Project_Module2.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nagababumo/iiitfmmlassignments/blob/main/Project_Module2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmWIufUKnVK6"
      },
      "source": [
        "# Module 2: Appreciating, Interpreting and Visualizing Data\n",
        "## Project\n",
        "\n",
        "```\n",
        "Coordinator: Shreeya Pahune \n",
        "```\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzcKLePrnV3U"
      },
      "source": [
        "For the last 2 years we have been severely affected by the COVID-19 pandemic. And if you would know a little about it, you would have heard about **SARS-CoV-2** (Severe Acute Respiratory Syndrome Coronavirus 2) which is the virus responsible for the COVID-19 disease.\n",
        "\n",
        "A little biology might intrigue you further. This SARS-CoV-2 is a RNA virus and when this virus enters your body, it interacts with the ACE-2 receptor using its Spike protein. When this happens, it gets access to be able to multiply inside your body, ultimately resulting in you getting infected with the COVID-19 disease.\n",
        "\n",
        "Before I explain you the details of the project and what you will be doing, let me first introduce you to what a virus genome is. Every living organism in this world has someting called a Nucleus which produces its characteristic genome sequence. Even your body has a genome and it will be very different from that of lets say a cat. Now, this SARS-CoV-2 virus also has its characteristic genome sequence and it is roughly of the length 29000 bases. A little more Class 9 biology lesson: Each genome is built up with just 4 characters called bases (A, T, G and C).\n",
        "\n",
        "For this project, I will provide you with some SARS-CoV-2 genomes collected from infected people in India and you will use just the Visualization techniques you learnt in the module to analyze the genomes and infer the spread of the COVID-19 disease in the country."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIiHGVQKnY1t"
      },
      "source": [
        "685 genome sequences from India downloaded from GISAID (https://www.gisaid.org/) in early to mid 2020 have been provided to you in the _sequences.fasta_ file and some cleaned metadata regarding these sequences has been given in the _INDIA_685.csv_ file. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2OHuH6nnRD_"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3FqnXuP7nfL7",
        "outputId": "d716dac4-eddf-41c4-f3d8-6b7980d6537b"
      },
      "source": [
        "# Upload the INDIA_685.csv file here by running this cell\n",
        "\n",
        "uploaded1 = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "     <input type=\"file\" id=\"files-ebb82402-4ee5-4208-8669-c3ca6d6c8c83\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ebb82402-4ee5-4208-8669-c3ca6d6c8c83\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving INDIA_685.csv to INDIA_685 (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Ojhce7sOnrF3",
        "outputId": "c941c8e6-6d8d-4ae4-fd7a-a2bc270be94b"
      },
      "source": [
        "# Upload the sequences.fasta file here by running this cell\n",
        "\n",
        "uploaded2 = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-45753844-491a-4188-8e7f-044982a0b2db\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-45753844-491a-4188-8e7f-044982a0b2db\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving sequences.fasta to sequences.fasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBFPYuhEn9lg",
        "outputId": "6894bd33-ea05-45aa-b258-9640d3ed4aa6"
      },
      "source": [
        "!pip3 install biopython"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting biopython\n",
            "  Downloading biopython-1.80-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0 MB 35.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from biopython) (1.21.6)\n",
            "Installing collected packages: biopython\n",
            "Successfully installed biopython-1.80\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZqOz_ZDoBBz"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from Bio import SeqIO"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "fb2dcglpoBIs",
        "outputId": "650c5562-a3f5-48bf-c7a0-d593aa390d87"
      },
      "source": [
        "data = pd.read_csv(\"INDIA_685.csv\")\n",
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              StrainID    Accession_ID  \\\n",
              "680  hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...  EPI_ISL_436137   \n",
              "681  hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...  EPI_ISL_436138   \n",
              "682  hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...  EPI_ISL_436139   \n",
              "683  hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...  EPI_ISL_436140   \n",
              "684  hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...  EPI_ISL_436141   \n",
              "\n",
              "                     Location      State   Host  Gender Patient age  \\\n",
              "680  Asia / India / Karnataka  Karnataka  Human  Female          28   \n",
              "681  Asia / India / Karnataka  Karnataka  Human    Male          38   \n",
              "682  Asia / India / Karnataka  Karnataka  Human    Male          20   \n",
              "683  Asia / India / Karnataka  Karnataka  Human  Female          52   \n",
              "684  Asia / India / Karnataka  Karnataka  Human  Female          27   \n",
              "\n",
              "    Patient status Collection_date             Originating lab  \\\n",
              "680   Asymptomatic       4/21/2020  District Surveillance Unit   \n",
              "681   Asymptomatic       4/23/2020  District Surveillance Unit   \n",
              "682    Symptomatic       4/23/2020  District Surveillance Unit   \n",
              "683   Asymptomatic       4/24/2020  District Surveillance Unit   \n",
              "684   Asymptomatic       4/24/2020  District Surveillance Unit   \n",
              "\n",
              "                                               Authors  \\\n",
              "680  Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...   \n",
              "681  Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...   \n",
              "682  Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...   \n",
              "683  Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...   \n",
              "684  Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...   \n",
              "\n",
              "                                              Nuc_muts  \n",
              "680  ['C1281T', 'C4158T', 'G11083T', 'C13730T', 'C2...  \n",
              "681  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...  \n",
              "682  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...  \n",
              "683  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...  \n",
              "684  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...  "
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StrainID</th>\n",
              "      <th>Accession_ID</th>\n",
              "      <th>Location</th>\n",
              "      <th>State</th>\n",
              "      <th>Host</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Patient age</th>\n",
              "      <th>Patient status</th>\n",
              "      <th>Collection_date</th>\n",
              "      <th>Originating lab</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...</td>\n",
              "      <td>EPI_ISL_436137</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/21/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C1281T', 'C4158T', 'G11083T', 'C13730T', 'C2...</td>\n",
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              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...</td>\n",
              "      <td>EPI_ISL_436138</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>38</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/23/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...</td>\n",
              "      <td>EPI_ISL_436139</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>Symptomatic</td>\n",
              "      <td>4/23/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...</td>\n",
              "      <td>EPI_ISL_436140</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>52</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/24/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...</td>\n",
              "      <td>EPI_ISL_436141</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
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              "      <td>27</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/24/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXBttf4aoBLy",
        "outputId": "1e1ced04-8831-4d22-cc12-edab6376d948"
      },
      "source": [
        "fasta_sequences = SeqIO.parse(open(\"sequences.fasta\"),'fasta')\n",
        "\n",
        "counter = 0\n",
        "for fasta in fasta_sequences:\n",
        "    counter +=1\n",
        "    \n",
        "print(\"Number of sequences is:\", counter)    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of sequences is: 685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LN32UzxtoJ11"
      },
      "source": [
        "You might have heard in the news that the SARS-CoV-2 is mutating and might have heard the words Double Mutant, Delta Variant and so on. These are nothing but very slight differences in the genomes caused due to Mutations. Some of these mutations end up making the virus deadlier or more infectious.\n",
        "\n",
        "Our goal here is to analyze these genomes and find the ones closer to one another (similar mutations in both) or distant (different profile of mutations). These mutations in turn help us to understand the spread of the Virus and the Demographics of it. Imagine a sequence from Italy has the same set of 10 random mutations as a sequence in India. We can then infer that the source of infection for both the people could be the same. This sort of analysis helps us analyze hotspots and predict if a large outbreak is about to happen.\n",
        "\n",
        "We will try to look at these genome sequences and be able to cluster similar ones together and distant ones furhter away. In this project you will be using two methods to do this:\n",
        "\n",
        "**1. k-mer based methods** <br>\n",
        "**2. Mutation Profile based methods**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KG-Wrc18oNTz"
      },
      "source": [
        "# kmer based methods\n",
        "\n",
        "In bioinformatics, k-mers are substrings of length k contained within a biological sequence. A sequence of length L will have ```L-k+1 k-mers``` and ```4^k total possible k-mers```. Here we, will be using kmers of size 7. We will represent each genome in this kmer space. Basically a genome of size 30000 bases would have a total of 29994 7-mers. \n",
        "\n",
        "**Now how do you use this information to cluster genome sequences?** <br>\n",
        "We would be looking at the counts of different kmers in the genomes and the ones which have similar counts for different kmers should end up closer than the others.\n",
        "\n",
        "For **k=7**, there are **16384** possible kmers. We will represent each genome sequence as a vector of size 16384 with each position scoring the frequency of the kmer corresponding to that position.\n",
        "\n",
        "**But how do we find out the position in the array for a kmer?**<br>\n",
        "We will be using a hash function to map each kmer to a position in the array (0 to 16383). Whenever that kmer will be observed in a sequence, the value at the corresponding position in the array would be increased by 1.\n",
        "\n",
        "#### Hash function:\n",
        "\n",
        "A: 0 <br>\n",
        "G: 1<br>\n",
        "T: 2<br>\n",
        "C: 3<br>\n",
        "\n",
        "Multiply the character value by ```4^(position of the character in the kmer)``` <br>\n",
        "therefore, TAG would equal = (4^0 * 2) + (4^1 * 0) + (4^2 * 1) = 2 + 0 + 16 = 18\n",
        "\n",
        "**Can you find out the hash value for CATCGAG?**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ANSWER:-** CATCGAG = (4^0*3)+(4^1*0)+(4^2*2)+(4^3*3)+(4^4*1)+(4^5*0)+(4^6*1) = 4579"
      ],
      "metadata": {
        "id": "c71CtZJrC0J3"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sddXsg9QoFIr",
        "outputId": "b660694b-c821-48bd-a64b-16cf6ca08908"
      },
      "source": [
        "def calc_score(examplesequence):\n",
        "\n",
        "    position = 0\n",
        "    score = 0\n",
        "    value = 0\n",
        "    flag = 0\n",
        "    \n",
        "    for base in examplesequence:\n",
        "\n",
        "        if(base == \"A\" or base == \"a\"):\n",
        "            value = 0\n",
        "        elif(base == \"G\" or base == \"g\"):\n",
        "            value = 1\n",
        "        elif(base == \"T\" or base == \"t\"):\n",
        "            value = 2\n",
        "        elif(base == \"C\" or base == \"c\"):\n",
        "            value = 3\n",
        "        else:\n",
        "            flag=1\n",
        "\n",
        "        score += (4 ** position)* value\n",
        "        position+=1\n",
        "\n",
        "    return (flag,score)\n",
        "    \n",
        "calc_score(\"ATAGTAG\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 4680)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfZYG8IxoFLZ"
      },
      "source": [
        "k=7\n",
        "big_matrix = []\n",
        "sequences_name = []\n",
        "\n",
        "fasta_sequences = SeqIO.parse(open(\"sequences.fasta\"),'fasta')\n",
        "\n",
        "for fasta in fasta_sequences:\n",
        "    \n",
        "    embedding = np.zeros(4**k, dtype=int)\n",
        "    sequence = str(fasta.seq)\n",
        "    sequence_name = fasta.id\n",
        "    sequences_name.append(sequence_name)\n",
        "\n",
        "    for j in range(len(sequence)-k+1):\n",
        "        kmer = sequence[j:j+k]\n",
        "        flag,score = calc_score(kmer)\n",
        "        if(flag!=1):\n",
        "            embedding[score]+=1\n",
        "\n",
        "    embedding = embedding.tolist()\n",
        "    big_matrix.append(embedding)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ny9at6TVoFN6",
        "outputId": "cb65c5ea-f26b-477d-f0fd-180569412065"
      },
      "source": [
        "print(\"Number of sequences:\", len(sequences_name))\n",
        "print(\"Number of features for each sequence:\", len(big_matrix[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of sequences: 685\n",
            "Number of features for each sequence: 16384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clSklu6EofaE"
      },
      "source": [
        "So essentially we reduced a **29000 base sequence** into a **vector of size 16384**. Now we need to further reduce the dimensionality to 2 or 3 dimensions to be able to visualize it. \n",
        "\n",
        "**But how dow we do that??**\n",
        "\n",
        "We can use any of the methods taught to you in the Labs. But here I will explain how to do this using PCA. Then you will use the knowledge from Lab3 on t-SNE to do a similar analysis yourself.\n",
        "\n",
        "### PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6XZ2uOtodUe"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA() # create a PCA object\n",
        "pca.fit(big_matrix) # do the math\n",
        "pca_data = pca.transform(big_matrix) # get PCA coordinates for the matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "UHbpD3-oodXA",
        "outputId": "642f1a4b-6f44-477f-ff16-9dd6e4131adc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)\n",
        "labels_all = ['PC'+str(x) for x in range(1, len(per_var)+1)]\n",
        "per_var=per_var[:10]\n",
        "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
        "\n",
        "with plt.style.context('dark_background'):\n",
        "    plt.figure(figsize = (15, 9))\n",
        "    plt.xlabel(\"Number of Principal Components\")\n",
        "    plt.ylabel(\"Percentage of variance explained\")\n",
        "    plt.bar(range(1,len(per_var)+1), per_var, tick_label=labels, color = \"aqua\")\n",
        "    plt.plot(range(1,len(per_var)+1), np.cumsum(per_var), color = \"red\")\n",
        "    plt.scatter(range(1,len(per_var)+1), np.cumsum(per_var), color = \"yellow\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHLyNlYaooVO"
      },
      "source": [
        "As you can see, the first principal component alone explains more than 50% of the variance in the dataset. To put this into context, more than **50% variance in 685 genomes of size 29000 bases could be explained with just one dimension**. This shows the amount of structure present in the dataset.\n",
        "\n",
        "We will be now visualizing the first 2 principal components and plot the genomes according to the State they belonged to."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NunbP88XodZ0",
        "outputId": "7f4147bf-3498-4ee1-b126-f1b4136ca183"
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"PC1\", \"PC2\", \"PC3\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"PC1\"] = pca_df.PC1.loc[sample]\n",
        "    dicti[\"PC2\"] = pca_df.PC2.loc[sample]\n",
        "    dicti[\"PC3\"] = pca_df.PC3.loc[sample]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "\n",
        "final_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           StrainID       PC1       PC2  \\\n",
              "0  hCoV-19/India/763/2020|EPI_ISL_420543|2020-03-03 -4.326235 -3.484139   \n",
              "1  hCoV-19/India/770/2020|EPI_ISL_420545|2020-03-03 -4.234399 -3.509996   \n",
              "2  hCoV-19/India/772/2020|EPI_ISL_420547|2020-03-03 -4.184090 -3.754896   \n",
              "3  hCoV-19/India/773/2020|EPI_ISL_420549|2020-03-03 -4.194559 -3.531680   \n",
              "4  hCoV-19/India/777/2020|EPI_ISL_420551|2020-03-03 -4.186462 -3.550434   \n",
              "\n",
              "        PC3      State  \n",
              "0 -1.786329  Stateless  \n",
              "1 -1.804263  Stateless  \n",
              "2 -1.696688  Stateless  \n",
              "3 -1.895380  Stateless  \n",
              "4 -1.888697  Stateless  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cc4704f-2860-4642-9111-2ecf76bde6aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StrainID</th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hCoV-19/India/763/2020|EPI_ISL_420543|2020-03-03</td>\n",
              "      <td>-4.326235</td>\n",
              "      <td>-3.484139</td>\n",
              "      <td>-1.786329</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hCoV-19/India/770/2020|EPI_ISL_420545|2020-03-03</td>\n",
              "      <td>-4.234399</td>\n",
              "      <td>-3.509996</td>\n",
              "      <td>-1.804263</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hCoV-19/India/772/2020|EPI_ISL_420547|2020-03-03</td>\n",
              "      <td>-4.184090</td>\n",
              "      <td>-3.754896</td>\n",
              "      <td>-1.696688</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hCoV-19/India/773/2020|EPI_ISL_420549|2020-03-03</td>\n",
              "      <td>-4.194559</td>\n",
              "      <td>-3.531680</td>\n",
              "      <td>-1.895380</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hCoV-19/India/777/2020|EPI_ISL_420551|2020-03-03</td>\n",
              "      <td>-4.186462</td>\n",
              "      <td>-3.550434</td>\n",
              "      <td>-1.888697</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-3cc4704f-2860-4642-9111-2ecf76bde6aa');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzUxyucCosjg"
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PIwDqixTosm9",
        "outputId": "2eb50485-2a8e-4c38-93e1-2aff1ce97a0c"
      },
      "source": [
        "for_x = final_df.PC1.tolist()\n",
        "for_y = final_df.PC2.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"Principal Component Axis\",\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"\n",
        "                })\n",
        "\n",
        "fig.show(renderer = \"colab\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "Even though the plot looks densely clustered, there are still a lot of obvious observations that you should be able to make.\n",
        "\n",
        "### The first one, is that for some reason the Blue dots (Odisha) are scattered away from the big main cluster.\n",
        "\n",
        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)\n",
        "\n",
        "### Also, we can observe a secondary cluster away from the primary cluster. On inspecting, we can see that the sequences forming this cluster are primarily from Telangana (red), Delhi (magenta), and Tamil Nadu (green).\n",
        "\n",
        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WBrbRuwrDZbWFxm2x2FpbHlapMHSLOAwPAVLYwjTOgqWZs3H5VlYGiyNkoDLzrM5HJZmE5TLMliaS4D2h8PS7LNyUwlLc0PP0VhYmiNcTothaZSOgrU0p61GVw9Lo+PmdBQszSkx6npYGjU6RwNhaY5wuSmGpbmhl3MsLA2WRkkgZ28xKYClMcGYMwSWlhMRqwJYGiuS1jmwNGs+DM/C0hjCzI6CpVE6CtbSspuJxxFYGg+q2ZmwtGwmnI7A0jiBNcXC0kxA+O3C0vixxXs8KRUN7/Hk2pSZ4bC0TBr8tmFp/NiakmFpJiCcdmFpnMBmx8LSspkwPIK1NEpRw1oawy60iIKlWcBheAqWxhCmdRQszZoPq7OwNFYkc+bA0nIiclMAS4OlURJw03b2x8LS7LNyUwlLc0PP0VhYmiNc1MWwNGp0TgfC0pwSc1QPS6N0FKylOeoz6mJYGjU6RwNhaY5wuSmGpbmhZ38sLM0+K5eVsDSXAK2Hw9JEW1r3qQZpxUZ9/Ax99FRl1qJgebX1DF3jZ2FpYhoAliaGs9cvwdLEoIalieHs9UuwNK6oYWlCLa21I6xOLCXf/y35398i//Pr5Cs/JK8N89W3cp3jgg6HpYmZPliaGM6wNGGcYWnCUMPSuKIWZWlqrLvh7KF9hz/ecWjDZ7WV7eG4lkh0N+0t27d6695Vqce2fZtOes/16tG4yvU5Mwmne8Wz53gdued3xHMj8dxw8fF/f02ds7StPeTtjjG5sH4WAksTM6GwNDGcYWnCOMPShKGGpXFFLcbSdLmpav38+a8NGv/om2P/MGT2m2uOVAaj0dN7R7015pGBY/4wcMxDL7/3/YcG/njWznWdWn+1tO6a8/rkUvLF715WtKSr3Up+8JvepZ+0tQYgatm9DkvLZsLjCCyNB9WrZuIVz6tiYX4QlsYcaV+BsLS+yDA5LsbSlMY189+YtX7WCV844qv6ZO1bQ2Y8UxMjl//pSnPlnNmrp+48Va8Y/dLSumqbyBvDyf9x9xULaekVNc+AyM7D3o4wk0ntTyGwNDGzCUsTwxlracI4w9KEoYalcUUtxtJUf31dtdffLhvEUNrK906bUPzgZ6HLkqZGqubPGbR878femGGQfmlpZOAQ4rnjylW0Sy96plzt+bc6z7VznexCDIeliZk1WJoYzrA0YZxhacJQw9K4ohZjaZd9jMS7TmzbOHjk/BGt2uWjrYfeHbN85v76Vo30W0v77gPJFzcvL55dqWieGwgszX+Vd3LA0rh+/6fDYWlpFLw38Ionb8KpfFiaGM54jydvzmItTY2eK9s4fmzpC2UNYd24ZGnKuQXFD604vK45qhtJS5MSmi8Uz/NHKKbIitYVtnud5Pu5LO2ltwPNnXn+rMVfXjSuEkJkpQBaQjwchl+xKyxH42pUVhlmIuqqBJKc46o/krjqWRxkRaAnmuiVtWBMYRWInL4IJBQ9BM7cpKU7LEuyZhhpZbqkTrn+15OrIOu8HDi9dcPwKUsGbT15NqLo6fNy/bx3Pxy57+yJWHJ1zTCIouq9spbnD1nRVU2XbF8necP0iueNV96gNoAcOiJF43n+rMVfnqImO0XTDPFf+pr6ipKsKapeEN96hT4viQuc44l8/xFX6JzlRLKlZQWcuRPQdCOhFMBv7cJtaTkhwNLUYN2W9WNnr568v74uKGe82ElIzYaHhyxfXNvZfWF1LbmWJmudwXieP4K9SvzCmp/N6wwfOk7+8DTx3HbhRc/byG+fMT6YRl4cTB7+K3ljRO/ST7o6gjajrqmy9FraNfWsxT9ZXyi5lhaJq+K/9LX2FSNSci2tOyxfa09c8PPtiSRicTXQmxD8da/BLydfWEu7Bp+4mKfsC4lYS9N6j5RNKVk5tuzk6ZB8eRXtwnKatGnGjRN3bGkKJS7s9tf70traQ+Gdh6Q5y+PFC6TS5aHdn3WdbgpW1ET2VPQcO43PS+vrpX3cl9YXGbbHcV8aW54WabgvzQIOw1O4L40hTOsovHvAmo/Ls0LuS9ODn02f9MgLIx8cPmfojMXDZyx5d8n2CSe6U695tpSO+P9mH9zb1pva7a+W5nKertnhsDQxUw9LE8MZ7/EUxhmWJgw1LI0raiGWZkRqP929ZMm6ooVrLzzWjV+3b/apQErL/Id3zjnqbYkosDSuM12g4bA0MRMHSxPDGZYmjDMsTRhqWBpX1EIsLeVf9v6LtTSu811w4bA0MVMGSxPDGZYmjDMsTRhqWBpX1LC0q3xGlx3idH/H004yajIJwNIyafDbhqXxY2tKxn1pJiCcdmFpnMBmx8LSspkwPAJLg6VREmDYhRZRsDQLOAxPwdIYwrSOgqVZ82F1FpbGimTOHFhaTkRuCmBplI6CtTQ3bWd/LCzNPis3lbA0N/QcjYWlOcJFXQxLo0bndCAszSkxR/WwNFgaJQFHfUZdDEujRudoICzNES43xbA0N/Tsj4Wl2WflshKW5hKg9XBYGqWjYC3NurFYnYWlsSJpnQNLs+bD8CwsjSFMiyhYmgUctqdgaWx5mtJgabA0SgKmTuK0C0vjBNYUC0szAeG3C0vjxzYzGZaWSYPrNiyNK15YGqWjYC2Na1+mw2FpaRRcN2BpXPFmhsPSMmnw24al8WNrSoalmYCw3YWlwdIoCbBtxL7SYGl9kWF7HJbGlqdFGizNAg7DU7A0hjCto2Bp1nxcnoWlUToK1tJcdp7N4bA0m6BclsHSXAK0PxyWZp+Vm0pYmht6jsbC0hzhcloMS4OlURJw2mp09bA0Om5OR8HSnBKjroelUaNzNBCW5giXm2JYmht6OcfC0igdBWtpOXuLSQEsjQnGnCGwtJyIWBXA0liRtM6BpVnzYXgWlsYQZnYULA2WRkkgu5l4HIGl8aCanQlLy2bC6QgsjRNYUywszQSE3y4sjR9br1+CpVE6CtbSuPZlOhyWlkbBdQOWxhVvZjgsLZMGv21YGj+2pmRYmgkI211YGiyNkgDbRuwrDZbWFxm2x2FpbHlapMHSLOAwPAVLYwjTOgqWZs3H5VlYGqWjYC3NZefZHA5LswnKZRkszSVA+8NhafZZuamEpbmh52gsLM0RLqfFsDRYGiUBp61GVw9Lo+PmdBQszSkx6npYGjU6RwNhaY5wuSmGpbmhl3MsLI3SUbCWlrO3mBTA0phgzBkCS8uJiFUBLI0VSescWJo1H4ZnYWkMYWZHwdJgaZQEspuJxxFYGg+q2ZmwtGwmnI7A0jiBNcXC0kxA+O3C0vixxXs86QUFa2lc+zIdDktLo+C6AUvjijczHJaWSYPfNiyNH1tTMizNBITtLtbSKEUNlsa2EftKg6X1RYbtcVgaW54WabA0CzgMT8HSGMK0joKlWfNxeRaWBkujJOCy82wOh6XZBOWyDJbmEqD94bA0+6zcVMLS3NBzNBaW5giX02JYGqWjYC3NaavR1cPS6Lg5HQVLc0qMuh6WRo3O0UBYmiNcbophaW7o5RwLS4OlURLI2VtMCmBpTDDmDIGl5UTEqgCWxoqkdQ4szZoPw7OwNIYws6NgaZSOwmQtrbUj3NnY6e2MZE8MjqQIwNLEdAIsTQxnr1+CpYlBDUsTw9nrl2BpXFHD0j4fS+to9PkrayMbdqgzF4Q37+05cKKzzuv1RblOdiGGw9LEzBosTQxnWJowzrA0YahhaVxRw9I+B0trbQsqC1eT7/yKeG648LiJ/J93kRGTu860QNRM7Q5LMwHhtAtL4wQ2OxZradlMeByBpfGgetVMWNpVsbA6CEv7HCxNH1NM/sfXLilaStQu/Pf3fwlU1LCa2v6RA0sTM4+wNDGcsZYmjDMsTRhqWBpX1LA00ZbWU3WWPPI88dx0FUvz3Br6ZA/X+S64cFiamCmDpYnhDEsTxhmWJgw1LI0raliaaEtLrPmEfOeBqylacjktPn9VW2uA65QXVjgsTcx8wdLEcIalCeMMSxOGGpbGFTUsTbSlxcr2kR893JelRVdt9vrwls/LkwJL4/r9nw6HpaVR8N7AfWm8CafyYWliOOM9nrw5w9IuC4Ej1tSfxNFd20gee5V4br6KqP34ocDBE44uo98Xw9LETDEsTQxnrKUJ4wxLE4Yaa2lcUcPSRFtaa0c4snYHefotcu9vyd0/J99/kPzuafLAk+S5QbFPdrU3dXGd74ILh6WJmTJYmhjOsDRhnGFpwlDD0riihqWJtjSvX2rtCAcrauRVm9T5K+UVG6K7DsbK9vVUncXH22b3OiwtmwmPI7A0HlSvmolXPK+KhflBWBpzpH0FwtL6IsPkOCztc7A0JjN3jYTA0sRMNCxNDGespQnjDEsThhqWxhW1KEvT5LCvva72bEVV3ZE6b0OPlCCEGAbRE8GWppqTdRU150609vTENcMg0bjK9TkzCae+L43JV792QmBpYuYaliaGMyxNGGdYmjDUsDSuqMVYmqG01W5ZOP/VgSMeeH7Y/YNnvbX+eE1cJ2pCbTu5dOKUp14f8eAro38/Y8v6s/64YcDSuE55YYXD0sTMFyxNDGdYmjDOsDRhqGFpXFGLsTSlceWcF6eumXq0IxTuPL5x9evvzXz2ZEwJd5+dMuKu6Qf3dvTKnTVrl2ycd+DMOQ2WRvkiLNdG+bzCYWliyMPSxHCGpQnjDEsThhqWxhW1IEvz1Z6saPI1xw1CtM7K/TMnffjz/Z0RX03R48NHNsU6FYNc+odXPLnOd8GFw9LETBksTQxnWJowzrA0YahhaVxRi7E0Q9c0Vdd1wyDx7uqyTe+Nmje8MRQ4v/vZhyeNXlHy3Gvv3ff8B79ffHB3W69hEEnWOoLxPH8EY0o8ofny/jrzHGPOy4vEVUKIrBRAS+R8Lvlc0BmSI3E1Glfz+SL7x7WFJTUSV7sjcv94Onn7LPzRRFRWA71K3l5hv7kwWdFDMaUTvw35EPCFZEnWDOPyYtalRa0c/+vJcf6qp9Xe5r1lxZPnvryx1hsLB6o3PHb/K99dcnhLbUPNgS0fjCkZueFYlaSrmi7JWp4/EkryOuN5f515jjHn5SmqTgjRdCNnJQrcEIgnNEXVFbUAvvXcPM18GJviHE/k+4+4fGDl5hpkRVM1PaGAM3cCmm4k8KODmwzEE5qcEGNpcqhu15ZxxUsHbzpeFYgbSiRwcvNDvxj29pkeb0LXY50H58x+Z/725W2KJGudwXieP4K9F9bSQvl+nXmOMeflRS+tpeWsRIEbAl0hOXphLc1NCMbaIRCRkmuW3RHZTjFqqAn0RBOx5FpagjoBA20SSFxaS7NZjzJHBLoEraWpkca926fPWTmurKq8M5r8GA4tFjx/4NXfjBjjjflUg5BYzeKP3pq7dW6Tgvd4cn2Ru7DCcV+amPnCfWliOOO+NGGccV+aMNS4L40rajH3pWnS8d0zSleM2XziRCCupV4MNRTJd27Nq4Pv33CmOiwnes6smzrrzcV7PvEn/48m1+fMJByfl8YEY84QWFpOREwKYGlMMNoJwd8esEPJfQ0szT1DmwmwNJug6MqEWJoRqpw1+Y+vjn1k3JJJC9cWLVw3ft2+2acCmhTx7Vj14tgFIxasLfpo7qtFK2cfamhR8Ekc+CSOywRgaXTf2E5HwdKcEqOuh6VRo3M0EJbmCJebYliaG3o5xwqxND1YuX5d0eSSgeNTj9IXSzcNrfQRXSNSV/mGdeOnz3tn5qqiPadPBeL4JI6cc3ZNFcDSxEw3LE0MZ7ziKYwzLE0YalgaV9RCLO2q7/fs4yAsjet8F1y4YEtr6Y41tYXPHG+r3lV/cl/D2ZqOxtZQwUGjuGBYGgU0uiFYS6Pj5nQULM0pMep6WBo1OjsDYWmXX1yzwytdg/vS0ii4bgi2tKb28Imdddve2bz8lx+tfnjxzlHbq/eca+qIcH2O+RAOSxM2C7A0MahhaWI4e/0SLI0ralgaLI2SANe+TIeLtLSWrt66qvb53yue9MWhE68bknqsf27V6YqW9PX01w1YmrCZhaWJQQ1LE8MZlsabMyyN0lGwlsa7NZP4GnkAACAASURBVFP5Ii2toSV4ZHVV2s9SG/PvnV6x9JiYJ/s5fhVYmjD4sDQxqGFpYjjD0nhzhqXB0igJ8G7NVL5ISzvf0HN4foXJ0kq+PunA7ENinuzn+FVgacLgw9LEoIalieEMS+PNGZZG6ShYS+Pdmql8kZbW1BGp2lNvsrTFv5hz9OOTYp7s5/hVYGnC4MPSxKCGpYnhDEvjzRmWBkujJMC7NVP5Ii2tpTtWX9e1/tlVk28YlnK1qQNGbhu2tb6uS8yT/Ry/CixNGHxYmhjUsDQxnGFpvDnD0igdBWtpvFszlS/S0rx+qbkzWlvesq/k4I7R27ePKDu0oOL08dZmX1TMk/0cvwosTRh8WJoY1LA0MZxhabw5w9JgaZQEeLdmKl+wpXn9Ukt3rKE5cPZUZ90182FpXr8ESxPTz/hUW2GcYWnCUOOTOLiihqVROgrW0rj2ZTpcvKWlv/Q1tQFLEzbdWEsTgxqWJoYz1tJ4c4alwdIoCfBuzVQ+LE0MZ1iaGM5YSxPGGZYmDDXW0riihqVROoqbtbS29pC/uj6881Bky57gviOd9W1M5rihKXByf2NVWV3VrvqzJzua+8Un5sPSmPRGzhBYWk5ErAqwlsaKpHUOLM2aD8OzsDSGMLOjYGnCLc0XDX1aQd4cSb7zK3L7feT+/9SmlLY3d3m7Y9nTk7xNyhdtaAk2tYVb+ihI3UrV1BY+NK981UOLFvxg+pKflpQN3XryUGNL4d/2Dku7alcwPwhLY460r0BYWl9k2B6HpbHlaZEGS7OA4/4ULE20pXWe7yBPDyKeW4nnhouPv/kXactub6f5j0U2d0bPN/ScOtJyZG31sbIzdSc7mtqv7mpNHZGThxqnf2XsxOsv/l2jyTcM2/ru5sbGHvct8vkmwNLE8IelieGMVzyFcYalCUMNS+OKGpYm2tIi+yrIzx+7rGhJV7uNDBzS2uxv7og0d0bTa2anj7VufmvjlJtGpD67a843io5vOd3QEmz2RZs7o03t4UZvqKEl2NgaOl/fvf3dLUU3XvyUr1T9qkcXV+2s59o9AsJhaQIg4z2eYiCnvgrW0sTQhqWJ4Yx3D/DmDEsTbWny+m3k+7/JtrTaTdWVa6qrd9c3NAVSs3544ZGpt45OfxT+pOuHrn1s6fGtp2v2nKveVndg1qENz65Z/YfF297+5Ni6k9mWNveeqfuK9/NuIN75sDTehFP5WEsTwxlracI4w9KEocZaGlfUsDTRlhYqrya/fvJKS7t9/VeHzrjjg2m3fzD9zrGrfll6bG3V+abArsm704qW2pj1rxNm/uv46XeOnX7H2OLbRk+5ZUTRjcOnDhg1+2uT1j++YvIN72fWT77h/em3j133zMrTx1q59hDXcFgaV7zpcFhaGgXvDayl8SacyoelieGMtTTenGFpfC2t3evv3Xmgd+cBr+/ibWdt7SH15feO33zD0Pv+4ae//8KPH7xh2pfum/LFi/eTTbxuyJQbhx16cELnqk8/nbo307omXjdk0j9d4WHps5OuHzr1n0df9ezsr03c8f423m3ELx+Wxo9tZjIsLZMG121YGle86XBYWhoF7w2spXElDEvjZWntTV3Skg3kR38iX/958vHAs9Gte9taA73b92999Y+/eeSLnldv9Lx9k+evtw+9+S9p30ptHPiHx9ofH73zpeWm4xS7RTcMW/yrOVx7iGs4LI0r3nQ4LC2NgvcGLI034VQ+LE0MZ6yl8eYMS+NlaeEte8n3fks8N156cXMAeexF/9HTkQlFg577t6SfDR3geevWm/7wzQ+uH2TSr0N/86u27/x12+9mmo5T7E7+4vvz751ed5TZi551R1tPbD59cs/5hvN+3t3p9UuwNAGQ8e4BMZBTXwWWJoY2LE0MZ1gab86wNF6WJpUsI54vXVK0Cx+68f98LVh24PzAl/7ziX9OKtrQAZ6Xb7v3vh+Z3GvKP75T5flJ+Ad/2f2IY0ubfvsHprvTJn1x6Mw7x218eW3F0mNuPuc2+dctm3r2fLhvw/OrV/x+wZo/L905Zsepg028GxSWxptwKh9raWI4490DwjjD0oShxiueXFHD0nhZWrx4UcZCWuqj0W6SFn1c++jvf/+H6y9a2ku3/fu9PzZZ2or/9+l2zze1/3ytcnLZnO9MMZ29Yvf6IZO+ODTzyJL/KFn/wuqVf1xU+s2iohsufzDH5BuGLbh3+tnazpauXrp+amoLH/24Zuad49MfyTbjK2N3jt5xjvOKGiyNbr6cjoKlOSVGXY+1NGp0jgbC0hzhclMMS3NDL+dYWBovS5PmryWeu65YS/PcQp58o+eue5765Rc8792SFLU3bh3wm29PuO69TNPa/Te/6/Xcob01pq66Y++UPUt/N++j703NLEhvF904vPjWUendidcNWfzT2ScPNJ480LjppbXTvjwm89TE64ac2HmWejntfGPPjqFbi266bH4Trxuy5ollJ/c35mwyNwWwNDf07I+Fpdln5bISluYSoM3hsDSboNyXwdLcM7RIgKXxsrTgwePk6dfJ//ga8dx0pav909Rv/ON/e/4Gz6CbPYMHeB6/y2Rp6/+vP3V77lGHFaWmraE5cOijw5kLYyn3+nDAyNK7J5feXWRSsaObTrV09e4t3l/y9UmmU5VrqpvawhbdYHHqfEPP1jc2mj849z8XV+04azHK/SlYmnuGdhJgaXYoMamBpTHBmDMElpYTEasCWBorklfNgaXxsjSvX/JXnSVDJxDPnVda2g1Rz/ULv/oP3/nz9Z63B/yXh79hcqnFf/dC449ei63bmpqwFl/09LHWJb+cM+POcR8OGPnhgJHTvjxm1l0TVj+2dNNL6z769odXDL9+yJF11c2d0cp1NYt/MSf96uTE64dO/dLo6k/PUa+lNTQFdo/ZMeWWi38IIfWxIBtfXnvmOJs/FX/V7sS7B/rCwvw4LI050r4CYWl9kWF7HJbGlqdFGizNAo77U7A0jpaWFLXyk+TL95ksjXhuIZ6bl975Bc+gAZ4/3TX+unczTWvh7cMrS674mwEtXb2NjT2fjt21+pHFax5ZvO3NTUeWHTt3puvYptpFP7ziHQbF/zz6+PY6r1861+Df9v6W4i+PKbppWNGNw6bd/sHG51Y3toaoO6alq/fsyY7SbxUV3Ty86MZhRTcNK/n6pH0zD1AH2hyItTSboFyWwdJcArQ/HJZmn5WbSliaG3qOxsLSHOFyWgxL42xplbXk2/dnWVryzQQr7rwueWvai195acATmZa26ZV1tYdtvXey/rRv5+gdU24anh6+/d3NZ2s6Uk3Q1Bau2V2/e/SOXSO3V66rdtoZ2fUt3bGkLH6wc/u7W3aP3lG97Qz166fZ4X0dgaX1RYbtcVgaW54WabA0CzgMT8HSGMK0joKlWfNxeRaWxtfSOho6SVHJVS3txN//448f/YJn8K2ep+/87d2/fOHWP0+8e/jG19edOtho83XJZl/0fH139afnDs8r3ztt37Ettefqu5s7o6meaOmONbWHzzf2nG/scbOKlu6wlu5YS1fv+abA+YZkZlNbOP2H4dM1zDdgacyRXjUQlnZVLDwOwtJ4UM3OhKVlM+F0BJbGCWwqFpbG19K8vmjXmRZlzjLy+MvEc7tJ12r+7roJ3/tfr//kH4bd88Vd19/iHT2zqfxsc/vFvyVlZ+JbumPNHZGG5mBSm9pFaJOdq2JYA0tjCNMiCpZmAYftKVgaW559pcHS+iLD/DgsjTnSzEBYGmdL80vert7Oho7g4SryxXtMlkY8/xTwXNf6t19o/dsvKJ7rye0/CeyuyJwebMPSxPQALE0MZ3yqrTDOsDRhqGFpXFHD0vhbmv/ilyD3PEA8N2eJWuoDb1P/vTG6covXd/ElS64TXyjhsDQxMwVLE8MZliaMMyxNGGpYGlfUsDRBlhbddYh8+YdZn52WqWjJ7d6lG1s7KD/SjGujfF7hsDQx5GFpYjjD0oRxhqUJQw1L44oalibI0sj748h/+arlQtoNxHNzdNU2rKVldjwsLZMGv21YGj+2pmTcl2YCwmkXlsYJbHYsLC2bCcMjsDRRlvboc8Rzm6Wl3Uy++3DP/uMMZ7cfRMHSxEwiLE0MZ6ylCeMMSxOGGpbGFbUoS9PksK+9rvZsRVXdkTpvQ4+kGIauxLrrTpefqD18PPk4WNNQ74/GDBKNq1yfM5NwfyQRk9XWS/ec5cwkL7xNPF+5mqUNILf8gHzrV+TbD0fL9rd7e3JGXVMFsDQx0w1LE8MZliaMMyxNGGpYGlfUYizNUNpqtyyc/+rAEQ88P+z+wbPeWn+8RlJiXWdXPPvsXY8Pvu/p9/7jmfd+8Nqs8fvrmvqppanL15Pbf0g8A4jnxkuudmNy984fS1s+5TrHBR0OSxMzfbA0MZxhacI4w9KEoYalcUUtxtKUhmWznpm0YmJFWzDUcXT9ytfenfn8qXDUVzv96ff+eibqlQ1y6Z/RTy3N65eSH2/71Z9f+LOedxDPHcTzVXL3r0lRCdcJLvRwWJqYGYSlieEMSxPGGZYmDDUsjStqQZbmqz1Z0eRrjhuEaJ2V+2dO+vDn+zvCncc/+OPwkU2xTuWasDSuE9lfw2FpYmYWliaGMyxNGGdYmjDUsDSuqMVYmqFrmqrrumGQeHd12ab3Rs0b1hINtlcM/tXAx94d+avH37z7qVEPLzm4v7PXMEgsrrb1SHn+CEQTkqx2BPL9OvMcY87LC0sKISSe0HJWosANgY5gPCwpYUlxE4KxdgikOPtCsp1i1FAT6I4kInHVH01QJ2CgTQJyQgtGE+15/1vb5tPJt7KOYFySNcO4vJh16aXHHP/ryXH+qqfV3ua9ZcWT5768sbYlIcf8J5cMnTFyU+X+2sYTOz8eOnLmkA1HqyRd1QxZ0fL8oai6pru6zkQoQg4fIVNnk+ISMn+p3BvP86f8uVyequmEEJeoP5crL7gvqmq6qukFd9kFd8Epzom8/xFXcGBNF5xQkv2sqGhp7r9Mdd0AZ1P7Md5NiLE0OVS3a8u44qWDNx2vCsR1Q9fU3u7mjpZgPK7pWrT909KZL83furgtIcmaLyTn+SMUU+IJrYv2Onva/MZHy5Kfc/t33yB/dzf5x2+TJ1/taezI82ct/vKicZUQIisF0BLi4TD8il1hORpXo3GVYSairkogIiU5+yP5/iPuqhdfQAd7osm34Qd7lQK65gK91ISih2PgzOs7uissC1lLUyONe7dPn7NyXFlVeWc0QQgxNE2OtPrCUd1IrpaQWNXi0oFzN89pSvTLT+IwvW4d2VNOHn3h0ps9U3+B4CvRbfva2gKmymt8F/eliWkA3JcmhjPuSxPGGfelCUON+9K4ohZzX5omHd89o3TFmM0nTgTiWurFUC3e662aNWn5nPNBf0JXO0+uKpr+5uI9W/3J/6PJ9TkzCXf6eWmZX7TneB0ZPI5cZ/7j68r8lZ0NnZmV2IaliekBWJoYzrA0YZxhacJQw9K4ohZiaUaoctbkP7469pFxSyYtXFu0cN34dftKanxS+5lVY4uemrpi7IJ1U0vmvjZ19ZzyxlbF6N+W1rt8I3nqDXLTD4jnlivX0m5QFqzqbISlXfHXIGBpXL//0+GwtDQK3hv4C1G8CafyYWliOHv9EiyNK2ohlqYHK9evK5pcMnB86lH6YummoZU+Q5Fjp8sXlC4eWjR38Ox1Mw7V14UT/fjz0rx+qbO+lTzwFPHcavKzi7t/fVv+cH5od3lHo4/rrBdQOCxNzGTB0sRwxlqaMM6wNGGoYWlcUQuxtKu+37OPg/3b0mLb9pJ/+emVinbjldL2z+SXT0U37sbfXE/1PSyN6/d/OhyWlkbBewNrabwJp/JhaWI4Yy2NN2dY2hWvr9nHTXdfWmLt5uT7Oj2ptwuk/nsL+d/fuvLIzcbb49qbuuxfTD+uhKWJmVxYmhjOWEsTxhmWJgw11tK4ooalCbW08MGj5J4HMv6U5w3JhbSbzG8jIE8ODBw7zXXiCyUcliZmpmBpYjjD0oRxhqUJQw1L44oalibU0pJ/zfOld8nffvXS4tnN5L/fTX74m0u7l9bYYGn+i/MCS+P6/Z8Oh6WlUfDewCuevAmn8mFpYjjjFU/enGFpoi2trTUQ+fQzMraYvDtGXrExtP8YeW/slZZ2szZ4Yru3h/fcF0Q+LE3MNMHSxHDGWpowzrA0YaixlsYVNSxNtKUlp7Mj3N7U1dHY2dYa8HZG/FX15KlXk39+wHMr+dJ9ZMxU/9HT3u4Y14kvlHBYmpiZgqWJ4QxLE8YZliYMNSyNK2pY2udhaZdezrs4tZ2RnqOngzsOhTZ+GtxV3nW6ubUjxHXWCygcliZmsmBpYjjD0oRxhqUJQw1L44oalpYHlmaSNuxmEIClcf3+T4fD0tIoeG/gvjTehFP5sDQxnHFfGm/OsDRYGiUB3q2ZyoelieEMSxPDGWtpwjjD0oShxloaV9SwNEpHofu8NK5z2S/DYWliphWWJoYzLE0YZ1iaMNSwNK6oYWmwNEoCXPsyHQ5LS6PgugFL44o3MxyveGbS4LcNS+PH1pQMSzMBYbsLS6N0FKylsW3EvtJgaX2RYXsclsaWp0UaLM0CDsNTsDSGMK2jYGnWfFyehaXB0igJuOw8m8NhaTZBuSyDpbkEaH84LM0+KzeVsDQ39ByNhaU5wuW0GJZG6ShYS3PaanT1sDQ6bk5HwdKcEqOuh6VRo3M0EJbmCJebYliaG3o5x8LSYGmUBHL2FpMCWBoTjDlDYGk5EbEqgKWxImmdA0uz5sPwLCyNIczsKFgapaNgLS27mXgcgaXxoJqdCUvLZsLpCCyNE1hTLCzNBITfLiyNH1uvX4KlwdIoCXDty3Q4LC2NgusGLI0r3sxwWFomDX7bsDR+bE3JsDQTELa7sDRKR8FaGttG7CsNltYXGbbHYWlseVqkwdIs4DA8BUtjCNM6CpZmzcflWVgaLI2SgMvOszkclmYTlMsyWJpLgPaHw9Lss3JTCUtzQ8/RWFiaI1xOi2FplI6CtTSnrUZXD0uj4+Z0FCzNKTHqelgaNTpHA2FpjnC5KYaluaGXcywsDZZGSSBnbzEpgKUxwZgzBJaWExGrAlgaK5LWObA0az4Mz8LSGMLMjoKlUToK1tKym4nHEVgaD6rZmbC0bCacjsDSOIE1xcLSTED47cLS+LHFezwpFc3rl2BpXPsyHQ5LS6PgugFL44o3MxyWlkmD3zYsjR9bUzIszQSE7S7W0ihFDZbGthH7SoOl9UWG7XFYGlueFmmwNAs4DE/B0hjCtI6CpVnzcXkWlgZLoyTgsvNsDoel2QTlsgyW5hKg/eGwNPus3FTC0tzQczQWluYIl9NiWBqlo2AtzWmr0dXD0ui4OR0FS3NKjLoelkaNztFAWJojXG6KYWlu6OUcC0uDpVESyNlbTApgaUww5gyBpeVExKoAlsaKpHUOLM2aD8OzsDSGMLOjYGmUjoK1tOxm4nEElsaDanYmLC2bCacjsDROYE2xsDQTEH67sDR+bPEeT0pFw3s8uTZlZjgsLZMGv21YGj+2pmRYmgkIp11YGiew2bGwtGwmDI9gLY1S1LCWxrALLaJgaRZwGJ6CpTGEaR0FS7Pmw+osLI0VyZw5sLSciNwUwNJgaZQE3LSd/bGwNPus3FTC0tzQczQWluYIF3UxLI0andOBsDSnxBzVw9IoHQVraY76jLoYlkaNztFAWJojXG6KYWlu6NkfC0uzz8plJSzNJUDr4bA0WBolAevGYnUWlsaKpHUOLM2aD8OzsDSGMC2iYGkWcNiegqWx5WlKg6VROgrW0kydxGkXlsYJrCkWlmYCwm8XlsaPbWYyLC2TBtdtWBpXvKIsTZPDvva62rMVVXVH6rwNPVKCZPxTe9vONZzw+jsl3TBINK5yfc5MwmFpTDDmDIGl5UTEpACWxgSjnRBYmh1K7mtgae4Z2kyApdkERVcmxtIMpa12y8L5rw4c8cDzw+4fPOut9cdr4volTdPlxs8mvjb4e1O2LmqUYGl0E9lfR8HSxMwsLE0MZ69fgqWJQQ1LE8PZ65dgaVxRi7E0pWHZrGcmrZhY0RYMdRxdv/K1d2c+d0q6aGla5EjptD89+dJNxWWwNK6TXYjhsDQxswZLE8MZliaMMyxNGGpYGlfUgizNV3uyosnXHDcI0Tor98+c9OF/HAikLE07tvqx8evnjBzxxOJdsDSuk12I4bA0MbMGSxPDGZYmjDMsTRhqWBpX1GIszdA1TdX15E1n8e7qsk3vjZr3fotCDJXEm0vfGDv6cMO+0uKXMyyt1S/l+aMnkpBkta0n368zzzHmvLxwTCGExBNazkoUuCHQHoiHYko4prgJwVg7BEIxJRRTOoNxO8WooSbQFZYjkuKPJKgT0gP3B6VXe+OeuHyrJI+O4me+mUA8oQWiiba8/62dntDC2mgPxCVZMwzj0k1idv/XY7cws07tbd5bVjx57ssba1sShhYL+zbM+vGsw+Ud0XMLp79yydI0zZAVLc8fiqZregFcZ55jzHl5qpa8f1EHas7fEQlV0zRd1fScM4IClwRUTdc0I6ECNd8f8oqa/BGtuG7pQ6r2PV316JrHuPDQ1V9pahvn70eXPSZ4uK4b+NHBm7kQS5NDdbu2jCteOnjT8apA3FBi4TOHBr9TWnIu5E/ozYsvW5oka76QnOePUEyJJ7SucL5fZ55jzHl50bhKCJGVAmiJnM8lnwu6wnI0rkbjaj5fZP+4tsgFzv4IfnTwJRCIJmKyGuxVXLbNo3Ii6WdEz3xUYPoyfkcnFD0cc8vZ5TT14+FdYVnIWpoaady7ffqclePKqso7owlCjEh3y8ezvvPwkCcmzH17yvzXX3jjey+Mvn/+/rKmCD6J46ovctef665cU31w9uHPFhyp/ay5uSNy1bJ+dhD3pYmZUNyXJoYz7ksTxpnVfWm3SbKHmC1tb5DygzaFPX2RXwj3pXGlLea+NE06vntG6Yoxm0+cCMS1C6+BGvHeYE35og07Uo/p7w39xXsznt1YfcwXg6WZprylq/fcef+mNzYs+lnJR/dMnX/v9LVPLq9cV3MtiBoszdQMnHZhaZzAZsfikziymfA4wtLSstbSYGmZUwZLy6TBfFuIpRnhqtIpj74x6Ympa0rWbP1ozbbZ28qX1ocy71jLfMUTlmaa5sbW0JFVJ4q/NHridUNSjyk3Dt/81qZzZ3ymyv63C0sTM6ewNDGcsZYmjDMrS3s9GveoSsbLnZpHVcoDMWFPJP+/ECyN6xwJsTQ9WLl+XdHkkoHjU4/SF0s3Da30ZVqa/+DOpYdOHepO4FNts+f7fEPP9ne3FN04LG1pE68bsvqxJTW7z2UX97MjsDQxEwpLE8MZliaMMytLOxiMvdIb9yhK0tVUxZNIFEekM35Y2uXXfGFpXLtaiKVl6liubVha9nyfb+zZ+f42k6WtfGRhVVlddnE/OwJLEzOhsDQxnGFpwjizsjSvX6rtiW0JxWaGpelhCato2TMIS8tmwvAILO3y/yFwhFXk3/FsbAkenvPZtC+PmXj9xVc8i24c9smbG+pP4xVPyulzNNfXQjEsTdgs4740MagZWpqYCy7crwJL4zp3sDTKX/MiLS3VAeueXDHrrgnT7xg7/c6xS35RWrH8GNfOyJNwrKWJmQhYmhjOWEsTxhmWJgw1LI0ralhawVia1y9Vbz97YtPp6l315852cW2L/AmHpYmZC1iaGM6wNGGcYWnCUMPSuKKGpRWSpTV3RpMPX7Slq5drW+RPOCxNzFzA0sRwhqUJ4wxLE4YalsYVNSytkCyNayvkZzgsTcy8wNLEcIalCeMMSxOGGpbGFTUsDZZGSYBrX6bDYWlpFFw3YGlc8WaG490DmTT4bcPS+LE1JcPSTEDY7sLSKB1F/LsH2E58oaTB0sTMFCxNDGespQnjDEsThhqWxhU1LA2WRkmAa1+mw2FpaRRcN2BpXPFmhmMtLZMGv21YGj+2pmRYmgkI211YGqWjYC2NbSP2lQZL64sM2+OwNLY8LdJgaRZwGJ6CpTGEaR0FS7Pm4/IsLA2WRknAZefZHA5LswnKZRkszSVA+8NhafZZuamEpbmh52gsLM0RLqfFsDRKR8FamtNWo6uHpdFxczoKluaUGHU9LI0anaOBsDRHuNwUw9Lc0Ms5FpYGS6MkkLO3mBTA0phgzBkCS8uJiFUBLI0VSescWJo1H4ZnYWkMYWZHwdIoHQVradnNxOMILI0H1exMWFo2E05HYGmcwJpiYWkmIPx2YWn82Hr9EiwNlkZJgGtfpsNhaWkUXDdgaVzxZobD0jJp8NuGpfFja0qGpZmAsN2FpVE6CtbS2DZiX2mwtL7IsD0OS2PL0yINlmYBh+EpWBpDmNZRsDRrPi7PwtJgaZQEXHaezeGwNJugXJbB0lwCtD8clmaflZtKWJobeo7GwtIc4XJaDEujdBSspTltNbp6WBodN6ejYGlOiVHXw9Ko0TkaCEtzhMtNMSzNDb2cY2FphWppp3pih4Ox44FYzjku6AJYmpjpg6WJ4Yy/ECWMMyxNGGpYGlfUsLTCs7TyYOzF3rgnkfAoiieRuEeS9wX7ravB0rh+/6fDYWlpFLw3sJbGm3AqH5YmhrPXL8HSuKKGpRWepb0QjXtUxUM0D9GT/9XVf4vFuXbJ5xgOSxMDH5YmhjPW0oRxhqUJQw1L44oallZ4luaREx4jpWj6RVFTFa5d8jmGw9LEwIelieEMSxPGGZYmDDUsjStqWBpnS+vqbWvtYTuFyRc6Ly6kXbI0XWX7JfInDZYmZi5gaWI4w9KEcYalCUMNS+OKGpbGy9I6Gn2BfZXhNdviH60IbdjlP17X2hFiMpfJtTSTpWEtzU85j0xmpB+EwNKETSLuSxODGpYmhjPuS+PNGZZG+dvd+pM42tpD0tqt5Nu/JJ4bQjFu/QAAIABJREFULj5eeNt/4qy3q9f9jP708n1pWuq+NE9Mdh+bnwlYSxMzL7A0MZyxliaMMyxNGGqspXFFDUvjYmnR3YfJw89dVrQLrqbOXtze5HM/nbU90ptRKbmipibf4/n93nh1D97jSTmP7qejfyTA0oTNI9bSxKCGpYnhjLU03pxhaZS/3a3X0uIfbyM/eshkaeStkd2nGnnPaD/Lx1qamAmFpYnhjLU0YZxhacJQYy2NK2pYmkBLGz6p60wL1+nsf+GwNDFzCksTwxmWJowzLE0YalgaV9SwNC6W1nPsNHlpyJVrabfEVm1tawtync7+Fw5LEzOnsDQxnGFpwjjD0oShhqVxRQ1L42JpbW2ByLZ95E8vkeu+Ta7/N/Ll+8ibo7qqzzN59wDXhsi3cFiamBmBpYnhDEsTxhmWJgw1LI0ralgaF0vz+qU2b0/gcFVs+cbYio2xtVu7a855O8Jc57JfhsPSxEwrLE0MZ1iaMM6wNGGoYWlcUcPSeFka12m7dsJhaWLmGpYmhjMsTRhnWJow1LA0rqhhabA0SgJc+zIdDktLo+C6AUvjijczHJ/EkUmD3zYsjR9bUzIszQSE7S4sjdJRrD+Jg+0kXctpsDQxsw9LE8MZa2nCOMPShKGGpXFFLcrSNDnsa6+rPVtRVXekztvQIyUIMXRN7w2cq6s/Un3ms5MNNe3BoKwZBonGC+CvUsLSuPZlOhyWlkbBdQOWxhVvZjjW0jJp8NuGpfFja0qGpZmAsN0VY2mG0la7ZeH8VweOeOD5YfcPnvXW+uM1kqbFAq071r06+IMHXxz+85c/eGj2tg31/phuwNLYznFBp8HSxEwfLE0MZ6ylCeMMSxOGGpbGFbUYS1Mals16ZtKKiRVtwVDH0fUrX3t35vMngxHvsYmPv/Pb3c1noqpUu33c+9PfXFtxVIalUb4Iy7VRPq9wWJoY8rA0MZxhacI4w9KEoYalcUUtyNJ8tScrmnzNcYMQrbNy/8xJH/58n08Ote/79GyLoiUIIXrT+mlzRy4/sLdXx1oa1ykvrHBYmpj5gqWJ4QxLE8YZliYMNSyNK2oxlmbomqbqum4YJN5dXbbpvVHz3m9RDENXFU03DIPIwYoN73wwb/TO2ha84unHWtplArA0rt//6XBYWhoF7w3cl8abcCofliaGM/7aOm/OYiyNXPyn9jbvLSuePPfljbUtCePiwZpPBg0a9d1nRz2y8NMdbZGEQTTdSKh6nj9ULWmdSt5fZ55jzHl5mp7sE70QWiLnc8nnAkXVNd0oiG+9fMZo59pSnPGjww4rNzWqlmxpVcv3XyVunmOejNUvcEZL85sOWdGSy1kO/3kc1hMih+p2bRlXvHTwpuNVgbieHh/uPHWq9tOtH4+dOHdyWfUpSZcSmi8k5/kjFFPkhNYVzvfrzHOMOS8vGlcJIbJSAC2R87nkc0F3WI7G1WhczeeL7B/XluLsjyT6x9PJ22cRiCZishqMKXl7hf3mwhKKHo4pXXn/W7tAgXeHZSn58Re8LU2NNO7dPn3OynFlVeWd0eSNaJqqBn1HT3eENV0nuhby7p41642F21d0JHBfGu8V1ALKxyueYiYLr3iK4Yz70oRxxiuewlDjvjSuqMW84qlJx3fPKF0xZvOJE4G4llpFU6Xo+YqxwxbObgz6FZ2EmrYVT3t+XtnSTgWWxnXKCyscliZmvmBpYjjD0oRxhqUJQw1L44paiKUZ4arSKY++MemJqWtK1mz9aM222dvKl9Z1xVpPLRo+8S9z109bs23h0qWDxs8fuf3ksRje43n53nmuc18Q4bA0MdMESxPDGZYmjDMsTRhqWBpX1EIsTQ9Wrl9XNLlk4PjUo/TF0k1DK32GIsdOly+du3jolLlvFy8fv726vDsm428P4D2eGQRgaVy//9PhsLQ0Ct4beI8nb8KpfFiaGM54jydvzkIsLf1GARsb+AtRvKe8sPJhaWLmC5YmhjPW0oRxhqUJQ421NK6oYWmULy/i73hy7ct0OCwtjYLrBiyNK97McKylZdLgtw1L48fWlAxLMwFhuwtLg6VREmDbiH2lwdL6IsP2OCyNLU+LNFiaBRyGp2BpDGFaR8HSrPm4PAtLo3QUrKW57Dybw2FpNkG5LIOluQRofzgszT4rN5WwNDf0HI2FpTnC5bQYlgZLoyTgtNXo6mFpdNycjoKlOSVGXQ9Lo0bnaCAszREuN8WwNDf0co6FpVE6CtbScvYWkwJYGhOMOUNgaTkRsSqApbEiaZ0DS7Pmw/AsLI0hzOwoWBosjZJAdjPxOAJL40E1OxOWls2E0xFYGiewplhYmgkIv11YGj+2Xr8ES6N0FKylce3LdDgsLY2C6wYsjSvezHBYWiYNftuwNH5sTcmwNBMQtruwNFgaJQG2jdhXGiytLzJsj8PS2PK0SIOlWcBheAqWxhCmdRQszZqPy7OwNEpHwVqay86zORyWZhOUyzJYmkuA9ofD0uyzclMJS3NDz9FYWJojXE6LYWmwNEoCTluNrh6WRsfN6ShYmlNi1PWwNGp0jgbC0hzhclMMS3NDL+dYWBqlo2AtLWdvMSmApTHBmDMElpYTEasCWBorktY5sDRrPgzPwtIYwsyOgqXB0igJZDcTjyOwNB5UszNhadlMOB2BpXECa4qFpZmA8NuFpfFji/d40gsK1tK49mU6HJaWRsF1A5bGFW9mOCwtkwa/bVgaP7amZFiaCQjbXaylUYoaLI1tI/aVBkvriwzb47A0tjwt0mBpFnAYnoKlMYRpHQVLs+bj8iwsDZZGScBl59kcDkuzCcplGSzNJUD7w2Fp9lm5qYSluaHnaCwszREup8WwNEpHwVqa01ajq4el0XFzOgqW5pQYdT0sjRqdo4GwNEe43BTD0tzQyzkWlgZLoySQs7eYFMDSmGDMGQJLy4mIVQEsjRVJ6xxYmjUfhmdhaQxhZkfB0igdBWtp2c3E4wgsjQfV7ExYWjYTTkdgaZzAmmJhaSYg/HZhafzY4j2elIrm9UuwNK59mQ6HpaVRcN2ApXHFmxkOS8ukwW8blsaPrSkZlmYCwnYXa2mUogZLY9uIfaXB0voiw/Y4LI0tT4s0WJoFHIanYGkMYVpHwdKs+bg8C0uDpVEScNl5NofD0myCclkGS3MJ0P5wWJp9Vm4qYWlu6DkaC0tzhMtpMSyN0lGwlua01ejqYWl03JyOgqU5JUZdD0ujRudoICzNES43xbA0N/RyjoWlwdIoCeTsLSYFsDQmGHOGwNJyImJVAEtjRdI6B5ZmzYfhWVgaQ5jZUbA0SkfBWlp2M/E4AkvjQTU7E5aWzYTTEVgaJ7CmWFiaCQi/XVgaP7Z4jyelouE9nlybMjMclpZJg982LI0fW1MyLM0EhNMuLI0T2OxYWFo2E4ZHsJZGKWpYS2PYhRZRsDQLOAxPwdIYwrSOgqVZ82F1FpbGimTOHFhaTkRuCmBpsDRKAm7azv5YWJp9Vm4qYWlu6DkaC0tzhIu6GJZGjc7pQFiaU2KO6mFplI6CtTRHfUZdDEujRudoICzNES43xbA0N/Tsj4Wl2WflshKW5hKg9XBYGiyNkoB1Y7E6C0tjRdI6B5ZmzYfhWVgaQ5gWUbA0CzhsT8HS2PI0pcHSKB0Fa2mmTuK0C0vjBNYUC0szAeG3C0vjxzYzGZaWSYPrNiyNK15YGiyNkgDXvkyHw9LSKLhuwNK44s0Mh6Vl0uC3DUvjx9aUDEszAWG7C0ujdBSspbFtxL7SYGl9kWF7HJbGlqdFGizNAg7DU7A0hjCto2Bp1nxcnoWlwdIoCbjsPJvDYWk2Qbksg6W5BGh/OCzNPis3lbA0N/QcjYWlOcLltFiUpWly2NdeV3u2oqruSJ23oUdKEGIYOomHmuvPVVaf+ezk+eq2QEDWDINE46rTpyG+HmtpYpjD0sRwhqWJ4ez1S7A0MahhaWI4e/0SLI0rajGWZihttVsWzn914IgHnh92/+BZb60/XhPX9Hi4+9Dmwe998OBLw+5/edTvZmxZW+eP6UbBW5qvt70h5jsZ852OtTfFuM5fvw+HpYmZYliaGM6wNGGcYWnCUMPSuKIWY2lKw7JZz0xaMbGiLRjqOLp+5Wvvznz+VDjqqy156s17PzlzLCL31m4fP3Tq22s+OyoXuKX5Yr5TvUpJlLwSJUOi8uLeNi9Ejf5FVVga1+//dDgsLY2C9wbW0ngTTuXD0sRwxloab86CLM1Xe7KiydccNwjROiv3z5z04c/2d8kx3/6yE2cVTSaE6E3ri0uGLdm7O6wX9FpaaG8vebGX3CtdfPx7jDzRy3sW+3E+LE3M5MLSxHDGWpowzrA0YaixlsYVtRhLM3RNU3VdNwwS764u2/TeqHnvtyiGoauKphmGQQjx7p0wce7wTcfr1MJeSzPeiZIfxi5b2r0x8utY8FCv14cVNZoVNVga1+//dDgsLY2C9wbW0ngTTuXD0sRwxloab85iLI1c/Kf2Nu8tK5489+WNtS2JpJtd/Ndzfm3xjGfn71jVGI4bhqoZckLL84ei6ppuvk69ppY83U7uzbQ0ifxSIvsSspTvzyg/gauaTgjJRp2fV1vAV6Voqqarml7ATyHvf2ik2KY4JxT8QOBLQFGTLa2oaGm+nOWEpusGOPP9yalcXM665Ey2/tdjqyqzSA7V7doyrnjp4E3HqwLx5O/e1D9vzcLiOa8s2r3qTJc/cWG5LaF1heU8f4QlRU5o3ZeuM9gRIM8OInf9jHxzZ8ZC2oXXPX8Zix+QugP5/ozyE3hMVgkhsqLn5+X1m6tKvWc5Jqv95hnl7ROJxtWYrPZEE3l7hf3jwoK9ipTQQjGlfzydfH4WCVWPSGr6t2E+X2ohXps/kpCSH3+RsbB1yZ6s/9ehpamRxr3bp89ZOa6sqrwzmkhl6woJ1K8s/ujNpTvXnO3ulDSS/HiOgvkkDklW23ouvopnzFxA/v7bxHMj+V9F5JtNl0Xtvhh5OOY7H/N24xVPvOJJQ4D3cnoqv60nHoopoZgi5stdy18lFFPCMaUjGL+WIQh47r6QnFSHiCzga13jXyJ5X1o00erP359vBT1BbYG4AEvTpOO7Z5SuGLP5xIlAPOliSR3TEpGOqnmzHi8tW9PQE1QuLq4VkKXFZDXdl+TBJ4jnNuK5gXh+Qv5nEbmjgnynjfw8Ql7oja7Buwfov3txX5qYny+4L00MZ7x7QBhn3JcmDDXePcAVtZD70oxwVemUR9+Y9MTUNSVrtn60ZtvsbeVLz3RGGg6Nefj5b4xfMX7Z5o/WbJ2zbvvSE021vQX5Hk/yo98Tz60XLO0m4vke8TxH7lmkTeiMbIGi0Sua1y/B0rh+/6fDYWlpFLw38O4B3oRT+bA0MZzx7gHenIVYmh6sXL+uaHLJwPGpR+mLpZve/6ylt6ly2riSNy8eLHlt4tz3d5w8ECxMS3tjGPHcccHSbrjw31vJYy91Nnbynr9+nw9LEzPFsDQxnLGWJowzLE0YaqylcUUtxNJSL3Ha+2+BvuIZPniM/PpJcv13yH/9F/L33yQ/eUQuWcp15q6RcFiamImGpYnhDEsTxhmWJgw1LI0ralga5etx2X/HM3zwKJk0i7zwNhk+IbrjQHtrwDRzjV3Rk22dR1qaj3pbznT4m7rxYmhu+LA0Uxdx2oWlcQKbHYtXPLOZ8DgCS+NB9aqZsLSrYmF1EJaWWxSuyjrb0q5alj7Y1N276/zp4XvnP7xh6OOfjJpW/vGh5ob0WWz0RQCW1hcZtsdhaWx5WqTB0izgMDwFS2MI0zoKlmbNx+VZWJogS6tp6/AseMwz8nbP0AHJx9i7/n3jCJeTdy0Mh6WJmWVYmhjOeMVTGGdYmjDUsDSuqGFpgixt6J5Sz4RvX1S0pKjdelPpQ+tOHeI6u/0gHJYmZhJhaWI4w9KEcYalCUMNS+OKGpYmyNKe3zreM/brGZY2wFP806LDK7nObj8Ih6WJmURYmhjOsDRhnGFpwlDD0riihqVxsbRgxUm9qIQ8+Rp5YZA+Z2lnU9f7e+Z4xn8r09JuLPndqpP7uc5uPwiHpYmZRFiaGM6wNGGcYWnCUMPSuKKGpbG3tND+SvLXt8kXvk08X05+iNqX7iPPvb27ptwz+wHP8NsuitqoO+5Z/capdh/X2e0H4bA0MZMISxPDGZYmjDMsTRhqJpZ2rKVl0uFVv17z9jcWPfOz9e+VVG4Sdv15/oVgaewtTZ21iAz4QcYn3N5I/varjfUt449s+M26d/9l/mPfWvTUnzePnV+zL8+bIx8uD5YmZhZgaWI4w9KEcYalCUPNxNKG7VnwX2f/2jPmXzzD/tkz9mtfX/D4ptMVwp5CPn8hWBp7S9NHTyX/7a4MS0v+NYLu43VeX/RAQ92Kmr0fnzp81OvN57bIn2uDpYmZC1iaGM6wNGGcYWnCULu3tPLmxm8tetbz/q2Xbwr64GtPbx0n7Cnk8xeCpXGwtAkzyd/ffaWl3eSvPOXtjORzK+TntcHSxMwLLE0MZ1iaMM6wNGGo3VvahtPlt815+LKiDR3gGXXnN1e8IOwp5PMXgqWxtzRpzWZy72+I55ZLojaA3Hl/W1N3PvdB3l4bLE3M1MDSxHCGpQnjDEsThtq9pe09X/u1BU94hmaspY35199tGCrsKeTzF4Klsbe0zoYOZXIJ+davyRfvIV/8Lvm338sfb8VCGt23ASyNjpvTUbA0p8So6/G3B6jRORoIS3OEy02xe0vz+qU/bxrjGXd38qa0oQOSb7Mr/um8Y1vcXFW/GQtLY29pqeZob+kOHKsNVJ7qN73yuTwRWJoY7LA0MZyxliaMMyxNGGomlvZZ07nfbhnnmf4zz6TveUp+M2j3bGHXn+dfCJbGy9LyfOIL5fJgaWJmCpYmhjMsTRhnWJow1EwsTdjVFtwXgqXB0igJiOl1WJoYzrA0MZxhacI4w9KEoYalcUUNS6N0FH8kEZPVVj/lcK6T2p/CYWliZhOWJoYzLE0YZ1iaMNSwNK6oYWmUmgVL49qX6XBYWhoF1w1YGle8meF490AmDX7bsDR+bE3JsDQTELa7sDRYGiUBto3YVxosrS8ybI/D0tjytEiDpVnAYXgKlsYQpnUULM2aj8uzsDRKR8FamsvOszkclmYTlMsyWJpLgPaHw9Lss3JTCUtzQ8/RWFiaI1xOi2FpsDRKAk5bja4elkbHzekoWJpTYtT1sDRqdI4GwtIc4XJTDEtzQy/nWFgapaNgLS1nbzEpgKUxwZgzBJaWExGrAlgaK5LWObA0az4Mz8LSGMLMjoKlwdIoCWQ3E48jsDQeVLMzYWnZTDgdgaVxAmuKhaWZgPDbhaXxY+v1S7A0SkfBWhrXvkyHw9LSKLhuwNK44s0Mh6Vl0uC3DUvjx9aUDEszAWG7C0uDpVESYNuIfaXB0voiw/Y4LI0tT4s0WJoFHIanYGkMYVpHwdKs+bg8C0ujdBSspbnsPJvDYWk2Qbksg6W5BGh/OCzNPis3lbA0N/QcjYWlOcLltBiWBkujJOC01ejqYWl03JyOgqU5JUZdD0ujRudoICzNES43xbA0N/RyjoWlUToK1tJy9haTAlgaE4w5Q2BpORGxKoClsSJpnQNLs+bD8CwsjSHM7ChYGiyNkkB2M/E4AkvjQTU7E5aWzYTTEVgaJ7CmWFiaCQi/XVgaP7Z4jye9oGAtjWtfpsNhaWkUXDdgaVzxZobD0jJp8NuGpfFja0qGpZmAsN3FWhqlqMHS2DZiX2mwtL7IsD0OS2PL0yINlmYBh+EpWBpDmNZRsDRrPi7PwtJgaZQEXHaezeGwNJugXJbB0lwCtD8clmaflZtKWJobeo7GwtIc4XJaDEujdBSspTltNbp6WBodN6ejYGlOiVHXw9Ko0TkaCEtzhMtNMSzNDb2cY2FpsDRKAjl7i0kBLI0JxpwhsLSciFgVwNJYkbTOgaVZ82F4FpbGEGZ2lChL0+Swr72u9mxFVd2ROm9Dj5QghBCDED0R7DhTe/aYL+pPGMlDBonG1ewLzbcjWEsTMyOwNDGcYWliOHv9EixNDGpYmhjOXr8ES+OKWoylGUpb7ZaF818dOOKB54fdP3jWW+uP18R1Q1PVQHvFpsXPP/7yXStrNndpsDSuk12I4bA0MbMGSxPDGZYmjDMsTRhqWBpX1GIsTWlYNuuZSSsmVrQFQx1H16987d2Zz52StN5w1+qpvxq/9J3nX/vhxydhaVxnukDDgzElQYg/odX5Y43+vH5xtkAJpy4bliZs+rCWJgY1LE0MZ6yl8eYsyNJ8tScrmnzNcYMQrbNy/8xJH/7HgYCmqj3NzQE9fGjc8Ic2wtKgIFchcC6mbCTkt7r2VUleG47x/n64ZvNhacKmHpYmBjUsTQxnWBpvzmIszdASCVnVVMMgsr9m+ydDRs19tzlhGMnXPHUjUg5LwyrR1QgcCMa+JSc8RPcQzWNoHl31SHJtD1ztKjrr8icFLM0lQPvDYWn2WbmphKW5oedoLF7xdITLabEYS0u+UyD5T+1t3ltWPHnuyxtrWy68V+DCUbOlqZoRT2h5/lBUXdML4DrzHKP15f1JVT2GesHS9LSrted9b1g/qbw9q2i6oul5e3n95sIUNclZRhtzJpBQNFXTE0q+/yrpB42t6Yai4kcHx06TE5phJN9e6eifx1F1slgO1e3aMq546eBNx6sCcf3yeLOlxRNad0TO80dYUmRF8+f9deY5RuvLuyuRSC6hJdfS0g9tfjzhBXbWBFLvWY7JqvWM4Kx7AjFZjclqoDfhPgoJFgSCMUVKaCFJsajBKSYEEqoekfCjg5e0+KMJSRZgaWqkce/26XNWjiurKu+MXvgYjrSmmS0tGldbe6Q8f/QkwalteX+deY7R+vLuiMnJVzkvK1rypc8tYakR2FkTaA/EwzElLCnWM4Kz7gmELnDuDMbdRyHBgkBXSL6gDgmLGpxiQiCe0AJRcOYlLe2BuABL06Tju2eUrhiz+cSJQDz5eRtX/LuKpTl94VZ8PT4vTQDzURHJoyoZlqZ5FOUk7ku72j18LqcD96W5BGh/OO5Ls8/KTSXuS3NDz9FY3JfmCJfTYiH3pRnhqtIpj74x6Ympa0rWbP1ozbbZ28qXng0okv/M9h0L1mwY9vxr3x0xf+CibcuONdZGdXyqrdNZ7K/15YHYO6l3DxhaUtcSiaci8Xo/3j2Adw+wJyDsmwiWJgY1LE0MZ7zHkzdnIZamByvXryuaXDJwfOpR+mLppqGV7XK49bOP5r0z4dLxiXPeL6s+2ANLK+DfQOz7NaaUEzJB04ZEpC0hkOFFAGtp7Fu3jyVPWJoY1LA0MZxhabw5C7G0K17fzLGDvxDFe8oLKx9/e0DMfMHSxHDG3x4QxhmWJgw1XvHkihqWRrk4gfvSuPZlOhyWlkbBdQOWxhVvZjjW0jJp8NuGpfFja0qGpZmAsN2FpcHSKAmwbcS+0mBpfZFhexyWxpanRRoszQIOw1OwNIYwraNgadZ8XJ6FpVE6CtbSXHaezeGwNJugXJbB0lwCtD8clmaflZtKWJobeo7GwtIc4XJaDEuDpVEScNpqdPWwNDpuTkfB0pwSo66HpVGjczQQluYIl5tiWJobejnHwtIoHQVraTl7i0kBLI0JxpwhsLSciFgVwNJYkbTOgaVZ82F4FpbGEGZ2FCwNlkZJILuZeByBpfGgmp0JS8tmwukILI0TWFMsLM0EhN8uLI0fW69fgqVROgrW0rj2ZToclpZGwXUDlsYVb2Y4LC2TBr9tWBo/tqZkWJoJCNtdWBosjZIA20bsKw2W1hcZtsdhaWx5WqTB0izgMDwFS2MI0zoKlmbNx+VZWBqlo2AtzWXn2RwOS7MJymUZLM0lQPvDYWn2WbmphKW5oedoLCzNES6nxbA0WBolAaetRlcPS6Pj5nQULM0pMep6WBo1OkcDYWmOcLkphqW5oZdzLCyN0lGwlpazt5gUwNKYYMwZAkvLiYhVASyNFUnrHFiaNR+GZ2FpDGFmR8HSYGmUBLKbiccRWBoPqtmZsLRsJpyOwNI4gTXFwtJMQPjtwtL4scV7POkFBWtpXPsyHQ5LS6PgugFL44o3MxyWlkmD3zYsjR9bUzIszQSE7S7W0ihFDZbGthH7SoOl9UWG7XFYGlueFmmwNAs4DE/B0hjCtI6CpVnzcXkWlgZLoyTgsvNsDoel2QTlsgyW5hKg/eGwNPus3FTC0tzQczQWluYIl9NiWBqlo2AtzWmr0dXD0ui4OR0FS3NKjLoelkaNztFAWJojXG6KYWlu6OUcC0uDpVESyNlbTApgaUww5gyBpeVExKoAlsaKpHUOLM2aD8OzsDSGMLOjYGmUjoK1tOxm4nEElsaDanYmLC2bCacjsDROYE2xsDQTEH67sDR+bPEeT0pF8/olWBrXvkyHw9LSKLhuwNK44s0Mh6Vl0uC3DUvjx9aUDEszAWG7i7U0SlGDpbFtxL7SYGl9kWF7HJbGlqdFGizNAg7DU7A0hjCto2Bp1nxcnoWlwdIoCbjsPJvDYWk2Qbksg6W5BGh/OCzNPis3lbA0N/QcjYWlOcLltBiWRukoWEtz2mp09bA0Om5OR8HSnBKjroelUaNzNBCW5giXm2JYmht6OcfC0mBplARy9haTAlgaE4w5Q2BpORGxKoClsSJpnQNLs+bD8CwsjSHM7ChYGqWjYC0tu5l4HIGl8aCanQlLy2bC6QgsjRNYUywszQSE3y4sjR/b/H2PZ2uPlOePnmhCktX2vL/OPMeY8/LCkkIIiSe0nJUocEOgPRAPS0pYUtyEYKwdAqFYknNnMG6nGDXUBLrCckRS/ZEEdQIG2iQQT2iBaKINvw35EGgPxCVZMwyDOPzncViPchAAARAAARAAARAAAREEYGkiKONrgAAIgAAIgAAIgIBTArA0p8RQDwIgAAIgAAIgAAIiCMDSRFDG1wC7/ve+AAAKSElEQVQBEAABEAABEAABpwRgaYT0+ruPlA0ZVzJwfOox550952rDybvd8Y8FATXacGrHmlWjiucPmrt5zhGvn0UoMswElI49az8ePeFiGz87Y+3gg80JcxH2aQkYuq6Emss2fjBtw+qWoE9N3QKsxdrPV25ZP2bavLdmrB73aX0nIRrtV8C4FAFDiUvNp5bOnPfw1rqGyIUWNuLe8n1LZ8xJ/Yh+deKC1+buqSIE7e2mZ7TeUOtnn86eseCtSR+9Pb9sU2MgqBhE6/WeqFizcNnQqYveX7Jj2bkeQHYDmclYWBohXY11a2f99+enTVi4dvLCtUULP55W6W2I4octkwYjxF+/a+3qUcULRy9cM2724kEzP57WGGUUjZgMAtHaGSOnPjuq9L1kD68ds2rX9BOdasZ5bLogoKtSsG7bhskzp/7iodFDT7TXJy5YWritcvumUZPmjFi4dtK85W+NmT/ybCSk6C6+0DU/NNEbOls596NlQ196xTPlwLHuWJKIET6yYsm7g8b9aeaaooVrJy/aOHn90bOEoL3p20WJ+GqPzJ4w8/WSNcWLVo8eMfGPCw8d6uoN1x9dOH/pwClLJy1YOXbWwlc+KvskhH6mx8xkJCyNkI7zJ9bO8cw8miDE8XtkmUxCvw6JHV4/fMbiITvr2uJypK5y+bTp/7T8dC9QM5/07uPjxi2dsb+ukXkyAokm93YfWrm25ODmV/88bnTVRUuTaw+Wls57ZFllo6Qo/ubyqWM8pVV1kQR+rdG3jBTsPrHvg6W7Pika6pl2uDplaXr3niUrRpd8srIHaOnRXjFS6m46tm/M7G1lXXFCEsFtc37y1pKFdY2H1y17fea6EUc7o/FIx/GDU4dO/NmhwBUDsSOcACyN6G1nK5d+6Bm7af++z9bvOrzx2Pn6UDyuQ9iYNKN2ftmc1+ZunHr2wvpZT3PlhsW3DdtahxeGmNDNDGk5NHT03Pfnb16997ONe4/urvW2SPiVlgnIzbauabKvJ0L0mnHPTB570dK09t2bx85a/JfKC7/G5Eh830LPK2v29PTG3Xypa3ysqqiRUFss7l84yjMzbWkdZfMWDxk5b9Ku8k27PttYfvpIt3SNc3L79JVY1N95siWUekFT/2z5g0OWLztzfMnseUOX7VsfSP7oUNoa980s8sytdfu1MN4dgWvM0tSEFI/3SpcfMVmJtdafWFT0rbeKB42a8uQ74x8aPm/0gfPnoxdvPHGH95ofbUjHSma8vaBsfuuF+/yiHae2r/7DwGWf4tUK5q3ReHD0B9P/Mqjo5eFFTw4q+kvR6pnVXXhpmSVmQ7vC0oz4uU8+Hjtj+aCzcvKrKDG5ev1PHytd2x0GdpfYjYTJ0tp3Llryzmtj/zqq+K+DJj0ybNZf1x33JgzV+QeEuryw/jfcMHQ97Dsyp+g/Svcf8J5Y+v+3d68/TZ1xHMD7Z+zV3u/V3JJlWXZNtkSji3HMbS7qFjNd5g1G1aEMBGHIpDLuICgrKDAgiAMBL8CAUWg7Siut0AvtqRxaSi+0tD290J7TLi3DIfJCOD3xRb/kvKBNf78TPjxpv89znrYlDflt4qFAfIJnJ6Ut1byrUgaXPl7qPz7JUprNKFeohqVTa4dSpJmbNS2S40Ml3Y8UnpUIZVVc++29gq52rQMT4gSMzIhXWl15obG/2Rzf5+dbVP9152BqUx92/iYA99kWdn1fz1CblJhd9ns1srby6l2Xe8cw13gWidWtjSnNp+nuKKxszTXEZyBhX1DTu/frmiabe5nVaVAcfS6luXQScVePZJhcDnrtuvuth765dIFccWJ4sxssETocclpU3Q2HLzY2qh1OStsiaMxvl4zEL9pHHKSstYpXOB5ASmPnzLI6uVIaPXm/rOx6ekFVavw4XXAts3mwxxxkGIZ+Oi2b7jt6saFWPmdhSYvymEBQdb3mXMO9OjK+su42T99r23emdRxraYkeHpEIQzMMszqMQzbVyL1z5+rLHLjomTjoDSktukL0dRVWtfA18atvISoo7/jg2xvddjeVuHMmZ6eNKS224PN0cIfdpPo2P5U3urywuuSTnEas/+pIhAk5LbN36j4+XlFD+pdCkShD3C4V5rWOD/pizxsR25y4qZxXNsH6VGjASiC5UtqmVLSPcs2Ts+6117Ppuwcv3qqSk0hpm3Jt9U5TZ+OpG11Xpl2xQhsh6RDyCocXotE17q32w+M3F2DcNq3Zaafi6zohq2q453xGfe3TUb15Ee7disDGlBa1i/ov1zYdGLPGugTcgYd1vIz7EqcPH16wFdZNHrsxpYW8lkUbYffG428spXXy014dpSwB7B7eRO8F72JcC4876z78sf6uiwmt7sNmnOL63zObh4TW2KUPH6nvLS3i/Tn3gg3xMI4EkNKifqN2uLrk7Zox7UqUCTomKgWvFT/4w7Ac32zCEXsytZ0ZFJQI0zrkU15qUf53RX7JOwMWGkvoCR8C6v7sImFev3qGCnp00uaa6o+KBx/hTTAJdH4upUWfKJrrhF+WD4g9Qb9F056RyesxzftwHY4t+saU5iW662/yK7qa5imasmsfNh88fPlXK+2mkdK2Sx10zE0M5GbVXZl1+2LfAB6XjNBLQ53p5S0nBwxLbpthuPfsmbKzRrwSbhc5QXVIaVHG57LIRq5mFX5+Omd3at6+/FulChMZwBNAgoZY0KUbeVBUUJSSdunT81VprRIZhc85SpDt+jYBh6y7PSuzIOVEzp50waHquzf1HuytXC+0/d9DHrdelH08c+cx/o5d379+6Ke3TpTkDGmmvR5ycrS+uPiLtNzdfMGBqn6RN4S3h2/fORoNWOZElVf2Hr3wyf7vXklJf/9I1hsVg716m2VSdLO07KtTOXtP5e3PvZ72UGcPR/7fpsLmlMlZu6CZFAre3PPDjiM/7zwWO949WSN4bCaW5sdut2dl/JJyOu+z7Fp+/8xiCFH4JQ8RpLRolKFpv9diMMiUGrFSJzdarf4QpsOJG5hMyOs0EcSkSvuPhtTZKUzNEme7vhMTcNkInV42pZFMG1WmpdhGE/wkRIAJhymH+tGMWPHfIZrSa+xeD82EfW4b+USu0kgeE0qLO4hFYnbgTMC/ROgka85ihXqUsC1SIdrvsZtIpUojVeom9WbCg5keO+gg5TEb1znPiJSE3h3w0WGfw2bUzk4odTKdybD63Q/sToVqlgJIaSwBUQ4BCEAAAhCAAAQ4EUBK44QVTSEAAQhAAAIQgABLAaQ0loAohwAEIAABCEAAApwIIKVxwoqmEIAABCAAAQhAgKUAUhpLQJRDAAIQgAAEIAABTgSQ0jhhRVMIQAACEIAABCDAUgApjSUgyiEAAQhAAAIQgAAnAkhpnLCiKQQgAAEIQAACEGApgJTGEhDlEIAABCAAAQhAgBMBpDROWNEUAhCAAAQgAAEIsBRASmMJiHIIQAACEIAABCDAiQBSGiesaAoBCEAAAhCAAARYCiClsQREOQQgAAEIQAACEOBE4F8PNMqwoivTWwAAAABJRU5ErkJggg==)\n",
        "\n",
        "### The third less obvious observation is that within the main cluster, there is a dense cluster of sequences from Gujarat.\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Z_H5O0epB43"
      },
      "source": [
        "## t-SNE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAvrdGToosqU",
        "outputId": "e795e0e9-3fba-4736-8c2e-c432911bd4d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Use these parameters\n",
        "random_state = 0\n",
        "n_components = 2\n",
        "perplexity = 30\n",
        "n_iter = 10000\n",
        "\n",
        "####### Enter code below ####### \n",
        "\n",
        "model = TSNE(n_components=2, random_state=random_state, perplexity=perplexity, n_iter=n_iter)\n",
        "tsne_data = model.fit_transform(big_matrix)\n",
        "\n",
        "################################"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning:\n",
            "\n",
            "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning:\n",
            "\n",
            "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LlFxnf4pEZG",
        "outputId": "3ace633e-f7b0-45c6-eed3-34db6801a859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        }
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"X\", \"Y\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "i = 0\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"X\"] = tsne_data[i][0]\n",
        "    dicti[\"Y\"] = tsne_data[i][1]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "    i+=1\n",
        "\n",
        "final_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              StrainID         X          Y  \\\n",
              "680  hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...  1.881189  11.271591   \n",
              "681  hCoV-19/India/MaxCov0047_CSIR-IGIB/2020|EPI_IS...  1.828764  11.692644   \n",
              "682  hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...  1.750070  12.592662   \n",
              "683  hCoV-19/India/MaxCov0049_CSIR-IGIB/2020|EPI_IS...  1.364001  12.977669   \n",
              "684  hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...  2.358655  10.985281   \n",
              "\n",
              "     State  \n",
              "680  Delhi  \n",
              "681  Delhi  \n",
              "682  Delhi  \n",
              "683  Delhi  \n",
              "684  Delhi  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b06fa825-92d8-4b0d-af0d-a8dc1e3700d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StrainID</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>1.881189</td>\n",
              "      <td>11.271591</td>\n",
              "      <td>Delhi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>hCoV-19/India/MaxCov0047_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>1.828764</td>\n",
              "      <td>11.692644</td>\n",
              "      <td>Delhi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>1.750070</td>\n",
              "      <td>12.592662</td>\n",
              "      <td>Delhi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>hCoV-19/India/MaxCov0049_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>1.364001</td>\n",
              "      <td>12.977669</td>\n",
              "      <td>Delhi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>2.358655</td>\n",
              "      <td>10.985281</td>\n",
              "      <td>Delhi</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us4Joar_pEcB",
        "outputId": "7cde17c3-a60f-4d9a-dbdc-ddc7fc39ef0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "for_x = final_df.X.tolist()\n",
        "for_y = final_df.Y.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"t-SNE\", height=600, width=1000,\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "\n",
        "fig.show(renderer = 'colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bCtiFsOpZaE"
      },
      "source": [
        "You should get a plot similar to this:\n",
        "\n",
        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)\n",
        "\n",
        "**Does making the 3 observations become easier in this plot? \n",
        "Can you make any other new observations from this plot?**\n",
        "\n",
        "Make sure you tweak the parameters to find out if a certain different setting is allowing you to get a more interpretable plot."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ANSWER:-yes, that 3 observations also easier in this t-SNE plot.**\n",
        "\n",
        "**1st** observation:for some reason the orange dots (MAHARASHTRA) are scattered away from two big main clusters.\n",
        "\n",
        "**2nd** observation: gujurat forms secondary main cluster at two places\n",
        "\n",
        "**3rd** observation : odisha forms primary small cluster"
      ],
      "metadata": {
        "id": "jIy3XlELIeBZ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECFR423VpcVd"
      },
      "source": [
        "# Mutation Profile based methods\n",
        "\n",
        "If you would have noticed, there is a column called Nuc_muts (Nucleotide mutations) in the INDIA_685.csv file. These are the specific mutations for each sequence which I have precomputed for you using the first SARS-CoV-2 sequence from Wuhan, China (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2?report=fasta) as the reference.\n",
        "\n",
        "A mutation C453T in a sequence means that the original reference sequence from Wuhan had a C at position 453 whereas the sequence has a T at that position. \n",
        "\n",
        "Now instead of using kmers for sequences, this time we will use the **presence/absence of all mutations** found in our dataset to represent the genomes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrbWFyL2pEed",
        "outputId": "5fb02120-4d81-4e86-b41d-0537312b83ca"
      },
      "source": [
        "## First we need to get the list of all unique mutations observed in our dataset.\n",
        "\n",
        "mutations = []\n",
        "for i in range(len(data)):\n",
        "\n",
        "    sample_muts = data[\"Nuc_muts\"][i][1:][:-1].split(\",\")\n",
        "\n",
        "    for j in sample_muts:\n",
        "        mut = j.strip()[1:][:-1]\n",
        "        if(len(mut)>=2):\n",
        "            if(mut not in mutations):\n",
        "                mutations.append(mut)\n",
        "\n",
        "print(len(mutations))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1279\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHS8f3jCphLc"
      },
      "source": [
        "So there are 1279 unique mutations in our sequences. We can now represent each genome as a ```vector of size 1279 where each position indicates the presence (1) or absence (0) of a specific mutation```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "uR3nnmgMpEhu",
        "outputId": "62d6adac-1489-4789-b214-1c3b068d157a"
      },
      "source": [
        "samplelist = data[\"StrainID\"].tolist()\n",
        "df = pd.DataFrame(columns = [*samplelist])\n",
        "df = df.replace(np.nan,0)\n",
        "\n",
        "for mutation in mutations:\n",
        "\n",
        "    dicti = dict(zip(samplelist, np.zeros(len(samplelist), dtype=int)))\n",
        "\n",
        "    for i in range(len(data)):\n",
        "        flag=0\n",
        "        strainid = data[\"StrainID\"][i]\n",
        "        sample_muts = data[\"Nuc_muts\"][i][1:][:-1].split(\",\")\n",
        "\n",
        "        for j in sample_muts:\n",
        "            mut = j.strip()[1:][:-1]\n",
        "            if(len(mut)>=2):\n",
        "                if(mut == mutation):\n",
        "                    flag = 1\n",
        "                    break\n",
        "\n",
        "        dicti[strainid]=flag  \n",
        "\n",
        "    df = df.append(dicti, ignore_index = True)\n",
        "    \n",
        "df = df.T\n",
        "\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   0    1    2    3    4     \\\n",
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              "                                                    ... 1269 1270 1271 1272  \\\n",
              "hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|202...  ...    0    0    0    0   \n",
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              "hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|202...  ...    0    0    0    0   \n",
              "\n",
              "                                                   1273 1274 1275 1276 1277  \\\n",
              "hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|202...    0    1    0    0    0   \n",
              "hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|202...    0    0    1    1    1   \n",
              "hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|202...    0    0    1    1    0   \n",
              "hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|202...    0    0    1    1    0   \n",
              "hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|202...    0    0    1    1    0   \n",
              "\n",
              "                                                   1278  \n",
              "hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|202...    0  \n",
              "hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|202...    0  \n",
              "hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|202...    1  \n",
              "hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|202...    0  \n",
              "hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|202...    0  \n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1279 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09f39025-c167-4fa1-8e3a-6490234d079c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-09f39025-c167-4fa1-8e3a-6490234d079c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09f39025-c167-4fa1-8e3a-6490234d079c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9CIPpHvpmc2"
      },
      "source": [
        "## PCA\n",
        "\n",
        "Performing PCA on this dataframe, we want to reduce the dimensionality of our genome sequences from 1279 to 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64svk1wEpjYC"
      },
      "source": [
        "pca = PCA()\n",
        "pca.fit(df)\n",
        "pca_data = pca.transform(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTEZQIDGpjbB",
        "outputId": "b5533988-6c68-4fae-dc1c-ef2d9875e21e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        }
      },
      "source": [
        "per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)\n",
        "labels_all = ['PC'+str(x) for x in range(1, len(per_var)+1)]\n",
        "per_var=per_var[:10]\n",
        "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
        "\n",
        "with plt.style.context('dark_background'):\n",
        "    plt.figure(figsize = (15, 9))\n",
        "    plt.xlabel(\"Number of Principal Components\")\n",
        "    plt.ylabel(\"Percentage of variance explained\")\n",
        "    plt.bar(range(1,len(per_var)+1), per_var, tick_label=labels, color = \"aqua\")\n",
        "    plt.plot(range(1,len(per_var)+1), np.cumsum(per_var), color = \"red\")\n",
        "    plt.scatter(range(1,len(per_var)+1), np.cumsum(per_var), color = \"yellow\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ],
            "image/png": 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nzp3k5eXRuHFjxo0bR5s2bVi+fDkDBgxg06ZNyYwhSYpJO+BSQplrB3wCPAY8APwTSAAL74Wi7pCTU/654mIYMSLVaSVJSk9J3/3ym9/8Jl26dCEvLw+A4cOHM336dDp27Mj06dMZPnx4siNIklKoAfATYCbwLnDDrtdLCWvoLgNmEAodwNixUFgIy5dDWVl4LSwM9yVJUuUkknUtW7Ys0aRJk33uLV68ONG8efMEkGjevHli8eLFkd8za9aspGX08vLy8jryKwsS34XEWEhsg0QCEm9D4peQaFkF8nl5eXl5eaX7dbBOlNSRukQiwdSpU5k9ezaFhYUANGvWjLVr1wKwdu1amjVrlswIkqQk+jLwJ2AV8CxwNnAPkAd8kXDW3KrY0kmSVDMkdU3d1772NdasWcPxxx/PtGnTWLx48X7PJBKJA362sLCQwYMHA5Cbm5vMmJKkQ9AUuISwe+VpwA5gMjAGeAYoiS+aJEk1UlJH6tasWQPAhg0bmDBhAl27dmXdunU0b94cgObNm7N+/foDfraoqIi8vDzy8vL48MMPkxlTkhShDnARMAlYDdxGKHNXAV8ALiQcEG6hkyQp9ZJW6o455hiOPfbYPf/+9re/zVtvvcVTTz1FQUEBAAUFBUycODFZESRJR6gnMBpYC4wHOgN/BE4lHC03CvgotnSSJAmSOP2yWbNmTJgwIfyS7GwefvhhnnvuOWbNmsX48eMZNGgQK1asYMCAAcmKIEk6DG0oP4agPVAMPE44hmAG4JngkiRVLUkrdcuWLeO0007b7/7HH3/M2WefnaxfK0k6DPUJ0yt/BPQiFLcZwE2EQlccXzRJkhQhqRulSJKqrkzgW4QidwFQD3gHGAE8CLwfXzRJknQILHWSVMN0IuxcOZCwycnHwN8J0ytfjzGXJEk6PJY6SaoBjgfyCWXudMIulZMJRW4yYSdLSZKUnix1klRN1QG+Ryhy5xD+C382MAR4BPCwGEmSqgdLnSRVM90J6+T+C2hMOFfuVsKo3Nsx5pIkSclhqZOkaqA18ENCmesIbAOeAMYAL+AxBJIkVWeWOklKU8cC/QlF7pu77s0A/hd4DPgkplySJCm1LHWSlEYygT6EInchcAywBPg14RiCFfFFkyRJMbHUSVIaOJWw4ckPgROAjYSplQ8Ar8aYS5Ikxc9SJ0lVVC7hGIIfAWcAO4FngaHAJGB7fNEkSVIVYqmTpCqkNnAeYVTuXKAWMJdQ5MYCG+KLJkmSqihLnSRVAV0pP4agCfABcDtheuVbMeaSJElVn6VOkmJyIuXHEJwCfApMIKyVmw6UxhdNkiSlEUudJKVQDvADwvTKbxB2s3wR+CPwKLA1tmSSJCldZcYdQJKqk/x8WLYMSkvDa35++TEEY4B1u15bAb8B2hLK3d+x0EmSpMPjSJ0kHSX5+VBUBDk54ec2n8L9X4C7joEm22AT4Sy5B4BXYswpSZKqF0udJB0lI0dCznbgfsJw3CyonQX1esGAF+Bp4LNYE0qSpOrIUidJRygT+DbQ+lrgKWAH0Bn4M5APdZvCo1kxBpQkSdWapU6SDlMH4DLC7pUnAKXTIeuKXTdPK39u5fIYwkmSpBrDUidJh6A+MIDQ284kHDvwLHA1kPMtuHtk+Zo6gOJiGDEihqCSJKnGsNRJUoQMoBehyP2AcCzBIuA64B/A2t0PjoedWWFtXatWsHJlKHRjx8aRWpIk1RSWOkmqQGvCeXIFQDtgM2H3yvuA1yr4zNixljhJkpRaljpJ2ks94ELCqFwfoAyYDvwaeBL4NL5okiRJB2SpkySgO6HIXQw0BN4DriecKbcyxlySJElRLHWSaqwWwKXAj4FTgWLgMcL0yplAIrZkkiRJlWepk1Sj1Ab6EkblvgtkAf8CLgceBT6JL5okSdJhsdRJqhFOIxS5gUATYBXwB+B+4D/xxZIkSTpiljpJ1VYuocT9mFDqPiNsdnIf8DxhExRJkqR0Z6mTVK1kEaZVXg58jzDdchbwU2AssCm+aJIkSUlhqZNULZxCmF55KWEDlHXAXYRRuYUx5pIkSUo2S52ktNUQ+C9CmesGlACTCUXuGWBnfNEkSZJSxlInKa1kEA4Fvwy4gHBY+ALg58CDwIb4okmSJMXCUicpLbQjbHhSALQCNgJ/J4zKzYkvliRJUuwsdZKqrBygP2FUrhdht8qpwLXARGB7fNEkSZKqDEudpCrna4QidxFQH1gCjAAeAFbHmEuSJKkqstRJqhJaAj8iTLHsAGwFxhGmV74SXyxJkqQqz1InKTZ1gH6EUblvAZnADOAm4HFgW3zRJEmS0oalTlLKnUEocvlAY2AFociNAZbFmEuSJCkdWeokpURT4IeEMvcl4FPCaNx9hNG5RHzRJEmS0pqlTlLSZAPnEYrcuUAt4N/AYMJ6uS3xRZMkSao2LHWSjrovEYrcDwkjdB8AfwbuBxbHF0uSJKlastRJOioaE9bIXUZYM7cDeIowvfI5oDS+aJIkSdWapU7SYcsEvk04hqAfYTfLN4CrgYeBj2JLJkmSVHNY6iQdsg6EIlcAnAB8CIwmjMrNjy+WJElSjZQZdwBJVVN+PixbBqWl4fWy/nA58C9gCXAdMBf4AfAFYBgWOkmSpDg4UidpP/n5UFQEOfWAmdDmPrj3GcgA3gauBR4E1saaUpIkSWCpk3QAI0dCzn+AK4BXgQaQcSms+Q50ujDmcJIkSdqHpU7SPnKA1ncBdwDHAUXAJcAx0LwszmSSJEk6ENfUSdrj+4TplRl/BgYRDpX7CXBMeH/lyriSSZIkqSKWOkm0Ap4EJgKbgBvPhuI/E0bqdikuhhEjYoknSZKkg7DUSTVYNvALwujc2bv+/VXgd89DYSEsXw5lZeG1sBDGjo0vqyRJkg7MNXVSDdWDcLbcVwgjdEOA9/d6f+xYS5wkSVI6cKROqmEaA38FXgEaAecD/di30EmSJCl9WOqkGuSHhL1PLgf+CHwReCrWRJIkSTpSljqpBjgZeAH4B/Af4HTgOqA4zlCSJEk6KipcU9elS5eDfvCNN9446mEkHV11gRHALwkFbjBwD5CIM5QkSZKOqgpL3a233gpA3bp1OeOMM5g/fz4ZGRl85StfYfbs2fTs2TNlISUdum8Do4CTgAcIO1tuiDWRJEmSkqHC6Ze9e/emd+/efPDBB5x++unk5eVxxhln0KVLF1avXp3KjJIOQXNgLPAcsBPoDRRgoZMkSaquItfUnXzyybz11lt7fl64cCGnnnpqUkNJOnSZwFWEjVD6AdcTjiuYEWcoSZIkJV3kOXVvvvkmRUVFPPjggwAMHDiQN998M+nBJFXe6YQz5/KAqcBPgXdjTSRJkqRUiSx1l112GVdeeSVDhw4FYObMmdx9991JDyYpWn3gJuD/A9YDFwPjY00kSZKkVIssddu3b2f06NE888wzLFmyJBWZJFVCf+AOwhq6UcCvgc2xJpIkSVIcItfU9e3bl3nz5jFlyhQAOnfuzMSJE5MeTNKBtQUmA48Ca4FuwBAsdJIkSTVVZKm78cYb6dq1K5s2bQJg/vz5tG3bNunBJO2rFvArYCFwFjAU6ArMjjOUJEmSYhc5/bKkpIQtW7bscy+R8OhiKZW+DtwNfJEwQjcMWBNrIkmSJFUVkSN1CxcuJD8/n6ysLNq3b8+dd97JK6+8kopsUo2XC9wHvAjUA84FBmChkyRJUrnIUjdkyBA6derE9u3bGTt2LFu2bGHYsGGpyCbVWBnA5YQz5wYCI4FOwLNxhpIkSVKVlAFU+bmUs2bNIi8vL+4YUkp0Iky1PAuYCVwJvB1rIkmSJMXtYJ0ock1dhw4d+MUvfkGbNm3Izi5/vE+fPkcvoSSOAa4HriHsZHkZcH+cgSRJkpQWIkvdo48+yujRo7nnnnsoLS095F+QmZnJ7NmzWb16NX379qVNmzY88sgjNGnShDlz5nDppZdSUlJyWOGl6uI84P+ANsC9wC+Bj+IMJEmSpLQRuaZu586djB49mlmzZjF37tw9V2UNHTqURYsW7fn5lltu4bbbbqNDhw5s3LiRQYMGHV5yqRo4AXgcmAQUE6Zc/gQLnSRJkiovstQ9/fTTXHnllTRv3pzGjRvvuSrjhBNO4LzzzuOee+7Zc69379489thjAIwZM4Z+/fodZnQpfWURjiVYBHwXGA50AV6KM5QkSZLSUuT0y4KCAgCuvfbaPfcSiQQnnXRS5JfffvvtXHfdddSvXx+AJk2asGnTpj3TOFetWsUJJ5xwwM8WFhYyePBgAHJzcyN/l5QuugJ/BU4DJgP/H7A8zkCSJElKa5Glrl27dof1xeeddx7r169n7ty59OrV65A/X1RURFFRERB2epHSXUPC0QRXAB8APwCeiDWRJEmSqoMKS903v/lNZsyYwQUXXHDA9ydMmHDQLz7zzDP5/ve/z7nnnkvdunVp0KABd9xxB40aNSIrK4vS0lJatmzJ6tWrj+wvkNJAPnAb4TDxO4AbgE9iTSRJkqTqosJS16tXL2bMmEHfvn33ey+RSESWuhEjRjBixIg93/WLX/yCH/7wh4wfP57+/fszbtw4CgoKmDhx4hH+CVLV1QEYBZwNvE5YPzcv1kSSJEmqjhLJvnr16pV4+umnE0Cibdu2iddeey2xdOnSxPjx4xO1a9eO/PysWbOSntHL62hedSBxAyQ+g8RGSFwBicwqkMvLy8vLy8vLyys9r4N1ooxd/zioc889l06dOlG3bt0992666aaojx01Bzs9Xapq+hBG5zoCDwM/B9bFmkiSJEnp7mCdKPJIg7vvvpuLL76YIUOGkJGRwUUXXUTr1q2Pekgp3TUDHgSeBzKAbwEDsdBJkiQpuSJLXc+ePSkoKGDjxo387ne/o0ePHnTs2DEV2aS0kEHY0XIx0B/4LfBlQrmTJEmSki2y1H366acAbNu2jRYtWlBSUkKLFi2SHkxKB52BV4C7gTnAV4DfANtjzCRJkqSaJfKcukmTJtGwYUP++Mc/MnfuXBKJBPfcc08qsklV1rGEEbmhwIeEaZYPx5pIkiRJNVWlNkrZrXbt2tStW5ctW7YkMdL+3ChFVUk/4E7gRGA08CtgU6yJJEmSVN0drBNVOFJX0aHju0WdUydVN62Bu4C+wHxgAPBqrIkkSZKkg5S6Ax06vltlDh+XqotswrEENwJlwDXAHUBpnKEkSZKkXSosdZdffnkqc0hV0pmEKZZfAiYQ1tC9H2siSZIkaV+Ru18ed9xx3HHHHcyZM4fZs2dz++23c9xxx6UimxSb44Ai4CWgPvB94EIsdJIkSap6IkvdI488woYNG/jBD35A//792bBhA+PGjUtFNikWPyKcOfdj4P8BXwSejjOQJEmSdBCRpa5FixbcfPPNLF++nOXLl/P73/+eZs2apSKblFKnADOAMcASoAvwS2BbnKEkSZKkCJGlburUqVx88cVkZGSQkZHBRRddxHPPPZeKbFJK1ANuJuxo+RXgJ8BZwFtxhpIkSZIqKfKcui1btpCTk0NpadjrLysri+LiYiDsgtmwYcOkh/ScOiXLd4BRQDvCCN21wIZYE0mSJEn7O6xz6nZr0KDBUQ8kxa0FcDvhrLnFwDeBf8YZSJIkSTpMkdMvP3+0QWZmJjfccEPSAknJkJ8Py5ZB6Q748Eb4T3Y4RPx/gM5Y6CRJkpS+Iktdnz59mDx5Ms2bN6dTp068+uqr1K9fPxXZpKMiPx+KiqDNh5DZA5r8Fmr3gl99D0YCO+IOKEmSJB2ByOmXAwcOZMCAASxYsIDi4mIuueQSXnnllVRkk46Kkb+HnL8SFsw1BcZB9kUwbAXcMSnmcJIkSdIRihypa9++PUOHDuXxxx9nxYoVXHrppdSrVy8V2aQjVgdofSNwDXABYQHdACADWrWKM5kkSZJ0dESWuqeffpobbriBK664gl69erF06VJmzZqVimzSEWlBWCuX8Q/gt8B4YK/NWleujCOVJEmSdHRFTr/s2rUrW7du3fPzn//8Z55++umkhpKO1BnAk4QO9+evwX9fAzl7/b8wiothxIiYwkmSJElHUeRIXb169bjnnnt49tlnATj11FM566yzkh5MOlwDgX8RNkDpAVzzEhQWwvLlUFYWXjP6aJsAACAASURBVAsLYezYOFNKkiRJR0dkqbv//vt57rnnaNGiBQBLlixh2LBhSQ8mHapM4P8BDwL/BvKAt3a9N3YstG0LWVnh1UInSZKk6iKy1OXm5vLoo49SVlYGQGlpKaWlpUkPJh2KhsAkwgaX/wd8G/go1kSSJElSakSuqSsuLua4444jkUgA0K1bNzZv3pz0YFJldQSeAtoBg4GieONIkiRJKRVZ6n7+85/z1FNPcdJJJ/HSSy9x/PHH079//1RkkyJ9FxhLWD/XG3gp3jiSJElSykWWujfeeINevXpx8sknk5GRwTvvvMPOnTtTkU06qGuAW4A3gX6AJxRIkiSpJoosdRDW0b399tvJziJVSl3gb8ClhKPnLgO2xZpIkiRJik/kRilSVfIF4EVCofs1cDEWOkmSJNVslRqpk6qCrsAEoD5wPmFzFEmSJKmmq9RI3cCBA7n++usBOPHEE8nLy0tqKOnzLiWM0H1KOFDcQidJkiQFkaVu1KhR9OjRg/z8fAC2bt3KX/7yl6QHkwCygD8BDwAvE0brFsaaSJIkSapaIqdfduvWja9+9avMnTsXgE2bNlG7du2kB5MaAY8A3wHuJOx26b6rkiRJ0r4iS11JSQmZmZl7Dh/Pzc2lrKws6cFUs50CTATaAD8B7o01jSRJklR1RU6/vPPOO5kwYQJNmzbl5ptv5qWXXmLkyJGpyKYa6hzgVaAh8E0sdJIkSdLBRI7UPfzww8yZM4c+ffqQkZFBv379WLx4cSqyqQa6DvhfYB7hQPH3440jSZIkVXmVWlO3cOFCRo0aBUD9+vXp2rUrr7/+etLDqeaoC9wDDCSso7ucsNOlJEmSpIOLnH55991388knn+z5+ZNPPuHuu+9OaijVLCcAM4F8YMSuVwudJEmSVDmRI3UZGRn7/JxIJMjO9sxyHR3dCQeKH0M4UHxSvHEkSZKktBM5Uvfee+8xZMgQsrOzyc7O5uqrr+a9995LRTZVcz8G/gl8Qih3FjpJkiTp0EWWuiuuuIKePXuyevVqVq1aRbdu3Rg8eHAqsqmaygL+DNxHmHbZFVgUayJJkiQpfUXOo9ywYQP5+fmpyKIaoDFhI5RvA7cDvwBKY00kSZIkpbfIUpebm0thYSFt2rTZZy3doEGDkhpM1c8pwFNAa8LulvfFG0eSJEmqFiJL3cSJE/nXv/7F888/T2mpYyo6POcBDwPbgG8A/441jSRJklR9RJa6Y445huHDh6cii6qp4cDvgbnABcCqeONIkiRJ1UrkRimTJk3inHPOSUUWVTP1CKNz/0tYR3cWFjpJkiTpaIssdUOHDmXSpEls27aNzZs3s2XLFjZv3pyKbEpjLYF/ARcDvwQGAp/FmkiSJEmqniKnXzZo0CAVOVSN9ACeIBwo/n1gcrxxJEmSpGotstQBNGrUiA4dOlC3bt099/71r38lLZTS1+XA3cAK4JvA4njjSJIkSdVeZKkbNGgQQ4cOpWXLlsybN4/u3bvz73//mz59+qQin9LE7gPFrwaeA/4L2BRrIkmSJKlmqNSaury8PFasWEHv3r3p0qULmzb5P9dV7jhgCqHQ3Uo4vsD/hEiSJEmpETlS99lnn7F9+3YAateuzTvvvMPJJ5+c9GBKD18kHCjeEigAHog3jiRJklTjRJa6VatW0bBhQ5588kmmTZvGxo0bWbFiRSqyqYrrCzwEfAL0Al6LN44kSZJUI0WWugsvvBCA3/72t8yYMYOGDRsyZcqUpAdT1TYCuAmYA/QD1sQbR5IkSaqxKix19evXZ+vWrTRu3HjPvQULFgBw7LHHsnHjxuSnU5VzDPB3wvlzDwKFeP6cJEmSFKcKS93DDz9M3759mTNnDolEgoyMjH1eTzrppFTmVBVwIjAR6AxcC/wp3jiSJEmSOEip69u3LwC9evXi/fffT1kgVU1nEg4UrwN8D3g23jiSJEmSdok80mDy5MmpyKEq7CfAC8BGoBsWOkmSJKkqiSx1c+fO5YwzzkhFFlUx2cBdQBGh1HUD3ok1kSRJkqTPi9z9slu3bgwcOJAVK1ZQXFy8Z01d586dU5FPMWkCjAd6E9bO/RIoizWRJEmSpAOJLHXf+c53UpFDVciXCBuifAH4EfCPeONIkiRJOojIUrdy5UoAjj/+eOrWrZv0QIrX+YSjCrYAXwdmxRtHkiRJUoTINXV9+/ZlyZIlLFu2jBdffJHly5fz7LNulVEd/Rp4ElgInIGFTpIkSUoHkaXupptuonv37ixZsoR27drRp08fXn311VRkU4ocQ1g/dxPwANAL+CDWRJIkSZIqK7LUlZSU8PHHH5OZmUlGRgb//Oc/3Q2zGmkFvAxcCFwDFADbY00kSZIk6VBErqnbtGkTOTk5zJw5k4ceeoj169dTXFycimxKsrOAx4FawHnAc/HGkSRJknQYIkfqzj//fLZt28bPfvYzpkyZwrvvvkvfvn1TkU1JNBiYDnwEdMVCJ0mSJKWryJG6//7v/2bcuHGsWbOGBx54IBWZlETZwB3AT4FngHzCTpeSJEmS0lPkSF39+vWZOnUqM2fO5KqrrqJp06apyKUkyAWmEQrdLUBfLHSSJElSuossdb/73e/40pe+xFVXXUWLFi148cUXmTZtWuQX16lTh9dee4158+bx1ltv8Zvf/AaANm3a8Oqrr7J06VIeeeQRatWqdcR/hKJ9mXBEQXfgh8BwoCzWRJIkSZKOhshSt9v69etZu3YtH330UaVG67Zv307v3r057bTTOO200/jud79Lt27duOWWW7jtttvo0KEDGzduZNCgQUf0ByjaBcArhA1RzgIeijeOJEmSpKMostRdeeWVzJgxg+nTp9OkSRMKCwvp3Llzpb589y6ZtWrVolatWiQSCXr37s1jjz0GwJgxY+jXr98RxNfBZAA3Ak8ACwgHis+ONZEkSZKkoy1yo5QTTzyRYcOGMX/+/EP+8szMTObMmUP79u35y1/+wrvvvsumTZsoLS0FYNWqVZxwwgmHnlqRcoAxwA+A+4Ar8fw5SZIkqTqKLHUjRow47C8vKyujS5cuNGzYkAkTJnDKKadU+rOFhYUMHjwYgNzc3MPOUBO1BiYCXwJ+BtwebxxJkiRJSVTpNXVHYvPmzcyYMYMePXrQqFEjsrKyAGjZsiWrV68+4GeKiorIy8sjLy+PDz/8MBUxq4VehCmWrYBzsNBJkiRJ1V2Fpa527dpH9MW5ubk0bNgQgLp16/Ktb32LRYsWMWPGDPr37w9AQUEBEydOPKLfo3JXEI4sWE84UDx6j1JJkiRJ6a7CUvfvf/8b4LAPHG/RogUzZsxg/vz5zJo1i2nTpjF58mR++ctf8vOf/5ylS5fSpEkT7r333sNLrj1qAXfvup4jHFvwn1gTSZIkSUqVCtfU1a5dm/z8fHr27MkFF1yw3/sTJkw46BcvWLCA008/fb/7y5Yto1u3bocRVQeSCzwOfB34X+DXeP6cJEmSVJNUWOquuOIKBg4cSKNGjejbt+8+7yUSichSp+TIz4eRI6FVK/hgCtQeAMcWQz7wSNzhJEmSJKVchaXu5Zdf5uWXX2b27Nn8/e9/T2UmVSA/H4qKICcHeAxOKICyRnD9mfDI1LjTSZIkSYpD5JEG//jHPxgyZAhf//rXAXjxxRcZPXo0O3fuTHo47WvkSMipB9wA3AT0gMwnoPAzGNk25nCSJEmSYhFZ6kaNGkWtWrUYNWoUAJdeeil33303hYWFSQ9X5SUSKf11bYHM0lImv34eqy8/gZ+OGsWOOnV2ZUlhkIyMFP4ySZIkSQcTWery8vI47bTT9vw8Y8YM5s2bl9RQqlhZVhb9nnyS7XXqWK4kSZIkRR8+XlpaSrt27fb83LZtW0pLS5MaSge3vW5dC50kSZIkoBIjdddeey0zZszgvffeIyMjg9atW3PZZZelIpskSZIkKUJkqXvhhRfo0KEDJ598MgDvvPMOO3bsSHowSZIkSVK0yFIHsGPHDhYsWJDsLJIkSZKkQxS5pk6SJEmSVHVZ6iRJkiQpjVWq1A0cOJDrr78egBNPPJG8vLykhpIkSZIkVU5kqRs1ahQ9evQgPz8fgK1bt/KXv/wl6cEkSZIkSdEiN0rp1q0bX/3qV5k7dy4AmzZtonbt2kkPJkmSJEmKFjlSV1JSQmZmJolEAoDc3FzKysqSHkySJEmSFC2y1N15551MmDCBpk2bcvPNN/PSSy8xcuTIVGSTJEmSJEWInH758MMPM2fOHPr06UNGRgb9+vVj8eLFqcgmSZIkSYoQWeoaN27M+vXrGTt2bPmHsrPZuXNnUoNJkiRJkqJFTr+cO3cuGzZsYMmSJSxdupQNGzawfPly5syZw+mnn56KjJIkSZKkCkSWumnTpnHuuedy/PHHk5ubyznnnMOkSZP46U9/yqhRo1KRUZIkSZJUgchS1717d6ZOnbrn52nTptGjRw9ee+016tSpk9RwkiRJkqSDi1xT98EHH3DdddfxyCOPAHDxxRezbt06MjMzPdpAkiRJkmIWOVJ3ySWX0LJlS5588kmefPJJWrVqxSWXXEJWVhYDBgxIRUZJkiRJUgUiR+o++ugjrr766gO+9+677x71QJIkSZKkyossdbm5uVx33XV06tSJunXr7rnfp0+fpAaTJEmSJEWLnH750EMPsXjxYtq2bctvf/tbli9fzqxZs1KRTZIkSZIUIbLUNWnShL///e+UlJQwc+ZMBg0aRO/evVORTZIkSZIUIXL6ZUlJCRB2wTz33HNZs2YNxx13XNKDSZIkSZKiRZa6m2++mQYNGnDNNddw11130aBBA4YNG5aKbJIkSZKkCJGlbuPGjWzZsoWFCxfumXbZs2fPpAeTJEmSJEWLXFN31113VeqeJEmSJCn1Khyp6969Oz179uT444/nZz/72Z77DRo0ICsrKyXhJEmSJEkHV2Gpq127NsceeyzZ2dnUr19/z/0tW7bQv3//lISTJEmSJB1chaVu5syZzJw5k/vvv5+VK1emMpMkSZIkqZIiN0qpU6cOf/3rX2nTpg3Z2eWP9+nTJ6nBJEmSJEnRIkvdo48+yujRo7nnnnsoLS1NRSZJkiRJUiVFlrqdO3cyevToVGSRJEmSJB2iyCMNnn76aa688kqaN29O48aN91ySJEmSpPhFjtQVFBQAcO211+65l0gkOOmkk5KXSpIkSZJUKZGlrl27dqnIIUmSJEk6DJHTL+vVq8f//M//8Ne//hWA9u3bc9555yU9mCRJkiQpWmSpu++++9ixYwc9e/YEYPXq1dx8881JDyZJkiRJihZZ6k466ST++Mc/UlJSAsCnn35KRkZG0oNJkiRJkqJFlrodO3ZQt25dEokEENbYbd++PenBJEmSJEnRIjdKufHGG5kyZQonnngiDz74IGeeeSY//vGPUxBNkiRJkhQlstQ9//zzzJ07l+7du5ORkcHQoUP56KOPUpFNkiRJkhQhcvplv3792LlzJ8888wyTJ09m586dnH/++anIJkmSJEmKEFnqbrzxRrZs2bLn582bN3PjjTcmNZQkSZIkqXIiS11m5v6PZGdHztqUJEmSJKVAZKmbPXs2t956K+3ataNdu3bceuutzJkzJxXZJEmSJEkRIkvdkCFD2LFjB+PGjeORRx7hs88+46qrrkpFNkmSJElShIPOo8zMzGTSpEn07t2bX/3qV6nKJEmSJEmqpIOO1JWVlVFWVkaDBg1SlUeSJEmSdAgidzz55JNPWLBgAdOmTaO4uHjP/aFDhyY1mCRJkiQpWmSpe+KJJ3jiiSdSkUWSJEmSdIgiS90DDzxA3bp1adWqFUuWLElFJkmSJElSJUXufvm9732PefPmMWXKFAA6d+7MxIkTkx5MkiRJkhQtstT95je/oWvXrmzatAmA+fPn065du6QHkyRJkiRFiyx1JSUlbNmyZZ97ZWVlSQskSZIkSaq8yFK3cOFC8vPzycrKon379tx555288sorqcgmSZIkSYoQWeqGDBlCp06d2L59Ow8//DCbN29m2LBhqcgmSZIkSYpQ4e6XderU4YorrqB9+/YsWLCAHj16UFpamspskiRJkqQIFY7UjRkzhjPOOIMFCxZwzjnn8Kc//SmVuSRJkiRJlVDhSN0Xv/hFvvKVrwBw77338vrrr6cslCRJkiSpciocqSspKdnzb6ddSpIkSVLVVOFIXefOndm8eTMAGRkZ1KtXj82bN5ORkUEikaBhw4YpCylJkiRJOrAKS112doVvSZIkSZKqiMgjDSRJkiRJVZelTpIkSZLSWNJKXcuWLXnhhRdYuHAhb731FldffTUAjRs3ZurUqSxZsoSpU6fSqFGjZEWQJEmSpGovaaVu586dXHPNNXTq1Inu3btz1VVXceqppzJ8+HCmT59Ox44dmT59OsOHD09WBEmSJEmq9pJW6tauXcsbb7wBwCeffMKiRYs44YQTOP/88xkzZgwQDjjv169fsiJIkiRJUrWXki0uW7duTZcuXXjttddo1qwZa9euBULxa9as2QE/U1hYyODBgwHIzc1NRUxJkiRJSjtJ3yglJyeHxx9/nGHDhrF169b93k8kEgf8XFFREXl5eeTl5fHhhx8mO6YkSZIkpaWklrrs7Gwef/xxHnroISZMmADAunXraN68OQDNmzdn/fr1yYwgSZIkSdVaUkvdvffey6JFi7jtttv23HvqqacoKCgAoKCggIkTJyYzgiRJkiRVa0lbU3fmmWfyox/9iDfffHPPhikjRozgD3/4A+PHj2fQoEGsWLGCAQMGJCuCJEmSJFV7SSt1L7/8MhkZGQd87+yzz07Wr5UkSZKkGiXpG6VIkiRJkpLHUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJkiRJacxSJ0mSJElpzFInSZIkSWnMUidJ0v/f3r0HR1kdbhx/NlcDmBBJIcolgFwEBixQEOWSioAgShjAKZfS7cCEtoMEqZ2SwVr4ozMFqzWDCh0xIHZAlCIEZMRgCJdpkaTJJtlcSAQDCYUkpNIYUG7h/P7g57aYECFK3j3J9zNzZpLdfd/zvCdh3MezuwEAwGKUOgAAAACwGKUOAAAAACxGqQMAAAAAi92xUpecnKzKykp5vV7fbZGRkUpNTVVJSYlSU1PVvn37OzU9AAAAALQKd6zUvfXWW5o4ceINtyUmJiotLU19+vRRWlqaEhMT79T0AAAAANAq3LFSd+jQIX3++ec33BYXF6eNGzdKkjZu3KipU6feqekBAAAAoFUIas7JOnXqpIqKCklSRUWFOnXqdNPHxsfHa8GCBZKkqKioZskHAAAAALZx9INSjDE3vW/dunUaNmyYhg0bpurq6mZMBQAAAAD2aNZSV1lZqejoaElSdHS0qqqqmnN6AAAAAGhxmrXU7dy5U263W5LkdruVkpLSnNMDAAAAQItzx0rd5s2bdfjwYfXt21fl5eWaN2+eVq5cqfHjx6ukpETjxo3TypUr79T0AAAAANAq3LEPSpk9e3aDt48bN+5OTQkAAAAArY6jH5QCAAAAAPhuKHUAAAAAYDFKHQAAAABYjFIHAAAAABaj1AEAAACAxSh1AAAAAGAxSh0AAAAAWIxSBwAAAAAWu2N/fBy4KWOcTuAMl8vpBAAAAGiB2KkDAAAAAItR6gAAAADAYpQ6AAAAALAYpQ4AAAAALEapAwAAAACLUeoAAAAAwGKUOgAAAACwGKUOAAAAACxGqQMAAAAAi1HqAAAAAMBilDoAAAAAsBilDgAAAAAsRqkDAAAAAItR6gAAAADAYpQ6AAAAALBYkNMBANwCY5xO4AyXy+kEAAAAfo+dOgAAAACwGKUOAAAAACxGqQMAAAAAi1HqAAAAAMBilDoAAAAAsBilDgAAAAAsRqkDAAAAAItR6gAAAADAYpQ6AAAAALAYpQ4AAAAALEapAwAAAACLUeoAAAAAwGKUOgAAAACwGKUOAAAAACxGqQMAAAAAi1HqAAAAAMBilDoAAAAAsFiQ0wEA4I4xxukEznC5nE4AAACaEaUOAPBfrbUIS5RhAIC1ePklAAAAAFiMnToAAL6r1rrDye4mAPgFduoAAAAAwGKUOgAAAACwGKUOAAAAACxGqQMAAAAAi1HqAAAAAMBilDoAAAAAsBilDgAAAAAsRqkDAAAAAItR6gAAAADAYpQ6AAAAALAYpQ4AAAAALBbkdAAAANBKGeN0Ame4XE4nANDCsFMHAAAAABZjpw4AAMAW7G4CaAClDgAAAC0bZRgtHC+/BAAAAACLUeoAAAAAwGKUOgAAAACwGO+pAwAAAFAf70W0Bjt1AAAAAGAxSh0AAAAAWIxSBwAAAAAWc6TUPf744zp69Kg+/fRTLV261IkIAAAAANAiNHupCwgI0Ouvv65Jkyapf//+mjVrlvr169fcMQAAAACgRWj2Ujd8+HAdO3ZMpaWlunLlirZs2aK4uLjmjgEAAAAALUKzl7rOnTurvLzc9/2pU6fUuXPn5o4BAAAAAC2CS1Kz/gGK6dOna+LEiYqPj5ck/fSnP9VDDz2kRYsW3fC4+Ph4LViwQJLUt29fFRcXN2dMvxcVFaXq6mqnY1iHdbt9rFnTsG5Nw7rdPtasaVi328eaNQ3r1jSsW30xMTHq2LHjTe83zTlGjBhh9uzZ4/s+MTHRJCYmNmuGljAyMzMdz2DjYN1YM9bNvwfrxpqxbv47WDPWjXXz39HsL7/MzMxU79691b17dwUHB2vmzJnauXNnc8cAAAAAgBYhqLknrKur0zPPPKOPPvpIgYGBWr9+vQoLC5s7BgAAAAC0CIGSVjT3pMeOHdNrr72m1atX69ChQ809fYuRnZ3tdAQrsW63jzVrGtataVi328eaNQ3rdvtYs6Zh3ZqGdbt1zf5BKQAAAACA70+zv6cOAAAAAPD9odRZJjk5WZWVlfJ6vU5HsUaXLl20b98+FRQUKD8/XwkJCU5HskJoaKiOHDminJwc5efna8WKFU5HskZAQICys7O1a9cup6NYo7S0VHl5efJ4PMrMzHQ6jjUiIiK0detWFRUVqbCwUCNGjHA6kl/r06ePPB6Pb9TU1Gjx4sVOx7LCs88+q/z8fHm9Xm3evFmhoaFOR7JCQkKCvF6v8vPz+V1rREPPbyMjI5WamqqSkhKlpqaqffv2Dia0g+Mfwcm49TF69GgzePBg4/V6Hc9iy4iOjjaDBw82kky7du1McXGx6devn+O5bBht27Y1kkxQUJD55JNPzEMPPeR4JhvGkiVLzKZNm8yuXbscz2LLKC0tNR06dHA8h23jrbfeMvPnzzeSTHBwsImIiHA8ky0jICDAnDlzxnTr1s3xLP4+7rvvPvPZZ5+Zu+66y0gy7777rnG73Y7n8vcxYMAA4/V6TVhYmAkMDDR79+41999/v+O5/HE09Px21apVZunSpUaSWbp0qVm5cqXjOf15sFNnmUOHDunzzz93OoZVKioq5PF4JEnnz59XUVGROnfu7HAqO1y4cEGSFBwcrODgYBljHE7k/zp37qzJkyfrzTffdDoKWrjw8HCNGTNGycnJkqQrV66opqbG4VT2eOyxx3T8+HGVlZU5HcUKQUFBCgsLU2BgoNq0aaPTp087Hcnv9evXT0eOHNFXX32luro6HThwQNOmTXM6ll9q6PltXFycNm7cKEnauHGjpk6d6kQ0a1Dq0KrExMRo8ODBOnLkiNNRrBAQECCPx6Oqqirt3btXGRkZTkfye0lJSfrtb3+ra9euOR3FKsYYpaam6p///Kfi4+OdjmOFHj166OzZs9qwYYOys7O1bt06tWnTxulY1pg5c6beeecdp2NY4fTp03rppZdUVlamM2fOqKamRnv37nU6lt/Lz8/X6NGjdc899ygsLExPPPGEunbt6nQsa3Tq1EkVFRWSrv8P+k6dOjmcyL9R6tBqtG3bVtu2bdOzzz6r2tpap+NY4dq1axo8eLC6dOmi4cOHa8CAAU5H8muTJ09WVVUVH8HcBKNGjdLQoUM1adIkLVy4UKNHj3Y6kt8LCgrSkCFDtHbtWg0ZMkQXLlxQYmKi07GsEBwcrClTpmjr1q1OR7FC+/btFRcXpx49eui+++5T27ZtNWfOHKdj+b2jR49q1apVSk1N1Z49e5STk6O6ujqnY1mLVws1jlKHViEoKEjbtm3Tpk2btH37dqfjWKempkbp6emaOHGi01H82siRIzVlyhSVlpZqy5YtGjt2rP761786HcsKX7+U6+zZs9q+fbuGDx/ucCL/d+rUKZ06dcq3g/63v/1NQ4YMcTiVHSZNmqTs7GxVVVU5HcUK48aNU2lpqaqrq3X16lW9//77euSRR5yOZYX169frRz/6vBgHRwAACxhJREFUkWJjY3Xu3DmVlJQ4HckalZWVio6OliRFR0fz7/VbUOrQKiQnJ6uoqEivvPKK01GsERUVpYiICEnSXXfdpfHjx+vo0aMOp/Jvy5YtU9euXdWjRw/NnDlT+/bt09y5c52O5ffatGmjdu3a+b6eMGGC8vPzHU7l/yorK1VeXq4+ffpIuv4escLCQodT2WHWrFm89PI2lJWVacSIEQoLC5N0/XetqKjI4VR2+MEPfiBJ6tq1q6ZNm6bNmzc7nMgeO3fulNvtliS53W6lpKQ4nMj/Of5pLYxbH5s3bzanT582ly9fNuXl5WbevHmOZ/L3MXLkSGOMMbm5ucbj8RiPx2MmTZrkeC5/HwMHDjTZ2dkmNzfXeL1e88ILLzieyaYRGxvLp1/e4ujRo4fJyckxOTk5Jj8/3yxbtszxTLaMBx980GRmZprc3Fyzfft20759e8cz+fto06aNqa6uNuHh4Y5nsWmsWLHCFBUVGa/Xa95++20TEhLieCYbxsGDB01BQYHJyckxY8eOdTyPv46Gnt/ec8895uOPPzYlJSVm7969JjIy0vGc/jxc//8FAAAAAMBCvPwSAAAAACxGqQMAAAAAi1HqAAAAAMBilDoAAAAAsBilDgAAAAAsRqkDgBbOGKOXXnrJ9/1zzz2n5cuXfy/n3rBhg6ZPn/69nKsxM2bMUGFhofbt23fD7TExMfryyy/l8XhUUFCgtWvXyuVy1Tv+3nvv1datW5s091NPPaWlS5c26diYmBh5vd4G7+vdu7d2796tkpISZWVl6d1331XHjh2bNI+/iIuLU79+/ZyOAQCtDqUOAFq4ixcvatq0aerQoYPTUW4QGBh4y4+dP3++4uPjNXbs2Hr3HT9+XIMHD9agQYPUv39/TZ06td48Z86c0dNPP92knLt27dKqVauadOzNhIaGavfu3Vq7dq369OmjoUOHas2aNb4/VGyrqVOnqn///k7HAIBWh1IHAC3c1atX9cYbb2jJkiX17vvmTlttba0kKTY2Vvv379eOHTt0/Phx/fGPf9Ts2bN15MgR5eXlqWfPnr5jxo0bp8zMTBUXF2vy5MmSpICAAL344ovKyMhQbm6uFixY4DvvwYMHlZKSosLCwnp5Zs6cqby8PHm9Xq1cuVKS9MILL2jUqFFKTk7Wiy++eNPrrKur0z/+8Q/16tVLbrdbKSkpSktLU1pa2g07Zm63W9u2bdOHH36okpKSGwrb448/rqysLOXk5Ojjjz/2Pf7VV1/1rdfatWvrXW9MTIwOHjyorKwsZWVl6eGHH270ZzJ79mwdPnxYH3zwge+2AwcOqKCgQKGhoVq/fr3y8vKUnZ2tH//4x74c27dvV2pqqkpLS7Vw4UItWbJE2dnZOnz4sCIjIyVJ6enpSkpKksfjkdfr1bBhwyRJkZGR2r59u3Jzc3X48GENHDhQkrR8+XIlJycrPT1dx48f16JFi3yZ5syZoyNHjsjj8egvf/mLAgKuP22ora3VH/7wB+Xk5Ojw4cPq2LGjHn74YU2ZMkV/+tOf5PF41LNnTy1atEgFBQXKzc3VO++80+iaAAC+G8f/AjqDwWAw7tyora01d999tyktLTXh4eHmueeeM8uXLzeSzIYNG8z06dNveKwkExsba86dO2eio6NNSEiIOXXqlFmxYoWRZBISEswrr7ziO/7DDz80LpfL9OrVy5SXl5vQ0FATHx9vnn/+eSPJhISEmMzMTNO9e3cTGxtrzp8/b7p3714v57333mtOnjxpoqKiTGBgoElLSzNxcXFGkklPTzdDhw6td0xMTIzxer1GkgkLCzMZGRlm4sSJxu12m/LychMZGVnvcW632xw/ftyEh4eb0NBQc+LECdOlSxcTFRVlysrKfNm+PtbtdptXX3210esNCwszoaGhRpLp1auXyczMrDfv/46XX37ZJCQkNPjz+vWvf22Sk5ONJNO3b19z8uRJExoaatxut/n0009Nu3btTFRUlPnPf/5jfvGLXxhJ5s9//rNZvHixb63eeOMNI8mMHj3aN//q1avN73//eyPJPProo8bj8RhJZvny5ebvf/+7CQkJMR06dDDV1dUmKCjIPPDAA2bnzp0mKCjISDKvv/66mTt3rpFkjDHmySefNJLMqlWrfD/rb/4+/etf/zIhISFGkomIiHD83wKDwWC01BEkAECLV1tbq7ffflsJCQn66quvbumYzMxMVVRUSLr+EsfU1FRJktfr1aOPPup73HvvvSdjjI4dO6bPPvtMDzzwgCZMmKBBgwZpxowZkqSIiAj17t1bly9fVkZGhk6cOFFvvmHDhmn//v2qrq6WJG3atEljxoxRSkpKoznvv/9+eTweGWOUkpKiPXv2yO12a+/evTp37lyDx6SlpemLL76QJBUWFiomJkaRkZE6ePCgL9vNjm3oektLS/Xaa6/phz/8oerq6tSnT59GMzdm1KhRvp3B4uJinTx50ne+9PR0nT9/XufPn1dNTY127dol6frPZNCgQb5zfL0rdujQIYWHhysiIkKjRo3y7cqmp6erQ4cOuvvuuyVJu3fv1uXLl/Xvf/9bVVVV6tSpkx577DENHTpUmZmZkqSwsDBVVVVJki5duuTbZczKytL48eMbvJa8vDxt2rRJO3bs0I4dO5q8JgCAxlHqAKCVSEpKUnZ2tjZs2OC77erVq76X1LlcLoWEhPjuu3Tpku/ra9eu+b6/du2agoL++58PY8wN8xhj5HK5tGjRIl8R/FpsbKwuXLjw/V2U/vueum9qbJ7/vba6urobrufbNHS9S5YsUWVlpR588EEFBATo4sWLjZ6joKBAsbGxtzzn177Lz+RWz/v1erhcLm3cuFHLli2r9/grV67Ue3xDJk+erDFjxuipp57S888/r4EDB6qurq7RLACA28d76gCglTh37pzee+89zZ8/33fbiRMnNHToUEnSlClTbih1t+rpp5+Wy+VSz5491bNnTxUXF+ujjz7Sr371K9+T/d69e6tNmzaNnicjI0OxsbHq0KGDAgICNGvWLB04cOC28zTVJ598ojFjxqh79+6S5HuP2jc1dL0RERE6c+aMjDGaO3fut5bEzZs365FHHtETTzzhu2306NEaMGCADh06pDlz5ki6vm7dunVTcXHxbV3LT37yE0nSyJEjVVNToy+++OKG88bGxqq6utr3HsqGpKWlacaMGb4Pb4mMjFS3bt0anbe2tta3++dyudS1a1ft379fS5cuVUREhNq1a3db1wEAuDXs1AFAK/Lyyy/rmWee8X2/bt06paSkKCcnR3v27NH58+dv+5xlZWXKyMhQeHi4fvnLX+rSpUt688031b17d2VnZ8vlcuns2bP1PpXymyoqKpSYmKj09HS5XC7t3r1bO3fuvO08TVVdXa0FCxbo/fffV0BAgKqqqjRhwoR6j2voetesWaNt27bpZz/72S2t48WLF/Xkk08qKSlJSUlJunLlivLy8rR48WKtWbNGa9euVV5enq5evaqf//znunz58m1dy8WLF5Wdna3g4GDNmzdPkrRixQqtX79eubm5+vLLL+V2uxs9R1FRkX73u98pNTVVAQEBunLlihYuXKiysrKbHrNlyxatW7dOCQkJmjlzppKTkxURESGXy6XVq1erpqbmtq4DAHBrXLr+5joAAPAtNmzYoA8++EDbtm1zOspNpaen6ze/+Y2ysrKcjgIAaCa8/BIAAAAALMZOHQAAAABYjJ06AAAAALAYpQ4AAAAALEapAwAAAACLUeoAAAAAwGKUOgAAAACwGKUOAAAAACz2f/tnwXeD44O+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_w644b-ptLV"
      },
      "source": [
        "The first principal component should be able to capture close to 30% of the variance in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edpEBZXSpjd2",
        "outputId": "f01d32d3-cdd6-40d8-8a3a-99dfdf520156",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        }
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"PC1\", \"PC2\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*samplelist], columns = labels_all)\n",
        "\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"PC1\"] = pca_df.PC1.loc[sample]\n",
        "    dicti[\"PC2\"] = pca_df.PC2.loc[sample]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "\n",
        "final_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              StrainID       PC1       PC2  \\\n",
              "680  hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...  1.530221  0.079550   \n",
              "681  hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20... -1.063943 -0.405022   \n",
              "682  hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20... -1.058232 -0.410670   \n",
              "683  hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20... -1.057465 -0.409620   \n",
              "684  hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20... -1.057465 -0.409620   \n",
              "\n",
              "         State  \n",
              "680  Karnataka  \n",
              "681  Karnataka  \n",
              "682  Karnataka  \n",
              "683  Karnataka  \n",
              "684  Karnataka  "
            ],
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              "      <th>StrainID</th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
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              "  <tbody>\n",
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              "      <th>680</th>\n",
              "      <td>hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...</td>\n",
              "      <td>1.530221</td>\n",
              "      <td>0.079550</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...</td>\n",
              "      <td>-1.063943</td>\n",
              "      <td>-0.405022</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...</td>\n",
              "      <td>-1.058232</td>\n",
              "      <td>-0.410670</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...</td>\n",
              "      <td>-1.057465</td>\n",
              "      <td>-0.409620</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...</td>\n",
              "      <td>-1.057465</td>\n",
              "      <td>-0.409620</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81c58abb-f027-4d92-930e-f80d834a21fa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81c58abb-f027-4d92-930e-f80d834a21fa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81c58abb-f027-4d92-930e-f80d834a21fa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TTC9hCppjgp"
      },
      "source": [
        "for_x = final_df.PC1.tolist()\n",
        "for_y = final_df.PC2.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, height=600, width=1200,\n",
        "                 title=\"Principal Component Axis\",\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "fig.show(renderer = 'colab')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5Gr0uSxp58y"
      },
      "source": [
        "You should get a plot like this:\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "**This makes the above observations so much easier. Can you list them with respect to this plot?**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ANSWER:-** from this plot ,\n",
        "\n",
        "**OBSERVATION 1:** gujurat (magenta dots) is scattered away from the big main cluster.\n",
        "\n",
        "**OBSERVATION 2:** maharshtra,telangana,karnataka,delhi,west bengal forms \n",
        "denser cluster.\n",
        "\n",
        "**OBSERVATION 3:** ladakh state doesn't form cluster."
      ],
      "metadata": {
        "id": "sZV5bM-aOY5Z"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fK7u44_Wp9_k"
      },
      "source": [
        "# t-SNE\n",
        "\n",
        "Now this time you will be expected to write more than just 3 lines of the code to get the t-SNE plot for this data. Make sure you play with the **perplexity** values enough to appreciate the algorithm. I hope you are able to retrieve the 3 observations and make some new ones from the plot.\n",
        "\n",
        "---\n",
        "\n",
        "You are encouraged to also implement **ISOMAP (from Lab-4)** and try to get more insights from the plots."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Use these parameters\n",
        "random_state = 0\n",
        "n_components = 2\n",
        "perplexity = 40\n",
        "n_iter = 10000\n",
        "\n",
        "####### Enter code below ####### \n",
        "\n",
        "model = TSNE(n_components=2, random_state=random_state, perplexity=perplexity, n_iter=n_iter)\n",
        "tsne_data = model.fit_transform(big_matrix)\n",
        "\n",
        "################################"
      ],
      "metadata": {
        "id": "iCDPBT4BRIuL",
        "outputId": "8a622313-6ef7-462c-8cc1-680ea062e1e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning:\n",
            "\n",
            "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning:\n",
            "\n",
            "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"X\", \"Y\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "i = 0\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"X\"] = tsne_data[i][0]\n",
        "    dicti[\"Y\"] = tsne_data[i][1]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "    i+=1\n",
        "\n",
        "final_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "Rzfk2dD1vLxm",
        "outputId": "9cb75edf-0828-400c-e976-099598bb2371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              StrainID         X         Y  \\\n",
              "680  hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...  9.326151  4.168576   \n",
              "681  hCoV-19/India/MaxCov0047_CSIR-IGIB/2020|EPI_IS...  9.449839  4.466097   \n",
              "682  hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...  9.745110  5.202258   \n",
              "683  hCoV-19/India/MaxCov0049_CSIR-IGIB/2020|EPI_IS...  9.680839  5.587805   \n",
              "684  hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...  9.526280  3.769078   \n",
              "\n",
              "     State  \n",
              "680  Delhi  \n",
              "681  Delhi  \n",
              "682  Delhi  \n",
              "683  Delhi  \n",
              "684  Delhi  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for_x = final_df.X.tolist()\n",
        "for_y = final_df.Y.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"t-SNE\", height=600, width=1000,\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "\n",
        "fig.show(renderer = 'colab')"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "when increased perplexity value to 40, then we get this t-SNE plot , as compared to perplexity value of 30, this graph doesn't formed a good clusters.\n",
        "\n",
        "\n",
        "**observation 1:** at 40 to 50 range above on y axis. we can see the main cluster. that the sequences formed this clusters are primarly from Tamil Nadu (green),Telangana(red) and delhi(magenta).\n",
        "\n",
        "\n",
        "**observation 2:**  secondary main cluster is formed at x-axix from 20 to 30 range by participating telangana(red),gujurat(magenta),tamil nadu(green) and rajasthan.\n",
        "\n",
        "\n",
        "**observation 3:** The third observation is that with in a secondary main cluster, there is a dense cluster of sequences from telangana\n",
        "\n",
        "**observation 4:** that we can see Maharastra(orange) are scattered away from secondary main cluster"
      ],
      "metadata": {
        "id": "BAEk2wZFv_P1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **implementing isomap**"
      ],
      "metadata": {
        "id": "jjAnaX9L5WrG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn import datasets\n",
        "n_points = 1000\n",
        "X, color = datasets.make_s_curve(n_points, random_state=0)\n",
        "fig = plt.figure(figsize=(15, 12))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\n",
        "ax.view_init(4, -72)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "RSuCQrVj5bas",
        "outputId": "933f740a-5ecb-4b2f-dfbd-60bf9b979666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Isomap(n_neighbors=10, n_components=2).fit_transform(X)\n",
        "\n",
        "ax = fig.add_subplot(111)\n",
        "plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n",
        "plt.title(\"Isomap on S curve\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "mpWLMKf65tkT",
        "outputId": "1e0b60b2-24ce-4cc7-9331-8503d992e09a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Z0ZBB-6qCSY"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "You might be wondering how these 3 observations help in understanding the demographic spread of the virus in the country. \n",
        "\n",
        "### This is not a Bioinformatics course, so you may choose to ignore the details mentioned below\n",
        "But the idea was to motivate you to understand the importance of visualization techniques even with no domain knowlege.\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "On performing genetic analysis on the sequences to understand why exactly these observations could have been happening I found the following:\n",
        "\n",
        "#### Observation 1: \n",
        "Samples from Odisha are different than the rest of the country, hence they were scattered away from the clusters in the plots.\n",
        "\n",
        "#### Reason: \n",
        "Genomic sequences of isolates from Odisha were shorter in length with missing bases (deletions); 1 - 29 bases in 5' UTR (38 isolates), 23842 - 24400 bases in S gene, 26306-26524 bases in E gene, 27527 – 28033 bases in ORF7b and ORF8 (8 samples), 28462 - 28680 bases in the N gene (10 samples), 29000 – 29685 in ORF10 gene (15 samples), and 29686 - 29903 bases in 3' UTR (39 isolates). These deletions were unique to the state and werent observed in samples from the rest of the country. \n",
        "\n",
        "#### Observation 2: \n",
        "A secondary cluster away from the primary cluster with sequences from Telangana, Delhi, and Tamil Nadu.\n",
        "\n",
        "#### Reason: \n",
        "About one-third of the Indian isolates (219/685) are part of a subclade I/A3i defined by the co-occurring mutations C6312A, C13730T, C23929T, and C28311T. The subclade's predominance is observed in Telangana (69/97, 71%), Delhi (52/76, 68%), and Tamil Nadu (16/34). Analysis of this subclade is important both scientifically and epidemiologically as the mutations defining it are found in 32% of the Indian samples sequenced, while outside India its distribution is very low (3.5%). This clearly hints at early community transmission due to some super spreader event during March-April, as it is highly unlikely that around one-third of the samples sharing the same set of mutations could have arisen by multiple independent entries with international travel history, especially when its presence globally is negligibly small.\n",
        "\n",
        "#### Observation 3:\n",
        "A dense cluster of sequences from Gujarat.\n",
        "\n",
        "#### Reason:\n",
        "Isolates from Gujarat are a part of a subclade, I/GJ-20A defined by a shared set of mutations C18877T, G25563T (ORF3a: Q57H), and C26735T. This set of mutations are not well represented in the rest of the country. Mutations present in Gujarat have also been found in samples from Europe and that could have been the possible route of transmission into India. Also, Gujarat has the highest COVID-19 death rate (5.12%) in the country (Avg: 2.67%), and this death rate in Gujarat is comparable to that in Europe. This high fatality could be due to these characteristic mutations increasing the deleteriousness of the virus.\n",
        "\n",
        "---\n",
        "\n",
        "**So I hope you appreciate the valuable insights visualization techniques provide us with.** \n",
        "\n",
        "Best of luck for the next Module......\n",
        "---"
      ]
    }
  ]
}