.ipynb_checkpoints/Vary elements PhOLED 300-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(304, 31)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import pickle\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_excel('C:/Users/acc_a/OneDrive/PhOLED 300.xlsx', sheetname='Sheet2')\n",
    "df = df[list(df.columns)[1:] ]\n",
    "df.dropna(axis=0, inplace=True)\n",
    "\n",
    "X = df[list(df.columns)[:-1]]\n",
    "y = df[['cd/A']]\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:  0.895240129661\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25, random_state=1)\n",
    "\n",
    "filename = 'C:/Anaconda3/projects/oled/PhOLED 300/rforest_300.sav'\n",
    "\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "\n",
    "print('R-squared: ', loaded_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### vary triplet level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(348840, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(38760,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.read_pickle('C:/Anaconda3/projects/oled/vary triplet/triplet.pkl')\n",
    "\n",
    "new_X = new_df[:]\n",
    "print(new_X.shape)\n",
    "\n",
    "y_pred = loaded_model.predict(new_X)\n",
    "y_pred = [round(float(i), 2) for i in y_pred]\n",
    "\n",
    "new_X['cd/a'] = y_pred\n",
    "\n",
    "triplet3 = new_X.loc[new_X['triplet_HOST'] == 3]\n",
    "triplet3['cd/a'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>triplet_HTL</th>\n",
       "      <th>LUMO_EML</th>\n",
       "      <th>HOMO_EML</th>\n",
       "      <th>triplet_HOST</th>\n",
       "      <th>thickness_EML</th>\n",
       "      <th>lumo_dopant</th>\n",
       "      <th>homo_dopant</th>\n",
       "      <th>triplet_dopant</th>\n",
       "      <th>dopant_percentage</th>\n",
       "      <th>triplet_ETL</th>\n",
       "      <th>total ETL</th>\n",
       "      <th>lumo_ETL1</th>\n",
       "      <th>homo_ETL1</th>\n",
       "      <th>thickness_ETL1</th>\n",
       "      <th>lumo_ETL2</th>\n",
       "      <th>homo_ETL2</th>\n",
       "      <th>thickness_ETL2</th>\n",
       "      <th>wf_1</th>\n",
       "      <th>thickness_EIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>348840.00000</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>3.488400e+05</td>\n",
       "      <td>3.488400e+05</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.0</td>\n",
       "      <td>348840.000000</td>\n",
       "      <td>348840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.76250</td>\n",
       "      <td>2.418824</td>\n",
       "      <td>5.907647</td>\n",
       "      <td>2.834314</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.801579</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.839474</td>\n",
       "      <td>6.517368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.839474</td>\n",
       "      <td>6.517368</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.326667</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.22027</td>\n",
       "      <td>0.250738</td>\n",
       "      <td>0.417254</td>\n",
       "      <td>0.116831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.212102e-11</td>\n",
       "      <td>1.607117e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.326617</td>\n",
       "      <td>0.406369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.326617</td>\n",
       "      <td>0.406369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857957</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.30000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>5.560000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.64750</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.83500</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.94000</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.700000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        triplet_HTL       LUMO_EML       HOMO_EML   triplet_HOST  \\\n",
       "count  348840.00000  348840.000000  348840.000000  348840.000000   \n",
       "mean        2.76250       2.418824       5.907647       2.834314   \n",
       "std         0.22027       0.250738       0.417254       0.116831   \n",
       "min         2.30000       2.020000       5.560000       2.640000   \n",
       "25%         2.64750       2.280000       5.710000       2.750000   \n",
       "50%         2.83500       2.400000       5.850000       2.850000   \n",
       "75%         2.94000       2.460000       5.900000       2.940000   \n",
       "max         3.00000       3.000000       7.500000       3.100000   \n",
       "\n",
       "       thickness_EML  lumo_dopant   homo_dopant  triplet_dopant  \\\n",
       "count       348840.0     348840.0  3.488400e+05    3.488400e+05   \n",
       "mean            20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "std              0.0          0.0  3.212102e-11    1.607117e-11   \n",
       "min             20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "25%             20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "50%             20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "75%             20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "max             20.0          3.0  5.700000e+00    2.700000e+00   \n",
       "\n",
       "       dopant_percentage    triplet_ETL  total ETL      lumo_ETL1  \\\n",
       "count           348840.0  348840.000000   348840.0  348840.000000   \n",
       "mean                10.0       2.801579       35.0       2.839474   \n",
       "std                  0.0       0.225838        0.0       0.326617   \n",
       "min                 10.0       2.500000       35.0       2.260000   \n",
       "25%                 10.0       2.690000       35.0       2.600000   \n",
       "50%                 10.0       2.750000       35.0       2.750000   \n",
       "75%                 10.0       2.800000       35.0       3.000000   \n",
       "max                 10.0       3.400000       35.0       3.500000   \n",
       "\n",
       "           homo_ETL1  thickness_ETL1      lumo_ETL2      homo_ETL2  \\\n",
       "count  348840.000000        348840.0  348840.000000  348840.000000   \n",
       "mean        6.517368             0.0       2.839474       6.517368   \n",
       "std         0.406369             0.0       0.326617       0.406369   \n",
       "min         5.850000             0.0       2.260000       5.850000   \n",
       "25%         6.130000             0.0       2.600000       6.130000   \n",
       "50%         6.500000             0.0       2.750000       6.500000   \n",
       "75%         6.800000             0.0       3.000000       6.800000   \n",
       "max         7.500000             0.0       3.500000       7.500000   \n",
       "\n",
       "       thickness_ETL2           wf_1  thickness_EIL  \n",
       "count        348840.0  348840.000000  348840.000000  \n",
       "mean             35.0       3.326667       0.666667  \n",
       "std               0.0       0.857957       0.471405  \n",
       "min              35.0       2.200000       0.000000  \n",
       "25%              35.0       2.200000       0.000000  \n",
       "50%              35.0       3.500000       1.000000  \n",
       "75%              35.0       4.280000       1.000000  \n",
       "max              35.0       4.280000       1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.iloc[:, 12:].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer(name): \n",
    "    column = name\n",
    "    HTL = {'50': device50[column], '45': device45[column] , '40': device40[column] , '35': device35[column] , \n",
    "           '30': device30[column] , '25': device25[column], '20': device20[column], '15': device15[column], \n",
    "           '10': device10[column], \n",
    "           '0': device[column]}\n",
    "    sequence = ['50', '45', '40', '35', '30', '25', '20', '15', '10', '0']\n",
    "    df = pd.DataFrame(data=HTL).reindex(columns=sequence)\n",
    "\n",
    "    # boxplot\n",
    "    f, ax = plt.subplots(figsize=(12, 10))\n",
    "    fig = sns.boxplot(data=df)\n",
    "    plt.xticks(rotation=90,fontsize=12)\n",
    "    plt.yticks(fontsize=12)\n",
    "    plt.title(name, fontsize=20);\n",
    "    return df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1st trio, triplet energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>50</th>\n",
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.360000e+02</td>\n",
       "      <td>7971.000000</td>\n",
       "      <td>13797.000000</td>\n",
       "      <td>4429.000000</td>\n",
       "      <td>5801.000000</td>\n",
       "      <td>6622.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.899733</td>\n",
       "      <td>2.852424</td>\n",
       "      <td>2.626471</td>\n",
       "      <td>2.507176</td>\n",
       "      <td>2.724112</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.131695e-15</td>\n",
       "      <td>0.089009</td>\n",
       "      <td>0.141478</td>\n",
       "      <td>0.216096</td>\n",
       "      <td>0.193313</td>\n",
       "      <td>0.241193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40            35           30            25           20  \\\n",
       "count  0.0  0.0  0.0  1.360000e+02  7971.000000  13797.000000  4429.000000   \n",
       "mean   NaN  NaN  NaN  2.800000e+00     2.899733      2.852424     2.626471   \n",
       "std    NaN  NaN  NaN  7.131695e-15     0.089009      0.141478     0.216096   \n",
       "min    NaN  NaN  NaN  2.800000e+00     2.580000      2.300000     2.300000   \n",
       "25%    NaN  NaN  NaN  2.800000e+00     2.800000      2.800000     2.580000   \n",
       "50%    NaN  NaN  NaN  2.800000e+00     2.940000      2.870000     2.580000   \n",
       "75%    NaN  NaN  NaN  2.800000e+00     2.940000      2.940000     2.870000   \n",
       "max    NaN  NaN  NaN  2.800000e+00     3.000000      3.000000     3.000000   \n",
       "\n",
       "                15           10    0  \n",
       "count  5801.000000  6622.000000  4.0  \n",
       "mean      2.507176     2.724112  2.3  \n",
       "std       0.193313     0.241193  0.0  \n",
       "min       2.300000     2.300000  2.3  \n",
       "25%       2.300000     2.580000  2.3  \n",
       "50%       2.670000     2.800000  2.3  \n",
       "75%       2.670000     2.940000  2.3  \n",
       "max       3.000000     3.000000  2.3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39af04da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_HTL')"
   ]
  },
  {
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>7971.000000</td>\n",
       "      <td>13797.000000</td>\n",
       "      <td>4429.000000</td>\n",
       "      <td>5801.000000</td>\n",
       "      <td>6622.000000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.976103</td>\n",
       "      <td>2.932431</td>\n",
       "      <td>2.810882</td>\n",
       "      <td>2.825439</td>\n",
       "      <td>2.806816</td>\n",
       "      <td>2.600587</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209204</td>\n",
       "      <td>0.272059</td>\n",
       "      <td>0.183932</td>\n",
       "      <td>0.194090</td>\n",
       "      <td>0.212171</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35           30            25           20  \\\n",
       "count  0.0  0.0  0.0  136.000000  7971.000000  13797.000000  4429.000000   \n",
       "mean   NaN  NaN  NaN    2.976103     2.932431      2.810882     2.825439   \n",
       "std    NaN  NaN  NaN    0.209204     0.272059      0.183932     0.194090   \n",
       "min    NaN  NaN  NaN    2.690000     2.690000      2.690000     2.690000   \n",
       "25%    NaN  NaN  NaN    2.800000     2.700000      2.700000     2.740000   \n",
       "50%    NaN  NaN  NaN    2.990000     2.800000      2.750000     2.750000   \n",
       "75%    NaN  NaN  NaN    2.990000     2.990000      2.800000     2.800000   \n",
       "max    NaN  NaN  NaN    3.400000     3.400000      3.400000     3.400000   \n",
       "\n",
       "                15           10     0  \n",
       "count  5801.000000  6622.000000  4.00  \n",
       "mean      2.806816     2.600587  2.75  \n",
       "std       0.212171     0.106000  0.00  \n",
       "min       2.500000     2.500000  2.75  \n",
       "25%       2.700000     2.500000  2.75  \n",
       "50%       2.750000     2.600000  2.75  \n",
       "75%       2.800000     2.600000  2.75  \n",
       "max       3.400000     3.400000  2.75  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39aeedc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_ETL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[new_X['triplet_HOST'] == 2.9]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]\n"
   ]
  },
  {
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1221.000000</td>\n",
       "      <td>15744.000000</td>\n",
       "      <td>10498.000000</td>\n",
       "      <td>3407.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>5152.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.902948</td>\n",
       "      <td>2.905484</td>\n",
       "      <td>2.803682</td>\n",
       "      <td>2.534458</td>\n",
       "      <td>2.560619</td>\n",
       "      <td>2.734472</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087899</td>\n",
       "      <td>0.086445</td>\n",
       "      <td>0.158706</td>\n",
       "      <td>0.171250</td>\n",
       "      <td>0.231268</td>\n",
       "      <td>0.230440</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25           20  \\\n",
       "count  0.0  0.0  0.0  1221.000000  15744.000000  10498.000000  3407.000000   \n",
       "mean   NaN  NaN  NaN     2.902948      2.905484      2.803682     2.534458   \n",
       "std    NaN  NaN  NaN     0.087899      0.086445      0.158706     0.171250   \n",
       "min    NaN  NaN  NaN     2.800000      2.580000      2.300000     2.300000   \n",
       "25%    NaN  NaN  NaN     2.800000      2.870000      2.580000     2.300000   \n",
       "50%    NaN  NaN  NaN     2.940000      2.940000      2.870000     2.580000   \n",
       "75%    NaN  NaN  NaN     3.000000      2.940000      2.940000     2.670000   \n",
       "max    NaN  NaN  NaN     3.000000      3.000000      3.000000     2.940000   \n",
       "\n",
       "                15           10    0  \n",
       "count  9578.000000  5152.000000  0.0  \n",
       "mean      2.560619     2.734472  NaN  \n",
       "std       0.231268     0.230440  NaN  \n",
       "min       2.300000     2.300000  NaN  \n",
       "25%       2.300000     2.580000  NaN  \n",
       "50%       2.670000     2.800000  NaN  \n",
       "75%       2.670000     2.940000  NaN  \n",
       "max       3.000000     3.000000  NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39aefc400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_HTL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1221.000000</td>\n",
       "      <td>15744.000000</td>\n",
       "      <td>10498.000000</td>\n",
       "      <td>3407.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>5152.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.951400</td>\n",
       "      <td>2.882322</td>\n",
       "      <td>2.791078</td>\n",
       "      <td>2.862797</td>\n",
       "      <td>2.743407</td>\n",
       "      <td>2.608389</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.199266</td>\n",
       "      <td>0.254073</td>\n",
       "      <td>0.148999</td>\n",
       "      <td>0.231343</td>\n",
       "      <td>0.208722</td>\n",
       "      <td>0.107746</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25           20  \\\n",
       "count  0.0  0.0  0.0  1221.000000  15744.000000  10498.000000  3407.000000   \n",
       "mean   NaN  NaN  NaN     2.951400      2.882322      2.791078     2.862797   \n",
       "std    NaN  NaN  NaN     0.199266      0.254073      0.148999     0.231343   \n",
       "min    NaN  NaN  NaN     2.690000      2.690000      2.690000     2.500000   \n",
       "25%    NaN  NaN  NaN     2.750000      2.700000      2.700000     2.700000   \n",
       "50%    NaN  NaN  NaN     2.990000      2.760000      2.750000     2.750000   \n",
       "75%    NaN  NaN  NaN     2.990000      2.950000      2.800000     2.950000   \n",
       "max    NaN  NaN  NaN     3.400000      3.400000      3.400000     3.400000   \n",
       "\n",
       "                15           10    0  \n",
       "count  9578.000000  5152.000000  0.0  \n",
       "mean      2.743407     2.608389  NaN  \n",
       "std       0.208722     0.107746  NaN  \n",
       "min       2.500000     2.500000  NaN  \n",
       "25%       2.600000     2.500000  NaN  \n",
       "50%       2.700000     2.600000  NaN  \n",
       "75%       2.760000     2.600000  NaN  \n",
       "max       3.400000     3.400000  NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39b955e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_ETL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[new_X['triplet_HOST'] == 2.8]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>14392.000000</td>\n",
       "      <td>9726.000000</td>\n",
       "      <td>3073.000000</td>\n",
       "      <td>7616.000000</td>\n",
       "      <td>5456.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.903655</td>\n",
       "      <td>2.905304</td>\n",
       "      <td>2.804019</td>\n",
       "      <td>2.523645</td>\n",
       "      <td>2.544102</td>\n",
       "      <td>2.731085</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084829</td>\n",
       "      <td>0.085503</td>\n",
       "      <td>0.159716</td>\n",
       "      <td>0.162128</td>\n",
       "      <td>0.223682</td>\n",
       "      <td>0.234851</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30           25           20  \\\n",
       "count  0.0  0.0  0.0  777.000000  14392.000000  9726.000000  3073.000000   \n",
       "mean   NaN  NaN  NaN    2.903655      2.905304     2.804019     2.523645   \n",
       "std    NaN  NaN  NaN    0.084829      0.085503     0.159716     0.162128   \n",
       "min    NaN  NaN  NaN    2.800000      2.580000     2.300000     2.300000   \n",
       "25%    NaN  NaN  NaN    2.800000      2.870000     2.580000     2.300000   \n",
       "50%    NaN  NaN  NaN    2.940000      2.940000     2.870000     2.580000   \n",
       "75%    NaN  NaN  NaN    3.000000      2.940000     2.940000     2.670000   \n",
       "max    NaN  NaN  NaN    3.000000      3.000000     3.000000     2.940000   \n",
       "\n",
       "                15           10    0  \n",
       "count  7616.000000  5456.000000  0.0  \n",
       "mean      2.544102     2.731085  NaN  \n",
       "std       0.223682     0.234851  NaN  \n",
       "min       2.300000     2.300000  NaN  \n",
       "25%       2.300000     2.580000  NaN  \n",
       "50%       2.670000     2.800000  NaN  \n",
       "75%       2.670000     2.940000  NaN  \n",
       "max       3.000000     3.000000  NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Q1MjrfPEvmT2iWUa6DCmVyerT1368vt3JVm7bOM8ya23vi/33PPpkdbZseOhJMnWrVeMvL3zzntJLr74NSOvB4zPrl2PZe/efXnbJ66e9Chj9fCeHVk7N8I3W77Okq4ZTpLW2roktyQ5J8kFCx57fpKfTvK/VNWXWmtXJfmV1trmqppb7GNu2DCT1atXHdPg0KuzztqU6enRQjhJHpt9LHsPrNw/2Kw9ZW1Onzl96SvMJGeeeWY2bVq/fEMd3NTMmpH34dq1g1I/ln0/M7Nm2T+vY5nrRDE9PbUix8WxOLjfj9f5VtLxvi+mpk7er5GpqeP3a+REsNQX0J2b5KNJvpjk/Kras2CR703yO/NeMHdTkp9P8vQkDy72cXfu3D3ywNC7n/qpN096hGW1ffujy76NV7ziVXnFK1617NuZb7k/r9nZRc87nPBmZ+dW5Lg4Fgf3+/E630o63vfFzMy6rMlMfvLvv33So4zV2z5xdVbPTB23+/14caQnC0t5Ad3GJHcnub2qLjpMCCfJ7yf5rtbaXxvevjDJfVW1aAgDAMCkLeXM8OVJzk2yZfj2aQddluTmqtpcVf+5tXZDkrtaa/uT7EjyivGPCwAA47OUF9Bdn+T6RR7ePG+5mzK4PAIAAE4IfgMdAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRr9VIWaq1dkuSaJHNJdie5qqrunff4a5NsnbfK05I8M8kzq+p/jm9cAAAYn6PGcGutJbkhyYuq6oHW2suT3J7k3IPLVNV7k7x3uPwpSX4zyc8IYQAAjmdLuUxiX5JLq+qB4e17k5zdWluzyPL/LMlXq+rmcQwIAADL5ahnhqvq/iT3J0lrbSrJtiR3VtX+hcu21s5McnWSFy1l4xs2zGT16lUjjAvA4UxPT016hGUzPT2VTZvWT3qMwzq434/X+VbS8b4vBvPNTXqMZXE8f42cCJZ0zXCStNbWJbklyTlJLlhksX+a5CNVdd9SPubOnbuXunkAjmB29uT8IZ8MPrft2x+d9BiHdXC/H6/zraTjfV/4GunbkZ4sLOndJFpr5yb5VJIDSc6vqq8tsuirkrxn1AEBAGASlvICuo1J7k5yS1W95QjLbUjynAyiGQAAjntLuUzi8gzeOWJLa23LvPsvS3JzVW0e3n5Okgeq6vExzwgAAMtiKS+guz7J9Ys8vHnecvdkEMQAAHBC8BvoAADolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6tnvQAADBO1113bXbu3LEi29qx46EkydatV6zI9jZs2Jhrr71uyctfddVleeSRh5dxokNmZ2eTJK973UUrsr0zznha3vGOm1dkW5zcxDAAJ5WdO3fkwYceTNadsfwbWzX4Mfrg3v3Lv61dj4y8yr59ezM7O5upqZX7h+C5uZXYxmz27du7/BuiC2IYgJPPujOy9qIrJz3FWO297caR11m37vRk1Uy++5+8fRkmmpzf+MDVWbfWlZ6MhyMJAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6NbqpSzUWrskyTVJ5pLsTnJVVd27YJnnJ/m3SZ6W5ECSy6rq98Y7LgAAjM9Rzwy31lqSG5JcUFWbk7w1ye0LlplJ8utJ3lZVL0xyXZJfHv+4AAAwPku5TGJfkkur6oHh7XuTnN1aWzNvme9J8qWq+vjw9p1JfnB8YwIAwPgd9TKJqro/yf1J0lqbSrItyZ1VtX/eYt+a5CuttV9K8oIkX0vyk+MeFgAAxmlJ1wwnSWttXZJbkpyT5IIFD5+S5OVJzq+qz7TWXpHk4621Z1XVvsU+5oYNM1m9etXoUwPwJNPTU5MeYdlMT09l06b1Iy1/srIvDjm2fTG3fANN0Kj7gidb6gvozk3y0SRfzCB49yxY5C+T/FFVfSZJquojrbVfTPItw3UOa+fO3cc0NABPNjt7cv6QTwaf2/btj460/MnKvjjEvjhk1H3RoyM9WVjKC+g2Jrk7ye1VddFhQjhJPpHk2a21Fw/X+c4Mnn7dd0wTAwDACljKmeHLk5ybZEtrbcu8+y9LcnNVba6qr7TWLkzyC8PLKfYl+UdVtXf8IwMAwHgs5QV01ye5fpGHN89b7jeTfMeY5gIAgGXnN9ABANAtMQwAQLfEMAAA3RLDAAB0SwwDANAtMQywBK997Q/mta/9wUmPAcCYiWEAALolhgGOYv4ZYWeHAU4uS/kNdACcCHbtzoEPfnj5t7Nv/+DPU9cs/7Z27U7Wzoy2yq7Hkr17s/e2G5dpqAnZ9Uh2HVg76SlOaI/s2ZG3feLqZd/Onv27kiSnrVm37Nt6ZM+ObJx5+rJv52QmhgFOAhs2bFyxbe3YvSdJsnHESD0ma2dW9HPj5LWSx9Hje/clSdbPnL7s29o483RfI0+RGAY4CVx77XUrtq2tW69IkmzbdtOKbXMU69adnj2r1mTtRVdOepSx2nvbjVm3dgXOxp+kfI2wGNcMAxzFe9/7Hw77dwBOfGIYAIBuuUwCYAmcEQY4OTkzDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0C0xDABAt8QwAADdEsMAAHRLDAMA0K3VS1motXZJkmuSzCXZneSqqrp3wTJvT/LKJDuGd1VVvWqMswIAwFgdNYZbay3JDUleVFUPtNZenuT2JOcuWPSlSS6qqk+Nf0wAABi/pVwmsS/JpVX1wPD2vUnObq2tObhAa+3UJC9M8hOttc+31n6ltbYwlgEA4Lhy1Biuqvur6mNJ0lqbSrItyZ1VtX/eYt+Y5D8n+edJNif5dJKPDJcHAIDj0pKuGU6S1tq6JLckOSfJBfMfq6r7krx83rI/l+TaJM9Oct9iH3PDhpmsXr1qpIEBmKzp6cF5jk2b1k94ksM7ON/JaHp6aqT9bl9MxvH+NcKTLfUFdOcm+WiSLyY5v6r2LHj8byV5QVW9b97dU0keP9LH3blz92jTAjBxs7NzSZLt2x+d8CSHd3C+k9Hs7NxI+92+mIzj/WukR0d6YnLUyyRaaxuT3J3k9qq6aGEID80meUdr7ZuHty9P8oWq+vIxzAsAACtiKWeGL8/gnSO2tNa2zLv/siQ3V9XmqvrD1tqPJ/loa21Vki8nuXj84wIAwPgcNYar6vok1y/y8OZ5y70/yfvHNBcAACw7v4EOAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBuiWEAALolhgEA6JYYBgCgW2IYAIBurV7KQq21S5Jck2Quye4kV1XVvYsse2GS91bVGWObEgAAlsFRzwy31lqSG5JcUFWbk7w1ye2LLPvcJD+3lI8LAACTtpRo3Zfk0qp6YHj73iRnt9bWzF+otTaT5P1Jto53RAAAWB5HvUyiqu5Pcn+StNamkmxLcmdV7V+w6M3D/74w3hEBAGB5LOma4SRpra1LckuSc5JcsOCxH0vyRFW9u7X27KV+zA0bZrJ69aqlLg7AcWB6eipJsmnT+glPcngH5zsZTU9PjbTf7YvJON6/Rniypb6A7twkH03yxSTnV9WeBYu8LslMa+1zSdYkOW3495dX1V8u9nF37tx9TEMDMDmzs3NJku3bH53wJId3cL6T0ezs3Ej73b6YjOP9a6RHR3pictQYbq1tTHJ3kluq6i2HW6aq/va85Z+d5A+HL7YDAIDj1lLODF+e5NwkW1prW+bdf1mSm0UvAAAnqqW8gO76JNcv8vDXhfDwBXenP7WxAABg+Xk/YAAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDolhgGAKBbYhgAgG6JYQAAuiWGAQDo1uqlLNRauyTJNUnmkuxOclVV3btgmSuTXD5c5ktJ3lBVXx3vuAAAMD5HPTPcWmtJbkhyQVVtTvLWJLcvWObFSX4iyUur6nlJ/iTJdeMfFwAAxmcpl0nsS3JpVT0wvH1vkrNba2sOLlBVv5fkuVX1cGttbZJvSvLQ2KcFAIAxOuplElV1f5L7k6S1NpVkW5I7q2r/guUeb61dmOQXMwjonx73sACM1623vi/33PPpkdbZsWNwrmPr1itGWu+8816Siy9+zUjrHLNdj2TvbTeOts6+PckTjy/PPAutPiU59bTR1tn1SLL2zJE3tXfXzvzGB64eaZ3H9+3KgSf2H33BMVi1ek1OOXXdSOvs3bUz69Y+fZkmerKT9muEv7Kka4aTpLW2LsktSc5JcsHhlqmqO5Lc0Vp7Q5JPttaeU1Wzi33MDRtmsnr1qtEmBmBsZmbWZHp6aqR11q5dmyQjrzczsyabNq0faZ1jcdZZm0aeLUkem308ew88sQwTfb21p6zO6TOnjrYYbIUqAAAHN0lEQVTSzKaceeaZI+3DY98X09l7YOTVjsmaU6Zz+sxoLbB+5syR98WxOhm/Rniyqbm5uaMu1Fo7N8lHk3wxyeuras+Cx5+T5Oyq+u3h7VVJ9ic5q6oWvVxi+/ZHj75xAAB4CjZtWr/oM5OlvIBuY5K7k9xeVRctDOGhZyS5rbV28N9vXp3kD48UwgAAMGlLuUzi8iTnJtnSWtsy7/7LktxcVZur6rdaa9cnuau19kSSv0xy4fjHBQCA8VnSZRLLxWUSAAAst6d0mQQAAJysxDAAAN0SwwAAdEsMAwDQLTEMAEC3xDAAAN0SwwAAdEsMAwDQLTEMAEC3xDAAAN0SwwAAdEsMAwDQLTEMAEC3xDAAAN0SwwAAdEsMAwDQLTEMAEC3xDAAAN0SwwAAdEsMAwDQram5ublJzwAAABPhzDAAAN0SwwAAdEsMAwDQLTEMAEC3xDAAAN0SwwAAdEsMAwAwVq2101trp016jqXwPsMAADxlrbXTk/xsklcnWT+8e2eSjyS5uqq+NqnZjmT1pAdYaa21b09ySZJnJjmQ5MtJ7qiq35roYCuotXZxVd3aWptOcmWSLUkeT3JbVb17stOtPMfEIY6NJ3NsOCYWckzAEf1SkvuSvCCDr41k8LVyWZL3Jvn+Cc11RF2dGW6tXZbkjUluy6H/Sd+U5KIk/66qfn5Ss62k1trvV9WLWmvXJvneJNuSTGXwg+63q+raiQ64ghwTT+bYOMSxMeCYOMQx8WSeKD2ZJ0pJa+2LVfU3F3ns/6uqb1vpmZaitzPDVyf52wtP07fW3pHkM0m6+kaW5AeS/G9V9UiStNY+nuRzSbr54RbHxGIcG46NhRwTjomFrklya5J/kQVPlFpr39zxE6U/GN79TUlubq319ERpf2vtW6rqz+bf2Vr76xk8UTou9RbDB5I8fJj7H81x/D9pGT2UZPe82/uSPDGhWSbFMXF4jg3HxkKOCcfEYjxR8kTpoP8zyX9trf1ukr8Y3veNSb4jyesnNtVR9BbDv57ko6219+TQ/6RnJPmRJP/vxKZaeee01v4oyWySn0vyxtba85O8Jcl/nehkK++TOfwx8cMZHC+9WezY+Jfp79jw/WLgSN8vPj3RyVbeYsfED6evY2IhT5Q8UUqSVNXHWmvfluR7kpyTwbuW/U6SN1TV9okOdwS9xfD/kcFF3Jdl8D/pW5LcncGrHN81wblWVFVtaq21JP9rDh0DL0zyxxn8gOvJ1iQ/mkPHxHQGP+TuSPLOCc41EcNj41uTvDSHjo3NSf4ogyDuycLvF1NJ/keS29Pf94tvTfKyDH7gJ4MXx/xxkv97UnNNyMFj4scyiOBVSf57Bj9DPjjBuSbFiZVDnFgZqqoHk3xg0nOMorcY/uWquri19qsZfPPam+QlSU4b3v7ykVY+mVRVJal5d72xql40qXkmpapmk/zC8L8kf/WCoRsnN9XktNbOzOAH/kNJ3tNa+1KSZ2VwDdy7kvz5BMdbaRsziL7fzSB+Ppnku4b3/2o62RettRcl+XCSv5bkztbah6rq/cPHfj9JT983Nif5qSRnZ/Az49J5lwb0ti8We/J88ImSEyuHnih1d2LlRNNbDLfhn9syCOO3J0lr7ccyeDuQ753UYCuptfZwBme55ptprT2SJFV1xspPNRmL7IvTetwXQ/8uyZ9m8K8md2XwA+3dGbxC+qYk/2Bik628+fvivyR5c5L3pL998Y4Mfsjfm8F1j7/WWntZVe3P13/tnOzm74ttST7RWju/032RJKmqP84gfg/efv8Ex5mYw51Y4cTRWwwf9NyqeuXBG1X1C621KyY50Ar7oSQ3ZHAG8A8y+Cb+sSQvn+RQE2JfPNlfr6otrbW1Sf6iqn5xeP97W2tvnORgE7BwX/zS8P7e9sVMVX1i+PdLWmsfyuBJwasnONOkzN8Xr+l8X6S1tvVIj1fVtpWaZdLsixNbb7+O+emttZck+dPW2nMO3tlae1YG1zx1oaruyCD2/q8kL6uq+5Psq6o/r6ou/un3IPvi60y31k6vqr1JLj14Z2ttY5JTJjfWRNgXA9OttbPm3f6hJN82fN/hft6ofsC+eLKDL6593vDv8/973gTnmgT74gTW25nh/5jBP229MMnaJP+gtXZJBi8C+fFJDrbSqupPWmt/N8m7Wmvfmf6Ohb9iXzzJu5J8obX23Kr6SJK01v5OBu+d+daJTrby7IuBn0vy2dbapVX1iara3Vr7/gxefPzMCc+20uyLearq9a21c5P8RlX98qTnmST74sTW1Znhqrq6ql6a5GkZ/LacJPl8ku+sqhPqlY/jUFW7q+q1SX4vyZ5JzzNJ9sXA8IWDP1BVB+bd/ZUkF1fVzRMaayLsi4HhNaDnJ/lv8+777xm8WOxNk5prEuyLw7oigxeiY1+csLr6dcwAADBfV2eGAQBgPjEMAEC3xDAAAN0SwwAAdEsMAwDQrf8fBmTmVWkRmA8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39b9b9a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_HTL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>14392.000000</td>\n",
       "      <td>9726.00000</td>\n",
       "      <td>3073.000000</td>\n",
       "      <td>7616.000000</td>\n",
       "      <td>5456.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.999678</td>\n",
       "      <td>2.884459</td>\n",
       "      <td>2.78743</td>\n",
       "      <td>2.883427</td>\n",
       "      <td>2.752139</td>\n",
       "      <td>2.602879</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.169828</td>\n",
       "      <td>0.253242</td>\n",
       "      <td>0.14768</td>\n",
       "      <td>0.247809</td>\n",
       "      <td>0.201342</td>\n",
       "      <td>0.098102</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.69000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.70000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>2.75000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.76000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.40000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30          25           20  \\\n",
       "count  0.0  0.0  0.0  777.000000  14392.000000  9726.00000  3073.000000   \n",
       "mean   NaN  NaN  NaN    2.999678      2.884459     2.78743     2.883427   \n",
       "std    NaN  NaN  NaN    0.169828      0.253242     0.14768     0.247809   \n",
       "min    NaN  NaN  NaN    2.690000      2.690000     2.69000     2.500000   \n",
       "25%    NaN  NaN  NaN    2.990000      2.700000     2.70000     2.700000   \n",
       "50%    NaN  NaN  NaN    2.990000      2.760000     2.75000     2.750000   \n",
       "75%    NaN  NaN  NaN    2.990000      2.990000     2.76000     2.990000   \n",
       "max    NaN  NaN  NaN    3.400000      3.400000     3.40000     3.400000   \n",
       "\n",
       "                15           10    0  \n",
       "count  7616.000000  5456.000000  0.0  \n",
       "mean      2.752139     2.602879  NaN  \n",
       "std       0.201342     0.098102  NaN  \n",
       "min       2.500000     2.500000  NaN  \n",
       "25%       2.600000     2.500000  NaN  \n",
       "50%       2.700000     2.600000  NaN  \n",
       "75%       2.760000     2.600000  NaN  \n",
       "max       3.400000     3.400000  NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39b9f7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_ETL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[new_X['triplet_HOST'] == 2.7]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>5747.000000</td>\n",
       "      <td>19245.000000</td>\n",
       "      <td>8617.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.798017</td>\n",
       "      <td>2.850780</td>\n",
       "      <td>2.841000</td>\n",
       "      <td>2.548498</td>\n",
       "      <td>2.727048</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.115144</td>\n",
       "      <td>0.167992</td>\n",
       "      <td>0.223294</td>\n",
       "      <td>0.230460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40   35          30           25            20           15  \\\n",
       "count  0.0  0.0  0.0  0.0  121.000000  5747.000000  19245.000000  8617.000000   \n",
       "mean   NaN  NaN  NaN  NaN    2.798017     2.850780      2.841000     2.548498   \n",
       "std    NaN  NaN  NaN  NaN    0.033705     0.115144      0.167992     0.223294   \n",
       "min    NaN  NaN  NaN  NaN    2.580000     2.300000      2.300000     2.300000   \n",
       "25%    NaN  NaN  NaN  NaN    2.800000     2.800000      2.800000     2.300000   \n",
       "50%    NaN  NaN  NaN  NaN    2.800000     2.800000      2.940000     2.670000   \n",
       "75%    NaN  NaN  NaN  NaN    2.800000     2.940000      2.940000     2.670000   \n",
       "max    NaN  NaN  NaN  NaN    3.000000     3.000000      3.000000     3.000000   \n",
       "\n",
       "                10    0  \n",
       "count  5030.000000  0.0  \n",
       "mean      2.727048  NaN  \n",
       "std       0.230460  NaN  \n",
       "min       2.300000  NaN  \n",
       "25%       2.580000  NaN  \n",
       "50%       2.670000  NaN  \n",
       "75%       2.940000  NaN  \n",
       "max       3.000000  NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39cb6e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_HTL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>5747.000000</td>\n",
       "      <td>19245.000000</td>\n",
       "      <td>8617.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.998264</td>\n",
       "      <td>2.906596</td>\n",
       "      <td>2.834888</td>\n",
       "      <td>2.757170</td>\n",
       "      <td>2.625499</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100014</td>\n",
       "      <td>0.229768</td>\n",
       "      <td>0.219454</td>\n",
       "      <td>0.211931</td>\n",
       "      <td>0.138290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40   35          30           25            20           15  \\\n",
       "count  0.0  0.0  0.0  0.0  121.000000  5747.000000  19245.000000  8617.000000   \n",
       "mean   NaN  NaN  NaN  NaN    2.998264     2.906596      2.834888     2.757170   \n",
       "std    NaN  NaN  NaN  NaN    0.100014     0.229768      0.219454     0.211931   \n",
       "min    NaN  NaN  NaN  NaN    2.690000     2.690000      2.500000     2.500000   \n",
       "25%    NaN  NaN  NaN  NaN    2.990000     2.740000      2.700000     2.600000   \n",
       "50%    NaN  NaN  NaN  NaN    2.990000     2.800000      2.750000     2.700000   \n",
       "75%    NaN  NaN  NaN  NaN    2.990000     2.990000      2.800000     2.800000   \n",
       "max    NaN  NaN  NaN  NaN    3.400000     3.400000      3.400000     3.400000   \n",
       "\n",
       "                10    0  \n",
       "count  5030.000000  0.0  \n",
       "mean      2.625499  NaN  \n",
       "std       0.138290  NaN  \n",
       "min       2.500000  NaN  \n",
       "25%       2.500000  NaN  \n",
       "50%       2.600000  NaN  \n",
       "75%       2.690000  NaN  \n",
       "max       3.400000  NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39cb6e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_ETL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[(new_X['triplet_HOST'] == 2.64) | (new_X['triplet_HOST'] == 2.65) ]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>2816.000000</td>\n",
       "      <td>21794.000000</td>\n",
       "      <td>10987.000000</td>\n",
       "      <td>5392.000000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.823991</td>\n",
       "      <td>2.847224</td>\n",
       "      <td>2.599183</td>\n",
       "      <td>2.720391</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.485978e-16</td>\n",
       "      <td>0.101919</td>\n",
       "      <td>0.165283</td>\n",
       "      <td>0.234598</td>\n",
       "      <td>0.232814</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40   35            30           25            20  \\\n",
       "count  0.0  0.0  0.0  0.0  5.000000e+01  2816.000000  21794.000000   \n",
       "mean   NaN  NaN  NaN  NaN  2.800000e+00     2.823991      2.847224   \n",
       "std    NaN  NaN  NaN  NaN  4.485978e-16     0.101919      0.165283   \n",
       "min    NaN  NaN  NaN  NaN  2.800000e+00     2.300000      2.300000   \n",
       "25%    NaN  NaN  NaN  NaN  2.800000e+00     2.800000      2.800000   \n",
       "50%    NaN  NaN  NaN  NaN  2.800000e+00     2.800000      2.940000   \n",
       "75%    NaN  NaN  NaN  NaN  2.800000e+00     2.800000      2.940000   \n",
       "max    NaN  NaN  NaN  NaN  2.800000e+00     3.000000      3.000000   \n",
       "\n",
       "                 15           10     0  \n",
       "count  10987.000000  5392.000000  1.00  \n",
       "mean       2.599183     2.720391  2.67  \n",
       "std        0.234598     0.232814   NaN  \n",
       "min        2.300000     2.300000  2.67  \n",
       "25%        2.300000     2.580000  2.67  \n",
       "50%        2.670000     2.670000  2.67  \n",
       "75%        2.800000     2.940000  2.67  \n",
       "max        3.000000     3.000000  2.67  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39b95fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_HTL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2816.000000</td>\n",
       "      <td>21794.000000</td>\n",
       "      <td>10987.000000</td>\n",
       "      <td>5392.000000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.917947</td>\n",
       "      <td>2.851602</td>\n",
       "      <td>2.758213</td>\n",
       "      <td>2.625243</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.213196</td>\n",
       "      <td>0.229393</td>\n",
       "      <td>0.198133</td>\n",
       "      <td>0.139155</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40   35     30           25            20            15  \\\n",
       "count  0.0  0.0  0.0  0.0  50.00  2816.000000  21794.000000  10987.000000   \n",
       "mean   NaN  NaN  NaN  NaN   2.99     2.917947      2.851602      2.758213   \n",
       "std    NaN  NaN  NaN  NaN   0.00     0.213196      0.229393      0.198133   \n",
       "min    NaN  NaN  NaN  NaN   2.99     2.690000      2.500000      2.500000   \n",
       "25%    NaN  NaN  NaN  NaN   2.99     2.750000      2.700000      2.690000   \n",
       "50%    NaN  NaN  NaN  NaN   2.99     2.950000      2.750000      2.740000   \n",
       "75%    NaN  NaN  NaN  NaN   2.99     2.990000      2.950000      2.760000   \n",
       "max    NaN  NaN  NaN  NaN   2.99     3.400000      3.400000      3.400000   \n",
       "\n",
       "                10     0  \n",
       "count  5392.000000  1.00  \n",
       "mean      2.625243  2.75  \n",
       "std       0.139155   NaN  \n",
       "min       2.500000  2.75  \n",
       "25%       2.500000  2.75  \n",
       "50%       2.600000  2.75  \n",
       "75%       2.690000  2.75  \n",
       "max       3.400000  2.75  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DxHyfS7J13jYfSvKhYxkgwLHYtevqXH/9B0fe7+DBg0ffaMzWrBntZxw9+clPy7ZtFyx5+8suuzTT00ecfzismZk7c+DAgZH3OxannnpqNm48feT9Nm3anEsvvezoGx6nXbuuzk033TjSPjMzdybJyJ/X2WefM9LfL7B8lvwCOgBOXNPTe3PbntuSjRtG2/Gee5Il/A/hONx1zz25a/+IL5yeObFfaH3oicSxRD5wYhDDwElr27YLzK7Nt3FDTnnmeZMexVjd+673rtixjuV62rHjoiTJzp1XLMeQgBUw2v/bAQAsMH9dbPU1ss7FyUcMAwBQlmUSAMBxMws6x7k4uZgZBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwDAcdu+/fxs337+pIcBIxPDAACUJYYBgOMyf0bY7DAnm7WTHgAAx29m5s5k//7c+673Tnoo4zWzLzP3Hhxpl8suuzTT03uXaUD3tXfvniTJjh0XrcjxNm3anEsvvWxFjsXArl1X56abbhxpn5mZO5MkGzeePtJ+Z599TrZtu2CkfTh+YhiAVWV6em9u23NbsvH+y3+wUwbfRm/bf/fyH2vm9uU/BmNx4MCBJKPHMJMhhgFWgY0bT89dp6zJKc88b9JDGat73/XebFy/YfQdN94/65918fgHNEH7r7l80kMoadu2C0aerT30PwU7d16xHENizKwZBgCOy1VXvfuwf4aTgRgGAKAsyyQAgONmRpiTlZlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKWruUjVprz0nykiSzSfYleVHv/eYF25yX5OVJDiaZTvK83vst4x0uAACMz1FnhltrLcmrkzyl9741ySuSvGfBNqcleXuSHx9u8/4krx//cAEAYHyWskziQAazvF8c3r45yYNaa+vmbXNKkqkkDxjePj3J/rGNEgAAlsFRl0n03m9NcmuStNamkuxM8v7e+93ztrmztfZzSf6ktbYngzj+nuUYMACwvHbtujo33XTjSPvMzNyZJNm48fSR9jv77HOybdsFI+0D47SkNcNJ0lrbmOTKJA9N8pQFH3tMkl9N8m9777e01l6U5H+31rb23mcXe8xNmzZk7dpTjmngAMxZs2Zq0kNYNmvWTGXLljNG2n61GvVcHKsNG9aNfB4PHDiQJDnjjNHGt2HDuhX5nFbSoXO32j6v1WqpL6B7WJIPJPnbJE/qvd+1YJMnJ/njeS+YuyLJ65KcmeS2xR53enrfyAMG4F87eHDReYeT3sGDs9m9+46Rtl+tRj0Xx+rcc5+Zc8995kj77NhxUZLkNa+5fOTjrcTntJIOXYOr7fM6mR3piclSXkC3OclHk7yn9/6sw4Rwknwiyfe31r5hePvpST7Xe180hAEAYNKWMjN8YZKHJTlv+PZph7wgyZt671t77/+3tfbqJDe01u5OsjfJueMfLgAAjM9SXkD3yiSvXOTDW+dtd0UGyyMAAOCk4CfQAQBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChr7aQHAAAsj8suuzTT03tX5Fh79+5JkuzYcdGKHG/Tps259NLLVuRYrG5iGABWqenpvdmzZ0/Wb9y07Mdac8q6JMnM/oPLfqz9M9PLfgzqEMMAsIqt37gpP/gfXjvpYYzV773zkkkPgVXEmmEAAMoSwwAAlCWGAQAoy5phgNViZl/ufdd7l/84B+4e/H7quuU/1sy+ZP2G0XaZuTPZvz/7r7l8mQY1ITO3Z+be9ZMeBavArl1X5/rrPzjyfgcPLv+LI+dbs2b0OdsnP/lp2bbtgpH2EcMAq8CmTZtX7Fh7992VJNk8YqQek/UbVvRzA+oRwwCrwEq+3+qh95HdufOKFTvmKDZuPD13nbIu65918aSHMlb7r7k8G9evwGw8q962bReMPHu6mlkzDABAWWIYAICyLJMAgFVqZubO7N9/YNX9kIr9M9PJvadOehisEmaGAQAoy8wwAKxSGzeenpyyYVX+OOaN683nMR6uJAAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoa+2kBwAAYzdze/Zfc/nyH+fAXYPfTz1t+Y81c3uy/oHLfxwoRgwDsKps2rR5xY61d98dSZLN69ct/8HWP3BFPzeoQgwDsKpceullK3asHTsuSpLs3HnFih0TGC9rhgEAKEsMAwBQlhgGAKAsa4YBCtu16+rcdNONI+2zd++eJHPrZZfq7LPPybZtF4y0D8dv/8x0fu+dlyz7cb56YCZJcr9TNy77sfbPTGfj+jNH2ueyyy7N9PTeZRrRfR3rv5FjtWnT5hVdK7/aiGEARnLqqadOeggs0cq+s8bdSZKN689Y9mNtXH/myJ/b9PTe7N2zJ/c/bfnPyf3WDP6N3LNvdtmPdftdKxP4q5kYBihs27YLzNauYt5Z477uf9rm/MKPvHbSwxirV123/LP+q501wwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQ1tqlbNRae06SlySZTbIvyYt67zcv2OYxSX4zyQOS3JvkBb33j493uAAAMD5HnRlurbUkr07ylN771iSvSPKeBdtsSPKRJK/qvX9HksuSvGP8wwUAgPFZyjKJA0me13v/4vD2zUke1FpbN2+bH05yS+/9Q8Pb709y/viGCQAA43fUZRK991uT3JokrbWpJDuTvL/3fve8zb4lyZdaa29J8tgkX07yC+MeLACw/Hbtujo33XTjSPvs3bsnSbJjx0Uj7Xf22edk27YLRtrnWMzM3Jn9+w/kVdddsuzHWklfuWtv1s+eOulhnNSWtGY4SVprG5NcmeShSZ6y4MP3S/LUJE/qvX+stXZukg+11h7eez+w2GNu2rQha9eeMvqoAeAEsGbNVJJky5YzJjyS8dqwYd3XPrelWr9+fZKMvN+GDetW5PxNTY02rpPJ1NTUqrsGV9JSX0D3sCQfSPK3GQTvXQs2+UKST/feP5Ykvff3tdbenOSRw30Oa3p63zENGgBOBAcPziZJdu++Y8IjGa9zz31mzj33mSt2vJU4fxs2bMy6bMgv/Mhrl/1YK+lV112StRumVt01OG5HerKwlBfQbU7y0STv6b0/6zAhnCTXJXlEa+1xw32ekME7T3zumEYMAAArYCkzwxcmeViS81pr5827/wVJ3tR739p7/1Jr7elJfmu4nOJAkh/vve8f/5ABAGA8lvICulcmeeUiH946b7s/SPLdYxoXAAAsOz+BDgCAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGWJYQAAyhLDAACUJYYBAChLDAMAUJYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAI7b9u3nZ/v28yc9DBiZGAYAoCwxDAAcl/kzwmaHOdmsnfQAAOBEsGvX1bnpphtH2mfv3j1Jkh07Lhppv7PPPifbtl0w0j4cv9vv2ptXXXfJSPvcdfdM7r73wDKN6L7WnXJqTlu3caR9br9rbzZvOHOZRlSDGAaAY3TqqadOeggs0aZNm49pvzWzydTBMQ9msWPdL1m7YWqkfTZvOPOYPzcGpmZnZyd28N2775jcwQGAsVi4NOKqq949oZHA4W3ZcsaizzKsGQYAjsv8+BXCnGzEMAAAZVkmAQDAqmaZBAAAHIYYBgCgLDEMAEBZYhgAgLLEMAAAZYlhAADKEsMAAJQlhgEAKEsMAwBQlhgGAKAsMQwAQFliGACAssQwAABliWEAAMoSwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICypmZnZyc9BgAAmAgzwwAAlCWGAQAoSwwDAFCWGAYAoCwxDABAWWIYAICyxDAAAGPVWju9tXbapMexFN5nGACA49ZaOz3JbyR5dpIzhndPJ3lfkkt671+e1NiOZO2kB7DSWmvfleQ5Sb4pyb1JPp/k2t77H050YCuotbat976rtbYmycVJzkvy1STX9N7fOtnRrTzXxBzXxn25NlwTC7km4IjekuRzSR6bwb+NZPBv5QVJrkryYxMa1xGVmhlurb0gyYuTXJO5v6SHJHlWkt/uvb9uUmNbSa21T/Tev7O1dmmSJyfZmWQqg290f9R7v3SiA1xBron7cm3McW0MuCbmuCbuyxOl+/JEKWmt/W3v/dsW+dhf996/faXHtBTVZoYvSfLvFk7Tt9Zen+RjSUp9IUvyjCTf13u/PUlaax9K8hdJynxzi2tiMa4N18ZCrgnXxEIvSbIryS9nwROl1tq/KfxE6VPDux+S5E2ttUpPlO5urT2y9/738+9srX1zBk+UTkjVYvjeJF85zP135AT+S1pGe5Lsm3f7QJJ7JjSWSXFNHJ5rw7WxkGvCNbEYT5Q8UTrkvyT509banyX5p+F935jku5P89MRGdRTVYvgjST7QWntb5v6SHpzkZ5P8n4mNauU9tLX26SQHk7wmyYtba49J8vIkfzrRka2863P4a+JnMrheqlns2vi11Ls2fL0YONLXixsnOrKVt9g18TOpdU0s5ImSJ0pJkt77B1tr357kh5M8NIN3LfvjJM/vve+e6OCOoFoM/+cMFnG/IIO/pEcm+WgGr3J84wTHtaJ671taay3Jv8/cNfAdST6TwTe4SnYk+bnMXRNrMvgmd22SN0xwXBMxvDa+JcnjM3dtbE3y6QyCuJKFXy+mkvxzkvek3teLb0nyxAy+4SeDF8d8Jsn/mNS4JuTQNfHCDCL4lCT/mMH3kHdNcFyTYmJljomVod77bUneOelxjKJaDL+j976ttfa7GXzx2p/knCSnDW9//kg7rya9956kz7vrxb3375zUeCal934wyW8NfyX52guGLp/cqCantfbADL7h70nyttbaLUkensEauDcm+YcJDm+lbc4g+v4sg/i5Psn3D+//3RQ5F604eey/AAACzUlEQVS170zy3iTfkOT9rbXf6b2/ffixTySp9HVja5JfTPKgDL5nPG/e0oBq52KxJ8+HniiZWJl7olRuYuVkUy2G2/D3nRmE8WuTpLX2wgzeDuTJkxrYSmqtfSWDWa75NrTWbk+S3vv9V35Uk7HIuTit4rkY+u0kf5fB/5rckME3tLdm8ArpK5L86MRGtvLmn4vfT/KyJG9LvXPx+gy+yd+cwbrHD7fWnth7vzv/+t/Oajf/XOxMcl1r7UlFz0WSpPf+mQzi99Dtt09wOBNzuIkVTh7VYviQR/Xef/LQjd77b7XWLprkgFbYc5O8OoMZwE9l8EX8g0meOslBTYhzcV/f3Hs/r7W2Psk/9d7fPLz/qtbaiyc5sAlYeC7eMry/2rnY0Hu/bvjn57TWfieDJwXPnuCYJmX+ubig+LlIa23HkT7ee9+5UmOZNOfi5FbtxzGf2Vo7J8nftdbOOnRna+3hGax5KqH3fm0GsfcrSZ7Ye781yYHe+z/03kv81+8hzsW/sqa1dnrvfX+S5x26s7W2Ocn9JjesiXAuBta01r5+3u3nJvn24fsO13mj+gHn4r4Ovbj20cM/z//16AmOaxKci5NYtZnh/5XBf219R5L1SX60tfacDF4E8p8mObCV1nv/bGvtB5K8sbX2hNS7Fr7GubiPNyb5ZGvtUb339yVJa+17MnjvzFdMdGQrz7kYeE2SP2+tPa/3fl3vfV9r7ccyePHxN014bCvNuZin9/7TrbWHJfm93vs7Jj2eSXIuTm6lZoZ775f03h+f5AEZ/LScJPnLJE/ovZ9Ur3wch977vt779iQfT3LXpMczSc7FwPCFg8/ovd877+4vJdnWe3/ThIY1Ec7FwHAN6JOS/M28+/4xgxeLvXRS45oE5+KwLsrgheg4FyetUj+OGQAA5is1MwwAAPOJYQAAyhLDAACUJYYBAChLDAMAUNb/B4ReOsLcv+xEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b39cae9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('triplet_ETL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.90    45600\n",
       "2.80    41040\n",
       "2.70    38760\n",
       "2.95    38760\n",
       "3.00    38760\n",
       "2.65    38760\n",
       "2.75    38760\n",
       "2.85    38760\n",
       "2.94     6840\n",
       "3.10     4560\n",
       "2.93     4560\n",
       "2.64     2280\n",
       "2.91     2280\n",
       "3.03     2280\n",
       "2.79     2280\n",
       "2.84     2280\n",
       "2.86     2280\n",
       "Name: triplet_HOST, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X['triplet_HOST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2nd HOMO energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[(new_X['HOMO_EML'] == 5.56) | (new_X['HOMO_EML'] == 5.57)]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>9213.000000</td>\n",
       "      <td>9264.000000</td>\n",
       "      <td>6396.000000</td>\n",
       "      <td>5284.000000</td>\n",
       "      <td>10384.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.862753</td>\n",
       "      <td>5.809096</td>\n",
       "      <td>5.770186</td>\n",
       "      <td>5.766057</td>\n",
       "      <td>5.382324</td>\n",
       "      <td>5.357646</td>\n",
       "      <td>5.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.183025</td>\n",
       "      <td>0.199798</td>\n",
       "      <td>0.191407</td>\n",
       "      <td>0.152980</td>\n",
       "      <td>0.342881</td>\n",
       "      <td>0.305564</td>\n",
       "      <td>0.044721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35           30           25           20  \\\n",
       "count  0.0  0.0  0.0  494.000000  9213.000000  9264.000000  6396.000000   \n",
       "mean   NaN  NaN  NaN    5.862753     5.809096     5.770186     5.766057   \n",
       "std    NaN  NaN  NaN    0.183025     0.199798     0.191407     0.152980   \n",
       "min    NaN  NaN  NaN    5.500000     5.500000     5.500000     5.500000   \n",
       "25%    NaN  NaN  NaN    5.700000     5.700000     5.700000     5.700000   \n",
       "50%    NaN  NaN  NaN    5.900000     5.700000     5.700000     5.800000   \n",
       "75%    NaN  NaN  NaN    6.100000     5.900000     5.900000     5.800000   \n",
       "max    NaN  NaN  NaN    6.100000     6.100000     6.100000     6.100000   \n",
       "\n",
       "                15            10         0  \n",
       "count  5284.000000  10384.000000  5.000000  \n",
       "mean      5.382324      5.357646  5.180000  \n",
       "std       0.342881      0.305564  0.044721  \n",
       "min       5.100000      5.100000  5.100000  \n",
       "25%       5.100000      5.100000  5.200000  \n",
       "50%       5.200000      5.200000  5.200000  \n",
       "75%       5.700000      5.700000  5.200000  \n",
       "max       6.100000      6.100000  5.200000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a7fb5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_HTL1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>9213.000000</td>\n",
       "      <td>9264.000000</td>\n",
       "      <td>6396.000000</td>\n",
       "      <td>5284.000000</td>\n",
       "      <td>10384.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.222227</td>\n",
       "      <td>6.631096</td>\n",
       "      <td>6.428042</td>\n",
       "      <td>6.433049</td>\n",
       "      <td>6.607555</td>\n",
       "      <td>6.468847</td>\n",
       "      <td>6.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.351259</td>\n",
       "      <td>0.390108</td>\n",
       "      <td>0.412614</td>\n",
       "      <td>0.379829</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>0.359877</td>\n",
       "      <td>0.297439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>6.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>6.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.560000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.790000</td>\n",
       "      <td>6.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.680000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35           30           25           20  \\\n",
       "count  0.0  0.0  0.0  494.000000  9213.000000  9264.000000  6396.000000   \n",
       "mean   NaN  NaN  NaN    7.222227     6.631096     6.428042     6.433049   \n",
       "std    NaN  NaN  NaN    0.351259     0.390108     0.412614     0.379829   \n",
       "min    NaN  NaN  NaN    6.500000     5.850000     5.850000     5.850000   \n",
       "25%    NaN  NaN  NaN    6.800000     6.500000     6.020000     6.120000   \n",
       "50%    NaN  NaN  NaN    7.500000     6.680000     6.500000     6.500000   \n",
       "75%    NaN  NaN  NaN    7.500000     6.800000     6.680000     6.680000   \n",
       "max    NaN  NaN  NaN    7.500000     7.500000     7.500000     7.500000   \n",
       "\n",
       "                15            10         0  \n",
       "count  5284.000000  10384.000000  5.000000  \n",
       "mean      6.607555      6.468847  6.148000  \n",
       "std       0.416761      0.359877  0.297439  \n",
       "min       5.850000      5.850000  6.010000  \n",
       "25%       6.300000      6.200000  6.010000  \n",
       "50%       6.560000      6.400000  6.020000  \n",
       "75%       6.800000      6.790000  6.020000  \n",
       "max       7.500000      7.500000  6.680000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a7fb1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_ETL2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[new_X['HOMO_EML'] == 5.8]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>15806.000000</td>\n",
       "      <td>13806.000000</td>\n",
       "      <td>10991.000000</td>\n",
       "      <td>12185.000000</td>\n",
       "      <td>7841.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.944468</td>\n",
       "      <td>5.810585</td>\n",
       "      <td>5.765377</td>\n",
       "      <td>5.587890</td>\n",
       "      <td>5.268149</td>\n",
       "      <td>5.572363</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.167072</td>\n",
       "      <td>0.196583</td>\n",
       "      <td>0.184312</td>\n",
       "      <td>0.293896</td>\n",
       "      <td>0.277660</td>\n",
       "      <td>0.314841</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  931.000000  15806.000000  13806.000000  10991.000000   \n",
       "mean   NaN  NaN  NaN    5.944468      5.810585      5.765377      5.587890   \n",
       "std    NaN  NaN  NaN    0.167072      0.196583      0.184312      0.293896   \n",
       "min    NaN  NaN  NaN    5.500000      5.500000      5.500000      5.100000   \n",
       "25%    NaN  NaN  NaN    5.900000      5.700000      5.700000      5.200000   \n",
       "50%    NaN  NaN  NaN    5.900000      5.700000      5.800000      5.700000   \n",
       "75%    NaN  NaN  NaN    6.100000      5.900000      5.900000      5.800000   \n",
       "max    NaN  NaN  NaN    6.100000      6.100000      6.100000      6.100000   \n",
       "\n",
       "                 15           10    0  \n",
       "count  12185.000000  7841.000000  0.0  \n",
       "mean       5.268149     5.572363  NaN  \n",
       "std        0.277660     0.314841  NaN  \n",
       "min        5.100000     5.100000  NaN  \n",
       "25%        5.100000     5.200000  NaN  \n",
       "50%        5.200000     5.700000  NaN  \n",
       "75%        5.200000     5.800000  NaN  \n",
       "max        6.100000     6.100000  NaN  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a7c11240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_HTL1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>15806.000000</td>\n",
       "      <td>13806.000000</td>\n",
       "      <td>10991.000000</td>\n",
       "      <td>12185.000000</td>\n",
       "      <td>7841.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.161096</td>\n",
       "      <td>6.618706</td>\n",
       "      <td>6.415375</td>\n",
       "      <td>6.517955</td>\n",
       "      <td>6.472860</td>\n",
       "      <td>6.484587</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.376842</td>\n",
       "      <td>0.387532</td>\n",
       "      <td>0.413791</td>\n",
       "      <td>0.413112</td>\n",
       "      <td>0.377801</td>\n",
       "      <td>0.349448</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.560000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.790000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  931.000000  15806.000000  13806.000000  10991.000000   \n",
       "mean   NaN  NaN  NaN    7.161096      6.618706      6.415375      6.517955   \n",
       "std    NaN  NaN  NaN    0.376842      0.387532      0.413791      0.413112   \n",
       "min    NaN  NaN  NaN    6.300000      5.850000      5.850000      5.850000   \n",
       "25%    NaN  NaN  NaN    6.800000      6.300000      6.020000      6.120000   \n",
       "50%    NaN  NaN  NaN    7.500000      6.670000      6.500000      6.560000   \n",
       "75%    NaN  NaN  NaN    7.500000      6.800000      6.680000      6.800000   \n",
       "max    NaN  NaN  NaN    7.500000      7.500000      7.500000      7.500000   \n",
       "\n",
       "                 15           10    0  \n",
       "count  12185.000000  7841.000000  0.0  \n",
       "mean       6.472860     6.484587  NaN  \n",
       "std        0.377801     0.349448  NaN  \n",
       "min        5.850000     5.850000  NaN  \n",
       "25%        6.130000     6.200000  NaN  \n",
       "50%        6.500000     6.400000  NaN  \n",
       "75%        6.790000     7.000000  NaN  \n",
       "max        7.500000     7.000000  NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a97c6b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_ETL2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[(new_X['HOMO_EML'] == 5.9) | (new_X['HOMO_EML'] == 5.92 ) | (new_X['HOMO_EML'] == 6.06 )]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>25812.000000</td>\n",
       "      <td>32052.000000</td>\n",
       "      <td>22656.000000</td>\n",
       "      <td>26461.000000</td>\n",
       "      <td>15091.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.979008</td>\n",
       "      <td>5.830699</td>\n",
       "      <td>5.759297</td>\n",
       "      <td>5.626002</td>\n",
       "      <td>5.284169</td>\n",
       "      <td>5.559466</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>0.194294</td>\n",
       "      <td>0.185858</td>\n",
       "      <td>0.282277</td>\n",
       "      <td>0.286819</td>\n",
       "      <td>0.317006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  1048.000000  25812.000000  32052.000000  22656.000000   \n",
       "mean   NaN  NaN  NaN     5.979008      5.830699      5.759297      5.626002   \n",
       "std    NaN  NaN  NaN     0.147368      0.194294      0.185858      0.282277   \n",
       "min    NaN  NaN  NaN     5.500000      5.500000      5.500000      5.100000   \n",
       "25%    NaN  NaN  NaN     5.900000      5.700000      5.700000      5.500000   \n",
       "50%    NaN  NaN  NaN     6.100000      5.900000      5.700000      5.700000   \n",
       "75%    NaN  NaN  NaN     6.100000      6.100000      5.900000      5.800000   \n",
       "max    NaN  NaN  NaN     6.100000      6.100000      6.100000      6.100000   \n",
       "\n",
       "                 15            10    0  \n",
       "count  26461.000000  15091.000000  0.0  \n",
       "mean       5.284169      5.559466  NaN  \n",
       "std        0.286819      0.317006  NaN  \n",
       "min        5.100000      5.100000  NaN  \n",
       "25%        5.100000      5.200000  NaN  \n",
       "50%        5.200000      5.700000  NaN  \n",
       "75%        5.200000      5.800000  NaN  \n",
       "max        6.100000      6.100000  NaN  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a83a6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_HTL1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>25812.000000</td>\n",
       "      <td>32052.000000</td>\n",
       "      <td>22656.000000</td>\n",
       "      <td>26461.000000</td>\n",
       "      <td>15091.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.263321</td>\n",
       "      <td>6.694422</td>\n",
       "      <td>6.416623</td>\n",
       "      <td>6.487191</td>\n",
       "      <td>6.494638</td>\n",
       "      <td>6.461866</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.341703</td>\n",
       "      <td>0.380078</td>\n",
       "      <td>0.399723</td>\n",
       "      <td>0.411898</td>\n",
       "      <td>0.381297</td>\n",
       "      <td>0.352073</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>6.790000</td>\n",
       "      <td>6.790000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  1048.000000  25812.000000  32052.000000  22656.000000   \n",
       "mean   NaN  NaN  NaN     7.263321      6.694422      6.416623      6.487191   \n",
       "std    NaN  NaN  NaN     0.341703      0.380078      0.399723      0.411898   \n",
       "min    NaN  NaN  NaN     6.500000      5.850000      5.850000      5.850000   \n",
       "25%    NaN  NaN  NaN     6.800000      6.500000      6.120000      6.120000   \n",
       "50%    NaN  NaN  NaN     7.500000      6.680000      6.500000      6.500000   \n",
       "75%    NaN  NaN  NaN     7.500000      6.800000      6.670000      6.790000   \n",
       "max    NaN  NaN  NaN     7.500000      7.500000      7.500000      7.500000   \n",
       "\n",
       "                 15            10    0  \n",
       "count  26461.000000  15091.000000  0.0  \n",
       "mean       6.494638      6.461866  NaN  \n",
       "std        0.381297      0.352073  NaN  \n",
       "min        5.850000      5.850000  NaN  \n",
       "25%        6.130000      6.200000  NaN  \n",
       "50%        6.500000      6.400000  NaN  \n",
       "75%        6.790000      7.000000  NaN  \n",
       "max        7.500000      7.000000  NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a7f3c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('homo_ETL2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3rd LUMO energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[(new_X['HOMO_EML'] == 5.56) | (new_X['HOMO_EML'] == 5.57 )]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>9213.000000</td>\n",
       "      <td>9264.000000</td>\n",
       "      <td>6396.000000</td>\n",
       "      <td>5284.000000</td>\n",
       "      <td>10384.000000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.109474</td>\n",
       "      <td>2.076789</td>\n",
       "      <td>2.062832</td>\n",
       "      <td>2.068609</td>\n",
       "      <td>2.078213</td>\n",
       "      <td>2.065195</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030720</td>\n",
       "      <td>0.049540</td>\n",
       "      <td>0.049486</td>\n",
       "      <td>0.049985</td>\n",
       "      <td>0.049325</td>\n",
       "      <td>0.049771</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35           30           25           20  \\\n",
       "count  0.0  0.0  0.0  494.000000  9213.000000  9264.000000  6396.000000   \n",
       "mean   NaN  NaN  NaN    2.109474     2.076789     2.062832     2.068609   \n",
       "std    NaN  NaN  NaN    0.030720     0.049540     0.049486     0.049985   \n",
       "min    NaN  NaN  NaN    2.020000     2.020000     2.020000     2.020000   \n",
       "25%    NaN  NaN  NaN    2.120000     2.020000     2.020000     2.020000   \n",
       "50%    NaN  NaN  NaN    2.120000     2.120000     2.020000     2.020000   \n",
       "75%    NaN  NaN  NaN    2.120000     2.120000     2.120000     2.120000   \n",
       "max    NaN  NaN  NaN    2.120000     2.120000     2.120000     2.120000   \n",
       "\n",
       "                15            10     0  \n",
       "count  5284.000000  10384.000000  5.00  \n",
       "mean      2.078213      2.065195  2.02  \n",
       "std       0.049325      0.049771  0.00  \n",
       "min       2.020000      2.020000  2.02  \n",
       "25%       2.020000      2.020000  2.02  \n",
       "50%       2.120000      2.020000  2.02  \n",
       "75%       2.120000      2.120000  2.02  \n",
       "max       2.120000      2.120000  2.02  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a80b9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('LUMO_EML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>9213.000000</td>\n",
       "      <td>9264.000000</td>\n",
       "      <td>6396.000000</td>\n",
       "      <td>5284.000000</td>\n",
       "      <td>10384.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.123927</td>\n",
       "      <td>2.750988</td>\n",
       "      <td>2.812816</td>\n",
       "      <td>2.787109</td>\n",
       "      <td>2.887651</td>\n",
       "      <td>2.935964</td>\n",
       "      <td>2.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.438583</td>\n",
       "      <td>0.320666</td>\n",
       "      <td>0.301860</td>\n",
       "      <td>0.277161</td>\n",
       "      <td>0.362155</td>\n",
       "      <td>0.319913</td>\n",
       "      <td>0.067082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.730000</td>\n",
       "      <td>2.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.730000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>2.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.910000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35           30           25           20  \\\n",
       "count  0.0  0.0  0.0  494.000000  9213.000000  9264.000000  6396.000000   \n",
       "mean   NaN  NaN  NaN    3.123927     2.750988     2.812816     2.787109   \n",
       "std    NaN  NaN  NaN    0.438583     0.320666     0.301860     0.277161   \n",
       "min    NaN  NaN  NaN    2.500000     2.260000     2.260000     2.260000   \n",
       "25%    NaN  NaN  NaN    2.600000     2.520000     2.700000     2.600000   \n",
       "50%    NaN  NaN  NaN    3.470000     2.700000     2.750000     2.730000   \n",
       "75%    NaN  NaN  NaN    3.470000     2.750000     2.910000     2.880000   \n",
       "max    NaN  NaN  NaN    3.470000     3.470000     3.470000     3.470000   \n",
       "\n",
       "                15            10         0  \n",
       "count  5284.000000  10384.000000  5.000000  \n",
       "mean      2.887651      2.935964  2.850000  \n",
       "std       0.362155      0.319913  0.067082  \n",
       "min       2.260000      2.260000  2.730000  \n",
       "25%       2.600000      2.730000  2.880000  \n",
       "50%       2.800000      2.880000  2.880000  \n",
       "75%       3.200000      3.000000  2.880000  \n",
       "max       3.500000      3.500000  2.880000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a7f15dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('lumo_ETL2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[new_X['HOMO_EML'] == 5.8]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>15806.000000</td>\n",
       "      <td>13806.000000</td>\n",
       "      <td>10991.000000</td>\n",
       "      <td>12185.000000</td>\n",
       "      <td>7841.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.273792</td>\n",
       "      <td>2.273334</td>\n",
       "      <td>2.273220</td>\n",
       "      <td>2.273518</td>\n",
       "      <td>2.273374</td>\n",
       "      <td>2.273154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023712</td>\n",
       "      <td>0.024834</td>\n",
       "      <td>0.025113</td>\n",
       "      <td>0.024947</td>\n",
       "      <td>0.024883</td>\n",
       "      <td>0.025109</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  931.000000  15806.000000  13806.000000  10991.000000   \n",
       "mean   NaN  NaN  NaN    2.273792      2.273334      2.273220      2.273518   \n",
       "std    NaN  NaN  NaN    0.023712      0.024834      0.025113      0.024947   \n",
       "min    NaN  NaN  NaN    2.240000      2.240000      2.240000      2.240000   \n",
       "25%    NaN  NaN  NaN    2.240000      2.240000      2.240000      2.240000   \n",
       "50%    NaN  NaN  NaN    2.280000      2.280000      2.280000      2.280000   \n",
       "75%    NaN  NaN  NaN    2.300000      2.300000      2.300000      2.300000   \n",
       "max    NaN  NaN  NaN    2.300000      2.300000      2.300000      2.300000   \n",
       "\n",
       "                 15           10    0  \n",
       "count  12185.000000  7841.000000  0.0  \n",
       "mean       2.273374     2.273154  NaN  \n",
       "std        0.024883     0.025109  NaN  \n",
       "min        2.240000     2.240000  NaN  \n",
       "25%        2.240000     2.240000  NaN  \n",
       "50%        2.280000     2.280000  NaN  \n",
       "75%        2.300000     2.300000  NaN  \n",
       "max        2.300000     2.300000  NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a868fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('LUMO_EML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>15806.000000</td>\n",
       "      <td>13806.000000</td>\n",
       "      <td>10991.000000</td>\n",
       "      <td>12185.000000</td>\n",
       "      <td>7841.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.075005</td>\n",
       "      <td>2.755228</td>\n",
       "      <td>2.817847</td>\n",
       "      <td>2.789506</td>\n",
       "      <td>2.850555</td>\n",
       "      <td>3.072231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.437568</td>\n",
       "      <td>0.315510</td>\n",
       "      <td>0.304105</td>\n",
       "      <td>0.300135</td>\n",
       "      <td>0.323363</td>\n",
       "      <td>0.288170</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.730000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.910000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40          35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  931.000000  15806.000000  13806.000000  10991.000000   \n",
       "mean   NaN  NaN  NaN    3.075005      2.755228      2.817847      2.789506   \n",
       "std    NaN  NaN  NaN    0.437568      0.315510      0.304105      0.300135   \n",
       "min    NaN  NaN  NaN    2.500000      2.260000      2.260000      2.260000   \n",
       "25%    NaN  NaN  NaN    2.600000      2.520000      2.700000      2.520000   \n",
       "50%    NaN  NaN  NaN    3.470000      2.700000      2.750000      2.730000   \n",
       "75%    NaN  NaN  NaN    3.470000      2.750000      2.910000      2.880000   \n",
       "max    NaN  NaN  NaN    3.470000      3.470000      3.470000      3.470000   \n",
       "\n",
       "                 15           10    0  \n",
       "count  12185.000000  7841.000000  0.0  \n",
       "mean       2.850555     3.072231  NaN  \n",
       "std        0.323363     0.288170  NaN  \n",
       "min        2.260000     2.260000  NaN  \n",
       "25%        2.700000     2.800000  NaN  \n",
       "50%        2.800000     3.000000  NaN  \n",
       "75%        3.000000     3.500000  NaN  \n",
       "max        3.500000     3.500000  NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a84088d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('lumo_ETL2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "triplet3 = new_X.loc[(new_X['HOMO_EML'] == 5.9) | (new_X['HOMO_EML'] == 5.92 ) | (new_X['HOMO_EML'] == 6.06 )]\n",
    "\n",
    "column = 'cd/a'\n",
    "device50 = triplet3.loc[triplet3[column] >= 50]\n",
    "device45 = triplet3.loc[(triplet3[column] >= 45) & (new_X[column] < 50)]\n",
    "device40 = triplet3.loc[(triplet3[column] >= 40) & (new_X[column] < 45)]\n",
    "device35 = triplet3.loc[(triplet3[column] >= 35) & (new_X[column] < 40)]\n",
    "device30 = triplet3.loc[(triplet3[column] >= 30) & (new_X[column] < 35)]\n",
    "device25 = triplet3.loc[(triplet3[column] >= 25) & (new_X[column] < 30)]\n",
    "device20 = triplet3.loc[(triplet3[column] >= 20) & (new_X[column] < 25)]\n",
    "device15 = triplet3.loc[(triplet3[column] >= 15) & (new_X[column] < 20)]\n",
    "device10 = triplet3.loc[(triplet3[column] >= 10) & (new_X[column] < 15)]\n",
    "device = triplet3.loc[triplet3[column] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>25812.000000</td>\n",
       "      <td>32052.000000</td>\n",
       "      <td>22656.000000</td>\n",
       "      <td>26461.000000</td>\n",
       "      <td>15091.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.549275</td>\n",
       "      <td>2.546096</td>\n",
       "      <td>2.539207</td>\n",
       "      <td>2.549583</td>\n",
       "      <td>2.553198</td>\n",
       "      <td>2.533876</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.222219</td>\n",
       "      <td>0.216898</td>\n",
       "      <td>0.213742</td>\n",
       "      <td>0.220386</td>\n",
       "      <td>0.220528</td>\n",
       "      <td>0.208951</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  1048.000000  25812.000000  32052.000000  22656.000000   \n",
       "mean   NaN  NaN  NaN     2.549275      2.546096      2.539207      2.549583   \n",
       "std    NaN  NaN  NaN     0.222219      0.216898      0.213742      0.220386   \n",
       "min    NaN  NaN  NaN     2.300000      2.300000      2.300000      2.300000   \n",
       "25%    NaN  NaN  NaN     2.400000      2.400000      2.400000      2.400000   \n",
       "50%    NaN  NaN  NaN     2.400000      2.400000      2.400000      2.550000   \n",
       "75%    NaN  NaN  NaN     2.670000      2.670000      2.670000      2.670000   \n",
       "max    NaN  NaN  NaN     2.950000      2.950000      2.950000      2.950000   \n",
       "\n",
       "                 15            10    0  \n",
       "count  26461.000000  15091.000000  0.0  \n",
       "mean       2.553198      2.533876  NaN  \n",
       "std        0.220528      0.208951  NaN  \n",
       "min        2.300000      2.300000  NaN  \n",
       "25%        2.400000      2.400000  NaN  \n",
       "50%        2.550000      2.400000  NaN  \n",
       "75%        2.670000      2.670000  NaN  \n",
       "max        2.950000      2.950000  NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a81b8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('LUMO_EML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>50</th>\n",
       "      <th>45</th>\n",
       "      <th>40</th>\n",
       "      <th>35</th>\n",
       "      <th>30</th>\n",
       "      <th>25</th>\n",
       "      <th>20</th>\n",
       "      <th>15</th>\n",
       "      <th>10</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>25812.000000</td>\n",
       "      <td>32052.000000</td>\n",
       "      <td>22656.000000</td>\n",
       "      <td>26461.000000</td>\n",
       "      <td>15091.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.185487</td>\n",
       "      <td>2.766813</td>\n",
       "      <td>2.803924</td>\n",
       "      <td>2.785055</td>\n",
       "      <td>2.862718</td>\n",
       "      <td>3.056172</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.413351</td>\n",
       "      <td>0.328851</td>\n",
       "      <td>0.304276</td>\n",
       "      <td>0.293368</td>\n",
       "      <td>0.330609</td>\n",
       "      <td>0.289244</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.730000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.910000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        50   45   40           35            30            25            20  \\\n",
       "count  0.0  0.0  0.0  1048.000000  25812.000000  32052.000000  22656.000000   \n",
       "mean   NaN  NaN  NaN     3.185487      2.766813      2.803924      2.785055   \n",
       "std    NaN  NaN  NaN     0.413351      0.328851      0.304276      0.293368   \n",
       "min    NaN  NaN  NaN     2.500000      2.260000      2.260000      2.260000   \n",
       "25%    NaN  NaN  NaN     2.600000      2.520000      2.700000      2.600000   \n",
       "50%    NaN  NaN  NaN     3.470000      2.700000      2.750000      2.730000   \n",
       "75%    NaN  NaN  NaN     3.470000      2.750000      2.910000      2.880000   \n",
       "max    NaN  NaN  NaN     3.470000      3.470000      3.470000      3.470000   \n",
       "\n",
       "                 15            10    0  \n",
       "count  26461.000000  15091.000000  0.0  \n",
       "mean       2.862718      3.056172  NaN  \n",
       "std        0.330609      0.289244  NaN  \n",
       "min        2.260000      2.260000  NaN  \n",
       "25%        2.700000      2.800000  NaN  \n",
       "50%        2.800000      3.000000  NaN  \n",
       "75%        3.000000      3.500000  NaN  \n",
       "max        3.500000      3.500000  NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290a80f8b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer('lumo_ETL2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}