preprocessing.ipynb

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          "output_type": "stream",
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AuLQLoR-xI93"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import librosa.display\n",
        "\n",
        "import os\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoaeBaCixI94"
      },
      "source": [
        "#S-1\n",
        "# Reading and Utilizing Provided Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2RTTu3R9xI95"
      },
      "outputs": [],
      "source": [
        "patient_data=pd.read_csv('/content/drive/MyDrive/Respiratory_Sound_Database/patient_diagnosis.csv',names=['pid','disease'])"
      ]
    },
    {
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        {
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          "data": {
            "text/plain": [
              "   pid  disease\n",
              "0  101     URTI\n",
              "1  102  Healthy\n",
              "2  103   Asthma\n",
              "3  104     COPD\n",
              "4  105     URTI"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "patient_data",
              "summary": "{\n  \"name\": \"patient_data\",\n  \"rows\": 126,\n  \"fields\": [\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 101,\n        \"max\": 226,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          174,\n          120,\n          217\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disease\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Healthy\",\n          \"Bronchiectasis\",\n          \"URTI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "patient_data.head()"
      ]
    },
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              "    1.962   6.553  1  1.1\n",
              "0   6.553  11.160  1    1\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6974aa9-ecbe-48ce-9b14-5112542e86dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"1.962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.425261461202038,\n        \"min\": 6.553,\n        \"max\": 15.401,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.553,\n          11.16,\n          15.401\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"6.553\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.340880133490597,\n        \"min\": 11.16,\n        \"max\": 19.841,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11.16,\n          15.401,\n          19.841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"1.1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/Respiratory_Sound_Database/audio_and_txt_files/160_1b4_Al_mc_AKGC417L.txt',sep='\\t')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kknO1ylhxI96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d210fca-18a2-40e6-db81-276372269412"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['160_1b4_Al_mc_AKGC417L',\n",
              " '160_1b3_Ar_mc_AKGC417L',\n",
              " '160_1b3_Tc_mc_AKGC417L',\n",
              " '160_1b3_Pr_mc_AKGC417L',\n",
              " '160_1b4_Ar_mc_AKGC417L']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import os\n",
        "path='/content/drive/MyDrive/Respiratory_Sound_Database/audio_and_txt_files/'\n",
        "files=[s.split('.')[0] for s in os.listdir(path) if '.txt' in s]\n",
        "files[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0m_MJSLxI96"
      },
      "source": [
        " have various information in txt  ,so we will extract that   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LkZ1T8aNxI96"
      },
      "outputs": [],
      "source": [
        "def getFilenameInfo(file):\n",
        "    return file.split('_')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LDGVIhINxI97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "951f02ba-1a52-4223-992d-0d1906c5d857"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['160', '1b4', 'Al', 'mc', 'AKGC417L']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "getFilenameInfo('160_1b4_Al_mc_AKGC417L')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7IS674zsxI97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "49917610-da0c-43f1-f394-7efed7dcde61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    start     end  crackles  weezels  pid mode                filename\n",
              "0   1.962   6.553         1        1  160   mc  160_1b4_Al_mc_AKGC417L\n",
              "1   6.553  11.160         1        1  160   mc  160_1b4_Al_mc_AKGC417L\n",
              "2  11.160  15.401         1        1  160   mc  160_1b4_Al_mc_AKGC417L\n",
              "3  15.401  19.841         1        1  160   mc  160_1b4_Al_mc_AKGC417L\n",
              "0   1.862   5.718         0        1  160   mc  160_1b3_Ar_mc_AKGC417L"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "    </tr>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbf16196-001d-4386-9af0-3ad96a817985')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dbf16196-001d-4386-9af0-3ad96a817985 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-dbf16196-001d-4386-9af0-3ad96a817985');\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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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "    animation:\n",
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              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
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              "        document.querySelector('#df-49123423-cc65-46ce-97c6-949369188c31 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "files_df",
              "summary": "{\n  \"name\": \"files_df\",\n  \"rows\": 6898,\n  \"fields\": [\n    {\n      \"column\": \"start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.871169326916663,\n        \"min\": 0.0,\n        \"max\": 84.365,\n        \"num_unique_values\": 2837,\n        \"samples\": [\n          12.566,\n          19.048,\n          35.445\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.032743670790671,\n        \"min\": 0.25,\n        \"max\": 86.2,\n        \"num_unique_values\": 2820,\n        \"samples\": [\n          9.631,\n          1.979,\n          28.161\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crackles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weezels\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 126,\n        \"samples\": [\n          \"102\",\n          \"179\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"sc\",\n          \"mc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 920,\n        \"samples\": [\n          \"199_2b3_Ll_mc_LittC2SE\",\n          \"205_1b3_Lr_mc_AKGC417L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "files_data=[]\n",
        "for file in files:\n",
        "    data=pd.read_csv(path + file + '.txt',sep='\\t',names=['start','end','crackles','weezels'])\n",
        "    name_data=getFilenameInfo(file)\n",
        "    data['pid']=name_data[0]\n",
        "    data['mode']=name_data[-2]\n",
        "    data['filename']=file\n",
        "    files_data.append(data)\n",
        "files_df=pd.concat(files_data)\n",
        "files_df.reset_index()\n",
        "files_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1hVxVdnmxI97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ccabefd-fcf0-4d43-9a1e-ac07a615ce18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 126 entries, 0 to 125\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   pid      126 non-null    int64 \n",
            " 1   disease  126 non-null    object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ]
        }
      ],
      "source": [
        "patient_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b-KbJKikxI97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3026d346-61ea-49c4-e07e-9784e3e3ce69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 6898 entries, 0 to 4\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   start     6898 non-null   float64\n",
            " 1   end       6898 non-null   float64\n",
            " 2   crackles  6898 non-null   int64  \n",
            " 3   weezels   6898 non-null   int64  \n",
            " 4   pid       6898 non-null   object \n",
            " 5   mode      6898 non-null   object \n",
            " 6   filename  6898 non-null   object \n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 431.1+ KB\n"
          ]
        }
      ],
      "source": [
        "files_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa23zgv_xI97"
      },
      "source": [
        "> Lets get **pid** and **101** to same type and merge both dataframes on pid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "raxFjcyLxI97"
      },
      "outputs": [],
      "source": [
        "files_df['pid'] = files_df['pid'].str.replace(r'\\D', '', regex=True)\n",
        "files_df['pid'] = files_df['pid'].astype('int32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1zh4GSIqxI97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0a17952f-ceee-48f4-d929-e32901bbbbab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    start     end  crackles  weezels  pid mode                filename disease\n",
              "0   1.962   6.553         1        1  160   mc  160_1b4_Al_mc_AKGC417L    COPD\n",
              "1   6.553  11.160         1        1  160   mc  160_1b4_Al_mc_AKGC417L    COPD\n",
              "2  11.160  15.401         1        1  160   mc  160_1b4_Al_mc_AKGC417L    COPD\n",
              "3  15.401  19.841         1        1  160   mc  160_1b4_Al_mc_AKGC417L    COPD\n",
              "4   1.862   5.718         0        1  160   mc  160_1b3_Ar_mc_AKGC417L    COPD"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6898,\n  \"fields\": [\n    {\n      \"column\": \"start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.871169326916663,\n        \"min\": 0.0,\n        \"max\": 84.365,\n        \"num_unique_values\": 2837,\n        \"samples\": [\n          10.256,\n          10.675,\n          28.685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.032743670790671,\n        \"min\": 0.25,\n        \"max\": 86.2,\n        \"num_unique_values\": 2820,\n        \"samples\": [\n          6.446,\n          12.864,\n          7.2357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crackles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weezels\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 126,\n        \"samples\": [\n          102,\n          179\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"sc\",\n          \"mc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 920,\n        \"samples\": [\n          \"197_1b1_Tc_sc_Meditron\",\n          \"204_2b5_Al_mc_AKGC417L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disease\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Bronchiolitis\",\n          \"Pneumonia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "data=pd.merge(files_df,patient_data,on='pid')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FDG8HUcRxI97"
      },
      "outputs": [],
      "source": [
        "os.makedirs('csv_data')\n",
        "data.to_csv('csv_data/data.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdNczmK4xI97"
      },
      "source": [
        "# S-2\n",
        "# Processing Audio files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MROq0wMAxI97"
      },
      "outputs": [],
      "source": [
        "def getPureSample(raw_data,start,end,sr=22050):\n",
        "    max_ind = len(raw_data)\n",
        "    start_ind = min(int(start * sr), max_ind)\n",
        "    end_ind = min(int(end * sr), max_ind)\n",
        "    return raw_data[start_ind: end_ind]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59mwCvw3xI97"
      },
      "source": [
        "input images to cnn and audio files must be of same length. ( (start - end) must be same )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZGhcERR5xI97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "e5118000-af6e-4fe7-8649-b9b30ad0dfa1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='None', ylabel='pid'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(x=(data.end-data.start), y=data.pid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PwwRqPpixI97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "42141353-b8ad-4c9b-dcb6-8d8fb14bba04"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='None'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(y=(data.end-data.start))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mseZBV6xI97"
      },
      "source": [
        "> Lets create a directory for storing our files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OctZGrrSxI98"
      },
      "outputs": [],
      "source": [
        "os.makedirs('processed_audio_files')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HN7E3Q_RxI98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78e0b92d-a4b6-4859-8f21-1b91e505213b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index -> 0\n",
            "Data->\n",
            " start                        1.962\n",
            "end                          6.553\n",
            "crackles                         1\n",
            "weezels                          1\n",
            "pid                            160\n",
            "mode                            mc\n",
            "filename    160_1b4_Al_mc_AKGC417L\n",
            "disease                       COPD\n",
            "Name: 0, dtype: object\n"
          ]
        }
      ],
      "source": [
        "for index,row in data.iterrows():\n",
        "    print(\"Index ->\",index)\n",
        "    print(\"Data->\\n\",row)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtnDTMaxxI98"
      },
      "source": [
        "\n",
        " using **Librosa** module for loading audio files and **Soundfile** module for writing to output path\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a-MJjIafxI98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a759997-d186-4158-d7c5-f2645cff134c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Files Processed:  6898\n"
          ]
        }
      ],
      "source": [
        "import librosa as lb\n",
        "import soundfile as sf\n",
        "i,c=0,0\n",
        "for index,row in data.iterrows():\n",
        "    maxLen=6\n",
        "    start=row['start']\n",
        "    end=row['end']\n",
        "    filename=row['filename']\n",
        "\n",
        "\n",
        "    if end-start>maxLen:\n",
        "        end=start+maxLen\n",
        "\n",
        "    audio_file_loc=path + filename + '.wav'\n",
        "\n",
        "    if index > 0:\n",
        "        #check if more cycles exits for same patient if so then add i to change filename\n",
        "        if data.iloc[index-1]['filename']==filename:\n",
        "            i+=1\n",
        "        else:\n",
        "            i=0\n",
        "    filename= filename + '_' + str(i) + '.wav'\n",
        "\n",
        "    save_path='processed_audio_files/' + filename\n",
        "    c+=1\n",
        "\n",
        "    audioArr,sampleRate=lb.load(audio_file_loc)\n",
        "    pureSample=getPureSample(audioArr,start,end,sampleRate)\n",
        "\n",
        "    #pad audio if pureSample len < max_len\n",
        "    reqLen=6*sampleRate\n",
        "    if len(pureSample) < reqLen:\n",
        "        padded_data = np.pad(pureSample, (0, reqLen - len(pureSample)), 'constant')\n",
        "    else:\n",
        "        padded_data = pureSample[:reqLen]\n",
        "\n",
        "    sf.write(file=save_path,data=padded_data,samplerate=sampleRate)\n",
        "print('Total Files Processed: ',c)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 5395.179099,
      "end_time": "2020-09-23T16:16:01.458198",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2020-09-23T14:46:06.279099",
      "version": "2.1.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}