04_ScoreMatrix.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1ef6809f-b4e1-4e33-a549-1a00ae11ec84",
   "metadata": {},
   "source": [
    "# Score model\n",
    "\n",
    "Use model to generate confusion matrix\n",
    "\n",
    "<figure>\n",
    "<img src=\"assets/score_matrix.png\" width=\"400\">\n",
    "<figcaption align = \"center\"> Confusion matrix for validation data </figcaption>\n",
    "</figure>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2714cb81-c84d-437b-ae86-9ef419dbb81b",
   "metadata": {},
   "source": [
    "### Val data image map\n",
    "\n",
    "The green dots within the red burn area represent false negatives -  we predicted the image at that location was not a fire (green) , but in reality it lies in the burn region (red)\n",
    "\n",
    "<figure>\n",
    "<img src=\"assets/plot_results_map.png\" width=\"600\">\n",
    "<figcaption align = \"center\"> plot of validation data </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fc2d53e-30c5-49c6-9c8b-173a6ae048de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-27 10:50:50,201 - numexpr.utils - INFO - Note: detected 224 virtual cores but NumExpr set to maximum of 64, check \"NUMEXPR_MAX_THREADS\" environment variable.\n",
      "2023-10-27 10:50:50,208 - numexpr.utils - INFO - Note: NumExpr detected 224 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "2023-10-27 10:50:50,212 - numexpr.utils - INFO - NumExpr defaulting to 8 threads.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device set to xpu by user.\n",
      "OMP_NUM_THREADS set to: 112\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import intel_extension_for_pytorch as ipex\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, models, transforms\n",
    "#from tqdm import tqdm\n",
    "from PIL import Image\n",
    "from data_loader import (\n",
    "    TRAIN_DIR,\n",
    "    VALID_DIR,\n",
    "    augment_and_save,\n",
    "    data_distribution,\n",
    "    imagenet_stats,\n",
    "    img_transforms,\n",
    "    plot_data_distribution,\n",
    "    show_data,\n",
    ")\n",
    "sns.set_theme(style=\"whitegrid\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102b8304-68ca-4b7c-878b-eb1654244574",
   "metadata": {},
   "source": [
    "# Define Predict Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "321d647d-a813-4db0-bddb-b848e363a2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(InFldr, ScoreDict, model):\n",
    "    import torchvision.transforms as transforms\n",
    "    import torch.nn as nn\n",
    "    import glob \n",
    "    model.eval()\n",
    "\n",
    "    scores = []\n",
    "    fns = []\n",
    "    transform = transforms.Compose([\n",
    "        transforms.Resize((1024, 1024)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(*imagenet_stats),\n",
    "    ])\n",
    "    \n",
    "    for fn in glob.glob(TEST_DIR+'*.png'):\n",
    "        img = Image.open(fn)\n",
    "        img_tensor = transform(img).reshape(1, 3, 1024, 1024)\n",
    "        score = 0\n",
    "        modelCPU = model.to(\"cpu\")\n",
    "        score = np.argmax(torch.softmax( modelCPU(img_tensor).detach(), dim =1).numpy().squeeze())\n",
    "        fns.append(fn)\n",
    "        scores.append(ScoreDict[score])\n",
    "    return (scores, fns)\n",
    "# model.eval()\n",
    "# with torch.no_grad():\n",
    "#     model_scripted = torch.jit.script(model.to(\"cpu\"))  # Export to TorchScript\n",
    "#     model_scripted.save(model_save_path)  # Jit Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d72bf02d-e15e-4216-b59f-c9e46cd3b820",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import os.path\n",
    "\n",
    "def most_recent_model_created(requestedFile):\n",
    "    path = 'models/'\n",
    "    check_file = os.path.isfile(f'{path}/{requestedFile}')\n",
    "    try: \n",
    "        if check_file:\n",
    "            return latest_file\n",
    "        else:\n",
    "            list_of_files = glob.glob(f'{path}*_jit.pt') # * means all if need specific format then *.csv\n",
    "            latest_file = max(list_of_files, key=os.path.getctime)\n",
    "            return latest_file\n",
    "    except:\n",
    "        print(\"No models exist yet - can't score!\")\n",
    "    return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6e45c26-687a-469f-8579-cc8f78e10224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models/model_acc_94_device_xpu_lr_0.000214_epochs_6_jit.pt\n"
     ]
    }
   ],
   "source": [
    "#model_save_path\n",
    "requestedFile = \"model_acc_86_device_xpu_lr_0.000214_epochs_6_jit.pt\"\n",
    "model_name =  most_recent_model_created(requestedFile)\n",
    "print(model_name)\n",
    "try:\n",
    "    model_read = torch.jit.load(model_name)\n",
    "except Exception as e:\n",
    "    print(f\"No models trained yet {e}\")\n",
    "#model_read.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d0e043-770a-43cf-877b-435b869d325f",
   "metadata": {},
   "source": [
    "# Scoring Individual Images with Model\n",
    "### Known Fire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e0d168f-938d-45f1-b9b9-3d63f5199c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_12.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_night_with_wild_fire_3.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_17.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_15.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_2.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_6.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_16.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_19.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_18.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_5.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire_0.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_4.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_smoky_conditions_4.png\n",
      "NoFire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_0.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_7.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_10.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_14.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_11.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_2.png\n",
      "NoFire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_9.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_early_morning_with_a_wild_fire,_no_clouds_3.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_13.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_mid-day_signs_of_wild_fire_8.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_1.png\n",
      "Fire\tdata/colorEnhanced/val/Fire/m_4012355_se_10_h_20160713_late_afternoon_with_signs_of_wild_fire,_no_clouds_1.png\n"
     ]
    }
   ],
   "source": [
    "Fire_true = []\n",
    "Fire_pred = []\n",
    "Fire_pred_dict = {}\n",
    "TEST_DIR =  'data/colorEnhanced/val/Fire/'\n",
    "scoreDict = {0:'Fire', 1:'NoFire'}\n",
    "scores, fns = predict(TEST_DIR, scoreDict, model_read)\n",
    "for score, fn in zip(scores, fns):\n",
    "    Fire_pred_dict[fn] = score\n",
    "    print(f\"{score}\\t{fn}\")\n",
    "    if score == 'Fire': \n",
    "        Fire_pred.append(1)\n",
    "    else:\n",
    "        Fire_pred.append(0)\n",
    "    Fire_true.append(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b500a614-2253-4efe-b736-9a52db7c4b9b",
   "metadata": {},
   "source": [
    "# Scoring Individual Images with Model\n",
    "### Known NoFire"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbc7762-8eba-4749-bbc2-d33b63ca18b8",
   "metadata": {},
   "source": [
    "# Define functions and normalization parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb4d34ef-4afb-4b54-ab5d-2c297cf81b83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_early_morning_with_clear_skies_with_no_signs_of_wild_fire_5.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_no_signs_of_fire_1.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_mid-day_with_clear_skies_4.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_sparse_vegetation_no_signs_of_wild_fire_9.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_mid-day_with_clear_skies_with_no_signs_of_wild_fire_7.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_mid-day_with_clear_skies_with_no_signs_of_wild_fire_2.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_sparse_vegetation_no_signs_of_wild_fire_19.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_early_morning_with_clear_skies_with_no_signs_of_wild_fire_15.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_early_morning_with_clear_skies_with_no_signs_of_wild_fire_10.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_dense_vegetation_without_any_signs_of_a_wild_fire_18.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_mid-day_with_clear_skies_with_no_signs_of_wild_fire_17.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_late_afternoon_with_clear_skies_3.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_sparse_vegetation_no_signs_of_wild_fire_14.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_late_afternoon_with_clear_skies_with_no_signs_of_wild_fire_1.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_night_2.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_mid-day_with_clear_skies_with_no_signs_of_wild_fire_12.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_early_morning_with_clear_skies_0.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_late_afternoon_with_clear_skies_with_no_signs_of_wild_fire_16.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_sparse_vegetation_no_signs_of_wild_fire_4.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_late_afternoon_with_clear_skies_with_no_signs_of_wild_fire_11.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_dense_vegetation_without_any_signs_of_a_wild_fire_13.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_dense_vegetation_without_any_signs_of_a_wild_fire_8.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_dense_vegetation_without_any_signs_of_a_wild_fire_3.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_early_morning_with_clear_skies_with_no_signs_of_wild_fire_0.png\n",
      "NoFire\tdata/colorEnhanced/val/NoFire/m_4012241_se_10_h_20160712_late_afternoon_with_clear_skies_with_no_signs_of_wild_fire_6.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEST_DIR =  'data/colorEnhanced/val/NoFire/'\n",
    "scoreDict = {0:'Fire', 1:'NoFire'}\n",
    "scores, fns = predict(TEST_DIR, scoreDict, model_read)\n",
    "for score, fn in zip(scores, fns):\n",
    "    Fire_pred_dict[fn] = score\n",
    "    print(f\"{score}\\t{fn}\")\n",
    "    if score == 'Fire': \n",
    "        Fire_pred.append(1)\n",
    "    else:\n",
    "        Fire_pred.append(0)\n",
    "    Fire_true.append(0)\n",
    "len(Fire_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "938f5e23-bd0f-4e32-ac3b-c0b14c115c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fireDict = {0:'Fire', 1:'NoFire'}\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "YTRUE = [fireDict[val] for val in Fire_true]\n",
    "YPRED = [fireDict[val] for val in Fire_pred]\n",
    "labels=['Fire', 'NoFire']\n",
    "cm = confusion_matrix(YTRUE, YPRED,  labels=labels)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "disp.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bc16d95-baf7-4ac6-b6aa-dbd728374543",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "usgs = pd.read_csv('data/USGS_NAIP_Sampled_Mapping.csv')\n",
    "usgs['Longitude'] = usgs['Centroid'].apply(lambda x: float(x.split('(')[-1].replace(')','').replace(' ','').split(',')[0]))\n",
    "usgs['Lattitude'] = usgs['Centroid'].apply(lambda x: float(x.split('(')[-1].replace(')','').replace(' ','').split(',')[1]))\n",
    "\n",
    "# ModisOutlineParadise = np.load('data/ModisOutlineParadise.npy')\n",
    "# ModisOutlineNorth = np.load('data/ModisOutlineNorth.npy')\n",
    "# ModisOutlineWest = np.load('data/ModisOutlineWest.npy' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8f68bda-150f-46de-b26c-910d27c7497c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Class</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-120.947196</td>\n",
       "      <td>40.004298</td>\n",
       "      <td>NoFire</td>\n",
       "      <td>m_3912045_ne_10_h_20160712_sparse_vegetation_n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-120.628079</td>\n",
       "      <td>40.068760</td>\n",
       "      <td>NoFire</td>\n",
       "      <td>m_3912045_ne_10_h_20160712_early_morning_with_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-123.394951</td>\n",
       "      <td>39.185473</td>\n",
       "      <td>NoFire</td>\n",
       "      <td>m_3912045_ne_10_h_20160712_dense_vegetation_wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-123.089519</td>\n",
       "      <td>40.527386</td>\n",
       "      <td>NoFire</td>\n",
       "      <td>m_3912045_ne_10_h_20160712_late_afternoon_with...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-120.278382</td>\n",
       "      <td>39.423949</td>\n",
       "      <td>NoFire</td>\n",
       "      <td>m_3912343_se_10_h_20160529_dense_vegetation_wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>-121.833440</td>\n",
       "      <td>39.263026</td>\n",
       "      <td>Fire</td>\n",
       "      <td>m_4012355_se_10_h_20160713_early_morning_with_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>-121.593185</td>\n",
       "      <td>40.095536</td>\n",
       "      <td>Fire</td>\n",
       "      <td>m_4012355_se_10_h_20160713_late_afternoon_with...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>-122.165480</td>\n",
       "      <td>39.737182</td>\n",
       "      <td>Fire</td>\n",
       "      <td>m_4012355_se_10_h_20160713_mid-day_signs_of_wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>-122.227585</td>\n",
       "      <td>39.798355</td>\n",
       "      <td>Fire</td>\n",
       "      <td>m_4012355_se_10_h_20160713_late_afternoon_1.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>-122.161015</td>\n",
       "      <td>40.291393</td>\n",
       "      <td>Fire</td>\n",
       "      <td>m_4012355_se_10_h_20160713_late_afternoon_with...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Long        Lat   Class  \\\n",
       "0   -120.947196  40.004298  NoFire   \n",
       "1   -120.628079  40.068760  NoFire   \n",
       "2   -123.394951  39.185473  NoFire   \n",
       "3   -123.089519  40.527386  NoFire   \n",
       "4   -120.278382  39.423949  NoFire   \n",
       "..          ...        ...     ...   \n",
       "245 -121.833440  39.263026    Fire   \n",
       "246 -121.593185  40.095536    Fire   \n",
       "247 -122.165480  39.737182    Fire   \n",
       "248 -122.227585  39.798355    Fire   \n",
       "249 -122.161015  40.291393    Fire   \n",
       "\n",
       "                                                  Name  \n",
       "0    m_3912045_ne_10_h_20160712_sparse_vegetation_n...  \n",
       "1    m_3912045_ne_10_h_20160712_early_morning_with_...  \n",
       "2    m_3912045_ne_10_h_20160712_dense_vegetation_wi...  \n",
       "3    m_3912045_ne_10_h_20160712_late_afternoon_with...  \n",
       "4    m_3912343_se_10_h_20160529_dense_vegetation_wi...  \n",
       "..                                                 ...  \n",
       "245  m_4012355_se_10_h_20160713_early_morning_with_...  \n",
       "246  m_4012355_se_10_h_20160713_late_afternoon_with...  \n",
       "247  m_4012355_se_10_h_20160713_mid-day_signs_of_wi...  \n",
       "248    m_4012355_se_10_h_20160713_late_afternoon_1.png  \n",
       "249  m_4012355_se_10_h_20160713_late_afternoon_with...  \n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fire_pred_dict\n",
    "colorEnhancedMap = pd.read_csv(\"data/ColorEnhanced_Mapping.csv\")\n",
    "colorEnhancedMap = colorEnhancedMap.drop(columns=['Unnamed: 0'])\n",
    "colorEnhancedMap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7697e1-5e8d-4b56-82c8-b36b54b47131",
   "metadata": {},
   "source": [
    "# Synthesize a Faux MODIS Burn Area\n",
    "\n",
    "### Predicting Coordinates within and without the Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27d0d661-f5ea-4a12-ab7f-df8a1974a599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from shapely.geometry import Polygon as GPoly\n",
    "from shapely.geometry import Point\n",
    "import random\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "N_rnd = 2000\n",
    "\n",
    "xrnd = 4*np.random.sample(N_rnd) - 124\n",
    "yrnd = 2*np.random.sample(N_rnd) + 39\n",
    "prnd = np.array(list(zip(xrnd, yrnd)))\n",
    "\n",
    "xmin = -124\n",
    "xmax = -120\n",
    "\n",
    "ymin = 39\n",
    "ymax = 41\n",
    "\n",
    "# Create star shape for synthetic MODIS\n",
    "import numpy as np\n",
    "pi = np.pi\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "for k in range(0,5):\n",
    "    r = 4/9\n",
    "    x.append( r*np.cos(2*pi*k/5 - 2*pi/10) - 122 )\n",
    "    y.append( r*np.sin(2*pi*k/5 - 2*pi/10) + 40 )\n",
    "    r = 1\n",
    "    x.append(  r*np.cos(2*pi*k/5) - 122)\n",
    "    y.append(  r*np.sin(2*pi*k/5) + 40)\n",
    "    \n",
    "polyStar =  np.array(list(zip(x,y))) \n",
    "polyStarP = Polygon(polyStar, closed=True,  fc=(1,0,0,0.3),  ec=(0,0,1,0.3))\n",
    "polyStarG = GPoly(polyStar)\n",
    "\n",
    "ax.add_patch(polyStarP)\n",
    "\n",
    "\n",
    "# Color polyStar points red\n",
    "test = []\n",
    "for point in prnd:\n",
    "    test.append(not polyStarG.contains(Point(point)))\n",
    "test = np.array(test)\n",
    "VertNoFire = np.array(list(zip(xrnd[test==True], yrnd[test==True])))\n",
    "\n",
    "# Color polyStar points red\n",
    "test = []\n",
    "for point in prnd:\n",
    "    test.append( polyStarG.contains(Point(point)))\n",
    "test = np.array(test)\n",
    "VertFire = np.array(list(zip(xrnd[test==True], yrnd[test==True])))\n",
    "\n",
    "for fn, score in Fire_pred_dict.items():\n",
    "    lat = colorEnhancedMap[colorEnhancedMap['Name'] == fn.split('/')[-1]]['Lat']\n",
    "    long = colorEnhancedMap[colorEnhancedMap['Name'] == fn.split('/')[-1]]['Long']\n",
    "    if score == 'Fire':\n",
    "        plt.scatter(float(long),float(lat), s = 3, c='r')\n",
    "    else:\n",
    "        plt.scatter(float(long),float(lat), s = 5, c='lime')\n",
    "\n",
    "# Set the aspect ratio and limits\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.xlim(-124, -120)\n",
    "plt.ylim(39.0, 41.0)\n",
    "plt.xticks(rotation = -60)\n",
    "# Set labels and title\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('MODIS Regions Ideal Sampling')\n",
    "plt.grid()\n",
    "ax.grid()\n",
    "# Show the plot\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0500f665-21dc-450e-95d3-66d0cc812d15",
   "metadata": {},
   "source": [
    "## Notices and Disclaimers\n",
    "\n",
    "Intel technologies may require enabled hardware, software or service activation.\n",
    "\n",
    "No product or component can be absolutely secure. \n",
    "\n",
    "Your costs and results may vary. \n",
    "\n",
    "© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-gpu",
   "language": "python",
   "name": "pytorch-gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
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