3_Machine_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3_Machine_Learning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFr35gKAR2x9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import google.colab as colab\n",
        "import shutil\n",
        "import os\n",
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "from keras import regularizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZ3xk-0Dczdg",
        "colab_type": "text"
      },
      "source": [
        "### Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxIyTxXml65l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mount_google_drive():\n",
        "\t'''\n",
        "\t# Functionality\n",
        "\t\tMount google drive. Since colab does not save files, we want to make it easier to directly access files in google drive.\n",
        "\t# Arguments\n",
        "\t\tNothing\n",
        "\t# Returns\n",
        "\t\tdrive_root: the working directory mounted\n",
        "\t'''\n",
        "\tmount_directory = \"/content/gdrive\"\n",
        "\tdrive = colab.drive\n",
        "\tdrive.mount(mount_directory, force_remount=True)\n",
        "\tdrive_root = mount_directory + \"/\" + list(filter(lambda x: x[0] != '.', os.listdir(mount_directory)))[0]\n",
        "\treturn drive_root\n",
        "\n",
        "def get_all_files_from_dir(directory):\n",
        "\n",
        "    '''\n",
        "    # Functionality\n",
        "    Get the whole list of files in a folder\n",
        "\t# Arguments\n",
        "\t\tdirectory: string.  (e.g.,Users/laowang/~vgg/data/flowers/102/102flowers.tgz)\n",
        "\t# Returns\n",
        "\t\tNone\n",
        "\t'''\n",
        "    file_paths = []\n",
        "    for root, dirs, files in os.walk(directory):\n",
        "        file_paths += [os.path.join(root, x) for x in files]\n",
        "    return sorted(file_paths)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4LE1Ewyc4tD",
        "colab_type": "code",
        "outputId": "a5e11bb7-e92c-46ea-ec2f-1763832b6587",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "ROOT_DIR =  mount_google_drive() + \"/ECE457B-Project/\"\n",
        "DATASET_DIR = ROOT_DIR + \"sec8ks/\"\n",
        "print(DATASET_DIR)"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/ECE457B-Project/sec8ks/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj2AiawKowl6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Hyperparams\n",
        "embedding_dim = 300\n",
        "max_length = 100\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "train_mode = \"nlp\" # \"nlp\" or \"ts\"\n",
        "split_size = 0.3 # training set proportion. we can tune it to see when our model overfits\n",
        "dp = 0.2 # dropout\n",
        "num_units_lstm = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d4wecU0ZT67",
        "colab_type": "text"
      },
      "source": [
        "## Load NLP Sentences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPIcouKZZWYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modified_results = np.load(ROOT_DIR + \"sec_modified.npy\", allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh7BHuA7Za1A",
        "colab_type": "code",
        "outputId": "d060b4a7-5c85-42ca-82a5-d464e2e81ebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "modified_results"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [datetime.date(2010, 1, 19),\n",
              "        'indenture salesforcecom inc us bank national association dated january 19 2010 purchase agreement dated january 12 2010 salesforcecom inc merrill lynch pierce fenner smith incorporated representative several initial purchasers named schedulea thereto form convertible bond hedge confirmation form warrant confirmation press release dated january 19 2010 announcing closing offering notes'],\n",
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              "        'agreement plan merger dated december7 2010 among salesforcecom inc hiiaka acquisition corporation heroku inc respect articles vii viii ix thereof john connors stockholder representative us bank national association escrow agent'],\n",
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              "        'share purchase agreement dated march30 2011 among salesforcecom inc salesforcecom canada corporation radian6 technologies inc radian6 technologies inc shareholders shareholder representative incorporated herein reference exhibit 21 companys current report form8k fileno 00132224 filed march 30 2011 '],\n",
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              "        'agreement plan merger dated june 3 2012 among salesforcecom inc bullseye merger corporation buddy media inc shareholder representative services llc'],\n",
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              "        'agreement plan merger dated june 3 2012 among salesforcecom inc bullseye merger corporation buddy media inc shareholder representative services llc'],\n",
              "       [datetime.date(2012, 8, 27),\n",
              "        'unaudited consolidated statements comprehensive income loss years ended january31 2012 2011 2010'],\n",
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              "        'indenture dated march 18 2013 salesforcecom inc us bank national association purchase agreement dated march12 2013 salesforcecom inc merrill lynch pierce fenner smith incorporated morgan stanley co llc representatives several initial purchasers named schedulea thereto form convertible bond hedge confirmation form warrant confirmation'],\n",
              "       [datetime.date(2013, 3, 21),\n",
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              "        'acquisition agreement dated june 3 2013 among salesforcecom inc excalibur acquisition corp exacttarget inc incorporated herein reference exhibit 21 companys current report form 8k filed commission june 4 2013 credit agreement dated july 11 2013 among salesforcecom inc guarantors time time party thereto lenders time time party thereto bank america na administrative agent press release dated july 12 2013'],\n",
              "       [datetime.date(2013, 9, 12),\n",
              "        'press release dated september 12 2013'],\n",
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              "       [datetime.date(2014, 4, 11), 'press release dated april 11 2014'],\n",
              "       [datetime.date(2014, 6, 30),\n",
              "        'employment offer letter dated june 11 2014 salesforcecom inc mark hawkins press release dated june 30 2014'],\n",
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              "       [datetime.date(2014, 12, 8),\n",
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              "        'agreement plan merger dated may 31 2016 among salesforcecom inc dynasty acquisition corp demandware inc commitment letter dated may 31 2016 among salesforcecom inc bank america na merrill lynch pierce fenner smith incorporated press release dated june 1 2016'],\n",
              "       [datetime.date(2016, 6, 3),\n",
              "        'amended restated certificate incorporation salesforcecom inc'],\n",
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              "        'amended restated 2013 equity incentive plan amended restated 2004 employee stock purchase plan'],\n",
              "       [datetime.date(2018, 3, 20),\n",
              "        'joint press release issued salesforcecom inc mulesoft inc dated march20 2018 investor presentation'],\n",
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              "        'underwriting agreement dated april5 2018 among company merrill lynch pierce fenner smith incorporated jp morgan securities llc acting representatives several underwriters named schedule thereto indenture dated april11 2018 company us bank national association trustee first supplemental indenture dated april11 2018 company us bank national association trustee form 2023 notes included exhibit 42 form 2028 notes included exhibit 42 opinion wachtell lipton rosen katz relating notes including consent required respect thereto '],\n",
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              "       [datetime.date(2018, 5, 2),\n",
              "        'consent kpmg llp independent registered public accounting firm press release dated may 2 2018 audited consolidated financial statements mulesoft incorporated reference mulesofts annual report form 10k year ended december31 2017 filed mulesoft sec february22 2018 unaudited pro forma combined condensed balance sheet january31 2018 unaudited pro forma combined condensed statement operations year ended january31 2018 case incorporated reference amendment no1 salesforces registration statement form s4 filed salesforce sec april23 2018 '],\n",
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              "        'amended restated bylaws salesforcecom inc press release dated august7 2018 press release dated august7 2018'],\n",
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              "       [datetime.date(2019, 6, 10),\n",
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              "        'consent pricewaterhousecoopers llp press release dated august1 2019 audited consolidated financial statements tableau incorporated reference tableaus annual report form 10k year ended december31 2018 filed tableau sec february22 2019 unaudited consolidated financial statements tableau incorporated reference tableaus quarterly report form 10q quarterly period ended june30 2019 filed tableau sec july31 2019 unaudited pro forma combined condensed balance sheet april 30 2019 unaudited pro forma combined condensed statement operations year ended january31 2019 three months ended april30 2019 case incorporated reference amendment 1 salesforces registration statement form s4 registration no333232530 filed salesforce sec july23 2019 ']],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVd3PyxVZa3O",
        "colab_type": "code",
        "outputId": "5f7f345f-d99c-422a-9d2c-222f0f2f4b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "sentences = modified_results[:, 1].tolist()\n",
        "sentences[:10]"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['indenture salesforcecom inc us bank national association dated january 19 2010 purchase agreement dated january 12 2010 salesforcecom inc merrill lynch pierce fenner smith incorporated representative several initial purchasers named schedulea thereto form convertible bond hedge confirmation form warrant confirmation press release dated january 19 2010 announcing closing offering notes',\n",
              " 'indenture salesforcecom inc us bank national association dated january 19 2010 purchase agreement dated january 12 2010 salesforcecom inc merrill lynch pierce fenner smith incorporated representative several initial purchasers named schedulea thereto form convertible bond hedge confirmation form warrant confirmation press release dated january 19 2010 announcing closing offering notes',\n",
              " 'press release dated april 21 2010',\n",
              " 'press release dated april 21 2010',\n",
              " 'press release dated october 27 2010',\n",
              " 'agreement plan merger dated december7 2010 among salesforcecom inc hiiaka acquisition corporation heroku inc respect articles vii viii ix thereof john connors stockholder representative us bank national association escrow agent',\n",
              " 'agreement plan merger dated december7 2010 among salesforcecom inc hiiaka acquisition corporation heroku inc respect articles vii viii ix thereof john connors stockholder representative us bank national association escrow agent',\n",
              " 'agreement plan merger dated december7 2010 among salesforcecom inc hiiaka acquisition corporation heroku inc respect articles vii viii ix thereof john connors stockholder representative us bank national association escrow agent',\n",
              " 'share purchase agreement dated march30 2011 among salesforcecom inc salesforcecom canada corporation radian6 technologies inc shareholders company shareholder representative',\n",
              " 'share purchase agreement dated march30 2011 among salesforcecom inc salesforcecom canada corporation radian6 technologies inc radian6 technologies inc shareholders shareholder representative incorporated herein reference exhibit 21 companys current report form8k fileno 00132224 filed march 30 2011 ']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5KQLWYVd6gF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set up tokenizer and convert text to numerial sequences\n",
        "tokenizer = Tokenizer(oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(sentences)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZxgTeb5exZq",
        "colab_type": "code",
        "outputId": "d8e88b2d-5774-4552-a954-4aff99195d36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "word_index = tokenizer.word_index\n",
        "vocab_size=len(word_index)\n",
        "vocab_size"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "303"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxBh-NCAe0f8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sequences = tokenizer.texts_to_sequences(sentences)\n",
        "padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pK7w6_IFe299",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "assert padded.shape[0] == len(sentences)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulphCnLAfNek",
        "colab_type": "code",
        "outputId": "d3a60dfa-8c99-4610-d397-d1b074ae1c9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "padded.shape"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(47, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6-FOnAhy5pe",
        "colab_type": "code",
        "outputId": "1564beed-0e8e-42c0-8b8f-03cf11bf4f33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(padded)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egwZ-g6EfDOw",
        "colab_type": "code",
        "outputId": "549c64e7-8c42-4277-e549-3f1610dfb013",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sequences = padded.tolist()\n",
        "dates = modified_results[:, 0].tolist()\n",
        "\n",
        "df_nlp = pd.DataFrame(list(zip(dates, sequences)), columns =['Date', 'Features']) \n",
        "df_nlp.set_index(\"Date\")\n",
        "df_nlp.head()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-01-19</td>\n",
              "      <td>[61, 4, 3, 30, 11, 25, 26, 2, 48, 75, 17, 36, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-19</td>\n",
              "      <td>[61, 4, 3, 30, 11, 25, 26, 2, 48, 75, 17, 36, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-04-21</td>\n",
              "      <td>[7, 8, 2, 79, 39, 17, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-04-21</td>\n",
              "      <td>[7, 8, 2, 79, 39, 17, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-10-27</td>\n",
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            "text/plain": [
              "         Date                                           Features\n",
              "0  2010-01-19  [61, 4, 3, 30, 11, 25, 26, 2, 48, 75, 17, 36, ...\n",
              "1  2010-01-19  [61, 4, 3, 30, 11, 25, 26, 2, 48, 75, 17, 36, ...\n",
              "2  2010-04-21  [7, 8, 2, 79, 39, 17, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "3  2010-04-21  [7, 8, 2, 79, 39, 17, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "4  2010-10-27  [7, 8, 2, 205, 206, 17, 0, 0, 0, 0, 0, 0, 0, 0..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StaW-rvEYfoj",
        "colab_type": "text"
      },
      "source": [
        "## Set up word embedding model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkngQNEmYVfj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Takes 3 mins to load\n",
        "embeddings_index = {};\n",
        "with open(ROOT_DIR + 'glove.840B.300d.txt', encoding='utf8') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = ''.join(values[:-300])\n",
        "        coefs = np.asarray(values[-300:], dtype='float32')\n",
        "        embeddings_index[word] = coefs;\n",
        "\n",
        "embeddings_matrix = np.zeros((vocab_size, embedding_dim));\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWYSVF1qTYK3",
        "colab_type": "text"
      },
      "source": [
        "## Load time series dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJf__3pjTRkb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_tseries = pd.read_csv(ROOT_DIR + 'CRM.csv',index_col=\"Date\",parse_dates=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnqP440vMnhe",
        "colab_type": "code",
        "outputId": "7bd0c605-8a7d-4d59-f31e-800b018d49db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_tseries.shape"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2549, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SmfONfdTrqY",
        "colab_type": "code",
        "outputId": "8bf7e750-a16d-4157-e58a-4f6fa0df43a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "df_tseries['Open'].plot(figsize=(16,6))"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ca4280390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hzcZ7a0ue3qakVERERETkFJBXVuufntt8P89xgxOJ9yZ61T0whfdoWa3/eFBAUgrg8Ha+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\nIiIi4jH/D79DnlDN4pG+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxEgWBP2ViY6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # convert column \"a\" of a DataFrame\n",
        "# df_tseries[\"Close\"] = df_tseries[\"Close\"].str.replace(',', '').astype(float)\n",
        "# df_tseries[\"Volume\"] = df_tseries[\"Volume\"].str.replace(',', '').astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jEL6DTXXm3f",
        "colab_type": "code",
        "outputId": "b7247716-a3f1-47da-d2ec-3e5bc8f3eff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "df_tseries['Open'].plot(figsize=(16,6))\n",
        "df_tseries.rolling(window=30).mean()['Close'].plot()"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c90e1a828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3/WzgnlJjzFEF0spnosMdFPlDaaUDf28Op0dK1dzcQ63yK1UUSkVEREREmqly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\nJEkqjUWpJEmSJKk0FqWSJEmSpNL8Px3UncwCEoY7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe3raLObXuz4",
        "colab_type": "code",
        "outputId": "3b1b69e5-f55a-48e4-dcbf-4ca84f31bbb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        }
      },
      "source": [
        "df_tseries['Close: 30 Day Mean'] = df_tseries['Close'].rolling(window=30).mean()\n",
        "df_tseries[['Close','Close: 30 Day Mean']].plot(figsize=(16,6))"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6b770a6f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QaQ+yw+rMijGvQ/sB8OE02PV9nTx/U0oWI2YuJT2nkHxfpTQkwDeT8GAiHEqG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\niEhgVJSKiIiIiIhIYP4PxiJDu8D0upYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3ANYLSeYSlE",
        "colab_type": "code",
        "outputId": "3a291a0f-45a5-4775-d24b-0a4dfa7fbf51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "source": [
        "# Optional specify a minimum number of periods\n",
        "df_tseries['Close'].expanding(min_periods=1).mean().plot(figsize=(16,6))"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ca42a7320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6zn5SEuO4ZmUp16wqZdP8AuLiXBxIkhR7Fk9JkqaQ0dHA1uPt3Lmtlvt2N9DS\nM0hCXMSlSwr57NVLuXRJoSObkqRJx+IpSdIkF0LgWGsfdzxfw90766lu7iUpIY4rlhZx3eoyNs0v\nIDXJsilJmrzGXTyjKIoHtgC1IYS3RFE0F7gVyAe2Ar8eQhicmJiSJM0sI6OBXbWd3Lu7gUf2N7O3\nvosognPn5vGRi+Zz+ZIistMSYx1TkqRxOZ0Rzz8A9gJZY/f/BvjHEMKtURR9A/gA8PUznE+SpBmj\nrXeQxw8289C+JrYcbae24yQJcRFrK3L5/DVLuXhRIZUF6bGOKUnSaRtX8YyiqBy4GvhL4BNRFEXA\nJcC7xw75DvBnWDwlSTottR0neWR/Ez/eXse24+0MjQTy05NYOyeXP7pyIefPL6AwMyXWMSVJel3G\nO+L5FeCPgcyx+/lARwhheOx+DVB2hrNJkjTthBDYVdvF/XsbuXtHHYebewGYW5DO+zfN5eoVJSwt\nySIhPi7GSSVJOnNetXhGUfQWoCmEsDWKootO9w2iKPoQ8CGAioqK0w4oSdJU9r/Xam451s72Ex08\ndbiFlp5Bogg2VOZxw/rZXLqkiHmz0jk1oUiSpOlnPCOe5wPXRlH0ZiCFU9d4fhXIiaIoYWzUsxyo\nfbkXhxBuAW4BWL9+fTgjqSVJmoRGRwPVLb3sa+hiV20X2463s7O2k77BEQDKclI5tyqfC+YX8Kbl\nxeSkJcU4sSRJZ8erFs8QwmeAzwCMjXj+UQjhPVEU/QC4nlMr294E/HgCc0qSNKmEEKjr7GfHiQ5e\nqOlkR00HO2s76e4/dRVKYnzEouJMblg/m7VzcllbkUN5blqMU0uSFBuvZx/PTwG3RlH0RWAb8K0z\nE0mSpMlnYHiEPXVdbDvewfYTHWw52kZdZz9wqmQuLs7i2lWlrCrPYXlZNlWz0klJdG9NSZLgNItn\nCOER4JGx76uBDWc+kiRJsdc7MMyWY+08vK+JbSc62FvXxeDIKACl2SmsLM/hw2/MZ9XsHBYXZ1oy\nJUn6FV7PiKckSdNKS88ATxxq4e4d9TxxqIXewRFSEuNYVZ7D+zZVsro8h9UVOZRkp8Y6qiRJU4rF\nU5I0YzV19bO7vounq1vZcrSdHTUdDI0ESrJTuGZVKVevLGHdnFzSkvx1KUnS6+FvUknSjBBC4ETb\nSZ6ubuXpI608d7SNE20ngVPXaK4qz+H9m+Zy1fISlpe6j6YkSWeSxVOSNG219gzwxOFWHt7XxJOH\nW2jsGgAgJy2RjVX53LSxkqWlWayZnUtqktdoSpI0USyekqRpo6t/iKcPt/LQvia2He/gQFM3IUBB\nRjIb5uaysSqf86rymTcrg4G6B2AAAB91SURBVLi4KNZxJUmaMSyekqQpaWQ0cKCxm23HO9hyrI2d\nNZ0cbOoBIDM5gQ1z87hqRTHnzy9gXUWuRVOSpBiyeEqSpoSu/iG2HG1j+/EOXqjpZMvRNnoHRwDI\nT09iRXk2160uZU1FLudU5pGU4DWakiRNFhZPSdKk1N0/xJaj7Ty8v4nHDjRztLUPgLgIFhVncd2a\nMtZW5LKhMo/ZealEkSOakiRNVhZPSdKk0NozwI6aTrafODV19tkjbQyNBNKT4llfmcc71s9mzewc\nVpRnk5mSGOu4kiTpNFg8JUlnXVf/ENuPd7CztpOtx9rZW99FfWf/i88vLcniNzZWcuHCWZxXlUdy\ngivOSpI0lVk8JUkTanQ0cKS1lycOtbCzppPnj7dT3dJLCKeerypI59y5eSwrzWZZWRYry3PISPbX\nkyRJ04m/2SVJZ9zBxm6erm7l0QPNPHOkje7+YeDU/pnr5+Rx3epT12euKM8mO9Vps5IkTXcWT0nS\n6zYyGnj8YDOPHWjhwX2NHBtbCGhOfhpvWlbM+spczqvKpyIvzUWAJEmagSyekqTXJITAE4dauXtn\nPffsrKfz5BCJ8REXLpjF+95QyUWLCpmTb9GUJEkWT0nSaWrrHeT2rSe49bkTVDf3kpQQx9UrSrhi\naREXLy4kJdGFgCRJ0i+yeEqSXtXQyCj37mrgf16o4+H9TQyNBDbMzeO3L5zHNatKSU2ybEqSpFdm\n8ZQkvaJdtZ3c9UId33vmON0Dw5TlpHLjhgresW42K8qzYx1PkiRNERZPSdIv2FnTyYP7Grl7Rz0H\nm3qIj4u4fEkR168r55LFhcTFec2mJEk6PRZPSZrhhkdG2VnbyeMHW7jrhToONfUQRbCuIpfPXr2E\nd6yf7ZYnkiTpdbF4StIMMzoaONTcw4N7m3i6upVnj7RxcmgEgA1z8/jcW5Zy/dpystMsm5Ik6cyw\neErSDNDQ2c/zx9u5f08jmw+10Nw9AMCiokzevq6MDXPzecO8fAoykmOcVJIkTUcWT0mapnbUdHDP\nzgbu3lnHibaTAGSnJvLGhbPYtKCA8+bmU5GfFuOUkiRpJrB4StI0sre+i/t2N/LDbTUca+0jIS7i\nvKp8btpYyTmVeSwuySQ5wa1PJEnS2WXxlKQpLITAthMdPLCnkR9vr6O249TI5nlVefzWprm8dU0Z\nmSleqylJkmLL4ilJU0zvwDAP7G3k0QPNPLK/mbbeQeIi2LRgFh9+YxVXrygh32s1JUnSJGLxlKQp\noLl7gHt3N/DwviYePdDMyGggLz2JTfML2DS/gDetKCbLkU1JkjRJWTwlaZIZGQ0cb+tjV20n2090\n8HR1K7vrugAoz03lA5vmctHCWZxXlU9cXBTjtJIkSa/O4ilJMTQ4PMrBpm62He9gT30Xu+u62Fff\nxcDwKABJCXGsLs/hU29azKb5Bawoz45xYkmSpNNn8ZSks6B3YJj6zn4aOvupbulhd20Xe+q7ONTU\nw8mhEQCyUhJYWprFe8+bw6KiTJaWZjG/MIOURFehlSRJU5vFU5Jep9aeAY629nK0pY+m7gGauvtp\n6h6guWuA5p4Bmrr66R0c+YXX5KUnsbQki3dtmM3ailxWlmdTkZdGFDl1VpIkTT8WT0k6DUMjoxxt\n6WVnbSdPV7ey5Wg71S29v3BMRnIChZnJFGQms6w0i4sXFTIrM5mS7BRKslOoyE+jOCvFkilJkmYM\ni6ckvYL23kF21Hay40QH+xu7OdTUw+HmHoZGAgDZqYmcU5nL9evLWVKSRUVeGiXZKaQl+aNVkiTp\n5/npSNKMF0LgRNtJtp1oZ9vxDnbVdlLd0ktb7+CLx1TkpVE1K52LFhWyqDiDRUVZLC7OdFVZSZKk\ncbB4SpqR2noHeepwK48daOaxg83Ud/YDkJIYx/LSbK5YWsS8WRksK8tiRVk2me6RKUmS9JpZPCVN\ne939Q2w91s72Ex1sPdbOgcZuGrsGAMhMSeCCBQV8dH4BK8tyWFqaRbyjmJIkSWeUxVPStDI6GjjU\n3MP2Ex3sru3kuaPt7KnvevH5pSVZvGFeAUtKMllbkcvq2TkkxMfFMLEkSdL0Z/GUNGWFEGjo6qe6\nuZfddZ08f6yDJw+30NU/DEBqYjyrZmfz8csWsnZODqtn5zhlVpIkKQYsnpKmjKbufvbUdbG7ros9\n9V08e6SN5u6BF5+fnZfKFcuK2VCZx/rKXCry0hzNlCRJmgRetXhGUZQCPAYkjx1/ewjh81EUzQVu\nBfKBrcCvhxAGX/lPkqRfrfPkEG29g7T1DnK8rZeGzgGauwc43NzD7rouWnp+sWSeOzePc+fmMW9W\nBguLMynISI5hekmSJL2S8Yx4DgCXhBB6oihKBDZHUfRT4BPAP4YQbo2i6BvAB4CvT2BWSdNEe+8g\nu+u6xvbG7OZAYw/VzT209w390rGpifFUFqRz0aJZLC3JYmlpFktKsshOdcqsJEnSVPGqxTOEEICe\nsbuJY18BuAR499jj3wH+DIunpJ/T3jvIoeYeDjWd+jrQ2M3+hm6afm56bE5aIguLMrlqRQmV+WkU\nZCSTm5bE7LxUSrJTSU/2igBJkqSpblyf6KIoiufUdNr5wD8Bh4GOEMLw2CE1QNmEJJQ0KY2MBjr6\nBunuH6ZnYJjW3sEXC+bhph4ON/fQ2vt/s++TE+KYNyuDTQsKWFSUyfKybBYXZ5Lv9FhJkqRpb1zF\nM4QwAqyOoigHuBNYPN43iKLoQ8CHACoqKl5LRklnQQiBrpPDNHX3v3idZWvvIK09g7T2DrzkdpD2\nvkFC+OU/JyctkfmzMrh8aRHzZmUwv/DUV1lOKnHujylJkjQjndYcthBCRxRFDwMbgZwoihLGRj3L\ngdpXeM0twC0A69evf5mPqZLOpqGRUQ40dnOstY8jLb3sb+hmV10nNe0nGRwefdnXZKcmkp+RREF6\nMvNmZbBhbhL5GcnkpyeRmZJAenICuWlJVM1KJz89iSiyYEqSJOn/jGdV21nA0FjpTAUuB/4GeBi4\nnlMr294E/Hgig0p6bUZHA7vruth8qIWnq1vZeqydnoHhF58vzU5heVk2ly0pojAzmVmZyeSnJ5Ob\nnsisjGRy05NIdEsSSZIkvQ7jGfEsAb4zdp1nHHBbCOEnURTtAW6NouiLwDbgWxOYU9I4jY4GdtZ2\n8nR1KztqO3nsQDPd/aeKZlVBOtetLuXcqnzmzUqnMj/dxXskSZI04cazqu0OYM3LPF4NbJiIUJLG\nb3Q0UN3Sy+aDzTx7tI2nDre+uC1JWU4qly8pYuO8fC5ZXOhCPpIkSYoJhzqkCdDZN8Sxtl7a+4bo\nGximb3CE/73scXg0MDIaCOHUtZO56YkUZiZTlJVCZsor7005MDxCTftJjrf1cby1j2OtfRxo7GZ3\nXeeLRbMoK5nLlhTxhvn5nFeVT0l26tn460qSJEm/ksVTOg0jo4HOk0P09A/TPXDqtrF7gBNtfZxo\n66O6pZfDTb+4jcjpSIiLSEqIO/UVH0difByDI6OnyuvQyC+sIpuaGM+8wnQuW1LEOXPzOG9uPhX5\naWfobypJkiSdORZPaUxb7yB1HSfp6BuivW+QjpND1LafpKPv1NYix1r7ONLa+4orvxZkJDEn/1QR\nrJqVTmXBqRVe05MTSE2MJ4ogBIiPi15crKfj5CBtPYM09wzQ0NlP58khBodHGRwZffE2OSGOtKQE\nMlMSmJ2bxpz8NCry05iVkezqsZIkSZoSLJ6asXoGhvnJC3U8fqiFZ6pbaen55VHKxPiI3LQkctIS\nqchL46JFsyjOPjUlNjMlgczkBPIzkpmdl0pa0un/cyrOTjkTfxVJkiRpUrN4akYJIfBUdSu3PnuC\nn+6qZ2gkUJiZzIULZ7GoKJO5BenkjBXNrJREZmUmEx/nqKIkSZL0elg8NSO09gxwz64Gvv/ccXbV\ndpGWFM97z5vDW1aWsrYixymrkiRJ0gSyeGraGh4Z5dEDzfx4ex1376xnZDSwtCSLL1y3jHesn01K\nYnysI0qSJEkzgsVT08rQyCgP7Wvi/j2N3LurgZ6BYXLTEnnvuRW8dU0Zq8pziHPqrCRJknRWWTw1\nLexr6OK7Tx3jru119AwMk52ayBVLi7hiWRGXLil6cRVZSZIkSWefxVNTVn3nSZ4eWyjomSNtJCfE\n8ZaVpVw5VjZdFEiSJEmaHCyemlIONHbz0L4m7tvdwPYTHYwGKM9N5TNXLeaG9bPJTU+KdURJkiRJ\nL2Hx1KR3qKmbn+5s4MF9TWw/0QHAqvJsPnrxfC5bWsSy0mxHNyVJkqRJzOKpSammvY97dtbzkx31\n7KjpBGBtRQ6fvHIRv7amjNKc1BgnlCRJkjReFk9NCqOjgd11XTxd3cojB5p44lArAKtn5/CZqxbz\n5hUlzM5Li3FKSZIkSa+FxVMx0z80wgN7G7lvdyOPHWymo28IgIVFGfzeJfO5bnUp8wszY5xSkiRJ\n0utl8dRZNToaePpIKz/eVse9uxvoPDlEZkoCb1pWzBvm53NOZR7luY5sSpIkSdOJxVNnzdZj7Xz1\nwYM8dqCZxPiIa1aW8ra15ZxXlUeC+2xKkiRJ05bFUxNud10nX33gIPftaSQlMY4/fctSrl9fTlZK\nYqyjSZIkSToLLJ6aEAPDIzy8r5l/23yEZ4+2kZeexEcumsfvXjyf9GRPO0mSJGkmsQHojBoZDXz7\niSPc+twJDjX1UJiZzCevXMS7N1SQm54U63iSJEmSYsDiqTNmT10Xf3XPXjYfamFpSRb//J61XLak\niKQEr9+UJEmSZjKLp163/qER/vXxav75kcPEx0V8/pqlvO/8ubGOJUmSJGmSsHjqNRsdDXz90cPc\nvrWGIy29bKzK5yvvWk1RVkqso0mSJEmaRCyeek1eONHBR7/3PCfaTrJ+Ti5/8uYlXLa0KNaxJEmS\nJE1CFk+dtr+/bz/fePQwhZkp3HzjGq5ZWUIURbGOJUmSJGmSsnhq3E4OjvA39+7j3588yptXFPNn\n1y6jMNNptZIkSZJ+NYunxqWlZ4BP3b6DB/c1cf26cv7m7SuJj3OUU5IkSdKrs3jqVT1+sJmP/Ofz\ndA8M8+fXLuOmN1TGOpIkSZKkKcTiqVf02IFmfrC1hv95oY7Zeanc8ZE3sLAoM9axJEmSJE0xFk/9\ngv6hEe7d1cAtj1Wzp76LzJQEPnjBXH7v0gVkpSTGOp4kSZKkKcjiKQCauwe464U6vv7IYVp6Blhc\nnMmfX7uMd59bQWJ8XKzjSZIkSZrCLJ4zWGNXP/ftaeSHz9ew/UQHIcCq2Tl8+R0ruXDBLBcPkiRJ\nknRGWDxnoJr2Pv7qnr3cv6eRoZFAZX4an7hsIW9aXswCr+GUJEmSdIZZPGeQEAJ/d99+/uXRapIS\n4rhpYyXv2lDBvFnpRJGjm5IkSZImhsVzhjjR1sfvfW8b20908La1ZXzi8oWU56bFOpYkSZKkGcDi\nOQP8x9PH+Pv79hMCfOGty3nvuRWOcEqSJEk6ayye01h1cw+fuO0Ftp/oYMPcPL5w3XIWFXsNpyRJ\nkqSzy+I5DY2OBr75eDVff/QwAJ+5ajHv3zTXbVEkSZIkxYTFc5rZ19DF5360i+eOtrOxKp8/v24Z\nC12pVpIkSVIMvWrxjKJoNvBdoAgIwC0hhK9GUZQHfB+oBI4CN4QQ2icuql7J8Mgod++s55uPV7Or\ntouctES+fP1Krl9X7rWckiRJkmJuPCOew8AfhhCej6IoE9gaRdH9wG8CD4YQvhRF0aeBTwOfmrio\neqkQAk8cauXLP9vHCzWdzMlP40/fspTrVpeSn5Ec63iSJEmSBIyjeIYQ6oH6se+7oyjaC5QB1wEX\njR32HeARLJ5nxcho4JnqVr7y4EGePdJGaXYKX3nnaq5dVUpcnCOckiRJkiaX07rGM4qiSmAN8AxQ\nNFZKARo4NRX35V7zIeBDABUVFa81p4DGrn7u2l7Hv26uprFrgFmZyfzFdct45zmzSU6Ij3U8SZIk\nSXpZ4y6eURRlAHcAHwshdP38tYMhhBBFUXi514UQbgFuAVi/fv3LHqNXtqu2k1ufO84Th1o50tIL\nwJqKHP7fm5dwxdJiUpMsnJIkSZImt3EVzyiKEjlVOv8rhPDDsYcboygqCSHUR1FUAjRNVMiZaH9D\nN1+8ew+bD7WQmhjPeVX5vGN9OVcuK2berIxYx5MkSZKkcRvPqrYR8C1gbwjhH37uqbuAm4Avjd3+\neEISzjBd/UN898mj3PzgITJSEvjYpQt536ZKslISYx1NkiRJkl6T8Yx4ng/8OrAziqLtY4/9P04V\nztuiKPoAcAy4YWIiTn8hBF6o6eT+PQ188/EjDA6P8paVJfz5tctcnVaSJEnSlDeeVW03A6+0VOql\nZzZO7HT0DfLNx6v56MULztp1k88fb+eOrTU8eqCZmvaTAFyyuJDfvXg+6+bknpUMkiRJkjTRTmtV\n2+nsoX1N/NPDh/nPp4/z8csW8Jvnz52Q9wkh8OiBZn6wtYZ7dtaTmhjP+fML+PCFVbxlZSm56UkT\n8r6SJEmSFCsWzzFvW1tOSXYqn79rF//+5NEJKZ4Nnf185L+28vzxDgoykrhpYyWfvHIR6cn+b5Ak\nSZI0fdl4fs7Gefm885wKvvCTPVQ391B1BlePvWNrDZ+5cyfxUcRf/doK3r6uzL03JUmSJM0IcbEO\nMNlcs6qE5IQ4rvnaZu7f0/i6/7yOvkF+6ztb+MMfvMD6Obn87GMX8u5zKyydkiRJkmYMRzxfojAz\nhe9/eCOfvmMHn/vRLs6ryiPzNLcyCSGwv7Gbu7bX8e0njjIyGvidi+bx+5ecvYWLJEmSJGmysHi+\njNWzc/js1Uu56dvP8pvffo7bPryR+LhXWtj3/zR09vOdp45y5/O1NHT1A/DW1aV88MIqlpVmT3Bq\nSZIkSZqcLJ6vYNOCAr70thV88vYdfPZHO/ncW5aSlvTy/7k6Tw7x/eeO83c/O8DgyCiXLy3iY4sX\nsL4yj/mFZ+46UUmSJEmaiiyev8L168p55kgb33v2BA2d/XzlnWvITvu/abedJ4e4Y2sN39p8hNqO\nk1y4cBZ/ce0yKgvSY5hakiRJkiYXi+evEEURf/eOVaypyOFP7tzFxi89yJqKHAaGRqnv7Kehq5+R\n0cDSkiy+/I6VbKzKJ4pefUquJEmSJM0kFs9xeM+5c1g3J5dvPnaEo629JMZHbJibR3luKlcuK2Z5\nmddvSpIkSdIrsXiO0+LiLP7+hlWxjiFJkiRJU477eEqSJEmSJpTFU5IkSZI0oSyekiRJkqQJZfGU\nJEmSJE0oi6ckSZIkaUJZPCVJkiRJE8riKUmSJEmaUBZPSZIkSdKEsnhKkiRJkiaUxVOSJEmSNKEs\nnpIkSZKkCRWFEM7em0VRM3DsrL3h6SsAWmIdQjOW559iyfNPseY5qFjy/FMsTbfzb04IYdZLHzyr\nxXOyi6JoSwhhfaxzaGby/FMsef4p1jwHFUuef4qlmXL+OdVWkiRJkjShLJ6SJEmSpAll8fxFt8Q6\ngGY0zz/FkuefYs1zULHk+adYmhHnn9d4SpIkSZImlCOekiRJkqQJZfGUJEmSJE0oi6ckSZKmvSiK\nolhnkGayGVc8oyiKH7v1h4/OOs87xVIURdljtzPuZ79iL4qiZVEUpcQ6h2a01FgH0MxlB5lBxTOK\novOjKPoO8NkoivKCqyrpLIqiaEMURd8EPhVF0axY59HMEUVRXBRFWVEU/QS4GSCEMBrjWJpBoiha\nGUXRZuCLQH6s82jmiaLovCiK7gD+KYqiK/63AEhngx3k/8yI4hlFURXwz8DDwP/f3r3GylHWcRz/\n/igFsa2gARQoiCSAiCAoApGgVUAwaKJGMBG5xWgEwRvIC6OiQdE3cjEI3oGgogQN3hIJwRdylQIW\nEiAgt6CACCKhIGA9/ftinpIjttDtOWfH3f1+kk3Ozs6cPJv9ZWb+M8/zzCuBU5Ic3G+rNAmSzEvy\nVbppsq8CXg+cnOTl/bZMk6IVmcuB+cBWSd4P3vXUUH0OuLiq3lNV98NkX/HXcCVZQncO+HPgduCD\nwEv7bJMmhzXIf5uUE483ALdV1XnACcAy4J1Jtu61VZoE6wH3AYe2/H0S2Bu7+2i4Xg08ApwBHJZk\nUVWt9ORfc6ndbd8OeKKqzmjLDkiyCTDxXc40NLsAS6vqR8AFdBfhnui3SZogb8Qa5FljWXi2LhU7\nTFu0FFicZOuq+gfdnafHgPf20kCNtefkbyVwYVXdkWTDqnoA+AuwaX8t1Dibnr9pJ/V3Av8C7mmv\nI5NsM8ndfTQ3puev3W1/BNg3ycFJLgFOpOvy/Zm2jhnUrFrNOeAVwCFJvgDcCGwBnJ3kkF4aqLGW\n5F1Jjkuyd1u0FNjaGqQzVoVnkk2S/Aa4DDg0ycL20dPAlcCh7f3twK3Ay5zoQLNldfmrqqmqegyg\nqp5Jsgh4FfBAn23V+FlN/hZMO6nfA3i8qm4BbgFOBs5JMt8ut5oNq8sfQFU9DpwLnAL8oKoOBL4H\n7D3txEyasTWdA1bVMuAgYFvg2KpaQnfyf1CSnXpqrsZMki2S/Ao4ia4r97lJDqyqu4FrsAYBxqzw\nBBYAlwLHt7/f3JY/DFwL7JJkz6qaAu4H9qmqp3tpqcbRc/O372rW2Qu4paoeSLIwyfbDbKDG2pr2\nf9B1916U5Kd0B8UbgDuqaoUTDWmWPF/+fk130r9qXN31wEPAM0Nsn8bfGo/BVXUdsBlwb1v0O2AR\n8ORwm6gxtgdwRVXtW1WnAGcCH26fXYE1CDAGhWeSI5K8JclL2qQF3wEuorvLuWeSrdqPfA3wR+D0\ndhVsZ+C+JC/urfEaeS+Qv72SbNnWW79tsgnw5yRH03W/2K2Pdms8rG3+6E74NwP+CuwOHAPs6NV+\nzcRa5G8rgKq6ma5r7XFJNqWb3OW1wN97arrGxADH4A2Bq4GPtU33o5theeJO/DV7Wv6WtHxdTjeG\neJW/A39qf/8BaxAAMorDK9q4pVcAP6YbQ3cX3dWtT1TVI22dfehua19fVRdM2/Y0YDHdzFJHVNXt\nQ26+RtyA+VtaVT+ctu0FwGHA+cDp7YRMWmvruv9Lsum0zxcCG1TVoz18BY2wGR5/Pw1sB2wPfKqq\nbh1y8zUGZrAP3JlumMErgBXAcVV12/C/gUbZC+UvyfyqWpHk48Brquqj07ad+Bpk5O54JpnXxi0t\nAu6vqv3ort4/SnelC4CquoquS8WOSTZuY+ugu+r6oaraaxJ/cM3MOuTv1emeobhqvPFv6Ga4Pdqi\nU4Oawf5vQTsgzkuyXlU9YdGpQc30+FtVp9EVnAdadGpdrGMGN0myURvjfiRwVFXtZ9GpQa1l/lYN\nXzkAuLhtt3lbdhITXoOMTOHZTphOBU5N8hZgR2AKoHWl/QTwpvbZKt8FFtINNL8zyZZtspflQ26+\nRtwM83c5cFeSLarqJ1V18ZCbrxE3C/u/u6ft/xzTqYHM1vG3rb9iqI3XWJiFDN7bhl491SZ7kdba\nIPmrqqkkG9DNL3NHkq8AlyV5aVX9e9JrkJEoPNuPfAPdOKU76WbHWwG8Ncme8Oy07V9sr1UOBo4F\nbgJ2aY+ykAYyC/lbRpe/B4fXao0L93/qk/lT32bxGHz/8FqtcTFg/r7UNnsRcBTdjYdFwP7tUSoT\nb/0XXuX/wkrg69P66e9O90iKLwDnAG9I90iAS4C3Jdm2qu6lGzS+f1X9vp9ma0yYP/XJ/KlP5k99\nM4Pq06D5WwxsCfwQOK09zkfNSNzxpLvScFGSee39VcA2VXUeMC/J8e1qw2Jgqu1wqKpfuMPRLDB/\n6pP5U5/Mn/pmBtWnQfK3sqr+UlXXVdURFp3/ayQKz6r6Z1U90/pRQzdg9+H299HATkl+DVwI3AjP\nzjolzZj5U5/Mn/pk/tQ3M6g+DZi/G8D8PZ9R6WoLdIN7gQJeDvyyLV4OfJbumWD3rOrD32adkmaN\n+VOfzJ/6ZP7UNzOoPpm/2TESdzynWQnMBx4Bdm1XGD5Pd2v7SgeOa46ZP/XJ/KlP5k99M4Pqk/mb\nBRm1ojzJ3sDV7XVuVX2/5yZpgpg/9cn8qU/mT30zg+qT+Zu5USw8FwOH080U9Uzf7dFkMX/qk/lT\nn8yf+mYG1SfzN3MjV3hKkiRJkkbLqI3xlCRJkiSNGAtPSZIkSdKcsvCUJEmSJM0pC09JkiRJ0pyy\n8JQkaQBJppIsS3JLkpuSnJDkeY+nSbZN8oFhtVGSpP83Fp6SJA3mqararap2Bg4A3gGc/ALbbAtY\neEqSJpaPU5EkaQBJnqiqhdPebwcsBTYFXglcACxoHx9XVVcnuRbYCbgHOB/4BvA1YAmwIfDNqvr2\n0L6EJElDZuEpSdIAnlt4tmWPATsCy4GVVfV0ku2BC6tqjyRLgBOr6p1t/Y8Am1fVl5NsCFwFHFJV\n9wz1y0iSNCTr990ASZLGyHzgrCS7AVPADmtY7+3Arkne195vDGxPd0dUkqSxY+EpSdIMtK62U8Df\n6MZ6PgS8jm4ehafXtBlwfFVdOpRGSpLUMycXkiRpHSXZDPgWcFZ1Y1c2Bh6sqpXA4cC8tupyYNG0\nTS8Fjkkyv/2fHZIsQJKkMeUdT0mSBrNRkmV03Wr/TTeZ0Gnts7OBnyU5Avgt8GRbfjMwleQm4Dzg\nTLqZbm9MEuBh4N3D+gKSJA2bkwtJkiRJkuaUXW0lSZIkSXPKwlOSJEmSNKcsPCVJkiRJc8rCU5Ik\nSZI0pyw8JUmSJElzysJTkiRJkjSnLDwlSZIkSXPKwlOSJEmSNKf+A7uj40m8TyOCAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J-R1Kv7kj8i",
        "colab_type": "text"
      },
      "source": [
        "## Merge two dataframes\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yS6WJf4FkZaz",
        "colab_type": "code",
        "outputId": "fda84cc0-7342-4ba7-89f3-ef01018c553e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(type(df_tseries))\n",
        "print(type(df_nlp))\n",
        "\n",
        "print(df_tseries.shape)\n",
        "print(df_nlp.shape)"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "(2549, 7)\n",
            "(47, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y06KykWNk4ap",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merged_df = df_tseries.join(df_nlp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwr4w1k6lqS9",
        "colab_type": "code",
        "outputId": "2da6c1a2-f1ea-4216-c86e-d46e6d3308b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "isnull = merged_df.Features.isnull()\n",
        "merged_df.loc[isnull, 'Features'] = [ [[0] * 100] * isnull.sum() ]\n",
        "merged_df"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Close: 30 Day Mean</th>\n",
              "      <th>Date</th>\n",
              "      <th>Features</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>18.652500</td>\n",
              "      <td>18.882500</td>\n",
              "      <td>18.547501</td>\n",
              "      <td>18.705000</td>\n",
              "      <td>18.705000</td>\n",
              "      <td>7906000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>18.612499</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>18.200001</td>\n",
              "      <td>18.625000</td>\n",
              "      <td>18.625000</td>\n",
              "      <td>7942400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>18.687500</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>18.495001</td>\n",
              "      <td>18.592501</td>\n",
              "      <td>18.592501</td>\n",
              "      <td>5122400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>18.629999</td>\n",
              "      <td>18.737499</td>\n",
              "      <td>18.385000</td>\n",
              "      <td>18.510000</td>\n",
              "      <td>18.510000</td>\n",
              "      <td>4840000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>18.517500</td>\n",
              "      <td>18.587500</td>\n",
              "      <td>18.315001</td>\n",
              "      <td>18.537500</td>\n",
              "      <td>18.537500</td>\n",
              "      <td>5510400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>189.710007</td>\n",
              "      <td>190.520004</td>\n",
              "      <td>186.770004</td>\n",
              "      <td>189.460007</td>\n",
              "      <td>189.460007</td>\n",
              "      <td>4535400</td>\n",
              "      <td>181.371334</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>188.500000</td>\n",
              "      <td>191.050003</td>\n",
              "      <td>188.419998</td>\n",
              "      <td>188.639999</td>\n",
              "      <td>188.639999</td>\n",
              "      <td>3132100</td>\n",
              "      <td>182.238000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>189.350006</td>\n",
              "      <td>190.250000</td>\n",
              "      <td>188.100006</td>\n",
              "      <td>189.949997</td>\n",
              "      <td>189.949997</td>\n",
              "      <td>3599400</td>\n",
              "      <td>183.003334</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>190.949997</td>\n",
              "      <td>191.500000</td>\n",
              "      <td>188.919998</td>\n",
              "      <td>191.089996</td>\n",
              "      <td>191.089996</td>\n",
              "      <td>4957000</td>\n",
              "      <td>183.834000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>192.000000</td>\n",
              "      <td>193.919998</td>\n",
              "      <td>191.800003</td>\n",
              "      <td>192.869995</td>\n",
              "      <td>192.869995</td>\n",
              "      <td>3903200</td>\n",
              "      <td>184.481333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2549 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open  ...                                           Features\n",
              "Date                    ...                                                   \n",
              "2010-01-04   18.652500  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-05   18.612499  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-06   18.687500  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-07   18.629999  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-08   18.517500  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "...                ...  ...                                                ...\n",
              "2020-02-12  189.710007  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-13  188.500000  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-14  189.350006  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-18  190.949997  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-19  192.000000  ...  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "\n",
              "[2549 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gppZ2LkfP4r9",
        "colab_type": "code",
        "outputId": "243e0429-9062-4c15-8b7d-211a6a23842f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "merged_df[[\"Open\", \"Features\"]]"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>Features</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>18.652500</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>18.612499</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>18.687500</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>18.629999</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>18.517500</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>189.710007</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>188.500000</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>189.350006</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>190.949997</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>192.000000</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2549 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open                                           Features\n",
              "Date                                                                     \n",
              "2010-01-04   18.652500  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-05   18.612499  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-06   18.687500  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-07   18.629999  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2010-01-08   18.517500  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "...                ...                                                ...\n",
              "2020-02-12  189.710007  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-13  188.500000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-14  189.350006  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-18  190.949997  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2020-02-19  192.000000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "\n",
              "[2549 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq5m8KUvNY93",
        "colab_type": "text"
      },
      "source": [
        "## Create Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9XuTvtjonEU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2XR4mfxl9-O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_price = []\n",
        "X_nlp = []\n",
        "y = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SWgQD3KqBH7",
        "colab_type": "code",
        "outputId": "cd0b058e-8491-4551-ef3d-8b76821931ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "prices = pd.DataFrame(merged_df['Open'])\n",
        "prices_scaled = sc.fit_transform(prices)\n",
        "nlp_features = np.array(merged_df['Features'].to_list())\n",
        "\n",
        "print(prices_scaled.shape)\n",
        "print(nlp_features.shape)"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2549, 1)\n",
            "(2549, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eVp8ITvqCbe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(60, merged_df.shape[0] - 1):\n",
        "  X_price.append(prices_scaled[i-60:i, 0])\n",
        "  X_nlp.append(nlp_features[i-60:i].flatten())\n",
        "  y.append(prices_scaled[i, 0])\n",
        "\n",
        "X_price, X_nlp, y = np.array(X_price), np.array(X_nlp), np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Y0_REVnqCZu",
        "colab_type": "code",
        "outputId": "49eefd79-e53d-4eeb-ad3a-cd9176d58efe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_nlp.shape # 60*100 in one timestep prediction"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2488, 6000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tq3iL3Xevk5-",
        "colab_type": "text"
      },
      "source": [
        "## Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6r2_p94UdJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train-test split\n",
        "# For time series data, we cannot do a random split\n",
        "total_num = X_price.shape[0]\n",
        "\n",
        "X_price_train = X_price[:int(total_num * split_size)]\n",
        "X_price_test = X_price[int(total_num * split_size):]\n",
        "X_nlp_train = X_nlp[:int(total_num * split_size)]\n",
        "X_nlp_test = X_nlp[int(total_num * split_size):]\n",
        "y_train = y[:int(total_num * split_size)]\n",
        "y_test = y[int(total_num * split_size):]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KC1aAPcFVzpc",
        "colab_type": "code",
        "outputId": "a20618cb-9bfc-4129-b9fb-1c216e2ca11d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_nlp_train.shape"
      ],
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(746, 6000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 341
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ihbs5Fq8NTuN",
        "colab_type": "text"
      },
      "source": [
        "## Build and Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VwcGhxyQfCd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Input, Dense, LSTM, Dropout, Conv1D, MaxPooling1D, Embedding, concatenate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yd6gnBt1H902",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build regressor to take care of price inputs\n",
        "regressor = Input(shape=(X_price.shape[1], 1))\n",
        "reg_input = regressor\n",
        "regressor = LSTM(units = num_units_lstm, return_sequences = True)(regressor)\n",
        "regressor = Dropout(dp)(regressor)\n",
        "regressor = LSTM(units = num_units_lstm, return_sequences = True)(regressor)\n",
        "regressor = Dropout(dp)(regressor)\n",
        "regressor = LSTM(units = num_units_lstm, return_sequences = True)(regressor)\n",
        "regressor = Dropout(dp)(regressor)\n",
        "regressor = LSTM(units = num_units_lstm)(regressor)\n",
        "regressor = Dropout(dp)(regressor)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBH_Tao2ItP0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build classifier\n",
        "classifier = Input(shape=(X_nlp.shape[1], ))\n",
        "cls_input = classifier\n",
        "classifier = Embedding(vocab_size, \n",
        "                       embedding_dim, \n",
        "                       input_length=max_length, \n",
        "                       weights=[embeddings_matrix], \n",
        "                       trainable=False)(classifier)\n",
        "classifier = Dropout(dp)(classifier)\n",
        "classifier = Conv1D(num_units_lstm, 5, activation='relu')(classifier)\n",
        "classifier = MaxPooling1D(pool_size=4)(classifier)\n",
        "classifier = LSTM(num_units_lstm)(classifier)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UG4JsuxHHz_o",
        "colab_type": "code",
        "outputId": "43e61ffa-98b3-4862-dd86-75fd3c1ba834",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(regressor)\n",
        "print(classifier)"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"dropout_98/Identity:0\", shape=(None, 64), dtype=float32)\n",
            "Tensor(\"lstm_99/Identity:0\", shape=(None, 64), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_Dkk4L2JWJN",
        "colab_type": "code",
        "outputId": "95d07d5f-7ca0-44b3-971f-4d5a11c73ae8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "source": [
        "# Integrate regressor and classifier\n",
        "if train_mode == \"nlp\":\n",
        "  merged = concatenate([regressor, classifier])\n",
        "  output = Dense(units=1)(merged)\n",
        "  model = Model(inputs=[reg_input, cls_input], outputs=[output])\n",
        "else:\n",
        "  merged = regressor\n",
        "  output = Dense(units=1)(merged)\n",
        "  model = Model(inputs=[reg_input], outputs=[output])\n",
        "\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "model.summary()"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_24\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_39 (InputLayer)           [(None, 60, 1)]      0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lstm_95 (LSTM)                  (None, 60, 64)       16896       input_39[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_95 (Dropout)            (None, 60, 64)       0           lstm_95[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lstm_96 (LSTM)                  (None, 60, 64)       33024       dropout_95[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "input_40 (InputLayer)           [(None, 6000)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_96 (Dropout)            (None, 60, 64)       0           lstm_96[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_19 (Embedding)        (None, 6000, 300)    90900       input_40[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lstm_97 (LSTM)                  (None, 60, 64)       33024       dropout_96[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout_99 (Dropout)            (None, 6000, 300)    0           embedding_19[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_97 (Dropout)            (None, 60, 64)       0           lstm_97[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_19 (Conv1D)              (None, 5996, 64)     96064       dropout_99[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "lstm_98 (LSTM)                  (None, 64)           33024       dropout_97[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_19 (MaxPooling1D) (None, 1499, 64)     0           conv1d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_98 (Dropout)            (None, 64)           0           lstm_98[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lstm_99 (LSTM)                  (None, 64)           33024       max_pooling1d_19[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_13 (Concatenate)    (None, 128)          0           dropout_98[0][0]                 \n",
            "                                                                 lstm_99[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_23 (Dense)                (None, 1)            129         concatenate_13[0][0]             \n",
            "==================================================================================================\n",
            "Total params: 336,085\n",
            "Trainable params: 245,185\n",
            "Non-trainable params: 90,900\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiSnuX__LarX",
        "colab_type": "code",
        "outputId": "e368c5ab-87b0-4dd0-c803-8144fd542e67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "if train_mode == \"nlp\":\n",
        "  history = model.fit([X_price_train, X_nlp_train], \n",
        "                    y_train, \n",
        "                    epochs = 20, \n",
        "                    batch_size = 32, \n",
        "                    validation_data=([X_price_test, X_nlp_test], y_test))\n",
        "else:\n",
        "  history = model.fit(X_price_train, \n",
        "                    y_train, \n",
        "                    epochs = 20, \n",
        "                    batch_size = 32, \n",
        "                    validation_data=(X_price_test, y_test))"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "24/24 [==============================] - 11s 438ms/step - loss: 0.0018 - val_loss: 0.1168\n",
            "Epoch 2/20\n",
            "24/24 [==============================] - 9s 383ms/step - loss: 5.5711e-04 - val_loss: 0.0117\n",
            "Epoch 3/20\n",
            "24/24 [==============================] - 9s 383ms/step - loss: 2.6570e-04 - val_loss: 0.0081\n",
            "Epoch 4/20\n",
            "24/24 [==============================] - 9s 382ms/step - loss: 2.2814e-04 - val_loss: 0.0113\n",
            "Epoch 5/20\n",
            "24/24 [==============================] - 9s 383ms/step - loss: 2.4617e-04 - val_loss: 0.0118\n",
            "Epoch 6/20\n",
            "24/24 [==============================] - 9s 379ms/step - loss: 1.9401e-04 - val_loss: 0.0080\n",
            "Epoch 7/20\n",
            "24/24 [==============================] - 9s 380ms/step - loss: 1.8493e-04 - val_loss: 0.0033\n",
            "Epoch 8/20\n",
            "24/24 [==============================] - 9s 380ms/step - loss: 1.7195e-04 - val_loss: 0.0038\n",
            "Epoch 9/20\n",
            "24/24 [==============================] - 9s 381ms/step - loss: 1.5344e-04 - val_loss: 0.0060\n",
            "Epoch 10/20\n",
            "24/24 [==============================] - 9s 380ms/step - loss: 1.8909e-04 - val_loss: 0.0037\n",
            "Epoch 11/20\n",
            "24/24 [==============================] - 9s 380ms/step - loss: 1.5982e-04 - val_loss: 0.0028\n",
            "Epoch 12/20\n",
            "24/24 [==============================] - 9s 379ms/step - loss: 1.7769e-04 - val_loss: 0.0032\n",
            "Epoch 13/20\n",
            "24/24 [==============================] - 9s 378ms/step - loss: 1.4846e-04 - val_loss: 0.0015\n",
            "Epoch 14/20\n",
            "24/24 [==============================] - 9s 379ms/step - loss: 1.5285e-04 - val_loss: 0.0047\n",
            "Epoch 15/20\n",
            "24/24 [==============================] - 9s 377ms/step - loss: 1.4453e-04 - val_loss: 0.0046\n",
            "Epoch 16/20\n",
            "24/24 [==============================] - 9s 378ms/step - loss: 1.6102e-04 - val_loss: 0.0054\n",
            "Epoch 17/20\n",
            "24/24 [==============================] - 9s 379ms/step - loss: 1.3896e-04 - val_loss: 0.0025\n",
            "Epoch 18/20\n",
            "24/24 [==============================] - 9s 378ms/step - loss: 1.2582e-04 - val_loss: 0.0015\n",
            "Epoch 19/20\n",
            "24/24 [==============================] - 9s 377ms/step - loss: 1.3172e-04 - val_loss: 0.0030\n",
            "Epoch 20/20\n",
            "24/24 [==============================] - 9s 379ms/step - loss: 1.4484e-04 - val_loss: 0.0018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTzUB1juo5Ae",
        "colab_type": "text"
      },
      "source": [
        "## View Train-test loss performacne"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppfhQgdvo7Cg",
        "colab_type": "code",
        "outputId": "a5276b55-9a1f-4b07-8f18-829c71fbb219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label=\"Train loss\")\n",
        "plt.plot(history.history['val_loss'], label=\"Test loss\")\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MSE')\n",
        "fig_name = \"Train and Test Loss (30/70 split)\"\n",
        "plt.title(fig_name)\n",
        "plt.legend()\n",
        "plt.grid()"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kNEGY2VQzW2FmK81sZpLlxWb2ULT8JTMbmrD8YDOrMrPvZDLOVtF4TCKSIzKW\nIMwsH7gdOB0YAZxrZiMSil0MfOzuhwE3AzcmLL8JeCJTMbZJSVm4iknDbYhIF5fJGsQEYKW7r3b3\nGuBBYFpCmWnAfdH0o8AUi55ramb/DLwDLM9gjK1XWg61n0BNVbYjERHJKPMMfRM2s7OAqe7+9ej9\nhcBEd78srsyyqMz66P0qYCJQDTwJnAp8B6hy958m2cclwCUAFRUV4x588ME2x1tVVUVpaWmz5So+\neIZPv3kLL024g096HNTm/bVWS+PLFsWXHsWXHsXXdieffPJidx+fbFmTz6TOouuAm929KqpQJOXu\ndwJ3AowfP94nT57c5h1WVlbSovVX1sKbtzBx1DA4+Ng276+1Whxflii+9Ci+9Ci+zMhkgngPGBz3\nflA0L1mZ9WZWAPQGNhNqEWeZ2U+APkC9mVW7+20ZjLdlGu6mVke1iHRtmUwQC4HhZjaMkAjOAc5L\nKDMbmA68CJwFPOOhzeuEWAEzu47QxJT95AAaj0lEckbGEoS715rZZcA8IB+4292Xm9n1wCJ3nw38\nGrjfzFYCWwhJpHPr0R8wjcckIl1eRvsg3H0uMDdh3jVx09XA2c1s47qMBNdW+QVhTCbVIESki9Od\n1G1RUq4+CBHp8pQg2qK0TOMxiUiXpwTRFqpBiEgOUIJoi9IK1SBEpMtTgmiL0rIw1EbNrmxHIiKS\nMUoQbVGieyFEpOtTgmiLUj2bWkS6PiWItigpCz+rNmY3DhGRDFKCaAsNtyEiOUAJoi0aahBqYhKR\nrksJoi3yC6H7AapBiEiXpgTRVrpZTkS6OCWItiot181yItKlKUG0VUmZahAi0qUpQbSVahAi0sUp\nQbRVSRns3g57qrMdiYhIRihBtJXuhRCRLk4Joq1KNNyGiHRtShBtVRrdLKcahIh0UUoQbdVQg1CC\nEJGuSQmirUpUgxCRrk0Joq0Ku0Fxb/VBiEiXpQSRjtIy1SBEpMtSgkhHSblqECLSZSlBpEM1CBHp\nwjKaIMxsqpmtMLOVZjYzyfJiM3soWv6SmQ2N5p9qZovNbGn085RMxtlmGtFVRLqwjCUIM8sHbgdO\nB0YA55rZiIRiFwMfu/thwM3AjdH8j4DPu/toYDpwf6biTEtpOVRvhdqabEciItLuMlmDmACsdPfV\n7l4DPAhMSygzDbgvmn4UmGJm5u7/cPf3o/nLge5mVpzBWNum4VJX9UOISNdTkMFtDwTWxb1fD0xM\nVcbda81sG9CPUIOI+RfgFXffnbgDM7sEuASgoqKCysrKNgdbVVXV6vX7ffQho4FFzz5BVc/D2rzv\nlmhLfB1J8aVH8aVH8WWIu9kRbloAABUWSURBVGfkBZwF/Cru/YXAbQlllgGD4t6vAvrHvR8ZzTu0\nuf2NGzfO0zF//vzWr7T2Zfdre7mvmJfWvluiTfF1IMWXHsWXHsXXdsAiT3FezWQT03vA4Lj3g6J5\nScuYWQHQG9gcvR8EzAK+6u6rMhhn22k8JhHpwjKZIBYCw81smJkVAecAsxPKzCZ0QkOocTzj7m5m\nfYA5wEx3fz6DMaZH4zGJSBeWsQTh7rXAZcA84A3gYXdfbmbXm9kXomK/BvqZ2UrgP4DYpbCXAYcB\n15jZq9GrPFOxtllRDyjqqU5qEemSMtlJjbvPBeYmzLsmbroaODvJejcAN2QytnZTqmdTi0jXpDup\n01VSrj4IEemSlCDSVVqm8ZhEpEtSgkhXSTlUbcx2FCIi7U4JIl2l5fDJFqjbk+1IRETalRJEuhqG\n2/io6XIiIvsZJYh0lUZX36qjWkS6GCWIdDXcLKeOahHpWjJ6H0ROiA23sXEZHDYFzLIbT0x9HdRU\nwe4q2L0jmt4e3tdE82KvPbtg0AT49OfDs7ZFRFCCSF/PAdCjPzx1Lfzjfhh7Phx5LvQ6KPP73rUF\n3vwzvDkXdmyIO/FXwZ6dLdtGXiHkF8HLd0L3A0LsR0+H8k9lNnYR6fSUINJV2A2++Rq8/jj847fw\n9A/gmf+Gwz4bksURp0NBOz7KYtcWeHMOLJ8F7/wN6muhzxAoOwKKDoHiUijuBUWlUNwzvC+K5hVH\n8xqW9Qyx1deHbb1yH7x8F/z9f2HwsTBuOoz45zCkiIjkHCWI9lBcCkddEF6bV8Grv4VXfw+PTIfu\nfWHMl8OyA0e3bfuffMyBG56GB26D1fP3JoXjLoOR/wwHjU2vaSsvDw49ObyqNsFrvw/J4vFvwBMz\nQ/zjprc9fhHZLylBtLd+h8KUa+Dk78Gq+fDqA7DobnjpDjhwDBx1IYw+C3r0bXo7n2yFFXNDTWHV\nfD5Vvwf6HAzH/huM/CIMOCoz/R2lZXD8FTDpcnj3eVh8H7zyG1h4FwwcB+NmwMgvhaQoIl2aEkSm\n5OXD8M+G164tsPTR0EfxxJXw1+/BEWeEZHHoyaEsREnhiSgpPAP1e6D3YDj2UhZXD2Hc57/ecZ3g\nZjD0M+F1+o2w5KGQLGZfDn+5OiS5cTNCohKRLkkJoiP06AsTLwmvD5aGvoolD4V+i54DYNSXQtPU\nqqehrgZ6DYKJ/xq+qQ88GszYUVmZvSukevSFY78BEy+FdS+H5qfXHoLF94Za0bjp5NcOzE5sIpIx\nShAd7cDRcPqP4dQfwFt/Ccni7/8bEsWES0Kn8KDxnedy2XhmcPDE8Jr6P7Dk4ZAs5nybYwt6Qrdv\nwzH/R81PIl2EEkS2FBTDiGnhtbsKCnuEzuL9RbfeMOH/wDFfh/WL2D7rKvo9dR08f2vow1CiENnv\n7UdnpC6suHT/Sg7xzGDwMSwdcw18/enQkf3UdfCz0fDczSH5ich+STUIaT+DxsMFj8L6RVD545Ao\nOmuNomYnrF8I774I618OlyMPPR6GfAb6D++cTXwiHUwJQtpfskTxws9h0hWhSSoLiaJgz/Zwg+G7\nL8DaF2HDa+F+EsuD8hGw8XVY9mgoXFIOQyaFK7iGHA9ln9p/a3j7u/p62LU5DIZZFb12fhiewVK1\nKZrexJiaAuhxfrg6sLcumGgvShCSOY0SxbXwwq0dkyi2rguJIEoIn9n0ZpifXxyawY7/Jhw8CQYf\nE/pT3GHLaljzXHi9+3y4ygxC7SI+YVSMUsKI2bQCXv9jGOrF8iGvIFy2bXlx07H5eQll8sO8vILQ\nFNkoCXwYhtH3usb7zS+G0opw307vgXRbtwTmfie8DhoLnzozJIuKkaoNpkEJQjIvlijWLYS/ZSBR\nuIcT1doXQpPR2hdh27qwrLgXDJ7A6tLxHDL5fBhwdPIBCc3CTY79Dg13jbvDx2tColjzPLz7XBj3\nCqBbn5AwhhwfmqUOHLP3XpZcsGU1LHssvD5cDhj06BdO5PX10c/aMGCk14HXt2y7+UXhpF9SBr0G\nhntsSsrDkPql5dF0lBSKe+1z4n95/nwmjxoQaokrnoD5P4L5Pww3lx5xRngNmQT5hZk5Jl2UEoR0\nnMHHwAV/SJIoLg/f+mp2Rq+qvdN7dsbNT1gWe+3eAXW7wz5KymHIcWEYkiHHRd/281lbWckhQya1\nPFYz6DssvI66IMzbui5KGAtC0lgxN8wv7gX9Dkt+ImvipJa2+rq9x6D7ARkdibe4+kN4/paQFDa8\nGmYOnghTbwxX4jU1OKX73mQRnzjqY6/aMN5Xtz5tPz5mYTyysiPghP+AHRvDZeQr5ob7dV66I9QU\nh58WksVhn4Vuvdq2rxyiBCEdr1GiuC512cIeUFQS/SwN00Wl4aTb8L4H9D8ifEPse0jmmhT6DIY+\n58CR54T329+PahfPw9Z3Ydt78N4rsOuj5N+aC7rt+424IZmUM+C9lfD8a42TX6MkWQU1u8J07Sd7\nt2350P9wOHBUuNemIvoZe6BVW2x/H5Y/Dssf47j1C8O8AUfDaTeE+3X6DG7Zdswgv4BwumnHgSub\n0rMi1ATHTQ/HatX8kCze+gssfSSMYjzshL21i47st6jbE2phm1bARytg01th1IQ+B0evIeFn78FZ\nHyhTCUKyJ5YoNr4O1dvCP0PDST9KCp256abXABhzdnjFq68Lw6tUbWzoRG00vXVt6JuJksnhAG9H\n6xeW7D0GsVdxT+h5YDg+saQZO1aF3UMfwAfLQhPb0kf2xlJasTdZxF79Dkt9XKs+DH0Kyx4LTXU4\nVIxm9bALOeTz3w41qv1NUQl8+p/Cq74ujAawYk4YJj/Wb9H74JAkeg3c+zN+ukf/1vc71eyCzW/D\nprcYtnoebPxVSAZbVoVaU0zvwaHp6805YSSFeCVljRNHw8/B4XefQTmfIGpq6/nX+xeR98luVuav\nZnDfHhzctweD+/agtDjnD0/HqBiR7QjaV15+aFKKPUyqKfV1sGszz7/wAsef9Nn2uWFy15bwAKsP\nloWhXTYuhRdvD99SIdRkykeE2kbF6NCRu/ntkBTWLAi1n7JPweSrwzAw/YeHJrr9MTkkyssPTY9D\njoNT/xs+ejvULD58PaoBLoI3Zjc+UecXhS8EiYkjNl27Gza9GdUK3grTW9cBDsDB5EG/Q8Jx/dSZ\ne5vD+g3f2wdXX7/3y8PWtaFWGpvesCRFAikPyWLwRJj6o3Y/XBk9A5rZVOAWIB/4lbv/OGF5MfAb\nYBywGfiKu6+Jll0NXAzUAVe4+7xMxLj1kxo2Ve1m1cZanl77xj7L+pYUNSSMg/t2Z/ABe5PHQb27\nUZCvK1kkTXn5UFrOnqI+7XdVV4++MOzE8IqprQknrg+WRsljCbzxpzBSb0zfQ+GEb4cxwLpa0k7G\nDMoOD694sUtrt68PSWP7+3HT78G6v8PyDXsTbrz84nAfzaBjYOwF0fY/xbPL1nPSKac2HU9eXujL\n6XVQGM4mUdIEEiWR6q1tPw5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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_f6BGZqWb8q",
        "colab_type": "text"
      },
      "source": [
        "## Visualize Training Fitting Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqZ5K-HcLvgE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_stock_prices = y_train.tolist()\n",
        "\n",
        "if train_mode == \"nlp\":\n",
        "  pred_stock_prices = model.predict([X_price_train, X_nlp_train]).tolist()\n",
        "else:\n",
        "  pred_stock_prices = model.predict(X_price_train).tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFuqGsoHPr3x",
        "colab_type": "code",
        "outputId": "45b5257f-d2f6-4073-b415-8203ebabc9f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Visualising the results\n",
        "plt.plot(real_stock_prices, color = 'red', label = 'Real Salesforce Stock Price')\n",
        "plt.plot(pred_stock_prices, color = 'blue', label = 'Predicted Salesforce Stock Price')\n",
        "plt.title('Salesforce Stock Price Prediction in Training set')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Salesforce Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OnTqRmJhISkpK0Lwedh+EP6cTVlyLCNvuch5y/fW+/RDXXguffQbvvQf2c2GF\n2Ni1C6pWzKHmiPDkRj9XvZjKA32Ah4DuGDPVWP+NVPUNVe2mqt3sL7vzkWHDhvHvf//bE9p669at\nZGVl0bdvX09I7X379vlEI7Xp1asXS5YsYaeVRCTNGnLvHxJ76NChnnSfgKfB7tu3Lx9++CEA3377\nrcfG72Tv3r0+Ib8TExNp2rQpYL5qGzRoQG5urifktpOiQl7v2LGDnj178uyzzxITE8OePXt89q1e\nvTo1a9Zk6dKlALz33nsB8ze0adOGffv2sWrVKk+d8vLygl5XJ7/++ivbtm0LeG7Oa9ijRw8WL17M\n4cOHyc/P56OPPqJfv35Brz+Y6L1Tp05l5MiR7N27t1C9T5fo6GgaN27M559/DhhNzM4zEihsu8t5\nwM6dYIekr17du9wOPKpqhANAUpK3D8IhIJq2qoA8+aewVC+cGkQqEOuYb2wtC4UUINFhnvoc6AW8\nVao1PEcYP348ycnJdOnSBVUlJiaGzz//nKuvvpoFCxbQvn17mjRpQu/evQvtGxMTwxtvvME111xD\nQUEBdevWZd68eVx55ZVcd911fPHFF7z66qu88sor3H333cTFxZGXl0ffvn2ZMmUKTz/9NGPGjKFD\nhw5cfPHFATWx3NxcHnroIfbu3UulSpWIiYlhypQpADz33HP07NmTmJgYevbs6SOUbD744APuuusu\nnn/+eXJzc7nhhhuIj4/n4YcfZtu2bagqgwYNIj4+vlDeinfffZc777yTEydO0KJFC955551C5Veo\nUIEZM2Zw7733cvLkSSpXrsz8+fODXlcnmZmZ3HvvvRw7dozy5cvTqlUr3njjDQBuv/12hg8fTsOG\nDVm4cCEvvPACAwYMQFUZMWIEV111FUDA629z6aWX8ve//50RI0Ywb968kEK4F8V7773HHXfcwVNP\nPUVkZCQzZ85k6NChbNmyxfN8VK1alffff5+6deue0bFczgIDBphW/sYbfQPude9uRk07l2VmBjQx\nWd8z4SFY58SZ/jDCJwljIrI7qTsE2XYajk5qTAf3eiDGmn8HuLuo4xWbk9rF5TeO+z6cY2zZ4u2Y\n/uUX83/ZZd5lc+aobt3qnb/9dtW33jLTycmqqlqzpqrDn6FEUEQnddhMTKqaB9wDfAdsAT5R1U0i\n8qyIjAQQke4ikgKMAqaKyCZr33yMeel7EfkZEODNcNXVxcXF5axy9Cg4c3PYDiRXX+2NzLphA7zy\nineb9HSvBlGhAhkZpphmzcJXzbAOlFPV2cBsv2VPOaZXYUxPgfadh3GBdXFxcbkw+OknWLoU/D0S\nbeeO+vXB9iR85BGTHAiMFD2jytkAACAASURBVEhP9+mD8HgwhdHEdK52UpcaeoFkzHNxORPc96AM\nefZZsCMfXHIJPPSQ1z/1Latb1dYUGjTw3XfTJrjzTmjZ0sRhcvRBuALiDKlUqRJHjhxxXw6X3zSq\nypEjR0rs4uxyhjz9NNx2m5m274EVyYCOHX23rVfP/DtjMrVqBbVqwb59PiYmy3EurALigo7F1Lhx\nY1JSUsIWN8jF5XyhUqVKNG4c0JrrEk4KHGOA8/NNa75pkxkMB0ZA9Ovnnbfd9Xv2NFrD3r0werQJ\nrTFzJthjhSIj2bnThGSqXz981b+gBURkZGTQAUouLi4uYcc5HmX9emjSxAiIVaugRQuIioIXXgDb\nhd3WMET44a1fOJkTwaBGgoweDY8+Cp9+ChEREBFBUhI0bw7W4PuwcEGbmFxcXFzKFOcAya5dwTFG\nhptvNv8BBvm+8QZc2r88Q4YKTz9tbdO6tVlpjYFISjIyJpxc0BqEi4uLS5ninwo0L887/ZTl0Okn\nIBIT4b77YMgQs+q556BXL7ischSnqEilChVQNYOw+/cPb/VdAeHi4uISLgJEFqBtW+PWatuGoqPJ\npxxL6UOF5XDTTSbV9AcfmLxAmzbBmDFQL/cTkqjP6wWPcM0RU3S4LeiuicnFxcUlXNgpQp980rus\ncWOvtxKACLfyNgNYxCWXGGelzz4z2kOlSma6e3doHJVGBzbxYNazrFhhdrWtTuHCFRAuLi4u4cIW\nEBMmQKwVms7P3XjdOpjOLdw5cCsffQQbNxonJpsWLWD+fFgw+C+8wzgytSq3327WdesW3uq7JiYX\nFxeXcLB4Mdx/v5mOjjYqwZ49nmxwNs8/bwK5vvDZRT4BXQsRFUUX1tG36hqW7OtKmza+ikg4cDUI\nFxcXl3Bwww3e6cjIgBrEpk3GhHTffRQtHMC4xAJPt/qQ5s3hz38u5foGwNUgXFxcXMKB/wAFO6qe\npUGoGstTdLRX0SgSS0AMbL6TpM9Kr5pF4QoIFxcXl3BiC4bmzVnGJdw16ykqroe6dc2wiFdfNV5L\nxWIJCKyUumcD18Tk4uLiEg6ys+Guu8DKWJjfvRd/4D2OnajgcV999FH4n/8JsTxbQFjZ5M4Grgbh\n4uLiEg5OnoSqVaG8aWaX5fQgGeHjRzYwelKDYnYOQKdO5t+ZZS7MuALCxcXFpbRRNRpE5cqeRQsX\nCSJw2aMlTHMzfLjJX3355aVUyeIJq4lJRIaLyK8isl1EHguwvq+IrBWRPBG5LsD6aiKSIiL/Cmc9\nXVxcXEqV7Gzz7xAQP/0E7dufQReCiLFHhTOFnB9hExAiEgG8BlwGtAfGiEh7v812A2OBD4MU8xyw\nJFx1dHFxcQkLJ0+af8ulVdUICOcAuPOBcGoQPYDtqpqkqjnAx8BVzg1UNVlVNwAF/juLSFegHjA3\njHV0cXEpLVJTYfv2sq7FuYGfBrFzp4nb16NHGdapBIRTQDQC9jjmU6xlxSIi5YB/AA8Vs93tIrJa\nRFa7SYFcSp38fG8OYJfiadzYBAfKzjafzABZWfDJJ2Vbr7LA1iAsAfHTT2b2ghQQItJURAZb05VF\nJDq81eJ/gNmqmlLURqr6hqp2U9VuMQFiqru4nBHdu59Vl8ILhsqV4Z//NNP3328yotkt5PmAMyR3\nSfETECtXGmuTf4bRc51iBYSI3AbMAqZaixoDn4dQdioQ65hvbC0Lhd7APSKSDPwduFlEXghxXxeX\n0mHdurKuwfnLN9+Y/927zb9/XoRzgd27vQ25zdKlJiyGnTO6pPj1Qfz0k8kXZOX6OW8IRYO4G7gE\nOA6gqtuAuiHstwpoLSLNRaQCcAPwZSiVUtUbVbWJqjbDmJmmq2ohLygXF5dzFPtT2dbAzqLvfkio\nmvzQV13lu3zOHPO/YEHJyn3nHXjzTZ8+iJwcWLvWKKTnG6EIiFNWJzMAIlIe0OJ2UtU84B7gO2AL\n8ImqbhKRZ0VkpFVWdxFJAUYBU0VkU0lOwsXFpYzJzPSdL2c1LbaAOHr07NanOGyNZt48X23BNi+V\nL8EQsR074NZb4fbbTd8LQFQU69YZeXHJJWdW5bIgFAGxWESeACqLyBBgJvBVKIWr6mxVvUhVW6rq\nJGvZU6r6pTW9SlUbq2oVVa2tqh0ClDFNVe8J/ZRcXErI0aPw179CQSGnOpeimDfPRJxzcuKE+bcF\nxLlmYnLmir70Uu+0LSBO1xaUnQ1XXumdt725YmNZtqzwYc4XQhEQjwGHgJ+BO4DZwJNF7uHicj4y\nYQI89phvYnlwPZkCkZ4OBw7Ae+/B0KGF19tf0LY30+HDZ69uoeAUEGA81sB7r09Xg7jhBtiyxTt/\n773mv1Ejli2DVq2gfv2SVbUsCeUqVAbeVtU3wTMArjJwIpwVc3E566Snm39/c8nJk+df72I4ycuD\nGjWK3iYrC265xevi6n9Ny5p9+3znd+0yqdtsAXE6HwUnTsAXXwRclZ1TjgUL4PrrS1jPMiYUDeJ7\njECwqQzMD091XFzKEFsI+Heo+nu6/Nbx//oOxNGjMH26d97WKM4V/DUaW2DYguF06vvrr77zbdua\nTvo9e/jsM5N1dNSokle1LAlFQFRSVY/4t6ajwlclF5cywjYrHDvmu/yEqyz7sGtX8dvYA1ftnJjn\nmoBISzP/ffqYf7u+dg5p+55nZnrNZMHwfz4SE2HDBnblN+axx6BDBxg8uHSqfbYJRUBkiUgXe8YK\ngeF+UrlceNgCYupU30bBFRC+JCcHX2e76tj2+EmToEuXc09AHDlihNeHVhg4W0CkWGNzs7Jg/XrT\n+T55ctFlOZ+PBx6AihU5li706wcZGTBtmtep63wjlD6I/wVmisheQID6wOiw1srFpSywTUnr1/tq\nEa6A8KUoDWLZMt9Um1WqmN+5JiDS0kwaNzsCww8/wG23wR4rOlBWFsyebabXrCm6LPu5WbPGCEPg\njTfMZVq+HLp1C0P9zxLFCghVXSUibYE21qJfVdV163C58LA7qcGk+rJxBYThhRdMrKXiTEyTJ7N2\nwnT20pARUVWQKlXOPTfXtDSoVcuTH5p33zWtut2/kpLi/Ugo7vPfL6wGwOefm7hLvXuXcr3PMkHP\nXEQGWv/XAFcCF1m/K61lLi7nPrt2GbU/lJG8x455beZvvuld7nZSG5Pb44/DddfBt9/6rrv4Yp/Z\n/+TeTHdWcSVf87ev2plUmeeaBnHggFd7+P3vyaYiMyveyPGCKqa+33/v9Uzy75Pyx/6AsATEqVNG\nmejbN0x1P4sUJRr7Wf9XBvhdEeZ6ubiUDhMnGhvy5yGED0tPL9TYAa4GAb5eP6mp8Mc/ehtYx7V9\n6SW47ZFaDGEe/VnIs+83Zw+x556ASE2FRlZw6csv50H+wfXM5Cq+QG8Z67ttcQLC/oCwckavW2e+\nR8537QGKEBCq+rQVdvtbVR3n97v1LNbRxaXk2BpBYmLx26anm0bvtdfM/K3WY+4KCHj7be90ixZG\nwzp40GgWMTGsvOgPXNZ6OxMmwLXXKF8ykre5lQIVHlp/E5mZ55CMyMoy99oSECei6/EutwCwiAH8\nmHCn2e6OO+B3v/M1PQbCT4NYscLMXtACAkBVC4BHzlJdXM5FcnLM6GLbLfB8w+4wdY5yDUZ6OlSv\nbtI6qnLi8eeYwD+57tlO7P54eXjrea6zdKl3ulYtn47oDz6AS3ZMZ/XRlvztb/DxDKECuTQnmcfv\nOsYnO7oRfSSZhg2Vn38ug7r7Y/czWAJixb6mZFGVGVxPNMeZuqit6YN49VUzIDBUDcISEL/8AnXq\nQIMG4TqBs0cozlfzReQhEYkVkVr2L+w1czk3+PRTE5/oyfM0uor99VecBpGTY+LpVK8OmA/j390W\nw0tM4NNt8dw85hyLRnq2cX5F25FKMV4648YZe/uOHfDww75RKp54TPnbLZu4n5cgP5/nnjuLdQ5G\nqpV1oGFDAH5IaoBQwFDmcuMVx/nkv5EcjWpkBk7GxBhNyQ7FEYgTJ8y21onv2GGUrAuBUATEaEzI\n7yXAGuu3OpyVcjmHyMgw/5s3l209SordsO3fH9p2loBYvBjmLYrkZe5jMv/LYvr/ttNDHDsGVaua\naeuLOSvLhJBo2hQ++wyqVXNs36gR1K1L+Xq1efiJSF5iAmN6JjF7dunk4zkjbDXG0iA2JFWlJTuo\nQTp3PNeY7GwTtTs1FWZlDOX/Tk1g6cz9wQfMnTzp6X8ASEqCli3DfRJnh2IFhKo2D/C7QOSjS7HY\nMXQWLzaJdc837JGx2dlFfwXaZgdLQHz6KURFKbfxJmOZRmVOMHVq8N0veNLTzZBg8GgQkyebRvTd\ndwOEZvrlF687bJUqAPQpWEJWlllVZpw8CffdZ6YtAbFxI3Qc1gi2bSMhAQYMgAcfNBlUR00ZzBP8\nH33HNGLOkH8ELjM93RPNNjfXG9bpQqAoN9eeIrJeRDJF5EcRaXc2K+ZyjvDee97p4jrrzkWc9uOi\nekmnT4eICE/P4tKl0Lu3UJlsapDOCL7hq6+02KgLFyzp6dDOagJ69CA723gsXXllYMcvqlb1ZFOz\nv667LDIN7Lp1GJOfSPGD0M6EHTtg2DDfZ8B5vOhosrNh2zbo2D3KhFwFZs6EZ5+Fl1+GlYuzOVSv\nI63ZyhPfDwx8/w8dgromh9qePeY75LegQbyGyeZWG/gn8NJZqZHLuUNurq/t/tSpsqtLSUhJMcmA\n7ZwERQmIHTtMA9iqFceOwYYN3jA9AMP4jr17hU2rfoMeTfn5RhNr2tRcz+nTmTnTjH27//4Q9rc6\nb1uyg3Ll1KRKePVVs+7TT8NX51atYO5cqFnTax6yn+c//Qkw2kx+PnTq5N21dm3485+NotGjbyXq\ntIvhEf7GOrqweEEALfTgQY+ASEoyiy54DQIop6rzVPWUqs4EYk63cBEZLiK/ish2ESmUMlRE+orI\nWhHJE5HrHMsTLK1lk4hsEBE3tEdZYJuXbHeM883d87PPjJCzR0UHCzmdmWkGRTVuDJg2UNU3wcsw\nvgNg7oxzLDPa2cDZP9OjB1StyvTp5it54MAQ9rdGK1cgl8Z1skn6NdfrNhsulczfKcHu+LDHczzz\nDGDMS+DNkBqQcuW4ifeJ5jgfvBsgiMTBg54xITt2mEW/BQFRQ0SusX8B5ovEyhvxGnAZ0B4YIyLt\n/TbbDYwFPvRbfgK42cowNxx4SUSKCUDvUurYDaqdKeuccWQPkTVrjJ25c2czH6z+f/+7+d+wAfD2\nYSYkAP/+N4wdSywptGIbi1dXCW+dz0XWrjX/lonp8GFYuBBGj/YNuxQUx0YtGmazc4fjKzxcITj8\nBY89KjotzXSYREQARkBERpoIIkEpV45KnOJKvjLfHFu2+x7HYWJKSjIKqz0G73ynKAGxGN/R0875\nUEZS9wC2q2qSldP6Y8AnQ7iqJqvqBqDAb/lWVd1mTe8FDlICDcblDLEFhD3Y7HzTII4eNV92tvdN\nMA3C9mN/8EHAOGzVq2dMDdx5pyfbSx+W8kNi1G8vI+mSJSYekRWp9b//NWaZ664rZr8ANG94iqRd\nEd4FH3wQnufKv8xRo0xngx2DyWLjRmjTpph8UJYwGcVM0rIqsbT97V6Hh/R0cywrXdyOHdC8+fkb\nvdWfokZS+4+ePt2R1I2APY75FGvZaSEiPYAKwI4A624XkdUisvqQHa7XpfSwG1Tr6+i8ExDHjpmv\nRcuL5uj+U4wfb0xIPmRmGjv1Aw8ARkB0cGZHtwRMH5Zy5HiFsvXCKQsWLzZRSi1PnVmzjHkpIeH0\ni2pe7wT7DkVykkrGIeDECW+o7dLE1hb/+Efvso0bCwmI9eshPr6YsqzWfjDzqcApvmGEeSf27vVG\nf23SBDAaxIViXoLQxkGUGSLSAHgPGGeN6vZBVd9Q1W6q2i0mxlUwSh37JbMExMbtlUKKeRcSycmm\njyCcpKWZht9q4CdNj+Wtt8zHpI8F4uBBj5akagREe6cx1OqbuBSTfd45qPiC5K23YOtW7/yGDabv\nAePhunixsTqGZF7yo0Vd80wl08zbyWO7IpcWql63ZYcwYPdub5hvzGRKSggCwvJWqEoW/VnEbC43\nO69d6xUQsbGoGg3iQvFggvAKiFQg1jHf2FoWEiJSDfgG+JOqrijlurmEgkOD+JIr6TTphqJTJ86Y\nASNHBh9vsHkzXH21aWW6dYNrry31KnuYNg02bTKdk5ZB+OsVpmHYs8fPu9LhhZKaasYGtnM6dcea\nx7gV26nH/gtfQIwf7201T540mpgdlmKFcWYLqXM6AM3qmGdqN008grfUBcTrr5tzAI8wOEklPvwq\nmtSfUj3HXb/ebFKsgHj0UfjpJwAuZza/0I4kmpsPKHtUdqNGpKWZU/lNaRAihb8TRKRiCGWvAlqL\nSHMRqQDcAHwZSqWs7f8LTFfVWaHs4xIG7KhjMTG8zv8A8OWXJlKyh4ICb5ymhx6Cr76CefMCl3fX\nXSby548/ejsnw2XQn2U9NuvWQe3aHG/SkV8P1eZ/bjNeKD/8YG1nO8JbAsIO2eSjQVghFARjZlq2\nLDxVPiewVSs7nIadq9nyZFuwwFhcShrKukkth4CwBG+pCwjn2B1Lg/gfXufG72/lYl1GRu+hwGkI\niHLloHt3aNaMEXwDYLSI9HTTzwVQu7Zx3+W3p0G85ZwRkarA7OJ2UtU84B7gO2AL8ImqbhKRZ0Vk\npFVWdxFJAUYBU0Vkk7X79UBfYKyIJFq/Elg8XUrEnDnGfjBpEgA5laqxkAFc3CgZcDSuAP/6l/lK\nu+Yab6N/8GDgcu1wBE5vonCNrbAN5JddBsDG7uMAGN54I7GxRkYBJlnwvn2FBEQ7/2GhlodTH5ay\na5c3M+UFh1P7a9sW/vIXM20JiIULjfJnDTgPHWssSoNqWURIAbtoGj4B4czfUasWR6nB+9xEJzaw\nm6a8tMc4Ya5cacIx2T4YxdKoEa3YQev6x/maK4xmlZ5uOrGjoti2zWxWpEfUeUYoAiJFRF4HEJGa\nwFzg/VAKV9XZqnqRqrZU1UnWsqdU9UtrepWqNlbVKqpa23JrRVXfV9VIVU1w/EKI1+xSKvzhDz6z\nPx+oSw4VubP9UiIjPdq2wbbV/Pe/3hczN0jCQVtAOF0bwyUgbLcUa0DW1oEmhHO74yu5+GITZI6j\nR73SrmZNwAiImjW9/fIeOnWCt96iK+Z8L9i4TE4B8euvpj8CoEEDsrJMozpgQAnKtVxly2sujSIP\nsLtOF4/nT6kLCOczVasWCxlAHpH8+7uWjByp/PPl8qSlGWHXv/9plGs9FKN67WEeQ9i5q5wREtWr\ngwjbthll4zdlYlLVp4BMEZmCEQ7/UNV3wl4zl7LDrw9h9c/GonhpnV9o08aY9j1cdFHh/YP1ZFve\nRB67LYRPQOTkGNOQNUhr92EjnGJTV9Crl+mH2H/1Xb7bYwRE27ZBOmDr1CEOo0lYCsWFR7BIeg0a\nsHixkf2DB5egXDvEa24uTXQXuyNbeqP7lbaAcPqsVq1KIgmUI58ufaowcaJw7BgMGmRMpVecTuqz\np5+GJk246+l6lKOAyZ80Mv0dljq1fbtxZqoYigH+PKGoWEzOQXErgV7AOkDdlKMXMKqFUmyuXg21\nyh2lWYW9tGljPiw9BBIGwTQIe9sFC7zLwikg7BAbGIFQr0IaFX9YQOdGxgS2bpN3vb3tli0BzEs2\nMTFEk0mL+ic89usLjmAOBnXqMH++afys4RCnh91o5+URq3vYnVXbOz6ltAWE/ZzdfTdERpJIAm0r\n7qRyZTNm8rrrzEDrFi2Mz0TIxMfDrl00TqjDrdU+5fXDo1hPnCdS4bZtnnBOFwxFaRD+KUbXAZG4\nKUcvbNau9Yn3T+XKJCZCl4qbkZxTtGljfL09csG5rU0wAWGHbDgbAuLUqUICIrZGBuzZQ8ITlwOw\nLtcx2OHRR0lLM90nQQVEnToAxDc6/NsQEI6MNyrlmDPHeKZaoZVOD1uDyMujSUEyKZk1yNdyZmxF\naQuIrCyTDfBf/wIgkQQSKnvddt9+20SgXbzYG0/wdPm/EcuoRRp3MoWCKtGoGs/gC6n/AcI7UM7l\nfMTufV2zxviSp6SwfTu0rrQHsrNp08a0IZ7I304BYce5KUpANGvmOwihpAMrkpKMETkYOTk+uv6e\nPRAbZ/oZqm9fQ8sWBazNsSK01a0L0dHBO6htLAHRodZetm8PfprnNU4Tk+3eI8LatUa7KsnoacAr\nILKzaVKwk9yC8sYbrlo1jh3KZeHCYsIyff+9X+dXEZw44R0cmV6OPTQhvqp3nG10NNx8s9fLtiTU\nGtadf/AgK+jNm3tHkJxsHu9iPaLOM0Jxc33XGQdJRGqKyNtF7eNyHmMnCIqOhgYNOCq1OHYMWlbZ\nB6eMBgEOM5OtAdxxhzcpQLBGPz0dunb1xkZy7n+6DBhgnPGD7e8wMamaMVKx7at5ood2aX6UdTmW\nBmE1inYMJp9R1E6sGD7NI1MpKDBlXnA4NQjbQF+nDu++a+Tt6JKGzbQFREYGTTAXbvduoFo1Bn7z\nAAMHwtdfF7H/4MHQs2dox8rK8jhE7IwyN7P1E0UN4CkB7dpxE+/Tj0U8lXq751ula9fSPUxZE4oX\nU5yqegKqq+pRoHMR27uczzgFBN7olC2rHAgsILKzTRjoKVMgIgJFeHD2ICZPDlC2nfPZ+elWUgFh\nj71YudJki/P//Dx1yqNBpKebMX+xsXiEU+fo7STlN+UY1T2awapVxmO3adMgxxSBOnVoLsnA+Zk/\nqVhsDeKeezyjp3NiGvHhh/C733mcvU6fIALi18iOrDtm3H4++CDIvs57W1z017w883FgCYhdKSaO\nUtOe9UtY8SC0aYMAz/MkB0/V4I9/NI9RScKPnMuEIiDKWe6tAFj5qMsXsb3L+YxtD/YTEC2qHYZT\np6hRw7wIts832dk+ppzvyw/jnysv4YEHTDQNH2wB4XSiL6mA6NbN/P/738ZW/vrrvusdGoRPuJxm\nzaBSJbp8/hQAibEjzbgPjIDo3r2YEBJ16tA8x0jHC1JA2BpE587GoF6xIvOvf4MjR+Cmm86gXFtA\nTJpEU0ymuaQkmHuqH2CiWQQ1MzkT/gQbY2NjO1hYJiZby7NCJZUe1avDjh1cuvpl/vAHU+m77/bN\nx30hEIqA+Afwo4g8JyLPA8uBv4W3Wi5lRkaGcea2vsA8AqL6EU9j3rq1Q0AcPuzT0/eFI2Cv1e4a\n8vPNZ3z16h73xkPU4cihEo6ktoXSxx+bf//R2wEERGwspvW/7DI6YwYyrLv9dWjenKws477bvXsx\nx61Zk8andhARcYEKCFuDKF/emNSys5mZ3J3q1WHo0DMo19FyVuc4DWueYPNm+D6jO80j9zB6tGn7\nPR7QU6Z4RzPuccT8tPu5gmEPwrSe3927Tae6FXGjdGnRArp2Zdo0YfNm4wV7oRHKOIjpwDXAAWAf\ncI2qvlf0Xi7nLRkZRnuwPqOTk62I2VUsN4233uKig8vYtk3NC/z99z6DApbpxQyJ/YUaNRw5WxYv\ntkam4REQP9ORZiTT9Oa+PnHhQsY/sqwjaTzgY2LyERAA//kPded/RKNGsGqzcbVcvtxE/ejVq5jj\nRkVR/lQWTZrAzg0Z3lALFwq2BmGFuFaFb74x3REOp7DTxy+edqdmGaxdC4sOd2Rg5DK67JgJWAMQ\n1683YVluucVs7FRF7eudnx84pIv9XFgaxK5dRnsoSWDBUClXzjg2hPMYZUWowfoiMaFoxJp2uVCx\nBYTF/v0mHAEVKxoT0fjxtN7xLampQtaPvqPFcnJgU35butZOJi7OITf69/cG77EExLM8xQmqkJVd\nnpdfLkE9/QWEv++lnwYREeHw2qxVCwYNYsAA08YUFBhtp2JF6NevmONGRcGJE7RooSR/szGEHc4z\nbAFhffHv3WuicRcrOIsjIsJn9uLWh9i0CdJzorhcvyFu8liEAtauyMET1Mg2F9l5PMErICZNMiqN\n02UaAmoQQfuUXIolFC+m+4EPgDpAXeB9Ebk33BVzKSOOHy8kIOrXx8eM1BpjX9qe7esnuHkz5FKB\nhBrJtG7t+157qF6dk5Vr8RVXci+vcFWXPcyeXYLMk36D+QrNOwTE7t1GyPm1UVx2mbFYrF4N335r\nZFiV4hLGWQKiab1sE0/Idn26ULBNTNbFCjmgXXH4ZdC5tr1JqlGtYjaXnfyMKpygLb+w9i/fel2n\n7X+nLc8WEKtWmX/bqcLG7qOwwv/v3h2G/offEKFoEH8Eeqrq01bYjV7AbeGtlkuZkZpqqQwGj4Bw\nNMAXYWxC2/b5tqa2SSmhWhLNmplQBieP+g2kq16dFQdbcIpKDGUuA1qnkJzsDRoaMidOwJgxxjbU\no4fXq8nGz8QUG1u4iGHDTDt4883Gxz+kUbWVK8PJkzSpdIh9NORU+QssBamfBmELiLi40j1Mh7Hd\nWb0aEh94j8qYZ6sLa1lLF++z5tQgbM83W0DYHdf+I90c0Wezs80z6AqIkhOKgBDAOf4+31rmciHi\n+ORSdQiI1as9m7TCmAC27bZeTiuhyrp1UEWyaFUphWbNzKpdiX42+urVWXmwOQAXs5wudYybiX+O\n+WI5ccJ0dvfubUxGdgDAY8dMv4ifiSlQI1G7Ntx2m3HZbdbMCIpisTWIfSYUekrzPqdZ8XMcPw0i\nMdGk0Dzt6K1FkZsLTZvStasp26Yz60gh1ptgzikg7LEz/gLCX3O0zVMNGnj6nlwTU8kJRUC8A6wU\nkYkiMhFYAbgD5S5EcnLMF5j1Rh09at7l+vXxCAEwmbUaVj7Kr6lWLB1rhFNiIsRV2kZE3imPgEhe\nV1hArEmpR3OSqMVR4qONHarI6KiB7E8nT3o7pmvXNgJi+XLjqD94sLEdVaxIQYEZHB5IgwAT7PXr\nr03qi2LNS+AREE32GgGxq3wxwf9PnDCR4U5bAjpYvtz04/ibU8KBXyd1SCk5TxenL6gdsA+jQQCs\nS7KkUUEBzJ/vjWFRBv59+wAAIABJREFUrZpXQNjXwtkXlZ8PzzxjpqOjw+fi+hsiFC+mfwLjgDTr\nN05VAw2DcjnfSUszjbFlv92/3yyuXx944w2z4MknAeh0ciUbdlsvcpUqFBSYNjAhaivk5noFxOfr\nfO3P1auzdmsVT9jsaqcO0bJlEQJizRrTKP/wAyxaZASYqmkYHAJiwcGOvPhUBplYrfzevVClCqmp\nxtoULIlL+fIwYsRp5ASIioKcHJrmG8G2+3iNordfutR0pFr5rotlwwbjDrNli4kn9Kc/meh4ixef\nnRCyDhNTVpZpm0t18FdnvzG2DgFhux6vTXakCR0yxGg1AwdCzZqkpBjPJE+iKaeAsNOMWvHIXQFx\n5oTSSf2eqq5V1Ves3zoRcd1cL0TsQWuWXddHQFSsaFrR556DpUtJIJGNh+qRUzEaIiJITjb9252j\nt0FuLg0aGM/G5JTyPvEHjhZUJ2lnObpUsGKGHz9OQkIRH9jLl5vOyksvNS9+w4YmJk9+vqdx2aat\nGJ41i0e+H8YEHN8uVap4R4KXVpYvy1uqceYvCAXsyirGwd6+iI7Ad0XyvpVq5csv4Z13vAl74OwM\nvHCYmDZuNLK41DSIgwf9sk3hIyBqkE5z2cm6VL/88u+/DyNGsLtKOzp9NYk2bWBjjhVm3pl8apcZ\ngMejj3pmRc4s5tJvnVBMTD6RaUQkAggp4oiIDBeRX0Vku4g8FmB9XxFZKyJ5InKd37pbRGSb9bsl\nlOO5nCG2gLA6d30EhJOEBDqzjlwqsKWS+SK0NYCE6CTIzSUiwny5JafX8kkcv26zKbvLlxNN4oX0\ndDp0MG2fvzkZ8EaAdWL7XFqjn/62eiAR5DOKT5jGWFKxOtmjojwm6VILw2xpLRXT9tGQvew42bDo\n7e2LGKqKYt+DQIMOzoaAcGgQHqeD0tIgYmIKuyM7BAQVK9JF17B2n0OYNmsGN94IwPNH7+ZYblVO\nnYKXMv5o1js1CFtAWCrD7t1GLp/R+I3fOEXlg3hcRDKAOBE5LiIZ1vxB4IviCrYEyWvAZUB7YIyI\ntPfbbDcwFvjQb99awNNAT6AH8LQz3IdLmLCD7FlvlN1ZGOP3QUfVqiQ0NZ2E6yJMyIs1a4y5plPN\nFPOlqEqzZpB8IsYngI+VWIwuXTA9n8eP0769sRgEHDBnN7CBqF2bQ4fgvdVtuZnpvMBj5BPBv7ES\nAVWpwvbtRpMJ1gdx2thmrePHac9mNucUI3k2bzb/ocZg8LsHPjgz8YULhwaxfr25RWHt5HUKiIYN\n6cJath+LIR1rufURsHMnvLN/OHfX/JDRo+Gr7MEUIL4Cwn5gLWHsurieOUWF+/4/VY0GXlTVaqoa\nbf1qq+rjIZTdA9iuqkmqmgN8DI44DOYYyaq6AfCPtzAMmKeqaVZwwHnA8NM5MZcS4KdBpKUZFb1G\nADN7q8HNqMFRlpUznderVpmsnJUuG2DGBmzdagTEqQZGQCxfDtOmsWqVeWljYjCNgyUgwNuW+nDg\ngPm/7z6PH6oCOURC7dq88grk5JVjApNpwU6urLGMqdxBNhUhKoodO4ynjP8YiBLjGLHdkY1s1nYU\n5BURLsRWrfwH9gXDFhD33FN4XbBsb6XB0aMmf8L06WY+IsI4HcSFeYSw0z2qYUNPP0RizFDT3zVj\nBmDGxUWUUx4v/yJDh8LBghh+oa3vdc3MNP/WOJ5du1wPpjOlKA2iqYhUt4WBiAwQkZdFZIKIhKK0\nNQIcQVRIsZaFQkj7isjtIrJaRFYf8vjGuZSYAALCinBdiIghA+nPIhakd0XVeMF2747XHpGWRrNT\nv3BA63Gial3jjnrLLaxc6YjaXK0apKVxUZ00ypULIiAOHzb9Dy+/DB9/zImfdzCQBVQhi/Evd+SV\nV+Dq/kdpiwmgd//rbThMDB/ye4iKYsuWUk7i4jCRdGATJ4li59YiEkPYnSDBBERiom8HTFH5McIp\nIDp1gnvv9fSBFJQrz4YNZyE6qWNQJo0aeZwXVmoP09/VsiVJSTBtGtzeeRWN0n6mz8XGDLaUPr4m\nyIwM8+xGRlJQENy92SV0iuqD+ASMS4iIJAAzMSahBOD1IvY7a6jqG6raTVW7xRSyg7icNn7mjbQ0\nn+4DXwYMYCAL2Jkby/z5xi29Wze8L3xGBq3enwjAjvxmgFEGdu3yRJE2AmLrVio2rE2rlhpYQDhD\nf1SowMtftWARA+jPIt76tCblysELf8r0VuuGenTkZ17kYY5oLTZv9gZ+LRUcGkSHyqZPYOOGIBrE\niRNeweDsTHXSuXNo+TFEwisgnHnCgaT9UWRlnYUEOM6vj9hY6nKItpHbWXzKG9vj+eeNmfCx3/0K\n+fm0qpRCPfYbAbF1qxHaV11lNAgrjemhQ+ZSugLizChKQFRWVctvjJuAt1X1HxiX1x7Bd/OQCjgt\nv42tZaFwJvu6lJQAGkRQAVG3LsMWPQF4B5gNHIi3Md+50zOgbnuESSJhJwTzaBAO80K7FtmejG4+\nZGR47NSqJlVk//7KvLRubNhguch39dqxReAFHuMX2nHpXy5DFS6+OOQrUDxOE1ONFCLIY9WqIHFC\nnJFHQzExbdsWPAtRnTrhExABvAPWbTMN7VnNkGaF0u2fO49lJzqTl2fGvU2fbvJRNWxnnhfZsZ0+\nLDUCYtMm4+X25ZfmWbEEhN2f75qYzoyiBITT8jgQ+B5AVUONz7wKaC0izS2T1A3AlyHu+x0w1Mpe\nVxMYai1zCSenIyCAi/o1YNAg04/cq5flSmoLiNWrvSOu88xw2ZUrzQejx+vV0UHZvmE627YFsLA4\nNIgtW8yo51GjBP6/vTMPj7K8+v/nJCEJJGHfE3YhLC6IiCCbFbHgghvurUsXra/aWn3ra/Wnoq3v\n29YFtdqqrVrUVkStigiKFZBNkbAoJCwieySQAAkEAtnu3x/3/UyeDJNkAplkAudzXXPNs86cmUme\n73POfe5zWrXilFN8Yxk+LmQmN/MKa7Ob07mzE666whdiSm4dzyCWs2BxFQPQ/rBnTQJRUgJ9+tgv\nycPvWaSkRE4gvESAiypazS9cmUyzZnVfYqNazj0XRDiHeewrSyYjo8J7+J//oaIEzKJFjGI+W+nG\nlj3JFecXFgb+VjyB6NmzHu0/DqlOIOaIyDQReQZoBcwBEJFOQI2NhI0xpcAd2Av7GmCaMSZTRB4V\nkQnutc4Uke3AlcCLIpLpzt0D/A4rMkuBR902JZLUJsTkmDLFjiUGuoG5Ozj+/ndaUkBbctmQbzu2\nLVliQ92Bm3Bf/Ll/212BO0bAugv5+ZUEwmtJeemlQUZ4E/GGDQtsep7befaeLfznP3XcxMVfVrxF\nC0YxnyUrmlRqzR3A8yCSkmoWCG+Sl5/ly2HkSPbSklcPXs0Fn90TdlvmWuG99003BTbNX57EsGH1\nlCJ6663WDW3XDmJjOZ/ZxMWU8fDDdkjkttvcNBJPIB5+mJEsANw4hIcvxOQVivSX8lBqT3UCcRfw\nb2AzMMIY443EdQQeCOfFjTEzjTF9jDG9jDGPuW0PGWOmu+Wlxpg0Y0ySy44a4Dv3FWPMSe7x6lF8\nNqW21NKDAEhNtWOJgTs1/6DjyJH0HtKabzfFUVxsE5lGjPCdXFIxuNs/xeYkBMYhpkyx2U++u8Kl\nS+37dA419SAnx5ZlcDTlEHc+2JJ+/Wr4zLXFLxDDhzOK+Rw+LKEv3J4H0aVLzQLx7LOV19PSOHgQ\nbu4xjzayh5/k/C+zck7n9tuPyfrQeLfbLp0snxZ8vTYhUKE94rzwgv29AUpLaUU+V4/IZvZsG4W8\nz5tB5ZuQcwqraNnsMDO5oOJ1fDcTmzbZbNfgNiFK7aguzdUYY6YaYyYbY7J921cYYzTcczziE4jy\ncpv5WJNAHEFCQsUte1oa6f1jycy0vYW8skQBfCGYvmWZiPgEYt68iuN8AlFlx7dQV4M6rTDn8L/H\n4MGMYCEihvnzsZM5RKxiQoUHkZYWOjvJP6/hqacqlvv0Yf9Xaxg3Dqa8HsOvfiUsPOkmHkn/FxkZ\nlSNXdcJ331m73e32IoZjjNSfQITguft38NRTtg1p+/Zuo8+diaWcmyfsYSrXcBeT2Uy3IzwIDS8d\nO+E2DFJOBHwhpoICG+WptUCIVIwJdO7MmWfaC9rLL9tI0Dnn+I799a/hggugaVOabc6ie3efQPjd\nhJQUcnNtBlRYGUkrV9oGD5HAPxM4Pp7W7CW9+2Fb7NYrJOeVx8jNtYMu7duHFogqRs8LkjrzwyuS\nWbwY3nwTJk+G4S0zGdXS1mI6lrp/Idm40bqCiYnQvTvzGUWTJqYimaABaNm7Hb/+dTVjIN9+y+8e\nLOZ6/slz3EF/sliW1y1wM6ECUTeoQCgV+DwI7+a21gIBFe0lO3UK3IW+/rqtOVdp0l2XLraf5dCh\nsHYt/fv7BMI/O6trV5bZ9PjwBOK002BchOZV+vsPuM/Zt+tBOwvcu7X3+mXn5dmZwImJodNXQ0wd\nL27TiQvlI5YutXPErr7a7YiLIz3RlpJYt66OPovHnj0V0+XnzGF+x6sZMuTIqhj1SlWlSV54AZ54\nAk46iaS+XXidG/iOXrQlj+tynqSsWQpFRXYORJ3V3zqBCUsgRKSpiKRH2hilgfEJhNd/56gEwnud\ntDQGDKi4Cww1ORiwV/3ly+nXq5j1612yzr59FftHjw60oxg06CjsqUu8AfE+fQIhj/S0A2zYAKU5\nLqTkiUhurr3wJiSQdbA7Q4fCO+9U//IPnv0Zi5Y344034IorfDvi4ugYs4uUlAgIhC899ED7HmTk\ndWfUqAZq+fLXv9ore1W112+9Fe65xy7HxMDEiXRjK09xN+tJ5/2coWRl2WhfvWZgHaeEU831YmAl\n8LFbHygi4aarKo0JX4jpmATCmxTWqxciNo48dy5cdVUVx48fDyUl9C9fzeHDsGmjsQLRurW9GjZr\nRkYGpKcfkdHaMKxcaQdVnED06VRISQlsWeMGoj0PwhOI+HgmF/6cJUvgF7/wRZuCLoJ7r/gZz37a\nlxtu8HkOHk2aIKUlpKWFTng6JnyDu19+aQW6wcYffvELXypbGLjv8DLeoyffMXn56IC3Wa9zOI5T\nwvEgJmEnxuUDGGNWApo8djziZRXFxQUEok0N1ayrfR0XBG7dOmjsIZjhwyE5mf7P2SJ7WT+fbEso\npKbaO3XsAHWdzog+Fk47zX4oz4PoZL2ddavcld8fYmrbFhOfwKcl59CkiR2XnjPHvc6AATZ/s1cv\n+P3v+eCiv3HokIT2tOLioLSUjh0rylPVGb5MMW8axtCh1RwfTbikgVjKuZ3nWbS1C08+af90NMR0\n7IQjECXGmOCay7VtMa80BkpK7IVI5Ng8iNmz4cYbK1VxrZb4eEhJoR92KnXW/FzrQTh34fvv7SNq\nBMLD8yDa28q26z915aY9N8d5EN8d6MgWuvG/jxni430CsX+/HajesAEeeIA5c+x4dsjP6ROIQIHb\nzZurr90ULr4Q08qVVtdDFWiMSny9Rm7iHzSNL2X9erjyyggXGTxBCEcgMkXkOiBWRHqLyJ+BxRG2\nS2kISksDA6+eQIR7ja/E2LG2ulpt/kNfeIHm7CeNbWQywHoQ7kJbqwHq+sQJRNvEQpKSYEueS4Fd\ntsymuu7eDW3b8uk2O3w34W8XM7TZN5UFwjdv5IsvrF6E/NqCBWLfPpuWeuedx/45fHasXFkPBfrq\nkp/8xH5xsbG0Zi/v3r2Yu+6Chx9uaMOOD8IRiDuxTYMOY/s2FGAn0SnHG54HgRWI5s3reBZydUyY\nAMBgMljM2XYWsZvH8NVXdjwyuFtlg+MEQkqK6doVthzydVZ66CH73K4dn246ia5sofe3H3Fu/rss\nX25sEVLfhXnXLutIVFk3qkmTgEAcOACFa1yx4+nHOBxYXm5fMCWF/futDY1KIEQqxcPGjyxk8uRG\n5AFFOeH0pD5ojHnAGHOme/w/Y0yowgJKYyfIgzgq7+EY+QFz2URPttA14EF8/rmNJFSV2NJgeBO3\niovp1g22lhzZVrSsdTvmbOjGWD5FgDNZijHCqi8KrRfg0kuXLrXHVxn793kQADu/2Vmx/WgpLbUT\n9IyB5GRWrbKLjUogPLxOeDrwUKeEk8X0qYi09K23EhGdSR1NGAOPP25j0seCz4Nw46v1zg+YC8Ac\nzoXERIqK7MBptYPcDYU33+PwYetBlB3Z/HhBbl8KDiXwQ1dr8lTsZLdv3llvf7fRowEClWxPPrmK\n94qLg5KSgEDkrM2v2H60zJgBv/mNXT7vvLpvMdoQ1FlvWQXCCzG1Ncbkeyuuw1v7ao5X6guvlENe\nHtx7b1Adi6PA50G48Hm9M4BM0tjGW1wNJSUsXmzHYaNSILyZZIcO0a2rIY+2HIyrnIf71pfdaBZf\nwgXMBCCVbFolF/P1zGw7huAKDK5bZ52JKr0250F488dytrvKrlX1mQgHb67JE0/AoEGsWGGTEtKO\n1LnoZ/Zs2xGvzloHKhCeQJSLSKDthoh0Q7OYGp6XX7ZXlKysiouEV8LyaAnyII4qxfUYicHwU15m\nNuezMa85U6fa0JK70Y4uvMq1hYV0TbUhjq2tKpLvS4nl3c9acPGgbJKwcyQEOK3LXr7Z28UWlnKC\nvG6dnedRJUEhppxcdyHMzT2i2U/YeN3YXEOPZctsKK9RZv+MHUtkKhme2IQjEPcDC0XkdRF5A5gP\nhNOTWokkr79un7dtq+jFe6z4PIiGCjEB/Jy/kcghJsy+g9dfh2uvjcLxB7BjELGxcOAA3Tra2eNb\nE/sEds/lB+TmxXD1iMoX8JM77ybzcC9MUkUvgxoFwg1Se6Kdl+8LLX1ylBFfTyBatODQIVi9ulLW\nqKJULxAiEgO0AAYBbwFTgTO0mmsU4M2WOny47gTCeRDFxTb60FACkcr3PMF/k1mQRlISPPhgw9hR\nIyLWiygspGtb6yFske5235gxTBv3KikpMP6svZVOS2+zm/0mhZwYW5AwP99mMYXjQcTF2TBUbkE8\n9OtnY+7vv3909ufn2zBZfDyrVtmfXwVC8VPtCJcxplxE7jXGTANm1PbFRWQc8AwQC/zdGPOHoP0J\nwGvAGcBu4GpjzGYRaQL8HStMccBrxpj/q+37H7cUFMDatXZ5z57KJah9JY9rjfMgvDkQ9S4QH3xg\nb2PHjuW/OnRgRL6t2VZV3baoICkJCgtJbXWQGMrYWm4D+KZLVz75LI1x4yCxVeWqd31a7QJg/aGu\ndKKitlKNAuFmqLdrB3kFidC+hY0Dbtt2dLYXFATyQb0sKhUIxU84Iab/iMh/i0gXEWntPWo6SURi\ngeeB8UB/4FoR6R902E+BvcaYk4DJwB/d9iuBBGPMKVjxuFVEuof1iU4ErruuYnnv3soeREqKzY45\nGpwH4Y191/sYxIQJcP/9NjbftSunnhrl4gBWjA8cIK6kiFSy2RLTHYBN8els2+YG14OUNj3R9p1e\nd9CKydEIRO7BZHtxT02t3RhEURHcdZedml5QEJhr8tln0LUrdO8e/kspxz/h5Mh5ZcP8I0AGqKna\n+hBggzFmI4CITAUuAbJ8x1yCrfUE8A7wnIiIe/0kEYkDmmJbnPrKe57gfPllxfLdd1eMR3hs2nR0\nxfCdB+EJREOFmBoVLsREURHdyGVr8gB47jk+j/s54ASiReUvskvJRhIpYl2+HXFet84OZVT7k8XH\nBwSibVvYeLi5vbinpdmB6sOHK2pAVccLL8Azz9h5A598AuedR2mpFQgtT6EEE85EuR4hHuFcfVIB\nv++73W0LeYzrYV0AtMGKxQFgB7AVeEJ7UvsYObLyuhdu8vCS6mti587KfQqcB+H1glCBCAMXYuLQ\nIbqylS27k+D225m3OJ527ewwQSVXrEkTYnbn0ptvWb/HfsFr1kDv3hXTKkKSmGh/n7IyG2IqaWFz\nUr2JYcF/A1WxfLl9znX1rm68kS+/tM7E2LG1/vTKcU44E+WaiMgvReQd97jDjRFEkiFAGdAZWzn2\nHhE5QpRE5BYRyRCRjNw678MYxezbZ5s7e3XxH3us8v5wBKKszPb4dSmOwBEeREOkuTY6kpNtcaS5\nc+nGFrbnJVJWZjumjh7t7sj9TYYSEyEvjz6sZ90uG//Pygq0g64a7zUOH6Zt63LyTGtM23aBqdfZ\ns77hhRdcL43q8CrterPi+vThn/+0Y9Xjx9fmgysnAuGMQfwVOw7wF/c4w22riWygi289zW0LeYwL\nJ7XADlZfB3xsjCkxxuwCFgFHlGozxrxkjBlsjBnczuuIdSKQn2/jz5dcAsAUbqC57KNn+0Iui/mA\n3713cqV2xyHxBHXatIptzoPwEqTUgwiD5s1tjOiBB+jBJkrLYpgzB7ZuhR/8IOjYwYPthX7nTtJZ\nx8adSRQW2vpHYQvEoUO0Sz5ICfEUJHW2k+1iY5nwzLncdhv87W81vM5B17Ni3TqIi+Nw5x689RZc\ndlmluoGKAoQnEGcaY240xsxxj5uBqlrH+1kK9BaRHiISD1wDBFcWmw7c6JYnAnOMMQYbVjoXQESS\ngKFAmD70CYAnEF26UEBz7uA5eqUe4sxzksiKGcBDi8cxZkwNlaA//NA++xrBex7E9u227HQ4Ie0T\nHt/U51NYBdjyRhAUssnPhwUL7Je6dSv9WENZeQwffmiduVoJRLydv5AX3xlEyGvahRU5tg5UjRmv\nBb7K/TfdxKw5CezdCz/6UQ3nKSck4QhEmYgEKmC5UE9ZTSe5MYU7gE+ANcA0Y0ymiDwqIhPcYS8D\nbURkA3A3cJ/b/jyQLCKZWKF51RjzTbgf6rjHyz7p3p33hj9JISm8+Ps83noL1p16FdMG/R9ffw3/\n/ncV58+aBbfcYpf94Q/nQWzf3kjLLTQEPoE4mdWIGD7+2GYEVSoL1KKF/a4TE2HfPs7A1jB/9VW7\nuzYC0VbscFxujE3xWhY/FEMMAwbAokW2QGuV5OdXLJ97Lm+8YW8GdPxBCUU4AvEbYK6IzBORz4E5\nwD3hvLgxZqYxpo8xppcx5jG37SFjzHS3fMgYc6Ux5iRjzBAv48kYU+i2DzDG9DfGPH50H+84xJhK\n6Ykfp/2MTh3LOfOGfnZ/u3ZcETedXr3g+edDnF9WBhdcULHerBk88oit0JaTE/AgVCDCxCcQyRzg\nnGF20P+qq6rICHJTwtNZR6tWhk8/tc5glUX6PPwexB6bF5uXZCvgrImxJ197ra26Uu20CJ9A5Hcf\nyIcfwjXX1GNZd6VRUaVAiMiVbnEj0Bv4JbY3RLoxZm492KaEorDQXuTdhSkjA4aPiKm4GLVtS8y6\nNfz0x8UsXAjbtwedn5lZeT0nByZNIu/r7RTvyKM8tgkbNmjV5LAJqq733J+KeOQReOCBKo53Azsx\nrVsxcaL90S69NIwac36B+M6mOeeK9SCyytNpE78v0Ec6KyvUC2BvLjyB6NiRWZv6UlxshUVRQlGd\nB+HVW3rXGHPYGPONexyu5hwl0nj/4C1bUlAA330XVJ55wgQoKODS3S8DIfrJeFNmfTzJ3bRnFz3Z\nyMe5Z1BUZNslK2EQ1JO1/8B4HnqomoY1XmpYq1Y8/jg8/TQ8+WQY7+MPMe1ZD0BunhWYNSUn0b/Z\nZnr3todUWbMxM9NmwP3lL7BtG3PnCS1a2HmJihKK6gRit4jMBnqIyPTgR30ZqAThE4hv3KhMpU5r\nEyaACH3//F/0Zn1gLDqAF3949FG49lp20JHfyh8YHbuQYuK58PN7ATjtNJRwCJ7q7R/TCYWXGpaU\nRIsW8Ktfhdn328sYOHyYpMKdNI05RF6edQoyi3rSL2Ej7dvbnIMqQ0xvv21b811+OcTFMW8ejBql\nFbKVqqku8nghthbS60A49zhKfeATiBUr7GIlDyIxEVJSkH37OI//8MaiPpSV+S4Cu3bZu9gHH4Ty\nct40yyiZ2oQXOz/K+m2JXMwMevbUmjxhE1yboqarredBNG1a/XHB+DwI8vNpm1jIrl2J7NoFe0ub\n0z92PTExduxo69YqXuPjj21P0w4dyMmBb7+FW2+tnRnKiUWVHoQxptgY8yVwtjHmc2PM58ACYIVb\nVuqbggICExxatmTlSpuB0im406Vr1Xk2i9m/39a/C5CbG2hzSUwMH+8+k/79oU/qAS7iIxZe9SyL\nF2vJhbA54suvAc+D8CashUuQQKSm7CM7u2JOZH9XwaZLl2o8iC1b3NTuinGKRt09Tok44WQxPSMi\nzd18hNVAloj8JsJ2KcHs2GED2/e5TGDnQQwcGOJi7mY8DWcRYFMfA+zaZVUFG55YtgyGDydwARp+\nton+AnnRREyMvSqHi+dBHKplW3dPIIqKYO9eurQqZNu2igt9vxIbb+zatQoPorjYllZJtdVuvMoc\nTi8UJSThCER/Y8w+4FJgFrb0xY8japVyJO+9Z59d6c/iZi3JzAwaf/BwSe3d2UzHDuUsWeLbt2tX\nwIPYts1WCx84kIDXodOnj4IqYzoh8ATicC1zPTp0sGK0fDmUlJDW9nBAIFLiD5G6ZxWUl9Oliy3u\nWhY8U+n77+2zy19eu9beR9TWAVJOLMIRiCau9tKlwHRjTAnacrR+MQZefLHSpqzsFpSUVBEieOIJ\nuOkmBDh9QHGg7A7G4J/k4G0//XRsKU/QW8qjZeZM+POfaz7OE+DaehAtW8KQIfDOOwB0Ty2hqAjm\nzoV+nQqQslLYtYuuXa047NgRdL5XWsV5j2vWQN++GkpUqiccgXgR2AwkAfNdT2otvV2fvP02gZQl\nx8pMWy8xpAfRpEmgENDA9CKystwNa0GBnUfhQiIrV9oLxCmnYGst5OTAoEER/CDHMePHwx131Hzc\n0YaYwAr7li0AnHKa/dfNyoLT+hTZ/dnZgQmOR8x/8d7PDY6vXWsFQlGqI5xy388aY1KNMRcYyxYg\nuAyZEklCtBRdscJOgq5UzsGPi1mf3ruQ0lIXq/ZCIT6B6N3b14BOBx8ijycQR1MZzze54tThFeeP\nHOFqa2zf7g3kXXQlAAAZE0lEQVQxHNlDyAtpJSSwf78VEBUIpSbCKffdQUReFpFZbr0/FQX2lPrA\nGx/wsXKlnatQZValE4iBPayzt2JGdkUfaxd4XrlSs1jqnebNYfJkmD279ue68ioAbQZ05IYbbJOh\nS65xLWezs6sWCM+DSExkvZ1npwKh1Eg4IaZ/YAvudXbr64G7ImWQEoKglMhypOaLuxOIXm0LSGpy\nmJUPvQvz59t9LVuSn28bz6lANAB33UVg2nNt8E/Pbt2aV1+1YwnNe7WzxZSys2nb1kYYq/MgvAwm\nFQilJsIRiLbGmGlAOQSqtNZYzVWpQ4pcjLl/f7j1Vjbf9if27ati/MHDCURM8SFOK8lgJQMrAtPN\nmweGNFQgGhE+DwIRYmJctfbYWOsVZmcjAp07V+9BrF1rT9F6W0pNhFPD8YCItMFlLonIUGxrUKW+\n8ARi7lxo354V7wJ/Dc+DYOdOTmc3r3ED5bvy7B1B8+aVM5iUxkGVBZ6wM7rdpIjU1Jo9iJ49td+H\nUjPheBB3Yxv79BKRRcBr2KquSn3hCYTLQFm50t4BVlsi2ivlMHUqA1nJfpqzaZu7H2jenBUr7Jh0\nx46RM1upY/weRDDjxtlCjDt2kJpaMe0hQJAHoeElJRzCyWJaDowGzgZuBQZo8556xmsT6S76mZk2\nhF1tOR/vYrJgAQOx7sLKHR1sP4LYWJYv1/BSo6M6D+K88+zzokUBD8L4Zys5D6I0NoH163W6ixIe\n1fWDuNx7ABOAdKAPcLHbViMiMk5E1onIBhG5L8T+BBF5y+1fIiLdfftOFZEvRCRTRFaJSA1lMo9j\niorsyKPr6pKVFcY/eGeXU7B3LyezmlhKWZHfA5o3D9RnOuusyJqt1DHVeRADB9q/kWXLSE21jYP2\nebOVPvss0AdkU05TioshPT3y5iqNn+rGIC6uZp8BqmpoCYCIxGJbh44FtgNLRWS6McbfzuSnwF5j\nzEkicg3wR+BqEYkD3gB+bIz52o2B1LK62XFEUVHAXSgutk3uJ06s4Rxfi7BEDjOI5XxSei6/b/06\nS5fatpTDhkXQZqXuqc6DiI+3cyv27yf1VLspOxtabFxR4V0AmevtBMsaO9gpCtUIhDHm5mN87SHA\nBq+NqIhMBS4B/AJxCTDJLb8DPCciApwPfGOM+drZsvsYbWnc+ATi22/DbHIfxFVM4zc8wXfDfsSX\ntiGZehCNjeo8CLAzJ4uKKs2F6J+7ptIhq7Ns0KC2fz/KiUk4g9SIyIUicq+IPOQ9wjgtFfAXHt7u\ntoU8xqXPFgBtsKEsIyKfiMhyEbm3CrtuEZEMEcnI9WrNHI8cPBgQCK96Z1j/4NOmBRavYhpCOc+U\n38nMmba8RlC3TCXaCTFhshJNm8LBg4HoYvb/PHtEzY3Vq6FHD9/seUWphnBmUr8AXI3NXBLgSqBb\nhO2KA0YA17vny0RkTPBBxpiXjDGDjTGD23k9Dho7paVwzz2V/7EPHAj8R2dl2fpJYcWQe/QILHZl\nG7fxV/78ShKLFtnSS0ojI6aGf9emTSt7ECt22r8jn7CsWqXhJSV8wpkHcbYx5lQR+cYY84iIPIkt\n+10T2YC/UH6a2xbqmO1u3KEFsBvrbcw3xuQBiMhMbHe7z8J438bNokXw1FO2rPeMGXbb/v2B2j1Z\nWfa6H1ZDsmbNKq3+H79lzzW3ExsLv/xlHdut1A8PP2yruobChZiaNoVW7OF7OsP2z2yRv6uv5uCy\nNaz7CC67rH5NVhov4QiES8LnoIh0xl7Aw6kivxToLSI9sEJwDXBd0DHTsXWdvgAmAnOMMUZEPgHu\nFZFmQDE2zXZyGO/Z+DlwwD5/9BHk5dny0IWFlQQi7BTFIIFozn7efLMObVXqn0mTqt7nPAhKSkgl\nm2xSK8q7P/QQGfOhbLqOPSnhE84YxAwRaQk8DizHlv7+V00nuTGFO7B1nNYA04wxmSLyqIhMcIe9\nDLQRkQ3YCXn3uXP3Ak9hRWYlsNwY81FtPlij5Tqfhj7yiH0uLITkZEpLrWMxYECYrxUkEMpxjhuD\n4PvvSSWbbXSp1P/jiy/sYUOHNqCNSqOiRg/CGPM7t/iuiMwAEo0xYZXaMMbMBGYGbXvIt3wIO6YR\n6tw3sKmuJw5ZWbZng4c38O48iO++s3X7ws5AUYE4sXAhJrZtI511LGQE5TtyiPEJxEknVbQkV5Sa\nqFIgRORMYJsxJset3wBcAWwRkUnGmD31ZOPxjzHw6KO2/yfYEehduyjJzWfndkjbvx+SkwMZTGF7\nEP6BilNPhfPPr1OzlSjDCzFt28YprOIAyWymOz3T0igthc8/1/EHpXZU50G8CJwHICKjgD9gM5kG\nAi9hxwyUuiAjo3JsefFiDl//E0bMf4yMLvBM7A38MiU+IBBh19HxN4v4+uu6slaJVpo2tTXcr7uO\nk7EDDas5mZ5paSxdCvn5tmSTooRLdWMQsT4v4WrgJWPMu8aYB4Gq+pgpR8OSJZXXU1J4LX8CGQcH\n0DKllAfLHmZ3TDuWL7dVOGudwz56dJ2ZqkQxPo9xALa0xipOgdRUZs2yWbK+SdWKUiPVCoRLPQUY\nA8zx7Qsn+0kJl32VW3ybuCY8vvYiBscsY8ElT7KfFJ7YMpFFi+Dss2v52nl58PHHdWerEr3cfntg\nMWXMWfRiA18xBJOaxjvvwPDh0Lp1A9qnNDqqE4g3gc9F5ANsqusCABE5Ce0HUbcECcSiRfBtfnvu\nLH+Gk7d8xJWtPuMP/+rGzp1HMYzQpk1Fbwjl+KZ3b7jmmsDy+czmM8bwxdpWrFkD117bsOYpjY8q\nBcIY8xhwD7bl6AhjAsWDY9B+EOFTqeZyFQQJxOuvQ7P4Ei7n37BgAZPOmUdysg0vXR5WHV3lhMWb\nPT9oEDfwGgdIZuQoISEhjAKPihJEtfMgjDFfGmPeM8Yc8G1b73pEKNWxcaMdW4iJgRUrqj923z57\n9QcOkcC0aXDZkO9Jxn7t/Ua1Y+NG2ygoKSnShiuNmocfhjffhJtvZihLuJNnadIEnnhC01uV2hNW\nsT6llsycaRv+ejOS5s2r/vh9+wL1cj7iQvLz4ccX7a3YP2gQ7doFJlMrStUkJNgwkyv3/mz6Xygq\ngjvuaGC7lEaJCkQkWFO5xHKgZaif8nKYPt0+e7WWsrJ4fcwUOnWCMedJxbEjR0bWXuX45Pvv4auv\nEKn5UEUJhQpEJAi+1T+iQTB2oOGSS+C552yCevPm7G7fj5nzk7nuOojr0MYed/HF6H+4clR06lRz\niXBFqQYViEgQXGo1lEBkZNjnBQtsZ5fUVKZOtaU0fvxjbP2cGTPgXzWWvVIURYkIKhCR4NChSqsr\nNqSQnm6zSIqL3UYvDJWTY2sude3KlCm2IsZpp7ljLrxQO7soitJgqEBEAm/MIS4OJkzgzsxbWb8e\n3n0X/vEPd4zXEGjhQgDWxJ7M0qVw4431bq2iKEpIVCAigScQBQV8ET+aReVn88zZUznlFHjtNawb\nEdQK8pVvRxIbC9dfX//mKoqihEIFoi5Zv94WxfMEIjGRNzJPpykH+clXt3HZZbB4sWFvQoeKxkDA\nnjPG8sK01kycCB06NJDtiqIoQahA1CXp6TBwoG3akphIOTF8sHcU4/iY5NJ8zh1VijHCAlza6uDB\nALxQcC2FhXD//Q1ou6IoShARFQgRGSci60Rkg4jcF2J/goi85fYvEZHuQfu7ikihiPx3JO2sc957\nD0pLWb4csnNiuXSUnfR2Vs4HJMSV8jmuuuqYMZQQx1+2XsjYsXaAWlEUJVqImECISCzwPDAe6A9c\nKyLBvdB+Cuw1xpyE7Tn9x6D9TwGzImVjxNiwAUpL+eQTuzr++YsASMz+jmHddjCPc6BbNxg3jk8Z\nS3Zxe38hTkVRlKggkh7EEGCDMWajMaYYmApcEnTMJcAUt/wOMEbEzgoTkUuBTeAK2zdCZs+GQYOg\n3ckd7ISl7dsZ3WUjKzid/IwNMGAA73MpKbEHtZGLoihRRyQFIhXY5lvf7raFPMYYU4otI95GRJKB\n/wEeqe4NROQWEckQkYxcr39zQ1FWVml1/90Ps3ixrzx3aipkZ3NOx7UYYlj4ZRzlbdoxvdWNjL8o\nloSE+jdZURSlOqK18c8kYLIxplCqKTNhjHkJ2/6UwYMHh1FX+yj48ENbsnv4cJue2qlT6OMKKrfI\nmHvynZSW+gSiY0fYtYuh/bNI4BDz5iXSsiXs3JvApVdHxHJFUZRjIpICkQ108a2nuW2hjtnuute1\nAHYDZwETReRPQEugXEQOGWOei6C9R7JjB0yYUHmbMbB7N8THV665tGdPxXK/fnz+TSsSE30d4FJS\nYNUqEhcu5CwuZ9680ZSV2Ze58MKIfxJFUZRaE8kQ01Kgt4j0EJF44BpgetAx0wFv7vBEYI6xjDTG\ndDfGdAeeBv633sUB4OmnQ29v2/bIlCNPIGbMgMxMFi6OYcgQKkJHKSm2oTxwLnNYtsy+/Pnnaz01\nRVGik4gJhBtTuAP4BFgDTDPGZIrIoyLi3Za/jB1z2ADcDRyRCtug7NpVabWA5iz9soxyBDZvrnys\nJxCtW3OwSFi+3EalAvhqKv1iZBZt2thKHDr3QVGUaCWiYxDGmJnAzKBtD/mWDwFX1vAakyJiXDjs\nrWjak0cbzmQpm4fF8ksm8wx3waRJ9gGVBOKrr6C0FEaM8L2WF45q0oQOc95kzV4oLKzoEKkoihJt\n6Ezq6sjPDyxOYhLbSeOc/jv5M3eyipPhEZdkdeAAPPWUXW7dmoULbQuHYcN8r+UJRLduEBdHu3Yq\nDoqiRDcqENWxdy+cdRb7fziRf3ATP+IN3p3wGs3Zx//j9/YYY+Cmm2DZMrveqhULF8KAAdCqle+1\nPIGoKgtKURQlylCBqI78fOjbl7cmvs0BkrmVF2m9ej6/4XGmcwlfMNT2fvj448ApZRLHF18EhZeg\nooBf9+71Zr6iKMqxoAJRFZ9/Dlu3QqtWvP8+9OpZzlnN18KMGfyKZ+nQtIBbeZGZ7x3GJCQGTlu9\nGvbtCxqghoqspzvvrL/PoCiKcgyoQITi3/+GV14BoOjHtzBnDlx4UQzSvRsAya3j+cvPV7KZ7lx4\nfUueLfqZPc+FlyCEB3HRRXas4swz6+czKIqiHCMqEMEUFcEVV9jOPv37M29nP4qK4IILgBYt7DEd\nO3L5ufnk0o7xw/J56OB97Bt1EXz9NXPmQNeudiz6CJo1q89PoiiKckyoQATj7/Q2YAAzZ9rr+ujR\nVAhEejo0b04CxTx60VfsowWvtruX8tQuzJ0LY8bYLCZFUZTGjApEMNtcfcE+fTBPPsVHH9kLfmIi\nFR5A376BrKTB/7qb4Szk2YWns2yZTXw699yGMV1RFKUuUYEIxhOIGTNYdyCNTZtg/Hi3b/du+9y3\nb0V9jMxM7uJpNu5M5oILbG2lH/6w3q1WFEWpc1QggvnuO4iJga5dmeVaFV1wgduXk2OfXYjJ41Le\np1/PQ+TlwS9+Ae3a1a/JiqIokSBay303HN9+a+cqJCQwaxb06+cbcB4yBDIzoXdv/A0c4ihj8ax9\nfL0jkZEjG8RqRVGUOkc9CD/FxfDll5CezoEDdipEILwE8PzzViBatz4iI6llz9aMHm2dD0VRlOMB\nvZz5WbjQVmm9+WbmzbN6UakVaNOm0N+11Q5OU4pTZ0xRlOMLFQiPAwfgjTfs8tChzJplnYRRo8I4\n9+9/j6hpiqIoDYHe9nrcfDO8/TYApnMqs2bZdNVqe0XHx1s3QxtKK4pyHKIehMeiRYHFtetj2Lgx\naPwhFJ4wqEAoinIcElGBEJFxIrJORDaIyBHd4kQkQUTecvuXiEh3t32siCwTkVXuOfJTz7wxhLg4\npkyB2Fi4/PIazomPt88qEIqiHIdETCBEJBZ4HhgP9AeuFZH+QYf9FNhrjDkJmAz80W3PAy42xpyC\n7Vn9eqTsLCmxBVb/VXQZZcRQlNSWKVPgwguhY8caTlaBUBTlOCaSHsQQYIMxZqMxphiYClwSdMwl\nwBS3/A4wRkTEGLPCGPO9254JNBWRiFyFc3PhtdcM1+c+zZksZeKALHJy4O67wzhZQ0yKohzHRFIg\nUoFtvvXtblvIY4wxpUAB0CbomCuA5caYw8FvICK3iEiGiGTk5uYelZGdO0P+Tb9mKleT17Yf/8lo\nxZ/+5Irz1YQnDImJ1R+nKIrSCInqLCYRGYANO50far8x5iXgJYDBgwebo3qTtWuRZ5/hauCq7Dco\ni6nFlAYvxOQ9K4qiHEdEUiCygS6+9TS3LdQx20UkDmgB7AYQkTTgPeAGY8x3EbOyb1/bIAiQ+Ca1\n+0I8D6KkpM7NUhRFaWgiKRBLgd4i0gMrBNcA1wUdMx07CP0FMBGYY4wxItIS+Ai4zxiziEhz2WVH\nd16rVva5rKzubFEURYkSIjYG4cYU7gA+AdYA04wxmSLyqIhMcIe9DLQRkQ3A3YCXCnsHcBLwkIis\ndI/2kbL1qJkyBe6/H4YObWhLFEVR6hwx5uhC99HG4MGDTUZGRkOboSiK0qgQkWXGmMGh9ulMakVR\nFCUkKhCKoihKSFQgFEVRlJCoQCiKoighUYFQFEVRQqICoSiKooREBUJRFEUJiQqEoiiKEpLjZqKc\niOQCW47hJdpi+1BEM9FuY7TbB2pjXaE21g3RYGM3Y0y7UDuOG4E4VkQko6rZhNFCtNsY7faB2lhX\nqI11Q7TbqCEmRVEUJSQqEIqiKEpIVCAqeKmhDQiDaLcx2u0DtbGuUBvrhqi2UccgFEVRlJCoB6Eo\niqKERAVCURRFCckJLxAiMk5E1onIBhG5r+YzImbHKyKyS0RW+7a1FpFPReRb99zKbRcRedbZ/I2I\nDKonG7uIyFwRyRKRTBH5VbTZKSKJIvKViHztbHzEbe8hIkucLW+JSLzbnuDWN7j93SNto3vfWBFZ\nISIzotS+zSKyynVzzHDbouZ3du/bUkTeEZG1IrJGRIZFk40iku7riLlSRPaJyF3RZGONGGNO2AcQ\nC3wH9ATiga+B/g1kyyhgELDat+1P2L7cYNux/tEtXwDMAgQYCiypJxs7AYPccgqwHugfTXa690p2\ny02AJe69pwHXuO0vALe55f8CXnDL1wBv1dN3eTfwL2CGW482+zYDbYO2Rc3v7N53CvAztxwPtIw2\nG322xgI5QLdotTGk3Q1tQIN+eBgGfOJb/y3w2wa0p3uQQKwDOrnlTsA6t/wicG2o4+rZ3g+AsdFq\nJ9AMWA6chZ2tGhf8u2N7pg9zy3HuOImwXWnAZ8C5wAx3QYga+9x7hRKIqPmdgRbApuDvIppsDLLr\nfGBRNNsY6nGih5hSgW2+9e1uW7TQwRizwy3nAB3ccoPb7UIdp2Pv0KPKThe+WQnsAj7Feon5xpjS\nEHYEbHT7C4A2ETbxaeBeoNytt4ky+wAMMFtElonILW5bNP3OPYBc4FUXqvu7iCRFmY1+rgHedMvR\nauMRnOgC0Wgw9pYiKnKSRSQZeBe4yxizz78vGuw0xpQZYwZi79SHAH0b0h4/InIRsMsYs6yhbamB\nEcaYQcB44HYRGeXfGQW/cxw2JPtXY8zpwAFsuCZAFNgIgBtPmgC8HbwvWmysihNdILKBLr71NLct\nWtgpIp0A3PMut73B7BaRJlhx+Kcx5t/RaieAMSYfmIsN2bQUkbgQdgRsdPtbALsjaNZwYIKIbAam\nYsNMz0SRfQAYY7Ld8y7gPazQRtPvvB3YboxZ4tbfwQpGNNnoMR5YbozZ6daj0caQnOgCsRTo7TJI\n4rFu4PQGtsnPdOBGt3wjNubvbb/BZT0MBQp8LmvEEBEBXgbWGGOeikY7RaSdiLR0y02xYyRrsEIx\nsQobPdsnAnPcXV1EMMb81hiTZozpjv17m2OMuT5a7AMQkSQRSfGWsfHz1UTR72yMyQG2iUi62zQG\nyIomG31cS0V4ybMl2mwMTUMOgETDA5s5sB4bp36gAe14E9gBlGDvjn6KjTV/BnwL/Ado7Y4V4Hln\n8ypgcD3ZOALrDn8DrHSPC6LJTuBUYIWzcTXwkNveE/gK2IB19RPc9kS3vsHt71mPv/k5VGQxRY19\nzpav3SPT+7+Ipt/Zve9AIMP91u8DraLQxiSsx9fCty2qbKzuoaU2FEVRlJCc6CEmRVEUpQpUIBRF\nUZSQqEAoiqIoIVGBUBRFUUKiAqEoiqKERAVCUY4CEWnjq9KZIyLZbrlQRP7S0PYpSl2gaa6KcoyI\nyCSg0BjzREPboih1iXoQilKHiMg5UtHjYZKITBGRBSKyRUQuF5E/ie2z8LErW4KInCEin7vCeJ94\nZRgUpaFRgVCUyNILW29pAvAGMNcYcwpQBFzoROLPwERjzBnAK8BjDWWsoviJq/kQRVGOgVnGmBIR\nWYVtGvOx274K2/8jHTgZ+NSWuiIWW3JFURocFQhFiSyHAYwx5SJSYioG/cqx/38CZBpjhjWUgYpS\nFRpiUpSGZR3QTkSGgS2nLiIDGtgmRQFUIBSlQTHGFGPLeP9RRL7GVsg9u2GtUhSLprkqiqIoIVEP\nQlEURQmJCoSiKIoSEhUIRVEUJSQqEIqiKEpIVCAURVGUkKhAKIqiKCFRgVAURVFC8v8BNTfHCuHS\nj1gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grtXnEzGWesQ",
        "colab_type": "text"
      },
      "source": [
        "## Visualizing Test Fitting Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3lpbOvxPzCk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_stock_prices = y_test.tolist()\n",
        "\n",
        "if train_mode == \"nlp\":\n",
        "  pred_stock_prices = model.predict([X_price_test, X_nlp_test]).tolist()\n",
        "else:\n",
        "  pred_stock_prices = model.predict(X_price_test).tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vRQeokfWkti",
        "colab_type": "code",
        "outputId": "9e0b2cb7-5a21-489d-be0c-bdc305fd99e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Visualising the results\n",
        "plt.plot(real_stock_prices, color = 'red', label = 'Real Salesforce Stock Price')\n",
        "plt.plot(pred_stock_prices, color = 'blue', label = 'Predicted Salesforce Stock Price')\n",
        "plt.title('Salesforce Stock Price Prediction in Test (30/70 split)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Salesforce Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hjh3dpp4TExN9rGe6aNOmDYsWLeLvv/8GjPljIJup5p49e7rdRALuirljx458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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4eOyJr5X3iP",
        "colab_type": "text"
      },
      "source": [
        "## Visualize Fitting in the full time range (train + test)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR2H9zuuW-D9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_stock_prices = y.tolist()\n",
        "\n",
        "if train_mode == \"nlp\":\n",
        "  pred_stock_prices = model.predict([X_price, X_nlp]).tolist()\n",
        "else:\n",
        "  pred_stock_prices = model.predict(X_price).tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVZpThUJX_tz",
        "colab_type": "code",
        "outputId": "f03ebed3-885f-4ae0-9eb5-e903c605bcbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Visualising the results\n",
        "plt.plot(real_stock_prices, color = 'red', label = 'Real Salesforce Stock Price')\n",
        "plt.plot(pred_stock_prices, color = 'blue', label = 'Predicted Salesforce Stock Price')\n",
        "plt.title('Salesforce Stock Price Prediction in Full Range (30/70 split)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Salesforce Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ul43j+PHjJCUlsWjRIr799lsA+vTp43bj7UlwcDCzZs1i5cqV/Prrrzz66KOs\nXr2aUaNGsWHDBkaMGEFCQgJJSUn07NnT69is3HRfddVVDB48mFtuuYUbb7wxU7nHjh0jISHB7Rn2\nzjvv9MpnwIABgFl+tVq1arRq1QqAMmXKZHldPV2MlypVitWrV7N48WLmz5/PgAEDeOWVVxg8eLCX\nLCtXrqRTp064VmQcNGgQixYtIjg42O/1/+yzz6hZsybff/89ISEhmc4P4K233uKLL76gdOnSTJ06\nFXEMlq5z8yQxMZHY2Fj69esHQLFixdzn6cvVvKu1ZrnIcHmbDWC1yvPBRacsCshDeUCurlWVsWPH\nZvrQ5uXSoWlpaSxfvtz9gTlXRITWrVvTunVrunfvzpAhQxg1ahSDBw/m+++/JyoqikmTJrFgwYJM\n5fpz0z1+/HhWrFjBTz/9RIsWLVi9evU5yRSIK3Rf1zUjwcHBdOrUiU6dOhEREcGnn36aSVnkhIiI\nCNauXUtMTIzfdUlcNouMnIsrdM3C1bzlIsTxxIyfCsj5xtosziM9e/bk/fffd7vj3rZtGydOnKBD\nhw5uN+D79u3z8prqom3btixatIi//voLMK6xgUxuvHv06OFe0hRwf7w7dOjAl19+CcDPP//stgl4\nEhcX5+WmfO3atdSuXRswtd1q1aqRnJzsdhPuSVZuunfu3EmbNm14/vnnqVSpEnv37vU6tmzZspQv\nX57FixcD8Pnnn/tcf6Jhw4bs27ePlStXumVKSUnxe1092bp1K9s9RpR4npvnNWzdujULFy7k8OHD\npKamMmXKFDp27Oj3+oPxMjxhwgT69u1LXFxcJrnPldKlSxMeHs73338PmBaaa50UX67mLRcphUxZ\nXHQti8LM0KFD2b17N82bN0dVqVSpEt9//z39+vVj3rx5NG7cmFq1atGuXbtMx1aqVIkPPviAG2+8\nkbS0NCpXrswvv/zCddddR//+/fnhhx8YO3YsY8aM4cEHHyQyMpKUlBQ6dOjA+PHjee655xg4cCBN\nmjThyiuvpFatWpnKSE5O5oknniAuLo5ixYpRqVIlxo8fD8ALL7xAmzZtqFSpEm3atPFSUC4mT57M\nAw88wIsvvkhycjK33norUVFRDB8+nO3bt6OqdO3alaioqEzrbnz66afcf//9nDx5krp16/LJJ59k\nyr9o0aJMnTqVYcOGcerUKYoXL87cuXP9XldPkpKSGDZsGAkJCRQpUoR69erxwQcfAHDvvffSq1cv\nqlevzvz583nllVfo3LkzqkqfPn24/vrrAXxefxdXX301r7/+On369OGXX34JyO18Vnz++efcd999\njBw5kpCQEKZNm0aPHj3YvJLUMM0AACAASURBVHmz+/koVaoUX3zxBZUrV85VWZZCijNar0DX3fbA\nuii3WC4y7PtwkTBqFIweDWlpeTIpz7oot1gslouR5GQIDgYRTp+G1q0hQ4P5vGKVhcVisRRGkpPd\nXVBr1oBjqiswLhplcaF1p1ks+YF9Dy4izp6FkBB++QWuuspEuf4LgotCWRQrVoz4+Hj7olguaVSV\n+Pj4HA+btuSCo0fBmVeUZyQnQ0gIzz9vNhs1Amf6T4FwUYyGCg8PJyYmJt/8GFksFwrFihUjPDy8\noMW4NPjzT6hTB0qXhgoVoEYNiInJu/wdZbFrl1lR1WPwXYFwUSiLkJAQv5OhLBaLJU956ilITIT3\n3oPOnWHePBMfG5u7fFNS4OWX4ZFHjALasoWkhBTiTsNLLxldVJBcFMrCYrFYzhuvvpoeXrQo7/Kd\nMgVGjoQjR+C332DlSg5jJo5WrZp3xeQUqywsFoslEFTB8VDgJjXVtAjyggcfNP+ffOJeUjWeigBU\nrJg3ReSGi8LAbbFYLPnOzJngw/EjS5bkTf4urwgea29bZWGxWCwXGvv2+Y531kEBvD7054TLD1QG\njpQy3VB+nEyfV6yysFgslkDw80H3YtasnOXtZznd+MdeBGzLwmKxWAqWHTvgueeMPSI7AknzxRdw\n/DisX2+2T5+G9u2NbydXnC+GDjX/nlphzhzig6sAtmVhsVgsBUvLlvD880ZpZIfHomJ+SU6G7t0h\nKspsN26cbtP46qvsj4+PTw+XKUN8PJQpUzi8lOershCRXiKyVUR2iMhTPvbXEpH5IvKHiKwXkd75\nKY/FYrG4SUpKtzEEogjGjcs+TWgo/P67CaelgbP+CWA8AWaHZ8uiZEmOHCkcrQrIR2UhIsHAOOAa\noDEwUEQaZ0g2AvhaVZsBtwLv5Zc8FovF4oWzLC/g/VHPDSdPpofffttMrnOxfTt07Qqvv+7/eM/Z\n9yVLEh9fOOwVkL8ti9bADlXdpapnga+A6zOkUcB1x8oCuV9mzGKxWALB0wbh+ZHPjmrV/O+bOzc9\n/Pjj6cNhAf79bzPbe/hw77JTU81/pUrg6YnCURa5XEcrz8hPZVED8Fw/M8aJ82QUcLuIxAAzgWG+\nMhKRe0VklYissv6fLBZLnpNhGd5MpKWlh//1r9yXN316eti1NO7o0fDss+nxJUty+PCl0bIIhIHA\nJFUNB3oDn4tIJplU9QNVbamqLSsVpNtFi8Vy4ZOUBFOnesdl17I4fTo9XLy4+Q8Ly7nP8FtuSZfB\ntZ57xYrQtKnpHmvWDEqUuGS6oWKBmh7b4U6cJ3cDXwOo6jKgGFBIGl0Wi+WiZNgwuPVW77jslMWp\nU+nh4GDzf/PNZqTT5MnG058/XN1Wd97pHX/rrXDoUHrZJUqYxY6OHYM1a0hOEY4duzSUxUqgvohc\nJiJFMQbsGRnS/A10BRCRRhhlYfuZLBZL/rFxY+a4c1EWri6pIOfzedtt8MwzmRWQi337oG5d3z6k\nvvsuPW9Xi8XB5e28sHiczzdloaopwEPAbGAzZtTTRhF5XkT6OskeB+4RkXXAFGCw2hWMLBZLfuK5\nPmmVKtCwYfY2iz170sMZlYWLpzLNDkgnNNT34kj33efVskhISC9q2zbzX7du1qKdL/LVZqGqM1W1\ngaperqovOXEjVXWGE96kqlepapSqRqvqnPyUx2KxWOjbNz385ptQtmzWLYs5c+Dqq034m2+gUycT\n7tfPK5lGRBojdUb+8x//yqJpU3fLQosV5+qr4YorzNIYixdDkSLQqtU5nFs+EpCyEJHaItLNCRcX\nkdLZHWOxWCyFEteHH4wxuWTJrJXF2rXp4ZIlSY2IZuECJa1jugPBtDSIiBSeOPpM5uNffNEoC08j\nuYv27d3KYsv+cmzcaJL973+m2EaNoFSpcz3B/CFbZSEi9wDTgQlOVDjwfX4KZbFYLPmGa14DmElz\nJUpkrSzKlUsPlyjBs8+axsUzz5jBUH/+CVu2GFPIG2/7WSIoNNR3V9f775v5GMD8NWXd0evWGVdS\nkZHncF75TCAtiweBq4DjAKq6Haicn0JZLBZLnpKcDF9/bSbDeRqaQ0ONstizx3c3EcCoUe5g3Mly\nvPGGCb/6Kixdar71y5alJz+L48hp7lxYtcqEQ0L8K6T9+wHYcyCUkBDT2Fi4EPbuvfCUxRlnBjYA\nIlIEM/PaYrFYCj9paWZI6oABULOmd3eQCNuSL2Pj4cpmKKwvPJTLql3lOXvWe/cvv6Q7jQX4C2cW\ndteu0KIFAH8nV+PEMSef666Dxx7LVEzcoaJUr258EG7ebOIuNGWxUESeAYqLSHdgGvC//BXLYrFY\n8og//kgPx8ZCnONVqF07iIykxU+jaccyYyjIiKqZC+GwLdYYEAYONNs33QS9HfenLk8d22jglcWm\nTVB/yUT6xjiOCG+4AXfzxIO4g8HUqOGtIC40ZfEUZu7Dn8B9GLccI/JTKIvFYskzMs5v+OQT8790\nKbsPFCcpuRiJlOEI5dPTR0fDzz+ndyMBPPgg2w6UJSwM+vQxUb17w08/wYoVsGiRicuoLL75Bs6m\nhTAvuQOpBJluLx/Exoq7ZeEiKzdU5xs/1hgvigMTVfVDcHuTLQ6cg+cti8ViKSB8jUJymD8/PfwX\nl1EBTEti3Tq46y748sv0BO++y7ZO0KCBaVlcfnn6sFaX9/GKFWF7ia7wz3S3REuXpmcRQzi1EpM4\neACqFCnipcji4qBnT6OnWrQwRnSRHJ91nhNIy+JXjHJwURyY6yetxWKxFC48Z1+7cNyEeyqLA5hV\n6dzuPFJSMrVKtm83yiIoCNq2TU/qokED2Favt3uCXloaLF8OVUITADhMGGO+qUHVqrAhKL2PKZFS\nJCZC9erGvLJyZdaezAuCQJRFMVVNcm04Yd/tKIvFYilsuFoWrmr644/D44+japRFy2IbADgQVN3Y\nN1wLIqWkeM3STkoytf8G3r1MXjRubPKMioIPPjCG6oQE6HPmGwCOUIFFZ9sA8GWD59zH7cP0N9Wo\n4S1qYSIQZXFCRJq7NkSkBeBDVVssFkshxOUOvIjT6169OmBWUo2JgYFlfgTgQFoYNG+ebrFOTk5v\nWUybxvbtJpiVsnj2WdONtH698eTRxugF+mNkOEIFThc3S9+9vKEvP31qjOdxVPcUrVASiLJ4BJgm\nIotFZAkwFePzyWKxWAo/kyeb/+Rk81+2LKdPQ48eZrNP8fmUIpH9VDURrvW4T5xIdx9eu7Z7OGtW\nyqJ2beOmY/RoEw4Ph5dfhmaYEVlHqOBevgLg+rvCOEWxC0JZZGvgVtWVInIF0NCJ2qqqyfkrlsVi\nseQTy5czo/Td7N5tav4NDu6gKvvTbRaejB0LwG8byzFoiIlq2DBzMk9KlYKRI83PxdmnjdKJpyIH\nDhhv5R07Ghv66slbiVtfAl69QJWFiHRR1XkicmOGXQ1EBFX9Np9ls1gslpyTmupz8ht33snamaZX\nauFCkAYpVOGAb2WRZMy1E2eYZXYqVDAG6HOl6LpVlIxK4kjDKzm4GypXNiOfANYcrkXsGeOiqnQh\n9rqXVcuiIzAPuM7HPgWssrBYLIWXFStgzJjM8bVrs26dcdIXGgqkplKFA2ymUea0jguQffFFKVvW\n26fgOREZSYWasLtxb85sNZ7Rq1eHqlVh9Wpjg69Ro3Aatl34VRaq+pyzxOnPqvr1eZTJYrFYco9r\npjbArbcy5KsexFGd2UWLsm4ddHY5jU1NpSr7WUCnzHk4I6mOHA+mTRvjLSSnlC9vHA6CaVmAsaev\nWWO8pBemCXi+yNLArappwL/PkywWi8WSd3j4ekpq151JDGEOPdmwoxixsWaZaxdVOMARKpKcsf7s\nUhbHgqlQIXfiVKiQriyqOD1ezZvDhg3w229mkl9hJpDRUHNF5AkRqSkiFVy/fJfMYrFY8oj1sekL\nWU+cWhIwrqEAmDOHKuVMd9PBjA61Hbfi8QnBuV4Lu0IF42oK0pXFDTek72/cOHf55zeBuPsY4Pw/\n6BGnQCFZ7M9isVgykME17O64dKv0lGlFCAnxaFlERFD1jh4wFvZTlRp4dF+dOEEawtEEyXXLonz5\n9LCrG6pFCzOi9/vv4Zprcpd/fhPI0NnLzocgFovFkmeMG+e1uadkerV9/35o2RKKFUvf73LH4WtE\n1DHKopp7ZeF5fKV011EUKQL9++cu7/OB324oEWkjIutEJElElomIj6ECFovFUgjxNG4Duw8WJyzM\nTJSDdMd/LqqEmHkQvpTFEeNeMNfdUK6WRcmS6ZPJLySyslmMA54AKgJvAm+fF4ksFoslt3h6mu3f\nnz0nKlG7dnq0yw2Hiyr9rgRIn8UNrArrxSLaE4/RErltWbiUTUbngxcKWSmLIFX9RVXPqOo0oFIW\naS0Wi6Xw8O675r9WLZg2jT1/C7Vrp3f3dOrknbxEqyZU5DB7qO2Oa3X4ZzqyiCPNugG5Vxau0U6e\nS4BfSGTVGCqXYfa217adwW2xWAoliYnp4X79SEuD3buNf8AXX4RHHjE6JCP12MEO6gGQRvrsuF1h\nps8qt91Q0dFG4TzzTO7yKSiyUhYL8Z697bltZ3BbLJbCx+efwz/+4d5MffV15sw23U/160Px4lCv\nnu9D67GDJVwNmHUnXKw9HA7kTTdUbKy3Yf1CIqsZ3EPOpyAWi8WSa95802tz9EtFeOEFE/anJFzU\n61mPL2fX4jShbqM2wM5jpklRrlzuxbtQFQUENinPYrFYLgxcLsUdZs9OD7dsmfWh9e9oixJEcU7z\nK13d8TuPVKBcuQtzBFNeYpWFxWK5MBk0yHjei41Njzt0yB3UChXZssUMk/3uu+xbBp4tj6+5xR3e\nk1A2111QFwPZKguRzH4QRSQ0f8SxWCyWAPnyS/M/a1Z6nMd623+n1uD4cbNmhKdbDX80bQqX1zZL\n9fyO90QMqywCa1l87LkhIqWAmfkjjsVisZwjxYunhz288a0f9CoAERGBZVOyJOxYGEcUazlNca99\nuR0JdTEQiLKIEZH3AESkPDAH+CJfpbJYLJZA8Rwq69EN9Wf5DoBpMQRMUBAViXdvlsLkbVsWASgL\nVR0JJInIeIyieENVP8l3ySwWi8UfLvetAMeOGYXxySdw/DiUKAHA+q2h1KkDZcqcQ75BQYRxGIDi\nRc5SA2MPscoia99QN7p+wAqgLfAHoD6WWvWXRy8R2SoiO0TkKT9pbhGRTSKyUUS+zMlJWCyWSwxn\nBTvAKIixY41xAuCddyAtjXUbgomMPMd8RdzKolzoKcphHAzabqisJ+VlXE71DyDEic92Up6IBGP8\nS3UHYoCVIjJDVTd5pKkPPA1cpapHRaSy79wsFovFg4SE9PDx43DypHvzdPlqzPpB2LIFbrnFx7FZ\n4dGyKB1ymhTnE1m9em4FvvDJz0l5rYEdqroLQES+Aq4HNnmkuQcYp6pHnTIP5rJMi8VyKZBRWXz6\nqXvz+f9F87KzGRV1jvmKUAnH7pGmbmVRtWoWx1wiBDJ09lMRKeexXV5EJgaQdw1gr8d2jBPnSQOg\ngYj8JiLLRaSXHxnuFZFVIrLqkIcBy2KxXKI461WM4AWGzPJuPqz5y/gCr1QJunU7x3yDgtzKIq1I\nUcYyjMZ1TnDllbmW+IInkNFQkarqVuNOK6BZFunPhSJAfaATMBD40FMxeZT5gaq2VNWWlSpZ57cW\nyyWP41X2JUYw6UBvEinl3rVxV3EGDIC9e8/RuA0gQmVMB0daufK0X/wyG/8qif3sBKYsgpwhswA4\n628HMvE9FqjpsR3uxHkSA8xQ1WRV/QvYhlEeFovFkiWpHp8vl7fYUxQjJkZo3BhCczJ1OCiImk6H\nSLduAldfnReiXhQEoizeAJaJyAsi8iKwFPhvAMetBOqLyGUiUhS4FZiRIc33mFYFIhKG6ZbaFaDs\nFovlEsZzVTvXokV/Y3yPX5bTxaCDgqjHTn4N7c1/A/nKXUIEsgb3ZyKyCuiCGQV1o+eIpiyOSxGR\nh4DZQDAwUVU3isjzwCpVneHs6yEim4BUYLiqxvvP1WKxWAx7PTou9lENBXbU7Qm7cqEsHO9GXYos\ngrK5l/FiIlA/iiHgXg0kJNDMVXUmGVyDOJP8XGEFHnN+FovFEjAxhLvD+6jGCw/H89w7ZvZcbpUF\nmV3iXfIEMhrqYWAyEAZUBr4QkWH5LZjFYrH4pXp1Ylqlzw0+QBW+mmMURVBQLuZFuGaGB1mH3BkJ\npGVxN9BGVU8AiMirwDJgbH4KZrFYLD75+2+IiyOmREmKFU2l1tkd7Kcq+/cbN+OffZaLhoFLWdiW\nRSYCUZ+CsSe4SAXslbRYLOeX3383/UuTJgEQs+MU4RVOUZX9rCeSo0dh2DBo1y4XZbg82N59d67F\nvdgIpGXxCbBCRL5ztm8AApmUZ7FYLHlHmzbm/7nnAIipfTXhpU5Saf8BFtERgEaNcllGaKhxHZKj\ncbcXN4GMhnpTRBYArgHHQ1T1j3yVymKxWLIhRqtzdXQQ5apGwK8mLtfKArzXx7C4yVZZiMjnqnoH\nsMZHnMViseQ/HivgAaQhxO4LJrwmlLyiEfxq1siukdGhkCXPCKQbqonnhuNNtkX+iGOxWCw+ePJJ\nr82DlSNIPgg1a0LRoiauZElrl85PslrP4mkRSQQiReS4iCQ62weBH86bhBaLxbJkiddmzEEz3Ss8\nPN2Uce2151uoS4usXJS/DLwsIi+r6tPnUSaLxXKp068fFCsGU6aY7T+MmfQI5dlOffZRDTDKIiIC\n5s6FFra/I1/JqmVRW0TKuhSFiHQWkXdE5FHH15PFYrHkD99/D199ZcKuVfFq16Yns2nLClZgmhPh\nziTurl2hXCZ/1Za8JKt5Fl8DJQFEJBqYBvwNRAPv5b9oFovlkiQtLT18/Djs2wfA4YdfYBWtAPic\nOwgNxboOP49kZeAurqpxTvh2jCPAN0QkCFib/6JZLJZLkl0ejqfXrIEQY59Ym1DHHR1LOPVrWYP2\n+SSrloXnbeiCM5JZVdN8J7dYLJY84A6PUfmdO7vXlFh30Ngp6vAXALVqnXfJLmmyUhbzRORrEXkH\nKA/MAxCRasDZ8yGcxWK5xEhJgeXLfe5au7cCNcLO0L24GRlVs6bPZJZ8Iitl8QjwLbAbuFpVk534\nqsB/8lkui8VyKfLQQ+7gKYqRQrB7e93OUkS1DqXhC6blERZ23qW7pMlq6KwCX/mIt64+LBZL/jBh\nAgCJlKIB2+jJbCYxhDMUZfOOEK67Efr3hzlzvPSK5TxgnbZbLJbCg9Nc+Hb0BvZTjU8ZTHLpCmyK\nHEhKihAVBbVrw+zZ5t9y/gh0pTyLxWLJf6pVg6NH+fHPdE2wdeF+1v5RBO6G6OgClO0SJ6CWhYgU\nF5GG+S2MxWK5xDlwgOTa9ZgzB9qyDICnng3hrruF4sXh8ssLWL5LmECWVb0OM69ilrMdLSIz8lsw\ni8VyibF+PRw8yKpGd3D8ODzccR0AP/1kdjdpAsHBWRxvyVcCaVmMAloDCQCquhbI6XLoFovFkk58\nvFlsCOC11wD4bUcVADpPvZ8BA6BsWfi//zPLpVoKjkBsFsmqeky8p0pqPsljsVguJcLCoG1b+O03\n+OILAJbUHkS9VKhSBT7/HBIToUKFApbTElDLYqOI3AYEi0h9ERkLLM1nuSwWy8XOxo3mf/ly+Phj\nwNRCf1tTzDVpm5AQqygKC4Eoi2GYBZDOAF8CxzAT9iwWiyXnNG2aHr73XgC2Nr6Rw4eFq64qIJks\nfglkDe6TmBnbdta2xWLJV+bf/QU8Dh07FrQklowEMhrqFxEp57FdXkRm569YFovloub33zPHvfMO\nX/+vOHXrQr16518kS9YE0g0VpqoJrg1VPQpUzj+RLBbLRY+jLHZSl3Ys5Sd6szkxnAULTI+UdT1e\n+AhEWaSJiNsZsIjUxo6GslgsgbB/PyQne8edOAHDhgHw325zWE47HuB9Po+/hqAguPPOApDTki2B\nKItngCUi8rmIfAEsAuya3BaLxT8bNpjmQbVqbsXAkSMmrlQpwNQ4Z24xU7L3UouX3ypO165QtWoB\nyWzJkiyVhbMqXlmgOTAV44W2hapam4XFYvFPRER6+NtvzToVFSt6JdnCFcTEwFtvpcfdfPN5ks9y\nzmSpLJxV8f6tqodV9Ufnd/g8yWaxWC4GoqNhx45M0bP/abwG9esH//qXaYTcdNP5Fs4SKIF0Q80V\nkSdEpKaIVHD9AslcRHqJyFYR2SEiT2WR7iYRURFpGbDkFoulcJKU5L39yy/QqFH6trPE3Zz1VWjQ\nwLgaf/tt2L3bTsArzATi7mOA8/+gR5wCdbM6SESCgXFAdyAGWCkiM1R1U4Z0pYGHgRWBCm2xWAox\ny5a5g9upxx804xammYhNm/iyy0fsAxasKsWQu0y0CBQtev5FtQROIJPycuo0sDWwQ1V3AYjIV8D1\nwKYM6V4AXgWG57Aci8VSWDh2DHr0AGB/eEuaxcznBKWoRVvasoL3f23AP/e/YdKehm7dClBWyzmR\nrbIQkRDgAaCDE7UAmOCxJrc/agB7PbZjgDYZ8m4O1FTVn0TEKguL5UJm9Wo4eBCAtJq1uaXOSk7E\nmF3z6UwDtvHkM8H07AmVK8PevdCnTwHKazknAumGeh8IAd5ztu9w4obmpmBnpNWbwOAA0t4L3AtQ\nq1atbFJbLJbzzsKF0KmTMTqI8PMbm1h8C3z4XAyvvRPCsrajKdPtORKfgJdfhmbNClpgy7kSiLJo\npapRHtvzRGRdAMfFAjU9tsOdOBelgabAAsf9eVVghoj0VdVVnhmp6gfABwAtW7a0EwItlkBZvtxM\nguvaNX/y37fPfP1LljTbR45AVBRvf1CCGjXgzv+Es2o/TJgA/5tlBkZZRXFhEoiySBWRy1V1J4CI\n1AVSAzhuJVBfRC7DKIlbgdtcO1X1GBDm2haRBcATGRWFxWLJBe3amf+xY+Ghh0xYnfpWbn1qzJzp\nsx9pww0jmDvaLFgUEgL33AM//ghXXQWjRuWuSEvBEcjQ2eHAfBFZICILgXnA49kdpKopwEPAbGAz\n8LWqbhSR50Wkb26EtlguOQ7nYHrT33+nh4cNM0rin/+EoCDzO3s2dzL5Mjj8859MOHwToaFur+O0\naAExMTB1qvcIWsuFhd+WhYjcrKrTgF1AfaChs2urqp4JJHNVnQnMzBA30k/aToHkabFcUiQkwPTp\npno+bRr07x/4sY+n1+nSEIKCMtQNQ0ONEsowszogrrsuc1zduvyv1zgm3WYm2uUkW0vhJauWhcv/\n0zeqekZV1zu/gBSFxWLJA5580igKOHdfGMWKATAs6F2qE0cMNTKnCQszo5jOhV27TL+Sw8x/L6AP\nPzKty3v07Wts3CNGnFuWlsJPVsoiXkTmAJeJyIyMv/MloMVyyaIKf/3lHbdiRbrNITtateI0obyb\n9iAHqMpYjEO/Lf/3LYMrzmAljsOEln4cJ8ydCz/8kDm+d2938NfvjnPjOx2ZSR9u+agnpUub1VKb\nNAlMRMuFQ1YG7j4YB4KfA2+cH3EsFgvJyWaE0XPPZd7Xti18+CEMDWDkenIyO7ncvfkxdzOKUTw5\n4ypmxFdmqTTiT21CKH5sF927m39P5fT337B1KwDzZp7m2htDqV/f2CU+/RQGDXI7lbVcZPhtWajq\nWVVdDlypqgtVdSGwGPjDCVsslvzglVe8FIUCd/MR1zCTs4T4dMrnxfLlcPIkHD/ONhoA8Ob924gn\njHd4mF/WhlG/PmzXejzD/52bbIsXAxBPBfoPCuXyy2HePHj3XaPDXnvt3LKzXDgEMhrqHREpIyIl\ngQ3AJjvb2mLJR9Z5T2NawtVM5G5mcQ0/cm3mxYQ82b/fDJctWRKef96tLO7+RzLtWMrTvMKp00GM\nGQOD+YT3+CfHKZ1115bnPsdIPqnNeI4ehcmToVIl05oYOtS2Ki5mAlEWjVX1OHAD8DNwGWYWt8Vi\nyQ+++QaAfdHXMJsejOR5ShQ5QxmO8VnovZntGC5OnoTRo72itlOfKlWUMu2aMPjZdO8HnTrBED7h\nNMWZygDSTp72zsdzDsaJE+nhP/9EgQ8O9+PKKyHKc7qu5aImEGUR4viHugGY4fiEsrOoLZZz5fnn\nITIy61r8XuNO7Ve6UHvjT/RiNgvozB13h3LP42X54Uwv7vuuJ2mnfdgZnnwSxo/3itpWLJIGDcyH\nf8Dj4TRvDk8/bQZKXblyDEIa9/Ihz3dd6LZFuGdju3DN8Th6FF5+mWVFO7FtZxH3IC3LJYKqZvkD\n/oWZgT0TEKA2sDi74/Lr16JFC7VYLghSUlQXL07fNmpCdd061VmzVFetUj17VvXEifQ0TZqognZv\nEqvh4arfvfCnDr8vQfftU92yRVVIVVCdP+jDzOW1b+8u4y9q62mKapXyp/Wuu/yL+O9b9yio1mCv\nOXbjxnQ5Xb9Vq0ziESNUQR9oNE+LF1c9fjxvLpPl/ACs0lx8e7NtWajqGFWtoaq9nTL3AJ3zUX9Z\nLBcHvXtD+/amS8ezW+fLL6FXLzNktWhR75r8VVeRShDL9lTj+uvhhhFN+e/4slStCg0bQkL/exDS\nWPxHBuNAUpLb+PzFPQu4jN20ZTkHjobSoIF/EV+dUouXir1ALOEkUNY95lWBv6hDHNU4u9KxoWzZ\nwllC+PpgJ/r2hdKl8+AaWS4YslUWIlJFRD4WkZ+d7cbAnfkumcVyoTNnju94fzaHQ4fggw/YUqoV\nSUlC69aZk5R59T80ZCsraeW9o1MnABIoy6PfdQRgLcZjn+dy2L5o3dkoq99JL3BEo2+py1/UII7r\nH6hmIv/8k3nNniA+Xhg0KOs8LRcfgdgsJmH8O1V3trcBj+SXQBbLxcxUbmH011dk3pGWZhZ5AFYm\nmf2tWmVORs2atGIlqzeXSI87c8Y9C3sygzh8GH7/Ha6+GurWdesRv7SZ9ADBksoiZ8maA1TmjR19\n3UpmFteQ2KoL7NnD+EIC5AAAHoZJREFUsiLtCQqCLl3O5awtFwOBKIswVf0aSAO3g8BAvM5aLJc8\naQiLaM+yEl3ZQkNuZSqjGM026nNtiXnU5G/2URWuucZ9zEpaUbq06XbKREgIUawjTqtxeNEm0/3k\nuPUAmFziHiIjjaKZO9fMpi5Rwkc+HpSuXJyWYbv5mls4RTHG8SBnU4KZPh1+LD0QgHWrzsLp06w+\nXo9GjTLbwC0XP4EoixMiUhFnBJSItAWO5atUFsuFRFyc8d909Kh3fKVKvMZwOrKIK0/OpRFb3Lve\nvnwsP53sTAw1eYeH3V1WSZRkKVfSooV7SkMmojA2hHU/7nWvU5GG8NnLsSw7Gc2AASZdaKiXHsmS\nURHfsp0GvMZw3i/zJNddBw0aQFSisYOsIwoFVsVVp0WLwPK0XGRkZwHHuPz4DaMgfsN0Q0Xmxqqe\nm58dDWUpdLz5phk1NHSo6u23q54+beKLFdOWVfZo06aqM2eqjhmj+upNK7QmZgRSkSJp2qHuXgXV\nJVypiZTUShxQUB0+3H9xBwlTUH1jwAr3iKWBTFZQrVtXNS4uB+ewaZN2YIF7ANT8+SY6DbQ88Xov\n4zWG6gqq77yTg/wtBQ65HA0VWCLjQ6oJZmW7kNwUmNufVRaWQsdLL3kPNb3iCtV27TSZYA0NOqOP\nP+6d/Pbb0xRUr79e9Y8JKxRUuzNbJzPQncX27VmU9/HHWpU4/Uez9arg/ogPG2ZG6+aUqdysoHr5\n5appaU4kaCfmKag2Z5WC6tKlOS/DUnDkVllktZ7FjX52NRARVPXbPGveWCwXMkUyvEZbTHfTFppw\nJq0o0dHeu2+/XViyxEyOi05J5gleYwz/QhFq1DC++vx1QQFw111E3/0zf/xRA9q1Y+ky43r8H/+A\n4OCcn8ZN73fnlUnf0uejG71G+t7ItyygM2sw/U8Zz8dyaZCV11kfq5u4UcAqC0vh4sQJM28hJOT8\nluuMYsrIWsxXNeOa0z17eoyeXZBML2bxOsOZS3ce6peNonBowwqeZyTHql3BXxWvgHg/BvFzIPj+\ne3jy/szxw3iXfySMZfNm45aqePHclWO5MPGrLFR1yPkUxGLJNaVKGbfa/uY35Bfq7b4jmSKsI4r3\neYBiISk0bJhFnUyVq1lCWFA8h9Mquo3T2XE1S1CCWP5XFfalVaFUqXyaJDd/PmzcSNmyxju65dIl\nq5aFGxHpg7FZuMdWqOrz+SWUxZJjfvkl7/NcujR9trUvUlOJoQYDmEpXfv3/9u47PIpqfeD4902j\nGFpoGooBETQgGIiCgKBioXjlgnoRFeFi74AF/IleFPSKoqiIV0ARUJqXotJEBbFcBInSmzSRTgSk\nJiHJvr8/ZrPZxPRks0n2/TzPPpk9M3PmnJ1lX87MmXMYIc+h6lzHuf3Wv16lyqBjR8o9P4Sl1wi7\nTzrPRuRF6/7NCJqYyvItEewPrc155+WvSnl21VW5P6hhAkKuwUJE3gMq4gzx8T5wC/CTj8tlTP4s\nX56+rJpxeI3C8M4nUwvCIyWFz+jOctqxnHYEB8GIEdChA7l3Mw0Kghde4BIglwetM6jU5+/Um7iH\nXQm1OZBQkciY3PcxpjDy8pxFW1W9Czimqi8AVwA5jDZjTDFLSYF27dLfb95c9IcghzvHjz7KRtLn\nER00CIYMgbZtnWcdfKJiRSLZzz7qsJ9IIiNz38WYwshLsEhw/z0jIpFAMuCrRq8x+ee+R5FIObbR\nCDZsKNLsR/EEVTjOpo1ZtCx27oSUFDbSlCaNUnniCWekcJ+rUIE67PMEC59dhjLGLS/BYr6IVAVe\nA34BfgOm+bJQxuTZ0qXQrRsAA3iTxmxjUq+FBctLFX75Jf39iRMAvMEgznAOM6ZkMYfE3r0osJGm\nXHkljBoF1asX7PD5EhpKHfaxlYtIoKK1LIzP5WWI8uGq+qeqzsaZy+IiVX3e90UzJhfbtnmGuzhI\nbT7E6cD3Lg8VLL+aNZ2bDGm9qSZO5BhVOeAeQ3P96mRYssSZtjSt7+uff7KXuhyhBpe2KsRDDvkV\nFkYk+z1vLVgYX8s2WIjIZSJyrtf7u4BPgOEiElEchTMmR+5Z5QDG8CjJEsZdTCaOWOL//b4zkmtW\nzp7N2IIAuO8+OHLEWU77W60aO2kIQAjJbPj6IAwdCitWOMO5qsKCBfxCSwBatizS2uUsU7CoU6cY\nj20CUk4ti3HAWQAR6QC8AkzBGSNqvO+LZowXVWfAPm/79gFwknD+U2EQPXsKj/AOShBL/u9reO21\nrPMaM8ZpQSxblp42YQIAa2jB7pEznF5QS5d6gkU3FrBTG5C4YjVHqcY47iMpqDyMH8/PtCIoSIt3\nPurQUAsWpljlFCyCVfWoe7kXMF5VZ6vqc0Aj3xfNGC9vvOH8Iu7YkZ62bx/7OY9Y4jiWUIHBg6Hl\nE9dQlWMsoRPMyWKQgTFj4MknneUtWzKs+onLiGENV68djQuBKVPYWS4acIKFi2C2cSGjGcgDjHNG\niwVW0oboaMl1KPAiFRZGHfZ53tplKONrOQYLEUl7DqMTsNRrXZ4e5jOmyIwb5/yNj3f+pqTAM88w\niX78ShMmTnTmcAgeNZKrWOYEi59+gri4jPlMnw7AJ9zKvJXuYTrcz0/MquXc69hFQ37AeTpuV/mL\nqVFDac1KAFYTQxyxgDPR0BEi+C7s2rRbJ8UnLIz6/O55a0NwGF/LKVhMB74Vkc9wus9+DyAijbD5\nLExxeeopGDbMuZkNTpAAzyWor7mWFi3gn16D09zAYnbRkA00dSLImjXOiqQkaNGC7VxALz7hpkk9\nSVy7FfbuBeB/Fa6jeeMEKnCG6fTmDBXYGdqEBg2EZmun0YCdvMIQfgh1polbRwuas47kVOGee4rj\nw/ASFkYFEvmS69ImyTPGt3IakhZoA/QAzvFKawy0LMxQt4V52RDlAcZr6O/fqK9/Tl+ompqqCppA\nOS0XlKSDBmXc5RA1NYgUvZdx6gLVL75QXbPGySc8XEdVfzl93gY6evIKC0nRJ59UvYOPFFTLkaDB\nkqK9ezv5LqSzZ78XeM6zPGlSsX8qzhjiaQUwJg8o5BDlOXadVdUVqjpXVU97pf2qqr/ktJ8xRS2B\n8lzGKi7tfRGuUW8A8Cl/J8kV9pdLQLVGDeYB3mMC9/EaT5Gy9yC89JKz8tQp1mpzzuEUQaTyLR0B\n+JlWnE0Jpl07eHL+1YSQTBLlSdVgGrnv0HWOWEUYSQD07FeFr+r1Z/586Nu3WD6CjIpqOBNj8qow\nkcYfL2tZBIANG1QvvVR1zhxVUBfoqzzp+Y/0OpppMsHaKHSXXtLMleWEP65bbtXuzFVQvZHPM7RQ\nmofv0C4s0JbE6VUs1Thaan/eV1A9dMidQZMmKqQqqE6b5k5LTNSlLQbo0H570icH8idQLVfO36Uw\npQTFMVNegTOHzsBWYDswJIv1g4BNwDpgCXB+bnlasAgAvXpl+HEfyVMKqk3YrKA6lgd1Ht0UVGfN\nyiaPlBQ98I/HPNkcpoYqaBKhGipndcjVP+ogRmWY4O6CC7z2b95cX+A5rRKenB5ASprPP1fdscPf\npTClRGGDRV6G+ygQEQkGxgJdgGigt4hEZ9psNRCrqs2BWcCrviqPKUUaNACcsZ5cCBMqDaJpoyRW\nt36QcznAj1zB5AoPUrMm3HRTNnkEB3PuxJf5EWcShm+4mlnczDUsJVlDufT+NnQcf6dn89t7pfLm\nm177t2/Pcwzn0Ldbs5vbyP/+9jfn4UBjioEvu8BeDmxX1Z0AIjID6I7TkgBAVb/x2n4FcCfGpKay\njI50YwHBpHLyZGXGvgIVvqpKG1bwMX2QBBcP353LpHgVKhBLHJU5zhsMYiXps/fExED9+rV5bAP0\n6QOxsZmG6hgxArnmGsrFZP7/jTGByWctC6AOsMfr/V53WnbuBhZltUJE7hOROBGJi0/rZ2/Kpt27\n4bXXeJn/4wzncJLKANx4IzBzJrf0cZ58i73olOfZumwFBRFCKp1YwkracC4HeODSFTz2GDRuDOXL\nw1tvOfMa/UW1anDzzXYj2Ri3EvFwnYjcCcSCu2tKJqo6HvcQI7GxsdnMQGPKhKgo9hHJ11zLcw/9\nwbEjSniDmtSvDxDGHVNuoMubEBFROc9ZjmAooW1iGfhcOG262tygxhSEL4PFPqCe1/u67rQMRORa\n4Fmgo6om+bA8pqRzOj3wX25FCaLPgBpceOFfN4vIzzCWCxYQ3aABMy+uXzRlNCZA+TJYrAIuFJEG\nOEHiNuB27w1EJAZnwMLOqnrYh2UxJcGcOc74Tq1bZ73+rDNfxM+1ulA3jCwDRb517VoEmRhjfHbP\nQlVTgEeAxcBm4BNV3SgiL4pIWh+W14Bw4L8iskZEPvdVeUwxiY+H5s1h1qyM6arOPYA2OVwGSkwE\nYENIDE2bZr+ZMab4+fSehaouBBZmSnvea/laXx7f+MG8ebB+Pdx6q+eyEgCHc2g4nj4NvXtD+/ak\nEsSW+Ag6NfN9UY0xeefL3lAmUJw+DSudUVnZupUkwujE10xp+Wb6BEVxcSjwHveze3em/SdPhnnz\ncA0ewk4akpgcYi0LY0oYCxamcFJSnDvObdpA//7w6qt8RweW0om+qwegAwc52w0cyAaa8SDv0b17\npg5tixbxMy2pyp88xLsAFiyMKWEsWJiC2bjReYI4NNRzYzrpw6kAzOdGz2ar4+s6C3v2MIeeAOzY\nkpyez9ix6Pz5PFZ+PCepzNdcB0C0PQtnTIliwcIUTMeOMH8+AAvoSmcWUZ4k2vI/3uZxWof+QnkS\n+Md3D3NcqkBioidYnEoK4+hRnDkqHnmE6fRmeWIrHmjszK9VvVIS4eH+qpgxJisWLEz+pabCkSP8\nQDuuZzE3soCNFS/jzlsT2cJFADw4oSVz6cEOGjGSwaynGetoQVcWAM49cH74gRSCGcxIWsWkMnbT\n1UwYuIlPZpeIZ0WNMV4sWJQUqvDpp+kzwRWl3buhWzf45BPYtCn37dOcPg0nTsD+/RnTDx4klSD6\nMYmVIe3o3x9+/aM6H80sx76QKBZ0H0+fPtCZxfRmGq8whDasICxM+TfPALBhvQsSEviRK9hLPQY/\nE0xQsHDPG9Fcc11wFoUxxviTBYuS4I8/ICoKevSAV14p2ryHDXPyXrgQevXK+53j2bMhPByqVHEe\npEuzcCHUrcsiurCDRoz/+Bw++MA9B7QIFU4epuucewgKApYtY8KMygzodYDwmhUYMUK4JPIo1TjK\n+qnr4McfWUQXQkKU668v2mobY4pYYcY398erzMxnsWeP6ooVzvLDD6dPqtCvX9EdY8QIVdBkgnUe\n3XQc92oSoXnb9847NZlgXUhnPUN51a5dnXTQXZyvlflTo+qlaGJiPst0zjnagWXalh9UQS9hrXbs\nmM88jDH5RkmdzyJghIc7/2PPrxtvdLqbnjgBIuygIS1Yw22TbsDlKoJy/fEHDB3KWppzMZv5G/O5\nn/HUYw//m7QNTp3Ker/jx2H5cvj4Y97icbqyiDasYOjCKzi4ag+cfz4z6cUJqvDlkmDKlctnua68\nkktYz3ouYSWXs57m9OhR6NoaY3ytMJHGH68S1bL49Vd1gW6hsfP+9GnVU6ec5fh41e3bs98XdD1N\ndeGojaqg9/MfT+Ni2oAVhStXfLzqtGl6hvJ6Pru0zrkpOuvhpTqX7hrCWb2JT1UbNcq2XC73jHLN\nWaOgWiEoQUG1Hd9rCkHaLnKHxsQUsGyHD+tUenvqWue8FD1ypMA1NcbkESV5WlVfvEpUsAB9h4cU\nVD++fYF6fgG//DJ9+fDhv+53+rQq6IVs9cwpXYPD+g9mamO2aOugnwpdLgV9hacVVJctc6fHxOhD\nvKMVOaWJhP11vy1bVEF7MstT/LffVtXt23UyfRRUn2W4BkmqPv98wYuXePS0dmq0Sxs1TNG1awue\njzEm7yxY+Mvx46qgHVimoHoNX2siYfoUI/W3h0aqC3QVrTR57cb0fV56yfnIZ8/WY1Tx/CC3YLWT\nPD1JRzFIQfW3HSkFLxvoaSpoBH9oly5e6cuX63y6eo676J3tqk89pdq6tdMiWrBA9xLpWR8Z6fL8\nr9/18VTtwgLPuri4ghfPGFP8LFj4y/LlqqA1OKygGkyy53/yN/GpvsljCqrP9Nun6nKpbt6sCpqK\n6EFq6Ty6Kahn/8qVXXrmjOq6tvcrqE6MHescZ9Uq1YMHneXUVNU1a1QXLFA9dCj7slWqpBPpl7FV\noaq6bZuepoLnB78Vq1RBTxCuKQSpXn65fkhfBdU1KxM1KSljtgnjp+hQXtQH+iepy1Wkn6Yxxscs\nWPjL++/rIWoqqN7BR54fYFCNZK9GsdMJBpUSNPmj6eoC/YRbNJafVEjVc9mv5cJSddWPydq6terk\nyU62qVe005oc0j5MVk1JcTKMjlbdvVv19tvTD5LWOymznTtVQS+rtEmjozXjj3p8vCroXLprb6Yq\nqI7nHq3Mn9qduaqg9zJOq1VzaWqqzz9BY0wxsmDhL2++qUu5SkH1i/d2aU0OKahey5ee3/PbmKag\nuoSrdTHXedKDSVZQ7dIpi36n9evrbUzTWhzUs8Ne0gxRCPQH2uq9jNNHeFtTsrpSFRWlcbRMv9+Q\n2aFDqpMmaTzVNYSzGbIfzz3arOIO7dy5qD8sY4y/FTZYWNfZgjpzho04D7g1uzGKRV+H8e4TO5hI\nfwDKl1fGcT8VOc0sbuF1nqBWTRfxi+I4Vu0CRpUfyrsTwv6ab1QUdzCVw9Rm4bCVfMZN1ON3fiGG\nD+lHe/7HBO7jHR7l2zdX/3X/WrV4jweoGJRAnz5ZlLtWLejZkxpVUxnOc3S86CCbuJgmbOE+JrDh\nTEPati3Cz8kYUzYUJtL441XsLYtvvlG9+WbVsWMzpg8dqg/wH61SxZV+qefUKdV27XT2v7c4vXyW\nLtVb+MTzP/eXX87D8Q4c0LMRtTWcE9qf9z2Xs3owW1uwWmMan9Q91/1TQXUUg1TnzUvfd+NG/ZPK\nWjE4Qe/uezbn4yQnq86d61ynOnhQk8Mq6qO8pbVD4nXXrjx/OsaYUgK7DOVDJ0+qgv5BhG6noboO\num8qx8Wp9uypHYK+17Ztc9jf5dLvaK/BJGtktdN67FgejztwoHZjnifIRLPBszxmjKredpvW5JD2\n53394aaRzuWoEydUwdObKt+9lZKTVQcMUNeu3/K5ozGmNChssLDLUDnZsoUDnEsDdtGIHXRs8SfJ\n02ehsbHMnePiO1d7muU0/acIV64azbZ3FrNma0WqVs3jcY8epRNLAGhWfjsTuNezqlcvoGJFotnE\nRO6m/edPM3YsMGMGB6nNCIZy3bVKq1b5rGtICIwejUSdn88djTGBwIJFTi67jA+4m5NU5m7e5/tD\njZn6+E98RB96MhdwpnXIUWwsDR7uRs2a+ThuZCS38l+68ylTv6tHG1ZwPYt5+mmcfCpWpCkbPZvP\nmwesXMm7PMQJqcKYdyS/NTXGmBxZsMjOokUAzKQXV7Y5y4Q2E7mYTUyI786E8o9RJ+wwC9u8WKBh\noXL1zDPUZR+fPhtH88vKEYSymM6MHOle36ABl/OTZ/MVK5SUXXv4tMLtXNkhiCZNfFAmY0xAs1lm\nsnLsGOzcya9cyAYu4a3bFGk2nH7XTmIwr0KiM5J4l8HP++b4lSo5tyjSbNsGSUnp7yMiuJ1paEQN\nko6e4oFT45iztArracQb3X1TJGNMYLOWRWb790NEBCse+YjXeQKAv/cQiImhL5OpyWEqhSVy113F\nWKZGjTLOQ9GsGaGk0O/5+nRzzzz3GG8D2AiuxhifsJaFKrz2GjRuDN27Q506LKQL3VgIQGzLVOrX\nDwYiqM1htnARrp83UeO8c/1X5thY+P13qFuXugMGcD6/sZsoOl2VQlSUnVJjTNGzlsXs2SQM/hdP\n9tjORx3f5yC1+ScfUotD9GAOr4/2muLz99+JOPk7NZr5MVCkqVcPRODpp3mWl4ituo3Rb1ugMMb4\nhqj3tfFSIDY2VuPi4gqegaozMVDlyhAWBkFBDONfvMAwABqzlT1Sn1Wvf0/TfpdBtWpFU3BfSpst\nKchivzEmayLys6rGFnT/wPt1eecdNtfqwKDyY9kU1JRjVGU0A7mC5QD8ShNGPnWEpgOvLx2BApwg\nYYHCGONDgdWyOHGClCoRNGcdm4kmnJNU5wi7iWINLVhNDL89M57nhocRHJx7dsYYU1oUtmURWBe5\nx4zhA+5mM9GMZgCf0Z1lXM1VV6bSYtQEWiQnQ7ssBvczxpgAFzDBYumMw/x3aAQzeZAObZN5PGwd\njy4bw1Tu4NoZUyDycn8X0RhjSqyACRabP1zBFO7inNBk3h0fioR/SHBUFHe1WAeR/i6dMcaUbD69\nKyoinUVkq4hsF5EhWawvJyIz3etXikiUr8ry8MwOHF/6C/tPV3Geb6tfH4YPh1mzfHVIY4wpM3wW\nLEQkGBgLdAGigd4iEp1ps7uBY6raCBgNjMRXqlYl5OorCQmVtALC0KHO09HGGGNy5MuWxeXAdlXd\nqapngRlA5pGLugOT3cuzgE4iYkOmGmNMCePLYFEH2OP1fq87LcttVDUFOA5Uz5yRiNwnInEiEhcf\nH++j4hpjjMlOqXiSS1XHq2qsqsbWzNfEEMYYY4qCL4PFPqCe1/u67rQstxGREKAKcMSHZTLGGFMA\nvgwWq4ALRaSBiIQBtwGfZ9rmc6Cve/kWYKmWtkfKjTEmAPjsOQtVTRGRR4DFQDAwUVU3isiLOBOH\nfw58AHwkItuBozgBxRhjTAnj04fyVHUhuCeGSE973ms5EbjVl2UwxhhTeKXiBrcxxhj/KnWjzopI\nPLC7gLvXAP4owuKUBoFYZwjMeludA0NB63y+qha4O2mpCxaFISJxhRmitzQKxDpDYNbb6hwY/FVn\nuwxljDEmVxYsjDHG5CrQgsV4fxfADwKxzhCY9bY6Bwa/1Dmg7lkYY4wpmEBrWRhjjCkACxbGGGNy\nFTDBIrdZ+0ozEflNRNaLyBoRiXOnRYjIVyKyzf23mjtdRORt9+ewTkRa+rf0eSMiE0XksIhs8ErL\ndx1FpK97+20i0jerY5UU2dR5mIjsc5/rNSLS1WvdM+46bxWRG7zSS813X0Tqicg3IrJJRDaKyOPu\n9DJ7rnOoc8k616pa5l84Y1PtABoCYcBaINrf5SrC+v0G1MiU9iowxL08BBjpXu4KLAIEaAOs9Hf5\n81jHDkBLYENB6whEADvdf6u5l6v5u275rPMw4Mksto12f6/LAQ3c3/fg0vbdB84DWrqXKwG/uutW\nZs91DnUuUec6UFoWeZm1r6zxnoVwMvB3r/Qp6lgBVBWR8/xRwPxQ1e9wBpv0lt863gB8papHVfUY\n8BXQ2felL5hs6pyd7sAMVU1S1V3Adpzvfan67qvqAVX9xb18EtiMM0lamT3XOdQ5O34514ESLPIy\na19ppsCXIvKziNznTqutqgfcyweB2u7lsvRZ5LeOZaXuj7gvuUxMuxxDGayziEQBMcBKAuRcZ6oz\nlKBzHSjBoqxrr6otgS7AwyLSwXulOm3XMt1HOhDq6PYf4ALgUuAA8Lp/i+MbIhIOzAYGqOoJ73Vl\n9VxnUecSda4DJVjkZda+UktV97n/Hgbm4jRHD6VdXnL/PezevCx9FvmtY6mvu6oeUtVUVXUBE3DO\nNZShOotIKM6P5lRVneNOLtPnOqs6l7RzHSjBIi+z9pVKInKOiFRKWwauBzaQcRbCvsBn7uXPgbvc\nvUjaAMe9mvelTX7ruBi4XkSquZv017vTSo1M95d64JxrcOp8m4iUE5EGwIXAT5Sy776ICM6kaJtV\n9Q2vVWX2XGdX5xJ3rv3dE6C4Xji9Jn7F6S3wrL/LU4T1aojT62EtsDGtbkB1YAmwDfgaiHCnCzDW\n/TmsB2L9XYc81nM6TlM8Geda7N0FqSPQH+eG4Hbgn/6uVwHq/JG7TuvcPwTneW3/rLvOW4EuXuml\n5rsPtMe5xLQOWON+dS3L5zqHOpeoc23DfRhjjMlVoFyGMsYYUwgWLIwxxuTKgoUxxphcWbAwxhiT\nKwsWxhhjcmXBwphsiEh1rxE/D3qNAHpKRN71d/mMKU7WddaYPBCRYcApVR3l77IY4w/WsjAmn0Tk\nKhGZ714eJiKTReR7EdktIj1F5FVx5hf5wj2MAyLSSkS+dQ/2uLg0jPRrjDcLFsYU3gXANcBNwMfA\nN6p6CZAAdHMHjDHALaraCpgIvOSvwhpTECH+LoAxZcAiVU0WkfU4E9B84U5fD0QBTYBmwFfOMEAE\n4wzjYUypYcHCmMJLAlBVl4gka/qNQBfOvzEBNqrqFf4qoDGFZZehjPG9rUBNEbkCnOGoRaSpn8tk\nTL5YsDDGx9SZ4vIWYKSIrMUZVbStf0tlTP5Y11ljjDG5spaFMcaYXFmwMMYYkysLFsYYY3JlwcIY\nY0yuLFgYY4zJlQULY4wxubJgYYwxJlf/D7reVKa9HQcxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rf-XXyhEZluf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}