{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ei_net import *\n",
    "from ce_net import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################\n",
    "############ PLOTTING SETUP ##############\n",
    "EI_cmap = \"Greys\"\n",
    "where_to_save_pngs = \"../figs/pngs/\"\n",
    "where_to_save_pdfs = \"../figs/pdfs/\"\n",
    "save = True\n",
    "plt.rc('axes', axisbelow=True)\n",
    "##########################################\n",
    "##########################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The emergence of informative higher scales in complex networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 06 - Causal Emergence and the Emergence of Scale\n",
    "\n",
    "## Network Macroscales\n",
    "First we must introduce how to recast a network, $G$, at a higher scale. This is represented by a new network, $G_M$. Within $G_M$, a micro-node is a node that was present in the original $G$, whereas a macro-node is defined as a node, $v_M$, that represents a subgraph, $S_i$, from the original $G$ (replacing the subgraph within the network). Since the original network has been dimensionally reduced by grouping nodes together, $G_M$ will always have fewer nodes than $G$.\n",
    "\n",
    "A macro-node $\\mu$ is defined by some $W^{out}_{\\mu}$, derived from the edge weights of the various nodes within the subgraph it represents. One can think of a macro-node as being a summary statistic of the underlying subgraph's behavior, a statistic that takes the form of a single node. Ultimately there are many ways of representing a subgraph, that is, building a macro-node, and some ways are more accurate than others in capturing the subgraph's behavior, depending on the connectivity. To decide whether or not a macro-node is an accurate summary of its underlying subgraph, we check whether random walkers behave identically on $G$ and $G_M$. We do this because many important analyses and algorithms---such as using PageRank for determining a node's centrality or InfoMap for community discovery---are based on random walking.\n",
    "\n",
    "Specifically, we define the *inaccuracy* of a macroscale as the Kullback-Leibler divergence between the expected distribution of random walkers on $G$ vs. $G_M$, given some identical initial distribution on each. The  expected distribution over $G$ at some future time $t$ is $P_m(t)$, while the distribution over $G_M$ at some future time $t$ is $P_M(t)$. To compare the two, the distribution $P_m(t)$ is summed over the same nodes in the macroscale $G_M$, resulting in the distribution $P_{M|m}(t)$ (the microscale given the macroscale). We can then define the macroscale inaccuracy over some series of time steps $T$ as:\n",
    "\n",
    "$$ \\text{inaccuracy} = \\sum_{t=0}^T \\text{D}_{_{KL}}[P_{M}(t) || P_{M|m}(t)] $$\n",
    "\n",
    "This measure addresses the extent to which a random dynamical process on the microscale topology will be recapitulated on a dimensionally-reduced topology.\n",
    "\n",
    "What constitutes an accurate macroscale depends on the connectivity of the subgraph that gets grouped into a macro-node. The $W^{out}_{\\mu}$ can be constructed based on the collective $W^{out}$ of the subgraph. For instance, in some cases one could just coarse-grain a subgraph by using its average $W^{out}$ as the $W^{out}_{\\mu}$ of some new macro-node $\\mu$. However, it may be that the subgraph has dependencies not captured by such a coarse-grain. Indeed, this is similar to the recent discovery that when constructing networks from data it is often necessary to explicitly model higher-order dependencies by using higher-order nodes so that the dynamics of random walks to stay true to the original data. We therefore introduce *higher-order macro-nodes* (HOMs), which draw on similar techniques to accurately represent subgraphs as single nodes.\n",
    "____________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"../figs/pngs/CoarseGraining.png\" width=800>\n",
    "\n",
    "- Top: The original network, $G$ along with its adjacency matrix (left). The shaded oval indicates that subgraph $S$ member nodes $v_B$ and $v_C$ will be grouped together, forming a macro-node, ${\\mu}$. All macro-nodes are some transformation of the original adjacency matrix via recasting it as a new adjacency matrix (right). The manner of this recasting depends on the type of macro-node. \n",
    "- Bottom left: The simplest form of a macro-node is when $W^{out}_{\\mu}$ is an average of the $W^{out}_{i}$ of each node in the subgraph. \n",
    "- Bottom center left: A macro-node that represents some path-dependency, such as input from $A$. Here, in averaging to create the $W^{out}_{\\mu}$ the out-weights of nodes $v_B$ and $v_C$ are weighted by their input from  $v_A$. \n",
    "- Bottom center right: A macro-node that represents the subgraph's output over the network's stationary dynamics. Each node has some associated ${\\pi}_{i}$, which is the probability of ${v}_{i}$ in the stationary distribution of the network. The $W^{out}_{\\mu}$ of a $\\mu | \\pi$ macro-node is created by weighting each $W^{out}_{i}$ of the micro-nodes in the subgraph $S$ by $\\frac{{\\pi}_{i}}{\\sum_{k \\in S} {\\pi}_{k}}$. \n",
    "- Bottom right: A macro-node with a single timestep delay between input $\\mu | j$ and its output $\\mu | \\pi$, each constructed using the same techniques as its components. However, $\\mu | j$ always deterministically outputs to $\\mu | \\pi$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different subgraph connectivities require different types of HOMs to accurately represent. For instance, HOMs can be based on the input weights to the macro-node, which take the form $\\mu | j$. In these cases the $W^{out}_{\\mu|j}$ is a weighted average of each node's $W^{out}$ in the subgraph, where the weight is based on the input weight to each node in the subgraph. Another type of HOM that generally leads to accurate macro-nodes over time is when the $W^{out}_{\\mu}$ is based on the stationary output from the subgraph to the rest of the network, which we represent as $\\mu | \\pi$. These types of HOMs may sometimes have minor inaccuracies given some initial state, but will almost always trend toward perfect accuracy as the network approaches its stationary dynamics. \n",
    "\n",
    "Subgraphs with complex internal dynamics can require a more complex type of HOM in order to preserve the network's accuracy. For instance, in cases where subgraphs have a delay between their inputs and outputs, this can be represented by a combination of $\\mu | j$ and $\\mu | \\pi$, which when combined captures that delay. In these cases the macro-node $\\mu$ has two components, one of which acts as a buffer over a timestep. This means that macro-nodes can possess memory even when constructed from networks that are at the microscale memoryless, and in fact this type of HOM is sometimes necessary to accurately capture the microscale dynamics.\n",
    "\n",
    "We present these types of macro-nodes not as an exhaustive list of all possible HOMs, but rather as examples of how to construct higher scales in a network by representing subgraphs as nodes, and also sometimes using higher-order dependencies to ensure those nodes are accurate. This approach offers a complete generalization of previous work on coarse-grains and also black boxes, while simultaneously solving the previously unresolved issue of macroscale accuracy by using higher-order dependencies. The types of macro-nodes formed by subgraphs also provides substantive information about the network, such as whether the macroscale of a network possesses memory or path-dependency.\n",
    "\n",
    "## Causal emergence reveals the scale of networks\n",
    "\n",
    "Causal emergence occurs when a recast network, $G_M$ (a macroscale), has more $EI$ than the original network, $G$ (the microscale). In general, networks with lower effectiveness (low $EI$ given their size) have a higher potential for causal emergence, since they can be recast to reduce their uncertainty. Searching across groupings allows the identification or approximation of a macroscale that maximizes the $EI$. \n",
    "\n",
    "Checking all possible groupings is computationally intractable for all but the smallest networks. Therefore, in order to find macro-nodes which increase the $EI$, we use a greedy algorithm that groups nodes together and checks if the $EI$ increases. By choosing a node and then pairing it iteratively with its surrounding nodes we can grow macro-nodes until pairings no longer increase the $EI$, and then move on to a new node.\n",
    "\n",
    "By generating undirected preferential attachment networks and varying the degree of preferential attachment, $\\alpha$, we observe a crucial relationship between preferential attachment and causal emergence. One of the central results in network science has been the identification of \"scale-free\" networks. Our results show that networks that are not \"scale-free\" can be further separated into micro-, meso-, and macroscales depending on their connectivity. This scale can be identified based on their degree of causal emergence. In cases of sublinear preferential attachment ($\\alpha < 1.0$) networks lack higher scales. Linear preferential attachment ($\\alpha=1.0$) produces networks that are scale-free, which is the zone of preferential attachment right before the network develops higher scales. Such higher scales only exist in cases of superlinear preferential attachment ($\\alpha > 1.0$). And past $\\alpha > 3.0$ the network begins to converge to a macroscale where almost all the nodes are grouped into a single macro-node. The greatest degree of causal emergence is found in mesoscale networks, which is when $\\alpha$ is between 1.5 and 3.0, when networks possess a rich array of macro-nodes.\n",
    "\n",
    "Correspondingly the size of $G_M$ decreases as $\\alpha$ increases and the network develops an informative higher scale, which can be seen in the ratio of macroscale network size, $N_M$, to the original network size, $N$. As discussed in previous sections, on the upper end of the spectrum of $\\alpha$ the resulting network will approximate a hub-and-spoke, star-like network. Star-like networks have higher degeneracy and thus less $EI$, and because of this, we expect that there are more opportunities to increase the network's $EI$ through grouping nodes into macro-nodes. Indeed, the ideal grouping of a star network is when $N_M=2$ and $EI$ is 1 bit. This result is similar to recent advances in spectral coarse-graining that also observe that the ideal coarse-graining of a star network is to collapse it into a two-node network, grouping all the spokes into a single macro-node, which is what happens to star networks that are recast as macroscales.\n",
    "\n",
    "Our results offer a principled and general approach to such community detection by asking when there is an informational gain from replacing a subgraph with a single node. Therefore we can define *causal communities* as being when a cluster of nodes, or some subgraph, forms a viable macro-node. Fundamentally causal communities represent noise at the microscale. The closer a subgraph is to complete noise, the greater the gain in $EI$ by replacing it with a macro-node. Minimizing the noise in a given network also identifies the optimal scale to represent that network. However, there must be some structure that can be revealed by noise minimization in the first place. In cases of random networks that form a single large component which lacks any such structure, causal emergence does not occur.\n",
    "____________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Causal Emergence in Preferential Attachment Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preferential_attachment_network(N, alpha=1.0, m=1):\n",
    "    \"\"\"\n",
    "    Generates a network based off of a preferential attachment \n",
    "    growth rule. Under this growth rule, new nodes place their \n",
    "    $m$ edges to nodes already present in the graph, G, with \n",
    "    a probability proportional to $k^\\alpha$.\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    N (int): the desired number of nodes in the final network\n",
    "    alpha (float): the exponent of preferential attachment. \n",
    "                   When alpha is less than 1.0, we describe it\n",
    "                   as sublinear preferential attachment. At\n",
    "                   alpha > 1.0, it is superlinear preferential\n",
    "                   attachment. And at alpha=1.0, the network \n",
    "                   was grown under linear preferential attachment,\n",
    "                   as in the case of Barabasi-Albert networks.\n",
    "    m (int): the number of new links that each new node joins\n",
    "             the network with.\n",
    "             \n",
    "    Returns\n",
    "    -------\n",
    "    G (nx.Graph): a graph grown under preferential attachment.\n",
    "    \n",
    "    \"\"\"\n",
    "    G = nx.Graph()\n",
    "    G = nx.complete_graph(m+1)\n",
    "\n",
    "    for node_i in range(m+1,N):\n",
    "        degrees = np.array(list(dict(G.degree()).values()))\n",
    "        probs = (degrees**alpha) / sum(degrees**alpha)\n",
    "        eijs = np.random.choice(G.number_of_nodes(), \n",
    "                                size=(m,), replace=False, p=probs)\n",
    "        for node_j in eijs:\n",
    "            G.add_edge(node_i, node_j)\n",
    "\n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nvals = sorted([30,60,90,120,150])\n",
    "alphas= np.linspace(-1,5,25)\n",
    "Niter = 2\n",
    "\n",
    "m     = 1\n",
    "pa_ce = {'alpha'  :[], \n",
    "         'N_micro':[],\n",
    "         'N_macro':[],\n",
    "         'EI_micro':[],\n",
    "         'EI_macro':[],\n",
    "         'CE'      :[],\n",
    "         'N_frac'  :[],\n",
    "         'runtime' :[]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: the following cell was run on a super-computing cluster. It is included as an example computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for N in Nvals:\n",
    "    for alpha in alphas:\n",
    "        for _ in range(Niter):\n",
    "            G      = preferential_attachment_network(N,alpha,m)\n",
    "\n",
    "            startT = dt.datetime.now()\n",
    "            CE     = causal_emergence(G, printt=False)\n",
    "            finisH = dt.datetime.now()\n",
    "\n",
    "            diff   = finisH-startT\n",
    "            diff   = diff.total_seconds()\n",
    "\n",
    "            pa_ce['alpha'].append(alpha)\n",
    "            pa_ce['N_micro'].append(N)\n",
    "            pa_ce['N_macro'].append(CE['G_macro'].number_of_nodes())\n",
    "            pa_ce['EI_micro'].append(CE['EI_micro'])\n",
    "            pa_ce['EI_macro'].append(CE['EI_macro'])\n",
    "            pa_ce['CE'].append(CE['EI_macro']-CE['EI_micro'])\n",
    "            pa_ce['N_frac'].append(CE['G_macro'].number_of_nodes()/N)\n",
    "            pa_ce['runtime'].append(diff)\n",
    "            \n",
    "NCE = pa_ce.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import cmocean as cmo\n",
    "# colorz = cmo.cm.amp(np.linspace(0.2,0.9,len(Nvals)))\n",
    "colorz = plt.cm.viridis(np.linspace(0,0.9,len(Nvals)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 342x307.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mult=0.95\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5.0*mult,4.5*mult))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.24, hspace=0.11)\n",
    "ymax_so_far = 0\n",
    "xmin_so_far = 0\n",
    "xmax_so_far = 0\n",
    "\n",
    "for i,Nn in enumerate(Nvals):\n",
    "    col = colorz[i]\n",
    "    means = [np.mean(NCE[Nn][i]['CE']) for i in NCE[Nn].keys()]\n",
    "    stdvs = [np.std(NCE[Nn][i]['CE'])  for i in NCE[Nn].keys()]\n",
    "    alphs = list(NCE[Nn].keys())\n",
    "\n",
    "    alphs = np.array([(alphs[i]+alphs[i+1])/2\n",
    "                      for i in range(0,len(alphs)-1,2)])\n",
    "    means = np.array([(means[i]+means[i+1])/2\n",
    "                      for i in range(0,len(means)-1,2)])\n",
    "    stdvs = np.array([(stdvs[i]+stdvs[i+1])/2\n",
    "                      for i in range(0,len(stdvs)-1,2)])\n",
    "    \n",
    "    xmin_so_far = min([xmin_so_far, min(alphs)])\n",
    "    xmax_so_far = max([xmax_so_far, max(alphs)])\n",
    "    ymax_so_far = max([ymax_so_far, max(means+stdvs)])\n",
    "\n",
    "    ax.plot(alphs, means,\n",
    "            markeredgecolor=col, color=col,\n",
    "            markerfacecolor='w',\n",
    "            markeredgewidth=1.5,markersize=5.0,\n",
    "           linestyle='-',marker='o',linewidth=2.2,label='N = %i'%Nn)\n",
    "    \n",
    "    ax.fill_between(alphs, means-stdvs, means+stdvs,\n",
    "                    facecolor=col, alpha=0.2, \n",
    "                    edgecolors='w', linewidth=1)\n",
    "\n",
    "cols = [\"#a7d6ca\",\"#dbb9d1\",\"#d6cdae\",\"#a5c9e3\"]    \n",
    "ax.fill_between([-2,0.90],[-1,-1],[3,3], \n",
    "                facecolor=cols[0],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([0.90,1.1],[-1,-1],[3,3],\n",
    "                facecolor=cols[1],alpha=0.7,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([1.1,3.0],[-1,-1],[3,3],\n",
    "                facecolor=cols[2],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([3.0,6],[-1,-1],[3,3],\n",
    "                facecolor=cols[3],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "    \n",
    "ax.text(-0.500, 2.65, '|', fontsize=14)\n",
    "ax.text(0.9425, 2.65, '|', fontsize=14)\n",
    "ax.text(0.9425, 2.72, '|', fontsize=14)\n",
    "ax.text(0.9425, 2.79, '|', fontsize=14)\n",
    "ax.text(2.4000, 2.65, '|', fontsize=14)\n",
    "ax.text(4.2500, 2.65, '|', fontsize=14)\n",
    "    \n",
    "ax.text(-1.1, 2.81,'microscale',fontsize=12)\n",
    "ax.text(0.35, 2.95,'scale-free',fontsize=12)\n",
    "ax.text(1.70, 2.81,'mesoscale',fontsize=12)\n",
    "ax.text(3.45, 2.81,'macroscale',fontsize=12)\n",
    "    \n",
    "ax.set_ylim(-0.025*ymax_so_far,ymax_so_far*1.05)\n",
    "ax.set_xlim(-1.075,5*1.01)\n",
    "ax.set_xlabel(r'$\\alpha$',fontsize=14)\n",
    "ax.set_ylabel('Causal emergence',fontsize=14, labelpad=10)\n",
    "ax.legend(loc=6,framealpha=0.99)\n",
    "ax.set_xticks(np.linspace(-1,5,7))\n",
    "ax.set_xticklabels([\"%i\"%i for i in np.linspace(-1,5,7)])\n",
    "ax.grid(linestyle='-', linewidth=2.0, color='#999999', alpha=0.3)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+\\\n",
    "        'CE_pa_alpha_labs.png', \n",
    "        dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+\\\n",
    "        'CE_pa_alpha_labs.pdf', \n",
    "        dpi=425, bbox_inches='tight')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 342x307.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mult=0.95\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(5.0*mult,4.5*mult))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.24, hspace=0.11)\n",
    "ymax_so_far = 0\n",
    "xmin_so_far = 0\n",
    "xmax_so_far = 0\n",
    "\n",
    "for i,Nn in enumerate(Nvals):\n",
    "    col = colorz[i]\n",
    "    means = [np.mean(NCE[Nn][i]['N_frac']) for i in NCE[Nn].keys()]\n",
    "    stdvs = [np.std(NCE[Nn][i]['N_frac'])  for i in NCE[Nn].keys()]\n",
    "    alphs = list(NCE[Nn].keys())\n",
    "\n",
    "    alphs = np.array([(alphs[i]+alphs[i+1])/2 \n",
    "                      for i in range(0,len(alphs)-1,2)])\n",
    "    means = np.array([(means[i]+means[i+1])/2 \n",
    "                      for i in range(0,len(means)-1,2)])\n",
    "    stdvs = np.array([(stdvs[i]+stdvs[i+1])/2 \n",
    "                      for i in range(0,len(stdvs)-1,2)])\n",
    "\n",
    "    xmin_so_far = min([xmin_so_far, min(alphs)])\n",
    "    xmax_so_far = max([xmax_so_far, max(alphs)])\n",
    "    ymax_so_far = max([ymax_so_far, max(means+stdvs)])\n",
    "    \n",
    "    ax.semilogy(alphs, means, markeredgecolor=col,\n",
    "                color=col,markerfacecolor='w',\n",
    "                markeredgewidth=1.5, markersize=5.0,\n",
    "                linestyle='-',marker='o',linewidth=2.0,\n",
    "                alpha=0.99,label='N = %i'%Nn)\n",
    "    ax.fill_between(alphs, means-stdvs, means+stdvs, \n",
    "                    facecolor=col,alpha=0.2,\n",
    "                    edgecolors='w',linewidth=1)\n",
    "    \n",
    "cols = [\"#a7d6ca\",\"#dbb9d1\",\"#d6cdae\",\"#a5c9e3\"]    \n",
    "ax.fill_between([-2,0.9],[-1,-1],[3,3],\n",
    "                facecolor=cols[0],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([0.9,1.1],[-1,-1],[3,3],\n",
    "                facecolor=cols[1],alpha=0.7,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([1.1,3.0],[-1,-1],[3,3],\n",
    "                facecolor=cols[2],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "ax.fill_between([3.0,6],[-1,-1],[3,3],\n",
    "                facecolor=cols[3],alpha=0.3,edgecolors='w',linewidth=0)\n",
    "    \n",
    "ax.text(-0.50, 1.036,'|', fontsize=14)\n",
    "ax.text(0.935, 1.036,'|', fontsize=14)\n",
    "ax.text(0.935, 1.170,'|', fontsize=14)\n",
    "ax.text(0.935, 1.320,'|', fontsize=14)\n",
    "ax.text(2.400, 1.036,'|', fontsize=14)\n",
    "ax.text(4.250, 1.036,'|', fontsize=14)\n",
    "\n",
    "ax.text(-1.1, 1.368, 'microscale', fontsize=12)\n",
    "ax.text(0.35, 1.750, 'scale-free', fontsize=12)\n",
    "ax.text(1.70, 1.368, 'mesoscale',  fontsize=12)\n",
    "ax.text(3.45, 1.368, 'macroscale', fontsize=12)\n",
    "    \n",
    "ax.set_ylim(0.009*ymax_so_far,ymax_so_far*1.075)\n",
    "ax.set_xlim(-1.075,5*1.01)\n",
    "ax.set_xlabel(r'$\\alpha$',fontsize=14)\n",
    "ax.set_ylabel('Size ratio: macro to micro',\n",
    "              fontsize=14, labelpad=2)\n",
    "ax.legend(loc=6,framealpha=0.99)\n",
    "ax.set_xticks(np.linspace(-1,5,7))\n",
    "ax.set_xticklabels([\"%i\"%i for i in np.linspace(-1,5,7)])\n",
    "ax.grid(linestyle='-', linewidth=2.0, color='#999999', alpha=0.3)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+\\\n",
    "        'Nfrac_pa_alpha_labs.png',\n",
    "        dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+\\\n",
    "        'Nfrac_pa_alpha_labs.pdf',\n",
    "        dpi=425, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Causal Emergence of Random Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ns = [20,30,40,50]\n",
    "ps = np.round(np.logspace(-3.25,-0.4,31),5)\n",
    "Niter = 40\n",
    "\n",
    "er_ce = {'p'  :[], \n",
    "         'N_micro':[],\n",
    "         'N_macro':[],\n",
    "         'EI_micro':[],\n",
    "         'EI_macro':[],\n",
    "         'CE_mean' :[],\n",
    "         'CE_stdv' :[],\n",
    "         'N_frac'  :[],\n",
    "         'runtime' :[]}\n",
    "ER_CE = {N:er_ce for N in Ns}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: the following cell was run on a super-computing cluster. It is included as an example computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for N in Ns:    \n",
    "    print(N, dt.datetime.now())\n",
    "    er_ce = {'p'  :[], \n",
    "             'N_micro':[],\n",
    "             'N_macro':[],\n",
    "             'EI_micro':[],\n",
    "             'EI_macro':[],\n",
    "             'CE_mean' :[],\n",
    "             'CE_stdv' :[],\n",
    "             'N_frac'  :[],\n",
    "             'runtime' :[]}\n",
    "\n",
    "    for p in ps:\n",
    "        print('\\t',p)\n",
    "        cee = []\n",
    "        for rr in range(Niter):\n",
    "            G      = nx.erdos_renyi_graph(N,p)\n",
    "            startT = dt.datetime.now()\n",
    "            CE     = causal_emergence(G,printt=False)\n",
    "            finisH = dt.datetime.now()\n",
    "\n",
    "            diff   = finisH-startT\n",
    "            diff   = diff.total_seconds()\n",
    "            ce = CE['EI_macro']-CE['EI_micro']\n",
    "            cee.append(ce)\n",
    "            \n",
    "        er_ce['p'].append(p)\n",
    "        er_ce['N_micro'].append(N)\n",
    "        er_ce['N_macro'].append(CE['G_macro'].number_of_nodes())\n",
    "        er_ce['EI_micro'].append(CE['EI_micro'])\n",
    "        er_ce['EI_macro'].append(CE['EI_macro'])\n",
    "        er_ce['CE_mean'].append(np.mean(cee))\n",
    "        er_ce['CE_stdv'].append(np.std( cee))\n",
    "        er_ce['runtime'].append(diff)\n",
    "        \n",
    "    ER_CE[N] = er_ce.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brennan/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:3099: UserWarning: Attempting to set identical left==right results\n",
      "in singular transformations; automatically expanding.\n",
      "left=100.0, right=100.0\n",
      "  self.set_xlim(upper, lower, auto=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import cmocean as cmo\n",
    "# colors = cmo.cm.thermal(np.linspace(0.1,0.95,len(Ns)))\n",
    "colors = plt.cm.viridis(np.linspace(0.0,1,len(Ns)))\n",
    "\n",
    "i = 0\n",
    "ymax = 0\n",
    "plt.vlines(100, -1, 1, \n",
    "           label=r'$\\langle k \\rangle=1$', linestyle='--',\n",
    "           color=\"#333333\", linewidth=3.5, alpha=0.99)\n",
    "for N in Ns:\n",
    "    CE1 = np.array(ER_CE1[N]['CE_mean'].copy())\n",
    "    CE2 = np.array(ER_CE2[N]['CE_mean'].copy())\n",
    "    CE3 = np.array(ER_CE3[N]['CE_mean'].copy())\n",
    "    CE4 = np.array(ER_CE4[N]['CE_mean'].copy())\n",
    "    CE5 = np.array(ER_CE5[N]['CE_mean'].copy())\n",
    "    CE6 = np.array(ER_CE6[N]['CE_mean'].copy())\n",
    "    CEs = (CE1 + CE2 + CE3 + CE4 + CE5 + CE6)/6\n",
    "    CEs = list(CEs)\n",
    "    CEs = [(CEs[i] + CEs[i+1])/2 for i in range(0,len(CEs)-1)]\n",
    "    CEs = [0] + CEs\n",
    "    CEs.append(0)\n",
    "    \n",
    "    x1 = np.array(ER_CE1[N]['p'].copy())\n",
    "    x2 = np.array(ER_CE2[N]['p'].copy())\n",
    "    x3 = np.array(ER_CE3[N]['p'].copy())\n",
    "    x4 = np.array(ER_CE4[N]['p'].copy())\n",
    "    x5 = np.array(ER_CE5[N]['p'].copy())\n",
    "    x6 = np.array(ER_CE6[N]['p'].copy())\n",
    "    xx = (x1 + x2 + x3 + x4 + x5 + x6)/6\n",
    "    xx = list(xx)\n",
    "    xx = [(xx[i] + xx[i+1])/2 for i in range(0,len(xx)-1)]\n",
    "    xx = [1e-4] + xx\n",
    "    xx.append(1)\n",
    "    \n",
    "    std1 = np.array(ER_CE1[N]['CE_stdv'].copy())\n",
    "    std2 = np.array(ER_CE2[N]['CE_stdv'].copy())\n",
    "    std3 = np.array(ER_CE3[N]['CE_stdv'].copy())\n",
    "    std4 = np.array(ER_CE4[N]['CE_stdv'].copy())\n",
    "    std5 = np.array(ER_CE5[N]['CE_stdv'].copy())\n",
    "    std6 = np.array(ER_CE6[N]['CE_stdv'].copy())\n",
    "    stds = (std1 + std2 + std3 + std4 + std5 + std6)/6\n",
    "    stds = list(stds)\n",
    "    stds = [(stds[i] + stds[i+1])/2 for i in range(0,len(stds)-1)]\n",
    "    stds = [0] + stds\n",
    "    stds.append(0)\n",
    "\n",
    "    ytop = np.array(CEs) + np.array(stds)\n",
    "    ybot = np.array(CEs) - np.array(stds)\n",
    "    ybot[ybot<0] = 0\n",
    "\n",
    "    ymax = max([ymax, max(ytop)])\n",
    "    \n",
    "    plt.semilogx(xx, CEs, label='N=%i'%N, \n",
    "                 color=colors[i], linewidth=4.0, alpha=0.95)\n",
    "    plt.vlines(1/(N-1), -1, 1, linestyle='--',\n",
    "               color=colors[i], linewidth=3.5, alpha=0.95)\n",
    "    i += 1\n",
    "\n",
    "plt.xlim(2.5e-4,max(xx))\n",
    "plt.ylim(-0.0015, ymax*0.6)\n",
    "plt.grid(linestyle='-', linewidth=2.5, alpha=0.3, color='#999999')\n",
    "plt.ylabel('Causal emergence', fontsize=14)\n",
    "plt.xlabel(r'$p$', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+\\\n",
    "        'CE_ER_p_N.png', dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+\\\n",
    "        'CE_ER_p_N.pdf', dpi=425, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brennan/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:3099: UserWarning: Attempting to set identical left==right results\n",
      "in singular transformations; automatically expanding.\n",
      "left=100.0, right=100.0\n",
      "  self.set_xlim(upper, lower, auto=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import cmocean as cmo\n",
    "# colors = cmo.cm.thermal(np.linspace(0.1,0.95,len(Ns)))\n",
    "colors = plt.cm.viridis(np.linspace(0.0,1,len(Ns)))\n",
    "\n",
    "i = 0\n",
    "ymax = 0\n",
    "plt.vlines(100, -1, 1, label=r'$\\langle k \\rangle=1$', linestyle='--',\n",
    "           color=\"#333333\", linewidth=3.5, alpha=0.99)\n",
    "for N in Ns:\n",
    "    CE1 = np.array(ER_CE1[N]['CE_mean'].copy())\n",
    "    CE2 = np.array(ER_CE2[N]['CE_mean'].copy())\n",
    "    CE3 = np.array(ER_CE3[N]['CE_mean'].copy())\n",
    "    CE4 = np.array(ER_CE4[N]['CE_mean'].copy())\n",
    "    CE5 = np.array(ER_CE5[N]['CE_mean'].copy())\n",
    "    CE6 = np.array(ER_CE6[N]['CE_mean'].copy())\n",
    "    CEs = (CE1 + CE2 + CE3 + CE4 + CE5 + CE6)/6\n",
    "    CEs = list(CEs)\n",
    "    CEs = [(CEs[i] + CEs[i+1])/2 for i in range(0,len(CEs)-1)]\n",
    "    CEs = [0] + CEs\n",
    "    CEs.append(0)\n",
    "    \n",
    "    x1 = np.array(ER_CE1[N]['p'].copy())\n",
    "    x2 = np.array(ER_CE2[N]['p'].copy())\n",
    "    x3 = np.array(ER_CE3[N]['p'].copy())\n",
    "    x4 = np.array(ER_CE4[N]['p'].copy())\n",
    "    x5 = np.array(ER_CE5[N]['p'].copy())\n",
    "    x6 = np.array(ER_CE6[N]['p'].copy())\n",
    "    xx = (x1 + x2 + x3 + x4 + x5 + x6)/6\n",
    "    xx = list(xx)\n",
    "    xx = [(xx[i] + xx[i+1])/2 for i in range(0,len(xx)-1)]\n",
    "    xx = [1e-4] + xx\n",
    "    xx.append(1)\n",
    "    \n",
    "    std1 = np.array(ER_CE1[N]['CE_stdv'].copy())\n",
    "    std2 = np.array(ER_CE2[N]['CE_stdv'].copy())\n",
    "    std3 = np.array(ER_CE3[N]['CE_stdv'].copy())\n",
    "    std4 = np.array(ER_CE4[N]['CE_stdv'].copy())\n",
    "    std5 = np.array(ER_CE5[N]['CE_stdv'].copy())\n",
    "    std6 = np.array(ER_CE6[N]['CE_stdv'].copy())\n",
    "    stds = (std1 + std2 + std3 + std4 + std5 + std6)/6\n",
    "    stds = list(stds)\n",
    "    stds = [(stds[i] + stds[i+1])/2 for i in range(0,len(stds)-1)]\n",
    "    stds = [0] + stds\n",
    "    stds.append(0)\n",
    "\n",
    "    ytop = np.array(CEs) + np.array(stds)\n",
    "    ybot = np.array(CEs) - np.array(stds)\n",
    "    ybot[ybot<0] = 0\n",
    "\n",
    "    ymax = max([ymax, max(ytop)])\n",
    "    \n",
    "    plt.semilogx(xx, CEs, label='N=%i'%N, color=colors[i], \n",
    "                 linewidth=4.0, alpha=0.95)\n",
    "    plt.fill_between(xx, ytop, ybot, facecolor=colors[i], \n",
    "                     linewidth=2.0, alpha=0.35, edgecolor='w')\n",
    "    plt.vlines(1/(N-1), -1, 1, linestyle='--',\n",
    "               color=colors[i], linewidth=3.5, alpha=0.95)\n",
    "    i += 1\n",
    "\n",
    "plt.xlim(2.5e-4,max(xx))\n",
    "plt.ylim(-0.0015, ymax)\n",
    "plt.grid(linestyle='-', linewidth=2.5, \n",
    "         alpha=0.3, color='#999999')\n",
    "plt.ylabel('Causal emergence', fontsize=14)\n",
    "plt.xlabel(r'$p$', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+\\\n",
    "        'CE_ER_p_N0.png', dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+\\\n",
    "        'CE_ER_p_N0.pdf', dpi=425, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in ER_CE1.keys():\n",
    "    ER_CE1[n]['k'] = np.array(ER_CE1[n]['p'])*n\n",
    "    \n",
    "for n in ER_CE2.keys():\n",
    "    ER_CE2[n]['k'] = np.array(ER_CE2[n]['p'])*n\n",
    "    \n",
    "for n in ER_CE3.keys():\n",
    "    ER_CE3[n]['k'] = np.array(ER_CE3[n]['p'])*n\n",
    "    \n",
    "for n in ER_CE4.keys():\n",
    "    ER_CE4[n]['k'] = np.array(ER_CE4[n]['p'])*n\n",
    "    \n",
    "for n in ER_CE5.keys():\n",
    "    ER_CE5[n]['k'] = np.array(ER_CE5[n]['p'])*n\n",
    "    \n",
    "for n in ER_CE6.keys():\n",
    "    ER_CE6[n]['k'] = np.array(ER_CE6[n]['p'])*n    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import cmocean as cmo\n",
    "# colors = cmo.cm.thermal(np.linspace(0.1,0.95,len(Ns)))\n",
    "colors = plt.cm.viridis(np.linspace(0.0,1,len(Ns)))\n",
    "\n",
    "i = 0\n",
    "ymax = 0\n",
    "\n",
    "for N in Ns:\n",
    "    CE1 = np.array(ER_CE1[N]['CE_mean'].copy())\n",
    "    CE2 = np.array(ER_CE2[N]['CE_mean'].copy())\n",
    "    CE3 = np.array(ER_CE3[N]['CE_mean'].copy())\n",
    "    CE4 = np.array(ER_CE4[N]['CE_mean'].copy())\n",
    "    CE5 = np.array(ER_CE5[N]['CE_mean'].copy())\n",
    "    CE6 = np.array(ER_CE6[N]['CE_mean'].copy())\n",
    "    CEs = (CE1 + CE2 + CE3 + CE4 + CE5 + CE6)/6\n",
    "    CEs = list(CEs)\n",
    "    CEs = [(CEs[i] + CEs[i+1])/2 for i in range(0,len(CEs)-1)]\n",
    "    CEs = [0] + CEs\n",
    "\n",
    "    x1 = np.array(ER_CE1[N]['k'].copy())\n",
    "    x2 = np.array(ER_CE2[N]['k'].copy())\n",
    "    x3 = np.array(ER_CE3[N]['k'].copy())\n",
    "    x4 = np.array(ER_CE4[N]['k'].copy())\n",
    "    x5 = np.array(ER_CE5[N]['k'].copy())\n",
    "    x6 = np.array(ER_CE6[N]['k'].copy())\n",
    "    xx = (x1 + x2 + x3 + x4 + x5 + x6)/6\n",
    "    xx = list(xx)\n",
    "    xx = [(xx[i] + xx[i+1])/2 for i in range(0,len(xx)-1)]\n",
    "    xx = [1e-4] + xx\n",
    "    \n",
    "    std1 = np.array(ER_CE1[N]['CE_stdv'].copy())\n",
    "    std2 = np.array(ER_CE2[N]['CE_stdv'].copy())\n",
    "    std3 = np.array(ER_CE3[N]['CE_stdv'].copy())\n",
    "    std4 = np.array(ER_CE4[N]['CE_stdv'].copy())\n",
    "    std5 = np.array(ER_CE5[N]['CE_stdv'].copy())\n",
    "    std6 = np.array(ER_CE6[N]['CE_stdv'].copy())\n",
    "    stds = (std1 + std2 + std3 + std4 + std5 + std6)/6\n",
    "    stds = list(stds)\n",
    "    stds = [(stds[i] + stds[i+1])/2 for i in range(0,len(stds)-1)]\n",
    "    stds = [0] + stds\n",
    "\n",
    "    ytop = np.array(CEs) + np.array(stds)\n",
    "    ybot = np.array(CEs) - np.array(stds)\n",
    "    ybot[ybot<0] = 0\n",
    "\n",
    "    ymax = max([ymax, max(ytop)])\n",
    "    \n",
    "    plt.semilogx(xx, CEs, label='N=%i'%N, \n",
    "                 color=colors[i], linewidth=4.0, alpha=0.95)\n",
    "    plt.fill_between(xx, ytop, ybot, \n",
    "                     facecolor=colors[i], \n",
    "                     linewidth=2.0, alpha=0.3, edgecolor='w')\n",
    "    i += 1\n",
    "\n",
    "plt.vlines(1, -1, 1, linestyle='--',label=r'$\\langle k \\rangle=1$',\n",
    "           color='k', linewidth=3.0, alpha=0.95)\n",
    "\n",
    "plt.xlim(1.0e-2,max(xx))\n",
    "plt.ylim(-0.0015, ymax*1.01)\n",
    "plt.grid(linestyle='-', linewidth=2.5, alpha=0.3, color='#999999')\n",
    "plt.ylabel('Causal emergence', fontsize=14)\n",
    "plt.xlabel(r'$\\langle k \\rangle$', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+\\\n",
    "        'CE_ER_k_N0.png', dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+\\\n",
    "        'CE_ER_k_N0.pdf', dpi=425, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import cmocean as cmo\n",
    "# colors = cmo.cm.thermal(np.linspace(0.1,0.95,len(Ns)))\n",
    "colors = plt.cm.viridis(np.linspace(0.0,1,len(Ns)))\n",
    "\n",
    "i = 0\n",
    "ymax = 0\n",
    "\n",
    "for N in Ns:\n",
    "    CE1 = np.array(ER_CE1[N]['CE_mean'].copy())\n",
    "    CE2 = np.array(ER_CE2[N]['CE_mean'].copy())\n",
    "    CE3 = np.array(ER_CE3[N]['CE_mean'].copy())\n",
    "    CE4 = np.array(ER_CE4[N]['CE_mean'].copy())\n",
    "    CE5 = np.array(ER_CE5[N]['CE_mean'].copy())\n",
    "    CE6 = np.array(ER_CE6[N]['CE_mean'].copy())\n",
    "    CEs = (CE1 + CE2 + CE3 + CE4 + CE5 + CE6)/6\n",
    "    CEs = list(CEs)\n",
    "    CEs = [(CEs[i] + CEs[i+1])/2 for i in range(0,len(CEs)-1)]\n",
    "    CEs = [0] + CEs\n",
    "    \n",
    "    x1 = np.array(ER_CE1[N]['k'].copy())\n",
    "    x2 = np.array(ER_CE2[N]['k'].copy())\n",
    "    x3 = np.array(ER_CE3[N]['k'].copy())\n",
    "    x4 = np.array(ER_CE4[N]['k'].copy())\n",
    "    x5 = np.array(ER_CE5[N]['k'].copy())\n",
    "    x6 = np.array(ER_CE6[N]['k'].copy())\n",
    "    xx = (x1 + x2 + x3 + x4 + x5 + x6)/6\n",
    "    xx = list(xx)\n",
    "    xx = [(xx[i] + xx[i+1])/2 for i in range(0,len(xx)-1)]\n",
    "    xx = [1e-4] + xx\n",
    "    \n",
    "    std1 = np.array(ER_CE1[N]['CE_stdv'].copy())\n",
    "    std2 = np.array(ER_CE2[N]['CE_stdv'].copy())\n",
    "    std3 = np.array(ER_CE3[N]['CE_stdv'].copy())\n",
    "    std4 = np.array(ER_CE4[N]['CE_stdv'].copy())\n",
    "    std5 = np.array(ER_CE5[N]['CE_stdv'].copy())\n",
    "    std6 = np.array(ER_CE6[N]['CE_stdv'].copy())\n",
    "    stds = (std1 + std2 + std3 + std4 + std5 + std6)/6\n",
    "    stds = list(stds)\n",
    "    stds = [(stds[i] + stds[i+1])/2 for i in range(0,len(stds)-1)]\n",
    "    stds = [0] + stds\n",
    "\n",
    "    ytop = np.array(CEs) + np.array(stds)\n",
    "    ybot = np.array(CEs) - np.array(stds)\n",
    "    ybot[ybot<0] = 0\n",
    "\n",
    "    ymax = max([ymax, max(ytop)])\n",
    "    \n",
    "    plt.semilogx(xx, CEs, label='N=%i'%N, \n",
    "                 color=colors[i], \n",
    "                 linewidth=4.0, alpha=0.95)\n",
    "    i += 1\n",
    "\n",
    "plt.vlines(1, -1, 1, linestyle='--',\n",
    "           label=r'$\\langle k \\rangle=1$',\n",
    "           color='k', linewidth=3.0, alpha=0.95)\n",
    "\n",
    "plt.xlim(1.0e-2,max(xx))\n",
    "plt.ylim(-0.0015, ymax*0.6)\n",
    "plt.grid(linestyle='-', linewidth=2.5, \n",
    "         alpha=0.3, color='#999999')\n",
    "plt.ylabel('Causal emergence', fontsize=14)\n",
    "plt.xlabel(r'$\\langle k \\rangle$', fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "if save:\n",
    "    plt.savefig(\n",
    "        where_to_save_pngs+'CE_ER_k.png', \n",
    "        dpi=425, bbox_inches='tight')\n",
    "    plt.savefig(\n",
    "        where_to_save_pdfs+'CE_ER_k.pdf', \n",
    "        dpi=425, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Chapter 06. In [Chapter 07](https://nbviewer.jupyter.org/github/jkbren/einet/blob/master/code/Chapter%2007%20-%20Estimating%20Causal%20Emergence%20in%20Real%20Networks.ipynb) we'll estimate causal emergence in real networks.\n",
    "\n",
    "_______________"
   ]
  }
 ],
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