{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b77c765-edb9-437f-bab9-9bb1217ab8a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:23.207656Z",
     "iopub.status.busy": "2023-07-12T19:18:23.207403Z",
     "iopub.status.idle": "2023-07-12T19:18:25.557392Z",
     "shell.execute_reply": "2023-07-12T19:18:25.556690Z",
     "shell.execute_reply.started": "2023-07-12T19:18:23.207621Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import skimage as ski\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from tqdm.auto import tqdm\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "BASEDIR = Path(\"/global/public/chabud-ecml-pkdd2023/\")\n",
    "fn = BASEDIR / \"train_eval.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fafd16b-c043-4c47-9e30-2c75b72eaccf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:25.562789Z",
     "iopub.status.busy": "2023-07-12T19:18:25.561013Z",
     "iopub.status.idle": "2023-07-12T19:18:25.571051Z",
     "shell.execute_reply": "2023-07-12T19:18:25.569973Z",
     "shell.execute_reply.started": "2023-07-12T19:18:25.562762Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def to_xarray(dataset, pretty_band_names=True):\n",
    "    \"\"\"Convert a single example into an xarray for easy access\"\"\"\n",
    "    \n",
    "    if pretty_band_names:\n",
    "        BANDS = [\"coastal_aerosol\", \"blue\", \"green\", \"red\",\n",
    "                 \"veg_red_1\", \"veg_red_2\", \"veg_red_3\", \"nir\", \n",
    "                 \"veg_red_4\", \"water_vapour\", \"swir_1\", \"swir_2\"]\n",
    "    else:\n",
    "        BANDS = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"8a\", \"9\", \"11\", \"12\"]\n",
    "        \n",
    "    post = dataset[\"post_fire\"][...].astype(\"float32\") / 10000.0\n",
    "    \n",
    "    # Da `pre_fire` manchmal fehlt ersetzen wir es durch 0 Werte was\n",
    "    # eh der Platzhalter für einen fehlenden Messwert ist.\n",
    "    try:\n",
    "        pre = dataset[\"pre_fire\"][...].astype(\"float32\") / 10000.0\n",
    "    except KeyError:\n",
    "        pre = np.zeros_like(post, dtype=\"float32\")\n",
    "        \n",
    "    # Da die Maske nur ein \"Band\" hat können wir die dritte Dimension einfach\n",
    "    # weglassen. Das erreichen wir in dem wir mit `0` am Ende indizieren.\n",
    "    mask = dataset[\"mask\"][..., 0].astype(\"bool\")\n",
    "    \n",
    "    return {\"pre\": xr.DataArray(pre, dims=[\"x\", \"y\", \"band\"], coords={\"x\": range(512), \"y\": range(512), \"band\": BANDS}),\n",
    "            \"post\": xr.DataArray(post, dims=[\"x\", \"y\", \"band\"], coords={\"x\": range(512), \"y\": range(512), \"band\": BANDS}),\n",
    "            \"mask\": xr.DataArray(mask, dims=[\"x\", \"y\"], coords={\"x\": range(512), \"y\": range(512)}),\n",
    "            \"fold\": dataset.attrs[\"fold\"]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385378a8-6c11-483c-a936-825af060f0c2",
   "metadata": {},
   "source": [
    "The following code was used and edited in order to analize the effect on the median and standart deviation if data with a too small masks are removed. This was done so that the Neuronal Network gets trained to predict larger masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a4eb708-4a8d-484a-a3d5-79318fa2e1ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:25.575871Z",
     "iopub.status.busy": "2023-07-12T19:18:25.574255Z",
     "iopub.status.idle": "2023-07-12T19:18:36.454225Z",
     "shell.execute_reply": "2023-07-12T19:18:36.453237Z",
     "shell.execute_reply.started": "2023-07-12T19:18:25.575846Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "res = []\n",
    "\n",
    "with h5py.File(fn, \"r\") as fd:\n",
    "    for name in fd:\n",
    "        ds = to_xarray(fd[name])\n",
    "        mask = ds[\"mask\"].values\n",
    "        burned = np.sum(mask) / (512*512)\n",
    "        #if(burned==1):\n",
    "            #print(name)\n",
    "        if(burned<=0.02):\n",
    "            continue;\n",
    "        res.append({\"burned\": burned})    \n",
    "        #break;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "450fe581-0d56-47e0-85d6-d82d3e22afde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:36.460360Z",
     "iopub.status.busy": "2023-07-12T19:18:36.458625Z",
     "iopub.status.idle": "2023-07-12T19:18:36.467408Z",
     "shell.execute_reply": "2023-07-12T19:18:36.466800Z",
     "shell.execute_reply.started": "2023-07-12T19:18:36.460317Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(res)\n",
    "del res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87df05e4-3401-428a-88ea-98a9510933c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:36.471822Z",
     "iopub.status.busy": "2023-07-12T19:18:36.470260Z",
     "iopub.status.idle": "2023-07-12T19:18:36.788703Z",
     "shell.execute_reply": "2023-07-12T19:18:36.787968Z",
     "shell.execute_reply.started": "2023-07-12T19:18:36.471799Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23417302673938228\n",
      "0.26324914249621933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x150591a3e230>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = np.mean(df[\"burned\"])\n",
    "std = np.std(df[\"burned\"])\n",
    "print(mean)\n",
    "print(std)\n",
    "#print(mean-std)\n",
    "fig_1 = sns.displot(df, x=\"burned\", kind =\"ecdf\", height=5, aspect=3)\n",
    "fig_1.ax.axvline(mean, color = \"red\")\n",
    "#fig_1.ax.axvline(mean-std, color = \"blue\")\n",
    "#fig_1.ax.axvline(std+mean, color = \"blue\")\n",
    "#fig_1.savefig('size_mask_no_2.eps' , format = 'eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "826fa232-df66-44ec-bbe0-6d43459512f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:36.792861Z",
     "iopub.status.busy": "2023-07-12T19:18:36.791384Z",
     "iopub.status.idle": "2023-07-12T19:18:36.796827Z",
     "shell.execute_reply": "2023-07-12T19:18:36.795906Z",
     "shell.execute_reply.started": "2023-07-12T19:18:36.792836Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# wir sehen, dass die meisten Masken zwiscehn 0 und 20% des Bildes abdecken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1574fdb1-6442-4803-b8d0-b8355e1c2377",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:18:36.801555Z",
     "iopub.status.busy": "2023-07-12T19:18:36.799900Z",
     "iopub.status.idle": "2023-07-12T19:18:36.805446Z",
     "shell.execute_reply": "2023-07-12T19:18:36.804561Z",
     "shell.execute_reply.started": "2023-07-12T19:18:36.801531Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ein treshhold von 2% für die Masken sorgt schon dafür, dass der Datensatz ausgeglichener wirkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e79099d4-e737-4909-9b18-1e900e6d73e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T19:24:35.628647Z",
     "iopub.status.busy": "2023-07-12T19:24:35.628293Z",
     "iopub.status.idle": "2023-07-12T19:24:35.854670Z",
     "shell.execute_reply": "2023-07-12T19:24:35.853911Z",
     "shell.execute_reply.started": "2023-07-12T19:24:35.628618Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tvDMDAAAtlsOyLKuhT/773/+u+Ph47dq1SwMHDpRlWUpMTFR2draefvppSf+8W+J2u7Vw4UJNmTJFlZWV6tixo9asWaMJEyZIkr744gt5vV5t2rRJ6enpVzyu3++Xy+VSZWWlYmJiGjr9enV+ZmOj7xNoLY4tuKe5pwCghQrl3+9reg9KZWWlJCk2NlaSVFJSIp/Pp7S0NHuM0+nUoEGDVFhYKEkqLi7WuXPngsYkJiYqOTnZHnOxQCAgv98ftAAAgNarwYFiWZZmzpypu+++W8nJyZIkn88nSXK73UFj3W63vc3n8ykiIkLt27evd8zF8vLy5HK57MXr9TZ02gAAoAVocKDMmDFD+/fv1xtvvHHJNofDEfTYsqxL1l3scmNycnJUWVlpL6WlpQ2dNgAAaAEaFChZWVl69913tWPHDnXq1Mle7/F4JOmSOyHl5eX2XRWPx6OamhpVVFTUO+ZiTqdTMTExQQsAAGi9QgoUy7I0Y8YMvfXWW9q+fbu6dOkStL1Lly7yeDwqKCiw19XU1GjXrl1KTU2VJPXp00dt2rQJGlNWVqaDBw/aYwAAwPUtPJTB06dP1+uvv6533nlH0dHR9p0Sl8ulyMhIORwOZWdna/78+eratau6du2q+fPnq23btnrggQfssZMnT9asWbPUoUMHxcbGavbs2UpJSdHw4cMb/wwBAECLE1KgLF++XJI0ePDgoPUrV65UZmamJGnOnDmqrq7WtGnTVFFRoX79+mnr1q2Kjo62xy9dulTh4eEaP368qqurNWzYMK1atUphYWHXdjYAAKBVuKbvQWkufA8K0Hz4HhQADfW9fQ8KAABAUyBQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgnPDmngAANIfOz2xs7ikARju24J5mPT53UAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgn5ED54IMPNHr0aCUmJsrhcOjtt98O2p6ZmSmHwxG03HXXXUFjAoGAsrKyFBcXp6ioKI0ZM0YnT568phMBAACtR8iBcvbsWfXs2VPLli2rd8zIkSNVVlZmL5s2bQranp2drQ0bNig/P1+7d+9WVVWVRo0apdra2tDPAAAAtDrhoT4hIyNDGRkZlx3jdDrl8Xjq3FZZWakVK1ZozZo1Gj58uCTptddek9fr1bZt25Senh7qlAAAQCvTJO9B2blzp+Lj49WtWzc99thjKi8vt7cVFxfr3LlzSktLs9clJiYqOTlZhYWFde4vEAjI7/cHLQAAoPVq9EDJyMjQ2rVrtX37di1evFhFRUUaOnSoAoGAJMnn8ykiIkLt27cPep7b7ZbP56tzn3l5eXK5XPbi9Xobe9oAAMAgIb/EcyUTJkyw/zs5OVl9+/ZVUlKSNm7cqHHjxtX7PMuy5HA46tyWk5OjmTNn2o/9fj+RAgBAK9bkHzNOSEhQUlKSjhw5IknyeDyqqalRRUVF0Ljy8nK53e469+F0OhUTExO0AACA1qvJA+XUqVMqLS1VQkKCJKlPnz5q06aNCgoK7DFlZWU6ePCgUlNTm3o6AACgBQj5JZ6qqiodPXrUflxSUqJ9+/YpNjZWsbGxys3N1f3336+EhAQdO3ZMc+fOVVxcnO677z5Jksvl0uTJkzVr1ix16NBBsbGxmj17tlJSUuxP9QAAgOtbyIGyd+9eDRkyxH787XtDJk2apOXLl+vAgQN69dVXdfr0aSUkJGjIkCFat26doqOj7ecsXbpU4eHhGj9+vKqrqzVs2DCtWrVKYWFhjXBKAACgpQs5UAYPHizLsurd/t57711xHzfeeKNefvllvfzyy6EeHgAAXAf4LR4AAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgn5ED54IMPNHr0aCUmJsrhcOjtt98O2m5ZlnJzc5WYmKjIyEgNHjxYhw4dChoTCASUlZWluLg4RUVFacyYMTp58uQ1nQgAAGg9Qg6Us2fPqmfPnlq2bFmd2xctWqQlS5Zo2bJlKioqksfj0YgRI3TmzBl7THZ2tjZs2KD8/Hzt3r1bVVVVGjVqlGpraxt+JgAAoNUID/UJGRkZysjIqHObZVl68cUX9eyzz2rcuHGSpNWrV8vtduv111/XlClTVFlZqRUrVmjNmjUaPny4JOm1116T1+vVtm3blJ6efg2nAwAAWoNGfQ9KSUmJfD6f0tLS7HVOp1ODBg1SYWGhJKm4uFjnzp0LGpOYmKjk5GR7zMUCgYD8fn/QAgAAWq9GDRSfzydJcrvdQevdbre9zefzKSIiQu3bt693zMXy8vLkcrnsxev1Nua0AQCAYZrkUzwOhyPosWVZl6y72OXG5OTkqLKy0l5KS0sbba4AAMA8jRooHo9Hki65E1JeXm7fVfF4PKqpqVFFRUW9Yy7mdDoVExMTtAAAgNarUQOlS5cu8ng8KigosNfV1NRo165dSk1NlST16dNHbdq0CRpTVlamgwcP2mMAAMD1LeRP8VRVVeno0aP245KSEu3bt0+xsbG6+eablZ2drfnz56tr167q2rWr5s+fr7Zt2+qBBx6QJLlcLk2ePFmzZs1Shw4dFBsbq9mzZyslJcX+VA8AALi+hRwoe/fu1ZAhQ+zHM2fOlCRNmjRJq1at0pw5c1RdXa1p06apoqJC/fr109atWxUdHW0/Z+nSpQoPD9f48eNVXV2tYcOGadWqVQoLC2uEUwIAAC2dw7Isq7knESq/3y+Xy6XKysomeT9K52c2Nvo+gdbi2IJ7mnsKjYLrHLi8prjWQ/n3m9/iAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgnEYPlNzcXDkcjqDF4/HY2y3LUm5urhITExUZGanBgwfr0KFDjT0NAADQgjXJHZQ77rhDZWVl9nLgwAF726JFi7RkyRItW7ZMRUVF8ng8GjFihM6cOdMUUwEAAC1QkwRKeHi4PB6PvXTs2FHSP++evPjii3r22Wc1btw4JScna/Xq1frmm2/0+uuvN8VUAABAC9QkgXLkyBElJiaqS5cu+ulPf6rPP/9cklRSUiKfz6e0tDR7rNPp1KBBg1RYWFjv/gKBgPx+f9ACAABar0YPlH79+unVV1/Ve++9p9///vfy+XxKTU3VqVOn5PP5JElutzvoOW63295Wl7y8PLlcLnvxer2NPW0AAGCQRg+UjIwM3X///UpJSdHw4cO1ceNGSdLq1avtMQ6HI+g5lmVdsu67cnJyVFlZaS+lpaWNPW0AAGCQJv+YcVRUlFJSUnTkyBH70zwX3y0pLy+/5K7KdzmdTsXExAQtAACg9WryQAkEAvrss8+UkJCgLl26yOPxqKCgwN5eU1OjXbt2KTU1tamnAgAAWojwxt7h7NmzNXr0aN18880qLy/XvHnz5Pf7NWnSJDkcDmVnZ2v+/Pnq2rWrunbtqvnz56tt27Z64IEHGnsqAACghWr0QDl58qR+9rOf6auvvlLHjh111113ac+ePUpKSpIkzZkzR9XV1Zo2bZoqKirUr18/bd26VdHR0Y09FQAA0EI1eqDk5+dfdrvD4VBubq5yc3Mb+9AAAKCV4Ld4AACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgnGYNlN/85jfq0qWLbrzxRvXp00cffvhhc04HAAAYotkCZd26dcrOztazzz6rP//5zxowYIAyMjJ04sSJ5poSAAAwRLMFypIlSzR58mQ9+uijuu222/Tiiy/K6/Vq+fLlzTUlAABgiPDmOGhNTY2Ki4v1zDPPBK1PS0tTYWHhJeMDgYACgYD9uLKyUpLk9/ubZH4XAt80yX6B1qCprrvvG9c5cHlNca1/u0/Lsq44tlkC5auvvlJtba3cbnfQerfbLZ/Pd8n4vLw8/fznP79kvdfrbbI5Aqib68XmngGA70NTXutnzpyRy+W67JhmCZRvORyOoMeWZV2yTpJycnI0c+ZM+/GFCxf09ddfq0OHDnWOR+vh9/vl9XpVWlqqmJiY5p4OgCbCtX59sCxLZ86cUWJi4hXHNkugxMXFKSws7JK7JeXl5ZfcVZEkp9Mpp9MZtO5f/uVfmnKKMExMTAx/aQHXAa711u9Kd06+1Sxvko2IiFCfPn1UUFAQtL6goECpqanNMSUAAGCQZnuJZ+bMmZo4caL69u2r/v3763e/+51OnDihqVOnNteUAACAIZotUCZMmKBTp07phRdeUFlZmZKTk7Vp0yYlJSU115RgIKfTqeeff/6Sl/gAtC5c67iYw7qaz/oAAAB8j/gtHgAAYBwCBQAAGIdAAQAAxiFQrnODBw9WdnZ2c0/je/X222/r1ltvVVhYmLKzs7Vq1Sq+Vwet0s6dO+VwOHT69Olr2s+xY8fkcDi0b9++RpmXySzL0uOPP67Y2Fj7nK/HvydNwJtkr3ODBw9Wr1699OKLLzb3VL43brdbDz/8sJ588klFR0crPDxcZ86cUXx8fHNPDWhUNTU1+vrrr+V2u6/pW7dra2v197//XXFxcQoPb9YvIG9ymzdv1r333qudO3fqBz/4geLi4uT3+9WmTRtFR0c39/SuK637f2loVWpra+VwOHTDDQ2/8VdVVaXy8nKlp6cHfdVyZGRkvc85d+6c2rRp0+BjAs0lIiJCHo/nmvcTFhbWKPtpajU1NYqIiLimffz1r39VQkJC0JeGxsbGNvlxcSle4oEuXLigOXPmKDY2Vh6PR7m5ufa2JUuWKCUlRVFRUfJ6vZo2bZqqqqrs7cePH9fo0aPVvn17RUVF6Y477tCmTZuueMxvbz1v3LhRPXv21I033qh+/frpwIED9phvX3r5wx/+oNtvv11Op1PHjx9XTU2N5syZo5tuuklRUVHq16+fdu7ceVXH/Pb/AQ0dOlQOh0M7d+685CWe3Nxc9erVS//zP/+jH/zgB3I6nbIsS5WVlXr88ccVHx+vmJgYDR06VH/5y1+u/AcMNJLBgwcrKytL2dnZat++vdxut373u9/p7NmzevjhhxUdHa1bbrlFmzdvlnTpSzyXu14rKir04IMPqmPHjoqMjFTXrl21cuVKSZe+xPPtft9//3317dtXbdu2VWpqqg4fPhw033nz5ik+Pl7R0dF69NFH9cwzz6hXr15Xda6ZmZkaO3asfv7zn9vX3JQpU1RTUxP05zFjxgzNnDlTcXFxGjFihCTp008/1Y9//GO1a9dObrdbEydO1FdffXVVx8zKytKJEyfkcDjUuXNn+zjffYmnc+fOmjdvnjIzM+VyufTYY49JkgoLCzVw4EBFRkbK6/XqySef1NmzZ6/qfHEpAgVavXq1oqKi9Kc//UmLFi3SCy+8YP8MwQ033KD/+q//0sGDB7V69Wpt375dc+bMsZ87ffp0BQIBffDBBzpw4IAWLlyodu3aXfWx//3f/12//vWvVVRUpPj4eI0ZM0bnzp2zt3/zzTfKy8vTf//3f+vQoUOKj4/Xww8/rI8++kj5+fnav3+/fvKTn2jkyJE6cuTIZY/13b9A169fr7Kysnp/WuHo0aN68803tX79evsv5XvuuUc+n0+bNm1ScXGxevfurWHDhunrr7++6vMFrtXq1asVFxenjz/+WFlZWXriiSf0k5/8RKmpqfrkk0+Unp6uiRMn6ptvvrnkuZe7Xp977jl9+umn2rx5sz777DMtX75ccXFxl53Ls88+q8WLF2vv3r0KDw/XI488Ym9bu3atfvnLX2rhwoUqLi7WzTffrOXLl4d0ru+//74+++wz7dixQ2+88YY2bNhwyS/br169WuHh4froo4/0yiuvqKysTIMGDVKvXr20d+9ebdmyRV9++aXGjx9/xeO99NJLeuGFF9SpUyeVlZWpqKio3rG/+tWvlJycrOLiYj333HM6cOCA0tPTNW7cOO3fv1/r1q3T7t27NWPGjJDOGd9h4bo2aNAg6+677w5ad+edd1pPP/10nePffPNNq0OHDvbjlJQUKzc3N+Tj7tixw5Jk5efn2+tOnTplRUZGWuvWrbMsy7JWrlxpSbL27dtnjzl69KjlcDisv/3tb0H7GzZsmJWTk3PF41ZUVFiSrB07dtjrVq5cablcLvvx888/b7Vp08YqLy+3173//vtWTEyM9Y9//CNof7fccov1yiuvXNU5A9fq4uv1/PnzVlRUlDVx4kR7XVlZmSXJ+uMf/2hfZxUVFZZlXf56HT16tPXwww/Xua2kpMSSZP35z3+2LOv/X7/btm2zx2zcuNGSZFVXV1uWZVn9+vWzpk+fHrSfH/3oR1bPnj2v6lwnTZpkxcbGWmfPnrXXLV++3GrXrp1VW1tr/3n06tUr6HnPPfeclZaWFrSutLTUkmQdPnz4isddunSplZSUFLRu0KBB1lNPPWU/TkpKssaOHRs0ZuLEidbjjz8etO7DDz+0brjhBvvPBKHhDgrUo0ePoMcJCQkqLy+XJO3YsUMjRozQTTfdpOjoaD300EM6deqUfdvyySef1Lx58/SjH/1Izz//vPbv3x/Ssfv372//d2xsrLp3767PPvvMXhcRERE0v08++USWZalbt25q166dvezatUt//etfQz73+iQlJaljx4724+LiYlVVValDhw5Bxy0pKWnU4wJX8t3rISwsTB06dFBKSoq97ttfhP/2Gv6uy12vTzzxhPLz89WrVy/NmTNHhYWFIc0lISEh6LiHDx/Wv/3bvwWNv/jxlfTs2VNt27a1H/fv319VVVUqLS211/Xt2zfoOcXFxdqxY0fQdfqv//qvktSo12pdx121alXQcdPT03XhwgWVlJQ02nGvJ7xJFpe8AdThcOjChQs6fvy4fvzjH2vq1Kn6xS9+odjYWO3evVuTJ0+2X4Z59NFHlZ6ero0bN2rr1q3Ky8vT4sWLlZWV1eD5fPfTBpGRkUGPL1y4oLCwMBUXFyssLCzoeaG8tHQlUVFRQY8vXLighISEOt/rwkeU8X2q63r97rpvr5cLFy5c8tzLXa8ZGRk6fvy4Nm7cqG3btmnYsGGaPn26fv3rX1/VXOo67sWfHLIa6UOj391vXdfq6NGjtXDhwkue921ENYa6jjtlyhQ9+eSTl4y9+eabG+241xMCBfXau3evzp8/r8WLF9ufnHnzzTcvGef1ejV16lRNnTpVOTk5+v3vf3/VgbJnzx774q2oqND//d//2f9vpy4//OEPVVtbq/Lycg0YMKABZ9UwvXv3ls/nU3h4uP3GOaAlutz12rFjR2VmZiozM1MDBgyw3yPWEN27d9fHH3+siRMn2uv27t0b0j7+8pe/qLq62v6U3Z49e9SuXTt16tSp3uf07t1b69evV+fOnb/Xj0T37t1bhw4d0q233vq9HbO14yUe1OuWW27R+fPn9fLLL+vzzz/XmjVr9Nvf/jZoTHZ2tt577z2VlJTok08+0fbt23Xbbbdd9TFeeOEFvf/++zp48KAyMzMVFxensWPH1ju+W7duevDBB/XQQw/prbfeUklJiYqKirRw4cKr+vRQQw0fPlz9+/fX2LFj9d577+nYsWMqLCzUf/zHf4T8ly7QXC53vf7nf/6n3nnnHR09elSHDh3SH/7wh5Cu5YtlZWVpxYoVWr16tY4cOaJ58+Zp//79IX0fS01NjSZPnmy/eff555/XjBkzLvtVA9OnT9fXX3+tn/3sZ/r444/1+eefa+vWrXrkkUdUW1vb4PO5kqefflp//OMfNX36dO3bt09HjhzRu+++e013k693BArq1atXLy1ZskQLFy5UcnKy1q5dq7y8vKAxtbW1mj59um677TaNHDlS3bt3129+85urPsaCBQv01FNPqU+fPiorK9O77757xe8TWLlypR566CHNmjVL3bt315gxY/SnP/1JXq+3Qed5NRwOhzZt2qSBAwfqkUceUbdu3fTTn/5Ux44ds1/zB0x3ues1IiJCOTk56tGjhwYOHKiwsDDl5+c3+FgPPvigcnJyNHv2bPXu3VslJSXKzMzUjTfeeNX7GDZsmLp27aqBAwdq/PjxGj16dNDXINQlMTFRH330kWpra5Wenq7k5GQ99dRTcrlc1/QdSlfSo0cP7dq1S0eOHNGAAQP0wx/+UM8991yjvqx0veGbZNEsdu7cqSFDhqiiooL3cADXiREjRsjj8WjNmjVXHJuZmanTp0/r7bffbvqJwUi8BwUA0Oi++eYb/fa3v1V6errCwsL0xhtvaNu2bfZ3LAFXwks8aBJTp04N+rjdd5epU6c22XEzMjLqPe78+fOb7LgAgn37suiAAQPUp08f/e///q/Wr1+v4cOHS1K912m7du304YcfNsmcTpw4cdnjnjhxokmOi4bhJR40ifLycvn9/jq3xcTENNkP8/3tb39TdXV1ndtiY2Ov+JsaAL4fR48erXfbTTfddNnfx2qo8+fP69ixY/Vu/74/+YPLI1AAAIBxeIkHAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJz/B+/9wvdVmH6ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_has_pre = []\n",
    "res_no_pre = []\n",
    "with h5py.File(fn, \"r\") as fd:\n",
    "    for name, ds in fd.items():\n",
    "        if \"pre_fire\" in ds:\n",
    "            res_has_pre.append(name)\n",
    "        else:\n",
    "            res_no_pre.append(name)\n",
    "        \n",
    "x = [\"has_pre_fire\", \"missing_pre_fire\"]  # X-axis values\n",
    "y = [len(res_has_pre), len(res_no_pre)]  # Y-axis values\n",
    "\n",
    "plt.bar(x, y)\n",
    "#plt.show()\n",
    "\n",
    "plt.savefig('missing_prefire.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "664466a8-906e-46c2-b269-336450464187",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f48ae8f-32e9-457f-bf9a-e6aeddf2e86f",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-07-12T19:18:37.515168Z",
     "iopub.status.idle": "2023-07-12T19:18:37.516757Z",
     "shell.execute_reply": "2023-07-12T19:18:37.516583Z",
     "shell.execute_reply.started": "2023-07-12T19:18:37.516563Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cc93207-9157-459f-aab1-c712e6714b10",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10 / DM",
   "language": "python",
   "name": "py310-dm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}