analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"svg.fonttype\"] = \"none\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>distance_m</th>\n",
       "      <th>min_azimuth_deg</th>\n",
       "      <th>max_azimuth_deg</th>\n",
       "      <th>min_altitude_deg</th>\n",
       "      <th>max_altitude_deg</th>\n",
       "      <th>min_distance_m</th>\n",
       "      <th>max_distance_m</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>center_x</th>\n",
       "      <th>center_y</th>\n",
       "      <th>center_z</th>\n",
       "      <th>yaw_rad</th>\n",
       "      <th>points_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "      <td>40570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3745.900099</td>\n",
       "      <td>29.523035</td>\n",
       "      <td>0.349493</td>\n",
       "      <td>6.917663</td>\n",
       "      <td>88.523277</td>\n",
       "      <td>92.972157</td>\n",
       "      <td>26.780119</td>\n",
       "      <td>28.930232</td>\n",
       "      <td>1.688902</td>\n",
       "      <td>1.528661</td>\n",
       "      <td>3.845704</td>\n",
       "      <td>28.190472</td>\n",
       "      <td>2.048284</td>\n",
       "      <td>-1.391330</td>\n",
       "      <td>-0.007033</td>\n",
       "      <td>431.475228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2144.109422</td>\n",
       "      <td>17.448112</td>\n",
       "      <td>20.861704</td>\n",
       "      <td>20.812059</td>\n",
       "      <td>12.100331</td>\n",
       "      <td>13.532669</td>\n",
       "      <td>16.944107</td>\n",
       "      <td>16.569554</td>\n",
       "      <td>0.440083</td>\n",
       "      <td>0.456467</td>\n",
       "      <td>2.379170</td>\n",
       "      <td>17.432736</td>\n",
       "      <td>8.435665</td>\n",
       "      <td>0.391436</td>\n",
       "      <td>1.952638</td>\n",
       "      <td>1379.834738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>-151.905000</td>\n",
       "      <td>-44.695000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-3.290000</td>\n",
       "      <td>-39.586000</td>\n",
       "      <td>-5.009000</td>\n",
       "      <td>-3.141000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1896.000000</td>\n",
       "      <td>15.697750</td>\n",
       "      <td>-10.070750</td>\n",
       "      <td>-6.245750</td>\n",
       "      <td>89.512000</td>\n",
       "      <td>91.542000</td>\n",
       "      <td>13.388500</td>\n",
       "      <td>16.021000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>14.478000</td>\n",
       "      <td>-2.955000</td>\n",
       "      <td>-1.616000</td>\n",
       "      <td>-1.501000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3753.000000</td>\n",
       "      <td>26.110500</td>\n",
       "      <td>2.936500</td>\n",
       "      <td>5.069000</td>\n",
       "      <td>90.199500</td>\n",
       "      <td>93.039000</td>\n",
       "      <td>23.655500</td>\n",
       "      <td>26.123500</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>1.620000</td>\n",
       "      <td>3.830000</td>\n",
       "      <td>24.833500</td>\n",
       "      <td>2.633000</td>\n",
       "      <td>-1.460000</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5588.000000</td>\n",
       "      <td>40.194500</td>\n",
       "      <td>14.073000</td>\n",
       "      <td>19.668000</td>\n",
       "      <td>90.810000</td>\n",
       "      <td>95.853000</td>\n",
       "      <td>37.269000</td>\n",
       "      <td>39.195750</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>38.587250</td>\n",
       "      <td>6.482000</td>\n",
       "      <td>-1.230000</td>\n",
       "      <td>1.539000</td>\n",
       "      <td>313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7480.000000</td>\n",
       "      <td>149.798000</td>\n",
       "      <td>48.765000</td>\n",
       "      <td>168.750000</td>\n",
       "      <td>100.918000</td>\n",
       "      <td>113.787000</td>\n",
       "      <td>79.921000</td>\n",
       "      <td>79.990000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>35.240000</td>\n",
       "      <td>146.945000</td>\n",
       "      <td>44.427000</td>\n",
       "      <td>2.729000</td>\n",
       "      <td>3.132000</td>\n",
       "      <td>42664.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              frame    distance_m  min_azimuth_deg  max_azimuth_deg  \\\n",
       "count  40570.000000  40570.000000     40570.000000     40570.000000   \n",
       "mean    3745.900099     29.523035         0.349493         6.917663   \n",
       "std     2144.109422     17.448112        20.861704        20.812059   \n",
       "min        0.000000      2.990000      -151.905000       -44.695000   \n",
       "25%     1896.000000     15.697750       -10.070750        -6.245750   \n",
       "50%     3753.000000     26.110500         2.936500         5.069000   \n",
       "75%     5588.000000     40.194500        14.073000        19.668000   \n",
       "max     7480.000000    149.798000        48.765000       168.750000   \n",
       "\n",
       "       min_altitude_deg  max_altitude_deg  min_distance_m  max_distance_m  \\\n",
       "count      40570.000000      40570.000000    40570.000000    40570.000000   \n",
       "mean          88.523277         92.972157       26.780119       28.930232   \n",
       "std           12.100331         13.532669       16.944107       16.569554   \n",
       "min            0.000000          0.000000        0.000000        0.000000   \n",
       "25%           89.512000         91.542000       13.388500       16.021000   \n",
       "50%           90.199500         93.039000       23.655500       26.123500   \n",
       "75%           90.810000         95.853000       37.269000       39.195750   \n",
       "max          100.918000        113.787000       79.921000       79.990000   \n",
       "\n",
       "             height         width        length      center_x      center_y  \\\n",
       "count  40570.000000  40570.000000  40570.000000  40570.000000  40570.000000   \n",
       "mean       1.688902      1.528661      3.845704     28.190472      2.048284   \n",
       "std        0.440083      0.456467      2.379170     17.432736      8.435665   \n",
       "min        0.760000      0.300000      0.200000     -3.290000    -39.586000   \n",
       "25%        1.460000      1.520000      3.360000     14.478000     -2.955000   \n",
       "50%        1.560000      1.620000      3.830000     24.833500      2.633000   \n",
       "75%        1.750000      1.710000      4.260000     38.587250      6.482000   \n",
       "max        4.200000      3.010000     35.240000    146.945000     44.427000   \n",
       "\n",
       "           center_z       yaw_rad  points_count  \n",
       "count  40570.000000  40570.000000  40570.000000  \n",
       "mean      -1.391330     -0.007033    431.475228  \n",
       "std        0.391436      1.952638   1379.834738  \n",
       "min       -5.009000     -3.141000      0.000000  \n",
       "25%       -1.616000     -1.501000     25.000000  \n",
       "50%       -1.460000     -0.001000     83.000000  \n",
       "75%       -1.230000      1.539000    313.000000  \n",
       "max        2.729000      3.132000  42664.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"output.csv\")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='type'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# typeを集計する\n",
    "df[\"type\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='distance_m', ylabel='points_count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Car, Van, Truckについて、横軸をdistance_m、縦軸をpoints_countとして散布図を描く\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle.plot.scatter(x=\"distance_m\", y=\"points_count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Pedestrians\n",
      "5deg = 0.9451749498551371\n",
      "10deg = 0.9973256073100066\n",
      "15deg = 1.0\n",
      "Pedestrians within 20m\n",
      "5deg = 0.9151138716356108\n",
      "10deg = 0.9958592132505176\n",
      "15deg = 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\367315968.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\367315968.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n"
     ]
    }
   ],
   "source": [
    "# Pedestrianについて、azimuthとaltitudeの差分を取る\n",
    "df_pedestrian = df[df[\"type\"] == \"Pedestrian\"]\n",
    "df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
    "df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
    "\n",
    "hist = pd.cut(df_pedestrian[\"azimuth_range\"], bins=np.arange(0, 21, 1), right=False).value_counts().sort_index()\n",
    "accum = hist.cumsum()\n",
    "accum_rate = accum / accum.max()\n",
    "# print(accum_rate.index)\n",
    "print(\"All Pedestrians\")\n",
    "print(f\"5deg = {accum_rate[5]}\")\n",
    "print(f\"10deg = {accum_rate[10]}\")\n",
    "print(f\"15deg = {accum_rate[15]}\")\n",
    "#print(f\"20deg = {accum_rate[20]}\")\n",
    "# plt.plot(np.arange(0, 20, 1), accum_rate.values)\n",
    "# for i, v in enumerate(accum_rate.values):\n",
    "#     plt.text(i, v, str(round(v, 3)))\n",
    "# plt.xlabel(\"azimuth_range(deg)\")\n",
    "# plt.ylabel(\"accum_rate\")\n",
    "# plt.title(\"azimuth_range(Pedestrian)\")\n",
    "# plt.show()\n",
    "\n",
    "df_pedestrian_20m = df_pedestrian[df_pedestrian[\"distance_m\"] < 20]\n",
    "hist = pd.cut(df_pedestrian_20m[\"azimuth_range\"], bins=np.arange(0, 21, 1), right=False).value_counts().sort_index()\n",
    "accum = hist.cumsum()\n",
    "accum_rate = accum / accum.max()\n",
    "# print(accum_rate.index)\n",
    "print(\"Pedestrians within 20m\")\n",
    "print(f\"5deg = {accum_rate[5]}\")\n",
    "print(f\"10deg = {accum_rate[10]}\")\n",
    "print(f\"15deg = {accum_rate[15]}\")\n",
    "#print(f\"20deg = {accum_rate[20]}\")\n",
    "# plt.plot(np.arange(0, 20, 1), accum_rate.values)\n",
    "# for i, v in enumerate(accum_rate.values):\n",
    "#     plt.text(i, v, str(round(v, 3)))\n",
    "# plt.xlabel(\"azimuth_range(deg)\")\n",
    "# plt.ylabel(\"accum_rate\")\n",
    "# plt.title(\"azimuth_range(Pedestrian)\")\n",
    "# plt.show()\n",
    "\n",
    "# # それぞれのヒストグラムを描く\n",
    "# plt.hist(df_pedestrian[\"azimuth_range\"], bins=20)\n",
    "# plt.title(\"azimuth_range\")\n",
    "# plt.show()\n",
    "# plt.hist(df_pedestrian[\"altitude_range\"], bins=20)\n",
    "# plt.title(\"altitude_range\")\n",
    "# plt.show()\n",
    "\n",
    "# # それぞれの散布図を描く\n",
    "# plt.scatter(df_pedestrian[\"distance_m\"], df_pedestrian[\"azimuth_range\"])\n",
    "# plt.xlabel(\"distance(m)\")\n",
    "# plt.ylabel(\"azimuth_range(deg)\")\n",
    "# plt.title(\"azimuth_range(Pedestrian)\")\n",
    "# plt.show()\n",
    "\n",
    "# plt.scatter(df_pedestrian[\"distance_m\"], df_pedestrian[\"altitude_range\"])\n",
    "# plt.xlabel(\"distance(m)\")\n",
    "# plt.ylabel(\"altitude_range(deg)\")\n",
    "# plt.title(\"altitude_range(Pedestrian)\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azimuth\n",
      "1m: 30407 / 32750 = 0.9284580152671755\n",
      "2m: 30407 / 32750 = 0.9284580152671755\n",
      "3m: 30407 / 32748 = 0.9285147184560889\n",
      "4m: 30407 / 32610 = 0.9324440355719105\n",
      "5m: 30407 / 32292 = 0.9416264090177133\n",
      "6m: 30407 / 31835 = 0.955143709753416\n",
      "7m: 30390 / 31377 = 0.968543837843006\n",
      "8m: 30260 / 30871 = 0.9802079621651388\n",
      "9m: 29960 / 30337 = 0.9875729307446353\n",
      "altitude\n",
      "1m: 31343 / 32750 = 0.9570381679389313\n",
      "2m: 31343 / 32750 = 0.9570381679389313\n",
      "3m: 31343 / 32748 = 0.9570966165872725\n",
      "4m: 31339 / 32610 = 0.9610242256976388\n",
      "5m: 31338 / 32292 = 0.9704570791527313\n",
      "6m: 31338 / 31835 = 0.984388251923983\n",
      "7m: 31236 / 31377 = 0.9955062625490009\n",
      "8m: 30831 / 30871 = 0.9987042855754592\n",
      "9m: 30320 / 30337 = 0.9994396281768138\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\138025622.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\138025622.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n"
     ]
    }
   ],
   "source": [
    "# Car, Van, Truckをあわせたものについて、azimuthとaltitudeの差分を取る\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
    "df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
    "\n",
    "print(\"azimuth\")\n",
    "for distance in range(1, 10):\n",
    "    df_vehicle_distance = df_vehicle[df_vehicle[\"distance_m\"] >= distance]\n",
    "    under_20deg_count = len(df_vehicle_distance[df_vehicle_distance[\"azimuth_range\"] < 20])\n",
    "    all_count = len(df_vehicle_distance)\n",
    "    print(f\"{distance}m: {under_20deg_count} / {all_count} = {under_20deg_count/all_count}\")\n",
    "\n",
    "print(\"altitude\")\n",
    "for distance in range(1, 10):\n",
    "    df_vehicle_distance = df_vehicle[df_vehicle[\"distance_m\"] >= distance]\n",
    "    under_20deg_count = len(df_vehicle_distance[df_vehicle_distance[\"altitude_range\"] < 16])\n",
    "    all_count = len(df_vehicle_distance)\n",
    "    print(f\"{distance}m: {under_20deg_count} / {all_count} = {under_20deg_count/all_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Vehicles\n",
      "10deg: 0.8147786259541985\n",
      "20deg: 0.9344732824427481\n",
      "30deg: 0.9652824427480916\n",
      "40deg: 0.9804885496183207\n",
      "Vehicles within 20m\n",
      "10deg: 0.4270080125073285\n",
      "20deg: 0.7918702364666796\n",
      "30deg: 0.8891928864569083\n",
      "40deg: 0.9376587844440102\n",
      "Vehicles over 10m\n",
      "10deg: 0.8979358184328383\n",
      "20deg: 0.9953530659662592\n",
      "30deg: 0.9994612250395662\n",
      "40deg: 0.9997979593898374\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\3113039397.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_8624\\3113039397.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n"
     ]
    }
   ],
   "source": [
    "# Car, Van, Truckをあわせたものについて、azimuthとaltitudeの差分を取る\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
    "df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
    "\n",
    "hist = pd.cut(df_vehicle[\"azimuth_range\"], bins=np.arange(0, 181, 1), right=False).value_counts().sort_index()\n",
    "accum = hist.cumsum()\n",
    "accum_rate = accum / accum.max()\n",
    "print(\"All Vehicles\")\n",
    "print(f\"10deg: {accum_rate[10]}\")\n",
    "print(f\"20deg: {accum_rate[20]}\")\n",
    "print(f\"30deg: {accum_rate[30]}\")\n",
    "print(f\"40deg: {accum_rate[40]}\")\n",
    "# plt.plot(np.arange(0, 180, 1), accum_rate.values)\n",
    "# for i, v in enumerate(accum_rate.values):\n",
    "#     plt.text(i, v, str(round(v, 3)))\n",
    "# plt.xlabel(\"azimuth_range(deg)\")\n",
    "# plt.ylabel(\"accum_rate\")\n",
    "# plt.title(\"azimuth_range(Car, Van, Truck)\")\n",
    "# plt.show()\n",
    "\n",
    "df_vehicle_20m = df_vehicle[df_vehicle[\"distance_m\"] < 20]\n",
    "hist = pd.cut(df_vehicle_20m[\"azimuth_range\"], bins=np.arange(0, 181, 1), right=False).value_counts().sort_index()\n",
    "accum = hist.cumsum()\n",
    "accum_rate = accum / accum.max()\n",
    "print(\"Vehicles within 20m\")\n",
    "print(f\"10deg: {accum_rate[10]}\")\n",
    "print(f\"20deg: {accum_rate[20]}\")\n",
    "print(f\"30deg: {accum_rate[30]}\")\n",
    "print(f\"40deg: {accum_rate[40]}\")\n",
    "\n",
    "df_vehicle_over_10m = df_vehicle[df_vehicle[\"distance_m\"] > 10]\n",
    "hist = pd.cut(df_vehicle_over_10m[\"azimuth_range\"], bins=np.arange(0, 181, 1), right=False).value_counts().sort_index()\n",
    "accum = hist.cumsum()\n",
    "accum_rate = accum / accum.max()\n",
    "print(\"Vehicles over 10m\")\n",
    "print(f\"10deg: {accum_rate[10]}\")\n",
    "print(f\"20deg: {accum_rate[20]}\")\n",
    "print(f\"30deg: {accum_rate[30]}\")\n",
    "print(f\"40deg: {accum_rate[40]}\")\n",
    "# plt.plot(np.arange(0, 180, 1), accum_rate.values)\n",
    "# for i, v in enumerate(accum_rate.values):\n",
    "#     plt.text(i, v, str(round(v, 3)))\n",
    "# plt.xlabel(\"azimuth_range(deg)\")\n",
    "# plt.ylabel(\"accum_rate\")\n",
    "# plt.title(\"azimuth_range(Car, Van, Truck) < 20m\")\n",
    "# plt.show()\n",
    "\n",
    "# # それぞれのヒストグラムを描く\n",
    "# plt.hist(df_vehicle[\"azimuth_range\"], bins=20)\n",
    "# plt.title(\"azimuth_range\")\n",
    "# plt.show()\n",
    "# plt.hist(df_vehicle[\"altitude_range\"], bins=20)\n",
    "# plt.title(\"altitude_range\")\n",
    "# plt.show()\n",
    "\n",
    "# # それぞれの散布図を描く\n",
    "# plt.scatter(df_vehicle[\"distance_m\"], df_vehicle[\"azimuth_range\"])\n",
    "# plt.xlabel(\"distance(m)\")\n",
    "# plt.ylabel(\"azimuth_range(deg)\")\n",
    "# plt.title(\"azimuth_range(Car, Van, Truck)\")\n",
    "# plt.show()\n",
    "\n",
    "# plt.scatter(df_vehicle[\"distance_m\"], df_vehicle[\"altitude_range\"])\n",
    "# plt.xlabel(\"distance(m)\")\n",
    "# plt.ylabel(\"altitude_range(deg)\")\n",
    "# plt.title(\"altitude_range(Car, Van, Truck)\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\740568791.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\740568791.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\740568791.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_each_distance_azimuth = df_pedestrian.groupby(df_each_distance)[\"azimuth_range\"].mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\740568791.py:15: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_each_distance_altitude = df_pedestrian.groupby(df_each_distance)[\"altitude_range\"].mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pedestrianについて、距離ごとのazimuth_rangeの平均を棒グラフで描く\n",
    "df_pedestrian = df[df[\"type\"] == \"Pedestrian\"]\n",
    "df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
    "df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
    "\n",
    "df_each_distance = pd.cut(df_pedestrian[\"distance_m\"], bins=np.arange(0, 101, 1), right=False)\n",
    "# binごとにazimuth_rangeの平均を取る\n",
    "df_each_distance_azimuth = df_pedestrian.groupby(df_each_distance)[\"azimuth_range\"].mean()\n",
    "plt.bar(np.arange(0, 100, 1), df_each_distance_azimuth.values)\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"mean azimuth_range(deg)\")\n",
    "plt.title(\"Pedestrian\")\n",
    "plt.show()\n",
    "\n",
    "df_each_distance_altitude = df_pedestrian.groupby(df_each_distance)[\"altitude_range\"].mean()\n",
    "plt.bar(np.arange(0, 100, 1), df_each_distance_altitude.values)\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"mean altitude_range(deg)\")\n",
    "plt.title(\"Pedestrian\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\543889984.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\543889984.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\543889984.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_each_distance_azimuth = df_vehicle.groupby(df_each_distance)[\"azimuth_range\"].mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\543889984.py:15: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_each_distance_altitude = df_vehicle.groupby(df_each_distance)[\"altitude_range\"].mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Car, Van, Truckについて、距離ごとのazimuth_rangeの平均を棒グラフで描く\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
    "df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
    "\n",
    "df_each_distance = pd.cut(df_vehicle[\"distance_m\"], bins=np.arange(0, 101, 1), right=False)\n",
    "# binごとにazimuth_rangeの平均を取る\n",
    "df_each_distance_azimuth = df_vehicle.groupby(df_each_distance)[\"azimuth_range\"].mean()\n",
    "plt.bar(np.arange(0, 100, 1), df_each_distance_azimuth.values)\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"mean azimuth_range(deg)\")\n",
    "plt.title(\"Car, Van, Truck\")\n",
    "plt.show()\n",
    "\n",
    "df_each_distance_altitude = df_vehicle.groupby(df_each_distance)[\"altitude_range\"].mean()\n",
    "plt.bar(np.arange(0, 100, 1), df_each_distance_altitude.values)\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"mean altitude_range(deg)\")\n",
    "plt.title(\"Car, Van, Truck\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:2: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:7: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:12: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_ped_azi_group = df_pedestrian.groupby(df_ped_dist)[\"azimuth_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:13: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_vehicle_azi_group = df_vehicle.groupby(df_vehicle_dist)[\"azimuth_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:14: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_ped_alt_group = df_pedestrian.groupby(df_ped_dist)[\"altitude_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3530855746.py:15: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_vehicle_alt_group = df_vehicle.groupby(df_vehicle_dist)[\"altitude_range\"]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pedestrian = df[df[\"type\"] == \"Pedestrian\"]\n",
    "df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
    "df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
    "\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
    "df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
    "\n",
    "bins = np.arange(0, 31, 5)\n",
    "df_ped_dist = pd.cut(df_pedestrian[\"distance_m\"], bins=bins, right=False)\n",
    "df_vehicle_dist = pd.cut(df_vehicle[\"distance_m\"], bins=bins, right=False)\n",
    "df_ped_azi_group = df_pedestrian.groupby(df_ped_dist)[\"azimuth_range\"]\n",
    "df_vehicle_azi_group = df_vehicle.groupby(df_vehicle_dist)[\"azimuth_range\"]\n",
    "df_ped_alt_group = df_pedestrian.groupby(df_ped_dist)[\"altitude_range\"]\n",
    "df_vehicle_alt_group = df_vehicle.groupby(df_vehicle_dist)[\"altitude_range\"]\n",
    "\n",
    "df_ped_dist_azi_u20 = df_ped_azi_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_vehicle_dist_azi_u20 = df_vehicle_azi_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_ped_dist_alt_u20 = df_ped_alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_vehicle_dist_alt_u20 = df_vehicle_alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "\n",
    "df_ped_dist_azi_u10 = df_ped_azi_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_vehicle_dist_azi_u10 = df_vehicle_azi_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_ped_dist_alt_u10 = df_ped_alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_vehicle_dist_alt_u10 = df_vehicle_alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "\n",
    "plt.plot(bins[:-1], df_ped_dist_azi_u20.values, marker=\"o\", color=\"red\", label=\"Pedestrian (<= 20deg)\")\n",
    "plt.plot(bins[:-1], df_ped_dist_azi_u10.values, marker=\"o\", color=\"blue\", label=\"Pedestrian (<= 10deg)\")\n",
    "plt.plot(bins[:-1], df_vehicle_dist_azi_u20.values, marker=\"x\", color=\"red\", label=\"Vehicle (<= 20deg)\")\n",
    "plt.plot(bins[:-1], df_vehicle_dist_azi_u10.values, marker=\"x\", color=\"blue\", label=\"Vehicle (<= 10deg)\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"azimuth_range\")\n",
    "plt.legend()\n",
    "plt.savefig(\"azimuth_range.svg\", bbox_inches=\"tight\", pad_inches=0.05, transparent=True, dpi=300)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(bins[:-1], df_ped_dist_alt_u20.values, marker=\"o\", color=\"red\", label=\"Pedestrian (<= 20deg)\")\n",
    "plt.plot(bins[:-1], df_ped_dist_alt_u10.values, marker=\"o\", color=\"blue\", label=\"Pedestrian (<= 10deg)\")\n",
    "plt.plot(bins[:-1], df_vehicle_dist_alt_u20.values, marker=\"x\", color=\"red\", label=\"Vehicle (<= 20deg)\")\n",
    "plt.plot(bins[:-1], df_vehicle_dist_alt_u10.values, marker=\"x\", color=\"blue\", label=\"Vehicle (<= 10deg)\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"altitude_range\")\n",
    "plt.legend()\n",
    "plt.savefig(\"altitude_range.svg\", bbox_inches=\"tight\", pad_inches=0.05, transparent=True, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4201619344.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4201619344.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4201619344.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  azi_group = df_pedestrian.groupby(df_each_distance)[\"azimuth_range\"]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pedestrianについて、距離ごとのazimuth_rangeがN度以下の割合を棒グラフで描く\n",
    "df_pedestrian = df[df[\"type\"] == \"Pedestrian\"]\n",
    "df_pedestrian[\"azimuth_range\"] = df_pedestrian[\"max_azimuth_deg\"] - df_pedestrian[\"min_azimuth_deg\"]\n",
    "df_pedestrian[\"altitude_range\"] = df_pedestrian[\"max_altitude_deg\"] - df_pedestrian[\"min_altitude_deg\"]\n",
    "\n",
    "bins = np.arange(0, 31, 5)\n",
    "df_each_distance = pd.cut(df_pedestrian[\"distance_m\"], bins=bins, right=False)\n",
    "azi_group = df_pedestrian.groupby(df_each_distance)[\"azimuth_range\"]\n",
    "df_each_distance_azi_u20 = azi_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_each_distance_azi_u10 = azi_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_each_distance_azi_u5 = azi_group.apply(lambda x: (x <= 5).sum() / x.count())\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u20.values, marker=\"o\", label=\"azimuth_range <= 20deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u10.values, marker=\"o\", label=\"azimuth_range <= 10deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u5.values, marker=\"o\", label=\"azimuth_range <= 5deg\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"Pedestrian\")\n",
    "plt.xticks(bins[:-1], [f\"[{i}-{i+5})\" for i in bins[:-1]])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3048266312.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  alt_group = df_pedestrian.groupby(df_each_distance)[\"altitude_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3048266312.py:5: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u20 = alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3048266312.py:6: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u10 = alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3048266312.py:7: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u5 = alt_group.apply(lambda x: (x <= 5).sum() / x.count())\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pedestrianについて、距離ごとのaltitude_rangeがN度以下の割合をグラフで描く\n",
    "bins = np.arange(0, 31, 1)\n",
    "df_each_distance = pd.cut(df_pedestrian[\"distance_m\"], bins=bins, right=False)\n",
    "alt_group = df_pedestrian.groupby(df_each_distance)[\"altitude_range\"]\n",
    "df_each_distance_alt_u20 = alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_each_distance_alt_u10 = alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_each_distance_alt_u5 = alt_group.apply(lambda x: (x <= 5).sum() / x.count())\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u20.values, marker=\"o\", label=\"altitude_range <= 20deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u10.values, marker=\"o\", label=\"altitude_range <= 10deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u5.values, marker=\"o\", label=\"altitude_range <= 5deg\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"Pedestrian\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  azi_group = df_vehicle.groupby(df_each_distance)[\"azimuth_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:9: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_azi_u20 = azi_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:10: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_azi_u10 = azi_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\4011409848.py:11: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_azi_u5 = azi_group.apply(lambda x: (x <= 5).sum() / x.count())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29463961fa0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vehicleについて、距離ごとのazimuth_rangeがN度以下の割合をグラフで描く\n",
    "df_vehicle = df[df[\"type\"].isin([\"Car\", \"Van\", \"Truck\"])]\n",
    "df_vehicle[\"azimuth_range\"] = df_vehicle[\"max_azimuth_deg\"] - df_vehicle[\"min_azimuth_deg\"]\n",
    "df_vehicle[\"altitude_range\"] = df_vehicle[\"max_altitude_deg\"] - df_vehicle[\"min_altitude_deg\"]\n",
    "\n",
    "bins = np.arange(0, 31, 1)\n",
    "df_each_distance = pd.cut(df_vehicle[\"distance_m\"], bins=bins, right=False)\n",
    "azi_group = df_vehicle.groupby(df_each_distance)[\"azimuth_range\"]\n",
    "df_each_distance_azi_u20 = azi_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_each_distance_azi_u10 = azi_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_each_distance_azi_u5 = azi_group.apply(lambda x: (x <= 5).sum() / x.count())\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u20.values, marker=\"o\", label=\"azimuth_range <= 20deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u10.values, marker=\"o\", label=\"azimuth_range <= 10deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_azi_u5.values, marker=\"o\", label=\"azimuth_range <= 5deg\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"Car, Van, Truck\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3356719221.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  alt_group = df_vehicle.groupby(df_each_distance)[\"altitude_range\"]\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3356719221.py:5: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u20 = alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3356719221.py:6: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u10 = alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
      "C:\\Users\\hykwy\\AppData\\Local\\Temp\\ipykernel_23244\\3356719221.py:7: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  df_each_distance_alt_u5 = alt_group.apply(lambda x: (x <= 5).sum() / x.count())\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vehicleについて、距離ごとのaltitude_rangeがN度以下の割合をグラフで描く\n",
    "bins = np.arange(0, 31, 1)\n",
    "df_each_distance = pd.cut(df_vehicle[\"distance_m\"], bins=bins, right=False)\n",
    "alt_group = df_vehicle.groupby(df_each_distance)[\"altitude_range\"]\n",
    "df_each_distance_alt_u20 = alt_group.apply(lambda x: (x <= 20).sum() / x.count())\n",
    "df_each_distance_alt_u10 = alt_group.apply(lambda x: (x <= 10).sum() / x.count())\n",
    "df_each_distance_alt_u5 = alt_group.apply(lambda x: (x <= 5).sum() / x.count())\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u20.values, marker=\"o\", label=\"altitude_range <= 20deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u10.values, marker=\"o\", label=\"altitude_range <= 10deg\")\n",
    "plt.plot(bins[:-1], df_each_distance_alt_u5.values, marker=\"o\", label=\"altitude_range <= 5deg\")\n",
    "plt.xlabel(\"distance(m)\")\n",
    "plt.ylabel(\"ratio\")\n",
    "plt.title(\"Car, Van, Truck\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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