Trying UTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Universal Transverse Mercator coordinate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'utm'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-2a1e37e6e408>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mutm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'utm'"
     ]
    }
   ],
   "source": [
    "import utm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import folium\n",
    "from folium import plugins\n",
    "\n",
    "import time\n",
    "import random\n",
    "from statistics import mean, median\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: utm in c:\\users\\tay\\documents\\github\\trajectory-data-mining\\traj-env\\lib\\site-packages (0.7.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 10.0.1, however version 20.3.3 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install utm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from auxiliary_functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We try the UTM package on GeoLife dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GEOLIFE_DATA_PATH =r\"C:\\Users\\Tay\\Documents\\GitHub\\Trajectory-Data-Mining\\Geolife Trajectories 1.3\\Data\"\n",
    "GEOLIFE_DATA_PATH =r\"C:\\Users\\tay.yq.XTRAMAN\\Documents\\GitHub\\Trajectory-Data-Mining\\Geolife Trajectories 1.3\\Data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users in Geolife dataset: 182\n"
     ]
    }
   ],
   "source": [
    "users = os.listdir(GEOLIFE_DATA_PATH)\n",
    "print(\"Number of users in Geolife dataset: \" + str(len(users)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readUserTraj(path_to_user):\n",
    "    '''\n",
    "    :param path_to_user: a path to the user's dataset according to the Geolife dataset file system\n",
    "    :return: a dataframe containing all of the user's trajectories\n",
    "    '''\n",
    "    trajs = os.listdir(path_to_user + r\"\\Trajectory\")\n",
    "    col_names = [\"lat\", \"lon\", \"alt\", \"date\", \"time\"]\n",
    "    userTraj = pd.DataFrame(columns=[\"lat\", \"lon\", \"alt\", \"time\"])\n",
    "    \n",
    "    for i in range(len(trajs)):\n",
    "        TRAJ_PATH = path_to_user + r\"\\Trajectory\" + r\"\\\\\" + trajs[i]\n",
    "        traj = pd.read_csv(TRAJ_PATH, skiprows=6, header=None, usecols=[0,1,3,5,6], names=col_names)\n",
    "        \n",
    "        traj[\"full_date\"] = traj[\"date\"].str.cat(traj[\"time\"], sep=\" \")\n",
    "        traj[\"full_date\"] = traj[\"full_date\"].apply(lambda x: pd.to_datetime(x))\n",
    "\n",
    "        traj = traj.drop(columns = [\"date\", \"time\"])\n",
    "        traj = traj.rename(columns = {\"full_date\": \"time\"})\n",
    "        \n",
    "        userTraj = userTraj.append(traj)\n",
    "        userTraj = userTraj.reset_index(drop=True)\n",
    "    \n",
    "    return userTraj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "trajDataFrames = [readUserTraj(GEOLIFE_DATA_PATH + r\"\\\\\" + users[i]) for i in range(5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some useful functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395201.31038112973, 5673135.241182375, 32, 'U')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utm.from_latlon(51.2, 7.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51.20000000399309, 7.499999999862897)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utm.to_latlon(395201.31038112973, 5673135.241182375, 32, 'U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39.984702, 116.318417],\n",
       "       [ 39.984683, 116.31845 ],\n",
       "       [ 39.984686, 116.318417],\n",
       "       ...,\n",
       "       [ 40.000443, 116.327186],\n",
       "       [ 40.000522, 116.327132],\n",
       "       [ 40.000543, 116.327148]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = trajDataFrames[0][[\"lat\", \"lon\"]].to_numpy()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11.9 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([['441807.05662297236', '4426281.714311392', '50', 'S'],\n",
       "       ['441809.8580366453', '4426279.583938533', '50', 'S'],\n",
       "       ['441807.0430475015', '4426279.938450423', '50', 'S'],\n",
       "       ...,\n",
       "       ['442568.93811357446', '4428023.143781784', '50', 'T'],\n",
       "       ['442564.3948573248', '4428031.946912262', '50', 'T'],\n",
       "       ['442565.7782140618', '4428034.267425094', '50', 'T']],\n",
       "      dtype='<U32')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "UTM_np = np.apply_along_axis(lambda x: utm.from_latlon(x[0], x[1]), 1, temp)\n",
    "UTM_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>easting</th>\n",
       "      <th>northing</th>\n",
       "      <th>zone_number</th>\n",
       "      <th>zone_letter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>441807.05662297236</td>\n",
       "      <td>4426281.714311392</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>441809.8580366453</td>\n",
       "      <td>4426279.583938533</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>441807.0430475015</td>\n",
       "      <td>4426279.938450423</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>441804.3125899241</td>\n",
       "      <td>4426280.181319337</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>441793.86824508826</td>\n",
       "      <td>4426276.5982440645</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173865</th>\n",
       "      <td>442574.79445660365</td>\n",
       "      <td>4428018.659661258</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173866</th>\n",
       "      <td>442570.89301981183</td>\n",
       "      <td>4428022.01904634</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173867</th>\n",
       "      <td>442568.93811357446</td>\n",
       "      <td>4428023.143781784</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173868</th>\n",
       "      <td>442564.3948573248</td>\n",
       "      <td>4428031.946912262</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173869</th>\n",
       "      <td>442565.7782140618</td>\n",
       "      <td>4428034.267425094</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173870 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   easting            northing zone_number zone_letter\n",
       "0       441807.05662297236   4426281.714311392          50           S\n",
       "1        441809.8580366453   4426279.583938533          50           S\n",
       "2        441807.0430475015   4426279.938450423          50           S\n",
       "3        441804.3125899241   4426280.181319337          50           S\n",
       "4       441793.86824508826  4426276.5982440645          50           S\n",
       "...                    ...                 ...         ...         ...\n",
       "173865  442574.79445660365   4428018.659661258          50           T\n",
       "173866  442570.89301981183    4428022.01904634          50           T\n",
       "173867  442568.93811357446   4428023.143781784          50           T\n",
       "173868   442564.3948573248   4428031.946912262          50           T\n",
       "173869   442565.7782140618   4428034.267425094          50           T\n",
       "\n",
       "[173870 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UTM_df = pd.DataFrame(data=UTM_np)\n",
    "UTM_df.columns = [\"easting\", \"northing\", \"zone_number\", \"zone_letter\"]\n",
    "UTM_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'T', 'R'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UTM_df.zone_letter.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for i in range(1, len(UTM_df)):\n",
    "    if UTM_df.zone_letter[i-1] != UTM_df.zone_letter[i]:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>easting</th>\n",
       "      <th>northing</th>\n",
       "      <th>zone_number</th>\n",
       "      <th>zone_letter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>460</th>\n",
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       "      <td>4427926.873683271</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>442342.71374168317</td>\n",
       "      <td>4427920.849734608</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>442341.52709383355</td>\n",
       "      <td>4427921.968706575</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>442337.0458428309</td>\n",
       "      <td>4427927.663562783</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>442347.4649654593</td>\n",
       "      <td>4427928.250593063</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>442350.76462077076</td>\n",
       "      <td>4427924.340662985</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>442354.65633569704</td>\n",
       "      <td>4427930.971052773</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>442357.95724282204</td>\n",
       "      <td>4427938.493903998</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>442364.7537436889</td>\n",
       "      <td>4427945.435286278</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>442372.04306259315</td>\n",
       "      <td>4427949.819989162</td>\n",
       "      <td>50</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>441972.69421692146</td>\n",
       "      <td>4428508.511698289</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>441992.3325939926</td>\n",
       "      <td>4428509.249905189</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>442005.74683616584</td>\n",
       "      <td>4428510.923596544</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>442017.5562516623</td>\n",
       "      <td>4428514.829506236</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>442030.5437040958</td>\n",
       "      <td>4428516.506494143</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>442045.4951539236</td>\n",
       "      <td>4428518.279539165</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>442057.6848117722</td>\n",
       "      <td>4428516.07771704</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>442068.2941542326</td>\n",
       "      <td>4428519.326857165</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>442071.863848704</td>\n",
       "      <td>4428528.512516592</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>442068.72687805136</td>\n",
       "      <td>4428542.522168896</td>\n",
       "      <td>50</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                easting           northing zone_number zone_letter\n",
       "460   442338.7471803688  4427926.873683271          50           S\n",
       "461  442342.71374168317  4427920.849734608          50           S\n",
       "462  442341.52709383355  4427921.968706575          50           S\n",
       "463   442337.0458428309  4427927.663562783          50           S\n",
       "464   442347.4649654593  4427928.250593063          50           S\n",
       "465  442350.76462077076  4427924.340662985          50           S\n",
       "466  442354.65633569704  4427930.971052773          50           S\n",
       "467  442357.95724282204  4427938.493903998          50           S\n",
       "468   442364.7537436889  4427945.435286278          50           S\n",
       "469  442372.04306259315  4427949.819989162          50           S\n",
       "470  441972.69421692146  4428508.511698289          50           T\n",
       "471   441992.3325939926  4428509.249905189          50           T\n",
       "472  442005.74683616584  4428510.923596544          50           T\n",
       "473   442017.5562516623  4428514.829506236          50           T\n",
       "474   442030.5437040958  4428516.506494143          50           T\n",
       "475   442045.4951539236  4428518.279539165          50           T\n",
       "476   442057.6848117722   4428516.07771704          50           T\n",
       "477   442068.2941542326  4428519.326857165          50           T\n",
       "478    442071.863848704  4428528.512516592          50           T\n",
       "479  442068.72687805136  4428542.522168896          50           T"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UTM_df.iloc[460:480, :]"
   ]
  }
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