prl900/rainfields_check.ipynb

## rainfields_check.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51f3b3fa-cc1b-41f8-aba2-3b6fa9f4a3be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Terrakio API at: https://terrakio-server-candidate-d4w6vamyxq-ts.a.run.app\n",
      "Requesting data with expression: RainfieldsCBR.precipitation_m15@(year=2024,month=11)#(day, sum)\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",
       "        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>time</th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>var0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-11-01</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>103.850006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-11-02</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>451.850006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-11-03</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>209.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>160.300018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>105.349991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-11-06</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>495.100006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-11-07</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>153.100006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-11-08</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>8.400001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-11-09</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>15.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-11-10</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>154.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-11-11</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>76.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-11-12</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>39.900002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-11-13</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>102.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-11-14</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>65.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-11-15</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>57.250008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-11-16</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>97.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2024-11-17</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>133.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2024-11-18</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2024-11-19</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>71.900002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024-11-20</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>134.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2024-11-21</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2024-11-22</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>8.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2024-11-23</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>234.850006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2024-11-24</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>134.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024-11-25</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>611.950012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2024-11-26</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>305.799988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2024-11-27</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>915.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2024-11-28</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>110.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2024-11-29</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>218.150009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2024-11-30</td>\n",
       "      <td>-34.7525</td>\n",
       "      <td>149.0175</td>\n",
       "      <td>542.049988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          time        y         x        var0\n",
       "0   2024-11-01 -34.7525  149.0175  103.850006\n",
       "1   2024-11-02 -34.7525  149.0175  451.850006\n",
       "2   2024-11-03 -34.7525  149.0175  209.649994\n",
       "3   2024-11-04 -34.7525  149.0175  160.300018\n",
       "4   2024-11-05 -34.7525  149.0175  105.349991\n",
       "5   2024-11-06 -34.7525  149.0175  495.100006\n",
       "6   2024-11-07 -34.7525  149.0175  153.100006\n",
       "7   2024-11-08 -34.7525  149.0175    8.400001\n",
       "8   2024-11-09 -34.7525  149.0175   15.700001\n",
       "9   2024-11-10 -34.7525  149.0175  154.500000\n",
       "10  2024-11-11 -34.7525  149.0175   76.400002\n",
       "11  2024-11-12 -34.7525  149.0175   39.900002\n",
       "12  2024-11-13 -34.7525  149.0175  102.300003\n",
       "13  2024-11-14 -34.7525  149.0175   65.250000\n",
       "14  2024-11-15 -34.7525  149.0175   57.250008\n",
       "15  2024-11-16 -34.7525  149.0175   97.150002\n",
       "16  2024-11-17 -34.7525  149.0175  133.050003\n",
       "17  2024-11-18 -34.7525  149.0175    2.700000\n",
       "18  2024-11-19 -34.7525  149.0175   71.900002\n",
       "19  2024-11-20 -34.7525  149.0175  134.350006\n",
       "20  2024-11-21 -34.7525  149.0175    0.250000\n",
       "21  2024-11-22 -34.7525  149.0175    8.900000\n",
       "22  2024-11-23 -34.7525  149.0175  234.850006\n",
       "23  2024-11-24 -34.7525  149.0175  134.350006\n",
       "24  2024-11-25 -34.7525  149.0175  611.950012\n",
       "25  2024-11-26 -34.7525  149.0175  305.799988\n",
       "26  2024-11-27 -34.7525  149.0175  915.250000\n",
       "27  2024-11-28 -34.7525  149.0175  110.099998\n",
       "28  2024-11-29 -34.7525  149.0175  218.150009\n",
       "29  2024-11-30 -34.7525  149.0175  542.049988"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from terrakio_api import Client\n",
    "from shapely.geometry import Point\n",
    "\n",
    "# Initialize the client\n",
    "client = Client(url=\"https://terrakio-server-candidate-d4w6vamyxq-ts.a.run.app/\")\n",
    "\n",
    "# Create a geographic feature\n",
    "point = Point(684750.0, 6152750.0)\n",
    "\n",
    "# Make a WCS request\n",
    "ds = client.wcs(\n",
    "     expr=\"RainfieldsCBR.precipitation_m15@(year=2024,month=11)#(day, sum)\",\n",
    "     feature=point,\n",
    "     in_crs=\"epsg:28355\",\n",
    "     output=\"csv\"\n",
    ")\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5fb65ca0-4b17-4d36-bdb9-574870200c72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-11-27 00:00:00\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accumulated precipitation')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "date = pd.to_datetime(\"2024-11-27 00:00:00\")\n",
    "\n",
    "arr = np.zeros((512,512), dtype=np.float32)\n",
    "for t in pd.date_range(start=f\"{date.strftime('%Y-%m-%d')} 00:00:00\", end=f\"{date.strftime('%Y-%m-%d')} 23:45:00\", freq=\"15min\"):\n",
    "    print(t)\n",
    "    ds = xr.open_dataset(f\"/home/pablo/Downloads/40_{date.strftime('%Y%m%d')}.prcp-m15/40_{t.strftime(\"%Y%m%d_%H%M00\")}.prcp-m15.nc\")\n",
    "    arr += ds.precipitation.values\n",
    "\n",
    "plt.imshow(arr, cmap=\"Blues\")\n",
    "plt.colorbar()\n",
    "plt.title(\"Accumulated precipitation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d51d88b8-8a21-499d-bd25-4a77d90e32b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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