{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pytorch Geometric (PyG) \n",
    "\n",
    "Pytorch Geometric (PyG) is a geometric deep learning extension library for PyTorch. It consists of various methods for deep learning on graphs and other irregular structures. It implements plenty of graph neural networks from the literature and allows to easily prototype new ones.\n",
    "\n",
    "Adapted from tutorials and notebooks from https://github.com/rusty1s/pytorch_geometric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Message Passing Networks\n",
    "\n",
    "Graph neural networks can be defined in terms of a *neighborhood aggregation* or *message passing* scheme.\n",
    "With $\\mathbf{x}^{(k-1)}_i \\in \\mathbb{R}^F$ denoting node features of node $i$ in layer $(k-1)$ and $\\mathbf{e}_{j,i} \\in \\mathbb{R}^D$ denoting (optional) edge features from node $j$ to node $i$, message passing graph neural networks can be described as\n",
    "\n",
    "$$\n",
    "  \\mathbf{x}_i^{(k)} = \\gamma^{(k)} \\left( \\mathbf{x}_i^{(k-1)}, \\square_{j \\in \\mathcal{N}(i)} \\, \\phi^{(k)}\\left(\\mathbf{x}_i^{(k-1)}, \\mathbf{x}_j^{(k-1)},\\mathbf{e}_{j,i}\\right) \\right),\n",
    "$$\n",
    "\n",
    "where $\\square$ denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and $\\gamma$ and $\\phi$ denote differentiable functions such as MLPs (Multi Layer Perceptrons).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The \"MessagePassing\" Base Class\n",
    "\n",
    "PyTorch Geometric provides the `MessagePassing` base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation.\n",
    "The user only has to define the functions $\\phi$ , *i.e.* `message`, and $\\gamma$ , *i.e.* `update`, as well as the aggregation scheme to use, *i.e.* `aggr=\"add\"`, `aggr=\"mean\"` or `aggr=\"max\"`.\n",
    "\n",
    "This is done with the help of the following methods:\n",
    "\n",
    "* `MessagePassing(aggr=\"add\", flow=\"source_to_target\", node_dim=-2)`: Defines the aggregation scheme to use (`\"add\"`, `\"mean\"` or `\"max\"`) and the flow direction of message passing (either `\"source_to_target\"` or `\"target_to_source\"`).\n",
    "  Furthermore, the `node_dim` attribute indicates along which axis to propagate.\n",
    "* `MessagePassing.propagate(edge_index, ...)`:\n",
    "  The initial call to start propagating messages.\n",
    "  Takes in the edge indices and all additional data which is needed to construct messages and to update node embeddings. \n",
    "* `MessagePassing.message(...)`: Constructs messages to node $i$ in analogy to $\\phi$ for each edge in $(j,i) \\in \\mathcal{E}$ if `flow=\"source_to_target\"` and $(i,j) \\in \\mathcal{E}$ if `flow=\"target_to_source\"`.\n",
    "  Can take any argument which was initially passed to `propagate`.\n",
    "  In addition, tensors passed to `propagate` can be mapped to the respective nodes $i$ and $j$ by appending $_i$ or $_j$ to the variable name, *.e.g.* $x_i$ and $x_j$.\n",
    "  Note that we generally refer to $i$ as the central nodes that aggregates information, and refer to $j$ as the neighboring nodes, since this is the most common notation.\n",
    "* `MessagePassing.update(aggr_out, ...)`: Updates node embeddings in analogy to $\\gamma$ for each node $i \\in \\mathcal{V}$.\n",
    "  Takes in the output of aggregation as first argument and any argument which was initially passed to `MessagePassing.propagate`.\n",
    "\n",
    "Let us verify this by re-implementing two popular GNN variants, the `GCN layer from Kipf and Welling <https://arxiv.org/abs/1609.02907>`_ and the `EdgeConv layer from Wang et al. <https://arxiv.org/abs/1801.07829>`_.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing the GCN Layer\n",
    "\n",
    "The GCN layer from Kipf and Welling is a popular GNN that was the first \n",
    "to bring the idea of layerwise convolutions to graphs processing. The layer\n",
    "is mathematically defined as\n",
    "\n",
    "$$\n",
    "    \\mathbf{x}_i^{(k)} = \\sum_{j \\in \\mathcal{N}(i) \\cup \\{ i \\}} \\frac{1}{\\sqrt{\\deg(i)} \\cdot \\sqrt{\\deg(j)}} \\cdot \\left( \\mathbf{\\Theta} \\cdot \\mathbf{x}_j^{(k-1)} \\right),\n",
    "$$\n",
    "\n",
    "where neighboring node features are first transformed by a weight matrix $\\mathbf{\\Theta}$, normalized by their degree, and finally summed up.\n",
    "This formula can be divided into the following steps:\n",
    "\n",
    "1. Add self-loops to the adjacency matrix.\n",
    "2. Linearly transform node feature matrix.\n",
    "3. Compute normalization coefficients.\n",
    "4. Normalize node features in $\\phi$.\n",
    "5. Sum up neighboring node features (`\"add\"` aggregation).\n",
    "\n",
    "Steps 1-3 are typically computed before message passing takes place.\n",
    "Steps 4-5 can be easily processed using the `MessagePassing` base class.\n",
    "The full layer implementation is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages.\n",
    "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "!pip install -q torch-geometric\n",
    "\n",
    "import torch\n",
    "from torch_geometric.nn import MessagePassing\n",
    "from torch_geometric.utils import add_self_loops, degree\n",
    "\n",
    "class myGCNConv(MessagePassing):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(myGCNConv, self).__init__(aggr='add')  # \"Add\" aggregation (Step 5).\n",
    "        self.lin = torch.nn.Linear(in_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        # x has shape [N, in_channels]\n",
    "        # edge_index has shape [2, E] (sparse adjacency matrix as a list of edges)\n",
    "\n",
    "        # Step 1: Add self-loops to the adjacency matrix.\n",
    "        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n",
    "\n",
    "        # Step 2: Linearly transform node feature matrix.\n",
    "        x = self.lin(x)\n",
    "\n",
    "        # Step 3: Compute normalization.\n",
    "        row, col = edge_index\n",
    "        deg = degree(col, x.size(0), dtype=x.dtype)\n",
    "        deg_inv_sqrt = deg.pow(-0.5)\n",
    "        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0\n",
    "        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n",
    "\n",
    "        # Step 4-5: Start propagating messages.\n",
    "        return self.propagate(edge_index, x=x, norm=norm)\n",
    "\n",
    "    def message(self, x_j, norm):\n",
    "        # x_j has shape [E, out_channels]\n",
    "\n",
    "        # Step 4: Normalize node features.\n",
    "        return norm.view(-1, 1) * x_j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "zF5bw3m9UrMy"
   },
   "outputs": [],
   "source": [
    "# Helper function for visualization.\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def visualize(h, color, epoch=None, loss=None):\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    if torch.is_tensor(h):\n",
    "        h = h.detach().cpu().numpy()\n",
    "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
    "        if epoch is not None and loss is not None:\n",
    "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
    "    else:\n",
    "        nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,\n",
    "                         node_color=color, cmap=\"Set2\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoW2Z7P70LNQ"
   },
   "source": [
    "# Example: node classification\n",
    "\n",
    "Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction.\n",
    "\n",
    "PyTorch Geometric provides an easy access to this dataset via the [`torch_geometric.datasets`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets) subpackage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H_VTFHd0uFz6",
    "outputId": "e9472a23-e3c8-4ef2-86c1-64282d80de5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: KarateClub():\n",
      "======================\n",
      "Number of graphs: 1\n",
      "Number of features: 34\n",
      "Number of classes: 4\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.datasets import KarateClub\n",
    "\n",
    "dataset = KarateClub()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('======================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7cjjyFVnpKB0"
   },
   "source": [
    "After initializing the [`KarateClub`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.KarateClub) dataset, we first can inspect some of its properties.\n",
    "For example, we can see that this dataset holds exactly **one graph**, and that each node in this dataset is assigned a **34-dimensional feature vector** (which uniquely describes the members of the karate club).\n",
    "Furthermore, the graph holds exactly **4 classes**, which represent the community each node belongs to.\n",
    "\n",
    "Let's now look at the underlying graph in more detail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gUFSrDPxuQ23",
    "outputId": "98c222c0-d955-4607-8b0e-cb98c012cb79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(edge_index=[2, 156], train_mask=[34], x=[34, 34], y=[34])\n",
      "==============================================================\n",
      "Number of nodes: 34\n",
      "Number of edges: 156\n",
      "Average node degree: 4.59\n",
      "Number of training nodes: 4\n",
      "Training node label rate: 0.12\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    }
   ],
   "source": [
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print(data)\n",
    "print('==============================================================')\n",
    "\n",
    "# Gather some statistics about the graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Number of training nodes: {data.train_mask.sum()}')\n",
    "print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MY4pZma9p3Ax"
   },
   "source": [
    "Each graph in PyTorch Geometric is represented by a single [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data) object, which holds all the information to describe its graph representation.\n",
    "We can print the data object anytime via `print(data)` to receive a short summary about its attributes and their shapes:\n",
    "```\n",
    "Data(edge_index=[2, 156], x=[34, 34], y=[34], train_mask=[34])\n",
    "```\n",
    "We can see that this `data` object holds 4 attributes:\n",
    "(1) The `edge_index` property holds the information about the **graph connectivity**, *i.e.*, a tuple of source and destination node indices for each edge.\n",
    "PyG further refers to (2) **node features** as `x` (each of the 34 nodes is assigned a 34-dim feature vector), and to (3) **node labels** as `y` (each node is assigned to exactly one class).\n",
    "(4) There also exists an additional attribute called `train_mask`, which describes for which nodes we already know their community assigments.\n",
    "In total, we are only aware of the ground-truth labels of 4 nodes (one for each community), and the task is to infer the community assignment for the remaining nodes.\n",
    "\n",
    "\n",
    "We can further visualize the graph by converting it to the `networkx` library format, which implements, in addition to graph manipulation functionalities, powerful tools for visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Y9MOs8iSwKFD",
    "outputId": "7bd4a2ef-08a4-458c-dfb5-09ea82fed059"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "G = to_networkx(data, to_undirected=True)\n",
    "visualize(G, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kPbYXBn1yYIJ"
   },
   "source": [
    "## Implementing a GCN \n",
    "\n",
    "We implement a GCN as a `torch.nn.Module` class that contains a sequence of GCNConv layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkQAVluLuxT_",
    "outputId": "b049292d-b2da-4926-f974-776012a3ec60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): myGCNConv(\n",
      "    (lin): Linear(in_features=34, out_features=4, bias=True)\n",
      "  )\n",
      "  (conv2): myGCNConv(\n",
      "    (lin): Linear(in_features=4, out_features=4, bias=True)\n",
      "  )\n",
      "  (conv3): myGCNConv(\n",
      "    (lin): Linear(in_features=4, out_features=2, bias=True)\n",
      "  )\n",
      "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.nn import Linear\n",
    "#from torch_geometric.nn import GCNConv\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(112233)\n",
    "        self.conv1 = myGCNConv(dataset.num_features, 4)\n",
    "        self.conv2 = myGCNConv(4, 4)\n",
    "        self.conv3 = myGCNConv(4, 2)\n",
    "        self.classifier = Linear(2, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = self.conv1(x, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv2(h, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv3(h, edge_index)\n",
    "        h = h.tanh()  # Final GNN embedding space.\n",
    "        \n",
    "        # Apply a final (linear) classifier.\n",
    "        out = self.classifier(h)\n",
    "\n",
    "        return out, h\n",
    "\n",
    "model = GCN()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hjsb3Fst2P8k"
   },
   "source": [
    "Here, we first initialize all of our building blocks in `__init__` and define the computation flow of our network in `forward`.\n",
    "We first define and stack **three graph convolution layers**, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 \"hops\" away).\n",
    "In addition, the `myGCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `myGCNConv` layer is enhanced by a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html?highlight=tanh#torch.nn.Tanh) non-linearity.\n",
    "\n",
    "After that, we apply a single linear transformation ([`torch.nn.Linear`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear)) that acts as a classifier to map our nodes to 1 out of the 4 classes/communities.\n",
    "\n",
    "We return both the output of the final classifier as well as the final node embeddings produced by our GNN.\n",
    "We proceed to initialize our final model via `GCN()`, and printing our model produces a summary of all its used sub-modules.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dt8fPEmc4m5l"
   },
   "source": [
    "### Embedding the Karate Club Network\n",
    "\n",
    "Let's take a look at the node embeddings produced by our GNN.\n",
    "Here, we pass in the initial node features `x` and the graph connectivity information `edge_index` to the model, and visualize its 2-dimensional embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "nwHtX5siwe2v",
    "outputId": "080bbcb1-8278-4dc5-dd4c-f163e9bb8372"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding shape: [34, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = GCN()\n",
    "\n",
    "_, h = model(data.x, data.edge_index)\n",
    "print(f'Embedding shape: {list(h.shape)}')\n",
    "\n",
    "visualize(h, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-W5Kfhu5I-P"
   },
   "source": [
    "Remarkably, even before training the weights of our model, the model produces an embedding of nodes that closely resembles the community-structure of the graph.\n",
    "Nodes of the same color (community) are already closely clustered together in the embedding space, although the weights of our model are initialized **completely at random** and we have not yet performed any training so far!\n",
    "This leads to the conclusion that GNNs introduce a strong inductive bias, leading to similar embeddings for nodes that are close to each other in the input graph.\n",
    "\n",
    "### Training on the Karate Club Network\n",
    "\n",
    "But can we do better? Let's look at an example on how to train our network parameters based on the knowledge of the community assignments of 4 nodes in the graph (one for each community):\n",
    "\n",
    "Since everything in our model is differentiable and parameterized, we can add some labels, train the model and observse how the embeddings react.\n",
    "Here, we make use of a semi-supervised or transductive learning procedure: We simply train against one node per class, but are allowed to make use of the complete input graph data.\n",
    "\n",
    "Training our model is very similar to any other PyTorch model.\n",
    "In addition to defining our network architecture, we define a loss critertion (here, [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)) and initialize a stochastic gradient optimizer (here, [`Adam`](https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam)).\n",
    "\n",
    "Note that our semi-supervised learning scenario is achieved by the following line:\n",
    "```\n",
    "loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "```\n",
    "While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.\n",
    "Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`.\n",
    "\n",
    "Let us now start training and see how our node embeddings evolve over time (best experienced by explicitely running the code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "etxOsz8QIbMO",
    "outputId": "a961884c-7520-460a-d591-94834563c4a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9WpK06eDzJYWVk9PJ/j49f/LoqNeLqYWAAc5C2bgJa7ScpG2dberOpLXp0HPKhKUtrz/MauPBnWe0bkwdBAxwFppbVaOUJc5oGakgoQM9J9STHShrviN9XWe0XkwdBAxwFrpk7tJR9cEUyo2iPyUwO+P1YmogYICzUF1FlV4xe4GSNvpDQNaFaqqaqcbKmSXPE8i0pKZx1OvE1ELAAGepd6y4TLOrZo46ZOZV12pedZ1ubF2tiqC05rZEEOj6BatGtT5MPQQMcJaqTqb0Py54jS6Y3aqkBUqVGBKSVBEkdPPCdZKkV81dqopg5Gu2kxZoeV1T0Ys0MT1xJT9wFqtOpvTu1VeoayCtze17daj3pB7v2Ke+bEbhEH00FUFCl85dqlc2LZQkVSVTet/51+uzT/1a6SHmS1lCc6tr9N7VV3ndHkwuVs5FT+vXr3dbtmzxWB0AEy2dy+i7z2/W7zr2SWYvXUBZmUgqkOm1C9fq1a2rZQWd9Z39Pbpr7zY91r73pZtmSk4JC3Td/HP16tbVqkjwnXa6MbPHnXPri04jYAAU05Pp12Pte3W075QSFmhx7WxdOLtVyRGa0tLZjHZ3HVN/mFVNslLL6mYrOIPBBJjchgsYvk4AKGpmqlLXzl9Z9nxVyZRWz2r2UCNMNXytAAB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMAEyB0TjkXTnQ1vEpOdAUA4GzRn8tqc/se3b3/GR1Nd8kkVScrdGXzcl07/1zNqpwx0VUcUwQMAIyDI32n9Lmnfq2+bFYDYVaS5CT1Zge0sW2n7j/4nG5b+SpdNGfRxFZ0DBEwAODZqYG0Pv3kverJ9MsVmZ51oeSkbz/3G81IVmj1rOZxr6MP9MEAgGd3H9ihvmymaLjky4Q53fnCb+XcSCWnBgIGADzKhDk9fPiFkjv0uwb69WJXh+dajQ8CBgA82t99XJKVXL4/zGp75yF/FRpHBAwAeNSfy5YRL5F0LuOlLuONgAEAj2orKhWW0aeSsEB1FdUeazR+CBgA8GjBjAZVJ1Mllw/MdFHTQo81Gj8EDAB4ZGa6sXW1UkFi5LKSFtc0ak51rf+KjQMCBgA8u6ZlhVpnNihlQx9yTVJVIqXbVl42fhXzjIABAM+SQULvO+96rWpoVipIKCjo9q8MkmqsnKkPXXDTtDl7kbiSHwDGRUUiqT9fd40O957UxoM7tetUh0IXak5Vra6bf67ObZinwModbza5ETAAMI6aZ9Tr1nMumehqjAuayAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOBFcqIrAAAYH+mBrB554qAOtXfLOWl2Q5Uuv7BVdTUVXtZHwADANBeGoX5w73M6cLj7tNePdvbqmRc71VhfpbfevEpVFWMbCTSRAcA0FoahvvXjp18WLvk6T6b1je8/pfRAdkzXTcAAwDR2zyN7dKp7YMRymWyo79+9c0zXTcAAwDQVhqGeebGz5PLtnX3qTWfGbP0EDABMUzv3HJdz5c3z6NaDY7Z+AgYApqn2zt6y5zne1T9m6ydgAGCaSiTKP8QnAhuz9RMwADBNLV1QV/Y8rfNqx2z9BAwATFPz59aqsiJRcnmT9Mo1c8ds/QQMAExjV76yteSy61Y2KQjGLhYIGACYxs4/d47mz5lRUtnqSq7kBwCUKD2Q1cH20kaTPbbtsI6fTI/ZugkYAJjG7n1kT1nlNz5aXvnhEDAAMI29sPdEWeX3DXPPsnIRMAAwjZV5If+YImAAAF4QMAAALwgYAJjGWubMLKt867yaMVs3AQMA09j1ly4qq/xrLl86ZusmYABgGps7e6ZWL2ssqeyFq+eorrZyzNY9tpdtAgAmnZsuX6ogMO3Ydazo78OYpIvWztOVF5V+W5lSEDAAMM0Fgemmy5fq4nXNenz7ET2397iyuVCpZKB15zTpglVzVVczdmcugwgYADhLNNZX68YNS3TjhiXjsj76YAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvzBW789lQhc3aJe31Vx0AwBSz2Dk3p9iEsgIGAIBS0UQGAPCCgAEAeEHAAAC8IGCmCDO7zczcEI8TE1y3283swBgv89+Z2XfN7DkzC83sgSJlEmb2ATO7z8yOmFmXmf3OzP7EzF722TaztWZ2j5l1m9kxM/u2mZX2W7IvX9aSeN+/azTzTyQze4OZPWFmaTPba2YfMbNEmctImdm2YvvAzN5sZj+Ml91nZjvN7B/NrLbIctaa2Y/M7KCZ9ZjZdjN7v5klC8pVmdlnzOxQvMzfmNlVo9sDGC/84NjU8xZJhQfz7ERUxLM3SLpA0qOSqoYoUy3pI5LukPQlSd2Sbpb0DUmrJP23wYJmNl/SA5KelfRmSQ2SPiPpLjO7wjkXjv0mTD5mdpOkH0r6pqT3SbpQ0j9IqpX0wTIW9QFJTcNM2yfpbxR9Vi+U9HFJ15rZhsF9nfeetEn6r5I6JF2v6H2ZW1Cfb0q6RdF7+qKk/yLpbjN7lXNuaxn1xnhyzvGYAg9Jt0lyks6Z6LoUqdvtkg6M8TKDvP9/WNIDRcokJDUWef1bktKSqvNe+4KkE5Ia8l67Kt6nbxpF/ZbE875rovd/mfV+QtKmgtc+KmlAUnOJy1gmqUfSrcX2gaQ5Reb5j3HZ6/Jee0/82sqCst+TdCjv+Svicu/Mey0paaekn030PuUx9IMmsmkmryntKjP7SV5z0JfNrLqgbIuZ3WFmHWbWb2ZPmdnbiyxzqZl9x8wOx+VeNLMvFSl3oZk9ZGa9Zva8mb13tNvhSjijcM7lnHOdRSZtllSp079hv07Sz51zJ/Lmf1DRN+3Xj7aeIzGzt5vZk3FzVEe8H1sKyrwtbrLqNrOTcdPTn+ZNv9jM7o3fx954/39lFHVZqOis8M6CSd+RlJL02hIX9VVFIfD/ik10zrUXeXlz/HdB3msV8d9TBWVP6PTm+9dJykj617x1ZOM63GRmY/9j8hgTBMzUkzCzZMGj2Pt4p6QXJL1J0bf3dys6MEiSzGympE2KDip/o6hJapuk75jZe/LKLZX0mKJv+x+Ly/+tXt48Uifpu/F6X6/ogPJVM7s2v1AcfrePbtNLdrWig9SheJ3VkpZKerpI2e2S1vioRLwfvyPpGUXvw4ck3SRpk5nVxGWuULTPNil6D96iqImvIZ5eI+luSTlFZ7E3S/qECpq3S9yva+O/p+0H59xuSb0qYT+Y2a2S1qu85jQpek+kaF8M+r6iZrH/HX+JqTOzN0p6h6TPFdR7t3Out2CZ2xWF1Dll1gXjZaJPoXiU9tDvm8iKPe4qUu5rBfN/WNFBamX8/M/jctcUlPu1pKOSEvHzOxT1bcwfpm63x8u6Nu+1SkUHj/9TUDYr6ZtlbnvRJrIhyt4kKZT04bzX5sf1e2+R8ndK2jWK92OJhmkiU9R8d0TS/QWvXxHP95fx8w9I6hxmPevj8uePUJ8R96ukt8XLWlVk2oES5p8Vb9O7StkHefMtiD9T9xaZdo6ioBj8LIeSPlpQ5h5JjxaZ94Z4nivLff94jM+DTv6p5416eSf/iSLl/q3g+fck/b2kSyQ9p+iMpM0590BBuTslfVvRt9ltkl6tKMAOjlCvXufc/YNPnHP9Zva8pEX5hZxz3j5zZrZG0r8o6jj+X/mTBldfbDZP1TlXUUf1h/NfdM49bGZ7FX2j/ydFZ3qzzOxORe/Rwy6vGU/S84re36+b2ZcV9Z/sL1xZifv1TPfDZyTtUtThXpL4DOynigLwnQXT5kj6kaL+nDdLOibpOkkfMbN+59zge2hnUGdMIAJm6nnaOfdCCeWODPF8sA28UXETUoHDedMlabZeHmjFHC/yWr+GHgE2psxsmaR7Je2W9AYXtdHn183p99uUb5akYv04Z2pwXUPt40ZJcs5tMrO3SPoLST+WJDPbJOl9zrmnnHMn42bG/ynpK5JqzWy7pI85535YZp0Gt7PYfmjQMPvBzC5VdHZ8vaR6M5OiZlFJqjazBkknXXxqEc9TJelnigYFXO2cK/wc/XdFZ0GLnXODn58H4iHTf2dm33TOdcT1WqSXm1WwXZhk6IOZvuYN8bwt/tspqbnIfIOvHYv/duj0jtlJx8xaJW1U1Fn8GufcaZ3GLmq736Pf90HkWyNph4dqDR70htrHg/tXzrkfOOeuVnTAfKOkFkm/Guxbc85tdc79e0XB8CpFZxH/ZmbryqzT9vjvafvBzJZImqHh98NqRc1+DygK7OOSnoyn/VP8vD5vmSlFw6EvkXSzc25bkWWeJ+mFvHAZ9JiiQQeDfSvbJS01sxkF5dYoGv1WyhcuTAACZvr6w4Lnb1XUvv1Y/HyTpFYzu7yg3NsUtZcPdsbeI+kPCkc+TRZxM8uv46c3uuIjmKTom/QtZpZ/ELxC0uJ42ljbqeis8a35L5rZhnidmwpncM51O+fukvR1RSEzu2B61jn3qKKzmUDRQb9kzrl9ikLh1oJJb1c0SuuXw8z+K0nXFjz+Qzzts/HzbkmKg/GfFZ3tvD6uczGHJZ1jZrMKXr80/jv4ZehnigLnLYMF4gsx/0jSPc65/mHqjQlEE9nUc4GZFbvAbUtBs9DNZvYZRQFxiaIRYHc4556Lp98u6a8k/cjMPqyoGexWSTdK+lPnXC4u9zFFF7g9Ymb/oOjb4gJFZwovG9I8EjPLSvq/zrk/GaHcYkkXx09nSwrN7M3x883Oub3x6LC7FTWz/LGiwGzNW8yOvLOZzyg6kP7MzP5R0bftTysK3B/nrXeJoma2v3XOfbyETbrIit9J4WeKri/5ety/cqei/fZJRf0q347X9wlFZ5f3SzooqVXSX0ra6pxrN7M/UHS9yE/ies2Mp3dJ+k1evUvar4pGDN5lZl9X1F91oaKLVb/knBtsHpWZfTSu/3Ln3N542uH8BcX7SpJ2FvTlfVlRGHxSUo+ZXZY37UBeU9nXFH3m7ok/q8ckXaNo4MOPB/uanHNbzexfJX0xPjPaLenPFI0MLAxLTCYTPcqAR2kPDT+KzElqKih3laLO1W5FzTVfVt6Fh3HZFkXDaDsU9Zc8JentRda9XNHBaLDci5K+kDf9dhW50FJRc8oDBa85Sbef4fbeFpdZMsI+uaZgmecp6qfpUdSkc7uk2QVl1mqIEWcF5UZa9+D78XZFZw39ig6g35HUkrecWxSF5KG4zH5Fnejz4+nnKrr+Y7eii0fbJf1C0qWj2a9x2Tfl1WmfoiBJFJT5eLzMJSXsg8ILLfcMs18+XlD2snh7DsXvy3ZFgVf4Wa2W9HlFIZeW9NvC95fH5HvwezDTjJndpujb8QpX2mAA5ImvXfmkoo7nwusuAJSBPhjgdFcrOjsjXIAzRB8MkMc5R5s+MEZoIgMAeEETGQDACwIGAOAFAQMA8IKAAQB4QcAAALz4/4/iw/7SbebkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xdgi6bvIMSdIbpy1QSayw+1sSFtMdtQ2XVC8KQ8AAGBGVqTJ9ePGqgoMhm0maO36yppSOkyTdMGWW4gXeqW9mWlFHwAwHAgbAiJk/Yar+93V3a/HEOiUsVnDYlMST+oP5N7/2PBmL60OLV+X92u5syVhcfzj/ZlWVlF9Su1EY7uQHMCqc6erQntYmne3p1NNHXtHZ7k51uwvvaymJJ1QSS+ijS1ZrekXVRds4cLZZ//jKczrX3anuIP3avLKSeEKpWFx/MP9mXRt1q2Fo9HcnPwEDYNRxzmnv2ZNa17hTh9tPK3BO1aXjdOf0hbp20ox+x26cc9p/rlmbmg7qbHenyhIp3TBlphZNrOMrvD3oL2D41DcAo46Zaf6EqRd93lih69ZXVqu+stpDy1AMxmAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAGAUSpwgc6nu5VxwUg3ZVASI90AAMDrnHPaeea4nmjcoV1nmhQzU+ACNUyYqntmXK3FE6cpZjbSzSwIAQMAo0QmCPSNXb/SjtNH1RVkwmXOSZL2tDbp8LkWNVRW64OLVygZi49kUwtCFxkAjBJrdz+vbVnhkqsrSGt3a5P+YeeGYW7Z4BAwADAKHG1v1YunGtXTR7j06nEZ7TpzXAfPnRqmlg0eAQMAo8BTR3YpExQ2mN8TZPRE407PLbp0BAwAjALbTh9VIFdQWSdp55njfhs0BAgYABgFBuoay5UusvxIIGAAYBSoSJYUVb48kfLUkqFDwADAKHBHbX3BU48TFtNtNfWeW3TpCBgAGAWKCQwz04q6Bo+tGRoEDACMAhXJEj3ccNOAVzGpWFwPzbteVSXlw9SyweNOfgAYJW6pmau4xfTonl9LCm+s7JWKJSQ5vaP+DbqtdvRfvUgEDACMKjdOna1rJ0/XlubD2njigNrT3SpPpHTT1Dm6sXq2SuKXz2H78mkpAFwhUvGEltfM0/KaeSPdlEvCGAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwIvESDcAADA8OjrT+uXmV7W/sVXpTKBUMq7FDVN0y9I6JRJDf71BwADAFeAnv9ivVw60XLCsJx1o09Zj2rT1mFYum6E3LK4d0joJGAAY437w1B7tb2ztt8z6zY0KnNON19QNWb2MwQDAGHbwaOuA4dLrl1uOqLM7PWR1EzAAMIatf+HVospv+M2RIaubgAGAMezUmc6iyu/cd2rI6iZgAGCMCoKg6HXSmeLX6QsBAwBjVCxW/CE+ZjZ09Q/ZlgAAo04qWdxhfsrEsiGrm4ABgDHsuoXVRZVffu20IaubgAGAMayzK1NU+W17m4esbgIGAMawYgNj7+EzQ1Y3AQMAY1jgRq5uAgYA4AUBAwDwgoABgDFs+tSKospPriodsroJGAAYw+65bU5R5e+9fe6Q1U3AAMAYVlVZpkX1kwsq2zCzSjWTi7vi6Q/fBwMAY9w9t81RPGbauqfvKcsL507S/XcM3dWLRMAAwJhnZlpz6xzdtKROm7Ye064DLcoETvGYaf7sKt28dJomVg7d2EsvAgYArhATxpdoza1ztObWOcNSH2MwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBfmXOHfp2lmJyUd8tccAMBlZrZzrjrfC0UFDAAAhaKLDADgBQEDAPCCgAEAeEHAXCbM7D1m5vp4nBnhtq01s8Yh3F6lmX3CzJ4zs1Nmdib6/1v6KH979Pp5MztuZp81s7I85Rab2RNm1hZt95/NbNIg2zgn2vfvG8z6I8nM3mJmL5pZp5kdMrO/NLN4Aes9bGa/MrOTZtZlZgfN7B/NbGaesrdF+7rJzM6a2W/M7I9zyow3s8+Y2bNRGWdmq/Jsa7aZ/VfU1vNm1hytc9+l7Af4R8Bcfh6StDzncdeItmjozZL03yWtl/SwpN+XtFvS983sQ9kFzWyppHWSmiQ9KOkvJf03SWtzyk2T9KykMkm/J+lDCvfbj83sivk7MLN7JP2npBck3SfpCwr32SMFrD5Z0lOS3ifp7mideyQ9Z2bjs+pYKulJSUlJ75f0tqi+b5rZB3O298eS0gp/h30ZJ6k5auf9kt4rqU3ST83srQW0GyPFOcfjMnhIeo8kJ6lhpNuSp21rJTUO4fYqJJXnWf6UpMM5y74vaY+kZNayP4z21Q1Zyz4n6YykqqxlK6Jybx1EG+dE675vpPd/ke1+UdL6nGWfkNQtqXYQ27sn2g9vy1r2SLS9cTllN0p6Puu5Zf3/rmg7qwqsNyHpVUk/Gul9yqPvxxVz5nalyOpKW2FmP8jqDvpybreRmdWZ2beiLocuM/utmT2cZ5tzzezRqPupy8z2m9kX8pS73sx+aWYdZrbHzP50MD+Dc67dOdeR56XNkqZl1ZeUdK+k7zjnerLKfUfhAe53spa9WdJPnHNnsur5haTDOeWGVNSt9HLUHdUc7ce6nDLvirqs2sys1cy2mtmfZL1+o5mti36PHdH+/8og2jJT0nWSHst56VGFVxuD6XI6Ff2bvf9T0fPzOWXPKKvXxEVJMRjOubSk1px6McoQMJefuJklch75fo+PSdor6a0Kz97fL+mrvS+aWYXCLqj7JP2FpLdI2irpUTP7QFa5uZI2KTzb/6uo/F9LmpJTX6Wkf4vq/R2FXSJfNbM3ZheKwm/t4H50rZC0K+t5vaRSSduyCznnOiXtk7QoqrNM0tzccpHtveWGWrQfH5W0U+Hv4f8oPONfb2bjojK3K9xn6xX+Dh6S9A1JVdHr4yQ9Limj8Cr2fkl/o/AMPruuQvbr4ujf3P11QFKHCtwPZhY3s5KoK+yzknZIeiKrSG87vmhm08ysyszeL+lOhe/FQTGzWPR+rzWzj0taIOnLg90ehsFIX0LxKOyh17vI8j1+nKfc13LW/5jCg9SC6PmHladLQmHfeZOkePT8Wwr7u6f107a10bbemLWsRGG/+T/klE1L+uYgfv4PRHW8O2vZrdGye/OU3yDpqej/06Jyf5qn3GOS9g2iPXPUTxeZpLikE5KeyVl+e7TeR6LnfyappZ96lkXllw7QngH3q6R3Rdu6Ks9rjYX+XqLfa+977wVJdXnK3Bhts7dct6T39rPNAbvIJH0ma3vnNIiuTR7D++AK5vLzuwr/eLMfH81T7js5z/+fwivWm6LnKyQdcc49m1PuMUnVev1s9m6FAXZ0gHZ1OOee6X3inOtSODYyK7uQcy7hnHvvANu6QDSz6IuSHnXO/Wv2S72bzbfaIMoNpYWSpkrKbq+ccxsUftzSymjRC5ImmtljZvagmVXlbGePwq6lr0fdbRfN2Iq2W8h+Har9cKfCcH+vwiutddntNrP5CicSbJf0JoXh8TVJXzOzdxdRT67PK3y/v0nSzyT9m5k9eAnbg2cEzOVnm3Nuc85jb55yJ/p4Pj36d5KkY3nWO571uhTO9ClkCvLpPMu6FHZhDZqZ3Sjph5KeVnhAy9YS/ZtvqvHErNdPKzyoDlRuKPXW1dc+niRJzrn1CrvFZiqcsHDSzJ6Mup/knGuV9EZJRyV9RdJhM9tmZm8bRJv6219VKnA/OOdeds4975z7J4UnIIskZY+3PaJwbORB59yPnXNPOec+ovCk5wuDnbXnnGuM3u8/ds69XeGkgc8MZlsYHgTM2FXTx/Mj0b8tkmrzrNe7rHfwtlmvh9KwMrMlCscfXlI4Syl3QHefwhBbnLNeqaR5CscG5MIJAwdzy0UW9ZYbYr0H6772ce/+lXPuu865lQrD7ncl1Un6ee+B2Dn3knPubQqDYbnCn/s7ZnZNkW3aHv2bu7/mSCrXIPaDC8dvWiQ1ZC1eIunlPL+vTQpPWKYWW08fNufUi1GGgBm73p7z/B2SAoV/5FI4qDzDzG7LKfcuhWMwO6PnT0h6MHfmk29RN8s6SfsVngnnzkiSc65b0s8lvd3Msge9f0/hGNAPs5b9UNIDZjYhq47bJc3OKTdUXlF41fiO7IVmdmtU5/rcFZxzbc65H0v6usKQmZzzeto5t1HSxxX+7V5dTIOcc4clvSwpt5vqYYVXHD8rZntSePNq1M59WYuPS7rOzFI5xW+W1KkhuGKMwvf2nHoxyiQGLoJR5jozy53BJUmbXTh1s9f9ZvZphQFxk8IZYN9yzu2OXl8r6X9K+p6ZfUxhN9i7Ja2R9CfOuUxU7q8kPaDwZrpHFM5Mm65wYP2iKc0DMbO0pH/pb7zAzKYqDJdUVP8iswuGCF6Mxngk6ZOSnld4Rv9lhYPvn5b0Xefclqx1Pq3wQPpDM/s7SRMk/V+Fgfv9rLrnSDog6a+dc58s4Ed6g+X/JIUfKry/5Otm9pjCsa3pkj6lcFzln6P6/kbh1eUzCrvBZkj6iKSXnHMnozGGD0j6QdSuiuj1c9HP3dvuAfdr5C8U3lz6dUnflnS9whsYv+Cc6+0elZl9Imp/vXPuULRsg8J9tUthUCyV9L8Uvne+kVXHlyT9h6QfRdOpzyucJv5OSZ+LTgx667kv+pmWRItWRu/vdufcz6Iyn1R49fYrheFVq7C79CaFJ0QYrUZ6lgGPwh7qfxaZkzQlp9wKSf+lcAZYi8LpnGU526xTOI22WWFX028lPZyn7nqFB6PecvsVHih6X1+rPDdaKrxz/tmcZU7S2gF+1lUD/KxzcsqvUHiw7VR41fB55b9Rc4nC4GpXOC6zVtLknDKL1ceMs5xycwr8fTys8KqhS2G32KPKmnWlMLwfVzhW06Xw5sFvKpq1p3CywL8rDJdOSScl/VTSzcXu16yyb81q02GFQRLPKfPJ3H0t6e8VTmU/F72vdigM7ql56rgv+v2fjMq/pPDTGXLrOdjH/juYVebNCsfgmqI2H1IY4LeN9N8lj/4ffB/MGGNm71F4djzf5R/8Rz+ie1c+pfBLlPLd7AmgQIzBABdaqfDqjHABLhFjMEAW59yl3KcBIAtdZAAAL+giAwB4QcAAALwgYAAAXhAwAAAvCBgAgBf/H6t7YmjsgB47AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yk7Ih6C11i/Tuhos1OVVxyjLbWo7oocaXtefkcWVCUHWqQmvnnK+r6hdpUrJsDB/R+DPUT8UQMADOCV19ab3SelTdfb2qTJbp/Jp6frhyFAwVMOxdAOeESckyraqdN9ZlnFMYgwEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC6SY10AAGBwbekuvdJ6VD2ZPk1OlWtFzSyVJc6Ot+6zo0oAOMcc7mzTj3Zv1MsnDikZRcqGoMhM2RB0Zf1i3d6wUpXJsrEuc0gEDACMM7vbm/TFFx9VOtOnIKkvkz3l/icP79BLzQf156tu0ZSyirEpsgiMwQDAONLRm9aXX3xMPblwKSQTglp6OvXVLY+fydJKRsAAwDjy6yM71Reyw7bLKOhQZ6v2tB8/A1WNDAEDAOPIIwe2qTebKaptbzarRw5sc65o5AgYABgnsiGoJd1VdPugoMaOFr+CThMBAwBwQcAAwDgRmWlq2aSi25ukOZVT/Qo6TQQMAIwjN8w5X6koUVTbVJTUjXOXOVc0cgQMAIwj18x6kxJmw7aLzFQ3abIWTak9A1WNDAEDAOPI5FS5/vSCtSqPBv8efMJM1akKffTCtbIiwmisEDAAMM4smTpTn1h1s5bX1CtpkcqjpFJRpIooqVSU0OUzG/QXb761pPGascBPxQDAODS3qkZ/dtFb1dzToW0tR+Ifu0yW6cLpczUpmRrr8opCwADAODa9vEpX1S8e6zJGhC4yAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgAsCBgDggoABALggYAAALggYAIALAgYA4IKAAQC4IGAAAC4IGACACwIGAOCCgAEAuCBgAAAuCBgAgIvkWBcAADgz0uk+Pfn8Ab2674T6+oLKUpEuOG+GLr9wtpLJ0T/eIGAA4BzwyNN7tHl70ym39fRm9MymQ3pm0yGtuXSeVl84a1S3ScAAwAR3/xO7tHVX85BtfvVcozLZrK64eM6obZcxGACYwA4dOzlsuPR7auNBdXb3jdq2CRgAmMAe++3+kto/+Vxp7YdCwADABHa4qaOk9tt2F3e0UwwCBgDwmkwmjNq6CBgAwOts9FZFwADABFaWKu1tvmZK+ahtm4ABgAls5rTKktpXTUqN2rYJGACYwA43nSypfeOR0toPhYABgAkskx27bRMwAAAXBAwAwAUBAwAT2KwZVSW1ZxYZAKAot16zqKT2b7umYdS2TcAAwAQ2bWqFzm+YVlTbhbOrNaduyqhtm4ABgAnu7WsWa/ni6UO2WTxvqu646bxR3S7ngwGACc7MdOu1i3XVqrn69QsH9OreE8pkg6LItHjuVF1z6TxNq64Y9e0SMABwjpg6pVy3XrtYt157ZrZHFxkAwAUBAwBwQcAAAFwQMAAAFwQMAMAFAQMAcEHAAABcEDAAABcEDADABQEDAHBhIYTiG5sdk7TXrxwAwFlmYQhhZqE7SgoYAACKRRcZAMAFAQMAcEHAAABcEDBnCTO728zCIJeWMa5tnZk1juL6qs3sU2b2azM7bmYtuf/fXqDtpwfZJ/9aoO0FZvaQmZ3Mrfe7Zjb0af4Gr7Eht50PjmT5sWRmt5vZRjPrNrO9ZvZfzSxRxHJ3mdlTZnbMzHrMbI+Z/YOZzc9r9/gQr9UH8tpOy62jycw6zOwRM7sor81qM/t7M9tmZp1mts/Mvm9mpZ1sHmccJxw7+/yOpPw3876xKMTRAkl/Ium7kj4rKSvp30j6sZl9JIRwT4FlrpGUGXC9eeCdZjZH0uOStkl6n6QaSX8j6edmdk0IITvKj2FcMrNbJP1I0rcl/QdJl0j6H5KmSPrEMIvXSvqlpL+W1CLpfEl/IekWM1sRQmjPtfsTSdV5y14p6W8l/XRALZa7vkjSn0o6IenPJT1mZqtCCP2v8zslXSDpK5K2SJqb2+6GXLv9JewCnEkhBC5nwUXS3ZKCpCVjXUuB2tZJahzF9VVJqixw+y8l7cu77dO5/ZIcZp1fVPymWDPgtjW5Ze8YQY0NuWU/ONb7v8S6N0pan3fbpySlJc0awfpuye2H9w7T7tuSeiRNH3Dbu3PLrh1w21TFHw6+MuC2mQXWt1DxB4/PjPU+5TL4hS6yCWZAV9oaM/vXAd1B95jZpLy2s83se7nuiR4z22xmdxVY5yIzu9fMDufa7TKzLxdod4mZPZHrxnjVzP5oJI8hhNARQugscNcGSXNGsk5J75L0/0IILQO28ytJ+xS/0bnIdSttynVHNeX24+y8Nu/PdVmdNLNWM3vRzD484P7LzOzh3PPYmdv/XxtBLfMlrZJ0X95d90pKSbq19Eeo47l/e4fY7iTFR94/CyEMPLJ8l6SDIYTH+m8IIbRK+pkGPCchhGP56wwh7JV0TPHRDMYpAubskzCzZN6l0PN4n6Qdku5Q/On930n6ev+dZlYlab3iN5X/Iul2SS9KutfMPjSg3SJJv1X8af+/5dr/d0kz8rZXLemfctt9t6RnJX3dzNYObJQLv3Uje+hao7iLq5D9ZpbJjSl8fmCY5v6/SNJLBZbbImnFCOsZUm4/3itpq+Ln4T8r/sS/3swm59pco3ifrVf8HPyOpG8p7sJTrt2Dirv/7pb0dkmfUV73dpH79YLcv6fshxDCbkmdKnI/mFnCzMrN7GLF3V4vS3poiEXuUNwF948F6hnsOVnQv48GqWG5pDrF+xbj1VgfQnEp7qLXu8gKXX5eoN038pb/pOI3qaW56x/Jtbs+r90jko5KSuSuf0/SSUlzhqhtnd7Y1VEuqUnS3+e17ZP07RE8/g/ltvH7ebffpXjs4GZJNykeV0lLenhAmzm5Zf+owHrvk7RzBPU0aIguMkkJSUckPZZ3+zW55T6au/5xSc1DbGd1rv3Fw9Qz7H6V9P7cupYVuK+x2Ocl97z2v/aelTR7mPYP5vZFMu/27ZJ+UKD9B3Prnj/I+pKKA/mopGmlPndcztyFQf6zz3v0xkH+lgLtfph3/QeSPifpcsV/2GskHQghPJ7X7j7Fg+srFB/R3Kw4wA4OU1dnOLWro8fMXlU8YK8Bt5f8mjOz6xUP8N4bQvh+3vryu3setnhG25fM7MYQwiOSrL95odWXWk+Rzlf8CfuTA28MITxpZnslXaf4MT0raZqZ3af4OXoyDOjGk/Sq4uf3m2Z2j+LxkzcMahe5X0drP7xVUqWk5YoH5R/OTZRoyW+Ym1xxo6QvhxDyJ6PYCGv5O0lXSbothHCihLpxhtFFdvZ5KYSwIe+yo0C7I4Nc7++zni7pUIHlDg+4X4pnDhUzBbnQH3qPpIoilh2UmV2meKbRo5L+sMjF/nfu38sG1Bb0+mMaaJryZpyNkv5tDbaPp0tSCGG94m6x+ZJ+LOlYbqruxbn7WyWtlXRQ0tck7TOzl8zsvSOoqf9xFtoPNSpyP4QQNoUQng4hfEfxB5AVkgYbb7tL8ftMfvdYfz2DPSdSgdeUmf2V4qPZPwghDNUth3GAgJm46ge5fiD3b7OkWQWW67+tf/C2SWM0kJr7PsSDkl5QPEtp0IHkQQRJCvGEgT16fQxioBWKxxBGW/+b9WD7uH//KoTwf0II1yl+Y32PpNmSHugfWwshvBBCeK/iN+MrJe2U9EMzu7DEmrbk/j1lP5hZg+IjkpL3Q4jHb5olLRmkyQckbQohbBqknsGek30hhJN5dX5S8TjWvw8h3FtqrTjzCJiJ63fzrt+peFrnb3PX10uaZ2ZX57V7v+K+7f7B04ckvSN/5pM3MztP0sOSdkl6Rwihq4TFfz/3728G3PZTSbeZ2dQB27hG8XTXn2r0vaL4qPHOgTea2VW5ba7PXyCEcDKE8HNJ31QcMrV59/eFEJ5R/B2QSHEXVdFCCPskbdLr+6ffXYpngd1fyvqk+MuruTp3FrhvteIAKXT0IsX7fa6ZXTdgmWpJ71Tec2JmH1XcxfvJEMJXS60TY2SsB4G4FHfR64P375P0lgKXZF67fYoHvG9SPA6QlvTdAeurUjwWc0TxoOrbFM94CpI+NKBdg+KjmN2KZ6KtVfyGdN+ANutU4Hswir/Y+HjebcUMRtcpPuJolnRbgcdaPqDtRkkfUzy76lbFs5p6Jd2ft865ucexPvdYf0/xqSd+IynKe7xB0qeHqbG/3ddzz0n+pUyvT0y4L7fNP1TcPbZd0uTcej6jOFDuVDwu9n7Fs/825u5/h+I32z/I7ft3KA79NknzStmvuXZvV/xB45uSrs/tu25Jf5PX7lO5dS4ccNuTkv5j7jl5a27ZRkn7Jc0osK2v5J6L+kFqiST9Orf8nYpn2D2ee97nD2jX/+Ho/gKvhRVj/bfJZYjX21gXwKXIJ2roWWSh/w98QLs1kn6ieAZYs6R7JE3KW+dsxaHSpHi8ZLOkuwps+02KxzX62+2S9MUB969T8QETJK0b5rFeP8xjbRjQ9geKPz135mp7WfEn/PIC671I8VFRh+L+/XWSavPaXKBBZpzltWso8vm4S/FRQ4/ibrF7NWDWVe7N+kHFYzU9uTfbbys3a0/xZIF/Vhzw3Yq/+/ELSVeUul8HtL1jQE37FIdJIq/Npwvs6y8onvjRnntdvaz4Q0xdgW2kcrX+bJhapkv6Tu412qn4y7Qr89qsG2I/P17MY+YyNhfOBzPBmNndimeBnRcKD/5jCLnvrvyl4k/uhb7sCaBIjMEAp7pO8dEZ4QKcJr4HAwwQQsgfAAcwQnSRAQBc0EUGAHBBwAAAXBAwAAAXBAwAwAUBAwBw8f8BVhFZZgcc110AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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muLw+jv5weNwjJMejsbpOtyxcqaoiTbZpCzS/bua0vBD4jsVXltxk7SS9Zf6yiq2boxWAccuUedfwlAUVbYIqxb3L1uvNza2jhkx1kNai+kZ9as1t3vpCJtPymXN1zZyWoiFbFaT088uuVW0Fm8im39YEMGFWNy0s66DcNqOyv5VUisBMH155vf7Lmlu1tmmRAjOlLZDJtKR+tj686np9+po7VJ+p3IH1UmJm+ujlN2r93CXKBKkLBjGkk0sK7mlbp1sWrqrout/wfTAAxu+2RZdr05FdJZWtDtK6s2VyrocyM62aNU+rZs1TGEXqD4dUFaRVlXpjHAJTFuijl9+kO1q69P3kXmTDUajadEY3zl+mWxb6uRfZG2PrAvBibk2DNsxr0+aTB8a8Ujx1CfVxpIJADYHf0VuXqsX1jfqFVTdM2PpoIgNwUT60coPWzF6k6lGuOakK0lpYN1OfWnPbG/KHx97IOIMBcFFSFugTV27Uq6eP6JFD27S3pyO59sKpuaZBdy1ZreuaW7n49g2IgAFw0cxMa5sWa23TYg2Ew+rPDqs6la7oRXuYeggYABVVk8qohl8uheiDAQB4QsAAALygiQwAPDva163HD+/Uq52HNRyFqs9Ua+P85XrLguWqT1dpZ/dxPda+Q4fPdsnJaV7tDN2++IrkwtCpex5AwACAJ2EU6R92/URbOg6dd7v+s9khffvAS/rOgZdUl67WUJg974fRuob6daCnU/WZKn1q7W2aXztzst7CRZm60QgAlzDnnL688yn9tOOQhqPwXLiMiJxT6Jx6hgcK/urmYJTV6cE+/fGLj6qzjLtWX0oIGADwYGf3cb3SeWTMOxwU4yQNZIf1T3s2V65iE4iAAQAPHm3fflHhMiKS09bTR3VmaKACtZpYBAwAVFjknLadPlax5QVmeqXzcMWWN1EIGACosKEoq0r+7E3oIp3NDlVugROEgAGACqsK0hX95c6UBVPy7ggEDABUWGCmVbPmV2x5kXNaM3thxZY3UQgYAPDgjpYri/5McalWzGxWU019RZY1kQgYAPBg9eyFWjVrni62K6Y6ldb7l19XkTpNNAIGADwIzPTLq2/WspnN45q/KkirLpXRr619mxbVz6pw7SYGt4oBAE8yQUq/cc3b9dD+l/Rvh7ZqtG7/lAWqTWXiK/qd0+zqer295QptmNc2JTv3RxAwAODZO9uu0VVNi/SvB1/Vzq7j537dM+sitdQ36u6la7WmadEk17LyCBgAmADLZzbrk2tuVfdQv471nVHknJprGzS3pmGyq+YNAQMAE2hWVa1mVdVOdjUmBJ38AAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOBFerIrAAAo31CY1ZG+bmWjUDOrajWvdsZkV+kCBAwATCGnB/v08KFtevr4HgVmMpmyLtKc6nrdtWS1rp93mQKzya6mJAIGAKaMQ72n9ecvP66hKKvQufNeO9Z/Rl/b/bxe6GjXL63eqJRNfg/I5NcAAFBU7/CAPv/K99UfDl8QLiOGolDbu47q63u2THDtCiNgAGAK2HR0l4bDsGi5oSjUU8f2qHd4YAJqNTYCBgAucZFz+sHhnRp2xQNGksxMTx7b47lWxREwAHCJ6x0e0GBUWrhI0nAU6rXuEx5rVBoCBgAucaFzKndcWDaKvNSlHAQMAFziGjLVikbp2C8kkGn+JXBdDAEDAJe4TJDS+rlLZCWex6SDQDcvXOm5VsURMAAwBdzRslqZoPghO5BpQd0sLWmYPQG1KlYXAMAlb0nDbN3Ttk6ZIDVqmcBM9Zkq/afVN09gzUbHlfwAMEXctvhy1Weq9Y29WzQUhRoMs5KklAUySStmNeujq25UY3Xd5FY0QcAAwBRy/bw2vbl5qbaePqqdXSc0GGbVVF2nN89r1dyahsmu3nkIGACYYgILtLZpsdY2LZ7sqoyJPhgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALxIT3YFAAAT4/ips/rhc4d0rOOsXORkgWnJggbdumGpmmbVVnx9BAwATHNRFOkbj7ymwyd6z5vuIqcDR3p0/7e3akVro9751hUVXS9NZAAwzX394R0XhEu+3Qe69NAPd1d0vQQMAExj+w936+jJvpLK7j7YpVNd/RVbNwEDANPYpucPlVX+h88drNi6CRgAmMZOdQ+UVb79+NhNaeUgYAAA50SRq9iyCBgAwDlmlVsWAQMA01h1przDfE1VqmLrJmAAYBobDqOyyg8MhhVbNwEDANNYVF6+qHI9MAQMAMATAgYA4AUBAwDTWPPs8m5iObO+qmLrJmAAYBq762cuK6v8229qrdi6CRgAmMaaZ9epbfHMksoubK5X66JZFVs3AQMA09w9b1up5UvGDo6W+Q36wM9eUdH18nswADDNmZneddtKdfcM6vvPHtDBoz2KIqfApEXzGnT7DUvV1FhX8fUSMADwBjFrRrXec/uqCVsfTWQAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAF+Zc6T+QaWYnJR3wVx0AwBTT6pxrLvRCWQEDAECpaCIDAHhBwAAAvCBgAABeEDBThJl9xMzcKI+uSa7b/WbWXuFl/rGZvWxmXWbWZ2Y7zOy3zeyCX0Uys41m9rSZ9ZvZMTP7vJnVFih3lZk9ama9ZnbKzL5iZk3jrF9bsu0/Pp75J5OZvdvMXjCzATM7YGb/w8xSJcz3ITN7ysxOmtmgme03s781syV55e41s39Olt1vZjvN7I/MbEaBZa4zs4eTfXLGzB4ysxWjrP9KM/uGmXXkLPe/jn9LwDd+cGzq+XlJ+Qfz7GRUxLOZkr4iaaekQUk3SfqMpOskvWukkJldLekxSY9IulvSZZL+VNJiSe/PKbdI0o8k7ZB0r6TGpNx3zWyjcy7y/YYuBWZ2p6R/lvRlSb8m6VpJn5M0Q9JvFpl9jqTvS/oTSV2SLpf025LuNLPVzrmepNyvSzoo6bcUf1avlfRZSbea2U0j29rMVkp6QtKrkv694uPR70r6sZmtc86dyKn3dZJ+oHgfflxSt6SVkhrGtyUwIZxzPKbAQ9JHJDlJKya7LgXqdr+k9glYzx8l22BuzrRvSdolKZMz7ReScutzpv2F4oNiY860m5Ny7xlHXdqSeT8+2du/zHq/IGlT3rTfkTQkacE4lndnsh3emzOtuUC5kX1yW860vy2wT1okDUj6k5xpgaStkr412duPR3kPmsimmZymtJvN7Ns5zUFfyG82MrOFZvYPSZPDYNIk9aECy7zMzL6aND8NmtleM/vLAuWuNbMnkiatXWb2yxV+e6eSf4eT9WUk3SXpQefccE65BxUfMN+VM+2dkv7VOdc1MsE592PF37Rzy1VU0qz0UtIc1ZFsx4V5ZT6YNFn1mlm3mb1iZr+U8/qbzeyxZD/2Jdv/r8ZRlyWS1kl6IO+lr0rKSPrZ8t/h+ftEkpxzJwuUez75d3HOtBskPZO3T9oVn9Hck1PurZJWS/r8OOqHSUTATD0pM0vnPQrtxwck7Zb0HsXf3n9R0hdHXjSzekmbFB9UfkvSuyW9IumrZvaJnHKXSXpO8bf9303K/56kuXnrmynpa8l636X4gPJFM7s1t1ASfveX+maT99dgZrcrbtL5O+dcd/Lyckk1ig9I5zjnBiTtUXxQUhKsl+WXS2wdKVdpyXb8qqTtivfDpxV/499kZg1JmY2Kt9kmxfvg5yX9jeImPCXlHpEUKj6LfYek31de83aJ2/Wq5N/87bVPUp9K3A5mljKz6qR58vOStkl6tMhstyT/bs+ZFir+IpBvUNJyM6tJnm9M/q0xs5+Y2bCZnTCz/53/pQmXmMk+heJR2kOvN5EVeny3QLn78ub/jOI/6FXJ819Jyr01r9zjkk5ISiXP/0FSr6RFY9Tt/mRZt+ZMq5bUIemv88pmJX25xPe8Ju99/v1IvZLXb0qm31Vg3iclfT/5/6Kk3C8XKPeApD3j2B9tGqOJTFJK0nFJP8ybvjGZ75PJ81+X1DnGeq5Lyl9dpD5Ft6ukDybLuqLAa+1l7JeOnH3yvKSFRcovTj5Tj+VNfzBZb27z5gzFzWZuZLmS7kuedyoO17cm261PNJtd0g/OYKaeeyS9Oe/xqQLlHsx7/k+Kz1g3JM9vlnTYOfejvHIPSGrW699m71AcYEeK1KvPOffDkSfOuUHFfSNLcws559LOuY8VWdaI3Yrf31sVn2XdozjwRtjIYgvMa+MoV0mXS5on6R9zJzrnnlR8u6WRb/TPS5ptZg+Y2d1m1pi3nF2KD7hfSprblqiAErdrpbbD2xSH+8cUn2k9VqDe8ULjM7DvKA7Aj+a9/JeKw+c+M1tsZq2KB3aMdNyPDLwYOU494Jz7Hefcj5xzf6b4TPrdZublDBQXj4CZel51zm3Oe+wuUO74KM9H2sCbJB0tMN+xnNeleORQKUOQTxeYNqi4CWtcnHMDyfvb5Jz7I0mflPRBM7shKdKZV9dcs3NeP634oFqsXCWNrGu0bdwkSc65TYqbxZYoHrBw0sweT5qf5OLmwFslHZH0V5IOmtmrZvbecdRprO3VqBK3g3PuJefcM865v1P8BWS1pAv625ImrockLZN0p4v7V3KX85Sk/6x4VF+7pP1JPf5ecdPZSH1G+nkey1vFSLPculLqjYlHwExf80d5fjj5t1PSggLzjUwb+aPu0Pkds5Npc/LvyHUSexSH2FW5hZID2zLFfQNyzvUpPnidVy6xeqRchY0cHEfbxiPbV865bzrnblEcdvdIWijp4ZG+Nefci8659yoOhhsVv+8HzWxNmXXamvybv73aJNVpHNvBxf03nXp9n4wsM6N4OPQGSe9wzr0yyvx/pfhMb42kpc652xU3aT7rXh+4MVLv/DOvkbOuN8QQ86mIgJm+3pf3/AOK/xCfS55vktRiZm/JK/dBxe3lI52xj0q6O3/k0yQZaVbaI0nOuSFJD0t6n5nldnrfq7gP6KGcaQ9J+jkzmzUyIelgb80rVyk7FZ81fiB3opndlKxzU/4Mzrle59x3JX1JccjMyXs965z7ieJrTwJJV5ZTIefcQUkvKb7mJNeHFI8C+145y5Pii1eTeu7JmRYobhp8m6R3JXUeq16DzrmtzrlDZrZW0u3KGZCS1GtQ8YjBXHcm/24WLk2T3QnEo7SHXu+8v1fx8M78Rzqv3EHFFxK+XXEH/5Ckr+Qsr17Sa4oPgh9X/Mf71WTeT+SUa1N8FrNP8Ui0WxUfkB7IKXO/ClwHo/iiuB/lTSulM/pqxcH2i4oPUu+Q9MeS+iX9W17Zdcn0f0nKfkzxN+pv5JVbnLyPTcl7fb/ivpBnJQV579dJ+myROo6U+2KyT/IfVZI+kZR5IFnnxxQ3j70mqSFZzu8rDpQPKO4X+6DivqcXktfvVhyA/zHZ9ncn2+aMpJZytmtS7h2Kv2h8SXHf1q8qvu7kT/PK/U6yzNacaU9K+m+Sfi7Z1r+quGnrkM6/NumLyfv+A134Oc2tc4ukP0yWd7viCz27JX29QL1/N6nP55Kyn072+/2T/bfJY4zP22RXgEeJO2rsUWRu5A88p9zNijtXe5MD7hck1eYtc6HiUOlQ/A3xZUkfKrDu5ZL+b065vZL+Iuf1+1V6wLhiBwXFzXlfUxxq/Yqbk55X3F5fXaD8zZKeSQ6UxyX9L0l1BcqtVdyOf1Zxv8z9kubklblKo4w4yyvXVuL++JDis4bB5H18VTmjrpKD6yOK+2oGk4P1l5WM2lM8WODrybYYkHRS0r9Jur7c7ZpT9j05dTqoOExSeWU+myyzLWfanyseyt6TfK62Kf4SMy9v3v1jbJfP5u3nx3M+V9sUB1i6QJ1N8TD13Yq/LB1QHM6ZUt4zj8l58Hsw04yZfUTxSJyVrnDnP8aQXLvyh4q/ufdNdn2AqYw+GOB8tyg+OyNcgIvEzS6BHM65/A5wAONEExkAwAuayAAAXhAwAAAvCBgAgBcEDADACwIGAODF/wc+EoGFM5bW1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#from IPython.display import Javascript  # Restrict height of output cell.\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 430})'''))\n",
    "\n",
    "model = GCN()\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.\n",
    "\n",
    "def train(data):\n",
    "    optimizer.zero_grad()  # Clear gradients.\n",
    "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
    "    loss.backward()  # Derive gradients.\n",
    "    optimizer.step()  # Update parameters based on gradients.\n",
    "    return loss, h\n",
    "\n",
    "for epoch in range(401):\n",
    "    loss, h = train(data)\n",
    "    if epoch % 10 == 0:\n",
    "        visualize(h, color=data.y, epoch=epoch, loss=loss)\n",
    "        time.sleep(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NgcpV4rjAWy-"
   },
   "source": [
    "As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.\n",
    "\n",
    "Note that we do not need to reimplement standard GNN architectures, the library provides implementations for the most popular ones, including the GCN Layer:\n",
    "\n",
    "`torch_geometric.nn.GCNConv`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: graph classification \n",
    "\n",
    "Let's now have a closer look at how to apply **Graph Neural Networks (GNNs) to the task of graph classification**.\n",
    "Graph classification refers to the problem of classifiying entire graphs (in contrast to nodes), given a **dataset of graphs**, based on some structural graph properties.\n",
    "Here, we want to embed entire graphs, and we want to embed those graphs in such a way so that they are linearly separable given a task at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common task for graph classification is **molecular property prediction**, in which molecules are represented as graphs, and the task may be to infer whether a molecule inhibits HIV virus replication or not.\n",
    "\n",
    "The TU Dortmund University has collected a wide range of different graph classification datasets, known as the [**TUDatasets**](https://chrsmrrs.github.io/datasets/), which are also accessible via [`torch_geometric.datasets.TUDataset`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.TUDataset) in PyTorch Geometric.\n",
    "Let's load and inspect one of the smaller ones, the **MUTAG dataset**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.chrsmrrs.com/graphkerneldatasets/MUTAG.zip\n",
      "Extracting data/TUDataset/MUTAG/MUTAG.zip\n",
      "Processing...\n",
      "Done!\n",
      "\n",
      "Dataset: MUTAG(188):\n",
      "====================\n",
      "Number of graphs: 188\n",
      "Number of features: 7\n",
      "Number of classes: 2\n",
      "\n",
      "Data(edge_attr=[38, 4], edge_index=[2, 38], x=[17, 7], y=[1])\n",
      "=============================================================\n",
      "Number of nodes: 17\n",
      "Number of edges: 38\n",
      "Average node degree: 2.24\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch_geometric.datasets import TUDataset\n",
    "\n",
    "dataset = TUDataset(root='data/TUDataset', name='MUTAG')\n",
    "\n",
    "print()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')\n",
    "\n",
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print()\n",
    "print(data)\n",
    "print('=============================================================')\n",
    "\n",
    "# Gather some statistics about the first graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset provides **188 different graphs**, and the task is to classify each graph into **one out of two classes**.\n",
    "\n",
    "By inspecting the first graph object of the dataset, we can see that it comes with **17 nodes (with 7-dimensional feature vectors)** and **38 edges** (leading to an average node degree of 2.24).\n",
    "It also comes with exactly **one graph label** (`y=[1]`), and, in addition to previous datasets, provides addtional **4-dimensional edge features** (`edge_attr=[38, 4]`).\n",
    "However, for the sake of simplicity, we will not make use of those.\n",
    "\n",
    "PyTorch Geometric provides some useful utilities for working with graph datasets, *e.g.*, we can shuffle the dataset and use the first 150 graphs as training graphs, while using the remaining ones for testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training graphs: 150\n",
      "Number of test graphs: 38\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(12345)\n",
    "dataset = dataset.shuffle()\n",
    "\n",
    "train_dataset = dataset[:150]\n",
    "test_dataset = dataset[150:]\n",
    "\n",
    "print(f'Number of training graphs: {len(train_dataset)}')\n",
    "print(f'Number of test graphs: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini-batching of graphs\n",
    "\n",
    "Since graphs in graph classification datasets are usually small, a good idea is to **batch the graphs** before inputting them into a Graph Neural Network to guarantee full GPU utilization.\n",
    "In the image or language domain, this procedure is typically achieved by **rescaling** or **padding** each example into a set of equally-sized shapes, and examples are then grouped in an additional dimension.\n",
    "The length of this dimension is then equal to the number of examples grouped in a mini-batch and is typically referred to as the `batch_size`.\n",
    "\n",
    "However, for GNNs the two approaches described above are either not feasible or may result in a lot of unnecessary memory consumption.\n",
    "Therefore, PyTorch Geometric opts for another approach to achieve parallelization across a number of examples. Here, adjacency matrices are stacked in a diagonal fashion (creating a giant graph that holds multiple isolated subgraphs), and node and target features are simply concatenated in the node dimension:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 13.11.53.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This procedure has some crucial advantages over other batching procedures:\n",
    "\n",
    "1. GNN operators that rely on a message passing scheme do not need to be modified since messages are not exchanged between two nodes that belong to different graphs.\n",
    "\n",
    "2. There is no computational or memory overhead since adjacency matrices are saved in a sparse fashion holding only non-zero entries, *i.e.*, the edges.\n",
    "\n",
    "PyTorch Geometric automatically takes care of **batching multiple graphs into a single giant graph** with the help of the [`torch_geometric.data.DataLoader`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.DataLoader) class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1:\n",
      "=======\n",
      "Number of graphs in the current batch: 64\n",
      "Batch(batch=[1185], edge_attr=[2624, 4], edge_index=[2, 2624], ptr=[65], x=[1185, 7], y=[64])\n",
      "\n",
      "Step 2:\n",
      "=======\n",
      "Number of graphs in the current batch: 64\n",
      "Batch(batch=[1146], edge_attr=[2538, 4], edge_index=[2, 2538], ptr=[65], x=[1146, 7], y=[64])\n",
      "\n",
      "Step 3:\n",
      "=======\n",
      "Number of graphs in the current batch: 22\n",
      "Batch(batch=[383], edge_attr=[832, 4], edge_index=[2, 832], ptr=[23], x=[383, 7], y=[22])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.data import DataLoader\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
    "\n",
    "for step, data in enumerate(train_loader):\n",
    "    print(f'Step {step + 1}:')\n",
    "    print('=======')\n",
    "    print(f'Number of graphs in the current batch: {data.num_graphs}')\n",
    "    print(data)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Graph Neural Network (GNN)\n",
    "\n",
    "Training a GNN for graph classification usually follows a simple recipe:\n",
    "\n",
    "1. Embed each node by performing multiple rounds of message passing\n",
    "2. Aggregate node embeddings into a unified graph embedding (**readout layer**)\n",
    "3. Train a final classifier on the graph embedding\n",
    "\n",
    "There exists multiple **readout layers** in literature, but the most common one is to simply take the average of node embeddings:\n",
    "\n",
    "$$\n",
    "\\mathbf{x}_{\\mathcal{G}} = \\frac{1}{|\\mathcal{V}|} \\sum_{v \\in \\mathcal{V}} \\mathcal{x}^{(L)}_v\n",
    "$$\n",
    "\n",
    "PyTorch Geometric provides this functionality via [`torch_geometric.nn.global_mean_pool`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.glob.global_mean_pool), which takes in the node embeddings of all nodes in the mini-batch and the assignment vector `batch` to compute a graph embedding of size `[batch_size, hidden_channels]` for each graph in the batch.\n",
    "\n",
    "The final architecture for applying GNNs to the task of graph classification then looks as follows and allows for complete end-to-end training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(7, 64)\n",
      "  (conv2): GCNConv(64, 64)\n",
      "  (conv3): GCNConv(64, 64)\n",
      "  (lin): Linear(in_features=64, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345678)\n",
    "        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv3(x, edge_index)\n",
    "\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "\n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we again make use of the [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv) with $\\mathrm{ReLU}(x) = \\max(x, 0)$ activation for obtaining localized node embeddings, before we apply our final classifier on top of a graph readout layer.\n",
    "\n",
    "Let's train our network for a few epochs to see how well it performs on the training as well as test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001, Train Acc: 0.6467, Test Acc: 0.7368\n",
      "Epoch: 002, Train Acc: 0.6467, Test Acc: 0.7368\n",
      "Epoch: 003, Train Acc: 0.6467, Test Acc: 0.7368\n",
      "Epoch: 004, Train Acc: 0.6467, Test Acc: 0.7368\n",
      "Epoch: 005, Train Acc: 0.6467, Test Acc: 0.7368\n",
      "Epoch: 006, Train Acc: 0.7533, Test Acc: 0.7632\n",
      "Epoch: 007, Train Acc: 0.7200, Test Acc: 0.7895\n",
      "Epoch: 008, Train Acc: 0.7667, Test Acc: 0.7895\n",
      "Epoch: 009, Train Acc: 0.7200, Test Acc: 0.7895\n",
      "Epoch: 010, Train Acc: 0.7000, Test Acc: 0.7632\n",
      "Epoch: 011, Train Acc: 0.7533, Test Acc: 0.7895\n",
      "Epoch: 012, Train Acc: 0.7200, Test Acc: 0.7895\n",
      "Epoch: 013, Train Acc: 0.7267, Test Acc: 0.7895\n",
      "Epoch: 014, Train Acc: 0.7400, Test Acc: 0.7895\n",
      "Epoch: 015, Train Acc: 0.7267, Test Acc: 0.8158\n",
      "Epoch: 016, Train Acc: 0.7533, Test Acc: 0.7895\n",
      "Epoch: 017, Train Acc: 0.7333, Test Acc: 0.7895\n",
      "Epoch: 018, Train Acc: 0.7267, Test Acc: 0.7632\n",
      "Epoch: 019, Train Acc: 0.7533, Test Acc: 0.8158\n",
      "Epoch: 020, Train Acc: 0.7600, Test Acc: 0.7895\n",
      "Epoch: 021, Train Acc: 0.7400, Test Acc: 0.7632\n",
      "Epoch: 022, Train Acc: 0.7667, Test Acc: 0.8158\n",
      "Epoch: 023, Train Acc: 0.7533, Test Acc: 0.7632\n",
      "Epoch: 024, Train Acc: 0.7533, Test Acc: 0.7632\n",
      "Epoch: 025, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 026, Train Acc: 0.7600, Test Acc: 0.7632\n",
      "Epoch: 027, Train Acc: 0.7733, Test Acc: 0.7895\n",
      "Epoch: 028, Train Acc: 0.7600, Test Acc: 0.7632\n",
      "Epoch: 029, Train Acc: 0.7400, Test Acc: 0.7105\n",
      "Epoch: 030, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 031, Train Acc: 0.7600, Test Acc: 0.7895\n",
      "Epoch: 032, Train Acc: 0.7467, Test Acc: 0.7632\n",
      "Epoch: 033, Train Acc: 0.7200, Test Acc: 0.7632\n",
      "Epoch: 034, Train Acc: 0.7667, Test Acc: 0.7895\n",
      "Epoch: 035, Train Acc: 0.7667, Test Acc: 0.8158\n",
      "Epoch: 036, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 037, Train Acc: 0.7333, Test Acc: 0.7632\n",
      "Epoch: 038, Train Acc: 0.7733, Test Acc: 0.7632\n",
      "Epoch: 039, Train Acc: 0.7667, Test Acc: 0.7895\n",
      "Epoch: 040, Train Acc: 0.7600, Test Acc: 0.7632\n",
      "Epoch: 041, Train Acc: 0.7333, Test Acc: 0.7632\n",
      "Epoch: 042, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 043, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 044, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 045, Train Acc: 0.7733, Test Acc: 0.7632\n",
      "Epoch: 046, Train Acc: 0.7667, Test Acc: 0.7632\n",
      "Epoch: 047, Train Acc: 0.7733, Test Acc: 0.7895\n",
      "Epoch: 048, Train Acc: 0.7733, Test Acc: 0.7895\n",
      "Epoch: 049, Train Acc: 0.7733, Test Acc: 0.7632\n",
      "Epoch: 050, Train Acc: 0.7733, Test Acc: 0.7632\n"
     ]
    }
   ],
   "source": [
    "#from IPython.display import Javascript\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "         loss = criterion(out, data.y)  # Compute the loss.\n",
    "         loss.backward()  # Derive gradients.\n",
    "         optimizer.step()  # Update parameters based on gradients.\n",
    "         optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "     model.eval()\n",
    "\n",
    "     correct = 0\n",
    "     for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  \n",
    "         pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "         correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "     return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "\n",
    "for epoch in range(1, 51):\n",
    "    train()\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, our model reaches around **76% test accuracy**.\n",
    "Reasons for the fluctations in accuracy can be explained by the rather small dataset (only 38 test graphs), and usually disappear once one applies GNNs to larger datasets.\n"
   ]
  }
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