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    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
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   "id": "cedb94af",
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   "source": [
    "# Introduction to clustering\n",
    "\n",
    "Clustering is a type of Unsupervised Learning that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. \n",
    "\n",
    "## Introduction\n",
    "\n",
    "In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans. \n",
    "\n",
    "Think a minute about how you might have encountered clustering 'in the wild', in a banking, e-commerce, or business setting.\n",
    "\n",
    ":::{note}\n",
    "Interestingly, cluster analysis originated in the fields of Anthropology and Psychology in the 1930s. Can you imagine how it might have been used?\n",
    ":::\n",
    "\n",
    "Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.\n",
    "\n",
    "## Getting started with clustering\n",
    "\n",
    "Scikit-learn offers a large array of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-learn and their appropriate use cases:\n",
    "\n",
    "| Method name                  | Use case                                                               |\n",
    "| :--------------------------- | :--------------------------------------------------------------------- |\n",
    "| K-Means                      | general purpose, inductive                                             |\n",
    "| Affinity propagation         | many, uneven clusters, inductive                                       |\n",
    "| Mean-shift                   | many, uneven clusters, inductive                                       |\n",
    "| Spectral clustering          | few, even clusters, transductive                                       |\n",
    "| Ward hierarchical clustering | many, constrained clusters, transductive                               |\n",
    "| Agglomerative clustering     | many, constrained, non Euclidean distances, transductive               |\n",
    "| DBSCAN                       | non-flat geometry, uneven clusters, transductive                       |\n",
    "| OPTICS                       | non-flat geometry, uneven clusters with variable density, transductive |\n",
    "| Gaussian mixtures            | flat geometry, inductive                                               |\n",
    "| BIRCH                        | large dataset with outliers, inductive                                 |\n",
    "\n",
    "How we create clusters has a lot to do with how we gather up the data points into groups. Let's unpack some vocabulary:\n",
    "\n",
    "**'Transductive' vs. 'inductive'**\n",
    "\n",
    "Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases. \n",
    "\n",
    "An example: Imagine you have a dataset that is only partially  labelled. Some things are 'records', some 'cds', and some are blank. Your job is to provide labels for the blanks. If you choose an inductive approach, you'd train a model looking for 'records' and 'cds', and apply those labels to your unlabeled data. This approach will have trouble classifying things that are actually 'cassettes'. A transductive approach, on the other hand, handles this unknown data more effectively as it works to group similar items together and then applies a label to a group. In this case, clusters might reflect 'round musical things' and 'square musical things'. \n",
    "\n",
    "**'Non-flat' vs. 'flat' geometry'**\n",
    " \n",
    "Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' (Euclidean) or 'non-flat' (non-Euclidean) geometrical methods. \n",
    "'Flat' in this context refers to Euclidean geometry (parts of which are taught as 'plane' geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a 'flat' or 'non-flat' way, depending on the nature of the data. Euclidean distances are measured as the length of a line segment between two points. Non-Euclidean distances are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.\n",
    "\n",
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/flat-nonflat.png\n",
    "---\n",
    "name: Flat vs Nonflat Geometry Infographic\n",
    "---\n",
    "Flat vs Nonflat Geometry Infographic by Dasani Madipalli\n",
    ":::\n",
    "\n",
    "**Distances**\n",
    "\n",
    "Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured in a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways.\n",
    "\n",
    "**Constrained**\n",
    "\n",
    "Constrained Clustering introduces 'semi-supervised' learning into this unsupervised method. The relationships between points are flagged as 'cannot link' or 'must-link' so some rules are forced on the dataset.\n",
    "\n",
    "An example: If an algorithm is set free on a batch of unlabelled or semi-labelled data, the clusters it produces may be of poor quality. In the example above, the clusters might group 'round music things' and 'square music things' and 'triangular things' and 'cookies'. If given some constraints, or rules to follow (\"the item must be made of plastic\", \"the item needs to be able to produce music\") this can help 'constrain' the algorithm to make better choices.\n",
    "\n",
    "**Density**\n",
    "\n",
    "Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. This article demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.\n",
    "\n",
    "\n",
    "## Clustering algorithms\n",
    "\n",
    "There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let's discuss some of the major ones:\n",
    "\n",
    "- **Hierarchical clustering**. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members' distance to and from other objects. Scikit-learn's agglomerative clustering is hierarchical.\n",
    "\n",
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/hierarchical.png\n",
    "---\n",
    "name: Hierarchical clustering Infographic\n",
    "---\n",
    "Hierarchical clustering Infographic by Dasani Madipalli\n",
    ":::\n",
    "\n",
    "- **Centroid clustering**. This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. K-means clustering is a popular version of centroid clustering. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.\n",
    "\n",
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/centroid.png\n",
    "---\n",
    "name: Centroid clustering Infographic\n",
    "---  \n",
    "Centroid clustering Infographic by Dasani Madipalli\n",
    ":::\n",
    "\n",
    "- **Distribution-based clustering**. Based in statistical modeling, distribution-based clustering centers on determining the probability that a data point belongs to a cluster, and assigning it accordingly. Gaussian mixture methods belong to this type.\n",
    "\n",
    "- **Density-based clustering**. Data points are assigned to clusters based on their density, or their grouping around each other. Data points far from the group are considered outliers or noise. DBSCAN, Mean-shift and OPTICS belong to this type of clustering.\n",
    "\n",
    "- **Grid-based clustering**. For multi-dimensional datasets, a grid is created and the data is divided amongst the grid's cells, thereby creating clusters.\n",
    "\n",
    "## Exercise - cluster your data\n",
    "\n",
    "Clustering as a technique is greatly aided by proper visualization, so let's get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.\n",
    "\n",
    "1. Open the _notebook.ipynb_ file in this folder.\n",
    "\n",
    "2. Import the `Seaborn` package for good data visualization."
   ]
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   "source": [
    "# Install the necessary dependencies\n",
    "\n",
    "import os\n",
    "import sys\n",
    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dedce48",
   "metadata": {},
   "source": [
    "3. Append the song data from _nigerian-songs.csv_. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data:"
   ]
  },
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sparky</td>\n",
       "      <td>Mandy &amp; The Jungle</td>\n",
       "      <td>Cruel Santino</td>\n",
       "      <td>alternative r&amp;b</td>\n",
       "      <td>2019</td>\n",
       "      <td>144000</td>\n",
       "      <td>48</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.8510</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.534000</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-6.699</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>133.015</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>shuga rush</td>\n",
       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
       "      <td>Odunsi (The Engine)</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2020</td>\n",
       "      <td>89488</td>\n",
       "      <td>30</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.683</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LITT!</td>\n",
       "      <td>LITT!</td>\n",
       "      <td>AYLØ</td>\n",
       "      <td>indie r&amp;b</td>\n",
       "      <td>2018</td>\n",
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       "      <td>40</td>\n",
       "      <td>0.836</td>\n",
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       "      <td>0.564</td>\n",
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       "      <td>0.1100</td>\n",
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       "      <th>3</th>\n",
       "      <td>Confident / Feeling Cool</td>\n",
       "      <td>Enjoy Your Life</td>\n",
       "      <td>Lady Donli</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>175135</td>\n",
       "      <td>14</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.7980</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>-4.961</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>111.087</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wanted you</td>\n",
       "      <td>rare.</td>\n",
       "      <td>Odunsi (The Engine)</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2018</td>\n",
       "      <td>152049</td>\n",
       "      <td>25</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.3480</td>\n",
       "      <td>-6.044</td>\n",
       "      <td>0.0447</td>\n",
       "      <td>105.115</td>\n",
       "      <td>4</td>\n",
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       "                       name                         album  \\\n",
       "0                    Sparky            Mandy & The Jungle   \n",
       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
       "2                     LITT!                         LITT!   \n",
       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
       "4                wanted you                         rare.   \n",
       "\n",
       "                artist artist_top_genre  release_date  length  popularity  \\\n",
       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
       "2                 AYLØ        indie r&b          2018  207758          40   \n",
       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
       "\n",
       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
       "\n",
       "   speechiness    tempo  time_signature  \n",
       "0       0.0829  133.015               5  \n",
       "1       0.3600  129.993               3  \n",
       "2       0.0424  130.005               4  \n",
       "3       0.1130  111.087               4  \n",
       "4       0.0447  105.115               4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/nigerian-songs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29076ed",
   "metadata": {},
   "source": [
    "Check the first few lines of data:\n",
    "\n",
    "4. Get some information about the dataframe, calling `info()`:"
   ]
  },
  {
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   "execution_count": 3,
   "id": "db352ce9",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 530 entries, 0 to 529\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              530 non-null    object \n",
      " 1   album             530 non-null    object \n",
      " 2   artist            530 non-null    object \n",
      " 3   artist_top_genre  530 non-null    object \n",
      " 4   release_date      530 non-null    int64  \n",
      " 5   length            530 non-null    int64  \n",
      " 6   popularity        530 non-null    int64  \n",
      " 7   danceability      530 non-null    float64\n",
      " 8   acousticness      530 non-null    float64\n",
      " 9   energy            530 non-null    float64\n",
      " 10  instrumentalness  530 non-null    float64\n",
      " 11  liveness          530 non-null    float64\n",
      " 12  loudness          530 non-null    float64\n",
      " 13  speechiness       530 non-null    float64\n",
      " 14  tempo             530 non-null    float64\n",
      " 15  time_signature    530 non-null    int64  \n",
      "dtypes: float64(8), int64(4), object(4)\n",
      "memory usage: 66.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd60fcc2",
   "metadata": {},
   "source": [
    "5. Double-check for null values, by calling `isnull()` and verifying the sum being 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db6f8b91",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                0\n",
       "album               0\n",
       "artist              0\n",
       "artist_top_genre    0\n",
       "release_date        0\n",
       "length              0\n",
       "popularity          0\n",
       "danceability        0\n",
       "acousticness        0\n",
       "energy              0\n",
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       "dtype: int64"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a6235e",
   "metadata": {},
   "source": [
    "6. Describe the data:"
   ]
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.131688</td>\n",
       "      <td>39696.822259</td>\n",
       "      <td>18.992212</td>\n",
       "      <td>0.117522</td>\n",
       "      <td>0.208342</td>\n",
       "      <td>0.148533</td>\n",
       "      <td>0.090321</td>\n",
       "      <td>0.123588</td>\n",
       "      <td>2.464186</td>\n",
       "      <td>0.092939</td>\n",
       "      <td>23.518601</td>\n",
       "      <td>0.333701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>89488.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>-19.362000</td>\n",
       "      <td>0.027800</td>\n",
       "      <td>61.695000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>199305.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681000</td>\n",
       "      <td>0.089525</td>\n",
       "      <td>0.669000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075650</td>\n",
       "      <td>-6.298750</td>\n",
       "      <td>0.059100</td>\n",
       "      <td>102.961250</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>218509.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>0.220500</td>\n",
       "      <td>0.784500</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.103500</td>\n",
       "      <td>-4.558500</td>\n",
       "      <td>0.097950</td>\n",
       "      <td>112.714500</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>242098.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.829500</td>\n",
       "      <td>0.403000</td>\n",
       "      <td>0.875750</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.164000</td>\n",
       "      <td>-3.331000</td>\n",
       "      <td>0.177000</td>\n",
       "      <td>125.039250</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>511738.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.966000</td>\n",
       "      <td>0.954000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.811000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>206.007000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       release_date         length  popularity  danceability  acousticness  \\\n",
       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
       "\n",
       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
       "\n",
       "            tempo  time_signature  \n",
       "count  530.000000      530.000000  \n",
       "mean   116.487864        3.986792  \n",
       "std     23.518601        0.333701  \n",
       "min     61.695000        3.000000  \n",
       "25%    102.961250        4.000000  \n",
       "50%    112.714500        4.000000  \n",
       "75%    125.039250        4.000000  \n",
       "max    206.007000        5.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104bb834",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "If we are working with clustering, an unsupervised method that does not require labeled data, why are we showing this data with labels? In the data exploration phase, they come in handy, but they are not necessary for the clustering algorithms to work. You could just as well remove the column headers and refer to the data by column number. \n",
    ":::\n",
    "\n",
    "Look at the general values of the data. Note that popularity can be '0', which show songs that have no ranking. Let's remove those shortly.\n",
    "\n",
    "7. Use a barplot to find out the most popular genres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1630f0f",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf3e72a",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "If you'd like to see more top values, change the top `[:5]` to a bigger value, or remove it to see all.\n",
    ":::\n",
    "\n",
    "Note, when the top genre is described as 'Missing', that means that Spotify did not classify it, so let's get rid of it.\n",
    "\n",
    "8. Get rid of missing data by filtering it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16503ad7",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[df['artist_top_genre'] != 'Missing']\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top.index,y=top.values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8e6615",
   "metadata": {},
   "source": [
    "9. By far, the top three genres dominate this dataset. Let's concentrate on `afro dancehall`, `afropop`, and `nigerian pop`, additionally filter the dataset to remove anything with a 0 popularity value (meaning it was not classified with a popularity in the dataset and can be considered noise for our purposes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31988514",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
    "df = df[(df['popularity'] > 0)]\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top.index,y=top.values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dff7b63",
   "metadata": {},
   "source": [
    "10. Do a quick test to see if the data correlates in any particularly strong way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6458e19c",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Aw6NyDAAAAADOwEzl2J6oHAMAAAAADI/kGAAAAABgeLRVAwAAAIAzYEEuu6JyDAAAAAAwPJJjAAAAAIDh0VYNAAAAAM6A1artisoxAAAAAMDwqBwDAAAAgDOgcmxXVI4BAAAAAIZHcgwAAAAAMDzaqgEAAADACVgsuY4OoVijcgwAAAAAMDySYwAAAACA4dFWDQAAAADOgNWq7YrKsR21aNFCI0aMcHQYSk5Olslk0p9//unoUAAAAACgSCI5LmaKSkIOAAAA4A6zmIvOVgyRHAMAAAAADI/kuJBkZ2drzJgxqlSpknx9fdWwYUMlJydb9y9fvlylSpXSJ598ooiICPn5+alt27bKzMy0zrl8+bKGDx+uUqVKKSAgQM8++6wee+wxde7cWZIUHx+vrVu3at68eTKZTDKZTDp+/Lj1+NTUVEVHR8vHx0eNGzdWenp6Id09AAAAABRtJMeFpG/fvtq+fbtWrVqlAwcOqFu3bmrbtq0OHz5snXPhwgW99NJLeuutt/TFF18oIyNDo0ePtu6fOXOmVqxYoWXLlmn79u06d+6c1q1bZ90/b948xcTEaODAgcrMzFRmZqaCg4Ot+8eNG6dZs2Zpz549cnNzU79+/Qrl3gEAAADcAWZz0dmKIVarLgRHjhzRypUr9fPPP6tixYqSpNGjR2vjxo1atmyZpk+fLknKycnRK6+8orvvvluSNHToUE2ZMsV6ngULFighIUEPPfSQJOnll19WUlKSdb+/v788PDzk4+OjChUq5Ilj2rRpat68uSRp7NixeuCBB3Tp0iV5eXnZ58YBAAAAwEmQHBeCvXv3ymKxqHr16jbjWVlZCggIsH728fGxJsaSFBQUpFOnTkmSzp49q5MnT+qee+6x7nd1dVWDBg1kvsnf3NSpU8fm3JJ06tQpVa5cOd/5WVlZysrKshmzWCwymUw3dT0AAAAAcBYkx4XAbDbL1dVVqampcnV1tdnn5+dn/dnd3d1mn8lkksViyTP2T//efz3/PP/V81wvsU5MTNTkyZNtr+/iJ5NryZu+JgAAAIA7pJiuEl1U8MxxIahfv75yc3N16tQpVa1a1WbLr/05P/7+/goMDNSuXbusY7m5udq3b5/NPA8PD+Xm5t6RuBMSEnT27FmbzeRS4o6cGwAAAACKEirHhaB69erq3bu34uLiNGvWLNWvX1+nT5/WZ599psjISLVv3/6mzjNs2DAlJiaqatWqqlGjhhYsWKA//vjDppocGhqqlJQUHT9+XH5+fipTpswtx+3p6SlPT0+bMVqqAQAAABRHVI4LybJlyxQXF6enn35a4eHh6tixo1JSUmxWk76RZ599Vj179lRcXJxiYmLk5+en2NhYmwW1Ro8eLVdXV9WsWVPlypVTRkaGPW4HAAAAQGFz9ArVxXy1apOlIA+tokgxm82KiIhQ9+7dNXXq1EK5pptHpUK5jiNd/PVLR4dQKMZFj3N0CIXisPlvR4dQKJrI39Eh2N0Dnr87OoRCMTfH19EhFIrIXM8bTyoG6udccnQIuEO+8jDG2z0C7szTeUVe31/ednQIt+TipoWODsHKu81gR4dwx9FW7UR+/PFHbdq0Sc2bN1dWVpZefvllHTt2TL169XJ0aAAAAADsjQW57Iq2aifi4uKi5cuX6z//+Y+aNGmigwcP6tNPP1VERISjQwMAAAAAp0bl2IkEBwdr+/btjg4DAAAAAIodkmMAAAAAcAbFdCGsooK2agAAAACA4ZEcAwAAAAAMj7ZqAAAAAHAGtFXbFZVjAAAAAIDhUTkGAAAAAGfAe47tisoxAAAAAMDwSI4BAAAAAIZHWzUAAAAAOAMW5LIrKscAAAAAAMMjOQYAAAAAGB7JMQAAAAA4A4u56GwFtHDhQlWpUkVeXl5q0KCBvvzyy+vOz8rK0rhx4xQSEiJPT0/dfffdWrp06a1+czeFZ44BAAAAAHazevVqjRgxQgsXLlSTJk20ePFitWvXTocOHVLlypXzPaZ79+46efKkXn/9dVWtWlWnTp3S5cuX7RonyTEAAAAAwG5mz56t/v37a8CAAZKkuXPn6pNPPtGiRYuUmJiYZ/7GjRu1detWHT16VGXKlJEkhYaG2j1O2qoBAAAAwBmYzUVmy8rK0rlz52y2rKysPCFnZ2crNTVVbdq0sRlv06aNduzYke9trl+/XtHR0XrhhRdUqVIlVa9eXaNHj9bFixft8rVeRXIMAAAAACiQxMRE+fv722z5VYFPnz6t3NxcBQYG2owHBgbqxIkT+Z776NGj2rZtm7755hutXbtWc+fO1XvvvachQ4bY5V6uoq0aAAAAAJzBLSyEZS8JCQkaNWqUzZinp+c155tMJpvPFoslz9hVZrNZJpNJK1askL+/v6QrrdkPP/yw/vvf/8rb2/s2o88fyTEAAAAAoEA8PT2vmwxfVbZsWbm6uuapEp86dSpPNfmqoKAgVapUyZoYS1JERIQsFot+/vlnVatW7faCvwbaqgEAAAAAduHh4aEGDRpo8+bNNuObN29W48aN8z2mSZMm+vXXX/X3339bx77//nu5uLjorrvuslusJMcAAAAA4AyKwEJc1q0ARo0apddee01Lly5VWlqaRo4cqYyMDA0aNEjSlRbtuLg46/xevXopICBAffv21aFDh/TFF1/omWeeUb9+/ezWUi3RVg0AAAAAsKMePXrozJkzmjJlijIzM1W7dm0lJSUpJCREkpSZmamMjAzrfD8/P23evFnDhg1TdHS0AgIC1L17dz3//PN2jdNksVgsdr0CipVvwjo4OgS7e9NcwtEhFIppe6Y5OoRCsS5yvKNDKBRn3PJf0KI4Waszjg6hULQ3BTg6BNxBR1xyHB2C3ZWUq6NDKBTBl43RcFnXfMHRIRSKRr+ucXQIt+Tie/ZNDgvC++HnHB3CHUflGAAAAACcQQHbmVEwxvgVGAAAAAAA10HlGAAAAACcAU/E2hWVYwAAAACA4ZEcAwAAAAAMj7ZqAAAAAHAGLMhlV1SOAQAAAACGR3IMAAAAADA82qoBAAAAwBnQVm1XVI4BAAAAAIZHcgwAAAAAMDzaqgEAAADAGVhoq7YnKscAAAAAAMOjcgwAAAAAzoAFueyKyjEAAAAAwPBIjgEAAAAAhkdbNQAAAAA4A4vF0REUa1SOAQAAAACGR3IMAAAAADA82qoBAAAAwBmwWrVdUTkGAAAAABgeybGDtGjRQiNGjLjt80yaNEn16tW77fMAAAAAKOLM5qKzFUMkx05u9OjR2rJli/VzfHy8Onfu7LiAAAAAAMAJ8cyxk7JYLMrNzZWfn5/8/PwcHQ4AAAAAODXDVY5btGihoUOHaujQoSpVqpQCAgL03HPPyfL/3hn2xx9/KC4uTqVLl5aPj4/atWunw4cPW49fvny5SpUqpXXr1ql69ery8vJS69at9dNPP1nn5Fe9HTFihFq0aHHNuN5++21FR0erRIkSqlChgnr16qVTp05Z9ycnJ8tkMumTTz5RdHS0PD099eWXX9q0VU+aNElvvPGGPvjgA5lMJplMJiUnJ+v+++/X0KFDba535swZeXp66rPPPrvFbxIAAABAobKYi85WDBkuOZakN954Q25ubkpJSdH8+fM1Z84cvfbaa5KuJLZ79uzR+vXr9dVXX8lisah9+/bKycmxHn/hwgVNmzZNb7zxhrZv365z587pkUceua2YsrOzNXXqVH399ddat26djh07pvj4+DzzxowZo8TERKWlpalOnTo2+0aPHq3u3burbdu2yszMVGZmpho3bqwBAwbof//7n7KysqxzV6xYoYoVK+q+++67rbgBAAAAoDgwZFt1cHCw5syZI5PJpPDwcB08eFBz5sxRixYttH79em3fvl2NGzeWdCWJDA4O1rp169StWzdJUk5Ojl5++WU1bNhQ0pVkOyIiQrt27dI999xzSzH169fP+nNYWJjmz5+ve+65R3///bdN2/SUKVPUunXrfM/h5+cnb29vZWVlqUKFCtbxrl27atiwYfrggw/UvXt3SdKyZcsUHx8vk8l0S/ECAAAAQHFiyMpxo0aNbJLCmJgYHT58WIcOHZKbm5s16ZWkgIAAhYeHKy0tzTrm5uam6Oho6+caNWqoVKlSNnMKat++ferUqZNCQkJUokQJawt2RkaGzbx/XvdmeXp66tFHH9XSpUslSfv379fXX3+db2X6n7KysnTu3DmbLduSW+DrAwAAALh9FrOlyGzFkSGT44KyWCx5Kqz5VVyvjrm4uFifYb7qn23Z/3b+/Hm1adNGfn5+evvtt7V7926tXbtW0pV263/y9fW9pXsYMGCANm/erJ9//llLly5Vy5YtFRISct1jEhMT5e/vb7O99ueRW7o+AAAAABRlhkyOd+7cmedztWrVVLNmTV2+fFkpKSnWfWfOnNH333+viIgI69jly5e1Z88e6+f09HT9+eefqlGjhiSpXLlyyszMtLnG/v37rxnPd999p9OnT2vGjBlq2rSpatSoYbMYV0F4eHgoNzdvdTcyMlLR0dFasmSJ/ve//9m0cV9LQkKCzp49a7MNKHX3LcUFAAAA4DY5+t3GvOe4+Pnpp580atQopaena+XKlVqwYIGeeuopVatWTZ06ddLAgQO1bds2ff3113r00UdVqVIlderUyXq8u7u7hg0bppSUFO3du1d9+/ZVo0aNrM8b33///dqzZ4/efPNNHT58WBMnTtQ333xzzXgqV64sDw8PLViwQEePHtX69es1derUW7q30NBQHThwQOnp6Tp9+rRNxXrAgAGaMWOGcnNz9dBDD93wXJ6enipZsqTN5mFyvaW4AAAAAKAoM2RyHBcXp4sXL+qee+7RkCFDNGzYMD3++OOSrixU1aBBA3Xo0EExMTGyWCxKSkqSu7u79XgfHx89++yz6tWrl2JiYuTt7a1Vq1ZZ98fGxmr8+PEaM2aM/vOf/+ivv/5SXFzcNeMpV66cli9frnfffVc1a9bUjBkz9NJLL93SvQ0cOFDh4eGKjo5WuXLltH37duu+nj17ys3NTb169ZKXl9ctnR8AAAAAiiOT5d8PxxZzLVq0UL169TR37txbOn758uUaMWKE/vzzzzsaV2H46aefFBoaqt27dysqKuqWzvFNWIc7HFXR86a5hKNDKBTT9kxzdAiFYl3keEeHUCjOuBX/lefX6oyjQygU7U0Bjg4Bd9ARl2uvOVJclJQxusqCLxujplTXfMHRIRSKRr+ucXQIt+TComGODsHK58kFjg7hjjPkq5yMJicnR5mZmRo7dqwaNWp0y4kxAAAAABRXxvgVmMFt375dISEhSk1N1SuvvOLocAAAAACgyDFc5Tg5Ofm2jo+Pj7/h+4GLmhYtWuR5tRQAAAAAJ1NM3y9cVFA5BgAAAAAYHskxAAAAAMDwDNdWDQAAAABOyWx2dATFGpVjAAAAAIDhUTkGAAAAAGdA5diuqBwDAAAAAAyP5BgAAAAAYHi0VQMAAACAM7DwnmN7onIMAAAAADA8kmMAAAAAgOHRVg0AAAAAzoDVqu2KyjEAAAAAwPCoHAMAAACAMzCzIJc9UTkGAAAAABgeyTEAAAAAwPBoqwYAAAAAZ2BhQS57onIMAAAAADA8kmMAAAAAgOHRVg0AAAAAzoDVqu2KyjEAAAAAwPBIjgEAAAAAhkdbNQrk5HlfR4dgd4c9/3Z0CIViXeR4R4dQKDofnOroEArFrAYTHB2C3UWZSjs6hEKRY5CFSL9zueToEApFZYuno0Owu92Ws44OoVD85Fb8/ywlySPHx9EhFIpGjg7gFlnMBvmPhINQOQYAAAAAGB6VYwAAAABwBizIZVdUjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAZWFiQy56oHAMAAAAADI/kGAAAAABgeLRVAwAAAIAzYLVqu6JyDAAAAAAwPCrHAAAAAOAMzCzIZU9UjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAZsCCXXVE5BgAAAAAYHskxAAAAAMDwaKsGAAAAAGdgYbVqe6JyDAAAAAAwPCrHAAAAAOAMWJDLrqgcAwAAAAAMj+QYAAAAAGB4tFUDAAAAgBOwmFmQy56KXeW4RYsWGjFihKPDKJCbiTk0NFRz5861fjaZTFq3bp0k6fjx4zKZTNq/f7/dYgQAAACA4ozKsZPYvXu3fH19890XHByszMxMlS1bVpKUnJys++67T3/88YdKlSpViFECAAAAgHMiOXYS5cqVu+Y+V1dXVahQoRCjAQAAAFDoWK3arpy6rfr8+fOKi4uTn5+fgoKCNGvWLJv9b7/9tqKjo1WiRAlVqFBBvXr10qlTp6z7k5OTZTKZtGXLFkVHR8vHx0eNGzdWenq6zXnWr1+v6OhoeXl5qWzZsurSpYt1X3Z2tsaMGaNKlSrJ19dXDRs2VHJysnX/mTNn1LNnT911113y8fFRZGSkVq5cmedeLl++rKFDh6pUqVIKCAjQc889J4vl//+H/99t1f/0z7bq48eP67777pMklS5dWiaTSfHx8XrzzTcVEBCgrKwsm2O7du2quLi463/RAAAAAFDMOXVy/Mwzz+jzzz/X2rVrtWnTJiUnJys1NdW6Pzs7W1OnTtXXX3+tdevW6dixY4qPj89znnHjxmnWrFnas2eP3Nzc1K9fP+u+jz76SF26dNEDDzygffv2WRPpq/r27avt27dr1apVOnDggLp166a2bdvq8OHDkqRLly6pQYMG2rBhg7755hs9/vjj6tOnj1JSUmxieOONN+Tm5qaUlBTNnz9fc+bM0WuvvVbg7yQ4OFjvv/++JCk9PV2ZmZmaN2+eunXrptzcXK1fv9469/Tp09qwYYP69u1b4OsAAAAAQHHitG3Vf//9t15//XW9+eabat26taQrCeZdd91lnfPPJDcsLEzz58/XPffco7///lt+fn7WfdOmTVPz5s0lSWPHjtUDDzygS5cuycvLS9OmTdMjjzyiyZMnW+fXrVtXknTkyBGtXLlSP//8sypWrChJGj16tDZu3Khly5Zp+vTpqlSpkkaPHm09dtiwYdq4caPeffddNWzY0DoeHBysOXPmyGQyKTw8XAcPHtScOXM0cODAAn0vrq6uKlOmjCSpfPnyNs8c9+rVS8uWLVO3bt0kSStWrNBdd92lFi1a5HuurKysPJXmbEuuPEyuBYoJAAAAwB1AW7VdOW3l+MiRI8rOzlZMTIx1rEyZMgoPD7d+3rdvnzp16qSQkBCVKFHCmgRmZGTYnKtOnTrWn4OCgiTJ2n69f/9+tWzZMt8Y9u7dK4vFourVq8vPz8+6bd26VUeOHJEk5ebmatq0aapTp44CAgLk5+enTZs25YmhUaNGMplM1s8xMTE6fPiwcnNzC/rVXNPAgQO1adMm/fLLL5KkZcuWKT4+3ua6/5SYmCh/f3+bbeX5tDsWDwAAAAAUFU5bOf7n87j5OX/+vNq0aaM2bdro7bffVrly5ZSRkaHY2FhlZ2fbzHV3d7f+fDVRNP+/d4h5e3tf8xpms1murq5KTU2Vq6ttNfVqZXrWrFmaM2eO5s6dq8jISPn6+mrEiBF5YigM9evXV926dfXmm28qNjZWBw8e1IcffnjN+QkJCRo1apTN2Paq/a4xGwAAAIBdWXjPsT05bXJctWpVubu7a+fOnapcubIk6Y8//tD333+v5s2b67vvvtPp06c1Y8YMBQcHS5L27NlT4OvUqVNHW7Zsyfe53Pr16ys3N1enTp1S06ZN8z3+yy+/VKdOnfToo49KupJQHz58WBERETbzdu7cmedztWrV8iTdN8PDw0OS8q06DxgwQHPmzNEvv/yiVq1aWb+b/Hh6esrT09P23LRUAwAAACiGnLat2s/PT/3799czzzyjLVu26JtvvlF8fLxcXK7cUuXKleXh4aEFCxbo6NGjWr9+vaZOnVrg60ycOFErV67UxIkTlZaWpoMHD+qFF16QJFWvXl29e/dWXFyc1qxZo2PHjmn37t2aOXOmkpKSJF1J4jdv3qwdO3YoLS1NTzzxhE6cOJHnOj/99JNGjRql9PR0rVy5UgsWLNBTTz11S99NSEiITCaTNmzYoN9++01///23dV/v3r31yy+/aMmSJTbPZAMAAACAkTltcixJL774opo1a6aOHTuqVatWuvfee9WgQQNJV94LvHz5cr377ruqWbOmZsyYoZdeeqnA12jRooXeffddrV+/XvXq1dP9999vs9L0smXLFBcXp6efflrh4eHq2LGjUlJSrBXZ8ePHKyoqSrGxsWrRooUqVKigzp0757lOXFycLl68qHvuuUdDhgzRsGHD9Pjjj9/S91KpUiVNnjxZY8eOVWBgoIYOHWrdV7JkSXXt2lV+fn75xgEAAACgiDJbis5WDJksN3p4F8VO69atFRERofnz5xf42C2BPewQUdHysucFR4dQKHrllHR0CIWi88GCd4w4o1kNJjg6BLs7azLGc1YBZqf+vfVN+87lkqNDKBSVLZ43nuTkdlvOOjqEQlHGpfj/WUpSi5xrr7dTnPT95W1Hh3BL/h7V0dEhWPnNXn/jSU7GaZ85RsH9/vvv2rRpkz777DO9/PLLjg4HAAAAAIoMkmMDiYqK0h9//KGZM2favPIKAAAAQNFnKabtzEUFybGBHD9+3NEhAAAAAECRRHIMAAAAAM6AyrFdGWPVDwAAAAAAroPkGAAAAABgeLRVAwAAAIAzMBvjtYaOQuUYAAAAAGB4JMcAAAAAALtauHChqlSpIi8vLzVo0EBffvnlTR23fft2ubm5qV69evYNUCTHAAAAAOAczJaisxXA6tWrNWLECI0bN0779u1T06ZN1a5dO2VkZFz3uLNnzyouLk4tW7a8nW/tppEcAwAAAADsZvbs2erfv78GDBigiIgIzZ07V8HBwVq0aNF1j3viiSfUq1cvxcTEFEqcJMcAAAAAgALJysrSuXPnbLasrKw887Kzs5Wamqo2bdrYjLdp00Y7duy45vmXLVumI0eOaOLEiXc89mshOQYAAAAAZ+DoVup/bImJifL397fZEhMT84R8+vRp5ebmKjAw0GY8MDBQJ06cyPc2Dx8+rLFjx2rFihVycyu8FyzxKicAAAAAQIEkJCRo1KhRNmOenp7XnG8ymWw+WyyWPGOSlJubq169emny5MmqXr36nQn2JpEcAwAAAIATsFgKthCWPXl6el43Gb6qbNmycnV1zVMlPnXqVJ5qsiT99ddf2rNnj/bt26ehQ4dKksxmsywWi9zc3LRp0ybdf//9d+Ym/oW2agAAAACAXXh4eKhBgwbavHmzzfjmzZvVuHHjPPNLliypgwcPav/+/dZt0KBBCg8P1/79+9WwYUO7xUrlGAAAAABgN6NGjVKfPn0UHR2tmJgYvfrqq8rIyNCgQYMkXWnR/uWXX/Tmm2/KxcVFtWvXtjm+fPny8vLyyjN+p5EcAwAAAIAzKOD7hYuKHj166MyZM5oyZYoyMzNVu3ZtJSUlKSQkRJKUmZl5w3ceFwaSYwAAAACAXQ0ePFiDBw/Od9/y5cuve+ykSZM0adKkOx/Uv/DMMQAAAADA8KgcAwAAAIAzcNK2amdB5RgAAAAAYHhUjgEAAADACVioHNsVyTEKxN1kdnQIdtdE/o4OoVCcMci//bMaTHB0CIXi6dQpjg7B7hpGxjk6hELR2jPY0SEUiiY5Xo4OoVBsd7/k6BDsrleOMf67+YVHjqNDKBQ/uBf/v+sB10JbNQAAAADA8AxSOwIAAAAAJ0dbtV1ROQYAAAAAGB7JMQAAAADA8GirBgAAAABnwHppdkXlGAAAAABgeFSOAQAAAMAJ8J5j+6JyDAAAAAAwPJJjAAAAAIDh0VYNAAAAAM6Atmq7onIMAAAAADA8kmMAAAAAgOHRVg0AAAAAzoD3HNsVlWMAAAAAgOGRHAMAAAAADI+2agAAAABwAhZWq7YrKscAAAAAAMOjcgwAAAAAzoAFueyKyjEAAAAAwPBIjgEAAAAAhkdbNQAAAAA4ARbksi8qx3YWHx+vzp07OzoMAAAAAMB1UDm+Q44fP64qVapo3759qlevnnV83rx5slj4DQ8AAAAAFGUkx3bm7+/v6BAAAAAAFAesVm1XTtVWvXHjRt17770qVaqUAgIC1KFDBx05csS6/+eff9YjjzyiMmXKyNfXV9HR0UpJSbHuX7Roke6++255eHgoPDxcb731lnXf8ePHZTKZtH//fuvYn3/+KZPJpOTkZEnSH3/8od69e6tcuXLy9vZWtWrVtGzZMklSlSpVJEn169eXyWRSixYtJOVtqzabzZo5c6aqVq0qT09PVa5cWdOmTbOJYc2aNbrvvvvk4+OjunXr6quvvrL5Hnbs2KFmzZrJ29tbwcHBGj58uM6fP2/dv3DhQlWrVk1eXl4KDAzUww8/bN333nvvKTIyUt7e3goICFCrVq1sjgUAAAAAI3Kq5Pj8+fMaNWqUdu/erS1btsjFxUUPPfSQzGaz/v77bzVv3ly//vqr1q9fr6+//lpjxoyR2Xzl1ytr167VU089paefflrffPONnnjiCfXt21eff/75TV9//PjxOnTokD7++GOlpaVp0aJFKlu2rCRp165dkqRPP/1UmZmZWrNmTb7nSEhI0MyZM63n+t///qfAwECbOePGjdPo0aO1f/9+Va9eXT179tTly5clSQcPHlRsbKy6dOmiAwcOaPXq1dq2bZuGDh0qSdqzZ4+GDx+uKVOmKD09XRs3blSzZs0kSZmZmerZs6f69euntLQ0JScnq0uXLrR9AwAAAE7AYi46W3HkVG3VXbt2tfn8+uuvq3z58jp06JB27Nih3377Tbt371aZMmUkSVWrVrXOfemllxQfH6/BgwdLkkaNGqWdO3fqpZde0n333XdT18/IyFD9+vUVHR0tSQoNDbXuK1eunCQpICBAFSpUyPf4v/76S/PmzdPLL7+sxx57TJJ09913695777WZN3r0aD3wwAOSpMmTJ6tWrVr64YcfVKNGDb344ovq1auXRowYIUmqVq2a5s+fr+bNm2vRokXKyMiQr6+vOnTooBIlSigkJET169eXdCU5vnz5srp06aKQkBBJUmRk5E3dOwAAAAAUZ05VOT5y5Ih69eqlsLAwlSxZ0trKnJGRof3796t+/frWxPjf0tLS1KRJE5uxJk2aKC0t7aav/+STT2rVqlWqV6+exowZox07dhQo/rS0NGVlZally5bXnVenTh3rz0FBQZKkU6dOSZJSU1O1fPly+fn5WbfY2FiZzWYdO3ZMrVu3VkhIiMLCwtSnTx+tWLFCFy5ckCTVrVtXLVu2VGRkpLp166YlS5bojz/+uGYcWVlZOnfunM2Wbckt0D0DAAAAgDNwquT4wQcf1JkzZ7RkyRKlpKRYnyfOzs6Wt7f3DY83mUw2ny0Wi3XMxcXFOnZVTk6Ozfx27drpxx9/1IgRI/Trr7+qZcuWGj169E3HfzMxSpK7u3uemK+2h5vNZj3xxBPav3+/dfv66691+PBh3X333SpRooT27t2rlStXKigoSBMmTFDdunX1559/ytXVVZs3b9bHH3+smjVrasGCBQoPD9exY8fyjSMxMVH+/v4224rz3930/QIAAAC4g8xFaCuGnCY5PnPmjNLS0vTcc8+pZcuWioiIsKl61qlTR/v379fvv/+e7/ERERHatm2bzdiOHTsUEREh6f9vi87MzLTu/+fiXFeVK1dO8fHxevvttzV37ly9+uqrkiQPDw9JUm7utSur1apVk7e3t7Zs2XITd5y/qKgoffvtt6patWqe7WoMbm5uatWqlV544QUdOHBAx48f12effSbpSrLdpEkTTZ48Wfv27ZOHh4fWrl2b77USEhJ09uxZm623b41bjh0AAAAAiiqneea4dOnSCggI0KuvvqqgoCBlZGRo7Nix1v09e/bU9OnT1blzZyUmJiooKEj79u1TxYoVFRMTo2eeeUbdu3dXVFSUWrZsqQ8//FBr1qzRp59+KulKVbdRo0aaMWOGQkNDdfr0aT333HM2MUyYMEENGjRQrVq1lJWVpQ0bNliT6/Lly8vb21sbN27UXXfdJS8vrzyvcfLy8tKzzz6rMWPGyMPDQ02aNNFvv/2mb7/9Vv3797+p7+HZZ59Vo0aNNGTIEA0cOFC+vr5KS0vT5s2btWDBAm3YsEFHjx5Vs2bNVLp0aSUlJclsNis8PFwpKSnasmWL2rRpo/LlyyslJUW//fab9R7+zdPTU56enjZjHibXm4oTAAAAAJyJ01SOXVxctGrVKqWmpqp27doaOXKkXnzxRet+Dw8Pbdq0SeXLl1f79u0VGRmpGTNmyNX1SjLXuXNnzZs3Ty+++KJq1aqlxYsXa9myZdZXLknS0qVLlZOTo+joaD311FN6/vnnbWLw8PBQQkKC6tSpo2bNmsnV1VWrVq2SdKVaO3/+fC1evFgVK1ZUp06d8r2P8ePH6+mnn9aECRMUERGhHj16WJ8nvhl16tTR1q1bdfjwYTVt2lT169fX+PHjrc8mlypVSmvWrNH999+viIgIvfLKK1q5cqVq1aqlkiVL6osvvlD79u1VvXp1Pffcc5o1a5batWt309cHAAAA4BiOXqG6uK9WbbLwHh8UwBcVujk6BLvb5eF540nFgJ9B/s3/02l+BXh7nk6d4ugQ7K5hZJyjQygUrT2DHR1CoaiRY4xOpO3ulxwdgt21zDbGfze/8Mi58aRioIzzNJbelmnH/+foEG7J6XbNHR2CVdmPtzo6hDvOIH9tBAAAAADg2ozxqyEAAAAAcHbFtJ25qKByDAAAAAAwPCrHAAAAAOAEiutCWEUFlWMAAAAAgOGRHAMAAAAADI+2agAAAABwArRV2xeVYwAAAACA4ZEcAwAAAAAMj7ZqAAAAAHACtFXbF5VjAAAAAIDhUTkGAAAAAGdgMTk6gmKNyjEAAAAAwPBIjgEAAAAAhkdbNQAAAAA4ARbksi8qxwAAAAAAwyM5BgAAAAAYHm3VAAAAAOAELGZWq7YnKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBFit2r6oHAMAAAAADI/KMQAAAAA4AYuFBbnsicoxAAAAAMDwqByjQJ52OenoEOzuTU8/R4dQKEZcsjg6hEIRZSrt6BAKRcPIOEeHYHcpB990dAiFYk3keEeHUCh+dXd0BIXjvx896egQ7G5e+2WODqFQtL/k6ugQCsUBTyqTMC6SYwAAAABwAizIZV+0VQMAAAAADI/kGAAAAABgeLRVAwAAAIATsJh5JtyeqBwDAAAAAAyPyjEAAAAAOAGLMV424jBUjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJsCCXfVE5BgAAAAAYHskxAAAAAMDwaKsGAAAAACdAW7V9UTkGAAAAABgelWMAAAAAcAK859i+qBwDAAAAAAyP5BgAAAAAYHi0VQMAAACAE2BBLvuicgwAAAAAMDySYwAAAACA4dFWDQAAAABOwGKhrdqeqBwDAAAAAAyP5BgAAAAAYHi0VQMAAACAE7CYHR1B8Ubl2Mnl5OQ4OgQAAAAAcHokx3eQxWLRCy+8oLCwMHl7e6tu3bp67733JEnJyckymUzasmWLoqOj5ePjo8aNGys9Pd3mHB9++KEaNGggLy8vhYWFafLkybp8+bJ1v8lk0iuvvKJOnTrJ19dXzz//vCTp+eefV/ny5VWiRAkNGDBAY8eOVb169SRJX3zxhdzd3XXixAmbaz399NNq1qyZHb8RAAAAAHeK2WIqMltxRHJ8Bz333HNatmyZFi1apG+//VYjR47Uo48+qq1bt1rnjBs3TrNmzdKePXvk5uamfv36Wfd98sknevTRRzV8+HAdOnRIixcv1vLlyzVt2jSb60ycOFGdOnXSwYMH1a9fP61YsULTpk3TzJkzlZqaqsqVK2vRokXW+c2aNVNYWJjeeust69jly5f19ttvq2/fvnb8RgAAAADAOZAc3yHnz5/X7NmztXTpUsXGxiosLEzx8fF69NFHtXjxYuu8adOmqXnz5qpZs6bGjh2rHTt26NKlS9Z9Y8eO1WOPPaawsDC1bt1aU6dOtTleknr16qV+/fopLCxMISEhWrBggfr376++ffuqevXqmjBhgiIjI22O6d+/v5YtW2b9/NFHH+nChQvq3r27Hb8VAAAAAHAOJMd3yKFDh3Tp0iW1bt1afn5+1u3NN9/UkSNHrPPq1Klj/TkoKEiSdOrUKUlSamqqpkyZYnP8wIEDlZmZqQsXLliPi46Otrl2enq67rnnHpuxf3+Oj4/XDz/8oJ07d0qSli5dqu7du8vX1/ea95SVlaVz587ZbGZWAQAAAAAcwmIxFZmtOGK16jvEbL6SNH700UeqVKmSzT5PT09rguzu7m4dN5lMNseazWZNnjxZXbp0yXN+Ly8v68/5JbRXz3WVxWKx+Vy+fHk9+OCDWrZsmcLCwpSUlKTk5OTr3lNiYqImT55sMxbkV1mVSoRc9zgAAAAAcDYkx3dIzZo15enpqYyMDDVv3jzP/n9Wj68lKipK6enpqlq1aoGuHR4erl27dqlPnz7WsT179uSZN2DAAD3yyCO66667dPfdd6tJkybXPW9CQoJGjRplM3ZfePsCxQYAAAAAzoDk+A4pUaKERo8erZEjR8psNuvee+/VuXPntGPHDvn5+Skk5MbV1gkTJqhDhw4KDg5Wt27d5OLiogMHDujgwYPWVanzM2zYMA0cOFDR0dFq3LixVq9erQMHDigsLMxmXmxsrPz9/fX8889rypQpN4zH09NTnp6eNmMuJjrxAQAAAEewmItnO3NRQaZzB02dOlUTJkxQYmKiIiIiFBsbqw8//FBVqlS5qeNjY2O1YcMGbd68Wf/5z3/UqFEjzZ49+4aJde/evZWQkKDRo0crKipKx44dU3x8vE0rtiS5uLgoPj5eubm5iouLu+X7BAAAAIDihsrxHWQymTR8+HANHz483/3/fg64Xr16ecZiY2MVGxt7zWv8e/5V48eP1/jx462fW7dunW97dmZmptq3b29dDAwAAACAc7hGKoA7hOS4GLhw4YJeeeUVxcbGytXVVStXrtSnn36qzZs3W+ecPXtWu3fv1ooVK/TBBx84MFoAAAAAKHpIjosBk8mkpKQkPf/888rKylJ4eLjef/99tWrVyjqnU6dO2rVrl5544gm1bt3agdECAAAAQNFDclwMeHt769NPP73unBu9tgkAAABA0caCXPbFglwAAAAAALtauHChqlSpIi8vLzVo0EBffvnlNeeuWbNGrVu3Vrly5VSyZEnFxMTok08+sXuMJMcAAAAAALtZvXq1RowYoXHjxmnfvn1q2rSp2rVrp4yMjHznf/HFF2rdurWSkpKUmpqq++67Tw8++KD27dtn1zhpqwYAAAAAJ2C2OGdb9ezZs9W/f38NGDBAkjR37lx98sknWrRokRITE/PMnzt3rs3n6dOn64MPPtCHH36o+vXr2y1OKscAAAAAgALJysrSuXPnbLasrKw887Kzs5Wamqo2bdrYjLdp00Y7duy4qWuZzWb99ddfKlOmzB2J/VpIjgEAAAAABZKYmCh/f3+bLb8q8OnTp5Wbm6vAwECb8cDAQJ04ceKmrjVr1iydP39e3bt3vyOxXwtt1QAAAADgBCxFqK06ISFBo0aNshnz9PS85nyTyTZ2i8WSZyw/K1eu1KRJk/TBBx+ofPnytxbsTSI5BgAAAAAUiKen53WT4avKli0rV1fXPFXiU6dO5akm/9vq1avVv39/vfvuu2rVqtVtxXszaKsGAAAAACdgsRSd7WZ5eHioQYMG2rx5s8345s2b1bhx42set3LlSsXHx+t///ufHnjggVv9ygqEyjEAAAAAwG5GjRqlPn36KDo6WjExMXr11VeVkZGhQYMGSbrSov3LL7/ozTfflHQlMY6Li9O8efPUqFEja9XZ29tb/v7+douT5BgAAAAAYDc9evTQmTNnNGXKFGVmZqp27dpKSkpSSEiIJCkzM9PmnceLFy/W5cuXNWTIEA0ZMsQ6/thjj2n58uV2i5PkGAAAAACcgLO+51iSBg8erMGDB+e7798Jb3Jysv0DygfPHAMAAAAADI/kGAAAAABgeLRVAwAAAIATKErvOS6OqBwDAAAAAAyPyjEAAAAAOIGCvF8YBUflGAAAAABgeCTHAAAAAADDo60aAAAAAJyAM7/n2BlQOQYAAAAAGB7JMQAAAADA8GirRoH0d63s6BDsbm5OlqNDKBTtTZ6ODqFQ5JgdHUHhaO0Z7OgQ7G5N5HhHh1Aouhyc6ugQCsU7dSY4OoRCMbv9MkeHYHcXTcZYPvcnd1dHh1AoShrkv5vOivcc2xeVYwAAAACA4VE5BgAAAAAnwIJc9kXlGAAAAABgeCTHAAAAAADDo60aAAAAAJyAMZa/cxwqxwAAAAAAwyM5BgAAAAAYHm3VAAAAAOAEWK3avqgcAwAAAAAMj+QYAAAAAGB4tFUDAAAAgBOw0FZtV1SOAQAAAACGR+UYAAAAAJyA2dEBFHNUjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJWMSCXPZE5RgAAAAAYHgkxwAAAAAAw6OtGgAAAACcgNni6AiKNyrHAAAAAADDo3IMAAAAAE7AzIJcdlWgynGLFi00YsQIO4ViLMePH5fJZNL+/fsdHQoAAAAAGF6BkuM1a9Zo6tSpd+TCJpNJ69atuyPncqTk5GSZTCb9+eefjg4FAAAAAHCLCtRWXaZMGXvFka+cnBy5u7sX6jUBAAAAoCjiPcf2dctt1aGhoZo+fbr69eunEiVKqHLlynr11Vetc7OzszV06FAFBQXJy8tLoaGhSkxMtB4rSQ899JBMJpP186RJk1SvXj0tXbpUYWFh8vT0lMViUWhoqObOnWsTS7169TRp0iTrZ5PJpMWLF6tDhw7y8fFRRESEvvrqK/3www9q0aKFfH19FRMToyNHjtic58MPP1SDBg3k5eWlsLAwTZ48WZcvX7Y572uvvaaHHnpIPj4+qlatmtavXy/pSmv0fffdJ0kqXbq0TCaT4uPjJUkbN27Uvffeq1KlSikgIEAdOnTIc+1/ulqB3rJli6Kjo+Xj46PGjRsrPT29QPFOmjRJlStXlqenpypWrKjhw4db9y1cuFDVqlWTl5eXAgMD9fDDD18zHgAAAAAwkttarXrWrFmKjo7Wvn37NHjwYD355JP67rvvJEnz58/X+vXr9c477yg9PV1vv/22NQnevXu3JGnZsmXKzMy0fpakH374Qe+8847ef//9Aj+PO3XqVMXFxWn//v2qUaOGevXqpSeeeEIJCQnas2ePJGno0KHW+Z988okeffRRDR8+XIcOHdLixYu1fPlyTZs2zea8kydPVvfu3XXgwAG1b99evXv31u+//67g4GC9//77kqT09HRlZmZq3rx5kqTz589r1KhR2r17t7Zs2SIXFxc99NBDMpvN172HcePGadasWdqzZ4/c3NzUr1+/m473vffe05w5c7R48WIdPnxY69atU2RkpCRpz549Gj58uKZMmaL09HRt3LhRzZo1K9D3CwAAAADF1W2tVt2+fXsNHjxYkvTss89qzpw5Sk5OVo0aNZSRkaFq1arp3nvvlclkUkhIiPW4cuXKSZJKlSqlChUq2JwzOztbb731lnVOQfTt21fdu3e3xhMTE6Px48crNjZWkvTUU0+pb9++1vnTpk3T2LFj9dhjj0mSwsLCNHXqVI0ZM0YTJ060zouPj1fPnj0lSdOnT9eCBQu0a9cutW3b1tpqXr58eZUqVcp6TNeuXW1ie/3111W+fHkdOnRItWvXvuY9TJs2Tc2bN5ckjR07Vg888IAuXbokLy+vG8abkZGhChUqqFWrVnJ3d1flypV1zz33SJIyMjLk6+urDh06qESJEgoJCVH9+vUL/B0DAAAAcIzrl9lwu26rclynTh3rzyaTSRUqVNCpU6ckXUko9+/fr/DwcA0fPlybNm26qXOGhITcUmL873gCAwMlyVo5vTp26dIlnTt3TpKUmpqqKVOmyM/Pz7oNHDhQmZmZunDhQr7n9fX1VYkSJaz3eS1HjhxRr169FBYWppIlS6pKlSqSriSpN3sPQUFBkmS91o3i7datmy5evKiwsDANHDhQa9eutbZct27dWiEhIQoLC1OfPn20YsUKm3vMT1ZWls6dO2ez5Vhyr3sMAAAAADij20qO/71YlslksrYNR0VF6dixY5o6daouXryo7t2739Qzrr6+vnmDdHGRxWKxGcvJybluPCaT6ZpjV2M0m82aPHmy9u/fb90OHjyow4cPy8vL66bu81oefPBBnTlzRkuWLFFKSopSUlIkXamMX8/txBscHKz09HT997//lbe3twYPHqxmzZopJydHJUqU0N69e7Vy5UoFBQVpwoQJqlu37nVX2U5MTJS/v7/N9sm5b68bPwAAAAA4o9tqq76RkiVLqkePHurRo4cefvhhtW3bVr///rvKlCkjd3d35ebeXBWyXLlyyszMtH4+d+6cjh07dtvxRUVFKT09XVWrVr3lc3h4eEiSzb2cOXNGaWlpWrx4sZo2bSpJ2rZt2+0Fq5uL19vbWx07dlTHjh01ZMgQ1ahRQwcPHlRUVJTc3NzUqlUrtWrVShMnTlSpUqX02WefqUuXLvmeKyEhQaNGjbIZe6PmE7d9HwAAAAAKjtWq7ctuyfGcOXMUFBSkevXqycXFRe+++64qVKhgfS43NDRUW7ZsUZMmTeTp6anSpUtf81z333+/li9frgcffFClS5fW+PHj5erqetsxTpgwQR06dFBwcLC6desmFxcXHThwQAcPHtTzzz9/U+cICQmRyWTShg0b1L59e3l7e6t06dIKCAjQq6++qqCgIGVkZGjs2LF2j3f58uXKzc1Vw4YN5ePjo7feekve3t4KCQnRhg0bdPToUTVr1kylS5dWUlKSzGazwsPDr3k9T09PeXp62oy5m27/ewcAAACAoua22qqvx8/PTzNnzlR0dLT+85//6Pjx40pKSpKLy5VLzpo1S5s3b1ZwcPANF4ZKSEhQs2bN1KFDB7Vv316dO3fW3XfffdsxxsbGasOGDdq8ebP+85//qFGjRpo9e7bN4mE3UqlSJU2ePFljx45VYGCghg4dKhcXF61atUqpqamqXbu2Ro4cqRdffNHu8ZYqVUpLlixRkyZNVKdOHW3ZskUffvihAgICVKpUKa1Zs0b333+/IiIi9Morr2jlypWqVavWbccFAAAAwP7MRWgrjkyWfz/MC1zHK8GPOjoEu9vnmuXoEApFTbPnjScVA3lXJyiefnMp/ovlRWXZ7fe5RUqXg1MdHUKheKfOBEeHUCgy3G88x9ldNBnjr5IVco3Rzlr8/2tyxbCf3nZ0CLdkY+Ajjg7Bqu3JVY4O4Y4zxt80AAAAAAC4DrsuyAUAAAAAuDOKaztzUUHlGAAAAABgeCTHAAAAAADDo60aAAAAAJwA7zm2LyrHAAAAAADDo3IMAAAAAE7ATOHYrqgcAwAAAAAMj+QYAAAAAGB4tFUDAAAAgBMwsyCXXVE5BgAAAAAYHskxAAAAAMDwaKsGAAAAACdgcXQAxRyVYwAAAACA4ZEcAwAAAAAMj7ZqAAAAAHACZkcHUMxROQYAAAAAGB6VYwAAAABwAmYT7zm2JyrHAAAAAADDIzkGAAAAABgebdUAAAAA4AR4z7F9UTkGAAAAABgeyTEAAAAAwPBoqwYAAAAAJ8B7ju2L5BgF8qtr8X/SITLX09EhFIpcg7wJ4DuXS44OoVA0yfFydAh296u7oyMoHO/UmeDoEApF9wNTHB1CoRgXPc7RIdhd3WxXR4dQKI67F/+/A0lSBaP8BQHIB8kxAAAAADgBM7+7sCueOQYAAAAAGB7JMQAAAADA8GirBgAAAAAnYBZ91fZE5RgAAAAAYHgkxwAAAAAAw6OtGgAAAACcgDFeKOY4VI4BAAAAAIZH5RgAAAAAnADvObYvKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBMyODqCYo3IMAAAAADA8kmMAAAAAgOHRVg0AAAAAToD3HNsXlWMAAAAAgOGRHAMAAAAADI+2agAAAABwAmaToyMo3qgcAwAAAAAMj8oxAAAAADgB3nNsX1SOAQAAAACGR3JcAC1atNCIESMkSaGhoZo7d65D4wEAAAAA3Bm0Vd+i3bt3y9fX19FhAAAAADAI2qrti+T4FpUrV87RIQAAAAAA7hDaqm/RP9uqe/bsqUceecRmf05OjsqWLatly5ZJkiwWi1544QWFhYXJ29tbdevW1XvvvWedn5ycLJPJpC1btig6Olo+Pj5q3Lix0tPTbc774YcfqkGDBvLy8lJYWJgmT56sy5cvW/dPmjRJlStXlqenpypWrKjhw4db9y1cuFDVqlWTl5eXAgMD9fDDD9/prwUAAAAAnBKV4zugd+/e6t69u/7++2/5+flJkj755BOdP39eXbt2lSQ999xzWrNmjRYtWqRq1arpiy++0KOPPqpy5cqpefPm1nONGzdOs2bNUrly5TRo0CD169dP27dvt57z0Ucf1fz589W0aVMdOXJEjz/+uCRp4sSJeu+99zRnzhytWrVKtWrV0okTJ/T1119Lkvbs2aPhw4frrbfeUuPGjfX777/ryy+/LMyvCQAAAMBtsPCeY7siOb4DYmNj5evrq7Vr16pPnz6SpP/973968MEHVbJkSZ0/f16zZ8/WZ599ppiYGElSWFiYtm3bpsWLF9skx9OmTbN+Hjt2rB544AFdunRJXl5emjZtmsaOHavHHnvMeo6pU6dqzJgxmjhxojIyMlShQgW1atVK7u7uqly5su655x5JUkZGhnx9fdWhQweVKFFCISEhql+//nXvKysrS1lZWTZjly25cjO53pkvDgAAAACKCNqq7wB3d3d169ZNK1askCSdP39eH3zwgXr37i1JOnTokC5duqTWrVvLz8/Pur355ps6cuSIzbnq1Klj/TkoKEiSdOrUKUlSamqqpkyZYnOOgQMHKjMzUxcuXFC3bt108eJFhYWFaeDAgVq7dq215bp169YKCQlRWFiY+vTpoxUrVujChQvXva/ExET5+/vbbNvPfntnvjQAAAAABWIuQltxROX4Dundu7eaN2+uU6dOafPmzfLy8lK7du0kSWbzlX98PvroI1WqVMnmOE9PT5vP7u7u1p9NJpPN8WazWZMnT1aXLl3yXN/Ly0vBwcFKT0/X5s2b9emnn2rw4MF68cUXtXXrVpUoUUJ79+5VcnKyNm3apAkTJmjSpEnavXu3SpUqle89JSQkaNSoUTZjMyIfL8C3AgAAAADOgeT4DmncuLGCg4O1evVqffzxx+rWrZs8PDwkSTVr1pSnp6cyMjJsWqgLKioqSunp6apateo153h7e6tjx47q2LGjhgwZoho1aujgwYOKioqSm5ubWrVqpVatWmnixIkqVaqUPvvss3yTbelK4v7v5J2WagAAAADFEcnxHWIymdSrVy+98sor+v777/X5559b95UoUUKjR4/WyJEjZTabde+99+rcuXPasWOH/Pz8rM8Q38iECRPUoUMHBQcHq1u3bnJxcdGBAwd08OBBPf/881q+fLlyc3PVsGFD+fj46K233pK3t7dCQkK0YcMGHT16VM2aNVPp0qWVlJQks9ms8PBwe30lAAAAAO4gZ25nXrhwoV588UVlZmaqVq1amjt3rpo2bXrN+Vu3btWoUaP07bffqmLFihozZowGDRpk1xh55vgO6t27tw4dOqRKlSqpSZMmNvumTp2qCRMmKDExUREREYqNjdWHH36oKlWq3PT5Y2NjtWHDBm3evFn/+c9/1KhRI82ePVshISGSpFKlSmnJkiVq0qSJ6tSpoy1btujDDz9UQECASpUqpTVr1uj+++9XRESEXnnlFa1cuVK1atW6o98BAAAAAPzT6tWrNWLECI0bN0779u1T06ZN1a5dO2VkZOQ7/9ixY2rfvr2aNm2qffv26f/+7/80fPhwvf/++3aN02SxWCx2vQKKlQmhvR0dgt2VzzXGGvkGuU0ddLnk6BAKRZMcL0eHYHd/GuSpjvKXbzynOOh+YIqjQygU46LHOToEu6ubbYx/OY+7G+OvzBUM8heE/j+/7egQbsnLwY86OgSroT/d/HfYsGFDRUVFadGiRdaxiIgIde7cWYmJiXnmP/vss1q/fr3S0tKsY4MGDdLXX3+tr7766vYCvw4qxwAAAADgBCxFaMvKytK5c+dstn+/BlaSsrOzlZqaqjZt2tiMt2nTRjt27Mj3Pr/66qs882NjY7Vnzx7l5OTc5LdVcCTHAAAAAIACye+1r/lVgU+fPq3c3FwFBgbajAcGBurEiRP5nvvEiRP5zr98+bJOnz59527iX1iQCwAAAABQIPm99vXfb7r5p6uvqb3KYrHkGbvR/PzG7ySSYwAAAABwAuYi9Eh4fq99zU/ZsmXl6uqap0p86tSpPNXhqypUqJDvfDc3NwUEBNx60DdAWzUAAAAAwC48PDzUoEEDbd682WZ88+bNaty4cb7HxMTE5Jm/adMmRUdHy93d3W6xkhwDAAAAgBMwF6GtIEaNGqXXXntNS5cuVVpamkaOHKmMjAzre4sTEhIUFxdnnT9o0CD9+OOPGjVqlNLS0rR06VK9/vrrGj16dAGvXDC0VQMAAAAA7KZHjx46c+aMpkyZoszMTNWuXVtJSUkKCQmRJGVmZtq887hKlSpKSkrSyJEj9d///lcVK1bU/Pnz1bVrV7vGSXIMAAAAALCrwYMHa/DgwfnuW758eZ6x5s2ba+/evXaOyhbJMQAAAAA4gYK2M6NgeOYYAAAAAGB4JMcAAAAAAMOjrRoAAAAAnIDF0QEUc1SOAQAAAACGR+UYAAAAAJyA2eToCIo3KscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBHjPsX1ROQYAAAAAGB7JMQAAAADA8GirBgAAAAAnwHuO7YvKMQAAAADA8KgcAwAAAIATMFM7tisqxwAAAAAAw6NyjAI5qWxHh2B3sTnG+I3cai9XR4dQKCpbPB0dQqHY7n7J0SHY3X8/etLRIRSK2e2XOTqEQjEuepyjQygU0/ZMc3QIdjejwXhHh1Ao6mUZ4yU6P3gY4+8HQH5IjgEAAADACRjjVzSOQ1s1AAAAAMDwSI4BAAAAAIZHWzUAAAAAOAFjrIzjOFSOAQAAAACGR3IMAAAAADA82qoBAAAAwAmwWrV9UTkGAAAAABgelWMAAAAAcAJmk6MjKN6oHAMAAAAADI/kGAAAAABgeLRVAwAAAIATMPOmY7uicgwAAAAAMDySYwAAAACA4dFWDQAAAABOgKZq+6JyDAAAAAAwPCrHAAAAAOAEzI4OoJijcgwAAAAAMDySYwAAAACA4dFWDQAAAABOgPcc2xeVYwAAAACA4ZEcX0OLFi00YsQIu14jPj5enTt3tus1AAAAAAA3Rls1AAAAADgBmqrti8oxAAAAAMDwSI5vwh9//KG4uDiVLl1aPj4+ateunQ4fPmzdP2nSJNWrV8/mmLlz5yo0NNT6OTc3V6NGjVKpUqUUEBCgMWPGyGKx/d1PixYtNHz4cI0ZM0ZlypRRhQoVNGnSJJs5Z8+e1eOPP67y5curZMmSuv/++/X1119b93/99de67777VKJECZUsWVINGjTQnj17JEk//vijHnzwQZUuXVq+vr6qVauWkpKS7syXBAAAAABOjOT4JsTHx2vPnj1av369vvrqK1ksFrVv3145OTk3fY5Zs2Zp6dKlev3117Vt2zb9/vvvWrt2bZ55b7zxhnx9fZWSkqIXXnhBU6ZM0ebNmyVJFotFDzzwgE6cOKGkpCSlpqYqKipKLVu21O+//y5J6t27t+666y7t3r1bqampGjt2rNzd3SVJQ4YMUVZWlr744gsdPHhQM2fOlJ+f3x34hgAAAADYm7kIbcURzxzfwOHDh7V+/Xpt375djRs3liStWLFCwcHBWrdunbp163ZT55k7d64SEhLUtWtXSdIrr7yiTz75JM+8OnXqaOLEiZKkatWq6eWXX9aWLVvUunVrff755zp48KBOnTolT09PSdJLL72kdevW6b333tPjjz+ujIwMPfPMM6pRo4b1HFdlZGSoa9euioyMlCSFhYXd4rcCAAAAAMULyfENpKWlyc3NTQ0bNrSOBQQEKDw8XGlpaTd1jrNnzyozM1MxMTHWMTc3N0VHR+dpra5Tp47N56CgIJ06dUqSlJqaqr///lsBAQE2cy5evKgjR45IkkaNGqUBAwborbfeUqtWrdStWzfdfffdkqThw4frySef1KZNm9SqVSt17do1z/X+KSsrS1lZWTZjuZZcuZpcb+q+AQAAANw5vOfYvmirvoF/J6//HDeZTJIkFxeXPPMK0nL9T1dboK8ymUwym680LpjNZgUFBWn//v02W3p6up555hlJV55//vbbb/XAAw/os88+U82aNa3t2wMGDNDRo0fVp08fHTx4UNHR0VqwYME1Y0lMTJS/v7/Ntu/sd7d0XwAAAABQlJEc30DNmjV1+fJlpaSkWMfOnDmj77//XhEREZKkcuXK6cSJEzYJ8v79+60/+/v7KygoSDt37rSOXb58WampqQWKJSoqSidOnJCbm5uqVq1qs5UtW9Y6r3r16ho5cqQ2bdqkLl26aNmyZdZ9wcHBGjRokNasWaOnn35aS5Ysueb1EhISdPbsWZutvn+NAsUMAAAAAM6A5PgGqlWrpk6dOmngwIHatm2bvv76az366KOqVKmSOnXqJOnKKtO//fabXnjhBR05ckT//e9/9fHHH9uc56mnntKMGTO0du1afffddxo8eLD+/PPPAsXSqlUrxcTEqHPnzvrkk090/Phx7dixQ88995z27NmjixcvaujQoUpOTtaPP/6o7du3a/fu3dYkfsSIEfrkk0907Ngx7d27V5999pl1X348PT1VsmRJm42WagAAAMAxLEVoK45Ijm/CsmXL1KBBA3Xo0EExMTGyWCxKSkqytkBHRERo4cKF+u9//6u6detq165dGj16tM05nn76acXFxSk+Pl4xMTEqUaKEHnrooQLFYTKZlJSUpGbNmqlfv36qXr26HnnkER0/flyBgYFydXXVmTNnFBcXp+rVq6t79+5q166dJk+eLOnK66SGDBmiiIgItW3bVuHh4Vq4cOGd+ZIAAAAAwImZLNd6qBbIxxOhN7c6tzOLu2SMfyVWexmjC6CsxRjrDmaYsm48ycn996MnHR1CoZjdftmNJxUDf5hyHR1CoZi2Z5qjQ7C7GQ3GOzqEQlEvq7i+vMbWDx7G+PvByIy3HR3CLRkZ+oijQ7Cac3yVo0O444zxt0YAAAAAcHLG+BWN49BWDQAAAAAwPCrHAAAAAOAELMV2KayigcoxAAAAAMDwSI4BAAAAAIZHWzUAAAAAOAEW5LIvKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBMysVm1XVI4BAAAAAIZH5RgAAAAAnAB1Y/uicgwAAAAAMDySYwAAAACA4dFWDQAAAABOgAW57IvKMQAAAADA8EiOAQAAAACGR1s1AAAAADgBs6MDKOaoHAMAAAAADI/kGAAAAABgeLRVAwAAAIATsLBatV1ROQYAAAAAGB6VYwAAAABwAizIZV9UjgEAAAAAhkdyDAAAAAAwPNqqUSB3WTwcHUIhyHJ0AIWipFwdHUKh2G056+gQCkWvHH9Hh2B389ovc3QIheKiyRiLrdTNNsb/B81oMN7RIdjd2NSpjg6hUKyoO8HRIRQKP/p2izQW5LIvKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBOh6ty8qxwAAAAAAw6NyDAAAAABOwGxhQS57onIMAAAAADA8kmMAAAAAgOHRVg0AAAAAToCmavuicgwAAAAAMDySYwAAAACA4dFWDQAAAABOwExjtV1ROQYAAAAAGB7JMQAAAADA8GirBgAAAAAnYKGt2q6oHAMAAAAADI/KMQAAAAA4AbOjAyjmqBwDAAAAAAyP5BgAAAAAYHi0VQMAAACAE+A9x/ZF5RgAAAAAYHgkxwAAAAAAwyM5tpPjx4/LZDJp//7915yzfPlylSpVqtBiAgAAAOC8LEXof8URybED9ejRQ99//72jwwAAAAAAw2NBLgfy9vaWt7e3o8MAAAAA4AR4z7F9OXXl+L333lNkZKS8vb0VEBCgVq1a6fz584qPj1fnzp01efJklS9fXiVLltQTTzyh7Oxs67EWi0UvvPCCwsLC5O3trbp16+q9996zOf+hQ4fUvn17+fn5KTAwUH369NHp06et+81ms2bOnKmqVavK09NTlStX1rRp02zOcfToUd13333y8fFR3bp19dVXX1n3/butetKkSapXr57eeusthYaGyt/fX4888oj++uuvm477jz/+UO/evVWuXDl5e3urWrVqWrZsmSQpOztbQ4cOVVBQkLy8vBQaGqrExMTb+0MAAAAAgGLAaZPjzMxM9ezZU/369VNaWpqSk5PVpUsXWSxX+t+3bNmitLQ0ff7551q5cqXWrl2ryZMnW49/7rnntGzZMi1atEjffvutRo4cqUcffVRbt261nr958+aqV6+e9uzZo40bN+rkyZPq3r279RwJCQmaOXOmxo8fr0OHDul///ufAgMDbeIcN26cRo8erf3796t69erq2bOnLl++fM37OnLkiNatW6cNGzZow4YN2rp1q2bMmHHTcV+N5eOPP1ZaWpoWLVqksmXLSpLmz5+v9evX65133lF6errefvtthYaG3t4fBAAAAADcIX/88Yf69Okjf39/+fv7q0+fPvrzzz+vOT8nJ0fPPvusIiMj5evrq4oVKyouLk6//vprga/ttG3VmZmZunz5srp06aKQkBBJUmRkpHW/h4eHli5dKh8fH9WqVUtTpkzRM888o6lTp+rixYuaPXu2PvvsM8XExEiSwsLCtG3bNi1evFjNmzfXokWLFBUVpenTp1vPuXTpUgUHB+v7779XUFCQ5s2bp5dfflmPPfaYJOnuu+/WvffeaxPn6NGj9cADD0iSJk+erFq1aumHH35QjRo18r0vs9ms5cuXq0SJEpKkPn36aMuWLZo2bZrOnz9/w7gzMjJUv359RUdHS5JN8puRkaFq1arp3nvvlclksn5vAAAAAIq+q4XA4qxXr176+eeftXHjRknS448/rj59+ujDDz/Md/6FCxe0d+9ejR8/XnXr1tUff/yhESNGqGPHjtqzZ0+Bru20yXHdunXVsmVLRUZGKjY2Vm3atNHDDz+s0qVLW/f7+PhY58fExOjvv//WTz/9pFOnTunSpUtq3bq1zTmzs7NVv359SVJqaqo+//xz+fn55bn2kSNH9OeffyorK0stW7a8bpx16tSx/hwUFCRJOnXq1DWT49DQUGtifPWYU6dOSbrS5n2juJ988kl17dpVe/fuVZs2bdS5c2c1btxYkhQfH6/WrVsrPDxcbdu2VYcOHdSmTZtrxp6VlaWsrCybscuWXLmZXK97zwAAAABQUGlpadq4caN27typhg0bSpKWLFmimJgYpaenKzw8PM8x/v7+2rx5s83YggULdM899ygjI0OVK1e+6es7bXLs6uqqzZs3a8eOHdq0aZMWLFigcePGKSUl5brHmUwmmc1XHmX/6KOPVKlSJZv9np6ekq5UcB988EHNnDkzzzmCgoJ09OjRm4rT3d3d5tpXz30z8/8d783E3a5dO/3444/66KOP9Omnn6ply5YaMmSIXnrpJUVFRenYsWP6+OOP9emnn6p79+5q1apVnmetr0pMTLRpRZekFiVr6/5SdfKdDwAAAMAY8iukeXp6WvOSW/HVV1/J39/fmhhLUqNGjeTv768dO3bkmxzn5+zZszKZTAV+ba7TPnMsXUkcmzRposmTJ2vfvn3y8PDQ2rVrJUlff/21Ll68aJ27c+dO+fn56a677lLNmjXl6empjIwMVa1a1WYLDg6WJEVFRenbb79VaGhonjm+vr6qVq2avL29tWXLlkK735uJW5LKlSun+Ph4vf3225o7d65effVV676SJUuqR48eWrJkiVavXq33339fv//+e77XS0hI0NmzZ222Zv617H6fAAAAAPIyy1JktsTEROtzwVe3213s98SJEypfvnye8fLly+vEiRM3dY5Lly5p7Nix6tWrl0qWLFmg6ztt5TglJUVbtmxRmzZtVL58eaWkpOi3335TRESEDhw4oOzsbPXv31/PPfecfvzxR02cOFFDhw6Vi4uLSpQoodGjR2vkyJEym8269957de7cOe3YsUN+fn567LHHNGTIEC1ZskQ9e/bUM888o7Jly+qHH37QqlWrtGTJEnl5eenZZ5/VmDFj5OHhoSZNmui3337Tt99+q/79+9vlnm8m7gkTJqhBgwaqVauWsrKytGHDBkVEREiS5syZo6CgINWrV08uLi569913VaFChWv+RiW/3/zQUg0AAAAgISFBo0aNshm7VtV40qRJeTpS/2337t2S/v9u23+yWCz5jv9bTk6OHnnkEZnNZi1cuPCG8//NaZPjkiVL6osvvtDcuXN17tw5hYSEaNasWWrXrp1Wr16tli1bqlq1amrWrJmysrL0yCOPaNKkSdbjp06dqvLlyysxMVFHjx5VqVKlFBUVpf/7v/+TJFWsWFHbt2/Xs88+q9jYWGVlZSkkJERt27aVi8uVgvv48ePl5uamCRMm6Ndff1VQUJAGDRpk1/u+UdweHh5KSEjQ8ePH5e3traZNm2rVqlWSJD8/P82cOVOHDx+Wq6ur/vOf/ygpKcl6PwAAAABwMwrSQj106FA98sgj150TGhqqAwcO6OTJk3n2/fbbb3neCvRvOTk56t69u44dO6bPPvuswFVjSTJZiuGSZ/Hx8frzzz+1bt06R4dS7EwN6e3oEOzu/n89O1Fcfezl4egQCsXX5nOODqFQ9Lrs7+gQ7O4n9xv/xrg4+NtU7P6znK/wbGP8eR51L/5/nmNTpzo6hEKxou4ER4dQKHKM8a+mBv78tqNDuCUPVu7g6BCsPszYcMfPmZaWppo1ayolJUX33HOPpCsdw40aNdJ33313zWeOrybGhw8f1ueff65y5crd0vUpGQIAAAAAHC4iIkJt27bVwIEDtXPnTu3cuVMDBw5Uhw4dbBLjGjVqWNeaunz5sh5++GHt2bNHK1asUG5urk6cOKETJ04oOzu7QNd32rZqAAAAADASi4p/N8qKFSs0fPhw6ytnO3bsqJdfftlmTnp6us6ePStJ+vnnn7V+/XpJUr169Wzmff7552rRosVNX7tYJsfLly93dAgAAAAAgAIqU6aM3n77+m3v/3wyODQ0VHfqSWHaqgEAAAAAhlcsK8cAAAAAUNyYDdBW7UhUjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJ3KmFp5A/KscAAAAAAMOjcgwAAAAATsDs6ACKOSrHAAAAAADDIzkGAAAAABgebdUAAAAA4AQsvOfYrqgcAwAAAAAMj+QYAAAAAGB4tFUDAAAAgBMw01ZtV1SOAQAAAACGR+UYAAAAAJyAxULl2J6oHAMAAAAADI/kGAAAAABgeLRVAwAAAIATYEEu+6JyDAAAAAAwPJJjAAAAAIDh0VYNAAAAAE7AQlu1XZEco0DK55ocHYLdfeXh5egQCkXwZUdHUDh+cvN0dAiF4guPHEeHYHftL7k6OoRC8ZO7Me7zuLsx/oJXL8vs6BDsbkXdCY4OoVD0/nqKo0MoFGsixzs6BMBhaKsGAAAAABgelWMAAAAAcAJmizG6bhyFyjEAAAAAwPCoHAMAAACAE6BubF9UjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJmGmstisqxwAAAAAAwyM5BgAAAAAYHm3VAAAAAOAEaKu2LyrHAAAAAADDo3IMAAAAAE7AYqFybE9UjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJsCCXfVE5BgAAAAAYHskxAAAAAMDwaKsGAAAAACdgoa3arqgcAwAAAAAMj+QYAAAAAGB4tFUDAAAAgBOwWGirticqxwAAAAAAwyM5tqMWLVpoxIgRjg4DAAAAQDFglqXIbMURyTEAAAAAwPBIju0kPj5eW7du1bx582QymWQymXT8+HEdOnRI7du3l5+fnwIDA9WnTx+dPn3aelyLFi00bNgwjRgxQqVLl1ZgYKBeffVVnT9/Xn379lWJEiV099136+OPP7Yek5ycLJPJpI8++kh169aVl5eXGjZsqIMHD9rE9P7776tWrVry9PRUaGioZs2aVWjfBwAAAAAUZSTHdjJv3jzFxMRo4MCByszMVGZmptzd3dW8eXPVq1dPe/bs0caNG3Xy5El1797d5tg33nhDZcuW1a5duzRs2DA9+eST6tatmxo3bqy9e/cqNjZWffr00YULF2yOe+aZZ/TSSy9p9+7dKl++vDp27KicnBxJUmpqqrp3765HHnlEBw8e1KRJkzR+/HgtX768sL4SAAAAALfBYrEUma04Ijm2E39/f3l4eMjHx0cVKlRQhQoVtHjxYkVFRWn69OmqUaOG6tevr6VLl+rzzz/X999/bz22bt26eu6551StWjUlJCTI29tbZcuW1cCBA1WtWjVNmDBBZ86c0YEDB2yuOXHiRLVu3VqRkZF64403dPLkSa1du1aSNHv2bLVs2VLjx49X9erVFR8fr6FDh+rFF18s1O8FAAAAAIoikuNClJqaqs8//1x+fn7WrUaNGpKkI0eOWOfVqVPH+rOrq6sCAgIUGRlpHQsMDJQknTp1yub8MTEx1p/LlCmj8PBwpaWlSZLS0tLUpEkTm/lNmjTR4cOHlZubm2+8WVlZOnfunM2WY8l/LgAAAAA4M95zXIjMZrMefPBBzZw5M8++oKAg68/u7u42+0wmk82YyWSynu9Grs61WCzWn6+6UTtEYmKiJk+ebDP2QIlIPViyzjWOAAAAAGAvxXWV6KKCyrEdeXh42FRlo6Ki9O233yo0NFRVq1a12Xx9fW/7ejt37rT+/Mcff+j777+3VqZr1qypbdu22czfsWOHqlevLldX13zPl5CQoLNnz9psbUvUuu04AQAAAKCoITm2o9DQUKWkpOj48eM6ffq0hgwZot9//109e/bUrl27dPToUW3atEn9+vW7ZmtzQUyZMkVbtmzRN998o/j4eJUtW1adO3eWJD399NPasmWLpk6dqu+//15vvPGGXn75ZY0ePfqa5/P09FTJkiVtNndT/ok0AAAAAPuyFKH/FUckx3Y0evRoubq6qmbNmipXrpyys7O1fft25ebmKjY2VrVr19ZTTz0lf39/ubjc/h/FjBkz9NRTT6lBgwbKzMzU+vXr5eHhIelK1fqdd97RqlWrVLt2bU2YMEFTpkxRfHz8bV8XAAAAAJwdzxzbUfXq1fXVV1/lGV+zZs01j0lOTs4zdvz48Txj+T0vfO+99+qbb7655rm7du2qrl27XnM/AAAAABgVyTEAAAAAOAFzMX2/cFFBWzUAAAAAwPCoHBcDLVq0uOFrmQAAAAAA10ZyDAAAAABOoLiuEl1U0FYNAAAAADA8KscAAAAA4ARYkMu+qBwDAAAAAAyP5BgAAAAAYHi0VQMAAACAE2BBLvuicgwAAAAAMDySYwAAAACA4dFWDQAAAABOgNWq7YvKMQAAAADA8EiOAQAAAACGR1s1AAAAADgBVqu2LyrHAAAAAADDo3IMAAAAAE6ABbnsi8oxAAAAAMDwSI4BAAAAAIZHWzUAAAAAOAEW5LIvKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBCwWs6NDKNaoHAMAAAAADI/KMQrkF7fivwhAlRyTo0MoFBGWC44OoVB45Pg4OoRC8YN78f9N8gFPY/y7WbL4/1FKkirkGuPP8wcPV0eHYHd+Bvlndk3keEeHUCi6HJzq6BBwHWYW5LIrKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBCwW2qrticoxAAAAAMDwSI4BAAAAAIZHWzUAAAAAOAFWq7YvKscAAAAAAMMjOQYAAAAAGB5t1QAAAADgBFit2r6oHAMAAAAADI/KMQAAAAA4ATOVY7uicgwAAAAAMDySYwAAAACA4dFWDQAAAABOwMJ7ju2KyjEAAAAAwPBIjgEAAAAAhkdbNQAAAAA4Ad5zbF9UjgEAAAAAhkflGAAAAACcgJkFueyKyjEAAAAAoEj4448/1KdPH/n7+8vf3199+vTRn3/+edPHP/HEEzKZTJo7d26Br01yDAAAAAAoEnr16qX9+/dr48aN2rhxo/bv368+ffrc1LHr1q1TSkqKKlaseEvXdmhynJycLJPJVKDfBBSmoh4fAAAAAOOwWCxFZrOHtLQ0bdy4Ua+99ppiYmIUExOjJUuWaMOGDUpPT7/usb/88ouGDh2qFStWyN3d/ZauX6jJcYsWLTRixAjr58aNGyszM1P+/v6FGcZNc1R8x48fl8lk0v79+wv1ugAAAABwM7KysnTu3DmbLSsr67bO+dVXX8nf318NGza0jjVq1Ej+/v7asWPHNY8zm83q06ePnnnmGdWqVeuWr+/QyrGHh4cqVKggk8nkyDCuqajHdzOys7MdHQIAAACAYiYxMdH6XPDVLTEx8bbOeeLECZUvXz7PePny5XXixIlrHjdz5ky5ublp+PDht3X9QkuO4+PjtXXrVs2bN08mk0kmk0nLly+3aVtevny5SpUqpQ0bNig8PFw+Pj56+OGHdf78eb3xxhsKDQ1V6dKlNWzYMOXm5lrPnZ2drTFjxqhSpUry9fVVw4YNlZycfFNx/fjjj3rwwQdVunRp+fr6qlatWkpKSpKUf1v1kiVLFBwcLB8fHz300EOaPXu2SpUqZd0/adIk1atXT2+99ZZCQ0Pl7++vRx55RH/99Zd1zsaNG3XvvfeqVKlSCggIUIcOHXTkyBHr/ipVqkiS6tevL5PJpBYtWkjKW3mXpM6dOys+Pt76OTQ0VM8//7zi4+Pl7++vgQMHSpJ27NihZs2aydvbW8HBwRo+fLjOnz9/U98RAAAAAMczWyxFZktISNDZs2dttoSEhHzjnjRpkjUHvNa2Z88eScq3MGmxWK5ZsExNTdW8efOsueXtKLTkeN68eYqJidHAgQOVmZmpzMxMBQcH55l34cIFzZ8/X6tWrdLGjRuVnJysLl26KCkpSUlJSXrrrbf06quv6r333rMe07dvX23fvl2rVq3SgQMH1K1bN7Vt21aHDx++YVxDhgxRVlaWvvjiCx08eFAzZ86Un59fvnO3b9+uQYMG6amnntL+/fvVunVrTZs2Lc+8I0eOaN26ddqwYYM2bNigrVu3asaMGdb958+f16hRo7R7925t2bJFLi4ueuihh2Q2myVJu3btkiR9+umnyszM1Jo1a254H//04osvqnbt2kpNTdX48eN18OBBxcbGqkuXLjpw4IBWr16tbdu2aejQoQU6LwAAAABIkqenp0qWLGmzeXp65jt36NChSktLu+5Wu3ZtVahQQSdPnsxz/G+//abAwMB8z/3ll1/q1KlTqly5stzc3OTm5qYff/xRTz/9tEJDQwt0T4X2nmN/f395eHjIx8dHFSpUkCR99913eebl5ORo0aJFuvvuuyVJDz/8sN566y2dPHlSfn5+qlmzpu677z59/vnn6tGjh44cOaKVK1fq559/tq5KNnr0aG3cuFHLli3T9OnTrxtXRkaGunbtqsjISElSWFjYNecuWLBA7dq10+jRoyVJ1atX144dO7RhwwabeWazWcuXL1eJEiUkSX369NGWLVusiXTXrl1t5r/++usqX768Dh06pNq1a6tcuXKSpICAAOt3VRD333+/NUZJiouLU69evaxV52rVqmn+/Plq3ry5Fi1aJC8vr3zPk5WVlee5gcuWXLmZXAscEwAAAABjKlu2rMqWLXvDeTExMTp79qx27dqle+65R5KUkpKis2fPqnHjxvke06dPH7Vq1cpmLDY2Vn369FHfvn0LFGeRe5WTj4+PNTGWpMDAQIWGhtpUcwMDA3Xq1ClJ0t69e2WxWFS9enX5+flZt61bt9q0Kl/L8OHD9fzzz6tJkyaaOHGiDhw4cM256enp1j+kq/79WbrS2nw1MZakoKAga7zSlcpyr169FBYWppIlS1rbqDMyMm4Y782Ijo62+Zyamqrly5fbfD+xsbEym806duzYNc+T33ME289+e0diBAAAAFAwjl6h2t6rVUdERKht27YaOHCgdu7cqZ07d2rgwIHq0KGDwsPDrfNq1KihtWvXSrpSUKxdu7bN5u7urgoVKtgcczMKrXJ8s/697LbJZMp37GoLstlslqurq1JTU+XqalvRvFZ79D8NGDBAsbGx+uijj7Rp0yYlJiZq1qxZGjZsWJ65+fW65/cPxvXilaQHH3xQwcHBWrJkiSpWrCiz2azatWvfcPEsFxeXPNfLycnJM8/X19fms9ls1hNPPJHvA+qVK1e+5vUSEhI0atQom7EZkY9fN0YAAAAAuFUrVqzQ8OHD1aZNG0lSx44d9fLLL9vMSU9P19mzZ+/4tQs1Ofbw8LBZSOtOqF+/vnJzc3Xq1Ck1bdr0ls4RHBysQYMGadCgQUpISNCSJUvyTY5r1KhhfR74qqsPjt+sM2fOKC0tTYsXL7bGu23bNps5Hh4ekpTnuypXrpwyMzOtn3Nzc/XNN9/ovvvuu+41o6Ki9O2336pq1aoFitXT0zPPcwO0VAMAAACOYZZ9KrZFSZkyZfT2229fd86NKtfHjx+/pWsXalt1aGioUlJSdPz4cZ0+fdqmmnqrqlevrt69eysuLk5r1qzRsWPHtHv3bs2cOdO66vT1jBgxQp988omOHTumvXv36rPPPlNERES+c4cNG6akpCTNnj1bhw8f1uLFi/Xxxx8XaFW00qVLKyAgQK+++qp++OEHffbZZ3mqs+XLl5e3t7c2btyokydPWn8rcv/99+ujjz7SRx99pO+++06DBw+2WUn7Wp599ll99dVXGjJkiPbv36/Dhw9r/fr1+f4CAAAAAACMqFCT49GjR8vV1VU1a9ZUuXLl7tgztsuWLVNcXJyefvpphYeHq2PHjkpJScl3Nex/y83N1ZAhQ6z97eHh4Vq4cGG+c5s0aaJXXnlFs2fPVt26dbVx40aNHDnymgta5cfFxUWrVq1SamqqateurZEjR+rFF1+0mePm5qb58+dr8eLFqlixojp16iRJ6tevnx577DHFxcWpefPmqlKlyg2rxpJUp04dbd26VYcPH1bTpk1Vv359jR8/XkFBQTcdNwAAAAAUZyaLvZ6mNoiBAwfqu+++05dffunoUArFhNDejg7B7qrk3N770ZxFhOWCo0MoFGkmH0eHUCh+cL/9TpyizsdijH83S5qNcZ8+Bvnbx7kit/TpnedX/P/vR5JUMtcY/9B2OTjV0SEUCvey135DTVFW0rfoxH3u/FFHh3DHFbkFuYq6l156Sa1bt5avr68+/vhjvfHGG9esNAMAAAAAnEOx/31mu3btbF5h9M/tRu9Azs+uXbvUunVrRUZG6pVXXtH8+fM1YMAAO0QOAAAAACgsxb5y/Nprr+nixYv57itTpkyBz/fOO+/cbkgAAAAAUGBmnoi1q2KfHFeqVMnRIQAAAAAAirhinxwDAAAAQHFgMcB7jh2p2D9zDAAAAADAjZAcAwAAAAAMj7ZqAAAAAHACLMhlX1SOAQAAAACGR3IMAAAAADA82qoBAAAAwAlYaKu2KyrHAAAAAADDo3IMAAAAAE6A9xzbF5VjAAAAAIDhkRwDAAAAAAyPtmoAAAAAcAIsyGVfVI4BAAAAAIZHcgwAAAAAMDzaqgEAAADACdBWbV9UjgEAAAAAhkdyDAAAAAAwPNqqAQAAAMAJ0FRtX1SOAQAAAACwAEXYpUuXLBMnTrRcunTJ0aHYjRHu0WLhPosbI9ynEe7RYuE+ixsj3KcR7tFi4T4BRzBZLCx5hqLr3Llz8vf319mzZ1WyZElHh2MXRrhHifssboxwn0a4R4n7LG6McJ9GuEeJ+wQcgbZqAAAAAIDhkRwDAAAAAAyP5BgAAAAAYHgkxyjSPD09NXHiRHl6ejo6FLsxwj1K3GdxY4T7NMI9StxncWOE+zTCPUrcJ+AILMgFAAAAADA8KscAAAAAAMMjOQYAAAAAGB7JMQAAAADA8EiOAQDA/9fevYfFmP5/AH9P51JJFG3bUUS2yFlWq5xZcviyZCXnFqGkWKrdVE6bZFmHWsSuw5bTrmNWhJxVKoVU1FIOJVGoZp7fH13Nz+y0ljW5Z575vK6r62ruZ/54P9fQPPfz3PfnQwghhCg9mhwTQgghhBBCCFF6NDkmhJAP5OnpiTNnzrCO0aBevnyJyspK8et79+5hzZo1SEhIYJiKEKLMysrKWEdoMBzHge8NZaqqqnDr1i3U1NSwjkKIGE2OiVyqqanBn3/+iU2bNuH58+cAgAcPHuDFixeMk8leVVUV/vrrLxQUFEj88MHp06dZR/gonj9/jv79+6NVq1YIDw/H/fv3WUeSOTc3N2zfvh1A7QVpt27dEBERATc3N2zYsIFxOtnp3bs3tm/fjpcvX7KO8lGVl5fjwIEDyM7OZh2lQSnLefLRihUrsGfPHvHrMWPGoGnTpjA1NcX169cZJpOt7du3w97eHtra2tDW1oaDgwN27NjBOpZMVVZWYsqUKdDR0UG7du3E1zxz5szB8uXLGacjSo8jRM7cvXuXa9OmDaejo8Opqqpyubm5HMdx3Ny5c7kZM2YwTic7t2/f5j7//HNORUVF4kcgEHAqKiqs48mEpqYmZ21tzS1dupQrKChgHadBPXnyhFuzZg3XoUMHTk1NjRs4cCAXFxfHVVVVsY4mE02bNuUyMzM5juO46OhozsHBgRMKhdxvv/3GtWnThnE62fH19eWaN2/O6evrc1OnTuUuXLjAOlKDGD16NPfjjz9yHMdxlZWVXKtWrTh1dXVOTU2Ni4+PZ5xOdpTlPI8ePcqdPXtW/HrdunVc+/btuXHjxnGlpaUMk8mOlZUVl5yczHEcxyUkJHAGBgbc8ePHuSlTpnD9+vVjnE42IiIiOB0dHc7f3587ePAgd+DAAW7BggWcjo4Ot3r1atbxZGbOnDlcp06duLNnz3KNGjUSX+cdPHiQ69ChA+N0RNnR5JjIHTc3N+7rr7/mXr9+zenq6or/aJ4+fZqzsbFhnE52nJycOGdnZ+7IkSNcamoql5aWJvHDByUlJVxUVBTn6OjIqaqqcv379+f27NnDvX79mnW0BpWSksLNnj2b09LS4po1a8bNmzePu337NutYH0RbW5u7d+8ex3G1E47vvvuO4ziOKygo4LS1tVlGk7mamhruwIEDnJubG6eurs61bduWW7VqFVdcXMw6msw0b95c/Hfm119/5WxsbLiKigrup59+4tXFqbKc52effcYdPnyY4ziOS09P5zQ1NblFixZx3bp14zw9PRmnkw0tLS3xTdY5c+Zw06dP5ziO427dusUZGBiwjCYzlpaWXGxsrNT4tm3bOEtLSwaJGoa5ubn4xuOb13k5OTmcnp4ey2iEcLSsmsidc+fOYcmSJdDQ0JAYt7Cw4NVy1bS0NGzatAmDBg1Chw4d0L59e4kfPjA0NMScOXOQkpKCq1evwtbWFrNmzYKJiQnmzJnDq6VwdYqKipCQkICEhASoqqpi8ODBuHHjBuzs7BAZGck63n9mY2ODAwcOoLCwEMePH0f//v0BAI8ePYK+vj7jdLKlqqoKNzc3HDhwAPfv34e7uzsCAwNhZmaG4cOHIzExkXXED/bs2TMYGhoCAI4dO4ZRo0ZBR0cHQ4YMQU5ODuN0sqMs55mfnw87OzsAwN69e/Hll18iPDwcP/30E44ePco4nWw0adIEhYWFAGo/y759+wKo3ZsrFApZRpOZoqIiODk5SY07OTmhqKiIQaKG8fjxYxgbG0uNV1RUQCAQMEhEyP+jyTGROyKRqN4vur/++gt6enoMEjUMOzs7PHnyhHWMj6ZDhw5YuHAhZs2ahYqKCmzZsgWdOnVCr169cOPGDdbxPkh1dbX4gtTCwgJxcXHw8fFBUVERYmNjkZCQgB07diAkJIR11P8sKCgIfn5+sLS0RLdu3dCjRw8AQEJCAhwdHRmnaxiXL19GUFAQfvjhBxgbG2PRokUwNjbG0KFD4efnxzreBzEzM8OFCxdQUVGBY8eOiW92PH36FFpaWozTyY6ynKeGhoa4YN6ff/4pPk9DQ0OUl5ezjCYzI0eOhLu7O/r164eSkhIMGjQIQO2NZhsbG8bpZMPGxga//fab1PiePXvQqlUrBokaRpcuXXD48GHx67oJcXR0tPi7hRBmWD+6JuTvxowZw02bNo3juNrlNnl5edzz5885V1dXhV8e9uzZM/HPyZMnuR49enCnTp3injx5InHs2bNnrKPKTFVVFRcXF8cNGjSIU1NT47p3785FR0dzL1684AoKCrhx48Zxbdu2ZR3zgzRt2pRr0qQJN3PmTC41NbXe95SWlir8sriioiIuJSWFEwqF4rFLly5x2dnZDFPJ1sOHD7kffviBa9euHaehocGNGjWKO3r0KCcSicTvOXHiBNeoUSOGKT/c+vXrOTU1Nc7AwIBr3769+DNdu3Yt17t3b8bpZEdZznPo0KHcgAEDuJCQEE5dXZ3766+/OI7juOPHj3OtWrVinE42qqqquFWrVnFz5szhUlJSxOORkZFcdHQ0w2SyEx8fz6mqqoo/y6VLl3IDBgzg1NTUuH379rGOJzPJycmcnp4e5+XlxWlpaXFz587l+vbtyzVq1Ii7evUq63hEyQk4jud14onCefDgAVxcXKCqqoqcnBx07twZOTk5aNasGc6cOVPvUhxFoaKiIrFkiOM4qSVEdWN8WCbm7e2NXbt2AQC+/vprTJ06FZ999pnEewoKCmBpaQmRSMQiokzs2LEDo0eP5tWTqH9TXl6OxMRE2Nraom3btqzjyIyGhgZatmyJyZMnw9PTE0ZGRlLvKS8vh5ubG06dOsUgoexcvXoVhYWF6NevH3R1dQEAhw8fhoGBAXr27Mk4newow3kWFBRg5syZKCwsxJw5czBlyhQAgI+PD4RCIdauXcs4IXlX165dQ2RkJLKzs8FxHOzs7DB//nzerdDJzMzEqlWrcO3aNYhEInTs2BEBAQGwt7dnHY0oOZocE7n08uVL7N69W+KP5vjx46Gtrc062gdJSkp65/d+8cUXDZjk4+jTpw+mTp2KUaNGSe0hr1NTU4Pk5GSFPt/JkycjKipKatl/RUUFvL29sWXLFkbJZGfMmDFwdnbG7Nmz8fLlS7Rv3x53794Fx3HYvXs3Ro0axTqiTJw9exa9evViHeOjEwqFyMjIgIWFBZo0acI6ToNRlvPko9jYWDRr1gxDhgwBAPj7+2Pz5s2ws7PDrl27YGFhwTgheRfV1dWYPn06AgMDYW1tzToOIVJockzkzpkzZ+Dk5AQ1NTWJ8ZqaGpw/fx7Ozs6MkslWQUEBzMzM6n1yXFhYCHNzc0bJZEdZPktVVVUUFRVJrWp48uQJWrRogZqaGkbJZKdFixY4fvw42rdvj507dyI4OBjXr19HbGwsNm/ejNTUVNYRyXuYN28e7O3tMWXKFAiFQnzxxRc4f/48dHR0cOjQIfTu3Zt1RJlQlvNMSUmBurq6+KnbwYMHsXXrVtjZ2eG77777x5uTisTW1hYbNmyAq6srLly4gD59+mDNmjU4dOgQ1NTUsG/fPtYRZUIoFGL//v3Izs6GQCBA27Zt4ebmJvU9qsgMDAyQkpJCk2Mil6ggF5E7Li4uKC0tlRp/9uwZXFxcGCRqGFZWVnj8+LHUeGlpKaysrBgkkj2+f5bl5eV49uwZOI7D8+fPUV5eLv55+vQpjhw5otDbAN6kLFV/HR0d0bFjR6mfTp06oWfPnpg4caLCL6cGgPj4eHFV/D/++AP5+fm4efMm5s2bh8WLFzNOJzvKcp4zZszA7du3AQB5eXkYO3YsdHR0EBcXB39/f8bpZKOwsFBceOvAgQP43//+h+nTp2PZsmU4e/Ys43SykZmZidatW2PixInYv38/9u3bh4kTJ6JVq1bIyMhgHU9mRowYgQMHDrCOQUi9+HMbivBGfftwAaCkpASNGjVikKhh/NN5vnjxgjd7V/n+WRoYGEAgEEAgEKB169ZSxwUCAb7//nsGyWSvruqvoaEhjh07ht27dwPgX9XfgQMHYsOGDbC3t0fXrl3BcRyuXr2K9PR0eHp6IisrC3379sW+ffvg5ubGOu5/VreqAQCOHDmC0aNHo3Xr1pgyZQqv9qcqy3nevn0bHTp0AADExcXB2dkZO3fuRHJyMsaOHYs1a9YwzScLurq6KCkpgbm5ORISEuDj4wMA0NLSwsuXLxmnk42pU6eiXbt2uHr1qnjZ/9OnT+Hp6Ynp06fjwoULjBPKho2NDZYuXYrz58+jU6dOUtcDc+bMYZSMEJocEzkycuRIALUTCk9PT2hqaoqPCYVCpKen19v/T9H4+voCqD3PwMBA6OjoiI8JhUJcunRJfJGjqJTlszx16hQ4joOrqyv27t0rfrIK1BZ2srCwwCeffMIwoezMmzcP48ePh66uLszNzcXLUc+cOcOrAipPnjzB/PnzERgYKDEeGhqKe/fuISEhAcHBwVi6dKlCT46bN2+OrKwsmJiY4NixY/jpp58AAJWVlVBVVWWcTnaU5Tw5jhMXNfzzzz/x5ZdfAqi9qcWXloH9+vXD1KlT4ejoiNu3b4v3Ht+4cQOWlpZsw8nI9evXJSbGQG1/57CwMHTp0oVhMtmKiYmBgYEBrl27hmvXrkkcEwgENDkmTNHkmMiNxo0bA6j9ktfT05MovqWhoYHu3btj2rRprOLJTN3eTI7jkJGRIbEXTENDA+3bt1f4HqrK8lnWFRHLz8+Hubl5vU/J+WLmzJno2rWruOqvikrtrhxra2uEhoYyTic7v/32m9TFGgCMHTsWnTp1QnR0NMaNG4fVq1czSCc7kyZNwpgxY2BiYgKBQIB+/foBAC5duoQ2bdowTic7ynKenTt3RmhoKPr27YukpCRs2LABQO3fpubNmzNOJxvr16/HkiVLUFhYiL1796Jp06YAaqs7jxs3jnE62bC1tcXDhw/Rrl07ifFHjx7xppczUPvvkhB5RQW5iNz5/vvv4efnx4tlt28zadIkREVFQV9fn3WUBsPnzzI9PR2fffYZVFRUkJ6e/tb3Ojg4fKRUDa+qqgr5+flo2bIlrwrE1GnevDlWrVoFDw8PifHt27djwYIFePjwIbKysuDs7KzwT+Ti4+NRWFiI0aNH49NPPwVQWxHYwMBAoZ+K/50ynGd6ejrGjx+PgoIC+Pr6Ijg4GEBtO72SkhLs3LmTcULyLo4cOQJ/f39899136N69OwDg4sWLCAkJwfLly/H555+L38vnawdCWKLJMSGE/AcqKiooLi6GsbGxuH91fX9O+dKzurKyEt7e3oiNjQVQu8fR2toac+bMwSeffIKFCxcyTigboaGhCA8Px7Rp09ClSxcIBAJcvnwZMTEx+Pbbb7F48WJERkbiyJEjOHHiBOu4MvHq1Ste7Rv/J8pynm969eoVVFVVoa6uzjqKTJw9exabNm1CXl4e4uLiYGpqih07dsDKykpi4qio6lbkABCvRKr7XnnztaJ/r0yePPmtx/nQ/pAoLv7d9ie8EB8fj99++w0FBQWoqqqSOJaSksIolWzV7cv9O4FAAC0tLdjY2MDd3R22trYfOdmH6dixI06ePIkmTZrA0dHxrUuNFfmzzM/Ph5GRkfh3vlu0aBGuX7+O06dPY+DAgeLxvn37Ijg4mDeT4yVLlsDKygrr1q3Djh07ANQudYyOjoa7uzsAwMvLC9988w3LmB9MKBQiPDwcGzduxMOHD8U3OwIDA2FpaYkpU6awjigTynKeAFBWVob4+Hjk5uZiwYIFMDQ0RFZWFpo3bw5TU1PW8T7Y3r17MWHCBIwfPx4pKSl4/fo1AOD58+cIDw/HkSNHGCf8cHyohP8unj59KvG6uroamZmZKCsrg6urK6NUhNSiyTGRO2vXrsXixYsxceJEHDx4EJMmTUJubi6uXLmCWbNmsY4nM/r6+jhw4AAMDAzQqVMncByH1NRUlJWVoX///tizZw9WrFiBkydPomfPnqzjvjM3NzdxAa7hw4ezDdOALCws6v2drw4cOIA9e/age/fuEjc87OzskJubyzCZ7NTU1CAsLAyTJ0/G+PHj//F9b+6hV1RhYWGIjY3FypUrJfb/29vbIzIykjeTRmU5z/T0dPTp0wcGBga4e/cupk2bBkNDQ+zfvx/37t3D9u3bWUf8YKGhodi4cSM8PDzE1fIBwMnJCSEhIQyTyU5dHQu+279/v9SYSCTCzJkzqfcxYY6WVRO506ZNGwQHB2PcuHHQ09PD9evXYW1tjaCgIJSWlmLdunWsI8rEwoULUV5ejnXr1omXUolEIsydOxd6enoICwuDl5cXbty4gXPnzjFO+/6EQiHOnTsHBwcHicqbfPH777+/83uHDRvWgEk+Dh0dHWRmZsLa2lri/+X169fh7OyMZ8+esY4oE7q6usjMzORN9dt/YmNjg02bNqFPnz4Sn+fNmzfRo0cPqSc7ikpZzrNv377o2LEjVq5cKXGe58+fh7u7O+7evcs64gfT0dFBVlYWLC0tJc4xLy8PdnZ2ePXqFeuIMvHq1Sukp6fj0aNH4grkdfjwXfI2t27dQu/evVFUVMQ6ClFi9OSYyJ2CggJxmx9tbW08f/4cADBhwgR0796dN5Pjn3/+GcnJyRJ7jFRUVODt7Q0nJyeEh4dj9uzZ6NWrF8OU/52qqioGDBiA7OxsXk6O3/WpuKLvDavTpUsXHD58GN7e3gD+f/9bdHQ0evTowTKaTPXt2xenT5+Gp6cn6ygN6v79+/VWvxWJRKiurmaQqGEoy3leuXIFmzZtkho3NTVFcXExg0SyZ2Jigjt37kjduDp37hxvnjYeO3YMHh4e9Rb748t3ydvk5uaipqaGdQyi5GhyTOROixYtUFJSAgsLC1hYWODixYto37498vPz6y14pKhqampw8+ZNtG7dWmL85s2b4i9ALS0thW4PZG9vj7y8PFhZWbGOInN/v6PPd8uWLcPAgQORlZWFmpoaREVF4caNG7hw4QKSkpJYx5OZQYMGYdGiRcjMzESnTp2kKq3z5clNu3btcPbsWaktAXFxcXB0dGSUSvaU5Ty1tLRQXl4uNX7r1i1xbQRFN2PGDMydOxdbtmyBQCDAgwcPcOHCBfj5+SEoKIh1PJmYPXs2Ro8ejaCgIN604KqPr6+vxGuO41BUVITDhw9j4sSJjFIRUosmx0TuuLq64o8//kDHjh0xZcoU+Pj4ID4+HlevXv3HIlaKaMKECZgyZQq+/fZbiaq44eHh4jYySUlJUv0OFUlYWBj8/PywdOnSeica1IpCcTg5OSE5ORk//PADWrZsiYSEBHTs2BEXLlyAvb0963gyU1doq74+xnx6chMcHIwJEybg/v37EIlE2LdvH27duoXt27fj0KFDrOPJjLKcp5ubG0JCQvDbb78BqP23WlBQgIULF2LUqFGM08mGv78/nj17BhcXF7x69QrOzs7Q1NSEn58fZs+ezTqeTDx69Ai+vr68nhgDQGpqqsRrFRUVGBkZISIi4l8rWRPS0GjPMZE7IpEIIpFI3EP1t99+w7lz52BjYwMvLy9oaGgwTigbQqEQy5cvx7p16/Dw4UMAtT1Wvb29ERAQAFVVVRQUFEBFRUXcm1PR1NeWAuBHK4q1a9di+vTp0NLSwtq1a9/63jlz5nykVIS8u+PHjyM8PBzXrl2DSCRCx44dERQUhP79+7OOJlPKcJ7l5eUYPHgwbty4gefPn+OTTz5BcXExevTogSNHjvCq13xlZSWysrIgEolgZ2cHXV1d1pFkZvLkyejZsydvCsURoohockyIHKhbDse3J6n/ttxWkStzWllZ4erVq2jatOlbl40LBALk5eV9xGQNRyQS4c6dO/UWinF2dmaUquEoY19cotgSExORkpIivgnQt29f1pHIe6isrMTo0aNhZGQEe3t7qf7UfLnR6urqin379sHAwEBivLy8HMOHD0diYiKbYISAJsdETqSnp7/zex0cHBowCSGkPhcvXoS7uzvu3bsntfdf0VcBvEmZ+uICQFVVVb03O8zNzRklahjKcp58VlFRgeXLl+PkyZP1fpZ8uAkZExMDLy8vaGtro2nTphIrrvh0o1VFRQXFxcUwNjaWGH/06BFMTU15VSyPKB7ac0zkQocOHSAQCMTLbd+GLxfhDx8+hJ+fn/iL/u8TDr6cJ1B7N7ygoABVVVUS43y80VH3OSpyIbX6eHl5oXPnzjh8+DBMTEx4d351lKUvbk5ODiZPnozz589LjPNhy8OblOU8AeDkyZP/OHHcsmULo1SyM3XqVCQlJWHChAm8/Ru0ZMkShISEYOHChRLbkvjizQchWVlZEpXUhUIhjh07BlNTUxbRCBGjyTGRC/n5+eLfU1NT4efnhwULFohbxFy4cAERERFYuXIlq4gy5+npiYKCAgQGBvL2i/7x48eYNGkSjh49Wu9xPl2Y/vzzz4iMjEROTg4AoFWrVpg3bx6mTp3KOJls5OTkID4+vt62OHyyfft2bN68GX369IGXl5d43MHBATdv3mSYTLY8PT2hpqaGQ4cO8fbvD6A85/n9998jJCQEnTt35u15Hj16FIcPH0bPnj1ZR2kwVVVV+Oqrr3g5MQb+/0GIQCCAq6ur1HFtbW38+OOPDJIR8v9ockzkwpttNkaPHo21a9di8ODB4jEHBweYmZkhMDDwnfvLyrtz587h7Nmz6NChA+soDWbevHl4+vQpLl68CBcXF+zfvx8PHz5EaGgoIiIiWMeTmcDAQERGRsLb21viho6Pjw/u3r2L0NBQxgk/XLdu3XDnzh3eT46VpS9uWloarl27hjZt2rCO0qCU5Tw3btyIbdu2YcKECayjNJgmTZrA0NCQdYwGNXHiROzZswfffvst6ygNoq4lp7W1NS5fvizRZkxDQwPGxsZQVVVlmJAQmhwTOZSRkVFvgSMrKytkZWUxSNQwzMzMeNW3uT6JiYk4ePAgunTpAhUVFVhYWKBfv37Q19fHsmXLMGTIENYRZWLDhg2Ijo7GuHHjxGPDhg2Dg4MDvL29eTE59vb2xvz581FcXFxvoRi+LJFXlr64dnZ2ePLkCesYDU5ZzrOqqgpOTk6sYzSopUuXIigoCLGxsdDR0WEdp0EIhUKsXLkSx48fh4ODg9Tf2fpazCmSur+rf1/2T4g8ockxkTtt27ZFaGgofv75Z3Gl2NevXyM0NBRt27ZlnE521qxZg4ULF2LTpk2wtLRkHadBVFRUiAtuGBoa4vHjx2jdujXs7e2RkpLCOJ3sCIVCdO7cWWq8U6dOqKmpYZBI9up6pb7Zg/LNOgF8WSKvLH1xV6xYAX9/f4SHh9d7s4MvlfOV5TynTp2KnTt3IjAwkHWUBhMREYHc3Fw0b94clpaWUp8lH75TMjIyxDfhMjMzJY7xcal8VlZWvfVIhg0bxigRIVStmsihy5cvY+jQoRCJRGjfvj0A4Pr16xAIBDh06BC6du3KOKFsNGnSBJWVlaipqYGOjo7UF31paSmjZLLTpUsXhIaGYsCAARg+fLj4ifHatWsRHx+P3Nxc1hFlwtvbG+rq6lJ39f38/PDy5UusX7+eUTLZuXfv3luP//1JqyJThr64dXsa/37BzbebHcpynnPnzsX27dvh4ODAyyeOQO2+6rcJDg7+SEnIh8rLy8OIESOQkZEhvskK/P//U778vySKiSbHRC5VVlbil19+wc2bN8FxHOzs7ODu7o5GjRqxjiYzsbGxbz0+ceLEj5Sk4fz666+orq6Gp6cnUlNTMWDAAJSUlEBDQwPbtm3DV199xTrif+br6yv+vaamBtu2bYO5uTm6d+8OoLb1UWFhITw8PKjACJE7fO5B/iZlOU8XF5d/PCYQCKhvrIK5c+cOcnNz4ezsDG1t7Xfq5KFIhg4dClVVVURHR4v3H5eUlGD+/Pn44Ycf0KtXL9YRiRKjyTFRWEOGDEFMTAxMTExYRyHvqLKyEjdv3oS5uTmaNWvGOs4HedvF6Jv4cmG6bNkyNG/eXGJZNVDbIubx48cICAhglKxhUF9cQsjHVlJSgjFjxuDUqVMQCATIycmBtbU1pkyZAgMDA94UsmzWrBkSExPh4OCAxo0b4/Lly7C1tUViYiLmz5+P1NRU1hGJEqM9x0RhnTlzBi9fvmQd44Pk5uZi69atyM3NRVRUFIyNjXHs2DGYmZmhXbt2rOPJnI6ODjp27Mg6hkycOnWKdYSPatOmTdi5c6fUeLt27TB27FjeTI753Bf3zR6j/4YvBdYA4OzZs9i0aRPy8vIQFxcHU1NT7NixA1ZWVvj8889ZxyNvYWhoiNu3b6NZs2Zo0qTJW5+e8mErko+PD9TV1VFQUCBRY+Wrr76Cj48PbybHQqEQurq6AGonyg8ePICtrS0sLCxw69YtxumIsqPJMSGMJCUlYdCgQejZsyfOnDmDsLAwGBsbIz09HTExMYiPj2cd8T95c7nxv+HDPjhlUVxcXO8qDSMjIxQVFTFI1DD43Be3rsfovy0YU/SbAG/au3cvJkyYgPHjxyMlJQWvX78GADx//hzh4eE4cuQI44T/3ciRI9/5vfv27WvAJA0nMjISenp6AGqLWPJdQkICjh8/jk8//VRivFWrVv9a90GRfPbZZ0hPT4e1tTW6deuGlStXQkNDA5s3b4a1tTXreETJ0eSYEEYWLlyI0NBQ+Pr6ir/8gdrlulFRUQyTfZh3XQ7Fp0kHAFy5cgVxcXH1Vt5U1AvTN5mZmSE5OVmqzVpycjI++eQTRqlkj899cfPz81lH+OhCQ0OxceNGeHh4YPfu3eJxJycnhISEMEz24Ro3biz+neM47N+/H40bNxZXzr927RrKysreaxItb96svcGHOhz/pqKiot42VU+ePIGmpiaDRA1jyZIlqKioAFD7f/TLL79Er1690LRpU+zZs4dxOqLsaHJMCCMZGRn1LlM1MjJCSUkJg0SyoWzLjQFg9+7d8PDwQP/+/XHixAn0798fOTk5KC4uxogRI1jHk4mpU6di3rx5qK6uhqurKwDg5MmT8Pf3x/z58xmnkx0+98XlU0Xxd3Xr1i04OztLjevr66OsrOzjB5KhrVu3in8PCAjAmDFjsHHjRqiqqgKoXbo6c+ZM3rSrAmr74965c6feegD1fc6KxtnZGdu3b8fSpUsB1N5EFolEWLVq1TvXuVAEAwYMEP9ubW2NrKwslJaW/uvSeUI+BpocE8KIgYEBioqKpJ7EpaamwtTUlFEq8l+Eh4cjMjISs2bNgp6eHqKiomBlZYUZM2bwpmCcv78/SktLMXPmTPGTcS0tLQQEBGDRokWM08mOsvTFrcP3PqMmJia4c+eOVC/5c+fO8Wr55pYtW3Du3DnxxBgAVFVV4evrCycnJ6xatYphOtm4ePEi3N3dce/ePamtAXzZCrBq1Sr07t0bV69eRVVVFfz9/XHjxg2UlpYiOTmZdbwGZWhoyDoCIQBockwIM+7u7ggICEBcXJz47nBycjL8/Pzg4eHBOp5MuLi4vPUuMB+qOAO1hdWGDBkCANDU1ERFRQUEAgF8fHzg6ur6r/05FYFAIMCKFSsQGBiI7OxsaGtro1WrVrxa6gcAffv2BQC4urpK/NvlQ0GuNylLn9EZM2Zg7ty52LJlCwQCAR48eIALFy7Az88PQUFBrOPJTE1NDbKzs2Fraysxnp2dLfWEVVF5eXmhc+fOOHz4MO/qAdTR1dVFWloaNm3aBFVVVVRUVGDkyJGYNWsWqqurWceTmYqKCixfvhwnT56sdxVAXl4eo2SE0OSYKLBvv/1Woe80hoWFwdPTE6ampuJezjU1NRg/fjyWLFnCOp5MdOjQQeJ1dXU10tLSkJmZyav9Y4aGhnj+/DkAwNTUFJmZmbC3t0dZWRkqKysZp5MtXV1ddOnShXWMBqMs2wLmzp0LKysr/Pnnn/X2GeULf39/PHv2DC4uLnj16hWcnZ2hqakJPz8/zJ49m3U8mZk0aRImT56MO3fuSPRaX758OSZNmsQ4nWzk5OQgPj4eNjY2rKM0GCsrKxQVFUndUC0pKcGnn37Km5tWU6dORVJSEiZMmMDbGx1EcVGfYyKXduzYgY0bNyI/Px8XLlyAhYUF1qxZAysrK7i5ubGOJ1N5eXlISUmBSCSCo6MjWrVqxTpSg/vuu+/w4sUL3lyEu7u7o3PnzvD19UVYWBiioqLg5uaGEydOoGPHjgpbkGvkyJHYtm0b9PX1/7Woj6KeY33qWv/k5uYiPj6el61/lK3PaGVlJbKysiASiWBnZyduI8MXIpEIP/zwA6KiosTV401MTDB37lzMnz9fYrm1onJ1dYW/vz8GDhzIOkqDUVFRQXFxMYyNjSXG7927Bzs7O3ERK0VnYGCAw4cPo2fPnqyjECKFnhwTubNhwwYEBQVh3rx5CAsLE98pNTAwwJo1axR6cvxvbY4uXrwo/p3PbY6+/vprdO3alTeT43Xr1uHVq1cAgEWLFkFdXR3nzp3DyJEjERgYyDjdf9e4cWPxHX19fX2luLv/Zuuf1NRUXrX+eZOy9BmNjY3F//73PzRq1EhcxZmPVFRU4O/vD39/f5SXlwPgx/74N3tze3t7Y/78+SguLq63HoAi9+auuzYQCAQICgqSqFgtFApx6dIlqZVYiqxJkyYKvfKP8Bs9OSZyx87ODuHh4Rg+fDj09PRw/fp1WFtbIzMzE71791boSrLvWm1SIBDwZj9ufXbs2IGAgAA8ePCAdRRCJDg6OsLHxwceHh4Sf3/S0tIwcOBAFBcXs44oE7169cL8+fMxfPhwuLu74+nTp1iyZAk2b96Ma9euITMzk3VEmTAyMkJlZSWGDh2Kr7/+GgMHDoSaGj0XUBQqKipv7c1dd0zR6wHUXRskJSWhR48e0NDQEB/T0NCApaUl/Pz8eLOy7JdffsHBgwcRGxtbb+sqQliibwgid/Lz8+Ho6Cg1XlfoSJEpy37GOn9fistxHIqKinD16lWFfqJan9zcXGzduhW5ubmIioqCsbExjh07BjMzM7Rr1451vA/m6uqKffv2wcDAQGK8vLwcw4cP583NHD63/nnT2/qMvtkPWNEVFRXh2LFj2LVrF8aOHQttbW2MHj0aX3/9NZycnFjHkxkrK6u3ruxQ1AJHytKbu+7aYNKkSYiKiuLFU/+3iYiIQG5uLpo3bw5LS0upVQApKSmMkhFCk2Mih6ysrJCWlibVk/Po0aOws7NjlIr8F40bN5Z4raKiAltbW4SEhKB///6MUsleUlISBg0ahJ49e+LMmTMICwuDsbEx0tPTERMTg/j4eNYRP9jp06el2v0AwKtXr3D27FkGiRqGsrT+UZY+o2pqavjyyy/x5ZdforKyEvv378fOnTvh4uKCTz/9FLm5uawjysS8efMkXldXVyM1NRXHjh3DggUL2ISSAWXrzf1m72o+Gz58OOsIhPwjmhwTubNgwQLMmjULr169AsdxuHz5Mnbt2oVly5YhJiaGdTzyHpTli37hwoUIDQ2Fr68v9PT0xOMuLi6IiopimOzDvbnnLysrS2JZsVAoxLFjx3jVl1tZWv9MnjwZUVFREv9eDQ0NUVFRAW9vb2zZsoVhuoaho6ODAQMG4OnTp7h37x6ys7NZR5KZuXPn1ju+fv16XL169SOnaRjLli1D8+bNMXnyZInxLVu24PHjxwgICGCUjLyv4OBg1hEI+Ue055jIpejoaISGhqKwsBBAbXuc7777DlOmTGGcjPwXV69eRXZ2NgQCAdq2bYtOnTqxjiRTurq6yMjIgJWVlcQ+1bt376JNmzbiYl2KqG7PH4B69/1pa2vjxx9/lLpgVWSLFy9GZGSk+HOra/2zdOlSxslkR1VVFUVFRVJVcZ88eYIWLVqgpqaGUTLZq3ti/Ouvv+LPP/+EmZkZxo0bh/Hjx6Nt27as4zWovLw8dOjQQVykS5FZWlpi586dUsvhL126hLFjxyrNEmxCSMOiJ8dELk2bNg3Tpk3DkydPIBKJpC7giGL466+/MG7cOCQnJ4v3qpaVlcHJyQm7du2CmZkZ24AyYmBggKKiIlhZWUmMp6amKvxT1fz8fHAcJ+6Fa2RkJD6moaEBY2NjXrSJeVNYWBgWL17My9Y/5eXl4DgOHMfh+fPn0NLSEh8TCoU4cuQIr/7ejhs3Dn/88Qd0dHQwevRonD59mld7jf9NfHw8b6oCFxcXw8TERGrcyMhI3L6KKIZ/2r4hEAigpaUFGxsbeHp68qZHN1EsNDkmcufly5fgOA46Ojpo1qwZ7t27hzVr1sDOzo5X+1SVweTJk1FdXY3s7GzY2toCqC14NHnyZEyZMgUJCQmME8qGu7s7AgICEBcXB4FAAJFIhOTkZPj5+cHDw4N1vA9St+dPJBIxTvJx6ejo8LL1j4GBAQQCAQQCAVq3bi11XCAQ4Pvvv2eQrGEIBALs2bMHAwYM4HWVakdHR4nJBsdxKC4uxuPHj/HTTz8xTCY7ZmZmSE5OlroJmZycjE8++YRRKvJfBAUFISwsDIMGDULXrl3BcRyuXLmCY8eOYdasWcjPz8c333yDmpoaTJs2jXVcomRoWTWRO/3798fIkSPh5eWFsrIy2NraQkNDA0+ePMHq1avxzTffsI5I3pG2tjbOnz8vVX08JSUFPXv2xMuXLxklk63q6mp4enpi9+7d4DgOampqEAqFcHd3x7Zt23jxZDU2NhbNmjXDkCFDAAD+/v7YvHkz7OzssGvXLqUrnKOokpKSwHEcXF1dsXfvXomnihoaGrCwsKCJhgL6+w0NFRUVGBkZoXfv3mjTpg2jVLK1YsUKrFq1CqtWrYKrqysA4OTJk/D398f8+fOxaNEixgnJuxo1ahT69esHLy8vifFNmzYhISEBe/fuxY8//ojNmzcjIyODUUqirGhyTOROs2bNkJSUhHbt2iEmJgY//vgjUlNTsXfvXgQFBfGqiArf2draYseOHejatavE+OXLl+Hu7o47d+4wStYw8vLykJKSApFIBEdHR970pARqP8sNGzbA1dUVFy5cQJ8+fbBmzRocOnQIampq2LdvH+uI5D3cu3cPZmZmUFFRYR1F5tauXYvp06dDS0sLa9eufet758yZ85FSkQ/FcRwWLlyItWvXiivna2lpISAggFfF8pSBrq4u0tLSYGNjIzF+584ddOjQAS9evEBubi4cHBwUvoUnUTw0OSZyR0dHBzdv3oS5uTnGjBmDdu3aITg4GIWFhbC1tUVlZSXriOQdHTx4EOHh4Vi/fj06deoEgUCAq1evwtvbGwEBAdTOQYG8+f8yICAARUVF2L59O27cuIHevXvj8ePHrCOS91RWVobLly/j0aNHUsvmFXk7gJWVFa5evYqmTZtKLcF9k0AgUNj+v/URCoU4cOCAuPihnZ0dhg0bxouVK2968eIFsrOzoa2tjVatWkFTU5N1JPKezM3N4ePjAx8fH4nxyMhIREZGoqCgAOnp6ejfv79EhwRCPgb+bsAhCsvGxgYHDhzAiBEjcPz4cfEfz0ePHkFfX59xOvI+PD09UVlZiW7duon3+9XU1EBNTQ2TJ0+WqHBcWlrKKuYH+9///ofOnTtj4cKFEuOrVq3C5cuXERcXxyiZ7Ojq6qKkpATm5uZISEgQ/7/U0tLizfJ4ZfLHH39g/PjxqKiogJ6ensR+VYFAoNCT4zerFitLBeM7d+5g8ODBuH//PmxtbcFxHG7fvg0zMzMcPnwYLVu2ZB1RZoqLi1FaWgpnZ2doamqC4zhe9eZWBoGBgfjmm29w6tQpdO3aFQKBAJcvX8aRI0ewceNGAMCJEyfwxRdfME5KlBE9OSZyJz4+Hu7u7hAKhejTp4+4aNOyZctw5swZHD16lHFC8q5iY2Pf+b0TJ05swCQNy8jICImJibC3t5cYz8jIQN++ffHw4UNGyWRn/PjxuHnzJhwdHbFr1y4UFBSgadOm+P333/Htt98iMzOTdUTyHlq3bo3BgwcjPDwcOjo6rOPIlK+v7zu9TyAQICIiooHTfByDBw8Gx3H49ddfxfvIS0pK8PXXX0NFRQWHDx9mnPDDlZSUYMyYMTh16hQEAgFycnJgbW2NKVOmwMDAgDefpbJITk7GunXrcOvWLXAchzZt2sDb21upqskT+USTYyKXiouLUVRUhPbt24v3xF2+fBn6+vq8KS5C+ENbWxtpaWniitx16iaTfHiyWlZWhiVLlqCwsBDffPMNBg4cCAAIDg6GhoYGFi9ezDgheR+NGjVCRkYGrK2tWUeRORcXl3d6n0AgQGJiYgOn+TgaNWqEixcvSt2gu379Onr27IkXL14wSiY7Hh4eePToEWJiYtC2bVtxP/m6lSw3btxgHZEQwgO0rJrIpRYtWqBFixYSY38v6kQUgzLsg/vss8+wZ88eqaIwu3fvhp2dHaNUsmVgYIB169ZJjfOp7Y8yGTBgAK5evcrLyfGpU6dYR/joNDU18fz5c6nxFy9eQENDg0Ei2UtISMDx48fx6aefSoy3atUK9+7dY5SKvKvy8nLx1rjy8vK3vpe20BGWaHJM5NKVK1cQFxeHgoICcVXKOlQVV3Eoyz64wMBAjBo1Crm5uRItRnbt2sWL/cYAcObMmbced3Z2/khJiCwMGTIECxYsQFZWFuzt7aGuri5xfNiwYYySkf/iyy+/xPTp0/Hzzz+LbyRfunQJXl5evPksKyoq6t0C8OTJEyrKpQCaNGmCoqIiGBsbi/ut/13d/nGhUMggISG1aFk1kTu7d++Gh4cH+vfvjxMnTqB///7IyclBcXExRowYga1bt7KOSN6RMuyDq3P48GGEh4cjLS0N2tracHBwQHBwMG8KitTX8ufNixu6mFEsb2vhRBeniqesrAwTJ07EH3/8Ib7RUV1dDTc3N2zduhUGBgZsA8rAkCFD0LFjRyxduhR6enpIT0+HhYUFxo4dC5FIhPj4eNYRyVskJSWhZ8+eUFNTQ1JS0lvfy5fvTaKYaHJM5I6DgwNmzJiBWbNmQU9PD9evX4eVlRVmzJgBExMTWsapQJRhH5yyePbsmcTr6upqpKamIjAwEGFhYejTpw+jZISQOnfu3EF2djY4joOdnZ1UH1lFlpWVhd69e6NTp05ITEzEsGHDcOPGDZSWliI5OZk3K5EIIWzRsmoid3JzczFkyBAAtfuoKioqIBAI4OPjA1dXV5ocKxBl2Af3pqqqqnp7xpqbmzNKJDuNGzeWGuvXrx80NTXh4+ODa9euMUhFZOHVq1fQ0tJiHYO8p3+ryn369Gnx76tXr27gNA3Pzs4O6enp+Omnn6CqqoqKigqMHDkSs2bNgomJCet45D0cO3YMurq6+PzzzwEA69evR3R0NOzs7LB+/Xo0adKEcUKizGhyTOSOoaGheEJlamqKzMxM2Nvbo6ysDJWVlYzTkfehDPvgACAnJweTJ0/G+fPnJcaVYf+UkZERbt26xToGeU9CoRDh4eHYuHEjHj58iNu3b8Pa2hqBgYGwtLTElClTWEck/yI1NfWd3senHsAtWrRASEgI6xjkAy1YsAArVqwAUNvy0NfXF/Pnz0diYiJ8fX1p+xxhiibHRO706tULJ06cgL29PcaMGYO5c+ciMTERJ06coKWbCmbt2rWYOHEievToIbUPLioqinE62fH09ISamhoOHToEExMTXl2M1klPT5d4zXEcioqKsHz5crRv355RKvJfhYWFITY2FitXrsS0adPE4/b29oiMjKTJsQJQxqrcZ8+exaZNm5CXl4e4uDiYmppix44dsLKyEj+FJPIvPz9f3Mlh7969GDp0KMLDw5GSkoLBgwczTkeUHU2OidxZt24dXr16BQBYtGgR1NXVce7cOYwcORKBgYGM05H3YWBggIMHD+LOnTvIysoCAN7tgwOAtLQ0XLt2jdc9uDt06ACBQIC/l6no3r07tmzZwigV+a+2b9+OzZs3o0+fPvDy8hKPOzg44ObNmwyTEVK/vXv3YsKECRg/fjxSUlLw+vVrAMDz588RHh6OI0eOME5I3pWGhoZ4JeCff/4JDw8PALUrB/+tzRMhDY0mx0Tu1FU1Bmorqvr7+8Pf359hIvIhfv75Z0RGRiInJwdAbU/KefPmYerUqYyTyY6dnR2ePHnCOkaDys/Pl3itoqICIyMj2quqoO7fv1/vTSqRSITq6moGiQh5u9DQUGzcuBEeHh7YvXu3eNzJyYmWWiuYzz//HL6+vujZsycuX76MPXv2AABu374t1ceakI/tn3s5EMJQbm4ulixZgnHjxuHRo0cAags43Lhxg3Ey8j4CAwMxd+5cDB06FHFxcYiLi8PQoUPh4+ODJUuWsI4nMytWrIC/vz9Onz6NkpISlJeXS/zwgYWFhcSPmZkZTYwVWLt27XD27Fmp8bi4ODg6OjJIRMjb3bp1q95+6vr6+igrK/v4gch/tm7dOqipqSE+Ph4bNmyAqakpAODo0aMYOHAg43RE2dGTYyJ3kpKSMGjQIPTs2RNnzpxBWFgYjI2NkZ6ejpiYGOplqEA2bNiA6OhojBs3Tjw2bNgwODg4wNvbG6GhoQzTyU7fvn0BQGpPPN8KciUlJeGHH35AdnY2BAIB2rZtiwULFqBXr16so5H3FBwcjAkTJuD+/fsQiUTYt28fbt26he3bt+PQoUOs4xEixcTEBHfu3IGlpaXE+Llz52Btbc0mFPlPzM3N6/07ExkZKfF6+fLl8PLy4kWfbqI4aHJM5M7ChQsRGhoKX19f6OnpicddXFx4VcRJGQiFQnTu3FlqvFOnTqipqWGQqGEoQ2GcX375BZMmTcLIkSMxZ84ccByH8+fPo0+fPti2bRvc3d1ZRyTvYejQodizZw/Cw8MhEAgQFBSEjh074o8//kC/fv1YxyNEyowZMzB37lxs2bIFAoEADx48wIULF+Dn54egoCDW8UgDCA8Px5gxY2hyTD4qAff36iqEMKarq4uMjAxYWVlBT08P169fh7W1Ne7evYs2bdqIi3UR+eft7Q11dXWpHpt+fn54+fIl1q9fzygZeV9t27bF9OnT4ePjIzG+evVqREdHIzs7m1EyQoiyWLx4MSIjI8XXAZqamvDz88PSpUsZJyMN4c1rQEI+FnpyTOSOgYEBioqKYGVlJTGempoq3pdCFMfPP/+MhIQEdO/eHQBw8eJFFBYWwsPDA76+vuL3/X0CrYgqKytRUFCAqqoqiXEHBwdGiWQnLy8PQ4cOlRofNmwYvv32WwaJiKy8ePECIpFIYkxfX59RGkL+WVhYGBYvXoysrCyIRCLY2dlBV1eXdSxCCI/Q5JjIHXd3dwQEBCAuLg4CgQAikQjJycnw8/MTl/sniiEzMxMdO3YEUFtkDQCMjIxgZGSEzMxM8fsUvS/w48ePMWnSJBw9erTe43zYc2xmZoaTJ09KVTg+efIkzMzMGKUi/1V+fj5mz56N06dPS6zG4ds+ecI/Ojo6aN68OQQCAU2MCSEyR5NjInfCwsLg6ekJU1NTcBwHOzs7CIVCuLu786rCsTJQhr24ADBv3jw8ffoUFy9ehIuLC/bv34+HDx8iNDQUERERrOPJxPz58zFnzhykpaXByckJAoEA586dw7Zt26gWgAIaP348AGDLli3iiQYh8qympgbff/891q5dixcvXgCo3Ybl7e2N4OBgqKurM05ICOED2nNM5FZubi5SU1MhEong6OiIVq1asY5ESL1MTExw8OBBdO3aFfr6+rh69Spat26N33//HStXrsS5c+dYR5SJ/fv3IyIiQry/uK5atZubG+Nk5H3p6uri2rVrsLW1ZR2FkHfi5eWF/fv3IyQkBD169AAAXLhwAd999x3c3NywceNGxgmJrNGeY8ICPTkmcqtly5Zo2bIl6xiE/KuKigoYGxsDAAwNDfH48WO0bt0a9vb2SElJYZxOdkaMGIERI0awjkFkoEuXLigsLKTJMVEYu3btwu7duzFo0CDxmIODA8zNzTF27FiaHPNQr169oK2tzToGUTI0OSZy4c3CTP+GD4WbCL/Y2tri1q1bsLS0RIcOHbBp0yZYWlpi48aNMDExYR1PJq5cuQKRSIRu3bpJjF+6dAmqqqr1tuwi8ismJgZeXl64f/8+PvvsM6klqXwoIkf4RUtLS6rHMQBYWlpCQ0Pj4wciHyQ3Nxdbt25Fbm4uoqKiYGxsjGPHjsHMzAzt2rUDABw5coRxSqKMaHJM5EJqauo7vY/2xRF5NG/ePBQVFQEAgoODMWDAAPzyyy/Q0NBAbGws43SyMWvWLPj7+0tNju/fv48VK1bg0qVLjJKR/+Lx48fIzc3FpEmTxGMCgYAKchG5NWvWLCxduhRbt26FpqYmAOD169cICwvD7NmzGacj7yMpKQmDBg1Cz549cebMGYSFhcHY2Bjp6emIiYlBfHw864hEidGeY0IIkbHKykrcvHkT5ubmaNasGes4MqGrq4v09HSpvV/5+flwcHDA8+fPGSUj/4WdnR3atm0Lf3//egtyWVhYMEpGSP1GjBiBkydPQlNTE+3btwcAXL9+HVVVVejTp4/Ee/ft28ciInlHPXr0wOjRo+Hr6yuxr/jKlSsYPnw47t+/zzoiUWL05JjIrTt37iA3NxfOzs7Q1tYWP9EgRB4o21YATU1NPHz4UGpyXFRUBDU1+ipRNPfu3cPvv/8u1ZqLEHllYGCAUaNGSYxRGznFlJGRgZ07d0qNGxkZoaSkhEEiQv4fXdEQuVNSUoIxY8bg1KlTEAgEyMnJgbW1NaZOnQoDAwPetMYhiu3vWwGuXbsGoVAoLnB0+/ZtqKqqolOnTiziyVy/fv2waNEiHDx4EI0bNwYAlJWV4dtvv0W/fv0YpyPvy9XVFdevX6fJMVEYP/30E0QiERo1agQAuHv3Lg4cOIC2bdtiwIABjNOR92FgYICioiJYWVlJjKempsLU1JRRKkJq0eSYyB0fHx+oq6ujoKAAbdu2FY9/9dVX8PHxockxkQtv9nBevXo19PT0EBsbiyZNmgAAnj59ikmTJqFXr16sIspUREQEnJ2dYWFhAUdHRwBAWloamjdvjh07djBOR97X0KFD4ePjg4yMDNjb20sV5Bo2bBijZITUz83NDSNHjoSXlxfKysrQvXt3qKur48mTJ1i9ejW++eYb1hHJO3J3d0dAQADi4uIgEAggEomQnJwMPz8/eHh4sI5HlBztOSZyp0WLFjh+/Djat28vsRclPz8f9vb2ePHiBeuIhEgwNTVFQkKCuMJmnczMTPTv3x8PHjxglEy2Kioq8Ouvv+L69evQ1taGg4MDxo0bJzWxIvJPRUXlH49RQS4ij5o1a4akpCS0a9cOMTEx+PHHH5Gamoq9e/ciKChI3H+dyL/q6mp4enpi9+7d4DgOampqEAqFcHd3x7Zt26Cqqso6IlFi9OSYyJ2Kigro6OhIjT958kRcoZIQeVJeXo6HDx9KTY4fPXrEq0JVjRo1wueffw5zc3NUVVUBAI4ePQqAnjQqGpFIxDoCIe+lsrISenp6AICEhASMHDkSKioq6N69O+7du8c4HXkf6urq+PXXXxESEoLU1FSIRCI4OjqiVatWrKMRQpNjIn+cnZ2xfft2LF26FADES25WrVoFFxcXxukIkTZixAhMmjQJERER6N69OwDg4sWLWLBgAUaOHMk4nWzk5eVhxIgRyMjIkGj5U4eeNBJCGpKNjQ0OHDiAESNG4Pjx4/Dx8QFQexNSX1+fcTryX7Rs2RItW7ZkHYMQCbSsmsid7OxsfPHFF+jUqRMSExMxbNgw3LhxA6WlpUhOTqY/pETuVFZWws/PD1u2bEF1dTUAQE1NDVOmTMGqVavEBWQU2dChQ6Gqqoro6GhYW1vj0qVLKC0txfz58/HDDz/wZm+1sggJCXnr8aCgoI+UhJB3Ex8fD3d3dwiFQvTp0wcJCQkAgGXLluHMmTPiVSxE/nEch/j4eJw6dQqPHj2SWslCrbgISzQ5JnKluroa/fv3x7Jly3D06FFcu3YNIpEIHTt2xKxZs2BiYsI6IiH/qKKiArm5ueA4DjY2NryYFNdp1qwZEhMT4eDggMaNG+Py5cuwtbVFYmIi5s+fL1W9m8i3uqJqdaqrq5Gfnw81NTW0bNkSKSkpjJIR8s+Ki4tRVFSE9u3bi/fNX758Gfr6+mjTpg3jdORdzZkzB5s3b4aLi0u9fda3bt3KKBkhNDkmcsjIyAjnz5+nvSeEyJEmTZrg2rVrsLa2RsuWLRETEwMXFxfk5ubC3t4elZWVrCOSD1ReXg5PT0+MGDECEyZMYB2HEMJThoaG+OWXXzB48GDWUQiR8s/lKglhxMPDAz///DPrGISQN3z22WdIT08HAHTr1g0rV65EcnIyQkJCYG1tzTgdkQV9fX2EhIQgMDCQdRRCCI81btyYvjeI3KKCXETuVFVVISYmBidOnEDnzp2llqauXr2aUTJClNeSJUtQUVEBAAgNDcWXX36JXr16oWnTptizZw/jdERWysrK8OzZM9YxCCE89t133+H777/Hli1boK2tzToOIRJoWTWRO2+rSC0QCJCYmPgR0xBC/klpaSmaNGkitV+MyL+1a9dKvOY4DkVFRdixYwecnZ2xa9cuRskIIXxXWVmJkSNHIjk5GZaWllBXV5c4TjUPCEs0OSaEEEKUjJWVlcRrFRUVGBkZwdXVFYsWLRL3kyWEEFkbM2YMTp06hf/973/1FuQKDg5mlIwQmhwTQgghhBBCPpJGjRrh+PHj+Pzzz1lHIUQKFeQihBBClEhNTQ3U1NSQmZnJOgohRAmZmZlBX1+fdQxC6kWTY0IIIUSJqKmpwcLCAkKhkHUUQogSioiIgL+/P+7evcs6CiFSaFk1IYQQomS2bt2KuLg4/PLLLzA0NGQdhxCiRJo0aYLKykrU1NRAR0dHqiBXaWkpo2SE0OSYEEIIUTqOjo64c+cOqqurYWFhIdUyj6rFEkIaSmxs7FuPT5w48SMlIUQa9TkmhBBClIybmxu14CKEMEGTXyLP6MkxIYQQQgghpMGUl5eLi3CVl5e/9b1UrIuwRJNjQgghRMlYW1vjypUraNq0qcR4WVkZOnbsiLy8PEbJCCF8pKqqiqKiIhgbG0NFRaXelSscx0EgEFCxQMIULasmhBBClMzdu3frvQB9/fo1/vrrLwaJCCF8lpiYKC7+t3XrVpiZmUFVVVXiPSKRCAUFBSziESJGT44JIYQQJfH7778DAIYPH47Y2Fg0btxYfEwoFOLkyZM4ceIEbt26xSoiIYTn3nyK/KaSkhIYGxvTk2PCFD05JoQQQpTE8OHDAQACgUCqKI66ujosLS0RERHBIBkhRFnULZ/+uxcvXkBLS4tBIkL+H02OCSGEECUhEokAAFZWVrhy5QqaNWvGOBEhRFn4+voCqL05FxgYCB0dHfExoVCIS5cuoUOHDozSEVKLJseEEEKIksnPz5caKysrg4GBwccPQwhRCqmpqQBqnxxnZGRAQ0NDfExDQwPt27eHn58fq3iEAKA9x4QQQojSWbFiBSwtLfHVV18BAEaPHo29e/fCxMQER44cQfv27RknJITw1aRJkxAVFUUtm4hcoskxIYQQomSsra3xyy+/wMnJCSdOnMCYMWOwZ88e/PbbbygoKEBCQgLriIQQQshHR8uqCSGEECVTVFQEMzMzAMChQ4cwZswY9O/fH5aWlujWrRvjdIQQQggbKqwDEEIIIeTjatKkCQoLCwEAx44dQ9++fQHU7gWkNiqEEEKUFT05JoQQQpTMyJEj4e7ujlatWqGkpASDBg0CAKSlpcHGxoZxOkIIIYQNmhwTQgghSiYyMhKWlpYoLCzEypUroaurC6B2ufXMmTMZpyOEEELYoIJchBBCCCGEEEKUHj05JoQQQpTQ7du3cfr0aTx69AgikUjiWFBQEKNUhBBCCDv05JgQQghRMtHR0fjmm2/QrFkztGjRAgKBQHxMIBAgJSWFYTpCCCGEDZocE0IIIUrGwsICM2fOREBAAOsohBBCiNygyTEhhBCiZPT19ZGWlgZra2vWUQghhBC5QX2OCSGEECUzevRoJCQksI5BCCGEyBUqyEUIIYQoGRsbGwQGBuLixYuwt7eHurq6xPE5c+YwSkYIIYSwQ8uqCSGEECVjZWX1j8cEAgHy8vI+YhpCCCFEPtDkmBBCCCGEEEKI0qNl1YQQQogS8PX1xdKlS9GoUSP4+vr+4/sEAgEiIiI+YjJCCCFEPtDkmBBCCFECqampqK6uFv/+T97seUwIIYQoE1pWTQghhBBCCCFE6VErJ0IIIYQQQgghSo8mx4QQQgghhBBClB5NjgkhhBBCCCGEKD2aHBNCCCGEEEIIUXo0OSaEEEIIIYQQovRockwIIYQQQgghROnR5JgQQgghhBBCiNKjyTEhhBBCCCGEEKX3f8gZhg8ZwvPqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da23f18",
   "metadata": {},
   "source": [
    "The only strong correlation is between `energy` and `loudness`, which is not too surprising, given that loud music is usually pretty energetic. Otherwise, the correlations are relatively weak. It will be interesting to see what a clustering algorithm can make of this data.\n",
    "\n",
    ":::{note}\n",
    "Note that correlation does not imply causation! We have proof of correlation but no proof of causation. An amusing web site has some visuals that emphasize this point.\n",
    ":::\n",
    "\n",
    "Is there any convergence in this dataset around a song's perceived popularity and danceability? A FacetGrid shows that there are concentric circles that line up, regardless of genre. Could it be that Nigerian tastes converge at a certain level of danceability for this genre?  \n",
    "\n",
    "Try different datapoints (energy, loudness, speechiness) and more or different musical genres. What can you discover? Take a look at the `df.describe()` table to see the general spread of the data points.\n",
    "\n",
    "### Exercise - data distribution\n",
    "\n",
    "Are these three genres significantly different in the perception of their danceability, based on their popularity?\n",
    "\n",
    "1. Examine our top three genres data distribution for popularity and danceability along a given x and y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "506106cf",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "g = sns.jointplot(\n",
    "    data=df,\n",
    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
    "    kind=\"kde\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf2bdf4",
   "metadata": {},
   "source": [
    "You can discover concentric circles around a general point of convergence, showing the distribution of points.\n",
    "\n",
    ":::{note}\n",
    "Note that this example uses a KDE (Kernel Density Estimate) graph that represents the data using a continuous probability density curve. This allows us to interpret data when working with multiple distributions.\n",
    ":::\n",
    "\n",
    "In general, the three genres align loosely in terms of their popularity and danceability. Determining clusters in this loosely-aligned data will be a challenge:\n",
    "\n",
    "2. Create a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d12909be",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28523a37dc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 451.375x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"artist_top_genre\") \\\n",
    "    .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
    "    .add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889fdc7f-e023-4716-bfad-02937fcff40f",
   "metadata": {},
   "source": [
    "A scatterplot of the same axes shows a similar pattern of convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb025fa",
   "metadata": {},
   "source": [
    "In general, for clustering, you can use scatterplots to show clusters of data, so mastering this type of visualization is very useful. In the next section, we will take this filtered data and use k-means clustering to discover groups in this data that see to overlap in interesting ways.\n",
    "\n",
    "## Your turn! 🚀\n",
    "\n",
    "[customer-segmentation-clustering](../../assignments/ml-advanced/unsupervised-learning/customer-segmentation-clustering.ipynb)\n",
    "\n",
    "## Self study\n",
    "\n",
    "Before you apply clustering algorithms, as we have learned, it's a good idea to understand the nature of your dataset.There are several resources available. \n",
    "\n",
    "- [right clustering algorithm](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
    "- [clustering algorithms behave](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) \n",
    "\n",
    "## Acknowledgments\n",
    "\n",
    "Thanks to Microsoft for creating the open-source course [Machine Learning for Beginners](https://github.com/microsoft/ML-For-Beginners). It inspires the majority of the content in this chapter."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}