Bitcoin_Price_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bitcoin Price Prediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/paulgureghian/Google_Colab_Notebooks/blob/master/Bitcoin_Price_Prediction.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "YRMRRLse647F",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Click \"File\" > \"Save a copy in Drive\", then press \"Runtime\" > \"Run all\", in the copy.**"
      ]
    },
    {
      "metadata": {
        "id": "4QOK6-txWk-S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Created by Paul A. Gureghian on 9/26/2018.** \n",
        "\n",
        "**This Python script uses Keras to predict Bitcoin prices.**\n",
        "\n",
        "**I picked a Recurrent Neural Network and a Bitcoin dataset.**"
      ]
    },
    {
      "metadata": {
        "id": "68Qtb-rdj_Zt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### install pydrive \n",
        "!pip install -U -q Pydrive "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KVkLAyG9Ub8K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### install mlflow\n",
        "!pip install -U -q mlflow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eZU11Sf2X01s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e38fcd29-21be-4009-e36a-d910f9dfa70b"
      },
      "cell_type": "code",
      "source": [
        "### import packages \n",
        "import mlflow \n",
        "import mlflow.keras\n",
        "import mlflow.tracking\n",
        "from mlflow import pyfunc\n",
        "from mlflow.models import Model\n",
        "\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "from matplotlib import pyplot as plt \n",
        "\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from google.colab import auth\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive  \n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ELIzMbqSYEye",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### **Click on the link, login to your google account, copy the verification code, paste the code into the box, hit enter** ###  \n",
        "\n",
        "### instantiate the pydrive client \n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth() \n",
        "gauth.credentials = GoogleCredentials.get_application_default() \n",
        "drive = GoogleDrive(gauth)   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZAbBsqcY4d9Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cde96da7-1529-4086-e185-e7be37c6409e"
      },
      "cell_type": "code",
      "source": [
        "### get a list of the csv files and their IDs used in this demo\n",
        "file_list = drive.ListFile({'q' : \"'1GC3wKpqR1QP1B25JStHzI996bEgMgIJL' in parents and trashed=false\"}).GetList()\n",
        "for file1 in file_list:\n",
        "    \n",
        "    print('title: %s, id: %s' % (file1['title'], file1['id']))       "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "title: bitstamp.csv, id: 1YYtQOjmVxlTYG5FfqZBsY8CFExk3BMB6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8ODhIjCM8o7D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8bf1cf19-98b9-46a1-c8f9-64539d7827bd"
      },
      "cell_type": "code",
      "source": [
        "### pull the files into Google Colab, save the files in the notebook, and set the file names\n",
        "bitstamp_downloaded = drive.CreateFile({'id' : '1YYtQOjmVxlTYG5FfqZBsY8CFExk3BMB6'})\n",
        "bitstamp_downloaded.GetContentFile('bitstamp.csv')\n",
        "\n",
        "print(\"File successfully imported.\") "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File successfully imported.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "27gI5R2C4qlD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "fd0b5c97-d72c-4441-c0bc-c35b455673df"
      },
      "cell_type": "code",
      "source": [
        "### read in the dataset to a dataframe \n",
        "pd.set_option('display.max_columns', 8)\n",
        "pd.set_option('display.width', 1000)\n",
        "df = pd.read_csv('bitstamp.csv')\n",
        "\n",
        "print(df.head())\n",
        "print('')\n",
        "print(df.shape) \n",
        "print('') "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  Weighted_Price\n",
            "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0            4.39\n",
            "1  1325317980  4.39  4.39  4.39   4.39      0.455581                2.0            4.39\n",
            "2  1325318040  4.39  4.39  4.39   4.39      0.455581                2.0            4.39\n",
            "3  1325318100  4.39  4.39  4.39   4.39      0.455581                2.0            4.39\n",
            "4  1325318160  4.39  4.39  4.39   4.39      0.455581                2.0            4.39\n",
            "\n",
            "(3405857, 8)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xttY1Jjp421I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "dfb46393-a9bb-4a30-8878-d65ecff92365"
      },
      "cell_type": "code",
      "source": [
        "### encode the date \n",
        "df['date'] = pd.to_datetime(df['Timestamp'], unit ='s').dt.date\n",
        "group = df.groupby('date')\n",
        "Real_Price = group['Weighted_Price'].mean()\n",
        "\n",
        "print(Real_Price.head())\n",
        "print('') \n",
        "print(Real_Price.shape)  \n",
        "print('')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "date\n",
            "2011-12-31    4.472579\n",
            "2012-01-01    4.680778\n",
            "2012-01-02    5.000000\n",
            "2012-01-03    5.145917\n",
            "2012-01-04    5.195443\n",
            "Name: Weighted_Price, dtype: float64\n",
            "\n",
            "(2368,)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_yUIW35p49jW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "eeddca41-88df-4883-adc5-ba817794d299"
      },
      "cell_type": "code",
      "source": [
        "### split dataset into train and test sets \n",
        "prediction_days = 30 \n",
        "df_train = Real_Price[:len(Real_Price) - prediction_days]\n",
        "df_test = Real_Price[len(Real_Price) - prediction_days:]\n",
        "\n",
        "print(df_train.head())\n",
        "print('')\n",
        "print(df_train.shape)\n",
        "print('')\n",
        "print(df_test.head())\n",
        "print('')\n",
        "print(df_test.shape)\n",
        "print('')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "date\n",
            "2011-12-31    4.472579\n",
            "2012-01-01    4.680778\n",
            "2012-01-02    5.000000\n",
            "2012-01-03    5.145917\n",
            "2012-01-04    5.195443\n",
            "Name: Weighted_Price, dtype: float64\n",
            "\n",
            "(2338,)\n",
            "\n",
            "date\n",
            "2018-05-29    7292.843307\n",
            "2018-05-30    7432.153648\n",
            "2018-05-31    7499.828363\n",
            "2018-06-01    7480.738075\n",
            "2018-06-02    7596.898291\n",
            "Name: Weighted_Price, dtype: float64\n",
            "\n",
            "(30,)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AdKnQQxg5Cxo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "cf52ee22-2dba-4e10-d985-cb0c8e9d2870"
      },
      "cell_type": "code",
      "source": [
        "### preprocess the data by reshaping it \n",
        "training_set = df_train.values \n",
        "training_set = np.reshape(training_set, (len(training_set),1))\n",
        "\n",
        "print(\"Training set after reshaping:\")\n",
        "print('')\n",
        "print(training_set)\n",
        "print('')\n",
        "print(training_set.shape)\n",
        "print('')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training set after reshaping:\n",
            "\n",
            "[[4.47257894e+00]\n",
            " [4.68077778e+00]\n",
            " [5.00000000e+00]\n",
            " ...\n",
            " [7.48973292e+03]\n",
            " [7.31683633e+03]\n",
            " [7.25498905e+03]]\n",
            "\n",
            "(2338, 1)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RcaaATWc5IYg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "0add9eb3-abec-4ae5-bcc8-0f82f470f7db"
      },
      "cell_type": "code",
      "source": [
        "### preprocess the data by scaling it \n",
        "sc = MinMaxScaler() \n",
        "training_set = sc.fit_transform(training_set) \n",
        "X_train = training_set[0 : len(training_set) -1]\n",
        "y_train = training_set[1 : len(training_set)]\n",
        "X_train = np.reshape(X_train, (len(X_train),1, 1)) \n",
        "                       \n",
        "print(\"Scaled training set:\")\n",
        "print('')\n",
        "print(training_set)\n",
        "print('')\n",
        "print(\"Define X_train\")\n",
        "print('')\n",
        "print(X_train)\n",
        "print('')\n",
        "print(X_train.shape)\n",
        "print(\"Define y_train:\")\n",
        "print('')\n",
        "print(y_train)\n",
        "print('')\n",
        "print(y_train.shape)\n",
        "print('')\n",
        "print(\"X_train reshaped:\")\n",
        "print('')\n",
        "print(X_train) \n",
        "print('') \n",
        "print(X_train.shape)\n",
        "print('')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scaled training set:\n",
            "\n",
            "[[7.49021754e-06]\n",
            " [1.83441728e-05]\n",
            " [3.49860700e-05]\n",
            " ...\n",
            " [3.90233875e-01]\n",
            " [3.81220319e-01]\n",
            " [3.77996057e-01]]\n",
            "\n",
            "Define X_train\n",
            "\n",
            "[[[7.49021754e-06]]\n",
            "\n",
            " [[1.83441728e-05]]\n",
            "\n",
            " [[3.49860700e-05]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[3.90047749e-01]]\n",
            "\n",
            " [[3.90233875e-01]]\n",
            "\n",
            " [[3.81220319e-01]]]\n",
            "\n",
            "(2337, 1, 1)\n",
            "Define y_train:\n",
            "\n",
            "[[1.83441728e-05]\n",
            " [3.49860700e-05]\n",
            " [4.25930911e-05]\n",
            " ...\n",
            " [3.90233875e-01]\n",
            " [3.81220319e-01]\n",
            " [3.77996057e-01]]\n",
            "\n",
            "(2337, 1)\n",
            "\n",
            "X_train reshaped:\n",
            "\n",
            "[[[7.49021754e-06]]\n",
            "\n",
            " [[1.83441728e-05]]\n",
            "\n",
            " [[3.49860700e-05]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[3.90047749e-01]]\n",
            "\n",
            " [[3.90233875e-01]]\n",
            "\n",
            " [[3.81220319e-01]]]\n",
            "\n",
            "(2337, 1, 1)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dwh8yAao5UNE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5117
        },
        "outputId": "de2c83a7-bdbb-4fc6-8f65-9893306c9ae5"
      },
      "cell_type": "code",
      "source": [
        "### define the RNN model \n",
        "### instantiate the RNN model object \n",
        "regr = Sequential() \n",
        "\n",
        "### add the input and LSTM layers \n",
        "regr.add(LSTM(units =4, activation ='sigmoid', input_shape =(None, 1)))    \n",
        "\n",
        "### add the output layer\n",
        "regr.add(Dense(units =1))\n",
        "\n",
        "### compile the RNN \n",
        "regr.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy']) \n",
        "\n",
        "### fit the model on the training set \n",
        "regr.fit(X_train, y_train, batch_size =5, epochs =150)  \n",
        "\n",
        "### log the keras model with mlflow \n",
        "with mlflow.start_run() as run:\n",
        "  mlflow.keras.log_model(regr, \"Bitcoin Price Prediction\") "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "2337/2337 [==============================] - 2s 824us/step - loss: 0.0202 - acc: 4.2790e-04\n",
            "Epoch 2/150\n",
            "2337/2337 [==============================] - 1s 409us/step - loss: 0.0159 - acc: 4.2790e-04\n",
            "Epoch 3/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 0.0119 - acc: 4.2790e-04\n",
            "Epoch 4/150\n",
            "2337/2337 [==============================] - 1s 393us/step - loss: 0.0081 - acc: 4.2790e-04\n",
            "Epoch 5/150\n",
            "2337/2337 [==============================] - 1s 396us/step - loss: 0.0049 - acc: 8.5580e-04\n",
            "Epoch 6/150\n",
            "2337/2337 [==============================] - 1s 395us/step - loss: 0.0024 - acc: 8.5580e-04\n",
            "Epoch 7/150\n",
            "2337/2337 [==============================] - 1s 393us/step - loss: 9.4093e-04 - acc: 8.5580e-04\n",
            "Epoch 8/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 2.9566e-04 - acc: 8.5580e-04\n",
            "Epoch 9/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 1.1833e-04 - acc: 8.5580e-04\n",
            "Epoch 10/150\n",
            "2337/2337 [==============================] - 1s 396us/step - loss: 8.8657e-05 - acc: 8.5580e-04\n",
            "Epoch 11/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.5014e-05 - acc: 8.5580e-04\n",
            "Epoch 12/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.5652e-05 - acc: 8.5580e-04\n",
            "Epoch 13/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.5781e-05 - acc: 8.5580e-04\n",
            "Epoch 14/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.7899e-05 - acc: 8.5580e-04\n",
            "Epoch 15/150\n",
            "2337/2337 [==============================] - 1s 392us/step - loss: 8.7469e-05 - acc: 8.5580e-04\n",
            "Epoch 16/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.7579e-05 - acc: 8.5580e-04\n",
            "Epoch 17/150\n",
            "2337/2337 [==============================] - 1s 395us/step - loss: 8.8574e-05 - acc: 8.5580e-04\n",
            "Epoch 18/150\n",
            "2337/2337 [==============================] - 1s 392us/step - loss: 8.6611e-05 - acc: 8.5580e-04\n",
            "Epoch 19/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.8542e-05 - acc: 8.5580e-04\n",
            "Epoch 20/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.9752e-05 - acc: 8.5580e-04\n",
            "Epoch 21/150\n",
            "2337/2337 [==============================] - 1s 405us/step - loss: 8.8825e-05 - acc: 8.5580e-04\n",
            "Epoch 22/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 9.0474e-05 - acc: 8.5580e-04\n",
            "Epoch 23/150\n",
            "2337/2337 [==============================] - 1s 409us/step - loss: 8.8336e-05 - acc: 8.5580e-04\n",
            "Epoch 24/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.9085e-05 - acc: 8.5580e-04\n",
            "Epoch 25/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.7804e-05 - acc: 8.5580e-04\n",
            "Epoch 26/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.6714e-05 - acc: 8.5580e-04\n",
            "Epoch 27/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.9013e-05 - acc: 8.5580e-04\n",
            "Epoch 28/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 9.1036e-05 - acc: 8.5580e-04\n",
            "Epoch 29/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.8093e-05 - acc: 8.5580e-04\n",
            "Epoch 30/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.6380e-05 - acc: 8.5580e-04\n",
            "Epoch 31/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.6819e-05 - acc: 8.5580e-04\n",
            "Epoch 32/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.8165e-05 - acc: 8.5580e-04\n",
            "Epoch 33/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.7792e-05 - acc: 8.5580e-04\n",
            "Epoch 34/150\n",
            "2337/2337 [==============================] - 1s 396us/step - loss: 8.9833e-05 - acc: 8.5580e-04\n",
            "Epoch 35/150\n",
            "2337/2337 [==============================] - 1s 396us/step - loss: 8.8382e-05 - acc: 8.5580e-04\n",
            "Epoch 36/150\n",
            "2337/2337 [==============================] - 1s 394us/step - loss: 8.5515e-05 - acc: 8.5580e-04\n",
            "Epoch 37/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.6772e-05 - acc: 8.5580e-04\n",
            "Epoch 38/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.6301e-05 - acc: 8.5580e-04\n",
            "Epoch 39/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.6796e-05 - acc: 8.5580e-04\n",
            "Epoch 40/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.7965e-05 - acc: 8.5580e-04\n",
            "Epoch 41/150\n",
            "2337/2337 [==============================] - 1s 420us/step - loss: 8.5767e-05 - acc: 8.5580e-04\n",
            "Epoch 42/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.8079e-05 - acc: 8.5580e-04\n",
            "Epoch 43/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.6418e-05 - acc: 8.5580e-04\n",
            "Epoch 44/150\n",
            "2337/2337 [==============================] - 1s 405us/step - loss: 8.9605e-05 - acc: 8.5580e-04\n",
            "Epoch 45/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.9046e-05 - acc: 8.5580e-04\n",
            "Epoch 46/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.6870e-05 - acc: 8.5580e-04\n",
            "Epoch 47/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.5412e-05 - acc: 8.5580e-04\n",
            "Epoch 48/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.9042e-05 - acc: 8.5580e-04\n",
            "Epoch 49/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.5442e-05 - acc: 8.5580e-04\n",
            "Epoch 50/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.7124e-05 - acc: 8.5580e-04\n",
            "Epoch 51/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.5334e-05 - acc: 8.5580e-04\n",
            "Epoch 52/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.6388e-05 - acc: 8.5580e-04\n",
            "Epoch 53/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.9299e-05 - acc: 8.5580e-04\n",
            "Epoch 54/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.7753e-05 - acc: 8.5580e-04\n",
            "Epoch 55/150\n",
            "2337/2337 [==============================] - 1s 425us/step - loss: 8.9207e-05 - acc: 8.5580e-04\n",
            "Epoch 56/150\n",
            "2337/2337 [==============================] - 1s 405us/step - loss: 8.6677e-05 - acc: 8.5580e-04\n",
            "Epoch 57/150\n",
            "2337/2337 [==============================] - 1s 431us/step - loss: 8.6572e-05 - acc: 8.5580e-04\n",
            "Epoch 58/150\n",
            "2337/2337 [==============================] - 1s 423us/step - loss: 8.7334e-05 - acc: 8.5580e-04\n",
            "Epoch 59/150\n",
            "2337/2337 [==============================] - 1s 414us/step - loss: 8.6262e-05 - acc: 8.5580e-04\n",
            "Epoch 60/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.9294e-05 - acc: 8.5580e-04\n",
            "Epoch 61/150\n",
            "2337/2337 [==============================] - 1s 417us/step - loss: 8.6786e-05 - acc: 8.5580e-04\n",
            "Epoch 62/150\n",
            "2337/2337 [==============================] - 1s 415us/step - loss: 8.7632e-05 - acc: 8.5580e-04\n",
            "Epoch 63/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.8656e-05 - acc: 8.5580e-04\n",
            "Epoch 64/150\n",
            "2337/2337 [==============================] - 1s 421us/step - loss: 8.8044e-05 - acc: 8.5580e-04\n",
            "Epoch 65/150\n",
            "2337/2337 [==============================] - 1s 414us/step - loss: 8.7420e-05 - acc: 8.5580e-04\n",
            "Epoch 66/150\n",
            "2337/2337 [==============================] - 1s 420us/step - loss: 8.6448e-05 - acc: 8.5580e-04\n",
            "Epoch 67/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.7715e-05 - acc: 8.5580e-04\n",
            "Epoch 68/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.8040e-05 - acc: 8.5580e-04\n",
            "Epoch 69/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.7747e-05 - acc: 8.5580e-04\n",
            "Epoch 70/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.7888e-05 - acc: 8.5580e-04\n",
            "Epoch 71/150\n",
            "2337/2337 [==============================] - 1s 424us/step - loss: 8.5805e-05 - acc: 8.5580e-04\n",
            "Epoch 72/150\n",
            "2337/2337 [==============================] - 1s 409us/step - loss: 8.8347e-05 - acc: 8.5580e-04\n",
            "Epoch 73/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.6307e-05 - acc: 8.5580e-04\n",
            "Epoch 74/150\n",
            "2337/2337 [==============================] - 1s 411us/step - loss: 8.6330e-05 - acc: 8.5580e-04\n",
            "Epoch 75/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.6474e-05 - acc: 8.5580e-04\n",
            "Epoch 76/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.6501e-05 - acc: 8.5580e-04\n",
            "Epoch 77/150\n",
            "2337/2337 [==============================] - 1s 423us/step - loss: 8.7167e-05 - acc: 8.5580e-04\n",
            "Epoch 78/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.6902e-05 - acc: 8.5580e-04\n",
            "Epoch 79/150\n",
            "2337/2337 [==============================] - 1s 421us/step - loss: 8.8191e-05 - acc: 8.5580e-04\n",
            "Epoch 80/150\n",
            "2337/2337 [==============================] - 1s 409us/step - loss: 8.4673e-05 - acc: 8.5580e-04\n",
            "Epoch 81/150\n",
            "2337/2337 [==============================] - 1s 419us/step - loss: 8.8933e-05 - acc: 8.5580e-04\n",
            "Epoch 82/150\n",
            "2337/2337 [==============================] - 1s 416us/step - loss: 8.8345e-05 - acc: 8.5580e-04\n",
            "Epoch 83/150\n",
            "2337/2337 [==============================] - 1s 411us/step - loss: 8.6719e-05 - acc: 8.5580e-04\n",
            "Epoch 84/150\n",
            "2337/2337 [==============================] - 1s 423us/step - loss: 8.7034e-05 - acc: 8.5580e-04\n",
            "Epoch 85/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.5503e-05 - acc: 8.5580e-04\n",
            "Epoch 86/150\n",
            "2337/2337 [==============================] - 1s 411us/step - loss: 8.9797e-05 - acc: 8.5580e-04\n",
            "Epoch 87/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.7938e-05 - acc: 8.5580e-04\n",
            "Epoch 88/150\n",
            "2337/2337 [==============================] - 1s 398us/step - loss: 8.8359e-05 - acc: 8.5580e-04\n",
            "Epoch 89/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.9402e-05 - acc: 8.5580e-04\n",
            "Epoch 90/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.7668e-05 - acc: 8.5580e-04\n",
            "Epoch 91/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.6863e-05 - acc: 8.5580e-04\n",
            "Epoch 92/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.7532e-05 - acc: 8.5580e-04\n",
            "Epoch 93/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.8494e-05 - acc: 8.5580e-04\n",
            "Epoch 94/150\n",
            "2337/2337 [==============================] - 1s 413us/step - loss: 8.4776e-05 - acc: 8.5580e-04\n",
            "Epoch 95/150\n",
            "2337/2337 [==============================] - 1s 400us/step - loss: 8.8335e-05 - acc: 8.5580e-04\n",
            "Epoch 96/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.8195e-05 - acc: 8.5580e-04\n",
            "Epoch 97/150\n",
            "2337/2337 [==============================] - 1s 400us/step - loss: 8.6128e-05 - acc: 8.5580e-04\n",
            "Epoch 98/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.8683e-05 - acc: 8.5580e-04\n",
            "Epoch 99/150\n",
            "2337/2337 [==============================] - 1s 405us/step - loss: 8.7448e-05 - acc: 8.5580e-04\n",
            "Epoch 100/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.8339e-05 - acc: 8.5580e-04\n",
            "Epoch 101/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.6902e-05 - acc: 8.5580e-04\n",
            "Epoch 102/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.5589e-05 - acc: 8.5580e-04\n",
            "Epoch 103/150\n",
            "2337/2337 [==============================] - 1s 398us/step - loss: 8.7641e-05 - acc: 8.5580e-04\n",
            "Epoch 104/150\n",
            "2337/2337 [==============================] - 1s 400us/step - loss: 8.8586e-05 - acc: 8.5580e-04\n",
            "Epoch 105/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.9604e-05 - acc: 8.5580e-04\n",
            "Epoch 106/150\n",
            "2337/2337 [==============================] - 1s 422us/step - loss: 8.6891e-05 - acc: 8.5580e-04\n",
            "Epoch 107/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.5685e-05 - acc: 8.5580e-04\n",
            "Epoch 108/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.8766e-05 - acc: 8.5580e-04\n",
            "Epoch 109/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.6377e-05 - acc: 8.5580e-04\n",
            "Epoch 110/150\n",
            "2337/2337 [==============================] - 1s 420us/step - loss: 8.6147e-05 - acc: 8.5580e-04\n",
            "Epoch 111/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.6671e-05 - acc: 8.5580e-04\n",
            "Epoch 112/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.8473e-05 - acc: 8.5580e-04\n",
            "Epoch 113/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.6268e-05 - acc: 8.5580e-04\n",
            "Epoch 114/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.6964e-05 - acc: 8.5580e-04\n",
            "Epoch 115/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.7592e-05 - acc: 8.5580e-04\n",
            "Epoch 116/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.7730e-05 - acc: 8.5580e-04\n",
            "Epoch 117/150\n",
            "2337/2337 [==============================] - 1s 398us/step - loss: 8.6231e-05 - acc: 8.5580e-04\n",
            "Epoch 118/150\n",
            "2337/2337 [==============================] - 1s 390us/step - loss: 9.0594e-05 - acc: 8.5580e-04\n",
            "Epoch 119/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.6115e-05 - acc: 8.5580e-04\n",
            "Epoch 120/150\n",
            "2337/2337 [==============================] - 1s 395us/step - loss: 8.6857e-05 - acc: 8.5580e-04\n",
            "Epoch 121/150\n",
            "2337/2337 [==============================] - 1s 412us/step - loss: 8.7862e-05 - acc: 8.5580e-04\n",
            "Epoch 122/150\n",
            "2337/2337 [==============================] - 1s 411us/step - loss: 8.9197e-05 - acc: 8.5580e-04\n",
            "Epoch 123/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.8984e-05 - acc: 8.5580e-04\n",
            "Epoch 124/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.9608e-05 - acc: 8.5580e-04\n",
            "Epoch 125/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.8415e-05 - acc: 8.5580e-04\n",
            "Epoch 126/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.8572e-05 - acc: 8.5580e-04\n",
            "Epoch 127/150\n",
            "2337/2337 [==============================] - 1s 394us/step - loss: 8.6198e-05 - acc: 8.5580e-04\n",
            "Epoch 128/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.7365e-05 - acc: 8.5580e-04\n",
            "Epoch 129/150\n",
            "2337/2337 [==============================] - 1s 397us/step - loss: 8.6296e-05 - acc: 8.5580e-04\n",
            "Epoch 130/150\n",
            "2337/2337 [==============================] - 1s 399us/step - loss: 8.7940e-05 - acc: 8.5580e-04\n",
            "Epoch 131/150\n",
            "2337/2337 [==============================] - 1s 414us/step - loss: 8.6411e-05 - acc: 8.5580e-04\n",
            "Epoch 132/150\n",
            "2337/2337 [==============================] - 1s 407us/step - loss: 8.5979e-05 - acc: 8.5580e-04\n",
            "Epoch 133/150\n",
            "2337/2337 [==============================] - 1s 401us/step - loss: 8.6969e-05 - acc: 8.5580e-04\n",
            "Epoch 134/150\n",
            "2337/2337 [==============================] - 1s 392us/step - loss: 8.7839e-05 - acc: 8.5580e-04\n",
            "Epoch 135/150\n",
            "2337/2337 [==============================] - 1s 388us/step - loss: 8.7795e-05 - acc: 8.5580e-04\n",
            "Epoch 136/150\n",
            "2337/2337 [==============================] - 1s 390us/step - loss: 8.5904e-05 - acc: 8.5580e-04\n",
            "Epoch 137/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.8117e-05 - acc: 8.5580e-04\n",
            "Epoch 138/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.7602e-05 - acc: 8.5580e-04\n",
            "Epoch 139/150\n",
            "2337/2337 [==============================] - 1s 402us/step - loss: 8.6314e-05 - acc: 8.5580e-04\n",
            "Epoch 140/150\n",
            "2337/2337 [==============================] - 1s 405us/step - loss: 8.5404e-05 - acc: 8.5580e-04\n",
            "Epoch 141/150\n",
            "2337/2337 [==============================] - 1s 421us/step - loss: 8.4652e-05 - acc: 8.5580e-04\n",
            "Epoch 142/150\n",
            "2337/2337 [==============================] - 1s 424us/step - loss: 8.5482e-05 - acc: 8.5580e-04\n",
            "Epoch 143/150\n",
            "2337/2337 [==============================] - 1s 424us/step - loss: 8.8098e-05 - acc: 8.5580e-04\n",
            "Epoch 144/150\n",
            "2337/2337 [==============================] - 1s 410us/step - loss: 8.7448e-05 - acc: 8.5580e-04\n",
            "Epoch 145/150\n",
            "2337/2337 [==============================] - 1s 408us/step - loss: 8.6355e-05 - acc: 8.5580e-04\n",
            "Epoch 146/150\n",
            "2337/2337 [==============================] - 1s 403us/step - loss: 8.6223e-05 - acc: 8.5580e-04\n",
            "Epoch 147/150\n",
            "2337/2337 [==============================] - 1s 400us/step - loss: 8.6436e-05 - acc: 8.5580e-04\n",
            "Epoch 148/150\n",
            "2337/2337 [==============================] - 1s 406us/step - loss: 8.7808e-05 - acc: 8.5580e-04\n",
            "Epoch 149/150\n",
            "2337/2337 [==============================] - 1s 404us/step - loss: 8.5956e-05 - acc: 8.5580e-04\n",
            "Epoch 150/150\n",
            "2337/2337 [==============================] - 1s 394us/step - loss: 8.6439e-05 - acc: 8.5580e-04\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iNxy8aQB6YVQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4301
        },
        "outputId": "69bd0abb-5c1d-47cd-d3b7-1e6ad3367bae"
      },
      "cell_type": "code",
      "source": [
        "### create predictions on the test set \n",
        "test_set = df_test.values\n",
        "inputs = np.reshape(test_set, (len(test_set), 1)) \n",
        "inputs = sc.transform(inputs)\n",
        "inputs = np.reshape(inputs, (len(inputs), 1, 1))\n",
        "predicted_BTC_price = regr.predict(inputs) \n",
        "predicted_BTC_price = sc.inverse_transform(predicted_BTC_price)\n",
        "\n",
        "print(\"Test set after reshaping:\")\n",
        "print('')\n",
        "print(inputs)\n",
        "print('')\n",
        "print(inputs.shape)\n",
        "print('')\n",
        "print(\"Scaled inputs:\")\n",
        "print('')\n",
        "print(inputs) \n",
        "print('')\n",
        "print(\"Reshaped inputs:\")\n",
        "print('')\n",
        "print(inputs)\n",
        "print('')\n",
        "print(inputs.shape)\n",
        "print('')\n",
        "print(\"Predicted BTC price: \", predicted_BTC_price)\n",
        "print('')\n",
        "print(\"Scaled predicted BTC price: \", predicted_BTC_price)\n",
        "print('')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set after reshaping:\n",
            "\n",
            "[[[0.3799695 ]]\n",
            "\n",
            " [[0.38723211]]\n",
            "\n",
            " [[0.39076018]]\n",
            "\n",
            " [[0.38976495]]\n",
            "\n",
            " [[0.39582069]]\n",
            "\n",
            " [[0.4006918 ]]\n",
            "\n",
            " [[0.39468285]]\n",
            "\n",
            " [[0.3903278 ]]\n",
            "\n",
            " [[0.396307  ]]\n",
            "\n",
            " [[0.40079757]]\n",
            "\n",
            " [[0.39751313]]\n",
            "\n",
            " [[0.39754439]]\n",
            "\n",
            " [[0.3716342 ]]\n",
            "\n",
            " [[0.35218174]]\n",
            "\n",
            " [[0.35186076]]\n",
            "\n",
            " [[0.33562138]]\n",
            "\n",
            " [[0.33868236]]\n",
            "\n",
            " [[0.34127403]]\n",
            "\n",
            " [[0.33667128]]\n",
            "\n",
            " [[0.33909474]]\n",
            "\n",
            " [[0.33982241]]\n",
            "\n",
            " [[0.34999305]]\n",
            "\n",
            " [[0.34805214]]\n",
            "\n",
            " [[0.35095706]]\n",
            "\n",
            " [[0.33131082]]\n",
            "\n",
            " [[0.3196336 ]]\n",
            "\n",
            " [[0.31383072]]\n",
            "\n",
            " [[0.32313992]]\n",
            "\n",
            " [[0.32307741]]\n",
            "\n",
            " [[0.31620838]]]\n",
            "\n",
            "(30, 1, 1)\n",
            "\n",
            "Scaled inputs:\n",
            "\n",
            "[[[0.3799695 ]]\n",
            "\n",
            " [[0.38723211]]\n",
            "\n",
            " [[0.39076018]]\n",
            "\n",
            " [[0.38976495]]\n",
            "\n",
            " [[0.39582069]]\n",
            "\n",
            " [[0.4006918 ]]\n",
            "\n",
            " [[0.39468285]]\n",
            "\n",
            " [[0.3903278 ]]\n",
            "\n",
            " [[0.396307  ]]\n",
            "\n",
            " [[0.40079757]]\n",
            "\n",
            " [[0.39751313]]\n",
            "\n",
            " [[0.39754439]]\n",
            "\n",
            " [[0.3716342 ]]\n",
            "\n",
            " [[0.35218174]]\n",
            "\n",
            " [[0.35186076]]\n",
            "\n",
            " [[0.33562138]]\n",
            "\n",
            " [[0.33868236]]\n",
            "\n",
            " [[0.34127403]]\n",
            "\n",
            " [[0.33667128]]\n",
            "\n",
            " [[0.33909474]]\n",
            "\n",
            " [[0.33982241]]\n",
            "\n",
            " [[0.34999305]]\n",
            "\n",
            " [[0.34805214]]\n",
            "\n",
            " [[0.35095706]]\n",
            "\n",
            " [[0.33131082]]\n",
            "\n",
            " [[0.3196336 ]]\n",
            "\n",
            " [[0.31383072]]\n",
            "\n",
            " [[0.32313992]]\n",
            "\n",
            " [[0.32307741]]\n",
            "\n",
            " [[0.31620838]]]\n",
            "\n",
            "Reshaped inputs:\n",
            "\n",
            "[[[0.3799695 ]]\n",
            "\n",
            " [[0.38723211]]\n",
            "\n",
            " [[0.39076018]]\n",
            "\n",
            " [[0.38976495]]\n",
            "\n",
            " [[0.39582069]]\n",
            "\n",
            " [[0.4006918 ]]\n",
            "\n",
            " [[0.39468285]]\n",
            "\n",
            " [[0.3903278 ]]\n",
            "\n",
            " [[0.396307  ]]\n",
            "\n",
            " [[0.40079757]]\n",
            "\n",
            " [[0.39751313]]\n",
            "\n",
            " [[0.39754439]]\n",
            "\n",
            " [[0.3716342 ]]\n",
            "\n",
            " [[0.35218174]]\n",
            "\n",
            " [[0.35186076]]\n",
            "\n",
            " [[0.33562138]]\n",
            "\n",
            " [[0.33868236]]\n",
            "\n",
            " [[0.34127403]]\n",
            "\n",
            " [[0.33667128]]\n",
            "\n",
            " [[0.33909474]]\n",
            "\n",
            " [[0.33982241]]\n",
            "\n",
            " [[0.34999305]]\n",
            "\n",
            " [[0.34805214]]\n",
            "\n",
            " [[0.35095706]]\n",
            "\n",
            " [[0.33131082]]\n",
            "\n",
            " [[0.3196336 ]]\n",
            "\n",
            " [[0.31383072]]\n",
            "\n",
            " [[0.32313992]]\n",
            "\n",
            " [[0.32307741]]\n",
            "\n",
            " [[0.31620838]]]\n",
            "\n",
            "(30, 1, 1)\n",
            "\n",
            "Predicted BTC price:  [[7344.6978]\n",
            " [7484.4087]\n",
            " [7552.255 ]\n",
            " [7533.117 ]\n",
            " [7649.547 ]\n",
            " [7743.168 ]\n",
            " [7627.674 ]\n",
            " [7543.9414]\n",
            " [7658.8936]\n",
            " [7745.2007]\n",
            " [7682.077 ]\n",
            " [7682.679 ]\n",
            " [7184.2793]\n",
            " [6809.6113]\n",
            " [6803.426 ]\n",
            " [6490.3486]\n",
            " [6549.3794]\n",
            " [6599.3535]\n",
            " [6510.596 ]\n",
            " [6557.3325]\n",
            " [6571.364 ]\n",
            " [6767.431 ]\n",
            " [6730.0225]\n",
            " [6786.01  ]\n",
            " [6407.2026]\n",
            " [6181.8823]\n",
            " [6069.8677]\n",
            " [6249.551 ]\n",
            " [6248.345 ]\n",
            " [6115.7676]]\n",
            "\n",
            "Scaled predicted BTC price:  [[7344.6978]\n",
            " [7484.4087]\n",
            " [7552.255 ]\n",
            " [7533.117 ]\n",
            " [7649.547 ]\n",
            " [7743.168 ]\n",
            " [7627.674 ]\n",
            " [7543.9414]\n",
            " [7658.8936]\n",
            " [7745.2007]\n",
            " [7682.077 ]\n",
            " [7682.679 ]\n",
            " [7184.2793]\n",
            " [6809.6113]\n",
            " [6803.426 ]\n",
            " [6490.3486]\n",
            " [6549.3794]\n",
            " [6599.3535]\n",
            " [6510.596 ]\n",
            " [6557.3325]\n",
            " [6571.364 ]\n",
            " [6767.431 ]\n",
            " [6730.0225]\n",
            " [6786.01  ]\n",
            " [6407.2026]\n",
            " [6181.8823]\n",
            " [6069.8677]\n",
            " [6249.551 ]\n",
            " [6248.345 ]\n",
            " [6115.7676]]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "d7HRFBom6i4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "fadb6430-77c5-4dcd-c959-007828b63f6e"
      },
      "cell_type": "code",
      "source": [
        "### get evaluation of the model predictions \n",
        "model_evaluation = regr.evaluate(inputs, predicted_BTC_price) \n",
        "\n",
        "print(\"Model evaluation is: \", model_evaluation)  \n",
        "print('') "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r30/30 [==============================] - 0s 3ms/step\n",
            "Model evaluation is:  [48800232.0, 0.0]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UWhS9mL96oqA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1187
        },
        "outputId": "27d56e7d-67ed-4983-b01e-0bd074c79924"
      },
      "cell_type": "code",
      "source": [
        "### visualize the results \n",
        "print(\"Visualize the results:\") \n",
        "print('')\n",
        "\n",
        "### plot the actual and predicted prices \n",
        "plt.figure(figsize =(25, 15), dpi =80, facecolor ='w', edgecolor ='k')\n",
        "ax = plt.gca() \n",
        "plt.plot(test_set, color = 'red', label = \"Real BTC Price\") \n",
        "plt.plot(predicted_BTC_price, color = 'blue', label = \"Predicted BTC Price\") \n",
        "plt.title(\"BTC Price Prediction\", fontsize = 40)\n",
        "\n",
        "### reindex the 'df_test' dataframe \n",
        "df_test = df_test.reset_index() \n",
        "x = df_test.index\n",
        "\n",
        "### set labels\n",
        "labels = df_test['date']\n",
        "\n",
        "### set xticks \n",
        "plt.xticks(x, labels, rotation = 'vertical')\n",
        "\n",
        "### set fontsize for 'x' and 'y' ticks \n",
        "for tick in ax.xaxis.get_major_ticks():\n",
        "    tick.label1.set_fontsize(18)\n",
        "\n",
        "for tick in ax.yaxis.get_major_ticks():\n",
        "    tick.label1.set_fontsize(18)\n",
        "    \n",
        "### set plot labels \n",
        "plt.xlabel('Time', fontsize = 40)\n",
        "plt.ylabel('BTC Price(USD)', fontsize = 40)\n",
        "\n",
        "### set plot legend\n",
        "plt.legend(loc = 2, prop = {'size' : 25})  \n",
        "\n",
        "### show the plot \n",
        "plt.show()    "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Visualize the results:\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2+vjVq1exY8eOQuNZsWIFcnJy1OVGjRohKCgI7u7u6sTPy/7Hjh2L3bt3o2rVqjhy5Eix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m/5XuVnn6uiJyfxLjjqnVDnE2W5XHKsWiGVlUkul5o2tdS6talNm2zautXQscfWnQ9NAAAAAAAa\nLpI/QAO2a1ek2ufttyPVPhdeGKn2adEisd+4Mgzp6KMtHX10WOedV9U2rrBQWrmyaoZQXp5da9bY\n1K2boRkzSuV0/sKih6u0VBmDrpRjw7cq/80ZKpz0XMK060LdEzqlh/b84xOlXzdAzuW5aty7lwpm\nzlPwtN/EO7QDlZcr/fqBcqxaoWA3n/JffFmx+YEC6hinU6HMLDmX/k+O1SsV8nWXJHm9YW3aZFNu\nrl3HHhub1qEAAAAAABwJ3pEEGqhFixw680yP3n7bqcaNLT33XKlmzQokfOLnl6SlSaedFtaQIUFN\nmFCmDz4o0aZNRfrPfyJfO2LhsNJHDJXzf/9RqENHFcyaJ7lcMVgYDZl5bCvtfWeJAn37ybZrlzIu\nu1juObPiHVYVy1LayFuU9NknCrduo/x5r9X59nTAkQh597V+y1lWeayi9Zvfz6U1AAAAAKBu4A4V\naGB275aGD3frhhuStXOnTRdcENSnnxbrsssSo81brNntsetMlfLQA3K9+47Mo5orf/7rsho1js3C\ngMejwukzVXzvaBnBoNLuvF2p9/4xMvQqzlL+Mlbu11+R2bix8v/6pqwWLeIdElCjgvuqffaf++Pz\nRapMc3PtcYkJAAAAAICfIvkDNCDvvuvQGWek6K23nGrUyNLUqaWaPbt+V/vUluQXnpNn+hRZHo/y\n570qs03beIeE+sYwVDLqT8qfNV+WJ0XJLz2vjCv7ydizO24huWe+KM+kp2S53cqf86rC7TvELRag\ntlS0enPmViV/unWLJH/8frssfqUCAAAAAOoAkj9AA7B7t3TTTW4NHlxV7fPZZ8Xq379hVvvEWtLf\nFynlgXtk2WwqeH5m5RuDQE0o//1F2vPu+wof11pJn32iRhecLfvXa2s9jqTF7yr13j/KMgwVPPeS\nQqf2qPUYgHgId+goy+ORfe1qqaREktSokdSunak9ewx99x2/WAEAAAAA8UfyB6jnFi926MwzU/Tm\nm5FqnylTqPaJJcf//qP0m26QYVkqeuwplZ9/YbxDQgMQ7pqpPe99ovLTfyvHhm/V6IKzlfTBe7X2\n/JWve9NU0V/Gq7zPxbX23EDc2e0KZXllhMNyrMirPFzR+s3vp/UbAAAAACD+SP4A9dSePdLNN7t1\n3XXJ+vFHm3r3Dumzz4p1+eVU+8SKbcO3yhh0pYxAQCW3jlTg+iHxDgkNiNWsmfJf+5tKBw2WrahQ\n6ddcoeRnJ6mme07Zv1mnjIFXyCgtjbzuhwyv0ecD6qKKuT9O/7LKY8z9AQAAAADUJSR/gHpoyRK7\nzjwzRW+84VRGhqVnninVyy+XUu0TQ8auXcq4+jLZdu1SoG8/FT/wULxDQkOUlKSiJyeq8NEnJJtN\nqWNHK+3W4VIgUCNPZ+zYoYyrLpNt924F+vXndY8GK+TLliQ5cvZP/piSJL+fy2sAAAAAQPxxdwrU\nI3v2SCNGuHXttR7t2GHTeedFqn2uvJJqn5gqLVXGtVfJ8e03Kj/tNyqcPE2y8b9TxIlhKDBkuPJf\neUtmo0Zyv/ZXNbr097Jt/yG2z1NUpIyBl8v+3UaVn9FThZOe43WPBqsy+ePPqTyWlRWWYVjy++0y\nzXhFBgAAAABABO/aAPXEe+/Z1bNnil5/3an0dEuTJ5dq7txSHX001T4xZZpKv3W4nP/9SqH2HVQw\ne77kdsc7KkDBnr20571PFOrYSc6l/1Oj83vJkbvs0A88HKGQ0oddL2dujkJdTlTBzLmSyxWbtYEE\nFG53gsy0dNnXr5NRWCBJSk2VOnQwVVhoaMMGPnEBAAAAAIgvR7wDqCmdOnU65DkffvihWrVqpWee\neUbPPvvsz5537bXX6v7776/8d9M0NXv2bL355pvauHGjXC6XunfvrltvvVXdunU76PFvvfWW5s6d\nq2+++UaGYSgzM1PDhw/XGWecEd3mgP3s3Ss98IBbr77qlCSdd15ITz4Z0DHHkPSpCSkPj5Zr4dsy\nmx2l/AVvyGrcJN4hAZXMdsdr7+IPlXbTELnef0+N/nCBCidNVdml/aNf1LKUetcouT74h8Itj428\n7jMaxS5oIBHZbAr5spX02T/lyFuu4G8i13Rer6mvv7YrJ8euE04IxTlIAAAAAEBDVm+TP5MmTfrZ\nrz399NMqLCxUkyYHvml72223qX379ged37Zt2wP+ffTo0Xr99dd1/vnna8iQISosLNTLL7+sgQMH\navbs2crOzq48d+rUqZo0aZJ69OihBx54QOFwWK+88oqGDh2qiRMnqnfv3ke2UTRo779v1513uvXD\nDzalp1v6858DtHirQe6Xpsvz3DOykpOVP+9VmW3axjsk4CBWWroKXv6rUh55WJ5nJyp9+A0qXr1K\nJfc8EFWbNs/T45U8d7bM9AzlL3hDZstjayBqIPGEvPuSPznLKpM/Pl9Yr73mlN9vV//+JH8AAAAA\nAPFTb5M/F1xwQbXHP/jgA3333Xd67LHH5PF4DvjaKaecoh49evziujk5OXr99dd1wQUXHJBgOv/8\n89W7d2+NHTtWb731liRp69atmjp1qnw+n2bOnCm73S5J6tOnj/r06aOxY8fq7LPPltPpPJKtogHa\nu1caPdqtV16JvHbOOSekp5+m2qcmJS1+V6n33y3LZlPB9JkKZZ8U75CAn2e3q3jMWIW6nKi0O25T\nysQn5VizSoVTX5CVmnbYy7gWzFXK44/ISkpSwax5Cnc5sQaDBhJLsHLuT1V7RZ8vLEnKzaWzMgAA\nAAAgvhrUnWlRUZHGjRunk08+WZdeemlUa7z99tuSIq3g9teiRQude+65WrVqldatWydJWrRokYLB\noK655prKxI8kpaamqm/fvtq5c6e++OKLKHeDhur99yOzfV55xam0NEuTJpVq/vxSEj81yLHsf0q/\n6QYZpqmiR8ar/ILfxzsk4LCUXX6V9r79d4VbHC3Xkr+rUZ/zZPtu42E91vnR+0q74zZJUuHk5xQ8\no2cNRgoknpCvuyTJmZtTeaxrV1N2u6W8PLvC4XhFBgAAAABAA0v+TJ06VTt27NCYMWN+8bzy8nKV\nl5dX+7W8vDzZ7fZqZ/t4vV5Jkt/vrzxX0gFt4H7uXOBQ8vOl229365prPPrhB5vOPjukzz4r1tVX\n0+atJtk2blDGwCtklJaqZMTtCgwZFu+QgF8ldNIp2vuPTxT0ZcuxepUa9+4l5xef/eJjHMtzlXHD\ntTLCYRWNGaeyfpfXUrRA4jCPay2zSRPZN26QsWe3JMnjkTp1MlVSYmjdugZ1mQ0AAAAAqGPqbdu3\nn9q1a5fmz5+vvn37qlOnTtWes3jxYo0dO1br16+XJHXs2FFDhgxR3759K8/ZsmWLmjRpUm2rtmOO\nOUaStHnz5spzpUhV0E+1bNnygHMPB2/wH6jiv0ci/3c53D188IFdd9zh1rZtNqWlWRo3LqABA+Kb\n9DGKCpUy7kFpyd+V0bqNQl2zFMrsplBWlsKdukhud/yC+xV+6Xtg7N6ljKsvk23nTpX94VKVPDi2\nTr7eEv1nIdHjl+r+HqyWLZX/zhKljrpV7jdeU8bll6j40ScUuH6IpAPjt323URlX95dRUqzSG4cr\ncOvtdXZf+6vr34NDSfT4pQa4B8OIzP35+EM5l+cq2OtsSZHWb6tW2eX329Sli1lzwVYf0gF/J6JE\n30Oixy+xh7og0eOXEn8PiR6/xB7qgkSPX0r8PSR6/FLi7yHR45fYQ12Q6PHHU4NJ/rz44osqKyvT\nTTfd9LPnfPrppxo8eLDatGmjjRs3asaMGbr77ru1Y8cODRsW+bR/cXFxZZLnpypmCBUXF1f+bbfb\nlZSUdNC5ycnJB5x7KE2apMhu5xOk1Wna9PDnV9RVP7eHvXulO+6QZs6M/Hvv3tILLxg67rjkWoyu\nGh9+KA0ZIn33nSTJuXWrnF/+u+rrDofUpYvk81X98Xqlpk3jFPChHfQ9CASkSwdJ36yXfvtbuV6Z\nL1cdT2gl+s9Coscv1fU9pEmvvSI93l3Gffcp9U+jlLphnTRxorTvAw1NVS5dc7n04w6pXz8lT5ui\n5P3aliaCuv09OLREj19qYHv47enSxx8qY90qqf8lkqQzzpDmz5fWrk1Ws2Y1GOQvaFDfgzoq0eOX\n2ENdkOjxS4m/h0SPX2IPdUGixy8l/h4SPX4p8feQ6PFL7KEuSPT446FBJH/y8/O1YMEC9erVS23a\ntDno63/4wx/k9XqVnZ2ttLTIi6hnz57q06ePLrzwQk2ZMkVXXXWV0tPTazv0Srt3F5Pd/AnDiPzQ\n79pVKCtBx9380h4+/NCuUaMi1T6pqZbGji3TwIFBGYa0c2ec4i0qlOfhMUqe9ZIkKfibM+R8fpry\nv/9B9rw82VfkybFiuRyrVsrIy5Py8qQ5cyofHz62lUKZWQpndYtUCWVmyWzdJq6p+2q/B6aptGE3\nyPX55wqd0F75M+bKKgpKRcG4xflLEv1nIdHjlxJsDzfeoqTjjlfq8CGyTZ2q8uUrVDRjtpq0aqHg\n7/vIuXatgqeepvyJz0l7SuId7WFLqO9BNRI9fqlh7iGpY1elSyr74ksV7iyUJLVvb5OUon//O6yd\nO2v3Z6ghfg/qmkSPX2IPdUGixy8l/h4SPX6JPdQFiR6/lPh7SPT4pcTfQ6LHL7GHuiDR469pzZr9\nfFKsQSR/Fi1apNLSUl166aXVfr1NmzbVJoWaNm2q3r1769VXX9WyZcvUq1cvpaSkqLS0tNp1Skoi\nN/ipqamSpJSUFIXDYZWXlx9U/fPTcw8HL+7qWVbi/7fZfw8FBdKYMS7Nnx95zfTqFdLTTwfUqpVV\neW48OD/9RGmjbpV98yZZHo+KRo9V2Q03qlnzDAVbtFZ591OrTg6HZf/2m0giKG955O8Vy2Xf8r3s\nW76X3ltceaqZlq5QZta+P90UzsxSqGNnyeWq1f3t/z1IGfugXH97U2azZspf8IbMxk2lBHiNJfrP\nQqLHLyXOHsrOv1ChxR8qY9CVSvr8U2Wcf5bUubOc//lKofYdlP/yAlnu5IR43f9UonwPfk6ixy81\nrD0EvZG5jo7cZZXnd+liyum0tGKFTeXllYV1taohfQ/qqkSPX2IPdUGixy8l/h4SPX6JPdQFiR6/\nlPh7SPT4pcTfQ6LHL7GHuiDR44+HBpH8WbJkiZKSktSzZ89f/dim+9pUFRUVSZKOO+44rVy5stqE\nTsWMn7Zt2x5w7g8//KDWrVtXe251SSc0XB99FJnts3VrVbXPNdcE4z/b5+ExSp4dqfYp/80ZKpw4\nRWbbdj8fl92ucIeOCnfoqLJL+0eOWZZsO7ZHkkAr8uTYVyVk//YbJf37CyX9+4vKh1tOp8IdOx+Q\nFAp1zZTVqHEN71Zyz3hBnimTZCUnK3/OKzLbtqvx5wTiIdy5i/a897HSh1yrpC8+kzZulHlUc+Uv\neENWk7rbohGoS8yjj1G4eQvZt3wvY8cOWc2by+WSTjzRlN9v15o1NmVl1e7cHwAAAAAApAaQ/Cku\nLlZOTo6ys7PlrmZeRzAY1Pvvvy/DMHThhRce9PUNGzZIUuWcn+zsbOXl5cnv9+uUU0454NylS5dK\nkrp371557pIlS7Rs2bKDkj8V55500klHuEPUB4WFkWqfuXMjCcXf/S6kCROqqn3ipbpqn8DgGyVb\nFPOnDENmi6NV3uJo6Zzzqw4XFcq+alWkOmjlvrZxq1dF/nllnvRK1RLh41or1HW/hFBWN5mtjotZ\n27ik9xYr9b4/yTIMFUybodBJpxz6QUACs5o0Vf6rbyt17Gglf/qx8p+ZLrNN23iHBSQOw1Aou7vs\n7y2Wc3mOys/tLUnyesPy++3y++0kfwAAAAAAcVHvkz9r165VMBhUx44dq/260+nU5MmTtXXrVnXp\n0qWyakeS1q9frw8//FBHH320unXrJkm67LLLNGfOHM2aNeuA5M/GjRv10UcfqUePHpWJnosuukgT\nJkzQnDlzdNFFF8nhiPzn3rNnj9566y21bt1aPXr0qKGdI1H84x/SDTekaMsWm1JSLD38cJkGDaq7\n1T6xZqWmKXRqD4VO3e9nIRSS/Zv1cuT591UI5cmxwi/75k2yb94k15J3K081MxpVVQh13dc6rmMn\n6Vj7CmMAACAASURBVCeVeYfiyFmq9OGDZZimCh99QuUX9onVFoG6zelU8Z8fU3KzNIV3FiZkqzcg\nnkLebLneWyxHzrLK5I/PZ+rll6XcXJsGDoxzgAAAAACABqneJ382btwoSTr22GN/9pwHH3xQN954\nowYMGKABAwaoVatW2rBhg+bOnSvDMDRu3Dg59zVs79y5s66//nrNnDlTt9xyi8477zzt3btXM2fO\nlNvt1ujRoyvXbdasmf74xz/qz3/+swYPHqy+ffuqrKxM8+bNU1FRkSZMmCBbNBUUqBeCQem++1ya\nPVuSbOrZM1Ltc9xxdanaJ0VFox+OvtonWg6Hwp06K9yps8r6Xxk5Zlmy/bBt3/ygfQmhPL/sGzco\n6YvPIm2r9rGSkhTq1EWhzKzIDKGKtnHpGdU/34YNSr/mChklJSq56VYFhgyvhU0CAOqDkG/f3B9/\nTuUxrzcsSfL77XGJCQAAAACAep/8KSgokCSlpKT87Dmnn366Xn31VT333HOaM2eOioqK1KhRI515\n5pkaPny4unTpcsD5d999t1q1aqVXXnlFo0ePVnJysk499VSNHDlS7du3P+DcQYMGqXHjxpo1a5bG\njh0ru90un8+ncePGVbaHQ8P05JNJmj07Samp0kMPBepetc9vz1ThhGfrzswbw5B5TEuVH9NS5edd\nUHW4sED2lSvlWLlfUmj1Sjnz/HLm+Q9YIty6bVWVUJZXocwsKcUjXXyhbD/+qLKLLlHxQ3+u7Z0B\nABJY0Bu5nnPk5kSmjxqGOnc25XJZWrXKprIyyeWKc5AAAAAAgAan3id/rr/+el1//fWHPK9r1656\n9tlnD2tNwzA0cOBADTzMPh4XXXSRLrroosM6Fw3D55/bNXFikpKSLP3zn4Zatw7KimPBT52o9omS\nlZau0GmnK3Ta6VUHg0HZ131dVSW0cl+V0KaNsm/aKNffF1Y93umUgkEFT+mhginPJ8SeAQB1h3XU\nUQq3Ok727zfLtm2rzJbHyumUMjNNLV1q16pVNmVnM/cHAAAAAFC76n3yB6hrdu0yNGKEW5ZlaMyY\ngLp3d2vnzvjEUuerfaLldCp8YleFT+yqsiuujhyzLNm2btlXHbRcjrxIYsi+aaPUubMK5vxVSk6O\na9gAgMQU8mbL/v1mOXJzVN4y0mrY6w1r6VK7cnPtJH8AAAAAALWO5A9QiyxLGjnSrR9+sOm880Ia\nNiwoyR2XWBK52icqhiHz2FYqP7aVyntfWHnYVlSgpsceJaugjEH3AICoBH3Zcr37jhz+ZSr/faTa\n2+ermPtTT3+vAgAAAADqNJI/QC2aMcOp995zqHlzU5MmBeIy46feVvtEyUpLl5KSJJXFOxQAQIIK\n+SJzf5y5OZXHfL5ItU9urj0uMQEAAAAAGjaSP0AtWbHCpocecskwLE2dGlCzZrVfZtLgqn0AAKgF\nIa9PkuTIXRYp8zUMdehgyuOxtHatTSUlkscT5yABAAAAAA0K7/gCtaC4WBo+3K2yMkO33Vaunj3D\ntfr8RlGhUv80So36/0H2zZtU/tsztfuTfykwZBiJHwAAjpDVqLHCbdvJtmePbJu+kyTZ7VJWVljh\nsKGVK/ldCwAAAACoXdyJArVgzBiX1q2zq3v3sO6+u7xWn9v56Sdq/LvTlTz7JVmeFBU++qTy31jY\nYNu8AQBQE4LZkdZvDv/Brd/8flq/AQAAAABqF8kfoIa9845Dc+YkKTXV0rRppXI6a+d5qfYBAKD2\nhLz75v7kLKs85vVGKn1zckj+AAAAAABqFzN/gBq0ebOhO+5wS5KefDKgtm1rZ84Ps30AAKhdIV+2\npJ9W/kSSP34/v38BAAAAALWL5A9QQ0Ih6aabklVQYOiqq4Lq1y9U489pFBUq5eExSp79kiSp/Ldn\nqnDCs7R4AwCghoW6eWUZhhz+XMk0JZtNxx9vKS3N0rp1NhUVSamp8Y4SAAAAANBQ8DFEoIY8+WSS\n/vtfu44/3tRf/hKo8ec7aLbPY08x2wcAgFpipaYp3KGjbIUFsm/4RlKk4NbrDcuyDOXl0foNAAAA\nAFB7SP4ANeCLL+yaMCFJTqel558vrdFP+lY72+ef/1bghqG0eQMAoBaFvPtavx0w98eUJOXm8jsZ\nAAAAAFB7uAsFYmz3bmnECLcsy9Do0WXq1s2ssef62WqfNm1r7DkBAED1fnnuD5U/AAAAAIDaw8wf\nIIYsSxo50q1t22w655yQhg0L1sjzVDvbZ+IUkj4AAMRR0NddkuTMrUr+eL2R5E9ODskfAAAAAEDt\nIfkDxNCMGU4tWeLUUUeZmjw5UCNd15yffqK0UbfKvnmTLE+KisaMVeD6IbR4AwAgzkJds2TZ7XLk\n+aVQSHI41KaNpcaNLW3YYNPevVKjRvGOEgAAAADQEPBuMRAjK1fa9NBDLknSlCkBHXWUFdP1me0D\nAEAd5/Eo3KmLjJIS2dd9LUkyjKrqn+XLqf4BAAAAANQO3jEGYqCkRBo+3K2yMkO33lqmXr3CMV2f\n2T4AACSGYHak9Vt1c39yc0n+AAAAAABqB8kfIAZGj3bp66/tys4O6557ymO2LtU+AAAklpA3W5Lk\nzFlaeczrNSVJfj+/uwEAAAAAtYOZP8ARWrjQoTlzkpSaamnatFIlJcVmXWb7AACQeEK+SPKnusof\nv5/KHwAAAABA7SD5AxyBzZsN3XGHW5I0fnxA7dod+Zwfo6hQKQ+PUfLslyRJ5Wf0VOGEZ2nxBgBA\nAgh16SrL6ZRj5QqpvFxKSlLLlpaaNTO1aZNNu3YZato0tnMBAQAAAAD4KUoIgCiFQtLNN7uVn2/o\niiuC6t8/dMRrHjTb5/Gnlf/6OyR+AABIFC6XQl0zZZSVybF2tSTJMCSfj9ZvAAAAAIDaw90nEKWn\nnkrSf/7jULt2ph57LHBEax002+eMnpHZPoNvpM0bAAAJJuTtLkly5CyrPOb1Rlq/5ebS+g0AAAAA\nUPN4VxmIwr/+ZdeECUlyOi09/3ypUlOPYLEPP1SjnlT7AABQX1Q39yc7uyL5w+U3AAAAAKDmMfMH\n+JV275ZGjHDLNA2NGROQ12tGt1AopJT775Jmvii7mO0DAEB9EfTtq/zJrUr+VFwv+P1U/gAAAAAA\nah4fPQR+BcuSRo1ya+tWm846K6SbbgpGvZbnyUeVPPNFKSVFReOp9gEAoL4Id+osy+2WY/VKKRBp\nDduihaVjjjG1bZtN27cbcY4QAAAAAFDfkfwBfoVZs5xavNipZs1MPfNMIOpxPM7PP5VnwpOynE7p\nn/9ktg8AAPWJw6FQZjcZoZAcK/MqD1fM/fH7+Z0PAAAAAKhZ3HkCh2nVKpvGjHFJkqZMCah5cyuq\ndYxdu5Q2YqgMy1LxmLHSSSfFMkwAAFAHBLMPbv3m80Vav+Xm0voNAAAAAFCzSP4Ah6GkRLrpJrfK\nygzdcku5zjorHN1ClqW0kSNk/2Gbys7rrcDwEbENFAAA1Akhb7YkyZm7rPKYzxe5fiD5AwAAAACo\naSR/gMPw4IMurVljl88X1r33lkW9jvul6XK9t1jhFkercNJzkkHPfwAA6qOQb1/lj7+q8sfrraj8\nscmKroAYAAAAAIDDQvIHOIRFixyaPTtJKSmWpk0rVVJSdOvY85Yr9aEHZBmGCqc8L6tZs9gGCgAA\n6ozwCe1lpqTK/vVaqahIktS0qaXWrU3t3GnT1q18AAQAAAAAUHNI/gC/4PvvDY0a5ZYkjR8f0PHH\nR/kx3eJipQ8fLKO8XKW336Fgz16xCxIAANQ9drtCXp8M05RjRV7lYa+X1m8AAAAAgJpH8gf4GaGQ\nNGKEW/n5hi6/PKjLLw9FvVbq/XfJsX6dgiedouK77othlAAAoK6qmvuztPJYRes3v5/LcAAAAABA\nzeGuE/gZEyYk6csvHWrXztTjjweiXsf11utKnj9HZlq6Cqa9JDmdMYwSAADUVSFfJPnjyK2a++Pz\nUfkDAAAAAKh5JH+Aanz5pV1PPZUkh8PS9OmlSk2Nbh3bxg1K/eNISVLR05NltmkbuyABAECdFvR1\nlyQ5/FXJn27dIskfv98uK8pusgAAAAAAHArJH+An9uyRbr7ZLdM0dP/9ZfL5zOgWCgaVfvMQ2QoL\nVDrwOpVd0i+2gQIAgDrNbNtOZkYjOb5ZLyN/rySpUSOpXTtTe/YY+u47I84RAgAAAADqK5I/wH4s\nS7rjDre2bLGpV6+Qbr45GPVaKY8/IufS/ynUoaOKxj0WwygBAEBCMIzKuT+O5f7KwxWt3/x+Wr8B\nAAAAAGoGyR9gPy+/7NS77zrVrJmpZ54JyBblT4jznx8r+ZkJslwuFUyfKaWkxDZQAACQEJj7AwAA\nAACIB5I/wD5r1tg0erRLkvTsswG1aBFdI37jxx+VdsswGZaloof+rHBmVizDBAAACaS6uT8VLWX9\nfi7FAQAAAAA1gztOQFJpqTR8uFuBgKGbby7X2WeHo1vINJV+23DZd2xX2QW/V+CGYbENFAAAJJSK\nyh9n7rLKY1lZYRmGJb/fLjPK0YIAAAAAAPwSkj+ApIcecmn1aru83rDuv78s6nWSp09V0kcfKHxM\nSxVOnCIZDHIGAKAhM49tJbNZM9k3fSdj1y5JUmqq1KGDqcJCQxs2cK0AAAAAAIg9kj9o8P7+d4dm\nzkySx2Np+vRSJSVFt47Dn6OUPz8oy2ZT4XMvymrSNLaBAgCAxGMY1bZ+83ojJT85Ocz9AQAAAADE\nHskfNGhbthgaOdItSXr88YCOPz7KOT9FhUobNlhGMKiSUX9S8DdnxDJMAACQwELeg1u/+XyRFrN+\nP8kfAAAAAEDskfxBgxUOSyNGuLV3r6HLLgvqiitCUa+Ves8f5djwrYI9TlfJnXfHMEoAAJDoQhWV\nP7lVlT8VyZ/cXC7HAQAAAACxx90mGqwJE5L073871KaNqfHjA1GP53G99le5X10gM6ORCp57UXI4\nYhsoAABIaCFfpPJn/7ZvXbuastst5eXZFQ7HKzIAAAAAQH1F8gcN0pdf2vXkk0lyOCJzftLSolvH\n/u16pd51hySpcMKzMlsdF8MoAQBAfWC2OFrhY1rKvm2rbNt/kCR5PFKnTqZKSgytW8clOQAAAAAg\ntrjTRIOzd690881umaahe+8tV/fuZnQLlZcrbfgQ2YqLVHrdEJVf9IfYBgoAAOqNirk/tH4DAAAA\nANQG7jTRoFiWdMcdbm3ZYlPPniHdckt51GulPPKwnP4chTp3UdHYv8QwSgAAUN9Utn7LXVZ5zOuN\nfADF77fHJSYAAAAAQP1F8gcNypw5Ti1a5FSzZqamTAnIFuVPgPOj9+V57hlZbrcKnp8lJSfHNE4A\nAFC/BH3dJR0496eq8ofkDwAAAAAgtkj+oMFYs8amBx5wSZImTw6oRQsrqnWM7duVfutNkqSicY8p\n3LlLzGIEAAD1U0XbN2fuskgpsqQTTzTldFpascKmYDCe0QEAAAAA6huSP2gQSkul4cPdCgQMDR9e\nrnPPDUe3kGkq/dZhsu38UWUXXaLAtYNjGygAAKiXrKZNFW7dRradO2Xb8r0kyeWKJIDKygytWcNl\nOQAAAAAgdrjLRIPw8MMurV5tV1ZWWA88UBb1OslTJivpnx8rfGwrFT49WTKMGEYJAADqs8rWb7lV\nrd+83sgHUpj7AwAAAACIJZI/qPcWL3ZoxowkeTyWnn++VC5XdOs4lv5XKY+OlWWzqeC5l2Q1ahzb\nQAEAQL12QOu3fXw+U5KUm8tlOQAAAAAgdrjLRL22dauhkSPdkqTHHgvohBOinPNTkK/04UNkhEIq\n+dO9Cp12eizDBAAADUDIF0n+UPkDAAAAAKhpJH9Qb4XD0ogRbu3ZY6hfv6CuvDIU3UKWpdS7Rsm+\naaPKf3OGSkb+MbaBAgCABiHUzStJcvhzJCvygZTOnU25XJZWrbKpLPrOtAAAAAAAHIDkD+qtSZOS\n9K9/OdS6tanx4wNRj+dxvTJf7jdfl9m4sQqnviDZ+WQuAAD49ayMRgqd0F62/L2ybdwgSXI6pcxM\nU8GgoVWruDQHAAAAAMQGd5iol776yq4nnkiSw2Fp+vRSpadHt459/Tql3XOnJKlw0nMyWx4bwygB\nAEBDU93cn4rWb7m5fMAEAAAAABAbJH9Q7+TnSzff7FY4bOiee8p10klmdAuVlSlt2GAZJSUqHTJM\n5Rf8PraBAgCABqe6uT8+X8XcHy7NAQAAAACxwR0m6hXLku68063vv7fpzDNDuvXW8qjXShk3Rs4V\nyxU6MVNFD/45hlECAICGKug7SdK+uT/7+HyRD6pQ+QMAAAAAiBWSP6hX5s1z6p13nGra1NSUKQHZ\nonyFJ/1jsTzPPyfL41HB8zMltzu2gQIAgAYplJkly2aTw58rhSMVPx06mPJ4LK1da1NJSZwDBAAA\nAADUCyR/UG98/bVN99/vkiRNnhzQ0UdbUa1j+2Gb0m6/WZJU9Mh4hTt2ilmMAACggUtNVbhjJ9mK\ni2T/Zr0kyW6XsrLCCocNrVzJ5TkAAAAA4Mhxd4l6IRCQhg1zq7TU0LBh5TrvvHB0C4XDShsxVLbd\nuxW4pJ8CAwbFNlAAANDghXzdJUmO3GWVx2j9BgAAAACIJZI/qBfGjnVp1Sq7MjPDGj26LOp1PM9M\nUNLnnyrcuo2KnpokGUYMowQAAJCC3mxJByZ/vN7IB1dI/gAAAAAAYoHkDxLee+/Z9eKLSfJ4LE2f\nHpDLFd06jv98Jc/jj8iy21Uw7SVZ6RmxDRQAAEBSyBdJ/jhzcyqP+XyR5I/fz+U5AAAAAODIcXeJ\nhLZli3T77W5J0l/+ElCHDmZU6xj5e5V+8xAZ4bCK73lAoZNPjWWYAAAAlUJds2Q5HHKsWC6FQpKk\n44+3lJZmad06m4qK4hwgAAAAACDhkfxBwgqHpUGDpN27berbN6irrw5Ft5BlKfXO/5N98yaVn9lL\npbeNim2gAAAA+3O7FerSVUYgIPvaNZIkmy3S+s2yDOXl0foNAAAAAHBkSP4gYU2b5tTHH0utW5t6\n4olA1ON53HNny/3OWzKbNlXhlOmRd18AAABqUFXrt/3n/kQqmHNzuRYBAAAAABwZ7iyRsHJy7HK5\npGnTSpUR5Xge+9o1Sn3gbklS4TPTZB59TAwjBAAAqF7IG0n+OKqd+0PlDwAAAADgyDjiHQAQrcmT\nA3I6nXI6TVlWFAuUlip92GAZpaUqGT5C5ef2jnmMAAAA1Qlld5ckOfz7V/5Ekj85OSR/AAAAAABH\nhsofJCyPRzrmCAp1Uh9+QI7VKxXM8qr4gYdjFxgAAMAhhDp1keVyybFyhVRWJklq08ZS48aWNmyw\nae/eOAcIAAAAAEhoJH/QICX9fZGSZ7wgy5OiwudnSC5XvEMCAAANSVKSQl0zZQSDcqxeKUkyjKrq\nn+XLqf4BAAAAAESP5A8aHNuW75U2coQkqfCxJxU+oUOcIwIAAA3RL839yc0l+QMAAAAAiB7JHzQs\n4bDSRgyVbe9eBS67QmVXDoh3RAAAoIEKZp8kSXL4q5I/Xq8pSfL7uUwHAAAAAESPu0o0KJ6nxyvp\n318o3LadisY/HemvAgAAEAcVlT/OnGWVxyoqf/x+Kn8AAAAAANEj+YMGw/nlv+R56nFZDocKps+Q\nlZYe75AAAEADFu7QUZbHI/va1VJJiSSpZUtLzZqZ2rTJpl27+JAKAAAAACA6JH/QIBh7divt5htl\nmKaK73tQoX1tVgAAAOLG4VAoyysjHJZjZZ6kSFGyz0frNwAAAADAkeGOEvWfZSlt1G2yb/le5Wed\no9IRt8U7IgAAAElS0Lev9VtuVes3rzfS+i03l9ZvAAAAAIDokPxBveee9ZJcf18os9lRKnhmumTj\nZQ8AAOqGirk/jtycymPZ2RXJH65ZAAAAAADR4Y4S9Zp91UqljrlXklQw5XlZzZvHOSIAAIAqoezu\nkiSHvyr54/VWtH2j8gcAAAAAEB2SP6i/SkqUPnywjLIyldzyfwqedU68IwIAADhAuN0JMtPSZV/3\ntYyiQklSixaWjjnG1LZtNm3fbsQ5QgAAAABAIiL5g3ordfS9cqxdo2B2dxXfOzre4QAAABzMZlPI\n65NhWXIs91cerpj74/dzuQ4AAAAA+PW4m0S9lLTwbSXPmSkzNU0F02ZISUnxDgkAAKBa1c398fki\nrd9yc2n9BgAAAAD49Uj+oN6xbd6ktDtulyQVPTFBZrvj4xwRAADAzwtWzv1ZVnnM54tU/pD8AQAA\nAABEg+QP6pdQSOk3DZEtf68CVw5Q2WVXxDsiAACAX1RR+ePMqUr+eL0VlT82WVZcwgIAAAAAJDCS\nP6hXPE8+Kud/v1Lo+BNU+OiT8Q4HAADgkMzWbWQ2aSL7xg0y9u6RJDVtaql1a1M7d9q0dasR5wgB\nAAAAAImG5A/qDecXn8kz4UlZTqcKn58ppabGOyQAAIBDM4yquT/+3MrDXi+t3wAAAAAA0SH5g3rB\n2LVLaTffKMOyVDxmrELdfPEOCQAA4LAFffuSP7kHt37z+7lkBwAAAAD8OtxJIvFZltJGjpD9h20q\nO/d8lQ4bEe+IAAAAfpWQt7skyZmbU3nM56PyBwAAAAAQHZI/SHjuF6fL9d5ihVscrcLJ0ySDvvgA\nACCxhCoqf/xVyZ9u3SLJH7/fLsuKS1gAAAAAgARF8geJLTdXKQ89IMswVDjleVnNmsU7IgAAgF/N\nPKalws1byP79Zhk//ihJatRIatfO1J49hr77jg+3AAAAAAAOH8kfJK7iYumqq2SUl6v09jsU7Nkr\n3hEBAABExzAqq3+c/qq5PxWt3/x+Wr8BAAAAAA4fyR8krJTHH5HWrlXw5FNUfNd98Q4HAADgiIS8\n+1q/MfcHAAAAAHCESP4gcYXDUuvWKpz2kuR0xjsaAACAIxLK7i7pwLk/Pp8pSfL7uWwHAAAAABw+\n7iKRsIofeVzauFFmm7bxDgUAAOCIBbvtq/zJWSZZliQpKyssw7Dk99tlmvGMDgAAAACQSEj+ILEZ\nDD8GAAD1g9W8ucLHtpJ9x3bZftgmSUpNlTp0MFVYaGjDBq57AAAAAACHh+QPAAAAUEeEfPtav+03\n98frjZT85OQw9wcAAAAAcHhI/gAAAAB1RNC3r/Vb7tLKYz5fWJLk95P8AQAAAAAcHpI/AAAAQB0R\n8kaSP879Kn8qkj+5uVy6AwAAAAAOD3eQAAAAQB0R8vokSQ5/jmRZkqSuXU3Z7Zby8uwKh+MZHQAA\nAAAgUZD8AQAAAOoIq3EThdu2k233btk2b5IkeTxSp06mSkoMrVvH5TsAAAAA4NC4ewQAAADqkKq5\nP8sqj9H6DQAAAADwa3D3CAAAANQhIW93SQfO/fF6TUmS32+PS0wAAAAAgMRC8gcAAACoQ0LZkeSP\nw1+V/Kmq/CH5AwAAAAA4NJI/AAAAQB0SyuomyzDkyM2RzEjFz4knmnI6La1YYVMwGOcAAQAAAAB1\nHskfAAAAoA6x0tIVbt9BtsIC2Td8I0lyuSIJoLIyQ2vWcAkPAAAAAPhl3DkCAAAAdUzImy1Jkeqf\nfbzeSOs35v4AAAAAAA7FEe8AakqnTp0Oec6HH36oVq1aSZKKi4v1wgsvaOHChdq+fbuSk5OVlZWl\noUOH6vTTT698zJtvvql77733Z9c855xzNHXq1AOOvfXWW5o7d66++eYbGYahzMxMDR8+XGeccUaU\nuwMAAEB9FsruLr3+ihy5y1R22RWSJJ/P1MsvS7m5Ng0cGOcAAQAAAAB1Wr1N/kyaNOlnv/b000+r\nsLBQTZo0kSQFAgENGDBA69evV79+/dS9e3dt375dL7/8sm644QZNmzZNv/vd7w5Y45prrtGpp556\n0NotWrQ44N+nTp2qSZMmqUePHnrggQcUDof1yiuvaOjQoZo4caJ69+4dg90CAACgPgl6u0uSnFT+\nAAAAAACiUG+TPxdccEG1xz/44AN99913euyxx+TxeCRJs2bN0po1a3TPPfdo8ODBlef26tVLl1xy\niZ555pmDkj+ZmZk/+xwVtm7dqqlTp8rn82nmzJmy2yM36n369FGfPn00duxYnX322XI6nUeyVQAA\nANQzocwsWXa7HHl+KRyW7HZ17mzK5bK0apVNZWWROUAAAAAAAFSnQc38KSoq0rhx43TyySfr0ksv\nrTyempqq3r17q3///gec37lzZzVv3lxr166N6vkWLVqkYDCoa665pjLxU/F8ffv21c6dO/XFF19E\ntxkAAADUXx6Pwp26yCgpkX3d15Ikp1PKzDQVDBpatapBXcYDAAAAAH6lBnXXOHXqVO3YsUNjxow5\n4PjAgQM1efJkpaWlHXA8HA6rtLRUqampP7tmMBhUWVlZtV/Ly8uTJGVnZx/0Na/XK0ny+/2/ag8A\nAABoGIK+yDWkI3dZ5bGK1m+5ubR+AwAAAAD8vAaT/Nm1a5fmz5+vvn37qlOnTof1mEWLFqmwsFAX\nX3zxQV/78ssv1b9/f2VlZalbt24699xzNWvWLFmWVXnOli1bJB08B0iSWrZsKUnavHlzNNsBAABA\nPRfyRpI/zv2SPz5fxdyfBnMZDwAAAACIQr2d+fNTL774osrKynTTTTcd1vkrV/4/e3ceHXV973/8\n9Z0tCQlbiKyyKwhiFtYoywxQ0Qq3QrV1oxSl7liX49r7Q1v0XqXHupwqtlUrm2gBRS1argrMsMgi\nkATZBdlXCWtCyGzf3x9DBkaWBCbJzCTPxzk5nXw/n+933h+895zMec3n816jsWPHqkWLFnrggQfO\nGF+wYIHuvPNO/f73v9eePXs0efJkvfjii9qyZYv+9Kc/SZKKi4tltVrlcDjOuD8lJSU8p6IMo8JT\na4Wyf49E/ndJ9DUkev0Sa4gHiV6/lPhrSPT6pcRfQ6LXL7GGqhDo2lWSZCvIC9eUkxOUJBUUWM+o\nM97qvxiJvoZEr19iDfEg0euXEn8NiV6/xBriQaLXLyX+GhK9finx15Do9UusIR4kev2xZJin8Gk/\nQwAAIABJREFUb1WpoY4cOSKn06mrr75ab731VrnzFy1apIceekjJycmaOHGiLr/88vDYvn37tGHD\nBnXq1EmXXHJJ+PqJEyf0i1/8Qtu3b9fnn3+u9u3b67rrrtOOHTu0du3aM95j27ZtGjRokAYMGFCh\nmgKBoKxWvuEJAABQa5SWSnXrShaLdPSo5HAoEJDq1QsNHT0q1akT6yIBAAAAAPGoVuz8mTVrlkpK\nSjRs2LBy586YMUPPPfecmjVrpnfeeUdt2rSJGG/SpMlZj3FLTk7WL3/5S7366qtasmSJ2rdvr9TU\nVAUCAXm93jN2/xw/flySzttP6HQHDxaTbv6EYUiNGtVVYeExJWqEmehrSPT6JdYQDxK9finx15Do\n9UuJv4ZEr19iDVWlfucushfk6dDCZQpkhnpGXnVVipYutcnjKVaPHsHw3His/0Il+hoSvX6JNcSD\nRK9fSvw1JHr9EmuIB4lev5T4a0j0+qXEX0Oi1y+xhniQ6PVXtYyMuuccqxXhz+zZs+VwONSvX7/z\nzpswYYJefPFF5eTkaPz48UpPT7+g92nUqJEkqaioSJLUsmVLrVmzRnv37lWrVq0i5pb1A2rdunWF\nn8//cZ+daSb+v02iryHR65dYQzxI9PqlxF9DotcvJf4aEr1+iTVUNn9WjuwFebLl58l/VSj8yc4O\naulSKS/Pqu7dg2fcE0/1X6xEX0Oi1y+xhniQ6PVLib+GRK9fYg3xINHrlxJ/DYlev5T4a0j0+iXW\nEA8Svf5YqPHniBUXFysvL0/Z2dlKTk4+57xPPvlEL730kvr27av33nvvnMHP119/rRkzZpx1bMuW\nLZKkZs2aSZJyckJNeleuXHnG3BUrVkiSunXrVvHFAAAAoFbx55zs+5OfF76WlRWQJOXnW2NSEwAA\nAAAg/tX48GfDhg3y+Xzq0KHDOeds3rxZzz77rDIzM/XGG28oJSXlnHM/+OAD/fd//7eWL18ecf3A\ngQP66KOPlJKSoj59+kiShgwZouTkZE2ePFl+vz8899ChQ5o5c6ZatWqlXr16RblCAAAA1FS+rNCX\niWz5p75MlJ0dCn8KCmr8n/IAAAAAgItU449927p1qySpRYsW55zz2muvqbS0VP369ZPb7T7rnJ49\neyo9PV1PPfWU8vLydM899+j222/XZZddpj179uj999/XkSNH9MILL4R3DWVkZOjxxx/XCy+8oDvv\nvFNDhw5VaWmp3n//fRUVFenVV1+VxcKHdgAAAJxdoOMVMpOTZVu3RjpxQkpOVrt2purWNfX99xYV\nFUkVbCEJAAAAAKhFanz4c/ToUUlSamrqOeesWbNGkvTXv/71nHMmTZqkXr16qUOHDpo5c6bGjx+v\nmTNn6vDhw0pLS1N2drbuuuuuM3by/OY3v1HDhg01YcIEjR07VlarVdnZ2Xr++efVtWvXSlghAAAA\naiy7Xf4umbIvXybb2tXyd+0uiyV09NvChTZ9951VV18diHWVAAAAAIA4U+PDn5EjR2rkyJHnnTN3\n7twLembr1q01bty4Cs8fMmSIhgwZckHvAQAAAEiSLzsnFP7krZS/a3dJUlZWUAsXSvn5FsIfAAAA\nAMAZOHMMAAAAiGP+k31/7AV54Wun+v5YY1ITAAAAACC+Ef4AAAAAccyfHToq2HZa+JOVFQp/8vII\nfwAAAAAAZyL8AQAAAOJY4LLLFUxNk3XDeqm4WJLUurWphg1Nbdli0eHDMS4QAAAAABB3CH8AAACA\neGa1yp+ZJSMYlO27VZIkwzi1+2fVKnb/AAAAAAAiEf4AAAAAce5U35+V4WtlfX/y8wl/AAAAAACR\nCH8AAACAOOfPOdn3J//0vj9BSVJBAX/SAwAAAAAi8UkRAAAAiHO+kzt/bPln7vwpKGDnDwAAAAAg\nkq0qHx4IBLR//34VFxerpKREXq9XDodDKSkpSk1NVePGjWW18mEVAAAAOJ9g23YK1m8g2+ZNMo4e\nkVmvvpo3N5WREdT27RYVFhrKyDBjXSYAAAAAIE5UWvhTXFysZcuWKT8/X6tWrdK2bdu0f/9+BQKB\nc95jsVjUuHFjtW7dWllZWcrMzFRubq5SU1MrqywAAAAg8RmG/Fk5csyfJ9uqAvn69JNhSNnZQX39\ntUUFBRYNHHjuv7sBAAAAALVLVOGP3+/X7Nmz9fnnn+ubb76R1+sNj5lm+d88DAQC2rNnj/bu3aul\nS5dKkux2u6655hoNHjxY119/vex2ezQlAgAAADWCP/tk+JO3Ur4+/SRJWVkBff21Tfn5VsIfAAAA\nAEDYRYU/xcXFmjJlit5//339+OOPkk6FPYZhRPzv+UKgsjllTNOU1+uVx+ORx+PRn//8Zw0fPlzD\nhw9nNxAAAABqtXDfn4K88LWcnFDgk59PK08AAAAAwCkXHP7MmDFDr732mgoLCyMCH8MwZJpm+JrV\nalWTJk3UvHlzpaamKjk5WUlJSSotLdWJEydUXFys3bt3a9++fRFHw50eGv3444967bXXNGnSJD3y\nyCP61a9+VRlrBgAAABKOPzsU/tjzT4U/WVlBSVJBAX00AQAAAACnVDj82bFjh5544gkVFBTINM0z\nAp/mzZurb9++ysrKUnZ2tlq3bi2rtfwPoYFAQNu2bVNBQYHy8/O1YMEC7d69W9KpIKiwsFDPPvus\nZsyYoZdfflktW7a8yOUCAAAAiSl4aUsFMzJk3b5VxsFCmemN1KSJqWbNgtqzx6K9ew1lZMS6SgAA\nAABAPKhQ+PPJJ5/o+eef1/Hjx8PBj2maql+/vm666SbdcMMN6tKly0UVYLVa1a5dO7Vr107Dhg2T\nJK1Zs0ZffPGFPvroIx0+fDj8fgUFBbrxxhs1ZsyY8FwAAACgVjAM+bJylDTnK9ny8+Qb8DNJob4/\ne/ZYtGqVRRf5JzkAAAAAoIYp93DwF198Uc8884yKi4vD15o2bao//vGP8ng8evLJJy86+DmXK6+8\nUk888YQ8Ho/Gjh2r5s2bSwrtBDp+/Lj+8Ic/6H//93/P208IAAAAqGn8J/v+2E/r+5OdHTr6LT+f\no98AAAAAACHnDX/uu+8+TZo0Kfx7SkqKHn74Yc2ePVu33nqrkpOTq7S4pKQk/frXv9Z//vMfPfbY\nY0pNTZUU6gc0efJk3X///VX6/gAAAEA88ed0kyTZ8k8Pf0L9M/PyCH8AAAAAACHnDX/cbrekUNjS\no0cPzZo1S/fff7+SkpKqo7Ywh8Ohe+65R7NmzVJubm64Jo/HU611AAAAALHkz8qWJNnyV4avZWWF\ndv4UFFjExngAAAAAgFSBY9+sVquefPJJTZo0KXz8Wqw0bdpUEyZM0NNPPy2brULtigAAAIAaI9i0\nmQJNm8m6Z7cs+/ZKkho1MtWqVVA//mjRzp0xLhAAAAAAEBfOG/7Ur19f77zzju666y4ZhlFdNZVr\n5MiReuedd1SvXr1YlwIAAABUK392qO+P7bS+P1lZoaPfli+PSUkAAAAAgDhz3vBn+vTp4WPW4k1u\nbq6mT58e6zIAAACAauXP7ipJsuWdefQb4Q8AAAAAQCon/GnVqlV11XFR4r0+AAAAoLL5zrLzJzub\nnT8AAAAAgFOqpHHO8ePHVVJSIr/fr5SUFNWtWzeujo0DAAAAEpU/K7Tzx56fJ5mmZBjKzDwV/phm\nLKsDAAAAAMSDqMMfn88nj8ejRYsWqaCgQNu3b1dxcXHEHKvVqqZNm6pTp07q1q2bBg4cqJYtW0b7\n1gAAAECtYzZqpECr1rJu3ybL7l0KtrhUDRpIbdsGtWWLRVu3GmrThgQIAAAAAGqziw5//H6/pkyZ\nor/97W86cuSIJMk8x9cM/X6/du7cqV27dunrr7/WuHHj1KdPHz300EPKzMy82BIAAACAWsmflSPr\n9m2y5a2Ut8WlkqQePQLassWib76xqk0bf4wrBAAAAADE0nl7/pzLkiVLdMMNN2jcuHE6fPiwTNOU\naZoyDOO8P5LCcxcuXKhbbrlF999/vw4ePFipiwIAAABqMl9WqO+P/bS+P05nKPDxeKrkZGcAAAAA\nQAK54PDniy++0N13360dO3ZEBD7SqWDnfD+SIua73W7ddNNN+u677ypxWQAAAEDN5c8J9f2x5a8M\nX+vXL9T3Z/58q4LBmJQFAAAAAIgTF/S1wE8//VTPPPOMgsFgOPQxTVMNGzbUNddco2uuuUZXXnml\nGjZsqAYNGshut+vw4cM6cuSIdu7cqeXLl2vp0qUqKCgIB0eStGfPHg0fPlwTJkxQTk5OlSwUAAAA\nqCn8mVmSJFtBnmSakmGoaVNTV10lffedRatXW5SZSQIEAAAAALVVhcOfnTt36k9/+lM4+DFNUxkZ\nGbr77rt12223yeFwnPW+9PR0paenq23bturbt68kadOmTXrnnXf073//O/y80tJSPfTQQ/r444/V\nuHHjylkdAAAAUAOZ9RvI3669bD9slmXrFgXbtpMkXXut9N13ktttU2amN8ZVAgAAAABipcLHvj3z\nzDM6fvx4OPjp3r27Zs2apd/+9rfnDH7O5bLLLtNLL72kiRMnqkmTJpJCR8EdOHBAjz/++IWtAAAA\nAKiF/Nln9v259trQ/3o81liUBAAAAACIExUKf5YsWaJvv/02fEyby+XSe++9pwYNGkT15t27d9fH\nH3+s1q1bh699++23+uabb6J6LgAAAFDT+bPL+v6cCn/69ZMcDlNLl1p1/HisKgMAAAAAxFqFwp9/\n/etfkiTTNNW4cWONGzdOdru9UgpIT0/X22+/rQYNGoR3Fb3xxhuV8mwAAACgpjoV/qwMX6tTR+rV\nKyCv19CSJez+AQAAAIDaqtzw5/Dhw/rqq69kGIYMw9BTTz2l+vXrV2oRLVu21COPPCLTNCVJeXl5\n2rRpU6W+BwAAAFCT+LpkyrRYZCvIl4LB8HWnMyAp1PcHAAAAAFA7lRv+5OXlye/3yzRNXXLJJbru\nuuuqpJCbb7454vg3t9tdJe8DAAAA1AhpaQp06ChLcZGsm099ccrl8kui7w8AAAAA1Gblhj+rVq2S\nJBmGoSFDhshqrZoPkVarVYMHDw7/np+fXyXvAwAAANQU/qwcSZItb0X42lVXBdWoUVDr1lm1b58R\nq9IAAAAAADFU4fBHkrp27VqlxTidzvDrH374oUrfCwAAAEh0vuyT4U9BXviaxSL16xc6+o3dPwAA\nAABQO5Ub/uzfvz/8OjMzs0qL6dChgyTJNM2I9wUAAABwprKdP/b8vIjrTmfo6Df6/gAAAABA7VRu\n+HP06FFJoWPfLrnkkiotJiUlRSkpKZKkEydOVOl7AQAAAInOf+VVMm022Vavkvz+8HWnM7TzZ/58\nq0wzVtUBAAAAAGKlwuFPamqqDKPqzwyvV6+eJCkQCFT5ewEAAAAJLSVF/is6yygpkXXD+vDlFi1M\nXX55QPv3W7RuXbl/8gMAAAAAaphyPwmWlJTIMAylpqZWRz0AAAAALoC/rO/PGUe/hb5M5XbT9wcA\nAAAAahu+BggAAAAkMH92V0mSrWBlxPWyvj8eD31/AAAAAKC2IfwBAAAAElh4509eZPjTu3dANpup\nJUusop0mAAAAANQuhD8AAABAAvNf0VmmwyHbmtVSaWn4elqa1L17QCUlhpYt4+g3AAAAAKhNCH8A\nAACAROZwyN/lKhk+n7R6dcSQyxXq++PxEP4AAAAAQG1C+AMAAAAkOH9W6Og3ffttxHX6/gAAAABA\n7VThT4EHDhzQddddV5W1SJIKCwur/D0AAACAmsSX3VUpkrR8uXTzHeHr2dlB1a9vatUqqw4cMJSR\nYcasRgAAAABA9alw+OP3+7Vt27aqrEWSZBhGlb8HAAAAUJOEd/4sXx5x3WqV+vb1a9YsuxYssGrY\nMH8MqgMAAAAAVLcKH/tmGEa1/AAAAAC4MIEOHWXWqRPq+XP8eMSY0xnq++N2c/QbAAAAANQWFQp/\nTNOs1h8AAAAAF8Bmk79LphQIyFaQHzF0qu+PVfypDQAAAAC1Q7lf/5s0aVJ11AEAAAAgCr7efWRf\ntkSO+fPky70mfL1NG1Nt2gS1datFmzZZdPnlwRhWCQAAAACoDuWGPz179qyOOgAAAABEwesaqDqv\nviy7e6705H9HjDmdfm3d6pDbbSX8AQAAAIBaoMI9fwAAAADEL3/3HlJammwrV8g4cjhizOUK9f3x\neOj7AwAAAAC1AeEPAAAAUBM4HJLLJSMYlH3B/IihPn38slhMLVpkldcbo/oAAAAAANWG8AcAAACo\nKQYNkiQ5PPMiLtevL+XkBFVcbGjFCmssKgMAAAAAVKOYhD/FxcU6cuRILN4aAAAAqLmuvVaS5HDP\nOWPI5fJLkjwewh8AAAAAqOkuOvxZvXr1Bc1fvny5fv/736tnz57q3r27cnNzlZmZqTvuuENTp06V\nz+e72FIAAAAASFLHjgq0uFTWbVtl2fJDxJDTGer743bT9wcAAAAAaroLDn9mzJihAQMGaMSIERW+\n53/+53/0m9/8Rl999ZWOHj0q0zRlmqa8Xq9Wrlyp559/XsOGDdOGDRsutBwAAAAAZQxDPtcASWce\n/datW0Bpaaby8y06fDgWxQEAAAAAqkuFw5+ioiLdfffdGjNmjHbv3q2SkhJt37693PteeOEFTZ48\nORz4GIYR8SNJpmlq06ZNuvPOO7V169aLXgwAAABQ23md/SVJDvfciOt2u9Snj1/BoKEFC9j9AwAA\nAAA1WYXCH9M09fDDD2vBggUyTTN8fePGjee9b/HixZoyZUpE2FMWAp3+UzZ28OBBPfbYYxHvAQAA\nAKDifP1cMg1D9gUeye+PGDt19Bt9fwAAAACgJqtQ+PPhhx9q0aJFEbt1rFarioqKznvfW2+9FX5d\nFvTcfPPN+uyzz1RQUKCFCxfqL3/5i9q2bRsOfNatW6dp06Zd7HoAAACAWs1s1Ej+rGxZjh2VLW9F\nxJjLFQqDPB52/gAAAABATVZu+BMMBvX2229H7NoZPHiw5s6dq6FDh57zvrVr12rZsmXh+wzD0P33\n368XXnhBHTp0UFJSkjIyMjR48GDNmDFDWVlZkkIh0ZQpUypvhQAAAEAt43Oe7Pvzk6Pf2rUzdeml\nQW3fbtGWLUYMKgMAAAAAVIdyw5+VK1dq9+7dkiTDMDRy5Ej95S9/UePGjc9738cffxzxe/v27TV6\n9Oizzq1Tp45eeeUV2WyhbyBu2rRJ69evr9ACAAAAAETyus4e/hiG5HSGdv+43ez+AQAAAICaqtzw\nZ+HChZJCO3JatmypJ598skIPdrvdEbt+RowYIav13GeLt2jRQj//+c/Dvy9evLhC7wMAAAAgkq97\nT5l1UmVbuVzG0SMRYy5XqO+Px0PfHwAAAACoqcoNfzZu3CgptOvnlltukcVSfpugzZs3a+fOneHf\nrVarrr/++nLv69+/f/j12rVry50PAAAA4CySkuS9preMQED2hQsihvr29cswTC1YYJPfH6P6AAAA\nAABVqtwkZ9u2beHXvXv3rtBDly5dGn5tGIa6dOmievXqlXtfZmZm+PX3339fofcCAAAAcCZf+Oi3\nORHX09OlzMygjh0zlJdX/he7AAAAAACJp9xPe4cOHQpNtFjUvn37Cj10xYoVkkJHxUlSjx49KnRf\n8+bNw31/Dh8+XKF7AAAAAJzJ6xooSbJ75p0x5nKFtvx4PPT9AQAAAICaqNzwp6SkRJKUmpoqu91e\noYcuX75chmGEf+/evXvFirFYlJqaKtM0VVRUVKF7AAAAAJwpcHkHBZo1l23LD7Js3RIx5nSG+v64\n3fT9AQAAAICaqNzwx+v1yjAMJSUlVeiBu3bt0r59+8K/G4ahnJycChfkcDgknQqdAAAAAFwEw5C3\n7Oi3n+z+6dEjoDp1TK1YYdWxY7EoDgAAAABQlcoNf5KTk2Wapk6cOFGhB5Yd+Vamffv2Fer3U6Ys\n9CkLgQAAAABcHN85wp+kJOnqqwMKBAwtWsTuHwAAAACoacoNf1JTUyWFQplgMFjuA08PfwzDUNeu\nXStcTCAQ0PHjx2UYhlJSUip8HwAAAIAzefu6JEn2BR7J748YczpDv7vd9P0BAAAAgJqm3PCnWbNm\nkkLBzJYtW8qZLS1YsECGYcg0TUkV7/cjSdu2bQsHTA0aNKjwfQAAAADOZGZkyJeZLcuRw7Llr4wY\nc7lCfX88HsIfAAAAAKhpyg1/2rVrF369bNmy885dtWqVdu/eferhFot69+5d4WJWrVoVft2mTZsK\n3wcAAADg7M519FvHjkE1bRrU5s0W7dhhxKI0AAAAAEAVKTf8ycrKCr+eMWPGeedOmDAh/NowDOXk\n5Cg9Pb3CxcyePTv8un379hW+DwAAAMDZeZ39JUkO99yI64Yh9evH7h8AAAAAqInKDX+cTmf49dq1\nazV58uSzznO73friiy8ijnwbPHhwhQvZsGGDPB5P+Pfc3NwK3wsAAADg7Hw9c2WmpMi2fJmMY0cj\nxlyuUN8fj8cai9IAAAAAAFWkQj1/yoIY0zT10ksvady4cdq6datM01RhYaEmTpyoRx99VIZx6riI\nRo0a6aabbqpQEcXFxXriiSfCoVGdOnXUs2fPi1kPAAAAgNMlJcl7TR8ZgYDsixZGDJXt/Jk/36ZA\nIBbFAQAAAACqQrnhjySNHj1apmnKMAwFAgFNmDBBP//5z9W5c2f16dNHL730kkpKSiQpPO/uu++W\nw+Eo99nr16/Xbbfdpo0bN0oKHRf361//Wna7PYplAQAAACjjCx/9NifieuPGpjp3DujQIUPffVeh\njwYAAAAAgARQoU943bt31z333BMOdkzTPOPn9F0/3bt314gRI877zL1792rUqFH65S9/qe+//z58\nf926dTVq1KgolgQAAADgdF7XQEmS/Sd9fyTJ5aLvDwAAAADUNBX+et+jjz6qu+66K/y7YRgRP2Uh\nUK9evfTWW29FhEFnk5KSokWLFikYDEo6tWNo7NixysjIuMjlAAAAAPipQMcrFGjaTLYfNsuyfVvE\nmNMZ6vvjdtP3BwAAAABqigqHP4Zh6Mknn9SUKVP085//XKmpqeHAx2q1KicnRy+99JImTJigtLS0\ncp9Xv359XXrppeHgyGKx6Nlnn9X1118f1YIAAAAA/IRhnDr6zTMvYig3N6CkJFPLlllVXByL4gAA\nAAAAle2Cz3bo1q2bunXrJkkqKirSiRMn1KBBA9lsF35MRJcuXbRz50517NhRY8aMUffu3S/4GQAA\nAADK53UNUPK/psrhnqsTvxkZvp6SIvXqFdD8+TYtXmzVz34WiFmNAAAAAIDKEVVX17S0NGVkZFxU\n8CNJt99+u6ZMmaJPP/2U4AcAAACoQt5+oZ0/9gVuKRAZ8Did9P0BAAAAgJokqvAnWj179iT0AQAA\nAKqBeckl8nXJlOXwYdkK8iLGXK5Q3x+Ph74/AAAAAFATxDT8AQAAAFB9fK4BkiSHe27E9SuvDCoj\nI6j1663as8eIQWUAAAAAgMpE+AMAAADUEt6T4Y/dMy/iusUi9etXdvQbu38AAAAAINER/gAAAAC1\nhK9nrszkZNm/XSqj6FjE2Kmj3+j7AwAAAACJjvAHAAAAqC2Sk+W7urcMv1/2RQsjhpzOUzt/gsFY\nFAcAAAAAqCzlfq2vU6dOlfdmNpvS0tLUvHlzdenSRU6nU06nU1YrR0sAAAAA1cHrGijHvDmye+bK\ne93Pw9ebNTPVsWNAGzZYtXatRV26kAABAAAAQKIqd+ePaZqV9uPz+XTo0CGtWbNG06ZN04MPPqhB\ngwbpyy+/rI61AgAAALWe19lfkuRwzz1jrGz3j9vNl7MAAAAAIJFV6Ng3wzAq/UcKBUu7du3Sww8/\nrNGjR8vr9VbpYgEAAIDaLtCpswKNm8i26XtZdu6IGHM66fsDAAAAADVBhcKfytz9c/pPWRBkmqbm\nzJmjUaNGqaSkpKrXDAAAANRehiFf2e4fz7yIoauvDshuN7V0qVX8WQ4AAAAAiavcr/R5PJ5KezOv\n16tDhw5p3759WrJkiebOnas9e/aEA6Dly5frxRdf1NixYyvtPQEAAABE8roGKHn6h7K75+rEHSPC\n19PSpB49AvrmG5uWLrXK5QrEsEoAAAAAwMUqN/xp0qRJpb5hy5YtJUnXXnutnn76aX3wwQd65ZVX\nVFpaKtM0NX36dN1www3Kzc2t1PcFAAAAEOLtd3Lnz/x5UiAgWU/1+HE6Q+GPx2Mj/AEAAACABFWh\nY9+qit1u14gRIzRx4kSlpaWFdwC9/fbbsSwLAAAAqNHMJk3k79xFlkOHZPuuIGLM5Srr+2M9260A\nAAAAgAQQ0/CnTFZWlv74xz/KNE1J0uLFi7V9+/YYVwUAAADUXF7XAEmSwz034npmZlANGphavdqq\n/fuNGFQGAAAAAIhWXIQ/kjR48GBlZmZKkkzTrNReQwAAAAAilYU/ds+8iOtWq9SvX2j3z4IF7P4B\nAAAAgEQUN+GPJP36178Ov87Pz49hJQAAAEDN5ut1tcykJNmXLZGKiiLGnM5Qrx+Pp9wWoQAAAACA\nOBRX4U/Pnj3Drzdu3BjDSgAAAIAaLiVFvtxrZPh8cixeGDHkdIZ2/rjdVp08mRkAAAAAkEDiKvxp\n1aqVbLbQtwsPHToU42oAAACAms3rGijpzKPfWrUy1a5dUHv3WrRxY1x9ZAAAAAAAVEDcfZKrV6+e\nTNPUkSNHYl0KAAAAUKN5nf0lSQ733DPGTt/9AwAAAABILHEX/gQCofPF7XZ7jCsBAAAAarZA5ysV\nvKSxbBs3yLJ7V8QYfX8AAAAAIHHFVfjj9/t17NgxGYah+vXrx7ocAAAAoGazWMK7f3569FufPn5Z\nraa++caq0tJYFAcAAAAAuFhxFf6sW7dOwWBQktS0adMYVwMAAADUfKeOfpsTcb1ePalr16COHze0\nfDlHvwEAAABAIomr8MftdodfX3XVVbErBAAAAKglfK4BkiTHfLd08otYZcr6/ng8hD9W8q/2AAAg\nAElEQVQAAAAAkEjiJvwpKirShx9+GP69W7duMawGAAAAqB2CTZrK3+lKWQoLZVu9KmLM5SoLf+j7\nAwAAAACJJG7CnzFjxqiwsFCS1KBBA/Xv3z/GFQEAAAC1Q7jvj3tuxPWuXYOqW9dUfr5FBw/GojIA\nAAAAwMWIefhTVFSkhx9+WLNnz5YkGYahm2++WQ6HI8aVAQAAALWDt+zot5+EPzab1KePX6ZpaOFC\ndv8AAAAAQKKIySe4QCCgVatWaf78+Zo6daqOHj0q0zRlGIaaNWum+++/P+r36NixY7lz5syZo0sv\nvVSSdOLECf3973/XF198oV27diktLU25ubl6+OGH1bZt24j7gsGgJk6cqI8//lhbt25VUlKSunbt\nqtGjRyszM/OM95k5c6amTJmizZs3yzAMdenSRffee6/69OkT9ToBAACAaPlyr5GZlCT7siVScbGU\nmhoeczoD+s9/7HK7rfrFL/wxrBIAAAAAUFHlhj/XXXddpb1ZMBjU0aNHVVRUpODJZrJloY8kORwO\nvfTSS6pTp07U7/X666+fc+yVV17RsWPHlJ6eHq7hgQce0DfffKNf/vKXevDBB7V//37985//1K23\n3qrp06erVatW4fvHjBmjGTNmaNCgQRo1apSOHTumSZMmafjw4Zo4caJycnLCc8ePH6/XX39dvXr1\n0v/7f/9PgUBA//rXv3T33Xfrtddeq9R/XwAAAOCi1KkjX8+r5VjglmPJInkHDgoPnd73xzRLdfJP\ndwAAAABAHCs3/Nm2bZsMw5BpmlVSQNmzU1JS9Oabb6pnz56V8tzrr7/+rNe//vprbdu2LSJk+vzz\nz7Vo0SKNGjVKTz75ZHju1VdfrZtuukl//vOf9cYbb0iS8vLyNGPGDF1//fURAdOgQYN03XXXaezY\nsZo5c6Ykaffu3Ro/fryys7P13nvvyWq1SpIGDx6swYMHa+zYsRowYIDsdnulrBkAAAC4WF7XADkW\nuGV3z40If9q2NdWqVVDbt1u0ZYuhdu2q5nMBAAAAAKDyVLjnj2EYlf4jhXbd5ObmasaMGbrmmmuq\nbKFSqL/Q888/r+7du2vYsGHh65988okkacSIERHzr7zySuXk5Mjtduvo0aPnndukSRP97Gc/09q1\na/X9999LkmbNmiWfz6c77rgjHPxIUlpamoYOHaoDBw5o0aJFlb9QAAAA4AKF+/545kVcNwzJ6Qzt\n/pk3j74/AAAAAJAIKhT+mKZZ6T/p6em6+eab9e6772rChAlq3759Va9V48eP1/79+/Xss89GXP/u\nu+/UrFkzNW3a9Ix7srKy5PP5tGbNmvBcq9V61t4+WVlZkqSCgoLwXEkRx8Cday4AAAAQS4EruyiY\nkSHb+nWy7NkdMeZ0BiRJHo/1bLcCAAAAAOJMuV/dmzRpUqW9mdVqVcOGDZWRkaF69epV2nMrorCw\nUFOnTtXQoUPVsWPH8PWioiIdPnxYbdu2Pet9zZo1kyTt3LlTkrRr1y6lp6ef9ai2srk7duwIz5VC\nu4J+qnnz5hFzK4Lz1SOV/Xsk8r9Loq8h0euXWEM8SPT6pcRfQ6LXLyX+GhK9fok1xIOo67da5HX2\nV/JH0+XwzFPpbXeEh/r188swTC1aZJPfL1XVqcW1/r9BHGANsZfo9UuJv4ZEr19iDfEg0euXEn8N\niV6/lPhrSPT6JdYQDxK9/lgqN/yprB48sfbOO++otLRU9913X8T14uJiSVJycvJZ7yvrC1Q2r7i4\nOBzyVGSu1WqVw+E4Y25KSkrE3PKkp6fKaq3wKX21SqNGdWNdQtQSfQ2JXr/EGuJBotcvJf4aEr1+\nKfHXkOj1S6whHkRV/38Nlj6arrpLFqjuQ6f+bs7IkLp3l779Vvrhh7rq3bsSCj2PWv3fIE6whthL\n9PqlxF9DotcvsYZ4kOj1S4m/hkSvX0r8NSR6/RJriAeJXn8s1IpDu48cOaIPPvhALpdLrVu3jnU5\nF+XgwWLSzZ8wjND/0xcWHpOZoH2HE30NiV6/xBriQaLXLyX+GhK9finx15Do9UusIR5URv2WrrlK\nlxT88ksd3H9Espz68lGfPg59+22SPv20VB07eiun6J/gv0HssYbYS/T6pcRfQ6LXL7GGeJDo9UuJ\nv4ZEr19K/DUkev0Sa4gHiV5/VcvIOHcoVivCn1mzZqmkpETDhg07YywtLU2SVFJSctZ7y3bmpKam\nhv/3XHOPHz8e8czU1FQFAgF5vd4zdv/8dG5F8H/cZ2eaif9vk+hrSPT6JdYQDxK9finx15Do9UuJ\nv4ZEr19iDfEgmvoDTZvLf0Un2davk3X1d/JflRUeczoDevVVad48m554omrCnzK1+b9BvGANsZfo\n9UuJv4ZEr19iDfEg0euXEn8NiV6/lPhrSPT6JdYQDxK9/lg47zlio0ePPmfQEWslJSX6/e9/X6G5\ns2fPlsPhUL9+/c4YS01NVXp6uvbu3XvWe3fvDjW7bdOmjSSpZcuWKiwslNd75gfesh4/p8+VdNZn\nl81N1J1IAAAAqJm8zgGSJLt7XsT17t0DqlPHVF6eRUePxqIyAAAAAEBFnTf8+frrr3X77beHA5B4\nsWfPHt1+++366quvyp1bXFysvLw8ZWdnn7OvT05Ojvbu3XvWdS5fvlzJycnq3LlzeG4wGFRBQcEZ\nc1esWCFJ6tq1a3iuJK1cufKcc7t161buGgAAAIDq4nP1lyQ53HMjrjscUu/eAQUChhYurBUHCAAA\nAABAwjpv+CNJ69ev14033qhPP/20Ouop1+eff66hQ4dq3bp1FZq/YcMG+Xw+dejQ4Zxzbr75ZknS\nhAkTIq4vW7ZMa9as0Q033BA+9u2mm26SYRhnzN26davmzp2rXr16qVWrVpKkIUOGKDk5WZMnT5bf\n7w/PPXTokGbOnKlWrVqpV69eFVoHAAAAUB28ub1lOhyyL/1GOnlUcRmnM/Q3rdttjUVpAAAAAIAK\nOu9X9urWrauioiIdO3ZMTz/9tObMmaOnn35azZs3r676wvbt26eXXnpJs2fPlnnycL969eqVe9/W\nrVslSS1atDjnnAEDBmjQoEGaOHGiioqKlJubq927d+uf//ynmjZtqsceeyw894orrtDIkSP13nvv\n6cEHH9S1116rw4cP67333lNycrLGjBkTnpuRkaHHH39cL7zwgu68804NHTpUpaWlev/991VUVKRX\nX31VFku5+RsAAABQfVJT5et1tRwLPLIv+Ua+AT8LDzmdAUmSx2OTVBqjAgEAAAAA5Tlv+PPhhx/q\nvvvu044dO2Sapr766it5PB6NHDlSo0aNqlD4Eq2ioiK9++67mjBhgk6cOBEOflq2bKm//e1v5d5/\n9OSB5GU7d87lL3/5i/7xj3/o3//+tz777DPVq1dPLpdLjz76qC655JKIuU899ZQuvfRS/etf/9KY\nMWOUkpKinj176pFHHtFll10WMfc3v/mNGjZsqAkTJmjs2LGyWq3Kzs7W888/Hz4eDgAAAIgnXmd/\nORZ45HDPjQh/OnQIqlmzoLZssWjbNkOtW9NxFQAAAADi0XnDn/bt22vatGl6+OGHtWzZMklSaWmp\n/vGPf2jSpEkaNmyY7rjjDrVv377SC/vhhx/0/vvva+bMmSopKZFpmjIMQ5LUo0cPvf7660pPTy/3\nOSNHjtTIkSPLnedwODR69GiNHj263LmGYWj48OEaPnx4uXOl0PFvQ4YMqdBcAAAAINZ8rgHSC3+U\nwzNXxaddN4zQ7p8PP7TI47FpxAhfrEoEAAAAAJxHuWeONWzYUBMnTtRjjz0mq9UaDmBKSkr0wQcf\naMiQIfrFL36hN954Q6tWrZLPd3EfAP1+v1atWqU33nhDN954owYPHqypU6fq+GnnjFutVj3yyCOa\nNGlShYIfAAAAABfO3yVTwUaNZFu3VpZ9eyPGXK5Q3x+Ph74/AAAAABCvzrvzp4xhGLrnnnvUt29f\njR07Vnl5eeEQyDRNbdy4Ud9//73efPNN2e12de7cWW3btlWLFi3UvHlzpaWlKTk5WUlJSSotLdWJ\nEydUVFSk3bt3a9euXdqyZYvWrVsnr9cbfmbZ+xqGIdM0lZ2drWeffVadO3euon8KAAAAAJIki0Xe\nfi4lz/xIdvdcld5ye3iob99Q358FC2wKBCQrGRAAAAAAxJ0KhT9lOnXqpA8++ECff/65/vrXv2rr\n1q2SFBEEeb1eFRQUqKCg4IIKOVvgY5qmWrdurYceeohj0wAAAIBq5HUNVPLMj+TwzIsIfy65xFSX\nLgGtXm1VQYFFXbsGY1glAAAAAOBsyj327WwGDx6s2bNn680331Rubm44rJFOBUFlykKcs/2c7vQA\nyTAM9erVS2+++aZmz55N8AMAAABUM5+zvyTJ4ZknBSMDnlNHv13Qd8kAAAAAANUkqk9rAwcO1MCB\nA3Xw4EF9+eWXWrhwoQoKCvTjjz9W6P7TA6CMjAxlZWWpT58+GjRokBo1ahRNaQAAAACiEGzeQv4O\nHWXbuEHWtWsU6HJVeMzpDOiNNyS326pHH41hkQAAAACAs6qUr+qlp6fr1ltv1a233ipJ2rt3r7Zs\n2aJdu3Zp7969Ki4uVklJibxerxwOh1JSUpSamqqmTZuqRYsWatOmjZo1a1YZpQAAAACoJF7XANk2\nbpDDM08lp4U/vXoFlJxsavlyq4qKpLS0GBYJAAAAADhDlZzT0LRpUzVt2rQqHg0AAACgmvic/aV/\nvCWHe45KHvx9+HpyspSbG5DbbdPixVZde20gdkUCAAAAAM5wUT1/AAAAANR83qv7yLTbZV/yjVRS\nEjHmdIb6/rjd9P0BAAAAgHhD+AMAAADg7NLS5OuZK6O0VPaliyOGnM7Qbh+PxxqLygAAAAAA50H4\nAwAAAOCcfM7+kiSHe27E9c6dg7rkkqA2brRq924jBpUBAAAAAM6lys5oKCws1IYNG7Rp0yYVFhaq\nuLhYdrtdTz31VFW9JQAAAIBK5nUNUOr/jpXDPVfFp123WKR+/QL66COLPB6rbrvNH6sSAQAAAAA/\nUanhj9fr1dSpU/XFF19o9erVMk0zYrxJkyZnDX9efvllSdIDDzygOnXqVGZJAAAAAKLgvypLwfR0\n2daulrFvn8wmTcJjLpdfH31kl8djI/wBAAAAgDhSace+zZs3T9dff73GjRun7777TsFgUKZpRvyc\ny//93//p3Xff1bBhw7Rt27bKKgkAAABAtKxWefu6JEmO+fMihk7v+xMMVndhAAAAAIBzqZTwZ/r0\n6Ro9erR2794dDnkM49S536e//qnt27drx44dkqRt27Zp1KhROnz4cGWUBQAAAKAS+FwDJJ3Z96dp\nU1NXXBFQYaFFa9bQThQAAAAA4kXUn9BWrFihZ599VoFAIBzymKapFi1a6MYbb9RDDz103l0/RUVF\nysjICM/ZtWuXXnnllWjLAgAAAFBJvM7+kiS7Z570k7/ty3b/uN1V1k4UAAAAAHCBogp/AoGAnnvu\nOZmmKcMwZJqmrr32Wn3yySf6+uuvNW7cOD344IOSzr37p3Pnzvryyy/Vs2dPSaHg6OOPP9aPP/4Y\nTWkAAAAAKknw0pbyX3a5rPv3ybpubcSYyxXq9eN2W2NRGgAAAADgLKIKf5YuXapNmzaFg53HH39c\nf/3rX3XFFVdc0HPq1Kmj8ePHq3nz5pJCodKsWbOiKQ0AAABAJfKe4+i33NyAHA5Ty5ZZVVISg8IA\nAAAAAGeIKvz58ssvw6/79++v3/3udxf9rLS0NN16663h35cvXx5NaQAAAAAqUbjvjycy/ElNlXr2\nDKi01NCSJez+AQAAAIB4EFX4s2rVqvDrO++8M+piBg8eHH69cePGqJ8HAAAAoHL4rukj02aTffEi\n6cSJiDH6/gAAAABAfIkq/Dl48KAkyWq1KicnJ+pimjZtKovFItM0deTIkaifBwAAAKBymGl15evR\nS8aJE7IvXRwxVtb3x+Nh5w8AAAAAxINKCX/q1asnmy36b/lZrVY1aNBAknT8+PGonwcAAACg8pw6\n+m1exPWrrgoqPT2otWut2rfPiEVpAAAAAIDTRBX+JCUlSZK8Xm+lFCNJx44dkySlpqZW2jMBAAAA\nRM/r7C9Jsrsj+/5YLFLfvqGj3+bPZ/cPAAAAAMRaVOFPw4YNJUnFxcXauXNn1MVs27ZNPp9PhmGE\nnw0AAAAgPvizchRs0ED21atk7N8fMeZyhcIfj4e+PwAAAAAQa1GFPx07dgy//uKLL6Iu5rPPPgu/\nvvzyy6N+HgAAAIBKZLXK2y+0+8exwB0x5HSe6vtjmtVdGAAAAADgdFGFPz169JAkmaapt99+W3v2\n7LnoZ23evFnvvvtu+Perr746mtIAAAAAVAHfyaPfHD85+u3SS021bx/Uvn0WrV8f1ccMAAAAAECU\novpUNmTIECUlJckwDB07dkwjRozQ5s2bL/g5K1eu1G9/+1uVlpZKkpKTkzVo0KBoSgMAAABQBSL6\n/vxki4/LdWr3DwAAAAAgdqIKf9LT0/WrX/1KpmnKMAzt2LFDN954o5577jktWbJExcXF57x3//79\nmj17th544AHdcccdOnDgQPg5t912mzIyMqIpDQAAAEAVCLZqLX/7y2Tdt1fWDesjxsqOfnO76fsD\nAAAAALEU9aeyJ554Qt9++602bNggwzDk9/s1bdo0TZs2TZJkt9slhY6GO3DggFwul44ePaqSkpLw\nM8pCH9M01blzZz300EPRlgUAAACgivic/WXbvEkO9xyVXNEpfL1374BsNlOLF1tVWiolJcWuRgAA\nAACozaI+jDspKUn/+Mc/1KVLl3CII4UCHdM05fV6w3P9fr/27t2r48ePh8clhYOfTp066e9//7tS\nUlKiLQsAAABAFfG6Bko6s+9P3bpSt24BlZQYWraMo98AAAAAIFYqpRNrkyZNNHXqVP3ud79TSkpK\nRKhzvh8pFBIlJyfr3nvv1bRp0zjuDQAAAIhzvt59ZNpssi9eJJ3s21nG6QxIou8PAAAAAMRSpYQ/\nkuRwOPT4449r3rx5evrpp9WvXz+lpqaGd/j89Cc9PV0ul0tjxozRggUL9Oijj4aPiAMAAAAQv8y6\n9eTv1kNGSYnsy5ZEjLlcob4/Hg99fwAAAAAgVir9E1n9+vU1cuRIjRw5UqZp6uDBgzp8+LCOHTsm\ni8WiunXrqn79+kpPT6/stwYAAABQTbyuAbIvXSyHe658fZ3h69nZQdWrZ2rVKosKCw01amTGrkgA\nAAAAqKUqbefP2RiGoUaNGql9+/bKzs5WZmam2rZtS/ADAAAAJDivs78kye6ZF3HdZpP69PHLNA0t\nWMDRbwAAAAAQC1Ua/gAAAAComfzZXRWs30D2VfkyDhyIGHO56PsDAAAAALFU6eHPhg0bNH369ArN\nXb58uV5//XUVFBRUdhkAAAAAqpLNFj7uzTE/cveP0xnq++N222Ry6hsAAAAAVLtKC382bdqk3/72\ntxo6dKheeeWVCt2ze/duvfXWW7r11lt1yy23aN26dZVVDgAAAIAqdq6j39q2NdW6dVC7dlm0ebMR\ni9IAAAAAoFarlPDnq6++0k033aRly5bJNE0dPXr0gu43TVMFBQW65ZZb9NVXX1VGSQAAAACqmNc1\nQJLkcM/VT7f4nL77BwAAAABQvaIOfwoKCvToo4+qtLQ0fC0YDF5QAGQYhgzDkNfr1WOPPabVq1dH\nWxYAAACAKhZs3Ub+tu1k3bNb1u83Row5nWV9fwh/AAAAAKC6RRX+mKapZ555Rn6/X4ZhyDRN1a9f\nX//1X/8lq7X85q65ubl6+umn1alTJ5mmKcMw5PP59Ic//CGasgAAAABUE9/Jo98c7jkR1/v29cti\nMbVwoVU+XwwKAwAAAIBaLKrwx+1264cffggHP0OGDNG8efP05z//WampqeXe37hxY40cOVIff/yx\nHn30UZknj4r4/vvvNXfu3GhKAwAAAFANvK6BkiS7O/Lv9wYNpJycoIqLDa1YUf4XwwAAAAAAlSeq\n8Of0/jyDBg3Syy+/rDp16lzUs+69914NHjw4/PucOXPOMxsAAABAPPD16SvTapXjm4XSaUdBS6f3\n/SH8AQAAAIDqFFX4k5+fH3593333RV3MXXfdFX6dl5cX9fMAAAAAVC2zXn35u3aXcfy47MuXRYy5\nXPT9AQAAAIBYiCr8OXDggCQpKSlJnTt3jrqYzp07Kzk5WaZpqrCwMOrnAQAAAKh6XtcASZLjJ0e/\ndesWUGqqqbw8iw4fjkFhAAAAAFBLRRX+FBcXyzAMpaWlVUoxhmGEewUdP368Up4JAAAAoGqVhT92\nT2T4Y7dLvXsHFAwaWriQ3T8AAAAAUF2iCn/q1Kkj0zR15MiRSikmEAjo0KFDMgxDDRs2rJRnAgAA\nAKha/pxuCtarL1tBvoyf7OB3uej7AwAAAADVLarw59JLL5Uk+f1+rV+/Pupivv32WwWDQUlSRkZG\n1M8DAAAAUA1sNvn69JNhmnIscEcMOZ30/QEAAACA6hZV+JOZmRl+PXXq1KgKCQaDGj9+fPj3nJyc\nqJ4HAAAAoPqcOvptXsT1yy4LqkWLoLZts2jLFiMWpQEAAABArRNV+HPttdeGX0+fPl3Tpk27qOd4\nvV4988wzWrZs2VmfDQAAACC+eZ39JUkO91zJNMPXDUNyOkNHv7H7BwAAAACqR1ThT58+fdShQwdJ\nkmmaeu6553Tvvfdq4cKFKi0tLff+3bt3a/LkyRo8eLA+++wzSZJhGOrSpYtyc3OjKQ0AAABANQq2\nbadA6zay7top66bvI8ZOHf1G3x8AAAAAqA5Rf/Xu+eef1/Dhw+X3+2WapubPn6/58+fLbrerVatW\nat68uerWraukpCSZpqmSkhL9+OOP2rFjh3788UdJoeDIMEJHQNhsNo0dOzbasgAAAABUM69roFIm\nviu7Z64Cl3cIX+/XLyDDMLVwoU1+v2RjAxAAAAAAVKmoP3ZlZWVp3LhxevLJJxUIhL7RZ5qmvF6v\nNm3apM2bN5/1PjPiKAhDpmnK4XDotddeU6dOnaItCwAAAEA18zr7K2Xiu3K45+rE7+4LX2/UyNRV\nVwW1apVV+fkWde8ejF2RAAAAAFALRHXsW5kbbrhBkyZNUps2bcKhjmEY4d08Z3P6uGma6tSpkyZP\nnqwBAwZURkkAAAAAqpmvbz+ZFoscCxdIXm/EGH1/AAAAAKD6VEr4I0ldu3bVv//9b7388svq3bu3\n7Ha7TNM878//Z+++w6Mq0z6Of89UUgmhigiKosDSe00CqCDEhg2WtawF1HVZxYq49l1BxcKrYF1B\nXbuAEHQRgSQQBKUXAVFQmiIQQkhhypnz/jEQCEETQpKZCb/PdeVicp5T7gfWOTtzn/t+nE4n/fr1\n44UXXmDq1Km0bdu2osIREREREZEqZtVMwN+hE0ZBPs5l3xYbS0kJdglIT9e6PyIiIiIiIpWtQh+7\nczgcpKamkpqaisfj4fvvv+enn35i3759FBYWYhgGMTEx1KpVi3POOYemTZviUMNvEREREZFqw5vc\nB+fSb3Cmz8XXvWfR9i5dTKKiLJYts5OXB7GxIQxSRERERESkmqu0zIvb7aZ169a0bt26si4hIiIi\nIiJhxpvSj5jx43Clz6Ng9MNF291u6N7dZN48B1lZdvr3N0MXpIiIiIiISDVXYW3fRERERERE/B06\nEoiLx7FyBca+7GJjh9f9SU9X9b+IiIiIiEhlUvJHREREREQqjtOJr2dvDMvCuSCj2FBycrDaJyND\n6/6IiIiIiIhUJiV/RERERESkQnlT+gLgSp9XbHuLFgHq1Qvwww92tm83QhCZiIiIiIjIqeEP+y2M\nHj266LVhGPz73//+3fGKdrzriYiIiIhI+CtK/mTMB8sCI5joMYxg9c/HH9vIyHAwbJgvlGGKiIiI\niIhUW3+Y/Jk2bRqGYWBZ1nGTMYfHK9rvXU9ERERERMJf4KymmI2bYN/6M/bNP2Ce3axoLDnZz8cf\nO8nIsCv5IyIiIiIiUknU9k1ERERERCqWYeBNDlb/OI9p/XZ43Z/MTDuBQJVHJiIiIiIickooNflj\nWVap4xX9IyIiIiIika1Y67ej1K9v0aKFSXa2jTVr9CyaiIiIiIhIZfjDtm9vv/32Hx5c2riIiIiI\niJyafL2TsGw2nAsywecDp7NoLCXFZP16OxkZDtq29YYwShERERERkerpD5M/Xbp0+cODSxsXERER\nEZFTk5VQC3/7DjiXLcW57Ft83XoUjSUn+5k0yUV6up2RI0MYpIiIiIiISDWlPgsiIiIiIlIpvMl9\ngJLr/nTrZuJyWXzzjZ2CglBEJiIiIiIiUr0p+SMiIiIiIpXCl9IPAFdG8eRPdDR07Wri9RosXmwP\nRWgiIiIiIiLV2kklf1JTU+nSpQtdunSha9eu7Ny5s6LiEhERERGRCOfr2JlATCyOFcsxcvYVG0tO\nNgGYP/8PO1GLiIiIiIhIOZxU8uenn37iwIED5ObmkpubS506dSoqLhERERERiXROJ75evTECAZwL\nMosNpaT4AcjMVOWPiIiIiIhIRTup5E9CQkKx303TPKlgRERERESkevGm9AXAdcy6P61aBahdO8D6\n9XZ27TJCEJmIiIiIiEj1dVLJn44dO2JZVtHv33333UkHJCIiIiIi1YfvcPInYx4c9dnBZoOkpODD\nY+npqv4RERERERGpSCeV/Lnttttwu90YRvBJvUmTJlVIUCIiIiIiUj2YTc/BbHQG9q0/Y9uyudhY\ncnKw9VtGhtb9ERERERERqUgnlfxp3rw5r776KnXr1sWyLLKysvjHP/5BTk5ORcUnIiIiIiKRzDB+\nt/VbcnKw8icjw350UZCIiIiIiIicpJNK/gB069aNL7/8kn/+85+0aNGC2bNn06dPH0aPHk1aWhrf\nf/89eXl5BAKBiohXREREREQiTFHyJ2N+se2nn27RrJnJ7t02vvvupD+aiIiIiHD9zkMAACAASURB\nVIiIyCEn1V/hxhtvLPZ7QkICDRs2ZOfOnUyfPp3p06eX+9yGYWgNIRERERGRasDXKwnLMHAuzASf\nD5zOorGUFJNNm+xkZNhp1UoPjImIiIiIiFSEk0r+LFq0qGi9n6MZhoGlvg0iIiIiIgJYibXxt2uP\nc8VyHMuX4e/arWgsOdnP66+7SE938Le/+UIYpYiIiIiISPVRab0VDMMo94+IiIiIiFQvR1q/FV/3\np0cPE4fDYvFiOwcPhiIyERERERGR6uekkz+WZVX4j4iIiIiIVC++5EPJn/TiyZ/YWOjc2eTgQYNv\nvrGHIDIREREREZHq56Tavm3YsKGi4hARERERkWrM16kLVnQMjuVLMfbnYNVMKBpLTjb5+msH6el2\nBg8OYZAiIiIiIiLVRKW1fRMRERERESnicuHt1RsjEMC5cEGxoeRkPwDp6Sf1bJqIiIiIiIgcouSP\niIiIiIhUCV9yH6Bk67d27QLUrGmxZo2d334LQWAiIiIiIiLVjJI/IiIiIiJSJbwp/QBwpc8ttt1u\nh969g9U/c+cee5SIiIiIiIicKPVVEBERERGRKmGe0wzz9EbYf/4J25bNBM5qWjSWkmKSlubk6ach\nK8tNfLxV9FOzpkV8PEe9toiJAZseZRMRERERETmuciV/LMti1apVLFu2jD179lBQUECtWrVo3Lgx\nPXv2pH79+hUdp4iIiIiIRDrDwJvch6j33sGVMZ+DRyV/+vb143BYrFxpsHKlq9RT2WwWcXEUJYOO\n/BTfVrPmkf2Cry1q1gwmkpzOypysiIiIiIhI6JxQ8sc0TT744ANeeeUV9uzZ87v7de/enZEjR9Ku\nXbuTDlBERERERKoPX0rfYPInfR4Hb7ipaHujRhZz5hTwyy8xbN9+kNxcg/37ITfXOPTaOPS6+Lb9\n+41yxxIdXTxpdHRlUfA1h5JFJRNH8fEW0dFglP/yIiIiIiIilabMyZ+8vDxGjhzJ119/jWVZxcYM\nwyi2bdGiRSxevJg777yT4cOHV1y0IiIiIiIS0by9U7AMA+fCTPD7wXHkI0mrVgFSUmDPHh/HfOQ4\nLp+PoiTRgQPFE0RHXhu/s09wv4ICG7/+Wr65OBwlE0dNmsADDxjUq1eGCYiIiIiIiFSSMiV/LMvi\njjvuYPHixUAw2XN4+9F/Hj0WCAR4/vnnsSyLESNGVGjQIiIiIiISmazatfG3aYdz1QocK5bh79y1\n3OdyOqF2bYvatQFOPNliWZCfT1EF0dGVRccmiQ5vCyaQKHqdnW0jO/vIORcsgLp1nYwe7S33vERE\nRERERE5WmZI/b7/9NosXLy6W9ImLi6Nz5840adKE6OhocnNz2bhxIytXrsTr9RZVA02YMIEuXbrQ\nvn37Sp2IiIiIiIhEBl9KX5yrVuBKn3dSyZ+TZRgQGwuxsRann16+Sh2Ph6IE0Zo1doYPjyItzaHk\nj4iIiIiIhFSpyR+/388bb7xRlMyx2Wzcfvvt3HzzzURFRZXYPzs7m//7v//j/fffxzAMTNPkxRdf\nZPLkyZURv4iIiIiIRBhvSl+iXxyPK2M+BfeODnU4J8Xthrp1LerWhbPP9vPkk7Bpk52NG22cd14g\n1OGJiIiIiMgpylbaDvPmzWP37t1AsKXbY489xt///vfjJn4AEhMTeeSRRxg1alRRO7glS5bw/fff\nV2DYIiIiIiISqXydumBFR+NY9i1G7v5Qh1NhDAMGDw6+Tksr8/KqIiIiIiIiFa7U5M8333xT9Lp7\n9+5cddVVZTrx8OHDadmyZdHvWVlZ5QhPRERERESqHbcbb49eGKaJc+GCUEdToa64Ivinkj8iIiIi\nIhJKpSZ/Vq5cWfR6yJAhJ3Tya665puj1smXLTuhYERERERGpvnwpfQFwZcwLcSQVq0cPqFcvwLp1\ndrZsMUIdjoiIiIiInKJKTf7s2bOn6HXHjh1P6ORH73+4dZyIiIiIiIg3OZj8caZXr+SPzQaDBvkB\nSEtzhjgaERERERE5VZWa/Nm/P9iD2+12U7t27RM6eYMGDYpe5+TknGBoIiIiIiJSXZnnnod5WkMc\nWzZj+/mnUIdToVJTg8mfWbPU+k1ERCJTIABLl9rQ13kiIpGr1ORPYWEhhmEQFxd3wiePjY0teu3x\neE74eBERERERqaYMA29R67f5IQ6mYvXoYZKYGGD5cjs7dqj1m4iIRJYFC+xceGE0F10Uw4gRoY5G\nRETKq9Tkz2F2u70y4xARERERkVOML7kPAK5q1vrN4YABA1T9IyIikWXjRhvDhkVxxRXRrF4d/B5w\n1izwekMcmIiIlEuZkz8iIiIiIiIVyZsUTP44F2SAaYY4mop1uPVbWpqSPyIiEt527TK4+243ycnR\nzJnjoE6dAGPHHqR9e5P8fFi2TA+Ei4hEIiV/REREREQkJKw6dfC1bottfw6OlctDHU6F6t3bJC7O\nYskSO7t2qfWbiIiEn7w8ePppF127xvDOOy5cLrjzTg9LluRz440++vYNPsiQkaHkj4hIJFLyR0RE\nREREQsZ3eN2fatb6ze2GCy/0Y1kGX3yh6h8REQkffj+8846Tbt1iePZZN4WFMGSIj8WL83nwQS+H\nl/1OSgpW5WZk6D4mIhKJlPwREREREZGQ8VbT5A+o9ZuIiIQXy4I5c+z06RPN3XfX4LffbKSk+Jk7\nt4AJEw7SsKFVbP9OnUxiYmD5chu5uSEKWkREyk3JHxERERERCRlfl25YUVE4ln2LcaB6fbPUp4+f\n6GiLrCw72dmhjkZERE5lq1fbuOKKKIYNi2bjRjstWph88EEBH31USKtWgeMe43JBSgoEAgYLF+pB\nBhGRSKPkj4iIiIiIhI7bja97Twy/H2fWwlBHU6Gio6FfPz+maTB7tr40ExGRqrdtm8Ftt9Xg/PNj\nWLjQQYMGAV58sZB58wro29cs9fjzzw/+mZmpdX9ERCJNmT+BmKbJb7/9hmVZpe9cQcfXr1+/XNcS\nEREREZHI4U3pi2veVzjnz4W/XBPqcCpUaqqfmTOdpKU5GTrUH+pwREQigscD06c76NMH9NVQ+ezf\nDy++6OL11114PAYxMRYjR3oZMcJLdHTZz3PBBcE/g+v+eColVhERqRxlTv7s2bOH5OTkcl3EsqwT\nPt4wDL777rtyXU9ERERERCKHNzm47o8zY36II6l4F1zgx+22yMiwk5sL8fGhjkhEJPw9/bSL//s/\nNwBt2kRzzTU+Lr/cT5065Xsg+VTi9cLkyU6ee85FdrYNu93ir3/1cs89XurWPfG/v5YtoX79AD/+\naGP7doNGjfRvICISKcrc9s2yrHL9nMzxIiIiIiJS/ZnNW2DWb4Djxx/gp59CHU6Fio2FlBQTr9fg\nyy/V+k1EpDS7dhm88YYLm83itNNg9Wo7Y8bUoE2bGK69NoqZMx14VIBSgmXBjBkOevWK4aGHapCd\nbWPAAB+ZmQWMG+cpV+IHwDAgOTnYHk6t30REIkuZkz+GYVTZj4iIiIiInEIMA19KsPqHOXNCG0sl\nGDTIB0BampI/IiKlmTDBRWGhwVVX+dm2DT76qIArrvDhdMLs2Q5uuimK1q1jufdeN99+a0PPDsM3\n39gYNCiam2+O4qefbLRvb/LZZwW8/fZBmjULnPT5k5ODbUuDrd9ERCRSlOldO5KrcDIyMnj99ddZ\nt24dDoeDFi1acNttt9G9e/eiffr27cuOHTv+8Dxvv/02Xbt2ZerUqYwePfp39+vXrx8TJ04stm3a\ntGm8++67/PjjjxiGQatWrRgxYgS9evU6ucmJiIiIiFQT3uQ+1PjwPXj0UeI/+IhAzZpYcTWxatYk\nEB+PFV8TKz4++Puh7VZ8PIH4mhATE3w0OUwNGODH4bCYP99Bfn4wXBERKWn7doMpU5w4HBb33uvB\nbnfSp49JSorJgQPBJPqHHzpZtMjBlCkupkxx0bRpgKuv9nHllT4aN47c76/KY/NmgyeecDNrlhOA\nxo0DjBnj4dJL/djK/Lh36ZKSgpU/CxbYCQSo0HOLiEjlKTX5s2HDhqqIo1J88sknjBkzhs6dOzNm\nzBjy8/OZMmUKt9xyC2+++SZdu3YF4JFHHqGwsPC455g8eTLfffcdZ5xxRrHtw4YNo0uXLiX2r3/M\nSoQTJ07kxRdfpGvXrjz00EOYpsmHH37ILbfcwgsvvED//v0raLYiIiIiIpHL2+d8AjUTsO3ciWvn\nzhM61rLbg4mh+JoE4g8lhuLiSySOAjUTirYfThwdHsPprKSZQUIC9Oplkp7uYN48Bxdf7K+0a4mI\nRLLnnnPh9Rpcf72XJk2KJ3Li4mDoUD9Dh/rZutXgk0+cfPSRk82bbYwd62bsWDc9e/q5+mofqal+\n4uJCNIkqsGePwfjxLqZMceL3GyQkWNx1l4cbb/Thdlf89Ro0sGje3GTDBjvr1tlo3frkq4lERKTy\nVdt6zd27d/Ovf/2LHj168Oabb2I79FhC3759ueaaa0hPTy9K/iQnJx/3HGvXrmX16tXcfvvtNGzY\nsNhYq1atGDBgwB/GsHPnTiZOnEi7du146623sNuDvVEHDRrEoEGDePzxx+nbty/OSvygKSIiIiIS\nCazatdm3Yi2187LJ+fkXjP05GLm5GPv3Y8vdH3x96E/b/hyMA8ExIzcXW+5+bPv2wb59lHc1Ais6\n+lAy6OhkUcVVH6Wm+klPd5CWpuSPiMjxbN5s8P77Ttxui1GjvH+4b+PGwX3uusvL0qU2PvrIyfTp\nTrKyHGRlOXjgAYuBA4OJoKQkE3s1WaqmsBBef93Fiy+6OHDAwOWyuO02L3fd5SEhoXKvnZwcTP5k\nZNiV/BERiRDVNvkzbdo0CgoKuOOOO4oSPwBnnHEGixYtKvV40zR5+OGHadSoEcOHDy9XDGlpa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SptrzeIia+H8kdmlL9MQJEAhQeOMtZC9ZScGd90B09PGPMww4/3wAXMdU/yQlBe9hmZn2EoeJ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\nPB4PQ4YMyQx+AKpXr87atWvP+riffPIJEG4Fd7JKlSp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/Xq1aNLly6sXbuWqVOnsnz5cm6//XZ++MMfJly+2YABA7Z7TWlpKf/8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\nx/QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f135da92080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FsTss2xI7EuA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "69cfbb6d-5687-416b-a393-0b50ccf2356c"
      },
      "cell_type": "code",
      "source": [
        "### end of notebook \n",
        "print(\"This is the end\") "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is the end\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "13rEJH3qUQ1n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2635
        },
        "outputId": "6b0e6395-a3c3-4cd6-cebb-75e311cdb76c"
      },
      "cell_type": "code",
      "source": [
        "!pip freeze"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "absl-py==0.5.0\n",
            "altair==2.2.2\n",
            "astor==0.7.1\n",
            "beautifulsoup4==4.6.3\n",
            "bleach==3.0.1\n",
            "boto3==1.9.21\n",
            "botocore==1.12.21\n",
            "cachetools==2.1.0\n",
            "certifi==2018.8.24\n",
            "chardet==3.0.4\n",
            "Click==7.0\n",
            "configparser==3.5.0\n",
            "crcmod==1.7\n",
            "cycler==0.10.0\n",
            "cymem==1.31.2\n",
            "cytoolz==0.9.0.1\n",
            "databricks-cli==0.8.2\n",
            "decorator==4.3.0\n",
            "defusedxml==0.5.0\n",
            "dill==0.2.8.2\n",
            "docutils==0.14\n",
            "entrypoints==0.2.3\n",
            "filelock==3.0.9\n",
            "Flask==1.0.2\n",
            "future==0.16.0\n",
            "gast==0.2.0\n",
            "gitdb2==2.0.4\n",
            "GitPython==2.1.11\n",
            "google-api-core==1.4.1\n",
            "google-api-python-client==1.6.7\n",
            "google-auth==1.4.2\n",
            "google-auth-httplib2==0.0.3\n",
            "google-auth-oauthlib==0.2.0\n",
            "google-cloud-bigquery==1.1.0\n",
            "google-cloud-core==0.28.1\n",
            "google-cloud-language==1.0.2\n",
            "google-cloud-storage==1.8.0\n",
            "google-cloud-translate==1.3.1\n",
            "google-colab==0.0.1a1\n",
            "google-resumable-media==0.3.1\n",
            "googleapis-common-protos==1.5.3\n",
            "grpcio==1.15.0\n",
            "gunicorn==19.9.0\n",
            "h5py==2.8.0\n",
            "httplib2==0.11.3\n",
            "idna==2.6\n",
            "ipykernel==4.6.1\n",
            "ipython==5.5.0\n",
            "ipython-genutils==0.2.0\n",
            "itsdangerous==0.24\n",
            "Jinja2==2.10\n",
            "jmespath==0.9.3\n",
            "joblib==0.12.5\n",
            "jsonschema==2.6.0\n",
            "jupyter-client==5.2.3\n",
            "jupyter-core==4.4.0\n",
            "Keras==2.1.6\n",
            "Keras-Applications==1.0.6\n",
            "Keras-Preprocessing==1.0.5\n",
            "Markdown==3.0.1\n",
            "MarkupSafe==1.0\n",
            "matplotlib==2.1.2\n",
            "mistune==0.8.3\n",
            "mleap==0.8.1\n",
            "mlflow==0.7.0\n",
            "mpmath==1.0.0\n",
            "msgpack==0.5.6\n",
            "msgpack-numpy==0.4.4.1\n",
            "murmurhash==0.28.0\n",
            "nbconvert==5.4.0\n",
            "nbformat==4.4.0\n",
            "networkx==2.2\n",
            "nltk==3.2.5\n",
            "nose==1.3.7\n",
            "nose-exclude==0.5.0\n",
            "notebook==5.2.2\n",
            "numpy==1.14.6\n",
            "oauth2client==4.1.3\n",
            "oauthlib==2.1.0\n",
            "olefile==0.46\n",
            "opencv-python==3.4.3.18\n",
            "pandas==0.22.0\n",
            "pandas-gbq==0.4.1\n",
            "pandocfilters==1.4.2\n",
            "patsy==0.5.0\n",
            "pexpect==4.6.0\n",
            "pickleshare==0.7.5\n",
            "Pillow==4.0.0\n",
            "plac==0.9.6\n",
            "plotly==1.12.12\n",
            "pluggy==0.7.1\n",
            "portpicker==1.2.0\n",
            "preshed==1.0.1\n",
            "prompt-toolkit==1.0.15\n",
            "protobuf==3.6.1\n",
            "psutil==5.4.7\n",
            "ptyprocess==0.6.0\n",
            "py==1.6.0\n",
            "pyasn1==0.4.4\n",
            "pyasn1-modules==0.2.2\n",
            "PyDrive==1.3.1\n",
            "Pygments==2.1.3\n",
            "pygobject==3.26.1\n",
            "pymc3==3.5\n",
            "pyparsing==2.2.2\n",
            "pystache==0.5.4\n",
            "python-apt==1.6.2\n",
            "python-dateutil==2.5.3\n",
            "pytz==2018.5\n",
            "PyWavelets==1.0.1\n",
            "PyYAML==3.13\n",
            "pyzmq==16.0.4\n",
            "querystring-parser==1.2.3\n",
            "regex==2017.4.5\n",
            "requests==2.18.4\n",
            "requests-oauthlib==1.0.0\n",
            "rsa==4.0\n",
            "s3transfer==0.1.13\n",
            "scikit-image==0.13.1\n",
            "scikit-learn==0.19.2\n",
            "scipy==0.19.1\n",
            "seaborn==0.7.1\n",
            "simplegeneric==0.8.1\n",
            "simplejson==3.16.0\n",
            "six==1.11.0\n",
            "smmap2==2.0.4\n",
            "spacy==2.0.12\n",
            "statsmodels==0.8.0\n",
            "sympy==1.1.1\n",
            "tabulate==0.8.2\n",
            "tensorboard==1.11.0\n",
            "tensorflow==1.11.0\n",
            "tensorflow-hub==0.1.1\n",
            "termcolor==1.1.0\n",
            "terminado==0.8.1\n",
            "testpath==0.4.2\n",
            "Theano==1.0.3\n",
            "thinc==6.10.3\n",
            "toml==0.10.0\n",
            "toolz==0.9.0\n",
            "tornado==4.5.3\n",
            "tqdm==4.26.0\n",
            "traitlets==4.3.2\n",
            "typing==3.6.6\n",
            "ujson==1.35\n",
            "uritemplate==3.0.0\n",
            "urllib3==1.22\n",
            "vega-datasets==0.5.0\n",
            "virtualenv==16.0.0\n",
            "wcwidth==0.1.7\n",
            "webencodings==0.5.1\n",
            "Werkzeug==0.14.1\n",
            "wrapt==1.10.11\n",
            "xgboost==0.7.post4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HvABDKa0uxJp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}