From 120bed10edc78c9af989e4119c4fe203a6b4b1cf Mon Sep 17 00:00:00 2001
From: pripky <asthay6677@gmail.com>
Date: Sun, 1 Jun 2025 00:15:16 +0530
Subject: [PATCH] Add files via upload

---
 .../240221_AsthaYadav_Week2 (1).ipynb         | 1554 +++++++++++++++++
 1 file changed, 1554 insertions(+)
 create mode 100644 Week 2 preprocessing/Week 2 Assignment/240221_AsthaYadav_Week2 (1).ipynb

diff --git a/Week 2 preprocessing/Week 2 Assignment/240221_AsthaYadav_Week2 (1).ipynb b/Week 2 preprocessing/Week 2 Assignment/240221_AsthaYadav_Week2 (1).ipynb
new file mode 100644
index 0000000..bdfd464
--- /dev/null
+++ b/Week 2 preprocessing/Week 2 Assignment/240221_AsthaYadav_Week2 (1).ipynb	
@@ -0,0 +1,1554 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import mne\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.signal import butter, filtfilt\n",
+        "from scipy.interpolate import interp1d\n",
+        "from scipy.interpolate import CubicSpline\n",
+        "from sklearn.preprocessing import MinMaxScaler"
+      ],
+      "metadata": {
+        "id": "hU09Gq1tovo5"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_mu = pd.read_csv(\"/content/MU.txt\", sep='\\s+', header=None, engine='python')\n",
+        "df_mu.columns = ['id', 'event', 'device', 'channel', 'code', 'size', 'data']"
+      ],
+      "metadata": {
+        "id": "PaokDcaooyq1"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display (df_mu)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "GGdOY6nxpacV",
+        "outputId": "20922fd2-c969-4d0d-edec-16811057b8e1"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "             id   event device channel  code  size  \\\n",
+              "0        978111  132669     MU     TP9     6   459   \n",
+              "1        978112  132669     MU     FP1     6   459   \n",
+              "2        978113  132669     MU     FP2     6   459   \n",
+              "3        978114  132669     MU    TP10     6   459   \n",
+              "4        978115  132670     MU     TP9     7   493   \n",
+              "...         ...     ...    ...     ...   ...   ...   \n",
+              "163927  1142038  173650     MU    TP10    -1   460   \n",
+              "163928  1142039  173651     MU     TP9    -1   460   \n",
+              "163929  1142040  173651     MU     FP1    -1   460   \n",
+              "163930  1142041  173651     MU     FP2    -1   460   \n",
+              "163931  1142042  173651     MU    TP10    -1   460   \n",
+              "\n",
+              "                                                     data  \n",
+              "0       475,474,477,486,486,476,479,483,489,483,482,48...  \n",
+              "1       468,487,493,493,498,493,491,490,492,487,483,48...  \n",
+              "2       482,475,490,500,485,470,470,482,490,484,478,48...  \n",
+              "3       470,470,478,489,487,475,469,478,488,483,474,48...  \n",
+              "4       506,499,495,491,492,507,496,500,498,496,499,50...  \n",
+              "...                                                   ...  \n",
+              "163927  537,531,530,531,533,537,537,555,534,522,531,54...  \n",
+              "163928  521,517,526,524,514,519,520,526,526,514,519,52...  \n",
+              "163929  525,518,522,525,523,523,512,523,519,522,523,52...  \n",
+              "163930  502,493,500,511,510,512,523,507,497,503,502,50...  \n",
+              "163931  523,516,519,524,511,520,519,521,522,523,520,53...  \n",
+              "\n",
+              "[163932 rows x 7 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-79f3d67c-790b-4d24-a560-e1c53759b166\" 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>id</th>\n",
+              "      <th>event</th>\n",
+              "      <th>device</th>\n",
+              "      <th>channel</th>\n",
+              "      <th>code</th>\n",
+              "      <th>size</th>\n",
+              "      <th>data</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>978111</td>\n",
+              "      <td>132669</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP9</td>\n",
+              "      <td>6</td>\n",
+              "      <td>459</td>\n",
+              "      <td>475,474,477,486,486,476,479,483,489,483,482,48...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>978112</td>\n",
+              "      <td>132669</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>FP1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>459</td>\n",
+              "      <td>468,487,493,493,498,493,491,490,492,487,483,48...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>978113</td>\n",
+              "      <td>132669</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>FP2</td>\n",
+              "      <td>6</td>\n",
+              "      <td>459</td>\n",
+              "      <td>482,475,490,500,485,470,470,482,490,484,478,48...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>978114</td>\n",
+              "      <td>132669</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP10</td>\n",
+              "      <td>6</td>\n",
+              "      <td>459</td>\n",
+              "      <td>470,470,478,489,487,475,469,478,488,483,474,48...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>978115</td>\n",
+              "      <td>132670</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>493</td>\n",
+              "      <td>506,499,495,491,492,507,496,500,498,496,499,50...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163927</th>\n",
+              "      <td>1142038</td>\n",
+              "      <td>173650</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP10</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>460</td>\n",
+              "      <td>537,531,530,531,533,537,537,555,534,522,531,54...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163928</th>\n",
+              "      <td>1142039</td>\n",
+              "      <td>173651</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP9</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>460</td>\n",
+              "      <td>521,517,526,524,514,519,520,526,526,514,519,52...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163929</th>\n",
+              "      <td>1142040</td>\n",
+              "      <td>173651</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>FP1</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>460</td>\n",
+              "      <td>525,518,522,525,523,523,512,523,519,522,523,52...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163930</th>\n",
+              "      <td>1142041</td>\n",
+              "      <td>173651</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>FP2</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>460</td>\n",
+              "      <td>502,493,500,511,510,512,523,507,497,503,502,50...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163931</th>\n",
+              "      <td>1142042</td>\n",
+              "      <td>173651</td>\n",
+              "      <td>MU</td>\n",
+              "      <td>TP10</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>460</td>\n",
+              "      <td>523,516,519,524,511,520,519,521,522,523,520,53...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>163932 rows × 7 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79f3d67c-790b-4d24-a560-e1c53759b166')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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",
+              "      fill: #1967D2;\n",
+              "      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",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-79f3d67c-790b-4d24-a560-e1c53759b166 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-79f3d67c-790b-4d24-a560-e1c53759b166');\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",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-94ef4c61-80dd-417d-bcd3-f018a661950f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-94ef4c61-80dd-417d-bcd3-f018a661950f')\"\n",
+              "                title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --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-94ef4c61-80dd-417d-bcd3-f018a661950f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_2cdef590-d6c1-43b7-851b-be0b01650f96\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_mu')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2cdef590-d6c1-43b7-851b-be0b01650f96 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_mu');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_mu"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_mu['signal'] = df_mu['data'].apply(lambda x: list(map(int, x.split(','))))\n",
+        "print(df_mu['signal'].head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Qjx18455Oj5J",
+        "outputId": "3b9e8eeb-f335-4853-e231-720b385a406f"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0    [475, 474, 477, 486, 486, 476, 479, 483, 489, ...\n",
+            "1    [468, 487, 493, 493, 498, 493, 491, 490, 492, ...\n",
+            "2    [482, 475, 490, 500, 485, 470, 470, 482, 490, ...\n",
+            "3    [470, 470, 478, 489, 487, 475, 469, 478, 488, ...\n",
+            "4    [506, 499, 495, 491, 492, 507, 496, 500, 498, ...\n",
+            "Name: signal, dtype: object\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def bandpass_filter(signal, sfreq=220.0, lowcut=0.1, highcut=25):\n",
+        "    nyquist = 0.5 * sfreq\n",
+        "    low=lowcut / nyquist\n",
+        "    high=highcut / nyquist\n",
+        "    b, a = butter(4, [low, high], btype='band')\n",
+        "    y = filtfilt(b, a, signal)\n",
+        "    return y\n",
+        "    #highcut=25 because spike appears at 50 (the spike in the example shown in the preprocessing article appeared at 60\n",
+        "    #and they kept the highcut at 30)"
+      ],
+      "metadata": {
+        "id": "IEcZJHXANMRB"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_mu['filtered_signal'] = df_mu['signal'].apply(lambda x: bandpass_filter(np.array(x)))"
+      ],
+      "metadata": {
+        "id": "ompexVflROrZ"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_index=56 #can tweak this to verify"
+      ],
+      "metadata": {
+        "id": "ykeEWacCjItH"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12,5))\n",
+        "plt.plot(df_mu.loc[sample_index, 'signal'], label='Original Signal')\n",
+        "plt.plot(df_mu.loc[sample_index, 'filtered_signal'], label='Filtered Signal')\n",
+        "plt.legend()\n",
+        "plt.title('Bandpass Filter on EEG Signal')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "bMT2ANc3RwuQ",
+        "outputId": "af6af604-3155-45e6-d143-224b6fbcb65e"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#grouping according to the digit being shown\n",
+        "groups = df_mu.groupby('code')\n",
+        "for code_value, group_df in groups:\n",
+        "  print(f\"Code Value: {code_value}\")\n",
+        "  print(group_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QFYerGYzSyKR",
+        "outputId": "4bafe30d-181a-4466-e44c-e0c879f8db05"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Code Value: -1\n",
+            "             id   event device channel  code  size  \\\n",
+            "119520  1097631  162549     MU     TP9    -1   460   \n",
+            "119521  1097632  162549     MU     FP1    -1   460   \n",
+            "119522  1097633  162549     MU     FP2    -1   460   \n",
+            "119523  1097634  162549     MU    TP10    -1   460   \n",
+            "119524  1097635  162550     MU     TP9    -1   460   \n",
+            "\n",
+            "                                                     data  \\\n",
+            "119520  522,540,548,539,535,545,553,554,551,552,554,54...   \n",
+            "119521  494,559,562,558,556,557,558,558,555,556,560,56...   \n",
+            "119522  556,558,606,558,542,590,590,590,542,542,574,57...   \n",
+            "119523  537,533,553,548,525,520,545,554,532,522,536,55...   \n",
+            "119524  482,473,473,488,496,488,484,491,488,492,480,47...   \n",
+            "\n",
+            "                                                   signal  \\\n",
+            "119520  [522, 540, 548, 539, 535, 545, 553, 554, 551, ...   \n",
+            "119521  [494, 559, 562, 558, 556, 557, 558, 558, 555, ...   \n",
+            "119522  [556, 558, 606, 558, 542, 590, 590, 590, 542, ...   \n",
+            "119523  [537, 533, 553, 548, 525, 520, 545, 554, 532, ...   \n",
+            "119524  [482, 473, 473, 488, 496, 488, 484, 491, 488, ...   \n",
+            "\n",
+            "                                          filtered_signal  \n",
+            "119520  [-0.8177034834069461, 6.862322308814219, 13.62...  \n",
+            "119521  [14.466067461813982, 43.00194940206231, 66.197...  \n",
+            "119522  [20.679139381270048, 28.02581334049313, 34.428...  \n",
+            "119523  [6.384238219930515, 7.651893874169301, 8.33786...  \n",
+            "119524  [-9.564042232381736, -9.514347334354934, -8.82...  \n",
+            "Code Value: 0\n",
+            "         id   event device channel  code  size  \\\n",
+            "60   978171  132684     MU     TP9     0   476   \n",
+            "61   978172  132684     MU     FP1     0   476   \n",
+            "62   978173  132684     MU     FP2     0   476   \n",
+            "63   978174  132684     MU    TP10     0   476   \n",
+            "100  978211  132694     MU     TP9     0   459   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "60   516,533,532,526,528,528,532,541,540,536,532,52...   \n",
+            "61   514,523,525,521,521,524,522,524,525,522,523,52...   \n",
+            "62   520,515,516,512,513,512,515,516,515,509,517,51...   \n",
+            "63   533,507,506,511,511,513,513,518,524,511,506,51...   \n",
+            "100  524,530,534,537,535,538,533,530,528,523,516,51...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "60   [516, 533, 532, 526, 528, 528, 532, 541, 540, ...   \n",
+            "61   [514, 523, 525, 521, 521, 524, 522, 524, 525, ...   \n",
+            "62   [520, 515, 516, 512, 513, 512, 515, 516, 515, ...   \n",
+            "63   [533, 507, 506, 511, 511, 513, 513, 518, 524, ...   \n",
+            "100  [524, 530, 534, 537, 535, 538, 533, 530, 528, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "60   [-2.3889427323326444, 2.6752883990087484, 7.07...  \n",
+            "61   [4.06729518431948, 7.825209257882252, 10.89949...  \n",
+            "62   [7.113357842782651, 4.360561814129578, 2.09628...  \n",
+            "63   [8.514645838853287, -2.438088381917153, -10.91...  \n",
+            "100  [7.881250410196705, 13.303189542931118, 17.792...  \n",
+            "Code Value: 1\n",
+            "        id   event device channel  code  size  \\\n",
+            "16  978127  132673     MU     TP9     1   493   \n",
+            "17  978128  132673     MU     FP1     1   493   \n",
+            "18  978129  132673     MU     FP2     1   493   \n",
+            "19  978130  132673     MU    TP10     1   493   \n",
+            "32  978143  132677     MU     TP9     1   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "16  502,503,498,500,493,507,514,506,501,502,499,49...   \n",
+            "17  506,511,513,518,505,522,526,509,517,514,514,51...   \n",
+            "18  513,512,506,509,504,507,512,505,514,512,507,50...   \n",
+            "19  501,504,494,489,485,509,513,487,495,503,503,49...   \n",
+            "32  514,506,504,510,516,511,508,509,511,514,515,51...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "16  [502, 503, 498, 500, 493, 507, 514, 506, 501, ...   \n",
+            "17  [506, 511, 513, 518, 505, 522, 526, 509, 517, ...   \n",
+            "18  [513, 512, 506, 509, 504, 507, 512, 505, 514, ...   \n",
+            "19  [501, 504, 494, 489, 485, 509, 513, 487, 495, ...   \n",
+            "32  [514, 506, 504, 510, 516, 511, 508, 509, 511, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "16  [-3.7783473034218895, -5.047913904341275, -5.6...  \n",
+            "17  [-0.06699306945382752, 3.252631522724486, 6.21...  \n",
+            "18  [-0.9911521525017524, -3.649796252934577, -5.7...  \n",
+            "19  [-8.108721360210737, -11.168247697975321, -13....  \n",
+            "32  [5.982306914528173, 3.6919052929416636, 1.9252...  \n",
+            "Code Value: 2\n",
+            "        id   event device channel  code  size  \\\n",
+            "56  978167  132683     MU     TP9     2   476   \n",
+            "57  978168  132683     MU     FP1     2   476   \n",
+            "58  978169  132683     MU     FP2     2   476   \n",
+            "59  978170  132683     MU    TP10     2   476   \n",
+            "84  978195  132690     MU     TP9     2   459   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "56  500,507,492,507,513,495,513,506,501,511,495,50...   \n",
+            "57  503,519,514,518,525,519,538,513,520,524,515,52...   \n",
+            "58  518,499,496,503,513,509,510,502,508,511,506,49...   \n",
+            "59  508,498,493,512,505,492,514,507,506,499,499,50...   \n",
+            "84  498,504,501,499,500,505,502,500,498,495,501,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "56  [500, 507, 492, 507, 513, 495, 513, 506, 501, ...   \n",
+            "57  [503, 519, 514, 518, 525, 519, 538, 513, 520, ...   \n",
+            "58  [518, 499, 496, 503, 513, 509, 510, 502, 508, ...   \n",
+            "59  [508, 498, 493, 512, 505, 492, 514, 507, 506, ...   \n",
+            "84  [498, 504, 501, 499, 500, 505, 502, 500, 498, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "56  [-15.315623304497628, -14.167152530537404, -12...  \n",
+            "57  [-2.310927096726438, 4.779189112804734, 10.940...  \n",
+            "58  [3.8957855125622003, -2.647406715610452, -7.53...  \n",
+            "59  [-11.00039583295662, -14.148635863885083, -16....  \n",
+            "84  [-6.590639001002224, -4.987291225509174, -3.69...  \n",
+            "Code Value: 3\n",
+            "         id   event device channel  code  size  \\\n",
+            "40   978151  132679     MU     TP9     3   459   \n",
+            "41   978152  132679     MU     FP1     3   459   \n",
+            "42   978153  132679     MU     FP2     3   459   \n",
+            "43   978154  132679     MU    TP10     3   459   \n",
+            "104  978215  132695     MU     TP9     3   493   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "40   506,505,518,512,511,503,508,523,518,525,507,51...   \n",
+            "41   501,511,511,510,505,507,508,512,507,517,507,51...   \n",
+            "42   508,503,497,501,492,497,504,497,498,509,501,51...   \n",
+            "43   509,498,503,508,508,499,493,520,514,520,503,50...   \n",
+            "104  499,508,510,506,511,510,504,512,509,517,514,50...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "40   [506, 505, 518, 512, 511, 503, 508, 523, 518, ...   \n",
+            "41   [501, 511, 511, 510, 505, 507, 508, 512, 507, ...   \n",
+            "42   [508, 503, 497, 501, 492, 497, 504, 497, 498, ...   \n",
+            "43   [509, 498, 503, 508, 508, 499, 493, 520, 514, ...   \n",
+            "104  [499, 508, 510, 506, 511, 510, 504, 512, 509, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "40   [-2.132234929248638, -0.5279212532803811, 0.75...  \n",
+            "41   [4.131486859691155, 7.492023735635791, 10.1284...  \n",
+            "42   [2.0853298093345862, -2.555059344312715, -6.38...  \n",
+            "43   [1.6088825088408105, -1.4676763565156383, -3.8...  \n",
+            "104  [-2.208535774591993, 1.8664822576783004, 5.188...  \n",
+            "Code Value: 4\n",
+            "         id   event device channel  code  size  \\\n",
+            "224  978335  132725     MU     TP9     4   476   \n",
+            "225  978336  132725     MU     FP1     4   476   \n",
+            "226  978337  132725     MU     FP2     4   476   \n",
+            "227  978338  132725     MU    TP10     4   476   \n",
+            "328  978439  132751     MU     TP9     4   493   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "224  516,517,522,518,522,526,516,518,521,528,522,51...   \n",
+            "225  520,525,529,524,526,531,524,525,524,528,528,52...   \n",
+            "226  528,507,520,514,508,521,511,509,519,520,515,51...   \n",
+            "227  523,511,508,507,513,515,506,511,511,518,515,50...   \n",
+            "328  479,501,506,501,503,505,502,499,493,500,506,50...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "224  [516, 517, 522, 518, 522, 526, 516, 518, 521, ...   \n",
+            "225  [520, 525, 529, 524, 526, 531, 524, 525, 524, ...   \n",
+            "226  [528, 507, 520, 514, 508, 521, 511, 509, 519, ...   \n",
+            "227  [523, 511, 508, 507, 513, 515, 506, 511, 511, ...   \n",
+            "328  [479, 501, 506, 501, 503, 505, 502, 499, 493, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "224  [0.3078741398831336, 2.0318958313577604, 3.498...  \n",
+            "225  [3.9296152847768693, 6.792154169053837, 9.1313...  \n",
+            "226  [7.641844071940069, 1.1300585289014529, -4.137...  \n",
+            "227  [4.096876660722616, -2.0267246752548065, -6.88...  \n",
+            "328  [-3.376203156381087, 7.627975382311337, 16.438...  \n",
+            "Code Value: 5\n",
+            "        id   event device channel  code  size  \\\n",
+            "64  978175  132685     MU     TP9     5   459   \n",
+            "65  978176  132685     MU     FP1     5   459   \n",
+            "66  978177  132685     MU     FP2     5   459   \n",
+            "67  978178  132685     MU    TP10     5   459   \n",
+            "76  978187  132688     MU     TP9     5   459   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "64  481,492,490,487,486,490,491,488,484,489,496,49...   \n",
+            "65  498,508,505,505,508,508,512,507,501,505,505,50...   \n",
+            "66  508,498,494,491,491,501,498,498,502,490,491,49...   \n",
+            "67  494,484,480,479,477,475,481,479,479,478,482,48...   \n",
+            "76  504,505,509,514,510,507,508,508,503,503,501,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "64  [481, 492, 490, 487, 486, 490, 491, 488, 484, ...   \n",
+            "65  [498, 508, 505, 505, 508, 508, 512, 507, 501, ...   \n",
+            "66  [508, 498, 494, 491, 491, 501, 498, 498, 502, ...   \n",
+            "67  [494, 484, 480, 479, 477, 475, 481, 479, 479, ...   \n",
+            "76  [504, 505, 509, 514, 510, 507, 508, 508, 503, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "64  [-13.02208149595159, -9.421547047116219, -6.65...  \n",
+            "65  [-7.675124803865947, -3.7604190252621117, -0.4...  \n",
+            "66  [-6.01177475670532, -12.326052337629617, -17.2...  \n",
+            "67  [-13.155368370522655, -19.500756201280087, -24...  \n",
+            "76  [4.410329048744597, 7.202728549907036, 9.44735...  \n",
+            "Code Value: 6\n",
+            "       id   event device channel  code  size  \\\n",
+            "0  978111  132669     MU     TP9     6   459   \n",
+            "1  978112  132669     MU     FP1     6   459   \n",
+            "2  978113  132669     MU     FP2     6   459   \n",
+            "3  978114  132669     MU    TP10     6   459   \n",
+            "8  978119  132671     MU     TP9     6   459   \n",
+            "\n",
+            "                                                data  \\\n",
+            "0  475,474,477,486,486,476,479,483,489,483,482,48...   \n",
+            "1  468,487,493,493,498,493,491,490,492,487,483,48...   \n",
+            "2  482,475,490,500,485,470,470,482,490,484,478,48...   \n",
+            "3  470,470,478,489,487,475,469,478,488,483,474,48...   \n",
+            "8  520,533,528,517,522,534,534,531,522,528,536,53...   \n",
+            "\n",
+            "                                              signal  \\\n",
+            "0  [475, 474, 477, 486, 486, 476, 479, 483, 489, ...   \n",
+            "1  [468, 487, 493, 493, 498, 493, 491, 490, 492, ...   \n",
+            "2  [482, 475, 490, 500, 485, 470, 470, 482, 490, ...   \n",
+            "3  [470, 470, 478, 489, 487, 475, 469, 478, 488, ...   \n",
+            "8  [520, 533, 528, 517, 522, 534, 534, 531, 522, ...   \n",
+            "\n",
+            "                                     filtered_signal  \n",
+            "0  [-16.384348518146272, -14.28677015299674, -12....  \n",
+            "1  [-12.355924481489554, -1.1196275855661515, 8.1...  \n",
+            "2  [-8.902057950255045, -6.571550261856517, -5.30...  \n",
+            "3  [-14.862239327190066, -10.1729854827539, -6.50...  \n",
+            "8  [11.732361011063364, 14.118893083372951, 16.25...  \n",
+            "Code Value: 7\n",
+            "        id   event device channel  code  size  \\\n",
+            "4   978115  132670     MU     TP9     7   493   \n",
+            "5   978116  132670     MU     FP1     7   493   \n",
+            "6   978117  132670     MU     FP2     7   493   \n",
+            "7   978118  132670     MU    TP10     7   493   \n",
+            "44  978155  132680     MU     TP9     7   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "4   506,499,495,491,492,507,496,500,498,496,499,50...   \n",
+            "5   505,515,513,506,512,520,512,521,517,512,517,52...   \n",
+            "6   511,501,503,503,498,514,506,511,511,500,505,50...   \n",
+            "7   500,506,490,487,492,507,497,493,496,499,495,48...   \n",
+            "44  482,491,478,478,481,490,491,488,485,490,485,48...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "4   [506, 499, 495, 491, 492, 507, 496, 500, 498, ...   \n",
+            "5   [505, 515, 513, 506, 512, 520, 512, 521, 517, ...   \n",
+            "6   [511, 501, 503, 503, 498, 514, 506, 511, 511, ...   \n",
+            "7   [500, 506, 490, 487, 492, 507, 497, 493, 496, ...   \n",
+            "44  [482, 491, 478, 478, 481, 490, 491, 488, 485, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "4   [-2.8461685103313803, -7.70979962293131, -11.4...  \n",
+            "5   [0.7195349899218323, 3.492396300168519, 5.9850...  \n",
+            "6   [0.2719353119824829, -3.6320274978373805, -6.3...  \n",
+            "7   [-5.984146989345621, -8.676245568757967, -10.6...  \n",
+            "44  [-22.412128443116003, -22.749599776110024, -22...  \n",
+            "Code Value: 8\n",
+            "        id   event device channel  code  size  \\\n",
+            "28  978139  132676     MU     TP9     8   442   \n",
+            "29  978140  132676     MU     FP1     8   442   \n",
+            "30  978141  132676     MU     FP2     8   442   \n",
+            "31  978142  132676     MU    TP10     8   442   \n",
+            "92  978203  132692     MU     TP9     8   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "28  516,520,516,517,529,521,524,522,520,518,513,51...   \n",
+            "29  511,521,514,517,524,515,515,515,518,512,513,51...   \n",
+            "30  508,516,500,502,510,502,508,506,504,505,508,51...   \n",
+            "31  513,523,513,520,525,511,512,520,522,511,505,51...   \n",
+            "92  487,501,499,503,508,504,500,511,503,506,508,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "28  [516, 520, 516, 517, 529, 521, 524, 522, 520, ...   \n",
+            "29  [511, 521, 514, 517, 524, 515, 515, 515, 518, ...   \n",
+            "30  [508, 516, 500, 502, 510, 502, 508, 506, 504, ...   \n",
+            "31  [513, 523, 513, 520, 525, 511, 512, 520, 522, ...   \n",
+            "92  [487, 501, 499, 503, 508, 504, 500, 511, 503, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "28  [-1.942080876253038, 0.10712782440252919, 2.02...  \n",
+            "29  [5.062872918162999, 8.524314348029614, 11.2509...  \n",
+            "30  [-0.8310023751563308, -1.8685638003418346, -2....  \n",
+            "31  [3.0541040274857005, 5.811252000635518, 7.9305...  \n",
+            "92  [0.8500088198422158, 7.679462851546148, 13.387...  \n",
+            "Code Value: 9\n",
+            "        id   event device channel  code  size  \\\n",
+            "36  978147  132678     MU     TP9     9   493   \n",
+            "37  978148  132678     MU     FP1     9   493   \n",
+            "38  978149  132678     MU     FP2     9   493   \n",
+            "39  978150  132678     MU    TP10     9   493   \n",
+            "52  978163  132682     MU     TP9     9   442   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "36  521,524,532,524,522,537,531,540,534,532,533,53...   \n",
+            "37  510,517,526,516,512,526,521,528,526,522,521,52...   \n",
+            "38  518,506,520,515,502,514,511,515,521,516,512,51...   \n",
+            "39  529,519,520,530,528,528,523,535,539,528,525,53...   \n",
+            "52  537,525,538,541,531,521,521,528,525,528,529,52...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "36  [521, 524, 532, 524, 522, 537, 531, 540, 534, ...   \n",
+            "37  [510, 517, 526, 516, 512, 526, 521, 528, 526, ...   \n",
+            "38  [518, 506, 520, 515, 502, 514, 511, 515, 521, ...   \n",
+            "39  [529, 519, 520, 530, 528, 528, 523, 535, 539, ...   \n",
+            "52  [537, 525, 538, 541, 531, 521, 521, 528, 525, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "36  [17.003944016879277, 19.150406097027016, 21.31...  \n",
+            "37  [4.56686351757058, 8.14046139048667, 11.307095...  \n",
+            "38  [7.344760141391687, 4.36329635746246, 1.911523...  \n",
+            "39  [19.752293876946077, 17.393641735853123, 15.87...  \n",
+            "52  [23.61698009200334, 22.328310041013964, 20.724...  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#need to remove data with code value = -1 (random or no stimulus was shown)\n",
+        "df_mu_filtered = df_mu[df_mu['code'] != -1]\n",
+        "groups=df_mu_filtered.groupby('code')\n",
+        "for code_value, group_df in groups:\n",
+        "  print(f\"Code Value: {code_value}\")\n",
+        "  print(group_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cka-c0NTTV_x",
+        "outputId": "5b37e929-606f-4e55-8e61-3658929e1782"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Code Value: 0\n",
+            "         id   event device channel  code  size  \\\n",
+            "60   978171  132684     MU     TP9     0   476   \n",
+            "61   978172  132684     MU     FP1     0   476   \n",
+            "62   978173  132684     MU     FP2     0   476   \n",
+            "63   978174  132684     MU    TP10     0   476   \n",
+            "100  978211  132694     MU     TP9     0   459   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "60   516,533,532,526,528,528,532,541,540,536,532,52...   \n",
+            "61   514,523,525,521,521,524,522,524,525,522,523,52...   \n",
+            "62   520,515,516,512,513,512,515,516,515,509,517,51...   \n",
+            "63   533,507,506,511,511,513,513,518,524,511,506,51...   \n",
+            "100  524,530,534,537,535,538,533,530,528,523,516,51...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "60   [516, 533, 532, 526, 528, 528, 532, 541, 540, ...   \n",
+            "61   [514, 523, 525, 521, 521, 524, 522, 524, 525, ...   \n",
+            "62   [520, 515, 516, 512, 513, 512, 515, 516, 515, ...   \n",
+            "63   [533, 507, 506, 511, 511, 513, 513, 518, 524, ...   \n",
+            "100  [524, 530, 534, 537, 535, 538, 533, 530, 528, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "60   [-2.3889427323326444, 2.6752883990087484, 7.07...  \n",
+            "61   [4.06729518431948, 7.825209257882252, 10.89949...  \n",
+            "62   [7.113357842782651, 4.360561814129578, 2.09628...  \n",
+            "63   [8.514645838853287, -2.438088381917153, -10.91...  \n",
+            "100  [7.881250410196705, 13.303189542931118, 17.792...  \n",
+            "Code Value: 1\n",
+            "        id   event device channel  code  size  \\\n",
+            "16  978127  132673     MU     TP9     1   493   \n",
+            "17  978128  132673     MU     FP1     1   493   \n",
+            "18  978129  132673     MU     FP2     1   493   \n",
+            "19  978130  132673     MU    TP10     1   493   \n",
+            "32  978143  132677     MU     TP9     1   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "16  502,503,498,500,493,507,514,506,501,502,499,49...   \n",
+            "17  506,511,513,518,505,522,526,509,517,514,514,51...   \n",
+            "18  513,512,506,509,504,507,512,505,514,512,507,50...   \n",
+            "19  501,504,494,489,485,509,513,487,495,503,503,49...   \n",
+            "32  514,506,504,510,516,511,508,509,511,514,515,51...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "16  [502, 503, 498, 500, 493, 507, 514, 506, 501, ...   \n",
+            "17  [506, 511, 513, 518, 505, 522, 526, 509, 517, ...   \n",
+            "18  [513, 512, 506, 509, 504, 507, 512, 505, 514, ...   \n",
+            "19  [501, 504, 494, 489, 485, 509, 513, 487, 495, ...   \n",
+            "32  [514, 506, 504, 510, 516, 511, 508, 509, 511, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "16  [-3.7783473034218895, -5.047913904341275, -5.6...  \n",
+            "17  [-0.06699306945382752, 3.252631522724486, 6.21...  \n",
+            "18  [-0.9911521525017524, -3.649796252934577, -5.7...  \n",
+            "19  [-8.108721360210737, -11.168247697975321, -13....  \n",
+            "32  [5.982306914528173, 3.6919052929416636, 1.9252...  \n",
+            "Code Value: 2\n",
+            "        id   event device channel  code  size  \\\n",
+            "56  978167  132683     MU     TP9     2   476   \n",
+            "57  978168  132683     MU     FP1     2   476   \n",
+            "58  978169  132683     MU     FP2     2   476   \n",
+            "59  978170  132683     MU    TP10     2   476   \n",
+            "84  978195  132690     MU     TP9     2   459   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "56  500,507,492,507,513,495,513,506,501,511,495,50...   \n",
+            "57  503,519,514,518,525,519,538,513,520,524,515,52...   \n",
+            "58  518,499,496,503,513,509,510,502,508,511,506,49...   \n",
+            "59  508,498,493,512,505,492,514,507,506,499,499,50...   \n",
+            "84  498,504,501,499,500,505,502,500,498,495,501,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "56  [500, 507, 492, 507, 513, 495, 513, 506, 501, ...   \n",
+            "57  [503, 519, 514, 518, 525, 519, 538, 513, 520, ...   \n",
+            "58  [518, 499, 496, 503, 513, 509, 510, 502, 508, ...   \n",
+            "59  [508, 498, 493, 512, 505, 492, 514, 507, 506, ...   \n",
+            "84  [498, 504, 501, 499, 500, 505, 502, 500, 498, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "56  [-15.315623304497628, -14.167152530537404, -12...  \n",
+            "57  [-2.310927096726438, 4.779189112804734, 10.940...  \n",
+            "58  [3.8957855125622003, -2.647406715610452, -7.53...  \n",
+            "59  [-11.00039583295662, -14.148635863885083, -16....  \n",
+            "84  [-6.590639001002224, -4.987291225509174, -3.69...  \n",
+            "Code Value: 3\n",
+            "         id   event device channel  code  size  \\\n",
+            "40   978151  132679     MU     TP9     3   459   \n",
+            "41   978152  132679     MU     FP1     3   459   \n",
+            "42   978153  132679     MU     FP2     3   459   \n",
+            "43   978154  132679     MU    TP10     3   459   \n",
+            "104  978215  132695     MU     TP9     3   493   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "40   506,505,518,512,511,503,508,523,518,525,507,51...   \n",
+            "41   501,511,511,510,505,507,508,512,507,517,507,51...   \n",
+            "42   508,503,497,501,492,497,504,497,498,509,501,51...   \n",
+            "43   509,498,503,508,508,499,493,520,514,520,503,50...   \n",
+            "104  499,508,510,506,511,510,504,512,509,517,514,50...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "40   [506, 505, 518, 512, 511, 503, 508, 523, 518, ...   \n",
+            "41   [501, 511, 511, 510, 505, 507, 508, 512, 507, ...   \n",
+            "42   [508, 503, 497, 501, 492, 497, 504, 497, 498, ...   \n",
+            "43   [509, 498, 503, 508, 508, 499, 493, 520, 514, ...   \n",
+            "104  [499, 508, 510, 506, 511, 510, 504, 512, 509, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "40   [-2.132234929248638, -0.5279212532803811, 0.75...  \n",
+            "41   [4.131486859691155, 7.492023735635791, 10.1284...  \n",
+            "42   [2.0853298093345862, -2.555059344312715, -6.38...  \n",
+            "43   [1.6088825088408105, -1.4676763565156383, -3.8...  \n",
+            "104  [-2.208535774591993, 1.8664822576783004, 5.188...  \n",
+            "Code Value: 4\n",
+            "         id   event device channel  code  size  \\\n",
+            "224  978335  132725     MU     TP9     4   476   \n",
+            "225  978336  132725     MU     FP1     4   476   \n",
+            "226  978337  132725     MU     FP2     4   476   \n",
+            "227  978338  132725     MU    TP10     4   476   \n",
+            "328  978439  132751     MU     TP9     4   493   \n",
+            "\n",
+            "                                                  data  \\\n",
+            "224  516,517,522,518,522,526,516,518,521,528,522,51...   \n",
+            "225  520,525,529,524,526,531,524,525,524,528,528,52...   \n",
+            "226  528,507,520,514,508,521,511,509,519,520,515,51...   \n",
+            "227  523,511,508,507,513,515,506,511,511,518,515,50...   \n",
+            "328  479,501,506,501,503,505,502,499,493,500,506,50...   \n",
+            "\n",
+            "                                                signal  \\\n",
+            "224  [516, 517, 522, 518, 522, 526, 516, 518, 521, ...   \n",
+            "225  [520, 525, 529, 524, 526, 531, 524, 525, 524, ...   \n",
+            "226  [528, 507, 520, 514, 508, 521, 511, 509, 519, ...   \n",
+            "227  [523, 511, 508, 507, 513, 515, 506, 511, 511, ...   \n",
+            "328  [479, 501, 506, 501, 503, 505, 502, 499, 493, ...   \n",
+            "\n",
+            "                                       filtered_signal  \n",
+            "224  [0.3078741398831336, 2.0318958313577604, 3.498...  \n",
+            "225  [3.9296152847768693, 6.792154169053837, 9.1313...  \n",
+            "226  [7.641844071940069, 1.1300585289014529, -4.137...  \n",
+            "227  [4.096876660722616, -2.0267246752548065, -6.88...  \n",
+            "328  [-3.376203156381087, 7.627975382311337, 16.438...  \n",
+            "Code Value: 5\n",
+            "        id   event device channel  code  size  \\\n",
+            "64  978175  132685     MU     TP9     5   459   \n",
+            "65  978176  132685     MU     FP1     5   459   \n",
+            "66  978177  132685     MU     FP2     5   459   \n",
+            "67  978178  132685     MU    TP10     5   459   \n",
+            "76  978187  132688     MU     TP9     5   459   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "64  481,492,490,487,486,490,491,488,484,489,496,49...   \n",
+            "65  498,508,505,505,508,508,512,507,501,505,505,50...   \n",
+            "66  508,498,494,491,491,501,498,498,502,490,491,49...   \n",
+            "67  494,484,480,479,477,475,481,479,479,478,482,48...   \n",
+            "76  504,505,509,514,510,507,508,508,503,503,501,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "64  [481, 492, 490, 487, 486, 490, 491, 488, 484, ...   \n",
+            "65  [498, 508, 505, 505, 508, 508, 512, 507, 501, ...   \n",
+            "66  [508, 498, 494, 491, 491, 501, 498, 498, 502, ...   \n",
+            "67  [494, 484, 480, 479, 477, 475, 481, 479, 479, ...   \n",
+            "76  [504, 505, 509, 514, 510, 507, 508, 508, 503, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "64  [-13.02208149595159, -9.421547047116219, -6.65...  \n",
+            "65  [-7.675124803865947, -3.7604190252621117, -0.4...  \n",
+            "66  [-6.01177475670532, -12.326052337629617, -17.2...  \n",
+            "67  [-13.155368370522655, -19.500756201280087, -24...  \n",
+            "76  [4.410329048744597, 7.202728549907036, 9.44735...  \n",
+            "Code Value: 6\n",
+            "       id   event device channel  code  size  \\\n",
+            "0  978111  132669     MU     TP9     6   459   \n",
+            "1  978112  132669     MU     FP1     6   459   \n",
+            "2  978113  132669     MU     FP2     6   459   \n",
+            "3  978114  132669     MU    TP10     6   459   \n",
+            "8  978119  132671     MU     TP9     6   459   \n",
+            "\n",
+            "                                                data  \\\n",
+            "0  475,474,477,486,486,476,479,483,489,483,482,48...   \n",
+            "1  468,487,493,493,498,493,491,490,492,487,483,48...   \n",
+            "2  482,475,490,500,485,470,470,482,490,484,478,48...   \n",
+            "3  470,470,478,489,487,475,469,478,488,483,474,48...   \n",
+            "8  520,533,528,517,522,534,534,531,522,528,536,53...   \n",
+            "\n",
+            "                                              signal  \\\n",
+            "0  [475, 474, 477, 486, 486, 476, 479, 483, 489, ...   \n",
+            "1  [468, 487, 493, 493, 498, 493, 491, 490, 492, ...   \n",
+            "2  [482, 475, 490, 500, 485, 470, 470, 482, 490, ...   \n",
+            "3  [470, 470, 478, 489, 487, 475, 469, 478, 488, ...   \n",
+            "8  [520, 533, 528, 517, 522, 534, 534, 531, 522, ...   \n",
+            "\n",
+            "                                     filtered_signal  \n",
+            "0  [-16.384348518146272, -14.28677015299674, -12....  \n",
+            "1  [-12.355924481489554, -1.1196275855661515, 8.1...  \n",
+            "2  [-8.902057950255045, -6.571550261856517, -5.30...  \n",
+            "3  [-14.862239327190066, -10.1729854827539, -6.50...  \n",
+            "8  [11.732361011063364, 14.118893083372951, 16.25...  \n",
+            "Code Value: 7\n",
+            "        id   event device channel  code  size  \\\n",
+            "4   978115  132670     MU     TP9     7   493   \n",
+            "5   978116  132670     MU     FP1     7   493   \n",
+            "6   978117  132670     MU     FP2     7   493   \n",
+            "7   978118  132670     MU    TP10     7   493   \n",
+            "44  978155  132680     MU     TP9     7   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "4   506,499,495,491,492,507,496,500,498,496,499,50...   \n",
+            "5   505,515,513,506,512,520,512,521,517,512,517,52...   \n",
+            "6   511,501,503,503,498,514,506,511,511,500,505,50...   \n",
+            "7   500,506,490,487,492,507,497,493,496,499,495,48...   \n",
+            "44  482,491,478,478,481,490,491,488,485,490,485,48...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "4   [506, 499, 495, 491, 492, 507, 496, 500, 498, ...   \n",
+            "5   [505, 515, 513, 506, 512, 520, 512, 521, 517, ...   \n",
+            "6   [511, 501, 503, 503, 498, 514, 506, 511, 511, ...   \n",
+            "7   [500, 506, 490, 487, 492, 507, 497, 493, 496, ...   \n",
+            "44  [482, 491, 478, 478, 481, 490, 491, 488, 485, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "4   [-2.8461685103313803, -7.70979962293131, -11.4...  \n",
+            "5   [0.7195349899218323, 3.492396300168519, 5.9850...  \n",
+            "6   [0.2719353119824829, -3.6320274978373805, -6.3...  \n",
+            "7   [-5.984146989345621, -8.676245568757967, -10.6...  \n",
+            "44  [-22.412128443116003, -22.749599776110024, -22...  \n",
+            "Code Value: 8\n",
+            "        id   event device channel  code  size  \\\n",
+            "28  978139  132676     MU     TP9     8   442   \n",
+            "29  978140  132676     MU     FP1     8   442   \n",
+            "30  978141  132676     MU     FP2     8   442   \n",
+            "31  978142  132676     MU    TP10     8   442   \n",
+            "92  978203  132692     MU     TP9     8   476   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "28  516,520,516,517,529,521,524,522,520,518,513,51...   \n",
+            "29  511,521,514,517,524,515,515,515,518,512,513,51...   \n",
+            "30  508,516,500,502,510,502,508,506,504,505,508,51...   \n",
+            "31  513,523,513,520,525,511,512,520,522,511,505,51...   \n",
+            "92  487,501,499,503,508,504,500,511,503,506,508,50...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "28  [516, 520, 516, 517, 529, 521, 524, 522, 520, ...   \n",
+            "29  [511, 521, 514, 517, 524, 515, 515, 515, 518, ...   \n",
+            "30  [508, 516, 500, 502, 510, 502, 508, 506, 504, ...   \n",
+            "31  [513, 523, 513, 520, 525, 511, 512, 520, 522, ...   \n",
+            "92  [487, 501, 499, 503, 508, 504, 500, 511, 503, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "28  [-1.942080876253038, 0.10712782440252919, 2.02...  \n",
+            "29  [5.062872918162999, 8.524314348029614, 11.2509...  \n",
+            "30  [-0.8310023751563308, -1.8685638003418346, -2....  \n",
+            "31  [3.0541040274857005, 5.811252000635518, 7.9305...  \n",
+            "92  [0.8500088198422158, 7.679462851546148, 13.387...  \n",
+            "Code Value: 9\n",
+            "        id   event device channel  code  size  \\\n",
+            "36  978147  132678     MU     TP9     9   493   \n",
+            "37  978148  132678     MU     FP1     9   493   \n",
+            "38  978149  132678     MU     FP2     9   493   \n",
+            "39  978150  132678     MU    TP10     9   493   \n",
+            "52  978163  132682     MU     TP9     9   442   \n",
+            "\n",
+            "                                                 data  \\\n",
+            "36  521,524,532,524,522,537,531,540,534,532,533,53...   \n",
+            "37  510,517,526,516,512,526,521,528,526,522,521,52...   \n",
+            "38  518,506,520,515,502,514,511,515,521,516,512,51...   \n",
+            "39  529,519,520,530,528,528,523,535,539,528,525,53...   \n",
+            "52  537,525,538,541,531,521,521,528,525,528,529,52...   \n",
+            "\n",
+            "                                               signal  \\\n",
+            "36  [521, 524, 532, 524, 522, 537, 531, 540, 534, ...   \n",
+            "37  [510, 517, 526, 516, 512, 526, 521, 528, 526, ...   \n",
+            "38  [518, 506, 520, 515, 502, 514, 511, 515, 521, ...   \n",
+            "39  [529, 519, 520, 530, 528, 528, 523, 535, 539, ...   \n",
+            "52  [537, 525, 538, 541, 531, 521, 521, 528, 525, ...   \n",
+            "\n",
+            "                                      filtered_signal  \n",
+            "36  [17.003944016879277, 19.150406097027016, 21.31...  \n",
+            "37  [4.56686351757058, 8.14046139048667, 11.307095...  \n",
+            "38  [7.344760141391687, 4.36329635746246, 1.911523...  \n",
+            "39  [19.752293876946077, 17.393641735853123, 15.87...  \n",
+            "52  [23.61698009200334, 22.328310041013964, 20.724...  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\nMissing values in each column:\")\n",
+        "print(df_mu_filtered.isnull().sum())\n",
+        "#no missing values"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7ScWzGozUVFx",
+        "outputId": "0b453bf1-d134-4145-894b-920a2219e150"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Missing values in each column:\n",
+            "id                 0\n",
+            "event              0\n",
+            "device             0\n",
+            "channel            0\n",
+            "code               0\n",
+            "size               0\n",
+            "data               0\n",
+            "signal             0\n",
+            "filtered_signal    0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#resampling to median length using CubicSpline\n",
+        "median_length=int(np.median(df_mu_filtered['filtered_signal'].apply(len)))\n",
+        "print(\"Median Length =\",median_length)\n",
+        "\n",
+        "def resample_signal(signal, target_length=median_length):\n",
+        "    original_length = len(signal)\n",
+        "    if original_length == target_length:\n",
+        "        return signal\n",
+        "    x_old =  np.linspace(0, 1, original_length)\n",
+        "    x_new = np.linspace(0, 1, target_length)\n",
+        "    cs = CubicSpline(x_old, signal)\n",
+        "    resampled_signal = cs(x_new)\n",
+        "    return resampled_signal\n",
+        "\n",
+        "df_mu_filtered['resampled_signal']=df_mu_filtered['filtered_signal'].apply(lambda x: resample_signal(np.array(x), median_length))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XsYczhCzXHMY",
+        "outputId": "70e5a0a7-0474-4fb1-fb77-3426b584b10e"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Median Length = 476\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-16-4f1ae0435947>:15: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_mu_filtered['resampled_signal']=df_mu_filtered['filtered_signal'].apply(lambda x: resample_signal(np.array(x), median_length))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(df_mu_filtered.loc[sample_index, 'resampled_signal']))\n",
+        "#can check by any signal, they have all been resampled to the median length i.e. 476"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FE7ZIeT0e2fi",
+        "outputId": "e5074de4-8a59-48ea-82d3-1ae67afa9daa"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "476\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#normalising\n",
+        "def normalise_signal(signal):\n",
+        "  signal=np.array(signal).reshape(-1,1)\n",
+        "  scaler=MinMaxScaler()\n",
+        "  scaler.fit(signal)\n",
+        "  signal=scaler.transform(signal)\n",
+        "  return signal\n",
+        "\n",
+        "df_mu_filtered['data_normalised']=df_mu_filtered['resampled_signal'].apply(normalise_signal)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cVlX0BG5fXbf",
+        "outputId": "8cbeaff8-f660-4d94-f4da-06123ab50855"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-18-67ee54a43d6f>:9: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_mu_filtered['data_normalised']=df_mu_filtered['resampled_signal'].apply(normalise_signal)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=df_mu_filtered"
+      ],
+      "metadata": {
+        "id": "CVlu5vaemFM4"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(type(df['data'].iloc[0]), type(df['resampled_signal'].iloc[0]), type(df['data_normalised'].iloc[0]))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GIGREb4dhuqP",
+        "outputId": "91f3aeb7-e160-45d5-a048-e5eabef3b43e"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'str'> <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['data'] = df['data'].apply(lambda x: list(map(int, x.split(','))) if isinstance(x, str) else x)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jh91s18MhxKn",
+        "outputId": "84662d74-21cd-451f-86a5-03c35d25de32"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-22-832cb8f72477>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df['data'] = df['data'].apply(lambda x: list(map(int, x.split(','))) if isinstance(x, str) else x)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(14,2))\n",
+        "original=df['data'].iloc[0]\n",
+        "resampled=df['resampled_signal'].iloc[0]\n",
+        "normalised=df['data_normalised'].iloc[0]\n",
+        "\n",
+        "plt.subplot(1,3,1)\n",
+        "plt.plot(original)\n",
+        "plt.title('Original')\n",
+        "\n",
+        "plt.subplot(1,3,2)\n",
+        "plt.plot(resampled)\n",
+        "plt.title('Resampled')\n",
+        "\n",
+        "plt.subplot(1,3,3)\n",
+        "plt.plot(normalised)\n",
+        "plt.title('Normalised')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "M44ww0Y6f_Iw",
+        "outputId": "d3987cb9-1564-48e8-8c7e-350349f172db"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x200 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#plotting for a sample index\n",
+        "df=df_mu_filtered\n",
+        "plt.figure(figsize=(14,2))\n",
+        "original=df['data'].iloc[sample_index]\n",
+        "resampled=df['resampled_signal'].iloc[sample_index]\n",
+        "normalised=df['data_normalised'].iloc[sample_index]\n",
+        "\n",
+        "plt.subplot(1,3,1)\n",
+        "plt.plot(original)\n",
+        "plt.title('Original')\n",
+        "\n",
+        "plt.subplot(1,3,2)\n",
+        "plt.plot(resampled)\n",
+        "plt.title('Resampled')\n",
+        "\n",
+        "plt.subplot(1,3,3)\n",
+        "plt.plot(normalised)\n",
+        "plt.title('Normalised')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "8wIx9zEwgu8f",
+        "outputId": "724c51b4-c33b-44f6-a0db-953e8bf02053"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x200 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file