{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4197519b-44fa-49b2-aec8-3fb402248bab",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Copyright CNES\n",
    "\n",
    "# Plot a lake's geometries from multiple Lake/River Single Pass products\n",
    "\n",
    "In this notebook, we show how to read the SWOT-HR River or Lake Single Pass vector products with geopandas and dask geopandas and how to represent a variable in time.\n",
    "The example is based on Lake Single Pass Prior products, but it would be the same methodology for all vector products relying on a feature ID"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c5902d-a63b-4a1e-845b-666a7051c4eb",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da42166b-989a-4a5f-a2bf-4b7c71516808",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from tqdm import tqdm\n",
    "\n",
    "from dask import dataframe as dd\n",
    "import dask_geopandas as dgpd\n",
    "from datetime import datetime as dt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec548ad4-dfa1-43a3-8d54-db049666c6c4",
   "metadata": {},
   "source": [
    "## Select all Lake Single-Pass products within our directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "345c3d75-f8c4-416e-91d0-f0438ceb38cb",
   "metadata": {},
   "source": [
    "### Filter by filename pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0884d32-63a5-4511-8186-e411f1592c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\n"
     ]
    }
   ],
   "source": [
    "filename_list = glob.glob(\"../docs/data/swot/SWOT_L2_HR_LakeSP_Prior/*PIC0_01/*PIC0_01.zip\")\n",
    "print(len(filename_list))\n",
    "# one can use zip files, but note it is slower"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cee9499",
   "metadata": {},
   "source": [
    "# Load all data\n",
    "now we want to load all data from all file in lazy mode, otherwise it will not fit into RAM.  \n",
    "For this, we will iteratively read all files with geopandas, and store data in a dask dataframe.  \n",
    "The drawback of this approach is that you cannot keep the geometries. However, if you are fine with that, it is very efficient.  \n",
    "One could hack that by changing geometries into strings and, once the data has been filter, converting back to geometries and a geopandas.Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb55e839",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_layers(files):\n",
    "    \n",
    "    ddf = None\n",
    "    for file in tqdm(files):\n",
    "\n",
    "        gdf = gpd.read_file(\n",
    "            file,\n",
    "            engine='pyogrio',\n",
    "            use_arrow=True,\n",
    "            )   \n",
    "        \n",
    "        ddf_tmp = dgpd.from_geopandas(gdf, chunksize=10000)\n",
    "        \n",
    "        if ddf is None:\n",
    "            ddf = ddf_tmp\n",
    "        else:\n",
    "            ddf = dd.concat([ddf, ddf_tmp ], ignore_order=True)\n",
    "\n",
    "    del ddf_tmp, gdf\n",
    "\n",
    "    return ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dcdc3f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:38<00:00,  1.27it/s]\n"
     ]
    },
    {
     "data": {
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       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lake_id</th>\n",
       "      <th>reach_id</th>\n",
       "      <th>obs_id</th>\n",
       "      <th>overlap</th>\n",
       "      <th>n_overlap</th>\n",
       "      <th>time</th>\n",
       "      <th>time_tai</th>\n",
       "      <th>time_str</th>\n",
       "      <th>wse</th>\n",
       "      <th>wse_u</th>\n",
       "      <th>wse_r_u</th>\n",
       "      <th>wse_std</th>\n",
       "      <th>area_total</th>\n",
       "      <th>area_tot_u</th>\n",
       "      <th>area_detct</th>\n",
       "      <th>area_det_u</th>\n",
       "      <th>layovr_val</th>\n",
       "      <th>xtrk_dist</th>\n",
       "      <th>ds1_l</th>\n",
       "      <th>ds1_l_u</th>\n",
       "      <th>ds1_q</th>\n",
       "      <th>ds1_q_u</th>\n",
       "      <th>ds2_l</th>\n",
       "      <th>ds2_l_u</th>\n",
       "      <th>ds2_q</th>\n",
       "      <th>ds2_q_u</th>\n",
       "      <th>quality_f</th>\n",
       "      <th>dark_frac</th>\n",
       "      <th>ice_clim_f</th>\n",
       "      <th>ice_dyn_f</th>\n",
       "      <th>partial_f</th>\n",
       "      <th>xovr_cal_q</th>\n",
       "      <th>geoid_hght</th>\n",
       "      <th>solid_tide</th>\n",
       "      <th>load_tidef</th>\n",
       "      <th>load_tideg</th>\n",
       "      <th>pole_tide</th>\n",
       "      <th>dry_trop_c</th>\n",
       "      <th>wet_trop_c</th>\n",
       "      <th>iono_c</th>\n",
       "      <th>xovr_cal_c</th>\n",
       "      <th>lake_name</th>\n",
       "      <th>p_res_id</th>\n",
       "      <th>p_lon</th>\n",
       "      <th>p_lat</th>\n",
       "      <th>p_ref_wse</th>\n",
       "      <th>p_ref_area</th>\n",
       "      <th>p_date_t0</th>\n",
       "      <th>p_ds_t0</th>\n",
       "      <th>p_storage</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
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       "</table>\n",
       "<div>Dask Name: concat, 97 expressions</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                lake_id reach_id  obs_id overlap n_overlap     time time_tai time_str      wse    wse_u  wse_r_u  wse_std area_total area_tot_u area_detct area_det_u layovr_val xtrk_dist    ds1_l  ds1_l_u    ds1_q  ds1_q_u    ds2_l  ds2_l_u    ds2_q  ds2_q_u quality_f dark_frac ice_clim_f ice_dyn_f partial_f xovr_cal_q geoid_hght solid_tide load_tidef load_tideg pole_tide dry_trop_c wet_trop_c   iono_c xovr_cal_c lake_name p_res_id    p_lon    p_lat p_ref_wse p_ref_area p_date_t0  p_ds_t0 p_storage  geometry\n",
       "npartitions=165                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
       "                 string   string  string  string    string  float64  float64   string  float64  float64  float64  float64    float64    float64    float64    float64    float64   float64  float64  float64  float64  float64  float64  float64  float64  float64     int32   float64      int32     int32     int32      int32    float64    float64    float64    float64   float64    float64    float64  float64    float64    string    int32  float64  float64   float64    float64    string  float64   float64  geometry\n",
       "                    ...      ...     ...     ...       ...      ...      ...      ...      ...      ...      ...      ...        ...        ...        ...        ...        ...       ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...        ...       ...       ...        ...        ...        ...        ...        ...       ...        ...        ...      ...        ...       ...      ...      ...      ...       ...        ...       ...      ...       ...       ...\n",
       "...                 ...      ...     ...     ...       ...      ...      ...      ...      ...      ...      ...      ...        ...        ...        ...        ...        ...       ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...        ...       ...       ...        ...        ...        ...        ...        ...       ...        ...        ...      ...        ...       ...      ...      ...      ...       ...        ...       ...      ...       ...       ...\n",
       "                    ...      ...     ...     ...       ...      ...      ...      ...      ...      ...      ...      ...        ...        ...        ...        ...        ...       ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...        ...       ...       ...        ...        ...        ...        ...        ...       ...        ...        ...      ...        ...       ...      ...      ...      ...       ...        ...       ...      ...       ...       ...\n",
       "                    ...      ...     ...     ...       ...      ...      ...      ...      ...      ...      ...      ...        ...        ...        ...        ...        ...       ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...        ...       ...       ...        ...        ...        ...        ...        ...       ...        ...        ...      ...        ...       ...      ...      ...      ...       ...        ...       ...      ...       ...       ...\n",
       "Dask Name: concat, 97 expressions\n",
       "Expr=Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[Concat(frames=[df, df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True), df], ignore_order=True)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf = load_layers(filename_list)\n",
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f8a8a41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2210007102\n",
       "1        2210007142\n",
       "2        2210007192\n",
       "3        2210007522\n",
       "4        2230000012\n",
       "            ...    \n",
       "33951    2230021903\n",
       "33952    2830061673\n",
       "33953    2940020732\n",
       "33954    2220006512\n",
       "33955    2220008312\n",
       "Name: lake_id, Length: 1456270, dtype: string"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf.lake_id.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1702f29a-b62b-42da-a9af-a5f0020ea58c",
   "metadata": {},
   "source": [
    "## Focus on our lake of interest\n",
    "If you do not know the ID of your lake of interest, you can get it the SWOT Prior Lake Database.\n",
    "This database can be viewed and/or downloaded from the hydroweb.next platform.\n",
    "You will find the SWOT Prior Lake Database (PLD) among the 'Results' from [hydroweb.next](https://hydroweb.next.theia-land.fr?pid=SWOT_PRIOR_LAKE_DATABASE). If you do not want to download it, click on \"+Project\" icon to add it to your project and click on the \"EYE\" icon to display this vector layer into the current map selection. Note that you may have to place this product on top of the products in your \"Project\" panel or unselect the \"EYE\" icon of the other products, in order to view the PLD layer on the map. On the map click inside the water body of interest (here the \"river-lake\" we are studying) and you will see more details about it in the 'Select' panel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0314b71d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\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>lake_id</th>\n",
       "      <th>reach_id</th>\n",
       "      <th>obs_id</th>\n",
       "      <th>overlap</th>\n",
       "      <th>n_overlap</th>\n",
       "      <th>time</th>\n",
       "      <th>time_tai</th>\n",
       "      <th>time_str</th>\n",
       "      <th>wse</th>\n",
       "      <th>wse_u</th>\n",
       "      <th>...</th>\n",
       "      <th>p_res_id</th>\n",
       "      <th>p_lon</th>\n",
       "      <th>p_lat</th>\n",
       "      <th>p_ref_wse</th>\n",
       "      <th>p_ref_area</th>\n",
       "      <th>p_date_t0</th>\n",
       "      <th>p_ds_t0</th>\n",
       "      <th>p_storage</th>\n",
       "      <th>geometry</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24386</th>\n",
       "      <td>2940021962</td>\n",
       "      <td>no_data</td>\n",
       "      <td>294094R099897</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.617207e+08</td>\n",
       "      <td>7.617207e+08</td>\n",
       "      <td>2024-02-20T05:05:48Z</td>\n",
       "      <td>117.685</td>\n",
       "      <td>0.791</td>\n",
       "      <td>...</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>44.416265</td>\n",
       "      <td>34.690836</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>119.034902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((44.56503 34.55532, 44.56499 34...</td>\n",
       "      <td>2024-02-20 05:05:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19967</th>\n",
       "      <td>2940021962</td>\n",
       "      <td>no_data</td>\n",
       "      <td>294094L099732;294094L099863;294094L099955;2940...</td>\n",
       "      <td>42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...</td>\n",
       "      <td>58</td>\n",
       "      <td>7.626653e+08</td>\n",
       "      <td>7.626653e+08</td>\n",
       "      <td>2024-03-02T03:28:37Z</td>\n",
       "      <td>113.316</td>\n",
       "      <td>0.030</td>\n",
       "      <td>...</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>44.416265</td>\n",
       "      <td>34.690836</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>119.034902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((44.34098 34.84831, 44.34081 34...</td>\n",
       "      <td>2024-03-02 03:28:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27266</th>\n",
       "      <td>2940021962</td>\n",
       "      <td>no_data</td>\n",
       "      <td>294094L099739;294094L099973;294094L099832;2940...</td>\n",
       "      <td>42;1;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...</td>\n",
       "      <td>60</td>\n",
       "      <td>7.644680e+08</td>\n",
       "      <td>7.644680e+08</td>\n",
       "      <td>2024-03-23T00:13:40Z</td>\n",
       "      <td>114.799</td>\n",
       "      <td>0.053</td>\n",
       "      <td>...</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>44.416265</td>\n",
       "      <td>34.690836</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>119.034902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((44.26363 34.82676, 44.26342 34...</td>\n",
       "      <td>2024-03-23 00:13:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11426</th>\n",
       "      <td>2940021962</td>\n",
       "      <td>no_data</td>\n",
       "      <td>294094L099894;294094L099975;294094L099435;2940...</td>\n",
       "      <td>42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...</td>\n",
       "      <td>57</td>\n",
       "      <td>7.680734e+08</td>\n",
       "      <td>7.680734e+08</td>\n",
       "      <td>2024-05-03T17:43:47Z</td>\n",
       "      <td>119.463</td>\n",
       "      <td>0.120</td>\n",
       "      <td>...</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>44.416265</td>\n",
       "      <td>34.690836</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>119.034902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((44.27089 34.81043, 44.27067 34...</td>\n",
       "      <td>2024-05-03 17:43:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8346</th>\n",
       "      <td>2940021962</td>\n",
       "      <td>no_data</td>\n",
       "      <td>294094L099811;294094L099884;294094L099963;2940...</td>\n",
       "      <td>42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...</td>\n",
       "      <td>44</td>\n",
       "      <td>7.698761e+08</td>\n",
       "      <td>7.698761e+08</td>\n",
       "      <td>2024-05-24T14:28:53Z</td>\n",
       "      <td>113.857</td>\n",
       "      <td>0.021</td>\n",
       "      <td>...</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>44.416265</td>\n",
       "      <td>34.690836</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>119.034902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((44.33459 34.84880, 44.33441 34...</td>\n",
       "      <td>2024-05-24 14:28:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          lake_id reach_id                                             obs_id  \\\n",
       "24386  2940021962  no_data                                      294094R099897   \n",
       "19967  2940021962  no_data  294094L099732;294094L099863;294094L099955;2940...   \n",
       "27266  2940021962  no_data  294094L099739;294094L099973;294094L099832;2940...   \n",
       "11426  2940021962  no_data  294094L099894;294094L099975;294094L099435;2940...   \n",
       "8346   2940021962  no_data  294094L099811;294094L099884;294094L099963;2940...   \n",
       "\n",
       "                                                 overlap n_overlap  \\\n",
       "24386                                                  0         1   \n",
       "19967  42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...        58   \n",
       "27266  42;1;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...        60   \n",
       "11426  42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...        57   \n",
       "8346   42;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0...        44   \n",
       "\n",
       "               time      time_tai              time_str      wse  wse_u  ...  \\\n",
       "24386  7.617207e+08  7.617207e+08  2024-02-20T05:05:48Z  117.685  0.791  ...   \n",
       "19967  7.626653e+08  7.626653e+08  2024-03-02T03:28:37Z  113.316  0.030  ...   \n",
       "27266  7.644680e+08  7.644680e+08  2024-03-23T00:13:40Z  114.799  0.053  ...   \n",
       "11426  7.680734e+08  7.680734e+08  2024-05-03T17:43:47Z  119.463  0.120  ...   \n",
       "8346   7.698761e+08  7.698761e+08  2024-05-24T14:28:53Z  113.857  0.021  ...   \n",
       "\n",
       "       p_res_id      p_lon      p_lat     p_ref_wse  p_ref_area  p_date_t0  \\\n",
       "24386 -99999999  44.416265  34.690836 -1.000000e+12  119.034902    no_data   \n",
       "19967 -99999999  44.416265  34.690836 -1.000000e+12  119.034902    no_data   \n",
       "27266 -99999999  44.416265  34.690836 -1.000000e+12  119.034902    no_data   \n",
       "11426 -99999999  44.416265  34.690836 -1.000000e+12  119.034902    no_data   \n",
       "8346  -99999999  44.416265  34.690836 -1.000000e+12  119.034902    no_data   \n",
       "\n",
       "            p_ds_t0     p_storage  \\\n",
       "24386 -1.000000e+12 -1.000000e+12   \n",
       "19967 -1.000000e+12 -1.000000e+12   \n",
       "27266 -1.000000e+12 -1.000000e+12   \n",
       "11426 -1.000000e+12 -1.000000e+12   \n",
       "8346  -1.000000e+12 -1.000000e+12   \n",
       "\n",
       "                                                geometry                date  \n",
       "24386  MULTIPOLYGON (((44.56503 34.55532, 44.56499 34... 2024-02-20 05:05:48  \n",
       "19967  MULTIPOLYGON (((44.34098 34.84831, 44.34081 34... 2024-03-02 03:28:37  \n",
       "27266  MULTIPOLYGON (((44.26363 34.82676, 44.26342 34... 2024-03-23 00:13:40  \n",
       "11426  MULTIPOLYGON (((44.27089 34.81043, 44.27067 34... 2024-05-03 17:43:47  \n",
       "8346   MULTIPOLYGON (((44.33459 34.84880, 44.33441 34... 2024-05-24 14:28:53  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MY_LAKE_ID = '2940022112'\n",
    "MY_LAKE_ID = '2940021962'\n",
    "\n",
    "df_vs = ddf[ddf['lake_id'] == MY_LAKE_ID].compute()\n",
    "\n",
    "# Interpreting the dates as dates with the datetime library\n",
    "df_vs['date'] = df_vs['time_str'].apply(\n",
    "    lambda t: np.nan if t =='no_data' else dt.fromisoformat(t.strip('Z'))\n",
    "    )\n",
    "\n",
    "# sorting values based on dated\n",
    "df_vs.sort_values('date', inplace=True)\n",
    "\n",
    "# interpreting NaNs (shapefiles have no system to identify fillvalues)\n",
    "df_vs = df_vs[df_vs['wse']>-1e10] \n",
    "\n",
    "df_vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2d2e1990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Water Level for Lake ID 2940021962')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_vs.plot(\n",
    "    column='wse',\n",
    "    figsize=(12,8),\n",
    "    cmap='viridis',\n",
    "    alpha=.5,\n",
    "    legend=True,\n",
    "    facecolor='None',\n",
    ")\n",
    "ax.set_axis_off()\n",
    "ax.set_title(f'Water Level (m) for Lake ID {MY_LAKE_ID}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b835e651",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}