CaImAn_OSI.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "79378e6a-b14c-4378-a820-989bcb165b01",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79378e6a-b14c-4378-a820-989bcb165b01",
        "outputId": "e058486f-5b35-4068-85d2-92752c31df08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.13.1)\n",
            "Requirement already satisfied: numpy<2.3,>=1.22.4 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.4)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "!pip install scipy\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import seaborn as sns\n",
        "from scipy.optimize import curve_fit\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b42e04b-1240-45c7-b0da-5d1431def3c8",
      "metadata": {
        "id": "0b42e04b-1240-45c7-b0da-5d1431def3c8"
      },
      "source": [
        "# Alinear transitorios con estímulos\n",
        "## alínea con los frames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "a1d1199d-9706-4495-8daa-c8055fafd96f",
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          "height": 443
        },
        "id": "a1d1199d-9706-4495-8daa-c8055fafd96f",
        "outputId": "206a4c93-d135-40dc-a41d-2c46b48be554"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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      "source": [
        "C_traces='/content/drive/MyDrive/Python/Calcio/Shank3_IGF/L1_988_directions_C_traces2.csv'\n",
        "C=pd.read_csv(C_traces)\n",
        "C"
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    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "a4d782ca-16da-4460-8d5a-705efafc2f0c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
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        "id": "a4d782ca-16da-4460-8d5a-705efafc2f0c",
        "outputId": "e6fd8245-5c96-4e29-aeda-2d6d43c18bb5"
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              "  </tbody>\n",
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              "<p>5732 rows × 100 columns</p>\n",
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      "source": [
        "max_len = len(C)\n",
        "C.insert(0, 'Frame', range(0, max_len ))\n",
        "\n",
        "C"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "78a27ff7-d393-41d5-9131-001e44295271",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "78a27ff7-d393-41d5-9131-001e44295271",
        "outputId": "511897ce-3bd3-4d09-cffd-a4595f391e76",
        "collapsed": true
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  [   0. 1389.]\n",
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            "\n",
            " [[  30.  577.]\n",
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            "  [  30. 5335.]]\n",
            "\n",
            " [[  60.  217.]\n",
            "  [  60. 1337.]\n",
            "  [  60. 1532.]\n",
            "  [  60. 2395.]\n",
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            "  [  60. 5232.]]\n",
            "\n",
            " [[  90.  268.]\n",
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            "\n",
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            "\n",
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            "  [ 180. 2602.]\n",
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            "  [ 180. 4523.]\n",
            "  [ 180. 5181.]]\n",
            "\n",
            " [[ 210.  114.]\n",
            "  [ 210. 1029.]\n",
            "  [ 210. 1583.]\n",
            "  [ 210. 2807.]\n",
            "  [ 210. 3413.]\n",
            "  [ 210. 4122.]\n",
            "  [ 210. 4368.]\n",
            "  [ 210. 5438.]]\n",
            "\n",
            " [[ 240.  474.]\n",
            "  [ 240. 1235.]\n",
            "  [ 240. 1995.]\n",
            "  [ 240. 2704.]\n",
            "  [ 240. 2950.]\n",
            "  [ 240. 3865.]\n",
            "  [ 240. 4832.]\n",
            "  [ 240. 5026.]]\n",
            "\n",
            " [[ 270.  423.]\n",
            "  [ 270. 1131.]\n",
            "  [ 270. 1686.]\n",
            "  [ 270. 2550.]\n",
            "  [ 270. 3002.]\n",
            "  [ 270. 3762.]\n",
            "  [ 270. 4317.]\n",
            "  [ 270. 5644.]]\n",
            "\n",
            " [[ 300.  371.]\n",
            "  [ 300.  771.]\n",
            "  [ 300. 1841.]\n",
            "  [ 300. 2447.]\n",
            "  [ 300. 3517.]\n",
            "  [ 300. 3916.]\n",
            "  [ 300. 4420.]\n",
            "  [ 300. 5386.]]\n",
            "\n",
            " [[ 330.   62.]\n",
            "  [ 330. 1080.]\n",
            "  [ 330. 1943.]\n",
            "  [ 330. 2190.]\n",
            "  [ 330. 3053.]\n",
            "  [ 330. 4071.]\n",
            "  [ 330. 4574.]\n",
            "  [ 330. 5540.]]\n",
            "\n",
            " [[ 360.  165.]\n",
            "  [ 360.  874.]\n",
            "  [ 360. 1480.]\n",
            "  [ 360. 2241.]\n",
            "  [ 360. 3465.]\n",
            "  [ 360. 4174.]\n",
            "  [ 360. 4883.]\n",
            "  [ 360. 5489.]]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 8, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "stimsnframes = np.load(r'/content/drive/MyDrive/Python/Calcio/Shank3_IGF/L1_988_result.npy')\n",
        "\n",
        "print(stimsnframes)\n",
        "stimsnframes.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "9664ed74-9abc-44e6-b9f4-c4a995136d72",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "9664ed74-9abc-44e6-b9f4-c4a995136d72",
        "outputId": "15404c48-a0a7-4226-db69-ee9cd6dd1c0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Frame   Stim         time           1           2           3  \\\n",
              "62       62  330.0    12.402164  517.354639    0.105744    0.000000   \n",
              "114     114  210.0    22.803978    4.106293  102.400030    0.000000   \n",
              "165     165  360.0    33.005758    0.035769   98.978720   43.139549   \n",
              "217     217   60.0    43.407573    0.000284  117.757776  133.246210   \n",
              "268     268   90.0    53.609353    0.000002  419.624790  273.458878   \n",
              "...     ...    ...          ...         ...         ...         ...   \n",
              "5438   5438  210.0  1087.789775    0.000007   38.913414    5.568842   \n",
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              "\n",
              "               4           5           6          7  ...            89  \\\n",
              "62     37.149153    6.072857  543.148091  10.169523  ...  2.431463e+02   \n",
              "114   126.326047  141.026372  137.350395  19.222176  ...  2.096523e+02   \n",
              "165    43.752066  106.130961    7.740019   0.654714  ...  4.118688e+02   \n",
              "217   295.892097  114.975272   41.121761  25.405078  ...  1.811530e+01   \n",
              "268   322.313275   22.430922    6.457019   0.926318  ...  1.885236e+01   \n",
              "...          ...         ...         ...        ...  ...           ...   \n",
              "5438  208.082050   29.114994    6.006783   1.083970  ...  2.265222e-07   \n",
              "5489   27.567159   33.759471    0.027967   1.119660  ...  1.729225e+02   \n",
              "5540    5.234180    0.942696   14.440477   0.006235  ...  3.083220e+00   \n",
              "5592    9.669998   11.707624    1.569402   0.411817  ...  5.080028e-02   \n",
              "5644   17.921417   95.187079   16.768717  26.215246  ...  8.370041e-04   \n",
              "\n",
              "              90          91          92         93            94          95  \\\n",
              "62      8.661128  337.526990    6.410677   6.160086  5.365032e+02   51.691815   \n",
              "114     2.989437  128.375093   21.050458  12.040120  3.631418e+01    1.095725   \n",
              "165   183.115555  859.329707  101.022136   0.692821  4.189858e+01  107.353736   \n",
              "217    18.359315  124.392607    9.580563   7.590092  5.348174e+00    2.275606   \n",
              "268     0.311710    5.438270    0.075494   1.181515  2.221996e-01    0.051947   \n",
              "...          ...         ...         ...        ...           ...         ...   \n",
              "5438   13.870201  409.224023   47.121231   1.381036  1.936959e-04    9.321421   \n",
              "5489   25.311295  449.203558   29.304345   0.095250  8.047450e-06  151.519513   \n",
              "5540    0.039026   88.913668    0.230917   0.111228  3.343459e-07   63.650698   \n",
              "5592   56.854005   59.195838    0.001655   0.010137  1.305107e-08   99.559360   \n",
              "5644  478.554999    2.433907  372.844827  14.966200  5.094440e-10  155.753560   \n",
              "\n",
              "             96          97          98  \n",
              "62     3.037140  104.075806    3.741094  \n",
              "114   32.012273   36.154576    1.437387  \n",
              "165   94.514218   63.987374    0.001621  \n",
              "217    6.228882    1.437865    0.838700  \n",
              "268    0.050103    0.034757    0.000946  \n",
              "...         ...         ...         ...  \n",
              "5438   3.631778    0.618743    2.520013  \n",
              "5489   0.029213    0.014957   12.005367  \n",
              "5540   0.000235    7.563419    0.013539  \n",
              "5592   6.515033    1.211551    0.508708  \n",
              "5644   0.414925    0.027225  126.765103  \n",
              "\n",
              "[104 rows x 101 columns]"
            ],
            "text/html": [
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              "      <th>114</th>\n",
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              "      <td>137.350395</td>\n",
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              "      <td>...</td>\n",
              "      <td>4.118688e+02</td>\n",
              "      <td>183.115555</td>\n",
              "      <td>859.329707</td>\n",
              "      <td>101.022136</td>\n",
              "      <td>0.692821</td>\n",
              "      <td>4.189858e+01</td>\n",
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              "      <td>117.757776</td>\n",
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              "      <td>6.228882</td>\n",
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              "      <td>409.224023</td>\n",
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              "      <td>1.936959e-04</td>\n",
              "      <td>9.321421</td>\n",
              "      <td>3.631778</td>\n",
              "      <td>0.618743</td>\n",
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              "      <td>0.027967</td>\n",
              "      <td>1.119660</td>\n",
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              "      <td>14.440477</td>\n",
              "      <td>0.006235</td>\n",
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              "      <td>0.039026</td>\n",
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              "      <td>0.230917</td>\n",
              "      <td>0.111228</td>\n",
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              "      <td>63.650698</td>\n",
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              "      <td>11.707624</td>\n",
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              "      <td>5.080028e-02</td>\n",
              "      <td>56.854005</td>\n",
              "      <td>59.195838</td>\n",
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              "      <td>1.305107e-08</td>\n",
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              "      <td>6.515033</td>\n",
              "      <td>1.211551</td>\n",
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              "\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-f212599b-cb69-4041-ab91-769a7dda6c0a 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-f212599b-cb69-4041-ab91-769a7dda6c0a');\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-f879a270-9814-4e97-9d4d-27189ea246a1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f879a270-9814-4e97-9d4d-27189ea246a1')\"\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-f879a270-9814-4e97-9d4d-27189ea246a1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_567291e3-7b6c-42a8-b4f3-16951561ec26\">\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('C_stim_frames')\"\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_567291e3-7b6c-42a8-b4f3-16951561ec26 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('C_stim_frames');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "C_stim_frames"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "stim_frame = stimsnframes[:, :, 1].flatten()\n",
        "C_stim_frames = C[C['Frame'].isin(stim_frame)].copy()\n",
        "tf_stimulus = []\n",
        "\n",
        "for time_frame in C_stim_frames['Frame']:\n",
        "    index = np.where(stimsnframes[:, :, 1] == time_frame)[0][0]\n",
        "\n",
        "    tf_stimulus.append(stimsnframes[index, 0, 0])\n",
        "\n",
        "if 'Stim' in C_stim_frames.columns:\n",
        "    C_stim_frames = C_stim_frames.drop(columns=['Stim'])\n",
        "\n",
        "C_stim_frames['Stim'] = tf_stimulus\n",
        "cols = ['Frame', 'Stim'] + [col for col in C_stim_frames.columns if col not in ['Frame', 'Stim']]\n",
        "C_stim_frames = C_stim_frames[cols]\n",
        "\n",
        "C_stim_frames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "ebe79ebd-cd7e-42fa-bd32-dded5680eeea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "ebe79ebd-cd7e-42fa-bd32-dded5680eeea",
        "outputId": "489ac919-3d8c-45af-cab4-144cc7169975"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "duracion_estimulo = 4\n",
        "\n",
        "C_stim_frames['End_time'] = C_stim_frames['time'] + duracion_estimulo\n",
        "columnas = [col for col in C_stim_frames.columns if col != 'End_time']\n",
        "columnas.insert(3, 'End_time')\n",
        "C_stim_frames = C_stim_frames[columnas]\n",
        "\n",
        "columnas = [col for col in C_stim_frames.columns if col != 'End_time']\n",
        "columnas.insert(3, 'End_time')\n",
        "C_stim_frames = C_stim_frames[columnas]\n",
        "C_stim_frames.columns\n",
        "colors = sns.color_palette(\"husl\", 13)  #<-- Cambia a la cantidad de estimulos\n",
        "stimuli = C_stim_frames['Stim'].unique()\n",
        "color_map = dict(zip(stimuli, colors))\n",
        "\n",
        "neurona='55'\n",
        "plt.figure(figsize=(14, 3))\n",
        "sns.lineplot(data=C,x='time', y=neurona)# Y es el número de neurona a graficar\n",
        "plt.xlabel('Tiempo (s)')\n",
        "plt.ylabel(f' Neurona {neurona}')\n",
        "for stimulus in stimuli:\n",
        "    stimulus_times = C_stim_frames[C_stim_frames['Stim'] == stimulus]['time'].values  # Tiempo de cada estímulo\n",
        "\n",
        "    for start in stimulus_times:\n",
        "        end = start + 4  # Duración del estimulo\n",
        "        plt.axvspan(start, end, color=color_map[stimulus], alpha=0.8)\n",
        "\n",
        "handles = [mpatches.Patch(color=color_map[stimulus], label=stimulus) for stimulus in stimuli]\n",
        "plt.legend(handles=handles, title=\"Estímulos\",  loc='upper right')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "b9d11f0d-ec85-4886-a292-8019ed6153cb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9d11f0d-ec85-4886-a292-8019ed6153cb",
        "outputId": "28539e1d-b3c4-400f-afa5-c8f202b5d130"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Frame', 'Stim', 'time', 'End_time', '1', '2', '3', '4', '5', '6',\n",
              "       ...\n",
              "       '89', '90', '91', '92', '93', '94', '95', '96', '97', '98'],\n",
              "      dtype='object', length=102)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "C_stim_frames.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "f337db9b-4aec-470c-8af0-e538fed6d9d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "f337db9b-4aec-470c-8af0-e538fed6d9d1",
        "outputId": "c71bc3dc-e29c-49fd-b5ab-9b22a4f56727"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Frame   Stim         time     End_time           1           2  \\\n",
              "62       62  330.0    12.402164    16.402164  517.354639    0.105744   \n",
              "114     114  210.0    22.803978    26.803978    4.106293  102.400030   \n",
              "165     165  360.0    33.005758    37.005758    0.035769   98.978720   \n",
              "217     217   60.0    43.407573    47.407573    0.000284  117.757776   \n",
              "268     268   90.0    53.609353    57.609353    0.000002  419.624790   \n",
              "...     ...    ...          ...          ...         ...         ...   \n",
              "5438   5438  210.0  1087.789775  1091.789775    0.000007   38.913414   \n",
              "5489   5489  360.0  1097.991555  1101.991555  825.569595    0.202264   \n",
              "5540   5540  330.0  1108.193334  1112.193334    7.191283    0.001051   \n",
              "5592   5592   90.0  1118.595149  1122.595149    0.057078    1.591901   \n",
              "5644   5644  270.0  1128.996964  1132.996964    0.000453    0.007464   \n",
              "\n",
              "               3           4           5           6  ...            89  \\\n",
              "62      0.000000   37.149153    6.072857  543.148091  ...  2.431463e+02   \n",
              "114     0.000000  126.326047  141.026372  137.350395  ...  2.096523e+02   \n",
              "165    43.139549   43.752066  106.130961    7.740019  ...  4.118688e+02   \n",
              "217   133.246210  295.892097  114.975272   41.121761  ...  1.811530e+01   \n",
              "268   273.458878  322.313275   22.430922    6.457019  ...  1.885236e+01   \n",
              "...          ...         ...         ...         ...  ...           ...   \n",
              "5438    5.568842  208.082050   29.114994    6.006783  ...  2.265222e-07   \n",
              "5489    0.038711   27.567159   33.759471    0.027967  ...  1.729225e+02   \n",
              "5540    3.644172    5.234180    0.942696   14.440477  ...  3.083220e+00   \n",
              "5592    1.459064    9.669998   11.707624    1.569402  ...  5.080028e-02   \n",
              "5644    7.294271   17.921417   95.187079   16.768717  ...  8.370041e-04   \n",
              "\n",
              "              90          91          92         93            94          95  \\\n",
              "62      8.661128  337.526990    6.410677   6.160086  5.365032e+02   51.691815   \n",
              "114     2.989437  128.375093   21.050458  12.040120  3.631418e+01    1.095725   \n",
              "165   183.115555  859.329707  101.022136   0.692821  4.189858e+01  107.353736   \n",
              "217    18.359315  124.392607    9.580563   7.590092  5.348174e+00    2.275606   \n",
              "268     0.311710    5.438270    0.075494   1.181515  2.221996e-01    0.051947   \n",
              "...          ...         ...         ...        ...           ...         ...   \n",
              "5438   13.870201  409.224023   47.121231   1.381036  1.936959e-04    9.321421   \n",
              "5489   25.311295  449.203558   29.304345   0.095250  8.047450e-06  151.519513   \n",
              "5540    0.039026   88.913668    0.230917   0.111228  3.343459e-07   63.650698   \n",
              "5592   56.854005   59.195838    0.001655   0.010137  1.305107e-08   99.559360   \n",
              "5644  478.554999    2.433907  372.844827  14.966200  5.094440e-10  155.753560   \n",
              "\n",
              "             96          97          98  \n",
              "62     3.037140  104.075806    3.741094  \n",
              "114   32.012273   36.154576    1.437387  \n",
              "165   94.514218   63.987374    0.001621  \n",
              "217    6.228882    1.437865    0.838700  \n",
              "268    0.050103    0.034757    0.000946  \n",
              "...         ...         ...         ...  \n",
              "5438   3.631778    0.618743    2.520013  \n",
              "5489   0.029213    0.014957   12.005367  \n",
              "5540   0.000235    7.563419    0.013539  \n",
              "5592   6.515033    1.211551    0.508708  \n",
              "5644   0.414925    0.027225  126.765103  \n",
              "\n",
              "[104 rows x 102 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frame</th>\n",
              "      <th>Stim</th>\n",
              "      <th>time</th>\n",
              "      <th>End_time</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>...</th>\n",
              "      <th>89</th>\n",
              "      <th>90</th>\n",
              "      <th>91</th>\n",
              "      <th>92</th>\n",
              "      <th>93</th>\n",
              "      <th>94</th>\n",
              "      <th>95</th>\n",
              "      <th>96</th>\n",
              "      <th>97</th>\n",
              "      <th>98</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>62</td>\n",
              "      <td>330.0</td>\n",
              "      <td>12.402164</td>\n",
              "      <td>16.402164</td>\n",
              "      <td>517.354639</td>\n",
              "      <td>0.105744</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>37.149153</td>\n",
              "      <td>6.072857</td>\n",
              "      <td>543.148091</td>\n",
              "      <td>...</td>\n",
              "      <td>2.431463e+02</td>\n",
              "      <td>8.661128</td>\n",
              "      <td>337.526990</td>\n",
              "      <td>6.410677</td>\n",
              "      <td>6.160086</td>\n",
              "      <td>5.365032e+02</td>\n",
              "      <td>51.691815</td>\n",
              "      <td>3.037140</td>\n",
              "      <td>104.075806</td>\n",
              "      <td>3.741094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>114</td>\n",
              "      <td>210.0</td>\n",
              "      <td>22.803978</td>\n",
              "      <td>26.803978</td>\n",
              "      <td>4.106293</td>\n",
              "      <td>102.400030</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>126.326047</td>\n",
              "      <td>141.026372</td>\n",
              "      <td>137.350395</td>\n",
              "      <td>...</td>\n",
              "      <td>2.096523e+02</td>\n",
              "      <td>2.989437</td>\n",
              "      <td>128.375093</td>\n",
              "      <td>21.050458</td>\n",
              "      <td>12.040120</td>\n",
              "      <td>3.631418e+01</td>\n",
              "      <td>1.095725</td>\n",
              "      <td>32.012273</td>\n",
              "      <td>36.154576</td>\n",
              "      <td>1.437387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>165</td>\n",
              "      <td>360.0</td>\n",
              "      <td>33.005758</td>\n",
              "      <td>37.005758</td>\n",
              "      <td>0.035769</td>\n",
              "      <td>98.978720</td>\n",
              "      <td>43.139549</td>\n",
              "      <td>43.752066</td>\n",
              "      <td>106.130961</td>\n",
              "      <td>7.740019</td>\n",
              "      <td>...</td>\n",
              "      <td>4.118688e+02</td>\n",
              "      <td>183.115555</td>\n",
              "      <td>859.329707</td>\n",
              "      <td>101.022136</td>\n",
              "      <td>0.692821</td>\n",
              "      <td>4.189858e+01</td>\n",
              "      <td>107.353736</td>\n",
              "      <td>94.514218</td>\n",
              "      <td>63.987374</td>\n",
              "      <td>0.001621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>217</td>\n",
              "      <td>60.0</td>\n",
              "      <td>43.407573</td>\n",
              "      <td>47.407573</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>117.757776</td>\n",
              "      <td>133.246210</td>\n",
              "      <td>295.892097</td>\n",
              "      <td>114.975272</td>\n",
              "      <td>41.121761</td>\n",
              "      <td>...</td>\n",
              "      <td>1.811530e+01</td>\n",
              "      <td>18.359315</td>\n",
              "      <td>124.392607</td>\n",
              "      <td>9.580563</td>\n",
              "      <td>7.590092</td>\n",
              "      <td>5.348174e+00</td>\n",
              "      <td>2.275606</td>\n",
              "      <td>6.228882</td>\n",
              "      <td>1.437865</td>\n",
              "      <td>0.838700</td>\n",
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              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>268</td>\n",
              "      <td>90.0</td>\n",
              "      <td>53.609353</td>\n",
              "      <td>57.609353</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>419.624790</td>\n",
              "      <td>273.458878</td>\n",
              "      <td>322.313275</td>\n",
              "      <td>22.430922</td>\n",
              "      <td>6.457019</td>\n",
              "      <td>...</td>\n",
              "      <td>1.885236e+01</td>\n",
              "      <td>0.311710</td>\n",
              "      <td>5.438270</td>\n",
              "      <td>0.075494</td>\n",
              "      <td>1.181515</td>\n",
              "      <td>2.221996e-01</td>\n",
              "      <td>0.051947</td>\n",
              "      <td>0.050103</td>\n",
              "      <td>0.034757</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5438</th>\n",
              "      <td>5438</td>\n",
              "      <td>210.0</td>\n",
              "      <td>1087.789775</td>\n",
              "      <td>1091.789775</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>38.913414</td>\n",
              "      <td>5.568842</td>\n",
              "      <td>208.082050</td>\n",
              "      <td>29.114994</td>\n",
              "      <td>6.006783</td>\n",
              "      <td>...</td>\n",
              "      <td>2.265222e-07</td>\n",
              "      <td>13.870201</td>\n",
              "      <td>409.224023</td>\n",
              "      <td>47.121231</td>\n",
              "      <td>1.381036</td>\n",
              "      <td>1.936959e-04</td>\n",
              "      <td>9.321421</td>\n",
              "      <td>3.631778</td>\n",
              "      <td>0.618743</td>\n",
              "      <td>2.520013</td>\n",
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              "    <tr>\n",
              "      <th>5489</th>\n",
              "      <td>5489</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1097.991555</td>\n",
              "      <td>1101.991555</td>\n",
              "      <td>825.569595</td>\n",
              "      <td>0.202264</td>\n",
              "      <td>0.038711</td>\n",
              "      <td>27.567159</td>\n",
              "      <td>33.759471</td>\n",
              "      <td>0.027967</td>\n",
              "      <td>...</td>\n",
              "      <td>1.729225e+02</td>\n",
              "      <td>25.311295</td>\n",
              "      <td>449.203558</td>\n",
              "      <td>29.304345</td>\n",
              "      <td>0.095250</td>\n",
              "      <td>8.047450e-06</td>\n",
              "      <td>151.519513</td>\n",
              "      <td>0.029213</td>\n",
              "      <td>0.014957</td>\n",
              "      <td>12.005367</td>\n",
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              "    <tr>\n",
              "      <th>5540</th>\n",
              "      <td>5540</td>\n",
              "      <td>330.0</td>\n",
              "      <td>1108.193334</td>\n",
              "      <td>1112.193334</td>\n",
              "      <td>7.191283</td>\n",
              "      <td>0.001051</td>\n",
              "      <td>3.644172</td>\n",
              "      <td>5.234180</td>\n",
              "      <td>0.942696</td>\n",
              "      <td>14.440477</td>\n",
              "      <td>...</td>\n",
              "      <td>3.083220e+00</td>\n",
              "      <td>0.039026</td>\n",
              "      <td>88.913668</td>\n",
              "      <td>0.230917</td>\n",
              "      <td>0.111228</td>\n",
              "      <td>3.343459e-07</td>\n",
              "      <td>63.650698</td>\n",
              "      <td>0.000235</td>\n",
              "      <td>7.563419</td>\n",
              "      <td>0.013539</td>\n",
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              "    <tr>\n",
              "      <th>5592</th>\n",
              "      <td>5592</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1118.595149</td>\n",
              "      <td>1122.595149</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>1.591901</td>\n",
              "      <td>1.459064</td>\n",
              "      <td>9.669998</td>\n",
              "      <td>11.707624</td>\n",
              "      <td>1.569402</td>\n",
              "      <td>...</td>\n",
              "      <td>5.080028e-02</td>\n",
              "      <td>56.854005</td>\n",
              "      <td>59.195838</td>\n",
              "      <td>0.001655</td>\n",
              "      <td>0.010137</td>\n",
              "      <td>1.305107e-08</td>\n",
              "      <td>99.559360</td>\n",
              "      <td>6.515033</td>\n",
              "      <td>1.211551</td>\n",
              "      <td>0.508708</td>\n",
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              "    <tr>\n",
              "      <th>5644</th>\n",
              "      <td>5644</td>\n",
              "      <td>270.0</td>\n",
              "      <td>1128.996964</td>\n",
              "      <td>1132.996964</td>\n",
              "      <td>0.000453</td>\n",
              "      <td>0.007464</td>\n",
              "      <td>7.294271</td>\n",
              "      <td>17.921417</td>\n",
              "      <td>95.187079</td>\n",
              "      <td>16.768717</td>\n",
              "      <td>...</td>\n",
              "      <td>8.370041e-04</td>\n",
              "      <td>478.554999</td>\n",
              "      <td>2.433907</td>\n",
              "      <td>372.844827</td>\n",
              "      <td>14.966200</td>\n",
              "      <td>5.094440e-10</td>\n",
              "      <td>155.753560</td>\n",
              "      <td>0.414925</td>\n",
              "      <td>0.027225</td>\n",
              "      <td>126.765103</td>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-80c09835-0641-4187-94b2-05d4a94826b3 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-80c09835-0641-4187-94b2-05d4a94826b3');\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",
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              "\n",
              "\n",
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              "            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",
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              "    </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-4d092675-3092-415f-8ef0-93b4ca2b8d3f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d84727c8-ca41-4a91-9388-053cb31ea1ca\">\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('C_stim_frames')\"\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_d84727c8-ca41-4a91-9388-053cb31ea1ca button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('C_stim_frames');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "C_stim_frames"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "C_stim_frames"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "617c262f-3cda-465d-968b-b217e8af132c",
      "metadata": {
        "id": "617c262f-3cda-465d-968b-b217e8af132c"
      },
      "source": [
        "# Calcular **OSI** Ortiz-Cruz (2022)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "3b5da3dd-febb-4afb-a579-f25677c8934b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b5da3dd-febb-4afb-a579-f25677c8934b",
        "outputId": "8f2c5247-4e60-4a3a-d6dd-47f300fe0a3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[330. 210. 360.  60.  90. 120. 300. 270. 240.   0.  30. 180. 150.]\n",
            "Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',\n",
            "       '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25',\n",
            "       '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37',\n",
            "       '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49',\n",
            "       '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61',\n",
            "       '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73',\n",
            "       '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',\n",
            "       '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97',\n",
            "       '98'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Crear una lista de estímulos únicos\n",
        "stimuli = C_stim_frames['Stim'].unique()\n",
        "duracion_basal = 2\n",
        "\n",
        "neuronas = C_stim_frames.columns[4:]\n",
        "\n",
        "print(stimuli)\n",
        "print(neuronas)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "191dc92a-1ecc-48ec-98a1-2e071b820a0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "191dc92a-1ecc-48ec-98a1-2e071b820a0f",
        "outputId": "5f990057-575c-4bc3-ff90-012f943b2731"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'1': [(330.0, -0.676696790509353), (210.0, -0.39523617113205034), (360.0, -0.6345624290606274), (60.0, 3.6124315636142375), (90.0, -0.3963676801108498), (120.0, -0.6311616847309187), (300.0, 1.592183916090659), (270.0, 0.6357855880962876), (240.0, -0.624294123825325), (0.0, -0.7098513955246718), (30.0, -0.355092425021884), (180.0, 0.6098302091116258), (150.0, 0.7149618836364048)], '2': [(330.0, -0.6658180858067491), (210.0, 0.8176791059313814), (360.0, 0.9275137349848026), (60.0, -0.33163109248626965), (90.0, -0.3701264235135935), (120.0, -0.041799049159792864), (300.0, 0.17007437238619194), (270.0, 0.45777275677225937), (240.0, -0.3534148739360203), (0.0, -0.662901098091358), (30.0, 0.17115992393344856), (180.0, 0.29636578443603595), (150.0, -0.42711331106684336)], '3': [(330.0, -0.2903485513901377), (210.0, 1.359168656035122), (360.0, 1.3708889356732918), (60.0, -0.5006674064849942), (90.0, -0.08140734673193785), (120.0, -0.2572562274206647), (300.0, -0.15028842435933756), (270.0, 0.44036151630388237), (240.0, 0.39602877300073536), (0.0, -0.551792563117648), (30.0, -0.012377501761096742), (180.0, 0.6365514044419108), (150.0, -0.31388903087347986)], '4': [(330.0, 0.0008014954116764243), (210.0, -0.016995917447254004), (360.0, 0.19527557404744786), (60.0, -0.4288869269338081), (90.0, 0.03884523318498694), (120.0, 0.3122302484614586), (300.0, -0.07605622191510143), (270.0, -0.2390783501148411), (240.0, -0.27856610708733553), (0.0, -0.20288006079348334), (30.0, -0.09596798344049767), (180.0, -0.10405208880616354), (150.0, -0.272003879182336)], '5': [(330.0, 3.614846853311073), (210.0, 0.1976464267102484), (360.0, 0.336659396029123), (60.0, 0.2242686115725883), (90.0, -0.2364405799995904), (120.0, -0.3874406450737395), (300.0, -0.2533372630555859), (270.0, 0.5130688958778901), (240.0, 1.1398042067412917), (0.0, 1.1255128364249694), (30.0, 0.06910183957316574), (180.0, 0.8657786979824921), (150.0, 0.0432091000214221)], '6': [(330.0, 0.6485591104522485), (210.0, 0.2201435628459807), (360.0, 6.648007363862868), (60.0, 3.091752476078013), (90.0, 0.45429773108544397), (120.0, -0.1308767881724649), (300.0, -0.38641230470058646), (270.0, 1.4600078643608505), (240.0, -0.6511116496579864), (0.0, -0.45805922455542775), (30.0, -0.6244822161600466), (180.0, 0.41002589664850503), (150.0, 0.3508847686134902)], '7': [(330.0, 2.2613628188727675), (210.0, -0.5018321301505962), (360.0, -0.0607784862126459), (60.0, 1.9936366478754302), (90.0, 1.1732565260820451), (120.0, 0.7170941225299857), (300.0, -0.38942488857044444), (270.0, -0.1270775549971495), (240.0, -0.5131037313824404), (0.0, 0.5774925030621126), (30.0, 1.274461961933403), (180.0, -0.0019250278973515238), (150.0, -0.25435941257591477)], '8': [(330.0, 1.8093851624144872), (210.0, 11.189709676657241), (360.0, -0.4070669317232187), (60.0, -0.5545518661868966), (90.0, 3.4034845923785664), (120.0, 42.34191772914011), (300.0, 9.816841569142055), (270.0, 0.153571356844162), 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(270.0, 1.4144828247212495), (240.0, -0.3123552578316341), (0.0, -0.5511974364802097), (30.0, -0.1627788966038662), (180.0, -0.40140377779642306), (150.0, -0.48594280699223485)], '80': [(330.0, 0.5840780199417684), (210.0, -0.0036302605821559365), (360.0, 0.220346792007002), (60.0, -0.3408669481368072), (90.0, 0.30822924731102586), (120.0, 1.015213331106799), (300.0, -0.1702809077271921), (270.0, 0.2776877257045409), (240.0, 0.07663119481652574), (0.0, 0.2780812876252781), (30.0, 2.636103973211072), (180.0, -0.06487705469022254), (150.0, 0.8395948710327309)], '81': [(330.0, -0.20358961136188528), (210.0, -0.3198669587255633), (360.0, 0.06711531460470024), (60.0, 0.5653141352713464), (90.0, -0.21064875949746553), (120.0, -0.11443088111366033), (300.0, 0.26554096033624114), (270.0, 0.17098101781815755), (240.0, -0.3827125914040859), (0.0, 0.19815938291050916), (30.0, 0.7902585539324875), (180.0, -0.3140368441884257), (150.0, -0.3115753329086739)], '82': [(330.0, -0.39258450588495336), (210.0, -0.10972310014525352), (360.0, -0.10471870247428963), (60.0, -0.20686766799352285), (90.0, -0.2645341715400227), (120.0, -0.22017972688763354), (300.0, -0.48965411855523217), (270.0, 5.889614652944761), (240.0, -0.4896541513823647), (0.0, -0.41925159974714005), (30.0, -0.4896541138348047), (180.0, -0.08388067515662671), (150.0, -0.33336688531475456)], '83': [(330.0, -0.06989385734352628), (210.0, -0.20384195911319794), (360.0, -0.27397280315457073), (60.0, -0.24324349615898286), (90.0, -0.2591192453903616), (120.0, 0.0809574909123664), (300.0, 0.5195481014839166), (270.0, -0.37069490341985756), (240.0, -0.012610820647374371), (0.0, -0.345219089880955), (30.0, 0.46035913729325384), (180.0, -0.28074622660402565), (150.0, -0.23036584351440748)], '84': [(330.0, -0.6062776060512975), (210.0, 0.9782258377659503), (360.0, -0.18618492172685772), (60.0, 0.3185004676911029), (90.0, -0.4462322740559555), (120.0, 0.3138166675856933), (300.0, -0.009917527424654515), (270.0, 0.2113500448922398), (240.0, -0.006361165780880404), (0.0, -0.3046341059887073), (30.0, -0.26407190968467315), (180.0, -0.22314401251972335), (150.0, -0.6220900666540169)], '85': [(330.0, -0.1390998407627237), (210.0, -0.2037819602562609), (360.0, -0.35544555163676345), (60.0, -0.21631646738204618), (90.0, 0.10972313632271607), (120.0, 0.5669732647010157), (300.0, 0.1927721860134739), (270.0, -0.20881248173728445), (240.0, 0.03567127377519911), (0.0, -0.2999231953121726), (30.0, -0.13709140825875182), (180.0, -0.0006687266621552338), (150.0, -0.30928789567904136)], '86': [(330.0, 46.216398239471715), (210.0, 10.785978252881804), (360.0, 0.695866365359349), (60.0, 0.985843617138833), (90.0, -0.7178019227839398), (120.0, -0.5031980000673972), (300.0, 18.272841529880456), (270.0, 1.8866559643507268), (240.0, 6.981519282854743), (0.0, 9.718438971475376), (30.0, 8.165158931521438), (180.0, 0.7894272563401956), (150.0, 0.17466325465815064)], '87': [(330.0, -0.5830527023684153), (210.0, 4.022475987828468), (360.0, 0.6058295347931), (60.0, -0.0009260243935745139), (90.0, -0.2778109661518466), (120.0, -0.4666342510148127), (300.0, 0.7672188080976843), (270.0, 0.8372394497481324), (240.0, -0.34970826646934877), (0.0, -0.6982985365274204), (30.0, -0.16436156141257105), (180.0, 0.27819676346475025), (150.0, -0.4641789310735472)], '88': [(330.0, 0.7065588228708761), (210.0, 0.7143681302345425), (360.0, 0.09715283967094886), (60.0, -0.48186586300148315), (90.0, -0.6025265539482839), (120.0, 3.847556493879864), (300.0, 1.7812353837747292), (270.0, 0.8032838191673546), (240.0, -0.056763846556104246), (0.0, 1.630674803630145), (30.0, 0.6069878908306278), (180.0, 0.37418365687662863), (150.0, 2.6141750812403237)], '89': [(330.0, -0.5285885502514045), (210.0, -0.22969713440487283), (360.0, -0.1373920738302944), (60.0, 2.1256358658721974), (90.0, -0.5566651551192763), (120.0, -0.04265731378368461), (300.0, 2.0027163735970746), (270.0, 1.2077656663639855), (240.0, -0.4154253045334643), (0.0, -0.37594272558342395), (30.0, -0.654933671983906), (180.0, -0.3934222208570423), (150.0, -0.654933610066691)], '90': [(330.0, 10.028277833227047), (210.0, 6.906669003059861), (360.0, 1.8664617363517642), (60.0, 0.5600823364603887), (90.0, 3.241514731789249), (120.0, 0.1446937503951011), (300.0, 4.082131372159831), (270.0, 0.4354067088816545), (240.0, 2.3169504812022392), (0.0, 9.148241351149732), (30.0, 0.4122029555093101), (180.0, 1.3190104563319942), (150.0, -0.10770394556013749)], '91': [(330.0, -0.4027912573710635), (210.0, 0.048810197455315715), (360.0, 0.05021007658758115), (60.0, 0.14422603762042993), (90.0, -0.15494118582351038), (120.0, 0.12198981269748728), (300.0, 1.6726576704709049), (270.0, 1.0394916913336256), (240.0, -0.5307396454233957), (0.0, -0.3199655338127748), (30.0, -0.23079841409754437), (180.0, -0.2441999444849801), (150.0, -0.2679128618910086)], '92': [(330.0, 2.5282679243728974), (210.0, 4.504769987245485), (360.0, 0.40974468910445544), (60.0, 0.6515483761718699), (90.0, 1.4126683755214036), (120.0, 0.14044139496099808), (300.0, 2.1228635711260457), (270.0, 0.20668014610507282), (240.0, 0.6419344312948737), (0.0, 1.81177410254903), (30.0, -0.04381256195950029), (180.0, 0.15420466894461768), (150.0, -0.17373484051868146)], '93': [(330.0, 4.472369650860939), (210.0, 6.20093025229277), (360.0, 5.804853566548532), (60.0, 2.2891983401931286), (90.0, 1.3612283397656257), (120.0, 1.648748215979085), (300.0, 4.393529061424967), (270.0, 0.5019155866202515), (240.0, 1.113334877907108), (0.0, 9.597272790646215), (30.0, 0.12002431938718272), (180.0, 2.156907954690352), (150.0, 0.38745758460704305)], '94': [(330.0, -0.5152056852967626), (210.0, -0.4334869667647217), (360.0, 0.0503140624266571), (60.0, -0.5740625064841699), (90.0, 0.3071169177183154), (120.0, 2.547769164696863), (300.0, -0.16448410129714708), (270.0, -0.21575440858080286), (240.0, -0.42981386219136325), (0.0, -0.3570471811119388), (30.0, -0.5452469259736428), (180.0, -0.4290245549171843), (150.0, -0.5740624549134731)], '95': [(330.0, 0.9131833172478647), (210.0, 0.6227181452932208), (360.0, 0.2162041785383183), (60.0, 0.15494142736460886), (90.0, 0.1511911397845294), (120.0, -0.06331108972145093), (300.0, 0.9629345948316053), (270.0, 0.563908463990436), (240.0, 0.2485371170532027), (0.0, 0.6342212395607247), (30.0, 0.08013792310613882), (180.0, 0.9407752254582177), (150.0, 0.9078667181214957)], '96': [(330.0, 14.840953984365813), (210.0, 9.50712141736104), (360.0, -0.04687443049960459), (60.0, 0.9695338413540919), (90.0, 0.1852798572530723), (120.0, 1.3073691976906403), (300.0, 3.1488535911542095), (270.0, 4.42799985268679), (240.0, 0.856659349526827), (0.0, 6.80664893309233), (30.0, 2.8661264897067666), (180.0, 1.1522272224884496), (150.0, 0.23013710479012292)], '97': [(330.0, -0.1428743761376293), (210.0, 3.4721793262736065), (360.0, 1.594050371624887), (60.0, 7.263295260847589), (90.0, -0.14551285883169673), (120.0, 1.2023470312583535), (300.0, -0.32148594931313806), (270.0, 0.3523932578975759), (240.0, -0.18637696576115179), (0.0, 0.29214565270264803), (30.0, 0.4417758540556513), (180.0, -0.09319996817797427), (150.0, 0.2400810533210942)], '98': [(330.0, 3.7108399623244903), (210.0, 1.2250950125773572), (360.0, 7.764697170128803), (60.0, 0.8118259081925687), (90.0, 2.5690912723637775), (120.0, 3.4054266175326053), (300.0, 144.7319655253056), (270.0, 0.525017058734758), (240.0, 5.569684213785851), (0.0, 7.358086309217362), (30.0, 0.5736644600179811), (180.0, 2.26650693700166), (150.0, 0.5619675439054975)]}\n",
            "<class 'dict'>\n"
          ]
        }
      ],
      "source": [
        "delta_f_f0_vals = {stimulus: {} for stimulus in stimuli}\n",
        "neuronas_normalizadas = {}\n",
        "\n",
        "\n",
        "neuronas_moduladas = {stimulus: [] for stimulus in stimuli}\n",
        "\n",
        "C_stim_frames['End_time'] = C_stim_frames['time'] + duracion_estimulo\n",
        "time = C['time']\n",
        "neuronas_normalizadas = {columna: [] for columna in neuronas}\n",
        "\n",
        "for stimulus in stimuli:\n",
        "    C_stim_frames_stimulus = C_stim_frames[C_stim_frames['Stim'] == stimulus]\n",
        "\n",
        "    for columna in neuronas:\n",
        "        all_basal_activity = []\n",
        "        all_stim_activity = []\n",
        "\n",
        "        for _, row in C_stim_frames_stimulus.iterrows():\n",
        "            start_stim = row['time']\n",
        "            end_stim = row['End_time']\n",
        "\n",
        "            basal_mask = (time >= (start_stim - 2)) & (time < start_stim)\n",
        "            actividad_basal = C.loc[basal_mask, columna].values\n",
        "            all_basal_activity.extend(actividad_basal)\n",
        "\n",
        "            stim_mask = (time >= start_stim) & (time < end_stim)\n",
        "            actividad_estimulo = C.loc[stim_mask, columna].values\n",
        "            all_stim_activity.extend(actividad_estimulo)\n",
        "\n",
        "        F0 = np.mean(all_basal_activity) if all_basal_activity else np.nan\n",
        "        F = np.mean(all_stim_activity) if all_stim_activity else np.nan\n",
        "\n",
        "        if not np.isnan(F0) and not np.isnan(F):\n",
        "            delta_F_F0 = (F - F0) / F0\n",
        "\n",
        "            delta_f_f0_vals[stimulus][columna] = delta_F_F0\n",
        "\n",
        "            neuronas_normalizadas[columna].append((stimulus, delta_F_F0))\n",
        "        else:\n",
        "            neuronas_normalizadas[columna].append((stimulus, np.nan))\n",
        "\n",
        "print(neuronas_normalizadas)\n",
        "print(type(neuronas_normalizadas))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check\n",
        "estimulo_interes = 90.0\n",
        "\n",
        "# Encuentra el intervalo de tiempo correspondiente a este estímulo\n",
        "estimulo_row = C_stim_frames[C_stim_frames['Stim'] == estimulo_interes]\n",
        "if not estimulo_row.empty:\n",
        "    start_time = estimulo_row['time'].values[0]\n",
        "    end_time = estimulo_row['End_time'].values[0]\n",
        "\n",
        "plt.figure()\n",
        "sns.lineplot(data=C,x='time', y='1')\n",
        "plt.xlabel('Tiempo (s)')\n",
        "plt.ylabel(f' Neurona 1')\n",
        "plt.xlim(start_time, end_time)\n",
        "\n",
        "for stimulus in stimuli:\n",
        "    stimulus_times = C_stim_frames[C_stim_frames['Stim'] == stimulus]['time'].values  # Tiempo de cada estímulo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "eVbLgqER6xLA",
        "outputId": "69a24cff-3ed1-4411-8d66-6c68157379b1"
      },
      "id": "eVbLgqER6xLA",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calcular_osi_dsi(actividades):\n",
        "    if not isinstance(actividades, dict):\n",
        "        raise ValueError(\"El argumento 'actividades' debe ser un diccionario.\")\n",
        "\n",
        "    resultados = {}\n",
        "    for neurona, respuestas in actividades.items():\n",
        "        respuestas = [(stim, max(delta_f, 0)) for stim, delta_f in respuestas]\n"
      ],
      "metadata": {
        "id": "HLADKi3nciL6"
      },
      "id": "HLADKi3nciL6",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# doble Gaussiana\n",
        "def doble_gaussiana(theta, b, c, d, a, theta_pref):\n",
        "    return b + c * np.exp(-((theta - theta_pref) ** 2) / (2 * d ** 2)) + \\\n",
        "           d * np.exp(-((theta - (theta_pref + 180)) ** 2) / (2 * a ** 2))\n",
        "\n",
        "# calcular R^2\n",
        "def calcular_r2(y_observado, y_ajustado):\n",
        "    ss_total = np.sum((y_observado - np.mean(y_observado)) ** 2)\n",
        "    ss_residual = np.sum((y_observado - y_ajustado) ** 2)\n",
        "    return 1 - (ss_residual / ss_total)\n",
        "\n",
        "# Calcular OSI y DSI\n",
        "def calcular_osi_dsi(actividades):\n",
        "    resultados = {}\n",
        "\n",
        "    for neuronas, respuestas in actividades.items():\n",
        "        respuestas = [(stim, max(delta_f, 0)) for stim, delta_f in respuestas]  # Ajuste a 0\n",
        "        orientaciones = np.array([stim for stim, _ in respuestas])\n",
        "        valores = np.array([delta_f for _, delta_f in respuestas])\n",
        "\n",
        "        # Asegurarse de tener datos válidos para el ajuste\n",
        "        if len(valores) > 0 and np.all(~np.isnan(valores)):\n",
        "            # Ajuste de doble Gaussiana\n",
        "            try:\n",
        "                popt, _ = curve_fit(doble_gaussiana, orientaciones, valores, p0=[0, max(valores), 1, 45, orientaciones[np.argmax(valores)]])\n",
        "                valores_ajustados = doble_gaussiana(orientaciones, *popt)\n",
        "\n",
        "                # Cálculo de R^2\n",
        "                r2 = calcular_r2(valores, valores_ajustados)\n",
        "\n",
        "                # OSI y DSI\n",
        "                R_pref = valores[np.argmax(valores)]\n",
        "                R_ortho = valores[np.argmin(np.abs(orientaciones - (orientaciones[np.argmax(valores)] + 90)) % 360)]\n",
        "                R_opuesto = valores[np.argmin(np.abs(orientaciones - (orientaciones[np.argmax(valores)] + 180)) % 360)]\n",
        "\n",
        "                OSI = (R_pref - R_ortho) / (R_pref + R_ortho)\n",
        "                DSI = (R_pref - R_opuesto) / (R_pref + R_opuesto)\n",
        "\n",
        "                resultados[neurona] = {\n",
        "                    \"OSI\": OSI,\n",
        "                    \"DSI\": DSI,\n",
        "                    \"R^2\": r2,\n",
        "                    \"Ajuste\": popt\n",
        "                }\n",
        "            except RuntimeError:\n",
        "                resultados[neurona] = {\"Error\": \"Ajuste fallido\"}\n",
        "        else:\n",
        "            resultados[neurona] = {\"Error\": \"Datos insuficientes o NaN\"}\n",
        "\n",
        "    return resultados\n",
        "\n"
      ],
      "metadata": {
        "id": "1XaJybJYbbEs"
      },
      "id": "1XaJybJYbbEs",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultados = calcular_osi_dsi(neuronas_normalizadas)\n",
        "for neurona, datos in resultados.items():\n",
        "    print(f\"Neurona {neurona}: OSI = {datos.get('OSI', 'N/A')}, DSI = {datos.get('DSI', 'N/A')}, R^2 = {datos.get('R^2', 'N/A')}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "077oTZ90AvLA",
        "outputId": "4a7d9be7-838a-4727-d842-6afe0ebe17cb"
      },
      "id": "077oTZ90AvLA",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-412168b2de74>:25: OptimizeWarning: Covariance of the parameters could not be estimated\n",
            "  popt, _ = curve_fit(doble_gaussiana, orientaciones, valores, p0=[0, max(valores), 1, 45, orientaciones[np.argmax(valores)]])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Neurona 55: OSI = 0.9921040298290447, DSI = 0.9540234026227624, R^2 = 0.9969829745053611\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Función de doble Gaussiana\n",
        "def doble_gaussiana(theta, b, c, d, a, theta_pref):\n",
        "    return b + c * np.exp(-((theta - theta_pref) ** 2) / (2 * d ** 2)) + \\\n",
        "           d * np.exp(-((theta - (theta_pref + 180)) ** 2) / (2 * a ** 2))\n",
        "\n",
        "# Cálculo de OSI y DSI\n",
        "def calcular_osi_dsi(actividades):\n",
        "    resultados = {}\n",
        "\n",
        "    for neurona, respuestas in actividades.items():\n",
        "        # Ajustar valores negativos a 0\n",
        "        respuestas = [(stim, max(delta_f, 0)) for stim, delta_f in respuestas]\n",
        "        orientaciones = np.array([stim for stim, _ in respuestas])\n",
        "        valores = np.array([delta_f for _, delta_f in respuestas])\n",
        "\n",
        "        if len(valores) > 0 and np.all(~np.isnan(valores)):\n",
        "            # Ajuste de doble Gaussiana\n",
        "            try:\n",
        "                popt, _ = curve_fit(doble_gaussiana, orientaciones, valores,\n",
        "                                    p0=[0, max(valores), 1, 45, orientaciones[np.argmax(valores)]])\n",
        "                valores_ajustados = doble_gaussiana(orientaciones, *popt)\n",
        "\n",
        "                # Cálculo de R^2\n",
        "                ss_total = np.sum((valores - np.mean(valores)) ** 2)\n",
        "                ss_residual = np.sum((valores - valores_ajustados) ** 2)\n",
        "                r2 = 1 - (ss_residual / ss_total)\n",
        "\n",
        "                # Identificar R_pref, R_ortho y R_oppo\n",
        "                theta_pref = popt[4]\n",
        "                R_pref = max(valores_ajustados)\n",
        "                R_ortho = valores_ajustados[np.argmin(np.abs(orientaciones - (theta_pref + 90)) % 360)]\n",
        "                R_oppo = valores_ajustados[np.argmin(np.abs(orientaciones - (theta_pref + 180)) % 360)]\n",
        "\n",
        "                # Calcular OSI y DSI\n",
        "                OSI = (R_pref - R_ortho) / (R_pref + R_ortho)\n",
        "                DSI = (R_pref - R_oppo) / (R_pref + R_oppo)\n",
        "\n",
        "                resultados[neurona] = {\n",
        "                    \"OSI\": OSI,\n",
        "                    \"DSI\": DSI,\n",
        "                    \"R^2\": r2,\n",
        "                    \"Ajuste\": popt\n",
        "                }\n",
        "            except RuntimeError:\n",
        "                resultados[neurona] = {\"Error\": \"Ajuste fallido\"}\n",
        "        else:\n",
        "            resultados[neurona] = {\"Error\": \"Datos insuficientes o NaN\"}\n",
        "\n",
        "    return resultados\n",
        "\n"
      ],
      "metadata": {
        "id": "Y8KebWuWq4h8"
      },
      "id": "Y8KebWuWq4h8",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Llamar a la función\n",
        "resultados = calcular_osi_dsi(neuronas_normalizadas)\n",
        "\n",
        "# Mostrar resultados\n",
        "for neurona, datos in resultados.items():\n",
        "    print(f\"Neurona {neurona}: OSI = {datos.get('OSI', 'N/A')}, DSI = {datos.get('DSI', 'N/A')}, R^2 = {datos.get('R^2', 'N/A')}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E3RC7PQwmGi",
        "outputId": "67dedfd6-ba66-4471-ab5a-fef9a31c2c19"
      },
      "id": "1E3RC7PQwmGi",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-bfb1cdc5be71>:19: OptimizeWarning: Covariance of the parameters could not be estimated\n",
            "  popt, _ = curve_fit(doble_gaussiana, orientaciones, valores,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Neurona 1: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 2: OSI = 0.7066563406245625, DSI = 0.7066563406245625, R^2 = 0.43090603114879444\n",
            "Neurona 3: OSI = 0.7062557322449663, DSI = 0.7062557322449663, R^2 = 0.38059114965129137\n",
            "Neurona 4: OSI = 0.0, DSI = 0.9999629938601747, R^2 = 0.03669621029174652\n",
            "Neurona 5: OSI = 0.8114512609553416, DSI = 0.8114512609553416, R^2 = 0.8225054293972061\n",
            "Neurona 6: OSI = 0.8464569007940743, DSI = 0.8464569003782266, R^2 = 0.7923305040994105\n",
            "Neurona 7: OSI = 0.6510178921084083, DSI = 0.6510178921084083, R^2 = 0.3661867837993723\n",
            "Neurona 8: OSI = 0.8380605393593469, DSI = 0.6236655074305588, R^2 = 0.8359305426963669\n",
            "Neurona 9: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 10: OSI = 0.8338290287289161, DSI = 0.8338290287289161, R^2 = 0.5754157396226253\n",
            "Neurona 11: OSI = 0.787954948078667, DSI = 0.787954948078667, R^2 = 0.4585072971713896\n",
            "Neurona 12: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 13: OSI = 0.8529163027259571, DSI = 0.8529163027259571, R^2 = 0.7650048761956362\n",
            "Neurona 14: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 15: OSI = 0.9999999989459237, DSI = 0.9999999977044708, R^2 = 0.9999060630375437\n",
            "Neurona 16: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 17: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 18: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 19: OSI = 0.4649450139222098, DSI = 0.8114921523511384, R^2 = 0.6232301625611288\n",
            "Neurona 20: OSI = 0.5894526278228929, DSI = 1.154276854872803, R^2 = 0.14779408779374636\n",
            "Neurona 21: OSI = 0.6856429914311141, DSI = 0.6856429914311141, R^2 = 0.509269298328827\n",
            "Neurona 22: OSI = 2.349806366641041e-16, DSI = 0.9999685423956776, R^2 = 0.08653441023636133\n",
            "Neurona 23: OSI = 0.8849424802895405, DSI = 0.8849424802895405, R^2 = 0.9304081927487192\n",
            "Neurona 24: OSI = 0.09169423201992491, DSI = 1.4877710545726088, R^2 = 0.09969567807573576\n",
            "Neurona 25: OSI = 0.738877039479503, DSI = 0.0, R^2 = 0.33683533493175266\n",
            "Neurona 26: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 27: OSI = 0.8632445326282698, DSI = 0.8632445326282698, R^2 = 0.6210575628548205\n",
            "Neurona 28: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 29: OSI = 0.9022414270911995, DSI = 0.9022414270911995, R^2 = 0.7781759249663841\n",
            "Neurona 30: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 31: OSI = 0.7852576224905137, DSI = 0.9573173111177672, R^2 = 0.8811642952318619\n",
            "Neurona 32: OSI = 0.835628762938472, DSI = 0.5793861745800675, R^2 = 0.6586829721567614\n",
            "Neurona 33: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 34: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 35: OSI = 0.7160844043280341, DSI = 0.7160844043280341, R^2 = 0.6480697315984163\n",
            "Neurona 36: OSI = 0.6600988024274552, DSI = 0.6600985605689215, R^2 = 0.40738188536115294\n",
            "Neurona 37: OSI = 0.0, DSI = 0.9999697846532084, R^2 = 0.10981315062658725\n",
            "Neurona 38: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 39: OSI = 0.8451115895799961, DSI = 0.32999287906416896, R^2 = 0.8241128732157255\n",
            "Neurona 40: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 41: OSI = 0.7308755298842757, DSI = 0.7308755298842757, R^2 = 0.6085883855058745\n",
            "Neurona 42: OSI = 0.7332744853518074, DSI = 0.7332744853518074, R^2 = 0.5077136867566177\n",
            "Neurona 43: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 44: OSI = 0.7855285715566513, DSI = 0.7855285715566513, R^2 = 0.45584797361889684\n",
            "Neurona 45: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 46: OSI = 0.9169300803786673, DSI = 0.9169300803786673, R^2 = 0.7731455990914211\n",
            "Neurona 47: OSI = 0.7428510006147199, DSI = 0.7295715738616588, R^2 = 0.3518133691406392\n",
            "Neurona 48: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 49: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 50: OSI = 0.6838037231944576, DSI = 0.692986165399511, R^2 = 0.6413621782058027\n",
            "Neurona 51: OSI = 0.9800642685521611, DSI = 0.9800642685521611, R^2 = 0.996712946951982\n",
            "Neurona 52: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 53: OSI = 0.4685372415929806, DSI = 0.48000841177372133, R^2 = 0.1717447465522055\n",
            "Neurona 54: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 55: OSI = 0.762415179137218, DSI = 0.762415179137218, R^2 = 0.6487964122514703\n",
            "Neurona 56: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 57: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 58: OSI = 0.05588471185186942, DSI = 1.7494493985241515, R^2 = 0.28362135916938724\n",
            "Neurona 59: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 60: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 61: OSI = 0.5969451823068745, DSI = 0.5969451823068745, R^2 = 0.5266711601588392\n",
            "Neurona 62: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 63: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 64: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 65: OSI = 0.6948521175245627, DSI = 0.6948521175245627, R^2 = 0.47875318936730304\n",
            "Neurona 66: OSI = 0.86418951244731, DSI = 0.8641561495360531, R^2 = 0.7417600510311351\n",
            "Neurona 67: OSI = 0.8360791894408117, DSI = 0.8360791894408117, R^2 = 0.6505842400186126\n",
            "Neurona 68: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 69: OSI = 0.31849342808488157, DSI = 1.6063305082341033, R^2 = 0.20899986500095924\n",
            "Neurona 70: OSI = 0.6321223566068626, DSI = 0.24229108020647108, R^2 = 0.5009543020967295\n",
            "Neurona 71: OSI = 0.9493676529569357, DSI = 1.0259513424479105, R^2 = 0.9370441997817841\n",
            "Neurona 72: OSI = 0.9126310118357672, DSI = 0.9126310118357672, R^2 = 0.8205926383569568\n",
            "Neurona 73: OSI = 0.985448290896883, DSI = 0.9854482908968927, R^2 = 0.992264932111417\n",
            "Neurona 74: OSI = 1.0000000000053577, DSI = 1.0000000000053577, R^2 = 1.0\n",
            "Neurona 75: OSI = 0.9865146697131558, DSI = 0.9865146697131558, R^2 = 0.9933640896719629\n",
            "Neurona 76: OSI = 0.816269983910214, DSI = 0.8111251337495601, R^2 = 0.6663947579160729\n",
            "Neurona 77: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 78: OSI = 0.9573269755475156, DSI = 0.9573269755475156, R^2 = 0.9755241629772757\n",
            "Neurona 79: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 80: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 81: OSI = 0.7034006612751511, DSI = 1.000037602325729, R^2 = 0.37636078289037245\n",
            "Neurona 82: OSI = 0.9999999922974038, DSI = 1.0000000006994156, R^2 = 1.0\n",
            "Neurona 83: OSI = 0.8402216645405035, DSI = 0.8402216645405035, R^2 = 0.5170601648553181\n",
            "Neurona 84: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 85: OSI = 0.0, DSI = 0.0, R^2 = -4.440892098500626e-16\n",
            "Neurona 86: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 87: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 88: OSI = 0.7029758294707775, DSI = 0.3670632925411845, R^2 = 0.6026949335530495\n",
            "Neurona 89: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 90: OSI = 0.0, DSI = 0.0, R^2 = 4.440892098500626e-16\n",
            "Neurona 91: OSI = 0.8691842362578822, DSI = 0.8691842362578822, R^2 = 0.7001371760812576\n",
            "Neurona 92: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 93: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 94: OSI = 0.9768996393404715, DSI = 0.9767979421910401, R^2 = 0.9854827797456498\n",
            "Neurona 95: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 96: OSI = 0.6997561790895749, DSI = 0.6997561790895749, R^2 = 0.5874743688533328\n",
            "Neurona 97: OSI = N/A, DSI = N/A, R^2 = N/A\n",
            "Neurona 98: OSI = 0.966686098500536, DSI = 0.9666088476649805, R^2 = 0.9969829745053611\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "            if delta_F_F0 > (F0 + 3 * std_basal):\n",
        "                modulado = True\n",
        "\n",
        "           if modulado:\n",
        "            neuronas_moduladas[stimulus].append(columna)\n",
        "\n",
        "        # Guardar las respuestas promediadas\n",
        "        neuronas_normalizadas[columna] = np.mean(responses)\n",
        "\n",
        "# Mostrar neuronas moduladas\n",
        "for stimulus, neuronas in neuronas_moduladas.items():\n",
        "    print(f\"Estímulo {stimulus}: Neuronas moduladas: {', '.join(neuronas) if neuronas else 'Ninguna'}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "xG3YloeW6dpH",
        "outputId": "c55bcbc0-3805-4989-f72e-2edb6ba35b8d"
      },
      "id": "xG3YloeW6dpH",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndentationError",
          "evalue": "unexpected indent (<ipython-input-18-badad39e072b>, line 4)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-18-badad39e072b>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    if modulado:\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "07a7df2a-892c-49b6-9b47-97ed234c4051",
      "metadata": {
        "id": "07a7df2a-892c-49b6-9b47-97ed234c4051"
      },
      "outputs": [],
      "source": [
        "neuronas_normalizadas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac31b10e-b064-4da1-942d-63b8f92db984",
      "metadata": {
        "id": "ac31b10e-b064-4da1-942d-63b8f92db984"
      },
      "outputs": [],
      "source": [
        "delta_f_f0_vals = {stimulus: {} for stimulus in stimuli}\n",
        "neuronas_moduladas = {stimulus: [] for stimulus in stimuli}\n",
        "theta_pref = {}\n",
        "C_stim_frames['End_time'] = C_stim_frames['time'] + duracion_estimulo\n",
        "time = C['time']\n",
        "\n",
        "for stimulus in stimuli:\n",
        "    C_stim_frames_stimulus = C_stim_frames[C_stim_frames['Stim'] == stimulus]\n",
        "\n",
        "    # Número de ensayos para este estímulo\n",
        "    num_ensayos = len(C_stim_frames_stimulus)\n",
        "    min_ensayos_modulados = num_ensayos /2   # <--Mínimo de ensayos modulados requeridos\n",
        "\n",
        "    for columna in neuronas:\n",
        "        responses = []\n",
        "        auc_scores = []\n",
        "\n",
        "        for _, row in C_stim_frames_stimulus.iterrows():\n",
        "            start_stim = row['time']\n",
        "            end_stim = row['End_time']\n",
        "\n",
        "            basal_mask = (time >= (start_stim - 2)) & (time < start_stim)\n",
        "            actividad_basal = C.loc[basal_mask, columna]\n",
        "\n",
        "            if actividad_basal.empty:\n",
        "                continue\n",
        "\n",
        "            F0 = actividad_basal.mean()\n",
        "            std_basal = actividad_basal.std()\n",
        "\n",
        "            if np.isclose(F0, 0):\n",
        "                continue\n",
        "\n",
        "            stim_mask = (time >= start_stim) & (time < end_stim)\n",
        "            actividad_estimulo = C.loc[stim_mask, columna]\n",
        "\n",
        "            if actividad_estimulo.empty:\n",
        "                continue\n",
        "\n",
        "            F = actividad_estimulo.mean()\n",
        "            delta_F_F0 = (F - F0) / F0\n",
        "\n",
        "            if not np.isnan(delta_F_F0):\n",
        "                responses.append(delta_F_F0)\n",
        "                auc_scores.extend([\n",
        "                    (delta_F_F0, 1) for _ in range(len(actividad_estimulo))\n",
        "                ])\n",
        "                auc_scores.extend([\n",
        "                    (val, 0) for val in actividad_basal\n",
        "                ])\n",
        "                if delta_F_F0 > (F0 + 3 * std_basal):\n",
        "                    neuronas_moduladas[stimulus].append(columna)\n",
        "\n",
        "        if responses:\n",
        "            delta_f_f0_vals[stimulus][columna] = np.mean(responses)\n",
        "\n",
        "        # AUROC Analysis\n",
        "        if len(auc_scores) >= 10:\n",
        "            random.shuffle(auc_scores)  # Mezclar para bootstrap\n",
        "            scores, labels = zip(*auc_scores)\n",
        "            scores = np.array(scores)\n",
        "            labels = np.array(labels)\n",
        "            valid_idx = ~np.isnan(scores)\n",
        "            if valid_idx.sum() > 0:\n",
        "                auc_value = roc_auc_score(labels[valid_idx], scores[valid_idx])\n",
        "\n",
        "                if auc_value >= 0.8:\n",
        "                    neuronas_moduladas[stimulus].append(columna)\n",
        "\n",
        "for columna in neuronas:\n",
        "    max_response = -np.inf\n",
        "    preferred_stimulus = None\n",
        "\n",
        "    for stimulus in stimuli:\n",
        "        if columna in delta_f_f0_vals[stimulus]:\n",
        "            avg_response = delta_f_f0_vals[stimulus][columna]\n",
        "            if avg_response > max_response:\n",
        "                max_response = avg_response\n",
        "                preferred_stimulus = stimulus\n",
        "\n",
        "    if preferred_stimulus is not None:\n",
        "        theta_pref[columna] = preferred_stimulus\n",
        "\n",
        "print(\"Neurona modulada por estímulo:\")\n",
        "for stimulus, neuronas in neuronas_moduladas.items():\n",
        "    print(f\"Estímulo {stimulus}: {', '.join(neuronas) if neuronas else 'Ninguna'}\")\n",
        "\n",
        "print(\"\\nOrientaciones preferidas (θpref):\")\n",
        "for neurona, stimulus in theta_pref.items():\n",
        "    print(f\"Neurona {neurona}: Estímulo preferido {stimulus}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "3d40ab83-473c-4c68-87e3-aa69ac86eabe",
      "metadata": {
        "id": "3d40ab83-473c-4c68-87e3-aa69ac86eabe",
        "outputId": "8f65d606-060f-4c8a-af0f-69ecf6bc6857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'responses' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-744961dbb0e7>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0morientations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mresponses_avg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mneurona\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponses\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdelta_f_f0_vals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# Convertir orientaciones y respuestas a valores numéricos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'responses' is not defined"
          ]
        }
      ],
      "source": [
        "orientations = np.array(list(responses.keys()), dtype=float)\n",
        "responses_avg = np.array(list(responses.values()), dtype=float)\n",
        "\n",
        "for neurona, responses in delta_f_f0_vals.items():\n",
        "    # Convertir orientaciones y respuestas a valores numéricos\n",
        "    orientations = np.array(list(responses.keys()), dtype=float)\n",
        "    responses_avg = np.array(list(responses.values()), dtype=float)\n",
        "\n",
        "    # Normalizar las respuestas entre 0 y 1\n",
        "    responses_norm = (responses_avg - responses_avg.min()) / (responses_avg.max() - responses_avg.min())\n",
        "\n",
        "    # Ajustar la curva bimodal Gaussian\n",
        "    try:\n",
        "        popt, _ = curve_fit(\n",
        "            bimodal_gaussian,\n",
        "            orientations,\n",
        "            responses_norm,\n",
        "            p0=[orientations[np.argmax(responses_norm)], 0, 1, 0.5, 20],\n",
        "            bounds=([-np.inf, 0, 0, 0, 1], [360, np.inf, 2, 1, 100])\n",
        "        )\n",
        "        theta_pref_fit, b_fit, c_fit, d_fit, a_fit = popt\n",
        "\n",
        "        # Predecir valores ajustados\n",
        "        responses_fit = bimodal_gaussian(orientations, *popt)\n",
        "\n",
        "        # Calcular R^2\n",
        "        r2 = r2_score(responses_norm, responses_fit)\n",
        "\n",
        "        if r2 > 0.7:\n",
        "            selective_neurons[neurona] = {\n",
        "                'theta_pref': theta_pref_fit,\n",
        "                'r2': r2,\n",
        "                'params': {\n",
        "                    'b': b_fit, 'c': c_fit, 'd': d_fit, 'a': a_fit\n",
        "                }\n",
        "            }\n",
        "    except RuntimeError:\n",
        "        print(f\"No se pudo ajustar una curva para la neurona {neurona}\")\n",
        "\n",
        "print(\"Neurona selectiva con R^2 > 0.7:\")\n",
        "for neurona, data in selective_neurons.items():\n",
        "    print(f\"Neurona {neurona}: θpref={data['theta_pref']:.2f}, R^2={data['r2']:.2f}\")"
      ]
    }
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