Fitness_Data_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.7",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "4"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "I1boM6AQ_fD7"
      },
      "source": [
        "## 1. Obtain and review raw data\n",
        "<p>One day, my old running friend and I were chatting about our running styles, training habits, and achievements, when I suddenly realized that I could take an in-depth analytical look at my training. I have been using a popular GPS fitness tracker called <a href=\"https://runkeeper.com/\">Runkeeper</a> for years and decided it was time to analyze my running data to see how I was doing.</p>\n",
        "<p>Since 2012, I've been using the Runkeeper app, and it's great. One key feature: its excellent data export. Anyone who has a smartphone can download the app and analyze their data like we will in this notebook.</p>\n",
        "<p><img src=\"https://assets.datacamp.com/production/project_727/img/runner_in_blue.jpg\" alt=\"Runner in blue\" title=\"Explore world, explore your data!\"></p>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "4"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "n3oLM8t1_fES",
        "outputId": "12f58a18-4fd7-4a46-e7f3-ec8e4caffa55"
      },
      "source": [
        " import pandas as pd\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Define file containing dataset\n",
        "runkeeper_file = 'cardioActivities.csv'\n",
        "\n",
        "# Create DataFrame with parse_dates and index_col parameters\n",
        "df_activities = pd.read_csv(runkeeper_file, parse_dates=True, index_col='Date')\n",
        "\n",
        "# First look at exported data: select sample of 3 random rows\n",
        "display(df_activities.sample(3))\n",
        "\n",
        "df_activities.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-530ea835-d8bd-4069-9e9d-6fd4db79b98d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-530ea835-d8bd-4069-9e9d-6fd4db79b98d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving cardioActivities.csv to cardioActivities.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Activity Id</th>\n",
              "      <th>Type</th>\n",
              "      <th>Route Name</th>\n",
              "      <th>Distance (km)</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Average Pace</th>\n",
              "      <th>Average Speed (km/h)</th>\n",
              "      <th>Calories Burned</th>\n",
              "      <th>Climb (m)</th>\n",
              "      <th>Average Heart Rate (bpm)</th>\n",
              "      <th>Friend's Tagged</th>\n",
              "      <th>Notes</th>\n",
              "      <th>GPX File</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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>2016-10-15 17:14:34</th>\n",
              "      <td>467ce511-b823-4bf8-a6b8-3de45da696b9</td>\n",
              "      <td>Running</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.49</td>\n",
              "      <td>1:14:11</td>\n",
              "      <td>5:30</td>\n",
              "      <td>10.91</td>\n",
              "      <td>942.999999</td>\n",
              "      <td>182</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TomTom MySports Watch</td>\n",
              "      <td>2016-10-15-171434.gpx</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-25 17:02:44</th>\n",
              "      <td>c306e91d-482d-40b4-96f6-c6f2117eaab4</td>\n",
              "      <td>Running</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.22</td>\n",
              "      <td>1:16:38</td>\n",
              "      <td>5:48</td>\n",
              "      <td>10.35</td>\n",
              "      <td>917.999999</td>\n",
              "      <td>226</td>\n",
              "      <td>142.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TomTom MySports Watch</td>\n",
              "      <td>2016-06-25-170244.gpx</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-04 18:18:00</th>\n",
              "      <td>5a5804c3-ea87-4b47-92bb-c0f8e9f67419</td>\n",
              "      <td>Running</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.22</td>\n",
              "      <td>32:00</td>\n",
              "      <td>5:09</td>\n",
              "      <td>11.66</td>\n",
              "      <td>445.000000</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-11-04-181800.gpx</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Activity Id  ...               GPX File\n",
              "Date                                                       ...                       \n",
              "2016-10-15 17:14:34  467ce511-b823-4bf8-a6b8-3de45da696b9  ...  2016-10-15-171434.gpx\n",
              "2016-06-25 17:02:44  c306e91d-482d-40b4-96f6-c6f2117eaab4  ...  2016-06-25-170244.gpx\n",
              "2014-11-04 18:18:00  5a5804c3-ea87-4b47-92bb-c0f8e9f67419  ...  2014-11-04-181800.gpx\n",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 508 entries, 2018-11-11 14:05:12 to 2012-08-22 18:53:54\n",
            "Data columns (total 13 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Activity Id               508 non-null    object \n",
            " 1   Type                      508 non-null    object \n",
            " 2   Route Name                1 non-null      object \n",
            " 3   Distance (km)             508 non-null    float64\n",
            " 4   Duration                  508 non-null    object \n",
            " 5   Average Pace              508 non-null    object \n",
            " 6   Average Speed (km/h)      508 non-null    float64\n",
            " 7   Calories Burned           508 non-null    float64\n",
            " 8   Climb (m)                 508 non-null    int64  \n",
            " 9   Average Heart Rate (bpm)  294 non-null    float64\n",
            " 10  Friend's Tagged           0 non-null      float64\n",
            " 11  Notes                     231 non-null    object \n",
            " 12  GPX File                  504 non-null    object \n",
            "dtypes: float64(5), int64(1), object(7)\n",
            "memory usage: 55.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "12"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "NzMebh3p_fEV"
      },
      "source": [
        "## 2. Data preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "12"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "UxsdAKDx_fEY",
        "outputId": "e0727c92-5458-487e-da3a-117084cb9874"
      },
      "source": [
        "# Define list of columns to be deleted\n",
        "cols_to_drop = ['Friend\\'s Tagged','Route Name','GPX File','Activity Id','Calories Burned', 'Notes']\n",
        "\n",
        "# Delete unnecessary columns\n",
        "df_activities.drop(columns=cols_to_drop)\n",
        "\n",
        "# Count types of training activities\n",
        "display(df_activities['Type'].value_counts())\n",
        "\n",
        "# Rename 'Other' type to 'Unicycling'\n",
        "df_activities['Type'] = df_activities['Type'].str.replace('Other','Unicycling')\n",
        "\n",
        "# Count missing values for each column\n",
        "df_activities.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running    459\n",
              "Cycling     29\n",
              "Walking     18\n",
              "Other        2\n",
              "Name: Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Activity Id                   0\n",
              "Type                          0\n",
              "Route Name                  507\n",
              "Distance (km)                 0\n",
              "Duration                      0\n",
              "Average Pace                  0\n",
              "Average Speed (km/h)          0\n",
              "Calories Burned               0\n",
              "Climb (m)                     0\n",
              "Average Heart Rate (bpm)    214\n",
              "Friend's Tagged             508\n",
              "Notes                       277\n",
              "GPX File                      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "19"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "lNLYMdfF_fEZ"
      },
      "source": [
        "## 3. Dealing with missing values\n",
        "<p>As we can see from the last output, there are 214 missing entries for my average heart rate.</p>\n",
        "<p>We can't go back in time to get those data, but we can fill in the missing values with an average value. This process is called <em>mean imputation</em>. When imputing the mean to fill in missing data, we need to consider that the average heart rate varies for different activities (e.g., walking vs. running). We'll filter the DataFrames by activity type (<code>Type</code>) and calculate each activity's mean heart rate, then fill in the missing values with those means.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "19"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "Ct4UfSZG_fEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "588bf323-7d3b-410c-a830-361286c55b72"
      },
      "source": [
        "# Calculate sample means for heart rate for each training activity type\n",
        "avg_hr_run = df_activities[df_activities['Type'] == 'Running']['Average Heart Rate (bpm)'].mean()\n",
        "avg_hr_cycle = df_activities[df_activities['Type'] == 'Cycling']['Average Heart Rate (bpm)'].mean()\n",
        "\n",
        "# Split whole DataFrame into several, specific for different activities\n",
        "df_run = df_activities[df_activities['Type'] == 'Running'].copy()\n",
        "df_walk = df_activities[df_activities['Type'] == 'Walking'].copy()\n",
        "df_cycle = df_activities[df_activities['Type'] == 'Cycling'].copy()\n",
        "\n",
        "# Filling missing values with counted means\n",
        "df_walk['Average Heart Rate (bpm)'].fillna(110, inplace=True)\n",
        "df_run['Average Heart Rate (bpm)'].fillna(int(avg_hr_run), inplace=True)\n",
        "df_cycle['Average Heart Rate (bpm)'].fillna(int(avg_hr_cycle), inplace=True)\n",
        "\n",
        "# Count missing values for each column in running data\n",
        "df_run.sort_index(ascending=True)\n",
        "df_run.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Activity Id                   0\n",
              "Type                          0\n",
              "Route Name                  458\n",
              "Distance (km)                 0\n",
              "Duration                      0\n",
              "Average Pace                  0\n",
              "Average Speed (km/h)          0\n",
              "Calories Burned               0\n",
              "Climb (m)                     0\n",
              "Average Heart Rate (bpm)      0\n",
              "Friend's Tagged             459\n",
              "Notes                       237\n",
              "GPX File                      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "26"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "n93Ztb8x_fEd"
      },
      "source": [
        "## 4. Plot running data\n",
        "<p>Now we can create our first plot! As we found earlier, most of the activities in my data were running (459 of them to be exact). There are only 29, 18, and two instances for cycling, walking, and unicycling, respectively. So for now, let's focus on plotting the different running metrics.</p>\n",
        "<p>An excellent first visualization is a figure with four subplots, one for each running metric (each numerical column). Each subplot will have a different y-axis, which is explained in each legend. The x-axis, <code>Date</code>, is shared among all subplots.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "26"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "ngelaRkT_fEe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "185103f3-610b-4c09-b6e5-682e16bae737"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# Import matplotlib, set style and ignore warning\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "plt.style.use('ggplot')\n",
        "warnings.filterwarnings(\n",
        "    action='ignore', module='matplotlib.figure', category=UserWarning,\n",
        "    message=('This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.')\n",
        ")\n",
        "\n",
        "# Prepare data subsetting period from 2013 till 2018\n",
        "runs_subset_2013_2018 = df_run['2018':'2013']\n",
        "runs_subset_2013_2018.sort_index(ascending=True).head()\n",
        "\n",
        "# Create, plot and customize in one step\n",
        "runs_subset_2013_2018.plot(subplots=True,\n",
        "                           sharex=False,\n",
        "                           figsize=(12,16),\n",
        "                           linestyle='none',\n",
        "                           marker='o',\n",
        "                           markersize=3,\n",
        "                          )\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1152 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "33"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "mb5s8A5r_fEg"
      },
      "source": [
        "## 5. Running statistics\n",
        "<p>No doubt, running helps people stay mentally and physically healthy and productive at any age. And it is great fun! When runners talk to each other about their hobby, we not only discuss our results, but we also discuss different training strategies. </p>\n",
        "<p>You'll know you're with a group of runners if you commonly hear questions like:</p>\n",
        "<ul>\n",
        "<li>What is your average distance?</li>\n",
        "<li>How fast do you run?</li>\n",
        "<li>Do you measure your heart rate?</li>\n",
        "<li>How often do you train?</li>\n",
        "</ul>\n",
        "<p>Let's find the answers to these questions in my data. If you look back at plots in Task 4, you can see the answer to, <em>Do you measure your heart rate?</em> Before 2015: no. To look at the averages, let's only use the data from 2015 through 2018.</p>\n",
        "<p>In pandas, the <code>resample()</code> method is similar to the <code>groupby()</code> method - with <code>resample()</code> you group by a specific time span. We'll use <code>resample()</code> to group the time series data by a sampling period and apply several methods to each sampling period. In our case, we'll resample annually and weekly.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "33"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "u_Cy1hpL_fEj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "outputId": "dcba788b-2e96-48fd-f96a-e1cd49c74e1e"
      },
      "source": [
        "# Prepare running data for the last 4 years\n",
        "runs_subset_2015_2018 = df_run['2018':'2015']\n",
        "runs_subset_2015_2018.sort_index(ascending=True).head()\n",
        "\n",
        "# Calculate annual statistics\n",
        "print('How my average run looks in last 4 years:')\n",
        "display(runs_subset_2015_2018.resample('A').mean())\n",
        "\n",
        "# Calculate weekly statistics\n",
        "print('Weekly averages of last 4 years:')\n",
        "display(runs_subset_2015_2018.resample('W').mean())\n",
        "\n",
        "# Mean weekly counts\n",
        "weekly_counts_average = runs_subset_2015_2018.filter(['Distance (km)']).resample('W').count().mean()\n",
        "print('How many trainings per week I had on average:', weekly_counts_average)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "How my average run looks in last 4 years:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Distance (km)</th>\n",
              "      <th>Average Speed (km/h)</th>\n",
              "      <th>Calories Burned</th>\n",
              "      <th>Climb (m)</th>\n",
              "      <th>Average Heart Rate (bpm)</th>\n",
              "      <th>Friend's Tagged</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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>2015-12-31</th>\n",
              "      <td>13.602805</td>\n",
              "      <td>10.998902</td>\n",
              "      <td>932.906138</td>\n",
              "      <td>160.170732</td>\n",
              "      <td>143.353659</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31</th>\n",
              "      <td>11.411667</td>\n",
              "      <td>10.837778</td>\n",
              "      <td>796.152777</td>\n",
              "      <td>133.194444</td>\n",
              "      <td>143.388889</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>12.935176</td>\n",
              "      <td>10.959059</td>\n",
              "      <td>914.164706</td>\n",
              "      <td>169.376471</td>\n",
              "      <td>145.247059</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>13.339063</td>\n",
              "      <td>10.777969</td>\n",
              "      <td>952.359375</td>\n",
              "      <td>191.218750</td>\n",
              "      <td>148.125000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Distance (km)  ...  Friend's Tagged\n",
              "Date                       ...                 \n",
              "2015-12-31      13.602805  ...              NaN\n",
              "2016-12-31      11.411667  ...              NaN\n",
              "2017-12-31      12.935176  ...              NaN\n",
              "2018-12-31      13.339063  ...              NaN\n",
              "\n",
              "[4 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Weekly averages of last 4 years:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Distance (km)</th>\n",
              "      <th>Average Speed (km/h)</th>\n",
              "      <th>Calories Burned</th>\n",
              "      <th>Climb (m)</th>\n",
              "      <th>Average Heart Rate (bpm)</th>\n",
              "      <th>Friend's Tagged</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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>2015-01-04</th>\n",
              "      <td>9.780000</td>\n",
              "      <td>11.120000</td>\n",
              "      <td>654.000000</td>\n",
              "      <td>51.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-18</th>\n",
              "      <td>9.780000</td>\n",
              "      <td>11.230000</td>\n",
              "      <td>654.500000</td>\n",
              "      <td>51.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-01</th>\n",
              "      <td>9.893333</td>\n",
              "      <td>10.423333</td>\n",
              "      <td>707.624448</td>\n",
              "      <td>58.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-14</th>\n",
              "      <td>12.620000</td>\n",
              "      <td>10.840000</td>\n",
              "      <td>928.000000</td>\n",
              "      <td>146.5</td>\n",
              "      <td>157.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-21</th>\n",
              "      <td>10.290000</td>\n",
              "      <td>10.410000</td>\n",
              "      <td>764.000000</td>\n",
              "      <td>133.0</td>\n",
              "      <td>155.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-28</th>\n",
              "      <td>13.020000</td>\n",
              "      <td>10.730000</td>\n",
              "      <td>967.000000</td>\n",
              "      <td>170.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-04</th>\n",
              "      <td>12.995000</td>\n",
              "      <td>10.420000</td>\n",
              "      <td>963.500000</td>\n",
              "      <td>170.0</td>\n",
              "      <td>156.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-11</th>\n",
              "      <td>11.640000</td>\n",
              "      <td>10.535000</td>\n",
              "      <td>864.000000</td>\n",
              "      <td>149.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>202 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Distance (km)  ...  Friend's Tagged\n",
              "Date                       ...                 \n",
              "2015-01-04       9.780000  ...              NaN\n",
              "2015-01-11            NaN  ...              NaN\n",
              "2015-01-18       9.780000  ...              NaN\n",
              "2015-01-25            NaN  ...              NaN\n",
              "2015-02-01       9.893333  ...              NaN\n",
              "...                   ...  ...              ...\n",
              "2018-10-14      12.620000  ...              NaN\n",
              "2018-10-21      10.290000  ...              NaN\n",
              "2018-10-28      13.020000  ...              NaN\n",
              "2018-11-04      12.995000  ...              NaN\n",
              "2018-11-11      11.640000  ...              NaN\n",
              "\n",
              "[202 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "How many trainings per week I had on average: Distance (km)    1.5\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "40"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "N0sEgapz_fEm"
      },
      "source": [
        "## 6. Visualization with averages\n",
        "<p>Let's plot the long term averages of my distance run and my heart rate with their raw data to visually compare the averages to each training session. Again, we'll use the data from 2015 through 2018.</p>\n",
        "<p>In this task, we will use <code>matplotlib</code> functionality for plot creation and customization.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "40"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "D2Ac5Vr3_fEo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "1203d965-02b1-449a-be9a-ee40f59c04b6"
      },
      "source": [
        "# Prepare data\n",
        "runs_subset_2015_2018 = df_run['2018':'2015']\n",
        "runs_distance = runs_subset_2015_2018['Distance (km)']\n",
        "runs_hr = runs_subset_2015_2018['Average Heart Rate (bpm)']\n",
        "\n",
        "# Create plot\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, figsize=(12,8))\n",
        "\n",
        "# Plot and customize first subplot\n",
        "runs_distance.plot(ax=ax1)\n",
        "ax1.set(ylabel='Distance (km)', title='Historical data with averages')\n",
        "ax1.axhline(runs_distance.mean(), color='blue', linewidth=1, linestyle='-.')\n",
        "\n",
        "# Plot and customize second subplot\n",
        "runs_hr.plot(ax=ax2, color='gray')\n",
        "ax2.set(xlabel='Date', ylabel='Average Heart Rate (bpm)')\n",
        "ax2.axhline(runs_hr.mean(), color='blue', linewidth=1, linestyle='-.')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "47"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "DIkavC75_fEp"
      },
      "source": [
        "## 7. Did I reach my goals?\n",
        "<p>To motivate myself to run regularly, I set a target goal of running 1000 km per year. Let's visualize my annual running distance (km) from 2013 through 2018 to see if I reached my goal each year. Only stars in the green region indicate success.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "47"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "0LEdou2M_fEq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "35b70e3d-8522-4a4f-8ad0-0ade0fa744db"
      },
      "source": [
        "# Prepare data\n",
        "df_run_dist_annual = runs_subset_2013_2018['Distance (km)'].resample('A').sum()\n",
        "\n",
        "# Create plot\n",
        "fig = plt.figure(figsize=[5.0,8.0])\n",
        "\n",
        "# Plot and customize\n",
        "ax = df_run_dist_annual.plot(marker='*', markersize=14, linewidth=0, color='blue')\n",
        "ax.set(ylim=[0, 1210],\n",
        "       xlim=['2012','2019'],\n",
        "       ylabel='Distance (km)',\n",
        "       xlabel='Years',\n",
        "       title='Annual totals for distance')\n",
        "\n",
        "ax.axhspan(1000, 1210, color='green', alpha=0.4)\n",
        "ax.axhspan(800, 1000, color='yellow', alpha=0.3)\n",
        "ax.axhspan(0, 800, color='red', alpha=0.2)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "54"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "f_FEMq7k_fEr"
      },
      "source": [
        "## 8. Am I progressing?\n",
        "<p>Let's dive a little deeper into the data to answer a tricky question: am I progressing in terms of my running skills? </p>\n",
        "<p>To answer this question, we'll decompose my weekly distance run and visually compare it to the raw data. A red trend line will represent the weekly distance run.</p>\n",
        "<p>We are going to use <code>statsmodels</code> library to decompose the weekly trend.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "54"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "PRW4eEoT_fEt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "4180655b-a692-4625-f1ef-3129d677ea45"
      },
      "source": [
        "# Import required library\n",
        "import statsmodels.api as sm\n",
        "\n",
        "# Prepare data\n",
        "df_run_dist_wkly = runs_subset_2013_2018['Distance (km)'].resample('W').bfill()\n",
        "decomposed = sm.tsa.seasonal_decompose(df_run_dist_wkly, extrapolate_trend=1, freq=52)\n",
        "\n",
        "# Create plot\n",
        "fig = plt.figure(figsize=[12.0,5.0])\n",
        "\n",
        "# Plot and customize\n",
        "ax = decomposed.trend.plot(label='Trend', linewidth=2)\n",
        "ax = decomposed.observed.plot(label='Observed', linewidth=0.5)\n",
        "\n",
        "ax.legend()\n",
        "ax.set_title('Running distance trend')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAFRCAYAAAB9v8WBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZgU1bk/8G9V9d6zM+wggiBGEI0gEhFFGREV0Ru3qDHR3KvmmnjVJ3qjMQGzGFFDNIn4xHhvNNFEgzf+NBrXwTUuYVMgoBKUJbLNvvVWXVXn90dN9/S+TfVM98z38zw+Mr2e7tNV9dap97xHEkIIEBERERENA/JgN4CIiIiIaKAw+CUiIiKiYYPBLxERERENGwx+iYiIiGjYYPBLRERERMMGg18iIiIiGjYY/BLRsCFJEh5//PFBe//DDz8cP/nJT9L+TcUz2H1PRKWDwS8RFcWVV14JSZIgSRIURcGECRPwta99Dfv27Ru0Nh04cAAXXnjhoL1/ovXr1+Omm27K6bF/+9vfIEkSdu/eXdxGWejxxx+HJEmD3QwiojgMfomoaBYsWIADBw5g7969+OMf/4gPPvgAF1100aC1Z8yYMXC5XIP2/olGjhwJr9c72M0YdKqqDnYTiGgYYfBLREXjcDgwZswYjB8/HqeccgquueYavPfee+jq6gIA3HHHHZg6dWrccxJHOB999FHYbDa88847OP744+HxeDB79mysX78++pw33ngDkiTh1VdfxSmnnAKPx4Ojjz4aL774YtxrJ176liQJDz74IK644gpUVlZiwoQJuOuuu+Ke09raiosuugherxejR4/GD37wA3z9619HQ0NDxs++efNmnHTSSXA6nZg2bRrWrFmT9JjEtIdnn30WX/ziF+HxeFBTU4O5c+figw8+wO7du7FgwQIAwOTJkyFJEhYuXAgA2LRpE8466yyMGjUKFRUVOOGEE/DSSy8lvc/y5ctxww03oK6uDqNHj8ZNN90ETdPiHrd69WocffTRcDqdGDVqFC644ILofeFwGHfccQcmT54Ml8uFGTNm4KGHHkr7+d944w1cccUV0e9ZkiRceeWVAICFCxfi3//93/GDH/wAY8eOxWGHHQYA2LlzJy644ALU1NSgtrYWixcvxtatW6OvmctvAQBef/11zJo1Cy6XC7NmzcLrr7+etp1ENPww+CWiAbF//3783//9HxRFgaIoeT3XMAzcdttt+MUvfoFNmzZh1KhRuPjii5OCt5tvvhnf+973sHnzZpx44om45JJL0N7envG1f/jDH+KUU07Bhx9+iNtuuw3f+973sHbt2uj9V111FTZv3oznn38er732Gj7//HM888wzGV8zEAjg7LPPRk1NDdatW4ff//73uPfee9HU1JT2OQcPHsRFF12ESy+9FNu2bcN7772HG2+8ETabDRMnTsSzzz4LAFi3bh0OHDiAp59+GgDQ1dWFSy65BK+//jo2bdqEM888E8uWLcOOHTviXv9Xv/oVxo4di7///e/41a9+hQceeAC/+93vovevWLEC3/3ud3Hddddh69ateOmll3D88cdH77/66qvx9NNP46GHHsJHH32E5cuX47vf/S7+93//N+XnOemkk/DAAw8AMNNNDhw4gF/84hfR+9esWYPm5masXbsWr776Kg4dOoSTTz4Zo0aNwttvv433338f06dPx8KFC9Hc3Bx9Xrbfwv79+7F06VLMnj0bmzZtwqpVq3DDDTdk7C8iGmYEEVERfP3rXxeKogiv1yvcbrcAIACI73znO9HHrFixQhxxxBFxz3v77bcFALFr1y4hhBCPPPKIACA2btwYfcz7778vAIiPP/5YCCHE66+/LgCIP//5z9HHHDx4UAAQL730UvQ2AOKxxx6L+/v666+Pe/+jjjpK3HrrrUIIIXbs2CEAiMbGxuj9qqqKCRMmiEWLFqX97A8//LDwer2ira0tetvWrVsFAPHjH/84etukSZOif2/atCnucydK/F4ymTVrlvjJT34S9z7nnntu3GOWLFkivvKVrwghhOjp6REul0vce++9KV/vs88+E5IkiY8++iju9h/+8Ifi2GOPTduOxx57TKQ6zJx66qli2rRpQtf16G0rVqwQJ554YtzjDMMQU6ZMEffdd58QIrffwu233y4OO+wwEQ6Ho4957rnnkvqeiIYv24BH20Q0bJx44on43e9+h2AwiDVr1qCxsbGg6gaSJOHYY4+N/j1u3DgAwKFDhzB9+vTo7ccdd1z036NHj4aiKDh06FDG1459TuS1I8/Zvn07AGDevHnR++12O+bMmYPu7u60r7l9+3Z84QtfQG1tbfS2mTNnorq6Ou1zZs2ahTPPPBMzZ87EGWecgYULF+LLX/4yJk6cmLH9zc3NWLFiBV577TUcPHgQmqYhGAxiz549WT/nrl27AADbtm1DMBjE4sWLU77Hhg0bIITAnDlz4m7XNC3vUfyI2bNnQ5b7Lj6uX78eGzduREVFRdzjAoEA/vnPf0b/zvZb2L59O+bOnQubre/wdvLJJxfURiIamhj8ElHRuN3uaE7vzJkz8emnn+L666/Hww8/DACQZRlCiLjnhMPhpNeRZTkuyIpUEDAMI+5xDocj6bmJj0mU+BxJkpKeMxAVCxRFwYsvvoj169ejsbERf/7zn3HrrbfiqaeewtKlS9M+78orr8TevXtxzz33YPLkyXC73fjKV76SNIksl8+ZTuRx7777LjweT9LrFCJxop9hGFi0aFE0VSJW7ElDrr8FIqJ0mPNLRAPmjjvuwCOPPIINGzYAAEaNGoWmpibouh59zKZNmwareUmOPvpoAMB7770XvU3TNGzcuDHr8z766CN0dHREb9u2bRs6OzszPk+SJMydOxff+9738NZbb+HUU0/FI488AqAveI39rgDgrbfewnXXXYdly5bhmGOOwdixY/HZZ5/l/iF72+tyufDKK6+kvH/27NkAgL1792Lq1Klx/x1xxBFpXzddm1OZM2cOtm3bhgkTJiS9x8iRI/P6LOvWrYt7z3feeSfn5xPR0Mfgl4gGzLRp03Duuefi9ttvBwCcdtpp8Pv9WL58OT799FM89dRTWL169SC3sk+kvd/61rfw5ptvYvv27bj22mvR1dWVccTzsssuQ2VlJb761a9i8+bNeP/99/GNb3wDbrc77XPeffdd/PjHP8bf//537N27F2vXrsWWLVuiAfikSZMgyzJeeOEFNDU1RQPp6dOn4w9/+AO2bt2KDz/8EJdeemlOwWasiooKfOc738Edd9yB1atXY8eOHdi8eXO08sXUqVPxjW98A1dffTUee+wx7Ny5E5s3b8Zvf/tb3H333Wlfd/LkyQCAv/zlL2hubkZPT0/ax37729+Grus477zz8Pbbb2P37t3429/+httvvx3vvvtuzp/lP//zP9Hc3IxrrrkGH330EdauXRv9vRERAQx+iWiA3XLLLXjllVfwxhtvYPr06Xj44YfxxBNPYObMmfjtb3+Ln/70p4PdxDiPPPIIZs6cibPOOgsLFy7E+PHjccYZZ2SsF+zxePDCCy+gtbUVc+fOxeWXX46bbroJo0aNSvuc6upqvPfeezjvvPMwbdo0fOMb38Dll1+OH/zgBwDMHOa77roLK1euxNixY3HeeedF22cYBubOnYvzzz8fS5YswQknnJD35/zxj3+MO++8E7/85S8xc+ZMLF68OG4U/je/+Q1uuukm3HnnnTj66KOxaNEi/O53v8OUKVPSvuYJJ5yAG264Addeey1GjRqFb3/722kfO3r0aLz33nuor6/Hl7/8ZUyfPh2XX3459uzZg7Fjx+b8OcaPH4/nnnsO69atw3HHHYcbbrgBP//5z3N+PhENfZJITLgjIqK0dF3HUUcdhWXLlmHVqlWD3RwiIsoTJ7wREWXw1ltvoampCV/84hfR3d2N++67D7t3744u2EBEROWFwS8RUQa6ruMnP/kJdu7cCbvdjpkzZ+L111/HMcccM9hNIyKiAuSc9mAYBm699VbU1dXh1ltvRVNTE+6//350d3djypQpuP766+PqKhIRERERlZqcJ7y98MILGD9+fPTvxx9/HOeccw5+9atfwev14rXXXitKA4mIiIiIrJJT8Nva2opNmzZh0aJFAAAhBLZt2xZd9WjhwoVYv3598VpJRERERGSBnILfRx99FF/96lejdS27u7vh8Xiiq+zU1dWhra2teK0kIiIiIrJA1iTdjRs3orq6GlOmTMG2bdvyfoPGxkY0NjYCAFauXJm05CaVH5vNBk3TBrsZ1E/sx6GB/Th0sC+HFvbn4Eu15D2QQ/D7ySefYMOGDfjggw+gqioCgQAeffRR+P1+6LoORVHQ1taGurq6lM9vaGhAQ0ND9O+WlpYCPwKVivr6evbjEMB+HBrYj0MH+3JoYX8OvnHjxqW8PWvwe9lll+Gyyy4DYK5N/9xzz+G//uu/8POf/xzvv/8+5s+fjzfeeANz5syxtsVERERERBYreHnjyy+/HM8//zyuv/569PT04PTTT7eyXURERERElsurMO+MGTMwY8YMAH3rzBMRERFRfoQQCAaDMAwjWlCA8ieEgCzLcLlcOX+PXJWCiIiIaIAFg0HY7XYuEGYBTdMQDAbhdrtzenzBaQ9EREREVBjDMBj4WsRms8EwjJwfz+CXiIiIaIAx1cFa+XyfPOUgIiIiGmba2tpwySWXAACam5uhKEq0bO1f//rXtDVy83HjjTeioaEBS5cu7fdrWYnBLxEREdEwU1dXh1dffRUAsGrVKni9Xnzzm9+M3q9p2pBNyxian4qIiIiI8nLjjTfC6XRi27ZtmDNnDq688krcfvvtaG1thdvtxr333oupU6fixhtvRGVlJTZv3ozm5mbcfvvtWLp0KYQQ+P73v4+33noL48aNs2T0uBgY/BIRWWTDvh5MrnVihMc+2E0hIirIgQMH8Oyzz0JRFFx88cVYuXIlpkyZgk2bNuG2227DU089BQA4dOgQnnnmGezcuRNXXXUVli5dihdffBGffvop3njjDTQ3N+O0006LplaUEga/REQW6QxqCIQZ+BJRfvSrlxXldZWH/5L3c5YuXQpFUeDz+bBx40Zce+210ftUVY3+e8mSJZBlGUceeSSam5sBAO+//z7OP/98KIqCMWPGYP78+f3/EEXA4JeIyCK6AHIvtkNEVHo8Hg8AsxRbVVVVNC84UWxKgxBiQNpmFQa/REQW0Q0BwyivgwARDb5CRmiLrbKyEhMnTsRzzz2Hc889F0IIbN++PbrSbyrz5s3D448/josuuggtLS149913cf755w9gq3PDOr9ERBbRhQBDXyIaKh544AE8+eSTaGhowGmnnYZXXnkl4+PPOussTJ48GQsXLsQNN9yA2bNnD1BL8yOJAR6r3r9//0C+HRVBfX09WlpaBrsZ1E/sR+s9+1EbZo724Ig614C9J/tx6GBfDi3Z+tPv90dTDKj/Un2f48aNS/lYjvwSEVlEFwJGmeW+ERENNwx+iYgsYhgAU36JiEobg18iIovoQoADv0REpY3BLxGRRZj2QERU+hj8EhFZRDfAkV8iohLH4JeIyCK6IaAz+iUiKmkMfomILMI6v0RUTvbv34+rrroK8+fPx0knnYTly5dDVVX86U9/wu233z7YzUsybdo0S16HwS8RkUV0wWoPRFQehBC4+uqrsWTJErzzzjt4++234fP5cPfddxfl/TRNK8rrFoLBLxGRRQwub0xEZeJvf/sbnE4nLrnkEgCAoii444478OSTTyIQCGD//v248MILMX/+fPz85z8HYC4kccUVV6ChoQGnn346nn32WQDAli1bcMEFF2DJkiW47LLLcOjQIQDAhRdeiOXLl+Oss87CL3/5S8ydOxeGYURfa86cOQiHw9i9ezcuv/xyLFmyBP/2b/+GnTt3AgD27t2Lc889F4sWLbI0KLdZ9kpERMMc0x6IqFzs2LEDxxxzTNxtlZWVGD9+PHRdx4cffoi1a9fC7XbjnHPOwaJFi/D5559jzJgxeOyxxwAAXV1dCIfD+P73v49HHnkEI0aMwLPPPou77747GjCHw2G8+OKLAICtW7fivffew/z58/Hqq69i4cKFsNvt+O///m+sXLkSU6ZMwaZNm3DbbbfhqaeewvLly/G1r30NF110ER599FHLPjuDXyIii+gGOOGNiIaEBQsWoK6uDgBw1llnYd26dTj99NPxox/9CHfeeScaGhpw4okn4uOPP8Ynn3yCr3zlKwAAwzAwatSo6OssW7Ys7t9/+ctfMH/+fPzlL3/B17/+dfh8PmzcuBHXXntt9HGqqgIA1q9fj4cffhgAcMEFF+DOO++05LMx+CUisohucJELIirM2k870OQLW/Z6o7x2LDqiJu3906ZNw1//+te427q7u7Fv3z4oigJJkuLukyQJRxxxBF566SW89tpruOeee3DyySdjyZIlOPLII/Hcc8+lfB+PxxP99+LFi7Fy5Uq0t7djy5YtmD9/Pvx+P6qqqvDqq6+mfH5iO6zA4JeIyCJc5IKICpUpUC2GBQsW4K677sJTTz2Fiy66CLqu40c/+hEuvvhiuN1uvP3222hvb4fL5cLLL7+MVatW4eDBg6ipqcEFF1yAqqoqPPHEE/jWt76FtrY2bNiwIZrD+9lnn2H69OlJ7+n1enHsscdi+fLlaGhogKIoqKysxMSJE/Hcc8/h3HPPhRAC27dvx4wZM3DCCSfg2WefxQUXXICnn37ass/OCW9ERBbRBRe5IKLyIEkS/ud//gfPP/885s+fjwULFsDpdOLWW28FABx33HG4+uqr0dDQgLPPPhvHHnssPv74YyxduhRnnHEG7rvvPtxwww1wOBx46KGH8NOf/hQNDQ1YvHgxNmzYkPZ9ly1bhqeffjouHeKBBx7Ak08+iYaGBpx22ml45ZVXAAA/+tGP8Oijj2LRokU4ePCgdZ9diMy7alVVsWLFCmiaBl3XMW/ePFx88cVYvXo1tm/fHh3O/ta3voXDDz886xvu37/fkobT4Kmvr0dLS8tgN4P6if1ovXve3od5EytxyuFVA/ae7Mehg305tGTrT7/fH5cSQP2T6vscN25cysdmTXuw2+1YsWIFXC4XNE3D8uXLcdxxxwEArrjiCsybN8+CJhMRlT9FkpBlPIGIiAZZ1rQHSZLgcrkAALquQ9f1oiQfExGVO1niIhdERKUup5xfwzBwyy234D/+4z9wzDHHRJeXe+KJJ3DzzTfj0UcfRThs3QxFIqJyJElgnV8iohKXNec3ls/nw89+9jNcddVVqKysRE1NDTRNw0MPPYQxY8bgwgsvTHpOY2MjGhsbAQArV66M1m6j8mWz2UpqmUIqDPvRej99dQdmjavC0hljBuw92Y9DB/tyaMnWn4cOHYLT6RzAFg1toVAIo0ePjrvN4XCkfGxepc68Xi9mzJiBDz/8MDpLz26347TTTktb362hoQENDQ3Rv5nMX/44KWNoYD9aLxQKoau7Z0C/V/bj0MG+HFqy9aeqqhBCwGZj1dn+0jQN4XA46fsueMJbV1cXFEWB1+uFqqrYsmULzjvvPLS3t6O2thZCCKxfvx4TJ0605hMQEZUpM+eXiQ9ElJ3L5UIwGEQoFOJcqn4QQkCW5ej8tFxkDX7b29uxevVqGIYBIQS+9KUvYfbs2fjhD3+Irq4uAMCkSZNwzTXXFN5yIqIhQILEOr9ElBNJkuB2uwe7GcNS1uB30qRJuOeee5JuX7FiRVEaRERUrljtgYio9HGFNyIii8iyxLQHIqISx+CXiMgiHPklIip9DH6JiCwiAxCs9EtEVNIY/BIRWUSWJBjGYLeCiIgyYfBLRGQRSQIY+xIRlTYGv0REFpElTngjIip1DH6JiCzCCW9ERKWPwS8RkUUkyVxtiIiISheDXyIii8iQWOuBiKjEMfglIiIiomGDwS8RkUVY45eIqPQx+CUisoAhBCRJGuxmEBFRFgx+iYgsoBuAwtiXiKjkMfglIrKAIQQUjvwSEZU8Br9ERBbQhYAiM/glIip1DH6JiCygG+YiF0REVNoY/BIRWUAXAjZGv0REJY/BLxGRBXSDaQ9EROWAwS8RkQWY9kBEVB4Y/BIRWUBntQciorLA4JeIyAKRag8Mf4mIShuDXyIiCxhc5IKIqCww+CUisoAuBGQm/RIRlTwGv0REFtANwMacXyKiksfgl4jIAmbO72C3goiIsuGumojIArohIEsSxGA3hIiIMrJle4CqqlixYgU0TYOu65g3bx4uvvhiNDU14f7770d3dzemTJmC66+/HjZb1pcjIhqSOPJLRFQeskardrsdK1asgMvlgqZpWL58OY477jg8//zzOOecczB//nz85je/wWuvvYbFixcPRJuJiEqObgAOlnsgIip5WccpJEmCy+UCAOi6Dl3XIUkStm3bhnnz5gEAFi5ciPXr1xe3pUREJcwQXN6YiKgc5JSnYBgGvvvd7+LgwYM488wzMXr0aHg8HiiKAgCoq6tDW1tbURtKRFTKuLwxEVF5yCn4lWUZ9957L3w+H372s59h//79Ob9BY2MjGhsbAQArV65EfX19YS2lkmGz2diPQwD70VreTgkjqpzwtBkD+r2yH4cO9uXQwv4sXXnNUPN6vZgxYwZ27NgBv98PXdehKAra2tpQV1eX8jkNDQ1oaGiI/t3S0tK/FtOgq6+vZz8OAexHa7V3dqFCOOEP+Af0e2U/Dh3sy6GF/Tn4xo0bl/L2rDm/XV1d8Pl8AMzKD1u2bMH48eMxY8YMvP/++wCAN954A3PmzLGwuURE5UU3AJnVHoiISl7Wkd/29nasXr0ahmFACIEvfelLmD17NiZMmID7778fTz75JCZPnozTTz99INpLRFSSdENA4QpvREQlL2vwO2nSJNxzzz1Jt48ePRp33XVXURpFRFRudMHgl4ioHPAiHRGRBQwBLnJBRFQGuKsmIrIA0x6IiMoDg18iIgvoQkCWJTD8JSIqbQx+iYgsoBuAjXtUIqKSx101EZEFmPZARFQeGPwSEVlAFwIyg18iopLH4JeIyAI6qz0QEZUF7qqJiFL4uDmQ1+MFR36JiMoCg18iohRe3NE+2E0gIqIiYPBLRJTCv7pUCCHyfl7+zyAiooHE4JeIKIEQAge6VeiMZImIhhwGv0RECTqCOjx2GbqRe/TL5S2IiMoDg18iogT7u1VMrHZCzyPtQTDhgYioLDD4JSJKsL9LxcRqB3RjsFtCRERWY/BLRJSgxR/G6Ap7XiO/RERUHhj8EhElMARgl/PL+SUiovLA4JeIKAVFBtMeiIiGIAa/REQxDCEgS4AsSUx7ICIaghj8EhHFaPFpqPfYYZPzC35Z6oyIqDww+CUiirG/W8W4KgcUCTDySHuIlDpjCExEVNoY/BIRxdjfrWJ8pQOyLEHjhDcioiGHwS8RUYz2gIZqlwIbc36JiIYkBr9ERDEkCZAkCYpsljwjIqKhhcEvEVEMuTdrV5YKT3sQHDEmIipZDH6JiFKwyVJBi1zIksQRYyKiEsbgl4goRqRqgyIVlvYgFfg8IiIaGLZsD2hpacHq1avR0dEBSZLQ0NCAs88+G2vWrMHatWtRVVUFALj00ktx/PHHF73BREQDQZYlaOH8l3iTJak3gGbRMyKiUpQ1+FUUBVdccQWmTJmCQCCAW2+9FbNmzQIAnHPOOVi2bFnRG0lENNCUAqs9yODILxFRKcsa/NbW1qK2thYA4Ha7MX78eLS1tRW9YUREgyGyUptNzm+RiwhZNpdIJiKi0pRXzm9TUxN27dqFqVOnAgBefvll3HzzzXjwwQfR09NTlAYSEQ0U3RCQe7MVCl3kQgInvBERlbKsI78RwWAQq1atwpVXXgmPx4PFixfjwgsvBAD86U9/wu9//3tcd911Sc9rbGxEY2MjAGDlypWor6+3qOk0WGw2G/txCGA/JusJaairVlFfXw+/EkBzuDvn78jj8aG+vh5VlSpqa+tQ7bYXubUm9uPQwb4cWtifpSun4FfTNKxatQoLFizAiSeeCACoqamJ3r9o0SLcfffdKZ/b0NCAhoaG6N8tLS39aS+VgPr6evbjEMB+TNbqD0ML+dHS0oLuHhWdXQG0tOR2gczvN5/n9/nQ0ioj7Mp5bKFf2I9DB/tyaGF/Dr5x48alvD3rXl0IgV//+tcYP348li5dGr29vb09+u9169Zh4sSJFjSTiGjwqLqAy2buFmWp0Dq/nPBGRFTKsg5NfPLJJ3jrrbdw2GGH4ZZbbgFgljV75513sHv3bkiShJEjR+Kaa64pemOJiIopqBlw9ga/SqE5vxInvBERlbKswe9RRx2FNWvWJN3Omr5ENNQENSM68msrcASXK7wREZU2rvBGRNQrpAk4FbPcgywXWOdXAjjwS0RUuhj8EhH1ih35VaT0aQ+qnr4AsDnyy+iXiKhUMfglomFjw76ejJPYQjE5v5kWufjT1ta0ryFLAENfouLLdBJKlAmDXyIaNj444MP+bjXt/SFdwGXrTXtIs7yxEAI+VY+7zRACkmQ+TwIKSpcgovz8/J39ePzDZjT7woPdFCozDH6JaNjwhw3s6QilvT+oGXAqkVJnqas2qLpAOGH0WDcAJbIynCQx55doAEyqceKCGSOw5h+spUv5YfBLRMNGnduGfV3pR35jS51FRnIThXSRlAtsCAFFiowYs84v0UBx22XUugdmQRkaOhj8EtGwocjIWLtXNwTsSuqgN0LVjaS8Yc0QUOS+dAnBoV+iASOBtbUpPwx+iWjYyBzWAlLWR5jl0BIDaF2YgTUQWeSiwAYSUd5cNhlBjZPfKHcMfoloWHFmOFCKHOo0hDQjOe3BYNoD0WDx2BUEwgx+KXcMfoloWDms2oG9GSa9ZaPqAomxsy7i0x54CZaouGJTi9x2mcEv5YXBLxENC2HdDFAn1TgzVnzIJqQbSaXMdMMc8QVY55doIGiGgK13o/PYZfgZ/FIeGPwS0bDgC+vw2hWM9NrR1I+6oKom4JDjc4P1mGoPkiTBYN4DUVGFDQFHb6K9m8Ev5YnBLxENCz7VQIVDhpymhFmuQnpfObSI+LQH5vwSFZuqx4/8BjjhjfLA4HVUkOIAACAASURBVJeIhoUeVYfXofT7dUKaAUdCObTYRS4kAAYTH4iKKqz3lSV025jzS/lh8EtEw4JP1VHRG/xWuxR0BLWCXkfVRfLIb0ydX0WSOPJLVGSaIWCPy/nVszyDBpMhRFJ99MHE4JeIhoWe3rQHABhX6cCBDCu9ZRLSU4z8xuX8gssb05D3PxsODepiLnEjv6z2UPI2H/TjH03+wW5GFINfIhoWfDFpD3VuG9oKHPkNaX0TbSKMmEUuWOqMhoOPmgPY21nYCaQVwjGrMdoVGeESGlWkZO0BjSO/RFYwhMC2Q6VzJkmlzaca8PaO/Na5bWgPxAe/uY5iGUIgIesBuiGiE+k44Y2Ggy+McmP9vp5Be39VN6JpD9R//2wNFDV1pCOgZVxafqAx+KWyFdIENh3wDXYzqEyEDSM6YlvpVNAdit/Rx9YNzZdZ7cH8t7m8cens5ImKwWOX4VcHL882Nu0ByL50OWX2SUsATT2Fl4DMpj2oQS+h3SKDXypbqm5A1ZnnRfmTJCmpHkNQE3AlDunmyKz20DfhjbEvDWW6ISBDwrgqB/YVmDvfX+aEt77tlZtc/6iaQHcRT2Y6gjrTHoisoOoCaimdShbgvX91D3YTqFeq+r25MmLq/EpMe6AhLqAZcNtlzJ1Qib9/nn0fdrDb+gA5rIukiadUOFUXSVfDLCXA4JfICmbwW94jv/9gzjIAoNVfvMttuQpqRsEjv5rRV+1BliTW+R1EL+5oxxNbmvHklpbBbsqQZU4elVHlVNAZ1OHLMmL4wo52y9ug6gI2pj1YJqQb6Cpi8CvLYNoDkRVU3UBIK6GtqQDZDhrDxfOfWH9wzFdIE3DmOJIkIb6igx5T7UGSAKO8z8nKWntQw6WzRkLwBKRo/GEDXrtZOeWsaTV4+Z8deHjDobTBUzEup4dj6vwCTHuwQjFHfm2yxJFfIisMhbQHH2tTAjArMQw0uywhHHPlIKjllvYgYAa6sTtyI27klwfigSKEiOvDVPeT9fyqAU9v5ZQxlQ58ecYIzBjlRluaKzhdQeuDKs2In/BG/WNXpKJVYwjr5sACqz0QWSCklf+EN478mnyDsDpTrduGtphyZ6E80h5ssgQt5qenC/OyHgDIYJ3fgfJ5l4q398TnnEbCIUWSSuoy61DiC+vw2OO3FYciI5TmCy/KyK8eP/LLMLh0dYY01HnsJTUXgsEvlS1V7xttK1eDMeJZivxF/h4MISAlHB7NWr99B+WQJuCyZf89SYgEvzFpDwkjv6W0kx/KwrpIu7KXTZEQZvRbFD7ViC4VHuG0SWkHI4qRSxo2jLiRX1kqrcvq1Kc9oKHeYyupkV9btge0tLRg9erV6OjogCRJaGhowNlnn42enh7cd999aG5uxsiRI3HTTTehoqJiINpMBMDM+XXkEKyUsh6O/AIo/shvsHd2eqw6tw37u1UA7uhjcq32kJi/lri8MUd+B4aqCwRihuBj0xzssoSwIXp7l6zkDxtJI79ORU4b5BYj+FX1+FJnkSWOK5xKhmfRYOgM6hjhib/SNtiy7ukVRcEVV1yB++67D3feeSdefvllfP7553jmmWdwzDHH4Je//CWOOeYYPPPMMwPRXqIoVU9eZrbccOTXVOzvoSfUt7pbRF1C2kO+wW/scqqJyxsz9h0YYcNAMGbkN7b2q0ORM+YDU+HMtIf4INOhSAil+L51QyAYNizPv9aN+JUWPXY57kSISoc58msvqZH5rHv62tpaTJkyBQDgdrsxfvx4tLW1Yf369Tj11FMBAKeeeirWr19f3JYSJVDLvM6jbgiEdOsPCuVGCAF/kSf++cI6vAmXaRNXeQvpRk5pDwCgJKQ9aEx7GBRhXSAYE/AENAGX3eyHYk7gGe5STTZz2mSoKarvhHQDXocSd7JoFSkm7c1tk4u+HxnKinkk7QhqqHPbSioHP69hs6amJuzatQtTp05FZ2cnamtrAQA1NTXo7OwsSgOJ0glpRs6lqUqRqgt47PKwP0DrAilHjKzUo+qoSBj5TVzlLagJOHO8kpCU9mD0LXIhS5zwNlDCCWkPwXDfpEWbLJV9NZhykm7kV9UEqpwKgkUuS+m2y/APwsTZoUQqUt60Zgg4bXJJDfRkzfmNCAaDWLVqFa688kp4PJ64+yRJijsDi9XY2IjGxkYAwMqVK1FfX9+P5lIpsNlsJdGPdlcQ1bCXRFsK0e5XUeWyo6q2Dl5HzpuiZUqlHwNhHboBy9oihOjN8e0b6ZU7gLHVLtTXx89L8Lh90fd1u30YPWpk/P0eX1y7NEOgwutHXY0HFVVu1Nd7zed6QhhVPwJepw2KX4WnzRiw77ZU+rG/PjrYjSq3HeOrXTk/x9UmIJRQ9PN3ST6MVM3vY0QH4K1K7vNSVi59GbvdRDhDGuytyb/7cFcQdd4meKtqUF/ptK4NCdvmuLADPlVHfX2tZe/RX+XSn4D5fVa77XBU1KDWY7f8tevr65P6bDDldMTVNA2rVq3CggULcOKJJwIAqqur0d7ejtraWrS3t6OqqirlcxsaGtDQ0BD9u6WFq+6Uu/r6+pLox46uHuhClERbCtHUE4ZLAQ41taDKNfDBb6n0Y09Ih00GDjU1R0dP+2N7kx9rP+tElVPB7HEVmDnagwOtHRhp86JFCsY91h/wR78Dv9+f9H0k3uYP69BCAfh7dLToQdRIAQBAV3cPOtrtCNjMST9d3T0D9t2WSj/21we7O6AZAmcfmXvw0trRBV1Vo5//YEsA4ZCOlhYJAV8PmrUA6hL6vJSVS1/GbjcRYV2grbM76fYDnSG4FAMHmlughKwLfhO3zZAvhIOdIbR4S2f0t1z6EzC/z1qbE3sONEGvtq6fIq/d0tKSch9bbOPGjUt5e9ZrfEII/PrXv8b48eOxdOnS6O1z5szBm2++CQB48803ccIJJ1jUVKLcCJiXmkspiT4fId0sF1SMXLhyogkBr0Ox7BL15oM+fPOE0fjacSOxcX8PVN0wSzM5+z85UtXMy3dJpc6EgBzJ+QUXuShEd0jHwW41r+ck5p4GNQNuW2TCmwSNaQ8DxiYjZQpXJO0hVT6wlTx25vz2V+I8iKEs63DTJ598grfeeguHHXYYbrnlFgDApZdeivPPPx/33XcfXnvttWipM6KB5lDMvD63BSOGA03VBbwOedjXItUMM/c5pCeXI8uXEAKaAdh7c3dPm1KNN3Z1IRBOvYCFrXeVN3uGXF8hRDStK6SbeebJwW9MtQeZpc4KoeoGHIoc933n8pzY4DegGRjhNi/Z2mUJwWG+bRVLqt5J12eqbqDSoSBU5EoMblZ76Lcqp4IWX+pV+oaarMHvUUcdhTVr1qS8b/ny5ZY3iCgfTkWGakHQNBhCWvFmQZcTTRfw2lPPFM/XzrYgjqjru2R3WLUTaz/thEORoiOzsSILXYyqkFOO1pqT14BIfBXqLa+XOJlKxI789j6H8iMAjKm042BPGGMrHSkfc6hHxeiKvvsSV/kKhg24qsy/bYqEMOtoFyTxe06Uz887pJsjv+lWf7MKqz30X6VDwa52a9OEQlrmwYXBUnotIsqRBCk68lvKPm4O4ECKy7mqLsy0hxJvf7FF0h6sqPiwab8Px4+Ln+B08qRKfHDAl/LxibV+EykJlRvMtIfkkd9YEljqrFAzRnmwrcmf9v4/b2uL+zusi7gDa0gX0bQHe0ItZsrdsx+3W3b1QtUMVLmUuJJ0xaDIrLJSqEipxkqnYvmCJB1BDbWu0lt4hMEvlbV05XVKyd7OEDpSBFgh3YDXLg/7A7TWW/It1M+R30iVh8T0hmkj3Fg0pTrlc8yRX7NvUl20lWUg9pgd0o1ozm+6XHNzkYvh3aeFkACMqbDjYHf6y66HetTklfViujs2vcVc5IL9UIjuoJ52e9QMAVsey8qHdDEgaQ9AcWvVDmWR1VJdNsnyk5SOoI6aQZjQnQ2DXypbAgKONIXVS0lHUEt5yS+kmSOerPMLeBwK1H6exOztVDGpJvUs5bPSVBAYW+nAlkO+tEGSTZagxwSyIc2AI0XOb+yzuchFYQTS541GtPi1pJNdGX0jfrEnP1zkonBdqp42WPWrOjyO3EMHVTcGJO0B4ETTQqmamc6Va659PjoCGmrdDH6JLOUsg7SHzqCeMrhSdYMT3mCOJHntcr8Pjvu6QmmD33ScNhnnf6EOf9zSnPLAqUgSjJgAStXNhTCUhEvqsYcMWQKXN+6HOo8Nrf7k0V8hBLpCyduSwyZFRykN0bfYiJ2LXBSsO6SnnTzmCxtJSxtnEtIEKp2ZR35b/WG8s7er3yfAVJiQbuS0WuqGfT3445Zm/O/GQym30VTaAhqqmfZAZC1H74S3UtYV1KEayW0MaWbO73AfnepLe+hfPwbCfWWu8jG6woETJ1QiRRdBlhC3JGdIMy8P2mSkTXswV44b3n1aiMih9wsj3fi4OZB0vz9soNqVXBLPbZNTXqq1KdKwP7EslGaItJe/fap50p6rUIaR37Bu4Kl/tOD1XV2o99jx1D9asfbTjoLbzbSHwqi9E3mz2dEawGWzRuKiGSPw5q6unF67yRdGXQmO/JZei4jy4FAkdIVKO/iVJKRMzVB7c347gsN7RrpVdX4DWuFVP44a6cZRI91JtyemN4R6R35lKT4XmPrHTCcx+67ObUs56a3Fr2FcpSM5+LX3Bb+x93DCW+FcNgnBNJUT/GEd3jxGfs2SjgrCKQYpPmsPYWK1E/MmVgIApte78cctzYU1Gkx7KJR5RSv3U4cqlw3dqh63rHsqHUFz1LcY6RT9xZFfKmvlMOHNoaQ+CEcPCsP8AB0pdWbJyK/FJe8SZ5CrMRPehvuIvZW6VR2VTjOg8tgV+NXk30Kr3yyBFnulRwBw2WQEUgRqzPktjGaYV6TS1Uj2qQY8eWxnsakoiQJhI9rvNHhULb5edi6+ONaLzQdTV9GJeHt3FxZMSr3672Bj8EtlzWnrf5WAYjPLsSUfnA1hls1KNSIynGiGgMeCUmdhI77mqxVkCYhtVkgTKRe5KO1fYOnrCemodJhBkF2RoKVImm7xaxhflXrkN5KfGp97zaobhegJ6aj32NOO/PrCel5pD5kEwsmBdOmNEQ59kStaEblsNzNHe7D1UPqyhEIItAU0jPDYLWmj1Rj8UllLF1iWCt0wl8NNV5HCDKIGuFGD7JOWAP7Z2pfTqQlrFrmQkL1aQL5scnwgZtaVzVznl/LXFdKzLj/dFtAwymuP294lmCO/6QI1yl+XqmOk15Z2wps/bOSV9pBpiwxoyXn63KoGXqTUGQB47Qp8OWxPsiShxpW+Tvpn7SFMqXVZ2k4rMfilsmZOeCvd3WV37yiKmiZQMvMSh9eBe1d7EJ0xec6RCW+l2I+J1R4EzJXcEuv8crSqf3rUvpFfIPX3aQgBd4qTJLetb+S39H5B5ac7pGOk156+1Fme6UWZ+sQf1vNKocjGLvNKWiFiJ7xVOhV057jQxdGj3NjZmjw5FQDWf96DEydWpLyvFDD4pbIUuSzjUKSSrvPbEdRQ61bSXkYajjPSD3aH4/KcdWFe6i7FCgmyHF/tIXq7hLj6v6XX8vLSo+aW+5lqRcfYCW/Uf5HgN93Ib6YcXgn5rbKWKk9fltIvIBOhGyLlcuUeu8IljgtgBr/m91mVxypvZqWV1H0lkFsFicFSui0jykAzzMlIDkVKWUasVHQEdVRnWN3GLg+/Fd5a/VpcwK8ZAjaLc3Wtokip0xtKcfZyOesK6aiIGflNt0UkljZMnPDGXum/HlVHvceWduQ303fsyPNkPmzEL08NpC9dl/S8FPsMr0Nm8FuAyOI9QH4jv64yPvFk8EtlKWwYcBZpRRordQYzF/jOVC92qJJlxF2aDOuZy+UMJpucmPaQWmm2vnxEqmikI4SAEKlHfl1Me7BUV0hHldOW9rvM9B2bC47kHgyl2m7c9uwBbCT3PpHXrqBHHd6lIwuhxkx4G1vpwP5uNekxhhBJ/eVSGPwSDShzOcbSDzk6Q5nXNS/14N1qQoik+qu6MEd+SzFwSVzkgooj21bgCxuocChJwa8EM2VGNwQrO1gkpBlw2aSCTuicSvJKjfm+TmwOdzrmiHHqkV9fijJ5lFnshLeqNCO/akJFCMCstsTgl2gA5boc42DzqTq8WSZ0DKdDdkfQzCeMS3vQrUl7KMb3qCRUdSj9X1x5ytZ3rX4N9R5bxhUdUx2cKX8CkVUKU8uW9pBYsjDf7dJtT123OVZYN9KkPSjwhTnym69cykQGw8lXZyInnuWIewoqS7kuxzjYIgcSMh3sVnFYtTNu5DeS81uK35JNzj6Bx7wcWIqtL1+JudYtvjDqvfaUaQ8RQc2Aq4Dlrck6mco65iq34Dc5VxjgyG9/JB6nEq+kBPNYQbMcrsJwT0FlKXZ2arkbGp8iNwd6wphY7YCmx6c9KFL/Rm6LtbNNXOQilXT5h5S7xG/Pk5D32eLXMMJjS1pxL1aw93I99Y+U8P9EmbY0p01OGvmNvE6uW6gnl+A33YQ3uwIfc377baTXjmZffP3eoGbAmeP2FdRExhz+UlDarSNKQ9UMOEp84wKGV2Cbi2ZfGGMqHUi8Utbf0fFirO4GmCOQ2UqaDaUTscGS+L16HQr8MUFMe1BDbYbcecAsm+WyeHlryo9DkTKuuJnLSWpOOb9pTjhdNqlsc1BLyaQaJ/Z2huJuC+kCrhyvtvrD8dVbShH3FDkSQuCjpvRL+dHACpVJwFH6F38GVt/kthTlw/rxuqnqhVohcTGLVFTdKIsUnFKVKiBKHPlNVVs2Md0kpIuk1cKocKl+9YYQkDNsqc4MOdkOWc5pVcTc0x6S28EUM2tMrHYkBb/5nFz61ORlq0tNabeuhGiGwIb9vsFuBvVSYya8lerurhzynkpJf76tYIplUq2QuMhFqt+ayrSHflFTjCh57HLWiUthXcAW870HUkzIofwIkXmpmWwpPmaps75XiD1BcdoyjwpHmCc+Wfq+SFd6yt1rn3Va8joeu5J0AhLKI6feF9bh5cjv0KDqIu0ZLQ28cpjZHdREdGeRuMs3hIiOUjBE7r9ijfwqCatNpUt7cDL4LVhXSEdFwupuZtpDfnmfxToBGk5iL22n+kWHsuR9OhUp7jgZmxKUmA+cbqKoXck+Qsw8+9Q2HyzeAF0+Ob8c+R1CwroYdsvQlrJ88yw1Q2BLwo7hD5ub8cSWZvzsb/uKMkmiI6ilrfGr6gKOIThy0RbQ8M80a737VD3jDrH/aQ/WjzTY5L6c33Qj+Ux76J8eVUdlwiiROfKbOfjVEvYBrPbQf90hPbrMdKpfeyjLoIMZ4PY905ybYfZRYj5wpuoB2Y60YSN1qbNcnjuUNfvClr2WOdk3tr9yTyvyhw14HaW9LZZ260qIqoukZWgTc2Jo4ITynPDmV3VsTEhbkSTg0lkjMXdCJdoDWppn5u8Pm5vhD+voDOqo6V3dLXE3rWpD4xLtwW4VO1oC0A2BrYd8eO7jNmzY15P6sT1hjKlwJN0uYv5faKpIoFhpDzHVHjRDwCEnvwfTHvonNuCK8Nrl6Ampqhs5fe9BjRPe+iu2L2Qk57tnq69uBrhGzOP7SlImLojg78fVmnSlzoa7Zp9mWd3d0RUONMUE08E8jlk+VUdFisEIScpeOnKg8NeTo7AhkupLvvapNfk1lL98R34DmoGOhAA3sg3WuBS0B60LfrtDOp7e1ta7tLE58pu4ucdO2CvnsOmtPV1o8Yfxp3+0oKknjK8dNzLtyMvBbhVjK83gN9XlTocsJ51g5qpoaQ8xI78hTURHsWIx7aF/ulUdFQmjRB6HEp3w1h7QUONOPpDGpj3IkgSfylJn/dUVE/y67MkLVqhZSliZE976tuGQbkS3DWfCAhiBcPoT1my9yJzf1FTdsGx550k1Tuzt6Bvgyy/n14AnxcivkkPpyIHC4DdHYd1AOKHXsq0/TsWjxuxUcxEIG2kD3BqXDR0B69IeqlwKDq914s3dXah2pb4UH9KHxsivZgicdFgVLps1EouOqMk42zpSqxVAXLWHyDOcNqngAvnFGvm1xeT8mgfyFCOQZVJ2r1T1hIykkd/YdJP2gI46d3L6UFg3oiO/LpuMzqCW1D8SSmekqRz0qDHBr01GUEse+c2030qsw6xqIrptOG1yXNpDppHfrGkPvNqSUoVDQWfQmmPZhCoH/tWlRv/WRO4rcYbTpILFDiYMNu6xc2ROeIvvtP4so/h5Zwj/YtpEwSKrgkVku1we1ETc7NXYahE1bhs6LBr51Q2zFND8wyrhtsuo7j2QJE6cUrXyKNVmJTVN8Bj5VhxKcoH8XBVr5Nes9hAz8puiz4ZL2sO7e7uSBgCs0B3KXBO0LRBOHfwaAvbedAh3b2m0xHJoNkXKqbwWmbpDffnXLpuMYNKM//z2W7FXRVwJE94C4cInRWUa+U1cHXC4EEKgxqWgM2TNscxpk3Pa3m2ylPN8qFLqm8xVwwE8+OCD2LRpE6qrq7Fq1SoAwJo1a7B27VpUVVUBAC699FIcf/zxxW3pIAunyPnNNhs5k8/aQ/DYZUysdva3acOSBCk6ymjvvdSWaSZqYj5gZ1CPpiRUWLgkZndIR5VLgSRJuH7e2Ojt9t5lWd29O+yhMvKbD/OEJf39uZZCSqW41R5630NLfbDO9ypEufq0LYSjR3pQ47b2e1aNzNtCe0DHjFHJh6rYkw5XmoUR7LLUmyJlXXuHMjMFJXbkNyH41Q1UOOw5v17s1RJnwoS3QNhAtSv1a2VNe8hwwul1yPCrOqqyLIoy1IQNgXqP3bKR31xFfid2JftGZpMlGOUS/C5cuBBLlizB6tWr424/55xzsGzZsqI1rNSohkjaIIOaAd1ILr6ei/aABps8vDZOK8VeNnf2BpbODF9nQDPgtcvR/jIrMfRO7JBSL7pQiHQrUTkUCWG9L0CLzVkujV3BwEiVFhFNe8hQID8bPY9LcvlQYha5SBdgq8Nk8k0grMMXNlDjHpj3i/RmqglxQHwA5LalXhjBrkhxS2lTZroRf0KRGPyaOb95jPxqAh53bNpDzMhvmpNJoH9pD167Al/YQJUr52YOCSFNYKTX2uA3ly0n8jtJtY0mkiWgwPENy2XdYx999NGoqKgYiLaUNDXFLFdVFwUP4bcFtKQ0CiqMI4egKRg2MLrCgc6QuWPoCOhxZcis6on2gIbaFJdoHYoMNea3EtIyz5ouB0IknxD2h0OR4soklQJF6kt7yBT8lntf5iKgiayLDxQi2zcnICCnOGnSjL7v3WVPvaytQ5EKnkQ53KUb+c3nt5408qvHVnvQC87TT/ebAMyRX6smfZWTkG5ghMeG7lB+n72/+/FUv5N0clkxc6AUPFzx8ssv4+abb8aDDz6Inp7UpY2GEi2mZEuEqhsF71h7VJ3Br0UcvSO/mQQ1A2Mq7NGKD5lq8PZHW0BDbYqZ6Q4lfjKXOVJd3qOF2XI1M0lV7SFxZKgUxI5Up5tUN1zSHoKaYVl6UKxse8F096t6X61Xly31wgj55CNS/HftsklJqSShLNUeEiUtcpGQ9pAuVUlC4WUPvQ6lKL/TUhfSBNx2GUaKLcYf1tPWAM71ylW6PVw+wa9SQsFvQUf/xYsX48ILLwQA/OlPf8Lvf/97XHfddSkf29jYiMbGRgDAypUrUV9fX2BTB5ez2UCFJxzXfh27UV1Ti1pPcu3SbKq8nbC73GX5fdhstkFvt8fji7ahvh3wVLpRX+9N+3h5TxDT6r0wBFBfXwv1swCOmDA6uiP3uH2WfCZ1VwBTJ4xJOkCMaAe8VX1tdDTpGFNfjfpqF7wea947X/3tx84WHw4fnfwa6b7L2D5zx/679/GjtW60B8Kor6/Luy2eIn6HkddWDmgYP7oW9RXOuNsdLh/GjBoZFygXsz2JBmp7tNmbobi8lr9Xtt9L4v0ejw8jRoyAq8XAqPpK1Ne64VP8qHR3JL3OiHagoirzvqGUDPa+Nfa7Vu1BHAp3xrXHvieIcaNGwpPhpDf2t2/fH8bYUSMwwuuAIQRsuwPR+xwuH8aOHpXyNeqqg/BW16V9n0zb1wT4sLc9kPb+tTuasejIkWnbb6WB7M920YORhgMdWvJ38/c97fhXRwAXTkpuS1cwjLqqcOpt0J28n040OmiGkfX1NX3PS9M/NS0GqmrMbXawFRT81tT0fchFixbh7rvvTvvYhoYGNDQ0RP9uaWkp5C0HXVtnF/SwGtf+UFjDweZW6N7cJwBEGOEQ2ju1svw+6uvrB73dfr8/2oaQvweH9ACqkXplMQBo7+yBu07GR80BTK3Q0dnTg+6ONnRHXi/gR3Nzc8ZSXbno6I5/3YiQvwdNRl8bWzu64K8UaAnbEAwEcKipuaDc8f7obz9+ur8Hbpuc9Br+gD/l68b2Wdy/ex/v7wmhuTOElsr8R21iX89qkdduau9EoF5CS1CJu93v96O1tXXA2pNooLZHmwjjUFsHrH6rbL+XQML94WAQB5qa0dbZhR6Xjhbdh6A/DEXoSa8T8HXjkBFAVYZ9QykZzH2rECKuLwJBDS3t3Whp6QtA27rM/Zs/w74q9rff1tmNnk4ZIqAk3ZdpGxFqAP862IQRntTH1kzPVX1hHGj1oaU2dfte+Mc+HF09MHn6A9mfh1oCCIV1+AOBpPf8+PN2NPvCaGlJHqhr9YcRDiY/BwDUUAAHDjXBrshpt9OQL4COgI4WV1+ViXT94/f1oKU1DI8+cBP9x40bl/L2gnq/vb09+u9169Zh4sSJhbWqjKTK+bXJhZftiMxCpv7LJe1BQPTW801dBiYySaJY7CnSHiILJtjl8sxLbA9oqPMknz/LedZW7ZvwVno5v7FCmkhZ5L10W2wtj31gLycrkrlamJJwQuqwmdt7OKHaQ6q+sSsyJ7zFCGkGHvuwGQe61aT72oN63GTdVJezDZHfBO9QgUt/u9NU78iFLiln6QAAIABJREFU1yFnXK7erxo40GPdMsClIlMFoc6QlnaCYKY5C1VOBV29OcS5pj1oGYoAlFKd36wjv/fffz+2b9+O7u5ufPOb38TFF1+Mbdu2Yffu3ZAkCSNHjsQ111wzEG0dVGqKnF9bgUFLoHf1k1zzZCgzhyLnNBHHbZcRTDMxrsaloCOoFZzDmo1DlhDUEye8mb8nu2LmJZZbZZ72gJ5ycp/TZk4+8qRY3jKVyLfS35xf0dUB7NoB8dkOoPUQRNg8wEljxgPjJ0E6cgakmhGFv37CJBtZKp38tYHgsRcekGSS7qDqtsvY362iJuE3Fhk4SCx1lir4dZTpiWUx/LM1gDd3dWFyrROtfi262mJE7AqMQGRQIb6/8702pSeUN8y1J9z21NU7cnpulhxUl13Gvk4Vhw2xMqMhzYAnxf44It1FzZBmpFy5EgAqnQq6QzpGeOxp+y4x+PWrOrxp9v22cqrze+ONNybddvrppxelMaUsrMcX1Ra9pZUKGVVoD2ioc9uwP8XZN+XPoUhoD/Zvg6pxm6u8TaiyqFEJHDYZXWrfaENsaa5CT6IGm2qkHtWJrAyV5oplWrmM4McSoSDwj40QH7wPo6sWxkfPpH5c7P/HT4I0ez6kkxZBGpFf3l9iy0ppFGMgSFLhk5DSyXSFwOtQ8K9ONal0YKT4fuxCB4os4cj65NpWNkVCMMRBBgBY93kP/n32KPyzNZiyHNbBnjCOHtmXi5kqBayQ3i8klaw/wW+29xtX6cC+IXjsjUxGjCyolDj6KiP17eZCJKlHjGNHftNJDH59GRYvUWSgVA51ZTbWNHiE6CvTYR70zE4vJGhpY/BrKYdNgtrPEakal63fK+4JIZDuWB4Z3U1/39A5QEcPXGnmNAjDgCz6amT3LW+c28iv2L8XYu3zEO+/Dqi9fXb4GYDTBUyaCmnykcC4wyA5nRCaBhz4F8SeT4Ed/wD27YHYtwfiuSeAaUdDOmYOpC8cB4w/DJItv2jd3o+0p3KVqkpHfwQ1I+WILQB47TI+aw9i7oTKuNvtspkeoyXUem04oibxJcxR4mHWR5lIkgSPXcbBFJf9m3xhnHq4tWf/hX7zHruCljTVCfrLXExn6OxvI0K9VWciAWvkqpxmCCiShBEeG1r9GkZVxO/nMqU9VDoU7OtWzX11mpOKpOBXNeB1pAt+S2efyeA3RwJm2kMkn0XVzQ4upCPbAhqm1A6tSy6Dydm7wluuwnryggg1LgVbD/VvWchMRdsdGXK8zdzxfr31oEgX6Ge67Cg2r4Px2GrYqo9DsLED7pnHQnhnQYh68+QyxYsKIYCWQxBb1kOsfxv49OO+O6dMB754EqSKmZBPug6SHH+5LbaXRTgMfLIV4r3XIDa9B+zYBrFjGwR+Byg2YMx4oLoOUmUVYBgQWhiSzQ7hngFjVweEfRpEnc8cPZYkKDLKst8KEVm226rFYCL8YQPedDP6HTI+71JxZsKlXPNkV2Ss9RqR6aRzuIl8VeZS0MmjeanyeRO/uYGaktufnN/hKlI+s9qloDPYV2++qSeMUV47RnntONCjpgh+06eoVbls+Kg5kPEk1Z6whLgvnD7tQSmhVDEGv3lw9O5InTYzgPLY5YJ2rO0BDbXjvAO2Ixnq8r1c3hlKrvEbyW3qj3Q5sICZ9pBuIY6hOPIbTLhkKdQQxOb1MN76NQDAXqEivPMTuD7+AOLwM2A8uwk4fCqMkV+C8a8WQBhAVydEWzOw51OgI6aagsMJ6aTTIZ1+LqSxExDSDDg/aU8KfBNJdjsw83hIM4+H8PcA2z+E2LoRYudHQNN+YN8ec2Q4tt0AxOEuiN2vAoefAeP/vQrUj4Z0/JdgGzcP4QleoLMDxit/Aw7ug2g5BGnUWIjJZwKwtpySCIeBz3cBug5MnAzJOXBLWIV0Ay67hEDY4uBXTV07GTAnoR7oVlGdsHKUU5Fz3l4cSumMNA2myMkL0L+UglzIMZfdE49xuR7zitlGCb3btRD9ru5TSkKauaBItdMWXcwJAPZ3qxhX5UC9x4b1+3pw7Jj4sn8hXaDGlX7kt1vVMwa/ifyqgbGVqa+iKTIQ7t8Yk2UY/ObBFnMJTdUFPHaloB2rL6zDY5eHzSzxYnMoctzKQZkokoQWn4ZqV/wB1VziuH860qzuBvROvEmX9lCGOb/mgSP1fS6bjE5/CGJ/C1A3EvD1wHjwpxDyFMBmh/Tlr8ExZi607vmQPl4PtLrM4PbDVuDwKjPQTOSpAKbPhDTnZEizToDk6supyFQsPx3JUwHMORnSnJPNzxP0A4f2A53tED3dgKKYo75aGNIhB6Sja4D2SqC12hyFfuUZyGM+h/rUHoiRx8W1WXy0GWKPDv3NPUBlNSSP12y/txLS4VPN0Wo1BDQfhGg6ADQfAFQVursCD+pTMbLKBXg8AICuQBjXBLdAbN0A7N4J6L1HDkkGJkyCdMwcqCcvgrC7zPdS8puw2RnU4LEr0fSBsG6u4ladcHIYCJsHP6sDEnPkN3XfeewyfKqRNBppz6MqCBe5MLX6NdR7zT7NZ1GCQkJDp81cyc2T4mQ0157w2GX4ixD86oYZ8NY4FXQG9aTJlOUs1Ju+UO1S8Fl7XwrfgW4V0+urUeGQ0Zai2pGaYcJbZFQ3mMey1mZ8k/rE3Bz5LY2BnqHT8wPAETNCp0ZGfgsMWobSGedAM5dj7Pv+EldPy6TapWBvZwiTaqxPO2kLaJiUJp0l0wG7HMsxdQc1VKg9EB98ClTXAodPNWcyfLwFzg0fwL/nAIzP15kPttkBLQwcPQvy7T+DNGEy7J92QJ88F2LefCj/aIF82ZkQn++CtEeH9IUqQFKAikpIdSPNdISxEyHJqYOkdCuv5UNyeYBJU81/x94OQNrSDHnWl8z/X3kusPNjiA/eh21XB/ROA3C6Ic1fZOYb146A2LoJ+BzAZzsAIGkkOZ1uuxeTRx2Lc/a9G73tycPPgL67EdFT5bETzRSN/Xugfb4Htn/tQvsLT/U2VgaOmQ25YRlw1Kyc9jEv/rMDXUENZ0ytgRDAKzs7MNJrxwUz4qtiBNOUeesvf1hPe8nV41BSBsYORYJP1XMKpOyKjHCJHGwH08EeFaN7L3enOtH3pZmhX8heyaHIULNMeI0EoenY/V0IHTwAEdoBEVaBznagrRno6oDw98BwfgF64zZzW7DZzHz90eMgTZoKHHZE2pSsYO++YnyVA/u6kiuJlLNI2kqVy4bOoC96e7eqo9KZ/qQ404S3iFAeI7+Zcn5LaXnjodPzA8Aek1sa1s1yZRxVGHhhQ8AWk6CvyFLKJR1TqXHZsOWgD8eNsX7Fp/aghi+6Ur9uptSGchv5Nf7+Jlqe+X+okSpgNG8xb6ysNlMVerrhclYjUDcdqK0Hujp6A98vQlqwGNKE8QB6gxJdmFUvFBnS+MMgjT8McDdDnpVfNZlCRn4LJckKcOQMSEfOgH13Jwy7CqkFkI/tS3GQjpsHacM+yFWnAP4eCL8P8PcA7W0Qn30C7NkJuD3AqLGQRo4BRo4FXG5092io6TGAim7zQC8rqPS44Ju/BNXHfhGYfow5igzA3xPAs3/fiUsOvQtl1w5orU1ATxewZT2MLeuBCZMhnbEM0gmnmOkeaeiGwL/PHo2Xd3YAAK6eMxpr/pFcnD6oZf6OGz/tiJtwtub/s/emQXJc55XoublVZe1b7w10A2gABIiFoADuFCWS0EJSpmRJ1GJt4UWWw54Z+3levLEjxla8eDHhiXke+XnCS8SMn23JT5Zt2aRkWqJEihIlcd9BAiD2BhpLL9W1V2ZW5XLfj1tZXUtmVVZ3NboB9olgEF2VlZWVy73f/b7znfN2GqZFkVYMfGRnHJNx50yQoltIuURJAZFDwklKj/c+7i5XT71cK/W6mSxca5gr6bhpxH3Mmy3pGA7157cyvW4LVK+CqgprNi3mQUt50FkR1pknoJTK8NFhWK9fZAEszwN6FXRhFrhyEcgtMjqUUxUIACZTwPSb9T9p6/93PATzB2dBoglAFEFzGaCQg2JwkAITGAlW8frkLdi9PcGqU6oCOn0SWJxnzQyiBHLwTpDxLX05J1cTIYnrSY+7U8ObDbVL8Nv4hHUajzkCGOtEIWcj+PUIAlLn/ALshgkuk/awgZWhanR/WN0QlwWcy1YQk/uv51uumq4r3tbGnMbMtcCRa8KLnho66D/9NejTjyMb346ETIHdNzG6wOI822h4HIGD96Aycgj8LVuZ2kKpAETjIG8tBVUizwJ+o0UHdDlQuwRmqwWB52AGwgBp9fMDiCSB7NjD/u1xf8U5BTFKwQ9/sv5abLqAYtyHWIsmaZ7yUGKD4O77dSRrLlK0mAd95vugP/4ecPEc6F//P6B/9xdM+WJ4DAhFADkIBINAKAqy7yAAtnh8YIeLHVYNqt45u/7to4vYPRDAaETCQlmHxBN8dE8KZzIa5kp6x+A30CFLNBpud6SyqyhezqvIL0+O8kKugtmSjvdvjfb82fWIRcVwXEjYuFKsYlMH3VtqmcD5M6AnL8N89gVArwKmwehDoTBQ0RhdqJiHyA1DzUzDys20BbCRsTuwkD4KAJATO0GvvOT8hT4/WzzHbgURJba4TgywcSQYAlkIgPvY3YwGZBigFRW4dAH0whng/BlwFRXmhXfA0+ZxVZNT8NE8kudex2IpCOtxl+AaAP23fwS23QBy8x0gu/cDw5tAhO4hE7Us4Mw7oK8/j7yhw+IFYGAY5NZ7QILh9u1VBfTZp4D5KyxR4JdBpnazRXaod/UNltl3v+dbuc5ejEgqPem2uzeissyvp92sOjaCX4+goE0ZOt2kkMXlqT1sYGWoOLjtdUKjukPMz9cmZ+eHfSVNEBSd6Sxud4odCK418pqBjGqAAJiI+Zp+C710Htb/+zXgwlmAF5C952HsvvtW8BEfU2OYvcQ2HB5DgALaUdagRgQBiCXaypxijYtptChvLOfMq7qFSIeyXj9gWBRCy7W1pc76RWDKawbGIs3BXtTPo6CZQEsMllONNt4mCUdBHvo06Ac/DvrST0Gf/ld2vc6fBj1/uu37aDwF8sH/DV4a8zSDnWOR42pul0vPj25S3D0RwZNncvjigUE8dSaHh3clALDn7dSiu7VwuWq6KqQAwBcODLa95mS+4AaewFFB5GxGQ9TPIy4LjhO1aljIV9ZJZ04fYKGzM9tsScehsVD9b1ousQbQ6QKsF/4G9NgbQLkIOnkYmF6i5TiNWlJSYNKTvAD4ZWB8CxCOgIQiuDuUxJN7bsEtURMBSwbxHwQskzVx8jxIahgYHAEGhsG9vQh+n/O9SY4sgOxuqLY0vEcpRei1C1B970e4nAX0KkgsAUQTqOgSAhoFVzwDerIMSFfYwl3yMcrEyDjLQqfnQF/4CQtiz7yz9DuDYRaYcxw7blUBdJ1Rv6JxQFNZ/4LCaAda43F9+29A3nMn610Y3QxkF0HPngD92Q8BtYxG0Ce/w2hM23eB3HwnyJ33NfU5OJ4Th9d002oaXxOygIxqNFU0vGR+NcPquHjyOgbyG7SHaxNiw6BbtSiCIoe00tsAqejmqnDn3k1wctvrhMbMYMwvuNo82k0WbtJLq4VuerEFzUDkKti/PXY8gx0pGe8sqDg8FcV4xAda0UB/+Bjo9/4RMAwgNQTuV38XOW2gPoASQtikUQNP2s0LDKvZJKae+a3pZ9voZVh8a66MGwcDUHWrzmdcDRAQx1KebXLTr6E8XzGxu+U6R328ox54TjOhuSgvEFFkHOQ772OqFhfPs3KyUmKTslJiChcXzoC+8BNYl58B+dDHGQXDBfY5DkgclKoFSV46F+WqiYQsgAI4Oq9A5Ll6lijqFxwNFWx06yJvlSQEeqM9EELatImvFKt4ZrqA4ZCIC/kKfv1Q++9WDavjcV/LoIUcMHcF1g+fZUFXMISKOgTx3DSs9Dyj5lyZYdtOHgad/hn7YHIQZGIK5P59IHIA4DgWJJeKgM8HEo4CoQj81QCqwRC4Tazaw+/71fp3xwCor89DGQ8hWLXANQTcbce5zN9HCEEoHoMyMIJoS+WgMq9ADlBwu+8B8S+A//wD7t//8S+BvvEiU4U58RaQXQTKRfZfKxbnl6pfADtX77kD4e27kJ6bw/zJMxg/8gzTJn/hx5gJDGBcWVjafseNIAduByQfo3ycPAqcPr4kx/jkY+C++O9Adu33dA7sc8foLEvnYCQsYbaoNwW/1INldb84/zxplrO8Uqx6rnrGZb6NhnS5UMVrV0pQdQsDQRF+gUNOY+6FraoWrdgIfntAo41utRZQ9Zr5zapmfQW10fLWGwqaAUJIjaDffPYqBnXN2mr60gTrEzjHUirAAuOcZq5a8NuUnWgY2hu1SFmWeimDbFGKb729iC8fHFqVY2qExBPcvimMoMghoxgYPfY86D//DZDLsON/74dAPvklEH8AlTcXXH3kndCaXbC5mK2Z305ovL6UUjxxKocTaQ0RH7+qtAcKpoDQ+h0811/+WrFitmWwbZ3NVuQ0w9NvJoFQnaPcCGqarKz79iLoT38A+vMngcntAMfDSt0Kao0Aew/V1SPsIDUgcijrFmINSahSlT0zt20K4fd+eAF/eO+m+ntu2s3140A7JagbbNqD1zPfuuWrl0t4eFcCCVnA//fmguNnNN3q6mx1rYGePAr69OOgb7wAuuneZlWVycPNf4sSMDYBfnwS1m1fhrDnZsZRfysNbp9zxtWGb0GBqluuVbADo0E8d6GI2ze1UwD6haAL71XTLYRqz5hf4PDCTBH7h4OOzxLxyyC3vQ+47X0AatSPUpEptVgWy/7KQUAQ2BiZzzKVlnAMiMRACIGcSuHiiRm8NlrGI5/6DOjrLwLTp/Atugv/sfo6yOgmkJtuZTSHFlClDPrWK6A/fBS4cBbWf//PTO3mgU+CbGrnItf5zpQChglDN3B0XsG2xBLlaCQk4c3ZMnYP+EBNE4RSRmdTSuw3WWbt/xbLxlMLXFlDWaXwgYBmKWDWtqNW/d90gQN96wLTR58TQfVTjP7RuD/LBNEJDMUPazoHWBa+nR/ELWKB7cuitf9bjHfd+Bq18EOawK8Zx8Bb7O9/4LYiZaq41biCsKFhAX6o4LDNUPCEfwp7Cq+xff3p3zneIxvBbw+QeIK8tsT5TQX5nsvVGVWvB7/dPjmTr2A0LHVdlb0bcCKt4rkLRSRkAXOlKu6aaOZCHRoL4a9fm8cn9qSQVQ385Fwej+xJMc3ZFjWAQ+PO2YaxiIR/O5FBUOKxezCAAw0NIq9cKuFghyyFF7hd70Z745+fL2A0ImFnikUXpaqF8lWahO3ji5sqzvzb90Bf/jZ7YfM2cI/8CsjOPfVte2WG6FazG5fIM55zo81zJ+Q0A987mcVnaxPvpUIVB0dDCEocHj2WwZ2bV28iBZw5rzZX2835qFeYDhkYN3vRgkOg3AsIz8N66NPgU2dBhnXQF58Bzp5gbxpjsP7sr4F4CuSWu0EO3Q1Fj0MWOARFvs0gwe4ml3gO/+Xw5rZFUb9HL6lD86gXNCYg3KCtoK9gvaF8/jx8LzwH6+W/Zy9wHMtMTn4YCEVZJtMcB9n9ScapnZgCJraBCCLk4xlUt0UhSnzd4KkbfDyHk2kNql5wvPZ7BgP469cWcF8XPjUBW1h953gGlAIRP4/BYK3a1OWuGgyKeOJUDi9d4rEl7q8H2pphIVXbx0d3JXAmo+HfTmRRrSmCDAZFR6dAoNbsGnF+D0Oj7D8HzJV0LJR1kMFRkA9+DABw4l9Og/vYQx1pciQQBLn1HtD33An6g38BffxboK/8HPSVn7OG1qFRIBJlAZ6mglZHYH79h0C5gPjAe/Ct70exRZvHRO4kTMsELBMxi+LUtl/AvF6qf4/frML6i2dcjyM0ejvm5ATE6Sdhmc6utI3c7k6NipwYhDGwD/Ty8wCAxORhHHRramxBOLIZz/niuGvhTcz54wgkddx76dn6+41nn5sMA9PHOu5vI/jtAUKDmYJuMamzXpspsqrp2d3t5+cLeHBnYtX5jOsdL18s4Uqpii8dGHAdLPYMBTAZ9+E7xzMYCUsYi0hYUHRsjvraOlU/tN25uWd7Usb2pAxKKf7+rXRT8PuD07mOwa9utnNC3aDqVlOA0Jj5zagG/A1ZiJxqXFWnI/rGC4j+3f9CNrwTkAMgn/xlkDvvd5UZ8wq9hSfK+PMW9BbbTAKW7W7NBi4qBl67XMZn9qZACMGbswpu3xxGQhZWTYarEU5NdQJHoOiWK42mH3DLnLJAeWX7LlRMRFMJcL/8O6Af+wKwMAsAIEezgPI2MH8F9AePgv7gUai7HoY06UNg330o880Lz3LVqutm91INWC7sMWC5/PBGvVK5pl3cem27dbd7BTVN0Bd/wjicSgnwB4CRcXB3fYA1i4ICxQIwdxk0swBj382gcnipwmGawJULwPwsy87pOkgixXixw+OdewwqFdDvfhNXnnsJw/44e57v+wir4MxY9QwupRRcS0a3fn5qyYOgxNdNFLphOCziclHAWETCLQ6JBkIIPjAV7Si/BbD76p/eXsTHdydq8l0GFms0wz2DgY6fnYz78ZVbhkEpxbfeSjcFv/7a9ec5gh0pGTtSS2UMt0rASjBb0puqiYpughCgWPXWq0AEAeTBR0Bvfz+joP3sB6yh9eK55g0nDwN5VqX7wMJrrBmwZezgAfz6qUfZHxwHcHwtgx1gHGOOY5znhn9HAhKORkbhGx5hr/O1z9T/44HwEBA8AIvjgcAYEL8N4Di2YGjYTuAkmNwoyM4Ee59OgOwMN++LazmO2vfdQDg8X0jirsQ9eCofwycSZRDhjobvWPosmRXBfeL97G8XbAS/PUBqyNBVTRbAeC2+ffedDEpVE6pu4dbagMCBOE70NlTdgqqvLMPjBQXNwJlspSnYW084nVHxGZfGh0aEJB6/VJOcentOQVY1sDnqYwNeD2VxpwnlQq7CBn+XCTFfMbpqRtp7zWnNnddCA+c331LOzmrGVZHT04tFcK+/AOvn/xMBAMrUYXC/9UWQZHvD0XJQNVs5v9xSw1tD8BjxCShUzDYHvoxi4MbBAM5lK9ia8COrLp3D90723hHdK9w4v6ruLSBYj8hrBiK1oJXEk0CcafsSbQHcw38BnD4O+srPQF99DqaqgP/+dyC/cQS5T/8HoGGsKFbMtka99Yojc+UmLmAiICCrGpDF5uNvbRRaDujJt2F9/c+AuUvNb1y+AOvV5wBJAqrNmbRFAEgNsQyjqgDpOaas0Lhf+x+T28F97HPArpuam1M1BfT1F0Ef/xYwfwVzqb0Y3nMDuF/4PxgvFwBmloK8TmopPp7UjU28NEYBQEDk2ypzrXBLQDTiM/tSTUFj1C+0ma90Q+tYfjUWyq1gscLSccyXdOwakJFR9J7mdpIYAPn0r4E+/EvAlRnQ+ctAucz4V6IEUhkF95VPs+ZCqdaIXFFrFI3mILSXZEZ0tozC24sQf+MjrtuIb6Vh7f4k0oqOkQUV/NZfdN5ON0FP58DtYmMNdyQNbt8HPB/Le66U8UShiniKInBDwnW7IJeBsiXakcK4Efz2gMaGN92kkDwOjudzFfh4Dr+wr/li2Rk/N+cUzaCrakNpI6uZOJFW123wuxzEZaHeZa7VyPDLBa2tnhcVA6Muk3xWNRDvMjDbk1ZGbbZXtpUPAJY5KzQ02mRVo85RWw1QSkFfeRa5f/4mItIYIErgfvELIAO3uQa+Fu1d4UB34PzqFtP5bQyK4zKPrNpuP53VDBzeFsVzM0VsjvlWnPXsFapDtzPL/JoIXeUGyX4hXzER8znfs4Tj6lxh+ulfA/nxcYCcR+DyLMpPfAd08ot1zeFStfM5WE9WsqcXNXx6b6r+t9397vRcE7K846aGAfqvfw/6/W+zzNvAMMhDn2a8a1UBPfIy6/C3G6QCIVYyj8ZBzp4ATc+xoNfGwDAzeQlFAJ6vWX6fBqZPwfraHwKxJDA+yZql8hmm8GEHzGMTmL//C7jplimQhmvEnO8siDyHYsVE2OX6hX08SjXubKfF/2pgNXov+pXR7xWNls9zZR27BgJIKwYmu68B2kDkALB1J8jWnc2vH1kASTbwsQlhlYYVIuLjUexCvfOLHCqGhXNZDVs6VLZ5QmAXMpkCUG/Hsn84gB+dzeO3bnVvzgWAgaCAhbK+Efz2C2JDl3HFpJ7LnU+fzeNz+9szl1KtccNl/oGqW9A8OpetBKpuIdOjasV6R1zm679ppQOealgYj0hYVHXX4DejdtbRbERONZo0NW0LSQDgKiqsagX0igaEwshpBmL+/k8ClFJUj7wC6xt/CZw+hmxoDPGRGLjf+BrIyCbgiHv5L6+ZiLjdtC6omu2cXzvzyzcFvywTt6VlUsiqBobDEjTdwom0WudEXy24ZX4V3XK1tF7vyGveKFiE44GBIXD//g8Q+G//GUqxDOt//J/gfvU/giQHUNYtyAIBnb8Meu4UcO4kMzZQy4BpwD98B8pHdQQmJkA2b2OBXI82zP0ApRQWRdP9lpCFJivYRjAbYApZbB/nqVJi8n5+GRgaq/8e+tYrsP7xr9h7hAN58JMs8G3QhyWbtoB++BNMFsvvZ+e3hmQ8jvTrL4HqBo5WfZCiMewcb89w0YrGmtd++CiT1sotNm+wfTfIre8DufM+qMdybUFAQGR0D5Hnavxx53s47GOLUYDNea2Nxtca9C4ymXxDkNpPJGuLrIGgiPmSjj1DAZxa1Lp/cB0g7OO72mH7BQ6aaWEmX8WhMff+C4EjsCw7hrI8WybbIITgd+905lY3YiAoYq7sri8ObAS/PUFq6sr3xvU7mVaxJe5z3FbiuVrjhvNEoBrmVeF7qobl6Pl9LSMg8vVz1yvtoRUZxcBU0l/nmzkhp5qYSrg/aMAS7SFTULGndB7WqRxzQCvkYRVjMP/hcVgEohCsAAAgAElEQVQB1pVv/S1rAijf8Avwh6MwXzjFnLpCYSASBxkZBzZtAaJJVvbyWMqihg766nOgP/pXZM8x+10Ew8h/8FNI3HwIZLB79n9RMZAM9Br8OnF+KXPrazjshCzgrYLS9nmr1hg3EfPh+yez+He3jfT0/SuFqreLvNvBbz9oD71m0yldykwuF3nNQNTFkbA140kAkFAEwd/8T1C+/jhw4mewvvpbIHfeD7MYB/mrR2E5SUABiFmnkT0xA/npx1j1Q5KYFfS2XUDiEKiVaAoAlwNqmkA2zTruizkm51UqAokBUGsTqJXETFHHpmjz4jUuC8hcLjXvh9ia4Ixn6terwOUZ0JlzwMm3mUxcY7ApSkz/taIyqgIADI4yaaoWlQ0bhOOAQPu5JzyPc5FN+PG5PPYMBrCoW3jutXk8tDPeVL0iPj/Ihz8B+sGPAQtzwKVp1r0fSzJVhmjnlKIscFANCxEw2oob/zbqE3A+xxYHNtXvWkenTH4qKCCt6BgK9YfGY1iM1jgYEjFX0jEQFFGomNgc9eGli6XuO+gBq5UmC0s8/F3GOL/AQdMtJmnZIS7iGnS3e5Us7QWDQREn0u764sBG8OsJVm0iEDmCaoO6gxeJnucuFPH5m5z5qhLf2XqT5whUffU7/VXdxPUsKNHY5OAVjWXBjGpgKiHjQt45QwQwHm8nPho1TVgz0zB/9D9QUIcQPPeDpsGKTh4G5i8DU/uZfM7wGJDNsOyQpgLTLzRv7/ZFko+VQeNJ1hgTT7HSF88D06dAj75WF2AnoTBw/8Mg9z6E/OUqJgLeBvxFVe85+NVNipCvvcnPpICfb8785tT2e97+vYfGQ3hmunDVJ+FOmd9+qAKUKu7UgdbegIrBvpOQlZmjlFwcCX0Ch4pJHZ8ZfnAEuOdDQGABeP0F0B/9K2u0KRcZT3XLDpDJ7SCT24FYHOB4JC5kkZu7iNErb7GS/MIscOoY6KljoJMFWN/9LyD7D4HcdBtww76uYv4AQE0LSM/B+vo/MF3auUtMh9pp28nDsP7pRZzf8yFMTIyAcpPA8DhALcj5DEonzsF84km2jwxzIbS2P4CwOotMOYOBzNn2nUoSMDTGnqXF+aVgOBAEefBTIPc+CCIsj2r1/EwRv3zzYD1IOzRm4gencvjFG5Nt2xKOrysN9HIXyjVNc4A1Pg67BHsR22QFTE7yelHAcMNQLUjtV/A7X6xgICBgMCjWJQsJYXN7qxZ6r6CUwrDYWLqSRXA3+AQO0S6OqH6BeKpSNy48VuLU2g1uKjmN2Ah+PcDm9/Jc7zeZTyCuJRRJIKh0yOz6a53I/cbpRQ3bEksOXp28uK8HLKfJIe4XkFVNDIY4ZFUD2xJ+nMq4ryTdJLuoYYA+/zTj/wX3AtNvAJMfAJncDiQHQGxNSDqByiOHIVdSqFgU/L4vgFoWyAvngFIJ3IcPgupVoJhnIuiXZ4CZs0C5tOSQBDANyvnLwPxl9wB5bALk3gcx8MDHsVhigXBWVZosnyWOc+X4ZRSja5a79burVvNAZ8u7GSaF0BCASTxXlx1yQkDk8Z/eO9bxu1cDhsP1tRen/VB7yFdM18VTUOJQqph1o5O8xhoCq+bKewKcFvByLYvj9swQnx/cb/we8MaLoOdPg/h3s0abRMoxq5YIDOHCyBbwDz8IAKDFAnD2BKon3gZ/yQJKBdBnfwT67I9YZDA0BrJlB7Nf3n2gzi2mhRwzG3jjRdBMCDD0Zm3amosXIjGQSBQIhkHnr4CofqBUwOXLaRx86V9gWS1B8uQHgOnXaz+OsLR6tYJo9goWfVEW6A5vYq5cU7tAtu8Ghsfq2WqqlABVBWSZWdOuMIvNDmPpPAZEDpVlSru5zVcBka/fO50yv3Y5G2CLrtXsP1gPGA5JeONKufuGHnE5r2E4JCEVEJEuF/q2XwA4tqDicqGKw1Mx6BaFtEJFnk4YCHReyPkFDnPlak8NfBVz9ZqFvfD0N4LfGr7xxgIEjk0sX7mlmUytN/AV+7m+kjiuY+ZXrnHO+o1npvPYFB2o820U3ULYx9czndcbaAdFDTfEZQFZzcBgSERWM3rmddKKBvrCT1jQaze2DN4B8vnfBJFvBH9ovPkDRxaQG4oisahitqTXeGccEIqAC0dh3ZjsykOjlgVUNFb+zaRBs2nmSlTVau5sgyD7DoEMMs4U8ctALfjVreYSlN14NuxgCGIHX50g1Jr47OdGb+H82gtJw2oPKp1+ZeNrq1Uq6xUCx56dfmSh85pZlwtrRdQvIN8Q/DIeuIBi1ezKxVsO/LWSuIuiKYDa5HLgNpADt7U12rQiJgt4c3YpoCDhCLD/ECo7DyA4XQD3qcOgr78A+uZLwIUzwOxF0NmLoM8/zT4QCrPqRUMTmLnlg5DCEZCPfAZkz83A6Gb3jPGbC+A+/yD0F87DN2Iyq+f0HLPflXzMaOD+3wXZsh1IDAAgIK9dRmzsk5gucOBucJdYBGpGIoGVaYB3wkoaBfMVE1EHPq8ssgUVwCTFnCoArXAyF7oWQLqoKjUiIQsd6W294nJBw46IyPo6+pydPZlWYa+JKoZ743w/cNumzve3X+Dw0qUSbhv3rrdeNSmkVTzmbtgIfmsQOOAz+wbwzSMLbXy3qgOPxctt3C1LLAmko4yVLK5O5lfRraZJWzUsjIWleqZzPWE1yzmdkJAFzJV1AOx8BTxkxqlpAqeOMiHyF59hdAWA6XE++AjLkN00COLSTJZVWZCt1tyl7IBbFljmJ9Alo0RsvUZ5MwsGevi9rbAbz5yCXwvdG0JsTqFYawSqmlZbZoKAeBLOZ3zY9Tfp1nV+XY6/F6WAfMXAmIvzYNTPI6+Z2FRTqcrVFmO6Za1ofHB7svo97oQkDqVqewmyXLMSJ6ObWVb1wUdAdR24fB70nSMsGD57knF3S0WWgd22C2TPzTACNyGciIDb3U4FaAUhABkaBZkUwT18e/v7RxbaNG6JX0ZsOIl8NnNVVSpKFcPTWOMVC2W9bgzRCFngMF9i4xuFt8Cwco1yfm2KhxdVFp4jsFaY4vqXY4t472SEZXtLVdw2tLQoK1VNBGu9Ayud2RTdqqslrDYf+5YuQa1f5PDOgtqkpNINVY+60auFjeC3BT6eZWMbV1GtAv1e0Y3QLTVoKDpB5JkRQL+h6BbK+lJwZVgUQyERi6qOwdDyJcFWA4ZFIS6znCOvgDYSlwW800CYd5oAqVJiPNpzp0CvCLD+6ttMyN7GthtA7n0I5OCdrBRaW1i5IaMamIj7oOoWsqqBiI8HTwh8NT5Vl8rTitD66xKygEtFZzcfL5BFVjq3y2CtUmcAm3TZ9e088RY0s65Hu57Akxrn1yF7QQhhygIe46aCZmLXgPNwHPXxTdcip5nYEvejWFmdhlimctC//boFVk6LSiKKrBluYgr44C+yakYpDxSLwNBInUervzzbE92E9W24v++0UJF4bkWc6uXgfFbFeMSbCZIXzJd0Rw1mWeR6vneuVc5vSOJQ7iLH109kVQPPni/i4V2Jpowzpex62HOsj3enlnmB3fQKrP218fNcV8nDVqy2eohUO79u2Ah+WxAQ2UVsvCEbBfrtS+XlkqldMoY+nkNecyZl01r392oMvYpuQWnxPU8GRFzs0NC1VlB1C34HqaFGUKUE5HNLjjY+PxAMIebn6jI9vSLi45v0dgGAA2CcOwnujZdAj7wM1Bx2KMCafpQSy/LuvwXktveBjE82fV4grMHRLRjIagYO+INMek5lphlRP1/vpMVVVPeKywLenm9XXfAKO/Nro1XqzIYrV7ohGMmovdNO+g2nK8ZzxNX1iiO9Pbud7IqjfqHeLAPYzZU80uXVoUU1lsS7wWtlxmkrRTe7ZjkJxwGROPuvAXqPneLpsuHKWwzUAsFGNY+1qTcB0xkFm6P9MwyZL+s4MNquKrGc7H51FTmaq4mQxLSKh67S98kih5zWPu/wHMGVYhUjtQpPMiB01I7vhEVFR1wWkK/YMnRre238IofN0d4WbRXTQti3ehmdwaCIhbKOLS7vbwS/YA+1nV0MSjwU3UJjMa0xa9XLoKh0aSQTO6g9aAaFr88ZGBuWReudvjaSLby89QLVsCC3rIxpLsOE4l9/HjjzzpK8UAti8SmkRRFWcifMR99kCgg794Ls2sdoAR26sZvKX5oG68ffQ/SNC8ic/jmS1VrjgiAAm7aitOVGhBI3gvvyJ0EG3MW3JYFgQdEduZ0EQLnWfZ+QBVzIVZCrmT0QYFXug04IexA274TW7KFT5td+vZX2EBDZM2hrk/aiobxacHvu3YJ6W6HBqwmv2yIAYNeisXPZDvzYJKt72n/7cbfTUGzIAod0eWm/puW+YGM65cubdJWqheQyyxkcIT05sF0sVDDuEmQkZBEZxUAgyu635Zi49AuzBQ0HU8s3JqgYFi4Vqthaa0htDepttC5Ove17dXmlqwUW/Hofy2SBqy3Mlp8pnoj5MJ1t1vFNBQQcW1Cxv2YmlQwwWbXlBL+nFjXsSPnx8iVWaVzrayMLnKOVdSesdrZ6IChgvuw+Pm4Ev2CDcKBG+GclkuZBQe+iXee63y6ZX6nBMa4VWi3oW42gJyBxKLdIqAVrpSEvuJo8XEWtwD99Atbbp4G5y6DTp5hUUiNseS+xNpFqKqCWEa+WcEEYYmL7F88xP/S3XmGBDC8AI+MgYxPA2CQQDDIbyGqFWUYqRdDqKMzvPgNKtoBOP4lE4gYsxkeR2n0XyP5bgR17QEQRhVwFyXwFZKCzpafIMZ5dJyc4QghiftZsZ3OANcNaVb1nzSF7yRHiGPAxma3uwU5rZkm3nO1inRreYn7WbGcHv1nVwESsf6XgXkHgLktkWM4DOFcTDegHBI7UtTEb4V9GAGOjU4Nd6361DiYxndzBukHRLWz20GjlBEkgXekyjbhYqOJ9W6KO78VlHhnVwHgtc1VdQUC/UrSacPSK+bKOp87k8OVEZwesXqS2bNlHt2d4vSMocT1RuIZDEuZKOrbElx/83rYpjL8/ksZ4amlOGAyKePpsvk4NSMoiTi521qJ1w3SugveMhpaC3zXO/Io8wf3bOrXItmO1KwmDQbF+fpywEfyCNV7YQSrLOjUHgVWPhhat6LZ6tPnFTrDlx3KrYALTKHNjw2tTx6VCFS8vzOPQwOryp6imgD71r1BefB2+igGaPbn0pk9m1qsHbgPZe5DZgjocf1Kt4sipRZCyBm70AdDZi8DxN0BPHmUB9MVp0IvTAJ5xPojJw6jMTEPcPAHsPYjUgfsxN74LXIv9mNeyvMQTzJV0bHOQCWvMEYo8cxnKagbGoxJyGjyXoZeDXI1i4QWLioGkh239Aod8pXnV7XSNWMaz+bV4zQ3JDkayWrvd8dUEzwG6C3uG54jjAE4axNz7Dfs0+lfQmNYx+G3Zr2o0V7BEnqublnjlUhKgrePeayOpEySO9JQ1KnaglSRksW6FDjA79NZq01rDa8NnTjMwk/ce6HlJZER9Qr3ysB4sqntFyMejvOj9ORkKiZgt6djSwR3MDaZFwRMCv8DokyORpX0MhcSm5yoZEJCeWR4tz6wl5JhdMEXFsBC4xpwmV1vtwW7adsO1dbZWCYq+NIAHJa4tVa6btJ6F6uVSqbrV5MrTCrFD5tep3N8vBMX27LZX5DQDGdXdla4foG+/CusbfwZk0tDi2yEPDILcfhMwOAwyvgUYn/Rkjxr2iyhDAAmGQCYGQCa2Abfew75DU4HLF1jwe/kCy/gSjnWUB0JAMARow8g/cDcSgTHwOwcwoBk4Nt2u1ZhVDU92uxJPcKlQxcExb+WhXE1SrGLQpjJ0v9GLlNuiqiPhweDCK6fQMNGWTUrIAs5kllZ93VyDVhvM8tT5t/h4Zx1vjpC+ZX7dsJLKUL6DKUvrfls1f4MiB6VqQZI5FKtmk3mJGyI+AcUWLeOyB86vGySB83xP8ByB3uFetDO/Nhrt0J2C9tVCqwtiIxpNdzohX2sO1c32RaUTWNNR5w1tw4C14kGvFCGJR6lidqTvNGIoJOLI3PIogDYfHwA+vD2OLUNhoMKcD5MBsck8wydw9fn/ZFpFTjNwaCzUdYFhWkvNm0wJxljTasVyYZv1rBbcqpc2us5if/7nf47XXnsN0WgUf/zHfwwAKJVK+NrXvoaFhQUMDAzgd37ndxAKrZ7O4WqjXF3KQAQlvq38XzUpYlz/Ob9Sh8yvVvtsvwcc06IISM7lUi/fVayYKKj9LcFTSoErM4yScORl4ORR9sbEFLR7v4TEDVPgEr13e3XU5vTLwNadIFt3um7DH0kjMxRAojZAhX3O3DGvwaPEs4VVzKNygV6Tr1lJedsLcqqJkbA37mVGMRwz163wGpgZtUxJI2yNZRtrnWviOeKaoQi6VHY4wkrYq4lOtKluyFdMRxUAgC3KjYaDb12IB0QOZd1CTAZKVQvDHhRiYjXd6Mbgly1qlhn88sRz8CtyBKUOFyMg8k33KmuytWlwrFmqF/H+5eJyoYpNcedxLijVzrmH4Hf/cBDTOWau0ImSQtDZ4MJGpCa1d63CxxNUTKsjfacRdt/PcpBVl1SUppJ+pMI+pGvBr8ARPLKnXZpPNy389HwBN48E8VevzuOBHfGOPOCLhWpdEYRZcJu1xtu1Hil7Q9XDwms10fWb3/e+9+H3f//3m1577LHHsHfvXvzpn/4p9u7di8cee2zVDvBqoKyb9cxusMH20UbVpVmnG7qV9QSOBaNOUD0+qL1CMywERd613NWtDFasmChW+iMCThcXYP3DX8H6vV+D9Ye/Bfrtv2GBryiBfPyL4H7vv0Eb2oTAVZKoaUXUz2M6V6k3WxGXlaRmeHPIE/maLqzDA88yNc5a0n6RQ2UVuvptZDVn2oNdUmvEomr0NfNLHTSDAy3P4FpnnDgC1yqM23VnmV9vR95Lg1XjYmFF5gdd7LgboRnNiivBhgaiTrbMjYj7BeT6GEBJPPEsgSjypKfGOq0h2I/6BeQdOvdXAxcLVUy6Br+8p2pdsWLi5tEgTi9qmC/rHSuPFJ1VRmzYmd9rK7Ragv2ceB2ngeUvuLNdmnO3OiQOvncyhwe2x3HzaAhfunkQPz6Xx9tzzk3cWdXAE6ey2DvEmiJjfgE5zfCUwV9vsGh3jfeV4pE97rrDXUe/3bt3Y35+vum1l19+GV/96lcBAPfccw+++tWv4nOf+9zKjnIN0Zj5lWqrxEboy+T8dpM6cwumABY4D62C5q4dkLc2vAFLWY5OmYBixURAWpkUD82kQb/7TdAXfrxkyxuOMqemvQdBdh8ACbJKgmbQFdE/TAvL7oKNywJeuljCXRPeXWs6QeJZ6dIJBYdMnB07+QWyqpnfgubctGQPrKmGwEFz6R5vxUqykuuNVyhw7llGt+eb9JD5LVW7C/Db5fd8Q1l1JWBZMG/nWdUtJOSle2Bz1Ic3ZsvYmZI9a3vGZQHPXihi/3CgL9dX4jnPCQmR4zAS8s7w04wlpZFYi8GIqlt4YaaIUtUERwg+MBVzvTc0w8LlBuWFbrhcqOKBmIxCrr0JilHVui8eKChSAREZ1cB8WcdQh+AX8Jb5jfp4FCrGmi9CV4rVSig1IqsZ2JLw3pxbMSgkjtYzvQJH8Nl9KXzvZA5vzZWbni3NsFA1Kb5w02A9iI/5eVwuVhmF4BpU4lhtdGrQXBbnN5/PIx5nTT+xWAz5fH55R7ZOoOhm3d7RaWDWG8pzvdxeurV8u2B7ldrv27k1G92YnUrWGo06DYYV04K/dq6oXgXS80BuERAl5i6WHHS1GaUVDfTJx0C//891ji255R6Qex8Etuxgmp4taCxBLgcV00LMv7xFRFwWcD5X6To5eIXEc+BcHkYCNKlAhCQeV2odyiLnHjT3A26ObbbFcWoZclSdFnY9Hdsayk7ZYE1tzkfhnvkFvF6xdFnvmoVNBkT83RsLUHQLhzxyxrvBaxCqGbQp8zsQFLBQ46B7DaKHQyImYz58/Y0FCBwBR1bWxBkQOc8cx80xCSMh7wt2VV+SYIv6BVzMV1AxLHzneAYmpbhzcwQJWcBzM0XMlauOphRff30essjhUqGK375j1NP36haF5PKbghKPco+l+PmSXs8QOoGALbq3xjsHa0GJR6lydaUWVwMrkQO7mK/UTY9uGw8j5DIn5FTD0U7aDQfHgtiRbJ4vCSF4cGccim6isdWA49CWeIjJAnKqgap17WV+1xorbngjhHQcRJ966ik89dRTAIA/+qM/Qirl3f7uaoETFYwPD9bJ8AG53HSc0kwFwwMpRPwC5Np7gUDz/53Q6T0bQZdtuAsVjA0NIDJrIBpPLDuIbsWVagHDYgAL1QJSqRQ03UQ8rCGVSmFrVYKim0il4q6fl6U8zPNnwD3+T9CPH3HUc+JSQxCndkHafxDC5HaAUujvHEH5sW+CFnIAAN8d9yL0ua9AGBnveLyiv4yRwYFlZ4uG4wqSAWlZ950haVCMWQwOLFmfBuQydCkEAiAV8kHgSNv94oZBq4SRmOK4bTiYx5bRFFIpFthsHqTIm4X6toGA8+dWAkEQ2D3scvz7xRD+6Y1LOJqxYFGWVVKp4Pk4Gvfr9CwEAmVQCsf92c9FVtExktTXdNyIzpmIySJSqUTbe4lw1vHYIrMGYrE4UuHOgcX5rILnruTxlTsm4e+QUf9shzHGvo69oNvY1Pi+MGtgbDCOVGjpt8i1+zEQKGNgYMBtN0340MAAPtTTUbrj4WTS85hwt4dTI8tlJGv7FOZMjA5GkYr4wclVPH/lEk4fL+ILh7ZgoOEcLFo+UEKQSrVLqEXDZfzKbRP425dmEIklXIPaRgQ7XMtxTsGZdPdxxp6fklENGaWKyVF3awdZLsMSRUyMDCDeZYHrlxUQ4vysXgsIBMrwBcMYlilSqe6SXNGwinAsUV9gPXrqPD5y4zgu5TWcKuk4POZ8HiR/GSNDS89Dt2fz/X04n9w5FX4AQ4PensO1hpfY6WpgWcFvNBpFNptFPB5HNptFJOKub3r//ffj/vvvr/+dTqeX85WrirJSRmZxsf63oipNx5ktlFDMiagKHBRVwdz8AjRVRTqdhqqomF9YcOwiVRSl6+9122YxX4SSl2BVVczMLvSt4eJKusQI/bXfmFMNWFUN6XQafFXHubkytgXbMzLUNEF/9gOUX74IVCvQp99kS9HUEBBPAoYBKGUgPQsrPYdKeg6VF37SfgBbdoD7+Jdg7NyDHADUfnvFYA0JrRkwRVGw2HBteoXPqsLQjGXdd9SkCIqk6bMHhwQ8c/wiKAXOZTX80v6BtvvFDUqxApmYjtsaVQ2cVkI6zVQOBEOFny4dd+N98sPTORzeFq1P/vMlHRcLFdw82j0jeKVYxaPHMojLPAKBABRFQcQnOB6TD8Dnbmye2CuG5flcNp4Xp/tcURRQOI8J9vbTWQ2SWV3TcUNVSghCRDrdnv3iLd35+MslLGYouIp7UHE+V8Ez5/L4pf0DKOWzcFekdEdZUWAYvd/f3camxvcXsgWoSYK0tjQGEV3D9OU5T2PctQDOqOD85XmEfDwWcnmoMSBdLcG0KCqais/uS4FoRaS1Yv0zpFrBdLaCTf5mJZaqaUGvsDHVRys4fXHOk228orpfy6pqYHaxhHSaPfPfeGMBAsd0fb9yaLgepNnP3LDPxLNncp2vsapA0wiqJRFppfNCQqkZCV2r11pRFMymKSI+Hul0dw63n1Zxcma27samKAqEagnjPornTy3iQNLFsrtlLkilUqt+zhRVAcG1c23sMeNqjR2jo86Vl2UFvwcPHsQzzzyDj370o3jmmWdw6NChFR2cE3TTwmtXyjWxaR+mErJnsnr/j6VZaqnR9EKodUYvV7LDrTRsk8FZ41D3pgSvKFctpIJivZTcqOFp66vWj83QgZNHmZvaGy8Ci/PA5GFwiRRw92+BO3QXiNxcVqOmCcxfAT31NnDsTdDMAguSQxFw73sAuPGAY8bm9KKGubKOe7c6C9EvFyuxxRV50mauMB7x1cucxYqJb7yxgFQXXl3j/tyaITZHfU30iuGQ6Mrxe3tOwV0T4XoJbK5cxXTOW/B7uVDFvVujuGFAXtbA3E85HdLBwHswKOKbRxZQNSg+uL038fR+gyPEdey5zcXViCPdTQReu1zCJ/YkV73pY6VQHUxQdqRknFpcBRHyNUJCFpDRDIR8PDSdQq6Vx3mO4IsHBh0/E/cLeFNrl8RqbHpK1MbUbsFvt+bIoMg1Kc3wHPCZfQP46XQB82Udm1qsZaeS/q78Vr5mt77e779+oWJY8Hscq1M162E7+LWnLN7FcGatsf6OaP2ja2TwJ3/yJzh27BiKxSK+8pWv4JFHHsFHP/pRfO1rX8PTTz9dlzrrJ47PK3hupog7N0ewayCAc1kN3z66iM/ftDZpfYolfUAOpEmfjvExKVZLkIBJRq3s1n7lUqmuLasaFoINE7naIMcmlHMwZq/gmyePgeYWgYVZXBFj+A/HHwcHCgyOgDt4JxI7d6E65nMMCAjPM+e0kXHgve5FzhNptUkbN6sZyHUQpF4u9g4FViQ59YUO91zYx+NX3jPomYuXCoiuwf3hqeYALxkQXTvU85qBvLZkoJLXzKZFSyfYxhnrARTUddC+r0e3oNXEe0aDrkH/rkFnTiVrUOu8335oeS8nbHGylO4E6tCVPZXw47vvZJbx7esTiYCAjGJgc9SHqkcns5DEOUofZpSl4DcuC5gtdtfoVnTLVTYPcG8gHQ6JmCux4LdqWnXXu5DEO8pqNUIW241oOuFaDpEJmNKO14a3ZGDJ+KQXR9O1OEfuKYQNdELX4Pe3f/u3HV//gz/4g74fjI3LxSoe2hmvC0LvHw7i6Lyz9MfVhsATlKtLYuQC1y4H1Qu6PSzdJKOeOJXFB6dirvw3SimePpuvB7/lqlkPWimlUM6ehv/IKzCPPg0UcvhUyz3ToTwAACAASURBVOe/v/tBFD/0KcT3HwC27gB5O4uoLKJYMVeUiX/8RBZTCX99Us2qRt1FqJ9YaaayWyOSyHNdtTdt8BxZdvNco8OTZlDk1KWsRE4zXCXzWtFNimcD7QguY2XrVed3LZQtcj065jm5i/kEJr93vfTYJGQBxxeWVBa8XBe3bTKqUc/EJmQBx+a7W9hmu7hENn5Xo8X4UGjJIjdfM8WxsT3ZWRs90KND4LUcYMkih6xmeG6eTgYEvFBzXyu3qLFEfDwKmoFIyzPUS5C8gbXHupwFV9PdbKWQeIKybi5lfnkCfQXBb7dPyl0MDp67UMR4xIc9Ll29Zd3CQkEDfe150AtnoJaSkH72FjhjFJW/fRJlYQhRvQwUc0ytYXySuaht2gKy+yYMqDIysohE0l9/uKN+EcWqikEsX4otXdaR04x6drNUNR2lWjaGk3ZE/XyTZmqp0l0qy8ZKjAWWA0rpupMuuxogPej8Xm30ugByy8/3Iue23pFooHyt9G7NqAb2DQcBsOpQwYMueqaHa7KoGEjVtLZtDV6g2V3MC3pxCPRSyVjPCEocLqWrnuX9/AJXlzydK+sYbKBLTMZ9OJerYP9w8/UqepAsXA0QQmC6mGVtwB3rMvjVdLoieauVwhb3dyp9iRzL/NY5vxyBsQo3XqPBQVZz92rfmZLx6uWSY/BLKxqy3/w6ysYNsB77v9lrk4dBpn8OeewOKGoV2lgKw7tvBXf7bcDQaFugklzUsKjqmIKf6SSKBGG/gHRmZVnaikmRUY2exOffzbADEIvSuv6uDQsUXqvYV3OI9PMcKiaFXyCO2UOeI6tq3rGW4K9iYNhrkJ1Vja48dUIITIvRHdx+x1hEqsvxXetotJrtBU6nhmkfs/mrm8WqjYxqdM3U2kgrOpKBJeMdG3nN9GxcArBsqNdg0Mn59FpCSOKR1Yw6LaQXzJWq9SobAGyJ+fGjs3nsry1wbKxVVS0s8ZjJV676917rWJfBb9Wy2m5SrmEwXm0EJeYw5dRkVs/81jRpRW5lmd9u6MT5NSwKsdYU11rKpLMXYf3lf0W+RBDaNgF6wz5wW3cC3BTI/fsQMlPQNn0JmiohOB4GcQlCkwGhXlYr1tycon4RZ1dAUaCUIiHzzc11Ltu++3KGnVGsmWG0UkQo1l+WNeLnUdBM+EPOC1mJJyvSel3PYFlR93GhX1lhH8+h0qMBSkY1sD3VOdCSa5mvAOeeydo1IK+I8rVe0Y9f1PgcenkiM6qBuOwta5hWDOwaaL9+ec3Aph74/LLIIeJRkzbi46Ffw9nFUC14X874OF/WcdPIUqAbcrO570JdWS3EZB5vzV2f4+hqYl0Gv0A7nyrm55vK5P2C0yQUENmD4hT8ijyHctVCMMjes9UeWuG1qcRtC/v1Tpxf+2E7OBbCM+cKeHgX0yGlrz4L62/+FNBU5Kfei/EdW6HddT9CPh7kyAK4fQMIzhShBkVoJaUjxSRaC2AAoFhh7m9Rv+D48HuFolsYi/iQUa6Obej1hLxmIhkQ2lb6AZHRY7y4r10thGslWbdOd5HjVnXhuJZgag/u7/eL2uUXOSg9Pov5ioloF+65X1gad9y4/UMhqd6X8W5Fv5IyXp0TAWBR0ZEMNLtOUkp7z/wKnKOzoxMiLgHftQLWnLg8ow5mDNX9PGVU50XJaiPmF7COch7XDNaMW0ApxaPHvOu3JlpkuPoFJ8vDoMS5+qiLPGH81Ba1h7b96mZHa2MbbvOj/Xqn4HdRMZAMCIjLrAxuXZmB9Y0/h/WX/xXQVJCDd6Hwkc9jciyJrNbMZwtIzC6zm+UjRwis2tEUqyYiEo+IX1hRxi6jGtgS9yHfwFslAATifC43wEApdeX1JWSx62LianNQI7YtKqWOqzyRZ1JL1yO68WF74Xh2gl/goDhYlXeC5aDe0L5fZqk9k69ic9S7Xeu7DdEGzu3VQtWk9YY3dgwCChUTmtmbfW/ML3hWfon6hWvaQSwo9Z657jQf8Q6N7mtFe4j6eVcHyg24Y83u5oxq4HTGu05kQhZXJfhVdKutmzsk8q4TisR5U3vwulrsBpEjqFpuwa+OpEBhPfc0Bp79Nyz+X78H+tMnAJ4H+dSvgnz5f0fR5LE56kO25dwFRR6KbjnKGLnB9oEXeG5FWoe2Za7VwGMlIIjJAvINzSGmRR3NQ96NEDkOhkWRa+nothFvoZE4oVS1PGd6+oFIbVLWa/ScVqwWX349gCekg5BbTQ4rsPKJ0tYB7zf8IgdNp5jJV3oqpV/roD3aacdkoW1sbQXvkiDpF4ZCImZLes8UsZgs4LZN4e4bAhiPSG0c12sJIYmHLPZ2hhIB92s7HpFwsaX6VjWtvuqge0XML6zJ964E66EZeM1oDzP5ak9ctbjM43Smu2RMr1Cqzbq3AMuKumkzijyB0kHtgWoqcO4kyldykLkgqCYAug6qlIALZ0HPn2JOaJYFBMPApvfDLArgJrYBI5tAeB6WZbHPzF8GigVgVoOVfx0o5ICKCoAA1QoWsjL2vvMkqJLFcHwKVyIjSN5wK8i9D4Fs2gKAZZ9SAQFns+1lcqVHr3g7+F0pMqqBHUm5Pljb+435+XpgDDQbcLzb4RcJNIMirxmI+pcmIa1mQJCQl3Qp3XC1OWkRH49ixaxlqpybR69X2kO37viMamAq6V/x98g1ekK8z/GprQSwUNYx4NEY4FpHWOJRrFo9cX7jLc2njTJkNhK1ytxKz6PIsaa81nzAUEjEuWxlVZtZfQKHQRfu/rUAv0B6lixMBkSkFedFxda4H995J4NtiaVneKG8NjS+oMhdc9WZ1urFWmANg99KT4MB63Dvf4ajXDURaHkogiKPsu6clRb5FrWHcgHK+fOwfnIE9PRxYOYsYFkoR7fAT3hYudMdv5+vDkH/yU8hUpPVSjkOFSJAHLkd1sxPADCFBjr9ZPuxTx5GQMkCW3Zg5PYP4+jIXnA3tLsRxWUB2cvNTkQBkeu5e5d1Mfch+FUMxMeXbr1srdkj5heaqBBqD6Lk1zv8tWCksZMcYE0uMT+PuMx3zUBlrnLwG6yZALQ6JNoQeXLdctU4rrPDW79KpH6hxvntd/ArcsipJijwrqm+MKML76YPAEvKXCosJRac6CxxP6Psuc13XrNgQYmD4kDHGwyKeHFmOebY7x4QQupJFa9IBgS8eaXs2BQ4GpHwqb2ppmt3h8cser9BCMGDO+Nr8t3LhVtC5GpizYLfYrW3LCLfZTJZLsoOqg4BF84vtSyIi3MoXclCeOdRmKdeB1/hofsToOmjbCOOAyamoI7djKiWB8oyIPlAfH5gdDPIlu1ALAEQDsguQjqZhz5gQbxwAliYBUwTqihDJiaQHAQiMWBgFGTsPiAcA/w1Qr0ggHBT4H/zsyCxJAYtimfebudQU1AW6LaURgMS7znzK9d4hV64gl5QMZszujnNRNzPuMvnc0sTyXrWe77asPWe7WBE4Ah006rTIAIi31WzM6sauOEqNmTYMk+NzlONkHjiyUnrWkS3zG+/mhP9AkGxB9qDaXkr6/sFDqrRWyB4rSMhM0tbrgcCQcwvIKsunX+nBWYi0NnlrZu7m42gxKhNrRkzn8C5UuM2sIRem9FSAWZ84ubKuWEWtHxU1ogi0oh3/dVTdAvD4eYVoe18Q6sV4Nwp0JOLMH/8E+DsOxAQRHnnxyG+9TPAUCDEN0PfNAVyx36QqV3Alh0gfhnauTzGkn7wkQ7liLEJ+KUsjE13g5cFUNMEqIVKSUcgo4OvPXRcTaGhDUcWQGLMwrKT57itnNHIoRVq23sZ5pMBEYstzVQrWYbYn7X1lO2gzFb0sKHpG7QHGz6Bg9awWInWKiE5zUBS9pbRWCspHpb5bb+OAnf9Br8cAa4GrU0WOcwpJgBvgXSxYnqSt5JFDnnNuKabnHpFQhZwJlOCz6P2LdAeeGYUAxPx5jE/3sXlzetzGRQ5nM9V6gYXjaAU120VpV/4wFRvlukBkce5bAVDLmo1G1geeEKg6ta7N/O7XlCumvVVNy3kgNPHgdPHYC7IsP7824BpgE4eBqZfBQCIQ8Moh5PwfeIL4LbfACk6AmuxCq5ldei14a3Rs53wPAAemtVsHex1Du02+Km61aRA4fXWSwZYRsTJqGAlsJtFsjWNYpFvlr7ayPwuodWNKernkddM5DWzzjvrdp/02g3eL7hyfq/jzC+TOlv96Jdxfr0Hv15VJvwCh1OLGu6cWJtS7logLgu4XNSxKbJ8DklWM3BAbm4Ms7nvbvB6TYISj7fmlLpVfSMU3doI0lYBPIcmd7cNrBw8R6Do1povrNck+M1rBsIS0w1cK2F+SikwewmlkxchP/syzNNHgfnLSxtMHgYsExjfAjK5HeT+fSBTu+GXY1CeOA/p/YdAOAKxrEO32t1VWgNNN0g1F6ymz3oI+iyHrG3rXNuqllDWzWVlUlMBAW/PKU3d6yvRt7Q/YcvXaS6NbapubQw8NfhFDukGPqLt8pbXjK6arWsN3XJreOOuimnNWoAjQI/eE8sC0/n1Tk/wmmWUBQ6nFlV8Zl9qJYd3TUHiORQ1E3Jy+ZMyS6Y0f57rpvxRawDuhqDEMr8f2t6ewQxJnKMKzAZWhm0J/5qX56838Byb23upsKwG1uRpuZivYlPUh3NZDZpBmyRIOslb8SsIuKiuA+dPg54+xhrTzrwDlAqoTh6GaDeTST5GW9i+GyS4D9zv/DJIIFg3hgAAsWKiYlr1YxBdTC400/KkvSfx7TI4mm4h1CWgKThIXoVrGQabS81KnOzfBOyG88Ita4WTxnKophMc6XHAbWwQSMgC5sruEj1aFw3idxMaTQcAZvpyMV9hLn/rvDRdNS3Xhrfl2I1eC2CZX+fol1LaN0qEWON+e0VWMzxpu/IcYQYl77LFZzfd826gaDdo6gavjahBiceFfKVubdyIoZDkqP+9gZXhSwfaG8g3sDLYtId+KEetBGsS/M4UKjg0FsJcSYfSko3UOmQ9bTMHLy5vtFwETr+zFOxOnwJaGziicSYvduuvgEztZlleoeaZfmQBJNCuayjxzc5NQgcNRy+DoCRwqLakiFTDauoMdtpL2kEndDgkYrZURdjHsgiNhgg8YZNZK53CyzBt0xEatw1LPArLCH7LDXSQRK2hwA3qBue3Dr9AkNfM+sQcdVA/sRsT3eg2axVmVg3quJgTr2POL+nA+VUNC0GpP/d1r4FWL7zvbQn/dZuZd4Np0as+5rhVvloRFNk47LTYPTgWum4XkmuJteiRuN7BcwRl3XRcxF1NrMm3LyqM4yTXtGaTDe910na1JWNag19KKbAwC3r6GHD6OAt2r8y072B0M2tK27YLZPtuIDUE7q20czNZDaZFwTdMMCJHmrJYoovJhVdIPGmnPXgI+hYVHamWrMxwWMJ0toLttRJavrJkdxn1C5gt6ZiMLTVjWBY8Bx+mhabSdbgLj80Njfy2bly4lWZhrifIIofZUrV+bf0Ch4ppNQW0cZl1nq8ni+OgyJoYnSYRkSdY50nrZYPr4PCWUQwkPDYpekEvWWRGQfF20u+aiCzziK5dxGSh5zFH4jlUDNa9vprhp0/gMOJiJ92qWLSBDaxXCByp0R7ehZxfgGUsgg5GC1oHbddEQEC6rINGDWDmLOjp43jr3DwGzx3BYPp888aCCGzZDjK1i2V1t90AEuy9eaPaolHKc6QpMy2sUKhf4kmb3m5rud92CGo8jrRiYGeLdMtwSMSLM8X633nNxNYEC3ZjMo9j8yp2Dy59RjMspILeboFy1USiQUg77OuuK+uErLqUsXbiwjVOJBXDgn+NeUHrBT6ew1xJxyGHZhcbjJ6iY8yhYUfRzTVZSER8PBbKOnam3oUNby48z0xN17pfWChV8cw5AwJPcHA01LdJpdfu+OsBCVnoucnWNrEYcglMATaGlypmVzpbN0zEri0zgw1soBUcwYbag5PLmFOzF1XKwNl3EDt5CidmNVhHHweqVVggeG77wwiHduIzWhaoBbpkaheweRuIuPLsiu7AV2wsWfIcWZFVn8RzbbSJ1syv3ekv8ksDZ6FitK32pRa1hLxmIOZj1I24X8ClQqUpK0gIIAveBmOLoomjE/HxuJBrb/TrhozSrDVrWGgivttyZ0MhaVn8uesVPEeQUY2OTS0Jud3Jz0ZWNdekhBfx8Ti+oDpyfiWeXLdqHh0zv6pRz+D3A792xwQuzaWh6hb+4a00kgERW1vktgSeIOYXsCEH2xkjYbFnSkrMz+PJ03mYlLpaAH9gWwyPn8wClKnnLCh6XZ4s3gN17LPvogbEDVyfEGpqD++64LeR4yuLHDJatel9VbfgV/OwXnwVsPm6l84DlCIMgvzEfUC1CgyN4eXt78WdY2M4HhyHcvNXEPagX9kLKKWoOpQJl9M05gYn2oNJaVNGTK7pDjcGn27OS41xeLm6xC2MywIuFfSmTuSgyHvmtyUDQlOwHfbxKPboEAe0N3fkNAM7U0uBQLzGZR1yT3C+a1HQzKamFtNCU1NlXBaQuezs9NQvR7FeEfHzWFR0x1J7QOTx0DXmTOQVBO7OXf3O/KaCElDL9k8l/bhSrGKu1NzfUKyaOJetNFV+NtCO+7f1nu3emZIR8wvYEve5LtZjsoBP701B0U0UKyYGguKynPNiGxzUDVzj4EhN6uzdRntYVAwM1Di7QYlHuWKAXjgDeuo4cOY4yvM6koszoKVLSx/iBWBiG4Sp3aCRA+D+/ReAcBTHXpvHr75nCGOKjqfPFvDwrkTfjlMWmaOWbtK2RoJ+NasAzTq/NlqnTNvdq1dQLMnIxfw80oretNoKSJwnOTaABb+NwbcscDifq+KNK2XH7Xek/I7c02qL1qzIEcQaArq4LGC+/O5ylvIKgSd1DjfAFpKpwFKGLyBymC/p+PsjC6BgCyGeEHAcMJOv4jN7r37WKOLjsagajplf4PrN7Msih+cvlJocC21cyFfxi7tXj6M5EpYwEu6z3/EGXBGUeGxNeLueAZFfV5z8DWzgakOwpc7ebZnfbEFBdH4G1pGj8J09DaUchjX9dP19bfAmyBIH7D1Y4+vuAia3g0hskldencPJqg+nT+ZwS43/mAyIyGlGGy8WAE4tqvj5+SLunohgKun3fJxb437889EMbhyU29LzQamfmV8OFwtVnEgvqR60NoGFfTweP5FpcvKqGM5ZJZsr27qqEnkOyYDQFGwERM5z2fmeyUhTNpoQgo/sjKPiEJRbFPjnoxmMhEW8f0sUhLBMmFOgkwgITZngqJ9vOhcbWMJAQGi6BiGJb8oEEULw5UPDTZ8xrSVW9Vrwa8M+HmnFWPMS19XGUEjC7941utaHsYENbGAD6wo8IdBMa837Pa568Jv7y/+OTaVZUGUOfhCoE/cBA8Mg23YBU7tQCWxHYNcm8C7NA/dvjSKjGtgUlbB3KFB//f9v7+5jmjzfPYB/n+cpbSkVSgHfQKYe5Bh0ajKYLMo8bqj5bbpEz2K2ZefE5CzLBA/Zsj9El80tzsxNnboNg8kSjSTu5feHGpecbBoXzd4y5mbQ+QYeVI5GeanASlug7X3+QCqVF2F9Xmqf7+cv09Le981l7eX9XM91L5qaigNnmqN2MntDAlPT7Xh5bha+bbiDy21+/GOGK5KE9YbCSJKHTv7mTExBXoYd/3O5HfMmRddx3d/EPBZOq4yibGfUTW+Lp0WfFvfoBEfUWkcyyZmE/+vsiZz6NVD2fbtBE53WQe3ShjNUe53pQ4zRb9YEB+rb/Pjnn20A+g7lGOoIzoIsR9SNIi67BeeafcjLsOtyPOzD5P4batLsClwPuIHG6FZVyXePZWYbJiIiUuS+8xqMvuqne/LbYXVi9oQsSNMfR9KMAojQFCiPV0SeD1xoQ/IIX+hT0+2YOkSZ4NR0O/7rseGTseX/6sZ3De1o6uxB7t2uBXf8IbhGqL1zJCn491kZgx5f8IAWQKGwwGib3kiSNOxNEgN/ZrQKs52oOdMy6IYXAJiSFv3Y7FEm1H/XjIzkB97Yc/8cFFnCfxdPwh83u6J6HRPwb9Oi/94VZDmQaXCvxAeRJGnQFQciIjKn/uTXaLp/c3pX/CfS5k2E3L8TVNcS9XygV2jWkik71QqPL3gv+Q0Ex3Snbb+BN2gNxeMffACFXmwWGY/nOPHDtb8GPfcf84bvZxxP7BYZT+SOvS1doptz33+SRtp5jydGNzMnIqL4oEjAMFWbutL9djthsYx4KVZg+OONY+VOtkT1ph3LaUdj0eztNfRY0DkTU/C/dwKDShWMvgRO5jSaExmJiCjxKbKEcBzs/Oqe/BqZfvUdAqB98nvL24OJTmO/8FcVZERuCCQy0qRxTH6JiKjvxutQHNzQY6rrkTaLHNVWrLM7pMmxkC2+4KCjh/U2zqZEtSYjMooRLdaIiCj+KJIEY7dB+8SU/JaXl8Nut0OWZSiKgq1bt6o1L12EhdCkFCAUFoa38SCKF7zZjYiIAECRo091NUrMO7+bNm1CaurI3Q/iiR6b7fyuJyIiIoqmyJLhB1wABtT8ckeUiIiIyHwUSRryuHu9xbzzu2XLFgDAkiVLUFpa+sCfT7uvtZiMvp5venUikAAIoV2D5e5gGNZhDs4gIiIiMitLIpQ9bN68GW63Gx0dHXj//fcxefJkFBQURP3M8ePHcfz4cQDA1q1bMWV8OjIz7x0ckenqRkpaOpy2vqk4HF3IzNTuBpnx6d2wjUvHOJui2lgD3+eax4d/mSRpugajWSyWhF6fWTCOiYFxTByMZWJhPAcL2bqRmuIz/PcSU/LrdrsBAGlpaSgqKkJDQ8Og5Le0tDRqR1ju8aG19V7lbbjHj6ZbLZHTvHw+H1pbW2OZ1ohs4W403LgNl90COditylgD53zhhhfjbIqmazBaZmZmQq/PLBjHxMA4Jg7GMrEwnoN1+oMI96qTe43G5MmTh3z8b1+fDwQC8Pv9kT/X1dUhNzf3ga9z2aPbb6UkyfD19rUfEzr0fktPtsDjC6LdH4Rbgx6/t729mMBjeYmIiIiiKBJge5hrfjs6OrB9+3YAQCgUwsKFCzFv3rwHvu7+mt/kJBm+3hAAoCckNC+EdidbcKHFD0kCXBokvx3dQaTZ2V+XiIiIaCBFlmB9mGt+J0yYgG3bto35dXZLdHKbYlXg7e5Lfv3B8KDn1dZ/xLEsAfkZyZqMwb6mRERERNHsFhlFcXD6rOF7z8kDyh78vWEkJ2k7JZtFRncorOrRxhIkhOPguD4iIiKieKXIEqal242ehvHJryNJhj/Yl/wGgmEka7zz288fVC/Rtsh97dr0qFkmIiIior9P/aLXMXIkyejquVv2oMPOrxYsCtAbFggEBcZZWe9LREREFK8MT37tFhmBATu/jiR9kkc1q3LTbBb881wbukMCj8dBLQsRERERDc3w5FeWJPQXC/h7w8hwaN8mTAKgZoHC4ulpKr4bEREREWklrmoM+ro9aN8pIcWqoDvI+lwiIiIiszF853egvppf7cse3MkWdN8ttSAiIiIi84ir5Lev24P2O7/pyZZInTERERERmUdcJL/jrAq+qGtBV08YSToce5fntiMn1ar5OEREREQUX+Ii+V0x063reDaLDJtO/YSJiIiIKH4wAyQiIiIi02DyS0RERESmweSXiIiIiEyDyS8RERERmQaTXyIiIiIyDSa/RERERGQaTH6JiIiIyDSY/BIRERGRaTD5JSIiIiLTYPJLRERERKbB5JeIiIiITEMSQgijJ0FEREREpAddd34rKyv1HA579+7VdTyzjKl3HAH918g4aoNxVJ8Z4mjEmEaskd+RD/94A+kVTzPE8e+OPVwMErrs4bHHHuOYCULvNTKO2mAcE4MZfq+MY2KMyTgmzphqjp3QyW9hYSHHTBB6r5Fx1AbjmBjM8HtlHBNjTMYxccZUc2zl3XfffTf2qYze9OnT9RyONMI4JgbGMTEwjomDsUwsjKfxhooBb3gjIiIiItNI6LIHIiIiIqKBLLG8uLW1FVVVVWhvb4ckSSgtLcUzzzwDr9eLnTt3oqWlBVlZWXjjjTfgdDpx48YN7NmzB42NjXjhhRfw3HPPAQB6enqwadMmBINBhEIhFBcXY/Xq1aoskB5MrTj2C4fDqKyshNvtNuROdLNSM47l5eWw2+2QZRmKomDr1q0Grsxc1IxjV1cXqqur0dTUBEmSsHbtWuTn5xu4OnNRK5Y3b97Ezp07I+/b3NyM1atX49lnnzVqaaak5mfzm2++wYkTJyBJEqZMmYKysjJYrVYDV2cyIgYej0dcuXJFCCGEz+cTFRUVoqmpSdTU1IhDhw4JIYQ4dOiQqKmpEUII0d7eLurr68XBgwfFkSNHIu8TDoeF3+8XQgjR29srNmzYIC5duhTL1GgM1Ipjv6NHj4pdu3aJDz74QL9FkKpxLCsrEx0dHfougIQQ6sbx008/FcePHxdC9P3b6vV6dVwJqf1vqxBChEIh8corr4jm5mZ9FkERasWzra1NlJWVie7ubiGEEDt27BDff/+9vosxuZjKHtLT0yOFxMnJycjOzobH40FtbS0WLVoEAFi0aBFqa2sBAGlpacjLy4OiKFHvI0kS7HY7ACAUCiEUCkGSpFimRmOgVhwBoK2tDb///juefvpp/RZAANSNIxlHrTj6fD5cuHABTz31FADAYrEgJSVFx5WQFp/Js2fPYuLEicjKytJ+ARRFzXiGw2H09PQgFAqhp6cH6enp+i2EYit7GKi5uRmNjY3Iy8tDR0dHJJAulwsdHR0PfH04HMb69etx69YtLFu2DDNmzFBrajQGscZx//79ePnll+H3+7WeKo0g1jgCwJYtWwAAS5YsQWlpqWZzpeHFEsfm5makpqZiz549uHbtGqZPn441a9ZENhpIX2p8JgHgxx9/xIIFC7SaJo1SLPF0u91YsWIF1q5dC6vVJ6sxtAAABbRJREFUirlz52Lu3Ll6TJvuUuWGt0AggB07dmDNmjVwOBxRz0mSNKpdXFmWsW3bNlRXV+PKlSu4fv26GlOjMYg1jqdPn0ZaWhpbuxhMjc/j5s2b8eGHH2Ljxo349ttvcf78ea2mS8OINY6hUAiNjY1YunQpPvroI9hsNhw+fFjLKdMw1PhMAkAwGMTp06dRXFysxTRplGKNp9frRW1tLaqqqrB3714EAgGcOnVKyynTfWJOfoPBIHbs2IGSkhLMnz8fQN9W/507dwAAd+7cQWpq6qjfLyUlBbNmzcKZM2dinRqNgRpxvHTpEn777TeUl5dj165dOHfuHD755BPN5073qPV5dLvdkdcWFRWhoaFBu0nTIGrEMSMjAxkZGZGraMXFxWhsbNR24jSImt+Rf/zxB6ZNmwaXy6XZfGlkasTz7NmzGD9+PFJTU2GxWDB//nxcvnxZ87nTPTElv0IIVFdXIzs7G8uXL488XlhYiJMnTwIATp48iaKiohHfp7OzE11dXQD6Oj/U1dUhOzs7lqnRGKgVx5deegnV1dWoqqrC66+/jtmzZ6OiokLTudM9asUxEAhEylYCgQDq6uqQm5ur3cQpilpxdLlcyMjIwM2bNwH0feHm5ORoN3EaRK1Y9mPJg7HUimdmZibq6+vR3d0NIQTOnj3LnEdnMR1ycfHiRbzzzjvIzc2NbPO/+OKLmDFjBnbu3InW1taoth/t7e2orKyE3++P3OT28ccfo6WlBVVVVQiHwxBC4IknnsDzzz+v2iJpZGrFceDlnz///BNHjx5lqzMdqRXHv/76C9u3bwfQd+l84cKFWLVqlZFLMxU1P49Xr15FdXU1gsEgxo8fj7KyMjidToNXaB5qxjIQCKCsrAyfffbZoEvtpA814/n111/jp59+gqIomDp1Kl577TUkJSUZvELz4AlvRERERGQaPOGNiIiIiEyDyS8RERERmQaTXyIiIiIyDSa/RERERGQaTH6JiIiIyDSY/BIRERGRaViMngARkVmUl5ejvb0diqJAlmXk5OTgySefRGlpKWR55L2I5uZmrFu3Dl988QUURdFpxkREiYfJLxGRjtavX485c+bA5/Ph/Pnz2LdvHxoaGlBWVmb01IiITIHJLxGRARwOBwoLC+FyufDWW29h+fLlaG1txZdffonbt2/D4XBg8eLFWL16NQBg06ZNAIA1a9YAAN5++23k5+fjxIkTOHr0KNrb25GXl4dXX30VWVlZRi2LiCjuseaXiMhAeXl5cLvduHjxImw2G9atW4d9+/ahsrISx44dw6+//goAeO+99wAA+/fvR01NDfLz81FbW4tDhw7hzTffxOeff46ZM2di9+7dRi6HiCjuMfklIjKY2+2G1+vFrFmzkJubC1mW8cgjj2DBggU4f/78sK87duwYVq5ciZycHCiKgpUrV+Lq1atoaWnRcfZERA8Xlj0QERnM4/HA6XSivr4eBw8exPXr1xEMBhEMBlFcXDzs61paWrBv3z4cOHAg8pgQAh6Ph6UPRETDYPJLRGSghoYGeDwezJw5E9u2bcOyZcuwYcMGWK1W7N+/H52dnQAASZIGvTYzMxOrVq1CSUmJ3tMmInposeyBiMgAPp8Pp0+fxu7du1FSUoLc3Fz4/X44nU5YrVY0NDTghx9+iPx8amoqJEnC7du3I48tWbIEhw8fRlNTU+Q9f/75Z93XQkT0MJGEEMLoSRARmcHAPr+SJCEnJwclJSVYunQpZFnGL7/8ggMHDsDr9aKgoABZWVno6upCRUUFAOCrr77Cd999h1AohI0bNyI/Px+nTp3CkSNH0NraCofDgUcffZRt04iIRsDkl4iIiIhMg2UPRERERGQaTH6JiIiIyDSY/BIRERGRaTD5JSIiIiLTYPJLRERERKbB5JeIiIiITIPJLxERERGZBpNfIiIiIjINJr9EREREZBr/DxmlSR6lYk/XAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "61"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "KF002jnU_fEu"
      },
      "source": [
        "## 9. Training intensity\n",
        "<p>Heart rate is a popular metric used to measure training intensity. Depending on age and fitness level, heart rates are grouped into different zones that people can target depending on training goals. A target heart rate during moderate-intensity activities is about 50-70% of maximum heart rate, while during vigorous physical activity it’s about 70-85% of maximum.</p>\n",
        "<p>We'll create a distribution plot of my heart rate data by training intensity. It will be a visual presentation for the number of activities from predefined training zones. </p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "61"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "gRJoToNb_fEw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "1bb0ec4e-ec65-4094-be40-2e6c25d59b25"
      },
      "source": [
        "# Prepare data\n",
        "hr_zones = [100, 125, 133, 142, 151, 173]\n",
        "zone_names = ['Easy', 'Moderate', 'Hard', 'Very hard', 'Maximal']\n",
        "zone_colors = ['green', 'yellow', 'orange', 'tomato', 'red']\n",
        "df_run_hr_all = runs_subset_2015_2018['Average Heart Rate (bpm)']\n",
        "\n",
        "# Create plot\n",
        "fig, ax = plt.subplots(figsize=[8.0,5.0])\n",
        "\n",
        "# Plot and customize\n",
        "n, bins, patches = ax.hist(df_run_hr_all, bins=hr_zones, alpha=0.5)\n",
        "for i in range(0, len(patches)):\n",
        "    patches[i].set_facecolor(zone_colors[i])\n",
        "\n",
        "ax.set(title='Distribution of HR', ylabel='Number of runs')\n",
        "ax.xaxis.set(ticks=hr_zones)\n",
        "ax.set_xticklabels(labels=zone_names, rotation=-30, ha='left')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "68"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "KHiiUZte_fEx"
      },
      "source": [
        "## 10. Detailed summary report\n",
        "<p>With all this data cleaning, analysis, and visualization, let's create detailed summary tables of my training. </p>\n",
        "<p>To do this, we'll create two tables. The first table will be a summary of the distance (km) and climb (m) variables for each training activity. The second table will list the summary statistics for the average speed (km/hr), climb (m), and distance (km) variables for each training activity.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "68"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "collapsed": true,
        "id": "o15ZRu-Y_fEy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38c0106b-33f9-4b21-b6c8-b4ff35a496b7"
      },
      "source": [
        "# Concatenating three DataFrames\n",
        "df_run_walk_cycle = df_run.append([df_walk,df_cycle], ignore_index=False, sort=True)\n",
        "\n",
        "dist_climb_cols, speed_col = ['Distance (km)', 'Climb (m)'], ['Average Speed (km/h)']\n",
        "\n",
        "# Calculating total distance and climb in each type of activities\n",
        "df_totals= df_run_walk_cycle.groupby(['Type'])[dist_climb_cols].sum()\n",
        "\n",
        "print('Totals for different training types:')\n",
        "display(df_totals)\n",
        "\n",
        "# Calculating summary statistics for each type of activities\n",
        "df_summary = df_run_walk_cycle.groupby('Type')[dist_climb_cols + speed_col].describe()\n",
        "\n",
        "# Combine totals with summary\n",
        "for i in dist_climb_cols:\n",
        "    df_summary[i, 'total'] = df_totals[i]\n",
        "\n",
        "print('Summary statistics for different training types:')\n",
        "df_summary.stack()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Totals for different training types:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Distance (km)</th>\n",
              "      <th>Climb (m)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cycling</th>\n",
              "      <td>680.58</td>\n",
              "      <td>6976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Running</th>\n",
              "      <td>5224.50</td>\n",
              "      <td>57278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Walking</th>\n",
              "      <td>33.45</td>\n",
              "      <td>349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Distance (km)  Climb (m)\n",
              "Type                             \n",
              "Cycling         680.58       6976\n",
              "Running        5224.50      57278\n",
              "Walking          33.45        349"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Summary statistics for different training types:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Average Speed (km/h)</th>\n",
              "      <th>Climb (m)</th>\n",
              "      <th>Distance (km)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"9\" valign=\"top\">Cycling</th>\n",
              "      <th>25%</th>\n",
              "      <td>16.980000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>15.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19.500000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>20.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>21.490000</td>\n",
              "      <td>318.000000</td>\n",
              "      <td>29.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>24.330000</td>\n",
              "      <td>553.000000</td>\n",
              "      <td>49.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.125172</td>\n",
              "      <td>240.551724</td>\n",
              "      <td>23.468276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.380000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>11.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.257100</td>\n",
              "      <td>128.960289</td>\n",
              "      <td>9.451040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6976.000000</td>\n",
              "      <td>680.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"9\" valign=\"top\">Running</th>\n",
              "      <th>25%</th>\n",
              "      <td>10.495000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>7.415000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10.980000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>10.810000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.520000</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>13.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>459.000000</td>\n",
              "      <td>459.000000</td>\n",
              "      <td>459.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>20.720000</td>\n",
              "      <td>982.000000</td>\n",
              "      <td>38.320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.056296</td>\n",
              "      <td>124.788671</td>\n",
              "      <td>11.382353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.770000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.953273</td>\n",
              "      <td>103.382177</td>\n",
              "      <td>4.937853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total</th>\n",
              "      <td>NaN</td>\n",
              "      <td>57278.000000</td>\n",
              "      <td>5224.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"9\" valign=\"top\">Walking</th>\n",
              "      <th>25%</th>\n",
              "      <td>5.555000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.385000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.970000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.485000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.512500</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>1.787500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.910000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>4.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.549444</td>\n",
              "      <td>19.388889</td>\n",
              "      <td>1.858333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.040000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.459309</td>\n",
              "      <td>27.110100</td>\n",
              "      <td>0.880055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total</th>\n",
              "      <td>NaN</td>\n",
              "      <td>349.000000</td>\n",
              "      <td>33.450000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Average Speed (km/h)     Climb (m)  Distance (km)\n",
              "Type                                                            \n",
              "Cycling 25%               16.980000    139.000000      15.530000\n",
              "        50%               19.500000    199.000000      20.300000\n",
              "        75%               21.490000    318.000000      29.400000\n",
              "        count             29.000000     29.000000      29.000000\n",
              "        max               24.330000    553.000000      49.180000\n",
              "        mean              19.125172    240.551724      23.468276\n",
              "        min               11.380000     58.000000      11.410000\n",
              "        std                3.257100    128.960289       9.451040\n",
              "        total                   NaN   6976.000000     680.580000\n",
              "Running 25%               10.495000     54.000000       7.415000\n",
              "        50%               10.980000     91.000000      10.810000\n",
              "        75%               11.520000    171.000000      13.190000\n",
              "        count            459.000000    459.000000     459.000000\n",
              "        max               20.720000    982.000000      38.320000\n",
              "        mean              11.056296    124.788671      11.382353\n",
              "        min                5.770000      0.000000       0.760000\n",
              "        std                0.953273    103.382177       4.937853\n",
              "        total                   NaN  57278.000000    5224.500000\n",
              "Walking 25%                5.555000      7.000000       1.385000\n",
              "        50%                5.970000     10.000000       1.485000\n",
              "        75%                6.512500     15.500000       1.787500\n",
              "        count             18.000000     18.000000      18.000000\n",
              "        max                6.910000    112.000000       4.290000\n",
              "        mean               5.549444     19.388889       1.858333\n",
              "        min                1.040000      5.000000       1.220000\n",
              "        std                1.459309     27.110100       0.880055\n",
              "        total                   NaN    349.000000      33.450000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "dc": {
          "key": "75"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "FDAa0c80_fEz"
      },
      "source": [
        "## 11. Fun facts\n",
        "<p>To wrap up, let’s pick some fun facts out of the summary tables and solve the last exercise.</p>\n",
        "<p>These data (my running history) represent 6 years, 2 months and 21 days. And I remember how many running shoes I went through–7.</p>\n",
        "<pre><code>FUN FACTS\n",
        "- Average distance: 11.38 km\n",
        "- Longest distance: 38.32 km\n",
        "- Highest climb: 982 m\n",
        "- Total climb: 57,278 m\n",
        "- Total number of km run: 5,224 km\n",
        "- Total runs: 459\n",
        "- Number of running shoes gone through: 7 pairs\n",
        "</code></pre>\n",
        "<p>The story of Forrest Gump is well known–the man, who for no particular reason decided to go for a \"little run.\" His epic run duration was 3 years, 2 months and 14 days (1169 days). In the picture you can see Forrest’s route of 24,700 km.  </p>\n",
        "<pre><code>FORREST RUN FACTS\n",
        "- Average distance: 21.13 km\n",
        "- Total number of km run: 24,700 km\n",
        "- Total runs: 1169\n",
        "- Number of running shoes gone through: ...\n",
        "</code></pre>\n",
        "<p>Assuming Forest and I go through running shoes at the same rate, figure out how many pairs of shoes Forrest needed for his run.</p>\n",
        "<p><img src=\"https://assets.datacamp.com/production/project_727/img/Forrest_Gump_running_route.png\" alt=\"Forrest's route\" title=\"Little run of Forrest Gump\"></p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "75"
        },
        "tags": [
          "sample_code"
        ],
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEPxnBQq_fE1",
        "outputId": "2f1b1571-660a-4a61-cba1-d11e5ebf92e6"
      },
      "source": [
        "# Count average shoes per lifetime (as km per pair) using our fun facts\n",
        "average_shoes_lifetime = 5224/7\n",
        "\n",
        "# Count number of shoes for Forrest's run distance\n",
        "shoes_for_forrest_run = int(24700/average_shoes_lifetime)\n",
        "\n",
        "print('Forrest Gump would need {} pairs of shoes!'.format(shoes_for_forrest_run))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Forrest Gump would need 33 pairs of shoes!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_LD6507_uLa"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}