notebooks/ActionSpaceSearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "primary-uzbekistan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets/Economics.pdf\n",
      "datasets/ReadMe.pdf\n",
      "datasets/AICU/CropParameters.csv\n",
      "datasets/AICU/GreenhouseClimate.csv\n",
      "datasets/AICU/GrodanSens.csv\n",
      "datasets/AICU/LabAnalysis.csv\n",
      "datasets/AICU/Production.csv\n",
      "datasets/AICU/Resources.csv\n",
      "datasets/AICU/TomQuality.csv\n",
      "datasets/Automatoes/CropParameters.csv\n",
      "datasets/Automatoes/GreenhouseClimate.csv\n",
      "datasets/Automatoes/GrodanSens.csv\n",
      "datasets/Automatoes/LabAnalysis.csv\n",
      "datasets/Automatoes/Production.csv\n",
      "datasets/Automatoes/Resources.csv\n",
      "datasets/Automatoes/TomQuality.csv\n",
      "datasets/Digilog/CropParameters.csv\n",
      "datasets/Digilog/GreenhouseClimate.csv\n",
      "datasets/Digilog/GrodanSens.csv\n",
      "datasets/Digilog/LabAnalysis.csv\n",
      "datasets/Digilog/Production.csv\n",
      "datasets/Digilog/Resources.csv\n",
      "datasets/Digilog/TomQuality.csv\n",
      "datasets/IUACAAS/CropParameters.csv\n",
      "datasets/IUACAAS/GreenhouseClimate.csv\n",
      "datasets/IUACAAS/GrodanSens.csv\n",
      "datasets/IUACAAS/LabAnalysis.csv\n",
      "datasets/IUACAAS/Production.csv\n",
      "datasets/IUACAAS/Resources.csv\n",
      "datasets/IUACAAS/TomQuality.csv\n",
      "datasets/Reference/CropParameters.csv\n",
      "datasets/Reference/GreenhouseClimate.csv\n",
      "datasets/Reference/GrodanSens.csv\n",
      "datasets/Reference/LabAnalysis.csv\n",
      "datasets/Reference/Production.csv\n",
      "datasets/Reference/Resources.csv\n",
      "datasets/Reference/TomQuality.csv\n",
      "datasets/TheAutomators/CropParameters.csv\n",
      "datasets/TheAutomators/GreenhouseClimate.csv\n",
      "datasets/TheAutomators/GrodanSens.csv\n",
      "datasets/TheAutomators/LabAnalysis.csv\n",
      "datasets/TheAutomators/Production.csv\n",
      "datasets/TheAutomators/Resources.csv\n",
      "datasets/TheAutomators/TomQuality.csv\n",
      "datasets/Weather/Weather.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('datasets'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "functioning-adelaide",
   "metadata": {},
   "outputs": [],
   "source": [
    "AICUGC=pd.read_csv(\"datasets/AICU/GreenhouseClimate.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "adequate-press",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['%time', 'AssimLight', 'BlackScr', 'CO2air', 'Cum_irr', 'EC_drain_PC',\n",
       "       'EnScr', 'HumDef', 'PipeGrow', 'PipeLow', 'Rhair', 'Tair', 'Tot_PAR',\n",
       "       'Tot_PAR_Lamps', 'VentLee', 'Ventwind', 'assim_sp', 'assim_vip',\n",
       "       'co2_dos', 'co2_sp', 'co2_vip', 'dx_sp', 'dx_vip', 'int_blue_sp',\n",
       "       'int_blue_vip', 'int_farred_sp', 'int_farred_vip', 'int_red_sp',\n",
       "       'int_red_vip', 'int_white_sp', 'int_white_vip', 'pH_drain_PC',\n",
       "       'scr_blck_sp', 'scr_blck_vip', 'scr_enrg_sp', 'scr_enrg_vip',\n",
       "       't_grow_min_sp', 't_grow_min_vip', 't_heat_sp', 't_heat_vip',\n",
       "       't_rail_min_sp', 't_rail_min_vip', 't_vent_sp', 't_ventlee_vip',\n",
       "       't_ventwind_vip', 'water_sup', 'water_sup_intervals_sp_min',\n",
       "       'water_sup_intervals_vip_min', 'window_pos_lee_sp',\n",
       "       'window_pos_lee_vip'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AICUGC.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "talented-edward",
   "metadata": {},
   "outputs": [],
   "source": [
    "aicu_setpoint_columns=[\"assim_sp\",\"co2_sp\",\"dx_sp\",\"int_blue_sp\",\"int_farred_sp\", \\\n",
    "        \"int_red_sp\",\"int_white_sp\",\"scr_blck_sp\",\"scr_enrg_sp\",\"t_grow_min_sp\",\\\n",
    "        \"t_heat_sp\",\"t_rail_min_sp\",\"t_vent_sp\",\"water_sup_intervals_sp_min\",\\\n",
    "        \"window_pos_lee_sp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "trained-beatles",
   "metadata": {},
   "outputs": [],
   "source": [
    "AICUGC[aicu_setpoint_columns]=AICUGC[aicu_setpoint_columns].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "combined-davis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'assim_sp'}>,\n",
       "        <AxesSubplot:title={'center':'co2_sp'}>,\n",
       "        <AxesSubplot:title={'center':'dx_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_blue_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'int_farred_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_red_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_white_sp'}>,\n",
       "        <AxesSubplot:title={'center':'scr_blck_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'scr_enrg_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_grow_min_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_heat_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_rail_min_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'t_vent_sp'}>,\n",
       "        <AxesSubplot:title={'center':'water_sup_intervals_sp_min'}>,\n",
       "        <AxesSubplot:title={'center':'window_pos_lee_sp'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AICUGC[aicu_setpoint_columns].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "conscious-snake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assim_sp</th>\n",
       "      <th>co2_sp</th>\n",
       "      <th>dx_sp</th>\n",
       "      <th>int_blue_sp</th>\n",
       "      <th>int_farred_sp</th>\n",
       "      <th>int_red_sp</th>\n",
       "      <th>int_white_sp</th>\n",
       "      <th>scr_blck_sp</th>\n",
       "      <th>scr_enrg_sp</th>\n",
       "      <th>t_grow_min_sp</th>\n",
       "      <th>t_heat_sp</th>\n",
       "      <th>t_rail_min_sp</th>\n",
       "      <th>t_vent_sp</th>\n",
       "      <th>water_sup_intervals_sp_min</th>\n",
       "      <th>window_pos_lee_sp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>803.000000</td>\n",
       "      <td>6684.000000</td>\n",
       "      <td>733.0</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14490.000000</td>\n",
       "      <td>733.0</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>5828.000000</td>\n",
       "      <td>805.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.559151</td>\n",
       "      <td>665.795166</td>\n",
       "      <td>2.2</td>\n",
       "      <td>644.975403</td>\n",
       "      <td>914.892761</td>\n",
       "      <td>862.077637</td>\n",
       "      <td>485.372955</td>\n",
       "      <td>48.234169</td>\n",
       "      <td>34.407742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.917862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.936296</td>\n",
       "      <td>1434.824585</td>\n",
       "      <td>1.101118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>46.811234</td>\n",
       "      <td>176.854675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>465.043701</td>\n",
       "      <td>270.647705</td>\n",
       "      <td>324.092743</td>\n",
       "      <td>496.774658</td>\n",
       "      <td>45.538765</td>\n",
       "      <td>43.976063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.251362</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.594668</td>\n",
       "      <td>775.662842</td>\n",
       "      <td>0.369969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.200001</td>\n",
       "      <td>983.000000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>825.000000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.700001</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         assim_sp       co2_sp  dx_sp   int_blue_sp  int_farred_sp  \\\n",
       "count  803.000000  6684.000000  733.0  47795.000000   47795.000000   \n",
       "mean    67.559151   665.795166    2.2    644.975403     914.892761   \n",
       "std     46.811234   176.854675    0.0    465.043701     270.647705   \n",
       "min      0.000000   300.000000    2.2      0.000000       0.000000   \n",
       "25%      0.000000   525.000000    2.2      0.000000    1000.000000   \n",
       "50%    100.000000   650.000000    2.2   1000.000000    1000.000000   \n",
       "75%    100.000000   825.000000    2.2   1000.000000    1000.000000   \n",
       "max    100.000000  1000.000000    2.2   1000.000000    1000.000000   \n",
       "\n",
       "         int_red_sp  int_white_sp  scr_blck_sp  scr_enrg_sp  t_grow_min_sp  \\\n",
       "count  47795.000000  47795.000000   995.000000  1111.000000            0.0   \n",
       "mean     862.077637    485.372955    48.234169    34.407742            NaN   \n",
       "std      324.092743    496.774658    45.538765    43.976063            NaN   \n",
       "min        0.000000      0.000000     0.000000     0.000000            NaN   \n",
       "25%     1000.000000      0.000000     0.000000     0.000000            NaN   \n",
       "50%     1000.000000      0.000000    65.000000     0.000000            NaN   \n",
       "75%     1000.000000   1000.000000    95.000000    90.000000            NaN   \n",
       "max     1000.000000   1000.000000   100.000000   100.000000            NaN   \n",
       "\n",
       "          t_heat_sp  t_rail_min_sp     t_vent_sp  water_sup_intervals_sp_min  \\\n",
       "count  14490.000000          733.0  19999.000000                 5828.000000   \n",
       "mean      20.917862            0.0     22.936296                 1434.824585   \n",
       "std        2.251362            0.0      2.594668                  775.662842   \n",
       "min       10.000000            0.0     13.000000                   10.000000   \n",
       "25%       19.700001            0.0     21.200001                  983.000000   \n",
       "50%       21.100000            0.0     22.900000                 1950.000000   \n",
       "75%       22.400000            0.0     24.700001                 1950.000000   \n",
       "max       25.000000            0.0     28.000000                 2000.000000   \n",
       "\n",
       "       window_pos_lee_sp  \n",
       "count         805.000000  \n",
       "mean            1.101118  \n",
       "std             0.369969  \n",
       "min             0.000000  \n",
       "25%             1.200000  \n",
       "50%             1.200000  \n",
       "75%             1.200000  \n",
       "max             4.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AICUGC[aicu_setpoint_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "continent-blair",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "AICUGC_actions={}\n",
    "for x in auto_setpoint_columns:\n",
    "    AICUGC_actions[x]={\n",
    "        \"values\":AICUGC[x].unique(),\n",
    "        \"length\":len(AICUGC[x].unique())\n",
    "    }\n",
    "# pprint(AUTOGC_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "delayed-official",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except ImportError:  # Python 3.x\n",
    "    import pickle\n",
    "\n",
    "with open('AICUGC_actions.pickle', 'wb') as fp:\n",
    "    pickle.dump(AICUGC_actions, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cleared-visibility",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kaypee/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,22,23,25,27,29,31,33,35,37,39,41,43,44,45,47,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "AUTOGC=pd.read_csv(\"datasets/Automatoes/GreenhouseClimate.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ordered-elder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['%time', 'AssimLight', 'BlackScr', 'CO2air', 'Cum_irr', 'EC_drain_PC',\n",
       "        'EnScr', 'HumDef', 'PipeGrow', 'PipeLow', 'Rhair', 'Tair', 'Tot_PAR',\n",
       "        'Tot_PAR_Lamps', 'VentLee', 'Ventwind', 'assim_sp', 'assim_vip',\n",
       "        'co2_dos', 'co2_sp', 'co2_vip', 'dx_sp', 'dx_vip', 'int_blue_sp',\n",
       "        'int_blue_vip', 'int_farred_sp', 'int_farred_vip', 'int_red_sp',\n",
       "        'int_red_vip', 'int_white_sp', 'int_white_vip', 'pH_drain_PC',\n",
       "        'scr_blck_sp', 'scr_blck_vip', 'scr_enrg_sp', 'scr_enrg_vip',\n",
       "        't_grow_min_sp', 't_grow_min_vip', 't_heat_sp', 't_heat_vip',\n",
       "        't_rail_min_sp', 't_rail_min_vip', 't_vent_sp', 't_ventlee_vip',\n",
       "        't_ventwind_vip', 'water_sup', 'water_sup_intervals_sp_min',\n",
       "        'water_sup_intervals_vip_min', 'window_pos_lee_sp',\n",
       "        'window_pos_lee_vip'],\n",
       "       dtype='object'),\n",
       " 50)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUTOGC.columns,len(AUTOGC.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "intimate-hawaiian",
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_setpoint_columns=[\"assim_sp\",\"co2_sp\",\"dx_sp\",\"int_blue_sp\",\"int_farred_sp\", \\\n",
    "        \"int_red_sp\",\"int_white_sp\",\"scr_blck_sp\",\"scr_enrg_sp\",\"t_grow_min_sp\",\\\n",
    "        \"t_heat_sp\",\"t_rail_min_sp\",\"t_vent_sp\",\"water_sup_intervals_sp_min\",\\\n",
    "        \"window_pos_lee_sp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "proper-sodium",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOGC[auto_setpoint_columns]=AUTOGC[auto_setpoint_columns].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "applied-conversation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'assim_sp'}>,\n",
       "        <AxesSubplot:title={'center':'co2_sp'}>,\n",
       "        <AxesSubplot:title={'center':'dx_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_blue_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'int_farred_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_red_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_white_sp'}>,\n",
       "        <AxesSubplot:title={'center':'scr_blck_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'scr_enrg_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_grow_min_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_heat_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_rail_min_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'t_vent_sp'}>,\n",
       "        <AxesSubplot:title={'center':'water_sup_intervals_sp_min'}>,\n",
       "        <AxesSubplot:title={'center':'window_pos_lee_sp'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AUTOGC[auto_setpoint_columns].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "annual-reviewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assim_sp</th>\n",
       "      <th>co2_sp</th>\n",
       "      <th>dx_sp</th>\n",
       "      <th>int_blue_sp</th>\n",
       "      <th>int_farred_sp</th>\n",
       "      <th>int_red_sp</th>\n",
       "      <th>int_white_sp</th>\n",
       "      <th>scr_blck_sp</th>\n",
       "      <th>scr_enrg_sp</th>\n",
       "      <th>t_grow_min_sp</th>\n",
       "      <th>t_heat_sp</th>\n",
       "      <th>t_rail_min_sp</th>\n",
       "      <th>t_vent_sp</th>\n",
       "      <th>water_sup_intervals_sp_min</th>\n",
       "      <th>window_pos_lee_sp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47024.000000</td>\n",
       "      <td>47055.000000</td>\n",
       "      <td>47091.000000</td>\n",
       "      <td>47636.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47636.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47026.000000</td>\n",
       "      <td>47024.000000</td>\n",
       "      <td>46304.000000</td>\n",
       "      <td>47103.000000</td>\n",
       "      <td>47022.000000</td>\n",
       "      <td>35233.000000</td>\n",
       "      <td>46399.000000</td>\n",
       "      <td>47094.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.391972</td>\n",
       "      <td>471.933319</td>\n",
       "      <td>1.747822</td>\n",
       "      <td>508.773865</td>\n",
       "      <td>0.625588</td>\n",
       "      <td>519.662720</td>\n",
       "      <td>520.925842</td>\n",
       "      <td>31.419342</td>\n",
       "      <td>47.357178</td>\n",
       "      <td>23.321987</td>\n",
       "      <td>21.392904</td>\n",
       "      <td>6.404172</td>\n",
       "      <td>31.056786</td>\n",
       "      <td>762.612549</td>\n",
       "      <td>6.580428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.885262</td>\n",
       "      <td>336.202148</td>\n",
       "      <td>1.003199</td>\n",
       "      <td>494.698792</td>\n",
       "      <td>9.991517</td>\n",
       "      <td>491.070831</td>\n",
       "      <td>491.047272</td>\n",
       "      <td>44.583820</td>\n",
       "      <td>45.573273</td>\n",
       "      <td>15.991185</td>\n",
       "      <td>3.536992</td>\n",
       "      <td>10.635707</td>\n",
       "      <td>2.648991</td>\n",
       "      <td>619.633301</td>\n",
       "      <td>12.307339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.700001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>853.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>21.799999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           assim_sp        co2_sp         dx_sp   int_blue_sp  int_farred_sp  \\\n",
       "count  47024.000000  47055.000000  47091.000000  47636.000000   47795.000000   \n",
       "mean      53.391972    471.933319      1.747822    508.773865       0.625588   \n",
       "std       49.885262    336.202148      1.003199    494.698792       9.991517   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      1.500000      0.000000       0.000000   \n",
       "50%      100.000000    600.000000      1.500000    800.000000       0.000000   \n",
       "75%      100.000000    700.000000      1.500000   1000.000000       0.000000   \n",
       "max      100.000000   1200.000000    144.000000   1000.000000    1000.000000   \n",
       "\n",
       "         int_red_sp  int_white_sp   scr_blck_sp   scr_enrg_sp  t_grow_min_sp  \\\n",
       "count  47636.000000  47795.000000  47026.000000  47024.000000   46304.000000   \n",
       "mean     519.662720    520.925842     31.419342     47.357178      23.321987   \n",
       "std      491.070831    491.047272     44.583820     45.573273      15.991185   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "50%      800.000000    853.000000      0.000000     65.000000      32.000000   \n",
       "75%     1000.000000   1000.000000     91.000000     95.000000      35.000000   \n",
       "max     1000.000000   1000.000000   1080.000000    650.000000     100.000000   \n",
       "\n",
       "          t_heat_sp  t_rail_min_sp     t_vent_sp  water_sup_intervals_sp_min  \\\n",
       "count  47103.000000   47022.000000  35233.000000                46399.000000   \n",
       "mean      21.392904       6.404172     31.056786                  762.612549   \n",
       "std        3.536992      10.635707      2.648991                  619.633301   \n",
       "min       10.000000       0.000000     13.000000                    0.000000   \n",
       "25%       18.700001       0.000000     30.000000                   55.000000   \n",
       "50%       21.799999       0.000000     32.000000                 1080.000000   \n",
       "75%       23.900000      15.000000     32.000000                 1080.000000   \n",
       "max       88.000000      60.000000     36.000000                 1950.000000   \n",
       "\n",
       "       window_pos_lee_sp  \n",
       "count       47094.000000  \n",
       "mean            6.580428  \n",
       "std            12.307339  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             1.000000  \n",
       "75%             8.000000  \n",
       "max           100.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUTOGC[auto_setpoint_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "verified-multiple",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "AUTOGC_actions={}\n",
    "for x in auto_setpoint_columns:\n",
    "    AUTOGC_actions[x]={\n",
    "        \"values\":AUTOGC[x].unique(),\n",
    "        \"length\":len(AUTOGC[x].unique())\n",
    "    }\n",
    "# pprint(AUTOGC_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "charitable-breathing",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except ImportError:  # Python 3.x\n",
    "    import pickle\n",
    "\n",
    "with open('AUTOGC_actions.pickle', 'wb') as fp:\n",
    "    pickle.dump(AUTOGC_actions, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "choice-former",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kaypee/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,22,23,25,27,29,31,33,35,37,39,41,43,44,45,47,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "DIGIGC=pd.read_csv(\"datasets/Digilog/GreenhouseClimate.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "unsigned-jersey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['%time', 'AssimLight', 'BlackScr', 'CO2air', 'Cum_irr', 'EC_drain_PC',\n",
       "        'EnScr', 'HumDef', 'PipeGrow', 'PipeLow', 'Rhair', 'Tair', 'Tot_PAR',\n",
       "        'Tot_PAR_Lamps', 'VentLee', 'Ventwind', 'assim_sp', 'assim_vip',\n",
       "        'co2_dos', 'co2_sp', 'co2_vip', 'dx_sp', 'dx_vip', 'int_blue_sp',\n",
       "        'int_blue_vip', 'int_farred_sp', 'int_farred_vip', 'int_red_sp',\n",
       "        'int_red_vip', 'int_white_sp', 'int_white_vip', 'pH_drain_PC',\n",
       "        'scr_blck_sp', 'scr_blck_vip', 'scr_enrg_sp', 'scr_enrg_vip',\n",
       "        't_grow_min_sp', 't_grow_min_vip', 't_heat_sp', 't_heat_vip',\n",
       "        't_rail_min_sp', 't_rail_min_vip', 't_vent_sp', 't_ventlee_vip',\n",
       "        't_ventwind_vip', 'water_sup', 'water_sup_intervals_sp_min',\n",
       "        'water_sup_intervals_vip_min', 'window_pos_lee_sp',\n",
       "        'window_pos_lee_vip'],\n",
       "       dtype='object'),\n",
       " 50)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIGIGC.columns,len(DIGIGC.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "literary-hello",
   "metadata": {},
   "outputs": [],
   "source": [
    "digi_setpoint_columns=[\"assim_sp\",\"co2_sp\",\"dx_sp\",\"int_blue_sp\",\"int_farred_sp\", \\\n",
    "        \"int_red_sp\",\"int_white_sp\",\"scr_blck_sp\",\"scr_enrg_sp\",\"t_grow_min_sp\",\\\n",
    "        \"t_heat_sp\",\"t_rail_min_sp\",\"t_vent_sp\",\"water_sup_intervals_sp_min\",\\\n",
    "        \"window_pos_lee_sp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "other-indicator",
   "metadata": {},
   "outputs": [],
   "source": [
    "DIGIGC[digi_setpoint_columns]=DIGIGC[auto_setpoint_columns].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "sexual-trance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'assim_sp'}>,\n",
       "        <AxesSubplot:title={'center':'co2_sp'}>,\n",
       "        <AxesSubplot:title={'center':'dx_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_blue_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'int_farred_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_red_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_white_sp'}>,\n",
       "        <AxesSubplot:title={'center':'scr_blck_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'scr_enrg_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_grow_min_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_heat_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_rail_min_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'t_vent_sp'}>,\n",
       "        <AxesSubplot:title={'center':'water_sup_intervals_sp_min'}>,\n",
       "        <AxesSubplot:title={'center':'window_pos_lee_sp'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DIGIGC[digi_setpoint_columns].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eleven-january",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assim_sp</th>\n",
       "      <th>co2_sp</th>\n",
       "      <th>dx_sp</th>\n",
       "      <th>int_blue_sp</th>\n",
       "      <th>int_farred_sp</th>\n",
       "      <th>int_red_sp</th>\n",
       "      <th>int_white_sp</th>\n",
       "      <th>scr_blck_sp</th>\n",
       "      <th>scr_enrg_sp</th>\n",
       "      <th>t_grow_min_sp</th>\n",
       "      <th>t_heat_sp</th>\n",
       "      <th>t_rail_min_sp</th>\n",
       "      <th>t_vent_sp</th>\n",
       "      <th>water_sup_intervals_sp_min</th>\n",
       "      <th>window_pos_lee_sp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47272.000000</td>\n",
       "      <td>47293.000000</td>\n",
       "      <td>47293.000000</td>\n",
       "      <td>46855.000000</td>\n",
       "      <td>46855.000000</td>\n",
       "      <td>46855.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47277.000000</td>\n",
       "      <td>47276.000000</td>\n",
       "      <td>46532.000000</td>\n",
       "      <td>47292.000000</td>\n",
       "      <td>47270.000000</td>\n",
       "      <td>47287.000000</td>\n",
       "      <td>46563.000000</td>\n",
       "      <td>47264.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.993824</td>\n",
       "      <td>404.079926</td>\n",
       "      <td>1.779695</td>\n",
       "      <td>720.098206</td>\n",
       "      <td>652.929260</td>\n",
       "      <td>712.382874</td>\n",
       "      <td>720.836914</td>\n",
       "      <td>44.624256</td>\n",
       "      <td>17.889965</td>\n",
       "      <td>21.784901</td>\n",
       "      <td>18.299086</td>\n",
       "      <td>1.075145</td>\n",
       "      <td>21.698666</td>\n",
       "      <td>1041.301880</td>\n",
       "      <td>0.019381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.582706</td>\n",
       "      <td>323.586761</td>\n",
       "      <td>0.551336</td>\n",
       "      <td>445.476501</td>\n",
       "      <td>441.587677</td>\n",
       "      <td>451.767975</td>\n",
       "      <td>444.062683</td>\n",
       "      <td>44.061401</td>\n",
       "      <td>35.846298</td>\n",
       "      <td>17.638073</td>\n",
       "      <td>2.903765</td>\n",
       "      <td>6.064124</td>\n",
       "      <td>4.166959</td>\n",
       "      <td>954.912537</td>\n",
       "      <td>0.152048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.299999</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           assim_sp        co2_sp         dx_sp   int_blue_sp  int_farred_sp  \\\n",
       "count  47272.000000  47293.000000  47293.000000  46855.000000   46855.000000   \n",
       "mean      60.993824    404.079926      1.779695    720.098206     652.929260   \n",
       "std       48.582706    323.586761      0.551336    445.476501     441.587677   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      1.500000      0.000000       0.000000   \n",
       "50%      100.000000    600.000000      2.000000   1000.000000    1000.000000   \n",
       "75%      100.000000    650.000000      2.200000   1000.000000    1000.000000   \n",
       "max      100.000000   1200.000000      6.000000   1000.000000    1000.000000   \n",
       "\n",
       "         int_red_sp  int_white_sp   scr_blck_sp   scr_enrg_sp  t_grow_min_sp  \\\n",
       "count  46855.000000  47795.000000  47277.000000  47276.000000   46532.000000   \n",
       "mean     712.382874    720.836914     44.624256     17.889965      21.784901   \n",
       "std      451.767975    444.062683     44.061401     35.846298      17.638073   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "50%     1000.000000   1000.000000     75.000000      0.000000      33.000000   \n",
       "75%     1000.000000   1000.000000     90.000000      0.000000      37.000000   \n",
       "max     1000.000000   1000.000000    100.000000    100.000000      40.000000   \n",
       "\n",
       "          t_heat_sp  t_rail_min_sp     t_vent_sp  water_sup_intervals_sp_min  \\\n",
       "count  47292.000000   47270.000000  47287.000000                46563.000000   \n",
       "mean      18.299086       1.075145     21.698666                 1041.301880   \n",
       "std        2.903765       6.064124      4.166959                  954.912537   \n",
       "min      -10.000000       0.000000      0.000000                    0.000000   \n",
       "25%       16.000000       0.000000     19.000000                   60.000000   \n",
       "50%       17.500000       0.000000     20.299999                 1030.000000   \n",
       "75%       21.000000       0.000000     23.000000                 2000.000000   \n",
       "max       24.000000      50.000000     35.000000                 2000.000000   \n",
       "\n",
       "       window_pos_lee_sp  \n",
       "count       47264.000000  \n",
       "mean            0.019381  \n",
       "std             0.152048  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             0.000000  \n",
       "75%             0.000000  \n",
       "max             4.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIGIGC[digi_setpoint_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "plastic-disease",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "DIGIGC_actions={}\n",
    "for x in auto_setpoint_columns:\n",
    "    DIGIGC_actions[x]={\n",
    "        \"values\":DIGIGC[x].unique(),\n",
    "        \"length\":len(DIGIGC[x].unique())\n",
    "    }\n",
    "# pprint(AUTOGC_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "initial-longer",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except ImportError:  # Python 3.x\n",
    "    import pickle\n",
    "\n",
    "with open('DIGIGC_actions.pickle', 'wb') as fp:\n",
    "    pickle.dump(DIGIGC_actions, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "living-cliff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kaypee/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,22,23,25,27,29,31,33,35,37,39,41,43,44,45,47,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "IUACGC=pd.read_csv(\"datasets/IUACAAS/GreenhouseClimate.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "characteristic-image",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['%time', 'AssimLight', 'BlackScr', 'CO2air', 'Cum_irr', 'EC_drain_PC',\n",
       "        'EnScr', 'HumDef', 'PipeGrow', 'PipeLow', 'Rhair', 'Tair', 'Tot_PAR',\n",
       "        'Tot_PAR_Lamps', 'VentLee', 'Ventwind', 'assim_sp', 'assim_vip',\n",
       "        'co2_dos', 'co2_sp', 'co2_vip', 'dx_sp', 'dx_vip', 'int_blue_sp',\n",
       "        'int_blue_vip', 'int_farred_sp', 'int_farred_vip', 'int_red_sp',\n",
       "        'int_red_vip', 'int_white_sp', 'int_white_vip', 'pH_drain_PC',\n",
       "        'scr_blck_sp', 'scr_blck_vip', 'scr_enrg_sp', 'scr_enrg_vip',\n",
       "        't_grow_min_sp', 't_grow_min_vip', 't_heat_sp', 't_heat_vip',\n",
       "        't_rail_min_sp', 't_rail_min_vip', 't_vent_sp', 't_ventlee_vip',\n",
       "        't_ventwind_vip', 'water_sup', 'water_sup_intervals_sp_min',\n",
       "        'water_sup_intervals_vip_min', 'window_pos_lee_sp',\n",
       "        'window_pos_lee_vip'],\n",
       "       dtype='object'),\n",
       " 50)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IUACGC.columns,len(IUACGC.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "capital-metro",
   "metadata": {},
   "outputs": [],
   "source": [
    "iuac_setpoint_columns=[\"assim_sp\",\"co2_sp\",\"dx_sp\",\"int_blue_sp\",\"int_farred_sp\", \\\n",
    "        \"int_red_sp\",\"int_white_sp\",\"scr_blck_sp\",\"scr_enrg_sp\",\"t_grow_min_sp\",\\\n",
    "        \"t_heat_sp\",\"t_rail_min_sp\",\"t_vent_sp\",\"water_sup_intervals_sp_min\",\\\n",
    "        \"window_pos_lee_sp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "printable-difficulty",
   "metadata": {},
   "outputs": [],
   "source": [
    "IUACGC[iuac_setpoint_columns]=IUACGC[iuac_setpoint_columns].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "suspended-queens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'assim_sp'}>,\n",
       "        <AxesSubplot:title={'center':'co2_sp'}>,\n",
       "        <AxesSubplot:title={'center':'dx_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_blue_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'int_farred_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_red_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_white_sp'}>,\n",
       "        <AxesSubplot:title={'center':'scr_blck_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'scr_enrg_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_grow_min_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_heat_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_rail_min_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'t_vent_sp'}>,\n",
       "        <AxesSubplot:title={'center':'water_sup_intervals_sp_min'}>,\n",
       "        <AxesSubplot:title={'center':'window_pos_lee_sp'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IUACGC[iuac_setpoint_columns].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "organizational-final",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assim_sp</th>\n",
       "      <th>co2_sp</th>\n",
       "      <th>dx_sp</th>\n",
       "      <th>int_blue_sp</th>\n",
       "      <th>int_farred_sp</th>\n",
       "      <th>int_red_sp</th>\n",
       "      <th>int_white_sp</th>\n",
       "      <th>scr_blck_sp</th>\n",
       "      <th>scr_enrg_sp</th>\n",
       "      <th>t_grow_min_sp</th>\n",
       "      <th>t_heat_sp</th>\n",
       "      <th>t_rail_min_sp</th>\n",
       "      <th>t_vent_sp</th>\n",
       "      <th>water_sup_intervals_sp_min</th>\n",
       "      <th>window_pos_lee_sp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45369.000000</td>\n",
       "      <td>45368.000000</td>\n",
       "      <td>45350.000000</td>\n",
       "      <td>47569.000000</td>\n",
       "      <td>47569.000000</td>\n",
       "      <td>47569.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>45487.000000</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>45623.000000</td>\n",
       "      <td>734.0</td>\n",
       "      <td>45605.000000</td>\n",
       "      <td>46647.000000</td>\n",
       "      <td>45364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.443760</td>\n",
       "      <td>432.144867</td>\n",
       "      <td>3.802381</td>\n",
       "      <td>941.367249</td>\n",
       "      <td>0.712649</td>\n",
       "      <td>944.604675</td>\n",
       "      <td>947.726746</td>\n",
       "      <td>50.078087</td>\n",
       "      <td>32.497284</td>\n",
       "      <td>18.055555</td>\n",
       "      <td>19.812653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.858915</td>\n",
       "      <td>1077.993286</td>\n",
       "      <td>2.975791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.557144</td>\n",
       "      <td>369.309967</td>\n",
       "      <td>2.541841</td>\n",
       "      <td>233.569458</td>\n",
       "      <td>8.411808</td>\n",
       "      <td>227.346390</td>\n",
       "      <td>213.652954</td>\n",
       "      <td>45.095490</td>\n",
       "      <td>44.894405</td>\n",
       "      <td>19.852596</td>\n",
       "      <td>3.727424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.046603</td>\n",
       "      <td>941.153137</td>\n",
       "      <td>1.400459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.299999</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           assim_sp        co2_sp         dx_sp   int_blue_sp  int_farred_sp  \\\n",
       "count  45369.000000  45368.000000  45350.000000  47569.000000   47569.000000   \n",
       "mean      44.443760    432.144867      3.802381    941.367249       0.712649   \n",
       "std       49.557144    369.309967      2.541841    233.569458       8.411808   \n",
       "min        0.000000      0.000000      1.200000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      2.200000   1000.000000       0.000000   \n",
       "50%        0.000000    600.000000      2.900000   1000.000000       0.000000   \n",
       "75%      100.000000    800.000000      4.300000   1000.000000       0.000000   \n",
       "max      100.000000   1000.000000     13.400000   1000.000000     100.000000   \n",
       "\n",
       "         int_red_sp  int_white_sp   scr_blck_sp  scr_enrg_sp  t_grow_min_sp  \\\n",
       "count  47569.000000  47795.000000  45487.000000   736.000000     576.000000   \n",
       "mean     944.604675    947.726746     50.078087    32.497284      18.055555   \n",
       "std      227.346390    213.652954     45.095490    44.894405      19.852596   \n",
       "min        0.000000      0.000000      0.000000     0.000000       0.000000   \n",
       "25%     1000.000000   1000.000000      0.000000     0.000000       0.000000   \n",
       "50%     1000.000000   1000.000000     80.000000     0.000000       0.000000   \n",
       "75%     1000.000000   1000.000000     90.000000    95.000000      40.000000   \n",
       "max     1000.000000   1000.000000     99.000000    95.000000      40.000000   \n",
       "\n",
       "          t_heat_sp  t_rail_min_sp     t_vent_sp  water_sup_intervals_sp_min  \\\n",
       "count  45623.000000          734.0  45605.000000                46647.000000   \n",
       "mean      19.812653            0.0     22.858915                 1077.993286   \n",
       "std        3.727424            0.0      4.046603                  941.153137   \n",
       "min       10.000000            0.0     13.000000                    0.000000   \n",
       "25%       17.500000            0.0     20.100000                   50.000000   \n",
       "50%       18.400000            0.0     21.900000                 1950.000000   \n",
       "75%       22.400000            0.0     26.299999                 1950.000000   \n",
       "max       50.000000            0.0     35.000000                 2000.000000   \n",
       "\n",
       "       window_pos_lee_sp  \n",
       "count       45364.000000  \n",
       "mean            2.975791  \n",
       "std             1.400459  \n",
       "min             0.000000  \n",
       "25%             2.000000  \n",
       "50%             4.000000  \n",
       "75%             4.000000  \n",
       "max             4.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IUACGC[iuac_setpoint_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "floating-brook",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "IUACGC_actions={}\n",
    "for x in auto_setpoint_columns:\n",
    "    IUACGC_actions[x]={\n",
    "        \"values\":IUACGC[x].unique(),\n",
    "        \"length\":len(IUACGC[x].unique())\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "exotic-juice",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except ImportError:  # Python 3.x\n",
    "    import pickle\n",
    "\n",
    "with open('IUACGC_actions.pickle', 'wb') as fp:\n",
    "    pickle.dump(IUACGC_actions, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "crazy-canberra",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kaypee/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,22,23,25,27,29,31,33,35,37,39,41,43,44,45,47,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "ATORGC=pd.read_csv(\"datasets/TheAutomators/GreenhouseClimate.csv\",sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cloudy-privilege",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['%time', 'AssimLight', 'BlackScr', 'CO2air', 'Cum_irr', 'EC_drain_PC',\n",
       "        'EnScr', 'HumDef', 'PipeGrow', 'PipeLow', 'Rhair', 'Tair', 'Tot_PAR',\n",
       "        'Tot_PAR_Lamps', 'VentLee', 'Ventwind', 'assim_sp', 'assim_vip',\n",
       "        'co2_dos', 'co2_sp', 'co2_vip', 'dx_sp', 'dx_vip', 'int_blue_sp',\n",
       "        'int_blue_vip', 'int_farred_sp', 'int_farred_vip', 'int_red_sp',\n",
       "        'int_red_vip', 'int_white_sp', 'int_white_vip', 'pH_drain_PC',\n",
       "        'scr_blck_sp', 'scr_blck_vip', 'scr_enrg_sp', 'scr_enrg_vip',\n",
       "        't_grow_min_sp', 't_grow_min_vip', 't_heat_sp', 't_heat_vip',\n",
       "        't_rail_min_sp', 't_rail_min_vip', 't_vent_sp', 't_ventlee_vip',\n",
       "        't_ventwind_vip', 'water_sup', 'water_sup_intervals_sp_min',\n",
       "        'water_sup_intervals_vip_min', 'window_pos_lee_sp',\n",
       "        'window_pos_lee_vip'],\n",
       "       dtype='object'),\n",
       " 50)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATORGC.columns,len(ATORGC.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "protective-editor",
   "metadata": {},
   "outputs": [],
   "source": [
    "ator_setpoint_columns=[\"assim_sp\",\"co2_sp\",\"dx_sp\",\"int_blue_sp\",\"int_farred_sp\", \\\n",
    "        \"int_red_sp\",\"int_white_sp\",\"scr_blck_sp\",\"scr_enrg_sp\",\"t_grow_min_sp\",\\\n",
    "        \"t_heat_sp\",\"t_rail_min_sp\",\"t_vent_sp\",\"water_sup_intervals_sp_min\",\\\n",
    "        \"window_pos_lee_sp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "insured-error",
   "metadata": {},
   "outputs": [],
   "source": [
    "ATORGC[ator_setpoint_columns]=ATORGC[ator_setpoint_columns].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "impossible-reform",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'assim_sp'}>,\n",
       "        <AxesSubplot:title={'center':'co2_sp'}>,\n",
       "        <AxesSubplot:title={'center':'dx_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_blue_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'int_farred_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_red_sp'}>,\n",
       "        <AxesSubplot:title={'center':'int_white_sp'}>,\n",
       "        <AxesSubplot:title={'center':'scr_blck_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'scr_enrg_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_grow_min_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_heat_sp'}>,\n",
       "        <AxesSubplot:title={'center':'t_rail_min_sp'}>],\n",
       "       [<AxesSubplot:title={'center':'t_vent_sp'}>,\n",
       "        <AxesSubplot:title={'center':'water_sup_intervals_sp_min'}>,\n",
       "        <AxesSubplot:title={'center':'window_pos_lee_sp'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ATORGC[ator_setpoint_columns].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "sharing-cricket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assim_sp</th>\n",
       "      <th>co2_sp</th>\n",
       "      <th>dx_sp</th>\n",
       "      <th>int_blue_sp</th>\n",
       "      <th>int_farred_sp</th>\n",
       "      <th>int_red_sp</th>\n",
       "      <th>int_white_sp</th>\n",
       "      <th>scr_blck_sp</th>\n",
       "      <th>scr_enrg_sp</th>\n",
       "      <th>t_grow_min_sp</th>\n",
       "      <th>t_heat_sp</th>\n",
       "      <th>t_rail_min_sp</th>\n",
       "      <th>t_vent_sp</th>\n",
       "      <th>water_sup_intervals_sp_min</th>\n",
       "      <th>window_pos_lee_sp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47243.000000</td>\n",
       "      <td>47165.000000</td>\n",
       "      <td>47157.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47795.000000</td>\n",
       "      <td>47255.000000</td>\n",
       "      <td>47245.000000</td>\n",
       "      <td>46521.000000</td>\n",
       "      <td>47269.000000</td>\n",
       "      <td>47248.000000</td>\n",
       "      <td>47267.000000</td>\n",
       "      <td>46627.000000</td>\n",
       "      <td>47249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.650192</td>\n",
       "      <td>720.457825</td>\n",
       "      <td>3.461632</td>\n",
       "      <td>2.194811</td>\n",
       "      <td>470.950256</td>\n",
       "      <td>487.939453</td>\n",
       "      <td>472.435791</td>\n",
       "      <td>51.998688</td>\n",
       "      <td>0.505514</td>\n",
       "      <td>15.152834</td>\n",
       "      <td>19.724913</td>\n",
       "      <td>17.399889</td>\n",
       "      <td>21.346319</td>\n",
       "      <td>715.458679</td>\n",
       "      <td>0.652621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.368790</td>\n",
       "      <td>203.591812</td>\n",
       "      <td>1.733395</td>\n",
       "      <td>39.391045</td>\n",
       "      <td>498.506134</td>\n",
       "      <td>499.206207</td>\n",
       "      <td>498.590485</td>\n",
       "      <td>48.024448</td>\n",
       "      <td>6.893989</td>\n",
       "      <td>16.331579</td>\n",
       "      <td>2.253413</td>\n",
       "      <td>17.460819</td>\n",
       "      <td>2.658390</td>\n",
       "      <td>564.522278</td>\n",
       "      <td>0.948292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.200001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>836.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>21.700001</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           assim_sp        co2_sp         dx_sp   int_blue_sp  int_farred_sp  \\\n",
       "count  47243.000000  47165.000000  47157.000000  47795.000000   47795.000000   \n",
       "mean      57.650192    720.457825      3.461632      2.194811     470.950256   \n",
       "std       49.368790    203.591812      1.733395     39.391045     498.506134   \n",
       "min        0.000000    300.000000      0.900000      0.000000       0.000000   \n",
       "25%        0.000000    476.000000      2.300000      0.000000       0.000000   \n",
       "50%      100.000000    836.000000      3.000000      0.000000       0.000000   \n",
       "75%      100.000000    898.000000      4.000000      0.000000    1000.000000   \n",
       "max      100.000000    967.000000      9.400000   1000.000000    1000.000000   \n",
       "\n",
       "         int_red_sp  int_white_sp   scr_blck_sp   scr_enrg_sp  t_grow_min_sp  \\\n",
       "count  47795.000000  47795.000000  47255.000000  47245.000000   46521.000000   \n",
       "mean     487.939453    472.435791     51.998688      0.505514      15.152834   \n",
       "std      499.206207    498.590485     48.024448      6.893989      16.331579   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "50%        0.000000      0.000000     85.000000      0.000000       0.000000   \n",
       "75%     1000.000000   1000.000000    100.000000      0.000000      30.000000   \n",
       "max     1000.000000   1000.000000    100.000000     95.000000      40.000000   \n",
       "\n",
       "          t_heat_sp  t_rail_min_sp     t_vent_sp  water_sup_intervals_sp_min  \\\n",
       "count  47269.000000   47248.000000  47267.000000                46627.000000   \n",
       "mean      19.724913      17.399889     21.346319                  715.458679   \n",
       "std        2.253413      17.460819      2.658390                  564.522278   \n",
       "min        6.800000       0.000000     13.000000                    5.000000   \n",
       "25%       18.200001       0.000000     19.299999                   60.000000   \n",
       "50%       19.600000      30.000000     20.900000                  999.000000   \n",
       "75%       21.700001      35.000000     23.400000                  999.000000   \n",
       "max       27.500000      40.000000     37.500000                 2000.000000   \n",
       "\n",
       "       window_pos_lee_sp  \n",
       "count       47249.000000  \n",
       "mean            0.652621  \n",
       "std             0.948292  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             0.000000  \n",
       "75%             1.000000  \n",
       "max             5.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ATORGC[ator_setpoint_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "revised-colon",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "ATORGC_actions={}\n",
    "for x in auto_setpoint_columns:\n",
    "    ATORGC_actions[x]={\n",
    "        \"values\":ATORGC[x].unique(),\n",
    "        \"length\":len(ATORGC[x].unique())\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "local-testing",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except ImportError:  # Python 3.x\n",
    "    import pickle\n",
    "\n",
    "with open('ATORGC_actions.pickle', 'wb') as fp:\n",
    "    pickle.dump(ATORGC_actions, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "scientific-recipient",
   "metadata": {},
   "outputs": [],
   "source": [
    "action_pickles=[\"AICUGC_actions.pickle\",\"ATORGC_actions.pickle\",\"AUTOGC_actions.pickle\",\"DIGIGC_actions.pickle\",\"IUACGC_actions.pickle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "female-lindsay",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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