From a5135b14754f866931e837d479b27133855ca654 Mon Sep 17 00:00:00 2001
From: Chen Wang <cwang138@illinois.edu>
Date: Thu, 13 Apr 2023 13:46:10 -0500
Subject: [PATCH] 193 galveston community notebook (#196)

* galveston notebook for the community app
* update the notebook
* add a link to the notebook
* update bldg functionality with latest network
* update hua id
* add sector shock
* update building; add sector shock
* remove outputs
* update notebook to use clipped hua
* update sector shock id
* add CGE
* move to correct location
* Update galveston_community_app.ipynb
* Update CHANGELOG.md
* add clustering code for functionality
* udpate community app notebook with new clustering method
---------

Co-authored-by: Vismayak Mohanarajan <vismayak97@gmail.com>
---
 CHANGELOG.md                                  |   6 +
 manual_jb/content/notebooks_other.md          |   1 +
 ...ombined_wind_wave_surge_building_dmg.ipynb |   2 +-
 notebooks/galveston_community_app.ipynb       | 742 ++++++++++++++++++
 notebooks/slc_community_app.ipynb             |  54 +-
 5 files changed, 782 insertions(+), 23 deletions(-)
 create mode 100644 notebooks/galveston_community_app.ipynb

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3995b530..25b04a06 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,12 @@ 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/).
 
+## Unreleased
+
+### Added
+- Galveston community app example notebook [#196](https://github.com/IN-CORE/incore-docs/issues/196)
+
+
 ## [4.0.0] - 2023-03-15
 
 ### Added
diff --git a/manual_jb/content/notebooks_other.md b/manual_jb/content/notebooks_other.md
index d6c9cd17..ea35c5fb 100644
--- a/manual_jb/content/notebooks_other.md
+++ b/manual_jb/content/notebooks_other.md
@@ -14,6 +14,7 @@ Note that some Notebooks might not work with the latest version of pyIncore.
 [cumulative_building_dmg.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/cumulative_building_dmg.ipynb) <br />
 [epf_dmg.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/epf_dmg.ipynb) <br />
 [epn_functionality.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/epn_functionality.ipynb) <br />
+[galveston_community_app.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/galveston_community_app.ipynb) <br />
 [housing household recovery](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/housing_household_recovery.ipynb) <br />
 [housing_recovery.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/housing_recovery.ipynb) <br />
 [housingunitallocation.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/housingunitallocation.ipynb) <br />
diff --git a/notebooks/combined_wind_wave_surge_building_dmg.ipynb b/notebooks/combined_wind_wave_surge_building_dmg.ipynb
index 4978f7f8..108a0925 100644
--- a/notebooks/combined_wind_wave_surge_building_dmg.ipynb
+++ b/notebooks/combined_wind_wave_surge_building_dmg.ipynb
@@ -284,4 +284,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/notebooks/galveston_community_app.ipynb b/notebooks/galveston_community_app.ipynb
new file mode 100644
index 00000000..22b0bca4
--- /dev/null
+++ b/notebooks/galveston_community_app.ipynb
@@ -0,0 +1,742 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "957d73f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd \n",
+    "import numpy as np\n",
+    "import sys \n",
+    "import os \n",
+    "import matplotlib.pyplot as plt\n",
+    "import contextily as ctx\n",
+    "import copy\n",
+    "import math\n",
+    "from scipy.stats import norm\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from pyincore import IncoreClient, Dataset, DataService, HazardService, FragilityService, MappingSet, FragilityCurveSet\n",
+    "from pyincore import HHRSOutputProcess\n",
+    "from pyincore.utils.dataprocessutil import DataProcessUtil\n",
+    "\n",
+    "\n",
+    "# importing pyIncone analyses:\n",
+    "from pyincore.analyses.buildingdamage import BuildingDamage\n",
+    "from pyincore.analyses.buildingfunctionality import BuildingFunctionality\n",
+    "from pyincore.analyses.combinedwindwavesurgebuildingdamage import CombinedWindWaveSurgeBuildingDamage\n",
+    "from pyincore.analyses.epfdamage import EpfDamage\n",
+    "from pyincore.analyses.montecarlofailureprobability import MonteCarloFailureProbability\n",
+    "from pyincore.analyses.housingunitallocation import HousingUnitAllocation\n",
+    "from pyincore.analyses.populationdislocation import PopulationDislocation, PopulationDislocationUtil\n",
+    "from pyincore.analyses.housingrecoverysequential import HousingRecoverySequential\n",
+    "from pyincore.analyses.socialvulnerability import SocialVulnerability\n",
+    "from pyincore.analyses.capitalshocks import CapitalShocks\n",
+    "from pyincore.analyses.galvestoncge import GalvestonCGEModel\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e50116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = IncoreClient()\n",
+    "# IN-CORE caches files on the local machine, it might be necessary to clear the memory\n",
+    "# client.clear_cache() \n",
+    "data_service = DataService(client) # create data_service object for loading files\n",
+    "hazard_service = HazardService(client)\n",
+    "fragility_services = FragilityService(client)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351fe20a",
+   "metadata": {},
+   "source": [
+    "## Combined Building Damage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "565bcb53",
+   "metadata": {},
+   "source": [
+    "### Wind building damage for Galveston, TX using Hurricane Ike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39b41540",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hazard_type = \"hurricane\"\n",
+    "\n",
+    "# Galveston Hurricane Ike\n",
+    "hazard_id = \"5fa5a228b6429615aeea4410\"\n",
+    "\n",
+    "# Galveston inventory data \n",
+    "# bldg_dataset_id = \"63053ddaf5438e1f8c517fed\" # Full city\n",
+    "bldg_dataset_id = \"63ff6b135c35c0353d5ed3ac\" # Island Only\n",
+    "\n",
+    "# Building Wind Fragility mapping\n",
+    "mapping_id = \"62fef3a6cef2881193f2261d\"\n",
+    "fragility_service = FragilityService(client)\n",
+    "mapping_set = MappingSet(fragility_service.get_mapping(mapping_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8bb07e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wind building damage\n",
+    "w_bldg_dmg = BuildingDamage(client)\n",
+    "w_bldg_dmg.load_remote_input_dataset(\"buildings\", bldg_dataset_id)\n",
+    "w_bldg_dmg.set_input_dataset('dfr3_mapping_set', mapping_set)\n",
+    "w_bldg_dmg.set_parameter(\"result_name\", \"Galveston-wind-dmg\")\n",
+    "w_bldg_dmg.set_parameter(\"hazard_type\", hazard_type)\n",
+    "w_bldg_dmg.set_parameter(\"hazard_id\", hazard_id)\n",
+    "w_bldg_dmg.set_parameter(\"num_cpu\", 8)\n",
+    "w_bldg_dmg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fb045b4",
+   "metadata": {},
+   "source": [
+    "### Surge-Wave building damage for Galveston, TX using Hurricane Ike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58b1565c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Surge-wave mapping\n",
+    "mapping_id = \"6303e51bd76c6d0e1f6be080\"\n",
+    "mapping_set = MappingSet(fragility_service.get_mapping(mapping_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9812c012",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# surge-wave building damage\n",
+    "sw_bldg_dmg = BuildingDamage(client)\n",
+    "sw_bldg_dmg.load_remote_input_dataset(\"buildings\", bldg_dataset_id)\n",
+    "sw_bldg_dmg.set_input_dataset('dfr3_mapping_set', mapping_set)\n",
+    "sw_bldg_dmg.set_parameter(\"result_name\", \"Galveston-sw-dmg\")\n",
+    "sw_bldg_dmg.set_parameter(\"hazard_type\", hazard_type)\n",
+    "sw_bldg_dmg.set_parameter(\"hazard_id\", hazard_id)\n",
+    "sw_bldg_dmg.set_parameter(\"num_cpu\", 8)\n",
+    "sw_bldg_dmg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72fd8814",
+   "metadata": {},
+   "source": [
+    "### Flood building damage for Galveston, TX using Hurricane Ike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfb2b053",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# flood mapping\n",
+    "mapping_id = \"62fefd688a30d30dac57bbd7\"\n",
+    "mapping_set = MappingSet(fragility_service.get_mapping(mapping_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3445913d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# flood building damage\n",
+    "f_bldg_dmg = BuildingDamage(client)\n",
+    "f_bldg_dmg.load_remote_input_dataset(\"buildings\", bldg_dataset_id)\n",
+    "f_bldg_dmg.set_input_dataset('dfr3_mapping_set', mapping_set)\n",
+    "f_bldg_dmg.set_parameter(\"result_name\", \"Galveston-flood-dmg\")\n",
+    "f_bldg_dmg.set_parameter(\"hazard_type\", hazard_type)\n",
+    "f_bldg_dmg.set_parameter(\"hazard_id\", hazard_id)\n",
+    "f_bldg_dmg.set_parameter(\"num_cpu\", 8)\n",
+    "f_bldg_dmg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9e16601",
+   "metadata": {},
+   "source": [
+    "### Combine wind, wave and surge building damage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef7f95aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "surge_wave_damage = sw_bldg_dmg.get_output_dataset(\"ds_result\")\n",
+    "wind_damage = w_bldg_dmg.get_output_dataset(\"ds_result\")\n",
+    "flood_damage = f_bldg_dmg.get_output_dataset(\"ds_result\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36d3114e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_bldg_dmg = CombinedWindWaveSurgeBuildingDamage(client)\n",
+    "result_name = \"Galveston-combined-dmg\"\n",
+    "combined_bldg_dmg.set_input_dataset(\"surge_wave_damage\", surge_wave_damage)\n",
+    "combined_bldg_dmg.set_input_dataset(\"wind_damage\", wind_damage)\n",
+    "combined_bldg_dmg.set_input_dataset(\"flood_damage\", flood_damage)\n",
+    "combined_bldg_dmg.set_parameter(\"result_name\", result_name)\n",
+    "combined_bldg_dmg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f4ebe2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_dmg = combined_bldg_dmg.get_output_dataset(\"ds_result\")\n",
+    "combined_dmg_df = combined_dmg.get_dataframe_from_csv(low_memory=False)\n",
+    "\n",
+    "# Display top 5 rows of output data\n",
+    "combined_dmg_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "828e824b",
+   "metadata": {},
+   "source": [
+    "### Archetype Mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d975ad1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buildings = Dataset.from_data_service(bldg_dataset_id, data_service)\n",
+    "bldg_gdf = buildings.get_dataframe_from_shapefile()\n",
+    "bldg_gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b055f2d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arch_mapping = Dataset.from_data_service(\"63dc1f1362b9d001e6a1b485\", data_service).get_dataframe_from_csv()\n",
+    "arch_mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61854765",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_dmg_state_df = DataProcessUtil.get_max_damage_state(combined_dmg_df)\n",
+    "max_dmg_state_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53b21c48",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "ret_json = DataProcessUtil.create_mapped_dmg_result(bldg_gdf, max_dmg_state_df, arch_mapping, \"max_state\", \"arch_wind\")\n",
+    "ret_json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88a5d556",
+   "metadata": {},
+   "source": [
+    "## Electric Power Pole Damage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed824b73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EPF fragility mapping\n",
+    "epf_mapping_id = \"62fac92ecef2881193f22613\"\n",
+    "epf_mapping_set = MappingSet(fragility_service.get_mapping(epf_mapping_id))\n",
+    "\n",
+    "epf_dmg_hurricane_galveston = EpfDamage(client)\n",
+    "epf_dmg_hurricane_galveston.load_remote_input_dataset(\"epfs\", \"62fc000f88470b319561b58d\")\n",
+    "epf_dmg_hurricane_galveston.set_input_dataset('dfr3_mapping_set', epf_mapping_set)\n",
+    "epf_dmg_hurricane_galveston.set_parameter(\"result_name\", \"Galveston-hurricane-epf-damage\")\n",
+    "epf_dmg_hurricane_galveston.set_parameter(\"fragility_key\", \"Non-Retrofit Fragility ID Code\")\n",
+    "epf_dmg_hurricane_galveston.set_parameter(\"hazard_type\", hazard_type)\n",
+    "epf_dmg_hurricane_galveston.set_parameter(\"hazard_id\", hazard_id)\n",
+    "epf_dmg_hurricane_galveston.set_parameter(\"num_cpu\", 8)\n",
+    "\n",
+    "# Run Analysis\n",
+    "epf_dmg_hurricane_galveston.run_analysis()\n",
+    "epf_dmg_result = epf_dmg_hurricane_galveston.get_output_dataset(\"result\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e8e1ca0",
+   "metadata": {},
+   "source": [
+    "## Monte Carlo Failure Probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8699e6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mc = MonteCarloFailureProbability(client)\n",
+    "mc.set_input_dataset(\"damage\", combined_dmg)\n",
+    "mc.set_parameter(\"result_name\", \"combined_bldg_mc\")\n",
+    "mc.set_parameter(\"num_cpu\", 8)\n",
+    "mc.set_parameter(\"num_samples\", 100)\n",
+    "mc.set_parameter(\"damage_interval_keys\", [\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "mc.set_parameter(\"failure_state_keys\", [ \"DS_2\", \"DS_3\", \"DS_4\"])\n",
+    "mc.run_analysis()\n",
+    "\n",
+    "gal_bldg_damage_mcs_samples = mc.get_output_dataset('sample_failure_state')\n",
+    "gal_bldg_damage_mcs_samples_df = gal_bldg_damage_mcs_samples.get_dataframe_from_csv()\n",
+    "gal_bldg_damage_mcs_samples_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "507233a6",
+   "metadata": {},
+   "source": [
+    "### Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da807655",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret_json = DataProcessUtil.create_mapped_func_result(bldg_gdf, gal_bldg_damage_mcs_samples_df, arch_mapping, \n",
+    "                                                     arch_col=\"arch_wind\")\n",
+    "with open(\"galveston_mcs_cluster.json\", \"w\") as f:\n",
+    "    json.dump(ret_json, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46ed6e01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gal_bldg_failure_probability = mc.get_output_dataset('failure_probability')  # get buildings failure probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "837544c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mc = MonteCarloFailureProbability(client)\n",
+    "mc.set_input_dataset(\"damage\", epf_dmg_result)\n",
+    "mc.set_parameter(\"result_name\", \"epf_mc\")\n",
+    "mc.set_parameter(\"num_cpu\", 8)\n",
+    "mc.set_parameter(\"num_samples\", 100)\n",
+    "mc.set_parameter(\"damage_interval_keys\", [\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "mc.set_parameter(\"failure_state_keys\",[ \"DS_2\", \"DS_3\", \"DS_4\"])\n",
+    "mc.run_analysis()\n",
+    "\n",
+    "gal_pole_damage_mcs_samples = mc.get_output_dataset('sample_failure_state')\n",
+    "gal_pole_damage_mcs_samples_df = gal_pole_damage_mcs_samples.get_dataframe_from_csv()\n",
+    "gal_pole_damage_mcs_samples_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c772d4",
+   "metadata": {},
+   "source": [
+    "## Building Functionality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9412fc5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gal_bldg_func = BuildingFunctionality(client)\n",
+    "    \n",
+    "gal_bldg_func.set_input_dataset(\"building_damage_mcs_samples\", gal_bldg_damage_mcs_samples)\n",
+    "gal_bldg_func.set_input_dataset(\"poles_damage_mcs_samples\", gal_pole_damage_mcs_samples)\n",
+    "gal_bldg_func.load_remote_input_dataset(\"interdependency_dictionary\", \"63e5af0262d82d2f5a1058ea\")\n",
+    "\n",
+    "gal_bldg_func.set_parameter(\"result_name\", \"gal_mcs_functionality_probability\")\n",
+    "gal_bldg_func.run_analysis()\n",
+    "\n",
+    "gal_bldg_func_samples_df = gal_bldg_func.get_output_dataset('functionality_samples').get_dataframe_from_csv()\n",
+    "gal_bldg_func_samples_df.head()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "7af54393",
+   "metadata": {},
+   "source": [
+    "df_gal_bldg_func_probability = gal_bldg_func.get_output_dataset('functionality_probability').get_dataframe_from_csv()\n",
+    "df_gal_bldg_func_probability = df_gal_bldg_func_probability.rename(columns={\"building_guid\": \"guid\"})\n",
+    "func_prob_target = 0.40\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'].isna(), 'functionality'] = 1 # Assumed to be connected to an undamaged/unanalyzed power network for now.\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'] <= func_prob_target, 'functionality'] = 0 # Non-Functional\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'] > func_prob_target, 'functionality'] = 1 # Functional\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'].isna(), 'functionality_state'] = 'Functional' # Assumed to be connected to an undamaged/unanalyzed power network for now.\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'] <= func_prob_target, 'functionality_state'] = 'Non-Functional' # Non-Functional\n",
+    "df_gal_bldg_func_probability.loc[df_gal_bldg_func_probability['probability'] > func_prob_target, 'functionality_state'] = 'Functional' # Functional\n",
+    "df_gal_bldg_func_probability.head()\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "dca9720d",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "### Plot of the distribution of functionality state for buildings\n",
+    "indexes = df_gal_bldg_func_probability['functionality_state'].value_counts(normalize=True).mul(100).index.tolist()\n",
+    "values = df_gal_bldg_func_probability['functionality_state'].value_counts(normalize=True).mul(100).tolist()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 2.5), dpi=200)\n",
+    "\n",
+    "bars = ax.bar(x=indexes, height=values,)\n",
+    "\n",
+    "for bar in bars:\n",
+    "    ax.text(bar.get_x() + bar.get_width() / 2,\n",
+    "            bar.get_height() + 3,f'% {bar.get_height() :.1f}',\n",
+    "            horizontalalignment='center')\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "ax.set_ylim([0,100])\n",
+    "ax.set_xlabel('Damage State', labelpad=15)\n",
+    "ax.set_ylabel('Percentage', labelpad=15)\n",
+    "ax.set_title('Functionality Percentage (Buildings and Substations)', pad=15)\n",
+    "ax.set(frame_on=False);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbd1a228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret_json = DataProcessUtil.create_mapped_func_result(bldg_gdf, \n",
+    "                                                     gal_bldg_func_samples_df, \n",
+    "                                                     arch_mapping, \n",
+    "                                                     arch_col=\"arch_wind\")\n",
+    "with open(\"galveston_bldg_func_cluster.json\", \"w\") as f:\n",
+    "    json.dump(ret_json, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08f62ae4",
+   "metadata": {},
+   "source": [
+    "# Capital Shocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fada1a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Capital shocks analysis\n",
+    "capital_shocks = CapitalShocks(client)\n",
+    "building_to_sectors_id = \"640200da475d895dfc2de1bd\"\n",
+    "# Load remote datasets\n",
+    "capital_shocks.load_remote_input_dataset(\"buildings_to_sectors\", building_to_sectors_id)\n",
+    "capital_shocks.load_remote_input_dataset(\"buildings\", bldg_dataset_id)\n",
+    "capital_shocks.set_input_dataset(\"failure_probability\", gal_bldg_failure_probability)\n",
+    "\n",
+    "capital_shocks.set_parameter(\"result_name\", \"sector_shocks\") # name of csv file with results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bbd01cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run capital shocks analysis\n",
+    "capital_shocks.run_analysis()\n",
+    "\n",
+    "sector_shocks_result = capital_shocks.get_output_dataset(\"sector_shocks\")\n",
+    "sector_shocks_result.get_dataframe_from_csv()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ff5fc0a",
+   "metadata": {},
+   "source": [
+    "# CGE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "471efa22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "galveston_cge = GalvestonCGEModel(client)\n",
+    "\n",
+    "galveston_cge.set_parameter(\"model_iterations\", 1)\n",
+    "galveston_cge.load_remote_input_dataset(\"SAM\", \"641e1fccea308903b2b8c51a\")\n",
+    "galveston_cge.load_remote_input_dataset(\"BB\", \"641e1fff34810d74880b3985\")\n",
+    "galveston_cge.load_remote_input_dataset(\"MISCH\", \"641e21a702414a24a5436dfe\")\n",
+    "galveston_cge.load_remote_input_dataset(\"EMPLOY\", \"641e2152ea308903b2b8c51c\")\n",
+    "galveston_cge.load_remote_input_dataset(\"JOBCR\", \"641e209eea308903b2b8c51b\")\n",
+    "galveston_cge.load_remote_input_dataset(\"OUTCR\", \"641e20f134810d74880b3986\")\n",
+    "galveston_cge.set_input_dataset(\"sector_shocks\", sector_shocks_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd952c8f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "galveston_cge.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34636105",
+   "metadata": {},
+   "source": [
+    "## Population Dislocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5687353b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pop_dis = PopulationDislocation(client)\n",
+    "value_loss = \"60354810e379f22e16560dbd\"\n",
+    "bg_data = \"603545f2dcda03378087e708\"\n",
+    "# hua_result_id = \"63ee615d62b9d001e6a2b660\" # city\n",
+    "hua_result_id = \"63ff8e895367c2261b4cb2ef\" # island\n",
+    "\n",
+    "pop_dis.load_remote_input_dataset(\"block_group_data\", bg_data)\n",
+    "pop_dis.load_remote_input_dataset(\"value_loss_param\", value_loss)\n",
+    "pop_dis.load_remote_input_dataset(\"housing_unit_allocation\", hua_result_id)\n",
+    "\n",
+    "pop_dis.set_input_dataset(\"building_dmg\", combined_dmg)\n",
+    "\n",
+    "result_name = \"galveston-pop-disl-results\"\n",
+    "seed = 1111\n",
+    "\n",
+    "pop_dis.set_parameter(\"result_name\", result_name)\n",
+    "pop_dis.set_parameter(\"seed\", seed)\n",
+    "pop_dis.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be72da00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "population_dislocation_result = pop_dis.get_output_dataset(\"result\")\n",
+    "population_dislocation_result_df = population_dislocation_result.get_dataframe_from_csv(low_memory=False)\n",
+    "population_dislocation_result_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "123208de",
+   "metadata": {},
+   "source": [
+    "## Housing Houshold Recovery Sequentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "183e1e00",
+   "metadata": {},
+   "source": [
+    "### Social Vulnerabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf28d307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "social_vulnerability = SocialVulnerability(client)\n",
+    "\n",
+    "social_vulnerability.set_parameter(\"result_name\", \"gal_social_vulnerabilty\")\n",
+    "social_vulnerability.load_remote_input_dataset(\"national_vulnerability_feature_averages\", \"63d178c2a011a9746c948115\")\n",
+    "social_vulnerability.load_remote_input_dataset(\"social_vulnerability_demographic_factors\", \"63d17e81a011a9746c94811b\")\n",
+    "social_vulnerability.run_analysis()\n",
+    "sv_result = social_vulnerability.get_output_dataset(\"sv_result\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d697f61",
+   "metadata": {},
+   "source": [
+    "### HHRS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9011551e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Population dislocation result aggregated to the block group level.\n",
+    "# Transition probability matrix per social vulnerability level.\n",
+    "transition_probability_matrix = \"60f5e2ae544e944c3cec0794\"\n",
+    "# Initial mass probability function for stage 0.\n",
+    "initial_probability_vector = \"60f5e918544e944c3cec668b\" # this is only for galveston???\n",
+    "\n",
+    "# Create housing recovery instance\n",
+    "housing_recovery = HousingRecoverySequential(client)\n",
+    "\n",
+    "# Load input datasets\n",
+    "housing_recovery.set_input_dataset(\"population_dislocation_block\", population_dislocation_result)\n",
+    "housing_recovery.load_remote_input_dataset(\"tpm\", transition_probability_matrix)\n",
+    "housing_recovery.load_remote_input_dataset(\"initial_stage_probabilities\", initial_probability_vector)\n",
+    "\n",
+    "# Chain with SV output\n",
+    "housing_recovery.set_input_dataset('sv_result', sv_result)\n",
+    "\n",
+    "# Initial value to seed the random number generator to ensure replication\n",
+    "seed = 1111\n",
+    "# A size of the analysis time step.\n",
+    "t_delta = 1.0\n",
+    "# Total duration.\n",
+    "t_final = 90.0\n",
+    "\n",
+    "\n",
+    "# Specify the result name\n",
+    "result_name = \"housing_recovery_result\"\n",
+    "\n",
+    "# Set analysis parameters\n",
+    "housing_recovery.set_parameter(\"result_name\", result_name)\n",
+    "housing_recovery.set_parameter(\"seed\", seed)\n",
+    "housing_recovery.set_parameter(\"t_delta\", t_delta)\n",
+    "housing_recovery.set_parameter(\"t_final\", t_final)\n",
+    "\n",
+    "housing_recovery.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a0730a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HH_stagehistory_DF = housing_recovery.get_output_dataset(\"ds_result\").get_dataframe_from_csv()\n",
+    "timesteps = [\"1\", \"7\", \"13\", \"25\", \"49\", \"85\"] # t0, t6, t12, t24, t48\n",
+    "HHRSOutputProcess.get_hhrs_stage_count(timesteps, HH_stagehistory_DF)"
+   ]
+  }
+ ],
+ "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": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/slc_community_app.ipynb b/notebooks/slc_community_app.ipynb
index 3da9b204..ded9cc1c 100644
--- a/notebooks/slc_community_app.ipynb
+++ b/notebooks/slc_community_app.ipynb
@@ -390,6 +390,20 @@
     "bdmcs.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1a408f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret_json = DataProcessUtil.create_mapped_func_result(bldg_gdf, \n",
+    "                                                     bdmcs, \n",
+    "                                                     arch_mapping, \n",
+    "                                                     arch_col=\"occ_type\")\n",
+    "ret_json"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -740,18 +754,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7b51fee7",
+   "id": "d4c7cec9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bldg_func_probability = bldg_func.get_output_dataset('functionality_probability')\n",
-    "df_bldg_func = bldg_func_probability.get_dataframe_from_csv()\n",
-    "df_bldg_func = df_bldg_func.rename(columns={\"building_guid\": \"guid\"})\n",
-    "func_prob_target = 0.40\n",
-    "df_bldg_func.loc[df_bldg_func['probability'] <= func_prob_target, 'functionality'] = 0 # Non-Functional\n",
-    "df_bldg_func.loc[df_bldg_func['probability'] > func_prob_target, 'functionality'] = 1 # Functional\n",
-    "df_bldg_func.loc[df_bldg_func['probability'] <= func_prob_target, 'functionality_state'] = 'Non-Functional' # Non-Functional\n",
-    "df_bldg_func.loc[df_bldg_func['probability'] > func_prob_target, 'functionality_state'] = 'Functional' # Functional"
+    "ret_json = DataProcessUtil.create_mapped_func_result(bldg_gdf, \n",
+    "                                                     df_bldg_samples, \n",
+    "                                                     arch_mapping, \n",
+    "                                                     arch_col=\"occ_type\")\n",
+    "ret_json"
    ]
   },
   {
@@ -761,15 +772,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "bldg_func_probability = bldg_func.get_output_dataset('functionality_probability')\n",
+    "df_bldg_func = bldg_func_probability.get_dataframe_from_csv()\n",
+    "df_bldg_func = df_bldg_func.rename(columns={\"building_guid\": \"guid\"})\n",
+    "func_prob_target = 0.40\n",
+    "df_bldg_func.loc[df_bldg_func['probability'] <= func_prob_target, 'functionality'] = 0 # Non-Functional\n",
+    "df_bldg_func.loc[df_bldg_func['probability'] > func_prob_target, 'functionality'] = 1 # Functional\n",
+    "df_bldg_func.loc[df_bldg_func['probability'] <= func_prob_target, 'functionality_state'] = 'Non-Functional' # Non-Functional\n",
+    "df_bldg_func.loc[df_bldg_func['probability'] > func_prob_target, 'functionality_state'] = 'Functional' # Functional\n",
+    "\n",
     "df_bldg_func.head()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cff9fdd8",
+   "cell_type": "raw",
+   "id": "cf9c5a25",
    "metadata": {},
-   "outputs": [],
    "source": [
     "### Plot of the distribution of functionality state for buildings\n",
     "indexes = df_bldg_func['functionality_state'].value_counts(normalize=True).mul(100).index.tolist()\n",
@@ -1471,14 +1489,6 @@
     "#     stage = HH_stagehistory_DF[t]\n",
     "#     hhr_barchart_data[\"t\" + t] = [(stage==1.0).sum(),(stage==2.0).sum(),(stage==3.0).sum(),(stage==4.0).sum(),(stage==5.0).sum()]"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5958d1c4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1497,7 +1507,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,