ML_daily.ipynb

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      "name": "ML_daily.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgMrPZLBHnOo"
      },
      "source": [
        "# this notebook represents short-term forecasting (daily)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IKIY1WhO8z57"
      },
      "source": [
        "refs:\n",
        "\n",
        "\n",
        "1.   https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424\n",
        "2.   (sklearn)https://towardsdatascience.com/time-series-modeling-using-scikit-pandas-and-numpy-682e3b8db8d1\n",
        "3.   https://towardsdatascience.com/sktime-a-unified-python-library-for-time-series-machine-learning-3c103c139a55\n",
        "4.   https://stackoverflow.com/questions/63517126/any-way-to-predict-monthly-time-series-with-scikit-learn-in-python\n",
        "5.   https://paperswithcode.com/paper/forecasting-foreign-exchange-rate-a\n",
        "6. https://github.com/alan-turing-institute/sktime/blob/main/examples/01_forecasting.ipynb\n",
        "7. https://arxiv.org/pdf/2005.08067.pdf (sktime paper-metrics p9)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iMMCpIyL46i"
      },
      "source": [
        "fine tuning RF : \n",
        "\n",
        "*   https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74\n",
        "*   xgboost: https://machinelearningmastery.com/xgboost-for-time-series-forecasting/\n",
        "* to read: https://towardsdatascience.com/random-forest-hyperparameters-and-how-to-fine-tune-them-17aee785ee0d \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3mfVfCumG9k"
      },
      "source": [
        "Regarding a machine learning paper, consider starting by reviewing papers related to your work (Google Scholar may help) and write a section about it (keep in mind how your work relates to them and what innovation it brings to the field, and write about it). Then write sections for describing your dataset(s), for describing your algorithms and methodology, and why you have selected them. Also, don’t forget to write about your results (you will see how a machine learning paper is organized by reviewing other papers anyway).\n",
        "\n",
        "At the end, get ready to answer the reviews you will receive some time after submitting.\n",
        "\n",
        "It would be easier if you already have academic experience on researching or if you have a professor or experienced researcher to advise you with that. Also, at least talk to someone with academic experience to advise you WHERE you should submit your paper, given your work subject.\n",
        "\n",
        "In summary: yes, you can! But it will be a lot easier if you have an advisor, at least…\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYirXA1MqJYx"
      },
      "source": [
        "One also needs to choose the journal which is most suited to the subject field of the article. Is the article a review article or a piece of original investigation? Does the study provide incremental gains in knowledge or is there something exceptionally novel to report? Journals and fields will vary in what they consider appropriate and which formats they publish.\n",
        "\n",
        "Depending on the journal, choosing an appropriate editor(s) is the final step. There may be a list of editors and reviewers to choose from and it makes sense to choose someone who works in a closely related field."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jafUzMrprfnK"
      },
      "source": [
        "The stages of publishing research may differ by field and publisher, but broadly you'll follow these steps:\n",
        "\n",
        "Do the research\n",
        "Decide what research results you want to publish together as an account of your research findings. This should be decided independently of the direction of the results, i.e. whether or not you got the results you wanted.\n",
        "Discuss authorship with those who contributed to the research\n",
        "Draft the text, tables and figures and work with all the authors on revising and editing the paper. Anyone involved in this process who is not an author should be acknowledged.\n",
        "Optionally, get feedback from colleagues about the manuscript\n",
        "Optionally, get the paper edited by a language editing service\n",
        "Get approval from all the authors of the version to be submitted\n",
        "Agree among the authors where you will submit and how any page charges or article processing charges will be paid\n",
        "Have the underlying data and any code ready to share on request or ideally deposit this publicly. Also have documentation such as of ethics approval ready.\n",
        "Prepare a cover letter and the manuscript files\n",
        "Read the instructions for authors of the target journal, check you comply with their policies\n",
        "Submit the article via the online submission\n",
        "The article will undergo different initials checks / triage depending on the journal, such as scope or issues like reporting.\n",
        "If the paper is not rejected, it will be assigned to an editor, either a staff editor or an academic editor. In some journals, the Editor-in-Chief decides this allocation.\n",
        "The editor will decide if the paper is suitable for peer review. If not, they may reject the paper (often called a “desk reject”) or ask for revisions. Some journals discuss decisions or require approval by the EiC or a section editor, others give individual editors autonomy in their decisions.\n",
        "Most journals require peer review before a paper can be accepted, though some rely on editorial review. Peer reviewers are experts in the topic and/or methods of the research. They may be selected from a database of reviewers, researchers the editors knows of themselves, or chosen based on their publication record.\n",
        "Potential reviewers are asked to review, usually by email but occasionally by phone. If they agree, they will be sent the paper and given anything from a few days to several weeks to submit their review. The number of reviewers is usually two or three, but can vary. If reviewers decline, they may suggest alternatives. Publishing staff often support editors in this process.\n",
        "Reviewers will usually send their comments via the online system or email. They might be asked to answer specific questions and are often asked about their recommendation (accept/revise/reject).\n",
        "Once the editor has enough reviews, they will make their decision based on the reviews, the journal guidelines, and their own assessment of the paper and the reviewers' expertise. The editor may be able to overrule the reviewers. Their decision might need to be approved or discussed. They will send their decision, usually by email, to the authors.\n",
        "If the article needs revisions, the authors will be asked to resubmit by a certain date. That deadline can often be extended. Authors might need to do more research, a reanalysis, or revise the language.\n",
        "If the article is rejected, the authors might appeal the decision or be allowed to resubmit to the same journal if they address all the editor and reviewer concerns. Otherwise, go back to step 10 at the next journal of the authors' choice.\n",
        "If revised, editor might make the next decision themselves or send the paper for re-review\n",
        "Once accepted, the paper might be posted publicly as an “in press” version or this might wait until production is complete\n",
        "During production, typesetting, formatting, copyediting and other checks will be performed by the publisher\n",
        "The final article is then formally published, usually as HTML and/or PDF, and sent to indexing services"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scyRsTjNg2Jt"
      },
      "source": [
        "#ref 1\n",
        "#make zoom on pred vs test data\n",
        "#make cross-correlation between pred-test data\n",
        "#dont be overconfident with traditional accuracy metrics : r2, mae,...\n",
        "#ref2\n",
        "#A better alternative for cross validation on time series data (than K-fold CV) is \n",
        "#Forward Chaining strategy.\n",
        "\n",
        "#sktime tuto github [6]\n",
        "#1.2.1, 1.2.2, 1.3, 1.4.1, 3.3(tuning), 3.4 (ensembling)\n",
        "#LSTM can overcome the problem of back-propagated error decay through memory blocks of LSTM. In \n",
        "#contrast, MLP) models very often face the problem ofoverfitting, backpropagated error decay, \n",
        "#and it cannot automatically determine the optimal time lags while fitting time-series data[5]\n",
        "\n",
        "#ML mastery: xgboost, Random Forest"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HRWeLc2Gc10"
      },
      "source": [
        "\n",
        "from numpy import argsort\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.ensemble import VotingRegressor\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from numpy import concatenate\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.gaussian_process import GaussianProcessRegressor\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn import linear_model\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import ElasticNet\n",
        "import sklearn\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import matplotlib.dates as mdates"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAw-KeR0XqqF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad6d2203-eaac-4625-f3b2-8dbb1a4a4d6c"
      },
      "source": [
        "#read multivariate data\n",
        "df=pd.read_csv('/content/clean_daily_df_human_logic_supervised.csv')\n",
        "df.time=pd.to_datetime(df.time,dayfirst=True)\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'MIN_TEMPERATURE_C_t_7', 'clients_day_t_7',\n",
              "       'Holiday_Food_Related_t_7', 'consommation_t_7',\n",
              "       'pdl_total_daily_load_t_7', 'max_pdl_daily_load_t_7', 'weekend_t_7',\n",
              "       'MIN_TEMPERATURE_C_t_1', 'clients_day_t_1', 'Holiday_Food_Related_t_1',\n",
              "       'consommation_t_1', 'pdl_total_daily_load_t_1',\n",
              "       'max_pdl_daily_load_t_1', 'weekend_t_1', 'MIN_TEMPERATURE_C_t',\n",
              "       'clients_day_t', 'Holiday_Food_Related_t', 'consommation_t',\n",
              "       'pdl_total_daily_load_t', 'weekend_t', 'max_pdl_daily_load_t'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgKOf0xp8ZFC"
      },
      "source": [
        "data=pd.read_csv('/content/clean_daily_df_human_logic_supervised.csv',\n",
        "                 index_col='time')[['MIN_TEMPERATURE_C_t_7', 'clients_day_t_7',\n",
        "       'Holiday_Food_Related_t_7', 'consommation_t_7',\n",
        "       'pdl_total_daily_load_t_7', 'max_pdl_daily_load_t_7', 'weekend_t_7',\n",
        "\n",
        "       'MIN_TEMPERATURE_C_t_1', 'clients_day_t_1', 'Holiday_Food_Related_t_1',\n",
        "       'consommation_t_1', 'pdl_total_daily_load_t_1',\n",
        "       'max_pdl_daily_load_t_1', 'weekend_t_1',\n",
        "       \n",
        "        'MIN_TEMPERATURE_C_t','clients_day_t', 'Holiday_Food_Related_t',\n",
        "        'weekend_t', 'max_pdl_daily_load_t']].dropna()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9QfJ8FPyGYL",
        "outputId": "04359afc-5a16-4f95-da0b-4eb30e5674ce"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2032, 19)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRaIduTRqmPq"
      },
      "source": [
        "data.index=pd.to_datetime(data.index,dayfirst=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "XOKVBlf2-uTH",
        "outputId": "47ecdb1b-25f3-4ef5-c179-6ec4d0d37733"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MIN_TEMPERATURE_C_t_7</th>\n",
              "      <th>clients_day_t_7</th>\n",
              "      <th>Holiday_Food_Related_t_7</th>\n",
              "      <th>consommation_t_7</th>\n",
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              "      <th>consommation_t_1</th>\n",
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              "      <th>max_pdl_daily_load_t_1</th>\n",
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              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>clients_day_t</th>\n",
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              "      <th>time</th>\n",
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              "      <th>2015-01-08</th>\n",
              "      <td>8</td>\n",
              "      <td>3970</td>\n",
              "      <td>1</td>\n",
              "      <td>7068.81</td>\n",
              "      <td>16580</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7468.64</td>\n",
              "      <td>20928</td>\n",
              "      <td>1319.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1379.0</td>\n",
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              "      <th>2015-01-09</th>\n",
              "      <td>12</td>\n",
              "      <td>3970</td>\n",
              "      <td>0</td>\n",
              "      <td>7074.65</td>\n",
              "      <td>22006</td>\n",
              "      <td>1439.0</td>\n",
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              "      <td>7464.41</td>\n",
              "      <td>20809</td>\n",
              "      <td>1379.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1370.0</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-10</th>\n",
              "      <td>12</td>\n",
              "      <td>3970</td>\n",
              "      <td>1</td>\n",
              "      <td>7074.65</td>\n",
              "      <td>18629</td>\n",
              "      <td>1127.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>7464.41</td>\n",
              "      <td>20803</td>\n",
              "      <td>1370.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1181.0</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-11</th>\n",
              "      <td>15</td>\n",
              "      <td>3971</td>\n",
              "      <td>0</td>\n",
              "      <td>7075.31</td>\n",
              "      <td>18060</td>\n",
              "      <td>1099.0</td>\n",
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              "      <td>14</td>\n",
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              "      <td>0</td>\n",
              "      <td>7446.13</td>\n",
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              "      <td>1181.0</td>\n",
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              "      <td>3972</td>\n",
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              "      <td>1</td>\n",
              "      <td>1093.0</td>\n",
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              "      <th>2015-01-12</th>\n",
              "      <td>15</td>\n",
              "      <td>3971</td>\n",
              "      <td>0</td>\n",
              "      <td>7083.30</td>\n",
              "      <td>20122</td>\n",
              "      <td>1288.0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>3972</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "            MIN_TEMPERATURE_C_t_7  ...  max_pdl_daily_load_t\n",
              "time                               ...                      \n",
              "2015-01-08                      8  ...                1379.0\n",
              "2015-01-09                     12  ...                1370.0\n",
              "2015-01-10                     12  ...                1181.0\n",
              "2015-01-11                     15  ...                1093.0\n",
              "2015-01-12                     15  ...                1205.0\n",
              "\n",
              "[5 rows x 19 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
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      },
      "source": [
        "data.tail()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MIN_TEMPERATURE_C_t_7</th>\n",
              "      <th>clients_day_t_7</th>\n",
              "      <th>Holiday_Food_Related_t_7</th>\n",
              "      <th>consommation_t_7</th>\n",
              "      <th>pdl_total_daily_load_t_7</th>\n",
              "      <th>max_pdl_daily_load_t_7</th>\n",
              "      <th>weekend_t_7</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>clients_day_t_1</th>\n",
              "      <th>Holiday_Food_Related_t_1</th>\n",
              "      <th>consommation_t_1</th>\n",
              "      <th>pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>weekend_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>clients_day_t</th>\n",
              "      <th>Holiday_Food_Related_t</th>\n",
              "      <th>weekend_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-27</th>\n",
              "      <td>24</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12627</td>\n",
              "      <td>924.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>11167</td>\n",
              "      <td>781.0</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1010.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-28</th>\n",
              "      <td>25</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12536</td>\n",
              "      <td>935.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13174</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>986.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-29</th>\n",
              "      <td>25</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13369</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13097</td>\n",
              "      <td>986.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>873.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-30</th>\n",
              "      <td>26</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13587</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>12000</td>\n",
              "      <td>873.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>762.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-31</th>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>13226</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>5189</td>\n",
              "      <td>0</td>\n",
              "      <td>5719.4</td>\n",
              "      <td>11604</td>\n",
              "      <td>762.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>5189</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>528.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            MIN_TEMPERATURE_C_t_7  ...  max_pdl_daily_load_t\n",
              "time                               ...                      \n",
              "2020-07-27                     24  ...                1010.0\n",
              "2020-07-28                     25  ...                 986.0\n",
              "2020-07-29                     25  ...                 873.0\n",
              "2020-07-30                     26  ...                 762.0\n",
              "2020-07-31                     27  ...                 528.0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmwYalon6Y4C"
      },
      "source": [
        "# data scaling made difference and improved the performance of some ML models (KNN, SVR, Ridge), but scaling is not required for decision tree based models (decision tree, random forest, xgboost,..) https://towardsdatascience.com/do-decision-trees-need-feature-scaling-97809eaa60c6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "data_scaled = scaler.fit_transform(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "source": [
        "features=data_scaled[:,:-1]\n",
        "target=data_scaled[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDaUCPH6njFx"
      },
      "source": [
        "# split into train and test sets\n",
        "#keep last 7 months for test (january to july 2020)\n",
        "n_train_days = -30*7\n",
        "\n",
        "X_train,y_train = features[:n_train_days, :],target[:n_train_days]\n",
        "X_test, y_test = features[n_train_days:, :],target[n_train_days:]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KmrKiF8_LVs6"
      },
      "source": [
        "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
        "  def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "  def smape(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return 2/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum((np.abs(y_true)+np.abs(y_pred)))*100\n",
        "  def mase(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    naive_pred=y_true[1:]\n",
        "    return 1/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum(np.abs(y_true[:len(y_true)-1]-naive_pred))*(y_true.shape[0]-1)\n",
        "  #def owe(y_true,y_pred):\n",
        "   # return (mase(y_true,y_pred)+smape(y_true,y_pred))/2\n",
        "\n",
        "  print('Evaluation metric results:-')\n",
        "  print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
        "  print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
        "  rmse=np.sqrt(metrics.mean_squared_error(y_true, y_pred))\n",
        "  print(f'RMSE is : {rmse}')\n",
        "  mape=mean_absolute_percentage_error(y_true, y_pred)\n",
        "  print(f'MAPE is : {mape} %')\n",
        "  r2=metrics.r2_score(y_true, y_pred)\n",
        "  print(f'R2 is : {r2}',end='\\n\\n')\n",
        "  print('SMAPE is : '+str(smape(y_true,y_pred))+'\\n')\n",
        "  print('MASE is : '+str(mase(y_true,y_pred)))\n",
        "  #model_scores[model_name] = [rmse, mape, r2]\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0gtYxCKB8cy",
        "outputId": "2d85e451-4a5e-4820-a32b-22075f06d943"
      },
      "source": [
        "!pip install xgboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "5z6WpGP7BWs8",
        "outputId": "bfb9d993-6f16-4369-f15b-bdde25984322"
      },
      "source": [
        "# ML models to test\n",
        "models = []\n",
        "models.append(('LR', LinearRegression()))\n",
        "models.append(('Ridge R',Ridge()))\n",
        "models.append(('lasso',linear_model.Lasso()))\n",
        "models.append(('elastic net',ElasticNet()))\n",
        "#models.append(('GP',GaussianProcessRegressor())) (very poor)\n",
        "models.append(('NN', MLPRegressor(solver = 'lbfgs')))  #neural network\n",
        "models.append(('KNN', KNeighborsRegressor())) \n",
        "models.append(('RF', RandomForestRegressor(n_estimators = 1000))) # Ensemble method - collection of many decision trees\n",
        "models.append(('SVR', SVR(gamma='auto'))) # kernel = linear\n",
        "models.append(('Xgboost',XGBRegressor(objective='reg:squarederror', n_estimators=1000)))\n",
        "# Evaluate each model in turn\n",
        "results = []\n",
        "names = []\n",
        "for name, model in models:\n",
        "    # TimeSeries Cross validation\n",
        "  tscv = TimeSeriesSplit(n_splits=4)\n",
        "    #n_splits=3 : because in train data there are 3 steps of prediction\n",
        "    #1-2016 features -->predict 2017; 2-16,17-->predict 18; 3-16,17,18-->predict 19\n",
        "  cv_results = cross_val_score(model, X_train, y_train, cv=tscv, scoring='r2')\n",
        "  results.append(cv_results)\n",
        "  names.append(name)\n",
        "  print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))\n",
        "    \n",
        "# Compare Algorithms\n",
        "plt.boxplot(results, labels=names)\n",
        "plt.title('Algorithm Comparison')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR: 0.754763 (0.134222)\n",
            "Ridge R: 0.776340 (0.096618)\n",
            "lasso: -0.073958 (0.047768)\n",
            "elastic net: -0.073958 (0.047768)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NN: 0.579676 (0.205856)\n",
            "KNN: 0.673155 (0.062024)\n",
            "RF: 0.766707 (0.087140)\n",
            "SVR: 0.775836 (0.070023)\n",
            "Xgboost: 0.698753 (0.143009)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KLnoDe-63Mdo"
      },
      "source": [
        "# the best models are: ridge, linear reg, SVR,RF, XGBoost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w96doMAYVX_q"
      },
      "source": [
        "# Ridge"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIcS9xMVVph5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4b81be4-e5f1-4ece-d6b0-33d2cbd303d2"
      },
      "source": [
        "model = Ridge()\n",
        "param_search = { \n",
        "    'alpha': [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],\n",
        "    'solver': ['auto','svd','cholesky','lsqr','sparse_cg','sag','saga']\n",
        "\n",
        "}\n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "gsearch = GridSearchCV(estimator=model, param_grid=param_search, scoring = 'r2')#we \n",
        "#can define another metric as mentioned in the tuto\n",
        "gsearch.fit(X_train, y_train)\n",
        "best_score = gsearch.best_score_\n",
        "best_model = gsearch.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.83611e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.82551e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.85629e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.83611e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.82551e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.85629e-17): result may not be accurate.\n",
            "  overwrite_a=True).T\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zs1SEGhWCFP",
        "outputId": "618f0f7d-e1b7-440f-b19f-de3a158410c8"
      },
      "source": [
        "best_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.2, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='lsqr', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9VglUl3WEfN",
        "outputId": "0df82bfb-9ff2-49aa-914c-e8be2e1e5c1b"
      },
      "source": [
        "best_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8160830376830679"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRIOrotuWOQz"
      },
      "source": [
        "# make a prediction\n",
        "yhat = best_model.predict(X_test)\n",
        "yhat=yhat.reshape(yhat.shape[0],1)\n",
        "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "# invert scaling for forecast\n",
        "#inv_yhat = concatenate((test_X[:, :-1],yhat), axis=1)\n",
        "inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,-1]\n",
        "# invert scaling for actual\n",
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "#inv_y = concatenate((test_X[:, :-1],test_y), axis=1)\n",
        "inv_y = concatenate((X_test,y_test), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR6_lqYLWOa_",
        "outputId": "1275880f-5258-43ac-f817-2e1de3e6b01b"
      },
      "source": [
        "#overall performance\n",
        "timeseries_evaluation_metrics_func(inv_y, inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9793.225817372104\n",
            "MAE is : 67.835090921222\n",
            "RMSE is : 98.96072866229363\n",
            "MAPE is : 6.267734541004136 %\n",
            "R2 is : 0.7479558982384501\n",
            "\n",
            "SMAPE is : 0.026990746265290903\n",
            "\n",
            "MASE is : 0.7779168176974158\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "GLSO9JC3WyU6",
        "outputId": "3d724b3d-9c36-4e43-9d2d-caae4dd7015b"
      },
      "source": [
        "# plot real vs predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,inv_y,label='actual')\n",
        "plt.plot(time,inv_yhat,label='forecast')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gm93kP71WyU-"
      },
      "source": [
        "ZOOM on winter 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "wBcP3ab4WyU_",
        "outputId": "40d0fb99-ebd9-43e0-f3d2-7c507e1b50bd"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[:90],inv_y[:90],label='actual')\n",
        "plt.plot(time[:90],inv_yhat[:90],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e6JssO0ROIpFIJJJqsxhy59uKouxVFOXriqI0a+d1AadVVU0CqKIWdhLonuOyM4JQNGlkoOisbfaxSenn5t+8CV74et5tTmgVlLXmFk8ZcffHC1RecjEvDRwLxrjzf8SmyVH8TEUSmVwTuwvWvgZO74aYMNXOzGZXUABu2NxKWoXHD44WfUyJRCKRSJaThQqUa1RV3QLsAMYRvpJFRVGUjymKckr/FwqVt7RvIQS1CoqZnkYPXZYJ8Z+9+Xkjx8fDNHjsRqUCMNJkS1UqjCWBBULadPSlgQ+9Msybv/I00clBAC7YuIlUWjVECADtW8TpyH5AxNzbLApeR6Zddf3GFiyKHDeWSCQSyZnLggSKqqontdME8GXgau2iAaBdURQbgCJ6Jd2ISkmpywo9xr+qqtqp/zOvkF4qQtFklkEWhFm1u0arVJx8FoLDWZcfHwtltXdAZI9AZtS3EJkJnuLf1+omLxYFHtg7zGgwylvWitaTrV6YZCfMAqj1QnE6shcQSbJ1bntWu6rR5+SingaeODJGLJnKfjBp/JJIJBLJGcC8BYqiKF5FUepNZ/0+8BKAqqqjwIvAe7TL3gqcUlX1aKnL5nssi00wml9BAej2prWvVDjwc+P8qXCcqUgir02T8aAUN8keGwvhsltory0+5eO0WdnYVkuDx853P3gZ69xBsNhw1bdp928SKG0XiNPhfYCYICo0dfRe/0Hu4ROod1wE/7IRPtcN/9Qovo7OFD0WiUQikUiqQbljxncCrwfagIcURQkCrwXuUxTFCijAceBW081uA76pKMrfAgHg/WVetqwkU2lmE6m8CgpAhzcFWpeHA/fDpR8CMgmzuVUQY4pnjgrKmiZf1pLAQnzng5ditSiihfTwENS006iNLk+GY5kr1rSDpxGGRQVlZjZBR7077/5eNf1T6pR+ZtLrcNV3gt0DoREYOwgTx2DVjpLHI5FIJBLJUlKWQFFV9bYiF20vcZtDwBWVXrbchGOi5VFToILS5hL5KGFvF96+pyE8Ad5GIwl2dU4Fxe2w4rZbi3pQookUQzOzbOuuL3i5mUafM/OfwBA09BoVmvGQ6f4VBVovgFPPQzrFdCRhbDI2UFXqp/ayR13Lf3d8ja+8SxMjL34H7v8ohMfnPB6JRCKRSJYSuYsnh2BMtGMKtXhWeUSL5zuzV4KagkO/ADIVlLUFRoX9XkfRCsqpqQiqCj1+T/kHmEpAeAxq24uPMbddCIkIsdGjxJLprAkecYPjWGYnOeHaxO6TpsWBXm0IKzxW/vGsEPYNzvDV3xxb7sOQSCSSFYMUKDkYiwILtHgarKKV8qPYFSSwMr3rPkAYZC0KdDfmC416j72oQDmp7enprkSgBIcBFWo6aPSKqkqeQGkVPpTZU3u0Y3BkX37qBQCiLTsYnJ5lJBAV5/t0gTK/8eOBycg5m674vef6+fwvDzIWjM19ZYlEIpEsGClQcjAWBRaooBAXlZJP3/padqrn4zn1JE/uPcrxsTBdfg9OmzXvJn6vo2iS7MkJTaAUEDZFCYoUWWrbafCKyshEoQoKkBoSPpTa3ArKoBAoNesuA+AlvYpiVFAqb/H81xPHufqLj/OZXxyo+LbVYjwUY3gmOq/bhrTWny5gJRKJRLK0SIGSQ1BfFFiggkI8BBY7V2xYRder3oVDSfHTe77O8fFwnv9Ep8HjIBRLEotG4P4/hSMPG5f1z6eCEhgSpzUdOG1Waly2bJMsQNN5YLFjGRWTPPW5AuXUC+Bp5LwNQsi8NDAlzvc0idMKWzx7T83wxYcOAvD1p07wX08cr+j21eLPf/ASH/jm8/O67Wxc8x9JgSKRSCRVQQqUHDIVlPzRXOIhcAgh0n3l21EVC79jf4FUWmVNU+EcE90nknz4n+DFb8PO/zIuG5iMYLcqtNflT9kUxVRBAWj0OpgI5VRQbA5o3oh7QoS1ZXlQElEx4bPqYta31lDjtPFSv1ZBcXjA4atIoETiSf78By8BcPcfXsb6Fh+ffeAAP9s9WP73VCX6xiMMzczOfcUCROKygiKRSCTVRAqUHEp5UIiFwFkjvvY2ofRcxTWWl3nz5jretK0j//qICsqVln14d/2nOGN0v3FZ/0SEzgYP1jlGjLMwKihCoPi9jvwWD0DbBbhmh6kjZATGAUKcpBPQeQkWi8LWrnpeHpwmkUob31clAuWffr6f4+Nh/vqmjVy5rolvfeBS2mpdfPzePTx15MyaBpoMx+ddAdEFSiQuBYpEIpFUAylQcpjTg+IwtXI2vRFLKsqXd4yytavwqHCrPcK/2L9KyuaB7itgZgCiM6iqysnJCF2VtHfAVEERgsjvdTIVjuebUzWj7GZLf3YF5ZTW4ui8CIDt3fVEE2kODYvdPXiby/ag/HLvaX7w/ABXr2/ig69aDUBHvZtvfeBSXHYrt33nBfYNnhmhb7PxFLOJFImUmp+eW+btIeNFkUgkEsnSIgVKDnN6UBymVs6mN4jTA/cXvjNV5ZrD/4d2ZZJ9W/4OzrtJnD96gLFgjFgyXdmIMUDgNLgbwC7aQo1eB8m0SmA255O9ZpTdpJzMrqBoBlk6RPbJdi2D5aWTmg/F2ywqKHNM4wxNz/LJH+/F73XwL2/fmhU0t6Gthv+69WISaZX3feN5hqbn11ZZTCZNk1TheYiMSEJ6UCQSiaSaSIGSg15BKS5QTBWU2g7ovBQO/woSBd6E9/yAjsEH+WXqEl5ufgO0nC/OH3llfgZZgOAQ1GTaSX6f8LhM5BplDYHSnz3Fc+oFYaJ1C2GyrasByJnkSSdhdqroIaTSKn9xz25mZhN88a1baKl1CUFzeo8hbC5f08gX37qF8VCMe54fqOx7XAImTT4d/WdcCXoFRQoUiUQiqQ5SoOQQKhbUpqrZHhSdzW+ERBge/CQcejDTHpnqgwf+ioSnlb9J/CFTkQS0bhaXje6f34ixqgoPimaQBVFBgQJZKB4/07am7BZPaAym+2HVxcbV/F4Hq5u8vDRQ/qjxQ68M89yJSd57eQ83bG4VZx5/HO68Bvbea1zvlgvbcdut7DwxWf73uESYBdx8jK7SJCuRSCTVRQqUHPQ3IK8jR6AkYyI91pEzTnz+74KrHnZ9E77/TvjntfDlLfCtN0I8SPDmf2eaGiEgaleBsw5G9s+vgjI7BcmoYZAFaPQViLvX6LOtYb0yiFPRDLB6e6fz4qzrbe+q58R4WBxjGWmyfRMiD+ZtF3VmzpzqE6cv/9A4y2GzsKOnnhdPThFPpllOzAIuXKHRVVVVZhOygiKRSCTVRAqUHPRNxnnL++Ji306WBwWgbhV8/Ah8+Ndwy/+Frb8PVoeoVFz5Z3g23QBoCwMVRVRRRl9hQHuTr8gkm2OQBWGShQIVFOCI0otDScL4YXHGqSICRfOh7B6YElM8UFKgjAfFYzXVmPYDRbQqyfFfZ7WHLlvdSCyZ5uVTpkj9ZcD8/FRaBYkm0oYlR5pkJRKBqqr8w8/28en/2V9035hEshDKWha4kgjFkkUmeHSBUiCQzeaAju3iH2LDMYlZsLlwKQoeh1W0eABaz4eTzxAaP0mTz1P4sYoR0ARKTaEWT34E+yvpbt4OMLxPPO7gC2BzZ7wwGtu7Mz6U16ybu4IyHoplPTaQESjpBBx8ALa/G4BLV/sBeO7EJBf3+sv6NpeCrApKhQLFPFosKygSiSCWTPOtZ/oB+OELA/zpa9bxB1f2FkzUlkjmg6yg5BCKJotnoEC+B6UYdreomCCyUKb0N8gW4UNxTx6ax4ixloGSVUHRTbL5n2B2x7UWzMheSKdh8EXo2AbW7O9vQ1sNLrtFGGXL8KBMhGPUOG247KYXollNoCgW2P9T4+xtXfU4bBaeW2YfysIESqZqIgWKRCIIzIoPXdu66qlz2/k/Dxzk+n/5DT/fM3TO7uSSVBcpUHIoXkERLZmCFZQ58HsdmTfIVlG96Igdm9+IMWRVUIptNE6nVfZFm4grThHONnEEYgFYdVHe3dqtFrasqmf3wDQpI+6++MLA8WA8u70DEJkAqxPWXAfHHjfaPC67lW1d9ezqmySZWj4fSnaLp7I2je4/Ebc9AwRKIgrffnPW2gSJpNoEtGm4V61r4pGPvZq/ed1GZmYT/On3X+Jzvzy4zEcnOReQAiWHUDRZZMRYCzLL9aCUQYPXkdlo3LIJgA2WgfmNGENWBcVlt+Jz2vLi7kPxJEnVwrBrjWjxGAFtlxS86+099YRiSY6FHKIKUqLFMxGOZbd3QLR4PI1w/pszbR6Ny1b7CcdTvDIUgFQCnrszI7aqxKJVUM6EJNnJY2Jq6tADc19XIlkiglFRQal1i2rqba9ey2/+6jo66lw8dnB+G9ElEjNSoJhIp1VC8cWvoDR47ETiKaKJFLjqmPV0sFEZqLzFEzgtDLiexqyzC8Xdz2ielwnfBoiMZwRDjkFWZ7uWh/LiQEDcf5EWTyqtMhmO0+QrUEHx+GHjG0CxZrV5Llstjve5ExPw3Ffhl38Nj326vO95kZgMx3FrLamFeVDOAJOs5vdRK1zqKJEsJgEjMyqTs+T3OtjQVsPJiQiptGzzSBaGFCgmIokUqlok5j5WZIqnDBo8otowrYmGMfda1iqD9DY4St0sn+AQ1LQZ3hYd0ULKNsnOaP3hQN0GccbhB8HXJkadC5BJlJ3OpMkWYCoSJ61mxpsNZieFQPH4Yc2rtTaPmNzZ0VOPzaJw6Mhh+PXnxfX33Zcx1laByUicLr9I3620TTN7hnlQjvSL4LuRoeUPwJOsXIwKiit7sWpvk5d4Kn1GJEhLzm6kQDFh7OEp2OLRKijOygVKrk+kz9aDQ0mxmgrbHIHTBQVGo+ZxMRvTdDEUbdTC4dSUqJ4ohRcTtta6WFXv5iV91LiIQNEneLIqKKkkRGfArU3pnP8W0ebRWhAeh40LO+u4fuDfxTTUBW8TeS57flDRtz9fkqk005GE0VKrVKCcSSbZ0WCUHz65BwDL7Jm1jFGystDXa+S2xFc3iSqznpckkcwXKVBM6CmyNQVbPLoHZR4tHk2g6D6U/akuAPzho+XfSTImWjUmg6yO3+sgkVKNPUKQqaCkmk0jxQUMsma2dddzZDREwtUoBEcyfzJI97o0mSsoeu6J3nrS2zyvZNo8b/Of4BaeJth9A7zpK2Kf0At3zbnzZzHQR7xba13YrUrFIkOvoLjtVsLxFOllKl0nU2n+/Pu7sUVFZcqbKL6OQCJZajIelOwKiiFQxqVAkSwMKVBMBMqpoDjKHDM24fdkV1CeD7cBYBnbX/6dFAhpM+5fEwvmfTPTs+Lrmrp6aOgVZxYxyOpsaqtBVWHGom1mjuR/Qi9YQdFHjD3+zOmaV8Oxx0SbJ5XgzUNfIqbaebDrf4HdBdveLSaL+p4seUyLgS4MG70OvE7bPCoo4vrN2uRSJLE8PpR/ffgwzxyf4KoOUQXzqmEx0SORLAMBTaDkVlB6G4VAOS4FimSBSIFiwmjxOO35F8ZKBLXNQYO2TXgqEieVVnk20EgSK4xUIFAKjBjrNGlpsuZ9M3oFpc5tF7t3bC4tSK44bXXCozGl1IkzQvlOfD1SPztFdkKcms27m7VpnkO/hOfuxBs4yldTv8NjI5ox+OIPiNMX7ip5TIuBXvXxex14HbaKja66INEFynK0eR49MMJ//PoYWzvruKIj06aTRlnJchHUXi9zPSgd9W4cVousoEgWjBQoJvRP1ovtQTFaPOEEw4Eo4ZSFCVcPjL5S/p0YI8aFWzxA1qixPsVT73bA674Af/jonMfeXucCYDxdK84oMMlTMkXWbUqK1ds8L3wdfv05qO/hNy3vZueJSeGVaVwLa66FAz+H4EjJ41ooeuXK73Pic9rm3eJp1qpG1c5CGZiM8Bf37KbeY+cr796BNZpZGzA9Xt1xbYlERw9qy62gWC0K3Y0e+rSFqBLJfJECxYReQSnpQbHPL6gNRAVF32IcqjsPpk9CLFjenRgVlBItHtOocVYFxdsEbRfM+RCttUKgnE5qbawCn87Hg1qLp2AFxSRQvI2w+hqRvxIPweu+wPY1HUyE4xwb06pRF38A0kl46TtzHttC0CecRIvHOm+T7HJUUBKpNH/8vRcJRJN86Z3b6GzwZO06mrUsCg0AACAASURBVBkbrNqxSCRmgtEkDpslO1Fao7fRy8BkZFnDGSVnP1KgmAjOVUGxe8FS+VNWr7V4JsNxBrQtxqoWec/ogTIPTveg5FdQGgvE3U9HEihK/qebUrRpFZSBuCbCCgiUiXAch9WSLeJyPSg6579FnK6/CTa8ztjL8+xx7fobbhGjz7u+Beml83VMhoVYa/AID0qWwHjss/CDd5e8fa5AqWYF5eDpIHsHZ7j1ih6u29CiHVBmPDs0KSsokuUhEE1QW+T1ZXWTh2Ra5dSUHDWWzB8pUExkPChFclDm4T8BcNpE2utUJE7/pGgVubu2igtHymzzBLQWT5EpHsivoNS67PlbmUvgc9qocdo4Mav5RApVUEIxmnwOFPO4sl5BcecIlC3vgGv/Bn7n3wC4tDezOBAAqx123AozJ+HoI2UfZ6UYFRSfQ7R49EkcVYVd3xDj0CUE0myOSbaaYW36pMT6VpM5e3aStEX8zKPTS9sek0iKEYwm8/wnAKRTrGkUfrYTctRYsgCkQDGhjxkXTZKdh/9Ep95jZzIc5+Sk+ETRuEYzrI6WaZQNnhYmVJsz76JG3SQbyphkp2cTRuWmEtrqXBwOi0pKIQ/KRChOY16KbM6YsY7dDdd+0qj6NHgdbGitYeeJiUxmy0V/IKL1l9Asq1eW9AoKaMbXsYNChKnpjMgqQCSewqJkzM7VbPEYviinVkZXVZidIuFfB0Bqif07EkkxArOJwhXaO6/hxgN/C8CJMSlQJPNHChQT+ptB0V0886yggKhyTEcSnJwI01rrxNXYDc668isoM6eKpsC6HVY8DmtWi2cmEhf+kwppq3PRFwDV5s5bGKiqKmNaBSWLyARYbGVter5sjZ+RQIx+3UBX1wnn3QyHHxKenCVgKhKnxmnDYbMY4jMcS0LfU5krlZiGmU2k8DhshripZotH3/3jcWi/k7EApJNYWzYCYCkwCi6RVINgNJkVcw+ISvPIPpr6H2CjclKGtUkWhBQoJvSxOW+xCso8MlB0GjwOrYISEYmmiiIWB468MndYWSwEMwPQdF7Rq2RtTEa0eOYlUGpdROJpVE9+mmwoliSeTOfv4ZnVFgUWSak1o/tQdp4wxdxf/AFAhZfvqfh4y2EiFDeMxF6tEhGMJuHEE5krFRip1onEU7gdVuP3opoVFL2dZFT1NP+JrX4VYVzYo8UrPxLJUqHvLat157xWmj5k/In955yQo8aSBSAFiolQLInbbsVuLfC0LMCDAkJAzCZSTEUSdPu1+2ndDNHpjAG2GOOHxWnzxqJXaTQJlEQqTTiemncFBSDu8ue1ePQMlPwWz0S+/6QIGzQvxcCUaQSx5ypxOro0K9onw3HDp6Nn3ISjceh/OnOlEhWUSDyFx2HNVF/i1fOg6GLIEM36BI/bT8BSj0emyUqWgWAsiapCTW5m1HS/OLU6uMXyDLGx49U/OMk5gxQoJkLRZOEJnnQKkrML8qDoCwMBYycM+iTPXIFtY4fEafOGolfRNxqrqmqMGM/XgwIQtvk1f0amujNhpMjmtngm8/0nRdBFk56hAIDDA96WvBbPr14Z5tTUwrIUVFVlKhI30nx1L4c6ckAIKz1dt0QFZTYuhOvyVFByPCj6xJS7gbDdT116atmi9yUrl0zMfc7r5ZQmUK78M6ykeUP4PuJJOWosmR9SoJgIxZJFMlDmnyKr02ASC92aw51WbU/OXIFtY1ploUQFxe91Ek+Kyom+KHC+LR7Q4u5TceF50CgYc59OiU/1noay7l/f26GvFTBo6Ml8+kKMMN723V187pcLq6oEY0kSKdWooOgiwzn4W3GFC94qTsOlWzwehxWfo/oCJaS1eAwPimFI9hN3NuInyHhIjnJKqktmUWCRCsqO93Lau5F3WH7N4Kml8ZZJzn2kQDERLFZBWcAeHp0Gr7mCogkdo4Iyl0A5BBY7+FcXvUqjT0+TjWUqKG5H0esXQ6+gTKLF3ZvaPEbMvVmgRGcAtewKitNmwWG1ZFdQAOp7IDQCCfFmOxWOiyngvoW1MPT9RBkPivj51o08K6aHNr1RXLHAxJLObFw3yYoqRjVNsvoeoEyLJ5Paq3qbsCspRkeLi6uVxmggyoX/8BC/3CvzYZYSo4KS+3o51S8SpGs7ObHhQ7iUBOpzdy7DEUrOBaRAMRGKJYtnoMCCPSg6RovHXQ913XD65dI3HjsIjetEbkgRzGFtM9qiwIVUUIZT+WmyRsy9b46Y+xIoikKt22YsGjOo7xanWptHF1nDgShD0/OvEEyaFgWCMJsqpGkcfx7at4rlizZX0RaPqqpEEsIka7NacNosyzJm7HVoLZ5IJhTPWiOC26ZGZZqszpHREMFYMpO1I1kS9ApofgXlJNStAqsN2/lv4kS6lY4j3y0/MVsiMSEFioaqqsUFit7iWQQPisdhzfZwdGyF8UMQL+K1SMzCVF9J/wmYwtpC8UzM/Tw8KH6vA4fVwqCeJmt64y7Y4im0KHAOal12o0Rs0NAjTrUetvnyF0/Ov4qiV1D059/rtLFRGcCZmIHeq8XkkbelaIsnnkqTSqt4NIEgdvlU1yTrsluw6cZtUwXFWSe2Ygcnhqp2PGc6+uZqPbFZsjRkPCim1xhVFS2eevG33NtSw9dSb8CVDIi0aImkQqRA0YgmxBtR4RaPXkGZv0DRBUS335Odwtq+TQSFjewrfMPxI4A6p0BpNO3jmTYWBVYuUBRFobXOSX8sP+5+IhRHUbL9NEVj7ktQ47YXqKBoAkXrYc+YWkC7+hcgUML65FHGJHuFRTMlr75GnPqaIVR4ikdfFKgLFK/TVt0clFgKr8P0O2lM8TTg8wuBEp2RYW06U9rvvoxYX1oKLgqcnRKeNe3DRrPPyYPWa5my+OGZr0AyXuiuJJKiSIGiEdRSZAubZHUPykIqKOJNvUtv7+i0bxOnQ7sL37CMCR4QJlnQWzzzr6AAtNe6OVYgTXY8FMPvcWQ+zcM8Kyi2fA9KQ7ZAMQuYF09OM1/08Dr9+fE6bVxu2U8aK3RfLq7kbcmbWNLR9/C47Tbj9np4WjUIx5PZuTyRSbB7wO6irkksjkwFpEDRmdJ+3gNTkUxasWTR0TOjsqLudYNsfS8gPux0NDXwfeUWsY19771VPkrJ2Y4UKBrGHp5CFZRF8KA0+Zy8/aJO3nFxV/YFHZpAOV1MoMw9wQMZj8VkOGaqoFRukgVhlD1eYB/PRCieH9JWoQcFhDcmHE9lbzqt7QQUo8Wji6wmn5P9QzNEE/Nrq+glf33M2GtXuMxygAH3hkzyrbcJ0gmRSZNDJKeC4nNaqz5m7M1dzKg91w6txVMqw2Wlof+8I/FUVnChZHHRP0BkVVD0EWPdTwasbvLyn+FrUR0+2PXNKh7h2c3+oQBHR0PLfRjLTlkCRVGUOxRF6VMURVUUZVuBy9+vXfZm03ktiqI8qCjKEUVR9imKck05ly0XmZ0nBaoOi+BBsVgU/vntW7lxc2v2Bd4m8eZcrIIyfkhMmzSuK3n/egtpIhQ3qhPzMcmCECiT1Ir/mN78xkKxbIMsmCoo5QuUgqPGNoeI8tcrKNr3cO2GZhIplb2DM5V9Ez/5I7jnvUwFhRfBmOKZOkidEuGga0vmuj5tS3CBNo/e4nFrAsXjqG6LJxRLZTJQQMuc0Ua6vc0AMk3WhC7OAQZkm2fJMCoo5tcYPcdIr4YiBEoQD7ONF4hqsKxqzYmqqrzvGzv5xH1zDE+sAMqtoPwIeBXQn3uBoii9wIeAZ3Mu+jzwrKqq64H3A3crimIv47JloWQFZRE8KCXp2CYqJYkCL6hjh8C/puCSQDMehxWX3cJEOM70bAKHzYLLPr8CWVutixRWEs4Go8UTS6YIRpOFY+6hMoHiKhDWBloWSvYUz3UbhHh4sVIfyqEH4MD93DzwJRw2izEFY+0X+3detpkEilcTKAWMshFjF07GJBtNpLOrP0tIJJ7MZKCA6PPr1Sp3AykseBKTpGRYG5C90VsaZZcOvYKSNVRgtHgyAqW3UVSdJ5ydEJvJVFwlGSaOZQ0jDEzOMhqMMTwTXcaDOjMo6x1MVdUnVFU9lXu+oigW4L+BPwViORe/A/iqdvvngSHg1WVctiwE9UWBS+RBKUn7VlBT+Xkoybj45Z2jvQOi39vodTKpeVDq3PZsM24F6FkoUUeD8aY9Eco2mxpEJkWFx1lX9v3r6ZMFjbKzUxANGALlVeubsCgVTvIkoka75vrQz/mQ89HMc9H3JEms7Mb0nPpEJaJQqySSyDXJitNqxN2n0yqReCrzJpBKCBOiLgYtFiK2BvwEjAmrlc50JCNQpFF26QhGxcSj1WJ6jZnqB6sTfJkqcW+TECinLMIvxaSMvs9CVeGum+Cnf2Sc9dKAeK2bisgW5UI9KB8DnlZVdZf5TEVRGgG7qqrDprP7gO5Sly3wWBaEUUEpmIOizfAvwINSEsMo+1L2+ZPHhHCZwyCroy8MnI7E5zXBo6MLlKC1wXjTnigU0gZCoLj9YCn/VylTQclplRhZKP0Eokm8Dit1bjub2mvZ1T9dvukxpP1qbXsPA0o7H0t9HY49LlJv+3/LQcs6JhKm50drlZRu8WRMspCprCwlYSOkTY+51zwyJr9PzOmnkZkFZcWcS0xG4rTWit/RgQWuSZAUJzCbyA9pm+4Xf8Om14LVmkA5mtD+xiaPVesQzw5CI+I19uRz4vUJ2D0g/s4j8dS8vXfnCvMWKIqiXAC8FfjM4h1Owcf5mKIop/R/odDSGIcMD0qpJFnn/JNkS1LMKFumQVZH7OOJzXuTsY4e1jZFnahopBKmDJQCHpQK2jtg9qAUmeSZ6mdmNmFcb0d3A+OhWPmfiIOaQGnZyB+l/oqo4oZ7/wBe+QnEAux3bsn2kZRs8WgVFHumxQPVibsP58bcm/bw6KQ9TTQpAVkO1pgOJ1jX4sNtt8oWzxISjCazQ9pUVbRn67M/ZzZ47NS6bOyZ1ab8ZAUlmwlNsMWDWqRERqBAtqdqJbKQCsrVQC9wRFGUPuBy4GuKovyRqqoTQFJRlDbT9XuBk6UuK/Qgqqr+q6qqnfo/n29p2izBQj1VnUXYxVMSXwvUdMDQnuzzyxwx1mn0Oogm0kyE4/NaFKjTXOPEosCYqgmyyEThkDYQb5oVjBhDJh67YNw9wPRJgiaRtaOnHqigzaMJlLinlX3xNr7Vebuogv34QwAcdm/PFhiGSTZfoMzmeFD0CkqoCmFtegXF+J2M5Pt9LL4WapRZRibnP4p9rpBIpQnGkjR4HHQ2uGWLZwkJRJPZiwJDo5CMZhlkQbSeVzd52TmttYClQMnGXFEa3EU8meaVocz+s5Xe5pm3QFFV9T9VVW1XVbVXVdVehEn2w6qq/qd2lXuBjwAoinIJsAr4TRmXLQuGB6VYBcVin9OouiDat8LYAeGf0Bk7CCjQuL6su9AneVQ1x11fIXarhSafk6FEJu5+3PCgmJ4DVc20eCpgzgrKtFZB0T6hXdQt7r9so6wmUII2cbvR5qvg5s+LQDyLnUHfhdlpsK56sNgK7uOJ5EzxVHOjsf4Yhfbw6DjrRL8/MC53z+gv5rpAGZyalZuel4hANJFdQSlgkNVZ3eTlZEgh7WvNVAwkgolsgXLgdIB4Mm1UqqdW+Kh8uWPGdyqKcgroBB5SFOVoGTf7BHCloihHgG8C71FVNVHGZcvCnB6Upaqe6HRsg3Qye7Px2CHxpu3wFL+dCbN4mG8Gik57nYuBeCZNtmCLJzojPDKVtniKeVBq2sFiR53qIxDNtHi6/G6afA52lVtB0TwoE4qo7DR6HXDph+Hav4GrP4bdXUM8lc6sgbdYwNNUusWjtVl8VVwYmBl9z9/Do+NuEIXI2ZlhVjp6ObzB66DL7yGeSjMalObhxSaaSBFPprM9KHoGSkO+QNGNsrO+XllByWXymBgy8DTC4C6jvfPq87Q9Wyu8xVPg3TgfVVVvK+M61+b8fwR4bZHrFr1suZjTg7JU/hMdc6LsqosglRQ9yXXXl30XjaaFhAvxoAC01rroO+0FOxAeZyIkfA9ZLZ55jBhDiSkeixXqOlGn+kmkVON6iqKwo7uBRw+O5o/dFkKroIwr9cCQyEBRFLj2kwB4f7wXEBUKh017znzNhVs8uVM8jjPBg5J5vm21ooKSlHH3xqfNBo/dmMYbmIoYpm/J4hAstChwuk+cFqmgAEw4V+GNPqdl+VT2mnHOMnEc6rrEZvujD7OvVvwdX7exmftePGUsO12pyCRZjVA0icNqwWmz5l8YD1WnggIZo+zUCZFuWqb/BLI3Ji/EgwKigjKa1kRZaJTxUByf04bLnhMaBvPwoBTJQQFTFoqaJbJ29DSQSqu8fKqMwLbgMDh8jMXF86GnyOrobbw8o2yhMWPNB+J2VN8kG4nntni0CpL5xV2bQFJlmmwmNdjroMvvBlZGFsqu/imu+vxjVfteM4sCzRkomoWwgEDRs1BOKXLUOIt0WjwXjWvFh9J0kvDJ3fQ2eljTJLyWssUjAYQHpWD1BEQFZakyUHRq2kR+gJ4oaxhky5vggUxaKixCBaXOxYQpTXY8FCswwVN5zD2Ay27FabNkJ8nq1HdjSYRpIJgtULpFBaesxYHBYahpM0ajzcINTFWQeI5RNhHJTGxp5EbdV9Mkm2nx5JhkTVM8eJsAcEQnqhYed6ail8PrPQ46G0RbdGDy3DfKPnNsnMHpWV7or04IWqBQBWWqX7xGFqiM6C2eI0nNjC59KILgaUjOgn8trNoBQEtgH9u66mnwiudWmmQlgKigFPSfgNjFs9QVFBBtntEDkIxlRoybyq+gZLV4FqGCMqFqzvvwOOOheLZBFua1KFCn1m0vXEHRPoF1KWNZi8i2dNZhsyi8VI4PJXgafG15m4x19FyRkFkgaW/0uW0ePQfFZcsJaquqSVbPQZkEFHCZQvG0EWk/M4yt8LC2SVOLp0sTKKdWQBbKaW3EvFpizKig5Jpk63tEKzWHOredRq+Dl0JiGk9WUDT0CZ7GjEDZajkmBIpHmmRBChSDUKyIQFFVrcWzxBUU0IyyCRjdb6qgnFf2zf2L7EEJ4ial2FHDY0yGC1RQ5ulBATFqPFOwxdMLCIFi/h5cdivnd9Ty4sk5Atv0FNmaNqN/25DT4vE5i7R4IK/NE4mn8DisWLTETG+h2y4RepXGaPFEpsBdL7w6OpqwalQCDE2v7CyUadPPu85jp8ZlWxFhbcOGQKnO96qb242Jx3QKZk4VNMjqXNLr55ER7TVUChSBXknyrwV3A5PubrYqx9jW3YDLbsVtt654k6wUKBqhWLLwiHEyKiZVFrAosGzMRtmxg2KJYAXmXJ/ThsMmfqQLSZIFaK9zAwphewOp4ChplcWvoOSaZMFUQRnNG5Xe3t3AZDhO30SJF2I9RbamjclQHEURJX8zmVFhU5umSBbKrCZQ8m9bBQ9KoTHj3Haa3U3C5qWJmRUf1jZlmuIB6GrwrIgWj1FBqZIYy3hQtL/PwKCYQCzgP9F5zaYWQqqLqLNpRafJ9k+EMx9uzBUU4KBlPWssw2yqF5f7vQ7Z4lnuAzhTCEWLCBRjD081WjxbxenQizB+uCKDLOj7eMSL80IrKHqa7IxSbxgwC8bcQ8UeFBDl4bwxYzCSKDtzKigAF/UI70XJPJSgNs1SI1o8DR5H9r4QihhdvYX38UQSScMgC0X8K0uEEdTmMHlQClSrUu5mmpQAp2eW583453uG+Puf7i1/FcESMRWOY7cqxmLILr+b0zOzJM5xb85woLotHv2DhfF6aRhki28ruW5DC4qi7eRZoRWUZCrNG/79KT787RfE38rEcVCsUN+Nqqo8GRECzzkifIj1HrsUKMt9AGcCsWSKeCo9xx6eKlRQajvEG+XBB0TlpgKDrI5/kQSKW9uDM04dlsg4oBaOuUcRbYcKqXXbmdXyFLLwtZCwOIUHxZ3989ihCZQXSgoULbCspp3JSDzPIAtF2jTFBEo8hceeOQ6rRcFtt2ZXX5YIvcXjcVpFq9G8ydiE4mte1hbPd57t57vPnszaJLwcTEWEINUXQ3Y2eEirnNOVpWgiZTzveWIstTQiWh8zNnJQSmSg6DTXONnaWc/eiF/8Hq/ArcaBaJJgNMlvj03wm8NjooLS0ANWO/0TEZ6J9oorDr4IaBWUsGzxrHiMkLaSFZQqCBRFEW2eiJZoWmEFBWBts4/OBjc268J/tG21LkZSPqypKF6iBWLuC3giykR/cQvmtnkUhWlHe8EKSkedi55GD48dHCmeEKrv4fG1MhmO540YQxGja4kWj7mCIm5vq5pJ1mGzYLdaxIRRKlawgmKvbcFPgJGZcIF7WXqOjopVEH0Ty/P4OtORRJbfqKvh3B81HglkxFdahdO6SO1/Bj7TAgPPL/pj6uZ2wyRbIkXWzA2bWjiS1DYdT55Y9OM60zFXQ77wwH7UyRPCf4LYv3NA7Sat2GBQ7N6t9zgIxZL5H+JWEFKgYB7nLFB10PfwVMODApk8FJhXBeUzb7mAH//xlYtyKG11Lk7FxffdqAQKtHgm5tXeAXPcff4b/ZitTQgUV7YwUBSF11/YzkggVjxVVvOgpHxtTBWpoBgmWXObxqNN8eSkyUZyPCji9taqmGTDZuN2oRFjDYuvBZuSJjBV/SyUiVDM+ATfN77EQkD/pF6EyUjcGM8E6PJro8bnsFFWr5qtaRYt6JO6GDvwc+GdO/HrRX/MvKC2MiooAK/Z2EqfqguUldfm0ZOO/V4HgZE+lFTM8J/sHpgmhoNE8/lCoKgqfm0Sc3oFt3mkQMH8B7cMiwJz0X0oUNEEj06ty05LzeIkZ7bVuhhMifZNjzKSN64rPBGVG2ShdFjbsNKCU0nijuXvxnn9lnYAfvFykd0zWgVlxtaIqmZnw+gUNLpabUJs5ezjEcm1BSooVfKgZI8YU1gQau2pRKD6abJ69QSWuILy0vfg37aIN94CpNIqM7M5FRT/uZ+FMhwQ39ulveL3whBj/U+L05FXCt1sQQSiSexWBZdde/uY7he/l3MY+je11xDxCp+KOlHOtpRzi5lZITT++Nq1bHaJDxOJ+tUAvDQwjd/rwNF9iWgzzwwY5v6VPMkjBQoFArHMxHSBUqUKij7J42sr+Gm5mrTVuXghLdpMl1kOZFdQVFVUUOYZWV007h4YRLRblOn8Bdeb22tZ3eTlgb2nSRVq82gpspMJIYAKtXh8haZ4QLR5TC2eRCpNIqXizonWFy2eKmwzjqUMU24mtbfA74QmUJTwWNUNoUdMAuXE+BIJlFgIHv1H8fVL3y14lZnZBKqaPbHVqbV4zuUsFH2C5xJdoExGxI6s4ZfFFZZEoIhFgbrXh+mTJQ2yOoqisGbjFgBCpw8v+nGd6eh+kp5GL+89T7x+PDZaQyyZ4sBQgK2ddSidF4krD+4yqr+L4e1KpdWqtKUXGylQmGNRYDU9KAB1neBfA12XVufxStBe5+IVtZeA6uEqy/7s5WDxkMhsmWcFRfeXFJrk6Utp7ZYCJX29zTMajPFCXwGjXXAYfK1FU2QBnDYLNouS36bxNme1eIwUWXtui8dWtWWBpTYZG+hZKASqvhxPr6A4bBb6S41/L4Tf3gGhEXDWwtFHIDyRd5VMzH2mxeNx2Gj0OhiYOocrKLkCZWoWTj4nNncrFpg4mr0hfREIRpOZ14JkDAJDc7Z3dK6+YDVjah2R4ZUnUKZn9aRjO1fWi6WA/2+Pys4Tk8RTabZ1NYjIe4DBXca6ksVo8fzbo0e46guPnXXtIilQmGtRYJU9KIoCH3oM3vLV6jxeCVrrXKSx8Fx6ExdajqHEM5+WjQyUeVZ5jBZPgQrK0YQmeqYLew6MNs/eAm2e0DDUtBtvWHltKYTI8Tpt2UmyICoo0RnxoksmRbaQSTaeTC95tSKSJVAK7OHR0Qy+jUqA09PVfTM+MhrE47CyvauevvHw4o8aB4bg6TtEovLNnxN5G/t/kne16SKhfJ1+zzltkj09E8VmUVjV4Ka5xim+1/6nxIUbbhFCRU+lXiQCs4mM/2TmFKDOaZDVuWJNI/204w7mV0fPdWa039F6tx3b9AlSio39kVo+eZ9YXrqtux4a14OjBgZfzFRQFkFUHBsNMR1J8Kv9Z9dSUSlQgLSq0uCxZ0c361TbgwLiTb+aj1eEdm0L7G/Tm7GRFpMBOvNcFKhjmGQLeFCOxLQ34SICZWNbDWuavTywdzi7zZOIijfymjYmwoXfsHR8hXwkxqix8KHoy/ryPCiOpY+7T6dVwvEUPt2DEtEESgkPSpMyY5T8q8XR0RB/VPs0H018g2AsaTzvi8bjnxX7Sl77adj8JrC54eV78642qZXPc3/eXQ1uRoMxoomlb8ktB8MzUVprXVgtCl0NbtHO6ntarEO48O3iSovc5glGE5kIgKk+cVpmBcVltxKr6aFWDTAzubIWXJp3RTFxDIt/Natb6hjUPlRs66wHiwVWbYehl2hwibfn6UXwoOgfBIt6985QpEABfndHJy996rVcsbbAm63hQSk/0fVcQQ9reyZ9vjij74nMhQuIuYfMmHFu3H0ilWYo7mLW4i06taEoCm+4sJ3xUIydJ0xtnpwUWSjc4gExalywxQNGmyd3UWDmtksfdx9J6I+d2+Ip7kFpZKaqYW0zkTi3Rr7Fn4bu4Orxe6glRP9iGmWH9wpz7OprYP1rhQlz4y0w8Gze74ZeMTNP8UDGKHvqHG3znJ6J0qZ9kOj2e4iEAqhDL0H3ldB2objSIgqUdFolGEtS48wdMe4t+z5qOoT5f/eeXYt2XGcDeounzqnAVB9K41r++ibh8VvT5M3sT1t1ESQiNMfEc7sYHhT9dfbpo+Nn1X4fKVDmoppJsmcYdW47LruFQ2onIWsdnDAJlMWqoOS0eMRElcKUo71oBQXg9VvE6vZf7B0y3TiTIjtRZFGgTsEsEyMLRXyykcayDwAAIABJREFUm03oLZ58kywUMNkuIpFc43akhCB01aNabDRVM6wtlSTxkz/hT2z3k1TEc9yjjHJisUaNVRV+9ffi69d+NrOE7sJ3iNN9P8q6uv6im7vW4Fw2ysaTacZDMUOgdPk9XGQ5jKKmoPcqaFgNdg+MLp5ACceTqGrG5F5OimwuPeuFcDpxeN+iHVdFpFNiKWuVk4+nI3G8DiuO0KDw7/nXcuPmVm69oocPXbMmc0XNh1I/KYzOi5Emq1eqk2mVX+0fXvD9VQspUOai2h6UMwhFUWirdaFiYaDuIjj9csYLsYCYe8iMdOeaZHWlH3KvgpnBommY57X6WNfi48F9wyR1L4ieIqtloMAcLZ5cgWEsDCxdQdHbLktZQQkZe3hMY8ZWp3jDycViAU8TbdZgdkVpqYhH4J5303Tkhzye2srhiz4FiFH0vsWa5Dn6CBz/NWz9fWjfkjl/3fXid+7le7PeYPTyud/jEEI1LX4n9K3G56JRVg9pa9cqnV0NHi6zHBAX9lwlfi9aNi1qBSVQLAOlAoFSt0pUDcKnD2X+dqvJvh/Df1wOD3+qqiJlOpIQAtrYwbMGRVH4pzddwO9fanr+NIHiGH4Jp82yKBWPmdkEm9prsVsV/kdv8+z5AdyxHQJnbttHCpS50AWKfeVVUADj09lk82WAKvrbsKBFgQBOmxWX3ZJXQdGVftS7SgRNBQYL3l6f5hkPxTNvyqHsPTxehxWXvXDKrddh0z4Nml6gcuLuZ4t5UKqwMDCct8lY28NTYJ09gOJtpsMeYv/pwNJGu0cm4TtvhsMP8nLjzXwo8ZfUr94OQLcysjhZKKmkqJ7Y3PCav8++zGqH898MYwdgJPMJXDfJNg88CP+yAXbdBZhaPOegUVbfwaP/jXb63VxmOUDc6oU2TdS1ni9+n3MSkueLsSjQnCLrawN7BdlLflEtaEsNlV5bsVTMaFWf395RVZEyPRsXkzkTWkidliKbR20H+FpRhvfQ4HEsOAdFVVUC0SRdDW6uWd/Mb49NiLbRscdFYN7jn13Q/S8lUqDMRSwkxIllZT5Vug8lskpLp+17Upwu0IMC+sLA7D8+vYISr9E+UZRs84hpnv/Rp3m0CkrM3czg1GzBkDYdr9Mmolzi5o3GmkDRWjz6Ze4CY8awtALFqKA4TFM8papVvmbqVTG6+OtDpd+M7tt1it8eyw/BK4sHPg4Dz8GVf8aXvH+BxeagtWcTAJucE4sjUJ77TzF5cuVHoW5V/uV6m+flHxpnTYbjbLb043ngowghLSZZOupdKMq5mSY7pJkrxeZx6K5R2Koco9+7RQQPArReIE5HFqedolc8a8x7eMo0yBq4akm6m1itDPPYwcURThUxK/5OaFwvRMoj/1AVkSIqKPa8LcYF8a+FqT4aFmGjcSSeIpVWqXXbef2WdlJplYdeGc6053Z/b0nychaDlfmuWwnx8Ir0n+i0aS9+rraN4pOS7kNZ4JgxCB9KbtS9LlBUvWRcIt78vNYazmvNtHlULUX2lruOcnw8zEXdxY9Nf4HN3mhcrMWT40ExNhovoQclrrd4TCbZUmLQ24wtGcGjxHi8hEAZDUT5qx/t4UsPzzOHYvBFMfL72k9zZCzCmiYvVq8fnHWstY3RNx5Z2KjxqV3wyD9C4zq46n8Vvk7XZVDXDfvuM1o5qdA4/+34V5SUls0z9BIgKnVtta7qmGRHD4oKY5U+keuVMr2C0h7ch0NJ8bLt/MyVWrWvR/YvymMGzZuMI5Nib5h/zRy3ysfatI7VllGeOLwMkzxRTaC89yfQeSk8/W/wyO1L+nNLptIEo0ljggerE2o7i9+goQdmp+hwxRfc4pkx7U66YXMrDqtFTPNMnxR/K2paVJLOQKRAmYt4aEX6T3Reta6J3kYPmzrqxDTF6H5RYYhMgrNOlNznSa3LlldB0Vs+Fn+vOKNEBQXg9Rd2MBmO89XfHGP3/oOEVScTSQefesNmvvi2rUVv5y3kI7G7RBiYVg4vlYMC1amg+JxWYeqbnS69NVprT716lcJTR8aJJQuLp/v3DJFWMYLsKiKVEC9qjeuIxJOcmpplfWuNaDv5e+lQhwktZNR4dhp+9D4RMPb2bxb/u7NY4MK3ifZf/9OQSvDRic/QwRi84V9h3Y1i/FXzSXU2uJc+CyWdhu+9Db55C3z7TcKvtcToI+Ud9UKgWE/+FoAn46Yloy2bxekifULW/z5r3XY49YI4s2NHxfej+NfQQIDQdH7o3pIzOw0oULsK3nOfJlK+LNKKl0ik6CKh3q1VUBp6S1fltQ9oaxxTBKLJBWUu6T+zOreI0rjmvGZ2HhtGDQ4Jr9KF7xCer2OPzfsxlgopUOYiHlrRFZRXrW/i1391nYi5X321OLPvyYwnYgGICkrhFo+jabV4oxo9UPI+Xr+lDYD/+6vDeONjRF0t/Obj1/GBV63GYSv+6110EsfbZMpBKWaSXfoxY/24PA6bCI9DLd3i0QTKa7oUwvEUL/QV7u3/dLfw9IyH5pE4O31S+IIa13BsVLRy1jVrIqJhNXWJMZzE52eUVVW4/6PiMW7+XGZEthhbtDbP3nvhob9je2ovD3p+B3bcCh3CE8Pp3YAwj05FEkub/tv/NMwMiMrPiSfgzmvgJx/RgsyWhuGZKBYFmvUVFP1PE1VcPB7oyFSxPH6o6Vi0Fk/W3rJBTaB0Xlz5HWlVl4bYqaqvZyA6Da5aIRBctZpIuQSe+lKmhb3I6CPGfpciqsKl2jtgCJQei6gwLSQLZSaii0rxuvWGLe20MImipsXjXP+/RUXnV58SH4bOIKRAmYtYaEVmoBRk9TXi9MQTc7ccyqDObSeaSGd92jd63DV14k3q5LMlP9Wsa6nhnRd38aZtHax1h2ls68nkCZRAFxnBWM4fvrcl0+JJFDPJLn1QWzhmavGUGjE2DkoIlMtaxHP5eIHe/pGRIPsGA4CYxqh4jbu+gda/hqNjQQDWt2oCxb8aBZVOZWx+O3l2/pdYBHj+W4hvex+/OTxWulXUsglaL4Tdd8POO3k2vYmftn5UXKYLlMEXAZEmCyxtFeXle8TpO78HH3kS1lwLe74P/36R+N6WgNOBKC01LmxWi0g/PvU8J70XMhNXso2VrefD2KGiE3GVEDC1Czj1gnhj030uldAoBEqvMrwoY7QAjB2GJ/557irI7DS4TNVIVy1c/XHx9fTA4hxLDrqJu8syJkT+XG0xLZl3FbpAmf9zpLfRdWPz9ZtaWG3TKlf13eLf5R+Bkb2Z3+MzBClQ5mKFe1CyaOgVvf++J4UHZZ4jxjr6H0zQ5EPRKyh1brsIm4qMw/iRkvfzhbdt4d/euglrdApqWst6bMNHkrcwsFl8b+lU0RZPNUyyesqtz2krvYdHRxMoXY4QrbVOHivgQ9GrJxvbhOCuOADKJFCOjIjptvUtmQoKaKPGlRplh3bDr/5O3Mfv3ME//Hw/f3DXTn57bI7y/5a3QzpBuraLP47/ObVe4Zei7UJRfdN8KF1aFsqSCZTELOz/mdhE3rJRPP6tPxWfzD2N8Kv/vSStg+GZWcN/wuAuSEaZbLoEyPleWzdDKpYxZy4A/W+11mkVFZT2rWArbkYvij8jUBYjiAyAF+6Cxz4jPB6lKNQu1Tcxx4KLcyw56BWQ9qSW21RmBaU5KXx1C3mOAubXVMSI+PVtoj044xSDBrzqY+L15dFPixiBMwQpUEqRSoqY7RXsQclj9TViAVkyOu8RYx1jo7HJhxIwm/B6tMkhrbdeEmPEuL2sxy7qI/G2CNNYZKKoSdZjtHiWrhwaNueglNrDo6NNICmRca7b0MLxsXBWqms6rfKz3UO017n4i5YXeZVlb+VtHv2F37+WI6MhrBaFnkZNvPvNAqWCF7hoAO59n/j67d/giZMxvr9TTBccHJ7jzWL7e2HL7zH8hm8xSS0NemqwwwPNm4TwATobFpgmGw9DssQbxOEHIRaALb+Xff66G2DNdeI1JBaY32MXIZFKMxqMGeso9PH/RNcVQM7U0iJO8uifxutnT4rW43zaO5ARKJYRI/V5wejG/cgcwjY6nW/u1wVKfGkFSnNCi00oNmKsU7sKFCv+hBAoCxk1Nkyy7kxl+XK/+IDxxJgm6t318OpPQHAInv2PeT/WYiMFSikSKzdFtii6DwUW7kHRKijmuPvAbAKf0ybK1rpA6S9DoGgTPPjKq6AU9ZHoWSih0UwFJWfMWN9uvLQmWS0HxWErLxTPmxmRvm6jmEYyt3l2nZzi1NQsb9zazvVHP8dnbV9nslKBMnlcmz5YxbHREL2NnozPR6ugbHZNVOZBefFbMHUCbvwnAv4L+MR9L+Oyi/s8NhYqfVuPH373Tkbd4sU+K5SvYzsETkFo1EiTHZzPIsXTe+BL58N3fzerCvL+b+zkz38gKjTsuUdUbC54a/7t9YpecHGXtI0FY6hqZoKH/qfA5sK3RmxBH5g0fa/GJM/CjbL6BwjvmBB/8xYorjpiTj+9yvDi7W/ShfxsibDCdApiAY6H7NktTv1D6BJVUIxt21FttHeuCorVBnWrqIkOZd1+PphNsjpr7eI5+vExU67SxR8QwvGpLxtRC8uNFCilWMF7eIrSu4gCxYi7z27xGH9I3iZoOq88gWLs4Sm3glJEZPgyYW2ReBKnzYLVkh2OZrEoeB3W/GWDi0iWB6WczBlDoIxw1bom7FaFxw9lXmR+8pL45PbW8/8/e28eL8l5l/d+3973Pvs6Z/ZFM9pGm63FliwjySJeAhiwWQLB1+BAFicON0BCCPcmuZB7A2G5NwSHBBwccBJwbMBcC3mRbWzJkrWNpJnRbJr97Evve7354623uno93ae7z4w09Xw++oxOVXVVr1VPPb/n9/xieIw8u1xLFBe6bD1dOwcjeygYkvOrGQ5M2H4XsRlw+9jvWeluqvGSOWn3tg/xr/7iOPOJPL/0vpuJBTycXdqEoJjQbZgjdoIya/pQrr7EREyZSHXyasdYeEV15OTWVVnz1T8F4Nxymq++vsyfvXyVxYUrcOZJ2Pfu5uVFTZjT/Y0X1x080/GA6q669CzsuIcdY0oZqFFQRg+Ay9uXVuNUvkzY58Y9b87Rmd0iQQGKkTlmxUr/PCj6d5JrHf5WTKt1z1yt1KYu+2Pq3wERFH0TFslcBE9AGZc3w9AuQln1u+3lPaoqKFUl2Ju6TNoV46kLBVb1jYrHB4/8sjoHJgdn7u4GDkFphxt4Dk9LxGdVpwL0zYOSrFNQrBAoUCpK4lI1VKgVtILSoQelpY/EykJZJlusNBhkrc38nsEOCyyW8bldSqGwFJQ2mTMev1qfXiLi9/D2PaM8fW6VXLFCsWzwhWPz3DQV5WCoeuGKXfxy50+oUlYt3yN7eWMlgyFh/4St9Olyw9AuZlkkU6yw0qlsv3oGQqN89WKZ//6dy7zzwBg/9LY59k1EOLvcmRKjT95DdnO0NspefRG/x81wyMtSqgvFaPE1+NQH1Dng+35PmSqf/CUoZvhLMxhQSjj9lU+BUYbbPtR8PxZB6W8gWTUDJahKWaUs7H4HYxEfQa+71oPi8Smi3w8FJVdSMfeXn1OkuIuI+3qIyASjJFlN9Sn5WP9Osq0VlN99QhGrBOHai/6APSj6WP7kG0ql6CT4c2gX7mKSGJmeslB044GV/guwcYliZBYpqU3zPfwB+JlvV38/1xgOQWkHXY90PCi10CpKvzwotlbjGgUFVJ8+wIWn2+9Mz+HpUEGJBFr4SKyBgUvkSpUG/4m1WbNhg31EulCuncMDmxPCyJR1p/6uQ+MUywbfOrvCU68vkciV+J47ZqteHWB68anOn1DioroQ2w2yk3W/i5E9jJTmERidG2VXz1Ae3sfPf/YYUb+Hf/PB2xBCsG88wkq6YLVItoOuzw/bJ1dP3qJUA9MoOxENsNSBgrKQyJO78qoiJ4UUfOjTyoz78D9TuSvf/E3+4tg8QyEvsYCH0bOfU0nTN723+Q6jqg3eItB9gp5aPR0PVKeM73oAIQRzI01yXyZvVp9hPtHTcVP5EmOBiiI7s3e3HL3QCdyxSbyiQi7ZpywUS0FpTlC+cGyeJ194HYCEDNdGHHj86vtS6Ey16xYb2RJuKriSlzsPtjPJ3w6x3JMHJZkv4XGJ6s1WpQzJK3hHVafQ83aCIsTWTM8DgkNQ2sFSUByCUoPbflDdGU63DkLrBFUFRV3o9cwIu5mLncr0x4Vvtt+ZbZJxJ2htktUlniWyxUpDB4/98YOcZpwp2MhRJwoKKHJlvg+WD+X1JT730hWEgA/cPmMRlKz0M5t+FTIdXhzsLcZm6aVGQQEY3oPHKDLFemetxrl1yK7wQnqExWSBX3r/EWaGlF9k77hSLc+ubH7B0HeXNR4Uj191r1x9AaRkIuZnKVVoW3qqGJKf/o0/ovSf36su5B/6Qzj4HrXy7o/A+GGMv/5NUgvnePzmKX7sUJnDlddJ7Hm8tcpqEd7BlHimYgE49zU1t2hO+U/mhkNc2chRMWyvtU+Jssl8mdvcFxRZ3XFXT/vyxZW6VEn2wZ9jVKrkq4mCcnY5zT/5k5eZCyoVLUG4YVAp/uhASzyz/rzKHtHnmM1gEpQ97pWeFJRErkQs6EVoMpm8ArJCZGIvsYCnlqBcZ3AISjtYHhSHoNRg1/3ws6eszo2toupBUXcHGXNmRI2CMjSnWpsvdqCgeMNVqXYT6DbjdL2PxCIoK+TalnjcAw5qK1tlKHLrZmpvczXHQnRKqX7FDHvHwuwaDfFXry3ypRNL3LtnVF38TYLyBXkfLgzln+gEq7UERQjYN96ooADsci3WdBBtts+nVmK8+6YJvv+uavS33ncnPpTq5Oq6/JuZO9TrTc0zEQ2QLVbafmbLiSy/bfxrguUU84/9Dhz67upKtwce/xVclTy/4P1j3nvbNB/2PwPAF10Ptn5yAy7xTAalygraea8iZagBiaWKrPXcaIKy1FuZJ5UvcStm2/+Oe3ral9ssx8pMH94bPV8HGjwo2WKZn/708+RKFf7BA+r3nZDhGnM+YBKU/nZbaaxni8wFTONyp8qzOeNov2+tN5NsvSptlsvF8C7u3DXMK5cT5EvXV0CbhkNQ2sHxoAwUsUBtm3Ei1+g2B2DXfbByqr2zPL3YsXoC4HYJgl53o4LijyoTW3qJbLHc0MGjEfF7rHk5g0CmWFfiCXUw80hfDFMLCCF4+NAES6kCxbLB99wxY60DeNL/GAYCXv//W+7u3z7xOn/3j15QJy97BspSirnhUOOkaHsWykoHrcarZwB4Q07zv7/nUPUODxtB6cCHok/eDd8bmw9l0jTKtvOhrF8+wQ6xwh9WHuU3rxxq3GDfw3zL83be536G+9wnmb3056wwzG+fm8EwWigzvrAy2Q+gxDMW8eObf05lnOx9l7VuR7Pclz508hTLBvmSwU3l1wGxpYj7Gpg3A+7sFgdX2mEv69j+X0rJL/7PVzm1mOYfP3aIQzH1m00Qbkixxh+tTq/vMzayJWb9mqB06N3TcfeelZ5KPKl82TrXqidz0dr/3buGKVYMXr3SW+lvUHAISjs4HpSBor6Lpyal0g4rD6WNipKa74qggGl0rRtWiBBWmuxmJtlSRbacedMrMoVKdVBgdpNJxhr69adryzw+j4vHbzG9Oead/Gr0ICfEATjz5ZYZH3/28lW+cGyen/wv36GycgbcPsqRGd5YyVQD2uwwFZSbA2udlXhsBGXOTHvV2DUawuMSm7caA+sZdYfocdedzmwEZSJqEpRka4JSvKT8Kq+yj8++eKUhJ+b0YopfyHyIsvDg+ez/hlh/gzemv5vLyRLPnm8sK0gp+cXPvcKCjA9EQZmOB+DcU2rB3oesdfq9vGTPfYlOqxJhDwRFDwrcnT8B44dUAmsvMMtfvkIfPCj2sk62qqA88doin33xCu++aYKffmifpbQkZLhhDthASzzZElNe8/PotLkgOg0uL7NiuecunlgTBYWhndy5S934XK9lHoegtIPjQRkovG4XIZ+7iYJSV8qwjLIt2o3LBSXrdklQIq3KNOExZHqZQtloaZJtOcunD5BSKgVFHzu31tnUaJuCAvD2PSOMRXy879bpqrqQXgRflEgkzpOVOxQJb+LvkVKykMzjdQu+cXqF5QsnkEO7ubBRoFSR7K83yIIZzy044FvmwmoHrcZmsumKb7ZazjLhdbvYORriXCcEJVtsLO+AGpTn9psKisoLWWrTMeJafAWAPbfeT7Fs8IdP1w6q/MIr81yQUywc/ohFAife8eMAfM5s47bjT1+4wqefucj5QtSatN0PVAzJYqqgMlDOfU19N6Zus9bvbBbtL4QyDi8e33KqbTJfZpwNhkuLPbUXWzA75sKltdYKVKewl3VsCsrJBVWy+cSjB3G5hDXJuOiNtyjx9J+glCoGqUKZCY95PelUQXG5Ib6DablMIleq9RR1iHLFIF2o8/UlzDj/oTmOzg3hdonaTp7rCA5BaQfHgzJwxALVgYHJJomHgGprDo+3NspaIW3dKyhNs0wiE5Ca5899/5SfWvw/VHvpc/9JzSAyT2CDjLvPFitIaZKgUl61kHZyUrMUFHW3HvC6+fIn3sWvfNA2eC+9CJEJRiM+vlgyFYZTX2zY1Xq2RLFs8ME7d/CDd04xUprnWG6UE/PqhL+/3n8Cahp0bIY5s9V4ebMguNUzLIkxYrHmd+L7xiNcWM1uOkxuPVus7eDRcHtV7PzVF5mIqvXtFJTI+nGy0s973/VO5kaC/OEzF2pq8184Ns9o2MfU+/6Z+q5N3cquI2/nyHSML7wyX7Pt+ZUM/+LzKrl1WcYR+XVFpPuAlXSBiiHZGy6qLqXd71QXMxNzrWYPTRxRhHSzlv0WSOVLHHUp1WvLAW12mJlDI3KjsdzSLTQpEa4aslIww9gsNVJ7VQJDNflLgCIo5bzKlekjNBEac5nXk27iGYZ2MlaeR0rZSKg6QCrfrMX4IgTiEIgT8nk4Mh3jhQvrnWcXbSMcgtIOuh7peFAGhljQs7kHRQjVzbP4avM2SSsDZQsEpZkCcs9HKe24lxGR4tbk1+Cbvwlf+AR86v3wqzvhdx7g/Rf/bz7o+jr5lfNdHbMTVOfwuDtvMYYqQbN1jMRDXvweW5kqvQiRSUbDPk7KOcrRWeVDqTs56TbWmaEgv/JdI/hEheeSQ/zi59RF98BkCzPy8G7GSyr9sq0PRUpYPcs5Oa06UZpg33iEsiG52GaGjpSS9WyptoPHjpk7ILvKNMrn0DKsTUom0q9zUs4xMxLhIw/sYS1TtALuTi2mOL2U5vFbpvCEhuBjX4e/9TkQgu+9Y5ZUvsxT5vyjUsXg4595kWypwkce2MOSNNWvPpV5dAfPHcZrgKzxn4Aiz8Mhb21YG9h8KFuLvE/mytzRT4ISGKIsvIyJRO9psrrEE59ThL6k3qNCSREUnU6sFBSBOxhrLPH4BpMmq2PuR4R5Pekm4HJ4F34jxxDpLc3j0cTPHtLGxoWa/Jq7dg2zmil2N6Jim+AQlHbQBKXDzhAH3SMW8JIw2/2sqZv1BAVUmUcaKjGzHumtEZRIq7C1A48y/71/ygOF3+bf3vM1+HvPw4/8KTz2r+Cm90F2lVsXPsuv+f4D+z7zYN9jzDVpCnU6yVhDt7S2ej7loppTEp1kNOIHBOs7vkudsJZfr9m0GgQWwL2uDLKRmYPWybahxVhjeA/+coo46fZZKOlFKKY5XZ60yi/12Kdbjdt08uRKKohuqNUEa9OHMpZS7bUtTbKJy4SNJGdcewl43fzA3XNEAx5+7xvnMAzJXxxTOTvvvc308kQnVdIx8IGjMwhRTev9jS+d4uXLCT724D7+9v27WZbx6mvuAxZM8ngwY6a57n1XwzZzI6HauHuoBiz2oqCIM5TdQTXrqFcIQd43wphI9D4wUBN5/RrNv/OmR8wi6bkNCMSJhvzNPSgwAIJimrgx99sNQbFloXQ80Ti1CPPHgGqEg3XTVylD4oo1LRkUQYHr04fiEJR2cLp4Bo5YsFriaamggOrkgeY+lB4UlGLZaFpC0IMC/f4QjO2HA4/A/X9fZWN84gSff/iv+FT5UVxGCZZPdHXczaDLRhG/B3QLZifZCf4oeEOtMzcyZheUqaAAXB43W2RP1Xbz1ESpmx083//oQ3zfHbM8fGi8wTNiYWQ3oDt52hAUm0G2FUHZ20Enz1qzmHs7TILiW3iJeNDbWkFZUCf0qyHVvRPxe/jht+/k7HKGp04t8YVjVxmL+Hj7nsYW0clYgPv3jfLVk8s88doC//6ps9w6G+cTjx5kx3CQDbd5QeoTQbE+m7VvK8WgSfDX3HCIxVS+1sRtEioyW+uaSecK3OY6R3rkls1b3jtEKTDKmEiy2uvAQF3W0TNuTGKvFRS/x6agBIes0nJNWWNAcfea1EeMlCpB+eOdP9gkEnNiuXMS95c/C//pMSikqzH3usSTugqyUqOg3L1bE5Q2M4yuEToiKEKI3xJCnBdCSCHEUdvyvxJCHBNCvCSE+IYQ4g7bugNCiG8JIU4JIZ4TQtzcybrrCsUMuDzgvn6S9d5qiAU8ZvtipXUXDyiDnz+2CUHpLEVWo52PpDrJuEkXjxCIoTm+apg/hfXzXR13M2hVJ+xzd0e+hFBG2VYKiiYukUnGIqqr5WzoDpUf83qtD0VfyKdiVYLiGdvHr3/oKL//E29r/RyGq1kobRUUi6BMWS3A9bAUlDZG2Y1mKbJ2jB1UpM1sNV5uoaDI+ZcBSMSrysDfvn83Hpfg//zz45xdzvDdt0w3zGXS+J6jsxQrBj/zX18g6HXzmx8+is/jwuUS+IZqW7x7xUIizxSrBJPnYM9DTdNcd4wEkRKu2Dt5QiZB2WJbr2v1FBGRJz/ZY3uxDUZonHESrGd69OdopdH8/mnCUrAUFPNSl0tAYIhYUHXh5ez5H7pbs8+txhvmeS0uTziAAAAgAElEQVRUTihDcycx9xomQVEKSocelCsvqAnal55pHBRo6+DRmI4HmYkH3tQKyp8A7wAu1C3/QSnlbVLKo8CvA39gW/e7wCellAeBf9PFuusHhZSqS/YQ5+ygPexhbcl2CorLrcKorjwPpTrpustJxhoRM2ekWZnHmmTcos044ndzUZrHW3ujq+NuBp2vEvZ7uleHolOtFRTtgYhMMhoxTaN5YN/DcPnZmlTZeVuJh7VzKgY8tqN+j40wW41vCa7xRjsPSg1Baa6gDIV8jEV8bQnKWrMUWTvcHtXhcvUlJiL+lgpK6fJLlKSb8thN1rLpeJD33TZt1eat8k4TPH7LFH6Pi4oh+eX332ypPwCx8VkAcmtXWz6+G1xN5HnAZbYL29qL7dCdPDX+neCwuoPfooISXVETjI3Z3hJk7XBFJ/CLEulkj3fvuTWlTOgyp1niKZQNPC5RbUE3FRR9jqlJkx1wiSdQ2uh+PIhJJObEMmudlHiya9VBf+f/2jYoUBMUs4MnPlfzsLt2j3BqMd3RaIntREcERUr5dSllw3hDKaUtvo84IAGEEBPA3cCnzXV/CswJIfa3W7e1lzBAFDNOB8+AYY+7T+RK+NyuqqGtHjvvA6OkSIod6YWuUmQ12rUKa5LQMgfF5+GyHEciYL2/BEXPBwr7PdWyQKcdSpFJ5TNp1olgG6g4aiooq+miSkyVBpz+K2vThUSeiN+jBsOtnoXh3Z3J+uYd7CHfSvtW49WzGEK9h60ICqgyz9mldMv9tEyRtWP2TigkOBJcIdMiTVYsHOOMnGV8qLaj6KPvVOWT8aife3a39g5EA15+9rFD/J2H9vEDd9cSuYlpdRecXGlsRd4KFhI5HvabkfV7mhOUuWFFUK5s2Mi8y6XM1tmt5Y6MbqgymGdnGwWtS3hiiuQXNno0EGfXVZihbsc3FZV8qVJVTypllRQbGLLOOzWdMRZB6W+arFY+vMWN7gesRiaRbr85j6cDgmI3QL/xDZsqbf52mygoAHebPpQXLl5fKkrPhUQhxH8BHjb//Bvmv3PAvJSyDCCllEKIi8BOINFm3Zlen09fUUw7IW0Dhn1gYDJfIhb01CSK1kDnoTzzO7ByWpFHX0jd4Ucnu1a6dImnqYJSalPiwfSv4CXtnyDaZwVFl5yUgjIPiOqd4WawtxrHZ2vX2RUUsySymi7AgfeoY7z+l3D0hwDVxTMVD6gZJ+vnYd+7Ozt+aAQCcXYJFXS3nCow0YyArJ5lzTdDOedpWeIB1cnz7BtrrKSLjEcbt9Mn/6FWCgpYPpSbOQfsYymZJ2Jvk86s4s3M86rxoHrNNtwyG+cfPXKQ3WOhluUdjZ98sPkQuF1zOyhKN8WN/igo8xs57uNVZVRtMb1bp8leXq9TG8NjW1ZQptOvMS9HiI/Pbb5xhwgMqe9rpdfyV25d/Ua0AdWmoPh14rHuAAwON4zZAFTiL/R9YOBGrghI3IWN7gyyAC4XRnwHO1aW+WInHpQFleVDaAyuvkhuVmkI7Uo8UGuU1QGP1wN6NslKKX9MSjkH/CKqXNNXCCE+IYS4rP9LpwcTRdwUxYxjkB0wqgpKqTHxsB4zd6g7pJN/AX/xD+GzH4XP/LC6gMa7P2m2HBhI1YMSbDPNGGDNN6uO38cMgapJ1q38JOExlenRCdoNp7OpMQGvm7DPrdo7I+MqrffUE5BZRUrJvE4qTVxWqpU2H3aC4T2Ml9XFuKHVFRTpWTvHvEcRqIloawVlMx+KZZJt5UEBK5L9YF4pAA2dPAvKf/Ka3K1ecx0+/sgB/ubR2YblneLgVIwV4og+mGQNQxJJnWNUrjXt3tHQQxcbCEporMaDspjMc9e/fJInj2/y3IoZpgrneVnubzn+YSvwxkxCnWkzxqIT6DBDraBYHhSj1iALdSWeZgpKf0s869kSUXIIo9y9ggK4hnexQ6yw1omReMFUUO7+CMgKwysvAPYSzwVVCgsO1TzspqkoIZ+b71xnRtm+dfFIKT8FPCyEGAUuAdNCCA+AULfEO4GLm6xrtt9fl1Lu0P9FItuoaBTSTolnwIjb4u6TuXJzg6yGxwc/8234yBPwo5+FD30avveT8L5/B+/99a6PHW6joLQ1ydoeu+ydUZJwrn/SaMZe4knNdxdAp7dtZpRNL4JwW3Xw0YifFX3Su/sjaqbLS58mVSiTLVZU6cVMe+14RDzAyB4ihSX8FBtbXUHdxRklzstpRsM+fJ7Wp6F9ZjvzuRadPBudlHhG98HoAfYufwU3lUYfimmQfc1oTlB6xVQswKoYJlDofebMaqbI2zHvklv4T0CF9E1E/VyuJ4jhUfVdrajv/MuXNljNFPlOk6j+2gOfxYXBedeu1grnVmCGtXlzPbw39jBDTQDMuPtCqVKdGWULadMlj+Ylnv4SlES2xA6/+Tl0MlOrDmJoJyFRwOiExC28ArFZOPIBAGY3VDncOq9uXGxQTwA8bhdH54Z4+VJi02DE7cSWCYoQYkgIMWP7+3uAVWBNSrkEvAD8qLn6g8BlKeWZduu2+lwGAilVicchKANFLFiroDQ1yNoRnVRm2f3fBYffD7d/SF1cx7q3MLU3yapl7YYFAiy6TeNkH8s8Oqgt7HV3PQTRkvxbKSiRCauLYDTiY013Txx+v7q7fu4/sbihTqb2FuOuJlebPpQ5sdQ8ZG1VkZ6TpYm2/hOoJta2UlDWOynxCAG3fj/+wir3uY43dvKYmREn5E6m4sG2z2crEEKQ940RN9aRRm+jERYSeR5wvYqBu1rybIEdw8HmCgpYJRD9+bQboghY34Nl39aVpKYw4+79vczj0TcHwRFFMlye2hKPpaDo7bZXQdnIbWGSsR1mJ08g02ADrUW5CMsnVXryxM0QHGZP+kWCXre6CTAqkLyiJsQ3wd27hsmVKlZa9PWATtuMf1cIcRnYATwhhDiDMsV+TgjxihDiZeDvAe+TVTfbx4CPCSFOAT8P/IRtl+3WXR8o51W/uONBGSg0s19NF8mVKu1LPH2GnnXTdZsxKpnSJeCKMAlBH42ymjBFyKrvYQufQVO0U1BSizVeltGwn9V0URlQPX6488dg4wL5k08CuoPHfF0jXZR4RqpTjRvi1sHq4DmWH2/rPwFVqvB7XG0ISpGI39NWhQHglg8C8AHXtxoVlIVjqtzkj7XOd+kRRmQSLxVWlnvzWiysp7jXdZz14Vs3Hda3YzjEcqpQE8Ffn4WiCUrLfBgNk6CsBzro5OoGkeo8ni1HredsYYZCmEZgnYNiM8naFZSgNsnau3h0m3GfSzyZEjM+8/3dQolHKx6x3CYeppXXVTl28hZ1E7LrAXYXTzEdMFXS1DwY5aYKCnBdDg7stIvnY2aJxSOlnJRS7pdSXpBSvk1KeauU8nYp5SNSypdsj3ldSnmflPKglPJuKeUrnay7buCEtG0LtElWS9ENgwIHiE48KK2GBQohCPs9XKT/rcZZbZItmqbWbvJd6iYaW5DSirnXGIv4KBuy2mp5908AgpHjfwiYCsrqWXVH2o3Hx9bJ01xBUQTldGmiwZRaD7dLsGcs3JagtEyRtWPsAMbkbTzufo7VhO0CVEjB6llOsnvT59IL/EPqM7x04XxP+yldfoGYyJHd8Y5Nt9VG2ZpOnroslG4VlHSw+cVtywiOUMHNsNywfnNdQ2egaP9JcLhOQdEm2aoHpb1Jts8lnlyJSW+XgwLtGN6t/inNtx+qqP0nU+bsrd3vxI3Bfd7T6u8WBlmNO3YOIwTX1eBAJ0m2FfSX1CnxDBRaQblkEZTtU1CiAe1BaTwxbpaDAkqBOVcxFYk+hrWlCxW8boEvV01+7RjBEUUo6glKfkN5TGz70sbSFV3mGdoJBx9nZulr7BDLTMWC6sI0tKu75FBTQTnsX20sMQCsnsHwBFlkuK1BVmPfeITL67laJcDEeqbU3iBrg+u27ycmskwv24ZOLrwKSF4s7hyI/0QjOq6Uh6X58z3tx3tRPXf/gYc32VIpKFBnlA2bJQatoJgZL0ubKChy7RwJGdraBbYdXC5y3qHe4u7r51WFRqyyT75Uwe9tVFCalnjcHhXq1y1BkRKe/Y9NRwiUzGnC455s7XPsBjrunuX2QxV1B48mKHveCcDbMDNzNiEo8aCXgxNRnj9//QwOdAhKK1gKikNQBglNErSZsq1Jts9oq6Bs0masHu9mqRSEwFBfSzyZQlkpN1uJ8He5VF2/vm3T1mKsUZOFonHPRxFIfsj9ZaaiXvW6uungAYjOgNvPbvcyVxM5iuU6093qWbKRXUhcm3pQQHXySAlvNInOVwpKh0nPN38fAHelvlxdZkbcv1Te2XJoYT8wPq0uComl3rJQ4msvU8bF+E33bbpttdXYpmJZCsoqFUNa5CWZLzclgBpy9Szn5RTRYP9TtfO+EcboYWCg9qBoA2rQJChS1ioouaoHJexz43aJxgnBvkj3bcYrp1W8/Ld/t2GVboMfE1uYw6MRHqfkUlkobUnc4isqD0qn6Y4fZl1Gua1sEpdNCArAXbuHWUjmuZrYpOS3TXAISitYgwIdgjJIeNwuwj63NT13OxWUdlH3uWIZr1vgdbf+iUT8HvXY4d19N8lGalJku4vwJzrZqKA0ITtjEVsWisa+d7PkmebD7qcYLlyGSrG7Dh5QJGl4FzPGgopbt5cYSnlIXGI9qIx/U/H2HhSodvLUl3nypQrZYqV9B48dQ3Oc9N3MfaVnqxch0yA7qA4ejdiYUlAKPWShSCnZlT/JJc8eRAel56ZZKDYPykIyT9HWsbGUbFHmKWZwpRe4ICcZi/afoJSDY6aCssW4+/oST2gYjDLl7AZlQ1YVFKvEM4wQgljA06hI+KPdKyj6t5ZoNLEmcopQDKEnGW/BJCsE6cCMGdbWQkGRUikokzdbJvh8RfKMcRO7imdUBsyGGQTfhqA8sG+Md+wfI51vMkT1GsAhKK2gCYrjQRk4YkEvurS6nSZZv8eF2yVathlvlvcQ1tOQR/aoIVz1EfxbRKZQJux323JLuovwJzKlHmvYlAtLQak1yQK1d64uF5/3PM6oSCK++RtqWTcGWY3hPQwVruLCqDXKrr8ByI4yUDT26U6epVoFxZrD06mCArw6/ChBUaRw/C/UgvmXKQQnWSXO5AAJivUZphe3LJ/PXzrHpFhndeiWjrZvmoVi86Do8s6M+bqXUi3ums3y5Xk5ycwAupxkeJywKLCxsbH5xs1QX+IxiUoprTqDAvZJxsJleU1iQW9t1D1sjaDoXJnUfMMq/R2N6UnGwe7bjAHy4R3sECusp1t8RsmrSiHS5R1U+epp4wguDLjwtFJQfFGl+LbAe2+b5tMffTuHprpL5R4UHILSCqtme+VWGK+DrmAv62yngiKEIOxzW229dmSLlZYGWY2wqaBILalucYx9PTIF89j6hNctQYlOKrd+zpZtYQ0KrCooo5aCUisbf7rwTop44cX/qhZ0q6AAzN6JW5b4oPvrtUZZ0yB7AaUKdWJM3TPWPKytGnPfOUG5MvsYZemi8vKfQLkAyydYjar5O4NUUDQxHDbWahWlLrBwQg3KlNOdDetrmoWiSwyZFYs43m1G+Lc0ypoG2fPG1ECMxC6zS62Q6DzI7sR8kj98+rz6I6tLPJqgqH+LafX9ryooCQjELYUhFvA2lnj80e6j7nUyb7KRoGjFI1JJqmGnnQYu1qESm8MvSuTWG48BVCPup6rkNZkv8YxxRP1x/htqDs/QzjfVbDmHoLTCsc+oet7ezc1oDnpDzNa5s50eFFBlmlYm2Xb+E/1YQ0IppsoV/SrzZAq6xLOoCLKnS1ndajW2+VAsNcamoGiCYpPWc8UKF3IBXoq9G3O0VncZKBr3/jTl4Dg/5/kMS8u2C4/u4ClP4XULRjogF2G/h5l4oIagJLIlfucplafSTdkhMjLDt4ybCVx8Ci4+DUaZS/4DAMoUPCh4/BS8cSbEBqcXt5aGXb74HADxA5v7TzQaslDcXnUHnV3lwppSpO7Zre7qWxplzdya83JqIAqKL64ISrELgvIH3zzPP//8a2q4XW5dBRD6zbZrk6iU04o41LQZ29SDeNDbvMRTTHeXDJ2xKShGrd9KBwkG9STjrWJYnWOMVmZ800vFZFVBSeRKnJI7yHmH4I2vqRJUm/LO9QiHoDTD8utqIN2Rv+l4ULYBdtVkOxUUMMs0TZzx2VK5bQePeqxan42YP/o+GGWllGSKZomn2xRZjWi1nGChiUlWkwO7grJgXqROzP2AWuDyWEFRXSEQRz7yy4yJJHec/Z3qcpOgvJIfZyIawLXJfBuNfRMRzi1nMAzJF1+d55F/9zX+7OWrPHhwnA/cPrP5DkxMxvz8mXE/LqMEX/nXAJxyKQI2UAUFMMKTjJHg9cWttbFGV4+RkX52Hrqj48e0zELJrHDRNKbr/IvFTRSUC3KS6aH+v0cBswVbNsvuaQGtemZL5WrMvVYGTAXFMEs8NW3Gtoj3WNBDKl+mYm/d9UeV+ljuwiSqSzxGqWEQoy7x+ItbmGRsg3dsNwAican5BguvAgImj1iLVPlKsDL6NuVPMUoOQXlL4KU/Uv+ag9McDBbXqsQDukyzNQXFisoPmemafVBQ8iUDQ0LYt4UUWY1IE4KSWlB3mL6QtcjjdjEU8rJiM8kumO59OX2Xmh49fbS7FmMbvHf8MK+Igzy48TlYNFsdV89CcJizaS8Tm4S02bF3LEyuVOFv/8Fz/J1Pv0CxbPBrP3A7n/qJe9TE5Q4xEQ3wROUeKsILl58F4KXyLnweV2d5Kj3AG59iQmxwamELBMUw2Jk/yVnPfgL+zhWjllko2RUurmaYjPnZOaK+Ey1NsmvnyIkQKfdQR4pXt9ADA0W283k8mnDlihVlkrV3x5hKhWGaZwPe5gqKPu/UGEK3kiZrj6BP1nZpbZgmWU9xvacW7dC4KrP6Uq0Iyiuq287mmdTqUHL63up2DkF5k8OowLH/BvGdsGvzMCQHvcNujI0Eti+oDWAo5GUtW6RQriUp2WKl5aBADb/Z4ZPzT4Lb15csFG3YHfEU1XyRLRGUZiWepaYTkUfDvhqT7EJSXcimhkLwt/4n/Pifd398DZeLT4/+fQQS/vKfKNl89QxyZD8r6UJXbb26k+frp5b5G7dO8eQnHuSDd+3oei7MZMxPihBnhu5XCwJDvJaOMR0P9HfGTBN44tNERY4Li90PxktdOUGYHCvxm7t6XPMslDHIrnFpNc3OkRARv4eg193aJLv2BldcU0zFgx0rXt1AmN/Lbubx5EtG9d/cWm2+iEkEpOlN8XvcavZQMVWjoMSbhrWZinlXBMWmmtQZZTeyJfwUcZXzW8tAMRGeVAQlnG3Spl5IK5XLZpCF6pyh4o77qwsdgvImx7mvqi/Z0R+yzFQOBgs9uCsa8Gw60r7fuHfvKMWywdNnqycZKSW5UoXQJl08Ol69YAhVBulDiUe3PE8I0/i3FYLStMSz0LRcNBrx12QrzJsKynQ8AN5gjeKyFZQnbucz5Yfhwl/D878PmWXysT0Yko4yUDQeOzLFI4cn+A8/eif//kfu6qj7pxn0454OvkstmL6NhVR3ZGnLMJWtxNLl2rJCB1g6qQLaKh0aZDWaZ6GMgqxg5DaYGwkhhGAy5m+cUQSqMy15mbOVycGVwEyCEih2Pkm3qqCUlQelRkEx/980ifs9LmWQhVoFxYq773Eej206NMnaNvKNbIlheshAMeEKj5IlQLzQxCS7dByQKuLeBh1C55s6DGE1lLHVHJ7rFc4VuB66vHP7h6/t87iBoE8U222QBXj0iLpo2MfN50sGUrYPaYMqQSlWDGUkXb/QYJLrFlpBGcckKFvxoJgD2CwFpVxQJ/EmCspYxMd6tkjZzMPQJZ5+dWvsHAnx/5R/kLI/Dl/8BQA2QuourhuCMhUP8Hs/fg+P39JlJkwdgj430YCHp+SdMHsXxZu+l41saeD+E8AiKEOVteYjANqgeEEZZGP77t1ky1q0y0IZFUl2jaiSwEQ00LyLx1QFT5cnBvcehcYwEERKnROUnElQytmE8ozYDajm/7vM4YB+r8sW0lbdLtZ0YKBptO22xKP3W6+g5IrM6kGBPSgoCMFV1zS7iqfJ1nfyWAmyt9Us1sQrFvTBnoeUkXgrfrJrCIeg2JHbgJNfgJ33b6210sGWoInJdvtPQGVs7B0L86UTi9aci6yeZLwJQdEhbqWyocLaKgWVh9ID9DySEcNUdLaioHh86i5ZKyjaINtkX6NhP1JW2yHnE3ncLsFYpHN/SDvMjQRZJ8brhz9uGQ8XvSq0bLNBgYPCRNTPpTTwk1/hyr4PAQxkinEDzPd/XCQ41aVRNrx6jBUZY+/+w109rl0Wyggpdo6q9eMxpaQ1pP7WGGQH9B65PWTdMeJyo6HU2gpaQaloU6qdoHgD4A1ZBCXgcdfM4dHQym2tgtJliccwPTCaHCQbSzxzfj3JuLcxAWcPfpQ4aU598ico298ni6DUKyjqPBYLeuE9/xf82Of7P6pgwHAIih2v/U91Ej36w9f6mdxQ0G3GsW0cFGjHo0cmWUwWePWqkoE3m2SsUaOg6CyUHn0ousQzVDbvJrdCUKAa1gZNQ9o06luNFxJ5JqP+vpXatAHzW8Pvt1ogLwnVddONgtJPTMaqaoFOMJ7aDrJkvv9dG2XLBaZzZzjhOsB4l+9Z0ywUU0EZESl2WgqKev3L6ToVxZaBMjNAlSnrVXH365k2s2Zs0B4UI2ObZGxHcARPQZESpaBU5/BoNB0YqEs8xQ5bwXPrgFQ3tN5Qww3KRrbEtK8/BOWxD/1dno8/ytHc03z+P/9KNfBv8VV1Q1KXOJ3MlxACon6PKvuas3neTHAIih0v/RF4gqq92MG24VoqKNBY5tHy8WYmWZ+poBTLRjUrpMdOHl3iiZbNO8NuQ9o0opMqRwWahrRpjIZrW40Xkvm+hnFpgnJhvQA/8PvwyC9zUnZf4uknJqJ+UvkyuWLFVtLaBgXFfP8nxHpXrcblq8fwUmY51p1BVqMhC8Vsdx0RSevz0Z9FQxaKJihyaqDvUcE/yphI1GTytINWULQRtqF8EhquEpQWCkp1YGCzLp4Ow9p0B094XBGEBgWlyKQn0/w5dgkhBLf91H9k1T3Od1/5LX7/z7+sFJzF15T/pM7knciViPo9AzE2bxccgqKxcka1HR75AARi1/rZ3FC4lh4UUGPGR8M+i6B0q6CUahSU3giKVlDCRfPEt2UFZRJKGSVVNwlp09ADA1fSBYplg5V0gek+XojGo378HpcaBjl2AN7xj1hMKTJ0rUo81sU4lbdyX7bFg2Kal3f7M12Fta2dehqA0lR3BlmNhiwUU0GZcqeteUxaQWnwoaydo+gKskx8oO9RJTRGXGRZT3ZG3PRrEbm6OTwawRF8RU1QbB6UJm3GPZlkdUhbeAxiMzUKSrFskClWGHObBKUP5RVveJjQh36PgChyx3d+jj//ytdUt19dBw8oBWU7R4cMAg5B0XhZm2Od7JPtxnjUjxD9M2Z2C7dL8O6bJji5kOLSWtbyoGxKUEwFpVA2rKTHXhWUjCZHhRV10vVs8SKulZfUYlVJaepBqSooS6k8ssvums0ghGBuJFQzj2cxmSfsc3eVX9JPjJsX48VkwVJQtoWg+GPgCbDTl+TscrrjTp6CZZB925YO25CFYnpQdgZyVmu17m5qUFBWz7HimwWE5WcZCExjd2ZtYZMNq112UDXCNpZ4hvGVU7ipEPBuoqDUtBlrgtIhgdQdPLrEkk9AUX3XdQbKiOhhUGATBA++i/w9P8MdrjPs/Po/VgubEJRErnTNVOl+wSEooGSylz8DsR2w58Fr/WxuOEzGAvzxT97LR99x7YzJj9jKPDmTJGw2LNDyoJQN1ZIbne6bB8WXX+p+irEdmoykF9oOHdQKylqmOLCL9ZxZYtAm5MVk/pqVd6BWQZlP5PG4hPU+DBRCQGSSUamm7OpZQpshtPwy541JDuzeWoZFfRZKOaAu5jO+6vBFHZpXo6CUC5C4xBXXNH6Pq/Op0VuAO6oISn5jc4JSqkhruKi7oIlHfYlH/R0nYyoojR6UaFOT7FYVlHGImb9Xs5MnYRrPh6xBgf0zqIbe8y8ojh3hdpcqwcnJxvJfMle+Zqp0v+AQFIA3vq4SAG//MLjaX5QcDAb37h0lPuAkz3Z454Ex/B4XTx5ftJV42ntQrC6einm2HN7TtxKPN7u0df8J2BSUBWWSdXmaniDHbCbZ+T63GGvsHAlRrBgsmkFgC4lrS1Am6hSUiT6agjdFZJJ4RfmLmuaO1CO3wWj+Aq+yjz1jWxu7UZ+FcjUtSckg467qRXjSUlBsz2n9AiCtDJRBBtn5zDTZSnLzuPucLbZf+0yamWQBhkVKmWSbKCgBrxu/x1XXZtxLicdMlDbTZDfM/UaNJHgCPWcK1cDjx/eD/5mS8FGQHq54avNNDEOSyjsKylsD+YRST5zyzg2LkM/DOw+M8ez5NUtJ6LiLR7f8jexRtW59t7YFpAtlwuRwlTJb95+ATUFZUipKeKJp8GAs4MXjEqykiywOyI8xZxoxL65myRUrJPPla+Y/AZioU1C2tbQYnSRUWsdNpWbEQEtcfRGAhejNWyZR9VkoF9eyrMkoQzJhbRMLevB5XLVpsqZB9kRhbODvUWjYnMejO87aoGAjKNpn0uBBMQnLEGnVZpzbqB0oaEINDLSZZH1hQKjU2U5glXjGqoqnaZRdNwMQw5VkX9UTCxOHefrOX+MXyx/hxFLtdyldLGPIa9cZ2S84BAXg5u+Bf/gKjO2/1s/EwTXEI4cnqRiSv3xVnWA2y0GpaTOGvhhls8UKE8I86fZCUKx5PAstY+4BXC7BSNjHanpwCoomKJfWcxYJmrxGfiOoKihX1nOsZvprCt4UkSkEkhGSHSko6Te+DUBxsvMBgfWoz0K5uIpFj9UAACAASURBVJZljRihcpVICyGYiPpZtCsoa2qK8cnC+ECmGNsRHlGt567s5nH3usUYwF/aUJ2X3rrnZxKWYZGuthkH4g2dLrGgt7bEI4RSUTpWUEwze2hUmWTBMspqBSVQTgwsfyR+9AP8j8q7ODFf23WkVSGnxPNWgRNrf8Pjuw5PIgS8eFGduDs1yVZLPLvVvz34UNKFMlMu88KxlRRZjZoST/uhgyPmPB6tHG01Rr4VdCvrxbVslaD0+RjdIOz3EPF7ePVKAim32Zxtfi4TYqMjBSV//jnK0kV8711bPmR9FsqFtQyrMoq/sK7mI5mYiPprPShWi/Fgphjb4TI9KL785gTFXuLxl5LNL/72Eo9uM7aVdzRiAU9tiQe6JCirigy5PQ0Kivag+HocFNgOh6aiuAQcv1pPUJQq5JR4HDh4i2A86ufoXPUk1mmJp6DTN/uQhZIplNnpNaX3XhQUf0QNPls+CZViSwUFYCziZzVdZD6RYyzit15Xv6AVlMtrWRbNC+C19KCAMoWeX1UX7G3p4NGIVgnKpgqKlASXXuKk3MnBHa0/v05gz0K5ZCoowijWXIgnogFWMwVr7AFr56i4AywxNPicGHNWTLC4usmG1RZjgFAl0bx80swkG2gkKKrEU0dQfJHOCUp2xeqKIjIJwmUpKOvZIm4qeIqpwZR4UORz73iEEwu1BKUac+8QFAcO3jLQoW3QZVAb9KXEkylWmHWbJ5teCAqoE+bSCfP/W+9rNOIjXShzYTU7kIt1xO9hJOxTCopVRrp2HhSolnlgm8mSqaCMiw1W0pt08SSvEi6u8LKxj5umoj0d1p6FcmE1S8lnejZsJZXJmBp7YE23XjtHKjSHxDXQFFkAPH4yIkykvPk8nlqCkmyqjNQqKK7WCkrQS75k1Ebs+6OdtxlnlquD+Nwe5fUyFZSNXIkhdIvx4CLmj0zHuLCaJWUjWpp0OQqKAwdvITxmIyibTTP22oPaQJ2E/LHOFJTF4/BvD8KVF2oWZwplptymgtJLFw8oglMxLzZtFJTRsLpYr2aKAyt3zA0Ha0o8/S4jdQs7KdlWBcX8THd4OvCgXHkegEuhwz1nxtiNshdXs8iwmcmRqSoW2jy8mMxDuQgbF80MFLbFp5P2DBM3Epvmw+gSj5sKYSPdosSjCNiIyOChoqLrWygo0CRNthMFRc/hCdvyTWLTNW3GQzoDZUAKCsDhaWX8fd02PqGqoDgmWQcO3jLYNx5h96gqSWxqkq1XUIRQgW2deFAuP6e8IS//cc3iTKHMpDDDp/qhoGi02ZeexwODu1jPjYRYShW4YAa2TVzDLh6oVVC2t4tHfQ5zvtSmHpTyZUVQihNHez6szkJ57WqCVKGM1/R82BUUHWC3lCzAxkWQBleE8lVsB4nL+lTc/cYm+TDaJBujTYR8cAgDwagrrbo0zWX1aJkmW0xtPplcz+HRCgpAdEb5vowK69kiO/o0KLAdjswognLcZpR1TLIOHLwFIYTg448c4MP3zClpuA1qgto0hvdA4rK6A20HfWF4/Ys1RsV0ocwY66rjoL4zoVvYSUkbNWbMRlAGVe7QPpTnL6wzEvYp4+I1hH6dQmyzmhMaAwTTrsSmCkr2wosUpJehXbe03a4TaAXlW2eUYhIeNr8PmSpBqYm7Nw2yZ41JAl4XQ9uQUVQKjDJMmrVUtu12usQzLNqUT1xusiLMsMg0DWnT0ApD04GBpUzD9jWwOnjGbDucBlmBzDIb2RKzPvO19ClFthkOT6vna+/k0a3TTonHgYO3GL73jh386gdv2zSYylJQKjaCMrIHkOoOtB20tJ64aPlEpJRkixVGjbXeUmQ17GWdNgRlJFxVEwZ1p6w7edYyxWtukIWqWjAIU3BbuD0QHmdCrLOWLVYNqU3gXXmNk3KOw7O9331bBOWcIiSxUfP7VeNBqebDaIJyPD/GTDw40JA2jUpoHJeQJFfbp8lqgmL5O+ozUEwkXTGGRKo6h6eJglIt8WwhrM0e0qahW42TV0jkSkxrgjLAEs9ENMBYxM/x+erzTTomWQcObmw05KAAxM1Ex7qx6w2w5z2c+iKguoEqhiReWevdfwK1xth2HhSbgjI4D0o1RfNahrRVn4N6nVPXgixFJhkyNpBSEbamSC0SLKxw3NjFocneDLJQzUK5tKZKDuOT5oW0iYKymKwqKC9lRratBCbM72h2fb7tdlUFpX2EfJKIIjH5FmFu9DgwMNuEoEQ1QZlnPVtk3KMVlMERFFAqyusLScu/45R4HDi4weF2CdwuUVvi0ScrLf+2QmbFjL+OwKknAFXeCZInaPSYIqthtrTib18uGqtRUAZjhtQKClwjUlAHfTG+JgMqo5PEyquAZLmVD2XxFQBOyp19ySDRWSgaU9M71P9kqybZ4ZAPj0uwbCoo0u3nbCG2bUF2npj6vhY2mceTMz0o4572/o4NosRkapMSjx4YaE+TNUcKdKqg1Jd4gFLiCtlihTGX9sk0V3n6hSPTMfIlgzdW1PESuRI+t4uA9819iX9zP3sHDq4xvO56gmIa5tKbEZRl1ZK472G4/CxkVskUyv1JkdXQCkq0vRpTo6AMiDxMDwWsqPaJ64CgzAwFmR0Kcs/uwV44mmLsIB6jwF4x39qHsqAIykLooDXzqVfoMk/U72EoPqQIsk1BcbkE4zqsbe0sxdgu1WI84JA2DV9cfV+NVPt5PFpBmfHq8knzz3BDRvBTtLpqtqXEYyooxbXLgE3lGaAHBRqNssl8iVjQsy2luUHCISgOHPQAn9tVbTMGa2z8pgpKdlWd2A4+DtKAM0+SKVSYoA8pshqa5GxSLgr53AS8LuJB76adS1uF1+2y/C3XQ4kn4HXzzZ9/Nz/14L7tP/iOuwG403W6dRaKSVAyQ4f6d1izzLZzNIRwudSdf120/EQswGoiAxsXSYXV9OTtUpkiI6YvZpPfjiYoEyZBkS0Iypo0lRCdS9RMQQk0IyjmvJ6OSzy2Lh5TQSlvqBJvXKbUDKBAvP2+eoRuNdZG2WSu/Kb3n4BDUBw46Ak+j7v7Eo+U6u4rPAYHHgMEnPoimWIfW4xB3VlOHIGd97bdTAjBTDxYU4YZBPT+r4cSzzXFjrcBcIc401JBMRZe4bwxyfDIWNP1WzqsqaBYn3N4tCYHBVTpK5y5CEaZFa/KQBn0HB6NyKg6njvbGUEZdyuTbMHbJKgNWKuYr9P007RVUGq6eDot8ZjP0+6B8UfBF4WkIihRI6l+hwNWMvaOhfF5XFbkfSJXetP7TwDe3CkuDhxcY/jcotYkGxgCl6c9QSmmoVJQd7CRCZi9C858mczNuf6WeISAn3m6o03/3x++E59nsCdRZZRdvS66eK4p4juohKe4M3WaP2nmQSlmEatnOC7v7mt5xa6gAOr7t3K6ZpuJqJ9J8RoAp/w3Awx8Do+GL67UR3+hfdy9zkEZFspvUfDEaPYMV4yIugXXBKWJghIJqEtgU5NscZM0WfscHjti07gzykcTqgxuUKAdHreLQ5PRqoKSL3Gwx/Th6wGOguLAQQ/weVy1CorLpSTfdgTFql2bdemDj0MhiffKt6sEpR9dPF3gyEyM/RODPaH9zaMzPHZkkgOTkYEe57qHEDB3DwfFJZKJ9cb1SycQ0uC4sYvZof6pF4em1PtuxeaHx6CUhWI1d2QiGuAB16tI4eJ5YRKU2DZNe/aFyREgWGwfd6+TZOOkScoQuUojsS5XDFaNsPojcVmVWfyN32+3SxD1exqTZAEKyYbta5BdqS3vaESn8eeUj8ZfajEraAA4Mh1jKVVgIZEnW6y86TNQwCEoDhz0BJ/HVauggDrxtyMounNCu/8PvgeAkStfYaKfJZ7rDPfvH+OTP3b3NQ9pux7gnnsbbiEZWjvWuHJBLTshd/a1g+auXSP82d97gA/crkop1vfP5kOZinq4z3Wc1PAtnEt7Cfnc2xqXnnANES03IW025EsVXAIiRpJ1GamZzaNRrBisYxINaSgPSIsySyzobaqgyHyKz714pen+AfUbDzUpwcVm8JYzRMjiLWwM3CCroQPbvv2GOr/EAm/+AolDUBw46AENCgqou6p2XTyavGi/ytStEJ1hevFrTLBOxRsFX3gwT9jB9YE55UOZSb/SuG7xVQCOG7v73kFz244hq5vKUvBsnTx7K+cYEhkuD9/D1Y0c0/HAtnaCpLwjjMq1mnTleuTLBkGvm1AlyToRS1Gp2aZksCFtv6E2bb6x+onGPnWhv7SwyD/8by/x+9883/igZnN4NMyQxQPiCkJWILQ9nWJHZpQR95lzSoFyFBQHDm5weOu7eEB18pQyUGwRlZ2pc/8LAQffw1DuIre5zlHZ5vKOg2uA6aOU8bCvcKJx3cIrZNwx5hnpa4mnAZaCUvV8zG08B8Cp4J3MJ/LbloGicTVwkFGRxFg523KbfLFCwOsmUEqwIaNNFY5CucIGtpJOs4nHJuJBT9M2Y11++6vjTXJZms3h0TDTZG9ymWnS21TiuUkrKOdMBcUhKA4c3NjwuZspKLqTZ6XxAVCV1O3y8MHHAYiJ3Lb7TxxcA3gDXA7s5xZ5iqL9AmsYsPAq5z17Cfk8g70LbvI9HV58moL08s3ifrLFyvZOegbOxZSyVDr9pZbb5MsVYp4KXiPPBmHLNGtHoWSwLm1epyYGWY1YwEsyX0Zq1cbjB5eXfFr5wV66tKHi/+1oNofH2qEiKEdcl8xttoegxAJedgwHOWeGtb0Vung6IihCiN8SQpwXQkghxFFzWUAI8TkhxCkhxMtCiCeFEPttj5kQQnxRCHFaCPGqEOLBTtY5cPBmQssSD7T2odSbZAH2PkRRqHwQd6wPc3gcXPdYiN7GiEizccWmoqy/AaUMrxm7mBka8Ayceg9KuYDvyrd5Xh7gO1fVBXm7CcrCyD2UpBvOfqXlNrlihQmPugivt1RQDDIEqAjTh9FGQYkFvVQMSaZo7kcIpD9CJZ/E53EhJXzlxFLtg5qFtGmYJZ6b3SZB2SYFBZRRVuNGKvH8CfAO4ELd8k8Ch6SUtwOfB37Ptu5XgWeklAeAnwD+SAjh7WCdAwdvGqigtrp6eWSTsLZ6kyyAN8iJwB0AuByCckMgMXYUgMK5Z6oLTYPsdwqz1vycgaFeQbn8HKKc42XP7VZk+vSgn0Md3MEYL8gDeC/+NVRKTbfJlyuMulXn0UYLk6xaJih4zIC0NgpKszTZkjtCSOb4oXvmcLsEXzpRl27bbA6PhqmgHMQs8WyTSRaqgW3AtpqbB4WOCIqU8utSyst1y/JSyr+Uli7GM8Bu2yY/CPwHc9vngKvAQx2sc+DgTQPdxSPtpr5NFZRlcw5PrRH2We89AIi3YAePg0aUplSirOvKc9WFC8oge6y0k9lB54/oC6e+2J77GgBnIndZm2y3ghL2e/hG5VZcpQxcfq7pNvmSwbjbVFBamGQLpqpZ8JkEpZ2C0mRgYJogEXK869AEb98zwjdOr5At2lqRm83hsV7EOGVcRDA9aNtU4oFq5D3cQCWeDvFxlIqCEGIU8Eop7e6i88DOduv6+FwcONgWNJ1orO+q0ktNHoGZIjve0Pb4Rdc7+e+e98Mt3zeIp+rgOkNoYheLcojw0gvVhQuvYLh8nJUzg09wDcTB5a2myb7xNfDHSAzfam2y3SbZkM/NNwzz+C3KPPlihRGhQtSUgtLEg1JWpKXkM4lJOw+KqTTYFZT1ip+IyHF0bohHDk9SKBt847TNU9amxCOFi2VpO55T4tky+kJQhBD/FNgP/EI/9le3708IIS7r/9LpTdL9HDjYRuhBbjVlHmseTyuT7GpT2Xep6OVTsY9ZErGDtzbGo0FeMA4QS52pxqovvEI6tp8SnsGXeIRQ38Psijr+ledh1wOMx6sjD7YrRVYj7PPwitxLyRdvTVDKFWsI30YrBcUkLSW/2eLbtounUUFZLnqJiTzDYR+PHlGm9S8dt5V5ms3hMZHMl1mQNlKyjQrKjuEgUb8iXE4XDyCE+Fng+4DvllJmAaSUq0BZCGHXqncDF9uta7Z/KeWvSyl36P8ikRs8hdLBdQVLQelmHo+ew1OHVL5MxP/mrxs76AxjUR8vGAdwYcCVF9T3InWVxdABgMETFDBDBVfgwrfAKMPehxiPKlIS8Xu2vUwQ8rsxcLEyfp96T7K1qbLlikGpIomL9ibZvKmgVPy6xNMmB0UPDMyrEs5qusBy0acmIVdKzI2EuGkqyldOLlExzBuRZnN4TGxkiyxI2/HaHLvfEEJYPpQbPqhNCPEJ4IeAR6WUG3Wr/wfwd8zt7gFmga91sM6BgzcNfO4mBMXjB38cMk1KPMUMlHMNtWspJal8mehboG7soDOMhv28aJiNj5eftSYYv+HZCzDYDBSN0KhS9Ez/CXseYiKqusm2a4qxHWGfuqheGb0XkHDuqZr1efN3FpdKQVknQqGNgmIETALRziQbqjXJvnRpg5Q033tT2Xr0yCSrmSIvXDRTblvN4QHWs6WqguKPg3t7f9Mff+QAv/jew3jcb/4UkU7bjH9XCHEZ2AE8IYQ4I4TYAfwaMAR8VQjxkhDi27aH/RxwvxDiNPAHwI9KKUsdrHPg4E0DraA0hrWNNS/xtKhd50sGFUO+Je56HHQGn8fFpcBByrjh0nNWguxrxi6EgMm4f/BPIjymZs6ceVKVJicOWwRluw2yoDwoAOfjb1cL6so8Si2R7MsdQwo3qzLW3iQ7cgjcPhjZ2/KY9SbZFy9ukKGRoICtzNNqDg+wni1WCco2pcja8cD+MT76ztav982Ejs6GUsqPtVjVsklfSrkIPNbtOgcO3kzQCkqhPgslMgGrZxof0IKgpMyo7ahDUG4oxKIxzqT3ctPl55RpFfhOfobxiHt7ZhZpJW/lFNzy/SAEE+a06YGbdJsgbJY4l9wTMHoAzn5Vxd6bhvJcscL7XU8zlz1O7uhHyD0TaGuSTRz4PnjoQ9Z72wyWSdb8Db54aZ17XaYPx5xofOtsnMmYnyePL/ILf+OwKvGMHWq6v5oSzzYaZN+KePNrQA4cXEM09aCAIiDZVTWzw45mKbJU699OiefGwljEz/PGfsitweknYGgnZ5Lu7fGfQC1R3quSHvZPRDg0GeXBg80VgkEiaCoo2UIF9r0bkpdh5bS1vphP8/PePybvjlJ56OcBWs7iAfB7PW3JCdSaZCuG5OVLCWJxk1iYCooQgkcOT3JuJcPZxUTrOTzAeqbEIlpBcQhKL3AIigMHPaDaxdMkTVYaDSa/zRSUiKOg3FAYj/p5prhP/ZFPYEzeylKq0PchgS1h7ybbowhKxO/hiX/0IO+9bfsDA7UHJVMsK4ICNWWe8POfZFas8tzunyIQUwSq1SweAL9380tc0OvG4xIkc2VOL6VIF8qMj5m/T91dRbXM841jp2k5h4c6BWUbQ9reinAIigMHPaBpDgrYWo3rOnlaKCgpS0FxCMqNhLGInxeMA9bfqaHDSLmN5RVNlId3w/Cu7TlmG4T8NgVl9ztUTosmKMl5xl/+/zhnTHFm14fxuF143aJl1D1AoIMymRCCuDnR+MWLqtdjZtIkH4Wktd19+0YJ+9y8dNJUdJqFtKFMspflOIXdD8PB93T0uh00h0NQHDjoAW1LPNDYydNsDg+QLjglnhsR41E/VxijFFQXxIXgNrYYQ/Uiu+f6CPIOeRWhyBTL4I/A3Nvh/DegXICv/Evc5Sz/uvwj+PzKyBvwuFsOC4TOFBRQmSHJXIkXzS6d3dNmCkahmrvl97h56NA4C/NmqHqzmHuUSbaMB/Gjn4VbPtjR8R00h0NQHDjoAT63Mu81Kig67r6uk6fZHB4ck+yNirGIDxCsj6g5TOfMFuNtIyizd8HRH4F7f3p7jrcJPG4Xfo+LrB7ct+9hKGXh2U/CS3/E6sT9fNm4k6BJZAI+d9sclE6NxrGAxyQoG8wOBRkaNm8gbCUeUGWeEUxVpQVB2ciWiPg91s2Lg63DeQcdOOgBLRWUVgMDM8vg9oM/WrNYl3icNuMbC+NmS+9z+/4ufOC3OVtU3oVtyUAB8Abge/49TBzenuN1gLDfQ8ZUFC0fypO/BEJw7JafAwQBTVC8rrZJsoEuFJSVdJEzy2nu2Dmk1BtoIChH54YZFSZBaVniKTIUcpTQfsAhKA4c9ACfW50oG0s8poJizuOx7vJ0imzdHB7dxRPxOye2GwmaoJyTs3Dnj3E1kQfYPpPsdYiQz11VUKZvV6260oA7f5zlkDIUWwqKp7mCok2yvg7DymJBrzn0E+7YOVy9gSjWjlaZjgeqBKWNgjIc8nV0XAft4RAUBw56gNcs8TQNagPILPPXp1e4+V88wZmllDLJNnH2OyWeGxPjEUVQltMFAK5u5PB7XIyEb9wLXNjnUR4UAJcbbnqvIikP/zOLjGhvSdDXwoNSNvC4RMdpqvZI/zt3DlUJis0kCxDwupn2mFOK2wS1OQpKf+AQFAcOekDLEk9gyJwUu8KZpRQVQ3JyIaUispvceaWdLp4bEiNhH0LAcqpKUGaHggjRMgPzLY+Q3626eDTe9+/g4y9DZNwiKIFNFJR8qYK/Cw+IzkLxuV0cmYmBTxOUVMO2M15TVWkSwlYoV8gWK46C0ic4BMWBgx7Qss1YCHWHlVmy5ockkwkoZZrWrlP5snqIzyEoNxI8bhcjIR8r6QJSSq6s57bPIHudokZBATXLJqAG4OWK6rdkN8m2irrXJKYT6DTZm2djyljr9oAnWNPFozHuSrEmIxiicf8bWaWEDjsKSl/gEBQHDnpA02GBGuExyCxbd3j5DdMw20QaThWU89/lunHvnG9UjEf9LKcKJPNlMsXKDe0/gaoHRUrZsE5351QVFBf5UuO2hbKxJQXljjnb7Bx/tKmCMkySNRljNVNsWLeeVcuGHAWlL3AIigMHPaClggKqkyezYtXIyykzE6VJRHYqXybqd9STGxFjET8r6SJXN3LANrYYX6cI+z1UDNk434qq2dxSULxuDAmlSh1BKVXwd6Gg7BhWs3fu22f7bfojTQlKzNhglRgLpqHZjvWMo6D0Ew5BceCgB7T0oIBSSkpZjLw6yVXSzVNkwSQoTkjbDYnxqJ90oczZZVVOuNEJip5obHXy2FD1oJgmWZOE1Jd5ulVQHjwwxhf+wTt45PBEdWEzBcWoECglWJUxriZyDfvZMBWU4RvY5NxPOATFgYMesGmJB/DkVTibyDWfwwOaoDgKyo0IFdYGxy4ngG3MQLlOoScaW1koNuStfJNqDgooxcSObhUUIQQ3z8Rrzcn+GBTrCEpuHYFkTUabKyimB8Up8fQHDkFx4KAHtBwWCNY8Hn9BERRvvnmKLKg2Y4eg3JjQWSgvXTLnwNzgBKWdgpIr6oRY9bsL+PqjoDSFVlDs/pbkVQBWiTHflKCYCopT4ukLHILiwEEP8G9W4qFKUHyFdXN5LUEplg0KZcMp8dygGDOzUF69ohSU6fiNbZKtmWhch3y5QsDrspQOPQywPgul2zbjpvBFwChD2SQiUsKXfhmA7xiHmG9X4nEUlL7AISgOHPSAtiZZk6AEimsAhErNCYoOaYs4CsoNCa2gZIsVRsO+rtpj34qomWhch3ypYvlOQAW1QSsFpcf30QprM1uNn/2PcPbLcPRHeSVwVwsFRZd4nJuNfsAhKA4c9ACv5UFpbIkkoghKsKQISlwmkC6vqm3bkHJC2m5oaAUFnPIOVBWUbBMFJVeqzTcJmDcI9rC2csWgbMiO5/C0hD1NdukkPPnPYXg3fPevMh0PNvWgbGSLeFyCiNOR1xc4BMWBgx7QiYISMZWTUZHCCI42zOFJF/SgQOeu60aEVlDgxp7Bo9HOg1IoVWoJileXeKrb6t9i7wqKOTAwuwaf/UmoFOF7Pwn+KNPxAAuJPIZRe2Oyni0xFPLd0EnA/YRDUBw46AEel0AIKJYbT6baDBupKIIyQpKivzEeO+nM4bmhMRzyofP5HAXF1sXTVEGpJSi6xGMnKNqP4u9ZQTGVzif/OSwcg3f+LOx8OwBT8QDFisFatjasbT1bdAyyfYRDUBw46AFCCLxuV0NQFAAeHwSGiFVUd8bI/2rvzmMkOc86jv+eru7qmZ5Zr3e9ttfO2onldQxOIAnEgpDgEIUcBIhEAoE/IkS4jBAiKOIMAiLuQ4pQUBABCRxIhLgUQFwJRyAoBAgEBwgQ24C92cQHPtY7szPd08fLH3V0dXdVdW/X2zPdvd+PNNJsV3VNz9tT7z79vM/7vrajdnhi4jSGeK5sQc10TTzMc6VPMZYyGZSCGpTs0E0zp0g22cl4w1cNyrmPSDd+nvTS700P3RgXMo8P87CTsV8EKEBFzaCWP4tHkrau1dWDC2rqQMdsX7v1qydOSQKU7SafvK5USR0KGZTyDEq7OxgJPPKKZDu+MihhPMTTaEmv+5VoT6DY6ePR+5Ss/itJg4HTBXYy9ooABagorE8JUNzTusaiBZ92ascnTtlhiOeKl9ShEKBMWQel20+DEim/SDZZIr/yNONrb482DHzNz0mnzo4cSqaCP3JxmEHZafc0cEwx9okeEaioEdTyi2QlaftaHXc7uq21J/WlJzUZoOwyxHPFu/4YQzyJdB2UsZVkBwOng95gZIhnWIMyvP+SYKVykey1t0s/cD7a2XhMEqBkpxqnGwVukUHxhR4RqGhaBqVmTi9oPSbtSE8Mjk2cssMsnivet7/srF589tTIjJ4r1WZBBiXJjOTN4tnPyaBUnmYs5QYnUlQkK0kPZ4Z4nmKRNu8IUICKwnpxBmXQOqWapDuC85KkR/vbE+cwxINbTm3pllNbR/0ylkKzXlNQs4kMyn66UWB2iCf6vjMSoHjKoJRohXUd32yMZFAu7LGTsW/UoAAVRbN48gOU7kY01fhM7yFJ0me6kwHKxXiIZ4vFnQCZmVphMJFBSXcyzgQeG2H0X1hud2knogAAGMdJREFUkWzVGpQpbji+MVKDcmE/HuIhg+INAQpQUdkQT7LuyQ2d/5UkfarTmjhnp93TZiNIV6UFrnRbYX1iFk8ShGyGw/skb6G2dpJB8THEU+KG4xt6+Om2XLyZ4FOXkgwKAYov9IhARWXTjNvNayRJJ7qPSpIeak8WQe6ykzEwotUMJtZBycugbKY1KJl1UOLvK6+DMsXp45s66A305KUoc3KBnYy9I0ABKmrUrbAGZT8crhzbU6Bze2H6iSux0+4RoAAZeRmUZKZOdppxI4jqVXKnGS84g3Lj2Eye4UaBZFB8IUABKgpLMih7jWGAslc/roO+06WxsfWddk/bzOABUmU1KM2x3Z436rWxAGXxRbJSZiZPGqAkNSjcy74QoAAVJbN4xjMjkrRnLXVclB3Zj4OVpy6N7t+x0+7qKjIoQGqrWZ+YxTMc4hn9b2szDPL34llwkWyyqN4jT0dTjS/sdXWsWaeWzCNaEqioEdTknNQfTAYo7d5Aj8eLs3XjgtknMwFKfxBlVBjiAYZaYaBOb6BeZuh0WCQ7mhlp1oP8vXgah5NB+Uwmg8IibX4RoAAVhfEntbw6lE53oCdctCtqvxUVzD6V2QF1N/6UeIx9eIBUsprsXk5mZLz4dTMMchdqO4xpxtJww0A2CvSPAAWoKOkI8+pQOr1+GqCoFa2Jkg1QWKQNmNRqTu5o3C7IoGw0xmpQfG0WOO01pou1RUM8T+0dUCDrGQEKUFEy5pyXQWl3B3oiHuKpH4sClCfj9RKkzE7GBChAKt2PJzOTJ61BGQs8NhtB/jooCy6SlYZroXR6fe0d9Jli7BkBClBRGBRnUNrdvh53UYDSOHadpNEi2Z10o0A6NiBRlkEZDzw2GmM1KF2Pe/FMcToOUFikbTEIUICKwpIhnihAiYZ4Nk+cliQ9yRAPUCo/gzK5DoqUFMlOTjMOD2E2zQ3xYm3/8/iuJOn4Jh80fJrpHTSzd5jZg2bmzOz50x6Pj91mZn9nZveZ2UfN7DmzHANWTRKgdPv5s3g+NPhcXbr2BWrd+kUyG82g7KY7GROgAIlWuqPxMEDJ2yxQigKW3sCl+2F1egPVa6b6oQQoUaHsfz68I4lVZH2b9R38XUkvkfTQjI9L0rsk/bJz7tmSfkbSPTMeA1ZKY8oQz33uJp173R8quOq0rt5sjEwzvsgQDzAh2TjzUs4Qz/g6KMm/k+Ptbn/hU4wTyVTj//jMRUnSiS2GeHyaKUBxzn3IOXd+1sfN7DpJL5T0nvih35N0k5mdLTs2zy8AHLV0Fk+/P3EsmfKYdJgntsLcWTzb7GQMpMoyKONDPMm/k+Od3mDhU4wTNx6PFmv7z4ejAIVZPH4t6l28SdLDzrmeJLloic1zkm6ecgxYOckQT6cggyINC/ZOtsLcWTzUoABDeRmUok0Ak+A/OX6YAUqSQbn/MYZ4FmHpi2TN7C1mdj752t3dPeqXBIxIhnhya1DGOtUkg5Isiz8skqVjAxJ5GZR2t6+wXlOtZiPnbjTGMijd/sR+PYuS1KAk9z6zePxaVIDyKUk3mFldkszMFGVIzk05NsE593bn3Jnka3t7e0EvGZhP2TTjzlhh38lWqP7ApbUnu2RQgAnDWTzDDMp+tz9RfyINs5PtIxji2WrWRwrc2SjQr4W8i865xyR9TNIb44deL+m8c+6BsmOLeC3AopVOM04XjYrOSYrokpk8O+2ewqB2aEV9wCoYroMymkHJu0+S7GSSrTzMDIoUTTWWpHrNqCXzbNZpxu8ys/OSzkh6v5k9UPZ47G5Jd5vZfZK+X9KbZjwGrJThEE/+XjxhMExLn4w3E0vWQtlp91hFFhiTn0EZTBTISkdbJCsN61CuboWKBgTgy0w9o3Pu7st5PD72SUkvutxjwKop24un3euP7AmSjFEnGZSL7S7DO8CYzcZkDUqn258okJUmh3ja3f6hBig3Xh0FKBTI+rf0RbLAsktn8RTsxZNNS5+Mh3iezAzxEKAAo2o102YjmFgHZSMvg9II0uNSlEE5zCHT01dFQzwUyPpHgAJUlA7xFEwzzu4JktagxEM8u52ejjX55AWM22oGE+ug5BXJNjMBSq8/UG/gDjWDcsPVyRAP97FvBChARWmRbG4GZTQtfbKVZFC6cs5FAQoZFGBCK6yPZVDyMyPJ/bV/0E/vwcPYyTiRTDUmg+IfAQpQUfluxoPRGpTMLJ69g776A0eRLJCjFQ4zKM457Xf76XBOVlIk2+4N0pk8zUPYyThx88mWpGGxLPyhZwQqCutR5X7uLJ7eYCSDctVGXUHN9NTeQbqK7FUs0gZM2GrW9URcqzXcMqJ8HZRkJ+O8YtpFeeY1W3rvN3+BnvuM44f2M68UBChARWEQdYZFC7Vl09JmphOtMA5QklVkuQ2Bca0w0P5BsjpsdG/lTjPOrCTbOYIMiiS9+OypQ/15VwqGeICKSvfi6fUnPvWdaEU7Gu90WEUWKLIV1nXpoJcO70j5tSXZvXiSe/Awi2SxOLyLQEWNIH+Ipz9w6vbdxKqW0X483cxGgQzxAONazUDORXVc7bEtI7LSvXgO+qXnYfUQoAAVFS11n3aWY5/6TrZCXdg70NP70RAPy2MDk4aryfbSLSPyimTTGpRenwzKmuFdBCoqmmacdpbjQzxboQZOOv/UniSGeIA8w/14+mktSl6RbBjUZBZlUDq94qEgrB4CFKCiRi1/L57CDEq8H8+5J5IAhSEeYNxIBqWkSNYsWnW23RsMi2TJoKwF3kWgolrN1AiseIhnokg2Wgvl3JNRgHIVGRRgQisc7sdTFOwnNhqB2t1+OhREDcp6IEABPAiD2sQsnuRT33hnmezH8xAZFKDQVlybdakzLH4tmj68GQcoZFDWC+8i4EGjXpsc4ukVZFDiAOXhp/cliZVkgRwjGZSSIlkpClyihdqOZh0ULAbvIuBBGNRKhngmZ/FI0sBJNZO2csbVgStdWoPS6Wv/ID8bmdioB9FCbRTJrhUCFMCDsF4rnsUzlm5OhnikaIqxmS3+BQIrJp3Fk61BKQhQNsMgXi+leEl8rB5yy4AHYb2mbs+NPNYp6FRPZAIU6k+AfMNZPNGmmlLxEM9Go6Y204zXDgEK4EFZkex4Z7kVBtGQUH/AGihAga10HZRe+lhRZiSaZsxCbeuGdxHwIKyX1aCM3mZmphPxWigEKEC+ViaD0u6V16A0G4G6fZcGMxTJrgfeRcCDJCOSVTZunqyFwhAPkC8Z4tk76GVWki2oQYkfT7aPKFovBauFj2+AB42cWTxl6eakUJYMCpAvWTX2UqevZj2qQSka4kkevxAHKGRQ1gO9I+BB/hBPcVr6BAEKUCqs1xQGNe0d9DRw0T1UNs1Yki7sRQFKGBCgrAN6R8CDsHShtslONVkLZbvJEA9QpNUMdKnTl2Sq10yNgsAjybZc3O+qXjPVCVDWAgEK4EEY1NQbOA0GTrVatK5JUZGsRAYFmEWrEWjvoKd6YIVTjKXhh4AL+1324VkjhJmAB2FcZ5ItlE2HeHIK9k60oswJGwUCxVrNui4d9LV/0FdzhgDl6f0uU4zXCO8k4EEy5p1dC6VooTZJOrXdlCQdb4UTxwBEtsJAe51oL56y1WGTY/2BI0BZI7yTgAeNejSsk61DKZvF84o7rtdbX/NZeuUd1x/OCwRWUCuMMijt7qB0iCd7jCGe9UGAAngQBlGnmJ3J0+72FQa1tCYla6MR6FvvupXOFCix1QzSdVDK7pXssZAMytrgnQQ8SGtQsgFKr896DEAFrbCubt9pp92dUiQ7vM/KalWwWug9AQ/CYHKIp90dkCEBKkj247nY7pUG+9n7jBqU9cE7CXiQZFA6Y0M8bPsOzC/Zj0cqry3ZoAZlLdF7Ah7kTTPu9AZs+w5UsBUO759Zi2TJoKwP3knAg2SFyy4ZFMCbVjObQWGI50rDOwl4ULRQG7uqAvPLZlDKh3gyRbLcc2uDAAXwIFmo7WBsoTbGw4H5ZWtQZl8Hhf/W1gXvJOBBkkEZmcUzZfVLAOWSWTxS+fTh0SEePhSsC3pPwIPxpe77A6du37EmA1DBrBmUbN0Jaw+tD95JwIPxhdo6vWgfHgr2gPllMyhl2UgzS49zz62Pmd5JM3uHmT1oZs7Mnp95/DYz+zszu8/MPmpmz6l6DFhF6SyevpOU2cmYDAowt1nXQZGGGRbuufUxa6j5u5JeIumhscffJemXnXPPlvQzku7xcAxYOcMMSpQ5aSc7GTMeDsxta8YhHmkYmJBBWR8zvZPOuQ85585nHzOz6yS9UNJ74od+T9JNZnZ23mPVfhXg6IxPM04DFMbDgbm1ZhziiY4nAQofCtZFld7zJkkPO+d6kuScc5LOSbq5wjFgJYUM8QDebV3GEM9GOsTDh4J1sfTvpJm9xczOJ1+7u7tH/ZKACeN78VAkC1S30ajJLPl+WoCSFMnyoWBdVOk9PyXpBjOrS5KZmaIsyLkKxyY4597unDuTfG1vb1d4ycBijC/URgYFqM7M0izKrEWyfChYH3O/k865xyR9TNIb44deL+m8c+6BeY/N+1qAoza+UFu7Rw0K4EMrXu5+5iJZ7rm1UZ9+imRm75L05ZJOS3q/me04585KulvSPWb2VkkXJb0p87R5jwErpzGWQemkRbJkUIAqtpp1aaczNdhnmvH6mSlAcc7dXfD4JyW9yOcxYBWNL9SWDPEwHg5UkwQe0zIoTRZqWzu8k4AHzfEhHqYZA14kq8lO2zaCacbrh94T8CAZ4un0k1k8ZFAAH1ppkWz5f1ctphmvnZmGeACUC2qmoGaZIR4yKIAPt58+pv99/FI6U67I1955k67abOjmk61DemVYNAIUwJMwqGWGeJhmDPjwfa/+LH3Pq26XJQuiFLjt+mO67fpjh/SqcBgIUABPGkEmg9JjFg/gQ1AzBSoPTrCeyD8DnoT1gCEeAPCE3hPwpFkfDvFQJAsA1RCgAJ40AksDEzIoAFANvSfgSViv6SDJoCRFsmRQAGAuBCiAJ2G9NlKDEgY11WoU9wHAPAhQAE8a2WnGvT6blgFABfSggCdhUBvZi4cpxgAwPwIUwJPsEE+n12fTMgCogB4U8CRaSdZJIoMCAFURoACeJLN4nHNqd/tMMQaACuhBAU/CeEjnoD+IMihMMQaAuRGgAJ404t1Wu32nTrfPEA8AVECAAniSZlB6A3V6A4pkAaACelDAkzDOoLS7fR30KZIFgCoIUABPkgzKTrsnSSzUBgAV0IMCniQZlJ12V5LIoABABQQogCdJBuViEqAwiwcA5kaAAniSBij70RAP66AAwPzoQQFPGmNDPE0yKAAwNwIUwJPhEA8ZFACoih4U8KQZjNWgUCQLAHMjQAE8adRN0nCaMRkUAJgfPSjgSRhEGZOL+2RQAKAqAhTAk4mF2ljqHgDmRg8KeNIIkiGeeBYPGRQAmBsBCuDJxCwephkDwNwIUABPmulCbUkNCrcXAMyLHhTwZLhQWzKLhwwKAMyLAAXwJBni2e/2JRGgAEAVBCiAJ8luxglm8QDA/OhBAU8aYwEKGRQAmB8BCuDJeMaEIlkAmB89KOBJOB6gMM0YAOZGgAJ4kh3iCYOaajU7wlcDAKvNS4BiZq82s38ys381s783s+fFj19nZn9mZveb2b+b2V2Z5xQeA1ZRNoNCgSwAVFOvegEzOyHpvZLucs59wsy+OP73cyX9tKS/d8692szulPQ+M7vFOdedcgxYOfWayUxyjmXuAaAqHx/zbpX0hHPuE5LknPtbSTeb2edJeoOkX4of/6ikz0h6afy8smPAyjGzdJiHAlkAqMZHL3q/pGvM7IskycxeK+mYpFskNZxzj2TOfVBR8HJN0bHxi5vZW8zsfPK1u7vr4SUDi9FMAxQyKABQReUAxTn3tKSvlvRTZvbPkl4p6T8kbVe9dnz9tzvnziRf29teLgssRFKHQgYFAKqpXIMiSc65D0r6oCSZWVPSI5I+LKlnZqczmZJnSTrnnHvCzHKP+Xg9wFFJh3iYYgwAlfiaxXND5p8/JOmvnHMPSPodSd8Wn3OnpGdI+pv4vLJjwEpKMihNMigAUImXDIqkH41n79QlfUTSN8WPf5+k3zCz+yUdSHpjZpZO2TFgJaVDPGRQAKASX0M831Lw+KOKalIu6xiwqkKKZAHAC/LQgEcNhngAwAt6UcAjphkDgB8EKIBHaZEsS90DQCX0ooBHjSDaIJAMCgBUQ4ACeMQsHgDwgwAF8CiMAxNWkgWAauhFAY8Y4gEAPwhQAI+a7MUDAF7QiwIeJQu1NalBAYBKCFAAj9LNAsmgAEAl9KKAR8PNAsmgAEAVBCiAR0wzBgA/CFAAjzbjzMlWkwAFAKrwspsxgMjrP/+M6kFNz73x+FG/FABYaQQogEentpv6ppfcctQvAwBWHkM8AABg6RCgAACApUOAAgAAlg4BCgAAWDoEKAAAYOkQoAAAgKVDgAIAAJYOAQoAAFg6BCgAAGDpEKAAAIClQ4ACAACWDgEKAABYOgQoAABg6RCgAACApWPOuaN+DZfFzDqS/q/g8Lak3UN8OauEtlkM2rUYbXN5aK9itM1iHHa7Xuuca8568soFKGXM7Lxz7sxRv45lRNssBu1ajLa5PLRXMdpmMZa9XRniAQAAS4cABQAALJ11C1DeftQvYInRNotBuxajbS4P7VWMtlmMpW7XtapBAQAA62HdMigAAGANEKAAAIDl45zz+iVpQ9LvS7pP0scl/bmks/Gx6yT9maT7Jf27pLsyz/u1zHM+LOnOzLGWpN+U9EB8zleX/PzCcyV9o6R/k9ST9F1Tfg8v11nTtik8V9I9kj4t6d746+d8/42tWLv+pKT/in/GP0l6Vcl1apJ+QdJ/x9f6jsyxL5f0z5I6kn7+Cmybwt9f0tsUrY2U/M29d03/ln5C0X2X/J5ft+h7fU3bhv5rxnbNnPPZkvZU0vdoEf3Xghr7NRrWt3yHpL+Ov/9VSW+Lv79T0nlJjfjfr5VUj7//CkkPZq75w5Luib+/RdJjkq4p+PmF50p6XtzQvz7+h7mo66xp2xSeq+gGn7lNroC/uS+TtJlpt6clbRVc5+sl/aWkQNJJSQ9Jek587Nnx839clx+grEPbFP7+igKUmdtkhdvr6sx5z5B0UdKpOa6zjv3X5bQN/deM7Ro/1pD0t5Leq/IAxX//dQiN/8Kk4RStWHc6c+wfJX1pznNOSepmGv8Tkr4wc/y3JX1zwc+beu4sf4S+rrOObVN27lHc4KvQrvHjNUUd57MKrvPHynzyk/Szkn587Jy3Xc4Nvi5tU/b7V22TFW2v2yXtSLpu3utUvVdXtW3Kfv+qbbKOf3OSfkzSd067z7SA/uswalDeLOkPzOwaRZHfI5ljD0q6ueA5f+Kc68X/vllRNDbteZd7bhlf1ymzqm0zzZvN7F/N7I/M7PkLuP7Un6/lbNc3SfqfsXPnuU4Vq9o203yNmX3czP7KzF425zXyLFV7mdl3mtknJX1M0rc65x6b5zqerGrbTEP/FZ9rZl8g6UWKhm6m8f43V6/y5GnM7K2Szkp6uaTNGZ/zRklvkHTXAl/akVvjtvlBSQ875wZm9lWS/tTMbnPOHcp+D8varmb2ckk/IukVLv4ocdjWuG1+SdJPOOe6ZvZiSe8zszudc/MGO8nrWrr2cs69Q9I7zOx5kt5jZh9wzj2xiJ9VZo3bhv5reN2WpF9UVJPizMzn5WeysAyKmX23pNdJ+jLn3F78h9Izs9OZ054l6VzmOV+rYUf1aOa8c5KeOf48M7vDzO6Nv95Zdu6U1+rlOrNag7Yp5Jz7tHNuEH//PkVp+9unPc+HZW1XM3upooK2r3TOfTJ+7OrMdd43y3WqWIO2KeSce8Q5142//7Ckf1GUJp/bsrZXwjn3cUXFnF9C/zVqhrYpRP818jNuVZQB+aCZPSjpuyR9o5m9+9D6rwWNob1FUcXuibHH79Fowc+nNSz4eYOiSuVn5lzvbZos4ikqgJp6rmarQfFynXVsm7JzJZ3JfP+Fkh6XdHwRf2er0K6KPtWck/SCGX6Hb9Bkkdnn5Pysy6q3WIe2Kfv9x/7mbpP0qKRnr+Hf0h2Z826Nf8/PvtzrjP0+a9F/XU7blP3+ov8qPFfTa1C+QZ77r0U09BlJTtFUo2Sq1j/Ex66X9IG4UT8h6WWZ53UlfSrznHs1rNDekvRb8TXvk/SGkp9feG7cgOclXZJ0If4+t3P0dZ01bZvCcyX9hYbT/T6S/T0W9bXk7Xq/os4y+zM+p+A6gaR3KqrF+G9Jb84ce3nczhcVFQCel/TaK6htCn9/Se9WNAXzXkWdfOGUyhVvrz+Kf+69iqZlf82i7/U1bZvC31/0X4XnanqA4r3/Yql7AACwdFhJFgAALB0CFAAAsHQIUAAAwNIhQAEAAEuHAAUAACwdAhQAALB0CFAAAMDSIUABAABLhwAFAAAsnf8H3bpCpHyxUS8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4t056X0WyVB"
      },
      "source": [
        "ZOOM on Ramadan 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "BizJIJ7wWyVC",
        "outputId": "dcdf3af1-bf32-4135-c76f-65727522e8d6"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[112:142],inv_y[112:142],label='actual')\n",
        "plt.plot(time[112:142],inv_yhat[112:142],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zVwnycuSfE7vh0cqKVzec5s8zSQad+7JDMaRdL+LpkZ30zvJUEPIxoGA14kWSsgp4Ye3J29fV3cvFzGfqNV/J7VzLLSQ6La/alP5Yf1dUFXacldkeyZ3L7jIX5uqa6Go0CmP93IhNz+N8ck5DTq1BKNf0HDx4kKlTpwJga2tbsV1VVUaPHs3JkycrfhISEujcufNtYzV33y4Z9DQQgZ6ObHp2CBN6uLH5TBITP9/PqbjMavdPzipg6YErTF90iD7/t4PXNp7h0KVrDOrkxHtTenD0H6NY/cQAHhvSgRVz+9HG1oIFq0+w10DOo7mFJSzae4l2jlZM613HprhXIyBqK3SdyLgRw3mglwd7IlP5dt+lyvu5B4pbqesxODf0PFUHPSN9XTHVKM0+7S9p3uyNTMXa3IQ+7avX64zxK8t6tMBeXGPHjmXHjh2VBM5Hjx4FYNSoUaxbt46cnBxUVeW7775rrGk2CHJ5qwFxsBKl3CuOxPLOprNM/fYgr47vxqODfFAUhdi0PP4MT2JrRDInYkVAZGGqYVQ3V8b3cGNkV9eKflc34+Nkw/K5fZm+6DDzloeyfG4/+vi0vm0/ffjpYDQZecW8Mq4rFqZ1zfJ8Im6HvADA2/f6czo+i/9ui6SXVyv6dWgjtrv1ELdS12NwyoOe3t5Vvx8crM0Y0LENBy6mkVNQXKXTtkTSlMnKLyYsNoMRvi41nqtu2DQk8/zolmXT0KlTJ1auXMm8efPIy8ujqKiIoKAgVq5cyYQJEzh69CjBwcEtQsis3Gnl1B4eHmp8fHxjT6PeRCRm8czKE1y5dp0BHdqQlV/M2SQhdLYxN2FkN1fG+7sxrItzjWaAN3MiNoNZi49goiiseqI//mVGdPqSXVDMkA92Y29lyq4Xh9fNxTT9CnwRDD5D4OHfK+6+mJLLpC9DsLUwZctzQ27oSD4NADMrePpIneYsqZr7vznIhas5nHxjDBpN1Wnr5Ydj+Nev4XwxI4h7AqvWRADCYPLsr9BlHJg333YrkjuLzaeTeHrlcd6d7M/s/t417vvK+lOsDY1nz0vD8dGzZVBpaSlRUVF06dIFE5PmYzxraGp6nRRFSVBVtY5LB4ZBLm81En5tHfjj2cHc27Mthy6nkZCZz9ReHvzwcG/C/jWaL2YEMaGHu84BD0CQVysWP9SbwlItDy85yqXU3DrN7ceQaLLyi3l2ZOe627Yf/BxUbUWWp5xOLrb8574epOQUsnD1yRttPtwD4FoUFEnPGENRUFzKmfgsgr1bVRvwgChdBx0qW44vg/WPQugSQ05TIqkXeyr0PM617IlstiuRQU9jYmthyqfTe3Lw1ZGE/nMU/30gkLu6udZdNAwM7OTElzOCyMwv5sHFR0jIzNfr+Ky8YhaHXMa7jTVTgurYQyznKpz4WXRRbz/sts2Tg9oxo68XIRev8eAPR9hyJokS1wARJF2NqNtjSm4jPCGLolItvbxq9iVxtbekp6cjeyJTKSwprXqn0hLRRgQg9rCBZyqR1A1Rqp5KZxdbPFrdXqp+KwM7OmFjbsJWGfS0WGTQ08goikJbRyuDNsIb4+fG/x4IJCm7gNmLj5CaU6jzsT+EXCanoITn7upcqbeYXhz+CkoLRZanmkqAf9/TnXsC23LkSjpP/Xyc5/eJjE9K1NG6PabkNkLLRcw+tZuxjfVzI7ewhIOX0qre4eyvN4zY4o7CHbYsLmmeRCRmk5pTqFOWB4RNw4iuLpyIzSQ5q8DIs5M0RWTQ00yZHNSOtyf5ceXadR5acpSsvNpL5DOuF7HkQDQdnG2YVJO2oybyM+HYEnDyBd+J1e5maWbCFzOCOPTqSP4+risJlkJYuHP3du77+gCrj8aSW1hStzlIACFiNtEoouFrLYwtq2z5q6orYFWFkE/BzBr8psD1FMiMMfR0JRK9Ka9WHVFNqXpVlC9xbT+rX7anvJT7TtPBNjTlr09TLX2XQU8z5sEBPrw81pdzSdnMWXqU67UEEd/vv0xuYT2zPMe+h6IcGLwQNLWP4WJvyZPDO7Lh5ckUWTkz2DaRc0nZvLrxDH3/bwevrD9FWEy6PNHoiaqqHI/JoLu7PdbmtevCOjjb0tnFlu1nr97QWZVzYTtcPQO9HhEiZoC4Y4aftESiJ3siU7AxN6G3HtWqw31dMDfV6L3EpdFoMDMzIy0tjZKSEkpLS+XPLT8lJSWkpaVhZmaGRofzf2MgS9abOU8N70h2QTGL9l5m3vIwfnikd5VlnWm5hSw9GE1nF1vuDqhjlqcoDw5/Aw6e0OMBvQ5VFAVzjyA8L+/h6KvD+CM8lbXH4lgbGs/a0Hg6udgyrbcHU4I9pHOwDly5dp2060U1V2Pdwlg/N77cfZHjsRmVLQ9CPgaNGQx4RixbAsQfhQD9/scSiSHJyismLCaDUd1cMTfV/QvW1sKUIZ2c2BOVSsb1Ilrd6hBfA15eXsTGxpKenl6XKbcIzMzM8PKqm4N/QyCDnmaOoii8Oq4r2fklrDoay4JVJ/hqZvBtmZzv9l0mr6iUhaO6YFJDpU+NnFgOeWkw/u9gUge/F7cAuPAX9jmXmNWvB7P6eXM+OZs1x+L45UQC/9lyng+3RjKqmyvT+3gytItz3efazKnw59FBz1NOedCzLTz5RtATcwhiD0HQbHBoJ5a6bJyFrkciaUT2X0xFq1bvwlwTY/3d2Hk+hZ3nU5jaS/cKanNzczp16oRWq5XZ5ypQFKXJZnjKkUFPC0BRFN6d7E9uYQl/nErk7xvO8NHUgIoy5tScQn46FE1XNzvGl613601pMRz8AqydIKiKxqK6cLMzc5lhYVc3e/59jx+vju/KXxFXWRsax7azyWyNSMajlRUfTg1gYEenGgZtmdTmxFwV/u3saetgybazyfxjYjexJl/WRoRBC8VOigIefYXTdtF16dcjaTR2ny/rqq6jiPlmRnVzRaMId2Z9gp5ymvoXu6R65H+uhWCiUfh4WiAju7qw4Xg8b286W3Gl8u3eSxQUa1k4qkuNfi41cmYdZMVB//mic3pdKO/BlXS7M7OFqQn3BLZl+dx+7H9lBAvu6kzG9SJmLT7Ce1vOUVTWeFUiCI3JoJ2jFe4OVjofoygKY/zciEvP51xSjuiFduEv6D4JnG7q0ePZB9RSSDxhhJlLJLWj1arsjUrF19WOto66v8fLaW1jTr/2bdh3IbVWraOkeSGDnhaEmYmGr2cF0699a5YejOaTHRe4ml3AisMx+LW1r6jg0RutVlT3mNtBn8frPkFHb7B0qLUHl0cra14Y3YXNC4YQ6OHIon2Xue/rA1xMqZsZY3MjM6+Iiym5emV5yinvT7QtIln8TwEGVzaYxKOvuJVLXJJG4mxSNtdydS9Vr4px/m4UlWgN1q9Qcmcgg54WhqWZCYsf7k1AO3tidv9IxmeD+Z/yKe93PoeSn1G3QSO3wLVI6PMoWNVeHl0tiiJ0PclnRCBVCz5ONqybP4AFIztxLimbu7/Yz4rDMS1+rf14rP56nnLK+xOdPn0CIjZCx5HQtmflndoGgcYU4mUFl6Rx2H1euDAPq0fQU20D0tJi+OVJ+G44FMoLqeaGDHpaIHZpp9lg8RafmX+NV0ksE0yO0OPIy/BRR1gyXlzhp0bqZkCnqkL3YWIB/Z+u/+TcA6H4OqRfqn1fRPbqhTG+rJk3ACdbC/75aziPLwslLVd3Q8bmRmi0CHqCa3FirgpTEw13dXNlVMYa4ZB9a5YHxPKlq780KZQ0GnuiUrG1MK22ka4uuDtYEejpyK7zKTecyEsKYe1DcGqlWL7d96GBZixpKsigpyWRkyyuYL4fiVnScfJ6zuU/vuuJnBkKk74E3wmQdBJ2/Bu+6gufB8HW1+DyXnH1UxVX9kFCGATNArs6Lo/dzM1iZj3o49OaLc8N4b6gduw4l8LYT/dX9ORpaYTGZGBjbkJXN7s6HX9PBw1TTfaSYt8DfAZXvZNnX8i7dsOlWSJpIDLzijgRm8GgTm30KlWvinE3O5EX58PqmSJzHTAd3HvCoa8g5byBZi5pCsigpyVQXAD7/wdf9BJXMB1GwJMHsJ78Me/OHEq3Lp0g+EH428/wyhWYtR56z4XSIjj8NSybBB92hHVz4PRayLvJoyLkY1A0MHCBYeZax6AHwN7SjE+m9+Szv/WksLiUR348xlt/RFBQXE0/qbpyeQ982RfObTLsuAaguFTLqbhMgrxa1dlgclDqGiyUEn7U3FdtG5Ebuh65xCVpWPZduIZW1c+FuTrKdYy7Tl+BldPg4g5RfTr5G5j4MWhLYctLMqPZjJAl680ZVYXzm2DbP0TbgNYdYOxi4apb3ZeZmSV0Hi1+1P8JfU3UVoj8U2g8IjaCYgJe/cGjtwgAejwArdsbZs5tOol2B8m3V3Dpyr092xHs1YoX1p7kxwPRHLyYxmczetLVzd4wczz0ldAwrZkFg56DkW+ASdP4KEUkZlNYoiW4DiJmAPIzMD3+I4nm3iy62oVHcwpxtqvCDNKzj7iNPwqB0+s+YYlET8ozuPXR85TTwdmWni4apkQsAM5Dn8dg/EfCTd6jF/R6GMKWwpn10oyzmSAzPY1Ncji85wmLhsL2N+DSLuFsbIhxf7oH1swWmZnRb8NTh8F3fPUBz60oiigjH/YKPLEbXoyCSV+IoCnxxI2u24Ofr/98y9GYCL1I0ql6XV15trZm9RMDeGlMFy6m5jLpywMsCbmC9tYWC/qSly7+R579wKOPeA2W3Ss6yzcBQqNFFq53XYOeo4uhKJdE//loVQ07zlXzvBy9wcZFVnBJGhStVmVvZCpd3ez0smOolvxMvtG+SxDnSer2KEz4b+X2OXf9G6xaw1//gIKs+j+epNGRQU9jc2YdFGZDerT4Al1+H3zgDUvvhn0fQXwolOrhI3E9DTY9D4uGQHSISNUuOC4yEqb1bN9g5wrBD8GMlfDKZZi5DmauBVe/+o17K+4BkJ8BWfH1GsZEo/DMyM5seHIg7g6WvL3pLI8sPUZKdj26K5/7HbQl0GsOPLIF+s6DmJCy1/tAveZrCI7HZqBRIMirDlV0RXlw5Btw8KLziEcw1SiidL0qFEXoeq5GyAoXSYMRnphF2vWiOrkw30ZeOiybhHtuOF+VTOI7q7m3XxBat4bRb0HuVdj9n/o/pqTRkUFPY3PhL3HF/MplmLcPRr8DPkOEOHjXu7D4LviwPayaCUcWVV9VVVos+l59EQShS0Qm4ok9cO+XYGuAE8StmFlBlzHQZazhx66Hrqcqeno6smXBEKb19mBfVCrjPtvP9rN1zMyEbxCVal0ngKk5TPgQ7v9BfPH/dA8c+LzR1v9VVSU0OgNfN3vsLOvQBuT4MtFGZNACHOysGdCxDQcvppFTUI2I3bNvmUnh8fpNXCKpipxkSDlX6a49kXV3Ya5Ebqq4sEw6hTr8NVbbPcK2iKtV2130nC00bEe/q9I4VXJnIYOexiQjBlLOQucxQhPiHgiDFsCDG+Hv0fDIZhj6Cjh3FbqaP18RVVUfd4ON8+DkKshOhAs74JuBsPVVYRA4dQnM+fN2f5U7BbcyZ+Z66HpuxcbClA+nBvLNrGBKtSqPLwvl2VUniLqao/sgOclwZb/QO1k63Li/x1Sx/NemE2z/l1hSbIRUeHxGPik5hfTyrkOWp6RItBGxcRZ9toAxfm4UlWrZHVmNeZs0KZQYk7UPw9f9xfLxlX2gquyOTMHOwrROxpsVZCfB0gmQEgF3/Rtl+KuM9XMnMauAMwlVfG41Gpj4P/H75hd18hCTNF1k0NOYXPhL3FaVLTG1EOXCI/8Bj20XQdCM1dBvPlg6wunV8Ot8EQD9fD9kxsHw1+GZY+B/v+66naaISzdhfmegTM/NjO/hztaFQxjh68wfpxIZ88k+5i8PI7yqk92tnP0NUMXreyvOvvD4LrHt/CZhbJYcbujp10hoTLmepw7eJWfWQXY89H9SZPGAMd1vcmeuirY9pUmhxDgUZAuRvI2zuND46R5KvhtFm4SdDO7UBrM6ViaSFS8CnmtRMPY9GCJ8qMaV9Rys9r3uHgB9nxBzOvlz3R5b0iRoGiUnLZWobaAxg44jat/X0l6IkH3Hi79zksXVz+U9IkAa8iI46N84r0liaqUmx78AACAASURBVCECHyOlkt0drPhxTl9OxmXy5a4LbI0QDUxHdnXh6RGdqr+KDN8gKsuqW9KzsBVLXZ79RMXc4lFw98fQc6ZRnset1KXJKCCuXA98Chb2onqlDFd7S4K8HNlzPoWC4lIszUwqH2dmJbJy8cfEkt6dHGhLmhZxR4Q55pCXxOft4Ocox5ez2CyUzJTf4cyr0H2yflWTGdFiCTozVmRubnqvB3u1wtnOgq3hybw8tmvVx494HSJ+ET5mXScKvY/kjkNmehqLousiaPEZBBZ1MJGzc4OAaTD5a7j7k+YT8JTjFgg5iWLt3Uj09HRk8cN92LJgCBMD3NkdmcL93xxk1uLDHLqUVnl9PzNOnIh9x9fcWVxRoN88sbxo3Rp+fRL+eE54JRmZ0OgMXOws8GilZ1XL+U3iyrfP3MrLdsBYPzeuF5Vy6FJa1cd69hU6oPTLdZy1RFIF0SHi1meQsMO4+xPe7rCa70omYl+QABvmwpe9RTl5iQ7u62mX4McJ4nM86ctKAQ+ARqMwursrl1KvczGlmiVvSwcY8654v+98u37PT9JoyKCnsbi8F0oLRfm35HbKxczJhl/iupXube35amYw258fxpSgdhy+nM6M7w/zwLeH2BOZIoKfiF/EzlUtbVWFZx8hTO8wXJyYl4wRV5pGIrugmMirOfT2aYWiT8alvI2IqSX0f+q2zWP9akn7e5T59eig64lJu87ZxGzd5yZpuUSHiGV8F1EZqtWq/HFF5RfnJ9E8HwHDXhUVnn88B58FCu+soutVj5UaKQKenCSY8p0wYq2CcRXv9RqKHHo8IApNwpaKYhPJHYcMehqLC9vErTGqn5oD7mVi5gaslujkYsvH03uy68VhzOjryan4TB758RiTvjxAVuhqVAt76DRK9wFtnGD2RiFGTzoFi4aJJU0jcDI2E1WFXvrqea7sFZ5LQbOrrPJr72RDZxdbtp+9SmlVHkeeZWLm+OqDnrj0PF5ed4oR/93DvV+FcDo+U785Su44VFXl1xMJ3P/NwdsbetZGYa54T3oPrPDMOZ2QRfr1IlG1Zd0aRrwGz4eLaldVC9teh0/8Ye9HIhgqJzlcBDx510SBR8C0ah+2f4c22Fua1jxfRSnz8jGBTS8Ix2bJHYUMekC4DX89wKhX4pVQVfHl16azcEmW3I6rP6AYRcxcG95tbHhvSgB7Xx7BIwN9KLwahUNGBNu1ffg9Iq3qL//q0JgIMfrMdeLvldNg5zsGP1mG1lXPs/9j4bBdQxuRsX5upF0vqtAMVcLBE2zdqmxHcTW7gH/+eoaR/9vDurB4enu3RkHh2VUnqi+DNxYXdohGkvv+K7KshXpU7Un0IjEzn0eXHmPhmpOExWQwf0UY7/15jpJSHaue4o4IKwTvQRV3lbswV2o9YWEnql2fOy1aRljYwu534ZMesP3foqXET3cLH7Rpy8Hvvhof1txUNNs9k5BFfEYNBrEuXUVWNOkkhP2o23OSNBlk0APipJ9yVlSwNATJp0WqVWZ5qsfCVpSAG7BsXV/aOlrx5iQ/Ng4TV37rCvuyYNUJRn28l3WhcRTrehIH4Wk0b59oYrj/v7BqhkF1PmEx6ViaafBrq0erjYQwkenpMRVaeVe7W41LXOUmhSkRFYHEtdxC3t10lqEf7mbF4Vj82zmw8rF+rJ0/gH9M7EZMWh6v/xJetSeKMVBV2PaaqL7b9Y7oJfe+F3wzSCyPnFgBqVGyFLmeaLUqyw5FM/rjveyOTGVKUDu2LBhCT09HFu29zOwfjpCao4P+JqbM5NPn5qAnFTtLU4KrMt00sxR6tGePw33fgUM7Icxfcb8w3PzbKuGrpQPl7/W/alriAhj2d7BvJ7Q9RtQdSgyPDHpAVE9ZO4lmmg1xIi5f4pB6nppxDxQC2Ua2f7eN+h2s2/DhS8+yYGQnruUW8vL60wz/aA8rDsfo3tC0lTc8ug0CZ4jlzbUP6ibCrIWSUi0nYzMJ9HDUr5R3/8fitpY2Iv7t7GnnaMW2iOSqAxXPvqBqyb18lI+2nWfoh7tZHHKFjs62LHmkNxufHMjATk4APDTAmzHdXfnjVCJrjsXpPtf6EL1fCLX7PiFe/zHvQte7hSA1bCn89jR81Qc+9IHlU2DP+yJLkF9FZktSJRdTcpn+3SHe+C0CR2tzls7pw8fTe9K9rT1r5w3g4QHeHL6czsTP91e0SqmW6AOikrDMrystt5BT8ZkM7exccxNdEzPRB+7JQzB9hfgfz14PnXVfkh7WxRlLMw1bq9OwlWNhC2P/I85N29/QeXxJ4yNL1kF8WPzvh6OLRMqybZBxHy9qG1g4iKadkupxD4Dw9WJd/qarvgbl6llIPQe95tDK3oYXxvjy2NAOLDsYzQ8hV/jnr+F8sesCTwztyMy+XliZm9Q8npkl3Pu1yC6eXCGMDKevqFeLkPPJOVwvKqW3jx5LW6mRomrLd4KwB6gBRRGVLUsPRnM2KRu/tpUrvPJce2EN/LhmDV8V3EtnF1teGN2FsX5uaDTKbWN9ODWAiM9DePOPCIK9W9HFtQ7Vi/pwbLG47Tcf2nSs/LnLShB6pPhQUXofHQKXdt7Y7tRFmDB69BbLI1Z1MH5sxhSXavlu32U+23GBYq2WRwb68PJYX2wsbny1mJtqeOtef4K8WvHaxjP87bvDvD6hG3MG+dwuui/KExnIjiPE0jCw/8I1VFWPBqMaDXS7R/zoiZW5CcO7uLDtbDLXcgtxsq3hc9n9Xuh4F5xaKdrzeA/Q+/EkDY9Ol4WKonyuKEq0oiiqoig9b7r/L0VRTiuKclJRlP2KogTdtK2zoigHFUWJUhTlmKIofrpsazQCyjpFn15r3MfJTREf6k4jRbAlqR4Dt6OoExEbxe1NVVv2lmY8M7IzIX8fyT8ndkOrwjubzjL4g118vedi7XoVjUY0bu05WxhUrpldr4xPnfx5Qj4Vtzo2ix1bRWVLQXEp3+27xIgV6RSpJvQ2ucgn0wPZunAo43u43xbwlONobc7nM3pSXKryzMrj5BcZUQyanQTnN0PHkSLguRWHdiKYGft/MPcveC0eHtsF4z4A/6ni/3JyBWxaKDxeqqsQaoGcjs9k0pcH+GhbJF5trFk/fyBvTvKrFPDczOSgdvz69CC8Wlvz9qazPLPqBLmFt/QVjD8G2uIq9TzDu9S/q7oujPV3RVVhR22tahQFJnwEJubCqVmfHomSRkPXXPh6YDAQc8v901RVDVBVtSfwMbD0pm2LgO9UVe0CfKDHtsahXTC07ghn1hv3zXthO6DKpS1dMEI7Cr1QVWFIaOsmKkluwcbClMeGdGD/KyN4514/LEw1fLg1kkHv7+KT7VFk5hVVP3ZF4DOrLPCp+1JXuYg52EvHoCczDs6sBe/BN6qvaqGPTytaWZvxV0QyhSWlLDsUzdAPd/OfLefRmFuR3cqP/uaXua9nO0yqCXZuppd3a14Y3YWoq7m8vSlCt3nXhePLRIPY3nN129/UHDx6Qf/5MPUHWHgaXowSQu/k0/DLvBav/ckvKuW9LeeY/NUBLlzNYcHITmxeMFinoNvXzY7fnhnEOD83Np9OYvJXByr74lToeYYAUKpV2RuVil9be1zsLY3xdG5jZFdXTDUKv55MIC23ls9km44waKHQtB1d1CDzk9QPnYIeVVX3qap6W8trVVVvrj11AFQARVFcgN7AirJtGwBPRVE61bStbk/BQCiKyPZcT4Ere4z3OBe2AYp+pc8tFevW4ODVeJmepJNCU+R3X0WqvSoszUx4cIAPe14ewYf3B9DKxpzPdl5g0Pu7eP/P81yr7sRZHvgEzizT+DxUp8DneEwGnV1scbQ21+2Ag1+IQGCIblkeAFMTUdlyPjmH4R/t4Y3fItCq8OY93dn90nCcug5Gyc+AtIs6j/nksI4M7uTEqqNx/HEqUefjdKa0RGh27NvV7yLDzhVGvy2yfef+gN3/Z7Ap3mkcvHSNcZ/tY9G+y/Ro58CmBYN5YYwvFqa1LOvehJ2lGd/MDub1CV25cu069355gE2ny/7/0SFgbluR5T0dn0lGXnH9G4zqgYOVGYM7O3H4cjq93t3BgPd28thPoXy6I4rtZ6+SlJVfWds25AVw9Ibd74nMYn24dhH2fAB//VOWwxuJemt6FEVZBpT3USiXyHsCSaqqlgCoqqoqihILeAFZNWzT/YxpDAIegD3/EUtcxghKSorg4i5h6GbjZPjxmyPuAcJSoDi/oidUgxG+QdzqaEhobqphWh9PpgS3Y9PpJL7afZFv915i6cErzOjrxbyhHXFzuOVqVWMC934pfj+1UgQ+05bprPFJysonITOfv/Xx1O05Xb8msh9uAUKPoAcTe7izPiye/OJSXh3flYcH+NzQMN1sUujUWafxNBqFj6cHMuGz/by+8QyBHo54tbHWa041EvWncPUe8Q/92hVUhaLAvV9B+hVRfefsW6PnS3MjK7+Y97acY/WxOCzNNPxzYjfmDGqvU1avKhRF4YmhHQnwcOSZlSd4ZuUJTl25yuvxoSg+gyv+X7sruqrf7iFlTD68P4DNZ5KISMwmPCGLPZEp7Dh3Y7mrtY05fm3t8W/ngF9be/oMehvXzQ/DX/8QfkD6kBknzE/D11e+wOt+n8g6SgxKvYMeVVUfAlAU5WHEUpVutYE6oijKC8AL5X87ODjUsHc9ad1BiBbP/SEMsixsDTt+7EEoypGl6vrgHigEt1fPNuwJQKuF8F9Epsmjt16HmppomBzUjkmBbfnrbDJf7LrIjwei+flwLFN7e/DksI54tr7py70i8FHh1CrRXXraTzoFPqHReuh5tKWw9wMoyRdXp3r2yhru68zaeQPo5m6HneUterSbTQqDZuk8poudJZ9M78lDS47yzKrjrJ8/EHNTAxWVHlssGqIGP2SY8cysYMYq+G4E/PYMtPLReXnwTmZbRDL/+jWclJxCBnZsw/tTAgwWnPbv0IbNCwbzzMrjnD68E8WikBz3fpRL2/dGpmBvaUqQZ8MKyF3sLZkzqH3F3wXFpURdzakIgsITszl6JZ39F66V7WHGEovejAzfwLLCYdh2u4vx/u7VFzbkpggLhTPrIe6wuM/CXix3t24Pu96FmBAZ9BgBg1Vvqar6k6Io3yqK0gaIA9wVRTFVVbVEERJ9LyAWyK5hW1XjfozQCwHg4eFh3JrygGmw5ShEbjH8lVxUDV3VJVVToes51bAngPijouv4oIV1bqSp0SiM83dnrJ8buyNT+GLXRVYeiWXNsTgeH9KBv4/zvVG9ojERmQRVhdOrywKfZUJjUgPlIubePjU4MauqCBx3vQup54W1f7dJej8fRVHo276ax3HwALu2VZoU1saQzs48OawjX++5xEfbzvOPid31HuM2rl0UzXi7TwY7NzLzikjOLqCrmx4+RlVh5wYzV8OScbB6Jjy+Cxy96j/fJkhYTDrf7LnEjnMi8Pjw/gAe6O2hX5sTHXC1t2Tl4/058MMGSITnDlnzePs0Orvacjohiwk93GsuVW8ALM1MCPBwJMDjRvBVUqrlUup1IhKzCE/IZkPsMwxIeYKBke8z/owj68NcWT63341sWH6muKAO3yD8sVQtmFqB3xSRTe40SlR3FuWJJa7oEBj0XCM94+ZLnYMeRVEcAWtVVRPL/p4MpAHpZUtWx4HZCJHy/UC8qqoXy/atdluj43cfbH1VLHEZPOjZKvQFrv6GHbc501gVXHoubdWEoiiM7OrKCF8XDl1K4/2t5/l27yVKSrX8Y2K3yoHP5K/F76dX37TUVX3gExaTQRsbc3yqu/K+tFsYqCUeFx3ih7wIA5+tUaNUZzz7iqvXgmyw1C+4eH50Fw5fTuP7/VcY0LENI7u61m8uoWKJQe0zl99OJPDWHxFk5hfz36mB3N+rns153QPhvkXCZ2nVDOH9Y+iscCNRqlXZfjaZ7/Zd5niskGxO7OHOv+/pblQhsZmJhuEWUZSaWHK8uD2zfzjCkM5OqOotLsxNCFMTDb5udvi62TElGKA72r2X6LT7HT71CuHpS8P5YutJFnpeEueTizugtAg0ZkJj5n+/uL31vWNuLbLLMYeELq2+S7OSSuj0aiqKsgiYCLgB2xRFyQHuAtYpimIFaIFU4G71hsJrHrBUUZTXEdmdOTcNWdO2xsXGSUTcF7aLFGQV/YjqxLWLkH4Jej9a58xBi8TODWycG7QHF9pSiPhVOEK79TDYsIqiMLCTE6uf6M/DS46yOOQKlmYmvDTW98ZOFYGPCqfXwLqH4YGfqgx8rheWcDYpm5FdXW6/+o47Brvehiv7xEm27zwY+pLh3s9V4dkXzv56w2dFD8xMNHw+I4gJn+3nxbWn+PO5obfrn3SlKA9O/kxx6848sduC3VEnaWNjjpu9JS+vP4W5qYZ7AtvWbexyuk+Ckf8SDs8bH4fpP1f0iTI42Umi5YIRA6u8ohLWh8XzQ8gVYtLyMDfVMKOvJ3MHt6eTi5F9lEDoHeOOYeLVj/XjhzN/RRh7yvQ8wxqoVN0QaAY9C6dXMSF9Ocvtz9HryFE4WgiKBtoPE4FOt7vBqpblaO9BEHtIZLjbySUuQ6JT0KOq6rxqNlW7oK2qaiRQpVtTTduaBAHTRFYmfKMoXTUE5Q1GO8ulLb1QFHFlfWU/lBY3jLdRdIio4us9xygBqrW5KUse6cPsH47y5e6LWJhqePaum8S/GhOY/I34/fQaWPcIPLD0tsDnVHwmpVqV3jfrea5GwK7/g8jN4kTbc5awzK+hzYTB8CjX9RzTO+gB8GhlzUcPBDJveRjPrT7Bysf710koqw3fgKYgkw/zJrM78RpTgtrxz7u7k51fzPTvDrFwzUnMTTUV3kN1ZsiLwuTxzFrY+RaMfqt+491KaQkc/Ew4RNu6wfTl0LZn7cfpQWpOIcsORbP8cAyZecU4WpuxYGQnHhzgg7Nd3Q0z9SbxuNCa+Qymk4stvz09iP/bcg5zE03DzqO+mFrAhP+iLJ/MEPZzQvFlC4N4+NGFeHjq8Rn0GSwE89EHZNBjYGQbiqroMh7M7cQXjqGI2gqmltB+qOHGbCm4BUBpoWgl0BCUL235TTHaQ9hZmrFsTl+6u9vzv+1RfLfvUuUdygOfHtNEALPuEXE1fBNh0eV6nlaitH7D46KfVORmodl56rDIGjVEwAOi0s7EXDSMrCNj/dx4eIA3R66k88WuC3offyk1l8tbPiNPtWCv1aiKdgitbczxcbLh58f608rajGdWHmf3+ZQ6zxMQAfGkL0Tl2oFP4eTK+o13M6lRsGSsWJq0bwe5yeLvU6sNMvzFlBxe3XCaQR/s4otdF3G0MuOdyf4cevUuXhjj2/CBRnSIuC0zJbSxMOU/9/XgzUmN71urNx1HwGM7YWE42TM3s7hwFE9sjNW9XQ2IrKnG7MbrIjEYMuipCnNrkb5OPA7X9D/x3kZBFsQcFOlNcwOW5LYUGlLXU1IE534XuiuXrkZ9KAdrM1Y81o8urrb8Z8t5fjoYXXkHjQnc9y30eEAEMuvnVAp8QmMy8DDJJPDU2/BlH5Fx6DgSHt8tsgLOvjQophaioWr8sXoZ+L02oRvd3e35fOcFDl9O0+mY4lItX+2+yCufLaVTyQXOOo1l4wvjbyt17uRiy4rH+mFjYcq8FWEcuHitmhF1xMwS/rYS7D3g9wVCh1EftKVw8EtYNEQsEw58VgSvc7aCdRthjvjn30XWU09UVeXQpTQeXXqMUR/vY/WxOHq0c+Db2b3Y+eJwHuzvXXsbFWMRHSIuCptLVsOjNzh6MqyLMwvv6sLZpGze+C1c9+PNbcRrEXtI+vUYGBn0VEe5iNkQbSku7RZmcF3G1H+sloh7WQVXQ+h6Lu8RjSb9jZfluZnWNub8/Fh/OjjZ8O/fI1h19JYiRo2JEM32eEBUYJUFPtrcNIbFfsFOs4WYHv8R2gbDI5vhwY3CXbyx8Owrgvy0ul8sWJqZ8OXMICzNTHhu9YlaXXHPxGdVtEN4zHIXAL2nvoxtNe0QurrZs2JuPyxMNcz96RhHr9TSALM2bF1ERZeJOayZBRm3GtfrSPplWDpReL3Yt4VHt4rmqGaWonLxib3CRfvIt7DsXqE51IGSUi2/n0pk0pcHmPH9YXZHpjDe340NTw5kw5MDGefvVme/HYNQWiz8nTz6iOfazHh2ZCdh9xAaz5pjVRYpV43PICjMbjxH+maKDHqqw2cI2LmLJa76dl6PknqeetGqvWjQ2hCZngZY2roVZzsLVj7eH6/W1rz+yxk2Hr/F/FxjApO/Fb2gzm+CH8ejfh7Io/xOppUXzFgj+kb5DG6wOVfLzSaF9aCDsy3vTvbnanYhL607VWV39/J2CPd+FcKFqzm8PNSFcRwQ2qLyQLka/Ns5sOzRvpgoCnN+PMrx2Hp2VHfrAfcvhrx0WPU3UcGmK1otHP1eLE3GHhKi8/khtzcktnWGh36Ffk+Kdg2LhkF8WI1DX7iaw7CP9rBg1QkupOTw0ABvdr84nG9m99KvV5sxSTwJxdcr9dtqTmg0Cp9M60k7Ryv+9VsE4QlZuh1Y/nmWS1wGRQY91aExEUr7zJj6ncC1WtFbydUfHHV0zZVURlHEl0ryGeP2PSrOF80p2/USBmENiJuDJSsf70dbByteWnfqhi1/OSamIuPjPxUSQrlu2ooFRc9wcuIf4Duu6VQE3mxSWE+mBHswJbgduyNT+SHkSqVthy6lMb6sHYJ/Owf+eHYwT7c6ilJSAH1067MV5NWKH+f0RavCw0uO6v5lVB1dJ8CoNyHlLGx4TLdlicxYWH4vbHlJVI4+/AdM+FAsb1SFiRmMfx/u+w7y0+HHccJhuxq+3XuZhMx8Fo7qzKFX7+Lte/3xcapm7MYipuxL3ad5Bj0ArWzM+WZ2MKgwf0VYzX35yvHsJ8w1ZdBjUGTQUxMVndfrIWhOPA5516CzXNqqF+4Bws0640rt+9aVC9vFYzRgludmPFpZ8/Nj/XCyteC51SfZFpFceQcTU5jyHczdwdteP/K7diC9fNo0ylyrxb6t0LfUwaSwKt65158OzjZ8sPU8p+Iyycov5rWNp5nx/WGSswv4x4RubHxyIN1cbeHYD2DVWhgS6kjf9q1Z/HBvCku0zP7hCOeT9cjQVMWg5270Utv+RvX7qSqE/QRfDxS2Ar0egScP6l7oEDhdZPds3eD3Z+GPhbf1bcsvKmVreBKBno4sHNWFVjY69mZraKIPiKXB8ixhMyXAw5E3J/kRn5HP82tOotXWsoJgbiOWrWMOSl2PAZFBT0249QDnbhCx8bbKGZ2J2ipuZVf1+tEQYuaIjeLW7z7jPUYt+DjZsPLxGiqMNCbg2YdjcTm0d7LBybYJlvN69hXOzwX1zJwgqni+nBGMoig89fNxxnyyl1VH4xjYsQ3bFg7l8aEdhFvvlb3CBytott66kEGdnFj0YC+uF5Ywe/ERLqbk1n3CigL3fApeA+DQlyKwuZXsRPj5AfhjgfDfmb0B7vlM/K4P7oHwxB7oMBzCfoSld1dqeLn93FWuF5VyX896ehIZk9ISiD0ssqsN3VuvEZjR15P7gz3YHZnKV7t18OP1GVym6zlj/Mm1EGTQUxOKIgTN+RlwaWfdxojaKq4+9ezfJLmF8nYUxgp6CnMhcit4DQSHdsZ5DB2prcIoNaeQmLS8pqPJuBXPvoAK8aEGGa57W3v+NbEbCZn55BWV8sH9Pfj5sX54t7lpmebYYkAR3kp1YISvC1/NDCYzr5iZ3x8m+tr1uk/Y1AKmrxDtKTa/IDymQGR3Tq2Gr/vDxe3CQ+mpQ/VrbmzTBmZtEBmm+KPw3TARRAC/HI/HRKNwd32NGI1J8mmRXW2mep5bURSFdyf7083dno93RLH/QmrNB0hdj8GRQU9t9HhA3NZliSs7UUTonUcbx/a/JeHURZS0GquSIWqrMEdroKqt2qipwigsRvzeZIOem00KDcTs/t78OKcPO18YxvQ+XpUdqLMSIPJP6HSXaBpcR8b4ufHp33pyLbeQWYuPEJ+RV/cJ2zgJgbmplWhXEXsEVs8SJeemljBjtfBQsjJAI00TUxj9Nkz9EQpzYOlEcvd/w74LqQzt7NQ0s4HlxBwQt81Yz3MrVuYmfDMrGFsLUxasOkFCZn71O0tdj8GRQU9tOHqKMtHIP/VP15dXbckGo/XHxBRc/USmp77VdFURvkE4GOuhBzE2/u0c+KmKCqOKJqNNNehx6yG+2OtZwXUziqIwwtel6v5Px38CtRT6PFbvx7k7oC3/fSCQxKx8Zn5/hOSsgroP5todpi4R540lY4TXkv9U4bvjO77ec70N/ynw2A5w8MR256u8p1nElAAnwz+OIYkOEV/qnv0aeyYNio+TDf97IJCMvGKe+vk4hSXVaHYsbKFtEMRKXY+hkEGPLgRMg5IC0SFXH6K2gWICHe8yzrxaGu6BkJcmMmiGJD9DiJjbDxNlwU2I4CoqjEJjMnCwMqOjcxNtcmlqXmZSGGrcajsQHi9hP4GDp8GKBaYEe/Cf+3oQm57HzO8Pk5JTj8CnyxiY8BG06Syax079Aayr6VRvCFz94IndhJr3ZprpXiaGPgpZ8bUf1xhoS4WZY9vg6qvVmjFj/Nx4cnhHTsVl8n+bz1W/o89gEThf1cPcUFItMujRhe73iuoCfZa4ivOF0Z3XAMOksCXG0/Wc3wzaYoN0VDcGt1YYhSdkEezliKYxDeVqw7MPFGbBtUjjPs75zaJFQ69HDLqEPKOvF29N8uPytevMXnyE9Ot1LGQAkYF6NlScRxqAy7lmTMteyHanh9AknRB+PplxDfLYenE1XLxHWtDS1q28OLoLAzq0YdmhGH49kVD1TlLXY1Bk0KMLVo5iierKfqEf0IXoEKERkUtbhqO8gsvQup7wDaLPTbe7DTuuARnUyYlFs0WFUXGpSm8fI2YLDEG5rseAS1xVcmyxdee3TQAAIABJREFU+N8FP2TwoR8e6MPrE7oSdTWX2YuPEJeeV6VJYlPj15OJaNFgNfbfwtsp75pwcW5qRJfpebybgKlmI2FqouHzGUG42lvw2sYzRCbn3L6TZz+xYiCDHoMggx5dCZgOqBC+Xrf9Zam64XHpLj78hsz0XL8Gl/cKEaxVE9XIlDGiq6gw6uBsw1g/18aeTs0Y0KSwWlIjIXq/6JNn61L7/nXgiaEdeXG06J005MPd9Hp3Bw/+cIQPtp5n0+lEoq9dr91vpQFRVZVfTyTgYmfBgI5txHnLuSscXy4qFJsSMQfE59mrZel5bsXZzoKvZwVTXKrlyRVh5BTc0lfNwk7oemIOGn+5uAVQdXMaye10HgOWDqIX16Dnat5XVYWep5UPOHVukOm1CMwswaWbYXtwnf1NiGCb6NLWrYzxc2OMn1tjT6N27NzAwctgJoVVErpE3BpAwFwTz97VmU4utoRcvEZEYjZHr6Sz/8INGwE7C1O6tbXHv60Dfm3t8W/nQEdnG+Ef1MAcj80gNj2Px4e0v9FPq9882PQ8nF5t9NdKZ7RaEfS4B+rvT9QM6eXdmn9M7MZbf5zllfWn+XpWcOUKRZ/BkBAqlgRrabEiqRkZ9OiKqYUwrQtbCsnh4OZf/b4p5yArDvrNbzrtAZoLbgFwaiVcTxMeJfUlfKOoNDJGNU1Lx7OPWDrMzzB8Fq3oOpxcJcxDvQYYduwqGN/DnfE93AHRwPNS6nUiErMIT8gmIjGLs2XBUDkWphq6utvjXxYE9Wvfmg4NIDz/pUwXMjnoJq+pgOmw4004sgh6z20a56TUc+J9ETS7sWfSZHhkoA9hMRlsOp3E4v1XeHzoTfYLPkPgwKdiiUsGPfVCBj36EDBdBD1n1tYc9FQsbUk9j8FxDxRBT/Ip6DiyfmNlJ4qrze6T5NWmMfDsJ4Ke+DDoXA8Dvqo4s16IYPv8q8G/xE1NNPi62eHrZseUsob2Wq1KbHoeEYnZhCdmEZ6QRURiNqfiMgEwM1HY+OQgeng4GG1eRSVaNp1OwtfVju7u9jc2mNsIzdPBL+DSLrGU29hIPc9tKIrCB/cHcD45h/e3nmeMn+sNA06vMl1PzAEY8FTjTvQOR2p69MGzv0jZn1lf89pq1DYwt20xLqMNSvlVjiGWuCJ+BdQ7ZmnrjqO8l5KhdT2qKgTM5rY3+uM1MhqNgo+TDRMD3Pn7uK4sn9uPsH+O4tBrI/l0ek9KtCqf7Igy6hz2RqWSmVfM5KB2lZdGAPo8Lnyojiwy6hx0Jno/oNzeSb6FY2Nhytv3+lGqVSs32bWwg7Y9RaZH6nrqhQx69EGjgYAHIDvhhpPoreSli5N8h+FiSUxiWNx6iFtDiJnDN4gvTtkM1ji49RCOxIau4EoIExV8AdPA0r72/RsJRVFwd7BiclA7JvZwZ9f5FE6UGUwag/KS53ur6rXVyht8J4hGqGmXjDYHnVBVIcp16yHtPKpgQIc29GjnwNrQuMpWCT6DoSATUiIab3LNABn06EuPaeK2Os+eiztA1cqqLWNhYQetO9a/bD0jWggDu05sEY0OGwUTM1F1khBmWDfZY4vFbe+5hhvTyCwc1RlFgU92XDDK+Fn5xWw/d5X+HVrT1rGa93O/+eL26PdGmYPOpEaKMnqfIY07jyaKoig8PrQDBcVaVhyOubHBW/r1GAIZ9OiLS1chpj37GxRX4dRarueR2QPj4R4IaRdFn6G6EvGLuPVrGr22mi2efUSX6NTzhhkvL12Izz3716yra2J0crHj3sC27ItKJTQ6vfYD9GRreBJFJVruC6qhWa7PYHDxgxMr6vfZqS8xZV/aLdiUsDYm+LvRztGKnw5GU1BcdsHg1V8sUcqgp17IoKcuBEwXJ/LyAKec0hKR6WkbBHZN3EflTqZc15NcB1v23FTxpRn2k7AgqK8YWlIzhjYpPLECSgubTum1Hiy4qzMaBaNoe345kYC5qYZx/u7V76Qoony9KEdUvjUW0QcQeh7jV93dqZiaaJg7uD1p14vYeLzMENfSXrR3iTkgdT31QAY9dcH/fhFxn15b+f64I6JHilzaMi7lzsy66Hry0kXPtC2vwNcD4L+dYP2c/2/v3uPsKut7j39+M7Mn2ZNJZkMgBJhcIEEFuSrI/aKo4K3eQVus4gVsa9VSPT1tPae8emyPHl/lWHpR8HJUUFu1gpZSuagVRMNFCHdDICYhXIIEEsiFZCbznD/W3slOMjPZM7Nn9mStz/v1mlf2rLX3Ws8zszPznWc96/nBs7/Jhvs7Ose3rUU3p4kV1wcGsrV5uvbJ7rjbwxy8bzdvPaaXWx5ew6Jla5p23MfWbmLRsmd49aGz6CmXhn/yEe/Mlg+49Yut+cWZUvZLe7+Xjm8Nshw497g5zJjawZdvXrZ9Acz5p2S3+j/1QGsbtwcz9IzGjP2z4pRLr89+qdZ4q/rEmD1MOYoXnoMlP4Lr/hK+eCr8n4PhX8+D2y7LvldHvBPedCl8dDGc8ecT2+4i6p4FlXnNGelZ9pMsrL7sPXvsTQIfPXMh7W3BJTc81LSSFj9cnBXgfesxvbt/cmdXVqfsmUfgkR835fwjsuYRWL/aO1sbMG1KB+edMI9lT2/gxgdXZxtrdbiGupFGu2XoGa0jz82KVNbmhkAWgrr32/5LWeNj2kyY0ZuN9GzZkF1SvOGv4Euvgs/Og2+fC7/8x2wdnsPeDG/4O/jIHfCnv4a3fxle/l7Y+6DJsUhbEcx5BaxZuuMfCKNx+1eAgJef35RmtcK8mdN4x8t6ue03z/DLR8Y+2pNS4qq7VlHpKnH6i/Zt7EXHfiBb86UV9biczzMi7ztpPp3tbVx+07Jsw7Z5PTe3tmF7MEPPaB36xux23Nolrmd+k03WPOS12a3tGl/7Hwmr74fPzIUr356tVrrmkey23LM/C3/wS/jEUjjn69n8j30OMeS0ypxqbaVVd4zu9WtXZpcnH/pR9v9rr3nNa1sLfORVC+lo0mjPg088z0Or1/PGI/ens6PBnzuVOdnPr4dvhKfH526yIdUm4TrS05BZM6bylmMO4I4Vz/KrFc9m8xD3PyqbF+W8nlHxt/NoTZme3e786KIs8Cy9PtvufJ6JcejvwLR9YMGZ8NpPwwU/g/+2DN71TTjhw7DfYYbPyWK0ixSufgC+fyH8/dHZ5cnZR8Br/rr57Ztgc/bu4pzj5nDHimd3qOE1GlfdtQpg+Lu2BrPt9vXLx3T+EUkp+2W970uy/7tqyAXVchRfqo32zDsZNj2TlfLQiPlbYSyOrK7Zc+/3slWY2zuzRQk1/o5+N3zyYfi978BJf5ytVtrW3upWaTD7HQ6lrsbn9axcBN96F3zhxKxI5vyT4T1XZcF21kvGt60T5I9euZDO9rYxjfZsHUj8YPHjzNm7zMvmjrC22dwTsxC5+FvZzRcT4dnfwPOPO8ozQgtnTefMl8ziugee5DdPb9i+vtFy5/WMhqFnLBa8CrpmwuIrs2us80+BKeNfVFDao7R3wAEvG36RwpSyPxy+ejZ89azsUtahb4IP/gTe++/Z/7UcXZ48sFLmXa+Yw+JH1/JfS347qmP88pE1PPX8Zt569CBlJ3YnIhvt2bIe7vrmqM4/YrVf0vOttzVSHzrtYFKCr/x8mfN6xsjQMxbtpez29WeXw9YtXtqShjLnuOwX7FM7Dclv7c/mxX3hZPjWOdm8n2POgz+6Dc69Enpf3pr2ToA/PGMhnR2jH+0ZtKL6SBz+juyPttsua+6K2UOp3XHkSM+IHX/Q3hzV28N371jFmq3lbIHcFbdkfyxoRAw9Y1Vf8NBVmKXBbVuk8Nbs3y0bs3II/3AMfP9D2R8OJ34EPnY3vPmfYN8XtaypE2V2z1R+9xVzufexddz44FMjeu2mLVv50X1PcNScCgfvO8rR5dLU7E64Z5fD0htGd4yRWH4LzDzEhVtHoVaaYnP/AFcsWpGNlm1c07yVzgvE0DNWB74cZh2Wzajf+6BWt0aanGqTmR/5Cdz0Ofj8EXDtJ7IlB175KfiT++Csv4GeUY5a7KH+8IwFTKmO9mxbgK4BNzy4mg1btvLWwYqLjsRxE3T7+tqVsG6lt6qPwdkvnc2cvct845cr2Nx7UrbRkhQjZugZqwg4/1r4/R+0uiXS5NW9L+x1EPz6GvjJp7OJza/7HHz8Pjj9k4VdnXfWjKm854R5PPjEc1z/wJMNv+6qO1fR3ha88agxhp4ZB2RrWS37KTw1jqMGtfk885zPM1od7W184OSDeGbDFq5+Zi4QzusZBUNPM5T3yj4kDe3Y92cjPm+9HD56Jxx/QbZCcMFdePoCyqV2Pn/j0oZGe55ev5mblj7NaYfswz7dTViZetvt65eN/VhDWe6ihM1wznFz6CmX+MKip0mzj8jCpPN6RsTQI2linPxR+OCNcNS52U0AAmDf6VP4/ZPm8esnn+c/79v9aM81dz/O1oE0+gnMO5vziqyQ5d3/ktV1Gg8rfp6N9M0Y48hUwXV1dvCeE+axfM1GVkw/BjY+Db9d0upm7VEMPZLUYheetoBpne18/saH2Lqb0Z6rFj/OtM52XnvY7OacvHb7et/GrIp9s617LJss7ShPU7y3Wpri209VVyb3EteIGHokqcX2ntbJ+06ez9Kn1nPNPY8P+bxlv13P3Y+u5ezD96fc2cTFOA9/G0zbN1uhudm3r69wPk8z7Tt9Cm972YF8e3UviXAy8wgZeiRpEvjQqQfTPaWDv79xKf1bB6+rdPW2iupNvsutY0o252rtymxhyGZyPk/TffDUg3mObh7tXOB6PSNk6JGkSaDS1cn7TzmIZU9v4Id37zrak1Li6rseY9b0KZy4YGbzG3Ds+6GtAxZ9obnHXXELVOZmH2qKhbO6efWh+3HDxkNgw2/h6Yda3aQ9hqFHkiaJD5xyENOndvD3P951tOfOlc+y8pmNvPnoA2hvG4eSHNNnw0vfms0RWX1/c475/JOw5mEvbY2DC047mEUDh2afOK+nYYYeSZokesolPnTqwaxYs5HvV8tM1Iy57EQjarev39qk29dr83m8tNV0x83fi037H89ACjY/fFOrm7PHaCj0RMSlEbE8IlJEHF3dNjUiro6IhyLi7oi4ISIW1r1mVkT8KCKWRsR9EXFaI/skqcjOP3k+PeUSl/54KX3V0Z4t/QNcc88TvHi/6Ry2/4zxO3nvsdkq8/d8BzY+M/bj1ebzWG+r6SKC3z3jKH6d5tK/7Gbn9TSo0ZGe7wGnACt22n458OKU0lHAD4Av1+37DLAopXQIcD7wrYgoNbBPkgpr+tQSF5x2MKue3cT3frUKgJ899FvWbuzjLceMoqL6SB3/YejfBHd+Y+zHWn4LzDgQ9po/9mNpF2e9dDb3dx7BtL5neOEJ63A1oqHQk1K6KaW0aqdtL6SUrk3bywMvAubXPeUc4IvV594OPA6c3sA+SSq09540n726SvzjTx5mc/9Wrq5e2nrzWGttNeKwt0D3fnD7l2Fr/+iPs3YlPL0kG+UZ76BWUO1twT6HnwnA4p//e4tbs2do5pyej5GN9hARM4FSSql+edHlwNzh9jWxLZK0x+qe0sGFpy/gsbWb+MrPf8MND67mhIP35oBKefxP3tEJx34A1j0KS/6jsdekBE8/nC1u+MM/hn86PisqC3DQqePXVnHCGW8CYMOSm3a7sKWgoxkHiYi/ABYCZzbjeDsd+yLgotrnPT09zT6FJE06v3/iPL500zI+d90SUhqHtXmGc+z5cNPnsgnNh7151/19m+Dxu+DRW2Hlrdm/m+rmAPXMgcPfDvNOgiPPnbh2F1C5si+/7VrIERvu5br7nuD1R1rqYzhjDj0R8QngbcCrU0obAVJKayKiPyJm143ozAdWDrdvsOOnlC4BLql93tvba5SVlHtdnR38wRkL+PR/PEhnRxtnH77/xJ28e1YWWu75F3jinuzzlYvg0duygPPE3TDQlz23rQNmH5mFm7nHQ+8roGcCA5rofskrKd/5Ja756c287ohzxn/e1x5sTKGnOgrzbrLAs3an3d8FPgxcHBHHAQcCP2tgnyQJ+L3j53HlohUcf9BMesoTfK/H8RdkoeerZ2V1uWrKe8HCM7NCpXNOgAOOgc6uiW2bdlA+5HS480tUnrqV25e/hlcctHermzRpNRR6IuIy4A3AbOC6iHgeOAP4O2AZ8NNqstycUjq++rI/A66IiKXAFuC8lFJfA/skSUC5s50f/+kZjMdahLt14Muz0Z4n76sGnONh7gkwc6ETkyebuScBcHzbg1x+0zJDzzAi7WH39vf29qZVq1bt/omSJBXFP5/E2jVPcvSGS7nxojNYOKu71S3aRUQ8llLqbWUbXJFZkqQ93fxTqGxdw0HxJN+8decl9VRj6JEkaU9XLfVxUvuDPPrMxt08ubgMPZIk7emqpT5OKS1h7UanyA7F0CNJ0p5u2j4w6zCO437WbdzS6tZMWoYeSZLyYP4p7JOeYfqmQZe9E4YeSZLyoXqJ69AX7mVPuzN7ohh6JEnKg/mnMEAbF7ZdxQuP3tXq1kxKhh5JkvJg2j5cO/eTzI5nmPr1s+GOr2bFYLWNoUeSpJxYNu+dvGPLxfR1zYJr/gS+/yHYvL7VzZo0DD2SJOVEpavEPWkBd73uB/Di18O934UvvRJWP9Dqpk0Khh5JknKiVpj22YEueNe34LWfhjWPwJdeBYu/1eLWtZ6hR5KknKiFnrUb+7LCsCf9MZx/LZT3gqv/AH7wEejb1OJWto6hR5KknKh0dQKwdlPdqsxzT4AP3wwLzoS7roAvvxqefrhFLWwtQ48kSTlRqR/pqTdtH/i978ErPwVPPQCXnw73fb8FLWwtQ48kSTlR6cpCz7pNg5SiaGuD0z8J77kaSl3wvfPh2k9C/+YJbmXrGHokScqJ6VNLRAwy0lPv4NPhwz+H+afCbZfDV8+CZ5dPWBtbydAjSVJOtLcFM6aWWLdpN5XWp++Xjfic+gl4/C647DT49bUT08gWMvRIkpQjPeXS8CM9Ne0dcOb/yOb6RDv8y7vh+k/B1gZeu4cy9EiSlCOVrgZGeuod8prs7q7eV8Av/gGW3zx+jWuxjlY3QJIkNU9PucQjT42w9ERPb7aez9LrYcGrxqdhk4AjPZIk5Uilq5MNW7aypX9gZC9sL8FL3jA+jZokDD2SJOVIba2eEV3iKghDjyRJOTLsWj0FZ+iRJClHehzpGZKhR5KkHOkZqhSFDD2SJOXJtqKjhp5dGHokScqR2pyetV7e2oWhR5KkHNl299ZGJzLvzNAjSVKO9DjSMyRDjyRJOeJE5qEZeiRJypEpHe2US+3esj4IQ48kSTlT6Sp5eWsQhh5JknKmp1xyIvMgDD2SJOWMIz2DM/RIkpQzlXIn6zb1MTCQWt2UScXQI0lSzlS6SqQEz7/Q3+qmTCqGHkmScqany6KjgzH0SJKUM9vW6tnkZOZ6hh5JknKmUrbo6GAMPZIk5YxFRwdn6JEkKWcsOjo4Q48kSTmzreiol7d20FDoiYhLI2J5RKSIOHp326v7DomIX0TEQxFxe0S8tJF9kiRpbCpd1Tk9Xt7aQaMjPd8DTgFWNLgd4DLg8pTSi4DPAl9rcJ8kSRqD2t1b3rK+o4ZCT0rpppTSqka3R8Qs4FjgyuqmfwPmRMTC4faNpgOSJGlH0zrb6WgLL2/tZLzm9MwBnkgp9QOklBKwEpi7m327iIiLImJV7WP9+vXj1GRJkvIhIqh0lVjnOj07mPQTmVNKl6SUemsf3d3drW6SJEmTXk+55EjPTsYr9DwK7B8RHQAREWQjOSt3s0+SJDVBpavTicw7GZfQk1J6CrgTOK+66e3AqpTSw8PtG4+2SJJURJVyiXUb+8hmkQgav2X9sohYBfQC10XEw8Ntr7oQuDAiHgL+O3B+g/skSdIY9XSV2LJ1gE19W1vdlEmjo5EnpZQuHMn26r4lwIkj3SdJksau/rb1rs6Gft3n3qSfyCxJkkbOoqO7MvRIkpRDFUtR7MLQI0lSDtVCj2v1bGfokSQph2pzehzp2c7QI0lSDll0dFeGHkmScsiio7sy9EiSlEMVL2/twtAjSVIOzSg7kXlnhh5JknKovS2YMbXDkZ46hh5JknKq0tVp6Klj6JEkKacqXSUnMtcx9EiSlFM95RJrNzqnp8bQI0lSTvWUS2zYspW+rQOtbsqkYOiRJCmntpei8BIXGHokScotK63vyNAjSVJOWXR0R4YeSZJyyqKjOzL0SJKUU9uKjhp6AEOPJEm5ZdHRHRl6JEnKqdqcnrWGHsDQI0lSbtUqra9zgULA0CNJUm7VKq070pMx9EiSlFNTS+2US+1OZK4y9EiSlGOVrpIjPVWGHkmScqynXHJOT5WhR5KkHOspl7xlvcrQI0lSjlW6stAzMJBa3ZSWM/RIkpRjlXInAwme39zf6qa0nKFHkqQc21Z01Du4DD2SJOVZz7ZVmZ3MbOiRJCnHKmWLjtYYeiRJyjHrb21n6JEkKcestL6doUeSpBzrsejoNoYeSZJybNvlLef0GHokScqzSld1IrOXtww9kiTl2bTOdjrawpEeDD2SJOVaRFRLUTinx9AjSVLOzbDoKGDokSQp9yrlkpe3MPRIkpR7la5O1m7qI6ViV1pvKPRExKURsTwiUkQcXbf9kIj4RUQ8FBG3R8RLx7pPkiQ1V6VcYkv/AC/0DbS6KS3V6EjP94BTgBU7bb8MuDyl9CLgs8DXmrBPkiQ1kUVHMw2FnpTSTSmlVfXbImIWcCxwZXXTvwFzImLhaPeNrSuSJGkwFh3NjGVOzxzgiZRSP0DKLhSuBOaOYZ8kSWoyV2XOTPqJzBFxUUSsqn2sX7++1U2SJGmPYtHRzFhCz6PA/hHRARARQTZas3IM+3aRUrokpdRb++ju7h5DkyVJKp7anJ6iL1A46tCTUnoKuBM4r7rp7cCqlNLDo9032rZIkqShVcpe3gLoaORJEXEZ8AZgNnBdRDyfUloIXAh8LSL+AngOOL/uZaPdJ0mSmsiio5mGQk9K6cIhti8BTmzmPkmS1FyO9GQm/URmSZI0NjPKzukBQ48kSbnX3hZMn9rhSE+rGyBJksZfpctK64YeSZIKoFLudKSn1Q2QJEnjz5EeQ48kSYXQUy6xfnM/fVuLW2nd0CNJUgFUuixFYeiRJKkArLRu6JEkqRAsOmrokSSpECw6auiRJKkQLEVh6JEkqRC2FR019EiSpDyr3b1V5Errhh5Jkgqgdnlr3Ubn9EiSpByrVVp3pEeSJOXa1FI7U0tt3rIuSZLyr+hFRw09kiQVRNGLjhp6JEkqiJ5yibVOZJYkSXlXG+kZGEitbkpLGHokSSqISrmTgQTPb+5vdVNawtAjSVJBbKu/VdDJzIYeSZIKouiV1g09kiQVxPZSFMWczGzokSSpICrlYhcdNfRIklQQRS86auiRJKkgegpedNTQI0lSQWwb6fHyliRJyjPv3pIkSYXQPaWD9rZwTo8kScq3iKBSLrk4oSRJyr+erpLr9EiSpPyrlEtOZJYkSflX6epk7aY+UipepXVDjyRJBdJTLrGlf4AX+gZa3ZQJZ+iRJKlAinzbuqFHkqQCKXLRUUOPJEkFUikXd1VmQ48kSQVS6SpupXVDjyRJBdLTVZvT4+UtSZKUY17eGqOIODsi7oiIeyJiUUQcVd0+KyJ+FBFLI+K+iDit7jVD7pMkSeOjdvdWEetvdYz1ABGxF/BN4LSU0v0RcWr188OBzwCLUkpnR8RxwFURcVBKqW83+yRJ0jiozenxlvXRWQCsSSndD5BSuhmYGxEvA84BvljdfjvwOHB69XXD7ZMkSeNgxtRsvKOIRUebEXqWAjMj4iSAiPgdYDpwEFBKKT1Z99zlZIFo5lD7mtAeSZI0hI72NqZP7SjkOj1jvryVUloXEe8A/ndEdAO/BB4Ausd6bICIuAi4qPZ5T09PMw4rSVJhVbqKWXR0zKEHIKX0U+CnABExBXgSuAXoj4jZdSM684GVKaU1ETHovkGOfQlwSe3z3t7e4lVIkySpiSrlTp7ZULyRnmbdvbV/3af/A/hJSulh4LvAh6vPOQ44EPhZ9XnD7ZMkSeOkp1wq5ETmpoz0AH9dvWurg+zy1geq2/8MuCIilgJbgPPq7s4abp8kSRonPV0l1m/up2/rAKX24izZ16zLWx8aYvtq4LUj3SdJksZPbYHC5zb1MbN7SotbM3GKE+8kSRJQX2m9WBdYDD2SJBVMpVzMoqOGHkmSCqaoRUcNPZIkFUxRi44aeiRJKpgeQ48kSSqCohYdNfRIklQwlW1zegw9kiQpx7Zf3nIisyRJyrGppXamltpcp0eSJOVfpdzpRGZJkpR/RSw6auiRJKmAeroMPZIkqQAq5RJrN25hYCC1uikTxtAjSVIBVbpKDCRYv6W/1U2ZMIYeSZIKaNsChQWazGzokSSpgIpYisLQI0lSAdVWZV5boErrhh5JkgrIkR5JklQIlXLxio4aeiRJKqAiFh019EiSVEBFLDpq6JEkqYC2TWR2To8kScqz7ikdtLdFoSqtG3okSSqgiKBSLrk4oSRJyr+ecsl1eiRJUv4VrdK6oUeSpILKKq0beiRJUs5VujrZ3D/AC31bW92UCWHokSSpoIpWisLQI0lSQRWt6KihR5KkgnKkR5IkFULR6m8ZeiRJKqhtldYd6ZEkSXnW45weSZJUBBXn9EiSpCKodGWXt4pSdNTQI0lSQc2Y2gE4p0eSJOVcR3sb06d0OKdHkiTlX5GKjhp6JEkqsEpXcYqOGnokSSqwSrnTOT0jERGvj4g7I2JxRNwXEe+tbp8VET+KiKXV7afVvWbIfZIkaWL0dJV4fnM/fVsHWt2UcTfm0BMRAVwJvC+ldDTwRuCyiJgOfAZYlFI6BDgf+FZElKovHW6fJEmaALW1ep4rwLyeZl3eSkDBWfQhAAAKa0lEQVSl+ngGsAbYDJwDfBEgpXQ78DhwevV5w+2TJEkTYFvR0QKEno6xHiCllCLiXOD7EbEB2At4GzAdKKWUnqx7+nJgbkTMHGrfWNsjSZIaVys6WoTJzM24vNUBfAp4W0ppHnAmcAVNCFTV418UEatqH+vXr2/GYSVJEtuLjnp5qzFHAweklG6CbZeqVgFHAv0RMbvuufOBlSmlNUPt2/ngKaVLUkq9tY/u7u4mNFmSJEGxio42I/Q8CuwfEYcCRMRCYAGwBPgu8OHq9uOAA4GfVV833D5JkjQBilR0tBlzelZHxAXAdyJigCxIfSSltDIi/gy4IiKWAluA81JKta/qcPskSdIE2FZ01NDTmJTSt4FvD7J9NfDaIV4z5D5JkjQxahOZi1CKwhWZJUkqsG23rG90To8kScqxqaV2pnS0OdIjSZLyr9JVKsTihIYeSZIKrihFRw09kiQVXI8jPZIkqQgq5RJrN25hYCC1uinjytAjSVLBVbpKDCRYv6W/1U0ZV4YeSZIKrnbbet7n9Rh6JEkquNqqzHm/bd3QI0lSwfUUpP6WoUeSpIKrFKTSuqFHkqSCq5SLUXTU0CNJUsEVpeiooUeSpIIrStFRQ48kSQXX0+VEZkmSVADTp3TQ3hZe3pIkSfkWEfSU819/y9AjSZKolEuuyCxJkvIvq7TuRGZJkpRzWaV1R3okSVLO9ZRLbO4f4IW+ra1uyrgx9EiSpEIUHTX0SJKkQhQdNfRIkiReNm8v3nviPKZNaW91U8ZNpJRa3YYR6e3tTatWrWp1MyRJ0ghExGMppd5WtsGRHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiGHkmSVAiRUmp1G0YkIjYDvx2HQ3cD68fhuHuCVva9qF93+10sRe03FLfv9ntX+6aUpkxkY3a2x4We8RIRq1JKva1uRyu0su9F/brb72Ipar+huH2335OTl7ckSVIhGHokSVIhGHq2u6TVDWihVva9qF93+10sRe03FLfv9nsSck6PJEkqBEd6JElSIRh6JElSMaSUJs0HMBW4GngIuBu4AVhY3TcL+BGwFLgPOK3udf+v7jW3AMfV7esCvg08XH3OO4Y5/26fCxwKbAQ+P8xxPlpt473APcB5dfveB6wDFlc/ftrKflfP/e/Ac8BmYAPwkUHOvRR4odbvwc5d1+/7gLXAk7VzA9Oqr7kX+DXwGSCGayfwNeCxuq/V50bzvQPeXz1vP/DxPeH91sS+73KcgvT7kGq/7gbuB87NUb8HfT9X9/1NdV/tOO8qSL+vqjvGYmAAeHtO+v23ZD8z7wbuAM6q2/cG4FdkP7s/X7c9L9/z4fp+MdmafbXjfHOo4+xwzEaeNFEf1W/U69k+1+gjwH9VH38VuLj6+DhgFVCqfv47QEf18RuB5XXH/J/A16qPDwKeAmYOcf5hnwuUgJuBbzJ86DkT6Kk+ngM8DSyofv4+4OrJ0u/qua+oe+5fAVuAmbVzV/t9F1nYu3Soc9f6XT33d6r9Pr167kuAb5AFnRLwn8A7h2sn2X+Ojw/1dW70ewccRRZWv8GuoWdSvt+a2PddjlOQft8CfLD6eF9gBXBwTvo96Pu5uq9S9/hAsj9mDsx7v3d63rFkP3um56TfrwPKdV+DdcC06ucvqm77NLuGnrz3/WKG+T085DFH+oKJ/Ki+eZdXH68HZtftuw149SCv2Qfoq/vG3Q+cULf/O1R/GA7y2mGfC/wvstGMEX2xydL0GdXH72On0DOZ+l099wbgg7Vz1/X7MeDfGj13rd/Vc98N/G7day4CfjhcO0f4n2O3/d3d8SbT+61ZfW/kODnt93rggLp9PwQuykO/R/B+fjHwPDCrYP3+AoP8fN7T+119XRtZkJ2/0/aLB+tznvu+uz4P9THZ5/R8DPhBRMwkS6JP1u1bDswd4jXXppT6q5/PJfsrb3evG/a5EXE8cCLwDyPpQES8GtgLuL1u8ykRsTgifhER7xyiD63q98eAB8l+YJaAeWzv91qyv54aOfdctvd7efW174yIKRHRDbwFmN9AOz8WEfdExDURcfQQ7R9pf4cyad5vtWM3qe+7O04e+/0r4DyAiDgYOIns/bZzH/bEfg8rIj4aEUuAO4ELUkpP7fSUXPYbICLKwLuBrwyyOw/9Ph9YttNxG5HXvr8zIu6OiJ9ExCsbOUhHgyebcBHxF8BCsksm5QZfcx5wDnBak9vSBfwz2XXJFBGNvu4Ismuk56aUNlQ3XwN8J6W0MSIOBa6PiEdTSouqr2lZv+vOfVPd5mH7PcS524D/S7Xf1dfdQjav51ayIcrbgFftpkl/CTyRUhqIiLcC/xkRh6SUml7PZjK936qa1fdhj5Pjfr8X+LuIWEz2Q/LHZHNBgFz3m5TSpcClEXEUcGVEXJ9SWgP57nfVO4CHUkr31m/MQ78j4kyy6QevSdWhjkbkuO9fBP4mpdQXEScDV0XEcSmlYQPhpBzpiYhPAG8DXpdS2lj9D9sfEbPrnjYfWFn3mnPZ/kVZXfe8lWSjFTu8LiIOq462LI6IfxruucACspT604hYDnwceH9EfD0iKnXHuaquPYeRBZz3p5R+XtueUno6pbSx+vhB4Frg5EnQ7z+vnRvoBZYAW6vHqPX7xcDJdf1eTvYX1W9q5672uwR8pq7f88kS+mXVz3uqfb5/mK85KaXHUkoD1cdXkQ1tvniE37vdmoTvt6b1fajjFKDfy1NKb08pHZ1SejPZe+7+nPS7ISmlu8kuSZ9RoH5/gJ1GefLQ74g4newP6DellJY0+sXIc99TSk+mlPqqj28hm3d67G6/KCO9HjbeH2RzPX4F7LXT9q+x4+Srx9g++eocspno8wY53sXsOqFqnyHO3dBz2f3100PJhvPOGmTfgXWP96u2+1Ut7vd1ZBP/9qp/7iDnfo7tE5l3OXddv68Y5Nzzga66bSuAo4drJ9Bbd+wTqm3sGe33jsEn9U7K91uz+j7UcQrQ7/2Aturjs8h+4Jbz0O/dvJ8Pq3u8AFhN9v8y1/2ubl9INl9lRs7+f59G9v49ZpivycXs9Dsp733f6TiHkL3XXzTceyelNLlCD9kIQwIeYfttaLdW9+0HXF/9htwPvLLudX3Ao+x4y2Jtpvg04F+rx3wIOGeY8zf03MHeYDvtvwF4dqf2nFXd97fV9i8mu539D1vZ77pzrye77fEFYOkQ5/4a229ZH+zc/1Xt9z3VfzeT3RlwDlnAeQh4oLr/bbtrJ3Aj22+//WV930fyvSObPL6KbIL22urjY1r5dZ/Avu9ynIL0+4Nkt8suIVsW4vAc9ft9DPJ+ru67hu0/X+4gu0My9/2u+9n69T3h98kI+72U7Bd6fXuOqO47s/p1eI5s0voqsjuwitD3r5PdLLOYLNwNeQt9/YdlKCRJUiFMyjk9kiRJzWbokSRJhWDokSRJhWDokSRJhWDokSRJhWDokSRJhWDokSRJhWDokSRJhWDokSRJhfD/AeA2uqTuW/qQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og_Fqe57bOrU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFRYgMQVbO2A"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqbvuVDybO2F",
        "outputId": "fdc994b6-3964-479a-e433-b31739b26d80"
      },
      "source": [
        "#model performance in peak period (winter)\n",
        "timeseries_evaluation_metrics_func(inv_y[:90], inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10112.135122177597\n",
            "MAE is : 70.77798441465414\n",
            "RMSE is : 100.55911257652185\n",
            "MAPE is : 5.670835206297269 %\n",
            "R2 is : 0.39464741516943536\n",
            "\n",
            "SMAPE is : 0.05928789387949646\n",
            "\n",
            "MASE is : 0.7069854784404282\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10A9_pFgkk-1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TC0n4auI7dFM"
      },
      "source": [
        "# **same methodology is repeated**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONvjD8x8g18r"
      },
      "source": [
        "# linear regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHvcg2zeiY04"
      },
      "source": [
        "best_model=LinearRegression()\n",
        "best_model.fit(X_train,y_train)\n",
        "# make a prediction\n",
        "yhat = best_model.predict(X_test)\n",
        "yhat=yhat.reshape(yhat.shape[0],1)\n",
        "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "# invert scaling for forecast\n",
        "#inv_yhat = concatenate((test_X[:, :-1],yhat), axis=1)\n",
        "inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,-1]\n",
        "# invert scaling for actual\n",
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "#inv_y = concatenate((test_X[:, :-1],test_y), axis=1)\n",
        "inv_y = concatenate((X_test,y_test), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivKD0YG8iY04",
        "outputId": "9d256003-4d12-46a7-d348-cec73af57475"
      },
      "source": [
        "#overall performance\n",
        "timeseries_evaluation_metrics_func(inv_y, inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9780.664777001073\n",
            "MAE is : 68.4684199043621\n",
            "RMSE is : 98.8972435257984\n",
            "MAPE is : 6.321034195014229 %\n",
            "R2 is : 0.7482791764101727\n",
            "\n",
            "SMAPE is : 0.027230775949216843\n",
            "\n",
            "MASE is : 0.7851796850486518\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0nmffOo_iY05",
        "outputId": "8ac95cbb-3214-4b2a-d6b7-ca8e6a3ed47f"
      },
      "source": [
        "# plot real vs predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,inv_y,label='actual')\n",
        "plt.plot(time,inv_yhat,label='forecast')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnTBvXX8iY05"
      },
      "source": [
        "ZOOM on winter 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "BJ9MX-3XiY05",
        "outputId": "153d1687-f606-4f1b-9892-859aee4f3836"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[:90],inv_y[:90],label='actual')\n",
        "plt.plot(time[:90],inv_yhat[:90],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyWuC8_GiY06"
      },
      "source": [
        "ZOOM on Ramadan 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "3UynKaNYiY06",
        "outputId": "9753021f-b414-4a36-b4d0-ed51a317c7e6"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[112:142],inv_y[112:142],label='actual')\n",
        "plt.plot(time[112:142],inv_yhat[112:142],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+xfvp9vpG5i4/xuZTqUT4OfPy2GB8XWx4/pfjrD1x0ahr/25PPBn5JTwxNNDgLA85KbD/K/AMw7vfdF4fH8rF7CLmLj96fV3dIxhKciFbGskam8t5xcRlFNSa0h8Z6omqwqaTMtsjuXHZUqHCXJuJrkajMDLEi4TMAk5fym3KpTUJup6e3bt3M3nyZADs7e0rt6uqyvDhwzl69GjlT3JyMh06dLjuXDe7bpcMepqI8DbO/PXUAMaEebH6xEXGfrKDY4lXat3/UnYRS3ZdYMqXe+jxv0288OsJ9py7TL9AN96aFMb+l4bx86N9eHhAO354qBet7K2Y8/MRthlJeTSvuIwvt53Dx9mGu7s3wBR32ztQVgS3vQIaDXd1b8Nd3XzZGpPOF9vPVd9Xp8wsPbiMztV+npqDnqFBnphrFP3T/sW5IiMnkbQgtsWkY2tpRo+2tffrjAipyHrcgl5cI0eOZNOmTdUanPfv3w/AsGHDWLFiBbm5uaiqyldffdVcy2wSZNDThDjZiFHu1yeEkppTzOQvdrNo54XK6a6EjAK+3HaOiZ/vovdbm5m36iRHE68wLNiTD6eEc/Dl4Xz7YE+m9vSrVooIcLPj+4d6YmtpzqzvD3IgrvGu5d/ujiOroJQnhwZiZW5glifjHBz+Hvz6QofhlXe/Nj6UIE8H/m99DPvOZ1zdX3pwmQxd0NP9miZmHU62FvRp34pdsRnk1pd9jN8Db/vDqVXGXqZE0mCyC0s5lJBF3/Zudb5XXZVpuPWCnsDAQJYuXcqsWbMIDw8nODiYjz76CIAxY8YwefJkIiMj6d69O35+DWhluIFQbrRxal9fXzUp6cYvg0SnZPPk0iNcuJxPn3atyC4s5eRF0ehsZ2nG0GBPRod6Maije51igFU5kpDF9IX7MFMUfnq0N6EVQnSGklNUyoB3tuBoY87fzw42XMV0xUyI/hUeXA9+vattik3LY9z8ndhbmbPm6QEieCvIhHfbQvhUmPhFg9YsqZk7F+zmbGouR18ZgUZTc9r6+73x/Of3KD6dGsEd4TX3RADw3Xg4vxW6PQB3fGyS9UokhrL6+EWeWHqYNyaEMqO3f537PrfyGMsPJrH1n4MJMNAyqLy8nDNnztCxY0fMzG4e4VljU9ffSVGUZFVVG1A6MB4y09NMhHg7seqp/ozv6s2e8xkkXylkcjdfFt3fnUP/Gc6nUyMYE9Za74AHIMLPhYX3dae4XMv9i/dzLj2vQWv7Zmcc2YWlPDW0g+EBT8pREfB0HHVdwAMQ6GHPmxPDSMst5pmfjwqbD1tXcGgttXqMTFFpOSeSson0d6k14AExug71TLYkHRIBD0DKESOuUiJpHFsr+3nc69kTabYrkUFPc2JvZc5HU7qy+/mhHHx5GP93Vzi3BXsa3jRchb6BbsyfGsGVwlLuXbiP5CuFBh2fXVDKwp3n8W9ly6SIBniI/f06oMDQ/9S6y4QIH6b29GNn7GXuXbSPNScuonXrBOkxwphUYhSikrMpKdfSza9uXRJPR2u6tnFma0w6xWW1/P13fiBu3YIgNRpKi4y8WonEcMSoejodPOzxdalBB+wa+rZ3w87SjHUy6LllkUFPM6MoCt7ONkY1whsR4sX7d4VzMaeIGQv3kZ5brPexi3aeJ7eojKdv61DNW0wv4nZC7Cbocjd4hda563/v6Mwd4d7su5DJ4z8eZmmcHZQVEhcbbdg1JbVyUNfEHFC/GNvIEC/yisvYfS7j+o1pp+D0XxA0FsImg7YMUqOMvVyJxGCiU3JIzy3WK8sDQqZhSCcPjiRc4VK2DNxvRWTQc5MyIcKH18aFcOFyPvct3k92Qf0j8ln5JSzeFUc7dzvG1dXbUROqCpteBY05DH6h3t2tLcz4dGoEe54fyr9HdSLFUrix/2/Jb0z8fBc/708gr7jMsDVIqnEoPgszjULXNs717juyYrJlQ03fgHd+KG4HzAXvSPHv5MPGWqZE0mB006pDahlVrwldiWvjScOyPbpR7hutD7ap0f19Wurouwx6bmLu7RPAv0YGcepiDjOX7Cf/2iDizAb4YoBoPD6/la+3x5JX3MAsT8xa4ZbebSa4ttX7MA9Ha2YPbs+/ZowHYKJPDqcu5vD8ryfo+b9NPLfyGIfiM+UbjYGoqsrh+Cw6t3bE1rL+vrB27vZ08LBn48lU0WelI/MCnFgJbQeBb3fwjhD3p8igR9L8bI1Jw87SjO4BNU8n1sTgIA8szTUGl7g0Gg0WFhZkZGRQVlZGeXm5/Lnmp6ysjIyMDCwsLNBoWmZ4IRWZm5vSItj3BXS6HdwCjX76xwe3J6eolC+3nWfW94dY9EB3rBQVtrwhvsGbW8Ol4xD9K/eonrg5Def2thGGXURbDptfAwtbGPivBq1TqTAeHeOZRf+HhrHqWArLDySy/GASyw8mEehhz93dfZkU6SuVg/XgwuV8MvJL6p7GuoaRIV7M3xLL4YQseug+RHZ/Amo5DHhW/G7XCpz9ZaZH0uxkF5RyKD6LYcGeWJrr/wFrb2XOgEA3tp5JJyu/BJeqCvH14OfnR0JCApmZjZcFuVmxsLBo0WPvMuhpbk6sgE3/ha1vCSG/Xo+BxnjjkIqi8PyoTuQUlvHT/gRe+X4Db6sfoyTsEaWKu5aAtow9Kz8kMOVPHiz+AT76CTqOhMj7IHA4mNXzNDmxAtJPiQ9GB8+GLdTKAZz9IO00jtYWTO/lz/Re/py+lMOyA4n8diSZN9ec5t11MQwL9mRKjzYM7OiOWR1TSbcylfo8evTz6NAFPeujLomgJ/cSHPkBfLpD26vy9XhHwMk/hFChlYOxly6R6MWO2HS0au0qzHUxMtSLzafT2Hw6jcnd9J+gtrS0JDAwEK1WK7PPNaAoSovN8OiQQU9zc36LuHXwgvUvwsk/YcLn0Kq90S6hKApvTAjFN3MPU+JeR1FyUHvOQhnxBphbkp5bzMyksbR3ncSqkXlojnwPZ9ZBzBoxSt51OkTeCy4B15+8rBi2/A+snaHvnMYt1D0Yzv0N5aVgZgFAJy9H/ntHCM+P7sSG6FSWH0xk/clLrIu+hK+LDe9O7kLf9m6Nu+5NSH1KzDUR6uOIt5M1609e4qWxwSh75kN5iQhmq9bnfSLh5O/CLy2gv7GXLpHoxZbTFa7qejYxV2VYsCcaRagzGxL06GjpH+yS2pH/c82JVgvnt4FXGMzeA71mQ+I+WNAX9nxmvPFtbTlmW9/k8aTnsNWU8VjJM7xadj9qRWDxxbZzFJVqeWp4ZzSdx8H0FfDMCRj8omhM3vF/8HG4EKeL+kUEOjoOLYErCdD/H2BTf8NsnXgEg7YUMs9ft8nK3Iw7wr35/qFe7HhuCHNu60BWfgnTF+7jrTWnKKkwXpUIDsZn4eNsQ2snG72PURSFESFeJGYWEnMhAQ4sBo/OQnOpKrKZWdLMaLUq286kE+TpgLez/s9xHa52lvRq24rtZ9Ov73VUVdj2Hqx8CMpKjLRiSUtBBj3NSWoUFFyGdkPA0hZGvw0PrBbZlfUvwpKxwtKhMeSmimBl+7soXmGYzd5Blv8oluyO48NNZ0nNKeKHvfGEeDtWTvAA4OQLg/8NTx+DGb9A8Dgxkr7yQXi/E6x7UXzobX9PrLfno41bJ4gPWKjXjsLXxZa5wzuyes4Awn2d+XL7eSZ+vovYtIaJMRpMTgpsfh3yaxjvbgFcKSghNi3PoCyPDp0/UdaW+VCaD/3nwrXfar27AopsZpY0Gycv5nA5T/9R9ZoYFepFSZm2ul9heRn8+ZToeYxaeVWfSnLTIIOe5kSncNtu8NX7AvrB7F2itydhDyzoB3sXiKyQoVzYDl/0h7gd0P0heGgjVh6BLLy/O118nfhk81nuXbSP4jItzwzrWPOIocYMAofBlO9h7mkY/ppQUN77GXw9BPLTYdBzImhrLBXNzPoqMwe42bHisT7MGRrIqYs53P7pDn7YG2/6WvuO90X2a8kYyDGus70xOJxgeD+Pjp4BrvjYlBGatFSUM0MmXr+TlQO4dZSZHkmzseW0UGEe1Iig5zoD0tIiWHE/HPke2t8mvoRtfw8uSU2qmwkZ9DQn57eAmRX4961+v6UdjH4HHlgjGoPXPW9Y1kerhW3vigxPaQHcuQhu/wAsrAFwsLZgycyedPCw50xqHmE+TgwL1qMZ0N4d+j0NTx6EmWuhyz3QeTxE3GvgA68Ft46gaAyyo7Aw0zB3RBDLZvXBzd6Kl3+P4pHvDpKRp78go0GoqhjPt7QXrvDfjIKsONNcq4EcjBNBT2Q9Ssw1YW6m4XmPvTioeWR0fbz2JnafSLgS32KzXZKbm61n0rG3Mq/VSFcfWjvZEN7Gmb9Pp1GcnwU/ThYinCGTYOrPMP4z8Xr/43HRZyi5KZBBT3NRWiRcq/16gUUtNemAfjB7N/ScBQm79cv65F+GH+8UzcXuwfDoVqGiew2udpb88HAvJkX48ObEMMOEpBRFBGqTvoS7v6tsOm40Fjbg2q5BHlw9AlxZ8/QAJkb4sOlUGiM/2lHpyWNULh6FnOSrpptZ8bB4NKSfMf61GsjB+CzsLM3o5NWAyarSIkZkryRVdeYPBtW+X6Vej/ThkjQtVwpKOJKQRb/AVgaNqtfEqBAvLIszKV50u8iI93gY7lwI5pYisO83RzTs75IGuzcLMuhpLpL2Q1mh6OepC0s7GPMu3P8X2HuIrM+3t9fY7Ev8blHOOve3GDd/ZDO4daj11J6O1nwwpSthvg1zYzcJ7p0g81yDvJ0crS34cEpXPr6nK8Wl5TzwzQFeXRVNUakR/bxi1orbTmNF4HPnQshPExmfi8eMd50GUlqu5VjiFSL8XAwXmAQ4thSrojSWqHew9lQdWiS6ZmYZ9EhMxd//g2/HQfKhandvP3sZrWqYCnNtjPErY4XlqzhmRsGgf8OY/6suGTLoeeE3t+0daYh8kyCDnubiXMWoervB+u3fdoDI+vR4BOJ3iazPvi9F1kerFUKDS26HomyY+BWM+7T2DFJLxqMzqFrIONvgU4zv6sOapwfQI8CFb3bFMX7+Lk5fyjHO+k6vARtX8O0pfg+bDFN+hOI8WHIHJOw1znUaSHRKDsVlWiIb0MRMeRns/AhsXLgYeA8H47Nq923zChOTfbKZWWIKtFo48DVc2AZf3warnoECEYTrMriN6ecBIO00/r9NoL3mIu8qD1I+6IXq0gwgWgLGfyb85v54QrxGJDc0MuhpLs5vFdo2rcP1P8bKHsb+H9y/CuzcYe1zIuvz0xTYNE9kdR7ZAuFTTLVq0+MRLG4b+a2qjastPz/ah3+O6Ehseh7j5u9i8c4LaLWNaHLOiofUE2KEu2qvS9AomLFSvDF+P1Fk2pqJg3Hig6F7Q4Ke6F9Fn06v2QwKCxB2aqdSa97XwloEqLKZWWIKMs5CYRYE3yFK6Ye+gfnd0R76ju2nU+nk5WCQHMN1JB0U2dn8NNZ0eI3PC4dVvnauo00P6P24yDjt/azh15S0CGTQ0xwUZIqyQLtBDVNfbjuwetbn7AYInwaP/H11AupGpTLoqXtsXR/MNApPDu3AL7P70trJmtf+OskDSw6QltNAd+Uz68Rt0Ojrt7UdCPf9Ifqblk6BU381fOGN4HBCFhoFIvwM1EzSamHHB6JBu+cjDA3yxFyjsL4ufyKfSMi7JEb4JRJjosuYht0tZDwmfgWKGZpVT/FF2cvc7Xul4ec+97com5UWwT0/4T/kAYC6vbiGvgyu7UXJ7XLDs9CS5kcGPc1B3A5A1b+0VRO6rM+D6+Gen2DiAtH/c6Pj2h40FpB22min7NrGmTVzBnB3d1+2n0ln1Mc72HiylgxGXZxeLabt2g+teXubHmLiztoJlt8Hx5Y1buEGoqoqB+OyCPJyxMHawObyM2uFlUj3B8HWFSdbC/q0b8Xu2Axyi2qZXJEihRJTkbhP3Pr1FiWn8Cnw1EGOet9DhHKWmdH3w5rnRDnfEKJ/gx/vFpna+36HjiPo3NqRNq42rI+6VLvchYWNKHOVl4gyl7GEYyVNjgx6moPKfp56mpj1wa83dBrT+PO0FMwtRZnOCJmeqthZmfPu5HAWTI+kXKvyyHcHeeqnI5xJzdXvBIVXRFat3WARcNaGV3McBTgAACAASURBVCjMXCcEG3+bBQcWGWP5epGUVUhabjHd/A3M8qiq0B4ys4I+T1TePSLEi5JyLVti0ms+TjquS0xFwl5waSuGN3RYO/Fq2X1M4R1Un26w/0v4tLv4cqGPNteBRbBiJti2EpIbfr0BoUQ+srMXKdlFnEiuI4jy7wO9ZomAbP9XjXyAkuZCBj3NwfmtwqnatW1zr6Rl4t5J9JYUG19heXRYa9Y9M4AhQe6sOpbCiA+389j3h4iq680OIHaT6NmpqbR1LW6B8OBa8f+7eq5oDm4CDsbr+nkM1C65sE30K0TMEB5wFYzoLMTbai1xeQSDubWc4JIYl7w0McFZEZToyMov4WjiFdw7dEfz4AYYNx/UcvjtUTHEUVsfoKoKkcHVc4Xg5oPrwDOk2i6jQsXzvs5yLghTaJcA2PRqzRO0khaPDHqamqw4yLoA7Y2Q5blZ0dlRXI4xyelbO9nwzcye/P5EP4YFe7Au+hK3f7qTB5ccqDTqvI7Tq8WtPkEPCMf4mevEY9n0X2FbYWKl6IaYjAIiy6OYCU2SKng6WhPh58zW02k1j/2bWYBXFxH0SMdpibHQlbba9Kp29/az6ahqhcGoRiNMkJ88KEqy8buEXMf6l6C4SvZWqxWWPn+/AZ5hoh2ghi+bkX4uuDtYXVVnrg1LOxFslRXCH081TClf0qzIoAdEILLlzeovFlNRk/WEpDpGmuCqj65tnFl4fw/WzBnA2C6t2RKTxp0LdjN94V72nMu4Wt8vKxGZHp/u1TIh9eLgKZowvSOFbcW65036JnkwLgsPByt8XQyYakk8IOxKwu4S32CvYWSIF/kl5ew5V4vysk+kmLLJutCwRTcVsZvhl0fg+HIoMpJ8gcQ06JqYr8n0bI3RuapXKXnZusLtHwpNMq8w2DMf5veAqF+FivLvs2Hv5+DXFx74S7wma0CjURje2ZNz6fnEptXzOdB2gLD1id8JB5uufC0xDjLoAfGGuO0dOPmn6a91fiugQNs61G5vdZoo6NHR2duRz6ZFsvEfg5gU4cPe85lM/Xovd32xh60xaahxO6E4R/8sT1VsXeH+P8G/P+z7QpgZmqAJMqeolJjUXLoHuBimrq0zVOz/jxo3jwypJ+1vQDNzfEY+J1OaKeDYNA9OLIdfH4H3AuGnaTIAaqkk7hNyHm5BlXfpXNWDWzvi6Wh9/TE+3eDhzTD2AygthJUz4aMwOP4zdBwN9/4KNnX3uo2qfK7rMeQw/FVw8oON/xVSFpIbBhn0AIROEk2cx34y7XW0Wji/TWjz2DbcM+amxyVA9Io0sQJqoIc9H0zpyt/PDmJqzzYcS7rCA98cYPUK8W1O27GBDeNWDkLHp8MIOPqDcKovKzHiyuFowhVUFboZ0s+TGg0xa6DT7bVKHbR1s6ODhz0bT6ZSXpPGkU/9ysyJmQX8a8UxhvzfVsZ/tpPjSY0YN24Iaafh0nHhqXT7hyKDcGZtlQBoqmiGlQGQ0VBVld+PJHPngt31l4yqUloIKUdFaUtz9ePpeHI2mfkldbuqa8ygx0PwVEV/Wu5FCJ8qzJL1EGrt3a4Vjtbm+q3XygHGfQyl+bBqjizv3kDIoAfAxkV8i4/bAVcSTHedS8ehMFOWtupDYwbuQc0m++7fyo63JnVh27+G8EAffyKL9hCv9WD00nT+PJZS84d/fVjYCOXmzhPg5O/w81QoKTDamg82pJ9n54fidsDcOncbGeJFRn5Jzf1Oru3ByrHGTE9qThEv/36Coe9vZcWhJLr7u6Kg8NRPR2ofgzcFJ5aL2+4Pip/7/4Rnz1QJgNaJZtj32lcJgAwchZZUknKlkAeXHOCZZUc5FJ/FYz8c4q21pygr16O0m3IEtKXCk7AKOhVmvawn7NzEePlzF2DCAr29AS3NNdwW7MmJ5GySsvR4bbYfKux+zm+Fw9/qdQ1J8yODHh1dp4lbU2qrnK8YVZdNzPXjHgy5KWJUvJnwdrZhXs9yvJUMUlsPJfFKIXN+OsKwD7ax4mAipfq8iVfF3BImLxbfQmM3CVdnI2UXDsVnYm2hIcTbUb8DMs9D1C9CNsGnW5271lni0mhE5vLiscqy3eW8Yt746yQD393CD3sTCPVxYunDvVj+WB9eGhtMfEYBL/4WVbsmijHRauHECnD0Af9+V++3d78aAP3zLNz+kVD+rQyAAmHpPXDsZxkA6YlWq/LdnjiGf7CNLTHpTIrwYc2cAXRt48yX284zY9G+2m1NdOj6edpc38/jYG1OpCGim7au19tK1IPuub5BnxIXwIg3xHNr/cuQnWTQtSTNgwx6dLQfKqwdjv1kulTl+a2ibHPNC1pSA7q+nnTjiRQ2iNNrAOg5+l52/Xsoc4YGcjmvmH+tPM7g97byw954wwxNNWZwx6fQa7aYOPluXKWnUEMpK9dyNOEK4b7OWOhrMrrrY+FxNuDZencN9XHEx9mG9dG1iLf5REJpPrlJJ3lv/WkGvruFhTsv0N7dnsUPdOfX2X3pG+gGwH19/BnR2ZNVx1JYdiDRkIfZMBL3iext2ORq5ZJq2LlB95lCUbtqAHR2g9Baei9QqGy3AEPZlkpsWh5TvtrDK39E42xryZKZPfhgSlc6ezuyfFYf7u/jz97zmYz9ZEftdg8g/r80FlfLpkBGXjHHkq4wsIN7w0x0DWBQR3esLTR1qzNXxdoJ7vgYSnJh1dOyzHUDIIMeHWYWQvI88xwkHTD++UsLIX4P+PURvkWSutGNrRtZpNBgYtaI8meb3rjYWTJ3RBC7nh/KP0d0pKCkjJd/j2LQe1tYtPMChSV6Bj8aDYx6S7g6pxyBb8ZArgF9D9dw+lIu+SXldA/Qs7SVkwJHlwrT1ID+9e6uKGKyJSmrkJMXr89MFXp0BeCdxUv5bMs5fJxtWDA9kr+e6s/QTp7VGqsVReHdyV3wcbZh3qpo/cUhG4qutBV2t377XxsA3fGxyBCd3Qg/3AnZyaZb6w1IabmWz7bEMubjHRyMz+KBvgFs+MfAahNWluYaXh0fykdTupJbVMY9X+1l8c4L1wfQWq3I9LQOr9aDs+PsZVTVCAajemBjacbgjh4ciMvkcl49WSkdHYYLG6DYTeJ11RiK8+DM+mazsbkV0CvoURTlE0VR4hRFURVF6Vrl/g2KohxXFOWooig7FEWJqLKtg6IouxVFOaMoygFFUUL02dasdJ0qbhv7xK2JhL1QXiz7efRF11jbTH09AFxJFH1YHUZWMxh1tLbgyaEd2Pnvobw8NhitCq//dZL+7/zN51tj9etXURQY8iIMf13YPywe1eApEIP1efZUyOkPeFbv9P/IGiZbikrL+Wr7Oe78vVBc3/wCH04JZ90zAxkd1hqNpuZzO9ta8snUrpSWqzy59LD+waKhlJUI2wGPEKGUbSh2raDbA8Ku4K5vID9d2IuU6flheJNzPOkK4+bv4r31Mfi1smXlY32ZNy4EOyvzGvefEOHD70/0w8/Vltf+OsmTPx0hr7iKa/nlM1B0pYZRddHPM7ij6YMegJGhnsJs1xCrmlFvgr0XrH8Bci7qf1x5mTA/3fae+PLzTgAsvRuWTZfihyZC30zPSqA/cO278t2qqnZRVbUr8AGwpMq2L4GvVFXtCLxjwLbmwysMPEOF23RpA00pa0P28xiGUxthftmcQY/OYLQWmw87K3MeHtCOHc8N4fXxIViZa3h3XQz93v6bDzee4UqBHhNa/eaIckpWnAh80s8YvExdE3Oknx5BT0EmHFwsnucdR+p9jR4BLrjYWrAh+hLFZeV8tyeOge9u4c01p8my8KTI0pXxHqlMjPDFrJZgpyrd/F2ZO7wjZ1LzeO2vaL3XYRCxm4SGUJe7Gn+uzuOh3zOQfBDW/rvx57uBKSwp5601p5jw2S7OpuYyZ2ggq+f01yvoDvJy4I8n+zEqxIvVxy8y4bNdV3VxEnX9PFebmMsrRtVDvB3xqGlU3QQM7STMdn8/mkyGvtkeGxfRGF+UDX/9o/Yyl6pCxjk4sBB+ng7vtoOFt8GWN8TUWvsh0GWK2Ddhn3EekKQaNYfk16Cq6nbgOv0PVVWrdpk6AWrFfh5Ad2BExbZfgPmKogQCObVtU1U1tmEPw4iET4UNL4myRugk4533/FawcRWqoJL6URTR19OcQc/p1WBmWbvBaAXWFmbc2yeAKT38+P1IMp9tjeXjzWdZuOM89/YJ4OEBbXGzt6r9BN1nihHYXx+Fbyo0RVqH673Mw/FZdPCwx9nWsu4dtVohx19aICa2DGjyNDcTky0rDyUx+L2tXMwuws3einl3dOaenn5YL+8unuNlJaJhWw9mD2rPnnMZ/LQ/kb7t3bgj3Fvv9ejF8YqhhDAjBD0AQ/8DF4/CoW9Ez0nkfcY57w3E7nOXeeHXE8RnFBDu68Q7k7vQyUvP5vkKHKwtWDAjkq93nOeddTGMn7+LdyZ34Xbdh3yVTM/xpCtkFZQyrZefMR9GnTjZWNC/gxtbY9Lp9sYmWjtZE+LtRKiPY+Wtl6P19XpYncaI59qJFeKnS0VJtSBTvDbObxG3uglhRSN0rtoPERUA357itXMlQTx3k/ZfrT5IjIZeQU9dKIryHaBLX+i+ErcBLqqqWgagqqqqKEoC4Adk17Gt+YOesLtg4ytiasNYQU9+Blw8DiETam+mlFyPR7Dor8pLF9M2TUlRNsTtrDAYddDrEEtzDXf3aMOkSB/+On6Rz7bE8sW2cyzZfYGpPf2YNbA9Xk61fFsNmywk7pffD0vugOkrrhvbrYmL2YUkXynknh5tat9JVUVPyubXIPWE8DbrPEGvx1SVsWGtWXkoicLScp4f3Yn7+wRgY2kmNnpHiMbf1KhqTah1odEofDAlnDEf7+DFX08Q7uuMXytbg9dVI0XZIlPn3x+cfI1zTjNzuHMxfDUYVj8ryma+dU++3SxkF5by1ppT/HwgEWsLDS+PDWZmv7Z6ZfVqQlEUHh3Yni6+zjy59AhPLj1CH6ftuLq2Q6liMrqlJhXmJuDdO7uw+sRFolNyiErOZmtMGptOXS13udpZEuLtSKiPk7j1dsLP1RbN6HdFYLP2OaGDdX5rRQN8RebHtZ2YGmw3RCg729SQHXNqI0pliSboLZU0PuhRVfU+AEVR7keUqoxq+a0oylygUkjEycnJmKe/HgdPCLxNpMbz0qq7/DaUC9sA1Tiu6rcS7roJrlNNH/TEbhJ6IQ1QYTY30zAhwodx4d5sOHmJT/+O5Ztdcfy4N4HJ3X2ZPag9bVxr+HAPGi2CnZ+mwvcT4J6l9ZZDD8bV08+TsFeYIybsFpODfecI9WWNmcGPa3CQO8tn9SG4tQMO1tdon3hXESnUM+gB8HCw5sMpXblv8X6e/OkwKx/ri6W5Eb4YnFoFZUXGKW1Vxa6VELtbPBKW3wuPbmv652YTsz76Ev/5PYq03GL6tm/F25O6GC047d2uFavn9Oel7zfTKj2JLcowOucUVaoub4tJw9HanIg2BoyqGwEPR2tm9rvq0VVUWs6Z1NzKICgqJYf9FzLZcfZy5T72VuZ09nZksucz3H3+Jdj1kQhqOo+vyOYMARf/+i+uKNCmh8g0F+fq/aVLoh+NDnp0qKr6raIoXyiK0gpIBForimKuqmqZIvKAfkACorxV27aazvsBol8IAF9fX9PPBIZPFd9ajy+Hvk82/nzSb6thVLWjaDuwaa9dMapOUMNjeI1GYVRoa0aGeLElJo1P/45l6b4Elh1I5JEB7fj3qKDrU+TtBonJoR/vFA2Ndy2BTmNrvYauibl7wDVKzKnRwuT0zFphJtrtATEt5tjwEpKiKPRsW4vic6Uy82HgIYPOO6CDO7MHtefzred4b/1pXhrbucFrrOT4clGa7DwegCsFJVzKKTK4FFMj3l1F/8bvs4Xdwb2/V2t0v1k4FJ/Jgq3n2HRKBB7v3tmFu7r7GmZzogeejtZ8MbgcVsD63AD+9ckOPp0aSQdPe44nZzMmrLXJR9Xrw9rCjC6+znTxvRp8lZVrOZeeT3RKNlHJOUSlZHMqJYfnitvyp+YF/H28eW3WNMzMG/Dc8O0pAvfkw+I9QWI0GvxKVRTFGbBVVTWl4vcJQAaQWVGyOgzMQDQp3wkk6Xp26trWIggaI/QXjv3c+KBHVUUt17WdflG+5CqVY+tN3NdTXirKQd6R4Ni60adTFIWhnTwZEuTBnnMZvL3uNF9sO0dZuZaXxgZf/yHSpocwKv1+Iiy7FyZ+cbU/4BoOxWfRys6SAN0378wLsPUt8aGPCiETYcjL4BbY6MdRJ/Ye4OgLybXbUdTFP4Z3ZO/5DL7ecYE+7VsxtFPNxpB6kZMiTFQ7jUW1duaPI8m8uiqaK4Wl/N/kcO7sZoRyV9dpkHxINKRu+i+M/F/jz9kCKNeqbDx5ia+2n+dwgmjZHBvWmv/e0dmkjcTmSaKfZ/ToCazakM+MRfsY0MENVdVThbkZMDfTEOTlQJCXA5MqYn6tViUhs4D5W3z58VASzptj+dfImi1e6qRNT3GbtF8GPUZG35H1LxVFSQJ8gfWKosQiGpd/VxTlhKIox4AngdvVq+ILs4BZiqKcAZ4HZlY5ZV3bmh8La+HTk3oCLp1o3LmyLojGtHaDjbGyWwt7D5EebuqgJ34XFGc3KstTE4qi0DfQjZ8f7U2PABcW7rzA+xtqmdbyCoOZa4Wr+6+Piomra8gvLuPkxRwi/V1Q8tNhzb+Ew/TxZaL5+tFtIlNk6oBHh0+EKEWW5Bt8qIWZhk+mRuBobc6zy49xKbsR05NRvwAqmYETK+0QNIqCl6M1/1p5jFXHUhp+7qqMfEt8I98zv+KaJqI4VwTiJqSgpIzv9sQx9P2tPPbDYaJScpjasw2b5g7ks+mRpp+cStgL1s4M6tuPP57sT1s3u0pX9UFNNKpuDDQahQA3O/43MZQuvk58tuWcYaPvOlp3FSKNifuNv8hbHL2CHlVVZ6mq6quqqrmqqp6qqgaqqhqvqmpPVVXDVFUNV1V1mKqqR6scE6Oqah9VVTuqqtpdVdUT+mxrMYTrNHsaaUJ6rmJUXfbzGI6iiGxP2qmmVTrVlbZqGVVvLLaW5ix+oAfhbZyZvyWWTzefrXlHtw7w4DpwbSvGYHd9XG3zsaQr2GrzmV2+FD4Oh/1fiamv+1eJCTDvrjWf11R4RwqV54vHG3S4r4st790VTlZBKU//fKRhHmeAenw5JeYO3PanZaUdwsa5g/jpkd64O1jxzLKjtbvGG4K5Jdz9Hdh7wh9PQqqRhTS1WjiwCN7vBPO7X30vMSLpucW8vyGGvm//zSt/RJNdWMqcoYHs+vdQ3prUhUCPJugnKS0Uzb4VJqOBHvb88UQ/pvXy44G+Abg71DH52EKxMjfj8+mRONta8I/lR0nIMNBnz8JavJaTDkiVZyMjR4lqo01PYaZ4YnnjvmWd3wooolNfYjgewSLrkmuA4FdjUFWIWQvO/lfLaybAwdqC72b2pHNrR97feIavtp+reUdnP5i5Tqxl4yvw9xtijaWFaHd+wnarZ4iMXyT2m/IjPLyp6fufdHhXaJOmXG8+qi8jQ7y4v48/+y5k8unftQSDdZAQcxjl0nF+KeqOra19pR2Cq50lAW52/Phwb1xsLXhy6WG2nE5r8DorcWwNd30rxB6XTTeeV1zmBWFRsnquMHTNSxPN7b/NbrRtCUBsWi7P/3Kcfu/8zad/x+JsY8HrE0LZ8/xtzB0R1LSBRvLhCpPRq6PqdlbmvDkxjHnjWoZubUPwdbHl43siyCsu47EfDhlmVwPiM6gwCzJaTufHzYAMempDUUS2Jz8dzv3dsHNoy0VvgXdEzaOJkvpx1ykzN5EdRWoUZCeI0paRGzavxcnWgh8e7kVHT3veXHOab3fH1byjg6fo8fGOFDo7y++FTyLpf+FjCrCm5I7PYPZuCL7d5GuuE13QU4PjuiG8MCaYzq0d+WTzWfaez9DrGJ0dwuofRTasqNMk1l9jhwAQ6GHPDw/3ws7KnFk/HGJX7OWaTmcY/n1EqSvzvPDq0hpoRFsVrRb2fQkL+kLcDug2E57YB4/vhcBhcGxpRQlzhcEZAFVV2XMugweXHGDYB9v5+UAiYT5OfDGjG5ufHcy9vf2vShA0JTpRQr+bz5NwUEd3nrmtIycv5vDKH1GGHezbQ9zKEpdRkUFPXeiaRxtqS3HxqJBVlyrMDaepm5lNXNq6Flc7S358uDft3Oz475/R/LS/xiFG4Rh9/59Cd+bUKtTyEt7mAeZ6LsKy24wGjaAbHRtnkR1NaVgzsw5rCzPmT4vA2sKMp38+Uq8q7omkbMbN38X/rT/FRLPdlNi2Zua0GdjXYofQycuRHx7qhZW5hoe+PcD+C43PnNDzEehyj9AG2v5uw86RcQ6WjBUaL3ZucN+fcMdHYO0ohiCmr4RJCwEVfn0Yfpysl3VJWbmWP4+lMG7+LqZ+vZctMWmMDvXil9l9+WV2X0aFejVYb8coJFSYjOqC5puMp4YGCrmHg0ksO1DL67smqjYzS4yGDHrqwsUfAgYIdebCLMOPr+znGWzMVd1aVI6tN5HbeswaMbnn16dprge4O1ix9JHe+Lna8uJvJ/j1cFLNO1o5wIyVMOUHzt6zky+KRtA1oBFTTqbAJ1KY9jayzNPO3Z43JoSSmlPMP1ccq9HdXWeHMP6znZxNzeWt7gV4qWlYRtxdrwhoqI8T3z3YEzNFYeY3+zmc0IDXd1UURYyxe4WJ6bmYdfofqy2H3fNFdidhN/ScBbP3XD+1oyhCd+jJg1cNLj/vLY4tL6vx1GdTcxn03lbm/HSEs2m53NfHny3PDmbBjG76e7WZEq1WOKt7d61mMnozodEofHh3V3ycbfjPH9FEJWfrd6CTLzh4S5FCIyODnvoInyrq9dG/GX7s+a1gblPNS0ZiILauolG0Kcpb2ckiO9dhJJhZ1L+/EfFysmbpI73wdrLhnyuO8dfxWiaMLGwg+A4OXBS+XpEt4YOrKlVFChvJpEhfJkX6sCUmnUU7L1TbtudcBqM/3s6X288T6uPEqqf6c4/VHrFR511UDxF+LnwzsydaFe5fvF//D6PasLSFKT+IUvavj4rMTX2knxFChxteEhpKM9fCmHfByr72Y2xdYeICuPc3MeG44SXh31RDA/kX286TfKWQZ4Z1YM/zt/Ha+FAC3Owa8SCNjM5k9CZ/j3Sxs2TBjEhQ4bEfDunnywdCviLtJBTlmHaBtxAy6KmPzuPAwtbwKa6SAvENxr8vmN940wctCo9gSD/duF4JfYhp2tLWtfi62PLjw71ws7fi6Z/rnjA6VJ8Sc3NRTaSw8bw+PpR27na8s+40xxKvkF1Yygu/Hmfq13u5lFPES2OC+XV2X4Ldra86qnvq3/zas60rC+/vTnGZlhmL9nH6UiM/XFwC4M5FUJwDy2ZAcV7N+5WXwc4P4Yv+wmW7z5Pw2C7xfqEv7YeKjFDfOUJa46vBotm9REwKFZaUsy7qIuFtnHlmWEdc7PTzRGtSEioC1Zuwn+dauvg6M29cCElZhfxj2VG0+kwn+vYEVGF0KzEKMuipDysHCL5D1FUvG9BFn7BbZIhkP0/j8egsTDKzDaiHN4SYtaK3oP1tpr1OHQS42bH0kfonjA4lZNHWza5uI9PmwCtMGCk2splZh52VOfOnRqIoCo//eJgRH26rMChtxfpnBvLIwHZCrTd2o8gYNMB2ol+gG1/e24384jJmLNxHbFotgYq+BN4Gt/1HfEP/86nrG45TT8Ki4bBpnpi6e2iDEDe0bIC1g6UtjHgdHt0i/va7PhZlsvNb2XgqlfySciZ2NbKRqzFJrDAZvckzPTqm9mzDnZG+bIlJ57Mtenye6P4ussRlNGTQow86zZ7jP+t/jLSeMB6VE1wmbGYuyhGTdm0HisbRZqS+CaP03GLiMwpaXpYHhGmqe7BRyls6Ons78p+xwSRfKaSgpJx37gzjx4d74d+qSpnm+HJx20BH9SFBHnw2LZIrBaVM+3ovcZcNF1isRv+50Ol2iP4V9nwm7isvhW3vwZcDRRm139Pw2I6rDauNoXU4PLwZRvwP8lLhu/G4bphDK00etxvbvd6YJOwVavXG8Di8AVAUhTcmhBLc2pEPNp1hx9n0ug9o3UXYqchmZqMhgx59aDsQHH2ELYW+JZZzW8HOXaTbJY2jcoLLhH095zYLrZBmKm1dS10TRofixb9bZNADQpk5JxlyG6BEWwszevvzzcwebJ47iCk9/KpbdxRliyxdIx3VR4R48dE9XbmcV8z0hftIyjJQUK4qigITFoBbR1FyOrAIvh4KW96AVoFCT2n4a8Zt3jUzF7Y5j++hJGAI/fM3ssX6OdwurDLeNYxJXppQrG/CoYGWgI2lGQumR2JvZc6cn46QfKWw9p3NrYQ6c9IB05f3bxFk0KMPGjMxvp6dCPE7698/L11YWLQdVO8UiUQP3IPErSknuHSj6h0Nd1U3FaE+Tnxbw4RRpcloSw16jNjMrENRFIYEedRsh3BqFZQX1+pPZgi3d/Hm/+4KJyW7kGlf72ucHYa1oxCMtLAVIoOp0TDwXzBrG/h0a/Raa8UlgKWBH/B0yeNYmSvwy0Mii9nSSKjQ57lFSltVCXCz4/0K9fHHfzxMcVkdwoVteorAPsNwsU7J9chPZH0JnyZu9WlovrBN3LYbbKrV3FpYO4JTG9OVt8pL4ex68Y3Kycc012ggkTVMGB2Mz8LJxoL27nVM+DQnRm5mrpfjy6o5qjeWSZG+vDkxjITMAqZ9vZe03EYEPu4d4e4lEDRW9N0MfblJBht+O3aRTeaDUGdWjM4f+dHk1zQYXT/PLdDEXBMjQryYPbg9xxKv8L/Vdby3SZFCoyKDHn1x7yi+nZ38o35DxfNSn8foeATD5Zha9UgaRcIe8U3KyAajxuLaCaOo5Gwi/ZzRNKegXF14hIggxEjNzHWSkwIXdkDHke2O5gAAIABJREFUkUIc0UhM7enHq+NCOH85nxkL95GZr+eIcU0EDoOpS0XfTRNwPj2PY4lXGBXaGuvWncSH5qlVDTKCNSkJe8V4f6sOzb2SZuPZ4R3p064V3+2J5/cjyTXvJEUKjYoMegwhfCqU5os3kNpQVdHP0yoQnNs02dJuejyCxTRc1oX69zWUJlZhbgj9At34coaYMCotV+ke4NrcS6odc0vwDBWZHlObJVY4qhPW+NLWtdzfN4AXx3TiTGoeMxbuIzGzoEaRxJbG70eFxtPEiIqsZZcp4n3r9OpmXNU1lBRUMxm9VTE30/DJ1Ag8Ha144dcTxFzKvX4nR29w9JWZHiNx6z7bGkLonWKkuS5bioxzkJMkXdWNjbtOmdnIzcyqCjGrwclPfFC3YIZ0EhNG7dztGBnSwpSYr8UnEgoy4IqJZQaOLxMK2h1GmOT0jw5sz7PDhXfSgHe30O2NTdy7aB/vrDvNX8dTiLucr5/eShOhqiq/H0nGw8GKPu1biTtDJoHGXPytWgopFSajt2A/z7W4O1jx+fRISsu1zP7hELlFNRhct+kptMqMZWh7C1OzOY2kZmxdIWgUnPoLspNqnhSRpS3TUGlHccpovRvifCfFB3PPWc1r1qknI0K8GBHi1dzLqB/vKn09Lv6muUbaKSHKF3kfWNTQ4GwknrqtA4Ee9uyMvUx0Sg77L2Sy4+xVGQEHK3OCvR0J9XYixNuRUB8n2rvbCf2gJuZwQhYJmQU8MqDtVT8tu1YQOFz0reWmCgPb5ibh5jUZbQjd/F15aWwwr646yXMrj/P59MjqE4ptegr5g+SDolwqaTAy6DGU8GmivHV8GQx49vrt57cKcba2A5p8aTc17kGAYvxm5hugtHVD4lNlgitkommuodPm0dN2ojGMDmvN6LDWgDDwPJeeT3RKNlHJOUSnZHOyIhjSYWWuoVNrR0IrgqBebV1p1wSN579V9IVMiLimIT98CpxZK8qBfR43+TrqJfHmNhltCA/0DeBQfBZ/Hb/Iwh0XeGRgu6sbfSv6ehIPyKCnkcigx1ACh4FtKzHF1X9u9exAeZloqvTpJlLuEuNhYQOubY0f9MSsBisn8O9n3PPe6rh1BAs70zUza7VwYqXodfAzwLrBCJibaQjyciDIy4FJkbrlqCRkFhCdkkNUSjZRydlEp+RwLFGUIyzMFH6d3Y8wX9O9L5SUafnr+EWCPB3o3Poagc2Oo8DKUQisNnfQU2kyGnHTmow2BEVReOfOLpy+lMvb604zIsTzqgCnVxiYW8tmZiMge3oMxdxSqL5mnIXkQ9W3pRyB4mzZz2MqPDpDRiyUFRvnfDkp4v+sw/AmNxi96dGYCefsi8dMI6qWuFfYkoTd2SIaYTUahQA3O8Z2ac2/R3Xi+4d6cejlYex5YSgfTelKmVblw01nTLqGbWfSuVJQyoQIn+qlERDBRefx4v/DlHpX+nA5RkxL+sl+nmuxszLntfEhlGvV6ia75pYVIoWHpEhhI2n+d4sbEZ0txbFrNHuk9YRp8QgGtVwEPsYgZq24laUt0+AdIYw3jfX/VZUmLG01FEVRaO1kw4QIH8aGtebv02kcqRCYNAW6kefxtXlt6f5Wzd3QXClKKPt5aqJPu1aE+Tix/GBidamENj3El+rLMc23uJsAGfQ0hNbhYproxMrqWYfzW0RKXycmJTEuxvbg0hmMyhq5adD1axhbpLCspEGO6s3JM8M6oCjw4SbTqOpmF5ay8VQqvdu54u1cS8nIv58oB55Y0bzZglvMZNRQFEXhkYHtKCrV8sPe+KsbKvt6ZImrMcigpyEoCnSdKlydz1QonhbniSdjQD+RipQYH2N6cBXnCuXsgP6y/8pU6JqZjd3XU+mobnxtHlMR6OHA+HBvtp9J52BcZv0HGMi6qIuUlGmvavPUhEYjXOizEyFht9HXoDcJe8C1Pdi7N98aWjhjQr3wcbbh291xFJVWWFS0kUGPMZBBT0PpMkVMaR2rcF6P3y10J9oNbs5V3dy0ChR6I8boSYjdLMQOO41t/LkkNePSVijuGtGDC6gozygQNtm45zUxc27rgEbBJL09vx1JxtJcw6jQ1nXv2OUecdtcJa7cVMiKk6Pq9WBupuGh/m3JyC/h18MVSs0OXuDsJ5uZG4kMehqKgxe0HwpnN0D+5Sr9PLKJ2WSYW4rAxxiZHl0/T8dRjT+XpGYURZS4Lh0X/mbGoCgbYtaJDF0jHNWbg3bu9kyM8GVXbAZ7z2cY7bzJVwrZez6TYcEeONnU05Dv0Qm8ukD0H1DaCE+xhpJ465qMGsqUHm1w/P/27j2+rrLO9/jnSbLT7vSSXQrl1tJCW5CLtFaKckfwgoqIIOAFBUTAcTzqYWaOc3V0bsc5voajOEcBxxm8jwgCDoPcERAFuVjutoVS2hRahNLS0NIkzXP+WGu3u2kuO81OdrLW5/165dVkrb3Wep5kN/32Wc96fuOb+Ld7lm1bAHP64fDSEthY+9HCvDD0DMW8D0N3V3KPfNmdMHH3bYvoaXhMOzD5n2LHxp0/x4r7kyX59zjUUiHDba8F0PV67eZhPfnzpKL6G8+ozflG2GdPnENjQ+CSW5fUrKTFz7eWnagyBM77UDIhtnxrfiStyHeR0cGYMK6Js986k2UvvcZtT61JNpZvcfV8clhVM/QMxRvem6x9cf9lyejDfsePiVV9x7RpBwFx555giBHu+xZc+Z4krJ7w1zVvnnqo9WTmx66qaUX1kTZz6gQ+uGA6v312Lb95ZuijPTFGrv1dG6WWAsftX+UcmUNOT27Nl5+AG0kr74PiLsk6ThrQuUfOormxgSvuXpZssOL6kBl6hqJQhINPTUYewPk8I2Fnn+DavAGuPg9u+nOYMgsuuCOpzK3hVcvJzMNUUX2kfeaEOTTVaLTnqRc2sGRNOycfuifNTVX+Op+0R/K7auktI3ubpLLIqP85rMq0yeM59U178eBzr/DQc6+kixQWndczBIaeoSqv2QOGnpGw9QmuQYSeF5+CK96WPOZ84ClwwZ2w+0HD0z5tb/JeMHGP2oz0PHY1EEf12jzVmLFLC2cunMGDz72yXQ2vnXHt79oA+n9qqzeHfih58OKJnw3p+oPy/MPJCKuLEg7KhWk5im/fvSxZRHWvN6WLFG6pc8vGJkPPUO1zBOx6QLJ2z+Q+FgVT7eyyLzSOqz70PHY1fPsEWLsM3vmPcOb3YPzkgY9T7ey9ANY8CZ2bhnaex64a1orqI+mP3zaH5saGIY32bOmOXL/oeWbsUmTBPlMGd/Ab3guFFnhkBJ/iclHCnTJn2iROfMM0bn5yNc++9FqySGHHhtqX5MkJQ89QhQDn/QLOvrbeLcmHhkbYbf+B/8J3dcCNfwbXnJ/Muzr3BjjyMw6r18NeC5KVtFc/vnPHv/gU/PjDSUX1g06FpnG1bV8d7F0q8qHDZ7Bo5Tp+ufgPO3WO3zzzMi9u2MwH5vdSdmIg4ybCge9LbpOsXbZT1x+0Ffcl87EsMjpoFxy7HzHCd361bNuTb97i2imGnlqYMDX50MiYdhC82gavv9r7/vVt8B/vht9eATOPhovuhpkjW5RSFfbeycnM61fB9X8M3zoSFt+YVGs/8Yu1b1+dfPr4OTQ37fxoT58V1atVXtxxJCY0d3cn/0jvOR8K44f/ehnzln13Yd70Vn76YBtrp8xLNq58oL6NGqMMPRp7yssC/KGXRQqfuQMuPxZWPQhHfR4+fj1M2n1k26ft7TXIycybXoFb/xa+sQB+94OkfMIFd8AZV8KEXYetmSNtj9bxfOTwfXhs1Xpue+rFQR27qWMLNz3+AvNmlNhvt4k714B9j0+W2Xj0J8mTjcPpD7+3yOgQlEtTbO7q5nuPbUwexnCkZ6cYejT27JaGnspFCru74a6vwvdPSxbCO+uH8I4vQ2NTfdqobVp2gdLMgUd6Ol+Hey+Fr8+He7+WLET50WvgnP+Cvd88Mm0dYZ8+fjbj0tGerQvQVeHWp9bwWscWPtBXcdFqNDbBIR9Mbm+1Pbjz56nGSufzDNVJB+/BjF2KfO83z9G118KkkK+LFA6aoUdjT3mkp1yOYuNa+PFZcOc/JAUoL/wlHHhyvVqn3uy9AF5a2vstye4tsOhH8I03w61/A+MmwQcuh4vugblvz/Q8rGmTx/Oxt87kqRde5ZYnV1d93LUPt9HYEDh53hAfnpg3QpXXXZRwyJoaGzj/qH1Z+1oHD3fPTTa2eYtrsAw9GntaZ0DzxGSkZ9XDcPlxyZoj8z4C598KU2fXu4Xqaa8FQIQXFm3bFiMsuQUuOwau+yPofC15wu4zDyarBjfk49fTRcfNplho5Gu3La1qtOel9s3cvfQljp27K7tOHOKk7j0OTda+evyaZPL/cFl5XzJyl6Hbk/Vw5sIZtBYLfPvZdA6pixQOWj5+qyhbGhpgtwOSv/D//i5oXw3v+zqc+k1obql369Sb8iKF5eKjbQ/ClSfDj86Atc/A0f8TPrsoecIuZxNdd5s0jo8fOZPfr97ALx4feLTnhkeeZ0t33PkJzJVCSNY92rQWnrl96OfrTbnIqLe2hqyluYmPvXUmd6ybRldjEVbeX+8mjTmGHo1N0w6Erk3J6rLn3wJvPjfTt0HGvD3nASEZ2bnq4/BvJ8KKX8OCj8Nnfwdv/9KYXmV5qC46djYTmhv52m1L2DLAaM+1i55nQnMj7zxoj9pcvFzH7JH/rM35eirP53ESc02cc+QsGhsLPNUwNxnp3tJV7yaNKYYejU1Hfg6O+VO48C7X/RgLxk1KRuee+xU8eT0c8F749H1wyjdc1BPYZUIz5x41i6UvtnPDo8/3+bplf2jnkZXrOOmQPSk2N9bm4qUZMOsYWPyL5AmrWnNRwprabdI4TluwN3dvmpXcEq58oEMDMvRobNptfzjxb5IngzQ2HH4hzH0XfOJm+PCPkhCkrS44Zj8mjmvi67ctpWtLd6+vuW5rRfUa3NqqdOiZSfX6J6+v7XkhCT3FXWDXubU/d0598pj9KiYzO69nMAw9kkbGwvPho1f5BE8fSi3NfOLofVn20mv8/JEdR3tijFz3u1VMmzSOI2bXeDHUA09JyrvUeqHCjo2w+lGLjNbYnGkTmTQnWXB1w9O/rnNrxhZDjySNEucfvS+Txjfx9dt3HO15eMUrrFi7kffP34vGhhoHiGIJDng3LL8H1q2s3XlXPZQWGTXo1tpH3raAZ7t3Z/OzTmYeDEOPJI0SrcUCFxyzH8+9vJGfpWUmyoZcdmIg5er1j/20dufcOonZ0FNrC2dNYXnxYHbtaOPlF1cNfICAKkNPCOHSEMLyEEIMIcxPt40PIVwXQlgSQngkhHBrCGFOxTHTQgg3hRCWhhAeDyEcW80+Scqz846aRWuxwKW3L6UzHe3p6Ormhkdf4IDdJ3HQnpOH58Jz3p7MvallWYoV9ydFRvecX5vzaasQArsdeAwA99xxY51bM3ZUO9JzNXA08FyP7VcAB8QY5wHXA/9Wse8rwH0xxrnAecCPQgiFKvZJUm5NGl/gwmP3o+2VTVz9UBsAdy35A+s2dnLqm3aionq1mprhkNOSOlmrHx36+bq7k7W09npT7tZeGikHHv52ANb+/lds6thS59aMDVWFnhjj3THGth7bXo8x3hi3lQe+D5hV8ZIzgcvS1z4APA8cV8U+Scq1c46cxZSWAv96x9Ns7trCdemtrfcPpdZWNQ79UPJnLSY0L78HNq9PJjFrWDTucTCdjS0c1L2Yqx+q4VysDKvlnJ7PkYz2EEKYChRijJXLiy4H9ulvXw3bIklj1sRxTVx03GxWrdvEd371LLc+tYa37rcLe5WKw3vh6YfBLvsl83p2dtG7dSvgZxfB994PBHiDdfCGTUMjDdMPY17DMv7jnqcHXNhSNQo9IYS/BOYAf1GL8/U498UhhLbyR3t7e60vIUmjzsePmMnUCc189ebFdHR1135tnt6Uy1K0r4Fn7xrcsRvXws1/lRSOffQ/Yfbb4KK7XIl5mDXuczhFNlN85ffc/ET1RWvzasihJ4Twp8BpwLtjjBsBYowvA10hhMp10mcBK/rb19v5Y4yXxBinlz8mTpw41CZL0qjX0tzEHx0/mxihuamBkw7Zc2QuXC5LUW3l9Y6NcM8l8PX58Jt/TUrEfOw6+Ni1afkRDasZhwNweNPTXH73MmKtJqFn1JBCTwjhYuDDwDtijOt67P4p8Kn0dQuBvYG7qtgnSQI++paZzJrawgfm701rcYSe9Zg6G6YfDk/9F2zuZ2R9Sxc8/L1kZOf2L0PLFDj9O3DBL5NRHo2M6QsBOGXqKh5ZuY4Hlr9S5waNbk3VvCiEcDnwXmAP4OYQwgbgeOBfgGXAnekTBZtjjOWxzC8A3w8hLAU6gLNjjJ1V7JMkAcXmRm7/k+Op9VqEAzr0TLjxt/D7/4Z5Z22/L0ZYfCPc9mV4aTG0TIV3/x9483nJE2AaWS27wNQ5HNL5e0KAK+5exuH7Wp6nL1WFnhjjRX3s6vOvYoxxDfDOwe6TJG1T89WXq3HI6XDTnye3uCpDz4r74Na/TRYdLLTAsf8LjvwfMH6Y1g5SdaYfTuGRH3Ha3ALXPLWGp19sZ840p4L0xhWZJUnba9kF5r4Tlt0JG1bDHxbDjz8C//4uaHsADvsEfHYRnPBXBp7RIJ3X80ez1wLww/t7LqmnsqpGeiRJOXPoWcltrB+eAWseh9gNB70fTvgi7Dpn4OM1ctLQM3vzkxQa38rKtRvr3KDRy9AjSdrR/ifBuNZkdeaZR8E7/i5Zx0ejz25vgOZJhLYHaC0ey7qNTpHti6FHkrSjwnj42M+gox32PS5Zw0ejU0MjTH8zrLifqcXA+k2Gnr44p0eS1Lvph8F+xxt4xoLph0PXJuYVVrLO0NMnQ48kSWNdOq9nPktYv7HTRQr7YOiRJGmsS+dbvaHrKTq2dLOp06rrvTH0SJI01hWnwK4HsN/rTwI4mbkPhh5JkrJgxkJKHavZjVcMPX0w9EiSlAXTk3k9CxqWsm5TR50bMzoZeiRJyoJ0MvOpjffSvuHVOjdmdDL0SJKUBbsewLrdDuPdjQ9w5M0nw9Lb6t2iUcfQI0lSFjQ0sPzkn/D3nWczrmMt/PB0uOocePWFerds1DD0SJKUEaUJRb6z5T1c8cafwIHvgyevg39dCPddBt0+xm7okSQpI0otBQDauqfAWT+AD/8keZz9pi/At98Gqx6ucwvry9AjSVJGTBpfIISKdXoOOAn++D446vOw5gn49glw45/B6+vr29A6MfRIkpQRjQ2ByeML2xcdbZ4A7/gyXHQPzHgL/PaK5JbX49dAzspVGHokScqQ1mKh98UJdz8IzvsFnPIN2NIBV38CfnAavPzMyDeyTgw9kiRlSKmlx0hPpYYGWPBx+MyDMP+j8Mwd8M0j4K6vQtfmkW1oHRh6JEnKkGSkZ4AVmSfsCqd+E879b5gyC+78B/jWUfDs3SPSxnox9EiSlCGllmZe69hCR1f3wC+edTR86ldw4hdh/Ur47vvg6duHv5F10lTvBkiSpNopFZPH1tdv6mS3SeMGPqCpGY75Ezj4NPjtt2G/44e1ffXkSI8kSRlSXqtn/WCLju6yL5z0T9DQOAytGh0MPZIkZUhrxUiPtmfokSQpQ8qhp9fH1nPO0CNJUoaUWpoBQ09vDD2SJGVIeU7POm9v7cDQI0lShmx9emugtXpyyNAjSVKGtDrS0ydDjyRJGeJE5r4ZeiRJypBxTY0UC40+st4LQ48kSRlTail4e6sXhh5JkjKmtVhwInMvDD2SJGWMIz29M/RIkpQxpWIz6zd10t0d692UUcXQI0lSxpRaCsQIG17vqndTRhVDjyRJGdPaYtHR3hh6JEnKmK1r9WxyMnMlQ48kSRlTKlp0tDeGHkmSMsaio70z9EiSlDEWHe2doUeSpIzZWnTU21vbqSr0hBAuDSEsDyHEEML8gban++aGEH4dQlgSQngghHBwNfskSdLQlFrSOT3e3tpOtSM9VwNHA89VuR3gcuCKGOP+wD8DV1a5T5IkDUH56S0fWd9eVaEnxnh3jLGt2u0hhGnAYcAP0k3XADNCCHP627czHZAkSdub0NxIU0Pw9lYPwzWnZwbwQoyxCyDGGIEVwD4D7NtBCOHiEEJb+aO9vX2YmixJUjaEECi1FFjvOj3bGfUTmWOMl8QYp5c/Jk6cWO8mSZI06rUWC4709DBcoWclsGcIoQkghBBIRnJWDLBPkiTVQKml2YnMPQxL6Ikxvgg8DJydbjodaIsxPt3fvuFoiyRJeVQqFli/sZNkFomg+kfWLw8htAHTgZtDCE/3tz11EXBRCGEJ8OfAeVXukyRJQ9TaUqBjSzebOrfUuymjRlM1L4oxXjSY7em+xcARg90nSZKGrvKx9Zbmqv65z7xRP5FZkiQNnkVHd2TokSQpg0qWotiBoUeSpAwqhx7X6tnG0CNJUgaV5/Q40rONoUeSpAyy6OiODD2SJGWQRUd3ZOiRJCmDSt7e2oGhR5KkDJpcdCJzT4YeSZIyqLEhMHl8kyM9FQw9kiRlVKml2dBTwdAjSVJGlVoKTmSuYOiRJCmjWosF1m10Tk+ZoUeSpIxqLRZ4rWMLnVu6692UUcHQI0lSRm0rReEtLjD0SJKUWVZa356hR5KkjLLo6PYMPZIkZZRFR7dn6JEkKaO2Fh019ACGHkmSMsuio9sz9EiSlFHlOT3rDD2AoUeSpMwqV1pf7wKFgKFHkqTMKldad6QnYeiRJCmjxhcaKRYancicMvRIkpRhpZaCIz0pQ48kSRnWWiw4pydl6JEkKcNaiwUfWU8ZeiRJyrBSSxJ6urtjvZtSd4YeSZIyrFRspjvChs1d9W5K3Rl6JEnKsK1FR32Cy9AjSVKWtW5dldnJzIYeSZIyrFS06GiZoUeSpAyz/tY2hh5JkjLMSuvbGHokScqwVouObmXokSQpw7be3nJOj6FHkqQsK7WkE5m9vWXokSQpyyY0N9LUEBzpwdAjSVKmhRDSUhTO6TH0SJKUcZMtOgoYeiRJyrxSseDtLQw9kiRlXqmlmXWbOokx35XWqwo9IYRLQwjLQwgxhDC/YvvcEMKvQwhLQggPhBAOHuo+SZJUW6VigY6ubl7v7K53U+qq2pGeq4Gjged6bL8cuCLGuD/wz8CVNdgnSZJqyKKjiapCT4zx7hhjW+W2EMI04DDgB+mma4AZIYQ5O7tvaF2RJEm9sehoYihzemYAL8QYuwBicqNwBbDPEPZJkqQac1XmxKifyBxCuDiE0Fb+aG9vr3eTJEkaUyw6mhhK6FkJ7BlCaAIIIQSS0ZoVQ9i3gxjjJTHG6eWPiRMnDqHJkiTlT3lOT94XKNzp0BNjfBF4GDg73XQ60BZjfHpn9+1sWyRJUt9KRW9vATRV86IQwuXAe4E9gJtDCBtijHOAi4ArQwh/CbwKnFdx2M7ukyRJNWTR0URVoSfGeFEf2xcDR9RynyRJqi1HehKjfiKzJEkamslF5/SAoUeSpMxrbAhMGt/kSE+9GyBJkoZfqcVK64YeSZJyoFRsdqSn3g2QJEnDz5EeQ48kSbnQWizQvrmLzi35rbRu6JEkKQdKLZaiMPRIkpQDVlo39EiSlAsWHTX0SJKUCxYdNfRIkpQLlqIw9EiSlAtbi44aeiRJUpaVn97Kc6V1Q48kSTlQvr21fqNzeiRJUoaVK6070iNJkjJtfKGR8YUGH1mXJEnZl/eio4YeSZJyIu9FRw09kiTlRGuxwDonMkuSpKwrj/R0d8d6N6UuDD2SJOVEqdhMd4QNm7vq3ZS6MPRIkpQTW+tv5XQys6FHkqScyHuldUOPJEk5sa0URT4nMxt6JEnKiVIx30VHDT2SJOVE3ouOGnokScqJ1pwXHTX0SJKUE1tHery9JUmSssyntyRJUi5MHNdEY0NwTo8kScq2EAKlYsHFCSVJUva1thRcp0eSJGVfqVhwIrMkScq+Uksz6zZ1EmP+Kq0beiRJypHWYoGOrm5e7+yud1NGnKFHkqQcyfNj64YeSZJyJM9FRw09kiTlSKmY31WZDT2SJOVIqSW/ldYNPZIk5UhrS3lOj7e3JElShnl7a4hCCCeFEB4MITwaQrgvhDAv3T4thHBTCGFpCOHxEMKxFcf0uU+SJA2P8tNbeay/1TTUE4QQpgA/BI6NMT4RQjgm/foQ4CvAfTHGk0IIC4FrQwj7xhg7B9gnSZKGQXlOj4+s75zZwMsxxicAYoz3APuEEBYAZwKXpdsfAJ4HjkuP62+fJEkaBpPHJ+MdeSw6WovQsxSYGkI4EiCEcAowCdgXKMQYV1e8djlJIJra174atEeSJPWhqbGBSeObcrlOz5Bvb8UY14cQPgj87xDCROA3wJPAxKGeGyCEcDFwcfnr1tbWWpxWkqTcKrXks+jokEMPQIzxTuBOgBDCOGA1cC/QFULYo2JEZxawIsb4cgih1329nPsS4JLy19OnT89fhTRJkmqoVGxm7Wv5G+mp1dNbe1Z8+TfAHTHGp4GfAp9KX7MQ2Bu4K31df/skSdIwaS0WcjmRuSYjPcDfpU9tNZHc3jo/3f4F4PshhKVAB3B2xdNZ/e2TJEnDpLWlQPvmLjq3dFNozM+SfbW6vXVBH9vXAO8c7D5JkjR8ygsUvrqpk6kTx9W5NSMnP/FOkiQBlZXW83WDxdAjSVLOlIr5LDpq6JEkKWfyWnTU0CNJUs7kteiooUeSpJxpNfRIkqQ8yGvRUUOPJEk5U9o6p8fQI0mSMmzb7S0nMkuSpAwbX2hkfKHBdXokSVL2lYrNTmSWJEnZl8eio4YeSZJyqLXF0CNJknKgVCywbmMH3d2x3k0ZMYYeSZJyqNRSoDtCe0dXvZsyYgw9kiTl0NYFCnM0mdnQI0lSDuWxFIWhR5KkHCqvyrwuR5XWDT2SJOWQIz2SJCkXSsVcGTdyAAALvklEQVT8FR019EiSlEN5LDpq6JEkKYfyWHTU0CNJUg5tncjsnB5JkpRlE8c10dgQclVp3dAjSVIOhRAoFQsuTihJkrKvtVhwnR5JkpR9eau0buiRJCmnkkrrhh5JkpRxpZZmNnd183rnlno3ZUQYeiRJyqm8laIw9EiSlFN5Kzpq6JEkKacc6ZEkSbmQt/pbhh5JknJqa6V1R3okSVKWtTqnR5Ik5UHJOT2SJCkPSi3J7a28FB019EiSlFOTxzcBzumRJEkZ19TYwKRxTc7pkSRJ2ZenoqOGHkmScqzUkp+io4YeSZJyrFRsdk7PYIQQ3hNCeDiEsCiE8HgI4Zx0+7QQwk0hhKXp9mMrjulznyRJGhmtLQU2bO6ic0t3vZsy7IYcekIIAfgBcG6McT5wMnB5CGES8BXgvhjjXOA84EchhEJ6aH/7JEnSCCiv1fNqDub11Or2VgRK6eeTgZeBzcCZwGUAMcYHgOeB49LX9bdPkiSNgK1FR3MQepqGeoIYYwwhnAX8LITwGjAFOA2YBBRijKsrXr4c2CeEMLWvfUNtjyRJql656GgeJjPX4vZWE/DXwGkxxpnAicD3qUGgSs9/cQihrfzR3t5ei9NKkiS2FR319lZ15gN7xRjvhq23qtqAQ4GuEMIeFa+dBayIMb7c176eJ48xXhJjnF7+mDhxYg2aLEmSIF9FR2sRelYCe4YQDgQIIcwBZgOLgZ8Cn0q3LwT2Bu5Kj+tvnyRJGgF5Kjpaizk9a0IIFwJXhRC6SYLUZ2KMK0IIXwC+H0JYCnQAZ8cYy9/V/vZJkqQRsLXoqKGnOjHGHwM/7mX7GuCdfRzT5z5JkjQyyhOZ81CKwhWZJUnKsa2PrG90To8kScqw8YVGxjU1ONIjSZKyr9RSyMXihIYeSZJyLi9FRw09kiTlXKsjPZIkKQ9KxQLrNnbQ3R3r3ZRhZeiRJCnnSi0FuiO0d3TVuynDytAjSVLOlR9bz/q8HkOPJEk5V16VOeuPrRt6JEnKudac1N8y9EiSlHOlnFRaN/RIkpRzpWI+io4aeiRJyrm8FB019EiSlHN5KTpq6JEkKedaW5zILEmScmDSuCYaG4K3tyRJUraFEGgtZr/+lqFHkiRRKhZckVmSJGVfUmndicySJCnjkkrrjvRIkqSMay0W2NzVzeudW+rdlGFj6JEkSbkoOmrokSRJuSg6auiRJEksmDmFc46YyYRxjfVuyrAJMcZ6t2FQpk+fHtva2urdDEmSNAghhFUxxun1bIMjPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRcMPZIkKRdCjLHebRiUEMJm4A/DcOqJQPswnHcsqGff8/p9t9/5ktd+Q377br93tFuMcdxINqanMRd6hksIoS3GOL3e7aiHevY9r993+50vee035Lfv9nt08vaWJEnKBUOPJEnKBUPPNpfUuwF1VM++5/X7br/zJa/9hvz23X6PQs7pkSRJueBIjyRJygVDjyRJyocY46j5AMYD1wFLgEeAW4E56b5pwE3AUuBx4NiK4/6j4ph7gYUV+1qAHwNPp6/5YD/XH/C1wIHARuBr/Zzns2kbHwMeBc6u2HcusB5YlH7cWc9+p9f+L+BVYDPwGvCZXq69FHi93O/erl3R78eBdcDq8rWBCekxjwG/B74ChP7aCVwJrKr4Xn11Z352wCfS63YBnx8L77ca9n2H8+Sk33PTfj0CPAGclaF+9/p+Tvf9Y7qvfJ4P5aTf11acYxHQDZyekX7/E8nvzEeAB4F3Vex7L/AQye/ur1Vsz8rPvL++f4lkzb7yeX7Y13m2O2c1Lxqpj/QH9R62zTX6DPDL9PN/B76Ufr4QaAMK6denAE3p5ycDyyvO+UXgyvTzfYEXgal9XL/f1wIF4B7gh/Qfek4EWtPPZwAvAbPTr88Frhst/U6v/f2K1/4t0AFMLV877ffvSMLepX1du9zv9NpXpf0+Lr32JcD3SIJOAfgFcEZ/7ST5y/H5vr7P1f7sgHkkYfV77Bh6RuX7rYZ93+E8Oen3vcAn0893A54D9stIv3t9P6f7ShWf703yn5m9s97vHq87jOR3z6SM9PvdQLHie7AemJB+vX+67R/YMfRkve9fop9/h/s852APGMmP9M27PP28HdijYt9vgbf3csyuQGfFD+4J4K0V+68i/WXYy7H9vhb4e5LRjEF9s0nS9PHp5+fSI/SMpn6n134N+GT52hX9XgVcU+21y/1Or/0I8JGKYy4Gft5fOwf5l2PA/g50vtH0fqtV36s5T0b73Q7sVbHv58DFWej3IN7PBwAbgGk56/e36OX381jvd3pcA0mQndVj+5d663OW+z5Qn/v6GO1zej4HXB9CmEqSRFdX7FsO7NPHMTfGGLvSr/ch+V/eQMf1+9oQwluAI4BvDKYDIYS3A1OAByo2Hx1CWBRC+HUI4Yw++lCvfn8OeIrkF2YBmMm2fq8j+d9TNdfeh239Xp4ee0YIYVwIYSJwKjCrinZ+LoTwaAjhhhDC/D7aP9j+9mXUvN/K565R3wc6Txb7/RBwNkAIYT/gSJL3W88+jMV+9yuE8NkQwmLgYeDCGOOLPV6SyX4DhBCKwIeB7/SyOwv9Pg9Y1uO81chq388IITwSQrgjhPC2ak7SVOXFRlwI4S+BOSS3TIpVHnM2cCZwbI3b0gJ8k+S+ZAwhVHvcG0nukZ4VY3wt3XwDcFWMcWMI4UDglhDCyhjjfekxdet3xbXvrtjcb7/7uHYD8H9J+50edy/JvJ77SYYofwucMECT/gp4IcbYHUL4APCLEMLcGGPN69mMpvdbqlZ97/c8Ge73OcC/hBAWkfySvJ1kLgiQ6X4TY7wUuDSEMA/4QQjhlhjjy5Dtfqc+CCyJMT5WuTEL/Q4hnEgy/eAdMR3qqEaG+34Z8I8xxs4QwlHAtSGEhTHGfgPhqBzpCSH8KXAa8O4Y48b0L2xXCGGPipfNAlZUHHMW274paypet4JktGK740IIB6WjLYtCCP+vv9cCs0lS6p0hhOXA54FPhBC+G0IoVZzn2or2HEQScD4RY/xVeXuM8aUY48b086eAG4GjRkG//6J8bWA6sBjYkp6j3O8DgKMq+r2c5H9Uz5avnfa7AHylot+zSBL65enXrWmfn+jne06McVWMsTv9/FqSoc0DBvmzG9AofL/VrO99nScH/V4eYzw9xjg/xvh+kvfcExnpd1VijI+Q3JI+Pkf9Pp8eozxZ6HcI4TiS/0C/L8a4uNpvRpb7HmNcHWPsTD+/l2Te6WEDflMGez9suD9I5no8BEzpsf1Ktp98tYptk6/OJJmJPrOX832JHSdU7drHtat6LQPfPz2QZDjvXb3s27vi893Tdp9Q537fTDLxb0rla3u59qtsm8i8w7Ur+v39Xq49C2ip2PYcML+/dgLTK8791rSNrTv7s6P3Sb2j8v1Wq773dZ4c9Ht3oCH9/F0kv3CLWej3AO/ngyo+nw2sIfl7mel+p9vnkMxXmZyxv9/Hkrx/39TP9+RL9Pg3Ket973GeuSTv9f37e+/EGEdX6CEZYYjAM2x7DO3+dN/uwC3pD+QJ4G0Vx3UCK9n+kcXyTPEJwE/Scy4Bzuzn+lW9trc3WI/9twKv9GjPu9J9/5S2fxHJ4+yfrme/K67dTvLY4+vA0j6ufSXbHlnv7dq/TPv9aPrnZpInA84kCThLgCfT/acN1E7gNrY9fvubyr4P5mdHMnm8jWSC9rr08zfV8/s+gn3f4Tw56fcnSR6XXUyyLMQhGer3ufTyfk733cC23y8Pkjwhmfl+V/xu/e5Y+PdkkP1eSvIPemV73pjuOzH9PrxKMmm9jeQJrDz0/bskD8ssIgl3fT5CX/lhGQpJkpQLo3JOjyRJUq0ZeiRJUi4YeiRJUi4YeiRJUi4YeiRJUi4YeiRJUi4YeiRJUi4YeiRJUi4YeiRJUi78f65vbDTfWbeBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMcT0wXbiY07"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgLGuQsMiY07"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-ygl44biY08",
        "outputId": "573919a6-90ac-4744-db7a-6388044eee88"
      },
      "source": [
        "#model performance in peak period (winter)\n",
        "timeseries_evaluation_metrics_func(inv_y[:90], inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9955.50621066046\n",
            "MAE is : 70.82333649792221\n",
            "RMSE is : 99.7772830390789\n",
            "MAPE is : 5.66329037917856 %\n",
            "R2 is : 0.40402384411352155\n",
            "\n",
            "SMAPE is : 0.05930243873495223\n",
            "\n",
            "MASE is : 0.7074384902710521\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OY4Srs6LklJF"
      },
      "source": [
        "# SVR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZsDFjbvklJG"
      },
      "source": [
        "X_train,y_train = features[:n_train_days, :],target[:n_train_days]\n",
        "X_test, y_test = features[n_train_days:, :],target[n_train_days:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyhqsxN2klJG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f26642b-7ad2-4c80-b9e8-447500018002"
      },
      "source": [
        "\n",
        "model = SVR()\n",
        "param_search = { \n",
        "    #'kernel' : ['linear', 'poly', 'rbf', 'sigmoid'],\n",
        "    'degree' : [1,2,3],\n",
        "    'gamma' : ['scale','auto'],\n",
        "    'C' : [0.25,0.5,0.75,1],\n",
        "    'epsilon':[0.0005,0.001,0.005],\n",
        "    'shrinking':[True,False]\n",
        "}\n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = 'r2')#we \n",
        "#can define another metric\n",
        "gsearch.fit(X_train, y_train)\n",
        "best_score = gsearch.best_score_\n",
        "best_model = gsearch.best_estimator_\n",
        "best_model.fit(X_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=0.5, cache_size=200, coef0=0.0, degree=1, epsilon=0.001, gamma='auto',\n",
              "    kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9Z0WFfOHpPG",
        "outputId": "58d23792-4c54-4b11-b2ae-df977e0dd391"
      },
      "source": [
        "best_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7963717366144116"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svm2v48eklJH"
      },
      "source": [
        "# make a prediction\n",
        "yhat = best_model.predict(X_test)\n",
        "yhat=yhat.reshape(yhat.shape[0],1)\n",
        "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "# invert scaling for forecast\n",
        "#inv_yhat = concatenate((test_X[:, :-1],yhat), axis=1)\n",
        "inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,-1]\n",
        "# invert scaling for actual\n",
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "#inv_y = concatenate((test_X[:, :-1],test_y), axis=1)\n",
        "inv_y = concatenate((X_test,y_test), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFqJ_WeRklJH",
        "outputId": "e141eea4-284a-47f1-8fa7-145e7ec37fcf"
      },
      "source": [
        "timeseries_evaluation_metrics_func(inv_y, inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9895.47038649003\n",
            "MAE is : 65.05567650236055\n",
            "RMSE is : 99.47597894210456\n",
            "MAPE is : 6.030752455816826 %\n",
            "R2 is : 0.7453244731019428\n",
            "\n",
            "SMAPE is : 0.02580415331242587\n",
            "\n",
            "MASE is : 0.7460431489159594\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nRWOuzyvklJH",
        "outputId": "ccfaaa96-f30b-410b-e139-4dcdaf090941"
      },
      "source": [
        "# plot real vs predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,inv_y,label='actual')\n",
        "plt.plot(time,inv_yhat,label='forecast')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wkVNHhFklJI"
      },
      "source": [
        "ZOOM on winter 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "1PKnVTWwklJI",
        "outputId": "ccb29b90-8881-4839-fba1-b68f29093edc"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[:90],inv_y[:90],label='actual')\n",
        "plt.plot(time[:90],inv_yhat[:90],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q2J8VGiklJI"
      },
      "source": [
        "ZOOM on Ramadan 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "__uNH0uZklJJ",
        "outputId": "d6ec8d90-fd08-4a3d-c9e1-310da5c64755"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[112:142],inv_y[112:142],label='actual')\n",
        "plt.plot(time[112:142],inv_yhat[112:142],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrlNzlzkklJJ"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luHUgeJOklJJ",
        "outputId": "df6a59cc-0778-47b3-9a41-717b0c46765e"
      },
      "source": [
        "#model performance in peak period (january to march)\n",
        "timeseries_evaluation_metrics_func(inv_y[:90], inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9537.709195802949\n",
            "MAE is : 68.37879672774038\n",
            "RMSE is : 97.66119595726313\n",
            "MAPE is : 5.447873265267526 %\n",
            "R2 is : 0.42903483336778936\n",
            "\n",
            "SMAPE is : 0.057347850343342624\n",
            "\n",
            "MASE is : 0.683020528481357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "645y0g8DKdJS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjtuycfBM0Mx"
      },
      "source": [
        "# Xgboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dcGst0mtTt-"
      },
      "source": [
        "#train data: all the data except the last 7 months\n",
        "#test data: the 7 last months\n",
        "\n",
        "X_train,y_train=data[:-30*7].drop(['max_pdl_daily_load_t'], axis = 1),data[:-30*7].max_pdl_daily_load_t\n",
        "X_test,y_test=data[-30*7:].drop(['max_pdl_daily_load_t'], axis = 1),data[-30*7:].max_pdl_daily_load_t"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGp8Ujc2zyWw",
        "outputId": "76aac1da-ba40-4221-847f-1dc630561fdf"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "model = XGBRegressor()\n",
        "param_search = { \n",
        "    'n_estimators': [40,50,60,70,80],#[10,20,30],#[10,50,100],#,\n",
        "    'max_depth' : [2,4,6],\n",
        "    'booster' : ['gbtree', 'gblinear', 'dart'],\n",
        "    'learning_rate':[0.001,0.01,0.1]\n",
        "}\n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = 'r2')#we \n",
        "#can define another metric as mentioned in the tuto\n",
        "gsearch.fit(X_train, y_train)\n",
        "best_score = gsearch.best_score_\n",
        "best_model = gsearch.best_estimator_\n",
        "best_model.fit(X_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:56:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[13:57:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=2, min_child_weight=1, missing=None, n_estimators=70,\n",
              "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "             silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_y--BGQzyW0",
        "outputId": "091bdf46-c470-4bac-c947-520710c4cfd6"
      },
      "source": [
        "y_true = y_test.values\n",
        "y_pred = best_model.predict(X_test)\n",
        "timeseries_evaluation_metrics_func(y_true, y_pred)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10530.20120821769\n",
            "MAE is : 70.08992454892113\n",
            "RMSE is : 102.61676865024396\n",
            "MAPE is : 6.374341136459491 %\n",
            "R2 is : 0.7289886749894423\n",
            "\n",
            "SMAPE is : 0.028032348109999758\n",
            "\n",
            "MASE is : 0.8037747177352272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Va5Gi-sQzyW3",
        "outputId": "3dd9a86e-818b-4c8a-ad65-8f506ec5cbd0"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,y_true,label='actual')\n",
        "plt.plot(time,y_pred,label='forecast')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sqeLJLV1kC8YI4bGxYwj2hCCldDZM0Q0Trza6Jr+6d4CHXtsPKEvBV+G8E5F0QYngaQ1Rdvak6ExkaamevF/akwXDlHiEshByBQNCvvHf5EJbWxsdHR309PSMf/IpTjAYpK2tbdKuN2UFwXbRDHcZ2VYAjHQZ2a9JWRSORiuDqD+VJ5EpUGUFmwHmt8QwpUpLPWN69ajjsC0Eezylu2dldZNohYn+q7/a4nw9kM6PEK8TGVuMT2uMwBar7fiM4zyo40DBMIlYac5TPWY0mfh8PmbPnn28h3FKcvIsOw+TTMHA7/WgecrTO8sEYZjLqNqnU08cHzphK6hcHfLRJAYZSOaIZwqOuwhgwTQVWN5ysHJgeXhQuUwQKkwSmbzBuj0DvKv+IB/SVtF/ksUR0tZ+Eqc1qErOzvipmW1kmNKxJl2XkcvJwJQVhGzeGGEdAERFcdvMsiwjKTnjFytZF/wUqwN/QY1XuXo8G+5lTeBPaex8mkSmQHWJILTXRwh4PWw5WDmOYAeiixZCUQQqba1on/sB7Rm+6P0p/UMn137PdlFgW20YnyboTJxc458sdFM6rkc39dTlZGDKCkI6b6iUU9OAZ/4V1t8PD38S77/N4lzfLoI+j7N6AyA3hC91kLXmPKaJfpbEn4Ztv4NHbwUgNLRHWQglQWnNI2itCdGTVBPe5x/awJNbytMsR1gIwwrURsM+t07L4BcGQwPdR/g0imzrGjrqwbq0Nf5IQKOpKkhXIjvOO6YmuimduhfXZeRyMjClYwghn6Za5z7zL+qg0ADBDb6X6AwuLm9Ol1IBrHv1ldR6h1je8d+wpw+aF5Hv3Eog001cKzDHCjLbhP0a6ZyOlJL7X9lHKmdw+cJi07rEsKByqcuo0k5atiDUeNLqPQP7gcUTfhY2u3tTXPHt1fzkD8/h0vlNR3y9Stguo7Dfy7TqYNleFJNCx1pI98G8qyb3upOIlBLDlE4syrUQXE4GxrUQhBB3CyG6hRBvjPLaXwshpBCiwfpeCCHuEEJsF0JsEEKsKDn3Y0KIbda/j03ubYzEyfzJWpXEV38NPv0yzLmMS+UrNEX95W9I9wEw4KnmHuMd1Ca3QSEN7/1PEt56Qvk+EtlylxFAxO8lldfJ6SamhG0lfY9gtKBycWKotJOWfW5EpgDQ4wcm8ARGYq/U9/anJ+V6lbBdRmG/RnN10GkkOGms/jr86rOTe81Jxq5SjroxBJeTiENxGf0EuHr4QSHEDOBKYG/J4WuAuda/PwZ+aJ1bB3wROA84F/iiEKL2SAY+Ho7LKGttgdl+MTTMhfnvZLrs5ObThq1aLQsh66/nQeMSkrHT4ZqvQeN80v56YnqfFVQuN6rCAY103nBaZu/oSTqTAZS0rsiPDCpXiiHYk4dft2ITQwcn8ARGYndx7TnKMYnSKvGWWJDOeHZy3VTJbmRiP0v/8VdjBvSPJ/oIQXAtBJcTn3EFQUq5Gugf5aVvA38DlP6l3wD8TCpeAmqEENOAq4DfSSn7pZQDwO8YRWQmk0zBUDUIGctCCFppofPfCcB7+n8Mq7+hYgzgCEIhUEeCKBuuewLOugWAbKCRGqOfbMEc1UJQgqCuk9dN9lkrcNOUDFmTsG0ZlMYQKk0S9nFfQVkb3vTktH849oLgpSUWJFMwSGQmcd/ZVA9CGkTyfezoSY5//nHAEYTg2ILwrd+9xecf2jDu9Xb3prj/lb3jnjcWUkq+8MhG1u9zN31yGZ0JBZWFEDcA+6WU64e9NB3YV/J9h3Ws0vHRrv3HQoi1Qoi1R1KYkskPcxmF1LaYxKZB+yXw1uPw1FeUPxocQdBDDQBESoLHeriJRqEsjdgwQbBjCKl8ccKz3UapvI6UIESphVCSZTSOIHjzavUbyExOgY7defVoC0Imrwr4gj6PU5DWOVmBZSmdn9V00UMye3Q3OJ8ohnFoFsJTW7t47PUD4+6t8cDaffztLzYekaURzxT475f28rvNp25/KZexOWxBEEKEgb8D/nHyhwNSyruklGdLKc9ubGyc8HUyhRKXkdDAXxIM/sgj8OlX1Ndd1n6tqV4IxAiH1ZZ4pfskEG2iViTxMzKGEPZrpPKG4zcH2NatXD12/KA+EiCrm2VVylC5T362YOJDx2OoSTSanxxBsCdPOytqonQMpNnVm6r4eipvEPZpiMG9LOpfBUyiIOSGQFfXahV9zjM+0ShY+ynbgjCYKdA3ynPf158hlTfYM05cx05OGEhPvCZlMK2u0XeS1bW4HDsmYiHMAWYD64UQu4E24FUhRAuwn/Ka1DbrWKXjR42MXYeQGVTuotKMIs2r4gnBmuIG3qkeiDRQFVATfmmLC0/1NAAaGSxLOwUIB7yk87rjMgLY3qUsBHuyaqoKYJiSglEuCJUshEzBoIriBFGj9x7u7Y9KMqcmhCO1EG5/bBN/cf/rFV9P5w1Cfi+8/CPm/P4zhMlOXnFaqiiObaLXccmdaNhxJHth8Z1V27jsG884sSZQk7zdH+uN/fExr2f/Lh1JkaItJv2pU7MuxGV8DlsQpJQbpZRNUsp2KWU7yv2zQkrZCTwGfNTKNjofiEspDwK/Aa4UQtRaweQrrWNHjYyTZRQvuotKEQJalkCnlTyV6oFIoxM0tpvbAfgtQWgSgyNcRhG/RsGQDFp/2A3RgOMysgPKjVaLjEzBKNspbawYQpVQgqALP/WyvyxQPZyCYfLa3oGKr9O3A4yCYyH0JnOHtP1nJToT2TH3OsjkdfX8BnYDMNfbVbbr3ESRUvLbVzY637eK3hPWZWTHEPyahxuXt/K2eY0ksjpPbi3WlOwrsQo2HTi0NupHIgi2hXCyVb67HDsOJe30XuBFYL4QokMI8fExTv81sBPYDvwH8KcAUsp+4CvAK9a/L1vHjhoZJ8tosBhQHk7zGdC9WQWWU31KECwLwG5dARCubwWgUQxSHfIqP7aVNWOf12utupfPqGFb9xC6YZId2M+5YovTMylXMA7JQsgWDGKWhZCItNPIIIPJyhPwL9cf4N0/eKFs32aH3u3wvbNhwwPOarpgSGdlOhH6k3n6krmKIpWyrbNBtcXo2+sHx91I6FDY15/hF6uVZZKTXqaLXkd0TzTs1teaR/Cdm87kJ7ecQ3MswC/XH+C+NXv5yv9tZl+/+nkFfR42HRjPQjjyydy2EFyXkUslxi1Mk1J+cJzX20u+lsCnK5x3N3D3YY5vQhQME92UalLKxpVraDRalqhag/6dykKYcQ43LJ9OTdhX1gMp1qC6CToWwv/eouoWPnifY0l0W4Jw4Zx6Vm3pYsvBIaav/y4PBO5nbd9bPMS1ykIo62VUuTAtJpSPPl0zl7rkW8R7D1AfO33U821//r7+NNNrQuUvbrgfpAl92xjKzncO9yRz1EaG1WIcIv3pPKZUk1Nj1cjtQzN5g7DP41gIZ0X6+NHeOHndxO+deHF8IlugwQrub5GzaBV9TuaUjR2nqdR99lhhWwheTf0eeTyCdy1p5Wcv7naq2W235NvnNfLK7gGklOXFkiVMpoVwMu6x4XJsmJKtK5xOp3ba6WguI4CWM9T/B9dDuhfCDSxpq+bWlXPLTgvVtmBKQaMYJBbQYPsq2P0s3HsTUc2gmX5O3/MAIFm5QFUAv7SzD1/qIDnp5ezO+/kz78NkC8V9EAJezxguI5NqoVaPRp0aS6qvo+L97h9Q545oESElbHxAfR3vIJnVHaHrnmCxWDqvO0JWKRaRzus0+9KQV26i07VOcro57u5y45HM6TSgBGGDeRozPL0kh1kIT7/ZzYqv/I5u+1nox2fys60nr6f4J3bdsmmqnUXAiynhsfUHqAp4uWh2DZdlVtE5ULknli0IdXt/C+mJGdeDloUwmCmM6YJ0OXWZkoKQ103qI37l/snGK7uMGheAxwu7VqtVdGT0rCah+RgUMVo8cYLxXWqim/9O2P0sKzZ+mbv9X+d9nd9kuuhjVn2Y2Q0RXt7Vhy/bx8vmQvbPuI5PaY9idm9xLIRYyDdmDKFOU5O8p2khALn+yjH4DstV1BkfJggdr6hVuvBAfD/JnM6MWmVB9CQnlvXTV7LZTaVspXTeoA3LV+7x0phT+fNH6jZKZnXqRYIBGaXP30qYLEam/Jp7+tKk8wYvbO+Bx/4MvrUA9GMfRC2UuIxsls+o4evvW8pDf3ohmkewqzdFW12Yt3k38U3/nWz49V0Vr5fIFoiR5IY3/wbWTszQHrAsBCmPLFvJZeoyJQWhIRpg3T9cwR+c3WbFECpYCN4ATD8b3nhIfR9pqHjNAa2OVi0OB15TB1b+A1z8V0zb9RCLPcpX3u5PIITgvNl1rNnVTyDXR5+o4cB5XyBFiJaX/omcbhDwegj5tDGzjOo0NWHH2pQgpPpVtfJo5r4d4B3RImLzo6AFYN7VykLI6cy2WlJPNNOodCKpbCEYtGHlus84H9/gLpqr/GMHvg+BZE5XLqNoE+ctXwZANFPe1sMuimt44Uvw6k+Va6932xF97kSwV+A+rSgIQgjef/YMTm+q4ozWGAAzakO0S2X9Bd96jM12cHn9/bBPpUabpiSZ02kSlvhZsZnDZbAkbuQGll1GY0oKgkMhA0a+soUAsOKjkLdM9QoWAkDC10i76FSC4AtDwzxY+QX6FtzMf+uXAzDDq/5gzzutjkS2QDDfT8ZXizfWzCpjBaHBN8kVTEcQxnIZ1XrSgKC6SWXrdvf1seVggrP+6Xes21N0GRimdCyDES6jvS/B9LOUJTR0gFQmR0t1iKDPM2FBKA1I9o5hITSbliCcvhKRH+LtrZLXj7BCdiinLIRIbQsXrlgOQFW+vMjKTus8q/eX0HqmOti9hWONHUMYvh+Hzbmz6wCYWReG3rcAuMjzBnf88mXlEnr007D634BigaNdHEm8svtwLAZLxLzvFN3W1GVsprYg2H2MKsUQABa/GwKWYIwhCMEzrmOGuR823KeC0ZoXPBoDK7/ON/Q/AKBNswRhdj0RsgTJc+HShQR9Gn1U48/2kSsYBHwaQZ+nclBZN4h5MhCIOQV1/YODPLW1G1OWN6frSmSdyaes+EvPIQ9uYGdwEVS3gakTyPWyJP86LVHvxC2EEkEYGhi9YC6d12nSOyHcANPUxL040H3E9Q8pK4bgqWqCKtVRNprvG/bZBn4KhMiSmnm5cgl2bz6iz50IujEyhlDKOe1KEGbUhZUFE67Hi0Ht3ieIr3sQzAJ0qXHb8YNGLEGdoCAMpPO0WpXjroXgMhpTXBDsPkZjCII/DMs+oL6ONlc8beFVn4BwPWQGiitPVL3CIFFy0keLR63cW2tC/PwD7QC0z5pNyKfRK2N4zAIinyDg9RAcw2WUzRtUi7SybLx+TOFFzyZ57HXlHintC2S7i+oj/rIYgjzwOsLM883NMYwq1SXkSvk8N2+9lZu1p5ysqFIefq2DTdt3wZZfVnwO9kRyaWgHf/36NbD/1bLXTVOSKRjUFw5C7SxVAAgsTzxFNp8fM5i5YddBOnr6Kr6ezCqXkRZthIgK3lcbA2XXTOV0qlFZVzsyYaifCz1bK17zaKFblcpebXQL4aLTG3jnkhYund+oLIT511CoPo3/p/2K/BorRpDogMyAIwgzA5YlG+9w0p4Ph8F0gTlN1gLDjSG4jMIUFwTLQhjLZQSw8gtw070Qqa98ji8EZ/+R+tpa9YJdhyDokjU0U/SRr6i3JvtIIyG/Rp9UPmN/rt8RhFwlQdANYiIDQfUe0xsiTI43u9SEkCjxBdu1B2fOrKV7KOsUnO16/fcArNHnMOhTls/7tdUAXJF/0pnYdcNUNRMFg799cCObf/kduP/DMDC6n7ovlcfrEdwUfBkPJux5fsTYpYSa/AGobYfqGbD8wyzr/AXf9X23YiHZwXgG8yfXUv/DJfDE50ed8DKZNNUijYg2g9dPxldDkxgoSz1N5w0W16nnujWuQdPC42IhFLOMRheESMDLDz50FrPCeZXy3DAf3413MNPTQ+PQFph5gTqxe4tTYT47aLULKaQPP9Mon6Y6vYfZDRGm0Ueo44UJ3ZfL1GZqC0JmWGO7SgSrYcE7x7/e+X+qOqCWbMwS9qt8907qaJQlq9uUlWUTbSTo1ehDTe7BXD8BrzZ2UDlvUCVTjiyERpUAACAASURBVJBpgShhUVzRJbIFpJR0J7J0WCmnK2bVUDCks/Lr2ryaDtlAD7XsN5XQLfCo/oKz829Sk9oFwB/9dC2fe3ADr+4dIG+YBAa2qw/peGXUsQ2k8tSFvZyff1EdOFDewsJ22VRlO6F2tqoIv/H77Jj5Pi73vEoiM7rb6Ou/eZPp9BAwM/DSDyAxyh4QaauFhxX8zwUbaBKDZcVpqbxOi089k+0JLzQtUplW+cq9l44GtsuoUgzBodd63g1zYfYlPL/kq7xptvHWsr9Vx7s2OU0J23wl1d7xfRwO+sv/wS/4HK3BArcFH+TGTZ9RfaFcXEqY2oLgWAjjCMKhEq6D6/5d/W/h0zz4NQ9dspZas2TVZvfciTQS9Hvok2pyD+b7Cfg8Y8cQCiYRUiqGAAh/mJaQgdcjiAW9JDI6a/cMcO4/P8n9r+yjNuwr2dA+S7ZgMCuziQPRJQB0ZPwYPvX6gZnXYaJxU+5/Ib6fbV1DvLlxDdMe+QBR0sxCTcTPPPkrfv/WyBhBXyrPBcE91Bh9ZAgUs64sMnmDuaIDj9SLdR5Arm4+AaGTGugccc23uoZ46NX9hMgR91g/q8LIqms9Y02IgSp1Skh1oS2zEHIG9VbK7uZBDZoWqBeOsdtId7KMxvkTswLKNMwD4Kx3fZybvN/mK6+FIFQLXW84LqNmzyBZqSrpt7556FbPvv406e6dBEWBOYW3WC524JUFVU/j4lLCFBeEQ4ghTALhgKYEQe8tujqS1mQabsCveRwLIaQPqCwj/xgxBN0gbBYtBPxh5lTDRy9opzkWJJEtsLdPBZb39qdprQnRHFPBwq5ElnjXblpFP/lpZwNwIJ4lF1btNwZPu443m9/JjZ5nkd85g4bUdt4m1zF7aC3X1uzhNKHSW2v7X+e3m0ZO3gOpPJfzMobQ+Jn+DujfURRe1Ar9DM9u9U3LUue4p1pVexcGRgZEt3UlEZiERY4BYd1zYeSK3shZx3xhAMxwE41isMwNlcrr1HvUeXsyAQaiVnX3Mc400kepQ3B49edw99Xw8l2qwNHjg5pZAFQFfXz6stN5dnsf8dg86NrkWEA15iCbrMYADz39MsbBN+DJL8PLP6o4jqFsgXf++7Ns2rYDgPbEK8yQVk3Lm49P0t26TBWmriCY5qHHEI6QiN9Lp6zDL7PFz0z1KCHy+hFCkPGpDeIihQECXo2Ad4y007xBWKacGAL+KG0RyT9et4hYyEciW3BiAD8K/Dv/oH+XlphqIdGZyJLf/TIA2sxzCPs1DgxmyYRaMKTAmHEBr535T3ww//cIabJIbuN0j7IK3l+1kSqRYUBGWST20NU3Mk20P5VnkbGV/pplvGDald7FDV7SeYPFYje6N6JcRhZarUqfNQZHCkLBMAmSx4OkH+tnlR/ZDlq3j/mU+MmqZhoZZKgkppLOG9RYbT/iMsLWbD34ImVjPBboY8UQNtwP+9bA45+D9fcqd5FW7CLz4fNnMa06yLOJZmTXZoYy6mcdyffxltlGRvq5VK7Bc9fb4NlvwuN/A5sfG3Ucj60/wFBOR1jutll7foEHSZdohLd+A8aJ2QvK5fgwNQUhn4LvnwubHlZpm9q4LZuOiLBfWQhAcbvLVDdEixvZe30BMlqUqhILYaxK5aBRYiH4wiqQCI7LqD+dx6cJ3hF8k/MTv6Fp231owqQrnsWzfy1Z6UObttTZ5H5f80ruM1YSjtVRGwnwkrkQ3RtmgdjLfE0JwtLBpwF41LgQnzCI9G0cMba+VJ7mwn7yNaex0bQm/BK3USZvsNizm3TdIihJuQzUzVRfxIdVXCd7INNHBBVbsIPv9v2WYjqCoCwELTaNgNDJJouxm1ROp1okkQiGCLOpM8lWz2kkdrw06rM+WjhB5eEuIylVy/UzPwy3roU/+Dm898dlpwR9Gn92+VyeirciCilCfZvweSTeXD89VHOQBi7UNqN7w/AXG6F1BTx6KwyNtOjuW7OPgNdDPargzZ9Tbs0fyPcoC/rn74YXf3AUnoDLycjUFIRsXK26ujePWVswWYQDykIAisHQVG/ZZwd9GkNaLVXmIEGfRtCr2mbbroVSPHpKZfAEbAsh7KyYbQthIJWnLVRAyw2CFkD71V+yI/Bhlmz7IaGuV9koZ1MTi9JaE+JAPMsb097L3+sfpyrgpTrsQ+KhL3Qa88U+5mtKxHwFZd0svPpPAJiefKNsL2TdMDEycar0fjwNp9NPjEy4lfjOV7j6O6uJpwuksjkWir3kG8+glEhtEznpRRsaJggPfYIV628nJFTKbLep4gOjCYLMW3EFn2q/4atuAcCMFyfCdN4gJpMQqiEa9PMfz+7k96mZhPs3H9O+RnbrihEWQrxDTcQtS9Tv6KLroXnxiPe//6w2dtZeiIGHmd1PMzOQQkgTIk2EG9sBWNP0fvaZDew+/8uQi8Pu58qu8cb+OBv3x/mrK+bRIBLkrPhDtmoWj3Mxz3ImeudmeP47k/8AXE5KpqYgxFrhg/fCp16ED/z3Uf+4iF+jC9tCsCanZPcwQfAw5Kmm2hi0LAT16LN6uSBIKfEXrOwPx0KIOD71WNBHIlOgL5VnQchy6Vz9L/CO23nDu5i39d5L9eAmXjPnUhv201od4sBgxglMVgV91IZVl9M93lmc5dmG30hBo2qRgTfEeReuJO1voF120FtS0TqQLjBLqMrgYLOqLxgMzyLfu4utnUPs6E2iDewkLHKYzUvK7qsqFKBT1hFIHyx/eEMHCWW6HAuh07BEcBSXkSMSloXgr1VxEWk9cyklqbxOlRxCBGuY11xFVyLHenOOCqJ2vTHymkcJo1Klsj2GkvjKaHg1Dx+89EzWmvNo73maGQEVUP+zGy6mZfZi0oS4h3fx6f95lT/6lRVst7rL2nxn1VtEA15uOquVWjHEC+YiAIKzzuZ/P3M5H8t9jjWN74VkFxQmaUc7l5OaqSkINs2LyjJdjhZlLqOONWDoI1xGQZ/GoKeGahm3soxUuupwt1HeMKmyCquKMYSihVAbMElkdfpTeeb4rKym1uVw8V9yT9Nf4ZM5NDPPq+ZcasI+ptUE6U3mGEjn0TyCoM/jCMIbehsBYfmQl1tdzuvngMdDrrqddk8n+waKE3NvMsdsoSbf6DSVFdOvNRDMKJGIpwuE+jYB4Gkt1moA+L0eukS9c65DLolXTxFmbAvBNCUevdxCCFmC4El3W8/SREoIm0kI1TKvWRVhbZBz1Pv2r+NYMbz9tUPnRkCo381xuGRuI78xzmG2uYdzhZWNFGmClX/Pv8/5Mav2FNjQEWdn3MQIN5UJwvPbe1m1pZtbV55OtVTuIu+8d6i4zryrmVUfYU5jlC0ZK+EicVQ3MHQ5SZjagnCMCPu95PDT3fw2WPcT+Fq7cluVWAghn8YAMWplgoBXIyaTfEZ7iEymfGWWLZjF7TOHxxAG9vDnay5judzK/oEMMzXLd16t/PNm3TyeFKqgaZtvAT7NQ2t1CClhe3eSaMCLEIKasHIdrEmVVGYvfjdofqhXWTmifg6zRadT5wAqo8m2EHyNp1Mb9tFNPZF8LxoGA+k8gcHtGFIQmLZgxHPq9TRSlSsKwu7eFOSH8OtDRCyXUa+0s4zKBSFdMAhiWSteJQjCqiz3WoKQsva1DhsJCNVy2fwmlrVVs2D+YhWsHlZVfTTRHZfRsD+xzg1Qd5qTOjsWrTUhNlZdDMB78o+qg9EmCNXSNHsxed10doYdCLSWCcJ3Vr1FW22IWy5sd1KgLzlrGfz567BUtVo5Y3o1rwxae40P7p3YjbpMKVxBmATsTXK2rPwx3PQ/qhXGuX8CS97vnBPya/RTTQ0Jgppk8e6f8Ne+B/F0vFh2LbU5jjUZ2j2W/BE1QfZtR5M6b9fW05nIMp0eNTlahVqNVQH+PvdRftz6ZQoR5V9vtTbMWbdnwBGCoNVL6ZW02hpUBmKqovj678JFf67G2zKPRhGnq6e45ePevjSzPZ2Y0WngD9MQDXDAqMWDqXZ1SxcIDe3mAI1Ew+ERz2nQ10is0AOmwZudQ1z6jaeRuSQ+PUV4nKByMqsXBcGyEAhUkSFAIKMmvLS1r3VQT0CohisXt/DorRfT3hBhvZyD7FgzoZYPE6Fic7vOjYdltc6as5D/0q+ixbBcbZYILpqmntMNy1qpCfvYYzaVVZdv605y2fwmZYmW1MSUcsb0at5IWb9jh1no5jI1ObrpN6cI9jaakYAP2t8FC9414pygT6PbrEITknqjj/Zd1sY1/bvLzssWjNEtBHAC1iuEaufcZHZBzUzsZWJTLEC3GePhbBu1YXVsWo1K0czkDf71PUW/fm3Yz8F4NX1UU984T11j2U3O64EmFSPIdW0HlPtnT3+Ks7UuPA3KimisCrA7pVwO00Q/g+k8Vam97Pe0MmOUnb+G/M1oeQOS3fQmfQQoIKSBX2ao9ihLJE4E6fEhhsUQkjmdkLCqnL3qnhCCAU8doXyvcw6Av6AsBJuW6iC/1c/ksr7/hDd+AUveV7zwo59WRXDvm9zN/EZrf00uqVbxyz98yNc5b3Ydn1v3Mfwzz+JDs9MQUCv65TNquOaMFv70stPJFAw27qnmLKMDjAJ5qTGYLhR3s0vZFd7DBKE1RqesRQoPwrUQXHAthEkhYrWvKN2HeTinNUbYOqQmsvMP/JeT0eMd3FV2XrZgFi0EJ4agqoxtP+9yzw48mNTlO5UgWDRG1QSwrTvpbI85qy7MzefN5GcfP5drlkxzzq2x4gj3Bj+oWnIMp0753UX/TufQnr60iiHUqXTTxqoA27NqjC2WINTnOujxTx/1GaSClosqcYC8bhKh6C5r8agAeVoGMb2hcgvh4AbSyQRB8hhasCyddVCrJ5o5gGlK0nkdgYmvkCgrRmyKBbnfuIxs41J44jb4/vlwz/uda7N38lNSR7UQ7Ay02lmHfJ3zT1NtR95qfhdc8SXneMiv8cMPn8W85irOm13PG5k6tclTfB99KSWcDVFbEGwLoXy/j8XTqzGEl6S/EQZdC8HFFYRJIRywLYTK+/ieNbOWHlOt+BcdeIhE/TJ2mNPwJXaXnZcptRDstFPbQrDM+qjIMk90UJU7CDUznPc2WcVped10AsdezcM/v3uJM7HY1ISU++i5uhvhjPeMHHDdaQCEhorj6+/roUbGHbFojAbYklIr1mmin8JQL2GZIh6aMeJygFMtTaKDnG4SEcX4hCMIBNC1EkEoZODHlxPddA8hckosSvDMOIfT9W3c98JWUnmDKjIqPbPEQmiuCmDiYctZX1b9rfq2FQPMmQEltKO0yjgSRm1/nbQy0MboqjucttoQN50zg8sWNFU857IFTRwQykW4a9smeoeUa61oIfSoNuDDKvajAS+zGyJ0ikbXZeQCuIIwKTRGA/g0QU2o8qb1K2bV8qqcyzcK7+eFpf/M1nf8lN2yhUCivKuoHUMwPX6nIhe/LQjFKt9LPBvw5wfLLISmqqDztR0vqERtRL1eHwmMfoI/TMLXSG1WTRS6YRKIW9ZCfdFl1FkIk5U+WkQ/oSFl7aSjo6+Apb1CTfWQN0yipRaCUJ1iMwSUFWC7jHJDapOjZBch8k5A2Wb++e/ELwye/O0vMbf8iks969ULpYJgtfXY4ZsLf/2mipOk+8E0il1Dh6VsHimGqQK+ZRaCnZJcNW30N42CEIJ/fe9SLp1fWRBmN0T4woevAWDVCy8726POzGyBe/5A3Vuk0XEtlrJwWoxdhXrXQnAB3BjCpHD98laWzaimeoxJuCEaYHp9jO/1vZvZs5YxKxRmj2zm0qHVKtBp/bFmCgYx0hiBWFGtrU1yiO/HqJrOQGKImzRVVVzmMqoqTu514criBEWXkS0MozEUnsnM3EGyBYOeoRxzsfzMViGV+jzBQVnHNNFPKqVez1efNur1PLYgpAfIe0wiFFflTQxgekOYeCh4gkULwepSauaGVAzBVy4IYtYFSKFxhfk8F7/+PBfYt1PS4ba0zxORNmsvBakmaLtnUv9O1Sp7kiiYcmRRmiMILZP2OTaL5s1HFz7OSDxH0/MHiXEVM958ALb9RlkHFe5tRm2Y7fk6rkg8hzD0o17V73Ji41oIk4BP83B60/hphCtmqlVrwOfBq3nYLZvR9LQqYrPI2RaCP1byAXZQeT+ecB2PGhc59QA0zHdOC/o0qoLqD7omMo4gWC6jukoWApCpmkW76GQwXWBPX5qFYg+GN+I0YrMFqFPWM8sXJ5bZhyEFWt3oFkI0HCIpgxipXvK6SVQULYQGBpBWrCTvCY0QBJEbIkgB4S8XBAJV6C3Lucn7DD5ZIGjXVZRYCCG/RizopdveUS5suc/6SvZaLomVjEnXpkPq/2OYcmSG0VCn+lkeQsrpYePxkAi2coF8jTn7HuTPvQ8R2vkb9ZqpV6zYb60JstesR0ij2HbF5ZTFFYRjyIpZliB4NbwewR5prRQHrMDyU/9E1f7nqCKNDJQ05LNdRoU0IlzLNz23sCz3H+T/5PkRKYxN1iQ9noVgxxjqxrBqCtWzqBdDJBJx9vSnWOjZi95Y7FFkC8JB6mj19NNU6KBDNlJTFRn1elVBHwOyCj3ZS8EoDyrXywGEzxaEQNFlZAtCfoggOTz+kems3jlvB+BXxnm8ZVoB7RJBAGUldCWsLCV7I6TewxSEVC/ceQm8+rNxT9UNiW94DUKyU1kHo7huJoM9cz7Mj/R38VrwfD7ufRyhZ2DFx9SLlQShOsR+aVlubqbRKY8rCMeQt89rpK02xJzGCD7LQgDUZCQlPPdtWvc+prKMgqUWQskEG6olFvRBIIZ/2sh8dnuSrh0nhmDHGGrHsCREtQoOZ3v3sLc3xQKxD19rMXXVzmrqlHXUGr3MF/vYLVuoq3DNqqCXAaIYqX6VZVQSVPahIwLqPrMEi0Fey6XjzScJi/yogiAWXEuKMN/V3803jD9ARlsgVp7p1BwLFvecti0Eey8C4VE/g00Pw57RdxJ7cUcf//X4syANOLi+0iNz0E0TbXiV8lAnRCffXWRTOOv/8S/6h/hq1koSqJkF13xNPQtrK9PhtNaUCIJbrXzK4wrCMWRGXZjn/nYlpzVG8WqC/bIBU2hqMsolwNQJpvdTRRpPaBQLASBURyzkrTiR24HlsSZ6KFoIFYPKgNfqUKoP7CXdt49qkcLTUmzEVhv2o3kECV8jmtSZ59nPU+aZ1Ff47FjQx6CMQrp/RFAZQPijBLwesgSKvn3LQvDqSSKefNF9VkrbWfzF7MfYKmfygvcCxF9vLRdUrBoNRxCsCdC2EBoXqkn+F5+A1V8fdey/3niQ516z+hD1vDnqOaXolWIIRyF+YDOjTrnT1mbbuLfqFrjiyyrm8pl1cPFfj/qe6TWhksaMriCc6riCcJzweTzoeEmHWqF/F6RVG4pI5iAxMUwQSifBUC3VIV/FVXiTYyGMLQgXzKnnk2+fw9nttRXPCTW0A2AO7KUqbu041ly0SjweQX3Ez+7ochJVc/hM/lZ+alw1poXQTxUi06/STq2gsok1cfrDRAJetRPbMJeRT1cWglOUNoxZ9cq6CPu1UV0yjdEAvdYeEkULwRKEtrNV+qlZGNme22IgnacRq5lgz9ZxK54NQ5annEp51AWhuSqI32q3/VzLx2DxjeoFX6isdqOUWMgL/ggZrarivbucOriCcJyw3QmZQIPqNplWaZeRXBfVpNBK94H2l7uM/uqK+fzN1fMZjQXTYtRF/BUnZZtIwMtt1yxwmuyNRrShDV160Ib205i0Js+m8qZsM+vCyKZFvHbdb/ileSFQ2eqwYwje3IDlMsqiCz9pYd2fP0rYr5GWAfRcSmUFWYIQMNIq7XQ0CwGYVR927ms0YiEfed1UzQR9QZW5lbDSeNvUznJ4vGqVPMpkH88UaLIFITuofmZjUDDN8qBybkhZPUdREDwewfRaZSWUZpyNhRCC1poQfVrj6PtYu5xSjCsIQoi7hRDdQog3So59XQixVQixQQjxsBCipuS1zwshtgsh3hRCXFVy/Grr2HYhxG2TfysnFz5rssj461ThkGUhaNIgIAqI0l3eND8Ia+IO13HBnHounNMw/JIAvHfFdF78/Er83iPX+qpwkE7qCKT205bfTr9/2ghXzPduXsE/3bjEiVl4PUKtOkchGvAyKKP4CkMUCnmiZMh6wqSFlTnkjxANeBkyfch8mm/+9k1HEIJmigD5EWmnNjNLLYRRiFlZVQlrO0pnX2x/FOZeCQuvg/M+Cflk2ZagNgPpPM1WrQQw7h7NhinLO53aAnIYNQgToc0ShIbo2AuCUlptt1Fi5G52LqcWhzJr/AS4etix3wFnSCmXAm8BnwcQQiwCbgIWW+/5gRBCE0JowPeBa4BFwAetc09Z7J200r46lXaa7is/oVQQhChaCaHKLh51qiDgrbzqPxw8HkGXaCSUPsgicxvdVSOD2C3VQRqrAk5RXm1EbRk6GkGfhwFUTYU3N0hEZMl5QqSFter3hQn7NTpSAp8wWL+720k/9VNQ24pWEIRZdZaFUKF9SMxKx01krP2X7ThCqE6t2j/w3zD9LHVsFF/6YLpAkxgkH7TcTePEEUbEEIbKm9MdLWZYz+FQLQSA1uoge/Ua10JwGV8QpJSrgf5hx34rpbR3Nn8JaLO+vgG4T0qZk1LuArYD51r/tkspd0op88B91rmnLPbqMeWrh0x/sa2BzbCVuOMqCdUdg9EV6dWaaE6/xXTRS6K+8qYuNU7lc+WVacCnMSBVDr4vN0CULFlPmCTWvfkjRAJeujJK0A72DpBNJZz3ezEquoxaa0J4ROX2IbaFELf2X5ZWHMEMF59nt8cSifjIlfJgukCjGGAwtlC1gOjeUvE+QVV2l8UQho6NhTDTEgSnj9Eh0FoTYle+Rlmqeu5oDc3lJGAyYgh/BDxufT0dKK2B77COVTo+AiHEHwsh1goh1vb09EzC8E5M7Bz1pNeakHreUumPNqUWAhyyhTDZxP3NhKVapedbVlQ8ryrgRfOIMWMXykJQguDPDxIhQ0aESgRBxRAyqGuEyNE30F9+Ed/oQWW/10N7faRiML16mMto0BpHn6GeayZvcMtD1ip+mCAUDJNkTqdZDJLw1UPjgnEthLLCtE2PwPp71ddVR9dCOK1B3Y/d9vxQaK0J0cmwLWBdTkmOqE5dCPH3gA7cMznDASnlXcBdAGefffaxaV5/HLAthKTXmuB7tkCkkWQ6TdQcKja2s7FTT4+xICSD0yADuvTgnb684nlCCGrGyH4C8GseBi2XUaAQJyyyZEQ9SWmtZv1hIn4vaev7qCfP4OBg+cqhgoUAcNdHzyZaKagctATBshDSvlpqgYSoohG4d81etibD6EEN7zCX0WC6gMCkgTj7PXVQH4UdT1UcByiXkU8Tap/j/7WKw5oWjfy5TjKXL2zm3k+cz8Jph/45rTVBDkjLFZY44HSzdTn1mLAgCCFuAa4FLpfFndj3A6WtLtusY4xx/JTE9i8nHEF4E2rb6c5GlSAMtxB8x8dCyIRbYQC2ypnU1VSPee7nrprP7IbRq5RBiUZKU9cIFpTLqI8QCWxBiBAOaPRY3y9u9JJKJsovUiGGAHB6U7Tia3ag2xYEexwDxMgWDO78/Q6k8NApa2nq30uprKUPbqWFAXzCoE/UQrig0lRHwyjA/nXohsAnTHj87yDWBre+Ul5PcpTQPIIL5tSPf2IJsaDPrUVwASboMhJCXA38DXC9lLJ0J5PHgJuEEAEhxGxgLrAGeAWYK4SYLYTwowLPjx3Z0E9uhBB4PYIhzUrQKqQhXE+XsFoMjHAZhVVGjPfQs0cmg0JUtax+3ZwzZnwA4KZzZ3LeaWNPRhmvJQh6nIjIkiLIkGl3dY0qC8EShKVNPgqZIaSnpOp6DAthLBwLIatCX0lLEHrNCC/s6KV7KMfHLmjnoKwj21vSwkHP0/bA1XzT90MAumSNiuPo2WKtRClbfgl3X8XczOtcln8GujbClV8+JmIwUbya4KBjIbiCcCpzKGmn9wIvAvOFEB1CiI8D3wOqgN8JIV4XQtwJIKXcBDwAbAaeAD4tpTSsAPStwG+ALcAD1rmnNF5NkNCKK34jWMsBbEEYJah8jK0DAL26ndfNOTxhnud0SD0ivGEKwk+oECdChiQhEtJa9fvChEtcRtOjEJBZCuESv/sYFsJYBH0aAa/HsRDiQj3frkKYvX1qYn/fWW0ckA2I0kkxsR9NT3OhthmAA0ZNMWU1Myy+YZ0PcFnqCd6VfhiaFsPiUfabOIHwejykCZL3xdzitFOccV1GUsoPjnL4P8c4/6vAV0c5/mvg14c1uimOz+MhLUPoniBeM0tKq+ZpTuf02BDL/MM6Yp7z8ePyx1oViXBj/is0RP0ju3dOgIBfIyVjRIw4EbIkZZBBMwgaVpaRplpXAHW+AhGypPz1+LECvRUqlQ+FWMjnBJUHLEE4mA9hDGYIeD0sbo3xitZIKPsKmKaq7h22ccy+QgxC1joq3Q/VbWWv251rL8o+g4YJ591x1JrZTRa2+zITbMY/WlA5l7SqnScnndnlxMWtVD6OaJpAl1IVpwFZXw3P6ot4aM5XR7YamLMSVnzkmI/Rzs4Zq+fR4RD0agx5YtQZPWhCkjSDxEtcRqc1RvAElHulWisQIseAVuKGmqDLCFQtgp12uts/l5/oV/Lb7CI6BjK01YYQQiBj0/HKQnHbSasDaFyqz92Tqyqm/o5mIaR6weNDwyTpqYKlfzDh8R4rvE7VfD2kukee8KNL4JFRtll1mXK4gnAc8Xo8FAypitNQgc5M3iA0xt7MxxpHEA6j8nUsgj4PQyJGk6HqLgbNAB2yERMPVLWwckEzj/7FlQBUeQtERJZeI0pBWqvTCbqMQN2LXZgWz2vcrt/CznSQXb0ppteqCT/QqDb3MfusdtiD+5AI/sX4MG9UXUJPlqLLKD2aIHRDyxmsC3Dr9QAAIABJREFU9l3ML2s/ekTjPVbY9RJ5b5VqsVFKPqWaL264D7avOg6jczmWuIJwHPFpAt0wSVmCEBcx8oZJaIz+Qscaexe4+sModBqLgE9jl3c2000lCAO6n5fMhdxz4ePO5vO2hRAVecJk6c37GKIYZ5gopS6jZM5wjr/VNeS0fIhOXwBAfL9VeBbfR8Jbz6rglfxy4dcZTBdKLIRRMo2S3RBp4iuhv2F17XsnPNZjiW0h5LUwZIdlddlbawoNfvVZ5UpzmbK4gnAc8WoC3ZQMWcVpPcbY/XiOB0WX0WRZCBr3BG+mE1UV3F/wAWJY4Nh6DiJLiDxdWS9JJ/A88RV3LOhzXEapnO4cN6VqAw3gq2snLzXM3u3qxcG99HmbqAn7iYV85HSTrM/KAKvkMoo0jr5j2gmKHUPIadGRFoIdQ1n8brWRU7r3GI/O5VjiCsJxxOfxqCpYqxbhYEFNhKETUBAOp1naWAS8HvqNIP+o/Rn9MsoOU7Vy8JU249N8IDR82QE8QtKd9RSrmY/IQvAW6xDyelkDQNtCCAUD7JXNaP1FQTgomqgJ+ZxNheIFjxKt9DALQUoVe4g2WoVpJ8efl91XK+uJQH4IzKL1xOAe9f+Mc9X/yVFiDC5ThpPjN3aK4tUEuiGdoOme3IlnITTHglwyt+Gwi50qEfRp5HSTl4wFrMj9iO1SZen4SzuDCgGhGiegOySDJS6jiWcZVYd8JLI6UkqGsjrt9UVxcQTBr7FLTsM/uFNNjIn9dMgGasJ+p4GfchvVjrQQsoNqT4VII7phnnQWQlazCvvyyeKLg3tVt117H4xx2n67nNycONHLUxCvx4NumrxafQWP78iTyFYDvSeUIPg0Dz//+HmTdr2g10OuYJA3TKA4YY5YTdfPhYOvA5CWATJ2R1TvkbmMDFOSyhukcjrtDRG2dSeREtqsoHLYr7FetrByaKOqKTB19uj11IRLLIRMAcK1I4PKSZWZJCONxDOFim00TjQcQfBYzzibKBZGDu6D6hnFfRxcC2FK41oIxxGfJigYkiEZ4lfm+XRbm8CfSFlGk03Qp5HVTfK6SekCeoQgNMx1irzSBJU7wxusuPPXoeDsiZApkMrpVId81Ef8+DWPsz902K+xU7aimXnY8yIA23J11IZ9jvtsMJ1XgeXhFoKVstljxkjlDeY2V26lcSJhWzJpj9V2pDSOMLgXamZAtEl9P1pa6uEQ3+92VD2BcQXhOOLVPBimpGCozI2uIbXn74lkIUw2QZ+HVE7HlOW7m40QhMbijnBpAiS9Nart9BFQ2vE0mdOJBrw0VQVprQnisSbFoE9jl2mthnc+rf7T62iIBpz3dyWyKvV0WJZRd6cqntuRVivtBS3DigtPUOw2Ko6FkCvJNBrcCzUzVdsUX/jILIRCFr5/Hjz37SMbsMtRwxWE44jXIygYpuU+sXzTcEKlnU42Aa+KIYBqmW3j9w7ztzcUBSElgzwc/SB8+MEj+my7n1E8XRSES+c38o6FxQynsN/LLmntWbDxfzF8UTpkI/+/vTOPk6yqD/333K32rl6np2df2AQE2UFwwwUwKJpoouYpIRqemvie5vkx+rJoor4Pbkk0m8EtaJS4K24QxA1EkQFx2IQZGJiZnpnet+ra7z3vj3NuLd1V1QOzVNXM+X4+/anqe0/de+pW1fnd375xIM5IOsrWoQQf/e9HmSO1zGT0rTuUievhBaVtnDTcHQIBlD+r0so01BBKOaUR9G5Qfp3E0KEJhP33Kaf1rtsPfcKGI4IRCG3EtS3KgaRYrq/y3UlRRoebqFv9yiWjrTSEkypPc0QIYgOw+pmHdO6w4unYQqGiobzr8lP4qyurzfvins0EaVWEL9LDXc++ngIeG/oTOLbF5/7ofFxbcPOuonYiV+PyI4UpAim4YzRgbW+MVNRdNodOxbGsaue6sIVo2BcivUE9JocbOpWllGzfO4ts0Iu6jj13qcfRe1RVWEPHYQRCG7EtlZgWmoxCjm2TUfW9JVuZjNIbKnWLFonWCY+nS6gh7J/N6fMvv84Rx0IIwVdP+xd48x3cJ5SmskFHJG0YiPPiU1czmo+ADJRQKBfh119ksDzGNCl+vGO6a8xFIY4tWAxDe0MNIQw57Q0FwqplGkKxHPDXX/opiesvpHjdVvjO25ufZM+v1GM5B2MPNB9naBtGILSR0KlcLC8RCO6x61SO1AqEmjvo2pwAQDmPB04EVJTR4YjYWdWjTDk7x1VYZaLBMYUQxFybJ92tkF7L7qksg0mv7vwJz2a8rM0ruRnYfSd8+638jvwpkzKNlHBytwkES7AolvgQdJbyJ+7J83+/eT+P5xLLnMofu/UR9j74c7Za+1Xl3ns+17Cb3K8en2J2x50EGy5WG/bc3XpC+7fD3Z+BB76h8jsMRwUjENpIGHa6VEM4pk1GNQt/nQ+hURKXNhtliR4WgRD3HFalIjywTy14zY4Z92xyJZWc9eRUttKnuHb/WCgQstNVEwswJVUV1e4TCBZZGQVERUMYG92FLwX/ek+G79y3j5seK0F2qs7cc//eOZ7dr8b/S9+7kE6UsZs/usx89PNt99LrT7N75DLVVzo0HzVASsnU598A3/tz+No1K/avNhw+jEBoI2FiWrFGIDiWWH63fAwROViTEcC68/CjfeSIHBaTEcCmgQQ7xhaWnb+WmGeTKyqBsHu6gUCIOMxKHVKam1bloYFPlq/kluQrADh9bevucp2GbQnKEtXiU9czmj2wi3H6+ME7LuWdl53MhNRRXovV8hX7ZnOc4ExQEFF+ODXInvWvoHfnN3hwx86644tRpRHcnt8C686DR2+B/3wVjD20bC7X33ofA7ld/Dpyntow88Rhf7/NKJR9vn///mU3accLx+7K0wW4tkUpCOpMRseydgD1GkJ92GmDrN7zryX/5m0Iy6YvfngctBsH4pQDuez8tcRdh2yxTKHss28ux4aB+ragcc9mDr0tP1fJ7L2+fCUnPvf3+e7bLmHrUHfkIIS4tqAcBKoxk9YQ3Mx+xuln00CcqGsxIbWQ047lIJDsm8uzngMsxNaxdzbPN0oXEBFlCnu3V44dBJLI7E58Kfju/jSc80cwfCrsvHVZBdX7987x45+obT+ynq02LulJUUcpd9iS5YrlgLf+57289Yv38sVfPnlYjtltGIHQRhxruYZwLDuUYYlTuVWUEYBlk+gd5EtvuoDXnL/hsJx/U03P52ZaR8yzyRZ9RmdySAkbl5mMHBbCYnuF+YpACJ3f3aYdQBjgICGSqvgQ4oUxZpxVCCGIujaTFYGgFuDJxQLFcsBQaR9+7yYAvvO4Erb+fDUaac9MlqFggknRzz17M3zgtyNclfsbEFaduQ3gjp2TPFOo0uO3+WeCHWktEL56DXzmxep54DeuxlrKH1RU03Vf/znFR3/IS+KP8s1f7Vg5aqqGHz8yXtEquxkjENqIY6t+CCU/qJiJjuUcBKgXCPV5CM2/ihdsGahECB0qG2vqFzU1GbnKZPTkdHbZa0A5lTNhbaXCAhQySOFQwCXepVnmKgQ6UCajwjxISW9pnMWoytGIujbjhCYjJRD2zeYRBKRye4kNnwDAeKBbv4YNhoCH9s2zhklkz1rKgeTTd+ziN3vnkJH0MoGw7YlpLortYc5bzeO5uOpIN9tEIOy4FR79gTIpFRaUz+HGP6jsHp/P85ffvJ/Cpy+Dm9/T8v1v3zvLWQ98gC9413F98D5unP0f7Pv+hw/q2u2aXOSaz93Nl361e+XBHY4RCG3EtQW+NhmF5aWP5bIVoMI6Q1pmKh8hNtWYf5qajLRTeY8WCEt9CDHPJkcEKWy1EBUz+G4CECS69PML/VlEUsqHkJ8jKvOU4ipru15DUHf/+2ZzDDODHRRJjpxE3LNZIEZBuljZGoGwf5611hT9a7aQijoMpVS0V9nrqcuKDgLJPbtnOMPaxWTPqeRLAX56XTUfopYgUIu8pa/31GPwxM/rIpzu2jXNjXc9gTV2P/kHv9c0WklKyQe+9zCn23sob3wO2Vf/F/sYQj7w9YO6dg/pIIX79swe1PhOxgiENuJYFmVfUvIl/VogHL8mo6NTGXRDzd1+vIk2FjqVx+cLWAIGlzQHUoJEUHaTFQ2h7OjieA1yG7oB21JJkqEPQeo6UrJnLaC0pjyeEoLFRUAJhI1CaQvWwGZOWZ1i40CCadGDk6s6nh8enWVETOP1b+An73w+H3/NswAo2Ik6DeGxiQxBdpbB4l4W+k9TYxJrG5qMyuOPwNQOZk+/GoBPf/27yOnHIVddlPMln2FmcPGJZvfx7Z/8ouF7H53Nce+ucTaKMZwN5xM/7QpGU88klp9oOH4pvz2gBMJvjEAwHAquLSpO5eNHIDQOOz1aGkJPVBW0S0acSv2ipcS1D2E6W6Qv7i0bF5r1yo4WCMUFSrbSPLqlwulSXEs7lbUPITuhzB92rxII6nMT+HZUOXJRC+mJrl40+zbz/leczidecxazoo9IYapy7AMHRvEoQXo9A8kI63qV8Fy0knUCYduTM5xt7QCguEoJjcXoiNJISvm6+d71i58C8B1fOZ5Xj/8MIX0ozFX6OeRLPutEdVG//YffYny+/jgAmUKZjWIMS5ZhUIU6y+RqeuUM+OVl45fy231KEOyezjKzWFxxfCdjBEIbqQ07rZiMjiMfwopO5SPExoE4iRZ38nFPRRnNLBYrgrqW0NRUdBJaICyq9pN0r0CvOpWVhpCZUFE28UHlzA8/t1qBsG82x6mxKWW2Sa/ntDVpzlzfS8bpJV6q1nlK5verJ2klXMIEwQXqNYRtT8zwysjdyEgP/vqLAJjzlMnqKz/6ZWXcQr7Ezu13UpAOdyyuIRMd4fnWb6pvRh8zXwpYrzUYKWwutB5mx3hNrwdNruizVexT/wyqZEg7vQYbyfzUvobXS0rJDbfcSfb6y/mbJ69mTVJ9f7ePzjUc3y0YgdBGQjVdaQjV8svHMlFneR6CbYmj2kzmwi0DPGOkp+n+qKt8CFOLRfoaCITwMypYCWUDL2QoWMrJ3K0+hLCuFpEeKOcpTDyOLwW9w+uBGq3IitRpCJvtKeX4tavve9HtJ1lWlWCllPSV9V16WjVDiro2AwmP2SBWJxDGZ+d5IXcjTn4p6ZRK7Ju0hwDYvevRyrgv372HzeXH2W1v4OHxAgecNSRFzZ1/Xt2xKw1hEomguPlSnmf9huEfvWNZcb1cyWer0EJLZ8fH+5Xwmtj/xPKLVS6y9/sf4ao7X4W371esl/t552YVGbW9ldmomIVPPgd+8a/Nx7QZIxDaiFuzCPbGXSxxHDiVa4vbaYFwtPwHIe+6/BT+45rzm+6PezYlXzKxUKA/3lwg5O14xamcF0ogdK8PQVR9CIAz9QgT9LIqrRbm8HMrWVFViwgVZbTKmlNF72rIewP0BHMglfY7gvYnpNdXxqxOR5kqxypJcAAnZe4hKTNw2isrzYgezqnIJi9TdSyPzeU4zXqS4tDp7J7O8kip/vyhHyFf9tlgTSBSI3inXsmQmOOEfTctK7+dK/pstfapvt76/adXqbnOjTeIcPrFP7P+7g9yX3AClxWuY1QO8LyF77NlKMFv9rbQEH76ITiwHfbd23xMmzECoY04NWYSz7E4aTjF1qFEi1d0P7VRRqloKBA662sYLvijszn6G/SSDkNLcyJecSrnRExlmXfYezlYXFsVWiSiBEBibgcHZH/FvBNqCCWtIeSKPtOLRXqDWVUWu4ZSdACXMuRmyBV91ohJylZUtR3VjKSjjJUiqhy2ttOfl7tdNenZ+gL6tCC+ezpKIAWJ3P7Ka53cOANiHjFyBgD3ZFR71ympy4VoDSFXDNhgTUDvBsTZr+fNfddzc/KV8MQdFcc4hBrCPop9J1a2Da7ZpPZNjda9t3zJp/jwD9gut3LjSX/PHns9Xyk/n/4DP+eSgUV2ji/UjWf0HvjaG+GzV8Cd/6S2Le2010F057f3GKH2ztizLW5++3N503O2tHFGRx4hREUoRF0bSzSpY9RGwmzxYjloqCHYlnoPWVHVEBaJEfdshOiOPspLsS2BH5qMgHR+lP3W6orvIHwsCSUQphZV17NEeabaTU3jx7WAWJwkW/QZEVNkY6tVTwXNSDrGgYK+tjr0dNAfYyyyGZwIUdfCcyy2788xTi+9pbFKolj/vAot7d1yDgCPS+Vn2BboHho1GsI6MQF9G8GyiQ6fxPcLzwK/AI//tDKXXKHMVjFKMFAVCOnBEXwpKM/V+xBe9fFbsEa38TP/dN76/BN49Tnr+FH8JQBcnL2tUgMLgCfvhE9dSrDjv9k1neMO+zzyw2ct77TXQXTWL/E4w6kxGbnHcP2ipYSLi2dbuLbVcbWbav04jZzKoBzLi8RqBEK0aV5DN6CSJAPY8jy45B3cMPhOPpt6S2W/a1vYlqAoPCjlyBTK2PhEijPLNASRVP8X5w+QLfqsFVPk42vqxqxORxkrqPLmoR8hEuQq4btCCPriLntncozKQVbLyUoDqVVZFYk0fMLZRByLh4ON+JbLj4Nn6eMpgVAsFhhmqlK+e+tQkh8sbEZ6SdhxS2UucnGcHpFDDFYFgrBdZq0+rMyBunmvnbsXRwRsOf9KzliX5r0vO43Pv/13ERsu5Mz5n5Av1WRK3/cl8FL8ce9nefH0u3hz6c/5+XQPgdEQDI2oNRlFOuwu+UgSdS1cW2DpQn6dZjKqjfRqJhBirs5WLmXBL5KR0a4OCHBCH0IkBS96H98QlxLtrb/zV7kIUSjnWSyU6SODQC4TCFZKvS43s598yWe1mKaUWF03ZiQdZZ76hjwRmSdwYpUxvTF17ffJAdaKScYXlFYSL06TFTHseC8nDicZo5/ptzzI7dEXqBdqDSGeG8MmgN6NAGwZSlLCYWHNc+DR/64kqsmscoA7qfr3u+AOEslVw1b9QHKh3E7JivLSl16FEOr725fw4NRXsDr/GCMl7XPwS/Db78LJV3D/JLz63PX8++vPYXcuSmlhkoY8/B342Uca7ztKrPhLFEJ8VggxLoR4oGZbvxDiViHEDv3Yp7cLIcQnhBA7hRDbhRBn17zmaj1+hxDi6iPzdrqLWpORu7SF5DFMxLErZqKIYx11p/JK1Dr2G0UZASQiNvNBdfFaCCLdrSHoJMmQ+VyJ3iXmsqhrURCu1hB8BoV2oC4RCE6PcvIW58bIFn16ySBj/XVjVqejzMtqgUA/kMRkgcCpJg6GjuVROcSImGJ8XmWOu+UFskIVDzx9TZqhVITBwVWsHx6giFvpdZ3Maft/qCGsUufbGz8FFvZBWQmYQFerdWP1Jcvz0SFS5erinS/5XGI9wP7es8GpT1bk1JcD8CL5C4JAwq6fQm4G/xlXMZ0tMpSKcPEJg5QjfUT8xUptpdt3TPDX33pAmcPu/gz89CNt7SZ3MLdm/wFcvmTbu4HbpJQnArfp/wGuAE7Uf9cC/wZKgADvBS4AzgfeGwqR4xnHqnEq2917d/lUCe3DUDUbdRK1d/oDzTQEz2FOVgXCXNDdGkKl2qmmWA7qAgBAmfryUvkQMvlyVSAs8SHEeoaU/X1+jFxukagoIWL1Bf9G0rGqhlCYJ1ssExMFpFcNqggFQj6xhogoszCp7PlRP0NOJwL+xeWn8OVrL0QIwRnrepmVCcr6jj9R1Iu5zrbeNJBACDiQ14JbV3UNCsrBbEfrK9TK5GoG5TRzObVA54plNogx5ntOYhk9a9jfcyYvtu9RPcMf+jZ4KaZGLkFKGNLBCTlHhztroXXbw+N84ZdP8uhYBiZ+q/wbbez/sOIvUUr5M2Cp0esq4Ab9/AbgFTXbPy8VvwR6hRAjwGXArVLKaSnlDHAry4XMcYdTqyF02F3ykSTq2lWB4HSeD6HWZNRUQ/BsZv1o5f853+vaLGWocSprCmV/2ecSdW1yuFDOsVgoM0BjDSGdiDJNiiAzQXlRjbFivXVjBpMeCzUmo2zRJ0EevKqGEEYaJYY2qWGTT6h5BIvkbbV49yU8tuhS4+du7GNOJpifUWaeSEnnBMT7K/NPx1xmy/ruvqgjgnS1Wtz6mlVOeoQhMc+eSZ3ols0QEWWCaP17CZlLncgaMUm+5KtFfe3ZTOYs/X7VOfOufq32IxTKygn9w18/Cgs6kmrfrxse/2jwdH+Jw1LKMA7sABAGAq8FagN39+ptzbYvQwhxrRBimxBi28TEwdUS6VbcJWGnxwtRp14gdLKG0CjKKBxTKxBmypGurXQKujeHXysQGmkIFrlQQyiUm5qM0jGXSZnGWpygnFWLshOvNwgkIw4ZUTUZLeaLxEQRq05DUNc+ObwZgGBGLSHxIEPRXt5v4pyNfcyRIDunymbEyvMECIhWtZNErWanNQRZVKYovPpjJodULsK+vSpru5jR98VNBIIf7aOPDPlSWXWWSwwxmVFmqUFd0K/g6dfqSKPQCf3I/duqB+pCgVBBqliww9b0VEp5vZTyXCnluUNDQyu/oIupzc7ttNDLI0nErQoBryN9CEogxFy7acOiuOcwXa7akadLXstyGJ3OUg2hWA6W3aTEXJts4EI5TyZfZFDMI22vbsEFJRBmZQorP4OvHbxuon4RFUJgRVJqwc7Pkc+qu3QRqS7KockopQWCk1E+gXiwSNFd3qJ0IBmh5PbghyYjf46clQSr+rnEPZv5QH9u2ncgSjonwavPARpYrZzRU/uVQChp4SZiTQRCrA9HBBQyM0ogxAeqAiEZVnhtrCHE5nboN70R9t/X8PhHg6e7Co1pUxD6MWxZNAqsrxm3Tm9rtv24pi4P4TjSEAYSXsUcsL4vzrq++AqvOLrEXXWn3yzCCNTCMlkrEMpeV2sIji0qbSOllFpDqBdwUddmUaprUshlWWXNIxJDdfkFAD0xlxmSuIVZyCktwksudxmm4hHyVlwLBHW3btcIhLBL3qqhVSyKBNFFtWQkyFJyGves9pL9OMU5gkAS9xfIOvUlSuIRh5my1uy0hmCFSWpe/ffQ61OhsplJlSVd1hrCUvNXiIyqBLny3AEVOVUnENR186PLNYSBhMeJYq9K3jv15XDggYrD+2jzdFehm4AwUuhq4Ns129+go40uBOa0aekW4CVCiD7tTH6J3nZcU+tU7jSzyZHkr688lX9+3VkA/NNrz+K6331mm2dUT6gVtBYIDlOlqkCYLLokutip7NRoCKHpqJFTeTFQQq+QX2TYml9mLgL1Xc5YKWXDD0NKk/3LxqVjLotCVTwtZFVymhOt3qWfu6mfi7YM8IyRFDPuMD2FAyAlKbmoeik0oKdvkJRc5LGJDKlgnrxTr70kPJsZX3+u2ndgl7XJyF1SJSA1oq7HjBJEgXYE24km8TAJ9R7FtO4nHe9nMlMk4lgV/5If1dchGwoEn/X9cU4Uo0zHNsLacyAowfjyXtNHg4MJO70R+AVwshBirxDijcB1wIuFEDuAF+n/Ab4PPA7sBD4FvBVASjkNvB+4W//9nd52XOMcrxpCMsJIWtlxHduqy8foBDzHwrFEU4cyKA1hvFjt4paRUeJd7FR2dKFFpR0oM8ZSM6YSCOqalPKLDDYRCABZp5doeR5RUGYWO778rjodc8mQgPw85by6S3drIn22DiW58doLSUVdFqMjDJTHKeczOCIg8BprCKm+VfSILLsnF0jJBQpLBELcs6uCXGdI236WEi44Sz7v+CABNtbiAYJAIrX5y2kiEKz4oDreTCgQBphcKDCYjFQy2J1IkiJOjYbgk4jYnGyNciCyEdadp15bk0l9NFnxGyylfG2TXS9sMFYCf9rkOJ8FPvuUZneMU6sVHE8aQjcQ82z6483bdsYjNosygkQgkGSIdb2GAFSq70J9IUKAqGOx4Kslo1zIMiDnloWchhTdXmzfJ5HTpR+iy/tM98Rc5lEVT4s5Zb7xYo0X+mJyLRvnfs3czBQDgIw0HudqTSS7ME0PC2QiJ9ftj3sOo6WwZIbSEJxyjqIVZdmnbVnkooP0Z2bYP5+vJLx5ieXaDoClt3szj+mTDTCRKVQcyqBNVjLFcLZqMhqJllgtprjH3sgZ6XUwcqZKarvk7Q3PcyQxq1AbqS1dsVQ9N7SXPzh3PZefPtJ0f8JzkFhIL4kUdlf3U4Zq1rwfKP8BLNcQYp7Ngq+WzXI+Q6+cg8Rgw+P5UXUX3ZPbSxl7WUgnqGZFYQlsX2sIXrzxQu+n1tIjsszuV2WmZWS5gIGqryI7O0kvGUpevWaSiNhMFVXHu9CH4AVZilZs6aHUeRLDDIsZHhvPVCqzRlKNBYKbUtciOq/mqHwIxUoOAqhrOCOTyFAglH02SeWjeEyo8uCc8jLYezfMVwv6HS3MKtRGjIbQufzVlady+emrm+4P/QyBlyII+yl3cZRReHNS8oPmGoJrM19WQi9amMSlBInGGgI6M7mvMKrCSxsU/UvHXKb8GDI/g59Xi3OkiUCQupdCYf+DejKNBUI0pRy7ubkxekSOsrfUZOSwWAxUiKn2Ibh+jqLdWCC4vWtYJWZ4bCKDVZhlXsaIRhqbEr14LyVpk6gIhH4mM4W6Fqxxz2aWJH4YZVQKWF9WUUyPlHW9p2e8TD0+8r2G5zmSmFWojRyvPoRjgbARju+m8B3ljOxuDUF9F+s1hOVRRgtlpSGki2NqY3yg4fHspNo+VBwlKxqXdE/HXGaCJORmCXQuQDOBYGuBYE2oLF4r3lgguEl1l+5qs03FiatJeDbZkq9MTtqHEJF5ynbjSDevby2rrVkem8jgFOeYJ9G0q2HUc5glSaSkHOlBtI/pxWKdQIh5DjMyWRd2ura8m5JweaSor+XQyTBwgqptdJTp3m/wMUB9lFFnxeIbWhMmr5XcBEFJlTYYSkVavaSjqWoINT6ERolpqLvjdFFHmjcRCI42n8TlIvvsNQ3H9MQc9skUopjB0hVKhddYeLi6g1lsVsXr27HGAoF+VT6ExziPAAAWuklEQVR+1fz9AMhYvQM4HnGQEqSXQGgfQiTI4TuNBYJIjdDHAlNzCzjFeeZlgrVNfqtR12JMphgSc+AlmS3Z+IGshJwCxF1lMiKntIJ8KWB14UkmIxuZyenSIULAxW+HYOV+zocbIxDayPGah3AsUOmaFlvN1KJFf8Lj5OHGd7fdQL0PQUcZLRUIjk1eC4QBf0LZF5oIhGhP1beQb5IzkI65PISKKoqHzucGvgaAWL/SEPoWVQSPE2sW+jnIPEm25B8MX1i/W39uvpvCKixQ8gNi5PGdJkmwKWU2tDJjeKV5pkWyac+LqKvMQeoN9S/LUgb1vRkjhZ2fASnJl3xW5Xcx2nMms/tKSCnV8c9+feP5HGHMKtRGasMtXct8FN1EaB6678z38bbi27hoywDWUewLfbhp6ENYmqns2RUNYUiq8hDEGy/MifQAvlTHLDQoMwHaZCTVvlRBm6CaaAg9yQQTMk1PWZlanGTj5DCEYL+7js3+bvXvkvmFn5vvJKCYIVfyiVMgaCKIwlwELzeOV14gYzV+L6Cu13TYtS0+wM5xpYHUm4xsZmUCEZQo5xeIBFnSxQMs9pyAH0gyhaOvFdRiVqE2Ev4Iw94Ahu5h81CCnqjDx24f59EFl4tPaBxt0y3U+RB0xvLy4nYWeZ2pPCJCgdBYQ+hPRplDLe6lBmUmQAsE1L508QAlHLAbh/qmog77pDpXUdpEo40FB8CEtx5LqOQ6O1E/v9DxX3QSUFggV/RJiDyyqUBQGkK8MEG0vEC2hUAQQjAvVMJcKdLH333nIbYMJnjW+qrwintOpahfYXGOrUJpRmH7zrAJULswAqGNVAWC+Ri6jWTE4X8+bysP71eOyYtPaLwwdguhP6scBBRKjQWCapCj7nbXiCkC4VRabi6lP+Exo++WS26TrOKoWxnTXxqjIKINx4H6jUwIdY0XiBNt4cCfiW+qPLeS9Z9LqCGU7LjqhV1UGkIzzSTUEJLFCWL+QqXKajMylnqv9884TC0W+MRrz6p0CFTnt8no4nrFxTlOslTIqRw6BaBSartdmJWojTg1Bd4M3cc1F29iMBlhbW+MDf2dVY/pqVKXmOaHJqP6aJqIW/UhREWJkpduGE4KWiDou3+/idCoNRn1BdMUmuQChEzbSgubl/GmkT4ACwlVDK8o7WWJbqGGkLcTUJgnVywTJ4/wmiz08X584TDkjxGRefJO4/cSkrGVs3tPPsZFWwc5fW298zvm6U57QCk3xxaxH184eENbgfZrCMap3EZCp7LRELqTuOfwqTecQzmQTR2N3UJ4c1L2JQXdKH5ZlJFjU8JW5RzwVfevJsfri3s8oBd7v0ndoVTUqTphoWlyWMi8uwoKSkPobyEQ8mklEOZILtMkYrpwYV7EoJghn8/iiKBpdBNCkIsM8Yyy8kkUmjjIQ3JOGnwY8xMNM91rNYRydo5eFih5vfQm1bbZXLHl8Y80RiC0kVBNP55KXx9rnLXh2Gj8V+dU9ps7lUFQsiJEgixBs0gfVMRNxtKLZ5OsYse2cCNxilYML8hRbpIcFpKJKIEwL+NEvea/maBvM2VpMSOTJJcIjlBDyIo4BGXKur+xFWluCpoeOIsLcjcDKK2oBVknDQU4UEosa0EKqpJuqCGUc/P0iCxlr6dS6rvdGoJZidpIqCEYk5Gh3dQ5lZv4EKI6c7kglF4gY639JjlHO1Ob5QygC9zZSoNYSSDkYsrBmyHe8iYqGU+wRw4xQ6rOfg9VH0JWqHPJhQMAOE1qKAFMb/odHKGuSbmJPyQk66kw19FisrLI1xLzbBa0QAhy8/SQxfd6SMfUWONDOI4RQmBbwmgIhrZj1yamNfEhhHb7gvYjWE2KvIWUIkogiCZlJkAVuFsQajEOnNYCoZRQDt5Fq3kuQHjMj5d/j8+VL68IsZBQQwjv0tECYWk/5VqKG19ARiqHdzPzV8ho9BT+X/Qd/Cg4q9LzoxbPscgL5W8K8gv0iEVkpIeIY6uyFtn2mozMStRmHEvgOt1tfzZ0P25tYlpTDUGbW3ToqZ1sHWoblo2wmzSUAUjHHGa1ryFY2o9gCTKpNIS81XpcT9TlW8El3BycT3Rpkx/HRgjIBGqBtxdV/oPbQiD09KS4LThbzbFJ+8zK8T2bLyxeSAmnoYYA4IcO7MICKXJI7XTvjbnGZHS849qW0RAMbaeiIQQBRd/HtkRdi1egsrhmA7XQucnWJqMnBp7DJ8tXEgyc1HRMT9Rl3FcLZNNcAE0snuCDpdfxQ+8FLcf1xJRZyHOsZfk9liWIuzazWiDs3r1LvZcWJqOeqMsN5ZewLTiJfKJxGY6QqGuT0075RhoCgOdFKAkPCkpDCHs0p+Mes8ZkdHzj2MJEGRnaTpgp7/tKQ2hUjj105C5ogeAkW/c8d3pGuK78OmLR5o2G1vfH2VfU5psVBEJPzOVT/pXsjZzYelxUzS/axDcXjzg8Mq0S13LTqhtaJNHcFJSOudwrT+JVxffhtUiIA+rCYZsJhLjnkLMSiKLyIQjtY+mNucwZDeH4xrGEcSob2k7oVC4HKsqo0Xcy4tj8yXM2k4hX6/W0oj+hFuZ4i8ZBb3/RiUR6lGARLSJ9oLrQx1ZoRNSjHbRLHcohCc9m+6QSCKuEbnrTwmQU9+yKttTsmCGRmv3NTEYx1yYn4ji5KSKiXOnR3Bt32x52alaiNuNYxmRkaD9LO6Y1a9j0l79zKqdt1H0iYisJBBWN1CqJLBV1uerZqqf2mlWtTVChKWipX2D5MdW4ZoIj7jmMF9RiPawFgtVCIAgh6AmPuYJAqHViN2vBGvNssiJGLKsa4ISVW3vjxodw3OPYRkMwtJ+6xLRyYw2hgqtLTDSpYxTyglOG+MMLNrBpcAUzi9YQEi3MNlBjClpBQ3Bti7hnNxUciYjNvK4ntNWZUBubJaZpwrDQlbSTUGC4tmjaUjXu2SwSI5FXAsFJaB9CTPkQVCfi9mBWojYz3BNluKd5DReD4WiwtNrp0pDT+sHa5r+CyWgkHeODr3zmyj6ysCKpt7IPASDmrrxs9UTdZSGnITHPIU+EbZELiAeqIikrRDhVz72ShqD2p2Ne09DYmGuzIGPESjPq1Pr998ZdiuWAvI7yagcmU7nN/Mc15xmnsqHt1HdM81ubMd0YCKtpG8unTGh6WmlRPkizDSjzUqSFDwHgrg1v4twdd4EdAbv1UphewS8REgqhvib+A1AawoKs3gRace1D0OeYzRWJea1zMo4URiC0mVS0+RfHYDhaVMNOlcloaT/lOjY/VzWotw5TD+nh0+GCN8PWS1sO6zlIsw3A6WvTpCKNl7cwWzm68TyQL4Kxh1Y83sE6tEOB0SzCSB3DYS6oWfCjVR8CwMxiiZG0EQgGg6FNVMNOA+VDaKUhnPpy9Xe4cDy44kMrDgudxSvdpQP8/e8/q+m+MFt5y1ACzv5UJVu5FU/VZNQswgiUhjAbRCG0KOnEtHRMCZF2RhoZgWAwGLDt+iijcPHtJCKOzZahBFuGWoenrkSoIZwwlIR4fEVfCFQjnA5WILTSEOKezawfqa6+SzSEduYidN6nbjAYjjpupUGOMhkNrhDa2S5+9H+ef8jHOH1tD6et6WFN78GbZaomo9b+vjAZrjfRXENIRBz26RLYJRxcVz2vVDxtY7ayEQgGg6HiQyj7AcWy3zQP4VjgyjPWcOUZrUtQLKVf5xQkmvglQkIfQysNYV1fjEe1QFgUCXp1NFJvaDIyGoLBYGgntYlphRaJaccrLz9zDemYu6Kzt2oyaq4hbBlMcpOutpq1koTl8qKuhedYbfUhmE/dYDBgWQJLqMS04kqJacchiYjDS585suK4UDNY3UJwbB5MVMpv11ZuFUK0vZ7RIX3qQoh3CCEeFEI8IIS4UQgRFUJsFkLcJYTYKYT4shDC02Mj+v+dev+mw/EGDAbD4cGxLUpBYDSEQ+CEVUlu+rOLee6JzUuDxzybWEI5knN2vYO83eUrnvanLoRYC/wv4Fwp5emADbwG+BDwD1LKE4AZ4I36JW8EZvT2f9DjDAZDh+BaAt9oCIfMGet6V+yx3d+vBMbSHs29Ma+rTUYOEBNCOEAc2A9cCnxN778BeIV+fpX+H73/haLbO5MbDMcQtiVU2Km/QukKwyEzNKQEQtGp1xDS3aohSClHgY8Cu1GCYA64B5iVUpb1sL3AWv18LbBHv7asxy+rjiWEuFYIsU0IsW1iYuLpTs9gMDxFXNuiUPbxA2k0hCPMmlWrACgu6dHcG3Pb2lf5UExGfai7/s3AGiABXH6oE5JSXi+lPFdKee7QUOsGHAaD4fBhW4LFgur2ZQTCkWXD8CB3+qeyP3VG3fau9SEALwJ2SSknpJQl4BvAxUCvNiEBrANG9fNRYD2A3p8Gpg7h/AaD4TDi2hbZolLujVP5yLJlVZLXlf6KJ1bV12/qjXvkSj553YbzaHMon/pu4EIhRFz7Al4IPAT8GHiVHnM18G39/Cb9P3r/j2Q7C38bDIY6jIZw9FiTjnHOxj7OWFtfMTbMVp5vk9noaSemSSnvEkJ8DbgXKAO/Bq4Hvgf8lxDiA3rbZ/RLPgN8QQixE5hGRSQZDIYOwbFFjYZgnMpHEssSfP0tz162vZKtnCuxqg19Ug4pU1lK+V7gvUs2Pw6c32BsHnj1oZzPYDAcORxLsFg0GkI7qdQzapMfwXzqBoMBUP29swXjQ2gnYSOe2Wx7chHMp24wGABlMjIaQnuJ6+J4uS50KhsMhmMIxxImyqjNhG0/C23qq2w+dYPBAOhaRr4K/DMCoT2E/RTy5fZoCKb8tcFgAOCNl2ymN+aSK/mcsCq18gsMh51QQ2hXHoIRCAaDAYDLTlvNZaetbvc0jmtCDcGYjAwGg+E4x7EtHEu0zWRkBILBYDB0EBHHIm80BIPBYDBEXZuC0RAMBoPBEHVtoyEYDAaDITQZGQ3BYDAYjnsirk2hbDQEg8FgOO4xGoLBYDAYAIi6lslDMBgMBoOJMjIYDAaDxuQhGAwGgwHQYadGQzAYDAZD1LGND8FgMBgMEHEtoyEYDAaDQTuVjYZgMBgMhoijNAQp5VE/txEIBoPB0EFEXRspoegffS3BCASDwWDoIML2pe0oX2EEgsFgMHQQ7WyjaQSCwWAwdBDtbKNpBILBYDB0EFGtIbSjfIURCAaDwdBBhD6EdpSvOCSBIIToFUJ8TQjxWyHEw0KIi4QQ/UKIW4UQO/Rjnx4rhBCfEELsFEJsF0KcfXjegsFgMBw7RLvYh/Bx4GYp5SnAmcDDwLuB26SUJwK36f8BrgBO1H/XAv92iOc2GAyGY46ujDISQqSB5wKfAZBSFqWUs8BVwA162A3AK/Tzq4DPS8UvgV4hxMjTnrnBYDAcg3SrhrAZmAA+J4T4tRDi00KIBDAspdyvxxwAhvXztcCemtfv1dsMBoPBoKkKhC7SEAAHOBv4NynlWcAiVfMQAFLlXj+l/GshxLVCiG1CiG0TExOHMD2DwWDoPqomo+7SEPYCe6WUd+n/v4YSEGOhKUg/juv9o8D6mtev09vqkFJeL6U8V0p57tDQ0CFMz2AwGLqPrtQQpJQHgD1CiJP1phcCDwE3AVfrbVcD39bPbwLeoKONLgTmakxLBoPBYED1VIb2+BCcQ3z924AvCiE84HHgGpSQ+YoQ4o3Ak8Dv67HfB14K7ASyeqzBYDAYaog4YWLa0dcQDkkgSCnvA85tsOuFDcZK4E8P5XwGg8FwrFNNTOsuH4LBYDAYDjOWJfAcq7vyEAwGg8FwZIg4ltEQDAaDwaDbaHZZ2KnBYDAYjgARxzLlrw0Gg8GgNIS80RAMBoPBEHWt7kpMMxgMBsORIeIYH4LBYDAYaJ+GcKiZygaDwWA4zFy0ZYBssftKVxgMBoPhMPNnl57YlvMak5HBYDAYACMQDAaDwaAxAsFgMBgMgBEIBoPBYNAYgWAwGAwGwAgEg8FgMGiMQDAYDAYDYASCwWAwGDRCdbbsTIQQE6i+zAfDIDB5BKdzJOimOXfTXEO6bc7dNl/orjl301xDnu6cN0oph57qizpaIDwVhBDbpJSN+jt3LN00526aa0i3zbnb5gvdNedummvI0Z6zMRkZDAaDATACwWAwGAyaY0kgXN/uCTwNumnO3TTXkG6bc7fNF7przt0015CjOudjxodgMBgMhkPjWNIQDAaDwXAIGIFgMBgMBoWUsi1/wHrgx8BDwIPA/9bb+4FbgR36sU9v/0NgO3A/cCdwZs2xLgceAXYC725xzqv1cXcAV9ds/yCwB8h0w5yBOPA94Ld6Htd16lz19puB3+h5fBKwO/n6Ltl/E/BAp88X+Il+/X36b1UXzNlD2cgfRX2Xf68T5wqkaq7rfai8gH/s9HVCb3+tPu521O9wsOUa12rnkfwDRoCzay74o8CpwIfDNw68G/iQfv7smgt4BXCXfm4DjwFb9BfsN8CpDc7XDzyuH/v08/B4F+r5rCQQOmLOKIHwgpof1e3AFZ04V72vRz8K4OvAazr5+tbs/13gSzQXCB0zX5RAOLfLfnd/C3xAP7dYslh10lyXjLsHeG6nX2NUR8zx8Lrq87+v5fdjpS/Q0foDvg28GCUNR2ou7CMNxvYBo/r5RcAtNfveA7ynwWteC/x7zf//Drx2yZiWAqET56y3fxz4k06fK+AC3wH+oNOvL5AE7kD9kBsKhA6b7084CIHQYXPeAyS6Ya41207S8xadPm/U720C2Ii6GfskcG2ruXaED0EIsQk4C7gLGJZS7te7DgDDDV7yRuAH+vla1AcUsldvW8rBjuuqOQsheoGXAbd18lyFELeg7lYWgK81m2sHzfn9wMeA7Epz7ZD5AnxOCHGfEOKvhRCik+esv7cA7xdC3CuE+KoQotE52z7XJWNeA3xZ6pV3Jdo5byllCXgLymS0D3Vz85lW8227QBBCJFFmhLdLKedr9+mLLpeMfwHqov3FUZvkEjplzkIIB7gR+ISU8vFOnquU8jLUXVEEuLTV2HbPWQjxLGCrlPKbBzm+E67xH0opnwk8R/+9vsPn7ADrgDullGcDvwA+2qFzreU1qN/cirR73kIIFyUQzgLWoPwI72n1mrYKBD3hrwNflFJ+Q28eE0KM6P0jqLvKcPwZwKeBq6SUU3rzKMqJE7IOGBVCXKDvlu4TQry82bgun/P1wA4p5T92wVyRUuZR6vNVjebbQXO+CDhXCPEEymx0khDiJx08X6SU4eMCyu9xfqP5dtCcp1DaV3j+rwJnd+hcw2OfCThSynuWzrND5/0sACnlY1oAfQXlr2jOwdrBDvcfyqb1eZZ464GPUO94+bB+vgHlaX/2kvEOyomymarj5bQG5+sHdqFsdH36ef+SMSs5lTtmzsAHUF84q5PnirLFj9Qc68vAn3XynJeM2URzp3JHzFe/PnQcuiiT3Js7ec56338Bl+rnfwR8tVPnqvdfB/xtqzWik+aN0gr2A0N63PuBj7Wc+0pv7kj9AZegVKbtVMO5XgoMoOzhO4Af1nx5Pg3M1IzdVnOsl6I8+Y8Bf9ninH+sL/xO4Jqa7R9G2d0C/fi+Tp4z6g5AAg/XHPtNHTrXYeBuPY8HgH9C3WV17PVdsn8TzQVCR8wXSKAiX7ajwhw/TvPQ3o6Ys96+EfiZnsttwIZOnave9zhwSpetbW9GrRPbUQEdA63mbkpXGAwGgwHoAKeywWAwGDoDIxAMBoPBABiBYDAYDAaNEQgGg8FgAIxAMBgMBoPGCASDwWAwAEYgGAwGg0Hz/wG9DNsrWYaRNgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QY1b2mPgzyXA"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukpGUEanzyXE",
        "outputId": "b795e64c-9b88-4c5e-f67c-51f873bf2fbe"
      },
      "source": [
        "#model performance in peak period (winter)\n",
        "timeseries_evaluation_metrics_func(y_true[:90], y_pred[:90])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10825.370331854787\n",
            "MAE is : 78.28616943359376\n",
            "RMSE is : 104.04503991952133\n",
            "MAPE is : 6.1393055540079695 %\n",
            "R2 is : 0.3519503217708968\n",
            "\n",
            "SMAPE is : 0.06623553770982199\n",
            "\n",
            "MASE is : 0.7819830616823619\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nrmb638qyllV"
      },
      "source": [
        "# Random forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeRULN6z-QEh"
      },
      "source": [
        "#train data: all the data except the last 7 months\n",
        "#test data: the 7 last months\n",
        "\n",
        "X_train,y_train=data[:-30*7].drop(['max_pdl_daily_load_t'], axis = 1),data[:-30*7].max_pdl_daily_load_t\n",
        "X_test,y_test=data[-30*7:].drop(['max_pdl_daily_load_t'], axis = 1),data[-30*7:].max_pdl_daily_load_t"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8ErcWUENUAE",
        "outputId": "a458a94c-7184-45b4-b92b-ac887d661204"
      },
      "source": [
        "#fine tuning, grid search\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "model = RandomForestRegressor()\n",
        "param_search = { \n",
        "    'n_estimators': [10,20,30],#[10,20,30],#[10,50,100],#,\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [10,20,30,40,50],#[60,62,65,67,69],#[11,12,13],#[10,12,14,16,18,20],\n",
        "    #'min_samples_split' : [2, 5, 10],\n",
        "    'min_samples_leaf' : [6,8,10,12],#[5],#[4,5,6],[1, 2, 3,4],\n",
        "    'bootstrap' : [True, False]\n",
        "    #default_criterion for evaluation: mse\n",
        "}\n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = 'r2')\n",
        "gsearch.fit(X_train, y_train)\n",
        "best_score = gsearch.best_score_\n",
        "best_model = gsearch.best_estimator_\n",
        "best_model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=40, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=6,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=20, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--b74PDqJrCI",
        "outputId": "0a59139d-27f2-437c-946c-ad16ff51ac9d"
      },
      "source": [
        "y_true = y_test.values\n",
        "y_pred = best_model.predict(X_test)\n",
        "timeseries_evaluation_metrics_func(y_true, y_pred)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9992.33188385424\n",
            "MAE is : 67.04548244849039\n",
            "RMSE is : 99.96165206645117\n",
            "MAPE is : 6.2061466265546725 %\n",
            "R2 is : 0.7428315898014133\n",
            "\n",
            "SMAPE is : 0.026759357198174863\n",
            "\n",
            "MASE is : 0.7688617740320709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "fTTj1mleNojo",
        "outputId": "3856bb8a-33e3-4cfa-e671-52c9ed703b4b"
      },
      "source": [
        "# plot real vs predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,y_true,label='actual')\n",
        "plt.plot(time,y_pred,label='forecast')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vt0fLb6C3i8O"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lY4lK5vbBaJZ"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ITKfChrBaJc",
        "outputId": "fea2b81e-17cd-418d-c7d0-841dc3fbb853"
      },
      "source": [
        "#model performance in peak period (winter)\n",
        "timeseries_evaluation_metrics_func(y_true[:90], y_pred[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9710.421024737487\n",
            "MAE is : 71.92019159648403\n",
            "RMSE is : 98.54146855378951\n",
            "MAPE is : 5.70911886070638 %\n",
            "R2 is : 0.41869561708822844\n",
            "\n",
            "SMAPE is : 0.06046977180437596\n",
            "\n",
            "MASE is : 0.7183947308739707\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKJF4X2nAnXL"
      },
      "source": [
        "# ensembling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6E8xL4UmvW2"
      },
      "source": [
        "\n",
        "*   SVR(C=0.5, cache_size=200, coef0=0.0, degree=1, epsilon=0.001, gamma='auto',kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)\n",
        "*   LinearRegression()\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJs1niaYIYyu"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "data_scaled = scaler.fit_transform(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyiYsmvkIYy6"
      },
      "source": [
        "features=data_scaled[:,:-1]\n",
        "target=data_scaled[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DwyD0ScIYy7"
      },
      "source": [
        "# split into train and test sets\n",
        "\n",
        "n_train_days = -30*7\n",
        "\n",
        "X_train,y_train = features[:n_train_days, :],target[:n_train_days]\n",
        "X_test, y_test = features[n_train_days:, :],target[n_train_days:]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AWyekCLBy5U"
      },
      "source": [
        "# get a list of base models\n",
        "def get_models():\n",
        "  models = list()\n",
        "  models.append(('lr', LinearRegression()))\n",
        "\n",
        "  models.append(('svr', SVR(C=0.5, cache_size=200, coef0=0.0, degree=1, epsilon=0.001, gamma='auto',\n",
        "                            kernel='rbf', max_iter=-1, shrinking=False, tol=0.001, verbose=False)))\n",
        "  return models\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TX49iAXYBy65"
      },
      "source": [
        "def evaluate_models(models, X_train, X_test, y_train, y_test):\n",
        "  scores = list()\n",
        "  for name, model in models:\n",
        "    model.fit(X_train, y_train)\n",
        "    yhat = model.predict(X_test)\n",
        "    yhat=yhat.reshape(yhat.shape[0],1)\n",
        "    X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "    inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "    inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "    inv_yhat = inv_yhat[:,-1]\n",
        "    y_test = y_test.reshape((len(y_test), 1))\n",
        "    inv_y = concatenate((X_test,y_test), axis=1)\n",
        "    inv_y = scaler.inverse_transform(inv_y)\n",
        "    inv_y = inv_y[:,-1]\n",
        "    mae = mean_absolute_error(inv_y, inv_yhat)\n",
        "    scores.append(-mae)\n",
        "  return scores\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNr0sQNoBy-z",
        "outputId": "5d9044fa-cde1-4783-f6b2-cdf672ff62cb"
      },
      "source": [
        "# create the base models\n",
        "models = get_models()\n",
        "# fit and evaluate each model\n",
        "scores = evaluate_models(models, X_train, X_test, y_train, y_test)\n",
        "print(scores)\n",
        "ranking = 1 + argsort(argsort(scores))\n",
        "print(ranking)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-68.4684199043621, -65.05567650236055]\n",
            "[1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ho2Zq_uBzAU",
        "outputId": "57ef1a95-458b-4919-f093-b5a593d6b3f6"
      },
      "source": [
        "# create the ensemble\n",
        "weighted_ensemble = VotingRegressor(estimators=models, weights=ranking)\n",
        "#verbose --> time consumed while fitting will be printed\n",
        "# fit the ensemble on the training dataset\n",
        "weighted_ensemble.fit(X_train, y_train)\n",
        "# make predictions on test set\n",
        "weighted_yhat = weighted_ensemble.predict(X_test)\n",
        "yhat=weighted_yhat.reshape(yhat.shape[0],1)\n",
        "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "weighted_inv_yhat = inv_yhat[:,-1]\n",
        "\n",
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "inv_y = concatenate((X_test,y_test), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]\n",
        "# evaluate predictions using MAE (Mean absolute error)\n",
        "score = mean_absolute_error(inv_y, weighted_inv_yhat)\n",
        "print('Weighted Avg MAE: %.3f' % (score))\n",
        "# evaluate each standalone model\n",
        "scores = evaluate_models(models, X_train, X_test, y_train, y_test)\n",
        "for i in range(len(models)):\n",
        "\tprint('>%s: %.3f' % (models[i][0], -scores[i]))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weighted Avg MAE: 64.062\n",
            ">lr: 68.468\n",
            ">svr: 65.056\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRwGk3L03fvJ",
        "outputId": "71f7ae79-e6db-4552-8894-630c83640dc5"
      },
      "source": [
        "# evaluate equal weighting (simple average ensemble)\n",
        "average_ensemble = VotingRegressor(estimators=models)\n",
        "average_ensemble.fit(X_train, y_train)\n",
        "average_yhat = average_ensemble.predict(X_test)\n",
        "\n",
        "yhat=average_yhat.reshape(yhat.shape[0],1)\n",
        "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n",
        "inv_yhat = concatenate((X_test,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "simple_inv_yhat = inv_yhat[:,-1]\n",
        "\n",
        "y_test = y_test.reshape((len(y_test), 1))\n",
        "inv_y = concatenate((X_test,y_test), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]\n",
        "score = mean_absolute_error(inv_y, simple_inv_yhat)\n",
        "print('Voting MAE: %.3f' % (score))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Voting MAE: 64.731\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "th7Dh_LyJ8pd"
      },
      "source": [
        "# **weighted ensembling**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IClKZwUBzIq",
        "outputId": "6662022f-6346-4786-fe66-8d4423261ba8"
      },
      "source": [
        "#general model performance \n",
        "timeseries_evaluation_metrics_func(inv_y, weighted_inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9593.217347265043\n",
            "MAE is : 64.0619605273783\n",
            "RMSE is : 97.94497101569351\n",
            "MAPE is : 5.957418537026763 %\n",
            "R2 is : 0.7531034314550754\n",
            "\n",
            "SMAPE is : 0.02543271509045272\n",
            "\n",
            "MASE is : 0.7346474485718556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Qec9rrnSKQZJ",
        "outputId": "b795a3dc-b7b7-48c8-a11c-94a383796c51"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,inv_y,label='actual')\n",
        "plt.plot(time,weighted_inv_yhat,label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpkzNVzpq3mo"
      },
      "source": [
        "**peak period**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4G-pXDoptjj",
        "outputId": "5766a000-fa23-49b7-880e-a43703137fa3"
      },
      "source": [
        "#model performance in peak period (winter)\n",
        "timeseries_evaluation_metrics_func(inv_y[:90], weighted_inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9560.963901945563\n",
            "MAE is : 68.31165913993604\n",
            "RMSE is : 97.78018153974537\n",
            "MAPE is : 5.45389818675933 %\n",
            "R2 is : 0.4276427142650656\n",
            "\n",
            "SMAPE is : 0.057260775386857414\n",
            "\n",
            "MASE is : 0.6823499061115943\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CXNaYN0OqGZy",
        "outputId": "cf5b8e32-f136-4736-f385-8dcd58d56c8c"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.index[-30*7:][:90]\n",
        "plt.plot(time,inv_y[:90],label='actual')\n",
        "plt.plot(time,weighted_inv_yhat[:90],label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQdYJY7xLYIy"
      },
      "source": [
        "# **simple average ensembling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aRWLbbkmLM3y",
        "outputId": "5107db28-57d8-4f3f-c4be-8e99bc6bc3a3"
      },
      "source": [
        "timeseries_evaluation_metrics_func(inv_y, simple_inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9541.0850161758\n",
            "MAE is : 64.73050332418993\n",
            "RMSE is : 97.67847775316629\n",
            "MAPE is : 6.013277436780422 %\n",
            "R2 is : 0.7544451391627457\n",
            "\n",
            "SMAPE is : 0.025709619958666403\n",
            "\n",
            "MASE is : 0.7423141396299421\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KNe_YwMaLM-D",
        "outputId": "6bb8e6d6-1a53-4491-9cdb-d7514c0b17f5"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.index[-30*7:]\n",
        "plt.plot(time,inv_y,label='actual')\n",
        "plt.plot(time,simple_inv_yhat,label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7WYFt6B3_GM",
        "outputId": "24c00bef-91b3-45dc-e698-0cd5eb7e682c"
      },
      "source": [
        "timeseries_evaluation_metrics_func(inv_y[:90], simple_inv_yhat[:90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 9616.09536707058\n",
            "MAE is : 68.65843313736299\n",
            "RMSE is : 98.06169163883816\n",
            "MAPE is : 5.485633636389597 %\n",
            "R2 is : 0.4243423257204416\n",
            "\n",
            "SMAPE is : 0.05753600153003041\n",
            "\n",
            "MASE is : 0.6858137541218073\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pkEnVs9T4EOy",
        "outputId": "d9a82c06-950b-41ad-d068-ee90f61e5cd0"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.index[-30*7:][:90]\n",
        "plt.plot(time,inv_y[:90],label='actual')\n",
        "plt.plot(time,simple_inv_yhat[:90],label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRtcRML85iAE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}