Growing Degree Days .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Growing Degree Days\n",
    "This program is for calculating growing degree days in a green house tomato. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymysql\n",
    "pymysql.install_as_MySQLdb()\n",
    "import sys\n",
    "import datetime as dt\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bkcharts import * \n",
    "from bokeh.palettes import PuBu\n",
    "\n",
    "from bokeh.models.widgets import Tabs, Panel\n",
    "import pandas, datetime\n",
    "from bokeh.models import HoverTool,Range1d,ColumnDataSource,HoverTool\n",
    "from bokeh.layouts import gridplot\n",
    "from time import gmtime,strftime\n",
    "from calendar import timegm\n",
    "from bokeh.layouts import row, column\n",
    "from bokeh.embed import components\n",
    "from time import gmtime,strftime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Connection to MySQL Database to get the data\n",
    "#conn = pymysql.connect(host = 'localhost', port= 8889, user ='root', passwd = 'root', db = 'test_DB')\n",
    "conn = pymysql.connect(host = '', port= 3306, user ='', passwd = '', db = '')\n",
    "\n",
    "cursor1 = conn.cursor()\n",
    "cursor2 = conn.cursor()\n",
    "\n",
    "cursor1.execute('SELECT sdate,ROUND((max_temp+min_temp)/2,1), sensor FROM daily_temp ORDER BY sdate ASC')\n",
    "#cursor1.execute('SELECT sdate,ROUND((max_temp+min_temp)/2-10,1), sensor FROM daily_temp ORDER BY sdate ASC')\n",
    "cursor2.execute('SELECT DISTINCT block,vid,planting, cutting FROM toguchi_plant WHERE  planting IN (SELECT Planting FROM toguchi_plant)')\n",
    "result1 = cursor1.fetchall()\n",
    "result2 = cursor2.fetchall()\n",
    "\n",
    "df1 = pd.DataFrame( [[ij for ij in i] for i in result1] )\n",
    "df2 = pd.DataFrame( [[ij for ij in i] for i in result2] )\n",
    "\n",
    "df1.rename(columns={0: 'Date', 1:'Mean_T', 2:'sensor'}, inplace=True);\n",
    "df2.rename(columns={0: 'block', 1: 'vid', 2: 'startHarv', 3: 'endHarv'}, inplace=True);\n",
    "if cursor1 and conn:\n",
    "    cursor1.close()\n",
    "if cursor2 and conn:\n",
    "    cursor2.close()\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean_T</th>\n",
       "      <th>sensor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>27.5</td>\n",
       "      <td>02:00:00:28:8c:e6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>23.7</td>\n",
       "      <td>02:00:00:28:8c:e6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>23.4</td>\n",
       "      <td>02:00:00:28:8c:e6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-04</td>\n",
       "      <td>24.0</td>\n",
       "      <td>02:00:00:28:8c:e6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>24.1</td>\n",
       "      <td>02:00:00:28:8c:e6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Mean_T             sensor\n",
       "0  2017-06-01    27.5  02:00:00:28:8c:e6\n",
       "1  2017-06-02    23.7  02:00:00:28:8c:e6\n",
       "2  2017-06-03    23.4  02:00:00:28:8c:e6\n",
       "3  2017-06-04    24.0  02:00:00:28:8c:e6\n",
       "4  2017-06-05    24.1  02:00:00:28:8c:e6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>block</th>\n",
       "      <th>vid</th>\n",
       "      <th>startHarv</th>\n",
       "      <th>endHarv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>v1</td>\n",
       "      <td>2017-06-11</td>\n",
       "      <td>2017-08-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>v1</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>2016-08-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>v1</td>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>2016-09-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>v1</td>\n",
       "      <td>2016-06-21</td>\n",
       "      <td>2016-10-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>v1</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>2016-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  block vid   startHarv     endHarv\n",
       "0     A  v1  2017-06-11  2017-08-02\n",
       "1     F  v1  2016-04-20  2016-08-20\n",
       "2     E  v1  2016-05-22  2016-09-12\n",
       "3     D  v1  2016-06-21  2016-10-12\n",
       "4     C  v1  2016-07-22  2016-12-04"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2_A = df2.query('block == \"A\" & vid == \"v1\"')\n",
    "df1_A = df1.query('sensor == \"02:00:00:28:8c:e6\"')\n",
    "\n",
    "df2_B = df2.query('block == \"B\" & vid == \"v1\"')\n",
    "df1_B = df1.query('sensor == \"70:ee:50:27:2e:da\"')\n",
    "\n",
    "df2_C = df2.query('block == \"C\" & vid == \"v1\"')\n",
    "df1_C= df1.query('sensor == \"03:00:00:05:05:30\"')\n",
    "\n",
    "df2_D = df2.query('block == \"D\" & vid == \"v1\"')\n",
    "df1_D = df1.query('sensor == \"03:00:00:02:c5:f8\"')\n",
    "\n",
    "df2_E = df2.query('block == \"E\" & vid == \"v1\"')\n",
    "df1_E = df1.query('sensor == \"02:00:00:27:39:82\"')\n",
    "\n",
    "df2_F = df2.query('block == \"F\" & vid == \"v1\"')\n",
    "df1_F = df1.query('sensor == \"03:00:00:04:f2:00\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>block</th>\n",
       "      <th>vid</th>\n",
       "      <th>startHarv</th>\n",
       "      <th>endHarv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>v1</td>\n",
       "      <td>2017-06-11</td>\n",
       "      <td>2017-08-02</td>\n",
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      "text/plain": [
       "  block vid   startHarv     endHarv\n",
       "0     A  v1  2017-06-11  2017-08-02"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_A.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Drop\n",
    "df = df1_A.drop('sensor',1)\n",
    "clean_df1_B = df1_B.drop('sensor',1)\n",
    "clean_df1_C = df1_C.drop('sensor',1)\n",
    "clean_df1_D = df1_D.drop('sensor',1)\n",
    "clean_df1_E = df1_E.drop('sensor',1)\n",
    "clean_df1_F = df1_F.drop('sensor',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_df2_A = df2_A.drop(['block','vid'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>startHarv</th>\n",
       "      <th>endHarv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-11</td>\n",
       "      <td>2017-08-02</td>\n",
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      ],
      "text/plain": [
       "    startHarv     endHarv\n",
       "0  2017-06-11  2017-08-02"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clean_df2_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "startHarv_A= clean_df2_A['startHarv'].iloc[0]\n",
    "endHarv = dt.date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_range = pd.date_range(startHarv_A, endHarv)\n",
    "df.set_index(['Date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_intepl = df.sort_index().reindex(full_range).interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def HDH (Tcurr,Tref=10.0):\n",
    "    if Tref <= Tcurr:\n",
    "        return ((Tcurr-Tref))\n",
    "    else:\n",
    "        return (0)\n",
    "\n",
    "df_intepl['GDD'] = df_intepl.apply(lambda row: HDH(row['Mean_T']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_intepl = df_intepl.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_intepl['Cum_GDD'] = df_intepl.GDD.cumsum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"18b17e9f-6636-40a8-b96c-b212e7491729\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"43b373a5-e21a-4d46-bfec-d931d71208d5\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"43b373a5-e21a-4d46-bfec-d931d71208d5\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"43b373a5-e21a-4d46-bfec-d931d71208d5\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '43b373a5-e21a-4d46-bfec-d931d71208d5' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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        <div style=\\\"background: #FFFFFF;\\\">\\n            <span style=\\\"font-size: 18px; color: blue;\\\">CGDD, @Cum_GDD{int}</span><br />\\n        </div>\\n    \"},\"id\":\"6e08ca4a-e439-4098-bdfb-0cc0cd7a8ef8\",\"type\":\"HoverTool\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"9b37509b-94e3-4cc0-8be8-80b3a3e2c92d\",\"type\":\"AdaptiveTicker\"}],\"root_ids\":[\"a3224b7a-7f08-4a9d-b551-f8d940a32390\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.6\"}};\n",
       "            var render_items = [{\"docid\":\"afbf68dd-2313-4776-8cc2-93cc68f96047\",\"elementid\":\"43b373a5-e21a-4d46-bfec-d931d71208d5\",\"modelid\":\"a3224b7a-7f08-4a9d-b551-f8d940a32390\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"43b373a5-e21a-4d46-bfec-d931d71208d5\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outputs in Jupyter    \n",
    "output_notebook()\n",
    "#Create the source data with formats for date\n",
    "Date1 = df.index\n",
    "GDD = df.GDD\n",
    "Cum_GDD = df.Cum_GDD\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        GDD = GDD,\n",
    "        Cum_GDD = Cum_GDD,\n",
    "        Date1 = pd.to_datetime(df['index'], format='%Y-%m-%d'),\n",
    "    )\n",
    ")\n",
    "#Hover with specfied tooltips\n",
    "hover4 = HoverTool(\n",
    "    tooltips=\"\"\"\n",
    "         <div style=\"background: #FFFFFF;\">\n",
    "            <span style=\"font-size: 18px; color: blue;\">CGDD, @Cum_GDD{int}</span><br />\n",
    "        </div>\n",
    "    \"\"\")\n",
    "\n",
    "# Create figures\n",
    "p1 = figure(x_axis_label='日付', x_axis_type='datetime',\n",
    "            y_axis_label='積算温度',\n",
    "            background_fill_color='#BFE6F6',\n",
    "            tools=[hover4, 'xpan','reset','previewsave'],\n",
    "            plot_width=1000,\n",
    "            logo=None)\n",
    "p1.line(x='Date1', y='Cum_GDD',source=source,line_width=1,line_color=\"blue\")\n",
    "p1.circle('Date1','Cum_GDD',source=source)\n",
    "p1.xaxis.axis_label_text_font_size = \"20pt\"\n",
    "p1.yaxis.axis_label_text_font_size = \"20pt\"\n",
    "\n",
    "#Create\n",
    "tab1 = Panel(child=p1, title='積算温度')\n",
    "\n",
    "# Using Returned TAB create layout\n",
    "layout = Tabs(tabs=[tab1])\n",
    "output_file('toguchi_CGDD.html')\n",
    "show(layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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