predict-movie-earnings-with-posters.ipynb

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  "metadata": {
    "colab": {
      "name": "0. combined-2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ate-_Gt2a2rb",
        "colab_type": "text"
      },
      "source": [
        "# About this notebook\n",
        "\n",
        "## Motivation\n",
        "- Posters communicate important features of the movies.\n",
        "- Movie posters provide the first impression before people decide to watch the movie or not.\n",
        "- We want to explore if there are features in the movie posters that convey the genre/budget/earnings of the movies.\n",
        "\n",
        "This notebook will explore the following:\n",
        "- Can model identify genre by poster?\n",
        "- Can a model predict budget of the movie by poster?\n",
        "- Can model predict the earnings of the movie by poster?\n",
        "\n",
        "## Data\n",
        "\n",
        "List of movies and images are crawled from The Movie Database (TMDb) - https://www.themoviedb.org\n",
        "\n",
        "## Sections\n",
        "\n",
        "This notebook is split into 5 main parts\n",
        "1. data acquisition (data filtering, and crawling poster images)\n",
        "2. data analysis (distributions and deciding which cropping method)\n",
        "3. predict genre classifier\n",
        "4. predict budget classifier\n",
        "5. predict earnings classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DCP6KrHaejI",
        "colab_type": "text"
      },
      "source": [
        "# Initialization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zzWL6PYYDFC",
        "colab_type": "code",
        "outputId": "a7255169-87a7-42c4-da40-e2ec94b8dfb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "%cd /content/gdrive/'My Drive'/Postgrad/CE7454/themoviedb\n",
        "# %cd /content/gdrive/'My Drive'/'CE7454'/themoviedb"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/Postgrad/CE7454/themoviedb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvDTJTubYXKS",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Load Python packages\n",
        "\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "import pandas as pd\n",
        "import os\n",
        "import re\n",
        "import json\n",
        "import time\n",
        "from PIL import Image\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time\n",
        "import copy\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", \"Palette images with Transparency\", UserWarning)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8i6ww2qOWBEE",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Load common functions\n",
        "\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import numpy as np\n",
        "import copy\n",
        "\n",
        "class Trainer(object):\n",
        "    def __init__(self, model, pretrain_model_dict={'format_pretrain_model':False}, optimizer=None, criterion=None, scheduler=None, learning_rate=0.001, verbose=False):\n",
        "\n",
        "        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "        self.verbose = verbose\n",
        "\n",
        "        if pretrain_model_dict['format_pretrain_model']:\n",
        "            model = self.format_pretrain_model(model, pretrain_model_dict)\n",
        "\n",
        "        self.model = model.to(self.device)\n",
        "\n",
        "        params_to_update = self.get_parameters()\n",
        "\n",
        "        if optimizer:\n",
        "            self.optimizer = optimizer\n",
        "        else:\n",
        "            self.optimizer = optim.Adam(params_to_update, lr=learning_rate)\n",
        "\n",
        "        if criterion:\n",
        "            self.criterion = criterion\n",
        "        else:\n",
        "            self.criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "        if scheduler:\n",
        "            self.scheduler = scheduler\n",
        "        else:\n",
        "            self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, step_size=5, gamma=0.1)\n",
        "\n",
        "    def _loop(self, data_loader, train_mode=True, topk_accuracy=1):\n",
        "        running_loss = 0.0\n",
        "        running_corrects = 0\n",
        "        total_data_count = 0\n",
        "\n",
        "        for X, Y in data_loader:\n",
        "            inputs = X.to(self.device)\n",
        "            labels = Y.to(self.device)\n",
        "\n",
        "            if train_mode:\n",
        "                self.optimizer.zero_grad()\n",
        "\n",
        "            outputs = self.model(inputs)\n",
        "\n",
        "            # this is for top one\n",
        "            # _, preds = torch.max(outputs, 1)\n",
        "            # running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            # this get top k\n",
        "            running_corrects += torch.sum(self.computes_accuracy(outputs, labels.data, topk_accuracy))\n",
        "\n",
        "            loss = self.criterion(outputs, labels)\n",
        "            running_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "            total_data_count += len(X)\n",
        "\n",
        "            if train_mode:\n",
        "                loss.backward()\n",
        "                self.optimizer.step()\n",
        "\n",
        "        epoch_loss = running_loss / total_data_count\n",
        "        epoch_acc = running_corrects.double() / total_data_count\n",
        "\n",
        "        return epoch_loss, epoch_acc\n",
        "\n",
        "    def computes_accuracy(self, outputs, targets, k=1):\n",
        "        _, preds = outputs.topk(k, 1, True, True)\n",
        "        preds = preds.t()\n",
        "        correct = preds.eq(targets.view(1, -1).expand_as(preds))\n",
        "        correct_k = correct[:k].view(-1).float()\n",
        "        return correct_k\n",
        "\n",
        "    def train(self, data_loader, topk_accuracy):\n",
        "        self.model.train()\n",
        "        return self._loop(data_loader, train_mode=True, topk_accuracy=topk_accuracy)\n",
        "\n",
        "    def validate(self, data_loader, topk_accuracy):\n",
        "        self.model.eval()\n",
        "        return self._loop(data_loader, train_mode=False, topk_accuracy=topk_accuracy)\n",
        "\n",
        "    def fit(self, dataloaders_dict, num_epochs=10, early_stopping=2, topk_accuracy=1):\n",
        "        print(\"Starting training...\")\n",
        "\n",
        "        time_fit_start = time.time()\n",
        "        train_losses, test_losses, train_accuracies, test_accuracies = [], [], [], []\n",
        "        early_stopping_counter = early_stopping\n",
        "\n",
        "        best_epoch_info = {\n",
        "            'model_wts':copy.deepcopy(self.model.state_dict()),\n",
        "            'loss':1e10\n",
        "        }\n",
        "\n",
        "        for epoch in range(num_epochs):\n",
        "            time_epoch_start = time.time()\n",
        "\n",
        "            train_loss, train_acc = self.train(dataloaders_dict['train'], topk_accuracy)\n",
        "            val_loss, val_acc = self.validate(dataloaders_dict['val'], topk_accuracy)\n",
        "\n",
        "            train_losses.append(train_loss)\n",
        "            test_losses.append(val_loss)\n",
        "            train_accuracies.append(train_acc)\n",
        "            test_accuracies.append(val_acc)\n",
        "\n",
        "            current_learning_rate = self.optimizer.param_groups[0]['lr']\n",
        "\n",
        "            print(\"Epoch {:2} in {:.0f}s || Train loss={:.3f}, acc={:.3f} | Val loss={:.3f}, acc={:.3f} | LR={}\".format(epoch+1, time.time() - time_epoch_start, train_loss, train_acc, val_loss, val_acc, current_learning_rate))\n",
        "\n",
        "            if val_loss < best_epoch_info['loss']:\n",
        "                best_epoch_info = {\n",
        "                    'model_wts':copy.deepcopy(self.model.state_dict()),\n",
        "                    'loss':val_loss,\n",
        "                    'epoch':epoch,\n",
        "                    'metrics':{\n",
        "                        'train_loss':train_loss,\n",
        "                        'val_loss':val_loss,\n",
        "                        'train_acc':train_acc,\n",
        "                        'val_acc':val_acc\n",
        "                    }\n",
        "                }\n",
        "\n",
        "\n",
        "            if early_stopping:\n",
        "                if len(test_losses) > 1:\n",
        "                    if val_loss-test_losses[len(test_losses)-2] >= 0:\n",
        "                        early_stopping_counter -= 1\n",
        "                    else:\n",
        "                        early_stopping_counter = early_stopping\n",
        "\n",
        "            if early_stopping_counter == 0:\n",
        "                print(\"Early Stop\")\n",
        "                break\n",
        "            else:\n",
        "                if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):\n",
        "                    self.scheduler.step(val_loss)\n",
        "                else:\n",
        "                    self.scheduler.step()\n",
        "\n",
        "        time_elapsed = time.time() - time_fit_start\n",
        "        print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
        "\n",
        "        self.draw_training_chart(len(train_losses), train_losses, test_losses, train_accuracies, test_accuracies)\n",
        "\n",
        "        self.model.load_state_dict(best_epoch_info['model_wts'])\n",
        "        print('epoch with lowest val loss:', best_epoch_info['epoch'])\n",
        "        for m in best_epoch_info['metrics']:\n",
        "            print(m, best_epoch_info['metrics'][m])\n",
        "\n",
        "    def draw_training_chart(self, epochs, train_losses, test_losses, train_accuracies, test_accuracies):\n",
        "        fig = plt.figure(figsize=(14,10))\n",
        "\n",
        "        colors  = ['b','g']\n",
        "\n",
        "        x = np.arange(1,epochs+1)\n",
        "\n",
        "        plt.subplot(2, 2, 1)\n",
        "        plt.title('losses')\n",
        "        lines = [train_losses, test_losses]\n",
        "        labels  = ['train_losses','test_losses']\n",
        "        for i,c,l in zip(lines,colors,labels):\n",
        "            plt.plot(x,i,c,label='l')\n",
        "            plt.legend(labels)\n",
        "\n",
        "        plt.subplot(2, 2, 2)\n",
        "        plt.title('accuracies')\n",
        "        lines = [train_accuracies, test_accuracies]\n",
        "        labels  = ['train_accuracies','test_accuracies']\n",
        "        for i,c,l in zip(lines,colors,labels):\n",
        "            plt.plot(x,i,c,label='l')\n",
        "            plt.legend(labels)\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "    def save_checkpoint(self, filepath='checkpoint.pth'):\n",
        "        checkpoint = {\n",
        "            'input_size': self.model.input_size,\n",
        "            'output_size': self.model.output_size,\n",
        "            'hidden_layers': [each.out_features for each in self.model.hidden_layers],\n",
        "            'state_dict': self.model.state_dict()\n",
        "        }\n",
        "        torch.save(checkpoint, filepath)\n",
        "\n",
        "    def format_pretrain_model(self, model, pretrain_model_dict):\n",
        "\n",
        "        bool_freeze_layers = pretrain_model_dict['bool_freeze_layers'] if 'bool_freeze_layers' in pretrain_model_dict.keys() else False\n",
        "        num_classes = pretrain_model_dict['num_classes'] if 'num_classes' in pretrain_model_dict.keys() else 10\n",
        "\n",
        "        # freeze layers\n",
        "        if bool_freeze_layers:\n",
        "            for param in model.parameters():\n",
        "                param.requires_grad = False\n",
        "\n",
        "        # replace last layer\n",
        "        num_ftrs = model.fc.in_features\n",
        "        model.fc = nn.Linear(num_ftrs, num_classes)\n",
        "        return model\n",
        "\n",
        "    def get_parameters(self):\n",
        "        print(\"Layers with params to learn:\")\n",
        "        params_to_update = []\n",
        "        for name, param in self.model.named_parameters():\n",
        "            if param.requires_grad == True:\n",
        "                params_to_update.append(param)\n",
        "                if self.verbose:\n",
        "                    print(\"\\t\",name)\n",
        "        print('\\t', len(params_to_update), 'layers')\n",
        "        return params_to_update\n",
        "\n",
        "\n",
        "\n",
        "def show_image_tensor(img):\n",
        "    img = img.numpy()\n",
        "    img = np.rollaxis(img, 0, 3)\n",
        "\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "\n",
        "    print('shape', img.shape)\n",
        "    print('mean', img.mean())\n",
        "    print('std', img.std())\n",
        "\n",
        "\n",
        "def show_image_numpy(img):\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "\n",
        "    print('shape', img.shape)\n",
        "    print('mean', img.mean())\n",
        "    print('std', img.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRj5U4x8WAzR",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Define global variables\n",
        "\n",
        "random_seed = 7\n",
        "input_size = 224\n",
        "validation_split = .2\n",
        "shuffle_dataset = True\n",
        "batch_size = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKaLV6G-cPjS",
        "colab_type": "text"
      },
      "source": [
        "# I. Data acquisition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr-SrHQYxApY",
        "colab_type": "text"
      },
      "source": [
        "## Crawl movie posters by genre "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kqfrZ9taGPb",
        "colab_type": "text"
      },
      "source": [
        "### Read CSV File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3qJP_vAYZ3y",
        "colab_type": "code",
        "outputId": "738c9b9b-23d2-4464-b7ab-ad6b10c8c636",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "movies_metadata = pd.read_csv('movies_metadata.csv')\n",
        "movies_metadata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>tagline</th>\n",
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              "      <td>1995-12-15</td>\n",
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              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
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              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>3.85949</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>81452156.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11862</td>\n",
              "      <td>tt0113041</td>\n",
              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>8.38752</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-02-10</td>\n",
              "      <td>76578911.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "2  False  ...       92.0\n",
              "3  False  ...       34.0\n",
              "4  False  ...      173.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiTFi4kYaV0S",
        "colab_type": "text"
      },
      "source": [
        "**Choose only the relevant columns (ie. id, original_title, genres, poster_path, popularity)**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVsgMuG4YfAS",
        "colab_type": "code",
        "outputId": "b81ed9e7-7581-419a-f575-a2575a0ee24f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "movies_title_genres_poster = movies_metadata[['id','original_title','genres','poster_path','popularity']]\n",
        "movies_title_genres_poster.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genres</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>862</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>21.9469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>17.0155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15602</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>11.7129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31357</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>3.85949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11862</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>8.38752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  ... popularity\n",
              "0    862  ...    21.9469\n",
              "1   8844  ...    17.0155\n",
              "2  15602  ...    11.7129\n",
              "3  31357  ...    3.85949\n",
              "4  11862  ...    8.38752\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCTVSAIHapPi",
        "colab_type": "text"
      },
      "source": [
        "### **Data Pre-processing**\n",
        "due to **Inconsistent behaviour among the number of data in each column**\n",
        "\n",
        "\n",
        "*   missing values in 'poster_path' and 'popularity' columns -- replace with 0 if missing values\n",
        "*   drop the entire row if 'poster_path' or 'popularity' has the value of 0\n",
        "*   format 'original_title' for consistentcy and drop duplicates\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYWQJtxKYh-S",
        "colab_type": "code",
        "outputId": "db29cab0-63b9-476b-b1af-1310fdaf6f39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "movies_title_genres_poster.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                45466\n",
              "original_title    45466\n",
              "genres            45466\n",
              "poster_path       45080\n",
              "popularity        45461\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miSEDmJfY-CC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# replace missing values\n",
        "movies_title_genres_poster['poster_path'].fillna(0,inplace=True)\n",
        "movies_title_genres_poster['popularity'].fillna(0,inplace=True)\n",
        "\n",
        "# drop the entire row if 'poster_path' or 'popularity' has the value of 0\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['poster_path']==0].index)\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['popularity']==0].index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuWI1VqXgLrW",
        "colab_type": "code",
        "outputId": "601ecdab-3f23-462b-c24f-57506b22e37b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#format 'original_title\" \n",
        "for index, row in movies_title_genres_poster.iterrows():\n",
        "    title = row['original_title'].lower()\n",
        "    title = title.replace(' ','-')\n",
        "    movies_title_genres_poster.loc[index, 'original_title'] = title\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop_duplicates(['original_title'], keep='last')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:205: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Mn-Hb8IJ4jY",
        "colab_type": "code",
        "outputId": "4f738299-511d-42f6-b4bf-3cd4080c3f7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "movies_title_genres_poster.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                42924\n",
              "original_title    42924\n",
              "genres            42924\n",
              "poster_path       42924\n",
              "popularity        42924\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kB4qYknyptTt",
        "colab_type": "text"
      },
      "source": [
        "**Format Genre**\n",
        "\n",
        "\n",
        "*   drop the entry if there is no genre indicated\n",
        "*   choose only the first genre from the genres list\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPzGY1I5pAhb",
        "colab_type": "code",
        "outputId": "a1ed1130-7876-438a-8ed2-eb67e5f3ad30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# there are movies without genre and drop them\n",
        "movies_title_genres_poster.loc[movies_title_genres_poster['genres']=='[]'].count()\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['genres']=='[]'].index)\n",
        "movies_title_genres_poster.count()\n",
        "\n",
        "# for each movie choose first genre from the genres list\n",
        "movies_title_genres_poster = movies_title_genres_poster.loc[movies_title_genres_poster['genres']!='[]'] \n",
        "\n",
        "list_movie_genre = []\n",
        "for index, row in movies_title_genres_poster.iterrows():\n",
        "    genres = json.loads(row['genres'].replace(\"'\",'\"'))\n",
        "\n",
        "    genre = 'nil'\n",
        "    if len(genres) > 0:\n",
        "        genre = genres[0]['name'].lower()\n",
        "\n",
        "    this_movie = {\n",
        "        'id':row['id'],\n",
        "        'genre':genre\n",
        "    }\n",
        "    list_movie_genre.append(this_movie)\n",
        "\n",
        "df_movie_genre = pd.DataFrame(list_movie_genre)\n",
        "df_movie_genre.head()\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster.join(df_movie_genre.set_index('id'), on='id')\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster[['id','original_title','genre','poster_path','popularity']]\n",
        "#print(subset_data.count())\n",
        "movies_title_genres_poster.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genre</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>862</td>\n",
              "      <td>toy-story</td>\n",
              "      <td>animation</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>21.9469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>17.0155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15602</td>\n",
              "      <td>grumpier-old-men</td>\n",
              "      <td>romance</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>11.7129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31357</td>\n",
              "      <td>waiting-to-exhale</td>\n",
              "      <td>comedy</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>3.85949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11862</td>\n",
              "      <td>father-of-the-bride-part-ii</td>\n",
              "      <td>comedy</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>8.38752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  ... popularity\n",
              "0    862  ...    21.9469\n",
              "1   8844  ...    17.0155\n",
              "2  15602  ...    11.7129\n",
              "3  31357  ...    3.85949\n",
              "4  11862  ...    8.38752\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gl_004IcXRl",
        "colab_type": "code",
        "outputId": "fdfb3529-0416-4021-d94b-6fb88cfef771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "movies_title_genres_poster.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                40727\n",
              "original_title    40727\n",
              "genre             40727\n",
              "poster_path       40727\n",
              "popularity        40727\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJCG0JuYzNIp",
        "colab_type": "text"
      },
      "source": [
        "**Count the number of movies in each genre**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h43EKH3bzHRD",
        "colab_type": "code",
        "outputId": "f24ebe54-0234-4cba-f5ef-14f2ee6aa8ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "movies_title_genres_poster.groupby(['genre']).size().reset_index(name='counts')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genre</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>action</td>\n",
              "      <td>4209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adventure</td>\n",
              "      <td>1413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>animation</td>\n",
              "      <td>1087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>comedy</td>\n",
              "      <td>8486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>crime</td>\n",
              "      <td>1578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>documentary</td>\n",
              "      <td>3298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>drama</td>\n",
              "      <td>11243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>family</td>\n",
              "      <td>498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>fantasy</td>\n",
              "      <td>634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>foreign</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>history</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>horror</td>\n",
              "      <td>2445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>music</td>\n",
              "      <td>469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>mystery</td>\n",
              "      <td>516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>odyssey media</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>romance</td>\n",
              "      <td>1129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>science fiction</td>\n",
              "      <td>618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>thriller</td>\n",
              "      <td>1547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>tv movie</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>war</td>\n",
              "      <td>369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>western</td>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              genre  counts\n",
              "0            action    4209\n",
              "1         adventure    1413\n",
              "2         animation    1087\n",
              "3            comedy    8486\n",
              "4             crime    1578\n",
              "5       documentary    3298\n",
              "6             drama   11243\n",
              "7            family     498\n",
              "8           fantasy     634\n",
              "9           foreign     115\n",
              "10          history     271\n",
              "11           horror    2445\n",
              "12            music     469\n",
              "13          mystery     516\n",
              "14    odyssey media       1\n",
              "15          romance    1129\n",
              "16  science fiction     618\n",
              "17         thriller    1547\n",
              "18         tv movie     366\n",
              "19              war     369\n",
              "20          western     435"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EC8BmVgj6qSM",
        "colab_type": "code",
        "outputId": "4c8fdfb7-9991-4fb4-d664-05b106fb66f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "pd.Series(movies_title_genres_poster['genre']).value_counts().plot('bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f81091f1dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
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S7PNrq+PTn/70b3+96U9/+nfs/l27+svXt/XRoF2tTDz77LP6y1/+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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 393,
              "height": 324
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FDVmtjgzkoU",
        "colab_type": "code",
        "outputId": "18589ad3-7dbc-4db2-959e-bcbd2387ba81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "movies_title_genres_poster = movies_title_genres_poster[~movies_title_genres_poster['genre'].isin(['fantasy', 'science fiction', 'mystery','family','music','western','war','tv movie','history','foreign','odyssey media','aniplex'])]\n",
        "pd.Series(movies_title_genres_poster['genre']).value_counts().plot('bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f81071193c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9+Mc/1vDhw5WZman9+/eroqJCvXr10lNPPaUZM2aoW7dubuO9vLy0fPlyZWVl\n6Te/+Y3++Mc/ytPTUxEREZo8ebJiYmKaPFdycrJGjBihdevWKS8vTw6HQ+Hh4YqNjVVcXFyDVYl6\nSUlJGjBggFavXq2ioiJVVVUpLCxM8fHxSkxMlK+vb4v2BAAAAGgLLA8TkvTggw/qwQcfvOHxnp6e\nmjp1aqMrCdcTExNzzcDRlDFjxjjvnwAAAABg8T0TAAAAANouwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIARwgQAAAAAI4QJAAAAAEYIEwAAAACM\nECYAAAAAGCFMAAAAADBCmAAAAABghDABAAAAwIi31RMAcPPZbEGt5jilpRdaYCYAAMAKrW5l4he/\n+IUGDBigAQMGaNWqVU2O27hxoyZPnqwRI0Zo+PDheuyxx7Ru3To5HI5rHn/nzp16+umnFRUVpcjI\nSE2cOFFvvvmmqqurr1lXWFioWbNmadSoURoyZIgefPBB/exnP9OFC7wRAgAAQMfUqlYm9uzZo5Ur\nV8rDw0N1dXVNjktJSVFWVpY6deqkUaNGydvbW7m5uUpNTVVubq6WLl0qT8+GOSk9PV1paWny8vJS\nVFSUgoODlZ+fryVLluiTTz7RmjVr5O/v36Duo48+0ty5c3X58mXdfffdCgkJUWFhoVatWqWcnBxl\nZ2ere/fuLdoL4GaImf2hZefeuOg7lp0bAAC0jFYTJqqrq/Xiiy+qe/fuGjp0qHJychodt2XLFmVl\nZclmsykzM1N9+/aVJJ09e1bTpk3T1q1btXbtWiUkJLjVFRUVadGiRfL391dGRoYiIyMlSRcvXtQz\nzzyj/Px8LV68WPPmzXOrKykp0fz581VXV6dly5YpOjpaklRbW6sXXnhBmzZt0oIFC7Rs2bIW7ggA\nAADQurWarzm98cYb+vvf/ydjzQsAACAASURBVK6UlBQFBTX9PewVK1ZIkubMmeMMEpLUo0cPJScn\nS7qyAvHPX3dKT09XXV2dpk+f7gwSkhQQEKCFCxfK09NTWVlZOn/+vFtdRkaGKisr9cgjjziDhCR5\ne3vr5ZdfVmBgoHJycnT48GHTSwcAAADapFYRJgoLC7V69WpNnDhRDzzwQJPjSkpKVFxcLB8fH02Y\nMKHB/qioKIWEhKi0tFQFBQXO7dXV1dq5c6ckadKkSQ3qwsLCNGzYMNXU1GjHjh1u++pXSBqrCwwM\n1P333+82DgAAAOgoLA8TVVVV+vGPf6wuXbpo/vz51xy7b98+SVL//v3l5+fX6JghQ4ZIkvbv3+/c\nduTIEVVUVKhr167q06fPNevqzyFJdrtdx48fd9t/I3UAAABAR2D5PROLFy/WkSNHtHjxYnXr1u2a\nY0+ePClJ6tWrV5NjQkND3cZe/bp+X2Pqj3nq1KkGdcHBwQoMDLxm3dXna2m+vt4t9ihPK7WHa2gp\n9MKlvfSivVxHS6AXLvTChV640AsXeuHSVnth6crE7t27lZGRoejoaD300EPXHX/p0iVJavSJS/UC\nAgIkXbmxujl1nTt3brE6AAAAoCOwbGWisrJSP/nJTxQYGKiXXnrJqmm0CdXVtTp3rsKotjWlXKt/\nnIxeuNCLllPfy7Z+HS2BXrjQCxd64UIvXOiFS2vpRZcu/vL1bX40sGxl4he/+IWOHj2qF198UT17\n9ryhmvpVgIqKpt9Y168Q1K9Q3Ghd/SpES9QBAAAAHYFlKxM5OTny9PTU+vXrtX79erd9n3/+uSQp\nOztbn3zyifr06aNXX31VvXv3liSdPn26yeOWlJRIknPs1a/PnDnTZF39vsbqzp8/L7vd3uh9E/V1\nt956a5PHBgAAANojS2/AdjgcysvLa3L/iRMndOLECedvPwwaNEiSdOjQIVVWVjb6RKeioiJJ0sCB\nA53bwsPD5efnp/Lych0/frzRJzrt2bOnQV1QUJD69Omj48ePq6ioSKNGjbqhOgAAAKAjsOxrTh9/\n/LEOHDjQ6D+PPvqoJGnu3Lk6cOCAPvzwQ0lXnsYUERGhmpoabd68ucEx8/LyVFJSIpvNpuHDhzu3\n+/r6asyYMZKkDRs2NKg7ceKECgoK5OPjo7Fjx7rtGzduXJN1drtd27dvlySNHz/eoAsAAABA22X5\n70w014wZMyRJaWlpOnbsmHN7WVmZUlJSJElJSUny9HS/tKSkJHl4eGjlypXO1QTpyj0W8+bNk8Ph\n0OTJkxUcHOxWl5CQID8/P61fv17btm1zbq+trdWCBQtkt9sVHR2tO+64o8WvFQAAAGjNLP+dieaa\nMGGC4uLilJ2drZiYGI0ePVre3t7Kzc11vrGfOnVqg7qhQ4dq9uzZSktL05NPPqmRI0cqKChI+fn5\nKisrU2RkpJ5//vkGdaGhoXr11Vc1d+5czZo1SyNGjFDPnj1VWFioU6dO6bbbblNqaurNuHQAAACg\nVWlzYUKSkpOTNWLECK1bt055eXlyOBwKDw9XbGys4uLiGqxK1EtKStKAAQO0evVqFRUVqaqqSmFh\nYYqPj1diYqJ8fX0brZs4caLCwsK0YsUK7d69W4WFhQoNDVViYqJmzpypoKDW85hNAAAA4GZplWHi\ntdde02uvvXbNMTExMYqJiWn2sceMGeO8f6I5IiMjtXz58mbXAQAAAO1Vm7tnAgAAAEDrQJgAAAAA\nYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAAAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAA\nYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAAAABGCBMAAAAAjHhbPQEAsJLNFtRqjlNaeqEFZgIA\nwM3DygQAAAAAI6xMAICkmNkfWnbujYu+Y9m5AQD4V7AyAQAAAMAIYQIAAACAEcIEAAAAACOECQAA\nAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAA\nAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAA\nAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAA\nAABGCBMAAAAAjHhbPQEAgPVstqBWdazS0gstMBMAwNeNlQkAAAAARliZAAA4xcz+0NLzb1z0HUvP\nDwBoHlYmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAAACOECQAAAABGLPud\niZqaGn366afasWOH8vLydPToUVVXV+uWW27R8OHDNWXKFN13331N1m/cuFHZ2dk6cOCAHA6H+vXr\np9jYWMXFxcnTs+mMtHPnTq1Zs0Z79+5VVVWVwsLC9PDDDysxMVG+vr5N1hUWFurtt9/W7t27Zbfb\nFRoaqujoaM2cOVNBQS33y7EAAABAW2FZmMjPz9dTTz0lSbLZbLr33nvl7++vv//979qyZYu2bNmi\n73//+/rhD3/YoDYlJUVZWVnq1KmTRo0aJW9vb+Xm5io1NVW5ublaunRpo4EiPT1daWlp8vLyUlRU\nlIKDg5Wfn68lS5bok08+0Zo1a+Tv79+g7qOPPtLcuXN1+fJl3X333QoJCVFhYaFWrVqlnJwcZWdn\nq3v37i3fJAAAAKAVsyxMeHh46Nvf/ramTZume+65x23fpk2bNGfOHC1fvlz33XefRo4c6dy3ZcsW\nZWVlyWazKTMzU3379pUknT17VtOmTdPWrVu1du1aJSQkuB2zqKhIixYtkr+/vzIyMhQZGSlJunjx\nop555hnl5+dr8eLFmjdvnltdSUmJ5s+fr7q6Oi1btkzR0dGSpNraWr3wwgvatGmTFixYoGXLlrV0\niwAAAIBWzbJ7JkaNGqWlS5c2CBKS9NBDD+nRRx+VJG3YsMFt34oVKyRJc+bMcQYJSerRo4eSk5Ml\nXVmBcDgcbnXp6emqq6vT9OnTnUFCkgICArRw4UJ5enoqKytL58+fd6vLyMhQZWWlHnnkEWeQkCRv\nb2+9/PLLCgwMVE5Ojg4fPtz8JgAAAABtWKu9AXvQoEGSpC+++MK5raSkRMXFxfLx8dGECRMa1ERF\nRSkkJESlpaUqKChwbq+urtbOnTslSZMmTWpQFxYWpmHDhqmmpkY7duxw25eTk9NkXWBgoO6//363\ncQAAAEBH0WrDxNGjRyVduZ+i3r59+yRJ/fv3l5+fX6N1Q4YMkSTt37/fue3IkSOqqKhQ165d1adP\nn2vW1Z9Dkux2u44fP+62/0bqAAAAgI7AsnsmrqW0tFQffPCBJOnBBx90bj958qQkqVevXk3WhoaG\nuo29+nX9vsbUH/PUqVMN6oKDgxUYGHjNuqvP19J8fb1ls7X9J0a1h2toKfTChV640AuX9tKL9nId\nLYFeuNALF3rh0lZ70epWJupvbL5w4YJGjRqlBx54wLnv0qVLktToE5fqBQQESLpyY3Vz6jp37txi\ndQAAAEBH0OpWJl566SXl5uYqNDRUP//5z62eTqtQXV2rc+cqjGpbU8otLb1g6fnphQu9cKEXV7Sm\nPkjW/7n4V9X3s61fR0ugFy70woVeuLSWXnTp4i9f3+ZHg1a1MvHKK6/o//7v/2Sz2bRmzRq3+yUk\n1ypARUXTb6zrVwjqVyhutK5+FaIl6gAAAICOoNWEiddee01r165Vt27dtGbNGrfHvtbr3bu3JOn0\n6dNNHqekpMRt7NWvz5w502Rd/b7G6s6fPy+73X7NultvvbXJYwMAAADtUasIEz/72c+0evVqde3a\nVatXr9Ydd9zR6Lj6x8UeOnRIlZWVjY4pKiqSJA0cONC5LTw8XH5+fiovL3c+nemf7dmzp0FdUFCQ\n8+lP9ce9kToAAACgI7A8TKSlpWnVqlXq0qWLVq9erbvuuqvJsaGhoYqIiFBNTY02b97cYH9eXp5K\nSkpks9k0fPhw53ZfX1+NGTNGUsMfwZOkEydOqKCgQD4+Pho7dqzbvnHjxjVZZ7fbtX37dknS+PHj\nr3+xAAAAQDtiaZhYvHix0tPTFRwcrHfeece58nAtM2bMkHQlhBw7dsy5vaysTCkpKZKkpKQkeXq6\nX1pSUpI8PDy0cuVK52qCdOUei3nz5snhcGjy5MkKDg52q0tISJCfn5/Wr1+vbdu2ObfX1tZqwYIF\nstvtio6ObnI1BQAAAGivLHua07Zt2/TWW29Jkvr06aPMzMxGx4WHhzsDhCRNmDBBcXFxys7OVkxM\njEaPHi1vb2/l5uY639hPnTq1wXGGDh2q2bNnKy0tTU8++aRGjhypoKAg5efnq6ysTJGRkXr++ecb\n1IWGhurVV1/V3LlzNWvWLI0YMUI9e/ZUYWGhTp06pdtuu02pqakt1BUAAACg7bAsTJw7d875eu/e\nvdq7d2+j46KiotzChCQlJydrxIgRWrdunfLy8uRwOBQeHq7Y2FjFxcU1WJWol5SUpAEDBmj16tUq\nKipSVVWVwsLCFB8fr8TERPn6+jZaN3HiRIWFhWnFihXavXu3CgsLFRoaqsTERM2cOVNBQa3rkYoA\nAADAzWBZmHjsscf02GOPGdfHxMQoJiam2XVjxoxx3j/RHJGRkVq+fHmz6wAAAID2yvIbsAEAAAC0\nTYQJAAAAAEYIEwAAAACMECYAAAAAGCFMAAAAADBi2dOcAABojWy2lnvcd0scq7T0QgvMBAC+HoQJ\nAADQKIIVgOshTAAA0IiY2R9aev6Ni75j6fkB4EYQJgAAwDURrAA0hRuwAQAAABghTAAAAAAwwtec\nAAAAroOb0YHGsTIBAAAAwAgrEwAAADeIm9EBd6xMAAAAADBCmAAAAABghDABAAAAwAhhAgAAAIAR\nwgQAAAAAI4QJAAAAAEYIEwAAAACM8DsTAAAAuGH8GjiuxsoEAAAAACOsTAAAAKDZ+DVwVmkkViYA\nAAAAGGJlAgAAAPgXdORVGlYmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAA\nACOECQAAAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAA\nACOECQAAAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAA\nACOECQAAAABGCBMAAAAAjBAmAAAAABghTAAAAAAwQpgAAAAAYIQwAQAAAMAIYQIAAACAEcIEAAAA\nACPeVk+gLdm4caOys7N14MABORwO9evXT7GxsYqLi5OnJ7kMAAAAHQth4galpKQoKytLnTp10qhR\no+Tt7a3c3FylpqYqNzdXS5cuJVAAAACgQyFM3IAtW7YoKytLNptNmZmZ6tu3ryTp7NmzmjZtmrZu\n3aq1a9cqISHB2okCAAAANxEfpd+AFStWSJLmzJnjDBKS1KNHDyUnJ0uS0tPT5XA4LJgdAAAAYA3C\nxHWUlJSouLhYPj4+mjBhQoP9UVFRCgkJUWlpqQoKCiyYIQAAAGANwsR17Nu3T5LUv39/+fn5NTpm\nyJAhkqT9+/fftHkBAAAAViNMXMfJkyclSb169WpyTGhoqNtYAAAAoCPwqKurq7N6Eq3ZW2+9pcWL\nFysmJkZpaWmNjlm8eLHeeustffe731VqaupNniEAAABgDVYmAAAAABghTFxH586dJUkVFRVNjrl4\n8aIkKSAg4KbMCQAAAGgNCBPX0bt3b0nS6dOnmxxTUlLiNhYAAADoCAgT1zFo0CBJ0qFDh1RZWdno\nmKKiIknSwIEDb9q8AAAAAKsRJq4jNDRUERERqqmp0ebNmxvsz8vLU0lJiWw2m4YPH27BDAEAAABr\nECZuwIwZMyRJaWlpOnbsmHN7WVmZUlJSJElJSUny9KSdAAAA6Dh4NOwNSk5OVnZ2tjp16qTRo0fL\n29tbubm5stvtio6O1tKlS+Xl5WX1NAEAAICbhjDRDBs3btS6det08OBBORwOhYeHKzY2VnFxcaxK\nAAAAoMMhTAAAAAAwwsfpAAAAAIwQJgAAAAAYIUwAAAAAMEKYAAAAAGCEMAEAAADACGECAAAAgBHC\nBAAAAAAjhAkAAAAARggTAAAAAIwQJgAAAAAYIUwAAAAAMEKYAAAALcJut8tut1s9DQA3EWECAAC0\niHvuuUdPPfWU1dMAcBMRJgCgGRwOh9VTaDW2bdumHTt2WD0NtCIBAQG67bbbrJ4GgJvI2+oJAK3V\n888/rylTpuiee+6xeiqtyuXLl1VeXq6qqqomx/Tq1esmzujmuv/++/Xd735XTzzxhHr06GH1dCz1\n7LPPavTo0frWt75l9VRalWPHjunLL79U165d1a9fP6unc1Pdfvvt+uKLL6yeRqtUXV2t8vJy+fr6\nqmvXrlZPxzJ5eXnKzMxUQUGBvvzyS02aNEk//elPJUl/+tOf9Je//EXx8fGy2WwWz/Tr117+PiVM\noFFVVVXatWuXjh49Krvdrrq6ugZjPDw8NGvWLAtmd3P87ne/0+bNm9W/f39NnjxZkyZNUufOna2e\nlmUKCwu1dOlSffrpp6qurm5ynIeHh/bt23cTZ3ZzffHFF/rlL3+pN998U+PHj9fkyZM7bODs0qVL\nh35TdLXa2lq99dZbysrK0ldffSVJeuSRR7Rw4UJJ0oYNG5SVlaXU1FTdeeedVk71a/X444/rpZde\n0t69ezV48GCrp9MqrF+/XmvXrtVnn30mh8Ph9udi69at2rx5s370ox8pLCzM4pl+/X75y19q+fLl\nbu8prn4dFBSk9PR0hYSEaMqUKVZM8aZob3+fEibQwJYtW/TSSy/p3LlzTY6pq6tr92Fi/vz5ysrK\n0sGDB5WSkqK0tDQ98sgjiouL0+2332719G6qv/71r3rqqaec/9Pr0qWLAgICLJ6VNTZs2KDMzEx9\n9NFH2rRpk373u9912MA5dOhQHTp0yOppWK62tlYzZsxQbm6uvLy8dPvtt+vw4cNuY+6++27NnTtX\nv//979t9mDhw4ICefvppTZ8+XePHj1fv3r3l6+tr9dQs8eKLL+rDDz9UXV2dOnfurEuXLrnt79ev\nn377299q4MCBmj59ukWzvDk+/vhjLVu2TKGhoXrxxRd17733avTo0W5jhg4dqm7dumn79u3tNky0\nx79PPeoa+8gZHVZhYaEmT54sDw8PTZgwQYcOHdLBgwc1Y8YMHTt2TH/+85914cIFxcbG6hvf+Iae\nffZZq6f8tcvNzVVWVpa2b9+u2tpaeXh46L777tOUKVM0btw4eXq2/1uPvve972nXrl164okn9MMf\n/lDdu3e3ekqWs9vt+uCDD5Sdna3PP/9cHh4eCggI6FCBMz8/XwkJCUpJSdHjjz9u9XQss2bNGr32\n2msaPXq0XnvtNfXs2VN33XWXHn30Uecn0JL07W9/W7fccoveffddC2f79Ro4cOANj20rn7qa+uCD\nD/STn/xEAwcO1CuvvKJBgwZp4MCBDf5c3H///QoLC9OvfvUrC2f79Xvqqaf017/+VR988IHz/4+N\n/XeSmJioEydO6Pe//71VU/1atce/T1mZgJtVq1bJ4XDozTff1NixY/WTn/xEBw8e1PPPPy9J+vLL\nLzVv3jzt2LFDH3zwgcWzvTlGjRqlUaNG6YsvvtCvf/1r/e///q927dqlv/zlL+rZs2eH+P78nj17\ndPvttys1NdXqqbQagYGBio+PV3x8vHbt2qWsrCx9/PHHyszM1Lp16xQVFaWpU6e2+8D55JNPasGC\nBdqyZYvGjx+vXr16yc/Pr9Gx9957702e3c2xYcMGde3aVUuWLFFwcHCT48LDw7V///6bOLObrzmf\nT7b3zzLfe+89BQQE6K233lJISEiT4+68884GK1ntUXFxsSIjI6/7QUu3bt20e/fumzSrm689/n1K\nmICbv/3tb+rfv7/Gjh3b6P5u3bopLS1N48aN09KlS9vVfwzXExISoh/84Af6/ve/r61btyorK0v5\n+fnO788/+OCDSkhI0NChQ62eaourq6vTgAEDrJ5GqzVy5EiNHDlS//jHP/T2228rMzNTeXl5ysvL\nU8+ePTVt2jRNmTKlyTfZbVV8fLw8PDxUV1enP/7xj/rTn/7U5Nj2/Cn0kSNHFBUVdc0gIV150tGX\nX355k2Zljc8++8zqKbQaBw8e1LBhw64ZJKQr9wmcPXv2Js3KOpWVlerWrdt1x13rK9btQXv8+5Qw\nATdfffWV7r77bue/e3l5SbryP4H6N0KBgYG699579Yc//MGSOVqtrq5O1dXVzu871tXVqaamRr/9\n7W+1adMm/cd//IdeeeWVdvXd+QEDBqi0tNTqabRq+/bt07p167Rp0yZJkqenp8LDw3Xo0CGlpaXp\n3XffVXp6uvr27WvtRFtQe11pMOHh4XHdMf/4xz/UqVOnmzAbtAa1tbU39PfAl19+KW/v9v92zGaz\n6fPPP7/uuMOHD6t37943YUbWaI9/n7b/P71oli5durg9WSAoKEiSVFJS4vYmyMPDQ2VlZTd7epY6\nefKk3n33Xb3//vsqLy9XXV2dhg0bpvj4eI0aNUobN27UO++8o9/97ncKCgpSSkqK1VNuMdOmTdOc\nOXO0f//+Zn0nur2rrq7Wpk2blJWVpaKiItXV1alr166Kj4/X5MmT9Y1vfEOfffb/27v7uBrv/w/g\nr6uVr9QmKZ2SkLtSiXSzTckqEjYVw3wVmw1fUi39RuYnd182ZEzLMLkd0tRxU6skJZmViFAyI92p\nRuVUON2c3x/9OnN033RddZ338y+d69Pj8Vqrc5339fl83p9M7NixAxcuXMCmTZuwe/durmO/MYcP\nH+Y6Qqegq6uLu3fvora2tsklbS9evMDdu3flYi8NqaOtrY2srKxmx9TU1ODevXvQ09NjKRV3rKys\nEB4ejkuXLsHa2rrRMZGRkcjPz4e7uzvL6djDx/spfxfyknYRCAQoKCiQfj106FBIJBLEx8dLX6us\nrERqamqLU7d8kZCQgIULF8LR0RE//fQTKioqMHXqVPzyyy84fvw4Jk+eDHV1dcydOxcRERHQ0dFB\nbGws17HfqEmTJmHRokX49NNPcfToUeTn53MdiVM5OTnYvHkzxo4dCz8/P9y8eROGhobYuHEjLl68\nCB8fHwgEAgB1Gwx37doFExMTpKamcpycdAQ7Ozs8fvwYwcHBTY756aef8OzZM9jZ2bGYjDvZ2dn4\n9ttv8cknn8DR0RGbN2+WXrtx4wZCQkLw7NkzDhN2PGtrazx69AinTp1qcszx48dRXFwsF2e1zJ8/\nH4qKivDy8kJISIi0hTIAPH/+HEKhEKtXr4aysjLc3Nw4TNqx+Hg/pZkJIsPS0hKHDx/G06dPoa6u\njnHjxkFZWRkBAQEoLi6GQCCAUChESUkJxo8fz3XcDrV3716EhIQgLy8PEokEWlpa+OSTTzBjxowm\n133WLwFr7ubRFb369GT9+vVYv359k2P5vDYeAL744gskJSWhtrYWioqKmDhxItzc3GSWBzZmyJAh\nuHXrFkspCZs+/fRThIWFISAgABkZGXB0dARQt2w0ISEBUVFREAqF0NbWxuzZszlO2/FCQ0Oxbt06\nVFVVAah7T3j9g+OaNWugqKiIadOmcRWzw33++ecQCoX4+uuvcf/+fenvhVgsxv379/Hrr79i9+7d\n0tlMvhs0aBC++eYbrFixAmvWrMHatWvBMAzOnDkDoVAIoG5p9ebNm3l95gYf76fUGpbIuHnzJr77\n7jvMnz9fOg15/PhxmSU7EokE2traOHnyZKs2U3VVBgYGAIDRo0djzpw5mDBhgnQPSXP27duH+Ph4\nXi0Bqf9ZtBafN2EaGBigd+/emDFjBmbNmtXqGbq4uDjcuXOHl+2U//jjDxw6dAi///47ioqKAAB9\n+vSBlZUV3NzcMGTIEI4Tdry7d+9i8eLFyMvLa7B/ov49c/fu3bw+YwKo66Hv5uaGHj16YPHixTA3\nN8eMGTNk2n/W1tbi3XffhYWFBX744QeOE3esK1euwNPTEyKRqME1iUQCVVVVBAUFwdLSkoN03Lh7\n9y527dqFS5cuoby8HADQvXt3vPfee1iyZAnvDzvk4/2UignSKunp6YiJiUFZWRn09fXh6uraYueS\nru7rr7+Gm5tbm//wCb+dOnUKkyZNgpKSEtdROoX6p9DV1dWNtvpUUlLC6tWr5eIcipcvX+LkyZO4\nePEicnNzUVNTA21tbYwdOxYzZszgVVOGpvznP//BxYsXceTIEYwaNQpA42cJzJ07F4WFhYiKiuIq\nKmuKi4tx4MCBBr8XNjY2+Pzzz6VLIuWNRCJBSUkJamtr0atXr1Y9rCOdExUThBDSBh4eHtDU1IS/\nvz/XUTh348YNfPLJJwCAiRMnYtq0adLlCTk5OTh58iSioqLAMAyOHj0KU1NTLuMSFrz33nvo37+/\nzMF8jRUTy5YtQ3x8PO0jIoQHaAM2IYS0QXx8vMz6b3m2b98+SCQSbN26Fdu2bcOYMWOgp6cHPT09\njBkzBtu2bUNAQABqamqa3ZxM+EMkErXqSXtlZSVqampYSMQdDw8PrFmzhusYnUZZWRlSUlJQWFjY\n5JjCwkKkpKTwfnM+39AGbNKkmpoalJaW4uXLl02O0dHRYTFRxwoMDGz39zIMgyVLlrzBNJ1TVVUV\noqOjkZycLL0haGlpwdLSEo6OjnKx9EdLSwvV1dVcx+gUrl27BhMTE0yaNKnJMU5OTti/fz89gZYT\nvXv3Rm5ubovjHjx4wPuOgPHx8XBwcOA6Rqdx6NAhBAUFITQ0tMn/98XFxXB3d8fSpUuxePFilhOy\ni0/3UyomSANXr17FDz/8gNTUVGk3jsZ0lS4DrRUYGCg9zfdVLR1GJZFI5KKYuHXrFry8vJCfn9/g\nZxQaGort27djx44dCYZknwAAIABJREFUMDIy4ighOz744AOcPXsWz58/h7KyMtdxOFVaWop33323\nxXF6enq8eq9oTHZ2Nvbu3SvdhP7qeT2v4tv75uvMzMwQHR2N9PR0mJiYNDomKSkJDx8+5P0+Gnrw\nICs+Ph56enrNbrA2NjaGnp4e4uPjeV1M8O1+SsUEkXHp0iUsWrRI+gaopqYmF5sGATTaZSc3NxdC\noRDdu3fHmDFjpKdy5uXl4fLly3jx4gVcXFx4fVonUHdo4fz581FWVgYdHR18+OGH0NXVBVD3Mzpz\n5gxyc3Mxf/58nDp1itdPHD08PBAfHw9PT0+sW7cO2traXEfijJqaGh49etTiuJycHKipqbGQiBvp\n6emYO3cunj9/3ugm9FfxfZvivHnzEBUVhaVLl2LDhg14//33Za6npKRg5cqVUFRUxJw5czhKyQ56\n8CArLy8PI0eObHHcwIEDcfPmTRYScYOP91MqJoiMHTt2oLq6Gp999hkWLlyInj17ch2JNa8XE/n5\n+XB1dYWjoyP8/f0btMF9+vQp1q5diwsXLuDkyZNsRmXdnj17UFZWBjc3N3z11VcNpl+XLl2KzZs3\n49ChQ9izZw/+93//l6OkHe/bb7/F4MGDER8fjwkTJsDIyAg6Ojr417/+1WAswzDYuHEjBynZMWrU\nKMTGxiImJgYTJkxodExsbCxu3LjR5HU+2LJlCyorKzFp0iR88cUX6N+/v9w8hHmdqakp/ud//geb\nN2/GF198AVVVVTAMg/Pnz+P9999HSUkJJBIJVqxYgWHDhnEdt0PRgwdZFRUVUFFRaXGciopKo610\n+YKP91Pq5kRkmJqaYvDgwbz/cNwaX331Fa5cuYLY2Fh069at0TFisRgODg6wtLTE1q1bWU7InvoD\nCmNiYppc9lVbWwtHR0dIJBLenQD+KgMDg0aXwzWGYRhkZGSwkIob165dw5w5c8AwDCZPngwXFxeZ\nJ2xCoRARERGora3Fzz//LG0VyjcjR45E3759ERERwXWUTiMhIQGBgYFIT0+XeX3o0KHw8vKCvb09\nR8nY4+fnh5KSEsTHx0NJSUmuHzwAdSfFq6io4MyZM82O++ijj1BaWoqLFy+ylIxdfLyf0swEkaGq\nqooBAwZwHaNTuHz5MiwtLZssJACgW7duGD16NH777TcWk7GvsLAQ48ePb3b/iIKCAkaMGIFz586x\nmIx9r7a3lHdmZmZYtWoVNm7ciDNnzjT4kCCRSKCoqIhVq1bxtpAAgH/96190Hs1rbG1tYWtri5KS\nEuTm5qK2thYCgaBLLNl4U8LDw6XvmVVVVUhLS0NaWlqjY+WhmBg1ahQiIyORkJAAW1vbRsckJCQg\nKysLTk5OLKdjDx/vp1RMEBnm5ubIysriOkan8OzZM1RUVLQ4rrKyktdTskDd6aSlpaUtjistLUX3\n7t1ZSMQdFxcXriN0KrNnz4aZmRkOHTqEq1evynQlsbCwkIuDH0eMGIGcnByuY3RKvXr1Qq9evbiO\nwQl68CBr7ty5iIyMhI+PD5YvXw5nZ2fpwzqxWAyhUIjNmzeDYRi4ublxnLbj8PF+SsUEkeHh4YGZ\nM2fiwIEDmDdvHtdxONWvXz/8/vvvyMnJkR7E9bqcnBxcuXKlyet8MWzYMCQnJ+P+/fsYNGhQo2P+\n/PNPJCcnt2qDHeEXAwMD3j9Vbc6iRYswd+7cZveOEPlDDx5kjRgxAt7e3vjuu+/g7++P9evXS9vL\nFxQUoKqqChKJBJ6enjAzM+M4bcfh4/2UigkiY8iQIQgODoaPjw+io6NhY2MDgUAABYXGzzd0dnZm\nOSF7XF1dsWXLFri5ucHb2xtTpkyBomLdn0x1dTUiIiKwY8cOiMVi3t80pk2bhpSUFMydOxfe3t74\n6KOPpE+UqqqqcPr0aenmfb63eyR/8/DwgIaGhtwfzDV69Gh89913WLVqFc6dOwdra+tm3zctLCxY\nTthxhELhP/p+Pt9DSEMLFy6Evr4+AgMDcffuXWRnZ0uvDRs2DB4eHtI9BXzFx/spbcAmDfz0008I\nCgrC8+fPWxzL582l1dXV0m4cDMNAQUEBffr0AQAUFRWhtrYWEokEtra2CAoKwltvvcVx4o61bNky\nRERESH8WmpqaYBhG5mcxZcoUXm9Ef1VhYSHOnz+Phw8fory8vNEN2XxfB21sbAwHBwds376d6yic\ni4yMxObNm5s93Rfg3zkT9Q0J2ovP9xDSvL/++gv5+fkA6g7A1dDQ4DgRe/h2P6WZCSLj+PHj0l9e\nAwMD6OnptaqVGx8pKipi165dOHLkCA4ePIjc3FwUFBRIr+vq6sLNzQ1ubm5NPoHkk4CAAJiZmWH/\n/v3Izc3F48ePpdf69euHefPm4d///jeHCdlz4MABBAQEyBxIVV9M1H+wqj/MkM/FBB3KVSc6Ohq+\nvr6ora2Fmpoa+vbtKzetYZ2dnf9RMcFnfn5+rR7L9/eKxmhoaMhVAfEqvt1PaWaCyJg0aRIePXqE\noKAgjB07lus4nUphYaH0D15LSwsCgYDjRNwpLCyU2WgrTx1aEhMTpf3z//3vfyM5ORlpaWlYu3Yt\nsrOzERMTg9zcXLi5ucHQ0JDXS+A2bNiAs2fP4sKFC3J9KJerqysyMjKwevVqzJw5Uy4eLpCWtdR4\n4PUHDzRLI5/4cD+lYoLIMDU1lVbLhJCGFixYgMTERISEhGDEiBHw8/ODUCiUfhAQi8VYv349IiIi\nEBYWxutWy6WlpZg+fToGDhwo14dyjRw5EkZGRvj555+5jkI6kfDw8EZfr62tRX5+Pi5evIj09HS4\nu7vz/sHDq65fv47ffvsNRUVFePnyZaNj5HGmpiujZU5Ehrq6OtTU1LiO0emIRCKkp6fj6dOn0NHR\n4XWnCdK89PR0GBsbY8SIEY1e79atG1avXi09tKurrHltDzoNvI6qqqpcz1SSxrVUHCxduhTbtm3D\n0aNH5eKgWLFYDG9vb1y4cAEAmj34k8/vF3xExQSRYW9vj5iYGIjF4mYPa5MXIpFIeiBXTU0NgLo1\nwvXFRGhoKHbs2IHAwMAu08KtNeo7tDg4OEBVVbXNHVv43KFFJBLJtAJWUlICUHfeSP06eSUlJZiZ\nmeH333/nJCNb6FCuOtbW1rh27Zp0uYo8qd9A2171rUHllbe3N86ePYsdO3Zg27ZtXMfpUDt37kRc\nXBx69OiBqVOnQl9fH6qqqlzH6nDycD+lYoLI8PT0RFJSEpYvXw5/f3+5nqWorKyEm5sbMjMz0bt3\nbxgbGyMhIUFmzLhx47B69WrExsbyqphYsWIFGIaBqakpVFVVpV+3Vld482uvXr16oby8XPp1z549\nAQB5eXkYMmSI9PWXL1/i2bNnrOdjEx3KVefLL7+Eq6srvv32W/j6+kpbSMsDOzu7dhdQfOts1R4K\nCgoYPnw4rly5wnWUDhcZGQllZWX88ssv0NfX5zoOa+Thfio/73ikVTZt2oRBgwYhKioKiYmJMDY2\nhkAgaPQXn89PGgEgODgYmZmZ+Oijj7B27VooKys32FCnqamJwYMH8+5GUN+h5e2335b5mgB9+/aV\neRpraGgIiUSCiIgIeHt7AwCePHmC5ORk3j91lZc13i0JDQ3F2LFjcfDgQcTGxsLKyqrZ980lS5Zw\nkLJj8P13nA3l5eUyDyj4qqioCFZWVnJVSADycT+lDdhERn3P8Nb8WvC9+8SUKVPw7NkzxMbGSpd8\nGRgYwMXFReaJ7NKlS5GWlobExESuohIW7dixAz/++CPOnz8PHR0dVFRUwM7ODs+ePYOjoyMEAgFi\nYmJQUFCAzz//HMuWLeM6cofZtGkT3n77bXh4eHAdhVOted+sv873903SNteuXYO7uzv09PQQGRnJ\ndZwOZWtri1GjRtG5NDxEMxNEBi1b+FtOTg6sra1b3DvSrVs3lJaWspSKcG3KlCkoLi5Gfn4+dHR0\noKKigv/+97/w9fVFVFSUdNzw4cPxn//8h8OkHe/IkSOws7PjOgbnlixZwrsnjeSfCwwMbPJaZWUl\n/vzzT1y6dAk1NTWYNm0ai8m4YWtri4SEBFRXV8vVUkB5QP83iQxatvA3RUXFJtvWverx48e8P6DK\n3d0dNjY2+OKLL5odt2/fPiQkJODQoUMsJWPfoEGDsGHDBpnXHBwcEB0djQsXLqCsrAz6+vqws7Pj\n/anoGhoavP9vbI2lS5dyHYF0QoGBgS3OWCkoKGD27NmYP38+i8m44eXlhYSEBKxbtw6rVq2S2yYv\nfLyfUjFBSBMGDhyIjIyMZjtblZWVITMzE8OHD2c5HbuSk5PRt2/fFsc9ePAAKSkpLCTqfLS0tDBr\n1iyuY7Dq/fffR1JSEj1pJKQRzc1YKSkpQUtLC1ZWVnJzPsuxY8dgbW2N0NBQXLp0CVZWVtDR0ZGL\nvUWv4uP9lN79CWmCo6MjAgICsGXLFnz99deNjtm2bRsqKyvh5OTEcrrOSSwW8/5JtZ+fH0aPHo3p\n06c3Oy4sLAwpKSm8Xjq4dOlSxMXFwd/fH19//TXvZ+hIQ/b29mAYBvv370e/fv1gb2/f6u9lGAax\nsbEdmI5bNGMl69WZmvz8/EYP9Xt1bxFfi4nW6kr3UyomSKMKCwtx/vx5PHz4EOXl5Y1O0/K9m9Oc\nOXMgFApx5MgR3Lp1CxMmTABQ1wL06NGjiIqKQkpKCoYOHdriB0t5UFtbi9u3b/O+nXD9DbCl/+fX\nrl2DUCjkdTERFhYGGxsbhIWFIS4uDu+//36zh9bx5cOBPPSNb628vDwwDIPq6mrp163F930m+fn5\n6NGjR4vviWVlZaioqOB9Zyx5b9TQFl3tfkrdnEgDBw4cQEBAgPTmAPx9UmX9m7+8dCUpLCyEl5cX\n0tLSZJ6YAHU/AyMjIwQFBUFLS4vjpG+eu7u79N/JycnQ0NBosqVfTU0NsrOz8eTJEzg5OfH68KXG\nOno1ZsWKFThz5gxu377NUjL2yWsXo/r/7sjISAwcOFD6dWvx5ecA/F08aGlpQVFRsU3FBIBWLffo\nqgwNDeHi4tLiQ7dVq1YhLCxM7s/c4DO+309pZoLISExMxDfffANVVVV89tlnSE5ORlpaGtatW4fs\n7GzExMQgNzcX7u7uMDQ05Dpuh9PS0sLx48dx8eJFXLx4ETk5OaipqYG2tjbGjh0LBwcH3j5dS05O\nlv6bYRj89ddf+Ouvv5r9nuHDh8PX17ejo3UJ9+7dk/YV5yt57WIkD33jW+v1YoDPxUFbSSSSVrVZ\nrx9L+Ivv91OamSAyFixYgMTERISEhGDEiBHw8/ODUCiUPkkTi8VYv349IiIiEBYWhgEDBnAbmHSY\n+jc/iUSCuXPnNtt9on4zIV+n6f38/KT/Dg8PR//+/WFmZtbo2JqaGty/fx937tyBra0tfvzxR7Zi\nEsI5Dw8PaGhoYM2aNVxH4VxrZzE9PT2RkJCAGzdusJSMsI3v91OamSAy0tPTYWxsjBEjRjR6vVu3\nbli9ejUSEhIQGBiIrVu3spyQsMXS0lL6bwsLC1haWsq8Jk9e3SjIMAyys7ORnZ3d7PdoaGjgyy+/\n7OhopBPIzMyEgoIChg4dynUUzsXHx8PBwYHrGJzJz8+X+bqysrLBa/XqHzwkJSVBV1eXjXidwsuX\nL3HlypUW92TyZY8VwP/7KRUTRIZIJEK/fv2kXyspKQGoe0Os79SipKQEMzMz/P7775xk5EJNTQ1K\nS0ubPXeiKz1FaKvDhw9zHYFT9U8WJRIJVq5c2Ww3p/qnSqampnLXR72oqAiFhYUA6pYI9unTh+NE\n7HB2doa5uTmOHDnCdRTOaWlpyey3kzd2dnYyS95iYmIQExPT7PdIJBJ8+OGHHR2tU4iOjoa/vz/K\nysqaHMP3bk58vJ9SMUFk9OrVC+Xl5dKve/bsCaBuk92QIUOkr798+RLPnj1jPR/brl69ih9++AGp\nqamoqqpqchzDMLzePFdWVoasrCzo6ek1udm8sLAQjx49wrBhw/DOO++wnLBjvXqYY2BgIExNTemA\nx1ecOHEC+/btw6NHj2Re79+/P+bPn4+PP/6Yo2TseOeddyAQCLiO0Sl88MEHOHv2LJ4/fw5lZWWu\n47Du1YdKBQUF6N69O3r16tXo2PoHD+PHj8ecOXPYisiZGzduwMfHBwzDYPLkybh37x6ysrKwYMEC\nZGdn4/LlyxCJRJg+fTr9PXUxVEwQGX379pWZkjU0NIREIkFERAS8vb0BAE+ePEFycjKvn8QDwKVL\nl7Bo0SLpUzY1NTW57aN/6NAhBAUFITQ0tMliori4GO7u7li6dCkWL17MckL2xMXFcR2hU1mxYgVO\nnTolfZpYPxtRVFSEhw8fYvXq1bh27RqvW+QaGBggJyeH6xidgoeHB+Lj4+Hp6Yl169bJzYFs9V59\nfzAwMMDEiRN5/bvfFvv27UNtbS127dqFcePGwc/PD1lZWdLloE+fPsXKlSuRkJDQ6BkUfMOnFvxU\nTBAZ7733Hn788Ufk5+dDR0cHtra26NmzJ3bv3o2HDx9CIBAgJiYGlZWVvF8Xu2PHDlRXV+Ozzz7D\nwoULpbM08ig+Ph56enowNjZucoyxsTH09PQQHx/P62KC/O3s2bMQCoXo3bs3li5dCldXV+nSLrFY\njLCwMAQGBkIoFMLa2hqTJ0/mOHHHcHd3h4eHBy5evIixY8dyHYdT3377LQYPHoz4+HhMmDABRkZG\nzZ490hU+KLXXpk2boKenx3WMTuP69esYMmQIxo0b1+h1dXV1bN26Ffb29vj++++xbt06dgOyqC0t\n+LvC3wgVE0TGlClTUFxcLC0mVFRU8N///he+vr6IioqSjhs+fDj+85//cJi042VlZcHIyAhfffUV\n11E4l5eXh5EjR7Y4buDAgbh58yYLibh3/fp1/PbbbygqKmpyL01XuRG014kTJ6CkpISDBw9i8ODB\nMte6deuGWbNmwdzcHM7OzggJCeFtMTF8+HDMmTMHixcvxrRp0zB+/Hjo6Oige/fujY7n86xueHi4\n9MNQVVUV0tLSkJaW1uhYvv990FJIWSUlJTJd8OpPd37x4oX0b0VVVRUWFhZITEzkJCMb+NiCn4oJ\nImPQoEHYsGGDzGsODg6Ijo7GhQsXUFZWBn19fdjZ2XWZY97bS1VVlVrf/r+KigqoqKi0OE5FRQUi\nkYiFRNwRi8Xw9vbGhQsXADTfH57vH5YyMzNhaWnZoJB41eDBg2FlZYX09HQWk7HL3t4eQN3vwokT\nJ3DixIkmx/J9fxUt6WmcvDfxAOr2YIrFYunX9ee0PH78WOZeyzAMnjx5wnY81hw+fBgMwyA4OFja\ngj8tLQ0zZswAAHh5eWH9+vX45ZdfEBYWxnHa1qFigsio33ytqqoq87qWlhZmzZrFRSTOmJubIysr\ni+sYnYKmpmarfhb37t1rcrMhX+zcuRNxcXHo0aMHpk6dCn19/QZ/L/Li+fPnUFNTa3GcmpoaXrx4\nwUIibsjbvoDm0NN4WTdu3MD333+Pq1evynyQfh3fi0wAEAgEKCgokH49dOhQSCQSxMfHY968eQDq\nOkempqY2uTePD/jYgp+KCSLD3NwcJiYmCA0N5ToK5zw8PDBz5kwcOHBA+kYnr0aNGoXIyEgkJCTA\n1ta20TEJCQnIysqCk5MTy+nYFRkZCWVlZfzyyy/Q19fnOg6ntLS0cPPmTena3sZIJBKkp6fzuk0s\nbconjUlNTcWnn34qLSJ69uzZqhlevrK0tMThw4fx9OlTqKurY9y4cVBWVkZAQACKi4shEAggFApR\nUlKC8ePHcx23w/CxBT8VE0SGiooK+vfvz3WMTmHIkCEIDg6Gj48PoqOjYWNjA4FAAAUFhUbHOzs7\ns5yQPXPnzkVkZCR8fHywfPlyODs7y2y0FQqF2Lx5MxiGgZubG8dpO1ZRURGsrKzkvpAAAGtra4SE\nhGDz5s3w9fVtsPSxtrYWW7duRU5OjtzNbBKyc+dOiMVizJgxA15eXujduzfXkTjl5OSEzMxM3Llz\nB9bW1ujVqxeWL1+OtWvXIjg4GEDdwwdtbW14eXlxnLbj8LEFPxUTRMagQYOkh06RunMmSktLUVBQ\n0OQmwnp8LiZGjBgBb29vfPfdd/D398f69eul63sLCgpQVVUFiUQCT09PmQ12fKSuri63y5pet2DB\nAkRGRuLAgQM4d+4cpkyZAl1dXTAMg5ycHERERCA3NxfvvPMOFixYwHXcDpOQkIB9+/Zh8eLFePfd\ndxsd89tvv2HXrl1YuHAhxowZw3JCdtXU1ODXX39tVYOCgwcPspyOPTdv3sSgQYN43ZWoLUaMGIH9\n+/fLvDZr1iwYGRkhJiZGuifT1dWVd2cVvYqPLfipmCAyPv74Y/j7++PWrVvNtgGVB8ePH5euVTQw\nMICenp5cT1EvXLgQ+vr6CAwMxN27d5GdnS29NmzYMHh4ePB6arqera0tEhISUF1dDUVF+X4L1dHR\nwZ49e+Dt7Y3c3Fzs3r1b5nr9U8bt27fzel9BWFgYbt261eQaaKDug1R6ejrCwsJ4XUyUlZXhs88+\nw507d5ptTgCgyaVxfCGRSDBs2DCuY3R6JiYmMDEx4ToGa/jYgp+RtPTXTuTOhg0bcPr0aXz++ecY\nP348+vbtK13SIk8mTZqER48eISgoSO57x7/ur7/+kj5Z0dHRgYaGBseJ2PPkyRO4urrC1tYWq1at\nksu/jdeJxWL8+uuvSElJkc5samlpwcLCAk5OTrz/GTk4OKBPnz44evRos+Nmz56N4uJinDt3jqVk\n7FuzZg2OHz8ObW1t/Pvf/26xQYGlpSWL6dg1a9YsKCkp4fDhw1xHIZ3I/fv3sX//fjg7O8Pc3BwA\nEBsbC19fX5lGFcOHD8eRI0e6xGG5VEwQGW3pacz37hOmpqYwMzNrMC1L5FtgYCAKCgoQFhYGbW1t\nWFlZQUdHp9GnrAzDYMmSJRykJGwyNTWFg4MDAgICmh23bNkyxMXF4fr16ywlY9/YsWPx8uVLnD17\nFpqamlzH4VRkZCR8fX1x8uTJLnNeAFuoVW5DhYWFXbYFv3zP0ZMG2lJb8r0OVVdXb1XbS3ny8uVL\nXLlyBQ8fPkR5eXmjvwN8/wAdGBgIhmEgkUiQn5+P8PDwBmPqr/P9Z0HqdOvWrVXnq4hEoiYbOPBF\nSUkJrK2t5b6QAOpmt//44w98+umn8PT0xLhx4+TuA/Lrrl69ih9++AGpqamoqqpqchzfH1Y2piu3\n4KdigsjIzMzkOkKnYW9vj5iYGIjFYt4v02iN6Oho+Pv7o6ysrMkx8vAB2sPDg+sInZI8P2kcNGgQ\nUlNTIRKJpAdxva68vBypqam8PwizT58+cr+XqN6rsxHr16/H+vXrmxwrDx+eL126hEWLFqG6uhpA\n3fkzXWEJD2kZ/cUT0gRPT08kJSVh+fLl8Pf3l+tZihs3bsDHxwcMw2Dy5Mm4d+8esrKysGDBAmRn\nZ+Py5csQiUSYPn06BAIB13E7FBUTsuhQLmD8+PFIS0vDypUrERAQ0ODhg1gsxsqVK1FZWQlHR0eO\nUrJjwoQJCA8Px4sXL9C9e3eu43CKZvpl7dixA9XV1fjss8+wcOFCaUtUefb48eNmO54BgIWFBYuJ\n2of2TBDSBD8/P4hEIpw/fx4qKiowNjaGQCBocm38xo0bOUjJDk9PT5w7dw67du3CuHHj4OfnB6FQ\niIyMDADA06dPsXLlSty+fRvh4eFytSFbnrX1UC6+Hu72/PlzuLi4IDs7G3379sWHH34oPYfkwYMH\nOH36NPLy8qCnp4fw8HBeP42tqKjAJ598Ah0dHfz3v/+V+7MVyN9MTU0xePBgnDx5kusonDt79ix2\n7tyJR48eNTuuqzyEoWJCzv2TTWFd5Ze8vQwMDKRr31vCMIz0gzUf2djYoFevXjh9+jQANCgmgLpl\nHPb29nB0dJSbvuoikQjp6el4+vQpdHR0eH/GxuvmzZuHK1eu0KFcAPLz87FkyRJkZGQ0eOAgkUhg\naGiInTt3QldXl6OEHcPPz6/BayKRCLGxsVBRUYGRkVGzDQr4/BCGyBozZgzefffdFhsV8N3p06ex\nfPlySCQS9OzZE7q6us0+YOgK3cBomZOc+ye1JN/r0E2bNnEdodMoKSmR+aBc32Hi1aUMqqqqsLCw\nQGJiIicZ2SQSibBx40acOXMGNTU1AOoOLaz/GYWGhmLHjh0IDAzEyJEjuYzaoehQrr/p6OggLCwM\n58+fR2JiIvLz88EwDLS1tWFjYwN7e3tenqvQWAOCehUVFUhOTm7yOhUT8sXc3BxZWVlcx+Dc3r17\nAQCrV6/GzJkzu0zHpuZQMSHnGttw/c033yAkJASzZs3C1KlT0bdvXwB1R72fOnUKx48fx6xZs7B8\n+XK247LKxcWF6widRs+ePWXWw9dvMn38+LHMhlKGYfDkyRO247GqsrISbm5uyMzMRO/evWFsbIyE\nhASZMePGjcPq1asRGxvL62KCDuWSxTAMHBwcusxBU28CPXRpWXZ2No4fP460tDQ8ffoU9vb2+Oqr\nrwDU7TnKzMyEk5MTr099Bur2m82cORMHDhzAvHnzuI7DmezsbIwePRqzZ8/mOsobQ8UEkREaGorD\nhw/j4MGD0sNU6hkYGMDAwAD29vaYO3cuBg4ciBkzZnCUlLBJIBCgoKBA+vXQoUMhkUgQHx8vvSlU\nVlYiNTUVWlpaHKVkR3BwMDIzM/HRRx9h7dq1UFZWhoGBgcwYTU1NDB48GFeuXOEoJTuGDRuG4uJi\nrmMQDr3+0CU/Px89evRosWFFWVkZKioqOjJapxAaGop169ZJ26AyDIOSkhLp9efPn2PNmjVQVFTE\ntGnTuIrJiiFDhiA4OBg+Pj6Ijo6GjY0NBAJBk+2SnZ2dWU7IDjU1Nd7dJ6mYIDKOHTuG0aNHNygk\nXmVubo7Ro0fj2LFjclNMiMVi3L59W+Z0XyMjI7lpGWtpaYnDhw/j6dOnUFdXx7hx46CsrIyAgAAU\nFxdDIBBAKBTJURsNAAAYG0lEQVSipKQE48eP5zpuh4qKikKfPn2wYcOGZv//DxgwAGlpaSwmY5+7\nuzt8fX2RkZFBh3IRAHUttV1cXFpcvrRlyxaEhYXxet9damoq/P390aNHD3z55ZcwNzdvcM+0tLTE\n22+/jbi4ON4XE0DdOROlpaUoKCho8f2Rr8WEtbU1UlJSpK3U+YCKCSLjwYMHsLOza3GcpqYm0tPT\nWUjEraqqKgQGBuLnn39u8BStR48emDNnDjw8PKCkpMRRQnY4OTkhMzMTd+7cgbW1NXr16oXly5dj\n7dq1CA4OBlC35EVbWxteXl4cp+1YOTk5sLa2brGQ7NatG0pLS1lKxQ06lIu8TiKRtHo/Hd/33f30\n009gGAZ79+7FqFGjGh2joKAAQ0ND3L9/n+V07Dt+/Di2bt0KoG6lg56eXrPd3/jK09MT06dPx7ff\nfgtfX19enMvS9f8LyBvVrVu3VnUlysjI4P1T+ZqaGixatAiXL1+GRCKBpqYm+vXrB6DuA2VxcTH2\n7NmDW7duYc+ePbzYRNWUESNGYP/+/TKvzZo1C0ZGRoiJiUFZWRn09fXh6urK+3W/ioqKzfYEr/f4\n8WPetQBtbvaBDuUibfHs2TPe30PS0tJgYmLSZCFRT0NDA7du3WIpFXcOHToERUVFBAUFYezYsVzH\n4YxAIMCxY8ewaNEixMbGwsrKqtm2813hEFgqJogMc3NzxMXFYfv27fDy8mq0xeH333+PP//8E/b2\n9hylZEdISAiSkpIwYMAAfP3117CxsZG5npiYiI0bN+Ly5cs4ceIEPvnkE46ScsfExAQmJiZcx2DV\nwIEDkZGR0ezJ6GVlZcjMzMTw4cNZTtexqPsbaUx+fr7M15WVlQ1eq1dTU4P79+8jKSmJd21yXycS\niVp1iGdlZaW0Kxyf5eXlwcLCQq4LCQCora1FcHAwHjx4gNraWuTm5jYYU9+WnooJ0iV5eXkhKSkJ\nu3fvxq+//opJkyZJ3/Dz8vIQERGBR48eoXv37vD09OQ4bcc6deoUlJWVcfDgwUY3S9nY2ODAgQOY\nOHEihEKhXBYT8sjR0REBAQHYsmULvv7660bHbNu2DZWVlXBycmI5XcdqrPsbIXZ2djIPnmJiYhAT\nE9Ps90gkEnz44YcdHY1TvXv3bvSD4usePHjAuw25jVFXV29xY7482L17N44dOwZFRUV88MEHvFju\nRcUEkTF06FDs2bMHvr6+yM7Oxo8//ihzvX65z5YtW3jfEvKPP/6AlZVVs2/yWlpasLKyQkpKCovJ\nCJfmzJkDoVCII0eO4NatW5gwYQKAumL76NGjiIqKQkpKCoYOHYrp06dznJaQjvfqPpmCggJ0794d\nvXr1anSskpIStLS0MH78eMyZM4etiJwwMzNDdHQ00tPTm5zBTUpKwsOHD/Hxxx+znI599vb2iImJ\naXZWVx6cPHkSysrKOHbsWINOgF0VFROkAUtLS5w7d076oejx48cA6j44W1hYYOLEidKDyvisuroa\nysrKLY5TVlZGdXU1C4lIZ6CsrIzg4GB4eXnh+vXr0o4kKSkp0g4dRkZGCAoKkusbJpEfcXFx0n8b\nGBhg4sSJdP4E6k6Ij4qKwtKlS7Fhwwa8//77MtdTUlKwcuVKKCoq8r6wAuo2HiclJWH58uXw9/eX\n21mK4uJiWFlZ8aaQAABGQgtZCWmUk5MTysvLcf78+SY/FIrFYtjb20NVVRW//vorywkJ1y5evIiL\nFy8iJycHNTU10NbWxtixY+Hg4MCbln+EtEV4eDj09PQwevRorqN0CsHBwdi8eTMYhoGqqirKy8vx\n9ttvQ1FRESUlJZBIJFixYoVcHOLm5+cHkUiE8+fPQ0VFBcbGxs1uPObr6egTJkyAgYEBvv/+e66j\nvDFUTBDShC1btmDfvn2YNGkS1qxZ06BLkUgkwtq1axEREYH58+fD19eXo6SEEEI6q4SEBAQGBjZo\npz506FB4eXnxvplJPQMDA+nG4pYwDNOqzpJd0fbt23Hs2DHExcV1+b0S9aiYIKQJJSUlcHFxQWFh\nIVRUVPDBBx9AV1cXDMMgJycHFy5cQEVFBQQCAcLDw+V2ypYQQkjLSkpKkJubi9raWggEArnYdP2q\n8PDwNo1//XR1vhCLxfj000+hoKCAdevWYeDAgVxH+seomCCkGdnZ2Vi2bJm0B3j9dGz9n42JiQkC\nAgKgp6fHWUbCnZqaGpSWljZ77gQd4kYIIaSeu7s7qqqqcP36dbz11lvQ0dFpdrnXwYMHOUjZNlRM\nENIKV69eRUpKCgoLCwH8vRnd3Nyc42SEC1evXsUPP/yA1NRUVFVVNTmODmojRH55eXnB1dUVNjY2\nUFBQ4DoO6STasvG6qyz3omKCEELa4NKlS1i0aJG0g5eamlqzJ12/2umGECI/6vcIaGho4KOPPoKL\niwsGDx7MdaxOQSwW4/bt2zIP6IyMjOSiA15ycnKbxltaWnZQkjeHiglCmpCQkIB9+/Zh8eLFePfd\ndxsd89tvv2HXrl1YuHAhxowZw3JCwoWPP/4Y6enp+Oyzz7Bw4UL07NmT60iEkE7oyJEjCA8Px+3b\ntwHUPWU2NjaGi4sLpkyZ0qCphzyoqqpCYGAgfv75Z1RUVMhc69GjB+bMmQMPDw8oKSlxlJC0BxUT\nhDTBy8sLiYmJuHTpUpNPnisqKmBtbQ07OzsEBASwnJBwwdTUFIMHD8bJkye5jkII6QLu37+PkydP\n4syZMyguLgbDMFBSUoKdnR1cXFzkZhlUTU0NFixYgMuXL0sPwO3Xrx8AICcnR/qzef/997Fnzx68\n9dZbHCcmrcX/315C2un27dswMDBodgmLiooKDA0NcfPmTRaTES6pqqpiwIABXMcghHQRgwYNwldf\nfYWEhATs3bsXTk5OYBgGUVFRWLRoEWxtbbFlyxauY3a4kJAQJCUloX///ti7dy8SExNx9OhRHD16\nFImJidi7dy8GDBiAy5cv48SJE1zHJW1AMxOENMHU1BQODg4tzjgsW7YMcXFxuH79OkvJCJe8vLzw\n559/4syZM1xHIYR0UeXl5YiMjER4eDiuX7/eZTba/hMzZ85EVlYWoqKimmyLW1hYiIkTJ2Lo0KEI\nCQlhOWHH8PPzA8Mw8PHxgYaGBvz8/Fr9vV3l8D5FrgMQ0ll169YNIpGoxXEikUgupqhJHQ8PD8yc\nORMHDhyQi1NrCSFvXk1NDaqqqprtBsc3f/zxB6ysrJo9X0NLSwtWVlZISUlhMVnHCg8PB8Mw+OKL\nL6ChodGm8zaomCCkixs0aBBSU1MhEonw9ttvNzqmvLwcqamptOxFjgwZMgTBwcHw8fFBdHQ0bGxs\nIBAImiwonZ2dWU5ICOmMamtrcfHiRYSFhSE+Ph5VVVWQSCTQ1tbG1KlTuY7X4aqrq6GsrNziOGVl\nZWm3PD7YtGkTAEBTU1Pmaz6hYoKQJowfPx5paWlYuXIlAgICGrSsE4vFWLlyJSorK+Ho6MhRSsKF\nq1evorS0FAUFBUhLS2t2LBUThMi3rKwshIeH48yZM3jy5AkkEgm6d++OyZMnw9XVFe+9916jB5bx\njY6ODq5evQqxWNxkC1ixWIyrV6/y6rDP10/y5uPJ3lRMENKE2bNnIzQ0FLGxsZg0aRI+/PBD6Ovr\nAwAePHiA06dPIy8vD3p6epgzZw7HaQlbjh8/jq1btwKo6yOvp6cHFRUVjlMRQjqbw4cPIzw8HBkZ\nGajfnjpy5Ei4urpi0qRJUFVV5Tghu+zs7LBv3z6sWLECa9asadAaVyQSYe3atfjrr7/kYqaGT2gD\nNiHNyM/Px5IlS5CRkdHgyZFEIoGhoSF27twJXV1djhIStk2aNAmPHj1CUFAQxo4dy3UcQkgnVX/S\nsZaWFqZOnQoXFxcMHDiQ41TcKSkpgYuLCwoLC6GiooIPPvgAurq6YBgGOTk5uHDhAioqKiAQCBAe\nHg41NTWuI5NWomKCkBZIJBKcP38eiYmJyM/PB8Mw0NbWho2NDezt7eViepr8zdTUFGZmZti/fz/X\nUQghnZiPjw9cXFwwZswYatLx/7Kzs7Fs2TLcunULAKT3z/qPoiYmJggICICenh5nGdly/fp1/Pbb\nbygqKsLLly8bHdNVNmBTMUEIIW3wwQcfYOTIkfjuu++4jkIIIV3S1atXkZKSgsLCQgB1szcWFhYw\nNzfnOFnHE4vF8Pb2xoULFwD8XUg1pqu0DKY9E4QQ0gb29vaIiYlpdhMhIYQQWeXl5QDqDv40NzeX\ni8KhMTt37kRcXBx69OiBqVOnQl9fv8vvn6GZCUIIaYNnz55h5syZMDAwgL+/P63rJYQAAAIDA9v9\nvQzDYMmSJW8wTedjYGAAExMThIaGch2FU/b29nj69ClOnjwpberS1dHMBCGEtMGmTZswaNAgREVF\nITExEcbGxhAIBI3unekq610JIf9cYGAgGIZpsGylpX11EolELooJFRUV9O/fn+sYnCsqKoKVlRVv\nCgmAiglCCGmT+tNMJRIJysvLceXKlSbHUjFBiPzw8PBo8Fpubi6EQiG6d++OMWPGoG/fvgCAvLw8\nXL58GS9evICLi4v0dT4bNGiQdI+EPFNXV+/yy5peR8UEIYS0AR9PLyWE/HOvFxP5+flwdXWFo6Mj\n/P39oa6uLnP96dOnWLt2LS5cuICTJ0+yGZUTH3/8Mfz9/XHr1i0YGxtzHYcztra2SEhIQHV1NRQV\n+fExnPZMEEIIIYS8YV999RWuXLmC2NjYZk98dnBwgKWlpfQwTD7bsGEDTp8+jc8//xzjx49H3759\n5a6RxZMnT+Dq6gpbW1usWrWKF//9VEwQQgghhLxh1tbWsLS0xLZt25od9+WXXyI5ORlJSUksJeOG\noaFhq8cyDIM7d+50YBruBAYGoqCgAGFhYdDW1oaVlRV0dHSa3HfXFfbS8GN+hRBCCCGkE3n27Bkq\nKipaHFdZWQmRSMRCIm615dk1n59zv7pRPz8/H+Hh4Q3G1F+nYoIQQnjIz8+v1WNpAzYh8qtfv374\n/fffkZOTg379+jU6JicnB1euXGnyOp9kZmZyHaFTaGyjfldHy5wIIaQNDAwMmr1eP1Vd/1SpK5xe\nSgh58/bt24ctW7ZAIBDA29sbU6ZMkW64ra6uRkREBHbs2IGCggIsW7YMn3/+OceJCWkfKiYIIaQN\nGpuSBoDa2lrk5+fj4sWLSE9Ph7u7OwwNDeHi4sJyQkJIZ1BdXQ0PDw/Ex8eDYRgoKCigT58+AOrO\nGqitrYVEIoGtrS2CgoLw1ltvcZyYkPahYoIQQt6wbdu24ejRozh58iQd0kSIHJNIJDhy5AgOHjyI\n3NxcmWu6urpwc3ODm5sbFBQUOEpIuPLy5UtcuXIFDx8+RHl5eZP7RLrCsigqJggh5A2rra2Fg4MD\nRo4c2WInF0KIfCgsLMTjx48BAFpaWhAIBBwnIlyJjo6Gv78/ysrKmhzTlZbK0gZsQgh5wxQUFDB8\n+PBmT8cmhMgXLS0taGlpcR2DcOzGjRvw8fEBwzCYPHky7t27h6ysLCxYsADZ2dm4fPkyRCIRpk+f\n3mUKTiomCCGkA5SXl6O8vJzrGIQQjrVmOUtXaQFK/rl9+/ahtrYWu3btwrhx4+Dn54esrCx8+eWX\nAOpORl+5ciUSEhKa3KPX2VAxQQghb9i1a9dw9epV6OnpcR2FEMKhtixnoWJCPly/fh1DhgzBuHHj\nGr2urq6OrVu3wt7eHt9//z3WrVvHbsB2oGKCEELaIDAwsMlrlZWV+PPPP3Hp0iXU1NRg2rRpLCYj\nhHQmfFzOQv65kpISmJmZSb+u7+L14sULdO/eHQCgqqoKCwsLJCYmcpKxraiYIISQNnj19NKmKCgo\nYPbs2Zg/fz6LyQghnQkfl7OQf65nz54Qi8XSr99++20AwOPHjzFgwADp6wzD4MmTJ2zHaxcqJggh\npA2aa9OnpKQELS0tWFlZQVtbm8VUhJDOho/LWcg/JxAIUFBQIP166NChkEgkiI+Px7x58wDUzXKn\npqZ2mQ37VEwQQkgbdIWe34QQ7vFxOQv55ywtLXH48GE8ffoU6urqGDduHJSVlREQEIDi4mIIBAII\nhUKUlJRg/PjxXMdtFTolhRBCCCHkDWtuOcurutJyFvLPOTk5wcLCAnfu3AEA9OrVC8uXL0d1dTWC\ng4OxceNG3L59GwKBAF5eXhynbR2amSCEkDYoKytDVlYW9PT0mpyCLiwsxKNHjzBs2DC88847LCck\nhHQGfFzOQv65ESNGYP/+/TKvzZo1C0ZGRoiJiUFZWRn09fXh6uraZe4fVEwQQkgbHDp0CEFBQQgN\nDW3yA0BxcTHc3d2xdOlSLF68mOWEhJDOgI/LWUjHMTExgYmJCdcx2oWWORFCSBvEx8dDT08PxsbG\nTY4xNjaGnp4e4uPj2QtGCOlU+LichZDG0MwEIYS0QV5eHkaOHNniuIEDB+LmzZssJCKEdEZ8XM5C\nSGOomCCEkDaoqKiAiopKi+NUVFQgEolYSEQI6Uq68nIWQhpDy5wIIaQNNDU1kZWV1eK4e/fuoVev\nXiwkIoQQQrhDxQQhhLTBqFGj8McffyAhIaHJMQkJCcjKysLo0aNZTEYIIYSwj4oJQghpg7lz5wIA\nfHx8cOLECZk+8mKxGCdOnMCyZcvAMAzc3Ny4ikkIIYSwgpFIJBKuQxBCSFeye/dufPfdd2AYBoqK\nitDR0QEAFBQUoKqqChKJBJ6entQWlhBCCO9RMUEIIe1w7tw5BAYG4u7duzKvDxs2DB4eHtQ3nhBC\niFygYoIQQv6Bv/76C/n5+QAAHR0daGhocJyIEEIIYQ8VE4QQQgghhJB2oXMmCCHkHygqKkJhYSEA\nQEtLC3369OE4ESGEEMIeKiYIIaQdTpw4gX379uHRo0cyr/fv3x/z58/Hxx9/zFEyQgghhD20zIkQ\nQtpoxYoVOHXqFCQSCRiGkc5GFBUVSV9zdnbGpk2bOE5KCCGEdCyamSCEkDY4e/YshEIhevfujaVL\nl8LV1RXdunUDUHfORFhYGAIDAyEUCmFtbY3JkydznJgQQgjpODQzQQghbeDu7o7r168jPDwcgwcP\nbnTMH3/8AWdnZ5iZmeHQoUMsJySEEELYQydgE0JIG2RmZsLS0rLJQgIABg8eDCsrK2RmZrKYjBBC\nCGEfFROEENIGz58/h5qaWovj1NTU8OLFCxYSEUIIIdyhYoIQQtpAS0sLN2/eRHMrRCUSCdLT06lN\nLCGEEN6jYoIQQtrA2toaubm52Lx5M2pqahpcr62txZYtW5CTkwMbGxsOEhJCCCHsoQ3YhBDSBvn5\n+XB2doZIJELfvn0xZcoU6OrqgmEY5OTkICIiArm5uXjnnXcgFAqhra3NdWRCCCGkw1AxQQghbZSW\nlgZvb288fvwYDMPIXJNIJNDW1sb27dthamrKUUJCCCGEHVRMEEJIO4jFYvz6669ISUlBYWEhgLr9\nFBYWFnBycpKePUEIIYTwGRUThBBCCCGEkHahDdiEEEIIIYSQdlHkOgAhhHRmhoaG7f5ehmFw586d\nN5iGEEII6VyomCCEkGb8k5WgtIqUEEII39GeCUIIaaNvvvkGISEhmDVrFqZOnYq+ffsCAPLy8nDq\n1CkcP34cs2bNwvLlyzlOSgghhHQsmpkghJA2CA0NxeHDh3Hw4EGYm5vLXDMwMICBgQHs7e0xd+5c\nDBw4EDNmzOAoKSGEENLxaGaCEELawNXVFaqqqjh06FCz49zd3SESiRAeHs5SMkIIIYR91M2JEELa\n4MGDB9DU1GxxnKamJh4+fNjxgQghhBAOUTFBCCFt0K1bN2RkZLQ4LiMjgw6uI4QQwntUTBBCSBuY\nm5vjwYMH2L59e6PdmiQSCXbs2IE///yzwZ4KQgghhG9ozwQhhLRBVlYWZsyYgZcvX0JPTw+TJk2C\nrq4ugLpuThEREXj06BH+9a9/ISQkBMOGDeM4MSGEENJxqJgghJA2Sk5Ohq+vL4qKisAwjMw1iUQC\nTU1NbNmyBe+++y5HCQkhhBB2UDFBCCHt8PLlS0RFRSElJQWPHz8GAGhpacHCwgITJ05E9+7dOU5I\nCCGEdDwqJgghhBBCCCHtQhuwCSGEEEIIIe1CxQQhhBBCCCGkXaiYIIQQQgghhLQLFROEEEIIIYSQ\ndqFighBCCCGEENIuVEwQQgghhBBC2oWKCUIIIYQQQki7UDFBCCGEEEIIaRcqJgghhBBCCCHtQsUE\nIYQQQgghpF2omCCEEEIIIYS0CxUThBBCCCGEkHb5PzYQhDqk6alqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 393,
              "height": 314
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cst8ZG2vayaS",
        "colab_type": "code",
        "outputId": "fac94907-e523-41dc-c3b2-1e2598973f08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "sns.countplot(x='genre', data=movies_title_genres_poster)\n",
        "plt.xticks(rotation=-45)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text xticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ICAgICFSOTCaDSPR2nlsqlUJdXb0GU/Tv4MaNGxg9ejQA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\nRdksHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 422,
              "height": 316
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bc6nGv6c2A8j",
        "colab_type": "text"
      },
      "source": [
        "**Choose only top 10 genres based on size**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kEQklnS2Qjf",
        "colab_type": "text"
      },
      "source": [
        "**Rank the movies in each genre based on popularity**\n",
        "\n",
        "**Choose only top 1000 for each genre based on popuarity**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxRMosO60YTJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_title_genres_poster['popularity'] = movies_title_genres_poster['popularity'].apply(pd.to_numeric, errors='coerce')\n",
        "movies_title_genres_poster['rank'] = movies_title_genres_poster.groupby('genre')['popularity'].rank(method='first')\n",
        "movies_title_genres_poster.sort_values(\"genre\", inplace = True)\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster[movies_title_genres_poster['rank']>1000].index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Arbfozk10TDy",
        "colab_type": "code",
        "outputId": "af94c9f8-49a3-4024-94df-de5c5d47733f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "movies_title_genres_poster.groupby(['genre']).size().reset_index(name='counts')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genre</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>action</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adventure</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>animation</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>comedy</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>crime</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>documentary</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>drama</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>horror</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>romance</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>thriller</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         genre  counts\n",
              "0       action    1000\n",
              "1    adventure    1000\n",
              "2    animation    1000\n",
              "3       comedy    1000\n",
              "4        crime    1000\n",
              "5  documentary    1000\n",
              "6        drama    1000\n",
              "7       horror    1000\n",
              "8      romance    1000\n",
              "9     thriller    1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYypjFf2aTHr",
        "colab_type": "code",
        "outputId": "f62993cf-da7b-41a6-ecf0-81af5fe7be74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "pd.Series(movies_title_genres_poster['genre']).value_counts().plot('bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f810741ceb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LFjXmAQAAACaw7EhBaWmpXnjhBbVs2VIzZsywqowGVV5eqVOnSq5qbmNKz4WF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\naWuQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 386,
              "height": 314
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zsnhvZ64j7N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_title_genres_poster.drop('popularity',axis=1,inplace=True)\n",
        "movies_title_genres_poster.drop('rank',axis=1, inplace=True)\n",
        "movies_title_genres_poster.to_csv('imdb_dataset.csv', index=None, header=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yeEY0fknPfSI",
        "colab_type": "text"
      },
      "source": [
        "### Download Images\n",
        "\n",
        "*   Here, we try to download images from the imdb website based on 'poster_path'\n",
        "*   There are images files that are not valid.\n",
        "*   Hence,we get a list of files that are not valid (due to broken link) and remove them from the folder.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhgua8_BQgO7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_metadata = pd.read_csv('imdb_dataset.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBcU8zc6Pald",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "downloaded_image_dir = \"posters\"\n",
        "if not os.path.exists(downloaded_image_dir):\n",
        "    os.makedirs(downloaded_image_dir)\n",
        "\n",
        "def download_poster(title,genre,poster_path):\n",
        "\n",
        "    if not os.path.exists(downloaded_image_dir+'/'+genre):\n",
        "        os.makedirs(downloaded_image_dir+'/'+genre)\n",
        "\n",
        "    imgUrl = 'http://image.tmdb.org/t/p/w185/' + poster_path\n",
        "\n",
        "    local_filename = re.sub(r'\\W+', ' ', title).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "\n",
        "    try:\n",
        "        session = requests.Session()\n",
        "        r = session.get(imgUrl, stream=True, verify=False)\n",
        "        with open(downloaded_image_dir +'/'+genre+'/'+local_filename, 'wb') as f:\n",
        "            for chunk in r.iter_content(chunk_size=1024):\n",
        "                f.write(chunk)\n",
        "    except:\n",
        "        print('PROBLEM downloading', title,genre,poster_path)\n",
        "\n",
        "for index, row in movies_metadata.iterrows():\n",
        "    download_poster(row['original_title'],row['genre'],row['poster_path'])\n",
        "    time.sleep(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30_GOCIuR3u9",
        "colab_type": "text"
      },
      "source": [
        "**Obtain a list of all images in the folder**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tnz4k7qyRDqt",
        "colab_type": "code",
        "outputId": "1b56c202-b57a-4fe2-98d1-2ca1b6411f74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# get all files in directory\n",
        "def get_all_files():\n",
        "    path = downloaded_image_dir\n",
        "    #path = resized_image_dir\n",
        "    #path = \"two_class_training/comedy_horror\"\n",
        "\n",
        "    files = []\n",
        "    # r=root, d=directories, f = files\n",
        "    for r, d, f in os.walk(path):\n",
        "        for file in f:\n",
        "            files.append(os.path.join(r, file))\n",
        "\n",
        "    print(len(files))\n",
        "    ii = 5\n",
        "    for f in files:\n",
        "        print(f)\n",
        "        ii-=1\n",
        "        if ii == 0:\n",
        "            break\n",
        "    \n",
        "    return files\n",
        "\n",
        "files = get_all_files()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9998\n",
            "posters/action/san-antonio.jpg\n",
            "posters/action/cheating-death-stealing-life-the-eddie-guerrero-story.jpg\n",
            "posters/action/lucky-break.jpg\n",
            "posters/action/china-o-brien.jpg\n",
            "posters/action/sumpah-orang-minyak.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CovT4FWR-Sg",
        "colab_type": "text"
      },
      "source": [
        "**Verify if each image is valid and obtain a list of invalid images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GA8zZxYPRM4I",
        "colab_type": "code",
        "outputId": "19379406-c2f6-4a46-ce4d-210badcdd0b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "download_again = []\n",
        "\n",
        "for index, row in movies_metadata.iterrows():\n",
        "\n",
        "    local_filename = re.sub(r'\\W+', ' ', row['original_title']).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "    genre = row['genre']\n",
        "    path = downloaded_image_dir+'/'+genre+'/'+local_filename\n",
        "    #path = 'two_class_training/comedy_horror/'+genre+'/'+local_filename\n",
        "\n",
        "    # if not exist\n",
        "    if path not in files:\n",
        "        download_again.append({\n",
        "            'original_title':row['original_title'],\n",
        "            'genre':row['genre'],\n",
        "            'poster_path':row['poster_path']\n",
        "        })\n",
        "    \n",
        "    # if exist, try opening it\n",
        "    try:\n",
        "        with open(path, 'rb') as f:\n",
        "            img = Image.open(f)\n",
        "            img.convert('RGB')\n",
        "    except:\n",
        "        download_again.append({\n",
        "            'original_title':row['original_title'],\n",
        "            'genre':row['genre'],\n",
        "            'poster_path':row['poster_path']\n",
        "        })\n",
        "\n",
        "print(len(download_again))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RALJlvO5SLDJ",
        "colab_type": "text"
      },
      "source": [
        "**Remove the invalid files from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCNFyZa4RQfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for row in download_again:\n",
        "    local_filename = re.sub(r'\\W+', ' ', row['original_title']).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "    genre = row['genre']\n",
        "    path = downloaded_image_dir+'/'+genre+'/'+local_filename\n",
        "    #path = 'two_class_training/comedy_horror/'+genre+'/'+local_filename\n",
        "    os.remove(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkzDNuVkSQKm",
        "colab_type": "text"
      },
      "source": [
        "**Final dataset that we will be using for training and validating**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jZ6KrRxRU3m",
        "colab_type": "code",
        "outputId": "0d5ff8b1-8f4a-493e-d6d9-49044c302711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "files = get_all_files()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9002\n",
            "posters/action/san-antonio.jpg\n",
            "posters/action/cheating-death-stealing-life-the-eddie-guerrero-story.jpg\n",
            "posters/action/lucky-break.jpg\n",
            "posters/action/china-o-brien.jpg\n",
            "posters/action/sumpah-orang-minyak.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0GNRved0n5F",
        "colab_type": "text"
      },
      "source": [
        "###Two Class Dataset (Comedy and Horror)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_kEo7J1ArG1",
        "colab_type": "code",
        "outputId": "62a0b56f-edad-4325-b5f2-b37f3ad07d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "movies_metadata = pd.read_csv('movies_metadata.csv')\n",
        "movies_title_genres_poster = movies_metadata[['id','original_title','genres','poster_path','popularity']]\n",
        "movies_metadata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>genres</th>\n",
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              "      <th>id</th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>video</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
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              "      <td>5415.0</td>\n",
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              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>[{'name': 'TriStar Pictures', 'id': 559}, {'na...</td>\n",
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              "      <td>1995-12-15</td>\n",
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              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
              "      <td>0</td>\n",
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              "      <td>15602</td>\n",
              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
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              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
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              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>3.85949</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
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              "      <td>1995-12-22</td>\n",
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              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
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              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
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              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>8.38752</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-02-10</td>\n",
              "      <td>76578911.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "2  False  ...       92.0\n",
              "3  False  ...       34.0\n",
              "4  False  ...      173.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1VIUAbHBL_F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# replace missing values\n",
        "movies_title_genres_poster['poster_path'].fillna(0,inplace=True)\n",
        "movies_title_genres_poster['popularity'].fillna(0,inplace=True)\n",
        "\n",
        "# drop the entire row if 'poster_path' or 'popularity' has the value of 0\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['poster_path']==0].index)\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['popularity']==0].index)\n",
        "\n",
        "#format 'original_title\" \n",
        "for index, row in movies_title_genres_poster.iterrows():\n",
        "    title = row['original_title'].lower()\n",
        "    title = title.replace(' ','-')\n",
        "    movies_title_genres_poster.loc[index, 'original_title'] = title\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop_duplicates(['original_title'], keep='last')\n",
        "\n",
        "# there are movies without genre and drop them\n",
        "movies_title_genres_poster.loc[movies_title_genres_poster['genres']=='[]'].count()\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster.loc[movies_title_genres_poster['genres']=='[]'].index)\n",
        "movies_title_genres_poster.count()\n",
        "\n",
        "# for each movie choose first genre from the genres list\n",
        "movies_title_genres_poster = movies_title_genres_poster.loc[movies_title_genres_poster['genres']!='[]'] \n",
        "\n",
        "list_movie_genre = []\n",
        "for index, row in movies_title_genres_poster.iterrows():\n",
        "    genres = json.loads(row['genres'].replace(\"'\",'\"'))\n",
        "\n",
        "    genre = 'nil'\n",
        "    if len(genres) > 0:\n",
        "        genre = genres[0]['name'].lower()\n",
        "\n",
        "    this_movie = {\n",
        "        'id':row['id'],\n",
        "        'genre':genre\n",
        "    }\n",
        "    list_movie_genre.append(this_movie)\n",
        "\n",
        "df_movie_genre = pd.DataFrame(list_movie_genre)\n",
        "df_movie_genre.head()\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster.join(df_movie_genre.set_index('id'), on='id')\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster[['id','original_title','genre','poster_path','popularity']]\n",
        "#print(subset_data.count())\n",
        "movies_title_genres_poster.head()\n",
        "\n",
        "\n",
        "movies_title_genres_poster = movies_title_genres_poster[~movies_title_genres_poster['genre'].isin(['fantasy', 'science fiction', 'mystery','family','music','western','war','tv movie','history','foreign','odyssey media','aniplex'])]\n",
        "\n",
        "movies_title_genres_poster['popularity'] = movies_title_genres_poster['popularity'].apply(pd.to_numeric, errors='coerce')\n",
        "movies_title_genres_poster['rank'] = movies_title_genres_poster.groupby('genre')['popularity'].rank(method='first')\n",
        "movies_title_genres_poster.sort_values(\"genre\", inplace = True)\n",
        "movies_title_genres_poster = movies_title_genres_poster.drop(movies_title_genres_poster[movies_title_genres_poster['rank']>2000].index)\n",
        "\n",
        "two_class = movies_title_genres_poster[(movies_title_genres_poster['genre'] == 'comedy')|(movies_title_genres_poster['genre']=='horror')]\n",
        "\n",
        "two_class.drop('rank',axis=1, inplace=True)\n",
        "two_class.to_csv('comedy_horror.csv', index=None, header=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDS-r45eCSII",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "downloaded_image_dir = \"two_class_training/comedy_horror\"\n",
        "def download_poster(title,genre,poster_path):\n",
        "\n",
        "    if not os.path.exists(downloaded_image_dir+'/'+genre):\n",
        "        os.makedirs(downloaded_image_dir+'/'+genre)\n",
        "\n",
        "    imgUrl = 'http://image.tmdb.org/t/p/w185/' + poster_path\n",
        "\n",
        "    local_filename = re.sub(r'\\W+', ' ', title).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "\n",
        "    try:\n",
        "        session = requests.Session()\n",
        "        r = session.get(imgUrl, stream=True, verify=False)\n",
        "        with open(downloaded_image_dir+'/'+genre+'/'+local_filename, 'wb') as f:\n",
        "            for chunk in r.iter_content(chunk_size=1024):\n",
        "                f.write(chunk)\n",
        "    except:\n",
        "        print('PROBLEM downloading', title,genre,poster_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_eNhXD6CVWS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists(downloaded_image_dir):\n",
        "     os.makedirs(downloaded_image_dir)\n",
        "\n",
        "for index, row in two_class.iterrows():\n",
        "    if index % 100 == 0:\n",
        "         print('downloaded', index)\n",
        "    download_poster(row['original_title'],row['genre'],row['poster_path'])\n",
        "    time.sleep(0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTjojlBrCZy3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get all files in directory\n",
        "def get_all_files():\n",
        "    path = \"two_class_training/comedy_horror\"\n",
        "\n",
        "    files = []\n",
        "    # r=root, d=directories, f = files\n",
        "    for r, d, f in os.walk(path):\n",
        "        for file in f:\n",
        "            files.append(os.path.join(r, file))\n",
        "\n",
        "    print(len(files))\n",
        "    \n",
        "    return files\n",
        "\n",
        "files = get_all_files()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpvEC8tVCknv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "download_again = []\n",
        "\n",
        "for index, row in two_class.iterrows():\n",
        "\n",
        "    local_filename = re.sub(r'\\W+', ' ', row['original_title']).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "    genre = row['genre']\n",
        "    path = 'two_class_training/comedy_horror/'+genre+'/'+local_filename\n",
        "\n",
        "    # if not exist\n",
        "    if path not in files:\n",
        "        download_again.append({\n",
        "            'original_title':row['original_title'],\n",
        "            'genre':row['genre'],\n",
        "            'poster_path':row['poster_path']\n",
        "        })\n",
        "    \n",
        "    # if exist, try opening it\n",
        "    try:\n",
        "        with open(path, 'rb') as f:\n",
        "            img = Image.open(f)\n",
        "            img.convert('RGB')\n",
        "    except:\n",
        "        download_again.append({\n",
        "            'original_title':row['original_title'],\n",
        "            'genre':row['genre'],\n",
        "            'poster_path':row['poster_path']\n",
        "        })\n",
        "\n",
        "print(len(download_again))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsGr6rvZCnRv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for row in download_again:\n",
        "    local_filename = re.sub(r'\\W+', ' ', row['original_title']).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "    genre = row['genre']\n",
        "    path = 'two_class_training/comedy_horror/'+genre+'/'+local_filename\n",
        "    os.remove(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMgETPwTCqnp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files = get_all_files()\n",
        "len(files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qA4F2OJ-xNc-",
        "colab_type": "text"
      },
      "source": [
        "## Prepare data for Budget prediction task\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3YRlJefc4T2",
        "colab_type": "text"
      },
      "source": [
        "### Read CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztoU3fGwZAj9",
        "colab_type": "code",
        "outputId": "5e1fdc60-7830-4e24-b27b-b96bb660757f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "data = pd.read_csv('movies_metadata.csv')\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>adult</th>\n",
              "      <th>belongs_to_collection</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>video</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
              "      <td>30000000</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "      <td>http://toystory.disney.com/toy-story</td>\n",
              "      <td>862</td>\n",
              "      <td>tt0114709</td>\n",
              "      <td>en</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
              "      <td>21.9469</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>[{'name': 'Pixar Animation Studios', 'id': 3}]</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-10-30</td>\n",
              "      <td>373554033.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>False</td>\n",
              "      <td>7.7</td>\n",
              "      <td>5415.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65000000</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8844</td>\n",
              "      <td>tt0113497</td>\n",
              "      <td>en</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "      <td>17.0155</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>[{'name': 'TriStar Pictures', 'id': 559}, {'na...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-15</td>\n",
              "      <td>262797249.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15602</td>\n",
              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>3.85949</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>81452156.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11862</td>\n",
              "      <td>tt0113041</td>\n",
              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>8.38752</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-02-10</td>\n",
              "      <td>76578911.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "2  False  ...       92.0\n",
              "3  False  ...       34.0\n",
              "4  False  ...      173.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcUkIessdEfj",
        "colab_type": "text"
      },
      "source": [
        "### Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5QPyaXTZd7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['budget'] = pd.to_numeric(data['budget'],errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkFra_BSdVM5",
        "colab_type": "text"
      },
      "source": [
        "**Select relevant columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUKg1tPcZkU5",
        "colab_type": "code",
        "outputId": "6f115be7-8ee5-4558-95ac-43461c247d48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "select_columns = ['id','original_title','budget','genres','poster_path']\n",
        "select_conditions = (data['budget']>0) & (data['genres']!='[]')\n",
        "movie_budget = data.loc[select_conditions, select_columns]\n",
        "movie_budget.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                8832\n",
              "original_title    8832\n",
              "budget            8832\n",
              "genres            8832\n",
              "poster_path       8826\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDdUxguKaWzS",
        "colab_type": "code",
        "outputId": "deefc25f-3835-45d4-cac9-1b1c9f259209",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "movie_budget['poster_path'].fillna(0,inplace=True)\n",
        "movie_budget = movie_budget.drop(movie_budget.loc[movie_budget['poster_path']==0].index)\n",
        "movie_budget.count()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                8826\n",
              "original_title    8826\n",
              "budget            8826\n",
              "genres            8826\n",
              "poster_path       8826\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McfzPmGtasmn",
        "colab_type": "code",
        "outputId": "f664198e-cb1d-46b8-fd3b-c45be8a34a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#format 'original_title\" \n",
        "for index, row in movie_budget.iterrows():\n",
        "    title = row['original_title'].lower()\n",
        "    title = title.replace(' ','-')\n",
        "    movie_budget.loc[index, 'original_title'] = title\n",
        "\n",
        "movie_budget = movie_budget.drop_duplicates(['original_title'], keep='last')\n",
        "movie_budget.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                8622\n",
              "original_title    8622\n",
              "budget            8622\n",
              "genres            8622\n",
              "poster_path       8622\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIRzHyyNa5f3",
        "colab_type": "code",
        "outputId": "fd96d925-8f56-4c5d-b909-501f25ccb0b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "list_movie_genre = []\n",
        "for index, row in movie_budget.iterrows():\n",
        "    genres = json.loads(row['genres'].replace(\"'\",'\"'))\n",
        "\n",
        "    genre = 'nil'\n",
        "    if len(genres) > 0:\n",
        "        genre = genres[0]['name'].lower()\n",
        "\n",
        "    this_movie = {\n",
        "        'id':row['id'],\n",
        "        'genre':genre\n",
        "    }\n",
        "    list_movie_genre.append(this_movie)\n",
        "\n",
        "df_movie_genre = pd.DataFrame(list_movie_genre)\n",
        "df_movie_genre.head()\n",
        "\n",
        "movie_budget = movie_budget.join(df_movie_genre.set_index('id'), on='id')\n",
        "\n",
        "movie_budget = movie_budget[['id','original_title','genre','poster_path','budget']]\n",
        "#print(subset_data.count())\n",
        "movie_budget.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genre</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>budget</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>862</td>\n",
              "      <td>toy-story</td>\n",
              "      <td>animation</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>30000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>65000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31357</td>\n",
              "      <td>waiting-to-exhale</td>\n",
              "      <td>comedy</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>16000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>949</td>\n",
              "      <td>heat</td>\n",
              "      <td>action</td>\n",
              "      <td>/zMyfPUelumio3tiDKPffaUpsQTD.jpg</td>\n",
              "      <td>60000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9091</td>\n",
              "      <td>sudden-death</td>\n",
              "      <td>action</td>\n",
              "      <td>/eoWvKD60lT95Ss1MYNgVExpo5iU.jpg</td>\n",
              "      <td>35000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id     original_title  ...                       poster_path      budget\n",
              "0    862          toy-story  ...  /rhIRbceoE9lR4veEXuwCC2wARtG.jpg  30000000.0\n",
              "1   8844            jumanji  ...  /vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg  65000000.0\n",
              "3  31357  waiting-to-exhale  ...  /16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg  16000000.0\n",
              "5    949               heat  ...  /zMyfPUelumio3tiDKPffaUpsQTD.jpg  60000000.0\n",
              "8   9091       sudden-death  ...  /eoWvKD60lT95Ss1MYNgVExpo5iU.jpg  35000000.0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV8qdKh-bMHa",
        "colab_type": "code",
        "outputId": "342ed350-2fd2-46fc-fe12-8f9a18faa954",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "pd.Series(movie_budget['genre']).value_counts().plot('bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb08e25b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DUQoAAAAAw1EKAAAAAMNRCgAAAADDUQoAAAAAw1EKAAAAAMN5uTsAYEW+vj5O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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 386,
              "height": 320
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC2VzIrEdeJc",
        "colab_type": "text"
      },
      "source": [
        "**Plot of 'budget' for each genre**\n",
        "\n",
        "\n",
        "*   to have a summary statistics of the minimum, maximum, 25th percentile, median, 75th percentile\n",
        "*   have a good overview of the distribution of the budget\n",
        "*   **result: choose adventure or action for training as they have greater spread**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkyejD52PxXj",
        "colab_type": "code",
        "outputId": "9d9b7b41-df69-4868-966f-bf48687774c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "sns.boxplot(x=\"budget\", y=\"genre\",data=movie_budget,width=0.6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffb06805320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3fwvABx/M4l//epT8/NvMnTsTlaoYN7fa7N9fss/k5Mnv4ePTjE2b1gPQq1df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\nP4ADBw4wfvx4WrZsSc2aNfHx8aFhw4aMGjWKixcv6s3n4cOHzJ8/n9a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Vis6dO+uNc+fO\nHWbMmEGLFi2oUqUK/v7+tG7dmsWLF5OWlqZTPyIiApVKRXR0NFeuXGHYsGG8/fbb+Pj40KRJE+bP\nn2/0kc59+/YxYMAA6tati4+PD3Xq1KFjx47Mnz+f1NRUvec5dOhQ6tWrh4+PD7Vq1aJv374cPnxY\naVg1Zs+erckZYPTo0ahUKlQqFRERme/tXLt2DZVKRWBgoN4YKSkpfPfdd3To0IFq1apRuXJlgoKC\nGDRoEHv37gWejHVsbOZvobt06aLpR6VScfDgQZP6un79OpGRkQQFBeHj40P16tXp3LkzP/zwg9Hz\nmz17Nv/++y9jx47VjFdgYCDTpk3j4cOHJo/XsyAqKorbt28XdBriKRIdvYbExDtER68xWGfbti0k\nJyfpHE9KSmLHjq1m5xAff5Xdu3fqHN+9eyfXrsUbLFeS1V7JoUMH9daLj79KXFys1c9fiVL/SuNn\nLlPim3KNLJVPfjN3fK19fYQQwlqe6cniuXPnaNu2LRs3bsTR0ZGGDRvyzjvv4OHhwa+//sqaNbo/\n+MybN48OHTqwZcsWXFxcaNSoEX5+fiQlJfH1119z/vx5k/tu2bIl3377Lffu3aNGjRpUr16dv//+\nm8mTJ9OrVy8ePXqkt+2ZM2cIDQ3l+PHj1KxZk6pVqxIfH8/06dOZNGmSTv2MjAzGjRtHz5492bVr\nFyVLlqRx48aUL1+eGzduMH36dP7991+tNosWLaJDhw5s27aN4sWLExgYSNmyZdm7dy+dO3fWOzb6\nVKhQgdatW1OmTBkAqlatSuvWrWndujUBAQGK7a9fv05oaChffPEF58+fx9/fn6CgIDw8PNi3bx8L\nFy4EoHjx4rRu3ZrixYsDULduXU0/2Y8bc+zYMd577z1WrlwJQKNGjfD19eXo0aMMHz6ckSNHkpGR\nobdtQkICoaGh/PTTT/j7+1OzZk1u377NggULNO9qPi8ePHhQ0CmIp0zWL5v0/dIJ4PHjx6xcaXiR\nrxUrlvD48eM895+RkcHy5Yv1/v1MT08nKup7g+VK0tPTFdump6ezcOFcg+Xz53/NypVLDZabe/5K\nlMZ/+fLFREUZHr/ly/M2dlmUrs/y5YtN7scS+eQ3U87f2PmY214IIQrSM/0Y6uLFi0lOTuajjz6i\nT58+WmXJyck6E79du3bx5Zdf4uzszPTp03XuTp04cYISJUoo9puamkq/fv24desWw4YNo3v37tjb\nZw5lYmIiQ4cO5cCBA8ybN4+BAwfqtF+6dCkDBgygf//+2NpmztcPHTpEly5dWLFiBT179sTT01NT\nf8mSJaxatYrixYvz9ddf4+/vrynLyMjg999/p0iRIppje/fu5fPPP6dEiRLMmTMHPz8/TVlcXBy9\ne/dmwoQJVK9enddee83ouQYHBxMcHExERARXr16lXbt2Ju9JmJ6ezoABA7h06RJBQUFMnjxZK8+k\npCROnsxcSOKNN95gypQpdO7cmX///ZfevXvnaoGbhw8fMmTIEO7du0fXrl0ZNWoUdnZ2AJw/f55u\n3bqxadMmqlatSseOHXXar1+/nnbt2jF27FgKFSoEwMWLF2nbti0//fQTcXFxJk2OhXgezZ07k/R0\nw5Ohx48f8+23s+nff0ie4h87FsepUycMlhsrM8XJk8c5duwIVaro/zscHb1G7127LFmrPxti7vkr\nURr/9PR0/vjD8Bgpnb8Spetz8uRxoqPXmHydzM0nv5ly/sbOx9z2QghRkJ7pyeJ///0HQL169XTK\nXFxcqFKlitaxOXPmADBy5Ei9jzFWrlzZpH6jo6O5du0aTZs2pXfv3lpl7u7uTJkyhaCgIJYvX86A\nAQOwsbHRquPr66tzvHr16tStW5dffvmFgwcP8t577wGgVqv59ttvAZg8ebLWRBHAxsaG2rVr6z3P\niRMnak0UAQICAujXrx9ffPEFq1ev1jxKag179uzh9OnTlC5dmi+//BJHR0etcldXV53c82rbtm0k\nJCRQunRpRowYoZkoApQrV46BAwcSGRnJokWL9E4WPT09GTNmjGaiCJkT2FatWrFy5Up+++03mSw+\ng16E7UGsLSnpPr//fkCx3m+//UrXrj1yvZWEWp3G8uWG79pZyvLlS/D1rayzVUFq6gN++GGD2fHz\nev5KTB1/JYbOX4mp1ye3Y5jXfPKbqedv6HzMbS+EEAXtmX4MNWtyN27cOPbv32/wsU+Af/75h7Nn\nz+Lg4EDr1q3N6veXX34BoEmTJnrLS5YsSdmyZblz5w6XL1/WKa9fv77OBBLg9ddfB+DWrVuaY6dO\nneLOnTuUKlVK76Q4p9u3b3PixAlcXV2pW7eu3jrVq1cHMh/btKascWrRooXORNHSDh06pOlL30qp\noaGh2NjYcOXKFW7evKlTXqtWLb056rsmQgghhBBCvAie6clijx49qF27NsePH6d79+4EBATQvn17\npk6dyrlz57Tq/v3330DmHSRzJy7x8Zkvow8ePFhrEZbsfy5cuACgdyGR7I+YZufq6gqgtaBKVt5K\nj4tmuXbtGpD5iGfFihX15tauXTuDuVlSVu5ZEy5rypoAenl56S0vXLiw5hFjfZNFpWti7BcRQjzP\nXF3dqFXrbcV6tWvXzdNdNXt7B8LCuuQltVwJC+uq966No6MTLVqY9wtEyPv5KzF1/JUYOn8lpl6f\n3I5hXvPJb6aev6HzMbe9EEIUNKs+hvrvv/8SGxtLQkICDx48YMCAARaN7+TkxOLFizl+/Dj79u3j\nyJEjHD16lOPHj7Nw4UIGDhyo6VPfnby8ylrIoEGDBhQtWtRoXXd3d51jWe8pWkPWaqpubm4EBwcb\nrauUu7ksOebWZs1rIgpOVNQ6i8f08MicEPzzz32Lxy4oSo/rfvjhYGJjDxp8b87Ozo6+fXXfzzaV\nv38APj6VDb7X5eOT+RRJXt9d9PX1w9+/qsHy0ND27Nq13eB7iy4uLjx48MDgatXmnr8SpfG3tbWl\nQgUfg+8tKp2/EqXr4+vrR2hoey5cOG/SNTI3n/xmyvkbOx9z2wshREGyymTx4cOHfPbZZ6xfv15r\nhbjsk8V79+4RFBREcnIy27Zt07t/n6n8/Pw07+Y9evSILVu28OmnnzJnzhyaNWvG66+/rrlzlJCQ\nQGpqqll3Fz09Pbl06RKdOnWiQYMGeY5jildeeQWAS5cumVQ/6zzt7e2ZMmWK1fLKTS6m5m6OkiVL\nAk/urOb08OFDzaOkWXVfVE5OTrIiqtDi6Oho9N9FOzs7wsK6smzZIr3l77/fVes94dyysbEhPPwD\nRo/+SGdVSFtbW8LDPwDQW67E1taWsLBuRn95ZWtrS+/e/Zgx4wu95b179+fff/+12vkrURr/sLBu\nVKrka3D8lM5fidL1CQvrprlOStfIEvnkN1PO39j5mNteCCEKksVvp6jVanr16sWaNWtwcHCgZs2a\nWouGZHnppZdo164d6enpbN1quT2qChUqRGhoKH5+fmRkZGgeR/Xw8EClUpGWlsbGjRvN6iPr3cHt\n27ebna+SSpUqUbRoUW7cuMG+fcqbjZcsWZJy5cpx584dzd6EBeWddzI3a9+8ebPJexVmvW+Y22Xo\ns97D3LJlC2q1Wqd8w4YNZGRkULZs2Rd+shgeHk6xYsUKOg3xFAkNbY+7e1FCQ9sbrBMS0gwXF1ed\n466uroSENDM7By8vb4KCGuscDwpqjJeXt8FyJVntlQQE1NBbz9u7DAEBNax+/kqU+lcaP3OZEt+U\na2SpfPKbueNr7esjhBDWYvE7i+vWrSM2NpZXX32VBQsW4O3tTd26dTUrl2bXrFkzFi1axO+//86H\nH36Y676WL19O7dq1dd6Ji4+P17wzWLp0ac3xAQMGMHDgQL744gs8PT2pX7++VruTJ0/i4eFBqVKl\njPbbvn17lixZwoYNGyhdujQ9e/bEyclJJ4cjR47QqlWrXJ9Xdg4ODvTp04cpU6YwevRovvnmG61V\nWzMyMjh48CCVKlXCzS3z8bjBgwfTv39/RowYwWeffaaz0M3jx4+JjY3FyclJZ3VVSwoKCqJChQqc\nOXOG4cOH89lnn2lyhCdbZ2RfETVrInfx4kXeftv093SaNm3KV199xbVr15g+fTojRozQPFp64cIF\nZs+eDUD37t0tcWrPtLZt29K2bVtCQkI0xx5/q/yLCEPMaSueDs2ataRZs5aK9UaMGE1k5Cdax4YP\nH22xPNq06cCBA7+SkpK5VYWzs4vWBDZnuZOTMwAPHqTo/ZyzvZLBg4czYoT2vqqDBg3TfG3t81ei\n1L/S+JnLlPg562Rn6Xzym7nja+3rI4QQ1mDxyeKmTZuwsbHh008/xdvb+G/Lypcvj52dHRcvXsxT\nX2vWrGHChAl4e3vz1ltv4ezszL///ktcXBxpaWk0b95ca2LVuHFjBg4cyOzZs+nduzflypXjrbfe\nIjk5mUuXLnHlyhWWLl2qOFl0cXFh3rx59O3blzlz5hAVFUW5cuUoUaIEycnJ/PXXX1y5cgU/Pz+z\nJ4sA3bp14+LFi6xdu5b27dvj4+ND2bJlSUxM5OLFiyQkJLB7927NRCxrX8SpU6fSo0cPXn31VV57\n7TVcXFz4559/OHPmDPfu3SMyMtKqk0VbW1tmz55Njx492LlzJ/v37ycgIAA3NzcSEhI4e/YsPj4+\nWpPF4OBgoqOj+eKLL9i/fz8vv/wykLmYkbGFcgoXLsxXX31Fr169WLRoETExMfj6+nL37l0OHjxI\nWloarVq1okOHDlY7XyGed2++qcLLy5tr1zIX+fL2LsObb6osFt/N7SVCQ9sRFbUYyLzj6eb2ksHy\nNm06ABkGP+dsr8TTszQBAdWJi8tcXblatRp4ej75haO1z1+JUv9K42cuU+LnrBMQUIO4uFir5JPf\nzB1fa18fIYSwBotPFi9cuICdnZ1JG6rb29vj6upKYmJinvoaMmQIP/30EydOnODo0aMkJSVRvHhx\natSoQdePAesAACAASURBVLt27bTunGQZMGAAtWrVYtmyZcTFxbFz505cXV3x8vJi4MCBqFSm/cev\nUqnYvHkzK1asYPfu3Zw+fZqjR49SrFgxPD09ad68ud7+88LGxoaJEycSFBTEqlWrOHHiBGfPnsXd\n3Z0yZcoQHh6Oh4eHVpsPPviA2rVrs2zZMmJjYzlw4AB2dnaUKFGCatWqERgYSKNGjSySnzHe3t5E\nR0cTFRXFzp07OXz4MOnp6RQvXpwGDRoQGhqqVT8oKIhx48axevVqfvvtN1JTUwFo2bKl4qqq/v7+\nbNy4kfnz57Nv3z527tyJo6Mj/v7+tG/fnhYtWsh7IUKYacyYCYwdm7k/6yefTLB4/ODgEM3f++Bg\n3X9DdcszFD7nzocfDtK8u9i37yCdcmufv5IxYyYQGTnaYP9K42cuU+Jnr9Okybts377FavnkN3PH\n19rXRwghLM0mI7erBSioXLkyDg4OxMXFaY5lPYZ65swZnfr+/v7Y2dlp1RfiRfLokZq7d/NvwZnn\ncTXP/CTjZz4ZQ/PI+JlHxs88Mn7mkfEzj4xf3hUp4kShQrm/T2jxBW48PDxISUkx6W7h2bNnSU1N\n1az4KYQQQgghhBDi6WDxyWKNGjWAzNUnlcyePRsbGxvq1Klj6TSEEEIIIYQQQpjB4pPF7t27Y2Nj\nw9dff82BAwf01rl16xbDhw9n9+7dODg40KVLF0unIYQQQgghhBDCDBZf4Oatt97i448/ZuLEifTo\n0YO33nqLe/fuAZmLy/z999+cP3+ex48fY2Njw/jx4+UxVCGEEEIIIYR4ylh8sgiZm36XKlWKzz77\njPPnz2uOx8TEaL729PTk008/JTAw0BopCCGeAYmJdxg5cghpaY9o1KgJarWaXbu2A1C4sCO2tjZ4\neXmTmJhIrVpvk5SUxJ49uwCYOHEqXl5ebNy4jgMHfuXll4vz3nttmTx5PACRkZN58823SE19oFnd\nsl279xk//mMAJk/+Umcz7KSk+5qVLidMmIKjoyObN0dz7txZVKryNGvWkhUrfgCgYcMm2Ns7KJ6j\nWp3Gli2bAKhbtz6ffDIcgM8//wp396JadbPnOnToSBwdnQzGevfdVjr9K5UrxTeFUh95rSvyh1wT\nIYQQuWHx1VCzS09P5+DBgxw9epR//vlHs2WCv78/tWvXxt7eKnNVYSVZ24qcO3eugDN5vhTEaqiW\n2NYlKmqd2TH69+/J3bt52zrHxsaGsLCumj3L9ImKWseMGZ9r9s3LztbWlqVL12gdi4gYqrWHXf36\ngVrxs+8ZFx7+AU2aNFfMc/v2LZoYjo5OpKZmXmt3d3fmzFmoVTd7rtWq1WDIkJEGY+nrX6lcKb4p\nlPowpa6sZmcec8YvN9fveSXff+aR8TOPjJ95ZPzy7qlZDVUruK0ttWvXpl+/fowbN47x48czcOBA\n3nnnHZkoCvGCO378SJ4nigAZGRmsWhVltM6CBd/onShC5i+ztm7drPl84cI5zUQRID7+KmvXrtRq\nkzVRBIiOXsP9+/eM9n///j2io9dqPmdNFAESExM5fvyI5nNCwnWtXA8fjiUh4brBWDn7VypXim8K\npT7yWlfkD7kmQgghcsvik8Xq1atTs2ZN4uPjlSsLIV5YM2ZMNTuGWq02Wr537x6j5StWLNV8PXXq\nZJ3yhw8fGmybkpJMdPQag+UA69evJiUl2WD5V189GYOZM6fplM+aNd1grJz9K5UrxTeFUh95rSvy\nh1wTIYQQuWXxyWJaWhqPHz/G29tbubIQIt+tW6f9+Kh9n/d0Phv6YykrVy5DrU6zWDxzTJkynm3b\ntpCcnJTrtrt379S6G5ldfPxVdu/eabR9Wloaq1cv59Chg3rjxMdfJS4u1mCsrP6VypXim0Kpj7zW\nFflDrokQQoi8sPhk0dPTk7S0p+OHQGE9W7dupUOHDlSpUoUqVarQtWtXDh8+bLD+9evXiYyMJCgo\nCB8fH6pXr07nzp354Ycf9NafPXs2KpWK2bNnc/36dUaPHk29evWoWLEikyZNAiA6OhqVSkVERAR3\n7txh4sSJBAYG4uPjQ79+/bTiHTlyhIEDB1KnTh18fHyoU6cOgwYN4tixY3r779y5MyqVioMHD3Lo\n0CF69+5NzZo1KV++vNZCTc+iqCjjj25am1qt5scfNxVoDtmdOnWS5csX56lteno6y5cvJuer3xkZ\nGXqP6/PDDxtYuHCuwfL58782GCur/6io7w2WL1u2yGj8BQu+IT093WiOxs4n5xjkpq7IH3JNhBBC\n5JXFJ4uBgYE8evSI/fv3Wzq0eErMnDmTYcOG4eDgQP369SlVqhS///473bp14+jRozr1jx07xnvv\nvcfKlZnvfzVq1AhfX1+OHj3K8OHDGTlypMEfVC5fvkzr1q3Zt28f/v7+NGzYkJdeekmrzp07d2jb\nti0//PAD5cuXJzAwkOLFi2vKV6xYQVhYGDt37sTT05OQkBA8PT3ZsWMHnTp1Ys0aw49hbd++nS5d\nupCQkECdOnWei4WZHjzIv8V09Jk8ObJA+7e0kyePc+zYEa1jx47FcerUCZNjGLurmZycbDTWyZPH\n+eOPkwbL//jjpNH4SUlJbNiw1mA5KJ9P9jEwpW5srGl3M4Vl5Ob6CSGEENlZ/Kfevn37smPHDj79\n9FMWLFjAG2+8YekuRAFbsWIFa9euxcfHB8j8zfS4ceNYs2YNs2bN4vvvv9fUffjwIUOGDOHevXt0\n7dqVUaNGYWdnB8D58+fp1q0bmzZtomrVqnTs2FGnry1bthAaGsr48eMpVKiQ3nx+/vln6taty8yZ\nM3F1ddUqO3v2rOZO5FdffUXTpk01ZT/++CPDhw9nwoQJ+Pv7U65cOb3nOmHCBDp06JDLUXr+hYe3\nLegUnhrLly/B17cy9vYOqNVpLF++VLnRU2Tz5miaN2+pdysNU89n+fIlVKhQ0aS68+fPp2rVqnnK\nVeRObq5f1vewEEIIkcXik8Xdu3fTqVMnvv76a9577z3q1auHv78/xYoV00wS9HnvPcu9DyWsa+DA\ngZqJImSuejt48GDWrFnD4cOHSUtLw8Eh8weObdu2kZCQQOnSpRkxYoTW90C5cuUYOHAgkZGRLFq0\nSO9k0d3dnU8++cTgRBHAwcGB8ePH60wUAZYuXYparebdd9/VmigCNG/enJiYGLZu3crSpUuZOHGi\nTvs6derIRFEIIYQQQryQLD5ZjIiIwMbGRvNY4Z49e9izx/iKhCCTxWdJgwYNdI4VL16cIkWKcPfu\nXRITE/Hw8ADg0KHMpfpbtGihmUBml3XX8MqVK9y8eZOSJUtqlb/99tt6J4HZVaxYES8vL71lWf2H\nhobqLW/Tpg1bt241+Fhco0aNjPYtBEBYWFfNHRl7ewfCwrowffqUAs7KdC1bhuq9qwimn09YWFcc\nHZ1Mqtu7d+////cgNa8pCxPl5vrJXUUhhBA5WXyyWL16dUuHFE+ZV155Re9xV1dX7t69q7XdwM2b\nNwEMTuYKFy5MiRIluHnzpt7JoqG+TK2j1H/Wqr1Z9XIT+0UXFbVOuZIearWabt107yI/q3x9/fD3\n136k0t8/AB+fyia/t+ji4mrwvUIXFxdee+0Ng7F8ff1IT083+N5ipUq+XL58yWB8V1dXWrduZzQ/\npfPJPgam1K1Ro4bR/oRl5eb6CSGEENlZfLK4bNkyS4cUTxlbW4uvi2SQo6OjRerY2NhYrX+RO/b2\n9rRo0ZoffthQ0KkA4OPjS5Uq1Vm2bFGu29ra2hIW1k3n+8vGxobw8A8YPfojxVUmW7RozZtvvsWM\nGV/oLe/duz8lS3rqjZXVP2CwvHPn7ty8mWAwfq9e/RT/Ths7n5xjkJu6In/INRFCCJFX+fdTv3gh\nZd0pvHbtmt7yhw8fcuvWLa261ug/Pt7QXnjxVuv7aeXkpP9xw/zUoUNYQaegERExjpCQZri4GH/c\nWZ+goMZ4eenfU9bLy5ugoMZG2zs4ONChQxgBATX0xvH2LqMp0xcrq3+lcqX4plDqI691Rf6QayKE\nECIvZLIorCrrseQtW7agVqt1yjds2EBGRgZly5a1yoQtq/+NGzfqLY+OjgZ4oR6LCw8PL+gUAKhf\nP9DsGErbmCj18f77XTRfjxgxWqe8cOHCBts6O7sQGtreaPw2bTrg7OxisHzIkBGarwcPHq5TPmjQ\nMIOxcvavVK4U3xRKfeS1rsgfck2EEELkltUni6mpqdy6dYu///7b6B/xfGratCmenp5cu3aN6dOn\na23+feHCBWbPng1A9+7drdJ/ly5dsLe358cff2TXrl1aZdu2bWPbtm04ODjQuXNnq/T/NGrbVnvL\nC/W8jTqfDf2xpF69+lGkiHue29vY2NCxo/GJb69e/QgI0P8eta2tLc2atdR8fvNNldYdFm/vMrRr\n10mrTfa7cKGh7XFz097zMyc3t5cIDX3yPmD2RWTc3d3x83vynpinZ2mtXKtVq4GnZ2mDsXL2r1Su\nFN8USn3kta7IH3JNhBBC5JZVdhe/f/8+8+bNY8eOHQYfP8zOxsaG06dPWyMVUcAKFy7MV199Ra9e\nvVi0aBExMTH4+vpy9+5dDh48SFpaGq1atbLa9hTly5fn448/5n//+x8DBgzAz88Pb29vrl69yokT\nJ7C1teXTTz9FpVJZpX9h3KRJUxk5cghpaY9o1KgJarWaXbu2A1C4sCO2tjZ4eZUhMfEOtWrVISnp\nPnv2ZE76p0z5kpIlS5GUdJ8DB36leHEPWrVqy+TJkQB89tk0AD78cJDmfb327cOIjMy8g/jFF1/p\n5DNmzATGjo0A4JNPJuDoWJiUlGTOnTuLSlWBZs1asm9fZv8NG4aYdI7BwSGkpmau+vnOO/X/j707\nD4uy6h8//gYGZVNpEUNRe9IcU8AFUXMNRPHB0hhRI8Alc8lcMw2zfMys1DRTLE2tXMCFzTVNhcw0\ne0QwFc21b7mlZT8WBURZ5vcHz0yMM8MAMzgqn9d1eV3MfZb7cx+k+HjOfQ5vv/3m/559vl7d0rGO\nHj2+zL4CAvTvb6rcVP/lYeoela0r7g35ngghhKgIG7Wp3Rcq6Pr164SGhnLlyhWTGzuUdvr0aUuG\nIaqAJqE6c+aMwXJ/f3+uXLlCcnKy3u6jV65cYfny5ezfv5+//voLBwcHmjdvzsCBA3nhhRf0NleI\niopiyZIljB07lnHjxhm8X2JiItOmTSM4OJg5c8reFj4tLY2vv/6aI0eOkJ2dTZ06dfDx8eGVV16h\nTZs2evUjIiJISUlhzZo1dOjQocy+zXXnTiHZ2beq9B6l1a1bi8BA839JrOxuqA+6unVrAXD9+k0r\nR/LgkjE0j4yfeWT8zCPjZx4ZP/PI+FVenTqO1KhR8XlCiyeL7777LnFxcdSuXZvXXnuNgIAA6tWr\nV+ah6kJUZ9ZIFkH+Q1tZMn7mkzE0j4yfeWT8zCPjZx4ZP/PI+FVeZZNFiy9D3bdvHzY2NsydOxc/\nPz9Ldy+EEEIIIYQQ4h6w+AY3mZmZ1KhRg+7du1u6ayGEEEIIIYQQ94jFk0U3NzdsbW3v6cHtQggh\nhBBCCCEsy+IZXUBAAPn5+Rw/ftzSXQshhBBCCCGEuEcs/s7imDFj2LNnDzNnzmTVqlXUri1nOAkh\nBEBhYQHbt28B4Pnn+6FQ2Feonqn2ptoVFhZw9uxpbGxsmTRpqs65j+bGXNn6lm4vhBBCCMuxeLJ4\n9uxZJk6cyOzZswkKCuKll17C09MTZ2fnMtv5+ho+OFsIUbXCw0OsHYKOh/lIjqSkXcTHbwDAwcGR\n3r37VKieqfblaaexbFkUEydOtVjMla1v6fZCCCGEsByLJ4sRERHaM/PUajWfffaZyTY2Njb88ssv\nlg5FCCHuGzdv3iAxMU77OTExls6du1KrVu1y1Sv52nj78rbTSE1N4erVK7i7NzA75srWt3R7IYQQ\nQliWxd9ZrF+/Pu7u7ri7u+t8XdafJ554wtJhCCHEfSUhYSN5ebnaz3l5uSQmxpa7nqn25W1X2uLF\nCywSc2XrW7q9EEIIISzL4jOL3333naW7FEJYUHx8PAkJCfTu/TxBQX11yuxHDNarX7BiTZnlllD6\nHg+aHTu2smPHNoKCXtAbT41Lly6SnLxb73py8m569AjEw6NhmfWSknYZ7FfTXq1WV6hd6bjS0lLw\n8Wlf6ZgrW9/c+wkhhBCi6sn5Fg8opVKJUqkEIC4ujgEDBtC2bVuUSiU3btzQ1svIyODjjz+md+/e\neHt707ZtWwYOHEhMTAyFhYV6/SYmJqJUKomMjCQ7O5vZs2fz3HPP4e3tzb///W/Wr1+vrXvu3Dkm\nTJhAp06d8Pb2JiQkhP379xuM99ixY8ydOxeVSkWnTp3w9PSkS5cujB8/nqNHjxpsExUVhVKpJCoq\nir///psZM2bQrVs3PD098ff3Z/78+dy+fdvoGB07dozJkyfj5+eHp6cnHTp0QKVSsXjxYjIzM/Xq\n//rrr7z99tv4+/vj5eWFr68vQ4cOJTk52eg9HkTR0dFkZGTIjI2FJCbGkpWVaXQ81Wo1MTGrUKvV\nemXFxcXasrLqacqNtY+O/rpC7UpbseJziouLKxVzZesbitOc9kIIIYSoGpIsPuDef/99ZsyYQY0a\nNfDz86Nly5bad0YvXLiASqVi5cqV5OTk4Ofnh6+vL2fPnmXWrFmMGDGCO3fuGOz3xo0bDBo0iG+/\n/ZZWrVrRpk0bLl68yMyZM1m+fDk///wzAwcO5Ndff6VDhw40bdqU9PR0Ro0axeHDh/X6W7hwIatX\nr6awsBBvb2/8/f155JFH2LVrFy+//DI7d+40+oxXr15FpVKxd+9eWrduTYcOHcjIyGDFihVMmDDB\nYJsvvviCQYMGsX37dpydnenZsyetWrUiJyeHzz77jLNnz+rU/+abb+jXrx8JCQk4OTnh5+eHUqkk\nNTWVMWPGsGjRovJ+S+57t27dAiA/P9/KkTwcNONobDyPHk3jxAnjRwmlpx/j6NEjJuuV1f7kyfQK\nt9PIyclh0ybddxrLG3Nl69/N3PZCCCGEqBoWX4aqoVar2bNnDz/++CPXrl0jPz+f1atXa8vz8vI4\nefIkNjY2tGvXrqrCeOht2bKFjRs34u3trVc2efJkrl69Su/evZk3bx41a9YESpKvoUOHcvDgQaKi\nopg8ebJe2+TkZAIDA/n444+17fbt28fIkSNZunQpGzZsYNy4cbzyyivaNnPnzuWrr75iyZIlOt9r\ngFdeeYX58+fz+OOP61z/7rvvGD9+PDNnzuS5557D0VF/K/+EhAQGDBigTYqhZBYwJCSEvXv3kpaW\nho+Pj7b+nj17+OSTT3BycmLBggX4+/vr9Hf8+HHc3Ny0n0+fPs1bb72Fvb09n332Gd27d9eWnTt3\njhEjRvD555/ToUMHOnbsqBffg+x+2wkV7s+YKquwsICYGNNLbGNiVmHNibOtWxPp06cvDg6OFYh5\nNV5e3v/7uvz1DR33YU57IYQQQlSdKplZ/P333+nbty8TJkxg48aN7Nu3j5SUFJ06NWvWZPr06URE\nRJCamloVYVQLr776qsFEMTU1lfT0dJydnXnvvfe0CR+Au7s706dPByAmJsbgUk5nZ2dmzpyp0657\n9+40b96cvLw83NzcdBJFgFGjRgGQlpZGQUGBTlm3bt30EkUAf39/AgMDycrK4tChQwaf0d3dnXfe\neUebKAI0adKEfv36AfDTTz/p1F+yZAkAU6dO1UsUAby9vXU2VVq2bBkFBQVMmTJFJ1EEePrpp4mM\njARKxkoIIYQQQojqwuLJYnZ2NsOGDePcuXM0a9aM8ePH4+LiolfPzs6O0NBQ1Go1u3frb2ogyqdn\nz54Gr2uScz8/P1xdXfXKu3XrRt26dcnNzeXEiRN65Z6enjz66KN61xs3bgxAly5d9MpcXV1xdXWl\noKCArKwsvfKS9+QSmTt3LtOnTycyMpLIyEjOnTsHlPwjgyEdO3bEwcFB7/pTTz0FwF9//aW9dv36\ndU6fPo29vT3BwcEG+yutuLiY/fv3Y2NjQ+/evQ3Wad++ZPOPn3/+2WR/QpSmUNgTFmZ6U6CwsKGE\nhw+5BxEZ1revCgeHkln98sc8BIXCvsL172ZueyGEEEJUHYsvQ/3qq6+4evUqXbp0YdmyZSgUCtat\nW0durv7W7f7+/sydO1d+CTdDgwaGz0j7888/AfDw8DDatmHDhly/fl1btzRjx5k4OTmVWe7s7ExW\nVpbebOWGDRuYM2eO9n05Q3Jycgxed3d3N3hd848Qpd+7/OOPP7RtDCWYd8vKytLe99lnny2zrqFN\ncR500dHx992yz+joeGuHUKa6dWsBcP36TcD0stnWrX3w9PQ2+k6el1crWrduC1BmPWO8vFpRXFxc\n6fcWXVxcCA4eUOmYK1P/bua2F0IIIUTVqJKjM2xsbIiMjEShKLv7xo0bY29vz8WLFy0dRrVhKiHS\nbHZTUba2ZU86myov7fjx48ycOROFQsHUqVPx8/PjiSeewNHRERsbGz755BO++OILo7sdVuReFX3e\noqIioGSmu29fw8cePGwcHR25detWuZJpYZqDgwP5+flGx9PGxobw8GFMm/aG3t9xW1tbwsKGav/e\nGqunKTfWHqhQu9JGjBij9zNWkZgrU/9u5rYXQgghRNWweLJ4+fJlatasSdOmTctV39nZ2eiMkqi8\nevXqAXDp0iWjdTRlmrpVZffu3ajVaiIiIhg+fLhe+YULFyx2L80s5NWrV8v8BV7jkUce0f6y/+67\n7+Ls7GyxWO5X4eHh2nMWhflUqoHacxaN8fBoSI8evfTOPezRo5fO+YHG6gUElJylWFb7irTTaNiw\nkcEzFisSc2XrW7q9EEIIISyvSja4ufvMLmMKCwvJycmpFr+g32ua9+z27t1Ldna2Xvn+/fu5fv06\nTk5OeHp6VmksmvsbWrqakZHBwYMHLXavunXrolQqKSgoYPPmzSbrKxQK7fLTXbvKPsD8YRESEsL6\n9euNHiAvKiYoqC9LlqwwOZ79+w/Cyemf/9Y5OTmjUg0sdz1T7cvbrrTx4/V3Qq5MzJWtb+n2Qggh\nhLAsi88senh4cP78eS5dukTDhmX/a/BPP/1EYWEhTZo0sXQY1V67du3w8vIiPT2dWbNm8dFHH2l3\nE/3zzz/58MMPgZJZptI7nlYFzUY0W7ZsISQkRPuPAzk5Obz99tvcuHHDovcbO3Ys48aNY968ebi7\nu+vtcJqenk7dunW1yevrr7/OgQMH+OCDD6hZsyZBQUE6S97UajXp6encuHHD4MY+D5OCFWUfYWCq\nXBhXq1ZtVKoBREevAkpmJGvVql2hemW1L287jXbt2uPubvid54rGXNn6lm4vhBBCCMuyeLL43HPP\nce7cOVatWsW7775rtF5eXh7z5s3DxsaGHj16WDoMASxYsIDBgwezfft2Dh06RLt27cjPz+fQoUPk\n5eXx7LPPMm7cuCqPQ6VSsXr1ak6ePElAQAA+Pj6o1WpSU1Oxt7enf//+JCQkWOx+vXr1Yty4cURF\nRTFy5EiaNWvG008/TW5uLr/99hsXLlxgzZo12mTRy8uLuXPn8vbbb/PGG2+wYMECmjRpQp06dcjM\nzOTUqVP8v//3/xgxYsRDnyyKqhUQEEh+fr7264rWM9XeVLvCwgLOnj2DjY0No0ePt2jMla1v6fZC\nCCGEsByLJ4vDhg0jNjaWdevW4eLiwtChQ3XKc3Jy2L9/P4sXL+a3336jXr16hIaGWjoMQckGQps2\nbWLlypUkJyeTnJyMQqGgadOmvPjiiwwcOBB7+6rfir5OnTokJCSwaNEifvzxR77//nsee+wxevbs\nyfjx49m4caPF7zl27Fg6duzI2rVrSUtLY/fu3bi4uODh4cG4ceNQKpU69fv06YOXlxdr1qzh4MGD\nHD58GIDHH3+cZ555hu7duxMYKL+4CvMoFPa8+KLp3WeN1TPVvrLtKhOLpepbur0QQgghLMdGXdY2\neZWUmprKa6+9Rk5ODra2tqjVatRqNbVq1SInJ0f7uU6dOnz55ZdV/s6cEPezO3cKyc42fqSIpd19\n9IOoGBk/88kYmkfGzzwyfuaR8TOPjJ95ZPwqr04dR2rUqPg8YZVscNOuXTu2bNlCnz59sLW1pbi4\nGLVazY0bNyguLsbOzo6goCASExMlURRCCCGEEEKI+5DFl6Fq1K9fn/nz5zN79mxOnDjB9evXUavV\nPPbYY3h6esoOqEIIIYQQQghxH6uyZFHDwcGBdu3aVfVthBBCCCGEEEJYkMWTxT/++KNC9WvUqEHt\n2rW1xzoIIYQQQgghhLA+iyeLlT0Go379+nTu3JmIiAiefvppC0clhDAmPNw6O09GR8db5b7mKCws\nYN26dQD4+fUGYPv2LQA8/3w/vc8KheHdhgsLCwzWy8+/xcKF81Cri2nWrDkKhb1OubF2pvotT7mp\ntkIIIYSofiyeLFZ2c9UrV64QGxvLpk2bmDFjBgMGDLBwZOJ+pznO4syZM1aORAjDkpJ2ER29GoCi\nIltATXz8BgAcHBz1Pvfu3cdoP4bqLV26mJMn0wH45ZeTeuXG2pnqtzzlptoKIYQQovqx+G6op0+f\nZvHixdSuXZvmzZszZ84ckpKSOH78OMePHyc5OZm5c+fSokUL6tSpw5IlSzh8+DBff/01nTt3pqCg\ngJkzZ3Lq1ClLhyaEEJV28+YNEhPjtJ8TEjaSkBBr9HNiYiw3b94w2Y+m3tWrV0hLO6xXX1NurJ2p\nfstTbqqtEEIIIaoni88sHjlyhDfeeIOuXbsSFRWFQqF7iwYNGtCgQQOef/55xo0bx6RJk4iJieHZ\nZ5/l2WefZfz48ezevZs1a9bw0UcfWTo8IUQZao4Yy+0VS3Q+W1Lpvh80CQkbycvL1X6+dStPp/zu\nz3l5uSQmxjJkyKtl9qOpd+rUSYP31ZSr1WqD7TT9G+u3POWm2gohhBCierL4zOLy5cspKipixowZ\nZsrFXwAAIABJREFUeoliaXZ2drzzzjsUFBTwxRdfaK+PHz8egJSUFEuHVm0olUrtks7ExERUKhWt\nW7emc+fOvP3222RkZABw+/ZtFi9eTGBgIF5eXjz33HMsXLiQgoICnf6GDBmCUqnkm2++MXrPOXPm\noFQqmTdvnvba7du3Wb58OcHBwbRp0wZPT0+6dOnCoEGDWLhwIbdv3wYgKipKG2/p+Es/R2nHjh1j\n0qRJdOvWDU9PTzp27Mjo0aNJTU01OR5xcXEMGDCAtm3bolQquXbtGj4+PrRo0YJr164ZfT6VSoVS\nqWTfvn1G6zwo4uPjCQ0NZceOrdYOxSp27NjK2LEjKvT8ly5dJDl5d4XvlZy8m8uXL5nsZ8+eXTr1\n7paUtMtgO03/xvotT3lqakqZbYUQQghRfVk8WTx27Bi1a9fG3d3dZN369etTu3Ztfv75Z+21pk2b\n4ujoyN9//23p0Kqdjz/+mBkzZuDq6krXrl2xsbEhISGBoUOHkpuby5AhQ4iOjqZp06Z07NiRrKws\nli1bxqxZs3T6iYiIANBu7HG3/Px8EhMTsbW1JTQ0FIDi4mJGjhzJggULuHTpEr6+vgQGBtKkSROu\nXr3KsmXLuHGjZJnbM888Q3BwsLa/4OBgnT+lffXVVwwaNIidO3fy+OOP4+/vT+PGjdm3bx8RERHE\nxsZizPvvv8+MGTOoUaMGfn5+tGzZEmdnZ1QqFUVFRWzcuNFgu6NHj3Ly5EkaNmxI165dTYz6/S86\nOpqMjAwSE42P1cMsMTGWrKzMcj+/Wq0mJmZVpd7HLi4u1rYtu5+y+9a0N9Z/dPTXRsujo782et/i\n4mJWrvy8zL4r+x66EEIIIR58Fl+GmpeXR1FREXfu3DF5HMbt27fJy8vDzs5O57qdnR22thbPY6ud\nzZs3s2XLFpo0aQJAdnY2gwYN4syZM7z00kvUqlWL5ORkatWqBcCpU6cICQkhLi6O0aNH06BBAwD8\n/Pxo0KABqampnD17lmbNmuncZ/v27WRnZ/Pcc8/RsGFDANLS0vjvf/9Ly5YtiY6OxsnJSVtfrVZz\n5MgRXFxcAAgICCAgIIBNmzYBJbOUhuzbt4+5c+fi5ubGkiVLaNWqlbYsLS2NkSNHMmvWLHx9ffnX\nv/6l137Lli1s3LgRb29vnethYWGsXbuWuLg4xowZg7297i6QmiQ5NDT0ofh7eevWLaAkya+ONM9d\n3uc/ejSNEyeOV/p+6enHOHr0CKA2q5+y+i+LqXvm5OSU2ffRo0do08anUrEJIYQQ4sFm8WSxcePG\nnDt3jtjYWMLDw8usGxcXR2FhIU899ZT2Wk5ODjk5OTRq1MjSoVU748eP1yaKAHXq1OGll17io48+\n4vz582zfvl2bKELJDF+3bt347rvvOHz4sDZZtLOzIzQ0lPnz57Nu3Tpmzpypcx9NMvXyyy9rr2lm\nhn18fHQSRQAbGxt8fCr+y+eSJSXvu82ePVsnUdTcZ8yYMcybN4+NGzcSGRmp1/7VV1/VSxQBnnzy\nSbp27coPP/zAnj17CAoK0pZlZGSwc+dOatasSf/+/Ssc8/3OWsdmWPve5VVYWEBMzBqz+4mJWcWD\nOkEXE7MaLy9vOUpDCCGEqIYsPk2iUqlQq9XMmTOH5cuXa2cxSrt16xYrV65kzpw52NjYoFKptGWa\nJal3z16JijO0ZLJx48ZAyRLg0omkxpNPPgnAX3/9pXN9wIABODg4sHXrVp2ZiGPHjhlcotmyZUvs\n7OyIj48nJibG7GXFGRkZHD9+HBcXF7p06WKwjq+vL1CybNSQnj17Gu1f8w8b69ev17keHx/PnTt3\n6NOnD66urpUJXQghhBBCiAeSxWcWBw8ezI8//sj+/ftZuHAhn3/+Oc2bN8fNzQ0oSUJOnz7N7du3\nUavVdOnShcGDB2vbx8WVbN9uLCEQ5ffEE0/oXdPM8hkqK12u2XxGw9XVlRdeeIG4uDi2bNlCWFgY\nYHyJZqNGjZg2bRpz585l1qxZzJo1i4YNG9KmTRt69OhBz5499ZYfl+Xy5ctAycxzixYtyqyr2cDn\nbpqZUkO6devGk08+SUpKCufPn6dp06YUFxezYUPJuXOlZ01F9aFQ2BMWNpgFCwwvjS6vsLChgNrs\nfqwhLGyIzCoKIYQQ1ZTFk0VbW1uWLl3K559/ztdff82tW7cMzvQ4OjoydOhQxowZo5NkfPrpp6jV\n6golEsKwst6vq8y7d+Hh4cTFxbFhwwbCwsLIzMwsc4lmREQEvXv3JikpibS0NI4cOcLWrVvZunUr\nzzzzDNHR0dr3Fk0pLi4GoFatWgQEBJRZ95FHHjF43cHBwWgbGxsbwsLC+OCDD1i3bh0zZsxg3759\nXLlyBS8vL7y8vMoV54MmOjreastBo6PjrXLfij5v69Y+eHp6V/p9Qy+vVrRu3RbArH7K6r+4uJiT\nJ9MNlnt6liy9NnZfFxcXo+8tlo5dCCGEENWPxZNFAIVCwfjx4xk+fDg//vgjv/zyC5mZmUDJL/It\nWrSgc+fOODs767V9GDYQeVg1b94cX19fDh8+zOHDhzl69Ci3b99GpVIZXaJZt25dQkNDtbuknj59\nmilTpnDq1CmWL1/OG2+8Ua57a3bXVSgURjfAMZdKpWLhwoVs2bKFyZMna2dNNbOoDwtHR0du3bpV\nZvL8MHNwcCA/P7/cz29jY0N4+DCmTXujwjuD2traEhY2FBsbG4Ay+rGhrB1RNe3vbqfpHzDYr62t\nLeHhw8osHzFiDJ9++rHRvjX3FkIIIUT1U6WZmbOzM7169WLixIm89957vPfee0ycOJFevXoZTBTF\n/U/zbl90dHSllmg2b95cu+z49OnTOmWaXUgLCwv12tWrV49mzZqRmZnJoUOHKhW7KS4uLgQHB5OT\nk8Nnn33GgQMHcHV11dnw5mEQHh7Oo48+iko10NqhWIVKNRBX10cq9PweHg3p0aNXhe/Vo0cvPDwa\nmuynZ89AnXp3CwgINNhO07+xfstT7uPTvsy2QgghhKi+qmRmUTy8AgICcHd359tvvwUwukTzp59+\n4s6dO3Tu3BmF4p+/ZkVFRfzwww9AySY7pbm5uXHlyhV+/fVXlEqlXp8TJkzg9ddfZ8qUKXz44Yd6\n77UWFRWRkpKCo6MjrVu3rtTzhYWFsW7dOr788ksA+vfvT82aNSvV1/0qJCSEkJAQrl+/qVd2e8WS\nMj8/DIKC+hIU1LfC7fr3H8TBgwfIy8sFwNGx5P3eW7fyDH52cnI2mJDe3Y+mXk7OTaZMmaBXv3Q/\nhtqZ6rc85abaCiGEEKJ6kjWfokIUCoV2SSkYX6J55swZRo4cSceOHRk8eDCTJ0/m9ddfp3v37uze\nvZu6desyYsQInTaa3UqHDh3KpEmTmD59OtOnT9eWBwQEEBkZyd9//83w4cMJDAxk9OjRTJ48mcGD\nB9OxY0eGDh3KqVOnKv18TZo0oXPnzkDJMrzSzyqqt1q1aqNSDdB+7t9/EP37DzT6WaUaSK1atU32\no6nn7t4AHx9fvfqacmPtTPVbnnJTbYUQQghRPcnMoqiwTp068cknn5S5RNPf35+bN29y+PBhLl68\nyM8//4yTkxP169fnpZde4uWXX+bRRx/VaTNp0iRsbGzYs2cPe/bsoaCgAIAPPvhAW2fYsGE8++yz\nrF27lpSUFA4ePIidnR1ubm60a9cOf3//Mo/IKO/zHThwgG7dutGwoSzDE/8ICAjEzq7k3T4/v0BA\nTX5+vrZM/7PxfgzVe+218SxcOA+1uphmzZ5BoVDolBtrZ4lyU22FEEIIUf3YqCu6Y4Oo9j788ENW\nr17Nq6++ypQpU6wdjsW9+OKL2g14unfvXuX3u3OnkOxs/fNIq0rdurUADC5DFabJ+JlPxtA8Mn7m\nkfEzj4yfeWT8zCPjV3l16jhSo0bF5wllGaqokKtXrxIXF4e9vb12s5uHyZ49ezh16hRNmjShW7du\n1g5HCCGEEEIIq5FlqKJc5s+fz7Vr1zh48CB5eXkMHz5ce5zFgy4zM5P58+eTnZ3Nvn37AJg6daoc\nGSCEEEIIIao1SRZFuezYsYM//vhDuzHNhAn6uzY+qHJzc4mPj0ehUNCoUSNGjRrFc889Z+2whBBC\nCCGEsCpJFkW5fPfdd9YOocp4eHhw5swZa4chhBBCCCHEfUXeWRRCCCGEEEIIoUdmFkWFJSYmMm3a\nNIKDg5kzZ45F+758+TI9evSgQYMGerOZ/v7+XLlyheTkZDw8PLTXIyMj2bRpEx999BEqlcqi8VQH\n4eEh1g4BgOjoeGuHYLb8/Fuljr5ojkJhz/PP90OhsAcgJ+cmM2ZEAjBr1hxcXGqVu08oOVpj6dLF\nAEyaNBUHB0eduqb6z8rK5K23JgIwd+6nuLo+olNeWFjA9u1bKC4uAsDW1k4n/rvjmTRpKgqFgu3b\ntwDo1TUkKyuT0aOHAjBnzkK9GEypzBhWxN3Pd/cYW1thYQHr1q0DwM+vt8nxFpah+dmA8v09F0KI\nh4Uki8IgY4mZEMK4pUsXc/JkOgC//HISAAcHR3r37gPA7Nkz+OuvPwH44IP/8NFHn1Soz+nTp5Cd\nnQXAsmVRTJw4Vaeuqf6nT59Cbm4uAO+8M4UlS1bqlCcl7SI+foPOtdLx3x3PsmVRNG/eQtvm7rqG\nTJ8+hZycHKMxmFKZMayIu5/v7jG2tqSkXURHrwagqMjW5HgLyyj9s1Gev+dCCPGwkGWo4r5Sr149\nduzYwapVq6wdihAVcvXqFdLSDutdT0yM5ebNG5w/f4bLly9pr1+6dJHz58t+V/buPjWJIkBqagpX\nr17RfjbV/7FjR3TaZ2VlcezYEe3nmzdvkJgYZzR+Q/GkpqYQH7/RYF1DTMVgSmXGsCIMPV/pMba2\nu79HpsZbWIaMuxCiOpOZRXFfsbe3p0mTJtYO46EWHx9PQkICvXs/T1BQX50yp5Hv6nzOW/6+0TJL\nKN3/g2jHjq3s2LGNoKAX+OGHvQbr5OXlkpgYy8GDB/TK5s//iGXLVhntf9Gi+WXef/HiBdqZtY8/\n/qjM/hcu/Fiv/NNPP+brr9cDkJCwkby8XKPxDxnyqsF48vNvGaxriKkYTDH1jOYy9Hylx9ja7v4e\nmRpvYRky7kKI6kxmFoWOxMRElEolV66U/Gt6jx49UCqV2j+XL1/WqZ+Tk8PcuXPx9/fH09OTrl27\n8p///IesrCyjfUdGRpKZmcns2bO17caMGQOUvLOoVCrx9/e32DMdO3aMSZMm0a1bNzw9PenYsSOj\nR48mNTXVYH3NswLExcUxYMAA2rZti1Kp5MaNB/9fk6Ojo8nIyCAxMdbaoTzwEhNjycrKJDZ2nc6M\n192SknaRm5ujdz0nJ4ddu3YYbHP48KEy+4SSmbW0tBR27txeZv/r16+lsLBAr7ygoICNG2O4dOki\nycm7jd4nOXk33377jcl4NHUN1TMVgymmntFcxsZbM8bWZux7ZGy8hWXIuAshqjtJFoWORo0aERwc\njJOTEwCBgYEEBwdr/2iuA9y8eZPQ0FASExN55pln6NKlC/n5+WzYsIFXXnmFggL9XwwBMjMzCQkJ\nYdu2bTRv3hx/f38ef/zxKnmer776ikGDBrFz504ef/xx/P39ady4Mfv27SMiIoLYWOMJ0/vvv8+M\nGTOoUaMGfn5+tGzZEhsbmyqJ8166datkJig/P9/KkTz4NGNYWFhYZj21Wm20bN261RQVFelcKy4u\nZuXKpeWKYdmyKNavX1tm/998s8Vo+bZtm1iz5ssyYywuLmb9+jXliqe4uJiYmFU6/RUWFpqMoawx\nLCoqMvmMd49hRZga7xUrPqe4uLjS/ZtLrVbrjamGofEWliHjLoQQsgxV3KVdu3a0a9eOlJQU8vLy\nmDp1qtENbpKSkujevTsbNmzA2dkZgD///JNBgwZx8uRJdu7cSd++ffXaff/993Tp0oVFixbh4uJS\nZc+yb98+5s6di5ubG0uWLKFVq1basrS0NEaOHMmsWbPw9fXlX//6l177LVu2sHHjRry9vassRiGK\niopYtiyK11+fqL2WmBhrcBbNEE3yX1b/ppw6ddJknYokY+npxzh69Aht2vgA8NFHM022mTNnFu+8\nM8tg2dKli7Q7tBqL7e4xrAhT452Tk8OmTXH07z+oUv2b6+jRNE6cOG60/O7xFpYh4y6EEJIsCjM4\nOTnxwQcfaBNFKNmgJiwsjPnz5/PTTz8ZTBbt7e157733qjRRBFiyZAkAs2fP1kkUAXx8fBgzZgzz\n5s1j48aNREZG6rV/9dVXH/pE8X45NgPur1jutZ9+OsCQIcNxcalFfv4ttm3bZO2QzBYTsxovL29y\ncnI4c+a0yfqnT/9CVlam3lEaOTk3+e9/D5psX3oMK6K84711ayJ9+vS950dpFBYWEBNjelZXM95y\npINlyLgLIUQJWYYqKs3T05O6devqXX/qqacA+Ouvvwy2a9GiRZUfx5GRkcHx48dxcXGhS5cuBuv4\n+voCcPToUYPlPXv2rLL4hBBCCCGEuN9Jsigqzd3d3eB1zYzhnTt3DJbXr1+/ymLS0GzEk5OTQ4sW\nLXQ26dH8GTBgAFCSWBrSoEGDKo9TCIBnn+2inRFzcHDkhReCrRyR+cLChqBQ2OPq+ghKZXOT9Zs3\nb6E3qwjg4lKLjh07mWxfegwrorzj3bev6p7PKgIoFPaEhQ02WU8z3sIyZNyFEKKELEMVlWZrW7l/\na3BwcLBwJPo0m1HUqlWLgICAMus+8oj+L6hwb+K0tujo+Ptm+Wd0dLy1QyiXunVLEpLr129aZOzs\n7OwYPXqczjWVaiB79nxbrvcWHR0duX37jtF3+uzs7Ey+b/jMMy1NvrdYnn40vLxa0bp1W+3nadNm\nMnToS2W2iYycYbTstdcmkJJyqMxnvHsMK8LUeLu4uBAcPKDS/ZurdWsfPD29jb4/d/d4C8uQcRdC\nCEkWxUNKM+upUCiYM2eOlaO5vzg6OnLr1q1qkQxXNQcHB/Lz81EoFGXu5mljY2N018SXXx6CnZ2d\nzjVbW1tGjhzDwoXzTMYwevQ4/v77b9au/cpo/1lZmUbfy3vhhWA6d+7GtGlvGI3R1taWl18eYvQe\nd9cNCxuqs3OwQqHghReCy4xBoTD+vyM7OzvCwozf39AYVoSp8R4xYkyl/3HMEmxsbAgPH2bwe2Ro\nvIVlyLgLIYQsQxVG2NuXLKsxZzt6a6pXrx7NmjUjMzOTQ4cOWTuc+0p4eDiPPvooKtVAa4fywFOp\nBuLq+ggDB76Mh0dDo/UCAgJxdtbf0MnFxYXAwCCDbXx82pfZJ0DDho3w8WlPYGBQmf0PGhRmcKmc\nvb09gwaF4eHRkB49ehm9T48evQgMDDIZj6auoXqmYjDF1DOay9h4a8bY2ox9j4yNt7AMGXchRHUn\nM4vCIDc3N37//Xd+/fVXGjdubO1wKmXChAm8/vrrTJkyhQ8//FBvo5uioiJSUlJwdHSkdevWVory\n3gsJCSEkJITr12/qleUtf99ou7LKqqugoL4EBZXs+NumjQ9TpkzQq+Pk5IxKNZDOnbsyc+Z0nbI3\n35xWZv8TJrxpsE+N8eMna7+eMmVamf1PmjSFjz/+UKd84sQp2q/79x/EwYMHyMvLNRi/sXgcHBzJ\nz7+lV9cQUzGYYuoZzWXo+UqPsbXd/T0yNd7CMmTchRDVmcwsCoM0O4G++eabjB8/nunTpzN9+nQy\nMzOtHFn5BQQEEBkZyd9//83w4cMJDAxk9OjRTJ48mcGDB9OxY0eGDh3KqVOnrB2qeAi4uzfAx8dX\n77pKNZBatWrTtKlSZyaiYcNGNG2qrFCfdeq4ar9u16497u7/bMJkqv9WrdrqtHd1daVVq3/et6pV\nqzYqlf57eZr4DcXTrl17QkIGGaxriKkYTKnMGFaEoecrPcbWdvf3yNR4C8uQcRdCVGcysygMCg8P\nJycnh23btrF3717tzqavvfaalSOrmGHDhvHss8+ydu1aUlJSOHjwIHZ2dri5udGuXTv8/f3liAxh\nMa+9Np6FC+ehVhfTrNkzKBQKAgICteXvvDOLGTNKzvScPt3wAfTG+iz5egJLly4CYPTo8Xp1TfX/\nwQcf89ZbE//39Xy98oCAQPLz8/+3kYwNtra2OvHfHc/o0eNRKOzIz8/Xtjflgw8+JjJyktEYTKnM\nGFbE3c93vwkICMTOruT9OT8/0+MtLEPzs6H5WgghqgsbtbEdDYQQ98SdO4VkZ9+6Z/crvZunqDgZ\nP/PJGJpHxs88Mn7mkfEzj4yfeWT8Kq9OHUdq1Kj4PKEsQxVCCCGEEEIIoUeSRSGEEEIIIYQQeiRZ\nFEIIIYQQQgihR5JFIYQQQgghhBB6ZDdUIaqxnJybvPPOFP7++28AFIqS/yQ0bfo0b775NgqFgsTE\nWJKTd+Po6Mhjjz1OZmYmnTp14cUXQwC05U5OzvznPx/w/ffJADz/fD+9Q9gLCwvYvn2L0XJLuVf3\nEUIIIYR4mEmyeBelsuTMrjNnzlg5EiGqRmCg8W3fCwsLATh9+hTLlkXRvHkLtm7dBEBubq42qdy8\nOQEXl9qAWqd8+vQpZGdnASWHtffu3Uen/6SkXcTHbzBabin36j5CCCGEEA8zWYYqrC4xMRGlUklk\nZKS1QxGlpKamEBu7zmh5fPx6YmPX61zTJIpQMuN48+YN7eebN2+QmBhntNxS7tV9hBBCCCEedpIs\nCiGMunPnjtGy/Px87ty5bbQ8Ly+XxMRY7eeEhI3k5eUaLbeUe3UfIYQQQoiHnSSLQogqk5y8m8uX\nL3Hp0kWSk3cbLbeUe3UfIYQQQojqoFomi2fOnOH111+nffv2tG7dmuDgYOLi4spsc+XKFWbOnEmP\nHj3w9PTE19eXiIgItm3bVma7Y8eOMXnyZPz8/PD09KRDhw6oVCoWL15MZmamtl5UVBRKpZKoqCiD\n/Rhbqln6enZ2NrNnz+a5557D29ubf//736xf/88ywXPnzjFhwgQ6deqEt7c3ISEh7N+/32jseXl5\nrFixgv79+9O2bVu8vb3p06cPUVFR5Obm6tUv/Qx///03M2bMoFu3bnh6euLv78/8+fO5fVt3Jsrf\n359p06YBsGnTJpRKpfZP6Wc9f/48ixYt4qWXXqJLly54enrSsWNHRowYwQ8//GByzDIzM5k9ezb+\n/v54enoyZswYNm/ejFKpZPjw4UbH4MyZMyiVSrp27ap9n0+UX3FxMdHRXxMTswq1Wm2w3FhZRanV\n6ntyHyGEEEKI6qLabXCTkpLCiBEjyM/P51//+hctWrTg+vXrzJgxg/Pnzxtsc/ToUUaMGMGNGzfw\n8PCgZ8+eZGdnk5KSQkpKCvv372fu3LnY2NjotPviiy9YuHAharWap59+mtatW5Obm8vvv//OZ599\nRocOHejQoYNFnuvGjRsMGjSInJwcfHx8yMrKIjU1lZkzZ3Lz5k18fX155ZVXaNCgAR06dODChQuk\np6czatQoVq9eja+vr05/165dY/jw4Zw/f55HH32UNm3aUKNGDdLT01myZAl79uxh7dq11KlTRy+W\nq1evolKpUKvVtGnThtzcXNLS0lixYgXnz59n2bJl2rqBgYEcPXqUI0eO0KhRI3x8fLRlpb/++uuv\niY+Pp0mTJjRv3hwXFxcuXbrEDz/8wA8//EBkZCTDhg0zODaZmZmEhIRox8bT0xNXV1eCgoKYN28e\nP/74IxcvXqRRo0Z6bWNiYgAYNGiQdqdQUTEnThwvszw9/RhHjx6hTRufMuuZcvRoWpn3stR9hBBC\nCCGqi2r1229+fj5vvvkm+fn5jBo1ikmTJmkTvJSUFEaOHKnX5vbt20ycOJEbN24wZMgQ3nrrLezs\n7AA4e/YsQ4cOZcuWLbRt25aXXnpJ227Pnj188sknODk5sWDBAvz9/XX6PX78OG5ubhZ7tuTkZAID\nA/n444+pWbMmAPv27WPkyJEsXbqUDRs2MG7cOF555RVtm7lz5/LVV1+xZMkSVq9erb2uVquZOHEi\n58+fJzw8nClTpuDg4ACUjOG7777L1q1b+eijj5gzZ45eLAkJCQwYMIAZM2ZQo0YNAH799VdCQkLY\nu3cvaWlp2kTwrbfeIjExkSNHjuDj42OwP4B+/frx2muv4eHhoXP92LFjvPLKKyxYsIB///vfPPHE\nE3ptv//+e7p06cKiRYtwcXHRKRs4cCBLly5l/fr1vPXWWzplOTk5bNu2DXt7ewYOHGh44B8wOTk3\nrR2CQTExq/Hy8q70EReFhQXExKyp8vsIIYQQQlQn1WoZ6rfffsuff/5Jo0aNmDBhgs5MYPv27XWS\nPY2dO3dy9epVGjRowJQpU7SJIkCzZs0YN24cAF999ZVOuyVLlgAwdepUvUQRwNvb22BiU1nOzs7M\nnDlTmygCdO/enebNm5OXl4ebm5tOoggwatQoANLS0igoKNBe/+GHH/j5559p3bo106dP1yaKAA4O\nDrz33ns89thjbNu2jezsbL1Y3N3deeedd7SJIkCTJk3o168fAD/99FOFn699+/Z6iSJAq1atCA8P\np6CggOTkZINt7e3tee+99/QSRYDQ0ND/nSWYqLdEdtOmTeTl5dGjRw+LJvZCCCGEEEI8CKpVsnj4\n8GEAgoKCdJI+DU0yY6jNCy+8gL29/myESqXCxsaGCxcu8OeffwJw/fp1Tp8+jb29PcHBwZZ8BKM8\nPT159NFH9a43btwYgC5duuiVubq64urqSkFBAVlZ/xx5oHkHsFevXtja6v8VcXJywtPTk8LCQtLT\n0/XKO3bsqJNgajz11FMA/PXXX+V8Kl05OTl88803zJ8/n3fffZfIyEgiIyM5dOgQAL/99pvBdi1a\ntDCYaALUq1ePnj17kpWVxTfffKNTpnnfMywsrFLx3o9cXGpZOwSDwsKGmDXbp1DYExY2uMrvI4QQ\nQghRnVSrZajXrl0DMJo4NGjQQO+aJgE01qZmzZq4ubnx559/8ueff1KvXj3++OMPoGSGzVD9Ja4w\nAAAgAElEQVTSVBWMzVI6OTmVWe7s7ExWVpbOrNqlSyW7Rs6bN4958+aVed+MjAy9a+7u7gbramb2\nyjqOwZikpCSmT5+uk9TezdCmOwD169cvs++IiAh27tzJunXrUKlUAPz3v//l119/5emnn6Z9+/YV\njlf8w9PTGzD+7qKXVytat25r9n1at/bB09O7yu8jhBBCCFFdVKtk8V65e6MbSyguLi6z3NAMYEXK\nSysqKgJKln4aSqBLM5SIVeRe5XHt2jUmT56sfde0T58+NGjQACcnJ2xtbdm4cSMzZswwutOlqYTd\nx8eHFi1akJ6ezokTJ/D09GTdupLD6ENDQy36LNWNra0t4eElGw9Nm/aG3vfI1taWsLChFvmZsbGx\nITx8WJXfRwghhBCiuqhWyWK9evWAkmMwDDF0XdPm8uXLBtvcvn1bu6xSU1czs3b16lXy8/PLNbuo\nWeKal5dnsFwzW3kvaOLv3bv3fbEEc+/eveTn5xMYGMgbb7yhV37hwgWz7xEeHs7bb79NTEwMEydO\nJDk5GWdnZ4NLk0X59ejRCw+Phtqvk5J2GS23BA+PhvfkPkIIIYQQ1UG1emdRs5xwx44d2tmz0rZu\n3ap3TXOkxPbt2w2es7dp0ybUajWNGzfWJot169ZFqVRSUFDA5s2byxWbpu3//d//6ZWp1eoyz0O0\ntG7dugElGwLdC5pE2dg5hppNdAwtpb1z5w67d+sfwl5Rzz//PK6uruzYsYMVK1ZQWFjIiy++aHBT\nnOqk9CZFd3NwcKBGjZpGy52cnFGp/tlFtn//QTg5ORstt5R7dR8hhBBCiIddtUoWAwMDqVu3Lhcu\nXCAqKkpnqVpqaiobNmzQa/Pvf/8bd3d3Ll++zIIFC3SWg54/f56oqCgAvZ1Gx44dC5S897dv3z69\nftPT07XvUAJ06NABW1tb9u/fT1pamvZ6UVERCxcu5Pjxss+qs6SAgABatmxJSkoKM2bMMPie4PXr\n14mNjbXI/cpKlOGfjXF2797N33//rb1+584d3n//fe07luaoWbMmAwcOJD8/n7Vr1wLw8ssvm93v\ng6xdu/YMHGh8DEJCQhk4UHeZbp06rtqvVaqB1KpVW/u5Vq3aqFQDjJZbyr26jxBCCCHEw65aLUN1\ndHTk448/ZtSoUSxdupRdu3bRokUL/vrrL1JTUxk8eDCrVq3SaVOzZk0+/fRTRowYwVdffUVSUhJe\nXl5kZ2dz6NAhCgoK6NevH4MGDdJp16tXL8aNG0dUVBQjR46kWbNmPP300+Tm5vLbb79x4cIF1qxZ\no50tq1+/PqGhocTExDBkyBB8fHxwcXHhl19+ITs7m4iICG0SU9VsbW35/PPPGTFiBBs3bmT79u00\nb94cd3d3bt++ze+//8758+d57LHHLHL+YOvWralbty4nT55EpVLx9NNPo1AoaNu2Lf3798ff358W\nLVrwyy+/0KtXL9q3b0/NmjU5cuQIOTk5Fhubl19+mS+//JKioiLat29P06ZNze7zQaNQlPwnoWnT\nZowePR6Fwo4bN7JJTt6No6MTjz32GJmZmXTq1JWAgEBArS13cnJm5swP2bs3CeB/5boCAgLJz883\nWm4p9+o+QgghhBAPs2qVLAI8++yzxMbGsmjRIlJTU0lKSuLJJ59kxowZhIaG6iWLUJLMbN68meXL\nl7N//352796Ng4MDrVu3ZuDAgbzwwgsGN84YO3YsHTt2ZO3ataSlpbF7925cXFzw8PBg3LhxKJVK\nnfrvvPMO7u7uJCQkkJaWhouLCx06dGDixIn8/PPPVTUkBj3xxBPEx8cTHx/Pzp07OXv2LMePH8fV\n1RU3NzeGDRtGz549LXKvGjVqsHLlShYuXMjRo0c5deoUxcXFFBUV0b9/fxQKBWvXrmXp0qUkJSXx\n448/UqdOHdq3b8/YsWM5evSoReJwd3fnqaee4ty5c/fFu5r3UnR0vNGygQPDGDjQ+HjcXf7iiyFG\n6yoU9mWWW8q9uo8QQgghxMPMRm1sC0khqpnTp0/Tr18/3Nzc2Lt3r3aWrarduVNIdvate3IvgLp1\nS85avH795j2758NExs98MobmkfEzj4yfeWT8zCPjZx4Zv8qrU8eRGjUq/rtttXpnUYiyLFq0CCg5\nd/FeJYpCCCGEEELcr+Q3YlGtJScnk5yczNmzZ0lPT6dBgwaEh4dbOywhhBBCCCGsTpJFUa398ssv\nJCQk4OzsTNeuXXn77bdxcnKydlhCCCGEEEJYnSSLolobN24c48aNs3YYQgghhBBC3HckWRRC6AgP\nl11E75WydqEVwpD8/Fu89dZsAMaOnYyDg2OF2hcWFrB9+xYAnn++HwqFvUXL8/NvsXDhPAAmTZpa\n4fiqWmFhAevWrQPAz6+3XvxCCCF0SbIoKiQpKYmVK1dy9uxZcnNzAdi8eTPPPPOMlSMrcejQIQYP\nHkz79u31zl7UHFVy5swZa4QmhBBmW7p0sfa4oGXLopg4cWqF2icl7SI+fgMADg6O9O7dx6LlS5cu\n5uTJ9ErHV9WSknYRHb0agKIiW734hRBC6JLdUEW5/fLLL0yYMIH09HTatGlDcHAwwcHB1KlTx9qh\nCSHEQ+/q1SukpR3Wfk5NTeHq1Svlbn/z5g0SE+O0nxMTY7l584bFys2Nr6qZil8IIYQ+mVkU5ZaU\nlERhYSGjR49m0qRJ1g7HIG9vb3bs2IGj4/219OlB1Xf0mirtf+uywffsXveL0s8sREUsWjRf79ri\nxQv46KNPytU+IWEjeXm52s95ebkkJsYyZMirFik3N76qZip+IYQQ+mRmUZTb1atXAWjcuLGVIzHO\n0dGRJk2aUL9+fWuHct+Kj48nNDSUHTu2WjsUIR54O3ZsZezYEVX+83T48CEuX76kd/3SpYukpaWY\nbH/p0kWSk3frXU9O3s3ly5fMLjc3vqpmKn4hhBCGSbIoTIqKikKpVJKYmAjAtGnTUCqVKJVKIiMj\nAdi1axfTpk2jT58+tGvXDi8vL3r27Ml7772nTTLvFhERgVKp5NChQ6SlpTF8+HB8fX3x8fFh+PDh\nnDp1Slt38+bN9O/fnzZt2tC+fXvefPNNrl+/rtfnoUOHUCqVREREmHyu3NxcfHx8aNGiBdeuXTNa\nT6VSoVQq2bdvn8k+HwTR0dFkZGSQmBhr7VCEeOAlJsaSlZVZpT9PxcXFrFy51Gj5ihWfU1xcbLRc\nrVYTE7MKtVptsO+YmFVER39ttDw6+usy20dHf21WfFWtPM9vqEwIIYQki6IcnnnmGYKDg2nUqBEA\nbdu21b6v6OPjA8CkSZP49ttvcXR0pFOnTnTu3Jk7d+6wbt06goOD+e2334z2v3fvXiIiIrhx4wZd\nunTBzc2NAwcOEB4ezu+//87cuXN55513qF27Nl26dMHe3p5t27YxbNgw7ty5U+nncnZ2RqVSUVRU\nxMaNGw3WOXr0KCdPnqRhw4Z07dq10ve6n9y6dQuA/Px8K0cixINP83NUlT9PiYmx5ObmGC3Pyclh\n06Y4o+VHj6Zx4sRxo+Xp6ce0m9IYcuLE8TLbnzhx3Kz4qlp5nv/o0SP3MCIhhHhwyDuLwqSAgAAC\nAgKIjIzk4sWLDBgwAJVKpVNn/vz5+Pn56bwrWFhYyJIlS1i6dCkffPABK1euNNj/qlWr+PTTT+nd\nuzdQ8i+9U6ZMYfv27YwbN46MjAw2b95M06ZNAcjKymLQoEGcO3eOHTt28OKLL1b62cLCwli7di1x\ncXGMGTMGe3vdbdQ1W6yHhoZia/vw/duKHJNhXTL+wpT8/Fts27bJZL2tWxPp06ev3lEVhYUFxMRY\n/31gY/FVtfI+f0zMary8vOUoDSGEuMvD99uvsIqgoCC9TWUUCgUTJ07Ezc2NH3/8kZwcw//y3KdP\nH22iCGBra8urr5ZsOHD27FnGjx+vTRQBXF1dCQ0NBUqWnZrjySefpGvXrly/fp09e/bolGVkZLBz\n505q1qxJ//79zbqPEEIIIYQQDxpJFoXF/Pbbb6xZs4bZs2czbdo0IiMjiYyMpKioiOLiYi5evGiw\nnaHlnZolr8bKNZvs/PXXX2bHHR4eDsD69et1rsfHx3Pnzh369OmDq6ur2fcRQoiKcnBw5IUXgk3W\n69tXZXDWTqGwJyzM+jvwGouvqpX3+cPChsisohBCGCDLUIXZCgsLee+994iLiytzkwBjM4v16tXT\nu+bs7Kz9+oknntArd3JyAjDrnUWNbt268eSTT5KSksL58+dp2rQpxcXFbNhQcvD0yy+/bPY97lfR\n0fF612Rp5L1jaPyrg7p1awFw/fpNK0divnvx86JSDWTPnm+Nvhfo4uJCcPAAo+1bt/bB09Pb6Ht7\nXl6tKC4uNvreoqenN4DR9p6e3vz22/9VOr6qVp7nb9267T2OSgghHgwysyjMtmbNGmJjY6lbty4L\nFy7k+++/Jz09nTNnznDmzBnatGkDYDSRNPUuYFW/K2hjY0NYWBjwzzuK+/bt48qVK3h5eeHl5VWl\n97/XNMuFHRwcrByJEA8+zc9RVf482draMnLkGKPlI0aMKfO/kzY2NoSHD8PGxsZg32FhQ4mIeMVo\neXj4sDLbh4cPMyu+qlae5zdUJoQQQpJFYQHffvstALNmzSIoKAh3d3dq1KihLb9w4YK1Qis3lUqF\nk5MTW7ZsITc3V5s0apLIh0l4eDiPPvooKtVAa4cixANPpRqIq+sjVf7z5OPTHg+PhnrXGzZshI9P\ne5PtPTwa0qNHL73rPXr0wsOjodnl5sZX1UzFL4QQwjBJFoXZsrOzAXB3d9cr+/HHH8nIyLjXIVVY\nyTKpYHJycvjss884cOAArq6uBAUFWTs0iwsJCWH9+vUEBfW1dihCPPCCgvqyZMmKe/LzNGHCm3rX\nxo+fXO72/fsPwsnpnyX+Tk7OOkmuueXmxlfVTMUvhBBCn7yzKMz2r3/9i99//51169Yxc+ZM7XKj\nixcv8p///MfK0ZVfWFgY69at48svvwSgf//+1KxZ08pRWdfWZfduY4x7eS8hHkTu7g3w8fElLe0w\nAO3atcfdvUG529eqVRuVagDR0auAklnRWrVqW6zc3Piqmqn4hRBC6JNkUZht1KhRHDhwgI0bN3Lo\n0CFatGhBdnY2KSkptGnThscff5yff/7Z2mGa1KRJEzp37syBAwewtbXVHs8hhBD3i9deG8+SJQsA\nGD16fIXbBwQEkp+fr/3a0uWvvTaehQvnVTq+qhYQEIidXcn7835++vELIYTQJcmiMFubNm2Ij49n\n4cKFnDhxguTkZDw8PBg9ejQjR45k+PDh1g6x3Dp16sSBAwfo1q0bDRvKeyxCiPuLg4Mjc+fOBSq3\nm6xCYc+LLxrfwdXccgcHR6ZNu39XlCgU9todrh+G3XiFEKKq2ajLOutAiGrmxRdf5NSpUyxfvpzu\n3bvfk3veuVNIdvate3IveLiOLbAGGT/zyRiaR8bPPDJ+5pHxM4+Mn3lk/CqvTh1HatSo+DyhbHAj\nxP/s2bOHU6dO0aRJE7p162btcIQQQgghhLAqWYYqqrXMzEzmz59PdnY2+/btA2Dq1Kly5pYQQggh\nhKj2JFkU1Vpubi7x8fEoFAoaNWrEqFGjeO6556wdlhBCCCGEEFYnyaKo1jw8PDhz5oy1wxBCCCGE\nEOK+I8miENVYeLjxXQ3LIzo63kKRPLgKCwvYvn0LAL179+Hbb78B4Pnn+6FQ2FszND2lY7VWfIWF\nBaxbtw4AP7/e990YmcvcMb4fvkdCCCGEhiSL4r7g7+/PlStXtMduCPGgSEraRXz8BgB+++3/SEtL\nAUqOEOjdu481Q9NTOlZrxZeUtIvo6NUAFBXZ3ndjZC5zx/h++B4JIYQQGrIbqhBCVNLNmzdITIzT\nftYkigCJibHcvHnDGmEZdHes1ojvfoihKpn7fA/7+AghhHjwSLIoRDUTHx9PaGgoO3Zs1bk+8ZUY\nJr4SY/Ba6T+WtmPHVsaOHaEXz4MgIWEjeXm5Bsvy8nJJTIy9xxEZd3es1ojvfoihKpn7fA/7+Agh\nhHjwSLL4kCoqKsLX15eWLVuSk5OjU5acnIxSqUSpVGqPi9DIycmhZcuW+Pr6UlxcrL2el5fHihUr\n6N+/P23btsXb25s+ffoQFRVFbq7+L8tFRUWsX7+el156CR8fHzw9PenUqRPBwcHMmTOHjIwMABIT\nE1EqlVy5cgWAHj16aGNTKpVcvnxZp99ff/2Vt99+G39/f7y8vPD19WXo0KEkJycbHAd/f39tP0lJ\nSURERODr64tSqeTUqVMAREREoFQqOXToECdOnGD06NF06NABLy8v+vbtS1xcnMG+H1TR0dFkZGTc\nN7+EJibGkpWVed/EU16XLl0kOXl3mXWSk3dz+fKlexSRccZivZfx3Q8xVCVzn+9hHx8hhBAPJkkW\nH1J2dna0b9+ewsJCUlJSdMp++ukng18DpKSkUFhYSIcOHbC1Lfnrce3aNQYMGMD8+fP5448/aNOm\nDZ07dyY7O5slS5YQGhpKdna2Tj/Tp09n5syZnDp1ilatWhEYGEjz5s25efMmX3/9NRcvXgSgUaNG\nBAcH4+TkBEBgYCDBwcHaP5rrAN988w39+vUjISEBJycn/Pz8UCqVpKam/n/27jysqmr/4/gbBAUU\np0TFHEoTnAVRFDUzRSUcSjQ1AYfKBittMNOGXzZrWWqa11tmWpJpivNUopXXCVScccwRTUkTRWSS\n8/uD55zEc44Mh5nP63l4Lmevvdf+rrX3Tb5n7bU2I0eOZNq0aVb747vvvuOFF14gKSmJTp064ePj\nY/Yuxc2bNzNo0CBiY2Pp0KEDTZs25ciRI7z99tvMmTMnu11f5N28eROApKSkQo4kgzGOohJPdhgM\nBsLC5mIwGO66X3p6erb2y093i7Wg4isKMeQnW9tX0vtHRESKLy1wU4L5+fmxYcMGtm3bRpcuXUzb\nt2/fTrVq1TAYDGzdujXTMcbk0c/PD8j4I+bll1/m+PHjhISE8Prrr+Pk5ARk/HH/zjvvsGLFCj75\n5BMmTpwIQGxsLEuXLsXd3Z3FixdTrVq1TOeIiYmhevXqALRu3ZrWrVsTGRlJYmIiY8eOtbjAzeHD\nh3njjTdwdHTkq6++4qGHHjKVHTt2jBEjRjBz5kzatm1Lu3btzI7/6aef+O9//3vXdyh+8803fPTR\nR/Tv/+8KocuXL2fs2LF89dVXPPHEEzg7O1s9XkqPjFHofdnad//+vezZsxtvb598jsqyPXt23TXW\ngoivKMSQn2xtX0nvHxERKb6ULJZgxoTv9tHDuLg4jh07Rq9evTAYDKxZs4bLly9zzz33ABmJJGBK\nuP744w+io6Px8vLirbfeMo02Ajg5OfHee++xZcsWVq5cyfjx46lUqRKXL18GoEmTJmaJIkDjxo1z\n3JZZs2aRmprKm2++mSlRBGjYsCHjxo1j9OjRhIWFWUwWg4KC7pooQsao5u2JIsCjjz7Kf//7X06c\nOMGBAwdo06ZNjmMvyWx99UZxlJqayjfffJOjY8LC5tG8eYsCfw1CWloqYWHfZ7lffsZXFGLIT7a2\nr6T3j4iIFG96DLUEa9CgAdWrV+fYsWPExcUB/yaDfn5++Pn5YTAYTMnk33//zdGjR6lRowYNGjQA\nMpJFgO7du2dKFI1cXFxo1qwZaWlp7N+/H4D69etTvnx5fv/9d2bNmmWaj5hb6enpbN68GTs7OwIC\nAizu4+vrC0B0dLTF8m7dumV5HmvJZP369QG4dOlSNqIVERERESkZlCyWcHeOLhr/t3379rRv3x7A\n9Cjq7Ymk0dmzGQsrfPrpp5kWnrn9x7hIjnHRmgoVKvDxxx9Trlw5pkyZQpcuXejUqROjRo0iPDyc\n5OTkHLXh6tWrJCQkYDAY8PPzsxiDMeZ//vnHYh333ntvludxd3e3uL1ChQoAOY5bSiZHR0dGjBiR\no2OCg4cWyoiQg4MjwcFDstwvP+MrCjHkJ1vbV9L7R0REijc9hlrCtW/fnuXLl7Nt2zb69OnD9u3b\nqVevHrVq1QIyFpgxJol3zleEjFVNIWPkLquEy1gnQEBAAO3btyciIoKoqCh2797N+vXrWb9+PTNm\nzCAsLMxqcnYnYwxlypShT58+2Wx5ZuXKlctyH0sjp3J38+cvtrmO4vgoa9u2bWnWrEW25i02b94S\nL69WBRCVZV5ePneNtSDiKwox5Cdb21fS+0dERIovJYslnDHx2759O2fOnCE2NpZBgwZlKl+4cCGn\nTp2ymCwaE7qAgACCg4NzdO6KFSuaVjUFOHPmDG+//TY7duxg8uTJfP7559mqp0qVKjg5OZkW1Clf\nvnyO4pDMnJ2duXnzpqlPC5sxDuPCScWBnZ0dISHDGT/+1buuUmlvb09w8DCzlXcL0t1iLaj4ikIM\n+cnW9pX0/hERkeJLQyklXI0aNbj//vs5f/48CxYsADIng8bfFy5cSGxsLPXr16dGjRqm8k6dOgGw\nbt06m2OpW7cuzz//PJCxuuntHB0zHq8yjiLezsHBwRTn+vXrbY6jtAsJCaFq1aoEBQ0o7FAACAoa\nQOXKVYpMPNlVu3Ydunbtftd9unbtTu3adQooIuusxVqQ8RWFGPKTre0r6f0jIiLFk5LFUsA4NzEs\nLAx7e/tMq4W2a9cOOzs7wsLCgMyJJIC/vz9NmzYlMjKS//u//+Pq1atm9cfFxbFo0b8vVD906BBr\n1qyxOGq1ceNGIPMjq4DpVRonTpyw2IYXXngBR0dHPvroI1avXm327bvBYGDfvn3873//s9wJYtK/\nf38WLFhAYGDmR3qnzglm6pxgi9tu/8lrgYF9mDHjG7N4ioN+/Qbi4mJ5pNvFpXyRSoDvjLUw4isK\nMeQnW9tX0vtHRESKHz2GWgr4+fkRFhZGcnIyTZs2pXLlyqayKlWq0LhxYw4dOgT8m1ga2dvbM3Pm\nTEaMGMHChQtZtWoVjRo1wt3dneTkZE6dOsXx48e55557GDAg44+a8+fP88orr+Ds7EyTJk1wd3cn\nNTWVQ4cOcfbsWcqXL8+oUaMynadbt25ERkYyZswYOnbsiKurKwBjxoyhSpUqNG/enEmTJvHmm2/y\n6quv8vnnn9OgQQMqVarEP//8Q0xMDJcvX2bEiBF07NgxP7tTxMTVtSJBQY8zf/5cAHx8fNm1KxLI\nGDF1da1YiNFldmeshRFfUYghP9navpLePyIiUvwoWSwF2rZti729Penp6WYjh5CRTB46dAh7e3vT\nKyhuV7NmTRYvXszixYtZu3YtR48eZd++fVSuXJnq1aszfPjwTK+maNmyJa+99hqRkZH8+eefHDx4\nEEdHR9zd3XnyyScJCQkxWywnJCSEhIQEVq5cyaZNm0hJSQHg+eefp0qVKgD07NmT5s2b8/3337N1\n61aioqIAqFatGo0bN+ahhx6iR48eedZvItnh79/DNIoeENCLdetWmbYXNbfHWljx+fv3oEyZjCcD\nHn646PWRrWzt46JwjURERIzsDHdbnUFE8l1KShrx8TcL7HxubhmjtnFx1wvsnCWJ+s926kPbqP9s\no/6zjfrPNuo/26j/cq9SJWfKls35OKHmLIqIiIiIiIgZJYsiIiIiIiJiRsmiiIiIiIiImFGyKCIi\nIiIiIma0GqqISCmRlpbKqlXLAejV61EcHBwLvI6kpJu88caHALz44ms4OTnnOAaxLi+usYiIiJGS\nRRExCQnpX9gh5Nr8+YsLO4Qib8OG9Sxe/BMATk7OBAT0LPA6/vOfL9mzZw8As2ZN5+WXx+Y4BrEu\nL66xiIiIkR5DFREpBa5fv0Z4+M+mz+Hhi7h+/VqB1nHhQiy7dkWZPu/cGcmFC7E5ikGsy4trLCIi\ncjsliyIipcCSJQtJTLxh+pyYeIPw8EUFWse0aZPNtn355ec5ikGsy4trLCIicjs9hipSyixevJgl\nS5YQENCLwMA+Vveb+PgP2apv3M+hOT4mr9x+7tJizZoVrFmzksDA3ne9frc7e/YMERG/mG2PiPiF\nrl17ULt2nXyvIypqB+fOnbVY765dkfj4+GYZg1iXF9dYRETkThpZlCLn1q1btGnThqZNm5KQkJCp\nLCIiAk9PTzw9Pfn9998zlSUkJNC0aVPatGlDeno6AHv37mXSpEkEBQXRvn17mjVrRseOHRk1apRp\n3tSdpk+fjqenJ9OnTyc2Npbx48fTqVMnmjRpwkcffZQ/jS5A8+fP58qVKxpxKKbCwxdx9eo/2b5+\nBoOBsLC5GAwGs7L09HSrZXlZR3p6OrNn/8dq+TffzDT9f1ZyLi+usYiIiCVKFqXIKVOmDL6+vqSl\npREZGZmpbNu2bRZ/B4iMjCQtLY22bdtib59xa0+ZMoV58+aRlpZGixYt6NKlC1WqVGH9+vUMHjyY\ntWvXWo3j1KlT9O3bl82bN+Pl5cXDDz9MxYoV87ClhePmzZsAJCUlFXIkkhvG65bd67dnzy4OHNhn\ntXz//r3s2bM7X+sID1/EjRsJVssTEhJYuvRnq+Vyd3lxjUVERCzRY6hSJPn5+bFhwwa2bdtGly5d\nTNu3b99OtWrVMBgMbN26NdMxxuTRz8/PtO3JJ59k8uTJVKtWLdO+GzduZNSoUUyYMIHOnTvj7Gy+\nfP+qVasICgrivffeo2zZsnnZvCKjOK9+eqeS1Ja8kpaWSljY91nuFxY2j+bNW1h8zYKtdSQl3WTl\nyqVZHr9iRTg9e/bRqzRyKC+usYiIiDUaWZQiyZjw3T56GBcXx7Fjx2jXrh3t2rXj6NGjXL582VS+\nfft2ANq1a2fa1qlTJ7NEEaBLly706NGDq1evsmPHDosxVK5cmbfeeqvEJooiIiIiInejZFGKpAYN\nGlC9enWOHTtGXFwc8G8y6Ofnh5+fHwaDwZRM/v333xw9epQaNWrQoEGDTHVlzM8LZ9KkSbz11luM\nGzeOcePGcezYMSDjcVNL2rdvT4UKFfKphSL5z8HBkeDgIVnuFxw81OqIk611ODk50+pQ6wAAACAA\nSURBVLt33yyP79MnSKOKuZAX11hERMQaPYYqRZafnx/Lly9n27Zt9OnTx5QYtm/f3rRYw9atW+nV\nq1emRPJ2P/30ExMnTjTN07PkzkV0jGrVqpUXzSjS7nyRfXF+lPPOtuQXNzdXAOLirhfI+e6U02vk\n5eVDs2YtrM5pa968JV5erfK1jqCgAfz66zqr8xYrVKhA376P3zUGsS4vrrGIiIglGlmUIqt9+/bA\nv4+ibt++nXr16lGrVi3uvfde6tata0oSLc1X3LdvHxMmTCAtLY2xY8eydu1aoqOjOXz4MEeOHOHZ\nZ58FsLpKoJOTU761TaSg2NnZERIyHDs7O7Mye3t7goOHWSzLyzrs7e155pmRVstHjBhpWpRKci4v\nrrGIiIgl+tdZiixj4rd9+3bOnDlDbGxspmTQz8+P2NhYTp06ZTFZ/OWXXzAYDISGhvLUU09Rv359\nXFxcTH80nT59ugBbU3QYF/NRMlw8Ga9bTq5f7dp16Nq1u9n2rl27Z/v9e7bW4ePja3G/OnXq6h2L\neSAvrrGIiMidlCxKkVWjRg3uv/9+zp8/z4IFCwDMkkWAhQsXEhsbS/369alRo4apPD4+HoCaNWua\n1X3lyhWz1VRLi5CQEKpWrUpQ0IDCDkVyIShoAJUrV8nx9evXbyAuLuVNn11cyhd4HaNHjzHbNmrU\nazmKQazLi2ssIiJyO81ZlCKtffv2nDx5krCwMOzt7TOtdNquXTvs7OwICwsDzOcr1q9fH4Dly5fT\nv39/ypfP+CMqISGBN998k2vXrhVQK4qW/v37079//yzn3I37OTTHdefmGMmZwMA+BAb2yfFxrq4V\nCQp6nPnz5wIZSaera87eG2prHe7u9+Lj04Zdu6IAaN3aF3f3e3MUg1iXF9dYRETkdkoWpUjz8/Mj\nLCyM5ORkmjZtSuXKlU1lVapUoXHjxhw6dAj4d46jUVBQEPPmzePgwYP4+/vj4+ODwWBg586dODo6\n0q9fP5YsWVKg7REpTP7+PUhKSjL9Xhh1PP/8KGbM+ByA554blasYxLq8uMYiIiJGShalSGvbti32\n9vakp6ebjRxCRjJ56NAh7O3t8fXNPO+pUqVKLFmyhGnTprFlyxZ+++037rnnHrp168aoUaNYuHBh\nQTVDpEhwcHDkscdsW/HW1jqcnJyZNGkSUHgrypZkeXGNRUREjOwM1paCFJECkZKSRny89Vd75LXC\nfvVDcaf+s5360DbqP9uo/2yj/rON+s826r/cq1TJmbJlcz5OqAVuRERERERExIySRRERERERETGj\nZFFERERERETMKFkUERERERERM1oNVUQkn6SlpbJiRThHjhzG07MRgYF9WLduNQC9ej2Kg4NjIUco\nIiIiYp2SRSl1PD09AThy5EghR1L4QkIKd4n9+fMXF+r589uGDesJD/8ZgIMH93P69Gl27YoEMl4h\nERDQszDDExEREbkrPYYqIpIPrl+/xpIlizJtMyaKAOHhi7h+/VpBhyUiIiKSbUoWpdRZs2YNa9as\nKewwpIRbsmQhN28mWi1PTLxBePgiq+UiIiIihU2PoUqp06BBg8IOoVAtXryYJUuWEBDQK9P2uV3n\nZOv4YRFP5vgYa8cXhjVrVrBmzUoCA3sTGNgnX85x9uwZIiJ+yXK/iIhf6Nq1B7Vr18mXOERERERs\noZFFyRVPT0/T3L/w8HCCgoLw8vKiQ4cOvPnmm1y5cgWA5ORkvvzyS3r06EHz5s3p3LkzU6ZMITU1\n1azO0NBQPD092bFjh8Vzjhs3Dk9PT8LDwzNtT05O5uuvv6Zv3754e3vTrFkzOnbsyMCBA5kyZQrJ\nyclWY79TamoqCxcuJDQ0FF9fX5o1a0bnzp159tlnWbFiRY77qSiaP38+V65cKbWjWuHhi7h69Z98\na7/BYCAsbC4GgyHLfdPT07O9r4iIiEhB08ii2OSzzz5j3rx5+Pr68uCDDxIdHc2SJUs4cOAACxYs\n4KmnnuLPP/+kTZs21K1bl6ioKGbNmsWVK1f44IMPbD5/eno6zzzzDNu3b8fV1ZU2bdrg6urK33//\nzcmTJ5k1axYhISG4ubllWVd8fDzPPvss0dHRlC1bllatWnHPPfdw6dIldu/ezbFjx+jTJ39GogrS\nzZs3AUhKSirkSAqHsd351f49e3Zx4MC+bO+/f/9e9uzZjbe3T77EIyIiIpJbShbFJsuWLWP58uWm\nRzvj4+MZOHAgR44cYdCgQbi6uhIREYGrqysAMTEx9O/fn59//pnnnnuOe++916bz79q1i+3bt9O0\naVPmz5+Pi4uLqcxgMLB7924qVKiQrbrGjx9PdHQ03t7eTJs2jRo1apjKkpOT2b59u02xirnCXo01\nr6WlpRIWNi/Hx4WFzaN58xZ6lYaIiIgUKXoMVWwyatSoTHMAK1WqxKBBgwA4fvw4H3zwgSlRBGjc\nuDGdOnXCYDAQFRVl8/n//vtvAHx8fDIligB2dnb4+Pjg7OycZT0xMTFERERQvnx5Zs6cmSlRBChX\nrhwPPfSQzfGKiIiIiBQXShbFJg8++KDZtnr16gFQq1Yti4vJ3HfffQBcunTJ5vM3bdqUMmXKsHjx\nYsLCwkzJY05t3rwZgC5dulC1alWb45LSycHBkeDgoTk+Ljh4qEYVRUREpMjRY6hik5o1a5ptM47w\nWSq7vfzOhWdyo27duowfP55Jkybx/vvv8/7771OnTh28vb3p2rUr3bp1o0yZMlnWExsbC0D9+vVt\njkmyb/78xQV+zvx+9NXLy4dmzVpke95i8+Yt8fJqla8xiYiIiOSGRhbFJvb21m+hu5XlRnp6usXt\noaGhbNq0iQkTJtC7d2/S09NZsWIFo0ePpl+/fiQkJGRZt52dXZ7GKqWXnZ0dISHDs3VP2dvbExw8\nTPefiIiIFElKFqXIcHTMeAwvMdHyi8zPnz9v9Vg3NzeeeOIJJk+ezMaNG1m+fDkeHh7ExMTw9ddf\nZ3nuWrVqAXDy5MlcRF68GOdwOjk5FXIkhcPY7vxsf+3adejatXuW+3Xt2l3vWBQREZEiS8miFBnG\nRWX+/PNPs7K///6bgwcPZruuRo0aMWTIEAAOHz6c5f4dO3YEICIiwvSOyJIqJCSEqlWrEhQ0oLBD\nKRRBQQOoXLlKvre/X7+BODu7WC13cSlfaq+BiIiIFA+asyhFRrt27QgPDycsLIzevXtTvXp1AK5e\nvcobb7xhccRx27ZtpKSk0KFDBxwc/r2db926xR9//AH8O2p4N02aNOHhhx9m06ZNvPjii0ydOtV0\nfvj31RklYUXU/v37079/f+LirvPjj9+btg+LeDLHdeXmmMIWGNiHwMD8f1+mq2tF+vUbwPz5c03b\nfHx82bUrEshIWl1dK+Z7HCIiIiK5pWRRioxHHnmEuXPncujQIXr16kWrVq1ITU1l//79VK9eHX9/\nfzZs2JDpmCNHjvDJJ5/g6upKkyZNcHNzIykpib179xIXF4ebmxsjRozI1vknTpzI008/za5du/D3\n98fHx4eqVaty6dIlDh8+jKurKxs3bsyPpksJ5e/fg8TEGxw5chhPz8YEBvZh3bpVpjIRERGRokzJ\nohQZZcuW5bvvvmPq1KlERETwv//9Dzc3Nx577DFGjRrFhx9+aHZMly5duH79OlFRUZw5c4bo6Ghc\nXFyoVasWgwYNYvDgwdl+FUblypX58ccfWbRoEatWrWLfvn2kpKRQrVo1fHx86N27d143WUo4BwdH\ngoIGZtr22GP5uxqriIiISF6xMxgMhsIOQqQ0S0lJIz7+ZoGdz83NFYC4uOsFds6SRP1nO/WhbdR/\ntlH/2Ub9Zxv1n23Uf7lXqZIzZcvmfJxQC9yIiIiIiIiIGSWLIiIiIiIiYkbJooiIiIiIiJhRsigi\nIiIiIiJmtBqqSCnTo4flVzbMn7/YbFtaWiqrVi0HoFevR3FwcMzX2ERERESk6FCyKABs2LCB2bNn\nc/ToUW7cuAHAsmXLaNy4cSFHlpmnpyeQ8X5FyX8bNqxn8eKfAHByciYgoGchRyQiIiIiBUXJonDo\n0CFGjx4NQLt27XBzcwOgUqVKhRmWFLLr168RHv6z6XN4+CI6dHgQV9eKhRiViIiIiBQUJYvChg0b\nSEtL47nnnuOVV14p7HDuas2aNYUdQqmxZMlCEhNvmD4nJt4gPHwRQ4c+XYhRiYiIiEhB0QI3woUL\nFwCoV69eIUeStQYNGtCgQYPCDqNEWrNmhen3s2fPEBHxi9k+ERG/cO7c2YIMS0REREQKiZLFUmz6\n9Ol4enoSHh4OwPjx4/H09MTT05Nx48aZ9tu9ezcvvfQSHTp0oFmzZnTo0IFRo0axZ88ei/WGhobi\n6enJjh07iIqK4plnnqFt27Y0atSIDRs2ZNp38+bNPPfcc7Rv355mzZrRsWNHXn31VatzEo3xWXL2\n7FnGjBmDn58fLVq0oGfPnnz77bfcunWLLl264Onpyblz5zIdc/v2LVu2MHToUHx8fGjZsiUDBgwg\nIiIi2/1Z3IWHLwLAYDAQFjYXg8Fgtk96errVMhEREREpWZQslmKNGzemb9++1K1bF4BWrVrRt29f\n+vbti4+PDwA//vgjwcHB/PLLL7i7u9OjRw/c3d1Zv349TzzxBIsWLbJa/7p16xgyZAgXLlygQ4cO\n+Pn54eDw75PPH374IU8//TSbN2+mbt26dO3aFTc3N1avXs3jjz/O77//nu22HD16lP79+7Ny5Uqc\nnJzo2rUrNWvWZOrUqbz66qtZHr948WKeeuopEhMTeeihh6hfvz579+7lhRdeYN26ddmOozhLSkoC\nYM+eXRw4sM/qfvv372XPnt0FFZaIiIiIFBLNWSzF/P398ff3Z9y4cZw5c4bHH3+coKAgU/nhw4f5\n6KOPAJg6dSqPPPKIqWz16tWMGTOG999/Hy8vLzw8PMzq//HHH3n//fcZOHCgWdmCBQv44YcfaNiw\nIdOmTcv0aOmGDRsYPXo0Y8aMYcOGDVkutGMwGBg7dixXr16lX79+vPfeezg6Zrzi4c8//2TIkCHE\nxcXdtY7Zs2fz9ddf06lTJ9O2mTNnMm3aNL744gsCAgLuenxJkZaWSljY91nuFxY2j+bNW+hVGiIi\nIiIlmEYWxarvv/+etLQ0AgMDMyWKAD179iQgIIDU1FS+/95yctGhQweLieKtW7f46quvgIwk9M45\niP7+/gwcOJBr166xYsUKs+PvtHPnTmJiYqhUqRJvvvmmKVEEqF+/PiNHjsyyjtDQ0EyJIsDTTz+N\nq6srp0+f5vz581nWISIiIiJSkihZFKuioqIAMo023q5fv34AREZGWizv1q2bxe0xMTHExcXRsGFD\nHnjgAYv7tGnTBsDqvMjbGc/fuXNnKlSoYFbeu3fvLOvo3Lmz2bayZctSp04dAC5dupRlHSWBg4Mj\nwcFDstwvOHioRhVFRERESjg9hipWXbx4EYDatWtbLDcmUsb97lSrVi2L28+ezVhN89ixY1YXqzG6\ncuVKtuO0dj5XV1dcXV25fv261TqsHWtMPpOTk7OMo6Tw8vKhWbMWVuctNm/eEi+vVgUclYiIiIgU\nNCWLkiU7O7tcHefk5GRxe3p6OgA1atSgffv2d62jfv362T7f3eK0t7/7IHpu21iSGK+XnZ0dISHD\nGT/+VbNVT+3t7QkOHqb+EhERESkFlCyKVTVq1ODMmTOcPXvWtGLq7YwjhDVq1MhRvTVr1gTAzc2N\niRMn2hxn9erVAazOK0xISCA+Pt7m85R0QUEDTL/Xrl2Hrl27s2HD+kz7dO3andq16xR0aCIiIiJS\nCDRnUawyzhtctmyZxXLj+xl9fX1zVG+LFi2oXLkyMTExnD592rYg+TfO3377jRs3bpiVr1q1yuZz\nlAaBgX0yfe7XbyAuLuVNn11cymdKKEVERESkZFOyKFYNGTIEBwcHVq9eza+//pqpbO3ataxduxZH\nR0dCQ0NzVK+joyMjR47k1q1bvPDCC+zbZz43LiUlhYiICE6cOJFlfb6+vnh4eHD16lUmTpxIWlqa\nqezUqVOmlVclZ1xdKxIU9Ljpc1DQAFxdKxZiRCIiIiJSkPQYqljVqFEj3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/46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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 453,
              "height": 267
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JACfsHAjPDsD",
        "colab_type": "code",
        "outputId": "71d4880a-3b46-404d-9ede-1233e14edd2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(movie_budget['budget'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHkrPs3Mbgky",
        "colab_type": "code",
        "outputId": "0a1d85f1-9e64-40ae-eb8b-19bfe88ac628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "select_conditions = (movie_budget['genre']=='action')\n",
        "#select_conditions = (movie_budget['genre']=='adventure')\n",
        "movie_budget = movie_budget.loc[select_conditions, :]\n",
        "movie_budget.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genre</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>budget</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>65000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>710</td>\n",
              "      <td>goldeneye</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/5c0ovjT41KnYIHYuF4AWsTe3sKh.jpg</td>\n",
              "      <td>58000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>10530</td>\n",
              "      <td>pocahontas</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/qk95miOV8wDABbO5gyCpCrvbVFu.jpg</td>\n",
              "      <td>55000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>11359</td>\n",
              "      <td>the-indian-in-the-cupboard</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/l16BtfILyWxYwSqzofL3V1rMSwe.jpg</td>\n",
              "      <td>45000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>17414</td>\n",
              "      <td>dunston-checks-in</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/lXSWbUA3T2o792SYdbfFKFTcSC1.jpg</td>\n",
              "      <td>16000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id  ...      budget\n",
              "1    8844  ...  65000000.0\n",
              "9     710  ...  58000000.0\n",
              "47  10530  ...  55000000.0\n",
              "59  11359  ...  45000000.0\n",
              "86  17414  ...  16000000.0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpX0f86jbyJ9",
        "colab_type": "code",
        "outputId": "09703319-1674-43f1-9a0d-d8335f799b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_budget['budget'].median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28000000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fd9qmE8feaxD",
        "colab_type": "text"
      },
      "source": [
        "**Split the movies into 2 categories based on their budget**\n",
        "\n",
        "\n",
        "1.   **Higher or equal to the median**\n",
        "2.   **Lower than the median**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lku_THpqbyM3",
        "colab_type": "code",
        "outputId": "ef0033ea-37bf-47cf-84c5-8d6e7ef75861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "movie_budget['budget_range'] = np.where(movie_budget['budget']>=movie_budget['budget'].median(), 'high', 'low')\n",
        "movie_budget.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genre</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>budget</th>\n",
              "      <th>budget_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>65000000.0</td>\n",
              "      <td>high</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>710</td>\n",
              "      <td>goldeneye</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/5c0ovjT41KnYIHYuF4AWsTe3sKh.jpg</td>\n",
              "      <td>58000000.0</td>\n",
              "      <td>high</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>10530</td>\n",
              "      <td>pocahontas</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/qk95miOV8wDABbO5gyCpCrvbVFu.jpg</td>\n",
              "      <td>55000000.0</td>\n",
              "      <td>high</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>11359</td>\n",
              "      <td>the-indian-in-the-cupboard</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/l16BtfILyWxYwSqzofL3V1rMSwe.jpg</td>\n",
              "      <td>45000000.0</td>\n",
              "      <td>high</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>17414</td>\n",
              "      <td>dunston-checks-in</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/lXSWbUA3T2o792SYdbfFKFTcSC1.jpg</td>\n",
              "      <td>16000000.0</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id              original_title  ...      budget budget_range\n",
              "1    8844                     jumanji  ...  65000000.0         high\n",
              "9     710                   goldeneye  ...  58000000.0         high\n",
              "47  10530                  pocahontas  ...  55000000.0         high\n",
              "59  11359  the-indian-in-the-cupboard  ...  45000000.0         high\n",
              "86  17414           dunston-checks-in  ...  16000000.0          low\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ys6l06rRhWj6",
        "colab_type": "code",
        "outputId": "8110011d-4c28-4ae2-8024-cc882684f026",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "movie_budget.groupby(['budget_range']).agg(['mean', 'count'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">budget</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>budget_range</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>high</th>\n",
              "      <td>9.403580e+07</td>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low</th>\n",
              "      <td>9.822701e+06</td>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    budget      \n",
              "                      mean count\n",
              "budget_range                    \n",
              "high          9.403580e+07   268\n",
              "low           9.822701e+06   259"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ai8j6XD6iRZm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "downloaded_image_dir = \"posters_budget_action\"\n",
        "#downloaded_image_dir = \"posters_budget_adventure\"\n",
        "\n",
        "if not os.path.exists(downloaded_image_dir):\n",
        "        os.makedirs(downloaded_image_dir)\n",
        "\n",
        "def download_poster(title,budget_bin,poster_path):\n",
        "\n",
        "    if not os.path.exists(downloaded_image_dir+'/'+ budget_bin):\n",
        "        os.makedirs(downloaded_image_dir+'/'+ budget_bin)\n",
        "\n",
        "    imgUrl = 'http://image.tmdb.org/t/p/w185/' + poster_path\n",
        "\n",
        "    local_filename = re.sub(r'\\W+', ' ', title).lower().strip().replace(\" \", \"-\") + '.jpg'\n",
        "\n",
        "    try:\n",
        "        session = requests.Session()\n",
        "        r = session.get(imgUrl, stream=True, verify=False)\n",
        "        with open(downloaded_image_dir+'/'+budget_bin+'/'+local_filename, 'wb') as f:\n",
        "            for chunk in r.iter_content(chunk_size=1024):\n",
        "                f.write(chunk)\n",
        "    except:\n",
        "        print('PROBLEM downloading', title, budget_bin,poster_path,imgUrl)\n",
        "    \n",
        "    try:\n",
        "        local_path = downloaded_image_dir+'/' + budget_bin +'/'+local_filename\n",
        "        with open(local_path, 'rb') as f:\n",
        "            img = Image.open(f)\n",
        "            img.convert('RGB')\n",
        "    except:\n",
        "        print('PROBLEM opening', title,budget_bin,poster_path,imgUrl)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy3F89DOiTmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for index, row in movie_budget.iterrows():\n",
        "\n",
        "    download_poster(\n",
        "        str(row['original_title']),\n",
        "        str(row['budget_range']),\n",
        "        row['poster_path']\n",
        "    )\n",
        "\n",
        "    time.sleep(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x-qIWv235VL",
        "colab_type": "code",
        "outputId": "b9ee36a5-ea6c-4037-a158-ede0375ef372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "do_transforms = transforms.Compose([\n",
        "        transforms.ToTensor()\n",
        "])\n",
        "dataset = datasets.ImageFolder(downloaded_image_dir, transform = do_transforms)\n",
        "\n",
        "print(len(dataset))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "527\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwanYqT3xf47",
        "colab_type": "text"
      },
      "source": [
        "## Prepare data for Earnings prediction task\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FvvoYltyoNc",
        "colab_type": "text"
      },
      "source": [
        "### Dataset for Predicting Earnings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlFKeKbJtdpr",
        "colab_type": "code",
        "outputId": "dc7b7658-41ef-4d3a-f698-3246ca7ac24d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "source": [
        "df = pd.read_csv('movies_metadata.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>adult</th>\n",
              "      <th>belongs_to_collection</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>video</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
              "      <td>30000000</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "      <td>http://toystory.disney.com/toy-story</td>\n",
              "      <td>862</td>\n",
              "      <td>tt0114709</td>\n",
              "      <td>en</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
              "      <td>21.9469</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>[{'name': 'Pixar Animation Studios', 'id': 3}]</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-10-30</td>\n",
              "      <td>373554033.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>False</td>\n",
              "      <td>7.7</td>\n",
              "      <td>5415.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65000000</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8844</td>\n",
              "      <td>tt0113497</td>\n",
              "      <td>en</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "      <td>17.0155</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>[{'name': 'TriStar Pictures', 'id': 559}, {'na...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-15</td>\n",
              "      <td>262797249.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15602</td>\n",
              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>3.85949</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>81452156.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11862</td>\n",
              "      <td>tt0113041</td>\n",
              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>8.38752</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-02-10</td>\n",
              "      <td>76578911.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "2  False  ...       92.0\n",
              "3  False  ...       34.0\n",
              "4  False  ...      173.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDdbD0PGkH4S",
        "colab_type": "text"
      },
      "source": [
        "**Select relevant columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rmiy1RRiMYQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['budget'] = pd.to_numeric(df['budget'],errors='coerce')\n",
        "df['popularity'] = pd.to_numeric(df['popularity'],errors='coerce')\n",
        "df['revenue'] = pd.to_numeric(df['revenue'],errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IieV2Qdltp65",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# filter\n",
        "select_columns = ['id','original_title','budget','popularity','revenue','genres','poster_path']\n",
        "select_conditions = (df['budget']>0) & (df['revenue']>0) & (df['genres']!='[]')\n",
        "subset_data = df.loc[select_conditions, select_columns]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "385b9n4uuLp4",
        "colab_type": "code",
        "outputId": "356724b1-27ea-421a-f19d-b80a00dd51fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "subset_data.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                5369\n",
              "original_title    5369\n",
              "budget            5369\n",
              "popularity        5369\n",
              "revenue           5369\n",
              "genres            5369\n",
              "poster_path       5369\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J55SilHNu_jT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#format 'original_title\" \n",
        "for index, row in subset_data.iterrows():\n",
        "    title = row['original_title'].lower()\n",
        "    title = title.replace(' ','-')\n",
        "    subset_data.loc[index, 'original_title'] = title\n",
        "\n",
        "subset_data = subset_data.drop_duplicates(['original_title'], keep='last')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6OqkdHVvT5x",
        "colab_type": "code",
        "outputId": "bd71cbfc-cbe5-47d9-9cfb-025cf859eb4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "list_movie_genre = []\n",
        "for index, row in subset_data.iterrows():\n",
        "    genres = json.loads(row['genres'].replace(\"'\",'\"'))\n",
        "\n",
        "    genre = 'nil'\n",
        "    if len(genres) > 0:\n",
        "        genre = genres[0]['name'].lower()\n",
        "\n",
        "    this_movie = {\n",
        "        'id':row['id'],\n",
        "        'genre':genre\n",
        "    }\n",
        "    list_movie_genre.append(this_movie)\n",
        "\n",
        "df_movie_genre = pd.DataFrame(list_movie_genre)\n",
        "df_movie_genre.head()\n",
        "\n",
        "subset_data = subset_data.join(df_movie_genre.set_index('id'), on='id')\n",
        "\n",
        "subset_data = subset_data[['id','original_title','genre','poster_path','popularity','budget','revenue']]\n",
        "#print(subset_data.count())\n",
        "subset_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>original_title</th>\n",
              "      <th>genre</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>popularity</th>\n",
              "      <th>budget</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>862</td>\n",
              "      <td>toy-story</td>\n",
              "      <td>animation</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>21.946943</td>\n",
              "      <td>30000000.0</td>\n",
              "      <td>373554033.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8844</td>\n",
              "      <td>jumanji</td>\n",
              "      <td>adventure</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>17.015539</td>\n",
              "      <td>65000000.0</td>\n",
              "      <td>262797249.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31357</td>\n",
              "      <td>waiting-to-exhale</td>\n",
              "      <td>comedy</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>3.859495</td>\n",
              "      <td>16000000.0</td>\n",
              "      <td>81452156.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>949</td>\n",
              "      <td>heat</td>\n",
              "      <td>action</td>\n",
              "      <td>/zMyfPUelumio3tiDKPffaUpsQTD.jpg</td>\n",
              "      <td>17.924927</td>\n",
              "      <td>60000000.0</td>\n",
              "      <td>187436818.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9091</td>\n",
              "      <td>sudden-death</td>\n",
              "      <td>action</td>\n",
              "      <td>/eoWvKD60lT95Ss1MYNgVExpo5iU.jpg</td>\n",
              "      <td>5.231580</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>64350171.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id     original_title      genre  ... popularity      budget      revenue\n",
              "0    862          toy-story  animation  ...  21.946943  30000000.0  373554033.0\n",
              "1   8844            jumanji  adventure  ...  17.015539  65000000.0  262797249.0\n",
              "3  31357  waiting-to-exhale     comedy  ...   3.859495  16000000.0   81452156.0\n",
              "5    949               heat     action  ...  17.924927  60000000.0  187436818.0\n",
              "8   9091       sudden-death     action  ...   5.231580  35000000.0   64350171.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVQzie-Gvra9",
        "colab_type": "code",
        "outputId": "b385e39d-5dcf-44d9-f593-62e0bd0cb290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "subset_data.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                5272\n",
              "original_title    5272\n",
              "genre             5272\n",
              "poster_path       5272\n",
              "popularity        5272\n",
              "budget            5272\n",
              "revenue           5272\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1j1HlQ0wHu-",
        "colab_type": "code",
        "outputId": "05a0fd04-6a6b-4b58-b0c2-ea8aa96d1842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "pd.Series(subset_data['genre']).value_counts().plot('bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0675b7ff28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X1taqoKBAkjRhwoRm88LDwzVixAjV1dVp69at7r4UAAAAwNIsWwr2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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 386,
              "height": 320
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRoQ1ZqpZ096",
        "colab_type": "code",
        "outputId": "f95a0442-a163-4c06-d3d7-ca68aed9ded2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "sns.jointplot(x='popularity',y='budget',data=subset_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f94f4416cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZGZZkfVR2VFt2H1HJPqcS43vrqksHaMyI/gE7DiEMAAAAoUA4gtfW/atc/9jq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\nARSOREREREREAIUjERERERERAP4HhK9kWSRsdOkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 419,
              "height": 419
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "As7U0G5GlA5n",
        "colab_type": "text"
      },
      "source": [
        "**Plot 'budget' for each genre**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-Q_nenpk8gv",
        "colab_type": "code",
        "outputId": "2c46a1e8-6e20-486c-baef-bf9e0423d549",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "sns.boxplot(x=\"budget\", y=\"genre\",data=subset_data,width=0.6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f067e1f62e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3k9my1K1InarE1PFb+vgIIYSofJIsCiFEFRUXN6fU8uLiYp1fq3z/fcnlhMnJ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\nWBQREREREREzKhZFRERERETEzP8Dh2/GioX2k8MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 453,
              "height": 267
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OrjuNiGk0Ka",
        "colab_type": "text"
      },
      "source": [
        "**Plot 'revenue' for each genre**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dPKnV83knlY",
        "colab_type": "code",
        "outputId": "d6cc67ad-be43-4b54-b351-f2ccecfc7ec3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "sns.boxplot(x=\"revenue\", y=\"genre\",data=subset_data,width=0.6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f067e3e95f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mi0IIIYQQQgghjMhqqEKIeisv7ypz5sygqKiIpk2bce1aPmVlZUD5So/lr1bQ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\nRERExIKKRREREREREbHw/w9C5+fPCyObAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 453,
              "height": 267
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7dHFRNbXt-6",
        "colab_type": "code",
        "outputId": "34ac2a26-8d64-4663-8134-2a51266d88c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "grid = sns.FacetGrid(subset_data, col='genre',hue='genre',margin_titles=True)\n",
        "grid.map(plt.scatter,'popularity','budget')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f94f1a6c6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FRTp8+LAOHz6stWvX6u6777bn+oaHhyszM1NFRUVq0qRJietT+ppWV98U85mA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\nERXUCDYMYEGUMpT5/cGa8wsOZZjCOXPm81kra13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