{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movesのデータを分析する\n",
    "\n",
    "## 1. 下準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# JupyterNotebookでデータ分析するときのの定番\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 今回使用\n",
    "import requests\n",
    "import secret # INIファイルの読み書き "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# アクセストークンをINIファイルから取得\n",
    "access_token = secret.readAccessToken()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取得したアクセストークンの中身\n",
    "print(access_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. ユーザープロフィールの取得"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_url = 'https://api.moves-app.com/api/1.1/user/profile?access_token=' + access_token \n",
    "requests.get(api_url).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 2017年4月1ヶ月分のデータを取得\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "api_url = 'https://api.moves-app.com/api/1.1/user/summary/daily/201704?access_token=' + access_token \n",
    "\n",
    "data = requests.get(api_url).json()\n",
    "#requests.get(api_url).text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "さて，これをどう料理するか"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(data) # ドキュメントによると日々のデータのリストになっている"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(data[0]) # 4/1のデータはdict型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4/1のデータだけ見る"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(data[0]['summary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(data[0]['summary'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各日付ごとにlist，その中に日付やサマリーがdictで入っていて，サマリーの値はアクティビティごとにlist,アクティビティの中はまたdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[0]['summary'][0]['duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "こうすればウォーキング，移動，バイクの3つのリストにデータを抽出できる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "walking = list()\n",
    "transport = list()\n",
    "motorcycle = list()\n",
    "for d1 in data:\n",
    "    date = d1[\"date\"]\n",
    "    for d2 in d1[\"summary\"]:\n",
    "        activity = d2[\"activity\"]\n",
    "        distance = d2[\"distance\"]\n",
    "        duration = d2[\"duration\"]\n",
    "\n",
    "        if activity==\"walking\":\n",
    "            steps = d2[\"steps\"]\n",
    "            walking.append((date, activity, distance, steps))\n",
    "        elif activity==\"transport\":\n",
    "            transport.append((date, activity, distance, duration))\n",
    "        elif activity==\"motorcycle\":\n",
    "            motorcycle.append((date, activity, distance, duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "walking[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transport[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "motorcycle[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "あとでpandasのDataFrameに入れることを考えると，列ごとのリストにしたほうがよい"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date = list()\n",
    "activity = list()\n",
    "distance = list()\n",
    "duration=list()\n",
    "steps=list()\n",
    "\n",
    "for d1 in data:\n",
    "    for d2 in d1[\"summary\"]:\n",
    "        date.append(d1[\"date\"])\n",
    "        ac = d2[\"activity\"]\n",
    "        activity.append(ac)\n",
    "        distance.append(d2[\"distance\"])\n",
    "        duration.append(d2[\"duration\"])\n",
    "        if ac==\"walking\":\n",
    "            steps.append(d2[\"steps\"])\n",
    "        else:\n",
    "            steps.append(np.nan)\n",
    "\n",
    "        \n",
    "df = DataFrame({'Date': date,\n",
    "               'Activity': activity,\n",
    "               'Distance': distance,\n",
    "               'Duration': duration,\n",
    "               'Steps': steps},\n",
    "              columns=['Date','Activity', 'Distance', 'Duration', 'Steps'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これでpandasのDataFrameに格納できる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 位置情報の取得\n",
    "\n",
    "今度は位置情報を取得。4/15はバイクでツーリングに行った日。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "api_url2 = 'https://api.moves-app.com/api/1.1/user/storyline/daily/20170415?trackPoints=true&scope=activity&access_token=' + access_token\n",
    "\n",
    "posdata = requests.get(api_url2).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(posdata[0][\"segments\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(posdata[0][\"segments\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "posdata[0][\"segments\"][1][\"activities\"][0][\"trackPoints\"][0][\"lat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データを見てみると，位置情報があるのはactivityとplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat=list()\n",
    "lon=list()\n",
    "time=list()\n",
    "activity=list()\n",
    "for d1 in posdata:\n",
    "    for d2 in d1[\"segments\"]:\n",
    "        if \"activities\" in d2:\n",
    "            for d3 in d2[\"activities\"]:\n",
    "                for d4 in d3[\"trackPoints\"]:\n",
    "                    activity.append(d3[\"activity\"])\n",
    "                    lat.append(d4[\"lat\"])\n",
    "                    lon.append(d4[\"lon\"])\n",
    "                    time.append(d4[\"time\"])\n",
    "        if \"place\" in d2:\n",
    "            if \"name\" in d2[\"place\"]:\n",
    "                name = d2[\"place\"][\"name\"]\n",
    "            else:\n",
    "                name = \"Unknown\"\n",
    "            if name != \"ホーム\": # 自宅データは除外\n",
    "                activity.append(\"Place:\" + name)\n",
    "                lat.append(d2[\"place\"][\"location\"][\"lat\"])\n",
    "                lon.append(d2[\"place\"][\"location\"][\"lon\"])\n",
    "                time.append(d2[\"endTime\"])\n",
    "                                                  \n",
    "            \n",
    "df2 = DataFrame({'Time': time,\n",
    "               'Lat': lat,\n",
    "               'Lon': lon,\n",
    "               'Activity': activity},\n",
    "              columns=['Time','Lat', 'Lon', 'Activity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "地図に表示するためにfoliumパッケージを使う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m = folium.Map(location=[35.01, 139], zoom_start=9)\n",
    "for i, row in df2.iterrows():\n",
    "    if row[\"Activity\"][0:5]==\"Place\":\n",
    "        folium.Marker([row[\"Lat\"],row[\"Lon\"]], popup=row[\"Activity\"][6:]).add_to(m)\n",
    "    else:\n",
    "        folium.CircleMarker([row[\"Lat\"],row[\"Lon\"]], radius=2, color=\"#336699\").add_to(m)\n",
    "    #print([row[\"Lat\"],row[\"Lon\"]])\n",
    "m"
   ]
  }
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