volvo_project_V_L.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**File 2017-05-11 also gives left turn**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualize all tracks in file '2017-05-17_00h_tracks.csv' :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3057: DtypeWarning: Columns (0,2,3,4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               ID                              Time        x        y\n",
      "131072  200019253  2017-05-17 07:47:05.246000+02:00  26.2837  11.5698\n",
      "131073  200019253  2017-05-17 07:47:05.498000+02:00  26.3034  11.5635\n",
      "131074  200019253  2017-05-17 07:47:05.751000+02:00  26.3205  11.5549\n",
      "131075  200019253  2017-05-17 07:47:06.004000+02:00  26.3366  11.5538\n",
      "131076  200019253  2017-05-17 07:47:06.257000+02:00  26.3449  11.5518\n",
      "Selected paths saved to csv !\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "\n",
    "num_TOs = 1500\n",
    "\n",
    "# Loading original tracks data:\n",
    "cols = ['ID','Time','x', 'y','Speed','Type','Estimated']\n",
    "data = pd.read_csv('2017-05-17_00h_tracks.csv',index_col=False, header=None,sep=';',names=cols )\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "df = data[(data['Type'] == 2)]  # only selecting TO of type 2 (car)\n",
    "\n",
    "\n",
    "# creating new data frame for plotting trajectories: \n",
    "df = df[['ID', 'Time','x', 'y']]\n",
    "\n",
    "print(df.head())\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'selected_path.csv':\n",
    "df.to_csv(os.path.join(path, r'xy_all.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conversion to lat-long coord:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conversion completed!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "import os\n",
    "import csv\n",
    "\n",
    "\"\"\" Converts data x-y coordinates into latitudinal and longitudinal positions.\n",
    " Specify below the path where the csv file is to be saved. Requires Pandas and Numpy package.\"\"\"\n",
    "\n",
    "########################################################################################################################\n",
    "# lat <=> y, long <=> x\n",
    "ref_position = np.array([59.916559, 10.734520])\n",
    "lat = 59.916687\n",
    "lon = 10.734836\n",
    "position = [lat, lon]\n",
    "\n",
    "diff_lat = position[0] - ref_position[0]\n",
    "diff_lon = position[1] - ref_position[1]\n",
    "\n",
    "diff_x = 23.41\n",
    "diff_y = -20\n",
    "\n",
    "const_lat = np.round(diff_lat/diff_y, 6)\n",
    "const_lon = np.round(diff_lon/diff_x, 6)\n",
    "\n",
    "theta = np.pi + np.pi/2\n",
    "\n",
    "########################################################################################################################\n",
    "\n",
    "cols = ['ID','Time','x', 'y']\n",
    "\n",
    "# Add the csv file to be converted here:\n",
    "df = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_L_2017_05_17_filtered.csv', index_col=False, header=None, sep=',', names= cols)\n",
    "df = df.drop(df.index[0])   # drops the first row\n",
    "\n",
    "df['lat'] = df['y'].astype(float)\n",
    "df['long'] = df['x'].astype(float)\n",
    "df['x'] = df['long'].astype(float)\n",
    "df['y'] = df['lat'].astype(float)\n",
    "\n",
    "# rotate x-y-coordinates by pi/2:\n",
    "#df['x_new'] = df['x'].multiply(np.cos(theta)) - df['y'].multiply(np.sin(theta))\n",
    "#df['y_new'] = df['x'].multiply(np.sin(theta)) + df['y'].multiply(np.cos(theta))\n",
    "\n",
    "df['const_lat'] = const_lat\n",
    "df['const_long'] = const_lon\n",
    "df['ref_lat'] = ref_position[0]\n",
    "df['ref_long'] = ref_position[1]\n",
    "\n",
    "df['lat'] = df['y'].multiply(df['const_lat'])\n",
    "df['lat'] = df['lat'].add(df['ref_lat'])\n",
    "\n",
    "df['long'] = df['x'].multiply(df['const_long'])\n",
    "df['long'] = df['long'].add(df['ref_long'])\n",
    "\n",
    "#df = df.drop(['const_lat', 'const_long', 'ref_lat', 'ref_long', 'x', 'y', 'x_new', 'y_new'], axis=1)\n",
    "df = df.drop(['const_lat', 'const_long', 'ref_lat', 'ref_long', 'x', 'y'], axis=1)\n",
    "\n",
    "# rounding positions coordinates to 6 decimals:\n",
    "df = df.round({'lat': 6, 'long': 6})\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'LatLongData.csv':\n",
    "df.to_csv(os.path.join(path, r'LatLongData_2017_05_17_left.csv'), index=False)\n",
    "\n",
    "print('conversion completed!')\n",
    "df_converted = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating geojson file:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "geojson created!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import csv\n",
    "\n",
    "\n",
    "''' This script converts the a data frame with columns: 'ID', 'lat', long' into geojson file for plotting '''\n",
    "\n",
    "########################################################################################################################\n",
    "'''\n",
    "# example data:    note: the 'lat' and 'long' must not be of typ numpy.int64 !\n",
    "Data = {'ID': [1, 1, 1, 1, 2, 2, 2, 2, 2, 2], 'lat':  [10.734221, 10.734474, 10.734661, 10.734940,  10.734114,\n",
    "                                                       10.734527, 10.734436, 10.734168, 10.733894, 10.733749],\n",
    "        'long': [59.916174, 59.916397, 59.916545, 59.916757,  59.916206, 59.916521, 59.916615, 59.916679, 59.916765,\n",
    "                 59.916798]\n",
    "        }\n",
    "df = DataFrame(Data, columns=['ID', 'lat', 'long'])\n",
    "\n",
    "'''\n",
    "########################################################################################################################\n",
    "cols = ['ID','Time','lat', 'long']\n",
    "# Add the csv file to be converted here:\n",
    "df = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\LatLongData_2017_05_17_left.csv', index_col=False, header=None, sep=',', names=cols)\n",
    "df = df.drop(df.index[0])   # drops the first row\n",
    "del df['Time']\n",
    "#df = df.loc[:, ['ID', 'lat', 'long']]\n",
    "\n",
    "\n",
    "# choose number of traffic objects to plot:\n",
    "num_TO = 900\n",
    "TO_unique = df.ID.unique()\n",
    "batch = TO_unique[0:num_TO]\n",
    "df = df[df['ID'].isin(batch)]\n",
    "\n",
    "df['lat'] = df['lat'].astype(float)\n",
    "df['long'] = df['long'].astype(float)\n",
    "def df_to_geojson(df):\n",
    "    # extracting unique TO IDs:\n",
    "    TO_unique = df.ID.unique()\n",
    "    MultiLineString = []\n",
    "    for object in TO_unique:\n",
    "        # converts the the data frame to an array  [[ , ],[ , ],...[ , ]] :\n",
    "        string = df.loc[df['ID'] == object].values\n",
    "        # deleting first element in the array (corresponding to the ID value):\n",
    "        string = np.delete(string, np.s_[0], 1)\n",
    "        num_rows = len(string)\n",
    "        object_string = []\n",
    "        for row in range(num_rows):\n",
    "            string[row][0] = np.round(string[row][0], 6)\n",
    "            string[row][1] = np.round(string[row][1], 6)\n",
    "            object_string.append([string[row][1], string[row][0]])\n",
    "        MultiLineString.append(object_string)\n",
    "\n",
    "    geojson_dict = {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'properties': {}, 'geometry':\n",
    "        {'type':'MultiLineString', 'coordinates': MultiLineString}}]}\n",
    "\n",
    "    return geojson_dict\n",
    "\n",
    "geojson_dict = df_to_geojson(df)\n",
    "\n",
    "geojson_str = json.dumps(geojson_dict, indent=2)\n",
    "\n",
    "# save the geojson result to a file\n",
    "output_filename = 'TO_paths_2017_05_17_left.geojson'\n",
    "\n",
    "\n",
    "#with open(output_filename, 'wb') as output_file:\n",
    "with open(output_filename, 'w') as output_file:\n",
    "    output_file.write('{}'.format(geojson_str))\n",
    "\n",
    "print('geojson created!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecting x-y coordinates which only contain LEFT lower quadrant turn in '2017-05-17_00h_tracks.csv' :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'x'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-c50aa49bbf9d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m#df1 = data[(data['Type'] == 2)]  # only selecting TO of type 2 (car)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'int32'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'int32'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'x'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "\n",
    "#num_TOs = 1500\n",
    "x_left_bound = 5\n",
    "x_right_bound = 33\n",
    "y_upper_bound = -30\n",
    "y_lower_bound = 2\n",
    "\n",
    "x_condition_2 = 22\n",
    "y_condition_2 = -15\n",
    "theta = 0.785    # radians to rotate\n",
    "\n",
    "# Loading original tracks data:\n",
    "cols = ['ID','Time','x', 'y','Speed','Type','Estimated']\n",
    "data = pd.read_csv('2017-05-17_00h_tracks.csv',index_col=False, header=None,sep=';',names=cols )\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "#df1 = data[(data['Type'] == 2)]  # only selecting TO of type 2 (car)\n",
    "df1 = data\n",
    "df1['x'] = df['x'].astype('int32')\n",
    "df1['y'] = df['y'].astype('int32')\n",
    "print(df1.dtypes)\n",
    "# rotate x-y-coordinates by theta to align the road with the x-y axis in the map:\n",
    "df1['x_rot'] = df1['x'].multiply(np.cos(theta)) - df1['y'].multiply(np.sin(theta))\n",
    "df1['y_rot'] = df1['x'].multiply(np.sin(theta)) + df1['y'].multiply(np.cos(theta))\n",
    "\n",
    "\n",
    "# creating new data frame for plotting trajectories: \n",
    "df = pd.DataFrame(columns=['ID', 'Time', 'x', 'y'])\n",
    "\n",
    "grouped = df1.groupby(['ID'])   # selecting only TO ID:s that fullfill x-y coord. condition for right turn\n",
    "for ID,group in grouped:\n",
    "\n",
    "    df_temp = pd.DataFrame({#'x_cond':group['x'].between(x_left_bound, x_right_bound),\n",
    "                            #'y_cond':group['y'].between(y_upper_bound, y_lower_bound),\n",
    "                            'x_cond2':[not(group['x'] > 1).any()]\n",
    "                            #,'x_cond3':[(group['x'] <= -20).any()]\n",
    "                            #,'x_cond4':not(group['x_rot'] < -5).any()\n",
    "                            #,'x_cond5':not(group['x_rot'] > 5).any()        \n",
    "                            #,'y_cond2':(group['y'] < -15).any()\n",
    "                           })\n",
    "    \n",
    "    if df_temp.all(axis=None):   # if boundary conditions for x and y are fullfilled\n",
    "        df = df.append(group[['ID','Time','x_rot','y_rot']])\n",
    "df = df.rename(columns={\"x_rot\": \"x\", \"y_rot\": \"y\"})\n",
    "df = df.iloc[:,[0,1,3,5]]\n",
    "print(df.head())\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'selected_path.csv':\n",
    "df.to_csv(os.path.join(path, r'xy_L_2017_05_17_filtered.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further filtering based on xy-coord. :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(2017-05-17 L lower quadrant) Checking if time matches between state vector file and xy-file , seems like state vector is ~ 0,632 seconds ahead:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [ID, Time]\n",
      "Index: []\n",
      "\n",
      "\n",
      "new\n",
      "Empty DataFrame\n",
      "Columns: [ID, Time id, State time]\n",
      "Index: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import csv\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import os\n",
    "\n",
    "# Loading original tracks data:\n",
    "data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_L.csv')\n",
    "\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "\n",
    "\n",
    "df1 = data[['ID','Time']]\n",
    "\n",
    "# Loading data with occupancy grid:\n",
    "data2 = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\result_2017_05_17_L_large.csv')\n",
    "\n",
    "data2['ID'] = data2['Object id']\n",
    "\n",
    "del data2['Object id']\n",
    "del data2['State time']\n",
    "\n",
    "# choosing ID:s in df2 that also exists in df1:\n",
    "df2 = data2.loc[data2['ID'].isin(df1['ID'])]\n",
    "df2['ID'] = df2['ID'].astype('int32')\n",
    "#print(df2.dtypes)\n",
    "# choosing ID:s in df1 that is also existing in df2:\n",
    "df1 = df1.loc[df1['ID'].isin(df2['ID'])]\n",
    "df2 = df2[['ID', 'Time id']]\n",
    "#print(df1.iloc[150:200])\n",
    "\n",
    "temp = df2.loc[df2['ID']==200027566]\n",
    "\n",
    "# calculating state time for data containing occupancy grid:\n",
    "temp['Time id'] = temp['Time id'].astype(float)\n",
    "start_time = pd.to_datetime('2017-05-17 00:00:01.285') \n",
    "temp['State time'] = start_time + timedelta(seconds=0.3)*temp['Time id'] - timedelta(seconds=0.632)\n",
    "print(df1.loc[df1['ID']==200027566].iloc[0:4])\n",
    "print('\\n')\n",
    "print('new')\n",
    "print(temp.iloc[0:4])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(2017-05-17 L lower quadrant) Selecting LEFT turn paths from the file above that corresponds to the time interval calculated from the GridExtractor, also choosing the data from GridExtractor that matches with the file above :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ID                              Time                    x  \\\n",
      "1  200018371  2017-05-17 07:47:23.813000+02:00  -10.411235833310052   \n",
      "2  200018371  2017-05-17 07:47:24.065000+02:00  -10.445467267791543   \n",
      "3  200018371  2017-05-17 07:47:24.317000+02:00  -10.562461066744072   \n",
      "4  200018371  2017-05-17 07:47:25.025000+02:00  -10.513713007147663   \n",
      "5  200018371  2017-05-17 07:47:25.277000+02:00  -10.455204527423877   \n",
      "\n",
      "                    y  \n",
      "1   2.716263217827384  \n",
      "2   2.616605289785248  \n",
      "3  2.5033093373959905  \n",
      "4     2.0483202083945  \n",
      "5  1.7537545425653518  \n",
      "   Unnamed: 0  Time id  P_11  P_12  P_13  P_21  P_22  P_23  P_31  P_32  ...  \\\n",
      "0           0       24   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "1           1       25   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
      "2           2       26   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "3           3       27   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "4           4       28   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
      "\n",
      "   H_21  H_22  H_23  H_31  H_32  H_33   Velocity  Acceleration  Delta theta  \\\n",
      "0   0.0   0.0   1.0   0.0   0.0   0.0  19.391417     -6.295319    -0.870940   \n",
      "1   0.0   0.0   1.0   0.0   0.0   0.0  17.992960     -5.549434    -1.003166   \n",
      "2   0.0   0.0   0.0   0.0   0.0   0.0  18.461326      1.851251    -0.339635   \n",
      "3   0.0   0.0   0.0   0.0   0.0   0.0  18.716580      0.874156     1.292475   \n",
      "4   0.0   0.0   0.0   0.0   0.0   0.0  16.380679     -6.778000    -0.605779   \n",
      "\n",
      "          ID  \n",
      "0  200000003  \n",
      "1  200000003  \n",
      "2  200000003  \n",
      "3  200000003  \n",
      "4  200000003  \n",
      "\n",
      "[5 rows x 42 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:58: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected paths saved to csv !\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "coordinates for restricted area in 'result_L.csv' is : [[[-35,-20],[8,-20]],[[-35,20],[8,20]]]\n",
    "'''\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import os\n",
    "\n",
    "\n",
    "# num_TOs = 1500\n",
    "\n",
    "# Loading file which contains x-y coord for right turn paths:\n",
    "cols = ['ID','Time','x','y']\n",
    "data1 = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_L.csv',index_col=False, header=None,sep=',',names=cols )\n",
    "data1 = data1.drop(data1.index[0])   # drops the first row\n",
    "print(data1.head())\n",
    "#df1 = data1[(data1['Type'] == 2)]  # only selecting TO of type 2 (car), allready filltered out above\n",
    "df1 = data1\n",
    "df1['Time'] = pd.to_datetime(df1['Time']) \n",
    "df1['Time'] = df1['Time'].astype('datetime64[ns]')\n",
    "df1['ID'] = df1['ID'].astype('int32')\n",
    "df1['ID'] = pd.to_numeric(df1['ID'])\n",
    "\n",
    "# Loading data with occupancy grid:\n",
    "data2 = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\result_2017_05_17_L_large.csv')\n",
    "data2['ID'] = data2['Object id']\n",
    "del data2['Object id']\n",
    "del data2['State time']\n",
    "print(data2.head())\n",
    "\n",
    "# choosing random TO IDs in df2:\n",
    "#TO_unique = df2.ID.unique()\n",
    "#rand_ID = np.random.choice(TO_unique, num_TOs, replace=False)\n",
    "#df2 = df2.loc[df2['ID'].isin(rand_ID)]\n",
    "\n",
    "\n",
    "# choosing ID:s in df2 that also exists in df1:\n",
    "\n",
    "df2 = data2.loc[data2['ID'].isin(df1['ID'])]\n",
    "df2['ID'] = df2['ID'].astype('int32')\n",
    "#print(df2.dtypes)\n",
    "# choosing ID:s in df1 that is also existing in df2:\n",
    "df1 = df1.loc[df1['ID'].isin(df2['ID'])]\n",
    "\n",
    "# check if they have identical ID:s:\n",
    "#ID_df1 = df1.ID.unique()\n",
    "#ID_df2 = df2.ID.unique()\n",
    "#ID_df1 = np.sort(ID_df1, axis=None)\n",
    "#ID_df2 = np.sort(ID_df2, axis=None)\n",
    "#print(np.array_equal(ID_df1, ID_df2))\n",
    "\n",
    "\n",
    "# calculating state time for data containing occupancy grid:\n",
    "df2['Time id'] = df2['Time id'].astype(float)\n",
    "start_time = pd.to_datetime('2017-05-17 00:00:08.485') \n",
    "df2['State time'] = start_time + timedelta(seconds=0.3)*df2['Time id'] - timedelta(seconds=0.632)\n",
    "\n",
    "\n",
    "#df2['Time_id_unique'] = (df2['Time id'].diff() != 1).cumsum()  # rows with consequtive increasing values gets same index\n",
    "\n",
    "\n",
    "# creating new data frame for plotting trajectories in map: \n",
    "df = pd.DataFrame(columns=['ID', 'Time', 'x', 'y'])\n",
    "\n",
    "# creating empty data frame for state vector:\n",
    "df_state = pd.DataFrame()\n",
    "\n",
    "# selecting x-y data that corresponds to the output from GridExtractor (for visualization of tracks in map)\n",
    "# also selecting the corresponding state vectors stored in df_state for clustering:\n",
    "grouped = df2.groupby(['ID'])  \n",
    "for ID,group in grouped:\n",
    "    df_temp = df1.loc[df1['ID'] == ID, ['ID', 'Time', 'x', 'y']]\n",
    "    #df_temp = df_temp.loc[(df_temp['Time'] >= group['State time'].iloc[0]) & (df_temp['Time'] <= group['State time'].iloc[-1])]\n",
    "    #if len(df_temp.index) > 5:    # only using ID:s containing more than 30 data points\n",
    "    df = df.append(df_temp)\n",
    "    df_state = df_state.append(group)\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'xy_right_time_matching.csv':\n",
    "df.to_csv(os.path.join(path, r'xy_L_time_matching.csv'), index=False)\n",
    "\n",
    "# the file is currently named 'state_vector_left_turn.csv':\n",
    "df_state.to_csv(os.path.join(path, r'state_vector_L.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Doing further filtering based on angle of deviation, theta :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:58: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import csv\n",
    "%matplotlib notebook\n",
    "\n",
    "plot_step = 3   # specify here the n:th number of step that will be used in the plot ( this is to remove noice)\n",
    "                    # if plot_step = 1 all data points will be used\n",
    "\n",
    "# Loading original tracks data:\n",
    "data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_L_time_matching.csv')\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "df_xy = data\n",
    "\n",
    "# Loading file which contains state vector for left turn paths:\n",
    "state_data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\state_vector_L.csv',index_col=False, header=None,sep=',')\n",
    "df_state = state_data\n",
    "df_state.columns = df_state.iloc[0]\n",
    "df_state = df_state.drop(df_state.index[0])   # drops the first row\n",
    "\n",
    "df_state['Delta theta'] = df_state['Delta theta'].astype(float)\n",
    "df_state['Velocity'] = df_state['Velocity'].astype(float)\n",
    "#print(df_test.dtypes)\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "# creating empty data frame to store path where outliers are removed (needed to plot paths in map):\n",
    "df_map = pd.DataFrame()\n",
    "df_filtered = pd.DataFrame()\n",
    "\n",
    "grouped = df_state.groupby(['ID'])  \n",
    "for ID,group in grouped:\n",
    "    theta = group['Delta theta']\n",
    "    theta = theta.to_numpy()\n",
    "    theta = np.insert(theta,0,0)\n",
    "    theta = theta*180/np.pi\n",
    "    theta_sum = np.sum(theta)\n",
    "    \n",
    "    if theta_sum >= 70 :  \n",
    "        theta = np.cumsum(theta)\n",
    "        theta_last = theta[-1]\n",
    "        time = np.arange(len(group)+1)*0.3\n",
    "        time_last = time[-1]\n",
    "        velocity = group['Velocity'].to_numpy()\n",
    "        velocity = np.insert(velocity,0,0)\n",
    "        v_plot = velocity[-1]\n",
    "        plot_data = pd.DataFrame({'Velocity': velocity, 'Cum_theta': theta, 'Time': time})\n",
    "        time_plot = plot_data['Time'].iloc[::plot_step].to_numpy()\n",
    "        velocity_plot = plot_data['Velocity'].iloc[::plot_step].to_numpy()\n",
    "        theta_plot = plot_data['Cum_theta'].iloc[::plot_step].to_numpy()\n",
    "        ax.plot3D(theta_plot, time_plot,velocity_plot, 'green')\n",
    "        df_temp = df_xy.loc[df_xy['ID'] == ID, ['ID', 'Time', 'x', 'y']]\n",
    "        df_map = df_map.append(df_temp)\n",
    "        df_temp2 = pd.DataFrame({'cumulative theta': theta[1:len(theta)],'time elapsed': time[1:len(time)]})\n",
    "        group['theta plot'] = theta_last\n",
    "        group['time plot'] = time_last\n",
    "        group['v plot'] = v_plot\n",
    "        group = group.reset_index(drop=True)\n",
    "        group = pd.concat([df_temp2,group], axis=1)\n",
    "        df_filtered = df_filtered.append(group)\n",
    "              \n",
    "# label axes\n",
    "ax.set_xlabel('Cumulative theta', fontsize=20)\n",
    "ax.set_ylabel('Time', fontsize=15)\n",
    "ax.set_zlabel('Velocity', fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'xy_right_time_matching.csv':\n",
    "df_map.to_csv(os.path.join(path, r'xy_L_filtered_2017_05_17.csv'), index=False)\n",
    "\n",
    "# the file is currently named 'state_vector_right_turn.csv':\n",
    "df_filtered.to_csv(os.path.join(path, r's_vect_L_2017_05_17.csv'), index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(2017-05-17 L lower quadrant) Doing some further filtering and make 3D plot :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ID                     Time                    x                   y\n",
      "1  200018361  2017-05-17 05:47:08.203  -11.358349050145092    20.1766929315972\n",
      "2  200018361  2017-05-17 05:47:08.456  -11.163352783649854   19.83313206017017\n",
      "3  200018361  2017-05-17 05:47:08.708  -11.118241429081287    19.0100603990802\n",
      "4  200018361  2017-05-17 05:47:08.960  -11.112277911044538  18.606524415271664\n",
      "5  200018361  2017-05-17 05:47:09.213   -11.14180365828064  18.389214189230948\n"
     ]
    },
    {
     "data": {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
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       "\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
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       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
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       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
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       "\n",
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       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
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       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
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       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='0970cf22-299f-44ab-ad57-403019bbdf6f'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:57: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:58: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-102-df3c1db964ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Delta theta'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtheta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m     \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m     \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtheta\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m180\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(arr, obj, values, axis)\u001b[0m\n\u001b[0;32m   4598\u001b[0m         \u001b[0mslobj2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4599\u001b[0m         \u001b[0mslobj2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4600\u001b[1;33m         \u001b[0mnew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslobj2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4601\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4602\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import csv\n",
    "%matplotlib notebook\n",
    "\n",
    "plot_step = 1   # specify here the n:th number of step that will be used in the plot ( this is to remove noice)\n",
    "                    # if plot_step = 1 all data points will be used\n",
    "\n",
    "# Loading file which contains x-y coord for left turn paths:\n",
    "cols = ['ID','Time','x','y']\n",
    "xy_data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_L_lower_time_matching.csv',index_col=False, header=None,sep=',',names=cols )\n",
    "df_xy = xy_data\n",
    "df_xy = df_xy.drop(df_xy.index[0])   # drops the first row\n",
    "\n",
    "# Loading file which contains state vector for left turn paths:\n",
    "state_data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\state_vector_L_lower.csv',index_col=False, header=None,sep=',')\n",
    "df_state = state_data\n",
    "df_state.columns = df_state.iloc[0]\n",
    "df_state = df_state.drop(df_state.index[0])   # drops the first row\n",
    "\n",
    "df_state['Delta theta'] = df_state['Delta theta'].astype(float)\n",
    "df_state['Velocity'] = df_state['Velocity'].astype(float)\n",
    "#print(df_test.dtypes)\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "# creating empty data frame to store path where outliers are removed (needed to plot paths in map):\n",
    "df_map = pd.DataFrame()\n",
    "df_filtered = pd.DataFrame()\n",
    "\n",
    "grouped = df_state.groupby(['ID'])  \n",
    "for ID,group in grouped:\n",
    "    theta = group['Delta theta']\n",
    "    theta = theta.to_numpy()\n",
    "    theta = np.insert(theta,0,0)\n",
    "    theta = theta*180/np.pi\n",
    "    theta = np.cumsum(theta) \n",
    "    theta_sum = theta[-1]\n",
    "    time = np.arange(len(group)+1)*0.3\n",
    "    if not((theta < -300).any() or (theta > 100).any()) or (theta > -20).all() or (group.shape[0] < 25):  # filtering out outliers for cumulative theta values \n",
    "        time_last = time[-1]\n",
    "        velocity = group['Velocity'].to_numpy()\n",
    "        velocity = np.insert(velocity,0,0)\n",
    "        v_plot = velocity[-1]\n",
    "        plot_data = pd.DataFrame({'Velocity': velocity, 'Cum_theta': theta, 'Time': time})\n",
    "        time_plot = plot_data['Time'].iloc[::plot_step].to_numpy()\n",
    "        velocity_plot = plot_data['Velocity'].iloc[::plot_step].to_numpy()\n",
    "        theta_plot = plot_data['Cum_theta'].iloc[::plot_step].to_numpy()\n",
    "        ax.plot3D(theta_plot, time_plot,velocity_plot, 'green')\n",
    "        df_temp = df_xy.loc[df_xy['ID'] == ID, ['ID', 'Time', 'x', 'y']]\n",
    "        df_map = df_map.append(df_temp)\n",
    "        df_temp2 = pd.DataFrame({'cumulative theta': theta[1:len(theta)],'time elapsed': time[1:len(time)]})\n",
    "        group['theta plot'] = theta_sum\n",
    "        group['time plot'] = time_last\n",
    "        group['v plot'] = v_plot\n",
    "        group = group.reset_index(drop=True)\n",
    "        group = pd.concat([df_temp2,group], axis=1)\n",
    "        df_filtered = df_filtered.append(group)\n",
    "              \n",
    "# label axes\n",
    "ax.set_xlabel('Cumulative theta', fontsize=20)\n",
    "ax.set_ylabel('Time', fontsize=15)\n",
    "ax.set_zlabel('Velocity', fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'xy_right_time_matching.csv':\n",
    "df_map.to_csv(os.path.join(path, r'xy_L_filtered_2017_05_17.csv'), index=False)\n",
    "\n",
    "# the file is currently named 'state_vector_right_turn.csv':\n",
    "df_filtered.to_csv(os.path.join(path, r's_vect_L_2017_05_17.csv'), index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(file: 2017-05-11) Selecting x-y coordinates which only contain LEFT turn :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-216b45ee26c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     46\u001b[0m                            })\n\u001b[0;32m     47\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m   \u001b[1;31m# if boundary conditions for x and y are fullfilled\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m         \u001b[0mdf0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Time'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;31m# doing 2nd filtering:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2932\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2933\u001b[0m             indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[1;32m-> 2934\u001b[1;33m                                                    raise_missing=True)\n\u001b[0m\u001b[0;32m   2935\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2936\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[0;32m   1352\u001b[0m                 kwargs = {'raise_missing': True if is_setter else\n\u001b[0;32m   1353\u001b[0m                           raise_missing}\n\u001b[1;32m-> 1354\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1355\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1356\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1152\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_for_reindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1154\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer_for\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1155\u001b[0m             \u001b[0mkeyarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1156\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer_for\u001b[1;34m(self, target, **kwargs)\u001b[0m\n\u001b[0;32m   4453\u001b[0m         \"\"\"\n\u001b[0;32m   4454\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4455\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4456\u001b[0m         \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4457\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer\u001b[1;34m(self, target, method, limit, tolerance)\u001b[0m\n\u001b[0;32m   2716\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclean_reindex_fill_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2718\u001b[1;33m         \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2719\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2720\u001b[0m             \u001b[0mtolerance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_tolerance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mensure_index\u001b[1;34m(index_like, copy)\u001b[0m\n\u001b[0;32m   5362\u001b[0m             \u001b[0mindex_like\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex_like\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5364\u001b[1;33m         \u001b[0mconverted\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mall_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclean_index_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex_like\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5366\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconverted\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mall_arrays\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.clean_index_list\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.infer_dtype\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_spec\u001b[1;34m(name, path, target)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mfind_spec\u001b[1;34m(cls, fullname, path, target)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_get_spec\u001b[1;34m(cls, fullname, path, target)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mfind_spec\u001b[1;34m(self, fullname, target)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_path_stat\u001b[1;34m(path)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "\n",
    "num_TOs = 1500\n",
    "# coordinate boundaries below are according to 'coordinate_system.pgn' after matrix rotation of xy-data by angle theta:\n",
    "x_left_bound = -35\n",
    "x_right_bound = -1\n",
    "y_upper_bound = -20\n",
    "y_lower_bound = 22\n",
    "\n",
    "x_condition_2 = -20\n",
    "y_condition_2 = 0\n",
    "\n",
    "y_condition_3 = 2\n",
    "y_condition_4 = -8\n",
    "x_condition_3 = 0\n",
    "theta = 0.588\n",
    "\n",
    "# Loading original tracks data:\n",
    "cols = ['ID','Time','x', 'y','Speed','Type','Estimated']\n",
    "data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\2017-05-11_00h_tracks.csv', index_col=False, header=None, sep=';', names=cols)\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "df1 = data[(data['Type'] == 2)]  # only selecting TO of type 2 (car)\n",
    "\n",
    "\n",
    "\n",
    "# rotate x-y-coordinates by theta to align the road with the x-y axis in the map:\n",
    "#df1['x_rot'] = df1['x'].multiply(np.cos(theta)) - df1['y'].multiply(np.sin(theta))\n",
    "#df1['y_rot'] = df1['x'].multiply(np.sin(theta)) + df1['y'].multiply(np.cos(theta))\n",
    "\n",
    "\n",
    "# creating new data frame for plotting trajectories: \n",
    "df0 = pd.DataFrame(columns=['ID', 'Time', 'x', 'y'])\n",
    "\n",
    "grouped = df1.groupby(['ID'])   # selecting only TO ID:s that fullfill x-y coord. condition for right turn\n",
    "for ID,group in grouped:\n",
    "    \n",
    "    df_temp = pd.DataFrame({'x_cond':group['x'].between(x_left_bound, x_right_bound),\n",
    "                            'y_cond':group['y'].between(y_upper_bound, y_lower_bound)\n",
    "                            ,'x_cond2':(group['x'] < 3).all()\n",
    "                            ,'y_cond2':(group['y'] > -20).all()\n",
    "                            # ,'y_cond3':(group['y'] < 17).all()\n",
    "                           })\n",
    "    if df_temp.all(axis=None):   # if boundary conditions for x and y are fullfilled\n",
    "        df0 = df0.append(group[['ID','Time','x','y']])\n",
    "\n",
    "# doing 2nd filtering:\n",
    "'''\n",
    "# creating new data frame for plotting trajectories: \n",
    "df = pd.DataFrame(columns=['ID', 'Time', 'x', 'y'])\n",
    "\n",
    "grouped = df0.groupby(['ID'])   # selecting only TO ID:s that fullfill x-y coord. condition for right turn\n",
    "for ID,group in grouped:\n",
    "    \n",
    "    df_temp = pd.DataFrame({'y_cond3':[(group['y_rot'] < y_condition_3).any()] \n",
    "                           ,'y_cond4':[(group['y_rot'] < y_condition_4).any()] \n",
    "                           ,'x_cond3':[(group['x_rot'] > x_condition_3).any()] })\n",
    "                          \n",
    "    if df_temp.all(axis=None):   # if boundary conditions for x and y are fullfilled\n",
    "        df = df.append(group[['ID','Time','x','y']])\n",
    "'''\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'selected_path.csv':\n",
    "df0.to_csv(os.path.join(path, r'xy_L_11.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Doing some further filtering after visualizing in map :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "[200027095 200029706 200048743 200055744 200067566 200069815]\n",
      "6\n",
      "[200027095 200029706 200048743 200055744 200067566 200069815]\n",
      "40\n",
      "40\n",
      "hej\n",
      "40\n",
      "Selected paths saved to csv !\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "'''\n",
    "After visualizing file: xy_left_turn.csv on map, one can set some conditions on xy-coord. to remove outliers\n",
    "'''\n",
    "\n",
    "\n",
    "# Loading original tracks data:\n",
    "#cols = ['ID','Time','x', 'y','Speed','Type','Estimated']\n",
    "data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_left_turn.csv')\n",
    "data = data.drop(data.index[0])   # drops the first row\n",
    "\n",
    "df1 = data\n",
    "# rotate x-y-coordinates by theta to align the road with the x-y axis in the map:\n",
    "#df1['x_rot'] = df1['x'].multiply(np.cos(theta)) - df1['y'].multiply(np.sin(theta))\n",
    "#df1['y_rot'] = df1['x'].multiply(np.sin(theta)) + df1['y'].multiply(np.cos(theta))\n",
    "#df_temp = df1[['ID','x','y']].loc[(df1['x_rot'] < -5).any() and (df1['x_rot'] > -12).any() and (df1['y_rot'] < -2).any()]\n",
    "\n",
    "df_temp = df1.loc[df1['x'] <2]\n",
    "df_temp = df_temp.loc[df1['x'] > - 5]\n",
    "df_temp = df_temp.loc[df1['y'] < - 4.5]\n",
    "#df_temp3 = df1[['ID','x','y']].loc[(df1['x'] < -17)& (df1['x'] > -30) & (df1['y'] < -16.127)]\n",
    "#df_temp2 = df1[['ID','x','y']].loc[(df1['x'] > -13) & (df1['y'] > 0)]\n",
    "\n",
    "#del df1['x_rot']\n",
    "#del df1['y_rot']\n",
    "\n",
    "print(len(pd.unique(df_temp['ID'])))\n",
    "\n",
    "print(pd.unique(df_temp['ID']))\n",
    "\n",
    "print(len(pd.unique(df_temp['ID'])))\n",
    "\n",
    "print(pd.unique(df_temp['ID']))\n",
    "\n",
    "print(len(pd.unique(df1['ID'])))\n",
    "\n",
    "'''\n",
    "df = df1.loc[df1['ID']!=200029706 ]\n",
    "\n",
    "df = df.loc[df['ID']!= 200041829 ]\n",
    "\n",
    "df = df.loc[df['ID']!= 200048743 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200055744 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200067566 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200069815 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200070553 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200070974 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200073761 ]\n",
    "\n",
    "\n",
    "df = df.loc[df['ID']!=200075824 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200027095 ]\n",
    "\n",
    "df = df.loc[df['ID']!=200048743]\n",
    "\n",
    "\n",
    "df = df.loc[df['ID']!=200025273]\n",
    "\n",
    "df = df.loc[df['ID']!=200039921]\n",
    "\n",
    "df = df.loc[df['ID']!=200070339]\n",
    "df = df.loc[df['ID']!=200077290]\n",
    "'''\n",
    "\n",
    "df = df.loc[df['ID']!=200048743]\n",
    "\n",
    "df = df.loc[df['ID']!=200027449]\n",
    "\n",
    "df = df.loc[df['ID']!=200070339]\n",
    "\n",
    "df = df.loc[df['ID']!=200103456]\n",
    "\n",
    "print(len(pd.unique(df1['ID'])))\n",
    "\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'selected_path.csv':\n",
    "df.to_csv(os.path.join(path, r'xy_left_turn_filtered.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')\n",
    "\n",
    "#print(pd.unique(df1['ID'].loc[df1['x']> 14.2]))\n",
    "#print('\\n')\n",
    "\n",
    "#print(len(pd.unique(df1['ID'].loc[df1['x']> 14.2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecting LEFT turn paths from the file above that corresponds to the time interval calculated from the GridExtractor, also choosing the data from GridExtractor that matches with the file above , some error to check later , could not extract xy-time matching with conditions  :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected paths saved to csv !\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "coordinates for restricted area in 'result_L.csv' is : [[[-35,-20],[8,-20]],[[-35,20],[8,20]]]\n",
    "'''\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import os\n",
    "\n",
    "\n",
    "# num_TOs = 1500\n",
    "\n",
    "# Loading file which contains x-y coord for right turn paths:\n",
    "cols = ['ID','Time','x','y']\n",
    "data1 = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_left_turn_filtered.csv',index_col=False, header=None,sep=',',names=cols )\n",
    "data1 = data1.drop(data1.index[0])   # drops the first row\n",
    "\n",
    "#df1 = data1[(data1['Type'] == 2)]  # only selecting TO of type 2 (car), allready filltered out above\n",
    "df1 = data1\n",
    "df1['Time'] = pd.to_datetime(df1['Time']) \n",
    "df1['Time'] = df1['Time'].astype('datetime64[ns]')\n",
    "df1['ID'] = df1['ID'].astype('int32')\n",
    "df1['ID'] = pd.to_numeric(df1['ID'])\n",
    "\n",
    "# Loading data with occupancy grid:\n",
    "data2 = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\result_2017_05_17_L_large.csv')\n",
    "data2['ID'] = data2['Object id']\n",
    "del data2['Object id']\n",
    "del data2['State time']\n",
    "\n",
    "# choosing random TO IDs in df2:\n",
    "#TO_unique = df2.ID.unique()\n",
    "#rand_ID = np.random.choice(TO_unique, num_TOs, replace=False)\n",
    "#df2 = df2.loc[df2['ID'].isin(rand_ID)]\n",
    "\n",
    "# choosing ID:s in df2 that also exists in df1:\n",
    "df2 = data2.loc[data2['ID'].isin(df1['ID'])]\n",
    "df2['ID'] = df2['ID'].astype('int32')\n",
    "#print(df2.dtypes)\n",
    "# choosing ID:s in df1 that is also existing in df2:\n",
    "df1 = df1.loc[df1['ID'].isin(df2['ID'])]\n",
    "\n",
    "# check if they have identical ID:s:\n",
    "#ID_df1 = df1.ID.unique()\n",
    "#ID_df2 = df2.ID.unique()\n",
    "#ID_df1 = np.sort(ID_df1, axis=None)\n",
    "#ID_df2 = np.sort(ID_df2, axis=None)\n",
    "#print(np.array_equal(ID_df1, ID_df2))\n",
    "\n",
    "\n",
    "# calculating state time for data containing occupancy grid:\n",
    "df2['Time id'] = df2['Time id'].astype(float)\n",
    "start_time = pd.to_datetime('2017-05-17 00:00:08.485') \n",
    "df2['State time'] = start_time + timedelta(seconds=0.3)*df2['Time id'] - timedelta(seconds=0.632)\n",
    "\n",
    "\n",
    "#df2['Time_id_unique'] = (df2['Time id'].diff() != 1).cumsum()  # rows with consequtive increasing values gets same index\n",
    "\n",
    "\n",
    "# creating new data frame for plotting trajectories in map: \n",
    "df = pd.DataFrame(columns=['ID', 'Time', 'x', 'y'])\n",
    "\n",
    "# creating empty data frame for state vector:\n",
    "df_state = pd.DataFrame()\n",
    "\n",
    "# selecting x-y data that corresponds to the output from GridExtractor (for visualization of tracks in map)\n",
    "# also selecting the corresponding state vectors stored in df_state for clustering:\n",
    "grouped = df2.groupby(['ID'])  \n",
    "for ID,group in grouped:\n",
    "    df_temp = df1.loc[df1['ID'] == ID, ['ID', 'Time', 'x', 'y']]\n",
    "    #df_temp = df_temp.loc[(df_temp['Time'] >= group['State time'].iloc[0]) & (df_temp['Time'] <= group['State time'].iloc[-1])]\n",
    "    #if len(df_temp.index) > 5:    # only using ID:s containing more than 30 data points\n",
    "    df = df.append(df_temp)\n",
    "    df_state = df_state.append(group)\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'xy_right_time_matching.csv':\n",
    "df.to_csv(os.path.join(path, r'xy_left_time_matching.csv'), index=False)\n",
    "\n",
    "# the file is currently named 'state_vector_left_turn.csv':\n",
    "df_state.to_csv(os.path.join(path, r'state_vector_left_turn.csv'), index=False)\n",
    "\n",
    "print('Selected paths saved to csv !')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Doing some more removal of outliers and creating 3D plot with state vectors and xy-file for trajectory plotting (left turn):**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "window.mpl = {};\n",
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
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       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
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       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "\n",
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       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
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       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
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       "\n",
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       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import csv\n",
    "%matplotlib notebook\n",
    "\n",
    "plot_step = 9   # specify here the n:th number of step that will be used in the plot ( this is to remove noice)\n",
    "                    # if plot_step = 1 all data points will be used\n",
    "\n",
    "# Loading file which contains x-y coord for right turn paths:\n",
    "cols = ['ID','Time','x','y']\n",
    "xy_data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\xy_left_time_matching.csv',index_col=False, header=None,sep=',',names=cols )\n",
    "df_xy = xy_data\n",
    "df_xy = df_xy.drop(df_xy.index[0])   # drops the first row\n",
    "\n",
    "# Loading file which contains state vector for right turn paths:\n",
    "state_data = pd.read_csv(r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L\\state_vector_left_turn.csv',index_col=False, header=None,sep=',')\n",
    "df_state = state_data\n",
    "df_state.columns = df_state.iloc[0]\n",
    "df_state = df_state.drop(df_state.index[0])   # drops the first row\n",
    "df_state['Delta theta'] = df_state['Delta theta'].astype(float)\n",
    "df_state['Velocity'] = df_state['Velocity'].astype(float)\n",
    "#print(df_test.dtypes)\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "# creating empty data frame to store path where outliers are removed (needed to plot paths in map):\n",
    "df_map = pd.DataFrame()\n",
    "df_filtered = pd.DataFrame()\n",
    "grouped = df_state.groupby(['ID'])  \n",
    "for ID,group in grouped:\n",
    "    theta = group['Delta theta']\n",
    "    theta = theta.to_numpy()\n",
    "    theta = np.insert(theta,0,0)\n",
    "    theta = theta*180/np.pi\n",
    "    theta = np.cumsum(theta)\n",
    "    time = np.arange(len(group)+1)*0.3\n",
    "    if not((theta > 150).any() or (time > 23).any() ):  # filtering out outliers for cumulative theta values \n",
    "        velocity = group['Velocity'].to_numpy()\n",
    "        velocity = np.insert(velocity,0,0)\n",
    "        \n",
    "        plot_data = pd.DataFrame({'Velocity': velocity, 'Cum_theta': theta, 'Time': time})\n",
    "        time_plot = plot_data['Time'].iloc[::plot_step].to_numpy()\n",
    "        velocity_plot = plot_data['Velocity'].iloc[::plot_step].to_numpy()\n",
    "        theta_plot = plot_data['Cum_theta'].iloc[::plot_step].to_numpy()\n",
    "        ax.plot3D(theta_plot, time_plot,velocity_plot, 'green')\n",
    "        df_temp = df_xy.loc[df_xy['ID'] == ID, ['ID', 'Time', 'x', 'y']]\n",
    "        df_map = df_map.append(df_temp)\n",
    "        df_temp2 = pd.DataFrame({'cumulative theta': theta[1:len(theta)],'time elapsed': time[1:len(time)]})\n",
    "        group = group.reset_index(drop=True)\n",
    "        group = pd.concat([df_temp2,group], axis=1)\n",
    "        df_filtered = df_filtered.append(group)\n",
    "              \n",
    "# label axes\n",
    "ax.set_xlabel('Cumulative theta', fontsize=20)\n",
    "ax.set_ylabel('Time', fontsize=15)\n",
    "ax.set_zlabel('Velocity', fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "# path to to put saved file:\n",
    "path = r'C:\\Users\\Desktop\\CAS\\Project_course_math_stat_modelling\\2017_05_17_L'\n",
    "\n",
    "# the file is currently named 'xy_right_time_matching.csv':\n",
    "df_map.to_csv(os.path.join(path, r'xy_left_filtered_cut.csv'), index=False)\n",
    "\n",
    "# the file is currently named 'state_vector_right_turn.csv':\n",
    "df_filtered.to_csv(os.path.join(path, r'state_vector_L_filtered_cut.csv'), index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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