Dicoding_BPML-Submission2.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Dicoding - Time Series.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3otC9cFGeuas",
        "colab_type": "text"
      },
      "source": [
        "# Australia Weather Forecasting\n",
        "### By Fahmi Jabbar (rootmawar77@gmail.com)\n",
        "---\n",
        "**Disclaimer :** \n",
        "- Any code below is done by purposed to complete *Dicoding* Submission and made 100% by myself\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-Tv_j5RWBVD",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "**Dataset Information** : \n",
        "\n",
        "![Dataset Information](https://image.prntscr.com/image/XwiEbOFdR9uiuPiRvnLDsg.png)\n",
        "\n",
        "Link : https://www.kaggle.com/jsphyg/weather-dataset-rattle-package\n",
        "\n",
        "Consist 142k time series weather data between 1 Nov 2007 - 25 Jun 2017"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0puFpdHZmAc4",
        "colab_type": "text"
      },
      "source": [
        "## Import Important Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pv001pGBUQ2k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import os, sys, datetime\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPqi-s6PmC6Y",
        "colab_type": "text"
      },
      "source": [
        "## Download & Unzip Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98pww8y3UG4P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.environ['KAGGLE_USERNAME'] = \"fahmijabbar\"\n",
        "os.environ['KAGGLE_KEY'] = \"70e97bf263ab4ed3b437c10c73941268\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u-I0Ymse9DR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0c366310-b04f-4d1b-d549-d639d4527eae"
      },
      "source": [
        "!kaggle datasets download -d jsphyg/weather-dataset-rattle-package"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading weather-dataset-rattle-package.zip to /content\n",
            "\r  0% 0.00/3.87M [00:00<?, ?B/s]\n",
            "\r100% 3.87M/3.87M [00:00<00:00, 59.2MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_gvX9vyfPBw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fb5a88da-a4c7-4002-a6c4-202eb7c99bbe"
      },
      "source": [
        "!unzip /content/weather-dataset-rattle-package.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/weather-dataset-rattle-package.zip\n",
            "  inflating: weatherAUS.csv          \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG3YauLpmF7F",
        "colab_type": "text"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyC9N6sUmHT1",
        "colab_type": "text"
      },
      "source": [
        "### txt to pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xu99f-vKUVFG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/weatherAUS.csv')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eK4QzbKGUcYQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "a0152a15-7003-4f16-ba32-b7bfdc459dec"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "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>Date</th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
              "      <th>WindSpeed9am</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>RISK_MM</th>\n",
              "      <th>RainTomorrow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-01</td>\n",
              "      <td>Albury</td>\n",
              "      <td>13.4</td>\n",
              "      <td>22.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>W</td>\n",
              "      <td>44.0</td>\n",
              "      <td>W</td>\n",
              "      <td>WNW</td>\n",
              "      <td>20.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1007.7</td>\n",
              "      <td>1007.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.9</td>\n",
              "      <td>21.8</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-02</td>\n",
              "      <td>Albury</td>\n",
              "      <td>7.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WNW</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NNW</td>\n",
              "      <td>WSW</td>\n",
              "      <td>4.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1010.6</td>\n",
              "      <td>1007.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.2</td>\n",
              "      <td>24.3</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-03</td>\n",
              "      <td>Albury</td>\n",
              "      <td>12.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WSW</td>\n",
              "      <td>46.0</td>\n",
              "      <td>W</td>\n",
              "      <td>WSW</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1007.6</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-04</td>\n",
              "      <td>Albury</td>\n",
              "      <td>9.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NE</td>\n",
              "      <td>24.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>E</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1012.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>No</td>\n",
              "      <td>1.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-05</td>\n",
              "      <td>Albury</td>\n",
              "      <td>17.5</td>\n",
              "      <td>32.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
              "      <td>ENE</td>\n",
              "      <td>NW</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1010.8</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>No</td>\n",
              "      <td>0.2</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date Location  MinTemp  ...  RainToday  RISK_MM  RainTomorrow\n",
              "0  2008-12-01   Albury     13.4  ...         No      0.0            No\n",
              "1  2008-12-02   Albury      7.4  ...         No      0.0            No\n",
              "2  2008-12-03   Albury     12.9  ...         No      0.0            No\n",
              "3  2008-12-04   Albury      9.2  ...         No      1.0            No\n",
              "4  2008-12-05   Albury     17.5  ...         No      0.2            No\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3h4fBrPmMJz",
        "colab_type": "text"
      },
      "source": [
        "### Check for empty value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHgvg-9OhJL3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "b6ceae66-1acc-450f-b55c-f34ac2cdc452"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 0\n",
              "Location             0\n",
              "MinTemp            637\n",
              "MaxTemp            322\n",
              "Rainfall          1406\n",
              "Evaporation      60843\n",
              "Sunshine         67816\n",
              "WindGustDir       9330\n",
              "WindGustSpeed     9270\n",
              "WindDir9am       10013\n",
              "WindDir3pm        3778\n",
              "WindSpeed9am      1348\n",
              "WindSpeed3pm      2630\n",
              "Humidity9am       1774\n",
              "Humidity3pm       3610\n",
              "Pressure9am      14014\n",
              "Pressure3pm      13981\n",
              "Cloud9am         53657\n",
              "Cloud3pm         57094\n",
              "Temp9am            904\n",
              "Temp3pm           2726\n",
              "RainToday         1406\n",
              "RISK_MM              0\n",
              "RainTomorrow         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48kNRr0A-taK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop any null or empty data\n",
        "df = df.dropna(how='any')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYBWhOHy-zdi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "9a0ac74b-e1db-42d0-dabe-415e8d701946"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date             0\n",
              "Location         0\n",
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustDir      0\n",
              "WindGustSpeed    0\n",
              "WindDir9am       0\n",
              "WindDir3pm       0\n",
              "WindSpeed9am     0\n",
              "WindSpeed3pm     0\n",
              "Humidity9am      0\n",
              "Humidity3pm      0\n",
              "Pressure9am      0\n",
              "Pressure3pm      0\n",
              "Cloud9am         0\n",
              "Cloud3pm         0\n",
              "Temp9am          0\n",
              "Temp3pm          0\n",
              "RainToday        0\n",
              "RISK_MM          0\n",
              "RainTomorrow     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uymUerEmmP0d",
        "colab_type": "text"
      },
      "source": [
        "## Submission objective\n",
        "\n",
        "**Please read this**\n",
        "\n",
        "I would like to forecast the max temprature from time to time, regardless to any other condition\n",
        "\n",
        "I will make this dataset as univariate dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZFzxFKQzGL4",
        "colab_type": "text"
      },
      "source": [
        "### Removing Unnecessary columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHOeapcxx3_A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "removecol = []\n",
        "for columns in df.columns:\n",
        "  if (columns == 'Date' or columns == 'MaxTemp'):\n",
        "    pass\n",
        "  else:\n",
        "    removecol.append(columns)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xTgk2UDyYsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(removecol, axis=1, inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNRv7bmp0x32",
        "colab_type": "text"
      },
      "source": [
        "### Sort by date time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SG2tGWay0gk7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.sort_values('Date')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTiteWA5yiRc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8f20f2d2-d05f-4731-c899-eff7d617d2ac"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "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>Date</th>\n",
              "      <th>MaxTemp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44351</th>\n",
              "      <td>2007-11-01</td>\n",
              "      <td>24.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44352</th>\n",
              "      <td>2007-11-02</td>\n",
              "      <td>26.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44353</th>\n",
              "      <td>2007-11-03</td>\n",
              "      <td>23.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44354</th>\n",
              "      <td>2007-11-04</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44355</th>\n",
              "      <td>2007-11-05</td>\n",
              "      <td>16.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date  MaxTemp\n",
              "44351  2007-11-01     24.3\n",
              "44352  2007-11-02     26.9\n",
              "44353  2007-11-03     23.4\n",
              "44354  2007-11-04     15.5\n",
              "44355  2007-11-05     16.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4FUZZoX_atl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ddb3100e-10cd-410f-d1ed-fb89349aa850"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 56420 entries, 44351 to 78750\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Date     56420 non-null  object \n",
            " 1   MaxTemp  56420 non-null  float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHkf7ZFw01GD",
        "colab_type": "text"
      },
      "source": [
        "### Temprature Visualization from time to time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJwFafdW1lr-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "60916697-4312-4456-e604-6d1927d04314"
      },
      "source": [
        "plot_features = df['MaxTemp']\n",
        "plot_features.index = df['Date']\n",
        "_ = plot_features.plot(figsize=(20,8), subplots=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fFbNDrVQ+/Y9rzhQRkYuTjpH3fnvh4O3HHs4qO6zjhu983ewQYBIuPqzvxKN07AHCKqVl/fi8E+UbxxxmdhhIkOOPpLePXVx+xhizQ0ACXfvt480OAS6mpeLHIyKPqKr6bRH5roj8WVGUb4vIOBFZrarqGSKyeuBrJND3Tz9Wto2/Tn74reOGXWDN/+P3TIwKGO5PPzzN7BAAhHBx0jFhv//Nr4VOFhx7+MGybfx1g1+T9rGOA/ejoNtN/nb1GWaHALjC175yUMz3ve/yU+Td3/xP2GMYkgAjRTwzUFV1j6qqWwb+3S4iO0TkJBG5VUSmDxw2XUR+YlSQCO2rhx44+G//YutxR7DSA+vgQ8we0h69UvfHpADE+k746qFhv3/BN44K+/3Az6BwI8Sv/Bar2onyj2vOlP1C9HZ59taz5ewTj0xwRDBS9pPXyE8u6J8C9Mavzpc/X8liC2CU5L9dHvN9jz38YDlgf5LyME9Uzz5FUZJE5AIR2SQix6uqumfgW7Ui4oraNSs2d/Zb+Y8r5NVfnMfFFobRYzoA/SGdj20giFe4z56pd9PHIlF+efE3Qn7v1DGHy5S7LkpgNDDCPT8I/rl+6/knyT+vPyvB0QDucfABsSdu7g7xugUSRfOzV1GUw0Vkvoj8XVXVtsDvqaqqigRvfqMoyv2KomQpipLV0NAQV7AI7/TjDpefXzjW0skpJN6RhxwY+aAIzh0bftUfgDNdEaZSh0UGwBznjh1qyn7oQfubGAngLkfHsdXroDiSRoAeND0DFUU5UPqTPh+rqrpg4OY6RVFOGPj+CSJSH+y+qqpOVlX1IlVVLxozxv6l3uRUYHcXnBx9EufA/bnCQ/R41ljfed8IP9XrpxeM1fxY/L2t4ajDwif72X5rH5sevzriMYcdRBN9AEBkWqZ6KSLyoYjsUFV1UsC3FonIXQP/vktEFuofHgC9ff6nH0Q85tADh68ghuvdAcB+yl+6ScpfuknGHh16i9/LPz83qsfcnwSxJRxyIBUgThFqOtcBA6+1I5icCMDBzhsbfnEK0dFS8fMDEblTRK5SFCVn4H83ishLInKtoijFInLNwNeOp9p0H9V1jA+ETqb+/mJ589cXmB0GbIAmhvb1+I1nye0Xhe4VE2jxXy+TCT/9jhxAMzDAcNPvuURuOPvr8pcrT5f1464yOxwAOvra4bFvJXOihX+5zOwQHEXLVK/1qqoqqqqeq6rq+QP/W6KqapOqqlerqnqGqqrXqKranIiAEZuJt59ndgiwkEhbAcK59NRj5MfnnRj0e6cfd3jMjwtnmX7PJcMmPgU6/siDExwNwln818sk+W/DT66i2Q70nZO+Kr+59JtsIbKJww6mIsiuvnrogXLFmWPkgP33k/+7/lsh32MBmOsf15wpN597QlT3eeeO/5GfDkzoc6vxt3xbnrr525qOZTNC9FiOdSA1SCeiI3Ro8AtE8sMz7d/HC/q4IsxzgQSBtXznpK/KaWOiT9r+/vtJw74OdRIWS18xGEePhv9whktOOUbOPvFIs8MAHOe4Iw+WS0/9WlT3uencE0RxeTYjms8nm27CMRWJnyjxHIMbjGzmrPV5f+E3j9Y/GBjuILZkucrP/mf0iuLIc00t555/vvL0YV/vH2KrV7i+Yn+96vSQ30P0tF4zHHs4VXd2FOo1FquD9t9PTo0h6QsgsnCvVpfnd0Li92IszvajZIfsIqvpCBSsAiySeX/8vvzph6cZEA2syO2lxW5z3+WnGvK4v77k5KiO/5+Tj5IHruB9Rk9z7v+e2SHAQHonSg+gITtgGBtcMloOiR9jkfhxkW+fQDmvWx0bZbO4M48/Qh694aywx3xy36Wy5pEr4gkrpAe5GARCOuXYr8R1/2ALGHosasQyTYpzPEC7w3We4nXs4QfzGgQAlyDxEzXr529DVXjMeeC7CY4EVnXHwMp8PFuzvn/asaNKxLW+Oo44JPzJ67gfnSUnHXVojJHB6mKpQsOQc04yf7zp9WfHPykyVC+D7556TNyPDWC0j35/kYiIfOv4I0SExKsTrH/sSrNDgAHceg7MrhVjkfiJkh22eo30xZ/7+ysENni+5BROrN3k6VvOFpGhi7V/Xv8tKZ7wIzltjLbKATPehmk4aZxDDoz/rd+/8nxoDFUednwftRIr/Pp+//1TDHnc9++8UD75A4sUgN5e+fm5ctVZx0vxhB/JPZclmR0OdDL26MPMDgGhxHGyc/Ix7v27qpwkGobEjwvsH2RVde4D9AFwkx+fd6KUv3STvH9n/2qfoihy4P77iden7f4Tbz8v6p/5m0uj6/eBxNlPh03UV511nIiIHHoQo6HNYuSJYaTJInrsw3/9l+ePuu2g/feT/XRuYOsmBx/AaR2C+5+BCt8D999v2Kp6sOfMaoO2cUM/f73qdPn8T983OwwEePPXFwz++7A4z42OibJFg1PQ48dYnCFEyawcZCyr6kAkwbLq7/32wsF/z3vwezLvwe/J2KMPk8tOP1ZEQicNlj50+bDFje9YYDsKrIm1HPz9mjPkGy5e0dTb1WcdJy/89Bw5dyzvu06m16jnwO22Zw5s+xr2fd6kLe+R674lF5zcn8xb88gVw87dYKyZ914S9PYfn3fi4L9vOffEoMf4RXqNTfjJd6KOC4hE3y5xLmDWh+GB+yviVfeTXo/GEg1gwHnfOCrk93xBntA3fOfrg/++KGloS+D7d14ouxo7QzZw/dbxR0hZQ2cckcIsl51xbNT3+d5pX5NF23YbEA20Cvb61Uuoy8uDDtDncyhk6Kz2xeTIQw+UO6iyRJRYXXeGU8ccPqzn4rGHHySNHb0mRuRsl58xJuIx0VSunveNo2TyncMTd0cd5s6KHxiLip8o2WHf4cEHUB2EIReFaeD8m+9+c/Df28ZfJ3n/vj7ksV85+ACqeBzqlvPCr0wFc923Rzf3XfSXH2i674Unx95UHEOi/Ti6+dwTRETk5K/FXmlzfphEMqyp8LkbzA4BOvneaV+L+b4nfPWQoLdf/f+OC3o7leb29eLPzjU7BIj26uZvHnOYHH/k6NfnqRr7cLrRforI0zd/2+wwbIfET4z0HqnpF2rs9sFRfADvPyLLfNYJo8t44R7hSsMvDqjo+eqhB8b1vFYleK+AM48/fPTBGrASaT8Haegv8terTpfjjjw4AdE4X7SvkRvPOUHKX7oprtf5iz87R244++vyPyTvbCNYleahB3H6ZxeB20rimfRz2EHBX/enjjlcyl+6adTtgdW/iN8NZ/f/PkOd52uhtYgkcIDD/2fvzuPjqMs/gH+e3c193/fZpEnTpm2SHumR3nda6MXRFkoppZQbCi1XOQtS8cADERFUUBEVEBUVERFBFBFBRBQVoYoigqIC+gOBfn9/zGwyu9ljdnd2Z3b28369lGSzSb7NfnfmO8883+d5/4beuH8fRddUrr0nywtie10vO2xi3L/zlAXj4v5et3j+6mFsn5ucJhNuxjN/jJKf7xP6qL5tdmvcvzzLy5eZUsNf8NfI5/WMaRtfkpc15nlEFBszGT/GII8VCavjqgpxw7EDpoJ8ADAlTM0Z/1CCg1eM96bGur5GAMAOLpwdz8y2klhV69kF4QrE52d7Eyrcz2yhsRrKtABBKhoi+GsyAsCs9ti3clPytVbGn3m7Z3mXhSOxnzFhwUwds09u6U/mcFyNEYEY+RfOqV6cnsDFGaUB455mY8HIzx0/HV/dNdpJrruWWWhu0hWiOCg5Q3DQ1SwrMu5OXjAOX9nFDpKpEOvL5dOP1ezK5yzGY+k9p8+15GcagwB+C7uqccv2GTh5QUfI76kvzcPE+uK4f+fHDd2NSOO/fuipM/d3XdUbf8aV8eKZ2dPJUaFn+JRHqMUzwfBad1bHl/0eilVF3p0iP8uLYX0rupl/2creuuQOyMUY+EkT4QrqWmlBV+i7SvsPjz8dkTKX8YKzKDcrYFsZ2S94SygATGowv9AvzNUySU5bGPrCIZI0KJXmGvuGJ6BXr82lYkgbDZ4d7ZUFmD8+tsyD81Z0s+YcUQyM7aCtqql387ZpePLipWMenz++KuR5wG+wPf56QgVJKofgBtNby/HYhYujPq+igFuinexcPeumolB7nUK9k/yv9VOXLMM3LQrkuhbXhSnBwE+MYlk4xyNSEPdU/QJrYZgATaI+d3zo9oS8XUBmxFL4vKoodJFJI6aKJ0+47Z/ZMWwLzfF5cfAA91jbIU+v37DSRA2O+tK8kew7XwxdRoI9cO4C3LI9zDnCQm67k5muBtvLsX0O39upluW1fv7n+Lwoi7H+CFnvhKE29DaUYF1fw8hWu2CbZkTvzPf+Db04enqT1cOjGHn1c1Xw2vfA+t6A17G6OBcl+VkRb+CfuaQzOYNMEwU5vpHraxFgXV+DzSNyLwZ+4mXD2rS5QtsfbFV9lMOnxt7Jh9LDttmtMV3Ep1pxXvS7gZcfNikFI8lMR89I3aLxm6eNvctlDKBXFY3e1dw5rz0lY0p3Fw334PRFHThimrnX8ZLVPThjUQeW9oQPFAUvSpMdgKkuCn03eyjEthSKz9Xre+PeLnT8nDZcsqYHpfmsx0axY/x2rIbSPHzz9LkjGSKhdFYXYuNAY8Sfc9T0ZhzYYL5rV06Wc9eCbhBcr+7oGc24er35gtpdNUUYV2XdNrB0dPvOwZGPBRLxPUKJ4dEgTpVJmpSpPFeGCiD52/1SetuzvCumO4cz21K7DavDxEmuhBccSWN1UHCOfrFeFmKve29QcV+z9Q0ovJK8LJyzrMt0F5GS/CzsXtYVcVtHqm0Oc2fb46AxprtNM5oT3i5039nzLBoNpRs2YbDOlKbSMY/ddcpsPLx34ZjHJ4cpiA/EF5CvNpFhTXHQX4p8vU7a/PFjm5uQOa2VBRho0a5DwhWcJ2sw8BMrPbRb4/B2xCcOxZei3VZZMPZBFuRIqaIcH4pyY98fX18S38n90QsWJ7SFw0kXk5Rc4ebYhau68fDehagJk77u9+C5C1iUz0JVRTl4eO/CkXayVkr2HXsGeNJDdVEuF+IpVBUmE84Oidzg5Ls70O0nDo55rL+5DE18bznKrDjqWuVn+/Cj8xbGlOVjWgZdfm2f04of7lkw5mZhsAfPXZCaAbkUAz8xGknpS9JpzarF9kXDPVGfc+xgS8SvHzMYfa8xWe/758zHQ3vG3gUa6oy8BeLi1aOvuQjw4aOmYkJdMfKj1MqpLcm1tHj4gq4qXMGC4I5gdfe0NVNCbw/1eT0RF7Dzx1dh/+ET0aoHls3EkuMJfmaipvJ8+DzWnMovXdODtsoC1JXkYsXE+DvKhLO4e+wd0VwWfzYt0jm5rtTau/qDbaMXQA+FyEqg5CjKdXaWTZHJos2s1RXIbPe84cl18Oh/O69HWGcrTTSW5SPbx0vqRIgIWipCJB8A+Ozx0zG+phC/v2rlyDqS4sNZGqORdu5JOqclK6AUSrg3GLnD8om1+M6ZQ0m/s95aEXjB/7njZ2DrrFbT3z8vxk5BZF60YGEw42L91jBZYJes7sHXTpkd08+9ZfsMHBtmTjSVjWarGGfqXSfH9jsyWWNZ+IyfWM5Vx89pww/OXYCfXLA4bPHRRBweomCjxyNh5xqZF65ge7y41Tb1rtvs3BboH9vUh/dv6MWTl4ztDhZKvslABwWqKc7FEdMacfycVuxeNt7u4RA5wsKuatx39nzLz3OZiH/BOCXzZkZOGkWNN6WwSGym2hGha5Kx8GZ9qfXbPcz40s5BfHrrNPhMHJBDXaAuYOAnabpr46+nE3LbJ4Dtc9vQ11wW9881Oml+O246bnrIr3XWWJut5GbXberHJzb32z0MAEAZAwZJY+Wu685qrc5aR7W5oqIteoDfKfPMTW7aOg23bJ+B1ZO1jMr7zp6HO3bNsnlUgeZ2VOKo6c2mM3msakWfiXJ8Xly6ZiKKHZ795UbJ7txMo/wZ6V912LHO7dInwuAQdrZzt8KjFyzGExeHv2MzJ0pHla2zAreHsb5L8g1FCIxUGIq7hioemArVRblY2lNj6rnfPnMoZDFDSo71/c5sibl4grblZ83k+oACxcz+ik9JfhaGHVKY/8Fzw7+/w50tuCvEHCv/Tit76/CtM+bisDDbN4P563dVFLItuNWW9NRgvuHYN76mCNNaU9twwch4LGEgN3HnmsjcsXpbNsUnnuA6y6DG56u7ZuGHexZguo3HukzEwE+MRrZ6JavGT1J+6qjaktyRCy3jIvKR8xcBAAajFDYrMLm/m+zl1BNRcW7WmFowyyaaCxpR7MLdnV3b1xDxWPOVk8LcgTFxgLpgZXfUReyi7ho8d9XKMXeF53RUYllPDY6ermUSzh4Xe6FFsldJfhaGOitDZiqGC1w49XjlNFavOybWl5jO4DhrcSfysrzoqWdXPrdrYcFhS5kp2P3tM4bwh/etSvh3Xbl2UtyNPig2vF+RmKLcLEtLjty2Y6ZlP8vNeBUfJ7fdoWywaZsQhSDmO964oYBiYxkXmanUUV2IcVWht3dU6nfz8xIo9n3S/HE4af64qM8LtzXwxq3TRj6+7cRBHHbdj/DLP/877vFQ6n3+BC7A0tU3TpuD7/36b2Men91Rid/sXwFAa0O9/vofp3polCLGZUVNcS7++d934HXBWsMuZgLbVtViPGawBcdEadxC8fno0VPxznv23qW49qgptv5+J/IIcEixU6hZzPiJUbLf8iLiuqASRXf41MB0+5K80fRq5eLb4QV6Acj1IYq+Uupds3EKDqzvjdpOkygeLj6U2a7VojunkxtLcc6yrojP6beoxhc5363bZ+AjR01lse8YWFmkO/h6gJcHyRXpFHX41AZsHGhM2VhCWdfXiHV99o7BaezcFpuOGPiJUaovwicncAGWjK4KPOkkR6ZVqvdnKi2fpLWMLjS07r73rKGRj7O8nHFWi/QXLcnLwtEzwreMJkoG3uwwxxfiePjs/hW4Y9esMTcPiKxQXZyLtYYbM3yrRvboBYtHinQTpZJx7UwUTmZdbVrAH/ZJ5habxRO0micrJtbizpNn41k9vTpWv7x0GX59xfKwX4/2LzhiQKuzYSy4Glx8NZXt5zOVG7ZzRVNVOLoH3tiJqrIw+t54So5UTruhzkrTBcIpfWXAoSypfCFS2XOzvJjWWp4R5wkrPX3ZMruHkJbMbKeY3pq5GWG1rK9DNumuLcZvrliBG45h50UKj4GfOCVriVVWkIVWvW3qpIZiZHk9yI2z3obP60F+dvxlnKY0leLggeGA4lvGdtzG7UiUmHjnU6lLXoOcLB6KUiWe60N/x5lkBXo/f8JMfNpQ24fic8aiDly/pR8HDwyPPJYOwfl0GCMFKkhCRnEqFbFVdliJvh/rSlgzkuxl7BaaiN9dudKSnxOv6zbHFsTJy/bC5+F6msLj7IhVknd63Xzc9LBfqyrU7iS0Vlqzl9+T4B3C5ezGlBKRthde75LIPmt/pF66JghMqGNXoXB2L+vCqt7Atu55aXCBnq5z0U6VNrdVD+7OuG94gk0jIbLH106Zbep5PL6lXkFO4HnPdNmAeNq5x3lhWKh3Se6K0AW1p74Y22a3xvRzK2w+N1gpWodYAOis1pqVlLIOmSkM/MTI/wZP1oG8pjh8mujczkp8/oQZOG1hhyW/K54K6MZ0ct6ltU6o00bwyxMqld+/ReqwKem9p7y3IXQtK84w6/nft+lSV2pRd/XIx8OT6yI8k5yuvTJ0Nzm+z2N371nzcM/pc+0eRlrbszxyEetMFe+F7Oj3Z4Y+FjlPG0smRL5R7d/NMDiuwvTPTHR7bWNZPr68cxDv3zA54vPOX9kNANjQb66oc19zGS5alf6B+LtPnYPbdw5Gfd4la3pw24kzA8pEUHhs5x4nuxaqQ51V0Z9Eac1/Yf70ZcuhADx+8LXwzxXBExcvRVFuer+VZ3dU2j2EjKMFft4z9Vw7F/JnLOrElpkt8HkEhbk+bPgk20inqwl12t27MRfcjPzErLIwhzUEkDdgAAAgAElEQVTQ4nTzcdq20mKbzpsT6opRlp+FH//hH7b8/mRzcydSSg/xTsGN/Y04ZrAZM676/shjUxpLkO1Lzo2yme3RA025WV48cfHSmI5XszvMB7CcampTqann5fi8mD2O1xBmpcctXwcZbK/AtJYyXOCCaGqiFFTEFEVKTEGObyQVNJLyguwx2RtuSy1mCqd1Dmzojft77ZhXHo+gqigHZfo8z/QittHuXDqZiODggWGcalHWKlE8/HUT4zmWZFuQKfmdM4dw24nR72QbffCIKThqWlPCv9uMaNncZyzuTMk4nKapfGztohUTa3FTEuvT5QbVP7zpONbCM8Ns0CCYCFBdlAuvIeX+66fNxVd3mdvWlyzlBdnwxXDs4Y4MCoeBnxjlZ/twx8mzMb4mswIed548Gx85amrAY3UleUmLglN88uIsBO50bRbVtaL0T0/P9OXMMpO11e48eRY+evTU6E+ktPKFE2bixmMH7B4GqorSP9uoLD/2WhjXb7Gnrt7GgUa8f2PkLSGpsnvpePREqLXWoG+bsetvlYhbts8IuOiP5oZjB7AkiR0pT1s4GmSrKc7Bgq7qCM8mv+C1cKwx3nRaZ3z7jCEcWB//DT3KLLxqd6BJ9Vq9EycVMR1oKcPavoaAg+Hpi3jXNhXcmDTdXqUFcvpNBiGu2TAZn90WvvA5JVdtsXMu8jI84QfjqswFQQdaynH41IYkj4ZSbW5nJZZNrLV7GDgiRdkndvHFUQORNOcs1bZyBheaTxf5DrqBlg7F+d0ondbdPfXFOHpGs93DiAm3KduHgR8HWtlbhx/uWYDFNqb0rzZRRDWWtMNIrEidTndm9iMHL0NTlfadDP3NZXhoz0JsmRn5ZLV9bhsA7Q7iwm7e6Qrmb7OebC0Vzsm4yvTLsYGWcruHYDmmpaefdH7FzIw90a6n6ayvOb5tMn6ZmAk+HCHIZdUxe/uctpiev7i7ekyTkEwR/PaN9xyTwYeBpLp4dXLLpbRW5Ed/UobKvKOzQwQHVq5cOwkLu0Yv4uy80Dp4YBjXbY6coltXEr77WKyK89K7MHE83h9UZ2Xe+NgLkzkl7TtezRX5UWss7Bhqx8EDw8jPzrw5Eot4Fnef0TOobt0+I+LzGkrzsL6/AQ2ledjsgLtKOb7MuAN68oJxdg+BHMZJ9a3MbIdxwpY0qznoJbCcf0u1ma3V/uDOt85I785yZy3pDOgc2R8h6BUpePCJLf04eGA45Nc6qkN3M4zVSfNjOyfcvG06nr869JjcLvjGdCzb9wBzweFF3dVoKs/DrhhfF6tsn9OGE+ZGDwYmGshNhmR3lXXSudJpGPixSXBg5ZjBFnz2+MgXYE7gP9k3lYWOps4yUaE+WCbfWfMrNdQaYIo5mZXIW2egpQwHDwxjnomsobqSPDxy/iI0ldt/F+Xao9xft2b7nDYWQM5Qe1d0YcfcNpw4NHZB76RuSct6arBjbht2zmsHEHgOG3mOA7akUezMzLJPbOnHqQvHRaz1kw7OWjIeNx83DWcvGY8HzpmPotzwjSTsWqrmc7tXzM5b0R3w+Y4Qx9NElRdk4+G9i2yr+XrJmh5cvLon4nO6a4vwtVPmpGhE5iX7reSkc6XTMPBDMakszMENxwzgU2Hu5O1d0RXy8Ugm1qf3wiEewXeOjJ+VFQQuoP3ZVVPi7FKQLtpN1i6hUf6g6VCnuS1fnz3efJ0kf0e5me3O2lpUa2G2oZU+vqnPsp91yZoeUx39yH1OWdCBfat7MLvD2e1pfV4P9q3uwd7lXfjQEVOwvs9d9aSmt6V3EfxkayjNw57l3a64sy4iOHNJJ9qrrMnMAaxtSPG93fOjZuZG88A58y0aTXooyctCtaEAfXdt5OuMQf2mdUHQeZfxg8SV5I0NprrgsJG2GPihmK2YVDsSnPC3TLxucx/u3z0/rouyrbNarRxeWgsVBOuuLcZ9Z8/DmS5vofr1U+fg4b0L7R5GWvEI8OC5C3DDMea2VCyMoSNIaX42vn/OfFzNbhGmjDNx0TCzLbYgmtlCzukuUxeB8WTIOo3P68GGgUZ4XJapum848p10N3PXK2mPb5xmXZZFQ2meqczcSKwMarmBMVPtsYsW46p1k/D9c+ajXL+2ydRzUjI8tGchlo7pfJfcPzDjdeEx8EMJGV9ThOeuWonVk+vj38fMA+yI8oLQ7WXH1xTFtEc5He9SFOVmmd5K1GVTaq0TtVYWJK3zx7iqwoypqWPGTVunhf2aMrHUyImxW8zeoHT1TFKaH37LhVv4vDHWneDViKUGI2QzhsvYGGhhJlC66jS5Rj1zydibbNk+D46MoaFGUW4WVvXW4phB++viZSrjmjn40HnU9NHXsrooFzk+r6mbNxS7kvws1I9JCkjDixSXYOCHEpZody8ntc60i1WHwEy5Lpjb6extEFa6feeg3UMgXVlBYsGIWPedZ8jbecQMQ0aU22q/jb3j6b6OllVF6dWityJCS+HcMOuSULWMjGLZur7/8Im4+bjwwWSyx6xxgZl4uVke/O7KlaYDR37XbxnAlWvTI2PWje21L10TPmvP7OnFzA0dit0h/llt465VR4p9ZhtP2GbsXzsp7NdyfJ6AxT6RGaUh9gy71aALtoNkAoHgthNn2j2MtFRZqF1MGy+a3VacMdQ2yxqH1quK192nzsEnonQEdbsN/Y0BHVojOXZWKxZPGBsQtIvb3nNWqyl21/s1kPte++BAdHet+UzxeNu/U2jBsyvZhxqvy24cWYmBnwTUl+bZPYS0cOxgS9ivZXs9TF8n09ZOrQeQfneWk2f0vdNSYX/HrUw3e1zmZKJZqaO6CF8/dQ4uXDVh5LHmMNs+c3zpt2w5YqARm2aY3yYSjtNrAjWU5mF4cl3U5zWX5+PRCxanYETRxVL09sFzFwAAPn+C8zuwJsIta7LprfFtywt30e/2BhvuE/g6fvmkWTaNw35Om7vJzqRKdrv4dMa/TAplckrv5pnc5xwgSesqt9+wY72Z8Lg/PfkSeX/lRdjSyroh2sLUuFirKwl9Y6UuDbNkprWWWXIxvbDbfHF2O128uififG8ozXNMd75IRW+Lgjr8+IORZrso+k1qyLzOpU4QqTU7AWUR6qi5ramDVyRkd6lo0nVN7b8ReIbeFCbWbYrJloq/6+xxzr5RYhcGfhIQy8Q9eGDYUSm9qfa+db14dv8KLOupQV+zsyLPqfbpCAVireKSG3ZEaSv4Yq8oN3x79nA3BeaNr8Jwbx3uO3seVkysxU8vdEaWRCqEO4ZdYMgKumxND65aF34rsVNk2raBE+a24Tf7V4T9uidNVp5PX7585OMN/dE7l7VU5OOIaY0Bj62YWIvPbJuelPFZzd+t1Wdhh7bPHW/fvz34X8F1UaAnL1mGT27px1khillvmtGMpnJ37Groay4d896Nev2W5nOlIMeHgweGsao3egZmKgT/OZNdU0pB8f0eRpqcfskNcrO8uHHrNHztFOvaXKajBSb3/xNR+uptKAn4vKu2CJ3VsXWjy83y4hNb+jG+pgg3HDvg8hoTgcItzBsMW6yPm92KLTPDbyV2DC5AA/gLd7dXhe6c5SQrJtYCMJeV98M9C8dkmdxw7ACqi9LjffupYwawf+0k0901zVjQVY1zlo637OfZIVTg1sLYmK1W9tbhrCXp/fpQdKnIsLni8Immn5us94+/k1teti9ts7WSjYGfBLhhUt2/ex4eOX+R3cNIO/edPc90AcdQgo95/hTy+eMZFDLDBW89y0U7jxbmhM84IXPqEqjr9r51vTh/ZTeyQ9SoccO5JFXS8W8Vzxq3RT8nDE+uw6MXLMZ3z5pn7aBs5H8N79w1296BmFCqb4fJhLvH1cW5EWsyxmvXgnGW/8xkqiyK3LkNAH7ikBpVsSrI9oacy6EyQ+LZGmW36zb34aE9CwMeM54z+jN0x0EqupNtndWa9N8RzQePmAwAqCnKScu1Qiow8JMAN7T566guCriDmmo7htpt+92JaKssiOmu2BMXL8WOuW0AQlebb6sswOP7luAE/TkUWiYsvmNh5u/RVqndVd/i8jpbpUH1CpJRzDC2Y2Xgi5Ob5UW2zxO2QO9Tly5LYGSZKV2K0MYzzvaqQjy+bwmu29SH2pJcdMXQkSZd+LcWOcltO2bil5eNvhfX9TUAiF5Ye82U+qSOK53Z2WFnquE8EJy5E+6cWF2Ui5lRus2mY/blry5fjsf3LcUzly/HM4ZtjABw6ZqJePLipQGPFadhjaTWigI06/VtQk27L+0cNHWuTY8zS/rxd3HurEnO+Swva/QGpxuu0ZOBgR8HcFq19UTEWstgfX9DkkaSfLvmjzNdlLW8IBv7Vvfg4IHhsHUCKgtz0uZChlLro0dPxZFBtSMAc4uTmmJtL/V8l28xDO7i8CH9zk8ksWxBiFYo0Bv0vl4T1N3I/9YOtxRJx7uryZSb5Z7lSbj3abQ7kjwnJEnQn/SjR08d+Tjb5wm44J3ZXoGDB4bRWhl5W9pQ52hHP//2w3qHFLDOZHtXdI18PK468DU8clr4TntXr+8dqdNWGKE+WzopzPEhL9uL/GwfCoIygL0eQVlBNk6a347L1vQAcF5BYCvk+LymzrVuKwxud505/6lu1aRaHDwwjPIUBP2Z8ROae1ZWNrBqUlVnYGvqxjLnFY07c/HYAneR1Jfm4c6T40tVj3cxf+t2d7eRjcfEevd3TDl8agOu2TjF7mGklQ4T9XROW9Rh+ucF1+wJFvyWLg5aXPqLGaoQJw6uT8Y6aX56bQ+h9BGcyXD41MRvQK3vG/0ZlfqarirE2s7fbYdSry1K8M6ovaoQ95w+hEvX9OCrGdQG/IKVE7BtjpZ57paW2PGcX+/YNQuXrulBboTuhDTKf4PRbv5OdU3l+Qz8hOGOdzWlnYVdWltaf6ebw6fanyZ9tkUFCLO8yYusz2MNoDF2DIXfHrc/hmJzqeb1iKVtvKe6KHMwVWIKwKbghtn9u+fj3rOGkv+L0oC/HTgvlN3H7iSmSVGCuPHwmbxIjvdmEcVuRtB2rbOWjMeNxw5gcqP51//4OW1Rs73ImcwcZpZMqA75eGtlAY6f457SC3Zve0rVIX9mewU+vXUazlvRbfu/2akY+LHRlWud34Y2WS5d04Mfn78Ipflaul9+tjNSaU9dmPhd5vcOxXawqU3DveJ2Gda3z0xvHQ2YtFSEX5RNqHNuNlBdSS6mNMYWrInUNWGdvm0yeHvBphlaHYOOKvelbRsle2FhVap0pFbJHdWF6K517px1Aidmi5IzPHDOfLuHEFWy2xiT5r6z52HnvMAaklleD5ZNrMUx+lY8K7uXUep1mN2KFiL1Y1Cv2bWddTUttWlG+O2TqbK0pwbZPg8zfsJg4MdG/gv+bJekU8bC5/Wg3sai0uF4LLgVaSbu4/8t6/sa8OiF6dkdwg5DnVU4eGA4YBtPWX42Dh4YDvn8cPWU0lWk1Otw2SuHT23AwQPDqGaAMamCFxnhDiVWHGMymRMLAkeTyS95jx58v+f0uUn/XcHbK+3SXVuE8TWF2Le6x+6hOIId87+xLA8iErKw9JHTm1JWZ4SSJ9r2okgZvV21RTh4YBizx1WGfQ7Fzlhc2W6M+4SWeREHB1nYXY2d89oj3sWn1KowuRBgJDk9xJpR43TBy5hMvqBMtVjThvOzfdg6S7uzPKdjtDB0qLvMvAAZy/jXNntcpvTg9oLVuVle3Hf2fExvjdwZipJvems5JjUU44KV3XYPhZKgqTzwBnJeNmvyOImxcH6qHeKFWkgM/NjI6xFcuGoCKpj66xjHzmpNabFgHpaSy2UJPxEV6tsl/SnM0doPU2yitSQO9eUrDp+EG44ZwPWbB0Yeu3h1D5ZMqMGHj2SxbjMEQH+IWljBXdScJNzQHjx3AW7fOZjawdjMX2fFmKnl3FfO/DmZWw7Tg8cjuOf0IRaLd6k7d83GZ4+fPvL5OJdvaY+bTRcb/uOkHb+egffQGPhJAIOJ7uP1iKmU9ERvWLr8hmdKheqS5FZ9zeGLQZfkZ+GBc+bj6g29+OGeBbh527QUjiy1ts1uTfnv9BcE7YlSNyq4k8+KSbUoyR/dguL1CG46bhrW9zdaP0iX2q0X3ve3F370gsV4/KIlSf2dputHhPDbK1fi+i39WBvUtKC1smAkMAsAS3sCC4sW5jgnTd4qFw1PwP2756PBsLU7xzd26bmwK70aF3z7zCE8cv4iAMAUFtbPGI9dtBg/35fcYw+ZV12cO9Ishsho7/Iuu4fgSAz8EAUJlYZeWZiNrpoiUxkkC4IWsKX5Y+sO+H+F1fGfAxt60VCahxxf5qa7fnrrNHxg42Q0p0Hhxs0zxxbCy4+QqtxVG7lFeXtVIXJ8XrRUFDimYHoyXLqmB8+/b9WYx5OZ5dRZo/3tT5rfHvF5/k6FmRSQTDZ/QWx/faTaktyk1/qJVIQ7miyvB6t667BjqB0F2V7M7QxdR2JRd03API703k9XWV7PmCBatiHwc8nqHkysL455i8b1W/rHPBYqK8+Khg2hFOdmoaE0Dy9cvQp3n8JOXWalquFCstZA1UW5GZulzwAnhRO+dqEY/j+1fF4PMzNDYOAnAWwVN5Yvia3MrXLNhskxf88xgy347tnzTBVmbQgqWn3bjrGp/at667BlZjMuGp4Q81giOXxqAx45f5Gjt0FYpS1Mi1UBcMS0Jjy0d2FqBxSjbbNbA4pU+/36ihUjGQ7RZOoCVERCFu7evczc383oBJNdPdr1+dYZ4jUzumnrNJw41BZ2flLsxlUVYsfcNnzq2IHoT3aQSQ0leOaKFaguCiysfvvOQVy4Sqs54rYC9Gasndow8vH2uW341hlDMf+MUH+2UDdZ9iyPrbZLrPFaETFVs+i6zX3c3gng9EUdKfk9mbAGSrWCNA1M80rNur9BuILanz9hhkW/ITn89RaB0e7AmYqBH7JUOrQq9Xd0SJbgPcY9IWoG5fi8uGpdb9wX7lP0bSeUvkJl5HznzNgugPZZHDhMd/FkaVQXmXsP+i/uSkJcXBq1VxXiouEe1xewTSWPR7BvdQ9aUxhMS2bC1mB7BXbOG5uJkilTJidr7NJzQm3imSBOfs+tnlzP7Z1J4M+wpORLhyxqI+ceDdLXnI5KfP3UOfjqrlkBj4e6iekkJw4ZMrUN5/ZpIeoHuh0DPwlgJn/miOW1PnrG2O07Vrrv7Hn4wo6ZSf0dmaA7yrapZAuVMehPgze7YHHzdq5YiURud58IY1ep4Iw+cic7MnrF5Zcq/m1XrRVjA3inLIw1E8Tdfysyp6UivYIR6WzJhBq7hxDA7PGS12rWFqOf0lQatnCyg2Pv8AhQFuLGXaZ1/GPghzJKX/PoHuVYsyvMSvbifXxNEYpyI2cdZJLgc7rZc3yq7xSmW/HSdNRUltqLgEzr0JRJeLFgvXOXdeHXVyxHU4jMgVRtzXnY4VuAicgaTg5CpFpNcW70J1nIX28xkSYJVnt2/0r89MIlY27qZIdoNuBmmfWvtRjXhdYptiFdN54ig2a2smV5BccOtkR9HiUm0XP6NRunYF1fQ/QnWmRaazkuWd0z5vE9IToPcMESn+I8H7bOasGdJ4cuthrq77ppZnPcv8+YYcSXzFr+bXul+eaLOD992bK4ftdJ88YW7I53MZhIJmFBTnrW0DBLRCzJUlzf3zCmiYJZuVmR/8bGm0PkfKECtB/b1Jf6gRA5WCLXq1ObSnHl2kkAgPNWmMuOWTyhGgcPDKMkT7tJffriTqzqrcXGacndERFJts8z5rwuknk3eRj4Icudubgz5u8522Qx21QJV0Pnq7tm4QMbJ0fcUiIi2K8fJCn1zHRTGu6tQ1tlAa49amoKRqTJ8grqS8fedYl0ITIUphsQhSYiuOLwSRiIYd92sYnsuQxbFzhCU3k+rlo3CTduNV/QOdpFfVghonbhUtmjmdsR+3v2HP38Z3ZRnemOHWyJ/7WOIlnbRTOFE+6eHzal3u4huFK6ngfZiCcxIlqDm4MHhkNmDm2fM7ZBRvAyvLIwB9dvGUBhDssT2I2vQALYrje0KpPFUo3iWcT98rJlmHzZfTF/nxmrJ9fjqT//e8zjTeX5Y9LUpzSV4qkX/4WnLl2GF/7+H3aTsEltcS5efv0tU8+d2R79oq4kLwv//r93Eh1WgOUTa8c85t9zvKxn7P75ySzinRLZXg/+996hmL+vriSx9OmfXLAIb771bkI/w822zEw8c/LW7TOw9TOPRXzO9JZyfArPj3w+0FIWd9ZdPG3mT1nYgf6WMsyJI2jkJj+5YBFmXf2A3cOgON171hDKY8jQI0omt9dMs9vDexfinfcOod3Q0CYd/uLBl+6ZlmHPwA+llaqiHLz6xtsAzN2pD2a2dXMsvnLSIN5+9xCKc7MwtYlp4naZ1FBiOvBjhtUnA0Hotr/+zIJts1ut/YU0RrhYfWGuD6/9539hvy/cVKhPsNBzXUkewNieZUK9TtGCvD/ft2RMd8XSvNTWUPN6JOODPoAWvE+24jwue5Ol24LObJQe5nRU4JHn/mH3MCgFwq1/QtVqc5rTFnbg/LueDlumIxODg/bnZKaxnvpizOmosHsYGaMo1xfXNjKj1ZMDU4BXThqbgRFKpNyuHJ83riAUJaZQrwvliSNCY/wOfxG6UFLR+aupPB8HDwxjNi/8EhIqhTj4sY9v6gtZw4PZm5krOOgDZN4dQKcLTqINFUC/9qgppn5Wjs/ddZSczoq3lpUdiii8vOzA94rxPPnFHfY3NrhkzdiaiUZdtUUY6qzE1esmp2hEznXY5Pi3Pxak8faso2c04+CB4YC5nOnn9/R9NR0gx+fFLcfPQMdF37F7KI5lZdHmzx0/Hc+98qZlP4/S26eOHcA3n3oJrfG0czVx5E/WuWHTjGZ86bE/mXou4xHm7BuegBUhgrjBr+G01jKsmVKP1vO/FfB4rH/mO3bNwit65iE5W6ru6LVW5OPgP/6L6ji2OlNkKybV4ttPvxzxOev6GhP6HZ/ZNo31fdJEZWEO/vzP/7N7GK53yoIOeETw0e//3u6hBLhq3STMbCtHR3XkG3PZPg8+f8LMFI3K2UpCtDE368NHmq+FWadnQcdT8iNVMn1dzcBPgszMn0Xd1SMff/esecjyZk648Xu759s9hLjE0/GLUquuJA87540b87jxPRnqzjBgLqgT7nsT1d9cii899id0Rli0JOt3u9WOobFdmUIJd8KPthAI/vK0OAv/UroY+/774BFTcO5Xnwr7Hffvno8Hnn0FS0PU6qL45Wd78YGNU3D09GZ86L7fhqy9F8o9p88d6ShjxqJuvm7pgqfH1MjL9uLspeMdF/ixovYbmVOc64spiLNtdisay/JC1qx0pAw8ljDwkwLG7iBdKdg6YjfjYitUBfh4JRqlvWbj2HTPcD9zbmclFnVX44FnX0nsl1LKxLIYND43XMcHQXLuDGwcaMRge4Wp/dGRfv0MBh8sw61e7hXPReLGgQY89sI/Ax7riXIzwOf1YFmI4u2UmOqiHBTk+DBvfBVefv0tPHXHL9Fs4tg5qYHFs5zI6iMtD92p4/9Tx9I1kzKL1yMhm5iQczDwkyAfOziNMb+rCuOqCvCxTX1xff+G/ka8/tY7+N6v/xb1uaX5WfjXf811XjJbz8fPuI/8qOlNMX0vOZvxxBQpvT83K/Brs9or0F1XhHffU/j8o3+M6Xf6A0wiYklRvPNWsvWzVXjt4A5WZModPDAMACOBn7qSXAxGqANGqXPktCYcOS3yuXjb7FYUWbjFnIgClZvsXHjYlHp846mXkjwaSqaPHG1+m1e6MAaLL1w1Ab/4k3auP25WZmSScUNzgswsNMsLMqvwb3FuFr5/zgJMrI/vjpvXA3x667SQXyvJCzzh/PTCxaZ/blGcBZgvP2wiGhLs3kPOYqzwf+XaSWGfd/0xAzjDUFD8SzsHcemaidi/dhLysqIXCTXznHj1sYOcdRj5cZ1vnjYXl63pMZ3Jfev2GfhoiEXuCXPbcO1RgY+ft0ILum4cSKyuDIXWXlmAdX0Npp+/sKtq5OPLDpuIc5Z1BXx93/AEy8ZGsRORmG+8kfvtXdEV/UmUUntXdKHIUMzZzVtgP7mlPyM7MTPwk0SD7eX4yFFTccQAs0Vi4fWEnpYKwPKJgQehHJ8XG/rDL76HOtkpiSILtx1RBGgozcPupePj/tnGhMBYisyOr9G2hIbqKvaDcxfgjl2z4GG2YczCxXeixX34l04/vY0l2DanzfTz542vwuFTowcbsr2eke3MmVSvLxVEBJ89fjq+fNKskWDNrHHRs62i3YBrrSiwZHyUPLedaL4I7/QI25zbq/haW+3+3fNw1ymzo26rG55cF9PPjbZ9Nha37xzEw3sXWvbzMtXOoXZk+xgacLOor66IfEZEXhGRXxkeKxeR74nI7/X/csNnCF6PYG1fAy/QYhQuWKNU7Kn8q3pjOxFZyXgXkpJvzRStXWW0FuzBhbtz48zKOZTE4gJLe2pw39nzQl6ItlUWsLiwxaLV+GFCUPoKdc547CLzmaKjP0f7Ly8sk2thVzWqinJQUZiDH5y7AJcfFj4j048rrPQ3pdH8nfe9y0czRYzH5ntOn4uvnzrHwlERAHRUF6G/efQyz6r3WzxLqLqS0DfqzNZNpMh8GdDZMFxdz0xh5hX+HIAVQY+dD+D7SqlOAN/XPydKiL8NbqyF44xvYuPe41ii1tdv6Y/pd5rxmW3T8cLVqyz/uRTaYVPq8cLVq9AS5e7uXSfPNvkTIy9vvCYCuonUHPFn/VD8rt7Qm9D3+1/ijqpCC0ZDyWb23VZdFL3pwOaZTSjM8WFliJsHmb5wTJW2ygJT53F2QbTXC1evirrWMXORn59t7iZMuIvT+tK8uLf0U3T+t5nHovdbPDfPWMw7+TLlTzwyn/WFXqacR6KeUZVSDwF4LReVTroAACAASURBVOjhwwHcon98C4C1Fo+LKC5PXLx05OPvnjVvpMjj3I7IW76S8XYXkYw5kDiFmb+32Zck2vPu2GU2gER2WT25PuBzYzF+Y52ncAudOfpxIz8n+gUJ3+ru0lFdhF9dvjxKfTe+6E4QLQbvf38HF+sna8Sy1on0rEQv6svyGfRJpkXd1Th2sAVXrJ1o6vkFUc6bh+J4vRl0T5yxbiVpTQM2z2zG2UviL+uQTuI9C9Yopf6qf/wyAPdWf6KUiftwHvSN3zpjLm7fOYi2ygKsmlSH963rxc3bQheL7qnXtv3UhEgf5Z0FiqSnvhjnLht7ovjyzkHcvnMQANuEO0l3bdFIPacbjhnA108b3RLAl8m9rA7NcK44T7SYg/84HO0GEKU33mhLriyvB/vXThrJmLx1+4yIz5/RFnlLuv99OafDfNdEHn8Tl0jdSjcInkO5WV68b10vSjIkcJzw7Q+lvXPDvhVFZKeIPC4ij7/66quJ/rq0smKSffVl0lm4U7fZi+iJ9SUj7Xc9HsHmmc3I8YW+83Dqwg5847Q5AfuXx4yHa4mMZHzZwxUQD1UfaGZ7BSY1xNfRjpLHWC9pxaTagEwO3kV0l/X95jtCmcXzgHPVlZjtuskX0Qn6m0PX81nUXR31e48ZbLZ6OBSneeOrInY2jNbQwp/xU5Dti/g8ouTIzPNBvIGfv4lIHQDo/30l3BOVUjcqpaYppaZVVWVWsdtjZvIElQprY2j7GszrEUyOoaggpZ/fXrkCSyYklpR4zcbJeHZ/cKmz2CztYWKkU4WLKSc6byi1RIBn96/ABzZOCXiM3K3CUNsvlH69buAJc813eaPUi9aM49n9K3CFiWLflDrXbJiMDx4xJeTX/Ddgw/HfzI3lGM1bNPFrKM3DORme7QNwDsUbZv0GgOMAHND/+3XLRuQiTDuNTUVBNl59421TXdCMRZxZBJciyfF5kWijAq9H4PXE1/3Lr7WS3YCcKtRC4OCBYfzv3UP4xYv/yvjU6HQSqUvfeSu60VaZeOeXwhxt6VRZGDngQM5QWZiDgweG7R4GRREt8zLUe/ucpeOx49bHkzUkisLjEeSEKMAe6v22qrcW33765ZHPF02oxtqp9di7ohvffeZvSR0nAY+cv2jMYyIhtj5lSDv3TL1Ejxr4EZEvAVgAoFJE/gzgUmgBn6+IyAkA/gjgyGQOkjLDLdtn4MHfvhJQdNXIf2z68JFTMJ3trCnJEjkp+IsIT24sxU+e/4dFI6KkCXO9ke3z4Nqjpkb81sOm1OMbT72EghymqzuR8QbMyQvGWfIzl0+sxTlLx+PYWS34+APPWfIziTJFqMNttLvwpy4M/d6tKgq9XiTn6WsqCwj85Pi8+MjRfRG/Z0N/I7pri3DVt38DIFM351jvs9umo6IwG/nZPvzmr6/j9C89OfK1wXEVuOuJv+CS1T02jpCSxUxXr01KqTqlVJZSqlEpdbNS6h9KqcVKqU6l1BKlVHDXL6KY1RTn4qjp0bfHre9vRFN54ndtKTOZacOeqNwsL+4+dQ5u3DqQ9N+VyfrC1IqIVSI1ft6/YTLuPHlWDHVGyA5rp9aPeSyWLkD+GxLLJ9YgN8uL0xd3ojSfGT9EVolWEyYUf8F+sk9nTSEAYChE8fSO6sKEfna2z4MT57WPfJ7p23SssrC7GpMbS9FRXYg1UwLPjf73ob8rsttkeoFwd76qDrCM9TyIHCkrxL6vHXPbcNOPXgh4LJ5FqNHUJtaOSrYv75yFt999z9RzF08IXzg0kYVAXrYXAy3MQHSypy5dhoLswG0iv75iOTwxpPVVFubgF5csRXFuZnT+SCdLJ3K9lY5u2joNZ9z+JP77v+jH8JVhmqXUluSirbIAkxvZUMEu3bXFeOLipSjLz8L5dz0d8LXxNYV47pU3LftdmX7RTtbJ1OwxBn6SJNJFBlkrU/dpknk757Xju8/8bczFn9++1T04f2U3Oi76zshjnFfOl+3zINvkfvRItcBK8rLwj//8z6phkU3C1dUryRsbrMmPo5NMqAyf81d282LEZt21xXYPgaI4f2U3Xn/rHcwbX4UbH34eAFBWkG364itSp8wfnLsg8QFSQsqjFFi3Dg+2RInIjApO5ApcXFO8qou0dHCrt2aYDTqQs9158mzUl3DLAMVu1/xxltUOInKr1soC3HbiIApyfGiv1Lb/uHUrCRE5V2uFViqkLGXBSmfhUZeIKAQzdyL7mrQ2wXuWd+ED3/1tcgdEcfne2fOidgpsrSzAMbNacM292ms4oLd/pvRw71lDyPUl1nWPiFLjyrWTcNjU+jFZmK0WdNwjIopk74puzO6oyNgmQbxdTUSEsQnEa/saon5Pb2MJHjl/EU6JcMf/l5ctw1OXLktwdBSvzpoijKsyX2DyiIFGfHHHzCSOiKzWXVuM1soCu4dBRCbkZXsxf3zVmMcn1pcEtJxmRm3mOXfZ+JCPr5hYCwBoqeBxPlXcuski2+fBou7MrQvHo6oF9q7osnsIGSHcTftk1WLZMdSG9qoCrOoNXVSQ3G1Bl7k6XQ2leWFriwBAcW5WyBoj5EwVhTnIzWL2CBFRsuUF1d1rKB3tjvi7K1emejiUoAtWdmPTjKa4v79C754YbH2/diOujJ0Uk+LkBeNwkt49jfUt3Y1bvSzQUs4IdLKdunBc2LS8qjAnikS1VBTggXMWJOVnExEREWWyL580C/c98zcU5oxejnzjtDl46sV/2TgqitdJ8wOzn+tK8sI8k5zkvBXddg+BUoQZPxZoKueBLRnuPWto5OM9y7vD1umIlG1BRERE7lOWz0zKdDeuqnBMcfTJjaU4dlarPQMiS+1Z3oWa4tGbs53V5rddA8CkBnbsI7ISAz8WmNxYiqHOSruH4Tps0UpWMRMbDO4ax3hiZpk9TjuGh6o9QUTO8+CehfjphYvtHgYRhZGb5cWayfWmn9/XXDry8f2752HzjGYAwIQ67XpgzRSWXiBKBLd6WWRcVSEe/v3f7R4GEVmEcZ/MMrWpFAcPDNs9DCIyqSSP9dOInM6rZ+ubuZlmvOHbUT3a9a2pPJ/nZyILMPBjkfICreCYR4BDbi2FTpRBmPFDREREFL9TF3XgzbffxZaZLfjh717F7195Ex89emrY599z+lw88OwrKRwhUeZg4Mciu+aPQ2VhDp740z9xx8//bPdwiCjFumuL8OzLb9g9DCIiIiJHKM7NwlXregEAH9/Uhwd/+yoOn9oQ9vmTGkowqaEkVcMjyigM/Fgk2+fB5pnN+MWL/7R7KBmpONeHd5lqRTb6yq5ZePWNt+0eBhEREZHjlOZnY21f+KAPESUXAz/kCj+/eKndQyAHqyrKQbbPg/NWhm9ZqRAcOIxtr1dxbhaKc1lvgoiIiIjSzzGDLbjj539m0yKXYuDHYnuWd+N/7x7CYVMY0U6lLC8b1FF4OT4vfnflSruHQURERETkSGx04W4M/FisqigHHzm6z+5hEFGCWNyZiIjIPl87ZTbeeOtdu4dBROQKTJMgIiIiIiJH6Wsuw7zxVWMeX9A19jEiIoqMGT9ERERERJQWPr11Gt565z27h0EWmdpUioqCbLuHQeR6DPwQEQHI9nqwZko9vvnUSwBiLe1MREREqZDl9bC2o4vcfeocu4dAlBF41CQiAiAi+PimPhTnMh5ORERERETuwcAPEZFBW2UBAC0QRERERERElO4Y+CEiIiIiIiIicikGfoiIDJTdAyAiIiIiIrIQAz9ERCFwoxcREREREbkBAz9ERAaD7RUAgIpCthYlIiIiIqL0x/Y1REQGe5d3YdOMZjSW5ds9FCIiIiIiooQx44eIyMDn9Yx09iIiIiIiIkp3DPwQEREREREREbkUAz9ERERERERERC7FwA8RERERERERkUsx8ENERERERERE5FIM/BARERERERERuRQDP0RERERERERELsXADxERERERERGRSzHwQ0RERERERETkUgz8EBERERERERG5FAM/REREREREREQuxcAPEREREREREZFLMfBDRERERERERORSDPwQEREREREREbkUAz9ERERERERERC7FwA8RERERERERkUv57B4AUSQd1YX413/fsXsYRERERERERGmJgR9ytPt3z7d7CERERERERERpi1u9iIiIiIiIiIhcioEfIiIiIiIiIiKXYuCHiIiIiIiIiMilGPghIiIiIiIiInIpBn6IiIiIiIiIiFyKgR8iIiIiIiIiIpdi4IeIiIiIiIiIyKUY+CEiIiIiIiIicikGfoiIiIiIiIiIXIqBHyIiIiIiIiIil2Lgh4iIiIiIiIjIpRj4ISIiIiIiIiJyKQZ+iIiIiIiIiIhcioEfIiIiIiIiIiKXYuCHiIiIiIiIiMilGPghIiIiIiIiInIpUUql7peJvArgjyn7hclVCeDvdg+CyAKcy+QmnM/kJpzP5Cacz+QmnM/kRC1KqapQX0hp4MdNRORxpdQ0u8dBlCjOZXITzmdyE85nchPOZ3ITzmdKN9zqRURERERERETkUgz8EBERERERERG5FAM/8bvR7gEQWYRzmdyE85nchPOZ3ITzmdyE85nSCmv8EBERERERERG5FDN+iIiIiIiIiIhcioEfogwiImL3GIiIiIiIiCh1GPghygAicpWITFDc20kuICKVIuLVP2Ywk9KaiLxfRIb0jzmfiYgcQERaRCRH/5jHZkp7DPwEEZETReR6ERln91iIEiUim0XkIQCnADjG7vEQJUJEtojILwB8EMBNAMBgJqUrfT4/AOBE/X+cz5S2RGSniOwXkTy7x0KUCP3Y/EsA1wC4A+CxmdzBZ/cAnECP4noAbASwF8BfAcwUkb8opd6ydXBEcRCRYgAfANAK4AIAEwCU6F8TnsAoXejHZx+AkwAcAeA0AD8B8LyIzFJK/cTO8RHFQkQ8APIA7AMwHcBFALIBrBaRQqXUm3aOjygWhuPzDgDnAXgLwH0AHrZzXESx0ueyADga2nrjZKXUIyLyiogsUUrdz/UzpbuMz/gRkVyleQ/AEwBmAvgkgHnQLpaJ0o5S6nUAn1ZKLVdKPQJAAThS/xpPWpQWRCRHPz6/A+AepdR8pdSPADQBeBzAq/aOkMg8fT4fUkr9B8AtSqkleuCyDMB8Bn0onYhItuH4/AS0NfOnABwvIhX2jo7IPBHJ1+fyIQD3KqXm6UGfSgA/BfB/ANfPlP4yOvAjIhcDuFdETheRiUqp3yulXoOW1icAhkSkzN5REpkjIheKyEz9Y69S6nHDl+8E8K6ITLZndESxEZELANwlImfox+eDopkO4C4AOQDeJyKX6M/P6PMZOVvQfJ6klHrWXzNCKXU3gDx/nR8ipxORSwHcJiLbRKRcKfVTpdT/Qbtx2ghgCY/JlA5E5Dxo14L7RGSxUuo1fa0xHsC3APwPwG4ReZ+IFNo7WqLEZOxBWUS2A1gMLTW1CsBVItIKAPrdizsBDADoD/o+FvciRxGROhG5E9o2xS8AgJ7BZlQG4AVk8Hue0oOItOl1TyZCq+UzHsA2ESnS77b9BcBipdRqaHP+dBGp1+/UETlKmPl8nL6tS+nPKQbwAIAa+0ZKZI6InA1gDrQgz2IAl4pIHQDo5RE+C2AztK3mRI4kIvUi8jUAU6CtJd4GcJKIVOrH5r8CWKGU2gDgVACrAHTYNmAiC2TkRaAevGkCcL1S6qfQinf9CsDV/ucope4DcBBAr4gMi8ip+uNM8yOn+TeAryqlSgH8S0R2A4CIjNTwUkq9AKAFwFT9axn53qe08Bq0bV3HKKV+AOCbAOoBvAsASqmXlFL/1D8+COAhAG02jZUomrDz2ZDx8zq0oE89oGVs2jVYokj0udkH4HKl1PcB7AfwXwBn+Z+jlPoSgNcBzBeR6SKyxZbBEkX2OoDvKKU2K6UeBfAdaFu6CgBAKfWGYa3xMoDfQMtmI0pbGXnxZwjebNU/fxPARwGME5EFhqfeC+BCAJ+GVnyRyHGUUv+Flo4KAGcDuEjfe/+uiHgMFxFfBbBU/x5mR5Dj6IUT/w3tmOv3DLRAvS/oubkici20bLZnUjdKotCCM4KjzOcspZQyBOG/BWATEDJjk8h2+nx+D8DfoBVzBoDnoG29nSAiA4an3wrgev1ruSkdKFGQMMfmNwF8yfDwS9DqVP0n6LmFIvIhaBlsTyR5qERJlXGBH8Ob/wCAdhGZp3/+d2jbZJbpz6uClgn0TQAdSqlrUz1WolBCZesopd7QT2Q/AvBDADfojx8yXES8DeBr3K5ITiEiXcb57A/KK6XeMDxtJoAXjY+JyGEAHtQ/Xa2U+lcKhksUzcgFrrH7S6T5bAjCvwzgE8zGJKcQkRn6NkQAATdNbwTQKCID+vw9COAxjGYUd0DLBPoCgC6l1M0pHTjRWCM37yMcm8cD+ItS6u+G586DlgnkhbbF/KUUjZcoKVy7wBCRtSKyP8zXfEqptwF8AlrLa/8J7T0A/9Cf9jqAtUqpE/SMCiLbiMhh/i1cQY+LIZDjz+w5GcBaEakUkQki0qs//hml1B3crkh2E5GlIvJTaHeNPYbHxX/ha9iq2ALgKf2xOSLSAuDHAI5QSp3N4zPZTUSWiciPAVzn39aiZ/P4osznWSIyUX/8AaXUF5iNSXYTkfki8msAOwEUGx73H6v/COB70OqiQCn1KoBqw494Ddr6+UQen8lOIrJKRO4F8DERORYYOTZ7Dcdm/9q5GdrchogsFJEp0Dp6HaGUOotzmdzAdYEffWvLDmhFFM+XoC4Zeru+d0WkTin1CQD/EZEDIjIXwGHQ/yZKqbeVUv8Y8wuIUki/cDgPwMcAfFBEpiqlDvlPVPp8VnqGmk9/7G/Q0qtfAXALgEP64+/Y8o8gwkhQJ0tEroC2BeD9Sqk9Sql39a97/e1U9UKh/uyJVgDFInIjgMsAFCil/q6UetGGfwZRAP3YewW0DOEvAjhKtA5eUEq9G2U+Xw7Af+eZ27vIdiKSC+BMAFcopXYopf6sP+41BCVLAHweQIWIXCQi4wB0YbQO22t6TRQiW+hr5wuhHWM/Aq0W4CoRWQ1ox1v92NwMvaYPtNpVeSJyA4BLoW3HfZtzmdzEdYEf/cT0e2hv4FOgLchG6FHeawDcKVoXrx3Q0lSvAvCQUuoDqRwvUST6RfHvAHQD2I3RLVzvASPz+WPQaki064HPYwEsAXCeUmqGUor1T8h2elDnHWiByDuUUncBgIgMiUgW9AtgfS/9lwF0iUgRgI0AjgDwjFJqqVLq1/b8C4gC6dmWNQCeUkrdrZR6AMD5APboASGIyIcRfj4v43wmh2kA8A+l1O0ikici6/W57M+OuB7ATdCO12cCyIc2vx9RSt1i16CJjPS18/MAjlZK3QutbMdL0Ld86YGhy6Ft4+oTkWwA86CtnX+jlFqglHrcntETJY+4YdeHiGyEtmf+p/rnWf7sBhH5GYAb/HuMRaQLwEkA9vurteuPZyul/pf60RMFEpEzoHV3eUIp9ZWg+fwCgIuUUrfpn0+B1k1jt38+i8g0AM+x7gk5gT6fewH8TCl1o4jUQquxBgAD0ALv/4S2deDbAC4BcJlhPp8M4CvMwCQnEJHjALyklPqe/nkrtIuKBf45qt8xzgNwGoArwflMDmVYbzyulLpDz4B4AMDxAPZB63L0HwDPQqvt8wEAZwatn3P08glEtglxbM6GloXmVUq9IyJfBvBNpdQX9OP2MQA+YTg2bwHwbePcJnKbtA78iEg1tE5FndAKy63XU/cE2r/tkIishLbta27wm1lPXWV6NTmCPm/PgnZH+FpoxRHfD+1E9Ir+nHUArlNKNYT4fp9/2wyRE4jINgC7oG3RugBacOc6AAugZT3sB/BbaNtsTwKww188kcF4chIRKYOWWTkHwKsA+gyZl7dCS2o7Tv+8HMB3oa1JXtQf43wmxwiz3vigUupmPetyGMBpSqn7RaQHwFcArFNK/V7/fq6fyRHCHZtFxOPfnqhvYbwdwPlKqWeDvp/HZsoYab3VS78Y/jqAFQD+Cu3CARgN+ohS6jsAfgNgp4gUiciRQEBbSiJHUFoUdiGAfUqpO6C1Zp8MYLnhOV8D8DsRORfQiuTq//Uw6EMOtBhaLZ97AZwDIAfALqXU3QB2KqWe1ef909CyfgCMHJ+5ECPH0G8c3Qet3e/PoWWm+Z0GrX7EdP3zNwE8idHti5zP5Cjh1hsichS0mijtGK0b+GsAP4JeC0Vfb3D9TI4Q5djsVwYgVyn1rIg06jtFeGymjJO2gR8Z7S7wcQC/hvamH9aLNh/Sv+5/znkAroZW+6cGCGhLSWQ7w3x+HMAQAOgXy78HMFHfouh3MoBrRORlaCnaxpbARLYzzOcnAfiLKT4O4BEAbSIyRyn1H8O3bIW2NeY1/bk8PpNj6NkRAHCrvoX2egDrReswB6XU69CKiF6sbzfYB2AiAH/Lds5ncowI641nAUyD1tV2H4DdIjJRRC4GMAnAi/pzud4gR4h0bNavBf3dFNsBFInIWdC25lYBPDZT5kmbwI+I9ItIhf9z/4lHKfWOnunwY2gnrTP8X9dT/cYB+CSAuwH0K6U+nvrREwUSvSuX/6RlWEg9B+3k5G/B/kNoHTSK9OdPhZbSeie0+cxiiuQIhosJ43x+BIBHRObpn/8KWnZmvf49G0TkKWiLspOVUm+lcMhEIRkuJgCMXhz456dS6mfQioJeZXjOddAyJQagtWw/Qin171SNmSgcESnR/+vvBhppvVEMoE0pdQ2ALwA4FUAHtPnMulRkK71G4Mh6I9qx2ZAJ3w9gFrS5PKyU+mRqR07kDI6v8SMifdCKyc0H0Ki0VtXGC2Z/KrUX2v7Os6AFf94C8D8AAqBGKfW71I+eKJCIzAGwE1q3gY8rpV7TH8/Si891ADgBwN8BfFQp9a6IfANanZ8bRKQJQKlS6mm7/g1EfiIyA8CgUupjQY979Ltt5QC2A2gDcIYejP8kgBeUUtfoFxyFSqmfpH70RIH0+XwigD8AuFkp9ar+uP8i45Dhuc3Q6p74j9dFSqnnWPuEnECfs4UAboPWpes4w9e8+rE43HrjXqXU9fpzR5pLENlFvxb8IIDnlVInGh4fqelqeCz42PwugDoAZUqph1M6cCKHcWzGj4jk6J0xPg0tde8haMXm/CctpZRS+vNylFLvKaUeAvAMtLvKD0ML+PybQR9yAhFphzaXfwDtjvB+EVkFaJlr+n+fg5Z+PQ5aW2AAeBvAH/Wvv8igDzmBnjL9NQD79CL6oe4ovwHtWJwD4IOitW0vg7YYg1LqaQZ9yG4i4hWRq6F1LXoE2t3hS0XEvzX8kB7IzBORQv2xP0Gb/09jNFMCDPqQE+jH4Degta9u0Gv3+JtAvKc/J9x643nDz2HQh2wjmmsB3ArglqCgj0e/FIx0bH4IQItS6lcM+hA5OPADLTr7c2jduO6C1g2mwliUWUQuBXCT/lyIyC5oBZ4/BWCy0rsPEDnENAC/UUp9DsC5AH4BYI2I+OfvlSJyM7R5/zEAM0Tk59DqnnzXniEThfUHaPV7ToZ+0WC86BWRy6Hdbf43gIuhBXwe1j/nFkVyEg+0+iVH6sfnswEMQqs7BWBkvfFFaNsSISKbAJwC7S50r1LqiRSPmSiabmhdjj4KYIuIFPm3vojI/gjrjfvsGjCRkb6rowjAk0qpWwFARMZJYMeuyxH+2DyJx2aiUb7oT0kd0TpuNQN4SCn1GLRsH78CACV6lk8WtHbA4wFcopQ6qD/nOQCz9bsYRLYSkUEArxkyzh4DcIaINCul/iQij0C703a0iPwM2klrZD6LyGYAPr1gHZGtQszne6BdMP8GwAkicoZS6mP6FoOJADqhtU79g/792wEUKKXesGH4RAGC5vMhALcppf6lZxD/RUT+DKASwEERmQygC8Ae/3wG8AKABUqpF2z5BxAZGOezfoNUQVsT/w9aBs8LALbpW7maoK09uN4gxwmx1tgN4Gcicgm0Lrd/A/CmiHwEWgfFTgB7Ddd+PDYTheGIjB89zfoSaN23DgG4WUTW61/zj/FuAIeJSIGeevqQUmqLUuoPhu0F9zPoQ3YTkVIR+Ra0LLUj/emn0OpO/QjAEfrnv4W2NbEYwNNKqc16jQh/PYk3uQgju4WYzwX+L+lbbN8C8CFowZ9KfVuMfz7/wVgfhUEfsluo47M+j/8FAEqpt0WkCFpdqpf0x35pmM/+9cajvLAgu4U6PvtrX0Lv0KWUegbaWuMSANcB+AXXG+Q04dYaSuua+AkAGwBcAGATtCYRRwD4p2Eu89hMFIUjAj/69oAuAOcopT4M4FIAp4vIBEOtiFcAPAAtdRVKqbeBkT2e3FNPTlIAbWvW6frH/o5GrwJ4FECviMzU5+1fAMxTevcXY/oqkUOEnM9B8/RBaHP7dGCkSC70O8+cz+QkwfN5KMRzZgB4Rin1kogUikgnMDKfud4gJwm33gCAP0Hr2vVlAHsBPAHgOaXUmwDXG+Q4Yeey0hpILFBKPaRf/90NLbD5X4DXgkRm2Rb4EZGtIjJfREr1h/4GoEwvPHcXtLsTR/kjuAD+A60Nn39PZ3AbbCLbGOZzsVLqL9CKhH4FWpbPDBFp0E9KPwHwJIAP65lAEwH8UUTyAc5ncoYo83mmiPjbsfuPw+8BuBLAeSLybwD9hu0GRLaKYT77t7+XAXhRRI4H8DMAU4HRLqJEdjI7n6HN4yoALwPoA7ALQJeITAC43iD7xTCXoZT6p+FbB6DVZfMXKudcJjIhpe3c9YuEWmgFPw9BKw5aAK0g8xnQag59TN9n3wXtzb9CKfVX/fu/BuBHSqkPpWzQRGFEmM9nKqX+rj9nDoAjATyulPq84Xs/DKARWnevrUqp36Z4+EQBYpzPP1NKfUF/zAOtPtVnodWTOEux8xzZLN75rD/+eQBboBUhv1Yp9csUD58oQLzrDX377RoJUAAABX9JREFUrf/rhQCylVKv2fBPIAKQ0FojB8AsaEWb/wptlwi7NhPFIGUZP6K3YIdWnf0vSqnF0LrBvA6to8D1AGYDmCwi+fqF8LMYrYcCAMcx6ENOEGE+vwbtjgUAQCn1CICD0O6ylei1IwBgD4ATlFIzGfQhu8Uxn7v1+Zyv32l7HVqh0MUM+pDd4pzPxYZ6bN+C1uHreAZ9yG4JrDcKlFJ/1+toevQ6Pgz6kG0SWGvk6Vu8/gfgSqXUGgZ9iGKX9IwffavWfgBeAN+GVsh2o1LqOMPX/wpgIbTUvUEAP1RKfVlEvggtA+inSR0kkUkm5rMHWkHQo5RSP9QfK4S2DWY2tAyfPqXUSzYMnyiARfN5QCn1ZxuGTxQgwfk8B1pX0an+LGMiO3G9QW5h0bGZc5koQUnN+BGR+QB+Dm2f8XPQ3vTvAFjoL/6p14a4HMAHlFK3ArgPwFYReRLa1i/ePSZHMDmfDwG4TP+f3zCAUwA8BaCXJy5yAgvnM4M+ZDsL5vMvoM1nBn3IdlxvkFtYeGzmXCZKkC/6UxJyCMCHDHuN+6C1SL0EwCcBDOhR3juhHQCalFJ3i8ijAPKVUs8neXxEsTA7n+8GsEhEWpVSB6EVqVuilHrInmEThcT5TG7C+UxuwvlMbsG5TOQQya7x83MAX5HRzlyPAGhWSn0OgFdETtejvI0A3lFKvQgASqmXGfQhB4plPr+nn7iglPo6T1zkQJzP5Cacz+QmnM/kFpzLRA6R1MCPUuq/Sqm39e1cALAUwKv6x8cDmCAi9wD4EoAnkjkWokTFM5/17gVEjsP5TG7C+UxuwvlMbsG5TOQcyd7qBWCkqJcCUAPgG/rDbwC4EMAkAC8opf6SirEQJSqW+aySXT2dKEGcz+QmnM/kJpzP5Bacy0T2S1U790MAsgD8HVq79nsAXAzgkFLqRwz6UJrhfCY34XwmN+F8JjfhfCa34FwmslnS27mP/CKRQQA/1v/3WaXUzSn5xURJwPlMbsL5TG7C+UxuwvlMbsG5TGSvVAZ+GgEcC+DDSqm3U/JLiZKE85nchPOZ3ITzmdyE85ncgnOZyF4pC/wQEREREREREVFqparGDxERERERERERpRgDP0RERERERERELsXADxERERERERGRSzHwQ0RERERERETkUgz8EBERERERERG5FAM/RERElDFE5D0R+YWIPCMiT4nIOSIScT0kIq0isjlVYyQiIiKyEgM/RERElEn+T/1/e/evalcRhnH496EhSJQ0amsQEhFBU3gBaRQEC4tYaikI2tnbWFgJShTtFAsre3MBiiAE9QZSitoIUUjhyVjsVQQhaBB2jvs8Tzf/FjPty8y31rq41nqqeq56oXr7H9acqwQ/AMD/0qy17vUeAAD2YmZ+X2s9eFv78eq76uHqserz6sw2/MZa65uZ+bZ6srpefVZ9UL1bXapOVx+utT7Z2yEAAO6C4AcAODH+Hvxsfb9VT1Q3qltrrZszc776Yq317Mxcqt5aa724zX+tenSt9c7MnK6+rl5ea13f62EAAP6F++/1BgAAjolT1ZWZuVgdVRfuMO/56umZuby1z1bn290IAgA4VgQ/AMCJtT31Oqp+aVfr5+fqmXZ1EG/eaVn15lrr6l42CQDwHyjuDACcSDPzSPVxdWXt3r6frX5aa92qXqnu26beqB66benV6vWZObV958LMnAkA4Bhy4wcAOEkemJnv2z3r+rNdMef3trGPqi9n5tXqq+qPrf/H6mhmfqg+rd5v96evazMz1a/VS/s6AADA3VDcGQAAAOBAeeoFAAAAcKAEPwAAAAAHSvADAAAAcKAEPwAAAAAHSvADAAAAcKAEPwAAAAAHSvADAAAAcKAEPwAAAAAH6i9sj+DbAyR4egAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27mfrKsI_izI",
        "colab_type": "text"
      },
      "source": [
        "As you can see, it's seasonal dataset\n",
        "\n",
        "if the max temp is low that means winter, otherwise summer. \n",
        "\n",
        "Anything in between is autumn or spring."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "an7fbD8vBG6Y",
        "colab_type": "text"
      },
      "source": [
        "### Requirements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21qOExHbBI9e",
        "colab_type": "text"
      },
      "source": [
        "Find minimum data and maximum data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD5MeOBi_10t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0613d3c6-9a58-40ac-d7be-c01eaf864a81"
      },
      "source": [
        "minscale = df['MaxTemp'].min()\n",
        "print(minscale)\n",
        "maxscale = df['MaxTemp'].max()\n",
        "print(maxscale)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.1\n",
            "48.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6dPXOGfBMQr",
        "colab_type": "text"
      },
      "source": [
        "My minimum MAE is"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1xNuX95A1N2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4cb37d20-ffb1-4d75-ddd8-c3bc3a2db1d8"
      },
      "source": [
        "maeRequirements = (maxscale - minscale) * 10/100\n",
        "maeRequirements"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OSxlw7gBdQ1",
        "colab_type": "text"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOd6uwk7BhyU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Important function, sources : https://github.com/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%204%20Exercise%20Answer.ipynb\n",
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "    series = tf.expand_dims(series, axis=-1)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
        "    return ds.batch(batch_size).prefetch(1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-Nu_jpnVG8N",
        "colab_type": "text"
      },
      "source": [
        "### Split Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riSB6snnCvFB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "split_time = int(len(df) * 0.8)\n",
        "window_size = 64\n",
        "batch_size = 256\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AZauPZUEsES",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date_train = df['Date'].values[:split_time]\n",
        "x_train = df['MaxTemp'].values[:split_time]\n",
        "date_val = df['Date'].values[split_time:]\n",
        "x_val = df['MaxTemp'].values[split_time:]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGky28_8FGcW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ac494003-b454-43dd-8045-e6859e552d0d"
      },
      "source": [
        "print(len(x_train))\n",
        "print(len(x_val))\n",
        "print(len(date_train))\n",
        "print(len(date_val))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45136\n",
            "11284\n",
            "45136\n",
            "11284\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucpQKwgHW8JC",
        "colab_type": "text"
      },
      "source": [
        "The dataset reduced to around 56k due to lots of null rows and I remove it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ystIx1IiVJH7",
        "colab_type": "text"
      },
      "source": [
        "### Change dataset to windowed dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qufS7bViF6d8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdccsT9rVNIt",
        "colab_type": "text"
      },
      "source": [
        "## Model Creation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYoJhhlTVRKR",
        "colab_type": "text"
      },
      "source": [
        "### Model Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t84XQosRGQi6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=64, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n",
        "  tf.keras.layers.Dense(16, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(8, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 400)\n",
        "])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcgA3bIzVO-6",
        "colab_type": "text"
      },
      "source": [
        "### Callback Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isk3A3TIG7vD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Learning Rate Scheduler\n",
        "lrs_callback = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-8 * 10**(2))\n",
        "\n",
        "#Tensorboard\n",
        "!rm -rf /content/logs\n",
        "log_dir=\"/content/logs/\"\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "#Custom callback\n",
        "class stopTraining(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('mae') is not None and logs.get('mae') <= maeRequirements):\n",
        "      print(\"\\n\\nReached\", maeRequirements, \"MAE, i think it's enough\\n\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "custom_callback = stopTraining()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4O3bC78nXZ7o",
        "colab_type": "text"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVjrVP5WVfUp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
        "\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16zEf09WVjGc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "0843902b-6ef5-459b-982e-c9836346240d"
      },
      "source": [
        "model.fit(train_set, epochs=100, callbacks=[lrs_callback, tensorboard_callback, custom_callback], batch_size=512)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "      1/Unknown - 0s 54us/step - loss: 37.4883 - mae: 37.9870WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
            "Instructions for updating:\n",
            "use `tf.profiler.experimental.stop` instead.\n",
            "      2/Unknown - 1s 271ms/step - loss: 37.6791 - mae: 38.1779WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.1773s vs `on_train_batch_end` time: 0.3644s). Check your callbacks.\n",
            "177/177 [==============================] - 31s 177ms/step - loss: 8.3167 - mae: 8.8035\n",
            "Epoch 2/100\n",
            "177/177 [==============================] - 32s 183ms/step - loss: 6.1464 - mae: 6.6297\n",
            "Epoch 3/100\n",
            "177/177 [==============================] - 32s 181ms/step - loss: 5.4182 - mae: 5.8997\n",
            "Epoch 4/100\n",
            "177/177 [==============================] - 31s 175ms/step - loss: 4.9972 - mae: 5.4776\n",
            "Epoch 5/100\n",
            "177/177 [==============================] - 31s 172ms/step - loss: 4.7449 - mae: 5.2244\n",
            "Epoch 6/100\n",
            "177/177 [==============================] - 30s 172ms/step - loss: 4.5850 - mae: 5.0641\n",
            "Epoch 7/100\n",
            "177/177 [==============================] - 31s 177ms/step - loss: 4.4592 - mae: 4.9376\n",
            "Epoch 8/100\n",
            "177/177 [==============================] - 30s 172ms/step - loss: 4.3563 - mae: 4.8344\n",
            "Epoch 9/100\n",
            "177/177 [==============================] - 30s 172ms/step - loss: 4.2599 - mae: 4.7376\n",
            "Epoch 10/100\n",
            "177/177 [==============================] - 31s 173ms/step - loss: 4.1753 - mae: 4.6527\n",
            "Epoch 11/100\n",
            "177/177 [==============================] - 30s 170ms/step - loss: 4.1024 - mae: 4.5795\n",
            "Epoch 12/100\n",
            "177/177 [==============================] - 30s 170ms/step - loss: 4.0320 - mae: 4.5089\n",
            "Epoch 13/100\n",
            "177/177 [==============================] - 30s 169ms/step - loss: 3.9664 - mae: 4.4431\n",
            "Epoch 14/100\n",
            "176/177 [============================>.] - ETA: 0s - loss: 3.9037 - mae: 4.3802\n",
            "\n",
            "Reached 4.4 MAE, i think it's enough\n",
            "\n",
            "177/177 [==============================] - 30s 169ms/step - loss: 3.9037 - mae: 4.3803\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe84d793550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3LnuDUrVUmg",
        "colab_type": "text"
      },
      "source": [
        "## Visualizing model using tensorboard\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sG9YQLawVan0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "1da2d90e-e2ef-42b8-af61-ec95aadc3afc"
      },
      "source": [
        "!tensorboard dev upload --logdir /content/logs \\\n",
        "  --name \"Australian Weather Forecasting Model\" \\\n",
        "  --description \"Dicoding Submission 2 - Time Series. Model made by Fahmi Jabbar\" \\\n",
        "  --one_shot"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-08-13 06:32:02.914319: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "\n",
            "***** TensorBoard Uploader *****\n",
            "\n",
            "This will upload your TensorBoard logs to https://tensorboard.dev/ from\n",
            "the following directory:\n",
            "\n",
            "/content/logs\n",
            "\n",
            "This TensorBoard will be visible to everyone. Do not upload sensitive\n",
            "data.\n",
            "\n",
            "Your use of this service is subject to Google's Terms of Service\n",
            "<https://policies.google.com/terms> and Privacy Policy\n",
            "<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n",
            "<https://tensorboard.dev/policy/terms/>.\n",
            "\n",
            "This notice will not be shown again while you are logged into the uploader.\n",
            "To log out, run `tensorboard dev auth revoke`.\n",
            "\n",
            "Continue? (yes/NO) y\n",
            "\n",
            "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=373649185512-8v619h5kft38l4456nm2dj4ubeqsrvh6.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email&state=D5XQVQH4ZOxCByKKlIN3NUHrBWScFh&prompt=consent&access_type=offline\n",
            "Enter the authorization code: 4/2wG28dFh2aZYGIDZixtDZ7ghFkSoiCxuIyDTY-MqmEs2WZiqlge5cnA\n",
            "\n",
            "Data for the \"graphs\" plugin is now uploaded to TensorBoard.dev! Note that uploaded data is public. If you do not want to upload data for this plugin, use the \"--plugins\" command line argument.\n",
            "Data for the \"histograms\" plugin is now uploaded to TensorBoard.dev! Note that uploaded data is public. If you do not want to upload data for this plugin, use the \"--plugins\" command line argument.\n",
            "Data for the \"hparams\" plugin is now uploaded to TensorBoard.dev! Note that uploaded data is public. If you do not want to upload data for this plugin, use the \"--plugins\" command line argument.\n",
            "Upload started and will continue reading any new data as it's added\n",
            "to the logdir. To stop uploading, press Ctrl-C.\n",
            "\n",
            "View your TensorBoard live at: https://tensorboard.dev/experiment/buPrwt4GSpq9FVQLQ9Wwjg/\n",
            "\n",
            "\u001b[1m[2020-08-13T06:32:22]\u001b[0m Uploader started.\n",
            "\u001b[1m[2020-08-13T06:32:23]\u001b[0m Total uploaded: 28 scalars, 196 tensors (207.8 kB), 1 binary objects (158.4 kB)\n",
            "\u001b[2K\u001b[33mListening for new data in logdir...\u001b[0m\n",
            "Done. View your TensorBoard at https://tensorboard.dev/experiment/buPrwt4GSpq9FVQLQ9Wwjg/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8eBvaY1dmvOe",
        "colab_type": "text"
      },
      "source": [
        "Please visit : https://tensorboard.dev/experiment/buPrwt4GSpq9FVQLQ9Wwjg/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaC5VirJnd9I",
        "colab_type": "text"
      },
      "source": [
        "Some screenshoots in case the web is expired\n",
        "\n",
        "**Loss Results**\n",
        "\n",
        "![Loss Graph](https://image.prntscr.com/image/bawy5NgrTOmMp1G5K4bYUw.png)\n",
        "\n",
        "**MAE Results**\n",
        "\n",
        "![MAE Graph](https://image.prntscr.com/image/rbsPRdzxRI_5xk5UYEaDtw.png)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJR48c0IoN8p",
        "colab_type": "text"
      },
      "source": [
        "## Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2NjNuzztOqD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#source : https://github.com/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%204%20Exercise%20Answer.ipynb\n",
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast\n",
        "\n",
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.xticks([])\n",
        "    plt.grid(True)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7cUlywEtRDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "series = np.array(df['MaxTemp'])\n",
        "forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "forecast = forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6vTnDG3tyZ_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "d9cdc1ac-2993-46a0-e46a-1f6db229234e"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(date_val, x_val)\n",
        "plot_series(date_val, forecast)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "829F5PX-vy8G",
        "colab_type": "text"
      },
      "source": [
        "As you can see, the model did quite well knowing the seasonal time/pattern, but the actual prediction is not 100% accurate due to high losses, but my MAE is below 10% data scale as explained in the code or results."
      ]
    }
  ]
}