E13_MatToPy_kpca_faultdetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab1cf29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc382fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_normal = pd.read_csv('./data/normal_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "295c433c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_faulty = pd.read_csv('./data/faulty_testing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85bfe898",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preparing\n",
    "df_train = train_normal[(train_normal.simulationRun>=1)].iloc[:500,3:]\n",
    "\n",
    "df_test = train_normal[(train_normal.simulationRun>5)&(train_normal.simulationRun<10)].iloc[:,3:]\n",
    "# IDV = 1\n",
    "# df_test = test_faulty[(test_faulty.faultNumber==IDV) & \n",
    "#                                (test_faulty.simulationRun==1)].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9523bee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 52)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f111439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25038</td>\n",
       "      <td>3674.0</td>\n",
       "      <td>4529.0</td>\n",
       "      <td>9.2320</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.402</td>\n",
       "      <td>2704.3</td>\n",
       "      <td>74.863</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33818</td>\n",
       "      <td>...</td>\n",
       "      <td>53.744</td>\n",
       "      <td>24.657</td>\n",
       "      <td>62.544</td>\n",
       "      <td>22.137</td>\n",
       "      <td>39.935</td>\n",
       "      <td>42.323</td>\n",
       "      <td>47.757</td>\n",
       "      <td>47.510</td>\n",
       "      <td>41.258</td>\n",
       "      <td>18.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25109</td>\n",
       "      <td>3659.4</td>\n",
       "      <td>4556.6</td>\n",
       "      <td>9.4264</td>\n",
       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>75.000</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33620</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
       "      <td>59.259</td>\n",
       "      <td>22.084</td>\n",
       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
       "      <td>17.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>74.771</td>\n",
       "      <td>120.42</td>\n",
       "      <td>0.33563</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24977</td>\n",
       "      <td>3661.3</td>\n",
       "      <td>4512.1</td>\n",
       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>75.224</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.33553</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>75.388</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.32632</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.24602</td>\n",
       "      <td>3651.9</td>\n",
       "      <td>4504.4</td>\n",
       "      <td>9.4721</td>\n",
       "      <td>26.600</td>\n",
       "      <td>42.312</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>74.623</td>\n",
       "      <td>120.40</td>\n",
       "      <td>0.32381</td>\n",
       "      <td>...</td>\n",
       "      <td>54.132</td>\n",
       "      <td>23.663</td>\n",
       "      <td>59.588</td>\n",
       "      <td>22.481</td>\n",
       "      <td>38.952</td>\n",
       "      <td>33.489</td>\n",
       "      <td>45.203</td>\n",
       "      <td>49.900</td>\n",
       "      <td>41.057</td>\n",
       "      <td>16.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.23457</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>4529.5</td>\n",
       "      <td>9.3440</td>\n",
       "      <td>26.872</td>\n",
       "      <td>42.434</td>\n",
       "      <td>2704.1</td>\n",
       "      <td>75.123</td>\n",
       "      <td>120.40</td>\n",
       "      <td>0.35454</td>\n",
       "      <td>...</td>\n",
       "      <td>54.028</td>\n",
       "      <td>22.776</td>\n",
       "      <td>60.566</td>\n",
       "      <td>22.237</td>\n",
       "      <td>42.978</td>\n",
       "      <td>38.017</td>\n",
       "      <td>42.637</td>\n",
       "      <td>50.075</td>\n",
       "      <td>41.339</td>\n",
       "      <td>17.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.23068</td>\n",
       "      <td>3656.4</td>\n",
       "      <td>4493.0</td>\n",
       "      <td>9.2978</td>\n",
       "      <td>26.920</td>\n",
       "      <td>42.536</td>\n",
       "      <td>2700.3</td>\n",
       "      <td>74.942</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.35859</td>\n",
       "      <td>...</td>\n",
       "      <td>54.465</td>\n",
       "      <td>23.158</td>\n",
       "      <td>62.102</td>\n",
       "      <td>21.639</td>\n",
       "      <td>42.483</td>\n",
       "      <td>41.016</td>\n",
       "      <td>46.782</td>\n",
       "      <td>50.098</td>\n",
       "      <td>41.277</td>\n",
       "      <td>18.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.24263</td>\n",
       "      <td>3632.5</td>\n",
       "      <td>4521.5</td>\n",
       "      <td>9.3915</td>\n",
       "      <td>26.956</td>\n",
       "      <td>42.317</td>\n",
       "      <td>2701.9</td>\n",
       "      <td>74.900</td>\n",
       "      <td>120.37</td>\n",
       "      <td>0.34057</td>\n",
       "      <td>...</td>\n",
       "      <td>54.532</td>\n",
       "      <td>23.386</td>\n",
       "      <td>63.164</td>\n",
       "      <td>22.661</td>\n",
       "      <td>39.558</td>\n",
       "      <td>37.892</td>\n",
       "      <td>47.468</td>\n",
       "      <td>50.312</td>\n",
       "      <td>40.557</td>\n",
       "      <td>19.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.23958</td>\n",
       "      <td>3629.7</td>\n",
       "      <td>4566.3</td>\n",
       "      <td>9.5071</td>\n",
       "      <td>27.219</td>\n",
       "      <td>42.757</td>\n",
       "      <td>2703.8</td>\n",
       "      <td>75.726</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.33665</td>\n",
       "      <td>...</td>\n",
       "      <td>53.996</td>\n",
       "      <td>23.686</td>\n",
       "      <td>59.553</td>\n",
       "      <td>22.749</td>\n",
       "      <td>40.036</td>\n",
       "      <td>37.977</td>\n",
       "      <td>52.375</td>\n",
       "      <td>50.728</td>\n",
       "      <td>41.145</td>\n",
       "      <td>19.029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     xmeas_1  xmeas_2  xmeas_3  xmeas_4  xmeas_5  xmeas_6  xmeas_7  xmeas_8  \\\n",
       "0    0.25038   3674.0   4529.0   9.2320   26.889   42.402   2704.3   74.863   \n",
       "1    0.25109   3659.4   4556.6   9.4264   26.721   42.576   2705.0   75.000   \n",
       "2    0.25038   3660.3   4477.8   9.4426   26.875   42.070   2706.2   74.771   \n",
       "3    0.24977   3661.3   4512.1   9.4776   26.758   42.063   2707.2   75.224   \n",
       "4    0.29405   3679.0   4497.0   9.3381   26.889   42.650   2705.1   75.388   \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "495  0.24602   3651.9   4504.4   9.4721   26.600   42.312   2707.2   74.623   \n",
       "496  0.23457   3663.0   4529.5   9.3440   26.872   42.434   2704.1   75.123   \n",
       "497  0.23068   3656.4   4493.0   9.2978   26.920   42.536   2700.3   74.942   \n",
       "498  0.24263   3632.5   4521.5   9.3915   26.956   42.317   2701.9   74.900   \n",
       "499  0.23958   3629.7   4566.3   9.5071   27.219   42.757   2703.8   75.726   \n",
       "\n",
       "     xmeas_9  xmeas_10  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6   xmv_7  \\\n",
       "0     120.41   0.33818  ...  53.744  24.657  62.544  22.137  39.935  42.323   \n",
       "1     120.41   0.33620  ...  53.414  24.588  59.259  22.084  40.176  38.554   \n",
       "2     120.42   0.33563  ...  54.357  24.666  61.275  22.380  40.244  38.990   \n",
       "3     120.39   0.33553  ...  53.946  24.725  59.856  22.277  40.257  38.072   \n",
       "4     120.39   0.32632  ...  53.658  28.797  60.717  21.947  39.144  41.955   \n",
       "..       ...       ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "495   120.40   0.32381  ...  54.132  23.663  59.588  22.481  38.952  33.489   \n",
       "496   120.40   0.35454  ...  54.028  22.776  60.566  22.237  42.978  38.017   \n",
       "497   120.39   0.35859  ...  54.465  23.158  62.102  21.639  42.483  41.016   \n",
       "498   120.37   0.34057  ...  54.532  23.386  63.164  22.661  39.558  37.892   \n",
       "499   120.39   0.33665  ...  53.996  23.686  59.553  22.749  40.036  37.977   \n",
       "\n",
       "      xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0    47.757  47.510  41.258  18.447  \n",
       "1    43.692  47.427  41.359  17.194  \n",
       "2    46.699  47.468  41.199  20.530  \n",
       "3    47.541  47.658  41.643  18.089  \n",
       "4    47.645  47.346  41.507  18.461  \n",
       "..      ...     ...     ...     ...  \n",
       "495  45.203  49.900  41.057  16.448  \n",
       "496  42.637  50.075  41.339  17.435  \n",
       "497  46.782  50.098  41.277  18.315  \n",
       "498  47.468  50.312  40.557  19.198  \n",
       "499  52.375  50.728  41.145  19.029  \n",
       "\n",
       "[500 rows x 52 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42a1a187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52, 500)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a1259c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_tr = df_train.mean(axis=0)\n",
    "std_tr = df_train.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d5bfa5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr = np.array(((df_train - mu_tr)/std_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7c92c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 52)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c467b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr = pd.DataFrame(X_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45d50a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007452</td>\n",
       "      <td>0.343515</td>\n",
       "      <td>0.501624</td>\n",
       "      <td>-1.429927</td>\n",
       "      <td>-0.084003</td>\n",
       "      <td>0.278264</td>\n",
       "      <td>-0.009547</td>\n",
       "      <td>-0.292480</td>\n",
       "      <td>0.510427</td>\n",
       "      <td>0.098332</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.488447</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>1.049039</td>\n",
       "      <td>-0.051767</td>\n",
       "      <td>-0.122613</td>\n",
       "      <td>1.318663</td>\n",
       "      <td>0.471088</td>\n",
       "      <td>-0.719850</td>\n",
       "      <td>0.279350</td>\n",
       "      <td>0.250147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017858</td>\n",
       "      <td>-0.103212</td>\n",
       "      <td>1.214437</td>\n",
       "      <td>1.034332</td>\n",
       "      <td>-0.865510</td>\n",
       "      <td>1.067806</td>\n",
       "      <td>0.107697</td>\n",
       "      <td>-0.032702</td>\n",
       "      <td>0.510427</td>\n",
       "      <td>-0.071306</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.236243</td>\n",
       "      <td>-0.024128</td>\n",
       "      <td>-1.589009</td>\n",
       "      <td>-0.185720</td>\n",
       "      <td>0.049574</td>\n",
       "      <td>0.089034</td>\n",
       "      <td>-1.226377</td>\n",
       "      <td>-0.759744</td>\n",
       "      <td>0.460137</td>\n",
       "      <td>-0.634230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.007452</td>\n",
       "      <td>-0.075674</td>\n",
       "      <td>-0.820695</td>\n",
       "      <td>1.239687</td>\n",
       "      <td>-0.149128</td>\n",
       "      <td>-1.228219</td>\n",
       "      <td>0.308685</td>\n",
       "      <td>-0.466930</td>\n",
       "      <td>1.022904</td>\n",
       "      <td>-0.120142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900641</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.029957</td>\n",
       "      <td>0.562396</td>\n",
       "      <td>0.098158</td>\n",
       "      <td>0.231278</td>\n",
       "      <td>0.029287</td>\n",
       "      <td>-0.740037</td>\n",
       "      <td>0.173742</td>\n",
       "      <td>1.720344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.029196</td>\n",
       "      <td>-0.045077</td>\n",
       "      <td>0.065155</td>\n",
       "      <td>1.683356</td>\n",
       "      <td>-0.693392</td>\n",
       "      <td>-1.259982</td>\n",
       "      <td>0.476176</td>\n",
       "      <td>0.392045</td>\n",
       "      <td>-0.514527</td>\n",
       "      <td>-0.128709</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030705</td>\n",
       "      <td>0.025687</td>\n",
       "      <td>-1.109583</td>\n",
       "      <td>0.302072</td>\n",
       "      <td>0.107446</td>\n",
       "      <td>-0.068218</td>\n",
       "      <td>0.380890</td>\n",
       "      <td>-0.648715</td>\n",
       "      <td>0.968488</td>\n",
       "      <td>-0.002532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.549243</td>\n",
       "      <td>0.496503</td>\n",
       "      <td>-0.324826</td>\n",
       "      <td>-0.084979</td>\n",
       "      <td>-0.084003</td>\n",
       "      <td>1.403588</td>\n",
       "      <td>0.124446</td>\n",
       "      <td>0.703021</td>\n",
       "      <td>-0.514527</td>\n",
       "      <td>-0.917785</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.683327</td>\n",
       "      <td>1.506313</td>\n",
       "      <td>-0.418150</td>\n",
       "      <td>-0.531976</td>\n",
       "      <td>-0.687757</td>\n",
       "      <td>1.198603</td>\n",
       "      <td>0.424319</td>\n",
       "      <td>-0.798676</td>\n",
       "      <td>0.725052</td>\n",
       "      <td>0.260028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-0.162872</td>\n",
       "      <td>-0.332695</td>\n",
       "      <td>-0.133709</td>\n",
       "      <td>1.613636</td>\n",
       "      <td>-1.428381</td>\n",
       "      <td>-0.130120</td>\n",
       "      <td>0.476176</td>\n",
       "      <td>-0.747566</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>-1.132832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390780</td>\n",
       "      <td>-0.360468</td>\n",
       "      <td>-1.324803</td>\n",
       "      <td>0.817665</td>\n",
       "      <td>-0.824935</td>\n",
       "      <td>-1.563413</td>\n",
       "      <td>-0.595413</td>\n",
       "      <td>0.428891</td>\n",
       "      <td>-0.080434</td>\n",
       "      <td>-1.160763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-0.571027</td>\n",
       "      <td>0.006940</td>\n",
       "      <td>0.514537</td>\n",
       "      <td>-0.010189</td>\n",
       "      <td>-0.163084</td>\n",
       "      <td>0.423467</td>\n",
       "      <td>-0.043045</td>\n",
       "      <td>0.200530</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>1.499991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155111</td>\n",
       "      <td>-0.682992</td>\n",
       "      <td>-0.539411</td>\n",
       "      <td>0.200975</td>\n",
       "      <td>2.051514</td>\n",
       "      <td>-0.086161</td>\n",
       "      <td>-1.666925</td>\n",
       "      <td>0.513004</td>\n",
       "      <td>0.424337</td>\n",
       "      <td>-0.464131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-0.709693</td>\n",
       "      <td>-0.195005</td>\n",
       "      <td>-0.428132</td>\n",
       "      <td>-0.595831</td>\n",
       "      <td>0.060204</td>\n",
       "      <td>0.886302</td>\n",
       "      <td>-0.679510</td>\n",
       "      <td>-0.142681</td>\n",
       "      <td>-0.514527</td>\n",
       "      <td>1.846979</td>\n",
       "      <td>...</td>\n",
       "      <td>1.145374</td>\n",
       "      <td>-0.544092</td>\n",
       "      <td>0.694087</td>\n",
       "      <td>-1.310421</td>\n",
       "      <td>1.697852</td>\n",
       "      <td>0.892257</td>\n",
       "      <td>0.063947</td>\n",
       "      <td>0.524059</td>\n",
       "      <td>0.313359</td>\n",
       "      <td>0.156980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.283714</td>\n",
       "      <td>-0.926291</td>\n",
       "      <td>0.307925</td>\n",
       "      <td>0.591932</td>\n",
       "      <td>0.227670</td>\n",
       "      <td>-0.107432</td>\n",
       "      <td>-0.411525</td>\n",
       "      <td>-0.222321</td>\n",
       "      <td>-1.539481</td>\n",
       "      <td>0.303098</td>\n",
       "      <td>...</td>\n",
       "      <td>1.297200</td>\n",
       "      <td>-0.461189</td>\n",
       "      <td>1.546936</td>\n",
       "      <td>1.272601</td>\n",
       "      <td>-0.391967</td>\n",
       "      <td>-0.126942</td>\n",
       "      <td>0.350407</td>\n",
       "      <td>0.626917</td>\n",
       "      <td>-0.975419</td>\n",
       "      <td>0.780208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.392437</td>\n",
       "      <td>-1.011965</td>\n",
       "      <td>1.464954</td>\n",
       "      <td>2.057305</td>\n",
       "      <td>1.451101</td>\n",
       "      <td>1.889111</td>\n",
       "      <td>-0.093292</td>\n",
       "      <td>1.343933</td>\n",
       "      <td>-0.514527</td>\n",
       "      <td>-0.032752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082597</td>\n",
       "      <td>-0.352105</td>\n",
       "      <td>-1.352910</td>\n",
       "      <td>1.495014</td>\n",
       "      <td>-0.050451</td>\n",
       "      <td>-0.099211</td>\n",
       "      <td>2.399474</td>\n",
       "      <td>0.826865</td>\n",
       "      <td>0.077083</td>\n",
       "      <td>0.660927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -0.007452  0.343515  0.501624 -1.429927 -0.084003  0.278264 -0.009547   \n",
       "1    0.017858 -0.103212  1.214437  1.034332 -0.865510  1.067806  0.107697   \n",
       "2   -0.007452 -0.075674 -0.820695  1.239687 -0.149128 -1.228219  0.308685   \n",
       "3   -0.029196 -0.045077  0.065155  1.683356 -0.693392 -1.259982  0.476176   \n",
       "4    1.549243  0.496503 -0.324826 -0.084979 -0.084003  1.403588  0.124446   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495 -0.162872 -0.332695 -0.133709  1.613636 -1.428381 -0.130120  0.476176   \n",
       "496 -0.571027  0.006940  0.514537 -0.010189 -0.163084  0.423467 -0.043045   \n",
       "497 -0.709693 -0.195005 -0.428132 -0.595831  0.060204  0.886302 -0.679510   \n",
       "498 -0.283714 -0.926291  0.307925  0.591932  0.227670 -0.107432 -0.411525   \n",
       "499 -0.392437 -1.011965  1.464954  2.057305  1.451101  1.889111 -0.093292   \n",
       "\n",
       "           7         8         9   ...        42        43        44  \\\n",
       "0   -0.292480  0.510427  0.098332  ... -0.488447  0.000961  1.049039   \n",
       "1   -0.032702  0.510427 -0.071306  ... -1.236243 -0.024128 -1.589009   \n",
       "2   -0.466930  1.022904 -0.120142  ...  0.900641  0.004234  0.029957   \n",
       "3    0.392045 -0.514527 -0.128709  ... -0.030705  0.025687 -1.109583   \n",
       "4    0.703021 -0.514527 -0.917785  ... -0.683327  1.506313 -0.418150   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "495 -0.747566 -0.002050 -1.132832  ...  0.390780 -0.360468 -1.324803   \n",
       "496  0.200530 -0.002050  1.499991  ...  0.155111 -0.682992 -0.539411   \n",
       "497 -0.142681 -0.514527  1.846979  ...  1.145374 -0.544092  0.694087   \n",
       "498 -0.222321 -1.539481  0.303098  ...  1.297200 -0.461189  1.546936   \n",
       "499  1.343933 -0.514527 -0.032752  ...  0.082597 -0.352105 -1.352910   \n",
       "\n",
       "           45        46        47        48        49        50        51  \n",
       "0   -0.051767 -0.122613  1.318663  0.471088 -0.719850  0.279350  0.250147  \n",
       "1   -0.185720  0.049574  0.089034 -1.226377 -0.759744  0.460137 -0.634230  \n",
       "2    0.562396  0.098158  0.231278  0.029287 -0.740037  0.173742  1.720344  \n",
       "3    0.302072  0.107446 -0.068218  0.380890 -0.648715  0.968488 -0.002532  \n",
       "4   -0.531976 -0.687757  1.198603  0.424319 -0.798676  0.725052  0.260028  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "495  0.817665 -0.824935 -1.563413 -0.595413  0.428891 -0.080434 -1.160763  \n",
       "496  0.200975  2.051514 -0.086161 -1.666925  0.513004  0.424337 -0.464131  \n",
       "497 -1.310421  1.697852  0.892257  0.063947  0.524059  0.313359  0.156980  \n",
       "498  1.272601 -0.391967 -0.126942  0.350407  0.626917 -0.975419  0.780208  \n",
       "499  1.495014 -0.050451 -0.099211  2.399474  0.826865  0.077083  0.660927  \n",
       "\n",
       "[500 rows x 52 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6fd183db",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ts = np.array(((df_test - mu_tr)/std_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb5eb5cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 52)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c38b8e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01208568,  0.40776988,  0.23561043, ..., -0.78041164,\n",
       "         0.10572277, -1.52566552],\n",
       "       [ 0.09699346, -0.69986809, -0.58309113, ..., -0.77127939,\n",
       "        -1.20095516, -2.03384709],\n",
       "       [ 0.01322353,  0.1293305 , -0.72771979, ..., -0.73186651,\n",
       "         1.79903418,  0.72727276],\n",
       "       ...,\n",
       "       [ 1.999462  , -0.13992956, -0.26025928, ...,  1.00470364,\n",
       "        -1.02553812,  0.45694839],\n",
       "       [ 0.15367183,  0.76882315,  0.7392281 , ...,  1.13832293,\n",
       "         0.78412132, -1.57295464],\n",
       "       [ 0.18361513,  0.05589594, -0.1337092 , ...,  1.13207455,\n",
       "        -1.65560749,  0.72656695]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3d161c",
   "metadata": {},
   "source": [
    "## Training Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd22c6cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_13236/2782355972.py:3: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  K = np.matmul(A, A.T)\n"
     ]
    }
   ],
   "source": [
    "# training proccess\n",
    "A = X_tr\n",
    "K = np.matmul(A, A.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a09954e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.232124</td>\n",
       "      <td>-1.004085</td>\n",
       "      <td>0.067264</td>\n",
       "      <td>-2.905314</td>\n",
       "      <td>7.166476</td>\n",
       "      <td>2.795394</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>1.448061</td>\n",
       "      <td>0.405459</td>\n",
       "      <td>1.577030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.591853</td>\n",
       "      <td>3.842471</td>\n",
       "      <td>-9.118121</td>\n",
       "      <td>4.283346</td>\n",
       "      <td>-3.392748</td>\n",
       "      <td>-10.650384</td>\n",
       "      <td>-3.097440</td>\n",
       "      <td>5.072833</td>\n",
       "      <td>-1.137402</td>\n",
       "      <td>-2.061092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.004085</td>\n",
       "      <td>15.616809</td>\n",
       "      <td>-1.640562</td>\n",
       "      <td>5.644505</td>\n",
       "      <td>2.585122</td>\n",
       "      <td>-0.439930</td>\n",
       "      <td>5.990194</td>\n",
       "      <td>4.597676</td>\n",
       "      <td>-2.429195</td>\n",
       "      <td>2.598958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.908481</td>\n",
       "      <td>1.023661</td>\n",
       "      <td>-2.706448</td>\n",
       "      <td>0.153809</td>\n",
       "      <td>0.318730</td>\n",
       "      <td>8.123094</td>\n",
       "      <td>5.361986</td>\n",
       "      <td>-5.625153</td>\n",
       "      <td>-8.417085</td>\n",
       "      <td>-1.240515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.067264</td>\n",
       "      <td>-1.640562</td>\n",
       "      <td>26.699551</td>\n",
       "      <td>17.842619</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>5.115206</td>\n",
       "      <td>3.047081</td>\n",
       "      <td>9.676234</td>\n",
       "      <td>-6.089012</td>\n",
       "      <td>3.920535</td>\n",
       "      <td>...</td>\n",
       "      <td>6.022803</td>\n",
       "      <td>5.870567</td>\n",
       "      <td>9.439036</td>\n",
       "      <td>-6.578546</td>\n",
       "      <td>5.755484</td>\n",
       "      <td>-1.733951</td>\n",
       "      <td>-1.246104</td>\n",
       "      <td>-3.112690</td>\n",
       "      <td>-1.142067</td>\n",
       "      <td>-3.645033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.905314</td>\n",
       "      <td>5.644505</td>\n",
       "      <td>17.842619</td>\n",
       "      <td>28.019440</td>\n",
       "      <td>-1.215484</td>\n",
       "      <td>1.026440</td>\n",
       "      <td>7.354669</td>\n",
       "      <td>7.863066</td>\n",
       "      <td>-6.383947</td>\n",
       "      <td>-0.043879</td>\n",
       "      <td>...</td>\n",
       "      <td>4.383819</td>\n",
       "      <td>5.620564</td>\n",
       "      <td>7.704904</td>\n",
       "      <td>0.810009</td>\n",
       "      <td>2.462522</td>\n",
       "      <td>7.425562</td>\n",
       "      <td>0.087649</td>\n",
       "      <td>-8.012049</td>\n",
       "      <td>-7.498938</td>\n",
       "      <td>1.317580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.166476</td>\n",
       "      <td>2.585122</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>-1.215484</td>\n",
       "      <td>40.382322</td>\n",
       "      <td>34.714218</td>\n",
       "      <td>-1.696429</td>\n",
       "      <td>-4.656149</td>\n",
       "      <td>5.297251</td>\n",
       "      <td>4.018798</td>\n",
       "      <td>...</td>\n",
       "      <td>2.643440</td>\n",
       "      <td>6.909011</td>\n",
       "      <td>-7.384464</td>\n",
       "      <td>0.622754</td>\n",
       "      <td>1.253632</td>\n",
       "      <td>-2.803456</td>\n",
       "      <td>-0.029599</td>\n",
       "      <td>7.955240</td>\n",
       "      <td>-3.665204</td>\n",
       "      <td>2.656389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-10.650384</td>\n",
       "      <td>8.123094</td>\n",
       "      <td>-1.733951</td>\n",
       "      <td>7.425562</td>\n",
       "      <td>-2.803456</td>\n",
       "      <td>-4.689299</td>\n",
       "      <td>1.352422</td>\n",
       "      <td>-0.148598</td>\n",
       "      <td>3.508259</td>\n",
       "      <td>8.030400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.762517</td>\n",
       "      <td>-4.202477</td>\n",
       "      <td>3.454964</td>\n",
       "      <td>-6.705873</td>\n",
       "      <td>17.998134</td>\n",
       "      <td>40.382474</td>\n",
       "      <td>7.844551</td>\n",
       "      <td>-8.141296</td>\n",
       "      <td>4.945185</td>\n",
       "      <td>8.019860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-3.097440</td>\n",
       "      <td>5.361986</td>\n",
       "      <td>-1.246104</td>\n",
       "      <td>0.087649</td>\n",
       "      <td>-0.029599</td>\n",
       "      <td>-1.630370</td>\n",
       "      <td>-1.134954</td>\n",
       "      <td>0.501412</td>\n",
       "      <td>-8.119684</td>\n",
       "      <td>-5.749619</td>\n",
       "      <td>...</td>\n",
       "      <td>2.456229</td>\n",
       "      <td>2.052526</td>\n",
       "      <td>3.397583</td>\n",
       "      <td>-1.030407</td>\n",
       "      <td>-0.649532</td>\n",
       "      <td>7.844551</td>\n",
       "      <td>29.274803</td>\n",
       "      <td>19.147968</td>\n",
       "      <td>0.827270</td>\n",
       "      <td>-3.450679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>5.072833</td>\n",
       "      <td>-5.625153</td>\n",
       "      <td>-3.112690</td>\n",
       "      <td>-8.012049</td>\n",
       "      <td>7.955240</td>\n",
       "      <td>2.708391</td>\n",
       "      <td>-6.143578</td>\n",
       "      <td>-6.828632</td>\n",
       "      <td>-4.196774</td>\n",
       "      <td>-12.126553</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.614045</td>\n",
       "      <td>4.985756</td>\n",
       "      <td>6.697375</td>\n",
       "      <td>11.271496</td>\n",
       "      <td>-6.032084</td>\n",
       "      <td>-8.141296</td>\n",
       "      <td>19.147968</td>\n",
       "      <td>36.529806</td>\n",
       "      <td>9.390213</td>\n",
       "      <td>3.957249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-1.137402</td>\n",
       "      <td>-8.417085</td>\n",
       "      <td>-1.142067</td>\n",
       "      <td>-7.498938</td>\n",
       "      <td>-3.665204</td>\n",
       "      <td>-1.291069</td>\n",
       "      <td>-4.374695</td>\n",
       "      <td>-4.959638</td>\n",
       "      <td>2.190599</td>\n",
       "      <td>-4.837514</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.130807</td>\n",
       "      <td>-8.844955</td>\n",
       "      <td>6.171358</td>\n",
       "      <td>-2.546988</td>\n",
       "      <td>6.338584</td>\n",
       "      <td>4.945185</td>\n",
       "      <td>0.827270</td>\n",
       "      <td>9.390213</td>\n",
       "      <td>33.924219</td>\n",
       "      <td>24.574407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-2.061092</td>\n",
       "      <td>-1.240515</td>\n",
       "      <td>-3.645033</td>\n",
       "      <td>1.317580</td>\n",
       "      <td>2.656389</td>\n",
       "      <td>-1.025183</td>\n",
       "      <td>-5.990094</td>\n",
       "      <td>-8.515507</td>\n",
       "      <td>-2.816364</td>\n",
       "      <td>-5.120780</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.922066</td>\n",
       "      <td>-2.100567</td>\n",
       "      <td>-0.784461</td>\n",
       "      <td>1.263230</td>\n",
       "      <td>1.116466</td>\n",
       "      <td>8.019860</td>\n",
       "      <td>-3.450679</td>\n",
       "      <td>3.957249</td>\n",
       "      <td>24.574407</td>\n",
       "      <td>51.801321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5    \\\n",
       "0    11.232124  -1.004085   0.067264  -2.905314   7.166476   2.795394   \n",
       "1    -1.004085  15.616809  -1.640562   5.644505   2.585122  -0.439930   \n",
       "2     0.067264  -1.640562  26.699551  17.842619  -0.313752   5.115206   \n",
       "3    -2.905314   5.644505  17.842619  28.019440  -1.215484   1.026440   \n",
       "4     7.166476   2.585122  -0.313752  -1.215484  40.382322  34.714218   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "495 -10.650384   8.123094  -1.733951   7.425562  -2.803456  -4.689299   \n",
       "496  -3.097440   5.361986  -1.246104   0.087649  -0.029599  -1.630370   \n",
       "497   5.072833  -5.625153  -3.112690  -8.012049   7.955240   2.708391   \n",
       "498  -1.137402  -8.417085  -1.142067  -7.498938  -3.665204  -1.291069   \n",
       "499  -2.061092  -1.240515  -3.645033   1.317580   2.656389  -1.025183   \n",
       "\n",
       "          6         7         8          9    ...       490       491  \\\n",
       "0    0.005684  1.448061  0.405459   1.577030  ...  0.591853  3.842471   \n",
       "1    5.990194  4.597676 -2.429195   2.598958  ...  0.908481  1.023661   \n",
       "2    3.047081  9.676234 -6.089012   3.920535  ...  6.022803  5.870567   \n",
       "3    7.354669  7.863066 -6.383947  -0.043879  ...  4.383819  5.620564   \n",
       "4   -1.696429 -4.656149  5.297251   4.018798  ...  2.643440  6.909011   \n",
       "..        ...       ...       ...        ...  ...       ...       ...   \n",
       "495  1.352422 -0.148598  3.508259   8.030400  ... -0.762517 -4.202477   \n",
       "496 -1.134954  0.501412 -8.119684  -5.749619  ...  2.456229  2.052526   \n",
       "497 -6.143578 -6.828632 -4.196774 -12.126553  ... -2.614045  4.985756   \n",
       "498 -4.374695 -4.959638  2.190599  -4.837514  ... -4.130807 -8.844955   \n",
       "499 -5.990094 -8.515507 -2.816364  -5.120780  ... -2.922066 -2.100567   \n",
       "\n",
       "          492        493        494        495        496        497  \\\n",
       "0   -9.118121   4.283346  -3.392748 -10.650384  -3.097440   5.072833   \n",
       "1   -2.706448   0.153809   0.318730   8.123094   5.361986  -5.625153   \n",
       "2    9.439036  -6.578546   5.755484  -1.733951  -1.246104  -3.112690   \n",
       "3    7.704904   0.810009   2.462522   7.425562   0.087649  -8.012049   \n",
       "4   -7.384464   0.622754   1.253632  -2.803456  -0.029599   7.955240   \n",
       "..        ...        ...        ...        ...        ...        ...   \n",
       "495  3.454964  -6.705873  17.998134  40.382474   7.844551  -8.141296   \n",
       "496  3.397583  -1.030407  -0.649532   7.844551  29.274803  19.147968   \n",
       "497  6.697375  11.271496  -6.032084  -8.141296  19.147968  36.529806   \n",
       "498  6.171358  -2.546988   6.338584   4.945185   0.827270   9.390213   \n",
       "499 -0.784461   1.263230   1.116466   8.019860  -3.450679   3.957249   \n",
       "\n",
       "           498        499  \n",
       "0    -1.137402  -2.061092  \n",
       "1    -8.417085  -1.240515  \n",
       "2    -1.142067  -3.645033  \n",
       "3    -7.498938   1.317580  \n",
       "4    -3.665204   2.656389  \n",
       "..         ...        ...  \n",
       "495   4.945185   8.019860  \n",
       "496   0.827270  -3.450679  \n",
       "497   9.390213   3.957249  \n",
       "498  33.924219  24.574407  \n",
       "499  24.574407  51.801321  \n",
       "\n",
       "[500 rows x 500 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10b6f273",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "K = pd.DataFrame(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b16b9ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5db5fff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62b8b7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = (len(X_tr),len(X_tr))\n",
    "unit = np.ones(s)/len(X_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d5a5c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit = pd.DataFrame(unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b2728c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit.shape\n",
    "# unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "069c5298",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_c = K-unit@K-K@unit+unit@K@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d0e66c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c33545f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.232124</td>\n",
       "      <td>-1.004085</td>\n",
       "      <td>0.067264</td>\n",
       "      <td>-2.905314</td>\n",
       "      <td>7.166476</td>\n",
       "      <td>2.795394</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>1.448061</td>\n",
       "      <td>0.405459</td>\n",
       "      <td>1.577030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.591853</td>\n",
       "      <td>3.842471</td>\n",
       "      <td>-9.118121</td>\n",
       "      <td>4.283346</td>\n",
       "      <td>-3.392748</td>\n",
       "      <td>-10.650384</td>\n",
       "      <td>-3.097440</td>\n",
       "      <td>5.072833</td>\n",
       "      <td>-1.137402</td>\n",
       "      <td>-2.061092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.004085</td>\n",
       "      <td>15.616809</td>\n",
       "      <td>-1.640562</td>\n",
       "      <td>5.644505</td>\n",
       "      <td>2.585122</td>\n",
       "      <td>-0.439930</td>\n",
       "      <td>5.990194</td>\n",
       "      <td>4.597676</td>\n",
       "      <td>-2.429195</td>\n",
       "      <td>2.598958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.908481</td>\n",
       "      <td>1.023661</td>\n",
       "      <td>-2.706448</td>\n",
       "      <td>0.153809</td>\n",
       "      <td>0.318730</td>\n",
       "      <td>8.123094</td>\n",
       "      <td>5.361986</td>\n",
       "      <td>-5.625153</td>\n",
       "      <td>-8.417085</td>\n",
       "      <td>-1.240515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.067264</td>\n",
       "      <td>-1.640562</td>\n",
       "      <td>26.699551</td>\n",
       "      <td>17.842619</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>5.115206</td>\n",
       "      <td>3.047081</td>\n",
       "      <td>9.676234</td>\n",
       "      <td>-6.089012</td>\n",
       "      <td>3.920535</td>\n",
       "      <td>...</td>\n",
       "      <td>6.022803</td>\n",
       "      <td>5.870567</td>\n",
       "      <td>9.439036</td>\n",
       "      <td>-6.578546</td>\n",
       "      <td>5.755484</td>\n",
       "      <td>-1.733951</td>\n",
       "      <td>-1.246104</td>\n",
       "      <td>-3.112690</td>\n",
       "      <td>-1.142067</td>\n",
       "      <td>-3.645033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.905314</td>\n",
       "      <td>5.644505</td>\n",
       "      <td>17.842619</td>\n",
       "      <td>28.019440</td>\n",
       "      <td>-1.215484</td>\n",
       "      <td>1.026440</td>\n",
       "      <td>7.354669</td>\n",
       "      <td>7.863066</td>\n",
       "      <td>-6.383947</td>\n",
       "      <td>-0.043879</td>\n",
       "      <td>...</td>\n",
       "      <td>4.383819</td>\n",
       "      <td>5.620564</td>\n",
       "      <td>7.704904</td>\n",
       "      <td>0.810009</td>\n",
       "      <td>2.462522</td>\n",
       "      <td>7.425562</td>\n",
       "      <td>0.087649</td>\n",
       "      <td>-8.012049</td>\n",
       "      <td>-7.498938</td>\n",
       "      <td>1.317580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.166476</td>\n",
       "      <td>2.585122</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>-1.215484</td>\n",
       "      <td>40.382322</td>\n",
       "      <td>34.714218</td>\n",
       "      <td>-1.696429</td>\n",
       "      <td>-4.656149</td>\n",
       "      <td>5.297251</td>\n",
       "      <td>4.018798</td>\n",
       "      <td>...</td>\n",
       "      <td>2.643440</td>\n",
       "      <td>6.909011</td>\n",
       "      <td>-7.384464</td>\n",
       "      <td>0.622754</td>\n",
       "      <td>1.253632</td>\n",
       "      <td>-2.803456</td>\n",
       "      <td>-0.029599</td>\n",
       "      <td>7.955240</td>\n",
       "      <td>-3.665204</td>\n",
       "      <td>2.656389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-10.650384</td>\n",
       "      <td>8.123094</td>\n",
       "      <td>-1.733951</td>\n",
       "      <td>7.425562</td>\n",
       "      <td>-2.803456</td>\n",
       "      <td>-4.689299</td>\n",
       "      <td>1.352422</td>\n",
       "      <td>-0.148598</td>\n",
       "      <td>3.508259</td>\n",
       "      <td>8.030400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.762517</td>\n",
       "      <td>-4.202477</td>\n",
       "      <td>3.454964</td>\n",
       "      <td>-6.705873</td>\n",
       "      <td>17.998134</td>\n",
       "      <td>40.382474</td>\n",
       "      <td>7.844551</td>\n",
       "      <td>-8.141296</td>\n",
       "      <td>4.945185</td>\n",
       "      <td>8.019860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-3.097440</td>\n",
       "      <td>5.361986</td>\n",
       "      <td>-1.246104</td>\n",
       "      <td>0.087649</td>\n",
       "      <td>-0.029599</td>\n",
       "      <td>-1.630370</td>\n",
       "      <td>-1.134954</td>\n",
       "      <td>0.501412</td>\n",
       "      <td>-8.119684</td>\n",
       "      <td>-5.749619</td>\n",
       "      <td>...</td>\n",
       "      <td>2.456229</td>\n",
       "      <td>2.052526</td>\n",
       "      <td>3.397583</td>\n",
       "      <td>-1.030407</td>\n",
       "      <td>-0.649532</td>\n",
       "      <td>7.844551</td>\n",
       "      <td>29.274803</td>\n",
       "      <td>19.147968</td>\n",
       "      <td>0.827270</td>\n",
       "      <td>-3.450679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>5.072833</td>\n",
       "      <td>-5.625153</td>\n",
       "      <td>-3.112690</td>\n",
       "      <td>-8.012049</td>\n",
       "      <td>7.955240</td>\n",
       "      <td>2.708391</td>\n",
       "      <td>-6.143578</td>\n",
       "      <td>-6.828632</td>\n",
       "      <td>-4.196774</td>\n",
       "      <td>-12.126553</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.614045</td>\n",
       "      <td>4.985756</td>\n",
       "      <td>6.697375</td>\n",
       "      <td>11.271496</td>\n",
       "      <td>-6.032084</td>\n",
       "      <td>-8.141296</td>\n",
       "      <td>19.147968</td>\n",
       "      <td>36.529806</td>\n",
       "      <td>9.390213</td>\n",
       "      <td>3.957249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-1.137402</td>\n",
       "      <td>-8.417085</td>\n",
       "      <td>-1.142067</td>\n",
       "      <td>-7.498938</td>\n",
       "      <td>-3.665204</td>\n",
       "      <td>-1.291069</td>\n",
       "      <td>-4.374695</td>\n",
       "      <td>-4.959638</td>\n",
       "      <td>2.190599</td>\n",
       "      <td>-4.837514</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.130807</td>\n",
       "      <td>-8.844955</td>\n",
       "      <td>6.171358</td>\n",
       "      <td>-2.546988</td>\n",
       "      <td>6.338584</td>\n",
       "      <td>4.945185</td>\n",
       "      <td>0.827270</td>\n",
       "      <td>9.390213</td>\n",
       "      <td>33.924219</td>\n",
       "      <td>24.574407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-2.061092</td>\n",
       "      <td>-1.240515</td>\n",
       "      <td>-3.645033</td>\n",
       "      <td>1.317580</td>\n",
       "      <td>2.656389</td>\n",
       "      <td>-1.025183</td>\n",
       "      <td>-5.990094</td>\n",
       "      <td>-8.515507</td>\n",
       "      <td>-2.816364</td>\n",
       "      <td>-5.120780</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.922066</td>\n",
       "      <td>-2.100567</td>\n",
       "      <td>-0.784461</td>\n",
       "      <td>1.263230</td>\n",
       "      <td>1.116466</td>\n",
       "      <td>8.019860</td>\n",
       "      <td>-3.450679</td>\n",
       "      <td>3.957249</td>\n",
       "      <td>24.574407</td>\n",
       "      <td>51.801321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5    \\\n",
       "0    11.232124  -1.004085   0.067264  -2.905314   7.166476   2.795394   \n",
       "1    -1.004085  15.616809  -1.640562   5.644505   2.585122  -0.439930   \n",
       "2     0.067264  -1.640562  26.699551  17.842619  -0.313752   5.115206   \n",
       "3    -2.905314   5.644505  17.842619  28.019440  -1.215484   1.026440   \n",
       "4     7.166476   2.585122  -0.313752  -1.215484  40.382322  34.714218   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "495 -10.650384   8.123094  -1.733951   7.425562  -2.803456  -4.689299   \n",
       "496  -3.097440   5.361986  -1.246104   0.087649  -0.029599  -1.630370   \n",
       "497   5.072833  -5.625153  -3.112690  -8.012049   7.955240   2.708391   \n",
       "498  -1.137402  -8.417085  -1.142067  -7.498938  -3.665204  -1.291069   \n",
       "499  -2.061092  -1.240515  -3.645033   1.317580   2.656389  -1.025183   \n",
       "\n",
       "          6         7         8          9    ...       490       491  \\\n",
       "0    0.005684  1.448061  0.405459   1.577030  ...  0.591853  3.842471   \n",
       "1    5.990194  4.597676 -2.429195   2.598958  ...  0.908481  1.023661   \n",
       "2    3.047081  9.676234 -6.089012   3.920535  ...  6.022803  5.870567   \n",
       "3    7.354669  7.863066 -6.383947  -0.043879  ...  4.383819  5.620564   \n",
       "4   -1.696429 -4.656149  5.297251   4.018798  ...  2.643440  6.909011   \n",
       "..        ...       ...       ...        ...  ...       ...       ...   \n",
       "495  1.352422 -0.148598  3.508259   8.030400  ... -0.762517 -4.202477   \n",
       "496 -1.134954  0.501412 -8.119684  -5.749619  ...  2.456229  2.052526   \n",
       "497 -6.143578 -6.828632 -4.196774 -12.126553  ... -2.614045  4.985756   \n",
       "498 -4.374695 -4.959638  2.190599  -4.837514  ... -4.130807 -8.844955   \n",
       "499 -5.990094 -8.515507 -2.816364  -5.120780  ... -2.922066 -2.100567   \n",
       "\n",
       "          492        493        494        495        496        497  \\\n",
       "0   -9.118121   4.283346  -3.392748 -10.650384  -3.097440   5.072833   \n",
       "1   -2.706448   0.153809   0.318730   8.123094   5.361986  -5.625153   \n",
       "2    9.439036  -6.578546   5.755484  -1.733951  -1.246104  -3.112690   \n",
       "3    7.704904   0.810009   2.462522   7.425562   0.087649  -8.012049   \n",
       "4   -7.384464   0.622754   1.253632  -2.803456  -0.029599   7.955240   \n",
       "..        ...        ...        ...        ...        ...        ...   \n",
       "495  3.454964  -6.705873  17.998134  40.382474   7.844551  -8.141296   \n",
       "496  3.397583  -1.030407  -0.649532   7.844551  29.274803  19.147968   \n",
       "497  6.697375  11.271496  -6.032084  -8.141296  19.147968  36.529806   \n",
       "498  6.171358  -2.546988   6.338584   4.945185   0.827270   9.390213   \n",
       "499 -0.784461   1.263230   1.116466   8.019860  -3.450679   3.957249   \n",
       "\n",
       "           498        499  \n",
       "0    -1.137402  -2.061092  \n",
       "1    -8.417085  -1.240515  \n",
       "2    -1.142067  -3.645033  \n",
       "3    -7.498938   1.317580  \n",
       "4    -3.665204   2.656389  \n",
       "..         ...        ...  \n",
       "495   4.945185   8.019860  \n",
       "496   0.827270  -3.450679  \n",
       "497   9.390213   3.957249  \n",
       "498  33.924219  24.574407  \n",
       "499  24.574407  51.801321  \n",
       "\n",
       "[500 rows x 500 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7155deb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls, eigvct = np.linalg.eig(K_c) # K_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ece98c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eigvls : in eigen values will be use in the test\n",
    "# eigvct : in eigen vector will be use in the train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54a925dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvls.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "003dfc71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0e7b71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvct = pd.DataFrame(eigvct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f33670a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls = pd.DataFrame(eigvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cb33de59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 16)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvct.iloc[:,:16].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b812c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_13236/3867140423.py:1: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  score_train = np.matmul(K_c, eigvct.iloc[:,:16])\n"
     ]
    }
   ],
   "source": [
    "score_train = np.matmul(K_c, eigvct.iloc[:,:16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99e77a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.636699+0.000000j</td>\n",
       "      <td>-57.279895+0.000000j</td>\n",
       "      <td>6.801064+0.000000j</td>\n",
       "      <td>30.806849+0.000000j</td>\n",
       "      <td>-50.814370+0.000000j</td>\n",
       "      <td>-1.955729+0.000000j</td>\n",
       "      <td>34.524197+0.000000j</td>\n",
       "      <td>14.593350+0.000000j</td>\n",
       "      <td>6.863549+0.000000j</td>\n",
       "      <td>-22.900072+0.000000j</td>\n",
       "      <td>6.938265+0.000000j</td>\n",
       "      <td>-17.644885+0.000000j</td>\n",
       "      <td>-7.591499+0.000000j</td>\n",
       "      <td>-4.826965+0.000000j</td>\n",
       "      <td>-12.133394+0.000000j</td>\n",
       "      <td>2.605697+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.102482+0.000000j</td>\n",
       "      <td>-12.697157+0.000000j</td>\n",
       "      <td>-21.717631+0.000000j</td>\n",
       "      <td>-54.801203+0.000000j</td>\n",
       "      <td>-17.393027+0.000000j</td>\n",
       "      <td>-2.718521+0.000000j</td>\n",
       "      <td>-9.2363730+0.0000000j</td>\n",
       "      <td>-16.236679+0.000000j</td>\n",
       "      <td>-26.451816+0.000000j</td>\n",
       "      <td>34.433300+0.000000j</td>\n",
       "      <td>-4.423900+0.000000j</td>\n",
       "      <td>6.535509+0.000000j</td>\n",
       "      <td>-11.486906+0.000000j</td>\n",
       "      <td>6.267477+0.000000j</td>\n",
       "      <td>16.483242+0.000000j</td>\n",
       "      <td>-15.466668+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.511178+0.000000j</td>\n",
       "      <td>-15.030580+0.000000j</td>\n",
       "      <td>-39.952632+0.000000j</td>\n",
       "      <td>50.696472+0.000000j</td>\n",
       "      <td>-31.786993+0.000000j</td>\n",
       "      <td>70.919995+0.000000j</td>\n",
       "      <td>-10.517666+0.000000j</td>\n",
       "      <td>-8.584178+0.000000j</td>\n",
       "      <td>-25.149494+0.000000j</td>\n",
       "      <td>28.083124+0.000000j</td>\n",
       "      <td>-31.686992+0.000000j</td>\n",
       "      <td>14.442525+0.000000j</td>\n",
       "      <td>-9.902951+0.000000j</td>\n",
       "      <td>-25.954889+0.000000j</td>\n",
       "      <td>16.115659+0.000000j</td>\n",
       "      <td>17.916747+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.229991+0.000000j</td>\n",
       "      <td>-8.0639210+0.0000000j</td>\n",
       "      <td>-31.901260+0.000000j</td>\n",
       "      <td>8.980417+0.000000j</td>\n",
       "      <td>-0.297943+0.000000j</td>\n",
       "      <td>24.679360+0.000000j</td>\n",
       "      <td>-0.5232200+0.0000000j</td>\n",
       "      <td>1.688813+0.000000j</td>\n",
       "      <td>-42.613742+0.000000j</td>\n",
       "      <td>45.036306+0.000000j</td>\n",
       "      <td>-25.990627+0.000000j</td>\n",
       "      <td>-2.015544+0.000000j</td>\n",
       "      <td>7.085784+0.000000j</td>\n",
       "      <td>-13.975932+0.000000j</td>\n",
       "      <td>25.854975+0.000000j</td>\n",
       "      <td>13.988025+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.779742+0.000000j</td>\n",
       "      <td>-103.177162+0.000000j</td>\n",
       "      <td>-34.325690+0.000000j</td>\n",
       "      <td>25.367360+0.000000j</td>\n",
       "      <td>-43.613848+0.000000j</td>\n",
       "      <td>-50.611448+0.000000j</td>\n",
       "      <td>37.419015+0.000000j</td>\n",
       "      <td>29.734129+0.000000j</td>\n",
       "      <td>39.350490+0.000000j</td>\n",
       "      <td>12.156158+0.000000j</td>\n",
       "      <td>-5.094993+0.000000j</td>\n",
       "      <td>57.027711+0.000000j</td>\n",
       "      <td>8.750487+0.000000j</td>\n",
       "      <td>21.490044+0.000000j</td>\n",
       "      <td>-22.296649+0.000000j</td>\n",
       "      <td>21.561991+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>57.616349+0.000000j</td>\n",
       "      <td>86.367733+0.000000j</td>\n",
       "      <td>-31.370777+0.000000j</td>\n",
       "      <td>-41.223525+0.000000j</td>\n",
       "      <td>74.161684+0.000000j</td>\n",
       "      <td>-37.912696+0.000000j</td>\n",
       "      <td>-60.605938+0.000000j</td>\n",
       "      <td>3.447153+0.000000j</td>\n",
       "      <td>-28.887516+0.000000j</td>\n",
       "      <td>58.495358+0.000000j</td>\n",
       "      <td>-14.932260+0.000000j</td>\n",
       "      <td>-17.949819+0.000000j</td>\n",
       "      <td>31.383810+0.000000j</td>\n",
       "      <td>17.199088+0.000000j</td>\n",
       "      <td>14.451924+0.000000j</td>\n",
       "      <td>-15.537569+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>11.502497+0.000000j</td>\n",
       "      <td>86.899702+0.000000j</td>\n",
       "      <td>-0.532217+0.000000j</td>\n",
       "      <td>-83.894587+0.000000j</td>\n",
       "      <td>-8.985749+0.000000j</td>\n",
       "      <td>57.302800+0.000000j</td>\n",
       "      <td>35.065081+0.000000j</td>\n",
       "      <td>-26.631721+0.000000j</td>\n",
       "      <td>-16.444364+0.000000j</td>\n",
       "      <td>2.904475+0.000000j</td>\n",
       "      <td>-11.575437+0.000000j</td>\n",
       "      <td>-8.561507+0.000000j</td>\n",
       "      <td>14.589257+0.000000j</td>\n",
       "      <td>27.871799+0.000000j</td>\n",
       "      <td>3.417315+0.000000j</td>\n",
       "      <td>6.520795+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-42.721295+0.000000j</td>\n",
       "      <td>41.969548+0.000000j</td>\n",
       "      <td>54.292086+0.000000j</td>\n",
       "      <td>-17.223082+0.000000j</td>\n",
       "      <td>-28.327301+0.000000j</td>\n",
       "      <td>47.942870+0.000000j</td>\n",
       "      <td>105.696016+0.000000j</td>\n",
       "      <td>11.900418+0.000000j</td>\n",
       "      <td>24.225741+0.000000j</td>\n",
       "      <td>-3.503348+0.000000j</td>\n",
       "      <td>-17.365393+0.000000j</td>\n",
       "      <td>-0.880877+0.000000j</td>\n",
       "      <td>20.098562+0.000000j</td>\n",
       "      <td>28.462241+0.000000j</td>\n",
       "      <td>-29.544152+0.000000j</td>\n",
       "      <td>9.583958+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>9.486255+0.000000j</td>\n",
       "      <td>20.438490+0.000000j</td>\n",
       "      <td>61.866810+0.000000j</td>\n",
       "      <td>36.634452+0.000000j</td>\n",
       "      <td>24.253080+0.000000j</td>\n",
       "      <td>18.548336+0.000000j</td>\n",
       "      <td>11.354000+0.000000j</td>\n",
       "      <td>-2.939992+0.000000j</td>\n",
       "      <td>49.028023+0.000000j</td>\n",
       "      <td>-2.356464+0.000000j</td>\n",
       "      <td>-15.509667+0.000000j</td>\n",
       "      <td>-25.506367+0.000000j</td>\n",
       "      <td>43.393306+0.000000j</td>\n",
       "      <td>10.924719+0.000000j</td>\n",
       "      <td>2.684610+0.000000j</td>\n",
       "      <td>-22.796027+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>41.763136+0.000000j</td>\n",
       "      <td>20.658138+0.000000j</td>\n",
       "      <td>34.861801+0.000000j</td>\n",
       "      <td>100.79089+0.00000j</td>\n",
       "      <td>58.652052+0.000000j</td>\n",
       "      <td>-25.419691+0.000000j</td>\n",
       "      <td>55.879085+0.000000j</td>\n",
       "      <td>12.174877+0.000000j</td>\n",
       "      <td>10.214084+0.000000j</td>\n",
       "      <td>19.540882+0.000000j</td>\n",
       "      <td>-46.463156+0.000000j</td>\n",
       "      <td>-3.226023+0.000000j</td>\n",
       "      <td>47.665824+0.000000j</td>\n",
       "      <td>28.413928+0.000000j</td>\n",
       "      <td>47.129524+0.000000j</td>\n",
       "      <td>-27.561404+0.000000j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0                     1                    2   \\\n",
       "0    23.636699+0.000000j  -57.279895+0.000000j   6.801064+0.000000j   \n",
       "1    40.102482+0.000000j  -12.697157+0.000000j -21.717631+0.000000j   \n",
       "2    33.511178+0.000000j  -15.030580+0.000000j -39.952632+0.000000j   \n",
       "3    39.229991+0.000000j -8.0639210+0.0000000j -31.901260+0.000000j   \n",
       "4    64.779742+0.000000j -103.177162+0.000000j -34.325690+0.000000j   \n",
       "..                   ...                   ...                  ...   \n",
       "495  57.616349+0.000000j   86.367733+0.000000j -31.370777+0.000000j   \n",
       "496  11.502497+0.000000j   86.899702+0.000000j  -0.532217+0.000000j   \n",
       "497 -42.721295+0.000000j   41.969548+0.000000j  54.292086+0.000000j   \n",
       "498   9.486255+0.000000j   20.438490+0.000000j  61.866810+0.000000j   \n",
       "499  41.763136+0.000000j   20.658138+0.000000j  34.861801+0.000000j   \n",
       "\n",
       "                      3                    4                    5   \\\n",
       "0    30.806849+0.000000j -50.814370+0.000000j  -1.955729+0.000000j   \n",
       "1   -54.801203+0.000000j -17.393027+0.000000j  -2.718521+0.000000j   \n",
       "2    50.696472+0.000000j -31.786993+0.000000j  70.919995+0.000000j   \n",
       "3     8.980417+0.000000j  -0.297943+0.000000j  24.679360+0.000000j   \n",
       "4    25.367360+0.000000j -43.613848+0.000000j -50.611448+0.000000j   \n",
       "..                   ...                  ...                  ...   \n",
       "495 -41.223525+0.000000j  74.161684+0.000000j -37.912696+0.000000j   \n",
       "496 -83.894587+0.000000j  -8.985749+0.000000j  57.302800+0.000000j   \n",
       "497 -17.223082+0.000000j -28.327301+0.000000j  47.942870+0.000000j   \n",
       "498  36.634452+0.000000j  24.253080+0.000000j  18.548336+0.000000j   \n",
       "499   100.79089+0.00000j  58.652052+0.000000j -25.419691+0.000000j   \n",
       "\n",
       "                       6                    7                    8   \\\n",
       "0     34.524197+0.000000j  14.593350+0.000000j   6.863549+0.000000j   \n",
       "1   -9.2363730+0.0000000j -16.236679+0.000000j -26.451816+0.000000j   \n",
       "2    -10.517666+0.000000j  -8.584178+0.000000j -25.149494+0.000000j   \n",
       "3   -0.5232200+0.0000000j   1.688813+0.000000j -42.613742+0.000000j   \n",
       "4     37.419015+0.000000j  29.734129+0.000000j  39.350490+0.000000j   \n",
       "..                    ...                  ...                  ...   \n",
       "495  -60.605938+0.000000j   3.447153+0.000000j -28.887516+0.000000j   \n",
       "496   35.065081+0.000000j -26.631721+0.000000j -16.444364+0.000000j   \n",
       "497  105.696016+0.000000j  11.900418+0.000000j  24.225741+0.000000j   \n",
       "498   11.354000+0.000000j  -2.939992+0.000000j  49.028023+0.000000j   \n",
       "499   55.879085+0.000000j  12.174877+0.000000j  10.214084+0.000000j   \n",
       "\n",
       "                      9                    10                   11  \\\n",
       "0   -22.900072+0.000000j   6.938265+0.000000j -17.644885+0.000000j   \n",
       "1    34.433300+0.000000j  -4.423900+0.000000j   6.535509+0.000000j   \n",
       "2    28.083124+0.000000j -31.686992+0.000000j  14.442525+0.000000j   \n",
       "3    45.036306+0.000000j -25.990627+0.000000j  -2.015544+0.000000j   \n",
       "4    12.156158+0.000000j  -5.094993+0.000000j  57.027711+0.000000j   \n",
       "..                   ...                  ...                  ...   \n",
       "495  58.495358+0.000000j -14.932260+0.000000j -17.949819+0.000000j   \n",
       "496   2.904475+0.000000j -11.575437+0.000000j  -8.561507+0.000000j   \n",
       "497  -3.503348+0.000000j -17.365393+0.000000j  -0.880877+0.000000j   \n",
       "498  -2.356464+0.000000j -15.509667+0.000000j -25.506367+0.000000j   \n",
       "499  19.540882+0.000000j -46.463156+0.000000j  -3.226023+0.000000j   \n",
       "\n",
       "                      12                   13                   14  \\\n",
       "0    -7.591499+0.000000j  -4.826965+0.000000j -12.133394+0.000000j   \n",
       "1   -11.486906+0.000000j   6.267477+0.000000j  16.483242+0.000000j   \n",
       "2    -9.902951+0.000000j -25.954889+0.000000j  16.115659+0.000000j   \n",
       "3     7.085784+0.000000j -13.975932+0.000000j  25.854975+0.000000j   \n",
       "4     8.750487+0.000000j  21.490044+0.000000j -22.296649+0.000000j   \n",
       "..                   ...                  ...                  ...   \n",
       "495  31.383810+0.000000j  17.199088+0.000000j  14.451924+0.000000j   \n",
       "496  14.589257+0.000000j  27.871799+0.000000j   3.417315+0.000000j   \n",
       "497  20.098562+0.000000j  28.462241+0.000000j -29.544152+0.000000j   \n",
       "498  43.393306+0.000000j  10.924719+0.000000j   2.684610+0.000000j   \n",
       "499  47.665824+0.000000j  28.413928+0.000000j  47.129524+0.000000j   \n",
       "\n",
       "                      15  \n",
       "0     2.605697+0.000000j  \n",
       "1   -15.466668+0.000000j  \n",
       "2    17.916747+0.000000j  \n",
       "3    13.988025+0.000000j  \n",
       "4    21.561991+0.000000j  \n",
       "..                   ...  \n",
       "495 -15.537569+0.000000j  \n",
       "496   6.520795+0.000000j  \n",
       "497   9.583958+0.000000j  \n",
       "498 -22.796027+0.000000j  \n",
       "499 -27.561404+0.000000j  \n",
       "\n",
       "[500 rows x 16 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "04925af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 16)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dc9bc302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.700454e+03+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.433701e+03+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.455603e+03+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.129258e+03+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.043778e+03+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-2.701266e-15-5.009920e-15j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-1.114526e-14+1.397340e-15j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-1.114526e-14-1.397340e-15j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-6.711123e-15+0.000000e+00j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-8.038289e-15+0.000000e+00j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "0    3.700454e+03+0.000000e+00j\n",
       "1    2.433701e+03+0.000000e+00j\n",
       "2    1.455603e+03+0.000000e+00j\n",
       "3    1.129258e+03+0.000000e+00j\n",
       "4    1.043778e+03+0.000000e+00j\n",
       "..                          ...\n",
       "495 -2.701266e-15-5.009920e-15j\n",
       "496 -1.114526e-14+1.397340e-15j\n",
       "497 -1.114526e-14-1.397340e-15j\n",
       "498 -6.711123e-15+0.000000e+00j\n",
       "499 -8.038289e-15+0.000000e+00j\n",
       "\n",
       "[500 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6085b06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\m477a\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\numpy\\core\\_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    }
   ],
   "source": [
    "a = eigvls.iloc[:16,:]\n",
    "b = np.zeros((16, 16), int)\n",
    "np.fill_diagonal(b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "371a51f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5caa9bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f91a9302",
   "metadata": {},
   "outputs": [],
   "source": [
    "# b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3adda0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "invegvldiag = np.linalg.inv(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f06c61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invegvldiag.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "818e9ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# T2_train = np.matmul((score_train/eigvls.iloc[:16,:]),score_train.T)\n",
    "T2_train = score_train@invegvldiag@score_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "480508a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "959d5849",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_tfinal = np.diag(T2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b60b0663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_tfinal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ba395315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.13049311+0.j,  8.08556841+0.j, 16.24430444+0.j, 10.05931997+0.j,\n",
       "       23.93515128+0.j, 35.53095842+0.j, 11.86190864+0.j, 16.28892614+0.j,\n",
       "       28.96755151+0.j, 36.6400294 +0.j, 26.13815261+0.j, 18.72508523+0.j,\n",
       "       13.39093062+0.j, 23.04078952+0.j, 29.95615616+0.j, 53.38529741+0.j,\n",
       "       19.03721181+0.j, 28.45215216+0.j, 21.62540987+0.j, 19.82515211+0.j,\n",
       "       21.37235584+0.j, 21.8309829 +0.j, 26.73865453+0.j, 25.00909794+0.j,\n",
       "       35.3697451 +0.j, 32.63004047+0.j, 21.81412674+0.j, 22.0906965 +0.j,\n",
       "       23.95394132+0.j, 19.17509118+0.j, 39.51381212+0.j, 37.10331939+0.j,\n",
       "       26.58104231+0.j, 30.52302487+0.j, 34.87661342+0.j, 27.31026099+0.j,\n",
       "       38.29348628+0.j, 33.85684143+0.j, 35.1740797 +0.j, 41.64130071+0.j,\n",
       "       42.40412426+0.j, 45.07819345+0.j, 28.9532172 +0.j, 35.13964816+0.j,\n",
       "       20.39580037+0.j, 54.31929501+0.j, 31.80249147+0.j, 49.35796103+0.j,\n",
       "       26.23466918+0.j, 30.07853618+0.j, 42.47119841+0.j, 40.02316961+0.j,\n",
       "       35.34928056+0.j, 33.604824  +0.j, 30.00631288+0.j, 28.05875939+0.j,\n",
       "       28.19238975+0.j, 19.41177079+0.j, 43.40478786+0.j, 44.63680146+0.j,\n",
       "       52.38296275+0.j, 60.09114393+0.j, 45.60390397+0.j, 50.06619114+0.j,\n",
       "       50.94364411+0.j, 64.05500592+0.j, 19.54301844+0.j, 20.22073801+0.j,\n",
       "       32.13712342+0.j, 39.64745476+0.j, 29.93894676+0.j, 24.51967938+0.j,\n",
       "       15.22356879+0.j, 16.41341189+0.j, 20.58769793+0.j, 12.00588957+0.j,\n",
       "       28.26953079+0.j, 10.81707103+0.j, 27.53944359+0.j, 24.07234144+0.j,\n",
       "       31.97469366+0.j, 33.01601638+0.j, 21.0484479 +0.j, 16.77011894+0.j,\n",
       "       17.39061936+0.j, 27.81231712+0.j, 32.11362551+0.j, 40.00485584+0.j,\n",
       "       42.8980995 +0.j, 29.69435943+0.j, 11.67923773+0.j, 27.69668769+0.j,\n",
       "       30.34199732+0.j, 26.23597702+0.j, 30.88276445+0.j, 21.96805946+0.j,\n",
       "        9.53758687+0.j,  9.05427401+0.j, 12.62939398+0.j,  4.47229015+0.j,\n",
       "       14.84055978+0.j, 16.54453377+0.j, 20.93130432+0.j, 11.00357542+0.j,\n",
       "       28.48447537+0.j, 22.10072482+0.j, 34.79545278+0.j, 41.18105258+0.j,\n",
       "       19.41296644+0.j, 10.87979502+0.j, 17.80471485+0.j, 22.33486939+0.j,\n",
       "       19.17242981+0.j, 18.60400368+0.j, 31.67321632+0.j, 37.5987557 +0.j,\n",
       "       31.8033567 +0.j, 31.32565591+0.j, 38.69440168+0.j, 56.9756411 +0.j,\n",
       "       42.59757155+0.j, 34.69524869+0.j, 38.02113061+0.j, 26.39938375+0.j,\n",
       "       32.30030128+0.j, 55.84759345+0.j, 48.36605812+0.j, 42.09272886+0.j,\n",
       "       49.31265782+0.j, 39.97844503+0.j, 38.01782208+0.j, 42.56347204+0.j,\n",
       "       29.80197775+0.j, 43.10769262+0.j, 20.97839623+0.j, 29.36912351+0.j,\n",
       "       20.97213397+0.j, 37.09659439+0.j, 41.13994795+0.j, 26.99909429+0.j,\n",
       "       30.12430384+0.j, 12.88476543+0.j, 40.04549858+0.j, 21.89598077+0.j,\n",
       "       11.94215394+0.j, 25.50442726+0.j, 33.25935906+0.j, 21.47563872+0.j,\n",
       "       22.40495192+0.j, 21.05621521+0.j, 17.70082313+0.j, 13.66903869+0.j,\n",
       "       15.07975884+0.j, 15.85287292+0.j, 16.95972932+0.j, 27.25361763+0.j,\n",
       "       20.34938272+0.j, 35.75782328+0.j, 17.37302612+0.j, 21.56778079+0.j,\n",
       "       69.74735085+0.j, 53.0526546 +0.j, 59.03650728+0.j, 52.92454625+0.j,\n",
       "       18.27798575+0.j, 36.20361295+0.j, 39.38078226+0.j, 38.23738113+0.j,\n",
       "       40.69627107+0.j, 43.95917007+0.j, 25.58036653+0.j, 36.45681075+0.j,\n",
       "       32.0232968 +0.j, 20.97683216+0.j, 55.75821625+0.j, 53.29126352+0.j,\n",
       "       37.89330046+0.j, 47.31458718+0.j, 33.43571246+0.j, 36.3409743 +0.j,\n",
       "       43.73163525+0.j, 30.38168999+0.j, 29.54973599+0.j, 32.66899007+0.j,\n",
       "       40.4934059 +0.j, 35.74353825+0.j, 19.58111139+0.j, 22.4663918 +0.j,\n",
       "       40.61504056+0.j, 30.96718929+0.j, 39.41099752+0.j, 29.84656507+0.j,\n",
       "       19.47234646+0.j, 31.95350531+0.j, 45.63975076+0.j, 38.08367402+0.j,\n",
       "       22.15177264+0.j, 26.91054383+0.j, 33.40446161+0.j, 30.13454338+0.j,\n",
       "       25.86168317+0.j, 40.94072758+0.j, 30.89078817+0.j, 20.31204166+0.j,\n",
       "       24.44767581+0.j, 41.84812336+0.j, 16.17746539+0.j, 12.52191365+0.j,\n",
       "       18.9927939 +0.j, 21.87777324+0.j, 27.37903606+0.j, 24.34736192+0.j,\n",
       "       29.10168274+0.j, 23.5177024 +0.j, 14.447741  +0.j, 26.19013321+0.j,\n",
       "       16.79157702+0.j, 26.2790149 +0.j, 33.91991132+0.j, 37.05304977+0.j,\n",
       "       27.2097939 +0.j, 22.93056188+0.j, 31.70987101+0.j, 35.95309514+0.j,\n",
       "       17.42302509+0.j, 32.29909243+0.j, 24.55482397+0.j, 16.49640339+0.j,\n",
       "       22.26270401+0.j, 22.78032801+0.j, 20.8255687 +0.j, 14.52859221+0.j,\n",
       "       21.16901067+0.j, 18.01461709+0.j, 20.12596191+0.j, 10.12648003+0.j,\n",
       "       16.5996328 +0.j, 24.62851576+0.j, 33.3199444 +0.j, 35.30774639+0.j,\n",
       "       18.06955433+0.j, 19.30646773+0.j, 33.85788015+0.j, 20.33603353+0.j,\n",
       "       23.53439407+0.j, 25.10872949+0.j, 36.78886211+0.j, 37.51177324+0.j,\n",
       "       37.94026257+0.j, 42.01476169+0.j, 26.82675593+0.j, 33.83811708+0.j,\n",
       "       42.64180003+0.j, 53.65420452+0.j, 44.91298121+0.j, 40.76630182+0.j,\n",
       "       40.18555102+0.j, 35.06213942+0.j, 38.51765646+0.j, 28.43069322+0.j,\n",
       "       52.66835169+0.j, 48.24833535+0.j, 56.60196981+0.j, 65.13272006+0.j,\n",
       "       66.44530868+0.j, 54.50297945+0.j, 42.91963502+0.j, 33.92397326+0.j,\n",
       "       32.7624555 +0.j, 32.0443793 +0.j, 40.50877668+0.j, 31.31362221+0.j,\n",
       "       40.60627271+0.j, 30.28647282+0.j, 27.24282601+0.j, 34.61669229+0.j,\n",
       "       46.03785132+0.j, 32.65487781+0.j, 32.68023298+0.j, 31.09605442+0.j,\n",
       "       30.09088436+0.j, 38.92185532+0.j, 17.45343032+0.j, 40.01252444+0.j,\n",
       "       18.57357797+0.j, 14.99828625+0.j, 13.04626477+0.j, 11.35373859+0.j,\n",
       "       27.96556351+0.j, 38.46094602+0.j, 25.13192929+0.j, 30.28116832+0.j,\n",
       "       26.52021469+0.j, 52.86524081+0.j, 34.25904788+0.j, 37.9252196 +0.j,\n",
       "       31.52823484+0.j, 40.15718037+0.j, 46.30683438+0.j, 47.28874324+0.j,\n",
       "       36.92831066+0.j, 40.57927864+0.j, 40.43833738+0.j, 38.26546265+0.j,\n",
       "       37.5178124 +0.j, 38.36284636+0.j, 23.24792843+0.j, 25.99805505+0.j,\n",
       "       26.97358747+0.j, 37.81465371+0.j, 36.67443929+0.j, 45.85862082+0.j,\n",
       "       37.04568676+0.j, 35.35500852+0.j, 54.02084633+0.j, 45.84451859+0.j,\n",
       "       26.14383175+0.j, 48.43697138+0.j, 40.05824078+0.j, 49.10959713+0.j,\n",
       "       33.64697929+0.j, 38.57090923+0.j, 47.73233445+0.j, 49.4319272 +0.j,\n",
       "       42.52557261+0.j, 48.45731866+0.j, 53.46267418+0.j, 59.1737464 +0.j,\n",
       "       75.64331461+0.j, 48.88068366+0.j, 72.26575785+0.j, 68.30130517+0.j,\n",
       "       53.39936402+0.j, 60.14789757+0.j, 59.90509423+0.j, 66.00759232+0.j,\n",
       "       53.83073259+0.j, 59.01736092+0.j, 43.69592741+0.j, 46.15800613+0.j,\n",
       "       53.3638162 +0.j, 50.57399557+0.j, 28.88147693+0.j, 31.35951071+0.j,\n",
       "       39.61318475+0.j, 41.51740022+0.j, 35.68618242+0.j, 30.26821348+0.j,\n",
       "       45.18968662+0.j, 34.70109421+0.j, 33.68284497+0.j, 51.93904454+0.j,\n",
       "       46.82613482+0.j, 46.39550677+0.j, 52.40482137+0.j, 35.75283669+0.j,\n",
       "       39.72519623+0.j, 29.87714563+0.j, 44.65286705+0.j, 32.00124863+0.j,\n",
       "       29.81999921+0.j, 39.94600834+0.j, 51.8790238 +0.j, 27.79169073+0.j,\n",
       "       31.26986185+0.j, 25.70113514+0.j, 46.79653614+0.j, 51.99197818+0.j,\n",
       "       66.83228041+0.j, 64.47333455+0.j, 39.93743299+0.j, 36.7160094 +0.j,\n",
       "       41.49243916+0.j, 35.30153795+0.j, 54.95876372+0.j, 68.72984247+0.j,\n",
       "       50.28115195+0.j, 53.79049683+0.j, 47.60830065+0.j, 46.42737093+0.j,\n",
       "       36.95095573+0.j, 47.06226725+0.j, 56.27106351+0.j, 45.66773858+0.j,\n",
       "       60.12233155+0.j, 70.63810625+0.j, 68.34110568+0.j, 53.43518294+0.j,\n",
       "       47.14367682+0.j, 38.49665276+0.j, 73.70883889+0.j, 58.9451449 +0.j,\n",
       "       73.68240947+0.j, 79.67545419+0.j, 54.90524798+0.j, 49.00294244+0.j,\n",
       "       55.6797104 +0.j, 65.58653205+0.j, 58.26032113+0.j, 63.49180046+0.j,\n",
       "       41.76436819+0.j, 45.99535178+0.j, 48.48056395+0.j, 52.46392804+0.j,\n",
       "       59.2081758 +0.j, 67.5138081 +0.j, 51.33882982+0.j, 47.02977489+0.j,\n",
       "       30.57042297+0.j, 40.13463592+0.j, 63.07593708+0.j, 67.18035483+0.j,\n",
       "       77.25572856+0.j, 72.86664183+0.j, 51.49666235+0.j, 36.94014655+0.j,\n",
       "       40.16534559+0.j, 39.49787018+0.j, 29.62663476+0.j, 24.85026855+0.j,\n",
       "       36.43832442+0.j, 34.36640426+0.j, 49.59603166+0.j, 38.25735999+0.j,\n",
       "       48.00984476+0.j, 44.32351399+0.j, 32.69708641+0.j, 22.32808665+0.j,\n",
       "       43.61424824+0.j, 43.70072998+0.j, 29.99802425+0.j, 38.79069135+0.j,\n",
       "       25.38539431+0.j, 23.57250338+0.j, 47.40473802+0.j, 39.53461372+0.j,\n",
       "       26.90122052+0.j, 51.63882078+0.j, 33.6731184 +0.j, 33.76852969+0.j,\n",
       "       38.50862535+0.j, 36.12841733+0.j, 42.12308309+0.j, 29.46434259+0.j,\n",
       "       31.35358341+0.j, 31.97655994+0.j, 34.51033983+0.j, 37.67761662+0.j,\n",
       "       37.95446749+0.j, 42.12393633+0.j, 42.48095504+0.j, 36.64738364+0.j,\n",
       "       45.33521959+0.j, 43.27147113+0.j, 39.36146357+0.j, 41.06992186+0.j,\n",
       "       31.50548924+0.j, 31.1151701 +0.j, 36.71625457+0.j, 26.64647231+0.j,\n",
       "       28.68246972+0.j, 33.16021304+0.j, 27.53847144+0.j, 32.76015223+0.j,\n",
       "       39.3108849 +0.j, 32.01030366+0.j, 32.93686847+0.j, 26.44082748+0.j,\n",
       "       29.73521304+0.j, 38.28654294+0.j, 48.95266441+0.j, 41.02105838+0.j,\n",
       "       37.04418607+0.j, 35.37161092+0.j, 16.47743202+0.j, 35.07447963+0.j,\n",
       "       23.13550394+0.j, 29.57261611+0.j, 28.20595884+0.j, 34.72601202+0.j,\n",
       "       21.63753728+0.j, 35.59946127+0.j, 50.6143939 +0.j, 45.72469487+0.j,\n",
       "       44.24477816+0.j, 28.78778025+0.j, 24.7526022 +0.j, 28.43239634+0.j,\n",
       "       12.6040965 +0.j, 22.78503892+0.j, 26.54541492+0.j, 33.20100246+0.j,\n",
       "       25.41402045+0.j, 41.92167578+0.j, 19.8494841 +0.j, 26.45118419+0.j,\n",
       "       17.44465568+0.j, 23.51309091+0.j, 13.91701681+0.j, 32.31368518+0.j])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_tfinal\n",
    "# SUPOSETEDLY USING TEST DATA NOT TRAINING DATA IN HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ff74bc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a5734dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f\n",
    "F = f.ppf(0.95, 16, df_train.shape[0]-16)\n",
    "T2_trlim = ((16*(df_train.shape[0]**2-1))/(df_train.shape[0]*(df_train.shape[0]-16)))*F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c6c6374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.511202863723632"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_trlim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8687941a",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_lim = 16*((len(X_tr)-1)/(len(X_tr)-16))*F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5639f36b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.456290283157315"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_lim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fecc4c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\m477a\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\numpy\\core\\_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(T2_tfinal,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd05858e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.694\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_tfinal>T2_trlim).sum()/T2_tfinal.shape[0]}')\n",
    "# ofcourse 100% because we using train and test in the same dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c22d1d1",
   "metadata": {},
   "source": [
    "## Testing Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "81fe000e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 52)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e71ffecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 52)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9f44e478",
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear kernel\n",
    "# Kt = X_ts@df_train.T\n",
    "Kt = X_ts@X_tr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "08ffa656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 500)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4faa0d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "92d225b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt = Kt.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "562b4d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centralize the kernel\n",
    "st = (len(X_ts),len(X_tr)) # 2000 or 960\n",
    "unit_t = np.ones(st)/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4fa25a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit_t = pd.DataFrame(unit_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b375aae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1      2      3      4      5      6      7      8      9    \\\n",
       "0     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "2     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "3     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "4     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "1995  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1996  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1997  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1998  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1999  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "\n",
       "      ...    490    491    492    493    494    495    496    497    498  \\\n",
       "0     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "2     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "3     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "4     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "1995  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1996  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1997  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1998  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1999  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "\n",
       "        499  \n",
       "0     0.002  \n",
       "1     0.002  \n",
       "2     0.002  \n",
       "3     0.002  \n",
       "4     0.002  \n",
       "...     ...  \n",
       "1995  0.002  \n",
       "1996  0.002  \n",
       "1997  0.002  \n",
       "1998  0.002  \n",
       "1999  0.002  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dc509651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 500)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "392e75b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unit_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e95484c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "afc96b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d3aac620",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = Kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bb416b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.695639</td>\n",
       "      <td>2.623756</td>\n",
       "      <td>-2.681584</td>\n",
       "      <td>3.997612</td>\n",
       "      <td>-1.243795</td>\n",
       "      <td>-0.885361</td>\n",
       "      <td>4.799502</td>\n",
       "      <td>1.178033</td>\n",
       "      <td>-0.860980</td>\n",
       "      <td>1.262205</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.981534</td>\n",
       "      <td>-0.402995</td>\n",
       "      <td>1.554334</td>\n",
       "      <td>7.084320</td>\n",
       "      <td>-5.609230</td>\n",
       "      <td>4.624945</td>\n",
       "      <td>-1.415091</td>\n",
       "      <td>-1.972167</td>\n",
       "      <td>-2.688193</td>\n",
       "      <td>-1.569975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.523094</td>\n",
       "      <td>1.887844</td>\n",
       "      <td>-7.711769</td>\n",
       "      <td>1.999139</td>\n",
       "      <td>6.058129</td>\n",
       "      <td>1.014034</td>\n",
       "      <td>-0.807084</td>\n",
       "      <td>-1.701939</td>\n",
       "      <td>5.986459</td>\n",
       "      <td>-6.903007</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.498930</td>\n",
       "      <td>-2.257303</td>\n",
       "      <td>-3.744538</td>\n",
       "      <td>15.449094</td>\n",
       "      <td>-3.309535</td>\n",
       "      <td>4.759863</td>\n",
       "      <td>-4.952008</td>\n",
       "      <td>1.060290</td>\n",
       "      <td>1.520902</td>\n",
       "      <td>16.759107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.842205</td>\n",
       "      <td>-4.254359</td>\n",
       "      <td>12.492652</td>\n",
       "      <td>13.846154</td>\n",
       "      <td>4.907164</td>\n",
       "      <td>0.816476</td>\n",
       "      <td>1.027335</td>\n",
       "      <td>-3.446869</td>\n",
       "      <td>-8.660779</td>\n",
       "      <td>3.763488</td>\n",
       "      <td>...</td>\n",
       "      <td>1.479031</td>\n",
       "      <td>4.307952</td>\n",
       "      <td>0.932493</td>\n",
       "      <td>8.184403</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>-4.897660</td>\n",
       "      <td>-7.185245</td>\n",
       "      <td>-1.132882</td>\n",
       "      <td>-4.969765</td>\n",
       "      <td>10.013809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.943712</td>\n",
       "      <td>9.613817</td>\n",
       "      <td>6.643203</td>\n",
       "      <td>12.540631</td>\n",
       "      <td>-3.057779</td>\n",
       "      <td>-8.944723</td>\n",
       "      <td>9.189780</td>\n",
       "      <td>10.857921</td>\n",
       "      <td>0.097814</td>\n",
       "      <td>-6.611868</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.788340</td>\n",
       "      <td>-10.982317</td>\n",
       "      <td>4.946113</td>\n",
       "      <td>-2.907392</td>\n",
       "      <td>-1.397147</td>\n",
       "      <td>4.041435</td>\n",
       "      <td>7.177668</td>\n",
       "      <td>-5.254921</td>\n",
       "      <td>-6.212297</td>\n",
       "      <td>-12.732895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.471688</td>\n",
       "      <td>6.088071</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>-6.089401</td>\n",
       "      <td>12.805172</td>\n",
       "      <td>9.621391</td>\n",
       "      <td>0.322932</td>\n",
       "      <td>8.760770</td>\n",
       "      <td>2.089753</td>\n",
       "      <td>0.253627</td>\n",
       "      <td>...</td>\n",
       "      <td>6.724578</td>\n",
       "      <td>1.095127</td>\n",
       "      <td>-6.466120</td>\n",
       "      <td>-10.715815</td>\n",
       "      <td>8.111396</td>\n",
       "      <td>2.977447</td>\n",
       "      <td>1.423037</td>\n",
       "      <td>-0.003008</td>\n",
       "      <td>-1.212142</td>\n",
       "      <td>-3.336056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-1.782030</td>\n",
       "      <td>-5.087269</td>\n",
       "      <td>0.650057</td>\n",
       "      <td>0.682216</td>\n",
       "      <td>-1.369587</td>\n",
       "      <td>-1.645744</td>\n",
       "      <td>-0.993376</td>\n",
       "      <td>0.317070</td>\n",
       "      <td>-0.436350</td>\n",
       "      <td>-8.879605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.163876</td>\n",
       "      <td>-1.651991</td>\n",
       "      <td>-6.997626</td>\n",
       "      <td>-9.416193</td>\n",
       "      <td>1.687341</td>\n",
       "      <td>-0.404758</td>\n",
       "      <td>-3.893521</td>\n",
       "      <td>-1.752026</td>\n",
       "      <td>7.802322</td>\n",
       "      <td>4.502515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-8.363253</td>\n",
       "      <td>7.368304</td>\n",
       "      <td>-7.912205</td>\n",
       "      <td>1.597145</td>\n",
       "      <td>0.996322</td>\n",
       "      <td>-3.226136</td>\n",
       "      <td>-2.028623</td>\n",
       "      <td>-7.769811</td>\n",
       "      <td>-0.551238</td>\n",
       "      <td>4.435573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.625231</td>\n",
       "      <td>4.531509</td>\n",
       "      <td>17.507241</td>\n",
       "      <td>15.937866</td>\n",
       "      <td>-1.914460</td>\n",
       "      <td>15.019301</td>\n",
       "      <td>7.916571</td>\n",
       "      <td>-1.357280</td>\n",
       "      <td>-2.530846</td>\n",
       "      <td>-2.831610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-4.524898</td>\n",
       "      <td>-3.239447</td>\n",
       "      <td>-0.125769</td>\n",
       "      <td>-0.576866</td>\n",
       "      <td>12.512392</td>\n",
       "      <td>8.817264</td>\n",
       "      <td>-14.875086</td>\n",
       "      <td>-9.626081</td>\n",
       "      <td>5.108003</td>\n",
       "      <td>-1.266279</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.771688</td>\n",
       "      <td>5.966198</td>\n",
       "      <td>21.363822</td>\n",
       "      <td>17.409025</td>\n",
       "      <td>-3.165359</td>\n",
       "      <td>-1.354441</td>\n",
       "      <td>-1.336142</td>\n",
       "      <td>11.121596</td>\n",
       "      <td>8.712929</td>\n",
       "      <td>22.623233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-0.349497</td>\n",
       "      <td>4.392246</td>\n",
       "      <td>-0.122470</td>\n",
       "      <td>-1.003586</td>\n",
       "      <td>6.767990</td>\n",
       "      <td>2.477811</td>\n",
       "      <td>1.697030</td>\n",
       "      <td>3.194447</td>\n",
       "      <td>-24.603042</td>\n",
       "      <td>-11.623890</td>\n",
       "      <td>...</td>\n",
       "      <td>4.264971</td>\n",
       "      <td>17.778868</td>\n",
       "      <td>-15.191829</td>\n",
       "      <td>2.533775</td>\n",
       "      <td>-9.579838</td>\n",
       "      <td>-7.780960</td>\n",
       "      <td>12.699933</td>\n",
       "      <td>11.034951</td>\n",
       "      <td>-6.870324</td>\n",
       "      <td>-3.370758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-3.940843</td>\n",
       "      <td>-7.539865</td>\n",
       "      <td>4.081290</td>\n",
       "      <td>-2.734833</td>\n",
       "      <td>-0.274603</td>\n",
       "      <td>-6.713764</td>\n",
       "      <td>-0.818715</td>\n",
       "      <td>2.872731</td>\n",
       "      <td>-12.349580</td>\n",
       "      <td>-20.702800</td>\n",
       "      <td>...</td>\n",
       "      <td>9.612196</td>\n",
       "      <td>7.856519</td>\n",
       "      <td>-10.656948</td>\n",
       "      <td>-13.040868</td>\n",
       "      <td>-13.822708</td>\n",
       "      <td>-8.965451</td>\n",
       "      <td>1.872880</td>\n",
       "      <td>8.737394</td>\n",
       "      <td>2.380629</td>\n",
       "      <td>5.743855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1          2          3          4         5    \\\n",
       "0     1.695639  2.623756  -2.681584   3.997612  -1.243795 -0.885361   \n",
       "1    -0.523094  1.887844  -7.711769   1.999139   6.058129  1.014034   \n",
       "2     4.842205 -4.254359  12.492652  13.846154   4.907164  0.816476   \n",
       "3     0.943712  9.613817   6.643203  12.540631  -3.057779 -8.944723   \n",
       "4     4.471688  6.088071   0.002204  -6.089401  12.805172  9.621391   \n",
       "...        ...       ...        ...        ...        ...       ...   \n",
       "1995 -1.782030 -5.087269   0.650057   0.682216  -1.369587 -1.645744   \n",
       "1996 -8.363253  7.368304  -7.912205   1.597145   0.996322 -3.226136   \n",
       "1997 -4.524898 -3.239447  -0.125769  -0.576866  12.512392  8.817264   \n",
       "1998 -0.349497  4.392246  -0.122470  -1.003586   6.767990  2.477811   \n",
       "1999 -3.940843 -7.539865   4.081290  -2.734833  -0.274603 -6.713764   \n",
       "\n",
       "            6          7          8          9    ...       490        491  \\\n",
       "0      4.799502   1.178033  -0.860980   1.262205  ... -6.981534  -0.402995   \n",
       "1     -0.807084  -1.701939   5.986459  -6.903007  ... -1.498930  -2.257303   \n",
       "2      1.027335  -3.446869  -8.660779   3.763488  ...  1.479031   4.307952   \n",
       "3      9.189780  10.857921   0.097814  -6.611868  ... -5.788340 -10.982317   \n",
       "4      0.322932   8.760770   2.089753   0.253627  ...  6.724578   1.095127   \n",
       "...         ...        ...        ...        ...  ...       ...        ...   \n",
       "1995  -0.993376   0.317070  -0.436350  -8.879605  ... -0.163876  -1.651991   \n",
       "1996  -2.028623  -7.769811  -0.551238   4.435573  ... -0.625231   4.531509   \n",
       "1997 -14.875086  -9.626081   5.108003  -1.266279  ... -3.771688   5.966198   \n",
       "1998   1.697030   3.194447 -24.603042 -11.623890  ...  4.264971  17.778868   \n",
       "1999  -0.818715   2.872731 -12.349580 -20.702800  ...  9.612196   7.856519   \n",
       "\n",
       "            492        493        494        495        496        497  \\\n",
       "0      1.554334   7.084320  -5.609230   4.624945  -1.415091  -1.972167   \n",
       "1     -3.744538  15.449094  -3.309535   4.759863  -4.952008   1.060290   \n",
       "2      0.932493   8.184403   0.313187  -4.897660  -7.185245  -1.132882   \n",
       "3      4.946113  -2.907392  -1.397147   4.041435   7.177668  -5.254921   \n",
       "4     -6.466120 -10.715815   8.111396   2.977447   1.423037  -0.003008   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1995  -6.997626  -9.416193   1.687341  -0.404758  -3.893521  -1.752026   \n",
       "1996  17.507241  15.937866  -1.914460  15.019301   7.916571  -1.357280   \n",
       "1997  21.363822  17.409025  -3.165359  -1.354441  -1.336142  11.121596   \n",
       "1998 -15.191829   2.533775  -9.579838  -7.780960  12.699933  11.034951   \n",
       "1999 -10.656948 -13.040868 -13.822708  -8.965451   1.872880   8.737394   \n",
       "\n",
       "           498        499  \n",
       "0    -2.688193  -1.569975  \n",
       "1     1.520902  16.759107  \n",
       "2    -4.969765  10.013809  \n",
       "3    -6.212297 -12.732895  \n",
       "4    -1.212142  -3.336056  \n",
       "...        ...        ...  \n",
       "1995  7.802322   4.502515  \n",
       "1996 -2.530846  -2.831610  \n",
       "1997  8.712929  22.623233  \n",
       "1998 -6.870324  -3.370758  \n",
       "1999  2.380629   5.743855  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5d24ed98",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = unit_t@K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "46a350bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-4.872769e-13</td>\n",
       "      <td>-4.321266e-13</td>\n",
       "      <td>-8.997247e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.220757e-13</td>\n",
       "      <td>4.120593e-13</td>\n",
       "      <td>-9.849482e-13</td>\n",
       "      <td>1.268263e-12</td>\n",
       "      <td>-1.950121e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.734546e-13</td>\n",
       "      <td>-1.917522e-12</td>\n",
       "      <td>3.371192e-13</td>\n",
       "      <td>-2.473577e-13</td>\n",
       "      <td>-8.965051e-14</td>\n",
       "      <td>1.233458e-13</td>\n",
       "      <td>8.049117e-14</td>\n",
       "      <td>2.970957e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-4.873879e-13</td>\n",
       "      <td>-4.324874e-13</td>\n",
       "      <td>-8.996137e-13</td>\n",
       "      <td>4.587997e-13</td>\n",
       "      <td>5.258016e-13</td>\n",
       "      <td>3.222977e-13</td>\n",
       "      <td>4.119483e-13</td>\n",
       "      <td>-9.848650e-13</td>\n",
       "      <td>1.268541e-12</td>\n",
       "      <td>-1.949996e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.736211e-13</td>\n",
       "      <td>-1.917466e-12</td>\n",
       "      <td>3.369527e-13</td>\n",
       "      <td>-2.471079e-13</td>\n",
       "      <td>-8.948398e-14</td>\n",
       "      <td>1.232625e-13</td>\n",
       "      <td>8.093526e-14</td>\n",
       "      <td>2.972067e-13</td>\n",
       "      <td>1.226297e-12</td>\n",
       "      <td>4.296008e-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0             1             2             3             4    \\\n",
       "0    -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "1    -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "2    -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "3    -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "4    -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995 -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "1996 -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "1997 -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "1998 -4.872769e-13 -4.321266e-13 -8.997247e-13  4.587997e-13  5.258016e-13   \n",
       "1999 -4.873879e-13 -4.324874e-13 -8.996137e-13  4.587997e-13  5.258016e-13   \n",
       "\n",
       "               5             6             7             8             9    \\\n",
       "0     3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "1     3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "2     3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "3     3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "4     3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "1996  3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "1997  3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "1998  3.220757e-13  4.120593e-13 -9.849482e-13  1.268263e-12 -1.950121e-12   \n",
       "1999  3.222977e-13  4.119483e-13 -9.848650e-13  1.268541e-12 -1.949996e-12   \n",
       "\n",
       "      ...           490           491           492           493  \\\n",
       "0     ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "1     ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "2     ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "3     ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "4     ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "...   ...           ...           ...           ...           ...   \n",
       "1995  ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "1996  ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "1997  ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "1998  ... -4.734546e-13 -1.917522e-12  3.371192e-13 -2.473577e-13   \n",
       "1999  ... -4.736211e-13 -1.917466e-12  3.369527e-13 -2.471079e-13   \n",
       "\n",
       "               494           495           496           497           498  \\\n",
       "0    -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "1    -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "2    -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "3    -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "4    -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995 -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "1996 -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "1997 -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "1998 -8.965051e-14  1.233458e-13  8.049117e-14  2.970957e-13  1.226297e-12   \n",
       "1999 -8.948398e-14  1.232625e-13  8.093526e-14  2.972067e-13  1.226297e-12   \n",
       "\n",
       "               499  \n",
       "0     4.296008e-13  \n",
       "1     4.296008e-13  \n",
       "2     4.296008e-13  \n",
       "3     4.296008e-13  \n",
       "4     4.296008e-13  \n",
       "...            ...  \n",
       "1995  4.296008e-13  \n",
       "1996  4.296008e-13  \n",
       "1997  4.296008e-13  \n",
       "1998  4.296008e-13  \n",
       "1999  4.296008e-13  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "58e48f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Kt@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "884ac9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "      <td>-9.777734e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "      <td>1.284528e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "      <td>-4.412026e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "      <td>1.063732e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "      <td>-4.197753e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "      <td>9.169696e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "      <td>-3.574085e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "      <td>-7.430168e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "      <td>-1.774303e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.050371e-13</td>\n",
       "      <td>-4.053147e-13</td>\n",
       "      <td>-4.053147e-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0             1             2             3             4    \\\n",
       "0    -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13   \n",
       "1     1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12   \n",
       "2    -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13   \n",
       "3     1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12   \n",
       "4    -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13   \n",
       "1996 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13   \n",
       "1997 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14   \n",
       "1998 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12   \n",
       "1999 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13   \n",
       "\n",
       "               5             6             7             8             9    \\\n",
       "0    -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13   \n",
       "1     1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12   \n",
       "2    -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13   \n",
       "3     1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12   \n",
       "4    -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13   \n",
       "1996 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13   \n",
       "1997 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14   \n",
       "1998 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12   \n",
       "1999 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13   \n",
       "\n",
       "      ...           490           491           492           493  \\\n",
       "0     ... -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13   \n",
       "1     ...  1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12   \n",
       "2     ... -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13   \n",
       "3     ...  1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12   \n",
       "4     ... -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13   \n",
       "...   ...           ...           ...           ...           ...   \n",
       "1995  ...  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13   \n",
       "1996  ... -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13   \n",
       "1997  ... -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14   \n",
       "1998  ... -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12   \n",
       "1999  ... -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13   \n",
       "\n",
       "               494           495           496           497           498  \\\n",
       "0    -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13 -9.777734e-13   \n",
       "1     1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12  1.284528e-12   \n",
       "2    -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13 -4.412026e-13   \n",
       "3     1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12  1.063732e-12   \n",
       "4    -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13 -4.197753e-13   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13  9.169696e-13   \n",
       "1996 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13 -3.574085e-13   \n",
       "1997 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14 -7.430168e-14   \n",
       "1998 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12 -1.774303e-12   \n",
       "1999 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.050371e-13 -4.053147e-13   \n",
       "\n",
       "               499  \n",
       "0    -9.777734e-13  \n",
       "1     1.284528e-12  \n",
       "2    -4.412026e-13  \n",
       "3     1.063732e-12  \n",
       "4    -4.197753e-13  \n",
       "...            ...  \n",
       "1995  9.169696e-13  \n",
       "1996 -3.574085e-13  \n",
       "1997 -7.430168e-14  \n",
       "1998 -1.774303e-12  \n",
       "1999 -4.053147e-13  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ce3dc683",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = unit_t@K@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "40087a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "      <td>9.977587e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>...</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "      <td>2.947295e-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0             1             2             3             4    \\\n",
       "0     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "2     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "3     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "4     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1996  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1997  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1998  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1999  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18   \n",
       "\n",
       "               5             6             7             8             9    \\\n",
       "0     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "2     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "3     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "4     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1996  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1997  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1998  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1999  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18   \n",
       "\n",
       "      ...           490           491           492           493  \\\n",
       "0     ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1     ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "2     ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "3     ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "4     ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "...   ...           ...           ...           ...           ...   \n",
       "1995  ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1996  ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1997  ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1998  ...  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1999  ...  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18   \n",
       "\n",
       "               494           495           496           497           498  \\\n",
       "0     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "2     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "3     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "4     9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1995  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1996  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1997  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1998  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18  9.977587e-18   \n",
       "1999  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18  2.947295e-18   \n",
       "\n",
       "               499  \n",
       "0     9.977587e-18  \n",
       "1     9.977587e-18  \n",
       "2     9.977587e-18  \n",
       "3     9.977587e-18  \n",
       "4     9.977587e-18  \n",
       "...            ...  \n",
       "1995  9.977587e-18  \n",
       "1996  9.977587e-18  \n",
       "1997  9.977587e-18  \n",
       "1998  9.977587e-18  \n",
       "1999  2.947295e-18  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ec806ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = Kt-unit_t@K-Kt@unit+unit_t@K@unit\n",
    "# the result are NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "89a9bb6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.695639</td>\n",
       "      <td>2.623756</td>\n",
       "      <td>-2.681584</td>\n",
       "      <td>3.997612</td>\n",
       "      <td>-1.243795</td>\n",
       "      <td>-0.885361</td>\n",
       "      <td>4.799502</td>\n",
       "      <td>1.178033</td>\n",
       "      <td>-0.860980</td>\n",
       "      <td>1.262205</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.981534</td>\n",
       "      <td>-0.402995</td>\n",
       "      <td>1.554334</td>\n",
       "      <td>7.084320</td>\n",
       "      <td>-5.609230</td>\n",
       "      <td>4.624945</td>\n",
       "      <td>-1.415091</td>\n",
       "      <td>-1.972167</td>\n",
       "      <td>-2.688193</td>\n",
       "      <td>-1.569975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.523094</td>\n",
       "      <td>1.887844</td>\n",
       "      <td>-7.711769</td>\n",
       "      <td>1.999139</td>\n",
       "      <td>6.058129</td>\n",
       "      <td>1.014034</td>\n",
       "      <td>-0.807084</td>\n",
       "      <td>-1.701939</td>\n",
       "      <td>5.986459</td>\n",
       "      <td>-6.903007</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.498930</td>\n",
       "      <td>-2.257303</td>\n",
       "      <td>-3.744538</td>\n",
       "      <td>15.449094</td>\n",
       "      <td>-3.309535</td>\n",
       "      <td>4.759863</td>\n",
       "      <td>-4.952008</td>\n",
       "      <td>1.060290</td>\n",
       "      <td>1.520902</td>\n",
       "      <td>16.759107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.842205</td>\n",
       "      <td>-4.254359</td>\n",
       "      <td>12.492652</td>\n",
       "      <td>13.846154</td>\n",
       "      <td>4.907164</td>\n",
       "      <td>0.816476</td>\n",
       "      <td>1.027335</td>\n",
       "      <td>-3.446869</td>\n",
       "      <td>-8.660779</td>\n",
       "      <td>3.763488</td>\n",
       "      <td>...</td>\n",
       "      <td>1.479031</td>\n",
       "      <td>4.307952</td>\n",
       "      <td>0.932493</td>\n",
       "      <td>8.184403</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>-4.897660</td>\n",
       "      <td>-7.185245</td>\n",
       "      <td>-1.132882</td>\n",
       "      <td>-4.969765</td>\n",
       "      <td>10.013809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.943712</td>\n",
       "      <td>9.613817</td>\n",
       "      <td>6.643203</td>\n",
       "      <td>12.540631</td>\n",
       "      <td>-3.057779</td>\n",
       "      <td>-8.944723</td>\n",
       "      <td>9.189780</td>\n",
       "      <td>10.857921</td>\n",
       "      <td>0.097814</td>\n",
       "      <td>-6.611868</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.788340</td>\n",
       "      <td>-10.982317</td>\n",
       "      <td>4.946113</td>\n",
       "      <td>-2.907392</td>\n",
       "      <td>-1.397147</td>\n",
       "      <td>4.041435</td>\n",
       "      <td>7.177668</td>\n",
       "      <td>-5.254921</td>\n",
       "      <td>-6.212297</td>\n",
       "      <td>-12.732895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.471688</td>\n",
       "      <td>6.088071</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>-6.089401</td>\n",
       "      <td>12.805172</td>\n",
       "      <td>9.621391</td>\n",
       "      <td>0.322932</td>\n",
       "      <td>8.760770</td>\n",
       "      <td>2.089753</td>\n",
       "      <td>0.253627</td>\n",
       "      <td>...</td>\n",
       "      <td>6.724578</td>\n",
       "      <td>1.095127</td>\n",
       "      <td>-6.466120</td>\n",
       "      <td>-10.715815</td>\n",
       "      <td>8.111396</td>\n",
       "      <td>2.977447</td>\n",
       "      <td>1.423037</td>\n",
       "      <td>-0.003008</td>\n",
       "      <td>-1.212142</td>\n",
       "      <td>-3.336056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-1.782030</td>\n",
       "      <td>-5.087269</td>\n",
       "      <td>0.650057</td>\n",
       "      <td>0.682216</td>\n",
       "      <td>-1.369587</td>\n",
       "      <td>-1.645744</td>\n",
       "      <td>-0.993376</td>\n",
       "      <td>0.317070</td>\n",
       "      <td>-0.436350</td>\n",
       "      <td>-8.879605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.163876</td>\n",
       "      <td>-1.651991</td>\n",
       "      <td>-6.997626</td>\n",
       "      <td>-9.416193</td>\n",
       "      <td>1.687341</td>\n",
       "      <td>-0.404758</td>\n",
       "      <td>-3.893521</td>\n",
       "      <td>-1.752026</td>\n",
       "      <td>7.802322</td>\n",
       "      <td>4.502515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-8.363253</td>\n",
       "      <td>7.368304</td>\n",
       "      <td>-7.912205</td>\n",
       "      <td>1.597145</td>\n",
       "      <td>0.996322</td>\n",
       "      <td>-3.226136</td>\n",
       "      <td>-2.028623</td>\n",
       "      <td>-7.769811</td>\n",
       "      <td>-0.551238</td>\n",
       "      <td>4.435573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.625231</td>\n",
       "      <td>4.531509</td>\n",
       "      <td>17.507241</td>\n",
       "      <td>15.937866</td>\n",
       "      <td>-1.914460</td>\n",
       "      <td>15.019301</td>\n",
       "      <td>7.916571</td>\n",
       "      <td>-1.357280</td>\n",
       "      <td>-2.530846</td>\n",
       "      <td>-2.831610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-4.524898</td>\n",
       "      <td>-3.239447</td>\n",
       "      <td>-0.125769</td>\n",
       "      <td>-0.576866</td>\n",
       "      <td>12.512392</td>\n",
       "      <td>8.817264</td>\n",
       "      <td>-14.875086</td>\n",
       "      <td>-9.626081</td>\n",
       "      <td>5.108003</td>\n",
       "      <td>-1.266279</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.771688</td>\n",
       "      <td>5.966198</td>\n",
       "      <td>21.363822</td>\n",
       "      <td>17.409025</td>\n",
       "      <td>-3.165359</td>\n",
       "      <td>-1.354441</td>\n",
       "      <td>-1.336142</td>\n",
       "      <td>11.121596</td>\n",
       "      <td>8.712929</td>\n",
       "      <td>22.623233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-0.349497</td>\n",
       "      <td>4.392246</td>\n",
       "      <td>-0.122470</td>\n",
       "      <td>-1.003586</td>\n",
       "      <td>6.767990</td>\n",
       "      <td>2.477811</td>\n",
       "      <td>1.697030</td>\n",
       "      <td>3.194447</td>\n",
       "      <td>-24.603042</td>\n",
       "      <td>-11.623890</td>\n",
       "      <td>...</td>\n",
       "      <td>4.264971</td>\n",
       "      <td>17.778868</td>\n",
       "      <td>-15.191829</td>\n",
       "      <td>2.533775</td>\n",
       "      <td>-9.579838</td>\n",
       "      <td>-7.780960</td>\n",
       "      <td>12.699933</td>\n",
       "      <td>11.034951</td>\n",
       "      <td>-6.870324</td>\n",
       "      <td>-3.370758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-3.940843</td>\n",
       "      <td>-7.539865</td>\n",
       "      <td>4.081290</td>\n",
       "      <td>-2.734833</td>\n",
       "      <td>-0.274603</td>\n",
       "      <td>-6.713764</td>\n",
       "      <td>-0.818715</td>\n",
       "      <td>2.872731</td>\n",
       "      <td>-12.349580</td>\n",
       "      <td>-20.702800</td>\n",
       "      <td>...</td>\n",
       "      <td>9.612196</td>\n",
       "      <td>7.856519</td>\n",
       "      <td>-10.656948</td>\n",
       "      <td>-13.040868</td>\n",
       "      <td>-13.822708</td>\n",
       "      <td>-8.965451</td>\n",
       "      <td>1.872880</td>\n",
       "      <td>8.737394</td>\n",
       "      <td>2.380629</td>\n",
       "      <td>5.743855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1          2          3          4         5    \\\n",
       "0     1.695639  2.623756  -2.681584   3.997612  -1.243795 -0.885361   \n",
       "1    -0.523094  1.887844  -7.711769   1.999139   6.058129  1.014034   \n",
       "2     4.842205 -4.254359  12.492652  13.846154   4.907164  0.816476   \n",
       "3     0.943712  9.613817   6.643203  12.540631  -3.057779 -8.944723   \n",
       "4     4.471688  6.088071   0.002204  -6.089401  12.805172  9.621391   \n",
       "...        ...       ...        ...        ...        ...       ...   \n",
       "1995 -1.782030 -5.087269   0.650057   0.682216  -1.369587 -1.645744   \n",
       "1996 -8.363253  7.368304  -7.912205   1.597145   0.996322 -3.226136   \n",
       "1997 -4.524898 -3.239447  -0.125769  -0.576866  12.512392  8.817264   \n",
       "1998 -0.349497  4.392246  -0.122470  -1.003586   6.767990  2.477811   \n",
       "1999 -3.940843 -7.539865   4.081290  -2.734833  -0.274603 -6.713764   \n",
       "\n",
       "            6          7          8          9    ...       490        491  \\\n",
       "0      4.799502   1.178033  -0.860980   1.262205  ... -6.981534  -0.402995   \n",
       "1     -0.807084  -1.701939   5.986459  -6.903007  ... -1.498930  -2.257303   \n",
       "2      1.027335  -3.446869  -8.660779   3.763488  ...  1.479031   4.307952   \n",
       "3      9.189780  10.857921   0.097814  -6.611868  ... -5.788340 -10.982317   \n",
       "4      0.322932   8.760770   2.089753   0.253627  ...  6.724578   1.095127   \n",
       "...         ...        ...        ...        ...  ...       ...        ...   \n",
       "1995  -0.993376   0.317070  -0.436350  -8.879605  ... -0.163876  -1.651991   \n",
       "1996  -2.028623  -7.769811  -0.551238   4.435573  ... -0.625231   4.531509   \n",
       "1997 -14.875086  -9.626081   5.108003  -1.266279  ... -3.771688   5.966198   \n",
       "1998   1.697030   3.194447 -24.603042 -11.623890  ...  4.264971  17.778868   \n",
       "1999  -0.818715   2.872731 -12.349580 -20.702800  ...  9.612196   7.856519   \n",
       "\n",
       "            492        493        494        495        496        497  \\\n",
       "0      1.554334   7.084320  -5.609230   4.624945  -1.415091  -1.972167   \n",
       "1     -3.744538  15.449094  -3.309535   4.759863  -4.952008   1.060290   \n",
       "2      0.932493   8.184403   0.313187  -4.897660  -7.185245  -1.132882   \n",
       "3      4.946113  -2.907392  -1.397147   4.041435   7.177668  -5.254921   \n",
       "4     -6.466120 -10.715815   8.111396   2.977447   1.423037  -0.003008   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1995  -6.997626  -9.416193   1.687341  -0.404758  -3.893521  -1.752026   \n",
       "1996  17.507241  15.937866  -1.914460  15.019301   7.916571  -1.357280   \n",
       "1997  21.363822  17.409025  -3.165359  -1.354441  -1.336142  11.121596   \n",
       "1998 -15.191829   2.533775  -9.579838  -7.780960  12.699933  11.034951   \n",
       "1999 -10.656948 -13.040868 -13.822708  -8.965451   1.872880   8.737394   \n",
       "\n",
       "           498        499  \n",
       "0    -2.688193  -1.569975  \n",
       "1     1.520902  16.759107  \n",
       "2    -4.969765  10.013809  \n",
       "3    -6.212297 -12.732895  \n",
       "4    -1.212142  -3.336056  \n",
       "...        ...        ...  \n",
       "1995  7.802322   4.502515  \n",
       "1996 -2.530846  -2.831610  \n",
       "1997  8.712929  22.623233  \n",
       "1998 -6.870324  -3.370758  \n",
       "1999  2.380629   5.743855  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "31008b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_13236/1658737890.py:1: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  score_test = np.matmul(Kt_c, eigvct.iloc[:,:16])\n"
     ]
    }
   ],
   "source": [
    "score_test = np.matmul(Kt_c, eigvct.iloc[:,:16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5b49eefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.986185+0.000000j</td>\n",
       "      <td>-43.000811+0.000000j</td>\n",
       "      <td>5.874231+0.000000j</td>\n",
       "      <td>-48.593418+0.000000j</td>\n",
       "      <td>37.517242+0.000000j</td>\n",
       "      <td>-30.595904+0.000000j</td>\n",
       "      <td>24.872044+0.000000j</td>\n",
       "      <td>11.049553+0.000000j</td>\n",
       "      <td>-3.197241+0.000000j</td>\n",
       "      <td>6.284813+0.000000j</td>\n",
       "      <td>-2.056365+0.000000j</td>\n",
       "      <td>-25.411024+0.000000j</td>\n",
       "      <td>-18.793389+0.000000j</td>\n",
       "      <td>3.321802+0.000000j</td>\n",
       "      <td>-14.528369+0.000000j</td>\n",
       "      <td>5.007990+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.101332+0.000000j</td>\n",
       "      <td>-46.373946+0.000000j</td>\n",
       "      <td>37.837082+0.000000j</td>\n",
       "      <td>13.302047+0.000000j</td>\n",
       "      <td>51.335107+0.000000j</td>\n",
       "      <td>-98.306881+0.000000j</td>\n",
       "      <td>72.430942+0.000000j</td>\n",
       "      <td>-0.722137+0.000000j</td>\n",
       "      <td>-5.625187+0.000000j</td>\n",
       "      <td>36.541643+0.000000j</td>\n",
       "      <td>0.740024+0.000000j</td>\n",
       "      <td>13.214829+0.000000j</td>\n",
       "      <td>13.065204+0.000000j</td>\n",
       "      <td>-11.372910+0.000000j</td>\n",
       "      <td>42.814119+0.000000j</td>\n",
       "      <td>10.524816+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.017356+0.000000j</td>\n",
       "      <td>-44.587818+0.000000j</td>\n",
       "      <td>-42.942569+0.000000j</td>\n",
       "      <td>98.033223+0.000000j</td>\n",
       "      <td>-2.292424+0.000000j</td>\n",
       "      <td>5.079248+0.000000j</td>\n",
       "      <td>48.021144+0.000000j</td>\n",
       "      <td>26.203044+0.000000j</td>\n",
       "      <td>-52.794590+0.000000j</td>\n",
       "      <td>-14.303105+0.000000j</td>\n",
       "      <td>-26.879627+0.000000j</td>\n",
       "      <td>-21.777840+0.000000j</td>\n",
       "      <td>-3.588051+0.000000j</td>\n",
       "      <td>-4.778161+0.000000j</td>\n",
       "      <td>-4.131287+0.000000j</td>\n",
       "      <td>26.252974+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.166582+0.000000j</td>\n",
       "      <td>-16.903688+0.000000j</td>\n",
       "      <td>9.581668+0.000000j</td>\n",
       "      <td>-71.195820+0.000000j</td>\n",
       "      <td>-64.374067+0.000000j</td>\n",
       "      <td>58.072123+0.000000j</td>\n",
       "      <td>-54.939257+0.000000j</td>\n",
       "      <td>-24.799956+0.000000j</td>\n",
       "      <td>-1.975343+0.000000j</td>\n",
       "      <td>-8.059053+0.000000j</td>\n",
       "      <td>-3.237475+0.000000j</td>\n",
       "      <td>-20.237674+0.000000j</td>\n",
       "      <td>1.300619+0.000000j</td>\n",
       "      <td>4.894027+0.000000j</td>\n",
       "      <td>17.288586+0.000000j</td>\n",
       "      <td>20.392397+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.888131+0.000000j</td>\n",
       "      <td>-15.661973+0.000000j</td>\n",
       "      <td>-44.206002+0.000000j</td>\n",
       "      <td>12.577171+0.000000j</td>\n",
       "      <td>-57.052073+0.000000j</td>\n",
       "      <td>14.777123+0.000000j</td>\n",
       "      <td>-34.855930+0.000000j</td>\n",
       "      <td>7.103158+0.000000j</td>\n",
       "      <td>26.019001+0.000000j</td>\n",
       "      <td>26.176471+0.000000j</td>\n",
       "      <td>34.117252+0.000000j</td>\n",
       "      <td>24.533519+0.000000j</td>\n",
       "      <td>-18.120853+0.000000j</td>\n",
       "      <td>-4.057446+0.000000j</td>\n",
       "      <td>4.232035+0.000000j</td>\n",
       "      <td>44.523593+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-123.848038+0.000000j</td>\n",
       "      <td>-1.230412+0.000000j</td>\n",
       "      <td>33.960611+0.000000j</td>\n",
       "      <td>10.387350+0.000000j</td>\n",
       "      <td>-22.148284+0.000000j</td>\n",
       "      <td>-8.556023+0.000000j</td>\n",
       "      <td>-32.507783+0.000000j</td>\n",
       "      <td>11.559652+0.000000j</td>\n",
       "      <td>-12.978623+0.000000j</td>\n",
       "      <td>-10.808107+0.000000j</td>\n",
       "      <td>-6.196501+0.000000j</td>\n",
       "      <td>3.756838+0.000000j</td>\n",
       "      <td>42.579375+0.000000j</td>\n",
       "      <td>0.965985+0.000000j</td>\n",
       "      <td>2.608485+0.000000j</td>\n",
       "      <td>25.300619+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-131.042474+0.000000j</td>\n",
       "      <td>30.038711+0.000000j</td>\n",
       "      <td>-93.114298+0.000000j</td>\n",
       "      <td>-102.627275+0.000000j</td>\n",
       "      <td>26.696256+0.000000j</td>\n",
       "      <td>-43.564276+0.000000j</td>\n",
       "      <td>3.047225+0.000000j</td>\n",
       "      <td>-42.004391+0.000000j</td>\n",
       "      <td>-14.970583+0.000000j</td>\n",
       "      <td>13.844793+0.000000j</td>\n",
       "      <td>1.410435+0.000000j</td>\n",
       "      <td>-17.348525+0.000000j</td>\n",
       "      <td>51.574440+0.000000j</td>\n",
       "      <td>34.014492+0.000000j</td>\n",
       "      <td>-30.916686+0.000000j</td>\n",
       "      <td>20.197845+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-115.478037+0.000000j</td>\n",
       "      <td>8.008511+0.000000j</td>\n",
       "      <td>-52.763190+0.000000j</td>\n",
       "      <td>41.561459+0.000000j</td>\n",
       "      <td>-24.414626+0.000000j</td>\n",
       "      <td>-38.413247+0.000000j</td>\n",
       "      <td>65.908274+0.000000j</td>\n",
       "      <td>-22.129850+0.000000j</td>\n",
       "      <td>18.121809+0.000000j</td>\n",
       "      <td>35.270634+0.000000j</td>\n",
       "      <td>-21.665796+0.000000j</td>\n",
       "      <td>37.684984+0.000000j</td>\n",
       "      <td>70.454808+0.000000j</td>\n",
       "      <td>54.453507+0.000000j</td>\n",
       "      <td>-1.943017+0.000000j</td>\n",
       "      <td>28.643082+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-80.289196+0.000000j</td>\n",
       "      <td>8.043660+0.000000j</td>\n",
       "      <td>-28.505129+0.000000j</td>\n",
       "      <td>-75.761783+0.000000j</td>\n",
       "      <td>-64.671384+0.000000j</td>\n",
       "      <td>-30.452543+0.000000j</td>\n",
       "      <td>56.130746+0.000000j</td>\n",
       "      <td>33.535003+0.000000j</td>\n",
       "      <td>-68.709428+0.000000j</td>\n",
       "      <td>-8.696782+0.000000j</td>\n",
       "      <td>-45.584343+0.000000j</td>\n",
       "      <td>39.855856+0.000000j</td>\n",
       "      <td>13.336046+0.000000j</td>\n",
       "      <td>-11.842143+0.000000j</td>\n",
       "      <td>-5.179646+0.000000j</td>\n",
       "      <td>-32.751171+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-111.970493+0.000000j</td>\n",
       "      <td>-16.554529+0.000000j</td>\n",
       "      <td>-22.035180+0.000000j</td>\n",
       "      <td>23.784894+0.000000j</td>\n",
       "      <td>32.932425+0.000000j</td>\n",
       "      <td>24.856543+0.000000j</td>\n",
       "      <td>15.218846+0.000000j</td>\n",
       "      <td>11.497608+0.000000j</td>\n",
       "      <td>-10.682684+0.000000j</td>\n",
       "      <td>-32.126752+0.000000j</td>\n",
       "      <td>-30.141694+0.000000j</td>\n",
       "      <td>42.526344+0.000000j</td>\n",
       "      <td>55.465577+0.000000j</td>\n",
       "      <td>-28.826292+0.000000j</td>\n",
       "      <td>25.352185+0.000000j</td>\n",
       "      <td>0.629223+0.000000j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0                    1                    2   \\\n",
       "0      48.986185+0.000000j -43.000811+0.000000j   5.874231+0.000000j   \n",
       "1      39.101332+0.000000j -46.373946+0.000000j  37.837082+0.000000j   \n",
       "2      10.017356+0.000000j -44.587818+0.000000j -42.942569+0.000000j   \n",
       "3      22.166582+0.000000j -16.903688+0.000000j   9.581668+0.000000j   \n",
       "4      68.888131+0.000000j -15.661973+0.000000j -44.206002+0.000000j   \n",
       "...                    ...                  ...                  ...   \n",
       "1995 -123.848038+0.000000j  -1.230412+0.000000j  33.960611+0.000000j   \n",
       "1996 -131.042474+0.000000j  30.038711+0.000000j -93.114298+0.000000j   \n",
       "1997 -115.478037+0.000000j   8.008511+0.000000j -52.763190+0.000000j   \n",
       "1998  -80.289196+0.000000j   8.043660+0.000000j -28.505129+0.000000j   \n",
       "1999 -111.970493+0.000000j -16.554529+0.000000j -22.035180+0.000000j   \n",
       "\n",
       "                        3                    4                    5   \\\n",
       "0     -48.593418+0.000000j  37.517242+0.000000j -30.595904+0.000000j   \n",
       "1      13.302047+0.000000j  51.335107+0.000000j -98.306881+0.000000j   \n",
       "2      98.033223+0.000000j  -2.292424+0.000000j   5.079248+0.000000j   \n",
       "3     -71.195820+0.000000j -64.374067+0.000000j  58.072123+0.000000j   \n",
       "4      12.577171+0.000000j -57.052073+0.000000j  14.777123+0.000000j   \n",
       "...                    ...                  ...                  ...   \n",
       "1995   10.387350+0.000000j -22.148284+0.000000j  -8.556023+0.000000j   \n",
       "1996 -102.627275+0.000000j  26.696256+0.000000j -43.564276+0.000000j   \n",
       "1997   41.561459+0.000000j -24.414626+0.000000j -38.413247+0.000000j   \n",
       "1998  -75.761783+0.000000j -64.671384+0.000000j -30.452543+0.000000j   \n",
       "1999   23.784894+0.000000j  32.932425+0.000000j  24.856543+0.000000j   \n",
       "\n",
       "                       6                    7                    8   \\\n",
       "0     24.872044+0.000000j  11.049553+0.000000j  -3.197241+0.000000j   \n",
       "1     72.430942+0.000000j  -0.722137+0.000000j  -5.625187+0.000000j   \n",
       "2     48.021144+0.000000j  26.203044+0.000000j -52.794590+0.000000j   \n",
       "3    -54.939257+0.000000j -24.799956+0.000000j  -1.975343+0.000000j   \n",
       "4    -34.855930+0.000000j   7.103158+0.000000j  26.019001+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995 -32.507783+0.000000j  11.559652+0.000000j -12.978623+0.000000j   \n",
       "1996   3.047225+0.000000j -42.004391+0.000000j -14.970583+0.000000j   \n",
       "1997  65.908274+0.000000j -22.129850+0.000000j  18.121809+0.000000j   \n",
       "1998  56.130746+0.000000j  33.535003+0.000000j -68.709428+0.000000j   \n",
       "1999  15.218846+0.000000j  11.497608+0.000000j -10.682684+0.000000j   \n",
       "\n",
       "                       9                    10                   11  \\\n",
       "0      6.284813+0.000000j  -2.056365+0.000000j -25.411024+0.000000j   \n",
       "1     36.541643+0.000000j   0.740024+0.000000j  13.214829+0.000000j   \n",
       "2    -14.303105+0.000000j -26.879627+0.000000j -21.777840+0.000000j   \n",
       "3     -8.059053+0.000000j  -3.237475+0.000000j -20.237674+0.000000j   \n",
       "4     26.176471+0.000000j  34.117252+0.000000j  24.533519+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995 -10.808107+0.000000j  -6.196501+0.000000j   3.756838+0.000000j   \n",
       "1996  13.844793+0.000000j   1.410435+0.000000j -17.348525+0.000000j   \n",
       "1997  35.270634+0.000000j -21.665796+0.000000j  37.684984+0.000000j   \n",
       "1998  -8.696782+0.000000j -45.584343+0.000000j  39.855856+0.000000j   \n",
       "1999 -32.126752+0.000000j -30.141694+0.000000j  42.526344+0.000000j   \n",
       "\n",
       "                       12                   13                   14  \\\n",
       "0    -18.793389+0.000000j   3.321802+0.000000j -14.528369+0.000000j   \n",
       "1     13.065204+0.000000j -11.372910+0.000000j  42.814119+0.000000j   \n",
       "2     -3.588051+0.000000j  -4.778161+0.000000j  -4.131287+0.000000j   \n",
       "3      1.300619+0.000000j   4.894027+0.000000j  17.288586+0.000000j   \n",
       "4    -18.120853+0.000000j  -4.057446+0.000000j   4.232035+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995  42.579375+0.000000j   0.965985+0.000000j   2.608485+0.000000j   \n",
       "1996  51.574440+0.000000j  34.014492+0.000000j -30.916686+0.000000j   \n",
       "1997  70.454808+0.000000j  54.453507+0.000000j  -1.943017+0.000000j   \n",
       "1998  13.336046+0.000000j -11.842143+0.000000j  -5.179646+0.000000j   \n",
       "1999  55.465577+0.000000j -28.826292+0.000000j  25.352185+0.000000j   \n",
       "\n",
       "                       15  \n",
       "0      5.007990+0.000000j  \n",
       "1     10.524816+0.000000j  \n",
       "2     26.252974+0.000000j  \n",
       "3     20.392397+0.000000j  \n",
       "4     44.523593+0.000000j  \n",
       "...                   ...  \n",
       "1995  25.300619+0.000000j  \n",
       "1996  20.197845+0.000000j  \n",
       "1997  28.643082+0.000000j  \n",
       "1998 -32.751171+0.000000j  \n",
       "1999   0.629223+0.000000j  \n",
       "\n",
       "[2000 rows x 16 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "84d68f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = score_test@invegvldiag@score_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3ebbf675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.737665+0.000000j</td>\n",
       "      <td>6.059376+0.000000j</td>\n",
       "      <td>-0.624068+0.000000j</td>\n",
       "      <td>-1.710140+0.000000j</td>\n",
       "      <td>-2.974467+0.000000j</td>\n",
       "      <td>4.602601+0.000000j</td>\n",
       "      <td>4.214573+0.000000j</td>\n",
       "      <td>4.188182+0.000000j</td>\n",
       "      <td>0.049609+0.000000j</td>\n",
       "      <td>-3.549030+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>3.987872+0.000000j</td>\n",
       "      <td>0.799982+0.000000j</td>\n",
       "      <td>3.264265+0.000000j</td>\n",
       "      <td>-5.296022+0.000000j</td>\n",
       "      <td>1.884573+0.000000j</td>\n",
       "      <td>-4.467418+0.000000j</td>\n",
       "      <td>4.048245+0.000000j</td>\n",
       "      <td>-4.697625+0.000000j</td>\n",
       "      <td>0.589270+0.000000j</td>\n",
       "      <td>-5.716099+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.059376+0.000000j</td>\n",
       "      <td>26.448288+0.000000j</td>\n",
       "      <td>3.380510+0.000000j</td>\n",
       "      <td>-12.364847+0.000000j</td>\n",
       "      <td>-4.337213+0.000000j</td>\n",
       "      <td>-0.513039+0.000000j</td>\n",
       "      <td>4.329561+0.000000j</td>\n",
       "      <td>12.830781+0.000000j</td>\n",
       "      <td>-0.284800+0.000000j</td>\n",
       "      <td>-1.384089+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.780098+0.000000j</td>\n",
       "      <td>-3.719455+0.000000j</td>\n",
       "      <td>1.141215+0.000000j</td>\n",
       "      <td>-12.483459+0.000000j</td>\n",
       "      <td>7.788583+0.000000j</td>\n",
       "      <td>-1.785352+0.000000j</td>\n",
       "      <td>-0.661054+0.000000j</td>\n",
       "      <td>8.713877+0.000000j</td>\n",
       "      <td>1.792220+0.000000j</td>\n",
       "      <td>2.074298+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.624068+0.000000j</td>\n",
       "      <td>3.380510+0.000000j</td>\n",
       "      <td>21.064772+0.000000j</td>\n",
       "      <td>-7.272530+0.000000j</td>\n",
       "      <td>-0.726617+0.000000j</td>\n",
       "      <td>-5.097894+0.000000j</td>\n",
       "      <td>-0.189443+0.000000j</td>\n",
       "      <td>0.217372+0.000000j</td>\n",
       "      <td>-3.922326+0.000000j</td>\n",
       "      <td>-0.140781+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.819238+0.000000j</td>\n",
       "      <td>4.523449+0.000000j</td>\n",
       "      <td>0.363500+0.000000j</td>\n",
       "      <td>-2.644922+0.000000j</td>\n",
       "      <td>1.710708+0.000000j</td>\n",
       "      <td>0.521285+0.000000j</td>\n",
       "      <td>-6.640414+0.000000j</td>\n",
       "      <td>5.328899+0.000000j</td>\n",
       "      <td>1.666441+0.000000j</td>\n",
       "      <td>4.819932+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.710140+0.000000j</td>\n",
       "      <td>-12.364847+0.000000j</td>\n",
       "      <td>-7.272530+0.000000j</td>\n",
       "      <td>18.232780+0.000000j</td>\n",
       "      <td>6.050323+0.000000j</td>\n",
       "      <td>1.466689+0.000000j</td>\n",
       "      <td>-5.423186+0.000000j</td>\n",
       "      <td>-6.448687+0.000000j</td>\n",
       "      <td>3.787201+0.000000j</td>\n",
       "      <td>4.332157+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.455443+0.000000j</td>\n",
       "      <td>-6.270291+0.000000j</td>\n",
       "      <td>-4.759597+0.000000j</td>\n",
       "      <td>5.570362+0.000000j</td>\n",
       "      <td>-6.124082+0.000000j</td>\n",
       "      <td>2.415347+0.000000j</td>\n",
       "      <td>2.394327+0.000000j</td>\n",
       "      <td>-7.502676+0.000000j</td>\n",
       "      <td>-0.250072+0.000000j</td>\n",
       "      <td>-4.132046+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.974467+0.000000j</td>\n",
       "      <td>-4.337213+0.000000j</td>\n",
       "      <td>-0.726617+0.000000j</td>\n",
       "      <td>6.050323+0.000000j</td>\n",
       "      <td>16.383388+0.000000j</td>\n",
       "      <td>12.864476+0.000000j</td>\n",
       "      <td>-4.555332+0.000000j</td>\n",
       "      <td>-4.082491+0.000000j</td>\n",
       "      <td>-2.121996+0.000000j</td>\n",
       "      <td>2.448952+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.814145+0.000000j</td>\n",
       "      <td>-2.946385+0.000000j</td>\n",
       "      <td>-7.590672+0.000000j</td>\n",
       "      <td>-0.597658+0.000000j</td>\n",
       "      <td>-9.428285+0.000000j</td>\n",
       "      <td>-1.032112+0.000000j</td>\n",
       "      <td>-4.485949+0.000000j</td>\n",
       "      <td>0.131957+0.000000j</td>\n",
       "      <td>-6.740269+0.000000j</td>\n",
       "      <td>-5.605631+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-4.467418+0.000000j</td>\n",
       "      <td>-1.785352+0.000000j</td>\n",
       "      <td>0.521285+0.000000j</td>\n",
       "      <td>2.415347+0.000000j</td>\n",
       "      <td>-1.032112+0.000000j</td>\n",
       "      <td>-3.644210+0.000000j</td>\n",
       "      <td>-1.835722+0.000000j</td>\n",
       "      <td>1.124363+0.000000j</td>\n",
       "      <td>0.715752+0.000000j</td>\n",
       "      <td>-0.218524+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>6.796399+0.000000j</td>\n",
       "      <td>5.148596+0.000000j</td>\n",
       "      <td>1.936940+0.000000j</td>\n",
       "      <td>5.268719+0.000000j</td>\n",
       "      <td>11.691123+0.000000j</td>\n",
       "      <td>11.513713+0.000000j</td>\n",
       "      <td>4.786850+0.000000j</td>\n",
       "      <td>7.274039+0.000000j</td>\n",
       "      <td>2.848327+0.000000j</td>\n",
       "      <td>7.395075+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>4.048245+0.000000j</td>\n",
       "      <td>-0.661054+0.000000j</td>\n",
       "      <td>-6.640414+0.000000j</td>\n",
       "      <td>2.394327+0.000000j</td>\n",
       "      <td>-4.485949+0.000000j</td>\n",
       "      <td>0.451817+0.000000j</td>\n",
       "      <td>5.321719+0.000000j</td>\n",
       "      <td>2.813191+0.000000j</td>\n",
       "      <td>-7.784538+0.000000j</td>\n",
       "      <td>-8.550423+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>14.724509+0.000000j</td>\n",
       "      <td>6.123599+0.000000j</td>\n",
       "      <td>6.027144+0.000000j</td>\n",
       "      <td>-0.112678+0.000000j</td>\n",
       "      <td>13.416419+0.000000j</td>\n",
       "      <td>4.786850+0.000000j</td>\n",
       "      <td>34.980369+0.000000j</td>\n",
       "      <td>15.877255+0.000000j</td>\n",
       "      <td>9.283966+0.000000j</td>\n",
       "      <td>2.235818+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-4.697625+0.000000j</td>\n",
       "      <td>8.713877+0.000000j</td>\n",
       "      <td>5.328899+0.000000j</td>\n",
       "      <td>-7.502676+0.000000j</td>\n",
       "      <td>0.131957+0.000000j</td>\n",
       "      <td>-0.124862+0.000000j</td>\n",
       "      <td>-1.705904+0.000000j</td>\n",
       "      <td>2.719387+0.000000j</td>\n",
       "      <td>-8.884287+0.000000j</td>\n",
       "      <td>-8.008882+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.532761+0.000000j</td>\n",
       "      <td>4.731570+0.000000j</td>\n",
       "      <td>-2.195964+0.000000j</td>\n",
       "      <td>0.653007+0.000000j</td>\n",
       "      <td>13.492731+0.000000j</td>\n",
       "      <td>7.274039+0.000000j</td>\n",
       "      <td>15.877255+0.000000j</td>\n",
       "      <td>34.298382+0.000000j</td>\n",
       "      <td>6.949470+0.000000j</td>\n",
       "      <td>9.452538+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.589270+0.000000j</td>\n",
       "      <td>1.792220+0.000000j</td>\n",
       "      <td>1.666441+0.000000j</td>\n",
       "      <td>-0.250072+0.000000j</td>\n",
       "      <td>-6.740269+0.000000j</td>\n",
       "      <td>-1.569461+0.000000j</td>\n",
       "      <td>11.551085+0.000000j</td>\n",
       "      <td>4.801223+0.000000j</td>\n",
       "      <td>-0.947634+0.000000j</td>\n",
       "      <td>-1.557829+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>6.415934+0.000000j</td>\n",
       "      <td>2.410049+0.000000j</td>\n",
       "      <td>10.377021+0.000000j</td>\n",
       "      <td>15.340873+0.000000j</td>\n",
       "      <td>5.788049+0.000000j</td>\n",
       "      <td>2.848327+0.000000j</td>\n",
       "      <td>9.283966+0.000000j</td>\n",
       "      <td>6.949470+0.000000j</td>\n",
       "      <td>31.56088+0.00000j</td>\n",
       "      <td>7.409399+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-5.716099+0.000000j</td>\n",
       "      <td>2.074298+0.000000j</td>\n",
       "      <td>4.819932+0.000000j</td>\n",
       "      <td>-4.132046+0.000000j</td>\n",
       "      <td>-5.605631+0.000000j</td>\n",
       "      <td>-14.958088+0.000000j</td>\n",
       "      <td>-3.781414+0.000000j</td>\n",
       "      <td>0.203397+0.000000j</td>\n",
       "      <td>-4.375738+0.000000j</td>\n",
       "      <td>-3.098228+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>3.818279+0.000000j</td>\n",
       "      <td>6.712548+0.000000j</td>\n",
       "      <td>4.893727+0.000000j</td>\n",
       "      <td>1.891525+0.000000j</td>\n",
       "      <td>11.660776+0.000000j</td>\n",
       "      <td>7.395075+0.000000j</td>\n",
       "      <td>2.235818+0.000000j</td>\n",
       "      <td>9.452538+0.000000j</td>\n",
       "      <td>7.409399+0.000000j</td>\n",
       "      <td>19.886219+0.000000j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 2000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0                    1                    2     \\\n",
       "0     8.737665+0.000000j   6.059376+0.000000j  -0.624068+0.000000j   \n",
       "1     6.059376+0.000000j  26.448288+0.000000j   3.380510+0.000000j   \n",
       "2    -0.624068+0.000000j   3.380510+0.000000j  21.064772+0.000000j   \n",
       "3    -1.710140+0.000000j -12.364847+0.000000j  -7.272530+0.000000j   \n",
       "4    -2.974467+0.000000j  -4.337213+0.000000j  -0.726617+0.000000j   \n",
       "...                  ...                  ...                  ...   \n",
       "1995 -4.467418+0.000000j  -1.785352+0.000000j   0.521285+0.000000j   \n",
       "1996  4.048245+0.000000j  -0.661054+0.000000j  -6.640414+0.000000j   \n",
       "1997 -4.697625+0.000000j   8.713877+0.000000j   5.328899+0.000000j   \n",
       "1998  0.589270+0.000000j   1.792220+0.000000j   1.666441+0.000000j   \n",
       "1999 -5.716099+0.000000j   2.074298+0.000000j   4.819932+0.000000j   \n",
       "\n",
       "                     3                    4                    5     \\\n",
       "0     -1.710140+0.000000j  -2.974467+0.000000j   4.602601+0.000000j   \n",
       "1    -12.364847+0.000000j  -4.337213+0.000000j  -0.513039+0.000000j   \n",
       "2     -7.272530+0.000000j  -0.726617+0.000000j  -5.097894+0.000000j   \n",
       "3     18.232780+0.000000j   6.050323+0.000000j   1.466689+0.000000j   \n",
       "4      6.050323+0.000000j  16.383388+0.000000j  12.864476+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995   2.415347+0.000000j  -1.032112+0.000000j  -3.644210+0.000000j   \n",
       "1996   2.394327+0.000000j  -4.485949+0.000000j   0.451817+0.000000j   \n",
       "1997  -7.502676+0.000000j   0.131957+0.000000j  -0.124862+0.000000j   \n",
       "1998  -0.250072+0.000000j  -6.740269+0.000000j  -1.569461+0.000000j   \n",
       "1999  -4.132046+0.000000j  -5.605631+0.000000j -14.958088+0.000000j   \n",
       "\n",
       "                     6                    7                   8     \\\n",
       "0      4.214573+0.000000j   4.188182+0.000000j  0.049609+0.000000j   \n",
       "1      4.329561+0.000000j  12.830781+0.000000j -0.284800+0.000000j   \n",
       "2     -0.189443+0.000000j   0.217372+0.000000j -3.922326+0.000000j   \n",
       "3     -5.423186+0.000000j  -6.448687+0.000000j  3.787201+0.000000j   \n",
       "4     -4.555332+0.000000j  -4.082491+0.000000j -2.121996+0.000000j   \n",
       "...                   ...                  ...                 ...   \n",
       "1995  -1.835722+0.000000j   1.124363+0.000000j  0.715752+0.000000j   \n",
       "1996   5.321719+0.000000j   2.813191+0.000000j -7.784538+0.000000j   \n",
       "1997  -1.705904+0.000000j   2.719387+0.000000j -8.884287+0.000000j   \n",
       "1998  11.551085+0.000000j   4.801223+0.000000j -0.947634+0.000000j   \n",
       "1999  -3.781414+0.000000j   0.203397+0.000000j -4.375738+0.000000j   \n",
       "\n",
       "                    9     ...                 1990                1991  \\\n",
       "0    -3.549030+0.000000j  ...   3.987872+0.000000j  0.799982+0.000000j   \n",
       "1    -1.384089+0.000000j  ...   1.780098+0.000000j -3.719455+0.000000j   \n",
       "2    -0.140781+0.000000j  ...   0.819238+0.000000j  4.523449+0.000000j   \n",
       "3     4.332157+0.000000j  ...   1.455443+0.000000j -6.270291+0.000000j   \n",
       "4     2.448952+0.000000j  ...  -4.814145+0.000000j -2.946385+0.000000j   \n",
       "...                  ...  ...                  ...                 ...   \n",
       "1995 -0.218524+0.000000j  ...   6.796399+0.000000j  5.148596+0.000000j   \n",
       "1996 -8.550423+0.000000j  ...  14.724509+0.000000j  6.123599+0.000000j   \n",
       "1997 -8.008882+0.000000j  ...  -1.532761+0.000000j  4.731570+0.000000j   \n",
       "1998 -1.557829+0.000000j  ...   6.415934+0.000000j  2.410049+0.000000j   \n",
       "1999 -3.098228+0.000000j  ...   3.818279+0.000000j  6.712548+0.000000j   \n",
       "\n",
       "                     1992                 1993                 1994  \\\n",
       "0      3.264265+0.000000j  -5.296022+0.000000j   1.884573+0.000000j   \n",
       "1      1.141215+0.000000j -12.483459+0.000000j   7.788583+0.000000j   \n",
       "2      0.363500+0.000000j  -2.644922+0.000000j   1.710708+0.000000j   \n",
       "3     -4.759597+0.000000j   5.570362+0.000000j  -6.124082+0.000000j   \n",
       "4     -7.590672+0.000000j  -0.597658+0.000000j  -9.428285+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995   1.936940+0.000000j   5.268719+0.000000j  11.691123+0.000000j   \n",
       "1996   6.027144+0.000000j  -0.112678+0.000000j  13.416419+0.000000j   \n",
       "1997  -2.195964+0.000000j   0.653007+0.000000j  13.492731+0.000000j   \n",
       "1998  10.377021+0.000000j  15.340873+0.000000j   5.788049+0.000000j   \n",
       "1999   4.893727+0.000000j   1.891525+0.000000j  11.660776+0.000000j   \n",
       "\n",
       "                     1995                 1996                 1997  \\\n",
       "0     -4.467418+0.000000j   4.048245+0.000000j  -4.697625+0.000000j   \n",
       "1     -1.785352+0.000000j  -0.661054+0.000000j   8.713877+0.000000j   \n",
       "2      0.521285+0.000000j  -6.640414+0.000000j   5.328899+0.000000j   \n",
       "3      2.415347+0.000000j   2.394327+0.000000j  -7.502676+0.000000j   \n",
       "4     -1.032112+0.000000j  -4.485949+0.000000j   0.131957+0.000000j   \n",
       "...                   ...                  ...                  ...   \n",
       "1995  11.513713+0.000000j   4.786850+0.000000j   7.274039+0.000000j   \n",
       "1996   4.786850+0.000000j  34.980369+0.000000j  15.877255+0.000000j   \n",
       "1997   7.274039+0.000000j  15.877255+0.000000j  34.298382+0.000000j   \n",
       "1998   2.848327+0.000000j   9.283966+0.000000j   6.949470+0.000000j   \n",
       "1999   7.395075+0.000000j   2.235818+0.000000j   9.452538+0.000000j   \n",
       "\n",
       "                    1998                 1999  \n",
       "0     0.589270+0.000000j  -5.716099+0.000000j  \n",
       "1     1.792220+0.000000j   2.074298+0.000000j  \n",
       "2     1.666441+0.000000j   4.819932+0.000000j  \n",
       "3    -0.250072+0.000000j  -4.132046+0.000000j  \n",
       "4    -6.740269+0.000000j  -5.605631+0.000000j  \n",
       "...                  ...                  ...  \n",
       "1995  2.848327+0.000000j   7.395075+0.000000j  \n",
       "1996  9.283966+0.000000j   2.235818+0.000000j  \n",
       "1997  6.949470+0.000000j   9.452538+0.000000j  \n",
       "1998   31.56088+0.00000j   7.409399+0.000000j  \n",
       "1999  7.409399+0.000000j  19.886219+0.000000j  \n",
       "\n",
       "[2000 rows x 2000 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bdb0658d",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "29c0a495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000,)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0af614b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.73766509+0.j, 26.44828794+0.j, 21.06477215+0.j, ...,\n",
       "       34.29838246+0.j, 31.56088039+0.j, 19.88621859+0.j])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "07cad87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.73766509, 26.44828794, 21.06477215, ..., 34.29838246,\n",
       "       31.56088039, 19.88621859])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final.real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7e4f49f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# T2_rd = round(T2_test_final, 4)\n",
    "T2_rd = []\n",
    "for data in T2_test_final:\n",
    "    x = round(data.real, 2) # rounded 4\n",
    "    T2_rd.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "98551afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_rd = np.array(T2_rd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "18b6e059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(T2_rd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ce553456",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\m477a\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\numpy\\core\\_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(T2_test_final,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "04a0d479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(T2_test_final.real,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0afed95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(T2_rd,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "78380554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.6965\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_test_final>T2_trlim).sum()/T2_test_final.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88374cf3",
   "metadata": {},
   "source": [
    "### Reconstruct new Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "22b35672",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reconstruct New Data\n",
    "# K_1 = score_train@score_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "385fb6b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K_1_ = K_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e441d7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a00de0f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# i = (500,500)\n",
    "# ii = np.ones(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1a25c743",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K_1 = K_1+ii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6f131bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dual_coef = np.linalg.solve(K_1, A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a0b5724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dual_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f68311f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K_2 = K_1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ac2acc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newData = K_2@dual_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "41bc97e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newData\n",
    "# newData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "56c888af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newData = pd.DataFrame(newData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "893d7a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newData.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4f230a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e24dc844",
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear kernel\n",
    "# Kt = np.matmul(df_test,newData.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6875b696",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "41764b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt = Kt.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "49e44279",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt_c = Kt-unit_t@K-Kt@unit+unit_t@K@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "728f43c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt_c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8372478e",
   "metadata": {},
   "source": [
    "## Let's try with the np.linalg.eig(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a910a789",
   "metadata": {},
   "outputs": [],
   "source": [
    "npegvls, npegvtr = np.linalg.eig(K_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "164b82dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npegvls.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "88561a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npegvtr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54154424",
   "metadata": {},
   "source": [
    "# Make experimental Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "56f2ecbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler, KernelCenterer\n",
    "# from sklearn.utils import extmath\n",
    "# from sklearn.metrics.pairwise import euclidean_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9fbb12d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class KPCA():\n",
    "   \n",
    "#     ###############\n",
    "\n",
    "#     def __init__ (self, a = 0.9):\n",
    "#         '''\n",
    "#         Constructor: function called every time a PCA object is initialized\n",
    "#         '''\n",
    "#         # if 0<=a<1, 'a' indicates the desired explained variance fraction\n",
    "#         # if a>=1, 'a' indicates the desired number of components\n",
    "#         self.a = a\n",
    "   \n",
    "#     ###############\n",
    "\n",
    "#     def fit(self, X, conf_Q = 0.99, conf_T2 = 0.99, plot = True):\n",
    "#         '''\n",
    "#         Model training function\n",
    "#         '''\n",
    "#         gamma = 0.01\n",
    "        \n",
    "#         # storing training averages and standard deviations\n",
    "#         self.mu_train = X.mean(axis=0)\n",
    "#         self.std_train = X.std(axis=0)\n",
    "       \n",
    "#         # normalizing training data\n",
    "#         X = np.array(((X - self.mu_train)/self.std_train))\n",
    "        \n",
    "#         # Calculate euclidean distances of each pair of points in the data set\n",
    "#         dist = euclidean_distances(X, X, squared=True)\n",
    "        \n",
    "#         # Calculate Gaussian kernel matrix\n",
    "#         K = np.exp(-gamma * dist)\n",
    "\n",
    "#         # centering kernel\n",
    "#         Kc = KernelCenterer().fit_transform(K)\n",
    "        \n",
    "#         # calculating the covariance matrix of the data\n",
    "# #         Cx = np.cov(X, rowvar=False)\n",
    "        \n",
    "#         # applying decomposition into eigenvalues ​​and eigenvectors\n",
    "#         # https://numpy.org/doc/stable/reference/generated/numpy.linalg.eig.html\n",
    "#         self.L, self.P = np.linalg.eig(Kc)\n",
    "        \n",
    "#         # fractions of explained variance\n",
    "#         fv = self.L/np.sum(self.L)\n",
    "        \n",
    "#         # fractions of accumulated explained variance\n",
    "#         fva = np.cumsum(self.L)/sum(self.L)\n",
    "       \n",
    "#         # defining number of components\n",
    "# #         if self.a>0 and self.a<1:\n",
    "# #             self.a = np.where(fva>self.a)[0][0]+1\n",
    "            \n",
    "#         # calculating detection limits\n",
    "\n",
    "#         # limit of the T^2 statistic\n",
    "#         from scipy.stats import f\n",
    "#         F = f.ppf(conf_T2, self.a, X.shape[0]-self.a)\n",
    "#         self.T2_lim = ((self.a*(X.shape[0]**2-1))/(X.shape[0]*(X.shape[0]-self.a)))*F\n",
    "        \n",
    "#         # Q statistic limit\n",
    "# #         theta = [np.sum(self.L[self.a:]**(i)) for i in (1,2,3)]\n",
    "# #         ho = 1-((2*theta[0]*theta[2])/(3*(theta[1]**2)))\n",
    "# #         from scipy.stats import norm\n",
    "# #         nalpha = norm.ppf(conf_Q)\n",
    "# #         self.Q_lim = (theta[0]*(((nalpha*np.sqrt(2*theta[1]*ho**2))/theta[0])+1+\n",
    "# #                                 ((theta[1]*ho*(ho-1))/theta[0]**2))**(1/ho))\n",
    "        \n",
    "#         # plotting explained variances\n",
    "#         if plot:\n",
    "#             fig, ax = plt.subplots()\n",
    "#             ax.bar(np.arange(len(fv)),fv)\n",
    "#             ax.plot(np.arange(len(fv)),fva)\n",
    "#             ax.set_xlabel('Number of components')\n",
    "#             ax.set_ylabel('Data variance')\n",
    "#             ax.set_title('PCA - Variance Explained');\n",
    "\n",
    "#     ###############\n",
    "            \n",
    "#     def predict(self, X):\n",
    "#         '''\n",
    "#         Function for model testing\n",
    "#         '''\n",
    "#         gamma = 0.01\n",
    "        \n",
    "#         # normalizing test data (using training parameters!)\n",
    "#         X = np.array((X - self.mu_train)/self.std_train)\n",
    "        \n",
    "#          # Calculate euclidean distances of each pair of points in the data set\n",
    "#         dist = euclidean_distances(X, X, squared=True)\n",
    "        \n",
    "#         # Calculate Gaussian kernel matrix\n",
    "#         K = np.exp(-gamma * dist)\n",
    "\n",
    "#         # centering kernel\n",
    "#         X = KernelCenterer().fit_transform(K)\n",
    "        \n",
    "#         # calculating T^2 statistic\n",
    "# #         T = X@self.P[:,:self.a] # score matrix\n",
    "#         T = X\n",
    "# #         self.T2 = np.array([T[i,:]@np.linalg.inv(np.diag(self.L[:self.a]))@T[i,:].T for i in range (X.shape[0])])\n",
    "#         self.T2 = np.array(T@np.linalg.inv(np.diag(self.P[:,:self.a]))@T.T )\n",
    "\n",
    "# #         # calculating Q statistic\n",
    "# #         e = X - X@self.P[:,:self.a]@self.P[:,:self.a].T\n",
    "# #         self.Q = np.array([e[i,:]@e[i,:].T for i in range(X.shape[0])])\n",
    "        \n",
    "# #         # calculating contributions to Q\n",
    "# #         self.c = np.absolute(X*e)\n",
    "                \n",
    "#     ###############\n",
    "\n",
    "#     def plot_control_charts(self, fault = None):\n",
    "#         '''\n",
    "#         Function to plot control charts\n",
    "#         '''\n",
    "#         fig, ax = plt.subplots(1,2, figsize=(15,3))\n",
    "\n",
    "#         ax[0].semilogy(self.T2,'.')\n",
    "#         ax[0].axhline(self.T2_lim,ls='--',c='r');\n",
    "#         ax[0].set_title('Control Letter $T^2$')\n",
    "        \n",
    "#         ax[1].semilogy(self.Q,'.')\n",
    "#         ax[1].axhline(self.Q_lim,ls='--',c='r')\n",
    "#         ax[1].set_title('Q Control Letter')\n",
    " \n",
    "#         if fault is not None:\n",
    "#             ax[0].axvline(fault, c='k')\n",
    "#             ax[1].axvline(fault, c='k')\n",
    "\n",
    "#     ###############\n",
    "            \n",
    "#     def plot_contributions(self, fault = None,\n",
    "#                            index = None,\n",
    "#                            columns = None):\n",
    "#         '''\n",
    "#         Function to plot contribution maps\n",
    "#         '''\n",
    "#         fig, ax = plt.subplots(figsize=(20, 6))\n",
    "        \n",
    "#         c = pd.DataFrame(self.c,\n",
    "#                          index = index,\n",
    "#                          columns = columns)\n",
    "    \n",
    "#         sns.heatmap(c, ax = ax,\n",
    "#                     yticklabels=int(self.c.shape[0]/10),\n",
    "#                     cmap = plt.cm.Blues);\n",
    "        \n",
    "#         ax.set_title('Partial contributions to Q')\n",
    "        \n",
    "#         if fault is not None:\n",
    "#             ax.axhline(y=c.index[fault],\n",
    "#                        ls='--', c='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "80df0bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train = train_normal[(train_normal.simulationRun>=1)&\n",
    "#                                  (train_normal.simulationRun<5)].iloc[:,3:]\n",
    "\n",
    "# df_test = train_normal[(train_normal.simulationRun>5)&\n",
    "#                                 (train_normal.simulationRun<10)].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "be291777",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kpca = KPCA(a = 16)\n",
    "# kpca.fit(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "763a346b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kpca.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "fda38024",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kpca.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "bb4b9c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kpca.plot_control_charts()\n",
    "# plt.suptitle('IDV(0)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f529133d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('False alarm rate\\n--------------')\n",
    "\n",
    "# print(f'T2: {(kpca.T2>kpca.T2_lim).sum()/kpca.T2.shape[0]}')\n",
    "# print(f'Q: {(kpca.Q>kpca.Q_lim).sum()/kpca.Q.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "56f4d723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IDV = 1\n",
    "\n",
    "# df_test = test_faulty[(test_faulty.faultNumber==IDV) & \n",
    "#                                (test_faulty.simulationRun==1)].iloc[:,3:]\n",
    "\n",
    "# kpca.predict(df_test)\n",
    "\n",
    "# kpca.plot_control_charts(fault=160)\n",
    "# plt.suptitle(f'IDV({IDV})');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8fe85ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.arange(9.0).reshape((3, 3))\n",
    "x2 = np.arange(9.0).reshape((3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4888d1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c56c49e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2.],\n",
       "       [3., 4., 5.],\n",
       "       [6., 7., 8.]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "cc4e0fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2.],\n",
       "       [3., 4., 5.],\n",
       "       [6., 7., 8.]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "cd22c007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1-x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a8e5a0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = 81.1008646"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "adeb4d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = round(x3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "2d54ce1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.1009"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}