Data_exploration_GLM_hopeful.ipynb

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                  "text/plain": "<Figure size 432x288 with 1 Axes>",
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    },
    "accelerator": "GPU",
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.system('rm -rf kcnischool2022-projectweek')\n",
        "os.system('git clone https://github.com/griffithslab/kcnischool2022-projectweek')\n",
        "os.chdir('kcnischool2022-projectweek/data')\n",
        "os.system('python download_ds000201_data.py')\n",
        "os.chdir('../')\n",
        "os.system('pip install nilearn hcp_utils')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xfag4ahHPx9",
        "outputId": "45a2a359-4eec-470d-bff2-15f18d44dcad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os,sys,glob,numpy as np,pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import nibabel as nib\n",
        "\n",
        "import hcp_utils as hcp\n",
        "from nilearn.plotting import plot_connectome\n"
      ],
      "metadata": {
        "id": "8tOd48V-HV08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8c32cad-c456-4e9b-9fd9-087afd9c9948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from kspw.utils import load_stockholm_data\n",
        "stuff = load_stockholm_data()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2u-EEPzdHYvh",
        "outputId": "54e3ae46-d607-4704-9448-b5c4adaae2e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "K-EsG6OEfED_"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install nilearn\n",
        "!pip install hcp_utils\n",
        "# @title Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "k9ql3pXENExn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import glob\n",
        "import os,sys\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import nibabel as nib\n",
        "import hcp_utils as hcp\n",
        "import scipy\n",
        "from nilearn.connectome import sym_matrix_to_vec\n",
        "# @title Importage"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = '/content/kcnischool2022-projectweek/data/'\n",
        "\n",
        "subj_info = data_dir + '/subjinfo/participants.tsv'\n",
        "subj_info = pd.read_csv(subj_info,sep='\\t',index_col=0)\n",
        "subj_info.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "YEJrni3DHk4U",
        "outputId": "380d9c6b-bd4d-4633-e1e3-1c74196e1333"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Sex AgeGroup         BMI1         BMI2  \\\n",
              "participant_id                                              \n",
              "sub-9001          Male    Young  19,78997095  19,78997095   \n",
              "sub-9002          Male      Old  21,79944511  21,46915048   \n",
              "sub-9003          Male      Old  20,49861496  20,49861496   \n",
              "sub-9004        Female      Old  22,94811574  22,94811574   \n",
              "sub-9005          Male      Old  24,75546432  24,75546432   \n",
              "\n",
              "                                                 EducationLevel  HADS_Anxiety  \\\n",
              "participant_id                                                                  \n",
              "sub-9001        Studerar för närvarande på universitet/högskola             0   \n",
              "sub-9002                            Har avslutat gymnasieskolan             2   \n",
              "sub-9003                   Har examen från universitet/högskola             2   \n",
              "sub-9004                   Har examen från universitet/högskola             1   \n",
              "sub-9005                            Har avslutat gymnasieskolan             0   \n",
              "\n",
              "                HADS_Depression  ISI KSQ_SleepQualityIndex  \\\n",
              "participant_id                                               \n",
              "sub-9001                      1   12                  4,75   \n",
              "sub-9002                      3    9                   5,5   \n",
              "sub-9003                      3   10                  5,25   \n",
              "sub-9004                      2   11                  4,25   \n",
              "sub-9005                      0    9                     6   \n",
              "\n",
              "               KSQ_WakeupSymptomIndex  ... PPIR_IR15 PPIR_IR40 BADD_Total  \\\n",
              "participant_id                         ...                                  \n",
              "sub-9001                  4,666666667  ...      11.0      29.0       16.0   \n",
              "sub-9002                  5,666666667  ...      10.0      32.0       33.0   \n",
              "sub-9003                  5,666666667  ...      12.0      26.0       13.0   \n",
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              "sub-9005                  5,666666667  ...       8.0      28.0       30.0   \n",
              "\n",
              "               BADD_Activation BADD_Attention BADD_Effort BADD_Affect  \\\n",
              "participant_id                                                          \n",
              "sub-9001                   6.0            2.0         2.0         6.0   \n",
              "sub-9002                   5.0            8.0         6.0         8.0   \n",
              "sub-9003                   4.0            4.0         4.0         1.0   \n",
              "sub-9004                   3.0            7.0         5.0         6.0   \n",
              "sub-9005                   9.0            5.0         7.0         3.0   \n",
              "\n",
              "               BADD_Memory Sl_cond Trial  \n",
              "participant_id                            \n",
              "sub-9001               0.0       2     B  \n",
              "sub-9002               6.0       2     B  \n",
              "sub-9003               0.0       1     B  \n",
              "sub-9004               3.0       1     B  \n",
              "sub-9005               6.0       2     B  \n",
              "\n",
              "[5 rows x 105 columns]"
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              "      <td>20,49861496</td>\n",
              "      <td>20,49861496</td>\n",
              "      <td>Har examen från universitet/högskola</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>5,25</td>\n",
              "      <td>5,666666667</td>\n",
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              "      <td>Female</td>\n",
              "      <td>Old</td>\n",
              "      <td>22,94811574</td>\n",
              "      <td>22,94811574</td>\n",
              "      <td>Har examen från universitet/högskola</td>\n",
              "      <td>1</td>\n",
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              "      <td>11</td>\n",
              "      <td>4,25</td>\n",
              "      <td>5,666666667</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>2</td>\n",
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              "<p>5 rows × 105 columns</p>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dwi_files = sorted(glob.glob(data_dir + '/dwiconn/*.csv'))\n",
        "subj2use = []\n",
        "\n",
        "for xx in range(len(dwi_files)):\n",
        "  subj2use.append('sub' + dwi_files[xx].split('sub')[1][:5])\n",
        "\n",
        "subj_info_filtered = subj_info.loc[subj2use]\n",
        "subj_info_filtered.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "2Aoe-8IFHxWX",
        "outputId": "c7241436-236e-459a-8480-0a6ec0cee6c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Sex AgeGroup         BMI1         BMI2  \\\n",
              "participant_id                                              \n",
              "sub-9002          Male      Old  21,79944511  21,46915048   \n",
              "sub-9003          Male      Old  20,49861496  20,49861496   \n",
              "sub-9004        Female      Old  22,94811574  22,94811574   \n",
              "sub-9005          Male      Old  24,75546432  24,75546432   \n",
              "sub-9008          Male      Old  22,34029707  22,64219297   \n",
              "\n",
              "                                      EducationLevel  HADS_Anxiety  \\\n",
              "participant_id                                                       \n",
              "sub-9002                 Har avslutat gymnasieskolan             2   \n",
              "sub-9003        Har examen från universitet/högskola             2   \n",
              "sub-9004        Har examen från universitet/högskola             1   \n",
              "sub-9005                 Har avslutat gymnasieskolan             0   \n",
              "sub-9008                 Har avslutat gymnasieskolan             1   \n",
              "\n",
              "                HADS_Depression  ISI KSQ_SleepQualityIndex  \\\n",
              "participant_id                                               \n",
              "sub-9002                      3    9                   5,5   \n",
              "sub-9003                      3   10                  5,25   \n",
              "sub-9004                      2   11                  4,25   \n",
              "sub-9005                      0    9                     6   \n",
              "sub-9008                      1    9                     5   \n",
              "\n",
              "               KSQ_WakeupSymptomIndex  ... PPIR_IR15 PPIR_IR40 BADD_Total  \\\n",
              "participant_id                         ...                                  \n",
              "sub-9002                  5,666666667  ...      10.0      32.0       33.0   \n",
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              "sub-9008                            4  ...      13.0      28.0       17.0   \n",
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              "               BADD_Activation BADD_Attention BADD_Effort BADD_Affect  \\\n",
              "participant_id                                                          \n",
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              "sub-9008                   4.0            5.0         3.0         1.0   \n",
              "\n",
              "               BADD_Memory Sl_cond Trial  \n",
              "participant_id                            \n",
              "sub-9002               6.0       2     B  \n",
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              "sub-9004               3.0       1     B  \n",
              "sub-9005               6.0       2     B  \n",
              "sub-9008               4.0       2     A  \n",
              "\n",
              "[5 rows x 105 columns]"
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              "      <td>Old</td>\n",
              "      <td>21,79944511</td>\n",
              "      <td>21,46915048</td>\n",
              "      <td>Har avslutat gymnasieskolan</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>5,5</td>\n",
              "      <td>5,666666667</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub-9003</th>\n",
              "      <td>Male</td>\n",
              "      <td>Old</td>\n",
              "      <td>20,49861496</td>\n",
              "      <td>20,49861496</td>\n",
              "      <td>Har examen från universitet/högskola</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>5,25</td>\n",
              "      <td>5,666666667</td>\n",
              "      <td>...</td>\n",
              "      <td>12.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub-9004</th>\n",
              "      <td>Female</td>\n",
              "      <td>Old</td>\n",
              "      <td>22,94811574</td>\n",
              "      <td>22,94811574</td>\n",
              "      <td>Har examen från universitet/högskola</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>4,25</td>\n",
              "      <td>5,666666667</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub-9005</th>\n",
              "      <td>Male</td>\n",
              "      <td>Old</td>\n",
              "      <td>24,75546432</td>\n",
              "      <td>24,75546432</td>\n",
              "      <td>Har avslutat gymnasieskolan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>5,666666667</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub-9008</th>\n",
              "      <td>Male</td>\n",
              "      <td>Old</td>\n",
              "      <td>22,34029707</td>\n",
              "      <td>22,64219297</td>\n",
              "      <td>Har avslutat gymnasieskolan</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 105 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/ThomasYeoLab/CBIG/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/'\n",
        "atlas2use = 'Parcellations/MNI/Centroid_coordinates/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.Centroid_RAS.csv'\n",
        "\n",
        "atlas  = pd.read_csv(url + atlas2use)\n",
        "atlas"
      ],
      "metadata": {
        "id": "khhVNT5KH3HB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coordinates = {'R': atlas['R'], 'A': atlas['A'], 'S': atlas['S']}\n",
        "coordinates = pd.DataFrame(coordinates)\n",
        "coordinates.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "8DSj2i38IXCt",
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      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "    R   A   S\n",
              "0 -26 -34 -18\n",
              "1 -26 -76 -14\n",
              "2 -18 -60  -6\n",
              "3 -26 -96  -4\n",
              "4  -6 -92  -2"
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    },
    {
      "cell_type": "code",
      "source": [
        "pts_dir = data_dir + '/ptseries/'\n",
        "ses2use = 'ses-1'\n",
        "\n",
        "\n",
        "all_ptsrs_fs = []\n",
        "\n",
        "for xx in subj2use:\n",
        "  all_ptsrs_fs.append(pts_dir + xx + '_' + ses2use + '_task-rest_atlas-SchaeferP100N7_desc-cleaneds0_bold.ptseries.nii')"
      ],
      "metadata": {
        "id": "7kQPcJ7OIYk_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "ci3QjJqcZ6lM"
      },
      "outputs": [],
      "source": [
        "# @title #Plot SC and FC\n",
        "\n",
        "# Select Subject\n",
        "sub = \"sub-9005\"  # @param ['sub-9002','sub-9003','sub-9004','sub-9005','sub-9008','sub-9009','sub-9011','sub-9014','sub-9018','sub-9020','sub-9023','sub-9025','sub-9026','sub-9028','sub-9029','sub-9032','sub-9033','sub-9034','sub-9036','sub-9038','sub-9039','sub-9040','sub-9041','sub-9042','sub-9045','sub-9046','sub-9047','sub-9048','sub-9049', 'sub-9055','sub-9058','sub-9061','sub-9062','sub-9064','sub-9065','sub-9068','sub-9069','sub-9071','sub-9072','sub-9075', 'sub-9079','sub-9080','sub-9081','sub-9084','sub-9085','sub-9086','sub-9087','sub-9088','sub-9089','sub-9092','sub-9093','sub-9094','sub-9096','sub-9098']\n",
        "\n",
        "subj_idx = [i for i, s in enumerate(all_ptsrs_fs) if sub in s][0]\n",
        "\n",
        "ts2use = nib.load(all_ptsrs_fs[subj_idx]).get_data()\n",
        "fc_matrix = np.array(pd.DataFrame(ts2use).corr())\n",
        "sc_matrix = np.array(pd.read_csv(data_dir + '/dwiconn/' + sub + '_Schaefer2018_100Parcels_7Networks.csv', sep = ' ' , header=None))\n",
        "sc_matrix = sc_matrix -2*np.diag(np.diag(sc_matrix)) + sc_matrix.T \n",
        "sc_matrix =  np.log1p(sc_matrix)\n",
        "\n",
        "label_stripped = []\n",
        "for xx in range(len(atlas['ROI Name'])):\n",
        "    label_stripped.append(atlas['ROI Name'][xx].replace('7Networks_',''))\n",
        "\n",
        "\n",
        "#sns.heatmap(fc_matrix,center=0,mask=np.eye(100),)\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(20, 7), sharey=True)\n",
        "fig.suptitle('connectome matrices')\n",
        "\n",
        "# SC\n",
        "sns.heatmap(sc_matrix,mask=np.eye(100), ax=axes.flat[0], square=True)\n",
        "axes.flat[0].set_title('Structural Connectivity for sub ' + sub)\n",
        "axes.flat[0].set_xticklabels(label_stripped, rotation=90)\n",
        "axes.flat[0].set_yticklabels(label_stripped, rotation=0)\n",
        "\n",
        "# FC (normalized between 0 and 3)\n",
        "sns.heatmap(fc_matrix, center=0,mask=np.eye(100), ax=axes.flat[1], square=True)\n",
        "axes.flat[1].set_title('Functional Connectivity for sub ' + sub)\n",
        "axes.flat[1].set_xticklabels(label_stripped, rotation=90)\n",
        "axes.flat[1].set_yticklabels(label_stripped, rotation=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subj2use.remove(\"sub-9100\")\n",
        "\n",
        "KSS_mean_ses1 = []\n",
        "KSS_mean_ses2 = []\n",
        "\n",
        "for sub in subj2use:\n",
        "  f = data_dir + '/subjinfo/%s_sessions.tsv' %sub\n",
        "  _df = pd.read_csv(f,sep='\\t',index_col=0).T\n",
        "  KSS_mean_ses1.append(np.mean(_df['ses-1'][1:7][:]))\n",
        "  KSS_mean_ses2.append(np.mean(_df['ses-2'][1:7][:]))\n",
        "\n",
        "KSS_mean_ses1 = np.array(KSS_mean_ses1)\n",
        "KSS_mean_ses2 = np.array(KSS_mean_ses2)\n",
        "\n",
        "\n",
        "KSS_df = {'ses-1': KSS_mean_ses1, 'ses-2': KSS_mean_ses2}\n",
        "KSS_df = pd.DataFrame(data=KSS_df)\n",
        "\n",
        "sns.lmplot(x='ses-1', y='ses-2', data=KSS_df)\n",
        "plt.title('r = ' + str(np.corrcoef(KSS_mean_ses1, KSS_mean_ses2)[0][1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "hIIUfZRMIeux",
        "outputId": "ad23521a-682f-429b-b09a-7181ec389d00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'r = 0.30852615502737674')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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oVzkxNL7oGcou6unk5PA4ncX85HVjpSoX9nQ2qolNpyEIEYlUMFnOBEeWOD3k7ddvoFx1RksVnOBruercfv2GBra2uRTAIhKZ0YkKh/tHOTU8wVJLXrdvXs2dN1/Jmu52BsfKrOlu586br0x0FYSGIESk4SbKVfpHJhgeb+zCl9s3r0504E6nABaRhqlUnf6RCQbHykvu8WaBAlhElqw2N+/pkWgnRU8bBbCILMngWIn+4VKi5+WdS+3su+ODY1y6pruh82noIJyILEpwgG2EvsFkT4o+lyln30WwqogCWETOy0Q5OH346JnRVM3NO5MpZ98ZDV9VREMQIrIg1apzKmMH2GY6+66Rq4oogEUaYCmrD89331ZY2Xixpw8n3Uxn3zVyVRENQYgs0VJWH57vvnGvbDxWqnDk9CgnFjBbWRpNOfvOafiqIgpgkSVayurD8903rpWNK1Xn+OAYr50eTeVSQAtVf/bdwGiJdcs7uOu2qxv2CURDECJLtJTVh+e7b7NXNlY977lqZ98tcUWMGakHLLJES1l9eL77NnNl48Gx0uS8DQrf5lAAiyzRUlYfnu++zVjZeGi8zKFTQT1v2hfBXIqBsRKDY6WGPmaki3KeLy3KKUlVq1RYzOrD8913KY89G3dncLzMmZH0rzy8FENjZb72wgn2HOhj38v9/Op738CP37hpMQ8146KcCmCRDKkF7+kUnzq8VINjJb72/En2HOjjiVf6KddVf7zj8jX8yU/fsJiHbf6qyCLSOobHy5wanlCPdwZzhW57IcfbNq3mvddcxPuuubih+1UAi6TcWKnCqeGJJa1GkUaDYyUeDUP3yZlCd/Nqdm3p5W2b1tDZlmd1dxvd7Y2NTAWwSEpFNSl6ktWH7hOv9E85ueRs6K7jbZtXTzn7LSoKYJGUKVWqnB5p/BH7pBoYLfG154MDaU+8evqc0L1h8xp2bultWujWUwCLpMRYqcKZ0ZJ6vMwduh210N3ay/ZNzQ/degpgkQRzd4bGywyMlTN9yjDAmbrQfXKO0H3bptV0xBi69RTAIgk0Ua4yNF5maKyc6XKyM6MlHj0YhO43D00L3WKOGzatYVfY022V0K2nAJbEaYXpGeNQrlQZnqgwPF7OdEVDfeg++Wo/9ZO0dRRzvL02vHBZa4ZuPQWwJEptesZi3qZMz3gXpDaEh8bLDIyWsh26IyUeCYcXvjlL6O7auo7tl62ivcVDt54CWBKlfnpGCJaIGZkoc+/eF1MVwDpVuC509x/nm4dOTwndzmKet18eVC8kLXTrKYAlUZo9PWOzZT14T49M8OjzJ9izv2/W0N21pZfrExy69RTAkigbVnVxfHBssgcM0U3P2GyDY8E8vFkL3tMjEzwSjul+a4bQfUfY001L6NZTAEuifGjHZj72xWcYmSjTWcwzWqo0fHrGZhsaL9OfsTka+kcmJg+kZS106ymAJVF2XbWOu6Dh0zM2m7szMBYcXMtK8PbX9XS/PS10u9rqQ3c1bYVsTFUeaQCb2UrgfuBNgAM/6e5/H+U+W1lWy6cabddV6xL7uo2VKgyOlRkeL2di1Ym5Qre77eyBtCyFbr2oe8B3Aw+6+w+bWRuQ/IG6Rcpi+ZScNTRe5sxoKRNnq50aPhu6Tx1W6M4lsgA2sxXADuDHAdx9ApiIan+tLivlUzJVVg6sBaHbF4bumXNC9x1XrGXnlrVsu1ShWy/KHvAmoA/4AzN7M/AEcKe7D9ffyMzuAO4A2LhxY4TNiVfay6dkqixMfn5qeIK9B86Gbv2ASnd7nhsvX8vOLb289dJVCt1ZRBnABeAtwM+7+zfM7G7gl4D/UH8jd78PuA+CJYkibE+s0lw+JWcNj5fpH5lgopzO4D05NF43vKDQXaooA/gwcNjdvxH+/DmCAM6kNJZPSaBSdYbGygyOl1IZvCeHxtkbhu7TCt2GiiyA3f2YmR0ys63uvh+4BfhuVPtrdWkpn5KzxssVzoyUGJ6o0EqL2zbCfKF70xVB6L5lo0J3KaKugvh54NNhBcSLwE9EvL+WluTyKTlrdCKY+HxkIl0Tn58YGmfvgSB0v3Nkauguay9w4xVrJnu6xbxCtxEiDWB3/xawLcp9SPq1Qv10qVJlaKzM0Hg5VQfW5grd5R0F3nF5MJ/uWzamO3Qfe/EUDzx+iKMDo1zU08nt129g++bVke9XZ8JJS4uzfrpSDVabGBpP12oTfYPjkyVj3zkycE7o3nj5WnZt7eW6jStTHbo1j714irsfOkghZ/R0FDg5PM7dDx3kTq6MPIQVwNLS4qifHp2oMDiWrrHdvsFx9h7sY8/+Pr7z2sCUbcs7CnVjuispZCB06z3w+CEKOZtcG652kPyBxw8pgCXbmlU/ncYhhrlCt6ejwI1XhD3dDdkL3XpHB0bp6ZgahR3FHMcGRiPftwJYWlqU9dPVqjM0EayrlpbVJvoGx9kTnhzxzAyhe9MVa9mp0J3iop5OTg6PT1kdeaxU5cKezsj3rQCWltbI+umxUoWRiQrj5QqlsqdmMcvjA2PsORhMYv7dozOE7pXB8IJCd2a3X7+Bux86yGipQkcxx1ipSrnq3H79hsj3rQCWlrbU+umJcpXh8XQNLUB96B7nu0cHp2xT6J6f7ZtXcydX8sDjhzg2MMqFTayCsFY6yLBt2zbft29f3M3InDjKvKLcZ21Zn4HRdJ2Z9vrAWHByxCyh+71X9rJzy1quVehGYnV3Gyu72hZ7d5vpSvWAMy6OMq+o9lmtnl1PLS3DC8cGxngkHNOdHrorOovcFB5Iu3bDSvK5Gf/GpYUpgDMujjKvRu8zbZOcHxsYm5xl7NkZQvd7r1zLri29vFmhm3gK4IyLY5rMRuyzNgHOwFg65to9dmZssnrhuWNTQ3dlGLo7FbqpowDOuDimyVzsPsfLFUYngkqGNJSN1UJ394E+9s8Wult7efN6hW5aKYAzLo5pMs9nn+PlCsPjFYZTUsVw9Mwoe8K5F6aH7qqu4mT1gkI3GxTAGRfHNJnz7bN2gsTgWDrmYJgM3f197H/93NCtVS9co9DNHJWhpUQrzBi2FNWqM1oKerpJnINh+mxa737jBZwenWDPgRMK3ZRQGZrMKIkrLleqzshEmdFShfFSNdHDC7XZtHCnXKnyzNEzfOvw6Sm3WdVVZMeVvezc2sv3XLJCoSuAAjgVkrDicrXqjJerwYG0UoWxUjVxvdyZvHZ6lLv/7iAnhscpVaY+n0LOeO81F7FrSy9vUujKDBTAKdCqKy6PlyuMTVQZnigzXk5H4AIcOT3Knv1BydjB40NTtuVzxrL2Asva85QrVe685cqYWilJoABOgVZYcdndGStVgyGFcjCskIaTImqO9I9Olow9Py10Czmjo5hjZWcbncUcZsZoqcJFK7TidVqYGWaN/wSjAE6BOErJakMKY6UKYykMXJg7dNd0t03W6Y6OV/idh5/HDLDgn1+zZtOSxsmZkc8Fl2I+R1s+R7FgFHI5inkFsMyiGaVkE+UqY+XgBIikHzSby2To7u/j+b5pobusLTiQtmUtV188dUw3ZxbLbFoyv3zOKOSDEG3L5yjkcxRyNhm4BuRiGp9XGZpMUakG8+SWK2cPmk2Uq1SqrfN70miH+0eC04D3n5gxdHde2cvOLb1cfUkPuQh6QbJ0ZkYhZ7QVchTzOQph2BbzuVY5+Hn+ZWhmlgc+CKwHHnT3r9Vt+1V3/y8NbaI03UQ5GLcdDU/vTdswwmwOnRqZnHvhhb7hKdtqobtray9vvDi9oRvXSsDnq35ooJAzcuHX2lBB7WsSzTcEcS/QBTwG3GNme9z9o+G2fwIogBPC3SlVfHK8drwcjBO30iegqNVCd/eBPl6cFrprl7WxY0svu7akO3Rr4lwJuBaoZ8Pz7M9mYBg5C24X19BAs8wXwNvd/RoAM/td4JNm9nng/czSpZbmc3cmKlUmysGlXHUq4aXqTtXJVNDWe7WupztT6O7cEgwvZCF060W9EnAhFwwDFPJGMfy+2FpDAi1hvgCePO/O3cvAHWb2MeAhYFmUDZNz1XqxpUowRluqVhkPQzerATuTV0/Whe6JqaHbu6ydHVvWZjJ06y11JeDamOvkWGshOLAVZcVAGs0XwPvM7FZ3f7B2hbvfZWavAf8r2qZl20Q5qDQohT3b8bBnq6Cd2Ssnh8PQPcFLs4Turq29vOGi7IZuvflWAi7kzpZgteVz5PNGvm4sVr3YxpgzgN39R2e5/n7g/khalCG1Hm25Gg4fhGGbtbHZxXr55PDkGWkvn5x61t+65Wd7ugrdc92+fQP3/N1BxssVOor58ExF+PDOzVy6plsB2yTnXQdsZve5+x1RNCaNKtVwyKAaTNRSC9xyxdWjXYT5Qrc2pnvVRcsVuiGzoDyrffKSZ3PvMi7s6WjqNKRyrsWciLGt4a1IqOCAV3XyIFe1CqVq9WzoVnxKWVdSyn5azUJCd9fWXq66cPm8Y49ZeQ+K+RxdbXm62wu0F3Izvi67rloXS+AmferURjrvEzHM7EF3vzWKxrTCiRgeVg0E1QNBqFbdqbhTCXut5Wr1vE+9rS/76SjmGCsFveI7b46+7CeJXjoxPHkg7ZUlhm5Nmt+DYj5HezFHZzFPRzHfsnWx9VOn1p82f9dtV6c9hBs2H/D3mVmPuw8ssUFNUT/OWir71B6rE/xcPRu4UYm67CcN5grdC3rqhhfOI3TrpeE9MAv+eRTzOdoKwQGytnwuMfWySZg6tZkWFMBm9ifAzwAV4HGgx8zudvffjLJx56M2vlqqVimFB7JqY6+tMM661LKfNHJ3Xj45Mjm88MqpxoduvaS9B7VTavO5oJa2sy0/63BCUrTq1KlxWWgP+I3uPmBmPwJ8Gfgl4Akg9gAenahwbGCsJUJ2LvOV/WSFu9f1dE/w6rTQvbCng51bglnGtl6w9NCt1+rvQVshR1dbgY5ijo5CPjG92vPRClOntpKFBnDRzIrADwK/6+4lM2uJxHNao4c7n9uv38DdDx1ktFSZMv6YhSkL60N39/4+DvVP7XFGGbr1Wu09yIfDIZ1tebraCpko/Ypj6tRWttAAvhd4Gfg2sNfMLgUSMQbcKrZvXs2dXJmZKQvdnRdrPd0ZQveiFR2TwwtbLljWlI/VrfAetBfzdIWh21HXE8+KOFbhbmWLno7SzArh6clz3eZlYJBg7Ljs7nOWsC2mCmJkosyxM2PndR+JRi10d4djuodnCd1dW3vpH5rgT/cdTn05WBZ7uTKjxVdBmNkFwH8DLnb395jZG4G3A7+/gLv/I3c/seBmSqK4Oy/2DbP7wPyhe+W6oKf72IunuOfh52OZiSsKtRMd2vI5avmazxkdxWz2cmXhFjoE8YfAHwD/Pvz5APCnLCyAJWXcnRf6zpaMTQ/di1eGobullyvWnTu8kPRysFrNbXshP3l2WZIrEyQ+Cw3gte7+WTP7ZQhmRjOzygLu58BXwwN297r7fdNvYGZ3AHcAbNy4cYHNkWZzd54/PjRZvXDk9NTQvWRlZ3AgbZbQrZe0crBCLkd3ezCE0FbQdIrSOAsN4GEzW0MQqJjZDcCZBdzvJnc/YmbrgL8xs+fcfW/9DcJQvg+CMeCFN12ittDQ3bV1HZf3di+4F9jq5WBwtiSsK6MHy6Q5FhrAHwW+CFxuZl8DeoEfnu9O7n4k/HrczL4AbAf2zn0viZO7czAM3b2zhO6urUH1wvmEbr1WKweDoDqho5CjvZins5hXL1eaYqEBfDnwHmAD8EPA2+a7r5l1Azl3Hwy/fzdw1xLaKhGphW6teuHotKqS9as6J8d0Ny8ydOu1QjkYMFmZ0N2Wp9CicydIui00gP+Du/+Zma0C/hHwcYIJ2d82x30uAL4Q/rEWgD+pn9hd4tXs0J1u++bVTQ/c2jwK3e0FulUSJi1goQFcO+D2XuB/u/tfm9mcC3K6+4vAm5fSOGmsuEM3DrWKha62Al3FdJ7eK8m10AA+Ymb3Au8Cft3M2gF9ZptBq8036+4ceH1osmRseuhuWNXJzq1B6G5am57Q7eko0t2uoQVpbQsN4H8G3Ap83N1Pm9lFwL+JrlnJFOdS3/Xcnf2vD7Jnfx97D544J3Q3ru6arF64bE1XKkLXzOhqy/nqd74AABHLSURBVNPTUaSzTVULkgwLCmB3HwE+X/fzUeBoVI1KqjhPMHB3njs2OFm9cGwg/aELQW93eUeBZe0F9XYlcRYzIbvMotknGNRCd/f+PvYe7OP1gfEp2y9d3RVMeLO1N1Whm88Zy8MhhvaCeruSXArgBmrGCQbzhu6arslZxjat7W7YfltBRzFPT2eR7rZ8av6ZSLYpgBsoqhMMshy6EATvqq42je1K6iQ6gHc/d5xP7n6BV04Nt0TFQSNPMKgP3T0H+jg+ODV0L1tTP7ywuNBttYqNmmI+x5Ov9POpf3iF186MsnF1d6bnjJX0WvR8wFE4n/mAa6ur5nNQyFkqVrh1d549OjhZMjY9dDet7WbnlrXs2LL40K1ptRWCc2Z0txdY3lHgH144mdWVcyW9GrYqckuora7aUchTqlQTN6VhTdWdZ48OTFYvzBa6O7f0cukSQ7deq0wJ2dmWZ1l7UMVQG9fVyrmSFYkN4NrqqvUd+Fae0rBe1Z3vvnY2dPuGmhO69eKcErJWxbC8o0BxhtIxrZwrWZHYAK6trtpRaN0pDevVQnf3gT72HujjxNDElO2b13ZPHkjbuCb6FWLjmBKyrZBjRWdxSm93Jlo5V7IisQFcW1216uUpY8CttMrwvKHbG4bulc0J3XrNnBKyq63Ais6Fn6GmlXMlKxJ7EA7OVkG8emq4ZVYZni90Lw9Dd8eWXjaujrdHV6uCiGJKSDNjWXsQvG2F8z9Dbfdzx7VyrqTJjB/5Eh3A0BqrIlfdeeZIOKZ7cPbQ3bmllw0xh27U8jljRWeR5R1FTfcocla6qiDiVnXnO0fOsOfACfYe7OPktNC9oncZO7euZceVjQndVq3ZrakF74rOYibPUqv12A/1j7Bhhh77fNslm9QDPg9TQvdAHyeHzw3d2nI9l6xq3MGsVqvZrddWyLG8PahoyOpcu7Wa9NnqlufbLpmgHvBiVKrOd147w579fTxy8MS5obtuGbu2ND5067VKzW5NbXy3p7OgyXCYv25Zdc0yGwXwDGqhuzsM3VMxhG69VlnGvZDL0dNZ0PjuNPPVLauuWWajAA5VqsHwwu4DM4fuleuWTR5Ia0bo1ot7GfeF1u9m1Xx1y6prltlkOoArVefpI+HwwvPnhu6WC5ZNloxdsjK+EzziWsb9fOt3s2q+umXVNctsMncQrlJ1njp8mj0HTvDIwT76R0pTttdCd+eWXi6OMXSni7Jmt17OjGUdBXo6Fle/m1Xz1S2rrjnzslsHPF/obr1g+eQsY60Uus1UW8hyWUfjlmtX6VVru+dvD3D/oy8xPFGhuy3PB2/axEfeuSXuZqVVtqogKlXn24dPs+dAH48ePDFr6O7c2stFK7IZumZGd3uwkGVHsbHDDPWlVys7ixwfHONjX3yGu0Ah3ALu+dsD3P3Q8+QMCrlgTPruh54HUAg3UaoCeN7QvXB5OLywNrOhC8EwQ09nkZ6O6BayVOlVa7v/0ZfC8A3e/5xBuVrl/kdfUgA3UeIDuFyp8uQr/ewJqxdOjyp0Z5Ozs2erRX3ShEqvWtvwRIXpQ/w5C66X5kl0AP/fv3+Z3/7bg+dUL1w1Gbq9XLiiI57GtZA46ndVetXautuCaoz6X4eqB9dL8yQ6gDuK+cnwfcNFyydLxi7sUehCvKsIq/SqtX3wpk3c/dDzlKtVchaEb9WD66V5Eh3A737jhfT943Gu27iSCxS6QOuUke26ah13gUqvWlRtnFdVEPHKRBlaq4hyRrNCLhdOA5ndSXFEWtiMf5SqtG+S2oxmJ4fH6ekocHJ4nLsfOshjL55a0uN2tRW4oKeDjWu6WNEV/cE1EWkcBXCT1M9oZgRfCznjgccPnfdjmQWLWq5f1cWFKzrobk/0SJJIZukvt0kaMaNZELwFVnYWI6vfFZHmUQA3yVJmNFPwiqST/pqb5PbrN1CuOqOlCk7wdb4ZzfI5Y1VXGxtXd7F2WbvCVyRl1ANuku2bV3MnVy5oRrNiPjd5qrDm3xVJr8gD2MzywD7giLu/L+r9tbLtm1fPWXZWzOdY1d3GMh1UE8mEZvyl3wk8C/Q0YV+JtaKzyKquNpWRicQgrqlTIx1UNLP1wHuB+6PcT5K1F/NcvLKTNcvaFb4iMahNnXp8cGzK1Km7nzse+b6jPqrzCeDfAtWI95M4hVyOdT0dXLKys+Fz8YrIwtVPnWoWfC3mjXv3vhj5viMLYDN7H3Dc3Z+Y53Z3mNk+M9vX19cXVXNayorOIhtWd2qsV6QFHOofmVIeCs2bOjXKHvCNwG1m9jLwAHCzmX1q+o3c/T533+bu23p7eyNsTvzai3kuWRUMN6i6QaQ1bFjVxWhp6jzIzZo6NbIAdvdfdvf17n4ZcDvwkLv/aFT7a1XFfI5VXW2sX9XFJSs7aS9ouEGklXxox2ZKFWdkoox78LVZU6fqM3BEivkcK7uKLGtXLa9IK4tz6tSmBLC77wZ2N2NfccuZsXpZGz0dxflvnHBxle5otWVptF1XrYvld0jntjZQd3uB9as6MxO+cZTuxFkyJNJoCuAGWNZe4OKVnVzQ05GZ+RriKt2Js2RIpNE0BrxItRnKVnQWKWYkdOvFteqxVluWNMlecizR9BnKshi+EF/pTpwlQyKNls30WIRCLseaZe1sWNXFqu62pi3v3qriKt2Js2RIpNE0BDGP9mKeFTEt7d7K4ird0WrLkiZaFXkW3e3B+K7maRCRBpix96Ye8DTLO4qs7MrmgTURaS4FcKi9mGdNd5t6vCLSNJkP4HzOWNWdjTPXRKS1ZDqAl3UUWNPdnvmKBhGJRyYDuJjPsXZZO51tGm4QkfhkKoDNjFVdRVZ0FlVSJiKxy0wAd7UVWLOsTdUNItIyUh/AhVyO1cvSv9S7pmiMnl5jabTUdgdzFszZkIW11zRFY/T0GksUUhfAZsaKziLrV3WyqrstE2O9mqIxenqNJQqp6RrmzOjpDA6wZa2sTFM0Rk+vsUQh8T3gnBlrlrWzcXUXqzM6S5mmaIyeXmOJQuIDuCOcrSyXweCt0RSN0dNrLFFIfABLOEXjbVezbnkHZ0ZLrFvewV23Xa0j9A2k11iikPjpKCV7VA4mCTTjR3T1gCVRVA4maaIAlkRROZikiQJYEuVQ/wid0+ZsVjmYJJUCWBJF5WCSJgpgSRSVg0maKIAlUVQOJmmSmlORJTt2XbVOgSupoB6wiEhMFMAiIjFRAIuIxEQBLCISEwWwiEhMFMAiIjFRAIuIxCSyOmAz6wD2Au3hfj7n7r8W1f4kGlmb+jFrz1fiFWUPeBy42d3fDFwL3GpmN0S4P2mwrE39mLXnK/GLLIA9MBT+WAwvrTP7u8wra1M/Zu35SvwiHQM2s7yZfQs4DvyNu39jhtvcYWb7zGxfX19flM2R85S1qR+z9nwlfpEGsLtX3P1aYD2w3czeNMNt7nP3be6+rbe3N8rmyHnK2tSPWXu+Er+mVEG4+2ngYeDWZuxPGiNrUz9m7flK/CILYDPrNbOV4fedwLuA56LanzRe1qZ+zNrzlfhFtiqymV0D/BGQJwj6z7r7XXPdp1VWRVYpkog02IyrIkdWB+zuTwHXRfX4UamVIhXzNqUU6S5QCItIQ+lMuGlUiiQizaIAnkalSCLSLArgaVSKJCLNogCeRqVIItIsCuBpVIokIs2iVZFnoFV3RaQZ1AMWEYmJAlhEJCYKYBGRmCiARURiogAWEYmJAlhEJCYKYBGRmGS2DlhTTqaX3ltJikz2gLX6bXrpvZUkyWQAa8rJ9NJ7K0mSyQDWlJPppfdWkiSTAawpJ9NL760kSSYDWFNOppfeW0mSTAawppxML723kiSRrYq8GK2yKrKISIPNuCpyJnvAIiKtQAEsIhITBbCISEwUwCIiMVEAi4jERAEsIhITBbCISEwUwCIiMVEAi4jERAEsIhITBbCISEwUwCIiMVEAi4jERAEsIhKTyFZFNrMNwB8DFwAO3Ofud0e1vyTQar3NoddZkiLKHnAZ+Nfu/kbgBuDnzOyNEe6vpWm13ubQ6yxJElkAu/tRd38y/H4QeBa4JKr9tTqt1tscep0lSZoyBmxmlwHXAd+YYdsdZrbPzPb19fU1ozmx0Gq9zaHXWZIk8gA2s2XAnwO/4O4D07e7+33uvs3dt/X29kbdnNhotd7m0OssSRJpAJtZkSB8P+3un49yX61Oq/U2h15nSZLIAtjMDPh94Fl3/62o9pMUWq23OfQ6S5JEtiqymd0EPAI8DVTDq3/F3b802320KrKIpNSMqyJHVgfs7o/OtlMREdGZcCIisVEAi4jERAEsIhITBbCISEwUwCIiMVEAi4jERAEsIhITBbCISEwiOxNuMcysD3hlEXddC5xocHOilLT2gtrcDElrL6jNC3XC3W+dfmVLBfBimdk+d98WdzsWKmntBbW5GZLWXlCbl0pDECIiMVEAi4jEJC0BfF/cDThPSWsvqM3NkLT2gtq8JKkYAxYRSaK09IBFRBJHASwiEpPEBrCZbTCzh83su2b2jJndGXeb5mNmHWb2mJl9O2zzf4q7TQthZnkz+6aZ/VXcbVkIM3vZzJ42s2+ZWSKWWDGzlWb2OTN7zsyeNbO3x92muZjZ1vD1rV0GzOwX4m7XXMzsF8O/u++Y2WfMrCP2NiV1DNjMLgIucvcnzWw58ATwg+7+3ZibNqtwnbxudx8KFyx9FLjT3f8h5qbNycw+CmwDetz9fXG3Zz5m9jKwzd0Tc4KAmf0R8Ii7329mbUCXu5+Ou10LYWZ54AjwNndfzIlUkTOzSwj+3t7o7qNm9lngS+7+h3G2K7E9YHc/6u5Pht8PAs8Cl8Tbqrl5YCj8sRheWvo/oJmtB94L3B93W9LKzFYAOwgWscXdJ5ISvqFbgBdaNXzrFIBOMysAXcBrMbcnuQFcz8wuA64DvhFvS+YXfpz/FnAc+Bt3b/U2fwL4t5xdWDUJHPiqmT1hZnfE3ZgF2AT0AX8QDvXcb2bdcTfqPNwOfCbuRszF3Y8AHwdeBY4CZ9z9q/G2KgUBbGbLgD8HfsHdB+Juz3zcveLu1wLrge1m9qa42zQbM3sfcNzdn4i7LefpJnd/C/Ae4OfMbEfcDZpHAXgL8L/c/TpgGPileJu0MOFwyW3An8XdlrmY2SrgBwj+2V0MdJvZj8bbqoQHcDiO+ufAp93983G353yEHzEfBs6ZoKOF3AjcFo6pPgDcbGafirdJ8wt7O7j7ceALwPZ4WzSvw8Dhuk9DnyMI5CR4D/Cku78ed0Pm8U7gJXfvc/cS8HngHTG3KbkBHB7Q+n3gWXf/rbjbsxBm1mtmK8PvO4F3Ac/F26rZufsvu/t6d7+M4GPmQ+4ee69hLmbWHR6UJfwY/27gO/G2am7ufgw4ZGZbw6tuAVr2YPI076fFhx9CrwI3mFlXmB23EBw3ilUh7gYswY3AjwFPh2OqAL/i7l+KsU3zuQj4o/CocQ74rLsnorQrQS4AvhD8jVEA/sTdH4y3SQvy88Cnw4/0LwI/EXN75hX+g3sX8KG42zIfd/+GmX0OeBIoA9+kBU5JTmwZmohI0iV2CEJEJOkUwCIiMVEAi4jERAEsIhITBbCISEwUwCLTmNm/MrPnzczNbG3c7ZH0UgCLnOtrBGdOtfrkMpJwCmBJvPDst78O51n+jpn9czN7q5ntCSfk+Uo4fSlm9pFwDumnzOyBmR7P3b/p7i839UlIJiX5TDiRmluB19z9vTA5veOXgR9w9z4z++fAfwV+kmCSm03uPl47LVwkLgpgSYOngf9hZr8O/BXQD7wJ+JvwlOQ8wRSEAE8RnPL7F8BfxNBWkUk6FVlSwcxWA98H/DTwEHCru5+zrE84D8cO4PsJZvL6HuCvCeaQ2OfuH6y77cskbGUNSRYFsCSemV0MnHL3sXAO458FtgA/5u5/H05buoVg9quN7v5yeN0rBEvUzLj6hAJYoqYhCEmD7wF+08yqQAn4MMGMV/eE48EFgpU9DgCfCq8z4J6ZwtfMPkKwCsiFwFNm9qX6nrFIo6gHLCISE5WhiYjERAEsIhITBbCISEwUwCIiMVEAi4jERAEsIhITBbCISEz+P+98Y78WPlSHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if ses2use=='ses-1':\n",
        "  Group = KSS_mean_ses1 > np.median(KSS_mean_ses1)\n",
        "  Group = np.multiply(Group, 1)\n",
        "  sleepy_idx = np.where(Group==1)[0]\n",
        "  not_sleepy_idx = np.where(Group==0)[0]\n",
        "elif ses2use=='ses-2':\n",
        "  Group = KSS_mean_ses2 > np.median(KSS_mean_ses2)\n",
        "  Group = np.multiply(Group, 1)\n",
        "  sleepy_idx = np.where(Group==1)[0]\n",
        "  not_sleepy_idx = np.where(Group==0)[0]"
      ],
      "metadata": {
        "id": "2Hy3AmcUImzZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sleepy = np.zeros((len(sleepy_idx), 100, 100))\n",
        "for xx in range(len(sleepy_idx)):               \n",
        "  sub = subj2use[sleepy_idx[xx]]\n",
        "\n",
        "  subj_idx = [i for i, s in enumerate(all_ptsrs_fs) if sub in s][0]\n",
        "\n",
        "  ts2use = nib.load(all_ptsrs_fs[subj_idx]).get_data()\n",
        "  sleepy[xx,:,:] = np.array(pd.DataFrame(ts2use).corr())\n",
        "\n",
        "not_sleepy = np.zeros((len(not_sleepy_idx), 100, 100))\n",
        "for xx in range(len(not_sleepy_idx)):               \n",
        "  sub = subj2use[not_sleepy_idx[xx]]\n",
        "\n",
        "  subj_idx = [i for i, s in enumerate(all_ptsrs_fs) if sub in s][0]\n",
        "\n",
        "  ts2use = nib.load(all_ptsrs_fs[subj_idx]).get_data()\n",
        "  not_sleepy[xx,:,:] = np.array(pd.DataFrame(ts2use).corr())"
      ],
      "metadata": {
        "id": "tw-rFpPTJUcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "import ipywidgets as widgets\n",
        "\n",
        "\n",
        "def refresh(window=3):\n",
        "    plt.imshow(not_sleepy[int(window)], interpolation=\"none\", cmap=\"bwr\", vmin=-1, vmax=1)\n",
        "    plt.title('not sleepy')\n",
        "    plt.show(sns)\n",
        "\n",
        "\n",
        "\n",
        "style = {'description_width': 'initial'}\n",
        "\n",
        "_ = widgets.interact(refresh, \n",
        "    window=widgets.FloatSlider(value=2, min=0, max=not_sleepy.shape[0], step=1, description=\"subj\", style=style),\n",
        ")  \n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313,
          "referenced_widgets": [
            "c5749db43b844bf88f2b915a01a80cfb",
            "78c9aef59b7543a5a4893d8b087be3f7",
            "3a6faa38fa4c4894933c584bbb3b58dc",
            "88c038bb21894d108e7f52b87d547991",
            "471d4ecf65694dd48eac5df1c12791ba",
            "fb90ca579dc44e29a2d7f9077a9ccb1d",
            "ceddb4660b5a4fb5acc2fcc6ed765775"
          ]
        },
        "id": "ww_Egfb1JXJK",
        "outputId": "1251bf8b-7571-42e4-8b37-13c91079bce2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=2.0, description='subj', max=28.0, step=1.0, style=SliderStyle(descrip…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5749db43b844bf88f2b915a01a80cfb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_stat, p_val = scipy.stats.ttest_ind(not_sleepy, sleepy, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0)\n"
      ],
      "metadata": {
        "id": "uOSBTAAFJbdq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p_level = 0.005\n",
        "thresh = 1 - p_level\n",
        "mat2plot = 1-p_val\n",
        "mat2plot = mat2plot[np.triu_indices_from(p_val, k=1)]\n",
        "\n",
        "mat2plot[mat2plot<=thresh] = 0\n",
        "edge_frac = mat2plot/mat2plot.shape[0]\n",
        "edge_frac_square = scipy.spatial.distance.squareform(edge_frac)*5000\n",
        "\n",
        "node_mask = np.amax(edge_frac_square, axis=0) >= thresh # find nodes that have at least one edge that passes the threshold\n",
        "node_size = edge_frac_square.sum(axis=0)*node_mask*20 # size nodes based on how many suprathreshold edges they have\n",
        "node_size = node_size\n",
        "\n",
        "plot_connectome(adjacency_matrix=edge_frac_square, edge_threshold=thresh,\n",
        "                node_color = 'red',\n",
        "                node_coords=coordinates, node_size=node_size,\n",
        "                display_mode= 'lzry',\n",
        "                edge_kwargs={\"linewidth\": 1, 'color': 'red'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "T4ExYbimJb5P",
        "outputId": "f735e1db-8110-44ba-b927-fffff5bb688b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<nilearn.plotting.displays._projectors.LZRYProjector at 0x7f45029cd8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 633.6x187.2 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regression"
      ],
      "metadata": {
        "id": "VTkXCkSYJsxf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7oqwhdEDgjmU"
      },
      "outputs": [],
      "source": [
        "\n",
        "def mk_kfold_indices(subj_list, k = 10):\n",
        "    \"\"\"\n",
        "    Splits list of subjects into k folds for cross-validation.\n",
        "    \"\"\"\n",
        "    \n",
        "    n_subs = len(subj_list)\n",
        "    n_subs_per_fold = n_subs//k # floor integer for n_subs_per_fold\n",
        "\n",
        "    indices = [[fold_no]*n_subs_per_fold for fold_no in range(k)] # generate repmat list of indices\n",
        "    remainder = n_subs % k # figure out how many subs are left over\n",
        "    remainder_inds = list(range(remainder))\n",
        "    indices = [item for sublist in indices for item in sublist]    \n",
        "    [indices.append(ind) for ind in remainder_inds] # add indices for remainder subs\n",
        "\n",
        "    assert len(indices)==n_subs, \"Length of indices list does not equal number of subjects, something went wrong\"\n",
        "\n",
        "    np.random.shuffle(indices) # shuffles in place\n",
        "\n",
        "    return np.array(indices)\n",
        "\n",
        "\n",
        "def split_train_test(subj_list, indices, test_fold):\n",
        "    \"\"\"\n",
        "    For a subj list, k-fold indices, and given fold, returns lists of train_subs and test_subs\n",
        "    \"\"\"\n",
        "\n",
        "    train_inds = np.where(indices!=test_fold)\n",
        "    test_inds = np.where(indices==test_fold)\n",
        "\n",
        "    train_subs = []\n",
        "    for sub in subj_list[train_inds]:\n",
        "        train_subs.append(sub)\n",
        "\n",
        "    test_subs = []\n",
        "    for sub in subj_list[test_inds]:\n",
        "        test_subs.append(sub)\n",
        "\n",
        "    return (train_subs, test_subs)\n",
        "\n",
        "\n",
        "def get_train_test_data(all_fc_data, train_subs, test_subs, behav_data, behav):\n",
        "\n",
        "    \"\"\"\n",
        "    Extracts requested FC and behavioral data for a list of train_subs and test_subs\n",
        "    \"\"\"\n",
        "\n",
        "    train_vcts = all_fc_data.loc[train_subs, :]\n",
        "    test_vcts = all_fc_data.loc[test_subs, :]\n",
        "\n",
        "    train_behav = behav_data.loc[train_subs, behav]\n",
        "\n",
        "    return (train_vcts, train_behav, test_vcts)\n",
        "\n",
        "\n",
        "def select_features(train_vcts, train_behav, r_thresh=0.2, corr_type='pearson', verbose=False):\n",
        "    \n",
        "    \"\"\"\n",
        "    Runs the CPM feature selection step: \n",
        "    - correlates each edge with behavior, and returns a mask of edges that are correlated above some threshold, one for each tail (positive and negative)\n",
        "    \"\"\"\n",
        "\n",
        "    assert train_vcts.index.equals(train_behav.index), \"Row indices of FC vcts and behavior don't match!\"\n",
        "\n",
        "    # Correlate all edges with behav vector\n",
        "    if corr_type =='pearson':\n",
        "        cov = np.dot(train_behav.T - train_behav.mean(), train_vcts - train_vcts.mean(axis=0)) / (train_behav.shape[0]-1)\n",
        "        corr = cov / np.sqrt(np.var(train_behav, ddof=1) * np.var(train_vcts, axis=0, ddof=1))\n",
        "    elif corr_type =='spearman':\n",
        "        corr = []\n",
        "        for edge in train_vcts.columns:\n",
        "            r_val = sp.stats.spearmanr(train_vcts.loc[:,edge], train_behav)[0]\n",
        "            corr.append(r_val)\n",
        "\n",
        "    # Define positive and negative masks\n",
        "    mask_dict = {}\n",
        "    mask_dict[\"pos\"] = corr > r_thresh\n",
        "    mask_dict[\"neg\"] = corr < -r_thresh\n",
        "    \n",
        "    if verbose:\n",
        "        print(\"Found ({}/{}) edges positively/negatively correlated with behavior in the training set\".format(mask_dict[\"pos\"].sum(), mask_dict[\"neg\"].sum())) # for debugging\n",
        "\n",
        "    return mask_dict\n",
        "\n",
        "\n",
        "\n",
        "def build_model(train_vcts, mask_dict, train_behav):\n",
        "    \"\"\"\n",
        "    Builds a CPM model:\n",
        "    - takes a feature mask, sums all edges in the mask for each subject, and uses simple linear regression to relate summed network strength to behavior\n",
        "    \"\"\"\n",
        "\n",
        "    assert train_vcts.index.equals(train_behav.index), \"Row indices of FC vcts and behavior don't match!\"\n",
        "\n",
        "    model_dict = {}\n",
        "\n",
        "    # Loop through pos and neg tails\n",
        "    X_glm = np.zeros((train_vcts.shape[0], len(mask_dict.items())))\n",
        "\n",
        "    t = 0\n",
        "    for tail, mask in mask_dict.items():\n",
        "        X = train_vcts.values[:, mask].sum(axis=1)\n",
        "        X_glm[:, t] = X\n",
        "        y = train_behav\n",
        "        (slope, intercept) = np.polyfit(X, y, 1)\n",
        "        model_dict[tail] = (slope, intercept)\n",
        "        t+=1\n",
        "\n",
        "    X_glm = np.c_[X_glm, np.ones(X_glm.shape[0])]\n",
        "    model_dict[\"glm\"] = tuple(np.linalg.lstsq(X_glm, y, rcond=None)[0])\n",
        "\n",
        "    return model_dict\n",
        "\n",
        "\n",
        "def apply_model(test_vcts, mask_dict, model_dict):\n",
        "    \"\"\"\n",
        "    Applies a previously trained linear regression model to a test set to generate predictions of behavior.\n",
        "    \"\"\"\n",
        "\n",
        "    behav_pred = {}\n",
        "\n",
        "    X_glm = np.zeros((test_vcts.shape[0], len(mask_dict.items())))\n",
        "\n",
        "    # Loop through pos and neg tails\n",
        "    t = 0\n",
        "    for tail, mask in mask_dict.items():\n",
        "        X = test_vcts.loc[:, mask].sum(axis=1)\n",
        "        X_glm[:, t] = X\n",
        "\n",
        "        slope, intercept = model_dict[tail]\n",
        "        behav_pred[tail] = slope*X + intercept\n",
        "        t+=1\n",
        "\n",
        "    X_glm = np.c_[X_glm, np.ones(X_glm.shape[0])]\n",
        "    behav_pred[\"glm\"] = np.dot(X_glm, model_dict[\"glm\"])\n",
        "\n",
        "    return behav_pred\n",
        "\n",
        "\n",
        "def cpm_wrapper(all_fc_data, all_behav_data, behav, k=10, **cpm_kwargs):\n",
        "\n",
        "    #assert all_fc_data.index.equals(all_behav_data.index), \"Row (subject) indices of FC vcts and behavior don't match!\"\n",
        "\n",
        "    subj_list = all_fc_data.index # get subj_list from df index\n",
        "    \n",
        "    indices = mk_kfold_indices(subj_list, k=k)\n",
        "    \n",
        "    # Initialize df for storing observed and predicted behavior\n",
        "    col_list = []\n",
        "    for tail in [\"pos\", \"neg\", \"glm\"]:\n",
        "        col_list.append(behav + \" predicted (\" + tail + \")\")\n",
        "    col_list.append(behav + \" observed\")\n",
        "    behav_obs_pred = pd.DataFrame(index=subj_list, columns = col_list)\n",
        "    \n",
        "    # Initialize array for storing feature masks\n",
        "    n_edges = all_fc_data.shape[1]\n",
        "    all_masks = {}\n",
        "    all_masks[\"pos\"] = np.zeros((k, n_edges))\n",
        "    all_masks[\"neg\"] = np.zeros((k, n_edges))\n",
        "    \n",
        "    for fold in range(k):\n",
        "        print(\"doing fold {}\".format(fold))\n",
        "        train_subs, test_subs = split_train_test(subj_list, indices, test_fold=fold)\n",
        "        train_vcts, train_behav, test_vcts = get_train_test_data(all_fc_data, train_subs, test_subs, all_behav_data, behav=behav)\n",
        "        mask_dict = select_features(train_vcts, train_behav, **cpm_kwargs)\n",
        "        all_masks[\"pos\"][fold,:] = mask_dict[\"pos\"]\n",
        "        all_masks[\"neg\"][fold,:] = mask_dict[\"neg\"]\n",
        "        model_dict = build_model(train_vcts, mask_dict, train_behav)\n",
        "        behav_pred = apply_model(test_vcts, mask_dict, model_dict)\n",
        "        for tail, predictions in behav_pred.items():\n",
        "            behav_obs_pred.loc[test_subs, behav + \" predicted (\" + tail + \")\"] = predictions\n",
        "            \n",
        "    behav_obs_pred.loc[subj_list, behav + \" observed\"] = all_behav_data[behav]\n",
        "    \n",
        "    return behav_obs_pred, all_masks\n",
        "\n",
        "\n",
        "def plot_consistent_edges(all_masks, tail, thresh = 1., color='gray'):\n",
        "    \n",
        "    edge_frac = (all_masks[tail].sum(axis=0))/(all_masks[tail].shape[0])\n",
        "    print(\"For the {} tail, {} edges were selected in at least {}% of folds\".format(tail, (edge_frac>=thresh).sum(), thresh*100))\n",
        "    edge_frac_square = scipy.spatial.distance.squareform(edge_frac)\n",
        "\n",
        "    node_mask = np.amax(edge_frac_square, axis=0) >= thresh # find nodes that have at least one edge that passes the threshold\n",
        "    node_size = edge_frac_square.sum(axis=0)*node_mask*20 # size nodes based on how many suprathreshold edges they have\n",
        "\n",
        "    plot_connectome(adjacency_matrix=edge_frac_square, edge_threshold=thresh,\n",
        "                    node_color = color,\n",
        "                    node_coords=coordinates, node_size=node_size,\n",
        "                    display_mode= 'lzry',\n",
        "                    edge_kwargs={\"linewidth\": 1, 'color': color})\n",
        "    \n",
        "#@title Defining Function for Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8sukWg-ngjj6",
        "outputId": "23a15d04-6462-4740-db1d-5de2ce36e2c2"
      },
      "outputs": [
        {
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              "\n",
              "              6         7         8         9     ...      4940      4941  \\\n",
              "sub-9002 -0.197855  0.006655  0.354120  0.297336  ...  0.700363  0.370715   \n",
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              "sub-9004 -0.126600  0.315456  0.053672  0.076178  ...  0.417258 -0.138106   \n",
              "sub-9005  0.049467  0.083227  0.210047  0.024197  ...  0.713522  0.656724   \n",
              "sub-9008 -0.224479  0.300339 -0.149283 -0.069398  ...  0.516570  0.561066   \n",
              "\n",
              "              4942      4943      4944      4945      4946      4947  \\\n",
              "sub-9002  0.671309  0.684687  0.500080  0.403040  0.668268  0.393627   \n",
              "sub-9003  0.539110  0.770461 -0.111735  0.148762  0.517436  0.456919   \n",
              "sub-9004  0.412602  0.703702  0.241610  0.040340  0.478144 -0.022711   \n",
              "sub-9005  0.523521  0.587850  0.522298  0.241902  0.573380  0.636687   \n",
              "sub-9008  0.523614  0.574633  0.508145  0.187284  0.492293  0.482336   \n",
              "\n",
              "              4948      4949  \n",
              "sub-9002  0.724631  0.680627  \n",
              "sub-9003  0.311614  0.659632  \n",
              "sub-9004 -0.120179  0.523175  \n",
              "sub-9005  0.739202  0.695635  \n",
              "sub-9008  0.560916  0.658201  \n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09b0a69b-ebb1-400a-aecb-7e32673fab02');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# @title #Variable that we want to regress\n",
        "\n",
        "# Select Subject\n",
        "var2regress = \"continous\"  # @param ['dichotomy', 'continous']\n",
        "\n",
        "\n",
        "if var2regress == 'continous':\n",
        "  fc = np.concatenate((sleepy, not_sleepy))\n",
        "\n",
        "  all_fc_data = np.zeros(((len(subj2use)), int(( len(atlas['ROI Name']) * (len(atlas['ROI Name']) - 1) ) / 2) )) #'n_nodes*(n_nodes-1)/2 \n",
        "\n",
        "\n",
        "  prova = sym_matrix_to_vec(fc[0], discard_diagonal=True)\n",
        "  for subject in range(fc.shape[0]):\n",
        "      all_fc_data[subject,:] = fc[subject][np.triu_indices_from(fc[subject], k=1)]\n",
        "\n",
        "  all_fc_data = pd.DataFrame(data=all_fc_data, index=subj2use)\n",
        "\n",
        "\n",
        "elif var2regress == 'dichotomy':\n",
        "\n",
        "  miss_value_idx = np.where(subj_info_filtered['SleepDeprivedSession1'].astype(str) == 'nan')[0]\n",
        "  subj_info_filtered_removed = subj_info_filtered.drop(['sub-9100'], axis=0)\n",
        "\n",
        "\n",
        "  for xx in miss_value_idx:\n",
        "    subj_info_filtered_removed = subj_info_filtered_removed.drop([subj2use[xx]], axis=0)\n",
        "\n",
        "\n",
        "  fc= np.zeros((len(subj2use), 100, 100))\n",
        "  for xx in range(len(subj2use)):\n",
        "    sub = subj2use[xx]\n",
        "    subj_idx = [i for i, s in enumerate(all_ptsrs_fs) if sub in s][0]\n",
        "    ts2use = nib.load(all_ptsrs_fs[subj_idx]).get_data()\n",
        "    fc[xx,:,:] = np.array(pd.DataFrame(ts2use).corr())\n",
        "\n",
        "  all_fc_data = np.zeros(((len(subj2use)), int(( len(atlas['ROI Name']) * (len(atlas['ROI Name']) - 1) ) / 2) )) #'n_nodes*(n_nodes-1)/2 \n",
        "\n",
        "\n",
        "  prova = sym_matrix_to_vec(fc[0], discard_diagonal=True)\n",
        "  for subject in range(fc.shape[0]):\n",
        "      all_fc_data[subject,:] = fc[subject][np.triu_indices_from(fc[subject], k=1)]\n",
        "\n",
        "  all_fc_data = pd.DataFrame(data=all_fc_data, index=subj2use)\n",
        "\n",
        "  for xx in miss_value_idx:\n",
        "    all_fc_data = all_fc_data.drop([subj2use[xx]], axis=0)\n",
        "\n",
        "\n",
        "\n",
        "all_fc_data.head() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWFHmU03gjor",
        "outputId": "f6973698-3f14-4155-a951-0fcc1165e8ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "doing fold 0\n",
            "doing fold 1\n",
            "doing fold 2\n",
            "doing fold 3\n",
            "doing fold 4\n",
            "doing fold 5\n",
            "doing fold 6\n",
            "doing fold 7\n",
            "doing fold 8\n",
            "doing fold 9\n"
          ]
        }
      ],
      "source": [
        "if var2regress == 'continous':\n",
        "  cpm_kwargs = {'r_thresh': 0.35, 'corr_type': 'pearson'} # these are the defaults, but it's still good to be explicit\n",
        "\n",
        "  ses = 'ses-1'\n",
        "  all_behav_data = KSS_df\n",
        "  all_behav_data.index = subj2use\n",
        "\n",
        "elif var2regress == 'dichotomy':\n",
        "\n",
        "  all_behav_data = {'SleepDeprivedSession1':subj_info_filtered_removed['SleepDeprivedSession1']}\n",
        "\n",
        "  all_behav_data = pd.DataFrame(all_behav_data)\n",
        "  all_behav_data = all_behav_data.replace('Surely', int(5))\n",
        "  all_behav_data = all_behav_data.replace('Likely', int(4))\n",
        "  all_behav_data = all_behav_data.replace('Equivocal', int(3))\n",
        "  all_behav_data = all_behav_data.replace('SurelyNot', int(1))\n",
        "  all_behav_data = all_behav_data.replace('LikelyNot', int(2))\n",
        "\n",
        "  cpm_kwargs = {'r_thresh': 0.4, 'corr_type': 'pearson'} # these are the defaults, but it's still good to be explicit\n",
        "\n",
        "\n",
        "  ses = 'SleepDeprivedSession1'\n",
        "  all_behav_data.index = subj_info_filtered_removed.index\n",
        "\n",
        "\n",
        "behav_obs_pred, all_masks = cpm_wrapper(all_fc_data, all_behav_data, behav=ses, **cpm_kwargs)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MNEcMu3RtFVE",
        "outputId": "c8bccb8c-e2d8-45a3-ed6c-85da6931d9b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ses-1 predicted (pos) ses-1 predicted (neg) ses-1 predicted (glm)  \\\n",
              "sub-9002              5.267318              5.408073              5.432922   \n",
              "sub-9003              4.649221              5.677953              5.293982   \n",
              "sub-9004              3.865195              4.263136              3.894222   \n",
              "sub-9005              5.514262              4.997549              5.247666   \n",
              "sub-9008              4.815304              4.777224               4.76608   \n",
              "\n",
              "         ses-1 observed  \n",
              "sub-9002            3.0  \n",
              "sub-9003            4.0  \n",
              "sub-9004       5.833333  \n",
              "sub-9005       5.166667  \n",
              "sub-9008       4.666667  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ses-1 predicted (pos)</th>\n",
              "      <th>ses-1 predicted (neg)</th>\n",
              "      <th>ses-1 predicted (glm)</th>\n",
              "      <th>ses-1 observed</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sub-9002</th>\n",
              "      <td>5.267318</td>\n",
              "      <td>5.408073</td>\n",
              "      <td>5.432922</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>sub-9003</th>\n",
              "      <td>4.649221</td>\n",
              "      <td>5.677953</td>\n",
              "      <td>5.293982</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
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              "      <th>sub-9004</th>\n",
              "      <td>3.865195</td>\n",
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              "      <td>3.894222</td>\n",
              "      <td>5.833333</td>\n",
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              "      <td>5.166667</td>\n",
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              "      <td>4.815304</td>\n",
              "      <td>4.777224</td>\n",
              "      <td>4.76608</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0bbf138f-503d-4273-ac13-3a724b7289bb');\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",
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          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "behav_obs_pred.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 279
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        "id": "VzdjDpFPt3mc",
        "outputId": "115de076-f268-4fe0-f9fc-ffbd599e34df"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x = behav_obs_pred.filter(regex=(\"obs\")).astype(float)\n",
        "y = behav_obs_pred.filter(regex=('glm')).astype(float)\n",
        "\n",
        "g = sns.regplot(x=x.T.squeeze(), y=y.T.squeeze(), color='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "uzDkIMOceXLH",
        "outputId": "bf531af6-cfa3-4583-b6e5-5e34c101e9a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the pos tail, 4 edges were selected in at least 80.0% of folds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 633.6x187.2 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_consistent_edges(all_masks, \"pos\", thresh = 0.8, color = 'red')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Suhayb's EDA"
      ],
      "metadata": {
        "id": "DbR7ay__CAl-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KSS_df_copy = KSS_df.copy()\n",
        "#subj_info_filtered = subj_info_filtered.drop('sub-9100')\n",
        "project_3_df = subj_info_filtered.copy()\n",
        "KSS_df_copy.index = project_3_df.index\n",
        "project_3_df = project_3_df.join(KSS_df_copy)\n",
        "project_3_df.rename(columns={'ses-1':'sleepy_ses_1', 'ses-2':'sleepy_ses_2'}, inplace = True)"
      ],
      "metadata": {
        "id": "IA4BtaBQZ6be"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "project_3_df['BMI1'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['BMI1']]\n",
        "project_3_df['KSQ_SleepQualityIndex'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['KSQ_SleepQualityIndex']]\n",
        "project_3_df['KSQ_WakeupSymptomIndex'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['KSQ_WakeupSymptomIndex']]\n",
        "project_3_df['KSQ_SnoringSymptomIndex'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['KSQ_SnoringSymptomIndex']]\n",
        "project_3_df['KSQ_SleepSymptomIndex'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['KSQ_SleepSymptomIndex']]\n",
        "project_3_df['IRI_EC'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['IRI_EC']]\n",
        "project_3_df['IRI_PT'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['IRI_PT']]\n",
        "project_3_df['IRI_PD'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['IRI_PD']]\n",
        "project_3_df['IRI_F'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['IRI_F']]\n",
        "project_3_df['ECS'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['ECS']]\n",
        "project_3_df['BMI2'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['BMI2'].astype(str)]\n",
        "project_3_df['BIS11_Total'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['BIS11_Total'].astype(str)]\n",
        "project_3_df['BIS11_Motor'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['BIS11_Motor'].astype(str)]\n",
        "project_3_df['BIS11_Nonplanning'] = [float(x.replace(\",\", \".\")) for x in subj_info_filtered['BIS11_Nonplanning'].astype(str)]\n",
        "\n",
        "project_3_df = project_3_df.drop(['EducationLevel'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['GeneralImpression1'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['GeneralImpression2'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['StrategyARROWS'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['Contraceptives'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['OtherDrugs'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_ConcernDisturbedSleep'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_Panic'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_NapFrequency'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_Worry'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_CompensatoryMorningSleep'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_ConcernFatigue'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_EnoughSleep'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_HealthProblem'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['KSQ_OverallSleepQuality'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['TestTimeType_1'], axis = 1)\n",
        "project_3_df = project_3_df.drop(['TestTimeType_2'], axis = 1)\n"
      ],
      "metadata": {
        "id": "K2FrVD5f2keA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = subj_info_filtered.copy()\n",
        "\n",
        "fig, ax = plt.subplots(ncols=3,nrows=3, figsize=(15,10))\n",
        "\n",
        "a = ax[0][0]\n",
        "project_3_df['KSQ_SleepQualityIndex'].plot(kind='kde', ax = a)\n",
        "a.set_title('Sleep Quality Index')\n",
        "\n",
        "b = ax[0][1]\n",
        "project_3_df['HADS_Anxiety'].plot(kind='kde', ax = b)\n",
        "b.set_title('Hamilton Anxiety Score')\n",
        "\n",
        "c = ax[0][2]\n",
        "project_3_df['HADS_Depression'].plot(kind='kde', ax = c)\n",
        "c.set_title('Hamilton Depression Score')\n",
        "\n",
        "d = ax[1][0]\n",
        "project_3_df['KSQ_SnoringSymptomIndex'].plot(kind='kde', ax = d)\n",
        "d.set_title('Snoring Index')\n",
        "\n",
        "e = ax[1][1]\n",
        "project_3_df['KSQ_WakeupSymptomIndex'].plot(kind='kde', ax = e)\n",
        "e.set_title('Wake-Up Index')\n",
        "\n",
        "f = ax[1][2]\n",
        "project_3_df['KSQ_SleepSymptomIndex'].plot(kind='kde', ax = f)\n",
        "f.set_title('Sleep Index')\n",
        "\n",
        "g = ax[2][0]\n",
        "project_3_df['BMI1'].plot(kind='kde', ax = g)\n",
        "g.set_title('BMI 1')\n",
        "\n",
        "h = ax[2][1]\n",
        "#project_3_df['Sex'].plot(kind='hist', ax = h)\n",
        "h.set_title('Gender Distribution')\n",
        "\n",
        "i = ax[2][2]\n",
        "#project_3_df['AgeGroup'].plot(kind='hist', ax = i)\n",
        "i.set_title('Age Group Distribution')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "qzWu0Qgj6zfw",
        "outputId": "ae1cb6e6-15e1-4795-ddc2-3f31ab6b1860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(subj_info_filtered['Sex'])\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "z2Jwb52nCDh7",
        "outputId": "c1dcb211-ff0b-4995-b06c-2e510f0ff846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(subj_info_filtered['AgeGroup'])\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "2vUZPyRPCDf2",
        "outputId": "9ee35995-575b-43d1-98f6-eab40aee1444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#recode the sleepyness score from sessions to conditions\n",
        "df3 = project_3_df.copy()\n",
        "df3[\"sleepy_dep\"] = \"\"\n",
        "df3[\"sleepy_nodep\"] = \"\""
      ],
      "metadata": {
        "id": "IiCnRrk05XVw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.loc[df3.Sl_cond == 1, 'sleepy_dep'] = df3['sleepy_ses_1'].copy()\n",
        "df3.loc[df3.Sl_cond == 2, 'sleepy_nodep'] = df3['sleepy_ses_1'].copy()\n",
        "df3.loc[df3.Sl_cond == 2, 'sleepy_dep'] = df3['sleepy_ses_2'].copy()\n",
        "df3.loc[df3.Sl_cond == 1, 'sleepy_nodep'] = df3['sleepy_ses_2'].copy()"
      ],
      "metadata": {
        "id": "_A259zTg5XEU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**t test**"
      ],
      "metadata": {
        "id": "woZHil2BjOcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "from nilearn.glm.second_level import SecondLevelModel\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "eav_RGgH5eSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#paired sample t test between conditions\n",
        "stats.ttest_rel(df3['sleepy_dep'], df3['sleepy_nodep'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tz2xVgLT5eAc",
        "outputId": "f6d319b3-cc57-4fa9-af84-e5d3698dd704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_relResult(statistic=9.060908864390798, pvalue=2.354432596567333e-12)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inde sample t test between age groups\n",
        "young_sleepy = df3[df3['AgeGroup']=='Young']['sleepy_dep']\n",
        "old_sleepy = df3[df3['AgeGroup']=='Old']['sleepy_dep']\n",
        "\n",
        "#test assumption\n",
        "stats.bartlett(young_sleepy, old_sleepy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGSC80H25d0A",
        "outputId": "934c8308-6aa8-47c8-9fa8-3efcc595f5ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BartlettResult(statistic=0.0014828531249729926, pvalue=0.9692827870877525)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(young_sleepy, old_sleepy, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBpwScAE5myL",
        "outputId": "2a47b6ef-b0e5-4004-9960-665735d24132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=3.843450312696103, pvalue=0.00033204833372784277)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inde sample t test between gender\n",
        "female_sleepy = df3[df3['Sex']=='Female']['sleepy_dep']\n",
        "male_sleepy = df3[df3['Sex']=='Male']['sleepy_dep']\n",
        "\n",
        "#test assumption\n",
        "stats.bartlett(female_sleepy, male_sleepy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLwtxlhw5mvz",
        "outputId": "74f0ad20-e65c-4bc7-ecec-47bd8d86a5a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BartlettResult(statistic=0.018003213577317292, pvalue=0.8932633604866065)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(female_sleepy, male_sleepy, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ypi14sOq5mtM",
        "outputId": "17f255db-9daf-4895-98ac-cc789a28e876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=0.34143216872373633, pvalue=0.734154159196901)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#paired sample t test on BMI btw sessions\n",
        "stats.ttest_rel(df3['BMI1'], df3['BMI2'], nan_policy = 'omit')"
      ],
      "metadata": {
        "id": "hMReJyzDh7O_",
        "outputId": "84ec9ec6-4682-49b3-ee19-5e86d427f7a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_relResult(statistic=1.0797755487119585, pvalue=0.2852250453364124)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Since there is no diff in BMI btw sessions, calculate the mean between BMI sessions\n",
        "bmi_mean = []\n",
        "\n",
        "bmi_mean.append(df3['BMI1'])\n",
        "bmi_mean.append(df3['BMI2'])\n",
        "\n",
        "bmi_mean = np.mean(np.array(bmi_mean), axis=0)\n",
        "\n",
        "df3['BMI_mean'] = bmi_mean\n",
        "df3['BMI_mean'].fillna(24.056935, inplace = True)"
      ],
      "metadata": {
        "id": "2vGC9iLWhEMd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Multiple linear regression between demographic variables and Sleepyness**"
      ],
      "metadata": {
        "id": "GEM6uiFajDwE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Multiple Linear Regression:\n",
        "#Input variable x:\n",
        "x_var = df3[['AgeGroup', 'Sex','HADS_Anxiety', 'HADS_Depression','ISI','KSQ_SleepQualityIndex', 'KSQ_WakeupSymptomIndex','KSQ_SnoringSymptomIndex','KSQ_SleepSymptomIndex']]\n",
        "#get dummy variables for categorical variables\n",
        "x_var = pd.get_dummies(data=x_var, drop_first=True)\n",
        "x = np.array(x_var)\n",
        "#output variable y:\n",
        "y = np.array(df3['sleepy_dep'])\n",
        "#run linear regression model\n",
        "LM_model = LinearRegression().fit(x, y)\n",
        "LM_R2 = LM_model.score(x, y)\n",
        "LM_intercep = LM_model.intercept_\n",
        "LM_coef = LM_model.coef_"
      ],
      "metadata": {
        "id": "1DyvtGK8iHAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LM_coef_1 = LM_coef.tolist()\n",
        "LM_coef_dp = LM_coef_1.insert(0, None)"
      ],
      "metadata": {
        "id": "JxCuF1c3tyXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#adjusted R2\n",
        "def adjustedR2(x,y, LM_model):\n",
        "  r2 = LM_model.score(x,y)\n",
        "  n = x.shape[0]\n",
        "  p = x.shape[1]\n",
        "  adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
        "  return adjusted_r2"
      ],
      "metadata": {
        "id": "UyAMadYVjtkY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculate p value\n",
        "params = np.append(LM_model.intercept_,LM_model.coef_)\n",
        "predictions = LM_model.predict(x)\n",
        "new_x = np.append(np.ones((len(x),1)), x, axis=1)\n",
        "M_S_E = (sum((y-predictions)**2))/(len(new_x)-len(new_x[0]))\n",
        "v_b = M_S_E*(np.linalg.inv(np.dot(new_x.T,new_x)).diagonal())\n",
        "s_b = np.sqrt(v_b)\n",
        "t_b = params/ s_b\n",
        "p_val =[2*(1-stats.t.cdf(np.abs(i),(len(new_x)-len(new_x[0])))) for i in t_b]\n",
        "p_val = np.round(p_val,3)"
      ],
      "metadata": {
        "id": "sLtQGE4mzTie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print out a summary table\n",
        "var_list = list(x_var.columns)\n",
        "var_list.insert(0, 'Intercept')\n",
        "\n",
        "column_values = ['Covariates']\n",
        "LM_summary = pd.DataFrame(data = var_list,\n",
        "                          columns = column_values)\n",
        "LM_summary['Coefficient'] = LM_coef_1\n",
        "LM_summary['p value'] = p_val\n",
        "print ('the adjusted R2 =', adjustedR2(x,y,LM_model))\n",
        "LM_summary"
      ],
      "metadata": {
        "id": "ptvtV1ZBzqO8",
        "outputId": "8924d264-6747-40c1-a0fe-f947225c5158",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the adjusted R2 = 0.29123098450013873\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Covariates  Coefficient  p value\n",
              "0                Intercept          NaN    0.085\n",
              "1             HADS_Anxiety    -0.057989    0.653\n",
              "2          HADS_Depression     0.058507    0.803\n",
              "3                      ISI     0.116078    0.374\n",
              "4    KSQ_SleepQualityIndex     1.152061    0.043\n",
              "5   KSQ_WakeupSymptomIndex    -0.610000    0.112\n",
              "6  KSQ_SnoringSymptomIndex     0.048083    0.920\n",
              "7    KSQ_SleepSymptomIndex    -1.089111    0.027\n",
              "8           AgeGroup_Young     0.647366    0.293\n",
              "9                 Sex_Male    -0.190975    0.634"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covariates</th>\n",
              "      <th>Coefficient</th>\n",
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              "      <td>-0.057989</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HADS_Depression</td>\n",
              "      <td>0.058507</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ISI</td>\n",
              "      <td>0.116078</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1.152061</td>\n",
              "      <td>0.043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>KSQ_WakeupSymptomIndex</td>\n",
              "      <td>-0.610000</td>\n",
              "      <td>0.112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>0.048083</td>\n",
              "      <td>0.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>0.027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>AgeGroup_Young</td>\n",
              "      <td>0.647366</td>\n",
              "      <td>0.293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sex_Male</td>\n",
              "      <td>-0.190975</td>\n",
              "      <td>0.634</td>\n",
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predict y by the model\n",
        "y_pred = LM_model.predict(x)\n",
        "plt.scatter (y, y_pred);\n",
        "plt.xlabel('Actual');\n",
        "plt.ylabel('Predicted')\n",
        "#sns.regplot(x=y,y=y_pred,ci=None, color = 'red')"
      ],
      "metadata": {
        "id": "DL5PgU54j-_1",
        "outputId": "74e5a50d-3850-424c-8759-5ee37e8d3fe1",
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          "height": 301
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model predict table\n",
        "pred_df=pd.DataFrame({'Actual Value':y,'Predicted Value':y_pred,'Difference':y-y_pred})\n",
        "pred_df"
      ],
      "metadata": {
        "id": "M0sFNc2_kI3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df3.to_csv('cleaned_csv_updated.csv')\n",
        "#files.download('cleaned_csv_updated.csv')"
      ],
      "metadata": {
        "id": "sFbuCKTP5mq3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nilearn.glm.second_level import SecondLevelModel\n"
      ],
      "metadata": {
        "id": "GU5oYFVyCDRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "second_level_model = SecondLevelModel().fit(cmap_filenames, design_matrix= design_matrix)"
      ],
      "metadata": {
        "id": "SmgmmswuCDPK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}