` | Electric Power Facility Damage State | MCS damage state of electric power facilities `pop_dislocation` * | `incore:popDislocation` | Population Dislocation | Population dislocation. + `dt_params` | `incore:dTParams` | Dislocation time parameters | Parameters for population dislocation time. `bldgs2elec` | `incore:bldgs2elec` | Building To Electric Power Facility | Relation between building and electric power facility. `bldgs2wter` | `incore:bldgs2wter` | Building To Water Facility | Relation between building and water facility. diff --git a/manual_jb/content/notebooks/create_local_hazard/create_local_hazard.ipynb b/manual_jb/content/notebooks/create_local_hazard/create_local_hazard.ipynb new file mode 100644 index 00000000..324acfb1 --- /dev/null +++ b/manual_jb/content/notebooks/create_local_hazard/create_local_hazard.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create Local Hazards" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial provides examples of how to create local hazards for tornadoes, earthquakes, tsunamis, floods, and hurricanes, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-05T17:23:46.971951Z", + "start_time": "2023-10-05T17:23:46.967669Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "from pyincore import Hurricane, Flood, Earthquake, Tornado\n", + "from pyincore.models.hazard.tsunami import Tsunami" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will need to indicate the local data path initially." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "dir = \"files\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Tornadoes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Inputs**: JSON representation of a dataset describing a tornado. Each available dataset in Shapefile format." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"pytest - Joplin Tornado\",\n", + " \"description\": \"Joplin tornado hazard\",\n", + " \"tornadoType\": \"dataset\",\n", + " \"threshold\": null,\n", + " \"thresholdUnit\": \"mph\"\n", + "}\n" + ] + } + ], + "source": [ + "with open(os.path.join(dir, \"tornado_dataset.json\"), 'r') as file:\n", + " tornado_dataset_json = file.read()\n", + " print(json.dumps(json.loads(tornado_dataset_json), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'demands': ['wind'], 'units': ['mph'], 'loc': '-94.37, 37.04', 'hazardValues': [102.62435891472981]}]\n" + ] + } + ], + "source": [ + "# create the tornado object\n", + "tornado = Tornado.from_json_file(os.path.join(dir, \"tornado_dataset.json\"))\n", + "\n", + "# attach dataset from local file\n", + "tornado.hazardDatasets[0].from_file((os.path.join(dir, \"joplin_tornado/joplin_path_wgs84.shp\")), \n", + " data_type=\"incore:tornadoWindfield\")\n", + "\n", + "payload = [\n", + " {\n", + " \"demands\": [\"wind\"],\n", + " \"units\": [\"mph\"],\n", + " \"loc\": \"-94.37, 37.04\"\n", + " }\n", + "]\n", + "\n", + "values = tornado.read_hazard_values(payload, seed=1234) # removing the seed will give random values\n", + "print(values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Earthquakes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Inputs**: JSON representation of a dataset describing an earthquake. Each available dataset in TIF format." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"pytest - Memphis Deterministic EQ\",\n", + " \"description\": \"Memphis dataset based deterministic hazard - Pytest\",\n", + " \"eqType\": \"dataset\",\n", + " \"hazardDatasets\": [\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"SA\",\n", + " \"demandUnits\": \"g\",\n", + " \"period\": \"0.2\",\n", + " \"eqParameters\": {\n", + " \"srcLatitude\": \"35.927\",\n", + " \"srcLongitude\": \"-89.919\",\n", + " \"magnitude\": \"7.9\",\n", + " \"depth\": \"10.0\"\n", + " }\n", + " },\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"PGA\",\n", + " \"demandUnits\": \"g\",\n", + " \"period\": \"0.0\",\n", + " \"eqParameters\": {\n", + " \"srcLatitude\": \"35.927\",\n", + " \"srcLongitude\": \"-89.919\",\n", + " \"magnitude\": \"7.9\",\n", + " \"depth\": \"10.0\"\n", + " }\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "with open(os.path.join(dir, \"eq-dataset.json\"), 'r') as file:\n", + " earthquake_dataset_json = file.read()\n", + " print(json.dumps(json.loads(earthquake_dataset_json), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'demands': ['PGA', '0.2 SA'], 'units': ['g', 'g'], 'loc': '35.03,-89.93', 'hazardValues': [0.3149999976158142, 0.4729999899864197]}]\n" + ] + } + ], + "source": [ + "# create the earthquake object\n", + "eq = Earthquake.from_json_file(os.path.join(dir, \"eq-dataset.json\"))\n", + "\n", + "# attach datasets from local files\n", + "eq.hazardDatasets[0].from_file((os.path.join(dir, \"eq-dataset-SA.tif\")),\n", + " data_type=\"ergo:probabilisticEarthquakeRaster\")\n", + "eq.hazardDatasets[1].from_file((os.path.join(dir, \"eq-dataset-PGA.tif\")),\n", + " data_type=\"ergo:probabilisticEarthquakeRaster\")\n", + "\n", + "payload = [\n", + " {\n", + " \"demands\": [\"PGA\", \"0.2 SA\"],\n", + " \"units\": [\"g\", \"g\"],\n", + " \"loc\": \"35.03,-89.93\"\n", + " }\n", + "]\n", + "\n", + "values = eq.read_hazard_values(payload)\n", + "print(values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Tsunamis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Inputs**: JSON representation of a dataset describing a tsunami. Each available dataset in TIF format." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"pytest - Seaside Probabilistic Tsunami - 100 yr\",\n", + " \"description\": \"pytest - Seaside dataset based probabilistic tsunami hazard. This is just a test!\",\n", + " \"tsunamiType\": \"dataset\",\n", + " \"hazardDatasets\": [\n", + " {\n", + " \"hazardType\": \"probabilistic\",\n", + " \"demandType\": \"Vmax\",\n", + " \"demandUnits\": \"m/s\",\n", + " \"recurrenceInterval\": \"100\",\n", + " \"recurrenceUnit\": \"years\"\n", + " },\n", + " {\n", + " \"hazardType\": \"probabilistic\",\n", + " \"demandType\": \"Mmax\",\n", + " \"demandUnits\": \"m^3/s^2\",\n", + " \"recurrenceInterval\": \"100\",\n", + " \"recurrenceUnit\": \"years\"\n", + " },\n", + " {\n", + " \"hazardType\": \"probabilistic\",\n", + " \"demandType\": \"Hmax\",\n", + " \"demandUnits\": \"m\",\n", + " \"recurrenceInterval\": \"100\",\n", + " \"recurrenceUnit\": \"years\"\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "with open(os.path.join(dir, \"tsunami.json\"), 'r') as file:\n", + " tsunami_dataset_json = file.read()\n", + " print(json.dumps(json.loads(tsunami_dataset_json), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'demands': ['hmax'], 'units': ['m'], 'loc': '46.006,-123.935', 'hazardValues': [2.9000000953674316]}]\n" + ] + } + ], + "source": [ + "# create the tsunami object\n", + "tsunami = Tsunami.from_json_file(os.path.join(dir, \"tsunami.json\"))\n", + "\n", + "# attach datasets from local files\n", + "tsunami.hazardDatasets[0].from_file((os.path.join(dir, \"Tsu_100yr_Vmax.tif\")),\n", + " data_type=\"ncsa:probabilisticTsunamiRaster\")\n", + "tsunami.hazardDatasets[1].from_file((os.path.join(dir, \"Tsu_100yr_Mmax.tif\")),\n", + " data_type=\"ncsa:probabilisticTsunamiRaster\")\n", + "tsunami.hazardDatasets[2].from_file((os.path.join(dir, \"Tsu_100yr_Hmax.tif\")),\n", + " data_type=\"ncsa:probabilisticTsunamiRaster\")\n", + "\n", + "payload = [\n", + " {\n", + " \"demands\": [\"hmax\"],\n", + " \"units\": [\"m\"],\n", + " \"loc\": \"46.006,-123.935\"\n", + " }\n", + "]\n", + "\n", + "values = tsunami.read_hazard_values(payload)\n", + "print(values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Floods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Inputs**: JSON representation of a dataset describing a flood. Each available dataset in TIF format." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"Lumberton Deterministic Flood - riverine flooding\",\n", + " \"description\": \"Lumberton dataset based deterministic hazard - 2 datasets\",\n", + " \"floodType\": \"dataset\",\n", + " \"hazardDatasets\": [\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"inundationDepth\",\n", + " \"demandUnits\": \"ft\",\n", + " \"floodParameters\": {\n", + " \"model\": \"riverine flooding\"\n", + " }\n", + " },\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"waterSurfaceElevation\",\n", + " \"demandUnits\": \"ft\",\n", + " \"floodParameters\": {\n", + " \"model\": \"riverine flooding\"\n", + " }\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "with open(os.path.join(dir, \"flood-dataset.json\"), 'r') as file:\n", + " flood_dataset_json = file.read()\n", + " print(json.dumps(json.loads(flood_dataset_json), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'demands': ['waterSurfaceElevation'], 'units': ['m'], 'loc': '34.60,-79.16', 'hazardValues': [41.970442822265625]}]\n" + ] + } + ], + "source": [ + "# create the flood object\n", + "flood = Flood.from_json_file(os.path.join(dir, \"flood-dataset.json\"))\n", + "\n", + "# attach datasets from local files\n", + "flood.hazardDatasets[0].from_file((os.path.join(dir, \"flood-inundationDepth-50ft.tif\")),\n", + " data_type=\"ncsa:probabilisticFloodRaster\")\n", + "flood.hazardDatasets[1].from_file(os.path.join(dir, \"flood-WSE-50ft.tif\"),\n", + " data_type=\"ncsa:probabilisticFloodRaster\")\n", + "\n", + "payload = [\n", + " {\n", + " \"demands\": [\"waterSurfaceElevation\"],\n", + " \"units\": [\"m\"],\n", + " \"loc\": \"34.60,-79.16\"\n", + " }\n", + " ]\n", + "\n", + "values = flood.read_hazard_values(payload)\n", + "print(values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Hurricanes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Inputs**: JSON representation of a dataset describing a hurricane. Each available dataset in TIF format." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"pytest - Galveston Deterministic Hurricane - Kriging \",\n", + " \"description\": \"Galveston dataset based deterministic hazard - 3 datasets\",\n", + " \"hurricaneType\": \"dataset\",\n", + " \"hazardDatasets\": [\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"waveHeight\",\n", + " \"demandUnits\": \"m\",\n", + " \"hurricaneParameters\": {\n", + " \"model\": \"Kriging\"\n", + " }\n", + " },\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"surgeLevel\",\n", + " \"demandUnits\": \"m\",\n", + " \"hurricaneParameters\": {\n", + " \"model\": \"Kriging\"\n", + " }\n", + " },\n", + " {\n", + " \"hazardType\": \"deterministic\",\n", + " \"demandType\": \"inundationDuration\",\n", + " \"demandUnits\": \"hr\",\n", + " \"hurricaneParameters\": {\n", + " \"model\": \"Kriging\"\n", + " }\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "with open(os.path.join(dir, \"hurricane-dataset.json\"), 'r') as file:\n", + " hurricane_dataset_json = file.read()\n", + " print(json.dumps(json.loads(hurricane_dataset_json), indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'demands': ['waveHeight', 'surgeLevel'], 'units': ['m', 'm'], 'loc': '29.22,-95.06', 'hazardValues': [1.54217780024576, 3.663398872786693]}, {'demands': ['waveHeight', 'surgeLevel'], 'units': ['cm', 'cm'], 'loc': '29.23,-95.05', 'hazardValues': [162.9628933899723, 369.7690109274975]}, {'demands': ['waveHeight', 'inundationDuration'], 'units': ['in', 'hr'], 'loc': '29.22,-95.06', 'hazardValues': [60.7156942134556, 18.346923306935572]}]\n" + ] + } + ], + "source": [ + "# create the hurricane object\n", + "hurricane = Hurricane.from_json_file((os.path.join(dir, \"hurricane-dataset.json\")))\n", + "\n", + "# attach datasets from local files\n", + "hurricane.hazardDatasets[0].from_file((os.path.join(dir, \"Wave_Raster.tif\")),\n", + " data_type=\"ncsa:deterministicHurricaneRaster\")\n", + "hurricane.hazardDatasets[1].from_file(os.path.join(dir, \"Surge_Raster.tif\"),\n", + " data_type=\"ncsa:deterministicHurricaneRaster\")\n", + "hurricane.hazardDatasets[2].from_file(os.path.join(dir, \"Inundation_Raster.tif\"),\n", + " data_type=\"ncsa:deterministicHurricaneRaster\")\n", + "\n", + "payload = [\n", + " {\n", + " \"demands\": [\"waveHeight\", \"surgeLevel\"],\n", + " \"units\": [\"m\", \"m\"],\n", + " \"loc\": \"29.22,-95.06\"\n", + " },\n", + " {\n", + " \"demands\": [\"waveHeight\", \"surgeLevel\"],\n", + " \"units\": [\"cm\", \"cm\"],\n", + " \"loc\": \"29.23,-95.05\"\n", + " },\n", + " {\n", + " \"demands\": [\"waveHeight\", \"inundationDuration\"],\n", + " \"units\": [\"in\", \"hr\"],\n", + " \"loc\": \"29.22,-95.06\"\n", + " }\n", + " ]\n", + "\n", + "values = hurricane.read_hazard_values(payload)\n", + "print(values)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/manual_jb/content/notebooks/create_local_hazard/files/Inundation_Raster.tif b/manual_jb/content/notebooks/create_local_hazard/files/Inundation_Raster.tif new file mode 100644 index 0000000000000000000000000000000000000000..c85597683aeb17ee85289b576a6e0cd0241bf413 GIT binary patch literal 526505 zcmeFa2UJzhk}rHX586InB-{1Io?D)vv>X2hR(=`U%U#K@$Cp3%!M}_f`7@rqGj?={W*BGB4!SL61 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[combined_wind_wave_surge_building_loss.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/combined_wind_wave_surge_building_loss.ipynb)
+[commercial_building_recovery.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/commercial_building_recovery.ipynb)
[cumulative_building_dmg.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/cumulative_building_dmg.ipynb)
[epf_dmg.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/epf_dmg.ipynb)
[epf_repair_cost.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/epf_repair_cost.ipynb)
diff --git a/manual_jb/content/tutorials.md b/manual_jb/content/tutorials.md index 4e3833c7..9671df78 100644 --- a/manual_jb/content/tutorials.md +++ b/manual_jb/content/tutorials.md @@ -10,20 +10,22 @@ You can also check out IN-CORE resources for other materials to help you navigat We encourage IN-CORE users to contribute improve the knowledge base. contact us with suggestions for expanding and extending Tutorials. 1. [Create Dataset](notebooks/create_dataset/create_dataset.ipynb) -2. [Create dfr3 Object](notebooks/create_dfr3_object/create_dfr3_object.ipynb) +2. [Create DFR3 Object](notebooks/create_dfr3_object/create_dfr3_object.ipynb) 3. [Create Earthquake](notebooks/create_earthquake/create_earthquake.ipynb) 4. [Create Tornado](notebooks/create_tornado/create_tornado.ipynb) -5. [Retrofit plan Joplin](notebooks/retrofit_plan_Joplin_2021_12_01/retrofit_plan_Joplin_2021_12_01.ipynb) -6. [Create network dataset](notebooks/create_network_dataset/create_network_dataset.ipynb) +5. [Retrofit Plan Joplin](notebooks/retrofit_plan_Joplin_2021_12_01/retrofit_plan_Joplin_2021_12_01.ipynb) +6. [Create Network Dataset](notebooks/create_network_dataset/create_network_dataset.ipynb) +7. [Create Local Hazard](notebooks/create_local_hazard/create_local_hazard.ipynb) More tutorials will be added soon. Download the full Notebooks: -- Create Dataset object: [create_dataset.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_dataset.zip) -- Create DFR3 object: [create_dfr3_object.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_dfr3_object.zip) +- Create Dataset Object: [create_dataset.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_dataset.zip) +- Create DFR3 Object: [create_dfr3_object.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_dfr3_object.zip) - Create Earthquake Hazard: [create_earthquake.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_earthquake.zip) - Create Tornado Hazard: [create_tornado.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_tornado.zip) -- Retrofit plan Joplin: [retrofit_plan_Joplin_2021_12_01.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/retrofit_plan_Joplin_2021_12_01.zip) -- Create network dataset: [create_network_dataset.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_network_dataset.zip) +- Retrofit Plan Joplin: [retrofit_plan_Joplin_2021_12_01.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/retrofit_plan_Joplin_2021_12_01.zip) +- Create Network Dataset: [create_network_dataset.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_network_dataset.zip) +- Create Local Hazard: [create_local_hazard.zip](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/create_local_hazard.zip) diff --git a/notebooks/commercial_building_recovery.ipynb b/notebooks/commercial_building_recovery.ipynb new file mode 100644 index 00000000..3f43e07c --- /dev/null +++ b/notebooks/commercial_building_recovery.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-29T15:02:55.785647Z", + "start_time": "2023-09-29T15:02:55.777915Z" + } + }, + "outputs": [], + "source": [ + "from pyincore import IncoreClient, RepairService, MappingSet\n", + "from pyincore.analyses.commercialbuildingrecovery.commercialbuildingrecovery import CommercialBuildingRecovery\n", + "\n", + "import pyincore.globals as pyglobals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Connect to IN-CORE service\n", + "client = IncoreClient()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Joplin Commercial Building Recovery." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Joplin\n", + "buildings = \"5dbc8478b9219c06dd242c0d\" # ergo:buildingInventoryVer6 5dbc8478b9219c06dd242c0d\n", + "\n", + "# Create commercial recovery instance\n", + "com_recovery = CommercialBuildingRecovery(client)\n", + "com_recovery.load_remote_input_dataset(\"buildings\", buildings)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Recovery mapping\n", + "mapping_id = \"60edfa3efc0f3a7af53a21b5\"\n", + "# Create repair service\n", + "repair_service = RepairService(client)\n", + "mapping_set = MappingSet(repair_service.get_mapping(mapping_id))\n", + "com_recovery.set_input_dataset('dfr3_mapping_set', mapping_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# input datsets ids\n", + "sample_damage_states = \"651dcf587faf5a0eae9e3443\" # 10 samples 28k buildings - MCS output format\n", + "mcs_failure = '651dced2adce7a5cb977f06e'\n", + "delay_factors = \"64ee0bcd553ecf0768e21e55\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load input datasets\n", + "com_recovery.load_remote_input_dataset(\"sample_damage_states\", sample_damage_states)\n", + "com_recovery.load_remote_input_dataset(\"mcs_failure\", mcs_failure)\n", + "com_recovery.load_remote_input_dataset(\"delay_factors\", delay_factors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "num_samples = 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the result name\n", + "result_name = \"joplin_commercial_test\"\n", + "\n", + "# Set analysis parameters\n", + "com_recovery.set_parameter(\"result_name\", result_name)\n", + "com_recovery.set_parameter(\"num_samples\", num_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the analysis (NOTE: with SettingWithCopyWarning)\n", + "com_recovery.run_analysis()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Retrieve result dataset\n", + "result = com_recovery.get_output_dataset(\"time_stepping_recovery\")\n", + "\n", + "# Convert dataset to Pandas DataFrame\n", + "df = result.get_dataframe_from_csv()\n", + "\n", + "# Display top 5 rows of output data\n", + "df.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/create_local_hazard.zip b/notebooks/create_local_hazard.zip new file mode 100644 index 0000000000000000000000000000000000000000..b07119569d8c34f7f2bd299dc486dd217e1a7c84 GIT binary patch literal 2625793 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