GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZtYFEhHoBGp"
   },
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "KJPfzpxLQI5O"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from deepgd import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z4eBEyB1oaJe"
   },
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Q_-MLFhpNvxF"
   },
   "outputs": [],
   "source": [
    "cuda_idx = 0\n",
    "canonicalization = Canonicalization(normalize=Standardization(norm_ord=1),\n",
    "                                    scale=False)\n",
    "config = StaticConfig({\n",
    "    \"name\": 'GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)',\n",
    "    \"uid\": None,\n",
    "    \"link\": None,\n",
    "    \"generator\": {\n",
    "        \"params\": {\n",
    "            \"num_blocks\": 9,\n",
    "            \"normalize\": canonicalization\n",
    "        },\n",
    "        \"pretrained\": {\n",
    "            \"name\": None,\n",
    "            \"epoch\": -1,\n",
    "        },\n",
    "        \"optim\": torch.optim.AdamW,\n",
    "        \"lr\" : {\n",
    "            \"initial\": 1e-3,\n",
    "            \"decay\": 0.99,\n",
    "        },\n",
    "    },\n",
    "    \"discriminator\": {\n",
    "        \"params\": {\n",
    "            \"conv\": [2, 16, 16, 16],\n",
    "            \"dense\": [2],\n",
    "            \"shared_depth\": 6,\n",
    "            \"enet_depth\": 10,\n",
    "            \"enet_width\": 64,\n",
    "            \"aggr\": \"add\",\n",
    "            \"normalize\": canonicalization\n",
    "        },\n",
    "        \"pretrained\": {\n",
    "            \"name\": None,\n",
    "            \"epoch\": -1,\n",
    "        },\n",
    "        \"optim\": torch.optim.AdamW,\n",
    "        \"lr\" : {\n",
    "            \"initial\": 1e-3,\n",
    "            \"decay\": 0.99,\n",
    "        },\n",
    "        \"noise\": {\n",
    "            \"std\": 0,\n",
    "            \"decay\": 0.95,\n",
    "        },\n",
    "        \"repeat\": 1,\n",
    "        \"complete\": True,\n",
    "        \"adaptive\": True\n",
    "    },\n",
    "    \"alternate\": \"epoch\",\n",
    "    \"batchsize\": 24,\n",
    "    \"epoch\": {\n",
    "        \"start\": -1,\n",
    "        \"end\": None,\n",
    "    },\n",
    "    \"log_interval\": 1,\n",
    "    \"test\": {\n",
    "        \"name\": \"test\",\n",
    "        \"epoch\": -1,\n",
    "    },\n",
    "    \"gan_flavor\": \"wgan\",\n",
    "    \"gp_weight\": 0,\n",
    "})\n",
    "data_config = StaticConfig({\n",
    "    \"sparse\": False,\n",
    "    \"pivot\": None,\n",
    "    \"init\": \"pmds\",\n",
    "    \"edge\": {\n",
    "        \"index\": \"full_edge_index\",\n",
    "        \"attr\": \"full_edge_attr\",\n",
    "    },\n",
    "})\n",
    "loss_fns = {\n",
    "    Stress(): 1\n",
    "}\n",
    "ctrler_params = {\n",
    "    \"tau\": 0.95,\n",
    "    \"beta\": 1,\n",
    "    \"exploit_rate\": 0.5,\n",
    "    \"warmup\": 2,\n",
    "}\n",
    "paths = StaticConfig({\n",
    "    \"root\": \"artifacts\",\n",
    "    \"checkpoints\": lambda: f\"{paths.root}/checkpoints/{config.name}\",\n",
    "    \"gen_pretrain\": lambda: f\"{paths.root}/checkpoints/{config.generator.pretrained.name}\",\n",
    "    \"dis_pretrain\": lambda: f\"{paths.root}/checkpoints/{config.discriminator.pretrained.name}\",\n",
    "    \"tensorboard\": lambda: f\"{paths.root}/tensorboards/{config.name}\",\n",
    "    \"visualization\": lambda: f\"{paths.root}/visualizations/{config.name}_{config.test.name}\",\n",
    "    \"log\": lambda: f\"{paths.root}/logs/{config.name}.log\",\n",
    "    \"metrics\": lambda suffix: f\"{paths.root}/metrics/{config.name}_{suffix}.pickle\",\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mdMEAbH10Qaq"
   },
   "outputs": [],
   "source": [
    "if \" \" in config.name:\n",
    "    raise Exception(\"Space is not allowed in model name.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WynW4ZAdBhep"
   },
   "source": [
    "# Prepare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DR6-vYtr_i_P"
   },
   "source": [
    "## Get log command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9-QpinlslcTO",
    "outputId": "48ae3716-212c-4d46-9efb-b7b4070f73de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cd /users/PAS0027/osu10203/deepgd && tail -n1000 -f 'artifacts/logs/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10).log'\n"
     ]
    }
   ],
   "source": [
    "print(f\"cd {os.getcwd()} && tail -n1000 -f '{paths.log()}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorboard dev upload --logdir 'artifacts/tensorboards/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)'\n"
     ]
    }
   ],
   "source": [
    "print(f\"tensorboard dev upload --logdir '{paths.tensorboard()}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "innqkwvH_ydD"
   },
   "source": [
    "## Set globals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Qjq7JlSZlkQR"
   },
   "outputs": [],
   "source": [
    "if cuda_idx is not None and torch.cuda.is_available():\n",
    "    device = f'cuda:{cuda_idx}'\n",
    "    pynvml.nvmlInit()\n",
    "    cuda = pynvml.nvmlDeviceGetHandleByIndex(cuda_idx)\n",
    "else:\n",
    "    device = 'cpu'\n",
    "    cuda =  None\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0da286l_ApEL"
   },
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RFuSHiZJMU4x",
    "outputId": "54065609-aaea-4536-d79c-73bc73b3f576"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load from 'cache/G_list.pickle'\n",
      "Load from 'cache/generate_data_list(list,sparse=False,pivot_mode=None,init_mode=pmds,edge_index=full_edge_index,edge_attr=full_edge_attr,pmds_list=ndarray,gviz_list=ndarray,noisy_layout=True,device=cpu).pickle'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/PAS0027/osu10203/.conda/envs/deepgd/lib/python3.9/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "G_list = load_G_list(data_path='data/rome', index_file='data_index.txt', cache='G_list', cache_prefix='cache/')\n",
    "data_list = generate_data_list(G_list, \n",
    "                               sparse=data_config.sparse, \n",
    "                               pivot_mode=data_config.pivot,\n",
    "                               init_mode=data_config.init,\n",
    "                               edge_index=data_config.edge.index,\n",
    "                               edge_attr=data_config.edge.attr,\n",
    "                               pmds_list=np.load('layouts/rome/pmds.npy', allow_pickle=True),\n",
    "                               gviz_list=np.load('layouts/rome/gviz.npy', allow_pickle=True),\n",
    "                               noisy_layout=True,\n",
    "                               device='cpu', \n",
    "                               cache=True,\n",
    "                               cache_prefix='cache/')\n",
    "train_loader = LazyDeviceMappingDataLoader(data_list[:10000], batch_size=config.batchsize, shuffle=True, device=device)\n",
    "val_loader = LazyDeviceMappingDataLoader(data_list[11000:], batch_size=config.batchsize, shuffle=False, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "djd1pLt2OxZq"
   },
   "outputs": [],
   "source": [
    "def draw_layout(G, method, draw=True):\n",
    "    if method == 'fa2':\n",
    "        layout = get_fa2_layout(G)\n",
    "    else:\n",
    "        try:\n",
    "            fn = getattr(nx.drawing.layout, f'{method}_layout')\n",
    "            layout = fn(G)\n",
    "        except:\n",
    "            layout = nx.drawing.nx_agraph.graphviz_layout(G, prog=method)\n",
    "    if draw:\n",
    "        nx.draw(G, pos=layout)\n",
    "    return layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "2vBBw77lR7Em"
   },
   "outputs": [],
   "source": [
    "methods = ['neato', 'dot', 'fdp', 'sfdp', 'twopi', 'circo', 'shell', 'spring', 'circular', 'spectral', 'kamada_kawai', 'fa2', 'pmds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "dx6VVA9XIc2O"
   },
   "outputs": [],
   "source": [
    "from functools import lru_cache\n",
    "\n",
    "@lru_cache(maxsize=None)\n",
    "def load_pos(method):\n",
    "    return np.load(f'layouts/rome/{method}.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "05a37aeeb20049dfa2f97287b3990556",
      "a75afae3adfd4810a0b0991a062535ee",
      "a24ae105ed124595992f7e2e5f909247",
      "01b4cabaf8004dd4b9269b4e6889c498",
      "f3c7c84f2b314bd8b97a7358deaf4fa1",
      "35e4a83e692b4a7bafb46fdf192b2586",
      "62847b5e2678459f845159eef6d4feab",
      "143e81f00313436dacfa246a57108944",
      "e05833f11803400b95ff22246cdc8f2b",
      "53aeede91e074378b5d7f626165655ee",
      "79a43768c1794a10a788a49e5883aa2c"
     ]
    },
    "id": "MSmP-jem9I0X",
    "outputId": "c698f0bf-e96e-4de9-aeda-0b046fc0a03f",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# best_list = []\n",
    "# best_layout_list = []\n",
    "# for idx, (G, data) in enumerate(zip(tqdm(G_list), data_list)):\n",
    "#     xing, stress, layout = {}, {}, {}\n",
    "#     for m in methods:\n",
    "#         batch = Batch.from_data_list([data])\n",
    "#         pos = load_pos(m)\n",
    "#         p = CanonicalizationByStress()(torch.tensor(pos[idx]).float(), batch)\n",
    "#         x = Xing()(p, batch).item()\n",
    "#         s = Stress()(p, batch).item()\n",
    "#         xing[m] = x\n",
    "#         stress[m] = s\n",
    "#         layout[m] = p.numpy()\n",
    "#         # plt.figure()\n",
    "#         # graph_vis(G, pos[idx])\n",
    "#         # plt.title(f'{m} stress={s:.2f} xing={x}')\n",
    "#     best, *_ = sorted(methods, key=lambda m: (xing[m], stress[m]))\n",
    "#     best_list.append(best)\n",
    "#     best_layout_list.append(layout[best])\n",
    "#     print(f'{best}, xing={xing[best]}, stress={stress[best]:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mx2bZNugdTqa",
    "outputId": "cec05a67-18e8-492f-aaa1-057274a8341a"
   },
   "outputs": [],
   "source": [
    "# pickle.dump(best_list, open('layouts/rome/best[xing,stress].pkl', 'wb'))\n",
    "# np.save('layouts/rome/best[xing,stress].npy', best_layout_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "7e0ae84dd0f3458d893c81b029fe1729",
      "18bdc3bf80fb434abde68de5b72a8870",
      "2e3552ded9594635a51832387ae754a0",
      "54f4aa5080c6498080a63be2cdeefee5",
      "3937586d0879451da5e2138ca3bfd0a0",
      "a9df6e84ce8c499eb2b2cf984995d791",
      "7af6b718dd3d4e01b264377a2fa938e2",
      "a774c86e64004bd6951645f1c24abad4",
      "c86575117cd041f2837762f37d1f387b",
      "6e9907cefb1241688fbd248b8281ed9f",
      "9b8e5405d8264944acab57e81f2f5ad6"
     ]
    },
    "id": "A1-w1fspC53p",
    "outputId": "09c2d5c3-1eef-41eb-dcee-581af1294804"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06647a70c5014b28b2ed32a4bc0eb7cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_layout_list = np.load('layouts/rome/best[xing,stress].npy', allow_pickle=True)\n",
    "for data, layout in zip(tqdm(data_list), best_layout_list):\n",
    "    data.gt_pos = torch.tensor(layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "y6EGh4U7exL_"
   },
   "outputs": [],
   "source": [
    "train_loader = LazyDeviceMappingDataLoader(data_list[:10000], batch_size=config.batchsize, shuffle=True, device=device)\n",
    "val_loader = LazyDeviceMappingDataLoader(data_list[11000:], batch_size=config.batchsize, shuffle=False, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 103,
     "referenced_widgets": [
      "38a33cf69aff48f4aabd4902a7ce48b1",
      "57e277bcb44c4e2fa2a12105d75153d1",
      "20ce017c46bd4a7c9b420270ac579405",
      "13f2df4fe0ce4af88b6e3bd1637065b0",
      "2867faa145ca4860a198d4d0bf830f89",
      "02838cf669174ed9afa41a1a8e680765",
      "7e9e3c031667455f8baca163d3a213e5",
      "4000135e7dfa46529fa6244fccc5cb57",
      "e523e9faac5b4ec38643c05ac5ba99f7",
      "e8e3ee9741e4480dae5003ccc19a7891",
      "9a569045221b43acb3ba2fbf116b2c74"
     ]
    },
    "id": "YEfRRUK0NiNm",
    "outputId": "93f53d05-7667-4cda-a38e-c3a953124f79"
   },
   "outputs": [],
   "source": [
    "# for m in methods:\n",
    "#     layouts = []\n",
    "#     for G in tqdm(G_list):\n",
    "#         layout = draw_layout(G, method=m, draw=False)\n",
    "#         layouts.append(np.array(list(layout.values())))\n",
    "#     np.save(f'layouts/rome/{m}.npy', layouts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "upK7AHYqPmC5",
    "outputId": "29a88e61-f55f-409c-9194-12c6d5762528"
   },
   "outputs": [],
   "source": [
    "# from collections import Counter\n",
    "\n",
    "# letter_counts = Counter(best_list)\n",
    "# df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "# df.plot(kind='bar', figsize=[12, 8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f-ODE2k8BFV6"
   },
   "source": [
    "## Create folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "3ykUMINYBRog"
   },
   "outputs": [],
   "source": [
    "mkdirs(paths.checkpoints(), paths.tensorboard(), paths.visualization(), f\"{paths.root}/logs\", f\"{paths.root}/metrics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BFq9k5nzBIh5"
   },
   "source": [
    "## Load checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "W5NbsKkobIfA"
   },
   "outputs": [],
   "source": [
    "class EdgeFeatureDiscriminator(nn.Module):\n",
    "    def __init__(self, \n",
    "                 conv, \n",
    "                 dense,\n",
    "                 shared_depth,\n",
    "                 enet_depth,\n",
    "                 enet_width,\n",
    "                 aggr='add', \n",
    "                 root_weight=True,\n",
    "                 normalize=None):\n",
    "        super().__init__()\n",
    "        self.enet = nn.Sequential(*[\n",
    "            DenseLayer(in_dim=in_d,\n",
    "                       out_dim=out_d,\n",
    "                       skip=nonlin,\n",
    "                       bn=nonlin,\n",
    "                       act=nonlin,\n",
    "                       dp=None)\n",
    "            for in_d, out_d, nonlin \n",
    "            in zip([self._get_feature_dim()] + [enet_width] * (shared_depth-1),\n",
    "                   [enet_width] * shared_depth,\n",
    "                   [True] * (shared_depth-1) + [False])     \n",
    "        ])\n",
    "        self.blocks = nn.ModuleList([\n",
    "            GNNLayer(nfeat_dims=(in_d, out_d),\n",
    "                     efeat_dim=enet_width,\n",
    "                     edge_net=EdgeNet(nfeat_dims=(in_d, out_d), \n",
    "                                      efeat_dim=enet_width, \n",
    "                                      depth=enet_depth, \n",
    "                                      width=enet_width),\n",
    "                     aggr=aggr,\n",
    "                     dense=False,\n",
    "                     skip=nonlin,\n",
    "                     bn=nonlin,\n",
    "                     act=nonlin,\n",
    "                     root_weight=root_weight) \n",
    "            for in_d, out_d, nonlin \n",
    "            in zip(conv[:-1], conv[1:], [True] * (len(conv)-2) + [False])     \n",
    "        ])\n",
    "        self.pool = (gnn.global_mean_pool if aggr == 'mean' \n",
    "                     else gnn.global_add_pool if aggr == 'add' \n",
    "                     else None)\n",
    "        self.dense = nn.Identity() if not dense else nn.Sequential(*[\n",
    "            DenseLayer(in_dim=in_d,\n",
    "                       out_dim=out_d,\n",
    "                       skip=nonlin,\n",
    "                       bn=nonlin,\n",
    "                       act=nonlin,\n",
    "                       dp=None)\n",
    "            for in_d, out_d, nonlin \n",
    "            in zip(conv[-1:] + dense[:-1], dense, [True] * (len(dense)-1) + [False])     \n",
    "        ])\n",
    "        self.normalize = normalize or IdentityTransformation()\n",
    "    \n",
    "    def forward(self, batch):\n",
    "        x = torch.ones_like(batch.pos)\n",
    "        e = self.enet(self._get_features(self._get_edge_info(batch, layout='pos')))\n",
    "        for block in self.blocks:\n",
    "            x = block(x, e, batch)\n",
    "        x = self.pool(x, batch.batch)\n",
    "        x = self.dense(x)\n",
    "        return x\n",
    "    \n",
    "    def _get_edge_info(self, batch, layout='gt_pos'):\n",
    "        pos = self.normalize(batch[layout].float(), batch)\n",
    "        src, dst = get_edges(pos, batch)\n",
    "        v, u = l2_normalize(dst - src, return_norm=True)\n",
    "        d = batch.edge_attr[:, :1]\n",
    "        return {\n",
    "            \"src\": src,\n",
    "            \"dst\": dst,\n",
    "            \"v\": v,\n",
    "            \"u\": u,\n",
    "            \"d\": d,\n",
    "        }\n",
    "\n",
    "    def _get_features(self, edges):\n",
    "        return torch.cat([edges['src'], edges['dst'], edges['d']], dim=1)\n",
    "        \n",
    "    def _get_feature_dim(self):\n",
    "        return self._get_features({\n",
    "            \"src\": torch.zeros(1, 2),\n",
    "            \"dst\": torch.zeros(1, 2),\n",
    "            \"v\": torch.zeros(1, 2),\n",
    "            \"u\": torch.zeros(1, 1),\n",
    "            \"d\": torch.zeros(1, 1),\n",
    "        }).shape[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "96ZActxlL0fJ"
   },
   "outputs": [],
   "source": [
    "class StressDiscriminator(nn.Module):\n",
    "    def __init__(self, normalize=CanonicalizationByStress(), **kwargs):\n",
    "        super().__init__()\n",
    "        self.dummy = nn.Parameter(torch.zeros(1))\n",
    "        self.normalize = normalize\n",
    "        self.stress = Stress(reduce=None)\n",
    "\n",
    "    def forward(self, batch):\n",
    "        return -self.stress(self.normalize(batch.pos, batch), batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "IJ6xCYBUuhcw"
   },
   "outputs": [],
   "source": [
    "def get_ckpt_epoch(folder, epoch):\n",
    "    if not os.path.isdir(folder):\n",
    "        os.mkdir(folder)\n",
    "    if epoch >= 0:\n",
    "        return epoch\n",
    "    ckpt_files = os.listdir(folder)\n",
    "    last_epoch = 0\n",
    "    if ckpt_files:\n",
    "        last_epoch = sorted(list(map(lambda x: int(re.search('(?<=epoch_)(\\d+)(?=\\.)', x).group(1)), ckpt_files)))[-1]\n",
    "    return last_epoch + epoch + 1\n",
    "\n",
    "def start_epoch():\n",
    "    return get_ckpt_epoch(paths.checkpoints(), config.epoch.start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JacDYXka9U01",
    "outputId": "ef4e31ab-2144-497c-8e4f-4d3740ce2d52"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from artifacts/checkpoints/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)/gen_epoch_171.pt...\n",
      "Loading from artifacts/checkpoints/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)/gen_optim_epoch_171.pt...\n"
     ]
    }
   ],
   "source": [
    "generator = Generator(**config.generator.params[...]).to(device)\n",
    "generator_optimizer = config.generator.optim(generator.parameters(), lr=config.generator.lr.initial * config.generator.lr.decay ** start_epoch())\n",
    "generator_scheduler = torch.optim.lr_scheduler.ExponentialLR(generator_optimizer, gamma=config.generator.lr.decay)\n",
    "if start_epoch() != 0:\n",
    "    gen_ckpt_epoch = start_epoch()\n",
    "elif config.generator.pretrained.name is not None and config.generator.pretrained.epoch != 0:\n",
    "    gen_pretrained_epoch = get_ckpt_epoch(paths.gen_pretrain(), config.generator.pretrained.epoch)\n",
    "    gen_ckpt_epoch = gen_pretrained_epoch \n",
    "else:\n",
    "    gen_ckpt_epoch = None\n",
    "if gen_ckpt_epoch is not None:\n",
    "    # Load generator\n",
    "    gen_ckpt_file = f\"{paths.checkpoints()}/gen_epoch_{gen_ckpt_epoch}.pt\"\n",
    "    print(f\"Loading from {gen_ckpt_file}...\")\n",
    "    generator.load_state_dict(torch.load(gen_ckpt_file, map_location=torch.device(device)))\n",
    "    # Load generator optimizer\n",
    "    gen_optim_ckpt_file = f\"{paths.checkpoints()}/gen_optim_epoch_{gen_ckpt_epoch}.pt\"\n",
    "    print(f\"Loading from {gen_optim_ckpt_file}...\")\n",
    "    generator_optimizer.load_state_dict(torch.load(gen_optim_ckpt_file, map_location=torch.device(device)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oWQhD7-88VXe",
    "outputId": "3ba01600-b546-4e45-a6bd-c7a65af1ae9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from artifacts/checkpoints/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)/dis_epoch_171.pt...\n",
      "Loading from artifacts/checkpoints/GAN(gan=wgan,data=best(xing,stress),dis=deep,share=6,embed=10)/dis_optim_epoch_171.pt...\n"
     ]
    }
   ],
   "source": [
    "discriminator = EdgeFeatureDiscriminator(**config.discriminator.params[...]).to(device)\n",
    "discriminator_optimizer = config.discriminator.optim(discriminator.parameters(), lr=config.discriminator.lr.initial * config.discriminator.lr.decay ** start_epoch())\n",
    "discriminator_scheduler = torch.optim.lr_scheduler.ExponentialLR(discriminator_optimizer, gamma=config.discriminator.lr.decay)\n",
    "if start_epoch() != 0:\n",
    "    dis_ckpt_epoch = start_epoch()\n",
    "elif config.discriminator.pretrained.name is not None and config.discriminator.pretrained.epoch != 0:\n",
    "    dis_pretrained_epoch = get_ckpt_epoch(paths.dis_pretrain(), config.discriminator.pretrained.epoch)\n",
    "    dis_ckpt_epoch = dis_pretrained_epoch # f\"{paths.dis_pretrain()}/dis_epoch_{dis_pretrained_epoch}.pt\"\n",
    "else:\n",
    "    dis_ckpt_epoch = None\n",
    "if dis_ckpt_epoch is not None:\n",
    "    # Load discriminator\n",
    "    dis_ckpt_file = f\"{paths.checkpoints()}/dis_epoch_{dis_ckpt_epoch}.pt\"\n",
    "    print(f\"Loading from {dis_ckpt_file}...\")\n",
    "    discriminator.load_state_dict(torch.load(dis_ckpt_file, map_location=torch.device(device)))\n",
    "    # Load discriminator optimizer\n",
    "    dis_optim_ckpt_file = f\"{paths.checkpoints()}/dis_optim_epoch_{dis_ckpt_epoch}.pt\"\n",
    "    print(f\"Loading from {dis_optim_ckpt_file}...\")\n",
    "    discriminator_optimizer.load_state_dict(torch.load(dis_optim_ckpt_file, map_location=torch.device(device)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N39dDHraedM6"
   },
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "BdIJSDSJedM-"
   },
   "outputs": [],
   "source": [
    "stress_criterion = StressDiscriminator().to(device)\n",
    "val_criterion = Stress(reduce=None)\n",
    "xing_criterion = Xing(reduce=None)\n",
    "dis_convert = DiscriminatorDataConverter(complete_graph=config.discriminator.complete, normalize=config.discriminator.params.normalize)\n",
    "tensorboard = SummaryWriter(log_dir=paths.tensorboard())\n",
    "epoch = start_epoch() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "OHRWj1h0edM-"
   },
   "outputs": [],
   "source": [
    "def gradient_penalty(interpolated, discriminator, weight=10):\n",
    "    interpolated.pos.requires_grad_()\n",
    "    prob_interpolated = discriminator(interpolated)\n",
    "    gradients = autograd.grad(outputs=prob_interpolated, \n",
    "                              inputs=interpolated.pos,\n",
    "                              grad_outputs=torch.ones_like(prob_interpolated),\n",
    "                              create_graph=True, \n",
    "                              retain_graph=True, \n",
    "                              allow_unused=True)[0]\n",
    "    gradients_norm = torch.sqrt(gnn.global_add_pool(gradients.square().sum(dim=1), batch.batch) + 1e-8)\n",
    "    return weight * ((gradients_norm - 1) ** 2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "o5wJJN9AedM_"
   },
   "outputs": [],
   "source": [
    "def get_gp_loss(batch, fake_pos, weight):\n",
    "    if weight > 0:\n",
    "        interp = dis_convert(batch, fake_pos, random.random())\n",
    "        return gradient_penalty(interp, discriminator, weight).mean()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "QwuA6sRoedM_"
   },
   "outputs": [],
   "source": [
    "def get_sgan_loss(batch, fake_pos, mode='discriminator'):\n",
    "    real = dis_convert(batch)\n",
    "    fake = dis_convert(batch, fake_pos)\n",
    "    pred = discriminator(merge_batch(real, fake)).view(2, -1).T\n",
    "    if mode == 'discriminator':\n",
    "        label = torch.zeros(pred.shape[0]).long()\n",
    "    elif mode == 'generator':\n",
    "        label = torch.ones(pred.shape[0]).long()\n",
    "    else:\n",
    "        raise Exception\n",
    "    return nn.CrossEntropyLoss()(pred, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "GuAP1YchedNA"
   },
   "outputs": [],
   "source": [
    "def get_rgan_loss(batch, fake_pos, mode='discriminator'):\n",
    "    real = dis_convert(batch)\n",
    "    fake = dis_convert(batch, fake_pos)\n",
    "    pred = discriminator(merge_batch(real, fake)).view(2, -1).T\n",
    "    real_pred, fake_pred = pred[:,0], pred[:,1]\n",
    "    if mode == 'discriminator':\n",
    "        losses = - F.logsigmoid(real_pred - fake_pred)\n",
    "    elif mode == 'generator':\n",
    "        losses = - F.logsigmoid(fake_pred - real_pred)\n",
    "    else:\n",
    "        raise Exception\n",
    "    return losses.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "KFllQ7BredNA"
   },
   "outputs": [],
   "source": [
    "def get_wgan_loss(batch, fake_pos, mode='discriminator'):\n",
    "    real = dis_convert(batch)\n",
    "    fake = dis_convert(batch, fake_pos)\n",
    "    pred = discriminator(merge_batch(real, fake)).view(2, -1).T\n",
    "    real_pred, fake_pred = pred[:,0], pred[:,1]\n",
    "    if mode == 'discriminator':\n",
    "        losses = fake_pred - real_pred \n",
    "    elif mode == 'generator':\n",
    "        losses = real_pred - fake_pred\n",
    "    else:\n",
    "        raise Exception\n",
    "    return losses.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "r5Kg_tYEedNA"
   },
   "outputs": [],
   "source": [
    "def get_ragan_loss(batch, fake_pos, mode='discriminator'):\n",
    "    real = dis_convert(batch)\n",
    "    fake = dis_convert(batch, fake_pos)\n",
    "    pred = discriminator(merge_batch(real, fake)).view(2, -1).T\n",
    "    real_pred, fake_pred = pred[:,0], pred[:,1]\n",
    "    if mode == 'discriminator':\n",
    "        losses = - F.logsigmoid(real_pred - fake_pred.mean()) - F.logsigmoid(real_pred.mean() - fake_pred)\n",
    "    elif mode == 'generator':\n",
    "        losses = - F.logsigmoid(fake_pred - real_pred.mean()) - F.logsigmoid(fake_pred.mean() - real_pred)\n",
    "    else:\n",
    "        raise Exception\n",
    "    return losses.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dgdv2_loss(batch, fake_pos, mode='discriminator'):\n",
    "    fake = dis_convert(batch, fake_pos)\n",
    "    pred = discriminator(fake)\n",
    "    if mode == 'discriminator':\n",
    "        gt = get_gt(batch, fake_pos)\n",
    "        loss = criterion(pred, gt)\n",
    "    elif mode == 'generator':\n",
    "        losses = pred.sum(dim=0)\n",
    "#         losses = torch.tensor(config.importance).to(device) * losses #/ losses.detach()\n",
    "        loss = losses.sum()\n",
    "    else:\n",
    "        raise Exception\n",
    "    return -loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "3IE_84f-edNB"
   },
   "outputs": [],
   "source": [
    "def get_gan_loss(batch, fake_pos, mode='discriminator'):\n",
    "    return {\"sgan\": get_sgan_loss,\n",
    "            \"wgan\": get_wgan_loss,\n",
    "            \"rgan\": get_rgan_loss,\n",
    "            \"ragan\": get_ragan_loss,\n",
    "            \"dgdv2\": get_dgdv2_loss}[config.gan_flavor](batch, fake_pos, mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "2NjekXgkedNB",
    "outputId": "da85b2ec-c0de-459c-c0dd-7ba3519419fa",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0ef85edf49049a4be6c6ff16e218ae2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(VBox(children=(Wrapper(), Hud())), HBox(children=(Output(),), layout=Layout(height='500px', over…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train_dis(batch, epoch):\n",
    "    generator.requires_grad_(False)\n",
    "    discriminator.zero_grad()\n",
    "    generator_output = generator(batch)\n",
    "    if config.discriminator.noise.std > 0:\n",
    "        generator_output = generator_output + torch.randn_like(generator_output) * config.discriminator.noise.std * config.discriminator.noise.decay ** epoch\n",
    "    discriminator_loss = get_gan_loss(batch, generator_output, mode='discriminator')\n",
    "    \n",
    "    # train discriminator\n",
    "    discriminator_loss.backward()\n",
    "    discriminator_optimizer.step()\n",
    "\n",
    "    # gradient penalty\n",
    "    if config.gp_weight > 0:\n",
    "        discriminator.zero_grad()\n",
    "        gp_loss = get_gp_loss(batch, generator_output, config.gp_weight)\n",
    "        gp_loss.backward()\n",
    "        discriminator_optimizer.step()\n",
    "\n",
    "    hud['dis_loss'] = format(discriminator_loss.item(), '.2e')\n",
    "    pbar().update()\n",
    "\n",
    "def train_gen(batch, epoch):\n",
    "    generator.requires_grad_(True)\n",
    "    generator.zero_grad()\n",
    "    discriminator.zero_grad()\n",
    "    generator_output = generator(batch)\n",
    "    if config.discriminator.noise.std > 0:\n",
    "        generator_output = generator_output + torch.randn_like(generator_output) * config.discriminator.noise.std * config.discriminator.noise.decay ** epoch\n",
    "    generator_loss = get_gan_loss(batch, generator_output, mode='generator') \n",
    "    \n",
    "    #train generator\n",
    "    generator_loss.backward()\n",
    "    generator_optimizer.step()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        dis_batch = dis_convert(batch, generator_output)\n",
    "        stress = stress_criterion(dis_batch).mean()\n",
    "        critic = discriminator(dis_batch).mean()\n",
    "    hud.append({'gen_loss': format(generator_loss.item(), '.2e'),\n",
    "                'stress': format(stress.item(), '.2e'),\n",
    "                'critic': format(critic.item(), '.2e')})\n",
    "    pbar().update()\n",
    "\n",
    "def cuda_memsafe_map(fn, *iterables, summary=False):\n",
    "    total, failed = 0, 0\n",
    "    iterator = zip(*iterables)\n",
    "    items = None\n",
    "    while True:\n",
    "        try:\n",
    "            items = next(iterator)\n",
    "            yield fn(*items)\n",
    "        except StopIteration:\n",
    "            if summary:\n",
    "                print(f'Iteration finished. {failed} out of {total} failed!')\n",
    "            break\n",
    "        except RuntimeError:\n",
    "            print('CUDA memory overflow! Skip batch...')\n",
    "            del items\n",
    "            failed += 1\n",
    "        torch.cuda.empty_cache()\n",
    "        total += 1\n",
    "    \n",
    "def validate(model, data_loader, criterion=val_criterion):\n",
    "    def val_one_batch(batch):\n",
    "        batch = preprocess_batch(model, batch)\n",
    "        pred = CanonicalizationByStress()(model(batch), batch)\n",
    "        gt = CanonicalizationByStress()(batch.gt_pos, batch)\n",
    "        loss = criterion(pred, batch)\n",
    "        gt_loss = criterion(gt, batch)\n",
    "        spc = (loss - gt_loss) / torch.maximum(torch.maximum(loss, gt_loss), torch.ones_like(loss)*1e-5)\n",
    "        return loss.mean().item(), spc.mean().item()\n",
    "    loss_all, spc_all = zip(*cuda_memsafe_map(val_one_batch, data_loader))\n",
    "    return np.mean(loss_all), np.mean(spc_all)\n",
    "\n",
    "def log(msg):\n",
    "    msg = f\"[{epoch:03}] {msg}\"\n",
    "    print(msg, file=open(paths.log(), \"a\"))\n",
    "    with log_out: \n",
    "        print(msg)\n",
    "\n",
    "print(f\"{'='*10} {config.link} {'='*10}\", file=open(paths.log(), \"a\"))\n",
    "hud = Hud()\n",
    "pbar = Wrapper(tqdm, total=len(train_loader)*2, smoothing=0)\n",
    "plot_out = Output()\n",
    "log_out = Output()\n",
    "tabs = {\"status\": VBox([pbar, hud]), \n",
    "        \"plot\": HBox([plot_out], layout=Layout(height='500px', overflow_y='auto')),\n",
    "        \"log\": HBox([log_out], layout=Layout(height='500px', overflow_y='auto'))}\n",
    "tab_bar = Tab(children=list(tabs.values()))\n",
    "[tab_bar.set_title(i, name) for i, name in enumerate(tabs)]\n",
    "display(tab_bar)\n",
    "while True:\n",
    "    if epoch % config.log_interval == 0:\n",
    "        torch.save(generator.state_dict(), f\"{paths.checkpoints()}/gen_epoch_{epoch}.pt\")\n",
    "        torch.save(generator_optimizer.state_dict(), f\"{paths.checkpoints()}/gen_optim_epoch_{epoch}.pt\")\n",
    "        torch.save(discriminator.state_dict(), f\"{paths.checkpoints()}/dis_epoch_{epoch}.pt\")\n",
    "        torch.save(discriminator_optimizer.state_dict(), f\"{paths.checkpoints()}/dis_optim_epoch_{epoch}.pt\")\n",
    "        generator.eval()\n",
    "        with torch.no_grad():\n",
    "            val_stress, val_stress_spc = validate(model=generator, data_loader=val_loader)\n",
    "            val_xing, val_xing_spc = validate(model=generator, data_loader=val_loader, criterion=xing_criterion)\n",
    "            with plot_out:\n",
    "                fig = plt.figure()\n",
    "                graph_vis(G_list[11100], generator(make_batch(data_list[11100]).to(device)).cpu())\n",
    "                plt.show()\n",
    "        # tensorboard.add_scalars('loss', {'train': train_loss, \n",
    "        #                                  'validation': val_loss}, epoch)\n",
    "        # for i, fn in enumerate(loss_fns):\n",
    "        #     tensorboard.add_scalars(type(fn).__name__, {'train': train_loss_comp[i].item(), \n",
    "        #                                           'validation': val_loss_comp[i].item()}, epoch)\n",
    "        hud.append({\n",
    "            'val_stress': format(val_stress, '.2f'),\n",
    "            'val_stress_spc': format(val_stress_spc, '.2%'),\n",
    "            'val_xing': format(val_xing, '.2f'),\n",
    "            'val_xing_spc': format(val_xing_spc, '.2%'),\n",
    "        })\n",
    "        log(f\"stress={hud.data['val_stress']}({hud.data['val_stress_spc']}) xing={hud.data['val_xing']}({hud.data['val_xing_spc']})\")\n",
    "        \n",
    "    # handle.update(tab_bar)\n",
    "    pbar().reset()\n",
    "    pbar().set_description(desc=f\"[epoch {epoch}/{config.epoch.end}]\")\n",
    "    hud(title=f\"epoch {epoch}\")\n",
    "    generator.train()\n",
    "    discriminator.train()\n",
    "    # proper: proper layout\n",
    "    for _ in range(config.discriminator.repeat):\n",
    "        for batch in train_loader:\n",
    "            train_dis(batch, epoch)\n",
    "            if config.alternate == 'iteration':\n",
    "                train_gen(batch, epoch)\n",
    "\n",
    "    if config.alternate == 'epoch':\n",
    "        for batch in train_loader:\n",
    "            train_gen(batch, epoch)\n",
    "\n",
    "    discriminator_scheduler.step()\n",
    "    generator_scheduler.step()\n",
    "\n",
    "    if epoch == config.epoch.end:\n",
    "        break\n",
    "    epoch += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qYK_X_hrq9o"
   },
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XEvmAtNjWhcY",
    "outputId": "9f4c86a4-a4b0-48f1-cb58-ab3679173184"
   },
   "outputs": [],
   "source": [
    "test_epoch = 931\n",
    "\n",
    "test_generator = Generator(**config.generator.params[...]).to(device)\n",
    "test_ckpt_epoch = get_ckpt_epoch(paths.checkpoints(), test_epoch)\n",
    "test_ckpt_file = f\"{paths.checkpoints()}/gen_epoch_{test_ckpt_epoch}.pt\"\n",
    "print(f\"Loading from {test_ckpt_file}...\")\n",
    "test_generator.load_state_dict(torch.load(test_ckpt_file, map_location=torch.device(device)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "9b7d18992dbf4a84931f916fbaaa37d8",
      "2c664d7e1e6d474fb87094fc0da270d3",
      "d375f5f82a4645d7b29c541596dc1c7f",
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      "4d98072bdf674792a13d77e85dd285d1",
      "f7ed92c383d741a0a1cef2536ff2db13",
      "2ba110f835c64ec4b82974996aed10af",
      "09397ceac9014af0aebca848d4d96d48",
      "fd0709f48d6c4527bc00b5f407f3c1b9"
     ]
    },
    "id": "ZubmSTcYrq9o",
    "outputId": "fc2f65a2-2085-43ac-b110-cbfe742318a8",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "rotate = RotateByPrincipalComponents()\n",
    "def test_callback(*, idx, pred, metrics):\n",
    "    # graph_vis(G_list[idx], pred, file_name=f\"{paths.visualization()}/{idx}_{metrics['stress']:.2f}_{metrics['resolution_score']:.2f}.png\")\n",
    "    pred = rotate(torch.tensor(pred), data_list[idx])\n",
    "    graph_vis(G_list[idx], pred)\n",
    "    plt.title(f\"[pred] idx: {idx}, stress: {metrics['stress']:.2f}({metrics['stress_spc']:.2%}), xing: {metrics['xing']:.2f}({metrics['xing_spc']:.2%})\")\n",
    "    plt.show()\n",
    "    gt_pos = rotate(data_list[idx].gt_pos, data_list[idx])\n",
    "    graph_vis(G_list[idx], gt_pos, node_color='orange')\n",
    "    plt.title(f\"[gt] idx: {idx}, stress: {metrics['gt_stress']:.2f}, xing: {metrics['gt_xing']:.2f}\")\n",
    "    plt.show()\n",
    "    \n",
    "test_metrics = test(model=test_generator, \n",
    "                    criteria_list=[], \n",
    "                    dataset=data_list, \n",
    "                    idx_range=range(10000, 11000), \n",
    "                    callback=test_callback,\n",
    "                    gt_pos=None)\n",
    "pickle.dump(test_metrics, open(paths.metrics(\"test\"), \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "t2rFXIr_PsrJ",
    "outputId": "6a4205b1-4630-4de4-ec72-bdba3d3ea114"
   },
   "outputs": [],
   "source": [
    "metrics = test_metrics\n",
    "print('stress:', metrics['stress'].mean())\n",
    "print('stress_spc:', metrics['stress_spc'].mean())\n",
    "print('xing:', metrics['xing'].mean())\n",
    "print('xing_spc:', metrics['xing_spc'].mean())\n",
    "print('l1_angle:', metrics['l1_angle'].mean())\n",
    "print('l1_angle_spc:', metrics['l1_angle_spc'].mean())\n",
    "print('edge:', metrics['edge'].mean())\n",
    "print('edge_spc:', metrics['edge_spc'].mean())\n",
    "print('ring:', metrics['ring'].mean())\n",
    "print('ring_spc:', metrics['ring_spc'].mean())\n",
    "print('tsne:', metrics['tsne'].mean())\n",
    "print('tsne_spc:', metrics['tsne_spc'].mean())\n",
    "print('reso_score:', metrics['resolution_score'].mean())\n",
    "print('min_angle:', metrics['min_angle'].mean())\n",
    "columns = [\n",
    "    'stress',\n",
    "    'stress_spc',\n",
    "    'xing',\n",
    "    'xing_spc',\n",
    "    'l1_angle',\n",
    "    'l1_angle_spc',\n",
    "    'edge',\n",
    "    'edge_spc',\n",
    "    'ring',\n",
    "    'ring_spc',\n",
    "    'tsne',\n",
    "    'tsne_spc',\n",
    "    'reso_score',\n",
    "    'min_angle'\n",
    "]\n",
    "df = pd.DataFrame(map(lambda m: metrics[m].mean().item(), list(metrics.keys())[:-1])).set_axis(columns).T\n",
    "df.style.format({c: \"{:.2f}\" for c in columns if 'spc' not in c} | {c: \"{:.2%}\" for c in columns if 'spc' in c})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5e7vPr0M3mmG"
   },
   "outputs": [],
   "source": [
    "metrics = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "methods = ['neato', 'dot', 'fdp', 'sfdp', 'twopi', 'circo', 'shell', 'spring', 'circular', 'spectral', 'kamada_kawai', 'fa2', 'pmds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433,
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    },
    "id": "s1UnzfID4Ai6",
    "outputId": "3e204e76-68fd-4111-f527-91f6e3b7a3ca"
   },
   "outputs": [],
   "source": [
    "for m in methods:\n",
    "    metrics[m] = test(model=test_generator, \n",
    "                      criteria_list=[], \n",
    "                      dataset=data_list, \n",
    "                      idx_range=range(10000, 11000), \n",
    "                      callback=None,\n",
    "                      gt_pos=load_pos(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dQ7hdCtk5u6K"
   },
   "outputs": [],
   "source": [
    "mean_metrics = {key : list(map(lambda m: metrics[key][m].mean().item(), list(metrics[key].keys())[:-1])) for key in metrics}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "OGyaCmLXTuvA",
    "outputId": "6c42d9f3-9869-4504-bc9c-d0eedaecfd90"
   },
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'stress',\n",
    "    'stress_spc',\n",
    "    'xing',\n",
    "    'xing_spc',\n",
    "    'l1_angle',\n",
    "    'l1_angle_spc',\n",
    "    'edge',\n",
    "    'edge_spc',\n",
    "    'ring',\n",
    "    'ring_spc',\n",
    "    'tsne',\n",
    "    'tsne_spc',\n",
    "    'reso_score',\n",
    "    'min_angle'\n",
    "]\n",
    "df = pd.DataFrame(mean_metrics).set_axis(columns).T\n",
    "df.style.format({c: \"{:.2f}\" for c in columns if 'spc' not in c} | {c: \"{:.2%}\" for c in columns if 'spc' in c})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L5S1HWnHOWIl"
   },
   "source": [
    "# Large Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_pefPcV1OZdD"
   },
   "outputs": [],
   "source": [
    "scalability = pd.read_csv(f\"/__artifacts__/data/scalability.csv\", index_col=\"index\")\n",
    "scalability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oMhWAsdvPnTX"
   },
   "outputs": [],
   "source": [
    "rescale = CanonicalizationByStress()\n",
    "stressfn = Stress()\n",
    "rotate = RotateByPrincipalComponents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LR83OcAaPID5"
   },
   "outputs": [],
   "source": [
    "stress_list = []\n",
    "spc_list = []\n",
    "pmds_list = np.load(\"layouts/new_large_graph/pmds.npy\", allow_pickle=True)\n",
    "gviz_list = np.load(\"layouts/new_large_graph/gviz.npy\", allow_pickle=True)\n",
    "with torch.no_grad():\n",
    "    for idx, col in tqdm(scalability.iterrows(), total=len(scalability)):\n",
    "        # if idx not in [406, 516]: continue\n",
    "        torch.cuda.empty_cache()\n",
    "        G = load_mtx(col['file'])\n",
    "        G.remove_edges_from(nx.selfloop_edges(G))\n",
    "        data = generate_data_list(G, \n",
    "                                sparse=data_config.sparse, \n",
    "                                pivot_mode=data_config.pivot,\n",
    "                                init_mode=data_config.init,\n",
    "                                edge_index=data_config.edge.index,\n",
    "                                edge_attr=data_config.edge.attr,\n",
    "                                pmds_list=pmds_list[idx],\n",
    "                                gviz_list=gviz_list[idx],\n",
    "                                device=device)\n",
    "        batch = Batch.from_data_list([data]).to(device)\n",
    "        # generator.train()\n",
    "        # generator(batch)\n",
    "        generator.eval()\n",
    "        pred = generator(batch)\n",
    "        pos = rotate(rescale(pred, batch), batch)\n",
    "        gt = rotate(rescale(batch.gt_pos, batch), batch)\n",
    "        stress = stressfn(pos, batch).item()\n",
    "        gt_stress = stressfn(gt, batch).item()\n",
    "        spc = (stress - gt_stress) / np.maximum(stress, gt_stress)\n",
    "        stress_list.append(stress)\n",
    "        spc_list.append(spc)\n",
    "\n",
    "        np.save(f\"/__artifacts__/gan_result/data/scalability_{idx}.npy\", pos.cpu().numpy())\n",
    "        graph_attr = dict(node_size=1, \n",
    "                        with_labels=False, \n",
    "                        labels=dict(zip(list(G.nodes), map(lambda n: n if type(n) is int else n[1:], list(G.nodes)))),\n",
    "                        font_color=\"white\", \n",
    "                        font_weight=\"bold\",\n",
    "                        font_size=12,\n",
    "                        width=0.1)\n",
    "\n",
    "        # gt_pos = pickle.load(open(f\"/__artifacts__/data/scalability_{idx}_gt.pkl\", \"rb\"))\n",
    "\n",
    "        plt.figure(figsize=[12, 9])\n",
    "        nx.draw(G, pos=gt.cpu().numpy(), node_color='orange', **graph_attr)\n",
    "        plt.title(f\"neato: large_{idx}\")\n",
    "        plt.axis(\"equal\")\n",
    "        plt.savefig(f\"/__artifacts__/gan_result/output/{idx}_{col['name']}_{col['n']}_{spc}_nx.png\", dpi=300)\n",
    "        plt.show()\n",
    "\n",
    "        plt.figure(figsize=[12, 9])\n",
    "        graph_vis(G, pos.cpu().numpy(), **graph_attr)\n",
    "        plt.title(f\"dgd: large_{idx}, spc={spc:.2%}\")\n",
    "        plt.axis(\"equal\")\n",
    "        plt.savefig(f\"/__artifacts__/gan_result/output/{idx}_{col['name']}_{col['n']}_{spc}_dgd.png\", dpi=300)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gRXTewkYeuq1"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "w_p3OD0woiSM",
    "DR6-vYtr_i_P",
    "innqkwvH_ydD",
    "f-ODE2k8BFV6",
    "N39dDHraedM6"
   ],
   "machine_shape": "hm",
   "name": "GAN(gan=rgan,data=best(xing,stress),dis=deep,share=16,embed=0,gp=0).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "DeepGD",
   "language": "python",
   "name": "deepgd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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          "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>val_stress</th>\n      <th>val_stress_spc</th>\n      <th>val_xing</th>\n      <th>val_xing_spc</th>\n      <th>dis_loss</th>\n      <th>gen_loss</th>\n      <th>stress</th>\n      <th>critic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>epoch 171</th>\n      <td>1546.64</td>\n      <td>76.97%</td>\n      <td>216.77</td>\n      <td>90.04%</td>\n      <td>-5.41e+17</td>\n      <td>-5.82e+17</td>\n      <td>-1.76e+03</td>\n      <td>2.57e+17</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
          "text/plain": "          val_stress val_stress_spc val_xing val_xing_spc   dis_loss  \\\nepoch 171    1546.64         76.97%   216.77       90.04%  -5.41e+17   \n\n            gen_loss     stress    critic  \nepoch 171  -5.82e+17  -1.76e+03  2.57e+17  "
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       "value": " 11531/11531 [00:00&lt;00:00, 57434.44it/s]"
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "54f818a26ee247a389afe6eac753693b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "551769a7c1e241e6b672b416347e9215": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c8ccc20125784f52904dbc43b696efee",
        "IPY_MODEL_1f203b839eac4541a8cfc7a3637cb685"
       ],
       "layout": "IPY_MODEL_1ee860169a5d4bf0bf4a7d9f24bc22da"
      }
     },
     "58f84da2032a43c7a6890ed85b5b9200": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e07d45278998447cb9b90ffd8efc7fb9",
       "outputs": [
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "df6fdc933af5470d8ae85abbbd01a698",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "  0%|          | 0/834 [00:00<?, ?it/s]"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6243406f4e574b87a3d78c85602227b4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6445801017b2467ab913d6287b58c20a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "66abd5cc60a4407c8abd5f3568b6b231": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_cb05f7bf193f47a8a9e933192b437e3d",
        "IPY_MODEL_6e70a686582442a2bd295e339bdcc965",
        "IPY_MODEL_a0b8e58e85164e729af56b602ab365f6"
       ],
       "layout": "IPY_MODEL_ff2934f2610e4108ab70e98ece478c66"
      }
     },
     "67af3940cd0b47bd807eecc9042a86d0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "68872491357248248e6df32df7f6253b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_58f84da2032a43c7a6890ed85b5b9200",
        "IPY_MODEL_d4f1eeead78f45de824c618536c537f3"
       ],
       "layout": "IPY_MODEL_1a9d8bcb3f654ef4aaf732f836c5298f"
      }
     },
     "6aa157c492794a7a86e6d2a64e19955c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6b9e76b93f164ad2bd5caaf6a134fa02": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9e1b3f6ab85944e9bb50722d13f22af2",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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r6wsfH59Se9/IyEh8/PHHiIyM1B+rXr06zp49i5o1a5ZaHZZMq9Vi5cqV8Pf3x507d/Qr+9jb26NLly5YtGiR/u/iwYMH+P3337F//35ERkbi4cOHEAQBlStXxptvvonOnTtj0KBBcHd3z/O9irLcmCiKyMjIQFJSEm7fvo2PdyZAJzVNa+95cqkEHzavxSkQZBAObnmJ2NnZITQ0FG3atEHTpk3h4eFRKu/74uAWAHByckJycrJFB19Jr0cZGRmJSZMm4cSJE/r9GqVSKerWrYtp06Zh8ODB0Ol02L17N6ZNm4a//voLsbGxyMzMhL29Pdzd3dG7d2/06dMH7du3h0yWfytMo9EgOTkZ+y/fhd/R+9DqgOw7fmlZWmw8ewvBp/+F9fkNSI05i7S0NGRkZOSaalNr8k6URF8E5/5RUTD4zEBRPkDr16+PgIAA/WLWTk5OJV5fXsFn6SM7j8Y8wqjgCGh0gn4EY/Z6lKERccW6J5WRkYF58+YhMDAQ9+/fR3ZnjaOjI/r06YMffvgBDx8+xB9//IEVK1Zg7NixePr0qb7LsmnTpvD19UWbNm2g0WiQkJCAe/fu4fTp09i6dSsSEhKQmJiIpKQkPH36FGlpaVCpVMjMzIRWq4W1cw24fLIYUoVN7uIkMogyGdI9BiMtJhIKRRasrKyg1WqRlZWFzMxMSCQSiOpMSAyYGlEcnPtHhmJXZzmX1wcokP+ggmzjxo3Dv//+ix07duQKJVMLDw/HqFGjEB4erj/WvXt3jBgxAl5eXiX63uXF87+cpGUV/gEskwDWChky1Lp8f5ERRRFHjx7FzJkz8ddff0GtVgP4r1XXp08f3L9/H3/99Rfu3r2rv5cnl8thbW0NKysrSKVSZGVlISsrCxqNBlKp9FkAiSJEUYRCoYC1tTVsbW1RoUIFWFtbQy6XQyKRQKfTIT09HWlpaUhJSYFjp89h18gzx+CWXAQtxOsnkRC2Ara2tnjrrbfQqlUreHp64s0338SCo3cQeukBdCXwqWNvLUfUd11Mf2F66TD4yjFDhpHnd1NfrVajQ4cO8PLywtSpU0u0znPnzmHs2LE4d+6c/thHH32ELl26YOjQoSX63uXB0ZhH+GLjBai1Aor7j0kKERJRQN2Hx/DPsW24d++efk5dtuzAyvXa/18lxdHREZUrV4ZSqYRSqYSzszNcXFxQtWpVVK1aFVZWVkhNTcW9e/dw584d3L17F3FxcXj48CGSk5P11xFFEVlZWZDL5ahUqRKqVKmCWrVq4e8Gn0IrKfz+nESbiWqnFiIrKwspKSlITU2FSqVCeno6FE6uqPLJYkjyajUagff4qCjY1VmOFWVB3xf/wWcvZv3OO+/g3XffRadOnUqsTkvt6oxNVMFv9984dC3B6GsJkAASGa5Wao37yZug1WphZWUFGxsbZGZmQq1WQyqVokqVKvDw8EDPnj0xYMAAODo6QqvVIj4+HpcuXcLly5dx7do1xMbGIiYmBklJSUhLS4NardaP9FUoFLCxsYGtrS3s7e1RrVo1VK9eHWq1Wh9SgiBAq9XiwYMHuH//Pi5duoRaDbwNuj8nyqyQlZUFBwcHVK9eHUqlEk5OTnB2doajoyPuIw2hj6yhFQGY6I6fQiaFd5vahZ9IBAZfuWbo4sChF+7i07dsIZfLczwcHR0RFBSEwYMHIzw8vMQGmlhi8B2NeYQRGy5AXcgvJkUlkcng8E4vJB38GWq1Wh94CsWzltajR4+wb98+7N27t8CRu1KpFDKZTN+1aW1tDVEUodVqodFooFarkZKSku/rZTIZ5HI5HBwc4OTkBFdXV8SLOugkhXeb29tY4cKFCwWeMypRhVm7onHE2L33/r8FnKHRwfvX85jV4020cq8MgJvdUv7Y1VmO1Z66x7CuM1GAdLMvtFptng+1Wq2ftPxiOOb1yP7QM/S8pKQkREVF4X//+5/+uStXriAzMxMdO3Ys1jVNdV5e5xo7xzE2UYX3Fx9HlrZkNgMWslS4u2gggNzdm/l1dxpKIpFAKpXq7+s5OzvD1dUV1atX17fOHBwc8nysjniK3VefoKBvuyhdjsX9cxRF8b92Yh5/l+M710OjGspi3Rsny8AWXzlmZy03aKCEvY0Vom7ezPd5QRDQo0cP1KtXD/Pnz883IF986HQ6g867cuUK4uPj0alTJ/2xtLQ0/Pvvv3B1dc11vexRgsa+b1HOff48qVRqVJgmub+PrCpvAwa0fopDYlVB/3Vhoff8LgjZ9/acnJzg4OAAa2trSKVSZGRk4MmTJzlGbWbvopCRkYH4+HgkJibi2rVr+lYiAP2fmVqtRmZmJtKldpB7zSjw/pyg1eB22BpMPCHLN0AdHR3h4OCAgL8SoSukRyMnEaIgPPtzL+CXl0WHruf7nFYQoRWebXbLCe+Wi8FXjvVq7Frogr5yqQS9m1Qv8DpSqRS//vormjZtitatW6Nfv34mrbNy5cr4888/MWzYMP0xZ2dnbNq0CV999ZVJ38tY2fvCGROknx9I/W8SW0nUqM6AVCrVB3R2CzX7Pp2TkxNeeeUVVKlSBS4uLnB2dkblypVRuXLlPL+2tbXN1cpNT09HYmIiEhMT8eTJkzy/zv5/lUqF5ORkJCXdQsX9S+DwwbhnK688v96mqIMUIjwyIqCUa5GV9Wwaw8OHD5GZmYm0tDSkpqbmeOh6z88R8oWTQCKV6bs3jZHfvXGyDAy+csynbR2ERsRBK+T/KWvoTf1KlSrhjz/+wP/+9z80atQIr732msnqNKd7fBKJBDKZDDKZDNbW1sW6Rua+PSau6jmCDjW09/Hx1KlwdXXN8ahSpQo0Gg0eP36Mx48fIzExMcfXMTExOY4nJiYiIeHZwJvsEMwrGLP/W6dOHf3XdnZ2ucJSEASkpKTgyq372Bgejz/vZCJLJ0IhEVBbmoDqqTHIfHwP/7wQoCqVSj+4xdnZGbVq1YKzszOOFXdkpwmW4+OEd8vG4CvH3JztEDDEo9B7FYZ21zRr1gyzZ89G3759cfbsWdjZmaabx5yCz1ixiaoSvb6VXIbeb1REZkIKrly5gv379yM+Ph7x8fFISEhApUqVcgWiq6srmjVrpv/axcUlx99Hdusur8C8fv06zpw5k+t5nU5XYFi2q1wZvdvkfN7e3j7P+6cajQZJSUm5WpKn/hEM3pi2JHDCu+Vi8JVzHeu7IMy3LYJO3irS4sD5+fzzz3Hq1CmMHDkS69evN8li1pYUfIEn8r+XagxDBl3odDo8evRIH4TZj/DwcP3XcXFxSE5ORpUqVfIMSFdXV7z55ptwdXVFpUqV8v37f74r9MXAvHHjBs6dO5crRLVabaHdrtmDaRo1aoSrdknYGvmg0JHLJUWizYK/vz88PT3RsGFD7iRiQTiq0wKlp6fj3XffxZdffokRI0YYfb2wsDAsXrwYYWFh+mOPHz9G/fr1kZiYaPT1y5PXZ+xBpgkaCrWdbZGQpjb6F5m8qNVqPHjwIFdAvvhIT09HtWrV8g3I7Iejo6NBoZCRkZGjm/XFwHzx2BO1DMrB8/NeAq0UVLYW8EbCMZw5sBMpKSno3Lmz/mHJa8xaAgafhfrnn3/QunVr7Nu3D82aNTPqWnv37sXy5cuxd+9e/TGNRoMKFSq8VHvybdq0CVMiHYz+fqxkUhwc367MRxRmZGTg/v37+QZjXFwc4uLiIIpijiCsXr16rnCsVq1asbrOD1y5h7GbL0OjE0tkGbOCSADYKJ7t5VfbOh2HDx/GoUOHcPjwYTg5OelDsGPHjlAqlaVbHJUoBp8FCw0NxVdffYULFy7A2dm52NfZvXs3fv75Z+zevTvHcXt7e9y/fx8ODg7GllqmRFHEDz/8gNWrV8P245XI0Bbvn4wEgJVcip8/ampWc8hSU1MLbT3Gx8fD2tq60NZjtWrVcg0qik1UIejkLWyNuAeVugSHy+bjxWX/BEHA5cuXcejQIRw6dAinT5/GG2+8oQ/Cli1bFntgFJUPDD4L99VXX+Hq1avYvXt3sRez3rlzJ4KCgrBz584cx2vUqIEzZ86YdbeRWq3GiBEjcPnyZezatQuLTsRjc0T8s2H1hVBIAZlMiiytYPLuzPJGFEUkJycXGIxxcXF48OABHB0d8w3Gh1JnzDuTgpJZGiBvhU26z8rKwpkzZ/RBGB0djVatWumDsFGjRgb92+FKMuUHg8/CaTQadOrUCV26dMH06dOLdY3t27dj3bp12L59e47jDRs2xKZNm/DWW2+ZoNLSl5ycjL59+8Le3h6//fYb7OzsMMj7S4RX9oROUvC4sL4erljYv0kpVWo+BEHA48ePCwzIhA5TTb6IdWGsJAKuze5mUIAlJSXh2LFj+iB88uQJ3nvvPXTu3Bmenp5wc3PL9Zri7rJCJYOjOi2cQqHA77//jmbNmqFFixbo3Llzka8hCEKe973MeWTnrVu30K1bN7z//vtYuHAhZDIZDhw4gLOHduP7oE8xPew2ZAoriMj5QSkB8HWX+hjVoW7ZFF7OSaVSuLi4wMXFBY0bN87zHIOX6jOhLOFZ1/ygQYOwcOHCPPexjE1Uwf/QP9h7+T40ghXg2BXyft3Qy90Rr2tu4ujR/Zg2bRocHR1z3B9MFa0xKjgiz11WuJJM2SjZjdrILLi6uiI4OBhDhw7FvXv3ivx6URTz/E3ZXIPv3LlzaN26NUaOHInFixdDJpMhLS0NI0aMwKpVq7Dz57lI+u1rDGxaHfbWckgkz/aCG9rCDccmdmDoFUNGRgY2btyIDh06QFBnlPr7O9hYYerUqdi1axecnZ3RqlUrnD17Vv/80ZhH8Fx8HDsuxUPzXItNK4g4eP0pVt19Bd7fLsKDBw+wdetWvPbaa1izZg1effVVdPGdj8wXdqJ/UfZKMlQ6GHwEAOjYsSPGjh2LAQMG6Dc8NVRe8/gA8wy+0NBQdO/eHatXr8aYMWP0x6dNm4b27dujRo0a2L17N8Z++iF+7OeBqO+64NYP3RD1XRf49WzI39iLKDIyEmPGjEHNmjURHByMMWPGYEjrepBLjRs5K5M8exhylexl/2bMmIGEhATs27cPKpUKrVq1Qo0aNTBr4QqM/P/9FvOj1gn4YuMF3HmSjrfeegvjx4/H3r178fjxY+DV5rl6Bl6UvZIMlQ4GH+lNnjwZzs7OmDRpUpFe9zIEnyiKWLBgAcaNG4cDBw6ge/fu+udOnz6NP/74A/7+/vD19YVMJsPXX39dhtWat5SUFKxatQrvvPMOvLy8ULlyZURERGDfvn3o27cvRrSvC4XMuI8mK7kMG4a/i56NXQs998Vl/7p06YLIyEjExsaiRYsWWHbwKjIMWOVFrc3darOyskKWgQNVuZJM6WHwkV72YtY7d+7E5s2bDX6duQefRqPBF198gY0bN+LMmTNo0uS/QSmZmZkYPnw4li5diujoaJw9exZjxozhvK4iEkURp0+fxmeffQY3NzccPHgQfn5+uHXrFmbOnIlatWrpz81eqq+CQpar5SeXSlBBIcP4zvUKfD5giAdauVfG4oFN8Msn7xR6bl4t9Zo1a2LLli2o8m53SAwY9CICebba7KwNG0phZ8UhF6WFf9KUg5OTE7Zs2YIuXbqgUaNGeP311wt9TUHBd//+/ZIo02RSUlLQv39/yGQynDx5Mtecwzlz5qBBgwbo06cPmjdvDgCYOHFiWZRqlhISErBhwwYEBQVBEAR4e3vjxx9/hItLwSMYDVmqr1fj6gYt5Wfssn/pRZhbmFerzVS7rJDpMPgoFw8PD/zwww/o27cvzp07B3t7+wLPLyj4rl69WlJlGu3OnTvo3r072rRpg6VLl0Iuz/nPITIyEqtWrUJkZCR27dqF27dv44svvkDlypXLqGLzIAgCDh8+jKCgIOzfvx89e/bEqlWr0KZNmyKteuPmbAe/ng3znV9X2PPFPfdFhu6LCeTdajPlLitkGuzqpDx5e3ujefPmGDFiRKE7fptjV+eFCxfQsmVLfPLJJ1ixYkWu0NNqtRg+fDh+/PFHvPLKK5g4cSKysrJ4b68A9+7dg5+fH9zd3TF58mR06NABt2/fxvr169G2bVuzXbquV2NXgwbJSIA8W22GdN0WZZcVMh6Dj/IkkUiwYsUKREVFYeXKlQWea27Bt2PHDnzwwQdYvnw5JkyYkOcHsr+/P5RKJT799FOsX78emZmZ+PTTT1GlSpUyqLj80mg02L59O7p3745GjRrh/v37CA0NRUREBEaOHPlS3Av1aVsH1vLCPyqt5Pm32rK7Wz9sXivHFJgPm9dCmG9bTl4vZVy5hQp048YNtGrVCrt379bf43rRmjVrcOrUKaxduzbH8fPnz2PEiBG4cOFCaZRaKFEUsXTpUsyfPx/bt2/HO++8k+d5//zzD1q1aoW//voLVatWhbu7O9LT0xEdHY3q1XkfBgCuX7+ONWvWYP369ahbty58fHzQr18/2NralnVpJeJozCOMKGBKg5VMilVDzWsNVkvGFh8VqG7duli1ahUGDBiQ7xZD5tDi02q1GDt2LAIDA3H69Ol8Q08QBPj4+GD69OmoU6cOli1bhooVK2Lw4MEWH3oZGRkIDg5Ghw4d0KZNG+h0Ohw9ehQnTpzAxx9//NKGHvCsxXZwXDv0auwKhey/HgK5VIJejV1xcHw7hp4ZYYuPDPL1118jKioKe/bsyRVyq1evxvnz57F69eocx8vLnnxpaWkYNGgQsrKysGXLFlSsWDHfc3/++WesX78eJ0+exNOnT1GvXj3odDpcunQJr776aukVXY5ERkYiKCgIv/32G9555x34+PjAy8sLVlZWZV0aUbGwxUcGmTt3LlQqFWbPnp3rufxafBUrVsTTp08LHRxTkuLi4tC2bVtUrVoVe/fuLTD07t69ixkzZmDNmjWQyWSYO3cuateujT59+lhc6KWkpGD16tX6SebOzs6IiIhAWFgY+vbty9Ajs8bpDGQQuVyeYzHr999/X/9cfsGnUChgY2ODtLS0MtmTLzIyEl5eXhg1ahQmT55c4KhCURQxcuRIjB07Fm+88Qbu3LmDNWvWQCKRICQkpBSrLjuiKOLMmTMICgrCtm3b0KlTJ/j5+cHT0xMyWeHbMBGZCwYfGaxatWoIDg7GoEGDEB4ert9nL7/gA/67z1fawbd3714MGzYMK1aswIABAwo9f9OmTbhz5w62bt0KAJg5cybefvttVK9eHXXrvtyLTj9+/Fg/yVyr1cLb2xtz587lCFZ6aTH4qEg6dOiA8ePHo3///jh+/DisrKwKDb6kpKRS3Yx25cqV+P7777Fz5060bNmy0PMTEhIwYcIE7Nq1C1ZWVvp7mQAQEBBQ0uWWiRcnmffo0QM///xzkSeZE5kjBh8V2aRJk3DmzBl89dVXWLZsWYHB5+TkVGojO3U6HSZNmoQ9e/bg5MmTcHd3N+h1vr6+GDp0qH6k5zfffIOWLVvC2toaDRo0KMmSS929e/ewbt06rFmzBkqlEj4+Pli1atVLMd+OyFAMPioyiUSCdevWoVmzZggJCTGoq7OkqVQqfPTRR0hKSsLp06dRqVIlg163a9cu/PXXX7h8+TIA4MSJE4iMjIRGo8G+fftKsuRSo9FosGfPHgQFBeH06dMYOHAgQkND4eHhUdalEZUJBh8Vi1KpxJYtW+Dp6YlPPvmkTIPvwYMH8PLywhtvvIHff//d4BGHT58+xahRo/Drr7/C1tYWoijql9pKTk7G22+/XaJ1l7QbN25gzZo1WLduHerWrQtvb2/8/vvvsLPj0lhk2TidgYqtcePGmDdvHn799Vdo8tlhuqSDLyoqCi1atECPHj2wbt26Ig2znzRpErp27YqOHTsCeLaUWVpaGo4ePYoZM2aUVMklKjMzE8HBwejYsSNatWoFjUaDI0eO4MSJExg2bBhDjwhs8ZGRPvvsM6xcuRIHDhyAKIq5BkaUZPAdPHgQQ4YMwaJFizBkyJAivfbo0aPYu3cvoqKiADxb2WXq1Knw9PTE9evX0axZs5IoucRcvnxZP8m8WbNmGD16NHr06MH5dkR5YIuPjObl5YUnT55gxYoVuZ4rqeALCgrC0KFDsWXLliKHXnp6Onx8fBAQEKCf0L5u3Tq4uLhg165dmD59usnrLQmpqalYvXo1mjdvjm7dusHJyQnnz59HWFgY+vXrx9AjygeDj4wmk8nQt29ffP/99zh79myO50wdfIIgYOrUqZg3bx6OHz+Odu3aFfkaM2fORPPmzeHl5QXgWRB+9913aN++PWrXro3WrVubrF5Ty55kPnz4cNSqVQv79+/HrFmzcPv2bcyaNcviVpghKg52dZLRBEGAs7MzAgMDMXDgQFy4cEG/Waspgy8jIwPDhg1DfHw8zpw5U6wNYcPDw7FhwwZcuXJFf2zp0qVo0aIFQkJCsGrVKpPUamrPTzLXaDTw9vbG1atXUbVq1bIujcjssMVHRsueztCzZ08MGjQIQ4YMgU73bLdpUwXfo0eP0KlTJ8jlchw6dKhYoadWqzF8+HD4+/vjlVdeAQAkJibip59+QqtWreDi4oIOHToYXaupCIKAQ4cOYeDAgahbty4uXryIlStXIiYmBpMmTWLoERUTg4+M9vw8vjlz5iArKwt+fn4ATBN8165dQ8uWLdG5c2ds3LgRNjY2xbrOvHnzUKtWLXz44Yf6Y3PnzkW/fv2wdu1aTJ8+vVysWnLv3j3Mnj0b7u7umDhxItq1a4dbt27h119/Rbt27cpFjUTmjF2dZDRBECCXP/tRksvlCAkJQdOmTdGiRQvUq1fPqOA7duwYBg4ciB9//BGffvppsa8THR2NpUuXIiIiQh8csbGx+OWXXzBv3jxcuHABXbp0Kfb1jaXRaLB3714EBQXh1KlTGDhwILZs2QIPDw8GHZGJMfjIaC+u3FK1alWEhISgf//+CAsLK3bwrV+/Hl9//TVCQkLQqVOnYten0+kwfPhw+Pn55VgzdObMmRg5ciRWrFiBWbNmlUnAPD/J3N3dHd7e3ggJCeF8O6ISxK5OMlpeS5a1bdsWEydOhI+PD54+fQpBEAy+niiKmDlzJmbNmoU///zTqNADgGXLlsHKygqff/65/tiVK1ewb98+NGrUCKIo6kd4lobMzEz89ttv6NSpE1q1agW1Wo0jR47g5MmT+OSTTxh6RCWMLT4ymiAIebaWvvrqK5w+fRqRkZFIS0uDo6NjodfKysrCZ599hn///RdnzpwxemucmzdvYvbs2Th9+nSOcJ46dSqmTp2KhQsXltq9vecnmTdt2hQjR45Ejx49YG1tXeLvTUT/YYuPjCaKYp5rdUokEvzyyy8QRRHr1q0r9DqJiYnw9PSEWq3G0aNHjQ49URTx+eefY9KkSXjttdf0x//880/8/fffqFu3LlJTU9GnTx+j3qcgqampCAwMxLvvvouuXbtCqVTi/Pnz2L9/P/r378/QIyoDDD4yWkG7M1SsWBGvvvoqZs6cib///jvfa1y/fh0tW7ZEy5Yt8fvvv6NChQpG1/XLL78gOTkZEyZM0B/LXoh69uzZmD9/PqZNm5Zv7cX14iTzffv2YebMmYiNjcX333/PSeZEZYxdnWS0goIPeDbYpV+/fujbty/Cw8Nz7cZ+8uRJ9OvXD99//32O+3DGiI+Px5QpU3Dw4EH9iFMA2LZtGzIzM+Hq6or79+9j4MCBJnk/4Nkk840bNyIoKAhqtZqTzInKKQYfGa2w4FMqlWjZsiUeP36sH7WYfU9t06ZN8PX1xcaNG/H++++bpB5RFDF69GiMGDEix9ZC2QtRL126FHPmzME333yTIxSLQxAEHDlyBEFBQQgLC4OXlxdWrFjB+XZE5RiDj4xmSPAlJydj2bJlaNWqFZYtW4YxY8Zgzpw5CAwMxOHDh/HWW2+ZrJ7Q0FBcu3YNISEhOY6vXbsWNWrUgIODA27cuIGPPvqo2O8RFxen38nc0dERPj4+WLlyJZycnIwtn4hKGIOPjGZo8NnY2GDLli1o2bIl9u/fj4cPH+Ls2bOoVq2ayWp58uQJxo4diy1btuQYOJKeno5Zs2Zh+/btmDlzJqZMmQKFQlGka2u1WuzduxeBgYE4deoUBgwYgM2bN6Np06Zs3RGZEQYfGc3Q4AMAJycnVKlSBUeOHEF0dLRJQw8AJkyYgP79+6NVq1Y5ji9ZsgRt2rSBVCrF5cuXsW3bNoOveePGDaxduxbr1q1DnTp1OMmcyMwx+MhohgRfXFwcbt68iW7duuGDDz5A165d4ePjg/3790Mmk5mkjv379+PPP//MsfMC8GyaxMKFC3HmzBlMmjQJkyZNKnQaQWZmJrZu3YqgoCBERUVh6NChOHToEN544w2T1EpEZYfTGchohgTfP//8g9atW2P06NFYtGgRZs+eDUEQ8N1335mkhtTUVIwYMQKrVq2Cvb19jud++OEHDBw4EBkZGTh79ix8fHzyvc6VK1fg6+uLGjVqYN26dRg5ciTu3r2LhQsXMvSIXhJs8ZHR8lu5JdvVq1dx8OBBbN26Fd26dQPwbDHrTZs2oWnTpmjZsiW6du1qVA3ffPMNOnbsmGtkaGxsLNatW4e///4bvr6+mDBhQq45gqmpqQgJCUFQUBDi4uLw2WefITw8HLVr1zaqJiIqnxh8ZLT8Vm4RRRELFizA+vXr0ahRI33oZatSpQpCQkLQt29fnDt3rtgTu0+ePInQ0FBERUXleu7bb7/F6NGjkZycjKNHj2LNmjX62s6dO4egoCCEhoaiQ4cOmDlzJrp06WKyrlciKp8YfGS0vLo6NRoNvvzyS5w7dw7r16/H9OnT83xtmzZtMHnyZPTv3x8nT54s8hJemZmZ8Pb2xvLly1GpUqUcz12+fBlhYWG4fv06vvzyS/j6+iIrKwtBQUEICgpCVlYWJ5kTWSDe4yOjvRh8T58+Rffu3XHv3j2cOHEC9evXL3BrovHjx8PNzQ3jxo0r8nv7+fmhYcOGea63OXXqVEybNg0PHz7Ejh07cPHiRbi7u+P8+fNYsWIF/vnnH0yePJmhR2RhGHxktOeD786dO2jTpg3q1q2LHTt2wMHBodBd2CUSCdauXYvDhw9jw4YNBr/vpUuXEBgYiOXLl+d67tixY7hy5QqSkpLQtGlT2NjYoEOHDrh16xY2btyI9u3bc+4dkYViVycZLTv4zp8/j549e2LixIkYN26cPlgqVqyo35Mvv9Gfjo6OCA0NRadOndC4ceNCV3LRarX47LPPMH/+/BwtNq1Wiz179uCTTz6BWq1GTEwMJBIJoqOj4ezsbLpvmojMFoOPjCYIAiIiIjBu3DgEBgaiV69eOZ6Xy+WwtbUtdE++t956C/7+/ujXrx+2HjiOTRcfYfuleKiytLCzlqNXY1f4tK0DN2c7LFy4EJUrV8awYcMAAP/++69+J3NHR0c4ODggKioKU6dOxciRIxl6RKQnEUVRLOsiyHyJoggPDw/ExsbiwIEDaNasWZ7n1axZE6dOnUKtWrUKvWbfL7/FRTsPSOVW0Ar//XjKpRIoZFJM61AF4wZ44tSpU4iIiEBQUBAuX76MoUOHYtiwYRgwYACWLVuGhg0bomHDhrh27RpcXFxM9j0TkXlji4+KTavVYty4cbh58ybmzp2bb+gB/y1bVljwxSaqEF2pJQSNAEHI+TuZVhChFXSYvu82XvNojdatW8PDwwMjRoxAz549YW1tjVWrVqFmzZrw9PTEhAkTMGzYMIYeEeXA4KNiSU1NxaBBg6DRaNC+fftCw6WwAS7ZAk/chEZXcCeEKJHAvqkXwlctzjHJXKVSYdasWdi5cycSEhKwfv36POf2EZFl46hOKrK4uDi0a9cOrq6u2LNnD2QyWaG7mCuVSiQlJRV67e2X4nN0b+ZFIlMg3rpmrpVVFi9ejHbt2qFZs2bw9/fHhx9+CFdX18K/ISKyKGzxUZFcunQJXl5e+PLLLzFp0iRIJJJ8V255npOTk0EtPlWW1qA6VOqc5z1+/BiLFi3C2bNnkZiYiMDAQFy8eNGgaxGRZWGLjwy2d+9eeHp6wt/fH5MnT9ZPVyhskWrA8K5OO2vDfhezs8p53pw5czBo0CDUrVsXS5YsQZ8+fQwaSENEloctPjJIQEAA/Pz8sHPnTrRs2TLHc6YMvl6NXRESfrfA7k65VILeTarr///WrVv49ddfER0djadPnyIgIADnzp0r9L2IyDKxxUcF0ul0mDBhApYtW4ZTp07lCj3AtMHn07YOFLKCr6WQSeHd5r/7e99++y3GjBmDKlWqYPny5ejatSvc3d0LfS8iskxs8RFiE1UIPHEz12TxIU2rYeoYb6SkpOD06dNwcnLK8/WGBt+LG8Tmxc3ZDgFDPDAqOAIanZDnPL6AIR5wc362+3lkZCQOHjyIgIAApKWlYcmSJTh+/HgRvnsisjQMPgt3NOZRrpBJy9IiJPwONp6+gbeqvI6wzd/Dysoq32uYssUHAB3ruyDMty0CDl9DyLlbkFpVgJ21HL2bVId3m9r60AOeLUQ9ffp0ODg4YMGCBejUqRNef/11g96HiCwTg8+CxSaqMCo4AhkaXa7ntAIAmRVuVOmA+6kauDmXXvABz1p+SYdWoatMhpULV+Z5ztGjRxETE4Pt27cjPT0d/v7+OHDggMHvQUSWiff4LNizyeJCgedodAKCTt4q8JySCL4jR44gLCwM8+bNy/N5URQxefJkzJkzB1ZWVggMDESLFi0KXdyaiIjBZ8EMmSyuFURsuxhX4DmCIBS6xU9Rgi89PR0+Pj5YuXJlvotah4aGQqvVYsCAAcjKysKCBQvy3eyWiOh57Oq0YMWdLP4iQyawFyX4ZsyYgRYtWqBbt255Pq/RaPDNN99gxYoVkEql+OWXX/D222+jadOmBl2fiCwbg8+C2VnLkWZA+L04WfxFhnR1Ojo6IiUlpdBzz507h+Dg4AJHgK5ZswZubm7w9PSERqPBjz/+iE2bNhX8TRAR/T92dVqwXo1dIZcW3EX54mTxvBgSfHK5HHZ2dkhNTc33HLVajeHDh2PRokV45ZVX8jwnLS0N33//PX788UcAwIYNG1C3bt085xcSEeWFwWfBijNZPC+GBB9QeHfn3LlzUadOHQwaNCjfcxYvXoz27dujadOm0Gq1mDt3LmbMmFHoexMRZWNXpwUr6mTx/BQ1+Nzc3HI9FxUVheXLl+PixYv5DpRJSEjA4sWL9cuR/f7776hWrRrat29f6HsTEWVj8Fm47MniQSdvYdvFOKjUWthZ5T1ZPD/Gtvh0Oh2GDx+OOXPmoEaNGvm+fs6cOfjwww/h7u4OQRAwZ84cLFmypND3JSJ6HoOP4OZsB7+eDeHXs2GxXm9s8C1duhS2trbw9vbO97W3bt3Chg0bcPXqVQDPpjM4ODigc+fOxaqZiCwXg4+MZkzw3bx5E3PmzMHZs2cLvMaMGTMwduxYuLi4QBRFzJ49G3PmzCl0/iAR0YsYfGS04gafKIrw8fHBlClTULdu3Xxfd/HiRRw+fBgrVz5bumzXrl2QyWT5zvMjIioIR3WS0QxZuQXIHXxr165FSkoKxo0bV+Drnl+IWhRF+Pn5Yfr06WztEVGxsMVHRjNk5RbgWfDdvXsXABAfH48pU6bg8OHDkMvz/zE8fPgwbty4AR8fHwDA/v37kZGRgV69epmkdiKyPGzxkdGK2tUpiiJGjhyJkSNHolGjRgVe9/mFqLNbe9OmTTPo/YiI8sIWHxmtqMH3xx9/4MaNG9i8eXOB52/ZsgUA0L9/fwDAsWPH8PjxYwwYMMD4oonIYjH4yGhFCb6EhAT4+vpi27ZtsLa2zvdcjUaDadOm4eeff9Zf28/PD9988w1kMpnJaiciy8P+IjJaUYIvOjoagwYNQosWLQo8NygoCHXq1MF7770HADh16hRu376NwYMHm6RmIrJcbPGR0QwNvkuXLiE1NRWzZ88u8Ly0tDT4+flhz549+mN+fn6YMmUKFAqF0fUSkWVj8JHRDAm+1NRUfPvtt7CxsYGdXcHLoC1atAgdO3ZEkyZNAADh4eGIjo7Gjh07TFYzEVkudnWS0QwJvqlTp8LT0xPp6ekQBCHf8xISErBkyRL4+fnpj82ePRuTJk0q8J4gEZGhGHxktMKC78SJE9i2bRv8/f0L3ZNv9uzZGDx4MOrUqQMAiIyMRHh4OIYPH27yuonIMrGrk4xW0MotmZmZ8Pb2xvLly+Hk5KSf0lCxYsVc5968eRMbN27UL0QNPAvCr776ChUqVCix+onIsrDFR0YraOWWWbNmoVGjRujduzeAgjejnTFjBnx9feHi4gIAiI6OxvHjx/HFF1+USN1EZJnY4iOj5dfVGRERgTVr1uDy5cv6Y/kF38WLF3HkyBGsWrVKf+yHH37AuHHjCh0MQ0RUFAw+MlpewafRaDB8+HAsWLAAVatW1R/PL/imTJmCGTNmwN7eHgBw/fp17N+/HwEBASVaOxFZHnZ1ktHyCr6ffvoJVapUwccff5zjuFKpRFJSUo5jhw4dws2bN/ULUQPA3LlzMXr0aDg6OpZc4URkkdjiI6O9GHzXrl3DwoULceHChVyDXpycnHK0+ARBwJQpUzBnzhz95PTbt29jx44duHHjRqnUT0SWhS0+MtrzwScIAry9vfHdd9/Bzc0t17kvdnX+8ccfkEgk6Nevn/7YvHnzMGLECDg5OZV47URkedjiI6M9H3zZu6SPGjUqz3OVSiViY2MBAGq1GtOmTcPq1av1r4+Li8PmzZtx7dq1UqiciCwRg4+KLTZRhcATN+HksxaNfjiOCgopki/9i43+K/Od3qBUKhEZGQkACAwMRN26ddGpUyf98/Pnz8enn36KV155pVS+ByKyPAw+KpajMY8wKjgCGp0AqbUtRADpGgHWb3bE6N1xCKhYBR3ru+R6XXZXZ/Zi1fv27dM/9+DBA2zYsAF///13KX4nRGRpeI+Piiw2UYVRwRHI0OigFcQcz4mQIkOjw6jgCMQmqnK9Njv4/P398d5776Fx48b65/z9/TFkyBBUq1atpL8FIrJgbPFRkQWeuAmNLv+FpgFAoxMQdPIW/Ho2zHFcqVTi8ePHWLp0Kc6fP68//vjxY6xZswaXLl0qiZKJiPTY4qMi234pPldL70VaQcS2i3G5jiuVSty5cwdDhw5F7dq19ccXL16Mfv36oWbNmiavl4joeWzxUZGpsrSGnafOfV5SUhLS0tIwbdo0/bHk5GSsXLkS4eHhJquRiCg/bPFRkdlZG/b7kp1V7vPmz58PAHB2dtYfW7p0Kby8vPRbERERlSS2+KjIejV2RUj43QK7O+VSCTo3cMH07Vew/VI8VFla2MglSMuqgYrV3ZGSkgKlUonU1FQsW7YMp06dKsXvgIgsmUQUxYJv1hC9IDZRhQ+WnECGRpfvOVYyKaSSZ/f6ng9IKUTotGrM96qHge3ewrx58xAZGYnffvutNEonImLwUfE8P4/v+WCTSyWQSSUQAai1+Y/8tJZJsH1Ec3R8pyEOHTqEhg0b5nsuEZEpMfio2GITVQg6eQvbLsZBpdbCzkqO3k2qIyVTgz2X7xc68lMKEc7J17Bl9ki4OXPPPSIqHQw+MrmG3+1HmoEjP2USwEouQ8AQjzxXeiEiMjWO6iSTM3S6AwDoRBS40gsRkakx+MjkDJ3u8LzslV6IiEoag49MrldjV8ilksJPfE5+K70QEZkag49MzqdtHShkRf/RymulFyIiU2Pwkcm5OdshYIgHKihkRWr55bXSCxGRqTH4qER0rO+CMN+2+LB5LYPCTy6VoHeT6qVQGRFZOk5noBJnyEovFRQyhPm25Xw+IipxbPFRiSuo61MulaCC4tk8PoYeEZUGtvio1OS30ot3m9oMPSIqNQw+IiKyKOzqJCIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii8LgIyIii/J/J8xNZ6Ydeb8AAAAASUVORK5CYII=\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6d2a7d01ed91451a8d7f62d262caecd3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "500px",
       "overflow_y": "auto"
      }
     },
     "6e70a686582442a2bd295e339bdcc965": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "layout": "IPY_MODEL_6243406f4e574b87a3d78c85602227b4",
       "max": 834,
       "style": "IPY_MODEL_8dd955b0a45c441fad3c8c16ad4e7e56",
       "value": 817
      }
     },
     "724ab53026bd4b5690adf1324385a99a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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