05 Extract Value - CHIRP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6096a9cc-13ea-4028-8d7b-c068f11b2e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import rasterio\n",
    "from rasterio.sample import sample_gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5da0b2f-323b-4579-bba2-3a88ce9487cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\\n       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso'],\\n      dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[41], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m## Get Data \u001b[39;00m\n\u001b[1;32m      3\u001b[0m df_ \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_compile_agro_CHIRP_Anomali.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m df_ \u001b[38;5;241m=\u001b[39m \u001b[43mdf_\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43midsubsegmen_repair\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mobs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtahun\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbulan\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m       \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mkdprov\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43midsubfinal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43midkab\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43midkec\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43midsegmen\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mis_puso\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      6\u001b[0m df_[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df_\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: Point(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m'\u001b[39m], row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      7\u001b[0m gdf_ \u001b[38;5;241m=\u001b[39m gpd\u001b[38;5;241m.\u001b[39mGeoDataFrame(df_, geometry\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 4108\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[1;32m   6248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[0;32m-> 6249\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6251\u001b[0m     not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m   6252\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\\n       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso'],\\n      dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "## Get Data \n",
    "\n",
    "df_ = pd.read_csv(\"data_compile_agro_climate.csv\", sep=';')\n",
    "df_ = df_[['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\n",
    "       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso']]\n",
    "df_['geometry'] = df_.apply(lambda row: Point(row['longitude'], row['latitude']), axis=1)\n",
    "gdf_ = gpd.GeoDataFrame(df_, geometry='geometry')\n",
    "print(gdf_.shape)\n",
    "gdf_.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d786c567-be7e-4f00-a589-c26eeb723b0e",
   "metadata": {},
   "source": [
    "### Get Sentinel 1 Pixel Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2efb3b6f-1344-4d9a-9911-4b18e5f3f263",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idsubsegmen_repair</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>obs</th>\n",
       "      <th>tahun</th>\n",
       "      <th>bulan</th>\n",
       "      <th>kdprov</th>\n",
       "      <th>idsubfinal</th>\n",
       "      <th>idkab</th>\n",
       "      <th>idkec</th>\n",
       "      <th>idsegmen</th>\n",
       "      <th>is_puso</th>\n",
       "      <th>geometry</th>\n",
       "      <th>year</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>320104008A1</td>\n",
       "      <td>-6.582085</td>\n",
       "      <td>106.646608</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23</td>\n",
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       "      <td>POINT (106.64661 -6.58209)</td>\n",
       "      <td>2023</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>320104008A2</td>\n",
       "      <td>-6.582958</td>\n",
       "      <td>106.646563</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>320104008A2239</td>\n",
       "      <td>3201</td>\n",
       "      <td>3201040</td>\n",
       "      <td>320104008</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (106.64656 -6.58296)</td>\n",
       "      <td>2023</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idsubsegmen_repair  latitude   longitude  obs  tahun  bulan  kdprov  \\\n",
       "0        320104008A1 -6.582085  106.646608  6.0     23      9      32   \n",
       "1        320104008A2 -6.582958  106.646563  6.0     23      9      32   \n",
       "\n",
       "       idsubfinal  idkab    idkec   idsegmen  is_puso  \\\n",
       "0  320104008A1239   3201  3201040  320104008        1   \n",
       "1  320104008A2239   3201  3201040  320104008        1   \n",
       "\n",
       "                     geometry  year  index  \n",
       "0  POINT (106.64661 -6.58209)  2023    273  \n",
       "1  POINT (106.64656 -6.58296)  2023    273  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_['year'] = \"20\" + gdf_['tahun'].astype(\"str\")\n",
    "gdf_['year'] = gdf_['year'].astype(\"int\")\n",
    "gdf_['index'] = 12*(gdf_.year-2001)+gdf_.bulan\n",
    "gdf_.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef95ac0a-1a1b-4d8a-be39-c40cf4c3f0a2",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "   270  271  272  273  274  275  \n",
       "0  109  113   43  174  689  197  \n",
       "1  109  113   43  174  689  197  \n",
       "\n",
       "[2 rows x 276 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load Get Value From Raster\n",
    "\n",
    "raster_path = \"chirp/Chirp_CH_montly_Jawa_2001-2023.tif\" \n",
    "with rasterio.open(raster_path) as src:\n",
    "    coordinates = [(geom.x, geom.y) for geom in gdf_.geometry]\n",
    "    pixel_values = list(src.sample(coordinates))\n",
    "     \n",
    "df_bands = pd.DataFrame(pixel_values)\n",
    "df_bands.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f5234fd-d5c0-415b-ae05-f930667fc0f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5399, 24)\n"
     ]
    },
    {
     "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PREC_23</th>\n",
       "      <th>PREC_22</th>\n",
       "      <th>PREC_21</th>\n",
       "      <th>PREC_20</th>\n",
       "      <th>PREC_19</th>\n",
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       "      <th>PREC_2</th>\n",
       "      <th>PREC_1</th>\n",
       "      <th>PREC_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PREC_23 PREC_22 PREC_21 PREC_20 PREC_19 PREC_18 PREC_17 PREC_16 PREC_15  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "  PREC_14  ... PREC_9 PREC_8 PREC_7 PREC_6 PREC_5 PREC_4 PREC_3 PREC_2 PREC_1  \\\n",
       "0     NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1     NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "  PREC_0  \n",
       "0    NaN  \n",
       "1    NaN  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_list = [f\"PREC_{i}\" for i in range(23, -1, -1)]\n",
    "df_prec = pd.DataFrame(index=gdf_.index, columns = prec_list)\n",
    "print(df_prec.shape)\n",
    "df_prec.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "170e24be-4f17-4c6a-9689-ffad0ae528c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,r in gdf_.iterrows():\n",
    "    ind = r['index']\n",
    "    df_prec.iloc[i,:] = df_bands.iloc[i,ind-24:ind]\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b9b62f7-04fa-4745-820f-20a0117c6065",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gdf_padi_ = gdf_[['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\n",
    "       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso', 'geometry',\n",
    "        'index']].join(df_prec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b6e662d6-873d-4b33-97c4-9a5666232fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_padi_.drop(['latitude','longitude','geometry'], axis='columns', inplace=True)\n",
    "gdf_padi_.to_csv(\"data_compile_agro_CHIRP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f366d2f1-a2dc-44d0-840c-fe93c495ea31",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-274</td>\n",
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       "      <td>135</td>\n",
       "      <td>-210</td>\n",
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      ],
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       "   0    1    2    3    4    5    6    7    8    9    ...  266  267  268  269  \\\n",
       "0  212  -25   41   73   29   20  106  -91  147  339  ...    2  -48  -86   34   \n",
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       "1  -63  -68 -274 -241  135 -210  \n",
       "\n",
       "[2 rows x 276 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load Get Value From Raster ANOMALY\n",
    "\n",
    "raster_path = \"chirp/Chirp_CH_montly_Jawa_2001-2023_Anomali.tif\" \n",
    "with rasterio.open(raster_path) as src:\n",
    "    coordinates = [(geom.x, geom.y) for geom in gdf_.geometry]\n",
    "    pixel_values = list(src.sample(coordinates))\n",
    "     \n",
    "df_bands = pd.DataFrame(pixel_values)\n",
    "df_bands.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7f2d0077-b87c-4c5c-9614-090e3308bc8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5399, 24)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PREC_23</th>\n",
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       "      <th>PREC_1</th>\n",
       "      <th>PREC_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PREC_23 PREC_22 PREC_21 PREC_20 PREC_19 PREC_18 PREC_17 PREC_16 PREC_15  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "  PREC_14  ... PREC_9 PREC_8 PREC_7 PREC_6 PREC_5 PREC_4 PREC_3 PREC_2 PREC_1  \\\n",
       "0     NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1     NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "  PREC_0  \n",
       "0    NaN  \n",
       "1    NaN  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_list = [f\"PREC_{i}\" for i in range(23, -1, -1)]\n",
    "df_prec = pd.DataFrame(index=gdf_.index, columns = prec_list)\n",
    "print(df_prec.shape)\n",
    "df_prec.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad9df886-c23e-4a67-a62e-918781da9ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,r in gdf_.iterrows():\n",
    "    ind = r['index']\n",
    "    df_prec.iloc[i,:] = df_bands.iloc[i,ind-24:ind]\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9780ce58-9cec-4d3c-8b49-f752f6766919",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_[['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\n",
    "       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso', 'geometry',\n",
    "        'index']].join(df_prec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "699ef0ba-e1e9-4963-a958-5fceee14fd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_padi_.drop(['latitude','longitude','geometry'], axis='columns', inplace=True)\n",
    "gdf_padi_.to_csv(\"data_compile_agro_CHIRP_Anomali.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f356bbc7-bc68-4726-92f6-1e122d7c612f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['idsubsegmen_repair', 'obs', 'tahun', 'bulan', 'kdprov', 'idsubfinal',\n",
       "       'idkab', 'idkec', 'idsegmen', 'is_puso', 'index', 'PREC_23', 'PREC_22',\n",
       "       'PREC_21', 'PREC_20', 'PREC_19', 'PREC_18', 'PREC_17', 'PREC_16',\n",
       "       'PREC_15', 'PREC_14', 'PREC_13', 'PREC_12', 'PREC_11', 'PREC_10',\n",
       "       'PREC_9', 'PREC_8', 'PREC_7', 'PREC_6', 'PREC_5', 'PREC_4', 'PREC_3',\n",
       "       'PREC_2', 'PREC_1', 'PREC_0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_padi_.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "73d6ed57-5c6a-484a-93d5-f636fc111f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(6.0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_puso.obs[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766a9b36-2c24-48b0-8d94-e4e0e7c5a3e5",
   "metadata": {},
   "source": [
    "#### Get Samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "141bce53-df04-4bf9-954d-06e26a752002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_puso = pd.read_csv(\"/data/raw/Workshop_PUSO.csv\", sep=';')\n",
    "sampling_rules = {\n",
    "    6.0: 2,\n",
    "    1.0: 1,\n",
    "    3.0: 1,\n",
    "    5.0: 1\n",
    "}\n",
    "df_puso['obs'] = pd.to_numeric(df_puso['obs'], errors='coerce')\n",
    "\n",
    "sampled_data = []\n",
    "\n",
    "for obs_value, sample_size in sampling_rules.items():\n",
    "    group = df_puso[df_puso['obs'] == obs_value]\n",
    "    if len(group) >= sample_size:  # Ensure there are enough rows to sample\n",
    "        sampled_data.append(group.sample(n=sample_size, random_state=42))  # Random seed for reproducibility\n",
    "    else:\n",
    "        print(f\"Not enough samples for obs={obs_value}. Available: {len(group)}, Requested: {sample_size}\")\n",
    "df_sampled = pd.concat(sampled_data).reset_index(drop=True)\n",
    "df_sampled.to_csv(\"sampel_puso_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3db3363-d17e-4072-9510-1a5663c8eca4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Catatan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3cc4fbc8-68b6-485f-a497-f155b7273716",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     idsubsegmen_repair  latitude   longitude  obs  tahun  bulan  kdprov  \\\n",
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       "\n",
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    "gdf_[['idsubsegmen_repair', 'latitude', 'longitude', 'obs', 'tahun', 'bulan',\n",
    "       'kdprov', 'idsubfinal', 'idkab', 'idkec', 'idsegmen', 'is_puso', 'geometry',\n",
    "        'index']].join(df_prec).iloc[2603:2605]"
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       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>327805003A22312</td>\n",
       "      <td>3278</td>\n",
       "      <td>3278050</td>\n",
       "      <td>327805003</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (108.2081 -7.34496)</td>\n",
       "      <td>2023</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>327808001C1</td>\n",
       "      <td>-7.316709</td>\n",
       "      <td>108.242268</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>327808001C12312</td>\n",
       "      <td>3278</td>\n",
       "      <td>3278080</td>\n",
       "      <td>327808001</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (108.24227 -7.31671)</td>\n",
       "      <td>2023</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>327808004C3</td>\n",
       "      <td>-7.305083</td>\n",
       "      <td>108.225835</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>327808004C32312</td>\n",
       "      <td>3278</td>\n",
       "      <td>3278080</td>\n",
       "      <td>327808004</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (108.22583 -7.30508)</td>\n",
       "      <td>2023</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     idsubsegmen_repair  latitude   longitude  obs  tahun  bulan  kdprov  \\\n",
       "2603        320319003C1 -6.835070  107.164869  6.0     23     12      32   \n",
       "2604        320505088B2 -7.457010  107.655826  6.0     23     12      32   \n",
       "2605        320714005B2 -7.388961  108.475277  6.0     23     12      32   \n",
       "2606        320714005C1 -7.388132  108.476177  6.0     23     12      32   \n",
       "2607        321103115B1 -6.872500  107.788132  6.0     23     12      32   \n",
       "...                 ...       ...         ...  ...    ...    ...     ...   \n",
       "2795        327312102C1 -6.921879  107.691642  4.0     23     12      32   \n",
       "2796        327703010A3 -6.864777  107.537640  8.0     23     12      32   \n",
       "2797        327805003A2 -7.344957  108.208097  8.0     23     12      32   \n",
       "2798        327808001C1 -7.316709  108.242268  8.0     23     12      32   \n",
       "2799        327808004C3 -7.305083  108.225835  2.0     23     12      32   \n",
       "\n",
       "           idsubfinal  idkab    idkec   idsegmen  is_puso  \\\n",
       "2603  320319003C12312   3203  3203190  320319003        1   \n",
       "2604  320505088B22312   3205  3205050  320505088        1   \n",
       "2605  320714005B22312   3207  3207140  320714005        1   \n",
       "2606  320714005C12312   3207  3207140  320714005        1   \n",
       "2607  321103115B12312   3211  3211031  321103115        1   \n",
       "...               ...    ...      ...        ...      ...   \n",
       "2795  327312102C12312   3273  3273121  327312102        0   \n",
       "2796  327703010A32312   3277  3277030  327703010        0   \n",
       "2797  327805003A22312   3278  3278050  327805003        0   \n",
       "2798  327808001C12312   3278  3278080  327808001        0   \n",
       "2799  327808004C32312   3278  3278080  327808004        0   \n",
       "\n",
       "                        geometry  year  index  \n",
       "2603  POINT (107.16487 -6.83507)  2023    276  \n",
       "2604  POINT (107.65583 -7.45701)  2023    276  \n",
       "2605  POINT (108.47528 -7.38896)  2023    276  \n",
       "2606  POINT (108.47618 -7.38813)  2023    276  \n",
       "2607   POINT (107.78813 -6.8725)  2023    276  \n",
       "...                          ...   ...    ...  \n",
       "2795  POINT (107.69164 -6.92188)  2023    276  \n",
       "2796  POINT (107.53764 -6.86478)  2023    276  \n",
       "2797   POINT (108.2081 -7.34496)  2023    276  \n",
       "2798  POINT (108.24227 -7.31671)  2023    276  \n",
       "2799  POINT (108.22583 -7.30508)  2023    276  \n",
       "\n",
       "[197 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_.loc[(gdf_.bulan == 12) & (gdf_.tahun == 23)]"
   ]
  }
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