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    "![](images/blue_brain_neurons.colorful.png)"
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    "# Analyzing Connectomics Data\n",
    "---\n",
    "The `netsci` package comes with an out-of-box dataset describing the [neuronal network](https://en.wikipedia.org/wiki/Neural_network) among ~2,000 neurons from a rat brain. In this notebook, we shortly explain the nature of this data and demonstrate a basic network analysis applied upon it.\n",
    "\n",
    "Essentially, after completing this tutorial, you will know:\n",
    "1. What are the brain's neural networks and what is the connectome.\n",
    "2. How to load the (toy) connectomics data and convert it to a network.\n",
    "3. How to identify and count three-node motifs in a network.\n",
    "4. That, in neuronal networks, a motif connectivity is highly related to the spatial embedding of its composing neurons.\n",
    "\n",
    "A deeper description of this network study, together with its neuroscientific context and implication, can be found in [this recent paper](https://doi.org/10.1101/656058).\n",
    "\n",
    "\n",
    "Let’s get started.\n",
    "\n",
    "\n",
    "## Connectomics\n",
    "> *You are more than your genes. You are your connectome.* --Sebastian Seung\n",
    "\n",
    "The human brain is composed of about 100 billion specialized nerve cells, [**neurons**](https://en.wikipedia.org/wiki/Neuron). Neurons can connect to up to 10,000 other target neurons each, altogether forming the vast and complex [biological neural networks](https://en.wikipedia.org/wiki/Neural_circuit). The inter-connections between neurons are formed by [**synapses**](https://en.wikipedia.org/wiki/Synapse) - structural \"junctions\" that allow transferring electric impulses, [**action potentials**](https://en.wikipedia.org/wiki/Action_potential), from one neuron the another. The precise wiring diagram of all neuronal connections, or [**the connectome**](https://en.wikipedia.org/wiki/Connectome), shapes the network-wide electric activity and underlies the different brain functions like information processing, memory, and, ultimately, behavior. However, how exactly does the network structure affect brain activity and function, is a long-standing and still open question in neuroscience for more than a century. \n",
    "\n",
    "<p style='text-align:center; margin: 20px 0'>\n",
    "    <img src='https://cdn.kastatic.org/ka-perseus-images/ee56b2d6b187d7bb192a2127ec38561e42ac7062.png'/>\n",
    "    from <a href='https://www.khanacademy.org/science/biology/human-biology/neuron-nervous-system/a/the-synapse'>www.khanacademy.org</a>\n",
    "</p>\n",
    "\n",
    "The biological connectome can be further modeled as a mathematical [**directed graph**](https://en.wikipedia.org/wiki/Directed_graph) whose [**nodes**](https://en.wikipedia.org/wiki/Glossary_of_graph_theory_terms#node) correspond to the neurons, and its [**edges**](https://en.wikipedia.org/wiki/Glossary_of_graph_theory_terms#edge) are the synaptic connections between those neurons. This network-based perspective of the brain allows utilizing tools from [**graph theory**](https://en.wikipedia.org/wiki/Graph_theory) and modern [**network science**](https://en.wikipedia.org/wiki/Network_science) to uncover key network structures that support brain functions. Such methods have already identified several network features in cortex architecture, such as the rare but highly-connected hub neurons, cliques of all-to-all connected neurons, and overall small-world topology of the cortical microcircuit. \n",
    "\n",
    "![](images/nn.4576.sketch.reduced.png)\n",
    "\n",
    "Arguably, one of the most basic, yet still not fully explained, network structures observed in neuronal circuits are the 3-neuron subgraphs (triplets). When counting the frequency of all possible 3-nodes connectivity patterns in the network, the distribution appears highly unexpected. Specifically, few specific configurations (out of all 16 possible) stand-out and are significantly over-expressed (motifs) when compared with reference randomized networks. More surprisingly, different microcircuits across different brain regions commonly display similar over- and under-expression of the same motifs. However, despite their cross-region universality, both the origin of these motifs and their functional implication have remained elusive.\n",
    "\n",
    "![](images/ANNs.cropped.png)"
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    "**So, what is the origin of neuronal network motifs?**\n",
    "\n",
    "In the absence of a concrete theory about the principles underlying these motifs, their emergence may be hypothetically associated with active plasticity and learning processes. But another, much more parsimonious,  possibility comes to mind. Most types of cortical neurons display highly asymmetric geometry with dendritic and axonal trees typically extending in different directions. As a consequence, the probability of forming a connection in one direction (e.g., down) may be higher than in the other direction (e.g., up). This symmetry breaking may distinctively promote some motifs while depressing others. Thus, it could be the case that the geometry per se “enforces” the complex profile of brain microcircuit motifs. \n",
    "\n",
    "The importance of this hypothesis is that if it is indeed so, then we can now see learning processes as operating on top of an innate, already structured, cortical skeleton rather than on a _tabula rasa_ network connectivity. This will strongly constrain the degree by which plasticity could further shape neural connectivity, and possible reduce the room for learning.\n",
    "\n",
    "We will test this hypothesis below. Toward this end, we will utilize the publicly available dataset of the [Blue Brain model](https://bbp.epfl.ch/nmc-portal/welcome). Specifically, we analyze here a subcircuit of it (pyramidal neurons for layer 5), that is now accessible via `netsci` API's."
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    "# # Install packages (uncomment if necessary, e.g., for Google Colab)\n",
    "# !pip install plotly\n",
    "# !pip install holoviews\n",
    "# !pip install netsci"
   ]
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       "</a>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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       "  <img src='data:image/png;base64,\n",
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       "z/8BE3VwbODCC/EAAAAASUVORK5CYII='\n",
       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "</a>\n",
       "\n",
       "\n",
       "\n",
       "<span style=\"float: left; margin-left: 5px; line-height: 15px; cursor: pointer; opacity: 0.7;\"\n",
       "      onmouseover=\"this.style.opacity='1'\"\n",
       "      onmouseout=\"this.style.opacity='0.7'\"\n",
       "      title=\"Extension loaded. This cell output contains code that enables plot interactivity, it should not be removed.\">ⓘ</span>\n",
       "</div>\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import holoviews as hv\n",
    "from holoviews import dim, opts\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from netsci.datasets import load_connectome\n",
    "import netsci.metrics.motifs as nsm\n",
    "\n",
    "\n",
    "hv.extension(\"plotly\")\n",
    "plt.rcParams[\"figure.dpi\"] = 144"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The connectome dataset is composed of two parts:\n",
    "1. The *nodes* are the neurons. The data lists of all neurons, their `gid`-s and 3d position $ (x,y,z) $.\n",
    "2. The *edges* depict the synapses interconnecting all neurons. The data lists all pairs of connected neurons and the number of contacts (synapses) realizing each connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:03.480376Z",
     "start_time": "2020-01-22T16:52:03.372062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': str,\n",
       " 'nodes': pandas.core.frame.DataFrame,\n",
       " 'edges': pandas.core.frame.DataFrame}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "connectome = load_connectome()\n",
    "display({k: type(v) for k, v in connectome.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magnitude of the network is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:03.494381Z",
     "start_time": "2020-01-22T16:52:03.487395Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connectome (L5-TTPC\n",
      "\t2,003 neurons\n",
      " \t102,732 connections (p = 2.56%)\n",
      "\t650,949 contacts\n"
     ]
    }
   ],
   "source": [
    "print(connectome[\"title\"])\n",
    "neurons, synapses = connectome[\"nodes\"], connectome[\"edges\"]\n",
    "\n",
    "n = len(neurons)\n",
    "print(\n",
    "    f\"\\t{n:,} neurons\\n\",\n",
    "    f\"\\t{len(synapses):,} connections (p = {len(synapses)/(n*(n-1)):.2%})\\n\"\n",
    "    f'\\t{synapses[\"contacts\"].sum():,} contacts',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:03.512607Z",
     "start_time": "2020-01-22T16:52:03.497909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>gid</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75211</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75214</td>\n",
       "      <td>305.869316</td>\n",
       "      <td>1200.585727</td>\n",
       "      <td>608.787329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75217</td>\n",
       "      <td>312.411025</td>\n",
       "      <td>1184.177732</td>\n",
       "      <td>647.120290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75218</td>\n",
       "      <td>275.261806</td>\n",
       "      <td>1110.341752</td>\n",
       "      <td>630.010095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75221</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>81314</td>\n",
       "      <td>472.198046</td>\n",
       "      <td>1087.211926</td>\n",
       "      <td>409.227347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>81317</td>\n",
       "      <td>308.608095</td>\n",
       "      <td>947.743964</td>\n",
       "      <td>624.308077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>81318</td>\n",
       "      <td>325.510390</td>\n",
       "      <td>1029.783942</td>\n",
       "      <td>574.410863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>81319</td>\n",
       "      <td>383.361952</td>\n",
       "      <td>1161.047906</td>\n",
       "      <td>655.450589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>81322</td>\n",
       "      <td>331.464765</td>\n",
       "      <td>1062.599933</td>\n",
       "      <td>651.448519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2003 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gid           x            y           z\n",
       "0     75211  341.846181   773.977844  636.437126\n",
       "1     75214  305.869316  1200.585727  608.787329\n",
       "2     75217  312.411025  1184.177732  647.120290\n",
       "3     75218  275.261806  1110.341752  630.010095\n",
       "4     75221  239.869853   880.629815  609.945456\n",
       "...     ...         ...          ...         ...\n",
       "1998  81314  472.198046  1087.211926  409.227347\n",
       "1999  81317  308.608095   947.743964  624.308077\n",
       "2000  81318  325.510390  1029.783942  574.410863\n",
       "2001  81319  383.361952  1161.047906  655.450589\n",
       "2002  81322  331.464765  1062.599933  651.448519\n",
       "\n",
       "[2003 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(neurons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *y-axis* denotes the cortical depth. The positive direction is defined to be the [_pia_](https://en.wikipedia.org/wiki/Pia_mater#Cranial_pia_mater) direction in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:04.252904Z",
     "start_time": "2020-01-22T16:52:03.517008Z"
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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99HRAIlD9g8jybkDtndFW5fN6QM4Y5gQtYn1ADaX2ItryfUDi6ZWyTMR0QIbJVMEocmdAVU0QdV9FdUBL5ljeVQqAQOPhPQfWKYBAwAXZsjwke0DvHwvRodR+QBo2yvoNv3VA7+apDjmacEDpDIy87GF8QKSpnsy/G2dANbitLbyIfkDIfat14hx3QORIZ2DkFXdAw2SqYJQtdUDdlzPb1W54QNJUT+YfQnVA8zgM5i8afUCyne+nhrt2QP4PsFbt5HpAG9gqwWL8bEDhKHl1TjRxQCWS6GUU/GZAwqONIxbUd0Cj6exk8KtvQIBHVKiuDHZAH/gYrDh1Z0BinwCKUet6QBMKEXCI02xAITtvY7P3gEBCsoAJXGZuQIf4hy09/XBAVU0QdZ8WcECFmbZ/pX1wQHDrbp6qZnZAoKnXLYKNckAcJ4V5j35vQHJtqBhnKHtAKowtBHlIfkBT6LzGbtp0QM0GmWRkEWhAE38UdSYeekBklj0JbP5oQGrbMAqCAmdAttlYiXmQekAib7n6ccF1QPdY+tCFknFA8UV7vFCWfUDfUWNCTEZ0QLwIU5RLcHtAPfGcLaBzfEB8REyJZEd3QJHyk2of53dAd4apLXWidUA3qP3WToJuQAPqzag5qXdA5dAi27nveUBi2GFMOsF3QDVG66hqDXRALEZday8bfED4VblQ+RdrQJijx+9tJnVAMGZLVsUKfEC3KLNBJmh4QML6P4dZv4BAw2SqYJTLcEDYKyy4n7tmQO84RUdysm1A73Tniac8eUD19ueioTBtQNCzWfW5dGFAdGA5QobkfUC8QbRWtDt0QGzoZn+g0XdAAMRdvQqnc0BBLQYPE4hyQNdnzvoUMHlA3SVxVsSWdkDlZOJWwYB7QAnh0caR5nFARuuoasKSc0BOY3staDB+QHvYCwWs4X5AWI/7Vuu2ckATfNP02ZRuQKJ6a2Brc3VAWRe30QBDfkAF+kSe5Il4QFMEOL1LaH1AHjNQGT8Ff0D9h/TbFyF6QBO4dTePZX9AXoQpykUwgEBenznrU6lcQCQqVDeX53NA3gIJip8uc0A4nzpW6b96QCkg7X8AHXVApONqZFcSbkDPEmQEFNp9QF7b2y1JiXdA14o2x/nXfEAZOnZQiVdoQLxXrUx4+n9AHmyx22f5dkAczvxqzhp+QM138BMHUWZAUwjkEkfDaUBcyCO4EbthQK6bUl5LXIFAfGRz1bwIYEChgy7hEDOCQM10r5N6uXJARSqMLUSfdkDymld11hJwQO6zykxpKnlA8RKc+kB1fECuK2aE9613QKG5TiOt7XxA7fDXZA3ybEBSYAFMGR98QC3saYd/AIBA0jWTb7bybkDxvb9BO1lyQPq4NlRsaIFAWMudmWCgZkBuhhvwOeF+QH12wHXFO3VAzTtO0VEJc0A/j1GeeTh2QIM0Y9E04nhA+RIqOLyYcUB8mShC6vNyQI7onnVNenRAMlncf2QkcUDEeqNWmM54QJYFE38Ue3FATMXGvE5KfEDovTEEAMN8QMX/HVEh4m9ApfW3BODicEDQ1sHBXj5lQKBU+3Q8R31AeXdkrDbgZEDAQXv1cdpmQLoQqz9CVIBA9BlQb8Ywa0COk8K8Rzp8QMakv5fCc2lARkCFIwjXeEB5r1qZcPRoQF+X4T+9woBAnaG4423wgEBCYOXQIqR4QIe/JmvU5nlAfNKJBNOneEDtSPWdX+V1QNEi2/k+3ntAuhXCamyCc0A2Ia0xaHdsQK4rZoQ3tH5AaF2j5QDHdUCyne+nRj13QANckC1LnH5AMsueBPZ7d0A8okJ1c2l7QM9Lxca8U35A4BKAf4qRfEBm22lrBH1/QDOoNjgRVXBAhXr6CHyNa0DPoKF/wq54QMhhMH/FXnNACFkWTHyDdkC9UStM37V3QCS4kbKF1XlAccyyJwFIakCmRBK9zAB7QCYd5WC2MXlAsBu2LQpifkCU+NwJdoxqQH+8V63MF29As++K4H+ka0DY74l1aoB8QEMaFThZb3NA09wKYXXQdED1EmOZ/g9sQA8KStEK0n5A5eyd0VYmc0Dmkqrthr+BQHgOZajKAH9Azt+EQsQPe0Aj93R1B192QILlCBlIhXJA66pALUYlc0A/HY8ZqGN1QHptNlbioXJAQkP/BBfLd0D6muWykWN5QE7VPbK5vHtA66pALQaYf0DU8gNXefRzQC/h0Fu8X2ZA2jujrUoyZUBREaeT7OV0QKiQK/Wszn5A5KCEmTZzakA0FHe8iZF8QECEuHJ2aoBA3Zcz21VLeED/XgoPGnl+QB+7C5TUGXxAvJAOD6E9cUCDwqBM4zNzQNV6v9EOjHhAkx6GViccekC5VKUtLjh9QHTS+8bXZX5A2T7kLZfTeED12mysRCtoQN9RY0IMRHVAA5Xx7/M5dUCdZ+xL9uN+QNgPscHCp2RATWa8rXSBZkBod0gxQOFrQBIwurx5PHtAjLysicVneUDTwI9qGJ1+QDpdFhObh2hApikCnD5ugEB8Qnbehgh/QAAfvHbpXmhAxvoGJregdkDgnudP24h6QInwL4LGJXhAmkARi5jGakARGOsbGF13QLIv2XhwaHZAJh3lYDa6bkAg0QSK2CpuQGwldJdEJHxAdELooEthf0AW3A94IAl8QJCiztzDmmdAAwXeyafudUDlCYSdAhCAQEQUkzfA0mxAaXHGMGf1cEBrD3uhACRzQCAIkKEjbHlA1sdD313TckAOT6+U5a5vQMzwn26gB3ZA38FPHEAzeUBR3PEmPyp6QD+Ne/Nb1XxA5SX/kz+5aUC2Zisv+Y5qQObPtwXLYWxAUdzxJr+0cUB/TdaohxBoQE8iwr/Ic3hAsvLLYAzvZkAS9u0kokxrQCBDxw6qvWRAh272B0rfe0CaP6a1aVt7QC0HeqhtLW1AAoOkT6u/dEB0XmOXqINxQNI2/kQlPXNAVPzfEZWVeUDzrnrA/HB6QByVm6ilcWdAd700RcDcfEDG3SBa6017QNdP/1nz8nZAPE1mvG1mgEBhbYyd8LqAQG2oGOdv8WZAzo5U33kvcECk374OHN9uQBZu+UjKRYFAlSwnobQ7dEBU5uYb0fZ3QHEfuTWpZXhAOZ1kqwtXe0DiPJzANCp0QB6NQ/0uoH5ABtUGJ6LJckDa44V0uBWAQMrcfCO6N2hA/wdYq/YyeEBiD+1jxdF+QLtHNldNPGVANnf0v1z7eUDM1CR4Q551QFBxHHi1V3xADcFxGbfqbkBhM8AF2bVuQNVcbjCUinpAvyzt1BzVcUAtk+F4vtF7QDICKhxBlX9AXK0Tl+NcfkBVih2NA7p/QPkSKjg8/W1Ai/m5oan8fkA43EduDfh3QG10zk8xYH9Am44AbhYTfkBRg2kY/nqAQNfBwd4Ev31AsRpLWJtcY0AijJ/GPQKBQPp8lBHXXX1APNwODQtCdkAlk1M7g6F0QOnRVE/mynNAkfP+Pw5RdEBoImx4+j9xQCwujspNjHpAl1KXjOOMcEAa3xeXagtxQPBMaJIYdnRA7iO3Jl1seUASZ0XUxBZ4QM0/+ibNNHtA5qxPOaaobEBm2ZPA5gV+QDYFMjvLVH5Ao7H2dzYmfEDMsieBzYRqQFDkSdK1O2RAZoUi3c9YakC+T1WhgZJyQLRVSWRfIXNAP3PWp9w6gUAD0v4HGKx6QHuH26HhFXhAVMkAUEWncEBPsP8698FyQF8M5US7onBAvLGgMKjcekAUXKyoQbRqQNs2jILgBnxAy6FFtrPifUDj4qjcRIRmQHSXxFmRdHtAvY44ZIPJeUBZh6Or9AF4QB+6oL5lJ2xA+pgPCHS1e0Bt5/upcfVtQKcDWU/te3ZAXcMMjafTc0BgIt46f1B4QIvdPqvMrnBAxyx7EtjTd0BwlLw6B7F5QPrRcMqcf3dAOSUgJiFedEBYObTI9npuQOSG3003KWxAh/iHLb39akBiLT4FwG9vQAe139rJ4YBAKXrgYzBTf0CsyVNWUwpkQG8MAcDx43NAQNr/AGt6XUB2a5kMh2B1QAAeUaH6snhAi6Td6OMYc0AmGM41DGl4QJ3zUxzHNnlAmpSCbi8SdkBPd554jrJ7QI1EaASbYG9A2SeAYuQQakA2sFWCxbp8QCYbD7YY5XxARWPt72yiZkCELAsmvklwQNp1b0WieHxAE0VI3c7GZkB6qkNuxtd4QLx4P27/v3pA9katMH3rf0BtGttrQcZnQItUGFuoUIBADVTGv08xfEDwplt2CIFiQDRnfcox6HlATz49tmUYfEBs6dFUzxl+QHB5rBmZO29A+yKhLWcrbkCcFrzoa7d9QFuXGqEfiHNAJsYy/VKRfkB6UFCK1l5vQIMyjSaXA3tAyotMwG/+eUCHhsWo65RkQMcsexIYP3lAOC140Vf1bEAA5e/e0S9yQLg81ozM235Az0iERhDjgEARj8TLEy+BQAGmDByQOHdAHt/eNegleUBXsI148sZ4QOxsyD+zEXVAOSuiJrrGckB88xsmWjF2QJgZNsr6SnxAswbvq/LlekDDDmPSXxJtQNTWiGCci2pAXW4w1CF/eECIY13cRoFnQB0FiIKZ9ntAz0iERjBAgEBL5ljelThyQO0NvjAZg39AlYEDWnrQeEDuzW+YqOmAQOT2yycLwIBAQ61p3vE2f0By++WTlSxhQLraiv1lnHJAlN3M6MfZdUDwbI/e8D9zQEcFTrZBxnZAo81xbhPIdUCjzXFu0/BwQHCaPjsgy3NAq7GEtXGme0AE4nX9giRxQGXDmsriHnhAJVryeJp/d0BtO22NSLR7QIpW7gVm2GVARZvj3KbVb0A74LpiJkiAQEZhF0WPC39ALXdmgqGJekBIbHcPUHtnQHvAPGTKgnNApONqZNfgfUDqdYvAWORyQLUX0XbM72JAARdkyzJ2gUCjyFpDqY1oQBQF+kQe0XRAR7Bx/fvUcEA6evzeJm90QBQF+kSeE3FA5IV0eAife0A4h2u1h8N7QJYJv9QPdnxATu/i/bhVcEB7h9uhIVJ0QKnaboKvnH1ADMwKRfpAdUA9YvTcQh1pQGlxxjAn53VAR40JMReFf0BYOEnzhyJ3QIsyG2QSp3pAhJuMKsOTXED0GrtE9Tt1QHNIaqHk33dAlSwnoXQieUA9YYkHlFZyQJHwvb/Bl3hAPzifOtZ9d0ADkxtFVihxQK7zb5f9qnFAZTVdT/SKdkByMnGrINp8QA/tYwW/om9A7lwY6YU/cEDLviuCf8JzQOer5GP3on5AnwPLEfKOdEABNEqXvuh9QEPFOH8TPmtA8Wd4s4ZvaUAcfjfdMpZmQFVtN8HXFYBAdlPKa6XIZ0C0OGOYs6qBQLpoyHjUjX5AR8fVyG66gUAKZ7eWSTx2QIVCBBxC7HdALUFGQIXZdED60XDKXH94QDf/rzpyi3BAM8NGWb/YckB/vcKCe6J5QOlJmdSQGnhAHhX/d0QlckCFe2Xeaot/QPj6Wpdaf35AM1Naf0tqbUCGkPP+XwqAQLFppRBIDGZAiV3b261KZ0BJ8lzfxwdxQKc8uhGWpnBAH/KWq5/IekAuG53z06B5QEhOJm6Vp3NAS633Gy0ac0A+d4L9lzdyQKn26XiMk3hAgJpatpYtdkAld9hEpod0QKIpO/0gkX1AMUEN3wL0fkCTj90FSlhoQFLt0/GYc2lA7Ulgc47dckDdsdgmlQV7QPDgJw7gCoBALPAV3foBZkBZUBiUaRJkQBke+1lsmXtAVYodjUPVe0AfuTXpdv1+QNczhGMWN3NA1edqK7ZmeUDJj/gVi8aBQHSaBdqdj3lAGhh5WVPecUCDL0ymik51QLtCHyxjw3lAx0lh3iMocEAkZCDPrup1QIMvTKYK5XpAgZcZNorae0Aydy0hXwhzQIyEtpwLHXdA2ERmLnBYa0BsIjMXuHVzQAXfNH328n1AYOrnTUXabkAuO8Q/rEJ5QI0MchdhX35A7ib4pikndEDSqwFKQ4lwQHAk0GBTEWRA6BN5kjRveUAofSHkPCl6QN481SG33XFAlltaDcnEf0BuwVJdQN6AQGMoJ9oVu31Axt0gWmtgcUC14EVfgfxyQBtHrMXnw3NAhAzk2SWKdEDTM73EmGVxQInrGFdc9W5AFM/ZAsJ2eEDPTDCcq2V4QO7RG+4jn3RA0qsBSgNifUB79lymZq95QBBZpIn3+29ALXqnAm4gekClL4Scd313QCSAm8WLDXFAZyrEI3Fib0CrQC0GjxRnQDG2EOQglHVAH/ZCAdsFgECvCz84nyppQP5fdeQIxHNA86s5QDA2fUDJdyl1ySVjQJS9pZwvy3JA5IIz+Dt/cECK5gEs8s5wQMhcGVTbUHdA+yMMA1aDeEA7jbRUnu94QBjMXyFzWXRAYK3aNWHMfED1RxgGrH1tQJLOwMjLSWpAAn6NJEFhbUDvb9BePQd+QNrGn6jsPmhAUMdjBqq1bEDg2omS0Ax6QA3jbhAtKGVA1XYTfPPsgECvfQG9cE5tQPPoRljU6mJAzsKedviEZkBEh8CRgJt8QIxoO6bufXRAEw69xQN5dkCG5jqNNAB1QIRLx5ynPHlAm1jgK/one0BqhH6mXvd6QKiOVUpPsHxAMZi/QmatbUDbwYh9wph0QF/QQgIGvHlAtYtppvtpfEAUCDvFKoVyQFacai1M2WZAdSFWfwTlbECoVImyt9VrQLwC0ZOyTm9AEeLK2Ts2fUC5iO/EbJR8QCfdlsgFYmpANCxGXWsagEAHtkqwOHBxQGWMD7MXTm5AttrDXqhmf0AeGED4EJ99QBRBnIfTJ3RAtVII5JIdc0BY5q26Tut3QJG0G31MY31AXW4w1CGufUAXoG01qwB1QDW4rS28zmdARfKVQAo7dEDsTKHzGtJxQF8mipBaCIBANsmP+JUHd0A7i96pgP9kQJVgcTjzFWtAtVII5FJ6c0DedTbkX3F+QAjovpzZGX9Az4HlCDnjgUDnU8cqpeFfQLvtQnMdQ3dAL4oe+BjteEBivVErDCJxQJ4MjpIXFHhAVMa/zzg0cEDP2m0XmvVwQCvZsRGIH3VA9PqT+Fz9e0CTGW8rPdd8QHmRCfj1yXVACTVDqqgbaUAkRs8t9GNnQIM1zqajeXRAwVPIlXrtbkB0Yg/tI8KAQF6dY0D2CH5AuiwmNh9ve0Bfl+E/3RJzQFvs9lnl5HJAN8MN+DxzfUBMcVXZd313QM791eO+WXFAbLBwkiYCckBGs7J9iAx1QCQLmMBtP3NASRKEKyAOcUATgH9KlTF7QBtkkpHzMXFA91llpvT5e0BznxwFiA1vQLKeWn01vnFAv5mYLsQFakCvtIzU+/R1QKJCdXMxfn5AKo2Y2ecifkCskPKTaqhmQOLkfociiHpAWDuKc5TAdEC0keumFN17QCyAKQOHsndAaYoApzdGeECOzY5Un7x3QBO54Az+UXBAkzoBTQSQa0CfHAWIwgl3QGVx/5EpGnpA0xQBTm8/a0ADJJpAkcR4QDSdnQzOcX1ALo81IwNSa0BKKH0hZJF8QFGgT+RJNH5ArBkZ5G6VfkCPqiaIOvN0QEdWfhkMvmtADmWoiqkXb0B0YaQXdZ1xQJG0G33Myn5AtW0YBUEra0BXXvI/uYt0QBg+IqbEIntA4gD6fT+TckD8VuvEZe90QHBcxk0N0GRASdi3kwikZkA/ARQjy4pqQPF/R1ToJ3BAMX2vIbjxeECDNc6mo2Z3QOSCM/h7S3ZAsVOsGoSrbECq1y0C4xF6QKM883IYTnhAlgZ+VMNHckBYHw9993RyQJG5Mqi2+2VAi1JCsCoxdUDP2JdsvH9mQB5rRgb5AIBAMzffiM40gUAbKzHPCmRyQFGE1O1sT3RA8DZvnJQae0BF1hpKbZN5QCLElbP3UG9AVkj5SXUhcED+mNam8Vh4QG/W4H2VgH1AT+rL0s5ubkBBKO/j6P59QGDmO/gJc3FAfAxWnGp/ekClaOVeIPd+QN5Zu+0CO3BAea2E7lJTekDPvBx2XyhkQOnUlc9y8nJA4jycwHSVeUDhKeRKvRB5QFN3ZRcMtHtAvHX+7fKadkCJQsu6/9FzQJIDdjW5eXpAXmQCfo2IfUDr4jYawOBvQD7ONGH7KW1AWW5pNaRyfUALDcSyGdtqQBuEud3LOYBAhXzQs1nFZUCKH2PuGnh4QO7RG+6j/3VAXmQCfs3Sd0BaSwFpf8txQKEsfH0tAHZAsd09QPd6eUA7/3bZb7NwQEc7bvgddnZAK98zEuEtdECC5nPuNol3QE6zQLtD9XJANsmP+BVGekBWKxN+6fl8QNsWZTZIMHVARzmYTQBfbUD1gk9zshl5QHiXi/jOC35AfQbUm1HRf0C30muzsRJoQB7f3jWobXhAArnEkUddeEBgOq3bIG5qQHWw/s/hOXdAv7fpz/7/ckCL4H8rWdVwQPZf56ZN5XNAafzCK8mKfEBNDwpK0V1xQJ+OxwzUQXBA66urArU+e0D/IJIhx/h0QLmMmxrol2ZArn5skl/keUCqZWt9kVZ/QKWD9X+OwWxAX+y9+KLHdkAwuycPC0doQAPN59xtKGZAQWfSpqrzfUCi8UQQZ2VmQL3IBPyakoBArMQ8K2l/ekAV5dL4BRJ8QIi4OZUMtmBAU1vqIC/bc0AnT1lNV5Z0QJXSM73EwXZAMGKfAIqudEASoRFs3KpxQEPhs3Ww/XZAJ09ZTZexcUDJ5T+kX9dsQNMupplupG9AdO52vbSHfUBYGvhRDX1uQGWJzjKLVHJAMNRhhZvqfUC1NLdCmDh7QFjIXBmUPXhAFhiyunXvgEBdM/lmm2ltQMEeEykNI4FAoYZvYZ0Bd0CwG7YtCrF2QMJqLGGt9XlAa5+Ox8wrcUDekEYFztBzQBQjS+ZYDXVAggGED+WtcEDRWtHmePh3QPRtwVJdL2xAk8X9RyYybkDw+WGEcM1rQGRZMPGHC3lAHvruVpZEfUBvgm+avtpmQM+goX+CxnhA3zZTIT6CgEB6pSxD3MB7QHBfB86ZPmRAQWFQplHxgEBaZhGKrcN0QDHvcaZJQ4BAjZyFPe25a0CwNzEkJ596QCB9k6ZBUYFAcM6I0t7JfEBhF0UPPPFzQEdy+Q9pDHdAF56Xis0beEANjpJXp3F8QLyQDg9h+HVA7BhXXBy7b0B87gT77751QKbxC6+k12pAe9tMhXiYbEBuMxXikXtrQBIWFXE6IGhApYP1f47bdUDudygKdBVjQOTZ5Vsf8WhAon2s4LfrY0DDDmPSH3x0QKpla31RR4BAj4mUZvMgZ0Ae3941aJeAQBkg0QQKZnZASfQyiqVHe0BoeR7cnZl4QMtHUtJDCHFASYJwBVQycECocASp1GJxQHhDGhU4in5AiC09mmqEbkASE9TwbVh5QH/bEyT2F35AnplgOFdNcEBky/J1mdZ9QE9AE2FDEH9AeJj2zX1Ub0A6kst/SGZ5QHQkl/+Q22lA1CtlGaKPfUBhbCHIwQ51QPRRRlyAF25A86ykFd+CZECDUN7HEbp+QCLH1jMEf2pAgoyACscbY0CVRszs8yZdQDcz+tEw33tA2ERmLnCkdUAtXFZh83x5QL99HTgnBHpAZHlXPWDubECTrMPRFdBwQGPuWkI+EHpAJ8Eb0mhXdUDjxi3mJ055QEOrkzOU4WlApPs5BbmNdkDlettMBWB+QIgOgSOBjnRAw7mGGdpYcECafLPNjRdkQFKbOLlfwG1AtY0/Udm+bkBvfy4acrV/QKxXkdGBGnRAAvVm1JzVgECIDoEjweF/QP30nzXfhYFACMxDprwJgUAXEFoPX+N2QC/BqQ9k9HJAw9UBELeFe0BrJ0pCoiN4QKEUrdzLN3lAB3sTQ3K/eUCjc36KowV3QA+Yh0w5ZXlAFLGIYQdNfkDWVBaFXY1/QIS9iSF5R4BAqg65GS7WeEC/mZguxKZ8QCZRL/hUEIFAKTxodh3Ke0DeIcUAiRhjQM9nQL3ZDn5APuqvVxivdEDy7zMu3EV2QIkkehnFt3BATwRxHs7zbkDWyRmKez18QM4Y5gTtfHFAm/7sRwq1bUC3RC44AzpxQIiDhCjfa2hAzaylgHQHeUDOjH40HPJqQCk8aHZd4HZAbXGNz8QMgECBkgILYI9jQLVTc7lBqW9AJ2w/GaO3f0D5LqUuGR59QBObj2uDWnhAjE0rhUAgdEC2R2+4D59lQMqmXOHdQWpAis3HtUGjgUC0rPvHQsF6QJSkayZ/+YFAADlhwuhmaUD5LqUumX1zQLSs+8dCRXRAJbGk3H3ydUDPTZtxGt1vQIP7AQ9M2nVAnIwqwzj0bUDcK/NW3SdyQN0Ii4o4CXhAyQG7mjzkb0AzqDY4EQp0QPHZOjhYzndA0R4vpEMQb0DABdmyfHZ4QLBwkuaPWHBADogQV05lfkAyIHu9+5dxQBsuck83GoBAytx8IzpSekDM1CR442eBQIR/ETRmg2pARdREn48Mf0Dc8pGUtLRwQNJUT+YfRnVA2C5tOOzNdkC2SrA4nCN4QL3CgvuBnnxASGx3D5A6c0CS6GUUi/J1QL5Nf/aju2lAhnZOswCifkBrLcxCOx1+QJOsw9HVj3dAS8tIvafUfkAEjgQabNp6QDS+Ly7V9XtAXcMMjeddgUBwzojS3iRzQJ/ouvCD5HFATvG4qJa2f0DQtpp1BhR8QKz+CMOAZ2pAkpbK2xFdd0DRkzKp4Wd0QBps6jyqCG9A3Esao3W/bkBA3qtWpqZ7QAcmN4pshX1AYASNmcSEbED3PlWFBj9rQI9wWvAiynFAqHNFKSGPaUDGavP/KnBuQEH0pEzq339AjLrW3keogEBe91Yk5l1zQMGsUKR7kGdAPWGJBzSagEDek4eFmsV0QPqa5bKR8XdAZyrEIzEtfkCGHFvP8A+AQCwtI/WeW2lASbvRx/zwdEDEz38P3v51QKVKlL3lRXlAmNu93OeHdkDqzD0kfCdzQB9JSQ8Du3NAt5kK8UgQc0BYGvhRTbZyQKExk6jXH3xAVvFG5pHfd0A8hVypJ0NxQH7IW65+l2pAPDCA8OEafECrPeyFguxtQBaInpTJAH9ANdQoJNmafkDtKw/S04Z0QCqPboTF9mdAQkP/BNd3eUBXBP9byUlmQMNKBRVVzWJAXP+uz3wdgECSeeQPxoN2QDbknxmEX3BA12fO+pSzdUACt+7m6Yt6QLNdoQ8W6HJAK76h8Nm9ekB9ryE4rshxQERpb/CFBHpA5BWInlSvfkB3LSEf9AxlQFG7XwU4dHdA1IBB0qfUcEDzBS0kYN1+QDUKSWb1D21A76tyodI/gkC38ScqG5psQIlA9Q/iYHZAUfpCyLmlcEBMNh5ssc9wQL74oj1evWxAvJaQDzqFdkBV203wDe97QBZu+UjKZWlAU+qScQylbkDWVBaF3UJqQPN0rigljGhAgQTFjzHZakAWhV0UPX5jQMnMBS4PInxAoImw4ekhYkAG19zRv1VxQFLRWPv7z39ARZ+PMuLSa0Apdck4Rl19QNYbtcJ0qGhAKuRKPctGeUAddXRczVN4QL6/QXv1NHdAWdsUj8sjfEDB5bFmJKd7QBYTm48r4XVA2PLK9XbGfEDEBaBROrlrQLTonQo4V3dAsYnMXKAteECzCwbX3FF9QIqvdhQnPHZAhXzQs9mGcUCZnxuaMrJ9QLxa7swEJWVA5US7CqndZUAOoUrNHoVlQOcYkL2+/4BArW2Kx2XFgEBHIF7Xr5NkQOmdCrgnlnxAz9xDwvckaUAHX5hMVep2QOPfZ1x4jnNAAyZw625aekDx1vm3S+52QHJw6ZhzoW1A8Z2Y9WJBfECNKsO42/x4QLiQR3Bj53pAJNHLKBYrbEBXX10VqJpsQI0qw7jbFHFASkONQpJecECfAfVmlJN6QCIzF7i8K31Aud+hKNAUaEBiFASPryh9QAaFQZkG33xAaK7TSMscakBA9+XM9kZlQJHSbB6HB3JA220XmitHd0CKr3YUJwyAQCHNWDSdf2pArBvvjozDYUABM9/BT2NsQG3IPzOI5HdA/Io1XCQXckAaFqOutWF1QICZ7+BnBXlAZMvyddkpd0DiHksf+vVzQDJZ3H8klHRA5X0czdH1ckD4/3HCRKF4QEcgXtcvNX1AhCo1eyBAd0ANi1HXWsRxQF4td2aCiH1AdXPxt/2LcUCtvyUA/y56QLr1mh7UT39AXyaKkLoZZkCnJOtwtLJ2QLsnDwu1oGNAlPjcCfaTdUA5RrJH6DpwQLH7juExhndAu9IyUm+TcUBmu0If7J5yQIOG/gluxnlAH9rHCv7VekDBV3TrdYt4QIv+0MzTTnBAY5y/CYVWbkAe/S/X4ghyQJXYtb2dN4BAVwT/W8kAf0AWiJ6UyeOAQDlGskfop3BAT8sPXGV+ZUAE5iFTPlxsQLA6cqSzi3pAVKuvrkozfEBkQPZ6N299QK8I/rcSy2lAE5uPa0MsgUDpK0gzVuV1QJZ5q67DIHVA4bN1cLCgdkDeHoSAfGZ6QN0LzApFlnhAe00PCooWe0BAaahRiN1wQHWPbK4a0HBAzJntCv2pb0Dc9dIUARF9QJM3wMx3AmxAdxGmKNc/fUAvMCsUKVR/QBvzOuIQOmRAIGKDhdMld0Bh4STNH8R/QJF8JZASKWVAARb59cPockCqtTALbbNlQNEeL6QDPXNAar5KPvZ8bEAEdcqj2wN7QEoNbQA2B2FA2LlpM87TdUD1S8RbJ/t2QDlGskeoz3FA6WFodTIEd0D4VE57Cjp6QP7viApVH3tAGCMShVb+c0BQqKePABhyQLa5MT3h4HdAvobguAxLcEB06zU9qBl/QJNwIY+ga3xAbsK9Mm8HaUCCcAUUarFuQGGMSBTaUmZAXru04bDqfECJQzaQLt1pQB7EzhS6m2JALq7xmSx5d0CJYBxcmpmAQNWT+UefE35AFCAKZoyFdUA7Hch66jd4QHjuPVwyvXZAeCefHlvIc0DejQWFwY90QNFXkGaslHJAL8Tqj3B/e0C6ERYVcS14QNnPYikSAHtADoXP1sHDcUCWsgxx7B55QL2pSIVxznBAlQ7W/3k9dUAt0VlmUdd5QPsCeuHOFWRAFHXmHnILgEBubHakuoZ4QApl4esrWXZArg6AuAsAgUCU93E0R+9nQDGVfsLZD2lAghq+hfXIdECf5Xlw9/N1QHx8QnYeSnhAdF34wXlLb0CduYeE7/FvQPZE14WfO29AshGI1/UAe0DGv8+4MPNwQEga3NbWKX5A2GSNegjAZkDaHr3hPtdlQPxR1Jl7anxAYFlpUgoOeEDLLhhc8wttQAbaHVKM4mZARS3NrRCzeUAMPPcebuB/QDenkgGgd15AlN3M6EdbgUAof/eOmvt2QNEINq4/vHRA02cHXNeWckDoFU89Ulp4QOCdfHoshnNAoPtyZjuSeUBAaahRSKxqQP8iaMzkpHtAPDPBcG6pekA012mkpRx9QO+NIQA4j3pA1O/C1uxFf0B8uOS4k+R1QM3MzMyMi35AbQGh9ZAUgUCAu+zXHVpjQJqZmZmZmnhAhetRuB5ZekDHDipxXRZyQArXo3C99HRAxVT6CSese0ADX9GtF+t0QGnFNxR+LX1A16NwPcqKfkCNKsO4Wz90QM5wAz4/mH5ASx3k9eAXakBEbLBwkqx4QIro19ZPUXNAXi13ZsILfUD8w5YezWZlQPVnP1KExnhAkj1CzRDTf0DxK9ZwEVZ2QHmSdM3kV2ZAw7ZFmU3QeEDCps6jYnWAQALXFTPCDHlABDxp4bLWZkDmBdhHJx6AQLb4FADjTXJAFW9kHnlWcECWeauuQ5B3QIHLY82IpnFAX38Sn3tDcEDt72yPHu51QIvFbwqrxn1AW86luCrWe0CbBG9Io/FqQF1Q3zLnPH1ABtodUszZdUBNu5hmek1wQM6pZACo9WtAgxQ8hZzEdUAjgnFw6Up2QEkw1cxaEYFA6rMDruv+fEBEpREz+whnQMFXdOu1DHpAvhQeNLssbEA9EFmkSZR+QAg57/+jAX5A5Lop5bVBdUBfz9cs151zQGIuqdqugHhAGO5cGKmXdkBIwr6dRMJ1QNUl4xjJ43dA8SxBRkA+a0Bk6NhB5Y99QAvxSLw8xn9AnPwWnSztcUACoIobN09mQIaTNH9MsHBAFXDP8ye2ZkANF7mnqwx/QJ8+An84UX5AUDi7tUy4a0DUSEvlLf10QC15PC3/pnVAN4yC4DGNdEAdc56x7+l2QDqvsUuUc3BAQu4iTNE0ekAY7lwYKUV7QIkI/yKoTXFABB2taolBdUDAe0eNidFpQBxEa0WbyWVAHy3OGGZVfUDRyr3ALH1sQIqO5PIf/WhAO3DOiFLdf0DXiGAcXEltQApnt5bpaYBAqFSJsnfec0DWbrvQXC1wQIqO5PIfJWZAu9QI/UzKbkDcfvlkBa9zQLa93ZLcLHhA2ekHdVEFcEDZImk3Oqx1QMiakUEuvXJANbIrLeNec0D3x3vVCph3QJijx++tZHJAJqyNsZOsdkAO2NXk6dRzQAGiYMZUvnNA5XtGInQReUAvF/GdmApzQN8zEqERoG1AorPMItRmdECMEvQXOt12QEypS8Yx5n1AlKC/0GPWdkCQTl35rFR5QHiazHjbt21A0c3+QPlzfkA4wMx38G2AQIULeQS3wnNAje21oPcEZ0ABF2TLcn5hQFqAttWsjXlAm8k321w4eUD7WwLwT1xyQFCJ6xhXW3lAGT230JWIdUARjINLxz56QFPovMbuE3tASDSBItZje0B5W+m1WXdrQF2/YDfs3HtAq15+p4mKdUBQ/1nzY7hpQE/Pu7GgF3hA6svSTu1fgEAaGk8EsX9zQNBFQ8Zj5n5Am3KFd9lRgEBVMZV+QrpwQBCWsaEbLmRAysABLd3Df0BfYcH9AOhpQN0HILUJDmFAg9+GGO8Ff0CJ8C+Cxi9pQHTv4ZIjrHNAjbYqiay9c0Dm5hvRfclxQOF7f4P2QW9AK01KQbfCc0ANpfYiGiJ5QKwCtRj8PXVAIhrdQSwNdUAhdqbQuex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\"]]}],[\"z\",{\"type\":\"map\",\"entries\":[[\"dtype\",\"f8\"],[\"bdata\",\"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       "  var render_items = [{\"docid\":\"65190a03-75ee-479e-b56f-5bbc39cf3307\",\"roots\":{\"6f9f080c-8a73-4588-bd57-851dd996c24a\":\"cf018b4f-738e-47bd-8d3d-f20302c29173\"},\"root_ids\":[\"6f9f080c-8a73-4588-bd57-851dd996c24a\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root.Plotly !== undefined))\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Scatter3D   [x,y,z]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "6f9f080c-8a73-4588-bd57-851dd996c24a"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hv.Scatter3D((neurons.x, neurons.y, neurons.z)).opts(\n",
    "#     opts.Scatter3D(azimuth=40, elevation=20, color='z', s=50, cmap='fire'))\n",
    "\n",
    "hv.Scatter3D((neurons.x, neurons.y, neurons.z)).opts(\n",
    "    cmap=\"fire\", color=\"y\", size=3, alpha=0.75, width=800, height=800\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synaptic connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:04.269558Z",
     "start_time": "2020-01-22T16:52:04.260977Z"
    }
   },
   "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>from</th>\n",
       "      <th>to</th>\n",
       "      <th>contacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75211</td>\n",
       "      <td>75276</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75211</td>\n",
       "      <td>75473</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75211</td>\n",
       "      <td>76012</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75211</td>\n",
       "      <td>76095</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102727</th>\n",
       "      <td>81322</td>\n",
       "      <td>80991</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102728</th>\n",
       "      <td>81322</td>\n",
       "      <td>81051</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102729</th>\n",
       "      <td>81322</td>\n",
       "      <td>81111</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102730</th>\n",
       "      <td>81322</td>\n",
       "      <td>81116</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102731</th>\n",
       "      <td>81322</td>\n",
       "      <td>81147</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102732 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         from     to  contacts\n",
       "0       75211  75221         5\n",
       "1       75211  75276         5\n",
       "2       75211  75473         5\n",
       "3       75211  76012         7\n",
       "4       75211  76095         3\n",
       "...       ...    ...       ...\n",
       "102727  81322  80991         5\n",
       "102728  81322  81051         5\n",
       "102729  81322  81111         4\n",
       "102730  81322  81116         9\n",
       "102731  81322  81147         4\n",
       "\n",
       "[102732 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(synapses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjaceny matrix \n",
    "\n",
    "Although representing the network as an edge list as above might space-efficient, sometimes it is more convenient to analyze the adjacency matrix directly. You could, obviously, transform the edge list to the matrix representation yourself (a quick exercise in [table operations](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html) acrobatics, or straightforward by nested loops). Still, we can simply get it using the `adjacency=True` flag as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:05.244741Z",
     "start_time": "2020-01-22T16:52:04.274168Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003x2003 adjacency matrix\n",
      " \t2,003 neurons\n",
      " \t102,732 connections\n",
      "\t650,949.0 contacts\n"
     ]
    }
   ],
   "source": [
    "connectome = load_connectome(adjacency=True)\n",
    "neurons, synapses, A, W = (\n",
    "    connectome[\"nodes\"],\n",
    "    connectome[\"edges\"],\n",
    "    connectome[\"A\"],\n",
    "    connectome[\"W\"],\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"{A.shape[0]}x{A.shape[1]} adjacency matrix\\n\",\n",
    "    f\"\\t{len(A):,} neurons\\n\",\n",
    "    f\"\\t{A.sum():,} connections\\n\" f\"\\t{W.sum():,} contacts\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for convenience, in addition to the binary adjacency matrix `A`, a weighted matrix `W`, whose values describe the number of synapses per connection, is also returned. For the rest of our analysis here, we consider only the connections (without the contacts)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motif embedding in space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scan triplet motifs\n",
    "Let's examine the how each motif is embedded in space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:53.102188Z",
     "start_time": "2020-01-22T16:52:05.250525Z"
    }
   },
   "outputs": [],
   "source": [
    "frequency, participations = nsm.motifs(A, participation=True)\n",
    "\n",
    "# Reorder according to nn4576 paper\n",
    "tid_order = nsm.triad_order_nn4576\n",
    "frequency = np.take(frequency, tid_order)\n",
    "participations = [participations[t] for t in tid_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:53.110935Z",
     "start_time": "2020-01-22T16:52:53.106713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4187617 2738360 3162677  281224  208612  174242   29961    4328    7555\n",
      "    7808    6694     689      15]\n"
     ]
    }
   ],
   "source": [
    "print(frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:57.915056Z",
     "start_time": "2020-01-22T16:52:53.126044Z"
    }
   },
   "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>R</th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>motif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75261</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75396</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809777</th>\n",
       "      <td>81129</td>\n",
       "      <td>80370</td>\n",
       "      <td>76956</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809778</th>\n",
       "      <td>78718</td>\n",
       "      <td>77948</td>\n",
       "      <td>77520</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809779</th>\n",
       "      <td>80751</td>\n",
       "      <td>79899</td>\n",
       "      <td>77546</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809780</th>\n",
       "      <td>80386</td>\n",
       "      <td>80335</td>\n",
       "      <td>77704</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809781</th>\n",
       "      <td>78422</td>\n",
       "      <td>77773</td>\n",
       "      <td>77708</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10809782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              R      G      B  motif\n",
       "0         75211  75221  75261      1\n",
       "1         75211  75221  75271      1\n",
       "2         75211  75221  75277      1\n",
       "3         75211  75221  75396      1\n",
       "4         75211  75221  75691      1\n",
       "...         ...    ...    ...    ...\n",
       "10809777  81129  80370  76956     13\n",
       "10809778  78718  77948  77520     13\n",
       "10809779  80751  79899  77546     13\n",
       "10809780  80386  80335  77704     13\n",
       "10809781  78422  77773  77708     13\n",
       "\n",
       "[10809782 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gids = neurons[\"gid\"].values\n",
    "motifs = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(gids.take(p), columns=[\"R\", \"G\", \"B\"]).assign(motif=i + 1)\n",
    "        for i, p in enumerate(participations)\n",
    "        if p\n",
    "    ],\n",
    "    ignore_index=True,\n",
    ")\n",
    "display(motifs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:52:58.242084Z",
     "start_time": "2020-01-22T16:52:57.920793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_a5743\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >motif</th>\n",
       "      <th id=\"T_a5743_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_a5743_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_a5743_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_a5743_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_a5743_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_a5743_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_a5743_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_a5743_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_a5743_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_a5743_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "      <th id=\"T_a5743_level0_col10\" class=\"col_heading level0 col10\" >11</th>\n",
       "      <th id=\"T_a5743_level0_col11\" class=\"col_heading level0 col11\" >12</th>\n",
       "      <th id=\"T_a5743_level0_col12\" class=\"col_heading level0 col12\" >13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a5743_level0_row0\" class=\"row_heading level0 row0\" >Frequency</th>\n",
       "      <td id=\"T_a5743_row0_col0\" class=\"data row0 col0\" >4,187,617</td>\n",
       "      <td id=\"T_a5743_row0_col1\" class=\"data row0 col1\" >2,738,360</td>\n",
       "      <td id=\"T_a5743_row0_col2\" class=\"data row0 col2\" >3,162,677</td>\n",
       "      <td id=\"T_a5743_row0_col3\" class=\"data row0 col3\" >281,224</td>\n",
       "      <td id=\"T_a5743_row0_col4\" class=\"data row0 col4\" >208,612</td>\n",
       "      <td id=\"T_a5743_row0_col5\" class=\"data row0 col5\" >174,242</td>\n",
       "      <td id=\"T_a5743_row0_col6\" class=\"data row0 col6\" >29,961</td>\n",
       "      <td id=\"T_a5743_row0_col7\" class=\"data row0 col7\" >4,328</td>\n",
       "      <td id=\"T_a5743_row0_col8\" class=\"data row0 col8\" >7,555</td>\n",
       "      <td id=\"T_a5743_row0_col9\" class=\"data row0 col9\" >7,808</td>\n",
       "      <td id=\"T_a5743_row0_col10\" class=\"data row0 col10\" >6,694</td>\n",
       "      <td id=\"T_a5743_row0_col11\" class=\"data row0 col11\" >689</td>\n",
       "      <td id=\"T_a5743_row0_col12\" class=\"data row0 col12\" >15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11d340f10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert np.array_equal(frequency, motifs.groupby(\"motif\").size())\n",
    "\n",
    "motifs.groupby(\"motif\").size().to_frame(\"Frequency\").T.style.format(\"{:,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Triplet motifs spatial embedding\n",
    "\n",
    "Lastly, we examine the positions of the neurons forming each of the motifs. Since the pyramidal neurons are vertical, we'll focus on the y-coordinate (cortical depth)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-22T16:53:02.299937Z",
     "start_time": "2020-01-22T16:52:58.248202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>R</th>\n",
       "      <th>G</th>\n",
       "      <th>B</th>\n",
       "      <th>motif</th>\n",
       "      <th>Rx</th>\n",
       "      <th>Ry</th>\n",
       "      <th>Rz</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>Bx</th>\n",
       "      <th>By</th>\n",
       "      <th>Bz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75261</td>\n",
       "      <td>1</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "      <td>203.276337</td>\n",
       "      <td>968.822791</td>\n",
       "      <td>481.909118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75271</td>\n",
       "      <td>1</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "      <td>320.183623</td>\n",
       "      <td>1180.844858</td>\n",
       "      <td>570.809167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75277</td>\n",
       "      <td>1</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "      <td>293.199873</td>\n",
       "      <td>1090.600882</td>\n",
       "      <td>580.749479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75396</td>\n",
       "      <td>1</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "      <td>393.450029</td>\n",
       "      <td>1067.014389</td>\n",
       "      <td>642.183122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75211</td>\n",
       "      <td>75221</td>\n",
       "      <td>75691</td>\n",
       "      <td>1</td>\n",
       "      <td>341.846181</td>\n",
       "      <td>773.977844</td>\n",
       "      <td>636.437126</td>\n",
       "      <td>239.869853</td>\n",
       "      <td>880.629815</td>\n",
       "      <td>609.945456</td>\n",
       "      <td>497.137587</td>\n",
       "      <td>1024.840713</td>\n",
       "      <td>725.135217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809777</th>\n",
       "      <td>81129</td>\n",
       "      <td>80370</td>\n",
       "      <td>76956</td>\n",
       "      <td>13</td>\n",
       "      <td>192.844296</td>\n",
       "      <td>948.897651</td>\n",
       "      <td>737.201974</td>\n",
       "      <td>178.475477</td>\n",
       "      <td>1061.766714</td>\n",
       "      <td>692.749867</td>\n",
       "      <td>248.743735</td>\n",
       "      <td>1104.252847</td>\n",
       "      <td>703.846591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809778</th>\n",
       "      <td>78718</td>\n",
       "      <td>77948</td>\n",
       "      <td>77520</td>\n",
       "      <td>13</td>\n",
       "      <td>472.534544</td>\n",
       "      <td>787.782031</td>\n",
       "      <td>597.521670</td>\n",
       "      <td>385.660359</td>\n",
       "      <td>797.075622</td>\n",
       "      <td>501.540998</td>\n",
       "      <td>359.745437</td>\n",
       "      <td>878.731038</td>\n",
       "      <td>649.375373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809779</th>\n",
       "      <td>80751</td>\n",
       "      <td>79899</td>\n",
       "      <td>77546</td>\n",
       "      <td>13</td>\n",
       "      <td>214.454828</td>\n",
       "      <td>907.813569</td>\n",
       "      <td>437.465360</td>\n",
       "      <td>226.882608</td>\n",
       "      <td>883.506021</td>\n",
       "      <td>430.744574</td>\n",
       "      <td>251.492190</td>\n",
       "      <td>1077.677984</td>\n",
       "      <td>499.352161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809780</th>\n",
       "      <td>80386</td>\n",
       "      <td>80335</td>\n",
       "      <td>77704</td>\n",
       "      <td>13</td>\n",
       "      <td>335.823705</td>\n",
       "      <td>1079.200210</td>\n",
       "      <td>626.482609</td>\n",
       "      <td>340.106908</td>\n",
       "      <td>1018.695726</td>\n",
       "      <td>604.070499</td>\n",
       "      <td>337.106262</td>\n",
       "      <td>998.329943</td>\n",
       "      <td>660.917740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809781</th>\n",
       "      <td>78422</td>\n",
       "      <td>77773</td>\n",
       "      <td>77708</td>\n",
       "      <td>13</td>\n",
       "      <td>277.609104</td>\n",
       "      <td>1140.425748</td>\n",
       "      <td>652.055906</td>\n",
       "      <td>271.051619</td>\n",
       "      <td>1144.976403</td>\n",
       "      <td>535.146850</td>\n",
       "      <td>355.084509</td>\n",
       "      <td>1117.287910</td>\n",
       "      <td>510.178296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10809782 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              R      G      B  motif          Rx           Ry          Rz  \\\n",
       "0         75211  75221  75261      1  341.846181   773.977844  636.437126   \n",
       "1         75211  75221  75271      1  341.846181   773.977844  636.437126   \n",
       "2         75211  75221  75277      1  341.846181   773.977844  636.437126   \n",
       "3         75211  75221  75396      1  341.846181   773.977844  636.437126   \n",
       "4         75211  75221  75691      1  341.846181   773.977844  636.437126   \n",
       "...         ...    ...    ...    ...         ...          ...         ...   \n",
       "10809777  81129  80370  76956     13  192.844296   948.897651  737.201974   \n",
       "10809778  78718  77948  77520     13  472.534544   787.782031  597.521670   \n",
       "10809779  80751  79899  77546     13  214.454828   907.813569  437.465360   \n",
       "10809780  80386  80335  77704     13  335.823705  1079.200210  626.482609   \n",
       "10809781  78422  77773  77708     13  277.609104  1140.425748  652.055906   \n",
       "\n",
       "                  Gx           Gy          Gz          Bx           By  \\\n",
       "0         239.869853   880.629815  609.945456  203.276337   968.822791   \n",
       "1         239.869853   880.629815  609.945456  320.183623  1180.844858   \n",
       "2         239.869853   880.629815  609.945456  293.199873  1090.600882   \n",
       "3         239.869853   880.629815  609.945456  393.450029  1067.014389   \n",
       "4         239.869853   880.629815  609.945456  497.137587  1024.840713   \n",
       "...              ...          ...         ...         ...          ...   \n",
       "10809777  178.475477  1061.766714  692.749867  248.743735  1104.252847   \n",
       "10809778  385.660359   797.075622  501.540998  359.745437   878.731038   \n",
       "10809779  226.882608   883.506021  430.744574  251.492190  1077.677984   \n",
       "10809780  340.106908  1018.695726  604.070499  337.106262   998.329943   \n",
       "10809781  271.051619  1144.976403  535.146850  355.084509  1117.287910   \n",
       "\n",
       "                  Bz  \n",
       "0         481.909118  \n",
       "1         570.809167  \n",
       "2         580.749479  \n",
       "3         642.183122  \n",
       "4         725.135217  \n",
       "...              ...  \n",
       "10809777  703.846591  \n",
       "10809778  649.375373  \n",
       "10809779  499.352161  \n",
       "10809780  660.917740  \n",
       "10809781  510.178296  \n",
       "\n",
       "[10809782 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding = (\n",
    "    motifs.merge(\n",
    "        neurons.set_index(\"gid\").add_prefix(\"R\"),\n",
    "        how=\"left\",\n",
    "        left_on=\"R\",\n",
    "        right_index=True,\n",
    "    )\n",
    "    .merge(\n",
    "        neurons.set_index(\"gid\").add_prefix(\"G\"),\n",
    "        how=\"left\",\n",
    "        left_on=\"G\",\n",
    "        right_index=True,\n",
    "    )\n",
    "    .merge(\n",
    "        neurons.set_index(\"gid\").add_prefix(\"B\"),\n",
    "        how=\"left\",\n",
    "        left_on=\"B\",\n",
    "        right_index=True,\n",
    "    )\n",
    ")\n",
    "embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Height (µm)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motifs_order = [3, 4, 7, 10, 11]\n",
    "colors = [\"#D1684C\", \"#A2CEA4\", \"#4192C6\"]\n",
    "\n",
    "data = embedding[embedding[\"motif\"].isin(motifs_order)].melt(\n",
    "    id_vars=\"motif\", value_vars=[\"Ry\", \"Gy\", \"By\"]\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(9, 3))\n",
    "sns.boxplot(\n",
    "    x=\"motif\",\n",
    "    y=\"value\",\n",
    "    hue=\"variable\",\n",
    "    data=data,\n",
    "    palette=colors,\n",
    "    saturation=1,\n",
    "    linecolor=\"k\",\n",
    "    width=0.6,\n",
    "    gap=0.2,\n",
    "    legend=False,\n",
    ")\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.ylabel(\"Height (µm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img 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    "The analysis uncovers a close match between the physical positions of the neurons and the synaptic connectivity they form. For instance, for motif #4 the “source” neuron (the presynaptic neuron that projects to the other two) is located, on average, above the other two cells whereas the “sink” neuron (the postsynaptic neuron receiving projections from the other two) is, on average, the lowest."
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    "## References and Further Readings\n",
    "Books\n",
    "1. [Networks of the Brain](https://mitpress.mit.edu/books/networks-brain#:~:targetText=In%20Networks%20of%20the%20Brain%2C%20Olaf%20Sporns%20describes%20how%20the,from%20a%20complex%20network%20perspective.&targetText=Networks%20of%20the%20Brain%20provides,essential%20foundation%20for%20future%20research.) by [Olaf Sporns](http://twitter.com/spornslab)\n",
    "2. [Connectome](http://connectomethebook.com/) by [Sebastian Seung](http://seunglab.org/)\n",
    "\n",
    "Papers\n",
    "* Markram, Muller, Ramaswamy, Reimann, Abdellah, Sanchez et al. (2015). [Reconstruction and Simulation of Neocortical Microcircuitry](https://www.cell.com/fulltext/S0092-8674(15)01191-5). *Cell*, **163**, 456–492. https://doi.org/10.1016/j.cell.2015.09.029.\n",
    "    * The _in silico_ digital reconstruction of the network of all synaptic connections in a cortical microcircuit.\n",
    "* Gal, London, Globerson, Ramaswamy, Reimann, Muller, Markram, and Segev. (2017). [Rich cell-type-specific network topology in neocortical microcircuitry](http://www.rdcu.be/tffO). *Nat Neurosci*, **20(7)**, 1004–1013. http://doi.org/10.1038/nn.4576.\n",
    "    * A network science analysis of the cortical microcircuit.\n",
    "* Gal, Perin, Markram, London, and Segev. (2019). [Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks](https://doi.org/10.1101/656058). *bioRxiv*, 656058. https://doi.org/10.1101/656058.\n",
    "    * Developing a general framework for spatially embedded networks that, when applied to biological neuronal networks, uncovers the geometric origin of network motifs universally emergence in different brain regions.\n",
    "\n",
    "Web resources\n",
    "1. [The neuron and nervous system](https://www.khanacademy.org/science/biology/human-biology#neuron-nervous-system). From the inspiring [Khan Academy](https://www.khanacademy.org/).\n",
    "2. [Blue Brain's portal](https://bbp.epfl.ch/nmc-portal/welcome)."
   ]
  }
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