{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各細胞の発火時系列が与えられたとき，2細胞の発火の時間的相関（temporal correlation）から，ネットワークの構造を推定することができる．\n",
    "\n",
    "本ノートブックでは，文献 {cite}`Kobayashi2019`で結合推定のデモデータとして使用されていた，ラット海馬のスパイクソーティング済みデータを用いる．元のデータの論文，データ詳細についてはCRCNSのホームページに記載されている．  \n",
    "http://crcns.org/data-sets/hc/hc-3/about-hc-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import gaussian_filter\n",
    "\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['figure.dpi'] = 140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>unit</th>\n",
       "      <th>spiketime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-99618.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-96536.180002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-95920.130002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-93842.150003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-92885.660004</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unit     spiketime\n",
       "0     0 -99618.990000\n",
       "1     0 -96536.180002\n",
       "2     0 -95920.130002\n",
       "3     0 -93842.150003\n",
       "4     0 -92885.660004"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datadir = '../datasets/02/'\n",
    "df = pd.read_csv(datadir + 'spikes_unit.csv', index_col=0)\n",
    "\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# filename = '../datasets/02/all.txt'\n",
    "# spiketime = {}\n",
    "\n",
    "# with open(filename) as f:\n",
    "#     neuron_id = 0\n",
    "#     spiketime[neuron_id] = []\n",
    "    \n",
    "#     for line in f:\n",
    "#         value = line.rstrip()\n",
    "        \n",
    "#         if value == ';':\n",
    "#             neuron_id += 1\n",
    "#             spiketime[neuron_id] = []\n",
    "#         else:\n",
    "#             spiketime[neuron_id].append(value)\n",
    "            \n",
    "# lst_neurons = []\n",
    "# lst_spiketime = []\n",
    "\n",
    "# for key, value in spiketime.items():\n",
    "#     lst_neurons.extend([key] * len(value))\n",
    "#     lst_spiketime.extend(value)\n",
    "    \n",
    "# df = pd.DataFrame({'unit': lst_neurons, 'spiketime': lst_spiketime})\n",
    "# df.to_csv('../datasets/02/spikes_unit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1400x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start, end = 0.0, 10000.0\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "df.query('@start <= spiketime <= @end').plot.scatter(x='spiketime', y='unit', c='k', ax=ax)\n",
    "ax.set_xlabel('spiketime [ms]')\n",
    "ax.set_ylabel('unit #')\n",
    "ax.set_yticks([0, 10, 19])\n",
    "\n",
    "plt.locator_params(axis='x', nbins=4)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Correlation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相互相関ヒストグラムを取る手順は次のようになる．\n",
    "\n",
    "1. 相互相関ヒストグラムの窓幅（50ms）を決める．\n",
    "2. 二つのspike train（reference unit: $j$, target unit: $i$）における各spikeについて，窓幅以内の時間差で発生した2つのspikeについて，相対発火時刻（target - reference）を列に格納する．\n",
    "3. 相対発火時刻の列について，特定のbin幅（1ms）によりヒストグラムを作成する．"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{tip}\n",
    "相対発火時刻を計算する際，spike trainが昇順で並んでいることを前提に探索を行うのが効率的である．\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spiketime_relative(spike_times_i, spike_times_j, window_size=50.0):\n",
    "    t_sp = []\n",
    "    i_min, i_max = 0, 0\n",
    "\n",
    "    for t_j in spike_times_j:\n",
    "        # reuse search index for next iteration to decrease the amount of elements to scan\n",
    "        i_min = _upper_bound_idx(lst=spike_times_i, upper=t_j - window_size, start_idx=i_min)\n",
    "        i_max = _upper_bound_idx(lst=spike_times_i, upper=t_j + window_size, start_idx=i_max)\n",
    "        t_sp.extend([(spike_times_i[i] - t_j) for i in range(i_min, i_max)])\n",
    "        \n",
    "    return t_sp\n",
    "\n",
    "def _upper_bound_idx(lst, upper, start_idx=0):\n",
    "    idx = start_idx\n",
    "    while idx < len(lst) and lst[idx] <= upper:\n",
    "        idx += 1\n",
    "    return idx\n",
    "\n",
    "def cross_correlogram(spike_times_i, spike_times_j, window=50., nbins=101):\n",
    "    t_sp = spiketime_relative(spike_times_i, spike_times_j)\n",
    "    hist, edges = np.histogram(t_sp, bins=np.linspace(-window, window, nbins))\n",
    "    return hist, edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i, j = 19, 9\n",
    "spike_times_i = df.query(f'unit=={i}').spiketime.values\n",
    "spike_times_j = df.query(f'unit=={j}').spiketime.values\n",
    "\n",
    "hist, edges = cross_correlogram(spike_times_i, spike_times_j)\n",
    "hist_g = gaussian_filter(hist, sigma=[20])\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.title(f'Cross-correlogram: from neuron {j} to {i}')\n",
    "plt.bar(edges[1:], hist, color='#C0C0C0', width=1.1, label='CC')\n",
    "plt.plot(edges[1:], hist_g, color='k', linewidth=2.0, linestyle='dashed', label='Gaussian Convolved')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r'time lag $\\tau$ [ms]')\n",
    "plt.ylabel(r'count $C(\\tau)$')\n",
    "plt.xlim(-50, 50)\n",
    "plt.xticks([-50, 0, 50])\n",
    "plt.yticks([0, 100, 200])\n",
    "plt.tick_params(direction='in')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上に示した相互相関ヒストグラムの見方は次の通りである．横軸$\\tau$は，**reference neuronのspike時刻を基準とした相対発火時刻**を表す．  \n",
    "neuron 9の発火はneuron 19の発火を促進する傾向（$\\tau > 0$）があり，一方でneuron 19の発火はneuron 9の発火を抑制する傾向（$\\tau < 0$）がある．"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effective Connectivity"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シナプス結合は，以下の3種類に大別することができる {cite}`sporns2004organization`．\n",
    "\n",
    "```{important}\n",
    "1. **構造的結合（structural connectivity）/ 解剖学的結合（anatomical connectivity）**:  \n",
    "    ニューロン間のシナプスを形成する軸索や樹状突起など、脳領域間の物理的結合を指す。\n",
    "2. **機能的結合（functional connectivity）**:  \n",
    "    領域間の統計的な依存関係や相関関係を指す．機能的結合は必ずしも解剖学的な直接的結合とは対応せず、異なる脳領域が機能的に結合または同期している度合いを反映する。\n",
    "3. **実効的結合（effective connectivity）**:  \n",
    "    ある脳領域が他の脳領域に与える因果的な影響のことで、脳内ネットワークにおける情報の流れの方向と強さに関する情報を提供する。\n",
    "```\n",
    "\n",
    "相互相関ヒストグラムにより推定される結合強度は，実効的結合である．実効的結合は，必ずしも神経伝達物質の放出量や受容体の密度といったシナプスの生理学的特性と対応するとは限らないが，発火活動における因果的な影響の指標として有効である．\n",
    "\n",
    "ここで，**$W_{ij}$をneuron $j$からneuron $i$への実効的結合強度とする**．相互相関ヒストグラムは，以下の3要素に分解できる{cite}`Spivak2022`．\n",
    "1. 多数の神経細胞から流入するbackground活動（baseline）\n",
    "2. neuron $j$からneuron $i$への因果的影響力（$\\tau > 0$）\n",
    "3. neuron $i$からneuron $j$への因果的影響力（$\\tau < 0$）\n",
    "\n",
    "1は，相互相関ヒストグラムを平滑化した曲線により近似することができ，簡易的にはガウシアンカーネルによる畳み込みがよく用いられる．  \n",
    "2, 3については，シナプス伝達遅れが一般に $\\tau=1 \\sim 5 $ msであることを踏まえ，簡易的には該当範囲でCCを加算した結果を正規化した値を用いたり，シナプス効果のモデル（e.g. アルファ関数）をフィッティングした係数が用いられたりする．\n",
    "\n",
    "以下では，{cite}`English2017`を元にした実装を述べる．シナプス効果を忠実にモデル化した例としては{cite}`Kobayashi2019`などがある．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シナプス効果が反映されたCCの領域として，以下の領域を採用する．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range of synaptic delay: \n",
      "[-5. -4. -3. -2.]\n",
      "[2. 3. 4. 5.]\n"
     ]
    }
   ],
   "source": [
    "print('range of synaptic delay: ')\n",
    "print(edges[1:][44:48])\n",
    "print(edges[1:][51:55])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x420 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i, j = 19, 9\n",
    "spike_times_i = df.query(f'unit=={i}').spiketime.values\n",
    "spike_times_j = df.query(f'unit=={j}').spiketime.values\n",
    "\n",
    "hist, edges = cross_correlogram(spike_times_i, spike_times_j)\n",
    "hist_g = gaussian_filter(hist, sigma=[20])\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))\n",
    "\n",
    "ax1.set_title(rf'from neuron $j={j}$ to $i={i}$')\n",
    "ax1.bar(edges[1:], hist, color='k', width=1.1, label='CC')\n",
    "ax1.plot(edges[1:], hist_g, color='lime', linewidth=2.0, label='Gaussian Convolved')\n",
    "ax1.legend(fontsize=10, loc='upper center')\n",
    "\n",
    "ax1.set_xlabel(r'$\\tau$ [ms]')\n",
    "ax1.set_ylabel(r'$C(\\tau)$')\n",
    "ax1.set_xlim(-50, 50)\n",
    "ax1.set_xticks([-50, 0, 50])\n",
    "ax1.tick_params(direction='in')\n",
    "\n",
    "hist_sub = hist - hist_g\n",
    "ax2.set_title('Baseline Subtracted')\n",
    "ax2.bar(edges[1:], hist_sub, color='k', width=1.1)\n",
    "\n",
    "ax2.bar(edges[1:][51:55], hist_sub[51:55], color='magenta', width=1.1, label=r'$W_{ij}$')\n",
    "ax2.bar(edges[1:][44:48], hist_sub[44:48], color='cyan', width=1.1, label=r'$W_{ji}$')\n",
    "ax2.legend(fontsize=10, loc='upper right')\n",
    "\n",
    "ax2.set_xlabel(r'$\\tau$ [ms]')\n",
    "ax2.set_xlim(-50, 50)\n",
    "ax2.set_xticks([-50, 0, 50])\n",
    "ax2.tick_params(direction='in')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "左図が2ニューロン間で作成された相互相関ヒストグラムとガウスカーネルにより平滑化された曲線，右側が相互相関ヒストグラムから平滑化されたベースラインの曲線を差し引いたヒストグラムである．シナプス効果が反映されていると考えられる領域を，マゼンタ・シアンで示した．この領域の合計を，シナプスの実効的結合強度とみなす．ただし，合計値はpresynaptic neuron（reference neuron）のspike数で割ることにより正規化する．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_connectivity(spike_times_i, spike_times_j, start=2, end=5, window=50., nbins=101):\n",
    "    # CC - CCG\n",
    "    hist, edges = cross_correlogram(spike_times_i, spike_times_j, window, nbins)\n",
    "    hist_g = gaussian_filter(hist, sigma=[20])\n",
    "    hist_sub = hist - hist_g\n",
    "\n",
    "    # range of synaptic delay\n",
    "    edges_ = edges[1:]\n",
    "    idx_ij = (start <= edges_) & (edges_ <= end)  # j --> i\n",
    "    idx_ji = (-end <= edges_) & (edges_ <= -start)  # i --> j\n",
    "    w_ij = np.sum(hist_sub[idx_ij]) / len(spike_times_j)  # normalize the sum with the number of presynaptic spikes\n",
    "    w_ji = np.sum(hist_sub[idx_ji]) / len(spike_times_i)\n",
    "    return w_ij, w_ji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "neurons = set(df.unit)\n",
    "n_neurons = len(neurons)\n",
    "w = np.zeros((n_neurons, n_neurons))\n",
    "\n",
    "for (i, j) in itertools.combinations(neurons, 2):\n",
    "    if i == j:\n",
    "        continue\n",
    "    else:\n",
    "        spike_times_i = df.query(f'unit=={i}').spiketime.values\n",
    "        spike_times_j = df.query(f'unit=={j}').spiketime.values\n",
    "        w_ij, w_ji = estimate_connectivity(spike_times_i, spike_times_j)\n",
    "        w[i, j] = w_ij\n",
    "        w[j, i] = w_ji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 560x420 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 3))\n",
    "\n",
    "# thresholding weight matrix\n",
    "w_ = np.zeros_like(w)\n",
    "\n",
    "thre_exc = 0.005\n",
    "thre_inh = 0.002\n",
    "w_[w > thre_exc] = w[w > thre_exc]\n",
    "w_[w < -thre_inh] = w[w < -thre_inh]\n",
    "\n",
    "# plotting\n",
    "sns.heatmap(w_, vmax=0.01, vmin=-0.01, center=0, ax=ax, cmap=plt.get_cmap('RdBu_r'), \n",
    "            linewidths=1.0, cbar_kws=dict(ticks=[0.01, 0, -0.01], label='effective connectivity'))\n",
    "\n",
    "ax.set_xticks([0.5, 10.5, 19.5])\n",
    "ax.set_yticks([0.5, 10.5, 19.5])\n",
    "ax.set_xticklabels([0, 10, 19])\n",
    "ax.set_yticklabels([0, 10, 19])\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "\n",
    "ax.set_xlabel('pre-unit')\n",
    "ax.set_ylabel('post-unit')\n",
    "ax.set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{tip}\n",
    "推定されたシナプス強度は，同じpre-unit（シナプス前細胞）について，興奮性あるいは抑制性の分類が一貫しているか確かめると良い．上の例では，列方向（縦）．例えば，neuron 17, 18, 19は抑制性細胞である可能性が高い．\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Connectivity Network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "推定された実効的結合強度に基づき，神経ネットワークをnetworkxにより可視化するコードの一例を以下に示す．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.DiGraph()\n",
    "\n",
    "for neuron in neurons:\n",
    "    G.add_node(neuron)\n",
    "\n",
    "for i in range(w.shape[0]):\n",
    "    for j in range(w.shape[1]):\n",
    "        w_ij = w[i, j]\n",
    "        if w_ij > thre_exc:\n",
    "            G.add_edge(j, i, weight=w_ij, color='magenta')  # exc\n",
    "        elif w_ij < -thre_inh:\n",
    "            G.add_edge(j, i, weight=w_ij, color='cyan')  # inh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 560x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_color = [G[u][v]['color'] for u, v in G.edges()]\n",
    "edge_width = [np.log10(abs(G[u][v]['weight'])) + 4 for u, v in G.edges()]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "nx.draw(G, pos=nx.circular_layout(G), with_labels=True, node_color='w', font_color='k', \n",
    "        edge_color=edge_color, width=edge_width, arrows=True, arrowsize=10, connectionstyle='arc3, rad=0.2')\n",
    "\n",
    "fig.set_facecolor('k')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ネットワークはBokehを使えばインタラクティブに可視化することもできる．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"p2704\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"p2704\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"p2704\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.plotting import curdoc, figure, from_networkx\n",
    "from bokeh.models import Circle, MultiLine, NodesAndLinkedEdges\n",
    "from bokeh.palettes import Spectral8\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_network_plot(G, pos):\n",
    "    node_highlight_color = Spectral8[4]\n",
    "    edge_highlight_color = Spectral8[4]\n",
    "    \n",
    "    HOVER_TOOLTIPS = [(\"neuron\", \"@index\"), (\"degree\", \"@degree\")]\n",
    "    plot = figure(tooltips = HOVER_TOOLTIPS, tools=\"pan,box_zoom,wheel_zoom,save,reset\", width=400, height=400)\n",
    "    \n",
    "    plot.axis.major_tick_line_color = None\n",
    "    plot.axis.minor_tick_line_color = None\n",
    "    plot.axis.major_label_text_color = None  \n",
    "    plot.xgrid.visible = False\n",
    "    plot.ygrid.visible = False  \n",
    "    \n",
    "    network_graph = from_networkx(G, pos, scale=8, center=(0, 0))\n",
    "    network_graph.node_renderer.glyph = Circle(size=20, fill_color='white')\n",
    "    network_graph.node_renderer.hover_glyph = Circle(size=20, fill_color=node_highlight_color, line_width=2)\n",
    "    network_graph.node_renderer.selection_glyph = Circle(size=20, fill_color=node_highlight_color, line_width=2)\n",
    "\n",
    "    network_graph.edge_renderer.glyph = MultiLine(line_color='edge_color', line_alpha=0.8, line_width='edge_width')\n",
    "    network_graph.edge_renderer.selection_glyph = MultiLine(line_color=edge_highlight_color, line_width='edge_width')\n",
    "    network_graph.edge_renderer.hover_glyph = MultiLine(line_color=edge_highlight_color, line_width='edge_width')\n",
    "    \n",
    "    network_graph.selection_policy = NodesAndLinkedEdges()\n",
    "    network_graph.inspection_policy = NodesAndLinkedEdges()\n",
    "    plot.renderers.append(network_graph)\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"e8206a48-2ce4-4ed3-9e05-313ec6e18197\" data-root-id=\"p2705\" style=\"display: contents;\"></div>\n"
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 const render_items = [{\"docid\":\"351c3d20-6547-48da-91f2-67d5af8eead4\",\"roots\":{\"p2705\":\"e8206a48-2ce4-4ed3-9e05-313ec6e18197\"},\"root_ids\":[\"p2705\"]}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
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   ],
   "source": [
    "# node attributes\n",
    "nx.set_node_attributes(G, name='degree', values=dict(nx.degree(G)))\n",
    "\n",
    "# edge attributes\n",
    "edge_color = {(u, v): G[u][v]['color'] for u, v in G.edges()}\n",
    "edge_width = {(u, v): np.log10(abs(G[u][v]['weight'])) + 8 for u, v in G.edges()}\n",
    "\n",
    "nx.set_edge_attributes(G, edge_color, \"edge_color\")\n",
    "nx.set_edge_attributes(G, edge_width, \"edge_width\")\n",
    "\n",
    "curdoc().theme = 'dark_minimal'\n",
    "p = make_network_plot(G, nx.circular_layout(G))\n",
    "show(p)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "```{bibliography}\n",
    ":filter: docname in docnames\n",
    "```"
   ]
  }
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