Clustering Case Study - Customer Segmentation with K-Means - Tier 3.ipynb

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    "# Clustering Case Study: Customer Segmentation with K-Means -  Tier 3\n",
    "***\n",
    "This case study is based on [this blog post](http://blog.yhat.com/posts/customer-segmentation-using-python.html) by the `yhat` blog. Please feel free to refer to the post for additional information, and solutions.\n",
    "\n",
    "Structure of the mini-project:\n",
    "\n",
    "1. **Sourcing and loading**\n",
    "    * Load the data\n",
    "    * Explore the data\n",
    "\n",
    "\n",
    "2. **Cleaning, transforming and visualizing**\n",
    "    * Data Wrangling: Exercise Set 1\n",
    "        - Creating a matrix with a binary indicator for whether they responded to a given offer\n",
    "        - Ensure that in doing so, NAN values are dealt with appropriately\n",
    "    \n",
    "\n",
    "3. **Modelling** \n",
    "    * K-Means clustering: Exercise Sets 2 and 3\n",
    "        - Choosing K: The Elbow method\n",
    "        - Choosing K: The Silhouette method\n",
    "        - Choosing K: The Gap statistic method\n",
    "    \n",
    "    * Visualizing clusters with PCA: Exercise Sets 4 and 5\n",
    "\n",
    "\n",
    "4. **Conclusions and next steps**\n",
    "    * Conclusions\n",
    "    * Other clustering algorithms (Exercise Set 6)"
   ]
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    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
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    "## 1. Sourcing and loading\n",
    "### 1a. Load the data\n",
    "The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset."
   ]
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   "source": [
    "df_offers = pd.read_excel(\"./WineKMC.xlsx\", sheet_name=0)"
   ]
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   "source": [
    "### 1b. Explore the data"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
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       "      <td>Oregon</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
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       "      <td>France</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   offer_id  campaign            varietal  min_qty  discount       origin  \\\n",
       "0         1   January              Malbec       72        56       France   \n",
       "1         2   January          Pinot Noir       72        17       France   \n",
       "2         3  February           Espumante      144        32       Oregon   \n",
       "3         4  February           Champagne       72        48       France   \n",
       "4         5  February  Cabernet Sauvignon      144        44  New Zealand   \n",
       "\n",
       "   past_peak  \n",
       "0      False  \n",
       "1      False  \n",
       "2       True  \n",
       "3       True  \n",
       "4       True  "
      ]
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   "source": [
    "df_offers.columns = [\"offer_id\", \"campaign\", \"varietal\", \"min_qty\", \"discount\", \"origin\", \"past_peak\"]\n",
    "df_offers.head()"
   ]
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    "We see that the first dataset contains information about each offer such as the month it is in effect and several attributes about the wine that the offer refers to: the variety, minimum quantity, discount, country of origin and whether or not it is past peak. The second dataset in the second worksheet contains transactional data -- which offer each customer responded to."
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_name</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>n</th>\n",
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       "      <th>0</th>\n",
       "      <td>Smith</td>\n",
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       "  customer_name  offer_id  n\n",
       "0         Smith         2  1\n",
       "1         Smith        24  1\n",
       "2       Johnson        17  1\n",
       "3       Johnson        24  1\n",
       "4       Johnson        26  1"
      ]
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   "source": [
    "df_transactions = pd.read_excel(\"./WineKMC.xlsx\", sheet_name=1)\n",
    "df_transactions.columns = [\"customer_name\", \"offer_id\"]\n",
    "df_transactions['n'] = 1\n",
    "df_transactions.head()"
   ]
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       "(324, 3)"
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   "source": [
    "df_transactions.shape"
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   "source": [
    "## 2. Cleaning, transforming and visualizing\n",
    "### 2a. Data Wrangling"
   ]
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   "source": [
    "We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.\n",
    "\n",
    "The first thing we need is a way to compare customers. To do this, we're going to create a matrix that contains each customer and a 0/1 indicator for whether or not they responded to a given offer. "
   ]
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   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set I</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Create a data frame where each row has the following columns (Use the pandas [`merge`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) and [`pivot_table`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) functions for this purpose):\n",
    "<ul>\n",
    "<li> customer_name\n",
    "<li> One column for each offer, with a 1 if the customer responded to the offer\n",
    "</ul>\n",
    "<p>Make sure you also deal with any weird values such as `NaN`. Read the documentation to develop your solution.</p>\n",
    "</div>"
   ]
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   "source": [
    "#your turn\n",
    "merged = pd.merge(df_offers,df_transactions)\n",
    "pivots = merged.pivot_table(index='customer_name', columns='offer_id', values = 'n', fill_value=0)"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "      <th>customer_name</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Peterson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Wood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>31</td>\n",
       "      <td>December</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Fisher</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>Young</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>Cooper</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>32</td>\n",
       "      <td>December</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>324 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     offer_id  campaign            varietal  min_qty  discount   origin  \\\n",
       "0           1   January              Malbec       72        56   France   \n",
       "1           1   January              Malbec       72        56   France   \n",
       "2           1   January              Malbec       72        56   France   \n",
       "3           1   January              Malbec       72        56   France   \n",
       "4           1   January              Malbec       72        56   France   \n",
       "..        ...       ...                 ...      ...       ...      ...   \n",
       "319        31  December           Champagne       72        89   France   \n",
       "320        32  December  Cabernet Sauvignon       72        45  Germany   \n",
       "321        32  December  Cabernet Sauvignon       72        45  Germany   \n",
       "322        32  December  Cabernet Sauvignon       72        45  Germany   \n",
       "323        32  December  Cabernet Sauvignon       72        45  Germany   \n",
       "\n",
       "     past_peak customer_name  n  \n",
       "0        False        Thomas  1  \n",
       "1        False       Jackson  1  \n",
       "2        False      Mitchell  1  \n",
       "3        False      Peterson  1  \n",
       "4        False          Wood  1  \n",
       "..         ...           ... ..  \n",
       "319      False        Fisher  1  \n",
       "320       True         Young  1  \n",
       "321       True        Cooper  1  \n",
       "322       True         Gomez  1  \n",
       "323       True         Kelly  1  \n",
       "\n",
       "[324 rows x 9 columns]"
      ]
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    "merged"
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     "data": {
      "text/plain": [
       "30    22\n",
       "22    21\n",
       "8     20\n",
       "7     19\n",
       "29    17\n",
       "31    17\n",
       "26    15\n",
       "18    14\n",
       "11    13\n",
       "4     12\n",
       "6     12\n",
       "24    12\n",
       "9     10\n",
       "1     10\n",
       "2     10\n",
       "14     9\n",
       "27     9\n",
       "17     7\n",
       "10     7\n",
       "3      6\n",
       "15     6\n",
       "13     6\n",
       "25     6\n",
       "28     6\n",
       "20     6\n",
       "16     5\n",
       "12     5\n",
       "19     5\n",
       "23     5\n",
       "5      4\n",
       "21     4\n",
       "32     4\n",
       "Name: offer_id, dtype: int64"
      ]
     },
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   "source": [
    "merged['offer_id'].value_counts()"
   ]
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   "source": [
    "## 3. Modelling \n",
    "### 3a. K-Means Clustering\n",
    "\n",
    "Recall that in K-Means Clustering we want to *maximize* the distance between centroids and *minimize* the distance between data points and the respective centroid for the cluster they are in. True evaluation for unsupervised learning would require labeled data; however, we can use a variety of intuitive metrics to try to pick the number of clusters K. We will introduce two methods: the Elbow method, the Silhouette method and the gap statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B7lnwb9HymX8"
   },
   "source": [
    "#### 3ai. Choosing K: The Elbow Sum-of-Squares Method\n",
    "\n",
    "The first method looks at the sum-of-squares error in each cluster against $K$. We compute the distance from each data point to the center of the cluster (centroid) to which the data point was assigned. \n",
    "\n",
    "$$SS = \\sum_k \\sum_{x_i \\in C_k} \\sum_{x_j \\in C_k} \\left( x_i - x_j \\right)^2 = \\sum_k \\sum_{x_i \\in C_k} \\left( x_i - \\mu_k \\right)^2$$\n",
    "\n",
    "where $x_i$ is a point, $C_k$ represents cluster $k$ and $\\mu_k$ is the centroid for cluster $k$. We can plot SS vs. $K$ and choose the *elbow point* in the plot as the best value for $K$. The elbow point is the point at which the plot starts descending much more slowly. \n",
    "\n",
    "**Hint:** the Elbow Method is discussed in part 2 of the Harvard Clustering lecture. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o5T4iBfqymX-"
   },
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set II</h3>\n",
    "\n",
    "<p><b>Exercise:</b></p> \n",
    "<ul>\n",
    "<li> What values of $SS$ do you believe represent better clusterings? Why?\n",
    "<li> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums) \n",
    "<li> Write code that applies the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering method from scikit-learn to this matrix. \n",
    "<li> Construct a plot showing $SS$ for each $K$ and pick $K$ using this plot. For simplicity, test $2 \\le K \\le 10$.\n",
    "<li> Make a bar chart showing the number of points in each cluster for k-means under the best $K$.\n",
    "<li> What challenges did you experience using the Elbow method to pick $K$?\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mG_tbcIdymX-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sum of Squares')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your turn\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "x_cols = pivots.to_numpy()\n",
    "SS = []\n",
    "results = {}\n",
    "\n",
    "for x in list(range(2,11)):\n",
    "    model = KMeans(n_clusters=x)\n",
    "    cluster = model.fit_predict(x_cols)\n",
    "    centers = model.cluster_centers_\n",
    "    SS.append(np.sum((x_cols - centers[cluster]) ** 2))\n",
    "    results[str(x)] = cluster\n",
    "    \n",
    "plt.plot(list(range(2,11)), SS)\n",
    "plt.xlabel('K Values')\n",
    "plt.ylabel('Sum of Squares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Counts')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## From the graph, we can estimate that 3 clusters is the region where we witness the bend in the elbow. This means \n",
    "## that including more than 3 clusters, will eventually lead to overfitting. \n",
    "## Lower values of SS means a better model. Better models yield more accurate predictons, which will minimize \n",
    "## the difference beween them. \n",
    "\n",
    "counts = np.bincount(results[str(3)])\n",
    "plt.bar(range(3), counts, align='center')\n",
    "plt.xlabel('Bins')\n",
    "plt.ylabel('Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OM08MgqiymYB"
   },
   "source": [
    "# 3aii. Choosing K: The Silhouette Method\n",
    "\n",
    "There exists another method that measures how well each datapoint $x_i$ \"fits\" its assigned cluster *and also* how poorly it fits into other clusters. This is a different way of looking at the same objective. Denote $a_{x_i}$ as the *average* distance from $x_i$ to all other points within its own cluster $k$. The lower the value, the better. On the other hand $b_{x_i}$ is the minimum average distance from $x_i$ to points in a different cluster, minimized over clusters. That is, compute separately for each cluster the average distance from $x_i$ to the points within that cluster, and then take the minimum. The silhouette $s(x_i)$ is defined as\n",
    "\n",
    "$$s(x_i) = \\frac{b_{x_i} - a_{x_i}}{\\max{\\left( a_{x_i}, b_{x_i}\\right)}}$$\n",
    "\n",
    "The silhouette score is computed on *every datapoint in every cluster*. The silhouette score ranges from -1 (a poor clustering) to +1 (a very dense clustering) with 0 denoting the situation where clusters overlap. Some criteria for the silhouette coefficient is provided in the table below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Prh3gX9NymYC"
   },
   "source": [
    "<pre>\n",
    "\n",
    "| Range       | Interpretation                                |\n",
    "|-------------|-----------------------------------------------|\n",
    "| 0.71 - 1.0  | A strong structure has been found.            |\n",
    "| 0.51 - 0.7  | A reasonable structure has been found.        |\n",
    "| 0.26 - 0.5  | The structure is weak and could be artificial.|\n",
    "| < 0.25      | No substantial structure has been found.      |\n",
    "\n",
    "</pre>\n",
    "Source: http://www.stat.berkeley.edu/~spector/s133/Clus.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LFdwQkQGymYC"
   },
   "source": [
    "**Hint**: Scikit-learn provides a function to compute this for us (phew!) called [`sklearn.metrics.silhouette_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html). Take a look at [this article](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) on picking $K$ in scikit-learn, as it will help you in the next exercise set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WQ1xRm9fymYD"
   },
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set III</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</p>\n",
    "\n",
    "<p><b>Exercise:</b> Compute the average silhouette score for each $K$ and plot it. What $K$ does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IZXVFtvXymYE"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9pc1PTk5m6NChFC9enA4dOnDjxg0aN26sJTeffPIJrVu3pnTp0tp3uHbt2gwcOJCXXnoJPz8/Ro0axYwZMzhz5gzVq1dP9f7NmjVDURS2bt1Knz592LVrF4qiEBkZSXR0NFu3bgVw2V7w008/5Y8//sBgMGjv7awxLVeuHEuWLMFgMPD4448TERHBiRMn0v2RY7Va+c9//sPzzz/PH3/8wfvvv6+t615mzpxJXFycdq65efMmgwYNStOGLrPOnTuHqqraTSvJycn8+eefWuyuytxZU/Z///d/hISEsHHjRvr378/s2bMZOXKktu6XX36Z1157DYBff/0VgCeffJIhQ4Zw6dIl1qxZQ4MGDVzGld3Lu//88w9RUVH07t0bb2/vbK0jq3LlriI/Pz9Gjx7N5s2bGTNmDJ06dcJoNHLgwIF0q31dURRF+zI6TzbO6rR9+/YB0KVLFwAMBoNWu+Ccd7e7x5N0Xs//4IMPCA8Pp1GjRiQkJHD06NF0qwHv5lzHnj17qFq1KuHh4VpVsKsvRtmyZWndujV79+6lT58+PP/884CjcWlK9evXx8vLC4PBoJ28nTVJjz/+OMWKFWPEiBG0aNGCY8eOuVwHQGhoKNWrV+fgwYNcvHiRNWvWAGgH9p49e2I0GnnllVdo2rQpc+fOpWzZspQtWzbNuvz8/OjatSvHjh3j0UcfpVu3bly+fJkaNWpkuv2K89Kg83Pz9vambdu2qeaBo5brXkmL00cffUS7du1ISkri888/d7mMt7c34eHh7N69W6tiddaYOTk/yyVLlmhVpHv27MFqtXL48OEcf3ZZLT9nPI0bN9ZqrsaMGcP//vc/l5fD7iU7n19mvyN3JwHpadq0KYCWDDjX4Tz5Oy8PeHp6ar/WXdm/fz92u51HHnlES9adz6OiolIldhl9l1KqUqUKjRs3ZtOmTTRp0oTevXuTmJionVAy+/k7j1nOBu1lypTRkmnnD4K739+53cHBwVSpUgWLxaJdtkvJ+Xk4LwE+9thjgOtjDThqphVF0RKDmjVrpvpREhQUpCVzAwYM0C7ZuDqWOD300EPUqVOHMWPG0LlzZ2bMmJHua0qVKkXVqlXZvn07R48e5fr161rD7927d7Nlyxa8vLxo3Lhxuu/nSt26dTGZTOh0OsqVKwfcOTek59FHHwXu7HuZrZk/efIkAE2aNAEcifBvv/3Gf//730zHm/Lc07hxY8xmM/Pnz6dRo0YMHToUvV6fJulLyfm5O+8Ic95ldvfnnvJ72K5dO0qVKsWkSZNo1KgRn376Kd7e3uk2nn3//fcZPHiwy4ezraIrzvOd8zieF9xe47J7926mT59O9erVGTBgAJ06daJTp05UqVKFb7/9VjvJZobRaNSyWudf5w6gKEqa5dOb57ymd/eO6jxo1q9fn8DAwFTzMvripuRsqFuuXDkefPDBVPPuXifAgQMHeOGFFyhZsiTPPfccvXv3dln1n7JWKuW222w27VJLr169+OSTTxg/fjz79u1Lk5g5derUiYMHD7Jq1Sr+/vtvSpUqRf369QHHiWT58uUsXbqUzZs389dff7Fw4ULeffddl41yv/jiC7p06cLq1avZvn07kyZNYubMmSxdulSrLcmITqdL8/m4+tx8fX3vuS6Afv368cwzz9CwYUPWrFnDmjVr+Oeff1ye9OrWrcuhQ4dYvHgxvr6+VKlSJdV852cZGhqapmGcp6dnjj87yFr5OV+T8hJgUlLSPe/USbkf3L3PZ/Xzy+x3JDOfl9Fo1BIk519nrM6yz+xg9c5E7m6uXp/R55GSXq9n2rRprFu3jrVr17Jjxw6+++47fv/9d5YtW8bJkycz9fk7Px/n/uy8/JZR3K5+KLla1rlc06ZNU+0HKd8jJUVR0mxryu/Z2bNn6dmzJ+BoJzFw4EDt//SMGTOGn376iXbt2jFw4EB27drFrFmz0v3smjVrxo8//qhdmnnllVfYsGEDa9as4ciRI7Rs2TLLDXBTbruznO617zjLKKs3Cbj6HiYmJqZb5k7O846qqqlu6PD19WXOnDmsWLGCf/75h+3bt7N161YWLlyY7g975+fubP/i5EzaUq7bqUyZMixdupTly5ezYcMGduzYwd9//8369eu1y+spbdq0yWWyDGiXS13Zvn073t7e2jklL7i9xsXHx4cVK1Ywbdo0Ll26pE13Fkh2b6u9m7OaddGiRYDjwOe8tu3MOp079+XLlwHStNsICwsDHDvDxIkTGTVqFKVLl6Zt27b4+vq6/EI4v/TOnbJy5coA+Pv7M3bsWCZOnEjt2rWpVasWtWvXThP3/PnziY+P54UXXqBPnz6ZTpCcDh06xK5duwgPD2fkyJHUq1eP8+fPZ/iajh07otPp+PHHH7l27RqPPfaYtm3fffcdo0eP5plnnuHXX39l3LhxgKONyd1Onz7Ne++9x5YtW/jwww9ZunQpjz/+OLGxsenWct2tWrVqqKqqfW7x8fFa+5uUvxbSO8DfrUaNGgCEhITQvXt3wHGp0tWvKWeblpiYGOrUqZPmPZyf5QMPPMDEiROZMGECISEh1K9fX/uFlJPPLqvl59w/t2zZoh00+/btS+PGjVM1tnZyHkid+3tsbCynTp3K0vvfvX/f6zvilJlqc1c/Npycv4LXrVsHOPaLv//+O93lnT8SNm/ezJUrVwDHgffq1as88MAD2aqR2rt3L++88w5nz57l888/Z/Xq1dSuXZsLFy5w6tSpHH/+GXE2Kr5w4QInTpzAw8MjzUkJ7nweTz/9NBMnTuT111+nfPnyqS6NpXR3ue7fv1/bP8BxqTU6OprOnTszYMCANJ+jq2PgzJkzURSFzz//nI4dO96z4abzR8SiRYvw9/endu3aVKtWjSVLlqCqqsv2LU6uEq/symj/y4izzDdu3AjArVu3ePjhh+nYsaPLdoV3n3cOHDiQahvWr1/PO++8g06nY8yYMaxfv56SJUty8OBBrl+/7rLMnTEMGjSIiRMn0qdPHx544AGthswp5ee3cOFC3nvvPSpWrMj48eNZu3Yter2eTZs2udzOtWvXcvToUZcP5xWPu0VFRXHx4kXCw8Nz9a7Ru7n9ncLDw2nXrh0rV66kQ4cO1K5dm8uXL3P8+HECAgK0k0tO9evXj+XLlzNp0iS2bt3KjRs3iIiIoHr16jz++OOA4+B26tQpXn/9dWrXrp3mzpsePXrwyy+/8NVXX7Fu3TrOnDnD5cuXtROAszHYzp076dmzJ9OnT9emzZo1i9OnT/Pll1/yyCOPsGnTJjp06IC/vz8HDhygbNmy9OnTJ03czoPRN998w+rVq7U2FnFxcZna7tKlS2MwGDh48CA9e/bkwoUL2p0b6TVOK126NA0aNNAa8KU8yMXExLBu3TqeeOIJqlatyqFDhwBcXgctWbIk69ev165Ne3t7s2vXLjw9PbUE4l4GDhzIzp07+fDDD1m0aJG247ds2ZL69etnulMsV4YMGcKiRYuIiIhg+vTpWhsgp5SNce9umAuOX7GhoaGsW7eOLl26oKoqx44do2bNmvTv3z/Hn11Wy+/hhx+mWrVq7N+/n86dO+Pp6cnBgwepWrUqYWFhaRpkOu9aWL9+PcOGDSMyMjLVPpGZ9w8KCuLs2bMMGjSIvn373vM74i5PP/00v/76KzNnzuTgwYNcunQpw+rpChUq0KVLFxYtWkSnTp0wm83s2bMHRVF4/fXXsxVDyZIlWbVqFYsWLWL16tXY7Xb2799PsWLFCA0NzfHnn5GJEyeyefNmTp8+TXJyMn369HFZC/H888+zfPly3njjDa1vrBs3bqTbKP3FF19k7969vPHGG9SuXZuDBw+m6gLAuU0zZszgyJEj2uVa5zb5+/uj1+s5c+YMzz33HJ999hnlypXjxIkTPP3005hMJo4cOQKkf/ypW7cuAQEB3Lx5k/r166PT6WjQoIGWLGfUH1ZQUBDXrl2jZ8+e+daFxnPPPccvv/zCzz//zP79+7l48SJxcXE89NBDLhN2Z1L9ww8/cPToUe1ymPNOscDAQFasWMHKlSuZO3cut27dIjo6mrCwMIKCgrQrDSnLvHfv3uzbt48XXniBWrVqsX//fuLj4zO8xObp6cmKFSvYsGGD9gPXZrPRsGFDt5XNhQsXgDs/+vJKrrRx+fLLL3n11VcpWbKkdhdHy5YtmTFjRqYuJ2SG2Wxm1qxZNG3aVGuY2r17d6ZNm6Z1YPTGG2/QoEEDLl68SEREBF9//XWqdQQHBzN58mRq1qypXWrp168fI0aMABxZbrdu3dDr9Vy4cIG4uDgee+wx6tWrx40bN7QapTFjxvDEE09w/fp1Tp48SdOmTZk2bZrLg3uvXr3o1KkT4Lh2+uKLL1KhQgWOHz+e6k6D9JQqVYpPPvmEMmXKcOzYMUJDQ3nyyScBMry13Pme5cqVS1Wz8fbbb/PKK69gMBjYunUrnp6eDBkyhL59+6ZZh6+vL7/88gstW7YkMjKSf//9l5o1azJp0iTKly9/z9jB0Q5hypQp1KlThwMHDmCxWOjTp0+qXm2zq3Tp0tot6j/88EOqGj9wnOycd7y4Slx0Oh2TJ0+mdevWREVFceHCBdq3b6/d3ZLTzy6r5acoChMnTqRNmzZcuHCBs2fP0rZtW3744QeXvx4rV67MyJEjCQwMZNu2bTRt2lRrS5TZ9x8wYAAlS5bk9OnTJCYm3vM74i4hISF8++23BAcHc/jwYRo3bkyPHj0AUnVIltJnn33G0KFD8fb2Zvfu3VSqVImxY8dq7T6yqnz58kybNo2GDRty5MgRTpw4wUMPPcSUKVPw9vbO8eefkc8++4xLly4RHx9P9+7d0z1JN2jQgG+//ZaQkBB27dqFt7c3b7zxhtbe5m4dOnRgxIgReHt7c/z4cQYOHJiqEe9jjz1G79698fLy4siRI3To0IG6detqPwR9fX15+eWX8fX1JSIigsTERD7//HPCw8O1ml7n5bI9e/a4jEGv1/Pwww8DaJcTnDGEh4dneE4YMmQIgYGBnDx58p5tWHJL6dKlmTx5spYwWCwWXnzxRd58802Xy3fv3p3u3btjtVrZtWsXb7zxRqoai9q1azNhwgSqVq3Kvn37OH/+PG3atNGOM67KvHPnznzwwQeUKFGCXbt2UapUKa13+vS0b9+eL774ggoVKrBz505iYmLo2rVruu0As8P54+LubiVym6K6qx5OFGijR4/m119/pV+/fi7vVhAiP23ZsoUDBw4QFhamNVR13p03ffp0HnrooXyOMHe0atWKc+fOsXHjRpd38gkh0sq7i1IFxLhx41w2TLqXNWvWUKFCBXr37s327dt5//33C3R3zE4LFixg2rRpWgdQzlshz549qzX02r17Nz4+PoCjBmb+/PlFMsG5cuUKJpMJf3//NPOctysXNO7e35KTk7l06ZLLW8bT4+xAbdeuXSQmJlKiRAkWLlyY6QbUmWGxWBgzZgw6nY7GjRuTkJDA3r17CQgISNVWLDY2lri4uFRt5Zzf6fbt2zN27Fi3xVQYFbbjk7ts27aNF154QattzKwtW7Zod0W50qRJk0z3HB4ZGUnZsmXTrSEU7nPfJS5ly5ZNcxssOG4rS05OplKlSi4b9hXWMVcsFot2O1+5cuUK5Mk5L0yfPp1x48Yxe/bsVIlLdHQ0o0ePJioqirlz5+ZjhLlv06ZNfPzxx7zwwguZPqlZrVZeeuklIiIi8PLyonLlyphMJrcmLeBowPnf//6X33//nR07dmidm7311lta3xBLlizhiy++4NNPP3VbI39xf7tw4YLWRYQrrn7k3M1isTBhwgSmTp3K1q1bJXHJA/dd4vLUU0+l6oDJyVllO2DAALc1IC4Inn76aR577LFcaVBZmKR3XXfDhg0sX748wz4Uioq7e03NjCNHjhAREYFOp2PevHnaXSq54aWXXuKll15Kd/4333xT5EabXrt2rVvX9+WXX5KQkCCXnTLJ2R4lJy5dunTPXp6Fe913icv9yM/PL9UonkJklrMjt+LFi+dq0iLcw9Ut1EIUNblyV5EQomhw9lNxv43QK4QouCRxyYGdO3fy8ssv07BhQ+rWrUv37t2ZPXt2uh0mRUVF8cEHH2ij2TZu3JgBAwZoA5BlRVRUFO+99x6dOnWiTp06NGjQgKeffpopU6akGVnWOUpuVqtEjx49yrBhw2jSpIk2cvSPP/6Y7nAIERERqbavUaNG9O3bl+XLl6dZ1jkKdHh4uMt+MI4dO6bNd2XHjh28+uqrPPzww9SoUYPmzZvzzjvvpLkU4hyx16lz587aSLOtWrXSxvk4ePAg4eHh2h0tTsnJyUyfPp0nn3ySunXrUqdOHbp168bUqVOz3AGZc+Rbu93OtGnTaN++PbVq1aJ169Z8+umnaW7fvpfMlve2bdsIDw/X+n359NNPCQ8P1zobdMX5+QwcOBBwdCDp/DxSNn68desW48aNo3PnztSuXVv7HkyZMsXl7avOEWgjIyMZPnw4derUoWHDhuneWgp39l9nJ5YDBw5Md9Tos2fPMnLkSJo2bUrNmjVp27YtX3/9dbp9rcTGxjJ+/Hgt/nr16tGzZ0/++OMPl52LubJlyxbCw8Np3Lhxut+NuXPnEh4eztNPP51q+pEjR3jvvfdo3749devWpWbNmrRo0YIRI0a4HLTwXuXnHCV45syZaV578OBBRowYwaOPPkqNGjVo0qQJAwcOdNnZpHOfSa+fkHXr1rn8vgCsWrWKl156iZYtW1KjRg2aNm3K4MGDtQ7csuLq1at89913dOzYURu9+vnnn0818nJGnOXl7IDvbo0bN06zT0Pmj69vv/12qt5s69Wr53I05dWrV9O/f38aN25MzZo1adOmDaNGjUrVGaCT85g1depUfvvtN5o1a0atWrXo1KmTdnzLyvG/KJJLRdm0ZMkSRo8ejYeHByEhIVy6dImDBw9y8OBBTp48yXvvvZdq+Q0bNjBs2DDi4+Px8vKiSpUqXLt2jb///pu///6boUOHpjtK8N1OnjzJs88+y82bNwkICCA0NJT4+HgOHDjA/v37WbVqFTNnzsxRI7Ft27Yxc+ZMVFWlcuXK3Lhxg5MnT/LNN9+wZ88eJk2alGr51atXM2LECBITE7Vxga5fv87mzZvZvHkzq1ev5n//+1+2ByZLyTngJjj6DzCbzVrj2mXLlvH9999rnVo5G2M7R6OtVq0anp6e+Pn5UaNGDYxGIxEREXh7e1O1atVUbQNu3LjByy+/zP79+9HpdAQHB+Pp6cnRo0c5dOgQS5cuZerUqVnuw+D999/nzz//pFixYoSFhXHixAlmzpzJypUrmT59eqYaUGelvP38/KhXrx7Hjh0jNjaW4OBgSpYs6XI8KicPDw/q1avHrVu3OHHiBCaTSeukznnZMSIigr59+3L+/Hn0ej1VqlTBbrdz6NAhDh48yKJFi5g6darL9hb//e9/OXDgAGazmYsXL2Z4iaN48eLUq1dPa0AfFhaGv78/xYsXT7XcyZMn6d69OzExMYSFheHh4UFkZCSTJ09m8+bN/P7776m+E2fPnqVfv36cOXMGg8FApUqVsNvt7Nmzhz179rBy5UomTpx4z9qmJk2aULZsWS5cuMCmTZtc9q3hPNE+8cQT2rT58+fz7rvvYrPZCAoKIiQkhNjYWM6ePcuSJUtYsWIFP//8s8sOw7JSfuDoMHP06NHYbDYCAgKoWrUqFy9eZN26daxbt47+/ftnmDxm1k8//aQNUFqhQgXCw8O1BrBr1qzJ0t1Ohw4dYsCAAVy+fBmj0UiVKlW4efMmO3fuZOfOnZw4cUIbUNCdsnJ8rVSpEjVq1NDGDKpbty6Komg3c6iqygcffMAff/wBODo5rFKlCqdPn2bGjBksWbJE6yfpbitXrmTv3r2UK1eO8uXLEx8fT3BwcJ4c/ws8VaiqqqotW7ZUzWazOnfu3AyX69Wrl2o2m1Wz2ayOGDFCvXnzpqqqqmqz2dQvvvhCNZvNanh4uHrp0iXtNVFRUWq9evVUs9msfvfdd2pSUpI2b/Xq1dq8VatWZSrWoUOHqmazWR01apSanJysTT948KDauHFj1Ww2q/Pnz9emz507VzWbzWq3bt1SxeTcjtjYWG36W2+9pU3v06dPqu349ddftXl79+7Vpp86dUqtWbOmajab1U8//VSNj4/X5q1fv15t0KCBajab1W+//fae7+909OhRbX5KK1asUM1ms1qvXj116dKl2vTk5GR1woQJ2rxz586lep1zXUePHk013VXZOA0YMEA1m81qjx491DNnzmjTz58/rz733HOq2WxWBw0alOZ16XHGYDab1a+//lq1WCyqqqrq1atX1d69e6tms1nt3r27arfbtdc497cZM2Zo07JT3umt617Wrl2rms1mtWXLlqmmJycnq+3bt1fNZrPaq1cv9cKFC6ni69y5s2o2m9Xnnnsu1euc37MaNWqou3fv1tYVExNzz1icr127dm2q6WPHjtXK9YknnlBPnz6tzVu1apUaHh6ums3mVPuL1WpVu3btqprNZnXgwIFqdHS0Nu/48ePq448/rn3HMuPrr79WzWaz+sYbb6SZFx0drT744INqtWrV1KtXr2rTateurZrNZnXq1Kmq1WrVlo+MjFS7dOmims1m9aWXXnJZBumVn6vPeOvWrWp4eLgaHh6uTpo0Sdvv7Ha7On/+fLVGjRqq2WxW//jjj1SvMZvNaqNGjVxur6v94vr162r16tXVmjVrqtu3b9emW61W9YcfflDNZrNap06dVPtrepKSkrT9a8CAAVq5qaqqLl26VH3wwQdVs9ms7ty5M8N409tnnBo1aqSazWZ169at2rSsHl8zOpZNmzZNNZvNatOmTdXNmzdr0+Pi4tSPPvpINZvNavPmzVPt/yn359GjR2vHA2cZZDW+okguFWVTpUqV+OKLL7Tb5XQ6HcOHD8ff3x9VVVONPTN16lRiY2Pp2rUrr732WqpfcK1bt9Z6Ic1s/zLOgSq7d++eKquuVq0aQ4cOpX379jm+fTsgIICxY8dqPc2CoxraOWZGyl4yJ0+eTFJSEo8++ijvvfdeqkHtmjVrpt3R8/PPP3P9+vUcxeWsaXnnnXdSDV1gNBoZPHiwNvbO9OnTc/Q+Bw4cYN26dQQGBjJx4sRUI1WXLVuWsWPH4uPjow0UlxVt27ZlxIgR2tgexYoVY+zYsQQEBPDvv/+mO5aIU16Wd3qWLl3K6dOnKVGiBBMnTkzV+2lISAg//fQT3t7e7Ny5k/Xr16d5fbt27bTei41Go1tur9br9YwfPz5V9/dt2rTRRgV21rqB49fsoUOHqFSpEt999502wjQ4esz+7rvv0Ol0zJ49O8OhB5ycNSlr1qxJcwlx+fLl2Gw2Hn30Ua2rBedluzp16tCvX79UNZHBwcH069cPuDMy8d2yUn4TJ05EVVV69OjBgAEDtP1OURS6du2qHX/Gjh2b6ctjrkRERGCxWAgJCUlVS6TX6xk4cCBt27blscce48aNG/dc14oVKzh9+jTly5fnu+++S9VFRYcOHbSBIBcuXJjteNPjruNrUlKSVjP91VdfpepE0dvbmw8//FAbC8tVVwxGo5HXXntN6yXbWQZ5cfwv6CRxyaaWLVumGVTKZDJpHXul/HI6r6927NjR5bo6duyIoigcPnzY5TXPuzlPoh999BHbt29PNfLo888/z9ixY7XxmrKrcePGLu9Ecl7GSHlC/OeffwDHmB6utGnThnLlypGYmMjWrVuzHVNkZCQnTpxAp9OlO6ics0t2Z0zZ5ezb4eGHH3bZr0/x4sW1Ye6z+l7OYQlSCgwM1IaFz2hwwZTvl9vlnRFnMtK5c2eX+0mZMmUy3J6Uw064S9WqVV0OBudMtlN+J523Ibdt29blQd5sNmM2m7FYLJkqw7CwMKpXr05cXFya7XVeJko5/EKHDh3Yu3cvv/76q8v1OZPR9NorZLb84uLitDGV0ttfevTogclk4vLlyy7b1WRW+fLl0ev1HDlyhK+//pqoqKhU88ePH8/nn3+e4SVKJ2cZduzY0WU3DkOGDOGvv/7igw8+yHa86XHX8XX37t3cuHGDEiVKaMeKuzmPY66OIWazWesYNDfiK8ykjUs2payJSMm5ozl/dcXGxmoDUX377bfp3u+v1+uxWq1ERESku26nV199la1bt7J371569+6Nn58fTZo0oVmzZrRq1SrVr8fsSi8GZ2dgztGXY2Njtb41nCN2u/Lggw9y/vx5IiIish3TiRMnAEftlvMX6d2cDULPnDmDqqrZHhHW+Ut3586dPPvssy6XcTbAO336dJbWnd6AlFWqVAHIsK+VvCzvjDjX6xxQzpVq1aqxcOFClzHkRj8jmd1n4c7n+9dff7Fr1y6Xr7t48SKQ+c/3iSee4ODBgyxdupT27dsDjn1kz549+Pr6pmrE6WQ0Gtm1axfHjh0jMjKSyMhIjhw5ou1bzlG675bZ8ouKisJqtWptRFzx8vIiNDRU67OnVq1amVq3q5h69+7N9OnTmTx5MpMnT6ZSpUo0bdqU5s2b89BDD2W63YUz6Ukv5mLFimVrBPDMcNfx1Xm8io+PT/cYcvPmTcD1PpbeZ5wXx/+CThKXbMrs7aEp72ZwjryckZiYmHsuU7t2bebPn88PP/zA2rVriYmJYdWqVaxatYqPPvqIDh068OGHH+ao75bsbJ+rXwdOzpNHTkbSdQ5kZ7VaU1X7u2K324mLi8v2JQjne12+fPmetWCZ+cycDAZDuuXknJ7R+vKyvDPiXG9GMTjnuYohN6qys3LLtvPzjYqKSlMzcLfMfr6dOnXif//7H+vXrycuLg4fHx+ttsVV9f3KlSsZM2ZMqsROp9NRpUoV2rVrx8qVK9N9r8yWn7Psvby80OnSr2B31/4ycuRIqlevzm+//cbevXuJiIggIiKCmTNnEhQUxOuvv84zzzxzz/U4a8ecceUldx1fnftYfHz8PY9XrgbpTO8zzovjf0EniUsuS9n+YOvWrW4bRbNy5cp8/fXXJCcns3fvXrZs2cL69es5ePAgixcvJiEhgQkTJrjlvTKS8sASGxub7pfF+cV0dSBSXdw+7upWWudrzWYzixcvzla8meX83N566610a3eyw2q1YrFYXP7ydJ40MtpH3FHe7uBcb0ajIjtP+Plx8rkX5+c7duxYrXYkp4oXL84jjzzC+vXrWbt2LZ07d2bJkiVA6stEABs3bmTYsGGoqkrLli1p37494eHhhISE4OXlxcaNGzNMXDLLmTwmJCRgt9vTTV6yur9kdMttly5d6NKlC1evXmXr1q1s2rSJdevWce3aNd5//32KFStGmzZtMly/8/KQu27tdXWMAdfHGXDP8dW5j7Vs2TLNXZg5VVCO//lF2rjkMn9/f61KM72Gdjabjc2bN3PmzJl7No6z2+1ERUVpjftMJhONGjXitddeY968eYwePRpw3C6bW7+2U/Lz89OqNNOrUVJVVZv3wAMPAKRqH5SyCt/JVS2H87VRUVEuXwOOgRR37tyZ5T5R0nuv9D4zcGzv4cOHMzx5u5LeOp2NfJ1tMlzJbnm7W0hICACHDx9Odxlne4mUDZsLisx8vnv27OHYsWPpntxccTbSXbVqFSdPnuTYsWOULVs2TX8o06ZNQ1VVunfvzqRJk+jWrRvVqlXTTnbOy1Q5FRwcjMFgwGKxaI067xYfH69dqnCWi7OxcHrfM1dDLyQkJHDo0CGtTIsXL07Hjh357LPPWL9+PQ8//DAAixYtumfczgbWzsstdzt06BA9e/bk448/znA9GW3HrVu30ny27jy+Orcho33s7Nmz7N27N1MNwN0dX2EmiUsecPYp8n//938u5y9evJi+ffvStWtX4uPjM1xXdHQ0bdu25cUXX3R5cnYeHCD96+Pu1qxZMwBmz57tcv7q1au5dOkSBoNBO4CnHLzM1fVdV2O4hIWFUb58eRISEtK9m2DMmDE8//zzDB8+PNV0Z1uXu395pfcL1NkXx8qVK7Vu71OKiYmhT58+dO3a1WUHexmZP39+mmnXrl1j9erVAFqj1vRkp7wh/TLIDuc+vWTJEpeXUi5evKg1cG7atGmO3w/cG7/z812wYIHLjgSjoqLo1asXnTt3TnUH3b20bt0aX19fNm7cqNW2dO7cOU1bK2dneq7aCKmqqu0jObnLBxw1Lg0aNADS31/++OMPLBYLgYGB2phdAQEBgCOpcZVEufp+zpgxg27duvHZZ5+lmWcymahfvz6QueOS806wZcuWuUw6li9fzp49e+75A8V5nMnsMSY7x9eUx5CU+2aDBg3w9vYmMjLSZSd/AO+++y49evTgiy++yHA7chJfUSSJSx546aWX8PDwYPHixXz77bepDpQbN27kk08+ARwDIt7rumTp0qVp1KgRdrudN954I9XOGxcXxzfffAM4OkLKq2uc/fv3x9PTkw0bNjBq1KhU1bv//PMP7777LgAvvvii1nDM2WkaOBotO09+VquVX3/91eXJXVEUBg8eDMBnn32WqvdMq9XKlClTtN5U776846wCP3/+vMvply9fTnWAbNy4MQ0bNuTWrVsMGDAgVYPZS5cuMXjwYG7evEnJkiXp3LlzpssKYObMmfz555/a/9HR0QwdOpSYmBhatGhxz8aR2SnvjMogOzp06EBISAhXrlxh8ODBqU5up0+f5pVXXiEhIYG6deu6bJSaHe6Mv1OnTlSqVIkzZ84wdOjQVDUIERERDB48GKvVyoMPPpjqNtZ78fT0pH379sTFxWm35N99mQju/BqfM2cOV65c0aZfvnyZESNGaHcCZbV3ZlcGDx6MTqfj999/56efftLuQlFVlQULFmgdxg0bNky7hBkaGkpgYCAAX3zxhRZHUlISY8aMcXmn1eOPP45er2fjxo1Mnjw51d0ux44d4/fffwfuJN4Z6dy5M+XLlycyMpK33norVXLs7JgPoE+fPhmux3nL+MyZM1PVfGzevNnlwKvZOb6mvLyWct/09fXV4nvjjTdSJS+JiYl89tlnbN26Fb1ez4svvpjhduQkPnDckXny5EmXP8IKI2njkgfCwsL48ssvefPNN5k0aRIzZswgJCSE69eva7+8Hn74Yd54441MrW/06NE89dRTbN++ndatW1OxYkWMRiORkZHEx8cTGBjIp59+mpublErlypX56quveOONN5gxYwZz586lcuXKXLt2Tdu+xx9/PE0tyLBhwxg6dCg7duygefPmVKpUiQsXLnDt2jVefPFF5s2bl+bX/FNPPcXx48eZPn06r7/+Op9//jmlS5fm7NmzWoO+V199Nc019PDwcHbv3s3rr79OaGgow4cPp2nTplSpUgVFUYiOjqZ9+/aUKVNG+2U6ZswY+vfvz/79+2nfvj1hYWHodDpOnTqFxWLB19eXyZMnZ3nU7ZCQEN59913GjRtH8eLFOXbsGBaLhfDw8Ex9btktb2fX57/88gtbtmzh8ccfZ8CAAVmK3clkMjFhwgReeuklbT8MCwvDbrdz/PhxVFUlPDycb7/91i29JTvjP3bsGF988QXz5s2jV69e2R7Z1xl///79Wb9+PS1atCAsLAyLxUJERAQ2m40yZcowceLELK/7iSeeYO7cucTHx1OtWjWXd8YMGjSITZs2cezYMVq1akVISIh2V6HVaqVRo0bs2rWL5ORkbty4oSUR2dG4cWPeffddRo8ezZgxY5g6dSoVK1bkwoULWsL24osv8vzzz2uv0ev1DBkyhFGjRrF8+XK2bNmiJRIxMTEMHTo0zZARwcHBjBw5klGjRvH1118zefJkKlSoQFxcnHaXX4sWLTL1mXl6ejJ+/Hj69+/PsmXLWLt2LZUrV+bq1atakjxs2DAaNWqU4Xr69OnD4sWLuXLlCl26dCEsLEzrnbhu3br4+PikGYogq8fXwMBAypQpw8WLF+nVqxfBwcF88cUXVKlShVdffZVTp07x119/0bdvX8qXL09gYCBnzpzRLjF//PHH6d5p6Ep2jv99+vTh3LlzDBkyhKFDh2b6vQoqqXHJI48//jgLFizgqaeeIjAwkKNHj3L9+nVq1qzJO++8w08//ZTpuyKCg4OZO3cuPXv2pFy5ckRFRREREUHp0qXp06cPS5YsSfc2wtzSrl27VNt35MgRrZO0cePG8d1336VpkNqmTRt++eUXHn30US0hqFChAv/73/9455130n2vkSNHMnXqVFq1aoXdbtfahjRt2pSJEycybNiwNK/57LPPaNy4MaqqEhERodWghISEMGrUKCpWrEh0dDRRUVHaL+DSpUszZ84c/vvf/1K9enXOnTvHqVOnKFWqFD169GDhwoUZ3g6cni+//JKhQ4ei1+s5ceIEFStW5D//+Q+//fbbPW+Fd8pOeb/yyit069YNX19fTp06lW6bh8yqXLkyCxYsYNCgQYSEhBAREcGFCxe0fXrOnDmZ6rMjs5zjwphMJk6fPp3l29DvFhYWxsKFCxk0aBChoaFEREQQGRlJxYoV6devH/Pnz8/WaMuNGjXSXueqtgUc/bDMnz+fdu3aUbx4cU6cOMGVK1eoW7cuo0eP5pdfftG6gU9vnJ2s6NWrF7///jsdO3bEaDRy+PBhdDod7du3Z/r06S6/b71792b8+PE0aNCA5ORkIiIiePDBB/npp5944YUXXL5P7969mThxIs2aNUOv12vHuQYNGjBq1Ch++OGHNP1fpcd5O/2LL75IqVKlOHbsGHFxcTzyyCNMnjyZV1999Z7rqFChAn/++SfdunUjKCiIkydPYjAYGDp0KL/++qvLO3eyc3wdO3YsNWvWJDExkaioKCIjIwFHW77vvvuOb7/9lkceeYS4uDiOHj2Kh4cHbdu2ZdasWWnGr7qXgnj8z2uK6o4LxkKIe3JeGlu8eDFmszmfoxFCiMJJalyEEEIIUWhI4iKEEEKIQkMSFyGEEEIUGgXirqKoqCji4+Px9vbWBikUQgghROGUm+f1AtE4d//+/VgsFnQ6XYHsHlwIIYQQmRcfH4/dbsdoNGZ74M70FIgaF2fvkHa7PcvdpwshhBCiYMpp78+uFIjERa/XawOA3Q81Ls7kLLsjF+eFwhCjJmWyW0DiLVTlVwBJ+eWclGHOSPnljLP83NUBZUoFInHx8PDAYrGk6ga+KNu1axdAgd7WwhBjQSbllzNSfjknZZgzUn454yw/V5385ZTcVSSEEEKIQkMSFyGEEEIUGpK4CCGEEKLQKBBtXIT72e125syZw/z58zl+/DhWq5XQ0FCefvppnn32WRRFye8Q3SfltuT/3f1CCCFykSQuRVBSUhKvvvoqGzZsQKfTERoaSnx8PIcOHeLjjz9mx44dfPPNN0UreRFCCHFfkEtFRdBXX33Fhg0bKFu2LPPnz2fp0qWsW7eOSZMm4e3tzbJly1i0aFF+hymEEEJkmSQuRUxUVBS//fYbBoOByZMnU7VqVW1ey5Yt6du3LwBz587NrxCFEEKIbJNLRUXMkiVLsNlsdO/enSpVqqSZ3717d0wmE2XLls2H6IQQQoickcSliNmyZQsArVu3djm/QoUKDBw4MC9DEkIIIdxGEpci5vjx4wCEhoYSExPD3Llz2blzJ/Hx8VSuXJkePXoQFhaWz1EKIYQQ2SOJSxGSlJTEtWvXALh48SJ9+vTh0qVL2vxNmzbx22+/8eGHH/LMM8/kV5hCCCFEtknj3CIkLi5Oe/7666/j6enJ5MmT2b9/P+vXr6dPnz5YrVY+/PBD7ZKSEEIIUZhIjUsRkpSUpD1PSEhgzpw5BAcHA1CmTBlGjhzJ1atXWbx4Md9++y0PPfRQuuuKtdq4kGCFc9G5HndO1U/xfFcBifd4zO3PooDEU9CoqFhsdpJsNpKsNhKtjr9JNhvFvT0Jstsx6eR3lRAiLUlcipCUo3A+8cQTWtKS0sCBA1m8eDH79u3j6tWrFC9e3OW6/omOIyo+mS1xR3MtXndJmbj8urdgxOus/SoM5ZdfdIqCTnH+VdDf/qvTKTxAMnUCvfI7RCFEASSJSxHi6+uLoiioqpruUOyVKlXCaDRisVg4d+5cuolLkt3OTasNNdmCroD3sPvm1Dna80SLNR8juSPZbgdAX0DiyambScmp/neVbKSexp3nKealfE16u5VBpyPUy5QHWyWEKIwkcSlCTCYTFSpUICoqKt1lUnbzbzDc++Mv4eOJSa93S3y5xq+i9rSg/Ea/YbcAEOjnnc+R5IzVbudWYjJGvY7y/r4YbychngYDHgY9ngY9Jr1O+985zdOgx0N/+2+K6R4ppuszuBS0a9euPNxKIURhIolLEVOrVi2ioqL4999/Xc4/f/48FosFnU5H+fLl8zg6UZCpqkqSzU6ixUqC1UaC1YrNruJlNFDB35du1UKoXaYEJr1OxrkSQuQbaf1WxHTo0AGAv/76K9Wt0E6zZs0CoGHDhgQEBORpbKLguhqfyIlrt7gQE0+izYaX0UB5P18eLBlE0wfK0rNmGI0qlMbDoJekRQiRr6TGpYhp1aoVdevWZc+ePQwYMICxY8dSsaLjUsqyZcuYOXMmAIMGDcrPMN3K78qdO3diSpTMx0gKF1VVsakqVrudG4nJBAf4UtrXmwcC/QgO8CE4wJdSPt7odZKoCCEKDklcihidTsf333/Piy++yOHDh3nssceoXLky8fHxnD17FoDXXnstw1uhC5s3enbSnn+4els+RlIw2G8nI1a7iu3231T/355vs6sYdDr0OgUPgx6DTqFfvaoEennc+02EECKfSOJSBJUuXZr58+czbdo0li9fTmRkJF5eXjRt2pQ+ffrw6KOP5neIIh2qqmJXVeyqIwGxaf+neNjTn2e1q6gqGPQKBsWRlBh0Ogw6BW+jPtX/Bp0OH5MRX5MRPw8jwQG+BHjK3TxCiIJNEpciysvLi1dffZVXX301v0O576i3EwibqnIrKTlTCYddBZuqoqpqOrcak+qWYqNOh0fK+SjodY7XmAw6fE0m/DyM+HmY8DUa8PNw/O9jMuJ3O1HxNhrlMpAQotCRxEWILHJeirHY7Fjsdqw2FYvd+dyOxa5it1rRKwq2JEuqfkxcJRzOxMSZqHgaDLdvMb5zK/HdtxZ76NPeYuxh0ONnMkoDWiFEkSaJixB3cdaYWJzJye0EJdlm09qJGPQ6jDodBp0Oo16Ht9GgTTPqdCTHxeCr1xH2QDBemUk+bj+XW42FECJjkriI+5aqqsRbrCQ5kxObTUtW9LcTEKP+TmIS4GnCqNdh0unx9zQR6Gki0NODQC/H34Db//t7mNi/dw8A9WuG5fNWCiFE0SKJi7hv3UqycC0hEW+TEZNeh4/JgFGnx6hX8PUwUczTg2LeHgR5eRDk5UmQp4lAL0diUtCHQRBCiKJKEhdR5N196cfZPiXOYqW0rxcNK5SmYoAvxbw8CPTyoJiXR8Ef5kAIIe5TkriIQk1V1VT/30pMvtM2JUWDWeelH2c7FE+jAT9PE8W9POj2YAgeBklUhBCiMJDERRQoqnZrsKODNMdtxY7nNruK1cXzlGItFow6vSMxud1GxaDT4e9hvN0exYNATxNBXh4EenpQ1s9bkhYhhChEJHERGTofE09etOZQ4XZCYkdRnH2SODpKcz436nV46gxafyWG29NTals5mEAvD4JSNJoN9PTAqJdhuYQQoiiQxEW45GvQU9xkwNfHO0/eT1HQEhQPgx5fkwEfo6PDNG+TAV+TER+jAR+TER9tngFvowFSXC56Jk+iFUIIkV8kcREutSzpy3WLjerVq+fJ++kUHEmJ0Si1I0IIIdIliYtwyaBTKOlhIDjAN79DEUIIITTy01YIIYQQhYbUuIjCb9euO8/r18+/OIQQQuQ6SVxE4degwZ3nd/XrIoQQomiRS0VCCCGEKDQkcRFCCCFEoSGJixBCCCEKDUlchBBCCFFoSOIihBBCiEJDEhchhBBCFBqSuAghhBCi0JDERQghhBCFhiQuQgghhCg0pOdc4ZKKikWxEmuJze9Q7sm7bBnteXwW4rXabfiZfNEr+twISwghRC6QxEW4dMV0lXh9PLEXC37iwrYZd55f3Jrpl+kUHQGmABqUkvGNhBCisJDERbhkUSwk6ZJRLTEoKPkdTrbYVJv2XKfoMCh69DoDBsWAQWdAURSCPIPyMUIhhBBZJYmLyJC/0R+9rnBdSrHZbcRaYzEoBrz1Xhh0Bjz1nviafPEz+uJr9MPP6Iun3hNFKZxJmRBC3K8kcRFFitVu5VbyLbwN3gSY/KkcUJkgj0A89B75HZoQQgg3kMRFFHrFV20GwG63c6pFNXyNvlTwLU94YDhGneziQghRlMhRvYi6fv06TZo0yXCZgQMHMnz48DyKKPfU7vuu9vzimeWU9i5FtaAH0Slyt78QQhQ1krgUUceOHQMgMDCQ0NBQl8uUL18+L0PKFVa7NdX/wb7BVA0Ml6RFCCGKKElciqijR48C0KlTJ95///18jsb97KqdOEscVtWSanr1oGrS4FYIIYowSVyKKGeNS1hYWD5H4n42u41bllt46DwIMPmnmidJixBCFG2SuBRRzsSlSpUq+RyJe6mqyi3LLbwMXpT0LEHN4jXyOyQhhBB5SBKXIkhVVY4fPw4UvRoXq+po0xJg8qduiToY5K4hIYS4r8hRvwiKiooiPj6ekiVLcvXqVaZMmcLhw4cBqFq1Kk8//TSVKlXK3yCzyGq3kmBLwGKz4G3wJtAUKEmLEELch+TIXwQ5LxPFxMTQuXNnbLY7Xd9v3LiRX375hffee4+ePXvmV4iZpqoq8dZ4kmxJeBk88fP0o6x3GSr7u75TSgghRNEm94wWQUeOHAEgMTGRp59+muXLl3PgwAFWrFhBjx49sFgsfPTRR6xbty6fI82Y1W7lRvINVOwU8wwiLCCMh0o34cGgqpj0pvwOTwghRD6QGpciqHr16vTo0QOz2UyvXr206ZUqVeKTTz7BaDQyc+ZMvvzyS1q2bJmPkWYs3hqvNcKtGhiOn8kvv0MSQgiRzyRxKYJatmyZYUIyYMAAZs6cyenTp4mIiCiQ7V1sdhtW1Yq34k34vZKWevXyLjAhhBD5ShKX+1CpUqUoXrw4V69e5fz58wUucUmyJRFnidMGSvQ1+mT8gl278iYwIYQQ+U4SlyLKYrGg0+nQ6/Uu56uqCoDRaMzLsO7JptqIs8QRYPKnnE85zIFm6b5fCCGERs4IRVDz5s2pUaMGy5cvdzn/0qVLXLt2DSDdcYzykqqqJNuSibXEciv5Fp4GT8r5lKVa0IMyurMQQohUJHEpgpydzi1YsMDl/J9//hmARo0aUbx48bwKS6OqqqNfFmsCN5Nvcj3pGom2BPSKHn+jH8U8ggj1D5Xu+4UQQqQhP2eLoP79+7Nx40Y2bNjAmDFjGDp0KCaTCZvNxvTp05k+fTp6vZ433ngjz2JSVRWL3UKyPRmL3TEwoklnxEvvhcloJMAjgGIexSjuWQw/o1/Wkpaffrrz/JVX3By5EEKIgkQSlyLo4YcfZvjw4Xz77bf89NNPzJ49m4oVK3L+/HmuX7+OwWBg9OjR1K5dO1fjUFUVq2olyZZEsi0ZvU6PSWfCy+iJp8GT4p7FKe5RjGKexTDqctDWZsCAO88lcRFCiCJNEpciauDAgdSpU4dffvmFPXv2cOzYMYKCgujUqRMvv/wyVatWzbX3VlWVRFsiibZEFBQ89R74eAQSYAqguGcxinsWz3qtihBCCEEGictDDz2U45UrisLmzZtzvB6RPU2aNKFJkyZ5/r421UaiLRE/ox8+Bh/K+pShjHcZvA1eeR6LEEKIoiXdxOX69es5Xrn8or4/qajoFB1BHoE0KFlf9gMhhBBuk27i8uuvv+ZlHKIIUFUVm2rDarcCoFN0krQIIYRwq3QTl0aNGuVlHKKQsKt27Kodm2rDptruPLfbUFHRK3r0ih4PnUk6jhNCCOF2OWqca7fbuXTpEjExMZjNZsDxq1t+ZRctdtVOsi2ZRFsiNtWmJSd6RY9RZ8BD8UCv6DEoBrwMnngZvPA2eFPWu0x+hy6EEKKIyVbicvDgQSZNmsSmTZtISEhAURQOHTrEuXPneOGFF+jbt2+qUYlF4RVnjcNqt2LUGfEx+uCh88Db6IWX3gtvgxdeKR4eOg9JWoUQQuSqLCcuf/75Jx999BFWqzXNvHPnznHu3DlGjx7NwYMH+fzzz90SpMg/OkUh0COQ4p7FKO9TjhKeJeQSkBBCiHyTpTPQwYMH+fDDDwF46aWXWLBgAXXq1NHmV69enSFDhqDX61mwYAELFy50a7AiZ5y918Zb4rmZdJPohCucj7vAmZhITtw8yeHrR9h/9QC7ondj1TkSU0+9F3WK16JuiTqU8iolSYsQQoh8laUalylTpmC32/n000956qmnANDp7pzIfHx8GDJkCMHBwbz11lvMnTuXJ554wr0Ri3Ql25K5nBDt6Kn2dtf6Vrv1dlf7Fix2C6pqx66qqNz1V7Vj587fJF0SHnYPTDojxTyL5femZaxTp/yOQAghRB7JUuKyY8cOihcvriUt6XniiSf46quvOHz4cI6CE5kTkxzD2bhzXIy/5GhAa7dhx46qqnf+qnZUVBQUFEVBh+NWZZ2iQ8Hx15BiekJSAqDSqFTDgl/LsnhxfkcghBAij2Qpcbl586Z299C9lC1bliNHjmQrKJF5x2+c4EzsGRKtSSTaEjHoDI4HBhRd6sTEmbRkhsLt5aSxrRBCiAIkS4lLUFAQUVFR91zObrcTFRVFsWIF/BJDIZdoSyQqNoobSTfx0HsQYApAr9Pnd1hCCCFErsnSNYCGDRsSExPD7NmzM1xu1qxZ3LhxgwYNGuQoOOGaTbURGRPF9ks7ibclaLcqS9IihBCiqMtSjcvLL7/MihUrGD16NFevXqVjx47Y7XZt/qVLl/i///s/Jk+ejE6no0+fPu6O975nsVvYeXkXN5NvEm919KHja/TN77Dy10cfuX4uhBCiyMlS4lK1alU++eQTPvjgAyZMmMCECRO0efXr1yc+Pl77/+2336ZmzZrui1QAEGeJI8YSQ6wlDl+jL0adUTp9+/jjO88lcRFCiCIty7eLdO/end9++41HH30Ug8GAqqqoqkpcXBw6nY4GDRowbdo0XnzxxdyI975nsVuwqyoGnQGT3iRJixBCiPtKtrr8r1WrFj/99BPJyclEREQQGxuLt7c3wcHB+Pj4uDvGIs+m2rDYLVjsVqy3+1tx/u987uyPJcGagFW1olekPYsQQoj7T44GWTSZTJm+Pfp+FWeJ51rS1VRJyGVTNHbFzuaLW7DYLbdHWU7bCZyqOvtiuWuequJn8svvTRNCCCHyXLYSl9jYWBYuXMiGDRs4ffo0CQkJ+Pv7ExYWRosWLejYsSNGo9HdsRZKB64d4HrSdWyqXUs6EgwJoEBifBJ21dG4OVWncLf/uuoYLmXHcUIIIcT9JsuJy5YtW/jvf//L1atXUVVVm3758mVOnDjBihUrmDx5MmPHjqVy5cpuDbawsat2ku0W4izxeOg90Cl6DDodFjUZRVXwN/mju93MSNqqCCGEEPeWpcTl5MmTDBw4kKSkJEJDQ+nSpQthYWH4+PgQFxfH4cOHmT9/PidPnqRv377Mnz+f4sWL51bsBYqqqli1tiqOx6X4iyRY4lEUBW+Dt5acJJIAIO1UhBBCiCzKUuIyadIkkpKS6Nq1K6NHj0avT33ibdOmDa+88grDhw9n7dq1TJw4kffff9+tAecF59g+joEJk9M0lE31sFm0QQwd4wHdGbTQptqJt8bjZ/KTGhUhhBDCDbKUuGzZsgV/f38+/vjjNEmLk4eHB//73/949NFHWbNmTaFKXC4nXObfa4cAR/KS3sjJ6Y2wnJ5YS2yq/5N1yQDYk67n3sbkkEr62yOEEELklywlLjExMZjNZjw8PDJcztfXl8qVK3Ps2LEcBZfXYi1xgHo7aXGcuHWKAooed17UuZV4CwB/k78b1+petxJvoVf1eOhM+R2KEEIIoclS4hIaGsqZM2dITk7GZEr/hGaz2Th//jwVK1bMcYB5KdQ/hFJeJbW7eXLL/sv7AahlrpVr75FT+y/vR6fqCsf4Ry+/nN8RCCGEyCNZSlz69OnDW2+9xahRo/jkk0/SXW7ChAlcu3aNoUOH5jjAvJYX4/4YVEexexm8cv29sssZY6Hw00/5HYEQQog8ku7Zaf369WmmBQYGUr9+febMmcOxY8d45plnePDBB/H29iYhIYHTp0+zcOFC1q9fT7t27WjatGmuBi+EEEKI+0u6icuAAQPSvRNGVVX27dvHvn370p2/atUqVq9ezaFDh9wTqRBCCCHue+kmLuXKlcvLOIQQQggh7indxGXt2rV5GYcQ2ffKK3eeS3sXIYQo0gpRC0wh0jF58p3nkrgIIUSRlqsj9e3cuTM3Vy+EEEKI+0yWa1wiIiL49ddfOXHiBImJidjt9lTzbTYbSUlJXLlyhdjYWGmcK4QQQgi3yVLiEhkZydNPP01sbKw2MrSiKKlGiU6pVKlSOY9QuMXu3bt5/vnnKVu2rLRfEkIIUWhlKXGZMmUKMTExlClThh49euDp6cmXX35Js2bNaNOmDRcvXmTp0qWcOXOGhx56iJ9//jm34hZZkJyczLvvvpumdkwIIYQobLI8yKJOp+PHH38kPDwcgJ9//pmbN2/yzDPPAI7+XwYMGMDWrVtZvXo1bdq0cX/UIkvGjh3LqVOn8jsMIYQQIsey1Dg3OjqacuXKaUkLQLVq1Th06BAWiwVwjA7tHA7g999/d2OoIjsOHjzIzz//jKenZ36HIoQQQuRYlu8qCgoKSvX/Aw88gNVq5cyZM9q0ihUrUrFiRQ4fPpzzCEW2WSwWRo4ciaIoDBo0KL/DEUIIIXIsS5eKihcvzqVLl1JNCw4OBuDYsWOEhYVp0318fDh37pwbQhTZ9eOPP3L06FEGDRqUqpYsMxTFjtEjGav9ei5FB6CiKCb0Su4PbCmEEKJoyFLiUrduXZYuXcqyZcvo0KEDAGFhYaiqyoYNG7RpsbGxREREUKxYMfdHLDLl2LFjTJo0idDQUAYPHsymTZuy9PrAUlfw8Eog3hqXSxE6KBjwNtRHrwvI1fcRQghRNGQpcXn22WdZsmQJb775JmvXruXzzz+nfv36lC5dmgULFhAcHMyDDz7IzJkzSUhIoH79+rkVt8iAzWbjnXfewWq1MmrUKEwmU5bXodPbMJiSsavxbo9Pxep4D8UHBSM6xSdnK/zwQzdEJYQQojDIUuJSv359hg0bxvjx41m1ahVff/01AIMHD+bDDz9k3LhxgGN0aJ1Ox+DBg90fsbinadOmceDAAZ5//vkcJ486xRdF0bspMse+YVNvoFd8MerK4amviqLkcOSJjz5yS2xCCCEKviyfMQYPHkzr1q3ZvHmzNq1Hjx4oisLUqVM5d+4cISEhDBs2jLp167o1WHFvERERjBs3jrJly/L666/ndzipqKqKnRgUjOh1xfDUV0dRlPwOSwghRCGSrZ+64eHhaRp7PvPMM1pfLiJ/qKrKu+++S1JSEh999BG+vgWn0auq2rGrsYCCXhd4u6ZFkhYhhBBZI6NDFyGzZs1i586ddOrUiRYtWuR3OBpVtWJTY1AUEwYlEC9DHbmTSAghRLakm7jMmjXLLW/w/PPPu2U94t5WrFgBwJIlS1iyZInLZc6dO6fVlq1Zs4YKFSrkWjyqasFOIqpqQaf4YNAVx0tfE53i5s7wOne+83zxYveuWwghRIGSbuLy6aefuqUqXxKXvGM2m7FarS7n3bp1ixMnTmAymahRowbg6OU4N6iqDbsai4odneKJXvHDqC+Ph66yWxv6atJJ0oQQQhQ96SYuDRs2zMs4hBu8//776c5bt24dAwcOpGTJksyePTvXYnC0ZYlBUTzQK76YdOUx6iqgU3InSRJCCHF/STdxmTFjRl7GIQo5VbWjkoRdTUCneGBQSuBtqIuiGPM7NCGEEEWINM4VOaKqKioJ2NVEx23Oij96XRBe+hqStAghhHA7SVxEtjn6ZYkF1Y5eCcSgK45JVxG9EiS3OgshhMgVkrjcJ1q2bMnRo0fduk6VBFBV9EoQXoYaGHTF3bp+IYQQ4m66/A5AFF4qVnSKJ56GcElahBBC5AlJXES2qaoN0KOTzuSEEELkEUlcRLaoqgrYUTCgwzu/wxFCCHGfyFLismfPHuLi4nIrFlFIqKoNlUQU9OgUbxRF8l8hhBB5I0uNc99++22uXr3K6tWrCQwMzKWQREHj6KPFgooFVAsqKgpGdIo3esU/v8ODH3/M7wiEEELkkSwlLhcuXCAkJESSlvuA3uAYOsCm3gJUFAwoihFF8URRPDAoQeiVIIy6MvkbKMArr+R3BEIIIfJIlhKX0qVLc/XqVWw2G3p9Low5I/KFsxM5mxqLXY3Bql5HUVTsNj06xQcdJvS6QPRKEAYlCJ3iJ5eHhBBC5IssnX3efPNNbty4wRtvvMGZM2dyKyaRi1RVxabGYrFfINF2nHjrHuKsG4i1bCHeuodE2xEstrOYPBOx2/R46s34GpvibaiLh74Sel2AJC1CCCHyTZZqXLZt20bVqlX566+/+OuvvwgICKBkyZLpjjKsKApz5sxxS6DCPZLtp0iynUbFClhvN7R1XBZSFMPtS0JeoCYCYNSVlq77hRBCFBhZSlxmzpyZ6v8bN25w48aNdJeXbt/znqMhrTVFQ1rHc1V1NK5NtkVhU2862qugR6d4AobCXYtSv/6d57t25V8cQgghcl2WEpfPP/88t+IQ6bCr8djVhFTJh4o1xfM708EOKoAdFRXHP87ndkBFVa2OhAWfVO+jqva83jT32b07vyMQQgiRR7KUuHTr1i234hAuWOwXSbQeJsNEBDuoKf9XM7Vum3rznsuoqtSYCSGEKFjcMshiXFwcPj4+915QZImCBygAOpRU09MseJsOBT3u6BD5VtxVrBYjCl45XpcQQgjhLtlKXC5evMj06dP5559/OHPmDKqqcujQIS5dusSIESPo168frVq1cnes9x2DLgg/Xct8ee+j5xxtRZRgqXURQghRcGQ5cdmwYQOvv/46sbGxt8erudMI9+zZs+zcuZNdu3bx6quvMmTIEPdGK4QQQoj7WpauKURGRjJs2DBiYmJ47LHHGD9+PNWqVdPmV6pUiW7duqGqKhMmTODvv/92d7xCCCGEuI9lKXH58ccfSUhI4D//+Q/ffvstbdq0wdPTU5tfvHhxPv/8c9544w1UVeW3335ze8Aicw4cOMDQoUN56KGHqFmzJm3atGHUqFFcunQpv0MTQgghsi1LicumTZsICAjg5ZdfznC5vn37EhgYyP79+3MUnMietWvX0qNHD1auXIndbicsLIzr168zY8YMOnfuzIEDB/I7RCGEECJbspS4XL16leDg4HuOU6TX66lQoQKxsbE5Ck5k3cWLF/nvf/+LzWZj8ODBbNq0ifnz57Nx40a6d+/OzZs3+c9//oPVas3vUIUQQogsy1Li4u/vz/nz5zO17KVLl2QU6XywaNEiYmNjadSoEa+99hoGg6P9tZeXFx9//DGBgYGcPXuWbdu25XOkQgghRNZlKXGpU6cO169f56+//spwuWXLlhEdHU3t2rVzFJzIulKlStG+fXt69OiRZp7JZKJixYoAXLhwIa9Dyz2LFt15CCGEKNKydDt0nz59WLNmDe+//z6JiYk8/vjjqeZbrVYWLFjA6NGjURSF559/3q3Binvr2rUrXbt2dTkvPj6eiIgIAB544IG8Cyq3de6c3xEIIYTII1lKXBo2bMiwYcMYO3YsI0eO5N1339X6cOnSpQtRUVEkJiaiqip9+/bl4YcfzpWgRdadPHmS0aNHc+vWLerVq0fDhg3zOyQhhBAiy7LcAd3gwYMJCwvj+++/5+TJk9r0Y8eOAVC+fHkGDx7Mk08+6b4oRbaNHz+eBQsWcPbsWVRVpVWrVnz22Wf5HZYQQgiRLdnq8r9du3a0a9eOqKgoTpw4QWxsLF5eXlSqVImwsDB3xyhyYPv27URFRWn/R0ZGsn37dtq3b5+PUQkhhBDZk6XEZceOHfj5+VG1alUAgoODCQ4Odrns5s2biYiI4Lnnnst5lCLbPvvsM0qWLMm5c+eYOXMmv/32G6+99hrffPMNHTp0yO/w3KNcuTvPM3nXmxBCiMIpS3cV9e7dm1GjRmVq2W+++YZvv/02W0EJ96lQoQIeHh6EhobywQcf0KtXL1RV5euvv8Zms+V3eO5x4cKdhxBCiCIt3RqXmJgYl93Dx8fHc+LEiXRXqKoq58+fT9X+RRQcr7zyCjNmzODcuXOcP38+3Rozkz4JH1McamL6n3W+U5PBnoCMXy2EEPePdBOX5ORkevbsSVxcnDZNURQOHz5M50zeftq4ceOcRyiy5ObNm5w5c4awsDC8vb3TzC9VqhTe3t7Ex8drPSG7UsrvMj6mWIi/ldsh54AOlIx7cRZCCFG0pHupqHjx4gwZMgRVVbUHkOp/Vw8Ab29v6taty8cff5w3WyE0HTt25Omnn+aff/5xOf/mzZskJCQAjiQmPTrFjlFvAdUOKnn8UMEWB/Z4sCeBanVMQwHFAIoH6LxB5wWKKbtFJYQQohDKsHFunz596NOnj/Z/1apVqV+/PrNmzcrtuEQ2NW7cmCVLljBnzhwee+yxNPNnzZqFqqqYzWbKpWzUmh6dd+7WaqjObMXu+KvaHYmKYgRDMdD7OBIUndedZOX2Q1GMuReXEEKIAilLdxUNGTKEsmXL5lYswg1efvllli9fzsaNG/nqq6947bXXMJlM2O12fv/9dyZMmICiKLzxxhu5E4CasurE7khEtL93TXMmKooCKDgu/egcf/U+4FkFxTM0d+IUQghRKGU5ccnIyZMnsdlshIWFodNl6YYl4SZVq1bl008/5YMPPmDKlCn83//9Hw888AAXL17k6tWr6PV63n33XZo3b+6eN3TWkKjJoFpu15aA4ypkikREe24AXcppiqN2RTE5HjrnXy/wKELDEgghhHCLLHdAFxMTw8yZMwkKCqJnz56AYyToQYMGcfjwYcDRe+7nn38u3crnkyeffJLw8HAmT57Mjh07OHbsGIGBgXTs2JF+/fpRo0aNnL2BagV7oiNRwQoYQWcEnc/tJMSQNhFRPBzLKB63l/G4Pc+IIg1shRBCZFKWEpebN2/yzDPPEBkZSatWrbTE5YMPPuDQoUMAGI1Gzp49y8svv8zixYvTvWtF5K4aNWrw/fffu3/Fqg1sN263M/F1JCX6ADAEOR76IBSdtD0RQgiRO7J0PWf69OmcOXOGihUr0q1bNwDOnTvH+vXr0el0/PLLL+zZs4e+ffuSmJjIlClTciVokQ9Uu+MuH9sNRyNZUzD4PgQBrVH8mqB4haMYS0nSIoQQIldlqcZl3bp1GAwGpk2bRvny5QFYs2YNAPXq1dP6bXn99deZO3cumzdvdnO4Is+pSWC3OC4LKR6g9wd9IHjXQdEVkFuRd+7M7wiEEELkkSwlLlFRUYSEhGhJC8CGDRtQFIVmzZpp04xGIxUqVJDecwsp1Z7o6MMFwJ4MOk/H5SBjKTCVB0NJFKUANb6uXz+/IxBCCJFHspS4qKqK0XjnUkBycjI7b//abdKkSapl4+Li5M6iQkBVLWCLSfG4BbabGHQWEixemLyLgZcZjOUKTg2LEEKI+1aWEpeKFSsSGRlJYmIinp6ebNmyhYSEBIoVK0atWrW05SIiIjh79iyhodIHR0Gj2mIhbqfjriDHlNu3M6d4YMVksGBL1oPfIyh6n/wMWQghhNBkqUqkcePGxMbG8s4777BmzRq++OILFEWhXbt22jL79+9n6NCh2O32VJePRAERvxds8bcHKIwHW+ztW5utjvmKARRPrDYDKoqMBSSEEKJAyVKNy4ABA1i5ciXLli1j+fLlqKpKUFAQgwYNAmDLli3069cPVVV54IEHeOmll3IlaJEDnmaI23P7NmZ/xx1CLsZXjr4SSbLVg0CdZ97HmFVKivhvj5clhBCiaMpS4lKsWDHmzp3L5MmTOX78OBUrVqRv376ULl0agJCQELy9vXnsscd48803CQgIyJWgRfYpxlIQ2P6ey12JTbznMkIIIURey3LPucWKFeOtt95yOa9MmTJs2bIFk0kacQohhBDC/dx+248kLUIIIYTILVmqcVmwYEGW36Br165Zfo0QQgghhCtZSlzefvttFCVtQ05XVFVFURRJXIQQQgjhNllKXKpVq5Zu4pKYmMiVK1e4efMmiqLQqVMnihUr5pYghRBCCCEgi4nLvHnz7rnMzp07efvtt/n333/5888/sx2YyJmTJ08yZcoUtm3bxuXLl/H09KRq1ao89dRTUgsmhBCi0HJ749wGDRrw/fffExERwYQJE9y9epEJa9eupVu3bsybN48rV64QGhqKh4cHO3bs4K233mLEiBGo0t+JEEKIQihXBhOqXr06oaGhrFy5MjdWLzJw5coV3njjDZKSknjmmWfYtm0bixYtYtOmTUyYMAEfHx+WLFnCjBkz8jtUIYQQIstybRREvV5PdHR0bq1epGPOnDnExcVRvXp1Pv74Y7y8vLR5bdq0YcSIEQBMnz49nyIUQgghsi9XEpfdu3dz4sQJSpQokRurFxnYvn07AG3btnU5OneLFi0AOHfuHDdv3szL0HLPuXN3HkIIIYq0LDXOnTVrVobzk5OTOX36NIsXLwagdevW2Y9MZMtrr71Gly5dqFGjhsv5CQkJ2nObzZZXYeWucuXyOwIhhBB5JEuJy6effpqpflxUVSU4OJjBgwdnOzCRPXXq1KFOnTrpzl+zZg3gGLohKCgoj6ISQggh3CNLiUvDhg0zXpnBgL+/P3Xr1uWpp57C19c3R8EJ94qOjmbKlCkAdOrUKdOdCQohhBAFRZYSF7kTpfCKj4/n1Vdf5datWwQFBTFgwIAMl/eLPY1/fCT2XYfzKMIciL5+53nJglGLVP7yZYDCUX4FUL6Wn3cxlKqPoSi5du+CECIHsjw6tCh84uLiGDhwIPv27UOv1/PVV1/ds+F0QOwpfOPPoSafzaMos0/f9VvtuW3B8HyM5A7/5GSAQlF+BVG+lp9faZSKjcGneN6/txDintJNXFI24syJlLfjirx37do1BgwYwP79+9HpdHz22Wc8+uijmXqtTrWCVwnQFaL81qdkfkcAQLI9BgAPH798jqRwyrfyi7kI0jejEAVaumekevXq5XjliqJw6NChHK9HZE9UVBT9+vUjMjISg8HAl19+SadOnbK2Er0J9MbcCTA3GDzyOwIA7LpEx5MCEk9hky/lZ00CRQFPP/AKzLv3FUJkSbqJizu6hJdu5fPPkSNHeOmll4iOjsbLy4vvv/+e5s2b53dYQhQsqgqq3fGwxIPBEyXoARSdPr8jE0KkI93ExXnbrCh8IiIi6NevH1evXiUgIIAff/yRunXr5ndYQuSOlMmH82G33TXNBva7/lftjmmKDnQ6x1+Tb+GqYRTiPpRu4lK+fPm8jEO4SUJCAgMHDuTq1asEBQUxffp0qlatmt9hCeE+iTGQdCt1QpIy+VD0KZ47/zeA0fV8xeABBk/Hw+SNUrZmfm+hECIDmW51mZiYiKenZ7rzN23aRPXq1QkMDHRHXCKbJk2axOnTp9HpdHz//feStIiiJ/EGeBdztL9yJiB6E4rR09EmxuAFRmcy4virGDxTTzN6Of7qPVBcDI0hhCi47pm4xMbGMm7cOP7880+WLl1KmTJl0ixjtVoZOnQoNpuNZ555huHDh+Pt7Z0rAYv0JScna8MyeHp68t1332W4/NixYylZsmDchSNEluhN6Or2AN9SYPCQNilC3EcyTFwuXbpEnz59iIiIQFVVtm/fTpcuXdIsd+rUKZKTk7FarcycOZNNmzbxyy+/yEkxjx09epSYGMdtpPHx8ezevTvD5ZOSkvIiLCHcQ1XBmui4PATgGYBikh9IQtxv0k1c7HY7gwcP5vTp0wQFBfHKK6/QrFkzl8uazWa2b9/OvHnzGDduHKdOnWLo0KHMnj1bupXPQzVr1uTo0aP5HYYQ7qHawWYBuwWS4yE5DnR68AwEgwmM6V+6FkIUXekmLosWLeLgwYM88MADzJgxg1KlSmW4Im9vb3r16kXz5s157rnn2LdvH8uXL6dDhw5uD1oIUUhpd/zYHH/vfn77f+8kZweYMbcb1nqDfzkU35JQsgpK6WooRuncUoj7UbqJy5IlS1AUhXffffeeSUtKwcHBvP/++wwbNoxFixZJ4iJynW3dmPwOQaQnOd7RmNaZlKA6ak0UveOv86E3OmpQdAZQ9MQrsaiKgaBS5cHkA4EVUEqawbeU1OIKcZ9LN3E5fPgw/v7+6V4eykjbtm0JCAjg33//zVFwQohCLvGGI/Ewet1OUoyOdikmH9D++qAYvVP8782Zg8ewKwaKN2iQ31sghChg0k1cbt68idlsztZKFUUhODiYI0eOZDswIUQRYfBAV6MLFAtx3JqciRoTu046gRNCuJZu4uLp6cmNGzeyveKYmBhMJlO2Xy+EyAfOYTpUO6De/v/2X1UF7Cmeq3c9t6edZnOM8oyHv7RJEUK4RbqJS2hoKAcPHuTGjRtZ7lTu+vXrnDt3jgceeCCn8Qlxb0ej7jwPD86/OHJLysRAdSYOLpKEu6e7SixSTnf1GlV1DDSI4vib6rnOxXRd6mXQgS7FMiYfR/sVk08+FJwQoihKN3Fp2rQpBw4c4Oeff2b48OFZWumMGTOwWq3Ur18/xwEKcS/6gd9pzwtcQ11X4+io6u2xclzNS+ehJQm6tMlCekmElmi4Wt7FNOf/OoOjQzedIfVD75xmvN1exTn9zjLK7XYsjga3t6d7BaJ4B+XbRyCEKFrSTVx69OjB1KlTmTZtGjVq1KBt27aZWuG6dev48ccfURSFp556ym2BCuFWLmseMrjkkan5dxINr+QkFNUOtqs4aiF0dyUedz10t8fPQX+7xuKu+Xojit50u5t6410JhSFtkqEzoLhKNNIsezvR0Bu0ZRRFusAXQhRc6SYupUuX5r333uP9999n+PDhdO/enT59+hAaGupy+ePHj/Pzzz+zcOFCbDYbTz/9NLVq1cq1wEUesVnutHvIM852Fs42FWR8CSSlmEt3zbenk3y4uCSSsuYiq5dE7qoRSYyJRVV0BAQGoug9HB2maX9vJyAGD+25os13vYx0aS+EEA4Zdvn/9NNPExERwbRp05gzZw5z5syhZMmSVKlShcDAQGw2G9evX+fkyZNcvXoVAFVV6dy5Mx9++GGebIDIPXZFDwnX8i+AlJc00lwC4fbfu2oHPHzSeY2LBESnR0lVa+Hi8ojLSyIpXuecruhTJRxn/j2CXWegboPG0u+IEEK40T0HWfzvf//LQw89xMcff0xUVBSXL1/m8uXLKIqCetcv8bCwMAYPHiydzhUBt3xDUBUdXvk13pSiT9Gm4q5EQm+83YGZAUVvAD7VXqar0yN1MpHm8sidNhi5mVDYDI47aCRpEUII97pn4gKOhrorV65k+/btbN26lVOnThETE4OnpyclSpQgJCSE5s2bp3sZSRQ+t3xDueUbSplC1sBaKRWe3yEIIYTIRZlKXMDxy7Fx48Y0btw4N+MRQgghhEiX3D4ghBBCiEJDEhchhBBCFBqSuAghhBCi0Mh0GxdReNntdnr06EFkZCTbtm3L73Dcr2zZ/I5ACCFEHpHE5T7w3XffsX///iyPOVVonD+f3xEIIYTII5K4FGGqqjJhwgR+/PHH/A5FCCGEcAtJXIqo6OhoPvzwQ9asWZPfoQghhBBuI41zi6CNGzfSvn171qxZQ8mSJRkxYkR+hySEEEK4hdS4FEEnTpwgPj6eJ554gpEjR3Ls2LH8Dil3LV5853nnzvkXhxBCiFwniUsRVKtWLebPn8+DDz6Y36HkjS5d7jzP85GshRBC5CVJXIqgevXq5XcIQgghRK6QNi5CCCGEKDSkxkW4pI++jPFsFLHXruR3KPfkm+J57KoV+RZHSp5nzgC4rfwUgx7Pug3QF9W+eIQQIpMkcREuee3egfFsJLEH9+V3KPeUKnFZtijf4kjJKyEBwG3lp3h6oVos+LZ73C3rE0KIwkoSF+GSYrWii48HTy/Q6/M7nMwzGPM7AgddsuNvduKx21CTkrAnJYLV5lidvx3VYnFjgEIIUThJ4iIypPP3RzGZ8juMTNMHBeV3CADYFQUgU5d2VLsd+62bqAkJqElJqDYriocnel8/FA9PFE8PdF7emEIr53LUQghR8EniIkQ+Ua1W7DdvYLtxA523NzpfP5TiJdB5eWMoUwZDmbIYSpfFULYs+hIlUQpTzZcQQuQSSVyEuAdVVR39w6gq2O2odjvY7aDab/+vpvlfFxeLoqpY4+Mcy6t2uL2c6nwO6P39MQZXxBRaGc869TCULYe+WHFJUoQQIh2SuIj7kqqq2G/ecLQbsd9OJNTUSUmqhAQFdAqKTgeKDnS6288V0KX8X4di0IHJhF3RofPzuzNPpzjmO5fXGzBWDsP7oYcxVqiY30UihBCFgiQuotCzlCub5deo8XHYb95E5x9wOyHR35VYpE0yFA8TismEYrz912SCFM+1h9FEdGQkqsFAhRo1Ui2vGE231+MhtSpCCJENkriIQkFNcakGVb1TM2K3c+2F525ffrHDtat3Lt9ol27uLJuyVkVfrDhejZpgqhyG4uGRJilJ9X8Wk4xkowcAHuaquVEcQghx35LE5T7QuHFjjh49mt9huGSPiXHc9qslF+qdJMSeIvHI6HKNs1ZEuV1TonNcrkl3uduXeBRvH3xatsZQomR+F4MQQohMksRF5Bs1ORlr9GX0gUFgMKLo0rYXSZl0pLpcY/JAMRodfz08UEy3nzvnmVIsZzLeXibFa0wmMBhQbt+2LIQQonCQxEXkGtVuB6sV9fYDqxXVZr0zzWJB5+WNx4PV8axd565kI8VfaRMihBDiNklcRI6pycnYY26lTlCsVlBVFIPBUbOhPYwonl53pps88H64KR4PVst+AD/9dOf5K6/kfIOEEEIUWJK4iBxRVRXrhfOODtQ8vVIlKRiN6P380fn5obv9V+/vrz3X+fqj8/V1LJsTAwbceS6JixBCFGmSuIhsU1UV+7VroNNhDA3Du2mzVImK4uUlbUiEEEK4lSQuIlNUVQWb7XYbFcdfNTYW1WbFUKYcPq3a4lm9Rn6HKYQQooiTxOU+pqoqanw89rhYxyM2FntcHPa4WHSxMQBYIs847vSx2UCvR9HrHZd29HoUT0+Mpcvg90R36a9ECCFEnpDE5T4Vu2oFCdu3OPpJsVpRbTawWVGtjr/62FhsxUrg7etzO1ExoPPxRufji87HF8XHB72/P56166EvViy/N0cIIcR9QhKX+5Q9LhZUHB2xGY0oRmOq+Ta9ARSFoFcGowsIROftLbcjCyGEyHeSuBRRN2/eZPz48axevZro6GiKFStG06ZNefXVVylfvjx+T3TH97GOjkECXYjcswcMeowVgvM4ciGEECJ9uvwOQLjfzZs3efbZZ/n111+5efMmZrOZxMRE5s6dS9euXTly5AiKoqDz9ETn4eHygdHo6LlWCCGEKEDkzFQEvf/++5w8eZLmzZvzzz//MG/ePDZs2ED37t25desWr7/+OjabLb/DFEIIIbJMEpci5uTJk6xcuRJvb2/+97//4evrC4CHhwejRo2icuXKnDx5klWrVuVzpEIIIUTWSeJSxCxatAhVVWnVqhWBgYGp5un1erp37w7AsmXL8iG6XNKp052HEEKIIk0a5xYx+/fvB6Bu3bou59epUweAXbt25VVIuW/x4vyOQAghRB6RGpci5syZMwBUqFDB5fxy5coBcOXKFeLi4vIsLiGEEMIdJHEpYq5fvw6Q5jKRU0BAQJplhRBCiMJCEpciJjExEQBPT0+X81NOT0pKypOYhBBCCHeRNi5FjF6vx263pzs/o3mF1kcfuX4uhBCiyJHEpYjx8vLCYrGkW5uSnJysPU+vVqbQ+fjjO88lcRFCiCJNLhUVMc62LTdu3HA5P+X0YjI4ohBCiEJGEpciJjQ0FIBz5865nH/+/HkASpYsiZeXV57FJYQQQriDJC5FTI0aNQDYt2+fy/l79+4FoHbt2nkVkhBCCOE20saliGnbti3jx49n1apVjBw5MtVt0Tabjfnz5wPQpUuXDNdjvRlP0qlLXIw35ma46VKtNpKu3iIx+iZBNUMo9UgNlHRGshZCCHH/kMSliKlatSrNmzdn/fr1DBs2jO+//56goCCSkpL46KOPOHnyJCEhIbRt2zbD9UT/so64/We45b0+jyJPTVEUdCYDOpORqGXbaPD5SwTVCMmXWIQQQhQckrgUQZ988gnPPfcc27Zto2XLloSGhnL27Flu3ryJn58f48ePR6fL+CqhLSYBy8UbJPlbUPQ5u6Ko2uxYYhJQU9yKreh06EwG9B5GdCYjOg8j+tuJit7DiM5oQNE5alh8Q8riG1ImRzEIIYQoGiRxKYLKlCnD3LlzmTBhAmvXruXYsWP4+fnRqVMnhg4dSqVKlTK9Lt8HSqP3NGXp/VVVJflmHJZb8Vhi4rElJGPw9cLo54XRzxuDlwd6Lw88SwbgVSoIz5IBeJYKxLNkIJ6lAvEqFYhH8QB0Bn0Wt1wIIURRJ4lLERUUFMR7773He++9l2fvqaoqSVdvEX/+Kopej0eQL74VS2MM8CHAXIGgmqEE1QzBt1IZTAE+0mZFCCFElkniIrJFtduxJVqwJSZjS0jCmpiMNTYBncmIb6Uy+IeVp2zLOgTVDCEgPBi9R9ZqbYQQQghXJHERGUq+FY8uIRnVasOamIQtIdmRrCRZ0HsY0Xt5oPc0YQrwwbtMMfwqlyPkmRaUaVEn7y71vPxy3ryPEEKIfCeJi3BNp6Dz9SD5RiwAil6PwcuEsUSAlqx4ly2OT3BJfCqUdPwNLoVfWDl0+jxum/LTT3n7fkIIIfKNJC7CpaDH62EsGUC5smUB0Ht5pEhSSuFVthh6U/708SKEEOL+JYmLcMmnTgg+dUKoU79+focihBBCaKTLfyGEEEIUGlLjIgq/V16581zauwghRJEmiYso/CZPvvNcEhchhCjS5FKREEIIIQoNSVyEEEIIUWhI4iKEEEKIQkMSFyGEEEIUGpK4CCGEEKLQkMRFCCGEEIWGJC5CCCGEKDQkcRFCCCFEoaGoqqrmdxD79+/HYrGg0+nw9vbO73ByXWysY8RlX1/ffI4kfYUhRs369XeeN2+ef3GkUKjKrwCS8ss5KcOckfLLGWf5GY1GatWq5dZ1F4jEZc+ePdjt9vwOQwghhBBupNPpqFu3rlvXWSC6/Pfw8CApKQm9Xo+Hh0d+hyOEEEKIHEhKSsJms+XKOb1A1LgIIYQQQmSGNM4VQgghRKEhiYsQQgghCg1JXIQQQghRaEjiIoQQQohCQxIXIYQQQhQakrgIIYQQotCQxEUIIYQQhYYkLkIIIYQoNCRxEUIIIUShIYmLEEIIIQoNSVyEEEIIUWhI4iKEEEKIQkMSFyGEEEIUGpK45LJly5bRo0cP6tevT6NGjRgwYAD79+/P0TpPnTpFzZo1mThxYpZet3nzZl544QUaN25MvXr16N27Nxs2bMjSOi5dusQHH3xA69atqVWrFu3bt2fChAkkJydnaT2FlTvKcO/evbz66qs8/PDD1KhRg6ZNmzJixAhOnjyZS1EXHO4ov7t98MEHhIeHM2/ePDdFWXC5o/wsFgs///wzTzzxBLVr16ZevXq8+OKLbNq0KZeiLljcUYYnT55k+PDhPPTQQ1SvXp1mzZrx3nvvceHChVyKuuCZN28e4eHh7Ny5M0uvc8c5RBKXXDRu3DiGDx/O8ePHady4MWazmfXr1/Pss8+yfv36bK3z2rVrDBkyJMuJwrx58+jbty979uyhVq1a1K1blz179vDSSy/x+++/Z2odFy9e5JlnnuH333/H39+fFi1aEBcXx9ixY+nfvz8WiyU7m1RouKMMFy1axHPPPcfq1aspXbo0zZs3x8fHhyVLlvDkk0+yY8eOXN6K/OOO8rvbP//8k+3XFjbuKL/k5GRefvllvvjiCy5evMgjjzxCWFgYW7dupX///qxevTqXtyJ/uaMM9+/fz1NPPcWyZcsoUaIELVu2xMPDgzlz5tCtWzdOnz6dy1uR//bs2cOnn36a5de57Ryiilxx4MAB1Ww2qy1btlQvXryoTV+3bp1arVo19eGHH1bj4+OztM6jR4+q7dq1U81ms2o2m9UJEyZk6nWXLl1Sa9SoodavX189evSoNn3fvn1qvXr11Jo1a6aKMT0DBgxI875xcXFqnz59VLPZrE6dOjVL21OYuKMMr169qtatW1etWrWqumrVKm263W5XJ02apJrNZrVFixZqcnJyrm1HfnHXPpjS9evX1UceeUT7PsydO9fdYRcY7iq/77//XjWbzWqvXr3UmJgYbfqyZcvU8PBwtVGjRqrFYsmVbchv7irDbt26qWazWf3555+1aVarVf3ggw9Us9msDhgwIDfCLzBWrFih1q1bV/ve7dixI9Ovddc5RGpccsnPP/8MwNChQyldurQ2vUWLFnTr1o0rV66wbNmyTK0rISGB8ePH88wzzxAREUGFChWyFMvMmTNJTk6mT58+mM1mbXqtWrV46aWXSEpKuuevjVOnTvH3339TsWJFBg4cqE339vZm9OjR6PV6Zs6cmaW4ChN3lOHKlSuJi4ujS5cutGnTRpuuKAoDBgygWrVqnD9/nj179uTaduQXd5Tf3T7++GNu3LhB7dq13R1ugeOO8ktMTGT69On4+/vz/fff4+vrq817/PHHad26Nb6+vpw4cSLXtiM/uaMMb9y4wcGDBwkMDKRPnz7adL1ez2uvvQZQZGtNL168yJtvvsnQoUOx2+2UKFEiS6935zlEEpdcsmHDBhRFoVWrVmnmtW3bFnBUc2fG8uXLGTduHL6+vowfP56uXbtmORYg1ckyq7Fs3LgRVVVp2bIlOl3q3aZcuXJUq1aNc+fOFdmDnjvK0G63U61aNZo0aeJy/gMPPADA5cuXcxJqgeSO8ktpyZIlLFu2jCFDhlClShX3BFmAues7HBcXxxNPPEGxYsXSzJ8wYQJr1qyhatWqboi44HFHGer1egDi4uKIiYlJNe/69esABAQE5DjWgui7775j4cKF1KhRg99//53Q0NAsvd6d5xBJXHLB5cuXuXnzJqVLl3a5Ezs/8GPHjmVqfYGBgQwdOpQVK1ZoX7DMUlWVEydOoNPpXO5olSpVQqfTceLECVRVTXc9zp0pvZNEVrepMHFXGT733HPMnz+fbt26pZlnt9s5ePAgAGXKlHFf8AWAu8rP6dKlS3zyySfUrl2bl19+OTdCLlDcVX7O/atmzZokJyezZMkSPvnkEz788EMWLlxYpNuouasM/fz8qFOnDhaLhaFDh3Ls2DESExPZv38/I0aMAKB///65th35KTQ0lC+//JI5c+YQHh6e5de78xxiyPK7i3uKjo4GoGTJki7nO6dfvXo1U+tr1aqVy5qbzLh58ybJyckUK1YMk8mUZr7BYCAoKIirV68SFxeXqvo4JWctQKlSpVzOd27TlStXshVnQeauMszIvHnziIyMpFSpUtSpU8cNURcc7i6/d999l6SkJL744gvtF3BR5q7yi4yMBBwn8SeffDLVCeL//u//mDZtGpMnT073O16YuXMf/OqrrxgwYABbtmyhc+fO2nQfHx/GjBlDp06dcmUb8tsrr7ySo9e78xwiiUsmjRgxQvvFkpG2bdvSvHlzALy8vFwu4+HhAUB8fLz7AkxHQkJChrEAeHp6AmT4hXWux7lseuvIi23Ka+4qw/T8+++/jB49GnDsZwZD0fpaurP8fvvtNzZs2MDIkSOzXFVdWLmr/JyXNkaPHk1gYCBTp06lTp06REVF8dlnn7F9+3aGDRvG7NmzURTFzVuRv9y5D5YqVYpu3boxduxYQkNDKV++PMeOHePs2bNMmzaNOnXqZLkd4v3AneeQonWEzEXnz5/P1G1u0dHRmf7SZ6ZaPKfuvpaY3Tic60lv25zryIttymvuKkNX9u/fz8svv0x8fDw9e/bMcvulwsBd5RcZGclXX31Fw4YNefHFF90RWqHgrvJLSkrS/k6bNo3g4GAAHnzwQSZPnszjjz/Onj172Lx5M4888kjOgi5g3FWGycnJ9O3bl3379vH111/ToUMHwHGpd/z48UyYMIF+/fqxZMkSlzU79zN3nkMkccmk2bNnZ3rZI0eOAHcOFHdzTs8o+3cXb2/vDGPJbDzO9SQmJmZ7HYWVu8rwbuvXr+c///kP8fHxdO/enQ8//DBngRZQ7ig/m83Gm2++CcDnn39e5GoEMuKu/c8579FHH9WSFidPT0+6dOnCpEmT2L59e5FLXNxVhnPnzmX37t0899xzWtICjpPysGHD2LVrF1u3bmXZsmVF8kdITrjzHCKJSy5wXsNL71rdvdrAuJOvry/e3t5cv34dq9Wa5jKE1Wrl+vXreHh44O/vn+56MrtNRfH6uLvKMKX/+7//45NPPsFms9G3b1/eeuutInsydkf5rVq1ij179hAcHMz333+fat6+ffsA+OOPP9i8eTPt2rWjXbt2ubMx+cBd+5/zTqLy5cu7nO+c7rw7pihxVxlu374dIN3ErlmzZmzdupXDhw9L4nIXd55D5K6iXFCsWDGKFy/OxYsXiY2NTTPf2bV7yr4EcouiKISFhWGz2YiIiEgz//Tp09jt9nvG4mwJnt6tanm5TXnNXWXoNH78eD788ENUVWXkyJG8/fbbRTZpAfeUn/O6d1RUFIsXL071cDY63bNnD4sXL+bo0aO5sh35xV37n3N+erfbO08cQUFBOQu4AHJXGd66dQsg3UbhzulF+Q6t7HLnOUQSl1zy6KOPYrPZWLduXZp5zm61nY148yKWlO+bnVic61i7di12uz3VvPPnz3P48GHKly9PWFiYO0IucNxRhgAzZsxg3LhxGI1GxowZk6oTq6Isp+XXvXt3jh496vLx1FNPAY5LSEePHmXo0KG5sAX5yx37X7NmzQDYtGlTmj5IwNHPBkCDBg1yFGtB5Y4ydDYIT2/Ils2bNwMU2b5wcsKd5xBJXHLJs88+i6IofP3110RFRWnT//77b+bPn0/JkiXT3DYXGRnJyZMnXR5UcqJ79+54eHgwefJk/v33X236gQMHmDJlCp6enjz33HPa9PPnz3Py5EmuXbumTQsODubRRx/l9OnTqarq4+Pjee+997RLHkWVO8rw6NGjfPnllwB8+eWXqa6RF3XuKL/7mTvKLzQ0lBYtWnDr1i3efvvtVG0NpkyZwu7du6lcuTIPP/xw3mxUHnNHGT711FPodDrmzJnDqlWrUq1/+vTprF+/nmLFit1X321XcvscoqhF8TaQAuKrr75iypQpeHl50aRJE+Li4tixYwcGg4EpU6ak6UG1VatWnDt3js8//5zu3bunu95x48Yxfvx4XnvtNQYPHpypWGbNmsUnn3yC0WikSZMmqKrKtm3bsFqtfPnllzzxxBPasr1792b79u0MGTIk1a/XqKgonn32WaKjozGbzYSEhLB7926io6Np1qwZP/zwQ5G7lTelnJbh8OHDWbZsGf7+/hn+suvZs2eR/NXrjn3QlXfffZc///zznt+bws4d5Xf58mVeeOEFTp8+TYkSJahTpw5nzpzh+PHj+Pv78/PPP1OjRo382Lw84Y4ynDVrFqNGjcJut/Pggw9SoUIFjh8/TkREBN7e3vz44480atQoPzYvTznLZ9asWWmOV7l9Dim6Z5kC4L///S9hYWHMmDGDLVu24OPjQ4sWLRg6dCjVq1fP01ief/55ypUrx5QpU9i1axcmk4l69eoxaNAgHnrooUytIzg4mDlz5jB27Fj++ecfzpw5Q3BwMC+88AIvvvhikU5aIOdl6GzYd+vWLRYvXpzucg8//HCRTFzcsQ/ez9xRfqVKleLPP/9k8uTJ/PXXX/zzzz8EBATwxBNP8Oqrr2rDThRV7ijD559/nqpVqzJ16lR2797N8ePHKVasGN26dWPgwIFUqlQpdzeiEHPXOURqXIQQQghRaEgbFyGEEEIUGpK4CCGEEKLQkMRFCCGEEIWGJC5CCCGEKDQkcRFCCCFEoSGJixBCCCEKDUlchBBCCFFoSOJShIwbN47w8PAsP86ePQs4ejsMDw9n5syZ+bwlmTdv3jzCw8NT9Zh69uxZbdvi4uK06W+//Tbh4eFat/tFyZUrV7QB4O7mHLzsfmWz2Rg3bhytWrWiRo0aPPLIIyxYsACALVu20KNHD+rWrUv9+vUZOHAg27ZtIzw8nMaNG+fofVu1akV4eLjL8cryUkH7/N1VvuL+VbS7Or3PlC1blnr16qWZ/u+//5KcnEylSpW0oe1T8vDwyIvwRC6ZPn0648aNY/bs2fj7+2vTo6OjGT16NFFRUcydOzcfI8xfkyZNYvz48QCEhYWh0+koW7YskZGRvPzyy1gsFooVK0a5cuWoUKFCPkfrPhEREXz00Uf4+/szduzY/A5HCLeRxKUIeeqpp7SRclNyjoE0YMCAIjeWS9u2balduzaenp75HUq++fzzz11O37BhA8uXL8/z4SUKmr/++guAgQMHMnz4cG36tGnTsFgsVKxYkSVLlmgJfEJCAsuWLcvxEBbTp0/HYrFQtmzZHK0nuxYvXsyWLVto3759vry/ELlFEhdRqPn5+eHn55ffYYgCzDlC7d3jPzmn16xZM1Wto5eXF5UrV87x+1asWDHH6xBCpCVtXIQQRZrVagXAZDKlmm6z2VxOF0IUbFLjIlzauXMnP/74I3v37sVqtRISEsLTTz9Nz549URQlzfJRUVFMnjyZjRs3cvnyZXx8fKhTpw59+vTJ8si/UVFR2nufPXsWg8FASEgI7du35/nnn8fLy0tbdt68eYwcOZLq1aszb968TL/H0aNHmTBhAtu3byc+Pp4KFSrwxBNP0K9fP4xGY5rlIyIimDZtmrZ93t7eVK9enWeeeYbHH3881bJnz56ldevWAOzevRsfH59U848dO0bnzp21OO62Y8cOpk+fzp49e7h16xbFixfnkUceYcCAAalG7x03bpzWdgPQ1vnrr78ycuRIzp07B8DBgwcJDw+nfPnyrF27Vls+OTmZ3377jcWLF3Pq1ClUVSUkJIROnTrRq1evLLd9io2N5bfffmP58uWcOXMGu91O5cqVefLJJ+nZsyc6XerfSbdu3eKXX35h5cqVREZGotPpCAkJoUOHDvTq1Svdy3+rV69m9uzZ/Pvvv8THx1O6dGlatGjBK6+8QqlSpbTlevfurY3IDfDCCy8AUL58ea1sAObPn8/8+fMBx+exbds2XnjhBQIDA9m2bVuq97ZYLPzxxx8sWrSI06dPk5iYyAMPPECnTp3o06dPqjJzXqKdNGkSLVu2TLWeI0eOMHnyZLZv387169cJCAigYcOGvPTSS9SoUSPVss79qUqVKixYsICff/6ZhQsXEhkZiZeXF/Xr12fQoEHUrFlTe014eLj2fMWKFYSHh9OoUSNmzJjhskwB3nzzTRYuXEivXr14//33XS7Tq1cvduzYwYcffshzzz0HOBLAJUuWsHTpUg4dOsSNGzcwmUwEBwfTunVr+vbtm6la0YzKC6Bx48bcuHGDX3/9NU3D3qwef5KTk5k1axYrVqzgzJkzxMXFUapUKZo0aULfvn3dUuMmco8kLiKNJUuWMHr0aDw8PAgJCeHSpUscPHiQgwcPcvLkSd57771Uy2/YsIFhw4YRHx+Pl5cXVapU4dq1a/z999/8/fffDB06lCFDhmTqvU+ePMmzzz7LzZs3CQgIIDQ0lPj4eA4cOMD+/ftZtWoVM2fOdJlcZNa2bduYOXMmqqpSuXJlbty4wcmTJ/nmm2/Ys2cPkyZNSrX86tWrGTFiBImJiXh7exMeHs7169fZvHkzmzdvZvXq1fzvf/9Dr9dnOyaniRMn8v333wMQFBSE2WzWGtcuW7aM77//nubNmwN3GmPv3r0bgGrVquHp6Ymfnx81atTAaDQSERGBt7c3VatWpWTJktr73Lhxg5dffpn9+/ej0+kIDg7G09OTo0ePcujQIZYuXcrUqVMJCgrKVNznzp3j5Zdf5uTJk+j1eipXrozFYuHff//l33//Zd++fanu5oqIiKBv376cP38evV5PlSpVsNvtHDp0iIMHD7Jo0SKmTp2aKmZVVfnggw/4448/AChZsiRVqlTh9OnTzJgxgyVLljB58mTtBG42m7Farezfvx+r1YrZbMbX15dmzZrxzz//cO7cOS5dukTx4sVTJYTpuXnzJgMHDtTKOzQ0FL1er+07W7ZsYfLkyffcN+fNm8f777+P1WrFz88Ps9nMxYsXWb58OStXruTjjz/m6aefTvM6q9XKgAED2LhxIyVKlKBy5cqcOHGCNWvWsGHDBmbMmEGdOnUAqFevHhcuXODChQsEBgYSGvr/7Z15UBTH28e/i4AGUUl5gIIHAkPAGMREUC6DoIgnpjAYMYLxKtaAJZIYwfKIRzAeWEQwnkE0l4pCgVdADR5cooKKQFSu5VTUIJcobL9/bE3/dthdWECT+FZ//qJ6emae7m26n36ep58ZDo7j2pTLw8MDcXFxOHfuHIKDgxXGc2VlJTIzM6GlpUWV9VevXsHPzw9XrlwBIHOP6evro7KyEnl5ecjLy0NSUhJOnDjxxixbHZ1/CCH48ssvkZycDE1NTQwdOhQGBgYoKirC8ePHkZCQgMOHD8PKyuqNyMt4DRDG/3ucnZ0Jx3EkJiamzXrz5s0jHMcRjuPIypUrSU1NDSGEkJaWFhIaGko4jiPm5uakqqqK3iORSMjo0aMJx3Fk165dpKmpiV5LSkqi1xITE9WS1d/fn3AcRzZt2kRevnxJy3NycoitrS3hOI6cOnWKlsfExBCO48isWbMEMvHtqKuro+WrVq2i5b6+voJ2REdH02tZWVm0vKCggIwcOZJwHEc2btxIGhoa6LXk5GTy0UcfEY7jSFhYWLvv58nPz6fX5Tl//jzhOI6MHj2anD59mpa/fPmSRERE0GtlZWWC+/hn5efnC8qV9Q3P0qVLCcdxxMvLixQXF9Py8vJyMnfuXMJxHPHz81O4TxX82Pn0009JaWkpLU9PTyejRo0iHMeRuLg42h43NzfCcRyZN28eqaiooPULCgrI9OnTCcdxZO7cuYJ3HDp0iHAcRxwcHEhKSgotr6+vJ+vXryccx5Hx48eT2tpawX02NjaE4ziSlpYmKOfH9KpVqwTlaWlphOM4YmNjIyjnx8+kSZMEfZ2Xl0fs7e0Jx3EkMjKSlvP/dxcvXqRl2dnZxNLSklhaWpKjR4+SlpYWQgghUqmUHDt2jIwYMYJYWlqS27dv03vkx9OoUaMEY6OqqopMmTKFcBxHFi9eLJA3PDyccBxH/P39iTq0tLQQR0dHwnGcoH95Dhw4oDAu+P8bOzs7kpubK6h/5swZYmFhQTiOE8isqn+V9Zc8yn7Hzsw/ly5dor+j/Nirra0lYrGYcBxH5s+f3153Mf5FWIwLQ4Fhw4YhNDSUHq3V0NDAihUr0Lt3bxBCkJ2dTesePHgQdXV18PDwwPLlywW7KhcXF6xcuRIABC6Ntvjrr78AAJ988olg52ppaQl/f3+4ubl1+fh2nz59EB4eruBWMDU1BQDcunWLlu/fvx9NTU1wdHTEmjVrBG4qJycneqLnp59+wrNnz7okF29pCQ4OxpQpU2i5lpYWxGIx3N3dUVdXh6ioqC69586dO7h06RL09PQQGRkpCCIdOHAgwsPD0bNnT1y4cAF5eXntPu/mzZvIyMiAjo4OIiMjYWhoSK/Z2NjQ3W5cXBwA4PTp0ygsLES/fv0QGRkJAwMDWt/Y2Bj79u2Djo4OMjMzkZycDABoamqilrBt27YJzP86OjpYt24drKysUFFR8UaOfldVVSE2NhYikQi7d+8WWC/Mzc2pFZJvoyp2795NLSfe3t7UfSYSiTB79mz4+PigublZwerH4+fnJxgbAwYMwJIlSwAAWVlZXWkiNDQ0MG3aNACy36g1fNnMmTNpWVpaGrp16wZ/f3+89957gvru7u6wsbEB8OZyyXRm/uHnGCcnJ8HY09XVxerVq2Fvbw8zM7M3Ii/j9cAUF4YCzs7OCkdBeZ81IHMz8PDJtaZOnar0WVOnToVIJEJubi4ePXrU7rv5RXT9+vXIyMiggZUA4O3tjfDwcIWYko5ia2ur1OfO+7XlFZDLly8DAPXnt8bV1RWDBg3CixcvkJaW1mmZSkpK8ODBA2hoaAgWJnn4RYWXqbNcuHABAGBnZ6c0r0/fvn0xduxYtd/FKxfOzs7o27evwnUvLy+cPn0ae/bsEdSfPn260t/BwMAAEydOBAD8+eefAGTK0d9//41+/fpR2VrD91tX+0cZycnJIITAyspK6aLm4uKCuLg4mthOGU1NTUhJSQGg+v+F/41TUlIEY5+HdxPKY2xsDEAWY9RVeKUkMTERr169ouWFhYXIyclB7969BfEnERERyM7OVpqGoaWlhcZ3NTY2dlk2ZXRm/uHnsZiYGBw/flwwnxkZGeHQoUMK7nDGfwsW48JQQN4SIQ8/CTU1NQGQTZQVFRUAgLCwMLowtaZbt25obm5GUVGRymfzLFu2DGlpacjKysLnn3+OXr16YezYsXBycsKECRPQr1+/zjaLokoGHR0dALLAPUDWvsePHwOQWXxUYWFhgfLychQVFXVapgcPHgCQ7Xq/+OILpXVevHgBACguLgYhRGmQtDrwu9/MzEx89tlnSuvw2ZQLCwvbfV5JSQkAqNyl6urqUmsWANpPFhYWKp9paWmJuLg4Wpfvn4aGBpUy19TUqC1zR2mvjVpaWgoWh9YUFRVRZSA4OFghWBkApFIpAFk7q6qqBNYrANDX11e4hw9i5k9JdQVzc3NYWFggNzcX165dw8cffwxAFvcGAJMnT1aIVdHS0kJNTQ0yMzNRUFCA0tJSFBQU4N69e1SZIoR0WbbWdHb+cXFxgZWVFbKzs7FmzRqsXbsWI0eOhIODA5ydnQVBzoz/JkxxYSigbhCdfDr9e/futVu/tra23TpWVlY4deoU9uzZg4sXL6K2thaJiYlITEzE+vXrMWXKFKxbt65LuVs6077WJ4Pk4RUe+fodhZ/gm5ubafCnKqRSKerr66Grq9uldz169KhdK5g6vxm/Y+X7oT34fmqrT/lrfF1e5oaGhnb753VYHlrT0TYqQ14uddw6yvq+K0Hp6jJz5kzk5ubi9OnTCoqLvJsIkG1idu7cid9//11gVenZsyesra1RXV2tlruxM3R2/tHW1kZ0dDQOHTqE2NhYFBcXIzs7G9nZ2YiIiICZmRk2bNiADz/88I3Izeg6THFhdBr5eI+0tDS1T6C0h4mJCbZv346XL18iKysLqampSE5ORk5ODuLj49HY2IiIiIjX8q62kF+k6urqVCpL/IKkbFFTttPkLSfK3sVxHOLj4zslr7rwv9uqVatUWnc6Ar/jV9cdwLe1LQWDX2T4urzMzs7OKuM/3iQdbaMy+Lbo6OgI4qj+a0ybNg3btm3DhQsX0NTUhAcPHqCoqAiGhoYKi3lwcDASEhKgo6ODpUuXwtraGiYmJjAyMoKGhgZWrlzZYcVFlXWm9f9NV+afHj16QCwWQywWo7CwEKmpqbh27RquXLmC+/fvY9GiRTh37pxSCxfj34fFuDA6Te/evWmMhKrgu5aWFqSkpKC4uLhdU7ZUKoVEIqG5N7S1tWFjY4Ply5fj5MmT2Lx5MwDZ8eSuWDfUpVevXvQ4rqodHSGEXuOP1MrHB/FuJ3mUWTn4eyUSidJ7ANmHFDMzM1FVVdWBVijCv6utgMl79+4hNzdXLevFsGHDAPzPndOaJ0+ewNPTE4GBgTQnEADk5uaqfGZOTg6A/8U88e9oS+bS0lJkZWXhyZMn7crcUXiZVbWxubkZc+bMQUBAAKqrq5XWGTx4MLp164aGhgZUVlYqrVNXV4f09HSUlpa+EfeKOvTv3x/jxo1DfX09UlNTcebMGQCymCR592RVVRUN2N27dy8CAwPh7OyMIUOGUDeYqnYqgz9+rWz8P3/+XEFx6ez88+zZM9y4cYNmTjY2NsbcuXMRERGBxMRE9O/fHw0NDUhKSlJbdsY/C1NcGF2CDxb87bfflF6Pj4/HggUL4OHhgYaGhjaf9fjxY0ycOBE+Pj5KF2c7Ozv6Nx8L8KZxcnICAPz6669KryclJaGqqgqampo0KZb8hw6VxVvIJ4HjMTU1haGhIRobG1WeTNmxYwe8vb0F39sBQBeT1gudshgKANT8/8cff9DJW57a2lr4+vrCw8MDZ8+eVfoMeRwdHQHIAmnlAx15EhMTcefOHTx8+BCampp0zCQkJCh1h1RWVtIAYgcHBwCydP06OjooKSmhAa6tCQkJgZeXF0JDQ9uVuaPY29tDJBIhKytLaSxTamoqbt26hfT0dJU7f11dXWqxUDWeoqKiMH/+fMyfP7/LY1zVuFAHDw8PALKxmpiYCACYMWOGoE5ZWRl9trIYsIcPH1KXmLJA49bw/zfq/s8AnZt/goKCMHfuXJw4cUKhvr6+PoYPHw7g9cQMMd4MTHFhdIlFixahe/fuiI+PR1hYGA3cBYCrV6/i22+/BQDMnj273bgUfX192NjYQCqVIigoSKC81NfXY+fOnQAAa2vrf+z7RAsXLkSPHj1w5coVbNq0SeAquHz5MkJCQgAAPj4+NHCYT1IHyIIG+cW5ubkZ0dHRNEurPCKRCGKxGACwZcsWwXHU5uZmHDhwgGYGbu3e4V0Q5eXlSssfPXok2MXa2tpizJgxeP78OZYuXYri4mJ6raqqCmKxGDU1Nejfvz/NxtsWdnZ2sLKyQm1tLQICAmhAMwBkZGRgx44dAIAFCxYAkJ3+MTY2RnV1NcRisWBXXlhYiCVLlqCxsRHW1tY0A7Guri58fX0ByBYeeeXlxYsX2LJlCz2a6+Pj067MHWXYsGGYPHkypFIp/P39BX2Wl5eHdevWAZAdq28rEaFYLIZIJML+/fsRHR1NF0dCCOLi4qgbzMfHp8sJDfk4odbjQh1cXV3Rs2dPxMfHo7i4GO+//75CNtmhQ4dS5Xjv3r2ChT49PR2LFy+mCov8vKAKa2trAMDRo0cFFpSUlBSVHxLtzPzDj+k9e/bg6tWrguedPXsWN27cgIaGBuzt7duVmfHvwGJcGF3C1NQUW7duxddff40ff/wRR44cgbGxMZ49e0bTqtvZ2SEoKEit523evBmenp7IyMiAi4sLhgwZAi0tLZSUlKChoQF6enrYuHHjm2ySABMTE2zbtg1BQUE4cuQIYmJiYGJigqdPn9L2ubu7K1hBAgIC4O/vj+vXr2P8+PEYNmwYKioq8PTpU/j4+ODkyZMK1gZPT0/cv38fUVFRCAwMxHfffQd9fX2UlpZSS8ayZcvg6uoquM/c3Bw3b95EYGAghg8fjhUrVsDBwQFmZmYQiUR4/Pgx3NzcYGBgQHf6O3bswMKFC3H79m24ubnB1NQUGhoaKCgowKtXr6Crq4v9+/er/dXtsLAw+Pr6Ij09Hc7OzjAzM0NtbS0kEgltG7+L19bWRkREBBYtWkR/Z1NTU0ilUty/fx+EEJibmyMsLEyweC9btgwFBQU4d+4cFixYAENDQ+jp6aG4uJi6tDZs2KCQMv91sWHDBkgkEty9exeTJ0+GqakpXr16RT9v4OjoSHOqqGLcuHH45ptvEBoais2bNyMiIgKDBw9GZWUlVfg8PDzo5wm6Aq883717F25ubjA3N0d4eLha977zzjuYNGkSVbJbB+UCsmPz3t7eOHLkCPbt24eYmBgMHDiQBn1rampizJgxuH79ulqpEHx9fREfH4/q6mrMmDEDpqamqKurQ2lpKaytrdGzZ08FRaMz88/MmTNx8eJFnD9/HgsXLoSBgQH69esnCFYPDAxkaf//wzCLC6PLuLu7IzY2Fp6entDT00N+fj6ePXuGkSNHIjg4GPv27VP7JM/gwYMRExODOXPmYNCgQZBIJCgqKoK+vj58fX2RkJDwjyeHmjRpkqB9eXl5NCndDz/8gF27dimc9nB1dcXhw4fh6OhIFQIjIyN8//33CA4OVvmu1atX4+DBg5gwYQKkUikNbHRwcEBkZCQCAgIU7tmyZQtsbW1BCEFRURG1BhgbG2PTpk0YMmQIHj9+DIlEQuMv9PX1cfz4cXz11VcYMWIEysrKUFBQgAEDBsDLywtxcXFtHldujaGhIU6ePIlly5Zh6NChePjwIZ48eYLRo0dj+/btND6Jx8TEBLGxsfDz84OxsTGKiopQUVFBx8zx48cxcOBAwT2amprYtWsXwsLCYG9vj/r6euTn56N79+6YOHEifv75Z6Wp8l8Xffr0wS+//IJVq1bBwsICEokE5eXlsLCwwNq1a7F37161xrmvry+OHTuGqVOnQltbG3l5eWhsbMSYMWMQGhqK0NDQTh91l8fOzg6BgYEwMDBAWVkZcnJyOuR+4pUVTU1NlXlSQkJCsHXrVnzwwQdobm5Gfn4+tLS0MH36dBw7doxaPK5fv95uvJSRkRFOnDiBWbNm4d1336WuRX9/f0RHR6tMPNnR+UckEmHHjh0ICQnBqFGjUFdXh7y8PBBCMHHiRERFRWHp0qVq9xPjn0dE/q0IMAaDwWAwGIwOwiwuDAaDwWAw3hqY4sJgMBgMBuOtgSkuDAaDwWAw3hqY4sJgMBgMBuOtgSkuDAaDwWAw3hqY4sJgMBgMBuOtgSkuDAaDwWAw3hqY4sJgMBgMBuOtgSkuDAaDwWAw3hqY4sJgMBgMBuOtgSkuDAaDwWAw3hr+D0pMUuwXYso1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IkRQuXJj27dtz584dGjRooCU348eP19q4OT7DNWrUYMiQIQwaNIiCBQvy6aefMm/ePC5cuEDVqlWTvX6TJk1QFIVdu3bRr18/9u3bh6IohIWFERERwa5duwBo2rRpitg/+eQTfvvtNwwGg/bajhrTUqVKsWrVKgwGA+3atSM0NJQzZ86k+iPHYrHw+uuv89JLL/Hbb7/x/vvva+t6mPnz5xMTE6N910RGRjJ06NAUbejS69KlS6iqql20kpiYyO+//67F7qzMHTVl//d//0eFChXYtm0bAwcOZOHChYwdO1Zb9yuvvMJrr70GwNy5cwF47rnnGDFiBNeuXWPjxo3UrVvXaVwZPb1rs9mw2WyYzWZ27NgBQPHixXkUwx9mS+PcggULMmHCBHbs2MGkSZPo2LEjRqORI0eOpFrt64yiKNqH0fFl46hOO3ToEADPPvssAAaDQatdcMx70IMF6jif/8EHH2Aymahfvz5xcXGcPHky1WrABznWceDAASpXrozJZNKqgp19MEqWLEnLli05ePAg/fr146WXXgLsjUuTqlOnDp6enhgMBu3L21GT1K5dOwoVKsSYMWNo1qwZp06dcroOgMDAQKpWrcrRo0e5evUqGzduBNAO7D179sRoNPLqq6/SuHFjFi9eTMmSJSlZsmSKdRUsWJAuXbpw6tQpnn76abp27cr169epVq1autuvOE4NOt43Ly8vWrdunWwe2Gu5Hpa0OHz00Ue0adOGhIQEJk6c6HQZLy8vTCYT+/fv16pYHTVmDo73ctWqVVoV6YEDB7BYLBw/fjzL711Gy88RT4MGDbSaq0mTJvHf//7X6emwh8nM+5fez8iDSUBqGjduDKAlA451OL78HacHPDw8tF/rzhw+fBibzUajRo20ZN3xODw8PFlil9ZnKalKlSrRoEEDtm/fTsOGDenTpw/x8fHaF0p633/HMcvRoL1EiRJaMu34QfDg6zu2OyAggEqVKmE2m7XTdkk53g/HKcBnnnkGcH6sAXvNtKIoWmIQEhKS7EeJv7+/lswNHjxYO2Xj7Fji8OSTT1KzZk0mTZpEp06dmDdvXqrPKVasGJUrV2b37t2cPHmS27dv07x5c8Be67Jz5048PT1p0KBBqq/nTK1atXBzc0On01GqVCng/ndDap5++mng/r6X3pp5R2PThg0bAvZE+JdffuE///lPuuNN+t3ToEEDgoODWbp0KfXr12fkyJHo9foUSV9SjvfdcUWY4yqzB9/3pJ/DNm3aUKxYMWbMmEH9+vX55JNP8PLySvUiiffff59hw4Y5vTnaKiY1e/Zstm3bRrdu3di3bx9Dhgxhw4YNqdYWupLLa1z279/PnDlzqFq1KoMHD6Zjx4507NiRSpUq8fXXX2tfsulhNBq1rNZx79gBFEVJsXxq8xzn9B7cUR0HzTp16mjn6xzS+uAm5WioW6pUKZ544olk8x5cJ8CRI0fo27cvRYsWpVevXvTp08dp1X/SWqmk2261WrVTLb1792b8+PFMnTqVQ4cOpZrpduzYkaNHj7J+/Xr++usvihUrRp06dQD7F8maNWv4448/2LFjB3/++SfLly9n3LhxThvlfv755zz77LNs2LCB3bt3M2PGDObPn88ff/yh1ZakRafTpXh/nL1v6T1POmDAAF544QXq1avHxo0b2bhxI3///bfTL71atWpx7NgxVq5cibe3N5UqVUo23/FeBgYGpmgY5+HhkeX3DjJWfo7nJD0FmJCQ8NArdZLuBw/u8xl9/9L7GUnP+2U0GrUEyXHviNVR9un9tfbgeXUHZ89P6/1ISq/XM3v2bDZv3symTZvYs2cP33zzDb/++iurV6/m7Nmz6Xr/He+PY392nH5LK25nP5ScLetYrnHjxsn2g6SvkZSiKCm2Nenn7OLFi/Ts2ROwt5MYMmSI9ndqJk2axA8//ECbNm0YMmQI+/btY8GCBam+d02aNOH777/XTs28+uqrbN26lY0bN3LixAmaN2+e4Qa4SbfdUU4P23ccZZTRiwScfQ7j4+NTLXMHx/eOqqrJLujw9vZm0aJFrF27lr///pvdu3eza9culi9fnuoPe8f77mj/4uBI2pKu26FEiRL88ccfrFmzhq1bt7Jnzx7++usvtmzZop1eT2r79u1Ok2VAO12alON0X+fOnfH29qZ79+7MmDEj1dOWruTyGpcCBQqwdu1aZs+ezbVr17TpjgLJ7GW1D3JUs65YsQKwH/gc57YdWadj575+/TpAinYbQUFBgH1nmD59Op9++inFixendevWeHt7O/1AOD70jp3S0Xrax8eHyZMnM336dGrUqEH16tWpUaNGiriXLl1KbGwsffv2pV+/fulOkByOHTvGvn37MJlMjB07ltq1a3P58uU0n9OhQwd0Oh3ff/89t27d4plnntG27ZtvvmHChAm88MILzJ07lylTpgBoVX9JnT9/nvfee4+dO3fy4Ycf8scff9CuXTuio6NTreV6UJUqVVBVVXvfYmNjtfY3SX8tpHaAf1C1atUAqFChAt26dQPspyqd/ZpytGmJioqiZs2aKV7D8V6WK1eO6dOnM23aNCpUqECdOnW0X0hZee8yWn6O/XPnzp3aQbN///40aNAgWWNrB8eB1LG/R0dHc+7cuQy9/oP798M+Iw7pqTZ39mPDwfErePPmzYB9v/jrr79SXd7xI2HHjh3cuHEDsB94b968Sbly5TJVI3Xw4EHeffddLl68yMSJE9mwYQM1atTgypUrnDt3Lsvvf1ocjYqvXLnCmTNncHd3T/GlBPffj+eff57p06fzxhtvULp06VTbFTxYrocPH9b2D7Cfao2IiKBTp04MHjw4xfvo7Bg4f/58FEVh4sSJdOjQ4aH9wTh+RKxYsQIfHx9q1KhBlSpVWLVqFaqqOm3f4uAs8cqstPa/tDjKfNu2bQDcvXuXp556ig4dOjhtV/jg986RI0eSbcOWLVt499130el0TJo0iS1btlC0aFGOHj3K7du3nZa5I4ahQ4cyffp0+vXrR7ly5bQaMoek79/y5ct57733KFu2LFOnTmXTpk3o9Xq2b9/udDs3bdrEyZMnnd4cZzyS8vHxAextdgDtmPRgc4Xs4PIaF5PJRJs2bVi3bh3t27enRo0aXL9+ndOnT+Pr66t9uWTVgAEDWLNmDTNmzGDXrl3cuXOH0NBQqlatSrt27QD7we3cuXO88cYb1KhRI8WVNz169ODnn3/myy+/ZPPmzVy4cIHr169rXwCOxmB79+6lZ8+ezJkzR5u2YMECzp8/zxdffEGjRo3Yvn077du3x8fHhyNHjlCyZEn69euXIm7Hweh///sfGzZs0NpYxMTEpGu7ixcvjsFg4OjRo/Ts2ZMrV65oV26k1jitePHi1K1bV8uEkx7koqKi2Lx5M507d6Zy5cocO3YMwOl50KJFi7Jlyxbt3LSXlxf79u3Dw8NDSyAeZsiQIezdu5cPP/yQFStWEB4eztWrV2nevDl16tRJd6dYzowYMYIVK1YQGhrKnDlztDZADkkb4z7YMBfsv2IDAwPZvHkzzz77LKqqcurUKUJCQhg4cGCW37uMlt9TTz1FlSpVOHz4MJ06dcLDw4OjR49SuXJlgoKCUvyycVy1sGXLFkaNGkVYWFiyfSI9r+/v78/FixcZOnQo/fv3f+hnxFWef/555s6dy/z58zl69CjXrl1zWj3tUKZMGZ599llWrFhBx44dCQ4O5sCBAyiKwhtvvJGpGIoWLcr69etZsWIFGzZswGazcfjwYQoVKkRgYGCW3/+0TJ8+nR07dnD+/HkSExPp16+f01qIl156iTVr1vDmm2+ycOFCTpw4wZ07d1JtlP7yyy9z8OBB3nzzTWrUqMHRo0eTdQHg2KZ58+Zx4sQJ7XStY5t8fHzQ6/VcuHCBXr168dlnn1GqVCnOnDnD888/j5ubm/bFldrxp1atWvj6+hIZGUmdOnXQ6XTUrVtXS5adtW9x8Pf359atW/Ts2TPHutDo1asXP//8Mz/99BOHDx/m6tWrxMTE8OSTTzpN2B1J9XfffcfJkye102GOK8X8/PxYu3Yt69atY/Hixdy9e5eIiAiCgoLw9/fXzjQkLfM+ffpw6NAh+vbtS/Xq1Tl8+DCxsbFpnmLz8PBg7dq1bN26VfuBa7VaqVevnkvKpXv37qxevZovv/ySTZs2ceTIEcD+Wc5u2dLG5YsvvmD48OEULVpUu4qjefPmzJs3L12nE9IjODiYBQsW0LhxY61hardu3Zg9ezZGoxGAN998k7p163L16lVCQ0P56quvkq0jICCAmTNnEhISop1qGTBgAGPGjAHsWW7Xrl3R6/VcuXKFmJgYnnnmGWrXrs2dO3e0GqVJkybRuXNnbt++zdmzZ2ncuDGzZ892enDv3bs3HTt2BOznTl9++WXKlCnD6dOnk11pkJpixYoxfvx4SpQowalTpwgMDOS5554DSPPScsdrlipVKlnNxjvvvMOrr76KwWBg165deHh4MGLECPr3759iHd7e3vz88880b96csLAw/v33X0JCQpgxYwalS5d+aOxgb4cwa9YsatasyZEjRzCbzfTr1y9Zr7aZVbx4ce0S9e+++y5ZjR/Yv+wcV7w4S1x0Oh0zZ86kZcuWhIeHc+XKFdq2batd3ZLV9y6j5acoCtOnT6dVq1ZcuXKFixcv0rp1a7777junvx4rVqzI2LFj8fPz459//qFx48ZaW6L0vv7gwYMpWrQo58+fJz4+/qGfEVepUKECX3/9NQEBARw/fpwGDRrQo0cPAO3z/KDPPvuMkSNH4uXlxf79+ylfvjyTJ0/W2n1kVOnSpZk9ezb16tXjxIkTnDlzhieffJJZs2bh5eWV5fc/LZ999hnXrl0jNjaWbt26pfolXbduXb7++msqVKjAvn378PLy4s0339Ta2zyoffv2jBkzBi8vL06fPs2QIUOSNeJ95pln6NOnD56enpw4cYL27dtTq1Yt7Yegt7c3r7zyCt7e3oSGhhIfH8/EiRMxmUxaTa/jdNmBAwecxqDX63nqqacAtFPUjhhMJlOa3wkjRozAz8+Ps2fPPrQNS3YpXrw4M2fO1BIGs9nMyy+/zFtvveV0+W7dutGtWzcsFgv79u3jzTffTFZjUaNGDaZNm0blypU5dOgQly9fplWrVtpxxlmZd+rUiQ8++IAiRYqwb98+ihUrpvVOn5q2bdvy+eefU6ZMGfbu3UtUVBRdunRJtR1gRj311FNMnToVk8nE4cOHKVasGB9//LH2GclOivoomgCLHDdhwgTmzp3LgAEDnF6tIERO2rlzJ0eOHCEoKEhrqOq4Om/OnDk8+eSTORxh9mjRogWXLl1i27ZtTq/kE0KklH1dmeZSU6ZMcdow6WE2btxImTJl6NOnD7t37+b999+nd+/e2RChay1btozZs2drHUA5LoW8ePGi1tBr//79FChQALDXwCxdujRfJjg3btzAzc1NOzeblONy5dzG1ftbYmIi165dc3rJeGocHajt27eP+Ph4ihQpwvLly13a0ZTZbGbSpEnodDoaNGhAXFwcBw8exNfXN1lbsejoaGJiYpK1lXN8ptu2bcvkyZNdFlNelNeOT67yzz//0LdvX622Mb127typXRXlTMOGDdPdc3hYWBglS5ZMtYZQuM5jl7iULFkyxWWwYL+sLDExkfLlyztt2JdXx1wxm83a5XylSpXKlV/Oj8KcOXOYMmUKCxcuTJa4REREMGHCBMLDw1m8eHEORpj9tm/fzscff0zfvn3T/aVmsVgYNGgQoaGheHp6UrFiRdzc3FzeO2aTJk34z3/+w6+//sqePXu0zs3efvttvLy8APtl6p9//jmffPKJyxr5i8fblStXtC4inHH2I+dBZrOZadOm8eOPP7Jr1y5JXB6Bxy5x6d69e7IOmBwcVbaDBw92WQPi3OD555/nmWeeyZYGlXlJaud1t27dypo1a9LsQyG/eLDX1PQ4ceIEoaGh6HQ6lixZol2lkh0GDRrEoEGDUp3/v//9L9+NNu3q0XS/+OIL4uLi5LRTOjnao2TFtWvXHtrLs3Ctxy5xeRwVLFiQggUL5nQYIg9ydORWuHDhbE1ahGs4u4RaiPwmW64qEkLkD45+Kh63EXqFELmXJC5ZsHfvXl555RXq1atHrVq16NatGwsXLky1w6Tw8HA++OADbTTbBg0aMHjwYG0AsowIDw/nvffeo2PHjtSsWZO6devy/PPPM2vWrBQjyzpGyc1olejJkycZNWoUDRs21EaO/v7771MdDiE0NDTZ9tWvX5/+/fuzZs2aFMs6RoE2mUxO+8E4deqUNt+ZPXv2MHz4cJ566imqVatG06ZNeffdd1OcCnGM2OvQqVMnbaTZFi1aaON8HD16FJPJpF3R4pCYmMicOXN47rnnqFWrFjVr1qRr1678+OOPGe6AzDHyrc1mY/bs2bRt25bq1avTsmVLPvnkkxSXbz9Mesv7n3/+wWQyaf2+fPLJJ5hMJq2zQWcc78+QIUMAeweSjvcjaePHu3fvMmXKFDp16kSNGjW0z8GsWbOcXr7qGIE2LCyM0aNHU7NmTerVq5fqpaVwf/91dGI5ZMiQVEeNvnjxImPHjqVx48aEhITQunVrvvrqq1T7WomOjmbq1Kla/LVr16Znz5789ttvTjsXc2bnzp2YTCYaNGiQ6mdj8eLFmEymFH1cnDhxgvfee4+2bdtSq1YtQkJCaNasGWPGjHE6aOHDys8xSvD8+fNTPPfo0aOMGTOGp59+mmrVqtGwYUOGDBnitLNJxz6TWj8hmzdvdvp5AVi/fj2DBg2iefPmVKtWjcaNGzNs2DCtA7eMuHnzJt988w0dOnTQRq9+6aWXko28nBZHeTk64HtQgwYNUuzTkP7j6zvvvJOsN9vatWs7HU15w4YNDBw4kAYNGhASEkKrVq349NNPk3UG6OA4Zv3444/88ssvNGnShOrVq9OxY0ft+JaR439+JKeKMmnVqlVMmDABd3d3KlSowLVr1zh69ChHjx7l7NmzvPfee8mW37p1K6NGjSI2NhZPT08qVarErVu3+Ouvv/jrr78YOXJkqqMEP+js2bO8+OKLREZG4uvrS2BgILGxsRw5coTDhw+zfv165s+fn6VGYv/88w/z589HVVUqVqzInTt3OHv2LP/73/84cOAAM2bMSLb8hg0bGDNmDPHx8dq4QLdv32bHjh3s2LGDDRs28N///jfTA5Ml5RhwE+wdVAUHB2uNa1evXs23336rdWrlaIzt6J66SpUqeHh4ULBgQapVq4bRaCQ0NBQvLy8qV66crG3AnTt3eOWVVzh8+DA6nY6AgAA8PDw4efIkx44d448//uDHH39MNmpterz//vv8/vvvFCpUiKCgIM6cOcP8+fNZt24dc+bMSVcD6oyUd8GCBalduzanTp0iOjqagIAAihYt6nQ8Kgd3d3dq167N3bt3OXPmDG5ublondY7TjqGhofTv35/Lly+j1+upVKkSNpuNY8eOcfToUVasWMGPP/7otL3Ff/7zH44cOUJwcDBXr15N8xRH4cKFqV27ttaAPigoCB8fHwoXLpxsubNnz9KtWzeioqIICgrC3d2dsLAwZs6cyY4dO/j111+TfSYuXrzIgAEDuHDhAgaDgfLly2Oz2Thw4AAHDhxg3bp1TJ8+/aG1TQ0bNqRkyZJcuXKF7du3O+1bw/FF27lzZ23a0qVLGTduHFarFX9/fypUqEB0dDQXL15k1apVrF27lp9++slph2EZKT+wd5g5YcIErFYrvr6+VK5cmatXr7J582Y2b97MwIED00we0+uHH37QBigtU6YMJpNJawC7cePGDF3tdOzYMQYPHsz169cxGo1UqlSJyMhI9u7dy969ezlz5ow2oKArZeT4Wr58eapVq6aNGVSrVi0URdEu5lBVlQ8++IDffvsNsHdyWKlSJc6fP8+8efNYtWqV1k/Sg9atW8fBgwcpVaoUpUuXJjY2loCAgEdy/M/1VKGqqqo2b95cDQ4OVhcvXpzmcr1791aDg4PV4OBgdcyYMWpkZKSqqqpqtVrVzz//XA0ODlZNJpN67do17Tnh4eFq7dq11eDgYPWbb75RExIStHkbNmzQ5q1fvz5dsY4cOVINDg5WP/30UzUxMVGbfvToUbVBgwZqcHCwunTpUm364sWL1eDgYLVr167JYnJsR3R0tDb97bff1qb369cv2XbMnTtXm3fw4EFt+rlz59SQkBA1ODhY/eSTT9TY2Fht3pYtW9S6deuqwcHB6tdff/3Q13c4efKkNj+ptWvXqsHBwWrt2rXVP/74Q5uemJioTps2TZt36dKlZM9zrOvkyZPJpjsrG4fBgwerwcHBao8ePdQLFy5o0y9fvqz26tVLDQ4OVocOHZriealxxBAcHKx+9dVXqtlsVlVVVW/evKn26dNHDQ4OVrt166babDbtOY79bd68edq0zJR3aut6mE2bNqnBwcFq8+bNk01PTExU27ZtqwYHB6u9e/dWr1y5kiy+Tp06qcHBwWqvXr2SPc/xOatWrZq6f/9+bV1RUVEPjcXx3E2bNiWbPnnyZK1cO3furJ4/f16bt379etVkMqnBwcHJ9heLxaJ26dJFDQ4OVocMGaJGRERo806fPq22a9dO+4ylx1dffaUGBwerb775Zop5ERER6hNPPKFWqVJFvXnzpjatRo0aanBwsPrjjz+qFotFWz4sLEx99tln1eDgYHXQoEFOyyC18nP2Hu/atUs1mUyqyWRSZ8yYoe13NptNXbp0qVqtWjU1ODhY/e2335I9Jzg4WK1fv77T7XW2X9y+fVutWrWqGhISou7evVubbrFY1O+++04NDg5Wa9asmWx/TU1CQoK2fw0ePFgrN1VV1T/++EN94okn1ODgYHXv3r1pxpvaPuNQv359NTg4WN21a5c2LaPH17SOZbNnz1aDg4PVxo0bqzt27NCmx8TEqB999JEaHBysNm3aNNn+n3R/njBhgnY8cJRBRuPLj+RUUSaVL1+ezz//XLtcTqfTMXr0aHx8fFBVNdnYMz/++CPR0dF06dKF1157LdkvuJYtW2q9kKa3fxnHQJXdunVLllVXqVKFkSNH0rZt2yxfvu3r68vkyZO1nmbBXg3tGDMjaS+ZM2fOJCEhgaeffpr33nsv2aB2TZo00a7o+emnn7h9+3aW4nLUtLz77rvJhi4wGo0MGzZMG3tnzpw5WXqdI0eOsHnzZvz8/Jg+fXqykapLlizJ5MmTKVCggDZQXEa0bt2aMWPGaIO9FSpUiMmTJ+Pr68u///6b6lgiDo+yvFPzxx9/cP78eYoUKcL06dOT9X5aoUIFfvjhB7y8vNi7dy9btmxJ8fw2bdpovRcbjUaXXF6t1+uZOnVqsu7vW7VqpY0K7Kh1A/uv2WPHjlG+fHm++eabZOOrBAUF8c0336DT6Vi4cGGaQw84OGpSNm7cmOIU4po1a7BarTz99NNaVwuO03Y1a9ZkwIAByWoiAwICGDBgAHB/ZOIHZaT8pk+fjqqq9OjRg8GDB2v7naIodOnSRTv+TJ48Od2nx5wJDQ3FbDZToUKFZLVEer2eIUOG0Lp1a5555hnu3Lnz0HWtXbuW8+fPU7p0ab755ptkXVS0b99eGwhy+fLlmY43Na46viYkJGg1019++WWyThS9vLz48MMPtbGwnHXFYDQaee2117Resh1l8CiO/7mdJC6Z1Lx58xSjjLq5uWkdeyX9cDrOr3bo0MHpujp06ICiKBw/ftzpOc8HOb5EP/roI3bv3p1s5NGXXnqJyZMna+M1ZVaDBg2cXonkOI2R9Avx77//BuxjejjTqlUrSpUqRXx8PLt27cp0TGFhYZw5cwadTpfqoHKO7qYdMWWWo2+Hp556ymm/PoULF9aGuc/oazmGJUjKz8+P1q1bA6Q5uGDS18vu8k6LIxnp1KmT0/2kRIkSaW5P0mEnXKVy5cpOB4NzJNtJP5OOy5Bbt27t9CAfHBxMcHAwZrM5XWUYFBRE1apViYmJSbG9jtNESYdfaN++PQcPHmTu3LlO1+dIRlNrr5De8ouJidHGVEptf+nRowdubm5cv37dabua9CpdujR6vZ4TJ07w1VdfER4enmz+1KlTmThxYpqnKB0cZdihQwen3TiMGDGCP//8kw8++CDT8abGVcfX/fv3c+fOHYoUKaIdKx7kOI45O4YEBwdrHYNmR3x5mbRxyaSkNRFJOXY0x6+u6Ohorly5AsDXX3+d6vX+er0ei8VCaGhoqut2GD58OLt27eLgwYP06dOHggUL0rBhQ5o0aUKLFi1cMjpnajE4OgNzjL4cHR2t9a3hGLHbmSeeeILLly8TGhqa6ZjOnDkD2Gu3HL9IH+RoEHrhwgVUVc30iLCOX7p79+7lxRdfdLqMowHe+fPnM7Tu1AakrFSpEkCafa08yvJOi2O9jgHlnKlSpQrLly93GkN29DOS3n0W7r+/f/75J/v27XP6vKtXrwLpf387d+7M0aNH+eOPP2jbti1g30cOHDiAt7d3skacDkajkX379nHq1CnCwsIICwvjxIkT2r7lGKX7Qektv/DwcCwWi9ZGxBlPT08CAwO1PnuqV6+ernU7i6lPnz7MmTOHmTNnMnPmTMqXL0/jxo1p2rQpTz75ZLrbXTiSntRiLlSoUKZGAE8PVx1fHcer2NjYVI8hkZGRgPN9LLX3+FEc/3M7SVwyKb2Xhya9msEx8nJaoqKiHrpMjRo1WLp0Kd999x2bNm0iKiqK9evXs379ej766CPat2/Phx9+mKW+WzKzfc5+HTg4vjyyMpKuYyA7i8WSrNrfGZvNRkxMTKZPQThe6/r16w+tBUvPe+ZgMBhSLSfH9LTW9yjLOy2O9aYVg2Oesxiyoyo7I5dsO97f8PDwFDUDD0rv+9uxY0f++9//smXLFmJiYihQoIBW2+Ks+n7dunVMmjQpWWKn0+moVKkSbdq0Yd26dam+VnrLz1H2np6e6HSpV7C7an8ZO3YsVatW5ZdffuHgwYOEhoYSGhrK/Pnz8ff354033uCFF1546HoctWOOuB4lVx1fHftYbGzsQ49XzgbpTO09fhTH/9xOEpdslrT9wa5duzJ8BUpqKlasyFdffUViYiIHDx5k586dbNmyhaNHj7Jy5Uri4uKYNm2aS14rLUkPLNHR0al+WBwfTGcHItXJ5ePOLqV1PDc4OJiVK1dmKt70crxvb7/9dqq1O5lhsVgwm81Of3k6vjTS2kdcUd6u4FhvWqMiO77wc+LL52Ec7+/kyZO12pGsKly4MI0aNWLLli1s2rSJTp06sWrVKiD5aSKAbdu2MWrUKFRVpXnz5rRt2xaTyUSFChXw9PRk27ZtaSYu6eVIHuPi4rDZbKkmLxndX9K65PbZZ5/l2Wef5ebNm+zatYvt27ezefNmbt26xfvvv0+hQoVo1apVmut3nB5y1aW9zo4x4Pw4A645vjr2sebNm6e4CjOrcsvxP6dIG5ds5uPjo1VpptbQzmq1smPHDi5cuPDQxnE2m43w8HCtcZ+bmxv169fntddeY8mSJUyYMAGwXy6bXb+2kypYsKBWpZlajZKqqtq8cuXKASRrH5S0Ct/BWS2H47nh4eFOnwP2gRT37t2b4T5RUnut1N4zsG/v8ePH0/zydia1dToa+TraZDiT2fJ2tQoVKgBw/PjxVJdxtJdI2rA5t0jP+3vgwAFOnTqV6pebM45GuuvXr+fs2bOcOnWKkiVLpugPZfbs2aiqSrdu3ZgxYwZdu3alSpUq2ped4zRVVgUEBGAwGDCbzVqjzgfFxsZqpyoc5eJoLJza58zZ0AtxcXEcO3ZMK9PChQvToUMHPvvsM7Zs2cJTTz0FwIoVKx4at6OBteN0y4OOHTtGz549+fjjj9NcT1rbcffu3RTvrSuPr45tSGsfu3jxIgcPHkxXA3BXx5eXSeLyCDj6FPm///s/p/NXrlxJ//796dKlC7GxsWmuKyIigtatW/Pyyy87/XJ2HBwg9fPjrtakSRMAFi5c6HT+hg0buHbtGgaDQTuAJx28zNn5XWdjuAQFBVG6dGni4uJSvZpg0qRJvPTSS4wePTrZdEdblwd/eaX2C9TRF8e6deu0bu+TioqKol+/fnTp0sVpB3tpWbp0aYppt27dYsOGDQBao9bUZKa8IfUyyAzHPr1q1Sqnp1KuXr2qNXBu3Lhxll8PXBu/4/1dtmyZ044Ew8PD6d27N506dUp2Bd3DtGzZEm9vb7Zt26bVtnTq1ClFWytHZ3rO2gipqqrtI1m5ygfsNS5169YFUt9ffvvtN8xmM35+ftqYXb6+voA9qXGWRDn7fM6bN4+uXbvy2WefpZjn5uZGnTp1gPQdlxxXgq1evdpp0rFmzRoOHDjw0B8ojuNMeo8xmTm+Jj2GJN0369ati5eXF2FhYU47+QMYN24cPXr04PPPP09zO7ISX34kicsjMGjQINzd3Vm5ciVff/11sgPltm3bGD9+PGAfEPFh5yWLFy9O/fr1sdlsvPnmm8l23piYGP73v/8B9o6QHtU5zoEDB+Lh4cHWrVv59NNPk1Xv/v3334wbNw6Al19+WWs45ug0DeyNlh1ffhaLhblz5zr9clcUhWHDhgHw2WefJes902KxMGvWLK031QdP7ziqwC9fvux0+vXr15MdIBs0aEC9evW4e/cugwcPTtZg9tq1awwbNozIyEiKFi1Kp06d0l1WAPPnz+f333/X/o6IiGDkyJFERUXRrFmzhzaOzEx5p1UGmdG+fXsqVKjAjRs3GDZsWLIvt/Pnz/Pqq68SFxdHrVq1nDZKzQxXxt+xY0fKly/PhQsXGDlyZLIahNDQUIYNG4bFYuGJJ55Idhnrw3h4eNC2bVtiYmK0S/IfPE0E93+NL1q0iBs3bmjTr1+/zpgxY7QrgTLaO7Mzw4YNQ6fT8euvv/LDDz9oV6GoqsqyZcu0DuNGjRqlncIMDAzEz88PgM8//1yLIyEhgUmTJjm90qpdu3bo9Xq2bdvGzJkzk13tcurUKX799VfgfuKdlk6dOlG6dGnCwsJ4++23kyXHjo75APr165fmehyXjM+fPz9ZzceOHTucDryameNr0tNrSfdNb29vLb4333wzWfISHx/PZ599xq5du9Dr9bz88stpbkdW4gP7FZlnz551+iMsL5I2Lo9AUFAQX3zxBW+99RYzZsxg3rx5VKhQgdu3b2u/vJ566inefPPNdK1vwoQJdO/end27d9OyZUvKli2L0WgkLCyM2NhY/Pz8+OSTT7Jzk5KpWLEiX375JW+++Sbz5s1j8eLFVKxYkVu3bmnb165duxS1IKNGjWLkyJHs2bOHpk2bUr58ea5cucKtW7d4+eWXWbJkSYpf8927d+f06dPMmTOHN954g4kTJ1K8eHEuXryoNegbPnx4inPoJpOJ/fv388YbbxAYGMjo0aNp3LgxlSpVQlEUIiIiaNu2LSVKlNB+mU6aNImBAwdy+PBh2rZtS1BQEDqdjnPnzmE2m/H29mbmzJkZHnW7QoUKjBs3jilTplC4cGFOnTqF2WzGZDKl633LbHk7uj7/+eef2blzJ+3atWPw4MEZit3Bzc2NadOmMWjQIG0/DAoKwmazcfr0aVRVxWQy8fXXX7ukt2RH/KdOneLzzz9nyZIl9O7dO9Mj+zriHzhwIFu2bKFZs2YEBQVhNpsJDQ3FarVSokQJpk+fnuF1d+7cmcWLFxMbG0uVKlWcXhkzdOhQtm/fzqlTp2jRogUVKlTQriq0WCzUr1+fffv2kZiYyJ07d7QkIjMaNGjAuHHjmDBhApMmTeLHH3+kbNmyXLlyRUvYXn75ZV566SXtOXq9nhEjRvDpp5+yZs0adu7cqSUSUVFRjBw5MsWQEQEBAYwdO5ZPP/2Ur776ipkzZ1KmTBliYmK0q/yaNWuWrvfMw8ODqVOnMnDgQFavXs2mTZuoWLEiN2/e1JLkUaNGUb9+/TTX069fP1auXMmNGzd49tlnCQoK0nonrlWrFgUKFEgxFEFGj69+fn6UKFGCq1ev0rt3bwICAvj888+pVKkSw4cP59y5c/z555/079+f0qVL4+fnx4ULF7RTzB9//HGqVxo6k5njf79+/bh06RIjRoxg5MiR6X6t3EpqXB6Rdu3asWzZMrp3746fnx8nT57k9u3bhISE8O677/LDDz+k+6qIgIAAFi9eTM+ePSlVqhTh4eGEhoZSvHhx+vXrx6pVq1K9jDC7tGnTJtn2nThxQuskbcqUKXzzzTcpGqS2atWKn3/+maefflpLCMqUKcN///tf3n333VRfa+zYsfz444+0aNECm82mtQ1p3Lgx06dPZ9SoUSme89lnn9GgQQNUVSU0NFSrQalQoQKffvopZcuWJSIigvDwcO0XcPHixVm0aBH/+c9/qFq1KpcuXeLcuXMUK1aMHj16sHz58jQvB07NF198wciRI9Hr9Zw5c4ayZcvy+uuv88svvzz0UniHzJT3q6++SteuXfH29ubcuXOptnlIr4oVK7Js2TKGDh1KhQoVCA0N5cqVK9o+vWjRonT12ZFejnFh3NzcOH/+fIYvQ39QUFAQy5cvZ+jQoQQGBhIaGkpYWBhly5ZlwIABLF26NFOjLdevX197nrPaFrD3w7J06VLatGlD4cKFOXPmDDdu3KBWrVpMmDCBn3/+WesGPrVxdjKid+/e/Prrr3To0AGj0cjx48fR6XS0bduWOXPmOP289enTh6lTp1K3bl0SExMJDQ3liSee4IcffqBv375OX6dPnz5Mnz6dJk2aoNfrteNc3bp1+fTTT/nuu+9S9H+VGsfl9C+//DLFihXj1KlTxMTE0KhRI2bOnMnw4cMfuo4yZcrw+++/07VrV/z9/Tl79iwGg4GRI0cyd+5cp1fuZOb4OnnyZEJCQoiPjyc8PJywsDDA3pbvm2++4euvv6ZRo0bExMRw8uRJ3N3dad26NQsWLEgxftXD5Mbj/6OmqK44YSyEeCjHqbGVK1cSHBycw9EIIUTeJDUuQgghhMgzJHERQgghRJ4hiYsQQggh8gxJXIQQQgiRZ+SKxrnHjh0jISEBvV6f74fjFkIIIfK7hIQErFYr7u7uaQ4Imxm5oh+XhIQEbDYbNpsNs9mc0+EIIYQQwgVc0Ynig3JF4qLX67UBwHLjgGyu5uh4KLMjFz8KeSHGLEk6vlA2bGO+L79sJuWXdVKGWSPllzWO8nNVB5RJ5YrExd3dHbPZnKwb+Pxs3759ALl6W/NCjLmZlF/WSPllnZRh1kj5ZY2j/LKj+Yc0zhVCCCFEnpEralyE67Ro0UIbr+Zh5s6dm2z0YCGEECK3k8Qln6lWrRrFixdPdf6lS5e4du0abm5uaS4nhBBC5EaSuOQzkydPTnXe7du36dSpEwDjxo2jfPnyjyiqXEhR7j/O+R4BhBBCpJO0cXmMjBs3joiICJo2bUrPnj1zOhwhhBAiwyRxeUysW7eOjRs34unpyYcffpjT4QghhBCZIonLY8BsNjNp0iQA+vXrR+nSpXM4IiGEECJzJHF5DPz666+Ehobi6+vLoEGDcjocIYQQItMkccnnbDYbc+bMAaBnz57SC6QQQog8TRKXfO6vv/4iPDwco9HISy+9lNPhCCGEEFkiiUs+t2DBAgDat28v/bYIIYTI86Qfl3zs7t277Nq1C4COHTtm6LmJVpWIeCs+N+5mR2g5rlKSx6edbGOc2cqduESCCheklG/+H/hTCCHyCklc8rG///4bi8WCr68vTz75ZIaeu+5SLKHRZtbfPJFN0eWsKUkeT96acht1Chj1OvQ6hbebV6OQl+sHChNCCJFxkrjkY5s3bwagZcuWGI3GDD03xmLjZoKNOCUeHcrDn5AHxJgtTqdHJ5ox6nUYdTr7vV6HTgEFhdK+XhR0z1jZCSGEyD6SuORjBw8eBMjSQIqlCnriZtC7KKKcY7bauHw3Dh8PA74ebvxv4Xr8PNzw9TDyXNkAfD3d8E9y8zTqUZT8kbAJIUR+IolLPhUVFcXFixcBCAkJyeFoHi2z1Ua8xWq/me33KuBh0OPtbmR0kyqU9PHM6TCFEEJkgiQu+dSJE/Z2G+7u7gQGBuZwNNnDpqokWuxJSoLFSoLVRkKSJMXDoMfX040SBj0FPYyU9vGicjEfSVqEECIPk8Qln4qIiACgePHi+eqUR2R8IlEJFhIsVsw2G256He4GPe4GPYXcDbgb9Hi7GSjt66Xdyvh6UdjLPV+VgxBCPK4kccmnbt++DZCv+m6x2Gxci4qneEEPihRwx92go5i3JyUKelCioCclfTwpUdATf0+3hycply/ff1yqVPYGLoQQwmUkccmnXnrppXzVU67FZuNadDweRj2Vi/nSvXo5inl7YNRnsg/FpANNqqprghRCCJHtJHEROU5VVWwqWFUVq82GxaZitan37m1YVZXoRAu+HkZK+XjSIqgEpaVTOCGEeCxJ4iKyjdWmkmi1YbHZUiQi95MT+zwUMCg69HoFg6Kg19276XW46xQKF3AnpIQ/HauUoZi3R05vmhBCiBwiiYvIEntyYiXRasNstZHouFlsqKhax272RESH4V4iYtAp6BUFw715bgYd3m5GvN0NeBkNeLsbKOBmv3m7GSnq7U5ZvwLSwFYIIR5zkriIdLHYbCRYbMkuO06anLjp9bjpdXi5GfC71/usp0FPEW8PfD2MWgJSwO3BpMSAl5v9aiAhhBDiYSRxESlYbSp3zTYAwu7EomJvg+JusF967GHQ4eNhxF2vw+NeclLEy53CBdwpfO++SAF3fNyNUkMihBDCpSRxESmcuB4JgK9Rh6+PJ+4GHV5GAyUK2i89Ln7vvqi3hyQnQgghHilJXEQKgYW9KedtJCLeyitPmShR0AO/9PSNIoQQQmQzSVxECp5GA21K2y83fqK4bw5HI4QQQtyXyd67hBBCCCEePUlchBBCCJFnyKki8XiSbv6FECJPksQln7LZbCxatIilS5dy+vRpLBYLgYGBPP/887z44ovS0FYIIUSeJIlLPpSQkMDw4cPZunUrOp2OwMBAYmNjOXbsGB9//DF79uzhf//7nyQvQggh8hxp45IPffnll2zdupWSJUuydOlS/vjjDzZv3syMGTPw8vJi9erVrFixIqfDFEIIITJMEpd8Jjw8nF9++QWDwcDMmTOpXLmyNq958+b0798fgMWLF+dUiLnDvn33b0IIIfIMOVWUz6xatQqr1Uq3bt2oVKlSivndunXDzc2NkiVL5kB0uUjduvcfS0NdIYTIMyRxyWd27twJQMuWLZ3OL1OmDEOGDHmUIQkhhBAuI4lLPnP69GkAAgMDiYqKYvHixezdu5fY2FgqVqxIjx49CAoKyuEohRBCiMyRxCUfSUhI4NatWwBcvXqVfv36ce3aNW3+9u3b+eWXX/jwww954YUXcipMIYQQItOkcW4+EhMToz1+44038PDwYObMmRw+fJgtW7bQr18/LBYLH374oXZKSQghhMhLpMYlH0lISNAex8XFsWjRIgICAgAoUaIEY8eO5ebNm6xcuZKvv/6aJ598MtV1RRLHLSUO8/Vj2R53TmiY5PGubNjGi4q95iu/ll92k/K7T0Ghok8pinjIgKdCgCQu+Yq7u7v2uHPnzlrSktSQIUNYuXIlhw4d4ubNmxQuXNjpui4pd7mjxHLrZoLT+Xld0sTl8M1zLl9/vBIPkG/LL7tJ+d1n0OlJtJp5umT1nA5FiFxBEpd8xNvbG0VRUFUVk8nkdJny5ctjNBoxm81cunQp1cRFRcWCDW+dHr2Sv88oeuiNLl+nhcRsW/fjIL+WnwrYVBs2VcWq2u4/xv44KQUFnaKgKAo25JJ9IRwkcclH3NzcKFOmDOHh4akuk7Sbf4Ph4W+/h94Ng07vkvhyK0+D+8MXyqAE4rNt3Y+D/FR+sZYEYsxxWFUVGyp6RUGHDp2iQ6/oMOruP9YpSpLHOjz0bnga3AjyKZ3TmyFEriGJSz5TvXp1wsPD+ffff53Ov3z5MmazGZ1OR+nScjAUIruo92pVYsxxeBu9cNMb0Ss63PVGPPRu9272x+6Ovw1u2jw3nUHGExPCCUlc8pn27dvzxx9/8Oeff/L6669TvHjxZPMXLFgAQL169fD1fXwb+8UVc36KTIiMcCQnFtWK1Wa/t9isWFUbVtWGTtFhUHQYdQZalKpJEQ9fdPn81KsQ2U0Sl3ymRYsW1KpViwMHDjB48GAmT55M2bJlAVi9ejXz588HYOjQoTkZZo5btfP3nA5B5HF3EqKJsyaiV3ToFT0Gnf3e3eiGQdGh1+nxMnjgbfCgsIcPRT38pAZFCBeQxCWf0el0fPvtt7z88sscP36cZ555hooVKxIbG8vFixcBeO2119K8FFoI8XBx1kSKe/rjbfS8fzMkfeyBPp+3DxMiJ0jikg8VL16cpUuXMnv2bNasWUNYWBienp40btyYfv368fTTT+d0iELkCwoKz5SpJwmKEI+QJC75lKenJ8OHD2f48OE5HYoQ+YKqqiTaLPab1SwXKAuRQyRxEY+lkht3aI+vtHwqByMRuV28NZFoczxmmwWDTo+7zkgBowf+OiOlChSWxrZCPGKSuIjHUuNXx2mPF53dnIORiNwuKjGOAkYPPPQF8XcvSFEPX4p4+FLUww8Pg1tOhyfEY0cSFyGESIOKipvOQOsydSjk7pPT4Qjx2JPERQjx2FFVFRUVW5J7m2rDhoqq3vub+13zAxgUaYArRG4giYsQIk+wJxv2GhCSPFadPLapNnsCkiQpuZ+g2JMS+1hAOnTamED3utzHPj6QUTGiQ6GAwQOjzoi7Xk4LCZEbSOIihMgUR62Fyv2kglQSiQeXBefTHXMSSEAFEuJuJ5uuOP4pCgrce2y/5950HWhJiAEdiu5+QpI0QTEqetz0Row6A+737o06A+46A0a9ETed4d7NiI+bF+75bMBHIfIqSVxEmm7E383pELLd1djbLl+n+d7oxvGxtocsmRc5Egns6YLiSCdInlQkmU7SZRQFexoBik5xknxAdII9DfL18EmWrOjvDUCovzdqeYqbTo9B0eOmtycdxnuJh/1vezJivDdPrgYSIm+SxCWfun37Ng0bNkxzmSFDhjB69Gin8wrgRjwWfDzzf2PEop5+Ll/nXXMkAD6e+W88qKRJicFpEqFHr3swqbBPc4zdo1fuPUeX5LGWfOg49u8xdCjUKlfz/nPvJS9CiMebJC751KlTpwDw8/MjMDDQ6TJpjQ5dTvWnjOpLjfI1syO8XKVL+UYuX+fBgwcBqJkPy0+HDoNOp9WCZAdP7KdlPA3u2bJ+IUTeJYlLPnXy5EkAOnbsyPvvv5/h5ysoGNDj8Rg0SMyObTSiz7Z1CyHE40xO8uZTjhqXoKCgHI5ECCGEcB2pccmnHIlLpUqVcjiSXKp27ZyOQAghRCZI4pIPqarK6dOnAalxSdW+fTkdgRBCiEyQxCUfCg8PJzY2lqJFi3Lz5k1mzZrF8ePHAahcuTLPP/885cuXz9kghRBCiEyQxCUfcpwmioqKolOnTlitVm3etm3b+Pnnn3nvvffo2bNnToUohBBCZIo0zs2HTpw4AUB8fDzPP/88a9as4ciRI6xdu5YePXpgNpv56KOP2LxZRkUWQgiRt0iNSz5UtWpVevToQXBwML1799amly9fnvHjx2M0Gpk/fz5ffPEFzZs3z8FIc9APP9x//OqrOReHEEKIDJHEJR9q3rx5mgnJ4MGDmT9/PufPnyc0NPTxbO8yePD9x5K4CCFEniGnih5DxYoVo3DhwgBcvnw5h6MRQggh0i/VGpcnn3wyyytXFIUdO3ZkeT0i48xmMzqdDr1e73S+fYReMBplxFshhBB5R6qJy+3bWR8xVwZEyxlNmzbl6tWrTJo0iY4dO6aYf+3aNW7dugWQ6jhGQgghRG6UauIyd+7cRxmHcKGgoCCuXr3KsmXLnCYuP/30EwD169fXThmlpKIYEkm0RmVjpDkn6QhC2bGNiiEhybptqNhQVRsqqv1v1Ype54m73s/lry2EEPlZqolL/fr1H2UcwoUGDhzItm3b2Lp1K5MmTWLkyJG4ublhtVqZM2cOc+bMQa/X8+abb6a6DqP/VXTu0VyJvfPoAn+EyiV5fCV2q8vX71b07r1139GmKSiAov0FUNgjhALGUi5/fSGEyK+ydFWRzWbj2rVrREVFERwcDNjbTsgpopz11FNPMXr0aL7++mt++OEHFi5cSNmyZbl8+TK3b9/GYDAwYcIEatSokeo6FL0ZxZhAoi3y3hdu7mBTLRlYWkkSu/3evm8m3x6LLYakCYWiLa9g35WTriPpfEBJ/hraPNWxrJ60Pg5GnXcGtkcIIUSmEpejR48yY8YMtm/fTlxcHIqicOzYMS5dukTfvn3p379/sv5DxKM3ZMgQatasyc8//8yBAwc4deoU/v7+dOzYkVdeeYXKlSunaz1uOl90ivMGvo+Sqlqxqgmoqg2DrsC9mO4nIQ/WZigoKIoOBR3cu1fQ3Z+WhK9bpSTT7y/jWN7ZtNSWdUyPOH0YUAgoVccejyTzQgjhEhlOXH7//Xc++ugjLJaUv3wvXbrEpUuXmDBhAkePHmXixIkuCVJkTsOGDWnYsGFOh5FhqqqiYsGmmrGplnu1LCo6xYi73o+CbuXxMpRKnlg8kERkJFko4lkzGzbCnuzZ4xNCCOEqGTqqHj16lA8//BCAQYMGsWzZMmrWrKnNr1q1KiNGjECv17Ns2TKWL1/u0mBF3qWqKqpqxaaasaqJWGzxWGyxmG0xJFqjSLRGkmC9Q7z1FvHWm5htMaiqDZ3ijpveF09DcQq6VaCoZz383avgrvfDTe+DUeeNUeeFQeeBXnFDpxjsCYzUcAghRL6UoRqXWbNmYbPZ+OSTT+jevTsAOt393KdAgQKMGDGCgIAA3n77bRYvXkznzp1dG7F4pKxqHDY1/fmt/eqZe1fO3HusYgO4VyuioNWQOE7nKMZk83ToMeoL4qbzxU3vi7veD4NSwLXJiJOrrYQQQuR+GUpc9uzZQ+HChbWkJTWdO3fmyy+/5Pjx41kKTuQcVVVQbQZQlXuX8KaPouhRFCXJaRzHY/29GhEjOsXt3mM39Pf+Tj7NHZ2SzaNRrFyZvesXQgiRLTL07RAZGaldPfQwJUuW1EYpFnmP5W5RdAleFPIvn6Hn2RMTo5aEOJITBYOcvhFCCJFlGUpc/P39CQ8Pf+hyNpuN8PBwChUqlOnARM5SzR5YzR74uacvURVCCCEehQw1zq1Xrx5RUVEsXLgwzeUWLFjAnTt3qFu3bpaCE0IIIYRIKkM1Lq+88gpr165lwoQJ3Lx5kw4dOmCz2bT5165d4//+7/+YOXMmOp2Ofv36uTpeIVzjo4+cPxZCCJGrZShxqVy5MuPHj+eDDz5g2rRpTJs2TZtXp04dYmNjtb/feecdQkJCXBepEK708cf3H0viIoQQeUaGe8fq1q0bv/zyC08//TQGg+Fe/xwqMTEx6HQ66taty+zZs3n55ZezI16RSfv37+eJJ56gRYsWOR2KEEIIkWmZuua0evXq/PDDDyQmJhIaGkp0dDReXl4EBARQoEABV8cosigxMZFx48YlO60nhBBC5EVZ6izDzc0t3ZdHi5wzefJkzp07l9NhCCGEEFmWqcQlOjqa5cuXs3XrVs6fP09cXBw+Pj4EBQXRrFkzOnTogNFodHWsIhOOHj3KTz/9hIeHB/Hx8TkdjhBCCJElGU5cdu7cyX/+8x9u3rx5r2t3u+vXr3PmzBnWrl3LzJkzmTx5MhUrVnRpsCJjzGYzY8eORVEUhg4dytdff53TIQkhhBBZkqHE5ezZswwZMoSEhAQCAwN59tlnCQoKokCBAsTExHD8+HGWLl3K2bNn6d+/P0uXLqVw4cLZFbt4iO+//56TJ08ydOhQTCZTTocjhBBCZFmGriqaMWMGCQkJdOnShZUrVzJkyBBatWrFk08+SatWrRg5ciRr1qyhRYsWXL9+nenTp2dX3OIhTp06xYwZMwgMDGTYsGE5HY4QQgjhEhlKXHbu3ImPjw8ff/wxer3e6TLu7u7897//xdPTk40bN7okSJExVquVd999F4vFwqeffoqbm1tOhySEEEK4RIYSl6ioKMqVK4e7u3uay3l7e1OxYkVu3bqVpeBE5syePZsjR47Qq1cv6tSpk9PhCCGEEC6ToTYugYGBXLhwgcTExDR/xVutVi5fvkzZsmWzHKDImNDQUKZMmULJkiV54403Mr0eb28LBQtagHx6GfUrPZL84fptLFnScQVXfis/P0AGTxVC5JwMJS79+vXj7bff5tNPP2X8+PGpLjdt2jRu3brFyJEjsxygSD9VVRk3bhwJCQl89NFHeHt7Z3pdhQqZ8fa2AtddF2Bu8sPrSf5w/TYWKmTOtnXnHAWIRxIXIUROSjVx2bJlS4ppfn5+1KlTh0WLFnHq1CleeOEFnnjiCby8vIiLi+P8+fMsX76cLVu20KZNGxo3bpytwYvkFixYwN69e+nYsSPNmjXL8vp0OhX7LpLhkSEee2ZzIgAeHvmlfZGKPWkRQoiclWriMnjwYBRFcTpPVVUOHTrEoUOHUp2/fv16NmzYwLFjx1wTqXiotWvXArBq1SpWrVrldJlLly5pl0Zv3LiRMmXKPGStkrhkhtXq+OxkqXPqh1CdPM6uaUnnCSFEzkn1qFqqVKlHGYdwgeDgYCwWi9N5d+/e5cyZM7i5uVGtWjWAhzayFpmlAir2vN+q/Z10XvJleci0tOYl/XGhpGOas3m6NOY9OM0DIYTISakmLps2bXqUcQgXeP/991Odt3nzZoYMGULRokVZuHDhI4wql3o1SRutHz5wssCDicaDt7TmqXh5OQa0jMP+hZ/0BplLGpxN0yVZb3ofZ+Q5D07LL6e+hBB5VXbWYwuRe81cfP/xD+8DNuy1I0nvIWWykfSmczLNvlxMzF1sNuVez9H6B26pJQUPSxrSek0hhHg8ZGvisnfvXurWrZudLyGEC0RjTwgcSYUxyd+Zu507dwCAwoVlqAUhhHClDCcuoaGhzJ07lzNnzhAfH4/NZks232q1kpCQwI0bN4iOjpbGuSIXerChaUHsbTc8AM8kN+e9QwshhMg5GUpcwsLCeP7554mOjtZGhlYUJdko0UkVK1Ys6xEKl2jevDknT57M6TByCfMDf1dDrpwSQoi8IUOJy6xZs4iKiqJEiRL06NEDDw8PvvjiC5o0aUKrVq24evUqf/zxBxcuXODJJ5/kp59+yq64hcggFXu7FRspExdJWoQQIq/IUOKyc+dOdDod33//vdYXyE8//URkZCQvvPACYO//ZfDgwezatYsNGzbQqlUr10ctRKocV/Y82NhW5f4VNdImXQgh8qoM/dSMiIigVKlSWtICUKVKFY4dO4bZbP8V6+7urg0H8Ouvv7owVCGSciQnFiAB+2XHMdgb2sYCifeWMWBvr1IQe1f1JYByORCvEEIIV8hwHbm/v3+yv8uVK4fFYuHChQvatLJly1K2bFmOHz+e9QiFSEbFnqQ8mKDosTeu9cY+EGBRoAxQATBhb8diwp60SNsrIYTIqzJUZ164cGGuXbuWbFpAQAAAp06dIigoSJteoEABLl265IIQhQB7cmLFnqgo2BMUI+DO/SuCHI/lVJAQQuRXGapxqVWrFhEREaxevVqbFhQUhKqqbN26VZsWHR1NaGgohQrJKLIio1Tsp38SsQ/qF4u9diUa+ykhHVAAqARUASoCpYHC2JMZSVqEECI/y9BR/sUXX2TVqlW89dZbbNq0iYkTJ1KnTh2KFy/OsmXLCAgI4IknnmD+/PnExcVRp06d7Ipb5DrOusHPzN9wvxGtnvuDPDpOBXkARbC3W8mCDz/M2vOFEELkiAwlLnXq1GHUqFFMnTqV9evX89VXXwEwbNgwPvzwQ6ZMmQLYR4fW6XQMGzbM9RGLRyyB+93KPywBcTYez4N/69IxP+lpH8fNiEu7t//oI9etSwghxCOT4Xr1YcOG0bJlS3bs2KFN69GjB4qi8OOPP3Lp0iUqVKjAqFGjqFWrlkuDFY+OzaZgNiu4uyftPTathEPhfpf5zu4djx/8+8H7pImNEEIIkVymGgSYTKZkl0QDvPDCC1pfLiLnHTlyhB9++IG9e/cSHR1N8eLFadasGa+88grFixd/6POvXXPDy0tPUJDj0mFHbYgkHEIIIXKOdBmaD23atIkePXqwbt06bDYbQUFB3L59m3nz5tGpUyeOHDny0HWYzToiI43YLysuir1dSSHAF3ufKF7cP4WjR5IWIYQQj0KqNS4LFixwyQu89NJLLlmPSJ+rV6/yn//8B6vVyrBhwxg+fDgGg4G4uDjGjx/PkiVLeP3111m7di0Gw2N8BU6nTvcfr1yZc3EIIYTIkFS/uT755BMUJeu/oiVxebRWrFhBdHQ09evX57XXXtOme3p68vHHH7Np0yYuXrzIP//8Q6NGjXIw0hy2alVORyCEECITUk1c6tWr9yjjEC5SrFgx2rZtS5s2bVLMc3Nzo2zZsty5c4crV67kQHRCCCFE1qSauMybN+9RxiFcpEuXLnTp0sXpvNjYWEJDQwH7UA1CCCFEXiONcx8TZ8+eZcSIEdy9e5fatWtLjZoQQog86TFunfl4mDp1KsuWLePixYuoqkqLFi347LPPcjosIYQQIlOkxiWf2717N+Hh4aiqvYfbsLAwdu/encNRCSGEEJkjiUs+99lnn3H48GHWrFnDSy+9xNmzZ3nttdeSDZQphBBC5BWSuORzZcqUwd3dncDAQD744AN69+6Nqqp89dVXWK3WnA5PCCGEyBBJXB4zr776KgCXLl3i8uXLORyNEEIIkTEZSlwOHDhATExMdsUiXCAyMpLDhw8TGxvrdH6xYsXw8vIC4ObNm48yNCGEECLLMnRV0TvvvMPNmzfZsGEDfn5+2RSSyIoOHToQERHBt99+yzPPPJNifmRkJHFxcYA9iXlsff99TkcghBAiEzKUuFy5coUKFSpI0pKLNWjQgFWrVrFo0SKnicuCBQtQVZXg4GBKlSqV6np8ddcoqItAvXI9O8PNOZ0C7j++ssblqy9jsJdbriw/VQXVAqo1yb2atXUWaYRSoKxr4hNCiDRkKHEpXrw4N2/exGq1otfrsysmkQWvvPIKa9asYdu2bXz55Ze89tpruLm5YbPZ+PXXX5k2bRqKovDmm2+muR5f3TW8dbcg6s6jCTyf8dUl2h/kyvJT0EbzVnT2x4qS/B4llem65Ms4HidcB0lchBCPQIYSl7feeovRo0fz5ptv8vrrr0u38blQ5cqV+eSTT/jggw+YNWsW//d//0e5cuW4evUqN2/eRK/XM27cOJo2bfrQdemwgqEQKJKkZlRCfBQA7saCWViLCjaz/abeuwfQuYHOQIqEAtKfiDxI0YFisK9X0dsfO+51D/79wDJ6T/AsnYXtFEKI9MtQ4vLPP/9QuXJl/vzzT/788098fX0pWrQo7u7uTpdXFIVFixa5JFCRfs899xwmk4mZM2eyZ88eTp06hZ+fHx06dGDAgAFUq1Yt/StzfFGJtKkqcO9277SLgmo/DaNNc8y3pT5Nm37vb8VwL1HxAjc30LmDmx8Y/UBnTJ5QKPoHkoqkSUfS6TqXjPwuhBA5IUPfSPPnz0/29507d7hz506qy8vBMedUq1aNb7/9NqfDyL06vnv/8YrxJE8oHrjHdj/JSDMBQavxcFfMgAIWHqj90CW/Vx44/eL42/HY6AtuhcG9kP3ezQ9FasCEEI+xDCUuEydOzK44hMgYR61EasnGQ+6Vf0Pvryrx5gOnWHT3Tp04kgg96JwlHw8mHfp7tSB6Im/cRkVHcd8y92pBHDUgjntjkhoSAyjGJPPuT1N0kqQIIURSGUpcunbtml1xCOGcarNf+WKz3LsCxnL/b0WXJOFwdu9IOB6o5Xiw+yLvwPvtRhSjPanQud1LLoz3TrcYnSceSRKOpDWMF6/uA6BE8TqPpJiEEOJx4ZLGCzExMRQoUMAVqxK5jTny3pf9I+a4VBeS11boPO8nD3rPBxKMJDfFeD8Z0RlBcTx2s89LQin97KPfPiGEEJmSqcTl6tWrzJkzh7///psLFy6gqirHjh3j2rVrjBkzhgEDBtCiRQtXxyoeIStGElVP3AxeOROAdgWLGxh9wFgQDAXv3dv/VnTGh69HCCFEvpLhxGXr1q288cYbREdHozqunrj3i/zixYvs3buXffv2MXz4cEaMGOHaaMUjc9UShIcSham4KWcC0LvbExS9hzTyFkIIoclQ4hIWFsaoUaOIi4ujXbt2dOjQgenTp3P8+HEAypcvT9euXVm6dCnTpk2jWrVqNGvWLDviFtnMipEYtZD0hiqEECJXydAgi99//z1xcXG8/vrrfP3117Rq1QoPDw9tfuHChZk4cSJvvvkmqqryyy+/uDxgIYQQQjy+MpS4bN++HV9fX1555ZU0l+vfvz9+fn4cPnw4S8EJIYQQQiSVocTl5s2bBAQEPHScIr1eT5kyZYiOjs5ScEIIIYQQSWUocfHx8eHy5cvpWvbatWsyirQQQgghXCpDiUvNmjW5ffs2f/75Z5rLrV69moiICGrUqJGl4ITINitW3L8JIYTIMzJ0VVG/fv3YuHEj77//PvHx8bRr1y7ZfIvFwrJly5gwYQKKovDSSy+5NFghXKZTp5yOQAghRCZkKHGpV68eo0aNYvLkyYwdO5Zx48ZpfWw8++yzhIeHEx8fj6qq9O/fn6eeeipbghZCCCHE4ynDHdANGzaMoKAgvv32W86ePatNP3XqFAClS5dm2LBhPPfcc66LUgghhBCCTHb536ZNG9q0aUN4eDhnzpwhOjoaT09PypcvT1BQkKtjFEIIIYQAMpi47Nmzh4IFC1K5cmUAAgICCAgIcLrsjh07CA0NpVevXlmPUghXK1Xq/uN0XiknhBAi52UocenTpw9169Zl/vz5D132f//7HxcuXJDEJYecPXuWWbNm8c8//3D9+nU8PDyoXLky3bt3p0uXLjkdXs67ciWnIxBCCJEJqSYuUVFRXLt2LcX02NhYzpw5k+oKVVXl8uXLydq/iEdr06ZNvP766yQkJODu7k5gYCA3b95kz5497Nmzh61bt/LVV1/J4IVCCCHynFQTl8TERHr27ElMTIw2TVEUjh8/Tqd0XkraoEGDrEcoMuTGjRu8+eabJCQk8MILL/Duu+/i6ekJwIYNG3jrrbdYtWoVNWrUoG/fvqmux+/aEQreOoPl6pZsj1m1msFmBUBfuxO6siHZ/ppCCCHyplQTl8KFCzNixAg+//xzbZqiKKiqmuYKFUXBy8sLk8nExx9/7LpIRbosWrSImJgYqlatyscff4xOd7+PwVatWjFmzBjGjx/PnDlz0kxcCkSG43X3MrbYq48ibPDwRilQCDXu7qN5PSGEEHlSmm1c+vXrR79+/bS/K1euTJ06dViwYEF2xyUyaffu3QC0bt06WdLi0KxZM8aPH8+lS5eIjIzE19c3jbWp4F8K9EbXBmk1Q2wkxEeBR0GUAv4ofiXRPdEUpZTJta8lhBAiX8lQ49wRI0ZQsmTJ7IpFuMBrr73Gs88+S7Vq1ZzOj4uL0x5brdaHr1DRo+jSHlQzNarNBtZEsDhuCfZ7VQVPX5Si5VFKVEIX1AClaAVpcyOEEOKhMpy4pOXs2bNYrVaCgoKc/toX2a9mzZrUrFkz1fkbN24EoFChQvj7+7v89dXEeIi5bU9SbFYwuIHBDcXgBl7+9r/dPdGVqoyuYn0U3+Iuj0EIIUT+leEO6KKiopg/fz7+/v707NkTsI8EPXToUI4fPw7Ye8+dOHEi9erVc220IksiIiKYNWsWAB07dsyeGo6Ym+DmheJTBIzuKAWLoBQsAgWLovjYb3j5Se2KEEKITMlQ4hIZGckLL7xAWFgYLVq00BKXDz74gGPHjgFgNBq5ePEir7zyCitXrky1gzrxaMXGxjJ8+HDu3r2Lv78/gwcPdvlrqKoK5kQUn+Lom/RFKRyQ6dNMQgghhDMZOp8zZ84cLly4QNmyZenatSsAly5dYsuWLeh0On7++WcOHDhA//79iY+P137di5wVExPD4MGDOXToEHq9ni+//JIiRYq49DVUVbWfIjK4ofgWRylSTpIWIYQQLpehGpfNmzdjMBiYPXs2pUuXBu63mahdu7bWb8sbb7zB4sWL2bFjh4vDFRl169YtBg8ezOHDh9HpdHz22Wc8/fTTLn0N1ZIId68DoPgUQ2d6KvefCtq7N6cjEEIIkQkZSlzCw8OpUKGClrQAbN26FUVRaNKkiTbNaDRSpkwZ6T03h4WHhzNgwADCwsIwGAx88cUXdOzY0fUvFHkNxbMg+JdGH9IKpXQV17+Gq9Wpk9MRCCGEyIQMJS6qqmI03u/TIzExkb33frk2bNgw2bIxMTFyZVEOOnHiBIMGDSIiIgJPT0++/fZbmjZt6tLXUG02iI+2X0HkWQZDi0Eo7gVc+hpCCCFEUhlKXMqWLUtYWBjx8fF4eHiwc+dO4uLiKFSoENWrV9eWCw0N5eLFiwQGBro8YPFwoaGhDBgwgJs3b+Lr68v3339PrVq1XLZ+1WaF6Fv2DuTcPFH8SqH4lZCkRQghRLbLUOLSoEEDTpw4wbvvvkuHDh20gfratGmjLXP48GHGjRuHzWZLdvpIPBpxcXEMGTKEmzdv4u/vz5w5c6hcubLL1q+aE+DOFfAogFK4LErRcugq1EYp5brXEEIIIVKTocRl8ODBrFu3jtWrV7NmzRpUVcXf35+hQ4cCsHPnTgYMGICqqpQrV45BgwZlS9AidTNmzOD8+fPodDq+/fZblyQtqtUMCTH2mznB3h9LyWD0Ndqh+JVwQdQ5IGnj4YeMvyWEECL3yFDiUqhQIRYvXszMmTM5ffo0ZcuWpX///hQvbu/9tEKFCnh5efHMM8/w1ltvPWQcHOFqiYmJ2jhSHh4efPPNN2kuP3nyZIoWLZr2Sm+GgaID9wIoXr7gXgBduZroqrdBcfUYRkIIIcRDZLjn3EKFCvH22287nVeiRAl27tyJm5tblgMTGXfy5EmioqIAe4dz+/fvT3P5hIQEp9NVmxVDYjSqokcpVAo8fdEVD0QpEYRSrCKKu5fLYxdCCCHSI8OJy8NI0pJzQkJCOHny5EOXU21WiLuLGhuJ7cIh1Ng7EBuJGhtpv4+7i2KzkOjph1dgXXS1OkhnckIIIXKFDCUuy5Yty/ALdOnSJcPPEa6lmuNRLx3Hduk4RN9Cjb8LFgtYzWC12Nuw3HuM1Qw2Kx7mROILFENnaiRJixBCiFwjQ4nLO++8k+4eUVVVRVEUSVxyiGq1oN4IQw0/gu3yCXtNSnwUmO+N2qzTg94AeqP93s3z/t86A+qVM/fWJH3xCCGEyD0ylLhUqVIl1cQlPj6eGzduEBkZiaIodOzYkUKFCrkkSJE+aswd1OtnUa+dxRYRCvHR9mQlLgoM7uBZELyLgN6Q+7vkF0IIIZzIUOKyZMmShy6zd+9e3nnnHf79919+//33TAcmUqfarBBzG/VuBOrd63DvXo26CQmxqAkxkBhrr0Fx94bCAXIFkBBCiHzB5Y1z69aty7fffstzzz3HtGnTUr0CSaSfarWgXjiEevuSPVmJugHmeLAkoloSwJxo73bfZgU3L3D3goJFUPQuf3uFEEKIHJUt32xVq1YlMDCQdevWSeKSRWpsJNY9S1CvnkV1jAtkSbS3UTG42U8BuReAAv5gcJNTQEIIIfK1bPtJrtfriYiIyK7VPxZUSyKWv+egRlywD2ZYwB88fewJigxgKYQQ4jGULYnL/v37OXPmDCVLlsyO1ed7qs0KdyOwXTkJd2/Yu9ovHCCXJbvSpUs5HYEQQohMyFDi4uhOPjWJiYmcP3+elStXAtCyZcvMR/YYUeOiUG9dRL192X67c1UbF0iNjwY3L0laXK1UqZyOQAghRCZkKHH55JNP0tWGQlVVAgICGDZsWKYDy890lgQ8Yq5jPRSBGnHBfmWQOR7VHG9vdGtOsLdhMbqDuyd4FMzpkIUQQohcIUOJS7169dJemcGAj48PtWrVonv37nh7e2cpuJygqirYLPYrdKyO+3u9ytosqNak8yz3721WVIu919lk05Msr9oskBBDwIVT6K0JWC/p7ZctWy1g9LDfvPzA6CE1LEIIIYQTGUpc5s2bl11xZIhqNdv7LomNTJFAqNYkycIDCYT6YMLxQHKhOh6rKqDa7x03bA/8rYJqe+Bv1Z74pLq8CjYrnvGx2HRGcPeDgsXA6C5XAz1qly/ffyynjYQQIs/I9R19qIlxqJHXIPIaquOm9WNiTpFEqA8mHGklEU4SD1ABBRTl/r3jhi7J4wfmKbr703Q6QO98nqIjTh8PioKHt2/OFKqA0qXvP1bVnItDCCFEhqSauMTFxbnkBTw9PTP1PDU+Gsufk+3JhTkBLAmojo7WLImu/bJR7v2nJRnJIrmX3ABYXfJyHolm+5oTb7lkfdlBsVlyOgQhhBAihVQTl9q1a2d55YqicOzYscw92ZJgv1dtYDCCwYjikeWQcoWEu3cBcPfxyeFIUhd/9y4Woxd45L12SkIIIfKvVBMX1QU1GllZh+JdGEOHN+y1LSlqQfK28EMHAahRo2aOxpGW8EMHsendKW6QMY6EEELkHqkmLhs3bnyUcTilOK60yWesRi8AFM/ce5mzI0YhhBAiN0k1cSmdtPGiEEIIIUQukO5zMPHx8WnO3759O3fu3MlqPEIIIYQQqXpo4hIdHc3EiRNp1KgRV69edbqMxWJh5MiRNG3alAkTJhAbG+vyQIUQQggh0kxcrl27xvPPP8/cuXOJiYlh9+7dTpc7d+4ciYmJJCQkMH/+fLp37y4jQwshhBDC5VJNXGw2G8OGDeP8+fP4+fnx9ttv06RJE6fLBgcHs3v3bt577z18fHw4d+4cI0eOdMmVSUIIIYQQDqkmLitWrODo0aOUK1eO5cuX079/f/z8/FJdkZeXF7179+b333+naNGiHDp0iDVr1mRHzEIIIYR4TKWauKxatQpFURg3bhzFihVL9woDAgJ4//33UVWVFStWuCRIIVwu2RAQQggh8opUE5fjx4/j4+OT6umhtLRu3RpfX1/+/fffLAUnhBBCCJFUqolLZGRkpvtyURSFgIAAuTxaCCGEEC6VauLi4eGRpcQjKioKNze3TD9fCCGEEOJBqSYugYGBXLt2LVPJy+3bt7l06RIlS5bMSmzCRWw2G88//zwNGjTI6VByj3377t+EEELkGal2+d+4cWOOHDnCTz/9xOjRozO00nnz5mGxWKhTp06WAxRZ980333D48OE0rwpLwWZDiYrCcuNGtsWVkwx162qPLdnQ55ASGWlfdz4tv/sU9IX8UXT5ayBUIUTulWri0qNHD3788Udmz55NtWrVaN26dbpWuHnzZr7//nsURaF79+4uC1RknKqqTJs2je+//z7Dz/X4eyuGCxe4vnFTNkSW80oleXz9629cvv4Cd+/a151Py89BUXR41qqJ33PdcjoUIcRjItXEpXjx4rz33nu8//77jB49mm7dutGvXz8CAwOdLn/69Gl++uknli9fjtVq5fnnn6d69erZFrhIW0REBB9++GGmR/nWRUaiu3ED8927KCguji53sVy67PJ16hIT7euOinb5uh8FVbWB1Wa/T4OxZEn0vr6PKCohhEgjcQF4/vnnCQ0NZfbs2SxatIhFixZRtGhRKlWqhJ+fH1arldu3b3P27Flu3rwJ2H/ld+rUiQ8//PCRbIBIadu2bYwaNYqYmBiKFi1K3759mTRpUqbW5Va6NEo+b2TtVqG8y9cZe8d+qsjNL+e/1FVVBZsN1WxGtVjAakW1Wu2JidUKNid/o6Do9ej0RtDpUfR60OtR9Dr7vU6PYjDgVqEC3k0z3mWCEEJkVpqJC8B//vMfnnzyST7++GPCw8O5fv06169fR1GUFF36BwUFMWzYMNq3b59tAYuHO3PmDLGxsXTu3JmxY8dy6tSpnA5JZDPVatUSE9VsBrMZ1Wy5N80MKihGI4rRAHq9lozojMbkCYljnpsRnZcXOk9P7aZ4eaHzcvztheLliXuFCihGY05vvhDiMfLQxAXsDXXXrVvH7t272bVrF+fOnSMqKgoPDw+KFClChQoVaNq0aaqnkcSjVb16dZYuXcoTTzyR06GIbGa+dh1bVBSo6r3ExAj37nWeXlqyonh4YPD3R+fjY09IvDyTJSZK0iTFy0uSESFErpWuxAXsnco1aNBALqnNA2rXrp3TIYhHQLVasUVG4hYYiOLpgcHPD32ym6/2WPH0RFHyd1slIcTjId2JixAiZ6k2G2p8PLbYWGwxsaiJiegKeKG4uVF87DuSmAghHguSuAiRy6mqiu3uXSwRN7S2J4aiRdF5F8CtbFk869SWpEUI8diQxEWIXEZVVfuVP4mJqImJ2GJiUM0WjAFlcAsIwC0wEPeKgbiVKydtUYQQjx1JXMRjyepdMKdDQFVVLTmx38z2e3MiKDoUNyOK0Q2lQAHcihbDp0N7PKqHSO2KEOKxJomLeCxFvPlmjr6+arVivnLV3k7Fwx3F6Ga/msfPz346yNsbQ5EiGAoXRl+kMJ7VqklHb0IIgSQuQmQfm81eg2K12jt+s1hRrRZUixVbXCy6At7aKR9DkSLoixTGULgwhiJF0Hl55XT0QgiRK0niIkQmqKqKGhdn7+TNagGLPSGxP7aij4tDQSXRwxPFYEAx6EFvQDEY0HkY0fv44FahPP49e6DPyOCXQgjxmJPERYgMsCUkYLt7F+vdu/c6d3NDMRjAaLR35KY3gEFPbEwMGAz4lglA510AXcGC6L290d276X0K4la+vDSuFUKIDJLERTyW3E+e1B4nmEypLqdaLNji4lDj4rHFxaJareh9fDAGBOBWqhSGUqXuJST25ERXoAD6ggW5cvo0GI1Urlv3UWyOEEI8NiRxEY8l/4W/aI+vfvSx9tjRZ4otNg5bXBzYbPaaFE8PDMWKoff1xSMkBM9aNTGWKpX6FT4XLmT3JgghxGNJEpfHQIMGDTiZpIZBOKeazZivXAVFQe9TEH3hQui9vTGWKYNb2bL2WpaAMnJ6RwghcpAkLkJg704/MSwcvb8fbuXL492oEcayARiKFUPR6XI6PCGEEPdI4iIEoCYkoBgNuFcIpPCrg+RyZCGEyKXkp6QQgJqQiKI3oPf3k6RFCCFyMalxEY+9xLBw1IQEjGVK41m7Vk6HI4QQIg2SuIjHgmq1osbHY4uPxxYXn2yeoXBhdAW88KheHY9q1XIoQiGEEOkhiYvIN1RVtXezb7XaR1dOSMQWH4caH49qtqB4eKDz8Egx5k+hgQNwK1cWnYdHDkUuhBAivSRxEbmaqqr2JMRisd9braiWe4mJ9d40i/VesmIBFDAYUPR6+2CFHp4o/v7oPL0wliqJsUxpjKVLw6QvtdfwMAXn3AYKIYTIEElcRK7hGP/HFhODLSYW1WJBtdnsSYheryUk6PUoBj06N8974//cm683oHN3t/di6+WFvlAhjKVL41amDIYSxe3LCCGEyNMkcRE5SlVVbNEx2KKjscXEoBiN6Ly97YmG0YiiN6Dz8rInIwUeuHkXQOdlv9ffm5buzuFq187eDRNCCJEtJHEROUK1WrFGRmK9E4liMKD38cFQpAiGEsXxMJlwr1QJQ9EiKJ6e2dMB3L59rl+nEEKIbCeJi3ikVKsV661bWCPvovMugLFUKYylS+FZqyYepsoYihTO6RCFEELkYpK4iEdCVVWst29jvXUbXcGCuJUvh3vlyhRoUB+3ihVTH6xQCCGESEISF5HtVJvNPuLy3SiMZcvi8URlCrZsgbFkyZwOTQghRB4jiYtIF1VVwXFpstWapL8UG6rNcXmyDWz37q1WVNu9e1QUvR5j6TL4de+GV61c0DvtDz/cf/zqqzkXhxBCiAyRxEWkYL52DSUqGoDE0Aug14PNCij2S5F1Ovu9Xg86nXaJss7odm+6DnSOS5R1KAYDOg9PPOvUzh1JC8DgwfcfS+IihBB5hiQuIoWEEycBFVuhQhh9fLVEROfuZu991tMLnacHiqcnOk/734qnJzoPD3RenigenujuzVM8PVHc3KQNixBCCJeQxEWkUOCpJ0kMD0PVG6jYsoWWlKS7jxQhhBAim0jiIlJQjEbMTzwBgKFYsRyORgghhLhPEpd8KjIykqlTp7JhwwYiIiIoVKgQjRs3Zvjw4ZQuXTqnwxNCCCEyJRu6JBU5LTIykhdffJG5c+cSGRlJcHAw8fHxLF68mC5dunDixImcDlEIIYTIFElc8qH333+fs2fP0rRpU/7++2+WLFnC1q1b6datG3fv3uWNN97AarXmdJhCCCFEhkniks+cPXuWdevW4eXlxX//+1+8vb0BcHd359NPP6VixYqcPXuW9evX53CkQgghRMZJ4pLPrFixAlVVadGiBX5+fsnm6fV6unXrBsDq1atzIDohhBAiayRxyWcOHz4MQK1UOnqrWbMmAPtkdGQhhBB5kFxVlM9cuHABgDJlyjidX6pUKQBu3LhBTEwMBQoUeGSx5SodO+Z0BEIIITJBEpd85vbt2wApThM5+Pr6Jlv2sU1cVq7M6QiEEEJkgpwqymfi4+MB8PDwcDo/6fSEhIRHEpMQQgjhKpK45DN6vT7N+Tab7RFFIoQQQrieJC75jKenJ5B6bUpiYqL2OLVaGSGEECK3kjYu+Yyfnx93797lzp07TucnnV6oUKFHE1Ru9NFHzh8LIYTI1SRxyWcCAwMJCwvj0qVLTudfvnwZgKJFi2q1M87cWrWXqG3Hueu/KlviTI01/n6NUMkWtan6+nPZ80Iff3z/sSQuQgiRZ0jiks9Uq1aNv/76i0OHDtGrV68U8w8ePAhAjRo10lxP7KFQYo9dJBHnCVB2UXQ63P0L4lHUl/gbkY/0tYUQQuR+krjkM61bt2bq1KmsX7+esWPHJrss2mq1snTpUgCeffbZh6/MpuJXpRw6DzfXBqmqWGLjSYyMwRwZgzXejNHHCzc/b9wLFaRQjSBKt6lD8aeru/Z1hRBC5HmSuOQzlStXpmnTpmzZsoVRo0bx7bff4u/vT0JCAh999BFnz56lQoUKtG7dOl3rU/Q6dHrXteFWbTbunrmMNcGMm583BQKK4ebnjV+V8hSpU4kSzWviUdj34SsSQgjxWJLEJR8aP348vXr14p9//qF58+YEBgZy8eJFIiMjKViwIFOnTkWne7QXlKmqiiUmntiLN1CMeorVCaZ44xCK1AnGP6QCBi+5wkkIIcTDSeKSD5UoUYLFixczbdo0Nm3axKlTpyhYsCAdO3Zk5MiRlC9fPttjUG02bBYbqsWCOSqO+Ig7qFYbHkX98AkuQ93PX8G7bPFsj0MIIUT+IolLPuXv7897773He++9l+V1qTYVm8WKarHcu7diM1tRrfZ7bVrSe5uKzqBHMegxeLlToGxxvEoVplTL2gR0ehLPYv4u2EohhBCPG0lcRApR5y5jvhEFwK3D50BRtCREd++mGO89djdiKOCRZL4BxaBD52bEzccLY0EvPIv7U7JFLYo2eAKdUXY5IYQQmSffIo8hVVWxxiVgjU/EEpeANe7+fdzVW5z+6U8SL93Eo1JJfLy90RkNuPkWwFjQC6NPAXtCci8pMfokmab97YXBywNFUXJ6U4UQQuQzkrjkATarFWt8YpIEIyHZY0tcopaIWOMSsMQ6kpJ7f99b3hp/f7pqs6FabffvkzyOu3ILS2QsxqK+NPh2BD5BpSUJEUIIkStI4pIL2CxW4q/fJvbyTWIu3SDu8k1iL98k9tINEu/GYE0wo1rt7UbsScYDj61qmolIssc2G6pNRdEpKDodil53//7eY8/ifiTE2UeZNnp75s+k5ZVXcjoCIYQQmSCJSw4wX4/kypTV3DYsAUBVwZZotteYPHCzma2oGRjR2VkionMzJJ+uU4DUkxFrggXVms9Hkf7hh5yOQAghRCZI4pID7qw7iDUqDos7WoKiqqo2XzHoMXh7YvBOfSyh7GYo7A16HXpX95orhBBCZIEkLjnA75laWO7E4ufti1epwniWLITePXclCOfOn8OthD/u/gVzOhQhhBBCI4lLDjAW8aHkiHbUqVMnp0NJ1e190u2+EEKI3EcSF/F4evXV+4+lvYsQQuQZkriIx9PMmfcfS+IihBB5xqMdaU8IIYQQIgskcRFCCCFEniGJixBCCCHyDElchBBCCJFnSOIihBBCiDxDEhchhBBC5BmSuAghhBAiz5DERQghhBB5hqImHd0vhxw+fBiz2YxOp8PLyyunw8l20dHRAHh7e+dwJKnLCzFmyZYt9x83bery1ef78stmUn5ZJ2WYNVJ+WeMoP6PRSPXq1V267lyRuBw4cACbzZbTYQghhBDChXQ6HbVq1XLpOnNFl//u7u4kJCSg1+txd3fP6XCEEEIIkQUJCQlYrdZs+U7PFTUuQgghhBDpIY1zhRBCCJFnSOIihBBCiDxDEhchhBBC5BmSuAghhBAiz5DERQghhBB5hiQuQgghhMgzJHERQgghRJ4hiYsQQggh8gxJXIQQQgiRZ0jiIoQQQog8QxIXIYQQQuQZkrgIIYQQIs+QxEUIIYQQeYYkLtls9erV9OjRgzp16lC/fn0GDx7M4cOHs7TOc+fOERISwvTp0zP0vB07dtC3b18aNGhA7dq16dOnD1u3bs3QOq5du8YHH3xAy5YtqV69Om3btmXatGkkJiZmaD15lSvK8ODBgwwfPpynnnqKatWq0bhxY8aMGcPZs2ezKercwxXl96APPvgAk8nEkiVLXBRl7uWK8jObzfz000907tyZGjVqULt2bV5++WW2b9+eTVHnLq4ow7NnzzJ69GiefPJJqlatSpMmTXjvvfe4cuVKNkWd+yxZsgSTycTevXsz9DxXfIdI4pKNpkyZwujRozl9+jQNGjQgODiYLVu28OKLL7Jly5ZMrfPWrVuMGDEiw4nCkiVL6N+/PwcOHKB69erUqlWLAwcOMGjQIH799dd0rePq1au88MIL/Prrr/j4+NCsWTNiYmKYPHkyAwcOxGw2Z2aT8gxXlOGKFSvo1asXGzZsoHjx4jRt2pQCBQqwatUqnnvuOfbs2ZPNW5FzXFF+D/r7778z/dy8xhXll5iYyCuvvMLnn3/O1atXadSoEUFBQezatYuBAweyYcOGbN6KnOWKMjx8+DDdu3dn9erVFClShObNm+Pu7s6iRYvo2rUr58+fz+atyHkHDhzgk08+yfDzXPYdoopsceTIETU4OFht3ry5evXqVW365s2b1SpVqqhPPfWUGhsbm6F1njx5Um3Tpo0aHBysBgcHq9OmTUvX865du6ZWq1ZNrVOnjnry5Elt+qFDh9TatWurISEhyWJMzeDBg1O8bkxMjNqvXz81ODhY/fHHHzO0PXmJK8rw5s2baq1atdTKlSur69ev16bbbDZ1xowZanBwsNqsWTM1MTEx27Yjp7hqH0zq9u3baqNGjbTPw+LFi10ddq7hqvL79ttv1eDgYLV3795qVFSUNn316tWqyWRS69evr5rN5mzZhpzmqjLs2rWrGhwcrP7000/aNIvFon7wwQdqcHCwOnjw4OwIP9dYu3atWqtWLe1zt2fPnnQ/11XfIVLjkk1++uknAEaOHEnx4sW16c2aNaNr167cuHGD1atXp2tdcXFxTJ06lRdeeIHQ0FDKlCmToVjmz59PYmIi/fr1Izg4WJtevXp1Bg0aREJCwkN/bZw7d46//vqLsmXLMmTIEG26l5cXEyZMQK/XM3/+/AzFlZe4ogzXrVtHTEwMzz77LK1atdKmK4rC4MGDqVKlCpcvX+bAgQPZth05xRXl96CPP/6YO3fuUKNGDVeHm+u4ovzi4+OZM2cOPj4+fPvtt3h7e2vz2rVrR8uWLfH29ubMmTPZth05yRVleOfOHY4ePYqfnx/9+vXTpuv1el577TWAfFtrevXqVd566y1GjhyJzWajSJEiGXq+K79DJHHJJlu3bkVRFFq0aJFiXuvWrQF7NXd6rFmzhilTpuDt7c3UqVPp0qVLhmMBkn1ZZjSWbdu2oaoqzZs3R6dLvtuUKlWKKlWqcOnSpXx70HNFGdpsNqpUqULDhg2dzi9XrhwA169fz0qouZIryi+pVatWsXr1akaMGEGlSpVcE2Qu5qrPcExMDJ07d6ZQoUIp5k+bNo2NGzdSuXJlF0Sc+7iiDPV6PQAxMTFERUUlm3f79m0AfH19sxxrbvTNN9+wfPlyqlWrxq+//kpgYGCGnu/K7xBJXLLB9evXiYyMpHjx4k53YscbfurUqXStz8/Pj5EjR7J27VrtA5Zeqqpy5swZdDqd0x2tfPny6HQ6zpw5g6qqqa7HsTOl9iWR0W3KS1xVhr169WLp0qV07do1xTybzcbRo0cBKFGihOuCzwVcVX4O165dY/z48dSoUYNXXnklO0LOVVxVfo79KyQkhMTERFatWsX48eP58MMPWb58eb5uo+aqMixYsCA1a9bEbDYzcuRITp06RXx8PIcPH2bMmDEADBw4MNu2IycFBgbyxRdfsGjRIkwmU4af78rvEEOGX108VEREBABFixZ1Ot8x/ebNm+laX4sWLZzW3KRHZGQkiYmJFCpUCDc3txTzDQYD/v7+3Lx5k5iYmGTVx0k5agGKFSvmdL5jm27cuJGpOHMzV5VhWpYsWUJYWBjFihWjZs2aLog693B1+Y0bN46EhAQ+//xz7Rdwfuaq8gsLCwPsX+LPPfdcsi+I//u//2P27NnMnDkz1c94XubKffDLL79k8ODB7Ny5k06dOmnTCxQowKRJk+jYsWO2bENOe/XVV7P0fFd+h0jikk5jxozRfrGkpXXr1jRt2hQAT09Pp8u4u7sDEBsb67oAUxEXF5dmLAAeHh4AaX5gHetxLJvaOh7FNj1qrirD1Pz7779MmDABsO9nBkP++li6svx++eUXtm7dytixYzNcVZ1Xuar8HKc2JkyYgJ+fHz/++CM1a9YkPDyczz77jN27dzNq1CgWLlyIoigu3oqc5cp9sFixYnTt2pXJkycTGBhI6dKlOXXqFBcvXmT27NnUrFkzw+0QHweu/A7JX0fIbHT58uV0XeYWERGR7g99eqrFs+rBc4mZjcOxntS2zbGOR7FNj5qrytCZw4cP88orrxAbG0vPnj0z3H4pL3BV+YWFhfHll19Sr149Xn75ZVeElie4qvwSEhK0+9mzZxMQEADAE088wcyZM2nXrh0HDhxgx44dNGrUKGtB5zKuKsPExET69+/PoUOH+Oqrr2jfvj1gP9U7depUpk2bxoABA1i1apXTmp3HmSu/QyRxSaeFCxeme9kTJ04A9w8UD3JMTyv7dxUvL680Y0lvPI71xMfHZ3odeZWryvBBW7Zs4fXXXyc2NpZu3brx4YcfZi3QXMoV5We1WnnrrbcAmDhxYr6rEUiLq/Y/x7ynn35aS1ocPDw8ePbZZ5kxYwa7d+/Od4mLq8pw8eLF7N+/n169emlJC9i/lEeNGsW+ffvYtWsXq1evzpc/QrLCld8hkrhkA8c5vNTO1T2sDYwreXt74+Xlxe3bt7FYLClOQ1gsFm7fvo27uzs+Pj6prie925Qfz4+7qgyT+r//+z/Gjx+P1Wqlf//+vP322/n2y9gV5bd+/XoOHDhAQEAA3377bbJ5hw4dAuC3335jx44dtGnThjZt2mTPxuQAV+1/jiuJSpcu7XS+Y7rj6pj8xFVluHv3boBUE7smTZqwa9cujh8/LonLA1z5HSJXFWWDQoUKUbhwYa5evUp0dHSK+Y6u3ZP2JZBdFEUhKCgIq9VKaGhoivnnz5/HZrM9NBZHS/DULlV7lNv0qLmqDB2mTp3Khx9+iKqqjB07lnfeeSffJi3gmvJznPcODw9n5cqVyW6ORqcHDhxg5cqVnDx5Mlu2I6e4av9zzE/tcnvHF4e/v3/WAs6FXFWGd+/eBUi1Ubhjen6+QiuzXPkdIolLNnn66aexWq1s3rw5xTxHt9qORryPIpakr5uZWBzr2LRpEzabLdm8y5cvc/z4cUqXLk1QUJArQs51XFGGAPPmzWPKlCkYjUYmTZqUrBOr/Cyr5detWzdOnjzp9Na9e3fAfgrp5MmTjBw5Mhu2IGe5Yv9r0qQJANu3b0/RBwnY+9kAqFu3bpZiza1cUYaOBuGpDdmyY8cOgHzbF05WuPI7RBKXbPLiiy+iKApfffUV4eHh2vS//vqLpUuXUrRo0RSXzYWFhXH27FmnB5Ws6NatG+7u7sycOZN///1Xm37kyBFmzZqFh4cHvXr10qZfvnyZs2fPcuvWLW1aQEAATz/9NOfPn09WVR8bG8t7772nnfLIr1xRhidPnuSLL74A4Isvvkh2jjy/c0X5Pc5cUX6BgYE0a9aMu3fv8s477yRrazBr1iz2799PxYoVeeqppx7NRj1irijD7t27o9PpWLRoEevXr0+2/jlz5rBlyxYKFSr0WH22ncnu7xBFzY+XgeQSX375JbNmzcLT05OGDRsSExPDnj17MBgMzJo1K0UPqi1atODSpUtMnDiRbt26pbreKVOmMHXqVF577TWGDRuWrlgWLFjA+PHjMRqNNGzYEFVV+eeff7BYLHzxxRd07txZW7ZPnz7s3r2bESNGJPv1Gh4ezosvvkhERATBwcFUqFCB/fv3ExERQZMmTfjuu+/y3aW8SWW1DEePHs3q1avx8fFJ85ddz5498+WvXlfsg86MGzeO33///aGfm7zOFeV3/fp1+vbty/nz5ylSpAg1a9bkwoULnD59Gh8fH3766SeqVauWE5v3SLiiDBcsWMCnn36KzWbjiSeeoEyZMpw+fZrQ0FC8vLz4/vvvqV+/fk5s3iPlKJ8FCxakOF5l93dI/v2WyQX+85//EBQUxLx589i5cycFChSgWbNmjBw5kqpVqz7SWF566SVKlSrFrFmz2LdvH25ubtSuXZuhQ4fy5JNPpmsdAQEBLFq0iMmTJ/P3339z4cIFAgIC6Nu3Ly+//HK+Tlog62XoaNh39+5dVq5cmepyTz31VL5MXFyxDz7OXFF+xYoV4/fff2fmzJn8+eef/P333/j6+tK5c2eGDx+uDTuRX7miDF966SUqV67Mjz/+yP79+zl9+jSFChWia9euDBkyhPLly2fvRuRhrvoOkRoXIYQQQuQZ0sZFCCGEEHmGJC5CCCGEyDMkcRFCCCFEniGJixBCCCHyDElchBBCCJFnSOIihBBCiDxDEhchhBBC5BmSuOQjU6ZMwWQyZfh28eJFwN7boclkYv78+Tm8Jem3ZMkSTCZTsh5TL168qG1bTEyMNv2dd97BZDJp3e7nJzdu3NAGgHuQY/Cyx5XVamXKlCm0aNGCatWq0ahRI5YtWwbAzp076dGjB7Vq1aJOnToMGTKEf/75B5PJRIMGDbL0ui1atMBkMjkdr+xRym3vv6vKVzy+8ndXp4+ZkiVLUrt27RTT//33XxITEylfvrw2tH1S7u7ujyI8kU3mzJnDlClTWLhwIT4+Ptr0iIgIJkyYQHh4OIsXL87BCHPWjBkzmDp1KgBBQUHodDpKlixJWFgYr7zyCmazmUKFClGqVCnKlCmTw9G6TmhoKB999BE+Pj5Mnjw5p8MRwmUkcclHunfvro2Um5RjDKTBgwfnu7FcWrduTY0aNfDw8MjpUHLMxIkTnU7funUra9aseeTDS+Q2f/75JwBDhgxh9OjR2vTZs2djNpspW7Ysq1at0hL4uLg4Vq9eneUhLObMmYPZbKZkyZJZWk9mrVy5kp07d9K2bdsceX0hsoskLiJPK1iwIAULFszpMEQu5hih9sHxnxzTQ0JCktU6enp6UrFixSy/btmyZbO8DiFEStLGRQiRr1ksFgDc3NySTbdarU6nCyFyN6lxEU7t3buX77//noMHD2KxWKhQoQLPP/88PXv2RFGUFMuHh4czc+ZMtm3bxvXr1ylQoAA1a9akX79+GR75Nzw8XHvtixcvYjAYqFChAm3btuWll17C09NTW3bJkiWMHTuWqlWrsmTJknS/xsmTJ5k2bRq7d+8mNjaWMmXK0LlzZwYMGIDRaEyxfGhoKLNnz9a2z8vLi6pVq/LCCy/Qrl27ZMtevHiRli1bArB//34KFCiQbP6pU6fo1KmTFseD9uzZw5w5czhw4AB3796lcOHCNGrUiMGDBycbvXfKlCla2w1AW+fcuXMZO3Ysly5dAuDo0aOYTCZKly7Npk2btOUTExP55ZdfWLlyJefOnUNVVSpUqEDHjh3p3bt3hts+RUdH88svv7BmzRouXLiAzWajYsWKPPfcc/Ts2ROdLvnvpLt37/Lzzz+zbt06wsLC0Ol0VKhQgfbt29O7d+9UT/9t2LCBhQsX8u+//xIbG0vx4sVp1qwZr776KsWKFdOW69OnjzYiN0Dfvn0BKF26tFY2AEuXLmXp0qWA/f34559/6Nu3L35+fvzzzz/JXttsNvPbb7+xYsUKzp8/T3x8POXKlaNjx47069cvWZk5TtHOmDGD5s2bJ1vPiRMnmDlzJrt37+b27dv4+vpSr149Bg0aRLVq1ZIt69ifKlWqxLJly/jpp59Yvnw5YWFheHp6UqdOHYYOHUpISIj2HJPJpD1eu3YtJpOJ+vXrM2/ePKdlCvDWW2+xfPlyevfuzfvvv+90md69e7Nnzx4+/PBDevXqBdgTwFWrVvHHH39w7Nix/2/v3MOiqr4+/h1u2oiKjyAYonI7BGWIJRg3I1S8ZFKPZmkJpEZi0BNaFvrzkpcoU5QEU9IM7KZiEuAl0ESMi5ICyi0FBrmONyBgEBlmv3/wnN0cZgYG0Mr32Z+/ePbZ55y192z2XnuttddBQ0MDDAwMYGFhAW9vbwQEBGhlFe2uvwDAxcUFDQ0NiI2NVQns7e38c//+fXz33Xc4deoUKioq0NLSghEjRmDSpEkICAh4IBY3xsODKS4MFZKSkrB582YMGDAAlpaWkEqlKCgoQEFBAUpLS7FmzRpB/fT0dISEhEAmk+Gxxx6Dra0t7t69i7Nnz+Ls2bMIDg7Gu+++q9W7S0tL8frrr6OxsRFDhw6FlZUVZDIZrly5gvz8fKSkpODgwYNqlQttyc7OxsGDB0EIgbW1NRoaGlBaWort27fj8uXL+OqrrwT1U1NTsWLFCty7dw9isRh2dnaor69HRkYGMjIykJqais8//xy6urp9loknOjoaO3fuBAAMGzYMHMfR4Nrjx49j586dmDx5MoC/g7EvXboEAHBwcMDAgQMxePBgPPXUU9DX14dEIoFYLMYTTzwBExMT+p6GhgYsXboU+fn50NHRgYWFBQYOHIiSkhIUFhYiOTkZ+/btw7Bhw7SSu7q6GkuXLkVpaSl0dXVhbW2N9vZ2XL16FVevXkVeXp7gNJdEIkFAQABqamqgq6sLW1tbKBQKFBYWoqCgAL/88gv27dsnkJkQgrVr1+LQoUMAABMTE9ja2qK8vBxxcXFISkpCTEwMXcA5joNcLkd+fj7kcjk4joOhoSE8PT1x7tw5VFdXQyqVYvjw4QKFUBONjY145513aH9bWVlBV1eXjp3MzEzExMT0ODaPHj2K//3vf5DL5Rg8eDA4jkNdXR1OnDiBX3/9FRs2bMC8efNU7pPL5QgMDMT58+dhbGwMa2trXL9+HadPn0Z6ejri4uIwfvx4AMCECRNQW1uL2tpaGBkZwcrKChzHdSuXr68vEhIScPLkSYSFhamM57q6OuTk5EBfX58q6+3t7Vi2bBnS09MBdLrHTE1NUVdXh+LiYhQXFyM1NRVHjhx5aJat3s4/hBC8++67SEtLg56eHsaMGQMzMzNIJBIcPnwYSUlJ+Pbbb+Ho6PhQ5GU8AAjj/z1eXl6E4zgSHx/fbb033niDcBxHOI4jK1asII2NjYQQQjo6Okh4eDjhOI7Y2dkRqVRK76msrCQTJkwgHMeRHTt2kLa2NnotNTWVXktJSdFK1uDgYMJxHNm0aRO5f/8+LS8oKCAuLi6E4zjy888/0/L4+HjCcRx5+eWXBTLx7Whubqblq1atouX+/v6CdsTGxtJrubm5tLysrIyMGzeOcBxHNm7cSGQyGb2WlpZGnn32WcJxHImIiOjx/TwlJSX0ujKnTp0iHMeRCRMmkOTkZFp+//59EhUVRa9VV1cL7uOfVVJSIihX1zc8gYGBhOM4Mn/+fFJRUUHLa2pqyIIFCwjHcWTZsmUq92mCHzuvvvoqqaqqouXZ2dlk/PjxhOM4kpCQQNvj4+NDOI4jb7zxBqmtraX1y8rKyOzZswnHcWTBggWCd+zfv59wHEfc3d1JRkYGLW9paSHr168nHMeRyZMnk6amJsF9zs7OhOM4kpWVJSjnx/SqVasE5VlZWYTjOOLs7Cwo58fPtGnTBH1dXFxM3NzcCMdxJDo6mpbz/3dnzpyhZXl5ecTBwYE4ODiQgwcPko6ODkIIIQqFghw6dIg8+eSTxMHBgeTn59N7lMfT+PHjBWNDKpWSmTNnEo7jyNKlSwXyRkZGEo7jSHBwMNGGjo4O4uHhQTiOE/Qvz9dff60yLvj/G1dXV1JUVCSof/z4cWJvb084jhPIrKl/1fWXMup+x77MP7/99hv9HZXHXlNTEwkKCiIcx5FFixb11F2MfxEW48JQYezYsQgPD6dHa3V0dPD+++9jyJAhIIQgLy+P1t23bx+am5vh6+uL9957T7Cr8vb2xooVKwBA4NLojj///BMA8Morrwh2rg4ODggODoaPj0+/j28PHToUkZGRKm4FGxsbAMDly5dpeUxMDNra2uDh4YE1a9YI3FSenp70RM8333yD+vr6fsnFW1rCwsIwc+ZMWq6vr4+goCDMmDEDzc3NOHDgQL/ec+XKFfz2228wMjJCdHS0IIh05MiRiIyMxKBBg3D69GkUFxf3+LxLly7hwoULEIvFiI6Ohrm5Ob3m7OxMd7sJCQkAgOTkZJSXl8PY2BjR0dEwMzOj9S0tLbF3716IxWLk5OQgLS0NANDW1kYtYVu3bhWY/8ViMdatWwdHR0fU1tY+lKPfUqkUx44dg0gkwq5duwTWCzs7O2qF5NuoiV27dlHLycKFC6n7TCQSYd68efDz84NcLlex+vEsW7ZMMDZGjBiBt99+GwCQm5vbnyZCR0cHL774IoDO36grfNmcOXNoWVZWFnR1dREcHIwnnnhCUH/GjBlwdnYG8PByyfRl/uHnGE9PT8HYMzQ0xMcffww3NzfY2to+FHkZDwamuDBU8PLyUjkKyvusgU43Aw+fXGvWrFlqnzVr1iyIRCIUFRXh5s2bPb6bX0TXr1+PCxcu0MBKAFi4cCEiIyNVYkp6i4uLi1qfO+/XVlZAzp07BwDUn9+VKVOm4PHHH8e9e/eQlZXVZ5lu3LiB69evQ0dHR7AwKcMvKrxMfeX06dMAAFdXV7V5fYYPH45JkyZp/S5eufDy8sLw4cNVrs+fPx/JycnYvXu3oP7s2bPV/g5mZmaYOnUqAODs2bMAOpWjhoYGGBsbU9m6wvdbf/tHHWlpaSCEwNHRUe2i5u3tjYSEBJrYTh1tbW3IyMgAoPn/hf+NMzIyBGOfh3cTKmNpaQmgM8aov/BKSUpKCtrb22l5eXk5CgoKMGTIEEH8SVRUFPLy8tSmYejo6KDxXa2trf2WTR19mX/4eSw+Ph6HDx8WzGejRo3C/v37VdzhjP8WLMaFoYKyJUIZfhJqa2sD0DlR1tbWAgAiIiLowtQVXV1dyOVySCQSjc/mWb58ObKyspCbm4s333wTgwcPxqRJk+Dp6YkXXngBxsbGfW0WRZMMYrEYQGfgHtDZvlu3bgHotPhowt7eHjU1NZBIJH2W6fr16wA6d71vvfWW2jr37t0DAFRUVIAQojZIWhv43W9OTg5ef/11tXX4bMrl5eU9Pu/GjRsAoHGXamhoSK1ZAGg/2dvba3ymg4MDEhISaF2+f2QymUaZGxsbtZa5t/TURn19fRWLQ1ckEglVBsLCwlSClQFAoVAA6GynVCoVWK8AwNTUVOUePoiZPyXVH+zs7GBvb4+ioiL8/vvveP755wF0xr0BwPTp01ViVfT19dHY2IicnByUlZWhqqoKZWVlKCwspMoUIaTfsnWlr/OPt7c3HB0dkZeXhzVr1mDt2rUYN24c3N3d4eXlJQhyZvw3YYoLQwVtg+iU0+kXFhb2WL+pqanHOo6Ojvj555+xe/dunDlzBk1NTUhJSUFKSgrWr1+PmTNnYt26df3K3dKX9nU9GaQMr/Ao1+8t/AQvl8tp8KcmFAoFWlpaYGho2K933bx5s0crmDa/Gb9j5fuhJ/h+6q5P+Wt8XV5mmUzWY/88CMtDV3rbRnUoy6WNW0dd3/cnKF1b5syZg6KiIiQnJ6soLspuIqBzE7N9+3b89NNPAqvKoEGD4OTkhNu3b2vlbuwLfZ1/DAwMEBsbi/379+PYsWOoqKhAXl4e8vLyEBUVBVtbW2zYsAHPPPPMQ5Gb0X+Y4sLoM8rxHllZWVqfQOkJa2trfPHFF7h//z5yc3ORmZmJtLQ0FBQUIDExEa2trYiKinog7+oO5UWqublZo7LEL0jqFjV1O03ecqLuXRzHITExsU/yagv/u61atUqjdac38Dt+bd0BfFu7UzD4RYavy8vs5eWlMf7jYdLbNqqDb4tYLBbEUf3XePHFF7F161acPn0abW1tuH79OiQSCczNzVUW87CwMCQlJUEsFiMwMBBOTk6wtrbGqFGjoKOjgxUrVvRacdFknen6f9Of+WfgwIEICgpCUFAQysvLkZmZid9//x3p6em4du0alixZgpMnT6q1cDH+fViMC6PPDBkyhMZIaAq+6+joQEZGBioqKno0ZSsUClRWVtLcGwYGBnB2dsZ7772Ho0ePYvPmzQA6jyf3x7qhLYMHD6bHcTXt6Agh9Bp/pFY5Poh3OymjzsrB31tZWan2HqDzQ4o5OTmQSqW9aIUq/Lu6C5gsLCxEUVGRVtaLsWPHAvjbndOVO3fuYO7cuQgNDaU5gQCgqKhI4zMLCgoA/B3zxL+jO5mrqqqQm5uLO3fu9Chzb+Fl1tRGuVyO1157DSEhIbh9+7baOhYWFtDV1YVMJkNdXZ3aOs3NzcjOzkZVVdVDca9og4mJCZ577jm0tLQgMzMTx48fB9AZk6TsnpRKpTRgd8+ePQgNDYWXlxdGjx5N3WCa2qkO/vi1uvH/119/qSgufZ1/6uvr8ccff9DMyZaWlliwYAGioqKQkpICExMTyGQypKamai0745+FKS6MfsEHC/74449qrycmJiIgIAC+vr6QyWTdPuvWrVuYOnUq/Pz81C7Orq6u9G8+FuBh4+npCQD44Ycf1F5PTU2FVCqFnp4eTYql/KFDdfEWykngeGxsbGBubo7W1laNJ1O2bduGhQsXCr63A4AuJl0XOnUxFACo+f/XX3+lk7cyTU1N8Pf3h6+vL06cOKH2Gcp4eHgA6AykVQ505ElJScGVK1dQWloKPT09OmaSkpLUukPq6upoALG7uzuAznT9YrEYN27coAGuXVm9ejXmz5+P8PDwHmXuLW5ubhCJRMjNzVUby5SZmYnLly8jOztb487f0NCQWiw0jacDBw5g0aJFWLRoUb/HuKZxoQ2+vr4AOsdqSkoKAOCll14S1KmurqbPVhcDVlpaSl1i6gKNu8L/32j7PwP0bf5ZuXIlFixYgCNHjqjUNzU1hZWVFYAHEzPEeDgwxYXRL5YsWYIBAwYgMTERERERNHAXAM6fP49PPvkEADBv3rwe41JMTU3h7OwMhUKBlStXCpSXlpYWbN++HQDg5OT0j32faPHixRg4cCDS09OxadMmgavg3LlzWL16NQDAz8+PBg7zSeqAzqBBfnGWy+WIjY2lWVqVEYlECAoKAgBs2bJFcBxVLpfj66+/ppmBu7p3eBdETU2N2vKbN28KdrEuLi6YOHEi/vrrLwQGBqKiooJek0qlCAoKQmNjI0xMTGg23u5wdXWFo6MjmpqaEBISQgOaAeDChQvYtm0bACAgIABA5+kfS0tL3L59G0FBQYJdeXl5Od5++220trbCycmJZiA2NDSEv78/gM6FR1l5uXfvHrZs2UKP5vr5+fUoc28ZO3Yspk+fDoVCgeDgYEGfFRcXY926dQA6j9V3l4gwKCgIIpEIMTExiI2NpYsjIQQJCQnUDebn59fvhIZ8nFDXcaENU6ZMwaBBg5CYmIiKigo89dRTKtlkx4wZQ5XjPXv2CBb67OxsLF26lCosyvOCJpycnAAABw8eFFhQMjIyNH5ItC/zDz+md+/ejfPnzwued+LECfzxxx/Q0dGBm5tbjzIz/h1YjAujX9jY2OCzzz7Dhx9+iK+++gpxcXGwtLREfX09Tavu6uqKlStXavW8zZs3Y+7cubhw4QK8vb0xevRo6Ovr48aNG5DJZDAyMsLGjRsfZpMEWFtbY+vWrVi5ciXi4uIQHx8Pa2tr3L17l7ZvxowZKlaQkJAQBAcH4+LFi5g8eTLGjh2L2tpa3L17F35+fjh69KiKtWHu3Lm4du0aDhw4gNDQUHz66acwNTVFVVUVtWQsX74cU6ZMEdxnZ2eHS5cuITQ0FFZWVnj//ffh7u4OW1tbiEQi3Lp1Cz4+PjAzM6M7/W3btmHx4sXIz8+Hj48PbGxsoKOjg7KyMrS3t8PQ0BAxMTFaf3U7IiIC/v7+yM7OhpeXF2xtbdHU1ITKykraNn4Xb2BggKioKCxZsoT+zjY2NlAoFLh27RoIIbCzs0NERIRg8V6+fDnKyspw8uRJBAQEwNzcHEZGRqioqKAurQ0bNqikzH9QbNiwAZWVlbh69SqmT58OGxsbtLe3088beHh40Jwqmnjuuefw0UcfITw8HJs3b0ZUVBQsLCxQV1dHFT5fX1/6eYL+wCvPV69ehY+PD+zs7BAZGanVvY899himTZtGleyuQblA57H5hQsXIi4uDnv37kV8fDxGjhxJg7719PQwceJEXLx4UatUCP7+/khMTMTt27fx0ksvwcbGBs3NzaiqqoKTkxMGDRqkomj0Zf6ZM2cOzpw5g1OnTmHx4sUwMzODsbGxIFg9NDSUpf3/D8MsLox+M2PGDBw7dgxz586FkZERSkpKUF9fj3HjxiEsLAx79+7V+iSPhYUF4uPj8dprr+Hxxx9HZWUlJBIJTE1N4e/vj6SkpH88OdS0adME7SsuLqZJ6b788kvs2LFD5bTHlClT8O2338LDw4MqBKNGjcLnn3+OsLAwje/6+OOPsW/fPrzwwgtQKBQ0sNHd3R3R0dEICQlRuWfLli1wcXEBIQQSiYRaAywtLbFp0yaMHj0at27dQmVlJY2/MDU1xeHDh/HBBx/gySefRHV1NcrKyjBixAjMnz8fCQkJ3R5X7oq5uTmOHj2K5cuXY8yYMSgtLcWdO3cwYcIEfPHFFzQ+icfa2hrHjh3DsmXLYGlpCYlEgtraWjpmDh8+jJEjRwru0dPTw44dOxAREQE3Nze0tLSgpKQEAwYMwNSpU/Hdd9+pTZX/oBg6dCi+//57rFq1Cvb29qisrERNTQ3s7e2xdu1a7NmzR6tx7u/vj0OHDmHWrFkwMDBAcXExWltbMXHiRISHhyM8PLzPR92VcXV1RWhoKMzMzFBdXY2CgoJeuZ94ZUVPT09jnpTVq1fjs88+w9NPPw25XI6SkhLo6+tj9uzZOHToELV4XLx4scd4qVGjRuHIkSN4+eWXMWzYMOpaDA4ORmxsrMbEk72df0QiEbZt24bVq1dj/PjxaG5uRnFxMQghmDp1Kg4cOIDAwECt+4nxzyMi/1YEGIPBYDAYDEYvYRYXBoPBYDAYjwxMcWEwGAwGg/HIwBQXBoPBYDAYjwxMcWEwGAwGg/HIwBQXBoPBYDAYjwxMcWEwGAwGg/HIwBQXBoPBYDAYjwxMcWEwGAwGg/HIwBQXBoPBYDAYjwxMcWEwGAwGg/HIwBQXBoPBYDAYjwz/B1IO9jjdWr6rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your turn.\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Make an empty list called avg_silhoute scores\n",
    "avg_silhoutte_scores = []\n",
    "\n",
    "# Iterate through Krange with variable K as before\n",
    "for k in list(range(2,11)):\n",
    "    fig, ax1 = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(9, 5)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    # Call set_xlim on ax1 \n",
    "    ax1.set_xlim([-0.25, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(x_cols) + (k + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    model_2 = KMeans(n_clusters=k, random_state=10)\n",
    "    cluster_labels = model_2.fit_predict(x_cols)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    # Make a variable called silhouette_avg. Set the parameters to silhouette_score to X, and cluster_labels\n",
    "    silhoutte_avg  = silhouette_score(x_cols, cluster_labels)\n",
    "    avg_silhoutte_scores.append(silhoutte_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    # Call sklearn.metrics.silhouette_samples with the below parameters\n",
    "    sample_silhouette_values = silhouette_samples(x_cols, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(k):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        # Make a variable called ith_cluster_silhouette_values\n",
    "        ith_cluster_silhouette_values =  \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        # Call sort() on this variable \n",
    "        ith_cluster_silhouette_values.sort()\n",
    "    \n",
    "        # Call shape[0] on ith_cluster_silhouette_values \n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0] \n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        cmap = cm.get_cmap(\"Spectral\")\n",
    "        color = cmap(float(i) / k)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    # Setting title, xlabel and ylabel \n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    # This requires axvline() \n",
    "    ax1.axvline(x=silhoutte_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % k),\n",
    "                 fontsize=14, fontweight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rol4xvqzymYH"
   },
   "source": [
    "#### 3aiii.  Choosing $K$: The Gap Statistic\n",
    "\n",
    "There is one last method worth covering for picking $K$, the so-called Gap statistic. The computation for the gap statistic builds on the sum-of-squares established in the Elbow method discussion, and compares it to the sum-of-squares of a \"null distribution,\" that is, a random set of points with no clustering. The estimate for the optimal number of clusters $K$ is the value for which $\\log{SS}$ falls the farthest below that of the reference distribution:\n",
    "\n",
    "$$G_k = E_n^*\\{\\log SS_k\\} - \\log SS_k$$\n",
    "\n",
    "In other words a good clustering yields a much larger difference between the reference distribution and the clustered data. The reference distribution is a Monte Carlo (randomization) procedure that constructs $B$ random distributions of points within the bounding box (limits) of the original data and then applies K-means to this synthetic distribution of data points.. $E_n^*\\{\\log SS_k\\}$ is just the average $SS_k$ over all $B$ replicates. We then compute the standard deviation $\\sigma_{SS}$ of the values of $SS_k$ computed from the $B$ replicates of the reference distribution and compute\n",
    "\n",
    "$$s_k = \\sqrt{1+1/B}\\sigma_{SS}$$\n",
    "\n",
    "Finally, we choose $K=k$ such that $G_k \\geq G_{k+1} - s_{k+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_e6PSKcVymYI"
   },
   "source": [
    "#### Aside: Choosing $K$ when we Have Labels\n",
    "\n",
    "Unsupervised learning expects that we do not have the labels. In some situations, we may wish to cluster data that is labeled. Computing the optimal number of clusters is much easier if we have access to labels. There are several methods available. We will not go into the math or details since it is rare to have access to the labels, but we provide the names and references of these measures.\n",
    "\n",
    "* Adjusted Rand Index\n",
    "* Mutual Information\n",
    "* V-Measure\n",
    "* Fowlkes–Mallows index\n",
    "\n",
    "**Hint:** See [this article](http://scikit-learn.org/stable/modules/clustering.html) for more information about these metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eHsqcXTIymYI"
   },
   "source": [
    "### 3b. Visualizing Clusters using PCA\n",
    "\n",
    "How do we visualize clusters? If we only had two features, we could likely plot the data as is. But we have 100 data points each containing 32 features (dimensions). Principal Component Analysis (PCA) will help us reduce the dimensionality of our data from 32 to something lower. For a visualization on the coordinate plane, we will use 2 dimensions. In this exercise, we're going to use it to transform our multi-dimensional dataset into a 2 dimensional dataset.\n",
    "\n",
    "This is only one use of PCA for dimension reduction. We can also use PCA when we want to perform regression but we have a set of highly correlated variables. PCA untangles these correlations into a smaller number of features/predictors all of which are orthogonal (not correlated). PCA is also used to reduce a large set of variables into a much smaller one.\n",
    "\n",
    "**Hint:** PCA was discussed in the previous subunit. If you need help with it, consult [this useful article](https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c) and [this visual explanation](http://setosa.io/ev/principal-component-analysis/). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O2A8l0xOymYJ"
   },
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set IV</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Use PCA to plot your clusters:</p>\n",
    "\n",
    "<ul>\n",
    "<li> Use scikit-learn's [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) function to reduce the dimensionality of your clustering data to 2 components\n",
    "<li> Create a data frame with the following fields:\n",
    "  <ul>\n",
    "  <li> customer name\n",
    "  <li> cluster id the customer belongs to\n",
    "  <li> the two PCA components (label them `x` and `y`)\n",
    "  </ul>\n",
    "<li> Plot a scatterplot of the `x` vs `y` columns\n",
    "<li> Color-code points differently based on cluster ID\n",
    "<li> How do the clusters look? \n",
    "<li> Based on what you see, what seems to be the best value for $K$? Moreover, which method of choosing $K$ seems to have produced the optimal result visually?\n",
    "</ul>\n",
    "\n",
    "<p><b>Exercise:</b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gPLWFI_symYK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1342114feb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your turn\n",
    "import sklearn.decomposition \n",
    "import matplotlib.colors\n",
    "\n",
    "model = sklearn.cluster.KMeans(n_clusters=3)\n",
    "clusters = model.fit_predict(x_cols)\n",
    "\n",
    "cmap = matplotlib.colors.ListedColormap(['red','blue','green'])\n",
    "pca = sklearn.decomposition.PCA(n_components=2)\n",
    "pca1, pca2 = zip(*pca.fit_transform(x_cols))\n",
    "plt.scatter(pca1, pca2, c=clusters.tolist(), cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Based on the graph, we see a clear represntation of the three clusters, which shows that K is the optimium number\n",
    "## of clusters for this data. Using the scatter plot proves to be the best way to viusalize K, as one can increase/decrease\n",
    "## the number of clusters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ip7bNDsDymYM"
   },
   "source": [
    "What we've done is we've taken those columns of 0/1 indicator variables, and we've transformed them into a 2-D dataset. We took one column and arbitrarily called it `x` and then called the other `y`. Now we can throw each point into a scatterplot. We color coded each point based on it's cluster so it's easier to see them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "30rRDyjzymYN"
   },
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set V</h3>\n",
    "\n",
    "<p>As we saw earlier, PCA has a lot of other uses. Since we wanted to visualize our data in 2 dimensions, restricted the number of dimensions to 2 in PCA. But what is the true optimal number of dimensions?</p>\n",
    "\n",
    "<p><b>Exercise:</b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GeC4yWmiymYO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion of variance explained')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your turn\n",
    "# Initialize a new PCA model with a default number of components.\n",
    "pca = sklearn.decomposition.PCA()\n",
    "pca.fit(x_cols)\n",
    "\n",
    "# Do the rest on your own :)\n",
    "variance = pca.explained_variance_ratio_\n",
    "plt.plot(range(len(variance)), variance)\n",
    "\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Proportion of variance explained')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we can see from the plot the amount of variance explained by the components decrease as teh number of components rise. \n",
    "## That is why the first handful of components provide the most information for the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_a0Q7XtFymYR"
   },
   "source": [
    "## 4. Conclusions and next steps\n",
    "### 4a. Conclusions\n",
    "What can you conclude from your investigations? Make a note, formulate it as clearly as possible, and be prepared to discuss it with your mentor in your next call. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gyu7Drl4ymYR"
   },
   "source": [
    "### 4b. Other clustering algorithms\n",
    "\n",
    "k-means is only one of a ton of clustering algorithms. Below is a brief description of several clustering algorithms, and the table provides references to the other clustering algorithms in scikit-learn. \n",
    "\n",
    "* **Affinity Propagation** does not require the number of clusters $K$ to be known in advance! AP uses a \"message passing\" paradigm to cluster points based on their similarity. \n",
    "\n",
    "* **Spectral Clustering** uses the eigenvalues of a similarity matrix to reduce the dimensionality of the data before clustering in a lower dimensional space. This is tangentially similar to what we did to visualize k-means clusters using PCA. The number of clusters must be known a priori.\n",
    "\n",
    "* **Ward's Method** applies to hierarchical clustering. Hierarchical clustering algorithms take a set of data and successively divide the observations into more and more clusters at each layer of the hierarchy. Ward's method is used to determine when two clusters in the hierarchy should be combined into one. It is basically an extension of hierarchical clustering. Hierarchical clustering is *divisive*, that is, all observations are part of the same cluster at first, and at each successive iteration, the clusters are made smaller and smaller. With hierarchical clustering, a hierarchy is constructed, and there is not really the concept of \"number of clusters.\" The number of clusters simply determines how low or how high in the hierarchy we reference and can be determined empirically or by looking at the [dendogram](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.cluster.hierarchy.dendrogram.html).\n",
    "\n",
    "* **Agglomerative Clustering** is similar to hierarchical clustering but but is not divisive, it is *agglomerative*. That is, every observation is placed into its own cluster and at each iteration or level or the hierarchy, observations are merged into fewer and fewer clusters until convergence. Similar to hierarchical clustering, the constructed hierarchy contains all possible numbers of clusters and it is up to the analyst to pick the number by reviewing statistics or the dendogram.\n",
    "\n",
    "* **DBSCAN** is based on point density rather than distance. It groups together points with many nearby neighbors. DBSCAN is one of the most cited algorithms in the literature. It does not require knowing the number of clusters a priori, but does require specifying the neighborhood size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8tRpy3OaymYS"
   },
   "source": [
    "### Clustering Algorithms in Scikit-learn\n",
    "<table border=\"1\">\n",
    "<colgroup>\n",
    "<col width=\"15%\" />\n",
    "<col width=\"16%\" />\n",
    "<col width=\"20%\" />\n",
    "<col width=\"27%\" />\n",
    "<col width=\"22%\" />\n",
    "</colgroup>\n",
    "<thead valign=\"bottom\">\n",
    "<tr><th>Method name</th>\n",
    "<th>Parameters</th>\n",
    "<th>Scalability</th>\n",
    "<th>Use Case</th>\n",
    "<th>Geometry (metric used)</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr><td>K-Means</span></a></td>\n",
    "<td>number of clusters</td>\n",
    "<td>Very large<span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span> with\n",
    "MiniBatch code</td>\n",
    "<td>General-purpose, even cluster size, flat geometry, not too many clusters</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Affinity propagation</td>\n",
    "<td>damping, sample preference</td>\n",
    "<td>Not scalable with n_samples</td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Mean-shift</td>\n",
    "<td>bandwidth</td>\n",
    "<td>Not scalable with <span class=\"pre\">n_samples</span></td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Spectral clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Medium <span class=\"pre\">n_samples</span>, small <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Few clusters, even cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Ward hierarchical clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Agglomerative clustering</td>\n",
    "<td>number of clusters, linkage type, distance</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints, non Euclidean\n",
    "distances</td>\n",
    "<td>Any pairwise distance</td>\n",
    "</tr>\n",
    "<tr><td>DBSCAN</td>\n",
    "<td>neighborhood size</td>\n",
    "<td>Very large <span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Non-flat geometry, uneven cluster sizes</td>\n",
    "<td>Distances between nearest points</td>\n",
    "</tr>\n",
    "<tr><td>Gaussian mixtures</td>\n",
    "<td>many</td>\n",
    "<td>Not scalable</td>\n",
    "<td>Flat geometry, good for density estimation</td>\n",
    "<td>Mahalanobis distances to  centers</td>\n",
    "</tr>\n",
    "<tr><td>Birch</td>\n",
    "<td>branching factor, threshold, optional global clusterer.</td>\n",
    "<td>Large <span class=\"pre\">n_clusters</span> and <span class=\"pre\">n_samples</span></td>\n",
    "<td>Large dataset, outlier removal, data reduction.</td>\n",
    "<td>Euclidean distance between points</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "Source: http://scikit-learn.org/stable/modules/clustering.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-vRkKRGGymYT"
   },
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set VI</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Try clustering using the following algorithms. </p>\n",
    "<ol>\n",
    "<li>Affinity propagation\n",
    "<li>Spectral clustering\n",
    "<li>Agglomerative clustering\n",
    "<li>DBSCAN\n",
    "</ol>\n",
    "<p>How do their results compare? Which performs the best? Tell a story why you think it performs the best.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LXJB660zymYT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10,  6,  3,  3, 12,  6,  8,  4,  2,  9, 13,  2, 12,  4,  6, 10,\n",
       "        6,  0,  1,  2,  8,  4,  9,  6, 10, 11,  4, 10, 13,  3,  8,  4,  7,\n",
       "       12,  5,  4,  2,  4,  5,  6,  6,  8, 12,  0,  7,  2,  3, 10, 13,  1,\n",
       "       12, 13,  6,  3,  8,  6,  3,  0,  8, 12,  8, 12,  0,  0, 13,  6, 11,\n",
       "        9, 10, 11, 10,  7,  3, 12,  3, 13,  8,  3, 13, 11, 10, 11, 13,  2,\n",
       "        5,  0, 10, 10,  8,  7,  0, 12,  3,  1, 12,  8, 12,  7, 10],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Your turn\n",
    "## affinity propagation\n",
    "## This algorithm allows data points to send signals to each other to communicate their distance \n",
    "## from each other. mathematically, this looks like a similarity matrix that shows the negative sum of the squared, \n",
    "## distance between the observations. To make sure the model chooses the smallest number of clusters possible, the diagonal\n",
    "## which should be 0, will be replaced the the smallest value from the similarity matrix. Then the data is used to \n",
    "## constrauct a resposibility matrix, then an availability matrix, and then a criterion matrix. this type of clustering is \n",
    "## well suited for data where optimum number of clusters is undetermined. \n",
    "from sklearn.cluster import AffinityPropagation\n",
    "af = AffinityPropagation(random_state=5)\n",
    "af.fit_predict(x_cols) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 0,\n",
       "       0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 2, 2, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       2, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 0, 0, 0, 2, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## spectral clustering\n",
    "## A much  more powerful tool than typical k-means clustering. This model essentially computes a similarity matrix from the \n",
    "## data, compute k eigenvectors from the Laplacian marix to define a feature vector for each observation, then run k-means\n",
    "## on the features to get k-clusters. \n",
    "from sklearn.cluster import SpectralClustering\n",
    "sc = SpectralClustering(n_clusters=3,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 2, 1, 1, 3, 0, 1, 2, 1, 3, 2, 1, 2, 3, 1, 3, 2, 1, 2, 0,\n",
       "       1, 1, 3, 1, 1, 1, 1, 3, 2, 0, 1, 1, 2, 0, 1, 2, 1, 2, 3, 3, 0, 1,\n",
       "       2, 1, 2, 2, 1, 1, 1, 1, 1, 3, 1, 0, 3, 2, 2, 0, 1, 0, 1, 2, 2, 3,\n",
       "       3, 1, 1, 1, 1, 1, 1, 2, 1, 2, 3, 1, 2, 3, 1, 1, 1, 3, 0, 0, 2, 1,\n",
       "       1, 0, 1, 2, 1, 2, 1, 1, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = SpectralClustering(n_clusters=4,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 4, 2, 0, 0, 4, 3, 0, 2, 0, 4, 2, 0, 0, 4, 0, 4, 2, 0, 2, 3,\n",
       "       0, 0, 4, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 3, 0, 2, 0, 3, 4, 4, 3, 0,\n",
       "       2, 1, 3, 0, 0, 0, 0, 0, 0, 4, 0, 3, 4, 0, 2, 3, 0, 3, 0, 2, 3, 0,\n",
       "       4, 0, 0, 0, 0, 0, 1, 2, 0, 2, 4, 0, 0, 4, 0, 0, 0, 4, 3, 3, 2, 0,\n",
       "       0, 3, 1, 2, 0, 2, 0, 0, 3, 0, 1, 0])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = SpectralClustering(n_clusters=5,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 0, 5, 1, 1, 0, 3, 1, 5, 1, 0, 5, 1, 1, 0, 1, 0, 5, 1, 5, 3,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 5, 3, 1, 2, 5, 4, 1, 5, 1, 4, 0, 0, 3, 1,\n",
       "       5, 2, 5, 5, 1, 1, 1, 1, 1, 0, 3, 3, 0, 5, 4, 3, 1, 3, 1, 4, 4, 1,\n",
       "       0, 1, 1, 1, 1, 1, 2, 5, 1, 5, 0, 3, 1, 0, 1, 1, 1, 0, 3, 4, 5, 1,\n",
       "       1, 3, 2, 5, 1, 5, 1, 1, 3, 1, 2, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = SpectralClustering(n_clusters=6,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 3, 4, 1, 3, 3, 4, 0, 3, 1, 3, 4, 1, 3, 3, 4, 3, 4, 1, 3, 1, 0,\n",
       "       3, 3, 4, 3, 3, 3, 3, 3, 1, 0, 3, 2, 1, 6, 3, 1, 3, 6, 4, 4, 0, 3,\n",
       "       1, 2, 1, 1, 3, 3, 3, 3, 3, 4, 5, 0, 4, 1, 6, 0, 3, 0, 3, 6, 6, 3,\n",
       "       4, 3, 3, 3, 3, 3, 5, 1, 3, 1, 4, 5, 3, 4, 3, 3, 3, 4, 0, 6, 1, 3,\n",
       "       3, 0, 2, 1, 3, 1, 3, 3, 0, 3, 2, 3])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = SpectralClustering(n_clusters=7,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 1, 7, 3, 1, 1, 7, 5, 4, 3, 4, 7, 3, 1, 1, 7, 1, 7, 3, 4, 3, 5,\n",
       "       5, 4, 0, 1, 1, 1, 1, 7, 3, 5, 4, 2, 3, 6, 4, 3, 4, 6, 7, 0, 5, 1,\n",
       "       3, 2, 3, 3, 1, 4, 4, 1, 1, 0, 5, 5, 0, 3, 6, 5, 1, 5, 1, 6, 6, 7,\n",
       "       0, 1, 4, 1, 1, 1, 5, 3, 1, 3, 7, 5, 1, 7, 1, 1, 1, 7, 5, 6, 3, 1,\n",
       "       1, 5, 2, 6, 1, 3, 4, 1, 5, 1, 2, 1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = SpectralClustering(n_clusters=8,random_state=5)\n",
    "sc_fit = sc.fit_predict(x_cols)\n",
    "sc_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 1,\n",
       "       0, 0, 2, 0, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 2, 2, 1, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 2, 2, 0, 1, 2, 1, 1, 0, 0, 1, 0, 1, 1, 2,\n",
       "       2, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 0, 0, 0, 2, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Agglomertaive Clustering \n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "ac = AgglomerativeClustering(n_clusters=3)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 1,\n",
       "       3, 0, 2, 0, 0, 0, 0, 2, 1, 1, 0, 3, 1, 1, 0, 1, 0, 1, 2, 2, 1, 0,\n",
       "       1, 3, 1, 0, 0, 0, 0, 0, 2, 2, 3, 1, 2, 1, 1, 0, 0, 1, 0, 1, 1, 2,\n",
       "       2, 0, 0, 0, 0, 0, 3, 1, 0, 1, 2, 3, 1, 2, 0, 0, 0, 2, 1, 1, 1, 0,\n",
       "       0, 1, 3, 1, 0, 1, 0, 0, 1, 0, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = AgglomerativeClustering(n_clusters=4)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 2, 4, 1, 1, 2, 4, 0, 4, 0, 2, 4, 1, 4, 2, 1, 2, 4, 1, 4, 4,\n",
       "       3, 0, 2, 0, 1, 1, 1, 2, 4, 4, 0, 3, 4, 4, 0, 4, 0, 4, 2, 2, 4, 1,\n",
       "       4, 3, 4, 1, 1, 1, 1, 1, 2, 2, 3, 4, 2, 4, 4, 1, 1, 4, 1, 4, 4, 2,\n",
       "       2, 1, 0, 1, 1, 1, 3, 4, 1, 4, 2, 3, 4, 2, 0, 0, 1, 2, 4, 4, 4, 0,\n",
       "       1, 4, 3, 4, 1, 4, 1, 1, 4, 1, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = AgglomerativeClustering(n_clusters=5)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 0, 2, 1, 4, 1, 4, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 1,\n",
       "       3, 4, 2, 5, 0, 0, 0, 2, 1, 1, 4, 3, 1, 1, 4, 1, 4, 1, 2, 2, 1, 0,\n",
       "       1, 3, 1, 0, 0, 0, 0, 0, 2, 2, 3, 1, 2, 1, 1, 0, 0, 1, 0, 1, 1, 2,\n",
       "       2, 0, 4, 0, 0, 0, 3, 1, 0, 1, 2, 3, 1, 2, 5, 5, 0, 2, 1, 1, 1, 5,\n",
       "       0, 1, 3, 1, 0, 1, 0, 0, 1, 0, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = AgglomerativeClustering(n_clusters=6)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 6, 6, 2, 1, 4, 1, 4, 2, 1, 0, 1, 2, 0, 2, 1, 0, 1, 1,\n",
       "       3, 4, 2, 5, 0, 0, 0, 2, 1, 1, 4, 3, 1, 1, 4, 1, 4, 1, 2, 2, 1, 0,\n",
       "       1, 3, 1, 6, 0, 0, 0, 6, 2, 2, 3, 1, 2, 1, 1, 0, 6, 1, 0, 1, 1, 2,\n",
       "       2, 0, 4, 0, 0, 0, 3, 1, 6, 1, 2, 3, 1, 2, 5, 5, 0, 2, 1, 1, 1, 5,\n",
       "       0, 1, 3, 1, 0, 1, 0, 6, 1, 6, 3, 6], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = AgglomerativeClustering(n_clusters=7)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 6, 6, 2, 0, 4, 0, 4, 2, 0, 7, 0, 2, 7, 2, 0, 1, 0, 0,\n",
       "       3, 4, 2, 5, 1, 7, 1, 2, 0, 0, 4, 3, 0, 0, 4, 0, 4, 0, 2, 2, 0, 7,\n",
       "       0, 3, 0, 6, 1, 7, 1, 6, 2, 2, 3, 0, 2, 0, 0, 7, 6, 0, 7, 0, 0, 2,\n",
       "       2, 1, 4, 1, 1, 1, 3, 0, 6, 0, 2, 3, 0, 2, 5, 5, 1, 2, 0, 0, 0, 5,\n",
       "       1, 0, 3, 0, 7, 0, 1, 6, 0, 6, 3, 6], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = AgglomerativeClustering(n_clusters=8)\n",
    "ac.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As we increased the number of clusters using Agglomerative clustering, we see the amount of noise in the dataset is \n",
    "## reduced. This means better segmentation between classes. Initially, at around 3 or 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1,  0, -1, -1, -1,  1,  2, -1, -1, -1, -1, -1, -1, -1,  0, -1,\n",
       "        1, -1, -1, -1, -1, -1, -1,  3, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1,  0,  4,  5, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1,  3, -1,  2,  4, -1,  6, -1, -1,  5, -1,  6, -1, -1,  3, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1,  7, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1,  5, -1, -1, -1,  7, -1, -1, -1, -1, -1, -1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## DBSCAN clustering \n",
    "from sklearn.cluster import DBSCAN\n",
    "dbs = DBSCAN(eps=0.2, min_samples=2)\n",
    "dbs_fit = dbs.fit_predict(x_cols)\n",
    "dbs_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## this model has poor perfomance in regard to seperating the data into groups that can identified. There are two parameters\n",
    "## that can be modified, which diminishes its ability to help analyze the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Based on the results, I would utilize Agglomerative clustering. We can see improvement in the learning ability of the \n",
    "## model as the number of clusters is increased. This tells us that the algorithm is learning new features, and can be help-\n",
    "## ful in finding new classifications. Another model that had good relative performance was Spectral Clustering, but this \n",
    "## model still had significant noise and therfore is not capturing the complete nature of the dataset. "
   ]
  }
 ],
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   "name": "Clustering Case Study - Customer Segmentation with K-Means - Tier 3.ipynb",
   "provenance": []
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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 },
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