From 16d222339e707d4f3af16049c5134e75817e5457 Mon Sep 17 00:00:00 2001
From: Barbaros Cetiner <bacetiner@ucla.edu>
Date: Fri, 30 Sep 2022 07:59:40 -0700
Subject: [PATCH 1/2] Updated the notebook for InventoryGenerator

---
 ...S_InventoryGenerator_SampleColabCall.ipynb | 835 ++++++++++++++++++
 1 file changed, 835 insertions(+)
 create mode 100644 notebooks/BRAILS_InventoryGenerator_SampleColabCall.ipynb

diff --git a/notebooks/BRAILS_InventoryGenerator_SampleColabCall.ipynb b/notebooks/BRAILS_InventoryGenerator_SampleColabCall.ipynb
new file mode 100644
index 0000000..202482c
--- /dev/null
+++ b/notebooks/BRAILS_InventoryGenerator_SampleColabCall.ipynb
@@ -0,0 +1,835 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/bacetiner/BRAILS/blob/master/notebooks/BRAILS_InventoryGenerator_SampleColabCall.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kVPwJknF2JZ-"
+      },
+      "source": [
+        "## Install the latest version of BRAILS:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qSKGJQUOvll9",
+        "outputId": "61e18965-f325-4af1-9025-341f43ebcb37"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting git+https://github.com/NHERI-SimCenter/BRAILS\n",
+            "  Cloning https://github.com/NHERI-SimCenter/BRAILS to /tmp/pip-req-build-oub2q6nk\n",
+            "  Running command git clone -q https://github.com/NHERI-SimCenter/BRAILS /tmp/pip-req-build-oub2q6nk\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (3.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.21.6)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.3.5)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (2.23.0)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.7.3)\n",
+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (0.11.2)\n",
+            "Requirement already satisfied: wget in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (3.2)\n",
+            "Requirement already satisfied: shapely in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.8.4)\n",
+            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (4.6.0.66)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (4.64.1)\n",
+            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (2.8.0)\n",
+            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (2.5.1)\n",
+            "Requirement already satisfied: webcolors in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.12)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (6.0)\n",
+            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (1.12.1+cu113)\n",
+            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (0.13.1+cu113)\n",
+            "Requirement already satisfied: timm in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (0.6.7)\n",
+            "Requirement already satisfied: yacs in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (0.1.8)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (7.1.2)\n",
+            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from BRAILS==2.0.1.dev479+gf677ee6) (0.0)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->BRAILS==2.0.1.dev479+gf677ee6) (4.1.1)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->BRAILS==2.0.1.dev479+gf677ee6) (3.0.9)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->BRAILS==2.0.1.dev479+gf677ee6) (1.4.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->BRAILS==2.0.1.dev479+gf677ee6) (2.8.2)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->BRAILS==2.0.1.dev479+gf677ee6) (0.11.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->BRAILS==2.0.1.dev479+gf677ee6) (1.15.0)\n",
+            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->BRAILS==2.0.1.dev479+gf677ee6) (2022.2.1)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->BRAILS==2.0.1.dev479+gf677ee6) (2022.6.15)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->BRAILS==2.0.1.dev479+gf677ee6) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->BRAILS==2.0.1.dev479+gf677ee6) (2.10)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->BRAILS==2.0.1.dev479+gf677ee6) (3.0.4)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn->BRAILS==2.0.1.dev479+gf677ee6) (1.0.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->BRAILS==2.0.1.dev479+gf677ee6) (3.1.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->BRAILS==2.0.1.dev479+gf677ee6) (1.1.0)\n",
+            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.2.0)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.35.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (3.4.1)\n",
+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.0.1)\n",
+            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (57.4.0)\n",
+            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.48.1)\n",
+            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (3.17.3)\n",
+            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.8.1)\n",
+            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (0.37.1)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (0.6.1)\n",
+            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (0.4.6)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (0.2.8)\n",
+            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (4.2.4)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (1.3.1)\n",
+            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (4.12.0)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (3.8.1)\n",
+            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (0.4.8)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->BRAILS==2.0.1.dev479+gf677ee6) (3.2.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install git+https://github.com/NHERI-SimCenter/BRAILS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vHKXTzgC2JaI"
+      },
+      "source": [
+        "## Initialize the InventoryGenerator for your location of interest:\n",
+        "If the provided API Key is valid and has the required APIs (i.e., Street View Static API, and Maps Static API) enabled, the following code will display work as expected.\n",
+        "\n",
+        "If you receive an API key error, please make sure you have a [valid API key](https://developers.google.com/maps/documentation/embed/get-api-key) and [have Street View Static API, and Maps Static API enabled](https://support.google.com/googleapi/answer/6158841?hl=en) for this key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gWzCiBa50b98",
+        "outputId": "3563c417-7984-43c9-ccbd-11771762994e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Searching for Berkeley, CA...\n",
+            "Found Berkeley, Alameda County, CAL Fire Northern Region, California, United States\n",
+            "\n",
+            "Fetching footprint data for Berkeley...\n",
+            "Found a total of 35346 building footprints in Berkeley\n",
+            "Randomly selected 100 buildings\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "# Import InventoryGenerator:\n",
+        "from brails.InventoryGenerator import InventoryGenerator\n",
+        "\n",
+        "# Initialize InventoryGenerator:\n",
+        "invGenerator = InventoryGenerator(location='Enter the Location You Want the Inventory for Here, e.g., Berkeley, CA',\n",
+        "                                  nbldgs=100, randomSelection=True,\n",
+        "                                  GoogleAPIKey=\"Enter Google API Key Here\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wPmu1-6L2JaL"
+      },
+      "source": [
+        "## Run InventoryGenerator for all enabled attributes "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "p9CUHE3L5uHw",
+        "outputId": "eac1bde3-e7d7-45bc-9e95-ac3572880f16",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Determining the number of floors for each building...\n",
+            "Loading default floor detector model file to tmp/models folder...\n",
+            "Default floor detector model loaded\n",
+            "Performing floor detections...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 100/100 [00:26<00:00,  3.74it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Total execution time: 00:00:31.71\n",
+            "\n",
+            "Determining the heights and roof pitch for each building...\n",
+            "Loading default facade parser model file to tmp/models folder...\n",
+            "Default facade parser model loaded\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 100/100 [00:16<00:00,  6.09it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Checking the existence of chimneys for each building...\n",
+            "Loading default chimney detector model file to tmp/models folder...\n",
+            "Default chimney detector model loaded\n",
+            "Performing chimney detections...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 100/100 [00:15<00:00,  6.32it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Total execution time: 00:00:16.72\n",
+            "\n",
+            "Determining the era of construction for each building...\n",
+            "Loading default era of construction classifier model file to tmp/models folder...\n",
+            "\n",
+            "Default era of construction classifier model loaded\n",
+            "Performing construction era classifications...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 100/100 [00:00<00:00, 272711.57it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n",
+            "\n",
+            "Checking the existence of garages for each building...\n",
+            "Loading default garage detector model file to tmp/models folder...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Default garage detector model loaded\n",
+            "Performing garage detections...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 100/100 [00:16<00:00,  6.09it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Total execution time: 00:00:17.31\n",
+            "\n",
+            "Determining the roof type for each building...\n",
+            "Loading default roof type classifier model file to tmp/models folder...\n",
+            "\n",
+            "Model found locally: ./tmp/models/transformer_rooftype_v1.pkl \n",
+            "Loading  ./tmp/models/transformer_rooftype_v1.pkl\n",
+            "                          image prediction  probability\n",
+            "0    tmp/images/satellite/0.png       flat     0.998295\n",
+            "1    tmp/images/satellite/1.png     gabled     0.999999\n",
+            "2    tmp/images/satellite/2.png       flat     0.999939\n",
+            "3    tmp/images/satellite/3.png       flat     0.992569\n",
+            "4    tmp/images/satellite/4.png     gabled     0.999990\n",
+            "..                          ...        ...          ...\n",
+            "95  tmp/images/satellite/95.png     hipped     1.000000\n",
+            "96  tmp/images/satellite/96.png     gabled     1.000000\n",
+            "97  tmp/images/satellite/97.png       flat     0.997960\n",
+            "98  tmp/images/satellite/98.png       flat     0.992386\n",
+            "99  tmp/images/satellite/99.png     hipped     0.853740\n",
+            "\n",
+            "[100 rows x 3 columns]\n",
+            "Results written in file ./tmp/roofType_preds.csv\n",
+            "Final inventory available in inventory.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "#invGenerator.enabledAttributes\n",
+        "invGenerator.generate(attributes='all')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ismcjgIp2JaN"
+      },
+      "source": [
+        "## Show a snippet of the computed attribute values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "aTs45AdMAr4f",
+        "outputId": "78558996-5f19-4212-9202-1f25adc8479b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                            footprint  \\\n",
+              "0   [[-122.2983117, 37.8657377], [-122.2982712, 37...   \n",
+              "1   [[-122.2793234, 37.8976833], [-122.2793214, 37...   \n",
+              "2   [[-122.2901031, 37.8681056], [-122.2900881, 37...   \n",
+              "3   [[-122.2877556, 37.8583993], [-122.2877415, 37...   \n",
+              "4   [[-122.2731439, 37.8598559], [-122.2730928, 37...   \n",
+              "..                                                ...   \n",
+              "95  [[-122.2912109, 37.8813178], [-122.2912095, 37...   \n",
+              "96  [[-122.2771664, 37.8989287], [-122.2770586, 37...   \n",
+              "97  [[-122.2738461, 37.8831535], [-122.2738486, 37...   \n",
+              "98  [[-122.2561551, 37.8580264], [-122.2559372, 37...   \n",
+              "99  [[-122.2691581, 37.8588693], [-122.2691722, 37...   \n",
+              "\n",
+              "               satellite_images             street_images  nstories  \\\n",
+              "0    tmp/images/satellite/0.png   tmp/images/street/0.png         1   \n",
+              "1    tmp/images/satellite/1.png   tmp/images/street/1.png         1   \n",
+              "2    tmp/images/satellite/2.png   tmp/images/street/2.png         1   \n",
+              "3    tmp/images/satellite/3.png   tmp/images/street/3.png         2   \n",
+              "4    tmp/images/satellite/4.png   tmp/images/street/4.png         3   \n",
+              "..                          ...                       ...       ...   \n",
+              "95  tmp/images/satellite/95.png  tmp/images/street/95.png         2   \n",
+              "96  tmp/images/satellite/96.png  tmp/images/street/96.png         3   \n",
+              "97  tmp/images/satellite/97.png  tmp/images/street/97.png         1   \n",
+              "98  tmp/images/satellite/98.png  tmp/images/street/98.png         1   \n",
+              "99  tmp/images/satellite/99.png  tmp/images/street/99.png         1   \n",
+              "\n",
+              "    buildingheight  chimneyExists  eraBuilt  garageExists  roofeaveheight  \\\n",
+              "0             16.6          False  Pre-1970          True            14.8   \n",
+              "1             32.1          False  Pre-1970         False            15.1   \n",
+              "2             20.1          False  Pre-1970         False            17.8   \n",
+              "3              NaN          False  Pre-1970         False             NaN   \n",
+              "4             62.4          False  Pre-1970         False            44.8   \n",
+              "..             ...            ...       ...           ...             ...   \n",
+              "95            37.9          False  Pre-1970          True            31.8   \n",
+              "96            51.2           True  Pre-1970          True            44.6   \n",
+              "97            13.4          False  Pre-1970          True            10.7   \n",
+              "98            19.9          False  Pre-1970          True            17.0   \n",
+              "99            18.1          False  Pre-1970         False            16.2   \n",
+              "\n",
+              "    roofpitch roofshape  \n",
+              "0        0.06      Flat  \n",
+              "1        0.65     Gable  \n",
+              "2        0.16      Flat  \n",
+              "3         NaN      Flat  \n",
+              "4        0.52     Gable  \n",
+              "..        ...       ...  \n",
+              "95       0.41       Hip  \n",
+              "96       0.23     Gable  \n",
+              "97       0.06      Flat  \n",
+              "98       0.18      Flat  \n",
+              "99       0.04       Hip  \n",
+              "\n",
+              "[100 rows x 11 columns]"
+            ],
+            "text/html": [
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+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>footprint</th>\n",
+              "      <th>satellite_images</th>\n",
+              "      <th>street_images</th>\n",
+              "      <th>nstories</th>\n",
+              "      <th>buildingheight</th>\n",
+              "      <th>chimneyExists</th>\n",
+              "      <th>eraBuilt</th>\n",
+              "      <th>garageExists</th>\n",
+              "      <th>roofeaveheight</th>\n",
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+              "      <th>roofshape</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[[-122.2983117, 37.8657377], [-122.2982712, 37...</td>\n",
+              "      <td>tmp/images/satellite/0.png</td>\n",
+              "      <td>tmp/images/street/0.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>16.6</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>True</td>\n",
+              "      <td>14.8</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>Flat</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>[[-122.2793234, 37.8976833], [-122.2793214, 37...</td>\n",
+              "      <td>tmp/images/satellite/1.png</td>\n",
+              "      <td>tmp/images/street/1.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>32.1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>False</td>\n",
+              "      <td>15.1</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>Gable</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>[[-122.2901031, 37.8681056], [-122.2900881, 37...</td>\n",
+              "      <td>tmp/images/satellite/2.png</td>\n",
+              "      <td>tmp/images/street/2.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>False</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>0.16</td>\n",
+              "      <td>Flat</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>[[-122.2877556, 37.8583993], [-122.2877415, 37...</td>\n",
+              "      <td>tmp/images/satellite/3.png</td>\n",
+              "      <td>tmp/images/street/3.png</td>\n",
+              "      <td>2</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Flat</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>[[-122.2731439, 37.8598559], [-122.2730928, 37...</td>\n",
+              "      <td>tmp/images/satellite/4.png</td>\n",
+              "      <td>tmp/images/street/4.png</td>\n",
+              "      <td>3</td>\n",
+              "      <td>62.4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>False</td>\n",
+              "      <td>44.8</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>Gable</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>[[-122.2912109, 37.8813178], [-122.2912095, 37...</td>\n",
+              "      <td>tmp/images/satellite/95.png</td>\n",
+              "      <td>tmp/images/street/95.png</td>\n",
+              "      <td>2</td>\n",
+              "      <td>37.9</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>True</td>\n",
+              "      <td>31.8</td>\n",
+              "      <td>0.41</td>\n",
+              "      <td>Hip</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>[[-122.2771664, 37.8989287], [-122.2770586, 37...</td>\n",
+              "      <td>tmp/images/satellite/96.png</td>\n",
+              "      <td>tmp/images/street/96.png</td>\n",
+              "      <td>3</td>\n",
+              "      <td>51.2</td>\n",
+              "      <td>True</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>True</td>\n",
+              "      <td>44.6</td>\n",
+              "      <td>0.23</td>\n",
+              "      <td>Gable</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>[[-122.2738461, 37.8831535], [-122.2738486, 37...</td>\n",
+              "      <td>tmp/images/satellite/97.png</td>\n",
+              "      <td>tmp/images/street/97.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>13.4</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>True</td>\n",
+              "      <td>10.7</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>Flat</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>[[-122.2561551, 37.8580264], [-122.2559372, 37...</td>\n",
+              "      <td>tmp/images/satellite/98.png</td>\n",
+              "      <td>tmp/images/street/98.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>19.9</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>True</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>0.18</td>\n",
+              "      <td>Flat</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>[[-122.2691581, 37.8588693], [-122.2691722, 37...</td>\n",
+              "      <td>tmp/images/satellite/99.png</td>\n",
+              "      <td>tmp/images/street/99.png</td>\n",
+              "      <td>1</td>\n",
+              "      <td>18.1</td>\n",
+              "      <td>False</td>\n",
+              "      <td>Pre-1970</td>\n",
+              "      <td>False</td>\n",
+              "      <td>16.2</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>Hip</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 11 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4698370-4efb-48d0-978f-4cc0720a4fee')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-c4698370-4efb-48d0-978f-4cc0720a4fee button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-c4698370-4efb-48d0-978f-4cc0720a4fee');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "invGenerator.inventory"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0cqfCy1F2JaP"
+      },
+      "source": [
+        "## Scroll through the images parsed by the InventoryGenerator"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "DL-ZALSqHGin",
+        "outputId": "567e3e11-dba2-4edf-8612-b471a03119ab",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 657
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "from IPython.display import Image\n",
+        "import os\n",
+        "images = ['tmp/images/street/' + im for im in os.listdir('tmp/images/street')]\n",
+        "Image(filename=images[-2]) "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lgmDaRcW2JaR"
+      },
+      "source": [
+        "## Download the created inventory table as a CSV file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "mU14EHF2Jd4u",
+        "outputId": "c5cf3b64-2d77-4ab5-9839-7b2f96959ede",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_59cf1b1f-3f96-4550-a944-3a16812daf86\", \"inventory.csv\", 26466)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "files.download('inventory.csv') "
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "machine_shape": "hm",
+      "name": "InventoryGeneratorLocalTesting.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "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": "3.8.12"
+    },
+    "gpuClass": "premium"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From dff10fb46d05c1bf8774e0da723c40274e9151a2 Mon Sep 17 00:00:00 2001
From: bacetiner <bacetiner@ucla.edu>
Date: Fri, 30 Sep 2022 08:02:15 -0700
Subject: [PATCH 2/2] Removed the old InventoryGenerator notebook

---
 .../InventoryGenerator_SampleColabCall.ipynb  | 176 ------------------
 1 file changed, 176 deletions(-)
 delete mode 100644 notebooks/InventoryGenerator_SampleColabCall.ipynb

diff --git a/notebooks/InventoryGenerator_SampleColabCall.ipynb b/notebooks/InventoryGenerator_SampleColabCall.ipynb
deleted file mode 100644
index 90a8652..0000000
--- a/notebooks/InventoryGenerator_SampleColabCall.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "view-in-github"
-   },
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/bacetiner/BRAILS/blob/master/notebooks/InventoryGeneratorLocalTesting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install the latest version of BRAILS:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "qSKGJQUOvll9",
-    "outputId": "666629d6-e158-4aeb-829f-028cf8a69bf6"
-   },
-   "outputs": [],
-   "source": [
-    "pip install git+https://github.com/NHERI-SimCenter/BRAILS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize the InventoryGenerator for your location of interest:\n",
-    "If the provided API Key is valid and has the required APIs (i.e., Street View Static API, and Maps Static API) enabled, the following code will display work as expected.\n",
-    "\n",
-    "If you receive an API key error, please make sure you have a [valid API key](https://developers.google.com/maps/documentation/embed/get-api-key) and [have Street View Static API, and Maps Static API enabled](https://support.google.com/googleapi/answer/6158841?hl=en) for this key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 235
-    },
-    "id": "gWzCiBa50b98",
-    "outputId": "6a74c5d7-c8bf-422c-af28-fb48ca77a36f"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Import InventoryGenerator:\n",
-    "from brails.InventoryGenerator import InventoryGenerator\n",
-    "\n",
-    "# Initialize InventoryGenerator:\n",
-    "invGenerator = InventoryGenerator(location='Enter the Location You Want the Inventory for Here, e.g., Berkeley, CA',\n",
-    "                                  nbldgs=100, randomSelection=True,\n",
-    "                                  GoogleAPIKey=\"Enter Google API Key Here\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run InventoryGenerator for all enabled attributes "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "p9CUHE3L5uHw"
-   },
-   "outputs": [],
-   "source": [
-    "#invGenerator.enabledAttributes\n",
-    "invGenerator.generate(attributes='all')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Show a snippet of the computed attribute values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "aTs45AdMAr4f"
-   },
-   "outputs": [],
-   "source": [
-    "invGenerator.inventory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scroll through the images parsed by the InventoryGenerator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DL-ZALSqHGin"
-   },
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image\n",
-    "import os\n",
-    "images = ['tmp/images/street/' + im im in os.listdir('tmp/images/street')]\n",
-    "Image(filename=images[-1]) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download the created inventory table as a CSV file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mU14EHF2Jd4u"
-   },
-   "outputs": [],
-   "source": [
-    "from google.colab import files\n",
-    "files.download('inventory.csv') "
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "authorship_tag": "ABX9TyM2CmHuPe6821wODxbNfdyi",
-   "collapsed_sections": [],
-   "include_colab_link": true,
-   "machine_shape": "hm",
-   "name": "InventoryGeneratorLocalTesting.ipynb",
-   "provenance": []
-  },
-  "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": "3.8.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}