Variational_Autoencoder_Compression_Regeneration.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "5Z81WPL1Rg0x",
        "colab_type": "text"
      },
      "source": [
        "# Image Compression and Generation using Variational Autoencoders in Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "zLZH8oPfRg0-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.utils.data\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.utils import save_image\n",
        "\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import os\n",
        "import shutil\n",
        "import random\n",
        "random.seed(5)\n",
        "\n",
        "import matplotlib\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'svg'\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "metadata": {
        "Collapsed": "false",
        "id": "KzgsPMEdRg1G",
        "colab_type": "code",
        "colab": {},
        "outputId": "8b518def-36aa-4eec-dc4c-4447b2d1d493"
      },
      "source": [
        "epochs = 1000\n",
        "batch_size = 64\n",
        "torch.manual_seed(17)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(f'Device: {device}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "zGctTDB6Rg1Q",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis and Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "hGdMKqiTRg1U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_img(img):\n",
        "    img = img.permute(1, 2, 0)\n",
        "    if img.shape[2]==1:\n",
        "        img = img.view(img.shape[0], img.shape[1])\n",
        "    plt.title(f'Image has size {img.cpu().numpy().shape}')\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    plt.axis('off')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "PO_NZ35LRg1b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transforms_all = transforms.Compose([\n",
        "    transforms.Grayscale(num_output_channels=1),\n",
        "    transforms.Resize(size=50),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "dummy_batch = torch.utils.data.DataLoader(\n",
        "    datasets.ImageFolder('Font/all/', transform=transforms.ToTensor()),\n",
        "    batch_size=1, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "jX1JrA2mRg1i",
        "colab_type": "code",
        "colab": {},
        "outputId": "1cb10cc2-c5fd-4c54-d637-ad7373841910"
      },
      "source": [
        "for batch in dummy_batch:\n",
        "    original_image = batch[0][0]\n",
        "    show_img(original_image)\n",
        "    show_img(transforms_all(transforms.ToPILImage()(original_image)))\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "l47EVYzxRg1s",
        "colab_type": "text"
      },
      "source": [
        "## Training/Validation Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "Cr1JdD4IRg1t",
        "colab_type": "code",
        "colab": {},
        "outputId": "21c6be99-10a3-41cd-c6fe-799bf2c10180"
      },
      "source": [
        "folders = os.listdir('Font/all')\n",
        "\n",
        "# ignore hidden files\n",
        "folders = [folder for folder in folders if folder[0]!='.']\n",
        "\n",
        "os.mkdir('Font/train')\n",
        "os.mkdir('Font/val')\n",
        "\n",
        "for folder in tqdm(folders):\n",
        "    os.mkdir(f'Font/val/{folder}')\n",
        "    os.mkdir(f'Font/train/{folder}')\n",
        "    \n",
        "    images = os.listdir(f'Font/all/{folder}')\n",
        "    \n",
        "    # ignore hidden files\n",
        "    images = [image for image in images if image[0]!='.']\n",
        "    \n",
        "    random.shuffle(images)\n",
        "    for image in images[:20]:\n",
        "        shutil.copy(f'Font/all/{folder}/{image}', f'Font/val/{folder}/{image}')\n",
        "        \n",
        "    for image in images[20:]:\n",
        "        shutil.copy(f'Font/all/{folder}/{image}', f'Font/train/{folder}/{image}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileExistsError",
          "evalue": "[Errno 17] File exists: 'Font/train'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-313-8e62dfd63e4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfolders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfolder\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfolder\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfolders\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Font/train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Font/val'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'Font/train'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "i8Pr_S12Rg10",
        "colab_type": "text"
      },
      "source": [
        "## Creating Data Loaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "6PP_74FVRg11",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transforms_set = transforms.Compose([\n",
        "    transforms.Grayscale(num_output_channels=1),\n",
        "    transforms.Resize(size=50),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.ImageFolder('Font/train', transform=transforms_set),\n",
        "    batch_size=batch_size, shuffle=True)\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "    datasets.ImageFolder('Font/val', transform=transforms_set),\n",
        "    batch_size=batch_size, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "QshJrTCMRg14",
        "colab_type": "text"
      },
      "source": [
        "##  VAE Architecture and Model Creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "CKTDxCtLRg15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class VAE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VAE, self).__init__()\n",
        "    \n",
        "        z = 32\n",
        "        \n",
        "        self.fc1 = nn.Linear(50*50, 1000)\n",
        "        self.fc21 = nn.Linear(1000, z)\n",
        "        self.fc22 = nn.Linear(1000, z)\n",
        "        self.fc3 = nn.Linear(z, 1000)\n",
        "        self.fc4 = nn.Linear(1000, 50*50)\n",
        "\n",
        "    def encode(self, x):\n",
        "        h1 = F.relu(self.fc1(x))\n",
        "        return self.fc21(h1), self.fc22(h1)\n",
        "\n",
        "    def reparameterize(self, mu, logvar):\n",
        "        std = torch.exp(0.5*logvar)\n",
        "        eps = torch.randn_like(std)\n",
        "        return mu + eps*std\n",
        "\n",
        "    def decode(self, z):\n",
        "        h3 = F.relu(self.fc3(z))\n",
        "        return torch.sigmoid(self.fc4(h3))\n",
        "\n",
        "    def forward(self, x):\n",
        "        mu, logvar = self.encode(x.view(-1, 50*50))\n",
        "        z = self.reparameterize(mu, logvar)\n",
        "        return self.decode(z), mu, logvar"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "nLbYp0MFRg1-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_function(recon_x, x, mu, logvar):\n",
        "    \n",
        "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 50*50), reduction='sum')\n",
        "\n",
        "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
        "\n",
        "    return BCE + KLD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "6FrDnO2BRg2C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = VAE().to(device)\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "5s-e7O-KRg2H",
        "colab_type": "text"
      },
      "source": [
        "##  Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "trWRs6opRg2H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(evaluate_data=val_loader):\n",
        "    model.eval()\n",
        "    val_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for i, (data, _) in enumerate(evaluate_data):\n",
        "            \n",
        "            data = data.to(device)\n",
        "            \n",
        "            recon_batch, mu, logvar = model(data)\n",
        "            \n",
        "            val_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
        "            \n",
        "            if i == 0:\n",
        "                n = min(data.size(0), 16)\n",
        "                comparison = torch.cat([data[:n],\n",
        "                                      recon_batch.view(batch_size, 1, 50, 50)[:n]])\n",
        "                save_image(comparison.cpu(),\n",
        "                         'Results/reconstruction_' + str(epoch) + '.png', nrow=n)\n",
        "\n",
        "    val_loss /= len(evaluate_data.dataset)\n",
        "    return val_loss\n",
        "\n",
        "\n",
        "def sample_latent_space(epoch):\n",
        "    with torch.no_grad():\n",
        "        sample = torch.randn(64, 32).to(device)\n",
        "        sample = model.decode(sample).cpu()\n",
        "        save_image(sample.view(64, 1, 50, 50),\n",
        "                   'Results/sample_' + str(epoch) + '.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "vs-uK1zdRg2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(epoch):\n",
        "\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "    \n",
        "    progress_bar = tqdm(train_loader, desc='Epoch {:03d}'.format(epoch), leave=False, disable=False)\n",
        "    for data, _ in progress_bar:\n",
        "        \n",
        "        data = data.to(device)\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        recon_batch, mu, logvar = model(data)\n",
        "        \n",
        "        loss = loss_function(recon_batch, data, mu, logvar)\n",
        "        loss.backward()\n",
        "        train_loss += loss.item()\n",
        "        \n",
        "        optimizer.step()\n",
        "\n",
        "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item() / len(data))})\n",
        "\n",
        "    average_train_loss = train_loss / len(train_loader.dataset)\n",
        "    tqdm.write('Training set loss (average, epoch {:03d}): {:.3f}'.format(epoch, average_train_loss))\n",
        "    val_loss = evaluate(val_loader)\n",
        "    tqdm.write('\\t\\t\\t\\t====> Validation set loss: {:.3f}'.format(val_loss))\n",
        "\n",
        "    train_losses.append(average_train_loss)\n",
        "    val_losses.append(val_loss)\n",
        "    \n",
        "    if epoch%50==0:\n",
        "        torch.save(model.state_dict(), f'Models/epoch_{epoch}.model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "_NhSvjAnRg2Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs=2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "DwqzZbfNRg2Y",
        "colab_type": "code",
        "colab": {
          "referenced_widgets": [
            ""
          ]
        },
        "outputId": "3a8d0092-d7dd-4982-af0b-8c7a92742fc7"
      },
      "source": [
        "train_losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(1, epochs+1):\n",
        "    train(epoch)\n",
        "    sample_latent_space(epoch)\n",
        "    \n",
        "np.savetxt('Models/training_losses.txt', np.array(train_losses), delimiter='\\n')\n",
        "np.savetxt('Models/validation_losses.txt', np.array(val_losses), delimiter='\\n')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 001', max=965.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training set loss (average, epoch 001): 266.831\n",
            "\t\t\t\t====> Validation set loss: 266.324\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 002', max=965.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training set loss (average, epoch 002): 265.057\n",
            "\t\t\t\t====> Validation set loss: 263.595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "Z4n9m9z0Rg2d",
        "colab_type": "code",
        "colab": {},
        "outputId": "caf4b2fb-de00-411d-84a2-17e5e1e818d2"
      },
      "source": [
        "model.load_state_dict(torch.load('Presaved_Models/epoch_950.model', map_location=torch.device('cpu')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "-DcHJug7Rg2j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(100):\n",
        "    sample_latent_space(f'-{i}')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "bP8pO-pORg2m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "Collapsed": "false",
        "id": "6mfvmfyTRg2t",
        "colab_type": "text"
      },
      "source": [
        "##  Results and Other Uses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "0sJ5jce0Rg2v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_losses = np.loadtxt('Presaved_Models/training_losses.txt')\n",
        "val_losses = np.loadtxt('Presaved_Models/validation_losses.txt')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "Collapsed": "false",
        "id": "5nPsLVroRg20",
        "colab_type": "code",
        "colab": {},
        "outputId": "09692eaa-1617-4c95-ae65-0b85ab3f263f"
      },
      "source": [
        "plt.plot(\n",
        "    range(1, len(train_losses)+1), \n",
        "    train_losses, \n",
        "    label='Training Loss',\n",
        "    linewidth=2, \n",
        "    alpha=0.7\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    range(1, len(val_losses)+1),\n",
        "    val_losses,\n",
        "    label='Validation Loss',\n",
        "    linewidth=2,\n",
        "    alpha=0.7\n",
        ")\n",
        "\n",
        "plt.title('VAE Font Training')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
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124.134797 219.200139 \nL 124.440998 219.563338 \nL 124.747198 217.889616 \nL 125.053399 220.422755 \nL 125.3596 220.571967 \nL 125.665801 217.435039 \nL 125.972002 219.200139 \nL 126.278203 220.083846 \nL 126.584403 216.298018 \nL 126.890604 215.380768 \nL 127.196805 218.616014 \nL 127.503006 218.646088 \nL 127.809207 218.391617 \nL 128.115408 218.901715 \nL 128.421609 218.627581 \nL 128.727809 219.363232 \nL 129.03401 220.798677 \nL 129.340211 221.375863 \nL 129.952613 217.231463 \nL 130.258814 220.701516 \nL 130.565014 219.178162 \nL 130.871215 219.311181 \nL 131.177416 218.079312 \nL 131.483617 219.88027 \nL 131.789818 219.955454 \nL 132.096019 219.048614 \nL 132.402219 216.027354 \nL 132.70842 221.493844 \nL 133.014621 220.255035 \nL 133.320822 213.823871 \nL 133.627023 218.029575 \nL 133.933224 218.252815 \nL 134.239425 218.034202 \nL 134.545625 219.150402 \nL 134.851826 219.589942 \nL 135.158027 219.484684 \nL 135.464228 219.840942 \nL 135.770429 218.398557 \nL 136.07663 218.721272 \nL 136.38283 217.542611 \nL 136.689031 218.189197 \nL 136.995232 221.250941 \nL 137.301433 216.631143 \nL 137.607634 217.314744 \nL 137.913835 217.29855 \nL 138.220035 216.979305 \nL 138.526236 218.281732 \nL 138.832437 218.946825 \nL 139.138638 218.722429 \nL 139.444839 219.371329 \nL 139.75104 220.368391 \nL 140.05724 220.320966 \nL 140.363441 218.848507 \nL 140.669642 218.329156 \nL 141.282044 218.773323 \nL 141.894446 218.884364 \nL 142.200646 218.427474 \nL 142.813048 220.775543 \nL 143.119249 218.18457 \nL 143.42545 219.621172 \nL 143.731651 220.115077 \nL 144.037851 217.034826 \nL 144.344052 219.636209 \nL 144.650253 219.414126 \nL 144.956454 216.491185 \nL 145.262655 220.069966 \nL 145.568856 214.205576 \nL 145.875056 218.705078 \nL 146.181257 220.31981 \nL 146.487458 217.777418 \nL 146.793659 218.57553 \nL 147.09986 217.797081 \nL 147.406061 218.693512 \nL 147.712262 220.006348 \nL 148.018462 220.411188 \nL 148.630864 219.107605 \nL 148.937065 219.905717 \nL 149.243266 218.729369 \nL 149.549467 221.522761 \nL 149.855667 218.316432 \nL 150.161868 219.120328 \nL 150.468069 218.622954 \nL 150.77427 219.279951 \nL 151.080471 218.563963 \nL 151.386672 219.759975 \nL 151.692872 220.067653 \nL 151.999073 219.225586 \nL 152.305274 220.345257 \nL 152.611475 219.666283 \nL 152.917676 221.054304 \nL 153.223877 221.002254 \nL 153.530078 219.924224 \nL 153.836278 216.63924 \nL 154.142479 219.365545 \nL 154.44868 217.562274 \nL 154.754881 218.087409 \nL 155.061082 219.814339 \nL 155.367283 219.200139 \nL 155.673483 215.694229 \nL 155.979684 221.098258 \nL 156.285885 218.61023 \nL 156.592086 220.630958 \nL 156.898287 219.235997 \nL 157.204488 220.772073 \nL 157.510688 217.219896 \nL 157.816889 221.270604 \nL 158.12309 219.242937 \nL 158.429291 220.016758 \nL 158.735492 218.824217 \nL 159.041693 219.821279 \nL 159.347893 220.042206 \nL 159.654094 219.03242 \nL 159.960295 218.686571 \nL 160.266496 220.059556 \nL 160.878898 217.238403 \nL 161.185099 217.369108 \nL 161.491299 216.812743 \nL 161.7975 217.583095 \nL 162.103701 218.939885 \nL 162.409902 218.433258 \nL 162.716103 220.751253 \nL 163.022304 221.264821 \nL 163.328504 219.685947 \nL 163.634705 220.268916 \nL 163.940906 219.924224 \nL 164.247107 216.368576 \nL 164.553308 219.27301 \nL 164.859509 219.950827 \nL 165.165709 218.007598 \nL 165.47191 218.849664 \nL 165.778111 220.415815 \nL 166.084312 220.34063 \nL 166.390513 221.282171 \nL 166.696714 220.883115 \nL 167.002915 220.192574 \nL 167.309115 220.074593 \nL 167.615316 219.696357 \nL 167.921517 218.415907 \nL 168.227718 219.289204 \nL 168.533919 218.15334 \nL 169.14632 221.166503 \nL 169.452521 219.718334 \nL 169.758722 220.922442 \nL 170.064923 218.458705 \nL 170.371124 218.212331 \nL 170.983525 220.426225 \nL 171.289726 219.561025 \nL 171.595927 222.025919 \nL 171.902128 220.704986 \nL 172.208329 218.722429 \nL 172.51453 218.296769 \nL 172.820731 218.458705 \nL 173.126931 221.128332 \nL 173.433132 221.356199 \nL 173.739333 220.699202 \nL 174.045534 219.187416 \nL 174.657936 218.806867 \nL 174.964136 216.704015 \nL 175.270337 217.364481 \nL 175.576538 221.732121 \nL 175.882739 220.895839 \nL 176.18894 219.476587 \nL 176.495141 220.384584 \nL 176.801341 217.099601 \nL 177.107542 219.947357 \nL 177.413743 220.244625 \nL 178.026145 218.594037 \nL 178.332346 220.662188 \nL 178.638547 218.80571 \nL 178.944747 220.360294 \nL 179.250948 220.425068 \nL 179.557149 220.255035 \nL 179.86335 219.075217 \nL 180.169551 219.192043 \nL 180.475752 220.183321 \nL 180.781952 220.300146 \nL 181.088153 217.558804 \nL 181.394354 220.473649 \nL 181.700555 219.400246 \nL 182.006756 219.316964 \nL 182.312957 220.156717 \nL 182.619157 219.91844 \nL 182.925358 219.111075 \nL 183.231559 221.043894 \nL 183.53776 219.661656 \nL 183.843961 218.912125 \nL 184.150162 217.857229 \nL 184.456362 219.074061 \nL 184.762563 218.35576 \nL 185.068764 221.051991 \nL 185.374965 218.396244 \nL 185.987367 220.971023 \nL 186.293568 219.189729 \nL 186.599768 216.455327 \nL 186.905969 219.724117 \nL 187.21217 215.95911 \nL 187.518371 221.390899 \nL 187.824572 221.529702 \nL 188.130773 221.294895 \nL 188.743174 218.391617 \nL 189.049375 221.179226 \nL 189.355576 218.710862 \nL 189.661777 217.883833 \nL 189.967978 220.760507 \nL 190.274178 218.567433 \nL 190.580379 220.170597 \nL 191.192781 219.89762 \nL 191.805183 220.297833 \nL 192.111384 220.339473 \nL 192.417584 220.914346 \nL 192.723785 217.72074 \nL 193.029986 219.045144 \nL 193.336187 219.739154 \nL 193.642388 219.340098 \nL 193.948589 219.584159 \nL 194.254789 221.167659 \nL 194.56099 218.264382 \nL 194.867191 220.588161 \nL 195.173392 220.76282 \nL 195.479593 219.024323 \nL 195.785794 220.866921 \nL 196.091994 219.577218 \nL 196.398195 219.624642 \nL 196.704396 218.560493 \nL 197.010597 215.259316 \nL 197.316798 220.687635 \nL 197.622999 219.371329 \nL 198.2354 219.726431 \nL 198.541601 220.901622 \nL 198.847802 220.266602 \nL 199.154003 219.225586 \nL 199.460204 221.224337 \nL 199.766405 217.742717 \nL 200.072605 219.017383 \nL 200.378806 219.727587 \nL 200.685007 218.637991 \nL 200.991208 220.126643 \nL 201.297409 220.436635 \nL 201.60361 219.756504 \nL 201.90981 220.348727 \nL 202.216011 217.481306 \nL 202.522212 219.105291 \nL 202.828413 218.761756 \nL 203.134614 220.665658 \nL 203.440815 220.447045 \nL 203.747016 219.934634 \nL 204.053216 221.688167 \nL 204.359417 219.92191 \nL 204.665618 221.304148 \nL 204.971819 220.36145 \nL 205.27802 220.206455 \nL 205.584221 220.480589 \nL 205.890421 219.504347 \nL 206.196622 220.664502 \nL 206.502823 218.917908 \nL 206.809024 219.172379 \nL 207.421426 220.973336 \nL 207.727626 220.104666 \nL 208.033827 218.34882 \nL 208.340028 219.71255 \nL 208.646229 219.370172 \nL 208.95243 219.855979 \nL 209.258631 221.8582 \nL 209.564831 220.039892 \nL 209.871032 220.038735 \nL 210.177233 218.647244 \nL 210.483434 219.673223 \nL 210.789635 221.713614 \nL 211.095836 217.974054 \nL 211.402037 219.620016 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221.289111 \nL 224.262472 220.647151 \nL 224.874874 220.184478 \nL 225.181074 217.859542 \nL 225.487275 220.176381 \nL 225.793476 220.605511 \nL 226.099677 219.072904 \nL 226.405878 220.714239 \nL 226.712079 221.557462 \nL 227.018279 218.231995 \nL 227.32448 218.13946 \nL 227.630681 219.587629 \nL 227.936882 220.418128 \nL 228.243083 219.239467 \nL 228.549284 219.91497 \nL 228.855484 219.551771 \nL 229.161685 221.109825 \nL 229.774087 218.930632 \nL 230.080288 221.518135 \nL 230.386489 222.988281 \nL 230.69269 221.661564 \nL 230.99889 218.671535 \nL 231.305091 219.166595 \nL 231.611292 218.717802 \nL 231.917493 219.198983 \nL 232.223694 221.253254 \nL 232.529895 217.889616 \nL 232.836095 220.257349 \nL 233.142296 218.360386 \nL 233.448497 221.21277 \nL 233.754698 221.752942 \nL 234.060899 219.69867 \nL 234.3671 220.398464 \nL 234.6733 219.824749 \nL 234.979501 219.532108 \nL 235.285702 221.056618 \nL 235.591903 217.512537 \nL 235.898104 220.474805 \nL 236.204305 221.275231 \nL 236.816706 218.754816 \nL 237.429108 220.701516 \nL 237.735309 220.692262 \nL 238.04151 219.968178 \nL 238.347711 221.116765 \nL 238.653911 221.259037 \nL 238.960112 215.813368 \nL 239.266313 220.384584 \nL 239.572514 219.547145 \nL 240.184916 219.02548 \nL 240.491116 218.12558 \nL 240.797317 219.536734 \nL 241.103518 219.393306 \nL 241.409719 220.593944 \nL 241.71592 219.986685 \nL 242.022121 218.408967 \nL 242.328322 219.373642 \nL 242.634522 219.438416 \nL 242.940723 219.88374 \nL 243.246924 217.847975 \nL 243.553125 219.43726 \nL 243.859326 219.44767 \nL 244.165527 220.083846 \nL 244.471727 217.374891 \nL 245.084129 220.400778 \nL 245.39033 218.182257 \nL 245.696531 218.557023 \nL 246.002732 220.331377 \nL 246.308932 220.384584 \nL 246.615133 221.172286 \nL 246.921334 218.547769 \nL 247.227535 221.137586 \nL 247.533736 220.484059 \nL 247.839937 220.463239 \nL 248.146138 219.589942 \nL 248.452338 220.031795 \nL 248.758539 218.983839 \nL 249.06474 219.692887 \nL 249.370941 218.910968 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221.271761 \nL 263.76238 220.841474 \nL 264.068581 217.971741 \nL 264.374782 219.077531 \nL 264.680983 221.591006 \nL 264.987184 219.476587 \nL 265.293385 220.445888 \nL 265.599585 220.634428 \nL 265.905786 222.11267 \nL 266.211987 220.654092 \nL 266.518188 220.804461 \nL 266.824389 219.835159 \nL 267.13059 218.098976 \nL 267.742991 220.369547 \nL 268.049192 220.587004 \nL 268.355393 218.680788 \nL 268.661594 221.070498 \nL 268.967795 221.781859 \nL 269.273996 214.617356 \nL 269.580196 221.578282 \nL 269.886397 220.608981 \nL 270.192598 220.792894 \nL 270.805 219.892993 \nL 271.111201 219.031263 \nL 271.417401 218.827687 \nL 271.723602 220.436635 \nL 272.029803 220.215708 \nL 272.336004 219.315808 \nL 272.642205 221.169973 \nL 272.948406 213.180754 \nL 273.254607 218.950296 \nL 273.560807 221.795739 \nL 273.867008 219.179319 \nL 274.173209 217.590035 \nL 274.47941 221.961145 \nL 274.785611 216.890241 \nL 275.091812 221.702048 \nL 275.398012 219.075217 \nL 275.704213 222.782391 \nL 276.010414 219.991311 \nL 276.316615 219.734527 \nL 276.622816 221.159563 \nL 276.929017 219.973961 \nL 277.235217 220.066496 \nL 277.541418 218.053865 \nL 277.847619 220.282796 \nL 278.15382 221.545895 \nL 278.460021 218.941042 \nL 278.766222 218.293299 \nL 279.072422 220.176381 \nL 279.378623 220.724649 \nL 279.684824 218.577843 \nL 279.991025 219.68826 \nL 280.297226 222.414565 \nL 280.603427 220.050302 \nL 280.909628 220.97565 \nL 281.215828 219.098351 \nL 281.522029 218.882051 \nL 281.82823 220.559243 \nL 282.134431 219.700984 \nL 282.440632 222.147371 \nL 282.746833 220.000565 \nL 283.053033 221.262508 \nL 283.359234 221.477651 \nL 283.665435 219.933477 \nL 283.971636 220.77323 \nL 284.277837 218.157967 \nL 284.584038 219.720647 \nL 284.890238 219.364388 \nL 285.196439 220.509506 \nL 285.808841 221.477651 \nL 286.115042 217.282357 \nL 286.727444 220.376487 \nL 287.033644 221.128332 \nL 287.339845 219.820122 \nL 287.646046 220.54999 \nL 287.952247 216.668157 \nL 288.258448 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        "id": "Jw0FwrrlRg28",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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      "cell_type": "markdown",
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      "source": [
        "![alt text](https://) "
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}