{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright CNES\n",
    "\n",
    "## Read and plot a SWOT-HR River Single Pass Reach product\n",
    "In this notebook, we show how to read the SWOT-HR River Single Pass Reach vector product with geopandas and how to represent a variable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read a SWOT-HR River Single Pass Reach product\n",
    "Note this is an extraction of the original file for demonstration purpose. It does not contain all variables and geometries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>4.993706</td>\n",
       "      <td>-53.027740</td>\n",
       "      <td>Le Sinnamary</td>\n",
       "      <td>2.958180e+01</td>\n",
       "      <td>7.493220e+00</td>\n",
       "      <td>7.492680e+00</td>\n",
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       "      reach_id          time      time_tai              time_str     p_lat  \\\n",
       "0  61670400011 -1.000000e+12 -1.000000e+12               no_data  5.043291   \n",
       "1  61670400023 -1.000000e+12 -1.000000e+12               no_data  5.041275   \n",
       "2  61670400033  7.685711e+08  7.685711e+08  2024-05-09T11:58:10Z  4.993706   \n",
       "3  61670400043  7.685711e+08  7.685711e+08  2024-05-09T11:58:10Z  4.921612   \n",
       "4  61670400053 -1.000000e+12 -1.000000e+12               no_data  4.955817   \n",
       "\n",
       "       p_lon    river_name           wse         wse_u       wse_r_u  ...  \\\n",
       "0 -53.048925       no_data -1.000000e+12 -1.000000e+12 -1.000000e+12  ...   \n",
       "1 -53.040153       no_data -1.000000e+12 -1.000000e+12 -1.000000e+12  ...   \n",
       "2 -53.027740  Le Sinnamary  2.958180e+01  7.493220e+00  7.492680e+00  ...   \n",
       "3 -53.016789  Le Sinnamary  3.064150e+01  1.184000e-01  7.693000e-02  ...   \n",
       "4 -53.042433       no_data -1.000000e+12 -1.000000e+12 -1.000000e+12  ...   \n",
       "\n",
       "     p_wid_var  p_n_nodes  p_dist_out      p_length         p_maf  p_dam_id  \\\n",
       "0   147791.751          3   89423.928    573.504801 -1.000000e+12         0   \n",
       "1   424227.915          9   91235.322   1811.393816 -1.000000e+12         0   \n",
       "2  2309349.443         50   88707.431  10027.236715 -1.000000e+12         0   \n",
       "3  4345107.297         39   96581.984   7874.552208 -1.000000e+12         0   \n",
       "4   482433.597         10   98607.588   2025.604217 -1.000000e+12         0   \n",
       "\n",
       "   p_n_ch_max  p_n_ch_mod  p_low_slp  \\\n",
       "0           7           4          0   \n",
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       "\n",
       "                                            geometry  \n",
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       "\n",
       "[5 rows x 127 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_swot = \"../docs/data/swot\"\n",
    "file_swot_reach = os.path.join(\n",
    "    dir_swot,\n",
    "    \"SWOT_L2_HR_RiverSP_Reach\",\n",
    "    \"SWOT_L2_HR_RiverSP_Reach_015_033_SA_20240509T114031_20240509T114037_PIC0_01_extract.shp\"\n",
    ")\n",
    "# read data with geopandas\n",
    "gdf = gpd.read_file(file_swot_reach)\n",
    "gdf.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not a number values are loaded as floats, it is safer to replace them by actual NaN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>4.993706</td>\n",
       "      <td>-53.027740</td>\n",
       "      <td>Le Sinnamary</td>\n",
       "      <td>29.5818</td>\n",
       "      <td>7.49322</td>\n",
       "      <td>7.49268</td>\n",
       "      <td>...</td>\n",
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      "text/plain": [
       "      reach_id          time      time_tai              time_str     p_lat  \\\n",
       "0  61670400011           NaN           NaN               no_data  5.043291   \n",
       "1  61670400023           NaN           NaN               no_data  5.041275   \n",
       "2  61670400033  7.685711e+08  7.685711e+08  2024-05-09T11:58:10Z  4.993706   \n",
       "3  61670400043  7.685711e+08  7.685711e+08  2024-05-09T11:58:10Z  4.921612   \n",
       "4  61670400053           NaN           NaN               no_data  4.955817   \n",
       "\n",
       "       p_lon    river_name      wse    wse_u  wse_r_u  ...    p_wid_var  \\\n",
       "0 -53.048925       no_data      NaN      NaN      NaN  ...   147791.751   \n",
       "1 -53.040153       no_data      NaN      NaN      NaN  ...   424227.915   \n",
       "2 -53.027740  Le Sinnamary  29.5818  7.49322  7.49268  ...  2309349.443   \n",
       "3 -53.016789  Le Sinnamary  30.6415  0.11840  0.07693  ...  4345107.297   \n",
       "4 -53.042433       no_data      NaN      NaN      NaN  ...   482433.597   \n",
       "\n",
       "   p_n_nodes  p_dist_out      p_length  p_maf  p_dam_id  p_n_ch_max  \\\n",
       "0          3   89423.928    573.504801    NaN         0           7   \n",
       "1          9   91235.322   1811.393816    NaN         0          10   \n",
       "2         50   88707.431  10027.236715    NaN         0          22   \n",
       "3         39   96581.984   7874.552208    NaN         0          33   \n",
       "4         10   98607.588   2025.604217    NaN         0           9   \n",
       "\n",
       "   p_n_ch_mod  p_low_slp                                           geometry  \n",
       "0           4          0  LINESTRING (-53.05135 5.04175, -53.05108 5.041...  \n",
       "1           2          0  LINESTRING (-53.04677 5.04475, -53.04650 5.044...  \n",
       "2           4          0  LINESTRING (-53.03375 5.03546, -53.03402 5.035...  \n",
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       "\n",
       "[5 rows x 127 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_value = gdf.wse.min()\n",
    "gdf.replace(fill_value, np.nan, inplace=True)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Plot data on maps\n",
    "The most straightforward is to use native geopandas methods.\n",
    "Check out geopandas documentation for more options: https://geopandas.org/en/v0.9.0/docs/user_guide/mapping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "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": [
    "fig, ax = plt.subplots(1, 1)\n",
    "gdf.plot('wse', ax=ax, legend=True, cmap='cividis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('geo-env')",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c53c0f632c0bd5c80f0fc28f8860901a2b42413fffd8e5b69bb54373659a6ea7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}