GpxtoCsv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORT NECESSARY PACKAGES\n",
    "import gpxpy\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from math import sqrt, floor\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas\n",
    "from shapely.geometry import Point,Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOAD DATA\n",
    "gpx_file = open('data/20210519234904-14242-Proposed data.gpx', 'r')\n",
    "gpx = gpxpy.parse(gpx_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CHECK LENGTH\n",
    "len(gpx.tracks)\n",
    "len(gpx.tracks[0].segments)\n",
    "len(gpx.tracks[0].segments[0].points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GET GPS TRACKS,SEGMENTS AND POINTS\n",
    "data = gpx.tracks[0].segments[0].points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Start Position\n",
    "start = data[0]\n",
    "## End Position\n",
    "finish = data[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATE A PANDAS DATAFRAME\n",
    "df = pd.DataFrame(columns=['lon', 'lat', 'ele'])\n",
    "for point in data:\n",
    "    df = df.append({'lon': point.longitude, 'lat' : point.latitude, 'ele' : point.elevation}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.337059</td>\n",
       "      <td>-0.796586</td>\n",
       "      <td>1907.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.337534</td>\n",
       "      <td>-0.797069</td>\n",
       "      <td>1902.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.337815</td>\n",
       "      <td>-0.797283</td>\n",
       "      <td>1902.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.337488</td>\n",
       "      <td>-0.797555</td>\n",
       "      <td>1898.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.337302</td>\n",
       "      <td>-0.797652</td>\n",
       "      <td>1899.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.337108</td>\n",
       "      <td>-0.797710</td>\n",
       "      <td>1899.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35.336862</td>\n",
       "      <td>-0.797812</td>\n",
       "      <td>1900.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.336772</td>\n",
       "      <td>-0.797742</td>\n",
       "      <td>1900.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.336498</td>\n",
       "      <td>-0.797420</td>\n",
       "      <td>1903.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lon       lat       ele\n",
       "0   35.336337 -0.797124  1904.768\n",
       "1   35.337059 -0.796586  1907.660\n",
       "2   35.337534 -0.797069  1902.705\n",
       "3   35.337815 -0.797283  1902.272\n",
       "4   35.337488 -0.797555  1898.250\n",
       "5   35.337302 -0.797652  1899.192\n",
       "6   35.337108 -0.797710  1899.809\n",
       "7   35.336862 -0.797812  1900.181\n",
       "8   35.336772 -0.797742  1900.589\n",
       "9   35.336498 -0.797420  1903.174\n",
       "10  35.336337 -0.797124  1904.768"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#READ\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ebf53e4220>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#PLOT\n",
    "plt.plot(df['lon'], df['lat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ebf5b741f0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#PLOT ELEVATION\n",
    "plt.plot(df['ele'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATE A GEODATAFRAME\n",
    "gdf = geopandas.GeoDataFrame(\n",
    "    df, geometry=geopandas.points_from_xy(df.lon, df.lat,df.ele))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         lon       lat       ele                                geometry\n",
      "0  35.336337 -0.797124  1904.768  POINT Z (35.33634 -0.79712 1904.76800)\n",
      "1  35.337059 -0.796586  1907.660  POINT Z (35.33706 -0.79659 1907.66000)\n",
      "2  35.337534 -0.797069  1902.705  POINT Z (35.33753 -0.79707 1902.70500)\n",
      "3  35.337815 -0.797283  1902.272  POINT Z (35.33782 -0.79728 1902.27200)\n",
      "4  35.337488 -0.797555  1898.250  POINT Z (35.33749 -0.79756 1898.25000)\n"
     ]
    }
   ],
   "source": [
    "print(gdf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\miniconda3\\envs\\gee\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "#PROJECT\n",
    "gdf.crs={'init': 'epsg:4326'} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: +init=epsg:4326 +type=crs>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- lon[east]: Longitude (degree)\n",
       "- lat[north]: Latitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PROJECT TO ZONE 36S\n",
    "geo_df = gdf.to_crs({'init': 'epsg:32736'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:32736>\n",
       "Name: WGS 84 / UTM zone 36S\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Between 30°E and 36°E, southern hemisphere between 80°S and equator, onshore and offshore. Burundi. Eswatini (Swaziland). Kenya. Malawi. Mozambique. Rwanda. South Africa. Tanzania. Uganda. Zambia. Zimbabwe.\n",
       "- bounds: (30.0, -80.0, 36.0, 0.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 36S\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>ele</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "      <td>POINT Z (760023.320 9911820.022 1904.768)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.337059</td>\n",
       "      <td>-0.796586</td>\n",
       "      <td>1907.660</td>\n",
       "      <td>POINT Z (760103.804 9911879.460 1907.660)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.337534</td>\n",
       "      <td>-0.797069</td>\n",
       "      <td>1902.705</td>\n",
       "      <td>POINT Z (760156.626 9911826.016 1902.705)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.337815</td>\n",
       "      <td>-0.797283</td>\n",
       "      <td>1902.272</td>\n",
       "      <td>POINT Z (760187.977 9911802.326 1902.272)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.337488</td>\n",
       "      <td>-0.797555</td>\n",
       "      <td>1898.250</td>\n",
       "      <td>POINT Z (760151.503 9911772.200 1898.250)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.337302</td>\n",
       "      <td>-0.797652</td>\n",
       "      <td>1899.192</td>\n",
       "      <td>POINT Z (760130.825 9911761.590 1899.192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.337108</td>\n",
       "      <td>-0.797710</td>\n",
       "      <td>1899.809</td>\n",
       "      <td>POINT Z (760109.130 9911755.128 1899.809)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35.336862</td>\n",
       "      <td>-0.797812</td>\n",
       "      <td>1900.181</td>\n",
       "      <td>POINT Z (760081.730 9911743.914 1900.181)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.336772</td>\n",
       "      <td>-0.797742</td>\n",
       "      <td>1900.589</td>\n",
       "      <td>POINT Z (760071.755 9911751.608 1900.589)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.336498</td>\n",
       "      <td>-0.797420</td>\n",
       "      <td>1903.174</td>\n",
       "      <td>POINT Z (760041.225 9911787.235 1903.174)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "      <td>POINT Z (760023.320 9911820.022 1904.768)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lon       lat       ele                                   geometry\n",
       "0   35.336337 -0.797124  1904.768  POINT Z (760023.320 9911820.022 1904.768)\n",
       "1   35.337059 -0.796586  1907.660  POINT Z (760103.804 9911879.460 1907.660)\n",
       "2   35.337534 -0.797069  1902.705  POINT Z (760156.626 9911826.016 1902.705)\n",
       "3   35.337815 -0.797283  1902.272  POINT Z (760187.977 9911802.326 1902.272)\n",
       "4   35.337488 -0.797555  1898.250  POINT Z (760151.503 9911772.200 1898.250)\n",
       "5   35.337302 -0.797652  1899.192  POINT Z (760130.825 9911761.590 1899.192)\n",
       "6   35.337108 -0.797710  1899.809  POINT Z (760109.130 9911755.128 1899.809)\n",
       "7   35.336862 -0.797812  1900.181  POINT Z (760081.730 9911743.914 1900.181)\n",
       "8   35.336772 -0.797742  1900.589  POINT Z (760071.755 9911751.608 1900.589)\n",
       "9   35.336498 -0.797420  1903.174  POINT Z (760041.225 9911787.235 1903.174)\n",
       "10  35.336337 -0.797124  1904.768  POINT Z (760023.320 9911820.022 1904.768)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:32736>\n",
       "Name: WGS 84 / UTM zone 36S\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Between 30°E and 36°E, southern hemisphere between 80°S and equator, onshore and offshore. Burundi. Eswatini (Swaziland). Kenya. Malawi. Mozambique. Rwanda. South Africa. Tanzania. Uganda. Zambia. Zimbabwe.\n",
       "- bounds: (30.0, -80.0, 36.0, 0.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 36S\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>ele</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "      <td>POINT Z (760023.320 9911820.022 1904.768)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.337059</td>\n",
       "      <td>-0.796586</td>\n",
       "      <td>1907.660</td>\n",
       "      <td>POINT Z (760103.804 9911879.460 1907.660)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.337534</td>\n",
       "      <td>-0.797069</td>\n",
       "      <td>1902.705</td>\n",
       "      <td>POINT Z (760156.626 9911826.016 1902.705)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.337815</td>\n",
       "      <td>-0.797283</td>\n",
       "      <td>1902.272</td>\n",
       "      <td>POINT Z (760187.977 9911802.326 1902.272)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.337488</td>\n",
       "      <td>-0.797555</td>\n",
       "      <td>1898.250</td>\n",
       "      <td>POINT Z (760151.503 9911772.200 1898.250)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.337302</td>\n",
       "      <td>-0.797652</td>\n",
       "      <td>1899.192</td>\n",
       "      <td>POINT Z (760130.825 9911761.590 1899.192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.337108</td>\n",
       "      <td>-0.797710</td>\n",
       "      <td>1899.809</td>\n",
       "      <td>POINT Z (760109.130 9911755.128 1899.809)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35.336862</td>\n",
       "      <td>-0.797812</td>\n",
       "      <td>1900.181</td>\n",
       "      <td>POINT Z (760081.730 9911743.914 1900.181)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.336772</td>\n",
       "      <td>-0.797742</td>\n",
       "      <td>1900.589</td>\n",
       "      <td>POINT Z (760071.755 9911751.608 1900.589)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.336498</td>\n",
       "      <td>-0.797420</td>\n",
       "      <td>1903.174</td>\n",
       "      <td>POINT Z (760041.225 9911787.235 1903.174)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.336337</td>\n",
       "      <td>-0.797124</td>\n",
       "      <td>1904.768</td>\n",
       "      <td>POINT Z (760023.320 9911820.022 1904.768)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lon       lat       ele                                   geometry\n",
       "0   35.336337 -0.797124  1904.768  POINT Z (760023.320 9911820.022 1904.768)\n",
       "1   35.337059 -0.796586  1907.660  POINT Z (760103.804 9911879.460 1907.660)\n",
       "2   35.337534 -0.797069  1902.705  POINT Z (760156.626 9911826.016 1902.705)\n",
       "3   35.337815 -0.797283  1902.272  POINT Z (760187.977 9911802.326 1902.272)\n",
       "4   35.337488 -0.797555  1898.250  POINT Z (760151.503 9911772.200 1898.250)\n",
       "5   35.337302 -0.797652  1899.192  POINT Z (760130.825 9911761.590 1899.192)\n",
       "6   35.337108 -0.797710  1899.809  POINT Z (760109.130 9911755.128 1899.809)\n",
       "7   35.336862 -0.797812  1900.181  POINT Z (760081.730 9911743.914 1900.181)\n",
       "8   35.336772 -0.797742  1900.589  POINT Z (760071.755 9911751.608 1900.589)\n",
       "9   35.336498 -0.797420  1903.174  POINT Z (760041.225 9911787.235 1903.174)\n",
       "10  35.336337 -0.797124  1904.768  POINT Z (760023.320 9911820.022 1904.768)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get projected values from geometry\n",
    "geo_df['x'] = None\n",
    "geo_df['y'] = None\n",
    "geo_df['e'] = None\n",
    "\n",
    "\n",
    "geo_df['x'] = geo_df.geometry.apply(lambda x: x.x)\n",
    "geo_df['y'] = geo_df.geometry.apply(lambda x: x.y)\n",
    "geo_df['e'] = geo_df.geometry.apply(lambda x: x.z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinate_list = [(x,y,e) for x,y,e in zip(geo_df.x , geo_df.y,geo_df.e)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(760023.3204170881, 9911820.02197887, 1904.768),\n",
       " (760103.8037010281, 9911879.460099561, 1907.66),\n",
       " (760156.6257695933, 9911826.016064063, 1902.705),\n",
       " (760187.9773967445, 9911802.326053752, 1902.272),\n",
       " (760151.5033769442, 9911772.200143933, 1898.25),\n",
       " (760130.8249364771, 9911761.589770054, 1899.192),\n",
       " (760109.1303804357, 9911755.12767041, 1899.809),\n",
       " (760081.7303998696, 9911743.914131414, 1900.181),\n",
       " (760071.7550257633, 9911751.608199755, 1900.589),\n",
       " (760041.2247859068, 9911787.23492929, 1903.174),\n",
       " (760023.3204170881, 9911820.02197887, 1904.768)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordinate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create list\n",
    "rows=[['760023.3204170881', '9911820.02197887', '1904.768'],\n",
    "      ['760103.8037010281', '9911879.460099561', '1907.66'],\n",
    " ['760156.6257695933', '9911826.016064063', '1902.705'],\n",
    " ['760187.9773967445', '9911802.326053752', '1902.272'],\n",
    " ['760151.5033769442', '9911772.200143933', '1898.25'],\n",
    " ['760130.8249364771', '9911761.589770054', '1899.192'],\n",
    " ['760109.1303804357', '9911755.12767041', '1899.809'],\n",
    " ['760081.7303998696','9911743.914131414', '1900.181'],\n",
    " ['760071.7550257633', '9911751.608199755', '1900.589'],\n",
    " ['760041.2247859068', '9911787.23492929', '1903.174'],\n",
    " ['760023.3204170881', '9911820.02197887', '1904.768']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760023.3204170881</td>\n",
       "      <td>9911820.02197887</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>760103.8037010281</td>\n",
       "      <td>9911879.460099561</td>\n",
       "      <td>1907.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>760156.6257695933</td>\n",
       "      <td>9911826.016064063</td>\n",
       "      <td>1902.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>760187.9773967445</td>\n",
       "      <td>9911802.326053752</td>\n",
       "      <td>1902.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>760151.5033769442</td>\n",
       "      <td>9911772.200143933</td>\n",
       "      <td>1898.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>760130.8249364771</td>\n",
       "      <td>9911761.589770054</td>\n",
       "      <td>1899.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>760109.1303804357</td>\n",
       "      <td>9911755.12767041</td>\n",
       "      <td>1899.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>760081.7303998696</td>\n",
       "      <td>9911743.914131414</td>\n",
       "      <td>1900.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>760071.7550257633</td>\n",
       "      <td>9911751.608199755</td>\n",
       "      <td>1900.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>760041.2247859068</td>\n",
       "      <td>9911787.23492929</td>\n",
       "      <td>1903.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>760023.3204170881</td>\n",
       "      <td>9911820.02197887</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0                  1         2\n",
       "0   760023.3204170881   9911820.02197887  1904.768\n",
       "1   760103.8037010281  9911879.460099561   1907.66\n",
       "2   760156.6257695933  9911826.016064063  1902.705\n",
       "3   760187.9773967445  9911802.326053752  1902.272\n",
       "4   760151.5033769442  9911772.200143933   1898.25\n",
       "5   760130.8249364771  9911761.589770054  1899.192\n",
       "6   760109.1303804357   9911755.12767041  1899.809\n",
       "7   760081.7303998696  9911743.914131414  1900.181\n",
       "8   760071.7550257633  9911751.608199755  1900.589\n",
       "9   760041.2247859068   9911787.23492929  1903.174\n",
       "10  760023.3204170881   9911820.02197887  1904.768"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert list to df\n",
    "my_df = pd.DataFrame(rows)\n",
    "my_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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>Eastings</th>\n",
       "      <th>Northings</th>\n",
       "      <th>Altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760023.3204170881</td>\n",
       "      <td>9911820.02197887</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>760103.8037010281</td>\n",
       "      <td>9911879.460099561</td>\n",
       "      <td>1907.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>760156.6257695933</td>\n",
       "      <td>9911826.016064063</td>\n",
       "      <td>1902.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>760187.9773967445</td>\n",
       "      <td>9911802.326053752</td>\n",
       "      <td>1902.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>760151.5033769442</td>\n",
       "      <td>9911772.200143933</td>\n",
       "      <td>1898.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>760130.8249364771</td>\n",
       "      <td>9911761.589770054</td>\n",
       "      <td>1899.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>760109.1303804357</td>\n",
       "      <td>9911755.12767041</td>\n",
       "      <td>1899.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>760081.7303998696</td>\n",
       "      <td>9911743.914131414</td>\n",
       "      <td>1900.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>760071.7550257633</td>\n",
       "      <td>9911751.608199755</td>\n",
       "      <td>1900.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>760041.2247859068</td>\n",
       "      <td>9911787.23492929</td>\n",
       "      <td>1903.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>760023.3204170881</td>\n",
       "      <td>9911820.02197887</td>\n",
       "      <td>1904.768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Eastings          Northings  Altitude\n",
       "0   760023.3204170881   9911820.02197887  1904.768\n",
       "1   760103.8037010281  9911879.460099561   1907.66\n",
       "2   760156.6257695933  9911826.016064063  1902.705\n",
       "3   760187.9773967445  9911802.326053752  1902.272\n",
       "4   760151.5033769442  9911772.200143933   1898.25\n",
       "5   760130.8249364771  9911761.589770054  1899.192\n",
       "6   760109.1303804357   9911755.12767041  1899.809\n",
       "7   760081.7303998696  9911743.914131414  1900.181\n",
       "8   760071.7550257633  9911751.608199755  1900.589\n",
       "9   760041.2247859068   9911787.23492929  1903.174\n",
       "10  760023.3204170881   9911820.02197887  1904.768"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the columns of \n",
    "my_df.columns = ['Eastings','Northings', 'Altitude']\n",
    "my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     760023.320417\n",
       "1     760103.803701\n",
       "2     760156.625770\n",
       "3     760187.977397\n",
       "4     760151.503377\n",
       "5     760130.824936\n",
       "6     760109.130380\n",
       "7     760081.730400\n",
       "8     760071.755026\n",
       "9     760041.224786\n",
       "10    760023.320417\n",
       "Name: Eastings, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_point_list.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eastings     object\n",
      "Northings    object\n",
      "Altitude     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (my_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to integer\n",
    "new_df=my_df.astype('float64').round(2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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>Eastings</th>\n",
       "      <th>Northings</th>\n",
       "      <th>Altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760023</td>\n",
       "      <td>9911820</td>\n",
       "      <td>1904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>760103</td>\n",
       "      <td>9911879</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>760156</td>\n",
       "      <td>9911826</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>760187</td>\n",
       "      <td>9911802</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>760151</td>\n",
       "      <td>9911772</td>\n",
       "      <td>1898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>760130</td>\n",
       "      <td>9911761</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>760109</td>\n",
       "      <td>9911755</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>760081</td>\n",
       "      <td>9911743</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>760071</td>\n",
       "      <td>9911751</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>760041</td>\n",
       "      <td>9911787</td>\n",
       "      <td>1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>760023</td>\n",
       "      <td>9911820</td>\n",
       "      <td>1904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Eastings  Northings  Altitude\n",
       "0     760023    9911820      1904\n",
       "1     760103    9911879      1907\n",
       "2     760156    9911826      1902\n",
       "3     760187    9911802      1902\n",
       "4     760151    9911772      1898\n",
       "5     760130    9911761      1899\n",
       "6     760109    9911755      1899\n",
       "7     760081    9911743      1900\n",
       "8     760071    9911751      1900\n",
       "9     760041    9911787      1903\n",
       "10    760023    9911820      1904"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Eastings'] = new_df['Eastings'].astype(int)\n",
    "new_df['Northings'] = new_df['Northings'].astype(int)\n",
    "new_df['Altitude'] = new_df['Altitude'].astype(int)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eastings     int32\n",
      "Northings    int32\n",
      "Altitude     int32\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (new_df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Easting=x=longitude\n",
    "\n",
    "Northing=y=latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_point_list =new_df.Northings\n",
    "lon_point_list = new_df.Eastings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\miniconda3\\envs\\gee\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    POLYGON ((760023.000 9911820.000, 760103.000 9...\n",
      "Name: geometry, dtype: geometry\n"
     ]
    }
   ],
   "source": [
    "#create polygon from point\n",
    "polygon_geom = Polygon(zip(lon_point_list, lat_point_list))\n",
    "crs = {'init': 'epsg:32736'}\n",
    "polygon = geopandas.GeoDataFrame(index=[0], crs=crs, geometry=[polygon_geom])       \n",
    "print(polygon.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "polygon.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12055.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygon.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon.to_file(filename='polygon2.shp', driver=\"ESRI Shapefile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "my_df.to_csv('Dumpsiteff.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}