2022-07-14 extract grid points.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f2ad27bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c158bf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "creds = json.load(open('dbcreds.json','r'))\n",
    "engine = create_engine(f\"postgresql://{creds['DB_USER']}:{creds['DB_PW']}@{creds['DB_SERVER']}:{creds['DB_PORT']}/{creds['DB_DBNAME']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1fb9e533",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = \"select ST_Y(geometry) as lat, ST_X(geometry) as lon from (select distinct geometry from imd_data) as a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f5736a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql(s1, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ecdfd943",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.00</td>\n",
       "      <td>80.50</td>\n",
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       "      <td>18.75</td>\n",
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       "      <th>3</th>\n",
       "      <td>23.50</td>\n",
       "      <td>69.75</td>\n",
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       "      <th>4</th>\n",
       "      <td>28.50</td>\n",
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      ],
      "text/plain": [
       "        lat    lon\n",
       "0     27.00  80.50\n",
       "1     18.75  78.25\n",
       "2     22.25  79.00\n",
       "3     23.50  69.75\n",
       "4     28.50  97.50\n",
       "...     ...    ...\n",
       "5012  17.50  76.75\n",
       "5013  19.75  75.75\n",
       "5014  11.25  78.75\n",
       "5015  17.25  79.50\n",
       "5016  18.50  77.50\n",
       "\n",
       "[5017 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c282f18f",
   "metadata": {},
   "outputs": [
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       "      <th>lon</th>\n",
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       "  </thead>\n",
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       "      <th>3</th>\n",
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       "      <td>23.50</td>\n",
       "      <td>69.75</td>\n",
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       "      <td>28.50</td>\n",
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       "      <td>78.75</td>\n",
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       "<p>5017 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "        sr    lat    lon\n",
       "0        1  27.00  80.50\n",
       "1        2  18.75  78.25\n",
       "2        3  22.25  79.00\n",
       "3        4  23.50  69.75\n",
       "4        5  28.50  97.50\n",
       "...    ...    ...    ...\n",
       "5012  5013  17.50  76.75\n",
       "5013  5014  19.75  75.75\n",
       "5014  5015  11.25  78.75\n",
       "5015  5016  17.25  79.50\n",
       "5016  5017  18.50  77.50\n",
       "\n",
       "[5017 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sr column, starting with 1\n",
    "df.index.name = 'sr'\n",
    "df.reset_index(drop=False, inplace=True)\n",
    "df['sr'] += 1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35475be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# good to go\n",
    "df.to_csv('grid_points.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21828833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d0bb4435",
   "metadata": {},
   "source": [
    "## let's also load these into the DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4eddc1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = \"select distinct geometry from imd_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57db9f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf1 = gpd.read_postgis(s2, con=engine, geom_col='geometry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9374a5f3",
   "metadata": {},
   "outputs": [
    {
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       "                       geometry\n",
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       "...                         ...\n",
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       "5015  POINT (79.50000 17.25000)\n",
       "5016  POINT (77.50000 18.50000)\n",
       "\n",
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     "execution_count": 31,
     "metadata": {},
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    "gdf1"
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  {
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   "execution_count": 32,
   "id": "c8dd50fc",
   "metadata": {},
   "outputs": [
    {
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       "<AxesSubplot:>"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b7e2c4e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf1.to_postgis('grid', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a189ce81",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}