Yield Estimation using statsmodel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "\n",
    "# YIELD ESTIMATION MODEL \n",
    "The yield estimation was done in a greenhouse Tomato farm.<br>\n",
    "using temperature data from the sensors that were installed in the farm<br>\n",
    "The greenhouse has six blocks with a rotational crop calender through out the year<br>\n",
    "\n",
    "## Using Different Model\n",
    "To estimate the Yield based on cumulative growing degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of Data set\n",
    "The farm is divided into Block(A,B,C,D,E,F) in the following "
    "The [Data ](https://github.com/DenisDPR/Sensor-Network-Project/blob/master/toguchi_harv%20.csv)"
    
   ]
  },
  {
   "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 bkcharts import * \n",
    "from bokeh.models.widgets import Tabs, Panel\n",
    "import pandas, datetime\n",
    "from time import gmtime,strftime\n",
    "from calendar import timegm\n",
    "from bokeh.layouts import row, column, gridplot\n",
    "from bokeh.models import Range1d\n",
    "from bokeh.models import ColumnDataSource,HoverTool\n",
    "from tabulate import tabulate\n",
    "\n",
    "import statsmodels.formula.api as smf\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 = 'my DB IP', port= 3306, user ='user', passwd = 'my-password', db = 'DB_NAME')\n",
    "cursor = conn.cursor()\n",
    "cursor.execute('SELECT sdate,block,vid,A_harv,B_harv,loss FROM toguchi_harv order by sdate DESC')\n",
    "result = cursor.fetchall()\n",
    "df = pd.DataFrame( [[ij for ij in i] for i in result] )\n",
    "df.rename(columns={0: 'date', 1: 'block', 2: 'vid', 3: 'A', 4: 'B', 5: 'Loss'}, inplace=True);\n",
    "if cursor and conn:\n",
    "    cursor.close()\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sum of A,B and Loss\n",
    "df['Total'] = df[[\"A\",\"B\",\"Loss\"]].apply(lambda row: np.sum(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Date column to datetime format\n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#QUERY To select data BLOCKWISE\n",
    "df_A = df.query('block == \"A\"')\n",
    "df_B = df.query('block == \"B\"')\n",
    "df_C = df.query('block == \"C\"')\n",
    "df_D = df.query('block == \"D\"')\n",
    "df_E = df.query('block == \"E\"')\n",
    "df_F = df.query('block == \"F\"')\n",
    "\n",
    "# QUERY to get Specific Varieties\n",
    "\n",
    "df_A_v1 = df_A.query('vid == \"v1\"')\n",
    "df_A_v2 = df_A.query('vid == \"v2\"')\n",
    "df_A_v3 = df_A.query('vid == \"v3\"')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop irrevant columns\n",
    "df_Av1 = df_A_v1.drop(['block','vid','A','B','Loss'], 1)\n",
    "df_Av2 = df_A_v2.drop(['block','vid','A','B','Loss'], 1)\n",
    "df_Av3 = df_A_v3.drop(['block','vid','A','B','Loss'], 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sort the values , Without sorting, the date index are in increasing order and may not match in merging \n",
    "df_Av1 = df_Av1.sort_values([\"date\", \"Total\"])\n",
    "df_Av2 = df_Av2.sort_values([\"date\", \"Total\"])\n",
    "df_Av3 = df_Av3.sort_values([\"date\", \"Total\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>79.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  Total\n",
       "805 2016-05-04    2.0\n",
       "804 2016-05-09   14.6\n",
       "803 2016-05-11   21.7\n",
       "802 2016-05-13   56.4\n",
       "801 2016-05-16   79.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Av1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the Data with the Cumulative GDD (CGDD) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfA_GDD = pd.read_csv('dfA_GDD.csv')\n",
    "\n",
    "dfA_GDD = dfA_GDD.drop(['Mean_T','GDD'], 1)\n",
    "# Renaming the Data set column\n",
    "new_names = ['date', 'Cum_GDD'] \n",
    "old_names = ['index', 'Cum_GDD'] \n",
    "dfA_GDD.rename(columns=dict(zip(old_names, new_names)), inplace=True)\n",
    "\n",
    "dfA_GDD = dfA_GDD.set_index(['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cum_GDD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-11</th>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-13</th>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>53.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cum_GDD\n",
       "date               \n",
       "2017-06-11     12.5\n",
       "2017-06-12     25.8\n",
       "2017-06-13     39.1\n",
       "2017-06-14     53.9\n",
       "2017-06-15     69.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the created indexed data\n",
    "dfA_GDD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to combine dataset\n",
    "def combine_two_df(df1, df2):\n",
    "    '''\n",
    "    This will return the result of combined dataset \n",
    "    based on column of 'Date'.\n",
    "    '''\n",
    "    return df1.merge(df2, how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set index that is used in merging \n",
    "df_Av1 = df_Av1.set_index(['date'])\n",
    "df_Av2 = df_Av2.set_index(['date'])\n",
    "df_Av3 = df_Av3.set_index(['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_dfv1 = combine_two_df(dfA_GDD,df_Av1)\n",
    "combined_dfv2 = combine_two_df(dfA_GDD,df_Av2)\n",
    "combined_dfv3 = combine_two_df(dfA_GDD,df_Av3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Calculate the Cumulative Harvest\n",
    "def full_df(df22):\n",
    "    df22 = df22.reset_index(drop=False)\n",
    "    df22['Cum_Yield'] = df22.Total.cumsum()\n",
    "    df = df22\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CALCULATE Cumulative YIELD \n",
    "combined_df_cumYield_v1 = full_df(combined_dfv1)\n",
    "combined_df_cumYield_v2 = full_df(combined_dfv2)\n",
    "combined_df_cumYield_v3 = full_df(combined_dfv3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop irrevant data column\n",
    "dfA_v1_GDD_clean11 = combined_df_cumYield_v1.drop('Total', 1)\n",
    "dfA_v2_GDD_clean22 = combined_df_cumYield_v2.drop('Total', 1)\n",
    "dfA_v3_GDD_clean33 = combined_df_cumYield_v3.drop('Total', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfA_v1_GDD_clean1 = dfA_v1_GDD_clean11.dropna()\n",
    "dfA_v2_GDD_clean2 = dfA_v2_GDD_clean22.dropna()\n",
    "dfA_v3_GDD_clean3 = dfA_v3_GDD_clean33.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Cum_GDD</th>\n",
       "      <th>Cum_Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>129.6</td>\n",
       "      <td>370.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>162.3</td>\n",
       "      <td>373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>196.7</td>\n",
       "      <td>383.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>244.6</td>\n",
       "      <td>416.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>279.6</td>\n",
       "      <td>430.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  Cum_GDD  Cum_Yield\n",
       "81 2017-06-19    129.6      370.9\n",
       "83 2017-06-21    162.3      373.0\n",
       "85 2017-06-23    196.7      383.6\n",
       "88 2017-06-26    244.6      416.5\n",
       "90 2017-06-28    279.6      430.6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA_v2_GDD_clean2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Let us stat Linear Regression Using  statsmodels.model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data set used is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Cum_GDD</th>\n",
       "      <th>Cum_Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>129.6</td>\n",
       "      <td>370.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>162.3</td>\n",
       "      <td>373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>196.7</td>\n",
       "      <td>383.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>244.6</td>\n",
       "      <td>416.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>279.6</td>\n",
       "      <td>430.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  Cum_GDD  Cum_Yield\n",
       "81 2017-06-19    129.6      370.9\n",
       "83 2017-06-21    162.3      373.0\n",
       "85 2017-06-23    196.7      383.6\n",
       "88 2017-06-26    244.6      416.5\n",
       "90 2017-06-28    279.6      430.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA_v2_GDD_clean2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Cum_GDD</th>\n",
       "      <th>Cum_Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>129.6</td>\n",
       "      <td>322.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>162.3</td>\n",
       "      <td>325.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>196.7</td>\n",
       "      <td>339.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>244.6</td>\n",
       "      <td>350.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2017-06-28</td>\n",
       "      <td>279.6</td>\n",
       "      <td>353.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  Cum_GDD  Cum_Yield\n",
       "80 2017-06-19    129.6      322.8\n",
       "82 2017-06-21    162.3      325.9\n",
       "84 2017-06-23    196.7      339.5\n",
       "87 2017-06-26    244.6      350.7\n",
       "89 2017-06-28    279.6      353.8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA_v3_GDD_clean3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Statsmodels.model\n",
    "Using statsmodel.model Linear Regression analysis is done <br>\n",
    "and the data is plotted using Bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              Cum_Yield   R-squared:                       0.979\n",
      "Model:                            OLS   Adj. R-squared:                  0.978\n",
      "Method:                 Least Squares   F-statistic:                     811.0\n",
      "Date:                Tue, 26 Sep 2017   Prob (F-statistic):           8.71e-16\n",
      "Time:                        13:01:10   Log-Likelihood:                -70.206\n",
      "No. Observations:                  19   AIC:                             144.4\n",
      "Df Residuals:                      17   BIC:                             146.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    347.4899      5.373     64.668      0.000     336.153     358.827\n",
      "Cum_GDD        0.2639      0.009     28.478      0.000       0.244       0.283\n",
      "==============================================================================\n",
      "Omnibus:                        2.785   Durbin-Watson:                   0.604\n",
      "Prob(Omnibus):                  0.248   Jarque-Bera (JB):                2.128\n",
      "Skew:                          -0.688   Prob(JB):                        0.345\n",
      "Kurtosis:                       2.109   Cond. No.                     1.32e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.32e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Intercept    347.489911\n",
      "Cum_GDD        0.263874\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/denisrubanga/anaconda/lib/python2.7/site-packages/scipy/stats/stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"a8bf0dc2-825c-48d8-9f09-20a25b73c663\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\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() + 5000;\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(\"a8bf0dc2-825c-48d8-9f09-20a25b73c663\");\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(\"a8bf0dc2-825c-48d8-9f09-20a25b73c663\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a8bf0dc2-825c-48d8-9f09-20a25b73c663' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"a8bf0dc2-825c-48d8-9f09-20a25b73c663\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\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(\"a8bf0dc2-825c-48d8-9f09-20a25b73c663\")).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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              Cum_Yield   R-squared:                       0.974\n",
      "Model:                            OLS   Adj. R-squared:                  0.972\n",
      "Method:                 Least Squares   F-statistic:                     625.6\n",
      "Date:                Tue, 26 Sep 2017   Prob (F-statistic):           7.53e-15\n",
      "Time:                        13:01:10   Log-Likelihood:                -59.574\n",
      "No. Observations:                  19   AIC:                             123.1\n",
      "Df Residuals:                      17   BIC:                             125.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    312.7697      3.162     98.909      0.000     306.098     319.441\n",
      "Cum_GDD        0.1348      0.005     25.012      0.000       0.123       0.146\n",
      "==============================================================================\n",
      "Omnibus:                        1.289   Durbin-Watson:                   0.631\n",
      "Prob(Omnibus):                  0.525   Jarque-Bera (JB):                1.081\n",
      "Skew:                          -0.399   Prob(JB):                        0.583\n",
      "Kurtosis:                       2.146   Cond. No.                     1.37e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.37e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Intercept    312.769695\n",
      "Cum_GDD        0.134816\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"b7d631c2-a16f-43ed-865e-2a31e89ffc04\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\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() + 5000;\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(\"b7d631c2-a16f-43ed-865e-2a31e89ffc04\");\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(\"b7d631c2-a16f-43ed-865e-2a31e89ffc04\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b7d631c2-a16f-43ed-865e-2a31e89ffc04' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"b7d631c2-a16f-43ed-865e-2a31e89ffc04\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\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(\"b7d631c2-a16f-43ed-865e-2a31e89ffc04\")).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=\"b18bb153-6bcf-43ab-8cfd-5fa20436af52\"></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(\"b18bb153-6bcf-43ab-8cfd-5fa20436af52\");\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(\"b18bb153-6bcf-43ab-8cfd-5fa20436af52\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b18bb153-6bcf-43ab-8cfd-5fa20436af52' 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 = {\"ef789b0f-add0-4721-a967-c6ad45b94d78\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"}]},\"id\":\"cb7abfaa-29d9-4dbb-a43f-c1f8747b81e3\",\"type\":\"Row\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6f450c48-5b24-4464-b5c3-24c8bc801ef6\",\"type\":\"BasicTicker\"}},\"id\":\"975f8343-b486-4cbc-b521-6b71a07f8b62\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"6f450c48-5b24-4464-b5c3-24c8bc801ef6\",\"type\":\"BasicTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"a559bf1f-92a3-4586-b55e-774246a48c4c\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"eb0065ab-33fa-49a7-9d5c-c8d419cb83a5\",\"type\":\"SaveTool\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"c9c658b7-a822-4484-a240-eca522eb07da\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"f18c4385-47a7-470c-b6bb-1501a1708417\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"xgLW2QHbd0BcVH/kEGV4QEFwDVdN9nhAMUuW84jAeUCr2ynlTVR6QAWjdinU7npAqVqCywPie0DgSCY6Yn18QCzgTAUtBn1A1oReW8yGfkBunCzVEhN/QL3Keh8EAIBAtmw8ywVQgEBo4MQVMqOAQIZ+froCIYFA8sPNo1l3gUA4rFttDs2BQF5iqM68U4JANK/Z7hCmgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"176401ae-d4de-4178-8987-392b661cf758\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_color\":{\"value\":\"black\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2a05d69d-3fe1-44a2-b3f3-dc4e9e9b426d\",\"type\":\"Circle\"},{\"attributes\":{\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"95efb1d1-c45a-4ba8-87c7-045d42531d45\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"xgLW2QHbd0BcVH/kEGV4QEFwDVdN9nhAMUuW84jAeUCr2ynlTVR6QAWjdinU7npAqVqCywPie0DgSCY6Yn18QCzgTAUtBn1A1oReW8yGfkBunCzVEhN/QL3Keh8EAIBAtmw8ywVQgEBo4MQVMqOAQIZ+froCIYFA8sPNo1l3gUA4rFttDs2BQF5iqM68U4JANK/Z7hCmgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"c9dbac85-b2b1-4b83-b314-3d477b8bba0a\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"afd9d968-9442-492e-9809-1d361b472510\",\"type\":\"Line\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"35757c39-599e-46f3-a97c-661c3a0b6ace\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"f1e75263-ab23-4cad-b2a3-ed8e550ed958\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"7886fd16-c8d2-46c4-96a1-cc4d91b2d632\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"b1b24505-2a0c-48e4-a170-365efad43891\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"ad4e4a4a-a54a-4104-b716-e8109e4ba5ce\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null},\"id\":\"9368bfa0-0775-4cc0-b05b-bbe346ddb2ac\",\"type\":\"DataRange1d\"},{\"attributes\":{\"line_color\":{\"value\":\"red\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"7886fd16-c8d2-46c4-96a1-cc4d91b2d632\",\"type\":\"Line\"},{\"attributes\":{\"items\":[{\"id\":\"478ff65e-e613-4b3b-bde5-adfc40d1f977\",\"type\":\"LegendItem\"},{\"id\":\"37e73b45-3c6f-4175-86bc-cf8734c673c3\",\"type\":\"LegendItem\"}],\"location\":\"top_left\",\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"4cbd4054-17a7-433b-b31d-ed6cb387d6b1\",\"type\":\"Legend\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"a72b5392-642a-40ca-ab7b-b54f0ce34d72\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2aba6820-36c5-40d3-b6fe-311df165d746\",\"type\":\"DatetimeTicker\"}},\"id\":\"9b5a3b6b-8a92-44ce-9b22-14074adb7801\",\"type\":\"Grid\"},{\"attributes\":{\"plot\":null,\"text\":\"YIELD OF V3 Vs GDD\"},\"id\":\"2ae69858-a65c-4bc0-9b27-246a2ad7ae7e\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"94fd16a0-d650-4aea-8eba-261b6b5c0c8d\",\"type\":\"LinearScale\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"68a49fb5-9313-48c6-8bf8-463ee91ee492\",\"type\":\"Line\"},{\"attributes\":{\"background_fill_color\":{\"value\":\"#C7C5D0\"},\"below\":[{\"id\":\"0972e4c8-f07a-4632-8d49-5421ebce352d\",\"type\":\"DatetimeAxis\"}],\"left\":[{\"id\":\"32c76008-4db6-4f5b-947d-a31ee1060527\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"0972e4c8-f07a-4632-8d49-5421ebce352d\",\"type\":\"DatetimeAxis\"},{\"id\":\"d92ab6f8-cbea-4342-887c-38a73fadcecf\",\"type\":\"Grid\"},{\"id\":\"32c76008-4db6-4f5b-947d-a31ee1060527\",\"type\":\"LinearAxis\"},{\"id\":\"e44bfe80-ec27-49e8-b957-a34b78e445e5\",\"type\":\"Grid\"},{\"id\":\"fa7baa9e-5c6f-43a4-b3d9-92bf267ac58a\",\"type\":\"Legend\"},{\"id\":\"c79e3f67-f7bf-470e-8963-babad55ea3b0\",\"type\":\"GlyphRenderer\"},{\"id\":\"7ae32c37-585d-4ecf-b5bf-8c6accba8e68\",\"type\":\"GlyphRenderer\"},{\"id\":\"8bc899ee-9db8-4359-ba1c-0187c4411a2b\",\"type\":\"GlyphRenderer\"},{\"id\":\"ad4e4a4a-a54a-4104-b716-e8109e4ba5ce\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"2ae69858-a65c-4bc0-9b27-246a2ad7ae7e\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"a1fe3665-7471-483e-8b4b-419f4690c7f9\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"4e68fe93-fe67-44b1-bb00-aaecf9d6caf9\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"887db8bd-919b-4c3d-adaf-0b4a65cbe0fd\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"b9cfe449-881b-4e78-a8fe-3a07096efd76\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"2c8cbcd4-17a5-474f-8db6-eba74e05deb1\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"2587f657-f286-4e34-ab0c-12578d52654c\",\"type\":\"LinearScale\"}},\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null},\"id\":\"2c8cbcd4-17a5-474f-8db6-eba74e05deb1\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"35688bd5-ba1b-494d-8c4b-1944845733c3\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"df757825-b5e5-4cc9-bb48-53088d026d93\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"c9dbac85-b2b1-4b83-b314-3d477b8bba0a\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"49de69cd-73db-42cf-965a-87906af360af\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"c9466866-6949-478d-80d7-904bc38ab69f\",\"type\":\"Circle\"},\"selection_glyph\":null},\"id\":\"9ebbaab7-1c6a-4fdb-9305-6321da5fc4f0\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\" DATE\",\"formatter\":{\"id\":\"f18c4385-47a7-470c-b6bb-1501a1708417\",\"type\":\"DatetimeTickFormatter\"},\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"73794198-c679-4cf6-9c94-00f8eab86a8e\",\"type\":\"DatetimeTicker\"}},\"id\":\"0972e4c8-f07a-4632-8d49-5421ebce352d\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"10bf6818-5cbf-4cb9-a9d8-1d46b37e4318\",\"type\":\"DaysTicker\"},{\"attributes\":{\"label\":{\"value\":\"Actual Yield\"},\"renderers\":[{\"id\":\"759f4434-8c25-43a5-a58e-f5132b438d4b\",\"type\":\"GlyphRenderer\"}]},\"id\":\"478ff65e-e613-4b3b-bde5-adfc40d1f977\",\"type\":\"LegendItem\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"ceabeab4-9c44-4380-b627-a1b4e1408fab\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"6cdbc117-b69d-416e-81d2-eeff069ed45a\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"b9cfe449-881b-4e78-a8fe-3a07096efd76\",\"type\":\"LinearScale\"},{\"attributes\":{\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6122806a-531e-48b5-9a81-e61c5c2f0adb\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null},\"id\":\"8eda9a6b-0b5a-4f3a-ab34-693b120b2d7a\",\"type\":\"DataRange1d\"},{\"attributes\":{\"label\":{\"value\":\"Actual Yield\"},\"renderers\":[{\"id\":\"c79e3f67-f7bf-470e-8963-babad55ea3b0\",\"type\":\"GlyphRenderer\"}]},\"id\":\"9f24c71a-f023-451c-a2d3-9cd61a6b038a\",\"type\":\"LegendItem\"},{\"attributes\":{\"plot\":null,\"text\":\"YIELD OF V2 Vs GDD\"},\"id\":\"f156002b-45e8-464d-8b54-6e7405a17db8\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"d92ac879-05dd-4c99-aa9f-8688386bf005\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{},\"id\":\"e871532b-3469-4180-92bb-5ca56b4fab3e\",\"type\":\"YearsTicker\"},{\"attributes\":{\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"63e876b5-ab6c-44fc-83fc-90aea58f0742\",\"type\":\"SaveTool\"},{\"attributes\":{\"data_source\":{\"id\":\"176401ae-d4de-4178-8987-392b661cf758\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2720ef3f-1a20-4510-84f0-33a5cd10ed0c\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"82b44d8d-ccab-4561-b7e0-b03cc430a905\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"6cbe39c3-64c1-4dcc-a2c9-ec06480c831d\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"4763a3ab-ae4e-443e-b63f-37aee8bfbbfe\",\"type\":\"ToolEvents\"},{\"attributes\":{\"axis_label\":\" DATE\",\"formatter\":{\"id\":\"d92ac879-05dd-4c99-aa9f-8688386bf005\",\"type\":\"DatetimeTickFormatter\"},\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2aba6820-36c5-40d3-b6fe-311df165d746\",\"type\":\"DatetimeTicker\"}},\"id\":\"e8649e37-dec2-4e99-adbf-ef08d5f90b5e\",\"type\":\"DatetimeAxis\"},{\"attributes\":{},\"id\":\"2587f657-f286-4e34-ab0c-12578d52654c\",\"type\":\"LinearScale\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"f88b0822-4bdf-49a8-82ee-d36fba496e71\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"label\":{\"value\":\"Predicted Yield\"},\"renderers\":[{\"id\":\"9ebbaab7-1c6a-4fdb-9305-6321da5fc4f0\",\"type\":\"GlyphRenderer\"}]},\"id\":\"37e73b45-3c6f-4175-86bc-cf8734c673c3\",\"type\":\"LegendItem\"},{\"attributes\":{\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"73794198-c679-4cf6-9c94-00f8eab86a8e\",\"type\":\"DatetimeTicker\"}},\"id\":\"d92ab6f8-cbea-4342-887c-38a73fadcecf\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"887db8bd-919b-4c3d-adaf-0b4a65cbe0fd\",\"type\":\"DataRange1d\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2ff17073-972c-4f40-ad35-d7c4d14621a2\",\"type\":\"Circle\"},{\"attributes\":{\"line_color\":{\"value\":\"red\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"e4a78b1d-a06e-41b9-a3f8-4aa47f782166\",\"type\":\"Line\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"2e0bb8db-ccd9-4479-bc3c-0d90d2833fca\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"f31b05bc-0900-46fc-922b-0a79a07414b0\",\"type\":\"DaysTicker\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"b1b24505-2a0c-48e4-a170-365efad43891\",\"type\":\"Line\"},{\"attributes\":{\"dimensions\":\"width\",\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"4774a923-92fd-44e9-afed-202fd2335e73\",\"type\":\"PanTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"ZmZmZmYud0AAAAAAAFB3QJqZmZmZ+XdAAAAAAAAIekCamZmZmel6QM3MzMzMhHtAMzMzMzMLfEDNzMzMzLx8QDMzMzMzk31AZmZmZmaefkAzMzMzM9N/QJqZmZmZWYBAZ2ZmZmaOgEA0MzMzM7uAQAEAAAAAAIFAzszMzMyAgUABAAAAAOSBQGdmZmZm9oFANDMzMzMTgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"f1e75263-ab23-4cad-b2a3-ed8e550ed958\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"label\":{\"value\":\"Predicted Yield\"},\"renderers\":[{\"id\":\"7ae32c37-585d-4ecf-b5bf-8c6accba8e68\",\"type\":\"GlyphRenderer\"}]},\"id\":\"455a44d3-2e52-4d62-9d59-a94991eaf977\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis_label\":\"Cum_Yield\",\"formatter\":{\"id\":\"4eb50690-100c-4b15-a099-d9a4d52e1d7c\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6f450c48-5b24-4464-b5c3-24c8bc801ef6\",\"type\":\"BasicTicker\"}},\"id\":\"bf0314e5-4688-49ec-8bed-4acced030608\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis_label\":\"Cum_Yield\",\"formatter\":{\"id\":\"35688bd5-ba1b-494d-8c4b-1944845733c3\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"502924f8-f603-44dd-ae92-fda9c59f2cef\",\"type\":\"BasicTicker\"}},\"id\":\"32c76008-4db6-4f5b-947d-a31ee1060527\",\"type\":\"LinearAxis\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"9609306e-a12c-4e75-ac34-a9e84fcb6791\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"background_fill_color\":{\"value\":\"#D1D1D8\"},\"below\":[{\"id\":\"e8649e37-dec2-4e99-adbf-ef08d5f90b5e\",\"type\":\"DatetimeAxis\"}],\"left\":[{\"id\":\"bf0314e5-4688-49ec-8bed-4acced030608\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"e8649e37-dec2-4e99-adbf-ef08d5f90b5e\",\"type\":\"DatetimeAxis\"},{\"id\":\"9b5a3b6b-8a92-44ce-9b22-14074adb7801\",\"type\":\"Grid\"},{\"id\":\"bf0314e5-4688-49ec-8bed-4acced030608\",\"type\":\"LinearAxis\"},{\"id\":\"975f8343-b486-4cbc-b521-6b71a07f8b62\",\"type\":\"Grid\"},{\"id\":\"4cbd4054-17a7-433b-b31d-ed6cb387d6b1\",\"type\":\"Legend\"},{\"id\":\"759f4434-8c25-43a5-a58e-f5132b438d4b\",\"type\":\"GlyphRenderer\"},{\"id\":\"9ebbaab7-1c6a-4fdb-9305-6321da5fc4f0\",\"type\":\"GlyphRenderer\"},{\"id\":\"6cbe39c3-64c1-4dcc-a2c9-ec06480c831d\",\"type\":\"GlyphRenderer\"},{\"id\":\"e1b32d8b-5eac-4822-b734-e267df84f21e\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"f156002b-45e8-464d-8b54-6e7405a17db8\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"4763a3ab-ae4e-443e-b63f-37aee8bfbbfe\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"c19b2634-646d-4e1f-b220-99f20809bb2f\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"9368bfa0-0775-4cc0-b05b-bbe346ddb2ac\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"94fd16a0-d650-4aea-8eba-261b6b5c0c8d\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"8eda9a6b-0b5a-4f3a-ab34-693b120b2d7a\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"9d30c215-de02-411e-ac7c-c0326e0737c2\",\"type\":\"LinearScale\"}},\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_color\":{\"value\":\"black\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"49de69cd-73db-42cf-965a-87906af360af\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"xgLW2QHbd0BcVH/kEGV4QEFwDVdN9nhAMUuW84jAeUCr2ynlTVR6QAWjdinU7npAqVqCywPie0DgSCY6Yn18QCzgTAUtBn1A1oReW8yGfkBunCzVEhN/QL3Keh8EAIBAtmw8ywVQgEBo4MQVMqOAQIZ+froCIYFA8sPNo1l3gUA4rFttDs2BQF5iqM68U4JANK/Z7hCmgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"2d7ee02a-4b1c-4455-8e90-4a5efa6cc0bf\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"502924f8-f603-44dd-ae92-fda9c59f2cef\",\"type\":\"BasicTicker\"},{\"attributes\":{\"items\":[{\"id\":\"9f24c71a-f023-451c-a2d3-9cd61a6b038a\",\"type\":\"LegendItem\"},{\"id\":\"455a44d3-2e52-4d62-9d59-a94991eaf977\",\"type\":\"LegendItem\"}],\"location\":\"top_left\",\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"fa7baa9e-5c6f-43a4-b3d9-92bf267ac58a\",\"type\":\"Legend\"},{\"attributes\":{\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"643bb060-b8e3-4ac6-9172-f3d563a6e9fb\",\"type\":\"ResetTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"logo\":null,\"tools\":[{\"id\":\"46b27276-8426-4f8d-b4e5-2bedd4fdf2e7\",\"type\":\"PanTool\"},{\"id\":\"95efb1d1-c45a-4ba8-87c7-045d42531d45\",\"type\":\"ResetTool\"},{\"id\":\"63e876b5-ab6c-44fc-83fc-90aea58f0742\",\"type\":\"SaveTool\"}]},\"id\":\"c19b2634-646d-4e1f-b220-99f20809bb2f\",\"type\":\"Toolbar\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"f8a0dbe6-d393-429b-b03a-1b3709b0a5ee\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"xgLW2QHbd0BcVH/kEGV4QEFwDVdN9nhAMUuW84jAeUCr2ynlTVR6QAWjdinU7npAqVqCywPie0DgSCY6Yn18QCzgTAUtBn1A1oReW8yGfkBunCzVEhN/QL3Keh8EAIBAtmw8ywVQgEBo4MQVMqOAQIZ+froCIYFA8sPNo1l3gUA4rFttDs2BQF5iqM68U4JANK/Z7hCmgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"fbe71f52-5843-4e1e-a9ab-c8015bece761\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"ZmZmZmYud0AAAAAAAFB3QJqZmZmZ+XdAAAAAAAAIekCamZmZmel6QM3MzMzMhHtAMzMzMzMLfEDNzMzMzLx8QDMzMzMzk31AZmZmZmaefkAzMzMzM9N/QJqZmZmZWYBAZ2ZmZmaOgEA0MzMzM7uAQAEAAAAAAIFAzszMzMyAgUABAAAAAOSBQGdmZmZm9oFANDMzMzMTgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"02024976-1741-4dc5-b660-74e6bfc08df3\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"a39f3a87-6a96-4119-807c-ee26d0961f30\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6122806a-531e-48b5-9a81-e61c5c2f0adb\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"35757c39-599e-46f3-a97c-661c3a0b6ace\",\"type\":\"Circle\"},\"selection_glyph\":null},\"id\":\"c79e3f67-f7bf-470e-8963-babad55ea3b0\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"ZmZmZmYud0AAAAAAAFB3QJqZmZmZ+XdAAAAAAAAIekCamZmZmel6QM3MzMzMhHtAMzMzMzMLfEDNzMzMzLx8QDMzMzMzk31AZmZmZmaefkAzMzMzM9N/QJqZmZmZWYBAZ2ZmZmaOgEA0MzMzM7uAQAEAAAAAAIFAzszMzMyAgUABAAAAAOSBQGdmZmZm9oFANDMzMzMTgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"637c4f87-f361-413d-97be-8baf27fd8d68\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"01634918-ff31-4bb2-8067-5ec032a6c55b\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"1dae604c-b123-4db4-b2b0-8e527cf43754\",\"type\":\"DaysTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"2d7ee02a-4b1c-4455-8e90-4a5efa6cc0bf\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"afd9d968-9442-492e-9809-1d361b472510\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"68a49fb5-9313-48c6-8bf8-463ee91ee492\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"8bc899ee-9db8-4359-ba1c-0187c4411a2b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2720ef3f-1a20-4510-84f0-33a5cd10ed0c\",\"type\":\"Line\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"dea1f13d-f8f5-47b2-841b-30dd0d4e169b\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"91764617-e22c-45dd-8293-3b5177f8c3ad\",\"type\":\"DaysTicker\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":{\"__ndarray__\":\"AAAAYtrLdUIAAIAtf8x1QgAAAPkjzXVCAABAKhvOdUIAAMD1v851QgAAQMFkz3VCAACA8lvQdUIAAAC+ANF1QgAAgIml0XVCAABAhkHTdUIAAMBR5tN1QgAAAIPd1HVCAACAToLVdUIAAAAaJ9Z1QgAAQEse13VCAADAFsPXdUIAAEDiZ9h1QgAAgBNf2XVCAAAA3wPadUI=\",\"dtype\":\"float64\",\"shape\":[19]},\"y\":{\"__ndarray__\":\"ZmZmZmYud0AAAAAAAFB3QJqZmZmZ+XdAAAAAAAAIekCamZmZmel6QM3MzMzMhHtAMzMzMzMLfEDNzMzMzLx8QDMzMzMzk31AZmZmZmaefkAzMzMzM9N/QJqZmZmZWYBAZ2ZmZmaOgEA0MzMzM7uAQAEAAAAAAIFAzszMzMyAgUABAAAAAOSBQGdmZmZm9oFANDMzMzMTgkA=\",\"dtype\":\"float64\",\"shape\":[19]}}},\"id\":\"a39f3a87-6a96-4119-807c-ee26d0961f30\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"fbe71f52-5843-4e1e-a9ab-c8015bece761\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2a05d69d-3fe1-44a2-b3f3-dc4e9e9b426d\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"2ff17073-972c-4f40-ad35-d7c4d14621a2\",\"type\":\"Circle\"},\"selection_glyph\":null},\"id\":\"7ae32c37-585d-4ecf-b5bf-8c6accba8e68\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1c89bf89-5572-444a-9c15-c07a6ac55d5d\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"9d30c215-de02-411e-ac7c-c0326e0737c2\",\"type\":\"LinearScale\"},{\"attributes\":{\"num_minor_ticks\":5},\"id\":\"73794198-c679-4cf6-9c94-00f8eab86a8e\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"logo\":null,\"tools\":[{\"id\":\"4774a923-92fd-44e9-afed-202fd2335e73\",\"type\":\"PanTool\"},{\"id\":\"643bb060-b8e3-4ac6-9172-f3d563a6e9fb\",\"type\":\"ResetTool\"},{\"id\":\"eb0065ab-33fa-49a7-9d5c-c8d419cb83a5\",\"type\":\"SaveTool\"}]},\"id\":\"4e68fe93-fe67-44b1-bb00-aaecf9d6caf9\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"a1fe3665-7471-483e-8b4b-419f4690c7f9\",\"type\":\"ToolEvents\"},{\"attributes\":{\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"902a4600-801d-40c1-9b93-a046ac7380d4\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"71aced6d-2b6c-4ae5-b915-adaf58d9e96e\",\"type\":\"DaysTicker\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"cec9964a-cc23-45f0-974d-a003ca6bef79\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"502924f8-f603-44dd-ae92-fda9c59f2cef\",\"type\":\"BasicTicker\"}},\"id\":\"e44bfe80-ec27-49e8-b957-a34b78e445e5\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"637c4f87-f361-413d-97be-8baf27fd8d68\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"e4a78b1d-a06e-41b9-a3f8-4aa47f782166\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"bace54de-a2fc-46bd-b5b7-4f3e8428db3b\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"e1b32d8b-5eac-4822-b734-e267df84f21e\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"02024976-1741-4dc5-b660-74e6bfc08df3\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4a112838-69e4-4218-bcd7-4fb0fb5f3130\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"df757825-b5e5-4cc9-bb48-53088d026d93\",\"type\":\"Circle\"},\"selection_glyph\":null},\"id\":\"759f4434-8c25-43a5-a58e-f5132b438d4b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"4a112838-69e4-4218-bcd7-4fb0fb5f3130\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"4eb50690-100c-4b15-a099-d9a4d52e1d7c\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"82b44d8d-ccab-4561-b7e0-b03cc430a905\",\"type\":\"Line\"},{\"attributes\":{\"num_minor_ticks\":5},\"id\":\"2aba6820-36c5-40d3-b6fe-311df165d746\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"dimensions\":\"width\",\"plot\":{\"id\":\"86df6c15-44b0-4849-85bc-08fe3f3ea2f8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"46b27276-8426-4f8d-b4e5-2bedd4fdf2e7\",\"type\":\"PanTool\"},{\"attributes\":{\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"10074cc9-c691-42ef-8f89-bd7838886536\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"ab65a4c7-308e-400a-a70b-7037106d73f0\",\"type\":\"DaysTicker\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"bace54de-a2fc-46bd-b5b7-4f3e8428db3b\",\"type\":\"Line\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"13251c90-7c74-4ea3-8461-bc9bf4eac12e\",\"type\":\"DaysTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"c9466866-6949-478d-80d7-904bc38ab69f\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"6590bf60-8961-413e-9255-f5523a4343d7\",\"type\":\"YearsTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"cd64f27e-d92a-4ef4-87ea-f057d3c59011\",\"type\":\"AdaptiveTicker\"}],\"root_ids\":[\"cb7abfaa-29d9-4dbb-a43f-c1f8747b81e3\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.6\"}};\n",
       "            var render_items = [{\"docid\":\"ef789b0f-add0-4721-a967-c6ad45b94d78\",\"elementid\":\"b18bb153-6bcf-43ab-8cfd-5fa20436af52\",\"modelid\":\"cb7abfaa-29d9-4dbb-a43f-c1f8747b81e3\"}];\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(\"b18bb153-6bcf-43ab-8cfd-5fa20436af52\")).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": [
    "#df = dfA_GDD_clean\n",
    "##################################################\n",
    "########                              ############\n",
    "#######          FOR V2 VARIETY      #############\n",
    "#######                              #############\n",
    "##################################################\n",
    "\n",
    "# Assign Features and Target variable\n",
    "y = dfA_v2_GDD_clean2['Cum_Yield'].values\n",
    "x = dfA_v2_GDD_clean2['Cum_GDD'].values\n",
    "xD = dfA_v2_GDD_clean2['date'].values\n",
    "\n",
    "# Create the Model\n",
    "lm = smf.ols(formula='Cum_Yield~Cum_GDD',data=dfA_v2_GDD_clean2)\n",
    "\n",
    "\n",
    "lm_result = lm.fit()\n",
    "\n",
    "print(lm_result.summary())\n",
    "print(lm_result.params)\n",
    "\n",
    "# Prediction on the data set\n",
    "y_predictions = lm_result.predict()\n",
    "y1 = y_predictions\n",
    "#Outputs in Notebook\n",
    "\n",
    "\n",
    "output_notebook()\n",
    "#Output the Plot result as html that will be displayed in the browser\n",
    "output_file(\"harvest_prediction.html\", title=\"CGDD Vs Harvest Yield Regression of Greenhouse Block A\")\n",
    "\n",
    "p = figure(title=\"YIELD OF V2 Vs GDD\",\n",
    "           x_axis_label=\" DATE\",\n",
    "           y_axis_label=\"Cum_Yield\",\n",
    "           x_axis_type='datetime',\n",
    "           background_fill_color='#D1D1D8',\n",
    "          # tools=[hover, 'xpan','reset','previewsave'],\n",
    "           tools=['xpan', 'reset','previewsave'],\n",
    "          # plot_width=1000,\n",
    "           logo=None\n",
    "          )\n",
    "\n",
    "p.scatter(xD, y, legend=\"Actual Yield\", marker=\"circle\",color=\"red\")\n",
    "\n",
    "p.scatter(xD, y_predictions, legend=\"Predicted Yield\", color=\"black\")\n",
    "\n",
    "p.line(xD, y_predictions, line_width=1,line_color=\"black\")\n",
    "\n",
    "p.line(xD, y, line_width=1,line_color=\"red\")\n",
    "#p.line(x, y, line_width=1, line_width=2)\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "\n",
    "output_file(\"TOGUCHI_harvest_prediction.html\")\n",
    "\n",
    "#show(p)\n",
    "\n",
    "##################################################\n",
    "########                              ############\n",
    "#######          FOR V3 VARIETY      #############\n",
    "#######                              #############\n",
    "##################################################\n",
    "\n",
    "# Assign Features and Target variable\n",
    "y3 = dfA_v3_GDD_clean3['Cum_Yield'].values\n",
    "x3 = dfA_v3_GDD_clean3['Cum_GDD'].values\n",
    "xD3 = dfA_v2_GDD_clean2['date'].values\n",
    "\n",
    "# Create the Model\n",
    "lm = smf.ols(formula='Cum_Yield~Cum_GDD',data=dfA_v3_GDD_clean3)\n",
    "lm_result = lm.fit()\n",
    "print(lm_result.summary())\n",
    "print(lm_result.params)\n",
    "# Prediction on the data set\n",
    "y3_predictions = lm_result.predict()\n",
    "y3 = y3_predictions\n",
    "\n",
    "#Outputs in Notebook\n",
    "output_notebook()\n",
    "\n",
    "#output_file(\"housing_prices.html\", title=\"LSTAT vs. Housing Prices Regression\")\n",
    "output_file(\"harvest_prediction.html\", title=\"CGDD Vs Harvest Yield Regression of Greenhouse Block A\")\n",
    "\n",
    "p3 = figure(title=\"YIELD OF V3 Vs GDD\",\n",
    "           x_axis_label=\" DATE\",\n",
    "            x_axis_type='datetime',\n",
    "           y_axis_label=\"Cum_Yield\",\n",
    "           background_fill_color='#C7C5D0',\n",
    "          # tools=[hover, 'xpan','reset','previewsave'],\n",
    "           tools=['xpan', 'reset','previewsave'],\n",
    "           #plot_width=1000,\n",
    "           logo=None\n",
    "          )\n",
    "\n",
    "p3.scatter(xD3, y, legend=\"Actual Yield\", marker=\"circle\",color=\"red\")\n",
    "\n",
    "p3.scatter(xD3, y_predictions, legend=\"Predicted Yield\", color=\"black\")\n",
    "p3.line(xD3, y_predictions, line_width=1,line_color=\"black\")\n",
    "p3.line(xD3, y, line_width=1,line_color=\"red\")\n",
    "\n",
    "p3.legend.location = \"top_left\"\n",
    "\n",
    "show(row(p, p3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Using Train/Test Split\n",
    "## The data is not that much but however, let us try some Machine Learning.\n",
    "You will later realise that the data set is too small to give goog results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.957233626191\n",
      "Root Mean Squared Error: 8.42607841353\n",
      "MODEL SCORE IS :0.957233626191\n",
      "Score:  0.957233626191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116945990>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088f0110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "y = dfA_v3_GDD_clean3['Cum_Yield'].values\n",
    "x = dfA_v3_GDD_clean3['Cum_GDD'].values\n",
    "x4 = dfA_v3_GDD_clean3['date'].values\n",
    "\n",
    "y = y.reshape(-1, 1)\n",
    "x = x.reshape(-1, 1)\n",
    "\n",
    "# Create training and test sets\n",
    "#X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.5,random_state=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)\n",
    "\n",
    "# Create the regressor: reg_all\n",
    "reg_all = linear_model.LinearRegression() \n",
    "\n",
    "# Fit the regressor to the training data to create a model\n",
    "model = reg_all.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test data: y_pred\n",
    "y_pred = reg_all.predict(X_test)\n",
    "\n",
    "# Compute and print R^2 and RMSE\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print(\"R^2: {}\".format(reg_all.score(X_test, y_test)))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse))\n",
    "print (\"MODEL SCORE IS :{}\" .format(reg_all.score(X_test, y_test)))\n",
    "print \"Score: \" ,model.score(X_test,y_test)\n",
    "\n",
    "#plt.scatter(y, y_pred)\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Above two Models\n",
    "### Train/Test Split and Statsmodels.model\n",
    "### Both have high R-Squared HOWEVER for such a small data set. ML is not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#y_test,y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statsmodels on the train/Test split data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOR 2-FOLD : \n",
      "FOR 2-fold scores: \n",
      "[ 0.33364129 -0.12058088]\n",
      "The AVERAGE SCORE IS: \n",
      "0.333641294442\n",
      "FOR 3-FOLD : \n",
      "FOR 3-fold scores: \n",
      "[ 0.0903894   0.57035744 -1.70271032]\n",
      "The AVERAGE SCORE IS: \n",
      "0.570357436161\n",
      "FOR 4-FOLD : \n",
      "FOR 4-fold scores: \n",
      "[  0.46610022   0.80292803   0.60428291 -54.09060611]\n",
      "The AVERAGE SCORE IS: \n",
      "0.802928027767\n",
      "FOR 5-FOLD : \n",
      "FOR 5-fold scores: \n",
      "[   0.20889532    0.68299154   -2.57212352    0.77360345 -121.02048183]\n",
      "The AVERAGE SCORE IS: \n",
      "0.773603445778\n",
      "FOR 6-FOLD : \n",
      "FOR 6-fold scores: \n",
      "[   0.20889532    0.37628521    0.46014849   -2.09624452    0.63305685\n",
      " -121.02048183]\n",
      "The AVERAGE SCORE IS: \n",
      "0.633056852203\n",
      "FOR 7-FOLD : \n",
      "FOR 7-fold scores: \n",
      "[ -1.54565525e+00   4.88963420e-02   7.29734403e-01  -6.16173274e+00\n",
      "   7.97567843e-01  -2.12022230e+01  -7.09624441e+02]\n",
      "The AVERAGE SCORE IS: \n",
      "0.797567843386\n",
      "FOR 8-FOLD : \n",
      "FOR 8-fold scores: \n",
      "[ -1.54565525e+00   4.88963420e-02   7.29734403e-01  -2.65371445e+01\n",
      "  -1.16546832e+00   6.73067142e-01  -2.12022230e+01  -7.09624441e+02]\n",
      "The AVERAGE SCORE IS: \n",
      "0.729734402608\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/denisrubanga/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary modules\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_validation import cross_val_score, cross_val_predict\n",
    "from sklearn import metrics\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "from beautifultable import BeautifulTable\n",
    "table = BeautifulTable()\n",
    "\n",
    "y = dfA_v3_GDD_clean3['Cum_Yield'].values\n",
    "x = dfA_v3_GDD_clean3['Cum_GDD'].values\n",
    "\n",
    "y = y.reshape(-1, 1)\n",
    "x = x.reshape(-1, 1)\n",
    "\n",
    "df = dfA_v3_GDD_clean3\n",
    "# Create a linear regression object: reg\n",
    "#reg = LinearRegression()\n",
    "reg = linear_model.LinearRegression() \n",
    "\n",
    "# Compute n-fold cross-validation scores: cv_scores\n",
    "C = np.arange(2,9)\n",
    "for c in C:\n",
    "    print('FOR %.0f-FOLD : ' % c )\n",
    "    # Make cross validated predictions\n",
    "    Predictions = cross_val_predict(reg, x, y, cv=c)\n",
    "    #print \"Cross-validated prediction are:\", Predictions\n",
    "    cvscores_5 = cross_val_score(reg,x,y,cv=c)\n",
    "    print('FOR %.0f-fold scores: ' % c )\n",
    "    print(cvscores_5)\n",
    "    print('The AVERAGE SCORE IS: ' )\n",
    "    print(\"{}\".format(np.max(cvscores_5)))\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116a122d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEGZmlssBwszMcjlAmJlZrv8POHH28f9mFh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112153d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y, Predictions)\n",
    "#plt.scatter(y_test, Predictions)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}