tutorials/tutorial_01_ML/tutorial_ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical ML: Scikit-learn\n",
    "## Introducing Pandas and Scikit-learn\n",
    "### Juan Antonio Cortés (jacortes@ugr.es)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_predict, GridSearchCV, StratifiedKFold\n",
    "from sklearn.metrics import classification_report, hamming_loss\n",
    "from sklearn import tree\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import imblearn\n",
    "from skmultilearn.dataset import load_dataset\n",
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from skmultilearn.adapt import MLTSVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n",
    "built on top of the Python programming language.\n",
    "\n",
    "It is the perfect tool to explore, clean and process tabular data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas tables (DataFrames from now on) are comprised of series (one-dimensional ndarray with axis labels). Below, an example of a series with a missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames can be built from different data types. For example, we can build a DataFrame with a date column (it will be the index of the DataFrame in this example) and four numerical columns (the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('20201101', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>0.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.138424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.461485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-1.945440</td>\n",
       "      <td>0.565144</td>\n",
       "      <td>-0.734308</td>\n",
       "      <td>-0.525013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01 -1.426951 -0.339902 -0.996485  0.145876\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.151100\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540\n",
       "2020-11-04 -1.059865  0.466425 -0.271732  0.138424\n",
       "2020-11-05 -1.476008 -0.366261  0.370270 -0.461485\n",
       "2020-11-06 -1.945440  0.565144 -0.734308 -0.525013"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also build a DataFrame from a Python dictionary. Thanks to the flexibility of Pandas we can mix different data types inside the same DataFrame. For example: integers, time stamps, floats, text..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'A': 1.,\n",
    "                    'B': pd.Timestamp('20201101'),\n",
    "                    'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "                    'D': np.array([3] * 4, dtype='int32'),\n",
    "                    'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "                    'F': 'foo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2020-11-01  1.0  3   test  foo\n",
       "1  1.0 2020-11-01  1.0  3  train  foo\n",
       "2  1.0 2020-11-01  1.0  3   test  foo\n",
       "3  1.0 2020-11-01  1.0  3  train  foo"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our DataFrame we can use different approaches to view it. Pandas offers multiples ways to accomplish this task:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can peek at the beginning of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>0.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.138424</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.461485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01 -1.426951 -0.339902 -0.996485  0.145876\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.151100\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540\n",
       "2020-11-04 -1.059865  0.466425 -0.271732  0.138424\n",
       "2020-11-05 -1.476008 -0.366261  0.370270 -0.461485"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.138424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.461485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-1.945440</td>\n",
       "      <td>0.565144</td>\n",
       "      <td>-0.734308</td>\n",
       "      <td>-0.525013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.151100\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540\n",
       "2020-11-04 -1.059865  0.466425 -0.271732  0.138424\n",
       "2020-11-05 -1.476008 -0.366261  0.370270 -0.461485\n",
       "2020-11-06 -1.945440  0.565144 -0.734308 -0.525013"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also list the name of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing the data is a trivial task with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2020-11-01</th>\n",
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       "      <th>2020-11-03</th>\n",
       "      <th>2020-11-04</th>\n",
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       "      <th>2020-11-06</th>\n",
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       "      <th>A</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>0.611093</td>\n",
       "      <td>0.749137</td>\n",
       "      <td>-1.059865</td>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-1.945440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.339902</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.565144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.996485</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.734308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.145876</td>\n",
       "      <td>-1.151100</td>\n",
       "      <td>1.673540</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>-0.461485</td>\n",
       "      <td>-0.525013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2020-11-01  2020-11-02  2020-11-03  2020-11-04  2020-11-05  2020-11-06\n",
       "A   -1.426951    0.611093    0.749137   -1.059865   -1.476008   -1.945440\n",
       "B   -0.339902    2.496005    0.715941    0.466425   -0.366261    0.565144\n",
       "C   -0.996485   -0.942824   -1.832690   -0.271732    0.370270   -0.734308\n",
       "D    0.145876   -1.151100    1.673540    0.138424   -0.461485   -0.525013"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as sort the DataFrame. For example, we can sort it by column in descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.145876</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-1.426951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>-1.151100</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>0.611093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>1.673540</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>0.749137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>0.138424</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-1.059865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-0.461485</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>-1.476008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-0.525013</td>\n",
       "      <td>-0.734308</td>\n",
       "      <td>0.565144</td>\n",
       "      <td>-1.945440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2020-11-01  0.145876 -0.996485 -0.339902 -1.426951\n",
       "2020-11-02 -1.151100 -0.942824  2.496005  0.611093\n",
       "2020-11-03  1.673540 -1.832690  0.715941  0.749137\n",
       "2020-11-04  0.138424 -0.271732  0.466425 -1.059865\n",
       "2020-11-05 -0.461485  0.370270 -0.366261 -1.476008\n",
       "2020-11-06 -0.525013 -0.734308  0.565144 -1.945440"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.461485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>0.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.138424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-1.945440</td>\n",
       "      <td>0.565144</td>\n",
       "      <td>-0.734308</td>\n",
       "      <td>-0.525013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-05 -1.476008 -0.366261  0.370270 -0.461485\n",
       "2020-11-01 -1.426951 -0.339902 -0.996485  0.145876\n",
       "2020-11-04 -1.059865  0.466425 -0.271732  0.138424\n",
       "2020-11-06 -1.945440  0.565144 -0.734308 -0.525013\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.151100"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also offers a quick statistic summary of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.758006</td>\n",
       "      <td>0.589559</td>\n",
       "      <td>-0.734628</td>\n",
       "      <td>-0.029960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.149787</td>\n",
       "      <td>1.043432</td>\n",
       "      <td>0.741988</td>\n",
       "      <td>0.964306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.945440</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.463744</td>\n",
       "      <td>-0.138320</td>\n",
       "      <td>-0.983069</td>\n",
       "      <td>-0.509131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.243408</td>\n",
       "      <td>0.515784</td>\n",
       "      <td>-0.838566</td>\n",
       "      <td>-0.161530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.193354</td>\n",
       "      <td>0.678242</td>\n",
       "      <td>-0.387376</td>\n",
       "      <td>0.144013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.758006  0.589559 -0.734628 -0.029960\n",
       "std    1.149787  1.043432  0.741988  0.964306\n",
       "min   -1.945440 -0.366261 -1.832690 -1.151100\n",
       "25%   -1.463744 -0.138320 -0.983069 -0.509131\n",
       "50%   -1.243408  0.515784 -0.838566 -0.161530\n",
       "75%    0.193354  0.678242 -0.387376  0.144013\n",
       "max    0.749137  2.496005  0.370270  1.673540"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another usual task whilst working with data is the selection. Pandas offers a \"pythonic\" selection interface so we can slice our DataFrame in multiple ways. With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-11-01   -1.426951\n",
       "2020-11-02    0.611093\n",
       "2020-11-03    0.749137\n",
       "2020-11-04   -1.059865\n",
       "2020-11-05   -1.476008\n",
       "2020-11-06   -1.945440\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>-0.996485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>-0.942824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>-1.832690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>-0.271732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>0.370270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-1.945440</td>\n",
       "      <td>-0.734308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2020-11-01 -1.426951 -0.996485\n",
       "2020-11-02  0.611093 -0.942824\n",
       "2020-11-03  0.749137 -1.832690\n",
       "2020-11-04 -1.059865 -0.271732\n",
       "2020-11-05 -1.476008  0.370270\n",
       "2020-11-06 -1.945440 -0.734308"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A', 'C']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-1.426951</td>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>0.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.151100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01 -1.426951 -0.339902 -0.996485  0.145876\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.151100\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by a combination of the previous method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.15110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.67354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C        D\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.15110\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.67354"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>-0.339902</td>\n",
       "      <td>-0.996485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>0.565144</td>\n",
       "      <td>-0.734308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2020-11-01 -0.339902 -0.996485\n",
       "2020-11-02  2.496005 -0.942824\n",
       "2020-11-03  0.715941 -1.832690\n",
       "2020-11-04  0.466425 -0.271732\n",
       "2020-11-05 -0.366261  0.370270\n",
       "2020-11-06  0.565144 -0.734308"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-11-01', '2020-11-02', '2020-11-03', '2020-11-04',\n",
       "               '2020-11-05', '2020-11-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.059865</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>-0.271732</td>\n",
       "      <td>0.138424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.476008</td>\n",
       "      <td>-0.366261</td>\n",
       "      <td>0.370270</td>\n",
       "      <td>-0.461485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.673540\n",
       "2020-11-04 -1.059865  0.466425 -0.271732  0.138424\n",
       "2020-11-05 -1.476008 -0.366261  0.370270 -0.461485"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2020-11-3':'2020-11-5']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also supports boolean indexing. With this feature we can select data based on conditions, complex or simple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can obtain the boolean mask that satisfies the following condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D\n",
       "2020-11-01  False  False  False   True\n",
       "2020-11-02   True   True  False  False\n",
       "2020-11-03   True   True  False   True\n",
       "2020-11-04  False   True  False   True\n",
       "2020-11-05  False  False   True  False\n",
       "2020-11-06  False   True  False  False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df > 0\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.673540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.138424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37027</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.565144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B        C         D\n",
       "2020-11-01       NaN       NaN      NaN  0.145876\n",
       "2020-11-02  0.611093  2.496005      NaN       NaN\n",
       "2020-11-03  0.749137  0.715941      NaN  1.673540\n",
       "2020-11-04       NaN  0.466425      NaN  0.138424\n",
       "2020-11-05       NaN       NaN  0.37027       NaN\n",
       "2020-11-06       NaN  0.565144      NaN       NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply these conditions over a subset of the data. For example, we can get all the values that satisfy the next constraint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.611093</td>\n",
       "      <td>2.496005</td>\n",
       "      <td>-0.942824</td>\n",
       "      <td>-1.15110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>0.749137</td>\n",
       "      <td>0.715941</td>\n",
       "      <td>-1.832690</td>\n",
       "      <td>1.67354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C        D\n",
       "2020-11-02  0.611093  2.496005 -0.942824 -1.15110\n",
       "2020-11-03  0.749137  0.715941 -1.832690  1.67354"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course the logical operators (and, or, exclusive-or) are supported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C, D]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['A'] > 0) & (df['C'] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can handle missing values without hassle. Given the Series introduced at the beginning of the notebook which contains one missing value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can obtain which values are missing (NaN):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we stated previously Pandas allows boolean indexing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[pd.isna(s)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we are left with three choices: do nothing, delete the missing values or replace them with a value. The first one is not an issue and with Pandas neither of them are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily delete them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or replace it with another value, in this case 5, but Pandas offers multiple options: previous/next valid value, mean or whichever function we pass to the `fillna` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    5.0\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas offers a shortcut to `matplotlib` so we can easily plot our data with the call of a method. Let's build a random time series dataset and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('11/1/2020', periods=1000))\n",
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot type is the `line` plot, perfect for the data at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot multivariate data and Pandas will handle the labels, legends, colors..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or change the plot type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation \"plot\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases we don't even need to use the binding with Matplotlib. For example, another usual plot we can make with Pandas and Pandas alone is the correlation plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.695958</td>\n",
       "      <td>-0.632552</td>\n",
       "      <td>0.317781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.695958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.363050</td>\n",
       "      <td>-0.334276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.632552</td>\n",
       "      <td>-0.363050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.582019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.317781</td>\n",
       "      <td>-0.334276</td>\n",
       "      <td>-0.582019</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "A  1.000000  0.695958 -0.632552  0.317781\n",
       "B  0.695958  1.000000 -0.363050 -0.334276\n",
       "C -0.632552 -0.363050  1.000000 -0.582019\n",
       "D  0.317781 -0.334276 -0.582019  1.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = df.corr()\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas supports styles (even with HTML and CSS) so creating a colored correlation plot is really easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_72ef1_row0_col0,#T_72ef1_row1_col1,#T_72ef1_row2_col2,#T_72ef1_row3_col3{\n",
       "            background-color:  #b40426;\n",
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       "            background-color:  #eb7d62;\n",
       "            color:  #000000;\n",
       "        }#T_72ef1_row0_col2,#T_72ef1_row2_col0{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_72ef1_row0_col3,#T_72ef1_row3_col0{\n",
       "            background-color:  #efcebd;\n",
       "            color:  #000000;\n",
       "        }#T_72ef1_row1_col2,#T_72ef1_row2_col1{\n",
       "            background-color:  #6f92f3;\n",
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       "        }#T_72ef1_row1_col3,#T_72ef1_row3_col1{\n",
       "            background-color:  #7597f6;\n",
       "            color:  #000000;\n",
       "        }#T_72ef1_row2_col3,#T_72ef1_row3_col2{\n",
       "            background-color:  #4358cb;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_72ef1_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >A</th>        <th class=\"col_heading level0 col1\" >B</th>        <th class=\"col_heading level0 col2\" >C</th>        <th class=\"col_heading level0 col3\" >D</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_72ef1_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
       "                        <td id=\"T_72ef1_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "                        <td id=\"T_72ef1_row0_col1\" class=\"data row0 col1\" >0.70</td>\n",
       "                        <td id=\"T_72ef1_row0_col2\" class=\"data row0 col2\" >-0.63</td>\n",
       "                        <td id=\"T_72ef1_row0_col3\" class=\"data row0 col3\" >0.32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_72ef1_level0_row1\" class=\"row_heading level0 row1\" >B</th>\n",
       "                        <td id=\"T_72ef1_row1_col0\" class=\"data row1 col0\" >0.70</td>\n",
       "                        <td id=\"T_72ef1_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "                        <td id=\"T_72ef1_row1_col2\" class=\"data row1 col2\" >-0.36</td>\n",
       "                        <td id=\"T_72ef1_row1_col3\" class=\"data row1 col3\" >-0.33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_72ef1_level0_row2\" class=\"row_heading level0 row2\" >C</th>\n",
       "                        <td id=\"T_72ef1_row2_col0\" class=\"data row2 col0\" >-0.63</td>\n",
       "                        <td id=\"T_72ef1_row2_col1\" class=\"data row2 col1\" >-0.36</td>\n",
       "                        <td id=\"T_72ef1_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "                        <td id=\"T_72ef1_row2_col3\" class=\"data row2 col3\" >-0.58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_72ef1_level0_row3\" class=\"row_heading level0 row3\" >D</th>\n",
       "                        <td id=\"T_72ef1_row3_col0\" class=\"data row3 col0\" >0.32</td>\n",
       "                        <td id=\"T_72ef1_row3_col1\" class=\"data row3 col1\" >-0.33</td>\n",
       "                        <td id=\"T_72ef1_row3_col2\" class=\"data row3 col2\" >-0.58</td>\n",
       "                        <td id=\"T_72ef1_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1248d4d3190>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you are wondering Pandas can also read (and write) from multiple file types: CSV, Excel, HDF5, Parquet... Let's load the SDSS (Sloan Digital Sky Survey) dataset from https://www.sdss.org/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv('data/sdss.csv')\n",
    "_ = df.pop('objid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this is not all about Pandas. Pandas is a huge library which offers endless resources to handle our data. Do not forget to visit the official documentation to take a glance of all that Pandas has to offer: https://pandas.pydata.org/docs/reference/index.html#api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start by taking a peek at the head of the data to get a notion of the data at hand. The data consists of 10000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar.\n",
    "\n",
    "Data from: https://www.sdss.org/\n",
    "\n",
    "The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one third of the sky, and spectra for more than three million astronomical objects. Learn and explore all phases and surveys—past, present, and future—of the SDSS.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
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       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
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       "      <th>rerun</th>\n",
       "      <th>camcol</th>\n",
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       "      <td>541</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.680207</td>\n",
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       "      <td>18.19169</td>\n",
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       "      <td>4</td>\n",
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       "      <td>513</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183.870529</td>\n",
       "      <td>0.049911</td>\n",
       "      <td>17.76536</td>\n",
       "      <td>16.60272</td>\n",
       "      <td>16.16116</td>\n",
       "      <td>15.98233</td>\n",
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       "      <td>4</td>\n",
       "      <td>269</td>\n",
       "      <td>3722365362331820032</td>\n",
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       "      <td>-0.000111</td>\n",
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       "      <td>510</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.102557</td>\n",
       "      <td>17.55025</td>\n",
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      ],
      "text/plain": [
       "           ra       dec         u         g         r         i         z  \\\n",
       "0  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342  15.22531   \n",
       "1  183.598371  0.135285  18.66280  17.21449  16.67637  16.48922  16.39150   \n",
       "2  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732  16.80125   \n",
       "3  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233  15.90438   \n",
       "4  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492  16.61326   \n",
       "\n",
       "   run  rerun  camcol  field            specobjid   class  redshift  plate  \\\n",
       "0  752    301       4    267  3722360139651588096    STAR -0.000009   3306   \n",
       "1  752    301       4    267   363814405953054720    STAR -0.000055    323   \n",
       "2  752    301       4    268   323274319570429952  GALAXY  0.123111    287   \n",
       "3  752    301       4    269  3722365362331820032    STAR -0.000111   3306   \n",
       "4  752    301       4    269  3722365912087633920    STAR  0.000590   3306   \n",
       "\n",
       "     mjd  fiberid  \n",
       "0  54922      491  \n",
       "1  51615      541  \n",
       "2  52023      513  \n",
       "3  54922      510  \n",
       "4  54922      512  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns description is as follows:\n",
    "* `ra`: right ascension.\n",
    "* `dec`: declination.\n",
    "* `u`: ultraviolet.\n",
    "* `g`: green.\n",
    "* `r`: red.\n",
    "* `i`: near infrared.\n",
    "* `z`: infrared.\n",
    "* `run`: run number.\n",
    "* `rerun`: rerun number.\n",
    "* `camcol`: camera column.\n",
    "* `field`: field number.\n",
    "* `specobjid`: object Identifier.\n",
    "* `class`: object class (galaxy, star or quasar).\n",
    "* `redshift`: final redshift.\n",
    "* `plate`: plate number.\n",
    "* `mjd`: MJD of observation.\n",
    "* `fiberid`: fiber ID.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()\n",
    "df_nan = df.mask(np.random.random(df.shape) < .05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After knowing a bit about the data we can check for missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ra    dec      u      g      r      i      z    run  rerun  camcol  \\\n",
       "0     False  False  False  False  False  False  False  False  False   False   \n",
       "1     False  False  False  False   True  False  False  False  False   False   \n",
       "2     False  False  False  False  False  False  False  False  False   False   \n",
       "3     False  False  False  False  False  False  False  False  False   False   \n",
       "4     False  False  False  False   True  False  False  False  False   False   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "9995  False  False  False  False  False  False  False   True  False   False   \n",
       "9996  False  False  False  False  False  False  False  False  False   False   \n",
       "9997  False  False  False  False  False  False  False  False  False   False   \n",
       "9998  False  False  False  False  False  False  False  False  False   False   \n",
       "9999  False  False  False  False  False  False  False  False  False   False   \n",
       "\n",
       "      field  specobjid  class  redshift  plate    mjd  fiberid  \n",
       "0     False      False  False     False  False  False    False  \n",
       "1     False      False  False     False  False  False    False  \n",
       "2     False      False  False     False  False  False    False  \n",
       "3     False      False  False     False  False   True    False  \n",
       "4     False      False   True     False  False  False    False  \n",
       "...     ...        ...    ...       ...    ...    ...      ...  \n",
       "9995  False      False  False     False  False  False    False  \n",
       "9996  False      False  False     False  False  False    False  \n",
       "9997  False      False  False     False  False  False    False  \n",
       "9998  False      False  False     False  False  False    False  \n",
       "9999  False      False  False     False  False   True    False  \n",
       "\n",
       "[10000 rows x 17 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df_nan) #.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another basic task during the EDA phase is to check the class distribution. We can easily plot the histogram with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEWCAYAAACKSkfIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAS6klEQVR4nO3df7RdZ13n8feHhhYGwbT0ktVJiokSl5ZRC2RKFRm1lf4CTVXAwoyNTDTqKjOoM2qZf6oUtOAPsIpdq9pqCkKtCLQKI8QUrKwltOkPi20pvQPUJgva0KQdOwx1tXznj/OEdSbem3tverN30uf9Wuuuu/d3P+ec714363N2nr3PPqkqJEl9eMrYDUiShmPoS1JHDH1J6oihL0kdMfQlqSMrxm7gQI4//vhau3bt2G1I0hHl5ptv/nJVzcy17bAO/bVr17Jjx46x25CkI0qSe+fb5vSOJHXE0Jekjhj6ktQRQ1+SOmLoS1JHFhX6Sb6Q5NNJbkuyo9WOS7ItyT3t97GtniSXJplNcnuSF049z6Y2/p4kmw7NLkmS5rOUI/0fqKqTq2pDW78Q2F5V64HtbR3gbGB9+9kCXAaTNwngIuDFwCnARfveKCRJw3gi0zsbga1teStw7lT9qpr4JLAyyQnAmcC2qtpTVXuBbcBZT+D1JUlLtNjQL+CjSW5OsqXVVlXVF9vyl4BVbXk1cN/UY3e22nx1SdJAFvuJ3O+tql1JngNsS/KZ6Y1VVUmW5dtY2pvKFoDnPve5y/GUi7b2wg8N+npD+8IlLx+7BUkjW9SRflXtar8fAD7AZE7+/jZtQ/v9QBu+Czhx6uFrWm2++v6vdXlVbaiqDTMzc946QpJ0kBYM/STPSPLMfcvAGcA/AtcB+67A2QRc25avA85vV/GcCjzcpoE+ApyR5Nh2AveMVpMkDWQx0zurgA8k2Tf+PVX110luAq5Jshm4F3h1G/9h4BxgFvgK8DqAqtqT5GLgpjbuTVW1Z9n2RJK0oAVDv6o+B3zXHPUHgdPnqBdwwTzPdSVw5dLblCQtBz+RK0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjqyYuwGpOWy9sIPjd3CIfWFS14+dgt6EvBIX5I6YuhLUkcWHfpJjkpya5K/auvrknwqyWySP0tydKsf09Zn2/a1U8/xxla/O8mZy743kqQDWsqR/huAu6bW3wq8vaqeB+wFNrf6ZmBvq7+9jSPJScB5wPOBs4A/SHLUE2tfkrQUiwr9JGuAlwN/1NYDnAa8rw3ZCpzblje2ddr209v4jcDVVfVoVX0emAVOWYZ9kCQt0mKP9N8B/DLwtbb+bOChqnqsre8EVrfl1cB9AG37w2381+tzPObrkmxJsiPJjt27dy9+TyRJC1ow9JO8Anigqm4eoB+q6vKq2lBVG2ZmZoZ4SUnqxmKu038J8MNJzgGeBjwL+F1gZZIV7Wh+DbCrjd8FnAjsTLIC+Ebgwan6PtOPkSQNYMEj/ap6Y1Wtqaq1TE7EXl9V/xH4GPDKNmwTcG1bvq6t07ZfX1XV6ue1q3vWAeuBG5dtTyRJC3oin8j9FeDqJG8GbgWuaPUrgHclmQX2MHmjoKruSHINcCfwGHBBVT3+BF5fkrRESwr9qvo48PG2/DnmuPqmqr4KvGqex78FeMtSm5QkLQ8/kStJHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyIKhn+RpSW5M8g9J7kjya62+Lsmnkswm+bMkR7f6MW19tm1fO/Vcb2z1u5Ocecj2SpI0p8Uc6T8KnFZV3wWcDJyV5FTgrcDbq+p5wF5gcxu/Gdjb6m9v40hyEnAe8HzgLOAPkhy1jPsiSVrAgqFfE4+01ae2nwJOA97X6luBc9vyxrZO2356krT61VX1aFV9HpgFTlmOnZAkLc6i5vSTHJXkNuABYBvwv4CHquqxNmQnsLotrwbuA2jbHwaePV2f4zHTr7UlyY4kO3bv3r3kHZIkzW9RoV9Vj1fVycAaJkfn33aoGqqqy6tqQ1VtmJmZOVQvI0ldWtLVO1X1EPAx4LuBlUlWtE1rgF1teRdwIkDb/o3Ag9P1OR4jSRrAYq7emUmysi0/HXgZcBeT8H9lG7YJuLYtX9fWaduvr6pq9fPa1T3rgPXAjcu0H5KkRVix8BBOALa2K22eAlxTVX+V5E7g6iRvBm4FrmjjrwDelWQW2MPkih2q6o4k1wB3Ao8BF1TV48u7O5KkA1kw9KvqduAFc9Q/xxxX31TVV4FXzfNcbwHesvQ2JUnLwU/kSlJHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRxYM/SQnJvlYkjuT3JHkDa1+XJJtSe5pv49t9SS5NMlsktuTvHDquTa18fck2XTodkuSNJfFHOk/Bvy3qjoJOBW4IMlJwIXA9qpaD2xv6wBnA+vbzxbgMpi8SQAXAS8GTgEu2vdGIUkaxoKhX1VfrKpb2vI/A3cBq4GNwNY2bCtwblveCFxVE58EViY5ATgT2FZVe6pqL7ANOGs5d0aSdGBLmtNPshZ4AfApYFVVfbFt+hKwqi2vBu6betjOVpuvvv9rbEmyI8mO3bt3L6U9SdICFh36Sb4B+Avg56vqf09vq6oCajkaqqrLq2pDVW2YmZlZjqeUJDWLCv0kT2US+H9aVe9v5fvbtA3t9wOtvgs4cerha1ptvrokaSCLuXonwBXAXVX1O1ObrgP2XYGzCbh2qn5+u4rnVODhNg30EeCMJMe2E7hntJokaSArFjHmJcBPAJ9Oclur/Q/gEuCaJJuBe4FXt20fBs4BZoGvAK8DqKo9SS4Gbmrj3lRVe5ZjJyRJi7Ng6FfVJ4DMs/n0OcYXcME8z3UlcOVSGpQkLR8/kStJHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyIKhn+TKJA8k+cep2nFJtiW5p/0+ttWT5NIks0luT/LCqcdsauPvSbLp0OyOJOlAFnOk/yfAWfvVLgS2V9V6YHtbBzgbWN9+tgCXweRNArgIeDFwCnDRvjcKSdJwFgz9qroB2LNfeSOwtS1vBc6dql9VE58EViY5ATgT2FZVe6pqL7CNf/1GIkk6xA52Tn9VVX2xLX8JWNWWVwP3TY3b2Wrz1f+VJFuS7EiyY/fu3QfZniRpLk/4RG5VFVDL0Mu+57u8qjZU1YaZmZnlelpJEgcf+ve3aRva7wdafRdw4tS4Na02X12SNKCDDf3rgH1X4GwCrp2qn9+u4jkVeLhNA30EOCPJse0E7hmtJkka0IqFBiR5L/D9wPFJdjK5CucS4Jokm4F7gVe34R8GzgFmga8ArwOoqj1JLgZuauPeVFX7nxyWJB1iC4Z+Vb1mnk2nzzG2gAvmeZ4rgSuX1J0kaVktGPqSdKitvfBDY7dwSH3hkpeP3cLXeRsGSeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcGD/0kZyW5O8lskguHfn1J6tmgoZ/kKOCdwNnAScBrkpw0ZA+S1LOhj/RPAWar6nNV9S/A1cDGgXuQpG6tGPj1VgP3Ta3vBF48PSDJFmBLW30kyd0D9TaG44EvD/VieetQr9QN/35Hrif73+6b5tswdOgvqKouBy4fu48hJNlRVRvG7kMHx7/fkavnv93Q0zu7gBOn1te0miRpAEOH/k3A+iTrkhwNnAdcN3APktStQad3quqxJK8HPgIcBVxZVXcM2cNhpotprCcx/35Hrm7/dqmqsXuQJA3ET+RKUkcMfUnqiKEvSR057K7Tf7JKclRVPT52H1KvkjwNeF5bna2qr47Zz1g80h/OzUm+e+wmtHySvCzJtrH70IElWZHkbUzuALAVuAq4L8nbkjx13O6GZ+gP52eA303yh0mOHbsZLV6S05J8NskjSd6d5DuS7AAuAS4buz8t6DeB44B1VfWiqnoh8C3ASuC3xmxsDF6yOaAkAX4W+O/A/wS+tm9bVf3XsfrSgSW5FfgF4O+Z3CH23cCFVfX7ozamRUlyD/CttV/Ytbv+fqaq1o/T2Tic0x/WccC/B3YDNzMV+jqsVVV9vC1/MMkuA/+IUvsHfis+nqS7o15DfyBJfhb4JSb/1dw81z9CHbZWJvnRqfUV0+tV9f4RetLi3Znk/Kq6arqY5D8Bnxmpp9E4vTOQJO8GfrGqHhi7Fy1Nkj8+wOaqqv88WDNasiSrgfcD/5fJ/7ABNgBPB36kqrq66aOhP5AkP1ZVfzFH/WjgV6rq4hHa0hOUZFVV3T92H1pYktOA57fVO6tq+5j9jMWrd4azJcmHk3zzvkKSs4HbgWeP15aWKsnKJJuTbAduHbsfHViSf5PkqVV1fVX9HvBR4DuT/MjYvY3BI/0BJXkN8GbgPcC/A54DXFBVt43ZlxaW5OlMvtrztcALgGcC5wI3VJUn5A9jSW5gch7tniTPA24E/pTJ93TfWFVvHLXBgRn6A2qXiP0a8PPAQ8BpVfXZMXvSwpK8B3gpkyPEq4HrmXyic92ojWlRkny6qr6jLV8MHFdVF7Sp1Zv3beuF0zsDSfK9wC1MpnJOBF4P/GWSNyU5ZtTmtJCTgL3AXcBd7XYaHi0dOab/VqcB2wCq6l/o8LJpQ3847wB+uqp+rqr2VtUHmUwTHAP8w5iN6cCq6mTg1UymdP4mySeAZyZZNWpjWqzbk/xWkl9kcu+dj8Lk3MyoXY3E6Z2BJHnKfHO/Sb6zqm4fuictTpJTq+qTU+svAl7D5I1gZ1V9z2jNaUHtfMwbmAT+pa18N/Ai4Fuq6l1j9TYGQ38k7ZYMpzE5MfiKqvKo8TCV5JZ2v5b96wFeWlU3jNCWFqndVO03gfOBzwMBVgG/X1W/keTkni6mcHpnYElOTXIpcC9wLXAD8G3jdqWDURMG/uHvt4FnAN80dcO1bwfWJbkM+MCo3Q3MI/2BJPl14FXAPwHvZfIPbYdXgBz+kjzE5M15TlX1w8N1o6VKMgusn+eGa18Gzp6evnuy8947w/kp4LNMbsX7l1X1aI83ezpC7WZytKgj09cOcMO13T0FPhj6QzoBeBmTE4DvSPIx4OlJVlTVY+O2pgU8UlV/O3YTOmgHuuHaXSP1NBqnd0bQrst/BZM3gJcC26vqteN2pfkkuR54bVV9qa2fD/wYk/Myv1pVe8bsTwfmDdf+f4b+yJI8C9jY22VjR5IktwA/WFV7kvwHJp/K/S/AycC3V9Urx+xPi+MN1yYM/cNAkn+qqueO3YfmluS29gEtkrwT2F1Vv7r/NulI4CWbh4eM3YAOaEWSfee/Tmdy752vbxuhH+mg+Q/28OB/tw5v7wX+NsmXmcwL/x1Au2Pjw2M2Ji2V0zsDSfJ7zB3uATZV1bMGbklLkORUJldgfbSq/k+rfSvwDVV1y6jNSUtg6A8kyaYDba+qrUP1Iqlfhv7IkjwN+KGq+vOxe5H05OeJ3BEkOSrJOUnexeRa7x8fuydJffBE7oCSfB+Tu2qew+Qr214CrKuqr4zamKRuOL0zkCQ7mdxs7TLgg1X1z0k+7w3XJA3J6Z3hvA/4t0ymcn4oyTPwUk1JA/NIf0DtSze+n8k9d84BVgKbgQ9V1SPjdSapF4b+SNq3+ZzJ5A3gzKo6fuSWJHXAE7kDSbIRWFNV72ylTwDPacu/ME5XknrjnP5wfhm4bmr9GCa3d/0+4CfHaEhSfzzSH87RVXXf1PonqupB4MF2UleSDjmP9Idz7PRKVb1+anVm4F4kdcrQH86nkvz0/sUkP8Pkg1qSdMh59c5AkjwH+CDwKLDvrowvYjK3f25V3T9Sa5I6YugPbL+vbLujqq4/0HhJWk6GviR1xDl9SeqIoS9JHTH0Jakjhr4kdeT/AS8xUODNVtEgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['class'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is clearly imbalanced towards the GALAXY and STAR classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GALAXY    4998\n",
       "STAR      4152\n",
       "QSO        850\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having an unbalanced dataset can degrade the quality of the prediction for the less represented class and even ignore completly the minority class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New examples can be synthesized from the existing examples. This is a type of data augmentation for the minority class and is referred to as the Synthetic Minority Oversampling Technique, or SMOTE for short.\n",
    "\n",
    "Multiple variations and improvements:\n",
    "\n",
    "- SMOTE\n",
    "- SMOTENC\n",
    "- SMOTEN\n",
    "- ADASYN\n",
    "- BorderlineSMOTE\n",
    "- KMeansSMOTE\n",
    "- SVMSMOTE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df.pop('class')\n",
    "X = df\n",
    "oversample = imblearn.over_sampling.SMOTE()\n",
    "X_smote, y_smote = oversample.fit_resample(X, y)\n",
    "y_smote.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "![](data/oversampling.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next figure shows the basic workflow while approaching a Machine Learning problem. We can distinguish two tasks, separate at first but intertwined in the last stages:\n",
    "\n",
    "* Optimization of the machine learning model (left) which will make the prediction using the data.\n",
    "* Construction of the dataset (accomplished during the EDA phase) and partitioning in training and test data (right)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/grid_search_workflow.png)\n",
    "From: https://scikit-learn.org/stable/modules/cross_validation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must first split the data in two sets: `X` and `y`. These names are a common convention. `X` for the variables and `y` for the target (class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv('data/sdss.csv')\n",
    "_ = df.pop('objid') # df.drop(['objid'], axis=1)\n",
    "\n",
    "y = df.pop('class')\n",
    "X = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to split the data, one more time, into train and test partitions. Scikit-learn can easily do that for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=24112020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the train partition (80% of our data) and the test partition (20% of our data) randomly selected from all the data and stratified (both partitions has the same class distribution) for better results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we train and optimize the desired model via the cross validation strategy. The next figure shows a graphical representation of this strategy:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/grid_search_cross_validation.png)\n",
    "From: https://scikit-learn.org/stable/modules/cross_validation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/knn_example.png)\n",
    "From: Cheng, Debo, et al. \"kNN algorithm with data-driven k value.\" International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to define the parameters which will be tested. In this case, the number of neighbors (`k`) that the algorithm will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_neighbors': [x for x in range (3, 16)]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we instantiate our classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can search for the best `k`. Sklearn offers the `GridSearchCV` method to accomplish this. This method will test all the combination of parameters via cross validation and will return the best parameters obtained during this training phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=KNeighborsClassifier(), n_jobs=4,\n",
       "             param_grid={'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                         14, 15]})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we get `k=15`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=15)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the training step we need to check how the model performs against data that has not been \"seen\" by the model (the test data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we get the predictions we compare them to the ground-truth to check the model's performance. Once again, sklearn has a classification report with all the common metrics used in this type of problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.75      0.96      0.85      1000\n",
      "         QSO       0.57      0.02      0.05       170\n",
      "        STAR       0.91      0.78      0.84       830\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.74      0.59      0.58      2000\n",
      "weighted avg       0.80      0.81      0.78      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what if we use a more complex (yet simple) model? The next figure shows a simple decision tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/dt_example.png)\n",
    "From: https://towardsai.net/p/programming/decision-trees-explained-with-a-practical-example-fe47872d3b53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion':('gini', 'entropy'), \n",
    "              'max_depth':[5, 10, 15, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(random_state=24112020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(random_state=24112020), n_jobs=4,\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [5, 10, 15, None]})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, random_state=24112020)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.98      0.99      0.99      1000\n",
      "         QSO       0.94      0.91      0.93       170\n",
      "        STAR       1.00      1.00      1.00       830\n",
      "\n",
      "    accuracy                           0.99      2000\n",
      "   macro avg       0.97      0.97      0.97      2000\n",
      "weighted avg       0.99      0.99      0.99      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the perks of this model is the interpretability of it. Thanks to sklearn, we can plot the final decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,20))\n",
    "tree.plot_tree(clf, feature_names=X.columns, class_names=['GALAXY', 'QSAR', 'STAR']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/rf_example.png)\n",
    "From: https://rpubs.com/Avalos42/randomforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "              'n_estimators': [5, 10, 15, 20],\n",
    "              'max_depth': [5, 10, 15, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(random_state=24112020)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(random_state=24112020), n_jobs=4,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [5, 10, 15, None],\n",
       "                         'n_estimators': [5, 10, 15, 20]})"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=15, n_estimators=15, random_state=24112020)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.99      0.99      0.99      1000\n",
      "         QSO       0.96      0.93      0.94       170\n",
      "        STAR       1.00      1.00      1.00       830\n",
      "\n",
      "    accuracy                           0.99      2000\n",
      "   macro avg       0.98      0.97      0.98      2000\n",
      "weighted avg       0.99      0.99      0.99      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model offers an useful trait: the feature importance. The higher the value the more important is the feature (variable) to the model so we can discard useless variables and improve the performance of the model. This is called \"feature selection\" and is one of the most powerful preprocessing techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00391372, 0.0032789 , 0.02165442, 0.02865361, 0.01082761,\n",
       "       0.10353049, 0.0497029 , 0.00244479, 0.        , 0.00107068,\n",
       "       0.00400284, 0.14607243, 0.56476699, 0.03300232, 0.02383341,\n",
       "       0.0032449 ])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = clf.feature_importances_\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>redshift</td>\n",
       "      <td>0.564767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>specobjid</td>\n",
       "      <td>0.146072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>i</td>\n",
       "      <td>0.10353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>z</td>\n",
       "      <td>0.049703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plate</td>\n",
       "      <td>0.033002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g</td>\n",
       "      <td>0.028654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mjd</td>\n",
       "      <td>0.023833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>u</td>\n",
       "      <td>0.021654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>r</td>\n",
       "      <td>0.010828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>field</td>\n",
       "      <td>0.004003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ra</td>\n",
       "      <td>0.003914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dec</td>\n",
       "      <td>0.003279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fiberid</td>\n",
       "      <td>0.003245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>run</td>\n",
       "      <td>0.002445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>camcol</td>\n",
       "      <td>0.001071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rerun</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature Importance\n",
       "12   redshift   0.564767\n",
       "11  specobjid   0.146072\n",
       "5           i    0.10353\n",
       "6           z   0.049703\n",
       "13      plate   0.033002\n",
       "3           g   0.028654\n",
       "14        mjd   0.023833\n",
       "2           u   0.021654\n",
       "4           r   0.010828\n",
       "10      field   0.004003\n",
       "0          ra   0.003914\n",
       "1         dec   0.003279\n",
       "15    fiberid   0.003245\n",
       "7         run   0.002445\n",
       "9      camcol   0.001071\n",
       "8       rerun        0.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_df = pd.DataFrame([X.columns, feature_importance]).T\n",
    "fs_df.columns = ['Feature', 'Importance']\n",
    "fs_df.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the `redshift` is the more important feature by a huge margin. Let's see what happens when we train another model with only this feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A support-vector machine (SVM) constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/svm_example.png)\n",
    "From: Ulas, Cagdas. (2013). INCORPORATION OF A LANGUAGE MODEL INTO A BRAIN COMPUTER INTERFACE BASED SPELLER. 10.13140/2.1.2017.6326. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'svc__kernel': ['rbf', 'linear', 'poly']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = make_pipeline(StandardScaler(), SVC(random_state=24112020))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('svc', SVC(random_state=24112020))]),\n",
       "             n_jobs=4, param_grid={'svc__kernel': ['rbf', 'linear', 'poly']})"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train['redshift'].values.reshape(-1, 1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svc', SVC(random_state=24112020))])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test['redshift'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see we obtain \"nearly perfect\" results using only one feature. This is a huge improvement in terms of training speed, prediction speed and model complexity (less complex models are often preferred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.98      0.98      0.98      1000\n",
      "         QSO       0.96      0.88      0.92       170\n",
      "        STAR       0.99      1.00      0.99       830\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.98      0.95      0.96      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering (K-Means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, lets try an unsupervised learning (a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision) model for clustering: K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the best number of cluster: the elbow method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow curve')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distorsions = []\n",
    "labels = []\n",
    "centroids = []\n",
    "X = X[['ra', 'dec', 'redshift']]\n",
    "X_test = X_test[['ra', 'dec', 'redshift']]\n",
    "for k in range(2, 10):\n",
    "    clf = KMeans(n_clusters=k, random_state=24112020)\n",
    "    clf.fit(X)\n",
    "    labels.append(clf.predict(X_test))\n",
    "    distorsions.append(clf.inertia_)\n",
    "    centroids.append(clf.cluster_centers_)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 10), distorsions)\n",
    "plt.grid(True)\n",
    "plt.title('Elbow curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x124e9a75940>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure('K-Means on SDSS Dataset', figsize=(10,10))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter(centroids[1][0][0], centroids[1][0][1], centroids[1][0][2], c='r', s=100)\n",
    "ax.scatter(centroids[1][1][0], centroids[1][1][1], centroids[1][1][2], c='r', s=100)\n",
    "ax.scatter(centroids[1][2][0], centroids[1][2][1], centroids[1][2][2], c='r', s=100)\n",
    "scatter = ax.scatter(X_test['ra'], X_test['dec'], X_test['redshift'], c=labels[1])\n",
    "fig.legend(handles=scatter.legend_elements()[0], labels=['GALAXY', 'QSO', 'STAR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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nnjiCT355JPlexHHEOgAAAAAUONKdw36STeJK+816Ovr63r3u9+tb3/72xocHNTs7Kz+8z//Uw899JA+8pGPHHns8UAPhUKqq6vT2NjYLc/7ta99TS9/+ct18+ZNSScHen19ve666y4tLi6W/HWdhEAHAAAAgBpX2ru4H4/08t7FPZfL6ZlnntGb3/xmPfDAA7rnnnv02te+Vn/2Z3+mTCZz5LHHA/3jH/+4Xv/615/4vEtLS7p+/bqsVqukkwP94OBAjz76qP7gD/6gxK/qZAQ6AAAAANS48q6D/q6yroN+OyHQAQAAAKDGlSrQpedPdz/IdeogY3/+f8u0tNrtiEAHAAAAgBpXykBH+RDoAAAAAFDjCPTqQKADAAAAQI0j0KsDgQ4AAAAANY5Arw4EOgAAAADUuHz4HV+WDOaSyWQIdAAAAACoZXt7ewqHw1pfXzd6V1DE+vq6wuGw9vZOvjM+gQ4AAAAANSAejxciPZPJKJvNMiaZTCZTiPN4PH7q75BABwAAAIAacHBwUIh0xpwTj8d1cHBw6u+QQAcAAACAGrK3t2f4EWPm1jnttPbDCHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAAAAAAEyAQAcAAAAAwAQIdAAAAAAATIBABwAAAADABAh0AAAAAABMgEAHAAAAAMAECHQAQE04ODgwehcAAACuhEAHAFS1g4MD3bhxQ5ubm9rc3FQ2m9WNGzcIdgAAUHUIdABA1drf31cul9POzo6SyaQ2Nja0sbGhRCKhVCqlra0tZbNZ3bx5k2AHAACmR6ADAKrOwcGB9vb2lM1mlclklMlklE6nlU6ntbm5qXQ6rWQyqUQicSTYt7e3lcvlCHYAAGBKBDoAoKocHBxod3e3EObZbFbZbPZIoB+e/NcJdgAAYHYEOgCgahw+pT2bzSqXyymXyxUN9GLBPj09rUAgQLADAABTINABAKZ3cHCgmzdvKpvN3hLnFw30wzMzMyOv11sI9vzRdYIdAAAYgUAHAJjaSae0H47zqwT67OysGhsbbznCnkqlTgz2nZ0d5XI57e3tEewAAKDkCHQAgGnlbwR30lHzUgR6JBK5JdBPOiX+cLAfvoZ9Z2dHu7u7BDsAACgJAh0AYDoHBwfa3t5WLBY7M86vGugej+dCP0OwAwCAciHQAQCmsr+/r93dXUWjUTU2Np4Z51cJ9Lm5uQsH+nmDPZ1OE+wAAOBCCHQAgCkcX9t8YWFBjY2NZ8b5VQO9oaHhSoFeLNjzy7olk0mCHQAAnIlABwAY7uDgQDdu3DhyI7hYLCaPx3PuQM/H8UVien5+Xm63u6SBflqw5282l0gkCHYAAHAiAh0AYKjDa5tnMplCdMfjcTU0NJQ10KPRaNkD/SLBnslktLu7q/39fYIdAIDbEIEOADDEWWubLy0tye12lz3QXS5XRQO9WLDnr19PJpPa3NxUJpPRjRs3CHYAAG4TBDoAoOLOs7b58vKyXC5XWQN9YWFB9fX1hgb6RYI9m80Wgh0AANQeAh0AUFH7+/vnWtt8ZWVF9fX1ZQ30xcVF0wU6wQ4AwO2LQAcAVET+lPZMJnOutc1XV1fldDrLHuhOp9PwCL9KsOfvEr+1tUWwAwBQ5Qh0AEDZneeU9uOztrYmh8NR1kCPxWJyOByGR3c5jrAT7AAAVB8CHQBQVvm1zc9z1PzwrK+vy263lzXQ4/F41Qf6ScGeX4P98F3iDwc7N5wDAMCcCHQAQFmctLb5eeM8H+g2m63sgW632w2P6koF+0lH2G/evEmwAwBgEgQ6AKDk8mub5+P8ImGen42NDVmt1gsFeiqVulC8Li0t1XygnyfYU6mUtra2lMvlCHYAAAxEoAMASubg4ODSp7Qfn0QiUZFAt9lshkezGYN9e3ubYAcAoMIIdABASVzmRnDFJplMymKxnOt5Lhvoy8vLslqthkeyWSadTheCfXJyUsFgkGAHAKCCCHQAwJXlT2m/6lHzw5NKpcoe6CsrKwT6KTM5Oanm5uYTbzpHsAMAUB4EOgDg0vJrm5filPbjk06nZbFYznUN+1UC3WKxGB7DZpzJyUn5/f4jX8sv6ZZMJgunwxPsAACUDoEOALiUUp/Sfnw2NzdlsVi0s7NTtkBfXV2VxWK58N3fb4c5KdCPz/FgP3wN+87OjnZ3d7W3t0ewAwBwTgQ6AODCSnUjuGKztbUli8VSODJbjkBfW1sj0K8Q6AQ7AAClRaADAM7tqmubX2S2t7dlsVgKy38Vm0wmo9nZWc3NzV0o0vOBftGwvx3mMoF+3mBPp9MEOwAAJyDQAQDncnxt83LGeS73/A3n8teHn3WkvaOjQ06nU06nUzabTS0tLRoeHtbi4mLRo+Pr6+uyWCxKJpOGB7HZphSBfp5gTyaTBDsAAP+DQAcAFHV4bfNKhPnho+L5089Pe8zy8rIaGhrU2tpaiL14PK7R0VG1trbK4XDI4XCotbVVo6OjisfjR4J9Y2ODQK9goBcL9vw67AQ7AOB2RqADAE6VvxHcwMCAxsfHKxbn+evK86efn/S9sbEx2Ww2jY2NKZvNamtr65ZT1dPptGKxmEZGRhQMBmW32+V0OtXW1qZwOKyFhQVZLBYlEgnDg9hsU4lAPy3YDy/plg/2TCaj3d1d7e/vE+wAgJpFoAMATnR4bfOenh4NDw9XLM7zkz+6ffhrm5ubam1tVUNDg5aXl4+c6n7WteSpVEoLCwsaGhpSIBCQzWaTxWJRe3u7xsfHtbKyYngYm2WMCPRiwZ6/fj1/tkMmk9GNGzcIdgBATSHQAQBHnLS2eV9fn4aGhioe6FarVYlEovDnWCwml8uljo6OW24ed55APz75a9D7+vrU3Nwsq9Uqt9utrq4uTU5Oam1tzfBQvp0D/SLBns1mC8EOAEC1ItABAAWnrW3e39+vwcHBige6zWbTxsaGstmsRkZGZLfbNTk5eeKp9tvb2xcO9GQyKYvFovX1dW1ubiqRSCgSiai/v18+n08Wi0Uej0c9PT2anp4uPO52GDMGOsEOAKh1BDoAQFLxtc1DoZBCoVDFA91utysWi6mlpUUej0erq6unPvYyR9BTqdSRQD8+GxsbmpmZUW9vrxobG2WxWOT1etXX16fZ2dmavna9GgL9rGDP33Rua2uLYAcAVAUCHQBuc4fXNj8pznO5nAYGBtTf32/IEXSHw6Hu7m5tb28XfexVAv28p7Kvr69renpaPT098ng8slqt8vl86u/vVyQSqam7wVdjoJ8V7Pkj7AQ7AMCsCHQAuI3t7++feEr78RkcHFRfX1/FwjyTyWhwcFAWi0Wjo6Pn+pnLBHo6nZbFYtHq6uqlAnBtbU2Tk5Pq6uqS2+2W1WpVc3OzBgYGND8/f+H9MdPUQqCf9PvOL+l2+C7xh4OdG84BAIxEoAPAbeiia5sPDw+rt7e3InGeSCTU3Nwsn88nh8OhlZWVsgX65ubmlQL9+KysrGh8fFwdHR2qr6+XzWZTIBDQ0NCQFhYWqirYazHQiwX7SUfYb968SbADACqKQAeA28zhU9rPE+e5XE4jIyPq6ekpe5zPzc3J4XCor69POzs7qq+vr0igl2N5tXQ6raWlJYXDYbW1tcnpdMputysYDGpkZESxWEzpdNrwSL2dA/2k39nxYE+lUoUVAwh2AEC5EegAcBs5vLZ5JpM5dziPjo6qu7u7bGG+s7Oj/v5+ORwOzc3NFb7ucrmOrHVejkC3Wq1aXl6uSPzFYjGNjIwoGAzKbrfL4XCora1N4XBYS0tLpgr2yclJNTc3G74fRs5pwZ6/HwLBDgAoNQIdAG4DJ61tfpGADofD6uzsLEucb2xsyOfzqbm5+cia57lcTm63W0tLS2UNdJvNVpFAPz6pVEqLi4saGhpSIBCQzWZTfX292tvbNTY2puXlZUODnUA/Oul0+kiwx+Nx9fT0EOwAgJIi0AGgxp22tvlFJn9ddanjfGZmRna7XQMDAyce0W9oaFA8Hj/Xc11mHfTNzecDfWlpyfAATKVSikajGhwclN/vl9VqlcvlUmdnpyYmJkp2nfx5h0AvPrFYTA6HgyPsAICSItABoIbt7+9f+qj54ZmYmFB7e3vJwnx7e1vd3d1yOp1aWFg49XEej0exWKysgW632xWPxw0PvuOTTCY1NzenUCikpqYmWa1WNTQ0qLu7W1NTU6eu3V6qIdCLTywWk9PpLPw5v6RbMpksxDrBDgC4KAIdAGpQ/pT2YmubX2QmJyfV1tZWkjhfW1tTY2OjAoGAUqlU0cc2NjZqcXGxrIHucDhMGejHJ5FIaHZ2Vn19ffJ6vbJYLGpsbFRvb69mZma0sbFR0u0R6MVncXHxSKAfn+PBfvgI+87OjnK5nPb29gh2AMARBDoA1JhSnNJ+fKamphQMBq/0HNlsVpOTk7Lb7RoeHj7XTeq8Xm/RI+yHZ2dn59KBHovFDA++i87Gxoamp6fV09Mjj8cji8Uin8+n/v5+RSIRJRKJKz0/gV58FhYWVF9ff+7HnxXsu7u7BDsAgEAHgFqSX9u8FEfND8/09LRaWlou/fNbW1vq7OyUy+U69ynruVxOPp9P0Wi0rIHudDqrMtCPz9ramiYnJ9XV1aWGhgZZrVY1NTUpFAppbm5OyWTyQs9HoBefhYUFuVyuS//8acGeTqcJdgC4jRHoAFADLrO2+UVmdnZWgUDgUj+7srKihoYGtba2Kp1OX+hnm5qaND8/X/ZAX1xcNDz4Sj0rKyuamJgofDBis9nk9/s1ODioaDR65t8VgV58otHolQL9+BDsAACJQAeAqpdf2zwf56UM8/xEIhH5/f4L/Uw2m9XY2JjsdrvGxsYu9aFBc3PzkXXRi8329vaFjxJvbm6qvr5eCwsLhgdfOSedTmt5eVljY2Nqb2+X0+mU3W5XS0uLhoeHtbi4eMuSbgR68Zmfn5fb7S7r7ywf7Pm7xCeTSYIdAB/4vpIAACAASURBVGocgQ4AVerg4KBsp7Qfn7m5OTU3N5/78Zubm2pra5Pb7dby8vKlt9vc3KxIJFLWQHe5XDUf6CfFXzwe1+joqFpbW+VwOORwONTa2qrR0VHF43FNTEwQ6EWm3IF+0u8slUoV7g6fSCQKwZ7JZLS7u6v9/X2CHQCqHIEOAFWoHDeCKzbz8/Py+Xznemw8HpfL5VJHR4e2trautN1AIKDZ2dmyB3o0GjU8+IycdDqtxcVFDQ8Pq6WlRXa7XXa7XU6nU2NjY1peXr7lCPvtPnNzc2poaDD0d5YP9vzp8Pn//jOZjG7cuEGwA0AVItABoMrkT2kv91Hzw7OwsCCv11v0MdlsViMjI7Lb7ZqYmCjJvgUCAc3MzJQ10N1ut+bn5w0PPjNNKpXSwMCAXC6X/H6/bDab6uvr1dHRofHxca2srBi+j0bP3NycPB6P4fuRn/MGOwDA3Ah0AKgSh28EV8k4z+VyWlxcVGNj46nfT6VSamlpkcfj0erqasm229LSounp6bIGekNDA4F+why+Bj2ZTGp+fl4DAwNqbm6W1WqV2+1WV1eXJicntba2Zvj+VnoikYipAv34FAv2bDZLsAOASRHoAFAF8qe09/f3KxQKVTTOc7mcYrGYPB7PqUfX6+vr1d3dre3t7ZJuNxgMViTQ5+bmDA8qs02xm8QlEglFIhH19/fL5/PJYrHI4/Gop6dH09PT2tjYMHz/yz2zs7NqbGw0fD/OO8eDPX/Tua2tLYIdAEyEQAcAkzt8I7hQKKRQKFTROM/lnr+u3O12H/laJpPR4OCg7Ha7pqeny/KhQWtrqyYnJ8sa6B6PR5FIxPCAMttc5C7uGxsbmpmZUW9vrxobG2WxWOT1etXX16fZ2VklEgnDX0+pZ3Z2Vl6v1/D9uOwQ7ABgTgQ6AJjUSWubDw4Oqr+/v+KBvrS0JJfLVfhzMpmU3++X1+vV+vp62bbb1tZ27kDf2dm5VKA3NjYS6CfMVZZZW19f19TUlLq7u9XQ0CCr1aqmpiaFQiHNzc1d6vdktpmZmanqQD8+6XS6sKTb4bvEHw52bjgHAOVHoAOACR1f2zx/dHp4eFi9vb0VD/SVlRXV19crl3v+ju4Oh0N9fX3a2dkp63bb29s1MTFR9kCfnZ01PJDMNqVcB311dVUTExPq7OyUy+WS1WpVc3OzBgYGND8/r1QqZfjrvejMzMzI5/MZvh/lmsPBfvgadoIdAMqLQAcAEzm8tvlJy6eNjIyou7u74oG+uroqh8Oh/v5+ORyOc69NftXJ3zW8nIHu9XoJ9BOmlIF+PPxWVlY0Pj6ujo4O1dfXy2azKRAIaGhoSAsLC1UR7NPT0zUd6Cf93o4HeyqVKiylePPmTYIdAEqAQAcAkzjP2ubhcFhdXV0VD/RoNCqLxaKmpiYlEomKbbezs1NjY2Pnemwmk7l0oM/MzBgeQGabcgX6SeG3tLSkcDistrY2ORwO2e12BYNBjYyMKBaLmXIN9qmpKTU1NRm+H0bNacGev1EkwQ4Al0OgA4AJnHdt87GxMXV0dFQ0zmdnZ2W322WxWJTJZCq67a6uLoXD4bIGus/n0/T0tOHBY7apVKCfFH6xWEwjIyMKBoOy2+1yOBxqa2tTOBzW0tKSKYJ9amrKkL8fM046nSbYAaBECHQAMNDBwYFu3rxZuEv7WXdCn5iYUHt7e0XieHt7Wz09PXI6nZqampLVaq1onOdyOXV3d2t0dPTcgX6ZU6ObmpoI9BPGqEA/PqlUSgsLCxoaGlIgEJDNZlN9fX3h8ofl5WVDgt0sfz9mnMPBHggEFA6HCXYAOCcCHQAMcp5T2o/P5OSkWltbyx7Ga2tramxsVCAQUCqVUjKZlMViqXig9/T0aGRkpOyBPjU1ZXjUmG3MGqCpVErz8/MaGBiQ3++X1WqVy+VSZ2enJiYmtLq6WpH9mJiYkN/vN/zvw+zj9/s1MTFRCPb80XWCHQBORqADgAEOr21+kfXDp6enFQwGyxbE2WxWU1NTstvtGh4eLpzSnkqlZLFYyrLWebHp7e3V8PBwWQO9ublZk5OThoeM2casgX58ksmk5ubmFAqF1NTUJKvVqoaGBnV3d2tqakrr6+tl2S6Bfr5pbm6+5QOw/BrsJwV7fmWIvb09gh3AbYlAB4AKOry2+UXjPJd7/nrwQCBQlhje2toqLIMVi8WOfC+dThtyDXpfX5+GhobKGuh+v59AP2GqJdCPTyKR0OzsrPr6+uT1emWxWNTY2Kje3l7NzMxoY2OjJNsZHx9XIBAw/PWafc5zj4fjwX74GvadnR3t7u4S7ABuGwQ6AFTI/v7+hU9pPz6RSER+v7/kIbyysqKGhgYFg0Gl0+lbvr+5uSmLxVI4HbVS09/fr8HBwbIH+sTEhOEhY7ap1kA/Puvr65qenlZPT488Ho8sFot8Pp/6+/sViUSUSCQu9bzj4+NqaWkx/PWZfS6zSgLBDuB2RqADQJmdtbb5RWZubk5NTU0lC+BsNqvx8XHZ7XaFw+FT9217e1sWi6Ww5nGlJhQKaWBgoKyBHggEND4+bnjImG1qJdCPz9ramiYnJ9XV1SW32y2r1arm5mYNDAxofn7+3CsBjI2NEejnmMbGRkUikSs9x2nBnk6nCXYANYdAB4AyOnxK+1XjPJd7fj1yn89Xkvjd3NxUW1ub3G63lpaWij52Z2dHFotFm5ubFQ/0UChEoBswtRrox2dlZUXj4+Pq6OhQfX29bDab/H6/BgcHtbCwcOp/U+FwWMFg0PD9N/t4PB7Nzc2V9DkPB3t+WbdkMkmwA6gJBDoAlMnhtc1Lde32wsKCGhsbr/w8S0tLcrvdam9v1+bm2dGdyWRksVhOPP29nDMwMKD+/v6yBnpLS4vGxsYMDxmzze0S6IcnnU5reXlZY2Njam9vl9PplN1uV0tLi4aHh7W4uFhY0i0cDqu1tdXwfTb7uN1uzc/Pl/33lkqlCjebSyQSBDuAqkWgA0CJXXRt84tMLBaTx+O59M9ns1mNjo7KbrdrYmLi3PuWzWZlsViUTCYrGuiDg4NlD/RgMEignzC3Y6Afn3Q6rXg8rtHRUbW2tsrhcMjhcKi1tVVtbW3y+/2GrMFeTeNyubSwsFDx39tJwb65ualMJqPd3V3t7+8T7ABMiUAHgBK6zNrmF5n8ke/L/Gw6nVYwGJTH49HKysqFf95qtSqRSFQ00IeGhtTb21vWQG9tbVU4HDY8ZMw2BPqtk06ntbi4qOHh4cIN55xOp9rb2zU2Nqbl5WWC/djU19drcXHR8N9bPtjz168fDvYbN24Q7ABMg0AHgBLZ398vy1Hzw7O8vCyXy3Xhn1tcXFR9fb26uroufSd2m82mjY2Nigb68PBwRQJ9dHTU8JAx2xDoxWdkZEStra2KRqMaHByU3++XzWaTy+VSZ2enJiYmtLKyYvh+Gj0Oh0OxWMzw/Tg8xYI9m80Wgh0AjECgA8AV5U9pv+za5heZ1dVVOZ3Ocz8+k8loaGhIdrtd09PTV9o3u92u9fX1igb6yMiIenp6yhrobW1tBPoJQ6AXn+Hh4cI9HPKTTCY1NzenUCikpqYmWa1Wud1udXV1aXJyUmtra4bvd6XHbrcrHo8bvh/F5rRg39raItgBVByBDgBXUO5T2o/P2tqaHA7HuR6bTCbl9/vl9Xq1trZ25W07HI6SPM9FZnR0VN3d3WUP9JGREcMjwWxDoBefoaEhdXR0FH1MIpFQJBJRf3+/fD6fLBaLPB6Penp6ND09rY2NDcNfR7nHZrNpeXnZ8P24yBwP9vxd4gl2AJVAoAPAJeXXNi/3UfPDs76+LpvNdubjotGonE6nent7tbOzU5JtO53OS127fpUJh8Pq6uoqa6C3t7dreHjY8Cgw2xDoxec8gX58NjY2NDMzo97eXjU2Nspiscjr9aqvr0+zs7NKJBKGv65Sj9VqrfpT/TnCDqCSCHQAuKBSr21+kUkkErJarUUjNRQKyeFwKBKJlHTbLpdLy8vLFQ30sbExdXZ2ljXQOzo6CPQThkAvPoODg+rs7LzSc6yvr2tqakrd3d1qaGiQ1WpVU1OTQqGQ5ubmCtdFV+uk02lZLBatrq4avi+lfl35NdgP3yX+cLBzwzkAl0WgA8AF7O/vK5VKqb29vWRrm19kUqmULBbLiR8KbGxsqKmpSU1NTWW5mZvb7dbS0lJFX+/4+Lg6OjrKHuhDQ0OGv+k32xDoxWdwcFBdXV0lfc7V1VVNTEyos7NTLpdLVqtVfr9fg4ODikajl/rv28jJ/3tV69feHw72/BH2VCpVCPabN28S7ADOjUAHgHM4ODgonNK+vr5+aiSXe/JHpI5/ODA7Oyu73a5QKFS2Dw4aGhoUj8cr+nonJibU3t5e1kDv7OzU4OCg4W/yzTYEevEZGBgoeaAfnnQ6reXlZY2Njam9vV1Op1M2m02BQEBDQ0NaWFgwfbAnk0lZLJbb4lr747+704I9l8sR7ACKItAB4AzHbwSXf9NpxBH0ra0tWSyWwlJpOzs76u3tldPpVDQaLeu2PR6PFhcXK/p6Jycn1dbWRqAbMAR68QmFQuru7q7Y9tLptJaWlhQOh9XW1iaHwyG73a5gMKiRkRHFYjHTrcGeSCRksVhq8tr6i/zeTgv2/L/jBDuAwwh0AChif3+/EML5I+abm5uyWCwlu/naRWZ7e1sWi0VbW1taW1uT1+uV3+9XMpks+7a9Xq8WFhYq+nqnpqbU2tpa1kDv6urSwMCA4W/kzTYEevEJhULq6ekxbPvpdFqxWEwjIyMKBoOy2+1yOp1qa2tTOBzW0tKS4cGeP9uo2q+lL/Xv7bRgT6fTnBIPgEAHgJPk1zY/6S7thyO50oGeyWRksVg0NjYmu92uoaGhih3J9/l8ZT9Kf3ymp6cVDAbLGujd3d0KhUKGv3E32xDoxae/v9/QQD8+qVRKCwsLGhoaUiAQkM1mU319vTo6OjQ+Pq6VlZWKB/va2posFovpT8U3cvLBnj/bIB6Pc4QduM0R6ABwzFlrm+cjOZ1OVzzQ86e4O53Oip9u3tTUpPn5+Ypuc2ZmRi0tLWUN9J6eHgL9hCHQi09/f796e3sN34/TJpVKaX5+XgMDA2pubpbVapXb7VZXV5cmJiYqcuO21dXVwr+VRv99mH3yN9RbXV1VKpVSMpksHF3PB3v+rK29vT2CHahhBDoAHHKetc2z2WzhtM1KxurKyoo8Ho8sFkvFlzvL5XJqbm7W3NxcRbc5OzurQCBw7kC/TAj09PSov7/f8DfoZhsCvfj09fWZOtCPTzKZVCQSUX9/v5qammSxWOTxeNTT06Pp6Wmtr6+XfJvLy8uyWq2Gv/ZqmPwR9OM31MuvwZ4P9sOnxO/s7Gh3d5dgB2oMgQ4Auvja5lartSxLmZ32gcD4+LjsdrtGR0dltVq1vr5e8UD3+/2anZ2teKD7/f5zB3r+Os6LvDHu7e0l0E8YAr349Pb2qq+vz/D9uOwkEgnNzs6qr69PXq9XFotFjY2N6u3t1czMTEnuvL60tCSbzWb4a62GOe/1+gQ7UPsIdAC3vfyN4M4b57lcTna7XWtra2UP1M3NTbW3tx9Zg9zhcFRk28enpaVFMzMzFd1mJBJRc3Nz2QO9mkOrXEOgF59a+2BnfX1d09PT6unpKZyp4/P51N/fr0gkcqk7scfjcTkcDsNfWzXMZS8HOC3Y0+k0wQ5UKQIdwG3r8Nrm5w3z/DidTq2urpY1TpeWluR2u9Xe3q7Nzc2KbvukCQaDmp6erug25+bm1NTUVNZAr7ZTlSs1BHrxqfVLI9bW1jQ5Oamuri653W5ZrVY1NzdrYGBA8/Pz57ozeywWk9PpNPy1VMOU6nKAk4I9mUwS7EAVIdAB3JbOuhHcWeNyucp2HXg2m9Xo6KjsdrvGx8dv2bdybrvYtLa2ampqqqLbnJ+fL3ugm/1mX0YNgV58brebC66srGh8fFwdHR2qr6+XzWZTIBDQ0NCQFhYWTrxB48LCgurr6w3f92qYeDwuu91e8ufNB3v+ZnMEO2B+BDqA285Ja5tfdNxut+LxeMmDNJ1OKxgMyuPxaGVl5dRt5093r+S0tbVpYmKiotuMRqPy+XxlD3QzLZdlliHQi8/tvDxfOp3W0tKSwuGw2tra5HQ6ZbfbFQwGNTIyosXFRaXTaUWjUblcLsP3txqmUmcbHA/2RCJROBsik8lod3dX+/v7BDtgIAIdwG2j2NrmFx2Px1PyZc4WFxdVX1+vzs7Owvq3p207FotVPNDz6ylXcpsLCwvyer3nemw2m71UoIdCIQL9hCHQi09XV5cGBgYM3w8zTDqdVjwe1+joqFpbW2W32+VwONTc3Cyn06l4PM5Sa2fMwsKCIR9mHA72/PXrh4P9xo0bBDtQYQQ6gNvCVU9pPz5er1fRaLQkEZrJZDQ0NCS73a6pqakz962xsVELCwsVD/TOzk6NjY1VdJuLi4tqbGw8d6Cn0+kLh8DAwIC6u7sNf4NutiHQi09XV5cGBwcN3w8zTiqV0uLiorq6umSz2WSz2eR0OtXe3q6xsTEtLy8T7Mdmfn5eDQ0Nhu9HsWDPZrOFYAdQPgQ6gJp3nrXNLzpNTU2an5+/8vMkk0kFAgF5vd5z35nd5/OV7MOBi0xXV5fC4XBFtxmLxeTxeMoe6F1dXYa/MTbbEOjFp7Ozk0A/Y2ZnZ9XY2KhUKqVoNKrBwUH5/X5ZrVa5XC51dnZqYmJCq6urhu+r0ROJRNTY2Gj4fhyf04J9a2uLYAfKhEAHULMOr21eyjjP5Z5fEzwSiVzpOaLRqJxOp3p7e7Wzs3Pun2tqatLc3FzFA727u1ujo6MV3WY8HldDQ0NZA31wcFCdnZ2GvxE22xDoxaejo0NDQ0OG74eZZ2ZmRl6v95avJ5NJzc3NKRQKqampSVarVQ0NDeru7tbU1JTW19cN33cj/q58Pp/h+3HWHA/2/E3nCHagdAh0ADVpf3+/pKe0H5+rrAmeyWQUCoVkt9s1Ozt74Z8vxYcDl5menh4NDw9XdJv5peYI9MoPgV58CPSzZ3p6Wk1NTWc+LpFIaHZ2Vn19ffJ6vbJYLGpsbFRvb6+mp6e1sbFh+Gsp90xNTZ3r78pswxF2oPQIdAA15Sprm19kgsHgpZYcSyQSampqUlNTkzY2Ni617UAgcKmwv+r09fVpaGioottcXl6Wy+Uqa6APDQ2po6PD8De6ZpupqSkCvci0t7dreHjY8P0w81z2Q56NjQ3NzMyot7dXjY2Nslgs8nq96uvr0+zsrBKJhOGvrdQzMTEhv99v+H5cddLptJLJ5C13iT8c7NxwDiiOQAdQMw6f0l7OOM/lLrfkWCQSkcPhUCgUUiaTufS2g8GgpqenKx7o/f39GhwcrOg2V1ZWVF9fX9ZAHx4eJtBPGAK9+LS3t2tkZMTw/TDzlCo619bWNDU1pe7ubjU0NMhqtaqpqUmhUEhzc3OFm5hV84yNjamlpcXw/Sj1HA72/BH2VCpVCPabN28S7MAxBDqAmnB4bfOrxO955yJLju3s7Ki3t1dOp7MkN5ZrbW3V5ORkxQM9FAppYGCgottcXV2V0+kse6C3t7cb/kbWbEOgF5+2tjYC/YwZHx9XIBAo+fOurq5qYmJCnZ2dcrlcslqt8vv9GhwcVDQaVSqVMvy1X3TyS9QZvR/lnmLBnsvlCHZABDqAKlfKtc0vMue9o/n6+rq8Xq/8fr+SyWRJtt3e3n7ho/elmIGBAfX391d0m2tra3I4HOd67Pb2tiYnJxWLxS4U6SMjI2prazP8javZhkAvPq2trRodHTV8P8w8Y2NjCgaDZd1GOp3W8vKyxsbG1N7eLqfTKZvNppaWFg0NDWlxcbEqgv12/aDwtGDf3t4m2HHbItABVK1Sr21+kenp6dHIyMip389ms5qenpbdbtfQ0FBJj+p3dHRUfD3yXC6nwcFB9fX1VXSb6+vrstvtZz4ukUjI5/PJ7XbLbrfL6XQWznI4awmn0dFRAv2EIdCLT2trq8LhsOH7YeYx4qhwOp1WPB4vbNvhcMhut6u1tVUjIyMX/gCvUsPNKjcLZ0AR7LjdEegAqtL+/n7Fj5ofnt7e3lNvmLa9va2uri7V19drcXGx5Ns2Yj3yXC6n4eFh9fb2VjzQbTZb0cfMz8/L4XCov79fyWRSyWRS0WhUAwMDam5uLizh1NPTc+IdoW+XU0svOgR68QkGgwT6GWOGs1PS6bRisZhGRkYUDAYLH+C1tbUpHA5raWnJFMEeCoXU3d1t+H6YaQh23K4IdABVJX9KeyQSUWtrqyFxnss9f8O0k67HXl1dlcfjUTAYVCqVKsu2u7u7ix69L9eMjIyop6enotvc2NiQ1Wo98XuZTEYDAwOy2+2am5s79Rr0/BJOh+8I7fP5CjeYyr9xN/rNqNmGQC8+wWBQY2Njhu+HmceMN2BMpVJaWFjQ0NCQAoGAbDab6uvrC2fcrKysGLJffX196u3tNfzvx8yTX9ItmUxqY2NDwWCwcAnDzs4OwY6aQaADqBqHT2mfnZ1Vc3OzIXGey916PXY2m9XExITsdrtGR0fL+sFBb29vxdcjz+VyGh0dVXd3d0W3mUgkZLFYbvl6KpWS3++X1+stLFeXzWYLb+KKvclbW1vT5OTkkRtMOZ3OwvWqZjiaZoYh0ItPS0sLgX7GVMNp28lkUvPz80fOuHG73erq6tLk5KTW1tYqsh89PT3q7+83/O+jmsZutysWixWC/fAR9p2dHe3u7mpvb49gR9Uh0AFUhfza5vlT2qPRqHw+n2GBPjQ0VLgee2trS+3t7XK5XIrH42XfthHLneVyOYXDYXV2dlZ0m8lkUhaL5cgHHouLi3I6nerp6SkcNblIoB+edDqtwcFBeTyewvWqDoejcPrr8vLybRvsBHrxCQQCGh8fN3w/zDwDAwPq6uoyfD8uMolEQpFIRP39/fL5fLJYLPJ4PIVLZNbX18uy3a6uLg0MDBj++qtl0um0rFbrkTMejh9hJ9hRrQh0AKZ22trmi4uLamxsNCzQ86d7Ly0tye12F66zrMS2jVjuLJfLaXx8XB0dHRXdZiqVKgR6NpvVyMiI7Hb7ievAXybQNzePLgWVSqW0uLiooaEh+f1+Wa1WuVyuwtG0cr05N+MQ6MXH7/drYmLC8P0w84RCIfX09Bi+H1eZjY0NzczMqLe3V16vVxaLRV6vV319fZqZmbnlnhaXnY6ODg0NDRn+eqtl8v/fUOzf5NOCPZ1OE+wwNQIdgGnl1zbPx/nhGIvH42poaDA00H0+n2w2m8bHxyt6LbwRy53lcjlNTEyovb29ottMp9OyWCxKpVIKBoPyeDxaXV098bGlCPTjk0wmbzma1tjYqL6+Ps3OziqRSBj+RrVcQ6AXHwL97Onv76+566rX19c1PT2tnp4eeTyewj0t+vv7FYlElEwmL/W8bW1tGhkZMfz1VctsbGzIYrFc6N/gk4I9mUwS7DAdAh2A6RwcHNxySvvxGFteXpbL5TIkztPptDwej+x2u1ZWViq+/cOn11dyJicn1dbWVtFtbm5uymKxyOVyqaOjQ1tbW6c+9rKBPjExIb/ff6E3593d3WpoaJDValVzc7MGBwcVjUarYr3l8w6BXnyam5s1OTlp+H6YeXp7e9XX12f4fpRz8ve06OrqktvtLvybMDAwoPn5+XMHO8v2Xfzv3WKxXOkSpMPBnr9LPMEOMyDQAZjKedc2X11dldPprHikxmIxuVwu+Xw+BYPBim8/l3t+ubNK3009l8tpamqqoq85m81qdHRUFotF4XD4zLMULhvok5OT5w7047OysqLx8XG1t7fL6XTKbrcrGAxqdHRU8Xi8qq9fJ9CLD4F+9tyONz7L/5vQ0dGh+vp62Ww2BQIBDQ0NaWFh4dQP8binwcVmeXlZNputpM+ZD/ZEIlGYfLBnMhnt7u5qf3+fYEfZEegATCN/Svt51jY/z/rYpZxMJqPh4WHZ7XZNTU1pcnJSra2thgS6EXdTz+Vymp6eVktLS0W2tbW1pY6ODrlcLlkslqJHzksR6KUI0cPrLbe0tBxZvmliYkKrq6uGv6m9yBDoxaepqUlTU1OG74eZp7u7W6FQyPD9MGrS6bSWlpYUDofV1tZ25EO8kZERxWKxwr9Xfr+fD3wuMPF4XA6Ho+y/v3yw569fz58RkclkdOPGDYIdZUGgAzBcfm3zYqe0H5+T7u5drkmlUgoEAmpsbNTa2ppyuZxmZmYqFqvHZ2xsrOJ3U8/lcpqdnVUgECj7dvJrybe2thZ+z5ubZ9+A77KBPjU1paamppK/uUulUoXlm5qammS1Wgt3gy7lzaXKNQR68fH5fJqenjZ8P8w8XV1dGhwcNHw/zDKHP8QLBoOy2+2FVSPcbrdGRkaq+qybSs7CwoJcLlfFf3+nBXs2my0EO3BVBDoAQ533lPbjk7952PGbx5V6otFoYUmv7e3twtcjkYj8fr8hgW7E3dQr8Zqz2aympqaOrCWfyWQK1xmWK9Cnp6fLEujHJ5FIFO4Gnb+5VFNTk0KhkObm5i59c6lyDYFefHw+n2ZmZgzfDzMPdyYvPodXjbDb7bJarXI6nWpvb9fY2NhtvczjWTM3N6eGhgZD94FgR7kQ6AAMc9aN4IrN1taWLBbLkWgu5WQyGQ0MDMhut2t2dvaW78/NzampqcmQQDfiZm3519zc3FyW597e3lZ3d7fq6+u1uLh4JLrzd3E/b6Bf9EZt09PT8vl8FX9zt7a2pomJCXV2dh65VnV4ePjIqa9GDYFefLxeL4F+xrS3t2t4eNjw/aiG8Xg8mpmZUTQa1eDg4JFlHjs7O6vyMplyzuzsrLxer+H7cXiOB3v+pnNbW1sEOy6EQAdQcaetbX6R2dnZOfepzxedRCKh5uZm+Xw+bWxsnPiYaDQqr9drSKBPTU0Zcv37/Py8fD5fyZ93fX1d3kxbSgAAIABJREFUXq9XgUDgxBC3WCxKJpNlC/SZmRnD3+jlr1UdHR1Va2vrkVNfjTqSVq5T/2tlvF6vZmdnDd8PM09bW5tGR0cN349qGLfbrfn5+SNfSyaTmpubUygUKlwm09DQoO7ubk1NTRVdA7zWpxr+fToe7NFoVP/n//wfra+vG/02DCZHoAOoqONrm1/2GvKLHFm9yEQiETkcDvX392tnZ+fUxy0uLqqxsdGQQK/kzdoOz8LCQsk/lIhEIrLb7RocHDz1cgWr1apEIlG2QDfjkZhUKqWFhYUjR9Lcbre6uroq9sa8Gt4AGzmNjY0E+hnD0mHnn/r6ei0sLBR9TCKR0OzsrPr6+uT1emWxWNTY2Kje3t6quK9FKWd8fFyBQMDw/bjITExMqK6uTplMxui3YjA5Ah1ARRxe2/wqYX6ZcDvP7OzsqK+vTw6HQ3Nzc2c+Ph6Pq6GhoeKRnMtV7mZtx6eUH0pkMhn19/fL4XBofn6+6GNtNtupZzIcnqsEemNjo+Fv3opNMplUJBJRf39/4Y251+tVX1+fIpFIWa5fJ9CLj8fjUSQSMXw/zDwtLS0aGxszfD+qYRwOh2Kx2IV+ZmNjQ9PT0+rp6Snc18Ln86mvr0+zs7NKJBKGv65yTTgcVmtrq+H7cZEJhUK68847Oc0dZyLQAZTdZW8Ed9bY7fbCXdWvMvlTrJubm88d/MvLy3K5XBWP5FyuvNeCF5tYLCaPx3Pl50kmk2publZTU9O5/r7tdrvW19fPfNxlAz0SiZg+0I/P+vq6pqam1N3dLbfbLavVKr/fr8HBwaJrLV9kCPTi4/F4NDc3Z/h+mHlY2/v8Y7PZtLS0dKXnWFtb0+TkpLq6utTQ0CCr1WrqG1FeZYaHh9Xe3m74flxkgsGgHnjgAZZlw5kIdABldZG1zS86TqdTKysrV3qOmZmZM0+xPmlWVlbkdDorHsm53PPXghtxg7p4PC63232l58jfFb+vr6/oJQSH57wfxFwl0D0ej+Fv3q4yKysrGhsbU3t7uxwOh+x2u1pbWzU6OqqlpaVLXb9OoBefhoYGAv2MYW3v801+VZKVlZWSPu/KykrhRpQul+vIB3nRaLQkH+QZNQMDA+rq6vr/7L17jHRZWf/7DiMgCihBRAhBQrg7ICoxIxAVMKgYQgwawCDDIYQgCM7RTMArhhDEaBDRcBDnhx6PnBHhnNlVu+73+/1+7brfum5dl66qvlTNMO/7PX/M2WV1vV3de1ftvVftftc3+fwxb++q9ezdVT3ru9aznod4HELQ6/V46UtfSg061bWiBp2KikoSbdPbXCgGgwHdbner165WDW+1WoJfPxgMwLKs7CZ5sZDmLDgfut3u1lkD5+fny1ZCl1XFvwqtViupQd+Hdj1iwvVaTqfTcLlcUKvV0Ol0y0rQg8GA1/tQg341lxX1olzEZrOhVCoRj2PfmUwmYBiG93dzG6bTKXq93nIhT6vVQq1Ww+VyIZ1O4/DwkHjnCCHEYjFEIhHicQjh+9//Pl7zmteQnp5RKUDUoFNRUYkuqVLa1zGZTGi324Jfd3R0BJPJBJfLtXWRudFoBJVKJbtJXizIFajr9/vQ6XSCXzedTuF0OmE2m3mlqq/DN1NiW4PeaDRgMBiIT96k4vj4GI1GY1kJmmEYmEymZWGpTedUqUG/GmrQr8dqtaJcLhOPY98Zj8dgGEbWIm/T6RSdTmfZOUKj0UCj0Swzbzqdzl4b9nA4jFgsRjwOIfzHf/wH3vzmN5OeolEpQNSgU1FRiapdepsLxWKxCNr9ns/nKBQKYFkWmUxmp/iOj4/BMIzk93gZpArUHR0dCU7rb7fb0Ov1CAaDW/es1+l0vI8ynJycUIN+DaPRCJVKZVlYijunmkgk0Gg0ls+PGvSr0ev1aDabxOPYZ2grOn4Mh8NlO0lSMUynUxweHi4zb1iWhVarhc/nQy6X2/qojFQEg0Ekk0nicQjhG9/4Bt7+9reTnqZRKUDUoFNRUYmi1d7mcpjzxWIBm812bQXwVePm9/uh1+vR6XR2Hps7Myjk3LpYdLvdnc+Cb8NgMIBGo+F17Xw+RzabBcuyKBaLO30e9Ho9er0e79+zUIPebDah1+uJT95IcXR0hEKhAL/fD51Ot0x79fv9MJvNezUp3yf0ev21bbHudcxmM610z4PBYACGYfbqu7be6pE7KuP3+3FwcCD6eXmh+Hw+ZDIZ4s9JCH/3d3+HX//1Xyc9XaNSgKhBp6Ki2lm3b9+WJaV9HYfDgVqtdu11vV4PBoMBXq8Xs9lMlLFPTk7AMMzWu8K7sG2q+a4Mh0Oo1eprr5vNZvB6vTAajTsX8VsshNUa2Magt1qte9qgr7Ka9mq1WsEwzHIXLZ/PE5+U7xN8+lbf69BCevzo9XpQqVTE47gK7qhMIpGA3W6HSqWCwWBAMBhEsViU9Pz8ZbjdbuRyOeLPRQh//dd/jfe///2kp2xUChA16FRUVFtLit7mQnC5XKhUKht/Pp/Pkc/nwbIs8vm8qPGdnp6CYRjMZuIYfiFsk2ouBuPx+Npz9/1+HwaDAT6fDycnJ6KMazQaeWc9bGvQdTod8cnbvlEqlWC1Wpe7aKuT8lAohFKphOFwSDxOUmi1WhweHhKPY5+h5/T50e12wbIs8TiEMB6PUavVEIvFlot5JpMJ4XAY5XJZ8r8NTqcThUKB+HMQwiOPPIKPfOQjpKduVAoQNehUVFRbaTWlnYQ5XywW8Hg8KBaLl/5sNpvB4/HAaDTyTo8Wwvn5ORiG2brI3C4Mh0MiFeS5c/eX/Wz1fL/YiyFCigFuY9APDw+h1WqJT972jcvOoHOT8mg0CovFAoZhYLFYEIvFUKvVblSf5evQarVot9vE49hn6DEAfrTbbcX/DeJqW0QiEZjN5uXfhmg0imq1urEY5bYosUPApz71KfzBH/wB6ekblQJEDToVFZVg3b59G8PhENFolMgZbA7uLNz6v3OFyfx+v2i7uJcZUq6oj9z3PRqNeKWaiw3XCmjdfJ+eni777Ipxvn8ds9mMw8NDXteenp4KNug3YXIsBXyKxA2HQ5RKJQSDwQt9llOpFFqtlqL7LF+HRqOhBv0aaJYBP27iMRvub0MoFILJZALDMLDZbKIt5imxAOFDDz2ERx55hPQUjkoBogadioqKt1Z7m3c6na17YotFIBBALpe7YJrT6bQohcn4oFarMRqNZL/vq3aypeSywniDwQBmsxkulwvT6VSScYVU699mB73dbkOj0RCfvO0bQqu4r/ZZ9nq9F9o27WMV6F3RaDTodDrE49hn6CIGP+6FThJcMUpuMVelUsFut9/VPYIvRqNRcccnfud3fgdf/OIXSU/lqBQgatCpqKh4ab23+dHREe+K3lIRDoeRyWSwWDy9u8v12j46OpJlfI1Gg8FgIPt9b9rJlprZbHahMF6lUgHLskin05LGYrVa0Ww2eV27jUHvdDrUoF/Crm3WVts2OZ1OqNVq6PV6BAIBFAoF2YtKiQ3LstSg02ckCrVaDWazmXgccjGdTtHv93FwcHChe4TT6eSdfaPEIo3vec978Pd///ekp3NUChA16FRUVNfq9u3bd/U2J5VmvUo0Gl3+z1yr1SIUCslaVV2r1YpSpXxbo3x2dibruFxhvMlkgkgkAq1WK6gP/bYIbacn1KArsUCTHIjdB/34+Bj1eh3xeHxZVMpsNiMSiUhyRlVq1Go1ut0u8Tj2GbVajV6vRzyOfadSqcBqtRKPgxTT6RTdbhe5XG6ZfcOyLNxuNzKZDNrt9l3ZN0rMYPnlX/5lfPOb3yQ9paNSgKhBp6Ki2igupf2y3uZcmjWJ4nAcsVgMNpsNLMteWc1dKvR6Pe/2X2JCqsXb2dnZsvCP3W6X7fy93W5HvV7n/Wy2MehqtZr45G3fENugrzMajVAulxEOh2EymaBSqWCz2bZOeZUbaj6vR6VS0dZ8PJD6u6Y0ptMp2u02MpkM3G43WJaFRqOB1+tdHpdhGEZx379f+IVfwHe+8x3SUzsqBYgadCoqqku1ntK+bsRnMzK7uBzj8Rh6vR5arRbD4ZBIDEajkXd1cSmM8mwmb4u3Wq0GhmEQiURkLQ5ot9t59btfLLYz6L1ejxr0S5DbNBwdHS1TXrVaLdRq9ZU7aKRRqVSKMwhywtWsODo6Ih7LvlMoFOBwOIjHsa9MJhO0Wi2kUqnlcRmGYeDxeJDP59Hr9fbu78NlvP71r8fjjz9OenpHpQBRg05FRXWXuN7m67vmq3DpzlJVSb+Ker0OjUYDm82GcDgs+/gcQqqLiwnX4k2qomyXjZdIJKDRaIhUrnc6nahWq7yu3dagq1Qq4pO3fYPkrt50OkWn00E2m13uoGm1Wvh8PhwcHOzFrizdHb4arlaG1P2wbwL5fB4ul4t4HEphOByCYRjEYjE4HA6oVKoL9S32cVFoOp3i5S9/OSwWC+kpHpUCRA06FRXVUkJ6m3NtxuTsA352doZoNAqNRoN6vY5sNotQKCS7QeYQUrxMTORs8TaZTOBwOGC1WjEajaBSqTAej2W9X6fTyfsIwzZt1vr9/jIjgfI/7FPa7WQyQbPZRDKZhN1uh0qlgtFoRCgUQrlcxmg0kj0mhmGoQb8C7hgUid+N0shms/B4PMTjUAqDwWA5/5jNLta3sNlsF/4+lEqlvVgkmk6neOELX4hAIEB6qkelAFGDTkVFBeDpQnCLxWJpzvmYITnN2nA4hNVqhd1uX46Zz+fh9/tlN8gcNpuN99losZHj2R8eHkKr1SIcDi+PMpBoLedyuVAul3ldu41BPzo6ogb9EvbJoK8zHo9RrVYRjUZhNpvBMAysVitisRjq9frOPZavg6ZvX89oNALDMIor/keCdDoNn89HPA6l0O/3r8x6Wv37YLFYLhSkrFQqRBaNptMpnvOc5yCTyZCe7lEpQNSgU1Hd47pz5w6vlPbLYFlWljZjXDuvZDJ5YfGgUCjA6/USMciLxQIOh4N36rXYsCwr2dn71X7y68ZYynE34Xa7USqVeF27jUHndmOUcIZRTvbZoF/2OywWiwgGg9Dr9RdaNh0eHor+u+XSt5XeKk5KuDRkqRdLbgLJZBKBQIB4HEpBaGvM4XB4oSDl6oJerVaTZRFpPB7j1q1bqNVqpKd9VAoQNehUVPewrisEdx06nU7SNmOnp6cIhUIb23mVy2W43W4iBnmxELazKzZS9WCfzWZwu90wmUyX9pMn0fvd4/GgWCzy/sxsa9D3vWq43CjJoK8ynU7R6/UutGxarQAtRkEper76eujCF3/i8ThCoRDxOJRCq9WCTqfb+vWrC3oGg2HZQSIej0uWgdNsNnHr1i0Mh0PSUz8qBYgadCqqe1RcSrvQXfNVDAYDOp2OJKZsMBjAbDbD6XRuPOderVbhdDplN8erxpHvzq7YSLE40u12odfr4ff7N7ZwI9H73ev18jboZ2dngo02t9NHDfpFlGrQ15lOpzg8PLxQAZorKFUsFrfaBefOV1ODvhla24E/0WgUkUiEeBxKodFowGg0ivZ+/X5/2UFCp9NBrVbD4XAgmUyi2WyK8v+GfD6PW7duYbFYkJ7+USlA1KBTUd1j4nqbb5PSvo4UVczn8zmKxSJYlkU6nb4yvnq9DrvdLrs55vD5fCgUCkTGFrMH+3w+Rz6fh1qtxsHBwZXPXK/Xo9fryf6cDw4OeF27i0GnqbgXuSkGfR2uoFQsFoPVal2eT41Go6hWq7zSXWkBtOuh3RH4Ew6HEYvFiMehFKrVKsxmsyTvzWXg5PN5+Hw+aLVasCwLl8uFdDq99ZGZaDSKZz/72bh9+zbpaSCVAkQNOhXVPaRdU9rXEbuK+cnJCfx+P/R6Pa/+4s1mE1arVXZzzBEIBJDP54mMbTQaRcle4J65wWDgZbwNBoNoCwN88fv9khp0Wszqcm6qQb/s98+dTzUajVCpVLDb7UgkEht3z8bjMTXo19DtdqFWq4nHoQSCwSASiQTxOJRCuVyW7W/TastHj8ezPDLj8XiQzWbR6XR4GXan04kXvOAFuHPnDumpIJUCRA06FdU9om0LwV2Fw+FArVYT5b16vR6MRuOy1Qyf1xweHsJsNstqFlcJBoPIZrNExjaZTLwWMa7i6OhI8DMXa2FACEIWQk5PTwXvhHNmixr0i9wrBn0dLt11dffM7XYjk8ksJ+N0Ued6hBbyupfx+/1IpVLE41AKhUIBTqeTyNjckZl0Og2XywW1Wg2tVgufz4d8Pr+xxoVWq8XLXvYyatCpeIkadCqqGy4hvc2F4nK5ePen3sRqenU+nxcUX6fTgdFolNUsrhIOh5FOp4mMbbFYLi2cx/eZl0olsCyLbDYr6JmbTCbRjzVcRzAYRC6X43XtLgad7oZe5F416Ktwu2eZTAZutxtqtRo6nQ5erxcMw6Db7RKPcV/h2jSSjkMJ+Hw+ZDIZ4nEohVwuB7fbTTyO2ezpgpHNZhPJZBIOh2NZ48Lv9+Ob3/zmsrbA9773Pbzuda+Tbe73N3/zN7h16xb+6I/+SLYxqcQTNehUVDdY673NxTTni4Ww4l2XMZvN4PV6eadXr9Pr9aDX62U1i6tEo1GkUikiY297vICrjK/T6bbagd9lYWBbQqEQ70yFbQw6PU98OdSg381kMkGj0UA0GgXDMGAYBkajEeFwGOVymX6GVti10va9hMfjQS6XIx6HUshkMnvbN361xsWb3/xm3H///XjJS16Cd77znXjDG96ATqcj+dwvFArhFa94Bd70pjdRg65QUYNORXUDtdrbXApjziHkbPA6nU5nucp8cnKy1XsMBgNoNBpZzeIq8XgciUSCyNh2ux31el3Qa4bDISwWy5WV8a9D7LoDfAiHw8hkMryu3cWg04rcF6EGfTNcYcHhcIhqtYpIJAKz2bzsryxluyal0Gw2odfricehBFwuFw4ODojHoRSU1De+0Wjg3/7t3/Ce97wHz3/+8/GMZzwDr3/96/HpT38a3//+90Vvu3ZycoJXv/rVMJvN+JVf+RVq0BUqatCpqG6YxC4EdxXbnMGez+fIZDJgWRbFYnGn+IbDIdRqtaxmcZVkMolYLEZkbIfDgWq1yvv6Wq0GlmWRTCZxfn6+9bg2mw2NRkPWe41EIryPEmxj0Lme1tu027rJUIO+Ga7H93oBOa6/ciAQgF6vh1qthtPp3Kn6s1IRuxXWTcbhcKBQKBCPQynEYjGEw2HicQjhb//2b/Gbv/mbGI/HePzxx/HZz34WDzzwAJ7xjGfgzW9+Mx555BFRzqd/5CMfwcMPPwwA1KArWNSgU1HdIInR21wq47RYLDCZTOByuWAymXB0dLTz+NzOpxz3ehmpVAqRSITI2HzP/5+fnyMWi0Gj0Yiy873Nzv2uCDlKsI1Bn06n1KBfAjXomzk6OgLDMFca7ul0im63i1wud6H6s9frvbKY1E2hVqvBZDIRj0MJ2Gw2lMtl4nEohUgkori2dF/4whfwu7/7u3fN23q9Hv7rv/4LX/rSl3aeAz722GN44IEHMJ/PAVCDrmRRg05FdQMkZm9zIcRiMd4p3tx5xFAohNPTU1HG54zVLjvCu5DJZBAKhYiM7Xa7USqVrrxmPB7DbrfDZrNhPB6LMq7QnXsxiMViSCaTvK7dxaAfHR0Rn8TtE9Sgb4Yz6EJeM5lM0Gq1kEql4HA4oFKpoNfrEQwGUSwWb9wRCyl7Vd80LBYLKpUK8TiUghLb0v3Jn/wJPvrRj0o2D2w2m/jJn/xJJJPJ5b9Rg65cUYNORaVwyZnSvg6fFO/z83Mkk0mwLLtzxfd1Tk5OwDCMaIZfKLlcDoFAgMjY1xXoazab0Gq1iEajODs7E21cMSr3C0XIWf+zs7Otzv0yDIN+v098ErdPUIO+mX6/L9igr3N8fIxarYZYLAaLxQKGYWCxWBCNRlGtVhXfwq1SqcBqtRKPQwmYTCbUajXicSgFv9+PdDpNPA4hfPKTn8SnP/1pyeaCjz/+OG7duoX7779/ya1bt3Dffffh/vvvx1NPPSXZ2FTiixp0KioFS4re5kJIp9NXpnhzO7gWiwXD4VD08c/OzpaTZLnvfbFY4ODgAH6/n8jYmwr0rS6ISLHT7Xa7US6XZb3XeDyOeDzO+zOxjUFXqVTUoK9BDfpmer0eVCqVqO85HA5RLpcRCoVgMBigUqlgt9uRTCbRarXuOu++75TLZfr54YnBYECj0SAeh1JQYtX73//938fnPvc5yeaDs9kM6XT6Am95y1vw4Q9/GOl0WrJxqaQRNehUVArUam9zUuZ8sXh6BzkYDF76s0ajAY1GI/oO7irz+XxZqInE/ReLRXi9XiJjBwIB5PP5C/82mUzgdDphNpslWRBZLBbweDzXptaLTSKR4F2MbxeD3uv1iE/i9glqsDbT6/WgVqslHaPf7yOfz8Pn80Gr1YJlWbjdbmSzWXQ6nb0/v14sFmG324nHoQR0Oh1arRbxOJSC0+lUXNX797///fjiF78o61yRprgrV9SgU1EpTLdv38YTTzwBj8eDZrNJzJwvFpfvIJ+dnS2LkslRTEylUol2vloo5XIZbrebyNjrFfTb7TZ0Oh2CwaCkKf9erxeFQkHWe00mk4hGo7yuPT8/38qgq9VqdLtd4pO4fYIa9M10u13JDfoq0+kU7XYbmUwGLpcLarUaOp0Ofr8fhUJhL+snFAoFOBwO4nEoAY1Gg3a7TTwOpWC321EqlYjHIYTf+I3fwFe/+lVZ54vUoCtX1KBTUSlE673NHQ4HarUaEXPIUSwW4fF4lv89Go1gtVpht9tlM80sy2IwGBC5/0qlAqfTSWRsrjf4fD5HNpsVpW0dHzal1kuJkGr52xp0lmWpQV+DGvTNdLtdsCxLbPzJZIJGo4FEIgGbzQaVSgWTyYRwOIxKpYLRaET8GR0cHMDlchGPQwnQBUJhKLGo3tvf/nZ861vfIj2VpFKIqEGnolKAVlPauUJwJM4CX2ZQXS4XFosFqtUqWJZFIpGQtaq6TqdDv98ncv+1Wg12u53I2JFIBPF4HF6vF0ajUbZncFlqvdRcV+tglfPz863O6rIsi06nQ3wSt09Qg76ZTqdD1KCvMx6PUalUEIlEYDKZwDAMbDYb4vE4Go0GkfPruVwObreb+LNRArRIpTBMJhPq9TrxOITw8z//83jsscdITyepFCJq0Kmo9lyrvc1Xja/P55M91fgyg2qz2RAOh6HVakXpsy0Ug8GAbrdL5P4bjQZsNhuRsf1+P1iWhc/nw8nJiWzjBoNB5HI5We9VSDu7bQ26RqOhBn0NatA30263odFoiMexicFggEKhgEAgAJ1OB7VaDZfLhXQ6jXa7Lcv59Ww2C4/HQ/xZ7DuTyQQMw2AwGBCPRSno9XrFndl/7WtfC5VKRXpKSaUQUYNORbWnuq63eSAQkN0orVMoFKBWq+F0OokVajOZTGi320TGbrVasFgsso45n89RKBSgUqngcDhkr0EQCoUunH2Xg2w2K7lB12q19AzoGtSgb6bdbkOr1RKPgw/T6RTdbndpmFmWhVarhc/nQz6fl2znNpPJwOv1Er//fWc8HoNhmL04lqAUlHZmfzqd4mUvexlsNhvpqSWVQkQNOhXVHopPb/NwOIx0Ok3EmM7ncxSLRajVamg0GllT2texWCxotVpExm632zCZTLKNd3p6ikAgAL1ej2AwyLuyuZiQ+Nxd1S1gnV0M+uHhIfGJ3D5BDfpmDg8PFWPQ15lMJmi1Wkgmk3A4HFCpVDAYDAgGgyiVShgOh6KMk0ql4Pf7id/vvjMajcAwzFa1M+5V1Gq1orpuTKdTvOAFL0AwGCQ9vaRSiKhBp6LaM92+fZtXb/NYLIZkMim7QTs5OVmaxIODAxgMBtljWMVms6HRaBAZu9PpyHb/g8EAZrMZbrcb0+lU0LlsMYlGo0ilUrKOmc/nEQgEeF27rUHX6XTUoK9BDfpmDg8PodPpiMchBsfHx6jVaohGo7BYLGAYBhaLBdFoFLVabWvjmEwmEQgEiN/fvjMYDMAwzN63zdsXptOp4o4ETKdTPPvZz0YulyM9xaRSiKhBp6LaE3Ep7Xx7myeTSdl3UPv9PoxG49Ik9vt96HQ62U3iKiSr2fd6Pej1esnHqVQqYFkW6XR6+bnIZDIIh8Oy3zOJhaHL2vltYheDrrQzjVJDDfpmWq3WjTHo6wyHQ5RKJQSDQRgMhuVxmmQyiVarxfv7lUgkEAwGid/PvtPr9aBSqYjHoRSUeCRgOBzi1q1baDQapKeaVAoRNehUVHsgPint68hp0ObzOQ4ODqBWq5HL5ZbxDYdDsCwru0lcxeVyEatmf3R0BK1WK9n7n52dIRKJQKvV3pXGn81mead9i0k8HkcikZB1zEKhAJ/Px+vabQ26Xq9Hs9kkPpHbJ6hB30yz2YReryceh9RMp1P0ej3k83n4fD5oNBqwLAuPx4NsNotut7tx5zcejyMcDhO/h32HdMs+pTEcDsEwDJHOBNtSr9dx69YtjMdj0tNNKoWIGnQqKsLiepvz2TVfJZfL8U773YXZbAav13tptfTxeAyVSiW7SVzF4/GgWCwSGVvKBYrVnvLHx8d3/VxI2reYJBIJ2TM3isUivF4vr2u3NegGg4Ea9DWoQd9Ms9mEwWAgHofcTKdTtNttpNNpuFwuqNVq6HQ6BAIBFAqFC2nHsVgMkUiEeMz7jpIKDu4D/X4fKpVKUUcCcrkcbt26hSeffJL0lJNKIaIGnYqKkC7rbS7EtAjZVdwW7oy13++/tJUX1x6GZJE4v9+Pg4MDImNLtUBRr9eh0WgQj8c3PtuDgwPJf/+XkUwmEY1GZR2zVCrB4/HwunYXg95oNIhP5PYJatA302g07kmDvs7x8TEajQbi8ThsNhsYhoHJZEIkEoHb7aY76DxotVr3RDaGWHQ6HcVlHITDYfzwD/8wbt++TXrqSaUQUYNORUViwiWlAAAgAElEQVRAXG9zzpxvY1rK5TLcbrckhmg+nyObzYJlWRQKhY2LBycnJ2AYBqenp7IbRQ6S7eaOj4/BMIxorc7Oz88Rj8eh0WhQr9evvFbIrrKYkChOJ+Szvq1BNxqNqNfrxCdy+wQ16Jup1+swGo3E49g3RqMRKpUKwuEwNBoNGIaBzWZDIpFAo9FQVFqyXNDFHmEosUCj3W7HC1/4Qty5c4f09JNKIaIGnYpKRt25c2frlPZ1arUaHA6H6GZoMpnA5XLBZDLh6OjoymvPzs7AMAxms5nsRpEjHA4jk8kQGZurJitGBsFkMoHD4YDVasVoNLr2eikXaK6CRHG6SqUCl8vF69ptDbrJZKIGfQ1q0DdTr9dhMpmIx7HPhEIhRCIRFAoF+P1+6HQ6qNVquFwuZDIZtNttRaUpS0WtVoPZbCYeh1JQ4oKGRqPBy1/+cmrQqXiLGnQqKpm0TSG4q2g2m7DZbKIaIW5lOhgM8toVn8/ny/6tchtFDhJtvzjEyiDgeipHIhGcnZ3xeo0Q0yom2WwWoVBI1jGr1SqcTieva3cx6LVajfhEbp+gBn0ztVqNGvRrCAQCSCaTy/+eTqfodDrIZrNwu91gWRZarRY+nw8HBwfo9/vEYyZBpVKBxWIhHodSUOKCxne/+1284Q1vID0NpVKQqEGnopJBXEr7rrvm66bObDaL8l7n5+dIJpNgWRblcllQjGq1GsPhUHajyEGiqjjHrhkE8/kc6XR6+dyFvFaqDIrrkKs44SrVapX3vW5r0M1mM6rVKvGJ3D5BDfpmlGgS5Mbv9yOVSm38+WQyQavVQjKZhN1uh0qlgsFgQCgUQrlcxnA4JH4PclAqlej3TABK/Lv07W9/G295y1tIT0WpFCRq0KmoJBTX21yMlPZ1ut0uDAbDzu9zfHwMh8MBi8WyldHWarXXpsJLCYl+8BxcBsFkMhH82ul0CrfbDZPJhMFgIPj19Xoddrtd9nsW0pNcLITc67YG3WKxUIN+AybCclGtVumu5zX4fD5kMhne14/HY1SrVUSjUVgsFjAMA4vFglgshlqthuPjY+L3JAWFQgEOh4N4HEpBic/r61//On71V3+V9JSUSkGiBp2KSiKJndK+jhg9uBuNhuDU6nX0ej16vZ7sRpGDRNGyVVQqFcbjsaDXdLtd6PV6BAKBrdPjm80mrFar7PcrR/eAder1Ou/jHJ1OB9FoFNVqFePxmPcEymKxoFKpEJ/I7RPUoG+GpiVfj9frRTab3fr1w+EQxWIRwWAQer0eKpUKDocDqVQKrVbrxhScy+fzcLlcxONQCvl8Hm63m3gcQvjKV76C3/qt3yI9LaVSkKhBp6KSQGIVgruK4XAItVq91WvPz88Ri8Wg0WhQq9V2isNkMqHdbstuFDmy2SyCwSCx8YWk+M/nc+TzebAsi4ODg50+G61WCxaLRfb7JVE9vtFoXGvQ5/M5CoUC1Go1fD4fjEbjhQn94eHhlQWprFYrNehrUIO+mUqlAqvVSjyOfcbtdiOXy4nyXtPpFL1eD/l8Hl6vFxqNBhqNBh6PB7lcDt1uV7EF57LZLDweD/E4lEImk4HX6yUehxD+8i//Eh/4wAdIT02pFCRq0KmoRNSuvc2FsG2Lr9FoBJvNBpvNJnjn9zIsFgtarZbsRpEjn8/LnnK9ikaj4ZWifnJyAp/PB4PBIErGQbvdhslkkv1+S6WS7NXjr8sWODs7QygUgl6vR7vdXu6s9fv9uyb0XEGqo6OjCxMoq9WKcrlMfCK3T1CDfvWzoQb9alwuF/L5vCTvPZ1OcXh4iHQ6DafTCbVavcxKKhaLGAwGxO+fL+l0Gj6fj3gcSiGZTCIQCBCPQwh//Md/jI997GOkp6hUChI16FRUImm9t7mU5nyxWGA2m4FhGEGp6dVqFSzLIh6Pi9IabLFYwGazXduzW0pI9QPn0Ol06Pf7V15zdHQEo9G43CURY9xOpwOj0Sj7/ZbLZdmrx1+VLTAej2G1WuFwODCZTDaeQb+sIJXJZEIkEkG1WoXVakWpVCI+kdsnqEGnz2YXnE4nDg4OZBnr+PgY9Xod8XgcVqsVDMPAbDYvv99CjrvIjRINJ0ni8TjC4TDxOITwiU98Ap/97GdJT1OpFCRq0KmodtRqb3M5jDmHkAriZ2dniEQi0Gq1aDabosbhdDpRrVZlN4qrhpFEP3AOg8GAbrd76c/m8zlKpRJYlkU2mxX1s9Hr9aDX62W/XyEtz8RiU8eCVqsFrVaLWCy2XHDiWyRuPB6jUqkgHA7DaDSCYRiYTCZe6fD3CtSEbqZUKsFutxOPY5+x2+0oFotExh6NRiiXy8vvt0qlgt1uRyKRQKPR2Kvz6/F4HKFQiHgcSiESiSAajRKPQwgf/vCH8ad/+qekp6tUChI16FRUO0jqQnBXwbcH+WAwgMVigdPplKRfudvtFtwiTOmGcZVNZ/BPT08RCoWg0+kkOaPf7/eh0+lkv18S7d3W0/nn8zmy2SxYlr1rcWjbKu5WqxXBYJBXOvy9AjXomykWi9SgX4PNZtubrJSjo6NlBwqtVguWZeF2u5HJZNDpdIguyEWjUUQiEeLPSCmEQiEkEgnicQjht3/7t/GlL32J9JSVSkGiBp2KaktJ0dtcKFcVKFvdvU2n06KltK/j8/lQKBSI3P9iQa7dGIfZbL7rDP5wOFwuimzTgo0Pg8EAGo3mnnjeq+n8Jycn8Hq9MBqNl7b329agOxwOFAoFzGbXp8Pvc7qsmFCDvhkltnqSm32t6zCdTtHpdJDJZOB2u8GyLLRa7XJBrt/vyxpPJBJBLBYj/lyUgt/vRyqVIh6HEN797nfjH//xH0lPW6kUJGrQqagESsre5kLZ1IP89PQUgUAAOp0Oh4eHksYQCASQz+eJPQNS7cY4rFbrhWMD3Dn/ZDIp2aLIYvH0IgDLskSeN9+WZ2LR7XZhMBgwGAxgMpng9XpxcnJy6bXn5+db7YY5nc6lQV9nPR1eSHV4JUMN+maoQb8ei8WCarVKPI7rmEwmaDabSCQSywU5o9GIcDiMcrmM0Wgk6fjBYFBxO8Ik8Xg8O7XvI8Hb3vY2PProo6Snr1QKEjXoVFQCdOfOHZydnSEcDuP09JSoOV8sLj//3O/3YTKZ4Ha7MZ1OJY8hFAohk8kQewabzifLhd1uR71ev9C6Tuxz/pcxHo+hUqlkv18S7d16vR40Gs0yG+Sq7x23gy7UNAspaHVZu6ebmA5PDfpmDg4O4HQ6icexz5jNZtRqNeJxCGU8HqNarSIajcJsNoNhGFitVsRiMdTrdRwfH4s6XiAQUNyOMElcLpdsxQfF4ud+7ufw3e9+l/QUlkpBogadioqnuEJw0+kUDMNs3MGTE7PZvNwhn8/nODg4AMuyyOVysi0eRKNRpFIpYs+g0+nAYDAQG58zdmK2ruPDtm32dkXuBZHz83MEg0EwDMNr4WNbg75tS6ibnA5PDfpmDg4O4HK5iMexzxiNRtTrdeJx7MpgMECxWEQwGIRer4darYbT6RQtg8bn8yGTyRC/T6VAsvjgtrz61a8Gy7Kkp7FUChI16FRU14hLaT8/P8fZ2RnOz8/BMIxkZ4uFwKVXc/+Tv6qiuFTE43HE43Fiz4BUNfPV3wHLshcqicsBt1Ak55iLhbz916fTKZxOJ0wmE+/z9tsadLfbLUrP5puUDk8N+mby+Tw16NdgMBjQaDSIxyEm0+kUvV4PuVzuQgaN1+tFLpdDr9cT/B33eDzI5XLE700pWK1WVCoV4nEI+cy89KUvhcPhID2dpVKQqEGnorpCt2/fvrRKu1qtxmg0ImYKORwOB9LpNAwGA3w+H2YzcXpsCyGVSiEajRJ7BqSKpZ2fnyOZTEKlUiEcDss+/mw2A8MwODs7k3VcuTIWuIWXYDC4THHn+3vZ1qBLMUleTYdfL0a17+nw1KBvJpfLwe12E49jn9Hr9Wi1WsTjkJLpdIrDw0OkUik4nU6oVKrl361isYjhcHjte2ybvXOvYjKZFJWZMZ1O8eM//uMIh8Okp7RUChI16FRUl+i63uabirPJyXw+X+7QHRwcEDsPn8lkEAqFiD2H4XAItVot65iTyQROpxMWi2V5Hk7u+z49PSVy1KLb7UqasTCfz1EsFsGy7PJzLaQg3rYGXY5dLKWlw1ODvplcLgePx0M8jn1Gq9Xi8PCQeBxycnx8jHq9jlgsBqvVCoZhYDabEY1GN37HVztIUK5Hr9cvMweVwHQ6xTOf+Uzk83nSU1sqBYkadCqqNd25cwdPPvnklb3NSaSSrzKdTpftYRKJBLE4FosF8vk8AoEAsfHlPovdbreh0+kQCoWW1fJJVLE/OzsDwzCYzeTNmpCy//rZ2dmyd3yn01n+u5BFmPl8vrVBl7sy8Gg0WqbDGwyGvUuHpwZ9M9lslhr0a9BoNGi328TjIMloNEK5XEYoFFouqNvtdiQSCTSbTUwmE9hstr1sR7evaLVaRX2uBoMBbt26hcPDQ9LTWyoFiRp0KqoVrfY2v+ps72W9r+Xi8PAQOp0OwWAQPp+PaIuzxWKBQqEAn89HbHy5zmLP53Nks1mwLItSqbRcEAiFQshms7Lf93w+J1IL4ejoCFqtVvT3HY/HsNlscDgcd93TaDTiXbGeK+Qo1Nx6vV7ihZr2LR2eGvTNZLNZeL1e4nHsMyzLotvtEo9jn+j3+zg4OIDP54NWqwXLsmBZFsFgEJ1Oh/iinBJQq9WK+lzVajXcunULk8mE9BSXSkGiBp2KCsJ7m9tsNtTrdVmN0fn5OVKpFFiWRblcxnw+RzgcJtribLFYoFwuw+12Exv/5OQEDMPg9PRUsjFms6cNnNFoRL/fv/CzSCSCdDpN5N4ZhpGtajyHFGf+Dw8PodVqNxbaG4/HYBiG13tta9D3rZIylw6/2ptZ7nR4atA3k8lkqEG/BrVajV6vRzyOfWU6naLdbkOr1cJqtUKtVkOn08Hv9yuiRgWpZ8YwjKKeTSaTwa1bt/CDH/yA9FSXSkGiBp3qntedO3cuLQR3FU6nE5VKRTZTdHx8DIfDAYvFgsFgsPz3WCyGZDJJxBxyVKtVOJ1OYuNLnerd6/WWRfguO+9N8ndAolihkPPg17GalXDV90nIMYZdDHo6nSY+mdsEiXT4crkMq9VK/N73kXQ6vSzMSbkchmHQ7/eJx7HvcNXuJ5MJGo0GEokEbDbbclEuHA6jXC5jNBoRj5U03P8LlPQsgsEgfuRHfgS3b98mPd2lUpCoQae6p3X79m3eu+areL1eFItFWQxRs9mEVqtFJBK5q2J3MplELBYjYg456vU6bDYbsfGlSvWez+coFApgWRb5fH7j54NkmzmWZS8s2MjBeDzmnW5+FScnJ/D5fDAajdcWXJxMJpIbdL/fj1QqRXwyxxeu1ZPH45EsHZ4a9M2k02n4/X7icewrStzpJIVOp7u02v14PEa1WkUkEoHZbAbDMLBarYjH46jX6zg+PiYeu9wMh0MwDKOoe7darXjRi16EO3fukJ7yUilI1KBT3ZNa720utMCYHIXBzs/PEY/HwbIsqtXqpdek02lEIhEi5pCj1WrBYrEQjUGlUom6k8wVf9Pr9dcWAyS5SEKimwC3g7HLewwGA5hMpmWRreuuF1JnYFuDHggEFGXQV1lNh1/deePS4bedzFKDvplUKkUN+hVwi2p82ozd6/AtpjcYDFAoFJb/b1Kr1XA6nUin03tRVFIOjo6OwDCMou6VZVm84hWvoAadSpCoQae657RNSvs6Up/95gpm2Wy2K41nLpdDMBgkYg452u02jEYj0RjE3EnmzKPb7cZ0Or32epKLJHq9/q4z8VIjZDf7Mmq1GliWRTqd5v0esxn/nu+7GPRkMkl8MicGq5Wjd0mHpwZ9M8lkEoFAgHgc+wpXN0JJqcikWC16NugNYPy/bPj6Z/4V//RHj8L23y6MhnfXm5hOp+h2uxeyaDQaDbxeL/L5PHq9nqJMLF+63S5YliUehxAee+wx/MzP/AzpqS+VwkQNOtU9Ja63+Ta75qtIee64VqtBo9EgHo9fu2NIuoL6YvH0GW0p+2LzQafTiWJUK5WKYPOYzWaJ9YEn0e5v26r55+fnSCQS0Gg0aDabgl4rpBDgtgY9GAwikUgQn8xJwbbp8NSgb4Ya9KsZjUbLIpakY9l3VCoV+v0+jroDfOWhr+Oh134GD73mD/GR1/whPvq6z+Lrn/lXjEdXP0cuiyaVSsHhcEClUsFgMCAYDKJYLN6YTAauoCjpOITw6KOP4hd/8RdJT3+pFCZq0KnuCfHpbS4EKdKaz87OEIlEoNVq0Wg0eL2GdAX1xUK6tltC2NWorj57oe3zSPaBNxqNaLfbso45m80EV82fTqdwuVywWCxbHUU4PT0FwzCXFulbhxr0q5lMJmg2m7zS4alB30wikUAwGCQex76ixLPCJOAykgaDATT/y4SHXvOH+N9/5S/wud/4Ij73G1/EZ9/+Z/jfXv9ZOP9fYQUJj4+PUavVEIvFYLFYwDAMLBYLotEoarWaYhdOms0mDAYD8TiE8LWvfQ3veMc7SE+DqRQmatCpbry43uacORfDpGQyGVF3TYfDISwWCxwOB46Pj3m/rlqtwuFwyGrQ1hmNRlCr1URjMJlMWxvV0WgEq9Uq+NlzcD1tSdy32WzG4eGhrGMKMcuLxf9kWAQCga1b4Qmp1L+tQQ+FQojH48Qnc3JzVTp8Op2GxWIhHuM+kkgkEAqFiMexrwwGA8WdFSbB6lGAv/3o1/HR131mac45fv/Vn8a/fO7/3Gmc4XCIUql04Xtut9uRTCbRarUwmUyIPws+1Go1mEwm4nEI4ctf/jLe+973kp4KUylM1KBT3VjduXNHtJT2dfL5PPx+/87vM5/PUS6XwbIsUqmU4AWERqNBtIL6YiGsBZZUWCwWwTvfi8XTFej5HifYRLFYhNfrJXbfQtPFd4WvWZ7P5ygWi2BZFgcHBzt9Ps7Pz5eT/euu3dagh8Phe9KgrzKdTi+kw6vVaqhUKtGrw98E4vE4NehX0O/3l38nKJvhjgIcHx/jKw9dbtA/IoJBv+z3k8/n4fP5oNVqwbIsPB4PstksOp3O3i6sVCoVxWX1/Pmf/zk++MEPkp4SUylM1KBT3UiJUQjuOlPm8Xh2eo/T01MEg0HodLqtd0EPDw9hNptlNWjrzGb8C3hJhc1m430sgDN88XgcGo0G9Xp9p7FJHjMQet9iwMcsn52dIRwOQ6fTodPp7DymkFZ62xr0SCSCWCxGfDK3T5RKJRiNxmU6PMMwMJvNiEajO1WHvwnEYjGEw2HicewrvV4PKpWKeBz7zmqmAfuvxqdT3H/1f1Lc/+jtf4aPvu6zcPw/HslimE6naLfbyGQycLlcUKvV0Ol08Pv9KBQKe7UwVywW4XA4iMchhIcffhgf//jHSU+LqRQmatCpbpy4lHaxd81XqVQqcDqdW7++3+8LqhS+iW63C4PBIKtBW0doyrMUOBwO1Go1XtceHx/D4XDAarWK0pqtWq3u9FnYBbvdzvu+xeI6s3x8fAybzQa73S5qb3pul4lPfNykU8gkKhKJIBqNEp/M7RPrZ9CvSodvt9t7u+smBbFYDJFIhHgc+4oSq22ToN/vLxcy+p0jfPn3v4aHXvuHeOi1n/n/i8R9Bl/79L9cWsldKiaTCRqNBuLx+IU6FeFwGJVKhWhl/nw+D5fLRfz3JoSPf/zjePjhh0lPjakUJmrQqW6MuN7mUqS0r1Ov17dKLZ/P5zg4OADLsshmszvH2O/3iRdo43ZUxTRjQnG5XCiXy9de12q1oNVqEYlERNvxr9frsNvtRO7b6XSiWq3KPq5KpcJ4PL7r37kKu9FoVLR6D9eNuc62Bj0ajVKDvsZVReLW0+GFVIe/CUSjUWrQr6DT6UCj0RCPY99ZX8g46g6g+zcLvvrJ/wNf+9S/wPx/O2Q155cxGo1QqVQQiURgMpmgUqlgs9kQj8fRaDRkPb+ezWbh9XqJ/96E8Hu/93v4i7/4C9JTZCqFiRp0qhshqVPaLzN6QlPLZ7MZfD6fqK2xhsMhWJaV3aBta56kwuv1olgsXmna0uk0WJZFpVIRdexmswmr1Urkvt1uN6+FCbFRq9UXsg/m8zlyuRxYlpUsnvUxr/pdz2bbGXRquC4ipIr7enX41XT4Wq1249Lh6YLO1SixHRYJ2u224p7TYDBAoVBAIBCATqeDWq2Gy+VCOp2WPJMmlUrB7/cTfwZCeN/73ocvf/nLpKfJVAoTNehUipdUheCuQmhqOXc9t/IrVhz7UKBtsViAZVkMBgNi4/v9fhwcHFz6s+l0CrfbDZPJJEmMrVYLFouFyH17PB6USiUiv+/hcIjF4ukjDn6/HwaDQZRe9HzGvIptDTo9U3w3u7RZuywd3ul03ph0eHok4mparRZ0Oh3xOPYdpT+n6XSKbreLbDZ7VyZNPp9Hv98XdTwlFmf8tV/7NfzTP/0T6akylcJEDTqVYiV2b3Mh8O39vbqzuGsl68uYzcgXaFssFtDpdJKas+sIBoPI5XJ3/Xu32925xdd1tNttmEwmIvft9XpRKBRkH1ej0WAwGGA4HMJsNsPtdmM2E2/h6TL4LgJta9Dj8Tg16GuI1Qd9Uzo8V4RqMBgQv1ehhMNhWlTwCpTYr5oEjUbjRj2nyWSCVquFZDIJh8MBlUoFg8GAUCiEUqmE4XC40/srMXPlrW99K7797W+TnjJTKUzUoFMpUuu9zeXeQebT+5vbuTUajZKZVyH9oaVEzLT9bQiHw8hkMhdMWj6fB8uyKBQKkn4+SBbquypzQEq0Wu1y4SmVSsny/dNqtTg6Orr2ul0MutJ2ZqRGLIO+zk1Ih6dt+a6mXq/DaDQSj2PfUWJfbyEcHx+jVqshGo3CYrGAYRhYLBbEYrGtvuuhUEhx37uf/dmfxX//93+TnjZTKUzUoFMpSqu9zUkYc47JZHJlanm73V7u3EpZ3VxI+ykpMZvNW7eKE4NoNIpUKoXFYoGTk5PlWf9eryf52P1+Hzqdjsh9BwKBSzMHpOT8/Bwsy4JlWVlbvGm1Wl4LXdsa9EQigWAwSHwyt09IZdDX2ZQOL8eZ1m1RolGQk5tuPMWiUqnAYrEQj0MuhsMhSqUSgsHghU4QyWQSrVbr2oJzfr8fqVSK+H3wZTqd4lWvehW0Wi3p6TOVwkQNOpViJHchuKs4OTkBwzB3pU2fn58vi5GVSiVZYuRbPEtKrFYrms0msfHj8TgSiQT6/T6MRqPoZ/2vYjAYQKPRELnvUCiEbDYr23iz2QwulwsqlUr26vF8j1FQgy4echn09QmtEtLhg8EgEokE8Tj2lWq1CrPZTDyOfadUKsFmsxGPgwTcdz2fz8Pr9UKj0YBlWXg8HmSzWXS73bv+jnu9XmSzWeKxC7nHn/qpn4LL5SI9haZSmKhBp1KE5OhtLoTLWotNJhM4nU6YzWZZC6bxTf2VEhL9uFdJJBJwuVyita8TAslK+pFIBOl0Wpaxer0eDAYDAoEADAYDOp2OrPeq1+t5HaPY1qAnk0kEAgHiE7p9goRBX2df0+GDwSCSySTx39G+UqlUiH92lEChUIDD4SAexz4wnU7RbreRTqfhdDqhVquh0+kQCASWi3Mulwv5fJ54rELu6fnPfz5isRjpaTSVwkQNOtXe6/bt27JXaeeDSqVa7lw3m01otVqEw2HJipFtgvT578Xi6T7kYrcv48vp6SksFgvUajXa7bbs44/HY6hUKiL3vpraLyWlUgksyyKfz2M+n8NkMsn+rPl+zjmDLrQ3rxLb90jNPhj0dfYlHT4QCFCDfgX38s6wEPL5PFwuF/E49pHj42PU63XE4/Hl4hzX0q1SqWA8Jtsfng+TyQT3338/CoUC6ak0lcJEDTrV3uv27dvEU9ovQ6PRoNfrIZFIgGVZ2VN+OUiYpXU8Hs+VfcilYjgcwmKxwGg0wufzEbn36+oRSAmX2i/V+5+dnSESiUCn0134jFksFrRaLVnv1Wg08tq1pwZdPPbRoK9CMh1eaWdh5aZYLMJutxOPY9/h2pORjkMJjEYjGAwGuFwumEwmqFQq2Gw2JBIJNBoNwX/z5aDX6+HWrVvodDqkp9JUChM16FR7rzt37hAxXteh0+lgNpths9mIngEnff57sSBTTbxarYJlWSSTSeRyOfj9fiL3PpuRa3WXSCQQj8clee/j42PYbDbY7fa7ihCS+MzxXYja1qCn02n4fD7iE7p9Yt8N+jpypsNTg341NHWbH/TvjjDMZjNqtRpmsxmOjo5QKBTg9/uh0+mWu+uZTGZviktWq1XcunULs9mM9FSaSmGiBp1KESJhvK6iVquBYRj4fD6cn58TjcXhcBA9/71YPN2HXK5iZefn54hGo9BqtUuTWCwW4fV6idz76ekpGIaRtFr/JpLJJKLRqOjv2263odVqEY1GL/1822w2WSu4Lxb8OwVsa9AzmQydKK+hNIO+jpTp8D6fD+l0mvg97is0dZsftPaFMAwGA5rN5l3/Pp1O0el0kM1m4Xa7l9k0Pp8PBwcH6Pf7ROJNpVK477778NRTT5GeRlMpTNSgUylCTzzxBBHztc7Z2Rmi0Sg0Gg2MRqPsJuUy3G43yuUy0RjkKlY2Ho9hs9lgs9kwHo+X/14ul+F2u4ncO1cwcDqdyj52Op1GJBIR7f3m8/myv/lVnym73Y56vS7rvfJNq9/FoHPV/ylPo3SDvorY6fA+nw+ZTIb4fe0ruVwObrebeBz7TjweRygUIh6HUtBqtTg8PLz2Oi6bJplMwm63Q6VSwWAwIBQKoVwuYzQayRKv3+/Hc5/7XNy+fZv0NJpKYaIGnUoR2geDzp13djgcOD4+htPpJHbufBWv14tCoUA0hlgshmQyKekYXCG+WCx2165utVqF0+kkcu8ke9Fns1mEQiFR3uv09BR+vx8Gg+HadtQSFOsAACAASURBVGYOh0P2zz7ftPptDTo9C3o3N8mgr7NrOrzX66UG/Qro94kfsVgMkUiEeBxKgWVZdLtdwa8bj8eoVquIRqMwm81gGAZWqxWxWAz1el2ybhBmsxkvfvGLcefOHdLTaCqFiRp0KkWIpEGfz+col8vL886cOfR4PCiVSsTi4ggEAsjn80RjSCaTiMVikrz3+fk5kskkWJbdmMpfr9dht9uJ3b9Kpbqwoy8XuVwOwWBw5/cZDocwm83LHa/rridRtZ9vWv22Bp3bWSU9Ad0nbrJBX2c1HV6v11+bDs/1aiYd975Cj4zwIxKJIBaLEY9DCUynUzAMI0q6+nA4RLFYRDAYXH7fHQ4HUqkUDg8PRTu/rlKp8MpXvpIadCrBogadShF68skniRiv09NThEIh6HS6u9JrSRRGu4xwOIxMJkM0BrFTrTm43vIWiwXD4XDjdc1mE1arldj9syx7ZXxScXBwsHNxvHq9DpZlkUqleFeiJ3Gsgm9a/S4GnabkXuReMuir8EmH93g8yOVyxGPdV2hXBH6EQiEkEgnicSiB4+NjMAyD4XAo6vuuft+9Xi80Gg00Gs3yO97tdrc27N/5znfwwAMPkJ5CUylQ1KBTKUIkDPrR0RFMJhNcLtel6cuhUEi2wmhXIUd6+XVks1lRdnJXabfb0Ol0CIVC1/aWPzw8hMlkInb/Wq0WR0dHso9bKBS2bi/HZSZoNBrBtRRIZI8IKYZ4cnIi2KDn83lq0Ne4Vw36OpPJBI1G40I6PMuycDqdoleHvynQ4mf8CAQCtBsAT0ajERiGkfz7Np1OcXh4iFQqBafTCbVaDb1ej0AggGKxKKhexbe+9S08+OCDpKfQVAoUNehUipCcBn0+n6NQKIBlWWQymY27itFolLgxXiyebrUlVXo5X8TYyV19/tlsFizLolQq8drV7XQ6MBgMxO5fr9ej1+vJPm6pVILH4xH8utlsBrfbDbPZvNXOv9frlb3vvZCaD9sY9IODA1p1eg1q0C9nNBrBYrHAbrdDr9dDrVaLVh3+ppBIJBAMBonHse/QYoP8OTo6WhZklXPc4+Nj1Ot1xONxWK3WZb2KSCSCarWK8Xi88bX/8A//gHe9612kp9BUChQ16FSK0A9+8ANZTMDJyQn8fj/0ej06nc6V1+6DMV4spEsvF4JYbc5ms6fPdhqNRkE70v1+Hzqdjtj9GwwGdLtd2cfdpnp9v9+HwWCA3++/NjNhEySOdzidTt7n3rc16E6nk/gkdJ+gBn0zLpcL+Xxe9OrwN4V4PI5wOEw8jn2HHpXgT7fbhVqtJh4HV68iHA7DaDRCpVLBbrcjkUig2WxeSMH/0pe+hPe9732SzY2/8Y1v4I1vfCOe97zn4XnPex4efPBB6HQ6ycajkk/UoFMpQnIY9F6vB4PBsGy1dN316XQa4XBYdlO2jliFwnahUqnA5XKJ8vx9Pp/gnuKDwQAajYbY/ZtMJrTbbdnHFVq9nit2mM/neZ83vwwShQmFnHvfxqAXCgU4HA7ik799ghr0zTidThwcHNz171w6/PpuG9/q8DeFaDRKq5PzgFvoIR2HEmi329BqtcTjWKff7y+zCB977DE85znPwYMPPohHHnkEn/rUp/ChD31IsrmxWq2GVqtFsVhEoVDAn/3Zn+GZz3wmMpmMZGNSySNq0KkUISkN+mrvZyHGJZfLIRAIyG7K1tnlHLJY1Gq1rauorx4p2NY4jkYjqNVqYvfPt0c3qed+fn6OSCQCrVYrykJCMBhELpeT9V7dbjfvc+/UoIsDNeibcTqdKBQK117H7bZx1aLvlXT4SCSCaDRKPI59x+Fw8PocUWZoNpvQ6/XE47iKyWQCq9WKz33uc/ilX/olPPvZz8ZznvMcfPCDH8Sjjz6Ker0u+Xz5BS94AR599FHJx6GSVtSgUylCUhn06XS6TKkWeoa4UCiIkta9K9ukOYvNtlXUT09PEQgEoNfrd0oR56q77rIrvAt8W4CJTaPRgM1mu/bZ2O122O12HB8fizIuiQKJHo+H97n309NTwQa9WCzCbrcTn+DtE9Sgb2YbYzWdTtHtdpHNZuF2u8GyLHQ63Y1Mhw+Hw7R9GA9sNhtKpRLxOJRAvV6HyWQiHocQPvrRj+IDH/gAvvCFL+Btb3sbfuiHfgivetWr8MlPfhLf//73MRqNRJsnP/XUU3jsscfwrGc9C9lsVrT3pSIjatCpFKGnnnpK9Al/u91eVuYUmlK9WDxtjHdN6xYDoWnOUnB4eAiz2SzoNYPBACaTiXfv7avg+qNyPerlxm63864wLibXLYxwlfAjkQjOzs5EGzccDiOdTst6r0IK022zg14qlahBX4Ma9M3Y7XYUi8Wd3uMmp8MHg0HaPowHFosFlUqFeBxKoFKpKO7v0Yc+9CH81V/91XIuO5vNoNFo8PDDD+OBBx7ABz/4wZ3nx6lUCj/6oz+K+++/Hz/2Yz8GrVa783tSkRc16FSKkJgGfT6fI51Og2VZFIvFrXdd6/X61mndYsJnF1Vqut2uoCrq3FnodDotyq73yckJGIbZuujZrggpYCYmmxZG5vM58vn8shK+2ONGo1GkUilZ79Xn8/EuTLeNQS+Xy7DZbMQndPsENeibEcOgrzMajVAqlW5EOnwgEEAymSQex75jMplQq9WIx6EElJjl9N73vhdf+cpXNs5tn3zyyZ3nx0888QRKpRIikQg+//nP4yd+4ifoDvoNEDXoVIqQWAZ9MpnA6XTCbDZjMBjs9F6tVgsWi0V2U8bXpMkJ3yrqZ2dnCIfD0Gq1ODw8FG38s7MzMAyD2Wy3nfhtEVLATEza7fZd/d+5YwMGgwH9fl+ScWOxmOwtBoVUjt/WoFMzSp8JX6ROTVZ6Orzf76f9vXlgMBjQaDSIx6EElNgK853vfCf++Z//Wdb58rve9S584hOfkHVMKvFFDTqVInT79u2dJ/itVgtarRbhcFiUndZOpwOj0Si7KVtH6O61FAwGA7Ase+U1o9EIVqsVDocDk8lE1PHn8zkYhhH9fflCoi/4Zb/74XAIs9ksyrGBq4jH40gkErLeq5DK8dsYdCWmT0oNNeibsdlsKJfLso2ntHR42t+bHzqdDq1Wi3gcSiCbzcLj8RCPQwgPPvgg/v3f/13W+fI73vEOPPTQQ7KOSSW+qEGnUoR2Mejn5+dIJBJgWVbUNGTSvbf3KY7xeAyVSrXx5/V6HRqNBvF4XLJz4iqVCqPRiMj9k+gLvlg83ZpOr9djsXj6qINGo0EymZT8LH4ikUAsFpP1XoVUjt/WoFssFuITun2CLlpsxmq1ymrQ19mUDp/JZNDpdIinw3s8HmSzWeK/p31Ho9Gg3W4Tj0MJpFIp+P1+4nEI4Y1vfCO+973vSTY3/vznPw+n04larYZUKoXPf/7zuO+++2AymSQbk0oeUYNOpQhta9DH4zHsdjusViuGw6GohmE4HF67aywH+xDHZDK5tEjb+fk54vE4NBoN6vW6pDGwLLvzsYVtCQQCsrcdWywWODo6gkajQSqVAsuykj9jjmQyiWg0Kuu9Cqkcv41Br1arMJvNxCd0+wQ16JvZp+Je+5gO73a7kcvliD+bfUetVqPb7RKPQwkkEgkEg0HicfBlOp3ila98JQwGg2Rz44997GP46Z/+aTzrWc/Ci170IrzrXe+i5vyGiBp0KkXozp07gif03K5tLBYTtYI1B+nWXvsUx2VF2rj2XlarVZadbZ1OJ9mZ6+sIhULIZDKyj9vpdJaprmIvQF1FOp1GJBKR9V7D4TDvZ7xNm7VarUYN+hrUoG/GYrGgWq0Sj+MyLkuHt1gsiMVisqXDu1wu5PN54s9i31GpVOj3+8TjUALRaBSRSIR4HHyZTqd48YtfDI/HQ3oKTaVAUYNOpQgJMehnZ2eIRqOS79rOZjOilcPX45BiEULIM18t0sad9xe7vddVGAyGnXqp70IkEpG97Vi/34deryfyGcxkMgiHw3v7jLc16ErrsSs11KBvxmw2761BX2c4HMqeDr9Nn/h7DS7zbN8L/u0L4XAY8XiceBx8mU6neO5zn4tEIkF6Ck2lQFGDTqUI8TXow+EQVqsVdrsd4/FYUsPAmdLpdCqrUbnMjJCsYL5Y/E+RtvF4vGxhJ3fbMZPJhHa7TeT+5a5qzrWpSyaTYBhG9vvNZrMIhUKyjimktds2Br1er1ODvgY16Jsxm82KbI+1KR0+EAiImg4vRRu6mwaX/TYajYjHogSU1rpvMpngvvvuQ6lUIj2FplKgqEGnUoyum5RXKpWlaZG6SNZicdGUym2QLouDVAVzDpVKBYfDAZPJROQsuMViQavVInLviUQC8Xhc8nHOz88RjUaXbeq4HRi5jzfk83kEAgFZxxSyCLKtQTcajcQndfsENeibuSn9q69Kh6/X61unw0vdhu4mMBqNwDDM3lXg31e8Xq+iOgN0Oh3cunULvV6P9PSZSoGiBp1KMbpqMh4KhaDVamU3aCQLk62iVquJVTBfLJ5u98UwDNxuN7GUf5vNhkajQWTsVColedG0yWQCu90Om82G4+NjLBYLTKfTS4vzSc3BwQH8fr+sYwpp7XZ2dibYoDcaDRgMBuKTun2CGvTNGI1G1Ot14nGIzWXp8C6XS3A6vNVq3ZsievvKYDBYLq6TjkUJuN1uRdU1KJfLuHXrFk5PT0lPn6kUKGrQqRSjJ5544q6J+NHREcxmM1wuF5EdZL1ej16vJ/u462i1WhwdHck+7nw+Rz6fB8uy0Gg0xM6ALxYLOBwOVKtVImOn02lJz2S3223odLq7zvRfVpxPDgqFAnw+n6xjxuNx3lkK2xj0ZrNJDfoa1KBvxmg0otFoEI9DStbT4dVq9TIdvlgsXpkOv89F9PaFfr8PlUpFPA6loLS6BolEAvfffz+eeuop0tNnKgWKGnQqxWjVoM/ncxSLRbAsi0wmQ6yCOclzz6vo9XrZzfHJyQl8Ph8MBgN6vR6MRiPRZ+FyuVAul4mMLdWZ7NUFkFKpdNfPSdUfKBaL8Hq9so4ppPf6Nga91WpBr9cTn9TtE9Sgb8ZgMNx4g76OkHT4m3IEQEq63S5YliUeh1Kw2Wwol8vE4+CL1+vF8573PNy5c4f09JlKgaIGnUox4gz6yckJ/H4/9Ho9Op0OEUPGYbVa0Ww2icawWMi/UNDv92E0GuH1ejGbPW0OSZ4BXywW8Hq9KBaLRMaW4kz26ekpAoHAcgHksmvOz8+JFCoslUpwu92yjimk9/rp6angc52tVgs6nY74pG6foAZ9M3q9Hs1mk3gcJLkqHV6v11ODfg3tdhsajYZ4HEpBaYUZTSYTXvKSl1CDTrWVqEGnUoyefPLJ5U6tx+PBbEauajmHw+FArVYjHodcCwWrmQvZbPZC5gLJM+CLxQJ+vx8HBwdExhY75Xs0GsFiscDlcl1pvrkCgdyZdLkol8twuVyyjplKpXj3Xt/GoB8eHlKDvgY16JvR6/VotVrE49gX1tPhGYaBRqNZpsMPh0PiMe4bdFFQGErLWnn88cfxqle9ihp0qq1EDTqVYnRwcAC1Wo18Pk8spX0dt9tNLK16FTkWCrhifDqd7tLdeqfTSewM+GKxQDAYRC6XIzJ2qVSCx+MR5b0ajQY0Gg3vbgQqlUr2TgLVahVOp1PWMdPptOQGXavVEp/U7RPUoG9Gp9NRg34FWq0WuVxO9OrwNwlamFIYOp0Oh4eHxOPgy3/+53/iTW96E+mpM5VCRQ06lWJ0cHCwFwXZVvH5fMR2bVeReqFgOBzCYrHA6XRuLMZHerEiHA4jk8kQGVuMHeX5fI5UKgWWZVGv13m/jmVZDIdDWe+3VqvB4XDIOmYmk+F9zn8bg07TTe+GGvTNcK0OScexr6w/H7Gqw98karUaTCYT8TiUAsuy6HQ6xOPgyze/+U289a1vJT11plKoqEGnUox+8IMfEDFfVxEMBpHNZonHIeX562q1CpZlkUqlrtzR9Xq9KBQKxJ5BNBpFKpUiMna1Wt3JsM5mM3g8HphMJsFmW6PRyN7qr16vw263yzom30J8s9nT7XiMRiMikQiq1Sovs97pdKhBX4Ma9M1otVq0223icewrGo1m4/O5rjr8vZIOX6lUYLFYiMehBKbTKVQqFfr9PvFY+PLVr34V7373u0lPnakUKmrQqRSjfTTokUiEmClcJRAIIJ/Pi/qe5+fniEaj0Gq1vM63SxGDEIS04RKbXQzr0f/H3ntHuXXd977MWkmc4rWcZv9hO7aT+64T39zcZ/v65dmx48QvN/GNm6xrWYkjxVaxbMmUZEuUIpKiukRRQ1ISu0RVSqJFSqIE4KD3Oui9DtqgA4MOTMGokN/3hwwYM8TMABjgbJzh+a71+YPDA+y9DzDk/p79K8UiJBIJpqenh2qXJhAIMDc3R+t6U6kUlEolrWMGAgGYzea+7qXBYEAkEoHVaoVYLAaPx4NGo4HP50M+n+95WpfL5diKyqtgDfrarGdAWd4/7czn831dO0h1+K1EJBKBUqkkPg8mUK/XweFwGPXw5sEHH8Tll19OeuvMiqFiDTorxmgSDTpJU9jNqMO7q9UqlEolVCpV3/nNFouFWIj58vL7Vb77bcM1alKpFBQKxcCvi8ViPQvuDYJIJKI99SOdTkMul9M65kaV8mdnZzv3cn5+vrOpbzQaKBQKK07rRCIRLBYLotEoKpUKmk225VEvWIO+Nnw+n1HhtnTD5XJRKBSGeu2lEg4/MzMDtVpNfB5MoFKpgMPhoFqtEp9Lv9x11124+uqrSW+dWTFUrEFnxRi99957RMzXegxSuGqcOBwOuN3ukbxXKpUCn8+Hw+Hoq0hZG5Ih5u3Pwmq1Ehk7k8lAJpP1fX13dEImk9nU2GKxmPZ2g4OudxSEQiEYjcaLft7O3efz+Z1Ij8XFxTVP3Wq1GmZnZ2G32yGTycDlcqFSqWC328HlcrfM5n8UsAZ9bZiWD0s3HA5nJOHIWzkcPhgMQqvVEp8HEyiVSp2WoqTn0i8333wzbrzxRtJbZ1YMFWvQWTFGk2jQ+wm7pQOXy7Xpk/ylpSW43W5QFDVURXjS0QR+v5/YZ5HNZiGRSPq6tl6vQ61WQ6lUjqT6ulQq7VlVf9zrlUqltI7Zq5Xd/Pw8DAbDRbn76xn01RSLxc5GmcPhQCAQwGg0Mn7zPwpYg742PB6v7xDuS41GowEOh4NSqTTy995K4fB+v7/TMpZlfQqFAng8HvF5DMK1116LO+64g/TWmRVDxRp0VozRJBr0Ufe/HpbNnuTX63VoNBrI5fKhK4K73W7Y7XZi92CtE1Y6yOfzEIlEG16Xy+UgFAphtVqxuLg4krHlcjnS6TSt683lchCLxbSOGQ6HYTAYOn8ul8uQyWTQ6/WYn59fce0gBr3N3NwcOBwOUqnUis2/QqGA0+lEKpVCvV4nvumjE9agrw2Pxxs6hHurU6vVaMsXLpfLCIfDjAyH9/l8mJ6eJj4PJsDELhtXXnkl7rvvPtJbZ1YMFWvQWTFGk2jQo9EodDod8Xls5iQ/m81CKBTCYrEMVaSsjc/nIxZivrx8sYGjk7m5OQiFwjX/vtVqIRQKgaIohMPhofPNe6FQKPoq4jdK+n0gMUq6e8230zDcbnfPe7m0tDS0Qe/+WXvzbzQaIRAIwOfzYTAYEAqFUCwWiW8Axw1r0NdmMznWW51qtQoOh9Op70AXTAuHd7vdMJlMxOfBBNLpNEQiEfF5DMI3v/lNTE1Nkd46s2KoWIPOijE6f/48EfO1HiT6QfciFAoNfJLfarXg9/tBURQikcimTeNGRbzGDcmHJaVSCXw+v+ffLSwswGQyja2Ym0qlGqhv+ijY6IHEuD5frVbb+c6ul4YxjEEvFovr5jg2Gg1kMhl4PB6oVCpwuVzIZDLY7XbGhdb2C2vQ14ZpLZ/oZFIKetXrdSQSiYkNh3e5XLBYLMQ/LyaQSCQgkUiIz2MQvva1r+H48eOkt86sGCrWoLNijCbRoA9bvXvURCKRgcxps/nrvtvFYnEkcyAd7h+Px6HRaIiMXalUwOPxev5cLpdDo9Gg0WiMZWy1Wj1UzYDNUCwWIRAIaB0zEolAKBRCLBZv+J1dWloaOBx90CJElUoF0Wh0RWitTqeD3+9HoVCY6NDafmEN+tqMqgjaVqRcLoPD4UxcSkg7IsZkMkEoFBIPh3c4HLDZbMTvCxOIx+OM6xn/N3/zN3jppZdIb51ZMVSsQWfFGE2iQSdRLKsXg5jTQqEAsVgMo9F4Ue7uZg0UyXD/zfQi3yztnMvuKIR2GLbL5RqoGv6gaDQaxGIxWte7XsTAOKhWqxCLxZ0cxI2u34xBH8ZUNBoN5HI5+Hw+aDQacLlciMViWK1WxGIx4ieJw8Ia9LU/bw6Hc0mkOQwDEypuT0I4vM1mg8PhIH4vmEAkEoFKpSI+j0H4q7/6K5w7d4701pkVQ8UadFaM0YULF4iYr/UoFAq05+L2IplMQqlUrntNdx50MBgcaR708jLZE+zlZbLRDPV6HRwOB0tLS2i1WvB6vUNXwx8UnU6HaDRK63rL5TIoiqJlrGw2C4FAAJ1O1/cDmGEMevvUbxRhr7VaDfF4HDabDRKJBFwuF2q1Gl6vF9lsdqKNSzesQe/NOKuUbwV61XOYdNrh8A6HA3K5nJZweIvFApfLRXztTCAUCkGj0RCfR780Gg186lOfglQqJb11ZsVQsQadFWM0iQa9VCrRZlTWY6O+1N150Pl8fixzSCQSGz4kGPc9IBXN0Gw2O6ev7dSBUqlEy9gGgwHhcJjW9VarVXC53LGO0f1AKRqNDhQhMYxBb+fNjmMjPjc3h0AgAL1eD4qiOid1kUiE9kJag8Aa9N60H8ixBr03TGyJtRo6wuFNJhPcbjfxtTKB9r+fpOfRL41GAx/5yEcwPT1NeuvMiqFiDTorxmgSDXq7Wu2oT6MHZb22V6VSCVKpFDqdDs3mxuHBw5JOpyGXyyfyHoybhYUFcDgciMViTE9PjzR1YCOmp6cxMzND63rbIf3jev/FxUVYrdYVD5QGeQC0GYM+7nD0dh/n7pM6pVIJt9uNdDo9UafrrEHvDZ1txJhIPp8HRVHE5zEqulNYtFrtyMLhp6en4fP5iK+PCXi9Xka1pGs0Gvj93/99eDwe0ltnVgwVa9BZMUokzNd6NJvvn5yOqqf1sKxVVTsajYKiKPh8vrE/RCCdj0+isnj3feZwOGu2/RonJpMJwWCQ1jHbJ4jjWGu9XodKpYJKpUK9Xu/8vJ80jjbDGPT2wza688VLpVKnwCKfzwefz+88dCF9Qssa9N60DfokRz+QhIk9qwf9/NcLh+/33x69Xg+/3098PUzA5XLBbDYTn8cg35Ft27YhFouR3jazYqhYg86KUSJhvtZjcXGxk2tHch6rc4LbJ5ACgQCZTIaWOZDojd0N3YXLlpffN4IOhwMCgaATHk33us1mMwKBAK1jtnNwR138rv0dslgsFz30SqVSfUdoDGPQJ8F0NRoNpNNpuFwuKJXKFRv/ZDJJe1Vs1qD3hlSfb6aQyWQgEAiIz4Muhg2H12q1CAaDxOfPBJhW8T6bzWLbtm0oFoukt82sGCrWoLNilN5++23aDdB6tFotYsasm+4q4pVKBQqFAmq1esUJ5Lgh0Xqrm7VanY2Ler0OtVoNhULRycmuVCq0r9tqtcLn89E6ZrP5fuTIwsLCyN6zHe0RCoV6nswPkkKxGYM+SWHLlUoFkUiks/GnKAp6vR6BQICWFl+sQe8NqWgLppBOpyEUConPgwSDhMOr1WrMzMwQnzMTsFqtjKp4Hw6HsW3bNiwtLZHeNrNiqFiDzopRmjSDvry8DIqiUC6Xic6h2XzfMM3OztLS2qsX5XKZVoO8ml6tzsZFLpe76KSXoijaCsN1Y7PZ4PV6aR2znXM/ilz77iiEbDa75nUbFUJc/Z6DGvR22P4kGfRuGo0GstksvF4v1Go1uFwupFIpbDYb4vH4WIrbsQa9N3TVK2AqyWQSYrGY+DxIUK/XEbDNQP2mAQbKjEQktWY4vFQqZQ16nzCtoJ7T6cRv/uZv4vz586S3zawYKtags2KUJtGgC4VCzM3NEZ1Ds/m+Qefz+Ugmk0TmQKdB7sW4wq676a4sHg6HV6xVIBCgWCzSvm6HwwG3203rmKNK7Wg2m9BoNJ0ohPWuHaTGwWYMOum8736pVquIxWKwWq0Qi8Xg8XjQaDTw+XzI5/MjKTbHGvTejLIl31YkkUhAIpEQnwfdVEoVvHGIhwf+9QD2XLYXe76zF1PXH4VF9uuT3+5weC6XCy6XC51OB7/fP7Lf260I0wrq6XQ6fOhDH8KFCxdIb5tZMVSsQWfFKL3zzju0G6CNkEgkyOVyxMav1WpQqVTgcDhja6HWD3QY5PWYn58fedh1NwsLCzCbzWu2qhOJRCgUCrSv2+l0wul00jrm0tISOBwOGo3G0O9RLBYhFothMpn6+sxyuRwkEknf8xvUoLe/v8VikfjmblAajQYKhQL8fj90Oh14PF4nwiMajQ6dK80a9N60DTrdNQGYwuzsLKRSKfF50I32rWnsuexRPHHTUzi56yU8ddcpPPhvBzB13RGkoumLrpdIJAgEAivC4UUi0aarw29FdDodAoEA8Xn0i1gsxkc/+lHWoLMaWqxBZ8UoTaJBl8vlSKfTRMZOp9MQCASw2WzEcqDbjDLseRjGWbCvUqlALpdDo9GsaUpJPahxu91wOBy0jtmuvTBsjYPZ2VlQFAW/3993xEU+n++7jd4wBr3ZbDLWoK+mXWXabrdDJpOBy+VCpVLB4/Egk8n0fUrHGvTelEol1qCvQzweh1wuJz4Pujm58yU8+K8HcHLXSx2euusU7v72Xui4xouuF4vFSCaTnT/3qg6vUCjgdDqJFImcJDQaDaPSAc6dO4dPf/rTpLfMrBgs1qCzYpQm0aCrVCokEgna8T+LfAAAIABJREFUDZLX6wVFUYjFYlhefj/EmkQOdLcp2oxpG8U9GUfBvlQqBT6fD6fTuW50gEwmo61ifjcejwd2u532cblc7oZh6b0+I7fbDT6fj1QqNdBrC4VC310ChjXoXC6XluJrdFMsFhEMBmEwGEBRFAQCAYxG44andLFY7JI0WhvRNuhsOHJvotHoRD7YySXzMFBmSF5WwkCZkUvmR/r+h25+Gnv/48kVBv3krpew57JHoXxNe9H1QqEQ6fTFJ+ttelWHv1TD4ZVKJaLRKPF59MtLL72Ez372s6S3zKwYLNags2KU3n33XdqNyEZotVpEo1Haxms0GtDpdJDJZCsMOakQ682atlGPP6oogu6HIPF4fMPr5XL5wKZzFPh8PlitVtrH5fF4A93r+fl56PV6SKXSoYoqDtLnfjMGvVAoEN/cjZN6vY5UKgWn0wmFQrHilC6VSq24b6xB702xWGQN+jpEIhEolUri8+gm7I7i2O3P4/7vT+GB7+/HA9/fj+M7XkDEExvZGJwTAuz+9l48ddepjjk/dPNJ3H/FFLzGi8Oz+Xw+stlsX+/dqzq8SCSC2WxGJBLZ8uHwcrkc8Xic+Dz65cSJE/jKV75CesvMisFiDTorRmkSDbrBYMDMzAwtY7Wrh/fK25VKpetWwaYD0hXt+Xz+SKII5ufnYTAYIJVK+34/pVJJpECf3++HxWKZ6M+6XC5DKpXCYDAMnQIxSBu/YQ06j8fb8gZ9Ne1TOqPRCIFAAD6fD4PBgFAohGAwyBr0HszNzXXSaVguJhwOQ6VSEZ9Hm3q9jhcfOIMH//UAnr/3NF68/1U8f+9pPHDlAZx68MzIQsfjgQSeuOkp7P7WI3j0R4fw0A8O4u5v78WrU2+iVr24oCBFUcjnhzvFv9TC4SUSyYp0gEnnwIED+PrXv056y8yKwWINOitGaRINuslkQiAQGOsYrVYLwWAQFEVhZmamZ96uQqEgcoLbjUAgIFrRfhQV9UulEiQSycBmUq1WY3Z2lvY1B4NBmEwm2sft92FIO0XA4/FsqsJ/qVQCn8/v69rNGPRhN8xbgUajgUwmA4/H0yk8yePxYLfbkUgk2Krlv2Jubg5cLpf4PCaVmZkZaDQa4vNoE/PPYt81h3Hijhfw4v2vdji+43k8ds0RxAOJkY715lE+Hr/xBI7d9hyUr+tQmut9uj3KlJqtHg6/UTrApHH//ffje9/7HuktMysGizXorBil9957j3YjshHj7kM9Pz+P6elpiMXidc2nSqUiYhC7EYvFRCvJb3b8eDwOiqLg8/kGNpNarbZTD4BOQqEQjEYj7eNu9DCm1WrB7/eDoqiRfC/L5TIoiurr2mENOkVRyOVyxDd3k0I4HIZYLO50L+je9BcKBcZv+oelUCiAx+MRn8ekEgwGodVenHNNiognhkd/+CSevuvUCoP+9H++iEd/eAhR7+hDpzf63RhnW8etGA7P5/MZ9W/znXfeiR/+8Iekt8ysGCzWoLNilCbRoDudTrhcrrG899zcXOc0t9lcvzo53bnwvZBKpUQKpXWPP0yY/9LSEpxOJwQCwdAV+fV6PSKRCO1rDofDMBgMtI+7Xs2DhYUFGI1GiMXikfWGL5fL4PF4fX+ewxh0pm0Cx013Dnr3pl+j0YDL5UIsFsNqtSIWi6FarRKfL13k83nWoK9DIBCATqcjPo82lXIVT991CnuvfmKFQd979RM4ufMlVMr0f3drtRo4HM7QLRAHHYvp4fBMqw+yfft23HTTTaS3zKwYLNags2KUJtGgj6OKdqvVQjgcBkVRCAQCfZ3mGgwGhMNhoveCdJj9MC3v6vU6NBoNFArFpgrc0VmLoJtIJAKdTkf7uGtFK1SrVcjl8s4J2qjGq1ar4HK5fV27GYPeb9GmS4H1isTVajXE43HYbDZIJBJwuVyo1Wp4vV5ks9ktfbqez+dBURTxeUwqfr+/81B5UnBqPDhww3E8cOV+PHbtYTxw5X4cuOE4nBoPkflUKpVO1xG6x2ZaOPw4ow3GxTXXXIM777yT9JaZFYPFGnRWjNL58+dpNyIbMeoiXQsLC52Q0kH6aptMJgSDQaL3gkTLuW6USuVA4+fzeYhEIlgsFiwuLm5qbKPRiFAoRPuaY7EYtFot7eP26vueyWQgEAg2bEk3DNVqFRwOp69rhzXoAoGANehdDFLFfW5uDoFAAHq9HhRFQSgUwmQyIRKJ0HJKSCe5XI416Ovg8/kwPT1NfB6rCTkj4J0U48UHzoB3UoyQM0JsLu1WfaRPr5kQDt/+t59JUTpXXHEFHnjgAdJbZlYMFmvQWTFKk2jQR5kDXC6XIZPJoNVq0Wg0BnqtxWKBz+cjei9I5WG3UavVfbVEa7VamJmZWbfo3qCYzeaxFwvsRTweh0ajoX3c7nSCVquFUCgEiqLGlmbRDgnt57PajEHPZDIX/bxWr8MQi0ITCaPEoE3iZhm2zVq9XkcymVwRUqtUKuFyuZBOpyfuhG5Qcrkc+Hw+8XlMKh6PB0ajkfg8JplJLTQ4ieHwk/IwYxC+8Y1v4MCBA6S3zKwYLNags2KUJtGgjyrEuF2gzOPxDHX6aLfb4Xa7id4LvV5PNMy+nzz8xcVFWCwWiESikRa0s1qtRB6QJBIJqFQq2sdtpxN038+1ctJHQTvMsZ/fjaWlJTQajYGNYK9KwdJQAF985gT+7NAB/NmhA/gfJw7jZbuV+AaQDkbVB71UKmFmZgbT09Pg8/ng8/mYnp7GzMwMo8JW22SzWQgEAuLzmFTcbjdMJhPxeUwyTEmTKJVKK8LhKYqiPRy+UChM5MOM9fj7v/97PPXUU6S3zKwYLNags2KUJtGgx+NxqNXqoV+/uLgIu90OgUCwqfxtl8sFp9NJ9F6QCvNus1EefrVahUKhgEajGThCYSPsdjs8Hg/ta04mk1AqlbSPq1AoEI1GoVKpoFKpUK/Xxzpeo9EYyKDX6/WBN48ikWiFQfem0/j0kcfx578y591IggHim8BxMyqD3k2j0UA6nYbL5YJSqQSHw4FcLofD4WBMwap2KgfpeUwqLpcLZrOZ+DwmmWw2y7goDFLh8EyMWPnCF76A06dPk94ys2KwWIPOilG6cOEC7UakH4OkUCiGem21WoVSqYRKpUKtVtvUPLxeL2w2G9F7QSrMu816DwjS6fTY8qOXl5fhcDjGVs1/PdLpNORyOe3jSqVS8Pl8WK3WsdzP1TSbTXA4nL5qBWzGoKdSqc6f75FL8H8dPniROf8vhw/i318/Q3wTOG7GYdBXU6lUEIlEVpzQ6fV6BAKBkfWIHjWZTAZCoZD4PCYVh8MBq/XSiDIZlnQ6zfjvEF3h8Ey8V5/5zGfw1ltvkd4ys2KwWIPOilGaRIOeyWQglUoHfl0qlQKfz4fD4RiJwQkEAjCbzUTvhdVqHWtP+I3o9YCg1WrB5/OBoqi+8tOHxeVyweFw0L7mYb9/myESiYDD4cBqtY4kf78f5ufnweFwsLCwsOG1wxp0sViMZDLZ+fN/nDt7kTlv88VnThDfBI4bOgx6N41GA9lsFl6vF2q1GlwuFxKJBDabDfF4nEjF614w0TDQid1uh81mIz6PSSaZTEIsFhOfxygZVzh8MpmERCIhvr5+aTQa+OQnPwm5XE56y8yKwWINOitGaRINersSeL/XLy0twe12g6IozM7OjmweoyxWNywOh4NoHvzqPPD5+XkYDAZIpVKUSqWxjj2Odnv9kMvlIJFIaBlraWkJDocDAoEAMplsrA88VrOwsAAOh4P5+fm+5jmMQZdIJCsM+k6JcM0T9O+fPU18Izhu6Dboq6lWq4jFYrBarRCLxeDxeNBoNPD5fETbQaXTaYhEIuKfz6Ris9lgt9uJz2OSmZ2dhVQqJT6PcTHKcPh4PE7036Fh1v4nf/InMJlMpLfMrBgs1qCzYpzoNkAbUSqVwOfz+7q23XNbLpejXC6PdB6RSAR6vZ7ovSB1itymOw+8VCpBKpXCYDD0Zeo2i8/ng9VqpX3Ngz4gGpZGo7GiXzzdFfsXFxfB4XDQbG7cW30zBj2RSHT+bE8m8F96GPQ/O3QAPJ+H+EZw3JA26Ks3vYVCAX6/HzqdbsWGPxqN0trKLZVKbbnTz1FitVrhdDqJz2OSmaTfLTrYTDh8JBKBUqkkvoZ+aTQa+N3f/V14vV7S22VWDBZr0FkxTm+//TbtJmg9qtUquFzuhtdls1kIhcKR9NzuBal2W92QzoN3Op1wOp2divg+n4+2EGxSKQZzc3MQCoVjH0MsFsNkMnVCzHU63dhaqvViaWkJHA6nr+J+rVZrKIMulUoxOzu74mdvelz46+OHO8b800cex9FpPfFNIB1Msolob/jtdjtkMhm4XC5UKhU8Hg8ymcxYT9e3YnjyKDGbzXC5XMTnsR7lYgUB2wz8liDmsvTXOmCa6Rw17c4ORqNxw3D4mZkZaDQa4nPul2q1im3btmF2dpb0dpkVg8UadFaM06QZ9I2qS7daLfj9flAUhUgkMjbDSKqadzd+vx8Wi4XY+C6XCwqFAnw+H+l0mtaxSaUYFItFCASCsb1/+2FHIBBY8d3V6/WIRCK0rbPVanV64fZz7TBt1mQy2UUGvdlsolitQhjwgev1IMPAtmDDMskG/aLPqFhEMBiEwWAARVEQCAQwGo0Ih8Mjry6dSCQYlRNLNyaTCW63m/g81sJvCeKF+36Jgz85jv0/Poand56CReagdQ4zMzNQq9XE78Uk0B0Or9FoLgqH93q90Ol0xOfZL+l0Gtu2bUO5XCa9XWbFYLEGnRXjNGkGfb3c2GazCb1eD6lUimKxONZ5kKrm3Q3JPPhGowGxWAw+n49qtUr7+DMzMzAYDLSPWy6XQVHUyN+31WrB7Xav+bBjo5Z244DD4fTV7WAzBj0ejxPf4E0KTDLo3dTrdaRSKTidTigUihXhtKlUatPVpROJxKbzhxuNBjzpFLg+D95wO2GIRVGqVonfu1FgNBrh8XiIz6MX6XgGT+88hSduOoHT+17Hq1PncOy253D4lpMI2mdom0cwGIRWqyV+PyaRXuHwfD5/5NXhx0UoFMK2bduwvLxMervMisFiDTorxmnSDPpaJ3uFQgFisRhGo5GWHGg6i4WtRTgcJmJS23nYCoUCJpOJyNpJ1QCoVCp9pVgMwvz8PPR6PWQyGSqVSs9rSPS853K5fT18Gdagy+Vy1qB3wVSDvppyuYxwOAyj0QiBQAA+nw+DwYBgMIhisTjw+42iwJcw4MNuqQi3CynsEPKxQ8THM2YjCjTm0o8Lg8EAn89HfB69MAqtmLruCM7sfxNnD77V4cCPj0H4vIy2eQQCAej1l0aqzGax2+1QqVR9hcNPAna7Hb/927+N8+fPk94us2KwWIPOinF65513iBiw9eDxeJ2ib61WC6FQCBRFIRQK0ZYDTUcu8kZEo1FotVraxmu1WgiHw6AoCjMzMwgGg8RO8GOxGK1rb1Or1cDhcEb2PSuXy30V1zOZTAgGg7SulcfjrfnAYPX3YhiDrlAoEIvFiG/wJoWtYtC7aTQayGQy8Hg8UKlU4HK5kMlksNvtSCQSfbVym52dhUw2vJmbyeVwj0yCh5VynDAacMJowJN6LW4TUpCFgsTv0WbR6/Xw+/3E59EL5es6TF1/ZIU5P3vwLRy+5STOHuTQNg+fz4fp6Wni94MJOBwOWK1WNJu/bsW4Vjg8ncUi10Kr1eIP//APceHCBdLbZVYMFmvQWTFOk2jQBQIB5ubmsLCwAKPRCJFIhHw+T+scxhXqPAizs7NQq9W0jLW4uAiLxbLiXpM6wad77d3U6/V1ayAMQjKZBEVR8Hg8Gxr+Xj3nxw1FUX11P9iMQY9Go8Q3eJPCVjToq6lUKohGozCbzRCJRODxeJ3TuUKh0PM7FI/HN2XQ5TMh3C6kOua8zR6pGIcNzA971ul0CAQCxOfRC5vSianrjuKXj73RMednDryJqWuPQPEafffe4/HAZDIRvx9MwGazweHoXSOgVqthdna2UyxykOrw40IoFOLjH/84a9BZbUqsQWfFOE2iQReLxYhGo5BKpZ1iJnTPoVqtjvQkdRiSySQUCgUta1UoFFCr1StSC6LRKHQ6HbG1kyjS12w2weFwNtUZoNVqwefzgaIoJBKJvl5jsVjg9/tpXStFUX31sx/WoCuVStagd3EpGPRuGo0G8vl853SOy+VCLBbDarUiFouh+qsc8c3eF1koiNt6GPR7ZGIc0jPfoGu1WoRCIeLz6EUxX8Ire1/HY9cewcldL+HZu1/G/h8fwzO7XkIinKJtHi6XCxaLhfj9YAKDdAXorg4vEAiIhMO//vrr+Mu//EvSW2VWDBdr0FkxTu+++y4RA7YeQqEQPB6P1rZeq9momjwdZDIZyGSysY6RTqchEAjgcDguWivJVnOpVIpIkb71ihT2+3qj0QiJRNKX+W1js9ng9XppXatAIOir2OJmDHokEiG+IZ0ULjWDvpparYZ4PA6bzQapVAoulwu1Wg2TyQSpVDr0Zj+QzWC3VIxHVYqOOT9s0OIXAh5EgcnM3R4EtVqNmRn6Cq4NSjqeAf9ZKU7c+SKO3f48zh2mEPHQm9ricDhgs9mI3wsmMD09Da/XO/Dr2uHwXq+X1nD4F198EZ///OdJb5VZMVysQWfFOE2SQV9cXITVagWXy6XdrIzaqI2CXC4HsVg8lvfublcXj8d7XpNIJKBSqYisPZPJQCqV0j7u4uIiOJz3cycHfW21WoVcLu9UEx7ktXa7HR6Ph9a1tlNJ+vmuDGPQVSoVwuEw8Q3ppHCpG/TVzM3NIRAIdCrDC4VCmEymgTf7jUYDHK8bd4mFuEPEx06xELcLKRw36pEdcUs4EjDl92guO4d8qncaw7CUixUY+GacfvR1vLL3dRgoM0pzF3+m64Vts6xEr9ePJGWCrnD4Y8eO4atf/SrprTIrhos16KwYp0kx6JVKpRNmrVKpEIvFiM5nkD7R42Jcherm5+dhMBggkUjWPUEldYq9vEyuiv7S0tJQn3smk4FAIIDT6Rwq6sLhcMDtdtO6VqFQOFaDrlarGWEs6II16L2JRqNQKBRIJpOdVlBcLhdKpRIulwvpdHrD71690YA1MYtXnXacslmgCs8gX2G+OW82mRGJkk3koOVM4+xBDrhPCeHSeVGrbc6gVUoVnN73BvZ891Hcf8UU7v/+FPZc9ihefuQ1lIsrH+BYLBY4nU7i94EJaDSasaRMjCscfmpqCv/yL/9CeqvMiuFiDTorxum9994jYsC6SSQS4PP5cLlcWFpaItITuhdcLrevKtfjolQqgc/nj/w9pVJppyXNeteSOsVeXn6/rZ5IJKJ93PaDmX76g7evDwaDoChqUw+VnE4nXC4XrWvtt/jiZgz6JIfm0g1r0HsTiUSgVCpX/Ky92Z+engafzwefz8f09DRmZmZQKpWIz5lOJr0bQjqewakHz+Cxaw/j8C0ncfCnJ/D4jScgP6vd1Gm6WWrHvZfvw9FfPItndr+MZ3a/jKO3PYd7vrsPRpF1xbUmkwlut5v4vWACKpVq7A981guHj0ajA0XI3HfffbjiiitIb5VZMVysQWfFOJE06EtLS3A6neDz+Ugmk52fk2g51Qs+nz9QHvGoqVarI+3JPTs7C4qi4PV6+8rtz+fzYwux34hisQiBQEBk7H4fzHRXvi8UCpsa0+Vywel00rpOsVg8VoM+rpMapnKpGPRGo4FIPg93KoVUH33Rw+EwVCrVuu+XTqfhcrmgVCrB4XAgl8vhcDiIVZamE5lMhng8Tnwea87vl2o8ds1hvDp1rlPJ/dhtz+H+K6bwxmEepgUWpOOZgd/33BE+7r38sY45b3Pv/3kMZx9f2cJt2LzqSxG5XE7796lXOLxSqewrHH7Hjh245pprSG+VWTFcrEFnxTiRMui1Wg0qlQpKpRLVanXF31mtVvh8PiLz6mYUxmsztFt+bbZQXveDkFQq1ffrSPaCJ9nmrp/2Y7VaDUql8qLK98Pidrtht9tpXadEIkEul9vwularhWazyRr0TXIpGPRMqYTnrWbcLRVjh5CP++QSnPO4UFmnH3o4HIZare57jEqlgkgkApPJBKFQCIqiOnm1c3NzxO/BqJFKpZidnSU+j140Gg08u+cVHN/xfMecP7fnNHZ+42Fc85e34O7v7MXUdUfxzO6XEXIOdmr71nEB9nz30YsN+uX78MZh3oprJ7lX/KQhkUiQSCSIzqFXOHz7d7hQKKww7DfddBO2b98+lv3v3r178YUvfAEf/OAH8eEPfxiXXXYZQqHQWMZiRVasQWfFOJ0/f552A9SuHG6z2Xq2syKRj9sLqVSKbDZLbPxm8/2WXwsLC0O/R6PRgEajgVwuHzhcfxwh9v1SqVRGGj0wCBtVN8/n8xAKhbDZbCOr8u/1emGz2WhdZ7/f72ENularRTAYJL4hnRS2ukFvNBp4zmLCzwU87NMocNigxUNKGX4h4EHgX7ua+szMzEAGffWY7VBatVoNLpcLiUQCm82GeDyO2joPBpjCJBiq9XjxgVdx9BfP/qoH+lu4/4op3Pylndj+xZ04fOszOLP/LTz+0xN4Ze/rqFaqfb+vQ+3GfVdM4cntT3fM+aGbT+K+703BqliZb87+W9M/IpEI6XSa+DzarA6HP3nyJD7ykY/gsssuw+HDh3H11VfjrrvuGsv+9+tf/zpeeOEF+Hw+uFwufOMb38AnPvEJLCwsjGU8VuTEGnRWjBOdBr3VasHr9W6Yr+t2u+FwOIiYs24UCsVAJ86jZjMVxZeXf53HbTabhzL5lUoFPB6PyNprtRqxPvTrFU8Lh8OgKArhcHikc/P5fLBarbSuUyaTIZPJbHjdsAZdp9Oxm+YutrpBD+Wy2CUV4TGNEseM+g73yiXYq1KgWO1tzkKhEDQazUjmUK1WEYvFYLVaIRaLwePxoNFo4PP5aOvbPGpEIhFSKfp6ig+KlmvEY9cewcuPvIYXH3gVO/6/e3HL3+7CHf/rfpx68AzOHuTg5Ydfw5Pbn0bUu35odTyQgFlqh03pRHY2h7eOC3D/FVPY85292HPZo7jve++HzdeqKx+8tOtdVMpVZBO5gR4EXGrw+Xxks1ni81iLfD6PF198EVdddRU+9alP4Td+4zfwsY99DHv27IFWq8U777wztv1wsVjEtm3boNFoxjYGKzJiDTorxokug95oNKDVaiGTyTbM6/b5fLBYLLQbs9WoVCokEgli4w9asKz7dW0jGQqFhjaSJE0yyT70vXKzl5aWYLfbIRQK+woLHxS/30/7d14ulyOdTvf1fWo2Bzfoo2rns1XY6gbdnkzgNiGFo9O6FQb9MY0Sd8vESK6Rjz5Kg95No9FAoVCA3++HTqfbVKEqkgiFwok68VxNMV/CG4d4OHDDMTz8g8dx/V//Ard+ZTcO3/oMzh7k4OxBDl7Z+zqe/NnTCLujPd+jVqtDelqFJ256Cnv/40ns/Y8ncez252FXuuDSecF/Tgr+c1I4td6LzHmz2YRcJscbJ7jY/+NjeOD7+/H4jSegekO/6UryWxEej4dCoUB8Hv3yz//8z/jBD36Aq666Ch/5yEfwwQ9+EN/+9rdx5MgRzMzM4MKFCyPbD0ciEWzbtg1er3dk78lqMsQadFaM04ULF8ZuBHK5HEQiEUwmU18nucFgEEajkXZjthqtVku83RuPxxsoNL3dS34URpKkSZ6fn990eP+wrM7NbqcJKJXKgR+W9EswGITJZKJ1nf1GiGzGoLN5ob9mqxv0aCGPu2Vi7FXLVxj0u2UiPKZRobxGuHkwGIRWqx37/Gq1GhKJRKdQFZfLhUqlgsfjQSaTQaPRQKVehy0xizc9brzlccOeSKBKuAidQCBAJjN4kTU6qZSrcOt9UL6uw8GfnsAjVz2BMwfe6hj0J7efxKkHz6BS6v1QxK5yYd81h3Hijhdw+tE38PLDr+HgT47j6C+eQyq68cOJQ7uO4xdfuxv3Xr4P+350CPdc9iju+peHID29dvHBS5H2/+lM6oTwd3/3dzh58iSA9w+UnE4nHnvsMfzjP/4jPvCBD2Bqamoke+Hz58/jm9/8Jr785S+P5P1YTZZYg86KcRqnQW+1WggEAgOHBIfDYej1etqN2Womod3bIJXkq9XqSAuXkTTJmw3v3wzdod9zc3MQi8Uwm8096yWMilAoRPtDKaVSuaJ7wloMa9ANBgNr0LvY6ga92Wzilw4bfi7g4QGFFPu1KuyRiXG7kA/FzNrFAoPBIHQ6He1zLRaLCAaDMBgMoCgKPD4fB/k8/IJzDv8ppLBLIsTdUjFedzmJmnQ+n49cLkf8s+2XkDOCp3eewtT1R3Do5pOYuv4oju94Hm7D2nUIzh7kYOq6Izj96BsdXtn7Oh65+gkY+OZ1x8smcvj5P+3C3d99BEd//myH+/7PY9j3o0Mo5pljRsdNtVoFh8NhTPRIs9nE5z//ebz66qs996+Li4uoVqsj2QvfeOON+OQnP4l0Oj2S92M1WWINOivGaVwGfX5+HtPT0xCLxWvm865FLBaDRqOh3ZitZhLavfVbST6TyUAgEMDhcIzsxLttkhuNBu3rXlpaAofDGcmDhkFph37H43FQFIVgMDj2MP92z2c619lvCsdmDLrPt/am/FLjUjDoxWoV5zwuPKiUYbdUhH1qJWShAGrrGNxAIAC9Xk903vV6HQqPGz97/QzuePU07nz5FPa8dgb3ct/CHRQHlji5PuQ8Hg/5fJ74ZzsIiZkkVG/o8eZRPuSvqhH1rZ97/sL9r+LJnz29wqCffvQNPHzVQbxxiAe3wYd8qndYttcUwE++eDv2Xf/kCoN+8IbjuPs7exF29Q6rvxQpl8ud/1dJz6Vf/uIv/gJcLnes++Dt27fj4x//OOLx+FjHYUVOrEFnxTiNw6DPzc1BIpHAYDCg2Rz8BDSRSECpVNJuzFZA/U7RAAAgAElEQVRjsVjg9/uJzmGjVlitVgt+v3/DwnvDMGwO/KggNbZCocD09DT4fH5fOdqjIBwOw2Aw0LpOtVqN2dnZvr4HzebgBp3tTbySS8Ggt8lXyogVCiitURiuG7/fT9ygN5tNvOF2YpdUhGctJjw1rcdBmQQPct7Ej154Dg+9+ksYDAYEg0EU++jtPkq4XO7QOcPedBpnXQ4cm9bjVacdrlSS+H3uhfS0Co/8++N4+ZHXOub8yZ89jR//j9vwn//8AHZ/6xHc970pvHmUj+qqHPSoL46fffVOPPiD/SsM+t6rn8ADV+5HKjbZ6QF0Mjc3By6Xy5hiiY1GA3/6p38KpVI5tv3v9u3b8dGPfhThcHgsY7CaDLEGnRUjNaoNf3dxskAgMPSpYzqdhkwmo92YrcZut8Pj8RCdw3qFvNpRChKJZN22YJuBy+UO3J5tVAyafz8Kms33Q0pFIhGtY0ciEdrTOjQaDeLx+IbXDWvQjUYja9C7iMfjl4xBHwS/3995mEuS191O7P6VQe9ml0SIV0xGeDweqFQqcLlcyGQy2O12zM7Ojr2VG4fDGaq/uz2RwD6NEvf/Kt3gAYUMj6gUMBKMBliLZCSNp+96CY9c9QSO/vxZ7P/xUVzzmVtxw2dvx+5vPYIb/+eduPrPb8LVf34TDtxwHLOhXz9oaDQauO9He/GLf9iDAz8+iqM/fxZT1x3Bjn+8D7987BzxtU0SuVwOFEURn0e/NBoN/NEf/RHMZvNY9r433XQTPvShD0GtViOfz3dYWloay3isyIk16KwYqbfffnvTm/2FhQWYzWaIRKJNFyfL5/MQi8W0mpVeuFwuOJ1OonNYK0+4XC5DKpV2Tp7GNf4gOfBMH7tUKkEikUAgECASidC61mg0Cq1WS+uYGo2mr6iLtkEfNCzSaHzf1JDe5E0KrEHvjc/nmwiDbp6NY5dEhCMGXcecHzbosFsqgjUx27muUqkgGo12/r/j8XjQ6XTw+/0oFAojPZ0ctqhXpV7H06ZpPKKS45TN0mGfWokj0/o1W96RZDaUBPWMBMd3vICH/v1x3Pylnbj/+1P48V/fhp/9zX/itn+4B9f+yrQ/u+cVVMq/XsPZV17H0dufxd3f3os7/+kB7PnOXjy35/SaYfGXKplMBkKhkPg8+qXRaOADH/gA/H7/WPa+27Zt68kLL7wwlvFYkRNr0FkxUps16OVyGTKZDFqtdiT5ysViEQKBgFaz0guv1wubzUZ0Dmq1+qJTzkQiAYqi4PV6x54bvV5P8HFD59jJZLJzT/s1rqMkHo/TXndBp9MhGo1ueN2wBt1kMsHtdhPf5E0KrEHvjc/nw/T0NPF5VGo1/NJhwy6JCLul77NLKsQZpx2VNb77jUYD+XwePp8PGo0GXC4XYrEYVqsVsVgM1U0a4Xary3K5PNDr4oUC9qoUeMpkWGHQnzEb8aBSjlBucvtg16o1yH6pxj2X78PPv3o3fvK5Hbj9a/fi9q/di59+/g5s/+JOPPzvj68oOsflcpHP5eEzB2EUWRGwzTAqz5oukskkxGIx8Xn0S6VSwbZt25BMJklvk1kxXKxBZ8VIbcagtwtpeTyekRUnq1Qq4PF4tJqVXvj9fpjNZqJz6DZRS0tLcLlc4PP5fbXHGgW9eoLThUgkGvvYrVYLXq8XFEV1Cqbp9XraT9BnZ2ehVqtpHVOn0/W1zmENutlsZg16F6xB743X64XRaCQ+j2bzfZNuno3jNZcTr7mcMM/G1zTnvajVaojH47DZbJBKpeByuVCr1fB6vchmswOfrg9bdTtVLGJKo8IRg26FQT9uNGCvSoFoYbKLztmUTtzz3X248X/ege3/710dg37df/s5dn/rETxw5QGYxDY0m8xsHUaK2dlZyGQy4vPol2QyiW3bto2sUjurS1esQWfFSL3zzjsDb+4XFxdht9shEAhGXkirXq+Dw+GM/XR4I0i0vlqNwWDAzMwMGo0GtFot5HI5rbnRUqkU2WyWyNolEslYx15YWOjk8HeH0pNor5dIJKBSqWgdU6/X97XOzRh0l8tFfJM3KbAGvTcej2diDPqomZub61SppygKQqEQJpMJkUikL9NdqVTA4XCGOok/43LgHrkEz5qNOGWz4DmLCffJJXjJZkF9wouElebKeGb3y7juv/0cP/r0zbj5S7tw/X//BW78wp2Yuu4I9v7HkwjYZtBs/jrKgEmtw0gRjUahVCqJz6NfAoEAtm3bhnfeeYf0NpkVw8UadFaM1KAGvd1vW6VSjaXKNsn+292QKNy1GpPJBIfDAbFYDJPJRPs9Wa9I3bjp7kc+aiqVCuRyeaf/cvffGY1GhEIhWteaSqVo71zQfviz0XXDGnSLxQKn00l8kzcpsAa9N263GyaTifg8xk29XkcymYTD4YBcLgeHw4FSqYTL5UI6ne55ur6ZtljJYhHPWUy4XyHFvXIJ7pNL8LTJiPiQFeHpJhXL4NiO5/Gjv7gFP/yv23HL3+7CA1cewP1XTOHVqTc796T9EGPcxfq2AjMzM1Cr1cTn0S9WqxW/8zu/g/Pnz5PeJrNiuFiDzoqRGsSgp1Ip8Pn8kfbbXg3JHtjdkMgLXm2M2lWDQ6EQkYgCpVLZV6/scaBQKMYSyp9Op8Hn8+FyuXp+h00mE4LBIK1rTafTkMvltI45PT3d14OIYQ261WplDXoXrEHvzaVi0FdTKpUwMzPTaenI5/M7v5PtcO1isQgOhzN04blyrQZ7MgFNJAxbYnYii8NthJYzjcO3PoPHrjuCJ256CtQzYhQyv65qXyqVGNfbmxTBYLDzUJoJqNVq/PEf/zEuXLhAepvMiuFiDTorRqofg760tAS32w0+n99X7+TNQrK9VxuS/dgXFxdhtVrB4/GI5sH3KlJHFyqVaqQPB1qtFoLB4IY94y0WC/x+P61rzWQytLcW7DdSYDMG3eFwEN/kTQqsQe+Ny+WC2WwmPg+SNBoNpNNpuN1uKJVKcDgcyOVyWCwW1nw2m6hWqkhF0yuMeZt2b2/Sc2QCk9IxoV/4fD4+8YlPsAad1abFGnRWjNS777677ga9Xq9Do9FALpejXC7TYh74fP7Yenv3C4lTzeXlZdRqtU4KgdVqhcvlInYP+q30PQ767dPdD4uLizCbzRCLxRtWhrdarfB6vbSuNZvNQiqV0jpmv5ECS0tLiMViSCQSAxkFm83GGvQuWIPeG5fLBYvFQnwek0SlUkEkEoHBYACHwwFFUdDr9QgEAkP1RN/K5PN58Hg84vNgAkyLVjl79iw+85nPkN4is9oCYg06K0ZqPYOezWYhFAphtVqxuLhIm3mgo4L3RuRyOUgkElrHzGQyEAgEsNvtnagFu91O7B6QKJjWRqvVjuThQPuBh1qt7qsNoN1uh8fjoXWtJL5rZrMZgUBg3WuWlpZgtVohEAggEAhAURQMBsOKMNy1sNvtsNvtxDd5kwJr0HvjdDpZg74GuVwOPB4PuVwOXq8XarUaXC4XEokENpsN8Xj8ks+9zmaz4PP5xOfBBJxOJ6xWK/F59MsLL7yAL3zhC6S3yKy2gFiDzoqR6mXQW60W/H4/KIoicoIqlUrHViCsX+bm5iAUCmkZq9VqIRAIXHS/fT4frFYrsXtAomBam1G0O8vlchAKhbDZbH3XTHA4HHC73bSuNZ/PQyQS0TrmRqH8jUYDGo0GKpUK5XIZtVoNmUymE4bL5XIhl8vhdDp7Frmy2+2w2WzEN3mTAmvQe+NwOBhlGuikl/msVquIxWKwWq0Qi8Xg8XjQaDTw+XzI5XJD56szhXq9DqfWC+5TIgiel8Kud0IoFBKfFxOw2WyMemh65MgR/MM//APpLTKrLSDWoLNipN57770VG/Nmswm9Xg+pVEoszFypVCKZTBIZu025XAZFUWMfZ2FhAUajsWf4dSAQgMlkInYP+jllHRfT09N9VRnvRavVQjgcBkVRA0cAuFwuOJ1OWtdK58OgNlarFT6fr+fflUqlTueAxcVFzM/PXxTeXi6XEQ6HYTQaIRAIwOfzYTQaMTMzg3K5DIfDwRr0LliD3hv2e7I27Yiqtf6+0WigUCjA7/dDp9OBx+NBJBLBbDYjGo1uudZjlXIVz91zGjd/aSd++rkd+MnnduCWr+zEkzuPbfkHE6OAaa0v9+3bh29+85ukt8istoBYg86Kkeo26IVCAWKxGEajEfPz80SM2fLyaPOPh6VarYLL5Y61enq5XIZMJuvZ7mt5eblT5ZfUPVjPxI2bYaupLy0twWazQSgUIpfLDfx6t9sNh8NB61qLxSIEAgGtY9pstp659qlUChRFwefzdb77vQx6N+0iVy6XCwqFAhwOB0KhEAqFAplMht08N+k36LFCAd50GvlKmfja14ONtFibdDoNkUjU9/W1Wg2JRAJ2ux0ymQxcLhcqlQput3uifw8bjQayiRxyyfy618lf1eCmL9yJu/73g3jo3w7iwX89gO1fugvXffZWBKwh4uuYdIxGIzweD/F59Ms999yDK6+8kvQWmdUWEGvQWTFS7733HlqtFkKhECiKItbSq5tRhDdvlkajAQ6HM7Z2colEAhRFwePxrHm/I5EIdDodsXtgt9tpD/duM0w19UajAbVaDaVSiVqtNtS4Xq8XNpuN1rWWSiXw+XzaP9vuXPvuNIvV1fM3MuirKZfL0Ol0kEgk4PP5EAgEMJlMiEQiW+5Ur1/oMujpUhFPm6Zxu5DCdoqDXRIhKJ8H9Qk1Z2ytgrVJJpMQi8VDv75YLCIUCsFgMICiKAgEAhiNRoTDYZTLk/HgJuyO4vUnuDh22/M4vuN5cI4LkQinel77+I0ncNP/85946N8Odth92cO46r/eiHNH2Dz0jdDr9fD7/cTn0S87duzAddddR3qLzGoLiDXorBip5eXlTog16cJsbUjmPrdZWFgAh8MZeSRBd8u6jcL4SfdidzqdtId7txm0mvrc3FwnvHMzBQ19Ph8sFguta6UrnaKb7lz7paUlWCwWiESinlXuFxYWBm711K7OXa/XkUql4HQ6IZfLO6d6Ho8H2Wx2Yk/1Rg0dBr1Wr+OQQYfr33oDd8vEeEglx+0iCjfx3oQsFCR+D3oxbF5svdGAJ52CKOiHKOiHJ52a2IcQw5JIJCCRSEbyXt2/h+0oF4VCAafTiVQqRaSVWzKSxjO7X8bBG47j+Xt/ief2nMb+64/i1INnkE8XLrr+kauewC1/u2uFQb/7u4/g6k/fiFf3v0n885p0tFotQiHmRBr89Kc/xa233kp6i8xqC4g16KwYqXw+D71ej2bz4hBrUpDoRb2apaWlTg/aUb1n+z/JflvWJRIJqFQqYveAZBX5Qaqpx2IxUBSFYDC46eiPQCBAe+/5djoFnWM6nU64XK5O1IFKpVrzuz7oCXqzuXZ/61KptOJUTygUdnJmq9Uq8U3huKDDoLtSSdxMcXC/QooprarDrQIuHlLKUZ3AftpWq3Xgdny1eh1vety4RybGXWIB7hILcK9MAo7XvaVM+uzsLGQy2Vjee3UNiXaHhmAwiGKxSMv6VG/oMXX9UZw58BbOHuTg7EEOXp16E1PXHYVZevFDm7MH38INn70dD3x/f8eg7/j6ffjhZ34GHddI/POadFQqFcLhMPF59MvVV1+NXbt2kd4is9oCYg06K0bq/PnzxEPae5kzUqHV3XC5XFSr1ZG819zcXKfw1sLCQl+vSaVSRHqxt/F6vcSqyLcN5HrXLC0twel0QiAQjKzqfzAYhNFopHWttVoNHA6H9vvb7g2/UdTBMAa9n5679XodyWQSDoejkzOrVqvh9Xq3XEVqOgy6NhLGT7jnVpjzKa0Ku6Ui3CkWIDchYc3dWK1WOJ3OgV5jTyRwt0yEJ/QaPGsx4VmLCY/rNNgjE8ORTBBfE5O+M83m+zngmUwGHo8HKpUKXC4XMpkMdrsds7OzY2vldu4whUM3n+yY8zYHbjgO2avqi66fDSVx7+X7cMP/fTt+8dU9uPXLu3HtX92C3Vc8hHJxZepMMV/CXI6eBw1MQS6XIxaLEZ9Hv1x++eV4+OGHSW+RWW0BsQadFSN14cIFIgZsPVwuF+2FunrB5/NRKpU2/T6RSGSoE95sNgupVEps/SROk9tsVKyt2WxCp9NBLpejUqmMbFwShfnq9To4HA6tD8qmp6fB5XLh9/s3HHdcBn01c3NzCAQC0Ov1nYrUFosFsViM8afrpE/QH1bKUZvAE3SLxTKwQT/ndmGXRNgx5212SoR4y+MmvqZREY1GoVAoaB+3UqkgGo120l54PB50Oh38fj8KhcLIHpxJX1HiwI+PrTDnZw68hanrjsLAvzj6ptlsIuafxcuPvIbd33oE918xhecefgVyya/vUTyYwKkHz2DPd/bi7m/vxXN7XsGMM0L8s5wEpFIpEgnmPMD6+te/jkOHDpHeIrPaAmINOitGahINOsmT225EIhEKhcLQr19cXOxUFM9mswO/nkR/7G5CoRDtp8nd34G1irWVSiVIJBJMT0/3HY3QL+FwGAaDgda1jrsgYTetVgt+vx9cLrfvAoTDGHSPxwOjcfiw01qthtnZWdhsNkgkEnC53E6/53w+z7jTdToMer3RwCGDDj/mvIHdUhEeUsomPgd9mNZPr7mc2C0VXWTQd0tFOOcezOxPMpFIBEqlkugcGo0G8vk8fD4fNBoNeDwexGIxrFbrph+chd1RHLvteRy+9Rmc3ncOr+x9HY/f+BRO7nwJqVimr/fw+XyYnp5Gs9lENpHDoz88hO1f3Im7v/0I9nxnL27+0k48+G8HkZhJEv88SSMSiZBK9S7AN4l85StfwbPPPkt6i8xqC4g16KwYqUk06MFgkGj/7zZSqXQoY728/H7YslKphEqlGrqiOIn2W92QMKtt/H5/z2Jt7er33W3ARkk0GqW9cn6z2QSHwxn5w4bVLC4udsLarVZr39XqhzHoXq93UwZ9Ne1+z1qttmMSbDYb4vH42EJwRwmtVdzNRuwQ8XEzxcHdUhEon3dic7OHMejGeAw7JUIcm9Z3zPnRaT12SUQwx+PE1zQqwuEwVCoV8Xl00/3gTCqVrkhLGaboo1PjwfP3nMbBn57AEzc9hVMPnUXANtP36z0eTydSR3pahVu+tAv7fnQI+68/iv3XH8Vj1x7GzV/aCe5T/ber26oIBAJks1ni8+iXz33uczhz5gzpLTKrLSDWoLNirEgYsPUgaQy7kcvlSKVSA78um81CIBDAbrdv6lSURHXvbkiY1TaBQGDFQ5pWqwWv1wuKojasfr8ZYrEY7ZXzx9UxoJtGowGVSgWVSoVGozFQlMowBr37ZGvU1Go1xONxWK1WiMXiTghuIBDA3Nwc8Y1lL+jugx4vFODLpFGY8LZ2JpMJbvdgYenlWg2nbBbcJRHiHpkE98gk2CkR4iWbFWUGPKzpl1AoBI1GQ3we69GdltIu+jhoS8VSoYSQI4wZZwSV8mAn8u1uEc1mEy89dBa3fmV3x5y3uf1r9+DYbc8Rv1ek4fF4yOfX7zU/SXz6058GRVGkt8estoBYg86KsSJhwDYySVqtlvg8VCrVRT2h16O7l3Q0Gt30+O3iYaSK+JFs89YdXr+wsACDwQCpVDqSmgDrMTs7C7VaTetaFxcXweFw0GyOp5NCsViEWCyGxWLpFIMbpJ3cMG3WfD4fDAbD2Ddxq0NwuVwupFIp7HY7EokEkfZRvaDboDMFk8kEj8cz8OuK1Sr00QhO2Sw4ZbNAH42gxPA6BasJBoPQarXE59Ev3UUf5XI5OBwOlEolXC4X0un0WNJSHA4HbDYbms0mzh3h45Yv7cLUdUdWGPRbv7wbLz18lvj9IUk7jYquCv2jmO/HPvYxqNVq0ttjVltArEFnxVi9/fbbREzYWpBuL9ZGq9UiFov1bWLa/eR79ZIeBjpzk9f6HJRKJZGx21EUlUoFMpkMOp1urCfMJNfcbunXaDTGsh6KohAIBFY86FkrhaAXwxh0v99Pi0FfTbVaRSwW6xS4oigKer2e1vZRvWANem+MRuNQBv1SIBAIQKfTEZ/HsJRKpU7RTT6fDz6fj+npaYRCIZRKpZGMYbPZOm36ArYZ7PyXh3DnP92Px645jMeuPYyd//tB3PG/7oddtXWKBw5D+2F/v1ENpGk0GviDP/gDWK1W0ttjVltArEFnxVhNmkFPp9NE24u1MRgMCIfDG15XLpc7JrLZHN0pKB2hz+tBss1bu4Ixn8+H2+2mLYoglUpBoVDQutZWqwUOh7NmH/Jh39Pv96+ZErA6hWA9hjXoer2e+CYvl8vB6/VCrVZ32kc5HA4kk0laT9dZg96b6elpeL1e4vOYREg95BoHjUYD6XQabrcbSqUSHA4Hcrl807+Lq7sAqM/pcc9lj+LWL+/GLX+7C7u/9QhEp+SMKyo5asrlMjgcDiPqdbS/L7/1W7+FYDBIenvMaguINeisGKtJM+i5XA4SiYT4PIxGI4LB4LrXJBKJsZnIcZ6s9kMmkyHS5q3VasFkMoHD4SAej9M6djqdhkwmo33NXC4X1Wp1JO/VXQxurZSAQQoxDmPQJ/H0r90+ymw2QygUgqIoGAyGkZ7orQVr0HszPT0Nn89HfB6TiNfrHVsdB9JUKhVEIhGYTKbO76Jerx+4jkSvGgaZ2SwMlBk6rhHJSJr4WieBubm5zl6C9Fz6oVQqYdu2bchkMqS3x6y2gFiDzoqxeuedd2g3JOsxNzcHoVBIfB4WiwV+v7/n37VaLbjdblAUNVCe+iC0T1ZHZdwGJZ/PQywW0zrm4uIiTCYTBAIBkdN7Ur3neTzeSPq51+t1qFQqqNXqdR/sDNJCb3FxcWCDHgwGJ86gd9NoNJDNZuF2u6FSqcDlciGXy+F0OpFKpUZ+us4a9N4YDAbWoK/BZlsVMoVekS4SiaSvLg1sBEZ/5PN5UBRFfB79kkgksG3bNtTrddLbY1ZbQKxBZ8VYTZpBJ129vI3dbofH47no581mEzqdDjKZDOVyeaxzoChq7GOsBd0PSmq1GhQKBTQaDaLRKJH891wuR/tDiVF9znNzcxCJRLBYLBvWLWjnhvbzvsMadCYVuKpUKgiHwzAajRAIBJ182ZmZGZTL5U2/P2vQe2MwGOD3+4nPYxJxu92dFmKXEu06Et1dGjQaDXw+H3K53IpTYL1ez35/+iCTyUAgEBCfR7/4fD5s27YN7777LuntMastINags2KsJs2gk65e3sblcsHpdF5kgsRiMYxG49j7Vi8vL0MgEIys6NyglEol8Pl8WsbK5XIrWtORqkNQKBQgEoloH5fP52+qQn27GFwwGOzr96bfVoatVgvz8/Mol8uo1Wp9G3UmtIhai3a+rMvlgkKhAIfDgUKh2FQ1atag96Yd1kx6HpOI0+nstBDrh0KlAmcqCUcygdwIHiqtRWY2C78liLA7ilptvHUcGo0GCoUC/H4/dDodeDweRCIRzGYzotEo1Go1gsEg8c9q0kmlUhCLxcTn0S8WiwW/93u/h/Pnz5PeHrPaAmINOivG6t1336XdkKxHs9kEh8OhxQCvh9frhc1m6/w5Go0OZIJGgVgsRj6fJ7L+SqUCHo831jFarRZmZmZAURQikUjn56RCzefm5iAQCGgfVygUDvUgptVqwefzgaIopFKpvl8XiUSg1+s3fO+FhQU0Gg1UKhWUy+UO1Wp1XbM+MzPDWIO+mnK5vKIatUAggNFoRDgc7rsqMmvQe9PuX096HpOIw+GA1Wrt61rLbBxP6rW4Vy7BvXIJHtdpMB2LjnQ+tWoNytd0OHHHC9h/wzE8uf1pnNn/FhLhFG33pFarIZFIwG63QyaTgcPhQCKRwO12I5PJMCbHmm5mZ2chk8mIz6NflEolPvzhD+PChQukt8estoBYg86KsZo0g066OFqbdiuqpaUl2Gw2CAQCZLNZWucglUppH7PNuCMZ2vdVKBRe9BCCRP778jK9UQPdiEQiFAqFgV7TzteXSCQDn75Ho1HodLo1/77VamFxcRHNZhMLCwuYn59Hs/l+r+NqtbrCsFcqlYtO12dmZqBWq4lv9EZNvV5HKpWC0+mEXC4Hl8uFSqWC2+1GNptd0yCwBr03Op1uqBNQfyaD11wOPKnX4pTNAmtidsuZM7vd3unxvR6RfB5TGhX2qhV43mrG81Yz9mmUeFStRDCbHdl8zBIbDtxwHCd3voQz+9/CSw+/hv3XH8XpfW+gUibTg14ul8NiscBgMICiqBUPz0aRmrJVaHdFIT2PfqEoCp/61KdYg85qJGINOivGatIMeqvVGmlV62EJhULQ6/VQqVRQqVSo1Wq0z0GhUAx0MjpKxtmHvV6vQ61WQ6lU9mwvRirUvFwujz1qoBeDRkrU63UolcrOKfWg48ViMWi12p5/1z45bzbfN+eLi4sXMT8/j3q9jlqt1vN0fasa9NWUSiWEQqGOQRAKhZ3w22r116aFSQY9ks/hDbcTT+q1eN5qhmU2Pjbzq9VqBzborlQSj6oV2CMTY59GifvkEtynkEA5EyJ+70ZJd4/v9ZCGArhHJsaLNssK7pVLIPCPpgBfvV7HSw+fxZGfP4uzBzkdXtn7Bh6/8QQC/z977xkk13Vei9Is/3GV7H+2Sj9tS7plW0+ucvnJvqp6Vdbzta+vr6Qr+VmWRdGWKWaQYBITAJIgARAECSIOcs45dPfpnHPOeTrnnLsnACC53g+oD6eBCd3T4XQPz1e1fmBwztl7n+6Z2mt/37eWiZp3LxaLEQ6HyTm2D88WtqYMS/hxkhAIBCbq7/Hly5fx53/+51RvjelYI0ETdDomNsaNoM/P99+TOwjY7XYwmUyYzWbMzMxQMgeZTDY0lfiV0Gw2h9Jq0CbfRqNxyfdaKBQoKTWvVCpgMpkjH1cgECCTyXR1bVsMzmQyrfrwJBKJQKFQPPTz2dnZFcn5YmS90Wh0kPW233GlUumpd32SUavVEDgsZqkAACAASURBVI/HYbVaIRKJwGQyIZfL4XK54PF4JqLE1JtOYatUhPVsJjYIeHiNS+ANPht833CEuBQKBfz+7sldrV7HCaMO74kFZLb4lMmAHXIpdiplQ+29HjVMJlOHx3cbvnQaLLcLp01GsFxOnDTq8b5E+BBB3yIV4YptZYLfDUqFMo6+dRbHN5zvIOhXdzHw6VMHYZE5BjJOrxCJRIhGo4vPuVTqEH5s2yr6fD4UCgXKP99RYtJEO0+ePInvfe97VG+N6VgjQRN0OiY2Pvvss5ETkpWwmpLfQWFubg4+nw9MJpOSPuiFUCgUCIfDlIw9jFaDdh+/3+9ftnSeKiX/Wq1GiUBht60M0Wh0IDoIkUgEcrmc/Pfc3BxmZ2fRbDbRbDa7JueLwev1gsViYXp6esVS+LWMQqEAn88HtVoNJpMJJpMJo9GIcDjckV0fJ5ww6vEyh4n9GhWmtGpMadV4VyzAZrEAiSGQGoVCgenp6a6vTxYL2C6TYL9G1UHQTxj1eEfEhzM5un7oYcNgMMBut3f8zJaIY69aiY/k99/BR3IJ3uJz8SLBwEmjniTnp0wGvCPkQx7o/t0uh3q9jut7mNj93OEOcn7m/SvY9+IxBJ1hSt4Rn89HPB7vav6pVApOp5O0VRQKhbBYLIhGo8taua0FeDweaDQayufRLfbt24cf/OAHVG+N6VgjQRN0OiY2xpGgU9V73Wq1oNfrwefz4fV6IZPJKH0ParW6QzxtlGj7sA+itH92dhY2mw0cDgepVGrF66nKZA+zrH85iMViJJPJZT8Ll8sFNpu97HXdIhaLkd/tdkl7v+S82WzCYrGAy+UinU4vmV1/UGjuq0DYQ6EQ+Hw+LBYLhEIhmEwmaR2VzWbHon86Wy5hs1iALVIxSc6ntGrs06jwMocFQyQy8DHlcnlPBD1bLuEThQy7VPIOgn5Yp8H7YiG86RTl73FQ0Ov1cDi+zEyXazWcNhmwS6nAFZuVxA65FM8zb2GDgIvdKgX2qBV4R8THQZ0aqWJxYPNx6bzY/9Jx7F13FKfeu4Qjb57Fx7+eAvMIl7LfYR6Ph2Qy2fN95XIZoVAIRqMRPB4PLBYLKpUKHo8HuVxuLH4fBwmn0zlRln3bt2/Hj370I6q3xnSskaAJOh0TG+NI0KnovS6VShCLxWQpGFVWXwuh1Wrh9/spG5/FYqFcLvf1jEbjfq+pWCzuWleAqkx2o0GNg8By3/dWqwWdTrcqMbilEIvFIJVKu+o37wa1Wg0qlQpisRilUmlZEt+t0NxaQiQS6Shxz+Vy8Hq9pHUUn8+HyWRCJBKhLJtXqFTwgUSIzRJhB0Hfo1biVS4L5tjipcT9QC6XIxAI9HTPLacdG4Q8HNFrccpkwHGDDu+I+Dim16Gyhr4/Op0OTqeT/Hc4l8MupRxnTcYOgn7WZMRWqQhnTUbsVimwSyXHTacd0Xxu4HNyqN24+PENHHztFI69fQ7iq0oUsoM5BKhWqj0T47Zwa7VaQzaZQ6Xce2VKvV5HNpuF2+2GQqHo+H0c52qXXtCrZR/V2LRpE/793/+d6q0xHWskaIJOx8TG559/PnLitxLkcjmi0ejIxovH42Cz2XA4HGT2NJPJQCAQUPoeDAYDvF4vZeP3qwVQLBYhEAh69o1vNBqUZLJbrRYYDAaazeZIx5VKpYjH4w/9vF8xuKUQj8chkUgGQs6LxSJEIhFUKhXq9XpPGfd6vb5sdp3qjeKg8CBBX4hqtYpIJAKz2Qw+nw8WiwWlUklm80Y5zyt2K9azmditUmBKq8Z+jQqv89j4SC5BfghERSaT9UzQU8UiTpkMeFfExyYhDxuFPExpVQhkMpR/zoOERqOB2/2lyFuskMcetQInjfoOgn7SqMcetQKxQh6FSgWFIRPKarWGdCyDYm4wxNxnmcatgxwcffscLmy/DoPQgmqlu0MqFosFOUOFw2+cwUe/2od9Lx6F5KqyL1X5arWKaDQKs9lMVru0tSSWc2oYZ1gsFlgsFsrn0S1effVVPPXUU1RvjelYI0ETdDomNsaRoKtUKoRCoaGPMzc3B6fTCYIgHhJjy+fz4HK5lL4Hk8kEl8tF2fir9eeen7+fpSUIAm63u+dM+LAE6lYCVRZ/i4kBtsX0zGbzwA8q4vE4uFwuIpFIT6T6QaRSKXA4HFitVjSbzVU/p1cbt0nDcgR9IR7M5jGZTAgEApjN5pH0yiaLBezXqPAKh4WX2Uy8xGbiA4kIllhsKONJpVIEg8Ge7ytXq7Al4pAHAzBEI8h16Uc/SVCr1fB4vhTnq9fruGa3YrtMggsWM67YrLhgMWO7TIJrdutEEkeP0YeDr57Ep08fwtG3zmL/+mP49OmDEF/tTtDs4LajePdfduD9n+3ErmcOYdtju/HOj7eDdZQ3sDnm83l4vV6o1WrSqUGv1yMYDKI8Id87o9G4qODguOLpp5/GK6+8QvXWmI41EjRBp2NiYxwJ+ihKuxuN+z68IpEIpVLpof8vFouUCJUthNVqhcPhoGz8Xu2/5ue/PPRgs9mrblOYmZkBg8FAozG4rHG3c2cwGItavw0TCoUCkUiE/HckEulKTG8165udnUWlUoHZbIZAICD7L30+37Ll6Q8iGAyCxWLB7/evmph/VbLr3RL0B1GpVBAOhzt6ZdVqNXw+H/L5/FDmWqhUoAmHwHK7IPL7ECsMZ5xG4z5BD4VClH8+4wiVSgWv19vxs2g+jzNmIz6SS7BdLsFHcgnOmI2IDum7MCgspjVRq9VwbTcDu549hKu7bpM4tuEcDr56EonQ8r3lpWIZb/z0PWz+149x7O1zJD558gC2/+dexIO996Z3s462U4NYLAaDwYBUKoXdbkcymRzbQ5IH2yXGHY899hg2btxI9daYjjUSNEGnY2Ljiy++oIwALgWDwQCPxzO05+fzefD5/GVLr6kSKlsIu90Om81G2fi9ivU1m01oNBoIhcJFDz26BVVEeX5+Hkwms+te+UFBqVQiHA53HG4MQgzuwXfaJr9tMbhWq4V8Pg+Px0NmbEUiEWw2G5LJ5KJZ8VarBYfDQR7ADJqcL5Vdr1arE5tdXy1BX4h6vY5MJgOXywW5XE5+VlarFfF4fNF3MK6EoQ2JREIT9CWwlAVdoVKBJR6DKhSEJR4bekl7P0jHMpDdUOPsB1dwbutVKG5rkE3eb9vIp/M4/PppnNh0voOgX/n0FnY+eQAOzfIe7hF/DC/+4E3sevZQB0E/8uZZbPrxdtiUrqGvr1gsYnp6GlqtFmw2G2w2m0wuFAco0NcvNBpNRzXGuOMnP/kJtm/fTvXWmI41EjRBp2NiYxwJutlshtPpHMqz21ZfXq932ewkVYreC+FyuWA2mykbXywWd50FL5VKEIlEUKvVA+nhpoIoz88PRhivV6hUKkxPT0On0/V9uLEYForBLVeKXqvVEIlEYDQaweVyyQ1nMBgkxZK0Wi0EAgHy+fzQyflShL1Wq01Udn0QBP1BVCoVhEIhGAwGcLlc0ufZ7/fDHYvhosWM9yVCfCSXgufzjCWRE4vFCIepsegad/SqcD9uyKfzuLrrNnY/fxjHN17AsQ3nsfv5w7i2h4lCtohSoYzjG8/j8OunOwj6he3XsOuZQ/BZll97KpbGy/9zAz761b4Ogr7vxWP44N92wm/rvXWiH9TrdSSTSTgcDkilUjAYDIjF4mUP0EYFpVIJn89H+XeiW/zDP/wDpqamqN4a07FGgibodExsjCNBt9lsA88cz87OwmKxdG31RZVg2EJ4PB4YjUbKxpdKpQ/1Ri+GZDJJiuwNqiSbIIiBE9Vu0K8w3mqgUCjA5/NJB4FBPnu1Su2tVguZTAZOp5PccLJYLPB4PMTj8b6E5YaRXV+MrI9Ldn0YBH0h6vU60uk0nE4nbgoEePL0Cfz89Am8cP0KXmbewvOs2zhp1KM6Bu9iIcRiMSJDsG9bC1iNgN44wSiyYs/zR3BtDxM3p9i4OcXG1d0M7Fl3BBbZffs46TUVPn5iCmc+uIyru27j4o4b+PSpgzj34bUVhd6KxSJ2vLwLG/73NuxZdwRH3zqLqZdPYOMPP8TJdy6gWqX2u14ulxEMBjsO0NRqNbxe79DaU5bCatwSqMT3v/99nDp1iuqtMR1rJGiCTsdEBxXkbzkMOnNcq9Ugk8kglUq79vVuC4ZRUWbdhs/ng06no2x8uVze0Rv9IObm5uDxeEAQxLLXrQZUEOX5+XlwOBwUCoWRjZfNZklf7EFXawzKRi2Xy4HL5UImk5HlnFwuF0ajEZFIBLVajXKyvjC7Pm6l8MMm6Atx2WbBc4yb2MrjYOOtG3jlwlk8e/Y0/uviWQgtZpRKJco34G2IRCKaoC+BSe/P554SYeqVkyQ5b2P/+uMQXZKj0WigkC2CcZiLveuOYudTB/Dp0wdxdssVhNwrfyfy+TyuXb6OiztuYMvPP8WmH2/Hez/dgU9+PQXeGREcGjflJL2NxdpT2uKPo7BWlEgkE1Wp8pd/+Ze4du0a1dtiOtZI0ASdjokOqgjgUvB6vTAYDAN5ViaTAZfLhclkwszMTE/3UlVm3UYgEIBGo6Fs/OXU9FutFvR6Pfh8/lAIbT8K8v2Ax+Mhl8uNZKy2GJxEIoHP5xvYc9ticK1Wi+w3Xy3pjUajpBp/+znNZhPJZBI2mw0ikYg8YPB4PMjn8xORXV9MuGpYGCVB3yYV420BF3vVyt9CgY8lIvzq8gXsZzLAYDAgkUjGQthKJBIhGh28v/pawKSX/0uuKrHvxaMPEfQ9645AcVtDXlet1jBtC8IktsKhcaOY7+4AKZvNgsVioV6vY9oWhOSaEjv+az/e+qcteP1/bMbb/2srjrxxBslIivJ38SDa4o8mkwl8Pp/8++l2u5HJZAb+OykUCifm96xer+Nb3/oWOBwO1dtiOtZI0ASdjomOO3fuUEYCF0NbeKVfkuLz+UAQBILB4KpKr6nK4rYRCoWgVCopG1+j0SAQCDz080qlAolEMnB/7oVYjYL8pIw7NzdHCq2lUino9fqBEfTFxOBWQ3BbrVZHdcRy15ZKJfj9fqjVarBYLPD5fFgsFsTjcTQaDcrJOtXZ9VES9D1qBV7jEgsIuhK7VQq8QDAgDfhRKpU6hK04HA50Oh0CgcDIbaMmiTiMGpNeXTBtC+LQb07hxDsXcGM/gRv7CBzbcB5H3jiDkKv/daXTabDZbDQa90ndqfcu4bUfvIuPfz2FfeuP4eNfT+G1H7yH05svj7VYYr1eRy6Xg8fjgUqlItuIDAYDQqHQQH4n+Xw+EokE5Wvt9n184xvfgFKppHpbTMcaCZqg0zHRMW4EPRQKQaVSrfr+hdndfrKho8ymLoZoNAq5XE7Z+Dqd7iG7u3Q6TXpfD1NAr1cF+UkZt9VqQavVdojBGQwGeL3evp/dLmlvk/N+yGzb2iubzfZ0b6PRQCwWg9lsBp/PJ63B/H5/TzZuwybro7RxGyVBl0z78SLBxBapiCTnv+Gy8a6I/5BlWq1WIyshxGIxmEwmpFIpHA4HUqnU0ImNQCBAbEge65OOST+8qNfr0PFMOL7xPPauO4q9647ixKbzMEkG48edTCbB5XLRaDQQ8cbw7k924MPH92Df+mMktj2+B+/+dAei/jjl76NbVKtVxGIxWCwWsjpJJpP19TvZ1t2hem3dfm/+4A/+ABaLheptMR1rJGiCTsdEx7gR9Gg0CplMtqp7y+UyxGIxlEol6vX6WJO1lRCPxyGVSikbfyFxnJubw/T0NAiCWLLsfZAQi8UDtxrrBiKRqCsRwdWgWq1CIpFApVKh0fiy8sBoNPZtK7iQnPdTYl6r1aBQKCCRSFAul/siwgtt3Nq9l2KxGHa7HalUqq9DhEGS9UajMdTs+igJerlaxQWLCb/hsfECwcALBAPviQXQRVYuly4Wi/D7/dBoNCAIAlwuF3q9fmCZvAchEAjIKgsaneDz+Wvi3SQjKdgUTtgUTqRjmYE9Nx6Pg8/no9FowGvy4+3/tRU7nz7YQdB3Pn0Qb//z1hUV4ccZhUKh43dyYcVLt3oSBEEgm81SvpZuUKvV8Lu/+7uYnp6meltMxxoJmqDTMdFx9+5dykjgYkgkEhCLxau6j81mw263DyS7SxVJbCOZTEIkElE2vslkgsvlwszMDEwmE5lRHcXYUqm0a4u3QUIikQxl3Gw2Cy6Xu2jlgdlshsvlWvWzByUGVygUIBQKodVq0WgMvjS9VqshHA7DYDCAw+GAzWZDp9MhGAyiWq1STtbbhH3QNm6jJOiNxv0slDuVhMjvgzwYQLJYWNVGOR6Pw2q1dmTynE7nwPpk1woJHQZ4PN7ElCUPAvV6HQ6NGzenCJx67yKYR3jwmpcm1tFoFEKhEI1GA7lUHtse2413/2VHB0F/96c78OEv9yCfHq1q+rBQq9WQSCRgs9kgkUhIPQmbzYZEIrHo36a2XWyh0PvfACqQz+fxyCOPIJPJUL0tpmONBE3Q6ZjoGDeCnk6nIRAIeiIoLpcLBEF0ZQvWLWQy2UCf1ysymUxP72HQsFgssFgskMlkkMlkI1W0p+rdS6VSxOPxgT4zHA6DIIhF+/nb79npdPb83IVicI1Gf+S8bZVnt9tHIvLWtnFzOBzkZlMmk8HlciGbzY6d0Fw/2fVRE/RhoFAowOfzQa1WgyAIsk82HA6jskqP9a8aCe0FXC4XyWSS8nmMCgahBfvXH8e+F47i2Ftnsfu5Izj46ik41O5Fr49EIhCLxeS/+eckePMfP8DGH36IbY/txoYfbsOb/3MLBOellK9tWCiVSggEAtDpdOBwOCAIAhqNBj6fjyTk1WoVDAZjrNwblkMkEsEjjzyCRqNB9baYjjUSNEGnY6Jj3Ah629apm2sbjQbUajWEQuHABd2USiXC4TBl7yGfz3f9HoYBvV4PgiBgNBoxM9ObAn6/UCgUA7du6wZyuRzRaHQgz2qLwbV7AJe6zmq1wuFw9PzsNonst6x9enoaLBYLwWCQMjJcqVQQCASg1WrJ8uq2jVu9XqecrLff9Wqy62uBoC9ErVYj+2SFQmGHCnU2m+06u87j8b5SJLQXTFLfcL8oZIs4+c5FHH79NK7vYZKYeuk4Lnx0A9XKwzZkoVAIUumX5LtarUF2Q41dzx7G+z/biV3PHob8ppoya8VRo16vI5VKwel0QiaTgclkQigUwmQygcFgjFwAcrVwOp34nd/5HXz22WdUb4vpWCNBE3Q6Jjru3btHGQlcDMViEQRBrHhdoVCAQCCAVqtFs9kc+DyWUjEf5Xtgs9mUjB0KhcBkMiGXy1elgN8vlrN4GyYGdTDQarWg0WggFApRLpeXvdZms8Fut3f97EGJwbVaLVitVpIMUE2AFxLhZDIJq9VKEkClUgmv14tCoTB22fXFyPrC7PpaI+gPIp/Pw+v1kirUfD4fJpNpRY/nr1qWuBew2WxkMoPr2R5nBOwh7F13FJc/vtlB0M9tvYoDr55APPjwdyQQCEAulz/082q1hlwqPzYe6FShUqkgFApBp9OBwWCAxWJBpVLB4/Egl8uNrbK9Xq/H1772NXz++edUb4vpWCNBE3Q6JjrGjaBXKhUwGIxliWG7bNjj8QyNQC6mYj5KlMtlMJnMkY45OztLkjaDwQCTyUTJ2qk6HBnEwUClUoFYLIZKperq4Mhut8Nms3X17EH1m9fr9Y7KE6oJ73IoFovw+XwkARQIBLBYLGR5NNXzaxP2pYTmQqHQmiboC1GtVhGNRjtU/JVKJUkMFl7L5XK/MlniXsFisSZG2KtfRLwxHHj5BM5tvdpB0E+9exFH3jiDTPzh9+D3+6FUKimf+7gjm82CyWQim83C7XZDqVR2HKL106IyDIjFYnz961/HF198QfW2mI41EjRBp2OiY9wIeqPRAIPBwMzMw2XVCwnksNS22xiEunY/qNVqKx5UDPq9K5VKSCQSVCoVeL1eGAwGStZO1eGIWq3u62Agm832bEPncDhgsVhWvG5Q5LxUKpFOB7VajXJy2wsajfsCUW3RQhaLBY1Gg+np6b5V5wdJ1huNL7PrFosFYrF4aDZu44qFHs8KhQJMJhMCgQBmsxnRaBRsNpsm6EuAyWQ+dKCxVlGr1XB9LwufPnMIlz+5het7mLi04wZ2PnUA7JPCRe/xer1Qq9WUz33c0bZFXfizhYdo7QoluVwOl8uFdDpNaXadyWTiT/7kT2iCTsfAgibodEx0fPbZZ5SQsKUwMzMDBoOBRqPR8fNarQa5XA6pVIpKpTL0eaxWvGtQaDTuH1S0Wq2hj1UoFMDn86HX68nx/H4/dDodJWvX6/Xw+XwjH1er1WJ6enpV94ZCoWXF4JaCy+WC2Wxe9ppBicFlMhlwuVyYTKaxsDnrB61WC7lcDm63+yEbt3Q6TXkp/MIWgng8PjQbt0lBpVJBJBIhD1fawoBerxf5/NpQ2h4E2srbX6V3EgskcOGjG9j93GHsevYQ9jx/BFd3MZa0ZnO73aTbBI2lkUgkwOPxlr2m3aLSFoBs2ysGg8GR965funQJ3/nOd6jeEtOxhoIm6HRMdIwbQZ+bmwODwUC1WiV/tpBYzMyMRrDMZrN1XXo8DCx1UDFoRKPRRdsFAoEANBoNJWunqnphNZn7ubk52O12cDgcpNPpnsd0u91LthIMUgyu3Rbi8/koJ6/DQK1WQygUgl6vB4fDAYfDIX28R23j1mg0oNVqH2ohCOayuOWw46hOg2tWC9zxeN82buOEVLGIaD6H8grrqNfrIAgCFouFPFwRCoWwWCyIxWIT/x76QbtyqlgsUj6XYSCfzsOucsEisyPq/9Jmr5gvwan1wCAww23woVxauvTa6XRCr9dTvpZxx0I7um6w0F5RLBaDwWBAKpXCbrcjmUwOPbt+/Phx/O3f/i3VW2I61lDQBJ2OiY5xI+jz8/MgCAKlUglzc3Pw+/1kZnKUgmVOp7Or0uNhoX1Q0be92Wwcdxoc3KnfwJ2WHvNzTfL5DocDbDZ7Ue/vUCgElUpFydr79QZfLQwGA7xeb9fXt8XgRCLRimJwS8Hj8cBoNC76+Q9KDG6hDSHVRHoUaLVaSKfTpI1b28fb7XYjl8sN9YCiWq1CJpNBLpd3HAzYkwlsEvHxLPMW1nNYeIZ5CxsEPJiikYnPrqeKRdx02PGJQobtMjEO67QwRCPL3kMQBCmE1ha1MhgM4HK5IAgCarW6wzJqLSFdKmE6k0FqERI+adZYvcCl9+LEOxfw6dMHsfOpAzjwygmIL8sXVWpfDna7HQaDgfL1jDvC4TAkEsmq7y8Wi5ienoZWqwWbzQabzYZWq4Xf7x/KAdKePXvw93//91RvielYQ0ETdDomOj7//HNKSNhy4HK5yGQyMBgM4PF4yOVyI5/DUsRplGAymasmfvPz87jTVOJe6QXcKzyGe8Vf4l7xP3G3+imajRwpElYqlRa9NxKJQKFQULLu1ViPDQK9ZO7bYnBqtbovFwGfzwe9Xt/xs3bmvNHor6S90bivjMvn85HL5SgnzlShXC5jenoaGo2G9PE2mUyIRqMDtXFrO0vodDo0Gl8K2NWbTXyilOMFgoFdKgV2q5XYpVLgBTYTnyjlqP/2EGY1Nm5Uo1Kr4azZiHeEfOzTKHFYp8GHMjE+lIlhS8SXvI8giEWF0Or1OtLpdIdllFgshtVqRTweR6lSgSuZhD4ShjOZWDFbP04oV6vg+7zYr1Fhh1yKvWolOB43CguEutoiqZNijdUt0rEMjm04d1+x/ZNbuLqLgRObLmDXs4dgkdl7epbVaoXZbKZ8TeOOQCAAmUw2kGfV63Ukk0k4HA5IpVIwGIyO38tB/H3atm0bfvzjH1O9JaZjDQVN0OmY6BhHgs7j8SAQCKBQKFCv1ymZA5U92G2w2ezV+7vPpnG39BLuFn+Nu+X3cLeyGXfLb2I++29wGrdCo9EsSyxjsRikUikl66aqvaDbzH0mkwGHw4HNZutaDG4pPPg9G5QYXDuTK5PJUKlUKCfJ44Jms4lEIgGr1QqBQAAmkwmVSgWfz9eXon1bkMlmsz30uQVyWbzCJbBNJsFutZLEhzIJXuESmM5lH5pjo3E/m1qpVJa1caMazmQCH0iEOKrX4ozZSOIDiRDnLaYl7+tWqbxcLiMYDEKv1+M6i4V3Ll3Ay9cu403WbXwg4uOs2YjEhGTZRX4vtkpFOKjT4JzFhMN6DbZIReB43B3rbbd4DWrcaq0GTyoFbTgEazzWcSAwTNTrdTI7bhJbsfPJA7iy8zau7mKQ2LvuKK7tZvT0XLPZDIvFQvnnOe4Yptp9+/fywaqXfjQlNm7ciMcee4zqLTEdayhogk7HRMe4EfREIgEGgwGtVts3+ekHgUAAarWa0nfRT/XAnaYY94q/xN3yu/fJeWUz6qk3kfb9FKXIM5hb4d0mEgmIxWJK1u1wOGC1Wkc+rsViWTFzHwwGQRAEgsHgQMZslxDOzw9ODC6fz5Oif43GeFiRjSsKhQK8Xi+USiXZC221WpFIJFZsLUiUihBN+3FBq8ahG9fh9noXvS6Qy+LVRQj6tiUI+mKHCkvZuFFN1jXhEN4TCzrI+RmzEbtUCuxRK1BdYm6rUSq/brdiA5fAxzw2tt68jjfPn8WzF85iSsBHIpEYW3/nRqOBXLmMA1o1prRqXLJZSBzWa7BXrSTL3YvFItnaNKhxr9gs+FAmxgcSEbZIRThm0CI4RBu3SrkCk9iKK5/ewtkProB7WgTOKRF2PnWgg5xf3cXAoddO4eyWKz0932g0wmazUf6Zjjs8Hs9I1O7r9ToymQxcLhepKdF2bIhEIl0fNr300kt45plnqN4S07GGgibodEx0jAtBn5ubI3tlBQIBotEopfMJh8OUlXi3IRAIkMlkVnXvnQYP9wq/vJ89L29GMfoKwvYn0Ew+gbullzA/t7zYXjqdhlAopGTd3SibDwM2mw12u33R/5udvHXSigAAIABJREFUnYXNZgOHw1n1Z7IY2mJ8s7OzAxGDi8fjYLPZcDqda1IMbpio1+sdNm4EQUCr1SIQCDxUhaCPRvCmgIvHLp3D/zl+BE9cv4KDOg2Ki1jXNZpN7FTJse6BEvd1BIMsce92jg/auC1WCj8Mwl6p1aAJh3DCqMeURgXC7UI0n4c9Ecf7EiGOG3QdBH2LVIQzpqX7hJlMZk+ZtkShgF0qOQ5pNbhgMeOCxYwzRj0+EfDwxo2ruMxggM1mQ6fTIRAIjF0PdzSfw06lDKdMhg6Cfs5iwg65FNO/7ccvFApgMBgDO2zgez14XyzACYP+/jszGbFFIsIpowGVIXxP6vU6xJfl2Lf+GI69fRan37+M/euPY/fzh7Htl3twdutVkpxf2Xkbn/z6AATnJQi5IzCJrbApXchnlq+I0Ov1cDgclH+m4w6n00m224wSlUoF4XAYJpMJfD4fTCYTCoUCbrcbmUxmye/2k08+iddee43qLTEdayhogk7HRMcXX3xBCQlbiEajQfZEF4tFKJVKhEIhSucUi8Ugk8konYNIJEIymVzd/TNe3Cs+hTvFl5CeXoeo8ym0sm/jXuEXuFs9suL92WwWfD6fknVT1f9vt9sXzdw3m02o1WqIxeKBW/wFg0GoVKq+yXmr1YLP5wNBEAiHw5ST3UlHq9VCNpuFy+Uie6ElEgkcDgd80SjeEnDx+MVzeP7caWwVCfC+RIgnGTfAcDsXfZ4zlcQ7YgGeZd3Ci2wmnmXewjtiARypZF/zHEV2vV6v46bTgQ0CHt4V8bFFIsIbfA52qxSYzqRxwqDHuyI+DurUOGHU4xOFDFskIhiXEYrr1UosnMvhI7mUJJptnDIasF0uQSCTRjKZhN1uh0QiIRWoHQ4HUqkU5dn1fKWCA1o19mtUHQT9oE6D/RoVmUHP5/NgMpkDGbNQqWC/RoV9auVD7+xDmRje9OB96KP+OI6+dRbnP7yG2wc5JPa+eBQfP7Efn/x6CgdfO4Wjb53FzicP4Ohb53BtNwN7X/jy/09sugCveXrJMbRaLVwuF6Wf5yTAbrfDaDRSOod6vY5cLgePxwOVSgUWiwUejweDwYBAIIBI5Mu/Eb/4xS/w7rvvDm2vq1Ao8MMf/hDf+MY38Mgjj+D27dtDG4uO8QiaoNMx0UE1QW8LK2m1WrInWqPRrNqPelBIJpOUlXi3IZVKEY/HV3f/3BxahcMoBn+EYuinuJN/CveKv8Dd8muYn1m5PDufz4PL5VKy7sWE00aBxZT7y+UyRCIRNBrNwD3p5+bmEI1GO/qgS6XSqkia2WwGl8tFOp2mnNyuRVSrVYRCIeh0Ouy9fg3/59hhPHvmFDYRTHwkl+BjpQyvcFl4TyJEc4lDlmihAMLjximzESyPC9FCYaBzbDab9/t+Byw058+ksVksxC6lHMcNOhw36HBUr8UbfA5uOh2I5vM4bzFhu0yMDyRC7FErIQ9ML0mK217fvSi0l6pVHNZrsVMh6yCbnyrlOKjTPNRX/aACddt2jwp/5zZkgWlslYqxX6PCaZMBU1o1tkrF4Pu85DXZbBYsFmsg42VKJexWKXBYp+l4Z+fMJnwgEcKZTAx8jXaVC/vWH+sg57cPcnDmgys4vvE8ZNdVOL35Eo5vOAfOKSH45yT45NdTOL7pPC7vvIULH13Hp88cwvGN55FLLX6Ao1ar4fF4KPkMJwkWi2XsxPSq1SpisRgsFguOHj2KRx99FN/5znfwwgsv4Ec/+hG2b98+tL0ul8vFpk2bcOvWLZqgf0WCJuh0THRQSdAjkciiHtx6vb4nu6thIJPJQCAQUDoHuVyOSCSyqnvT6TS4HBaCnpO4U96Ku+W3cbd2GvMz3VUmFItFsNlsStbt9/vJvuxR4sHS+rbwl91uH7jF30IxuHw+39EHLRKJYLfbkUqlVuyDrtVqUCqVEIvFqyL3NHpDpVLBFOM2/uXUcbxx6zrWXzqP586cwstXLuLZ61fwGw4L1cZglOH92Syu2K34VCXHabMRlkS86wqL5gJl+H6z64pgAG/xuSQ5b+NDmRifKuWo1+uo1esI5bLwplPIrUCA2wS9V6smQySCj+RSbJdJsF+twnaZBB/JJdCGQ8vet9DfWSQSkbZ7TqcT6XR6ZNn1Sq0GWWAaB7Vq7FTKcECrhsjvRXlBj24mkwFBEAMZr1av45zFhA+l4g6Cvl+twm6VYlGbt37hMfow9dJx3NjL6iDoxzecx8UdN1Ct3m/BqJTvH6ic3XoVe184iss7b5G4uOMGPn5iClb54mXsSqUSPp9vJJ/ZJMNkMo19r77NZsPWrVvxd3/3d/i93/s9fO1rX8Mvf/lLnDt3Drlcbmj7XpqgfzWCJuh0THyMmgjNzs7CarWCw+EsWsJNlQ/2QuTzefB4PErnoFKpei71X+gd30+bQLlcBovFomTd7b7sUY+7sLQ+EAj0/Q6XwnJicLVaDZFIBAaDARwOB2w2G3q9HqFQCLUH+puLxSJEIhHUavVA7cJoLI62+B5LIcd6NhMbhDx8rJThQ6kImwgm/r8zJ/HG+bPg8/kwm82IxWJoNFYn0udMJfGuWID1nPvjvMIl8DqfA0lgelXP6ye7rgmH8Cafg2N6bQdB/0AixH6NqudNea1WWxVBr9frsCXiuGAxY0qjwnmLGZZ4rGeCXSgU4PP5oFarQRAEuFwuDAYDQqEQKiNQOM9XKojmc4seZKTTabDZ7IGN5UwmsEslxxapCPvUSuyQS7FVKoZ42j+UtZUKZVz8+CamXjmBG/vuk/SLO25g3wvHoON1qvqXiiUcfPUkDr56soOgX955Cx8/MQWDYPHsr1wux/T00iXwNO5j0nr1/+Zv/gabNm3CO++8g7/+67/Go48+ir/6q7/Cxo0boVQqcffu3YHteWmC/tUImqDTMfFx586dkZGgWq0GuVwOiUSyZD/vcmJdowKVGeQ2NBoNAoFA19fPzMzAaDQOxDu+Wq2CwWAMPHPcDUKhEFQq1cjH9Xq90Ol05OHRIMXg5ufvH570IgbXarWQyWTgcDggkUjAZDIhl8vhdrsRDAbBZrNhtVppMbgRIJlMgs1mw+FwoNls4rzVjGeYt7COYOAVLoEnGTewUciHN51CPB6HxWKBQCAAi8XquX2h2WrhgFaNlzksTP1W+XtKq8ZGIR/vS4TIVfuzzXvQxm2l7HqyWMAOuRTvi+/bqe1SyfG+WIiXOSxIpnsnem2C3o+Q21Lq8KuZS7vkVigUdvyOZbPZkfeup1IpcDicgT7Tm07husOGKa0KZ81GGKIR1Ia4rpArgos7bmDqpePYt/4YDr9xGoLzUpSL9w8kKuUK9Hwzzm27hk0/+hAv/M1b2P/ScVz+5D45P/neRex57gimbcFFny+VShEMLv5/NL6ERqOB2+2mfB7d4rvf/S6uX79O7kvz+TwuXLiAxx9/HH/4h3+IK1euDGzPSxP0r0bQBJ2OiY9REfRsNgsejwej0YiZmaVVxBfrBR41KpUKmEwmpXPQ6XTw+XxdXVur1Ujf61qt1vfY7TJUKqzuIpEIJQr6Ho8HXC53KGJw7ZL2fsTgyuUypqenSREsDocDi8WCeDyORoO2UxsWQqEQWCwWAoEA+bNaswFpcBo7VXJslghxzmpGMJfruK/VaqFQKMDj8UChUJDtCzabDclkcsn2hVS5hI1CPj6USToI+l61Ei9zWLAlEwNdX7sUfrnsuiEawWaxED+7chH/dP4U/vHcKfzXzWu47rB1lGh3g/bhH1W94Muh3W6yUNDKZDL1ZBfVD5LJJHg83lCePUxS/iCK+RJcei+scgfCnmjHgY/shgp71x3B0TfP4uCrJ7Hue2/iye+8gu2P78H+l45jx6/2g3WUv2RVh0QiQTgcpvy7Mu5oHwxSPY9uUK/X8ad/+qfg8/mL7lE///xz3Lt3b2B7XpqgfzWCJuh0THwMm6DPzc1henoaBEEgEAismJWlSsV7IagkqG0YjUZ4PJ4Vr2sffJhMpoHNt9lsgsFgkMJ9owQVCvrlchlcLhdcLncoYnDtkvaVesqXQ6vVgt1uB5vNRiwWQywWg9lsvl92zWKR4orlcplyUrsW0Gq14HK5wGazkUj0T4rr9ToikQiMRiO4XC7YbDa0Wi2CwWCHjVu2UsG7YgG2SEUdBP1TlRyv8Qi40qmhrXkpG7dCsYgDahX+48ZVPM+6hS0SET5WyPC2gAvBApGzblCpVMaWoC9EtVpFNBqF2WyGQCAAk8mEUqmEx+NBLpcbSnY9Ho+Dz+dTvvZhIR3L4NiGczj13kXc3E/g5n4Cpzdfwlv/tAW/+X/fw8l3L0DN0qNUWPq7IRKJEI1GKV/LuGOSWgHq9Tq+/vWvQ61Wj2TPSxP0r0bQBJ2OiY9hEvSZmRkYDAbweDxks9mu7vH7/dDpdCMnhgvRarXAYDAGTtZ6gdlshtPpXPaaYDAIgiAwPT3dWzn63Czm55qYX+Ke2dlZ0o931OtOJBKQSCQjG68tBqdSqQZeWr+QnPdTit5o3LcXEggEKDyg/t1qtZDL5eB2uyGXy0lLMKfTiUwmQ5fAr5KoLmwXGfTz2+0LTqcTUqmUtAVrf2bnLCa8QDCwW6XAlFaNfRoVXuGysFMpR60xumqJdnbdHA3jP29cwb9duYCfX7mIX924incEPGyTiLBLJUexh97tNkEfRb/3ING2i1IqlWCxWBAIBDCbzYhGowPLrsdiMQgEAsrXOiz4rQHsXXcU1/cySYJ+cz+B8x9ew6HXTiEZuW/9loykoGRocX0vE5yTQrh0XjKjLhAIyKohGktDIpEgFFpeQHFcUK/X8bWvfQ02m20ke16aoH81gibodEx83L17dyjEp1KpQCKRQKFQ9ET02t7QoyaGC0ElQW1juV78hUJ76XS6++fONXGnwcHd8ibcLb2Eu9VPcaf1cDvB3NwcGAwGqtXqyNc9Sou7hWJwoVAISqVyYM8eFDkvl8uQSCSQy+WoVqsrXr/QEozNZoPL5cJoNCISidBicl2gXq+TyvijqkaoVqsIBoPkZ3aVxcLbt67jmZtX8SLBwHoOE9tkEriHmD1fCq1WC/s0KvzD2ZP41c2rePr2DTx2/RL+9fIFvM5lYYtYgGg2i1qt1pUyfLlcnkiCvhDVahWRSAQmk4msYFGpVPB6vT35uz+IaDQKkUhE+fqGhYg3hoOvncKF7dc7CPqJTRdw8t2LKOaKiAeTOP/hNexZdwRH3zqLAy+fwIFXTkLF0qHRaIDH4yGZTFK+lnHHJFUa1Go1PProowgGg0Pb5zabTdhsNthsNjzyyCPYvXs3bDYb4vH40Makg9qgCTodEx/DIOhtUSWbzdZz2XUkEoFcLh85MXwQTCZz4L3IvcDhcMBqtT7083q9DoVCsazQ3qKYm8Pd2mncK/4H7hafwt3SetwrPo57pedwp2V46HoWi4VyuTzydafTaQiFwqGO0T7g4HK5ZGXHoHrfexWDWw4LdRtWUx7fbDaRTCZhs9lIi6m2TVGxWKScDI8byuUyxGIxVCoVZYcZ7c9MbzbjKJOBDy9fxFEOAb3dhnw+P/KKiFA+jw1CHv7t6iX8+vYNvEAw8ALBwGPXr+BnVy5gj1KOfLHYtY1bm6CPoqd7FKjX68hms3C73aTWgFAohMViQSwW68l/PhwOQywWU76mYRKx2wc52P3cYVzacRM39xM4t/Uqdj97GLIbajQaDQgvyrBn3RHc3E/g1gE2bh1g4/TmSzi24RwS4RR5KE31WsYdfD5/YioNstksHnnkkaFaq8lkMjzyyCMP4Ve/+tXQxqSD2qAJOh0TH4Mk6HNzc3C73SAIYtUe3vF4fKQlzkuBzWajWCxSNr7b7YbJZOr4WdvqSa/X915+PxPGvdKzuFt6GXcr799HeTPuFf8Tdysf3i97H4P1Z7NZ8Pn8oT2/2WxCpVJBLBZ3VAgMovd9bm6OJFn9kvNoNAqCIODxeAZGyorFInw+X4fn+kqiZV8V5HI5UsthnN5FqVSC3++HWq0Gi8UCn88fqTigLZnARhEfr/M5+NnVi/jPW9fwFOMm/vXqRfz44llIAtM92biVSiUwGIyeiOskoVKpIBwOw2AwgMvlgiAIqNVq+Hw+FAqFZe8NhUKQSCSUr2GYSMcyuLGfwIFXTmDP80dw6DenwT0jRjFfQqVcwZn3L+PUexdJcn7rABs3pwjsfeEobEoXCIJANpulfB3jDi6Xi1QqRfk8ukEoFMIjjzyCVqtF9XaYjjUUNEGnY+Lj3r17AyM+Go0GQqGwL2KXSqWGnkHtBoOwK+sHXq8Xer2e/HckEgFBEPB6vauyP7vTVOJe8Ze4W978JUGvvI+7pVdwr7QO87P5juu5XC7y+Xxfa1gN8vk8uFzuUJ5dKpUgFAqh1WofOuCIx+OQSqWrfvYgxeA8Hg8IgkA0Gh0a8VpMtEyn0yEUCnVVSr+WEI/HwWaz4Xa7lzwMabVaKFSrKFPYJtBoNB4SB1Sr1fD7/V3buPWKYC6HD6Ri7FUrsUHIw3/duoZf3riKf7t2CRuEPFQane+jLTRXq9UWtXHL5/NrmqAvRL1eRzqdhtPphEwmIw/FrFYr4vH4Q+8gGAxCKpVSPu9ho1qtIeAIwaXzIuqPk4J71WoN57Zdw4lN5zsI+o39LOx78RicWg+YTGZfbQRfFUzSQYbD4cCjjz6Kzz77jOrtMB1rKGiCTsfExyAIerFYhFAohEaj6Vv5u13WO2pi+CAEAsHAvbB7gd/vh1arxdzcHBwOB9hsNpLJ5Kqfd6dlxL3ir3C3vPEBgr4Od0uvYH6u056Nz+d3Lew3SAzLgz6VSpFe1osdcCQSiVX3vg+q33yhOFk2mx0Z8VsoWtb2XJfJZHC73cjlcmtaaC4QCIDFYiEUCi15jSeTxmG9Fm/wOdgg5OGS3YoMxWr5rVYL+Xy+w8ZNLBbDbrcjlUoNrAqg2WrhgtWCt4Vc7NEoMaVV432JEBuEPKjCS78z8v4HsuvhcBgMBgPFYvGh7PpaR7lcRjAYhF6vB4fDAUEQ0Gg08Pv9KBaLmJ6ehlwup3yeVELJ1GHP80dwdddtMnt++PXTOP3+ZWSTOfK7Q/U8xxn1en2iDjK0Wi1+//d/H1988QXV22E61lDQBJ2OiY9+CXo7s+t2u1eV2R0VQesVYrG4L0LcL4LBIJRKJdRqNUQiEUqlUn/PnKvhbnnj/R708ju/LXF/E/cKj+FO7eJD1wuFQqRSqZGvu1QqgcViDex5C23+wuHwktelUimIRKJVPX8Q5LxarUIul0MikVBuldb2XNdoNGRZtdlsRiwWQ6OxNjzXW61Wx8HXUtcFcllsEPLwNPMm3hbw8AafgycZN7FLpUBljET3arUaWVrN4XDIiohgMLhiRUSr1UKkkMd0LotSvfbQ/+erVVx12LBdLsFmiRA7VXKIA9No9HgIEI/HQRAEnE7notn15XrX1xrq9TpSqRQcDgep5M/j8cDn85FMJodi4zYJyGcKYBzmYv/649j7wlHsXXcUJ9+9CJfOi2q1OhEWfVSjVquBwWCgVCpRPpduIBQK8Y1vfIMm6HQMNGiCTsfEx2effbYq4jM7OwubzQYOhzNQIlupVMBkMkdODB+ETCZDLBajbHyPx0P6Ww/Mj3zGjbvlt3Cv+B/3BeKKT+BudTfm5x4m/2KxGIlEYuTrHuTnPzs7C4vF0iEGtxTS6TQEAkHXz26LwQ2CnBcKBQgEAmi1WjQa40WAG437/swWi6WjrHqSPdebzSb0ej34fD7y+fyy1160WfAU4yY+VsjwiVKOT5RybJWJ8SzrFjSRMOVrWQwPVkQwGAzIZDK4XC5ks9mO72q8WMQZsxEfSEV4VyzALpUCqnBo0e9zslRCIJdFYRUtEKFQCCwWC8FgsONzqNVqXfWur3WUSiWyb33hAcv09PTEEK1BoVyqwKX3QkMYYBBaSPu1teAAMJL3N2Hv6fbt2/jmN79JE3Q6Bho0Qadj4mM1BL1Wq61OSbwL1Ot1MBiMntXfBw2FQrFsxnWYiMfjYLFY4PF4A6lK6MBcGXeaKtxp8DHfsmN+bmbR66RSKSUHFO3T/37X3Wg0oFQqIZFIurKL60WcbpBicIlEAmw2G3a7fexLydtl1Q96rjscjonxXG//7ZJKpahUKitev00uwStcgiTnbTzNvAmG20n5erpBpVJBIBCAVqsFQRCk9Z4/GMRBrRpvCbjYp1HhsF6LbTIJ3hMLYUnEBza+1+sFQRCIx5d+Zrt3fSmy/lXIrnu9XqjVatTrdSSTSdjtdvKARSqVwuFwIJVKfWWz68Vi8SujXzCI9zQp35OLFy/iu9/9LtVbYTrWWNAEnY6Jj14J+kLrp5mZxcldP2i1WmAwGIPLGq8SarUagUBgpGMuVMF3OBwj8wNfDAqFYtVK/P2g0WiAwWD09d1qi8HpdLqu1e67Fadrl7S3yXk/xGV6evqhrOIkoVarIRQKQa/Xg81mg8PhwGAwjK3neqlUgkgkglp939Kpm3sO6jR4jnW7g5zvUMjwFOMmhNM+ytfUK9o2blarFadYTPzHqePYdP0qPhHwcEijxAmjHu+IBThtNvY9VqvVIqusMplMz/NcSmhurZJ1t9sNjUazKOGanp6GVqslf8/0ej2CweBXqty7LTBI9TzGHblcDiwWi/J5dIujR4/i+9//PtVbYTrWWNAEnY6Jj88//7xr8tju5Z2enh58ZnfBOO1TcqrI6fz8PHQ6Hfx+/8jGa7Va0Ol0EAgEKBaLI/EDXw4qlQqhUGjk4/Z7QJNMJsFms+F0Onv6jhYKBXA4nGWvGVS/eavVgtVqBYfDQSqVopy0DYr4pVIp2O32Ds91r9eLQqFA+fwymQy4XC4sFktPn50mEsY6goE3+Fx8rJBhu1yKdQQDG4Q8JEqT7SWvCofwBpfATgEPm2/dwBsXzmHDlUt4/dZ1bBXw+jpkaTabMBgM4PP5fX/+K2XX1wpZd7lcZJvLUqjVakgkErDZbBCLxaSgo9PpRDqdnpis6WqQzWYninhShXQ6DTabTfk8usXu3bvxj//4j1RvhelYY0ETdDomProh6DMzMx3q0sMmaSwWq39RtD5hNBrh8XhGMlalUoFYLIZSqUSj0cD8/PD9wFeCRqMZeQXB/Pz9vvF2eV4v983NzcHv94MgiFVl/kulEgiCWPb5gyhpr9frpPBfsTjZBG85lEol+Hw+qFQqMJlMCIVCWK1WSjzXY7HYqj3lm60WGG4XfsNj42nmTTzDvIW3BFzs0ahw1mKCN5Om/F2vFvZkAptEfBzWa3HSZMBxgw57ZRI8f/US3rlykdTA6FVvoF6vQ6VSkS1Qg573Ws2uO51O6HS6nu4pFArw+/3QaDRk+4LBYEAoFJqYHuRuMWnEkyokk0nweDzK59EttmzZgp/85CdUb4XpWGNBE3Q6Jj5WIuiVSgUSiQQKhWJkWW0Oh0OJB/dCWCwWOJ3OoY+TSqXA4XBgs9k6+u67yegOE6OuIGhjNRUUs7OzMJvNfXnXl8vlRdXjBykGVyqVyIOYWu1htey1inq9jmg0+pDnejcK4/1i4aFNP8+JF4tQR8KY0qrx3cP78c39u/DN/bvw7andeFvI61nRfBxQazRw3KjHmwIudqsVOKBT4wOpCO9LRbAlEsjlch16A20bt3Q6veTvQbVahVQqhVKpHEmbw4M2bpOcXbfb7dDr9au+v1arIRaLwWKxkFUscrkcLpcLmUxm4rPrqVQKXC6X8nmMO2KxGIRCIeXz6BZvv/02Hn/8caq3wnSssaAJOh0TH1988cWSpCWZTC5KHocNPp9PqQf5/Pw8bDYbbDbbUIlomzwsJka3UkZ32DAYDPB6vZSMzWQyuxYfbDQapOhXN2JwS6FSqYDBYDz0GbVJQL+Z83Q6DS6XC7PZPPIM8jih1Wohm83C6XRCKpWSJboul2ugnusL+5/T6cFkuf3ZLP7s4F58a/8u/NmBPfizA3vw7and+NbUbhw16Ch/t6tBulzGJbsFH8okeF8qwpRWDUMs+tB1C/UGOBwO2QcdCoXIQ5aF2g9UfMfbpfCTml232WwwGo0De14+n4fX64VKpSJFR41GI8Lh8ERm1+PxOPh8PuXzGHeEw2GIxWLK59Et1q9fj+eee47qrTAdayxogk7HxMdiBH2hWBkVQmEikYhSD/L5+Xk4nU5YLJahPHtm5suWgaUyvm3COKxe/5VgMpngcrkoGZsgiK5aHEqlEgQCQU9icEvhQfX4QYrBhcNhEAQBn883EUrno8SDCuM8Hg8mk6kvz/VGowGtVguBQDDQ/vedKjm+PbUbf3ZgD/784F4S39y/C39/9iTl77IfpMolRAp51Lp4561WC+l0Gg6HAxKJhMyuEwRBGTlfDJNm42a1WmEymVZ1b7ZcgiORgC0RR7xQeOj/q9UqotEozGYzBAIBmEwmFAoF3G43stnsRGTXJy0zTBWCwSBkMhnl8+gWTzzxBF5//XWqt8J0rLGgCTodEx8PEvRmswmNRkOKlVFB0Kiy+FoIj8cDo9E48OfWajVIpVLI5fJly7iptpuzWCxwOByUjM3hcFAoFJa9pi0G53K5BnKI0Wh8qR7fzpw3Gv2LwTmdTrDZ7GUtpmh8SagSiQQsFgsEAgHpue73+7vuga5Wq5DJZJDJZAMvn39TwMW3p3Z3kPM/P7gX357aje8e3k/5+6MK4XAYLBYLIpGo45AlGo2OjZr/QqG5SqUyljZuFosFZrO55/ucyQROmwzYq1Zij0qBo3odVKEgasuQ7lwuB4/HA6VSCRaLBT6fD7PZjEgkgmq1SjlpWwyRSGSiMsNUwe/3Q6FQUD6PbvHzn/8cmzdvpnorTMcaC5qg0zHxsZCgF4tFCIVCaDQaSm3OqPQgb8Pv90On0w30mdlslixzXol4N5tNSu3mhl3ivxyWqyyYm5uDz+cDQRCIRqMDG3Ph+x7Z3Q1qAAAgAElEQVREv3mj0YBerwefz0c+n6ecoEwa2p7rHo8HCoWCzNI6HI4le6CLxSIEAgGphD3oOZ0wGfCtBzLof3ZgD741tRs/u3qJ8ndGBdrkvG0V2D5ksVqtEAqFpJq/z+cbK1HEpbLrVJbCm81mWK3Wnu5JFAo4adRjn1qJs2YjbjpsOGs2YkqrgjuV7OoZ1WoVkUgEJpMJfD4fLBYLKpUKXq8X+XyecgLXRigUglQqpXwe4w6v10taSU4CfvjDH+KTTz6heitMxxoLmqDTsSZifn4e0WgUBEHA7XZTVlbdBlUK4gsRCASg0WgG9rxgMAiCIBAIBLp6v6tVMx8UHA7H0Er8VwKfz1/ULWB2dhYmkwk8Hm/gIoIzMzNgMBhkb2Y/5LxSqQwti/tVRa1WQzgc7uiBNhgMCIfDqNVqSKfTpF7GsNoIctUK/vuJI/j21G58e2o3/tvUblIoju/3Uv6ORo32QVksFlvymkKhAK/XC6VS2aHmn0gkxqoUvtFY2sZtVITdaDTCZrP1dI88MI2X2Uz8hkvgNS6BTUIejui12K9RgutxI1UsIl4ooNLl/Ov1OrLZLNxuN3kwJhQKYbFYEIvFKK0yCAQCkMvllBPKcYfL5erZDYBK/OAHP8ChQ4eo3gbTscaCJuh0THx8/vnnsNvtYLPZSCQSlBCyB6HX6+Hz+SidQzgchlKp7Ps5s7OzsFgs4HK5PQnftdXMuxVLGzRcLhdMJhMlYwuFQqTT6Y6fNRpfisEN2k2gXdJOEASZpc1kMqsierlcDnw+n1Rjppp89EVcWi0oQkGcNBnA9LhQHpNy5XYPtN1uh1gsBoPBAIPBgEajQaFQGGqfvzeTxi+uXyFJ+v9z6hhuOB2Uv5NRv3+73Q4Oh4NMJtP1fW01//YhG0EQ0Gq1CAQCPdm4DRtU2bgZDAbY7Xby3+VqFc5kAmK/D9KAH950qqNsvVKrYa9GiV9ev4xNQh42iwV4ncvGa1wC74sF2CYV45hBh8M6Da7YrHAlu8uoL0SlUkE4HCY1UwiCgFqths/nQ2GRXvdhwu/3k1akNJaG3W6HwWCgfB7d4nvf+x7Onj1L9VaYjjUWNEGnY+Ljs88+g8lkoowILgaTyQS3203pHGKxGGQyWV/PqNfrfSmMdyuWNgx4vV4YDAZKxhaLxR0ige3SZb1ej5mZmYGOtVAMrlKpkErVbDYbXC4XRqMR0WgUjcbKZDsWi5F98ZMuBpcpl/HYjSv4vw7tw18cvI8fnDkOY3zpbOmo0Wq14PV6wWKxYDabSbVqgUAw9CxtrFiAN5OeSHu1ftBsNmEwGMDn8/sS4Gu1WsjlcnC5XJDJZGAymZBIJMu2MFC13kZj6ez6IMm6Xq+Hw+FAo9FAqVoFx+PGfo0KB7VqTGlUOKTVQB6YJkm6P5PGNqkYLxIMbBYLsVUqxlapGOsJBv7l8nm8xefggsWMKzYrDuk0OKrXYTqTWfX86vU6MpkMXC4Xab0nEolgtVoRj8eHnl2ftNJtqmC1WlelZUAVvvOd7+DmzZtUb4XpWGNBE3Q61kTcuXOHEiK2FKxWK2UCZW0kk0mIxeJV35/P58lM6szM6khlN2Jpw8IwevC7hVQqRTwex/z8PBKJBNhs9lBaL5YTg2s2m0gmk7DZbBAKhWRf5mKCZa1Wiyz3DYfDlJOKQeB1Pgd/cXAv/urIAfzNsUP4v48exF8c3Iu/P3sC1Qb1lQGtVqujMqX980bjvtrzg1naYDCISqVC+bwnGY1GA2q1GmKxeOAZ72q12nE49mALA9Vrn5n50m5xWNl1rVYLl8t1n2TFY9ivUeKGwwaW2wmW24lLVjMO6zQIZu+TbFsijt0qBT78LUl/ncfBm3wOfnHtMn544QyO67X4SC7BejYTL7OZeJ3PBuF2DoxYlctl8jPjcrkgCAIajQZ+vx/FYnHF+3OpPGKBBIr5Ulfjud1uUl+CxtIwmUw9axlQhXq9jj/+4z+GUCikehtMxxoLmqDTsSbi7t27lBCxpeBwOGC1WimdQzqdhkAgWNW9kUgEBEHA6/X2RSqX6sUeBQbdg98L5HI5IpFIR4/roMdoZ84bje76zQuFwqKCZalUCiaT6SGiOMnIVCr4q6MH8JeH9+Nvjx8m8ddHD+IvDu0D10dtv3Wj0YBGo4FIJEKpVFryurbn+sIsrVQqhcvlQjabHZss7SSgrY6vUCiGTpibzSZSqRTZwsBkMiGXy+F2u5HL5cbmc2s2m6jX6wPLrms0GrjdbjQaDRBuF47oNSQ5b2OfWgldJIxyrQZTLII9KgUuWczYr1biHSEfGwQ8PMO4iV/fuo7nmLfwz+dP439fOIN/vnAa/3D2BNaxbnfdj94r0Wpb70mlUvJvpM1mQyKR6HgXxVwR4styHN94HgdfPYkz71+Gjmda8X05nc6J6q2mCgsrMcYd9Xodf/RHfwStVkv1NpiONRY0QadjTcS4EXS32z0Ui7NekMvlwOPx/n/23ju6zetK93aaZ25uvsnNTObemcncNd/EVbZcEpckjlscyXKTLdtqtopVLatZkiVZvXeJvffeKxpBohAk0UiQYAN7772ChETFTvK7f1BERIuUKYkSaAbPWs+SFgG85+C8ANZ5zt772bcs+sbq+aejj/tEtdj3irW1tWg0GpuMnZmZSVZW1l0xg7t6ddQX4E6c2gcHB6mtrUWv1yMUChEKhWRnZ085FX6ms7Kjgye93fi1j8c4gf4bPy8e83QhojDfZnPr7+9HpVLdllDs7++npqbmhp7rs+W+3S329vYil8vvmjv+t7Gvr4+qqiq0Wu24lmCNjY0z5r6NpcLfSXRdq9VSVlbG0NCoQHfTqoksMJJQXGQV6C6aLGIKCwjJy8VVq2a3VMxuqZiQPANxhQX45eg5pVKwLjmBN8KCWJ0Qw4bkBNYkxjE/JJB5IQHEFBbQc80M85vsauuiSFNCRrwGvTSXurKG2xJevb29VFdXk52dTUpKChKJBL1eT1VVFUL/VBw+9SLwSCSR5xLw3ReKyxZfchU3j/p+V2qr+/v6KcwykR6rJluaR2frvXXC1+v11oOemU6z2cxPfvITiouLbb0NtmOWwS7Q7ZgVmGkCvaKiwmbp1WPs6elBIpFM+fnDw8NoNBoUCgV9fX3TMgelUmkz4776+noyMzPv+bhmsxmJRIJMJrsrZnBXrlyxpqreSSSup6cHhUKBRqOhsbFxwlT4m0V3ZzIHh4Z4JdifuZ6u4wT6r3zcedLbjTwb1aFfXzZyp7Xl17cDG+u5/l2/b3eDnZ2dSKVS8vPzZ0TkemhoiKamJvLz88fdt4qKihl13yZr43az6Lr1fQwMEJlvZKMgkZ0pIg7JU/HQawk35nEqXcGJdAXueg2hxlxcdRq2iJLZn5aCl16LvyEbWXkZaxPjeC04gE8SY1keF81rIQG8HOTL7wN82JicSHxRIW2941PL25s6kATICDoaRfjZOEJORBN5PoEiTckdi7CWlhaKiooQxonYt+QYpzc64HMkiPDzscS7ivDeE0zY6Tj6eic+OBgaGq2tzsvLs7movBnbmzrwPxjOoYVn2P/mKfa/dQrHTV6UG6vu2RzGWuTZei2mwoGBAb73ve9RV1dn622wHbMMdoFux6zATBPo1dXVaLVam86hv78foVA4peeO9Y/X6/VYLJZpm0NGRsZdSe+eChsbG1GpVPd0zJ6eHtLS0khNTaWysnJar329GdydivPW1tZJW3pNlgp/u67wtmJgnoG51wzifuXjwZNebjzu6comUZJN3kdLSwsSiYTi4uJpH99isVjbgV1/34qKimhtbZ0x7cBstealpaUz8rM7dt/KysqsbdwUCgWFhYW0tLTMmPs21TZuWVlZVFZWkl5VibMmiyNKGdvFQj4VJPFJYhxHFGmcVilx1aqJLy60Mig3h9MqJf45o47t3tk6ViXEMC8kgKWxUfwxJIDXgv1ZHhfFm2HBfJmagrMmC0VlxTixpE8xEHQ0krTwdOSRGcgjM4hzFhDnLKCjpXNaBFl5XiUOGz0JPBmO2z4fzm1z5Nx2Jy5udcNhiwdNtc2TvtZoNJKfn29zUXkzJrqL+fL1kzhv8cVrdzDuOwI48NZpXLf50dfTd0/mkJmZSVXVvTsQuBO2tbVx33330d3dbettsB2zDHaBbseswNdff20TETgZ6+rqyMrKsukcBgcHEQgEXLly5abPa2pqQiwWU1JSMu0mZllZWdTV1dnk/Tc3N9+RSd6tcmwdx4yAplOg32q9+c1YU1ODSCSiqqrqW587lgp/u67wthY/wcZc/hgawFPe7jzv78UJlYIeGxh21dbWTnnNp4NjPdcNBoO153pOTs6MMiy726yvr0ckElFdXW3zuUyVZrOZ+vp6cnNzkUql1rTq6urqGWUQOJnRXEZGBrkmE/6GbCILjCSbivE36Dmbmc5heRoXMlU4a7IIzjOME+gxRQV8JkziRLqc6EIjCcWF7EuT8IcgP1YlxLAoKow1SXF8FBfFwohQXLVqQo25BOcZ6L6W6j7QP0Ciu4R4V6FVnMsjM5CFqwg5Hk1F/vQIvvqKRrx2BxF2OpYEVxHxLkLCzsdybqMzpzZcIikhiYyMDEwmE21tbZivayt3O33i7yW72rs5t8qFMytd8NodbKXrNj8OLTxDsfbepJ2rVCpqa2ttvh5TYXV1Nffddx9Xrlyx9TbYjlkGu0C3Y1Zgpgl0W0Rvv8nh4WEEAsGkEfGRkRFKSkrumonZ1atX0Wq11NTU2OT9t7W1IZfL7/o4IyMjlJeXj1tHg8FAeXn5tF1/OsT5WO/nMX+B29mUT+YKP5NSc28QPcPDNPZ002u+98K0uacH33QlZ2KiKa2pscn7H+u5XlxcPM6wrKysjK6urhkZWb5TVlZWWr+Ptp7Lndy3jo4OTCYTKpUKgUCASqXCZDLdcTaLeXiYtr4+ugYG6DebKW9vI7OmGl19HabWllv6roxF1ysrKxGJRORVlOOqySKhIB8fvZZD8lQ+jovmlSA/fufvxfzQQFYnxHBIlsqqhFgWRoTwZkggrwb58kWKCCd1BmHGXGIL8lkRH838kEBeC/bnzfAg3okIYbdUTFJxERH5eQTm5dDZNxrVHRgYJMlDQpzz5AK9ubaF2tJ6akx16CQGJP4yJAFyjKpCOlo6MZvN9PXe3BzPbDYj8kvD4VMvQk7EEOOQTMChCJw3+6BPyaWnp4fKykp0Oh1isRipVIrBYLB6fsxk87O2xnZOLXfk4jqPcQLdc1cQ+988RV76vTlcUCgU1NfX23w9psKioiJ++MMf8pe//MXW22A7ZhnsAt2OWYGZJtBbWlpQKBQ2ncOVK1cQCASYzeYbHrNYLOj1emQyGT09PXdtDtMdSb4VdnR0kJaWdlfHuHz5Mrm5udaeymN/z83NpbS0dFru4XSI86GhIev9vpPez9dztqTC3y365Oh50tOFh90cedTDmWf9PIkqKrD5vHp7e6msrLQalslkMvLz8+9qz/Wh4WFymxoJLzDil5uDvLKC9rsQEbZYLBQXF5OSkkJbW5vN13o6OTAwQE1NDdnZ2dZsFoPBQH19/Q1ZEcMWC4093VR0tNPaN/7wrKStlaiifLxydJzLVLFfJmWjMIkV8bFsFCRyVCknotBIY8/UfyfGulU0NzfT2tuDf042zupMdogFfBAZzrO+Hsz1cuUpLzd+7e3Go+5OPOLmxEOuDvzS1YH/vo4PuDrwqLszrwT4slmQyE6JkE2CRDYJEnHSZJJYVIjAVIyrTkNK2fiIbk6akcAjkaSGKq0CPfpSEpHnEpAEyAg9EcPZVc5s//0BPnl0G+vn7mDb7/Zz9IMLeO0OIslDQoxDMkLfVIp1pZMK9c7WLlKCFPjsDcFtux/+B8PJTNIx0D8w7nmDg4M0NTVRUFCAQqFAIBCQlpZGSUkJ7e3t46LrM4GDg4P4fBnCwXfO4PlFkFWgn1npwumPnWiqabkn85DJZDQ1Ndl8PaZCrVbLT3/6U/7617/aehtsxyyDXaDbMSvw5z//2aZi2BbicCoUCoX09/eP+1tfXx9KpRKNRsPQ0NBdHT8nJ2faIsm3yq6uLqRS6V27vtlsJjMzk4yMjBvM4IxGIyUlJbd97ek0g+vr6yM9PZ2srCwGBgbuini4WSq82Wy2ubi510wtL2PONRHyKx93fuXjwRwPF57wciOnscHm8xvj0NBoz3Wj0Tiu5/p0plRbLBaEpSXsk0nZJ5NySJHGF6kS3PSaG8TjVDlssdBrHmTougOF4eFh62FZV1eXzdd2InYNDFDd2UFTT88dfaevz2ZRKBQIhUKysrIoKyujrqUFQakJz2wdTlo1PoZslNWVDAwNUdnRgVeODo9sHT6GbDaLk3k7PIT3osI4IJOyI0XEF1IxZzPTiS8uGre+k93bkpISJBLJuAMRZVUl2yRC1iXF8UqQH7/yced3/l68EuDLkx4uPHBNjP9yAoH+39c99pi7M6sTYkgqLsI3R4+rToNvjh4XrZqw/DzqO8fXlXe1dZEaoiTkeDShJ2MIOhZF9MVEIs7FE3A4As/dQex76xQrf7mZxf++ntWPbGPNo9tZ9/jnrHpoK8eXXiTOSUDYqVgCD0eQqxhfL242m2lrbKetcVRcN9e1UmOqm3J9e1ZWFjqdzno4lpqaSm5uLnV1dfRP4kp/r1mYZeLUckf2v3WK0x87c/i9cxxaeAZJgPyezUEqldLScm8OA+6UaWlp/Md//IddoNsx7bALdDtmBWaaQO/u7iYlJcXm85BIJOMi5GPmYEVFRd9amz4dzMvLm5ZI8u3wVl3sb/X+pqWlYTAYuHz58g2PFxQUUFxcfFvXHhkZsW7C71Scd3R0WDeB98pw6ruaCj9d7OvrY0lwAI+4O/GMrwfP+HryjK8nv/bxYI6nCwfkqTaf40S0WCx0dnZSWlo6rue6yWS6o57r1Z0dHFHKOJ+VgY8hGx9DNp7ZOnZJxYjKSm55jvnNTQQbc3HSqvEz5JDdUM/A4CBarRalUklfX5/1+d0DA+Q1NaKuq8HU2sLg0M19Exp7utHU1ZJRUz2l50+VQ8PDaOtq8TFk46DJwk2vJamkeMpZBN0DA2Q31BFbVEhSSTH5zU3j5jaWFaHRaDgdHcnnEWE4SSWE6rWEGg04atVo6+sQlZXgqlOTXGrCUZPFp8IklsRG8G5kKHvSJJxQKdgsTuZilgoPvZaGm2TbWCwWCgoKkEqldHZ2jnuspbeH40o5KxNieNbXg1/7uPNasD8vB/jwqLsTD14nwicS52N8yNWReaEBeOfoMDY2oKyqRFBiQltbQ1l9E1pTBWKTCUVlOeVtrQyazfR09VJqqEAvzcWoKqRIYyL0ZCyJ7hLOr3Fj34ITLP/PjSz+t3Us/vd1rPjvz/jg52tY/G/r+OLVI6SGKEnykOCyzReHjR4UZpkYGBikobIJabCC4KNRBB6OICVQTkPlrUV5x1zuh4ZGDfcaGxvJz89HLpdbD1lKS0vp6OiwaXS9JLucsNOxXFjrjveeYLSiHAYGpr/3/GSUSCS0t7fbXHxPhUlJSTz00EN2gW7HtMMu0O2YFZhpAr2vrw+RSGTzeUilUrq6uhgZGbGmId5L07Y7Eaoz9R6MmcGVlZVNaqpXVFREYWHhLV/7+nrzOxXU9fX11nnaMuV8Infx2ZoK39nZSWpqKvMCfZnj4WIV52N81MOZNUnxNp/nVDhRSvXtZEVk1FSzO1ViFedjPJ6uwEGTdUtzMjQ2cCYznbOZ6Xhka7mgzuCoIg1P0Whd/fWp3tWdHXjm6DimlHMsXc4JlYKwgjw6ByYWxblNjThr1ZxUKTmpUnI2M5244iL6piEDJL+5CQdNFn6GHGKLCgnLN3IhK2NKUeqewUHiiotw0GTika3lTGY6hxRpRBcV3HCA0NTTg4dei486E4+0VM7Gx3IyOpLjSQk4ytPw0mkIzDOQXGriTEY6GwQJvB8dzjuRIWyTCNglFfNxfDQ7JSLOZKZT29U54ZzGshXGSqS++fjg8BARhUbWJMbzlNdoSvsTni487unCHLe/CfQHbxI9f8DVgYfdHHkp0Jcj8jQKm0fFcE9XLyqBjuP7/di+1ZkvD/txNkqIp15LVk31DcK2Ir+KwCMRJLpLOP2xE18uOMmif/mED//POpb/50bWPvY57/98DR/877Wsm7uDoGNR7J1/gg1P7mLlLzfzxWtH8N0XSsjxaE6vcOLY4oscef88hxaewW9/GO1NHVMSch3NnYS5R5HoJ8KoKqS7o2fc452dnVY3f5FIRFpaGnl5edTX1zMwMHBHIvJ2aatDAqFQSFfXve29frsMDw/n6aeftvUW2I5ZCLtAt2NW4C9/+YvNxfD1HHNQn25X9FulTCajtbUVg8FgTf28l+PfrlCdDg4MDEzrPRgZGaGsrAyxWExTU9NNn2symcjPz7/l60+XGVxpaSlisZiGhpmTTn358uxOhW9ubkYikVBSUsKeVAlzPF34tc+NEfQzmek2n+utciwroqCgwBrtm2rvbnVdDbtTJXjn6McJ9CNKOa56zdQ/O0ND+ORkc0GdQXiBkfACI0GGbHZFhbMvIY626+YxODSEX24OJ1QKgvNzCSswEpBn4LAiDXF56Q3Xbu3rxVWvwVmnJqqogKiiAoKNuZxUKdDW193Z2lks+BtyOJelIqaogARTMQmmYiIL8nHSqqnu7Ljp6w2NDThqMgk25nJYkcY2sYCNwkRWJ8USUWBk4DqRXt3ZgbNWTXJJMYJSE8klJqKNubjKZRyKj+VQRBi7oyNwlaWyLTmRN8KDeCnQl+d8PXk7IoT3o8J5PTSID6LDWZUQi4deS2JJMfr6OjquRfuHh4fR6/UoFIpx2QrfZFheLi8EeDPHw5mHXB156Jrovl6UP+zmOGEU/cFrnOPuxMuBvhyUScm/9huhiMrk1DYvPt/hysmjQRzZ4cmRnV54JsnwydGPS3vvaOlEFq7i8HvnObnMgf1vnWbXq4dZ9M+rWfQvn7D439bx4f9Zy1s//oi3fvwRH/zrGlb8/5+x7Bcb2fDULtY/sZNNv9rNkv9Yz0f/tYnPXzzIqeWOnFvtyu4/HmPt4zu4uN4dtUBPc+3kKdnVRbX47Q/jizcPc3TxOc6uciHkRPSkrxkYGKC+vp68vDzS0tIQiUSo1WrKysro7JyednEzlWN7p56eHpvPZSr09vbmxRdftPUW2I5ZCLtAt2NWYKYJ9G9zUL9XlMvlyOVyMjMzJzSLu9s0mUwYjUabvHez2YxAIODy5RtT0G+Vly9fth5yXG8GNxlLS0vJy8ub8vWnS5wPDw9jMBhITU2lo+PmG39bczalwldXVyMSiaitreXy5csYmxp52sedRz2cedLbjSe93KxGcRUd7Taf751yLCvi+t7dk/Vcb+rp4aRKyXGVwirOXXUavkiVoKyeetu55t4eLqgz8DVkE15gxF+v5UhMFOckIk6qFFRe93mv6GjnpEpJQJ6BsAKjlS66URH+TZfy3KZGTmUoiSzMtwr0qKICHLVZBOYZbnudOgf6STYVsy45gXXJCexOleCq05BgKiLBVMwlTSam1pt3VEguNeGm13JEKWOLOJnDijSOp8tZmxzPZ6IkogrzqezoYHB4iPb+fvxycwgx5iIoNVnpnaMjqrAAY0M9Z2SpbIwOZ56PB896uPCEuzOPuTvzuKcLrwT5sSgqjOWx0SyNjWJdcjzOGjWO2iyiCvNp7u5CrVajUqlu6mcxNDzMYXkav/P34bf+Xjzh5crDbo7W2vNHXB15yM1x0tT2sej5k95uvBsRgpsmk9rWVnIURk6vdGLrB6fZvvYSp48GceliFIe2e3DpQhTOmixyG0bdv7vauxH5peG3P4yL69zZ+/oJtv5mH+vm7uC9n33CG/+4jNd/uJTXf7iEeT9YzLzvLWb+Dxbz5j8uZ+E/reTdn65k6S82sOX5L1n6iw0s/KeVbHhyFwffOcPh986x+dkv+ei/P2PHS4fw3htM9KUkGquaqCqsQRmThcBbilqYTUNlI6EnYzj/iStnNjnieyiE0JOxnFnhTJK75FsFoNlspqOjg9LSUmsWklwux2g00tDQYLPo+t1if38/AoFgxtTkfxsdHBxYsGCBrbfAdsxC2AW6HbMCM02gjzmof9M87F6yvb3dGum6F/XmE7GsrIzc3FybjG2xWBAIBAwPD9/RdQYHB8nIyLilQ46pvu8xMziLxXLH9eYDAwNkZmaiUqlmVM/kWxV936VU+DHX8Ila1ymqKlkQHsxcL1fmermyMCrsjqOxM5GDg4PW3t0pKSlIJBJycnKora21ppyr62o4li7ni1QJu1Ml7JWlEJRnuKWe9N0DAzhr1aNGYVo1h6IicJKl4p2j54I6Y5zreElbK8fS5fjn5nA6Q8nnKUK2iYV8KZNyNjP9hjT3nMYGTmUox4nzqKICnHVq/HNzbnlNhi0WBoeGSDQVcz5TxRdSCZ8KktguEbJZlIxnto6wfCOuOg313aOGdoPDQ9R0dlLR3j5ufpLyUk5nKNkmEVjF+X6ZlHejQnk/Opz1yfE4abJIKimma2CAjJpqnLRq/HOziSgw4pWjw12vpailGYvFgqq6io/jY5gXGsjrIQH8MdCHZzxcmOPqwHOernwSFc6ymAg+EyaySZiEV7aepBITl7JUeIlHI7nflu3SZzbzSVIc80IDWJUQy4cxEbwVHsKrQX487unC876ezPV0ualAf9zThZcDfdkrlaCvq6UkpxyX7b5sf+EAm948wupX97HlnWPsXefIjqXn2LX8PKcjBeRdE+hFmhL8D4aTEihHEiDH72A4Z1Y6s+nXu3n/558w/wdLmPf9xbx+/1Lm/2gJ83+wmPk/XML8Hy7h/Z+v4Z2frOD9f13LrleOsOKBzbzz/61g6+/2s+W5vWx+bi/bXzjA+id2snf+cYS+qfh8Gf1FNp8AACAASURBVErY6VgCDkXg82UIQUej8NwViPfeYE4tdyToaBRnP3PE73AokecS8Nkbgus2P9oab63Wur+/n7q6OnJzc0lNTUUkEqHVaqmoqPjOpIXfjD09Pda9k63nMhWeOHGCDz74wNZbYDtmIewC3Y5Zgb/+9a82E8KTUSgU0tfXZ5Oxq6urEYvFyOVy6uvrbbYGFRUVZGdn22Tsm7WZmyrHzOByc3O5fHnqkfiKigpycnJu+pzpNIPr6upCJpOh1+sZGpoeYytbi76Zngo/lq1wM9fwYYuFkrZWytvbZuwhw3TSYrHQ3t5OcXEx6enp1p7rpaWlFNfXI6+sIKW8DGNT47jU7KlSUl7KHlEyu8PDcEtX4J+bw1GlnOiignHr29HfzyVNJhsFSaxIiGFtcjzrkhN4JyKEnSliur4R/a3v7sJRk4W3QW8V5+GFRk6oFLcU5e8eGCCztoYgowFnrZrdUgkBuQY8s3VsEQvYKhawQZDIjhQR57NUiMpK6R4YQFFVwSmVgq1iAVtESRyQpZJUUszg0BCm1tHDhg2CBI6lyzmaLmdZXBSvhwaxSZjEF6kSIgqMOGqzSKusYHB4iJyGekLz8/DLzSGmqIDi1hbr+lR0tLM+OZ5NgkT2y6QcT5fzUXw0b4UH80qADwuD/Pi9lxvzfD2ZF+DDAYmQ2Pw8DsdFc1qQxOAE37/BoSHym5uINxURVViAuraG9YIEXg7yZVF0OIuiw1gcE8Gi6DB+5e3Gk56uPHCt1nyOuzNzPV148Jop3O8DfFgYGcI7ESFsTE7A15BNSWMzSR4SPHcFcfSD8xxceYm1i46x/MntfDR3G5/8djerX/6SHZ85IRNqMZvNpMepCTkeTVpoupVifxm7XjnC2z9ZMRox//6SUaH+vcXMv38Jb/34Y+b/YAnv/NMKFv50JYt+tpotz+9j1YNbeP/na/j0V7tZO+dz1s75nM+e3cvGp77g1EeOSAJkhJ2O5cDbpwk6EonYX4bYX4bIL40L69zZ+cphQk5Ec2aTA/5Hwog8l4D/gXCcN/vcNDX+22g2m2lvb6ekpITMzExrNktBQQFNTU3fGZF7PTs7OxEKhTafx1T55Zdfsnr1altvge2YhbALdDtmBWaiQE9JSZlSOvR0i1Kj0YhUKqW9vR2tVktNTY3N1qC6uhqdTmeTsUdGRhAIBAwMDNzW6xsbGxGLxZSXl99yHXtVVRV6vf6mcxuLmt+pGdxY7XNxcfGsFIEzMRV+cHAQtVpNenr6dzJb4V6xr6+Pqqoqa1uptLQ08vPzrT2Ob/V6RaWlHI+K5LBUwvF0Bacz0okoMNI+QS10eL6RhZFhfJwQzfrkBFbEx7AuOYEvUiXoG8ZnMlgsFhRVFZzNVHE2U8UlTeZo/boxd0KndYvFQr/ZPM7grdc8SExRAWcz03FQZ7JPJuWDmAj2y1KJLMjHQ6/jyzQp65MT2ChMRFZZTk1nJ0FGA2uS4lgQFsQfQwJ4OzKEj+KjWRE/WmM+ODREXHEhy2KjeC86jDfDg3nOz5M/BPvzdkQI65MTCC/IJzQ/D//cHOvhw+DQEN0DAzeY0FV3drJRmMguqZgTKgUnrh0MzAsN5Lf+XqxKjOHD6HCWRobxeqAvy4L8ORgRxpdR4bgpZHR1dY37nRkaHiatshxnrRrvnGz8cnNw0qr5OCGaxz1dmOvhwhxPFx5yc7qh3vxBVwcecXfi8WsGck94uvJKoC/HlDJ8c7IJzc/DRafBSSzDbX8IQp80Lqx154vXjrLud3tZ9MCnfPDwZ3z0wm4+++gMe5ee59C7Z0j2TkEckIb/ofBxAj3GMZl3/9cq3rh/GQvuX8aCHy1l/g9HBfq87y3mjX9czoL7l/Lmj5ez4EdLWfCjpSz+9/Wsf2oXO146xIYnd7HigS2semALW57/kj3zjhN8LIqUIDleu4PY/cdjJHlIrAJd7C8j7EwcO185gsMGT05/eomAY+FEnkvgwlp3wk7HTqs7el9fH7W1tRgMBqRSKWKxGJ1OR2Vl5Xempru9vR2J5NtT/2cKt27dyubNm229BbZjFsIu0O2YFZiJAj0tLY2Ojo57Nt5YX+6x+sCrV6+SnZ1NZWWlzdagtrYWjUZjs/FFItEtZzGMjIxYTdaam5tva9yamhq0Wu2k15+OlPbLly9TWVmJSCSipqbG5mLsXtHWqfC9vb0oFAq0Wu2MieZ/Fzg0NERTUxNGo9FqfKXT6aiqqrqp2djly98oJWhtpbark4LmZqo6OyZ1QU+tKGOnVMSeNAlbxQIOylPxzNZxLktFvKnohucPWywUt7aQXGoiuqgATV3NhI7vFR3tJJiK8crRE5RnQFdfZ40gH0+Xc0CWylaxgDVJcbwVHszS2EhcdBoSTMXEFhVyPiuDsHwjw8PDCMtKOChP4+P4aN6LCmNlQgyLosJZmRDDekECn0tEKKoqCcjLYXFMBM/5evKUtztPervxG39v5oUGsDoxjiNKGWHGPLxz9LR9y1oODg9xTClndWIcX8pSOKxMY0eKiD8E+/M7f282JCfwblQo70SG8mlSPIsDfNmfGM9RiZDQNCkikQiZTGY9aKloa8NdryWmqBBBaQmC0hL8DNnMC/HnWV8PHnV3vmmv84fdHHnc05VnfNx5ytuNZ3w98M7RkVRisvKsIIWju7wJOhLJiSWX2PzsXpb+YgOL/nUNyx/awp4Pz7J/0VmOvH+eL/5wlHOrXfDcE4Tb535EnItHHCgjJVDOzpcO8cY/LOPdn63m7Z98zPwfLuH1Hy21CvQF9y+zGsbN+/5i5n1/MW//z49ZP3cnnnsC2ffGKfbMP8bOVw6zZ94xvL8MQRwgI9krhUsbPDm13JFkr5RxAj3yfAIOGz25uM6dHQsOcHGjO2dXueD2uT8lOeV3TTiazWba2towmUzWtolKpZLCwkKam5tnbHS9paUFqVRq83lMlWvWrGHv3r223gLbMQthF+h2zBrYSgRORoVCQWtr6z0Zq6uri9TU1Bv6cufm5lJWVmazNaivryczM9Nm43+zD/y38fLly+Tk5JCWlnZLr/sm6+rqUKvVN/x9Os3g8vPzSUlJoa2tzebiy1a816nwHR0dSKVSjEbjPesrPxt5fc/1sdTc9PR0TCbTDQctw8PD5OXlkZqaOmkpwURUVVdxIl1BZGH+OJ7KUCIqu9HJfSqs6GjHTa/lojoDP0M27noN57JUpFaWIyw1sS45no3CJPakSdiblsKiqHBeCvLl8xQhUYUFeGbrcNaqKW5toaO/H2+DnnNZKt6PDmdhZBjvRIbyx9AAXg32Y0V8DCviozmilHM6M51NoiQWx0bwW38vnvH14NUgPxZGhLJLKmarRMBJlZLYokLMU/hcautq2Z0qYbMomW0SITtSRCyLjWR9cgKnM5Tsl0nZkBjHhwE+vB8axH5ZKuLyUvqutd1qbGy03hP3+Di+jI8lMCuT+MJ8BKUlHFHKeDXYn8UxEfzG15PHPZx56Lr68ge+UW/+0DWRPtfThZcCfYkrLiTeVERYvpGIAiM+2Xq+3O3F3vnHOfWRI06febP9hQN89F+b2PzsXo4tvsip5Y64bPblyHvnCD4eTcChCPwPhRNxNh6f/aG4fe7P+id28s4/rWDZf25k0T+vZsGPlo4K8WsCfUywz//BEt74x2Us+IdlLPzpSt772SpW/PdnHF9yEe+9wcQ4JBN0NAqffaH4fBmC/8FwxAEyoi4k4rc/DJFvGmJ/GcleUjx3BZIeq6bUUMH5HU74HQxFEiCnurj2ngrJvr4+ampqyMnJsXpF6PV6qqqq6O3ttbnQHWNjYyMymczm85gqlyxZwokTJ2y9/bVjFsIu0O2YNbCVCJyMKpXqW9txTQfr6uoQi8VUVFTckIptNBoxmUw2W4OmpiZUKpXNxh/rAz+V596OGdxk/ObBxPVmcHcqzs1mMxqNBoVCMWEP4r9X3u1U+LGSB1v3lZ+NHBgYoLa29oae62MZON/W0msi1nR2ckmdiatOYxXnntk6zmamY2ptnfA1fWYztV2dNHR3M/yNe2yxWIgqzOekSkF0YQFxxYXEFRcSmJuDq06DV46OD2MiOCBP5ZAijUOKNL5MS2FeaCCr4mNx1qnxNWSjq68brdXv78c7R4+rTsPCyFBeDPTlD8EB/DE0kBcDffhDcABvh4ewNy2Fs5kqtkuEfJIUx5KYSF4PC+SNsCDeCAvmk8Q4ViTEcFQpo7Rt4vf1TQ4ND6Orr8M7R8cF9Wh/de8cHRezVCSXmojMyeZwVAQnUsTsl6eirK5kcPjGkgSLxYK6opyjYgGXkpM4GRXJxaQENsVE81KAD0tiI3gpyJfn/bx43N35Wp25A49M1FrNzZG5nq6sSYrDO1vPoWst5TYIEvkkKY49JwPYu+AERxad5+j759n1ymE+fXo3O14+zI6XDuH0qTdnV7lwcqkjCa5iEt0lRJyJp760kbLcSjITdZxd6cTSX2xk2f/dyNJfbODt//kxC/5hNIL+5v/4iFUPb2PB/cuYd80wblS0L2bB/Ut593+t4uJ6dzx2BnBpvQci31SKtaXkpRdSqDZRkFWMKk5NwOEIPHYF4rUnGO89wSR7plh7pQuFwhnRJs1sNtPa2kpRURHp6ekIBALS09MpKiqipaXFZv3Ph4aGqK+vR6lU2nyNpsq3334bBwcHW29/7ZiFsAt0O2YN/vSnP9lMCE7ErKysu2rQduXKFYqKikhJSZk0Ul9YWEhRUZHN1qClpQWFQmGz8adaZjCWgZCbmzstjveNjY1kZGRw9er0msH19vaiVCrRaDRWh2w7J+atpsJbLBbK29swtbbekDY9VkpQVzf7XNhnGscOWvLz8xGJRAgEArKysqioqLjlAyltXS2O2iyOp4/WWl9UZ6KoqrhBfF++PNoaz9cw2mf9gjqD7RIhH0aHsyAsiEWRYWxMjuediFAWx0TwSVIcGwQJfC4RckiRxp5UCR56LR/FR7FdIuSgPJUDslS2S4SsSozleLqcsAIjntk6/PNykFdV0N7fR4KpGAdNFktjInnG14M/hgTwapAfLwf68U54CAvCgjiqSLMK9I2CRD6MieDD6AhWJsbyQXQEa5Pi2S4RIqssv+W17hkcpL67i/a+Pjr6+4kqzOeUTMq+yDAclTKctWpEZSUMDk3uF9DW10dAngE/Qw4JRQWEaNVsiYnieU8X3vPz4gUfD57xducxD2cecHXgUXdn5rg73eDa/ryfJysTYjieLmd1UiwbkhP4OD6aBWFBvBTkywcHnNm1wRHfoxEEHAwn+GgUDhs92fbb/Wx48gsOvnOG44svEXAonNQQJXHOQqIuJNLTOXo411zbSuS5BPbMO86H/3stH/zrGhb+0wre/B/LWfjTlWz93X42PL2L13/4t5T36zn/h0vY+PQXHHznNLtePcKe+SeQR2YgCZBx7MML7Jl/nKMfXsB1mx8hJ2OIcxYg9JFSrCulv6/f2vZzJtaC9/b2Wn1TJBIJKSkpZGdnU11dTV9f3z2dS01NDSqVyuZrMlW+8sor+Pj42Hr7a8cshF2g2zFrMNME+t00aBsaGkKj0aBUKm9aY20ymcjPz7fZGrS3tyOTyWw2vlwuo6tVxlcDHnzVf5yvBoO5erl63HMaGhomzUC4XTY3N5Oenj6tZnBtbW2kpKSQn59vT6++RX5bKnxuUyNL46L4ta8Hv/Lx4K2IEFIry7FYLOMOwWz9Pv5e2NfXh0KhQKfT0dnZSUVFxbie64WFhbS0tEzpe9DU00N2Qz36hjrquscbnA1bLOQ2NuCVrWOrKJnDChmBuTl8EB3OI+5OPHitTnpMSM5xd+Jpbzee9hrtbf+MjyfP+3rxcpAfX0jFbBAksDYpjnciQpkfFsjCyFCWx0axWyrGUZtFiDGPwLxcLmkyiTcVUd7ejm9uNh/HRzMvNIDnfD35jb8Xb4YFX7tWPAevtZLbKhGwM0XMkphI3goPZpMgic2iZE6ky4koyL+llnWTMb+sDIfYGDwy04koyEdbXzel65paWwjMG3Wud9aqcdKo2ShIZHFUGK/5ezPXzZGHrgnxR64zh3vay5UX/X1YnRhHRIERF52ac1npfCpMZElsFPNDA1kcG8FnwiQ+cfZn9coznL8QSVqYClmYCrFfGhfXeXBquSNnPnYm6nwiqSFKxP4y/PaFkZmg/du9Hh5GGZ2F/6EITn3kxKdPf8Hif1vP4n9bx7HFFwg8Gsmqh7aMF+bfHy/SF/7TSra/sJ+tv93HJ49sZdOv97D5ub18+foJTn3kyL43T7H52b2se3wH+986jes2P3z3hZIaqqSzvQuBQDCj0skn4uDgIM3NzRQWFqJUKhEKhahUKoqLi2ltbb3r0fWqqiqysrJsvg5T5XPPPUdERIStt792zELYBbodswYzTaDfLYO2np4ea0sti8Vy0+fasg/51atX6ezsJDU11WbjFxkuYmlbxdc9n/BV7ya+7lnJV707uXrZxMjICCUlJXdkBjcZW1tbUSgU05LSfvnyZWpra62HCLYWT991fjMVPiwpkee93Xncw4VnfT34jb8XT3i58ayvJ+EKOTKZjO7ubpvP+++FY9kseXl5Nwhws9lMQ0MDubm5SKVSJBIJ2dnZ1NbWMvCN1mmT0WKx0NjTTW5jA6dUSlbGxzA/NICXg3x5LyqcZbFRPObhwiNujtZ66W+anI2Zm81xd+LX3qOfmS2iZN6LDOOlIF9e8Pfm1WA/Xg8L4o3wYFYmxBBVWGA1PYsrLsJRk4WptZWmnh5OZ6SzI2W0T/sBuZSL6gx8Ddmcy0zHVafGQZPJYUUa6wWJvBcVxtsRIXwcF83e1BTiTIXjesDfLisqKqy/hebh4UnN9yZjR38/hS3N5Dc30dDdTU1HBx56LVvFAt6OCOb3/t78ysuVOW6OPOLqwONujrzq782nibH45ehJLjXhqMniYlYGTposNolGDyAOyFM5li5nU1wcnx/wYuu6SwSdiyfOUYD/wXBEvjKqi+tIcpfgfyAc/wPhBByKQBIgp6tt/Lp0t/eQHqMm6nwCwceiCT8dT8iJGMJOxeJ/IJw1cz5n3g9ujJ5fz/f+eTVr5mxn0b+sZsH9S3nrxx+x5Tf7OLLoPNt+u4/3f76GD//PWjY+PdqCLeRENF67g9BJDQgEAvr7+20uKm+FPT09VFZWotPpEIvFSKVScnJyqK2tvSvR9fLycjQajc3f91T52GOPkZycbOvtrx2zEHaBbseswVdffWUzITgR74ZB21gdbGlp6ZSivbbsQ3716uhhgkQisc34l9vpqFzGcOtavuo/Mcq+43zds4qrfRfIyclGJpPdkRncZByLdt9uO6nrxYTJZEIikdDU1GRz8TQb6ZCp4jEPZ572cOFxdyfmujvzay9X5rg7sSoseMrCz847Z2trKxKJBJPJ9K2HWmM9100mk7XnekZGBqWlpXR2dk74+p7BQZJLTVzSZPJxfAwvBvryRlgwz/p48LinC096ufKElyu/dHXgQVfHa//eKNDH+LCbI8/6evBKkB/vR4WxICyIlwP9rpm+hfB2RAiLYyJZER+Lm147zpncQZOJrn60ZCK3qRFHbRbhBUaSS03Em4pw0amJKsyntqsTaUWZNf3eRacm0VSEuq6Guq7OWxbSE62jyWQiJSWF9vb2ab2fwxYLrX29NPf2UNXZga6uFnF5Cb45erYkJ7AzIZZjsdEcjwzncEIcZ1NTiMgzcCYznc9TRFZxfkQpY7M4mUupCg65RBB4Po54ZxE6sYHOllHjwL7ufsoMleglucgjMlALsinSlN4g0oeHh2mtb6O+rJGu1tHHmmtaSHCTsPV3+3jzH5ffVKBPxvk/XMLC/7WKD/9tHcv/61O2v3CAPfOOcfojR4KPRRF1KZHk5OQZ654+FQ4ODtLU1ERBQQEKhcL6nRszd5yO6HppaSl6vd7m73UqNJvN/Nd//RdKpdLW2187ZiHsAt2OWYOZJtDz8/MpLi6elmtdH+29FeM5W/Yhv3r1Kn19fQiFwns/tiWfr3r3cKXlGUba/sjXPWv5qv8oX/WfYKRrBx2Vy9BpRnutTvfYV65cYXBwcFyULycnh7q6uluqGx8aGiI7O5u0tLRbcq+289a4I0XEg26jYux6PuzmyCs+HnfVFf7vlT2Dg9R0dtLc+7ea8rFSk6qqqtu65ljPdZ1OZ+25npObS019vfWQTFxWykmVEgdNJouiwnjc02XC1l+/nOD/E3Gupwu/D/Dh5UBfXgsOYFFUGIuiwtiblsKeVAlfpIpZHhfFktgITquUVnGeYCrmkiaL/ObRQ7fB4SHkVRV45uhw1Kpx0o6K8/rurnFr1mueXt8Ji8VCQUGB1UzzXn4GjE2N+Ofm4KTN4rxKySVZKpGyNEISE9gTFcHKiDDWJ8SyXyZls1jAfrkUN72GYGMuXT19DA7c+H1srmkhwVWE/4FwAg9H4n8gnARXEU3VLTedS4m+nIBDkfjuD2PZ/914WwL9er7zkxWsmfM5G5/6gnWP7+DYhxcIOh5JclKyTQ3Yppvd3d1UVFSg1WoRi8VWH5e6urrbzhQoLi7GYDDY/L1NVaD//Oc/Jycnx9bbXztmIewC3Y5Zg5km0IuKiigoKLjj61gsFvR6PXK5/JajvZO1+7pXHBwcRCAQTFtt95RoKeDr3m183f0hV1qeZaTtJf7c+TJfdy/D0nGQlrLl9NSt4srlzmkddyIzuLEoX3FxsbWeT61WU1FRcVNn8f7+flQqFZmZmfYI7l3m6sS4ce2frhdlH0aF3TVX+L9HDg0Pk1FTjbNWzQmVgvNZKuKKiygoLUUsFtPQ0DDpa3vNg5haWylsaaa17+br397bS3yugWPCZL6IDOd0dBSRsjS2JsazO0XExuQEHve4UZzfKh/zcOZ3Ad78LsCbtyOCeT86nKWxkRxRyjiilHFQnsqapHg+uObunmAqJq64CBfdqNC8vs+6xWKhqacHU2sLFe3tDAzdfubNVDg8PExubq41i8gWn4eO/n5Mra2UtLXSde13zmw2k1Vi4kBSAgv9fXjLz4tPIkLYLxbgmJVhPdSY6P2kBisJPBxJanA6aaEqUoPTCTwcSUqQ4qZ+BWmhKsJOxSL2l3F88UXmf//OBPq87y1mzZzP+fzFg6x/YgefPr0br71BiEQim4vKu8XBwUEaGxvJz89HLpcjFArJysqitLSUjo6OKR9MFBQUkJeXZ/P3M1WB/uMf/5iSkhJbb3/tmIWwC3Q7Zg2+/vprmwnRiVhSUkJeXt4dXWPMLGmsJutWX3+9m7gtODQ0hEAg4PLly/dmzJERvup34OueNXzVe4ShxlcZafs9X3cu4GrbyzSXLGWo+UP+NOAzreNO1Qyut7d3nOHVRM7inZ2dpKWlYTAY7GZw94ALI0Ot0dIHXB14wM3RKsA2iZKsz7tVV3g7b6S+oY6j6XIuqTPxz83BTadhW0Ish6MiaG6ZPMpZ1t6Gt0HPyQwlJ1UKnLRqMmqqJ3Rj7zebCS8wclCRxoWsDNx0GjYmxvN7Xy8ec3PkMTdHHr5DYX69+/hcL1feCAtiaWwkqxJj2SRMYodUxBGljJ1SMRsFieyTSXHUZOKoycJZpyaiIJ/qzg6b3YehoSF0Oh1KpZL+/n6bzeNmHBwaQl9fS2C2noupKVxKTsI/Id76vWtraxv3vWtv7CD8dBwCLylpoSorBV6phJ2Oo61+8vT91JB0wk/HIQtTEXAonHd/uvKOBfrbP/mYlQ9uYd3jOzj83lncd/mREJ1oc1F5r9jZ2WmtJx/LaMnLy6O+vp6BgYFJX2c0GikoKLD5/KfC/v5+7rvvPhoaGmy9/bVjFsIu0O2YNZhpAr2iooKcnJzbfn1LSwsSiYSioqLbbv3V0tKCUqm02RpYLBYEAsFtHS7cFkcG+ap3J1/17uKr/lN01aziSssfGGn9LSPNTzHS/iZf9R/n6pXpM4Ubi5wPDd2aGdzg4CB1dXXjnMXVajUikYji4mK76LtHfCM8mDnXWkCNRc8fcHPkEXcn9sukU7539lT4m9M8PIyPIZuzmSpC8/MIMeZyTJjMquAAlsdE4pOjp7Cl+QbR3d7fj7tey7ksFWH5eUQUGHHTazmblY62rhZ1bQ3BRgOBeQZU1VWkVZSzNjme9ckJbBAksj45gRcCfJjj6cITXm485u5007T1scOaZ3w9eHCCx+Z4ODPX04VH3J2Y6+HCc74e7JGKcdNpOKlSclQpY7MomRUJMSyNjeKwQoahoZ7ugQEqOtqp7uy4aduyu34fzGbUajUZGRnfuVaNAwMD47oxpKSkYDAYqKuro7G6ibBTsQi9U8cJdKFPGmEnY2mtb5v0usXaUquxXKxDMkt/sfFbzeKmUpO+9BcbOLzoLJHn43H+3Ifo4Fj6+/u/03Xot8OBgQEaGhowGo2kpaUhEolQq9WUlZXd0BfeYDBQVFRk8zlPhS0tLdx333309vbaevtrxyyEXaDbMWsw0wT67dZ/j4yMUF5ejlgsvuM+6m1tbcjlcputwcjICAKBgMHBwXsz5shlvuo7wFe9m/mq/xStldtpLltLZ+Ui/tSxgK8G/bk6Mnlbutt5f9Ph1D40NBo5EAqFSKVSRCIRWq2WqqqqGRvhmg20WCx8FhfNI9cE+Zg4f8zThbleroQXGL/1Gt90hbenwk/MnsFBHDSZo3XEeQYOJcazOMifpTERLIwMZWeKmJMqJfLK8Z0KchobOKFSEF5gJLIw38pDijS2ioWsSY7nrYgQXgz05YUAb17w9+aVYD+WxUXxRlgQT3i6jEa6PV14zs+TRz2ceehbBPqDrg684O/F876evBToyx+C/Xna240nPVx4zMOZp7xc+UOwP7tTxRySp6Kvr2NoeJj85iYiCow4azVcUmeQaCqixUbp4xNxcHAQlUqFWq3+zh8kDQ8P09raSlFREUqlj9iWKwAAIABJREFUkuSkZNwO+OD4uScC3xTSQkfT3IOPRSPylTE0NHk2Un/vALLwDAIPRRJ4OJK1j21n3vcX8/qPJu6JPhV+8K9rOPD2aU5/5ETAkXBObbiIJkNLX18fvb299Pb20tfXx8DAwN+VYDebzXR0dFBWVmbNRpLJZBiNRhoaGtDpdJSUlNh8nlNhVVUV9913H1evXrX19teOWQi7QLdj1mCmCfTbqf++fPkyBoPBagx2p3OwdZuzq1evIhQKb9qrfbr5J7OQr3tWM9K1i9qiDTSY1vGn7tV81XeQqyP90zbOlStXpkWcDw8Pk5eXh1QqtaZLd3V1UVpaSkZGxjh36q6uLntkfZo4NDSa5nsmPs6a1n59/flcL9dvrXWeiPZU+EnWe3gYX0M2p9IVnEiMZ314KEtjI9kkSmZV4qjLuatew4WsjHFtw9R1NZxUKcaJ88jCfD4TJvFWRAiLosKZHxrI62FB/C7Amzkezjzm4cwLAT78PsCHR9ydreULj7o78bC7Ew+6Ot5UoD/j4cJrPu685OXOawE+LIsKZ3F0BPNDA5kXEsDa5HhCjLlEFxVwSZ1JWfvforPm4WG6BwYYHLZdlHwijpVL6fX6WVk609fXhy49G4ed7hxcfpITa89zdqMTAcfCqS6u/dbXD/QNUGaoIDNRR/jZeN77509YcP/S246kb/3tPs6sdGHz83vZ+JtdOO30oKm6heHhYQYHBxkYGBgn1nt7e/8uo+v9/f3U1dWRm5tLamoqAoEAhUJBeXk5XV1dNp/fzVhQUMD999/PX/7yF1tvf+2YhbALdDtmDf785z/bVIh+k7da/z0wMIBKpSIrKwuz2Twtc7Bpm7NrlEgkd6WV2aQcMTPY4Up7xYd0V7/LQOMSvuo7xNXLpdNy/ZGREa5cuTLODO52N5WDg4Oo1WrS09Pp6+ubeOM4MGDNxhir5cvPz6e5uXlWbrTvBce+a5mZmWwSJjHHczQy+oCbIw+6OfKYhzNzvVwRlpXc0Tj2VPjxVFdV8ml4CDtjo1iTFMfH8TEsi4vmc4kQV50arxw9R5Uychr/ZhZnam3ldIYS7xw9pzPS+SJVzC6piAVhwbwZHsyrwf78IdifRVFh/DHEnye93HjE3YnHPV15zteDudeZwT3o6sCDbo43pLOP/f8hVwfmhQTyZVoKa5PieCXQh7eD/HnX34dVQQEsCQ1iWVQYJ5Sya+I8i9D8PPpn+L3s6elBJpORm5s7638z2urbyU7NI8lXTLx/MkkxydasloqKiikb4nnuCmDxv6/nzf+xnHm3aBr37s9Wc3zxRT791Re89y+rWPnIFna8dIjD755DEiDHPDj6eRkeHmZoaGhSsf73GF1XqVTodDoyMzMRCoXI5XLy8/NpbGyccWuhVqv52c9+xl//+ldbb3/tmIWwC3Q7Zg1mmkC/lfrv9vZ2UlJSyM/Pv+1684nY399vmzZn13Gshc+9Gq++vh6xWERddTrVpSFUlcZMW+T8m2ZwdyLOu7u7kcvl6HS6KYu1oaEhGhsbrRF3iURCdnY2tbW137l6Uluxu7sbmUxGdnY2Q0NDvBDgw6993HkpyJcXA314MdCHl4J8edLbjXNZqmkb9+89Fb6rqwtpairh6QpcdWqWxUWzODaStUnxrEmKZ2VCLJ8kxrEqIZbM2pq/va6/Hz9DDh/FR/NhTAQfx8fwdngIz/l68msfd572duc5P09+H+DDS4G+zPV04WE3Rx5wdeAhN0cedne6abT8UXcnXgr0ZadENHpoEBfNNomQFfExzA8NZG3SaD37hXQFW+NjeCvAl1WBfuyMicRRloqpvm5GZ0Z0dXUhlUopKCiY0fO8W7RYLNasljFzToVCQWFhIS0tLZMeWPR09hJyPIZVD25h/g+WTFmcL7h/KfvfPoXbDj+W/XIjHz/wGYcWnuXMCif2zj/B7j8cJT1GPeGYY9H1/v7+v9tUeJVKRU1NDUNDo9H12tpaDAYDUqkUsViMVquloqKC7u5um89VKpXyn//5n3aBbsddgV2g2zFrMNMEent7O2lpad8q+KqrqxGLxdTU1Ez7HMbanE2n6L9VymQy2tvb7/o4IyMjmEwmJBIJLS0tXL16leLiYvLz86ft+pcv37oZ3ERsaWkhJSWFwsLC277WRC3csrKyKC8vt1nbpJnO1tZWUlJSKCoqsq77gvBgnvJ246UgXytfDPThCS833PXauzaXyVLhv+lOPRvY1taGRCKxmh/2mc0klRSxITmB5bFRrBcksFUi4OP4aBZFh+Ofm01VRweJpmJc9Rp2p0r4MDqcDYIEPhMlsVUi4LWQAJ7ycuNpLzdeCPDmWV8Pq4/AN53WJ3Ngf9TNkae8XFmbFIevQU9UoZF1yfFsESVzSJHGvrQUPo6PZkVC9Kh7vCaLBFMRObW1ZBQWkHVd/+e8vDwaGxsZGpo5qe3t7e1IJBJKSkpm3Wfqdmk2m6mvryc3N9d6yKnX66murr7B72NoaBiBt5QV//3ZLZjDLWb7SwdY98x2Vjy4md1/PIbDp16j3ODJ7teO4bbdj67Wv5VxdLZ0UVVYS0NFE2O18t8WXR8cHJyVgl2pVFJfX3/D381mM21tbZhMJmvpl1KppLCwkObmZpusRUJCAo888oitt752zFLYBbodswZ/+ctfbCZCJ2JXVxcpKSmTPn7lyhWMRiNSqZSOjo67Mofh4WEEAgEWi8Vm66BQKKyC+W7RYrGQnZ2NXC6nt7fX+vfS0tI7bnV39er0mcFdvnyZ6upqRCIR1dXV07rxHGvhptForFGioqKiWSn4bod1dXWIRCKqqqrG/d1dr+VJbzee9/O0RtCf8nbjWT9Pytsnd36eTs7GVPh+sxlTawuKokIik5OprKwc93hnfz/bJULeiwrjo/hoPoqPZl1yAicyFBxUpHE6M50TKgUe2VrWJSfwfnQ4W8TJnMpQskmUxAfR/4+98w6Pqkzf/666ou7Psrvuul/9uusqCoiCFBEV0VW6ChZ6701AkCoiIqJISZue3uh1kpn03nvvlfReJjMTAvjd/fz+CDMmJAFSJ8R5ruu+Lk3Oec97zpkw7/0+z3PfJ5hga8lwkXm7aut3UmnX9aS/aS1hxeXzHA8JIreiApfUFI4GBXA4yJ+fAv04EhzAuaREQvJyyamoaKMyr9FoKCoqIi4uDi8vL73Ao6ErI4qKilAoFGRkZBhsDv0dWq2W8vLyVqTP39+f5ORkysrKqK2q5fTRS5iukzDrz0vvTNBvCst9/sxyNr2zm43jdvHjYvNfCfpaCV/POMSR5UJyU/JRqzWEuERit+8kgs3WSHfYc0nk1q4l3G8lu+7l5aXf6LodamtrycnJITIyEjc3N/1GS1ZWFtXV1X0yV0dHR0aPHm3opa8xBmgYCboxBkz0N4JeU1ODq6tru79TqVQEBgbi7+/fqwrnV69eRS6X91hPe1fg7+9PYWFhr41fX1+Pn58fwcHBqNWt7dzS09OJiorq9jPsCXKu1WpJSEjQZ/h7c+GpyxJFRUW1IXxqdf/J8PXVIjw1NRWFQkFhYWGb39c0qFivuMxrMiEjpAJelQh43UrCqcR4g8z31lJ4FxeXHi+Fz6+q5FhIIKtdLrLXx5PowoIevYfM8nLEEWFsk19kmb0N+9wVuKWn0dCinLimQcXhIH92eCjZ5eXGQX9fJJHhOMXHskp+gS/dXDlxU7l9m4eCWScdmWhnxfQT9rxhJWGkVMDLIjOG3KGEvT1yPkRoygsWx3lJYMJ4awnLL51DGB5KVX09ao2GjPIywq/kEVNYQFknXBR05dQtFaoNURlx5coVfVWWIT7D9yrq6+vJyckhIiICpVLJOecLHFx+jF0zvmfh8xuYOmheh5ZqU/7wq6Dc5PvnsOCf6/jsr8vZMHYnR1eLOL5WwqGFpuz9+CckX9lRlFtCfEASwi02OH5/hssiN84ev4xgszUXzBX6PvX2oNFoaGhoGJBCcx4eHhQXF3fqnIaGBkpKSkhKSsLf3x+5XI6fnx+JiYkUFxfT0NDQK3MVi8VMnDjR0EtfYwzQMBJ0YwyY6G8Evb6+HrlcTlNTU6uf65TVo6OjaWxs7NU5NDU14eLiQl1dz6mXdxaBgYFcuXKlV8YuLy/H3d29w979zMxMIiIiuvzsekoMrqGhgbCwMLy9vamqquryOF1BS8LXMsOXlZXVoTDdQEFLhfzy8vKOj9NqCcjJRhAeinV0JLmVFQafuw49XQofV1TIaEsRLwlNeeFmj/ZLQlOc7sJS7m5QrVIhCAvhy4vn2HXSGVFQAOZhIez39SLiSj6NjY0UVldzMTmJRefP8JGzPZ+cdmbmSSfWyC+y39eLBefOsM/Hg+/8vNnhoWT5pXOMt5IwWiZknJWYYSKzVoS7I19zXWb9hVsI+mALEwZbHGeIwJQ3rCRsUFzGMzO9x9+drjIiKioKNze3Vr7dvaUZkZOTg0Kh4MqVK70y/m8FGo2GrLRsDi45xrJXNjLryaV8+rdlzHlmFTMfX8ykB34l6FMeaNuj/umTy/n87yv56LHFfPHGbr6Z+RO7px/i+7nH8XTwQ61Wc/a4HMvdTsgl7sgl7lwUKrHdd4LDyyxICk2763mq1eoBk11XKpWUlZV1a4yamhqys7P1Gy06rZbs7Gxqamp6bK5Hjx5l+vTphl76GmOAhpGgG2PAxH//+1+Dk/KWUKvVbcrL8/LyUCgUZGZmtiHuvQWFQtGq7LuvERwcTG5ubo+Pq3uWWVlZHT7L7njR95QYXG1tLX5+fgQFBRlcyK1lhk+nkuvv709KSgoVFRUDqhS+oaGBkJAQfHx8BowIW0+Uwn925gQvCk2b7cjE5gwTmTFYYMJwcdds5W5FTEEBX5w7zdenTiALC8EhLgaHuBh+CPBFFhmOc1wss8+cYLRMyAiJBcPFFoy1FPOWdXN7wRRHW6Y52TLeSsJIqZBXJRYMF5vz4k3C/bLI7K5L2nWl7INvCsa9LDRliNCUFy1M9CXuH5904FxSAjUNvfu3qdVqKS0t1ft26zQj0tLSqKqq6pG/vYyMjD6p0Pkt4ZypC2tGfMXnT61g3rOrmfvsaj7561I+e3oZk3XE/L7ZbZTepw2az7rR25nz9GqWvrSJb2cdxnSdFDc7HypLq1DVqXA4cBqng2eRS9xx+uEs3885xo5JB1gz8iuOr5EQ7d15Yb97Pbvu4uJCRUVFj42nUqn0G9R+fn7I5XL8/f1JSkqipKSkW9n1AwcOMHv2bEMvfY0xQMNI0I0xYKK/EfSW5eVXr14lISEBNzc3SkpK+nQefa2ifivCwsLIzs7usfGamppISkpCqVTe8Vnm5uZ22ou+Zb95dy2JysrKcHd3JyYmpl/aG+lKOsPDw1EoFAPGwk23KRIcHGzwTZHeQldK4fOqKnnpJkl9WWyuh46kd7esX61W4+DlySone6wiwvTk3CEuhmMhgay5fIG3bWS8LDLjJYEpL920PBssMOFViQUjpQI+sLdhpLTZKu1ViQVDRWb6DLmuLP3Fm4T7bkj64JtEfJjQlFfE5rxhJWG8lYTx1hKWXDhLaF6uQTamampqyMzMJCQkBFdXV7y8vIiPj+/S355WqyU5ORk3NzfKytr2LxvRdRRmFXNw7jHmPr2aWX9exsLn1rNi2BaWDvmCjx9f/CtBb9GHPum+5jL3WX9ayupXt3FgzlGy4nMoyPr13Wq1WuRSd8Tb7Dhn6sL+T39m+/v72TfrMDsmHcB8oyXirbakR2d1ee4thebuhey6TtS2N3vIq6uryczMJCwsDIVCgZubG5GRkeTk5FBbW9upsXbs2MHy5csNvfQ1xgANI0E3xoCJ/kbQdeXllZWVBAcH4+vra5BS875SUe8IERERZGRk9MhYWq2W8PDwNmJwHSE/P5/AwMAukfPuLtqb7d4UpKen3xOZabVaTWFhIbGxsXh4eNyzFm6VlZV4enoSFRV1T28ydBZ3UwqfVVF+W4LuGBfT5evrdDVOebiz00OJSWiQnpxbRoaz6PwZRkoseKFFZvuFW4j0MKGZ3iZtuMiMYSJzBgtMWpWwv9AiO95R1nywoLmE/VWxORNsZUx1smXO2ZNMc7LnoxMOzDt7igN+3qSUlBj8vTU2trZP9PDwQKFQdKgsfiu0Wi1xcXF4eHhQWVlp8HsZiIjxjWffzJ+Y98wa5vzPahb9az2LX9jA4hc38uEfFzRn0u+7pSf9gTnMeGQBs59aiWynQ7vjZsRmY7nbkZ8Wm7P57b18PeMQX723n5+XWXBRoEC42RqFlVeP3YdOaK6/Ztfr6uqQy+WdJsrd2RAoLCwkPj4eHx8fXFxcCAgIIDk5mdLS0jtm1zds2MAXX3xh6KWvMQZoGAm6MQZUGJqU3wqdBU9ERITBlNR9fX17XUX9doiOjiYtLa3b49TX1+Pr60tISAgajeauzikoKMDf3/+uju0pct5SlKygoGfFt/oKLdWN/fz87hkLt6KiIpRKJcnJyffEpkhvoaNS+Pz8fD5wsGGwwERf4v6y2JwXhaYMEZmRXdFxn/7tUFtbi6+vL/aeHuz39eKT085McrRl6YWzzDrpxKsSC168W+uzm1Zpgy1M7pglb2+c4SIz3re3YoaTHT8E+LHby52D/j6klpWQVlpKZEE+6WX919lAq9VSUVFBSkpKK2XxlJQUysvLW81bo9EQFRWFl5fXgGnj6I/QarVkJ+bhcOA0u6YcZMuEvez77EdMvhaya/qBX0vdW+L+2Ux/eB6znlyC+FtrcnJyqK+vbzN2WlQmFl9YseH1XXzz8U+YrZdxzkTOJaESqz1OnDx8oVfu6U42bobIrtfU1CCXyw22UVBVVUVGRgahN+0T3d3diYqKIjc3l7q6ujbHL1u2jF27dvXqmlYkEvHPf/6TQYMGMW7cOKKionr1esboP2Ek6MYYUGFoQn4rOZTL5cTGxvZZv3l78Pf3p6CgwGDXj42NJSUlpVtj6MTg4uPjO+XpXlRUhK+v7x2JeU+JwekWzJ6enlRU9B+hse6itrZWX47bXy3ccnJycHV17bfK1RX1dXhmpuOTlUltH1qn3VoK//PpUwwRmPKiwITBAhNeFJryotCUQ4F+XRq/qqoKT09PLL08mX3mBO/aWfOWjZRRUqG+V7yjbHdPQJeRHyY0ZYxMyCQHG1ZePs9q+QWOBQdy0N+X8Ct5Bn//XUVdXV2rNhSd53peXh6hoaH6yixDz/O3hPT0dBQKBfk5+Xw35ygfPbqIqQ/eVHFvoeQ+79k17JjyHRdtXPTq4jobt5abLSkR6RxfLebEj+c5+dMFTh+5xCWhEvONlnifDOyTe+rIxq2mpoaM+CyC5eGEKaMoyCq8a8JbW11LRlwW6TGZVJZW3vH4yspKXFxcek11vbPZ9YKCAuLi4vQtRJ6enmzfvp3AwEBUKhWzZ8/m4MGDvbaePXPmDA8++CB2dnakpaWxZs0annjiCSorK3vtmsboP2Ek6MYYUHH9+nWDE/OmpiaSk5P1mavS0lKDzicoKIj8/HyDXT8hIYHExMQun5+bm4tCoehSH3tpaSne3t63fVe6xUl3yXl9fb3eOm8gL5hbWrjplKkN6dmt679VKpUUFRUZ/Pm0B4e4GN6wljBcbM5wsQUTbC1RpKcaZC5VVVVcighnnqMtr4stmGotRejtSUlJSac//6Wlpbi5uREWG8sG18u8Y2fJe3ZWTHK0ZYyluA2J7g1yPlJswWtSAW/aSPnohAOzTjnxySln1rlewjQ0GM/MjFb2bvcydJ7rsbGxuLq6IpfLCQ4ONrjn+m8J6enpKJVKSktLyU7MZeu7+9jw+k4+/9sKPvzjQmY9sYSPH1vE9Ifms2zIJrZO3IeXcwBarZa6ujqys7P1my26ypaogGgOzjvO8iGbWT1iG19O2MuuKd9jtsGSGN94Kkv63vlDrVZTU12Dq7UnP68Q8P284xycdxzT9VJCFZF3zHKnx2bhePAMpuukHF8rwXqvMzG+8bc9p6ysDIVCYXBy3tHmgZ+fH2+++SYPPvggf/vb33j99ddZtWoVWq22V9az48aNa1VC/5///Ienn36aw4cP98r1jNG/wkjQjTGgwtAEXaPR6O20ampq8PPzo6ioyKBzCg0NJScnx2DXT0pKIj4+vtPnNTU1kZiYiJubW5c3OcrLy/H09Oxw/J4Sg6usrMTLy4uIiAjU6t+Oz7hGo6GkpESfne1rCzeNRkN0dHS/rljwycpkhFSgVysfaynmZbE5oy1FJJc0q23HFRXiGBfD5dRkqvuw3/92pfB32mwpKChAoVCQkZFBZMEVZp1y4h1bS6Y52zPVyZ6hQrM2ZLqniPlgi+O8JGjupf/4hCOzTjnz6WknVl2+gCwyArf0NBKKCykegKS1vr4ef39/goODqaioaKM7kJiYSElJyW9Kf6GvkJqa2kqILyc5j23v7mvuHf/3fub97xpm/mkJ0x9awJQ/zGHm40uY/+xaNr/1NZdEylbvRFfZ4n7Oi69nH2TB4DXMeWYls/9nFQueW8fSlzaxcdxuDi+xwGKTFe72vtRV9+3Gb3xgMj8vs0C6wx7HH85i//1pTNZJOLZGRHp8ZodCcyVXSrH62gnT9TJOHr7A6aOXEG+zRbDFmoy4rA5JcElJCe7u7gYn43dCWVkZDg4OjBgxgieffJJBgwYxZcoUzM3Nyc7O7rG17P3338/ly5db/Xzp0qXMnDmzR65hjP4dRoJujAEVhiToNTU1+Pj4EBoaqu+RDgwMNGj2+tq1ZpG2zMxMg10/JSWFmJiYTp2j1WoJCwvDx8eH2traLl+7srISd3f325Lz7pZoFxYWGvueb+JWCzc/Pz+Sk5N7xcJNpVIRHByMn59fv/ZzX+d6meFic8ZZSXjDWsob1lLGWTVn07/z82aT0oVXJBYME5kzTGTOWzYyAnKy+3yet2623E4VPjs7G1dXV/Lzm33NQ/Jy+ez0CcZbS/nA0YbXLSWt+sef70GC/rzF8ZsicuYME5rxvr01Ky6fxzE2mvyqgS2SVltbi4+PD+Hh4W0IuEqlalPZEhkZ2aue678VaLVakpKScHNza7UR2KBq4PgaCevH7ODQIlO+/exnFvxzHVMfnMdHjy3iy4n7ODDnGJve3MMXb+wm4EIY5cUVNKiaN7/qa+s5efgC337yM19O/Ia9nxxiywdfM+cfK/n878tZ/NIGDi48jsUma36Yb4KrpWef3vcFCwU/L7PgxE/n9XD+8RwH55vgfy64Q6G5CI8Yjq4ScebYZc6ayPU4vkaCu71Ph8S3sLAQT09PgxPwu8WYMWM4efIkWVlZmJubM2XKFB588EEGDx7Mli1byM3N7fJatrS0lN/97neEh4e3+vnOnTsZN25cd5fKxrgHwkjQjTGg4saNGwYhocXFxSiVSpKSklr1m4eEhBg0e33tWs+JtHUV6enpREVF3fXxdXV1nRaD6wjV1dUolcpeI+eZmZkoFApyc3MNvojsb+jIwq2wsLDb2T2dKFlISIhByuo7gw9POvKKxEJPznUYJjJnspMdL4vNGSEVMMZSxGhLEcNEZrxhLaHMwJsO7anCJyYmEhMTg0KhaOW1nVVRznrXS7xhJeY1mZBRMqHeQq27Jey3kvOhIjNetxTzqsSCKY62yFOSye2FDaD+hurqajw9PYmOjr7j34/Ocz0pKUnvuR4YGEhaWhqVlZUD/ln1JLRaLQkJCXq70lt/nxSayr6ZP7F21HbWjtrOzCeWMPOJJWybuI9DC0zZ/+kR1o/ZwWd/Xc6qV7eydeI+fphvgsLamyivOMTb7Dgw+xh7pv/Aj4vMODjvOEtf/IJ5z65hxSub2Tb9G75b/hPfLTrMwUXHyE3L77N7P3n4AsfXiFsR9BM/nefQQjP8z4boS+Fv7V33OR3A4WUWiLbZcnSVCNN1Uuy+PYlwiw1nTS53SHjz8/Px8emYwPc3DB06FBcXl1ZrUK1Wi6urK+vXrycjI6PLa1kjQTeGkaAbY0BFXxP0pqamXwVj2smUh4eHGzR7fe1az4i0dQeZmZmEh4ff1bFlZWW4ubmRkJDQKTG4jlBbW4uLi4v+XV29erVHyLlGoyEuLk5ffm/oRWR/h0ajaWXhprOR6kjZ+HbQCQb2V2/5W7HFzbXdDPrLIjNelQh4WWzOWCuxHqMtRQwTm3fL8qynoVKpyM3NxdvbG7lcrtcd0JXCa7RaLiQn8m97a14SmPCSwPSOGfO7+X1LS7WhN+3hXhGb87qlmM9OnyAs/94Vf+sMKioq9CKZXfl3SyfyGBoaqvdcj4uL65Ln+m8JOgs7T09Pqqo67gPPTy/kklCJYLMVq17ZytpR2zm0wJSD80xYP2Yni57fwMwnlrDwXxvYNH4P60fv5JuPf8J8oyXHV4s5vEzA9ve/48dFZuz/7MhNG7eNbHx9F6brpNh864zZlxK+mrYPO6Fjn7UyBF0K59ACUxwPnuHET+ex/+4UR1eJ2D3tIP5ng9tcWyc0F6KIYPPbX7Nx/G42vbWHL97cw7b3vuXrDw/hdy64Q8Kbm5uLv7+/wYn33aChoYFnn30Wf3//XlnLGkvcjWEk6MYYUNGXBF2r1RIZGan/8m7vmKioKINmr69d675IW3eRk5NDaGjoHY/rjhhcR6ivr0cul3P16lX9AqK7YnAqlYqQkBB8fHyMwkxdXPTeauGmy+7dbhHc2NjcTqBQKEhNTW31DlNLSxBFhHE0OBC3jDRU6v6jAxCWn8drMqG+73y0TMTLYnNetxLzisSCV6WCVgR9rJWY4WILTEODDT53HdRqNeHh4Xh7e1NdXd1uKXxqejqbXC4xVGDKUJFZs9e6yIwhIrN2yfdtfcwtjjNGJmS4yIwhAlOmOtmxy8uNHR5Kvvfz5kRCrMErDPoKpaWlKJVKUlJSeiTzrVY3e67full2N57rvyVotVpiYmI6ZWGn1Wqx3O3E6hHs0xfeAAAgAElEQVTbODD7KHs//JGlL21i3rNr+PixxWx5+2sOzD7KV//ezzcf/8iRFUJ+XGTG0ZUidk89yJ7pP7D/05+Z/79rmP+PdeyaehDbfSdxPnQO6XZ7TNZKyUu/Ql5enr6VQalUEhkZSW5ubqc3O++EiuJK7Paf4of5JvywwJQNY3ey9MUv2PzW15itl6K08aa+tu01Ay6E8sUbu9k4bje7pnzP7mkHWTNyG5ve2kN8cGKHvetZWVkEBgYanHzfLUH/y1/+0qu2Z+PGjWPTpk36///Pf/7DM888YxSJ+42EkaAbY0DFL7/80ieks66uDj8/P4KCglCr1R0eFxsbS3JyskEJenJyMnFxcQa7fl5eHkFBQR3+vifE4DpCQ0MDcrm8Oct3k5x3Z8Gi0xm4F0qr7xXcauHm7e1NQkJCG1XxrKwsXF1dyctrnTU9mRDHWzYyRslEjJI1l4mvuHyBivr+QzbkqSm872DTTMglAmaccCAoN5sPTzoyTNQ6g64j80oDqbzfCpVKRVBQEP7+/u0SgKqqKkITEth59jTjxOYMEZgwTGDCUKEpr0kFjJRY8JLAhBESC14Vm/OCxXFetDDRk/QXWuB5i+O8aHGcCTYyNrhc4uOTjiy+cIaQvNwBKfh2JxQVFaFQKMjMzOyV8XWe66mpqa0815OTkykrK/vNlsLr7DK9vb07rW+RlZDLvpmHWfXKVpa8sJGP/t8iZjyygI8fW8yX7+xj/6c/s2vy9+yYfADxNjssvrDC/rvTfD/nGFsmfMPa17az8uUvWfnKVg4vtcB+/ynEX9lxcO5xLovd2rw/XSuDbrMzICCAlJSUHtP9KC+swMPBl60T97Fx3C6OrRJx6vAFHA6c5uhKEeHK6DbnOP94HvMNsuZ7ensvW9/dx/dzj/H93GNEece26l2vra3V965nZGQQEhJicPJ9twT9oYceIi0trdfWs2fOnGHQoEE4ODiQnp7O2rVreeKJJ6ioqOi1axqj/4SRoBtjQEVfEHSdtdDdeHInJCSQkJBgUIKelpZGdHS0wa5fUFBAQEBAu7/TaDSEhoZ2WwyuI2g0GuRyOdnZ2d0WStK997i4OGNZaC+hoaGBK1euEB0drRe6ioqKIiwsDIVCQUlJSavjM8rLeMtGxlgrMZMcbZjsZMu79laMkok4HhJk8PtpCZVGTUxhAfFFRWhuLpwvpiTdFIgzY6RU0PzfYnNmnXJCpTF8FUDLXn91i6qEmgYVqaUlpJaW4J+dxWr5Rd6yljJaJuIVkTmDb5JtXTZ8iIUJb1iJGSMTM1xkzssiM14WmTNEaMoIiQXDReaMsxLziticl8XmvG0jZbqzPQvOn0aemmzw52AI5Ofn4+rq2qf6FjrdiIiIiFaq/oayUDQENBoNERER+Pj4dLmi4Ep6AaJttsz73zXMeGQBMx9f3GzB9qelLPjHOhb8c11zln3OMU4fvURpfhmRnrF4OPji8P0ZhFus2ffJYbZM2Mue6T9gsk6Ki9SD6orbb1LV1taSlZWl1/3w8PAgJiam2+8vJSKdY6vFnD5ykXMmcj3EW21xOHAGtbr196HNNyf55uMf2Tn5AJvf+ppt7+3n52UCjq4UEuuXqO9dr6+vb0XWExMTCQ0NbTe73t9QW1vL7373OwoLC3t1TSsUCvnHP/7Bgw8+yLhx44iMjOzV6xmj/4SRoBtjQEVvEvSmpiaysrJQKBR3LfyWkpJCbGysQQl6RkYGkZGRBrt+UVERfn5+bX5eV1fXRvW+p9+XWq0mKSkJHx+fVqrUnc2K5Obm4urq2muZLCPaQqvVUlxcrH937b0/q+hIRslEenKuw+tWYqY42d0TGcDTifF8cDO7PlIqZIubK4XV1QafV1VVFZ6enkRFRVFVX0dZbS0NGg3RhQWYhASx28udNfKLTHWyY6KdJe/YWjLWSqzvEx8iMNET9GEWxxktMuM1kTmjJAJeFZsz7GYZ/DCRGe/ZW7P80nlmnLBnztkT7PX2xC4mitTfqL5DdnY2CoWCgoICg81BZwN2aytDRkYG1f3g89lb9xwWFoafn1+3ysU1Gg2yXQ6sfe0rVr2ylYXPrWPm40uY8oe5TH1wLnOfWcOqV7excvhWTh+9rD8v1i8RwWZrxF/Z4XDgDBYbrTi0yAzvEwFdmkNRURHx8fH69xccHExGRsYdW4luRXxgMsdWiVuR83Mmcqx2O2K1xxlVfWvyL/nKjhUvb2HP9EMcmH2Urz88xPqxO/hm5mEKs4vbnatuAzw5OblVdr2/kvXCwkJ+97vfUVdXZ+hlrzEGaBgJujEGVPzf//1fr5DMq1evEhMTg4eHB+Xl5Xd9XmcVzHsD2dnZhIWFGez6paWleHt7t/lZT4rB3UrM2xODu9UCzN/fn5SUlNuqGuvsdZRKJUVFRQZfQP6WoFKpCAwMxN/fn7q6Ov3706mK+/n5sV/hwiipkMmOtq0I+nhrCe/YWekz1X2JoppqrKMj+cbHC0F4KFkV5Xc8p0GjIbuinDID9wDXNTTglp7KET9vdpxw4nRgAOcTE/jSzZX5506x4NxpPjvtzLyzp1h4/jQfONgw/qbo3UQ7K8ZbN9vHDRebN5N0oSmDLY4zQmLBm1YS3raSMEEm4lWBGSOEZoyRCJhoa8l0Z3s+OunIRoWcwJzse2JjpbeQnp6OUqlspZLfH6BT9Q8ODsbFxQUfHx99K8pAqChSq9WEhITg7+/f7Wqr6ooadk87yOa39nLg86OsH7uTD/+4iMkPzGHyfXP45M/L2PjGHr6d9TOyXY5Ul9dQX1uP4/dnkO1y5KJAqYf1187YfXvyjtnzu3l/GRkZ+vfn7e1NfHz8XQkFFmQWIdxig923J1sR9GOrxG2s38qLKhFssWbn5O/ZMmEvX/17/83y+N18P+c4laVtNweqqqpwd3cnKSlJT4Bvza63tHEzNDlXq9VkZGTwu9/9jhs3bhh62WuMARpGgm6MARW9QdBVKhUBAQEEBASgUqk6da6u3MyQBD0vL4/g4GCDXb+8vBxPT0/9/+fk5HSqCqGz5Lyx8c5icPX19fqNC52qcXx8PMXFxfrFilqtJiIiAi8vr3btdYzoPVRXV+Pt7U1YWFir0uqW7y83NxdrTw9GiMwYK7ZgorWEf9tZ8YGDDa/JhGxWuhCYm4NtTBQXkpP6pCc9vqiI9x1sGCEV6PGmjQzvrP5feVFeX8dOTzfet7NivMSCd6ylTLC1ZIKNjIl2lrxlI2O4uLl8fZy1hDlnT/KmjZRxlmJGSgRMsJUxSiZknJWYUTIhYy1FjLeW8o5tc3Z9xaXzrJZf4JPTzqy5fAHb4EBO+fpw/NwZvj9zChtvT5IzM38zpdS3oqXXdllZmcHnczs0NDSQn5+vb0XpTaGyvoBarSY4OJjAwMAe+fzVVtex/f1mYnpg9lFWvbKVjx9bzMzHlzJt0Dw+f2oFG1/fhdUeJ0w3yMhJyqMotwTxNjtO/XyxFUE/c1yOcIsN+Wk9V02hayXSJR0UCgVhYWFkZWW1W12mVqux3O3EimFbmP/sWpa8sJGN43bx8zIBmfE5rY7NTy9AsNkah4NnsdhkxcF5JhxeYoFoqw3CLdZtMugtyXl739c6ZfiWNm79IbseGxvLQw89xH/+8x9DL3uNMUDDSNCNMaCipwl6RUUFHh4eREc3C6F09vzc3FxCQkIMStBv1wPeF6isrMTNzY2rV6+SkJCgX4D2BjnXarWdFoNTq9Vt+p7Dw8Px8vIiICDgnlxw3ssoKyvD3d2duLi4O2ZS1RoNGxWXeU0q4DWJBa+JzBkuNGWcVMgsZ3vGWjbbljWXwdsSktd7/bxarZaFF87wqlTAO3ZWvGtvzUQ7K0ZIBXzgaEttPyeesqhw3rWRMdVSzNKzp1h84Swjbgq8jbOW8J69NcMlFgy2OM5QoRkfn3Rkop0VYy3FDBWaMloqZKRUwKsSC1696fu+4PwZ9vl4slJ+gYP+vkgiwvDPzqKm4dcMpUajaVcVPjMz8zfjkqCz8/Lw8LjnNgO1Wi1lZWWthMoCAwNJTU29JzzXGxoaCAwMJCgoqEc3hyw2W7F6xDa2vbuPhc+t4+PHFjNt0HymPzyfuc+sYe7/rOar975FuMWGwuxiKkuqsNrjhP13p1sRdKcfziHb6UBpfu9s2rQnFOjn50dSUhKlpaWU5Jdiu+8Eiwdv5MM/LmLKzSqAaYPms3bUds6ZuLTK7leVVWP99Qls9p7ggoVCD6s9Tth9e5Laql83SnXkPDEx8a4+JxqNhoaGhn6RXQ8MDOQvf/kL//3vfw297DXGAA0jQTfGgIr//Oc/PUb4dLZfmZmZNDU1dWmMK1euGJQcX7vWcQ94X6GmpgZXV1dCQ0Px9fWlrq6uV8h5Y2Njq5L2ri5WdBl+hUKhn3dWVpbRgqgPcOXKFRQKBenp6Xd9Tk2DCklkODNPOfG+gw1bXC+z8sxJXhGZMVZszgRLEROspbwmFTLFyY7qbpavdoSM8jLGWIp5w1rKu/bWerxta8lIqbDfZNEr6+vxy87COjoSh9hoIq7kU6WqZ8FJJyZIhSw/f4a1LpdYcP40IyQWvCAw0ff06wj7iwITxlqKGCUVMUxkxlCRGWMsRbwmEzJMZM4bVhI2uF7mSFAAe709cIyLoeEu7Q11pdS6VgZfX189WejvZK8r0CmGd8bOqz9DJ1Smq07y9PQkLi6OwsLCdqthDAmVSoW/v38bEcSeQGJIKl/POMTSIZuY9edlTH94PlMfnMfMxxYz+6mVzHx8MZ/8aSlWXzvpP9fezgGYrJNy4qcLXBQoOfXzRUzWSVDaePfZM9FVJ/l7BXB4sylbJu1h5hOLmf7QfGb8cQGz/rSUz/66nBkPL+Tzv61g70c/EngxrNUYQZfCMV0nxWqPE84/nsdytyOm62WEKaL0x3SWnN8KndCcobLrSqWSf/zjH0aCboxeCyNBN8aAip4g6FevXiU+Pr5HbL+Kiorw9fU1KEFvrwe8r68vl8sJDQ1Fq9X2Cjm/td+8q9ARRJ3Ptq7vWZdZCAgIuGcyQ/caMjIyUCgU5Ofnd2ucapWKiXZWvGEt4QMHG96ztWSCpZhxYgteFZkh9fHqlb7ZpJJiRluKeNNG1oqgv3Mzi+6WnmbwZ1xRX4ckMoxtHkq+8fFkt5cHOzyVHHNT8KGVlOlOdqyWX2CtyyUWXzzLCKmAFyyO84aVhKlOdoy2FPGS0LRZ/E1g0krobYazHR842jDd2Z693h7s8/HihwBfnOO67lmuUqnIy8sjMjISpVKJm5sb0dHR5OfnD4hSeLVa3Wrj0tDz6Y37KywsJC4uDk9PT1xdXW9bSt2XqK+vx8/Pj7CwsF7roU8MTuGnxeZ89tflfP63Fcx8YmlzqfufljLjkQXM+ONCvv3kCFXlzaJ71RU1uFp6It5qi9kGGcIvbZBL3Kks6ZyoW/ffmwanQ+fY/v5+lg3dxOQ/zGHSfbOZdN9spj8yn1l/XsrMx5fw0aOL2PruPmz3naSlknuDqoEwRRR2+08h2W6Pw4EzRLjHoG5o3gTpLjlvD7pS+L7Krp87d45hw4YZeslrjAEcRoJujAEV3SXoul40Pz+/Hsn0lpaW4uXlZVCCrivTNxQ5VyqVyOVyGhs73yJwJ7QnBtcVaLVa0tLSbksQ6+rqyM7OJjQ0VN+33p5ftxGdf/YtN8S6O15RTTXjbaS8bStrJRz3gYMNIyUCjihdW1m49RTZU2nUTHO2Z4RUwMSbJe7v2lsz2lLEeBspRTWGV772ycrkK3cFoogwZFERSCPD2XHhLEsd7Fhw7hSjZCLes7dixgkHFp0/w1hLES9YHGe8lYTpzvaMsxIz2MKk2TpNYMpoqYiRUiHjrSVsdnPhYIAv+3y9iCsqJLW0hNzKyh4T6uuoFD4jI+OezDw3NDQQFBSk1zYx9Hx6G1qtlsrKSlJTU/VCnX5+fgbxXNc5iERERPS6wF1pYTnb3vuWhc+tZ96za5n991VMf2g+0wbN56P/t4j5/1iL1R4n6mubW6k0Gg356QWkRKSTl3rFIAJ8cf5JrB+zkyWDv+CjRxcx6fez22Dy/bOZOmguq0ZtxWyzlLrathtMdTX1lF4pp67m1zaxqqoqPDw8epSc34qObNx0ZL0nsuv29vaMHTvW0EteYwzgMBJ0Ywyo6A5Br6qqwsvLi8jIyB7L9FZUVODu7m5Qgl5dXY1Sqezz6+qsgrKyspDL5T2aPdcptd9JDO5uv8yjo6Nxd3envPzOituNje33rUdFRXHlypV+V8bZn6FWqwkPD8fb27vHrJs0Wi2fnTnBaJmoFUGfYCtjnJWEqIIraLVaSktLSUxMbGPB1x2yJ09NYayVmBFSAaMtRYyQChglEyGOCLvteWW1tV3OMt/pWRTX1FBWW4tWq0UQFsJeH09kURFIIsLYd+Ece842K7N/dtqZCbaWjJIJGS0TMlom4k1rKaNlQr3o2yiZkKFCM14VW/CaVMDbNjKmn7Dnk9PObFa6IAgP4YC/D/lVvd9HfS+XwtfX1/daafW9Al0p9a2e671dHVFbW4u3tzdRUVF9Rn4VVl4seG49Hz++mKmD5jFt0Dym3sRHjy5ixctb8D0dZPB3ooPdt6dY8M91LHxuPR8/tpjJ97Ul6M0kfQ7LXv4C86/FuLq63tGGry/IeXvoDaE5oVDIe++9Z+glrzEGcBgJujEGVPz3v//tEuHTlTanpaV1ud+8I3KsUCgMStBra2txcXHps+u1bBEoKyvj6tWryOVyGhoaeoycd0UMrj2oVCqCgoLw8/PrcsmlVqttldkz9q3fHerr6wkICCAwMLDLQnzVKhXiiDBmnXZmspMde7w9SC4pRp6azDhrCaNkIt6yld1UFhexxc213YVhdXV1K7Kny+yVl5d3eiEZmJvDRoWcKU52LLt0HnlqSodjxBcVseLyBUZbihhtKWL5pfPEFvaMWnNqaQmyqAj2+3rxpZsrG13lfH7mBDNPOrLTQ8muM6fYd/E8Pwb48oGjLV95KPgp0I/FF84y2dGWiXaWbHNXEJGfxzZ3BbPPnmTGCXv+7WDDWvlF5p87zYcnHFgrv8iC86dZ63KRPd4eWMdE9bm93b1UCl9bW9tn2dt7BS0913UbZjrP7p70XK+pqcHLy4uYmJg+JYhVZdXsnHyAD/+4kBkPL2CaLoP+6CJmPLKAz59aya4pB6itNvz3RYOqgZ+XC1j4rw189Ohipjwwt11yPun3s5nywBzWjf6KopziDm34dM4o1dXVeHh4kJCQYNANtDtl11Uq1V0R9iNHjjBjxgxDL3mNMYDDSNCNMaCiswS9qamJ5ORkvc91T5PVuro65HJ5j5L+zkKlUvXZHDQaDSEhIa3E4JqampDL5dTX1/cIOW9s7L4YXGNj826+zsqrpxbxLcs4b+1br6rq2z7C/gxdtUpERESXM4gqjZr1rpcZYynmdSsJ422kerX2pJJiXNJSmHv2FG/ZyPjAwYbjIUF3JRCnUqlaZfY8PDyIiYmhoKCgzVxzKytJKimmrgufn5yKCibYWTJcbMGomwR9uNiCt20tybzLSo6OkFtZyQ8BvuzwVLLLy52PTzryprWEMZYiXpVYMFpszkJnB6SR4WxWujDFyY7jIYFYR0diHR2JVVQEhwP9ORocgFqjoVqlIqawgNMJ8Wx1d0UcHoppWDCr5Bf47LQzkxxtWHrxLOZhIeRWVhj0s9WyFL5ldUR/KIWvqqrC09OzzwnivYbq6moyMjIICQlpl+x1dUydYF1fPPvK0iqyEnIpyCxCq9VitceJec+sae49f3gBH/6/Rcx4ZCEzHp7PvGfXsuqVrcQHJhv82TeoGjDbaMnsp1Z2SMwn/X42kx+Yw5z/WcX2979rO0YLGz53d3e94GpISIjBtQduRVez6/v372fu3LmGXvIaYwCHkaAbY0BFZwi6RqMhLCwMb29vampqeoWwqtXqXuu/7sx99nSJeXvQlQ6GhYW1uZarq2u3n3FPisEVFxejVCp7vdSurq6OrKwsfd+6t7c3CQkJ90QZbm+htLQUNze3bmdSvDIzGGsl5j17a6Y62zPV2Z7JTnaMkonY4+VOY+NNC6H6OlSarm0CaDQaioqK9CJXCoWC8PBwQpKSWHX5PK9bNZPeSY62nEjo3ML/SHAAr0gsGG8j5S1bGW/ZynjTRsorEgsOB/l36xlfTklmm7sCk5BAPnCw4SWhKf+yOK7H8xbHGSoyY+Xl83yhlLPs4lmkkeF6gm4dHcl3ft6IIsJa3ZNKrcYhNppdXu78GOjH9/4+rJJfYKubAveM9D7xmu8sbq2OMFQpvK7dydAZxHsNOs9uHdlTKpVERESQk5Nz15U3lZWVffbs1WoNgRfDEH1py8/LBZislXL2uJxzpi7seP87Pnp0MVMfnMvUB+cx7aH5zHh4ATMfX8KyIZsJuhRu0GddV12Hp6Mf2977lskPzLktQZ/+0Hw+e3I5Py0xv+2YVVVVuLm5ERAQgL+/Py4uLvj7+xtEe+BO6IyN2/bt21m5cqWhl7zGGMBhJOjGGHBxN2SvpqZGTyY1Gk2vkdbGxkbkcjlqtdpgBL2nS8zbQ0lJCW5ubiQlJbWbqXdzc6Oqqqpb99BT5DwrKwtXV1eys7P79Mtf17ceFRWl71uPjo7+TfWt5+fn660LuzuWSWgQYyxFenKuw3gbKZMcbXt87jq/4NjERN63kjBMYMoosTnjZCJG3uw5v5zaNgOmUqvbLfleevEcr0gs9ORch1ckFiy6cKZbc5VGhrPb0403bWQ834KYtyTnLwhMmHP2JInFxZiFBfO1tweSiDCsoyM5FhzIbi93gvNy2oxdrVLhnZmBWVgwx0ICuZSSREH1vVEdYqhSeJ1Yps4dwtDP4V6FznM9OTlZ77muq1CqqKho99lWVFTov5v64tlHe8Xx8zIBwi02OB48i/VeZ35caIrFZiuOrBAw539WMeX+Oc0EfdA8pj44j1l/Wsqi5zfie8qwfehyiRsHZh9l2ZBNtyXnk34/m2kPzeejRxfjeLDjf6t0Ze3x8fH6Z19fX09OTk4r7YGoqCjy8vL6lViiroWuo+z6qlWr2LRpk6GXu8YYwGEk6MYYcHEnsldUVIRSqSQ5ObnXy757sry7O3NwcXHpFf/xa9d+FYPLzc3t8BgPDw/Ky8u7NPeeEoPTarUkJCTg5uZGcXGxwb/8S0pKiI+Px8vLS28/lJ2d3eV+7P6Mlir5BQU902MtighjtKWIKU52rQj661YSZp1y6tbYBdVVXEhO4mJKEoW39MBeTk1mpFTI2zYyJlhLeVMmYpzYguFCMz5ysNGX4fpmZ7Hk4lnesJbybwcbTEJbl9h/5aHskKB/6ebarfmfSUxg1iknvSXav27Bi0JTXhKaMsZSjFarJbO8HIuwEPZ4u7PLy53v/X2Qp6bctvJAo9XScA/3UPdVKXxhYWGPbUoZ0Ro6Z42wsDAUCgWenp7Exsbq21HKy8txc3MjJSWlT+ajVmuw238Kk7USHA6cRrjFBuGXNljtceLn5QIcD57lsyeXM+WBuXqC/tFji/nkL8tY/PwGQlwiW41Xml9GZlwORbklvT73gqwijq0Ss+29b/nwjwvvTNAfnMeK4Vu5YKFodzxdS0FLcn4rdH+DiYmJ+Pr64uLiQlBQEGlpaf3OyrRldr2srIynn36ad99919DLXWMM4DASdGMMuLh+/XqHZE9HEq5cudJnBFmhUPRaCb0h53D16lXi4uL06ue3O9bb27vTnvK3isF158u6oaGB0NDQHlUL7ym07FvXlQAGBgaSlpY2IPrWNRoNsbGxnVLJvxsklxTzlq2MN6ylepL+nr01YyxFiO6gmn472MZE8batJa/JhIySiZhgZ4ljXIz+9+ZhIbwmE/KevbUe79pbM0YmYoxUgMLNjWPnzjJKYsGrYnNetxIzWtZsR7bO9bI+m+6XncVIqZCRUgFv2kh500bKSKmAkVIh3lkZ3Xo2KaUlvG0r4wULkzbkXIeXhKaMthShvkmy6xoaSCwuIrqwgCsD4HPXWfRGKXx+fj6urq7k5uYa/P4GOtrzXHdxcSE8PLzP+p7rqusQfWnLDwtM2TpxH2tHbWftqO1se3cfe6b/gKulB8uGbmbe/67hk78s46NHFzPrT0v59MkVLB+2hcTg5o2Eupp63O18EGy25ugKIRabrHC19KS6ovc0FFLC09n70U/M+981zHhkYbPv+W0I+qd/WcaBOUc5ayJvM9bdkPP2UFNTQ2Zmpr4l7NYNF0N/xhobmzeFZsyYwYgRI8jKyjL0ctcYAziMBN0YAy7aI+harZaIiAg8PT27VWrdFbi7u1NZWWlQgt7Tc1Cr1YSEhODn53dX1QG+vr6dEuHryX7z2tpafH19CQ4O7lcldLebb8u+dR8fHxITE+/JvnXdxoiPj0+vCHTZxUbzlq2M0ZYixliKGGslZp3LpbsSg2sPATnZjLOW8LqVmPcdbHjfwYaxlmLesJYSmp9HY2MjJxLiGCkV8o6dVSuS/ppUyIwTDqjVauacdma4yJxxEgHjxBaMlwkZK2u2KQvJayZrWq1WXwUwXGLBKxILRluKEISHdvs9xxReYaRU0CE512XR17teNvhnpD+iJ0rhdZVFPVUxYsTdo7i4WF+V1NJzvbe1BzQaDeKv7Fk+dDPLh25i8QsbWfjceub/Yy1LX/yCs8flfPXet3z21AqmPTSfyQ/MYcoDc5k2aB7znllNbko+jY2N+JwM5PBSC6z2OHHy8AWs9zpzeIkFSmuvXntmuSn57PjgOz75y7Jma7X7b9ODft9sFvxzHXum/UCEW0yrcbpKzm+FWq2moKCg1YZLaGhot60wuwOVSsXMmTMZOXIkNTU1hl7qGmOAh5GgG2PAxa0Eva6uDj8/P4KDgw3SC+7l5dXp7HFvzKGsrKxHxtL174eHh9+18FxAQAAFBTBETPcAACAASURBVAU9S861dTQ1+HGt1oJrtSY01Sto1LbO0JaVleHu7k5MTMw9aWmkU8ONiopq5RXcnzIKHUH3dxcUFNSrGyNJJcWIIsI4FhKIR2Z6lwXhGhsb2eXlziiZkEmOtnp84GDDazIh3/o2L47Lamv5t4MNI6QC3raVMdHOirFWYl6TCbGNiaKqvp5x1hLGWUl4z96aiXZWvG0t5Q2pgJeFpnx99jRJSUl6gaScigqc4mNxjIshu6J7FQYqjRr39DRmnLBn8G3I+fMWxxkusSA4z5jZvRO6UgqflpaGUqmkpKT3S5ONaI2ioiIUCkUrjRGdM0PLDZeoqKhe0R6w/+40n/11BZ8+uZx5z6xh3jNr+OTPS5n91EqsvnZi4xu7mPqHuXqiq8tUz3hkAd6nAqkqq0a8zQ7pDntOH72kh9UeJwSbrSnNL+uV51ZRUsnWid/c3Di4ffZ80n2z+fyplQi/tKG6/Ne/gZqamh4h57dCV2WWlpamr3DRbVx3R9m/M2hoaODzzz9n+PDhVFVVGXqZa4zfQBgJujEGXNy4cUNP9nSK0fHx8Vy9etUg5Liz2ePemkNxcXG3xykpKUGpVHYoBtcRgoKCyM/Pv+Nxdy0Gp1Vxrc6a69XruVG9mRvVW7lRvZZrtUdp1GTT1OBPeYEjoQECMtIHhmqyziv41r71zqgZ9xUqKyvx9PQkKirqntoYWX75PKNvWrW1xCiZiHWul/THRVzJZ7qzPa/JhIyQChhnJeFQgC9qjQaVRs07dpaMsRS1yrBPtLNihFSAJNBfTxR0m0fd3XDRaLUE5uZwJDiAf9tbMVRoymCL4wwWtF/i/raNlN1ebtT2M4/wewHtlcLrKlw0Gg1JSUm4ubn1aDuHEXeHgoKCO7YUtOx7bum5np6e3iMtRTb7TjL/2bUsfG49C/6xjoXPrWf1q9tY+Nx6flhgwqJ/rWfSfbOZfN8cPdmdfH9zJn3bv7+lMLsYs/VSHA6caUXQnX88x7HVYnKT83v8uRXnlrD/06PMeXo1U/7Qse+5br5THpjD+jE7Kcn7dbNAR877wsZOpVLpN667quzfGajVahYsWMDQoUMpLy839BLXGL+RMBJ0Ywy4uHHjBk1NTWRmZt5RvKwvEBAQ0Kc97+3B39+fwsLCLp/f1NREVlYWCoXiroj2rQgJCSEnJ+e243dGDO6qOobr1V9wveYg12uPN6PmMDeqFnG9ahX1BYsoTf8UTclyrtUeoVFTZPDFY09CpyiekpLSpm/d0D32Ogu7vlJN7kn8HBTAazIhHzjY6Mn5+zcz6Kahwa2OVanV+Odk4ZqW2sb7+4cAX0ZIBbxlI9OT85FSIRNsLSm+mXXtaMMlOzuburrO2ZX5ZWexw1PJJoWcUSJzXhGa8ZLQlKFCU4YKzXjhZtb8FbEFYyxFLLlwFmV6ap89V3WDGoWVFzsnH2DT+D1Y7XGmOK/U4O+7u7i1FN7V1RVXV1cyMjJ6VRXeiLbQ9fvn53eOwOr6nnWe697e3sTHx3c5M+t48CwLn1vPzkkH2Pbet2z/4Dt2TfmeZS9tYu+HP/K5zl/8ZvZ88n3N5HzSfbNZ9K8NVJVVI9vliGirbSuCLt1hj3ibLRXFlT3+7Cy+sOSzv65g5uNLmNKevdrNLP/k++Yw/eEFfPrkcuy+PdnqGfalx3xLtFT29/f3Ry6X623cysvLuz0ftVrNsmXLGDx4MCUlJYZe3hrjNxRGgm6MARdNTU1ER0fj4eFBRUWFQYnxtWvXCA4ONvgmwd1msNtDZ8TgOkJ4eDhZWVkdkvPGxsZOicE1qS5xvXrdr+S89jjXa45wo2IKmsJ3yUnciLr8ENdrDnGjehXX6mwMvoDsTdTW1rZaZBqqbz03N9cgFnY9hYyyMn0/+Vs2Mt6ykTFS2kzYO1N+XlFfx7JL5xglEzJSJmxWfbeV4Z6R3u7xLYUCAwICWtlH3UnNuLahgcNB/uzxcGODsyPjpELGWYkZLjbnRaEpr0osGCY05QWBCUOFprxrZ4VtdCQ1DX2jx6DRaDg47zjTBs1vxoPzmP7QfBb+cz1XMgoN/s576h4jIiLw8PAgJiamV1XhjWiL3NzcHun313mux8TEdDkzG3w5nJXDt7Jm5Fd8+c43fPnON6wZ+RXrRu9AsNmaWX9e1kx275/DlJs96Lp+72UvbaK6vIYQeQRHljdbtdntP4Voqy0/LxPge7rnbdhyU6+w8Ln1TLl/DlP+MJfpD89v038+45EFzHh4AR/+cRGL/rWBLyfsJSGo2VLSkOS8PdTV1ZGTk0N4eHirtrCutDNoNBpWr17Nv/71LwoKCgy9tDXGbyyMBN0YAy6io6MJCAhApVIZnJxfu3aNsLCwDslpXyE0NPS2GeyOoFarCQ4OvmsxuI4QGRlJenp6u+RcV9LeKltxhy/6JpUrN6rXtiLoTZV7aCwaR03uDBqrfuJ67dFm1HzN9ZodNGp7p3evv0HXt97TZdS3g1arJSUlBaVSSVHRvV2tEFlwhWWXzjHeRsp4GykrL18grqjzRFKt0eCdlYkwPBSHuJg2dm23Q0v7KFdXV7y8vDrM6uVVVbJd6cJmZ0f2u1zmk9NOjLEUMc5KwjCRGcNEZrwoMGWI0JSPnR3wSE/v04V0mDKa6Q/NZ8YjC5n5+BJmPr6Ejx9dzPSH5nN0lcjg77u7UKvVhIaG6v+N1P38dqXw/YHIDBTk5OSgUCgoLOzZzR6tVkt5ebk+M6vbNEtJSenQc72xsZGqsmqOrxGz9rXtLBn8hb7EfeeUA/idDWbO06tbEPS5v2as75vNnukHUTeoUTeoCVNEYbXHCfONlsh2ORJ4MQxVfc9WZWg0Gqz3OjNt0Dy9ddrUm5j8wBwm3Tebj/7fImY+tpiPH13M4uc3sv39/Zw6con62vp+R87bu7/i4uJW+hEt2xluN2eNRsPGjRt59tlnycvLM/Sy1hi/wTASdGMMuNDtkhqamN+JnPYlIiIiyMzM7NQ5OjG4iIiIuxaD6wgxMTGkpqZ2SM61Wi2NmnyaVJe5XvMT12t2ca3mZ5rqHGmqs+darSnX6h25qk6isbGRq+p0blR/xfXqPVyvOYam/CeK0+Zztfh1rlVu+pWc1x7les23XK/eRqNmYGTrurJA0SnhKhQKwsPDe7RXT6PRtKpYMfQ99wS0Wi0F1VUUVld3eeGZVlaKPDWFoNwcvZVZV6BTM26Z1YuMjCQvLw+VSkVabi4r7W3ZIb+IJDKcA37eTHK05TWZkFfEzcrwb9nIOODnRUV950rnewKCzdbMeHgBHz+2WE/QZz6+hOkPzefzv600+LvuDlQqFYGBgQQGBt5WCLEnVOGNaAtdG1txcXGvX0u3aRYeHo5CodBXS7S38enp7M+Kl7/ks7+tYPbfV7Hwn+vZ/sF3BF0KZ/3oHXz4x4X6zLkukz794QU4HzrX+po19ZTkl1Jb1Tt/txkxWXz13n5937kucz75vuZ5Tb5vDgufW8/+z45wcN5x7PafIkwZTU1lbb8n5+2hurqajIwMQkJC9BufcXFxREdHt9If0Gg0bN26laeffprs7GxDL2mN8RsNI0E3xoCLX375xeCkvCViY2NJSUkx6ByioqI6tUmg6yNOTk7ulBhcR4iLiyMpKalDct7U4MP1qk38Uv4Wv5SP4Jfy17hR/ga/lI/ll/K3uF45lxtVy7les5urDaE0arVcq3PgRuUirpZ+TGXWdOquLORG5RyuV29tQc6PcKN6Q3MfurZ/q573NnR968nJyfj5+en71tPT07vct97Q0EBISAi+vr595jXc31Hb0MBOTzfGWIp4RWLBSJmQmaecSCouIqG4CL/sLPKrutZHquu3TEpKwtfXF7lcjlwux8xNwSbXyxwJCkASGc53ft58ctqZJRfOIokII9mAauLCLTZM74Cgz/77qrv/rKka+hURqK+vx8/Pj5CQkE5VpnRFFd6ItkhPT0epVFJa2vdaBhqNhqKiolb6EToLsJLCUr6Z+ROLnt/AvGdWM/uplawY9iW7pnyPdIc9+z89wvJhW5j7P6uZ9aelzHxiKZ/8eRkrhm0hKyGnT+9BttOB2X9fydQH57UrCDftwXkIvrSmrqb1Zm5NTQ1eXl7Exsb2q7/JzkC38RkbG8v06dMZNGgQb7/9NgcOHGDz5s089dRTpKenG3o5a4zfcBgJujEGXPzf//2fwUl5SyQkJJCYmHhPbBK0FNfras96R88gISFBLwan1Wr1/eZX1QncqJx/k5i/eAuGNP+84n1uVM7kRtUyrtceoknlxvXqnTQUzqU6axLa4llcq/mRprpL3KjexI2q9Vyv/orr1Wu5Xr2dq+oYgy8I+hva61tvaf91N+f7+voSEhJizAK2wJHgAEZIBYy1FDPB1pLx1lKGi2/6nMtEjJQKecNawnd+3tR3o+UgPT0dV1dXYmNj8Q4IYM8JJ5Y62jLV3oqJtpbMPXuyw573vkSEe2ybEvePHl3M9IcWYLJWcsfzAy+EsWHsLqY/tIDPnlyOeJtdr2UU7xa1tbX66iKNRkN9bT1utt6Yb7TEfv8p8tPvvhfaWArfeaSmpqJUKikrM3zbklarpaqqSm8BdmynBZ/8dSkfPbqI6Q8vYPpD85n+yAI+f2olBz4/itMPZ9n+/n62TtzH+tE7WDdqO1+M34PN3hOo1X3jeNGgakAucWfJ4I3MeGQh0x9Z0FYQ7v45zPrTEjyd/VudOxDI+a3QaDQEBASwbds2hgwZwu9//3tefPFF9uzZQ0hICL/88ouhl7XG+A2GkaAbY8BFfyPoycnJxMXF9ftNgqtXrxIbG9sr4nrJycnExsbqvwxbisFdr9nLL+WjbiHlLUn6cG5UTuVG5afcqJjF9apVXKvaQEXuOjLjvkRVephrtT9yo3oDTSpvrjaEcq32CNdrvuFanS1X1ckGXwD0d9xagqvrWy8sLGxXybiiokJf4nkv2aj1NmobGphga8komYh37Kz0GCI05V8WxxklE/KOrSWvW4kZKRVyLCSw09fQarUkJCTg5ubWiqBkl5Yw0UbGSwITXhSYMERgwssiMw55efSa9sDdzvfwUgu9SNzUmyJxi5/fSFHO7UuT/c+FMP2h5nN0504bNJ8dkw4YjBxUVVXh6elJTEwMWq2WkvxSlg3ZzOT75jT38P5hLtMGzcPd3rfTYxtL4e/8WUpOTsbNza3fttNYbLZk2sPzm0vYH5jD5D80i69NeWAOS4dsIjUyA+dD5/l+zjH2TD/E/s+OYP21c58KJkZ7xXFg9lFWvLyFz/66go8fW9zcd37/HL26/Kw/LWXLhG/wPxeiP6+2tnbAkfOWn60ffviBP//5zwQFBXHmzBmWLFnCk0/+f/a+O7ypun0fB6KUofj61d878BUc7D0FJ3sjskqZKjLcrwounIAoo02a3Q1lFBltRpPOdO+9927TNB0ZJxRa4f790ebQdI+kGZz7uu5LaU9yPjknaZ778zzP/fwDTz31FLZv344LFy5AoVCYOsSl8ICAEugUrA7mJtAzMzMRFxdn0jWkpqZ2u0mgVqsRGhqK4OBgo5jrZWRkkDOx9QSdphBN1evQLHu5k+x5G8FevQhN8u1oql6OO9UboSh4B/mpX4CQ/4nb9fa4XW+PO4ovcEfxFW7XnWjJntd+g8aGi9AS5hnImSt15Zvt+9YLCgqgVCpRWloKoVCIjIwMqwvSBsoSRQ1mc5mYy2OR4nyeEwvjWseczeYyybnos7gMvO7K65ObukajQUxMDPz8/Dq0JRz188VEpgNmchmYw2NiFtsRkxztMdHxHLjX/kJERARyc3P7PMLNEFSrNZB4BOP79Sfxv7d+hNtPl1FV0r0rPkEQ+GDqF1gxdBvW2OzA2hF2WDuiJSu58rFtiBEnDPrrqK6uhq+vL1JSUsj3/i9bz2DF0G1Y/uhW8r/LHt6CVY/borKo/xleqhS+4/shJSUFvr6+kMsNP2rMUPx+/UnSYK1FmLcI9aUPbcaakTvg7xsAT3svHHv3D3y55Gf8ZnsOwV5hUKsGbxPt4u/X8Ou2s/jizWOwe+FQa8tJy+dqtc0ObByzByd2OeC3bWcRKYiFVntfnOs2pkx9nQ393jp16hSefPJJxMXFdYgno6Oj8cMPP2DWrFmYO3euiSJbCg8aKIFOwepw9+5dk4vytszJyUF0dLRJ15CRkdHlJoFCoYCfn59BzOA6Y2NjI/Ly8uDj49NBJNxqCESzbHo34lzH6a396bPRUPgW6grW45bivji/XXsGd+Rb0Cx7FU3yTbij+AR3FEfRVPMhbtezHvj+84EELm371nU9zzExMQ+kSOiJSo0aSz1cMJ3tSAr02VwGXqCdwXjaGSxwYpMCfaETG3N4zF6Pb9P1+wcFBXUQ2RqCwGwuE5NZNMzhMUnO5jIwiemA09KgDiPcenKjNjXllTUt5cGPbyfF+doRdlhjswOrHt8O95+vDOp6KioqyI0p3c/qFfVYOawl87hi6DaSLe7cW3H1nI/Bzv8gl8ITBIHExESIxWI9My9z5HdrT2DZw5tJAzid2drShzZj/eid4Pzojj2TP8Km/7cP7/5zH+xeOoT/vXUMITciB22Nbj9exsmdDvh9Nw0fTP0CO8cdwhqbHVjx2DastdmBfRM/xS9bz4D2Ea/FpM7Kxfm5c+cwevRoREVF9RhfNjY2DkIUS4ECJdApWCHMTaDn5+cjPDzcpGvIzs5GTExMh5+XlZVBKBQiPT3dIGZwnYlznRmcXC5HZmYmKRJCQkIgK/wZTbKJvRDoL6G58mXcqZgAVclqNFe/gabqLbgj34em6pVols1Fs2wC/pZNRrNsXuu/l6JJvh9NNQepMncDBDGpqakQCARITExEWFgYKRL60rf+INAlPhbT2Y6tveZsTGfT8QLtDF5xtMcbrrz7GXQOA2+4OaGum9LlHJkMx6VB2PHXZew67waer7DTUme1RtPi2t6FQD8Tfn9+ckNDg94IN50Tc1lZmVm1KzTUNWDdyJ1YOWxbB4G+8rFtuObAH7S1lJaWQiAQIDc3V+/nstJqLG8VYe0F+oqh24y2ifAglcITBIH4+HhIJBKL2BR0OMQlhfmK1paHFUNbHNvXjdqJ/TP+hw1P7cY7T+/Bxqd3Y+0oO6x7aicOv/kVQoJCkZeXZ/Qql+Cr4Ti28RS435zHsY2nsO1f+7H6CVusGLoN60btxNZ/fYBPF32HWL9EqxfnDAYDI0eORGhoqKlDVwoU9EAJdApWB3MT6EVFRQgJCTHpGnQBeVvhnJ2dDYFAgOLiYqMIc50ZHDlGrc0XY319PXJzc1GS+Qlul7+C5sqXWljVlThvYVPlRDRV2+KO7B00yyboHfN3p5yO5uq30Njwl8mDAUulrqxaIpHolZYqlUoUFBQgOjoaQqEQYrEYCQkJXfatPyjUEATYMVF43ZWHmRwG5rSWvE9h0TDfiY3FLlzM4bb0oDtEhnf5PDElxZjbOsv8ZfpZvEI/h8lMGpziYjo9/iD/JiYyHDC7tcR9Do+JaWw6JrNoCC8s6PQxnY1wi46ORkFBAZJLS3ApJQmCrAwouhkhZkye2kvHymHbsfoJW1Kcr3hsG9aN3InK4t5VHgyUhYWF4PP5KCws7PA7giBa+s9bZ1qTAv2RFkEW559k/PebFZfCEwSB2NhY+Pv7W8yUCD5XgpWPb2/pP28dobZi6DYsH7oVm/6vpd/73f/bh53jD2HXi4dh98JBrBu1E3bjDiEsIILcwA4ODkZaWhpkMpnBhbGsTA76J074dPF3ODT7a2z5fx/A7r+H8NH8ozi1l4Y/9jnip01/IJwfbdXinMvlYsSIEQgODjZ12EqBQgdQAp2C1eHevXsmF+VtWVpaiqCgIJOuobCwEGFhYbh9u8UMLj4+HmKxGHK53CjiXKvtaAbXGW/Xc9Akm3pfoLenrP2/J6JJ9lYXju+di/Rm2WTcrj0GrZUFGINBpVJJehN0l9Vp27cuFoshEAhIodfdfGhrpkKpREp5GUoUNSiuqcF+n+uYw2NiJscRC53ZOBkSDKWm89YLgiCw2esiJjDsMcXxHKazaJjFccQkpgOmsx1R2slYvOTyMsxzYmEi0wGTmA6YyGj57+e+/F4F17oRbonJydhz3g2T6OfwCv0cJjDs8aozB8H5uQO6Hv2hrEyOg7O/bjWWs8XKYS3l7v6efTfX6w9zc3MhEAhQWtq1gVfQlTAsbxXounLm5Y9uxZHlv5hE1OhK4dtWuVhiKbxGo0F0dDQCAgJM4pvQXyZJU7Dp//ZhxWOtrQ6PbMXyoVuxbtRO7J30KVY9bovVrVUgK4dtx8Yxu7Hhqd3Y9I99KMkrg1bbUuWSn5/fYeZ6cXGxQQwfYyWJ+H23Az6afxSbn3sfG57ag08Xfw+n7y/C41cvePzqhV+2ncHp/9GsVpy7ubnBxsYGEonE1CErBQqdghLoFKwO5ibQKyoq4O/vb9I1FBcXQyqVGt0MTlfS3sEMriuqs1sFd8dseadiXTYRTbIVraZyOrf3l3vIor+Epur10Gp6P/qIYss4nYCAAERERPQpKCQIAjKZDGlpaQgMDISPjw/CwsKsIqM3UGZVVSKiqBDlPVyHwho5JjMdMJF+DjNYdMzmMjCby8AsjiMmMOxxKaXzzGxGZQW+C5Bg2XlXbPG6CPfE+C43AboiMzoSk5kOmMaiYRabjulMB7xCP4sZTAeExMUNutBTKVUIuhIGztfuuHTqOkrzund+NxQzMzMhFApR0YsZ8qE3onBo7hEsH7oV7/7fPnC+9ugwO9oUtNRSeI1Gg8jISAQGBqKhwfTXsS+MFsVj90sf4Z1n9mHTM/vwzj/2YvOzLf//4cwvyTFmenx4M7aP/RA1VR033jqbuR4eHo6cnJx+/T0tySnD6fcZ+GMvHa7HLuLHd05h98sf44Np/8OZ/Sx4/OoFlx8v4pO3j4L3q7vViXOtVgtPT0/Y2NhAIBCYOlylQKFLUAKdgtXB3AS6TCaDWCw26RrKysrg7+8PiUSCmJiWElljifPOStq7Y2ODBM2yqV2Wtbfl7fKpuFX+dpeu710J9GbZDGhVpp8JbSmsqqqCr68vEhMTBxyg1dbWIjs7Wy+jZ6zSTWthTEYGJtDPYjLTgRTnegI9OdEo5yUIAm+5O2My0wHznFgk53CZmMiwx28+N8kxfHFxcQbL6JkTdW7hIpEIMlnfyujN+f1sKaXwarUaERER5CayqdfTV14954Pv1p7Ap4u/x+6XP8bulz7Ge5M/w94Jn2L7fz7sKM5b+f7Uz3v1/qmpqdGrkAgICEBKSgoqKip6tSkeci0SP77zB9x+vAS3Hy/h3AE2Ppz5JXa//DG+WfUbXH68iKObfsbRDb8iK37wq2aMTS8vLwwfPhw3btwwdahKgUK3oAQ6BauDuQl0hUIBoVBo0jVkZmbC29sbGRkZRjeD61eQqsxCk2xpR3HeToDfKn8VyqJFnWTWexLpr+C24k9oCfPNGpkLS0pKIBAIkJWVZXDB0bZvXSAQmK1BmSmZlZUFPp+PtefdMIFhj1kcR1KgT2I6YBqbjqIa/TFTdSoVwgsLEFVcCNUArqPObG4am64n0Oc5tfTQ/xEmhUajQXl5OZKTk8mMnm46g6X0CXfFtoZk5u4WPlCaYym8Wq1GWFgYpFKpRYpzrbZFoP+69Qy4R8/jj72OOG57DqffZ+Dz13/A2hF2XQr0Hf890Oe/gUqlEkVFRYiNjYVIJIJIJEJMTEy3rUX+niH4adN9ge764yX8uu0M9rz8MfZN+gyfvH0U3717HEFeYWa94dQfXr9+HcOHD4eXl5epw1QKFHoEJdApWCVMLcrbsr6+Hj4+PiY5t84Mjs/nG22ToDszuD6RIFBR4ovyzA1oqnylg/Bukq1Ck3wHmmRL0CybjObKlztm2at0GfPOsujz0VhvuLFH1sicnBwIBAIUFRUZ/VwajQalpaVISEiAWCzWMyiz1OB8IGybua2qqkJ4YQFmcRmYyHTARIY9JjLsMZlJg2N0hN7jLqckYYEzGxMZDpjIdMCb7k4Iyut/5mvTFU9MapdB120OXEtL7bDmmpoaZGZmIiQkhDS3MvcRbl29H6Ojoy3KkMxQ7KwUPjY2dlBL4VUqFUJCQhAaGmrW5fc9MT4gGT9vPg32l66kCGZ85oyvlvyEVY9v71Kgb/q/fQPapCQIApWVlUhNTSVbi0JCQpCZmQm5XE5+FpND0/DLltPgfO1Ors/le098tfRnHLM7gUuMqyjKsr52MIFAABsbG1y4cAH37t0zdYhKgUKPoAQ6BavEnTt3TC7MdVSpVPD29satW7cG9by3bt1CXFwcxGIxiouLIRKJDC7+tdremcH1JrjIzMwkXeW16izcrj2JO/L9uC0/iDs1n+OO4n+4U3sEt2t/axmvJlvRZTn831Wt1BPpL6NJ9ia06jKTBwvmRoIgkJycDJFIhMrKSpOcv6qqSi+4fJD61jUaDWJjYztkbtMrK3As0A+brnjisMAbfjnZeo8Lzs/FZCYNrzjaYxqLjqksOiYw7DGD44jsqqp+rcUnMx1TWXRMYjpgZms2fRLTAas83bsdCafVdjS3spQKCbVaTc6Yt7SeZ2O8Fwe7FF6pVEIqlSI8PNziWyaU9Ur8Ze+DHzb8jqMrf8N3607it+1ncemP61g78n4GfdkjW/T60W3HfmjQddTV1SE3NxcRERHg8/nw8/NDYmIiCvOLcOH4Vfz4zh/4Yy8dZz5g4PsNJ/H9tt8QLJFa1KZabykWizFixAi4urpS4pyCxYAS6BSsEuYk0AmCgLe3NzQazaBuCoSEhJClgnV1deDz+QYV530yg+uGGo2G3EjosueTqMUtdRZuafKg1VThTu0x3JZ/gGbZpDZmcffFOinQqzqK9Sb5LjQ2XIeWePCytJ1RrVYjKioKfn5+ZlPWqyu/DQ0NhY+PD4KCgpCWt49nDQAAIABJREFUlmZxWdneUKVSITw8HIGBgX12q/7Q50aLIGc7YiaHgZkcBmawHTGRYY+TIcH9XtOVlGQs8XDGVBYd09mO+NDnBgrk1ffvj1IJXlwMtv91Ge9euQiHyHBUtss6q9Vqi6iQUCqVCAkJQUhIiFmty1xo7FL4hoYGBAUF9dmM0lyprFdC6OyPYxtP4aMF3+CLN47B5dhFKKprYfv8gS4z6J+99r3R1qQbp5iQkACJRILrXjfg/LsH/thPx5kPGbA/wkCAMMjq/rZqtVoEBARg5MiRYLPZlDinYFGgBDoFq4Q5CfTGxkZ4e3sbxTW9MyoUig5mcEqlEt7e3gbpP9dlzgdc0q5tCc5CQ0MRFBTUp7LSxgZvNFW8hebKV9BcOa3rmeidCPU7FbPQKNtBlbu3Xn+pVAqpVGq2mUOlUmmSrKyGICDNz8O5iDDYR4YhKC8XSgMLiIaGBgQHByMsLKxf4nDZeVdMYNiT4lzHlx3P4QD/xsCuu0aN7KqqDiPd6lUq7LzmhUmt49x0XHPRo4NI17Gts39QUBB8fHwQGhqKrKwsKDoZGTdYtDZxaGwauhS+vr4egYGBiIqKMusKi75Q4hGEnzb9gbMfssD52gPnDrDx4zt/QMDzwzerf8Mamx1Y9mhr9vzhzVgxdCtWD98Bx0+dBmV9KqUKJYWlSE9PR3BwMG7evAmRSITU1FRUVVVZlUgPCQnB6NGjQafTKXFOweJACXQKVommpiaTC/O25PP5qK2tNfp5SktLIRAIkJmZqSfGNRoNvL29QRDEgMW5QfrNtS1utH5+foiKiup9cFxZiTt//om7L43DXe6zuFs6HnfzxuFu0Xg0V9w3jevKzf3vqpfQVDkB2tKZUBSsQ1ZmrEkFgimpUCj6fv1NTF1WNj4+Hr6+vhAKhYiJiUFhYaFB+1Yb1Gp8KRa2upc7YDz9LMbTz2IejwWnuBhoDBDE1tbWwt/fH9HR0f0WJx94X+8ig+6A49Igo9yDS8mJmMR0wCwuo0OP+rmIsF49R11dHXJychAeHq7nRD2YBmWGuP4PMgdaCl9XVwd/f3/ExMRYzfWXV9bg7IcsnN3PguuxSyTPHeTg9AdMePzqBduxB7DzxcPY8fwB2I49gO3//hC7XvwIUcI4o65N2aCC9K8IOH7qjNPvM8D77jzOMy4iKioK+fn5iI6O7jChwZK9ACIiIvDkk0/izJkzlDinYJGgBDoFq4S5CXSRSAS5XG60529sbCR7uEtKSjr8/tatW/D29oZKper3OQxmBqfVoqysDEKhECkpKb1+rkZvb9yzscG9hx7CvYcewt3pw3A3/QXczW3DovF6zu6dC/UJaJbNwe2KV5Gd/AdZtpmamvrAjP6qrKyESCRCcnKyxb7etn3rbQVCTk7OgE2+ziclYDaXiaksGl6kn20h7QzG0c5gOtsRzOjIAT1/dXW1QcbYBeTmYBLTARMY9pjGpmMai45XGPaYznFERmXP87v7w0+EPpjYzkRunhMLk5gO2Hj5Qqf3KT4gGeyv3MD8whWRwjg9QaZSqTo4URtbINTU1EAikSAhIcFi3//mxr6UwtfW1sLPzw9xcXFWdf2LMktwfIc9OF+56wl07tHz+G37WaRHZ+HkTnvYvXAI2/79Ibb9ez/2vPwxeN9cMPp18GaLcWzjKfy+ywGn9zPw8ZtH8fWan5AkvW/82HZCg7+/v96miyVtZMfExGDMmDE4efIkJc4pWCwogU7BKmFuAl0ikaCqqsooz63VahEXF0caTHUl4L29vdHQ0NAv8X/r1i2DmMFptVrk5uaCz+cjPz+/149p9PbGvUcewb2HHwaGDCF596d/4G7WC7hbNr6FFeNxt7I3In0qmquX4HbtT1Ap61FYWEiO/hKLxRZhbNVfFhUVQSAQICcnx+RrMSQ761vvr5v4rutemM5xxEv0c3iJfg6vONrjFcdzGE8/i0lMB7zl7gx5P1sCysvLIRQKkZGRYZCg3CMxHnN4TExodXpf7MKFJCfLaNf5MxG/S4H+7hVPvWM1Gg3OHmBjjc0OrBl+n79uPwuVsqP4bpuV9ff3N8oIN5lMBpFI1KfNQYp9Y3el8DKZzGo3R8gM+of6GXT7gxycfp+BqhIZlA1KBHmFgfO1O5y+vYAYycA26XrDktwy/L6bBvuDHHC/8cBPu3/Hyf3n8MuW03D6zrPL7zmFQoHs7Gy9mevJycm9nrluCsbHx+Mf//gHfv75Z0qcU7BoUAKdglXC3AR6QEAAysvLDf68KpWK7CHuqcddIBD0ucy+vRncQJ3ak5KSIBKJUFHRh+xeZWVL5rydOCdF+hob3L35L9xNfQF3817A3cr7Ir3LUnfZy2iWvY07iq+hJe4bc7Ud/aUroR7scUPGIkEQyMrKIqssTL0eQzKhtARnI0Lxc3AALqUkoUwu77Rvvby8vFeB5aYrnpjOpmM8/SxeJgW6PcbTz2Iai4a5PBYSy0r7vM7CwsI+b071hgqlEoF5uQgtyDd4n3x7Xk9PxSSmA2ZwHElxPqu1xJ0VE6V3bNCVMKwevgNrR9hh45g92DhmD9aN3InVw23B50p6PFf7EW4DNQusqKiAUChEZmbmgK9DfW0DCtKLoKi2/ikDA2HbTRc/Pz94e3tDIpEgKyvLKic0iN0DceydUzh3gA3uEQ/YH+Tg2MZT4HMlqFfUozSvHDVVg5uNTpKm4tjGU+AcdSfFufP3nnA4zMUf+xwhr+zZHFRX6RIXF6fXXlRQUGA2/iVJSUl49tln8e2331LinILFgxLoFKwSzc3NJhflbSmVSjstPR8IdWWasbGx0Gq1PR7v6+vbpzJ7Q5rBqVQqREREICAgoM+lcnf+/BP3HnqoU3Gu473HH8LdvaNx1/ffegK96wz6RDTLFuBO7TfQdvHadCXUKSkpCAgIILN5eXl5ZhOQ9JYEQSAxMRG+vr6o6uf4LXPl+aQEvO7Kw1weC3N4TMxzYmH7X5dR3OpIr3Mwbt+33t2my/GQIExl0fEi/SyZQX+5NYM+k+OIhc4cFMjlfVpndnZ2p5sjCqUS9KgIrLrghtdcefhMxEdCqfluoCg1ahwS3MRkJq1l9jqjxSRu69VLqGn3ufh16xmsfsIW7zy9V4+rn7DF18t+6dN5uxrhVlpa2qtNl5KSEggEAuTm9n9GvFarhVqlBu/oeawfvQvLH92K1U/Y4sx+Fupq+ubA/6BR19aRkJBgVFd4U7OhrgEiF3+c2c/Eb9vP4vQHTHhzxAi4GAKHQ1wctz2HP99nQMDzG7T3TGZsDn7c9Ae+3/4bKc5dvvfEH3vpsD/IQUNd377P2rYX6UwfQ0JCkJGRoTdzfTCZlpaGf/7zn/jyyy9x9+5dU4egFCgMGJRAp2CVMDeBHhYWhsLCQoM9ny7YbG8G1x37UmZvSDO42tpaBAYG9s+pmiBwd9y4HgU6hgzBvYeH4O6Ux3A37b/3RXqXAn0ymmWLcLvOvtdrqampQUZGBqRSKRmQZGZmmn1vnlqtRmRkJAICAqwuY5VXLcObbk5Y5MLF2oseWHfpPFZ5umMuj4lfgwM7HE8QBCorK8lNl6761nOrZVju4YoX6WcxjnYG42lnMJ5+lpwx/qmI3+s1EgSBlJQUiESiDpsjao0GH/rcwDQ2HTM4jq1973QscuEgrguRThAE0irKEVVcCIWJxoIpNWpcS0vFx0IfHOTfxPmkhE7X8s3q41g5bHsHgb7qcVscnne03+dvW+kiFoshEAhIs6vONs90lQtFRUUDfu2Mz12w/NGtWPbIFix/dCuWP7IFK4ZuxbGNv5v882Cu1LUVpKen67+PWkvh2/oPWEvFkqxMjvzUQshKqxF6Iwo/vvMHTu50AP0TJ/y5zxHfrzuJazT+oIhZRY0CP9qdxKdvfQv2l65w+d4T9I95+GH9Sfhd6P84Rh11M9cjIyPB5/P1Ns8Gw4A0MzMTY8eOxccffzxo4vz555/HkCFDOvDw4cMAgMbGRhw+fBhjxoyBjY0NNm3ahOrq6kFZGwXrACXQKVglzE2gR0ZGIi8vb8DP09YMrrS0tE+P7W2ZvSHN4KqqqsisSb961kpLexTmHUrexzyMu6H/7qbM/WU0yxagqcYWjQ03+/W66uvrkZubS7pQm6vJXH19PYKDgxEaGmqVM54vJCVgHo+FNa3iXMfXXHlY6uECpab74LB9CXVwcDDZt55ZWYFPRT6YwLDHi/SzmMhwwGwuE7tvXO0weqwrajQaPX+I9r/3z83BDI4jFjiz8YabE95wc8LrrjxMZdHxkdC7w/FpFRXYevUSprMdMZVFx6vOHHBjo3v9nsuWVcEjMR6eyYm9fg0DoefJv7D6CVtseGo3Kc43PrUHq4fbgvO1h0HO0dkIN93mWU1NDXJycsi/lwM9l7yiBquH78DyR7Zg5WPbSC5/tEWs5yYZtnXBGlhVVdWrtoLOXOHDwsJ67QpvrmyoawDtYx5O2NnD6dsLJM8dYOPkTnuU5JYZ9/wNDQgICID4hh84X7vj582ncWzjKRy3s8dVex/UKwybxddN2khMTIREIgGfz0dkZKRBfSTaMicnB//9739x4MCBQc2c19TUQCaTkQwICMCQIUMglUoBAAcPHsR//vMfBAUFISEhAQsWLMCrr746aOujYPmgBDoFq8Tff/9tclHeljExMcjOzh7Qc2i1WsTGxnZrBtcdg4ODuxX1hjaDKywshEAgQHZ2dr+f61ZWVp8FOinUN9uguXJKuyz6JDTLFuKO/CCaFP/DLXXmgAMElUplliZzcrkcEonEqsYYtadbQhzm8Vhk9lzHN9yc8La7Mxr6kL3RlVDrskB+fn5ISkpCUWkJ/HKycDE5EaEF+b0esaZWqxEREYHAwMAuA9OzEaGYznYkxbmOs7lMLHRm631uahoasMTDGZNZNMzmMjGXx8JUFh1T2XRcSUnudi0EQeBkSDCmsGiYwLDHBIY9prMd4Z4Yb9T7IyuTY++ET7F6uC1WtxrErX7CFjueP4iy/HKjnLPtCDdvb294e3sjJiYGFRUVA/6bliRNJTPmbQX6iqEtGXWJx8CzkdZEXc9/dnZ2nx/bF1d4c2ZFUSVO2NmD9hFPT6DzvjmPHzb8jrTITKOdu6GhAYGBgeQoQWWDCunRWYjzT0JBWpHRryNBEJDL5cjIyNDzkTDUzPWCggK8+OKL2LdvH/7++2+TxpyfffYZxo8fj3v37kGpVGLo0KH466+/yN9nZ2djyJAhiI6ONuEqKVgSKIFOwSphbgI9Pj4eGRkZ/X68UqmEVCpFSEhIv0elhYaGoqioqEtxbkgzuLS0NAiFwoGbkfUjg96W2rJcNNa5o6l6PZpli9BcvQJNNXtwR/EVGpW+Xfaf95fmYjKncwpPTU21qGC2r0wqK8Nrrly86e5EivM1Fz0wh8fEVxJhv59X17euM0Tqa+ltQ0MDpFJpj5UL7JgoTGPT8borT0+gz+Qw8La7s96xl1OSMIVFwzweCwudOSQns2hYf+l8t+u5lpaKicyWXvGZHEfM5DhiIsMek1k0xJQUG/UelRVUwP4QB9v+tR9bnnsff77niKJMw/bYF2WWwOV7T5zc5QC3Hy+hOKeUbCvIyspCXFycQUqoi7JKWsraH9XPoK9oLXWPlSSa/DNhLiwrKzNIz79Wa9ml8HU19Th7gI1Te2h6Ap32EQ+/2Z5DYYZxPn/txbmpr4NuTQUFBR1mrvfnPhYVFeGVV16BnZ2dycX5nTt38PTTT+PEiRMAgKCgIAwZMgQNDQ16x40dOxbnzp0zxRIpWCAogU7BKmFuAj0pKQmpqan9eqxcLodYLEZcXBy02p7N4LpiREQE8vPzuxTnarV6wF/karUaMTExkEgkqK6uHviXeh960PX60R96CHfHjbsvwAkNbqmz0KgUo1HpB60m3+DivD1NZTJXUFAAPp9vkMDY3EkQBE6EBGGBMxvzeCy86sLBXB4TKy+4IaXcMKWjbfvW24/+qq/vWB5aW1sLf39/REVF9dh/mVVVibk8FmZyGKRIX+jMwTQ2HX+ESfWO/SNMiqksup44X+jMwQyOI2ZxGd1m9u2ueWECwx6zuAySOpH+fUDPburmzEhhHGk8t+px25aS+jG74HLWXa+toLf+Az3xq6U/Y8XQFlG+YqiO27Dn5Y/NRgiZmjqPFENPK9BqLbMU3t9Tih/W/47THzDB+dodDoe4+G7tCVw48ZdRNlDNUZz35T725OtSWlqKyZMnY+vWrWhubjZ1uAkvLy888sgjqKysBABcvHgRjz32WIfj5s6diyNHjgz28ihYKCiBTsEqcffuXZOL8rZMTU1FUlJSnx+nC3SysrJ6bQbXFaOiopCTk9OlOB9ooFBfX0+OfOtMuPSXvXFx70yg3zl92uRBSFsa22SOIAhkZGRAKBQapN/WUqjWaHA9PRWHBDdh+9dl/B4ajGwjOtV31reucy+WyWTw9fVFYuL92ca1KiWupaXiuDQI5yLCkFSmv3HgkRiPuTwWprHpmM52xHS2I3Ze90J1g/5n6EJSAqawaJjvxNYT6FNYNKz2dO92zSvOu2JiO4E+i8vAK47ncIB/w+T3sL9UNqhg98IhrHrcFhvH7MbGMXuwdpQdVjy2DXsnfgq1umtxUlNTg6ysLISGhuqNcOvJR6K8sBL7p/8Py1tN4pY9sgU7nj9I9Z+3sri4GHw+H4WFhYNyPksohVfWK8HnSnBqDw0/vvMHju+wx8Xfr6G6vG+TIHpDSxDnXd3H7Oxs0tfF39+fHM3XNp4oLy/HtGnTsHHjRjQ1NZk61AQALF++HGvXriX/TQl0CoYAJdApWCXMTaBnZGQgLi6u18c3NjYiIyMDAoEAZWVlBllDbGwssrKyyH8b0gyuurqa7Hc2uGtrD3PQO7q5P4x7NjbQVlaaPOjoijrXW0OZzGk0GsTHx0MsFhumcoFir9jQ0IC8vDxERkbCx8cH3t7eCAkJIeetV9TVYsdfVzC7jSh+1YUDj3a93+mVFaBHReBUqBSCrIxOZ5nL6uvxuhsPk5k0zOG2jJObxqZjKovW4fna839iISYy7DGzNds+i8vADI4jJjDsYR8ZZpBroVapESNOQMDFEBSkD9wtvTeM80vE6idssX70rpYZ66N3Yu0oO6wbtRNrhu9AakRGr++jrvRW5yORkJDQ5Qg3jUaDSGEcrp71Rsi1SKiU5l1mPVjUueUXFxu3baIrmnspfGVRFTJjs1GcXfrAZs57Q5VKheLiYkRHR+PZZ5+FjY0NlixZguPHj2PmzJlYs2YNbt++beowEwBQUlKChx9+GN7e3uTPqBJ3CoYAJdApWCXMTaBnZ2cjOjq6V8dqtVqyTFyhUBhsDQkJCUhPTze4GZwuy5+Wlma0jEWjtzfuPfJIjyL93sMP494jj6DRx8fkQUZvqQsq+2syp1KpEB4e3q0ZGUXjsqioCD4+PkhMTNTrd/7iLy/MYNPxtpsTVlxww/LzrpjnxMIiFy5yZLI+nyeutASrPd0xjU3HFBYNc3hMnA4P6dG4LrGslBTkU1g00ixuoTMHJYqO7vJ9ZWZsNj6Y9gXWjdyJNTY7sOGp3fjzPQaU9ff772VlckQKYpEYnNptZrsvDPeJwapWgb5utB3WjbbDxjF7sP7JXVj9hC0SArs3z+uMGo0GZWVlpAt1TyPcKLYwPz/fYG75hqAllsIPhDpxHhUVZdHivD3VajXEYjH279+PsWPH4qGHHsL8+fNx4sQJpKam4t69eyaNNX/66Sc899xzeqX2OpO4a9eukT/LycmhTOIo9AmUQKdglbh3757JRXlb5uXlISIiosfjlEolgoODB2QG1xWTk5ORkpJCBi+GMIPLzs6GQCAYlHLGRm/vlkz6Qw91KHnX/eyejY1FifP27KvJXF1dHYKCgvo3Y56iQagb49XWEJEgCJSVl+N1JzZmMh2wiE3H6zwW3nblYZmHM2ZxGXCJj+3X+VQaDSKKCuGXk42yWoXeOQvk1ciqqoS6kwA9pCAfGy9fwORWgW537QqSDdCnX6+ox56XP8EaGztsemYftjz3PjY8tRtrR9jB5XtPEAQBj1+98O7/vYe1I1qy2x9M+wIpYekDPre8sgbvPL0XK4Ztw7rRO7FxzB5sHLMbqx7fji3/7wPU1w5MUBMEgerqaqSnpyM4OLjDCDdTv/fMhbm5uRAIBCgvN44zvyHYvhQ+ICDA7Erh+0trFec61tTUYPHixXjrrbdQUFAAZ2dnbNy4ETY2NvjPf/6DQ4cOQSgU4tatW4MaZ969exdjx47F0aNHO/zu4MGDGDt2LIKDg5GQkICFCxdi4cKFg7o+CpYNSqBTsEqYm0AvKipCaGhot8fozODi4+Nx69Ytg68hNTUVCQkJBjGD02g0pIisMmK/bwdWVuLO6dO4O26c/ki1ceNaes4Hcy1GZluTubbmZDqTuerqatI80BqDMnMnQRBITU2FSCRCZSftFGqNBq+78vCqMwfLPFzwlgsXr3GZeJVJwzSGA477CiGXyw0iDpLLy7Dzuler+RsD6y56wC+n42grgiAgzMrEzmtemMNjYpELF78GB6JqAJ4R/p4hWDvCDu8++x62/vMDkutG7YTt2APwZvti3cidWDdqJzY+vRdrRthh5bBt2Pbv/ZCVDqwdo7a2Fr8fPodVT2zHqidssXq4LVY9YYs1NnbwZvka/J7rWlMiIiLA5/PJPtmKiooH9jOYnZ0NoVCIiooKk6+ltzT3Uvi+sKGhAUFBQVYrzhUKBd5880289tpr0Gg0enFeY2MjJBIJPv74Y/z3v//FF198Mahxpp+fH4YMGYLc3NwOv2tsbMThw4fx1FNPYfjw4XjnnXcgk8kGdX0ULBuUQKdglTA3gV5aWorg4OAuf19cXEzODB+oGVxnbGxsRG5uLvh8PqKiolBQUNDvjKtSqURYWBgCAwNNVypIENCWleFWVha0ZWVGd2Q3B7Y1mdPNdw4PD6cyeSagRqNBbGwsnLxvgh0ZDs/kRBTWdDR8+txXgJkcBpadd8WKC25YccENi104mMNhwE0i7iDy+iPWSxUKvOnmhCksGubyWnrTp7LomMtjIapYv7IlpqQY85xYmMKiYRaHgRlsR0xh0bDt6qU+zYxvy8t/3sC6kTv1xPnWf37Qks1+ajc+nPElVtvswBobOyx9eDOWPnSfJ3bY9/se1NTUkL3igZdD8eXbP2HH8wdxZPkvCLsZZfT3gFqtRnFxscFGuFkiMzMzIRQKB3eT1sC05FJ4pVJp1eK8rq4OS5cuxYIFC6BSqXqM+RobGwcpwqRAwfigBDoFq4WpRXlbVlRUICAgoFPhnJ6eblAzuM7OQRAEVCoVZDIZ0tLSEBgYSI4Z6mpcVGdUKBQICAhARETEAxOEmhvz8vLg4+ODuLg4g5nMUew91Wo1QsPDsf+CB+Y7sTCby8BsLgOvufJwOSVJ79jEslIs8XDBLC4D853YmMNlYg6XiSN+vtC0GjQWFxfrZfLi4uJQXFzcwWyxuqEevLgY7Pe5jk9EfPhkpkOt0YAdE4WpLDoWuXDwmisXr7lysdiFg6ksGj73Feg9x2GBN6awaFjkzMFiFy4Wu3Axv1XQe2f0r+Q87GYU1o7ciU3P7NMT6GtH2GHPK5/g3Wf2YfVw2xZR/nAbPrQZK4dtR3p0Vp/PKZPJIBKJkJqaahbv9+5GuJm7yOvv601LS4NIJIKsH14K5kxLKYW3dnFeX1+PVatWYc6cOaivrzd1OEmBwqCDEugUrBamFuVtKZPJIJFI9H6m1WoRHR0NPz8/g5rBtRXm3Tm1KxQKZGZm9nrsV0VFBUQiEZKSkswqUHlQqAuKhUKhXq9ne5M5iUTSJ5M5ir1nQ0MDpFIpjt+4hjlcJha7cLH8vCuWnXfFXB4Li1w4SK3Q78NNqyjHD4F+WHvRA3bXrsAjMb5Tl3aCIMhMnp+fn15LQ5GsCmsvemAyk4aJDAdMYjpgMouGI36++FoiwmQWjRTnOs7gOGLlBTdky6rIEvZXnTmYyXEkxbmO01h0nAgJ6tc1UavUODzvKNaOsMPGMbux6Zl9WDdyJ9aO3IlrDnx8vOAbLHtkC5Y+tBnLHtnSwoe3tAr0bWB87tKn85WXl0MoFCIzM9Pk74euqBN5fR3hZgkkCAIpKSnw9fWFXG74MWHmRHMthdeJ88jISKv8G69UKrF+/XpMnz4dtbW1pg4lKVAwCSiBTsFqcefOHZMLcx1ramogEonIf+vM4EJDQ6FWq40izrXa3pvB1dfXdxj7lZaWhurqahAEgfz8fPD5fOTm5pr8y/tBpK6kWiKRdBsU60zm4uPje2UyR7H3rK2tRUBAAKKiosgebl3Zus6hfRaXAYfI8AGfiyAIyOVysqXhQ3cXTHA8hxlsOubxmJjvxCbL0z8R+WAqi47FbTPorlxMYNhjYqtr+0yOI474+WKphwumsel64nyRMwdTWXTQoyL6vd6y/HL8sOF3bHhqN9aN2oXt//kQnif/gkajgdgtkBTkyx6+L86XP7IFqx7fjuM7zvX6PLqJEXl5eSZ/P/SWSqWSHOEmFApJn5GSkhLDj6Q0MgmCQFJSEsRi8QPXWqMrhW9bJWGKUnhrF+cqlQrvvvsuJk+ejJqaGlOHkRQomAyUQKdgtTAngV5XVwc+n4/bt2+T5l4JCQlGMYPTlbTrxHl/AoCCggJERUWBz+dDKBTCx8cHWVlZFp/9sUTqev6DgoJ63Yqg1eqX3XZmMmfq12UpzKyqBC8yHN9fvgh+eBgIgsD6S+cx34mtJ9BXXHDDLC4DvwYHGnwNb7s7YRLDAbNYNMxg2GMm0wGzWHRMYjrgc18+5vJaytRfdWZjkQsHk5gOeIF2Bi/QzmBcGy5y4bT2pzOxyJmDRc4cTGPTMZvLRGZVR6O7vrIktwzp0Vmold8f90cQBA7M/Op+7/nDm7Fi6FZseHI3Vg/fgQtM4BknAAAgAElEQVQn/urVcxcUFIDP56OoaHBmrBuDljzCjSAIJCQkkOM/Tb0eU9MUpfDWLs7VajVsbW0xceJEylCNwgMPSqBTsFqYk0BXKpXw9vZGYWEhBAIBcnJyjGYG11VJe3++LCMjI+Hr64vIyEiIRCL4+voiPj4epaWlVhkgmBt1WVtD9Py3NZnrTUvDg06CIMCIjsBiZw6mM+wxi+2I1115YERH4Dt/MWZyGFjexvxtiYcLZnMZuJLS99nbPfEtd2dMYdEw34mNeU4szOEyMItFxwT6Wbzn4QpHsS9ebe0ln8KiYVwbcf4i/SxepJ8lRfq7Vzwxne2IaWw6prbOUr+UnGjUa5kSlo4NT+7GymHbsXakHdaP2onVT+zAjucPorKoZ4Mx3Si7srKBj4UzF3Y2wk0qlSIjI8Ng7v6GXGtcXBz8/Pyssqd+oByMUvgHQZzv2bMHL730EioqKkwdPlKgYHJQAp2C1aKpqcnkwlxHtVoNb29vclasMc7R2NhokPnmWu39+dqhoaFkZkej0aC8vByJiYkQi8UQCoWIiYmhyqeNRJlMBl9fXyQkJBg8IKurq0NOTg5lMtcNg/JysYDHwhwmDcvdnLD2ogded+VhkQsH7onxeMvdCTM5DCxy4WKhMwczuQxs8bqEmi4yoTElxfg5OACHBd44GxGKnD6Ya/0cFIBJTAfM5bEw34mN+U5szOQwMJlFw9W4WCQlJcHb1xcnLl/CLzevY3yrGH/J8RzJF2ln8QLtDD4R+kCanwdaVDic4mKQXz2wUWe9ZaQwDofmfI11o3Zi3ahdOLriV2QndF+qThAEMjIyIBQKOx1lZ46sldfhwvGrODz3CA7O/hpuP16CvKLncnBdm5FuhJufnx+SkpJQXl5uUkGm0WgQExODgIAA1NXVmWwdlkJjlMLrWuIiIiKsUpxrNBq8//77eOGFF1BaWmrq0JECBbMAJdApWC3MRaATBIGoqCh4e3ujsrLSKMK8OzO4vlImk/U4X7vtjO6AgACqfNrA1PXaZmZmGl0wd2UyZ2phYGp+dvMapjPssdLDBesunSc5l8fC9wESxJYU4yD/Jha7cPGGmxN+DPRHiaJzIXY1NblFxHMY5Kzyt92dEVNS3Ku1FNXIsdTDBZOYDiQnM2k4JLgJVes90vWtxyUnY3xrBv1FWkv2/KXWLPoLtDP4SOBtsmtKEARK88pRWdzz5gRBEEhOToavry+qB2kTYaCsq6nH4blHsHLYdqwYug0rhm7FymHb8d7kz3sl0nVsO8JN5yURExODwsLCfo/H7A81Gg2ioqIQGBhI/V3vJwdaCv8giPNDhw5h7NixKCwsNHXYSIGC2YAS6BSsFuYg0BsaGshMtLe3N+rq6gwuznVfcobInBcVFfVLGOrKp3WlmqGhocjOzqYyLv2grpy3sLBw0M/dncmcpRla9Zc6t/wtLjzM5TL0xPm61t7zw21ErkKp7HaGeFV9Pd50c8IcLhPLPFyw/Lwrlnq4YAbHETuve/X6c1aqUOBMeCg2e13EruteOJ+UAKWm43kzKiswk+NI9qC/SDtDCvbxtDNwjgo3+yoJXUm1pfU7Xz3ngxWPbcPq4bZYO8IOa0fYYfXwHVg5bDvcf77S72tRWVmJ1NRUcjzmYJiTqdVqREREICgoiBLnBmJfS+EfBHH+2Wef4V//+hfy8vJMHTJSoGBWoAQ6BatFc3OzScV5dXU1WaJ869YtiEQi1NTUGFScD8QMri11paQCgQDFxb3L6nXFuro6ZGdnkyOGgoODyb5KUwcE5kzd+CKRSISKigqzWM+DZjKn0WgQHx8PsVgMhzAp5vJYWO3pTorz1Z7umMtjgRUT1evnFGVlYhaXgSXuzlh+3pXkay5cLHBmI6/aMHOkNQSB49IgzOIyMJFhTwp0XS/6eNoZvMZj4TqfT3pJmKOTuC5ra4kl1UeW/4IVQ7eS4lzHFUO34vDcIwY5h0KhQHZ2NpmR1bWnVFVVGWzjRa1WIywsDFKpdFAz9g8SeyqFt3ZxThAEjhw5gueeew7Z2dmmDhcpUDA7UAKdgtXClAJdl4luawYnkUggk8kMmjk3REm7boSXWCyGrA99sb1hQ0MD8vPzERkZCT6fT5b3GTKYtAaq1WpER0fDz8/PbMcXdWYyl5WVZVEZzp7uQWRkJCkMSxQ12HDpPObwmHjdjYfX3XiYw2Niw6XzKO7DPRJmZWAmh4HX3Vr611935WGZhwtec20R6LkGEuhXU5MxjU3HLA4Di1rnnY9v7TufwqThQ58byK2WkV4SSUlJkEgk4PP53TqJEwSBG+lp2HfzL6zxdMcRP18klJYY7R6Eh4cjODjYIjeBvl19HCse29apQP944bcGP58uIxsTEwOhUGiQjReVSoWQkBCEhIRQ3iKDyPal8DofgvLycqv7riQIAj/88AOeeeYZpKenmzpUpEDBLEEJdApWC1MI9MbGRqSlpUEoFHYwgwsICEBFRYXBMueGEOcNDQ0ICQlBcHCw0bNVKpUKRUVFZDApFoupXuc290AqlVqMKLE2kzmlUgmpVIqQkBC9jGG2rArf+UuwxMMZSzyc8Z2/BNlVPbuOt2VhTTWmsegYTz+L8a294JOYDpjGpsP2ryvQGOh67bp+tXUe+v0Z5686czCZScP3AZJOH9PWSTwoKKjTjZfTYSGYzm5xh9f9d54TCyEF+Ua5B6GhoRYrDL1Zvi0l7k+0KXF/whYrh23HpVPXjXruthsvfn5+4PP5iIyMRF5eXq/HM+ruQVhYmNlVVjwo1GXOg4KCEBMTYzRXeFORIAj89ttvGDNmDJKTk00dJlKgYLagBDoFq8VgC3SdGZy/vz9qa2s7/D44OBilpaUDOochzeDkcjn8/PwQFRU16MFY+15nXQBSXFz8QAWGCoUC/v7+JrkHhqKlm8zV1dUhICAAkZGRXd4DpVoNZT/vz9nwUExmOpDO6rp+8AkMe0jzu3cx7wtXe7pjOltfoC924WIyk4avJKJeX4u2Gy+XRUJMZ9Ewo83zLmoV/Vu8LhpsM6a+vh6BgYHd3gNLoLJeia+W/owVj21r4dCW/36y8FvU1fROJBuCOsPAthUvPY1w0/mlREREWPQ9sGSqVCpIpVKEh4eT98AYrvCmIkEQOHXqFJ566inExcWZOkSkQMGsQQl0ClaLv//+e9DEuS640WUeOjsmNDQURUVF/c6a37p1y2BmcGVlZRAKhUhNTTV5xlPX65ycnAw/Pz8IBAJERUWhoKDAqvsfq6qqIBKJkJSUZPJ7YChamsmcXC6HWCxGfHy8UTYT6lQqvOXujLk8Fha7cDGd7YjJLBqmseiYwXFEYF6uwc51xM8XU1g0LHLmkGJ6oTMHU1l0OMXF9Pn5VCoV2KFSTHS0xyyGA+YwaZjHYWChEwuzuUzM4jL6VOrfFWtra+Hn54fY2FiL2NDpicp6JXw4Yny37iS+WXUcNxyFqFcMnjjvjPX19cjLyyNbjdqPcGtoaEBgYCCioqKs4h5YIjsT551xoK7wpiJBEDh37hxGjx6NqKgoU4eHFCiYPSiBTsFqMVgCXTevOjExEbdu3eryOJ3BVn/EuaHM4LTaFpdwPp+P/HzDlqga6ku8uroaaWlppGNxeHg4cnNze12maQksLi6GQCBAdna2yddizHtpziZzlZWVEAqFSEtLM1pgW1Qjx3wnNha7cLHUw4XkEndnzOA44lJyosHOlVRWhvlObExh0VoENIeBKSwalp13RWU/21f+SkvBFBYNC505WOjExnwOA3NYNEx1tMd0Jg3xGRkD2kTTbZAkJiaatbiwJqrVapSUlOhtogkEAkilUqv6G2tJ7K04b8++usKbigRBgMFgYOTIkQgNDTV1aEiBgkWAEugUrBaDIdALCwshEAiQm5vb47HR0dHIzs7uszjXag1jBkcQBJKSkszGJbw3VCgUyMzMREhICFmmmZmZadHGZFlZWQZxy7c0mpPJnG6DJDfXcBnsztigVmP5eVfM4TL1BPprLlzM4TERVmjYTbLwwgLYXfPCbC4Dc7hMHBZ497lnvi3La2sx34mNaSw6mZlv6Wt3wPaLHmTfemhoaJ/vpa6CxByqeB5UKhQKiMViBAYGDuoIN4r32V9x3p7mWgpPEAQ4HA5GjBiB4OBgU4eFFChYDCiBTsFqcffuXaMJ88bGRqSmpkIoFPba+C0uLg4ZGRl9zpwbQpyrVCpEREQgICDAYsVtfX09cnNz9YzJ0tLSUF1dbREBPkEQSExMhK+vL6oGIJqsgaY0mcvNzR2UDZKU8jKcCQ/Fu1c8MZlJw2wuA6+78rDQmYMZHEfsvn4VaiOUExMEgcq6OlQ3GCYbejklCbO5DExl0TCZ6YCpLDredndGemWF3r3sy9iv8vJyCIVCZGVlmfy9+KBSoVBAIpEgISGBvE+1tbVGH+FG8T4NJc67ur+mLoUnCAKurq6wsbGBRCIZ1PivoqICdnZ2GDNmDB5//HFMmTIF8fHx5O/v3buHY8eO4bnnnsPjjz+OJUuWULPYKZgVKIFOwWphLIFOEAQiIyO7NIPriomJiUhNTe3VsYY0g6utrUVgYCDCw8OtpqdbqVSioKAAUVFREAgEZE9lRUWFWQaSuhFe/v7+FrtBYsx7ORgmcwRBkBMWjF1BcjMjDa+58jCby8AsjiMmMRzwsuM5zOQwsMCZjU+EPii1oPdBYlkpjocE4X9iIdgxUSir7Xztbe+lblJDQkICSktLyXtZUlICgUCAvLzeGeSVF1YiKSQNlUUP9qaWIVlTUwOxWNyt/0VXI9weNCNPY1EnzgfDMd9UpfAXLlzA8OHDIRQKBzX2q6+vx/PPP4+9e/eSr9PPzw8FBQXkMadOncLo0aPh7e2N1NRUrF+/Hi+88AIaGxsHda0UKHQFSqBTsFoYQ6Dr3IbDw8Oh0Wj69NiUlBQkJSX1mDU3pBlcZWUl2R9vreY/up7KuLg4iEQiMpBsKwpMyYaGBgQHB3cY4UWxI41lMkcQBOLj4yEWiyGXy436Gqrq67HUwwXzeCysuuCG1Z7uWHnBDTM5jthx7QoK5NUmv86DdS/LysqQmJgIiUQCgUCA4OBg+Pj49EqcK2S1+H0XDRue3I21I+ywccwenNnPMrnhmqVTLpfD19cXKSkpvf5+6WyEW0REhNV5gwwWdbPmTTHOjiCIQSmF9/LywvDhw3Hz5s1Bj/2OHj2KxYsXd/n7e/fu4bnnnsPp06fJnymVSgwbNgyXL18ejCVSoNAjKIFOwWpx7949g4pznRlcUlJSt2ZwXTE9PR3x8fE9lrQbSpwXFBSAz+dbtRFZe+oCSZ0oEAqFiImJMZlpTk1NDfz8/BAdHW0WmwWWREOZzOmqFwICAgalF1OYlYE5XCZWnHfFak93kq+78rDYlWtRmXND3svk5GT4+PjAz8+vV6Lgl61nsMZmB9aP3oV3nt6LdaN2YY3NDpx+n2Hy12OplMlkEIlESE9P7/f3S1cj3NLT0y2m3ciUNKU474zGKIW/fv06hg8fjqtXr5ok9ps4cSI+//xzbN68Gc888wxmzJgBHo9H/r6wsBBDhgzpMIf99ddfx6effjrYy6VAoVNQAp2C1cKQAr2goIAsy+zvc2RnZyMmJqZbcW4oMzhdKW9paanJAwBTkSAIVFVVITU1FQEBAYPuIl5RUQGRSNSnTBXFrtkfkzmlUomQkBBIpVKDVi9oCAJeqcnYec0LSz1ccIB/gxyZ5p2RjjlcJla2Zs91fNPNCYtcuCis6ZjBr6yrQ2JZKcqt0JiLIAikp6dDKBSisrISWm3Xvc46D4KCtCKsH70L60fvxuZn3ye5ftQubHxqN8oLK03+uiyNVVVVEAqFyMzMNOjzNjQ06I1w07WolJWVUZuS7Whu4rw9DVEKLxAIYGNjA09PT9y7d88ksd+wYcMwbNgwfPvtt0hKSgKXy8Xjjz8Od3d3AEBkZCSGDBmCqqoqvcdt2bIFW7duNcWSKVDoAEqgU7BaGEKgNzY2IiUlBSKRCJWVlQN6Ll0QY0xxrlarER0dDYlEYvRSXkujTuDpymxDQ0ON5nBbWFgIPp9vdJfwB5WdmcylpKQgOjcH0vw85FdXo66uDoGBgYiIiDB4MGwfEYa5PBbm8JhY6MzBbC4Drzpz4J2RjuKaGrzhxsOrzhxSnK+64IbZXCZ237iq9xmvU6nwmzQQi1pd3V914eCnIH9U19dDlJWJo36++EIswMXkRCgssD1CNznC19cX1dWdl/br/CR0HgRisRgXaV5YPdwWm57ZqyfQ3/nHPqwdYYf4gGSTvzZLYkVFBYRCodGrqTob4RYdHY2CgoIHvr3H3MV5e/anFF4sFmPEiBFwc3MzmTgHgKFDh2LhwoV6P/vkk0+wYMECAJRAp2AZoAQ6BavGQAS1zgwuICAAdXV1Axb7hYWFCAsL0/uZIc3g6uvrIZVKIZVKzWLOtMFJKKHVFEFLDHzjoa6uTi+DFxQUhPT09AFvahAEgYyMDAgEgge6emEwqVQqEZ+ZiT0Xz2M+i47ZTBoWc5l4390VYUZoLSiqkeM1Vy4WOnOw8oIbVl5ww4rzrpjNZWLj5QtQatTgxkZjgTMbc3hMLHBiYzaXibfcnTqMVfspyB8zOI6Y58TCa648zHdiYzqbjqUeLpjJYWAam47pbDpmcBxhd80Lcgv6XGs0Gohv+OHMl3TcYAlRVSLr1WPKysogvCLG6uG2WDPCFhuf3t0q1N/D+tG7sX70LhRllZj89VkKy8vLB2WkYHu2rWDSjXDrzzg+a6ClifPOqFAo9L4zN2zYgPfeew/e3t5QqVQICAjAyJEjweVyTSrOAWDs2LF4//339X7GYrHwz3/+EwBV4k7BMkAJdApWjf6K6YGYwXXFkpISSKVSMmtuSDO46upqSCQSxMbGWmwA0CUJAo1KPpoUH6JZvglNNba4XUeHljCM2VZDQwPy8/PJEk1/f3+kpKT0ebSQRqMhM0cyWc9ihKJhSBAEPhXxscCZjZUXXLHK3RmvsuiYyXDAQQ83xMbGGsR5ukBejfNJCfhKIsI0Nh3Lz7uSAn3lBTe86eaEBc5sZFa19G5KcrLwmS8f2/+6jJ+C/JFcXqb3fKUKBRY6czDfiY0lHi4kZ3IcMY52BnN5LLzt7oy33Z3xmgsXMziOYMVEmfx694YqlQq/7j2NtaPssHZECzc/9z6Ezv69fo4jK37FmuE7sG7UTqx/aifWjLDFqie24+vVP1MzunvJ0tJSCAQC5Ofnm3wturYGXdWLKcZ+mYJqtdrixXl7KpVKsNlsrFq1CqNGjcKoUaPw9NNP47333oNSqTR12AdbW9sOJnGff/45mVXXmcSdOXOG/L1KpaJM4iiYFSiBTsGqcefOnT4L6aqqKohEIiQnJ/fLDK4rlpeXIzAwEI2NjdBqtQYT57qxRQMx/jFnNiqFaJavR7N8PZpqdqJJvgXN8hW4U/sTtAZ+vWq1GkVFReRoIbFY3Kt+St2c+cDAQEo8DDJTysvwlpsTVnm6YYOnO5ZymVh33hVL3F2w1N0ZYfFxpMlcZGRkjx4ESrUaV1KScYB/A7uue8E+Igwu8bFY6uGC+U5szOQwMJ5+FtPYdKxoI9Jfc+VikQun0x7zzhhRVIhZXAbecHPSE+jT2HS8QDuDhc4cUqC/7e6MmRxHbPG6ZPLr3RNVKhVYPzljtY0tNjy5G1v/tR9b/vkB1o3ciXee3ovM2N6VWVcWVeHrpT9j3aidWN0q1L94+wdIBH5k1UtaWhrZt27q121uLC4uBp/PR2FhYdfHEQS0paW4lZUFbWmpwf+edvceKSoqQmxsLDnCLS4uzupGuKnVaoSGhiI0NNSqXldbSqVSTJgwAW+//TYmTpyIoUOHYtmyZaDRaCgqKjJJ3BcXF4dHH30UJ06cQH5+Pi5evIjhw4fD09OTPObUqVN48skn4ePjg7S0NGzYsIEas0bBrEAJdApWjb4KdJ0ZXH5+vsGEeVvhL5FIyJL2gZbeEgSBrKwsCAQCFBUVmfyL2igk1LijOIxm+Vo0KQ7eZ81ONMs34pY60Wjn1pXbJiQkwNfXlzTMaR9E1tXVISgoCGFhYQ98n6Up6J+bg1edOVjj4YKlXCbWX3DDZq+LWHvRA2+4OSGtolzPeVrnQdCZyZyGIPCtvxgLnTlY6Mxp6Q3nMjGBYY/5TmxsuHQe6y+dxwSGPcbRzmAej4WVF9yw1MMFs7gMHBLc7PW686plmO/ExkJnjp5An8ykYRztDBa7cvUE+iwOA5uuePbrGik1aoQXFiAoLxdVRhyLpVQqIZVKcWDe/7B2hB22/ftDklv/tR/rRu4E+yu3Xj8fQRDIiMlGsFc4suJy9c5TUFCAqKgoCAQCypisHYuKisDn81FcXNz5MZWVuPPnn7g7bhwwZAjJu+PG4c6ff0JbOXgmfLrJG8nJyR1GuNXV1Zn8WvaXD4I4j46OxpgxY/D777+TZe0FBQWwt7fHkiVL8Oijj2Ly5Mk4ceLEoMd+AoEAU6ZMwbBhwzBhwgQ9F3egJYt+7NgxPPvssxg2bBiWLFmC3NzcQV8nBQpdgRLoFKwavRXojY2NSE5ONogZXFesrq6GQCBARUXFgIPItuXUVVVVJv+iNho1FWiu2YKmmh3tBPoBNMtXolEpGJR16EZ+tQ0io6KikJGRAbFYjNjY/8/edYc1ea5vWzsdrdphp+1RcbTugeLWuuoWbZ1QR+3Q9rQ9nT97erTnnNbTVkEIIwkQ9hC1QhICYe+994YAIYOR9SEKyv37A5OyRQkkhPe+rvuqJV+S9/ve5Mt7v8/z3E8SEQY6YnZNNVY52GOlvTV2e7hi/xVP7L/iiQ0ujtjm4QJRD4K0J5O57Oxs8LIzscqJgc1uLOzxdsceb3esdGJg2j0xrv7bRlcnGFlbwIhmgcUMW039eYHowb6LX/LYmG9PwwpHOta7OGKlEwNv217GDJoFVjr9FUFfy2Jivj0NFrHRD3x9YspKscPTFYsZtljEsMEaZyYcU5K0HnVWlwXFxcXh5JwvsetZs04C/cBrH2H72MO4YG6l1fdVqVQQCARITU1FYGCgxpisrKxsRG6YlZSUgMPhoLKy5zr9Zj8/tI0di7ZHHkHbI490Eujqv7WNHYtmP78hHztFUZBKpcjLy0NkZCT8/f0RHh4+7Fq4jQRxnpKSgueffx7nz5/vteZcLpfjypUrsLGxGeKVHwHB8AcR6AQGjZaWlvsKZ5VKhdjYWK2ZwfW2AaBUKpGYmKiJ+KSnp6OmpuaBFx1yuRzR0dEIDQ0d1hGGfpGSoUVqjhaJaReBfhytku24qXhwwTJQUhQFsViMpKQk+Pn5wc/PD9HR0SgsLETjIEYnCXuei5SUFJi5OGEZ0w6b3FjY5eWGd1wdscKRDqu4mPu+RkcX8S/cXbDQ9jK2sJjY4e6MPd7uWOFEx3TrS3jb9rJGoO/xdscqJwaWO9rj9+hIXMnKeCgDt9qGBnzKvgFjph0W0GlYyrTDSb9rOH7jKhbQaVhAp2EhnYb59jTs8XZHVf2DmWuVSyVY5+yABXQaVrOYWOvsgCUMWyxh2oKdl6u1eaivrwefz9dsVF0wt8KO8Ufw/qunNOL8vVc+xI7xR+H12/VB/Tx0NSZTfzcN/l7Z1ISioiJwOBxUVVX1+Hiznx/aRo9G26OPdhLmXdn26KNoGz1aJyK9I9X+IMMpU2IkiPP09HRMnjwZZ8+e1bkhHAGBoYIIdAKDxv0EemNjI0JCQhAbG6s1M7iuwlzt1K6uN1dHfJKTkxEQEAAej4eUlBQIBIL7LjqkUimCg4MRGxvb776kw523Gp3RKtmKFukhtEg/Rov0GFolW3G77jSaKN24WpeUlIDNZqOkpAT19fXIz8/XRHwiIiKQl5c34pyKh5pKpRLx8fEIDg5GZW0tzoUFY6OrE9Y4M7HV3RmWsdGQPeAC2S4hDksZttjsxMAGhi020G2ximmLadaX8JbNXwJ9t5cbjB3s8ENw4IDPg6IoZFZXIaAgD+lVVaAoCo0KBdzSU3H8xlUcvuoDWkIsahoe3NuAnpSABXQa1rs4dkqjX0Cn4cSNa1qZB4lEAh6Ph7S0NM1mY1ZsLvZPPoHt445g7/PHsOe5Y9gx7gjMjc4g2CMCVmeY+OOkDQJYIWisH7zvcF1dHfLz8xEVFdWpbn04RWP7y4KCAnC5XNTU1PR8jFDYHjm/jzjvJNLHjh3SdPe+qFQqO2VKcDgcvWvhphbnkZGRBivOs7Ky8Morr+Drr7/G3bt3db3EIyAwWBCBTmDQ6EugC4VCjRlcc3PzoIjzpqa+zeDU9XdpaWma9MzeXKerq6s14zW0xWWfpBpwq+EiWiX70CrZilbJTtyu+ww3lflDPhaKopCdnQ0ul9tjlKqxsRFFRUXdUqcNURDoknK5HFFRUd1aClbWSZFWJYDwIaOlaVUCrHV2wDpnB+z2csNOdxdscKRjhvUlzLa+hGX2NKxysIcx0w5b3FlIq9LvVnr/Dg/FAjqtkzh/x9UJS5l22OzGGvDrqw01s7OzNZ/vkqwyOJ71wD/W/YQDr57CrmfNsPe5Yzi74xf8ctQSe587hl3PmmHXM2bYPcEc37xzHtLawd/MGo7R2P4yLy8PXC63z3Kn27//3i2l/b4i/ZFHcPuPP3R+fl1JURREIhGys7MRFhamFy3clEoloqOjERkZabCb53l5eXj99dfx+eefE3FOQDDIIAKdwKDR2trao3hW1+kNhhmcWpyro+X9Xfyp0zMzMzM71TmXlpaioKAAbDZ7yHvZ6hNvqgrQrAjBTUVie0/0IX5/lUqF5ORkBAUF9atfesfU6YGWNRD+xYaGBoSGhiI2NlbrUSqKomCbEIc1zkwsc7CDMdMOyx3t8cGfvvglNBhbnR2wim6Dw05MuPJ4et/T2SklqVsEfYOLIxbQafiY3X9Du56o7q9dUGCg2AYAACAASURBVPCXK3syPw2H3/wEuyeat3OCGfa//CECnEKQEJCCvc8fw/6XTuLotNM4Ou00Dr7+EXZPMIf7f32H9LoolUpUVlZqfDy4XC4SExNRVlY27MRVTk4OAgIC+m7tSFG4O3XqQwn0u1OnDpm7+8Oyq6fEULdwGwnivLCwEG+88QY+/vhjIs4JCIYARKATGDS6CvSbN29qzOBqa2sHXZw/7OJAXeeclZWFgIAA+Pn5ISIi4r4toggHh+q6/7CwsIeqZVULgocpayD8i1KpFEFBQUhOTh6060ZRFOLKy/BHTCT+ExGK6zlZqLuXQktRFFQU1avJnL61/Kqqr8MmNxbm02lY6cTAahYTC+k2MHawQ3BR4UO/bkVFRbf+2kqlCqeNv8fuCeYwMzoD8xmfwczoDPZM+gDH3/oC1p87YNcEcxyZ+qlGoB+ddhq7J5rj9NLvdHaNOm6MhoSEwN/fHzExMXpft05RFLKyssDj8e6/YSgQPJAw78qmXmra9ZEdW7gFBAQgICBA08JtMMTzSBDnpaWlmDZtGk6cOIE7d+7oellHQDAiQAQ6gUGjo0BXm8GFhoaisbFxUMW5Uqkc8EJdoVAgLi4OwcHBEAgEnVpEjSTjoyZKiZuqIjSpSnUSyVFHbGNiYrSyAOtY1hAUFKSppRyO0buhpLokJSsrS69EcE+ZEmlpaaiurtaLzZe0KgEO+npjKdMOSxi22OzGgm9WxkO/ntp/oWsLr7ykAux78TgOTvkY5jM+0/Dw3z7B3ueO4V97/4ddz5p1EudHp53Gnuc+wEfzv9b5dVKzrq5O713EKYpCeno6AgMDIZVK73v8zfz8AQn0m/lDX06kDapUKtTU1CAjIwPBwcFab+E2EsR5eXk5Zs6ciaNHjxJxTkAwhCACncCgcefOHdy6dQsNDQ0ICQlBXFwcKIoaVDM4bYhzdW/tqKiobgY49fX1KCgoGBGmZDcVsbhd9wVapPvQIn0Pt+vP4qYyZ8jeXyQSaaLdgyG21LWUWVlZCAkJ0SwgSaZEZ1ZWVnZLp9ZHqg0g1anTAQEBvXpKDOm4KArZNdVIFlSicQBCoqCgoFeX8Jz4fOx74TgOvdFZoB/526fY+9wHcPrRE7snmuPAa6c04vzwm59g1wQz2H/jovO564kd69bZbDb4fD7S09N1uvlCURRSU1MRFBTU/3v+CIqg98WuLdwGYho4EsS5QCDA22+/jffffx+tra26Xs4REIwoEIFOYNC4c+eOJvKWmZmpMzO4B2FtbS14PF6/0ni7mpKFhYUhJycHEolEb6I9D8ubyiy0SA+hVbIDLdIP0CI1Q6vkXbRIP0STqnpIFiccDge5ublDdi2lUmmnTImoqCgUFBSgvv7BHbwNhcXFxWCz2SgvL9f5WB6EFEVBKBQiMzNTE72Li4sblpsvanNEdWlQT8co5Ap8tOBr7JnYJcX9uQ9gbnQG4moJ/rn7wr3adHPsmfQBdk8wx4dzv4KgePC/zwNlT3XrSUlJKC8vHzKBRlEUkpOTwefzH+yeYOA16A/D3kwD+1N2pFQqERMTY9DivLq6GvPmzcOePXvQ0tKi66UcAcGIAxHoBAaNqqoqcDgclJaW6o0ZXF8sLy8Hh8NBXl7eQ/VHLy0tRVxcHNhsNoKDg5GZmYna2tphKdZvNVjeE+SfoqXudDulH6NVsg3N8sE1lVL3Ey4rK9PZ+avrnKOjow1u86U/pCgKubm54HK5qK7WfwF3v3ORSCSdNl8iIyP13mROPfb09PR+1TpHXovD+6+ewu6J5tj7/DHsnmiO9146iQBWCJqamtBYL4OvhT/+se4nnFn2PRz+zx1VJcNvbjtuvnSsW9dW6nRPVKlUSExMREhIyEO9hyG5uA/Gte3awi0+Ph4lJSXdNtPU4jwiIsJgxblQKMSiRYuwfft23L59W9fLOAKCEQki0AkMGgqFAiKRSO/rzdVihMPhdKvtfBgqlUqUl5cjMTFx2DqI367/Cq2SvX+J83tslbyLWw3Wg/KeFEUhMzOz737COqBcLkdJSYnBbL70Zx7S0tLA4/EgFot1Oo7BeN3hYjKn7lzA5/P7vZGQFZsLi4/t8Y/1/8Jvx2lICXn4evfhwq6p0+q6dW1tpqlUKsTHx2v8Ux7qdYZ5H/ShYk8t3CIjI5GXlwexWGzw4lwkEmHZsmXYvHkzmpubdb2EIyAYsSACncCgcffuXb0X5yqVCklJSQgMDBwUMaJSqXp0ENf3/r+3G/6HVsn2zgJd+mm7QJd5DMp1SkxMBJ/P75fxkq6o3nxJSkoCl8tFYGAgUlNT9X4+H2QeEhISEBwcrJPUfhVF4Vp2Fj740xeb3Vg45X8d3PzBK3PoajJ3hcvBV9d8sY7FwDpnB/wUykexuI8WWk1NkCmVCCjIAyMpAf55OWjQgnhQKpWIi4sbmCgcgZTJZCguLtZspg20bl2pVCI2NhZhYWEDLo1o9vND2+jR9xXpbY8+irbRo9Hs76/z66lrdtxM8/PzA5vNRlpa2rDa7O4vJRIJVq5cifXr1+PmzZu6Xr4REIxoEIFOYNAYDIGuTTM4mUyGyMhIhIeHD8kiuKODeGBgoKaOUtcmVj3xpiIeLdJ97VF06Sm0SE/eq0c/2u7orsX3ksvlQzoP2pzPqqoqpKamdqqL1cf57O88REVFITw8XCNGEirKcT4sBKf8/8S/I0KRWDnwDJO+yEhKwConBlY40rHBxREmjnSscWbCJ3PwI8HC+nrscGNhjo0l5tLa+baNJd5xZqKklz7XJWIx9nq7YyGdhoV0Gyyg07DNwwXZNQ+fOq5QKBAdHY2IiIhuJpWE/adSqURFRYVmc1RtGtjfuvWO6dTamodmP7/2SPojj3RLeVf/rW3sWCLOe5iH8PBwlJaWPvR86jPr6uqwdu1arFmzBiqVStdLNwKCEQ8i0AkMGtoU6No2g5NIJODz+UhISNCJmFLXUWZkZIDP5+tlu69mORst0g/QKtmJVskutNR9hJuKWK0vTIKDgxEXFzcsRW1v88lmszV1lMNBZDU2Nmra2anngZOfi63uzljNYuAdVyesZjHxrrszAgsHp+1TTUM9trixsM7ZAaY+HhquZjGw29sd9YrBvY6OKUmYb0/DKqf26PlqJwaWM2zxNs0CZ1ydezSZO+l3DfPtrbHaiYENLo5Yw2Jivj0N713xhOoh7lFyuRzh4eGIjo7Wm/uAIbDj97Nry6+eNgUVCgWioqIGx4hMKMTtP/7A3alTOwn0u1Onttec92IEOBKpzmDouknSmw/BcGx/2tDQgI0bN8LExAQKhULXyzYCAgIQgU5g4Ghra9NLMziBQAAul6s3PZ0pioJYLEZ2drZmsaGzdl+UGM3yP3G74RfcarBAs5yHm4pY3FQmoonSbnRb7ZiflpamF/OgzfmUSCTIycnR1FFGR0fr7eJRKpUiKCgISUlJmu9Xg0KB9329sN7FAYeueuPwNR8cuuqNdc4OOHzVB7JB2EyJKCnGCic6dnm5dRLo2z1csJrFRFqVYFCvwxmuH+bZW2Ods0Mnzren4X0fj24mc6GpKVjCsIWJoz02uDhquNKJjsUM2wfONlC3o4yPjzeIcgl9prpuPSIiQtMuMzc3FxKJBDKZDBEREYiOjh7cTUOKQlNVVXuf86oqg3RrHwhVKlWP4ry3+czPz0dUVFSnFm765ivRlY2NjXj33XexdOlSyGQyXS/ZCAgI7oEIdAKDhjYEujpyro2U9qamJhQWFoLNZqO0VLtp2tpk13ZfQybuVDW4XfcPtEq2o1Wy495/9+BWI13ri8eKigpwOBzk5w9ONFafWF9fj/z8fI2JVUREBPLy8vSi1r62tlbTBrHj9yuxsgIbXByx74onDl/z0dDUxwPvuDoNilhOqqzAahYT2zxcOgn0LW4srHdxQIFocCOL3/N5PQr0efbWOOV/HU1NTZCrlEgrL0d0RgZcAnmYS7PEUjtrrHKww1oWE+tdHLGWxcQCOg3BRYX9fu+6ujrw+fx+tXck1C4bGxtRXFyM2NhYsNlsTe16VVWVXos7Q6ZanIeHhz9wBpJMJuvkK6H2CelPC7ehpEwmw86dO7FgwQI0NDToerlGQEDQAUSgExg0BirQtW0Gl5aWhoCAAAiHkTNufX09CgoKuom7wTDwutXIQqtkG1qkH6Gl7kw7pUfQIt2Hm8o0rb1PQUEBOBzOsOutrQ3KZDIUFRUhNjZW4yCelZWlk0iPutd8QUFBt8dSBZV4x9UJpj4enQT6Xm93bHR1QtYAaqx7o1Klgtn1K1juYK+Jou/0csUyB3uc5vgN+vX5MycLC+g0LGW2i+21LCaWOdhhAZ0G36wMcPJzsdfbHSaOdKx0ouPEjWtY4WiPhfbWWMGwxTI7ayy3t8ZCO2ssY9qiXNI/00mJRAIej4f09HQiCHVImUyGkJAQREREICkpSVPnnJycPGx9JYYjByLOe3qtqqoqpKWlISgoqM8WbkNJhUKBffv2Yc6cOZBKpUOyHjt37hxGjRrViTNnztQ83tzcjNOnT2PSpEkYO3YsTE1NIRaLh2RsBAT6BiLQCQwe+mAGJ5fLERMTg5CQEJ04U2uLjY2NKCoq0rSH0nZv7pa6T9Eq2feXOK87gxbpabRKtuGWzL3v56tK0SznolkeiCZVz+JN3dN5uG2SDBYVCgXKyso0kZ6hbMdXUlICNpvda695uUqJD/70xRoWEwfvpbgfvOqNNSwmTvhdg3KQIlHpVVXY6+0BE0d7rHCkw8SRjoNXvQc1ep4rrMEZrh+WMe3wlu1lzKBZYI6dFebb07CIYYPv+DyEFRdhpRMDS5i2WO/iiDXOTCxi2MDEkY4FdBoW0mntYt7eGnNtL+MTN+d2E6vERAjS0qDKyUGTQNAtE6W2thZcLhc5OTlEnOuQjY2NCAkJQUJCgibKSlEUampq+l23TjhwalOcd6W6lCwnJ6dTqcpQZzMpFAocPHgQs2fPHlIBfO7cObz99tsQiUQa1tXVaR7/5JNP8PrrryMsLAypqalYvnw5VqxYMWTjIyDQJxCBTmDweJio+c2bN7VmBldfX4+QkBDExMQMC7Ou/rKn3twDjcS21H3y4AKdonCr0R0t0oOa1PgW6VE0y9mdjlMqlYiPj0dwcHC/ezqPJCqVyk7t+AICApCSkoLKykqtpmVSFIXc3FxwuVxUV/cdBY8rL8Meb3esZjGxmsXAahYTpj4eg+7kLpY14mp2JuwT4+GXm4O6QfzeCurqsNmNhXn21ljmYIdlTDu8bXsZ8+k0/BgShKjSEqgoCp9z/bGYYYut7s5418MF73q4YLMbC4voNviSx8F2j/ZI/xZ3FhhJCVBUV0H+88+49dprnYzAbk+ZgqYLF9AkFKKqqqrXDAbCoWNDQwP4fD6SkpJ6vXeqfSVyc3O71a3rQ6mKIXAwxXlv867OZlL/hmZkZKCmpmbQUuGVSiXMzc1hZGQEoVA4pGuxc+fOYf78+T0+JpfL8fjjj+Pq1auavxUUFGDUqFFISEgYqiESEOgNiEAnMHjcvn37oc3gBirOhUIhAgICkJaWple1Z9qmQqFAeXk5EhMTBxSJbU9xf7d7irvEFDflwWiiOixEKTmaFZG4Xf8jWsVr0CLZgRbp6XuCfh9apAdwU5mFpqYmjelSZGSkTtMKhwvV7fjS09M1aZnacPhXZzDweDyIemkb1pWlEjGcUpLwa2Q4WKnJKJNKdH59tEm7xHjMp9OwztlBY/K2/p4x3L/DQzXHvevhDBNHukacq7mYYYsLUeFQURQkMhmUKlW/Wmm1PvUU4s+dQ15ens6vwXClUqlCY13jgH4n6uvrERQUhJSUlAd6HXU2U1dxJxQKSSbEQ3CoxXn3z9LAWvL19xxPnjyJqVOnQiAQDPla7Ny5cxgzZgxefvll/O1vf8Phw4c14wgLC8OoUaO6GdVNmTIFFhYWQz5WAgJdgwh0AoNHfwW6ts3gSktLwWazUVjYf6MmQ2DXSCyPx0Nqaiqqqqruv0mhqsbtuq/QKtl2r7XaNrSK16NVvBUtkvfQIjXHrUYGmlS1uN3wK1olu3FHZIw7orfRKlqCVsn2e6L+THvUvdERUqlUp+3shjspioJIJEJWVtaAHP5VKhUSEhIQHBw8rMs8tM0veWzMt7fu5MK+wcURi+g2eN/XS3PciRvXsIRp20mcb3F3xmKGLRySEzXHNfv5oW30aLQ9+mgnYd6VbY88grZHH0X8uXMIDQ1FdnY2xGIxEXf9YIO0EfRvXXHgtVPYPcEcp42/R5hP9AO/jlQqRWBg4IBr/9UbpKRu/eGoUqkQFxeHsLAwvchy66uF28PeO1UqFT799FNMmTIF5eXlOlmL8Xg8+Pr6IisrC0FBQTAxMcGUKVOgVCrh6emJJ554ottzli5diu+++04HoyUg0C2IQCcwePRHoGvTDI6iKGRlZYHL5UIgGNy2TPpOdSQ2LS0NPB4PXC4XSUlJfS4cbyqScVt8FK2ipWgVLUCraDFaRTvRIj2OFslBtIq3oUVyGK2i9WgR70OraE37MeJluCNajBbpIbTUfYZWyQ7IhRd6dAgnfHiqHf7VabaRkZHIz8/vc+GoUCgQHR2N8PBwksHQhT+Hh2C+PQ3rO0TQN7g4Yr49Dac5fprjbuRmw9jBDiaOdGxxd8ZGVycsZtjiHVenv7IKhML2yPl9xLlGpD/6KNrGjkV5eno3H4Lq6mqDzvp5WFIUhbM7fsH2sYexY9xR7HrWHNvGHMauZ8wQ4hnZ79dRG/Np+96kUqlQU1OD9PR08Pl8sNlsxMXF6aZl5jCgvonznlhXV9djC7fa2tp+fXZUKhX+/ve/49VXX0VxcbGul2QayGQyPPPMM3B0dCQCnYCgC4hAJzB4tLS0DJkZnFKpREJCAvh8PiQSw0rFHSjVUYGMjAzw+fzuadMUhVv11mgVzcYdkVEXzkSreCNaRat7eKwr30KraB1uCdcjJfY/Iy6DYSjZ0NCAwsJCREdH92oa2NjYiLCwMMTExGgtVdOQmFBRjqVMOyxm2GCdswPWOztgKdMWixg24Obnao6jKAq2CXFY68zEYoYtljLtsN3TFRElxZpjbv/+e7eU9vuK9Ecewe0//kBT01/ZLykpKeDxeJo0WxKJ/YupYZnYMf4odk/8APsnn8T+ySex78UT2DbmMD5a+HW/NjXEYjECAgKQnZ09qBuHHevWdWlKpq8cDuK8K+VyuaaFG5fLRWBgoMYrpKfvKEVR+Pbbb/HSSy+hoKBA18uxbliyZAl++OEHkuJOQNAFRKATGDx6E+jaNoNrbGxEeHg4qXPuB9Vutuq0aTabjexUB7QI3+6HAO8Ha2egSbAQQkGgzs91pLAn08Dk5GQEBgYiMTGRRGP7oEtaClY40rHAnob59u1u7BaxUT3ekwR1deAV5COsuAgNHTc8KAp3p07VCHTZmKdRM3EiWvqR6n536tRu7u5dN9TUkVhdt4fSNX1+v4HtY49g34snNAJ9/+ST2D3BHLueNUNtZd/eCmrX/Nzc3CEZb0eqTcnUXThCQkKQmZk5IuvWVSoV4uPjh5U47+kcOrZwc3V1xZo1a3DhwgUUFxeDoij8+OOPeOGFF5CTk6PrpVg3qFQqTJw4EVZWVhqTuGvXrmkeLywsJCZxBCMWRKATGDx6EujaNoMTi8UIDAxEUlISESIPQalUivry02itNcKdjnwIcd4qnInbNbNwW7QStxosdX5uI5FKpRK5ubnw9/cHm81GYGBg/30IRigrpFJ4Z6bDIyMNhf000OtEgQAYNQp148bh7HumWP2vszA5/0/s+epz+C9acN9IelNVVa+v3VMkNioq6r6lDYZIjgMf28cehukLxzsJ9J3jj8L0heNorO9980IoFILL5eqFa37HunUulwsej4fk5OReI7GGREMQ511JURTy8vLw97//HbNnz8ajjz4KIyMjjBkzBtevX0dbW5uul2L4+uuvERkZiYqKCsTFxWHjxo14/vnnNX3YP/nkE0yZMgXh4eFITU2FiYkJTExMdDxqAgLdgAh0AoNHa2trr/Xm2hALFRUV4HA4yM3NHXFRCG2yRXKiQwS8B/Yrcm6EW9Vz0CpaiFbxerTUfazz8xqJFAgE4HA4yM/P10R5UlNT++1DQPjgvJmfj5ZHH8UHH3+Ihb/8jBXnfsSaf/6Axf89h+U//4TAeXP6FOg38/P7/V5dSxtGksmcpEaK917+ENvHHsa+F9qj6HsmHcP2sYdx8ZRdr8+rrq4Gh8PRy5Kbjl0bDD1bwhDFeVdSFIWffvoJM2bMwOrVq/HUU09h2rRp+OqrrxAREYGWlhadrMUOHDiAl19+GU888QReffVVHDhwAKWlpZrHm5ubcfr0aUycOBFjxozB3r17IRKJdDJWAgJdgwh0AoNHR4GubTO4vLw8cDgclJeX6/xHebjzVv3vvYrufon1e4+1ihbjjmgRWsXrcLv+O52f10ijuntBaWlpt8c6uhMHBwd3EgKGulgeMgoEiJ45A0v+cw7rf/wOW77/WsPF/z2Pw2c+RttDRtD7Ysea2JFiMhd9Ix77J5/AtqcPYdvTh7B97GF8sepHiKt79h1Rb1iVlJTofOz3o7r8KCcnp1vdel1dnc7HNxB2FOeGtvHQcf4uXbqEZ599VpMa3tTUBDabjQ8//BCTJ0/GhAkTcPjwYcTExOh4dUZAQNAbiEAnMHioBbo2zeBUKpXGSKm2tlbnP8oGQWUZWkVL7xsh75OiGbijdn6XvItmOVv35zWCmJeXBy6Xi6p+iD112nROTg7CwsLg7++P6OhoFBYWoqGhQefnou9UqFSIKCnGjdxsZNVUo4mi4GS6B4v/e66TOF/34/dY/J9zMP75J6iefLLfNegPw5FiMleUUYrPV/wf3n3qILY+cQCmLxzH1cs932sqKyvB4XBQVlam83E/DNXZEl3r1vvrIK4vVIvz0NBQgxbnNBoN48ePR3R0dI/robt37yIxMRFnz54Fm80e4tUYAQFBf0EEOoHBo7W1VatmcHK5HFFRUQgLCyNCQsu8qUxCq2hV97py0Srcln6DVtGyPsV6a+0s3BHNR6tkK241WKOJIlFZbbJOLkeJWIy6LtFuiqKQnp4OHo8H0cPUTzc1ob6+Hvn5+YiMjIS/vz8iIiKQm5tL3KZ7YHZNDfb5eGAJ0xYL6DQsd7DHPwK58Lr4Oxb/9zw2/vANNv3wDeZf+DeMLH7DVMvfYWTxG459fBLCCc/26uKuTRqqyVyduB7m089g29OHsPMZM+yeYI5tTx/CjnFHEOgS1unY8vJycDgcVFRU6Hzc2qC6bj0xMVFTt96Xg7i+cKSIczqdjnHjxiEiIkLXyy4CAoIBggh0AoOHg4MDkpKStBI5l0qlCA4ORlxcHGkZNVikKNyUp6G5gYHmBns0yVPbo3sUhZvKDDTXX0KLeDeaa5bhpmAemmtWQll9AhV5Z1GY9hkyE/+FnEx/VFUJDDbFdqhZLpXgE/YNLGXYYhHdBu+6O8MpORHyez4OiYmJ4PP5WkuBlclkKC4uRmxsrCZql5WVBZFIpFdRO7GsET6ZGfhvRBgs46KRKqgc9PeUK5XY6+2BBfbWWMq0xRxbK8ygWWAmzQJnrl7Blh++waJfzmP+rz9jmsVvGi7873ks/OVnHP/oBO7ec3pX90FvEgoHdcyGZDLnTw/EtqcPYc/ED2D6/HENtz19CKeX/lVSU1paCg6Hg8rKwf9M6ILqunW1gziHw0F8fLxebMDIZXLwWCFg/csLkddiERMda/DinMViYezYseDz+bpechEQEGgBRKATGDTu3r2Lffv24amnnsLMmTPx7bffIj4+/qGEW3V1NbhcLjIzM/VKJIw0UhSFnJycHlOpezMk0/cIjz4zR1gDE0c6plpd1HCa1UXMs7PC5dgoREdHD2pNp0KhQFlZWbca55qaGp1+DyukUphd98UKJzqWO9rDxNEe77g6wiMjbVDfN6SoEIsZtlhgT8N060uYZn0J060vaebldytLbP3uH5hm+bsmcr7w15+x+Yevsfaf32PZv39C5pTX28X56NFo9vcf8ms3nE3m7L52xvZxRzqJc9Pnj2PHuKPYNcEMFEWhuLgYHA6nX6UehkB13Xp2dramXEW9ATPUdevBnpHY9vQhbHxkfzsf3Y/33zyJgowinV+nwaK7uzvGjh0LLper6yUXAQGBlkAEOsGIgEKhgKenJ0xNTTFmzBhMnToVX375JaKiovol1ouKisBms1FcXKzzH+ORTJVKpemtLRaL+zy2Y4qtOsKTkJCAsrIykv3QT1IUhVP+1zXibybNAjOsLTDN6iKmWV/CcjtrcMPCBvV61jY2gpOfC5/MDCRWlKOiogLJyckICAhAQECAzlpD/RIZBhNHe+zxdsd7Vzyx38cD650dsNmNhZL7fDYHQu/MdMyzt4bRPWE+k2aJGbT2OZlqdREHfL2QesUbCy78G/N+/TcW/vIzlv37J2w4+x02ff81Fv3yM/hz56Bt7FidiPOuHG4mc76X/LDt6UPYO+lYtwj68be+QGFhITgcDmpqanQ+Vl2xa916aGgosrKyBr1uPS+14C9h3oWnjb/X+82fh6GPjw/GjBmDGzdu6HqZRUBAoEUQgU4w4qBSqeDr64uDBw9i/PjxmDJlCj777DOEhIR0W+QrFAqYm5vjp59+QnV1tc5/jEcyFQoFYmJiEBoa+sC1/xRFQSQSISsrCyEhIWCz2YiNjSXu4fdhqUSsiZ7PsLbALBtLzLKxxEyaBaZaXcRsmgWSKgavg0FMWSne9/XCehdHrHdxwBZ3Fv4ZwkedXA6KolBTU4P09PQh34CRq5TY5uGCd1wd8d4VTw33+XhghRMdXpnpg/beiRXlmGdnjWnWlzDD2gIzaBYwsmqPpM+gWcDE0R4JFWWYbWOJqZf/wHSL3zHd4jfMuPg/LPnPYDz6FgAAIABJREFUOZj89zxyL/6BJj00txwOJnO1lSLsf+kktj19CLsnmmPvpGPYMe4Ito05DPqPzuByuRAOcsnAcKJcLkdZWZmmbj0wMBApKSkQCLRfgrRv8oleBfrmx99HXrJhRdGvXbuGsWPHwtfXV9fLKgICAi2DCHSCEY2bN2/Cz88PZmZmmDhxIl5++WV8/PHHCAwMhEAgwMqVK/Haa68hMTFR5z/GI5kNDQ0IDQ1FTEyMVsSXuh62q3t4Y2Ojzs9Vn1goEv0l0GkWGnFudC9a+5btZRSL728KV1Vfh4zqKtQ03L/euE4uBzc/FxdjIrHO2QGrWQwcueYDs+tXsM/HA+ucHeCYktTpOV03YPz9/REbG4uioqJBSb2XKZXY6u6MTa5OnQT6/nsC3T09ddDmhKIo7PZy12Q1TL+XzTDd+hIW2FtjpRMdZ0OC8JbNZUy3vgQj60uYYX2pPevB6iI+9v9T55+r/p6nvprMJQen48jfPsW2MYfx7lOHsOsZM/z32CVw2JyHNkkcCVSXIHWtWy8tLR3wRmmtQNSrOFczxi9B59dAW2Sz2Rg7diw8PT3R1tam66UUAQGBlkEEOgHBPdy6dQsBAQE4ceIEJkyYgAkTJmDWrFnw9PQkUVYdUiwWa6Iug5Hy2tU9PDIycliaVw0GlSoVDl71bq91vhdFN+pQi77Dw6XP59fJ5fg9JgLrXRywwJ6GBfY0HL7qg/QqQY/HC+rq8CnnBja4OMLYwQ5GNAvMs7PGTk9XmP/pC/M/fbHV3RmHrnpDea8jg7LDZ0LY0ICw4iLwc3OQlp2NiIgIrc2pqst7/V9wIJY72GOfj4dGoG92Y2GDiyOyawY32yavVog5tpc7lB5YahzdP+f64x1XR6x0tMdCOg1GNIt2kU6zwGwbS7gN4ubBYHGwTOaEFSLkJOT32r+8L8plCsRxkhDsGYFIfjQCAgLuW3ZD2HlORSJRt7r1goKCh5rTaL+E+wr0qhLDKDsIDAzEuHHj4OzsTMQ5AYGBggh0AoIuSEpKwksvvYRt27bh1KlTmDx5MiZNmgQzMzNcv36dRFmHkAKBAFwuF7m5uUNSP9jY2NjJvCosLAw5OTkjutVXeEkRljJsMdXqIv52j1OtLmIBnYaYstI+n3shMhwL6TaYbWOJt22t8JbtZcygWWANi9mjiP0tOgJrnR1w+KoPtro74y3by3jb1goL6O3C3vxPX+zycsMOT1fYJcbj0FVv7PZyxw/8QPwRHYndXu5Y5+yADS6OOHjVGxElxd0MydRzKpFI+vWZqqyT4vfoSOz2csNOTzf8JyIUBaJaZNdUY4+3O0wc6VjrzMRKJzpWs5j4IyYSFEWhXCpBfq0QcpX2U7Pz8/Pxs48XjJm2mG9vjQV0GhYxbLDLyw3pVQIsZdq1p75bX8L0ewJ+lSMdS5l2w1Kgd+VATebqxPW4eMoO+yefwO6J5njv5Q9h84UTZA0PFpmnKAoZGRng8XiQSB5c5BP+xfr6ehQUFHSa0wepW0+PzMKm0e/1Ks73vnBM5+eoDQYHB2P8+PFgMBhEnBMQGDCIQCcg6IArV65g7NixsLCw0Pz43blzBxEREfjss8/w6quv4tlnn8XBgwfh4+NDoqyDyOLiYrDZbJSW9i0CB4tyuRwlJSWIi4sDm83W21ZfQ8E/E+KwnWGHRXQaFjNsYX7dt9couJplUgm2uLE0Ansp0w5zbC53EvmbXZ1QWNter1uvkMPUxwM7Pd1w7M+rMPX2wBxbK8y3s8ZbtpexhsXEOy6OWMqwxVpnB6x1ZmKLGwvbPVxgzLTDLBtLrHVm4tBVb7zv64V1zg7Y7eXeybBNPafx8fFgs9ng8/nIyMiAUCiESqVCsViEIrEIqnvzK2psxAd/XsVKJwY2u7GwxY2FlU50vO/rhXKpBIUiES7FROH4jav4kseBf14O8oVCfBHAxjpnB6xhMfHeFU9w8nO1Mg8URSErKwsBAQGora1Fdk0NLGKj8a+wYLimpaC2sRFJlRWYbdMeXZ9Ja69RV6e6L3OwQ9p95m248WFM5v572BI7nzkK0+eP4f1XPsTe545h5zNmsPqM+UBzoU7VHmqnckNnxzlV162npqb2WrdOURTi4xNw4M1TvQr0/NThX38eERGBZ555BjQajYhzAgIDBxHoBAQA2tra8J///Afjx48Hm83u9bg7d+4gNjYWX331Fd58802MGzcO+/btg7u7O4mgaIlqEcLlcvXGmK+3Vl9CodCgxbo6QqgWhBRFQdHPMoOEinKYONjjbVsrLGHY4i0bS40478hpVhcRVJAPsawRu73csdfbHcf+vIqDvl6YZ2/dLXI/3foSZtlYYruHC8yuX4HZ9SswcbDHdOtLWOFEx9HrV3D0+hUcueaDNSwmHJMTkVhZAXpSAuhJCUisrGhPjVcqUVFRgaSkJNhfv4Z9TkyscbDDehYTH/pdQ1x5Gbwy07GaxcB7Vzxx6Kp3u/i/4olVTgzYJ8Z3O2eJTIb3fb1gzLTDRlcnbHFjYbmjPdY6OyCiZGAdINSCMDAwsM97zU+hfMy1+8vl3aiDw/tOT1eD/rz2x2SuNLsce58/hn0vHMfhNz7RcO9zx7D/pZMQVty/hpyiKKSkpIDP55NN2kFmx9aZgYGBGjNIdd06RVFITExESEgIksPScODVU50i6e8+dRD+9jydn8dAGRMTgwkTJnQKHhAQEBguiEAnIADA5XLx+uuvIysrq9/PuXv3LpKSkvDdd9/ByMgITz/9NHbt2gUnJydiFDSAxVhiYiKCgoL0dsNDLQKSk5PB5XLB4/GQmpqKqqoqvWwLNZC5SEpKAp/Pf6gIYYlYjLUsB8y2scQie1qP4lzNhXQaZAoFvg0KwDpnBxz09cJihg1mWl/qdqy6/n0p0xZHr7UL9MUMG8y0scQ8e2scvXZFI9JXOzFwwNcLG12dsJrFxGoWExtdnXAxJkpTT14qEbdH+m0uY6b1JRjdM15bYmeNE1e9scqJoRHnaq53dsAZjh8UKhW8MtLwwZ9XsdPTFQd8vbCIYYMdnq7Y4+2OPd7u2O3lhmUO9vh7AFsrc1FfXw+KoiBTKnsU2x/8eRVLGLZYw2Jivj0NM20sNaUCn3Ju6PxzNVTszWTuhiMXO585ioOvf9RJoB949RR2TzBHekRWv+YiODj4gbtJEA58TtV166GhofD39wePxwOPx0Ptva4Ekto6ePxyFb8ds4bjPz1QXTr8684TEhIwceJEXLhwgYhzAoIRAiLQCQjQHkGXyWQP/fy7d+8iIyMDP/74I2bPno0nn3wS27ZtA51OR01NjUFHrbRFuVyOqKgohIeHD5s6/47RHXXETld9ubVJdUu7sLCwAc3F/wUHtrdmo1n0KdCnWl1EeHERkgWVMPXxwCK6DaZbX+oUPe9KI+tLMPXxgNn1K1jlxIARzQIL7GkagX7oqjeWOthhKdMOOzxdNeJ6h6cr1rs4IrioAE1NTfiOz4PRvfGpxb/633NoFlhkcxk7WA4w9XDBAV8vHLrqjdUsJn4IDsRv0RFYxWJgNYuBja6OmGtnjenWl/COq5NGoO/xdsdaZwdsu4+hXm9UKpWIjY1FaGgo6hsa4JGRhn0+HljDYuJ9Xy/4ZGZ0ur/8FMrHQroNNrk6YbMbC5vdWNjk6oSFdBv8LypC558tXbCjyZyPgy+2jz+M3ZPM8P6rH+LglHahbvpCez/zyqKqXl9HpVIhISEBISEhw+YeZaikKApxcXHg8Xgag0+1F4EhlSGlpKTg+eefx88//0zEOQHBCAIR6AQEWkZbWxtyc3Nx/vx5zJ8/H48//jg2bdoEGxsbVFZWGszCQZusr69HSEgIYmNjh62wVUfsuvblLi8vH/S+3NqkTCZDeHg4oqOjBzxuUWMjPvS7hlm0ntPbO9I/LwdNTU1IFVRivr31fY//m9VFvOvenua+1Z2FGTQLLGbYYO+9qPUqJwaWMG0x356mEchb3Z2x1pmJefbW2OvtDlp8DBYzbDDt3mbA9Htp4WqhPsvGEsZMW6xzpGMDwxbGNpexyM4KxgwbOCbE4517Iljt4r7OmYlp1pcwz84au73+EujLHexx4sa1B75+CoUCUVFRiIiIgFwuh01CLIyZ7ZsOq1lMLGXawtjBDg7Jf7WBTBFUYqUTHYvoNljn7IB1zg5YSLfBahYD2TXDP5o4UFIUhW82ncf2cUew+zkz7HnBHLsmmmH7uMM4t/+3Xu/PKpUKcXFxCAsL03mbt5FOiqKQlJTUaaOkJy+CtLS0YZ3ZlJ6ejsmTJ+Ps2bNEnBMQjDAQgU5AMIhoa2tDUVERfvnlFyxZsgSPPfYY1q1bB0tLS5SVlRGx3tSE2tpa8Hg8pKWlGcz16NqXu2MPZ31u2VdXVwc+n4+EhAStLmpDigowrQ+h/ZbtZeTfM4sLLMzHrF7q1btyCcMWyx3sMc/eGvPtrbGUaYsVjnRsdWNhm4eLJqI9k2aBafdaxfUVle8o0P9mdRGzbSzxno8Hlt1r+aZ+fK6NJXYw7LDYzgrbXRxh6uWO9654Yo+XG2bZWGK69SVsdmVhl5cb1jozscKRjmvZfadOd6V6oyQmJgZKpRLChgasc3aAiSMd73q4aLjc0R7vuDpBLPsrohuQn4dtHi5YyrTFUqYddnq6IaSoUOefL31hdZkQ3248jz2TPsCO8Uexe6I5Pl/7A656XevRZE6pVCImJgbh4eF6/f0dCVSL8+Dg4F6zGFQqFQQCgaZuncvlIiEhAWVlZcNm/rKysvDKK6/gm2++wd27d3W9lCEgIBhiEIFOQDBEaGtrQ3l5Of744w+YmJhg9OjRWLlyJX7//XcUFRUZjDh9EFZWVoLD4SA/P9+gz18ikSAnJ0fT7zc6OhqFhYV6lSYrEonA4/GQnp4+KHNhHRfTqzD+OpCrOe6XyLA+xXxH7vR0hYmjPUwc7LHN3RnrnR2wmsXAFwH+2OjqhCVM2z4FeU+GdV1T79c5O2C1EwOL6DZYYG+NRXQbmDjYYzHDBgvsrLHJkY6NdFtsdrDHDhdHrHCwx2zby1juaI9lDvZ4x9URtglxGmf4/rChoQEhISGdNkoiS0uwlGmHTW6sTgL9HVcnGDvYIbGivNNryJRKJFSUI7GyYlBavQ13UhSFrNhchPlEIy+pffOiJ5M5tQFZRETEsMqEMUT2R5z39Jza2lpkZWVp6tajo6NRUFCgtx4CeXl5eP311/H5558TcU5AMEJBBDoBgQ7Q1taGqqoqXL58GWvWrMHo0aNhbGyMX3/9FXl5eQYtVtUsLCwEh8NBeXm5zscylKyvr0d+fr6mbjIyMhL5+fk6dYOuqqoCl8sd9M+eT0Ya5tr+1WrtLdvLuBAZjsYOwuff4aH9FtTz7a2xwcVR4+Zudv2Kpu3aUqYtpj+AOO+L063+ir5Ps7qIGTQLzKRZYD6dhpVODJh6u2O3uzM2OtKx0OYyTriy4BYeBt+UJJR3MTtUURQSKsrBTE6Ea1oKisSdDSWlUimCgoKQkpLSaS5SBZVY7miP9S6OnQT6OmcHrHCkI0dI0te1SYqiIBAIEBgYCDab3SkLpmuKe2O9DLHsRERdj4OoSj/NLYc7KYpCcnLyA4nznlhXV4f8/HxERUXB398fYWFhyM7Ohlgs1ovf3cLCQrzxxhv4+OOPiTgnIBjBIAKdgEDHaGtrQ21tLWxtbbFhwwY89thjWLhwIc6fP4/MzEy9WDRoe6Glbt0lFAp1Ph5dsrGxEYWFhYiOjtYsFnNzcyGVSodsDGVlZWCz2SgpKRnSz4BEJusxsuuXmwMj6/ubyk27x63uzjh6/Qr2+Xhgm4cLdni6Yp6dFWbQLDTHaUOkdzSRU7+mkfUlzKRZYpaNJRYzbLDSiYGDvl5wi4+Df3QUuFxuJ+PABpkMZ0OCsNKJAWMHOxg72GG9iwO8MtLQ1NQEsVgMHo+HjIyMbt97FUXhyDUfLGbYaKLoG92csIhhgxM3rhncfULXlMvlmhIDhUKhyYIJDw+Hv78/oqKiUFBQAL5XOA6/+Ql2jD+CHeOOYP9LJ+Hzx8hxyx8Kakuc9zTHJSUliI+P14u69dLSUkybNg0nTpzAnTt3dLYmuXDhAkaNGoUvvvhC87fm5macPn0akyZNwtixY2FqagqxWKyzMRIQGDqIQCcg0CO0tbVBKpWCyWRiy5YteOKJJzB37lz8+OOP3SJqw5FKpRLx8fHg8/lDKkKHA2UyGUpKShAbGws2m42QkBBkZWUNamSnoKAAHA4HAoFA5+evZr1Cjj1e7n3Wic+xs8KsezXhxkw7rHJiYI6dFWbbXMZsG0tNzbn6+AdJc9cGp1ldxFKGLT7j+iGntFRjHHjW0wOL7Kyx1omBHR4u2OnpCmNmex19aE42uFwucnNze53v7Joa7PZywxKmrYZ7fTxQcK/FFKF2KJPJEBYWhri4uB6FWkNDAwoLC/Gnhz92TjiCd58+iD3PfYB9Lx7HzvFHsetZM0Rei9P5eRgCO4rzwUxJVyqVEAgEmvIGLpeLxMRElJWVDUlpQ1lZGWbMmAEzMzOdivPk5GS8+eabmDdvXieB/sknn+D1119HWFgYUlNTsXz5cqxYsUJn4yQgMHQQgU5AoKdoa2tDQ0MDnJ2dsXPnTjz55JOYNWsWvvvuO62beA0FZTIZIiIiEBERQVyQ70OFQoGysjKNIzGfz0dGRgaEQqFWxDpFUcjMzERAQICmf7A+USxrxHtXPLtFv42sLTDvnhBfQKdhrp1VewSb1t4DfaE9DTN66J2uSy5m2CCooN1j4YivF5bY07CBaQdjW6tOKfgzrS7i3zzufee3Ti6HX2426EnxYOflol4xPEyvhgsbGxu71f/3Rub3btgx7ghMJx/H3hc+aHeFf94c28Ycwrdbzg+7e7S+cajEeU/vq65bDwkJgb+/P2JiYlBYWDgo46isrMRbb72FAwcOoLW1VWdrDpVKBSMjI4SEhGDt2rUagS6Xy/H444/j6tWrmmMLCgowatQoJCQk6Gq4BAQGDSLQCQiGCeRyOTw8PGBqaooxY8Zg2rRp+OqrrxAdHa33C0G1O3h8fPywbaOmKyqVSlRUVCApKQlcLhc8Hg+pqamdXKYfhCqVCklJSQgKCtL7LIbC2lpcjo3BgStemGdnjXn27VzpRIepjwdWONLxls1lzKRZ4C3by/1q56YLzrKxhHNqEja7sbCUaYuNrk49RvWNrC/hYmR4J0d2wqFjQ0MDgoODkZSU1K/v1q9HL2PbmMN4/5UPNdz/0glsH3cYB/92SlPeUFFRQe57D0hdifOeqK5bV/uGhIWFIScnRyvZTdXV1Zg7dy727t2LlpYWna4xzM3N8eWXXwJAJ4EeFhaGUaNGQSaTdTp+ypQpsLCwGPJxEhCMBBCBTkAwDKFSqeDr64sDBw5g/PjxeOONN/DZZ58hNDRU7xaCQqEQAQEBPdbVEj4YVSoVqqqqkJqaqnGZVtc390dQKBQKxMTEIDQ0VK8c5O/HErEY7/t6YS2LiV1ebtjr7YF1zg4w9XbHRlcnvOPihI2uTljmYKdzMd4bZ9IsMMfOqtd0e/Xfp1tdxFqGLX5k+6GypoZ8Z4aI9fX14PP5D1RK5HLeBzvGHcF7L53UCPT3Xj6JHeOO4Px7f0AoFCIjIwN8Pr9PkznCzqQoCikpKeDz+ToX512pLkXqWLfetS1ffykUCrFo0SLs2LEDt2/f1umawtvbG3PmzEFzczOAzgLd09MTTzzxRLfnLF26FN99992QjpOAYKSACHQCgmGOpqYm3LhxA0ePHsWECRPwyiuv4JNPPkFgYKDO2wKVl5eDw+GgsJD0YNY2KYqCUCjU1DdzOBwkJCSgvLy8x3lX99WOiooaNr2AOzKtSoB/BHKxw9MVOzxd8Y9ALlIqK3H8xlVsdHWC+XVfLGXY6lyI3y+S3p/jljJssMTeGqdcnLRe3kDYnWrn/LS0tAe6xpWFVTjw2kfYPvYI9j5/DKYvHMP2sUewZ6I5kvlpmuMoiurVZE6X3Rv0kfoszruya1s+dd16b/fgjhSJRDA2NsaWLVs0olhXqKqqwosvvoisrCzN34hAJyDQLYhAJyAwIDQ3N4PL5eL48eN47rnn8OKLL+LEiRPgcDhDKsooikJ+fj44HA4qKyt1vpAydFIUBZFIhKysLAQHB3eK1snlctTX1yM4OHhYehd0Pc/KOikq66QaIeWXm41NbixsdHXCIrqNzkW4toS8iaM9Nro6ISk/r1t5g65cpg2REomkV+f8/jA9IgufLPkWu541w65nzPDBzM8R7BHZ53PUJnMduzfoU6svXXE4ifOexi4UCpGZmdmtbj0/P7/bZ27lypXYsGEDbt68qetlA27cuIFRo0Zh9OjRGo4aNQqPPPIIRo8ejdDQUJLiTkAwxCACnYDAQNHS0oLg4GB89NFHmDx5MiZNmgRzc3P8+eefg5reTFEU0tLSwOPx9NKAbCRQHa0LCwuDv7+/JmI33Ba9/SFFUbiRm40P/vTFGhZT5+JaW5xu1W4wF1ZShKamv8ob0tLSEBgY2Clap29lLcOFYrEYAQEByM7OHpAwVqlUKEgtRk5CPhTyB8taksvlKC0t1UrK9HDmcBbnPVEqlSIvLw9XrlzBo48+ipkzZ+Lzzz8Hn8/H6tWrsWbNGqhUKl0vEwAASqUSOTk5nbhkyRIcPXoUOTk5GpO4a9euaZ5TWFhITOIICAYRRKATEIwAtLa2IiIiAmfOnMGrr76KZ599FocOHcKVK1e0mmKpVCoRFxeHkJAQkrqpB6yurgaHw0FMTAwiIiLg7++PyMhI5OfnG9z8KFQqVEilOM25oXNxrS1OtbqIz7j+UHQRamqX6czMzB4zJnQ9F8OBtbW1CAgIQG5urs7HombHlOmAgIARYzJHURRSU1MNRpx3ZUlJCf73v/9h9erVeOKJJ/DUU0/h9OnTiIiI0Klre1/omOIOtLdZmzJlCsLDw5GamgoTExOYmJjocIQEBIYNItAJCEYY7ty5g5iYGHz55Zd44403MH78eOzfvx8eHh4DcvVubGwc1jXOhsby8nKw2WyUlJRo/tY1tTY8PBy5ubl67+b+IKQoCl4Z6VjpQB/y/ueDwUV0GkKLi/o8364ZE9HR0SgoKDBIsaMNCoVCcLncbqnH+kR1yrShm8x1FOeGtmnYkY2Njdi6dSuWLFkCLy8vnDx5Ei+++CImTZoEMzMzXLt2TW8i6kB3gd7c3IzTp09j4sSJGDNmDPbu3QuRSKTDERIQGDaIQCcgGMG4e/cukpKS8O2332L69OkYM2YMdu/eDRaLBZFI1O/Fh0QiQVBQEBITE0dUWqa+sqCg4L71/zKZDMXFxYiNjYW/vz9CQkKQlZVlUHWwcpUS4cVFYKUk4eg1n2Ep2KdaXcT/oiL6fc4iiQQxGRkIvmdGZoibMAOhOqtkOBlXGqrJ3EgR5zKZDDt27MCCBQvQ0NCg+f29c+cO4uLi8P3332PWrFl48sknce7cOd0tCAgICPQGRKATEBAAaBfr6enpOHv2LGbPno0nn3wS27dvB4PBQE0f7Z64XC5u3LiBrKwsgxF2w5UURSEzMxMBAQEPVP+vUChQVlaGhIQEcDgcg3QOV6pU+Ck0CG/ZXoaRtYXOhfeDcLUTA6r7zIOKouCRkYY93u5Yw2JiixsLv0WGIzs/H7GxsWCz2ZpNmNraWoOZ1wdhVVUVOBwOiouLdT6WgdAQTObU4jwoKMigxblcLoepqSnmzJmDurq6Pn+Di4qKkJGRMUS/+AQEBPoMItAJCAi6oa2tDTk5OTh37hzmzZuHxx9/HJs2bYKtrS0EAoFmEWhhYYEnn3wSLi4uOl8IjXSqVCokJycjKChoQNFSpVKJiooKjXN4YGAg0tLSDMa0Kk9YA4vYKPwWHYEVjvY6F9/95X4fD1jHxWC3lzu2ujvjXFgwSiVizXm5pafCxJEOE0c6Nro6YTWLiaVMOxy/cRXu6akIzM9FYUkJEhMTweVyNW3FDGVe78fKykpwOByUlpbqfCzaZFeTOT6fj/T0dNTU1OjtvI4Uca5QKHDw4EHMnj0bYrFY1z/rBAQEwwhEoBOMKNjY2OCNN97Ak08+CWNjYyQlJel6SHqPtrY2FBYW4pdffsHixYvx2GOPYe3atTA1NcXTTz8NV1dXnS+ERjqVSiViY2MRGhqq1bpjtXO4us+v2rSqsrJSbxf/D0KpTIYNzg46F98Py1k0S1zJSodMqcQuLzescKRjl5cbdnm54V0PF8ymWWKq1UUstKfB2MEOB696o6C2FiqVCgKBoMd5NUQzMrUfQ3l5uc7HMpgcDiZz6i4fhi7OlUolzMzMYGRkBKFQqOufcQICgmEGItAJRgx8fHzwxBNPgMViIS8vD6dOncKECRMgkUh0PbRhg7a2NhQVFcHY2BhPPfUUXnvtNaxatQp//PEHiouLh016pSFRJpMhIiJi0M35KIpCTU0N0tPTERQUBA6HYzBtvsqlYvwnPBQHfD0xx85KI4CnW1/CW7aXMV0PxHhvfNvGEv55OVjNYmCTmxN2eblhsxsL060vdTvWyOoSdnq6dUqX7zivfD4fHA4HCQkJKC0tNQizx9LS0vv6MRgiezKZi4+P16nJ3EgR5yqVCidPnsTUqVMhEAh0/bNNQEAwDEEEOsGIgbGxMc6cOaP5/7t37+KVV17BhQsXdDiq4QWlUomtW7di7ty5EAgEEAgEsLS0xOrVqzF69GgsW7YMv/76K/Ly8ohYHwLW19cjODgY8fHxQyqSKYqCSCQyyDZfJWIxFjFsMN36EmbRLDDbxlIjdvXVZO6bwAC84+qEtc5MvOvhDKMexHlHHvT1RolY3O3cKYqCWCxGdnY2QkND4e/vj5iYGBQVFaGxsVHnc/OgLC4uBofDQVVVlc7Hokvqg8ncSBLnn376Kd544w2Ul5fr+iebgIBgmIKM5D8HAAAgAElEQVQIdIIRgdu3b2P06NG4ceNGp7+bm5tj165dOhrV8EJNTQ0WLFiATZs2QS6Xd3qsra0NQqEQNjY22LBhAx577DEsWrQIP//8MzIzM4lYHwSKxWLweDykpaXp9Pr21OZLLeqGazuokKJCrHdxwFu2lzHbxhJLmXb4gc/D0as+mEHTP4O5+fbW2O7pgoV0G8yzt+7Xc4yZdgjIz+vzOtTV1SEvLw8RERHw9/dHZGQk8vPzh4XAKiwsBIfDQXV1tc7Hom8capO5kSTO//73v+PVV19FSUmJjn6pCQgIDAFEoBOMCAiFQowaNQrx8fGd/v7tt9/C2NhYR6MaPsjJycFrr72G48ePo6Wlpc9j29raIJVKwWAwsGXLFjz++OOYO3cu/vnPfyI1NZWIdS2wuroaXC4Xubm5enc9exJ1w7End93/s3ffYU2d7R/AsSxlg6JYFeserVpbtWrf+qtFxYGiCCIOUFyorX3rauuota3WunHgwr2rVUNCmGHvIXvLHmGPJGqtkO/vD0pemSLrJHB/ruu+rpYc8EnOyfjmPOd+ysvhlpQITnwskvOrlxzMLy3Fl1cvYbht02eoO7qGnz7eoi8ORp85gfPBAc16PEpLS5GYmAhfX1+wWCy4u7tLbefw+Ph4cDgc5ObmMj4Waa+mmsy1xX4ViUSSy2I6czgXiUTYsWMH9PT0kJCQ0EHvzISQzooCOukSKKC3TlxcHA4dOgSxWPxOvycWi1FSUoKrV6/CyMgIysrKGDlyJL7//ntaM72FlZaWJjNLRdU9Uyfra3KXCQSYf/sGRp09yXgob6sabHsUpwP83ulxeDPUOTg4SNWyfLGxsQ0uM1iQWwjXW15wvOyK1Nh0xo8laay2bjL3ZjgvKipi/P61V4lEIuzevRu6urqIjY1tp3dhQkhXQgGddAk0xV06lJeX49atW1i0aBFUVFQwZMgQbN26FT4+PhTWm1E103ZlseFVWVkZkpOT4efnJ/VnYJuqwz5eGHXmBOPBui1riO1RPIqJatb9Lygrw73ICBzy9sT54EDE5+ZIluVzdHQEl8tFaGgoMjMzW/WcLi+rQHxIEp7FpDXr+BCJRIiOjoajoyPy61xf737XG8s/2IgFWiuxQHMlTPtY4/yOa/Sa85bHMycnp8VN5rpSON+/fz969uyJyMhIpt9iCSGdBAV00mVMmjQJX3/9teT/q6qq0K9fP2oSxxChUIj79+9jyZIlUFNTw8CBA/HNN9+Ax+PJfFfw9vgQGBUVBUdHx04xbbeiogKpqan1zsDm5eVJfVjPLSnB/FvXGA/VbV1fXr0EwRuBtVwggF9aKryepSC/rLpBXEp+Pszu38aki3aYePEsJl48i6+u2cMxIQ7Pn1dfg5udnY3w8HA4OTmBw+G0qNO/601PrP94G0z11sCs71p8P/tXxIckNfn8iIiIAJfLRUFBQa3bUqJSseT9dTDWtsSyD2ywfPBGLO69Ggt1rOB42ZXx40kWqqE+E001masJ505OTp0+nB88eBDa2toIDQ1l+i2VENKJUEAnXca9e/egrKyMa9euIT4+HuvXr4eWlhby8/OZHlqX9/z5czx69AjLly+HlpYW+vXrh40bN8LZ2RkVFRWMfxBjsoRCIUJCQuDs7FwvfHSGEggEkjOwHA4HTk5OCA8PR3Z2tlSe4RSJRHDx88MoKQjVbVkjTh/H2UB/FJWXw+tZChbdvYkp9ufw2aVzMLx5BXciwrHD2RETLp7F7JtXYXT7Oubduvbv7VeRW1yMCO9o/HWKA8fLrshJy0VeXl6Dnf6bOgMbwAnBkvfXYlHPVVj2wUZY6G/AQh0rrB3zHfhZ9Y//NxuQNXTpxNV9d2GsZYnlgzdixZBNkjLWssS2r/YxfjzJYpWUlCAhIaHBJnNCobDLhPOjR49CU1MTgYGBTL+FEkI6GQropEs5ffo09PX1oaSkhEmTJiEoKIjpIZE6Xr58CTabjdWrV0NHRwd9+vTBmjVrwOFwZH4Jr3ctgUAAPz8/uLu7y1yTtZaUUChEVlYWQkNDweVyJdfAZmRkSEVYFwqFCAwMrA6c0VEYIYUd3VtTY+xsMdX+/L9nyO1gePMqZt24gsmXzuGzi3YYY2eLj8+dxpdXL2HerWswun0dc25exaQjx7Fs5o8w1rbEQh0rmOqtgfXob+Hxp6/ksSsoKEBsbGy9Zb7qHtf7Fh/GQh0rWI34BqtGbsGqkVtgOexrLOq5Co9Oc2ptKxKJEBoaChcXl0YbkNluvogFmitrhfMVQzZhUa9VWD1qC+PHlKxX3SZzHA4HbDYbz549k/rZMC0tkUiEU6dOQV1dHb6+vky/ZRJCOiEK6IQQqfXPP//AxcUF69evR+/evdGzZ09YWlri8ePHMruEV3OrrKwMnp6e8PLy6nJfTNR8CM7JyZFcx9rS6dJtVQKBAL6+vuDxeJJjL6OoECv/uo+hUhCu27pGnj6OD8+ebHDt98G2RzH67EkYXLfH7O1/4Ms+K2GgtAQzupliRjdTzFJYAmMdS6wauQUZSfXXIK85A+vt7V2veaD1h/+Fqd4aSTivqUU9rXD2uyuSvyEUChEcHAxXV9cmv7x6dJoDYy1LWHxgIwnnywdXX4/+69JjrT4uKsor4HHfF6e32OPc9qsIdmZ22UMmn6/h4eHgcDgICAhokyZz0lgikQjnzp2DmpoaPD09mX6LJIR0UhTQCSEy4fXr1/D09MSmTZvw/vvvQ1NTE8uWLcP9+/c73dnl4uJiuLm5ISAgoNN8sG3th+K606UDAgLw7NmzDvnyory8HF5eXo1+WSIQCpFWUIAkfh4cE2Lxk7srJlw4y3jIbtc6cQQjv/0J01WXYnr3JTCQN5UE9DfLauQ3+Os0B7np/AYf2zebBzo4OGDDZ1uxQGslLIdvloRzq+HfYKGOFe4efoTnz/83k8HNzQ2lpaVN7ruCnEKsHftddXM4vTUwe38tFmiuhFnfNQj3aF5jvMaqtLgMPy36Awu1rWCstRILtCxh0msVznx7WSpmfHTk8zMiIgJOTk6Sywxa22ROGkskEuHy5ctQVVWFq6sr02+JhJBOjAI6IUTmVFZWwtfXF99++y309fWhrq4OMzMz3Lp1S2aX8KqpgoICODk50ZrxTXxILigoQHR0NNzd3cFiseDr64ukpKR2+eBfVlYGHo8HPz+/Zn9ZIhSJMNbuVK0zzowH6rask0cw6OQRjFnxPaarmOMrBVMYvNdwQK+ppQPWI8T1aZOPW0VFBR6cfQJjHUss0FkB0/7WMB+wFsballgxZBMykrIgFArh7+9faybD2+pZTBr2LT4MU701WNzbGt9M/RG+rKBWHxsPTjjAWNsS5v3XSc7Mm+pZY1HPVfDnhDD+XOmo52NkZGStcN7Yc7a5TeaktW7evAlVVVU4Ojoy/RZICOnkKKATQmRaVVUVgoKCsH37dgwZMgQqKipYuHAhrl69Wm+5JWmvnJwccDgcxMTEUDhvZhUVFSEuLg6enp5NXtvckiouLoarqysCAwPf6YxoWGamJJQP+bcYD9VtHM4HnTyCcabb8aXGUkzrueytAX1GN1NYDv8apcVNh2qRSIQ7h/7CsoE2MNa2hLH2SlgMXo/zv9sjKCgIPB4PPB6vRTMn8jL4yExuu+aD26b/BGMty3rXty/QXAnbzRcZf260dzUnnDf2vGqsyZy0vu7du3cPKioqePLkCdNveYSQLoACOiGk06iqqkJ4eDh+/PFHjBw5Et27d4eRkREuXryI3Nxcqf3w9/z5c6Snp4PNZiMpqfHlpKiarpKSEiQmJta7trkl3aQLCwvh7OzcopkMoZnpkkDbaQP6iSMYueUnfKlpgWm9luMrJTMYvCWgz+hmCtM+1vhx3m+IDohr8jHMTefD+1EAArlhKC0uRVZWFpydneHg4AA2m92hlzg0Vps/+x4LdazqBfT5Gitx2PoM48+H9qyacM7lcls1a+nNJnM1Sy4+ffoUOTk5UvN6/fDhQ6ioqODPP//s0PczOzs7jBkzBurq6lBXV8fkyZPB5XIlt798+RKbNm2Cjo4OVFVVYWJiQqvSENJJUEAnhHRKYrEYMTEx2LdvH8aMGQMlJSXMmjULdnZ2yMrKkpoPf8+fP0diYiLYbDbS09MZH0tnqTevbWaxWHB3d2/2WTo+nw9HR0dERka26DipEAox+swJSajtVFPc3ziDPmT/AUz+aC2+VFvarHBet+apLsPOWfsR4hLe9ONZUQEvLy94e3ujvLwc+fn5DV7i8Lbr0du6Lv5wEwu0VmLZGw3oluqvh7GWJTj2nXeN9bYK53VLIBAgIyMDISEhUtNkzsHBAaqqqrh9+3aHv4c5ODjA0dERycnJSEpKwq5du6CoqIjY2FgAgI2NDQYMGAAej4ewsDBMnjwZU6dO7fBxEkLaHgV0QkinJxaLkZiYiAMHDuCTTz6BgoICpk+fDltbW6SlpTEW1kUiEaKjo8HhcJCbm8v4B+/OWg2dpYuMjEReXl69fV9zmUFcXFyr/s2Tfj6dK5g3cAZ92K5fMXznfkzTXd6igD6jmylmvmcGE93V8GcHN7rvPDw84Ovr22BIq7nEwcvLCywWC15eXoiLi+uQNbgzk7Ox5qP/YoHmSizquQoLdaxgrGWJrdN/QmlRx35Z0FElEokQFRXV5uG8oX+nsSZzHTVrwsnJCWpqarh27RrEYjHTb2MAAG1tbdjb26O8vByKiop48OCB5LaEhATIycnRuuyEdAIU0AkhXYpYLMazZ8/wxx9/4LPPPoO8vDy++OILHD16FMnJyR0W1oVCIUJDQ+Hk5ISCggLGP3h3lRIIBEhPT0dwcDA4HA6cnJwQHh6O7OzsNr/M4ISfj2QJtsG2RzHmrC0+OXcaI06fwEhZXUP9jTPoQ/cfwIit+zBdeUmLA/qMbqaYKW+Kb/+zu9614TUN+vz9/Zt13XhpaSmSkpLg6+v7zrMmWloZSVmw23oFa8d+B5tPd+D6/nsoyJXtRpWNVUeF84b+3Y5uMufq6gp1dXVcvHhRKsJ5ZWUl7t69CyUlJcTFxYHH40FOTg5lZWW1ttPX18fx48cZGiUhpK1QQCeEdFlisRiZmZk4fvw4vvjiC8jLy2Py5Mk4ePAg4uPj2+1DvUAggL+/P9zc3DrdEnGyVEKhEJmZmQgNDQWbzcaTJ0/g4+ODzMzMNmkkdikkCJMu2cHo1jUY37khqc8u2WGLowN+5rnK3ln2NwP6rwfx8fzvWhXOa8pQcQm8HwVIHrvS0lK4u7sjMDAQ5eUVeOoVjUDHUORl5iM3LQ85aU33lHhz1gSbzYaLiwsiIiKkvheFtBZT4byhau8mcx4eHtDQ0MDp06cZD+fR0dFQVVWFvLw8NDU1JR3kb9++DSUlpXrbT5w4ETt37uzoYRJC2hgFdEIIQXVYz8nJwenTpzF9+nQoKCjg008/xS+//IKoqKg2+1D/tjW1qTq+EhMT4eDggLi4OISHh8PJyQkcDgdBQUFIS0tr8fWvB708MOmiXa1wbnznBr64fAGm927DNTEBI0+fkK2QXhPOf/oNnw+0wlfdTGHwb7UmoM98zxRWI75BRmIWYoLjceL7s7hy6CYCHUPx9ZQfYdrHGsbalpinugzG2pYw7WONzZO+h+stz7fuh5pZEzXXNnO5XISGhrbZFzGdvaQpnNettm4y5+vrCy0tLRw/fpzxcA4Ar169QkpKCsLCwvDDDz+gV69eiIuLo4BOSCdHAZ0QQuoQi8UoKCjA+fPnMWvWLCgqKmLs2LHYu3cvwsPDWxzWS0pK4ObmBn9/f8aaHlHVrtjY2Ho9AEQiEfLy8hAZGVnr+td37Rp+LTy0+gz67f+dQV9w+zomXbLDdmdHhGdlYvTZk7IT0GvOnh8/jM/1lkuCeVsE9LkqFljUaxW2/GcXFupaYp76MszuvhQz3zPDLMUlWKhjBUMlc8x873/Xrs98r/rM+17j31GY17xrzoVCIbKzsxv8IqaiooLx41HaqqZPBpfLlfpLcRprMpeRkdGs19uAgABoa2vj0KFDUhHOG2JgYID169fTFHdCOjkK6IQQ0gSxWIySkhJcuXIF8+bNg7KyMkaNGoUffvgBwcHBzT4DV1BQACcnJ4SGhtJZOymoNztR5+fnN7ldQUFBg13Dy8qaXtM7q7gI8+/cwMSLdph5/TIMb1zB5Evn8H9XL8E39RmcE+Px6YUzGHbqGPPh+x3C+aDjh/HJzG9g0M201hn01oT0+RorME9tGQyVlmCe2jLMVDBrtKncjG6mmPGeKWYrL8VMeTPMU12GExsvNLkvMpKyEMaLRGZydq19W/NFjJubGxwcHODn54fk5OS37tuuUrISzht63jbWZK6hfRsSEoKePXvil19+kdpwDgDTp0+HlZWVpEncw4cPJbclJiZSkzhCOgkK6IQQ8g7Ky8tx8+ZNLFy4ED169MDQoUOxfft2+Pr6Nhq8nzx5gkuXLiE6Opquf5WCEolECA0NhbOz8ztP2a3pGu7p6VmrWVVjvQSeZmXB+vFDTLtyEf+5fAEm926BHR8rue3zy+cx/PRx6T+LfvwwBv9xCIOOVQf0wb//ji/0VrTdWfT3TDFT3gyzlc0xq5FwXnf7uT0sMEthCWYrL8Xi3tZIeppSf3/xi3F07VksHbAei3VXw7z/ehxbb4ei/PrNxQoLCxEbGwsPD49m7dvOXrIazutW3SZzhoaGGD9+PHbt2oWIiAiEh4ejd+/e2L17t1SF8x9++AHe3t5IT09HdHQ0fvjhB3Tr1g2urq4AqpdZ09fXh4eHB8LCwjBlyhRMmTKF4VETQtoCBXRCCGkhgUCAe/fuwczMDGpqavjggw+wZcsWeHh4SML6hQsX0L17dxw5coTxD6pU1VOcAwMD26RBX0lJCRISEuDt7Q0WiwVPT88Gl/gSiUSIy8tFRHYWyt+YaisSibCZ8wQjz5zAENuG10sfduo4PrKzfWuAHmx7FKPOnMDQU8ckneNbWg1+WfDHIYzYuR/D9v6GofsPYsjPBzBs76+YOmhVm0xxr6l5qsswU745nd/NYKhkXuv/lw7YgIMrTuLbL3bDavjX+HHub9j21T4s0FyJuSrVYX6WwhLM6WGB31eefKd96+HhgdjYWJkPq82t6OhoODo6dsr7GxkZid27d+OTTz6BvLw8dHR08PnnnyMiIkKqArq1tTUGDhwIJSUl6OrqwsDAQBLOAeDly5fYtGkTtLW1oaKigkWLFoHP5zM4YkJIW6GATkgXs2/fPsjJydWqESNGSG6vedPX0dGBqqoqTExMkJ+fz+CIZcPz58/x119/Yfny5dDU1ET//v0xb948dO/eHXZ2dox/KKWqvkbV19cXPB6vzacwl5WVITk5+Z2X+MouLoblX/cx7FT1WfSaGnrqGKban8OEi2cx6uxJDD11DMNPH8ew08cxpB3PlA9pZLr94EPVAX34j79IasiB3/Gx8dY2C+czuplilsKS5m0nb1YvsEt+9z1TzFSoPhs/473aZ93fbEr36Ixjs/dtSkoK/P394eDgAFdXV0RGRiIvL69TzojpzOH8zYqNjcXQoUMxZ84cmJiYQEVFBYMHD8bWrVvh4+ODyspKpt9WCCFdFAV0QrqYffv24cMPPwSfz5dUUVGR5HYbGxsMGDAAPB4PYWFhmDx5MqZOncrgiGXPixcvYGFhASUlJYwYMQJ6enpYu3YtHB0dqXM7Q/Vm9/z2bgZWt7N0cwLdw+hImD+4i2lXLmL2zSs44uMF/7RU/OfyBYw4fRzDTx/HqLMnMersSYw4fQLDTh3DsFPHJGG+LcL5pAtnMe7cKYw9dwqfXbTDh2dP1priPnznfgz/vjqkD9v9K4Yc+B2ffbqhzcJ5W1Xds+uN1XyNFXC54YHnz6tnViSEJiHSNxalxY1/eVNRUYG0tDQEBQWBw+HAyckJ4eHhyM7O7hS9JWJiYrpEOE9ISMDAgQNhY2ODqqoqyes2m83GmjVroKuri169esHa2hrZ2dkMv6MQQroaCuiEdDH79u3DuHHjGrytpvHMgwcPJD9LSEigxjPvoLKyEhs2bEC/fv0QExODV69ewdnZGevWrZN86LOyssKTJ0+oEVUHVc2a2n5+fh3ePb9mia+GAl1DYb2kogKCf4OeSCTCBofHGPnv1PWRp09I/nus3SkMO3UM48+fbnWTucG2RzH+/GnMvHEZky7aYd6ta5h36xrm3rqGD8/+O73+5BEM/uOP6inu+37DkN9+x+iv92KG6tKOCd7vmcJQ2RxzeizFnB5v/zcbazL3Zs3pYQGbT3Yg0jcGmyftxNweS2GouATzNVbAftfNJvdrUX4xWOed8Pvqkziw5hiunb4JR0dHBAcHIz09XSZXaegq4TwlJQVDhgzBmjVrGj1LXllZCV9fX2zduhWlpaUd/C5CCOnqKKAT0sXs27cPKioq6Nu3LwYNGoRly5YhMzMTAGjpllZ68eIFFi5ciFGjRiErK6ve7a9fv4aHhwc2btyIvn37QktLC8uXL8eDBw+6bCOq9q7i4mK4uroiKCiI8TOcQqEQmZmZCA0NlSwD9bb1uNMKC7Dkz9uSKfBDTx3DyDMn8On5MxhjdwqfXz7/1gZzM69fxkd2to1uN+niWRz18cK0Kxfxf1cuSgJ6TX1y4QwMrttjjJ0thh89glEHDmHmd7/j/O/3kJ2ai7t/PKo+Y/1eG4XxRspIbTmWfWCDJf3WtcnfW6izCqZ9rGGmt6bB2/+wPtPgPslOzcXmz77HAs2VkjLRXY0z2+wR6BvUqqX5mKquEs5TU1MxfPhwrFy5kqawE0KkFgV0QroYLpeLP//8E1FRUXB2dsaUKVOgr68PgUCA27dvQ0lJqd7vTJw4ETt37mRgtLKjrKwMX3zxBaZOnYqSkpK3bl9ZWQkfHx9s2bIF+vr60NDQwJIlS3D79u16TcaoWlaFhYVwcnJCWFiY1F0r3NB63I2dfRWKRHgQHYGVf93H9GuXsPjeLZwPDsROFy4+vXC2ySnug22PYs2Th/j0wlkYXLPHJxfOYPSZkxh95gRGnD4Bq7/+RHRONoQiERbdvYXPLtnVCuezrl/GxIt24Pzbeb6grAyldS4RiPaPg7GWZbOau7W25qpYtNnfmt19KeZrrGj8LLy8GQpy6nf5P25zHgs0V8JCfz2Wf2CDBZor/12X3QxL+6+H/a6byMnKqbc0X2JiIkpLSxk/9upWbGwsHB0dm1xusDNURkYGRo8eDXNzc7x+/boD3hUIIaRlKKAT0sWVlZVBQ0MD9vb2FNBb4fnz59i1axeeP3/+zr9bVVWFwMBAbNu2DUOGDIGqqioWLlyIa9eudfoPze1VfD4fjo6OiIqKkrpwXrfeXI+77tnXpq6Xj83NwZxb1zDqzIlGA7rhjStgxcVg0iU7TL18HoY3r2LWjSuYeNEO065cRExujuTvXQ8PxaRLdvjc/jwMb1zBV9fs8emFszC5dwslTYxDJBJh3+I/MFO+GcujyVitG7cVPy06hH2L/8DPZkdgv+cmzPTWwKTXKqwYvBHzNVZgprwZZspXr9FupL4CC3WscH7HNcnjU1RUhPj4eHh5eTXZ7Z+J6irhPCsrC2PGjIGJiQn++eefdngHIISQtkMBnRCCCRMm4IcffqAp7lKgqqoKYWFh+PHHHzFixAh0794dRkZGuHTpEnJzc6U+bEpD5eTkgMPhIC4ujvGxvGuJRCLk5+fXOvvq5+eH5OTkBnsWJPL5+JnnVruh27814cJZlJSXQyQSwdbfF19cuYBPL5ypPpt+/TJYcTG1/pZQJIJtgC+mX7OvDvT252H56E/E5eW+ddzFBSU4bH260TPcsxTrd2efpbgEJr1XY77GCsxuRlM3aar5miuxVH+DZOm2WYpLMFPeDCa6q2HaxxpLB6wHP7N+6C0tLUVSUtI7d/tvj+oq4Tw3Nxfjx4+HkZERXr16xfRLPCGEvBUFdEK6OKFQCG1tbdja2kqaxD18+FBye2JiIjWJY4hYLEZ0dDR++uknfPTRR1BSUoKhoSHOnTuHrKwsCusNVEZGBthsNpKTkxkfS1tUYWEh4uLi4OHhARaLBW9vbyQkJNTrWVAuEOBxTBSsHj3A0j/v4mF0ZL2/9awgH3/FRMEhLhZ5TUy1zi4uhvezFIRnZUL4jsdYSVEJPO774PQWeywfvLF66bM616YbKi6Bhf4GrBu3FatHf4tFPa1gpLa82UusSUvNVbGo7hiv+G9IlzfDUv31WDbIBia9ViHCO7rJx6qm239gYCDYbDZcXFzw9OlT5OTktPtzu6uEcz6fj4kTJ8LQ0BAvX75k+iWdEEKahQI6IV3Mtm3b4OXlhfT0dPj7+2PGjBno1asXCgsLAVQvs6avrw8PDw+EhYVhypQpmDJlCsOjJmKxGAkJCfjtt98wfvx4KCoq4quvvsKpU6eQnp5OYf35czx79gwODg5IS0tjfCztUSUlJUhISIC3t7fUTZVuqEqLy3Dxp+tY3G815qouw5zu1V3SF/e2xtqx32H1qC1Y3Hs1Vo3cAjO9NVg20AaLdVfL3Nn0N2uWghnmqS2HsbYlgpzCmv1YCQQCZGRkICQkBI6OjuByuW9tINjSiouL6xLhvKCgAFOnTsVXX32FFy9eMP0STgghzUYBnZAuxtzcHH379oWSkhL69esHc3NzPHv2THL7y5cvsWnTJmhra0NFRQWLFi0Cn89ncMSkLrFYjJSUFBw6dAiTJk2CgoICvvjiCxw7dgwpKSldMqwnJiaCzWYjKyuL8bF0RJWVlTU4VbqgoEBq9n9CQgI4HA5yc3NRUliKZzFp+N3SFksHrMdi3dUw1VuDTRN3IsT1KVaN/Abm/dZh3ditWDvmO5j3W9fuXeE7opb0W4uE8HebzfFmA0FnZ2dwOBwEBQUhLS2tyZ4EzamuEs6Lioowbdo0TJs2DUKhkKqPxBoAACAASURBVOmXbEIIeScU0AkhRIaJxWJkZGTg2LFj+M9//gN5eXlMmTIFv//+OxISEqQmrLVXiUQixMbGSoIg0+NhomqmSgcEBMDBwQGurq6IjIxEXl4eY/u/Zp/k5eXV219xwQlwusaDLysI5aXVS5DdPfwIZnprYKq3BssHbYRZ37UwUluO2d07aJ31dqw5PZYiMzm7xcd3Xl4eoqKi4ObmBgcHhyZ7EjRVNeGcz+czfsy2ZxUXF8PAwABTp05FRUUF0y/RhBDyziigE0JIJyEWi5GTk4NTp07hyy+/hIKCAj799FP8+uuviI6O7nRhXSQSISIiAlwut9OfEWxuCQQCpKWlISgoCBwOB87Ozh12XXPNPomOjn7ns7RCoRCs8074evIPWD5oIzZO3Am7bVcbXaNc1srs/bV4aMtusHHcu1RhYSFiY2Pr9SQoLi5u8vfi4uLA4XA6fTgvLS3F7NmzMXHixHrNTgkhRFZQQCeEkE5ILBYjPz8f58+fx8yZM6GoqIixY8fip59+Qnh4uMyHdZFIhJCQEDg7O0vtNdhMl1AoRGZmJkJDQ9v9uuaafRIZGQkul4uCgoIWj7kovxhCoRAikQj2u252quXbjNSW44/Vp5GekNnqx7umJ4GPjw9YLBZ4PB5iYmLqXebQVcJ5WVkZjIyMMH78eJSUlDD9EkwIIS1GAZ0QQjo5sViM4uJiXL58GXPnzoWysjJGjx6NH3/8ESEhIe0S1tqzhEIhAgIC4ObmVq+bOVXjj1nNdc1OTk7gcDgIDg5Geno6BAJBq/++SCTC06dP4eTkhMLCwjYbt0gkwmM7rsx1eH9bGWtb4sy3l1FR3rprymuqrKwMKSkp8Pf3r3WZQ3h4ONhsdqcP5+Xl5TAxMcGYMWNQVFTE9EsuIYS0CgV0QgjpQsRiMcrKynDjxg0YGxujR48eGDZsGLZv3w4/Pz+pD+sCgQC+vr7w8PB452twqaqr5rrmyMhIuLi4wMHBAQEBAXj27FmLmpCJRCKEhobC2dn5rVOtW1pZKdnYY3yQ8WDdVjVb2RwLta1w88CDNn+sai5z4PF4ePLkCRwdHREeHo6srCypf363pCoqKmBubo5Ro0YhPz+f6ZdYQghpNQrohBDShQkEAty9exempqZQU1PDBx98gC1btsDT01PqPsyXl5fD09MT3t7ere5mTVVdIpEI+fn5iI6Ohru7O1gs1js1Iau51MDV1bVDZjPk5xTgZ7PD7RKaDTtweTdDpSUw6bUKq0ZugaCi9TMY6lZ8fLykcWJWVhbCwsLA5XLbfOYE0yUQCLBy5UoMGzYMubm5TL+cEkJIm6CATgghBAAgEonw8OFDLFu2DJqamhgwYAA2bdoEV1dXxj/Ml5aWwt3dHf7+/oyPpTNX3SZkPj4+SExMbDB8C4VCBAYGws3NDaWlpR0+1iJ+Ec7vvI5Vo75pfAp8M5dqm6e6DN5PAjFXxaJDAvqc7kuxpN9amPVdg/zsll2v31jVhPOGOujn5uYiIiKi3syJ8vJyxo+9dy2hUAhra2sMHjwYmZmZTL98EkJIm6GATgghpJ4XL16AxWLBysoK2tra0NPTw7p168Dlcjv87HVxcTFcXFwQHBwsdWf1O3MVFxcjISEBXl5eYLFY8PT0RFxcHIqKiiR9ANzd3aXiUoPighJc+ekOds07gF1GB3Bu21Us+8CmWdeuG2tZwp8djOfPn+PRGQ5mynfMWXQj9eVYN24rBIK2O6Zr1p6vG87r1pszJ3g8Xq0vY5j4suVdSygUwsbGBgMHDkRaWhrTL5eEENKmKKATQghp0qtXr+Ds7Iy1a9dCV1cXvXr1wqpVq/DkyZN2D2cFBQVwcnLqFJ3nZblKS0uRlJQEX19fsFgssNlsODk5ITc3V2r3S2xQAvYtPgxDxcZD+vqPt6GizhTzcM8ozGrid9qqZsqb4d6Rx212f5sbzhuq4uJixMfHN/hlDNP7sW4JhUJ888036N+/P1JSUph+eSSEkDZHAZ0QQkizvX79GjweDzY2Nujbty+0tLSwYsUKPHjwoM2vQc7Ly4OjoyOioqKkNgR2tRIIBPDy8oKLiwv8/PxqdQzPy8uTyv0kEolw47c/MbeHBWa+Z4qZ75lhtpI51o/fhoykrAa3/3HeAcxTWdbsKfItuw7dHLx7Pm1yH1sTzutW3S9j3N3dERUVhfz8fMb3r1AoxLZt26Cnp4fExMQOfe07ePAgJkyYADU1Nejq6sLY2LjeGF6+fIlNmzZBR0cHqqqqMDExocZ1hJB3RgGdEEJIi1RWVsLb2xtbtmzBgAEDoKGhAXNzc9y5c6fVZ96ys7PB4XAQHx/PeMCjqq6Kigp4eXnVatJX0zE8KCgIbDYbzs7OePr0KXJychgPc3WrrLQcrPNOuPbzPfg5BDXZnC3aPw6Ww7/GQh0rGKkvx0yFtl+LfZ7qMjhf92j1/WrLcN7QPk9NTUVgYCDj+1ckEmHXrl3o3bs3YmNjO/z1ztDQEFevXkVsbCwiIyMxd+5c6OvrQyQSSbaxsbHBgAEDwOPxEBYWhsmTJ2Pq1KkdPlZCiGyjgE4IIaTVqqqqEBAQgG3btmHw4MFQVVXFokWLcP36dRQUvFsTrIyMDLDZbCQnJzMe6qiqq6aDvq+vb6NN+oRCITIyMhASEgJHR0dwuVyEhobK7PJeqbHpsN91C7uMDuC/0/ZgntpyzFJqu6nvZnprkJ6Q2aoxJiYmtls4r1sCgaDB/ZuZmdnu+1ckEmH//v3Q0dFBZGQk0y93AIDCwkLIycnB29sbAFBeXg5FRUU8ePBAsk1CQgLk5OQQGBjI1DAJITKIAjohhJA2VVVVhdDQUPzwww8YPnw4evToASMjI9jb2791GnRwcDAcHByQlpbGeECjqq6ysjLweDz4+fk1u4O+UChEdnY2wsPD4eTkJPPLe7EvusBY2xLzVJe1zfXn75nh3PZrrRpTTTjPzc3t8MejZv8+ffoUzs7OYLPZCAwMRGpqaps3kRSJRDh48CC0tbURGhrK9MubREpKCuTk5BATEwMA4PF4kJOTQ1lZWa3t9PX1cfz4cSaGSAiRURTQCSGEtBuxWIzo6Gjs3bsXH374IZSUlDB79mycO3cO2dnZtcL6nj17oK2tjcTERMYDGVV11SxvFxAQ0OKzpHWX92rPMNdelZmcDfN+6zBffXmrw/ks+SVYoLkSWc9yWjweJsN5Q/uXz+cjKioKbm5ucHBwgJ+fH5KTk1vdRFIkEuHo0aPQ1NSUqrPQVVVVmDdvHj7//HPJz27fvg0lJaV6206cOBE7d+7syOERQmQcBXRCCAHg7e0NIyMj9O3bF3Jycnj8+HGt28ViMfbu3Qs9PT10794dBgYGSE5OrrVNSUkJli1bBnV1dWhqasLa2hpCobAj74ZUE4vFiI+Pxy+//ILx48dDUVERBgYGsLW1xfr166GmpgYHBwfGAwdVdZWUlMDNzQ1BQUFtNoX5zeW93NzcwGKx2izMtXf9eewJFve2hqGSecvC+XumMNVbA2NtK6wfv63Fy6tJUzhvqAoLCxEbGwsPDw+wWCx4e3sjISEBxcXF73ysnDp1ChoaGvD19WX65auWmiXesrOzJT+jgE4IaSsU0AkhBACXy8Xu3bvx6NGjBgP6oUOHoKmpiSdPniAqKgoLFizAoEGD8PLlS8k2s2fPxrhx4xAUFARfX18MHToUFhYWHX1XZIJYLEZKSgoOHjwIPT099OjRAwYGBjh27BhSUlKkrsFYV6uatedDQkLadV/UDXM1a3G39YoAbVUhrk9xdO1ZWI/eAkPFJZgpb1Zd75lVd3yvqW6mmPnGf8/oZoo5PSywUMcKJr1Wg33RpUX/vrSH87pVUlKCxMRE+Pj4gMVigcfjISYmBgUFBU0eVyKRCOfOnYOamho8PT2ZfrmqZfPmzejfv3+99ddpijshpK1QQCeEkDrqBnSxWAw9PT0cOXJE8rPy8nIoKyvj7t27AID4+HjIycnVukbSyckJ3bp1Q25ubscNXoa8fv0aq1evxqBBg+Dl5YVjx47h888/h7y8PKZOnYpDhw4hISGBwnoHV1FREZydnREWFtahj31xcTESEhLqrcX9rmdeO6qcrvGwcuhmzNdYgVkK1UF9TncLzJSvXsZtSb91WNp/ffXybvJmMNZaiXVjt4J13qlFj2tSUpJMhfO6VV5ejpSUFPj7+9dani8qKqpWXwKRSAR7e3uoqqrCzc2N6ZcpCbFYjM2bN+P999+vN3sK+F+TuIcPH0p+lpiYSE3iCCHvjAI6IYTUUTegp6amQk5ODhEREbW2mzZtGrZs2QIAuHz5MrS0tGrd/vr1a8jLy+PRo0ftP2gZ8/fff8PExASjR49GTk6O5OdisRg5OTk4deoU/u///g8KCgqYMGECfvvtN8TExFBYb+cqLCyEk5MTnj59yuhjXXct7jfPvDL9GL1ZRfxi+DwOgMd9H7jc9ATH3hV/nWZjzYf/xaKeq7BQxwrLBtrg1oEHSE/MbHJpt6ZK1sN53apZni84OBhjx45Fz549YWZmhjt37uDKlStQVVWFo6Mjg69Q9W3cuBGamprw8vICn8+X1IsXLyTb2NjYQF9fHx4eHggLC8OUKVMwZcoUBkdNCJFFFNAJIaSOugHd398fcnJyyMvLq7WdmZkZlixZAgA4cOAAhg8fXu9v6erqws7Orn0HLGOEQiFmzpyJiRMnori4uNHtxGIx+Hw+zp07hxkzZkBRURHjxo3Dvn37GA+QnbEKCgrA5XIRFRUlVY9t3TOvbm5uiIqKAp/Pl6px1hpzaTmCnMLg+yQQ+dmt+1IhKSkJbDa704TzulVaWopbt25h8eLF0NbWhqKiIqZPn47Hjx/j+fPnHfjK1DQ5ObkG6+rVq5JtXr58iU2bNkFbWxsqKipYtGgR+Hw+c4MmhMgkCuiEEFIHBfT2dezYMUyfPh0CgaDZvyMWi1FcXAx7e3vMmTMHSkpK+PDDD7Fr1y6EhITI5Drb0lR8Ph+Ojo6IiYlhfCxNVUVFBdLS0hAUFAQ2mw1nZ2c8ffoUOTk5UhvWW1M14Twnp+Ud32WlHjx4ABUVFfz222/YsWMHhg4dih49emDRokW4ceMGSktL2/FViRBCpAcFdEIIqYOmuLevqqoq/P333y3+fbFYjLKyMly/fh3Gxsbo3r07hg8fju3bt8Pf35/C+jtWXl4eOBwO4uLiGB/Lu5RAIEBGRgZCQkLg6OgILpeLsLAwZGVldYpjoCuFcxaLBVVVVdy5c6fW8zwmJgb79+/Hxx9/DAUFBcyYMaNW53RCCOmMKKATQkgdjTWJO3r0qORnFRUVDTaJCwsLk2zj4uJCTeI6QEVFBe7cuQNTU1Ooqqpi0KBB+Pbbb+Hl5dUpglp7Vk5ODjgcDhISEhgfS2tKKBQiOzsb4eHh4HK54HA4CA4ORnp6eq0GZLJSycnJXSacc7lcqKmp4fr16xCLxY0+z9PS0nDixIlWfblHCCGygAI6IYSg+rroiIgIREREQE5ODsePH0dERAQyMzMBVC+zpqWlBRaLhejoaBgbGze4zNr48eMRHBwMPz8/DBs2jJZZ62AikQgPHz6EhYUFNDQ0MGDAAGzevBlubm4yGdTas7KyssBms5GUlMT4WNqyRCIRcnNzERERARcXF7DZbAQGBiI1NRUVFRWMj+9t1ZXCuaurK9TV1XHx4sUmwzkhhHQlFNAJIQSAp6dngw2ArKysAFSfRd+7dy/69OkDZWVlGBgYICkpqdbfKCkpgYWFBdTU1KChoYHVq1dDKBQycG8IALx48QIsFguWlpbQ1tZG3759sX79ejg5OclEUGvPyszMBJvNxrNnzxgfS3uWSCRCfn4+oqKi4ObmBgcHB/j5+SElJQVlZWWMj69udaVw7uHhAQ0NDZw5c4bCOSGEvIECOiGEkE7v1atXcHJywpo1a9CrVy/o6upi9erVYLFYUhnU2rPS09Ph4OCAtLQ0xsfS0VVYWIjY2Fh4eHiAxWLBx8cHiYmJKC0tZXxsKSkpYLPZyM7OZnws7V0+Pj7Q0tLCiRMnKJwTQkgdFNAJIYR0Ka9fv4a7uzs2bNgAPT09aGtrY+XKlXj48KFUBLX2rNTUVLDZbKSnpzM+FqaruLgY8fHx8PLyAovFgqenJ+Lj41FcXNzhY+lK4TwgIADa2tr4448/KJwTQkgDKKATQgjpsiorK+Hl5YVvvvkG/fv3h6amJszNzXH37l0UFRUxHmbaIwRmZmYyPhZpq9LSUiQlJcHX1xcsFgs8Hg8xMTEoKGjdGubvsl+6QjgPCQlBz5498csvv1A4J4SQRlBAJ4QQQlC9/Ju/vz+2bt2KQYMGQU1NDSYmJrhx40aHBLX2rJolu7pCCGxtlZeXIyUlBf7+/nBwcICbmxuioqLA5/PbfK31rhTOw8PD0bt3b+zZs4fCOSGENIECOiGEEFJHVVUVQkND8f3332PYsGHo0aMH5s+fj8uXLyMvL4/xsPMulZCQAA6H0yUaj7V1VVRUIC0tDYGBgWCz2XB2dsbTp0+Rm5vb6rDelcJ5ZGQk+vbtix07dqCqqorppzchhEg1CuiEEEJIE6qqqhAZGYk9e/bgww8/hLKyMubMmYPz588jOzu7zc+qtmXFxcWBw+HI3JcK0lgCgQAZGRkICQmBo6MjuFwuwsLCkJWVBaFQ+E5/qyacZ2VlMX6/2rtiY2PRv39/fPvttxTOCSGkGSigE0IIIc0kFosRFxeH/fv34+OPP4aioiJmzJiB06dPIyMjQ2rCukgkQkxMDBwdHcHn8xkfT2croVCIrKwshIWFgcvlwtHRESEhIcjIyIBAIGjyd589e9ZlwnlCQgL09fWxceNGCueEENJMFNAJIYSQFhCLxUhOTsbBgwcxceJEKCgo4P/+7/9w/PhxPHv2jLGwLhKJEBkZCS6XK/PXzstCiUQi5ObmIiIiAs7OzmCz2QgMDERqaioqKipqbduVwnlKSgoGDx6MtWvXUjgnhJB3QAGdEEIIaSWxWIz09HQcPXoUU6dOhby8PKZOnYo//vgDiYmJHRbWRSIRnj59CicnJxQWFjIe0rpaiUQi8Pl8REVFwc3NDQ4ODvDz80NKSgoSExO7TDhPTU3F8OHDYWlpicrKSqafnoQQIlMooBNCCCFtSCwWIzs7G7a2tpg2bRoUFBQwceJEHDhwALGxse0W1kUiEcLCwuDs7NzploiT1SosLERsbCycnZ3x5MkT8Hg8JCYmorS0lPGxtVdlZGRg9OjRWLp0KV6/fs3005EQQmQOBXRCCCGknYjFYvD5fNjZ2cHAwACKior4+OOP8fPPPyMiIqLNwrpIJEJISAhcXFxQXFzMeEij+l/VTGtPSUlBfHw8vLy8wGKx4OXlhfj4+E61v7KysjBmzBiYmJjgn3/+YfrpRwghMokCOiGEENIBxGIxioqKYG9vjzlz5kBJSQkfffQRdu/ejdDQ0BaHdaFQiKCgILi5uaGkpITxkEb1v0pNTW1wWntpaSkSExPh4+MDFosFHo+H2NhYmb4sITc3F+PHj8f8+fPx6tUrpp9uhBAisyigE0IIIR1MLBajtLQU169fx4IFC9C9e3eMGDECO3bsQEBAQLOX7RIKhQgICIC7uzvKysoYD2lU/6uacJ6ZmdnkduXl5UhJSYG/vz8cHBzg5uaGqKgo8Pl8qVkV4G3F5/MxceJEzJ49Gy9fvmT66UUIITKNAjohhBDCsIqKCty5cweLFy+GqqoqBg8ejP/+97/w9vZuNKwXFhbC1tYWPB6PwrmUVXPDed2qqKhAamoqAgMDwWaz4eLigoiICOTm5kptWC8oKMCUKVNgYGCAFy9eMP1UIoQQmUcBnRBCCJEiIpEIDx48wNKlS6Gurg59fX18/fXXcHNzk6yxnZ+fjwkTJmDMmDGduuGYLFZLw3ndEggEyMjIQEhICDgcDrhcLsLCwpCVldXsGRbtXYWFhfjiiy8wbdo0CIXCDn+ueHt7w8jICH379oWcnBweP35c63axWIy9e/dCT08P3bt3h4GBAZKTkzt8nIQQ8i4ooBNCCGm2t30gtrKygpycXK0yNDSstU1JSQmWLVsGdXV1aGpqwtrampEP97LgxYsXePLkCVauXAltbW307dsXa9aswYcffoixY8ciJyeH8ZBG9b9KS0uDg4NDq8N53RIKhcjKykJYWBi4XC4cHR0REhKCjIwMyZc2HV3FxcX46quvMHXqVAgEAkaeH1wuF7t378ajR48afD06dOgQNDU18eTJE0RFRWHBggUYNGgQTcMnhEg1CuiEEEKa7W0fiK2srDB79mzw+XxJlZaW1tpm9uzZGDduHIKCguDr64uhQ4fCwsKiI++GTHr16hXu378PXV1d6Orqom/fvrC2toaDgwPKy8sZD6ddvWrCeUZGRrv+OyKRCLm5uXj69CmcnZ3BZrMRGBiItLQ0VFRUdMh9LS0thaGhISZNmoTy8nKmnxoAUO/1SCwWQ09PD0eOHJH8rLy8HMrKyrh79y4TQySEkGahgE4IIaRFGgvoxsbGjf5OfHw85OTkEBoaKvmZk5MTunXrhtzc3HYba2dQXFyMTz/9FPPmzYNAIICbmxs2bNiAPn36QEdHB5aWlvjrr79oyjsD1VHhvG6JRCLw+XxERUXBzc0NDg4O8Pf3R0pKSrt9aVNWVgYjIyOMHz8eJSUlTD8tJOq+HqWmpkJOTg4RERG1tps2bRq2bNnS0cMjhJBmo4BOCCGkRRoL6JqamtDV1cXw4cNhY2OD4uJiye2XL1+GlpZWrd95/fo15OXl8ejRow4ZtywqKCjAmDFjsGjRonpLWFVWVsLLywtff/01+vfvD01NTSxduhT37t3rVGtsS2ulpaWBzWZ3eDhvqAoKChATEwMejwcWiwUfHx8kJSW12Zc25eXlWLRoEcaMGYOioiKGng0Nq/t65O/vDzk5OeTl5dXazszMDEuWLOno4RFCSLNRQCeEENIiDQX0u3fvgsViITo6Go8fP8aoUaMwceJEVFZWAgAOHDiA4cOH1/tburq6sLOz65Bxy5rc3FyMHDkSS5cuxT///NPktlVVVfD398d3332HDz74AGpqali8eDFu3ryJgoICxgNkZyumzpw3p4qLixEfHw8vLy+wWCx4eXkhPj6+xV/aVFRUwNzcHKNGjUJBQUEHHf3NRwGdENJZUEAnhBDSIg0F9Lpqppm6u7sDoIDeEnZ2dli1apXkS47mqqqqQkhICHbu3Ilhw4ahR48eWLBgAS5fvgw+n894gJT1Sk9Pl9pwXrdKS0uRmJgIHx8fsFgs8Hg8xMbGorCwsFm/LxAIsGLFCgwbNqxe4JUWNMWdENJZUEAnhBDSIs0J6ADQq1cvnD9/HgBNcW8psVjcqt+vqqpCREQEdu/ejdGjR0NZWRlz587F+fPnkZOTI7VrbEtryVI4r1tlZWVISUmBn58fHBwc4ObmhqioKOTn5zd4HAiFQqxevRpDhgxBVlZWGx3Rba+xJnFHjx6V/KyiooKaxBFCpB4FdEIIIS3SnICenZ2Nbt26gcViAfhfk7iwsDDJNi4uLtQkrgOJxWLExcVh//79GDduHBQVFTFz5kycOXMGGRkZFNabGc7T09MZH0trq6KiAqmpqQgMDASbzcaePXtgaWkJFosFgUAAoVCIDRs2YODAgUhPT2f60K1HKBQiIiICERERkJOTw/HjxxEREYHMzEwA1cusaWlpSS67MTY2pmXWCCFSjwI6IYSQZmvqA7FQKMT27dsRGBiI9PR0uLu745NPPsGwYcPw999/S/7G7NmzMX78eAQHB8PPzw/Dhg2jZdYYIhaLkZycjIMHD2LChAlQUFDAl19+iRMnTiA1NZXCeicO53VLIBDgr7/+wrx586CmpoaePXti6tSp6NWrFxISEpg+VBvk6ekJOTm5emVlZQWg+vjeu3cv+vTpA2VlZRgYGCApKYnZQRNCyFtQQCeEENJsTX0gfvHiBWbNmgVdXV0oKipi4MCBWLduHfLz82v9jZKSElhYWEBNTQ0aGhpYvXo1hEIhQ/eI1BCLxUhLS8ORI0cwZcoUKCgo4PPPP8fhw4eRlJTU5cN6Zw7ndau4uBhWVlYYMGAAevXqhZ49e8La2hocDqfWl22EEELaHgV0QgghhNQiFouRlZWFkydPYtq0aVBQUMCkSZNw8OBBxMXFdbmwnpGR0WXCuUgkwq5du9C7d2/ExsaisrISvr6++O677zBw4ECoq6vDwsICDx48oC/WCCGkHVBAJ4QQQkijxGIx8vLycPbsWRgYGEBBQQHjx4/Hzz//jMjIyE4f1rtaOP/555/Rs2dPREVFNXgshIeHY9euXRgxYgQOHjzIwBFJCCGdGwV0QgghhDSLWCxGYWEhLl26hNmzZ0NJSQljxozB7t27ERoa2unCek04T0tLY3wsHRHODx48CG1t7VpNHJvy+vXrdj7iCCGk66GATgghhJB3JhaLUVpaimvXrmH+/Pno3r07Ro4ciZ07dyIwMBBCoZDx0EnhvPnh/MiRI9DU1ERQUBDThxYhhHRpFNAJIYQQ0moVFRW4ffs2TExMoKKigiFDhuC7776Dj4+PzIX1rhbObW1toaGhAT8/P6YPI0II6fIooBNCCCGkTQmFQvz5558wNzeHuro6Bg4ciK+//hru7u4QCASMh1IK5/8L5+fOnYO6ujo8PT2ZPmwIIYSAAjohhBBC2tGLFy/w+PFjrFy5ElpaWnj//fdhY2MDJycnVFRUMB5S64ZzNpvdZcK5vb09VFVV4ebmxvRhQggh5F8U0AkhhBDSIV6+fAkOhwNra2v07NkTvXv3hrW1NdhsNsrLyxkNrJmZmV0mnD9//hzXr1+HqqoqHB0dmT4sCCGEvIECOiGEEEI63D///AM3Nzds2LABffr0gY6ODiwtLfHo0SOUlpYyEs5TU1MZD84dcRGrCAAADtVJREFUUXfv3oWKigpYLBbThwEhhJA6KKATQgghhFGvX7+Gp6cnNm/ejH79+kFTUxMWFha4f/8+iouLKZy3YT148AAqKip4+PAh07udEEJIAyigE0IIIURqVFZWws/PD//973/xwQcfQF1dHaamprh16xYKCwspnLeinjx5AlVVVdy5c4fp3UwIIaQRFNAJIYQQIpWqqqoQHByMHTt2YOjQoVBRUYGxsTGuXLkCPp/fqrCalZXVpcK5o6MjVFVVcf36dYjFYqZ3LSGEkEZQQCeEEEKI1KuqqsLTp0+xe/dujBo1CsrKypg3bx4uXLiAnJwciESidw7nz549Yzw4d0S5uLhATU0Nly5donBOCCFSjgI6IYQQQmSKWCxGbGwsfv75Z4wdOxaKioqYOXMmzp49i8zMzCbDekhISJcK5zweDxoaGjhz5gyFc0IIkQEU0AkhhBAis8RiMZKSknDgwAF8+umnUFBQwJdffomTJ08iNTW1Vli/ffs2evToAXd3d8aDc0eUj48PNDU1cfLkSQrnhBAiIyigE0IIIe3k4MGDmDBhAtTU1KCrqwtjY2MkJibW2ubly5fYtGkTdHR0oKqqChMTE+Tn59faJjMzE3PnzkWPHj2gq6uL7du34/Xr1x15V2SCWCxGamoqDh8+jMmTJ0NBQQH/+c9/cOTIEZw7dw7du3fH0aNHGQ/OHVEBAQHQ1tbG4cOHKZwTQogMoYBOCCGEtBNDQ0NcvXoVsbGxiIyMxNy5c6Gvrw+RSCTZxsbGBgMGDACPx0NYWBgmT56MqVOnSm6vrKzERx99hBkzZiAiIgJcLhe9evXCjz/+yMRdkhlisRiZmZk4ceIExowZA3l5eUybNg0HDx5EXFzcO12zLmsVEhKCnj174tdff6VwTgghMoYCOiGEENJBCgsLIScnB29vbwBAeXk5FBUV8eDBA8k2CQkJkJOTQ2BgIACAy+Xivffeq3VW/dy5c9DQ0MCrV6869g7IIHd3d6iqqsLW1hZnzpzBV199BQUFBXzyySfYv38/IiMjO1VYDw8PR+/evbFnzx4K54QQIoMooBNCCCEdJCUlBXJycoiJiQEA8Hg8yMnJoaysrNZ2+vr6OH78OABg7969GDduXK3b09LSICcnh6dPn3bMwGUUj8eTLC1WQywWo7CwEBcvXoShoSEUFRUxZswY7NmzB2FhYTId1iMjI9G3b1/s3LkTVVVVDD7yhBBCWooCOiGEENIBqqqqMG/ePHz++eeSn92+fRtKSkr1tp04cSJ27twJAFi3bh1mzZpV6/bnz59DTk4OXC63fQctw3g8HtTU1GqF87rEYjFKSkpw9epVGBkZQVlZGSNHjsT333+PoKAgCIVCxkN3cys2Nhb9+/fHt99+K1Xh/MyZMxg4cCCUlZUxadIkBAcHMz0kQgiRahTQCSGEkA5gY2ODgQMHIjs7W/IzCujtg8/nQ11dHdeuXXun3ysvL8etW7dgYmICFRUVDBkyBFu3boWvr69Uh/WEhATo6+tj48aNUhXO7927ByUlJVy5cgVxcXFYt24dtLS0UFBQwPTQCCFEalFAJ4QQQtrZ5s2b0b9/f6SlpdX6OU1xbz9JSUmt+n2hUIj79+/D3Nwc6urqGDhwIL755hvweDwIBALGQ3lNpaSkYNCgQVi7dq1UhXMAmDRpEjZv3iz5/6qqKrz//vv4/fffGRwVIYRINwrohBBCSDsRi8XYvHkz3n//fSQnJ9e7vaZJ3MOHDyU/S0xMbLBJ3JtnHS9cuAANDQ38/fff7X8nCJ4/f45Hjx5hxYoV0NLSQr9+/bBx40Y4OzujoqKCsXCempqKYcOGwdLSEpWVlUw/TLW8evUK8vLyePz4ca2fW1paYsGCBQyNihBCpB8FdEIIIaSdbNy4EZqamvDy8gKfz5fUixcvJNvY2NhAX18fHh4eCAsLw5QpUzBlyhTJ7TXLrM2aNQuRkZFwdnaGrq4uLbPGkJcvX4LNZmP16tXQ0dFBnz59sGbNGnA4HJSXl3dYOE9PT8eoUaNgYWGB169fM/2w1JObmws5OTkEBATU+vmOHTswadIkhkb1/+3dT4hWdRvH4UOENlJj5UwLDQVtEaREpAMhzWDFlBaVRJsW7rRSIUGFCiIICly0CgrbTAuFXEmbYQgioxYu+mMZITWYTcaMQ6LFWOr4zLfV+7wMWK+ZztzxXhecxZzzO4f7N7sPPJwDUJ9AB4CrpGmaix4DAwPtNb///ns2b96cm266KfPmzcv69eszOjo67TnHjh3L2rVr09HRka6urmzfvr1klP2/OX/+fN5///1s2rQpt9xySxYsWJANGzZk//79OXXq1FWL85GRkaxYsSJPPPFEzp8/P9v/hosS6ACXR6ADAPxDk5OT+fDDD7N58+YsXLgw8+fPz1NPPZV9+/bl5MmTVyzOjx8/nrvuuiuPPvpozp07N9vb/lN+4g5weQQ6AMAVdOHChXz88cfZtm1blixZkhtuuCFPPvlk9u7dm/Hx8cuO89HR0axatSoPPfTQv+L9Az09Pdm6dWv771arlUWLFnlJHMBfEOgAAFdJq9XKwYMHs3Pnzixbtizz5s3L448/noGBgYyNjV1ynJ84cSL33HNP7r///mnvMKjs3Xffzdy5c/POO+/km2++yaZNm3LjjTdmbGxstkcDKEugAwDMgFarlc8++ywvvvhibr/99lx33XV55JFH8vbbb+enn37KxMTEReN8fHw89957b3p7ezMxMTHb2/hb3njjjSxevDhz5sxJT09PDh48ONsjAZQm0AEAZtjU1FQOHz6cl19+OStWrMicOXPS39+fN998MyMjI+1Y//nnn3Pfffdl9erV+fXXX2d7bACuMoEOADCLpqamcuTIkbz66qu5++67c+2112bNmjV5/fXXs2bNmvT09OT06dOzPSYAM0CgAwAUMTU1leHh4ezatSvLli1LZ2dnTp48OdtjATBDBDoAQEGtViunTp2a7TEAmEECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwBlvPbaa1m5cmWuv/76dHd357HHHsuRI0emrenr60vTNNOOp59+etqaH374IevWrUtHR0e6u7uzY8eOTE5OzuRWAOBvE+gAQBkPPvhgBgYG8vXXX+fQoUNZt25dFi9enImJifaavr6+bNy4MaOjo+3jl19+aV+/cOFCli9fngceeCBffPFFBgcH09XVlRdeeGE2tgQAl0ygAwBljY+Pp2mafPTRR+1zfX19ee655/70nsHBwVxzzTUZGxtrn3vrrbfS2dmZc+fOXdV5AeCfEOgAQFnfffddmqbJ4cOH2+f6+vrS1dWVBQsW5I477sjzzz+fM2fOtK+/9NJLufPOO6c95+jRo2maJp9//vmMzQ4Af5dABwBKarVaefjhh7N69epp53fv3p2hoaF89dVX2bNnTxYtWpT169e3r2/cuDH9/f3T7jlz5kyapsng4OCMzA4Al0OgAwAlPfPMM1myZEl+/PHHv1z3wQcfpGmaDA8PJxHoAPx7CXQAoJwtW7bk1ltvzdGjR//n2omJiTRNk6GhoSR+4g7Av5dABwDKmJqaypYtW7Jw4cJ8++23l3TPJ598kqZp8uWXXyb570viTpw40V6ze/fudHZ25uzZs1dlbgC4EgQ6AFDGs88+m/nz5+fAgQPTPqP222+/JUmGh4fzyiuv5NNPP83333+f9957L0uXLk1vb2/7Gf/5zFp/f38OHTqUoaGhdHd3+8waAOUJdACgjKZpLnoMDAwkSUZGRtLb25ubb745c+fOzW233ZadO3dO+w56khw7dixr165NR0dHurq6sn379kxOTs7CjgDg0gl0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFCAQAcAAIACBDoAAAAUINABAACgAIEOAAAABQh0AAAAKECgAwAAQAECHQAAAAoQ6AAAAFDAH6xDP6/KPKlHAAAAAElFTkSuQmCC\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-label Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/classi_paradigms.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we are going to use an especific music dataset. This dataset contains 593 different songs each one of them eliciting different emotions (multi-label): angry-fearful, relaxing-calm, happy-pleased...\n",
    "\n",
    "*From: Trohidis, K., Tsoumakas, G., Kalliris, G., & Vlahavas, I. P. (2008, September). Multi-label classification of music into emotions. In ISMIR (Vol. 8, pp. 325-330).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we need our train and test partitions. In this case, this dataset is part of the library so we can import the data with the `load_dataset` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emotions:train - exists, not redownloading\n",
      "emotions:test - exists, not redownloading\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, feature_names, label_names = load_dataset('emotions', 'train')\n",
    "X_test, y_test, _, _ = load_dataset('emotions', 'test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start transforming a multi-label classification problem with L labels into L single-label separate binary classification problems using the same base classifier (Support Vector in this case). The prediction output is the union of all per label classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = BinaryRelevance(classifier=SVC(), require_dense=[False, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BinaryRelevance(classifier=SVC(), require_dense=[False, True])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the six classifiers trained (one per label)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SVC(), SVC(), SVC(), SVC(), SVC(), SVC()]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.classifiers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finish with the prediction step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Hamming loss is the fraction of labels that are incorrectly predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3250825082508251"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hamming_loss(y_test, prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLTSVM (Twin multi-Label Support Vector Machines)\n",
    "*From: Chen, W. J., Shao, Y. H., Li, C. N., & Deng, N. Y. (2016). MLTSVM: a novel twin support vector machine to multi-label learning. Pattern Recognition, 52, 61-74.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM are all-terrain machines so of course there is a variation specific to the multi-label problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c_k': 0.125} 0.607071441399509\n"
     ]
    }
   ],
   "source": [
    "parameters = {'c_k': [2**i for i in range(-5, 5, 2)]}\n",
    "score = 'f1_macro'\n",
    "\n",
    "clf = GridSearchCV(MLTSVM(), parameters, scoring=score)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print (clf.best_params_, clf.best_score_)\n",
    "prediction = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27145214521452143"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hamming_loss(y_test, prediction)"
   ]
  }
 ],
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