Data_Cleaning_and_Engineering.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "14Uefty1xplar4RTue0IAQNpagI98mnO8",
      "authorship_tag": "ABX9TyOodEpODsbjXuw3SZAT7WYA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jbloewencolon/Analyzing-Bergson/blob/main/Data_Cleaning_and_Engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdzydofPPc5F",
        "outputId": "d24fa318-b18f-466e-9946-9420941d287d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: PyPDF2 in /usr/local/lib/python3.10/dist-packages (3.0.1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package words to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/words.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "!pip install PyPDF2\n",
        "\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords, words\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter\n",
        "from nltk import bigrams\n",
        "import warnings\n",
        "from PyPDF2 import PdfReader\n",
        "import os\n",
        "import pickle\n",
        "\n",
        "# Suppress specific warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module='PyPDF2')\n",
        "\n",
        "nltk.download('words')\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to read a PDF\n",
        "def pdf_to_text(filepath):\n",
        "    with open(filepath, 'rb') as f:\n",
        "        pdf_reader = PdfReader(f)\n",
        "        text_pages = []\n",
        "        for page_num in range(len(pdf_reader.pages)):\n",
        "            text_pages.append(pdf_reader.pages[page_num].extract_text())\n",
        "    return ''.join(text_pages)\n",
        "\n",
        "# Function to read a TXT file\n",
        "def txt_to_text(filepath):\n",
        "    with open(filepath, 'r') as f:\n",
        "        return f.read()\n",
        "\n",
        "# Check if a pickled file already exists\n",
        "pickle_file_path = r\"/content/drive/MyDrive/Colab Notebooks/Bergson/Bergson_text_dict.pkl\"\n",
        "\n",
        "if os.path.exists(pickle_file_path):\n",
        "    with open(pickle_file_path, 'rb') as handle:\n",
        "        text_dict = pickle.load(handle)\n",
        "    print(\"Loaded pickled dictionary. Exiting script.\")\n",
        "else:\n",
        "    print(\"Pickled dictionary not found. Proceeding with text processing.\")\n",
        "\n",
        "    # Initialize a dictionary to hold each file's text\n",
        "    text_dict = {}\n",
        "\n",
        "    # Define the directory path for files\n",
        "    folder_path = r\"/content/drive/MyDrive/Colab Notebooks/Bergson\"\n",
        "\n",
        "    # List all files in the directory\n",
        "    all_files = [f for f in os.listdir(folder_path)]\n",
        "\n",
        "    # Loop through each file and extract text\n",
        "    for file_name in all_files:\n",
        "        if file_name not in text_dict:  # Skip if already processed\n",
        "            full_path = os.path.join(folder_path, file_name)\n",
        "            print(f\"Processing {file_name}...\")\n",
        "            try:\n",
        "                if file_name.endswith('.pdf'):\n",
        "                    text = pdf_to_text(full_path)\n",
        "                elif file_name.endswith('.txt'):\n",
        "                    text = txt_to_text(full_path)\n",
        "                else:\n",
        "                    print(f\"Skipping unsupported file format: {file_name}\")\n",
        "                    continue\n",
        "\n",
        "                text_dict[file_name] = text\n",
        "            except Exception as e:\n",
        "                print(f\"Failed to process {file_name}: {e}\")\n",
        "\n",
        "    # Pickle the dictionary\n",
        "    with open(pickle_file_path, 'wb') as handle:\n",
        "        pickle.dump(text_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "    print(\"Pickled the dictionary.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uhn8aGUPMK-8",
        "outputId": "250953a3-80e0-4eda-cd35-d6677e46ad5c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pickled dictionary not found. Proceeding with text processing.\n",
            "Processing Other Books.lnk...\n",
            "Skipping unsupported file format: Other Books.lnk\n",
            "Processing Duration and simultaneity.pdf...\n",
            "Processing Mind-Energy_ Lectures and Essays.pdf...\n",
            "Processing Laughter.txt...\n",
            "Processing Two Sources.pdf...\n",
            "Processing The Creative Mind.pdf...\n",
            "Processing Dreams.txt...\n",
            "Processing The Meaning of War.txt...\n",
            "Processing Time and Free Will.txt...\n",
            "Processing Matter and Memory.pdf...\n",
            "Processing An Introduction to Metaphysics (1912).pdf...\n",
            "Processing Creative Evolution.pdf...\n",
            "Pickled the dictionary.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in pdf_text_dict.items():\n",
        "    print(f\"---{key}---\")\n",
        "    print(value[:100])  # print the first 100 characters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGqOe29k42jx",
        "outputId": "a5f3ae33-0f49-44e6-b898-ee8953dd745f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---Duration and simultaneity.pdf---\n",
            "BOBBS-MERRILL THELIBRARY OFLIBERAL ARTS\n",
            "BERGSON\n",
            "Duration and\n",
            "Simultaneity\n",
            ".B413\n",
            "Duration andSimultan\n",
            "---Mind-Energy_ Lectures and Essays.pdf---\n",
            "]?ELHI UNIVERSITY LIBRARY\n",
            "OJ.Np.S\n",
            ",. >s Date ofreleoBe foiloai\n",
            "Ad.Ko.|0B&\n",
            "This book should bereturne\n",
            "---Time and free will_ an essay on the immediate data of consciousness.pdf---\n",
            "MUIRHEAD LIBRARY OF PHILOSOPHY \n",
            "As may be seen from the original programme \n",
            "printed in Erdmann’s His\n",
            "---An Introduction to Metaphysics (1912).pdf---\n",
            "iii Tr ; METAPHYSICS saree ptrenr et smn \n",
            "HENRI BERGSON \n",
            "The Library | : \n",
            "SCHOOL OF THEOLOGY \n",
            "_ AT C\n",
            "---Creative Evolution.pdf---\n",
            "V)> U3^\n",
            "m<OU_166582>m\n",
            "z\n",
            "._i\n",
            "OSMANIA UNIVERSITY LIBRARY\n",
            "CallNO.-/T7$& S%\n",
            "Accession No.\n",
            "Author\n",
            "Thisboo\n",
            "---Laughter.pdf---\n",
            "DELHI UNIVERSITY\\\n",
            "LIBRARY]\n",
            "\\\n",
            "i\n",
            "-r ^\n",
            "ClNo‘\n",
            "Ac.No. Date ofrelease forloan\n",
            "Thisbook should beretursed o\n",
            "---The Meaning Of War.pdf---\n",
            "A free download from http://manybooks.net\n",
            "The Meaning of the War\n",
            "The Meaning of the War\n",
            "The Meaning \n",
            "---The Creative Mind.pdf---\n",
            "THE CREATIVE MIND  \n",
            "by HENRI BERGSON of the Ac adémie Francaise and the \n",
            "Académie des Sciences  mora\n",
            "---Matter and Memory.pdf---\n",
            "PILAKI (Jaipur State) \n",
            "Class No \n",
            "Book No > 6W6-M \n",
            "Accession No > XiDrar? of pbilosopbi?. \n",
            "EDITED BY \n",
            "---Two Sources.pdf---\n",
            "^TM srwf^^^ a^^ 1^ \n",
            "Lai Bahadut^fMsttj 7^a4fmy ft \n",
            "of Admjnistratidrl . ^ \n",
            "MUSSOOf^IE \n",
            "% \n",
            "lGo^8g^i \n",
            "\n",
            "---Dreams.pdf---\n",
            "22102108358 v4 ^‘-•*..,'3. Digitized by the Internet Archive \n",
            "in 2019 with funding from \n",
            "Wellcome Li\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all the text to lowercase and concatenate it\n",
        "all_text = ''.join([text.lower() for text in pdf_text_dict.values()])\n",
        "\n",
        "# Tokenization: Split the text into words\n",
        "tokens = word_tokenize(all_text)\n",
        "\n",
        "# Remove Punctuation and Numbers\n",
        "tokens = [word for word in tokens if word.isalpha()]\n",
        "\n",
        "# Remove Stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "filtered_tokens = [word for word in tokens if not word in stop_words]\n",
        "\n",
        "# Frequency Distribution\n",
        "freq = Counter(filtered_tokens)\n",
        "\n",
        "# Visualization\n",
        "common_words = freq.most_common(10)\n",
        "words = [word[0] for word in common_words]\n",
        "counts = [word[1] for word in common_words]\n",
        "\n",
        "plt.figure(figsize=(15, 6))\n",
        "plt.barh(words, counts, color='purple')\n",
        "plt.xlabel('Counts')\n",
        "plt.ylabel('Common Words')\n",
        "plt.title('Most Common Words in PDF Collection')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "qsPabfjlC5m_",
        "outputId": "f2a066ce-0727-4f5e-88d5-5994216067e9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to hold the most common bigrams for each text\n",
        "common_bigrams_per_text = {}\n",
        "\n",
        "# Loop through each text in your dictionary\n",
        "for pdf_file, text in pdf_text_dict.items():\n",
        "    # Tokenization: Split the text into words\n",
        "    tokens = word_tokenize(text.lower())  # Converting to lower case\n",
        "\n",
        "    # Remove punctuation and numbers\n",
        "    tokens = [word for word in tokens if word.isalpha()]\n",
        "\n",
        "    # Remove stopwords\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    filtered_tokens = [word for word in tokens if not word in stop_words]\n",
        "\n",
        "    # Generate bigrams\n",
        "    bigrams_list = list(bigrams(filtered_tokens))\n",
        "\n",
        "    # Count frequency of bigrams\n",
        "    bigram_freq = Counter(bigrams_list)\n",
        "\n",
        "    # Get top 5 bigrams\n",
        "    common_bigrams = bigram_freq.most_common(5)\n",
        "\n",
        "    # Add the top 5 bigrams for this text to the dictionary\n",
        "    common_bigrams_per_text[pdf_file] = common_bigrams\n",
        "\n",
        "    # Optional: Plot top 5 bigrams for the current text\n",
        "    plt.figure(figsize=(15, 6))\n",
        "    bigram_words = [f\"{bigram[0][0]} {bigram[0][1]}\" for bigram in common_bigrams]\n",
        "    bigram_counts = [bigram[1] for bigram in common_bigrams]\n",
        "    plt.barh(bigram_words, bigram_counts, color='blue')\n",
        "    plt.xlabel('Counts')\n",
        "    plt.ylabel('Common Bigrams')\n",
        "    plt.title(f\"Top 5 Bigrams in {pdf_file}\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1v7wCW4K5Ymf",
        "outputId": "65fd18fa-a61b-49b6-fd8b-4cfb84968184"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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TJ09Okuyzzz7z1XjVVVdl1qxZ9br/9773vdxyyy154YUX8tBDD9VryoJ5FvX3O+/74rPfE82bN8/qq6++2PdZ1PfM//73v3z44YcLPG7ttdde7PsAwPLMHI0AAPU0L3R75513luj8Jk2aZMstt8xFF12UyZMnZ7311lsqdX16FNs848aNyy677JJvfetbufTSS9OtW7c0b948o0aNmm+hjSRp2rTpAq/9ee3FpxbfOe+887Lvvvvm1ltvzV133ZXhw4fnzDPPzL/+9a/5QrPFsTj3XBoWdZ95I/COOuqozx159nnhb5Kss846ST6Zn2/eaLpFqaioSJMmdccEzJ07N507d8511123wHPmzd353nvvZeDAgWnXrl1OPfXUrLHGGmnZsmUmTpyYn/3sZ4s9ovCzunfvnm9+85u5+eab8/Of/zz/+te/MnXq1Pzyl7+sU2OSnHPOOfna1762wOt83jyQC7LXXnvluOOOy49//OOsuOKK2W677epd9xfVjwAAQSMA0AituuqqufvuuzNjxow6oxrnvRK66qqr1jl+3iirT/vPf/6TVq1afe7CKgsz75XLJTl3no8//jhJMnPmzAXu79y5c1q2bLnA1XEX1PZ5/vjHP6Zly5a58847U1FRUds+atSoela8ePr27Zu+ffvmhBNOyEMPPZQBAwbk8ssvz2mnnbZM7vdFmDeqrXnz5tlmm23qff7OO++cM888M7///e8XO2hckDXWWCN33313BgwYsMBQeZ6xY8fm7bffzp/+9Kd861vfqm2fMmXKfMd+dvXyRfnud7+bgw46KJMmTcpNN92UVq1aZeedd65TY/LJKM4l+Vl9Vs+ePTNgwICMHTs2P/3pT+dbqX1pmPd9MXny5Gy11Va17XPmzMmUKVOy4YYbLtZ1Fud7ZoUVVljgcZMmTVqS0gFguePVaQCg0dlhhx1SU1OTiy++uE77BRdckFKpNN8chg8//HCd+QhfffXV3Hrrrdluu+0+d7RTkgW+Vj1jxoxceOGFWWmllerMx1Yfc+bMyV133ZUWLVp87kqyTZs2zTbbbJO//OUvef3112vbX3jhhfnmoFyYpk2bplQq1ZmL8uWXX85f/vKXJar981RXV9eGp/P07ds3TZo0yaxZs5bqvb5onTt3zqBBg3LFFVdk2rRp8+1f1Ov3/fv3z5AhQ3LVVVct8Oc+e/bsHHXUUYusY88990xNTU1+8YtfzLfv448/znvvvZfk/x/B9+kRe7Nnz86ll14633mtW7eu16vMe+yxR5o2bZobbrght9xyS3baaae0bt26dv8mm2ySNdZYI+eee+4CQ/T6TlWQJKeddlpGjhyZQw89tN7nLo5+/fqlU6dOufzyy+tMZTB69Ojan+mnPf/885k6dep87Yv6nmnatGkGDx6cv/zlL3XOf+6553LnnXcu3YcCgAZiRCMA0OjsvPPO2XLLLXP88cfn5ZdfzoYbbpi77rort956a0aMGFE7qmqe9ddfP4MHD87w4cNTUVFRG7iccsopC73PJZdckr/85S/Zeeed07Nnz0ybNi1XX311pk6dmmuvvbbOwhEL8/e//712tOX06dNz/fXXZ/LkyTn22GNr57ZbkJNPPjl33XVXBgwYkJ/+9Ke14er666+fJ554YrHuveOOO+b888/PkCFD8r3vfS/Tp0/PJZdckjXXXDP//ve/F+sai+Pee+/NIYccku985ztZa6218vHHH+faa69N06ZNs8ceeyy1+zSUSy65JN/4xjfSt2/f/PjHP87qq6+eN998Mw8//HD++9//5sknn1zo+ddcc02222677L777tl5552z9dZbp3Xr1pk8eXJuvPHGTJs2Leeee+5CrzFw4MAccMABOfPMM/PEE09ku+22S/PmzTN58uTccsstueiii/Ltb387W2yxRTp06JB99tknw4cPT6lUyrXXXrvAV4U32WST3HTTTTniiCOy6aabpk2bNnVGKH5W586ds+WWW+b888/PjBkz8t3vfrfO/iZNmuSqq67K9ttvn/XWWy/77bdfVl555bz22mu577770q5du9x+++0Lfc4FPffAgQPrdU59NG/ePKeddloOOOCAbLXVVvnud7+bKVOmZNSoUQuco7FPnz4ZOHBgxo4dW6d9cb5nTjnllPzjH//IN7/5zRx00EH5+OOP8+tf/zrrrbfeUv33CAANRdAIADQ6TZo0yW233ZaTTjopN910U0aNGpXVVlst55xzTo488sj5jh84cGD69++fU045JVOnTs26666b0aNHZ4MNNljofQYMGJCHHnooV111Vd5+++20bt06m222Wa6++uo6r1guykknnVT755YtW2adddbJZZddlgMOOGCh522yySb5+9//nqOOOionnnhievTokVNPPTXPPffcYq8cvNVWW+W3v/1tzjrrrIwYMSK9evXKL3/5y7z88stLNdjYcMMNM3jw4Nx+++157bXX0qpVq2y44Yb5+9//nq9//etL7T4NZd11181jjz2WU045JaNHj87bb7+dzp07Z6ONNqrz9/t5OnXqlIceeiiXXnppbrrpphx//PGZPXt2Vl111eyyyy457LDDFquOyy+/PJtsskmuuOKK/PznP0+zZs2y2mqr5fvf/34GDBiQJFlxxRVzxx135Mgjj8wJJ5yQDh065Pvf/3623nrr+eaYPOigg/LEE09k1KhRueCCC7LqqqsuNGhMPnl9+u67707btm2zww47zLd/0KBBefjhh/OLX/wiF198cWbOnJmuXbtm8803X2Sfbyg/+clPUlNTk3POOSdHH310+vbtm9tuuy0nnnjiYl9jcb5nNthgg9x555054ogjctJJJ2WVVVbJKaeckmnTpgkaAfhSKBVmQQYAvsRKpVIOPvjg+V6zbsyGDh2aZ555ZoFzvQFfvC/j9wwALAlzNAIALMc+/PDDOtuTJ0/O3/72twwaNKhhCgIAgM/h1WkAgOXY6quvnn333Terr756XnnllVx22WVp0aJFjjnmmIYuDQAA6hA0AgAsx4YMGZIbbrghb7zxRioqKtK/f/+cccYZ6d27d0OXBgAAdZijEQAAAAAomzkaAQAAAICyCRoBAAAAgLKZo5H5zJ07N6+//nratm2bUqnU0OUAAAAA0ECKosiMGTPSvXv3NGmy8DGLgkbm8/rrr6dHjx4NXQYAAAAAy4lXX301q6yyykKPETQyn7Zt2yb5pAO1a9eugasBAAAAoKFUV1enR48etXnRwggamc+816XbtWsnaAQAAABgsabXsxgMAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQtmYNXQDLr8rKhq4AGoeiaOgKAAAAoOEZ0QgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdBYpkGDBmXEiBGfu//kk0/O1772tYVeY999983QoUOXal0AAAAA8EUSNC5jRx11VO65556GLgMAAAAAlqlmDV3A8mrOnDlp3rx52ddp06ZN2rRpsxQqAgAAAIDlV4OOaJwxY0aGDRuW1q1bp1u3brngggvmexV51qxZOeqoo7LyyiundevW2XzzzTN27Nja/aNHj0779u1z5513pk+fPmnTpk2GDBmSadOm1bnXVVddlT59+qRly5ZZZ511cumll9bue/nll1MqlXLTTTdl4MCBadmyZa677rq8/fbb2WuvvbLyyiunVatW6du3b2644YZ6PeNnX52uqanJEUcckfbt22fFFVfMMccck6Io6pwzd+7cnHnmmenVq1dWWGGFbLjhhvnDH/5Q5xr7779/7f611147F110UZ1rjB07Nptttllat26d9u3bZ8CAAXnllVfqVTsAAAAALK4GDRqPOOKIPPjgg7ntttsyZsyYjBs3LhMnTqxzzCGHHJKHH344N954Y/7973/nO9/5ToYMGZLJkyfXHvPBBx/k3HPPzbXXXpsHHnggU6dOzVFHHVW7/7rrrstJJ52U008/Pc8991zOOOOMnHjiifnd735X517HHntsDjvssDz33HMZPHhwPvroo2yyySb561//mqeffjo/+clP8oMf/CCPPvroEj/zeeedl9GjR+fqq6/OP//5z7zzzjv585//XOeYM888M9dcc00uv/zyPPPMMzn88MPz/e9/P/fff3+ST4LIVVZZJbfcckueffbZnHTSSfn5z3+em2++OUny8ccfZ+jQoRk4cGD+/e9/5+GHH85PfvKTlEqlBdY0a9asVFdX1/kAAAAAQL0UDaS6urpo3rx5ccstt9S2vffee0WrVq2Kww47rCiKonjllVeKpk2bFq+99lqdc7feeuviuOOOK4qiKEaNGlUkKV544YXa/ZdccknRpUuX2u011lijuP766+tc4xe/+EXRv3//oiiKYsqUKUWS4sILL1xk3TvuuGNx5JFH1m4PHDiwtt4FGTlyZLHhhhvWbnfr1q04++yza7fnzJlTrLLKKsWuu+5aFEVRfPTRR0WrVq2Khx56qM519t9//2Kvvfb63PscfPDBxR577FEURVG8/fbbRZJi7Nixi3yeeTUmWcCnqkgKHx+fRXwAAADgy6qqqqpIUlRVVS3y2Aabo/Gll17KnDlzstlmm9W2VVZWZu21167dfuqpp1JTU5O11lqrzrmzZs3KiiuuWLvdqlWrrLHGGrXb3bp1y/Tp05Mk77//fl588cXsv//++fGPf1x7zMcff5zKyso61+3Xr1+d7Zqampxxxhm5+eab89prr2X27NmZNWtWWrVqtUTPXFVVlWnTpmXzzTevbWvWrFn69euXoiiSJC+88EI++OCDbLvttnXOnT17djbaaKPa7UsuuSRXX311pk6dmg8//DCzZ8+ufUW7Y8eO2XfffTN48OBsu+222WabbbLnnnumW7duC6zruOOOyxFHHFG7XV1dnR49eizRMwIAAADw1bRcLwYzc+bMNG3aNBMmTEjTpk3r7Pv0AiufXbSlVCrVBnczZ85MkvzmN7+pE/Alme+arVu3rrN9zjnn5KKLLsqFF16Yvn37pnXr1hkxYkRmz55d3oMtxLx6//rXv2bllVeus6+ioiJJcuONN+aoo47Keeedl/79+6dt27Y555xz8sgjj9QeO2rUqAwfPjz/+Mc/ctNNN+WEE07ImDFj8vWvf32+e1ZUVNReGwAAAACWRIMFjauvvnqaN2+e8ePHp2fPnkk+GfH3n//8J9/61reSJBtttFFqamoyffr0fPOb31yi+3Tp0iXdu3fPSy+9lGHDhtXr3AcffDC77rprvv/97yf5ZG7E//znP1l33XWXqJbKysp069YtjzzySO0zfvzxx5kwYUI23njjJMm6666bioqKTJ06NQMHDvzcurbYYoscdNBBtW0vvvjifMdttNFG2WijjXLcccelf//+uf766xcYNAIAAABAuRosaGzbtm322WefHH300enYsWM6d+6ckSNHpkmTJrWLlqy11loZNmxY9t5775x33nnZaKON8r///S/33HNPNthgg+y4446Lda9TTjklw4cPT2VlZYYMGZJZs2blsccey7vvvlvnleHP6t27d/7whz/koYceSocOHXL++efnzTffXOKgMUkOO+ywnHXWWendu3fWWWednH/++Xnvvffq/FyOOuqoHH744Zk7d26+8Y1vpKqqKg8++GDatWuXffbZJ717984111yTO++8M7169cq1116b8ePHp1evXkmSKVOm5Morr8wuu+yS7t27Z9KkSZk8eXL23nvvJa4bAAAAABamQV+dPv/883PggQdmp512Srt27XLMMcfk1VdfTcuWLWuPGTVqVE477bQceeSRee2117LSSivl61//enbaaafFvs+PfvSjtGrVKuecc06OPvrotG7dOn379s2IESMWet4JJ5yQl156KYMHD06rVq3yk5/8JEOHDk1VVdWSPnKOPPLITJs2Lfvss0+aNGmSH/7wh9ltt93qXPMXv/hFOnXqlDPPPDMvvfRS2rdvn4033jg///nPkyQHHHBAHn/88Xz3u99NqVTKXnvtlYMOOih///vfk3wyZ+Xzzz+f3/3ud3n77bfTrVu3HHzwwTnggAOWuG4AAAAAWJhSMW8yw+XA+++/n5VXXjnnnXde9t9//4Yu5yururr6/y2UU5WkXUOXA8u95edbFAAAAJaueTlRVVVV2rVbeE7UoCMaH3/88Tz//PPZbLPNUlVVlVNPPTVJsuuuuzZkWQAAAABAPTX4qtPnnntuJk2alBYtWmSTTTbJuHHjstJKKzV0WQAAAABAPSxXr06zfPDqNNSPb1EAAAC+rOrz6nSTL6gmAAAAAOBLTNAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUTdAIAAAAAJRN0AgAAAAAlE3QCAAAAACUrVlDF8Dyq6oqadeuoasAAAAAoDEwohEAAAAAKJugEQAAAAAom6ARAAAAACiboBEAAAAAKJugEQAAAAAom6ARAAAAACiboBEAAAAAKJugEQAAAAAom6ARAAAAACiboBEAAAAAKJugEQAAAAAom6ARAAAAACiboBEAAAAAKJugEQAAAAAom6ARAAAAACiboBEAAAAAKFuzhi6A5VdlZUNXACxLRdHQFQAAAPBlYkQjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZBIwAAAABQNkEjAAAAAFA2QSMAAAAAUDZB4xdo9OjRad++/UKP2XfffTN06NAlvsegQYMyYsSIJT4fAAAAAJZEs4YugLouuuiiFEXR0GUAAAAAQL0IGpczlZWVDV0CAAAAANSbV6fLcMcdd6R9+/apqalJkjzxxBMplUo59thja4/50Y9+lO9///t1zrvzzjvTp0+ftGnTJkOGDMm0adNq93321em5c+fm7LPPzpprrpmKior07Nkzp59++kLrmjt3bo455ph07NgxXbt2zcknn1z+wwIAAADAQggay/DNb34zM2bMyOOPP54kuf/++7PSSitl7Nixtcfcf//9GTRoUO32Bx98kHPPPTfXXnttHnjggUydOjVHHXXU597juOOOy1lnnZUTTzwxzz77bK6//vp06dJloXX97ne/S+vWrfPII4/k7LPPzqmnnpoxY8Z87vGzZs1KdXV1nQ8AAAAA1IegsQyVlZX52te+Vhssjh07Nocffngef/zxzJw5M6+99lpeeOGFDBw4sPacOXPm5PLLL0+/fv2y8cYb55BDDsk999yzwOvPmDEjF110Uc4+++zss88+WWONNfKNb3wjP/rRjxZa1wYbbJCRI0emd+/e2XvvvdOvX7/PvUeSnHnmmamsrKz99OjRo/4/DAAAAAC+0gSNZRo4cGDGjh2boigybty47L777unTp0/++c9/5v7770/37t3Tu3fv2uNbtWqVNdZYo3a7W7dumT59+gKv/dxzz2XWrFnZeuut61XTBhtsUGd7YfdIPhk1WVVVVft59dVX63U/AAAAALAYTJkGDRqUq6++Ok8++WSaN2+eddZZJ4MGDcrYsWPz7rvv1hnNmCTNmzevs10qlT53lekVVlhhiWpa0D3mzp37ucdXVFSkoqJiie4FAAAAAIkRjWWbN0/jBRdcUBsqzgsax44dW2d+xvrq3bt3VlhhhYW+9gwAAAAAywMjGsvUoUOHbLDBBrnuuuty8cUXJ0m+9a1vZc8998ycOXPmG9FYHy1btszPfvazHHPMMWnRokUGDBiQ//3vf3nmmWey//77L61HAAAAAICyCRqXgoEDB+aJJ56oHb3YsWPHrLvuunnzzTez9tprl3XtE088Mc2aNctJJ52U119/Pd26dcuBBx64FKoGAAAAgKWnVHzeBIF8ZVVXV6eysjJJVZJ2DV0OsIz49gcAAGBR5uVEVVVVaddu4TmRORoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAsgkaAQAAAICyCRoBAAAAgLIJGgEAAACAstU7aHz11Vfz3//+t3b70UcfzYgRI3LllVcu1cIAAAAAgMaj3kHj9773vdx3331JkjfeeCPbbrttHn300Rx//PE59dRTl3qBAAAAAMDyr95B49NPP53NNtssSXLzzTdn/fXXz0MPPZTrrrsuo0ePXtr1AQAAAACNQL2Dxjlz5qSioiJJcvfdd2eXXXZJkqyzzjqZNm3a0q0OAAAAAGgU6h00rrfeern88sszbty4jBkzJkOGDEmSvP7661lxxRWXeoEAAAAAwPKv3kHjL3/5y1xxxRUZNGhQ9tprr2y44YZJkttuu632lWoAAAAA4KulVBRFUd+TampqUl1dnQ4dOtS2vfzyy2nVqlU6d+68VAvki1ddXZ3KysokVUnaNXQ5wDJS/29/AAAAvmrm5URVVVVp127hOVGzJblB06ZN64SMSbLaaqstyaUAAAAAgC+BegeNb7/9dk466aTcd999mT59eubOnVtn/zvvvLPUiqNhVVUliwiqAQAAACDJEgSNP/jBD/LCCy9k//33T5cuXVIqlZZFXQAAAABAI1LvoHHcuHH55z//WbsIDAAAAABAvVedXmeddfLhhx8ui1oAAAAAgEaq3kHjpZdemuOPPz73339/3n777VRXV9f5AAAAAABfPfV+dbp9+/aprq7OVlttVae9KIqUSqXU1NQsteIAAAAAgMah3kHjsGHD0rx581x//fUWgwEAAAAAkixB0Pj000/n8ccfz9prr70s6gEAAAAAGqF6z9HYr1+/vPrqq8uiFgAAAACgkar3iMZDDz00hx12WI4++uj07ds3zZs3r7N/gw02WGrFAQAAAACNQ6koiqI+JzRpMv8gyFKpZDGYL5Hq6upUVlamqqoq7dq1a+hyAAAAAGgg9cmJ6j2iccqUKUtcGAAAAADw5VTvoHHVVVddFnUAAAAAAI1YvYPGeZ599tlMnTo1s2fPrtO+yy67lF0UAAAAANC41DtofOmll7Lbbrvlqaeeqp2bMflknsYk5mgEAAAAgK+g+Vd2WYTDDjssvXr1yvTp09OqVas888wzeeCBB9KvX7+MHTt2GZQIAAAAACzv6j2i8eGHH869996blVZaKU2aNEmTJk3yjW98I2eeeWaGDx+exx9/fFnUCQAAAAAsx+o9orGmpiZt27ZNkqy00kp5/fXXk3yySMykSZOWbnUAAAAAQKNQ7xGN66+/fp588sn06tUrm2++ec4+++y0aNEiV155ZVZfffVlUSMNpLKyoSsAoDH6f9M3AwAAXzH1DhpPOOGEvP/++0mSU089NTvttFO++c1vZsUVV8xNN9201AsEAAAAAJZ/paIof9zBO++8kw4dOtSuPE3jVl1dncrKyiRVSdo1dDkANDJGNAIAwJfHvJyoqqoq7dotPCeq1xyNc+bMSbNmzfL000/Xae/YsaOQEQAAAAC+wuoVNDZv3jw9e/ZMTU3NsqoHAAAAAGiE6r3q9PHHH5+f//zneeedd5ZFPQAAAABAI1TvxWAuvvjivPDCC+nevXtWXXXVtG7dus7+iRMnLrXiAAAAAIDGod5B49ChQ5dBGQAAAABAY7ZUVp3my8Wq0wCUw28WAADw5bHMVp0GAAAAAFiQer863aFDh5RKpfnaS6VSWrZsmTXXXDP77rtv9ttvv6VSIAAAAACw/Kt30HjSSSfl9NNPz/bbb5/NNtssSfLoo4/mH//4Rw4++OBMmTIlP/3pT/Pxxx/nxz/+8VIvGAAAAABY/tQ7aPznP/+Z0047LQceeGCd9iuuuCJ33XVX/vjHP2aDDTbIr371K0EjAAAAAHxF1HsxmDZt2uSJJ57ImmuuWaf9hRdeyNe+9rXMnDkzL774YjbYYIO8//77S7VYvhgWgwGgHBaDAQCAL49luhhMx44dc/vtt8/Xfvvtt6djx45Jkvfffz9t27at76UBAAAAgEaq3q9On3jiifnpT3+a++67r3aOxvHjx+dvf/tbLr/88iTJmDFjMnDgwKVbKQAAAACw3Kr3q9NJ8uCDD+biiy/OpEmTkiRrr712Dj300GyxxRZLvUC+eF6dBqAcXp0GAIAvj/q8Or1EQSNfboJGAMrhNwsAAPjyqE/QuFivTldXV9deqLq6eqHHLuqGAAAAAMCXz2IFjR06dMi0adPSuXPntG/fPqVSab5jiqJIqVRKTU3NUi8SAAAAAFi+LVbQeO+999auKH3fffct04IAAAAAgMbHHI3MxxyNAJTDbxYAAPDlsdTnaPw877//fm666aZ8+OGH2W677dK7d+9yLgcAAAAANFJNFvfAqVOnZuDAgWnbtm223XbbTJ06NRtvvHF+9KMf5dBDD83Xvva1PPDAA8uyVgAAAABgObXYQeNRRx2V2bNn5/LLL0+rVq0yePDg9O7dO9OmTcubb76Z7bffPieffPIyLBUAAAAAWF4t9hyNXbt2zW233ZbNNtss77zzTlZaaaU8+OCD6d+/f5LkySefzNZbb5233nprmRbMsmeORgDKYY5GAAD48qjPHI2LPaJx+vTpWXXVVZMkHTt2TKtWrdKlS5fa/V27ds277767hCUDAAAAAI3ZYgeNSVIqlRb4ZwAAAADgq61eq06fdNJJadWqVZJk9uzZOf300//fK7bJBx98sPSrAwAAAAAahcWeo3HQoEGLNYrxvvvuK7soGpY5GgEohzkaAQDgy6M+czQu9ojGsWPHllsXi2ns2LHZcsst8+6776Z9+/YNXQ4AAAAALFK95mhk6Rs0aFBGjBjR0GUAAAAAQFkEjV8hc+bMaegSAAAAAPiSWu6DxlmzZmX48OHp3LlzWrZsmW984xsZP3587f6xY8emVCrlnnvuSb9+/dKqVatsscUWmTRpUp3r3Hrrrdl4443TsmXLrL766jnllFPy8ccff+59x48fn2233TYrrbRSKisrM3DgwEycOLHOMaVSKVdddVV22223tGrVKr17985tt91W55j7778/m222WSoqKtKtW7cce+yxtffdd999c//99+eiiy5KqVRKqVTKyy+/XHvuhAkTynqmUqmUyy67LLvssktat26d008/ffF+6AAAAABQT8t90HjMMcfkj3/8Y373u99l4sSJWXPNNTN48OC88847dY47/vjjc9555+Wxxx5Ls2bN8sMf/rB237hx47L33nvnsMMOy7PPPpsrrrgio0ePXmjwNmPGjOyzzz755z//mX/961/p3bt3dthhh8yYMaPOcaecckr23HPP/Pvf/84OO+yQYcOG1db22muvZYcddsimm26aJ598Mpdddll++9vf5rTTTkuSXHTRRenfv39+/OMfZ9q0aZk2bVp69OixVJ/p5JNPzm677ZannnqqzvmfNmvWrFRXV9f5AAAAAEC9FMuxmTNnFs2bNy+uu+662rbZs2cX3bt3L84+++yiKIrivvvuK5IUd999d+0xf/3rX4skxYcfflgURVFsvfXWxRlnnFHn2tdee23RrVu3xa6lpqamaNu2bXH77bfXtiUpTjjhhDr1Jin+/ve/F0VRFD//+c+Ltddeu5g7d27tMZdccknRpk2boqampiiKohg4cGBx2GGH1bnX0nqmJMWIESMW+WwjR44skizgU1V8snaoj4+Pj4/P4n8AAIAvj6qqqiJJUVVVtchjF3vV6U9777338uijj2b69OmZO3dunX177733kmae83nxxRczZ86cDBgwoLatefPm2WyzzfLcc8/VOXaDDTao/XO3bt2SJNOnT0/Pnj3z5JNP5sEHH6wz2q+mpiYfffRRPvjgg7Rq1Wq+e7/55ps54YQTMnbs2EyfPj01NTX54IMPMnXq1M+9b+vWrdOuXbtMnz49SfLcc8+lf//+KZVKtccMGDAgM2fOzH//+9/07Nlzoc+/NJ6pX79+C71Hkhx33HE54ogjarerq6vrjKwEAAAAgEWpd9B4++23Z9iwYZk5c2batWtXJ0QrlUpLNWisj+bNm9epI0ltCDpz5syccsop2X333ec7r2XLlgu83j777JO33347F110UVZdddVUVFSkf//+mT179ufed969Pxu+Lqml8UytW7de5H0qKipSUVFRbrkAAAAAfIXVO2g88sgj88Mf/jBnnHHGAkcCLk1rrLFGWrRokQcffDCrrrpqkk9WTh4/fnxGjBix2NfZeOONM2nSpKy55pqLfc6DDz6YSy+9NDvssEOS5NVXX81bb71Vr/r79OmTP/7xjymKojYofPDBB9O2bdusssoqSZIWLVqkpqamXtdNluyZAAAAAGBZqXfQ+Nprr2X48OHLPGRMPhmN99Of/jRHH310OnbsmJ49e+bss8/OBx98kP3333+xr3PSSSdlp512Ss+ePfPtb387TZo0yZNPPpmnn366dmGWz+rdu3euvfba9OvXL9XV1Tn66KOzwgor1Kv+gw46KBdeeGEOPfTQHHLIIZk0aVJGjhyZI444Ik2afLIOz2qrrZZHHnkkL7/8ctq0aZOOHTsus2cCAAAAgGWl3qtODx48OI899tiyqGWBzjrrrOyxxx75wQ9+kI033jgvvPBC7rzzznTo0GGxrzF48ODccccdueuuu7Lpppvm61//ei644ILaUZIL8tvf/jbvvvtuNt544/zgBz/I8OHD07lz53rVvvLKK+dvf/tbHn300Wy44YY58MADs//+++eEE06oPeaoo45K06ZNs+6666ZTp07zzQG5NJ8JAAAAAJaVUlEURX1O+O1vf5tTTz01++23X/r27TvfHIW77LLLUi2QL151dXUqKyuTVCVp19DlANDI1O83CwAAYHk2LyeqqqpKu3YLz4nqHTTOe+V3gRcrlZZovkGWL4JGAMohaAQAgC+P+gSN9Z6jcWmtqAwAAAAAfHnUe45GAAAAAIDPWqKg8f7778/OO++cNddcM2uuuWZ22WWXjBs3bmnXBgAAAAA0EvUOGn//+99nm222SatWrTJ8+PAMHz48K6ywQrbeeutcf/31y6JGAAAAAGA5V+/FYPr06ZOf/OQnOfzww+u0n3/++fnNb36T5557bqkWyBfPYjAAlMNiMAAA8OVRn8Vg6j2i8aWXXsrOO+88X/suu+ySKVOm1PdyAAAAAMCXQL2Dxh49euSee+6Zr/3uu+9Ojx49lkpRAAAAAEDj0qy+Jxx55JEZPnx4nnjiiWyxxRZJkgcffDCjR4/ORRddtNQLBAAAAACWf/UOGn/605+ma9euOe+883LzzTcn+WTexptuuim77rrrUi8QAAAAAFj+1XsxGL78LAYDQDn8ZgEAAF8e9VkMpt4jGj9t5syZmTt3bp22Rd0QAAAAAPjyqfdiMFOmTMmOO+6Y1q1bp7KyMh06dEiHDh3Svn37dOjQYVnUCAAAAAAs5+o9ovH73/9+iqLI1VdfnS5duqRUKi2LugAAAACARqTeQeOTTz6ZCRMmZO21114W9QAAAAAAjVC9X53edNNN8+qrry6LWgAAAACARqreIxqvuuqqHHjggXnttdey/vrrp3nz5nX2b7DBBkutOAAAAACgcah30Pi///0vL774Yvbbb7/atlKplKIoUiqVUlNTs1QLBAAAAACWf/UOGn/4wx9mo402yg033GAxGAAAAAAgyRIEja+88kpuu+22rLnmmsuiHgAAAACgEar3YjBbbbVVnnzyyWVRCwAAAADQSNV7ROPOO++cww8/PE899VT69u0732Iwu+yyy1IrDgAAAABoHEpFURT1OaFJk88fBGkxmC+H6urqVFZWJqlK0q6hywGgkanfbxYAAMDybF5OVFVVlXbtFp4T1XtE49y5c5e4MAAAAADgy6neczQCAAAAAHxWvUc0Jsn48eNz3333Zfr06fONcDz//POXSmEAAAAAQONR76DxjDPOyAknnJC11147Xbp0SalUqt336T8DAAAAAF8d9Q4aL7roolx99dXZd999l0E5AAAAAEBjVO+gsUmTJhkwYMCyqIXlTFVVsojFhAAAAAAgyRIsBnP44YfnkksuWRa1AAAAAACNVL1HNB511FHZcccds8Yaa2TddddN8+bN6+z/05/+tNSKAwAAAAAah3oHjcOHD899992XLbfcMiuuuKI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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "english_words = set(words.words())\n",
        "\n",
        "def split_fused_words(word):\n",
        "    for i in range(1, len(word)):\n",
        "        prefix = word[:i]\n",
        "        suffix = word[i:]\n",
        "        if prefix.lower() in english_words and suffix.lower() in english_words:\n",
        "            return prefix, suffix\n",
        "    return word, None\n",
        "\n",
        "# Test function\n",
        "word = \"appleorange\"\n",
        "split_fused_words(word)\n"
      ],
      "metadata": {
        "id": "kRjBaAIz8v7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbf7ee03-dc3c-47ea-f431-692ad7978c41"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('apple', 'orange')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_eqj1a5rCn1E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}