{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using LSTM for Time series prediction i.e. Sine Wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn \n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import keras\n",
    "from keras import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.utils import shuffle\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sine Wave')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MoqBUCmZmPZWdV+hsnrke7mVMWCsLX3peOJe8HLm12ZQbXOuczRsow+eMsTW+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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe = pd.read_csv(\"../datasets/sine-wave.csv\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(dataframe)\n",
    "plt.title(\"Sine Wave\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use a moving forward window of size 50, which means we will use the first 50 data points as out input X to predict y1 — 51st data point. Next, we will use the window between 1 to 51 data points as input X to predict y2 i.e., the 52nd data point and so on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'first 50 points')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4eHgSV1xQm799vob7Jy4lO0fTM4mUF1Yef0klJia6lJQUr8sQEfFLzjmem7GB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\nfkChTERERMQP/D+LM52FYk7G1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(dataframe[:50])\n",
    "plt.title(\"first 50 points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the dataset\n",
    "\n",
    "This includes:\n",
    "\n",
    "i. Normalization of the feature values\n",
    "\n",
    "ii. Convert the dataset in the time series(up to certain steps)\n",
    "\n",
    "iii. Split the dataset into training and test "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Normalization of the feature values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Preparing the dataset\n",
    "# normalize the values between -1 and 1\n",
    "# .fit_Transform function is used to find minimum and maximum values in the data and normalize according to that.\n",
    "# Since we are normalizing values b/w -1 and 1. \n",
    "# So after predictions, we have transform again into original form. If needed, the transform can be inverted. \n",
    "# This is useful for converting predictions back into their original scale for reporting or plotting. \n",
    "# This can be done by calling the inverse_transform() function\n",
    "scaler = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
    "scaled_data = scaler.fit_transform(dataframe.values)\n",
    "scaled_dataframe = pd.DataFrame(scaled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Convert the dataset in the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>...</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873749</td>\n",
       "      <td>0.902566</td>\n",
       "      <td>0.927822</td>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.902566</td>\n",
       "      <td>0.927822</td>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927822</td>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</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>4970</th>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4974</th>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4983</th>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4984</th>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4985</th>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4986</th>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4987</th>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4988</th>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4989</th>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4990</th>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991</th>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4992</th>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>0.841483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         0         0         0         0         0         0  \\\n",
       "0     0.873749  0.902566  0.927822  0.949416  0.967263  0.981292  0.991449   \n",
       "1     0.902566  0.927822  0.949416  0.967263  0.981292  0.991449  0.997693   \n",
       "2     0.927822  0.949416  0.967263  0.981292  0.991449  0.997693  1.000000   \n",
       "3     0.949416  0.967263  0.981292  0.991449  0.997693  1.000000  0.998360   \n",
       "4     0.967263  0.981292  0.991449  0.997693  1.000000  0.998360  0.992780   \n",
       "5     0.981292  0.991449  0.997693  1.000000  0.998360  0.992780  0.983282   \n",
       "6     0.991449  0.997693  1.000000  0.998360  0.992780  0.983282  0.969904   \n",
       "7     0.997693  1.000000  0.998360  0.992780  0.983282  0.969904  0.952697   \n",
       "8     1.000000  0.998360  0.992780  0.983282  0.969904  0.952697  0.931731   \n",
       "9     0.998360  0.992780  0.983282  0.969904  0.952697  0.931731  0.907088   \n",
       "10    0.992780  0.983282  0.969904  0.952697  0.931731  0.907088  0.878865   \n",
       "11    0.983282  0.969904  0.952697  0.931731  0.907088  0.878865  0.847173   \n",
       "12    0.969904  0.952697  0.931731  0.907088  0.878865  0.847173  0.812138   \n",
       "13    0.952697  0.931731  0.907088  0.878865  0.847173  0.812138  0.773898   \n",
       "14    0.931731  0.907088  0.878865  0.847173  0.812138  0.773898  0.732603   \n",
       "15    0.907088  0.878865  0.847173  0.812138  0.773898  0.732603  0.688418   \n",
       "16    0.878865  0.847173  0.812138  0.773898  0.732603  0.688418  0.641515   \n",
       "17    0.847173  0.812138  0.773898  0.732603  0.688418  0.641515  0.592081   \n",
       "18    0.812138  0.773898  0.732603  0.688418  0.641515  0.592081  0.540310   \n",
       "19    0.773898  0.732603  0.688418  0.641515  0.592081  0.540310  0.486407   \n",
       "20    0.732603  0.688418  0.641515  0.592081  0.540310  0.486407  0.430584   \n",
       "21    0.688418  0.641515  0.592081  0.540310  0.486407  0.430584  0.373061   \n",
       "22    0.641515  0.592081  0.540310  0.486407  0.430584  0.373061  0.314067   \n",
       "23    0.592081  0.540310  0.486407  0.430584  0.373061  0.314067  0.253833   \n",
       "24    0.540310  0.486407  0.430584  0.373061  0.314067  0.253833  0.192597   \n",
       "25    0.486407  0.430584  0.373061  0.314067  0.253833  0.192597  0.130601   \n",
       "26    0.430584  0.373061  0.314067  0.253833  0.192597  0.130601  0.068090   \n",
       "27    0.373061  0.314067  0.253833  0.192597  0.130601  0.068090  0.005310   \n",
       "28    0.314067  0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491   \n",
       "29    0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491 -0.120065   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4970 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584   \n",
       "4971 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061   \n",
       "4972 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067   \n",
       "4973 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833   \n",
       "4974 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597   \n",
       "4975 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601   \n",
       "4976 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090   \n",
       "4977 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310   \n",
       "4978 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310  0.057491   \n",
       "4979 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310  0.057491  0.120065   \n",
       "4980 -0.192597 -0.130601 -0.068090 -0.005310  0.057491  0.120065  0.182166   \n",
       "4981 -0.130601 -0.068090 -0.005310  0.057491  0.120065  0.182166  0.243547   \n",
       "4982 -0.068090 -0.005310  0.057491  0.120065  0.182166  0.243547  0.303967   \n",
       "4983 -0.005310  0.057491  0.120065  0.182166  0.243547  0.303967  0.363188   \n",
       "4984  0.057491  0.120065  0.182166  0.243547  0.303967  0.363188  0.420975   \n",
       "4985  0.120065  0.182166  0.243547  0.303967  0.363188  0.420975  0.477101   \n",
       "4986  0.182166  0.243547  0.303967  0.363188  0.420975  0.477101  0.531344   \n",
       "4987  0.243547  0.303967  0.363188  0.420975  0.477101  0.531344  0.583490   \n",
       "4988  0.303967  0.363188  0.420975  0.477101  0.531344  0.583490  0.633333   \n",
       "4989  0.363188  0.420975  0.477101  0.531344  0.583490  0.633333  0.680677   \n",
       "4990  0.420975  0.477101  0.531344  0.583490  0.633333  0.680677  0.725334   \n",
       "4991  0.477101  0.531344  0.583490  0.633333  0.680677  0.725334  0.767129   \n",
       "4992  0.531344  0.583490  0.633333  0.680677  0.725334  0.767129  0.805896   \n",
       "4993  0.583490  0.633333  0.680677  0.725334  0.767129  0.805896  0.841483   \n",
       "4994  0.633333  0.680677  0.725334  0.767129  0.805896  0.841483       NaN   \n",
       "4995  0.680677  0.725334  0.767129  0.805896  0.841483       NaN       NaN   \n",
       "4996  0.725334  0.767129  0.805896  0.841483       NaN       NaN       NaN   \n",
       "4997  0.767129  0.805896  0.841483       NaN       NaN       NaN       NaN   \n",
       "4998  0.805896  0.841483       NaN       NaN       NaN       NaN       NaN   \n",
       "4999  0.841483       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "             0         0         0    ...            0         0         0  \\\n",
       "0     0.997693  1.000000  0.998360    ...    -0.477101 -0.531344 -0.583490   \n",
       "1     1.000000  0.998360  0.992780    ...    -0.531344 -0.583490 -0.633333   \n",
       "2     0.998360  0.992780  0.983282    ...    -0.583490 -0.633333 -0.680677   \n",
       "3     0.992780  0.983282  0.969904    ...    -0.633333 -0.680677 -0.725334   \n",
       "4     0.983282  0.969904  0.952697    ...    -0.680677 -0.725334 -0.767129   \n",
       "5     0.969904  0.952697  0.931731    ...    -0.725334 -0.767129 -0.805896   \n",
       "6     0.952697  0.931731  0.907088    ...    -0.767129 -0.805896 -0.841483   \n",
       "7     0.931731  0.907088  0.878865    ...    -0.805896 -0.841483 -0.873749   \n",
       "8     0.907088  0.878865  0.847173    ...    -0.841483 -0.873749 -0.902566   \n",
       "9     0.878865  0.847173  0.812138    ...    -0.873749 -0.902566 -0.927822   \n",
       "10    0.847173  0.812138  0.773898    ...    -0.902566 -0.927822 -0.949416   \n",
       "11    0.812138  0.773898  0.732603    ...    -0.927822 -0.949416 -0.967263   \n",
       "12    0.773898  0.732603  0.688418    ...    -0.949416 -0.967263 -0.981292   \n",
       "13    0.732603  0.688418  0.641515    ...    -0.967263 -0.981292 -0.991449   \n",
       "14    0.688418  0.641515  0.592081    ...    -0.981292 -0.991449 -0.997693   \n",
       "15    0.641515  0.592081  0.540310    ...    -0.991449 -0.997693 -1.000000   \n",
       "16    0.592081  0.540310  0.486407    ...    -0.997693 -1.000000 -0.998360   \n",
       "17    0.540310  0.486407  0.430584    ...    -1.000000 -0.998360 -0.992780   \n",
       "18    0.486407  0.430584  0.373061    ...    -0.998360 -0.992780 -0.983282   \n",
       "19    0.430584  0.373061  0.314067    ...    -0.992780 -0.983282 -0.969904   \n",
       "20    0.373061  0.314067  0.253833    ...    -0.983282 -0.969904 -0.952697   \n",
       "21    0.314067  0.253833  0.192597    ...    -0.969904 -0.952697 -0.931731   \n",
       "22    0.253833  0.192597  0.130601    ...    -0.952697 -0.931731 -0.907088   \n",
       "23    0.192597  0.130601  0.068090    ...    -0.931731 -0.907088 -0.878865   \n",
       "24    0.130601  0.068090  0.005310    ...    -0.907088 -0.878865 -0.847173   \n",
       "25    0.068090  0.005310 -0.057491    ...    -0.878865 -0.847173 -0.812138   \n",
       "26    0.005310 -0.057491 -0.120065    ...    -0.847173 -0.812138 -0.773898   \n",
       "27   -0.057491 -0.120065 -0.182166    ...    -0.812138 -0.773898 -0.732603   \n",
       "28   -0.120065 -0.182166 -0.243547    ...    -0.773898 -0.732603 -0.688418   \n",
       "29   -0.182166 -0.243547 -0.303967    ...    -0.732603 -0.688418 -0.641515   \n",
       "...        ...       ...       ...    ...          ...       ...       ...   \n",
       "4970 -0.373061 -0.314067 -0.253833    ...          NaN       NaN       NaN   \n",
       "4971 -0.314067 -0.253833 -0.192597    ...          NaN       NaN       NaN   \n",
       "4972 -0.253833 -0.192597 -0.130601    ...          NaN       NaN       NaN   \n",
       "4973 -0.192597 -0.130601 -0.068090    ...          NaN       NaN       NaN   \n",
       "4974 -0.130601 -0.068090 -0.005310    ...          NaN       NaN       NaN   \n",
       "4975 -0.068090 -0.005310  0.057491    ...          NaN       NaN       NaN   \n",
       "4976 -0.005310  0.057491  0.120065    ...          NaN       NaN       NaN   \n",
       "4977  0.057491  0.120065  0.182166    ...          NaN       NaN       NaN   \n",
       "4978  0.120065  0.182166  0.243547    ...          NaN       NaN       NaN   \n",
       "4979  0.182166  0.243547  0.303967    ...          NaN       NaN       NaN   \n",
       "4980  0.243547  0.303967  0.363188    ...          NaN       NaN       NaN   \n",
       "4981  0.303967  0.363188  0.420975    ...          NaN       NaN       NaN   \n",
       "4982  0.363188  0.420975  0.477101    ...          NaN       NaN       NaN   \n",
       "4983  0.420975  0.477101  0.531344    ...          NaN       NaN       NaN   \n",
       "4984  0.477101  0.531344  0.583490    ...          NaN       NaN       NaN   \n",
       "4985  0.531344  0.583490  0.633333    ...          NaN       NaN       NaN   \n",
       "4986  0.583490  0.633333  0.680677    ...          NaN       NaN       NaN   \n",
       "4987  0.633333  0.680677  0.725334    ...          NaN       NaN       NaN   \n",
       "4988  0.680677  0.725334  0.767129    ...          NaN       NaN       NaN   \n",
       "4989  0.725334  0.767129  0.805896    ...          NaN       NaN       NaN   \n",
       "4990  0.767129  0.805896  0.841483    ...          NaN       NaN       NaN   \n",
       "4991  0.805896  0.841483       NaN    ...          NaN       NaN       NaN   \n",
       "4992  0.841483       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4993       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4994       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4995       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4996       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4997       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4998       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "4999       NaN       NaN       NaN    ...          NaN       NaN       NaN   \n",
       "\n",
       "             0         0         0         0         0         0         0  \n",
       "0    -0.633333 -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749  \n",
       "1    -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566  \n",
       "2    -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822  \n",
       "3    -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416  \n",
       "4    -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263  \n",
       "5    -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263 -0.981292  \n",
       "6    -0.873749 -0.902566 -0.927822 -0.949416 -0.967263 -0.981292 -0.991449  \n",
       "7    -0.902566 -0.927822 -0.949416 -0.967263 -0.981292 -0.991449 -0.997693  \n",
       "8    -0.927822 -0.949416 -0.967263 -0.981292 -0.991449 -0.997693 -1.000000  \n",
       "9    -0.949416 -0.967263 -0.981292 -0.991449 -0.997693 -1.000000 -0.998360  \n",
       "10   -0.967263 -0.981292 -0.991449 -0.997693 -1.000000 -0.998360 -0.992780  \n",
       "11   -0.981292 -0.991449 -0.997693 -1.000000 -0.998360 -0.992780 -0.983282  \n",
       "12   -0.991449 -0.997693 -1.000000 -0.998360 -0.992780 -0.983282 -0.969904  \n",
       "13   -0.997693 -1.000000 -0.998360 -0.992780 -0.983282 -0.969904 -0.952697  \n",
       "14   -1.000000 -0.998360 -0.992780 -0.983282 -0.969904 -0.952697 -0.931731  \n",
       "15   -0.998360 -0.992780 -0.983282 -0.969904 -0.952697 -0.931731 -0.907088  \n",
       "16   -0.992780 -0.983282 -0.969904 -0.952697 -0.931731 -0.907088 -0.878865  \n",
       "17   -0.983282 -0.969904 -0.952697 -0.931731 -0.907088 -0.878865 -0.847173  \n",
       "18   -0.969904 -0.952697 -0.931731 -0.907088 -0.878865 -0.847173 -0.812138  \n",
       "19   -0.952697 -0.931731 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898  \n",
       "20   -0.931731 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603  \n",
       "21   -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418  \n",
       "22   -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515  \n",
       "23   -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081  \n",
       "24   -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310  \n",
       "25   -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407  \n",
       "26   -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584  \n",
       "27   -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061  \n",
       "28   -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067  \n",
       "29   -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "4970       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4971       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4972       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4973       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4974       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4975       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4976       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4977       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4978       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4979       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4980       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4981       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4982       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4983       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4984       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4985       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4986       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4987       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4988       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4989       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4990       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4991       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4992       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4993       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4994       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4995       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4996       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4997       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4998       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4999       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[5000 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Window size \n",
    "#Fix the moving window size to be 50. \n",
    "# For this purpose we use pandas shift function that shifts the entire column by the number we specify. \n",
    "\n",
    "# we shifted the column up by 1 (hence used -1. we want to predict future values) \n",
    "# If we want to shift it down by 1, we will have to use +1) and then concatenate that to the original data.\n",
    "\n",
    "window_size = 50\n",
    "copied_dataframe = scaled_dataframe.copy()\n",
    "\n",
    "for i in range(window_size):\n",
    "    \n",
    "    scaled_dataframe = pd.concat([scaled_dataframe, copied_dataframe.shift(-(i+1))], axis=1)\n",
    "    \n",
    "scaled_dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.992780</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927822</td>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.949416</td>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.967263</td>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.981292</td>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.991449</td>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.997693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.998360</td>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.992780</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.969904</td>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.952697</td>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.931731</td>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.907088</td>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.878865</td>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.847173</td>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.812138</td>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.998360</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.773898</td>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.992780</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</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>4920</th>\n",
       "      <td>0.732603</td>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4921</th>\n",
       "      <td>0.688418</td>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.969904</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>0.641515</td>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.952697</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4923</th>\n",
       "      <td>0.592081</td>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.931731</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4924</th>\n",
       "      <td>0.540310</td>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.907088</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4925</th>\n",
       "      <td>0.486407</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.878865</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4926</th>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.847173</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4927</th>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.812138</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4928</th>\n",
       "      <td>0.314067</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.773898</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4929</th>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.732603</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>0.192597</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.688418</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4931</th>\n",
       "      <td>0.130601</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.641515</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4932</th>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.592081</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4933</th>\n",
       "      <td>0.005310</td>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.540310</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4934</th>\n",
       "      <td>-0.057491</td>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.486407</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>-0.120065</td>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.430584</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>-0.182166</td>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.373061</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-0.243547</td>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.314067</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4938</th>\n",
       "      <td>-0.303967</td>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253833</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4939</th>\n",
       "      <td>-0.363188</td>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.192597</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4940</th>\n",
       "      <td>-0.420975</td>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.130601</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4941</th>\n",
       "      <td>-0.477101</td>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.068090</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4942</th>\n",
       "      <td>-0.531344</td>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005310</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4943</th>\n",
       "      <td>-0.583490</td>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057491</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4944</th>\n",
       "      <td>-0.633333</td>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4945</th>\n",
       "      <td>-0.680677</td>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182166</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4946</th>\n",
       "      <td>-0.725334</td>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243547</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4947</th>\n",
       "      <td>-0.767129</td>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303967</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4948</th>\n",
       "      <td>-0.805896</td>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363188</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4949</th>\n",
       "      <td>-0.841483</td>\n",
       "      <td>-0.873749</td>\n",
       "      <td>-0.902566</td>\n",
       "      <td>-0.927822</td>\n",
       "      <td>-0.949416</td>\n",
       "      <td>-0.967263</td>\n",
       "      <td>-0.981292</td>\n",
       "      <td>-0.991449</td>\n",
       "      <td>-0.997693</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.420975</td>\n",
       "      <td>0.477101</td>\n",
       "      <td>0.531344</td>\n",
       "      <td>0.583490</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.680677</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.767129</td>\n",
       "      <td>0.805896</td>\n",
       "      <td>0.841483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4950 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         0         0         0         0         0         0  \\\n",
       "0     0.873749  0.902566  0.927822  0.949416  0.967263  0.981292  0.991449   \n",
       "1     0.902566  0.927822  0.949416  0.967263  0.981292  0.991449  0.997693   \n",
       "2     0.927822  0.949416  0.967263  0.981292  0.991449  0.997693  1.000000   \n",
       "3     0.949416  0.967263  0.981292  0.991449  0.997693  1.000000  0.998360   \n",
       "4     0.967263  0.981292  0.991449  0.997693  1.000000  0.998360  0.992780   \n",
       "5     0.981292  0.991449  0.997693  1.000000  0.998360  0.992780  0.983282   \n",
       "6     0.991449  0.997693  1.000000  0.998360  0.992780  0.983282  0.969904   \n",
       "7     0.997693  1.000000  0.998360  0.992780  0.983282  0.969904  0.952697   \n",
       "8     1.000000  0.998360  0.992780  0.983282  0.969904  0.952697  0.931731   \n",
       "9     0.998360  0.992780  0.983282  0.969904  0.952697  0.931731  0.907088   \n",
       "10    0.992780  0.983282  0.969904  0.952697  0.931731  0.907088  0.878865   \n",
       "11    0.983282  0.969904  0.952697  0.931731  0.907088  0.878865  0.847173   \n",
       "12    0.969904  0.952697  0.931731  0.907088  0.878865  0.847173  0.812138   \n",
       "13    0.952697  0.931731  0.907088  0.878865  0.847173  0.812138  0.773898   \n",
       "14    0.931731  0.907088  0.878865  0.847173  0.812138  0.773898  0.732603   \n",
       "15    0.907088  0.878865  0.847173  0.812138  0.773898  0.732603  0.688418   \n",
       "16    0.878865  0.847173  0.812138  0.773898  0.732603  0.688418  0.641515   \n",
       "17    0.847173  0.812138  0.773898  0.732603  0.688418  0.641515  0.592081   \n",
       "18    0.812138  0.773898  0.732603  0.688418  0.641515  0.592081  0.540310   \n",
       "19    0.773898  0.732603  0.688418  0.641515  0.592081  0.540310  0.486407   \n",
       "20    0.732603  0.688418  0.641515  0.592081  0.540310  0.486407  0.430584   \n",
       "21    0.688418  0.641515  0.592081  0.540310  0.486407  0.430584  0.373061   \n",
       "22    0.641515  0.592081  0.540310  0.486407  0.430584  0.373061  0.314067   \n",
       "23    0.592081  0.540310  0.486407  0.430584  0.373061  0.314067  0.253833   \n",
       "24    0.540310  0.486407  0.430584  0.373061  0.314067  0.253833  0.192597   \n",
       "25    0.486407  0.430584  0.373061  0.314067  0.253833  0.192597  0.130601   \n",
       "26    0.430584  0.373061  0.314067  0.253833  0.192597  0.130601  0.068090   \n",
       "27    0.373061  0.314067  0.253833  0.192597  0.130601  0.068090  0.005310   \n",
       "28    0.314067  0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491   \n",
       "29    0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491 -0.120065   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4920  0.732603  0.688418  0.641515  0.592081  0.540310  0.486407  0.430584   \n",
       "4921  0.688418  0.641515  0.592081  0.540310  0.486407  0.430584  0.373061   \n",
       "4922  0.641515  0.592081  0.540310  0.486407  0.430584  0.373061  0.314067   \n",
       "4923  0.592081  0.540310  0.486407  0.430584  0.373061  0.314067  0.253833   \n",
       "4924  0.540310  0.486407  0.430584  0.373061  0.314067  0.253833  0.192597   \n",
       "4925  0.486407  0.430584  0.373061  0.314067  0.253833  0.192597  0.130601   \n",
       "4926  0.430584  0.373061  0.314067  0.253833  0.192597  0.130601  0.068090   \n",
       "4927  0.373061  0.314067  0.253833  0.192597  0.130601  0.068090  0.005310   \n",
       "4928  0.314067  0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491   \n",
       "4929  0.253833  0.192597  0.130601  0.068090  0.005310 -0.057491 -0.120065   \n",
       "4930  0.192597  0.130601  0.068090  0.005310 -0.057491 -0.120065 -0.182166   \n",
       "4931  0.130601  0.068090  0.005310 -0.057491 -0.120065 -0.182166 -0.243547   \n",
       "4932  0.068090  0.005310 -0.057491 -0.120065 -0.182166 -0.243547 -0.303967   \n",
       "4933  0.005310 -0.057491 -0.120065 -0.182166 -0.243547 -0.303967 -0.363188   \n",
       "4934 -0.057491 -0.120065 -0.182166 -0.243547 -0.303967 -0.363188 -0.420975   \n",
       "4935 -0.120065 -0.182166 -0.243547 -0.303967 -0.363188 -0.420975 -0.477101   \n",
       "4936 -0.182166 -0.243547 -0.303967 -0.363188 -0.420975 -0.477101 -0.531344   \n",
       "4937 -0.243547 -0.303967 -0.363188 -0.420975 -0.477101 -0.531344 -0.583490   \n",
       "4938 -0.303967 -0.363188 -0.420975 -0.477101 -0.531344 -0.583490 -0.633333   \n",
       "4939 -0.363188 -0.420975 -0.477101 -0.531344 -0.583490 -0.633333 -0.680677   \n",
       "4940 -0.420975 -0.477101 -0.531344 -0.583490 -0.633333 -0.680677 -0.725334   \n",
       "4941 -0.477101 -0.531344 -0.583490 -0.633333 -0.680677 -0.725334 -0.767129   \n",
       "4942 -0.531344 -0.583490 -0.633333 -0.680677 -0.725334 -0.767129 -0.805896   \n",
       "4943 -0.583490 -0.633333 -0.680677 -0.725334 -0.767129 -0.805896 -0.841483   \n",
       "4944 -0.633333 -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749   \n",
       "4945 -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566   \n",
       "4946 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822   \n",
       "4947 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416   \n",
       "4948 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263   \n",
       "4949 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263 -0.981292   \n",
       "\n",
       "             0         0         0    ...            0         0         0  \\\n",
       "0     0.997693  1.000000  0.998360    ...    -0.477101 -0.531344 -0.583490   \n",
       "1     1.000000  0.998360  0.992780    ...    -0.531344 -0.583490 -0.633333   \n",
       "2     0.998360  0.992780  0.983282    ...    -0.583490 -0.633333 -0.680677   \n",
       "3     0.992780  0.983282  0.969904    ...    -0.633333 -0.680677 -0.725334   \n",
       "4     0.983282  0.969904  0.952697    ...    -0.680677 -0.725334 -0.767129   \n",
       "5     0.969904  0.952697  0.931731    ...    -0.725334 -0.767129 -0.805896   \n",
       "6     0.952697  0.931731  0.907088    ...    -0.767129 -0.805896 -0.841483   \n",
       "7     0.931731  0.907088  0.878865    ...    -0.805896 -0.841483 -0.873749   \n",
       "8     0.907088  0.878865  0.847173    ...    -0.841483 -0.873749 -0.902566   \n",
       "9     0.878865  0.847173  0.812138    ...    -0.873749 -0.902566 -0.927822   \n",
       "10    0.847173  0.812138  0.773898    ...    -0.902566 -0.927822 -0.949416   \n",
       "11    0.812138  0.773898  0.732603    ...    -0.927822 -0.949416 -0.967263   \n",
       "12    0.773898  0.732603  0.688418    ...    -0.949416 -0.967263 -0.981292   \n",
       "13    0.732603  0.688418  0.641515    ...    -0.967263 -0.981292 -0.991449   \n",
       "14    0.688418  0.641515  0.592081    ...    -0.981292 -0.991449 -0.997693   \n",
       "15    0.641515  0.592081  0.540310    ...    -0.991449 -0.997693 -1.000000   \n",
       "16    0.592081  0.540310  0.486407    ...    -0.997693 -1.000000 -0.998360   \n",
       "17    0.540310  0.486407  0.430584    ...    -1.000000 -0.998360 -0.992780   \n",
       "18    0.486407  0.430584  0.373061    ...    -0.998360 -0.992780 -0.983282   \n",
       "19    0.430584  0.373061  0.314067    ...    -0.992780 -0.983282 -0.969904   \n",
       "20    0.373061  0.314067  0.253833    ...    -0.983282 -0.969904 -0.952697   \n",
       "21    0.314067  0.253833  0.192597    ...    -0.969904 -0.952697 -0.931731   \n",
       "22    0.253833  0.192597  0.130601    ...    -0.952697 -0.931731 -0.907088   \n",
       "23    0.192597  0.130601  0.068090    ...    -0.931731 -0.907088 -0.878865   \n",
       "24    0.130601  0.068090  0.005310    ...    -0.907088 -0.878865 -0.847173   \n",
       "25    0.068090  0.005310 -0.057491    ...    -0.878865 -0.847173 -0.812138   \n",
       "26    0.005310 -0.057491 -0.120065    ...    -0.847173 -0.812138 -0.773898   \n",
       "27   -0.057491 -0.120065 -0.182166    ...    -0.812138 -0.773898 -0.732603   \n",
       "28   -0.120065 -0.182166 -0.243547    ...    -0.773898 -0.732603 -0.688418   \n",
       "29   -0.182166 -0.243547 -0.303967    ...    -0.732603 -0.688418 -0.641515   \n",
       "...        ...       ...       ...    ...          ...       ...       ...   \n",
       "4920  0.373061  0.314067  0.253833    ...    -0.983282 -0.969904 -0.952697   \n",
       "4921  0.314067  0.253833  0.192597    ...    -0.969904 -0.952697 -0.931731   \n",
       "4922  0.253833  0.192597  0.130601    ...    -0.952697 -0.931731 -0.907088   \n",
       "4923  0.192597  0.130601  0.068090    ...    -0.931731 -0.907088 -0.878865   \n",
       "4924  0.130601  0.068090  0.005310    ...    -0.907088 -0.878865 -0.847173   \n",
       "4925  0.068090  0.005310 -0.057491    ...    -0.878865 -0.847173 -0.812138   \n",
       "4926  0.005310 -0.057491 -0.120065    ...    -0.847173 -0.812138 -0.773898   \n",
       "4927 -0.057491 -0.120065 -0.182166    ...    -0.812138 -0.773898 -0.732603   \n",
       "4928 -0.120065 -0.182166 -0.243547    ...    -0.773898 -0.732603 -0.688418   \n",
       "4929 -0.182166 -0.243547 -0.303967    ...    -0.732603 -0.688418 -0.641515   \n",
       "4930 -0.243547 -0.303967 -0.363188    ...    -0.688418 -0.641515 -0.592081   \n",
       "4931 -0.303967 -0.363188 -0.420975    ...    -0.641515 -0.592081 -0.540310   \n",
       "4932 -0.363188 -0.420975 -0.477101    ...    -0.592081 -0.540310 -0.486407   \n",
       "4933 -0.420975 -0.477101 -0.531344    ...    -0.540310 -0.486407 -0.430584   \n",
       "4934 -0.477101 -0.531344 -0.583490    ...    -0.486407 -0.430584 -0.373061   \n",
       "4935 -0.531344 -0.583490 -0.633333    ...    -0.430584 -0.373061 -0.314067   \n",
       "4936 -0.583490 -0.633333 -0.680677    ...    -0.373061 -0.314067 -0.253833   \n",
       "4937 -0.633333 -0.680677 -0.725334    ...    -0.314067 -0.253833 -0.192597   \n",
       "4938 -0.680677 -0.725334 -0.767129    ...    -0.253833 -0.192597 -0.130601   \n",
       "4939 -0.725334 -0.767129 -0.805896    ...    -0.192597 -0.130601 -0.068090   \n",
       "4940 -0.767129 -0.805896 -0.841483    ...    -0.130601 -0.068090 -0.005310   \n",
       "4941 -0.805896 -0.841483 -0.873749    ...    -0.068090 -0.005310  0.057491   \n",
       "4942 -0.841483 -0.873749 -0.902566    ...    -0.005310  0.057491  0.120065   \n",
       "4943 -0.873749 -0.902566 -0.927822    ...     0.057491  0.120065  0.182166   \n",
       "4944 -0.902566 -0.927822 -0.949416    ...     0.120065  0.182166  0.243547   \n",
       "4945 -0.927822 -0.949416 -0.967263    ...     0.182166  0.243547  0.303967   \n",
       "4946 -0.949416 -0.967263 -0.981292    ...     0.243547  0.303967  0.363188   \n",
       "4947 -0.967263 -0.981292 -0.991449    ...     0.303967  0.363188  0.420975   \n",
       "4948 -0.981292 -0.991449 -0.997693    ...     0.363188  0.420975  0.477101   \n",
       "4949 -0.991449 -0.997693 -1.000000    ...     0.420975  0.477101  0.531344   \n",
       "\n",
       "             0         0         0         0         0         0         0  \n",
       "0    -0.633333 -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749  \n",
       "1    -0.680677 -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566  \n",
       "2    -0.725334 -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822  \n",
       "3    -0.767129 -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416  \n",
       "4    -0.805896 -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263  \n",
       "5    -0.841483 -0.873749 -0.902566 -0.927822 -0.949416 -0.967263 -0.981292  \n",
       "6    -0.873749 -0.902566 -0.927822 -0.949416 -0.967263 -0.981292 -0.991449  \n",
       "7    -0.902566 -0.927822 -0.949416 -0.967263 -0.981292 -0.991449 -0.997693  \n",
       "8    -0.927822 -0.949416 -0.967263 -0.981292 -0.991449 -0.997693 -1.000000  \n",
       "9    -0.949416 -0.967263 -0.981292 -0.991449 -0.997693 -1.000000 -0.998360  \n",
       "10   -0.967263 -0.981292 -0.991449 -0.997693 -1.000000 -0.998360 -0.992780  \n",
       "11   -0.981292 -0.991449 -0.997693 -1.000000 -0.998360 -0.992780 -0.983282  \n",
       "12   -0.991449 -0.997693 -1.000000 -0.998360 -0.992780 -0.983282 -0.969904  \n",
       "13   -0.997693 -1.000000 -0.998360 -0.992780 -0.983282 -0.969904 -0.952697  \n",
       "14   -1.000000 -0.998360 -0.992780 -0.983282 -0.969904 -0.952697 -0.931731  \n",
       "15   -0.998360 -0.992780 -0.983282 -0.969904 -0.952697 -0.931731 -0.907088  \n",
       "16   -0.992780 -0.983282 -0.969904 -0.952697 -0.931731 -0.907088 -0.878865  \n",
       "17   -0.983282 -0.969904 -0.952697 -0.931731 -0.907088 -0.878865 -0.847173  \n",
       "18   -0.969904 -0.952697 -0.931731 -0.907088 -0.878865 -0.847173 -0.812138  \n",
       "19   -0.952697 -0.931731 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898  \n",
       "20   -0.931731 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603  \n",
       "21   -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418  \n",
       "22   -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515  \n",
       "23   -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081  \n",
       "24   -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310  \n",
       "25   -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407  \n",
       "26   -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584  \n",
       "27   -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061  \n",
       "28   -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067  \n",
       "29   -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "4920 -0.931731 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603  \n",
       "4921 -0.907088 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418  \n",
       "4922 -0.878865 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515  \n",
       "4923 -0.847173 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081  \n",
       "4924 -0.812138 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310  \n",
       "4925 -0.773898 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407  \n",
       "4926 -0.732603 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584  \n",
       "4927 -0.688418 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061  \n",
       "4928 -0.641515 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067  \n",
       "4929 -0.592081 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833  \n",
       "4930 -0.540310 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597  \n",
       "4931 -0.486407 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601  \n",
       "4932 -0.430584 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090  \n",
       "4933 -0.373061 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310  \n",
       "4934 -0.314067 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310  0.057491  \n",
       "4935 -0.253833 -0.192597 -0.130601 -0.068090 -0.005310  0.057491  0.120065  \n",
       "4936 -0.192597 -0.130601 -0.068090 -0.005310  0.057491  0.120065  0.182166  \n",
       "4937 -0.130601 -0.068090 -0.005310  0.057491  0.120065  0.182166  0.243547  \n",
       "4938 -0.068090 -0.005310  0.057491  0.120065  0.182166  0.243547  0.303967  \n",
       "4939 -0.005310  0.057491  0.120065  0.182166  0.243547  0.303967  0.363188  \n",
       "4940  0.057491  0.120065  0.182166  0.243547  0.303967  0.363188  0.420975  \n",
       "4941  0.120065  0.182166  0.243547  0.303967  0.363188  0.420975  0.477101  \n",
       "4942  0.182166  0.243547  0.303967  0.363188  0.420975  0.477101  0.531344  \n",
       "4943  0.243547  0.303967  0.363188  0.420975  0.477101  0.531344  0.583490  \n",
       "4944  0.303967  0.363188  0.420975  0.477101  0.531344  0.583490  0.633333  \n",
       "4945  0.363188  0.420975  0.477101  0.531344  0.583490  0.633333  0.680677  \n",
       "4946  0.420975  0.477101  0.531344  0.583490  0.633333  0.680677  0.725334  \n",
       "4947  0.477101  0.531344  0.583490  0.633333  0.680677  0.725334  0.767129  \n",
       "4948  0.531344  0.583490  0.633333  0.680677  0.725334  0.767129  0.805896  \n",
       "4949  0.583490  0.633333  0.680677  0.725334  0.767129  0.805896  0.841483  \n",
       "\n",
       "[4950 rows x 51 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove missing values \n",
    "scaled_dataframe.dropna(axis=0, inplace=True)\n",
    "scaled_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the dataset for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3960"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_data = int(round(0.8*scaled_dataframe.shape[0]))\n",
    "split_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = scaled_dataframe.iloc[:split_data,:]\n",
    "test_data = scaled_dataframe.iloc[split_data:,:]\n",
    "\n",
    "train_data = shuffle(train_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train features shape  (3960, 50)\n",
      "train label shape  (3960,)\n",
      "test data y  (990,)\n"
     ]
    }
   ],
   "source": [
    "# Consider last column as output\n",
    "train_data_x = train_data.iloc[:,:-1]\n",
    "train_data_y = train_data.iloc[:,-1]\n",
    "test_data_x = test_data.iloc[:,:-1]\n",
    "test_data_y = test_data.iloc[:,-1]\n",
    "print \"train features shape \", train_data_x.shape\n",
    "print \"train label shape \", train_data_y.shape\n",
    "print \"test data y \", test_data_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshape train example x  (3960, 50, 1)\n",
      "Reshape test example x  (990, 50, 1)\n"
     ]
    }
   ],
   "source": [
    "# Reshape it for LSTM \n",
    "train_data_x = np.array(train_data_x)\n",
    "test_data_x = np.array(test_data_x)\n",
    "# print train_data_x.shape[0], train_data_x.shape[1]\n",
    "train_data_x = train_data_x.reshape(train_data_x.shape[0],train_data_x.shape[1],1)\n",
    "test_data_x = test_data_x.reshape(test_data_x.shape[0],test_data_x.shape[1],1)\n",
    "print \"Reshape train example x \", train_data_x.shape\n",
    "print \"Reshape test example x \", test_data_x.shape\n",
    "input_shape = (50,1)\n",
    "output = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Model  Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 50, 50)            10400     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 50, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 256)               314368    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 325,025\n",
      "Trainable params: 325,025\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(input_shape = input_shape, units= output, return_sequences = True))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(LSTM(256))\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation(\"linear\"))\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3564 samples, validate on 396 samples\n",
      "Epoch 1/6\n",
      "3564/3564 [==============================] - 20s 6ms/step - loss: 0.2740 - acc: 0.0084 - val_loss: 0.0724 - val_acc: 0.0202\n",
      "Epoch 2/6\n",
      "3564/3564 [==============================] - 20s 6ms/step - loss: 0.0369 - acc: 0.0202 - val_loss: 0.0047 - val_acc: 0.0202\n",
      "Epoch 3/6\n",
      "3564/3564 [==============================] - 17s 5ms/step - loss: 0.0144 - acc: 0.0202 - val_loss: 0.0017 - val_acc: 0.0202\n",
      "Epoch 4/6\n",
      "3564/3564 [==============================] - 16s 5ms/step - loss: 0.0079 - acc: 0.0202 - val_loss: 0.0045 - val_acc: 0.0202\n",
      "Epoch 5/6\n",
      "3564/3564 [==============================] - 20s 5ms/step - loss: 0.0075 - acc: 0.0202 - val_loss: 0.0020 - val_acc: 0.0202\n",
      "Epoch 6/6\n",
      "3564/3564 [==============================] - 18s 5ms/step - loss: 0.0055 - acc: 0.0202 - val_loss: 4.5101e-04 - val_acc: 0.0202\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f3550bebcd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "model.fit(train_data_x,train_data_y,batch_size=512,epochs=6,validation_split=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_output = model.predict(test_data_x)\n",
    "#converting predictions back into their original scale for reporting or plotting.\n",
    "# print \"predicted output shape \", predicted_output.shape\n",
    "predicted_output_scaled = scaler.inverse_transform(predicted_output)\n",
    "# print predicted_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ramesh/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# convert actual to its original form\n",
    "# test_data_y = test_data_y.values.reshape(-1,1)\n",
    "test_data_y = test_data_y.reshape(test_data.shape[0],1)\n",
    "# print test_data_y.shape\n",
    "actual_output_scaled = scaler.inverse_transform(test_data_y)\n",
    "# actual_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model.evaluate(test_data_x, test_data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean square error  0.00045264009063750757\n"
     ]
    }
   ],
   "source": [
    "print \"mean square error \", mean_squared_error(actual_output_scaled,predicted_output_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f34f8230810>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PgIqKTv1wOIzTykpw9RU34eab/x47d+7EBRdcMKVrv9P52m7MG90DfPrT476v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UYEcT7OzjTmPhnPcQVxNgNBOVtYiOcSKICc+b3nZWeHOkiZjL/X5UJQzuJaL9NpCvrEmXdKJWp3\nxJl33Fk4cRYOEvBZxmTrJE5ix9cKMryxixHSWH+s7N9FGTFcP4kTegf1un/XnGzvooo1nDif8++i\njNjaorO+/EwxvbNDI7txXWoRxMzLzg6wskJJdOJIUM5OnIjnLAIQnLM4d5DEmTnizAz32SbOvFMs\n0iy5OAsHCfgsI33mFJbRwsG1pn8X3d3FPjZx8kzav2ty4uQpnMAu9ncnvl0ytb+LPXUCq6u+XZIV\nQaSY3tmhB2JcZxG2toB63d8U03F/cG1t0eiun7PvSXAmHSQzxJk54swcv51pDezuijMTWi36irOz\nWZGAzzLcfXaN1/zbk5Y53MVB+kQs9yIAdH5hFiNUXz/07Zq52i6quZOxnkUA/G2Ik/CwB/xdnpIE\nZ90uPaD9IgnO9vdpQMYvkuBMghcz/HY2GNCqEXHmnUoFGA7FmQlx3wYxDxLwWcbiJTpXzk0a4geF\n+g5q+fjWjvzD5MxNTuMHy80dtJbiuecRkIBvFsSZOeLMnK0tWmpdrfpzvcmE/MfdWbVKnWc/aDaB\ndjv+zvysl+61xJl3khC8iLPwkIDPMlafoE/x6IZ/xwystHfRWYlv8LLyKDnrXfPJmdZY7e+ivxpf\nZ9IRN8dvZ1qLM1P6ferYizPvHB4Co1EynPk1+56ETuXWFgW17bY/10uKMz+XqCfFmZ/9jF3Kpxdr\nZ7MiAZ9l5C7QbJXe9mm2ajzG2nAPAye+wUvpCXI2uumTs2YTRd3BeCO+zqbn1b/beM7LeBz/WQS/\nnTUatNxRnHknCQ97v50loVMpzswRZ+acOEGzyH7NvifF2eGhf7PvSXA2KxLw2UaphK4qILN325fL\n6d09ZDDG6ORZX67HkfSZac3f9sdZ781b9MPZ+DpzM0Nu+zQpenBAS8fi3Ai7HSS/nCXhwXWKxmLE\nmQHizBxxZo44M0ecmeM683NgIZsFyvFMOj8XEvDZhlKoFM6gWL3ly+War9B10udO+3I9luRyqGY2\nkNv3x1ntRbrOwoX4OlOKGmJ5cHknl6Ozq8SZd8plejiLM++4/23izDvSETcnCGdKHS+vjSNBOFte\nBhYX/bkeR4JwFudjeeZBAj4LqZfOYa11w59rvUTBS/6RM75cjyvVxXNYqfnjrPUqOVt8LN7OTp0C\nbvszKZqIDhIgzkxJpei/T5x5Z2mJZuDFmXe2tuiz5qezXA5YW/PnehxxO+J+OtvYiO+xPEAwzuJc\nLwFxFiYS8FlIb/0stgY3fUnL3XmDatnyE/EOXlrOWZS7N325Vv8aOVt76pQv1+PK6dMyIm6KODPH\nb2epFJ33GGf8dlYqIbbH8gAUZGxtySyCCaur9Jnw01nc27IgZqvi7uz0dKGUOAseCfgsZHz6LE5h\nGwfb8+9yHb19C30sYP3xdR9KxpfB1lmcHt/w5VBsfesW9rCJE+dz81+MMe6STj8yjrnBy4n45rkB\n4P8y2HyeOuNxxm9nm5tAOu3P9bjit7MkdJDEmRlBLOuPu7NcjpapizPvrK/TgIw4Cx4J+Cwkc+Ec\nUtA4+O78NUTdvoXbOI2Tp2I8VAkAZ89hBU3svFqf+1LZXXK2seFDuRhz6hRliazV5r/Wzg4tf8rn\n578WZ06domyko9H813IfXHGeRQD8XwabhIe9ODNHnJkjzszxy1kSjuUB/F3WPxjQYfVxdzYrEvBZ\nSPExyg5Zf3H+JYoLh7exnTqD5eW5L8Wa3CPk7Oj5+Z0Vjm7jMHcGqZjXHj+XWiThwQWQM/cg63lJ\nkrNGA2i15r9Wkpzt7MCXZf1JciazCGb45SwJx/K4+OWs2QQ6HXFmQhKO5ZmHmHdZ48nqB88BAHpv\nzJ+EZKl2C9XimdjPIiw9Sc6aL8/vrNS8hfpKvPc8Av5upk5KB0mcmePnvpckORsO6fyqeUjKLAJA\nzg4PMfey/l4PODpKjrPbt+df1n94SEFfkpzNS1L2cAPiLCzmCviUUo5S6s+UUm9Mv78vZ5VS6keU\nUt+546unlPrU9G//QSl1/Y6/XZ6nPEmhfJlmqyZvzzlbpTXW2rfQLsX3eAEX50PkbPjWnM66XZSG\nFfSc+DvzsyO+vX18vTgjzszxy9loBOzvizMTajUKYJLkzO0Uzor7/qQ463aB+pw7IdzPaVKc+bGs\nP2nO/HpmutcT3s+8M3y/DOCLWutLAL44/ff3oLX+c631Za31ZQA/CqAD4P+74yX/1P271vo7c5Yn\nEWTXlnCk1pDZnnO2qlpFTvcx3Ir/bNXqEycxQhq4OaezaYuiT8ffmV+dyvGYRu9ifE79u/i1DLbR\noC9x5p3tbVriKM68c3M6/iXOvCPOzEmas8mEBp/mIWnO6nWg3Z7vOklyNgvzBnyfBPCZ6c+fAfCp\nB7z+pwF8XmvdmfO+iecgfxbFynyzVZMbdJ6cOhv/4EVl0thLn8bC7nzOum+Qs8xD8XdWKFCilXmX\nWuztUdB3Jv7KsLFBGSLndXaLPmaJcObXMlhxZo44M0ecmSPOzHGdnY7/YiLfBpdv3aJMqevxTjo/\nM/MGfFta6x0AmH5/0OlHPwPgv7znd7+mlHpeKfWbSql75rlXSv2iUuqqUurqwcHBfKWOAfXlsyg1\n5wteknLoukuleBZLtfmcHb1IzpYeT4YzP5ZaJGnULZ2moyfEmXeWl4HFRXFmgnu8iTjzjl+dStdZ\nkoIXP5y5ZyHGHT+dlctAsTh/mbjjp7MzZ+Kf2XpWHhjwKaW+oJR68S5fnzS5kVLqJICnAfzpHb/+\nFQCPA/gIAAfAL93r/Vrr39FaP6u1fnYj7vnwPdBZP4fN3nzLExuvTA9dfzwBQ0gAGmvnUG7P56z9\n+vTQ9Q8kw5kf2bOSNLoLiDNTlBJnpiws0HmDfjhLpeJ/PiZAneeFBX86laUSYp/ZGvC3I376NGKf\n2RrwdxlsEgZiAHEWFg+sflrrH9Naf+AuX58FsDcN5NyA7n6rlv8BgP+qtX73tHCt9Y4m+gD+PYCP\nzvefkxxGJ8/C0VUMjmZf9Nx7/QZGSGPj6QQ87QH0N85ia3gLejx7LvPRWzdQQwmnHl3ysWR88SN7\nVpJmEQD/nLmBUBLwy9nSUvwPqnfxy9mpUzT7Enfcg8T9WGqXlLasWARWV8WZCZubFNj64SwJg1eA\nv8tgk+JsFuYdb/kcgE9Pf/40gM/e57U/i/cs57wjWFSg/X8vzlmexJC6+BAA4OAbb89+kevXcRNn\ncfZCAp72ACbnHkIOAzRemz1NW+bmdVzHhcR0xN3zvubJOHbzJu0HdBz/ysWZ06ePZ5tm5eZNmnXJ\nZv0pE3f8cnb2bHKW8/jpLCmIM3PEmRnpNB0LIM68s7JCy/rncZak5HCzMm/A9+sAPqGUegPAJ6b/\nhlLqWaXU77ovUko9BOAsgL94z/v/s1LqBQAvAFgH8C/mLE9iWPzgwwCAo6tvzXyN/M51vK0uJGaD\na+5JcnbwtdmdLR5cx/bCBeTzfpWKN+fPU8axeZZauKNuSemInz9PGcfmSWWetJHK8+fpv3k8nv0a\nSXR2Y86kw+LMHHefUFKY19lkIp8zUzodoFpNTvCi1PzO3KMwkuJsFuYK+LTWFa31x7XWl6bfq9Pf\nX9Va/8Idr3tba31aaz15z/t/VGv99HSJ6D/UWrfmKU+ScJ69CADovTR78LJSvY6DpQuJWFcPACuX\nyVnruzM60xpO420crV7wsVS8OUfn1eOdd2a/RpJGKoFjZ/M8vJLobDyeb2Ahic6OjoBmc7b3a51M\nZzdvzj6w0OsBBwfJczZP+394CAwG4syEJO1HdhFnwZOQrn78OP3BMupYgX7r2mwX6Hax1ttFs5yc\n4OXE953HCGkMX5vR2d4e8pMuOlvJcXb+PH2ftyFO0sNenJkzr7PhENjdFWcmHB3RodpJczYazX74\nurvHKGnOqlWgNeNwfNL2cAPk7OZNmt2chaQ6m3dgGUiWM1Mk4LOUQlHhnczDyN+ecbbq7bcBAP1T\nyQleNk9ncRPnkHlnRmfXrwMAxueS42ze2Sp31iZJo25uR3xWZ/U6zdqIM+9sb9OMlTjzTpKOF3CZ\ntz0TZ+Yk1Zk7CDULSQxezp0DKpXZD19PojNTJOCzmIPlh7FanS14mbxFwYu6mJzgJZUCtgsPY2l/\nNme9V8hZ9tHkOCsW6RDTWUfednYo6EtSI7y1RenfZ3WWxAfXvEuHxZk5SXQ276yoODNHnJnjOktK\ncjjAnwGsJCWHmwUJ+CymuXERW53rM21IaL1AwUv+ieQELwBwtHYRG43ZAr7m8+Rs6QMP+Vgi/syz\nmTqJ6+pTKerciDPvLC7SOWnizDsnT1IWV3HmnXlnq5LobN6O+K1b9DlN0vHJfjjb2EBiksMB/jhL\nUnK4WZCAz2KG5x7GAobQN81z2XZeuo4u8ig/lYwz+Fy6px7G6qgyUwrFwWvXsYstnHqkGEDJ+DLP\n2vokju4C4mwWxJkZ7sDCPM4ymWQcuu6yvAysrc3nbG2NBiiSwsmT9DmZx9mZM8k4dN3Fj9n3JLVl\ngDgLgwRVwfiRfpSOGah/2zwJyejN63gbD+Hc+WQNh+iL5Gzw6gyJW96mM/jchikpuNmztDZ/r9t4\nJ9XZLLzzDp3l5B5GmxTmdVYq0XlOSWJeZ6dP02ctScwzsPDOO8lry9JpCtjEmXdWVujAenHmnVOn\n6LMmzoJDAj6LWfogHTNQ+6b5EsXMTQr4Ll70u1S8yT9J/8GVr5s7K0zPLUzaKNL588fnApny1lu0\npn511f9yceb8edq/OBiYv/ett+j9mYz/5eKMu3R4loGFt94CHn7Y/zJxZ57l1kl1du6cODNFnJkz\nq7PJBLh2LXnOMhkagJrFWbdLibuS5swUCfgsZuPDZzFEBr2XzYOX5cPr2MlfSNyI+Ooz1CK0nzd0\nNhqh1LiBo9ULiRwRB2Ybebt2DYkbVADImdbHe35MSLKzVouOCzAlyc62tykjoClJdjbLioXJhBI1\nJ9mZKe02HYgtzryzu0vnPYoz70wTqCfSmQkS8FnM+YczuIaLSL/5utkbKxUsDmpobSavdpx5cgV7\n2MTkVUNn77yDtB6jfyp5zubZTJ3EkUpAnM3CrM7GYzplJqnOJpPj8+G80moB+/vJddZsmm/j3t6m\nGfukOrt9m84wNMHtiCfV2aztP5DM4EWcBYsEfBaztga8nn4SK7deNnvjK68AAHoPPRFAqXhz5gzw\nMp5E4e3ZnI0fTZ6zWTdTux3xJDbCszprNIDDQ3FmgtsRF2feSfKI+KzOktypPHdutoGFpDur180H\nFpLu7NYt84GFJDszQQI+i1EK2HGewnrtDaDf9/y+8YsUvKSffjKoorFlYQF4Z/EplPdeNlrT0/0W\nOctdTl7AVy4DS0vHjapX3IY7iY3wuXOUlc7UWZI74hemJ8SYOkvyw16cmSPOzBFn5szjTKnjFQ9J\n4sIFGih2sy575do1ypybpKM/ZkECPstpnX8KaT0GXve+RLH1jVfQQQFrlxPYogConX4KxWHDaINV\n55uvYBdbOP2BtQBLxhOlgEuXgDfeMHtfkh/2Cwv0wDZ19tZ0a2kSnZXLtGpBnHnHTe4jzrxz6RJ9\nn6U9S6WSmQlwHmfLy1S3k8Y8zs6cAXI5/8vEnXmcXbwoZ/A9CAn4LCf19FMAgPHzL3l+z/CFV/Aa\nHsPFR5L5v3/0GDnDS96d4dVX8DKeTGQHCQAefdRoTAFAsgM+QJzNwqVLszlLp5N5BlMmQ5+VWZyV\nShRgJ41SCdjcnM3Z2bM0mJM0zp6lAGQWZ0ntiD/yCH2fxVkS9zwC9MwExFlQJLPHHyNKH30MI6TR\n+BvvwUvu2it4BU8ktlNZeJYCvs43PDrTGos3yJm7TCNpXLpE+/FMsgFeu0Yd0jNnAisWa9xZUZNs\ngNeuUSc8acdYuMw6k3zuHJDNBlMm7szqLKkdcWA+Z0kklaIARpx5Z3GRjhkQZ945cYK2j5g40zrZ\nzkyQgM9yHn4yhzfxCAbf9hi8tFpYrryD11NP4PTpYMvGlbOXy9jFFlpf9+hsZwf5Xh03ik+gVAq2\nbFy5dInW1rt7zLxw7Voyz5NzuXSJkrAcHHh/T9IfXI8+Svs3ej3v7xFnwJtvmg8sJNmZBHzmmDpL\n8sEsZ4kAABadSURBVDEWLqbOOh06vzWpzpQyH1jY26Nz+JLqzAQJ+Czn0iXgJTyFhTc9Bi+vvQYA\nqGw9mbjz5FxcZ6mXPTqbZuhsnU1ewhaXWdbWJ32ZhTgz59IlClzeMjgmU5xRR3F729vr3Y540p3t\n7NDxFF5wz5NLurO33qKBPy+458kl3ZlJ+//22/Q9ycGLqbOkb4MwQQI+yzlxAngj+xRKB296Gxaf\nBi+Dh5MbvFy8CLyMp7B8y2Omzqkz/XhynZmurdeaZh2S3AibOhsOk3uMhYsbJHt1Vq/TDKo48+7s\n1q3kHmPh4tZNrx3LJCe5cXn0UfrceD0n7c036XvSnR0eAkdH3l4vzsjZ9evet4+IM+9IwGc5SgFH\npz+AlJ4ALz/4bDn98isYIY2VDz8SQul4kssBO+UPIDdoeVqjOHzhFdSxghNXToZQOp6YZlDc2wOq\nVeCJ5MbIxhkU33yTHnJJdmY6K+o2eeLMuzM3V5U4E2cmiDNzZnX2+OPBlMcGTLePvPQS7d+WgO/B\nSMAXA9pPfZR++Nu/feBrO1/9Nl7BE3j8gwlMNXYHR5e8O+t/7dt4Hh/EUx9IaIaDKSZLLdwH11NP\nBVce7rgZFMWZd9wMiuLMO24GRXHmHTeDoomzdBp47LHgysSdWYKX5eVkZs91mcXZmTNIbK4AYDZn\njz2W3KRdJkjAFwPWLp/HLrYw/urX7v9CrZH5zjfxTTyT6Ic9ACx8+ANoowj9Nw9wNhoh/+p3xBnM\njhmQTiVh6kypZI+IA2ZHM7z0ElAoILHZc4HjDIomzra2knk2moubQdHE2SOPJPNsNJeTJ8mbibMn\nn0xuJliA9i8qZeZMnpn0XZz5jwR8MeDSowpfw/dj9NcPCF5u30auvo9v4hk8+WQ4ZePKw49l8A18\nBMMHOXv1VWQGXXw3/cy7o8JJ5dIlyqDY6Tz4tS+9BDgOdSyTjDsrOpk8+LUvvUSBS7EYfLk4Yxok\nP/EEBT1JxtSZdJDEmSlKiTNTcjngoYe8ORuPgVdfFWfu9hEvzlot2veedGdeSfhjMh48/TTwNXw/\ncm+/DlQq937hN78JALi1+QxWVsIpG1dcZ5kXvn3/ZDdTZ0cXn0ns8QIuTz9NyVhefPHBr3Uf9kke\n3QXIWbd7vLH8fkgHiXj6acrwt7//4NeKM+Lpp6mD9KDBmMl0q7c4I2cvvPDgrJO9HiVtEWfk7Lvf\nffDrDg7oS5x5d3btGn3Wku5MKe/Opvn0Eu/MKxLwxYCnngK+kf4B+sf99qR95SsYqAWMP/ThcArG\nmMuXgb/BDyA1GgLf+ta9X/iVr6CeWkXxwwneRT3lyhX6/u1v3/91WktH3MWrs8GAOuzijOom8GBn\ntRodRSDOyNlkQgHM/bhxg44YEGfkrN1+8GDMq6+SW3FGznZ2KCnX/ZAl/cdcvkynYT1oMEacHXP5\nMgV8DxqMEWdmSMAXAxYWgM6Tz2KMFPC1ey9R1H/5V7iKj+DRD+ZDLB1P1taA7TPfR/+4j7PJX/wV\n/mryQ3jyA1JVzp8HVlcf3BHf2aHOuDTCtIclmwW+8537v+6NN4DRSJwBxwHfg5zJw/4Yd2BBnHlH\nnJkjzsy5csXbYIzrLOnbbQBy1uk8eDDmpZeo/5vksx5NkF5sTHj8mUW8lPkQ9F/91d1f0OkA3/om\n/lL/HWmEp5x59gRuZi8A93J2cIDU66/iryHOAFpqcfmyPOxNWFggDw8KksXZMWtrNLggzrzjdTBG\nnB3jDsZ4cZbJHCeTSDIf+hB99+JsZYUS4yQdrysWXnoJOHeOMpsmHRNnjz+OxG+38YoEfDHhyhXg\nudGPA3/910Cj8f4XfPnLUMMh/hw/Ig/7KVeuAM8Nfxz6C1+gNXXv5c/+DADE2R1cuQI8//z9l1q4\nI5nijLhy5cFB8osvUuKRJJ+/dCdenS0uUrCTdLwOxrz4ImVbXFsLp1yccQdjvDi7dIlen3TW1igJ\nyYOcvfCC7OF2OX+evHl1JnhfGSPOzJCALyZcvgx8Hj8JNRq9G6h8D889h0G2iK+k/y6efjr88nHk\n8mXgOfwkVKt191m+555DM7+B15Y/kvgMnS6XL1MSktdeu/drrl6ls4Q2N8MrF2cuX6Y9Lzs7937N\n1av0kCsUwisXZ65coT2Nrda9X3P1Kr0u6Rk6XbwMxly9CjzzTHhl4s6VKzSLoPXd/6418I1viLM7\ncZ3di9GItsWLM8IdjLmfs3abkimJM2JhAfjAB+7vbHcXuHVLnJkgj8qY8KEPAV/FD6JTcIA/+qPv\n/eNkAvzJn+Bq6eN4/HJeOpVTrlwBvoiPY5gtvN/ZYAB8/vP4y+JP4NmPpqRTOcVLEpKvfx34vu8L\npzw28CBnWouz93L5Mnl5/vm7/30woE6lODvmQYMx9TolIBFnx1y+TNlg7zUYc+sWdSzF2TGXL9Oe\n43sNxrzyCu0gEWfHXL5MbdlodPe/f+tb1E0TZ8e4QfK9BmO+8Q36Ls68I93YmFAqAecuZvEXJ38W\n+Oxn6enu8uUvAzdu4HfbP4uPfjSyIrLjzBkg5yzh6rn/DviDP/je4xn+238DqlX8Vk2c3cnjj9PZ\nQvdKbFqpUApzcXaMu+/lXs6uXydv4uwYN0i+l7MXXgD6fXF2Jw9ydvUqdZ7E2TEPcvb1r9N3cXbM\nlSv0ObrXcjtx9n6uXKHuxauv3v3v4uz9XLlCR3vcvn33v3/960A6DXxYks57RgK+GPGDPwj866Of\no5blt3/7+A+/9VsYL5fwX7qfktGQO1CKnP1f/Z+jtJK/93vHf/yt30J//RQ+P/lxcXYH2SzwkY/Q\nVtG78dWv0vfv//7wysSdlRVannKv3EBf+Qp9F2fHnDlDX+LMO08+SZ+1+zlTiuqvQDzzDLVp92rP\nvvIVGuByB22E4zp3P2eOQ/seBeIHf5C+38/ZhQuyDeJOvDj74AeBYjG8MtmOBHwx4mMfA75w9Axa\nf+fvAb/xG3RI1Ve+AvzxH+PqD/yv6KGAj30s6lLy4mMfA/7jrR/F4Mr3Ab/6q0C1Cjz3HPCFL+DL\nH/7foFUaP/zDUZeSFx/7GJ1Hf7fcQF/6EpDPyzKL9/Kxj9GDazh8/9++9CWgXKagUCCUImdf/vLd\nl/R86UvAxYuU1U4g0mngh3+YnN2NL32JRs0lYcsxxSK1Vfdz9kM/REGfQGxsUFt1N2daA1/8ItVd\nSdhyzMWLNIB1N2fjMf3+R34k7FLx5vJlGsC6m7NulwaXxZkZEvDFCDeY+5O/+3/QLN+zzwJ//+8D\nDz2Ef536J/9/e3cfJFV15nH8+2RUkJAAOru64LhCRHnZLVHGFwKlI1IMhCiQ0NFkEy2U0kRwE2MS\nJVuprJsyxixRSUVTSalRiBUVRCEBcXXAmFTNUqBWXBAoKAhK1KARiUhEh3n2j+d2mOnpGWbsoe+d\n5vep6mrO6YvzcDxz7n1un3Mun/iEdrQrFG1mrJr241jMMWoUfO5zMGIE//3utZx1li6QCtXVxUmq\n2DcJDQ0wbpwukArV1cW6lvzUnbz8BdKFF2rzkUJ1dfEr+dJLrevzF0jjx6cRVbbV1cVmN4XToPbt\ng8ZGtVkxdXUx3bXwBtabb8bDn9VmbeVvYBVubr19O7z8stqsUMsbWM3NrT/7wx9g9261WaH8DaxV\nq9p+1tgYU/rVZl2jS4wKMmRIPIDygXUjYfnyeHDQBRfw3vIGVvz+40yYkHaE2TNqVEyjuP+lc2Lt\nY00NTJ7M7of/h9+u6a02K2Ls2NgO/9e/bl3/yiuxtuqii9KJK8vGj49nBRW22YYN0W5qs7YmToz3\nwjZrbIwlymqzturr472wzVatiotztVlb9fVxE+GJJ1rXr1wZ72qzturrY2fJ1atb169YEe86b7ZV\nXx+7Nec3G8nLt5mSl7bq62ODoMKNqFasiKnYmn3VNUr4KogZTJ8eJ/c9oy6I20lLl/LE5iHs3Quf\n/WzaEWZPVRVcckkMIPsvnBRTYBct4vG1g2hqUpsV07s3TJ4c+XHLu5WLF8e72qytAQPiDu9jj7We\norhoUfzeTpuWWmiZVVMTkxQee6x1/aJF8Q3ylCnpxJVlI0fCqacWb7P+/XVRWcyYMXHTb8mS1vWL\nFsWDw7WRRlsTJsRNv2L9bORIOP30dOLKsilT4qZfsTYbOzaejymtTZ0a7y3bzD3abOJEPaS+q0pK\n+MwsZ2YbzKzZzGo7OG6SmW02s61mdlOL+sFmtsbMtpjZw2amR5uWKJeLdUIPPniwbuHCWCOk+c7F\n5XLwzjvw6KMH6xYujAfM1rbbq49suVxsV56/C+4ebXbGGVqs355cLqbbNTZG+cCB+D09/3w48cR0\nY8uqXC6mwW7YEOX9++GRR2DSJJ3sizGLNmtoiKl1EFMVH388Lp708PC2qqriJtWyZTGNE2Iq8cqV\nMGOGploX07s3XHxxbG797rtRt317TPPP5dKNLasGDIhE+Ze/PLiW+8UX46U2K66mJjYJuv/+gzeX\nn302xja1WdeVOpStBz4DPNveAWZWBdwFTAZGAJ83sxHJx7cBd7j7UGA3cFWJ8Rzxzj47XvPnx6Cy\neXOc7K+5Ju4uSVsTJsTs19tvj0Fl7dqYqnLttVp43p5p02DgQJg3L5K9p5+OZ+bMnp12ZNn1hS/E\ntyzz5kV5yZJ4hMWcOenGlWVXXhkXlz/6UZQXLIgbDWqz9n35y/F+553x/rOfRdKn3832zZ4dy97v\nvjvK+fPnV76SblxZdt11sbn1ffdFed68uMaYNSvduLLsuutife1DD0X5ttugb1+4/PJ048qyOXPi\nOnb58ij/8IdQXa2E70Nx95JfwDNAbTufjQGebFGem7wMeBM4qthxHb1Gjx7t0r5ly9zBfeZM99pa\n9499zP3119OOKtsWLIg2mz3bffhw9+pq9z170o4q2+bPjzb75jfdTznFvabG/W9/SzuqbLv55miz\n73zH/YQToq81NaUdVbZdf3202fe+596/v/u557o3N6cdVbbNnOn+kY+4f//77n36uNfXpx1R9k2f\n7t6rl/utt7offbT7pZemHVG2NTe7jx/v3rev+y23RH+75pq0o8q2AwfcR492P/74g+eCG29MO6ps\n27/f/bTT3AcNcp87N9rs1lvTjipbgHXemVytMwcd8j/SccI3A7inRflLwE+AamBri/oaYH1nfp4S\nvkO74Yb4v9url/vSpWlHk33Nze6zZkWb9enj3tCQdkTZd+CAey4Xbdavn/uaNWlHlH3vv+8+aVK0\nWXW1+/r1aUeUffv2uY8bF202cKD7tm1pR5R9b7/tfuaZ0WZDhri/+mraEWXfrl3uw4ZFm40Y4f6X\nv6QdUfbt2OE+eHC02dlnu+/dm3ZE2bdpU4xj4F5X5/7ee2lHlH3PPx/nS3C/+GL3Dz5IO6Js6WzC\nZ17sIUctmNnTQLEVJv/h7kuTY54BvuHu64r8/RxQ7+6zkvKXgHOA/wIa3f3UpL4GWOHu/9pOHFcD\nVwOcfPLJo3fs2NFh3BJz6vv1i4egSuds2RIL+Pv1SzuSnmPTpnjGUN++aUfSM7jDxo3xoN1jj007\nmp7BPdbxDR2qR3501oED8UiLYcNiRzs5tKamGM+GD4+1fXJo+/fD1q3RZlrv2Dn79sGOHfG7qWUj\nnbN3b0yHPe00tVkhM3vO3Q+548QhE75O/rBnaD/hGwP8p7vXJ+W5yUc/AN4ATnT3psLjOlJbW+vr\n1rX5USIiIiIiIkeEziZ85bgfsxYYmuzIeQxwGbAs+RpyNTHlE+AKYGkZ4hERERERETkilPpYhulm\ntpPYcGW5mT2Z1A80sxUA7t4EzAGeBDYCj7h7ssk2NwJfN7OtwPHAvaXEIyIiIiIiIgd1y5TOctOU\nThEREREROZJlaUqniIiIiIiIpEAJn4iIiIiISIVSwiciIiIiIlKhlPCJiIiIiIhUKCV8IiIiIiIi\nFUoJn4iIiIiISIVSwiciIiIiIlKhlPCJiIiIiIhUKCV8IiIiIiIiFUoJn4iIiIiISIVSwiciIiIi\nIlKhlPCJiIiIiIhUKHP3tGPoMjN7A9iRdhxFVANvph2EVDz1MykH9TM53NTHpBzUz6Qc0upn/+zu\n/3Cog3pkwpdVZrbO3WvTjkMqm/qZlIP6mRxu6mNSDupnUg5Z72ea0ikiIiIiIlKhlPCJiIiIiIhU\nKCV83evnaQcgRwT1MykH9TM53NTHpBzUz6QcMt3PtIZPRERERESkQukbPhERERERkQqlhE9ERERE\nRKRCKeHrBmY2ycw2m9lWM7sp7Xik5zKzGjNbbWYbzWyDmX01qT/OzJ4ysy3J+4Ck3szsx0nfe9HM\nzkr3XyA9iZlVmdkLZvabpDzYzNYk/exhMzsmqe+VlLcmn5+SZtzSc5hZfzNbbGabknFtjMYz6W5m\ndn1yzlxvZr8ys94az6RUZnafme0ys/Ut6ro8fpnZFcnxW8zsijT+LUr4SmRmVcBdwGRgBPB5MxuR\nblTSgzUBN7j7cOA8YHbSn24CGtx9KNCQlCH63dDkdTXw0/KHLD3YV4GNLcq3AXck/Ww3cFVSfxWw\n291PBe5IjhPpjPnASncfBpxB9DeNZ9JtzGwQ8O9Arbv/C1AFXIbGMynd/cCkgroujV9mdhzwXeBc\n4Bzgu/kksZyU8JXuHGCru29z9/eBh4CpKcckPZS7v+buzyd/foe4OBpE9KkHksMeAKYlf54KLPDw\nv0B/M/unMoctPZCZnQRMAe5JygaMBxYnhxT2s3z/WwxclBwv0i4z+zhwPnAvgLu/7+5vo/FMut9R\nwLFmdhTQB3gNjWdSInd/FniroLqr41c98JS7v+Xuu4GnaJtEHnZK+Eo3CHilRXlnUidSkmSayZnA\nGuAEd38NIikE/jE5TP1PPqw7gW8BzUn5eOBtd29Kyi370t/7WfL5nuR4kY4MAd4AfpFMHb7HzD6K\nxjPpRu7+J2Ae8DKR6O0BnkPjmRweXR2/MjGuKeErXbG7QnrWhZTEzPoCjwJfc/e/dnRokTr1P+mQ\nmX0a2OXuz7WsLnKod+IzkfYcBZwF/NTdzwTe5eD0p2LUz6TLkulxU4HBwEDgo8T0ukIaz+Rwaq9f\nZaK/KeEr3U6gpkX5JODVlGKRCmBmRxPJ3oPuviSp/nN+alPyviupV/+TD2MscImZ/ZGYhj6e+Mav\nfzIlClr3pb/3s+TzfrSd5iJSaCew093XJOXFRAKo8Uy60wRgu7u/4e4fAEuAT6LxTA6Pro5fmRjX\nlPCVbi0wNNkN6hhiofCylGOSHipZR3AvsNHdb2/x0TIgv7PTFcDSFvWXJ7tDnQfsyU81EGmPu891\n95Pc/RRizFrl7v8GrAZmJIcV9rN8/5uRHK874tIhd38deMXMTk+qLgJeQuOZdK+XgfPMrE9yDs33\nM41ncjh0dfx6EphoZgOSb6MnJnVlZerjpTOzTxF3x6uA+9z9lpRDkh7KzMYBvwP+j4Nrq75NrON7\nBDiZOLnl3P2t5OT2E2IB8D5gpruvK3vg0mOZWR3wDXf/tJkNIb7xOw54Afiiu+83s97AQmJN6VvA\nZe6+La2Ypecws1HExkDHANuAmcTNZo1n0m3M7GbgUmKn6xeAWcQ6KY1n8qGZ2a+AOqAa+DOx2+bj\ndHH8MrMriWs5gFvc/Rfl/HeAEj4REREREZGKpSmdIiIiIiIiFUoJn4iIiIiISIVSwiciIiIiIlKh\nlPCJiIiIiIhUKCV8IiIiIiIiFUoJn4iIiIiISIVSwiciIiIiIlKh/h/ZG1gWz1mt9wAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(actual_output_scaled, '-b', label='actual output')\n",
    "# we just visualize prediction of first 200 points because predicted and actual output overlaps completely\n",
    "plt.plot(predicted_output_scaled[:200], '-r', label='predicted output')\n",
    "plt.title(\"Actual and predicted output for sine wave\")\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Till now, we only checked our model whether it has learned the trained data. We can see in above plot that model is able to reproduce the output similar as the original one. Now, we will use LSTM to predict the future output .\n",
    "\n",
    "Now we use moving forward window method. \n",
    "\n",
    "Use a moving forward window of size 50, which means we will use the first 50 data points as out input X to predict y1 — 51st data point. Next, we will use the window between 1 to 51 data points as input X to predict y2 i.e., the 52nd data point and so on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM for Future Predictions (Using Window Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g34VB11twFJd1W5eBmdvlwUv10fUGeMp1S2zXOuw1k3XbXNhr0+gOuP3YmPaT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\nYQyHwWAwGJri/wPmgxeOhB8saAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print test_data_x[0]\n",
    "def movingTestWindowPrediction(number_of_future_predictions, test_data_x):\n",
    "    \n",
    "    preds_moving = []\n",
    "    moving_test_window = [test_data_x[0,:].tolist()] # Take time predictions \n",
    "    \n",
    "    moving_test_window = np.array(moving_test_window)\n",
    "\n",
    "    for i in range(number_of_future_predictions):\n",
    "        \n",
    "        predicted_one_step = model.predict(moving_test_window)\n",
    "        \n",
    "#         print \"Predicted one step \", predicted_one_step\n",
    "#         print \"Appended predicted one step \", predicted_one_step[0][0]\n",
    "        # Append each prediction in list\n",
    "        preds_moving.append(predicted_one_step[0][0])\n",
    "        \n",
    "        predicted_one_step = predicted_one_step.reshape(1,1,1)\n",
    "\n",
    "        # way of concatenating 3d array\n",
    "        # we always maintain the size of window i.e. 50. \n",
    "        # each iteration we remove first element from window and add predicted output at the last of window\n",
    "        moving_test_window = np.concatenate((moving_test_window[:,1:,:], predicted_one_step), axis=1)\n",
    "\n",
    "    preds_moving = np.array(preds_moving)\n",
    "    preds_moving = preds_moving.reshape(preds_moving.shape[0],1)\n",
    "    preds_moving = scaler.inverse_transform(preds_moving)\n",
    "    \n",
    "    \n",
    "    \n",
    "    return preds_moving\n",
    "    \n",
    "preds_moving = movingTestWindowPrediction(500, test_data_x)\n",
    "    \n",
    "    \n",
    "plt.plot(actual_output_scaled)\n",
    "plt.plot(preds_moving)\n",
    "plt.title(\"Sine Wave prediction using LSTM for 500 steps\")\n",
    "plt.xlabel(\"Number of steps\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}