testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d4589e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2da467f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[494610,2520901,9,26.3],[494659,2520876,18,23.8],[494715,2520968,15,21.7],[494730,2521007,15,22.3],[494698,2521049,9,24.9],[494618,2521084,11,24.5],[494567,2521112,10,24.7],[494556,2521101,13,23.3],[494560,2521043,18,25.1],[494595,2520947,17,26.1]],columns=['X(Easting)','Y(Northing)','Altitude(m)','SNR Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5458cfc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>X(Easting)</th>\n",
       "      <th>Y(Northing)</th>\n",
       "      <th>Altitude(m)</th>\n",
       "      <th>SNR Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>494610</td>\n",
       "      <td>2520901</td>\n",
       "      <td>9</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>494659</td>\n",
       "      <td>2520876</td>\n",
       "      <td>18</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494715</td>\n",
       "      <td>2520968</td>\n",
       "      <td>15</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>494730</td>\n",
       "      <td>2521007</td>\n",
       "      <td>15</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>494698</td>\n",
       "      <td>2521049</td>\n",
       "      <td>9</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>494618</td>\n",
       "      <td>2521084</td>\n",
       "      <td>11</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>494567</td>\n",
       "      <td>2521112</td>\n",
       "      <td>10</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>494556</td>\n",
       "      <td>2521101</td>\n",
       "      <td>13</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>494560</td>\n",
       "      <td>2521043</td>\n",
       "      <td>18</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>494595</td>\n",
       "      <td>2520947</td>\n",
       "      <td>17</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X(Easting)  Y(Northing)  Altitude(m)  SNR Value\n",
       "0      494610      2520901            9       26.3\n",
       "1      494659      2520876           18       23.8\n",
       "2      494715      2520968           15       21.7\n",
       "3      494730      2521007           15       22.3\n",
       "4      494698      2521049            9       24.9\n",
       "5      494618      2521084           11       24.5\n",
       "6      494567      2521112           10       24.7\n",
       "7      494556      2521101           13       23.3\n",
       "8      494560      2521043           18       25.1\n",
       "9      494595      2520947           17       26.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04b12029",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('gps data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca1365b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.sort_values(['X(Easting)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2681483e",
   "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>X(Easting)</th>\n",
       "      <th>Y(Northing)</th>\n",
       "      <th>Altitude(m)</th>\n",
       "      <th>SNR Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>494556</td>\n",
       "      <td>2521101</td>\n",
       "      <td>13</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>494560</td>\n",
       "      <td>2521043</td>\n",
       "      <td>18</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>494567</td>\n",
       "      <td>2521112</td>\n",
       "      <td>10</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>494595</td>\n",
       "      <td>2520947</td>\n",
       "      <td>17</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>494610</td>\n",
       "      <td>2520901</td>\n",
       "      <td>9</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>494618</td>\n",
       "      <td>2521084</td>\n",
       "      <td>11</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>494659</td>\n",
       "      <td>2520876</td>\n",
       "      <td>18</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>494698</td>\n",
       "      <td>2521049</td>\n",
       "      <td>9</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494715</td>\n",
       "      <td>2520968</td>\n",
       "      <td>15</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>494730</td>\n",
       "      <td>2521007</td>\n",
       "      <td>15</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X(Easting)  Y(Northing)  Altitude(m)  SNR Value\n",
       "7      494556      2521101           13       23.3\n",
       "8      494560      2521043           18       25.1\n",
       "6      494567      2521112           10       24.7\n",
       "9      494595      2520947           17       26.1\n",
       "0      494610      2520901            9       26.3\n",
       "5      494618      2521084           11       24.5\n",
       "1      494659      2520876           18       23.8\n",
       "4      494698      2521049            9       24.9\n",
       "2      494715      2520968           15       21.7\n",
       "3      494730      2521007           15       22.3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99a6f9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1['X(Easting)'].to_list()\n",
    "x.append(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ecda504e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b0129ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df1['Y(Northing)'].to_list()\n",
    "y.append([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d129a05",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(x,y)\n",
    "plt.title(\"GPS Surveying:Closed Traverse\")\n",
    "plt.xlabel(\"Easting----->\")\n",
    "plt.ylabel(\"Northing----->\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64e565d2",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}