Covid.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0eHrA6fZCVkA"
      },
      "outputs": [],
      "source": [
        "import numpy as np #cálculos de matemática\n",
        "import matplotlib.pyplot as plt #plotar os gráficos\n",
        "import matplotlib.lines as mlines #plotar os markers nos gráficos\n",
        "import pandas as pd #data frames\n",
        "import os #comandos ! do bash\n",
        "import glob #faz as listas\n",
        "\n",
        "#as bibliotecas torch \n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import random_split\n",
        "from torchvision.utils import make_grid\n",
        "from PIL import Image\n",
        "\n",
        "from torchvision import models\n",
        "\n",
        "from torch import optim\n",
        "\n",
        "from torch.optim.lr_scheduler import OneCycleLR\n",
        "\n",
        "import copy\n",
        "\n",
        "\n",
        "\n",
        "covid_list = glob.glob('./PNGS/PNGs/*')\n",
        "pneumo_list = glob.glob('./Pneumonia/*')\n",
        "mass_list = glob.glob('./Mass/*')\n",
        "consolidation_list = glob.glob('./Consolidation/*')\n",
        "edema_list = glob.glob('./Edema/*')\n",
        "fibrosis_list = glob.glob('./Fibrosis/*')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WeVmSErCVkE"
      },
      "outputs": [],
      "source": [
        "numepoca=65\n",
        "balum = 'unbal'\n",
        "\n",
        "selectedname = 'pneumo'\n",
        "selected = pneumo_list\n",
        "#menorlista = min([len(covid_list),len(selected)])\n",
        "#covid_list = covid_list[:menorlista]\n",
        "#selected = selected[:menorlista]\n",
        "selected_label = torch.zeros(len(selected))#[:len(selected)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LhJF6mY3CVkF",
        "outputId": "6bda66ae-98f8-492a-98a7-4ea7b2a4acb3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<>:43: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:43: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<ipython-input-3-996ea14aaa43>:43: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  if x.mode is not 'RGB':\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "class my_Dataset(Dataset):\n",
        "    def __init__(self, transform=None):\n",
        "        covid_list = glob.glob('./PNGS/PNGs/*')#[:len(selected)]\n",
        "        covid_label = torch.ones(len(covid_list))#[:len(selected)]\n",
        "        \n",
        "        pneumo_list = glob.glob('./Pneumonia/*')#[:len(selected)]\n",
        "        pneumo_label = torch.zeros(len(pneumo_list))#[:len(selected)]\n",
        "        \n",
        "        mass_list = glob.glob('./Mass/*')#[:len(selected)]\n",
        "        mass_label = torch.zeros(len(mass_list))#[:len(selected)]\n",
        "        \n",
        "        consolidation_list = glob.glob('./Consolidation/*')#[:len(selected)]\n",
        "        consolidation_label = torch.zeros(len(consolidation_list))#[:len(selected)]\n",
        "\n",
        "        edema_list = glob.glob('./Edema/*')#[:len(selected)]\n",
        "        edema_label = torch.zeros(len(edema_list))#[:len(selected)]\n",
        "        \n",
        "        fibrosis_list = glob.glob('./Fibrosis/*')#[:len(selected)]\n",
        "        fibrosis_label = torch.zeros(len(fibrosis_list))#[:len(selected)]\n",
        "\n",
        "# Para fazer o balancemento de classes optamos por usar o menor valor entre os possíveis candidatos        \n",
        "#        self.images = covid_list[:len(pneumo_list)] + pneumo_list[:len(pneumo_list)] + mass_list[:len(pneumo_list)] + consolidation_list[:len(pneumo_list)] + edema_list[:len(pneumo_list)] + fibrosis_list[:len(pneumo_list)]\n",
        "#        self.labels = torch.cat([covid_label[:len(pneumo_list)], pneumo_label[:len(pneumo_list)], mass_label[:len(pneumo_list)], consolidation_label[:len(pneumo_list)], edema_label[:len(pneumo_list)], fibrosis_label[:len(pneumo_list)]])\n",
        "        #, mass_label, consolidation_label, edema_label, fibrosis_label])\n",
        "\n",
        "# Para fazer com as classes desblanceadas usamos todas as imagens        \n",
        "#        self.images = covid_list + pneumo_list + mass_list + consolidation_list + edema_list + fibrosis_list\n",
        "#        self.labels = torch.cat([covid_label, pneumo_label, mass_label, consolidation_label, edema_label, fibrosis_label])\n",
        "        #, mass_label, consolidation_label, edema_label, fibrosis_label])   \n",
        "\n",
        "# Para fazer com as classes uma a uma comparadas     \n",
        "        self.images = covid_list + selected\n",
        "        self.labels = torch.cat([covid_label, selected_label])\n",
        "        #, mass_label, consolidation_label, edema_label, fibrosis_label])   \n",
        "        \n",
        "        self.trfm = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        x = Image.open(self.images[index])\n",
        "        if x.mode is not 'RGB':\n",
        "            x = x.convert('RGB')\n",
        "        if self.trfm is not None:\n",
        "            x = self.trfm(x)\n",
        "        else:\n",
        "            x = transforms.ToTensor()(x)\n",
        "        y = self.labels[index].long()\n",
        "        return x, y\n",
        "\n",
        "trans = transforms.Compose([\n",
        "                            transforms.Resize((256,256)),\n",
        "                            transforms.RandomRotation(30),\n",
        "                            #transforms.RandomHorizontalFlip(p=0.1),\n",
        "                            transforms.ToTensor()\n",
        "])\n",
        "\n",
        "data = my_Dataset(trans)\n",
        "\n",
        "len_ds = len(data)\n",
        "len_train = int(0.8*len_ds)\n",
        "len_val = len_ds - len_train\n",
        "\n",
        "ds_train, ds_val = random_split(data, [len_train, len_val])\n",
        "\n",
        "train_dl = DataLoader(dataset=ds_train, batch_size=32, shuffle=True, num_workers=6, pin_memory=True)\n",
        "val_dl = DataLoader(dataset=ds_val, batch_size=32, shuffle=False, num_workers=6, pin_memory=True)\n",
        "\n",
        "def show(img, y=None, color=True):\n",
        "    npimg = img.numpy()\n",
        "    npimg_tr = np.transpose(npimg, (1,2,0))\n",
        "    plt.imshow(npimg_tr)\n",
        "    if y is not None:\n",
        "        plt.title(f'label:{y}')\n",
        "\n",
        "        \n",
        "grid_size = 5\n",
        "rnd_inds = np.random.randint(0,len(ds_train),grid_size)\n",
        "\n",
        "x_grid = [ds_train[i][0] for i in rnd_inds]\n",
        "y_grid = [ds_train[i][1] for i in rnd_inds]\n",
        "\n",
        "\n",
        "x_grid = make_grid(x_grid, nrow=grid_size, padding=1)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(15,15))\n",
        "show(x_grid,y_grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NkNNJSRrCVkH"
      },
      "outputs": [],
      "source": [
        "'''trans = transforms.Compose([\n",
        "                            transforms.Resize((256,256)),\n",
        "                            transforms.ColorJitter(brightness=(0.6,1.4),contrast=(0.6,1.4)),\n",
        "                            transforms.RandomRotation(30),\n",
        "                            transforms.ToTensor()\n",
        "])\n",
        "\n",
        "data = my_Dataset(trans)\n",
        "\n",
        "len_ds = len(data)\n",
        "len_train = int(0.8*len_ds)\n",
        "len_val = len_ds - len_train\n",
        "\n",
        "ds_train, ds_val = random_split(data, [len_train, len_val])\n",
        "\n",
        "train_dl = DataLoader(dataset=ds_train, batch_size=32, shuffle=True, num_workers=6, pin_memory=True)\n",
        "val_dl = DataLoader(dataset=ds_val, batch_size=32, shuffle=False, num_workers=6, pin_memory=True)        \n",
        "        \n",
        "        \n",
        "grid_size = 2\n",
        "rnd_inds = np.random.randint(0,len(ds_train),grid_size)\n",
        "\n",
        "x_grid = [ds_train[i][0] for i in rnd_inds]\n",
        "y_grid = [ds_train[i][1] for i in rnd_inds]\n",
        "\n",
        "\n",
        "x_grid = make_grid(x_grid, nrow=grid_size, padding=1)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(15,15))\n",
        "show(x_grid,y_grid);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iy2MuwcOCVkI",
        "outputId": "5b8e9e88-40b1-41cc-cff1-b4ece254cbf7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/bruno/anaconda3/lib/python3.8/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /opt/conda/conda-bld/pytorch_1607370172916/work/c10/cuda/CUDAFunctions.cpp:100.)\n",
            "  return torch._C._cuda_getDeviceCount() > 0\n"
          ]
        }
      ],
      "source": [
        "model_resnet18 = models.resnet18(pretrained=True,num_classes=2)\n",
        "\n",
        "num_classes = 2\n",
        "num_ftrs = model_resnet18.fc.in_features\n",
        "model_resnet18.fc = nn.Linear(num_ftrs, num_classes)\n",
        "\n",
        "bt = next(iter(train_dl))\n",
        "\n",
        "outtt = model_resnet18(bt[0])\n",
        "\n",
        "opt = optim.Adam(model_resnet18.parameters(), lr=1e-4)\n",
        "\n",
        "lr_scheduler = OneCycleLR(opt,max_lr=10, pct_start=0.2, steps_per_epoch=1, epochs=numepoca)\n",
        "\n",
        "def get_lr(opt):\n",
        "    for param_group in opt.param_groups:\n",
        "        return param_group['lr']\n",
        "    \n",
        "def metrics_batch(ouput, target):\n",
        "    pred = ouput.argmax(dim=1, keepdim=True)\n",
        "    corrects = pred.eq(target.view_as(pred)).sum().item()\n",
        "    return corrects\n",
        "\n",
        "los_func = nn.CrossEntropyLoss(reduction='sum')\n",
        "\n",
        "#helper function to compute the loss value per batch of data\n",
        "def loss_batch(loss_func, output, target, opt=None):\n",
        "    loss = loss_func(output, target)\n",
        "    metrics_b = metrics_batch(output, target)\n",
        "    if opt is not None:\n",
        "        opt.zero_grad()\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "        \n",
        "    return loss.item(), metrics_b\n",
        "\n",
        "def loss_epoch(model, loss_func, dataset_dl, sanity_check=False, opt=None):\n",
        "    running_loss = 0.0\n",
        "    running_metrics = 0.0\n",
        "    len_data = len(dataset_dl.dataset)\n",
        "    for xb, yb in dataset_dl:\n",
        "        xb = xb\n",
        "        yb = yb\n",
        "        #xb = xb.to('cuda')\n",
        "        #yb = yb.to('cuda')\n",
        "        output = model(xb)\n",
        "        loss_b, metric_b = loss_batch(loss_func, output, yb, opt)\n",
        "        running_loss += loss_b\n",
        "        if metric_b is not None:\n",
        "            running_metrics += metric_b\n",
        "        if sanity_check is True:\n",
        "            break\n",
        "            \n",
        "    loss = running_loss/float(len_data)\n",
        "    metric = running_metrics/float(len_data)\n",
        "    \n",
        "    return loss, metric\n",
        "\n",
        "def train_val(model, params):\n",
        "    num_epochs = params['num_epochs']\n",
        "    loss_func = params['loss_func']\n",
        "    opt = params['optimizer']\n",
        "    train_dl = params['train_dl']\n",
        "    val_dl = params['val_dl']\n",
        "    sanity_check = params['sanity_check']\n",
        "    lr_scheduler = params['lr_scheduler']\n",
        "    path2weights = params['path2weights']\n",
        "    \n",
        "    loss_history = {\n",
        "        'train':[],\n",
        "        'val':[]\n",
        "    }\n",
        "    metric_history = {\n",
        "        'train':[],\n",
        "        'val':[]\n",
        "    }\n",
        "    best_model_weights = copy.deepcopy(model.state_dict())\n",
        "    best_loss = float('inf')\n",
        "    \n",
        "    for epoch in range(num_epochs):\n",
        "        current_lr = get_lr(opt)\n",
        "        print(f'epoch {epoch}/{num_epochs}, current lr:{current_lr}')\n",
        "        model.train()\n",
        "        train_loss, train_metric = loss_epoch(model, loss_func, train_dl, sanity_check, opt)\n",
        "        loss_history['train'].append(train_loss)\n",
        "        metric_history['train'].append(train_metric)\n",
        "        \n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            val_loss, val_metric = loss_epoch(model, loss_func, val_dl, sanity_check)\n",
        "            loss_history['val'].append(val_loss)\n",
        "            metric_history['val'].append(val_metric)\n",
        "            \n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_model_weights = copy.deepcopy(model.state_dict())\n",
        "            torch.save(model.state_dict(), path2weights)\n",
        "            print('best model saved')\n",
        "        \n",
        "        lr_scheduler.step()\n",
        "        print(f'train loss: {train_loss:.6f}, val loss:{val_loss:.6f}, accuracy: {100*val_metric:.2f}')\n",
        "        print('+-+-'*10)\n",
        "        \n",
        "    model.load_state_dict(best_model_weights)\n",
        "    return model, loss_history, metric_history\n",
        "\n",
        "def find_lr(model, loss_fn, optimizer, init_value=1e-8, final_value=10.):\n",
        "    nunber_in_epoch = len(train_dl) -1\n",
        "    update_step = (final_value/init_value)**(1/nunber_in_epoch)\n",
        "    lr = init_value\n",
        "    optimizer.param_groups[0][\"lr\"] = lr\n",
        "    best_loss = 0.0\n",
        "    batch_num = 0\n",
        "    losses = []\n",
        "    log_lrs = []\n",
        "    for data in train_dl:\n",
        "        batch_num += 1\n",
        "        inputs, labels = data\n",
        "        inputs = inputs\n",
        "        #inputs = inputs.to('cuda')\n",
        "        labels = labels\n",
        "        #labels = labels.to('cuda')\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "        \n",
        "        if batch_num > 1 and loss > 4*best_loss:\n",
        "            return log_lrs, losses\n",
        "        \n",
        "        if loss < best_loss or batch_num == 1:\n",
        "            best_loss = loss\n",
        "        \n",
        "        losses.append(loss)\n",
        "        log_lrs.append(lr)\n",
        "        \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        lr *= update_step\n",
        "        optimizer.param_groups[0][\"lr\"] = lr\n",
        "    return log_lrs, losses\n",
        "\n",
        "def smooth(y, window_size, order, deriv=0, rate=1):\n",
        "    from math import factorial\n",
        "    try:\n",
        "        window_size = np.abs(np.int(window_size))\n",
        "        order = np.abs(np.int(order))\n",
        "    except(ValueError, msg):\n",
        "        raise ValueError(\"window_size and order have to be of type int\")\n",
        "    if window_size % 2 != 1 or window_size < 1:\n",
        "        raise TypeError(\"window_size size must be a positive odd number\")\n",
        "    if window_size < order + 2:\n",
        "        raise TypeError(\"window_size is too small for the polynomials order\")\n",
        "    order_range = range(order+1)\n",
        "    half_window = (window_size -1) // 2\n",
        "    # precompute coefficients\n",
        "    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])\n",
        "    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)\n",
        "    # pad the signal at the extremes with\n",
        "    # values taken from the signal itself\n",
        "    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )\n",
        "    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])\n",
        "    y = np.concatenate((firstvals, y, lastvals))\n",
        "    return np.convolve( m[::-1], y, mode='valid')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tn4X-4U8CVkJ"
      },
      "outputs": [],
      "source": [
        "logs, losses = find_lr(model_resnet18, los_func, opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WwxsInEECVkJ"
      },
      "outputs": [],
      "source": [
        "losses_y = np.array([x.detach().cpu().numpy() for x in losses])\n",
        "\n",
        "losses_y = smooth(losses_y, 21, 1)\n",
        "\n",
        "min_grad = np.gradient(losses_y).argmin()\n",
        "\n",
        "logs[min_grad]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.axvline(x=logs[min_grad], c='r',ls='--')\n",
        "ax.annotate(f'lr={logs[min_grad]:.3e}', xy=(logs[min_grad], losses_y[min_grad]),\n",
        "             xytext=(0.7, 0.9), textcoords='axes fraction', fontsize=20)\n",
        "ax.plot(logs, losses_y, lw=3.)\n",
        "plt.ylabel('Loss value', fontsize=26)\n",
        "plt.xlabel('Learning rate', fontsize=26)\n",
        "plt.xscale('log')\n",
        "plt.grid()\n",
        "for tick in ax.xaxis.get_major_ticks():\n",
        "    tick.label.set_fontsize(20)\n",
        "for tick in ax.yaxis.get_major_ticks():\n",
        "    tick.label.set_fontsize(20)  \n",
        "plt.savefig(f'lr {balum} covid and {selectedname} {numepoca} epochs.png', format='png');\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VhPxwuyXCVkK"
      },
      "outputs": [],
      "source": [
        "loss_func = nn.CrossEntropyLoss(reduction='sum')\n",
        "opt = optim.Adam(model_resnet18.parameters(), lr=logs[min_grad])\n",
        "\n",
        "lr_scheduler = OneCycleLR(opt,max_lr=10,pct_start=0.1, steps_per_epoch=len(train_dl), epochs=numepoca)\n",
        "\n",
        "params_train = {\n",
        "    'num_epochs':numepoca,\n",
        "    'loss_func':loss_func,\n",
        "    'optimizer':opt,\n",
        "    'train_dl':train_dl,\n",
        "    'val_dl':val_dl,\n",
        "    'sanity_check':False,\n",
        "    'lr_scheduler':lr_scheduler,\n",
        "    'path2weights':'models/resnet18.pt'\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LZCcHQtACVkL"
      },
      "outputs": [],
      "source": [
        "model_resnet18, loss_history, metrics_history = train_val(model_resnet18, params_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3KfXIJPYCVkL"
      },
      "outputs": [],
      "source": [
        "num_epochs = params_train['num_epochs']\n",
        "melhor_epoch = loss_history['val'].index(min(loss_history['val']))\n",
        "melhor_acc = metrics_history['val'][melhor_epoch]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "twin1 = ax.twinx()\n",
        "plt.title('Loss/Accuracy', fontsize=26)\n",
        "ax.plot(range(1,num_epochs+1), loss_history['train'],ls='-', \n",
        "        label='ResNet18-train loss', lw=3., color='seagreen',alpha=0.5)\n",
        "ax.plot(range(1,num_epochs+1), loss_history['val'],ls='-', \n",
        "        label='ResNet18-val loss', lw=3., color='brown',alpha=0.5)\n",
        "twin1.plot(range(1,num_epochs+1), metrics_history['train'],ls='-.', lw=2.5, \n",
        "           label='ResNet18-train acc', color='cadetblue')\n",
        "twin1.plot(range(1,num_epochs+1), metrics_history['val'],ls='-.', lw=2.5, \n",
        "           label='ResNet18-val acc', color='firebrick')\n",
        "\n",
        "\n",
        "#inserir os parâmetros do plot da legenda\n",
        "x_b,y_b = 0.65, 0.4\n",
        "\n",
        "\n",
        "bestmark = mlines.Line2D([], [], color='gold', marker='*', linestyle='None', markersize=15, label='Best Epoch')\n",
        "plt.plot([melhor_epoch+1],metrics_history['val'][melhor_epoch], \"*\", color='gold', markersize='15')\n",
        "beststar = plt.legend(bbox_to_anchor=(x_b+0.25, y_b),handles=[bestmark],fontsize=16)\n",
        "ax.annotate(f'learning rate = {logs[min_grad]:.3e}\\nbest epoch / total = {melhor_epoch+1} / {num_epochs}\\n with accuracy = {melhor_acc*100:.2f}%', xy=(logs[min_grad], losses_y[min_grad]),\n",
        "             xytext=(x_b,y_b), textcoords='axes fraction', fontsize=12, color='grey')\n",
        "\n",
        "\n",
        "x_a,y_a = x_b-0.05,y_b-0.36\n",
        "\n",
        "\n",
        "ax.set_ylabel('Loss (NLLLoss)', fontsize=26)\n",
        "twin1.set_ylabel(\"Accuracy\", fontsize=26)\n",
        "ax.set_xlabel('Training Epochs', fontsize=26)\n",
        "ax.legend(bbox_to_anchor=(x_a,y_a), loc='lower left',fontsize=16,framealpha=0.5)\n",
        "twin1.legend(bbox_to_anchor=(x_a, y_b-0.09), loc='upper left',fontsize=16, framealpha=0.5)\n",
        "ax.grid()\n",
        "plt.gca().add_artist(beststar)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for tick in ax.xaxis.get_major_ticks():\n",
        "    tick.label.set_fontsize(20)\n",
        "for tt1 in ax.yaxis.get_major_ticks():\n",
        "    tt1.label.set_fontsize(20)\n",
        "\n",
        "plt.savefig(f'{balum} covid and {selectedname} {num_epochs} epochs.png', format='png');\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "covid_enxuto.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}