├── readme.md
├── Discovery-Jp-Test.ipynb
└── sakura-predict-csv.ipynb


/readme.md:
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1 | readme
2 | 


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/Discovery-Jp-Test.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Discoveryでコレクション作成"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": null,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "# 個別設定\n",
 17 |     "# 管理コンソールのcredentail情報をコピーします。\n",
 18 |     "credencial = {\n",
 19 |     "  \"url\": \"https://gateway.watsonplatform.net/discovery/api\",\n",
 20 |     "  \"username\": \"xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx\",\n",
 21 |     "  \"password\": \"xxxxxxxxxxxx\"\n",
 22 |     "}"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": null,
 28 |    "metadata": {},
 29 |    "outputs": [],
 30 |    "source": [
 31 |     "# ライブラリ読み込み\n",
 32 |     "import json\n",
 33 |     "from watson_developer_cloud import DiscoveryV1\n",
 34 |     "# In the constructor, letting the SDK manage the IAM token\n",
 35 |     "discovery = DiscoveryV1(version = '2018-03-05',\n",
 36 |     "                        username = credencial['username'],\n",
 37 |     "                       password = credencial['password'])"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": null,
 43 |    "metadata": {},
 44 |    "outputs": [],
 45 |    "source": [
 46 |     "# Environment作成\n",
 47 |     "response = discovery.create_environment(\n",
 48 |     "    name=\"my_environment\"\n",
 49 |     ")\n",
 50 |     "# environment_idの設定\n",
 51 |     "environment_id = response['environment_id']\n",
 52 |     "print(environment_id)"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": null,
 58 |    "metadata": {},
 59 |    "outputs": [],
 60 |    "source": [
 61 |     "# Collection作成\n",
 62 |     "col_name = 'JP_TEST'\n",
 63 |     "new_collection = discovery.create_collection(\n",
 64 |     "    environment_id = environment_id, \n",
 65 |     "    name = col_name, \n",
 66 |     "    language='ja')\n",
 67 |     "\n",
 68 |     "# collection_idの設定\n",
 69 |     "collection_id = new_collection['collection_id']\n",
 70 |     "print(collection_id)"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": null,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "# 必要であれば、このタイミングでエンリッチ設定の変更を行います。\n",
 80 |     "# 一度コレクションを日本語で作ってしまえば、エンリッチ設定変更はUIから可能です。"
 81 |    ]
 82 |   },
 83 |   {
 84 |    "cell_type": "markdown",
 85 |    "metadata": {},
 86 |    "source": [
 87 |     "# CSVファイル読込み"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": null,
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "infile = 'data_xxxxxx.csv'\n",
 97 |     "\n",
 98 |     "# このセルは、ツール生成のものを一部手で修正します。\n",
 99 |     "\n",
100 |     "import sys\n",
101 |     "import types\n",
102 |     "import pandas as pd\n",
103 |     "from botocore.client import Config\n",
104 |     "import ibm_boto3\n",
105 |     "\n",
106 |     "def __iter__(self): return 0\n",
107 |     "\n",
108 |     "client_xxxxxx = ibm_boto3.client(service_name='s3',\n",
109 |     "    ibm_api_key_id='xxxxxx',\n",
110 |     "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
111 |     "    config=Config(signature_version='oauth'),\n",
112 |     "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
113 |     "\n",
114 |     "# Key=のパラメータは変数利用に修正しました\n",
115 |     "body = client_xxxxxx.get_object(Bucket='xxxxxxx',Key=infile)['Body']\n",
116 |     "# add missing __iter__ method, so pandas accepts body as file-like object\n",
117 |     "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
118 |     "\n",
119 |     "# DataFrame名は df_dataに修正しました\n",
120 |     "df_data = pd.read_csv(body)\n",
121 |     "df_data.head()"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "markdown",
126 |    "metadata": {},
127 |    "source": [
128 |     "# 読み込んだデータをDiscovey Collectionに書き込み"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": null,
134 |    "metadata": {
135 |     "scrolled": true
136 |    },
137 |    "outputs": [],
138 |    "source": [
139 |     "import os\n",
140 |     "import time\n",
141 |     "\n",
142 |     "for i in range(len(df_data)):\n",
143 |     "    item = df_data.iloc[i,:]\n",
144 |     "    data = {}\n",
145 |     "    \n",
146 |     "    # 以下はCSVの項目名によって修正して下さい\n",
147 |     "    data['pat_id'] = str(item['pat_id'])\n",
148 |     "    data['pat_name'] = item['pat_name']\n",
149 |     "    data['summury'] = item['pat_summury']\n",
150 |     "    data['text'] = item['pat_text']\n",
151 |     "    # print(data)\n",
152 |     "    filename = data['pat_id'] + '.json'\n",
153 |     "    f = open(filename, 'w')\n",
154 |     "    json.dump(data, f)\n",
155 |     "    f.close()\n",
156 |     "\n",
157 |     "    collection = discovery.get_collection(environment_id, collection_id)\n",
158 |     "    proc_docs = collection['document_counts']['processing']\n",
159 |     "    while True:\n",
160 |     "        if proc_docs < 20:\n",
161 |     "            break\n",
162 |     "        print('busy. waiting..')\n",
163 |     "        time.sleep(10)\n",
164 |     "        collection = discovery.get_collection(environment_id, collection_id)\n",
165 |     "        proc_docs = collection['document_counts']['processing']\n",
166 |     "\n",
167 |     "    # print(json.dumps(collection, indent=2))\n",
168 |     "    with open(filename) as f:\n",
169 |     "        add_doc = discovery.add_document(environment_id, collection_id, file = f)\n",
170 |     "    os.remove(filename)\n",
171 |     "    # print(json.dumps(add_doc, indent=2))"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": null,
177 |    "metadata": {},
178 |    "outputs": [],
179 |    "source": []
180 |   },
181 |   {
182 |    "cell_type": "markdown",
183 |    "metadata": {},
184 |    "source": [
185 |     "# 検索テスト"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": null,
191 |    "metadata": {},
192 |    "outputs": [],
193 |    "source": [
194 |     "query = '検索テスト'\n",
195 |     "filter = '検索テスト'"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": null,
201 |    "metadata": {},
202 |    "outputs": [],
203 |    "source": [
204 |     "my_query = discovery.query(\n",
205 |     "    environment_id = environment_id, \n",
206 |     "    collection_id = collection_id, \n",
207 |     "    query = query, \n",
208 |     "    filter = filter, \n",
209 |     "    return_fields = '['pat_id', 'pat_name']')\n",
210 |     "print(json.dumps(my_query, indent=2))"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": null,
216 |    "metadata": {},
217 |    "outputs": [],
218 |    "source": []
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "# データ削除用 (MAX100件)"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": null,
230 |    "metadata": {},
231 |    "outputs": [],
232 |    "source": [
233 |     "results = discovery.query(environment_id, collection_id, return_fields='id', count=100)[\"results\"]\n",
234 |     "ids = [item[\"id\"] for item in results]\n",
235 |     "\n",
236 |     "for id in ids:\n",
237 |     "    print('deleting doc: id =' + id)\n",
238 |     "    discovery.delete_document(environment_id, collection_id, id)"
239 |    ]
240 |   }
241 |  ],
242 |  "metadata": {
243 |   "kernelspec": {
244 |    "display_name": "Python 3.5",
245 |    "language": "python",
246 |    "name": "python3"
247 |   },
248 |   "language_info": {
249 |    "codemirror_mode": {
250 |     "name": "ipython",
251 |     "version": 3
252 |    },
253 |    "file_extension": ".py",
254 |    "mimetype": "text/x-python",
255 |    "name": "python",
256 |    "nbconvert_exporter": "python",
257 |    "pygments_lexer": "ipython3",
258 |    "version": "3.5.4"
259 |   }
260 |  },
261 |  "nbformat": 4,
262 |  "nbformat_minor": 1
263 | }
264 | 


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/sakura-predict-csv.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# 桜開花予想"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "## データの読み込み\n",
  15 |     "データ分析に必要なデータをCSVファイルから読み込む。"
  16 |    ]
  17 |   },
  18 |   {
  19 |    "cell_type": "code",
  20 |    "execution_count": 1,
  21 |    "metadata": {},
  22 |    "outputs": [
  23 |     {
  24 |      "data": {
  25 |       "text/html": [
  26 |        "<div>\n",
  27 |        "<style scoped>\n",
  28 |        "    .dataframe tbody tr th:only-of-type {\n",
  29 |        "        vertical-align: middle;\n",
  30 |        "    }\n",
  31 |        "\n",
  32 |        "    .dataframe tbody tr th {\n",
  33 |        "        vertical-align: top;\n",
  34 |        "    }\n",
  35 |        "\n",
  36 |        "    .dataframe thead th {\n",
  37 |        "        text-align: right;\n",
  38 |        "    }\n",
  39 |        "</style>\n",
  40 |        "<table border=\"1\" class=\"dataframe\">\n",
  41 |        "  <thead>\n",
  42 |        "    <tr style=\"text-align: right;\">\n",
  43 |        "      <th></th>\n",
  44 |        "      <th>location_name</th>\n",
  45 |        "      <th>prec_id</th>\n",
  46 |        "      <th>block_id</th>\n",
  47 |        "      <th>latitude</th>\n",
  48 |        "      <th>longitude</th>\n",
  49 |        "      <th>height</th>\n",
  50 |        "    </tr>\n",
  51 |        "  </thead>\n",
  52 |        "  <tbody>\n",
  53 |        "    <tr>\n",
  54 |        "      <th>0</th>\n",
  55 |        "      <td>稚内</td>\n",
  56 |        "      <td>11</td>\n",
  57 |        "      <td>47401</td>\n",
  58 |        "      <td>45.4150</td>\n",
  59 |        "      <td>141.6783</td>\n",
  60 |        "      <td>2.8</td>\n",
  61 |        "    </tr>\n",
  62 |        "    <tr>\n",
  63 |        "      <th>1</th>\n",
  64 |        "      <td>旭川</td>\n",
  65 |        "      <td>12</td>\n",
  66 |        "      <td>47407</td>\n",
  67 |        "      <td>43.7567</td>\n",
  68 |        "      <td>142.3717</td>\n",
  69 |        "      <td>120.0</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>2</th>\n",
  73 |        "      <td>札幌</td>\n",
  74 |        "      <td>14</td>\n",
  75 |        "      <td>47412</td>\n",
  76 |        "      <td>43.0600</td>\n",
  77 |        "      <td>141.3283</td>\n",
  78 |        "      <td>17.2</td>\n",
  79 |        "    </tr>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>3</th>\n",
  82 |        "      <td>網走</td>\n",
  83 |        "      <td>17</td>\n",
  84 |        "      <td>47409</td>\n",
  85 |        "      <td>44.0167</td>\n",
  86 |        "      <td>144.2783</td>\n",
  87 |        "      <td>37.6</td>\n",
  88 |        "    </tr>\n",
  89 |        "    <tr>\n",
  90 |        "      <th>4</th>\n",
  91 |        "      <td>釧路</td>\n",
  92 |        "      <td>19</td>\n",
  93 |        "      <td>47418</td>\n",
  94 |        "      <td>42.9850</td>\n",
  95 |        "      <td>144.3767</td>\n",
  96 |        "      <td>4.5</td>\n",
  97 |        "    </tr>\n",
  98 |        "  </tbody>\n",
  99 |        "</table>\n",
 100 |        "</div>"
 101 |       ],
 102 |       "text/plain": [
 103 |        "  location_name  prec_id  block_id  latitude  longitude  height\n",
 104 |        "0            稚内       11     47401   45.4150   141.6783     2.8\n",
 105 |        "1            旭川       12     47407   43.7567   142.3717   120.0\n",
 106 |        "2            札幌       14     47412   43.0600   141.3283    17.2\n",
 107 |        "3            網走       17     47409   44.0167   144.2783    37.6\n",
 108 |        "4            釧路       19     47418   42.9850   144.3767     4.5"
 109 |       ]
 110 |      },
 111 |      "execution_count": 1,
 112 |      "metadata": {},
 113 |      "output_type": "execute_result"
 114 |     }
 115 |    ],
 116 |    "source": [
 117 |     "# セル1-1. obs_station.csvを、PandasのDataframeとして読み込む（変数名をdf_stationとする）\n",
 118 |     "import sys\n",
 119 |     "import types\n",
 120 |     "import pandas as pd\n",
 121 |     "from botocore.client import Config\n",
 122 |     "import ibm_boto3\n",
 123 |     "\n",
 124 |     "def __iter__(self): return 0\n",
 125 |     "\n",
 126 |     "# @hidden_cell\n",
 127 |     "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
 128 |     "# You might want to remove those credentials before you share your notebook.\n",
 129 |     "client_1539ecb44f134a9c99ef6869a2839be9 = ibm_boto3.client(service_name='s3',\n",
 130 |     "    ibm_api_key_id='hWqSqualyGC4H1YfmJfirrY-KDDqP51f3NrvLc2vJHlH',\n",
 131 |     "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
 132 |     "    config=Config(signature_version='oauth'),\n",
 133 |     "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
 134 |     "\n",
 135 |     "body = client_1539ecb44f134a9c99ef6869a2839be9.get_object(Bucket='think2018aka2-donotdelete-pr-wlhe61fd1hungw',Key='obs_station.csv')['Body']\n",
 136 |     "# add missing __iter__ method, so pandas accepts body as file-like object\n",
 137 |     "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
 138 |     "\n",
 139 |     "df_station = pd.read_csv(body)\n",
 140 |     "df_station.head()"
 141 |    ]
 142 |   },
 143 |   {
 144 |    "cell_type": "code",
 145 |    "execution_count": 2,
 146 |    "metadata": {},
 147 |    "outputs": [
 148 |     {
 149 |      "data": {
 150 |       "text/html": [
 151 |        "<div>\n",
 152 |        "<style scoped>\n",
 153 |        "    .dataframe tbody tr th:only-of-type {\n",
 154 |        "        vertical-align: middle;\n",
 155 |        "    }\n",
 156 |        "\n",
 157 |        "    .dataframe tbody tr th {\n",
 158 |        "        vertical-align: top;\n",
 159 |        "    }\n",
 160 |        "\n",
 161 |        "    .dataframe thead th {\n",
 162 |        "        text-align: right;\n",
 163 |        "    }\n",
 164 |        "</style>\n",
 165 |        "<table border=\"1\" class=\"dataframe\">\n",
 166 |        "  <thead>\n",
 167 |        "    <tr style=\"text-align: right;\">\n",
 168 |        "      <th></th>\n",
 169 |        "      <th>location_name</th>\n",
 170 |        "      <th>y2007</th>\n",
 171 |        "      <th>y2008</th>\n",
 172 |        "      <th>y2009</th>\n",
 173 |        "      <th>y2010</th>\n",
 174 |        "      <th>y2011</th>\n",
 175 |        "      <th>y2012</th>\n",
 176 |        "      <th>y2013</th>\n",
 177 |        "      <th>y2014</th>\n",
 178 |        "      <th>y2015</th>\n",
 179 |        "      <th>y2016</th>\n",
 180 |        "      <th>y2017</th>\n",
 181 |        "      <th>prec_id</th>\n",
 182 |        "      <th>block_id</th>\n",
 183 |        "    </tr>\n",
 184 |        "  </thead>\n",
 185 |        "  <tbody>\n",
 186 |        "    <tr>\n",
 187 |        "      <th>0</th>\n",
 188 |        "      <td>旭川</td>\n",
 189 |        "      <td>2007-05-06</td>\n",
 190 |        "      <td>2008-04-23</td>\n",
 191 |        "      <td>2009-05-03</td>\n",
 192 |        "      <td>2010-05-11</td>\n",
 193 |        "      <td>2011-05-09</td>\n",
 194 |        "      <td>2012-05-02</td>\n",
 195 |        "      <td>2013-05-18</td>\n",
 196 |        "      <td>2014-05-02</td>\n",
 197 |        "      <td>2015-04-27</td>\n",
 198 |        "      <td>2016-05-03</td>\n",
 199 |        "      <td>2017-05-03</td>\n",
 200 |        "      <td>12</td>\n",
 201 |        "      <td>47407</td>\n",
 202 |        "    </tr>\n",
 203 |        "    <tr>\n",
 204 |        "      <th>1</th>\n",
 205 |        "      <td>宇都宮</td>\n",
 206 |        "      <td>2007-03-26</td>\n",
 207 |        "      <td>2008-03-28</td>\n",
 208 |        "      <td>2009-03-26</td>\n",
 209 |        "      <td>2010-04-02</td>\n",
 210 |        "      <td>2011-04-06</td>\n",
 211 |        "      <td>2012-04-08</td>\n",
 212 |        "      <td>2013-03-21</td>\n",
 213 |        "      <td>2014-03-29</td>\n",
 214 |        "      <td>2015-03-30</td>\n",
 215 |        "      <td>2016-03-28</td>\n",
 216 |        "      <td>2017-04-03</td>\n",
 217 |        "      <td>41</td>\n",
 218 |        "      <td>47615</td>\n",
 219 |        "    </tr>\n",
 220 |        "    <tr>\n",
 221 |        "      <th>2</th>\n",
 222 |        "      <td>横浜</td>\n",
 223 |        "      <td>2007-03-23</td>\n",
 224 |        "      <td>2008-03-23</td>\n",
 225 |        "      <td>2009-03-22</td>\n",
 226 |        "      <td>2010-03-22</td>\n",
 227 |        "      <td>2011-03-30</td>\n",
 228 |        "      <td>2012-04-02</td>\n",
 229 |        "      <td>2013-03-18</td>\n",
 230 |        "      <td>2014-03-25</td>\n",
 231 |        "      <td>2015-03-23</td>\n",
 232 |        "      <td>2016-03-23</td>\n",
 233 |        "      <td>2017-03-25</td>\n",
 234 |        "      <td>46</td>\n",
 235 |        "      <td>47670</td>\n",
 236 |        "    </tr>\n",
 237 |        "    <tr>\n",
 238 |        "      <th>3</th>\n",
 239 |        "      <td>岡山</td>\n",
 240 |        "      <td>2007-03-26</td>\n",
 241 |        "      <td>2008-03-26</td>\n",
 242 |        "      <td>2009-03-21</td>\n",
 243 |        "      <td>2010-03-22</td>\n",
 244 |        "      <td>2011-03-31</td>\n",
 245 |        "      <td>2012-04-03</td>\n",
 246 |        "      <td>2013-03-24</td>\n",
 247 |        "      <td>2014-03-28</td>\n",
 248 |        "      <td>2015-03-28</td>\n",
 249 |        "      <td>2016-03-26</td>\n",
 250 |        "      <td>2017-04-01</td>\n",
 251 |        "      <td>66</td>\n",
 252 |        "      <td>47768</td>\n",
 253 |        "    </tr>\n",
 254 |        "    <tr>\n",
 255 |        "      <th>4</th>\n",
 256 |        "      <td>下関</td>\n",
 257 |        "      <td>2007-03-23</td>\n",
 258 |        "      <td>2008-03-26</td>\n",
 259 |        "      <td>2009-03-18</td>\n",
 260 |        "      <td>2010-03-20</td>\n",
 261 |        "      <td>2011-03-30</td>\n",
 262 |        "      <td>2012-03-30</td>\n",
 263 |        "      <td>2013-03-19</td>\n",
 264 |        "      <td>2014-03-25</td>\n",
 265 |        "      <td>2015-03-25</td>\n",
 266 |        "      <td>2016-03-28</td>\n",
 267 |        "      <td>2017-03-29</td>\n",
 268 |        "      <td>81</td>\n",
 269 |        "      <td>47762</td>\n",
 270 |        "    </tr>\n",
 271 |        "  </tbody>\n",
 272 |        "</table>\n",
 273 |        "</div>"
 274 |       ],
 275 |       "text/plain": [
 276 |        "  location_name       y2007       y2008       y2009       y2010       y2011  \\\n",
 277 |        "0            旭川  2007-05-06  2008-04-23  2009-05-03  2010-05-11  2011-05-09   \n",
 278 |        "1           宇都宮  2007-03-26  2008-03-28  2009-03-26  2010-04-02  2011-04-06   \n",
 279 |        "2            横浜  2007-03-23  2008-03-23  2009-03-22  2010-03-22  2011-03-30   \n",
 280 |        "3            岡山  2007-03-26  2008-03-26  2009-03-21  2010-03-22  2011-03-31   \n",
 281 |        "4            下関  2007-03-23  2008-03-26  2009-03-18  2010-03-20  2011-03-30   \n",
 282 |        "\n",
 283 |        "        y2012       y2013       y2014       y2015       y2016       y2017  \\\n",
 284 |        "0  2012-05-02  2013-05-18  2014-05-02  2015-04-27  2016-05-03  2017-05-03   \n",
 285 |        "1  2012-04-08  2013-03-21  2014-03-29  2015-03-30  2016-03-28  2017-04-03   \n",
 286 |        "2  2012-04-02  2013-03-18  2014-03-25  2015-03-23  2016-03-23  2017-03-25   \n",
 287 |        "3  2012-04-03  2013-03-24  2014-03-28  2015-03-28  2016-03-26  2017-04-01   \n",
 288 |        "4  2012-03-30  2013-03-19  2014-03-25  2015-03-25  2016-03-28  2017-03-29   \n",
 289 |        "\n",
 290 |        "   prec_id  block_id  \n",
 291 |        "0       12     47407  \n",
 292 |        "1       41     47615  \n",
 293 |        "2       46     47670  \n",
 294 |        "3       66     47768  \n",
 295 |        "4       81     47762  "
 296 |       ]
 297 |      },
 298 |      "execution_count": 2,
 299 |      "metadata": {},
 300 |      "output_type": "execute_result"
 301 |     }
 302 |    ],
 303 |    "source": [
 304 |     "# セル1-2. cherry_blooming.csvを、PandasのDataframeとして読み込む（変数名をdf_cherry_bloomingとする）\n",
 305 |     "body = client_1539ecb44f134a9c99ef6869a2839be9.get_object(Bucket='think2018aka2-donotdelete-pr-wlhe61fd1hungw',Key='cherry_blooming.csv')['Body']\n",
 306 |     "# add missing __iter__ method, so pandas accepts body as file-like object\n",
 307 |     "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
 308 |     "\n",
 309 |     "df_cherry_blooming = pd.read_csv(body)\n",
 310 |     "df_cherry_blooming.head()"
 311 |    ]
 312 |   },
 313 |   {
 314 |    "cell_type": "code",
 315 |    "execution_count": 3,
 316 |    "metadata": {
 317 |     "scrolled": true
 318 |    },
 319 |    "outputs": [
 320 |     {
 321 |      "data": {
 322 |       "text/html": [
 323 |        "<div>\n",
 324 |        "<style scoped>\n",
 325 |        "    .dataframe tbody tr th:only-of-type {\n",
 326 |        "        vertical-align: middle;\n",
 327 |        "    }\n",
 328 |        "\n",
 329 |        "    .dataframe tbody tr th {\n",
 330 |        "        vertical-align: top;\n",
 331 |        "    }\n",
 332 |        "\n",
 333 |        "    .dataframe thead th {\n",
 334 |        "        text-align: right;\n",
 335 |        "    }\n",
 336 |        "</style>\n",
 337 |        "<table border=\"1\" class=\"dataframe\">\n",
 338 |        "  <thead>\n",
 339 |        "    <tr style=\"text-align: right;\">\n",
 340 |        "      <th></th>\n",
 341 |        "      <th>location_name</th>\n",
 342 |        "      <th>prec_id</th>\n",
 343 |        "      <th>block_id</th>\n",
 344 |        "      <th>date</th>\n",
 345 |        "      <th>atmospheric_pressure</th>\n",
 346 |        "      <th>precipitation</th>\n",
 347 |        "      <th>temperature_avg</th>\n",
 348 |        "      <th>temperature_highest</th>\n",
 349 |        "      <th>temperature_lowest</th>\n",
 350 |        "      <th>humidity</th>\n",
 351 |        "      <th>daylight_hours</th>\n",
 352 |        "      <th>blooming</th>\n",
 353 |        "    </tr>\n",
 354 |        "  </thead>\n",
 355 |        "  <tbody>\n",
 356 |        "    <tr>\n",
 357 |        "      <th>0</th>\n",
 358 |        "      <td>旭川</td>\n",
 359 |        "      <td>12</td>\n",
 360 |        "      <td>47407</td>\n",
 361 |        "      <td>2007-01-01</td>\n",
 362 |        "      <td>1004.1</td>\n",
 363 |        "      <td>3.5</td>\n",
 364 |        "      <td>0.5</td>\n",
 365 |        "      <td>4.1</td>\n",
 366 |        "      <td>-5.5</td>\n",
 367 |        "      <td>74.0</td>\n",
 368 |        "      <td>4.1</td>\n",
 369 |        "      <td>0</td>\n",
 370 |        "    </tr>\n",
 371 |        "    <tr>\n",
 372 |        "      <th>1</th>\n",
 373 |        "      <td>旭川</td>\n",
 374 |        "      <td>12</td>\n",
 375 |        "      <td>47407</td>\n",
 376 |        "      <td>2007-01-02</td>\n",
 377 |        "      <td>1001.5</td>\n",
 378 |        "      <td>1.0</td>\n",
 379 |        "      <td>-2.2</td>\n",
 380 |        "      <td>0.8</td>\n",
 381 |        "      <td>-9.6</td>\n",
 382 |        "      <td>83.0</td>\n",
 383 |        "      <td>1.6</td>\n",
 384 |        "      <td>0</td>\n",
 385 |        "    </tr>\n",
 386 |        "    <tr>\n",
 387 |        "      <th>2</th>\n",
 388 |        "      <td>旭川</td>\n",
 389 |        "      <td>12</td>\n",
 390 |        "      <td>47407</td>\n",
 391 |        "      <td>2007-01-03</td>\n",
 392 |        "      <td>1005.0</td>\n",
 393 |        "      <td>0.0</td>\n",
 394 |        "      <td>-5.5</td>\n",
 395 |        "      <td>-0.7</td>\n",
 396 |        "      <td>-11.1</td>\n",
 397 |        "      <td>79.0</td>\n",
 398 |        "      <td>3.1</td>\n",
 399 |        "      <td>0</td>\n",
 400 |        "    </tr>\n",
 401 |        "    <tr>\n",
 402 |        "      <th>3</th>\n",
 403 |        "      <td>旭川</td>\n",
 404 |        "      <td>12</td>\n",
 405 |        "      <td>47407</td>\n",
 406 |        "      <td>2007-01-04</td>\n",
 407 |        "      <td>1004.5</td>\n",
 408 |        "      <td>0.0</td>\n",
 409 |        "      <td>-3.1</td>\n",
 410 |        "      <td>0.5</td>\n",
 411 |        "      <td>-9.8</td>\n",
 412 |        "      <td>79.0</td>\n",
 413 |        "      <td>3.6</td>\n",
 414 |        "      <td>0</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>4</th>\n",
 418 |        "      <td>旭川</td>\n",
 419 |        "      <td>12</td>\n",
 420 |        "      <td>47407</td>\n",
 421 |        "      <td>2007-01-05</td>\n",
 422 |        "      <td>1009.0</td>\n",
 423 |        "      <td>0.0</td>\n",
 424 |        "      <td>-6.2</td>\n",
 425 |        "      <td>0.0</td>\n",
 426 |        "      <td>-10.5</td>\n",
 427 |        "      <td>82.0</td>\n",
 428 |        "      <td>1.9</td>\n",
 429 |        "      <td>0</td>\n",
 430 |        "    </tr>\n",
 431 |        "  </tbody>\n",
 432 |        "</table>\n",
 433 |        "</div>"
 434 |       ],
 435 |       "text/plain": [
 436 |        "  location_name  prec_id  block_id        date  atmospheric_pressure  \\\n",
 437 |        "0            旭川       12     47407  2007-01-01                1004.1   \n",
 438 |        "1            旭川       12     47407  2007-01-02                1001.5   \n",
 439 |        "2            旭川       12     47407  2007-01-03                1005.0   \n",
 440 |        "3            旭川       12     47407  2007-01-04                1004.5   \n",
 441 |        "4            旭川       12     47407  2007-01-05                1009.0   \n",
 442 |        "\n",
 443 |        "   precipitation  temperature_avg  temperature_highest  temperature_lowest  \\\n",
 444 |        "0            3.5              0.5                  4.1                -5.5   \n",
 445 |        "1            1.0             -2.2                  0.8                -9.6   \n",
 446 |        "2            0.0             -5.5                 -0.7               -11.1   \n",
 447 |        "3            0.0             -3.1                  0.5                -9.8   \n",
 448 |        "4            0.0             -6.2                  0.0               -10.5   \n",
 449 |        "\n",
 450 |        "   humidity  daylight_hours  blooming  \n",
 451 |        "0      74.0             4.1         0  \n",
 452 |        "1      83.0             1.6         0  \n",
 453 |        "2      79.0             3.1         0  \n",
 454 |        "3      79.0             3.6         0  \n",
 455 |        "4      82.0             1.9         0  "
 456 |       ]
 457 |      },
 458 |      "execution_count": 3,
 459 |      "metadata": {},
 460 |      "output_type": "execute_result"
 461 |     }
 462 |    ],
 463 |    "source": [
 464 |     "# セル1-3. weather_data.csvを、PandasのDataframeとして読み込む（変数名をdf_weatherとする）\n",
 465 |     "body = client_1539ecb44f134a9c99ef6869a2839be9.get_object(Bucket='think2018aka2-donotdelete-pr-wlhe61fd1hungw',Key='weather_data.csv')['Body']\n",
 466 |     "# add missing __iter__ method, so pandas accepts body as file-like object\n",
 467 |     "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
 468 |     "\n",
 469 |     "df_weather = pd.read_csv(body)\n",
 470 |     "df_weather.head()"
 471 |    ]
 472 |   },
 473 |   {
 474 |    "cell_type": "markdown",
 475 |    "metadata": {},
 476 |    "source": [
 477 |     "## データの理解\n",
 478 |     "分析対象のデータを可視化する。"
 479 |    ]
 480 |   },
 481 |   {
 482 |    "cell_type": "code",
 483 |    "execution_count": 4,
 484 |    "metadata": {
 485 |     "scrolled": false
 486 |    },
 487 |    "outputs": [
 488 |     {
 489 |      "data": {
 490 |       "text/html": [
 491 |        "<div>\n",
 492 |        "<style scoped>\n",
 493 |        "    .dataframe tbody tr th:only-of-type {\n",
 494 |        "        vertical-align: middle;\n",
 495 |        "    }\n",
 496 |        "\n",
 497 |        "    .dataframe tbody tr th {\n",
 498 |        "        vertical-align: top;\n",
 499 |        "    }\n",
 500 |        "\n",
 501 |        "    .dataframe thead th {\n",
 502 |        "        text-align: right;\n",
 503 |        "    }\n",
 504 |        "</style>\n",
 505 |        "<table border=\"1\" class=\"dataframe\">\n",
 506 |        "  <thead>\n",
 507 |        "    <tr style=\"text-align: right;\">\n",
 508 |        "      <th></th>\n",
 509 |        "      <th>location_name</th>\n",
 510 |        "      <th>y2007</th>\n",
 511 |        "      <th>y2008</th>\n",
 512 |        "      <th>y2009</th>\n",
 513 |        "      <th>y2010</th>\n",
 514 |        "      <th>y2011</th>\n",
 515 |        "      <th>y2012</th>\n",
 516 |        "      <th>y2013</th>\n",
 517 |        "      <th>y2014</th>\n",
 518 |        "      <th>y2015</th>\n",
 519 |        "      <th>y2016</th>\n",
 520 |        "      <th>y2017</th>\n",
 521 |        "      <th>prec_id</th>\n",
 522 |        "      <th>block_id</th>\n",
 523 |        "    </tr>\n",
 524 |        "  </thead>\n",
 525 |        "  <tbody>\n",
 526 |        "    <tr>\n",
 527 |        "      <th>旭川</th>\n",
 528 |        "      <td>旭川</td>\n",
 529 |        "      <td>2007-05-06</td>\n",
 530 |        "      <td>2008-04-23</td>\n",
 531 |        "      <td>2009-05-03</td>\n",
 532 |        "      <td>2010-05-11</td>\n",
 533 |        "      <td>2011-05-09</td>\n",
 534 |        "      <td>2012-05-02</td>\n",
 535 |        "      <td>2013-05-18</td>\n",
 536 |        "      <td>2014-05-02</td>\n",
 537 |        "      <td>2015-04-27</td>\n",
 538 |        "      <td>2016-05-03</td>\n",
 539 |        "      <td>2017-05-03</td>\n",
 540 |        "      <td>12</td>\n",
 541 |        "      <td>47407</td>\n",
 542 |        "    </tr>\n",
 543 |        "    <tr>\n",
 544 |        "      <th>宇都宮</th>\n",
 545 |        "      <td>宇都宮</td>\n",
 546 |        "      <td>2007-03-26</td>\n",
 547 |        "      <td>2008-03-28</td>\n",
 548 |        "      <td>2009-03-26</td>\n",
 549 |        "      <td>2010-04-02</td>\n",
 550 |        "      <td>2011-04-06</td>\n",
 551 |        "      <td>2012-04-08</td>\n",
 552 |        "      <td>2013-03-21</td>\n",
 553 |        "      <td>2014-03-29</td>\n",
 554 |        "      <td>2015-03-30</td>\n",
 555 |        "      <td>2016-03-28</td>\n",
 556 |        "      <td>2017-04-03</td>\n",
 557 |        "      <td>41</td>\n",
 558 |        "      <td>47615</td>\n",
 559 |        "    </tr>\n",
 560 |        "    <tr>\n",
 561 |        "      <th>横浜</th>\n",
 562 |        "      <td>横浜</td>\n",
 563 |        "      <td>2007-03-23</td>\n",
 564 |        "      <td>2008-03-23</td>\n",
 565 |        "      <td>2009-03-22</td>\n",
 566 |        "      <td>2010-03-22</td>\n",
 567 |        "      <td>2011-03-30</td>\n",
 568 |        "      <td>2012-04-02</td>\n",
 569 |        "      <td>2013-03-18</td>\n",
 570 |        "      <td>2014-03-25</td>\n",
 571 |        "      <td>2015-03-23</td>\n",
 572 |        "      <td>2016-03-23</td>\n",
 573 |        "      <td>2017-03-25</td>\n",
 574 |        "      <td>46</td>\n",
 575 |        "      <td>47670</td>\n",
 576 |        "    </tr>\n",
 577 |        "    <tr>\n",
 578 |        "      <th>岡山</th>\n",
 579 |        "      <td>岡山</td>\n",
 580 |        "      <td>2007-03-26</td>\n",
 581 |        "      <td>2008-03-26</td>\n",
 582 |        "      <td>2009-03-21</td>\n",
 583 |        "      <td>2010-03-22</td>\n",
 584 |        "      <td>2011-03-31</td>\n",
 585 |        "      <td>2012-04-03</td>\n",
 586 |        "      <td>2013-03-24</td>\n",
 587 |        "      <td>2014-03-28</td>\n",
 588 |        "      <td>2015-03-28</td>\n",
 589 |        "      <td>2016-03-26</td>\n",
 590 |        "      <td>2017-04-01</td>\n",
 591 |        "      <td>66</td>\n",
 592 |        "      <td>47768</td>\n",
 593 |        "    </tr>\n",
 594 |        "    <tr>\n",
 595 |        "      <th>下関</th>\n",
 596 |        "      <td>下関</td>\n",
 597 |        "      <td>2007-03-23</td>\n",
 598 |        "      <td>2008-03-26</td>\n",
 599 |        "      <td>2009-03-18</td>\n",
 600 |        "      <td>2010-03-20</td>\n",
 601 |        "      <td>2011-03-30</td>\n",
 602 |        "      <td>2012-03-30</td>\n",
 603 |        "      <td>2013-03-19</td>\n",
 604 |        "      <td>2014-03-25</td>\n",
 605 |        "      <td>2015-03-25</td>\n",
 606 |        "      <td>2016-03-28</td>\n",
 607 |        "      <td>2017-03-29</td>\n",
 608 |        "      <td>81</td>\n",
 609 |        "      <td>47762</td>\n",
 610 |        "    </tr>\n",
 611 |        "  </tbody>\n",
 612 |        "</table>\n",
 613 |        "</div>"
 614 |       ],
 615 |       "text/plain": [
 616 |        "    location_name       y2007       y2008       y2009       y2010       y2011  \\\n",
 617 |        "旭川             旭川  2007-05-06  2008-04-23  2009-05-03  2010-05-11  2011-05-09   \n",
 618 |        "宇都宮           宇都宮  2007-03-26  2008-03-28  2009-03-26  2010-04-02  2011-04-06   \n",
 619 |        "横浜             横浜  2007-03-23  2008-03-23  2009-03-22  2010-03-22  2011-03-30   \n",
 620 |        "岡山             岡山  2007-03-26  2008-03-26  2009-03-21  2010-03-22  2011-03-31   \n",
 621 |        "下関             下関  2007-03-23  2008-03-26  2009-03-18  2010-03-20  2011-03-30   \n",
 622 |        "\n",
 623 |        "          y2012       y2013       y2014       y2015       y2016       y2017  \\\n",
 624 |        "旭川   2012-05-02  2013-05-18  2014-05-02  2015-04-27  2016-05-03  2017-05-03   \n",
 625 |        "宇都宮  2012-04-08  2013-03-21  2014-03-29  2015-03-30  2016-03-28  2017-04-03   \n",
 626 |        "横浜   2012-04-02  2013-03-18  2014-03-25  2015-03-23  2016-03-23  2017-03-25   \n",
 627 |        "岡山   2012-04-03  2013-03-24  2014-03-28  2015-03-28  2016-03-26  2017-04-01   \n",
 628 |        "下関   2012-03-30  2013-03-19  2014-03-25  2015-03-25  2016-03-28  2017-03-29   \n",
 629 |        "\n",
 630 |        "     prec_id  block_id  \n",
 631 |        "旭川        12     47407  \n",
 632 |        "宇都宮       41     47615  \n",
 633 |        "横浜        46     47670  \n",
 634 |        "岡山        66     47768  \n",
 635 |        "下関        81     47762  "
 636 |       ]
 637 |      },
 638 |      "execution_count": 4,
 639 |      "metadata": {},
 640 |      "output_type": "execute_result"
 641 |     }
 642 |    ],
 643 |    "source": [
 644 |     "# セル2-1. DataFrameのIndexの変更\n",
 645 |     "\n",
 646 |     "pd.set_option(\"display.max_rows\", 500)\n",
 647 |     "\n",
 648 |     "# df_weatherのIndexを日付に変更する\n",
 649 |     "df_weather.index = pd.to_datetime(df_weather['date'].tolist())\n",
 650 |     "df_weather['year'] = df_weather.index.year\n",
 651 |     "\n",
 652 |     "# df_stationとdf_cherry_bloomingのIndexを観測所名に変更する\n",
 653 |     "df_station.index = df_station['location_name'].tolist()\n",
 654 |     "\n",
 655 |     "df_cherry_blooming.index = df_cherry_blooming['location_name'].tolist()\n",
 656 |     "df_cherry_blooming.head()"
 657 |    ]
 658 |   },
 659 |   {
 660 |    "cell_type": "code",
 661 |    "execution_count": 5,
 662 |    "metadata": {},
 663 |    "outputs": [
 664 |     {
 665 |      "data": {
 666 |       "text/html": [
 667 |        "<!--\n",
 668 |        "  ~ Copyright (c) 2015 IBM Corporation and others.\n",
 669 |        "  ~\n",
 670 |        "  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 671 |        "  ~ You may not use this file except in compliance with the License.\n",
 672 |        "  ~ You may obtain a copy of the License at\n",
 673 |        "  ~\n",
 674 |        "  ~     http://www.apache.org/licenses/LICENSE-2.0\n",
 675 |        "  ~\n",
 676 |        "  ~ Unless required by applicable law or agreed to in writing, software\n",
 677 |        "  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 678 |        "  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 679 |        "  ~ See the License for the specific language governing permissions and\n",
 680 |        "  ~ limitations under the License.\n",
 681 |        "  -->\n",
 682 |        "\n",
 683 |        "\n",
 684 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.css\">\n",
 685 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/sumoselect.css\">\n",
 686 |        "\n",
 687 |        "<style>\n",
 688 |        "    \n",
 689 |        "</style>\n",
 690 |        "\n",
 691 |        "<div id=\"controlsid8fca95cc-6981-11e8-ae66-3a5f3782500e\" class=\"brunel\"/>\n",
 692 |        "<svg id=\"visid8fca92a2-6981-11e8-ae66-3a5f3782500e\" width=\"1200\" height=\"400\"></svg>"
 693 |       ],
 694 |       "text/plain": [
 695 |        "<IPython.core.display.HTML object>"
 696 |       ]
 697 |      },
 698 |      "metadata": {},
 699 |      "output_type": "display_data"
 700 |     },
 701 |     {
 702 |      "data": {
 703 |       "application/javascript": [
 704 |        "/*\n",
 705 |        " * Copyright (c) 2015 IBM Corporation and others.\n",
 706 |        " *\n",
 707 |        " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 708 |        " * You may not use this file except in compliance with the License.\n",
 709 |        " * You may obtain a copy of the License at\n",
 710 |        " *\n",
 711 |        " *     http://www.apache.org/licenses/LICENSE-2.0\n",
 712 |        " *\n",
 713 |        " * Unless required by applicable law or agreed to in writing, software\n",
 714 |        " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 715 |        " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 716 |        " * See the License for the specific language governing permissions and\n",
 717 |        " * limitations under the License.\n",
 718 |        " */\n",
 719 |        "\n",
 720 |        "require.config({\n",
 721 |        "    waitSeconds: 60,\n",
 722 |        "    paths: {\n",
 723 |        "        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n",
 724 |        "        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n",
 725 |        "        'brunel' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.min',\n",
 726 |        "        'brunelControls' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.controls.2.3.min'\n",
 727 |        "    },\n",
 728 |        "    shim: {\n",
 729 |        "       'brunel' : {\n",
 730 |        "            exports: 'BrunelD3',\n",
 731 |        "            deps: ['d3', 'topojson'],\n",
 732 |        "            init: function() {\n",
 733 |        "               return {\n",
 734 |        "                 BrunelD3 : BrunelD3,\n",
 735 |        "                 BrunelData : BrunelData\n",
 736 |        "              }\n",
 737 |        "            }\n",
 738 |        "        },\n",
 739 |        "       'brunelControls' : {\n",
 740 |        "            exports: 'BrunelEventHandlers',\n",
 741 |        "            init: function() {\n",
 742 |        "               return {\n",
 743 |        "                 BrunelEventHandlers: BrunelEventHandlers,\n",
 744 |        "                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n",
 745 |        "              }\n",
 746 |        "            }\n",
 747 |        "        }\n",
 748 |        "\n",
 749 |        "    }\n",
 750 |        "\n",
 751 |        "});\n",
 752 |        "\n",
 753 |        "require([\"d3\"], function(d3) {\n",
 754 |        "    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n",
 755 |        "        function  BrunelVis(visId) {\n",
 756 |        "  \"use strict\";                                                                       // strict mode\n",
 757 |        "  var datasets = [],                                      // array of datasets for the original data\n",
 758 |        "      pre = function(d, i) { return d },                         // default pre-process does nothing\n",
 759 |        "      post = function(d, i) { return d },                       // default post-process does nothing\n",
 760 |        "      transitionTime = 200,                                        // transition time for animations\n",
 761 |        "      charts = [],                                                       // the charts in the system\n",
 762 |        "      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n",
 763 |        "\n",
 764 |        "  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n",
 765 |        "\n",
 766 |        "  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n",
 767 |        "\n",
 768 |        "  charts[0] = function(parentNode, filterRows) {\n",
 769 |        "    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 5, 43, 37, 62),\n",
 770 |        "      elements = [];                                              // array of elements in this chart\n",
 771 |        "\n",
 772 |        "    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n",
 773 |        "\n",
 774 |        "    var chart =  vis.append('g').attr('class', 'chart1')\n",
 775 |        "      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n",
 776 |        "    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n",
 777 |        "    var zoom = d3.zoom().scaleExtent([1/3,3]);\n",
 778 |        "    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n",
 779 |        "      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n",
 780 |        "      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n",
 781 |        "      .style('cursor', 'move').call(zoom)\n",
 782 |        "      .node();\n",
 783 |        "    zoomNode.__zoom = d3.zoomIdentity;\n",
 784 |        "    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n",
 785 |        "    var interior = chart.append('g').attr('class', 'interior zoomNone')\n",
 786 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n",
 787 |        "      .attr('clip-path', 'url(#clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_inner)');\n",
 788 |        "    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n",
 789 |        "    var gridGroup = interior.append('g').attr('class', 'grid');\n",
 790 |        "    var axes = chart.append('g').attr('class', 'axis')\n",
 791 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n",
 792 |        "    var legends = chart.append('g').attr('class', 'legend')\n",
 793 |        "      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n",
 794 |        "    vis.append('clipPath').attr('id', 'clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_inner').append('rect')\n",
 795 |        "      .attr('x', 0).attr('y', 0)\n",
 796 |        "      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n",
 797 |        "\n",
 798 |        "    // Scales //////////////////////////////////////////////////////////////////////////////////////\n",
 799 |        "\n",
 800 |        "    var scale_x = d3.scaleTime().domain([new Date('2014-10-01'), new Date('2017-10-01')])\n",
 801 |        "      .range([0, geom.inner_width]);\n",
 802 |        "    var scale_inner = d3.scaleLinear().domain([0,1])\n",
 803 |        "      .range([-0.5, 0.5]);\n",
 804 |        "    var scale_y = d3.scaleLinear().domain([0, 35.000003])\n",
 805 |        "      .range([geom.inner_height, 0]);\n",
 806 |        "    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n",
 807 |        "\n",
 808 |        "    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n",
 809 |        "\n",
 810 |        "    axes.append('g').attr('class', 'x axis')\n",
 811 |        "      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n",
 812 |        "      .attr('clip-path', 'url(#clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_haxis)');\n",
 813 |        "    vis.append('clipPath').attr('id', 'clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_haxis').append('polyline')\n",
 814 |        "      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n",
 815 |        "    axes.select('g.axis.x').append('text').attr('class', 'title').text('Date').style('text-anchor', 'middle')\n",
 816 |        "      .attr('x',geom.inner_rawWidth/2)\n",
 817 |        "      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n",
 818 |        "    axes.append('g').attr('class', 'y axis')\n",
 819 |        "      .attr('clip-path', 'url(#clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_vaxis)');\n",
 820 |        "    vis.append('clipPath').attr('id', 'clip_visid8fca92a2-6981-11e8-ae66-3a5f3782500e_chart1_vaxis').append('polyline')\n",
 821 |        "      .attr('points', '-1000,-10000, 10000,-10000, 10000,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+5) + ', -1000,' + (geom.inner_rawHeight+5) );\n",
 822 |        "    axes.select('g.axis.y').append('text').attr('class', 'title').text('Temperature Highest').style('text-anchor', 'middle')\n",
 823 |        "      .attr('x',-geom.inner_rawHeight/2)\n",
 824 |        "      .attr('y', 4-geom.inner_left).attr('dy', '0.7em').attr('transform', 'rotate(270)');\n",
 825 |        "\n",
 826 |        "    var axis_bottom = d3.axisBottom(scale_x).ticks(Math.min(10, Math.round(geom.inner_width / 99.0)));\n",
 827 |        "    var axis_left = d3.axisLeft(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 20)));\n",
 828 |        "\n",
 829 |        "    function buildAxes(time) {\n",
 830 |        "      var axis_x = axes.select('g.axis.x');\n",
 831 |        "      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_x));\n",
 832 |        "      var axis_y = axes.select('g.axis.y');\n",
 833 |        "      BrunelD3.transition(axis_y, time).call(axis_left.scale(scale_y));\n",
 834 |        "    }\n",
 835 |        "    zoom.on('zoom', function(t, time) {\n",
 836 |        "        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n",
 837 |        "        scale_x = t.rescaleX(base_scales[0]);\n",
 838 |        "        scale_y = t.rescaleY(base_scales[1]);\n",
 839 |        "        zoomNode.__zoom = t;\n",
 840 |        "        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n",
 841 |        "        build(time || -1);\n",
 842 |        "    });\n",
 843 |        "\n",
 844 |        "    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n",
 845 |        "\n",
 846 |        "    elements[0] = function() {\n",
 847 |        "      var original, processed,                           // data sets passed in and then transformed\n",
 848 |        "        element, data,                                 // brunel element information and brunel data\n",
 849 |        "        selection, merged;                                      // d3 selection and merged selection\n",
 850 |        "      var elementGroup = interior.append('g').attr('class', 'element1'),\n",
 851 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
 852 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
 853 |        "\n",
 854 |        "      function makeData() {\n",
 855 |        "        original = datasets[0];\n",
 856 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
 857 |        "        processed = pre(original, 0)\n",
 858 |        "          .filter('temperature_highest valid');\n",
 859 |        "        processed = post(processed, 0);\n",
 860 |        "        var f0 = processed.field('date'),\n",
 861 |        "          f1 = processed.field('temperature_highest'),\n",
 862 |        "          f2 = processed.field('blooming'),\n",
 863 |        "          f3 = processed.field('location_name'),\n",
 864 |        "          f4 = processed.field('precipitation'),\n",
 865 |        "          f5 = processed.field('#row'),\n",
 866 |        "          f6 = processed.field('#selection');\n",
 867 |        "        var keyFunc = function(d) { return f0.value(d) };\n",
 868 |        "        data = {\n",
 869 |        "          date:         function(d) { return f0.value(d.row) },\n",
 870 |        "          temperature_highest:function(d) { return f1.value(d.row) },\n",
 871 |        "          blooming:     function(d) { return f2.value(d.row) },\n",
 872 |        "          location_name:function(d) { return f3.value(d.row) },\n",
 873 |        "          precipitation:function(d) { return f4.value(d.row) },\n",
 874 |        "          $row:         function(d) { return f5.value(d.row) },\n",
 875 |        "          $selection:   function(d) { return f6.value(d.row) },\n",
 876 |        "          date_f:       function(d) { return f0.valueFormatted(d.row) },\n",
 877 |        "          temperature_highest_f:function(d) { return f1.valueFormatted(d.row) },\n",
 878 |        "          blooming_f:   function(d) { return f2.valueFormatted(d.row) },\n",
 879 |        "          location_name_f:function(d) { return f3.valueFormatted(d.row) },\n",
 880 |        "          precipitation_f:function(d) { return f4.valueFormatted(d.row) },\n",
 881 |        "          $row_f:       function(d) { return f5.valueFormatted(d.row) },\n",
 882 |        "          $selection_f: function(d) { return f6.valueFormatted(d.row) },\n",
 883 |        "          _split:       function(d) { return f2.value(d.row) },\n",
 884 |        "          _key:         keyFunc,\n",
 885 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
 886 |        "        };\n",
 887 |        "      }\n",
 888 |        "      // Aesthetic Functions\n",
 889 |        "      var scale_color = d3.scaleLinear().domain([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1])\n",
 890 |        "        .interpolate(d3.interpolateHcl)\n",
 891 |        "        .range([ '#045a8d', '#2b8cbe', '#74a9cf', '#bdc9e1', '#f8efe8', '#fef0d9', \n",
 892 |        "          '#fdcc8a', '#fc8d59', '#e34a33']);\n",
 893 |        "      var color = function(d) { return scale_color(data.blooming(d)) };\n",
 894 |        "      legends._legend = legends._legend || { title: ['Blooming'], \n",
 895 |        "        ticks: [1, 0.8, 0.6, 0.4, 0.2, 0]};\n",
 896 |        "      legends._legend.color = scale_color;\n",
 897 |        "\n",
 898 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
 899 |        "\n",
 900 |        "      function build(transitionMillis) {\n",
 901 |        "        element = elements[0];\n",
 902 |        "        var w = geom.default_point_size;\n",
 903 |        "        var x = function(d) { return scale_x(data.date(d))};\n",
 904 |        "        var h = geom.default_point_size;\n",
 905 |        "        var y = function(d) { return scale_y(data.temperature_highest(d))};\n",
 906 |        "\n",
 907 |        "        // Define selection entry operations\n",
 908 |        "        function initialState(selection) {\n",
 909 |        "          selection\n",
 910 |        "            .attr('class', 'element point filled')\n",
 911 |        "        }\n",
 912 |        "\n",
 913 |        "        // Define selection update operations on merged data\n",
 914 |        "        function updateState(selection) {\n",
 915 |        "          selection\n",
 916 |        "            .attr('cx',function(d) { return scale_x(data.date(d))})\n",
 917 |        "            .attr('cy',function(d) { return scale_y(data.temperature_highest(d))})\n",
 918 |        "            .attr('r',geom.default_point_size / 2)\n",
 919 |        "            .filter(BrunelD3.hasData)                     // following only performed for data items\n",
 920 |        "            .style('fill', color);\n",
 921 |        "        }\n",
 922 |        "\n",
 923 |        "        // Define labeling for the selection\n",
 924 |        "        function label(selection, transitionMillis) {\n",
 925 |        "\n",
 926 |        "          var tooltipLabeling  = {\n",
 927 |        "            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n",
 928 |        "            fit: true, granularity: 0,\n",
 929 |        "            content: function(d) {\n",
 930 |        "              return d.row == null ? null : '<span class=\"title\">Location Name: </span>'\n",
 931 |        "\t\t\t+ '<span class=\"field\">' + data.location_name_f(d) + '</span>'\n",
 932 |        "\t\t\t+ '<br/>'\n",
 933 |        "\t\t\t+ '<span class=\"title\">Date: </span>'\n",
 934 |        "\t\t\t+ '<span class=\"field\">' + data.date_f(d) + '</span>'\n",
 935 |        "\t\t\t+ '<br/>'\n",
 936 |        "\t\t\t+ '<span class=\"title\">Precipitation: </span>'\n",
 937 |        "\t\t\t+ '<span class=\"field\">' + data.precipitation_f(d) + '</span>'\n",
 938 |        "            }\n",
 939 |        "          };\n",
 940 |        "          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n",
 941 |        "        }\n",
 942 |        "        // Create selections, set the initial state and transition updates\n",
 943 |        "        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n",
 944 |        "        var added = selection.enter().append('circle');\n",
 945 |        "        merged = selection.merge(added);\n",
 946 |        "        initialState(added);\n",
 947 |        "        selection.filter(BrunelD3.hasData)\n",
 948 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
 949 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
 950 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
 951 |        "        label(merged, transitionMillis);\n",
 952 |        "\n",
 953 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
 954 |        "          .style('opacity', 0.5).each( function() {\n",
 955 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
 956 |        "        });\n",
 957 |        "      }\n",
 958 |        "\n",
 959 |        "      return {\n",
 960 |        "        data:           function() { return processed },\n",
 961 |        "        original:       function() { return original },\n",
 962 |        "        internal:       function() { return data },\n",
 963 |        "        selection:      function() { return merged },\n",
 964 |        "        makeData:       makeData,\n",
 965 |        "        build:          build,\n",
 966 |        "        chart:          function() { return charts[0] },\n",
 967 |        "        group:          function() { return elementGroup },\n",
 968 |        "        fields: {\n",
 969 |        "          x:            ['date'],\n",
 970 |        "          y:            ['temperature_highest'],\n",
 971 |        "          key:          ['date'],\n",
 972 |        "          color:        ['blooming']\n",
 973 |        "        }\n",
 974 |        "      };\n",
 975 |        "    }();\n",
 976 |        "\n",
 977 |        "    function build(time, noData) {\n",
 978 |        "      var first = elements[0].data() == null;\n",
 979 |        "      if (first) time = 0;                                           // no transition for first call\n",
 980 |        "      buildAxes(time);\n",
 981 |        "      if ((first || time > -1) && !noData) {\n",
 982 |        "        elements[0].makeData();\n",
 983 |        "        BrunelD3.addLegend(legends, legends._legend);\n",
 984 |        "      }\n",
 985 |        "      elements[0].build(time);\n",
 986 |        "    }\n",
 987 |        "\n",
 988 |        "    // Expose the following components of the chart\n",
 989 |        "    return {\n",
 990 |        "      elements : elements,\n",
 991 |        "      interior : interior,\n",
 992 |        "      scales: {x:scale_x, y:scale_y},\n",
 993 |        "      zoom: function(params, time) {\n",
 994 |        "          if (params) zoom.on('zoom').call(zoomNode, params, time);\n",
 995 |        "          return d3.zoomTransform(zoomNode);\n",
 996 |        "      },\n",
 997 |        "      build : build\n",
 998 |        "    };\n",
 999 |        "    }();\n",
1000 |        "\n",
1001 |        "  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n",
1002 |        "  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n",
1003 |        "  function buildAll() {\n",
1004 |        "    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n",
1005 |        "    updateAll(transitionTime);\n",
1006 |        "  }\n",
1007 |        "\n",
1008 |        "  return {\n",
1009 |        "    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n",
1010 |        "    dataPostProcess:    function(f) { if (f) post = f; return post },\n",
1011 |        "    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n",
1012 |        "    visId:              visId,\n",
1013 |        "    build:              buildAll,\n",
1014 |        "    rebuild:            updateAll,\n",
1015 |        "    charts:             charts\n",
1016 |        "  }\n",
1017 |        "}\n",
1018 |        "\n",
1019 |        "// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n",
1020 |        "\n",
1021 |        "var table1 = {\n",
1022 |        "   summarized: false,\n",
1023 |        "   names: ['date', 'temperature_highest', 'blooming', 'location_name', 'precipitation'], \n",
1024 |        "   options: ['date-YearMonthDay', 'numeric', 'numeric', 'string', 'numeric'], \n",
1025 |        "   rows: [['2015-01-01', 8.2, 0, '東京', 0], ['2015-01-02', 7.9, 0, '東京', null],\n",
1026 |        "  ['2015-01-03', 8.9, 0, '東京', null], ['2015-01-04', 9.3, 0, '東京', null],\n",
1027 |        "  ['2015-01-05', 12.9, 0, '東京', null], ['2015-01-06', 16, 0, '東京', 6.5],\n",
1028 |        "  ['2015-01-07', 10.2, 0, '東京', null], ['2015-01-08', 11.2, 0, '東京', null],\n",
1029 |        "  ['2015-01-09', 11, 0, '東京', null], ['2015-01-10', 11.2, 0, '東京', null],\n",
1030 |        "  ['2015-01-11', 10.8, 0, '東京', null], ['2015-01-12', 9, 0, '東京', null],\n",
1031 |        "  ['2015-01-13', 11.8, 0, '東京', null], ['2015-01-14', 11.5, 0, '東京', null],\n",
1032 |        "  ['2015-01-15', 8.2, 0, '東京', 35.5], ['2015-01-16', 13.2, 0, '東京', 0],\n",
1033 |        "  ['2015-01-17', 11.6, 0, '東京', 0], ['2015-01-18', 10.9, 0, '東京', null],\n",
1034 |        "  ['2015-01-19', 11.7, 0, '東京', null], ['2015-01-20', 11.2, 0, '東京', null],\n",
1035 |        "  ['2015-01-21', 3.8, 0, '東京', 4], ['2015-01-22', 5.6, 0, '東京', 7.5],\n",
1036 |        "  ['2015-01-23', 12.8, 0, '東京', 7.5], ['2015-01-24', 8.1, 0, '東京', null],\n",
1037 |        "  ['2015-01-25', 12.2, 0, '東京', null], ['2015-01-26', 12.9, 0, '東京', 0.5],\n",
1038 |        "  ['2015-01-27', 16.4, 0, '東京', 14.5], ['2015-01-28', 9.7, 0, '東京', 0],\n",
1039 |        "  ['2015-01-29', 8.2, 0, '東京', null], ['2015-01-30', 4.7, 0, '東京', 16.5],\n",
1040 |        "  ['2015-01-31', 10.5, 0, '東京', null], ['2015-02-01', 8.6, 0, '東京', null],\n",
1041 |        "  ['2015-02-02', 9.6, 0, '東京', null], ['2015-02-03', 10.3, 0, '東京', null],\n",
1042 |        "  ['2015-02-04', 9.6, 0, '東京', null], ['2015-02-05', 5.1, 0, '東京', 9],\n",
1043 |        "  ['2015-02-06', 12, 0, '東京', 0], ['2015-02-07', 9.6, 0, '東京', 1.5], ['2015-02-08', 6.8, 0, '東京', 6],\n",
1044 |        "  ['2015-02-09', 4.9, 0, '東京', null], ['2015-02-10', 8.1, 0, '東京', null],\n",
1045 |        "  ['2015-02-11', 10.9, 0, '東京', null], ['2015-02-12', 13, 0, '東京', null],\n",
1046 |        "  ['2015-02-13', 12.3, 0, '東京', null], ['2015-02-14', 10.6, 0, '東京', null],\n",
1047 |        "  ['2015-02-15', 11.7, 0, '東京', null], ['2015-02-16', 13.2, 0, '東京', null],\n",
1048 |        "  ['2015-02-17', 5.4, 0, '東京', 3], ['2015-02-18', 4.3, 0, '東京', 10.5],\n",
1049 |        "  ['2015-02-19', 13, 0, '東京', null], ['2015-02-20', 11.9, 0, '東京', 0],\n",
1050 |        "  ['2015-02-21', 10.5, 0, '東京', null], ['2015-02-22', 8, 0, '東京', 1.5],\n",
1051 |        "  ['2015-02-23', 19.2, 0, '東京', 16], ['2015-02-24', 12.7, 0, '東京', 0],\n",
1052 |        "  ['2015-02-25', 13.2, 0, '東京', 0], ['2015-02-26', 7.9, 0, '東京', 14.5],\n",
1053 |        "  ['2015-02-27', 15.3, 0, '東京', null], ['2015-02-28', 12.8, 0, '東京', null],\n",
1054 |        "  ['2015-03-01', 9.8, 0, '東京', 29], ['2015-03-02', 14.1, 0, '東京', null],\n",
1055 |        "  ['2015-03-03', 7.7, 0, '東京', 0.5], ['2015-03-04', 17.5, 0, '東京', 18.5],\n",
1056 |        "  ['2015-03-05', 14.3, 0, '東京', null], ['2015-03-06', 12, 0, '東京', 0],\n",
1057 |        "  ['2015-03-07', 8.2, 0, '東京', 4.5], ['2015-03-08', 10.9, 0, '東京', 0.5],\n",
1058 |        "  ['2015-03-09', 9.9, 0, '東京', 23], ['2015-03-10', 13.7, 0, '東京', 2.5],\n",
1059 |        "  ['2015-03-11', 12.2, 0, '東京', null], ['2015-03-12', 14.8, 0, '東京', null],\n",
1060 |        "  ['2015-03-13', 14.4, 0, '東京', null], ['2015-03-14', 13.5, 0, '東京', null],\n",
1061 |        "  ['2015-03-15', 13, 0, '東京', 0], ['2015-03-16', 16, 0, '東京', 10],\n",
1062 |        "  ['2015-03-17', 21.2, 0, '東京', 0.5], ['2015-03-18', 21.7, 0, '東京', 0],\n",
1063 |        "  ['2015-03-19', 15.9, 0, '東京', 5], ['2015-03-20', 14.9, 0, '東京', null],\n",
1064 |        "  ['2015-03-21', 16, 0, '東京', 0], ['2015-03-22', 18.7, 0, '東京', 0], ['2015-03-23', 17, 1, '東京', 0],\n",
1065 |        "  ['2015-03-24', 15.1, 1, '東京', null], ['2015-03-25', 14.3, 1, '東京', null],\n",
1066 |        "  ['2015-03-26', 15.8, 1, '東京', null], ['2015-03-27', 20.4, 1, '東京', null],\n",
1067 |        "  ['2015-03-28', 20.6, 1, '東京', null], ['2015-03-29', 21.1, 1, '東京', 0],\n",
1068 |        "  ['2015-03-30', 23.6, 1, '東京', null], ['2015-03-31', 23.2, 1, '東京', null],\n",
1069 |        "  ['2015-04-01', 18.7, 1, '東京', 1.5], ['2015-04-02', 17.4, 1, '東京', null],\n",
1070 |        "  ['2015-04-03', 20.6, 1, '東京', 0], ['2015-04-04', 19.1, 1, '東京', 0],\n",
1071 |        "  ['2015-04-05', 11.4, 1, '東京', 8], ['2015-04-06', 22.9, 1, '東京', 0],\n",
1072 |        "  ['2015-04-07', 13, 1, '東京', 4.5], ['2015-04-08', 7.6, 1, '東京', 20],\n",
1073 |        "  ['2015-04-09', 13.2, 1, '東京', null], ['2015-04-10', 12.1, 1, '東京', 9.5],\n",
1074 |        "  ['2015-04-11', 16.2, 1, '東京', 7.5], ['2015-04-12', 18, 1, '東京', null],\n",
1075 |        "  ['2015-04-13', 11.3, 1, '東京', 36.5], ['2015-04-14', 14.1, 1, '東京', 22.5],\n",
1076 |        "  ['2015-04-15', 21.4, 1, '東京', 2], ['2015-04-16', 21.5, 1, '東京', null],\n",
1077 |        "  ['2015-04-17', 24.9, 1, '東京', 3.5], ['2015-04-18', 19.7, 1, '東京', null],\n",
1078 |        "  ['2015-04-19', 17.2, 1, '東京', 0], ['2015-04-20', 20.7, 1, '東京', 10.5],\n",
1079 |        "  ['2015-04-21', 20.5, 1, '東京', 0.5], ['2015-04-22', 21.4, 1, '東京', 2.5],\n",
1080 |        "  ['2015-04-23', 24.8, 1, '東京', null], ['2015-04-24', 23.9, 1, '東京', null],\n",
1081 |        "  ['2015-04-25', 22.9, 1, '東京', 0], ['2015-04-26', 23.8, 1, '東京', null],\n",
1082 |        "  ['2015-04-27', 25, 1, '東京', null], ['2015-04-28', 27.2, 1, '東京', null],\n",
1083 |        "  ['2015-04-29', 24.3, 1, '東京', null], ['2015-04-30', 24, 1, '東京', null],\n",
1084 |        "  ['2015-05-01', 27.5, 1, '東京', null], ['2015-05-02', 27.6, 1, '東京', null],\n",
1085 |        "  ['2015-05-03', 27, 1, '東京', null], ['2015-05-04', 26.6, 1, '東京', 0],\n",
1086 |        "  ['2015-05-05', 22.4, 1, '東京', null], ['2015-05-06', 24.7, 1, '東京', null],\n",
1087 |        "  ['2015-05-07', 24.7, 1, '東京', 0], ['2015-05-08', 26.8, 1, '東京', null],\n",
1088 |        "  ['2015-05-09', 21.3, 1, '東京', 0], ['2015-05-10', 27, 1, '東京', null],\n",
1089 |        "  ['2015-05-11', 23.5, 1, '東京', null], ['2015-05-12', 22.7, 1, '東京', 58.5],\n",
1090 |        "  ['2015-05-13', 28.4, 1, '東京', 0], ['2015-05-14', 28.6, 1, '東京', null],\n",
1091 |        "  ['2015-05-15', 27.8, 1, '東京', null], ['2015-05-16', 23.7, 1, '東京', 3.5],\n",
1092 |        "  ['2015-05-17', 27.8, 1, '東京', null], ['2015-05-18', null, 1, '東京', 3.5],\n",
1093 |        "  ['2015-05-19', 24.2, 1, '東京', 4.5], ['2015-05-20', 27.3, 1, '東京', 0.5],\n",
1094 |        "  ['2015-05-21', 25.7, 1, '東京', 8.5], ['2015-05-22', 26, 1, '東京', null],\n",
1095 |        "  ['2015-05-23', 27.1, 1, '東京', null], ['2015-05-24', 26, 1, '東京', null],\n",
1096 |        "  ['2015-05-25', 26.1, 1, '東京', null], ['2015-05-26', 30.9, 1, '東京', null],\n",
1097 |        "  ['2015-05-27', 30.2, 1, '東京', null], ['2015-05-28', 26.8, 1, '東京', null],\n",
1098 |        "  ['2015-05-29', 22.8, 1, '東京', 9], ['2015-05-30', 29.6, 1, '東京', null],\n",
1099 |        "  ['2015-05-31', 32.2, 1, '東京', 0], ['2015-06-01', 27.7, 1, '東京', null],\n",
1100 |        "  ['2015-06-02', 28.7, 1, '東京', null], ['2015-06-03', 22.9, 1, '東京', 16],\n",
1101 |        "  ['2015-06-04', 29.1, 1, '東京', null], ['2015-06-05', 22.5, 1, '東京', 27],\n",
1102 |        "  ['2015-06-06', 24.6, 1, '東京', 23.5], ['2015-06-07', 25.5, 1, '東京', null],\n",
1103 |        "  ['2015-06-08', 25.3, 1, '東京', 0.5], ['2015-06-09', 21.9, 1, '東京', 33],\n",
1104 |        "  ['2015-06-10', 27.8, 1, '東京', null], ['2015-06-11', 27.5, 1, '東京', 0],\n",
1105 |        "  ['2015-06-12', 25, 1, '東京', 4.5], ['2015-06-13', 29, 1, '東京', 0],\n",
1106 |        "  ['2015-06-14', 26.8, 1, '東京', 1.5], ['2015-06-15', 30.8, 1, '東京', null],\n",
1107 |        "  ['2015-06-16', 27.2, 1, '東京', 31], ['2015-06-17', 24.8, 1, '東京', 11],\n",
1108 |        "  ['2015-06-18', 26.1, 1, '東京', 0], ['2015-06-19', 20.8, 1, '東京', 10.5],\n",
1109 |        "  ['2015-06-20', 26.9, 1, '東京', 0], ['2015-06-21', 22.8, 1, '東京', 0.5],\n",
1110 |        "  ['2015-06-22', 26.8, 1, '東京', 0], ['2015-06-23', 28.8, 1, '東京', 20],\n",
1111 |        "  ['2015-06-24', 29.9, 1, '東京', 0], ['2015-06-25', 28.7, 1, '東京', null],\n",
1112 |        "  ['2015-06-26', 25.6, 1, '東京', 13], ['2015-06-27', 25.5, 1, '東京', 3],\n",
1113 |        "  ['2015-06-28', 31.4, 1, '東京', null], ['2015-06-29', 26.3, 1, '東京', null],\n",
1114 |        "  ['2015-06-30', 26.7, 1, '東京', 0.5], ['2016-01-01', 12.1, 0, '東京', null],\n",
1115 |        "  ['2016-01-02', 13.4, 0, '東京', null], ['2016-01-03', 16.2, 0, '東京', null],\n",
1116 |        "  ['2016-01-04', 15.2, 0, '東京', null], ['2016-01-05', 15.4, 0, '東京', null],\n",
1117 |        "  ['2016-01-06', 11.3, 0, '東京', null], ['2016-01-07', 12.9, 0, '東京', null],\n",
1118 |        "  ['2016-01-08', 10.9, 0, '東京', null], ['2016-01-09', 11.6, 0, '東京', null],\n",
1119 |        "  ['2016-01-10', 12.9, 0, '東京', null], ['2016-01-11', 10.5, 0, '東京', 0],\n",
1120 |        "  ['2016-01-12', 6.1, 0, '東京', 0], ['2016-01-13', 9.7, 0, '東京', null],\n",
1121 |        "  ['2016-01-14', 11.4, 0, '東京', null], ['2016-01-15', 10.9, 0, '東京', 0],\n",
1122 |        "  ['2016-01-16', 11.8, 0, '東京', null], ['2016-01-17', 8.6, 0, '東京', 3],\n",
1123 |        "  ['2016-01-18', 6.2, 0, '東京', 67], ['2016-01-19', 8.6, 0, '東京', 0],\n",
1124 |        "  ['2016-01-20', 9.1, 0, '東京', null], ['2016-01-21', 9.5, 0, '東京', null],\n",
1125 |        "  ['2016-01-22', 10.3, 0, '東京', null], ['2016-01-23', 6.4, 0, '東京', 0],\n",
1126 |        "  ['2016-01-24', 9.2, 0, '東京', null], ['2016-01-25', 8.4, 0, '東京', null],\n",
1127 |        "  ['2016-01-26', 9.6, 0, '東京', null], ['2016-01-27', 13.4, 0, '東京', null],\n",
1128 |        "  ['2016-01-28', 14, 0, '東京', null], ['2016-01-29', 7.8, 0, '東京', 9.5],\n",
1129 |        "  ['2016-01-30', 5, 0, '東京', 5.5], ['2016-01-31', 11.2, 0, '東京', null],\n",
1130 |        "  ['2016-02-01', 6.7, 0, '東京', 0], ['2016-02-02', 10.7, 0, '東京', 0],\n",
1131 |        "  ['2016-02-03', 9.2, 0, '東京', null], ['2016-02-04', 9.5, 0, '東京', null],\n",
1132 |        "  ['2016-02-05', 13.3, 0, '東京', null], ['2016-02-06', 9.9, 0, '東京', 0],\n",
1133 |        "  ['2016-02-07', 9.4, 0, '東京', 0], ['2016-02-08', 9.3, 0, '東京', null],\n",
1134 |        "  ['2016-02-09', 14.7, 0, '東京', null], ['2016-02-10', 10.8, 0, '東京', null],\n",
1135 |        "  ['2016-02-11', 11.5, 0, '東京', null], ['2016-02-12', 11.9, 0, '東京', null],\n",
1136 |        "  ['2016-02-13', 21.8, 0, '東京', 0], ['2016-02-14', 23, 0, '東京', 12.5],\n",
1137 |        "  ['2016-02-15', 15.5, 0, '東京', 0.5], ['2016-02-16', 10, 0, '東京', null],\n",
1138 |        "  ['2016-02-17', 13.4, 0, '東京', null], ['2016-02-18', 10.7, 0, '東京', null],\n",
1139 |        "  ['2016-02-19', 16.5, 0, '東京', null], ['2016-02-20', 11.2, 0, '東京', 40.5],\n",
1140 |        "  ['2016-02-21', 15.7, 0, '東京', 0], ['2016-02-22', 9.8, 0, '東京', 1.5],\n",
1141 |        "  ['2016-02-23', 10, 0, '東京', 0], ['2016-02-24', 7.7, 0, '東京', 0.5],\n",
1142 |        "  ['2016-02-25', 8.4, 0, '東京', 0.5], ['2016-02-26', 11.4, 0, '東京', null],\n",
1143 |        "  ['2016-02-27', 12.5, 0, '東京', null], ['2016-02-28', 15, 0, '東京', 0.5],\n",
1144 |        "  ['2016-02-29', 14.8, 0, '東京', 0.5], ['2016-03-01', 10.3, 0, '東京', 0],\n",
1145 |        "  ['2016-03-02', 12.4, 0, '東京', null], ['2016-03-03', 16.4, 0, '東京', null],\n",
1146 |        "  ['2016-03-04', 15.9, 0, '東京', null], ['2016-03-05', 16.3, 0, '東京', null],\n",
1147 |        "  ['2016-03-06', 17.3, 0, '東京', 2], ['2016-03-07', 15.5, 0, '東京', 31.5],\n",
1148 |        "  ['2016-03-08', 20.8, 0, '東京', null], ['2016-03-09', 14.9, 0, '東京', 21.5],\n",
1149 |        "  ['2016-03-10', 8.5, 0, '東京', 0], ['2016-03-11', 6.1, 0, '東京', 0], ['2016-03-12', 8.4, 0, '東京', 0],\n",
1150 |        "  ['2016-03-13', 9.2, 0, '東京', null], ['2016-03-14', 6.9, 0, '東京', 37],\n",
1151 |        "  ['2016-03-15', 14.1, 0, '東京', 0.5], ['2016-03-16', 13.9, 0, '東京', null],\n",
1152 |        "  ['2016-03-17', 20.4, 0, '東京', null], ['2016-03-18', 21.4, 0, '東京', 2],\n",
1153 |        "  ['2016-03-19', 16.7, 0, '東京', 7.5], ['2016-03-20', 19, 0, '東京', 0.5],\n",
1154 |        "  ['2016-03-21', 13.8, 1, '東京', 0], ['2016-03-22', 17.5, 1, '東京', null],\n",
1155 |        "  ['2016-03-23', 18, 1, '東京', null], ['2016-03-24', 9.5, 1, '東京', 0.5],\n",
1156 |        "  ['2016-03-25', 14.4, 1, '東京', null], ['2016-03-26', 12.5, 1, '東京', 0],\n",
1157 |        "  ['2016-03-27', 16.1, 1, '東京', null], ['2016-03-28', 16.8, 1, '東京', 0],\n",
1158 |        "  ['2016-03-29', 18.6, 1, '東京', null], ['2016-03-30', 20, 1, '東京', null],\n",
1159 |        "  ['2016-03-31', 20.2, 1, '東京', null], ['2016-04-01', 18.4, 1, '東京', 2.5],\n",
1160 |        "  ['2016-04-02', 13.3, 1, '東京', 1.5], ['2016-04-03', 17, 1, '東京', 4],\n",
1161 |        "  ['2016-04-04', 21.9, 1, '東京', 8], ['2016-04-05', 12.8, 1, '東京', 1.5],\n",
1162 |        "  ['2016-04-06', 20.2, 1, '東京', null], ['2016-04-07', 20.1, 1, '東京', 33],\n",
1163 |        "  ['2016-04-08', 22.9, 1, '東京', null], ['2016-04-09', 23.5, 1, '東京', null],\n",
1164 |        "  ['2016-04-10', 23.9, 1, '東京', null], ['2016-04-11', 17.1, 1, '東京', 0],\n",
1165 |        "  ['2016-04-12', 15.1, 1, '東京', null], ['2016-04-13', 19, 1, '東京', 1.5],\n",
1166 |        "  ['2016-04-14', 17.6, 1, '東京', 20], ['2016-04-15', 21.4, 1, '東京', null],\n",
1167 |        "  ['2016-04-16', 20.9, 1, '東京', null], ['2016-04-17', 23.1, 1, '東京', 6],\n",
1168 |        "  ['2016-04-18', 26.8, 1, '東京', 0.5], ['2016-04-19', 21.9, 1, '東京', 0],\n",
1169 |        "  ['2016-04-20', 19.3, 1, '東京', null], ['2016-04-21', 20.2, 1, '東京', 6],\n",
1170 |        "  ['2016-04-22', 23.7, 1, '東京', 0], ['2016-04-23', 22.4, 1, '東京', 0],\n",
1171 |        "  ['2016-04-24', 19.1, 1, '東京', 13.5], ['2016-04-25', 23.1, 1, '東京', null],\n",
1172 |        "  ['2016-04-26', 25.1, 1, '東京', null], ['2016-04-27', 21.8, 1, '東京', 0],\n",
1173 |        "  ['2016-04-28', 15.4, 1, '東京', 22], ['2016-04-29', 21.4, 1, '東京', 0],\n",
1174 |        "  ['2016-04-30', 21.7, 1, '東京', null], ['2016-05-01', 25, 1, '東京', null],\n",
1175 |        "  ['2016-05-02', 21.4, 1, '東京', null], ['2016-05-03', 24.7, 1, '東京', null],\n",
1176 |        "  ['2016-05-04', 27, 1, '東京', 13.5], ['2016-05-05', 27.4, 1, '東京', null],\n",
1177 |        "  ['2016-05-06', 22.5, 1, '東京', 2.5], ['2016-05-07', 26.4, 1, '東京', 0],\n",
1178 |        "  ['2016-05-08', 27, 1, '東京', null], ['2016-05-09', 21.8, 1, '東京', 19],\n",
1179 |        "  ['2016-05-10', 22.6, 1, '東京', 6], ['2016-05-11', 27.1, 1, '東京', 5.5],\n",
1180 |        "  ['2016-05-12', 28, 1, '東京', null], ['2016-05-13', 26.5, 1, '東京', null],\n",
1181 |        "  ['2016-05-14', 24.8, 1, '東京', null], ['2016-05-15', 23.7, 1, '東京', null],\n",
1182 |        "  ['2016-05-16', 23.3, 1, '東京', null], ['2016-05-17', 20.5, 1, '東京', 31.5],\n",
1183 |        "  ['2016-05-18', 27.6, 1, '東京', null], ['2016-05-19', 25.1, 1, '東京', null],\n",
1184 |        "  ['2016-05-20', 20.8, 1, '東京', null], ['2016-05-21', 26.3, 1, '東京', 0],\n",
1185 |        "  ['2016-05-22', 29.3, 1, '東京', null], ['2016-05-23', 30.9, 1, '東京', null],\n",
1186 |        "  ['2016-05-24', 29.3, 1, '東京', null], ['2016-05-25', 24.5, 1, '東京', 0],\n",
1187 |        "  ['2016-05-26', 27.2, 1, '東京', null], ['2016-05-27', 22.4, 1, '東京', 36.5],\n",
1188 |        "  ['2016-05-28', 24.1, 1, '東京', null], ['2016-05-29', 27.9, 1, '東京', 0],\n",
1189 |        "  ['2016-05-30', 21.3, 1, '東京', 23], ['2016-05-31', 24.9, 1, '東京', null],\n",
1190 |        "  ['2016-06-01', 26.4, 1, '東京', null], ['2016-06-02', 26, 1, '東京', null],\n",
1191 |        "  ['2016-06-03', 24.2, 1, '東京', null], ['2016-06-04', 27.2, 1, '東京', 0],\n",
1192 |        "  ['2016-06-05', 23.4, 1, '東京', 3], ['2016-06-06', 23.9, 1, '東京', null],\n",
1193 |        "  ['2016-06-07', 20.9, 1, '東京', 0.5], ['2016-06-08', 27.3, 1, '東京', 0],\n",
1194 |        "  ['2016-06-09', 23.7, 1, '東京', 6.5], ['2016-06-10', 28.8, 1, '東京', null],\n",
1195 |        "  ['2016-06-11', 29.1, 1, '東京', null], ['2016-06-12', 28.3, 1, '東京', 0],\n",
1196 |        "  ['2016-06-13', 22.7, 1, '東京', 98.5], ['2016-06-14', 26.2, 1, '東京', null],\n",
1197 |        "  ['2016-06-15', 22.2, 1, '東京', 0], ['2016-06-16', 24.4, 1, '東京', 7.5],\n",
1198 |        "  ['2016-06-17', 29.2, 1, '東京', 2.5], ['2016-06-18', 33, 1, '東京', null],\n",
1199 |        "  ['2016-06-19', 29.3, 1, '東京', 5], ['2016-06-20', 28.8, 1, '東京', 4],\n",
1200 |        "  ['2016-06-21', 25.7, 1, '東京', 10], ['2016-06-22', 23.9, 1, '東京', 0.5],\n",
1201 |        "  ['2016-06-23', 26.9, 1, '東京', 9.5], ['2016-06-24', 25.3, 1, '東京', 1.5],\n",
1202 |        "  ['2016-06-25', 28.7, 1, '東京', 1], ['2016-06-26', 30, 1, '東京', null],\n",
1203 |        "  ['2016-06-27', 28.9, 1, '東京', null], ['2016-06-28', 22.8, 1, '東京', 23],\n",
1204 |        "  ['2016-06-29', 24.2, 1, '東京', 0.5], ['2016-06-30', 27.1, 1, '東京', 1],\n",
1205 |        "  ['2017-01-01', 13.8, 0, '東京', null], ['2017-01-02', 13.3, 0, '東京', null],\n",
1206 |        "  ['2017-01-03', 13.7, 0, '東京', null], ['2017-01-04', 14, 0, '東京', null],\n",
1207 |        "  ['2017-01-05', 10.4, 0, '東京', null], ['2017-01-06', 8.8, 0, '東京', null],\n",
1208 |        "  ['2017-01-07', 8.7, 0, '東京', null], ['2017-01-08', 6, 0, '東京', 20.5],\n",
1209 |        "  ['2017-01-09', 11.1, 0, '東京', 5.5], ['2017-01-10', 12.7, 0, '東京', null],\n",
1210 |        "  ['2017-01-11', 11, 0, '東京', null], ['2017-01-12', 12.1, 0, '東京', null],\n",
1211 |        "  ['2017-01-13', 12.7, 0, '東京', 0], ['2017-01-14', 6.3, 0, '東京', 0],\n",
1212 |        "  ['2017-01-15', 4.7, 0, '東京', null], ['2017-01-16', 8, 0, '東京', null],\n",
1213 |        "  ['2017-01-17', 10.9, 0, '東京', null], ['2017-01-18', 10.3, 0, '東京', null],\n",
1214 |        "  ['2017-01-19', 10.9, 0, '東京', null], ['2017-01-20', 3.8, 0, '東京', 0],\n",
1215 |        "  ['2017-01-21', 11, 0, '東京', null], ['2017-01-22', 12.9, 0, '東京', null],\n",
1216 |        "  ['2017-01-23', 7.9, 0, '東京', null], ['2017-01-24', 7.7, 0, '東京', null],\n",
1217 |        "  ['2017-01-25', 9.5, 0, '東京', null], ['2017-01-26', 10.7, 0, '東京', 0],\n",
1218 |        "  ['2017-01-27', 16, 0, '東京', null], ['2017-01-28', 13.6, 0, '東京', null],\n",
1219 |        "  ['2017-01-29', 12, 0, '東京', null], ['2017-01-30', 19.5, 0, '東京', 0],\n",
1220 |        "  ['2017-01-31', 10, 0, '東京', null], ['2017-02-01', 10.6, 0, '東京', null],\n",
1221 |        "  ['2017-02-02', 9.7, 0, '東京', null], ['2017-02-03', 13.7, 0, '東京', null],\n",
1222 |        "  ['2017-02-04', 13.4, 0, '東京', null], ['2017-02-05', 9.9, 0, '東京', 0],\n",
1223 |        "  ['2017-02-06', 15.7, 0, '東京', 0], ['2017-02-07', 10, 0, '東京', null],\n",
1224 |        "  ['2017-02-08', 11, 0, '東京', null], ['2017-02-09', 5.4, 0, '東京', 8],\n",
1225 |        "  ['2017-02-10', 9.3, 0, '東京', 0.5], ['2017-02-11', 10.1, 0, '東京', null],\n",
1226 |        "  ['2017-02-12', 11.2, 0, '東京', null], ['2017-02-13', 11.8, 0, '東京', null],\n",
1227 |        "  ['2017-02-14', 11.8, 0, '東京', null], ['2017-02-15', 12.8, 0, '東京', null],\n",
1228 |        "  ['2017-02-16', 13.7, 0, '東京', null], ['2017-02-17', 20.6, 0, '東京', null],\n",
1229 |        "  ['2017-02-18', 10.7, 0, '東京', 0], ['2017-02-19', 11.5, 0, '東京', 0],\n",
1230 |        "  ['2017-02-20', 19.7, 0, '東京', 6.5], ['2017-02-21', 9.9, 0, '東京', null],\n",
1231 |        "  ['2017-02-22', 10.6, 0, '東京', null], ['2017-02-23', 16.3, 0, '東京', 0.5],\n",
1232 |        "  ['2017-02-24', 11.3, 0, '東京', 0], ['2017-02-25', 12.7, 0, '東京', null],\n",
1233 |        "  ['2017-02-26', 14.2, 0, '東京', null], ['2017-02-27', 9.7, 0, '東京', null],\n",
1234 |        "  ['2017-02-28', 10.1, 0, '東京', null], ['2017-03-01', 11.1, 0, '東京', 4.5],\n",
1235 |        "  ['2017-03-02', 8.7, 0, '東京', 9.5], ['2017-03-03', 16, 0, '東京', null],\n",
1236 |        "  ['2017-03-04', 14.6, 0, '東京', null], ['2017-03-05', 15.9, 0, '東京', null],\n",
1237 |        "  ['2017-03-06', 11.5, 0, '東京', 1], ['2017-03-07', 10.9, 0, '東京', 0.5],\n",
1238 |        "  ['2017-03-08', 11.8, 0, '東京', null], ['2017-03-09', 13, 0, '東京', null],\n",
1239 |        "  ['2017-03-10', 14.8, 0, '東京', null], ['2017-03-11', 13.2, 0, '東京', null],\n",
1240 |        "  ['2017-03-12', 12.2, 0, '東京', 0], ['2017-03-13', 12.8, 0, '東京', 0.5],\n",
1241 |        "  ['2017-03-14', 11, 0, '東京', 2.5], ['2017-03-15', 7.5, 0, '東京', 1],\n",
1242 |        "  ['2017-03-16', 15.5, 0, '東京', 0], ['2017-03-17', 15.2, 0, '東京', 0],\n",
1243 |        "  ['2017-03-18', 16.2, 0, '東京', null], ['2017-03-19', 18.6, 0, '東京', null],\n",
1244 |        "  ['2017-03-20', 18.7, 0, '東京', null], ['2017-03-21', 12.9, 1, '東京', 25.5],\n",
1245 |        "  ['2017-03-22', 15, 1, '東京', null], ['2017-03-23', 15.7, 1, '東京', 0],\n",
1246 |        "  ['2017-03-24', 13.9, 1, '東京', null], ['2017-03-25', 12.7, 1, '東京', null],\n",
1247 |        "  ['2017-03-26', 8.1, 1, '東京', 12.5], ['2017-03-27', 8.3, 1, '東京', 21.5],\n",
1248 |        "  ['2017-03-28', 14.9, 1, '東京', 0], ['2017-03-29', 14.7, 1, '東京', null],\n",
1249 |        "  ['2017-03-30', 17.8, 1, '東京', null], ['2017-03-31', 12.3, 1, '東京', 6.5],\n",
1250 |        "  ['2017-04-01', 8.9, 1, '東京', 5.5], ['2017-04-02', 14.2, 1, '東京', null],\n",
1251 |        "  ['2017-04-03', 16.1, 1, '東京', 1.5], ['2017-04-04', 17.3, 1, '東京', null],\n",
1252 |        "  ['2017-04-05', 20.7, 1, '東京', null], ['2017-04-06', 21.2, 1, '東京', 0.5],\n",
1253 |        "  ['2017-04-07', 21.6, 1, '東京', 3], ['2017-04-08', 17.7, 1, '東京', 7],\n",
1254 |        "  ['2017-04-09', 15.2, 1, '東京', 9], ['2017-04-10', 15.7, 1, '東京', null],\n",
1255 |        "  ['2017-04-11', 10.7, 1, '東京', 46], ['2017-04-12', 21.6, 1, '東京', 0],\n",
1256 |        "  ['2017-04-13', 17.6, 1, '東京', 0], ['2017-04-14', 21.5, 1, '東京', null],\n",
1257 |        "  ['2017-04-15', 24.2, 1, '東京', 0], ['2017-04-16', 26.1, 1, '東京', null],\n",
1258 |        "  ['2017-04-17', 26.1, 1, '東京', 13], ['2017-04-18', 25.7, 1, '東京', 17.5],\n",
1259 |        "  ['2017-04-19', 26.1, 1, '東京', null], ['2017-04-20', 21.1, 1, '東京', 0],\n",
1260 |        "  ['2017-04-21', 19.2, 1, '東京', 0], ['2017-04-22', 18.4, 1, '東京', 15],\n",
1261 |        "  ['2017-04-23', 20.3, 1, '東京', 0.5], ['2017-04-24', 19.6, 1, '東京', null],\n",
1262 |        "  ['2017-04-25', 21, 1, '東京', null], ['2017-04-26', 19.4, 1, '東京', 0],\n",
1263 |        "  ['2017-04-27', 19.4, 1, '東京', 3.5], ['2017-04-28', 21.6, 1, '東京', null],\n",
1264 |        "  ['2017-04-29', 23.4, 1, '東京', 0], ['2017-04-30', 24.3, 1, '東京', null],\n",
1265 |        "  ['2017-05-01', 26, 1, '東京', 1], ['2017-05-02', 23.3, 1, '東京', null],\n",
1266 |        "  ['2017-05-03', 22.4, 1, '東京', null], ['2017-05-04', 23.3, 1, '東京', null],\n",
1267 |        "  ['2017-05-05', 25.6, 1, '東京', null], ['2017-05-06', 28.2, 1, '東京', null],\n",
1268 |        "  ['2017-05-07', 24.3, 1, '東京', 0], ['2017-05-08', 27.8, 1, '東京', null],\n",
1269 |        "  ['2017-05-09', 21.9, 1, '東京', 0], ['2017-05-10', 17.9, 1, '東京', 4],\n",
1270 |        "  ['2017-05-11', 27.4, 1, '東京', 0], ['2017-05-12', 28.3, 1, '東京', 0],\n",
1271 |        "  ['2017-05-13', 20.1, 1, '東京', 27], ['2017-05-14', 20.4, 1, '東京', 0],\n",
1272 |        "  ['2017-05-15', 22.6, 1, '東京', 0], ['2017-05-16', 23.4, 1, '東京', 0],\n",
1273 |        "  ['2017-05-17', 19.7, 1, '東京', null], ['2017-05-18', 23.6, 1, '東京', 1.5],\n",
1274 |        "  ['2017-05-19', 26.3, 1, '東京', null], ['2017-05-20', 28.7, 1, '東京', null],\n",
1275 |        "  ['2017-05-21', 30.9, 1, '東京', null], ['2017-05-22', 29, 1, '東京', null],\n",
1276 |        "  ['2017-05-23', 27.4, 1, '東京', null], ['2017-05-24', 26.2, 1, '東京', 0],\n",
1277 |        "  ['2017-05-25', 24, 1, '東京', 1], ['2017-05-26', 20.8, 1, '東京', 14.5],\n",
1278 |        "  ['2017-05-27', 27.3, 1, '東京', 0], ['2017-05-28', 26.7, 1, '東京', null],\n",
1279 |        "  ['2017-05-29', 26.9, 1, '東京', null], ['2017-05-30', 29.1, 1, '東京', null],\n",
1280 |        "  ['2017-05-31', 27.5, 1, '東京', null], ['2017-06-01', 26.4, 1, '東京', 15.5],\n",
1281 |        "  ['2017-06-02', 29.1, 1, '東京', null], ['2017-06-03', 26.6, 1, '東京', null],\n",
1282 |        "  ['2017-06-04', 26.7, 1, '東京', null], ['2017-06-05', 25.6, 1, '東京', 3.5],\n",
1283 |        "  ['2017-06-06', 24.2, 1, '東京', 0], ['2017-06-07', 22.6, 1, '東京', 0],\n",
1284 |        "  ['2017-06-08', 25.8, 1, '東京', 0], ['2017-06-09', 28.1, 1, '東京', null],\n",
1285 |        "  ['2017-06-10', 30.6, 1, '東京', 0], ['2017-06-11', 25.3, 1, '東京', 0],\n",
1286 |        "  ['2017-06-12', 26.3, 1, '東京', 0], ['2017-06-13', 19.4, 1, '東京', 9.5],\n",
1287 |        "  ['2017-06-14', 24.6, 1, '東京', 3.5], ['2017-06-15', 26, 1, '東京', null],\n",
1288 |        "  ['2017-06-16', 29, 1, '東京', 0], ['2017-06-17', 27.7, 1, '東京', null],\n",
1289 |        "  ['2017-06-18', 22.5, 1, '東京', 11.5], ['2017-06-19', 30.5, 1, '東京', 1],\n",
1290 |        "  ['2017-06-20', 28, 1, '東京', null], ['2017-06-21', 24.4, 1, '東京', 45.5],\n",
1291 |        "  ['2017-06-22', 27.9, 1, '東京', null], ['2017-06-23', 30.2, 1, '東京', null],\n",
1292 |        "  ['2017-06-24', 29, 1, '東京', 0], ['2017-06-25', 23.9, 1, '東京', 10],\n",
1293 |        "  ['2017-06-26', 25.9, 1, '東京', 0.5], ['2017-06-27', 25.6, 1, '東京', 1],\n",
1294 |        "  ['2017-06-28', 25, 1, '東京', 3], ['2017-06-29', 29.1, 1, '東京', null],\n",
1295 |        "  ['2017-06-30', 25.7, 1, '東京', 2]]\n",
1296 |        "};\n",
1297 |        "\n",
1298 |        "// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n",
1299 |        "\n",
1300 |        "var v  = new BrunelVis('visid8fca92a2-6981-11e8-ae66-3a5f3782500e');\n",
1301 |        "v.build(table1);\n",
1302 |        "\n",
1303 |        "    });\n",
1304 |        "});"
1305 |       ],
1306 |       "text/plain": [
1307 |        "<IPython.core.display.Javascript object>"
1308 |       ]
1309 |      },
1310 |      "execution_count": 5,
1311 |      "metadata": {},
1312 |      "output_type": "execute_result"
1313 |     }
1314 |    ],
1315 |    "source": [
1316 |     "# セル2-2. Brunelによるデータの可視化\n",
1317 |     "\n",
1318 |     "# グラフ作成用のライブラリーBrunelをインポート\n",
1319 |     "import brunel \n",
1320 |     "\n",
1321 |     "# 東京の2015年以降のデータだけを抽出する\n",
1322 |     "df_tokyo = df_weather[(df_weather.location_name == '東京') & (df_weather.year >= 2015)]\n",
1323 |     "\n",
1324 |     "# 横軸に日付、縦軸に最高気温を用いて、散布図を作成する\n",
1325 |     "%brunel data('df_tokyo') x(date) y(temperature_highest) color(blooming) tooltip(location_name,date,precipitation) ::width=1200"
1326 |    ]
1327 |   },
1328 |   {
1329 |    "cell_type": "code",
1330 |    "execution_count": 6,
1331 |    "metadata": {},
1332 |    "outputs": [
1333 |     {
1334 |      "data": {
1335 |       "text/html": [
1336 |        "<div>\n",
1337 |        "<style scoped>\n",
1338 |        "    .dataframe tbody tr th:only-of-type {\n",
1339 |        "        vertical-align: middle;\n",
1340 |        "    }\n",
1341 |        "\n",
1342 |        "    .dataframe tbody tr th {\n",
1343 |        "        vertical-align: top;\n",
1344 |        "    }\n",
1345 |        "\n",
1346 |        "    .dataframe thead th {\n",
1347 |        "        text-align: right;\n",
1348 |        "    }\n",
1349 |        "</style>\n",
1350 |        "<table border=\"1\" class=\"dataframe\">\n",
1351 |        "  <thead>\n",
1352 |        "    <tr style=\"text-align: right;\">\n",
1353 |        "      <th></th>\n",
1354 |        "      <th>location_name</th>\n",
1355 |        "      <th>prec_id</th>\n",
1356 |        "      <th>block_id</th>\n",
1357 |        "      <th>latitude</th>\n",
1358 |        "      <th>longitude</th>\n",
1359 |        "      <th>height</th>\n",
1360 |        "      <th>y2007</th>\n",
1361 |        "      <th>y2008</th>\n",
1362 |        "      <th>y2009</th>\n",
1363 |        "      <th>y2010</th>\n",
1364 |        "      <th>y2011</th>\n",
1365 |        "      <th>y2012</th>\n",
1366 |        "      <th>y2013</th>\n",
1367 |        "      <th>y2014</th>\n",
1368 |        "      <th>y2015</th>\n",
1369 |        "      <th>y2016</th>\n",
1370 |        "      <th>y2017</th>\n",
1371 |        "    </tr>\n",
1372 |        "  </thead>\n",
1373 |        "  <tbody>\n",
1374 |        "    <tr>\n",
1375 |        "      <th>下関</th>\n",
1376 |        "      <td>下関</td>\n",
1377 |        "      <td>81</td>\n",
1378 |        "      <td>47762</td>\n",
1379 |        "      <td>33.9483</td>\n",
1380 |        "      <td>130.9250</td>\n",
1381 |        "      <td>3.3</td>\n",
1382 |        "      <td>2007-03-23</td>\n",
1383 |        "      <td>2008-03-26</td>\n",
1384 |        "      <td>2009-03-18</td>\n",
1385 |        "      <td>2010-03-20</td>\n",
1386 |        "      <td>2011-03-30</td>\n",
1387 |        "      <td>2012-03-30</td>\n",
1388 |        "      <td>2013-03-19</td>\n",
1389 |        "      <td>2014-03-25</td>\n",
1390 |        "      <td>2015-03-25</td>\n",
1391 |        "      <td>2016-03-28</td>\n",
1392 |        "      <td>2017-03-29</td>\n",
1393 |        "    </tr>\n",
1394 |        "    <tr>\n",
1395 |        "      <th>京都</th>\n",
1396 |        "      <td>京都</td>\n",
1397 |        "      <td>61</td>\n",
1398 |        "      <td>47759</td>\n",
1399 |        "      <td>35.0150</td>\n",
1400 |        "      <td>135.7317</td>\n",
1401 |        "      <td>41.4</td>\n",
1402 |        "      <td>2007-03-25</td>\n",
1403 |        "      <td>2008-03-24</td>\n",
1404 |        "      <td>2009-03-19</td>\n",
1405 |        "      <td>2010-03-19</td>\n",
1406 |        "      <td>2011-03-28</td>\n",
1407 |        "      <td>2012-04-03</td>\n",
1408 |        "      <td>2013-03-22</td>\n",
1409 |        "      <td>2014-03-27</td>\n",
1410 |        "      <td>2015-03-27</td>\n",
1411 |        "      <td>2016-03-23</td>\n",
1412 |        "      <td>2017-03-31</td>\n",
1413 |        "    </tr>\n",
1414 |        "    <tr>\n",
1415 |        "      <th>仙台</th>\n",
1416 |        "      <td>仙台</td>\n",
1417 |        "      <td>34</td>\n",
1418 |        "      <td>47590</td>\n",
1419 |        "      <td>38.2617</td>\n",
1420 |        "      <td>140.8967</td>\n",
1421 |        "      <td>38.9</td>\n",
1422 |        "      <td>2007-04-06</td>\n",
1423 |        "      <td>2008-04-05</td>\n",
1424 |        "      <td>2009-04-07</td>\n",
1425 |        "      <td>2010-04-13</td>\n",
1426 |        "      <td>2011-04-12</td>\n",
1427 |        "      <td>2012-04-18</td>\n",
1428 |        "      <td>2013-04-09</td>\n",
1429 |        "      <td>2014-04-07</td>\n",
1430 |        "      <td>2015-04-03</td>\n",
1431 |        "      <td>2016-04-01</td>\n",
1432 |        "      <td>2017-04-07</td>\n",
1433 |        "    </tr>\n",
1434 |        "    <tr>\n",
1435 |        "      <th>佐賀</th>\n",
1436 |        "      <td>佐賀</td>\n",
1437 |        "      <td>85</td>\n",
1438 |        "      <td>47813</td>\n",
1439 |        "      <td>33.2650</td>\n",
1440 |        "      <td>130.3050</td>\n",
1441 |        "      <td>5.5</td>\n",
1442 |        "      <td>2007-03-24</td>\n",
1443 |        "      <td>2008-03-24</td>\n",
1444 |        "      <td>2009-03-19</td>\n",
1445 |        "      <td>2010-03-21</td>\n",
1446 |        "      <td>2011-03-22</td>\n",
1447 |        "      <td>2012-03-28</td>\n",
1448 |        "      <td>2013-03-18</td>\n",
1449 |        "      <td>2014-03-19</td>\n",
1450 |        "      <td>2015-03-22</td>\n",
1451 |        "      <td>2016-03-23</td>\n",
1452 |        "      <td>2017-03-30</td>\n",
1453 |        "    </tr>\n",
1454 |        "    <tr>\n",
1455 |        "      <th>函館</th>\n",
1456 |        "      <td>函館</td>\n",
1457 |        "      <td>23</td>\n",
1458 |        "      <td>47430</td>\n",
1459 |        "      <td>41.8167</td>\n",
1460 |        "      <td>140.7533</td>\n",
1461 |        "      <td>35.0</td>\n",
1462 |        "      <td>2007-04-30</td>\n",
1463 |        "      <td>2008-04-22</td>\n",
1464 |        "      <td>2009-04-25</td>\n",
1465 |        "      <td>2010-05-05</td>\n",
1466 |        "      <td>2011-05-02</td>\n",
1467 |        "      <td>2012-05-02</td>\n",
1468 |        "      <td>2013-05-08</td>\n",
1469 |        "      <td>2014-04-28</td>\n",
1470 |        "      <td>2015-04-21</td>\n",
1471 |        "      <td>2016-04-24</td>\n",
1472 |        "      <td>2017-04-27</td>\n",
1473 |        "    </tr>\n",
1474 |        "  </tbody>\n",
1475 |        "</table>\n",
1476 |        "</div>"
1477 |       ],
1478 |       "text/plain": [
1479 |        "   location_name  prec_id  block_id  latitude  longitude  height       y2007  \\\n",
1480 |        "下関            下関       81     47762   33.9483   130.9250     3.3  2007-03-23   \n",
1481 |        "京都            京都       61     47759   35.0150   135.7317    41.4  2007-03-25   \n",
1482 |        "仙台            仙台       34     47590   38.2617   140.8967    38.9  2007-04-06   \n",
1483 |        "佐賀            佐賀       85     47813   33.2650   130.3050     5.5  2007-03-24   \n",
1484 |        "函館            函館       23     47430   41.8167   140.7533    35.0  2007-04-30   \n",
1485 |        "\n",
1486 |        "         y2008       y2009       y2010       y2011       y2012       y2013  \\\n",
1487 |        "下関  2008-03-26  2009-03-18  2010-03-20  2011-03-30  2012-03-30  2013-03-19   \n",
1488 |        "京都  2008-03-24  2009-03-19  2010-03-19  2011-03-28  2012-04-03  2013-03-22   \n",
1489 |        "仙台  2008-04-05  2009-04-07  2010-04-13  2011-04-12  2012-04-18  2013-04-09   \n",
1490 |        "佐賀  2008-03-24  2009-03-19  2010-03-21  2011-03-22  2012-03-28  2013-03-18   \n",
1491 |        "函館  2008-04-22  2009-04-25  2010-05-05  2011-05-02  2012-05-02  2013-05-08   \n",
1492 |        "\n",
1493 |        "         y2014       y2015       y2016       y2017  \n",
1494 |        "下関  2014-03-25  2015-03-25  2016-03-28  2017-03-29  \n",
1495 |        "京都  2014-03-27  2015-03-27  2016-03-23  2017-03-31  \n",
1496 |        "仙台  2014-04-07  2015-04-03  2016-04-01  2017-04-07  \n",
1497 |        "佐賀  2014-03-19  2015-03-22  2016-03-23  2017-03-30  \n",
1498 |        "函館  2014-04-28  2015-04-21  2016-04-24  2017-04-27  "
1499 |       ]
1500 |      },
1501 |      "execution_count": 6,
1502 |      "metadata": {},
1503 |      "output_type": "execute_result"
1504 |     }
1505 |    ],
1506 |    "source": [
1507 |     "# セル2-3. データの結合\n",
1508 |     "\n",
1509 |     "#桜の開花日データを可視化するために、df_stationとdf_cherry_bloomingを結合する\n",
1510 |     "df_cherry_blooming = pd.concat([df_station, df_cherry_blooming[['y2007', 'y2008', 'y2009', 'y2010', 'y2011', 'y2012', 'y2013', 'y2014', 'y2015', 'y2016', 'y2017']]], axis=1)\n",
1511 |     "df_cherry_blooming.head()"
1512 |    ]
1513 |   },
1514 |   {
1515 |    "cell_type": "code",
1516 |    "execution_count": 7,
1517 |    "metadata": {},
1518 |    "outputs": [
1519 |     {
1520 |      "data": {
1521 |       "text/html": [
1522 |        "<!--\n",
1523 |        "  ~ Copyright (c) 2015 IBM Corporation and others.\n",
1524 |        "  ~\n",
1525 |        "  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n",
1526 |        "  ~ You may not use this file except in compliance with the License.\n",
1527 |        "  ~ You may obtain a copy of the License at\n",
1528 |        "  ~\n",
1529 |        "  ~     http://www.apache.org/licenses/LICENSE-2.0\n",
1530 |        "  ~\n",
1531 |        "  ~ Unless required by applicable law or agreed to in writing, software\n",
1532 |        "  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n",
1533 |        "  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
1534 |        "  ~ See the License for the specific language governing permissions and\n",
1535 |        "  ~ limitations under the License.\n",
1536 |        "  -->\n",
1537 |        "\n",
1538 |        "\n",
1539 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.css\">\n",
1540 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/sumoselect.css\">\n",
1541 |        "\n",
1542 |        "<style>\n",
1543 |        "    \n",
1544 |        "</style>\n",
1545 |        "\n",
1546 |        "<div id=\"controlsid95cf1934-6981-11e8-ae66-3a5f3782500e\" class=\"brunel\"/>\n",
1547 |        "<svg id=\"visid95cf16fa-6981-11e8-ae66-3a5f3782500e\" width=\"1200\" height=\"600\"></svg>"
1548 |       ],
1549 |       "text/plain": [
1550 |        "<IPython.core.display.HTML object>"
1551 |       ]
1552 |      },
1553 |      "metadata": {},
1554 |      "output_type": "display_data"
1555 |     },
1556 |     {
1557 |      "data": {
1558 |       "application/javascript": [
1559 |        "/*\n",
1560 |        " * Copyright (c) 2015 IBM Corporation and others.\n",
1561 |        " *\n",
1562 |        " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
1563 |        " * You may not use this file except in compliance with the License.\n",
1564 |        " * You may obtain a copy of the License at\n",
1565 |        " *\n",
1566 |        " *     http://www.apache.org/licenses/LICENSE-2.0\n",
1567 |        " *\n",
1568 |        " * Unless required by applicable law or agreed to in writing, software\n",
1569 |        " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
1570 |        " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
1571 |        " * See the License for the specific language governing permissions and\n",
1572 |        " * limitations under the License.\n",
1573 |        " */\n",
1574 |        "\n",
1575 |        "require.config({\n",
1576 |        "    waitSeconds: 60,\n",
1577 |        "    paths: {\n",
1578 |        "        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n",
1579 |        "        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n",
1580 |        "        'brunel' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.min',\n",
1581 |        "        'brunelControls' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.controls.2.3.min'\n",
1582 |        "    },\n",
1583 |        "    shim: {\n",
1584 |        "       'brunel' : {\n",
1585 |        "            exports: 'BrunelD3',\n",
1586 |        "            deps: ['d3', 'topojson'],\n",
1587 |        "            init: function() {\n",
1588 |        "               return {\n",
1589 |        "                 BrunelD3 : BrunelD3,\n",
1590 |        "                 BrunelData : BrunelData\n",
1591 |        "              }\n",
1592 |        "            }\n",
1593 |        "        },\n",
1594 |        "       'brunelControls' : {\n",
1595 |        "            exports: 'BrunelEventHandlers',\n",
1596 |        "            init: function() {\n",
1597 |        "               return {\n",
1598 |        "                 BrunelEventHandlers: BrunelEventHandlers,\n",
1599 |        "                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n",
1600 |        "              }\n",
1601 |        "            }\n",
1602 |        "        }\n",
1603 |        "\n",
1604 |        "    }\n",
1605 |        "\n",
1606 |        "});\n",
1607 |        "\n",
1608 |        "require([\"d3\"], function(d3) {\n",
1609 |        "    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n",
1610 |        "        function  BrunelVis(visId) {\n",
1611 |        "  \"use strict\";                                                                       // strict mode\n",
1612 |        "  var datasets = [],                                      // array of datasets for the original data\n",
1613 |        "      pre = function(d, i) { return d },                         // default pre-process does nothing\n",
1614 |        "      post = function(d, i) { return d },                       // default post-process does nothing\n",
1615 |        "      transitionTime = 200,                                        // transition time for animations\n",
1616 |        "      charts = [],                                                       // the charts in the system\n",
1617 |        "      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n",
1618 |        "\n",
1619 |        "  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n",
1620 |        "\n",
1621 |        "  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n",
1622 |        "\n",
1623 |        "  charts[0] = function(parentNode, filterRows) {\n",
1624 |        "    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 0, 0, 0, 110),\n",
1625 |        "      elements = [];                                              // array of elements in this chart\n",
1626 |        "\n",
1627 |        "    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n",
1628 |        "\n",
1629 |        "    var chart =  vis.append('g').attr('class', 'chart1')\n",
1630 |        "      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n",
1631 |        "    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n",
1632 |        "    var zoom = d3.zoom().scaleExtent([1/5,5]);\n",
1633 |        "    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n",
1634 |        "      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n",
1635 |        "      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n",
1636 |        "      .style('cursor', 'move').call(zoom)\n",
1637 |        "      .node();\n",
1638 |        "    zoomNode.__zoom = d3.zoomIdentity;\n",
1639 |        "    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n",
1640 |        "    var interior = chart.append('g').attr('class', 'interior zoomNone')\n",
1641 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n",
1642 |        "      .attr('clip-path', 'url(#clip_visid95cf16fa-6981-11e8-ae66-3a5f3782500e_chart1_inner)');\n",
1643 |        "    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n",
1644 |        "    var gridGroup = interior.append('g').attr('class', 'grid');\n",
1645 |        "    var legends = chart.append('g').attr('class', 'legend')\n",
1646 |        "      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n",
1647 |        "    vis.append('clipPath').attr('id', 'clip_visid95cf16fa-6981-11e8-ae66-3a5f3782500e_chart1_inner').append('rect')\n",
1648 |        "      .attr('x', 0).attr('y', 0)\n",
1649 |        "      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n",
1650 |        "\n",
1651 |        "    // Projection //////////////////////////////////////////////////////////////////////////////////\n",
1652 |        "\n",
1653 |        "    var base = d3.geoMercator()\n",
1654 |        "      .translate([geom.inner_width/2, geom.inner_height/2])\n",
1655 |        "      .scale(Math.min((geom.inner_width-4)/0.3039, (geom.inner_height-4)/0.3739))\n",
1656 |        "      .center([137.1217, 38.9699])\n",
1657 |        "    function projection(p) {\n",
1658 |        "        var q = base(p), t = d3.zoomTransform(zoomNode);\n",
1659 |        "        return q ? [t.k*q[0]+t.x, t.k*q[1]+t.y] : null;\n",
1660 |        "    };\n",
1661 |        "    function project_center(v) { return (v ? projection([v.c, v.d]) : null) || [-9e6, -9e6] };\n",
1662 |        "    var path = d3.geoPath().projection(BrunelD3.geoStream(projection));\n",
1663 |        "    var scale_x = d3.scaleLinear(), scale_y = d3.scaleLinear();\n",
1664 |        "    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n",
1665 |        "    zoom.on('zoom', function(t, time) {\n",
1666 |        "        t = t || d3.event.transform;\n",
1667 |        "        zoomNode.__zoom = t;\n",
1668 |        "        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n",
1669 |        "        build(time || -1);\n",
1670 |        "    });\n",
1671 |        "\n",
1672 |        "    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n",
1673 |        "\n",
1674 |        "    elements[0] = function() {\n",
1675 |        "      var original, processed,                           // data sets passed in and then transformed\n",
1676 |        "        element, data,                                 // brunel element information and brunel data\n",
1677 |        "        selection, merged;                                      // d3 selection and merged selection\n",
1678 |        "      var elementGroup = interior.append('g').attr('class', 'element1'),\n",
1679 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
1680 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
1681 |        "\n",
1682 |        "      function makeData() {\n",
1683 |        "        original = datasets[0];\n",
1684 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
1685 |        "        processed = pre(original, 0);\n",
1686 |        "        processed = post(processed, 0);\n",
1687 |        "        var f0 = processed.field('#row'),\n",
1688 |        "          f1 = processed.field('#selection');\n",
1689 |        "        var keyFunc = function(d) { return f0.value(d) };\n",
1690 |        "        data = {\n",
1691 |        "          $row:         function(d) { return f0.value(d.row) },\n",
1692 |        "          $selection:   function(d) { return f1.value(d.row) },\n",
1693 |        "          $row_f:       function(d) { return f0.valueFormatted(d.row) },\n",
1694 |        "          $selection_f: function(d) { return f1.valueFormatted(d.row) },\n",
1695 |        "          _split:       function(d) { return 'ALL' },\n",
1696 |        "          _key:         keyFunc,\n",
1697 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
1698 |        "        };\n",
1699 |        "      }\n",
1700 |        "\n",
1701 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
1702 |        "\n",
1703 |        "      function build(transitionMillis) {\n",
1704 |        "        element = elements[0];\n",
1705 |        "        // Read in the feature data and call build again when done\n",
1706 |        "        var features = {\n",
1707 |        "          'https://brunelvis.org/geo/2.3/med/Japan.json': {\n",
1708 |        "          }\n",
1709 |        "        };\n",
1710 |        "        if (BrunelD3.addFeatures(data, features, null, null, this, transitionMillis)) return;\n",
1711 |        "        main.attr('class', 'diagram map');\n",
1712 |        "\n",
1713 |        "        // Define selection entry operations\n",
1714 |        "        function initialState(selection) {\n",
1715 |        "          selection\n",
1716 |        "            .attr('class', 'element polygon filled')\n",
1717 |        "            .classed('nondata', function(d) {return !d || d.row == null})\n",
1718 |        "            .style('pointer-events', 'none')\n",
1719 |        "        }\n",
1720 |        "\n",
1721 |        "        // Define selection update operations on merged data\n",
1722 |        "        function updateState(selection) {\n",
1723 |        "          selection\n",
1724 |        "            .attr('d', function(d) { return path(d).replace(/,L/g, 'L').replace(/,Z/g, 'Z') });\n",
1725 |        "        }\n",
1726 |        "        // Create selections, set the initial state and transition updates\n",
1727 |        "        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n",
1728 |        "        var added = selection.enter().append('path');\n",
1729 |        "        merged = selection.merge(added);\n",
1730 |        "        initialState(added);\n",
1731 |        "        selection.filter(BrunelD3.hasData)\n",
1732 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
1733 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
1734 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
1735 |        "\n",
1736 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
1737 |        "          .style('opacity', 0.5).each( function() {\n",
1738 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
1739 |        "        });\n",
1740 |        "      }\n",
1741 |        "\n",
1742 |        "      return {\n",
1743 |        "        data:           function() { return processed },\n",
1744 |        "        original:       function() { return original },\n",
1745 |        "        internal:       function() { return data },\n",
1746 |        "        selection:      function() { return merged },\n",
1747 |        "        makeData:       makeData,\n",
1748 |        "        build:          build,\n",
1749 |        "        chart:          function() { return charts[0] },\n",
1750 |        "        group:          function() { return elementGroup },\n",
1751 |        "        fields: {\n",
1752 |        "          key:          ['#row']\n",
1753 |        "        }\n",
1754 |        "      };\n",
1755 |        "    }();\n",
1756 |        "\n",
1757 |        "    // Define element #2 ///////////////////////////////////////////////////////////////////////////\n",
1758 |        "\n",
1759 |        "    elements[1] = function() {\n",
1760 |        "      var original, processed,                           // data sets passed in and then transformed\n",
1761 |        "        element, data,                                 // brunel element information and brunel data\n",
1762 |        "        selection, merged;                                      // d3 selection and merged selection\n",
1763 |        "      var elementGroup = interior.append('g').attr('class', 'element2'),\n",
1764 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
1765 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
1766 |        "\n",
1767 |        "      function makeData() {\n",
1768 |        "        original = datasets[1];\n",
1769 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
1770 |        "        processed = pre(original, 1);\n",
1771 |        "        processed = post(processed, 1);\n",
1772 |        "        var f0 = processed.field('longitude'),\n",
1773 |        "          f1 = processed.field('latitude'),\n",
1774 |        "          f2 = processed.field('y2016'),\n",
1775 |        "          f3 = processed.field('location_name'),\n",
1776 |        "          f4 = processed.field('#row'),\n",
1777 |        "          f5 = processed.field('#selection');\n",
1778 |        "        var keyFunc = function(d) { return f4.value(d) };\n",
1779 |        "        data = {\n",
1780 |        "          longitude:    function(d) { return f0.value(d.row) },\n",
1781 |        "          latitude:     function(d) { return f1.value(d.row) },\n",
1782 |        "          y2016:        function(d) { return f2.value(d.row) },\n",
1783 |        "          location_name:function(d) { return f3.value(d.row) },\n",
1784 |        "          $row:         function(d) { return f4.value(d.row) },\n",
1785 |        "          $selection:   function(d) { return f5.value(d.row) },\n",
1786 |        "          longitude_f:  function(d) { return f0.valueFormatted(d.row) },\n",
1787 |        "          latitude_f:   function(d) { return f1.valueFormatted(d.row) },\n",
1788 |        "          y2016_f:      function(d) { return f2.valueFormatted(d.row) },\n",
1789 |        "          location_name_f:function(d) { return f3.valueFormatted(d.row) },\n",
1790 |        "          $row_f:       function(d) { return f4.valueFormatted(d.row) },\n",
1791 |        "          $selection_f: function(d) { return f5.valueFormatted(d.row) },\n",
1792 |        "          _split:       function(d) { return f2.value(d.row) },\n",
1793 |        "          _key:         keyFunc,\n",
1794 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
1795 |        "        };\n",
1796 |        "      }\n",
1797 |        "      // Aesthetic Functions\n",
1798 |        "      var scale_color = d3.scaleTime().domain([new Date('2016-03-19'), new Date('2016-03-28'), new Date('2016-04-06'), new Date('2016-04-15'), new Date('2016-04-24'), new Date('2016-05-03'), new Date('2016-05-13')])\n",
1799 |        "        .interpolate(d3.interpolateHcl)\n",
1800 |        "        .range([ '#FF0000', '#FFC0CB', '#edf8fb', '#b2e2e2', '#66c2a4', '#2ca25f', \n",
1801 |        "          '#006d2c']);\n",
1802 |        "      var color = function(d) { return scale_color(data.y2016(d)) };\n",
1803 |        "      legends._legend = legends._legend || { title: ['Y 2016'], dateFormat: BrunelData.util_DateFormat.YearMonthDay, \n",
1804 |        "        ticks: [new Date('2016-05-17'), new Date('2016-05-10'), new Date('2016-05-03'), new Date('2016-04-26'), new Date('2016-04-19'), new Date('2016-04-12'), new Date('2016-04-05'), new Date('2016-03-29'), new Date('2016-03-22'), new Date('2016-03-15')]};\n",
1805 |        "      legends._legend.color = scale_color;\n",
1806 |        "\n",
1807 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
1808 |        "\n",
1809 |        "      function build(transitionMillis) {\n",
1810 |        "        element = elements[1];\n",
1811 |        "        var w = geom.default_point_size;\n",
1812 |        "        var x = function(d) { return projection([data.longitude(d),data.latitude(d)])[0]};\n",
1813 |        "        var h = geom.default_point_size;\n",
1814 |        "        var y = function(d) { return projection([data.longitude(d),data.latitude(d)])[1]};\n",
1815 |        "        var labeling  = [{\n",
1816 |        "          index: 0, method: 'box', location: ['right', 'center'], inside: false, align: 'start', pad: 3, dy: 0.3,\n",
1817 |        "          fit: false, granularity: 2,\n",
1818 |        "          content: function(d) {\n",
1819 |        "            return d.row == null ? null : data.location_name_f(d)\n",
1820 |        "\t\t\t+ ', '\n",
1821 |        "\t\t\t+ data.y2016_f(d)\n",
1822 |        "          }\n",
1823 |        "        }];\n",
1824 |        "\n",
1825 |        "        // Define selection entry operations\n",
1826 |        "        function initialState(selection) {\n",
1827 |        "          selection\n",
1828 |        "            .attr('class', 'element point filled')\n",
1829 |        "        }\n",
1830 |        "\n",
1831 |        "        // Define selection update operations on merged data\n",
1832 |        "        function updateState(selection) {\n",
1833 |        "          selection\n",
1834 |        "            .attr('cx',function(d) { return projection([data.longitude(d),data.latitude(d)])[0]})\n",
1835 |        "            .attr('cy',function(d) { return projection([data.longitude(d),data.latitude(d)])[1]})\n",
1836 |        "            .attr('r',geom.default_point_size / 2)\n",
1837 |        "            .filter(BrunelD3.hasData)                     // following only performed for data items\n",
1838 |        "            .style('fill', color);\n",
1839 |        "        }\n",
1840 |        "\n",
1841 |        "        // Define labeling for the selection\n",
1842 |        "        function label(selection, transitionMillis) {\n",
1843 |        "          BrunelD3.label(selection, labels, transitionMillis, geom, labeling);\n",
1844 |        "\n",
1845 |        "          var tooltipLabeling  = {\n",
1846 |        "            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n",
1847 |        "            fit: true, granularity: 0,\n",
1848 |        "            content: function(d) {\n",
1849 |        "              return d.row == null ? null : '<span class=\"title\">Location Name: </span>'\n",
1850 |        "\t\t\t+ '<span class=\"field\">' + data.location_name_f(d) + '</span>'\n",
1851 |        "\t\t\t+ '<br/>'\n",
1852 |        "\t\t\t+ '<span class=\"title\">Y 2016: </span>'\n",
1853 |        "\t\t\t+ '<span class=\"field\">' + data.y2016_f(d) + '</span>'\n",
1854 |        "            }\n",
1855 |        "          };\n",
1856 |        "          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n",
1857 |        "        }\n",
1858 |        "        // Create selections, set the initial state and transition updates\n",
1859 |        "        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n",
1860 |        "        var added = selection.enter().append('circle');\n",
1861 |        "        merged = selection.merge(added);\n",
1862 |        "        initialState(added);\n",
1863 |        "        selection.filter(BrunelD3.hasData)\n",
1864 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
1865 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
1866 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
1867 |        "        label(merged, transitionMillis);\n",
1868 |        "\n",
1869 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
1870 |        "          .style('opacity', 0.5).each( function() {\n",
1871 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
1872 |        "        });\n",
1873 |        "      }\n",
1874 |        "\n",
1875 |        "      return {\n",
1876 |        "        data:           function() { return processed },\n",
1877 |        "        original:       function() { return original },\n",
1878 |        "        internal:       function() { return data },\n",
1879 |        "        selection:      function() { return merged },\n",
1880 |        "        makeData:       makeData,\n",
1881 |        "        build:          build,\n",
1882 |        "        chart:          function() { return charts[0] },\n",
1883 |        "        group:          function() { return elementGroup },\n",
1884 |        "        fields: {\n",
1885 |        "          x:            ['longitude'],\n",
1886 |        "          y:            ['latitude'],\n",
1887 |        "          key:          ['#row'],\n",
1888 |        "          color:        ['y2016']\n",
1889 |        "        }\n",
1890 |        "      };\n",
1891 |        "    }();\n",
1892 |        "\n",
1893 |        "    function build(time, noData) {\n",
1894 |        "      var first = elements[0].data() == null;\n",
1895 |        "      if (first) time = 0;                                           // no transition for first call\n",
1896 |        "      if ((first || time > -1) && !noData) {\n",
1897 |        "        elements[0].makeData();\n",
1898 |        "        elements[1].makeData();\n",
1899 |        "        BrunelD3.addLegend(legends, legends._legend);\n",
1900 |        "      }\n",
1901 |        "      elements[0].build(time);\n",
1902 |        "      elements[1].build(time);\n",
1903 |        "    }\n",
1904 |        "\n",
1905 |        "    // Expose the following components of the chart\n",
1906 |        "    return {\n",
1907 |        "      elements : elements,\n",
1908 |        "      interior : interior,\n",
1909 |        "      zoom: function(params, time) {\n",
1910 |        "          if (params) zoom.on('zoom').call(zoomNode, params, time);\n",
1911 |        "          return d3.zoomTransform(zoomNode);\n",
1912 |        "      },\n",
1913 |        "      build : build\n",
1914 |        "    };\n",
1915 |        "    }();\n",
1916 |        "\n",
1917 |        "  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n",
1918 |        "  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n",
1919 |        "  function buildAll() {\n",
1920 |        "    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n",
1921 |        "    updateAll(transitionTime);\n",
1922 |        "  }\n",
1923 |        "\n",
1924 |        "  return {\n",
1925 |        "    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n",
1926 |        "    dataPostProcess:    function(f) { if (f) post = f; return post },\n",
1927 |        "    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n",
1928 |        "    visId:              visId,\n",
1929 |        "    build:              buildAll,\n",
1930 |        "    rebuild:            updateAll,\n",
1931 |        "    charts:             charts\n",
1932 |        "  }\n",
1933 |        "}\n",
1934 |        "\n",
1935 |        "// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n",
1936 |        "\n",
1937 |        "var table1 = {\n",
1938 |        "   summarized: false,\n",
1939 |        "   names: ['_dummy_'], \n",
1940 |        "   options: ['numeric'], \n",
1941 |        "   rows: []\n",
1942 |        "};\n",
1943 |        "var table2 = {\n",
1944 |        "   summarized: false,\n",
1945 |        "   names: ['longitude', 'latitude', 'y2016', 'location_name'], \n",
1946 |        "   options: ['numeric', 'numeric', 'date-YearMonthDay', 'string'], \n",
1947 |        "   rows: [[130.925, 33.9483, '2016-03-28', '下関'], [135.7317, 35.015, '2016-03-23', '京都'],\n",
1948 |        "  [140.8967, 38.2617, '2016-04-01', '仙台'], [130.305, 33.265, '2016-03-23', '佐賀'],\n",
1949 |        "  [140.7533, 41.8167, '2016-04-24', '函館'], [139.06, 36.405, '2016-03-23', '前橋'],\n",
1950 |        "  [136.965, 35.1667, '2016-03-19', '名古屋'], [135.1633, 34.2283, '2016-03-22', '和歌山'],\n",
1951 |        "  [131.6183, 33.235, '2016-03-28', '大分'], [135.5183, 34.6817, '2016-03-23', '大阪'],\n",
1952 |        "  [135.8267, 34.6933, '2016-03-23', '奈良'], [139.8683, 36.5483, '2016-03-28', '宇都宮'],\n",
1953 |        "  [140.975, 42.3117, '2016-04-25', '室蘭'], [131.4133, 31.9383, '2016-03-24', '宮崎'],\n",
1954 |        "  [137.2017, 36.7083, '2016-03-29', '富山'], [140.345, 38.255, '2016-04-06', '山形'],\n",
1955 |        "  [136.7617, 35.4, '2016-03-20', '岐阜'], [133.9167, 34.66, '2016-03-26', '岡山'],\n",
1956 |        "  [143.2117, 42.9217, '2016-04-30', '帯広'], [132.4617, 34.3983, '2016-03-23', '広島'],\n",
1957 |        "  [136.2433, 35.275, '2016-03-30', '彦根'], [134.5733, 34.0667, '2016-03-30', '徳島'],\n",
1958 |        "  [139.0467, 37.9117, '2016-04-03', '新潟'], [142.3717, 43.7567, '2016-05-03', '旭川'],\n",
1959 |        "  [141.3283, 43.06, '2016-04-25', '札幌'], [139.76, 35.69, '2016-03-21', '東京'],\n",
1960 |        "  [132.7767, 33.8433, '2016-03-23', '松山'], [133.065, 35.4567, '2016-03-23', '松江'],\n",
1961 |        "  [139.6517, 35.4383, '2016-03-23', '横浜'], [140.4667, 36.38, '2016-03-28', '水戸'],\n",
1962 |        "  [136.5183, 34.7333, '2016-03-28', '津'], [130.7067, 32.8133, '2016-03-22', '熊本'],\n",
1963 |        "  [139.38, 36.15, '2016-03-23', '熊谷'], [138.5533, 35.6667, '2016-03-23', '甲府'],\n",
1964 |        "  [141.165, 39.6983, '2016-04-11', '盛岡'], [135.2117, 34.6967, '2016-03-26', '神戸'],\n",
1965 |        "  [136.2217, 36.055, '2016-03-27', '福井'], [130.375, 33.5817, '2016-03-19', '福岡'],\n",
1966 |        "  [140.47, 37.7583, '2016-03-30', '福島'], [140.0983, 39.7167, '2016-04-14', '秋田'],\n",
1967 |        "  [141.6783, 45.415, '2016-05-13', '稚内'], [144.2783, 44.0167, '2016-05-08', '網走'],\n",
1968 |        "  [136.6333, 36.5883, '2016-03-30', '金沢'], [144.3767, 42.985, '2016-05-10', '釧路'],\n",
1969 |        "  [140.8567, 35.7383, '2016-04-01', '銚子'], [129.8667, 32.7333, '2016-03-22', '長崎'],\n",
1970 |        "  [138.1917, 36.6617, '2016-04-03', '長野'], [140.7683, 40.8217, '2016-04-17', '青森'],\n",
1971 |        "  [138.4033, 34.975, '2016-03-27', '静岡'], [134.0533, 34.3167, '2016-03-26', '高松'],\n",
1972 |        "  [133.5483, 33.5667, '2016-03-24', '高知'], [134.2383, 35.4867, '2016-03-23', '鳥取'],\n",
1973 |        "  [130.5467, 31.5533, '2016-03-27', '鹿児島']]\n",
1974 |        "};\n",
1975 |        "\n",
1976 |        "// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n",
1977 |        "\n",
1978 |        "var v  = new BrunelVis('visid95cf16fa-6981-11e8-ae66-3a5f3782500e');\n",
1979 |        "v.build(table1, table2);\n",
1980 |        "\n",
1981 |        "    });\n",
1982 |        "});"
1983 |       ],
1984 |       "text/plain": [
1985 |        "<IPython.core.display.Javascript object>"
1986 |       ]
1987 |      },
1988 |      "execution_count": 7,
1989 |      "metadata": {},
1990 |      "output_type": "execute_result"
1991 |     }
1992 |    ],
1993 |    "source": [
1994 |     "# セル2-4. 地図上でのデータの可視化\n",
1995 |     "\n",
1996 |     "# 観測所の位置情報を元に、2016年の開花日データを日本地図にプロットする\n",
1997 |     "%brunel map('japan') + data('df_cherry_blooming') x(longitude) y(latitude) tooltip(location_name, y2016) \\\n",
1998 |     "color(y2016:[red,pink,greens]) label(location_name,y2016) ::width=1200, height=600"
1999 |    ]
2000 |   },
2001 |   {
2002 |    "cell_type": "markdown",
2003 |    "metadata": {},
2004 |    "source": [
2005 |     "## データの加工\n",
2006 |     "予測に有用な特徴量を作成する。"
2007 |    ]
2008 |   },
2009 |   {
2010 |    "cell_type": "code",
2011 |    "execution_count": 8,
2012 |    "metadata": {
2013 |     "scrolled": true
2014 |    },
2015 |    "outputs": [
2016 |     {
2017 |      "name": "stdout",
2018 |      "output_type": "stream",
2019 |      "text": [
2020 |       "['旭川' '宇都宮' '横浜' '岡山' '下関' '岐阜' '宮崎' '京都' '金沢' '釧路' '熊谷' '熊本' '広島' '甲府'\n",
2021 |       " '高松' '高知' '佐賀' '札幌' '山形' '鹿児島' '室蘭' '秋田' '松江' '松山' '新潟' '神戸' '水戸' '盛岡'\n",
2022 |       " '青森' '静岡' '仙台' '前橋' '帯広' '大阪' '大分' '稚内' '銚子' '長崎' '長野' '鳥取' '津' '東京' '徳島'\n",
2023 |       " '奈良' '函館' '彦根' '富山' '福井' '福岡' '福島' '名古屋' '網走' '和歌山']\n",
2024 |       "[2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017]\n"
2025 |      ]
2026 |     }
2027 |    ],
2028 |    "source": [
2029 |     "# セル3-1. ループ処理用のリスト作成\n",
2030 |     "\n",
2031 |     "# location_nameとyearの一覧を取得する\n",
2032 |     "location_list = df_weather['location_name'].drop_duplicates().values\n",
2033 |     "year_list = df_weather['year'].drop_duplicates().values\n",
2034 |     "\n",
2035 |     "print(location_list)\n",
2036 |     "print(year_list)"
2037 |    ]
2038 |   },
2039 |   {
2040 |    "cell_type": "code",
2041 |    "execution_count": 9,
2042 |    "metadata": {
2043 |     "scrolled": true
2044 |    },
2045 |    "outputs": [
2046 |     {
2047 |      "name": "stdout",
2048 |      "output_type": "stream",
2049 |      "text": [
2050 |       "Feature extraction is started at  2018/06/06 12:03:23\n",
2051 |       "Feature extraction is completed at  2018/06/06 12:03:55\n"
2052 |      ]
2053 |     }
2054 |    ],
2055 |    "source": [
2056 |     "# セル3-2. 特徴量の作成\n",
2057 |     "\n",
2058 |     "from datetime import datetime\n",
2059 |     "\n",
2060 |     "print('Feature extraction is started at ', datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\"))\n",
2061 |     "\n",
2062 |     "# 加工後のデータを格納するための空DataFrameを作成しておく\n",
2063 |     "df_weather_custom = pd.DataFrame(index=[], columns=[])\n",
2064 |     "\n",
2065 |     "for current_location in location_list:\n",
2066 |     "    for current_year in year_list:\n",
2067 |     "        df_one = df_weather[(df_weather.location_name == current_location) & (df_weather.year == current_year)].copy()\n",
2068 |     "\n",
2069 |     "        # NULL値（NaN）を0へ置換する。降水量（precipitation）に関して、0mmの降雨と降雨なし（Null）は異なるが、今回は同じとみなす\n",
2070 |     "        df_one = df_one.fillna(0)\n",
2071 |     "\n",
2072 |     "        # 「3日後に開花しているかどうか」をラベル（モデルが出力すべき正解の値）として定義する\n",
2073 |     "        df_one['label'] = df_one['blooming'].shift(-3)\n",
2074 |     "\n",
2075 |     "        # データ期間の終わりである6/27 - 6/30は「3日後のデータ」が存在せず、NaNとなるので1（開花）に変更する\n",
2076 |     "        df_one['label'] = df_one['label'].fillna(1)\n",
2077 |     "\n",
2078 |     "        # 気温0℃以下を一律0℃とみなす\n",
2079 |     "        df_one['temperature_highest'] = df_one['temperature_highest'].where(df_one['temperature_highest'] >=0,0)\n",
2080 |     "        df_one['temperature_avg'] = df_one['temperature_avg'].where(df_one['temperature_avg'] >=0,0)\n",
2081 |     "\n",
2082 |     "        # 最高気温の7日間移動平均を算出する\n",
2083 |     "        df_one['th_mean7'] = df_one['temperature_highest'].rolling(window=7, min_periods=1).mean()\n",
2084 |     "\n",
2085 |     "        # 2月以降、3月以降の平均気温の累積値をそれぞれ算出する\n",
2086 |     "        df_one['ta_cumsum2'] = df_one['temperature_avg'].where(df_one.index.month >= 2,0).cumsum()\n",
2087 |     "        df_one['ta_cumsum3'] = df_one['temperature_avg'].where(df_one.index.month >= 3,0).cumsum()\n",
2088 |     "        \n",
2089 |     "        # 気温3℃以下を一律3℃とみなす\n",
2090 |     "        df_one['temperature_highest'] = df_one['temperature_highest'].where(df_one['temperature_highest'] >=3,3)\n",
2091 |     "        df_one['temperature_avg'] = df_one['temperature_avg'].where(df_one['temperature_avg'] >=3,3)\n",
2092 |     "        \n",
2093 |     "        # 1月の平均気温の平均を算出する\n",
2094 |     "        df_one['ta_mean1'] = df_one['temperature_avg'].where(df_one.index.month == 1,0).mean()\n",
2095 |     "        \n",
2096 |     "\n",
2097 |     "        df_weather_custom = pd.concat([df_weather_custom, df_one])\n",
2098 |     "\n",
2099 |     "print('Feature extraction is completed at ', datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\"))"
2100 |    ]
2101 |   },
2102 |   {
2103 |    "cell_type": "code",
2104 |    "execution_count": 10,
2105 |    "metadata": {},
2106 |    "outputs": [
2107 |     {
2108 |      "data": {
2109 |       "text/html": [
2110 |        "<div>\n",
2111 |        "<style scoped>\n",
2112 |        "    .dataframe tbody tr th:only-of-type {\n",
2113 |        "        vertical-align: middle;\n",
2114 |        "    }\n",
2115 |        "\n",
2116 |        "    .dataframe tbody tr th {\n",
2117 |        "        vertical-align: top;\n",
2118 |        "    }\n",
2119 |        "\n",
2120 |        "    .dataframe thead th {\n",
2121 |        "        text-align: right;\n",
2122 |        "    }\n",
2123 |        "</style>\n",
2124 |        "<table border=\"1\" class=\"dataframe\">\n",
2125 |        "  <thead>\n",
2126 |        "    <tr style=\"text-align: right;\">\n",
2127 |        "      <th></th>\n",
2128 |        "      <th>prec_id</th>\n",
2129 |        "      <th>block_id</th>\n",
2130 |        "      <th>atmospheric_pressure</th>\n",
2131 |        "      <th>precipitation</th>\n",
2132 |        "      <th>temperature_avg</th>\n",
2133 |        "      <th>temperature_highest</th>\n",
2134 |        "      <th>temperature_lowest</th>\n",
2135 |        "      <th>humidity</th>\n",
2136 |        "      <th>daylight_hours</th>\n",
2137 |        "      <th>blooming</th>\n",
2138 |        "      <th>year</th>\n",
2139 |        "      <th>label</th>\n",
2140 |        "      <th>th_mean7</th>\n",
2141 |        "      <th>ta_cumsum2</th>\n",
2142 |        "      <th>ta_cumsum3</th>\n",
2143 |        "      <th>ta_mean1</th>\n",
2144 |        "    </tr>\n",
2145 |        "  </thead>\n",
2146 |        "  <tbody>\n",
2147 |        "    <tr>\n",
2148 |        "      <th>count</th>\n",
2149 |        "      <td>105682.000000</td>\n",
2150 |        "      <td>105682.000000</td>\n",
2151 |        "      <td>105682.000000</td>\n",
2152 |        "      <td>105682.000000</td>\n",
2153 |        "      <td>105682.000000</td>\n",
2154 |        "      <td>105682.000000</td>\n",
2155 |        "      <td>105682.000000</td>\n",
2156 |        "      <td>105682.000000</td>\n",
2157 |        "      <td>105682.000000</td>\n",
2158 |        "      <td>105682.000000</td>\n",
2159 |        "      <td>105682.000000</td>\n",
2160 |        "      <td>105682.000000</td>\n",
2161 |        "      <td>105682.000000</td>\n",
2162 |        "      <td>105682.000000</td>\n",
2163 |        "      <td>105682.000000</td>\n",
2164 |        "      <td>105682.000000</td>\n",
2165 |        "    </tr>\n",
2166 |        "    <tr>\n",
2167 |        "      <th>mean</th>\n",
2168 |        "      <td>52.358491</td>\n",
2169 |        "      <td>47665.188679</td>\n",
2170 |        "      <td>1007.262643</td>\n",
2171 |        "      <td>3.804390</td>\n",
2172 |        "      <td>11.547651</td>\n",
2173 |        "      <td>15.723993</td>\n",
2174 |        "      <td>6.867456</td>\n",
2175 |        "      <td>66.946188</td>\n",
2176 |        "      <td>5.443948</td>\n",
2177 |        "      <td>0.486336</td>\n",
2178 |        "      <td>2012.000000</td>\n",
2179 |        "      <td>0.502886</td>\n",
2180 |        "      <td>15.233320</td>\n",
2181 |        "      <td>576.027336</td>\n",
2182 |        "      <td>477.347698</td>\n",
2183 |        "      <td>0.822720</td>\n",
2184 |        "    </tr>\n",
2185 |        "    <tr>\n",
2186 |        "      <th>std</th>\n",
2187 |        "      <td>21.470656</td>\n",
2188 |        "      <td>140.433158</td>\n",
2189 |        "      <td>30.965654</td>\n",
2190 |        "      <td>10.629459</td>\n",
2191 |        "      <td>7.093331</td>\n",
2192 |        "      <td>8.180168</td>\n",
2193 |        "      <td>8.105448</td>\n",
2194 |        "      <td>14.122655</td>\n",
2195 |        "      <td>4.194424</td>\n",
2196 |        "      <td>0.499816</td>\n",
2197 |        "      <td>3.162451</td>\n",
2198 |        "      <td>0.499994</td>\n",
2199 |        "      <td>7.943180</td>\n",
2200 |        "      <td>628.167811</td>\n",
2201 |        "      <td>578.244244</td>\n",
2202 |        "      <td>0.278733</td>\n",
2203 |        "    </tr>\n",
2204 |        "    <tr>\n",
2205 |        "      <th>min</th>\n",
2206 |        "      <td>11.000000</td>\n",
2207 |        "      <td>47401.000000</td>\n",
2208 |        "      <td>0.000000</td>\n",
2209 |        "      <td>0.000000</td>\n",
2210 |        "      <td>3.000000</td>\n",
2211 |        "      <td>3.000000</td>\n",
2212 |        "      <td>-25.700000</td>\n",
2213 |        "      <td>0.000000</td>\n",
2214 |        "      <td>0.000000</td>\n",
2215 |        "      <td>0.000000</td>\n",
2216 |        "      <td>2007.000000</td>\n",
2217 |        "      <td>0.000000</td>\n",
2218 |        "      <td>0.000000</td>\n",
2219 |        "      <td>0.000000</td>\n",
2220 |        "      <td>0.000000</td>\n",
2221 |        "      <td>0.510989</td>\n",
2222 |        "    </tr>\n",
2223 |        "    <tr>\n",
2224 |        "      <th>25%</th>\n",
2225 |        "      <td>36.000000</td>\n",
2226 |        "      <td>47595.000000</td>\n",
2227 |        "      <td>1003.200000</td>\n",
2228 |        "      <td>0.000000</td>\n",
2229 |        "      <td>4.800000</td>\n",
2230 |        "      <td>9.100000</td>\n",
2231 |        "      <td>0.800000</td>\n",
2232 |        "      <td>57.000000</td>\n",
2233 |        "      <td>1.100000</td>\n",
2234 |        "      <td>0.000000</td>\n",
2235 |        "      <td>2009.000000</td>\n",
2236 |        "      <td>0.000000</td>\n",
2237 |        "      <td>9.185714</td>\n",
2238 |        "      <td>25.800000</td>\n",
2239 |        "      <td>0.000000</td>\n",
2240 |        "      <td>0.533702</td>\n",
2241 |        "    </tr>\n",
2242 |        "    <tr>\n",
2243 |        "      <th>50%</th>\n",
2244 |        "      <td>53.000000</td>\n",
2245 |        "      <td>47648.000000</td>\n",
2246 |        "      <td>1009.500000</td>\n",
2247 |        "      <td>0.000000</td>\n",
2248 |        "      <td>10.300000</td>\n",
2249 |        "      <td>15.200000</td>\n",
2250 |        "      <td>6.000000</td>\n",
2251 |        "      <td>67.000000</td>\n",
2252 |        "      <td>5.400000</td>\n",
2253 |        "      <td>0.000000</td>\n",
2254 |        "      <td>2012.000000</td>\n",
2255 |        "      <td>1.000000</td>\n",
2256 |        "      <td>14.685714</td>\n",
2257 |        "      <td>337.800000</td>\n",
2258 |        "      <td>208.700000</td>\n",
2259 |        "      <td>0.773077</td>\n",
2260 |        "    </tr>\n",
2261 |        "    <tr>\n",
2262 |        "      <th>75%</th>\n",
2263 |        "      <td>68.000000</td>\n",
2264 |        "      <td>47772.000000</td>\n",
2265 |        "      <td>1015.300000</td>\n",
2266 |        "      <td>2.000000</td>\n",
2267 |        "      <td>17.900000</td>\n",
2268 |        "      <td>22.800000</td>\n",
2269 |        "      <td>13.500000</td>\n",
2270 |        "      <td>77.000000</td>\n",
2271 |        "      <td>9.100000</td>\n",
2272 |        "      <td>1.000000</td>\n",
2273 |        "      <td>2015.000000</td>\n",
2274 |        "      <td>1.000000</td>\n",
2275 |        "      <td>22.328571</td>\n",
2276 |        "      <td>973.375000</td>\n",
2277 |        "      <td>836.700000</td>\n",
2278 |        "      <td>1.054696</td>\n",
2279 |        "    </tr>\n",
2280 |        "    <tr>\n",
2281 |        "      <th>max</th>\n",
2282 |        "      <td>88.000000</td>\n",
2283 |        "      <td>47895.000000</td>\n",
2284 |        "      <td>1034.200000</td>\n",
2285 |        "      <td>247.500000</td>\n",
2286 |        "      <td>31.300000</td>\n",
2287 |        "      <td>39.800000</td>\n",
2288 |        "      <td>28.500000</td>\n",
2289 |        "      <td>100.000000</td>\n",
2290 |        "      <td>15.000000</td>\n",
2291 |        "      <td>1.000000</td>\n",
2292 |        "      <td>2017.000000</td>\n",
2293 |        "      <td>1.000000</td>\n",
2294 |        "      <td>34.985714</td>\n",
2295 |        "      <td>2657.500000</td>\n",
2296 |        "      <td>2379.600000</td>\n",
2297 |        "      <td>1.690110</td>\n",
2298 |        "    </tr>\n",
2299 |        "  </tbody>\n",
2300 |        "</table>\n",
2301 |        "</div>"
2302 |       ],
2303 |       "text/plain": [
2304 |        "             prec_id       block_id  atmospheric_pressure  precipitation  \\\n",
2305 |        "count  105682.000000  105682.000000         105682.000000  105682.000000   \n",
2306 |        "mean       52.358491   47665.188679           1007.262643       3.804390   \n",
2307 |        "std        21.470656     140.433158             30.965654      10.629459   \n",
2308 |        "min        11.000000   47401.000000              0.000000       0.000000   \n",
2309 |        "25%        36.000000   47595.000000           1003.200000       0.000000   \n",
2310 |        "50%        53.000000   47648.000000           1009.500000       0.000000   \n",
2311 |        "75%        68.000000   47772.000000           1015.300000       2.000000   \n",
2312 |        "max        88.000000   47895.000000           1034.200000     247.500000   \n",
2313 |        "\n",
2314 |        "       temperature_avg  temperature_highest  temperature_lowest  \\\n",
2315 |        "count    105682.000000        105682.000000       105682.000000   \n",
2316 |        "mean         11.547651            15.723993            6.867456   \n",
2317 |        "std           7.093331             8.180168            8.105448   \n",
2318 |        "min           3.000000             3.000000          -25.700000   \n",
2319 |        "25%           4.800000             9.100000            0.800000   \n",
2320 |        "50%          10.300000            15.200000            6.000000   \n",
2321 |        "75%          17.900000            22.800000           13.500000   \n",
2322 |        "max          31.300000            39.800000           28.500000   \n",
2323 |        "\n",
2324 |        "            humidity  daylight_hours       blooming           year  \\\n",
2325 |        "count  105682.000000   105682.000000  105682.000000  105682.000000   \n",
2326 |        "mean       66.946188        5.443948       0.486336    2012.000000   \n",
2327 |        "std        14.122655        4.194424       0.499816       3.162451   \n",
2328 |        "min         0.000000        0.000000       0.000000    2007.000000   \n",
2329 |        "25%        57.000000        1.100000       0.000000    2009.000000   \n",
2330 |        "50%        67.000000        5.400000       0.000000    2012.000000   \n",
2331 |        "75%        77.000000        9.100000       1.000000    2015.000000   \n",
2332 |        "max       100.000000       15.000000       1.000000    2017.000000   \n",
2333 |        "\n",
2334 |        "               label       th_mean7     ta_cumsum2     ta_cumsum3  \\\n",
2335 |        "count  105682.000000  105682.000000  105682.000000  105682.000000   \n",
2336 |        "mean        0.502886      15.233320     576.027336     477.347698   \n",
2337 |        "std         0.499994       7.943180     628.167811     578.244244   \n",
2338 |        "min         0.000000       0.000000       0.000000       0.000000   \n",
2339 |        "25%         0.000000       9.185714      25.800000       0.000000   \n",
2340 |        "50%         1.000000      14.685714     337.800000     208.700000   \n",
2341 |        "75%         1.000000      22.328571     973.375000     836.700000   \n",
2342 |        "max         1.000000      34.985714    2657.500000    2379.600000   \n",
2343 |        "\n",
2344 |        "            ta_mean1  \n",
2345 |        "count  105682.000000  \n",
2346 |        "mean        0.822720  \n",
2347 |        "std         0.278733  \n",
2348 |        "min         0.510989  \n",
2349 |        "25%         0.533702  \n",
2350 |        "50%         0.773077  \n",
2351 |        "75%         1.054696  \n",
2352 |        "max         1.690110  "
2353 |       ]
2354 |      },
2355 |      "execution_count": 10,
2356 |      "metadata": {},
2357 |      "output_type": "execute_result"
2358 |     }
2359 |    ],
2360 |    "source": [
2361 |     "# セル3-3. データの統計量の確認\n",
2362 |     "df_weather_custom.describe()"
2363 |    ]
2364 |   },
2365 |   {
2366 |    "cell_type": "code",
2367 |    "execution_count": 11,
2368 |    "metadata": {
2369 |     "scrolled": false
2370 |    },
2371 |    "outputs": [
2372 |     {
2373 |      "data": {
2374 |       "text/html": [
2375 |        "<!--\n",
2376 |        "  ~ Copyright (c) 2015 IBM Corporation and others.\n",
2377 |        "  ~\n",
2378 |        "  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n",
2379 |        "  ~ You may not use this file except in compliance with the License.\n",
2380 |        "  ~ You may obtain a copy of the License at\n",
2381 |        "  ~\n",
2382 |        "  ~     http://www.apache.org/licenses/LICENSE-2.0\n",
2383 |        "  ~\n",
2384 |        "  ~ Unless required by applicable law or agreed to in writing, software\n",
2385 |        "  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n",
2386 |        "  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
2387 |        "  ~ See the License for the specific language governing permissions and\n",
2388 |        "  ~ limitations under the License.\n",
2389 |        "  -->\n",
2390 |        "\n",
2391 |        "\n",
2392 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.css\">\n",
2393 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/sumoselect.css\">\n",
2394 |        "\n",
2395 |        "<style>\n",
2396 |        "    \n",
2397 |        "</style>\n",
2398 |        "\n",
2399 |        "<div id=\"controlsidb4d092fe-6981-11e8-ae66-3a5f3782500e\" class=\"brunel\"/>\n",
2400 |        "<svg id=\"visidb4d08e62-6981-11e8-ae66-3a5f3782500e\" width=\"1200\" height=\"400\"></svg>"
2401 |       ],
2402 |       "text/plain": [
2403 |        "<IPython.core.display.HTML object>"
2404 |       ]
2405 |      },
2406 |      "metadata": {},
2407 |      "output_type": "display_data"
2408 |     },
2409 |     {
2410 |      "data": {
2411 |       "application/javascript": [
2412 |        "/*\n",
2413 |        " * Copyright (c) 2015 IBM Corporation and others.\n",
2414 |        " *\n",
2415 |        " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
2416 |        " * You may not use this file except in compliance with the License.\n",
2417 |        " * You may obtain a copy of the License at\n",
2418 |        " *\n",
2419 |        " *     http://www.apache.org/licenses/LICENSE-2.0\n",
2420 |        " *\n",
2421 |        " * Unless required by applicable law or agreed to in writing, software\n",
2422 |        " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
2423 |        " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
2424 |        " * See the License for the specific language governing permissions and\n",
2425 |        " * limitations under the License.\n",
2426 |        " */\n",
2427 |        "\n",
2428 |        "require.config({\n",
2429 |        "    waitSeconds: 60,\n",
2430 |        "    paths: {\n",
2431 |        "        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n",
2432 |        "        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n",
2433 |        "        'brunel' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.min',\n",
2434 |        "        'brunelControls' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.controls.2.3.min'\n",
2435 |        "    },\n",
2436 |        "    shim: {\n",
2437 |        "       'brunel' : {\n",
2438 |        "            exports: 'BrunelD3',\n",
2439 |        "            deps: ['d3', 'topojson'],\n",
2440 |        "            init: function() {\n",
2441 |        "               return {\n",
2442 |        "                 BrunelD3 : BrunelD3,\n",
2443 |        "                 BrunelData : BrunelData\n",
2444 |        "              }\n",
2445 |        "            }\n",
2446 |        "        },\n",
2447 |        "       'brunelControls' : {\n",
2448 |        "            exports: 'BrunelEventHandlers',\n",
2449 |        "            init: function() {\n",
2450 |        "               return {\n",
2451 |        "                 BrunelEventHandlers: BrunelEventHandlers,\n",
2452 |        "                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n",
2453 |        "              }\n",
2454 |        "            }\n",
2455 |        "        }\n",
2456 |        "\n",
2457 |        "    }\n",
2458 |        "\n",
2459 |        "});\n",
2460 |        "\n",
2461 |        "require([\"d3\"], function(d3) {\n",
2462 |        "    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n",
2463 |        "        function  BrunelVis(visId) {\n",
2464 |        "  \"use strict\";                                                                       // strict mode\n",
2465 |        "  var datasets = [],                                      // array of datasets for the original data\n",
2466 |        "      pre = function(d, i) { return d },                         // default pre-process does nothing\n",
2467 |        "      post = function(d, i) { return d },                       // default post-process does nothing\n",
2468 |        "      transitionTime = 200,                                        // transition time for animations\n",
2469 |        "      charts = [],                                                       // the charts in the system\n",
2470 |        "      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n",
2471 |        "\n",
2472 |        "  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n",
2473 |        "\n",
2474 |        "  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n",
2475 |        "\n",
2476 |        "  charts[0] = function(parentNode, filterRows) {\n",
2477 |        "    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 5, 35, 37, 148),\n",
2478 |        "      elements = [];                                              // array of elements in this chart\n",
2479 |        "\n",
2480 |        "    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n",
2481 |        "\n",
2482 |        "    var chart =  vis.append('g').attr('class', 'chart1')\n",
2483 |        "      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n",
2484 |        "    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n",
2485 |        "    var zoom = d3.zoom().scaleExtent([1/3,3]);\n",
2486 |        "    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n",
2487 |        "      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n",
2488 |        "      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n",
2489 |        "      .style('cursor', 'move').call(zoom)\n",
2490 |        "      .node();\n",
2491 |        "    zoomNode.__zoom = d3.zoomIdentity;\n",
2492 |        "    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n",
2493 |        "    var interior = chart.append('g').attr('class', 'interior zoomNone')\n",
2494 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n",
2495 |        "      .attr('clip-path', 'url(#clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_inner)');\n",
2496 |        "    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n",
2497 |        "    var gridGroup = interior.append('g').attr('class', 'grid');\n",
2498 |        "    var axes = chart.append('g').attr('class', 'axis')\n",
2499 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n",
2500 |        "    var legends = chart.append('g').attr('class', 'legend')\n",
2501 |        "      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n",
2502 |        "    vis.append('clipPath').attr('id', 'clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_inner').append('rect')\n",
2503 |        "      .attr('x', 0).attr('y', 0)\n",
2504 |        "      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n",
2505 |        "\n",
2506 |        "    // Scales //////////////////////////////////////////////////////////////////////////////////////\n",
2507 |        "\n",
2508 |        "    var scale_x = d3.scaleTime().domain([new Date('2016-01-01'), new Date('2016-06-30')])\n",
2509 |        "      .range([0, geom.inner_width]);\n",
2510 |        "    var scale_inner = d3.scaleLinear().domain([0,1])\n",
2511 |        "      .range([-0.5, 0.5]);\n",
2512 |        "    var scale_y = d3.scaleLinear().domain([0, 35.000003])\n",
2513 |        "      .range([geom.inner_height, 0]);\n",
2514 |        "    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n",
2515 |        "\n",
2516 |        "    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n",
2517 |        "\n",
2518 |        "    axes.append('g').attr('class', 'x axis')\n",
2519 |        "      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n",
2520 |        "      .attr('clip-path', 'url(#clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_haxis)');\n",
2521 |        "    vis.append('clipPath').attr('id', 'clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_haxis').append('polyline')\n",
2522 |        "      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n",
2523 |        "    axes.select('g.axis.x').append('text').attr('class', 'title').text('Date').style('text-anchor', 'middle')\n",
2524 |        "      .attr('x',geom.inner_rawWidth/2)\n",
2525 |        "      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n",
2526 |        "    axes.append('g').attr('class', 'y axis')\n",
2527 |        "      .attr('clip-path', 'url(#clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_vaxis)');\n",
2528 |        "    vis.append('clipPath').attr('id', 'clip_visidb4d08e62-6981-11e8-ae66-3a5f3782500e_chart1_vaxis').append('polyline')\n",
2529 |        "      .attr('points', '-1000,-10000, 10000,-10000, 10000,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+5) + ', -1000,' + (geom.inner_rawHeight+5) );\n",
2530 |        "\n",
2531 |        "    var axis_bottom = d3.axisBottom(scale_x).ticks(Math.min(10, Math.round(geom.inner_width / 99.0)));\n",
2532 |        "    var axis_left = d3.axisLeft(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 20)));\n",
2533 |        "\n",
2534 |        "    function buildAxes(time) {\n",
2535 |        "      var axis_x = axes.select('g.axis.x');\n",
2536 |        "      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_x));\n",
2537 |        "      var axis_y = axes.select('g.axis.y');\n",
2538 |        "      BrunelD3.transition(axis_y, time).call(axis_left.scale(scale_y));\n",
2539 |        "    }\n",
2540 |        "    zoom.on('zoom', function(t, time) {\n",
2541 |        "        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n",
2542 |        "        scale_x = t.rescaleX(base_scales[0]);\n",
2543 |        "        scale_y = t.rescaleY(base_scales[1]);\n",
2544 |        "        zoomNode.__zoom = t;\n",
2545 |        "        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n",
2546 |        "        build(time || -1);\n",
2547 |        "    });\n",
2548 |        "\n",
2549 |        "    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n",
2550 |        "\n",
2551 |        "    elements[0] = function() {\n",
2552 |        "      var original, processed,                           // data sets passed in and then transformed\n",
2553 |        "        element, data,                                 // brunel element information and brunel data\n",
2554 |        "        selection, merged;                                      // d3 selection and merged selection\n",
2555 |        "      var elementGroup = interior.append('g').attr('class', 'element1'),\n",
2556 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
2557 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
2558 |        "\n",
2559 |        "      function makeData() {\n",
2560 |        "        original = datasets[0];\n",
2561 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
2562 |        "        processed = pre(original, 0)\n",
2563 |        "          .reduce('date; temperature_highest; th_mean7; #series; #row; #count')\n",
2564 |        "          .series('temperature_highest,th_mean7;date, temperature_highest, th_mean7')\n",
2565 |        "          .sortRows('date:ascending');\n",
2566 |        "        processed = post(processed, 0);\n",
2567 |        "        var f0 = processed.field('#series'),\n",
2568 |        "          f1 = processed.field('#values'),\n",
2569 |        "          f2 = processed.field('date'),\n",
2570 |        "          f3 = processed.field('temperature_highest'),\n",
2571 |        "          f4 = processed.field('th_mean7'),\n",
2572 |        "          f5 = processed.field('#row'),\n",
2573 |        "          f6 = processed.field('#selection');\n",
2574 |        "        var keyFunc = function(d) { return f0.value(d) };\n",
2575 |        "        data = {\n",
2576 |        "          $series:      function(d) { return f0.value(d.row) },\n",
2577 |        "          $values:      function(d) { return f1.value(d.row) },\n",
2578 |        "          date:         function(d) { return f2.value(d.row) },\n",
2579 |        "          temperature_highest:function(d) { return f3.value(d.row) },\n",
2580 |        "          th_mean7:     function(d) { return f4.value(d.row) },\n",
2581 |        "          $row:         function(d) { return f5.value(d.row) },\n",
2582 |        "          $selection:   function(d) { return f6.value(d.row) },\n",
2583 |        "          $series_f:    function(d) { return f0.valueFormatted(d.row) },\n",
2584 |        "          $values_f:    function(d) { return f1.valueFormatted(d.row) },\n",
2585 |        "          date_f:       function(d) { return f2.valueFormatted(d.row) },\n",
2586 |        "          temperature_highest_f:function(d) { return f3.valueFormatted(d.row) },\n",
2587 |        "          th_mean7_f:   function(d) { return f4.valueFormatted(d.row) },\n",
2588 |        "          $row_f:       function(d) { return f5.valueFormatted(d.row) },\n",
2589 |        "          $selection_f: function(d) { return f6.valueFormatted(d.row) },\n",
2590 |        "          _split:       function(d) { return f0.value(d.row) },\n",
2591 |        "          _key:         keyFunc,\n",
2592 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
2593 |        "        };\n",
2594 |        "      }\n",
2595 |        "      // Aesthetic Functions\n",
2596 |        "      var scale_color = d3.scaleOrdinal()\n",
2597 |        "        .domain(['temperature_highest', 'th_mean7'])\n",
2598 |        "        .range([ '#00538A', '#C10020', '#F4C800', '#007D34', '#803E75', '#FF6800', \n",
2599 |        "          '#817066', '#FFB300', '#F6768E', '#93AA00', '#53377A', '#FF8E00', '#B32851', \n",
2600 |        "          '#CEA262', '#FF7A5C', '#7F180D', '#593315', '#F13A13', '#232C16']);\n",
2601 |        "      var color = function(d) { return scale_color(data.$series(d)) };\n",
2602 |        "      legends._legend = legends._legend || { title: ['Series'], \n",
2603 |        "        ticks: scale_color.domain()};\n",
2604 |        "      legends._legend.color = scale_color;\n",
2605 |        "\n",
2606 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
2607 |        "\n",
2608 |        "      function build(transitionMillis) {\n",
2609 |        "        element = elements[0];\n",
2610 |        "        var w = geom.default_point_size;\n",
2611 |        "        var x = function(d) { return scale_x(data.date(d))};\n",
2612 |        "        var h = geom.default_point_size;\n",
2613 |        "        var y = function(d) { return scale_y(data.$values(d))};\n",
2614 |        "        // Define paths\n",
2615 |        "        var path = d3.line().x(x).y(y);\n",
2616 |        "        var splits = BrunelD3.makePathSplits(data, path, x);\n",
2617 |        "\n",
2618 |        "        // Define selection entry operations\n",
2619 |        "        function initialState(selection) {\n",
2620 |        "          selection\n",
2621 |        "            .attr('class', 'element line')\n",
2622 |        "        }\n",
2623 |        "\n",
2624 |        "        // Define selection update operations on merged data\n",
2625 |        "        function updateState(selection) {\n",
2626 |        "          selection\n",
2627 |        "            .attr('d', function(d) { return d.path })\n",
2628 |        "            .filter(BrunelD3.hasData)                     // following only performed for data items\n",
2629 |        "            .style('stroke', color);\n",
2630 |        "        }\n",
2631 |        "\n",
2632 |        "        // Define labeling for the selection\n",
2633 |        "        function label(selection, transitionMillis) {\n",
2634 |        "\n",
2635 |        "          var tooltipLabeling  = {\n",
2636 |        "            index: -1, method: 'path', location: ['center', 'center'], inside: true, align: 'middle', pad: 0, dy: 0.3,\n",
2637 |        "            fit: true, granularity: 0,\n",
2638 |        "            path: path,\n",
2639 |        "            content: function(d) {\n",
2640 |        "              return d.row == null ? null : '<span class=\"title\">Date: </span>'\n",
2641 |        "\t\t\t+ '<span class=\"field\">' + data.date_f(d) + '</span>'\n",
2642 |        "\t\t\t+ '<br/>'\n",
2643 |        "\t\t\t+ '<span class=\"title\">Temperature Highest: </span>'\n",
2644 |        "\t\t\t+ '<span class=\"field\">' + data.temperature_highest_f(d) + '</span>'\n",
2645 |        "\t\t\t+ '<br/>'\n",
2646 |        "\t\t\t+ '<span class=\"title\">Th Mean 7: </span>'\n",
2647 |        "\t\t\t+ '<span class=\"field\">' + data.th_mean7_f(d) + '</span>'\n",
2648 |        "            }\n",
2649 |        "          };\n",
2650 |        "          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n",
2651 |        "        }\n",
2652 |        "        // Create selections, set the initial state and transition updates\n",
2653 |        "        selection = main.selectAll('.element').data(splits, function(d) { return d.key });\n",
2654 |        "        var added = selection.enter().append('path');\n",
2655 |        "        merged = selection.merge(added);\n",
2656 |        "        initialState(added);\n",
2657 |        "        selection.filter(BrunelD3.hasData)\n",
2658 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
2659 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
2660 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
2661 |        "        label(merged, transitionMillis);\n",
2662 |        "\n",
2663 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
2664 |        "          .style('opacity', 0.5).each( function() {\n",
2665 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
2666 |        "        });\n",
2667 |        "      }\n",
2668 |        "\n",
2669 |        "      return {\n",
2670 |        "        data:           function() { return processed },\n",
2671 |        "        original:       function() { return original },\n",
2672 |        "        internal:       function() { return data },\n",
2673 |        "        selection:      function() { return merged },\n",
2674 |        "        makeData:       makeData,\n",
2675 |        "        build:          build,\n",
2676 |        "        chart:          function() { return charts[0] },\n",
2677 |        "        group:          function() { return elementGroup },\n",
2678 |        "        fields: {\n",
2679 |        "          x:            ['date'],\n",
2680 |        "          y:            ['temperature_highest', 'th_mean7'],\n",
2681 |        "          key:          ['#series'],\n",
2682 |        "          color:        ['#series']\n",
2683 |        "        }\n",
2684 |        "      };\n",
2685 |        "    }();\n",
2686 |        "\n",
2687 |        "    function build(time, noData) {\n",
2688 |        "      var first = elements[0].data() == null;\n",
2689 |        "      if (first) time = 0;                                           // no transition for first call\n",
2690 |        "      buildAxes(time);\n",
2691 |        "      if ((first || time > -1) && !noData) {\n",
2692 |        "        elements[0].makeData();\n",
2693 |        "        BrunelD3.addLegend(legends, legends._legend);\n",
2694 |        "      }\n",
2695 |        "      elements[0].build(time);\n",
2696 |        "    }\n",
2697 |        "\n",
2698 |        "    // Expose the following components of the chart\n",
2699 |        "    return {\n",
2700 |        "      elements : elements,\n",
2701 |        "      interior : interior,\n",
2702 |        "      scales: {x:scale_x, y:scale_y},\n",
2703 |        "      zoom: function(params, time) {\n",
2704 |        "          if (params) zoom.on('zoom').call(zoomNode, params, time);\n",
2705 |        "          return d3.zoomTransform(zoomNode);\n",
2706 |        "      },\n",
2707 |        "      build : build\n",
2708 |        "    };\n",
2709 |        "    }();\n",
2710 |        "\n",
2711 |        "  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n",
2712 |        "  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n",
2713 |        "  function buildAll() {\n",
2714 |        "    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n",
2715 |        "    updateAll(transitionTime);\n",
2716 |        "  }\n",
2717 |        "\n",
2718 |        "  return {\n",
2719 |        "    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n",
2720 |        "    dataPostProcess:    function(f) { if (f) post = f; return post },\n",
2721 |        "    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n",
2722 |        "    visId:              visId,\n",
2723 |        "    build:              buildAll,\n",
2724 |        "    rebuild:            updateAll,\n",
2725 |        "    charts:             charts\n",
2726 |        "  }\n",
2727 |        "}\n",
2728 |        "\n",
2729 |        "// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n",
2730 |        "\n",
2731 |        "var table1 = {\n",
2732 |        "   summarized: false,\n",
2733 |        "   names: ['date', 'temperature_highest', 'th_mean7'], \n",
2734 |        "   options: ['date-YearMonthDay', 'numeric', 'numeric'], \n",
2735 |        "   rows: [['2016-01-01', 12.1, 12.1], ['2016-01-02', 13.4, 12.75], ['2016-01-03', 16.2, 13.9],\n",
2736 |        "  ['2016-01-04', 15.2, 14.225], ['2016-01-05', 15.4, 14.46], ['2016-01-06', 11.3, 13.933333],\n",
2737 |        "  ['2016-01-07', 12.9, 13.785714], ['2016-01-08', 10.9, 13.614286], ['2016-01-09', 11.6, 13.357143],\n",
2738 |        "  ['2016-01-10', 12.9, 12.885714], ['2016-01-11', 10.5, 12.214286], ['2016-01-12', 6.1, 10.885714],\n",
2739 |        "  ['2016-01-13', 9.7, 10.657143], ['2016-01-14', 11.4, 10.442857], ['2016-01-15', 10.9, 10.442857],\n",
2740 |        "  ['2016-01-16', 11.8, 10.471429], ['2016-01-17', 8.6, 9.8571429], ['2016-01-18', 6.2, 9.2428571],\n",
2741 |        "  ['2016-01-19', 8.6, 9.6], ['2016-01-20', 9.1, 9.5142857], ['2016-01-21', 9.5, 9.2428571],\n",
2742 |        "  ['2016-01-22', 10.3, 9.1571429], ['2016-01-23', 6.4, 8.3857143], ['2016-01-24', 9.2, 8.4714286],\n",
2743 |        "  ['2016-01-25', 8.4, 8.7857143], ['2016-01-26', 9.6, 8.9285714], ['2016-01-27', 13.4, 9.5428571],\n",
2744 |        "  ['2016-01-28', 14, 10.185714], ['2016-01-29', 7.8, 9.8285714], ['2016-01-30', 5, 9.6285714],\n",
2745 |        "  ['2016-01-31', 11.2, 9.9142857], ['2016-02-01', 6.7, 9.6714286], ['2016-02-02', 10.7, 9.8285714],\n",
2746 |        "  ['2016-02-03', 9.2, 9.2285714], ['2016-02-04', 9.5, 8.5857143], ['2016-02-05', 13.3, 9.3714286],\n",
2747 |        "  ['2016-02-06', 9.9, 10.071429], ['2016-02-07', 9.4, 9.8142857], ['2016-02-08', 9.3, 10.185714],\n",
2748 |        "  ['2016-02-09', 14.7, 10.757143], ['2016-02-10', 10.8, 10.985714], ['2016-02-11', 11.5, 11.271429],\n",
2749 |        "  ['2016-02-12', 11.9, 11.071429], ['2016-02-13', 21.8, 12.771429], ['2016-02-14', 23, 14.714286],\n",
2750 |        "  ['2016-02-15', 15.5, 15.6], ['2016-02-16', 10, 14.928571], ['2016-02-17', 13.4, 15.3],\n",
2751 |        "  ['2016-02-18', 10.7, 15.185714], ['2016-02-19', 16.5, 15.842857], ['2016-02-20', 11.2, 14.328571],\n",
2752 |        "  ['2016-02-21', 15.7, 13.285714], ['2016-02-22', 9.8, 12.471429], ['2016-02-23', 10, 12.471429],\n",
2753 |        "  ['2016-02-24', 7.7, 11.657143], ['2016-02-25', 8.4, 11.328571], ['2016-02-26', 11.4, 10.6],\n",
2754 |        "  ['2016-02-27', 12.5, 10.785714], ['2016-02-28', 15, 10.685714], ['2016-02-29', 14.8, 11.4],\n",
2755 |        "  ['2016-03-01', 10.3, 11.442857], ['2016-03-02', 12.4, 12.114286], ['2016-03-03', 16.4, 13.257143],\n",
2756 |        "  ['2016-03-04', 15.9, 13.9], ['2016-03-05', 16.3, 14.442857], ['2016-03-06', 17.3, 14.771429],\n",
2757 |        "  ['2016-03-07', 15.5, 14.871429], ['2016-03-08', 20.8, 16.371429], ['2016-03-09', 14.9, 16.728571],\n",
2758 |        "  ['2016-03-10', 8.5, 15.6], ['2016-03-11', 6.1, 14.2], ['2016-03-12', 8.4, 13.071429],\n",
2759 |        "  ['2016-03-13', 9.2, 11.914286], ['2016-03-14', 6.9, 10.685714], ['2016-03-15', 14.1, 9.7285714],\n",
2760 |        "  ['2016-03-16', 13.9, 9.5857143], ['2016-03-17', 20.4, 11.285714], ['2016-03-18', 21.4, 13.471429],\n",
2761 |        "  ['2016-03-19', 16.7, 14.657143], ['2016-03-20', 19, 16.057143], ['2016-03-21', 13.8, 17.042857],\n",
2762 |        "  ['2016-03-22', 17.5, 17.528571], ['2016-03-23', 18, 18.114286], ['2016-03-24', 9.5, 16.557143],\n",
2763 |        "  ['2016-03-25', 14.4, 15.557143], ['2016-03-26', 12.5, 14.957143], ['2016-03-27', 16.1, 14.542857],\n",
2764 |        "  ['2016-03-28', 16.8, 14.971429], ['2016-03-29', 18.6, 15.128571], ['2016-03-30', 20, 15.414286],\n",
2765 |        "  ['2016-03-31', 20.2, 16.942857], ['2016-04-01', 18.4, 17.514286], ['2016-04-02', 13.3, 17.628571],\n",
2766 |        "  ['2016-04-03', 17, 17.757143], ['2016-04-04', 21.9, 18.485714], ['2016-04-05', 12.8, 17.657143],\n",
2767 |        "  ['2016-04-06', 20.2, 17.685714], ['2016-04-07', 20.1, 17.671429], ['2016-04-08', 22.9, 18.314286],\n",
2768 |        "  ['2016-04-09', 23.5, 19.771429], ['2016-04-10', 23.9, 20.757143], ['2016-04-11', 17.1, 20.071429],\n",
2769 |        "  ['2016-04-12', 15.1, 20.4], ['2016-04-13', 19, 20.228571], ['2016-04-14', 17.6, 19.871429],\n",
2770 |        "  ['2016-04-15', 21.4, 19.657143], ['2016-04-16', 20.9, 19.285714], ['2016-04-17', 23.1, 19.171429],\n",
2771 |        "  ['2016-04-18', 26.8, 20.557143], ['2016-04-19', 21.9, 21.528571], ['2016-04-20', 19.3, 21.571429],\n",
2772 |        "  ['2016-04-21', 20.2, 21.942857], ['2016-04-22', 23.7, 22.271429], ['2016-04-23', 22.4, 22.485714],\n",
2773 |        "  ['2016-04-24', 19.1, 21.914286], ['2016-04-25', 23.1, 21.385714], ['2016-04-26', 25.1, 21.842857],\n",
2774 |        "  ['2016-04-27', 21.8, 22.2], ['2016-04-28', 15.4, 21.514286], ['2016-04-29', 21.4, 21.185714],\n",
2775 |        "  ['2016-04-30', 21.7, 21.085714], ['2016-05-01', 25, 21.928571], ['2016-05-02', 21.4, 21.685714],\n",
2776 |        "  ['2016-05-03', 24.7, 21.628571], ['2016-05-04', 27, 22.371429], ['2016-05-05', 27.4, 24.085714],\n",
2777 |        "  ['2016-05-06', 22.5, 24.242857], ['2016-05-07', 26.4, 24.914286], ['2016-05-08', 27, 25.2],\n",
2778 |        "  ['2016-05-09', 21.8, 25.257143], ['2016-05-10', 22.6, 24.957143], ['2016-05-11', 27.1, 24.971429],\n",
2779 |        "  ['2016-05-12', 28, 25.057143], ['2016-05-13', 26.5, 25.628571], ['2016-05-14', 24.8, 25.4],\n",
2780 |        "  ['2016-05-15', 23.7, 24.928571], ['2016-05-16', 23.3, 25.142857], ['2016-05-17', 20.5, 24.842857],\n",
2781 |        "  ['2016-05-18', 27.6, 24.914286], ['2016-05-19', 25.1, 24.5], ['2016-05-20', 20.8, 23.685714],\n",
2782 |        "  ['2016-05-21', 26.3, 23.9], ['2016-05-22', 29.3, 24.7], ['2016-05-23', 30.9, 25.785714],\n",
2783 |        "  ['2016-05-24', 29.3, 27.042857], ['2016-05-25', 24.5, 26.6], ['2016-05-26', 27.2, 26.9],\n",
2784 |        "  ['2016-05-27', 22.4, 27.128571], ['2016-05-28', 24.1, 26.814286], ['2016-05-29', 27.9, 26.614286],\n",
2785 |        "  ['2016-05-30', 21.3, 25.242857], ['2016-05-31', 24.9, 24.614286], ['2016-06-01', 26.4, 24.885714],\n",
2786 |        "  ['2016-06-02', 26, 24.714286], ['2016-06-03', 24.2, 24.971429], ['2016-06-04', 27.2, 25.414286],\n",
2787 |        "  ['2016-06-05', 23.4, 24.771429], ['2016-06-06', 23.9, 25.142857], ['2016-06-07', 20.9, 24.571429],\n",
2788 |        "  ['2016-06-08', 27.3, 24.7], ['2016-06-09', 23.7, 24.371429], ['2016-06-10', 28.8, 25.028571],\n",
2789 |        "  ['2016-06-11', 29.1, 25.3], ['2016-06-12', 28.3, 26], ['2016-06-13', 22.7, 25.828571],\n",
2790 |        "  ['2016-06-14', 26.2, 26.585714], ['2016-06-15', 22.2, 25.857143], ['2016-06-16', 24.4, 25.957143],\n",
2791 |        "  ['2016-06-17', 29.2, 26.014286], ['2016-06-18', 33, 26.571429], ['2016-06-19', 29.3, 26.714286],\n",
2792 |        "  ['2016-06-20', 28.8, 27.585714], ['2016-06-21', 25.7, 27.514286], ['2016-06-22', 23.9, 27.757143],\n",
2793 |        "  ['2016-06-23', 26.9, 28.114286], ['2016-06-24', 25.3, 27.557143], ['2016-06-25', 28.7, 26.942857],\n",
2794 |        "  ['2016-06-26', 30, 27.042857], ['2016-06-27', 28.9, 27.057143], ['2016-06-28', 22.8, 26.642857],\n",
2795 |        "  ['2016-06-29', 24.2, 26.685714], ['2016-06-30', 27.1, 26.714286]]\n",
2796 |        "};\n",
2797 |        "\n",
2798 |        "// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n",
2799 |        "\n",
2800 |        "var v  = new BrunelVis('visidb4d08e62-6981-11e8-ae66-3a5f3782500e');\n",
2801 |        "v.build(table1);\n",
2802 |        "\n",
2803 |        "    });\n",
2804 |        "});"
2805 |       ],
2806 |       "text/plain": [
2807 |        "<IPython.core.display.Javascript object>"
2808 |       ]
2809 |      },
2810 |      "execution_count": 11,
2811 |      "metadata": {},
2812 |      "output_type": "execute_result"
2813 |     }
2814 |    ],
2815 |    "source": [
2816 |     "# セル3-4. 特徴量の可視化\n",
2817 |     "df_tokyo_2016 = df_weather_custom[(df_weather_custom.location_name == '東京') & (df_weather_custom.year == 2016)]\n",
2818 |     "%brunel data('df_tokyo_2016') line x(date) y(temperature_highest, th_mean7) color(#series) tooltip(date,temperature_highest,th_mean7) ::width=1200"
2819 |    ]
2820 |   },
2821 |   {
2822 |    "cell_type": "markdown",
2823 |    "metadata": {},
2824 |    "source": [
2825 |     "## モデル作成\n",
2826 |     "機械学習のライブラリーを利用して予測モデルを作成する。"
2827 |    ]
2828 |   },
2829 |   {
2830 |    "cell_type": "code",
2831 |    "execution_count": 12,
2832 |    "metadata": {},
2833 |    "outputs": [],
2834 |    "source": [
2835 |     "# セル4-1. ライブラリーのインポート\n",
2836 |     "\n",
2837 |     "# 今回はscikit-learnを使用する\n",
2838 |     "import numpy as np\n",
2839 |     "from sklearn.linear_model import LogisticRegression\n",
2840 |     "from sklearn.preprocessing import StandardScaler"
2841 |    ]
2842 |   },
2843 |   {
2844 |    "cell_type": "code",
2845 |    "execution_count": 13,
2846 |    "metadata": {},
2847 |    "outputs": [
2848 |     {
2849 |      "data": {
2850 |       "text/plain": [
2851 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
2852 |        "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
2853 |        "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
2854 |        "          verbose=0, warm_start=False)"
2855 |       ]
2856 |      },
2857 |      "execution_count": 13,
2858 |      "metadata": {},
2859 |      "output_type": "execute_result"
2860 |     }
2861 |    ],
2862 |    "source": [
2863 |     "# セル4-2. モデルの定義と学習\n",
2864 |     "\n",
2865 |     "#ラベルと説明変数をNumpy Arrayとして用意\n",
2866 |     "label = np.array(df_weather_custom['label'])\n",
2867 |     "features = np.array(df_weather_custom[[\"ta_mean1\", \"th_mean7\", \"ta_cumsum2\", \"ta_cumsum3\"]])\n",
2868 |     "\n",
2869 |     "\n",
2870 |     "# 2016年以前のデータを学習用に、2017年のデータをテスト用に分割\n",
2871 |     "train_index = np.array(df_weather_custom['year'] <= 2016)\n",
2872 |     "test_index = np.array(df_weather_custom['year'] > 2016)\n",
2873 |     "\n",
2874 |     "# スケールの異なる説明変数を含むため標準化を行う\n",
2875 |     "sc = StandardScaler()\n",
2876 |     "sc.fit(features[train_index])\n",
2877 |     "features = sc.transform(features)\n",
2878 |     "\n",
2879 |     "#ロジスティック回帰モデルを学習\n",
2880 |     "classifier = LogisticRegression(C=1.0, penalty='l2')\n",
2881 |     "classifier.fit(features[train_index], label[train_index]) "
2882 |    ]
2883 |   },
2884 |   {
2885 |    "cell_type": "code",
2886 |    "execution_count": 14,
2887 |    "metadata": {},
2888 |    "outputs": [],
2889 |    "source": [
2890 |     "# セル4-3. テストデータでの予測\n",
2891 |     "df_prediction = df_weather_custom[test_index]\n",
2892 |     "# SettingWithCopyWarningを出さないための設定\n",
2893 |     "df_prediction.is_copy = False\n",
2894 |     "df_prediction['prediction'] = classifier.predict(features[test_index])"
2895 |    ]
2896 |   },
2897 |   {
2898 |    "cell_type": "code",
2899 |    "execution_count": 15,
2900 |    "metadata": {
2901 |     "scrolled": true
2902 |    },
2903 |    "outputs": [
2904 |     {
2905 |      "data": {
2906 |       "text/html": [
2907 |        "<div>\n",
2908 |        "<style scoped>\n",
2909 |        "    .dataframe tbody tr th:only-of-type {\n",
2910 |        "        vertical-align: middle;\n",
2911 |        "    }\n",
2912 |        "\n",
2913 |        "    .dataframe tbody tr th {\n",
2914 |        "        vertical-align: top;\n",
2915 |        "    }\n",
2916 |        "\n",
2917 |        "    .dataframe thead th {\n",
2918 |        "        text-align: right;\n",
2919 |        "    }\n",
2920 |        "</style>\n",
2921 |        "<table border=\"1\" class=\"dataframe\">\n",
2922 |        "  <thead>\n",
2923 |        "    <tr style=\"text-align: right;\">\n",
2924 |        "      <th></th>\n",
2925 |        "      <th>date</th>\n",
2926 |        "      <th>location_name</th>\n",
2927 |        "      <th>label</th>\n",
2928 |        "      <th>prediction</th>\n",
2929 |        "    </tr>\n",
2930 |        "  </thead>\n",
2931 |        "  <tbody>\n",
2932 |        "    <tr>\n",
2933 |        "      <th>2017-03-28</th>\n",
2934 |        "      <td>2017-03-28</td>\n",
2935 |        "      <td>奈良</td>\n",
2936 |        "      <td>0.0</td>\n",
2937 |        "      <td>0.0</td>\n",
2938 |        "    </tr>\n",
2939 |        "    <tr>\n",
2940 |        "      <th>2017-03-29</th>\n",
2941 |        "      <td>2017-03-29</td>\n",
2942 |        "      <td>奈良</td>\n",
2943 |        "      <td>0.0</td>\n",
2944 |        "      <td>0.0</td>\n",
2945 |        "    </tr>\n",
2946 |        "    <tr>\n",
2947 |        "      <th>2017-03-30</th>\n",
2948 |        "      <td>2017-03-30</td>\n",
2949 |        "      <td>奈良</td>\n",
2950 |        "      <td>0.0</td>\n",
2951 |        "      <td>1.0</td>\n",
2952 |        "    </tr>\n",
2953 |        "    <tr>\n",
2954 |        "      <th>2017-03-31</th>\n",
2955 |        "      <td>2017-03-31</td>\n",
2956 |        "      <td>奈良</td>\n",
2957 |        "      <td>1.0</td>\n",
2958 |        "      <td>1.0</td>\n",
2959 |        "    </tr>\n",
2960 |        "    <tr>\n",
2961 |        "      <th>2017-04-01</th>\n",
2962 |        "      <td>2017-04-01</td>\n",
2963 |        "      <td>奈良</td>\n",
2964 |        "      <td>1.0</td>\n",
2965 |        "      <td>1.0</td>\n",
2966 |        "    </tr>\n",
2967 |        "  </tbody>\n",
2968 |        "</table>\n",
2969 |        "</div>"
2970 |       ],
2971 |       "text/plain": [
2972 |        "                  date location_name  label  prediction\n",
2973 |        "2017-03-28  2017-03-28            奈良    0.0         0.0\n",
2974 |        "2017-03-29  2017-03-29            奈良    0.0         0.0\n",
2975 |        "2017-03-30  2017-03-30            奈良    0.0         1.0\n",
2976 |        "2017-03-31  2017-03-31            奈良    1.0         1.0\n",
2977 |        "2017-04-01  2017-04-01            奈良    1.0         1.0"
2978 |       ]
2979 |      },
2980 |      "execution_count": 15,
2981 |      "metadata": {},
2982 |      "output_type": "execute_result"
2983 |     }
2984 |    ],
2985 |    "source": [
2986 |     "# セル4-4. 予測結果の表示\n",
2987 |     "df_prediction[df_prediction.location_name == '奈良'][['date', 'location_name', 'label', 'prediction']].iloc[86:91]"
2988 |    ]
2989 |   },
2990 |   {
2991 |    "cell_type": "code",
2992 |    "execution_count": 16,
2993 |    "metadata": {
2994 |     "scrolled": false
2995 |    },
2996 |    "outputs": [
2997 |     {
2998 |      "data": {
2999 |       "text/html": [
3000 |        "<div>\n",
3001 |        "<style scoped>\n",
3002 |        "    .dataframe tbody tr th:only-of-type {\n",
3003 |        "        vertical-align: middle;\n",
3004 |        "    }\n",
3005 |        "\n",
3006 |        "    .dataframe tbody tr th {\n",
3007 |        "        vertical-align: top;\n",
3008 |        "    }\n",
3009 |        "\n",
3010 |        "    .dataframe thead th {\n",
3011 |        "        text-align: right;\n",
3012 |        "    }\n",
3013 |        "</style>\n",
3014 |        "<table border=\"1\" class=\"dataframe\">\n",
3015 |        "  <thead>\n",
3016 |        "    <tr style=\"text-align: right;\">\n",
3017 |        "      <th></th>\n",
3018 |        "      <th>label2017</th>\n",
3019 |        "      <th>prediction2017</th>\n",
3020 |        "      <th>diff2017</th>\n",
3021 |        "      <th>diff2017_days</th>\n",
3022 |        "    </tr>\n",
3023 |        "    <tr>\n",
3024 |        "      <th>location_name</th>\n",
3025 |        "      <th></th>\n",
3026 |        "      <th></th>\n",
3027 |        "      <th></th>\n",
3028 |        "      <th></th>\n",
3029 |        "    </tr>\n",
3030 |        "  </thead>\n",
3031 |        "  <tbody>\n",
3032 |        "    <tr>\n",
3033 |        "      <th>下関</th>\n",
3034 |        "      <td>2017-03-26</td>\n",
3035 |        "      <td>2017-03-23</td>\n",
3036 |        "      <td>3 days</td>\n",
3037 |        "      <td>3</td>\n",
3038 |        "    </tr>\n",
3039 |        "    <tr>\n",
3040 |        "      <th>京都</th>\n",
3041 |        "      <td>2017-03-28</td>\n",
3042 |        "      <td>2017-03-28</td>\n",
3043 |        "      <td>0 days</td>\n",
3044 |        "      <td>0</td>\n",
3045 |        "    </tr>\n",
3046 |        "    <tr>\n",
3047 |        "      <th>仙台</th>\n",
3048 |        "      <td>2017-04-04</td>\n",
3049 |        "      <td>2017-04-06</td>\n",
3050 |        "      <td>-2 days</td>\n",
3051 |        "      <td>-2</td>\n",
3052 |        "    </tr>\n",
3053 |        "    <tr>\n",
3054 |        "      <th>佐賀</th>\n",
3055 |        "      <td>2017-03-27</td>\n",
3056 |        "      <td>2017-03-22</td>\n",
3057 |        "      <td>5 days</td>\n",
3058 |        "      <td>5</td>\n",
3059 |        "    </tr>\n",
3060 |        "    <tr>\n",
3061 |        "      <th>函館</th>\n",
3062 |        "      <td>2017-04-24</td>\n",
3063 |        "      <td>2017-04-21</td>\n",
3064 |        "      <td>3 days</td>\n",
3065 |        "      <td>3</td>\n",
3066 |        "    </tr>\n",
3067 |        "    <tr>\n",
3068 |        "      <th>前橋</th>\n",
3069 |        "      <td>2017-03-30</td>\n",
3070 |        "      <td>2017-03-30</td>\n",
3071 |        "      <td>0 days</td>\n",
3072 |        "      <td>0</td>\n",
3073 |        "    </tr>\n",
3074 |        "    <tr>\n",
3075 |        "      <th>名古屋</th>\n",
3076 |        "      <td>2017-03-25</td>\n",
3077 |        "      <td>2017-03-25</td>\n",
3078 |        "      <td>0 days</td>\n",
3079 |        "      <td>0</td>\n",
3080 |        "    </tr>\n",
3081 |        "    <tr>\n",
3082 |        "      <th>和歌山</th>\n",
3083 |        "      <td>2017-03-27</td>\n",
3084 |        "      <td>2017-03-25</td>\n",
3085 |        "      <td>2 days</td>\n",
3086 |        "      <td>2</td>\n",
3087 |        "    </tr>\n",
3088 |        "    <tr>\n",
3089 |        "      <th>大分</th>\n",
3090 |        "      <td>2017-04-01</td>\n",
3091 |        "      <td>2017-03-24</td>\n",
3092 |        "      <td>8 days</td>\n",
3093 |        "      <td>8</td>\n",
3094 |        "    </tr>\n",
3095 |        "    <tr>\n",
3096 |        "      <th>大阪</th>\n",
3097 |        "      <td>2017-03-27</td>\n",
3098 |        "      <td>2017-03-24</td>\n",
3099 |        "      <td>3 days</td>\n",
3100 |        "      <td>3</td>\n",
3101 |        "    </tr>\n",
3102 |        "    <tr>\n",
3103 |        "      <th>奈良</th>\n",
3104 |        "      <td>2017-03-31</td>\n",
3105 |        "      <td>2017-03-30</td>\n",
3106 |        "      <td>1 days</td>\n",
3107 |        "      <td>1</td>\n",
3108 |        "    </tr>\n",
3109 |        "    <tr>\n",
3110 |        "      <th>宇都宮</th>\n",
3111 |        "      <td>2017-03-31</td>\n",
3112 |        "      <td>2017-04-01</td>\n",
3113 |        "      <td>-1 days</td>\n",
3114 |        "      <td>-1</td>\n",
3115 |        "    </tr>\n",
3116 |        "    <tr>\n",
3117 |        "      <th>室蘭</th>\n",
3118 |        "      <td>2017-04-29</td>\n",
3119 |        "      <td>2017-04-29</td>\n",
3120 |        "      <td>0 days</td>\n",
3121 |        "      <td>0</td>\n",
3122 |        "    </tr>\n",
3123 |        "    <tr>\n",
3124 |        "      <th>宮崎</th>\n",
3125 |        "      <td>2017-03-30</td>\n",
3126 |        "      <td>2017-03-21</td>\n",
3127 |        "      <td>9 days</td>\n",
3128 |        "      <td>9</td>\n",
3129 |        "    </tr>\n",
3130 |        "    <tr>\n",
3131 |        "      <th>富山</th>\n",
3132 |        "      <td>2017-04-02</td>\n",
3133 |        "      <td>2017-04-02</td>\n",
3134 |        "      <td>0 days</td>\n",
3135 |        "      <td>0</td>\n",
3136 |        "    </tr>\n",
3137 |        "    <tr>\n",
3138 |        "      <th>山形</th>\n",
3139 |        "      <td>2017-04-11</td>\n",
3140 |        "      <td>2017-04-10</td>\n",
3141 |        "      <td>1 days</td>\n",
3142 |        "      <td>1</td>\n",
3143 |        "    </tr>\n",
3144 |        "    <tr>\n",
3145 |        "      <th>岐阜</th>\n",
3146 |        "      <td>2017-03-25</td>\n",
3147 |        "      <td>2017-03-26</td>\n",
3148 |        "      <td>-1 days</td>\n",
3149 |        "      <td>-1</td>\n",
3150 |        "    </tr>\n",
3151 |        "    <tr>\n",
3152 |        "      <th>岡山</th>\n",
3153 |        "      <td>2017-03-29</td>\n",
3154 |        "      <td>2017-03-27</td>\n",
3155 |        "      <td>2 days</td>\n",
3156 |        "      <td>2</td>\n",
3157 |        "    </tr>\n",
3158 |        "    <tr>\n",
3159 |        "      <th>帯広</th>\n",
3160 |        "      <td>2017-04-28</td>\n",
3161 |        "      <td>2017-04-30</td>\n",
3162 |        "      <td>-2 days</td>\n",
3163 |        "      <td>-2</td>\n",
3164 |        "    </tr>\n",
3165 |        "    <tr>\n",
3166 |        "      <th>広島</th>\n",
3167 |        "      <td>2017-03-24</td>\n",
3168 |        "      <td>2017-03-24</td>\n",
3169 |        "      <td>0 days</td>\n",
3170 |        "      <td>0</td>\n",
3171 |        "    </tr>\n",
3172 |        "    <tr>\n",
3173 |        "      <th>彦根</th>\n",
3174 |        "      <td>2017-04-02</td>\n",
3175 |        "      <td>2017-04-01</td>\n",
3176 |        "      <td>1 days</td>\n",
3177 |        "      <td>1</td>\n",
3178 |        "    </tr>\n",
3179 |        "    <tr>\n",
3180 |        "      <th>徳島</th>\n",
3181 |        "      <td>2017-03-31</td>\n",
3182 |        "      <td>2017-03-24</td>\n",
3183 |        "      <td>7 days</td>\n",
3184 |        "      <td>7</td>\n",
3185 |        "    </tr>\n",
3186 |        "    <tr>\n",
3187 |        "      <th>新潟</th>\n",
3188 |        "      <td>2017-04-05</td>\n",
3189 |        "      <td>2017-04-05</td>\n",
3190 |        "      <td>0 days</td>\n",
3191 |        "      <td>0</td>\n",
3192 |        "    </tr>\n",
3193 |        "    <tr>\n",
3194 |        "      <th>旭川</th>\n",
3195 |        "      <td>2017-04-30</td>\n",
3196 |        "      <td>2017-05-03</td>\n",
3197 |        "      <td>-3 days</td>\n",
3198 |        "      <td>-3</td>\n",
3199 |        "    </tr>\n",
3200 |        "    <tr>\n",
3201 |        "      <th>札幌</th>\n",
3202 |        "      <td>2017-04-25</td>\n",
3203 |        "      <td>2017-04-26</td>\n",
3204 |        "      <td>-1 days</td>\n",
3205 |        "      <td>-1</td>\n",
3206 |        "    </tr>\n",
3207 |        "    <tr>\n",
3208 |        "      <th>東京</th>\n",
3209 |        "      <td>2017-03-18</td>\n",
3210 |        "      <td>2017-03-22</td>\n",
3211 |        "      <td>-4 days</td>\n",
3212 |        "      <td>-4</td>\n",
3213 |        "    </tr>\n",
3214 |        "    <tr>\n",
3215 |        "      <th>松山</th>\n",
3216 |        "      <td>2017-03-27</td>\n",
3217 |        "      <td>2017-03-23</td>\n",
3218 |        "      <td>4 days</td>\n",
3219 |        "      <td>4</td>\n",
3220 |        "    </tr>\n",
3221 |        "    <tr>\n",
3222 |        "      <th>松江</th>\n",
3223 |        "      <td>2017-03-29</td>\n",
3224 |        "      <td>2017-03-29</td>\n",
3225 |        "      <td>0 days</td>\n",
3226 |        "      <td>0</td>\n",
3227 |        "    </tr>\n",
3228 |        "    <tr>\n",
3229 |        "      <th>横浜</th>\n",
3230 |        "      <td>2017-03-22</td>\n",
3231 |        "      <td>2017-03-23</td>\n",
3232 |        "      <td>-1 days</td>\n",
3233 |        "      <td>-1</td>\n",
3234 |        "    </tr>\n",
3235 |        "    <tr>\n",
3236 |        "      <th>水戸</th>\n",
3237 |        "      <td>2017-03-31</td>\n",
3238 |        "      <td>2017-04-01</td>\n",
3239 |        "      <td>-1 days</td>\n",
3240 |        "      <td>-1</td>\n",
3241 |        "    </tr>\n",
3242 |        "    <tr>\n",
3243 |        "      <th>津</th>\n",
3244 |        "      <td>2017-03-31</td>\n",
3245 |        "      <td>2017-03-28</td>\n",
3246 |        "      <td>3 days</td>\n",
3247 |        "      <td>3</td>\n",
3248 |        "    </tr>\n",
3249 |        "    <tr>\n",
3250 |        "      <th>熊本</th>\n",
3251 |        "      <td>2017-03-29</td>\n",
3252 |        "      <td>2017-03-21</td>\n",
3253 |        "      <td>8 days</td>\n",
3254 |        "      <td>8</td>\n",
3255 |        "    </tr>\n",
3256 |        "    <tr>\n",
3257 |        "      <th>熊谷</th>\n",
3258 |        "      <td>2017-03-25</td>\n",
3259 |        "      <td>2017-03-25</td>\n",
3260 |        "      <td>0 days</td>\n",
3261 |        "      <td>0</td>\n",
3262 |        "    </tr>\n",
3263 |        "    <tr>\n",
3264 |        "      <th>甲府</th>\n",
3265 |        "      <td>2017-03-27</td>\n",
3266 |        "      <td>2017-03-25</td>\n",
3267 |        "      <td>2 days</td>\n",
3268 |        "      <td>2</td>\n",
3269 |        "    </tr>\n",
3270 |        "    <tr>\n",
3271 |        "      <th>盛岡</th>\n",
3272 |        "      <td>2017-04-14</td>\n",
3273 |        "      <td>2017-04-17</td>\n",
3274 |        "      <td>-3 days</td>\n",
3275 |        "      <td>-3</td>\n",
3276 |        "    </tr>\n",
3277 |        "    <tr>\n",
3278 |        "      <th>神戸</th>\n",
3279 |        "      <td>2017-03-31</td>\n",
3280 |        "      <td>2017-03-25</td>\n",
3281 |        "      <td>6 days</td>\n",
3282 |        "      <td>6</td>\n",
3283 |        "    </tr>\n",
3284 |        "    <tr>\n",
3285 |        "      <th>福井</th>\n",
3286 |        "      <td>2017-04-02</td>\n",
3287 |        "      <td>2017-04-01</td>\n",
3288 |        "      <td>1 days</td>\n",
3289 |        "      <td>1</td>\n",
3290 |        "    </tr>\n",
3291 |        "    <tr>\n",
3292 |        "      <th>福岡</th>\n",
3293 |        "      <td>2017-03-22</td>\n",
3294 |        "      <td>2017-03-21</td>\n",
3295 |        "      <td>1 days</td>\n",
3296 |        "      <td>1</td>\n",
3297 |        "    </tr>\n",
3298 |        "    <tr>\n",
3299 |        "      <th>福島</th>\n",
3300 |        "      <td>2017-04-05</td>\n",
3301 |        "      <td>2017-04-05</td>\n",
3302 |        "      <td>0 days</td>\n",
3303 |        "      <td>0</td>\n",
3304 |        "    </tr>\n",
3305 |        "    <tr>\n",
3306 |        "      <th>秋田</th>\n",
3307 |        "      <td>2017-04-13</td>\n",
3308 |        "      <td>2017-04-10</td>\n",
3309 |        "      <td>3 days</td>\n",
3310 |        "      <td>3</td>\n",
3311 |        "    </tr>\n",
3312 |        "    <tr>\n",
3313 |        "      <th>稚内</th>\n",
3314 |        "      <td>2017-05-06</td>\n",
3315 |        "      <td>2017-05-06</td>\n",
3316 |        "      <td>0 days</td>\n",
3317 |        "      <td>0</td>\n",
3318 |        "    </tr>\n",
3319 |        "    <tr>\n",
3320 |        "      <th>網走</th>\n",
3321 |        "      <td>2017-05-02</td>\n",
3322 |        "      <td>2017-05-05</td>\n",
3323 |        "      <td>-3 days</td>\n",
3324 |        "      <td>-3</td>\n",
3325 |        "    </tr>\n",
3326 |        "    <tr>\n",
3327 |        "      <th>金沢</th>\n",
3328 |        "      <td>2017-04-01</td>\n",
3329 |        "      <td>2017-03-31</td>\n",
3330 |        "      <td>1 days</td>\n",
3331 |        "      <td>1</td>\n",
3332 |        "    </tr>\n",
3333 |        "    <tr>\n",
3334 |        "      <th>釧路</th>\n",
3335 |        "      <td>2017-05-11</td>\n",
3336 |        "      <td>2017-05-11</td>\n",
3337 |        "      <td>0 days</td>\n",
3338 |        "      <td>0</td>\n",
3339 |        "    </tr>\n",
3340 |        "    <tr>\n",
3341 |        "      <th>銚子</th>\n",
3342 |        "      <td>2017-03-29</td>\n",
3343 |        "      <td>2017-03-26</td>\n",
3344 |        "      <td>3 days</td>\n",
3345 |        "      <td>3</td>\n",
3346 |        "    </tr>\n",
3347 |        "    <tr>\n",
3348 |        "      <th>長崎</th>\n",
3349 |        "      <td>2017-03-27</td>\n",
3350 |        "      <td>2017-03-22</td>\n",
3351 |        "      <td>5 days</td>\n",
3352 |        "      <td>5</td>\n",
3353 |        "    </tr>\n",
3354 |        "    <tr>\n",
3355 |        "      <th>長野</th>\n",
3356 |        "      <td>2017-04-11</td>\n",
3357 |        "      <td>2017-04-09</td>\n",
3358 |        "      <td>2 days</td>\n",
3359 |        "      <td>2</td>\n",
3360 |        "    </tr>\n",
3361 |        "    <tr>\n",
3362 |        "      <th>青森</th>\n",
3363 |        "      <td>2017-04-14</td>\n",
3364 |        "      <td>2017-04-16</td>\n",
3365 |        "      <td>-2 days</td>\n",
3366 |        "      <td>-2</td>\n",
3367 |        "    </tr>\n",
3368 |        "    <tr>\n",
3369 |        "      <th>静岡</th>\n",
3370 |        "      <td>2017-03-30</td>\n",
3371 |        "      <td>2017-03-23</td>\n",
3372 |        "      <td>7 days</td>\n",
3373 |        "      <td>7</td>\n",
3374 |        "    </tr>\n",
3375 |        "    <tr>\n",
3376 |        "      <th>高松</th>\n",
3377 |        "      <td>2017-03-30</td>\n",
3378 |        "      <td>2017-03-25</td>\n",
3379 |        "      <td>5 days</td>\n",
3380 |        "      <td>5</td>\n",
3381 |        "    </tr>\n",
3382 |        "    <tr>\n",
3383 |        "      <th>高知</th>\n",
3384 |        "      <td>2017-03-26</td>\n",
3385 |        "      <td>2017-03-21</td>\n",
3386 |        "      <td>5 days</td>\n",
3387 |        "      <td>5</td>\n",
3388 |        "    </tr>\n",
3389 |        "    <tr>\n",
3390 |        "      <th>鳥取</th>\n",
3391 |        "      <td>2017-03-30</td>\n",
3392 |        "      <td>2017-03-30</td>\n",
3393 |        "      <td>0 days</td>\n",
3394 |        "      <td>0</td>\n",
3395 |        "    </tr>\n",
3396 |        "    <tr>\n",
3397 |        "      <th>鹿児島</th>\n",
3398 |        "      <td>2017-04-02</td>\n",
3399 |        "      <td>2017-03-20</td>\n",
3400 |        "      <td>13 days</td>\n",
3401 |        "      <td>13</td>\n",
3402 |        "    </tr>\n",
3403 |        "  </tbody>\n",
3404 |        "</table>\n",
3405 |        "</div>"
3406 |       ],
3407 |       "text/plain": [
3408 |        "               label2017 prediction2017 diff2017  diff2017_days\n",
3409 |        "location_name                                                  \n",
3410 |        "下関            2017-03-26     2017-03-23   3 days              3\n",
3411 |        "京都            2017-03-28     2017-03-28   0 days              0\n",
3412 |        "仙台            2017-04-04     2017-04-06  -2 days             -2\n",
3413 |        "佐賀            2017-03-27     2017-03-22   5 days              5\n",
3414 |        "函館            2017-04-24     2017-04-21   3 days              3\n",
3415 |        "前橋            2017-03-30     2017-03-30   0 days              0\n",
3416 |        "名古屋           2017-03-25     2017-03-25   0 days              0\n",
3417 |        "和歌山           2017-03-27     2017-03-25   2 days              2\n",
3418 |        "大分            2017-04-01     2017-03-24   8 days              8\n",
3419 |        "大阪            2017-03-27     2017-03-24   3 days              3\n",
3420 |        "奈良            2017-03-31     2017-03-30   1 days              1\n",
3421 |        "宇都宮           2017-03-31     2017-04-01  -1 days             -1\n",
3422 |        "室蘭            2017-04-29     2017-04-29   0 days              0\n",
3423 |        "宮崎            2017-03-30     2017-03-21   9 days              9\n",
3424 |        "富山            2017-04-02     2017-04-02   0 days              0\n",
3425 |        "山形            2017-04-11     2017-04-10   1 days              1\n",
3426 |        "岐阜            2017-03-25     2017-03-26  -1 days             -1\n",
3427 |        "岡山            2017-03-29     2017-03-27   2 days              2\n",
3428 |        "帯広            2017-04-28     2017-04-30  -2 days             -2\n",
3429 |        "広島            2017-03-24     2017-03-24   0 days              0\n",
3430 |        "彦根            2017-04-02     2017-04-01   1 days              1\n",
3431 |        "徳島            2017-03-31     2017-03-24   7 days              7\n",
3432 |        "新潟            2017-04-05     2017-04-05   0 days              0\n",
3433 |        "旭川            2017-04-30     2017-05-03  -3 days             -3\n",
3434 |        "札幌            2017-04-25     2017-04-26  -1 days             -1\n",
3435 |        "東京            2017-03-18     2017-03-22  -4 days             -4\n",
3436 |        "松山            2017-03-27     2017-03-23   4 days              4\n",
3437 |        "松江            2017-03-29     2017-03-29   0 days              0\n",
3438 |        "横浜            2017-03-22     2017-03-23  -1 days             -1\n",
3439 |        "水戸            2017-03-31     2017-04-01  -1 days             -1\n",
3440 |        "津             2017-03-31     2017-03-28   3 days              3\n",
3441 |        "熊本            2017-03-29     2017-03-21   8 days              8\n",
3442 |        "熊谷            2017-03-25     2017-03-25   0 days              0\n",
3443 |        "甲府            2017-03-27     2017-03-25   2 days              2\n",
3444 |        "盛岡            2017-04-14     2017-04-17  -3 days             -3\n",
3445 |        "神戸            2017-03-31     2017-03-25   6 days              6\n",
3446 |        "福井            2017-04-02     2017-04-01   1 days              1\n",
3447 |        "福岡            2017-03-22     2017-03-21   1 days              1\n",
3448 |        "福島            2017-04-05     2017-04-05   0 days              0\n",
3449 |        "秋田            2017-04-13     2017-04-10   3 days              3\n",
3450 |        "稚内            2017-05-06     2017-05-06   0 days              0\n",
3451 |        "網走            2017-05-02     2017-05-05  -3 days             -3\n",
3452 |        "金沢            2017-04-01     2017-03-31   1 days              1\n",
3453 |        "釧路            2017-05-11     2017-05-11   0 days              0\n",
3454 |        "銚子            2017-03-29     2017-03-26   3 days              3\n",
3455 |        "長崎            2017-03-27     2017-03-22   5 days              5\n",
3456 |        "長野            2017-04-11     2017-04-09   2 days              2\n",
3457 |        "青森            2017-04-14     2017-04-16  -2 days             -2\n",
3458 |        "静岡            2017-03-30     2017-03-23   7 days              7\n",
3459 |        "高松            2017-03-30     2017-03-25   5 days              5\n",
3460 |        "高知            2017-03-26     2017-03-21   5 days              5\n",
3461 |        "鳥取            2017-03-30     2017-03-30   0 days              0\n",
3462 |        "鹿児島           2017-04-02     2017-03-20  13 days             13"
3463 |       ]
3464 |      },
3465 |      "execution_count": 16,
3466 |      "metadata": {},
3467 |      "output_type": "execute_result"
3468 |     }
3469 |    ],
3470 |    "source": [
3471 |     "# セル4-5. モデルの評価\n",
3472 |     "\n",
3473 |     "# 実測値（label）および予測結果（prediction）が開花（1）になっている日付をそれぞれ取得する\n",
3474 |     "label2017 = df_prediction[df_prediction.label == 1].groupby(['location_name']).min()['date'].astype('datetime64[ns]')\n",
3475 |     "prediction2017 = df_prediction[df_prediction.prediction == 1].groupby(['location_name']).min()['date'].astype('datetime64[ns]')\n",
3476 |     "\n",
3477 |     "# 観測所名（location_name）をIndexとして実測値と予測値の差を計算する\n",
3478 |     "prediction_summary = pd.concat([label2017,prediction2017],axis=1)\n",
3479 |     "prediction_summary.columns = ['label2017', 'prediction2017']\n",
3480 |     "prediction_summary['diff2017'] = pd.to_timedelta(prediction_summary['label2017'] -  prediction_summary['prediction2017'])\n",
3481 |     "prediction_summary['diff2017_days'] = (prediction_summary['diff2017'] / np.timedelta64(1, 'D')).astype(int)\n",
3482 |     "prediction_summary"
3483 |    ]
3484 |   },
3485 |   {
3486 |    "cell_type": "code",
3487 |    "execution_count": 17,
3488 |    "metadata": {
3489 |     "scrolled": false
3490 |    },
3491 |    "outputs": [
3492 |     {
3493 |      "data": {
3494 |       "text/html": [
3495 |        "<!--\n",
3496 |        "  ~ Copyright (c) 2015 IBM Corporation and others.\n",
3497 |        "  ~\n",
3498 |        "  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n",
3499 |        "  ~ You may not use this file except in compliance with the License.\n",
3500 |        "  ~ You may obtain a copy of the License at\n",
3501 |        "  ~\n",
3502 |        "  ~     http://www.apache.org/licenses/LICENSE-2.0\n",
3503 |        "  ~\n",
3504 |        "  ~ Unless required by applicable law or agreed to in writing, software\n",
3505 |        "  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n",
3506 |        "  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
3507 |        "  ~ See the License for the specific language governing permissions and\n",
3508 |        "  ~ limitations under the License.\n",
3509 |        "  -->\n",
3510 |        "\n",
3511 |        "\n",
3512 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.css\">\n",
3513 |        "<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/sumoselect.css\">\n",
3514 |        "\n",
3515 |        "<style>\n",
3516 |        "    \n",
3517 |        "</style>\n",
3518 |        "\n",
3519 |        "<div id=\"controlsidc1cfbce6-6981-11e8-ae66-3a5f3782500e\" class=\"brunel\"/>\n",
3520 |        "<svg id=\"visidc1cfbad4-6981-11e8-ae66-3a5f3782500e\" width=\"1200\" height=\"600\"></svg>"
3521 |       ],
3522 |       "text/plain": [
3523 |        "<IPython.core.display.HTML object>"
3524 |       ]
3525 |      },
3526 |      "metadata": {},
3527 |      "output_type": "display_data"
3528 |     },
3529 |     {
3530 |      "data": {
3531 |       "application/javascript": [
3532 |        "/*\n",
3533 |        " * Copyright (c) 2015 IBM Corporation and others.\n",
3534 |        " *\n",
3535 |        " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
3536 |        " * You may not use this file except in compliance with the License.\n",
3537 |        " * You may obtain a copy of the License at\n",
3538 |        " *\n",
3539 |        " *     http://www.apache.org/licenses/LICENSE-2.0\n",
3540 |        " *\n",
3541 |        " * Unless required by applicable law or agreed to in writing, software\n",
3542 |        " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
3543 |        " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
3544 |        " * See the License for the specific language governing permissions and\n",
3545 |        " * limitations under the License.\n",
3546 |        " */\n",
3547 |        "\n",
3548 |        "require.config({\n",
3549 |        "    waitSeconds: 60,\n",
3550 |        "    paths: {\n",
3551 |        "        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n",
3552 |        "        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n",
3553 |        "        'brunel' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.2.3.min',\n",
3554 |        "        'brunelControls' : '/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext/brunel.controls.2.3.min'\n",
3555 |        "    },\n",
3556 |        "    shim: {\n",
3557 |        "       'brunel' : {\n",
3558 |        "            exports: 'BrunelD3',\n",
3559 |        "            deps: ['d3', 'topojson'],\n",
3560 |        "            init: function() {\n",
3561 |        "               return {\n",
3562 |        "                 BrunelD3 : BrunelD3,\n",
3563 |        "                 BrunelData : BrunelData\n",
3564 |        "              }\n",
3565 |        "            }\n",
3566 |        "        },\n",
3567 |        "       'brunelControls' : {\n",
3568 |        "            exports: 'BrunelEventHandlers',\n",
3569 |        "            init: function() {\n",
3570 |        "               return {\n",
3571 |        "                 BrunelEventHandlers: BrunelEventHandlers,\n",
3572 |        "                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n",
3573 |        "              }\n",
3574 |        "            }\n",
3575 |        "        }\n",
3576 |        "\n",
3577 |        "    }\n",
3578 |        "\n",
3579 |        "});\n",
3580 |        "\n",
3581 |        "require([\"d3\"], function(d3) {\n",
3582 |        "    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n",
3583 |        "        function  BrunelVis(visId) {\n",
3584 |        "  \"use strict\";                                                                       // strict mode\n",
3585 |        "  var datasets = [],                                      // array of datasets for the original data\n",
3586 |        "      pre = function(d, i) { return d },                         // default pre-process does nothing\n",
3587 |        "      post = function(d, i) { return d },                       // default post-process does nothing\n",
3588 |        "      transitionTime = 200,                                        // transition time for animations\n",
3589 |        "      charts = [],                                                       // the charts in the system\n",
3590 |        "      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n",
3591 |        "\n",
3592 |        "  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n",
3593 |        "\n",
3594 |        "  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n",
3595 |        "\n",
3596 |        "  charts[0] = function(parentNode, filterRows) {\n",
3597 |        "    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 0, 0, 0, 104),\n",
3598 |        "      elements = [];                                              // array of elements in this chart\n",
3599 |        "\n",
3600 |        "    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n",
3601 |        "\n",
3602 |        "    var chart =  vis.append('g').attr('class', 'chart1')\n",
3603 |        "      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n",
3604 |        "    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n",
3605 |        "    var zoom = d3.zoom().scaleExtent([1/5,5]);\n",
3606 |        "    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n",
3607 |        "      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n",
3608 |        "      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n",
3609 |        "      .style('cursor', 'move').call(zoom)\n",
3610 |        "      .node();\n",
3611 |        "    zoomNode.__zoom = d3.zoomIdentity;\n",
3612 |        "    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n",
3613 |        "    var interior = chart.append('g').attr('class', 'interior zoomNone')\n",
3614 |        "      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n",
3615 |        "      .attr('clip-path', 'url(#clip_visidc1cfbad4-6981-11e8-ae66-3a5f3782500e_chart1_inner)');\n",
3616 |        "    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n",
3617 |        "    var gridGroup = interior.append('g').attr('class', 'grid');\n",
3618 |        "    var legends = chart.append('g').attr('class', 'legend')\n",
3619 |        "      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n",
3620 |        "    vis.append('clipPath').attr('id', 'clip_visidc1cfbad4-6981-11e8-ae66-3a5f3782500e_chart1_inner').append('rect')\n",
3621 |        "      .attr('x', 0).attr('y', 0)\n",
3622 |        "      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n",
3623 |        "\n",
3624 |        "    // Projection //////////////////////////////////////////////////////////////////////////////////\n",
3625 |        "\n",
3626 |        "    var base = d3.geoMercator()\n",
3627 |        "      .translate([geom.inner_width/2, geom.inner_height/2])\n",
3628 |        "      .scale(Math.min((geom.inner_width-4)/0.3039, (geom.inner_height-4)/0.3739))\n",
3629 |        "      .center([137.1217, 38.9699])\n",
3630 |        "    function projection(p) {\n",
3631 |        "        var q = base(p), t = d3.zoomTransform(zoomNode);\n",
3632 |        "        return q ? [t.k*q[0]+t.x, t.k*q[1]+t.y] : null;\n",
3633 |        "    };\n",
3634 |        "    function project_center(v) { return (v ? projection([v.c, v.d]) : null) || [-9e6, -9e6] };\n",
3635 |        "    var path = d3.geoPath().projection(BrunelD3.geoStream(projection));\n",
3636 |        "    var scale_x = d3.scaleLinear(), scale_y = d3.scaleLinear();\n",
3637 |        "    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n",
3638 |        "    zoom.on('zoom', function(t, time) {\n",
3639 |        "        t = t || d3.event.transform;\n",
3640 |        "        zoomNode.__zoom = t;\n",
3641 |        "        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n",
3642 |        "        build(time || -1);\n",
3643 |        "    });\n",
3644 |        "\n",
3645 |        "    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n",
3646 |        "\n",
3647 |        "    elements[0] = function() {\n",
3648 |        "      var original, processed,                           // data sets passed in and then transformed\n",
3649 |        "        element, data,                                 // brunel element information and brunel data\n",
3650 |        "        selection, merged;                                      // d3 selection and merged selection\n",
3651 |        "      var elementGroup = interior.append('g').attr('class', 'element1'),\n",
3652 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
3653 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
3654 |        "\n",
3655 |        "      function makeData() {\n",
3656 |        "        original = datasets[0];\n",
3657 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
3658 |        "        processed = pre(original, 0);\n",
3659 |        "        processed = post(processed, 0);\n",
3660 |        "        var f0 = processed.field('#row'),\n",
3661 |        "          f1 = processed.field('#selection');\n",
3662 |        "        var keyFunc = function(d) { return f0.value(d) };\n",
3663 |        "        data = {\n",
3664 |        "          $row:         function(d) { return f0.value(d.row) },\n",
3665 |        "          $selection:   function(d) { return f1.value(d.row) },\n",
3666 |        "          $row_f:       function(d) { return f0.valueFormatted(d.row) },\n",
3667 |        "          $selection_f: function(d) { return f1.valueFormatted(d.row) },\n",
3668 |        "          _split:       function(d) { return 'ALL' },\n",
3669 |        "          _key:         keyFunc,\n",
3670 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
3671 |        "        };\n",
3672 |        "      }\n",
3673 |        "\n",
3674 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
3675 |        "\n",
3676 |        "      function build(transitionMillis) {\n",
3677 |        "        element = elements[0];\n",
3678 |        "        // Read in the feature data and call build again when done\n",
3679 |        "        var features = {\n",
3680 |        "          'https://brunelvis.org/geo/2.3/med/Japan.json': {\n",
3681 |        "          }\n",
3682 |        "        };\n",
3683 |        "        if (BrunelD3.addFeatures(data, features, null, null, this, transitionMillis)) return;\n",
3684 |        "        main.attr('class', 'diagram map');\n",
3685 |        "\n",
3686 |        "        // Define selection entry operations\n",
3687 |        "        function initialState(selection) {\n",
3688 |        "          selection\n",
3689 |        "            .attr('class', 'element polygon filled')\n",
3690 |        "            .classed('nondata', function(d) {return !d || d.row == null})\n",
3691 |        "            .style('pointer-events', 'none')\n",
3692 |        "        }\n",
3693 |        "\n",
3694 |        "        // Define selection update operations on merged data\n",
3695 |        "        function updateState(selection) {\n",
3696 |        "          selection\n",
3697 |        "            .attr('d', function(d) { return path(d).replace(/,L/g, 'L').replace(/,Z/g, 'Z') });\n",
3698 |        "        }\n",
3699 |        "        // Create selections, set the initial state and transition updates\n",
3700 |        "        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n",
3701 |        "        var added = selection.enter().append('path');\n",
3702 |        "        merged = selection.merge(added);\n",
3703 |        "        initialState(added);\n",
3704 |        "        selection.filter(BrunelD3.hasData)\n",
3705 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
3706 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
3707 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
3708 |        "\n",
3709 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
3710 |        "          .style('opacity', 0.5).each( function() {\n",
3711 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
3712 |        "        });\n",
3713 |        "      }\n",
3714 |        "\n",
3715 |        "      return {\n",
3716 |        "        data:           function() { return processed },\n",
3717 |        "        original:       function() { return original },\n",
3718 |        "        internal:       function() { return data },\n",
3719 |        "        selection:      function() { return merged },\n",
3720 |        "        makeData:       makeData,\n",
3721 |        "        build:          build,\n",
3722 |        "        chart:          function() { return charts[0] },\n",
3723 |        "        group:          function() { return elementGroup },\n",
3724 |        "        fields: {\n",
3725 |        "          key:          ['#row']\n",
3726 |        "        }\n",
3727 |        "      };\n",
3728 |        "    }();\n",
3729 |        "\n",
3730 |        "    // Define element #2 ///////////////////////////////////////////////////////////////////////////\n",
3731 |        "\n",
3732 |        "    elements[1] = function() {\n",
3733 |        "      var original, processed,                           // data sets passed in and then transformed\n",
3734 |        "        element, data,                                 // brunel element information and brunel data\n",
3735 |        "        selection, merged;                                      // d3 selection and merged selection\n",
3736 |        "      var elementGroup = interior.append('g').attr('class', 'element2'),\n",
3737 |        "        main = elementGroup.append('g').attr('class', 'main'),\n",
3738 |        "        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n",
3739 |        "\n",
3740 |        "      function makeData() {\n",
3741 |        "        original = datasets[1];\n",
3742 |        "        if (filterRows) original = original.retainRows(filterRows);\n",
3743 |        "        processed = pre(original, 1);\n",
3744 |        "        processed = post(processed, 1);\n",
3745 |        "        var f0 = processed.field('longitude'),\n",
3746 |        "          f1 = processed.field('latitude'),\n",
3747 |        "          f2 = processed.field('diff2017_days'),\n",
3748 |        "          f3 = processed.field('location_name'),\n",
3749 |        "          f4 = processed.field('label2017'),\n",
3750 |        "          f5 = processed.field('prediction2017'),\n",
3751 |        "          f6 = processed.field('#row'),\n",
3752 |        "          f7 = processed.field('#selection');\n",
3753 |        "        var keyFunc = function(d) { return f6.value(d) };\n",
3754 |        "        data = {\n",
3755 |        "          longitude:    function(d) { return f0.value(d.row) },\n",
3756 |        "          latitude:     function(d) { return f1.value(d.row) },\n",
3757 |        "          diff2017_days:function(d) { return f2.value(d.row) },\n",
3758 |        "          location_name:function(d) { return f3.value(d.row) },\n",
3759 |        "          label2017:    function(d) { return f4.value(d.row) },\n",
3760 |        "          prediction2017:function(d) { return f5.value(d.row) },\n",
3761 |        "          $row:         function(d) { return f6.value(d.row) },\n",
3762 |        "          $selection:   function(d) { return f7.value(d.row) },\n",
3763 |        "          longitude_f:  function(d) { return f0.valueFormatted(d.row) },\n",
3764 |        "          latitude_f:   function(d) { return f1.valueFormatted(d.row) },\n",
3765 |        "          diff2017_days_f:function(d) { return f2.valueFormatted(d.row) },\n",
3766 |        "          location_name_f:function(d) { return f3.valueFormatted(d.row) },\n",
3767 |        "          label2017_f:  function(d) { return f4.valueFormatted(d.row) },\n",
3768 |        "          prediction2017_f:function(d) { return f5.valueFormatted(d.row) },\n",
3769 |        "          $row_f:       function(d) { return f6.valueFormatted(d.row) },\n",
3770 |        "          $selection_f: function(d) { return f7.valueFormatted(d.row) },\n",
3771 |        "          _split:       function(d) { return f2.value(d.row) },\n",
3772 |        "          _key:         keyFunc,\n",
3773 |        "          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n",
3774 |        "        };\n",
3775 |        "      }\n",
3776 |        "      // Aesthetic Functions\n",
3777 |        "      var scale_color = d3.scaleLinear().domain([-4, -1.875, 0.25, 2.375, 4.5, 6.625, 8.75, 10.875, 13])\n",
3778 |        "        .interpolate(d3.interpolateHcl)\n",
3779 |        "        .range([ '#045a8d', '#2b8cbe', '#74a9cf', '#bdc9e1', '#f8efe8', '#fef0d9', \n",
3780 |        "          '#fdcc8a', '#fc8d59', '#e34a33']);\n",
3781 |        "      var color = function(d) { return scale_color(data.diff2017_days(d)) };\n",
3782 |        "      legends._legend = legends._legend || { title: ['Diff 2017 Days'], \n",
3783 |        "        ticks: [15, 10, 5, 0, -5]};\n",
3784 |        "      legends._legend.color = scale_color;\n",
3785 |        "\n",
3786 |        "      // Build element from data ///////////////////////////////////////////////////////////////////\n",
3787 |        "\n",
3788 |        "      function build(transitionMillis) {\n",
3789 |        "        element = elements[1];\n",
3790 |        "        var w = geom.default_point_size;\n",
3791 |        "        var x = function(d) { return projection([data.longitude(d),data.latitude(d)])[0]};\n",
3792 |        "        var h = geom.default_point_size;\n",
3793 |        "        var y = function(d) { return projection([data.longitude(d),data.latitude(d)])[1]};\n",
3794 |        "        var labeling  = [{\n",
3795 |        "          index: 0, method: 'box', location: ['right', 'center'], inside: false, align: 'start', pad: 3, dy: 0.3,\n",
3796 |        "          fit: false, granularity: 2,\n",
3797 |        "          content: function(d) {\n",
3798 |        "            return d.row == null ? null : data.diff2017_days_f(d)\n",
3799 |        "          }\n",
3800 |        "        }];\n",
3801 |        "\n",
3802 |        "        // Define selection entry operations\n",
3803 |        "        function initialState(selection) {\n",
3804 |        "          selection\n",
3805 |        "            .attr('class', 'element point filled')\n",
3806 |        "        }\n",
3807 |        "\n",
3808 |        "        // Define selection update operations on merged data\n",
3809 |        "        function updateState(selection) {\n",
3810 |        "          selection\n",
3811 |        "            .attr('cx',function(d) { return projection([data.longitude(d),data.latitude(d)])[0]})\n",
3812 |        "            .attr('cy',function(d) { return projection([data.longitude(d),data.latitude(d)])[1]})\n",
3813 |        "            .attr('r',geom.default_point_size / 2)\n",
3814 |        "            .filter(BrunelD3.hasData)                     // following only performed for data items\n",
3815 |        "            .style('fill', color);\n",
3816 |        "        }\n",
3817 |        "\n",
3818 |        "        // Define labeling for the selection\n",
3819 |        "        function label(selection, transitionMillis) {\n",
3820 |        "          BrunelD3.label(selection, labels, transitionMillis, geom, labeling);\n",
3821 |        "\n",
3822 |        "          var tooltipLabeling  = {\n",
3823 |        "            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n",
3824 |        "            fit: true, granularity: 0,\n",
3825 |        "            content: function(d) {\n",
3826 |        "              return d.row == null ? null : '<span class=\"title\">Location Name: </span>'\n",
3827 |        "\t\t\t+ '<span class=\"field\">' + data.location_name_f(d) + '</span>'\n",
3828 |        "\t\t\t+ '<br/>'\n",
3829 |        "\t\t\t+ '<span class=\"title\">Label 2017: </span>'\n",
3830 |        "\t\t\t+ '<span class=\"field\">' + data.label2017_f(d) + '</span>'\n",
3831 |        "\t\t\t+ '<br/>'\n",
3832 |        "\t\t\t+ '<span class=\"title\">Prediction 2017: </span>'\n",
3833 |        "\t\t\t+ '<span class=\"field\">' + data.prediction2017_f(d) + '</span>'\n",
3834 |        "            }\n",
3835 |        "          };\n",
3836 |        "          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n",
3837 |        "        }\n",
3838 |        "        // Create selections, set the initial state and transition updates\n",
3839 |        "        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n",
3840 |        "        var added = selection.enter().append('circle');\n",
3841 |        "        merged = selection.merge(added);\n",
3842 |        "        initialState(added);\n",
3843 |        "        selection.filter(BrunelD3.hasData)\n",
3844 |        "          .classed('selected', BrunelD3.isSelected(data))\n",
3845 |        "          .filter(BrunelD3.isSelected(data)).raise();\n",
3846 |        "        updateState(BrunelD3.transition(merged, transitionMillis));\n",
3847 |        "        label(merged, transitionMillis);\n",
3848 |        "\n",
3849 |        "        BrunelD3.transition(selection.exit(), transitionMillis/3)\n",
3850 |        "          .style('opacity', 0.5).each( function() {\n",
3851 |        "            this.remove(); BrunelD3.removeLabels(this); \n",
3852 |        "        });\n",
3853 |        "      }\n",
3854 |        "\n",
3855 |        "      return {\n",
3856 |        "        data:           function() { return processed },\n",
3857 |        "        original:       function() { return original },\n",
3858 |        "        internal:       function() { return data },\n",
3859 |        "        selection:      function() { return merged },\n",
3860 |        "        makeData:       makeData,\n",
3861 |        "        build:          build,\n",
3862 |        "        chart:          function() { return charts[0] },\n",
3863 |        "        group:          function() { return elementGroup },\n",
3864 |        "        fields: {\n",
3865 |        "          x:            ['longitude'],\n",
3866 |        "          y:            ['latitude'],\n",
3867 |        "          key:          ['#row'],\n",
3868 |        "          color:        ['diff2017_days']\n",
3869 |        "        }\n",
3870 |        "      };\n",
3871 |        "    }();\n",
3872 |        "\n",
3873 |        "    function build(time, noData) {\n",
3874 |        "      var first = elements[0].data() == null;\n",
3875 |        "      if (first) time = 0;                                           // no transition for first call\n",
3876 |        "      if ((first || time > -1) && !noData) {\n",
3877 |        "        elements[0].makeData();\n",
3878 |        "        elements[1].makeData();\n",
3879 |        "        BrunelD3.addLegend(legends, legends._legend);\n",
3880 |        "      }\n",
3881 |        "      elements[0].build(time);\n",
3882 |        "      elements[1].build(time);\n",
3883 |        "    }\n",
3884 |        "\n",
3885 |        "    // Expose the following components of the chart\n",
3886 |        "    return {\n",
3887 |        "      elements : elements,\n",
3888 |        "      interior : interior,\n",
3889 |        "      zoom: function(params, time) {\n",
3890 |        "          if (params) zoom.on('zoom').call(zoomNode, params, time);\n",
3891 |        "          return d3.zoomTransform(zoomNode);\n",
3892 |        "      },\n",
3893 |        "      build : build\n",
3894 |        "    };\n",
3895 |        "    }();\n",
3896 |        "\n",
3897 |        "  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n",
3898 |        "  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n",
3899 |        "  function buildAll() {\n",
3900 |        "    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n",
3901 |        "    updateAll(transitionTime);\n",
3902 |        "  }\n",
3903 |        "\n",
3904 |        "  return {\n",
3905 |        "    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n",
3906 |        "    dataPostProcess:    function(f) { if (f) post = f; return post },\n",
3907 |        "    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n",
3908 |        "    visId:              visId,\n",
3909 |        "    build:              buildAll,\n",
3910 |        "    rebuild:            updateAll,\n",
3911 |        "    charts:             charts\n",
3912 |        "  }\n",
3913 |        "}\n",
3914 |        "\n",
3915 |        "// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n",
3916 |        "\n",
3917 |        "var table1 = {\n",
3918 |        "   summarized: false,\n",
3919 |        "   names: ['_dummy_'], \n",
3920 |        "   options: ['numeric'], \n",
3921 |        "   rows: []\n",
3922 |        "};\n",
3923 |        "var table2 = {\n",
3924 |        "   summarized: false,\n",
3925 |        "   names: ['longitude', 'latitude', 'diff2017_days', 'location_name', 'label2017', 'prediction2017'], \n",
3926 |        "   options: ['numeric', 'numeric', 'numeric', 'string', 'date-YearMonthDay', 'date-YearMonthDay'], \n",
3927 |        "   rows: [[130.925, 33.9483, 3, '下関', '2017-03-26', '2017-03-23'],\n",
3928 |        "  [135.7317, 35.015, 0, '京都', '2017-03-28', '2017-03-28'],\n",
3929 |        "  [140.8967, 38.2617, -2, '仙台', '2017-04-04', '2017-04-06'],\n",
3930 |        "  [130.305, 33.265, 5, '佐賀', '2017-03-27', '2017-03-22'],\n",
3931 |        "  [140.7533, 41.8167, 3, '函館', '2017-04-24', '2017-04-21'],\n",
3932 |        "  [139.06, 36.405, 0, '前橋', '2017-03-30', '2017-03-30'],\n",
3933 |        "  [136.965, 35.1667, 0, '名古屋', '2017-03-25', '2017-03-25'],\n",
3934 |        "  [135.1633, 34.2283, 2, '和歌山', '2017-03-27', '2017-03-25'],\n",
3935 |        "  [131.6183, 33.235, 8, '大分', '2017-04-01', '2017-03-24'],\n",
3936 |        "  [135.5183, 34.6817, 3, '大阪', '2017-03-27', '2017-03-24'],\n",
3937 |        "  [135.8267, 34.6933, 1, '奈良', '2017-03-31', '2017-03-30'],\n",
3938 |        "  [139.8683, 36.5483, -1, '宇都宮', '2017-03-31', '2017-04-01'],\n",
3939 |        "  [140.975, 42.3117, 0, '室蘭', '2017-04-29', '2017-04-29'],\n",
3940 |        "  [131.4133, 31.9383, 9, '宮崎', '2017-03-30', '2017-03-21'],\n",
3941 |        "  [137.2017, 36.7083, 0, '富山', '2017-04-02', '2017-04-02'],\n",
3942 |        "  [140.345, 38.255, 1, '山形', '2017-04-11', '2017-04-10'],\n",
3943 |        "  [136.7617, 35.4, -1, '岐阜', '2017-03-25', '2017-03-26'],\n",
3944 |        "  [133.9167, 34.66, 2, '岡山', '2017-03-29', '2017-03-27'],\n",
3945 |        "  [143.2117, 42.9217, -2, '帯広', '2017-04-28', '2017-04-30'],\n",
3946 |        "  [132.4617, 34.3983, 0, '広島', '2017-03-24', '2017-03-24'],\n",
3947 |        "  [136.2433, 35.275, 1, '彦根', '2017-04-02', '2017-04-01'],\n",
3948 |        "  [134.5733, 34.0667, 7, '徳島', '2017-03-31', '2017-03-24'],\n",
3949 |        "  [139.0467, 37.9117, 0, '新潟', '2017-04-05', '2017-04-05'],\n",
3950 |        "  [142.3717, 43.7567, -3, '旭川', '2017-04-30', '2017-05-03'],\n",
3951 |        "  [141.3283, 43.06, -1, '札幌', '2017-04-25', '2017-04-26'],\n",
3952 |        "  [139.76, 35.69, -4, '東京', '2017-03-18', '2017-03-22'],\n",
3953 |        "  [132.7767, 33.8433, 4, '松山', '2017-03-27', '2017-03-23'],\n",
3954 |        "  [133.065, 35.4567, 0, '松江', '2017-03-29', '2017-03-29'],\n",
3955 |        "  [139.6517, 35.4383, -1, '横浜', '2017-03-22', '2017-03-23'],\n",
3956 |        "  [140.4667, 36.38, -1, '水戸', '2017-03-31', '2017-04-01'],\n",
3957 |        "  [136.5183, 34.7333, 3, '津', '2017-03-31', '2017-03-28'],\n",
3958 |        "  [130.7067, 32.8133, 8, '熊本', '2017-03-29', '2017-03-21'],\n",
3959 |        "  [139.38, 36.15, 0, '熊谷', '2017-03-25', '2017-03-25'],\n",
3960 |        "  [138.5533, 35.6667, 2, '甲府', '2017-03-27', '2017-03-25'],\n",
3961 |        "  [141.165, 39.6983, -3, '盛岡', '2017-04-14', '2017-04-17'],\n",
3962 |        "  [135.2117, 34.6967, 6, '神戸', '2017-03-31', '2017-03-25'],\n",
3963 |        "  [136.2217, 36.055, 1, '福井', '2017-04-02', '2017-04-01'],\n",
3964 |        "  [130.375, 33.5817, 1, '福岡', '2017-03-22', '2017-03-21'],\n",
3965 |        "  [140.47, 37.7583, 0, '福島', '2017-04-05', '2017-04-05'],\n",
3966 |        "  [140.0983, 39.7167, 3, '秋田', '2017-04-13', '2017-04-10'],\n",
3967 |        "  [141.6783, 45.415, 0, '稚内', '2017-05-06', '2017-05-06'],\n",
3968 |        "  [144.2783, 44.0167, -3, '網走', '2017-05-02', '2017-05-05'],\n",
3969 |        "  [136.6333, 36.5883, 1, '金沢', '2017-04-01', '2017-03-31'],\n",
3970 |        "  [144.3767, 42.985, 0, '釧路', '2017-05-11', '2017-05-11'],\n",
3971 |        "  [140.8567, 35.7383, 3, '銚子', '2017-03-29', '2017-03-26'],\n",
3972 |        "  [129.8667, 32.7333, 5, '長崎', '2017-03-27', '2017-03-22'],\n",
3973 |        "  [138.1917, 36.6617, 2, '長野', '2017-04-11', '2017-04-09'],\n",
3974 |        "  [140.7683, 40.8217, -2, '青森', '2017-04-14', '2017-04-16'],\n",
3975 |        "  [138.4033, 34.975, 7, '静岡', '2017-03-30', '2017-03-23'],\n",
3976 |        "  [134.0533, 34.3167, 5, '高松', '2017-03-30', '2017-03-25'],\n",
3977 |        "  [133.5483, 33.5667, 5, '高知', '2017-03-26', '2017-03-21'],\n",
3978 |        "  [134.2383, 35.4867, 0, '鳥取', '2017-03-30', '2017-03-30'],\n",
3979 |        "  [130.5467, 31.5533, 13, '鹿児島', '2017-04-02', '2017-03-20']]\n",
3980 |        "};\n",
3981 |        "\n",
3982 |        "// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n",
3983 |        "\n",
3984 |        "var v  = new BrunelVis('visidc1cfbad4-6981-11e8-ae66-3a5f3782500e');\n",
3985 |        "v.build(table1, table2);\n",
3986 |        "\n",
3987 |        "    });\n",
3988 |        "});"
3989 |       ],
3990 |       "text/plain": [
3991 |        "<IPython.core.display.Javascript object>"
3992 |       ]
3993 |      },
3994 |      "execution_count": 17,
3995 |      "metadata": {},
3996 |      "output_type": "execute_result"
3997 |     }
3998 |    ],
3999 |    "source": [
4000 |     "# セル4-6. 地図上での誤差の表示\n",
4001 |     "\n",
4002 |     "# 予測の誤差と観測所の位置情報を結合する\n",
4003 |     "prediction_summary = pd.concat([prediction_summary, df_station], axis=1)\n",
4004 |     "prediction_summary.head()\n",
4005 |     "\n",
4006 |     "# 地図上に予測の誤差を表示する\n",
4007 |     "%brunel map('japan') + data('prediction_summary') x(longitude) y(latitude) \\\n",
4008 |     "tooltip(location_name,label2017,prediction2017) color(diff2017_days) \\\n",
4009 |     "label(diff2017_days) ::width=1200, height=600 "
4010 |    ]
4011 |   },
4012 |   {
4013 |    "cell_type": "code",
4014 |    "execution_count": null,
4015 |    "metadata": {},
4016 |    "outputs": [],
4017 |    "source": []
4018 |   }
4019 |  ],
4020 |  "metadata": {
4021 |   "kernelspec": {
4022 |    "display_name": "Python 3.5",
4023 |    "language": "python",
4024 |    "name": "python3"
4025 |   },
4026 |   "language_info": {
4027 |    "codemirror_mode": {
4028 |     "name": "ipython",
4029 |     "version": 3
4030 |    },
4031 |    "file_extension": ".py",
4032 |    "mimetype": "text/x-python",
4033 |    "name": "python",
4034 |    "nbconvert_exporter": "python",
4035 |    "pygments_lexer": "ipython3",
4036 |    "version": "3.5.4"
4037 |   }
4038 |  },
4039 |  "nbformat": 4,
4040 |  "nbformat_minor": 1
4041 | }
4042 | 


--------------------------------------------------------------------------------