FC_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import mne\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 files missing from root.txt in C:\\Users\\work\\mne_data\\MNE-fsaverage-data\n",
      "0 files missing from bem.txt in C:\\Users\\work\\mne_data\\MNE-fsaverage-data\\fsaverage\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# Download fsaverage files\n",
    "fs_dir = mne.datasets.fetch_fsaverage(verbose=True)\n",
    "subjects_dir = os.path.dirname(fs_dir)\n",
    "\n",
    "# The files live in:\n",
    "subject = 'fsaverage'\n",
    "trans = 'fsaverage'  # MNE has a built-in fsaverage transformation\n",
    "src = os.path.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')\n",
    "bem = os.path.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = mne.read_labels_from_annot('fsaverage', parc='aparc',\n",
    "                                            subjects_dir=subjects_dir, verbose=False)\n",
    "labels.pop(-1) # remove the unknown label\n",
    "label_names = [label.name for label in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lobe_map = {\"Frontal\":{\n",
    "                \"frontalpole\":\"FP\",\n",
    "                \"medialorbitofrontal\":\"MO\",\n",
    "                \"lateralorbitofrontal\":\"LO\",\n",
    "                \"paracentral\":\"ParaC\",\n",
    "                \"parsopercularis\":\"POp\",\n",
    "                \"parsorbitalis\":\"POr\",\n",
    "                \"parstriangularis\":\"PT\",\n",
    "                \"precentral\": \"PreC\",\n",
    "                \"rostralanteriorcingulate\":\"RAC\",\n",
    "                \"rostralmiddlefrontal\":\"RMF\",\n",
    "                \"superiorfrontal\":\"SF\",\n",
    "                \"caudalanteriorcingulate\":\"CACC\",\n",
    "                \"caudalmiddlefrontal\":\"CMF\",\n",
    "            },\n",
    "            \"Parietal\":{\n",
    "                \"superiorparietal\":\"SP\",\n",
    "                \"inferiorparietal\":\"IP\",\n",
    "                \"isthmuscingulate\":\"IC\",\n",
    "                \"postcentral\":\"PG\",\n",
    "                \"posteriorcingulate\":\"PCC\",\n",
    "                \"precuneus\":\"PCUN\",\n",
    "                \"supramarginal\":\"SG\",\n",
    "            },\n",
    "            \"Temporal\":{\n",
    "                \"bankssts\":\"BSTS\",\n",
    "                \"entorhinal\":\"ENT\",\n",
    "                \"fusiform\":\"FUS\",\n",
    "                \"middletemporal\":\"MT\",\n",
    "                \"parahippocampal\":\"PHG\",\n",
    "                \"superiortemporal\": \"STG\",\n",
    "                \"temporalpole\":\"TP\",\n",
    "                \"transversetemporal\":\"TTG\",\n",
    "                \"inferiortemporal\":\"ITG\",\n",
    "            },\n",
    "            \"Occipital\":{\n",
    "                \"cuneus\":\"CUN\",\n",
    "                \"lateraloccipital\":\"LOC\",\n",
    "                \"lingual\":\"LNG\",\n",
    "                \"pericalcarine\":\"PCAL\",\n",
    "            },\n",
    "            \"Insula\":{\n",
    "                \"insula\":\"INS\"\n",
    "            }\n",
    "}\n",
    "lobe_map_color = {\"Frontal\": (1,0,0),\n",
    "                  \"Insula\": (0,1,0),\n",
    "                \"Temporal\": (0,0,1),\n",
    "                \"Parietal\": (1, 1,0), \n",
    "                 \"Occipital\": (0,1,1),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 files missing from root.txt in C:\\Users\\work\\mne_data\\MNE-fsaverage-data\n",
      "0 files missing from bem.txt in C:\\Users\\work\\mne_data\\MNE-fsaverage-data\\fsaverage\n"
     ]
    }
   ],
   "source": [
    "# params\n",
    "fs_dir = mne.datasets.fetch_fsaverage(verbose=True)\n",
    "subjects_dir = os.path.dirname(fs_dir)\n",
    "\n",
    "labels = mne.read_labels_from_annot('fsaverage', parc='aparc',\n",
    "                                            subjects_dir=subjects_dir, verbose=False)\n",
    "labels.pop(-1) # remove the unknown label\n",
    "label_names = [label.name for label in labels]\n",
    "\n",
    "# all functions\n",
    "# example 1_al_ciplv_theta_1.npy\n",
    "def parse_filename(filename):\n",
    "    s = filename.split(\"_\")\n",
    "    pId = s[0]\n",
    "    label = s[1]\n",
    "    method = s[2]\n",
    "    freq = s[-1].split(\".\")[0]\n",
    "    \n",
    "    return {\"pId\": pId, \"label\": label, \"method\": method, \"freq\": freq}\n",
    "\n",
    "def read_file(filename): \n",
    "    data = np.load(filename)\n",
    "    return data\n",
    "\n",
    "def read_file2(filename): # use for lobe-level\n",
    "    data = np.load(filename)\n",
    "    data = data + data.T - np.diag(data.diagonal())\n",
    "    df_corr = pd.DataFrame(data, columns = label_names, index = label_names)\n",
    "    df_pair = df_corr.stack().reset_index()\n",
    "    df_pair.columns = [\"source\", \"target\", \"value\"]\n",
    "    return df_pair\n",
    "\n",
    "# get all the files in the folder output/\n",
    "def get_files(folder, filter = None):\n",
    "    '''\n",
    "    filter is a dictionary, has 2 keys: method and freq to filter the files\n",
    "    '''\n",
    "    files = glob.glob(os.path.join(folder, \"*.npy\"))\n",
    "    ret_files = []\n",
    "    if filter:\n",
    "        for f in files:\n",
    "            # get the filename\n",
    "            filename = os.path.basename(f)\n",
    "            f_info = parse_filename(filename)\n",
    "            # remove epoch 2 if no overlap\n",
    "            # if f_info[\"epoch\"] == \"2\":\n",
    "            #     continue\n",
    "            if (f_info[\"method\"] == filter[\"method\"]) and (f_info[\"freq\"] == filter[\"freq\"]):\n",
    "                ret_files.append(f)\n",
    "    else:\n",
    "        ret_files = files\n",
    "\n",
    "    return ret_files\n",
    "\n",
    "def feature_extraction(data):\n",
    "    \"\"\"\n",
    "    get lower part of diagonal matrix\n",
    "    \"\"\"\n",
    "    data_lower = data[np.tril_indices(data.shape[0], k=-1)]\n",
    "    return data_lower\n",
    "\n",
    "def get_lobe(name):\n",
    "    # get hemisphere and label\n",
    "    label = name.split(\"-\")[0]\n",
    "    hemisphere = name.split(\"-\")[1]\n",
    "\n",
    "    for lobe in lobe_map:\n",
    "        if label in lobe_map[lobe]:\n",
    "            return lobe, hemisphere\n",
    "    return None\n",
    "\n",
    "def get_lobe2(name):\n",
    "    # get hemisphere and label\n",
    "    label = name.split(\"-\")[0]\n",
    "    hemisphere = name.split(\"-\")[1]\n",
    "\n",
    "    for lobe in lobe_map:\n",
    "        if label in lobe_map[lobe]:\n",
    "            return lobe\n",
    "    return None\n",
    "\n",
    "def feature_extraction_lobe_level(data):\n",
    "    \"\"\"\n",
    "    data: pandas frame 3 columns: source, target, value. outputs from read_file2()\n",
    "    \"\"\"\n",
    "\n",
    "    # get the lobe level using the lobe_map\n",
    "    data[\"source_lobe\"] = data[\"source\"].apply(lambda x: get_lobe(x))\n",
    "    data[\"target_lobe\"] = data[\"target\"].apply(lambda x: get_lobe(x))\n",
    "\n",
    "    # average the value for the same lobe\n",
    "    data = data.groupby([\"source_lobe\", \"target_lobe\"]).mean().reset_index()\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lh_labels = ['lateraloccipital-lh','pericalcarine-lh','cuneus-lh','lingual-lh',\n",
    "            'inferiorparietal-lh','superiorparietal-lh','precuneus-lh','isthmuscingulate-lh','supramarginal-lh','postcentral-lh','posteriorcingulate-lh',\n",
    "            'bankssts-lh','fusiform-lh','inferiortemporal-lh','parahippocampal-lh','middletemporal-lh','transversetemporal-lh','superiortemporal-lh','entorhinal-lh','temporalpole-lh',\n",
    "            'insula-lh',\n",
    "            'paracentral-lh','precentral-lh','caudalmiddlefrontal-lh','parsopercularis-lh','caudalanteriorcingulate-lh','superiorfrontal-lh','lateralorbitofrontal-lh','parstriangularis-lh','rostralanteriorcingulate-lh','medialorbitofrontal-lh','parsorbitalis-lh','rostralmiddlefrontal-lh','frontalpole-lh']\n",
    "rh_labels = [label[:-2] + 'rh' for label in lh_labels]\n",
    "\n",
    "new_order = []\n",
    "new_order.extend(lh_labels[::-1])  # reverse the order\n",
    "new_order.extend(rh_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs = ['delta', 'theta', 'alpha', 'beta', 'gamma']\n",
    "method = 'pli'\n",
    "output_dir_prefix = \"output/no_split\"\n",
    "# condition = \"al\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "al_sum = np.zeros((len(labels), len(labels)))\n",
    "# get all the files\n",
    "files = glob.glob(f\"{output_dir_prefix}/*al*{method}*{freqs[0]}*.npy\")\n",
    "# print(files)\n",
    "for file in files:\n",
    "    al_sum += np.load(file)\n",
    "\n",
    "al_sum = al_sum / len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frontalpole-lh                 0\n",
       "rostralmiddlefrontal-lh        2\n",
       "parsorbitalis-lh               1\n",
       "medialorbitofrontal-lh         4\n",
       "rostralanteriorcingulate-lh    3\n",
       "                              ..\n",
       "rostralanteriorcingulate-rh    3\n",
       "medialorbitofrontal-rh         1\n",
       "parsorbitalis-rh               5\n",
       "rostralmiddlefrontal-rh        5\n",
       "frontalpole-rh                 0\n",
       "Length: 68, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.load(\"connection_alter_beta.npy\")\n",
    "df_corr = pd.DataFrame(data, columns = label_names, index = label_names)\n",
    "df_order = df_corr.reindex(new_order, axis=0).reindex(new_order, axis=1)\n",
    "(df_order>0).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frontalpole-lh Frontal\n"
     ]
    }
   ],
   "source": [
    "c_increase = (df_order>0).sum(axis=1)\n",
    "roi = c_increase.index[0]\n",
    "for lobe in lobe_map:\n",
    "    if roi.split(\"-\")[0] in lobe_map[lobe]:\n",
    "        print(roi,lobe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delta\n",
      "increase {'Frontal': 49, 'Parietal': 42, 'Temporal': 30, 'Occipital': 11, 'Insula': 2} 134\n",
      "decrease {'Frontal': 71, 'Parietal': 32, 'Temporal': 40, 'Occipital': 14, 'Insula': 5} 162\n",
      "increase {'Frontal': 0.366, 'Parietal': 0.313, 'Temporal': 0.224, 'Occipital': 0.082, 'Insula': 0.015}\n",
      "decrease {'Frontal': 0.438, 'Parietal': 0.198, 'Temporal': 0.247, 'Occipital': 0.086, 'Insula': 0.031}\n",
      "theta\n",
      "increase {'Frontal': 57, 'Parietal': 31, 'Temporal': 33, 'Occipital': 17, 'Insula': 2} 140\n",
      "decrease {'Frontal': 48, 'Parietal': 54, 'Temporal': 35, 'Occipital': 19, 'Insula': 0} 156\n",
      "increase {'Frontal': 0.407, 'Parietal': 0.221, 'Temporal': 0.236, 'Occipital': 0.121, 'Insula': 0.014}\n",
      "decrease {'Frontal': 0.308, 'Parietal': 0.346, 'Temporal': 0.224, 'Occipital': 0.122, 'Insula': 0.0}\n",
      "alpha\n",
      "increase {'Frontal': 45, 'Parietal': 30, 'Temporal': 41, 'Occipital': 13, 'Insula': 3} 132\n",
      "decrease {'Frontal': 59, 'Parietal': 56, 'Temporal': 36, 'Occipital': 11, 'Insula': 2} 164\n",
      "increase {'Frontal': 0.341, 'Parietal': 0.227, 'Temporal': 0.311, 'Occipital': 0.098, 'Insula': 0.023}\n",
      "decrease {'Frontal': 0.36, 'Parietal': 0.341, 'Temporal': 0.22, 'Occipital': 0.067, 'Insula': 0.012}\n",
      "beta\n",
      "increase {'Frontal': 51, 'Parietal': 69, 'Temporal': 27, 'Occipital': 14, 'Insula': 3} 164\n",
      "decrease {'Frontal': 44, 'Parietal': 30, 'Temporal': 39, 'Occipital': 16, 'Insula': 3} 132\n",
      "increase {'Frontal': 0.311, 'Parietal': 0.421, 'Temporal': 0.165, 'Occipital': 0.085, 'Insula': 0.018}\n",
      "decrease {'Frontal': 0.333, 'Parietal': 0.227, 'Temporal': 0.295, 'Occipital': 0.121, 'Insula': 0.023}\n",
      "gamma\n",
      "increase {'Frontal': 55, 'Parietal': 46, 'Temporal': 58, 'Occipital': 22, 'Insula': 2} 183\n",
      "decrease {'Frontal': 56, 'Parietal': 14, 'Temporal': 28, 'Occipital': 13, 'Insula': 2} 113\n",
      "increase {'Frontal': 0.301, 'Parietal': 0.251, 'Temporal': 0.317, 'Occipital': 0.12, 'Insula': 0.011}\n",
      "decrease {'Frontal': 0.496, 'Parietal': 0.124, 'Temporal': 0.248, 'Occipital': 0.115, 'Insula': 0.018}\n"
     ]
    }
   ],
   "source": [
    "d_change = {}\n",
    "for i, f in enumerate(freqs):\n",
    "    data = np.load(f\"connection_alter_{f}.npy\")\n",
    "    df_corr = pd.DataFrame(data, columns = label_names, index = label_names)\n",
    "    df_order = df_corr.reindex(new_order, axis=0).reindex(new_order, axis=1)\n",
    "    d_count_increase = {k:0 for k in lobe_map.keys()}\n",
    "    d_count_decrease = {k:0 for k in lobe_map.keys()}\n",
    "\n",
    "    c_increase = (df_order>0).sum(axis=1)\n",
    "    c_decrease = (df_order<0).sum(axis=1)\n",
    "    for k in range(len(c_increase)):\n",
    "        roi = c_increase.index[k]\n",
    "        for lobe in lobe_map:\n",
    "            if roi.split(\"-\")[0] in list(lobe_map[lobe].keys()):\n",
    "                d_count_increase[lobe] += c_increase[k]\n",
    "                break\n",
    "\n",
    "    for k in range(len(c_decrease)):\n",
    "        roi = c_decrease.index[k]\n",
    "        for lobe in lobe_map:\n",
    "            if roi.split(\"-\")[0] in list(lobe_map[lobe].keys()):\n",
    "                d_count_decrease[lobe] += c_decrease[k]\n",
    "                break\n",
    "    print(f)\n",
    "    print(\"increase\", d_count_increase, sum(d_count_increase.values()))\n",
    "    print(\"decrease\", d_count_decrease, sum(d_count_decrease.values()))\n",
    "    d_change[f] = [d_count_increase, d_count_decrease]\n",
    "\n",
    "    #convert to percentage\n",
    "    d_count_increase = {k: round(v/sum(c_increase), 3) for k, v in d_count_increase.items()}\n",
    "    d_count_decrease = {k: round(v/sum(c_decrease), 3) for k, v in d_count_decrease.items()}\n",
    "    # print(f)\n",
    "    print(\"increase\", d_count_increase)\n",
    "    print(\"decrease\", d_count_decrease)\n",
    "    # d_change[f] = [d_count_increase, d_count_decrease]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\work\\Anaconda3\\envs\\kia\\lib\\site-packages\\ipykernel_launcher.py:8: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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5k+XhEPM2btjzmJ+2tvLZjRs4e+NGrtm+jR7T3Oexl4WCnL5hA6duWM8D7e17bv9WSwtnb9zIL9ra9tx2f/vuj6ycSCYpJX86RA89fEkg/yAPlQ882rCwQW2+cUDWTi+kq0Bt4xRPxfiL8ybOUte52o+2RXcQ29KEGelh268XUHzshWDZmVo443TMYCcvPXxNldDjCCF4qelvfOe4q/c0u/nl7jb+X7l9/cbTC4u4Zvt22gyDr5VX8ERP957n+VR+Pl+vqMAtBD9ra+WBjnau22sBiSklt+3axe/GVlPp8XDB5k2cUFCAKSV+TfDEhAlctnULvaZJVEpWRyJ8paw86d8TS0r5QIMRWXJm3sEGbp+ZwI/Ya3pNGX4qdFMoUNtYjMtzZdGss2erngr7VzHvky+G4CoYwdwv3L/zTCr2LIFyb3lpzzD1F6PH7LlvmdvNn8aPB2C7Hv9I6B6T/2F+TfPn8ew+RqhN0SjjPF6qE22MTyss4sVgkJMKCohaEktKjMRqiXvaWrm6PPkX6zWktO4+2oi9dmJesregX9ewsOHZpgVNLyT5uMonUNMLKRKobRTAF4pmnnGMK1CsLqOdZF49eFCPf7y7izn5Hx9E7jJ0qjwfjk2q3G5aDZ2JPh+lbhef27yJuQUFbInHsYDD/MldkRWX0rj9RCv+2onDcgZQAA80LGxQPXhTSI10U2e2b3TdZ33VU4a8VVjZP68+9A5j97fvxiUEZxYVDepx3x5Zuef9r27byi1VVdzfvpu1sRifCuRzfknJUEsCICwt/bYzsD6Y6hvOtbU1wA9Q0wwpo0a6KRCobaxAiEsKpn/mCKG6kQ8L3xA7jP29u4uXg0HuGDWaff3XVLo97NQ/XBix0zA+trttSW8vh/n9hC3J1rjOL0aP4bneXiKWNZSSAOiWZuy784X8YKo3FQ3Jv9awsGF6Cp5HQYXusAvUNmrApfn1c2tdeUVqWmGY+IfQ7GZZKMjvOzr49Zix+91AMcXvZ7MeZ1s8TlxK/tnbwwn9rguqS8nDiV1vUcuiL7dNJLoc2oUsdmNGv32xS2yflLLWnm7gt6r/bmqo6YXhN114/FPzJh15hNOFZLPAAdo6frNlO6+Hw3SZJies/4Cry8r5bUc7upRctm0rYJ9Mu6WqilZD53s7d/KbsdW4heC7Iyu5fNtWLOCc4mJqfR8OPh/t7OSsYnvX22Sfj6hlcdbGjRxXkE/REM6VbhdG5OYvuT295e5U/24eCVxFv80/yvAQcoh/jZUDC9Q2eoHbi448e7Z/3NTZTteTTeq6gi39Vy/MePU7vSP07oxuibneZYR/+BWPLzKUtE6ObmBi04Km9gPeUxky9XJieM1x5Y8Y6xt72JAv3aMMTMAIZ3QjlyafHrr5Gq/fwcAFKAa+6+Dz5wQVusMkUNtYCJxfOPOMyULL4r6CacCyTNMn9Yz9Hi8viIduu9YX0NOjM89VDQsbJjhdRDZLh//kbHWap3z8SE9FjVoiNswsMxZ1uoaheqE0HrrzKn++dGvpsqrFC9zmdBHZTIXuMAjUNlYCnymccdo0tURs+EkjGne6hqF4Ykw89NsrAvlp2GTu8w0LG2Y6XUS2Srv/7Sxxrr9meqW7aOQhTheSE4xIRl2LR0rJw5P08J8uPujGNcNFAD92uohspUI3yQK1jZMQojH/sBPUErEUEXo4Y0LXklLeN90I/+P8pPdRSLZTGxY2qKZMw0CFbhIlNkL8V96kxpGuvMLKAz5ASQqXHtp3T8Y0Y0hp/uxTZvSl09M+cPvc4HQB2UiFbnJNByblTZg52elCcolLD6f9YvOYtIwfnWzpK+b6M+nSFec1LGxQU2RJpkI3SRJdxM72lFVLV0GZ+kFNIY/em9ahG5JW/PtnYbx71LA2rhkOLlQjnKRToZs8NUB1YPKxk9WChdTy6sG0/YZ3STP2nQsEGw73Zlrg9rmkYWGDmipLItV7IXmOF26v5a2omZ7KJ5VGnJ1/ugFp6GBZBCYfQ8mcC9G7drJ70R1YkV68VZMoP+MbCNdH9w+YkR7anrid+I51FDScROkpVyaOqdP6+K2YvbspnPFZCmd+FoD2Z+6hYPpp+KompfJLPCDfQbR1HE6tmNGbL3FpHaPdmXxZHD9wGWo1Q9Kk5Q9rpknsPjs2UHdsmXB7Ujtn5/JQ+V8/ZvSlv2LUF+8msvFNYtvX0PXSgxTNOosxVzyA5s8nuPr5jz1UuLyUzLmIESdc+pHbIxvfwjf2MEZd+iuC774IQLx1A9Ky0idw+/UMGUqHseG2TTMiN17hcmd44Pa5rGFhQ9q+msg0KnST4whA84+dkvJlYkIINK+d89IywDJBCKJbVhOoOxaAgiknEX7/Px97rOb14x97OGKvXBCaC6nHwDQhkW1dyx6hZM5Fw/vFDIKUck+PAr8eTKvQXefWw9++yuMNlqa8U9hwOQQ4yekisoUK3YOUWCZ2ureq1uXKLxnrRA3SMmn532vYds9F+Gum4y6pQvPlIzQ7l1yF5ZjBgTeO8k+YgdHdyo6Hr6No1pmE1y3HWzkRd2HZcH0JQyD3jLzyjE9u65hKb/v10M3X+Pyxgqy7Bt6XnC4gW2TLX2In1QIVgUNnH+5UAUJzMfqL92BFg7T+/UfoHdsO+ngV8+yT1tI02PXnmxl57k10LHkAs6eN/CknEahtTEbpQyflngFDwAin4uoKB/RqYTx091f8gTTqo5BM5zQsbChvWtC02+lCMp0a6R68k4Q3YHlKxzY4XYjmL8A/biqx7WuwYiGkZe8ZMHt34yoY2ii1d+ViCqacSKxlLZovn/KzbqBnxd+TWfYQ2aErpSTPiDi+MuC58njorq+mVeOaZPMC6TO/lMFU6B6EQG3jCGBWfv1xVcLlzAkTM9yNFbWvhGvpMaKbVuIpq8Y/roHwmlcACL6zhEDt0YM/djRI5IMV5E85EWnEQAgQwn7faRIBYJnxmCZwNOj+Vq2Hfnd5WjauSbb5TheQDdT0wsFpBPBWTqx3qgAz2MHuxb8AaYG0CNTNITDpKDzl49i96H/oWvYI3spDKJj6aQDC65YT37luz0mxbfddioyHkaZB+P3XGHnBrXjLxwHQ/eqjFH9qPkJo5E2YSe9bi9nx+6spmHGaU19uPzIRutEo4Mj0giWlXFhnRP55bl66Nq5JtqMbFjaMblrQ1OJ0IZlMXa5niAK1jS7gZ8KbZ5V/9rprhaZl24mTtDa5rb17nmdssR7atfvUFT8sT/Xzm1Ja9x5hxpadmlHbepPh6qYFTb92uohMlvWvh4bRWKDQP356pQrc1JOJka7QU9/WUZfS/OmcnAxcgHOdLiDTqdAdulpAeCsPSZPdAjknEbqp7TAWlZZ+66mW/tacnAxcgOMbFjak09rBjKNCd+gagR5PSdVEpwvJRSKxZCyVbR2D0orffA7WmiMyrnFNMrmAM50uIpOp0B2CQG1jAJjoLh2D5stXf/UdIBMjXY8eSslJiU7M2Lc/r7Gp3psWa4IddorTBWQyFbpDMxHAX92gRrlOkTIRusPfYWynMCI3fNGl7ZqQFX0UkuFk1Yth6FToDs0UwPSUV6v5XMfYoesb5tDdrBnhb1/h8XRVudNmq3EaGIn9O6AMgQrdQUo0Kz8SzdXpLqyY4HQ9uc5nBIdt5chajx767tUef2iES61n/7jjnS4gU6nQHbxKoMRfPaVcuNxqfs85EiBvmDqMrcyLh2651heI57vU78i+Hed0AZlK/QUfvImA8I46VF2Sx1kSwK+Hkv6yf1lxPPSrL2dt45pkOdbpAjKV+is+eLOAoLto5DinC8llAmGPdI1QUk9uPT0yHrrnq4FsblyTLKMaFjZUOV1EJlKhOwiB2kYvcDjQ5corHOl0PTlOAgSMcNLWzD42Ph568LJArvRRSIbpTheQiVToDk4V4HIVlvuF2xtwuphcJgSWZRmGVxoHPUVmSSl/V6+H//bfKnAHaYbTBWQiNac7OCMBvBU16uqoacAyYlGg4GCOYUpp/epIM/rqKXnqj+jgTXe6gEykQndwxgLSXVKlphacJ6URjR/MAeJSmnceb8VXHeNXgTs0aqQ7BGp6YXAmAiFXQZkKXacJJMbQO4xFpKX/8HRLX3WML1cb1yTDpIaFDeoP1iCp0B2gxKaIGiCk5aXVFRpzlLA0PWwM5ZG90op/7zxhvT89pxvXJEPf74QyCCp0B64AyAfimi8wwulicp0ASxtCh7F2zOiNX9DYcqhHbWxJjhqnC8g0ak534EoBS3h8buH2FTpdjCKkyxhch7EdwojcfKnb3T1S9VFIohqnC8g0KnQHbgQgPCPGlAih1s07TgjLqwcHHLobXUb4lis8vkixS13lI7lU/5FBUqE7cKWA5iquVFML6cAO3QH99XvPq4d+dKU3Tw+oPgrDoMbpAjKNCt2BGwPEXPklauVCGhAgvfHgAUN0RX489LOv+AOWV23rHSZqO/wgqdAduNFARHP71AmYNHGgDmMvlcRD937Zn48a4A6nUqcLyDQqdAeuEojicqvvWZrIM4L7PSH2j1Hx0MOXqG29KVDidAGZRgXIwPmBsNBUQ+t0kWeEP9ZhTErJoxON0BMXqMBNkWKnC8g06nXXwHkBE5dabpQu9m7raEkpf9tghJ+4IE8Fbup4GhY2qO/3IKhR2wAEahs17EtPS6Gp6YV0IJHSb0T8JJbvGVJadx9txF47UTWuccAIIOR0EZlCBcjA7Pk+qemF9OAyImiJwI1LafzPiZbRdHSe6qPgjCKnC8gkKkAGxk2iaTaaS00vpAGXYQ+swtLSbzsD64Opqo+Cg1SODIL6Zg2Mh0ToqpFuevDGg/RIM/b9+ZrYPkn1UVAyhwqQgfnw+6RCNy1orPffeLFL7B7rTuo10hRluKkAGZg90wtC09T0gkMOize1nxVaHDi94D23VmuWnDp6rOqCoWQcFboD82HQqpFuSh0eb9o933om+Jm8d0sri+Jl/U/ZjOiOt3SN8I12rjpFGTwVIAOjvk8pNCW+evcF1jPBU/PeKx1ZFC8Hyvd1v9N7g8afRqjp3DQwqBabuU6FycDs+T5JPR5GLUxKuobY27vny76g1fcbtP1dboQq/2iVmkITql2js9Qa3UFQoTtwAkDqUfUDliRTY6sSQdtcVlE8sKDtr1zgK+uKbe8o9Y8ZphKVgel2uoBMokJ3YEIkXkJZKnQPyrTYyrb58tnQp4cYtHs7szdkLSxVS3Qd1uV0AZlEhe7AhEn0qZDxsArdQZoRf7N1vvVs5JS8taXlxXoFUJGsY19mhCofNEsN4RLqZ9kZwaYFTYO+Vl0uUz+oAxMiMb1gxcJhh2vJCDNjb7TN57nQKXlrysuKjGFr/D5CwzuyK7atrcw/drieQ/lEXU4XkGlU6A5AeN1yI1DbGAXcVjSoRrr7IKXFrPhbrfN5NnJy3tqysmIjqSPaT3J2MMgDZWqKwSFqPneQVOgOXDfgsaK9KnQTpLQ4Mv5G63z5XPjkwPsVpcXDN6L9JAvMcOVvTakLl1AbV1KvxekCMo0K3YHrAirNUHdOh66UFkfFV+yaz/PRk/PeLx/hUND2VyzwVHVFt+4qy6t2upZ9seIWG2/fiDQk0pQUHVlE5TmVtL/QTvtz7cRb49TdU4e78OO/jsHmIDv/tHPPx7EdMaqvrKboiCK23r+V6LYohdMLqTqvCoDWRa34x/gpOiJljb82peqJsoUK3YHrBsaaoc5epwtJNSktGuMrds3nuehJeevKRxQblU7XtLdze0PivrL0XEAtPIKaG2pw+V1IQ7LhxxsobCgkUBugcFohG3+ycb+PLagvYNKtkwAwggbrblhHwZQColujaF6N2ttq2fjTjZhhEytuEVkfYeS8lP4d3JTKJ8sGKnQHrgN7eiEmLcsUmpbVC/KltDg6vnzXfJ6Pnpi3rnxEsZl2QdvfxVao6l6jLC7cIu0a4AghcPntHxdp2qNdBOSNH9wfiZ43eihoKEDzaeCyR9DSkkhDggatj7cy8pyUv/DYlOonzHQqdAeuA/uSPUgjHhJef/Y1bpambIy/3nqBHbQVJWketP0VCOEe0xXd2lKenlMM0pKs//564q1xSk8qJTBx8Be46F7eTdmpZQD4R/txF7pZ//31lHyqhPiuOFJK8mpSPtrfnOonzHQqdAeul74NEtHeNi1bQlea8uj48l0X8HzshMAHFSV5mRO0ezsvGNTuLk/TKQZNMOnWSZghky33bCG6LYp/7MBXXOhduj1/O6Vwz22jLhy15/3Nv9jM6EtG07qolejWKAWHF1A6NyVXR9+UiifJJip0B27PrjSjd/d2d1HFRIfrGTppytnx1xJBu76iOM+scrqkZLjIDFf90rBiwq2lbRccV76L/Pp8gk3BQYVu9+vdFM0sQrg/3syy560e/DV+rJhFvC3OuKvGsenOTZTMLrGnIoZPN2r1wqCp0B243X3vGJ0tLYypd7KWQRPSlMfE/r1rvnghdnxgQ9YEbX95mnCN64xu31oRGOd0Lf0ZPQbCJXDlu7DiFsF3g5SfPrjdz92vdVN5/sdfhEhD0v5cO+O/Pp7YrtiHt/fN9Q7vn5/VTQuaVIexQVKhO3CtgAm44js/aGHKSU7Xc0BCmvLY+Ks75/NC/PjAhpFFeVbWBe3e5geD7p9VpNcFgY1ug20PbENaEiQUH1VM0fQi2p9vp+3pNoxugw++9wGFUwsZc+kYIhsjdCztYMyldh+feFscvUMnf/LHr3TevqSdkmPsEa2/2o+MS9bdtI7CqYW48of9XO+q4X6CbCSkVH+oBipQ2/htYCTQXT7vhus0j6/A6Zr2JqQp58Rf2TmfJfHjAhtGFnms9JzkHCZRKa0jq6vjeDS1RW34Xda0oOkPTheRadRId3CagYlAtxnq2q6VVE52uiCwg/a4+LKd81miHxfYOLIwzxp14EdlJ78QWk1ntHXTyPSaYshSq5wuIBOp0B2cTSQa35i9bS0eB0NXk7p1XOzVXfPFkvicwMbKXA7avX0+FPTcTnpNMWQbKaUuhHjX6ToykQrdwdlzplbv2N7ir56S0ifXpG4dH1u2c7540ZiTv6myQAXtPp0vI1W361YYj6aSd5gIIVY1LWiKHfieyt5U6A7ObkAH3PGd61qYduqwP6EmdWtubNnO+doS49jA5sqCPEtdiPEAPEKIiR2RtvWV+eOdriWL/WsgdxJClAFLEh9WYZ+Mbkt8fJSUMj4MtQ2JEOIWICilvHM4n0eF7iCE1y23ArWNHwDVZrCj04pHujVvXnGyn8cl49bc2LKd52svGnMCmyvzVdAO2oXhoO+HfPxsv5I0AwpdKWU7MB1SF2qfRAjhklI62nRdhe7gNQP1QKcZ6tyerNB1ybh1QuxfO+drLxrHBLaooD1I58ho5Q/jVgivppI3yaSUphDi5aE+XghxBPBzoAD71eMlUsodQoiXgJXAHCAfuBj4NtAAPCalvEkIUQM8A7wJzATeBS6WUoaFECcBd2Ln2grgSillTAixCXgMOAW4QwhRCHwZe1v/B8AXpJQpuzjBsG5XyVJb+t4xOnds+aQ7Hojbipufjj7X8kD821tXey7Vf1fyh9GfLto0Lt9tpe2OqkzhFkJM7ozsPvA9lcESQrzRtKBpqM3LBXAPcJ6U8gjgD8CP+n0+LqWcBdwPPAlcBUwBLklMVQBMBu6VUtYDPcBXhRB+4EHgAillA3bwXtnvuO1SyplSyv8DHpdSHimlnIY9iLpsiF/LkKiR7uC1kFjBENm0ak3eIUd8ZjAPdltx86T40p3na0vNTwW2VgUCUo1oh8kXwkH/TWqKYTgsOfBd9suHHaLPCyEAXMCOfp9flPi3CXhXSrkDQAixAajG7mu9VUr5auJ+jwDXAs8DG6WU7yduX4gd2HclPn6s33NMEULcBpRgj7afPYivZ9BU6A5eB/aec7/Rub3bjPTscuUVfWKTGI8V2xO0swPbqgIBqS4ZngJnymjl92JmUPpcabeJJcM9dRCPFdhhOns/n+9bEWH1e7/v47682ntH10B2ePW/+MCDwNlSyreFEJcAcwfw+KRR0wuDFF63XAL/AcoA9PZta/Z1P48VM0+LPrP9D/Ebtq72XmrdX/LQmJOKto4LuGXa9XvNVpoQ1KsphqSSUrYArx3EIWJAhRBiNoAQwiOEOHyQxxjX93jgv4FXgLVAjRBiUuL2LwD7m3cuBHYIITzAhYN87oOmRrpDswo4FSC29Z01/rGHHQ920J4SX7LzfO0l6+jA9qo8NaJ13IJIMP8G1EA3WYQQfz/IJjcWcB5wtxCiGDuD7sI+ITZQa4GrhBB/AN4D7pNSRoUQXwT+IoToO5F2/34e/z1gOfbSteXYIZwyqvfCEARqGz3YJwPaAf2CTx954QLfUl9j/vaqPJdUF0dMM1NHjemRfld29D923klNC5pedOrJE6sXnpJSpnZnUhKp6YUhCK9brgMrQJblE6k6o+fP7rlF26pV4KanKZ2RDqdryAZSyg72/5JdGSAVukNUQLipXmyeXSe2zH51Y3jHgR+hOOXSaK+aX0gCIcQTTQuaHN1YIKXclMmjXFBzukM2Xaxf7cFYpSG3ftCG0RXNqy3xi8F1plZS4mT0ci1qdlt+V9J3D+aYh5wuIBuoke4QPfd+OOoS8hUhKAdo2mW+43RNyv5N6wx3Ol1DJpOW3NC0oElNLSSBCt2D828SF0T5x/vG25Y6K5m2LosEU3qGOtsITfzW6RqyhQrdg/M+EAG8a3ZbXZu79uyGUdLM8UIvc0VMNdodAimlgb2hQEkCFboHYdFaXcfutlQB8NT7+n+crUj5JDM7Qz1O15CRJIubFjTtcrqMbKFC9+C9jH1CUjy/wdzcFrLUJanT1JeiaophKIQm7nO6hmyiQvcgLVqr78BuR1cB8OJGU41209SnhFHqDhtqze4gSFM2Ny1oSmlDmGynQjc5ngH7olx/fld/LxiX6mVsmjqqM6z+bwZD+0jbRSUJVOgmxzpgK1CsW1jLt5nLnS5I2bfLY8ESp2vIFNKQO4QQjx34nspgqNBNgkVrdYndcLkE4JHV+pu6mT7XflI+NEsYJZ6Q0e50HRlBcEfTgibD6TKyjQrd5Hkbu4t9XntExpparZVOF6Ts2+yuUK/TNaQ7acpu4VJrc4eDCt0kSSwfe4rECbU/rtb/Y1hSjRLS0BWx4Aina0h3Uso7mhY0pey6YblEhW5yvQYYgHtdh9W9YrtayZCOpgqz2BvUVXPz/bB0q01zaz93uo5spUI3iRat1XuBF4AqgHtXxF8J6zLobFXKvhzTFVb/L/shDfm9pgVNUafryFYqdJPvn0Ac8HfHiD+33nCs4bOyf1+J9ZY6XUM6smLWRlee6wGn68hmKnSTLDHa/TOJ0e6Dq/RVu8NWRvXb3dptccLCEIf9Osjh9wb55Wv29QFveSnKmJ/3Mv3+INPvD/L0On2fj7/0yQgjf9rLlHs/Opi84fkoU+8LcvHfI3tue2R1nLtei+19iGF3mGYV+Xv11pQ/cfr7etOCJsvpIrKZCt3h8SqwEyixJPLRJj2jdvS4NfjZp/28d1UBr12Wz69X6LzXZveu/vrRXlZ9pYBVXyng9Np9Xyjjkukenrko8JHbuqOSt3aarL6yAK8LmnaZRHTJ/67SuepIZ67VeVx3KHLge+UOM2KuePfL7z7pdB3ZToXuMEisZHgEGEGiJ8P6DqvZ4bIGbFShxsxRLgAKfYL6Co3tPQPvWnnceDeleeIjt2kCdBOklIR1iccFd/47zjVHefG4xH6ONLyuiAdLVTdOm7SkieALTteRC1ToDp93sdfujgT47Zvx501LOnqpk6HY1GWxcodJ41g7hH/1epyp9wW59MkInZGBB1ahT3B6rZsZvwkxqkCj2CdYvt3k7DrnLit3qLAKA7266p4FGN3Gfe9d8d5ap+vIBSp0h0lil9pjgB9wNe+2Ole0ZNYSsmBc8rk/h7nrM36KfIIrZ3lZf20Bq76Sz6gCwXXPDe4E9/XH+Fj1lQJ+dqqf7y2N8cMTfPzurTjz/xLmtn+lfl4X4MTuUM6fpTcj5k4E1zldR65QoTuMFq3VtwPPAaMBfvGf+EsdESsjRla6aQfuhQ0ezq23R6OVBRouTaAJweVHeHl9+9AG7it3mEgJk8s0/vKezp/PD7C+02Jde+pfCFwRD5XLHJ5jkFJihsxL1/y/NWrbeoqo0B1+i7GXkAUiBuavXo8/nu471aSUXLYoSn25i2/M9u25fUfvhye1/96sM2Xk0H58vrc0xq0n+tAtMBOH1IDwvhdDDKsJmpVf0JO7UwxGt/HU2uvW/tPpOnKJCt1htmit3gP8L/YSMvFGi9W6ZIP5gsNlfaJXt5o8vFrnxY3GR5aHXf9CjIb7gky9L8jSTSa/ONUPQEuvxel//HDH6Of/Fmb270OsbbcY+/Nefv/Wh4OoJ9bozBqtMbpQo8QvmF7louG+IFFTMq3KlfKvFeDknpAzcxsOMyNmu4zLC52uI9eIHH5llTLzJnsE8CXgaGCrAO453X/RuGJtorOVKQBbpQifNmFsnhDCmWUUDpCWtCKbIvPW/2D9YqdryTVqpJsCiZNqjwLdQIkE7ng19kTUkKqhSBqoFjJQ1KPvdLqOVIq1xB5SgesMFbopsmitHgTuw167697SLYOPvaMvcrgsJeHT3cGcOZGkd+nrQ2tDlztdR65SoZtCi9bq64AngLEAf2s21jbtMt9ytCgFgCv00Ehpyazf/mrFrWhse+zsloda0vpkbjZToZt6TwEbSGya+MkrsWe6olK1GXTYKE3mlfTEs3qKQVpShteHr914x8Z3nK4ll6nQTbHEFuHfAh7A3xtH/8krsT9FDan6ADjs9J5Qxu0YHIzwuvAj7c+2/87pOnKdCl0HLFqr7wQWYm+a0N5rszp/91b8MTMHXt6ms8uMUIXMwK3aAxHdFn1z9z93X96zchBNNJRhoULXOa9iNzwfB/DcenPzP943nnK2pNxWKaS/tDv7phj0Tn1H12tdn+1Z2ZOT65HTjQpdh/RbRvYOMAbgDyv1lcu3Ga86WliOO6MnmFWvNsyw2duzsufstn+05eyuu3SjQtdBifnd3wAdQDnAj5fFX2huM1c7WlgO+5IRqpRpvk17oKyYFe16retLLQtbXne6FuVDKnQdlrjSxF3Y/xdFEvje0tiTW7qt9Y4WlqNKNbwVXbGMn2KwDEvv+nfXD4Org39xuhblo1TopoFFa/UdwM+BYiAQN7G+uyT657ZQZl3mJ1vM6w1m9MkmaUqz699dd/e+3XuHOnGWflTopolFa/UPgLux1+/6umPEv7Mk9nBryNrucGk554tmuEqa0oGeZwdPWtLq+k/Xg71v9X6nZ2VPVq7EyHQqdNPIorX629gdycYAnl0hGfnWc9GHWnqtzQ6XllNKBJ7KrljGnXiSpjS7Xu36Y88bPdf2rOzJmW3NmUaFbvr5F/BHoBrwdkaJf/O56CObu6x1DteVU87pDR74TmlEGlLveKnj0Z43e67qWdmjGimlMdXaMU3Nm+w5HrgU2AFEvS60n5zs+9ykUtdhDpeWE3olxuzx1ZZwCWcuVTwIlm7FO5Z0/DG0JvSNnpU9XU7Xo3wyNdJNU4vW6i8D92I3Pw/ETazrn4/99d1Wc5WzleWGQoF7dGc07acYrLgVbX+2/fehNaH/pwI3M6jQTWOL1urLgV9gr+EtMCzkd5bEnnxrh7nc4dJywueCwbT+/TBDZk/bU233hj8If6tnZU+v0/UoA6OmFzLAvMmew4BvAD2JN64/xnv8MdWuuTl0sYOUC1vSPGp8tSHcmu/A906t+O74rran2n5ldBl39qzsyfkrGmcSFboZYt5kzyTgm0AM6AT4XL178ucbPOd4XSLtQiFbnFZQsWVbRd44p+voL7Ixsq7t6bZfSl3+TvVTyDxp/fJJ+VBiHe/t2P9nI8Fugn7z0thv2sOZcVn3TDQ/FHTmapn7IKWUPSt73mx9svV6qcv7VOBmJjXSzTDzJnsqgK8CNcBWwCrw4r7pON8Zh1W4pjlaXBaKSmnNGlcdF27N72QdVtwKd7zUsSz0XuiWnpU9rzlZi3JwVOhmoHmTPT5gPnAK0AJEAS6f6Tni9Fr3aS5NpM3oLBt8tqB8y5aKgGNTDPHd8e1ti9ueMzqNH/es7PnAqTqU5FChm6ESl3WfDVwGhIF2gGOqXaO+eqR3fqFPlDhYXlZ5RPhb/qdm5OhUP6+UUgbfDa7qWNKxCMmve1b2tKW6BiX5VOhmuHmTPeOAq4FSYDsgK/NF3rfn+OYdMkKrc7a67BCT0ppVXR3Do+Wl6jnNqNnb8WLHv8Pvhx8EHlfberOHCt0sMG+ypwC4BDgS2AboAOcd5q77XL3ntHyvKHKwvKxwVn75lg0jh3+KQUpJZFPk3fbn2l+zItavelb2rBru51RSS4Vulpg32aMBpwLnAxGgDWCEH+/XjvadNK1KO1JTi3qH7DF8O26bUDlqOJ/DDJsdHUs7VoTXhZcBv+lZ2aOuEp2FVOhmmXmTPeOxR70TsPs2xADmjHONvnSG58yygFblYHkZS5dSzhxbHcGrBZJ9bCmlDK8Lr2p/vn211OWjwJKelT1ZcfUK5eNU6GaheZM9buA44L8ACewEpEdD++qR3qOPr3HNdWvC42iRGejcQNnmdZX545N5zHh7fFPH0o7VsW2x14AHe1b2qMb1WU6FbhabN9lTBnwee663DQgCHFqmFV99lPf0mhLtUCfryzSP49v1/QmVlck4lhk2d3f9p+uNYFNwK/AnYJlqOp4bVOhmucTSsmnYUw6F2Ot6TYATalzVF0zxnDi6UKtxrMAMYkgpZ4ytDuHVCoZ6DEu3IsGm4Oudr3RuweI14M9q7ja3qNDNEfMmewLAPOyTbVGgFXvqgdNr3YecW+8+cWS+NsbBEjPC/LzSzc1VBYOeYrB0KxxeF36zc1nnNitivY/dqP4DdQ2z3KNCN8fMm+ypBs4FZmBvqmjt+9wZh7onnnGoe87oQi2p85bZZLH0tt54SNXIgd7filvB0NrQ8s5lnS0yLjuxpxLeUFMJuUuFbg5KTDlMAD4HHA6ESCwxAzhxgqv6nDrPnPElWq1DJaYtS0qmjxnbK32uwk+6nxkxO0NrQm90vdrVIg0ZBhYBL/es7ImkplIlXanQzWGJ8D0UOAs4DHvaYReJaYdplVrZGYe6ZzRUuqYFPGLI85jZ5vN5pZvf2ccUg5RS6rv193ubelcHVwdD2CcunwD+rcJW6aNCV+k/8j0De9rBwJ520AHcGuKMQ92Tjh/vnlFTIg7N9YY6z0pv2zcPqaro+9iKW8HI5shb3cu71+u7dQ3oAh4Hlqv2i8reVOgqH5GY850LHAt4gV7spukSoKpA5J1b75l65GhtRllAS8ryqUw0beTo3ZEesyX0fqi59+3eIBYu4APgGaBJ9UpQ9keFbooIIUygCXADzcACKeWALpUthJgFXCylvPYT7jMdGC2lfPoAx5oLfFNKecYn3W/eZE8eMAU4CXsKQgK7sbcYAzB7rKvq5EPcDRNLxcTSvOwP4KAuzRUdbPtXO8t/u5G2XoN8IA4sBV4BtqvVCMqBqNBNESFEUEpZkHj/j8CbUsqfD+BxbinlAbeECiEuAWZJKa8+wP3mMoDQ7W/eZM9I7A0WpwDF2FuL20is9wWoLhL5J0xwTzy8QjtkfIk2MRvmgC0pZWdE7trSLbe80WK+/+gGq7jZ7akKadq7wDrgJWClmq9VBkOFborsFbpfAaYC/wRuwn4Z3w5cKKXcJYS4BZgIHAJsAX5DIiiFEPnAPdijUA9wS+I4HwB52O0dbwc2Ar8E/Nij0y9KKdcOJXT7zJvscQG1wBygERDYI+AuErvd+swarY2cPdZ9yORybeLoQjE+E7Yd66bUW0Ny26Yua+u7bdaWf281t3VEZAB7U4kwYcsGj6d5u8f9gtrQoAyVCt0U6QtdIYQb+Bv23N//AV1SSimE+BJQL6W8LhG6ZwLHSikj/YNSCPFj4D0p5SNCiBLgdeyTX+fTb6QrhCgCwlJKQwhxMnCllPJzBxO6/SWuXlED1GGPgvuafMex54D3nEDyutCmjNTKDi3TKsYXayOrCrSRZQFRUeSjVBPCkev06abUe+N0dkTk7g2d1tZVO80ty7eZrbpFIdA3ShfA+9hTB82L1urtTtSqZBe30wXkkDwhxKrE+8uA3wOTgceEEKOwR7sb+91/kZRyXy9bPw3ME0J8M/GxH9hXn9diYKEQohZ7NJrUkeaitXoMWJt4e3LeZE8x9sh8GjAT6JvjjcdNQm/tsDre2mG1Ae/1HcPvxtUwUis7tMw1srpYVJQHRGm+R+TneUR+npt8n5vAUNtRmpa0wjo93THZ2RWVnbvDsmtXUHZu7bE6N3RaXdt6ZAj7e16M/QrBlah5HbAa+/9i66K1+oDm3RVloFTopk5ESjm9/w1CiHuAn0spFyVGoLf0+3RoP8cRwOeklGv3OlbjXve7FVgqpTxHCFGDPf84bBat1buBlcDKeZM9C4Eq7BCeiL0cbQz2z5uFfUXjWNQgvKLF2r2ixWrd1zEFUJonfCV+fEU+4S30CV+BV/gEEDOlETMwooY0IgZG1MAI69IIxaUR1jF0CytxGBd2qPoTby5gBFCG/T1uAt7BnsZpWbRWVy0VlWGlQtdZxdhzsAALBviYZ4FrhBDXJKYlZkgpV2Iv7eq/S6r/sS9JRrEDtWitLrF7+e4AXoU9TdZHABXYl5CfkHgbjR2EffNcAjuUpQSzPSLN9ggGSBN723I08fm+Nw/gS7zfN8dsJf7VsNca7wLWY38/WrGnP9qB9kStipIyKnSddQvwFyFEJ/AidggdyK3AXcBqYc+HbsTe1LAUuDExhXE7cAf29MJNwOKkVz5Ii9bqFomgA9YA/4I9YVwE5Pd7CyT+Lej3fh52iMYTb7H9/GsAPdjB2gWEVLAq6USdSFMURUkhR84cK4qi5CoVuoqiKCmkQldRFCWFVOgqiqKkkApdRVGUFFKhqyiKkkIqdBVFUVJIha6iKEoKqdBVFEVJIRW6iqIoKaRCV1EUJYVU6CqKoqSQCl1FUZQUUqGrKIqSQv8fGNf7ntMYFRMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# pie chart of beta\n",
    "labels = list(d_count_increase.keys())\n",
    "sizes = list(d_count_increase.values())\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freqs</th>\n",
       "      <th>increase</th>\n",
       "      <th>decrease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>delta</td>\n",
       "      <td>134</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>theta</td>\n",
       "      <td>140</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha</td>\n",
       "      <td>132</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>beta</td>\n",
       "      <td>164</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gamma</td>\n",
       "      <td>183</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   freqs  increase  decrease\n",
       "0  delta       134       162\n",
       "1  theta       140       156\n",
       "2  alpha       132       164\n",
       "3   beta       164       132\n",
       "4  gamma       183       113"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_change = pd.DataFrame({\"freqs\": freqs, \n",
    "                        \"increase\": [sum(d_change[f][0].values()) for f in freqs], \n",
    "                        \"decrease\": [sum(d_change[f][1].values()) for f in freqs]})\n",
    "df_change                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_change['increase'] = -df_change['increase']\n",
    "df_change['diff'] = df_change['increase'] + df_change['decrease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\work\\Anaconda3\\envs\\kia\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", font_scale=2.5)\n",
    "# set figure size\n",
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "sns.set_color_codes(\"pastel\")\n",
    "rect2 = plt.bar(df_change[\"freqs\"], df_change['decrease'], color = \"r\", label = \"Increased connections\")\n",
    "rect1 = plt.bar(df_change[\"freqs\"], df_change['increase'], color = \"b\", label = \"Decreased connections\")\n",
    "# rect2 = sns.barplot(data = df_change, x= \"freqs\", y='decrease', color = \"b\")\n",
    "# sns.set_color_codes(\"muted\")\n",
    "# sns.barplot(data = df_change, x= \"freqs\", y='diff', color=\"r\")\n",
    "ax.bar_label(ax.containers[0], label_type='center')\n",
    "ax.bar_label(ax.containers[1], label_type='center')\n",
    "\n",
    "plt.ylabel(\"Number of connections\")\n",
    "plt.xlabel(\"Frequency band\")\n",
    "ax.set_ylim(-200, 240)\n",
    "\n",
    "ax.set_xticklabels([v.capitalize() for v in freqs])\n",
    "# put legend in the top middle\n",
    "plt.legend(loc='upper center',\n",
    "            ncol=2, fancybox=True, shadow=True)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'delta': [{'Frontal': 49,\n",
       "   'Insula': 2,\n",
       "   'Temporal': 30,\n",
       "   'Parietal': 42,\n",
       "   'Occipital': 11},\n",
       "  {'Frontal': 71,\n",
       "   'Insula': 5,\n",
       "   'Temporal': 40,\n",
       "   'Parietal': 32,\n",
       "   'Occipital': 14}],\n",
       " 'theta': [{'Frontal': 57,\n",
       "   'Insula': 2,\n",
       "   'Temporal': 33,\n",
       "   'Parietal': 31,\n",
       "   'Occipital': 17},\n",
       "  {'Frontal': 48,\n",
       "   'Insula': 0,\n",
       "   'Temporal': 35,\n",
       "   'Parietal': 54,\n",
       "   'Occipital': 19}],\n",
       " 'alpha': [{'Frontal': 45,\n",
       "   'Insula': 3,\n",
       "   'Temporal': 41,\n",
       "   'Parietal': 30,\n",
       "   'Occipital': 13},\n",
       "  {'Frontal': 59,\n",
       "   'Insula': 2,\n",
       "   'Temporal': 36,\n",
       "   'Parietal': 56,\n",
       "   'Occipital': 11}],\n",
       " 'beta': [{'Frontal': 51,\n",
       "   'Insula': 3,\n",
       "   'Temporal': 27,\n",
       "   'Parietal': 69,\n",
       "   'Occipital': 14},\n",
       "  {'Frontal': 44,\n",
       "   'Insula': 3,\n",
       "   'Temporal': 39,\n",
       "   'Parietal': 30,\n",
       "   'Occipital': 16}],\n",
       " 'gamma': [{'Frontal': 55,\n",
       "   'Insula': 2,\n",
       "   'Temporal': 58,\n",
       "   'Parietal': 46,\n",
       "   'Occipital': 22},\n",
       "  {'Frontal': 56,\n",
       "   'Insula': 2,\n",
       "   'Temporal': 28,\n",
       "   'Parietal': 14,\n",
       "   'Occipital': 13}]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder the legend\n",
    "lobe_order = ['Frontal', 'Insula', 'Temporal', 'Parietal', 'Occipital']\n",
    "# colors = [\"red\", \"lime\", \"blue\", \"yellow\", \"cyan\"]\n",
    "colors = np.array([(181,32,32), (221,147,27), (67,241,96), (65,92,214), (199,35,199)])/256\n",
    "\n",
    "d_change_order = {}\n",
    "for f in freqs:\n",
    "    d_change_order[f] = [d_change[f][0], d_change[f][1]]\n",
    "    d_change_order[f][0] = {k: d_change_order[f][0][k] for k in lobe_order}\n",
    "    d_change_order[f][1] = {k: d_change_order[f][1][k] for k in lobe_order}\n",
    "\n",
    "d_change_order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_inc = {k:v[1] for k,v in d_change_order.items()}\n",
    "d_dec = {k:v[0] for k,v in d_change_order.items()}\n",
    "\n",
    "df_inc = pd.DataFrame(d_inc)\n",
    "df_dec = pd.DataFrame(d_dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\work\\Anaconda3\\envs\\kia\\lib\\site-packages\\ipykernel_launcher.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = d_change_order['theta']\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", font_scale=2.5)\n",
    "# set figure size\n",
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "\n",
    "rect1 = plt.bar(lobe_order, np.array(list(data[1].values())), color = colors)\n",
    "rect2 = plt.bar(lobe_order, np.array(list(data[0].values()))*-1, color = colors)\n",
    "# rect2 = sns.barplot(data = df_change, x= \"freqs\", y='decrease', color = \"b\")\n",
    "# sns.set_color_codes(\"muted\")\n",
    "# sns.barplot(data = df_change, x= \"freqs\", y='diff', color=\"r\")\n",
    "# ax.bar_label(ax.containers[0], label_type='center')\n",
    "# ax.bar_label(ax.containers[1], label_type='center')\n",
    "\n",
    "plt.ylabel(\"Number of connections\")\n",
    "plt.xlabel(\"Brain lobe\")\n",
    "ax.set_ylim(-80, 80)\n",
    "\n",
    "ax.set_xticklabels([v.capitalize() for v in lobe_order])\n",
    "# put legend in the top middle\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib set colormap jet\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "sns.set(font_scale=2.5)\n",
    "\n",
    "inc = d_change_order[\"gamma\"][1]\n",
    "dec = d_change_order[\"gamma\"][0]\n",
    "# plot pie chart\n",
    "labels = list(inc.keys())\n",
    "sizes = list(inc.values())\n",
    "fig1, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10, 20))\n",
    "ax1.pie(sizes, autopct='%1.1f%%',startangle=90, pctdistance=1.2, labeldistance=1.2, colors=colors)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title(\"Increased connections\", y=1.05)\n",
    "\n",
    "labels = list(dec.keys())\n",
    "sizes = list(dec.values())\n",
    "\n",
    "ax2.pie(sizes, autopct='%1.1f%%', startangle=90, pctdistance=1.2, labeldistance=1.2, colors=colors)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.set_title(\"Decreased connections\", y=1.05)\n",
    "# plt.legend(labels, loc='best')\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "69.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load(f\"connection_alter_beta.npy\")\n",
    "\n",
    "df_corr = pd.DataFrame(data, columns = label_names, index = label_names)\n",
    "df_order = df_corr.reindex(new_order, axis=0).reindex(new_order, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_s =  df_order.stack().reset_index()\n",
    "df_s.columns = ['source', 'target', 'connectivity']\n",
    "df_s = df_s[df_s['connectivity']!=0].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "lobe_name = ['Frontal-rh', 'Insula-rh','Temporal-rh', 'Parietal-rh', 'Occipital-rh',\n",
    "             'Occipital-lh', 'Parietal-lh','Temporal-lh', 'Insula-lh',  'Frontal-lh',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d_count_intra_dec = {k:0 for k in lobe_name}\n",
    "# d_count_intra_inc = {k:0 for k in lobe_name}\n",
    "d_count_inter_dec = {k:{j:0 for j in lobe_name} for k in lobe_name}\n",
    "d_count_inter_inc = {k:{j:0 for j in lobe_name} for k in lobe_name}\n",
    "for i in range(len(df_s)):\n",
    "    source = df_s['source'][i]\n",
    "    target = df_s['target'][i]\n",
    "    val = df_s['connectivity'][i]\n",
    "    source_lobe, hemisphere1 = get_lobe(source)\n",
    "    target_lobe, hemisphere2 = get_lobe(target)\n",
    "    s = source_lobe + \"-\" + hemisphere1\n",
    "    t = target_lobe + \"-\" + hemisphere2\n",
    "\n",
    "    # if s == t:\n",
    "    #     if val < 0:\n",
    "    #         d_count_intra_inc[s] += 1\n",
    "    #     else:\n",
    "    #         d_count_intra_dec[s] += 1\n",
    "    # else:\n",
    "    if val < 0:\n",
    "        if d_count_inter_inc[s].get(t):\n",
    "            d_count_inter_inc[s][t] += 1\n",
    "        else:\n",
    "            d_count_inter_inc[s][t] = 1\n",
    "    else:\n",
    "        if d_count_inter_dec[s].get(t):\n",
    "            d_count_inter_dec[s][t] += 1\n",
    "        else:\n",
    "            d_count_inter_dec[s][t] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_count_inter_dec_2 = {k:{j:0 for j in lobe_name} for k in lobe_name}\n",
    "for i in d_count_inter_dec:\n",
    "    for j in d_count_inter_dec[i]:\n",
    "        d_count_inter_dec_2[i][j] = d_count_inter_dec[i][j] + d_count_inter_dec[j][i]\n",
    "\n",
    "d_count_inter_inc_2 = {k:{j:0 for j in lobe_name} for k in lobe_name}\n",
    "for i in d_count_inter_inc:\n",
    "    for j in d_count_inter_inc[i]:\n",
    "        d_count_inter_inc_2[i][j] = d_count_inter_inc[i][j] + d_count_inter_inc[j][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d_count_intra_dec = {k:0 for k in lobe_name}\n",
    "# d_count_intra_inc = {k:0 for k in lobe_name}\n",
    "d_count_inter_dec_3 = {k:{j:0 for j in lobe_map.keys()} for k in lobe_map.keys()}\n",
    "d_count_inter_inc_3 = {k:{j:0 for j in lobe_map.keys()} for k in lobe_map.keys()}\n",
    "for i in range(len(df_s)):\n",
    "    source = df_s['source'][i]\n",
    "    target = df_s['target'][i]\n",
    "    val = df_s['connectivity'][i]\n",
    "    source_lobe, hemisphere1 = get_lobe(source)\n",
    "    target_lobe, hemisphere2 = get_lobe(target)\n",
    "    s = source_lobe \n",
    "    t = target_lobe\n",
    "\n",
    "    if val < 0:\n",
    "        if d_count_inter_inc_3[s].get(t):\n",
    "            d_count_inter_inc_3[s][t] += 1\n",
    "        else:\n",
    "            d_count_inter_inc_3[s][t] = 1\n",
    "    else:\n",
    "        if d_count_inter_dec_3[s].get(t):\n",
    "            d_count_inter_dec_3[s][t] += 1\n",
    "        else:\n",
    "            d_count_inter_dec_3[s][t] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "intra_inc = {k:0 for k in lobe_map.keys()}\n",
    "inter_inc = {k:0 for k in lobe_map.keys()}\n",
    "for i in d_count_inter_inc_3:\n",
    "    for j in d_count_inter_inc_3[i]:\n",
    "        if i == j:\n",
    "            intra_inc[i] += d_count_inter_inc_3[i][j]\n",
    "        else:\n",
    "            inter_inc[i] += d_count_inter_inc_3[i][j]\n",
    "\n",
    "intra_dec = {k:0 for k in lobe_map.keys()}\n",
    "inter_dec = {k:0 for k in lobe_map.keys()}\n",
    "for i in d_count_inter_dec_3:\n",
    "    for j in d_count_inter_dec_3[i]:\n",
    "        if i == j:\n",
    "            intra_dec[i] += d_count_inter_dec_3[i][j]\n",
    "        else:\n",
    "            inter_dec[i] += d_count_inter_dec_3[i][j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frontal': 21, 'Parietal': 27, 'Temporal': 25, 'Occipital': 15, 'Insula': 3}"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frontal': 31, 'Parietal': 54, 'Temporal': 26, 'Occipital': 14, 'Insula': 2}"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.774390243902439"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(inter_dec.values())/(sum(inter_dec.values())+sum(intra_dec.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frontal': 23, 'Parietal': 3, 'Temporal': 14, 'Occipital': 1, 'Insula': 0}"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intra_inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4318181818181818"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intra_inc[\"Frontal\"]/sum(intra_inc.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frontal': 20, 'Parietal': 15, 'Temporal': 1, 'Occipital': 0, 'Insula': 1}"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intra_dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=2.5)\n",
    "\n",
    "# plot pie chart\n",
    "labels = [\"Intra-connections\", \"Inter-connections\"]\n",
    "sizes = [sum(intra_inc.values()), sum(inter_inc.values())]\n",
    "fig1, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10, 20))\n",
    "ax1.pie(sizes, autopct='%1.1f%%',startangle=90, pctdistance=1.2, labeldistance=1.2, colors=colors)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title(\"Increased connections\", y=1.05)\n",
    "\n",
    "labels = [\"Intra-connections\", \"Inter-connections\"]\n",
    "sizes = [sum(intra_dec.values()), sum(inter_dec.values())]\n",
    "\n",
    "ax2.pie(sizes, autopct='%1.1f%%', startangle=90, pctdistance=1.2, labeldistance=1.2, colors=colors)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.set_title(\"Decreased connections\", y=1.05)\n",
    "# plt.legend(labels, loc='best')\n",
    "fig.tight_layout()\n",
    "# fig.savefig(\"pie.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "# substract 2 dicts\n",
    "d_count_sum = {k:{j: d_count_inter_dec[k][j] + d_count_inter_inc[k][j] for j in lobe_name} for k in lobe_name}\n",
    "d_count_diff = {k:{j: d_count_inter_dec[k][j] - d_count_inter_inc[k][j] for j in lobe_name} for k in lobe_name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_count_sum_2 = {k:{j: d_count_inter_dec_2[k][j] + d_count_inter_inc_2[k][j] for j in lobe_name} for k in lobe_name}\n",
    "d_count_diff_2 = {k:{j: d_count_inter_dec_2[k][j] - d_count_inter_inc_2[k][j] for j in lobe_name} for k in lobe_name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frontal-rh</th>\n",
       "      <th>Insula-rh</th>\n",
       "      <th>Temporal-rh</th>\n",
       "      <th>Parietal-rh</th>\n",
       "      <th>Occipital-rh</th>\n",
       "      <th>Occipital-lh</th>\n",
       "      <th>Parietal-lh</th>\n",
       "      <th>Temporal-lh</th>\n",
       "      <th>Insula-lh</th>\n",
       "      <th>Frontal-lh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frontal-rh</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insula-rh</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal-rh</th>\n",
       "      <td>-9</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-7</td>\n",
       "      <td>-2</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal-rh</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-rh</th>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-lh</th>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal-lh</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal-lh</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
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       "    <tr>\n",
       "      <th>Insula-lh</th>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontal-lh</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-6</td>\n",
       "      <td>-8</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Frontal-rh  Insula-rh  Temporal-rh  Parietal-rh  Occipital-rh  \\\n",
       "Frontal-rh             1          0           -3            4            -1   \n",
       "Insula-rh              0          0            0            2             0   \n",
       "Temporal-rh           -9         -1           -1            0             0   \n",
       "Parietal-rh            3          0           -1           -1             0   \n",
       "Occipital-rh          -2          0            2           -1             0   \n",
       "Occipital-lh          -2          0            2            0            -2   \n",
       "Parietal-lh            5          0            2            3             0   \n",
       "Temporal-lh            0         -1            6            2             2   \n",
       "Insula-lh             -3         -1           -1            0             0   \n",
       "Frontal-lh             6          0           -6           -8            -1   \n",
       "\n",
       "              Occipital-lh  Parietal-lh  Temporal-lh  Insula-lh  Frontal-lh  \n",
       "Frontal-rh              -1            4            0          0          -3  \n",
       "Insula-rh                0            0           -1          0          -1  \n",
       "Temporal-rh              2           -1           -7         -2          -3  \n",
       "Parietal-rh              0            4            2          0          -3  \n",
       "Occipital-rh            -1            0            0          0           0  \n",
       "Occipital-lh            -3            0            1          0          -1  \n",
       "Parietal-lh              0            1            2          1           1  \n",
       "Temporal-lh              1            3           -4          1          -3  \n",
       "Insula-lh                0            0           -1          0           0  \n",
       "Frontal-lh               1           -2           -2          0          -8  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d_count_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frontal-rh': 2,\n",
       " 'Insula-rh': 0,\n",
       " 'Temporal-rh': -2,\n",
       " 'Parietal-rh': -2,\n",
       " 'Occipital-rh': 0,\n",
       " 'Occipital-lh': -6,\n",
       " 'Parietal-lh': 2,\n",
       " 'Temporal-lh': -8,\n",
       " 'Insula-lh': 0,\n",
       " 'Frontal-lh': -16}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the values of the diagonal\n",
    "d_count_diag = {k:d_count_diff_2[k][k] for k in lobe_name}\n",
    "d_count_diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndelta: frontal: 0.40540540540540543, insula: 0.02364864864864865, temporal: 0.23648648648648649, parietal: 0.25, occipital: 0.08445945945945946\\n\\ntheta: frontal: 0.3547297297297297, insula: 0.006756756756756757, temporal: 0.22972972972972974, parietal: 0.28716216216216217, occipital: 0.12162162162162163\\n\\nalpha: frontal: 0.35135135135135137, insula: 0.016891891891891893, temporal: 0.26013513513513514, parietal: 0.2905405405405405, occipital: 0.08108108108108109\\n\\nbeta: frontal: 0.32094594594594594, insula: 0.02027027027027027, temporal: 0.22297297297297297, parietal: 0.3344594594594595, occipital: 0.10135135135135136\\n\\ngamma: frontal: 0.375, insula: 0.013513513513513514, temporal: 0.2905405405405405, parietal: 0.20270270270270271, occipital: 0.11824324324324324\\n\\n'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Parietal-rh</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-rh</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-lh</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal-lh</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal-lh</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insula-lh</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontal-lh</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Frontal-rh  Insula-rh  Temporal-rh  Parietal-rh  Occipital-rh  \\\n",
       "Frontal-rh             8          1            6            8             3   \n",
       "Insula-rh              0          0            0            0             0   \n",
       "Temporal-rh            4          0            2            5             3   \n",
       "Parietal-rh            3          0            2            7             2   \n",
       "Occipital-rh           3          0            2            5             0   \n",
       "Occipital-lh           4          0            4            4             0   \n",
       "Parietal-lh            3          0            4            4             2   \n",
       "Temporal-lh            5          0            3            4             2   \n",
       "Insula-lh              0          1            0            1             0   \n",
       "Frontal-lh            15          1            7           10             1   \n",
       "\n",
       "              Occipital-lh  Parietal-lh  Temporal-lh  Insula-lh  Frontal-lh  \n",
       "Frontal-rh               1           12            7          0          10  \n",
       "Insula-rh                0            0            0          0           1  \n",
       "Temporal-rh              2            3            4          1           5  \n",
       "Parietal-rh              3            5            4          0           8  \n",
       "Occipital-rh             1            6            1          0           2  \n",
       "Occipital-lh             0            5            1          0           4  \n",
       "Parietal-lh              2            2            2          0           2  \n",
       "Temporal-lh              3            7            6          2           6  \n",
       "Insula-lh                0            1            3          0           2  \n",
       "Frontal-lh               5           10            8          0          10  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d_count_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_count_sum = pd.DataFrame(d_count_sum)\n",
    "d_count_diff = pd.DataFrame(d_count_diff)\n",
    "# get absolute value\n",
    "d_count_sum.to_csv(\"d_count_sum.csv\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_count_sum_2 = pd.DataFrame(d_count_sum_2)\n",
    "d_count_diff_2 = pd.DataFrame(d_count_diff_2)\n",
    "# get absolute value\n",
    "# d_count_sum_2.to_csv(\"d_count_sum_2.csv\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frontal-rh      101\n",
       "Insula-rh         4\n",
       "Temporal-rh      59\n",
       "Parietal-rh      82\n",
       "Occipital-rh     33\n",
       "Occipital-lh     39\n",
       "Parietal-lh      72\n",
       "Temporal-lh      74\n",
       "Insula-lh        11\n",
       "Frontal-lh      117\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_count_sum_2.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3694915254237288"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "109/295"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(4+11)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frontal-rh</th>\n",
       "      <th>Insula-rh</th>\n",
       "      <th>Temporal-rh</th>\n",
       "      <th>Parietal-rh</th>\n",
       "      <th>Occipital-rh</th>\n",
       "      <th>Occipital-lh</th>\n",
       "      <th>Parietal-lh</th>\n",
       "      <th>Temporal-lh</th>\n",
       "      <th>Insula-lh</th>\n",
       "      <th>Frontal-lh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frontal-rh</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insula-rh</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal-rh</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-4</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal-rh</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>10</td>\n",
       "      <td>-3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-rh</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occipital-lh</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parietal-lh</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal-lh</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>-4</td>\n",
       "      <td>3</td>\n",
       "      <td>-12</td>\n",
       "      <td>-3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insula-lh</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontal-lh</th>\n",
       "      <td>-9</td>\n",
       "      <td>-2</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Frontal-rh  Insula-rh  Temporal-rh  Parietal-rh  Occipital-rh  \\\n",
       "Frontal-rh            16          1            4           -1             4   \n",
       "Insula-rh              1          0            0            0             0   \n",
       "Temporal-rh            4          0           -4           -1             5   \n",
       "Parietal-rh           -1          0           -1           10            -3   \n",
       "Occipital-rh           4          0            5           -3             0   \n",
       "Occipital-lh           1          0            0            3            -1   \n",
       "Parietal-lh            5          0            3            9             2   \n",
       "Temporal-lh            6          0           -5            2            -3   \n",
       "Insula-lh              0          1            1            1             0   \n",
       "Frontal-lh            -9         -2           -4            6             3   \n",
       "\n",
       "              Occipital-lh  Parietal-lh  Temporal-lh  Insula-lh  Frontal-lh  \n",
       "Frontal-rh               1            5            6          0          -9  \n",
       "Insula-rh                0            0            0          1          -2  \n",
       "Temporal-rh              0            3           -5          1          -4  \n",
       "Parietal-rh              3            9            2          1           6  \n",
       "Occipital-rh            -1            2           -3          0           3  \n",
       "Occipital-lh             0            1           -4          0           3  \n",
       "Parietal-lh              1           -4            3         -1          -2  \n",
       "Temporal-lh             -4            3          -12         -3           4  \n",
       "Insula-lh                0           -1           -3          0           2  \n",
       "Frontal-lh               3           -2            4          2          -4  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_count_diff_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x276251e5888>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot correlation matrix\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the correlation matrix\n",
    "plt.matshow(d_count_diff_2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\work\\Anaconda3\\envs\\kia\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \n",
      "c:\\Users\\work\\Anaconda3\\envs\\kia\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#set upper triangle to zero\n",
    "d_count_sum_2.where(np.tril(np.ones(d_count_sum_2.shape)).astype(np.bool)).fillna(0).astype(int).to_csv(\"d_count_sum_2.csv\", sep=\" \")\n",
    "d_count_diff_2.where(np.tril(np.ones(d_count_diff_2.shape)).astype(np.bool)).fillna(0).astype(int).to_csv(\"d_count_diff_2.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_count_diff.to_csv(\"d_count_diff.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = np.array([(181,32,32), (221,147,27), (67,241,96), (65,92,214), (199,35,199)])/256\n",
    "# add alpha\n",
    "colors = np.hstack((colors, np.ones((5,1))))\n",
    "# matplotlib set colormap jet\n",
    "sns.set(font_scale=2.5)\n",
    "\n",
    "inc = d_change_order[\"beta\"][0]\n",
    "dec = d_change_order[\"beta\"][1]\n",
    "# plot pie chart\n",
    "labels = list(inc.keys())\n",
    "sizes = list(inc.values())\n",
    "fig1, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10, 20))\n",
    "ax1.pie(sizes, autopct='%1.1f%%',startangle=90, pctdistance=1.3, labeldistance=1.2, colors=colors)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "# ax1.set_title(\"increased connections\")\n",
    "\n",
    "labels = list(dec.keys())\n",
    "sizes = list(dec.values())\n",
    "\n",
    "ax2.pie(sizes, autopct='%1.1f%%', startangle=90, pctdistance=1.3, labeldistance=1.2, colors=colors)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "# ax2.set_title(\"decreased connections\")\n",
    "# plt.legend(labels, loc='right')\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pair = df_corr.stack().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>connectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>bankssts-rh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>caudalanteriorcingulate-lh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>caudalanteriorcingulate-rh</td>\n",
       "      <td>0.135901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bankssts-lh</td>\n",
       "      <td>caudalmiddlefrontal-lh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        source                      target  connectivity\n",
       "0  bankssts-lh                 bankssts-lh      0.000000\n",
       "1  bankssts-lh                 bankssts-rh      0.000000\n",
       "2  bankssts-lh  caudalanteriorcingulate-lh      0.000000\n",
       "3  bankssts-lh  caudalanteriorcingulate-rh      0.135901\n",
       "4  bankssts-lh      caudalmiddlefrontal-lh      0.000000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pair.columns = ['source', 'target', 'connectivity']\n",
    "df_pair.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>connectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>rostralmiddlefrontal-lh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>parsorbitalis-lh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>medialorbitofrontal-lh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>frontalpole-lh</td>\n",
       "      <td>rostralanteriorcingulate-lh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4619</th>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>rostralanteriorcingulate-rh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>medialorbitofrontal-rh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4621</th>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>parsorbitalis-rh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4622</th>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>rostralmiddlefrontal-rh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4623</th>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>frontalpole-rh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4624 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              source                       target  connectivity\n",
       "0     frontalpole-lh               frontalpole-lh           0.0\n",
       "1     frontalpole-lh      rostralmiddlefrontal-lh           0.0\n",
       "2     frontalpole-lh             parsorbitalis-lh           0.0\n",
       "3     frontalpole-lh       medialorbitofrontal-lh           0.0\n",
       "4     frontalpole-lh  rostralanteriorcingulate-lh           0.0\n",
       "...              ...                          ...           ...\n",
       "4619  frontalpole-rh  rostralanteriorcingulate-rh           0.0\n",
       "4620  frontalpole-rh       medialorbitofrontal-rh           0.0\n",
       "4621  frontalpole-rh             parsorbitalis-rh           0.0\n",
       "4622  frontalpole-rh      rostralmiddlefrontal-rh           0.0\n",
       "4623  frontalpole-rh               frontalpole-rh           0.0\n",
       "\n",
       "[4624 rows x 3 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order = df_pair.set_index(['source', 'target']).reindex(new_order, level=0).reindex(new_order, level=1).reset_index()\n",
    "df_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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