STINTSY MP - Copy.ipynb

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   "cell_type": "markdown",
   "id": "4d7fc418-3a2d-45b5-9add-f181cd2efa7e",
   "metadata": {},
   "source": [
    "Group: I-Need-More-Boolets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a793946-036b-4b4d-b554-393a9900b269",
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   "source": [
    "Members: <br>\n",
    "Go, Ryan Jefferson <br>\n",
    "Ejercito, Joshua Carl <br>\n",
    "Moraña, Anton Louis <br>\n",
    "Nieva, Samuel "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed8ac24-7138-45ea-923f-f940f71c8027",
   "metadata": {},
   "source": [
    "Section: S11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8599513-976f-4f17-96d7-bf21b4c8a75a",
   "metadata": {},
   "source": [
    "# Introduction to the problem/task and dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39f4459-40bb-41a5-a479-a89bf003dae9",
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   "source": [
    "This major course output tasked our group to select a real-word dataset from the selection given. This group has selected the stars dataset, which is a dataset containing spectral characteristics derived from pictures taken by the SDSS (Sloan Digital Sky Survey). This dataset's main use is spectral classification, i.e., classifying whether a celestial body is a star, a quasar or an entire galaxy.\n",
    "\n",
    "Using the dataset, the group is to perform the following:\n",
    "- Describe the dataset\n",
    "- Perform Data Pre-processing and Cleaning\n",
    "- Perform Exploratory Data Analysis\n",
    "- Select 3 Machine Learning Models\n",
    "- Perform model training\n",
    "- Perform hyperparameter tuning\n",
    "- Extract insights from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4d3d97-7961-43c6-8e17-8f37c290fc2b",
   "metadata": {},
   "source": [
    "# Description of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aacce68-3066-4d02-b0e6-3e19764ba70b",
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   "source": [
    "As stated earlier, the Stars datasets contains spectral characteristics from celestial bodies that can be used to classify it as either a quasar, star, or galaxy. This dataset is made up of 100,000 obersavations of space taken by the SDSS (Sloan Digital Sky Survey) with each observations having 18 features. The SDSS takes these observations through the use of a 2.5m wide located at Apache Point Observatory in New Mexico. The telescope is described in detail in a paper by Gunn et al. (2006), and it is through this powerful telescope that a robust astronomical survey is created, from which datasets such as this are derived.\n",
    "\n",
    "The following are the description of their features:\n",
    "1. obj_ID =  the unique value that identifies the object in the image catalog used by th CAS (Chinese Academy of Sciences).\n",
    "2. alpha = it contains the right ascension angle, it is measured from the start of a point which is called vernal equinox and go eastward. It is expressed in hours, minutes, and seconds. Vernal equinox is when the sun is exactly above the equator and day and night are of equal day. It is similar to the longitude in space.\n",
    "3. delta = it contains the declination angle, which shows the angle between the celestial equator and a point on the celestial sphere. It is like the latitude in space which results can either be positive or negative. Positive means that the object is located north while negative means it is located south.\n",
    "4. u – Ultraviolet filter in the photometric system.\n",
    "5. g – Green filter in the photometric system.\n",
    "6. r – Red filter in the photometric system.\n",
    "7. i – Near Infrared filter in the photometric system.\n",
    "8. z – Infrared filter in the photometric system.\n",
    "9. run_ID – Run Number used to identify the specific scan.\n",
    "10. rereun_ID – Rerun Number to specify how the image was processed.\n",
    "11. cam_col – Camera column to identify the scanline within the run.\n",
    "12. field_ID – Field number to identify each field.\n",
    "13. spec_obj_ID – Unique ID used for optical spectroscopic objects (this means that 2 different observations with the same spec_obj_ID must share the output class).\n",
    "14. class – object class (galaxy, star or quasar object). This is the label the group will be tasking the models to predict.\n",
    "15. redshift – redshift value based on the increase in wavelength.\n",
    "16. plate – plate ID, identifies each plate in SDSS (Sloan Digital Sky Survey).\n",
    "17. MJD – Modified Julian Date, used to indicate when a given piece of SDSS (Sloan Digital Sky Survey) data was taken.\n",
    "18. fiber_ID – identifies the fiber that pointed the light at the focal plane in each observation.\n",
    "\n",
    "The first few rows of the dataset look like this:"
   ]
  },
  {
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   "id": "ee4601ef-f0a8-4d4a-aaac-84e8b3ef7d95",
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    {
     "name": "stdout",
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     "text": [
      "stars dataset shape:  (100000, 18)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>rerun_ID</th>\n",
       "      <th>cam_col</th>\n",
       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>MJD</th>\n",
       "      <th>fiber_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.237661e+18</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.237665e+18</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.237661e+18</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.237663e+18</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.237680e+18</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         obj_ID       alpha      delta         u         g         r  \\\n",
       "0  1.237661e+18  135.689107  32.494632  23.87882  22.27530  20.39501   \n",
       "1  1.237665e+18  144.826101  31.274185  24.77759  22.83188  22.58444   \n",
       "2  1.237661e+18  142.188790  35.582444  25.26307  22.66389  20.60976   \n",
       "3  1.237663e+18  338.741038  -0.402828  22.13682  23.77656  21.61162   \n",
       "4  1.237680e+18  345.282593  21.183866  19.43718  17.58028  16.49747   \n",
       "\n",
       "          i         z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID  \\\n",
       "0  19.16573  18.79371    3606       301        2        79  6.543777e+18   \n",
       "1  21.16812  21.61427    4518       301        5       119  1.176014e+19   \n",
       "2  19.34857  18.94827    3606       301        2       120  5.152200e+18   \n",
       "3  20.50454  19.25010    4192       301        3       214  1.030107e+19   \n",
       "4  15.97711  15.54461    8102       301        3       137  6.891865e+18   \n",
       "\n",
       "    class  redshift  plate    MJD  fiber_ID  \n",
       "0  GALAXY  0.634794   5812  56354       171  \n",
       "1  GALAXY  0.779136  10445  58158       427  \n",
       "2  GALAXY  0.644195   4576  55592       299  \n",
       "3  GALAXY  0.932346   9149  58039       775  \n",
       "4  GALAXY  0.116123   6121  56187       842  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "seed_num = 12\n",
    "\n",
    "np.random.seed(seed_num)\n",
    "\n",
    "stars = pd.read_csv('stars.csv')\n",
    "print(\"stars dataset shape: \", stars.shape)\n",
    "stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe9c0c8-7fe7-4a20-b14d-2b5c8351d5e9",
   "metadata": {},
   "source": [
    "The following shows the number of data points for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35d324fc-f260-47f7-8460-40571b1073a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "GALAXY    59445\n",
       "STAR      21594\n",
       "QSO       18961\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42462967-688b-4032-901d-2f166a6a8ba7",
   "metadata": {},
   "source": [
    "The following shows the datatypes of each feature within the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1045fce1-9695-4cc7-8e53-1ead1e8ad4f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   obj_ID       100000 non-null  float64\n",
      " 1   alpha        100000 non-null  float64\n",
      " 2   delta        100000 non-null  float64\n",
      " 3   u            100000 non-null  float64\n",
      " 4   g            100000 non-null  float64\n",
      " 5   r            100000 non-null  float64\n",
      " 6   i            100000 non-null  float64\n",
      " 7   z            100000 non-null  float64\n",
      " 8   run_ID       100000 non-null  int64  \n",
      " 9   rerun_ID     100000 non-null  int64  \n",
      " 10  cam_col      100000 non-null  int64  \n",
      " 11  field_ID     100000 non-null  int64  \n",
      " 12  spec_obj_ID  100000 non-null  float64\n",
      " 13  class        100000 non-null  object \n",
      " 14  redshift     100000 non-null  float64\n",
      " 15  plate        100000 non-null  int64  \n",
      " 16  MJD          100000 non-null  int64  \n",
      " 17  fiber_ID     100000 non-null  int64  \n",
      "dtypes: float64(10), int64(7), object(1)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "stars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd6bd83-55ea-4845-aca7-bcdcc4aaf78c",
   "metadata": {},
   "source": [
    "# List of requirements\n",
    "\n",
    "The group will be using Python's scikit learn library for their classification models (I think), scipy, scikit, numpy and pandas array for processing data, and matplotlib for visualizing data (add stuff if need pa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bcd160-54cd-4934-afb6-7a77af3fea69",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c63a680-6ab0-4576-8a66-c70b579ba1b1",
   "metadata": {},
   "source": [
    "To begin, we assign a set of columns that will be the focus for data cleaning and preprocessing, namely the `u`, `g`, `r`, `i`, `z`, and `redshift` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3964d300-a2bc-40a8-ac4b-b6f42af5a6cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns_to_clean = ['alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'redshift']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6f17c7-1eee-4c7e-9664-8e744d815d97",
   "metadata": {},
   "source": [
    "## Replacement of `obj_ID` column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545a28ee-f353-4c4a-95b2-2012c4401187",
   "metadata": {},
   "source": [
    "The `obj_ID` column assigns a unique ID for each observation in the dataset, although it is represented in a floating point format. For the purposes of the study, this will be replaced with a similarly-named column that utilizes integer IDs instead in order to simplify the access to each element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f4bfc16-1442-4355-bb54-00fe02a1afdc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
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       "      <th>fiber_ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
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       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>99995</td>\n",
       "      <td>39.620709</td>\n",
       "      <td>-2.594074</td>\n",
       "      <td>22.16759</td>\n",
       "      <td>22.97586</td>\n",
       "      <td>21.90404</td>\n",
       "      <td>21.30548</td>\n",
       "      <td>20.73569</td>\n",
       "      <td>7778</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>581</td>\n",
       "      <td>1.055431e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9374</td>\n",
       "      <td>57749</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>99996</td>\n",
       "      <td>29.493819</td>\n",
       "      <td>19.798874</td>\n",
       "      <td>22.69118</td>\n",
       "      <td>22.38628</td>\n",
       "      <td>20.45003</td>\n",
       "      <td>19.75759</td>\n",
       "      <td>19.41526</td>\n",
       "      <td>7917</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>8.586351e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.404895</td>\n",
       "      <td>7626</td>\n",
       "      <td>56934</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99997</td>\n",
       "      <td>224.587407</td>\n",
       "      <td>15.700707</td>\n",
       "      <td>21.16916</td>\n",
       "      <td>19.26997</td>\n",
       "      <td>18.20428</td>\n",
       "      <td>17.69034</td>\n",
       "      <td>17.35221</td>\n",
       "      <td>5314</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "      <td>3.112008e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.143366</td>\n",
       "      <td>2764</td>\n",
       "      <td>54535</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>99998</td>\n",
       "      <td>212.268621</td>\n",
       "      <td>46.660365</td>\n",
       "      <td>25.35039</td>\n",
       "      <td>21.63757</td>\n",
       "      <td>19.91386</td>\n",
       "      <td>19.07254</td>\n",
       "      <td>18.62482</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>131</td>\n",
       "      <td>7.601080e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.455040</td>\n",
       "      <td>6751</td>\n",
       "      <td>56368</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>99999</td>\n",
       "      <td>196.896053</td>\n",
       "      <td>49.464643</td>\n",
       "      <td>22.62171</td>\n",
       "      <td>21.79745</td>\n",
       "      <td>20.60115</td>\n",
       "      <td>20.00959</td>\n",
       "      <td>19.28075</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>8.343152e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.542944</td>\n",
       "      <td>7410</td>\n",
       "      <td>57104</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       obj_ID       alpha      delta         u         g         r         i  \\\n",
       "0           0  135.689107  32.494632  23.87882  22.27530  20.39501  19.16573   \n",
       "1           1  144.826101  31.274185  24.77759  22.83188  22.58444  21.16812   \n",
       "2           2  142.188790  35.582444  25.26307  22.66389  20.60976  19.34857   \n",
       "3           3  338.741038  -0.402828  22.13682  23.77656  21.61162  20.50454   \n",
       "4           4  345.282593  21.183866  19.43718  17.58028  16.49747  15.97711   \n",
       "...       ...         ...        ...       ...       ...       ...       ...   \n",
       "99995   99995   39.620709  -2.594074  22.16759  22.97586  21.90404  21.30548   \n",
       "99996   99996   29.493819  19.798874  22.69118  22.38628  20.45003  19.75759   \n",
       "99997   99997  224.587407  15.700707  21.16916  19.26997  18.20428  17.69034   \n",
       "99998   99998  212.268621  46.660365  25.35039  21.63757  19.91386  19.07254   \n",
       "99999   99999  196.896053  49.464643  22.62171  21.79745  20.60115  20.00959   \n",
       "\n",
       "              z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "0      18.79371    3606       301        2        79  6.543777e+18  GALAXY   \n",
       "1      21.61427    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "2      18.94827    3606       301        2       120  5.152200e+18  GALAXY   \n",
       "3      19.25010    4192       301        3       214  1.030107e+19  GALAXY   \n",
       "4      15.54461    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "...         ...     ...       ...      ...       ...           ...     ...   \n",
       "99995  20.73569    7778       301        2       581  1.055431e+19  GALAXY   \n",
       "99996  19.41526    7917       301        1       289  8.586351e+18  GALAXY   \n",
       "99997  17.35221    5314       301        4       308  3.112008e+18  GALAXY   \n",
       "99998  18.62482    3650       301        4       131  7.601080e+18  GALAXY   \n",
       "99999  19.28075    3650       301        4        60  8.343152e+18  GALAXY   \n",
       "\n",
       "       redshift  plate    MJD  fiber_ID  \n",
       "0      0.634794   5812  56354       171  \n",
       "1      0.779136  10445  58158       427  \n",
       "2      0.644195   4576  55592       299  \n",
       "3      0.932346   9149  58039       775  \n",
       "4      0.116123   6121  56187       842  \n",
       "...         ...    ...    ...       ...  \n",
       "99995  0.000000   9374  57749       438  \n",
       "99996  0.404895   7626  56934       866  \n",
       "99997  0.143366   2764  54535        74  \n",
       "99998  0.455040   6751  56368       470  \n",
       "99999  0.542944   7410  57104       851  \n",
       "\n",
       "[100000 rows x 18 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = stars.drop(['obj_ID'], axis=1)\n",
    "stars['obj_ID'] = np.arange(len(stars))\n",
    "stars.insert(0, 'obj_ID', stars.pop('obj_ID'))\n",
    "stars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ba649a-ad32-496b-82f3-3867eecbb9e8",
   "metadata": {},
   "source": [
    "## Checking for empty observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b6e9a3-3237-48b5-bc07-e624a44c6ce6",
   "metadata": {},
   "source": [
    "Checking was done for any entries with missing/null features by instantiating another stars dataset whose entries are filtered off of null features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4dacb600-1948-4e71-aeca-42b7bba51dd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stars dataset shape (no null):  (100000, 18)\n"
     ]
    }
   ],
   "source": [
    "nan_variables = stars[stars.isnull().any(axis=1)].index\n",
    "stars_nonull = stars.drop(labels=nan_variables).reset_index(drop=True)\n",
    "print(\"stars dataset shape (no null): \", stars_nonull.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b7c843-0d10-476f-aa73-ca741a7c9249",
   "metadata": {},
   "source": [
    "As shown by the resulting filtered dataset and comparing its shape to the original dataset, it was shown that all entries have all their features accounted for."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2666107a-bea2-4c2d-9c94-75681dd1cfed",
   "metadata": {},
   "source": [
    "## Checking for duplicate entries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9188f66-83ed-455d-9aca-5be85832b4e9",
   "metadata": {},
   "source": [
    "Any duplicated entries was checked first by comparing all entries' columns to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c650c61-d409-49af-94db-0278e6f6bbee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e1b8f8-6265-4f83-8be4-a0e65e1ef4ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "As shown, there are no duplicated entries within the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3841516b-5097-473d-b411-7497cbdb0473",
   "metadata": {},
   "source": [
    "## Checking for outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649de04e-c502-499c-bf0a-bfc5e0785f7e",
   "metadata": {},
   "source": [
    "The removal of outliers aims to craft a dataset whose subset can be used for training data that is generally representative of each classes. We will first check for the existence of outliers in this instance, and any extreme outliers will be removed from analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1efc01d9-9548-4e52-9845-20eccc7bf5db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97884, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars[(np.abs(stats.zscore(stars[columns_to_clean])) < 3).all(axis=1)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5e8967-a212-410c-9c29-dba710f1eec9",
   "metadata": {},
   "source": [
    "There are 2116 data points that are considered as outliers based from the z-score metric (>= 3 standard deviations from the mean). In order to know more about these outliers, the features are drawn in a boxplot, displaying their minimum and maximum values respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57c92b10-1b57-49a5-91e7-a63e2ddc0f23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" alpha \" minimum value:  0.0055278279239701\n",
      "\" alpha \" maximum value:  359.999809770956\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" delta \" minimum value:  -18.7853280771825\n",
      "\" delta \" maximum value:  83.00051858898\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" u \" minimum value:  -9999.0\n",
      "\" u \" maximum value:  32.78139\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" g \" minimum value:  -9999.0\n",
      "\" g \" maximum value:  31.60224\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" r \" minimum value:  9.82207\n",
      "\" r \" maximum value:  29.57186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" i \" minimum value:  9.469903\n",
      "\" i \" maximum value:  32.14147\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" z \" minimum value:  -9999.0\n",
      "\" z \" maximum value:  29.38374\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" redshift \" minimum value:  -0.009970667\n",
      "\" redshift \" maximum value:  7.011245\n"
     ]
    }
   ],
   "source": [
    "for col in columns_to_clean:\n",
    "    stars.boxplot(col, figsize=(4, 4))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(\"\\\"\",col,\"\\\" minimum value: \", stars[col].min())\n",
    "    print(\"\\\"\",col,\"\\\" maximum value: \", stars[col].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46edc477-6428-4445-a529-eaa1a439b5d5",
   "metadata": {},
   "source": [
    "Some features have values equated to a whole value of -9999. This may indicate that these features were not recorded/not available for that observation, thus putting a default value for those features. To resolve this, we will remove the associated entries that has defaule values for these certain features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e76fdb2-2c40-415f-ae8b-00159a44bdc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99999, 18)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_value = stars['u'].min()\n",
    "stars = stars[stars['u'] != default_value]\n",
    "stars = stars[stars['g'] != default_value]\n",
    "stars = stars[stars['z'] != default_value]\n",
    "stars.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a831b3a-a2aa-4b98-8e2f-5768a96266b9",
   "metadata": {},
   "source": [
    "Based from the filtered dataset, there is only one entry that contains such default values. Any other outliers will be resolved by applying the robust scaling method for normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d63c578-8555-4bc1-9d9d-97698e25ec8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" alpha \" minimum value:  0.0055278279239701\n",
      "\" alpha \" maximum value:  359.999809770956\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" delta \" minimum value:  -18.7853280771825\n",
      "\" delta \" maximum value:  83.00051858898\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" u \" minimum value:  10.99623\n",
      "\" u \" maximum value:  32.78139\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" g \" minimum value:  10.4982\n",
      "\" g \" maximum value:  31.60224\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" r \" minimum value:  9.82207\n",
      "\" r \" maximum value:  29.57186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" i \" minimum value:  9.469903\n",
      "\" i \" maximum value:  32.14147\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" z \" minimum value:  9.612333\n",
      "\" z \" maximum value:  29.38374\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" redshift \" minimum value:  -0.009970667\n",
      "\" redshift \" maximum value:  7.011245\n"
     ]
    }
   ],
   "source": [
    "for col in columns_to_clean:\n",
    "    stars.boxplot(col, figsize=(4, 4))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(\"\\\"\",col,\"\\\" minimum value: \", stars[col].min())\n",
    "    print(\"\\\"\",col,\"\\\" maximum value: \", stars[col].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e69833-1761-49f5-b7b6-cb147d44fe79",
   "metadata": {},
   "source": [
    "The boxplots indicates that any extreme outliers that comes from the default values were successfully removed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff80bc99-e579-4e98-ba95-949cbe6336db",
   "metadata": {},
   "source": [
    "## Fixing the obj_ID after removal of outliers\n",
    "\n",
    "Since the outliers were removed, the row obj_ID may not reflect it's actual position in the dataset. To fix this, we simply reassign the obj_IDs incrementally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "197f48f0-7dec-4a9b-a377-fbd1f20f8748",
   "metadata": {
    "tags": []
   },
   "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>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>rerun_ID</th>\n",
       "      <th>cam_col</th>\n",
       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>MJD</th>\n",
       "      <th>fiber_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>99994</td>\n",
       "      <td>39.620709</td>\n",
       "      <td>-2.594074</td>\n",
       "      <td>22.16759</td>\n",
       "      <td>22.97586</td>\n",
       "      <td>21.90404</td>\n",
       "      <td>21.30548</td>\n",
       "      <td>20.73569</td>\n",
       "      <td>7778</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>581</td>\n",
       "      <td>1.055431e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9374</td>\n",
       "      <td>57749</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>99995</td>\n",
       "      <td>29.493819</td>\n",
       "      <td>19.798874</td>\n",
       "      <td>22.69118</td>\n",
       "      <td>22.38628</td>\n",
       "      <td>20.45003</td>\n",
       "      <td>19.75759</td>\n",
       "      <td>19.41526</td>\n",
       "      <td>7917</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>8.586351e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.404895</td>\n",
       "      <td>7626</td>\n",
       "      <td>56934</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99996</td>\n",
       "      <td>224.587407</td>\n",
       "      <td>15.700707</td>\n",
       "      <td>21.16916</td>\n",
       "      <td>19.26997</td>\n",
       "      <td>18.20428</td>\n",
       "      <td>17.69034</td>\n",
       "      <td>17.35221</td>\n",
       "      <td>5314</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "      <td>3.112008e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.143366</td>\n",
       "      <td>2764</td>\n",
       "      <td>54535</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>99997</td>\n",
       "      <td>212.268621</td>\n",
       "      <td>46.660365</td>\n",
       "      <td>25.35039</td>\n",
       "      <td>21.63757</td>\n",
       "      <td>19.91386</td>\n",
       "      <td>19.07254</td>\n",
       "      <td>18.62482</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>131</td>\n",
       "      <td>7.601080e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.455040</td>\n",
       "      <td>6751</td>\n",
       "      <td>56368</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>99998</td>\n",
       "      <td>196.896053</td>\n",
       "      <td>49.464643</td>\n",
       "      <td>22.62171</td>\n",
       "      <td>21.79745</td>\n",
       "      <td>20.60115</td>\n",
       "      <td>20.00959</td>\n",
       "      <td>19.28075</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>8.343152e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.542944</td>\n",
       "      <td>7410</td>\n",
       "      <td>57104</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99999 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       obj_ID       alpha      delta         u         g         r         i  \\\n",
       "0           0  135.689107  32.494632  23.87882  22.27530  20.39501  19.16573   \n",
       "1           1  144.826101  31.274185  24.77759  22.83188  22.58444  21.16812   \n",
       "2           2  142.188790  35.582444  25.26307  22.66389  20.60976  19.34857   \n",
       "3           3  338.741038  -0.402828  22.13682  23.77656  21.61162  20.50454   \n",
       "4           4  345.282593  21.183866  19.43718  17.58028  16.49747  15.97711   \n",
       "...       ...         ...        ...       ...       ...       ...       ...   \n",
       "99995   99994   39.620709  -2.594074  22.16759  22.97586  21.90404  21.30548   \n",
       "99996   99995   29.493819  19.798874  22.69118  22.38628  20.45003  19.75759   \n",
       "99997   99996  224.587407  15.700707  21.16916  19.26997  18.20428  17.69034   \n",
       "99998   99997  212.268621  46.660365  25.35039  21.63757  19.91386  19.07254   \n",
       "99999   99998  196.896053  49.464643  22.62171  21.79745  20.60115  20.00959   \n",
       "\n",
       "              z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "0      18.79371    3606       301        2        79  6.543777e+18  GALAXY   \n",
       "1      21.61427    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "2      18.94827    3606       301        2       120  5.152200e+18  GALAXY   \n",
       "3      19.25010    4192       301        3       214  1.030107e+19  GALAXY   \n",
       "4      15.54461    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "...         ...     ...       ...      ...       ...           ...     ...   \n",
       "99995  20.73569    7778       301        2       581  1.055431e+19  GALAXY   \n",
       "99996  19.41526    7917       301        1       289  8.586351e+18  GALAXY   \n",
       "99997  17.35221    5314       301        4       308  3.112008e+18  GALAXY   \n",
       "99998  18.62482    3650       301        4       131  7.601080e+18  GALAXY   \n",
       "99999  19.28075    3650       301        4        60  8.343152e+18  GALAXY   \n",
       "\n",
       "       redshift  plate    MJD  fiber_ID  \n",
       "0      0.634794   5812  56354       171  \n",
       "1      0.779136  10445  58158       427  \n",
       "2      0.644195   4576  55592       299  \n",
       "3      0.932346   9149  58039       775  \n",
       "4      0.116123   6121  56187       842  \n",
       "...         ...    ...    ...       ...  \n",
       "99995  0.000000   9374  57749       438  \n",
       "99996  0.404895   7626  56934       866  \n",
       "99997  0.143366   2764  54535        74  \n",
       "99998  0.455040   6751  56368       470  \n",
       "99999  0.542944   7410  57104       851  \n",
       "\n",
       "[99999 rows x 18 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = stars.drop(['obj_ID'], axis=1)\n",
    "stars['obj_ID'] = np.arange(len(stars))\n",
    "stars.insert(0, 'obj_ID', stars.pop('obj_ID'))\n",
    "stars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca72200f-a4f2-4470-9235-ae92153b6aa7",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc743d3-8d07-4a35-a067-e5d21b3c6054",
   "metadata": {},
   "source": [
    "Normalization is performed for the previously-specified columns in order to equally weight-in each of the features for the model training and evaluation. The robust scaler will be applied as normalization technique to account for the massive outliers for some features such as `alpha`, `delta`, `u`, `g,` and `z`.\n",
    "\n",
    "In order to begin the normalization process, the original dataset is split into training and test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40f96024-5110-4e21-b04e-3a6426b0acf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save for EDA\n",
    "og_stars = stars.copy()\n",
    "\n",
    "stars_train = stars.sample(frac=0.7,random_state=seed_num)\n",
    "stars_test = stars.drop(stars_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "857bf0b5-7511-416b-a93c-6d83d1708bb6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(69999, 18)\n",
      "(30000, 18)\n"
     ]
    }
   ],
   "source": [
    "print(stars_train.shape)\n",
    "print(stars_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "421e9510-dbc1-412a-9129-5c8626578740",
   "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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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>rerun_ID</th>\n",
       "      <th>cam_col</th>\n",
       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>MJD</th>\n",
       "      <th>fiber_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76074</th>\n",
       "      <td>76074</td>\n",
       "      <td>1.600838</td>\n",
       "      <td>-0.653444</td>\n",
       "      <td>0.201696</td>\n",
       "      <td>0.162024</td>\n",
       "      <td>-0.023905</td>\n",
       "      <td>-0.245544</td>\n",
       "      <td>-0.378337</td>\n",
       "      <td>4188</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>1.232976e+18</td>\n",
       "      <td>STAR</td>\n",
       "      <td>-0.651146</td>\n",
       "      <td>1095</td>\n",
       "      <td>52521</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59701</th>\n",
       "      <td>59701</td>\n",
       "      <td>-0.064724</td>\n",
       "      <td>-0.726566</td>\n",
       "      <td>0.034128</td>\n",
       "      <td>0.344053</td>\n",
       "      <td>0.360867</td>\n",
       "      <td>0.334026</td>\n",
       "      <td>0.458230</td>\n",
       "      <td>1140</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>140</td>\n",
       "      <td>4.266309e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.269980</td>\n",
       "      <td>3789</td>\n",
       "      <td>55269</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73664</th>\n",
       "      <td>73664</td>\n",
       "      <td>-1.528443</td>\n",
       "      <td>-0.960207</td>\n",
       "      <td>-0.911844</td>\n",
       "      <td>-1.141082</td>\n",
       "      <td>-1.132471</td>\n",
       "      <td>-1.071363</td>\n",
       "      <td>-1.058618</td>\n",
       "      <td>1740</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>142</td>\n",
       "      <td>3.224632e+18</td>\n",
       "      <td>STAR</td>\n",
       "      <td>-0.651942</td>\n",
       "      <td>2864</td>\n",
       "      <td>54467</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7228</th>\n",
       "      <td>7228</td>\n",
       "      <td>-0.191236</td>\n",
       "      <td>-0.123871</td>\n",
       "      <td>-0.711827</td>\n",
       "      <td>-1.017740</td>\n",
       "      <td>-1.127491</td>\n",
       "      <td>-1.145352</td>\n",
       "      <td>-1.239132</td>\n",
       "      <td>5313</td>\n",
       "      <td>301</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2.791195e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>-0.453333</td>\n",
       "      <td>2479</td>\n",
       "      <td>54174</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34292</th>\n",
       "      <td>34292</td>\n",
       "      <td>1.533221</td>\n",
       "      <td>-0.586932</td>\n",
       "      <td>-0.203909</td>\n",
       "      <td>0.160651</td>\n",
       "      <td>0.553611</td>\n",
       "      <td>0.640247</td>\n",
       "      <td>0.931627</td>\n",
       "      <td>7712</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>221</td>\n",
       "      <td>1.273059e+19</td>\n",
       "      <td>QSO</td>\n",
       "      <td>2.189334</td>\n",
       "      <td>11307</td>\n",
       "      <td>58430</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       obj_ID     alpha     delta         u         g         r         i  \\\n",
       "76074   76074  1.600838 -0.653444  0.201696  0.162024 -0.023905 -0.245544   \n",
       "59701   59701 -0.064724 -0.726566  0.034128  0.344053  0.360867  0.334026   \n",
       "73664   73664 -1.528443 -0.960207 -0.911844 -1.141082 -1.132471 -1.071363   \n",
       "7228     7228 -0.191236 -0.123871 -0.711827 -1.017740 -1.127491 -1.145352   \n",
       "34292   34292  1.533221 -0.586932 -0.203909  0.160651  0.553611  0.640247   \n",
       "\n",
       "              z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "76074 -0.378337    4188       301        5        49  1.232976e+18    STAR   \n",
       "59701  0.458230    1140       301        5       140  4.266309e+18  GALAXY   \n",
       "73664 -1.058618    1740       301        3       142  3.224632e+18    STAR   \n",
       "7228  -1.239132    5313       301        6        12  2.791195e+18  GALAXY   \n",
       "34292  0.931627    7712       301        5       221  1.273059e+19     QSO   \n",
       "\n",
       "       redshift  plate    MJD  fiber_ID  \n",
       "76074 -0.651146   1095  52521       420  \n",
       "59701  0.269980   3789  55269       999  \n",
       "73664 -0.651942   2864  54467       197  \n",
       "7228  -0.453333   2479  54174       324  \n",
       "34292  2.189334  11307  58430       152  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = RobustScaler().fit(stars_train[columns_to_clean])\n",
    "stars_train[columns_to_clean] = scaler.transform(stars_train[columns_to_clean])\n",
    "stars_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e0784af-5f29-45a8-b391-e82ffde052e8",
   "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",
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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>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>rerun_ID</th>\n",
       "      <th>cam_col</th>\n",
       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>MJD</th>\n",
       "      <th>fiber_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.342674</td>\n",
       "      <td>0.218320</td>\n",
       "      <td>0.781088</td>\n",
       "      <td>0.547752</td>\n",
       "      <td>0.847730</td>\n",
       "      <td>0.659242</td>\n",
       "      <td>1.057130</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.542278</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.541169</td>\n",
       "      <td>-0.074511</td>\n",
       "      <td>-0.819874</td>\n",
       "      <td>-1.109232</td>\n",
       "      <td>-1.255119</td>\n",
       "      <td>-1.287096</td>\n",
       "      <td>-1.406721</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>-0.477430</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.379617</td>\n",
       "      <td>-0.160517</td>\n",
       "      <td>1.079084</td>\n",
       "      <td>0.449480</td>\n",
       "      <td>0.175589</td>\n",
       "      <td>0.139040</td>\n",
       "      <td>0.019324</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>5.652162e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.050829</td>\n",
       "      <td>5020</td>\n",
       "      <td>55852</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1.411663</td>\n",
       "      <td>-0.398040</td>\n",
       "      <td>-0.402511</td>\n",
       "      <td>-0.739875</td>\n",
       "      <td>-0.904915</td>\n",
       "      <td>-0.897455</td>\n",
       "      <td>-0.967055</td>\n",
       "      <td>7773</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>123</td>\n",
       "      <td>5.702738e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>-0.421953</td>\n",
       "      <td>5065</td>\n",
       "      <td>55739</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>-1.689672</td>\n",
       "      <td>-0.593760</td>\n",
       "      <td>-0.535548</td>\n",
       "      <td>-0.217933</td>\n",
       "      <td>0.058517</td>\n",
       "      <td>0.243451</td>\n",
       "      <td>0.357378</td>\n",
       "      <td>7712</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>339</td>\n",
       "      <td>9.843824e+18</td>\n",
       "      <td>QSO</td>\n",
       "      <td>2.468444</td>\n",
       "      <td>8743</td>\n",
       "      <td>57663</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj_ID     alpha     delta         u         g         r         i  \\\n",
       "1        1 -0.342674  0.218320  0.781088  0.547752  0.847730  0.659242   \n",
       "4        4  1.541169 -0.074511 -0.819874 -1.109232 -1.255119 -1.287096   \n",
       "10      10  1.379617 -0.160517  1.079084  0.449480  0.175589  0.139040   \n",
       "13      13  1.411663 -0.398040 -0.402511 -0.739875 -0.904915 -0.897455   \n",
       "17      17 -1.689672 -0.593760 -0.535548 -0.217933  0.058517  0.243451   \n",
       "\n",
       "           z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "1   1.057130    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "4  -1.406721    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "10  0.019324    8102       301        3        27  5.652162e+18  GALAXY   \n",
       "13 -0.967055    7773       301        2       123  5.702738e+18  GALAXY   \n",
       "17  0.357378    7712       301        5       339  9.843824e+18     QSO   \n",
       "\n",
       "    redshift  plate    MJD  fiber_ID  \n",
       "1   0.542278  10445  58158       427  \n",
       "4  -0.477430   6121  56187       842  \n",
       "10  0.050829   5020  55852       525  \n",
       "13 -0.421953   5065  55739       200  \n",
       "17  2.468444   8743  57663       295  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = RobustScaler().fit(stars_test[columns_to_clean])\n",
    "stars_test[columns_to_clean] = scaler.transform(stars_test[columns_to_clean])\n",
    "stars_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960e010a-987a-454a-8c23-575c99056702",
   "metadata": {},
   "source": [
    "## Selecting the relevant features\n",
    "\n",
    "To train the models, the group has selected the following features (subject to change): `u`, `g`, `r`, `i`, `z` and `redshift`. So, these features must be isolated, along with the `obj_ID` which we will use as the index, and the `class` column which we will use as the labels. The classes will also be transformed to numerical representations of the data:\n",
    "\n",
    "Galaxy: 0 | Star: 1 | QSO: 2\n",
    "\n",
    "Our resulting dataset looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07129cb4-a90a-4ceb-9301-ce66516fcddc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HTMI\\AppData\\Local\\Temp\\ipykernel_17836\\3313585080.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  stars_df['class'] = stars_df['class'].map(labels)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>redshift</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obj_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>22.16759</td>\n",
       "      <td>22.97586</td>\n",
       "      <td>21.90404</td>\n",
       "      <td>21.30548</td>\n",
       "      <td>20.73569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>22.69118</td>\n",
       "      <td>22.38628</td>\n",
       "      <td>20.45003</td>\n",
       "      <td>19.75759</td>\n",
       "      <td>19.41526</td>\n",
       "      <td>0.404895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>21.16916</td>\n",
       "      <td>19.26997</td>\n",
       "      <td>18.20428</td>\n",
       "      <td>17.69034</td>\n",
       "      <td>17.35221</td>\n",
       "      <td>0.143366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>25.35039</td>\n",
       "      <td>21.63757</td>\n",
       "      <td>19.91386</td>\n",
       "      <td>19.07254</td>\n",
       "      <td>18.62482</td>\n",
       "      <td>0.455040</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>22.62171</td>\n",
       "      <td>21.79745</td>\n",
       "      <td>20.60115</td>\n",
       "      <td>20.00959</td>\n",
       "      <td>19.28075</td>\n",
       "      <td>0.542944</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>99999 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               u         g         r         i         z  redshift  class\n",
       "obj_ID                                                                   \n",
       "0       23.87882  22.27530  20.39501  19.16573  18.79371  0.634794      0\n",
       "1       24.77759  22.83188  22.58444  21.16812  21.61427  0.779136      0\n",
       "2       25.26307  22.66389  20.60976  19.34857  18.94827  0.644195      0\n",
       "3       22.13682  23.77656  21.61162  20.50454  19.25010  0.932346      0\n",
       "4       19.43718  17.58028  16.49747  15.97711  15.54461  0.116123      0\n",
       "...          ...       ...       ...       ...       ...       ...    ...\n",
       "99994   22.16759  22.97586  21.90404  21.30548  20.73569  0.000000      0\n",
       "99995   22.69118  22.38628  20.45003  19.75759  19.41526  0.404895      0\n",
       "99996   21.16916  19.26997  18.20428  17.69034  17.35221  0.143366      0\n",
       "99997   25.35039  21.63757  19.91386  19.07254  18.62482  0.455040      0\n",
       "99998   22.62171  21.79745  20.60115  20.00959  19.28075  0.542944      0\n",
       "\n",
       "[99999 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_use = ['obj_ID', 'u', 'g', 'r', 'i', 'z', 'redshift', 'class']\n",
    "columns_data = ['u','g','r','i','z','redshift']\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "stars_df = stars[columns_to_use]\n",
    "stars_df['class'] = stars_df['class'].map(labels)\n",
    "\n",
    "stars_df.set_index('obj_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36a60b3-1019-486a-9a02-e075c615cd3d",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b656dcf-5df8-44ff-a4f9-54b63332c5be",
   "metadata": {},
   "source": [
    "## Central Tendencies\n",
    "\n",
    "To gain a better understanding of the data, we can begin with examining the central tendencies of the data. These would be the mean, median and mode."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824f0aaf-d971-4263-9d20-f371880f7011",
   "metadata": {},
   "source": [
    "### Means\n",
    "\n",
    "For the means, we can take the means of each feature, pertaining to each class and compare them to one another. Putting the means together in a table, it would look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f136c20-b58b-44ba-9d1a-b438f2786896",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class         GALAXY         QSO        STAR\n",
      "alpha     177.954727  180.474314  174.232180\n",
      "delta      23.513911   26.808621   23.499671\n",
      "u          22.587379   21.547619   21.153830\n",
      "g          20.906101   20.926193   19.617142\n",
      "r          19.587552   20.624089   18.947005\n",
      "i          18.851990   20.431173   18.543760\n",
      "z          18.449156   20.266732   18.334295\n",
      "redshift    0.421596    1.719676   -0.000115\n",
      "\n",
      "\n",
      "class         GALAXY         QSO        STAR\n",
      "alpha     177.954727  180.474314  174.232180\n",
      "delta      23.513911   26.808621   23.499671\n",
      "u          22.587379   21.547619   21.153830\n",
      "g          20.906101   20.926193   19.617142\n",
      "r          19.587552   20.624089   18.947005\n",
      "i          18.851990   20.431173   18.543760\n",
      "z          18.449156   20.266732   18.334295\n",
      "redshift    0.421596    1.719676   -0.000115\n"
     ]
    }
   ],
   "source": [
    "means_df = og_stars.groupby('class').mean()\n",
    "means_df = means_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "normalized_means_df = stars.groupby('class').mean()\n",
    "normalized_means_df = normalized_means_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "print(means_df)\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(normalized_means_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668f9a35-147c-49ca-b3f5-59e8f7ab2314",
   "metadata": {},
   "source": [
    "We can then graph it to a bar plot to make it easier to compare to one another. It would look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8ce9bdf8-af0d-4caf-92f9-3eeace0b3f5b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=len(means_df), figsize=(8, 4 * len(means_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(means_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row {index}')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df2ba4a-5bb1-4a32-aa69-037bfedd9be1",
   "metadata": {},
   "source": [
    "Analysis goes here, will add later\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "276abc7e-d7d2-4f8a-a860-3b9d10eea5e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class    GALAXY       QSO      STAR\n",
      "0                                  \n",
      "u      24.63466  24.63466  24.63465\n",
      "g      22.41741  20.03421  25.11437\n",
      "r      20.72245  19.61753  24.80203\n",
      "i      17.35948  19.99037  24.36180\n",
      "z      22.82690  22.82690  22.82690\n",
      "\n",
      "\n",
      "class    GALAXY       QSO      STAR\n",
      "0                                  \n",
      "u      24.63466  24.63466  24.63465\n",
      "g      22.41741  20.03421  25.11437\n",
      "r      20.72245  19.61753  24.80203\n",
      "i      17.35948  19.99037  24.36180\n",
      "z      22.82690  22.82690  22.82690\n"
     ]
    }
   ],
   "source": [
    "mode_df = og_stars.groupby('class').apply(lambda x: x.mode().iloc[0])\n",
    "mode_df = mode_df[['u','g','r','i','z','redshift']].T\n",
    "\n",
    "normalized_mode_df = stars.groupby('class').apply(lambda x: x.mode().iloc[0])\n",
    "normalized_mode_df = normalized_mode_df[['u','g','r','i','z','redshift']].T\n",
    "\n",
    "print(mode_df.head())\n",
    "print(\"\\n\")\n",
    "print(normalized_mode_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd559f69-6d9f-4f7e-bc21-4f09a188829e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jx4/r/PnzeuaZZ5SSkqJjx44pISFBgwcPVrNmzdSvX7+yxgYAAABQgcpcLNLS0hQSEiJJ+vDDD9W2bVslJydrxYoViouLK9N77dq1S7fccotuueUWSVJUVJRuueUWTZs2Ta6urkpNTdVdd92l5s2ba8yYMerYsaOSkpKYkQAAAAAcTJkvhcrPz7f9YL9x40bdddddkqSWLVsqMzOzTO8VFhamwsLCYsfXr19f1ngAAAAATFDmGYs2bdpo0aJFSkpK0oYNG9S/f39J0smTJ1WvXr1yDwgAAADA8ZW5WMyePVtvv/22wsLCNGLECLVv316SFB8fb7tECgAAAEDVUuZLocLCwnTq1Cnl5uaqbt26tuPjxo1TzZo1yzUcAAAAAOdwTc+xKCws1O7du/X222/r3LlzkiR3d3eKBQAAAFBFlXnG4ocfflD//v11/PhxWa1W9enTR56enpo9e7asVqsWLVpUETkBAAAAOLAyz1g89dRT6tSpk3755RfVqFHDdvzuu+9WQkJCuYYDAAAA4BzKPGORlJSk5ORkubu72x1v0qSJfvzxx3ILBgAAAMB5lHnGoqCgQJcvX77i+IkTJ+Tp6VkuoQAAAAA4lzIXi759+yo2Nta2b7FYdP78eU2fPl0DBgwoz2wAAAAAnESZL4V644031K9fP7Vu3VoXLlzQyJEjdfjwYfn6+mrVqlUVkREAAACAgytzsQgMDNTevXu1evVqpaam6vz58xozZozuv/9+u8XcAAAAAKqOMhcLSXJzc9MDDzxQ3lkAAAAAOKkyF4vly5eXOP7ggw9ecxgAAAAAzqnMxeKpp56y28/Pz9evv/5qe/I2xQIAAACoesp8V6hffvnFbjt//rwOHjyoHj16sHgbAAAAqKLKXCyKctNNN+mVV165YjYDAAAAQNVQLsVC+n1B98mTJ8vr7QAAAAA4kTKvsYiPj7fbLywsVGZmpubNm6fu3buXWzAAAAAAzqPMxSIyMtJu32KxqH79+urdu7feeOON8soFAAAAwImUuVgUFBRURA4AAAAATqzc1lgAAAAAqLpKNWMRFRVV6jd88803rzkMAAAAAOdUqmLx7bfflurNLBaLoTAAAAAAnFOpisXmzZsrOgcAAAAAJ8YaCwAAAACGlfmuUJK0a9cuffjhhzp+/LguXrxoN/bRRx+VSzAAAAAAzqPMMxarV69Wt27dtH//fq1du1b5+fnat2+fNm3aJG9v74rICAAAAMDBlblYvPzyy3rrrbf06aefyt3dXXPmzNGBAwc0fPhwNWrUqCIyAgAAAHBwZS4W6enpuvPOOyVJ7u7uysvLk8Vi0aRJk/TOO++Ue0AAAAAAjq/MxaJu3bo6d+6cJOmGG25QWlqaJOns2bP69ddfyzcdAAAAAKdQ6mLxR4Ho2bOnNmzYIEkaNmyYnnrqKY0dO1YjRozQHXfcUTEpAQAAADi0Ut8Vql27durcubMiIyM1bNgwSdKUKVNUrVo1JScna+jQoZo6dWqFBQUAAADguEpdLBITE7V06VLFxMTopZde0tChQ/XII4/oueeeq8h8AAAAAJxAqS+FCg0N1ZIlS5SZmal//OMfOnbsmG6//XY1b95cs2fPVlZWVkXmBAAAAODAyrx4u1atWnrooYeUmJioQ4cOadiwYZo/f74aNWqku+66qyIyAgAAAHBwZS4Wf9asWTP9/e9/19SpU+Xp6anPP/+8vHIBAAAAcCKlXmPxV1u3btWSJUv073//Wy4uLho+fLjGjBlTntkAAAAAOIkyFYuTJ08qLi5OcXFxOnLkiLp166a5c+dq+PDhqlWrVkVlBAAAAODgSl0sIiIitHHjRvn6+urBBx/Uww8/rBYtWlRkNgAAAABOotTFolq1avrXv/6lgQMHytXVtSIzAQAAAHAypS4W8fHxFZkDAAAAgBMzdFcoAAAAAJAoFgAAAADKganFYuvWrRo0aJACAgJksVj08ccf240XFhZq2rRpatiwoWrUqKHw8HAdPnzYnLAAAAAAimVqscjLy1P79u01f/78IsdfffVVzZ07V4sWLdJXX32lWrVqqV+/frpw4UIlJwUAAABQkmt+QF55iIiIUERERJFjhYWFio2N1dSpUzV48GBJ0vLly+Xn56ePP/5Y9913X2VGBQAAAFACh11jcfToUWVlZSk8PNx2zNvbW126dNGOHTuKfZ3ValVubq7dBgAAAKBiOWyxyMrKkiT5+fnZHffz87ONFSUmJkbe3t62LSgoqEJzAgAAAHDgYnGtoqOjlZOTY9syMjLMjgQAAABc9xy2WPj7+0uSsrOz7Y5nZ2fbxori4eEhLy8vuw0AAABAxXLYYhEcHCx/f38lJCTYjuXm5uqrr75S165dTUwGAAAA4K9MvSvU+fPndeTIEdv+0aNHtWfPHvn4+KhRo0aaOHGiXnzxRd10000KDg7W888/r4CAAEVGRpoXGgAAAMAVTC0Wu3btUq9evWz7UVFRkqRRo0YpLi5Ozz77rPLy8jRu3DidPXtWPXr00Lp161S9enWzIgMAAAAogqnFIiwsTIWFhcWOWywWzZw5UzNnzqzEVAAAAADKymHXWAAAAABwHhQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhjl0sXjhhRdksVjstpYtW5odCwAAAMBfuJkd4GratGmjjRs32vbd3Bw+MgAAAFDlOPxP6W5ubvL39zc7BgAAAIASOPSlUJJ0+PBhBQQEqGnTprr//vt1/PjxEs+3Wq3Kzc212wAAAABULIcuFl26dFFcXJzWrVunhQsX6ujRowoNDdW5c+eKfU1MTIy8vb1tW1BQUCUmBgAAAKomhy4WERERGjZsmNq1a6d+/frpiy++0NmzZ/Xhhx8W+5ro6Gjl5OTYtoyMjEpMDAAAAFRNDr/G4s/q1Kmj5s2b68iRI8We4+HhIQ8Pj0pMBQAAAMChZyz+6vz580pPT1fDhg3NjgIAAADgTxy6WDz99NNKTEzUsWPHlJycrLvvvluurq4aMWKE2dEAAAAA/IlDXwp14sQJjRgxQqdPn1b9+vXVo0cPpaSkqH79+mZHAwAAAPAnDl0sVq9ebXYEAAAAAKXg0JdCAQAAAHAOFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGOUWxmD9/vpo0aaLq1aurS5cu2rlzp9mRAAAAAPyJwxeLf/7zn4qKitL06dP1zTffqH379urXr59++ukns6MBAAAA+D8OXyzefPNNjR07Vg899JBat26tRYsWqWbNmlqyZInZ0QAAAAD8HzezA5Tk4sWL2r17t6Kjo23HXFxcFB4erh07dhT5GqvVKqvVatvPycmRJOXm5lZsWAdSYP3V7AioYFXp73NVxHf4+sd3+PrGd/j6V5W+w3981sLCwque69DF4tSpU7p8+bL8/Pzsjvv5+enAgQNFviYmJkYzZsy44nhQUFCFZATM4B1rdgIARvAdBpxbVfwOnzt3Tt7e3iWe49DF4lpER0crKirKtl9QUKAzZ86oXr16slgsJiZDRcjNzVVQUJAyMjLk5eVldhwAZcR3GHBufIevf4WFhTp37pwCAgKueq5DFwtfX1+5uroqOzvb7nh2drb8/f2LfI2Hh4c8PDzsjtWpU6eiIsJBeHl58R80wInxHQacG9/h69vVZir+4NCLt93d3dWxY0clJCTYjhUUFCghIUFdu3Y1MRkAAACAP3PoGQtJioqK0qhRo9SpUyeFhIQoNjZWeXl5euihh8yOBgAAAOD/OHyxuPfee/Xzzz9r2rRpysrKUocOHbRu3borFnSjavLw8ND06dOvuPwNgHPgOww4N77D+DNLYWnuHQUAAAAAJXDoNRYAAAAAnAPFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCTmPBggVmRwAAAMXIzMzUhAkTzI4BE3G7WTgNHx8fde7cWUuXLlVAQIDZcQAY8PXXX2vVqlU6dOiQJKl58+YaOXKkOnXqZHIyACXZt2+fNm/eLHd3dw0fPlx16tTRqVOn9NJLL2nRokVq2rSp9u3bZ3ZMmIQZCziNtLQ0ubm5qW3btvrggw/MjgPgGj377LPq0qWL3nvvPZ04cUInTpzQu+++qy5dumjy5MlmxwNQjPj4eN1yyy363//9Xz322GPq1KmTNm/erFatWmn//v1au3YtpaKKY8YCTicuLk5RUVHq1auXpkyZIjc3+wfIt2vXzqRkAK5m2bJleuyxx/Taa6/p0UcfVbVq1SRJ+fn5WrhwoSZPnqy3335bDz74oMlJAfxVSEiIunfvrlmzZum9995TVFSU2rRpoyVLlqhz585mx4MDoFjAKW3cuFH9+/dXYWGhCgsLZbFYbP97+fJls+MBKEZISIhGjBihSZMmFTn+5ptvavXq1dq5c2clJwNwNd7e3tq9e7eaNWumy5cvy8PDQ+vWrVN4eLjZ0eAguBQKTufNN9/U4MGD9cADD+jQoUM6evSo/vvf/9r+F4Dj2rdvnwYPHlzseGRkJJdSAA7q3Llz8vLykiS5urqqRo0aatq0qcmp4Ejcrn4K4Bj++9//atSoUTp8+LBWrlxZ4g8nAByTq6urLl68WOx4fn6+XF1dKzERgLJYv369vL29JUkFBQVKSEhQWlqa3Tl33XWXGdHgALgUCk6jdu3a6t+/vxYtWiRfX1+z4wC4BmFhYQoNDdWsWbOKHJ86daq2bdumLVu2VG4wAFfl4nL1C124JLlqY8YCTmPRokV64IEHzI4BwICnn35akZGRslqt+tvf/iY/Pz9JUlZWlt544w3FxsZq7dq1JqcEUJSCggKzI8DBscYCTsNqtZY4fu7cOT3yyCOVlAbAtRg4cKDeeustzZkzRwEBAfLx8ZGPj49uuOEGzZ07V6+//roGDhxodkwA16CgoECfffaZ2TFgIi6FgtPw9vZWaGio3nvvPfn7+9uNrV+/XmPHjlXdunW1d+9ekxICKK0TJ05ozZo1Onz4sKTfH5A3dOhQBQUFmZwMQFkdOXJES5YsUVxcnH7++Wfl5+ebHQkmoVjAaRw7dkwPPfSQUlNTNW/ePI0YMULnzp3TxIkT9f777+vpp5/WjBkzbPfFBwAAFeO3337TmjVr9N5772n79u0KDQ3Vfffdp7vvvtt2iSOqHooFnE5sbKymTp2qsLAwfffdd6pdu7bi4uJ4OA/gBA4dOqSzZ88qJCTEdiwhIUEvvvii8vLyFBkZqb///e8mJgRQkq+//lrvvfeeVq9erRtvvFH333+/Jk+erNTUVLVu3drseDAZayzgdB599FH17dtXX3zxhc6cOaPVq1dTKgAnMXnyZLtrsI8ePapBgwbJ3d1dXbt2VUxMjGJjY80LCKBY7dq107Bhw1SvXj0lJyfrm2++0d/+9jdZLBazo8FBUCzgVLZv36727dvrwIEDWrdunSIiItS1a1fNmTPH7GgASmHXrl2KiIiw7a9YsULNmzfX+vXrNWfOHMXGxiouLs68gACKdfDgQfXs2VO9evVidgJFoljAafztb39T7969NWjQIH3zzTfq27evPvzwQy1evFgvvviiwsLCdPToUbNjAijBqVOnFBgYaNvfvHmzBg0aZNsPCwvTsWPHTEgG4Gr++9//qkWLFnr88ccVGBiop59+Wt9++y0zFrChWMBpfPLJJ9q4caPeeOMNVa9e3Xb83nvvVVpamry9vdWuXTsTEwK4Gh8fH2VmZkr6/daUu3bt0m233WYbv3jxolj6BzimG264QVOmTNGRI0f0/vvvKysrS927d9elS5cUFxenQ4cOmR0RJqNYwGmkpqYqNDS0yDE/Pz998sknWrBgQSWnAlAWYWFhmjVrljIyMhQbG6vLly8rLCzMNv7999+rSZMmpuUDULytW7fq0qVLkqTevXvrgw8+UGZmpubNm6dNmzapZcuW/IKviqNYwGnUrFmzxPH9+/fzDAvAwb300kvav3+/GjdurGeffVavvvqqatWqZRt///331bt3bxMTAihOr169dObMGbtj3t7eeuKJJ7Rr1y598803dr8oQNXD7Wbh1PLy8rR69WotXrxYKSkpat26tdLS0syOBaAEly5d0r59+1S/fn25u7vL3d1dXl5ekqS9e/cqMDBQ9erVMzklgL9ycXFRVlaWGjRoYHYUOChmLOCUtm/frocfflh+fn4aN26cunXrpu+//55SATiB8+fP65133lG7du3k5+enunXryt/fX9HR0brpppsoFYADY6E2SsKMBZzGTz/9pLi4OC1ZskQ5OTkaMWKERo4cqa5du2rv3r3c+g5wAmfOnFHXrl31448/6v7771erVq0k/b62YuXKlWrZsqW2bdum1NRUpaSk6H//939NTgzgDy4uLoqIiJCHh0eJ53300UeVlAiOxs3sAEBpNW7cWPfcc4/mzJmjPn36yMWFCTfA2cycOVPu7u5KT0+Xn5/fFWN9+/bV//zP/+jLL7/U3LlzTUoJoDienp6qUaOG2THgoCgWcBqNGzfWtm3b1KhRIzVu3FgtW7Y0OxKAMvr444/19ttvX1EqJMnf31+vvvqqBgwYoOnTp2vUqFEmJARQkrlz57LGAsXiV75wGgcOHLDd2q5z587q2LGj3nrrLUlc8wk4i8zMTLVp06bY8bZt28rFxUXTp0+vxFQASoP/r8XVUCzgVLp3764lS5YoMzNTjz32mNasWaPLly/riSee0Lvvvquff/7Z7IgASuDr61vik7WPHj3Kb0MBB8WyXFwNxQJOqXbt2ho7dqySk5O1b98+dezYUVOnTlVAQIDZ0QCUoF+/fpoyZYouXrx4xZjVatXzzz+v/v37m5AMwNW8+OKL2rlzp92x5cuXKzg4WA0aNNC4ceNktVpNSgdHwF2hcN24dOmS4uPjNWTIELOjACjGiRMn1KlTJ3l4eGj8+PFq2bKlCgsLtX//fi1YsEBWq1Vff/21GjVqZHZUAH/Rv39/9erVS5MnT5Ykfffdd7r11ls1evRotWrVSq+99poeffRRvfDCC+YGhWkoFrhuHDhwQHfddZcOHTpkdhQAJTh69KieeOIJffnll7ZLKywWi/r06aN58+apWbNmJicEUJSGDRvq008/VadOnSRJU6ZMUWJiorZt2yZJWrNmjaZPn67vv//ezJgwEXeFwnXDarUqPT3d7BgAriI4OFj/+c9/9Msvv+jw4cOSpGbNmsnHx8fkZABK8ssvv9jd0S0xMVERERG2/c6dOysjI8OMaHAQrLEAAJiibt26CgkJUUhICKUCcAJ+fn46evSoJOnixYv65ptvdNttt9nGz507p2rVqpkVDw6AYgEAAICrGjBggJ577jklJSUpOjpaNWvWVGhoqG08NTVVN954o4kJYTYuhQIAAMBVzZo1S0OGDNHtt9+u2rVra9myZXJ3d7eNL1myRH379jUxIczG4m04jbp165b4cJ5Lly4pLy9Ply9frsRUAABULTk5Oapdu7ZcXV3tjp85c0a1a9e2KxuoWpixgNOIjY01OwIAAFWet7d3kcdZKwVmLHBduXz58hW/QQEAAEDFY/E2rguHDh3S5MmTFRgYaHYUAACAKoliAaf166+/aunSpQoNDVXr1q2VmJioqKgos2MBAABUSayxgNNJSUnRe++9pzVr1qhRo0bav3+/Nm/ebHfLOwAAAFQuZizgNN544w21adNG99xzj+rWrautW7fqu+++k8ViUb169cyOBwAAUKWxeBtOw83NTZMnT9bMmTPtFmhXq1ZNe/fuVevWrU1MBwAAULUxYwGnMWvWLK1Zs0bBwcGaPHmy0tLSzI4EAACA/0OxgNOIjo7WoUOH9P777ysrK0tdunRR+/btVVhYqF9++cXseAAAAFUal0LBaeXm5mrVqlVavHixdu/erS5duuiee+7hzlAAAAAmoFjgupCWlqbFixdrxYoV+umnn8yOAwAAUOVwu1k4jd9++00JCQkaOHCgpN8vjbJarbZxNzc3paenmxUPAACgSqNYwGksW7ZMn3/+ua1YzJs3T23atFGNGjUkSQcPHlRAQIAmTZpkZkwAAIAqiUuh4DRCQ0P17LPPatCgQZIkT09P7d27V02bNpUkffDBB5o/f7527NhhZkwAAIAqibtCwWkcOXJEN998s22/evXqcnH5/3+FQ0JC9P3335sRDQAAoMrjUig4jbNnz9qtqfj555/txgsKCuzGAQAAUHmYsYDTCAwMLPGheKmpqQoMDKzERAAAAPgDxQJOY8CAAZo2bZouXLhwxdhvv/2mGTNm6M477zQhGQAAAFi8DaeRnZ2tDh06yN3dXRMmTFDz5s0l/X43qHnz5unSpUv69ttv5efnZ3JSAACAqodiAady9OhRPf7449qwYYP++KtrsVjUp08fLViwwHaHKAAAAFQuigWc0pkzZ3TkyBFJUrNmzeTj42NyIgAAgKqNYgEAAADAMBZvAwAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAACrMsWPHZLFYtGfPHrOjAAAqGMUCAAAAgGEUCwAAAACGUSwAAIYVFBTo1VdfVbNmzeTh4aFGjRrppZdeuuK8y5cva8yYMQoODlaNGjXUokULzZkzx+6cLVu2KCQkRLVq1VKdOnXUvXt3/fDDD5KkvXv3qlevXvL09JSXl5c6duyoXbt2VcpnBACUzM3sAAAA5xcdHa13331Xb731lnr06KHMzEwdOHDgivMKCgoUGBioNWvWqF69ekpOTta4cePUsGFDDR8+XJcuXVJkZKTGjh2rVatW6eLFi9q5c6csFosk6f7779ctt9yihQsXytXVVXv27FG1atUq++MCAIrAk7cBAIacO3dO9evX17x58/TII4/YjR07dkzBwcH69ttv1aFDhyJfP2HCBGVlZelf//qXzpw5o3r16mnLli26/fbbrzjXy8tL//jHPzRq1KiK+CgAAAO4FAoAYMj+/ftltVp1xx13lOr8+fPnq2PHjqpfv75q166td955R8ePH5ck+fj4aPTo0erXr58GDRqkOXPmKDMz0/baqKgoPfLIIwoPD9crr7yi9PT0CvlMAICyo1gAAAypUaNGqc9dvXq1nn76aY0ZM0Zffvml9uzZo4ceekgXL160nbN06VLt2LFD3bp10z//+U81b95cKSkpkqQXXnhB+/bt05133qlNmzapdevWWrt2bbl/JgBA2XEpFADAkAsXLsjHx0dz58696qVQTz75pL7//nslJCTYzgkPD9epU6eKfdZF165d1blzZ82dO/eKsREjRigvL0/x8fHl+pkAAGXHjAUAwJDq1atr8uTJevbZZ7V8+XKlp6crJSVFixcvvuLcm266Sbt27dL69et16NAhPf/88/r6669t40ePHlV0dLR27NihH374QV9++aUOHz6sVq1a6bffftOECRO0ZcsW/fDDD9q+fbu+/vprtWrVqjI/LgCgGNwVCgBg2PPPPy83NzdNmzZNJ0+eVMOGDfXYY49dcd6jjz6qb7/9Vvfee68sFotGjBihJ554Qv/5z38kSTVr1tSBAwe0bNkynT59Wg0bNtT48eP16KOP6tKlSzp9+rQefPBBZWdny9fXV0OGDNGMGTMq++MCAIrApVAAAAAADONSKAAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAJQoLi5OFovFtrm5uemGG27Q6NGj9eOPP5odDwDgINzMDgAAcA4zZ85UcHCwLly4oJSUFMXFxWnbtm1KS0tT9erVzY4HADAZxQIAUCoRERHq1KmTJOmRRx6Rr6+vZs+erfj4eA0fPtzkdAAAs3EpFADgmoSGhkqS0tPT7Y5v2rRJoaGhqlWrlurUqaPBgwdr//79tvHU1FRZLBbFx8fbju3evVsWi0W33nqr3XtFRESoS5cuV82yZs0atW7dWtWrV1fbtm21du1ajR49Wk2aNDHwCQEAZUGxAABck2PHjkmS6tatazu2ceNG9evXTz/99JNeeOEFRUVFKTk5Wd27d7ed37ZtW9WpU0dbt261vS4pKUkuLi7au3evcnNzJUkFBQVKTk5Wz549S8zx+eef695771W1atUUExOjIUOGaMyYMdq9e3f5fmAAQIm4FAoAUCo5OTk6deqULly4oK+++kozZsyQh4eHBg4caDvnmWeekY+Pj3bs2CEfHx9JUmRkpG655RZNnz5dy5Ytk4uLi7p3766kpCTb65KSkhQZGalPPvlEycnJ6t+/v61k/DEzUpzo6GjdcMMN2r59u2rXri1JuuOOOxQWFqbGjRtXwL8JAEBRmLEAAJRKeHi46tevr6CgIN1zzz2qVauW4uPjFRgYKEnKzMzUnj17NHr0aFupkKR27dqpT58++uKLL2zHQkND9c033ygvL0+StG3bNg0YMEAdOnSwFY6kpCRZLBb16NGj2EwnT57Ud999pwcffNBWKiTp9ttv180331yunx8AUDKKBQCgVObPn68NGzboX//6lwYMGKBTp07Jw8PDNv7DDz9Iklq0aHHFa1u1aqVTp07ZikRoaKguXbqkHTt26ODBg/rpp58UGhqqnj172hWL1q1b25WUv/rjz2zWrNkVY0UdAwBUHC6FAgCUSkhIiO2uUJGRkerRo4dGjhypgwcP2s0WlEanTp1UvXp1bd26VY0aNVKDBg3UvHlzhYaGasGCBbJarUpKStLdd99dER8FAFABmLEAAJSZq6urYmJidPLkSc2bN0+SbOsZDh48eMX5Bw4ckK+vr2rVqiVJcnd3V0hIiJKSkpSUlGRbRxEaGiqr1aoVK1YoOzv7qgu3//gzjxw5csVYUccAABWHYgEAuCZhYWEKCQlRbGysLly4oIYNG6pDhw5atmyZzp49azsvLS1NX375pQYMGGD3+tDQUH311VfavHmzrVj4+vqqVatWmj17tu2ckgQEBKht27Zavny5zp8/bzuemJio7777rpw+KQCgNCgWAIBr9swzzyg7O1txcXGSpNdee02nT59W165d9frrr2vWrFnq3bu3vL299cILL9i9NjQ0VL/99psyMjLsCkTPnj116NAhNWnSxLYwvCQvv/yyfvzxR3Xv3l2xsbGaPn26hgwZorZt28pisZTnxwUAlIBiAQC4ZkOGDNGNN96o119/XZcvX1Z4eLjWrVunevXqadq0aXr99dd12223afv27QoODrZ7bbdu3eTq6ipPT0+1b9/edvzPl0WVxqBBg7Rq1SpdvHhRzz33nD766CPFxcWpRYsWql69evl9WABAiSyFhYWFZocAAKC8dejQQfXr19eGDRvMjgIAVQIzFgAAp5afn69Lly7ZHduyZYv27t2rsLAwc0IBQBXEjAUAwKkdO3ZM4eHheuCBBxQQEKADBw5o0aJF8vb2VlpamurVq2d2RACoEniOBQDAqdWtW1cdO3bUe++9p59//lm1atXSnXfeqVdeeYVSAQCViBkLAAAAAIaxxgIAAACAYRQLAAAAAIZd92ssCgoKdPLkSXl6evKgJAAAAKAMCgsLde7cOQUEBMjFpeQ5ieu+WJw8eVJBQUFmxwAAAACcVkZGhgIDA0s857ovFp6enpJ+/5fh5eVlchoAAADAeeTm5iooKMj2M3VJrvti8cflT15eXhQLAAAA4BqUZkkBi7cBAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhphaLmJgYde7cWZ6enmrQoIEiIyN18OBBu3PCwsJksVjstscee8ykxAAAAACKYmqxSExM1Pjx45WSkqINGzYoPz9fffv2VV5ent15Y8eOVWZmpm179dVXTUoMAAAAoCimPnl73bp1dvtxcXFq0KCBdu/erZ49e9qO16xZU/7+/pUdDwAAAEApmVos/ionJ0eS5OPjY3d8xYoV+uCDD+Tv769Bgwbp+eefV82aNYt8D6vVKqvVatvPzc2tuMAAAAB/0eS5z82OgAp27JU7zY7gkBymWBQUFGjixInq3r272rZtazs+cuRINW7cWAEBAUpNTdXkyZN18OBBffTRR0W+T0xMjGbMmFFZsQEAAABIshQWFhaaHUKSHn/8cf3nP//Rtm3bFBgYWOx5mzZt0h133KEjR47oxhtvvGK8qBmLoKAg5eTkyMvLq0KyAwAA/IEZi+tfVZqxyM3Nlbe3d6l+lnaIGYsJEybos88+09atW0ssFZLUpUsXSSq2WHh4eMjDw6NCcgIAAAAomqnForCwUE8++aTWrl2rLVu2KDg4+Kqv2bNnjySpYcOGFZwOAAAAQGmZWizGjx+vlStX6pNPPpGnp6eysrIkSd7e3qpRo4bS09O1cuVKDRgwQPXq1VNqaqomTZqknj17ql27dmZGBwAAAPAnphaLhQsXSvr9IXh/tnTpUo0ePVru7u7auHGjYmNjlZeXp6CgIA0dOlRTp041IS0AAACA4ph+KVRJgoKClJiYWElpAAAAAFwrU5+8DQAAAOD6QLEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGuZkdAOWvyXOfmx0BFezYK3eaHQEAAMAOMxYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAw0wtFjExMercubM8PT3VoEEDRUZG6uDBg3bnXLhwQePHj1e9evVUu3ZtDR06VNnZ2SYlBgAAAFAUU4tFYmKixo8fr5SUFG3YsEH5+fnq27ev8vLybOdMmjRJn376qdasWaPExESdPHlSQ4YMMTE1AAAAgL9yM/MPX7dund1+XFycGjRooN27d6tnz57KycnR4sWLtXLlSvXu3VuStHTpUrVq1UopKSm67bbbzIgNAAAA4C8cao1FTk6OJMnHx0eStHv3buXn5ys8PNx2TsuWLdWoUSPt2LGjyPewWq3Kzc212wAAAABULFNnLP6soKBAEydOVPfu3dW2bVtJUlZWltzd3VWnTh27c/38/JSVlVXk+8TExGjGjBkVHRcAKkyT5z43OwIq2LFX7jQ7AgCUO4eZsRg/frzS0tK0evVqQ+8THR2tnJwc25aRkVFOCQEAAAAUxyFmLCZMmKDPPvtMW7duVWBgoO24v7+/Ll68qLNnz9rNWmRnZ8vf37/I9/Lw8JCHh0dFRwYAAADwJ6bOWBQWFmrChAlau3atNm3apODgYLvxjh07qlq1akpISLAdO3jwoI4fP66uXbtWdlwAAAAAxTB1xmL8+PFauXKlPvnkE3l6etrWTXh7e6tGjRry9vbWmDFjFBUVJR8fH3l5eenJJ59U165duSMUAAAA4EBMLRYLFy6UJIWFhdkdX7p0qUaPHi1Jeuutt+Ti4qKhQ4fKarWqX79+WrBgQSUnBQAAAFASU4tFYWHhVc+pXr265s+fr/nz51dCIgAAAADXwmHuCgUAAADAeVEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGmVostm7dqkGDBikgIEAWi0Uff/yx3fjo0aNlsVjstv79+5sTFgAAAECxTC0WeXl5at++vebPn1/sOf3791dmZqZtW7VqVSUmBAAAAFAabmb+4REREYqIiCjxHA8PD/n7+1dSIgAAAADXoswzFhkZGTpx4oRtf+fOnZo4caLeeeedcg32hy1btqhBgwZq0aKFHn/8cZ0+fbrE861Wq3Jzc+02AAAAABWrzMVi5MiR2rx5syQpKytLffr00c6dOzVlyhTNnDmzXMP1799fy5cvV0JCgmbPnq3ExERFRETo8uXLxb4mJiZG3t7eti0oKKhcMwEAAAC4UpmLRVpamkJCQiRJH374odq2bavk5GStWLFCcXFx5Rruvvvu01133aWbb75ZkZGR+uyzz/T1119ry5Ytxb4mOjpaOTk5ti0jI6NcMwEAAAC4UpmLRX5+vjw8PCRJGzdu1F133SVJatmypTIzM8s33V80bdpUvr6+OnLkSLHneHh4yMvLy24DAAAAULHKXCzatGmjRYsWKSkpSRs2bLDd/vXkyZOqV69euQf8sxMnTuj06dNq2LBhhf45AAAAAMqmzMVi9uzZevvttxUWFqYRI0aoffv2kqT4+HjbJVKldf78ee3Zs0d79uyRJB09elR79uzR8ePHdf78eT3zzDNKSUnRsWPHlJCQoMGDB6tZs2bq169fWWMDAAAAqEBlvt1sWFiYTp06pdzcXNWtW9d2fNy4capZs2aZ3mvXrl3q1auXbT8qKkqSNGrUKC1cuFCpqalatmyZzp49q4CAAPXt21ezZs2yXYoFAAAAwDFc03MsCgsLtXv3bqWnp2vkyJHy9PSUu7t7mYtFWFiYCgsLix1fv379tcQDAAAAUMnKXCx++OEH9e/fX8ePH5fValWfPn3k6emp2bNny2q1atGiRRWREwAAAIADK/Mai6eeekqdOnXSL7/8oho1atiO33333UpISCjXcAAAAACcQ5lnLJKSkpScnCx3d3e7402aNNGPP/5YbsEAAAAAOI8yz1gUFBQU+eTrEydOyNPTs1xCAQAAAHAuZS4Wffv2VWxsrG3fYrHo/Pnzmj59ugYMGFCe2QAAAAA4iTJfCvXGG2+oX79+at26tS5cuKCRI0fq8OHD8vX11apVqyoiIwAAAAAHV+ZiERgYqL1792r16tVKTU3V+fPnNWbMGN1///12i7kBAAAAVB3X9BwLNzc3PfDAA+WdBQAAAICTKnOxWL58eYnjDz744DWHAQAAAOCcylwsnnrqKbv9/Px8/frrr7Ynb1MsAAAAgKqnzHeF+uWXX+y28+fP6+DBg+rRoweLtwEAAIAqqszFoig33XSTXnnllStmMwAAAABUDeVSLKTfF3SfPHmyvN4OAAAAgBMp8xqL+Ph4u/3CwkJlZmZq3rx56t69e7kFAwAAAOA8ylwsIiMj7fYtFovq16+v3r1764033iivXAAAAACcSJmLRUFBQUXkAAAAAODEym2NBQAAAICqq1QzFlFRUaV+wzfffPOawwAAAABwTqUqFt9++22p3sxisRgKAwAAAMA5lapYbN68uaJzAAAAAHBirLEAAAAAYFiZ7wolSbt27dKHH36o48eP6+LFi3ZjH330UbkEAwAAAOA8yjxjsXr1anXr1k379+/X2rVrlZ+fr3379mnTpk3y9vauiIwAAAAAHFyZi8XLL7+st956S59++qnc3d01Z84cHThwQMOHD1ejRo0qIiMAAAAAB1fmYpGenq4777xTkuTu7q68vDxZLBZNmjRJ77zzTrkHBAAAAOD4ylws6tatq3PnzkmSbrjhBqWlpUmSzp49q19//bV80wEAAABwCqUuFn8UiJ49e2rDhg2SpGHDhumpp57S2LFjNWLECN1xxx0VkxIAAACAQyv1XaHatWunzp07KzIyUsOGDZMkTZkyRdWqVVNycrKGDh2qqVOnVlhQAAAAAI6r1MUiMTFRS5cuVUxMjF566SUNHTpUjzzyiJ577rmKzAcAAADACZT6UqjQ0FAtWbJEmZmZ+sc//qFjx47p9ttvV/PmzTV79mxlZWVVZE4AAAAADqzMi7dr1aqlhx56SImJiTp06JCGDRum+fPnq1GjRrrrrrsqIiMAAAAAB1fmYvFnzZo109///ndNnTpVnp6e+vzzz8srFwAAAAAnUuo1Fn+1detWLVmyRP/+97/l4uKi4cOHa8yYMeWZDQAAAICTKFOxOHnypOLi4hQXF6cjR46oW7dumjt3roYPH65atWpVVEYAAAAADq7UxSIiIkIbN26Ur6+vHnzwQT388MNq0aJFRWYDAAAA4CRKXSyqVaumf/3rXxo4cKBcXV0rMhMAAAAAJ1PqYhEfH1+ROQAAAAA4MUN3hQIAAAAAiWIBAAAAoBxQLAAAAAAYRrEAAAAAYJipxWLr1q0aNGiQAgICZLFY9PHHH9uNFxYWatq0aWrYsKFq1Kih8PBwHT582JywAAAAAIplarHIy8tT+/btNX/+/CLHX331Vc2dO1eLFi3SV199pVq1aqlfv366cOFCJScFAAAAUJIyPXm7vEVERCgiIqLIscLCQsXGxmrq1KkaPHiwJGn58uXy8/PTxx9/rPvuu68yowIAAAAogcOusTh69KiysrIUHh5uO+bt7a0uXbpox44dJiYDAAAA8FemzliUJCsrS5Lk5+dnd9zPz882VhSr1Sqr1Wrbz83NrZiAAAAAAGwcdsbiWsXExMjb29u2BQUFmR0JAAAAuO45bLHw9/eXJGVnZ9sdz87Oto0VJTo6Wjk5ObYtIyOjQnMCAAAAcOBiERwcLH9/fyUkJNiO5ebm6quvvlLXrl2LfZ2Hh4e8vLzsNgAAAAAVy9Q1FufPn9eRI0ds+0ePHtWePXvk4+OjRo0aaeLEiXrxxRd10003KTg4WM8//7wCAgIUGRlpXmgAAAAAVzC1WOzatUu9evWy7UdFRUmSRo0apbi4OD377LPKy8vTuHHjdPbsWfXo0UPr1q1T9erVzYoMAAAAoAimFouwsDAVFhYWO26xWDRz5kzNnDmzElMBAAAAKCuHXWMBAAAAwHlQLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhjl0sXjhhRdksVjstpYtW5odCwAAAMBfuJkd4GratGmjjRs32vbd3Bw+MgAAAFDlOPxP6W5ubvL39zc7BgAAAIASOPSlUJJ0+PBhBQQEqGnTprr//vt1/PhxsyMBAAAA+AuHnrHo0qWL4uLi1KJFC2VmZmrGjBkKDQ1VWlqaPD09i3yN1WqV1Wq17efm5lZWXAAAAKDKcuhiERERYfvndu3aqUuXLmrcuLE+/PBDjRkzpsjXxMTEaMaMGZUVEQAAAICc4FKoP6tTp46aN2+uI0eOFHtOdHS0cnJybFtGRkYlJgQAAACqJqcqFufPn1d6eroaNmxY7DkeHh7y8vKy2wAAAABULIcuFk8//bQSExN17NgxJScn6+6775arq6tGjBhhdjQAAAAAf+LQayxOnDihESNG6PTp06pfv7569OihlJQU1a9f3+xoAAAAAP7EoYvF6tWrzY4AAAAAoBQc+lIoAAAAAM6BYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMMwpisX8+fPVpEkTVa9eXV26dNHOnTvNjgQAAADgTxy+WPzzn/9UVFSUpk+frm+++Ubt27dXv3799NNPP5kdDQAAAMD/cfhi8eabb2rs2LF66KGH1Lp1ay1atEg1a9bUkiVLzI4GAAAA4P84dLG4ePGidu/erfDwcNsxFxcXhYeHa8eOHSYmAwAAAPBnbmYHKMmpU6d0+fJl+fn52R338/PTgQMHinyN1WqV1Wq17efk5EiScnNzKy6ogymw/mp2BFSwqvT3uSriO3z94zt8feM7fP2rSt/hPz5rYWHhVc916GJxLWJiYjRjxowrjgcFBZmQBqgY3rFmJwBgBN9hwLlVxe/wuXPn5O3tXeI5Dl0sfH195erqquzsbLvj2dnZ8vf3L/I10dHRioqKsu0XFBTozJkzqlevniwWS4XmReXLzc1VUFCQMjIy5OXlZXYcAGXEdxhwbnyHr3+FhYU6d+6cAgICrnquQxcLd3d3dezYUQkJCYqMjJT0e1FISEjQhAkTinyNh4eHPDw87I7VqVOngpPCbF5eXvwHDXBifIcB58Z3+Pp2tZmKPzh0sZCkqKgojRo1Sp06dVJISIhiY2OVl5enhx56yOxoAAAAAP6PwxeLe++9Vz///LOmTZumrKwsdejQQevWrbtiQTcAAAAA8zh8sZCkCRMmFHvpE6o2Dw8PTZ8+/YrL3wA4B77DgHPjO4w/sxSW5t5RAAAAAFACh35AHgAAAADnQLEAAAAAYBjFAgAAAIBhFAsAAAAAhlEs4DQWLFhgdgQAAAAUg7tCwWn4+Pioc+fOWrp0aakeKw/AcX399ddatWqVDh06JElq3ry5Ro4cqU6dOpmcDMC1yszM1EsvvaR58+aZHQUmYcYCTiMtLU1ubm5q27atPvjgA7PjALhGzz77rLp06aL33ntPJ06c0IkTJ/Tuu++qS5cumjx5stnxAJRg3759mjdvnt555x2dPXtWknTq1ClNmjRJTZs21ebNm80NCFMxYwGnExcXp6ioKPXq1UtTpkyRm5v9cx7btWtnUjIAV7Ns2TI99thjeu211/Too4+qWrVqkqT8/HwtXLhQkydP1ttvv60HH3zQ5KQA/io+Pl733HOPLl26JElq2rSp3n33XQ0fPlwdO3bUxIkT1b9/f5NTwkwUCziljRs3qn///iosLFRhYaEsFovtfy9fvmx2PADFCAkJ0YgRIzRp0qQix998802tXr1aO3furORkAK4mJCRE3bt316xZs/Tee+8pKipKbdq00ZIlS9S5c2ez48EBUCzgdN588009//zzGjZsmJ5//vkrZiwaN25sUjIAV1OrVi199913atq0aZHj//3vf3XzzTcrLy+vkpMBuBpvb2/t3r1bzZo10+XLl+Xh4aF169YpPDzc7GhwEG5XPwVwDP/97381atQoHT58WCtXrtTgwYPNjgSgjFxdXXXx4sVix/Pz8+Xq6lqJiQCU1rlz5+Tl5SXp9+9yjRo1iv0lAaomigWcRrt27dS/f3+tXbtWvr6+ZscBcA1uvfVWrVixQrNmzSpy/P3339ett95ayakAlNb69evl7e0tSSooKFBCQoLS0tLszrnrrrvMiAYHwKVQcBoffPCBHnjgAbNjADDgs88+U2RkpKKiovS3v/1Nfn5+kqSsrCy98cYbio2N1dq1azVw4ECTkwL4KxeXq99MlLWOVRu3m4XTsFqtJY6fO3dOjzzySCWlAXAtBg4cqLfeektz5sxRQECAfHx85OPjoxtuuEFz587V66+/TqkAHFRBQcFVN0pF1caMBZyGt7e3QkND9d5778nf399ubP369Ro7dqzq1q2rvXv3mpQQQGmdOHFCa9as0eHDhyX9/oC8oUOHKigoyORkAK5VQUGBvvjiC345UIVRLOA0jh07poceekipqamaN2+eRowYoXPnzmnixIl6//339fTTT2vGjBm2++IDAICKd+TIES1ZskRxcXH6+eeflZ+fb3YkmIRLoeA0mjRpos2bN+v555/X2LFjNXDgQLVt21YpKSnavn27Xn75ZUoF4OAOHTp0xTMqEhIS1KtXL4WEhOjll182KRmAsvjtt9+0fPly9ezZUy1atFBycrKmTZumEydOmB0NJqJYwOk8+uij6tu3r7744gudOXNGq1ev5sE8gJOYPHmyPvvsM9v+0aNHNWjQILm7u6tr166KiYlRbGyseQEBlOjrr7/Wo48+Kn9/f8XGxmrw4MGyWCxasGCBHnvsMdsNGVA1USzgVLZv36727dvrwIEDWrdunSIiItS1a1fNmTPH7GgASmHXrl2KiIiw7a9YsULNmzfX+vXrNWfOHMXGxiouLs68gACK1a5dOw0bNkz16tVTcnKyvvnmG/3tb3+TxWIxOxocBMUCTuNvf/ubevfurUGDBumbb75R37599eGHH2rx4sV68cUXFRYWpqNHj5odE0AJTp06pcDAQNv+5s2bNWjQINt+WFiYjh07ZkIyAFdz8OBB9ezZU7169VLr1q3NjgMHRLGA0/jkk0+0ceNGvfHGG6pevbrt+L333qu0tDR5e3urXbt2JiYEcDU+Pj7KzMyU9PsdZHbt2qXbbrvNNn7x4kVxTxHAMf33v/9VixYt9PjjjyswMFBPP/20vv32W2YsYEOxgNNITU1VaGhokWN+fn765JNPtGDBgkpOBaAswsLCNGvWLGVkZCg2NlaXL19WWFiYbfz7779XkyZNTMsHoHg33HCDpkyZoiNHjuj9999XVlaWunfvrkuXLikuLk6HDh0yOyJMRrGA06hZs2aJ4/v37+cZFoCDe+mll7R//341btxYzz77rF599VXVqlXLNv7++++rd+/eJiYEUJytW7fq0qVLkqTevXvrgw8+UGZmpubNm6dNmzapZcuWXDlQxfEcCzi1vLw8rV69WosXL1ZKSopat26ttLQ0s2MBKMGlS5e0b98+1a9fX+7u7nJ3d5eXl5ckae/evQoMDFS9evVMTgngr1xdXZWZmakGDRoUOb5nzx4tWbJEc+fOreRkcBTMWMApbd++XQ8//LD8/Pw0btw4devWTd9//z2lAnAC58+f1zvvvKN27drJz89PdevWlb+/v6Kjo3XTTTdRKgAHdbXfRXfo0IFSUcUxYwGn8dNPPykuLk5LlixRTk6ORowYoZEjR6pr167au3cvd6gAnMCZM2fUtWtX/fjjj7r//vvVqlUrSb+vrVi5cqVatmypbdu2KTU1VSkpKfrf//1fkxMD+IOLi4uys7NVv359s6PAQbmZHQAorcaNG+uee+7RnDlz1KdPH7m4MOEGOJuZM2fK3d1d6enpVzxIa+bMmerbt6/+53/+R19++SW/+QQc0OjRo+Xh4VHiOR999FElpYGjoVjAaTRu3Fjbtm1To0aN1LhxY7Vs2dLsSADK6OOPP9bbb79d5NN5/f399eqrr2rAgAGaPn26Ro0aZUJCACXx9PRUjRo1zI4BB0WxgNM4cOCAtm/frsWLF6tz585q3ry5HnjgAUniHtqAk8jMzFSbNm2KHW/btq1cXFw0ffr0SkwFoLTmzp1b7OJtgGtJ4FS6d++uJUuWKDMzU4899pjWrFmjy5cv64knntC7776rn3/+2eyIAErg6+tb4pO1jx49yg8tgIPil3i4GhZvw+nt379fixcv1vvvv68zZ84oPz/f7EgAivHwww8rPT1dGzZskLu7u92Y1WpVv3791LRpUy1ZssSkhACK4+LioqysLMo/ikWxwHXj0qVLio+P15AhQ8yOAqAYJ06cUKdOneTh4aHx48erZcuWKiws1P79+7VgwQJZrVZ9/fXXatSokdlRAfzFyy+/rHbt2mngwIG2Y8uXL9f06dOVl5enyMhI/eMf/7jq4m5cvygWuG4cOHBAd911lw4dOmR2FAAlOHr0qJ544gl9+eWXtvviWywW9enTR/PmzVOzZs1MTgigKP3791evXr00efJkSdJ3332nW2+9VaNHj1arVq302muv6dFHH9ULL7xgblCYhmKB68bevXt166236vLly2ZHAVAKv/zyiw4fPixJatasmXx8fExOBKAkDRs21KeffqpOnTpJkqZMmaLExERt27ZNkrRmzRpNnz5d33//vZkxYSLuCgUAMEXdunUVEhJidgwApfTLL7/Y3So6MTFRERERtv3OnTsrIyPDjGhwENwVCgAAAFfl5+eno0ePSpIuXryob775Rrfddptt/Ny5c6pWrZpZ8eAAKBYAAAC4qgEDBui5555TUlKSoqOjVbNmTYWGhtrGU1NTdeONN5qYEGbjUig4jbp165Z4D+1Lly5VYhoAAKqWWbNmaciQIbr99ttVu3ZtLVu2zO620UuWLFHfvn1NTAizsXgbTmPZsmWlOm/UqFEVnAQAgKorJydHtWvXlqurq93xM2fOqHbt2lc8owZVB8UC15XLly9f8R86AAAAVDzWWOC6cOjQIU2ePFmBgYFmRwEAAKiSKBZwWr/++quWLl2q0NBQtW7dWomJiYqKijI7FgAAQJXE4m04nZSUFL333ntas2aNGjVqpP3792vz5s12d6YAAABA5WLGAk7jjTfeUJs2bXTPPfeobt262rp1q7777jtZLBbVq1fP7HgAAABVGou34TTc3Nw0efJkzZw5026BdrVq1bR37161bt3axHQAAABVGzMWcBqzZs3SmjVrFBwcrMmTJystLc3sSAAAAPg/FAs4jejoaB06dEjvv/++srKy1KVLF7Vv316FhYX65ZdfzI4HAABQpXEpFJxWbm6uVq1apcWLF2v37t3q0qWL7rnnHu4MBQAAYAKKBa4LaWlpWrx4sVasWKGffvrJ7DgAAABVDrebhdP47bfflJCQoIEDB0r6/dIoq9VqG3dzc1N6erpZ8QAAAKo0igWcxrJly/T555/bisW8efPUpk0b1ahRQ5J08OBBBQQEaNKkSWbGBAAAqJK4FApOIzQ0VM8++6wGDRokSfL09NTevXvVtGlTSdIHH3yg+fPna8eOHWbGBAAAqJK4KxScxpEjR3TzzTfb9qtXry4Xl///VzgkJETff/+9GdEAAACqPC6FgtM4e/as3ZqKn3/+2W68oKDAbhwAAACVhxkLOI3AwMASH4qXmpqqwMDASkwEAACAP1As4DQGDBigadOm6cKFC1eM/fbbb5oxY4buvPNOE5IBAACAxdtwGtnZ2erQoYPc3d01YcIENW/eXNLvd4OaN2+eLl26pG+//VZ+fn4mJwUAAKh6KBZwKkePHtXjjz+uDRs26I+/uhaLRX369NGCBQtsd4gCAABA5aJYwCmdOXNGR44ckSQ1a9ZMPj4+JicCAACo2igWAAAAAAxj8TYAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQCgwhw7dkwWi0V79uwxOwoAoIJRLAAAAAAYRrEAAAAAYBjFAgBgWEFBgV599VU1a9ZMHh4eatSokV566aUrzrt8+bLGjBmj4OBg1ahRQy1atNCcOXPsztmyZYtCQkJUq1Yt1alTR927d9cPP/wgSdq7d6969eolT09PeXl5qWPHjtq1a1elfEYAQMnczA4AAHB+0dHRevfdd/XWW2+pR48eyszM1IEDB644r6CgQIGBgVqzZo3q1aun5ORkjRs3Tg0bNtTw4cN16dIlRUZGauzYsVq1apUuXryonTt3ymKxSJLuv/9+3XLLLVq4cKFcXV21Z88eVatWrbI/LgCgCDx5GwBgyLlz51S/fn3NmzdPjzzyiN3YsWPHFBwcrG+//VYdOnQo8vUTJkxQVlaW/vWvf+nMmTOqV6+etmzZottvv/2Kc728vPSPf/xDo0aNqoiPAgAwgEuhAACG7N+/X1arVXfccUepzp8/f746duyo+vXrq3bt2nrnnXd0/PhxSZKPj49Gjx6tfv36adCgQZozZ44yMzNtr42KitIjjzyi8PBwvfLKK0pPT6+QzwQAKDuKBQDAkBo1apT63NWrV+vpp5/WmDFj9OWXX2rPnj166KGHdPHiRds5S5cu1Y4dO9StWzf985//VPPmzZWSkiJJeuGFF7Rv3z7deeed2rRpk1q3bq21a9eW+2cCAJQdl0IBAAy5cOGCfHx8NHfu3KteCvXkk0/q+++/V0JCgu2c8PBwnTp1qthnXXTt2lWdO3fW3LlzrxgbMWKE8vLyFB8fX66fCQBQdsxYAAAMqV69uiZPnqxnn31Wy5cvV3p6ulJSUrR48eIrzr3pppu0a9curV+/XocOHdLzzz+vr7/+2jZ+9OhRRUdHa8eOHfrhhx/05Zdf6vDhw2rVqpV+++03TZgwQVu2bNEPP/yg7du36+uvv1arVq0q8+MCAIrBXaEAAIY9//zzcnNz07Rp03Ty5Ek1bNhQjz322BXnPfroo/r222917733ymKxaMSIEXriiSf0n//8R5JUs2ZNHThwQMuWLdPp06fVsGFDjR8/Xo8++qguXbqk06dP68EHH1R2drZ8fX01ZMgQzZgxo7I/LgCgCFwKBQAAAMAwLoUCAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwBAqcTFxclisdg2Nzc33XDDDRo9erR+/PFHs+MBAEzmZnYAAIBzmTlzpoKDg3XhwgWlpKQoLi5O27ZtU1pamqpXr252PACASSgWAIAyiYiIUKdOnSRJjzzyiHx9fTV79mzFx8dr+PDhJqezl5eXp1q1apkdAwCqBC6FAgAYEhoaKklKT0+3O75p0yaFhoaqVq1aqlOnjgYPHqz9+/fbxlNTU2WxWBQfH287tnv3blksFt1666127xUREaEuXbqUmGP06NGqXbu20tPTNWDAAHl6eur+++83+vEAAKVEsQAAGHLs2DFJUt26dW3HNm7cqH79+umnn37SCy+8oKioKCUnJ6t79+6289u2bas6depo69atttclJSXJxcVFe/fuVW5uriSpoKBAycnJ6tmz51WzXLp0Sf369VODBg30+uuva+jQoeX3QQEAJeJSKABAmeTk5OjUqVO6cOGCvvrqK82YMUMeHh4aOHCg7ZxnnnlGPj4+2rFjh3x8fCRJkZGRuuWWWzR9+nQtW7ZMLi4u6t69u5KSkmyvS0pKUmRkpD755BMlJyerf//+tpLxx8xISaxWq4YNG6aYmJjy/+AAgBIxYwEAKJPw8HDVr19fQUFBuueee1SrVi3Fx8crMDBQkpSZmak9e/Zo9OjRtlIhSe3atVOfPn30xRdf2I6Fhobqm2++UV5eniRp27ZtGjBggDp06GArHElJSbJYLOrRo0ep8j3++OPl9VEBAGXAjAUAoEzmz5+v5s2bKycnR0uWLNHWrVvl4eFhG//hhx8kSS1atLjita1atdL69etti6pDQ0N16dIl7dixQ0FBQfrpp58UGhqqffv22RWL1q1b25WU4ri5udkKDgCgclEsAABlEhISYrsrVGRkpHr06KGRI0fq4MGDql27dpneq1OnTqpevbq2bt2qRo0aqUGDBmrevLlCQ0O1YMECWa1WJSUl6e677y7V+3l4eMjFhcl4ADAD//UFAFwzV1dXxcTE6OTJk5o3b54kqXHjxpKkgwcPXnH+gQMH5Ovra7sFrLu7u0JCQpSUlKSkpCTbOorQ0FBZrVatWLFC2dnZpVq4DQAwF8UCAGBIWFiYQkJCFBsbqwsXLqhhw4bq0KGDli1bprNnz9rOS0tL05dffqkBAwbYvT40NFRfffWVNm/ebCsWvr6+atWqlWbPnm07BwDg2CgWAADDnnnmGWVnZysuLk6S9Nprr+n06dPq2rWrXn/9dc2aNUu9e/eWt7e3XnjhBbvXhoaG6rffflNGRoZdgejZs6cOHTqkJk2asG4CAJwAxQIAYNiQIUN044036vXXX9fly5cVHh6udevWqV69epo2bZpef/113Xbbbdq+fbuCg4PtXtutWze5urrK09NT7du3tx3/82VRAADHZyksLCw0OwQAAAAA58aMBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMczM7QEUrKCjQyZMn5enpKYvFYnYcAAAAwGkUFhbq3LlzCggIkItLyXMS132xOHnypIKCgsyOAQAAADitjIwMBQYGlnjOdV8sPD09Jf3+L8PLy8vkNAAAAIDzyM3NVVBQkO1n6pJc98Xij8ufvLy8KBYAAADANSjNkgIWbwMAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADDO1WMTExKhz587y9PRUgwYNFBkZqYMHD9qdExYWJovFYrc99thjJiUGAAAAUBRTi0ViYqLGjx+vlJQUbdiwQfn5+erbt6/y8vLszhs7dqwyMzNt26uvvmpSYgAAAABFMfV2s+vWrbPbj4uLU4MGDbR792717NnTdrxmzZry9/ev7HgAAAAASsmh1ljk5ORIknx8fOyOr1ixQr6+vmrbtq2io6P166+/FvseVqtVubm5dhsAAACAiuUwD8grKCjQxIkT1b17d7Vt29Z2fOTIkWrcuLECAgKUmpqqyZMn6+DBg/roo4+KfJ+YmBjNmDGjsmIDAAAAkGQpLCwsNDuEJD3++OP6z3/+o23btikwMLDY8zZt2qQ77rhDR44c0Y033njFuNVqldVqte3/8RjynJwcnrwNAAAAlEFubq68vb1L9bO0Q8xYTJgwQZ999pm2bt1aYqmQpC5dukhSscXCw8NDHh4eFZITAADgapo897nZEVDBjr1yp9kRHJKpxaKwsFBPPvmk1q5dqy1btig4OPiqr9mzZ48kqWHDhhWcDgAAAEBpmVosxo8fr5UrV+qTTz6Rp6ensrKyJEne3t6qUaOG0tPTtXLlSg0YMED16tVTamqqJk2apJ49e6pdu3ZmRgcAAADwJ6YWi4ULF0r6/SF4f7Z06VKNHj1a7u7u2rhxo2JjY5WXl6egoCANHTpUU6dONSEtAAAAgOKYfilUSYKCgpSYmFhJaQAAAABcK4d6jgUAAAAA50SxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYW5mB0D5a/Lc52ZHQAU79sqdZkcAAACww4wFAAAAAMMoFgAAAAAMM7VYxMTEqHPnzvL09FSDBg0UGRmpgwcP2p1z4cIFjR8/XvXq1VPt2rU1dOhQZWdnm5QYAAAAQFFMLRaJiYkaP368UlJStGHDBuXn56tv377Ky8uznTNp0iR9+umnWrNmjRITE3Xy5EkNGTLExNQAAAAA/srUxdvr1q2z24+Li1ODBg20e/du9ezZUzk5OVq8eLFWrlyp3r17S5KWLl2qVq1aKSUlRbfddpsZsQEAAAD8hUOtscjJyZEk+fj4SJJ2796t/Px8hYeH285p2bKlGjVqpB07dhT5HlarVbm5uXYbAAAAgIrlMMWioKBAEydOVPfu3dW2bVtJUlZWltzd3VWnTh27c/38/JSVlVXk+8TExMjb29u2BQUFVXR0AAAAoMpzmGIxfvx4paWlafXq1YbeJzo6Wjk5ObYtIyOjnBICAAAAKI5DPCBvwoQJ+uyzz7R161YFBgbajvv7++vixYs6e/as3axFdna2/P39i3wvDw8PeXh4VHRkAAAAAH9i6oxFYWGhJkyYoLVr12rTpk0KDg62G+/YsaOqVaumhIQE27GDBw/q+PHj6tq1a2XHBQAAAFAMU2csxo8fr5UrV+qTTz6Rp6enbd2Et7e3atSoIW9vb40ZM0ZRUVHy8fGRl5eXnnzySXXt2pU7QgG4bjV57nOzI6CCHXvlTrMjAEC5M7VYLFy4UJIUFhZmd3zp0qUaPXq0JOmtt96Si4uLhg4dKqvVqn79+mnBggWVnBQAAABASUwtFoWFhVc9p3r16po/f77mz59fCYkAAAAAXAuHuSsUAAAAAOdFsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGGZqsdi6dasGDRqkgIAAWSwWffzxx3bjo0ePlsVisdv69+9vTlgAAAAAxTK1WOTl5al9+/aaP39+sef0799fmZmZtm3VqlWVmBAAAABAabiZ+YdHREQoIiKixHM8PDzk7+9fSYkAAAAAXAuHX2OxZcsWNWjQQC1atNDjjz+u06dPmx0JAAAAwF+YOmNxNf3799eQIUMUHBys9PR0/f3vf1dERIR27NghV1fXIl9jtVpltVpt+7m5uZUVFwAAAKiyHLpY3HfffbZ/vvnmm9WuXTvdeOON2rJli+64444iXxMTE6MZM2ZUVkQAAAAAuoZLoTIyMnTixAnb/s6dOzVx4kS988475RqsKE2bNpWvr6+OHDlS7DnR0dHKycmxbRkZGRWeCwAAAKjqylwsRo4cqc2bN0uSsrKy1KdPH+3cuVNTpkzRzJkzyz3gn504cUKnT59Ww4YNiz3Hw8NDXl5edhsAAACAilXmYpGWlqaQkBBJ0ocffqi2bdsqOTlZK1asUFxcXJne6/z589qzZ4/27NkjSTp69Kj27Nmj48eP6/z583rmmWeUkpKiY8eOKSEhQYMHD1azZs3Ur1+/ssYGAAAAUIHKvMYiPz9fHh4ekqSNGzfqrrvukiS1bNlSmZmZZXqvXbt2qVevXrb9qKgoSdKoUaO0cOFCpaamatmyZTp79qwCAgLUt29fzZo1y/bnAwAAAHAMZS4Wbdq00aJFi3TnnXdqw4YNmjVrliTp5MmTqlevXpneKywsTIWFhcWOr1+/vqzxAAAAAJigzJdCzZ49W2+//bbCwsI0YsQItW/fXpIUHx9vu0QKAAAAQNVS5hmLsLAwnTp1Srm5uapbt67t+Lhx41SzZs1yDQcAAADAOVzTk7cLCwu1e/duvf322zp37pwkyd3dnWIBAAAAVFFlnrH44Ycf1L9/fx0/flxWq1V9+vSRp6enZs+eLavVqkWLFlVETgAAAAAOrMwzFk899ZQ6deqkX375RTVq1LAdv/vuu5WQkFCu4QAAAAA4hzLPWCQlJSk5OVnu7u52x5s0aaIff/yx3IIBAAAAcB5lnrEoKCjQ5cuXrzh+4sQJeXp6lksoAAAAAM6lzMWib9++io2Nte1bLBadP39e06dP14ABA8ozGwAAAAAnUeZLod544w3169dPrVu31oULFzRy5EgdPnxYvr6+WrVqVUVkBAAAAODgylwsAgMDtXfvXq1evVqpqak6f/68xowZo/vvv99uMTcAAACAqqPMxUKS3Nzc9MADD5R3FgAAAABOqszFYvny5SWOP/jgg9ccBgAAAIBzKnOxeOqpp+z28/Pz9euvv9qevE2xAAAAAKqeMt8V6pdffrHbzp8/r4MHD6pHjx4s3gYAAACqqDIXi6LcdNNNeuWVV66YzQAAAABQNZRLsZB+X9B98uTJ8no7AAAAAE6kzGss4uPj7fYLCwuVmZmpefPmqXv37uUWDAAAAIDzKHOxiIyMtNu3WCyqX7++evfurTfeeKO8cgEAAABwImUuFgUFBRWRAwAAAIATK7c1FgAAAACqrlLNWERFRZX6Dd98881rDgMAAADAOZWqWHz77belejOLxWIoDAAAAADnVKpisXnz5orOAQAAAMCJscYCAAAAgGFlviuUJO3atUsffvihjh8/rosXL9qNffTRR+USDAAAAIDzKPOMxerVq9WtWzft379fa9euVX5+vvbt26dNmzbJ29u7IjICAAAAcHBlLhYvv/yy3nrrLX366adyd3fXnDlzdODAAQ0fPlyNGjWqiIwAAAAAHFyZi0V6erruvPNOSZK7u7vy8vJksVg0adIkvfPOO+UeEAAAAIDjK3OxqFu3rs6dOydJuuGGG5SWliZJOnv2rH799dfyTQcAAADAKZS6WPxRIHr27KkNGzZIkoYNG6annnpKY8eO1YgRI3THHXdUTEoAAAAADq3Ud4Vq166dOnfurMjISA0bNkySNGXKFFWrVk3JyckaOnSopk6dWmFBAQAAADiuUheLxMRELV26VDExMXrppZc0dOhQPfLII3ruuecqMh8AAAAAJ1DqS6FCQ0O1ZMkSZWZm6h//+IeOHTum22+/Xc2bN9fs2bOVlZVVkTkBAAAAOLAyL96uVauWHnroISUmJurQoUMaNmyY5s+fr0aNGumuu+6qiIwAAAAAHFyZi8WfNWvWTH//+981depUeXp66vPPPy+vXAAAAACcSKnXWPzV1q1btWTJEv373/+Wi4uLhg8frjFjxpRnNgAAAABOokzF4uTJk4qLi1NcXJyOHDmibt26ae7cuRo+fLhq1apVURkBAAAAOLhSF4uIiAht3LhRvr6+evDBB/Xwww+rRYsWFZkNAAAAgJModbGoVq2a/vWvf2ngwIFydXWtyEwAAAAAnEypi0V8fHxF5gAAAADgxAzdFQoAAAAAJIoFAAAAgHJgarHYunWrBg0apICAAFksFn388cd244WFhZo2bZoaNmyoGjVqKDw8XIcPHzYnLAAAAIBimVos8vLy1L59e82fP7/I8VdffVVz587VokWL9NVXX6lWrVrq16+fLly4UMlJAQAAAJTkmh+QVx4iIiIUERFR5FhhYaFiY2M1depUDR48WJK0fPly+fn56eOPP9Z9991XmVEBAAAAlMBh11gcPXpUWVlZCg8Ptx3z9vZWly5dtGPHDhOTAQAAAPgrU2csSpKVlSVJ8vPzszvu5+dnGyuK1WqV1Wq17efm5lZMQAAAAAA2Djtjca1iYmLk7e1t24KCgsyOBAAAAFz3HLZY+Pv7S5Kys7PtjmdnZ9vGihIdHa2cnBzblpGRUaE5AQAAADhwsQgODpa/v78SEhJsx3Jzc/XVV1+pa9euxb7Ow8NDXl5edhsAAACAimXqGovz58/ryJEjtv2jR49qz5498vHxUaNGjTRx4kS9+OKLuummmxQcHKznn39eAQEBioyMNC80AAAAgCuYWix27dqlXr162fajoqIkSaNGjVJcXJyeffZZ5eXlady4cTp79qx69OihdevWqXr16mZFBgAAAFAEU4tFWFiYCgsLix23WCyaOXOmZs6cWYmpAAAAAJSVw66xAAAAAOA8KBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAID/x96dx0VVL/4ffw8guAGKKGi4Zu5XLRUzxdDcMDVz66rd1OtaWrfIK3E118qsTDQ1KxfUXG72zbLNDfeF3CXcUELTBHcBSUeB+f3RbX6RgtARzoy8no/HPLrnfD4zvqfrmG/O53PGMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwxy6WIwfP14WiyXLo1atWmbHAgAAAPAnbmYHuJu6detq/fr19mM3N4ePDAAAABQ6Dv+3dDc3N/n7+5sdAwAAAEAOHHoplCQdP35cFSpUULVq1dS3b1/9/PPPZkcCAAAA8CcOfcWiadOmioyMVM2aNZWYmKgJEyYoKChIsbGx8vT0vONzrFarrFar/TglJaWg4gIAAACFlkMXi5CQEPv/rl+/vpo2barKlSvrs88+08CBA+/4nMmTJ2vChAkFFREAAACAnGAp1B+VKlVKNWrU0IkTJ7KdEx4eruTkZPvj9OnTBZgQAAAAKJycqlhcu3ZN8fHxKl++fLZzPDw85OXlleUBAAAAIH85dLEYOXKkNm/erJMnT2rHjh16+umn5erqqt69e5sdDQAAAMAfOPQeizNnzqh37966dOmSypYtqxYtWig6Olply5Y1OxoAAACAP3DoYrF8+XKzIwAAAADIBYdeCgUAAADAOVAsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGOYUxWLWrFmqUqWKihYtqqZNm2rXrl1mRwIAAADwBw5fLP773/8qNDRU48aN0759+9SgQQO1b99e58+fNzsaAAAAgP9x+GLx/vvva/DgwRowYIDq1KmjOXPmqHjx4po/f77Z0QAAAAD8j5vZAXJy8+ZN7d27V+Hh4fZzLi4uatOmjXbu3HnH51itVlmtVvtxcnKyJCklJSV/wzqQTOuvZkdAPitMv58LIz7D9z8+w/c3PsP3v8L0Gf79vdpstrvOdehicfHiRWVkZMjPzy/LeT8/Px09evSOz5k8ebImTJhw2/mKFSvmS0bADN4RZicAYASfYcC5FcbPcGpqqry9vXOc49DF4q8IDw9XaGio/TgzM1OXL19WmTJlZLFYTEyG/JCSkqKKFSvq9OnT8vLyMjsOgDziMww4Nz7D9z+bzabU1FRVqFDhrnMdulj4+vrK1dVV586dy3L+3Llz8vf3v+NzPDw85OHhkeVcqVKl8isiHISXlxd/oAFOjM8w4Nz4DN/f7nal4ncOvXnb3d1djRo1UlRUlP1cZmamoqKi1KxZMxOTAQAAAPgjh75iIUmhoaHq16+fGjdurMDAQEVERCgtLU0DBgwwOxoAAACA/3H4YvHMM8/owoULGjt2rJKSktSwYUOtXr36tg3dKJw8PDw0bty425a/AXAOfIYB58ZnGH9kseXm3lEAAAAAkAOH3mMBAAAAwDlQLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEs4DRmz55tdgQAAJCNxMREjRgxwuwYMBG3m4XT8PHxUZMmTbRgwQJVqFDB7DgADNi9e7eWLVumuLg4SVKNGjXUp08fNW7c2ORkAHJy6NAhbdy4Ue7u7urVq5dKlSqlixcv6s0339ScOXNUrVo1HTp0yOyYMAlXLOA0YmNj5ebmpnr16unTTz81Ow6Av2jUqFFq2rSp5s6dqzNnzujMmTP65JNP1LRpU4WFhZkdD0A2Vq1apYcfflgvvfSShg0bpsaNG2vjxo2qXbu2jhw5opUrV1IqCjmuWMDpREZGKjQ0VK1atdLo0aPl5pb1C+Tr169vUjIAd7Nw4UINGzZM7777roYOHaoiRYpIkm7duqUPP/xQYWFh+uijj/Tcc8+ZnBTAnwUGBqp58+aaNGmS5s6dq9DQUNWtW1fz589XkyZNzI4HB0CxgFNav369OnToIJvNJpvNJovFYv9nRkaG2fEAZCMwMFC9e/fWK6+8csfx999/X8uXL9euXbsKOBmAu/H29tbevXtVvXp1ZWRkyMPDQ6tXr1abNm3MjgYHwVIoOJ33339fTz31lJ599lnFxcUpISFBP/30k/2fABzXoUOH9NRTT2U73rVrV5ZSAA4qNTVVXl5ekiRXV1cVK1ZM1apVMzkVHInb3acAjuGnn35Sv379dPz4cS1dujTHv5wAcEyurq66efNmtuO3bt2Sq6trASYCkBdr1qyRt7e3JCkzM1NRUVGKjY3NMqdLly5mRIMDYCkUnEbJkiXVoUMHzZkzR76+vmbHAfAXBAcHKygoSJMmTbrj+JgxY7Rt2zZt2rSpYIMBuCsXl7svdGFJcuHGFQs4jTlz5ujZZ581OwYAA0aOHKmuXbvKarXq1VdflZ+fnyQpKSlJU6dOVUREhFauXGlySgB3kpmZaXYEODj2WMBpWK3WHMdTU1M1aNCgAkoD4K/o1KmTpk2bpunTp6tChQry8fGRj4+PHnjgAc2YMUPvvfeeOnXqZHZMAH9BZmamvvnmG7NjwEQshYLT8Pb2VlBQkObOnSt/f/8sY2vWrNHgwYNVunRpHTx40KSEAHLrzJkzWrFihY4fPy7pty/I6969uypWrGhyMgB5deLECc2fP1+RkZG6cOGCbt26ZXYkmIRiAadx8uRJDRgwQDExMZo5c6Z69+6t1NRUvfzyy1q8eLFGjhypCRMm2O+LDwAA8sf169e1YsUKzZ07V9u3b1dQUJD+/ve/6+mnn7YvcUThQ7GA04mIiNCYMWMUHBysH3/8USVLllRkZCRfzgM4gbi4OF29elWBgYH2c1FRUXrjjTeUlpamrl276j//+Y+JCQHkZPfu3Zo7d66WL1+uBx98UH379lVYWJhiYmJUp04ds+PBZOyxgNMZOnSo2rVrp++++06XL1/W8uXLKRWAkwgLC8uyBjshIUGdO3eWu7u7mjVrpsmTJysiIsK8gACyVb9+ffXs2VNlypTRjh07tG/fPr366quyWCxmR4ODoFjAqWzfvl0NGjTQ0aNHtXr1aoWEhKhZs2aaPn262dEA5MKePXsUEhJiP16yZIlq1KihNWvWaPr06YqIiFBkZKR5AQFk69ixY2rZsqVatWrF1QncEcUCTuPVV19V69at1blzZ+3bt0/t2rXTZ599pnnz5umNN95QcHCwEhISzI4JIAcXL15UQECA/Xjjxo3q3Lmz/Tg4OFgnT540IRmAu/npp59Us2ZNPf/88woICNDIkSO1f/9+rljAjmIBp/HVV19p/fr1mjp1qooWLWo//8wzzyg2Nlbe3t6qX7++iQkB3I2Pj48SExMl/XZryj179ujRRx+1j9+8eVNs/QMc0wMPPKDRo0frxIkTWrx4sZKSktS8eXOlp6crMjJScXFxZkeEySgWcBoxMTEKCgq645ifn5+++uorzZ49u4BTAciL4OBgTZo0SadPn1ZERIQyMjIUHBxsHz98+LCqVKliWj4A2duyZYvS09MlSa1bt9ann36qxMREzZw5Uxs2bFCtWrX4AV8hR7GA0yhevHiO40eOHOE7LAAH9+abb+rIkSOqXLmyRo0apXfeeUclSpSwjy9evFitW7c2MSGA7LRq1UqXL1/Ocs7b21svvPCC9uzZo3379mX5QQEKH243C6eWlpam5cuXa968eYqOjladOnUUGxtrdiwAOUhPT9ehQ4dUtmxZubu7y93dXV5eXpKkgwcPKiAgQGXKlDE5JYA/c3FxUVJSksqVK2d2FDgorljAKW3fvl3//Oc/5efnpyFDhuixxx7T4cOHKRWAE7h27Zo+/vhj1a9fX35+fipdurT8/f0VHh6uhx56iFIBODA2aiMnXLGA0zh//rwiIyM1f/58JScnq3fv3urTp4+aNWumgwcPcus7wAlcvnxZzZo10y+//KK+ffuqdu3akn7bW7F06VLVqlVL27ZtU0xMjKKjo/XSSy+ZnBjA71xcXBQSEiIPD48c533xxRcFlAiOxs3sAEBuVa5cWT169ND06dPVtm1bubhwwQ1wNhMnTpS7u7vi4+Pl5+d321i7du30j3/8Q2vXrtWMGTNMSgkgO56enipWrJjZMeCgKBZwGpUrV9a2bdtUqVIlVa5cWbVq1TI7EoA8+vLLL/XRRx/dViokyd/fX++88446duyocePGqV+/fiYkBJCTGTNmsMcC2eJHvnAaR48etd/arkmTJmrUqJGmTZsmiTWfgLNITExU3bp1sx2vV6+eXFxcNG7cuAJMBSA3+G8t7oZiAafSvHlzzZ8/X4mJiRo2bJhWrFihjIwMvfDCC/rkk0904cIFsyMCyIGvr2+O36ydkJDAT0MBB8W2XNwNxQJOqWTJkho8eLB27NihQ4cOqVGjRhozZowqVKhgdjQAOWjfvr1Gjx6tmzdv3jZmtVr1+uuvq0OHDiYkA3A3b7zxhnbt2pXl3KJFi1S1alWVK1dOQ4YMkdVqNSkdHAF3hcJ9Iz09XatWrVK3bt3MjgIgG2fOnFHjxo3l4eGh4cOHq1atWrLZbDpy5Ihmz54tq9Wq3bt3q1KlSmZHBfAnHTp0UKtWrRQWFiZJ+vHHH/XII4+of//+ql27tt59910NHTpU48ePNzcoTEOxwH3j6NGj6tKli+Li4syOAiAHCQkJeuGFF7R27Vr70gqLxaK2bdtq5syZql69uskJAdxJ+fLl9fXXX6tx48aSpNGjR2vz5s3atm2bJGnFihUaN26cDh8+bGZMmIi7QuG+YbVaFR8fb3YMAHdRtWpVff/997py5YqOHz8uSapevbp8fHxMTgYgJ1euXMlyR7fNmzcrJCTEftykSROdPn3ajGhwEOyxAACYonTp0goMDFRgYCClAnACfn5+SkhIkCTdvHlT+/bt06OPPmofT01NVZEiRcyKBwdAsQAAAMBddezYUa+99pq2bt2q8PBwFS9eXEFBQfbxmJgYPfjggyYmhNlYCgUAAIC7mjRpkrp166bHH39cJUuW1MKFC+Xu7m4fnz9/vtq1a2diQpiNzdtwGqVLl87xy3nS09OVlpamjIyMAkwFAEDhkpycrJIlS8rV1TXL+cuXL6tkyZJZygYKF65YwGlERESYHQEAgELP29v7jufZKwWuWOC+kpGRcdtPUAAAAJD/2LyN+0JcXJzCwsIUEBBgdhQAAIBCiWIBp/Xrr79qwYIFCgoKUp06dbR582aFhoaaHQsAAKBQYo8FnE50dLTmzp2rFStWqFKlSjpy5Ig2btyY5ZZ3AAAAKFhcsYDTmDp1qurWrasePXqodOnS2rJli3788UdZLBaVKVPG7HgAAACFGpu34TTc3NwUFhamiRMnZtmgXaRIER08eFB16tQxMR0AAEDhxhULOI1JkyZpxYoVqlq1qsLCwhQbG2t2JAAAAPwPxQJOIzw8XHFxcVq8eLGSkpLUtGlTNWjQQDabTVeuXDE7HgAAQKHGUig4rZSUFC1btkzz5s3T3r171bRpU/Xo0YM7QwEAAJiAYoH7QmxsrObNm6clS5bo/PnzZscBAAAodLjdLJzG9evXFRUVpU6dOkn6bWmU1Wq1j7u5uSk+Pt6seAAAAIUaxQJOY+HChfr222/txWLmzJmqW7euihUrJkk6duyYKlSooFdeecXMmAAAAIUSS6HgNIKCgjRq1Ch17txZkuTp6amDBw+qWrVqkqRPP/1Us2bN0s6dO82MCQAAUChxVyg4jRMnTuhvf/ub/bho0aJycfn/v4UDAwN1+PBhM6IBAAAUeiyFgtO4evVqlj0VFy5cyDKemZmZZRwAAAAFhysWcBoBAQE5fileTEyMAgICCjARAAAAfkexgNPo2LGjxo4dqxs3btw2dv36dU2YMEFPPvmkCckAAADA5m04jXPnzqlhw4Zyd3fXiBEjVKNGDUm/3Q1q5syZSk9P1/79++Xn52dyUgAAgMKHYgGnkpCQoOeff17r1q3T7791LRaL2rZtq9mzZ9vvEAUAAICCRbGAU7p8+bJOnDghSapevbp8fHxMTgQAAFC4USwAAAAAGMbmbQAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAEC+OXnypCwWiw4cOGB2FABAPqNYAAAAADCMYgEAAADAMIoFAMCwzMxMvfPOO6pevbo8PDxUqVIlvfnmm7fNy8jI0MCBA1W1alUVK1ZMNWvW1PTp07PM2bRpkwIDA1WiRAmVKlVKzZs316lTpyRJBw8eVKtWreTp6SkvLy81atRIe/bsKZD3CADImZvZAQAAzi88PFyffPKJpk2bphYtWigxMVFHjx69bV5mZqYCAgK0YsUKlSlTRjt27NCQIUNUvnx59erVS+np6eratasGDx6sZcuW6ebNm9q1a5csFoskqW/fvnr44Yf14YcfytXVVQcOHFCRIkUK+u0CAO6Ab94GABiSmpqqsmXLaubMmRo0aFCWsZMnT6pq1arav3+/GjZseMfnjxgxQklJSfr88891+fJllSlTRps2bdLjjz9+21wvLy998MEH6tevX368FQCAASyFAgAYcuTIEVmtVj3xxBO5mj9r1iw1atRIZcuWVcmSJfXxxx/r559/liT5+Piof//+at++vTp37qzp06crMTHR/tzQ0FANGjRIbdq00dtvv634+Ph8eU8AgLyjWAAADClWrFiu5y5fvlwjR47UwIEDtXbtWh04cEADBgzQzZs37XMWLFignTt36rHHHtN///tf1ahRQ9HR0ZKk8ePH69ChQ3ryySe1YcMG1alTRytXrrzn7wkAkHcshQIAGHLjxg35+PhoxowZd10K9eKLL+rw4cOKioqyz2nTpo0uXryY7XddNGvWTE2aNNGMGTNuG+vdu7fS0tK0atWqe/qeAAB5xxULAIAhRYsWVVhYmEaNGqVFixYpPj5e0dHRmjdv3m1zH3roIe3Zs0dr1qxRXFycXn/9de3evds+npCQoPDwcO3cuVOnTp3S2rVrdfz4cdWuXVvXr1/XiBEjtGnTJp06dUrbt2/X7t27Vbt27YJ8uwCAbHBXKACAYa+//rrc3Nw0duxYnT17VuXLl9ewYcNumzd06FDt379fzzzzjCwWi3r37q0XXnhB33//vSSpePHiOnr0qBYuXKhLly6pfPnyGj58uIYOHar09HRdunRJzz33nM6dOydfX19169ZNEyZMKOi3CwC4A5ZCAQAAADCMpVAAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAO4b48ePl8ViMTsGABRKFAsAQK5FRkbKYrHYH25ubnrggQfUv39//fLLL2bHAwCYyGKz2WxmhwAAOIfIyEgNGDBAEydOVNWqVXXjxg1FR0crMjJSVapUUWxsrIoWLWpavvT0dKWnp5uaAQAKKzezAwAAnE9ISIgaN24sSRo0aJB8fX01ZcoUrVq1Sr169TItl5ubm9zc+E8bAJiBpVAAAMOCgoIkSfHx8VnOb9iwQUFBQSpRooRKlSqlp556SkeOHLGPx8TEyGKxaNWqVfZze/fulcVi0SOPPJLltUJCQtS0adMcc7DHAgDMQ7EAABh28uRJSVLp0qXt59avX6/27dvr/PnzGj9+vEJDQ7Vjxw41b97cPr9evXoqVaqUtmzZYn/e1q1b5eLiooMHDyolJUWSlJmZqR07dqhly5YF9p4AAHlDsQAA5FlycrIuXryoM2fO6P/+7/80YcIEeXh4qFOnTvY5//73v+Xj46OdO3fq3//+t8aOHauoqCglJydr3LhxkiQXFxc1b95cW7dutT9v69at6tq1qywWi3bs2CFJ9pLx+5URAIDjoVgAAPKsTZs2Klu2rCpWrKgePXqoRIkSWrVqlQICAiRJiYmJOnDggPr37y8fHx/78+rXr6+2bdvqu+++s58LCgrSvn37lJaWJknatm2bOnbsqIYNG9oLx9atW2WxWNSiRYsCfJcAgLxghxsAIM9mzZqlGjVqKDk5WfPnz9eWLVvk4eFhHz916pQkqWbNmrc9t3bt2lqzZo3S0tJUokQJBQUFKT09XTt37lTFihV1/vx5BQUF6dChQ1mKRZ06dbKUFACAY6FYAADyLDAw0H5XqK5du6pFixbq06ePjh07ppIlS+bptRo3bqyiRYtqy5YtqlSpksqVK6caNWooKChIs2fPltVq1datW/X000/nx1sBANwjLIUCABji6uqqyZMn6+zZs5o5c6YkqXLlypKkY8eO3Tb/6NGj8vX1VYkSJSRJ7u7uCgwM1NatW7V161b7PoqgoCBZrVYtWbJE586dY+M2ADg4igUAwLDg4GAFBgYqIiJCN27cUPny5dWwYUMtXLhQV69etc+LjY3V2rVr1bFjxyzPDwoK0g8//KCNGzfai4Wvr69q166tKVOm2OcAABwXxQIAcE/8+9//1rlz5xQZGSlJevfdd3Xp0iU1a9ZM7733niZNmqTWrVvL29tb48ePz/LcoKAgXb9+XadPn85SIFq2bKm4uDhVqVLFvjEcAOCYKBYAgHuiW7duevDBB/Xee+8pIyNDbdq00erVq1WmTBmNHTtW7733nh599FFt375dVatWzfLcxx57TK6urvL09FSDBg3s5/+4LAoA4NgsNpvNZnYIAAAAAM6NKxYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMMzN7AD5LTMzU2fPnpWnp6csFovZcQAAAACnYbPZlJqaqgoVKsjFJedrEvd9sTh79qwqVqxodgwAAADAaZ0+fVoBAQE5zrnvi4Wnp6ek3/5leHl5mZwGAAAAcB4pKSmqWLGi/e/UOTG1WEyePFlffPGFjh49qmLFiumxxx7TlClTVLNmTfuc4OBgbd68Ocvzhg4dqjlz5uTq1/h9+ZOXlxfFAgAAAPgLcrOlwNTN25s3b9bw4cMVHR2tdevW6datW2rXrp3S0tKyzBs8eLASExPtj3feecekxAAAAADuxNQrFqtXr85yHBkZqXLlymnv3r1q2bKl/Xzx4sXl7+9f0PEAAAAA5JJD3W42OTlZkuTj45Pl/JIlS+Tr66t69eopPDxcv/76a7avYbValZKSkuUBAAAAIH85zObtzMxMvfzyy2revLnq1atnP9+nTx9VrlxZFSpUUExMjMLCwnTs2DF98cUXd3ydyZMna8KECQUVGwAAAIAki81ms5kdQpKef/55ff/999q2bVuOt7LasGGDnnjiCZ04cUIPPvjgbeNWq1VWq9V+/PtO9uTkZDZvAwAAAHmQkpIib2/vXP1d2iGuWIwYMULffPONtmzZctf74zZt2lSSsi0WHh4e8vDwyJecAAAAAO7M1GJhs9n04osvauXKldq0aZOqVq161+ccOHBAklS+fPl8TgcAAAAgt0wtFsOHD9fSpUv11VdfydPTU0lJSZIkb29vFStWTPHx8Vq6dKk6duyoMmXKKCYmRq+88opatmyp+vXrmxkdAAAAwB+Yusciuy/aWLBggfr376/Tp0/r2WefVWxsrNLS0lSxYkU9/fTTGjNmTK73S+RlXRgAAACA/89p9ljcrdNUrFjxtm/dBgAAAOB4HGLzNgAAwP2iymvfmh0B+ezk20+aHcEhOdQX5AEAAABwThQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhphaLyZMnq0mTJvL09FS5cuXUtWtXHTt2LMucGzduaPjw4SpTpoxKliyp7t2769y5cyYlBgAAAHAnphaLzZs3a/jw4YqOjta6det069YttWvXTmlpafY5r7zyir7++mutWLFCmzdv1tmzZ9WtWzcTUwMAAAD4Mzczf/HVq1dnOY6MjFS5cuW0d+9etWzZUsnJyZo3b56WLl2q1q1bS5IWLFig2rVrKzo6Wo8++qgZsQEAAAD8iUPtsUhOTpYk+fj4SJL27t2rW7duqU2bNvY5tWrVUqVKlbRz505TMgIAAAC4nalXLP4oMzNTL7/8spo3b6569epJkpKSkuTu7q5SpUplmevn56ekpKQ7vo7VapXVarUfp6Sk5FtmAAAAAL9xmGIxfPhwxcbGatu2bYZeZ/LkyZowYcI9SgUABa/Ka9+aHQH57OTbT5odAQDuOYdYCjVixAh988032rhxowICAuzn/f39dfPmTV29ejXL/HPnzsnf3/+OrxUeHq7k5GT74/Tp0/kZHQAAAIBMLhY2m00jRozQypUrtWHDBlWtWjXLeKNGjVSkSBFFRUXZzx07dkw///yzmjVrdsfX9PDwkJeXV5YHAAAAgPxl6lKo4cOHa+nSpfrqq6/k6elp3zfh7e2tYsWKydvbWwMHDlRoaKh8fHzk5eWlF198Uc2aNeOOUAAAAIADMbVYfPjhh5Kk4ODgLOcXLFig/v37S5KmTZsmFxcXde/eXVarVe3bt9fs2bMLOCkAAACAnJhaLGw2213nFC1aVLNmzdKsWbMKIBEAAACAv8IhNm8DAAAAcG4UCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhrmZHQD3XpXXvjU7AvLZybefNDsCAABAFlyxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhmarHYsmWLOnfurAoVKshisejLL7/MMt6/f39ZLJYsjw4dOpgTFgAAAEC2TC0WaWlpatCggWbNmpXtnA4dOigxMdH+WLZsWQEmBAAAAJAbbmb+4iEhIQoJCclxjoeHh/z9/QsoEQAAAIC/wuH3WGzatEnlypVTzZo19fzzz+vSpUtmRwIAAADwJ6ZesbibDh06qFu3bqpatari4+P1n//8RyEhIdq5c6dcXV3v+Byr1Sqr1Wo/TklJKai4AAAAQKHl0MXi73//u/1//+1vf1P9+vX14IMPatOmTXriiSfu+JzJkydrwoQJBRURAAAAgJxgKdQfVatWTb6+vjpx4kS2c8LDw5WcnGx/nD59ugATAgAAAIWTQ1+x+LMzZ87o0qVLKl++fLZzPDw85OHhUYCpAAAAAOS5WJw+fVoWi0UBAQGSpF27dmnp0qWqU6eOhgwZkqfXunbtWparDwkJCTpw4IB8fHzk4+OjCRMmqHv37vL391d8fLxGjRql6tWrq3379nmNDQAAACAf5XkpVJ8+fbRx40ZJUlJSktq2batdu3Zp9OjRmjhxYp5ea8+ePXr44Yf18MMPS5JCQ0P18MMPa+zYsXJ1dVVMTIy6dOmiGjVqaODAgWrUqJG2bt3KFQkAAADAweT5ikVsbKwCAwMlSZ999pnq1aun7du3a+3atRo2bJjGjh2b69cKDg6WzWbLdnzNmjV5jQcAAADABHm+YnHr1i37FYP169erS5cukqRatWopMTHx3qYDAAAA4BTyXCzq1q2rOXPmaOvWrVq3bp06dOggSTp79qzKlClzzwMCAAAAcHx5LhZTpkzRRx99pODgYPXu3VsNGjSQJK1atcq+RAoAAABA4ZLnPRbBwcG6ePGiUlJSVLp0afv5IUOGqHjx4vc0HAAAAADn8Je+IM9ms2nv3r366KOPlJqaKklyd3enWAAAAACFVJ6vWJw6dUodOnTQzz//LKvVqrZt28rT01NTpkyR1WrVnDlz8iMnAAAAAAeW5ysW//rXv9S4cWNduXJFxYoVs59/+umnFRUVdU/DAQAAAHAOeb5isXXrVu3YsUPu7u5ZzlepUkW//PLLPQsGAAAAwHnk+YpFZmamMjIybjt/5swZeXp63pNQAAAAAJxLnotFu3btFBERYT+2WCy6du2axo0bp44dO97LbAAAAACcRJ6XQk2dOlXt27dXnTp1dOPGDfXp00fHjx+Xr6+vli1blh8ZAQAAADi4PBeLgIAAHTx4UMuXL1dMTIyuXbumgQMHqm/fvlk2cwMAAAAoPPJcLCTJzc1Nzz777L3OAgAAAMBJ5blYLFq0KMfx55577i+HAQAAAOCc8lws/vWvf2U5vnXrln799Vf7N29TLAAAAIDCJ893hbpy5UqWx7Vr13Ts2DG1aNGCzdsAAABAIZXnYnEnDz30kN5+++3brmYAAAAAKBzuSbGQftvQffbs2Xv1cgAAAACcSJ73WKxatSrLsc1mU2JiombOnKnmzZvfs2AAAAAAnEeei0XXrl2zHFssFpUtW1atW7fW1KlT71UuAAAAAE4kz8UiMzMzP3IAAAAAcGL3bI8FAAAAgMIrV1csQkNDc/2C77///l8OAwAAAMA55apY7N+/P1cvZrFYDIUBAAAA4JxyVSw2btyY3zkAAAAAODH2WAAAAAAwLM93hZKkPXv26LPPPtPPP/+smzdvZhn74osv7kkwAAAAAM4jz1csli9frscee0xHjhzRypUrdevWLR06dEgbNmyQt7d3fmQEAAAA4ODyXCzeeustTZs2TV9//bXc3d01ffp0HT16VL169VKlSpXyIyMAAAAAB5fnYhEfH68nn3xSkuTu7q60tDRZLBa98sor+vjjj+95QAAAAACOL8/FonTp0kpNTZUkPfDAA4qNjZUkXb16Vb/++uu9TQcAAADAKeS6WPxeIFq2bKl169ZJknr27Kl//etfGjx4sHr37q0nnngif1ICAAAAcGi5vitU/fr11aRJE3Xt2lU9e/aUJI0ePVpFihTRjh071L17d40ZMybfggIAAABwXLkuFps3b9aCBQs0efJkvfnmm+revbsGDRqk1157LT/zAQAAAHACuV4KFRQUpPnz5ysxMVEffPCBTp48qccff1w1atTQlClTlJSUlJ85AQAAADiwPG/eLlGihAYMGKDNmzcrLi5OPXv21KxZs1SpUiV16dIlPzICAAAAcHB5LhZ/VL16df3nP//RmDFj5OnpqW+//fZe5QIAAADgRHK9x+LPtmzZovnz5+v//u//5OLiol69emngwIH3MhsAAAAAJ5GnYnH27FlFRkYqMjJSJ06c0GOPPaYZM2aoV69eKlGiRH5lBAAAAODgcl0sQkJCtH79evn6+uq5557TP//5T9WsWTM/swEAAABwErkuFkWKFNHnn3+uTp06ydXVNT8zAQAAAHAyuS4Wq1atys8cAAAAAJyYobtCAQAAAIBkcrHYsmWLOnfurAoVKshisejLL7/MMm6z2TR27FiVL19exYoVU5s2bXT8+HFzwgIAAADIlqnFIi0tTQ0aNNCsWbPuOP7OO+9oxowZmjNnjn744QeVKFFC7du3140bNwo4KQAAAICc/OXvsbgXQkJCFBIScscxm82miIgIjRkzRk899ZQkadGiRfLz89OXX36pv//97wUZFQAAAEAOHHaPRUJCgpKSktSmTRv7OW9vbzVt2lQ7d+7M9nlWq1UpKSlZHgAAAADyl8MWi6SkJEmSn59flvN+fn72sTuZPHmyvL297Y+KFSvma04AAAAADlws/qrw8HAlJyfbH6dPnzY7EgAAAHDfc9hi4e/vL0k6d+5clvPnzp2zj92Jh4eHvLy8sjwAAAAA5C+HLRZVq1aVv7+/oqKi7OdSUlL0ww8/qFmzZiYmAwAAAPBnpt4V6tq1azpx4oT9OCEhQQcOHJCPj48qVaqkl19+WW+88YYeeughVa1aVa+//roqVKigrl27mhcaAAAAwG1MLRZ79uxRq1at7MehoaGSpH79+ikyMlKjRo1SWlqahgwZoqtXr6pFixZavXq1ihYtalZkAAAAAHdgarEIDg6WzWbLdtxisWjixImaOHFiAaYCAAAAkFcOu8cCAAAAgPOgWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIcuFuPHj5fFYsnyqFWrltmxAAAAAPyJm9kB7qZu3bpav369/djNzeEjAwAAAIWOw/8t3c3NTf7+/mbHAAAAAJADh14KJUnHjx9XhQoVVK1aNfXt21c///xzjvOtVqtSUlKyPAAAAADkL4cuFk2bNlVkZKRWr16tDz/8UAkJCQoKClJqamq2z5k8ebK8vb3tj4oVKxZgYgAAAKBwcuhiERISop49e6p+/fpq3769vvvuO129elWfffZZts8JDw9XcnKy/XH69OkCTAwAAAAUTg6/x+KPSpUqpRo1aujEiRPZzvHw8JCHh0cBpgIAAADg0Fcs/uzatWuKj49X+fLlzY4CAAAA4A8culiMHDlSmzdv1smTJ7Vjxw49/fTTcnV1Ve/evc2OBgAAAOAPHHop1JkzZ9S7d29dunRJZcuWVYsWLRQdHa2yZcuaHQ0AAADAHzh0sVi+fLnZEQAAAADkgkMvhQIAAADgHCgWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADHOKYjFr1ixVqVJFRYsWVdOmTbVr1y6zIwEAAAD4A4cvFv/9738VGhqqcePGad++fWrQoIHat2+v8+fPmx0NAAAAwP84fLF4//33NXjwYA0YMEB16tTRnDlzVLx4cc2fP9/saAAAAAD+x6GLxc2bN7V37161adPGfs7FxUVt2rTRzp07TUwGAAAA4I/czA6Qk4sXLyojI0N+fn5Zzvv5+eno0aN3fI7VapXVarUfJycnS5JSUlLyL6iDybT+anYE5LPC9Pu5MOIzfP/jM3x/4zN8/ytMn+Hf36vNZrvrXIcuFn/F5MmTNWHChNvOV6xY0YQ0QP7wjjA7AQAj+AwDzq0wfoZTU1Pl7e2d4xyHLha+vr5ydXXVuXPnspw/d+6c/P397/ic8PBwhYaG2o8zMzN1+fJllSlTRhaLJV/zouClpKSoYsWKOn36tLy8vMyOAyCP+AwDzo3P8P3PZrMpNTVVFSpUuOtchy4W7u7uatSokaKiotS1a1dJvxWFqKgojRgx4o7P8fDwkIeHR5ZzpUqVyuekMJuXlxd/oAFOjM8w4Nz4DN/f7nal4ncOXSwkKTQ0VP369VPjxo0VGBioiIgIpaWlacCAAWZHAwAAAPA/Dl8snnnmGV24cEFjx45VUlKSGjZsqNWrV9+2oRsAAACAeRy+WEjSiBEjsl36hMLNw8ND48aNu235GwDnwGcYcG58hvFHFltu7h0FAAAAADlw6C/IAwAAAOAcKBYAAAAADKNYAAAAADCMYgEAAADAMIoFnMbs2bPNjgAAAIBscFcoOA0fHx81adJECxYsyNXXygNwXLt379ayZcsUFxcnSapRo4b69Omjxo0bm5wMwF+VmJioN998UzNnzjQ7CkzCFQs4jdjYWLm5ualevXr69NNPzY4D4C8aNWqUmjZtqrlz5+rMmTM6c+aMPvnkEzVt2lRhYWFmxwOQg0OHDmnmzJn6+OOPdfXqVUnSxYsX9corr6hatWrauHGjuQFhKq5YwOlERkYqNDRUrVq10ujRo+XmlvV7HuvXr29SMgB3s3DhQg0bNkzvvvuuhg4dqiJFikiSbt26pQ8//FBhYWH66KOP9Nxzz5mcFMCfrVq1Sj169FB6erokqVq1avrkk0/Uq1cvNWrUSC+//LI6dOhgckqYiWIBp7R+/Xp16NBBNptNNptNFovF/s+MjAyz4wHIRmBgoHr37q1XXnnljuPvv/++li9frl27dhVwMgB3ExgYqObNm2vSpEmaO3euQkNDVbduXc2fP19NmjQxOx4cAMUCTuf999/X66+/rp49e+r111+/7YpF5cqVTUoG4G5KlCihH3/8UdWqVbvj+E8//aS//e1vSktLK+BkAO7G29tbe/fuVfXq1ZWRkSEPDw+tXr1abdq0MTsaHITb3acAjuGnn35Sv379dPz4cS1dulRPPfWU2ZEA5JGrq6tu3ryZ7fitW7fk6upagIkA5FZqaqq8vLwk/fZZLlasWLY/JEDhRLGA06hfv746dOiglStXytfX1+w4AP6CRx55REuWLNGkSZPuOL548WI98sgjBZwKQG6tWbNG3t7ekqTMzExFRUUpNjY2y5wuXbqYEQ0OgKVQcBqffvqpnn32WbNjADDgm2++UdeuXRUaGqpXX31Vfn5+kqSkpCRNnTpVERERWrlypTp16mRyUgB/5uJy95uJstexcON2s3AaVqs1x/HU1FQNGjSogNIA+Cs6deqkadOmafr06apQoYJ8fHzk4+OjBx54QDNmzNB7771HqQAcVGZm5l0flIrCjSsWcBre3t4KCgrS3Llz5e/vn2VszZo1Gjx4sEqXLq2DBw+alBBAbp05c0YrVqzQ8ePHJf32BXndu3dXxYoVTU4G4K/KzMzUd999xw8HCjGKBZzGyZMnNWDAAMXExGjmzJnq3bu3UlNT9fLLL2vx4sUaOXKkJkyYYL8vPgAAyH8nTpzQ/PnzFRkZqQsXLujWrVtmR4JJWAoFp1GlShVt3LhRr7/+ugYPHqxOnTqpXr16io6O1vbt2/XWW29RKgAHFxcXd9t3VERFRalVq1YKDAzUW2+9ZVIyAHlx/fp1LVq0SC1btlTNmjW1Y8cOjR07VmfOnDE7GkxEsYDTGTp0qNq1a6fvvvtOly9f1vLly/liHsBJhIWF6ZtvvrEfJyQkqHPnznJ3d1ezZs00efJkRUREmBcQQI52796toUOHyt/fXxEREXrqqadksVg0e/ZsDRs2zH5DBhROFAs4le3bt6tBgwY6evSoVq9erZCQEDVr1kzTp083OxqAXNizZ49CQkLsx0uWLFGNGjW0Zs0aTZ8+XREREYqMjDQvIIBs1a9fXz179lSZMmW0Y8cO7du3T6+++qosFovZ0eAgKBZwGq+++qpat26tzp07a9++fWrXrp0+++wzzZs3T2+88YaCg4OVkJBgdkwAObh48aICAgLsxxs3blTnzp3tx8HBwTp58qQJyQDczbFjx9SyZUu1atVKderUMTsOHBDFAk7jq6++0vr16zV16lQVLVrUfv6ZZ55RbGysvL29Vb9+fRMTArgbHx8fJSYmSvrtDjJ79uzRo48+ah+/efOmuKcI4Jh++ukn1axZU88//7wCAgI0cuRI7d+/nysWsKNYwGnExMQoKCjojmN+fn766quvNHv27AJOBSAvgoODNWnSJJ0+fVoRERHKyMhQcHCwffzw4cOqUqWKafkAZO+BBx7Q6NGjdeLECS1evFhJSUlq3ry50tPTFRkZqbi4OLMjwmQUCziN4sWL5zh+5MgRvsMCcHBvvvmmjhw5osqVK2vUqFF65513VKJECfv44sWL1bp1axMTAsjOli1blJ6eLklq3bq1Pv30UyUmJmrmzJnasGGDatWqxcqBQo7vsYBTS0tL0/LlyzVv3jxFR0erTp06io2NNTsWgBykp6fr0KFDKlu2rNzd3eXu7i4vLy9J0sGDBxUQEKAyZcqYnBLAn7m6uioxMVHlypW74/iBAwc0f/58zZgxo4CTwVFwxQJOafv27frnP/8pPz8/DRkyRI899pgOHz5MqQCcwLVr1/Txxx+rfv368vPzU+nSpeXv76/w8HA99NBDlArAQd3tZ9ENGzakVBRyXLGA0zh//rwiIyM1f/58JScnq3fv3urTp4+aNWumgwcPcocKwAlcvnxZzZo10y+//KK+ffuqdu3akn7bW7F06VLVqlVL27ZtU0xMjKKjo/XSSy+ZnBjA71xcXHTu3DmVLVvW7ChwUG5mBwByq3LlyurRo4emT5+utm3bysWFC26As5k4caLc3d0VHx9/2xdpTZw4Ue3atdM//vEPrV27lp98Ag6of//+8vDwyHHOF198UUBp4GgoFnAalStX1rZt21SpUiVVrlxZtWrVMjsSgDz68ssv9dFHH93x23n9/f31zjvvqGPHjho3bpz69etnQkIAOfH09FSxYsXMjgEHRbGA0zh69Ki2b9+uefPmqUmTJqpRo4aeffZZSeIe2oCTSExMVN26dbMdr1evnlxcXDRu3LgCTAUgt2bMmJHt5m2AtSRwKs2bN9f8+fOVmJioYcOGacWKFcrIyNALL7ygTz75RBcuXDA7IoAc+Pr65vjN2gkJCfylBXBQ/BAPd8PmbTi9I0eOaN68eVq8eLEuX76sW7dumR0JQDb++c9/Kj4+XuvWrZO7u3uWMavVqvbt26tatWqaP3++SQkBZMfFxUVJSUmUf2SLYoH7Rnp6ulatWqVu3bqZHQVANs6cOaPGjRvLw8NDw4cPV61atWSz2XTkyBHNnj1bVqtVu3fvVqVKlcyOCuBP3nrrLdWvX1+dOnWyn1u0aJHGjRuntLQ0de3aVR988MFdN3fj/kWxwH3j6NGj6tKli+Li4syOAiAHCQkJeuGFF7R27Vr7ffEtFovatm2rmTNnqnr16iYnBHAnHTp0UKtWrRQWFiZJ+vHHH/XII4+of//+ql27tt59910NHTpU48ePNzcoTEOxwH3j4MGDeuSRR5SRkWF2FAC5cOXKFR0/flySVL16dfn4+JicCEBOypcvr6+//lqNGzeWJI0ePVqbN2/Wtm3bJEkrVqzQuHHjdPjwYTNjwkTcFQoAYIrSpUsrMDDQ7BgAcunKlStZbhW9efNmhYSE2I+bNGmi06dPmxENDoK7QgEAAOCu/Pz8lJCQIEm6efOm9u3bp0cffdQ+npqaqiJFipgVDw6AYgEAAIC76tixo1577TVt3bpV4eHhKl68uIKCguzjMTExevDBB01MCLOxFApOo3Tp0jneQzs9Pb0A0wAAULhMmjRJ3bp10+OPP66SJUtq4cKFWW4bPX/+fLVr187EhDAbm7fhNBYuXJiref369cvnJAAAFF7JyckqWbKkXF1ds5y/fPmySpYsedt31KDwoFjgvpKRkXHbH3QAAADIf+yxwH0hLi5OYWFhCggIMDsKAABAoUSxgNP69ddftWDBAgUFBalOnTravHmzQkNDzY4FAABQKLF5G04nOjpac+fO1YoVK1SpUiUdOXJEGzduzHJnCgAAABQsrljAaUydOlV169ZVjx49VLp0aW3ZskU//vijLBaLypQpY3Y8AACAQo3N23Aabm5uCgsL08SJE7Ns0C5SpIgOHjyoOnXqmJgOAACgcOOKBZzGpEmTtGLFClWtWlVhYWGKjY01OxIAAAD+h2IBpxEeHq64uDgtXrxYSUlJatq0qRo0aCCbzaYrV66YHQ8AAKBQYykUnFZKSoqWLVumefPmae/evWratKl69OjBnaEAAABMQLHAfSE2Nlbz5s3TkiVLdP78ebPjAAAAFDrcbhZO4/r164qKilKnTp0k/bY0ymq12sfd3NwUHx9vVjwAAIBCjWIBp7Fw4UJ9++239mIxc+ZM1a1bV8WKFZMkHTt2TBUqVNArr7xiZkwAAIBCiaVQcBpBQUEaNWqUOnfuLEny9PTUwYMHVa1aNUnSp59+qlmzZmnnzp1mxgQAACiUuCsUnMaJEyf0t7/9zX5ctGhRubj8/9/CgYGBOnz4sBnRAAAACj2WQsFpXL16NcueigsXLmQZz8zMzDIOAACAgsMVCziNgICAHL8ULyYmRgEBAQWYCAAAAL+jWMBpdOzYUWPHjtWNGzduG7t+/bomTJigJ5980oRkAAAAYPM2nMa5c+fUsGFDubu7a8SIEapRo4ak3+4GNXPmTKWnp2v//v3y8/MzOSkAAEDhQ7GAU0lISNDzzz+vdevW6fffuhaLRW3bttXs2bPtd4gCAABAwaJYwCldvnxZJ06ckCRVr15dPj4+JicCAAAo3CgWAAAAAAxj8zYAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAg35w8eVIWi0UHDhwwOwoAIJ9RLAAAAAAYRrEAAAAAYBjFAgBgWGZmpt555x1Vr15dHh4eqlSpkt58883b5mVkZGjgwIGqWrWqihUrppo1a2r69OlZ5mzatEmBgYEqUaKESpUqpebNm+vUqVOSpIMHD6pVq1by9PSUl5eXGjVqpD179hTIewQA5MzN7AAAAOcXHh6uTz75RNOmTVOLFi2UmJioo0eP3jYvMzNTAQEBWrFihcqUKaMdO3ZoyJAhKl++vHr16qX09HR17dpVgwcP1rJly3Tz5k3t2rVLFotFktS3b189/PDD+vDDD+Xq6qoDBw6oSJEiBf12AQB3wDdvAwAMSU1NVdmyZTVz5kwNGjQoy9jJkydVtWpV7d+/Xw0bNrzj80eMGKGkpCR9/vnnunz5ssqUKaNNmzbp8ccfv22ul5eXPvjgA/Xr1y8/3goAwACWQgEADDly5IisVqueeOKJXM2fNWuWGjVqpLJly6pkyZL6+OOP9fPPP0uSfHx81L9/f7Vv316dO3fW9OnTlZiYaH9uaGioBg0apDZt2ujtt99WfHx8vrwnAEDeUSwAAIYUK1Ys13OXL1+ukSNHauDAgVq7dq0OHDigAQMG6ObNm/Y5CxYs0M6dO/XYY4/pv//9r2rUqKHo6GhJ0vjx43Xo0CE9+eST2rBhg+rUqaOVK1fe8/cEAMg7lkIBAAy5ceOGfHx8NGPGjLsuhXrxxRd1+PBhRUVF2ee0adNGFy9ezPa7Lpo1a6YmTZpoxowZt4317t1baWlpWrVq1T19TwCAvOOKBQDAkKJFiyosLEyjRo3SokWLFB8fr+joaM2bN++2uQ899JD27NmjNWvWKC4uTq+//rp2795tH09ISFB4eLh27typU6dOae3atTp+/Lhq166t69eva8SIEdq0aZNOnTql7du3a/fu3apdu3ZBvl0AQDa4KxQAwLDXX39dbm5uGjt2rM6ePavy5ctr2LBht80bOnSo9u/fr2eeeUYWi0W9e/fWCy+8oO+//16SVLx4cR09elQLFy7UpUuXVL58eQ0fPlxDhw5Venq6Ll26pOeee07nzp2Tr6+vunXrpgkTJhT02wUA3AFLoQAAAAAYxlIoAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAA3FVkZKQsFov94ebmpgceeED9+/fXL7/8YnY8AIADcDM7AADAeUycOFFVq1bVjRs3FB0drcjISG3btk2xsbEqWrSo2fEAACaiWAAAci0kJESNGzeWJA0aNEi+vr6aMmWKVq1apV69epmcDgBgJpZCAQD+sqCgIElSfHx8lvMbNmxQUFCQSpQooVKlSumpp57SkSNH7OMxMTGyWCxatWqV/dzevXtlsVj0yCOPZHmtkJAQNW3aNNsMmzZtyrJM64+PKlWq3IN3CQDIDa5YAAD+spMnT0qSSpcubT+3fv16hYSEqFq1aho/fryuX7+uDz74QM2bN9e+fftUpUoV1atXT6VKldKWLVvUpUsXSdLWrVvl4uKigwcPKiUlRV5eXsrMzNSOHTs0ZMiQbDPUrl1bixcvznLu6tWrCg0NVbly5e79mwYA3BHFAgCQa8nJybp48aJu3LihH374QRMmTJCHh4c6depkn/Pvf/9bPj4+2rlzp3x8fCRJXbt21cMPP6xx48Zp4cKFcnFxUfPmzbV161b787Zu3aquXbvqq6++0o4dO9ShQwd7yfj9ysid+Pn56dlnn7Uf22w2denSRR4eHoqMjLz3/xIAAHfEUigAQK61adNGZcuWVcWKFdWjRw+VKFFCq1atUkBAgCQpMTFRBw4cUP/+/e2lQpLq16+vtm3b6rvvvrOfCwoK0r59+5SWliZJ2rZtmzp27KiGDRvaC8fWrVtlsVjUokWLXGecNGmSvvnmG0VGRqpOnTr34m0DAHKBKxYAgFybNWuWatSooeTkZM2fP19btmyRh4eHffzUqVOSpJo1a9723Nq1a2vNmjVKS0tTiRIlFBQUpPT0dO3cuVMVK1bU+fPnFRQUpEOHDmUpFnXq1MlSUnKyevVqTZgwQeHh4erevfs9eMcAgNyiWAAAci0wMNB+V6iuXbuqRYsW6tOnj44dO6aSJUvm6bUaN26sokWLasuWLapUqZLKlSunGjVqKCgoSLNnz5bVatXWrVv19NNP5+r1EhIS1LdvX7Vt21ZvvPFGnt8bAMAYlkIBAP4SV1dXTZ48WWfPntXMmTMlSZUrV5YkHTt27Lb5R48ela+vr0qUKCFJcnd3V2BgoLZu3aqtW7fa91EEBQXJarVqyZIlOnfunFq2bHnXLNevX1e3bt1UqlQpLVu2TC4u/OcNAAoaf/ICAP6y4OBgBQYGKiIiQjdu3FD58uXVsGFDLVy4UFevXrXPi42N1dq1a9WxY8cszw8KCtIPP/ygjRs32ouFr6+vateurSlTptjn3M2wYcMUFxenlStXZrlDFQCg4FAsAACG/Pvf/9a5c+fsd2B69913denSJTVr1kzvvfeeJk2apNatW8vb21vjx4/P8tygoCBdv35dp0+fzlIgWrZsqbi4OFWpUsW+MTw73377rRYtWqSQkBDFxMTo008/tT++/PLLe/xuAQDZsdhsNpvZIQAAji0yMlIDBgzQ7t277XssfpeZmakaNWpI+m0JlKurq6KiojRu3Djt27dPRYoU0eOPP64pU6aodu3aWZ6bmpqq0qVLq3jx4rpy5YpcXV0lSUuWLNGzzz6rf/zjH1q0aFGust1J5cqV7d+1AQDIXxQLAAAAAIaxFAoAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhrmZHSC/ZWZm6uzZs/L09JTFYjE7DgAAAOA0bDabUlNTVaFCBbm45HxN4r4vFmfPnlXFihXNjgEAAAA4rdOnTysgICDHOfd9sfD09JT0278MLy8vk9MAAAAAziMlJUUVK1a0/506J/d9sfh9+ZOXlxfFAgAAAPgLcrOlgM3bAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMMzN7AC496q89q3ZEZDPTr79pNkRkI/4DN//+Azf3/gM3//4DN8ZVywAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYaYWi8mTJ6tJkyby9PRUuXLl1LVrVx07dizLnBs3bmj48OEqU6aMSpYsqe7du+vcuXMmJQYAAABwJ6YWi82bN2v48OGKjo7WunXrdOvWLbVr105paWn2Oa+88oq+/vprrVixQps3b9bZs2fVrVs3E1MDAAAA+DM3M3/x1atXZzmOjIxUuXLltHfvXrVs2VLJycmaN2+eli5dqtatW0uSFixYoNq1ays6OlqPPvqoGbEBAAAA/IlD7bFITk6WJPn4+EiS9u7dq1u3bqlNmzb2ObVq1VKlSpW0c+dOUzICAAAAuJ2pVyz+KDMzUy+//LKaN2+uevXqSZKSkpLk7u6uUqVKZZnr5+enpKSkO76O1WqV1Wq1H6ekpORbZgAAAAC/cZgrFsOHD1dsbKyWL19u6HUmT54sb29v+6NixYr3KCEAAACA7DhEsRgxYoS++eYbbdy4UQEBAfbz/v7+unnzpq5evZpl/rlz5+Tv73/H1woPD1dycrL9cfr06fyMDgAAAEAmFwubzaYRI0Zo5cqV2rBhg6pWrZplvFGjRipSpIiioqLs544dO6aff/5ZzZo1u+Nrenh4yMvLK8sDAAAAQP4ydY/F8OHDtXTpUn311Vfy9PS075vw9vZWsWLF5O3trYEDByo0NFQ+Pj7y8vLSiy++qGbNmnFHKAAAAMCBmFosPvzwQ0lScHBwlvMLFixQ//79JUnTpk2Ti4uLunfvLqvVqvbt22v27NkFnBQAAABATkwtFjab7a5zihYtqlmzZmnWrFkFkAgAAADAX+EQm7cBAAAAODeKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAwztVhs2bJFnTt3VoUKFWSxWPTll19mGe/fv78sFkuWR4cOHcwJCwAAACBbphaLtLQ0NWjQQLNmzcp2TocOHZSYmGh/LFu2rAATAgAAAMgNNzN/8ZCQEIWEhOQ4x8PDQ/7+/gWUCAAAAMBf4fB7LDZt2qRy5cqpZs2aev7553Xp0qUc51utVqWkpGR5AAAAAMhfDl0sOnTooEWLFikqKkpTpkzR5s2bFRISooyMjGyfM3nyZHl7e9sfFStWLMDEAAAAQOFk6lKou/n73/9u/99/+9vfVL9+fT344IPatGmTnnjiiTs+Jzw8XKGhofbjlJQUygUAAACQzxz6isWfVatWTb6+vjpx4kS2czw8POTl5ZXlAQAAACB/OVWxOHPmjC5duqTy5cubHQUAAADAH5i6FOratWtZrj4kJCTowIED8vHxkY+PjyZMmKDu3bvL399f8fHxGjVqlKpXr6727dubmBoAAADAn5laLPbs2aNWrVrZj3/fG9GvXz99+OGHiomJ0cKFC3X16lVVqFBB7dq106RJk+Th4WFWZAAAAAB3YGqxCA4Ols1my3Z8zZo1BZgGAAAAwF/lVHssAAAAADgmigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADAsz8Xi9OnTOnPmjP14165devnll/Xxxx/f02AAAAAAnEeei0WfPn20ceNGSVJSUpLatm2rXbt2afTo0Zo4ceI9DwgAAADA8eW5WMTGxiowMFCS9Nlnn6levXrasWOHlixZosjIyHudDwAAAIATyHOxuHXrljw8PCRJ69evV5cuXSRJtWrVUmJi4r1NBwAAAMAp5LlY1K1bV3PmzNHWrVu1bt06dejQQZJ09uxZlSlT5p4HBAAAAOD48lwspkyZoo8++kjBwcHq3bu3GjRoIElatWqVfYkUAAAAgMLFLa9PCA4O1sWLF5WSkqLSpUvbzw8ZMkTFixe/p+EAAAAAOIe/9D0WNptNe/fu1UcffaTU1FRJkru7O8UCAAAAKKTyfMXi1KlT6tChg37++WdZrVa1bdtWnp6emjJliqxWq+bMmZMfOQEAAAA4sDxfsfjXv/6lxo0b68qVKypWrJj9/NNPP62oqKh7Gg4AAACAc8jzFYutW7dqx44dcnd3z3K+SpUq+uWXX+5ZMAAAAADOI89XLDIzM5WRkXHb+TNnzsjT0/OehAIAAADgXPJcLNq1a6eIiAj7scVi0bVr1zRu3Dh17NjxXmYDAAAA4CTyvBRq6tSpat++verUqaMbN26oT58+On78uHx9fbVs2bL8yAgAAADAweW5WAQEBOjgwYNavny5YmJidO3aNQ0cOFB9+/bNspkbAAAAQOGR52IhSW5ubnr22WfvdRYAAAAATirPxWLRokU5jj/33HN/OQwAAAAA55TnYvGvf/0ry/GtW7f066+/2r95m2IBAAAAFD55vivUlStXsjyuXbumY8eOqUWLFmzeBgAAAAqpPBeLO3nooYf09ttv33Y1AwAAAEDhcE+KhfTbhu6zZ8/eq5cDAAAA4ETyvMdi1apVWY5tNpsSExM1c+ZMNW/e/J4FAwAAAOA88lwsunbtmuXYYrGobNmyat26taZOnXqvcgEAAABwInkuFpmZmfmRAwAAAIATu2d7LAAAAAAUXrm6YhEaGprrF3z//ff/chgAAAAAzilXxWL//v25ejGLxWIoDAAAAADnlKtisXHjxvzOAQAAAMCJsccCAAAAgGF5viuUJO3Zs0efffaZfv75Z928eTPL2BdffHFPggEAAABwHnm+YrF8+XI99thjOnLkiFauXKlbt27p0KFD2rBhg7y9vfMjIwAAAAAHl+di8dZbb2natGn6+uuv5e7urunTp+vo0aPq1auXKlWqlB8ZAQAAADi4PBeL+Ph4Pfnkk5Ikd3d3paWlyWKx6JVXXtHHH398zwMCAAAAcHx5LhalS5dWamqqJOmBBx5QbGysJOnq1av69ddf7206AAAAAE4h18Xi9wLRsmVLrVu3TpLUs2dP/etf/9LgwYPVu3dvPfHEE/mTEgAAAIBDy/VdoerXr68mTZqoa9eu6tmzpyRp9OjRKlKkiHbs2KHu3btrzJgx+RYUAAAAgOPKdbHYvHmzFixYoMmTJ+vNN99U9+7dNWjQIL322mv5mQ8AAACAE8j1UqigoCDNnz9fiYmJ+uCDD3Ty5Ek9/vjjqlGjhqZMmaKkpKT8zAkAAADAgeV583aJEiU0YMAAbd68WXFxcerZs6dmzZqlSpUqqUuXLnl6rS1btqhz586qUKGCLBaLvvzyyyzjNptNY8eOVfny5VWsWDG1adNGx48fz2tkAAAAAPksz8Xij6pXr67//Oc/GjNmjDw9PfXtt9/m6flpaWlq0KCBZs2adcfxd955RzNmzNCcOXP0ww8/qESJEmrfvr1u3LhhJDYAAACAeyzXeyz+bMuWLZo/f77+7//+Ty4uLurVq5cGDhyYp9cICQlRSEjIHcdsNpsiIiI0ZswYPfXUU5KkRYsWyc/PT19++aX+/ve//9XoAAAAAO6xPF2xOHv2rN566y3VqFFDwcHBOnHihGbMmKGzZ8/qk08+0aOPPnrPgiUkJCgpKUlt2rSxn/P29lbTpk21c+fOe/brAAAAADAu11csQkJCtH79evn6+uq5557TP//5T9WsWTPfgv2+GdzPzy/LeT8/vxw3ilutVlmtVvtxSkpK/gQEAAAAYJfrYlGkSBF9/vnn6tSpk1xdXfMzkyGTJ0/WhAkTzI4BAAAAFCq5Xgq1atUqPfXUUwVWKvz9/SVJ586dy3L+3Llz9rE7CQ8PV3Jysv1x+vTpfM0JAAAAwOBdofJT1apV5e/vr6ioKPu5lJQU/fDDD2rWrFm2z/Pw8JCXl1eWBwAAAID89ZfvCnUvXLt2TSdOnLAfJyQk6MCBA/Lx8VGlSpX08ssv64033tBDDz2kqlWr6vXXX1eFChXUtWtX80IDAAAAuI2pxWLPnj1q1aqV/Tg0NFSS1K9fP0VGRmrUqFFKS0vTkCFDdPXqVbVo0UKrV69W0aJFzYoMAAAA4A5MLRbBwcGy2WzZjlssFk2cOFETJ04swFQAAAAA8sph91gAAAAAcB4UCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABjm0MVi/PjxslgsWR61atUyOxYAAACAP3EzO8Dd1K1bV+vXr7cfu7k5fGQAAACg0HH4v6W7ubnJ39/f7BgAAAAAcuDQS6Ek6fjx46pQoYKqVaumvn376ueff85xvtVqVUpKSpYHAAAAgPzl0MWiadOmioyM1OrVq/Xhhx8qISFBQUFBSk1NzfY5kydPlre3t/1RsWLFAkwMAAAAFE4OXSxCQkLUs2dP1a9fX+3bt9d3332nq1ev6rPPPsv2OeHh4UpOTrY/Tp8+XYCJAQAAgMLJ4fdY/FGpUqVUo0YNnThxIts5Hh4e8vDwKMBUAAAAABz6isWfXbt2TfHx8SpfvrzZUQAAAAD8gUMXi5EjR2rz5s06efKkduzYoaefflqurq7q3bu32dEAAAAA/IFDL4U6c+aMevfurUuXLqls2bJq0aKFoqOjVbZsWbOjAQAAAPgDhy4Wy5cvNzsCAAAAgFxw6KVQAAAAAJwDxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGFOUSxmzZqlKlWqqGjRomratKl27dpldiQAAAAAf+DwxeK///2vQkNDNW7cOO3bt08NGjRQ+/btdf78ebOjAQAAAPgfhy8W77//vgYPHqwBAwaoTp06mjNnjooXL6758+ebHQ0AAADA/7iZHSAnN2/e1N69exUeHm4/5+LiojZt2mjnzp13fI7VapXVarUfJycnS5JSUlLyN6wDybT+anYE5LPC9Pu5MOIzfP/jM3x/4zN8/ytMn+Hf36vNZrvrXIcuFhcvXlRGRob8/PyynPfz89PRo0fv+JzJkydrwoQJt52vWLFivmQEzOAdYXYCAEbwGQacW2H8DKempsrb2zvHOQ5dLP6K8PBwhYaG2o8zMzN1+fJllSlTRhaLxcRkyA8pKSmqWLGiTp8+LS8vL7PjAMgjPsOAc+MzfP+z2WxKTU1VhQoV7jrXoYuFr6+vXF1dde7cuSznz507J39//zs+x8PDQx4eHlnOlSpVKr8iwkF4eXnxBxrgxPgMA86Nz/D97W5XKn7n0Ju33d3d1ahRI0VFRdnPZWZmKioqSs2aNTMxGQAAAIA/cugrFpIUGhqqfv36qXHjxgoMDFRERITS0tI0YMAAs6MBAAAA+B+HLxbPPPOMLly4oLFjxyopKUkNGzbU6tWrb9vQjcLJw8ND48aNu235GwDnwGcYcG58hvFHFltu7h0FAAAAADlw6D0WAAAAAJwDxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAk5j9uzZZkcAAADZSExM1IgRI8yOARNxu1k4DR8fHzVp0kQLFixQhQoVzI4DwIDdu3dr2bJliouLkyTVqFFDffr0UePGjU1OBiAnhw4d0saNG+Xu7q5evXqpVKlSunjxot58803NmTNH1apV06FDh8yOCZNwxQJOIzY2Vm5ubqpXr54+/fRTs+MA+ItGjRqlpk2bau7cuTpz5ozOnDmjTz75RE2bNlVYWJjZ8QBkY9WqVXr44Yf10ksvadiwYWrcuLE2btyo2rVr68iRI1q5ciWlopDjigWcTmRkpEJDQ9WqVSuNHj1abm5Zv0C+fv36JiUDcDcLFy7UsGHD9O6772ro0KEqUqSIJOnWrVv68MMPFRYWpo8++kjPPfecyUkB/FlgYKCaN2+uSZMmae7cuQoNDVXdunU1f/58NWnSxOx4cAAUCzil9evXq0OHDrLZbLLZbLJYLPZ/ZmRkmB0PQDYCAwPVu3dvvfLKK3ccf//997V8+XLt2rWrgJMBuBtvb2/t3btX1atXV0ZGhjw8PLR69Wq1adPG7GhwECyFgtN5//339dRTT+nZZ59VXFycEhIS9NNPP9n/CcBxHTp0SE899VS24127dmUpBeCgUlNT5eXlJUlydXVVsWLFVK1aNZNTwZG43X0K4Bh++ukn9evXT8ePH9fSpUtz/MsJAMfk6uqqmzdvZjt+69Ytubq6FmAiAHmxZs0aeXt7S5IyMzMVFRWl2NjYLHO6dOliRjQ4AJZCwWmULFlSHTp00Jw5c+Tr62t2HAB/QXBwsIKCgjRp0qQ7jo8ZM0bbtm3Tpk2bCjYYgLtycbn7QheWJBduXLGA05gzZ46effZZs2MAMGDkyJHq2rWrrFarXn31Vfn5+UmSkpKSNHXqVEVERGjlypUmpwRwJ5mZmWZHgINjjwWchtVqzXE8NTVVgwYNKqA0AP6KTp06adq0aZo+fboqVKggHx8f+fj46IEHHtCMGTP03nvvqVOnTmbHBPAXZGZm6ptvvjE7BkzEUig4DW9vbwUFBWnu3Lny9/fPMrZmzRoNHjxYpUuX1sGDB01KCCC3zpw5oxUrVuj48eOSfvuCvO7du6tixYomJwOQVydOnND8+fMVGRmpCxcu6NatW2ZHgkkoFnAaJ0+e1IABAxQTE6OZM2eqd+/eSk1N1csvv6zFixdr5MiRmjBhgv2++AAAIH9cv35dK1as0Ny5c7V9+3YFBQXp73//u55++mn7EkcUPhQLOJ2IiAiNGTNGwcHB+vHHH1WyZElFRkby5TyAE4iLi9PVq1cVGBhoPxcVFaU33nhDaWlp6tq1q/7zn/+YmBBATnbv3q25c+dq+fLlevDBB9W3b1+FhYUpJiZGderUMTseTMYeCzidoUOHql27dvruu+90+fJlLV++nFIBOImwsLAsa7ATEhLUuXNnubu7q1mzZpo8ebIiIiLMCwggW/Xr11fPnj1VpkwZ7dixQ/v27dOrr74qi8VidjQ4CIoFnMr27dvVoEEDHT16VKtXr1ZISIiaNWum6dOnmx0NQC7s2bNHISEh9uMlS5aoRo0aWrNmjaZPn66IiAhFRkaaFxBAto4dO6aWLVuqVatWXJ3AHVEs4DReffVVtW7dWp07d9a+ffvUrl07ffbZZ5o3b57eeOMNBQcHKyEhweyYAHJw8eJFBQQE2I83btyozp0724+Dg4N18uRJE5IBuJuffvpJNWvW1PPPP6+AgACNHDlS+/fv54oF7CgWcBpfffWV1q9fr6lTp6po0aL2888884xiY2Pl7e2t+vXrm5gQwN34+PgoMTFR0m+3ptyzZ48effRR+/jNmzfF1j/AMT3wwAMaPXq0Tpw4ocWLFyspKUnNmzdXenq6IiMjFRcXZ3ZEmIxiAacRExOjoKCgO475+fnpq6++0uzZsws4FYC8CA4O1qRJk3T69GlFREQoIyNDwcHB9vHDhw+rSpUqpuUDkL0tW7YoPT1dktS6dWt9+umnSkxM1MyZM7VhwwbVqlWLH/AVchQLOI3ixYvnOH7kyBG+wwJwcG+++aaOHDmiypUra9SoUXrnnXdUokQJ+/jixYvVunVrExMCyE6rVq10+fLlLOe8vb31wgsvaM+ePdq3b1+WHxSg8OF2s3BqaWlpWr58uebNm6fo6GjVqVNHsbGxZscCkIP09HQdOnRIZcuWlbu7u9zd3eXl5SVJOnjwoAICAlSmTBmTUwL4MxcXFyUlJalcuXJmR4GD4ooFnNL27dv1z3/+U35+fhoyZIgee+wxHT58mFIBOIFr167p448/Vv369eXn56fSpUvL399f4eHheuihhygVgANjozZywhULOI3z588rMjJS8+fPV3Jysnr37q0+ffqoWbNmOnjwILe+A5zA5cuX1axZM/3yyy/q27evateuLem3vRVLly5VrVq1tG3bNsXExCg6OlovvfSSyYkB/M7FxUUhISHy8PDIcd4XX3xRQIngaNzMDgDkVuXKldWjRw9Nnz5dbdu2lYsLF9wAZzNx4kS5u7srPj5efn5+t421a9dO//jHP7R27VrNmDHDpJQAsuPp6alixYqZHQMOimIBp1G5cmVt27ZNlSpVUuXKlVWrVi2zIwHIoy+//FIfffTRbaVCkvz9/fXOO++oY8eOGjdunPr162dCQgA5mTFjBnsskC1+5AuncfToUfut7Zo0aaJGjRpp2rRpkljzCTiLxMRE1a1bN9vxevXqycXFRePGjSvAVAByg//W4m4oFnAqzZs31/z585WYmKhhw4ZpxYoVysjI0AsvvKBPPvlEFy5cMDsigBz4+vrm+M3aCQkJ/DQUcFBsy8XdUCzglEqWLKnBgwdrx44dOnTokBo1aqQxY8aoQoUKZkcDkIP27dtr9OjRunnz5m1jVqtVr7/+ujp06GBCMgB388Ybb2jXrl1Zzi1atEhVq1ZVuXLlNGTIEFmtVpPSwRFwVyjcN9LT07Vq1Sp169bN7CgAsnHmzBk1btxYHh4eGj58uGrVqiWbzaYjR45o9uzZslqt2r17typVqmR2VAB/0qFDB7Vq1UphYWGSpB9//FGPPPKI+vfvr9q1a+vdd9/V0KFDNX78eHODwjQUC9w3jh49qi5duiguLs7sKABykJCQoBdeeEFr1661L62wWCxq27atZs6cqerVq5ucEMCdlC9fXl9//bUaN24sSRo9erQ2b96sbdu2SZJWrFihcePG6fDhw2bGhIm4KxTuG1arVfHx8WbHAHAXVatW1ffff68rV67o+PHjkqTq1avLx8fH5GQAcnLlypUsd3TbvHmzQkJC7MdNmjTR6dOnzYgGB8EeCwCAKUqXLq3AwEAFBgZSKgAn4Ofnp4SEBEnSzZs3tW/fPj366KP28dTUVBUpUsSseHAAFAsAAADcVceOHfXaa69p69atCg8PV/HixRUUFGQfj4mJ0YMPPmhiQpiNpVAAAAC4q0mTJqlbt256/PHHVbJkSS1cuFDu7u728fnz56tdu3YmJoTZ2LwNp1G6dOkcv5wnPT1daWlpysjIKMBUAAAULsnJySpZsqRcXV2znL98+bJKliyZpWygcOGKBZxGRESE2REAACj0vL2973ievVLgigXuKxkZGbf9BAUAAAD5j83buC/ExcUpLCxMAQEBZkcBAAAolCgWcFq//vqrFixYoKCgINWpU0ebN29WaGio2bEAAAAKJfZYwOlER0dr7ty5WrFihSpVqqQjR45o48aNWW55BwAAgILFFQs4jalTp6pu3brq0aOHSpcurS1btujHH3+UxWJRmTJlzI4HAABQqLF5G07Dzc1NYWFhmjhxYpYN2kWKFNHBgwdVp04dE9MBAAAUblyxgNOYNGmSVqxYoapVqyosLEyxsbFmRwIAAMD/UCzgNMLDwxUXF6fFixcrKSlJTZs2VYMGDWSz2XTlyhWz4wEAABRqLIWC00pJSdGyZcs0b9487d27V02bNlWPHj24MxQAAIAJKBa4L8TGxmrevHlasmSJzp8/b3YcAACAQofbzcJpXL9+XVFRUerUqZOk35ZGWa1W+7ibm5vi4+PNigcAAFCoUSzgNBYuXKhvv/3WXixmzpypunXrqlixYpKkY8eOqUKFCnrllVfMjAkAAFAosRQKTiMoKEijRo1S586dJUmenp46ePCgqlWrJkn69NNPNWvWLO3cudPMmAAAAIUSd4WC0zhx4oT+9re/2Y+LFi0qF5f//1s4MDBQhw8fNiMaAABAocdSKDiNq1evZtlTceHChSzjmZmZWcYBAABQcLhiAacREBCQ45fixcTEKCAgoAATAQAA4HcUCziNjh07auzYsbpx48ZtY9evX9eECRP05JNPmpAMAAAAbN6G0zh37pwaNmwod3d3jRgxQjVq1JD0292gZs6cqfT0dO3fv19+fn4mJwUAACh8KBZwKgkJCXr++ee1bt06/f5b12KxqG3btpo9e7b9DlEAAAAoWBQLOKXLly/rxIkTkqTq1avLx8fH5EQAAACFG8UCAAAAgGFs3gYAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAADkm5MnT8pisejAgQNmRwEA5DOKBQAAAADDKBYAAAAADKNYAAAMy8zM1DvvvKPq1avLw8NDlSpV0ptvvnnbvIyMDA0cOFBVq1ZVsWLFVLNmTU2fPj3LnE2bNikwMFAlSpRQqVKl1Lx5c506dUqSdPDgQbVq1Uqenp7y8vJSo0aNtGfPngJ5jwCAnLmZHQAA4PzCw8P1ySefaNq0aWrRooUSExN19OjR2+ZlZmYqICBAK1asUJkyZbRjxw4NGTJE5cuXV69evZSenq6uXbtq8ODBWrZsmW7evKldu3bJYrFIkvr27auHH35YH374oVxdXXXgwAEVKVKkoN8uAOAO+OZtAIAhqampKlu2rGbOnKlBgwZlGTt58qSqVq2q/fv3q2HDhnd8/ogRI5SUlKTPP/9cly9fVpkyZbRp0yY9/vjjt8318vLSBx98oH79+uXHWwEAGMBSKACAIUeOHJHVatUTTzyRq/mzZs1So0aNVLZsWZUsWVIff/yxfv75Z0mSj4+P+vfvr/bt26tz586aPn26EhMT7c8NDQ3VoEGD1KZNG7399tuKj4/Pl/cEAMg7igUAwJBixYrleu7y5cs1cuRIDRw4UGvXrtWBAwc0YMAA3bx50z5nwYIF2rlzpx577DH997//VY0aNRQdHS1JGj9+vA4dOqQnn3xSGzZsUJ06dbRy5cp7/p4AAHnHUigAgCE3btyQj4+PZsyYcdelUC+++KIOHz6sqKgo+5w2bdro4sWL2X7XRbNmzdSkSRPNmDHjtrHevXsrLS1Nq1atuqfvCQCQd1yxAAAYUrRoUYWFhWnUqFFatGiR4uPjFR0drXnz5t0296GHHtKePXu0Zs0axcXF6fXXX9fu3bvt4wkJCQoPD9fOnTt16tQprV27VsePH1ft2rV1/fp1jRgxQps2bdKpU6e0fft27d69W7Vr1y7ItwsAyAZ3hQIAGPb666/Lzc1NY8eO1dmzZ1W+fHkNGzbstnlDhw7V/v379cwzz8hisah379564YUX9P3330uSihcvrqNHj2rhwoW6dOmSypcvr+HDh2vo0KFKT0/XpUuX9Nxzz+ncuXPy9fVVt27dNGHChIJ+uwCAO2ApFAAAAADDWAoFAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAMDpVKlSRf3798/Tc/r376+SJUvmaq7FYtH48eOznNu9e7cee+wxlShRQhaLRQcOHMjTrw8A9zuKBQA4qcjISFksFvvDzc1NDzzwgPr3769ffvnF7Hj3lVu3bqlnz566fPmypk2bpsWLF6ty5cqaPXu2IiMjzY4HAA7BzewAAABjJk6cqKpVq+rGjRuKjo5WZGSktm3bptjYWBUtWtTseE7p+vXrcnP7//+JjI+P16lTp/TJJ59o0KBB9vOzZ8+Wr69vnq+eAMD9iGIBAE4uJCREjRs3liQNGjRIvr6+mjJlilatWqVevXqZnC6rtLQ0lShRwuwYd/XnQnb+/HlJUqlSpUxIAwDOgaVQAHCfCQoKkvTbT9n/aMOGDQoKClKJEiVUqlQpPfXUUzpy5Ih9PCYmRhaLRatWrbKf27t3rywWix555JEsrxUSEqKmTZvmmOP3PQ3x8fHq2LGjPD091bdvX0lSZmamIiIiVLduXRUtWlR+fn4aOnSorly5kuU1bDab3njjDQUEBKh48eJq1aqVDh06dNuvdevWLU2YMEEPPfSQihYtqjJlyqhFixZat27dbXN/+eUXde3aVSVLllTZsmU1cuRIZWRkZJnzxz0W/fv31+OPPy5J6tmzpywWi4KDg1WlShUdOnRImzdvti9HCw4OzvHfCQDcz7hiAQD3mZMnT0qSSpcubT+3fv16hYSEqFq1aho/fryuX7+uDz74QM2bN9e+fftUpUoV1atXT6VKldKWLVvUpUsXSdLWrVvl4uKigwcPKiUlRV5eXsrMzNSOHTs0ZMiQu2ZJT09X+/bt1aJFC7333nsqXry4JGno0KGKjIzUgAED9NJLLykhIUEzZ87U/v37tX37dhUpUkSSNHbsWL3xxhvq2LGjOnbsqH379qldu3a6efNmll9n/Pjxmjx5sgYNGqTAwEClpKRoz5492rdvn9q2bWufl5GRofbt26tp06Z67733tH79ek2dOlUPPvignn/++Tu+h6FDh+qBBx7QW2+9pZdeeklNmjSRn5+f0tLS9OKLL6pkyZIaPXq0JMnPzy+X/y8BwH3IBgBwSgsWLLBJsq1fv9524cIF2+nTp22ff/65rWzZsjYPDw/b6dOn7XMbNmxoK1eunO3SpUv2cwcPHrS5uLjYnnvuOfu5J5980hYYGGg/7tatm61bt242V1dX2/fff2+z2Wy2ffv22STZvvrqqxzz9evXzybJ9tprr2U5v3XrVpsk25IlS7KcX716dZbz58+ft7m7u9uefPJJW2Zmpn3ef/7zH5skW79+/eznGjRoYHvyySdzlWfixIlZzj/88MO2Ro0aZTknyTZu3Dj78caNG22SbCtWrMgyr27durbHH388x18XAAoLlkIBgJNr06aNypYtq4oVK6pHjx4qUaKEVq1apYCAAElSYmKiDhw4oP79+8vHx8f+vPr166tt27b67rvv7OeCgoK0b98+paWlSZK2bdumjh07qmHDhtq6dauk365iWCwWtWjRIlf5/nwlYMWKFfL29lbbtm118eJF+6NRo0YqWbKkNm7cKOm3qyw3b97Uiy++KIvFYn/+yy+/fNuvUapUKR06dEjHjx+/a55hw4ZlOQ4KCtJPP/2Uq/cCAMgexQIAnNysWbO0bt06ff755+rYsaMuXrwoDw8P+/ipU6ckSTVr1rztubVr19bFixftRSIoKEjp6enauXOnjh07pvPnzysoKEgtW7bMUizq1KmTpaRkx+3/tXfn0TXdi/vHnyORY0pSIaZmMA+hqCGqGlNRY6sDXcGXKkrLbUkHzXVRNQQtDY15iprS6rdat7011lTqmofUENQQFTNJKAdJfn/06/wEibBzss+R92utvbr23p+z9xNLNE8+e3B3txec2w4dOqTExEQVK1ZMvr6+6ZYrV67Yb5S+nbtChQrpPu/r65vuMi/p7ydjXb58WRUrVtRTTz2lDz/8UHv27LknT758+eTr65tuW+HChe+5twMA8PC4xwIAXFxwcLD9qVDt27fXc889p06dOungwYNZfiHcbXXq1FG+fPm0fv16BQQEqFixYqpYsaJCQkI0efJk2Ww2bdiwQS+//HKWjme1WpUnT/rfYaWmpqpYsWJasGDBfT9z9w/+WdGwYUMdOXJEP/zwg1asWKGZM2fqiy++0NSpU9M9HtbNze2hjw0AyBqKBQA8Rtzc3BQREaEmTZooKipKH3/8sQIDAyVJBw8evGf8gQMHVLRoUfsjYD08PBQcHKwNGzYoICDA/oSpkJAQ2Ww2LViwQGfOnFHDhg0fOWO5cuW0atUqNWjQQPnz589w3O3chw4dUtmyZe3bz507d98ZBh8fH3Xv3l3du3fXlStX1LBhQ33yySfpikV2u/MSLQDI7bgUCgAeM40bN1ZwcLAiIyN1/fp1lSxZUjVr1tTcuXN1+fJl+7jY2FitWLFCrVu3Tvf5kJAQ/fe//9WaNWvsxaJo0aKqUqWKxowZYx/zqDp27KiUlBQNHz78nn23bt2yZ2zWrJny5s2rL7/8UmlpafYxkZGR93zuwoUL6dYLFSqk8uXLy2azPXLOrChYsGC6P1MAyM0oFgDwGPrwww915swZRUdHS5I+++wzXbhwQfXr19fnn3+u4cOHq2nTpvL29ra/r+G2kJAQXbt2TfHx8ekKRMOGDRUXF6fSpUvfc9/Ew2jUqJF69+6tiIgItW7dWpGRkZo0aZL69++vwMBArVq1SpLs75j46aef1LZtW02aNEk9e/ZUdHS0ihYtmu6YQUFBev311zV27FjNnDlTffr00bfffqvQ0NBHzpkVtWvX1p49ezRixAjFxMTol19+cej5AMCZcSkUADyGXnnlFZUrV06ff/65evXqpWbNmmnZsmUaOnSohgwZorx586pRo0YaM2aMypQpk+6zzz77rNzc3FSgQAHVqFHDvj0kJETTpk0zNFtx29SpU1W7dm1NmzZN//znP+Xu7q7SpUurS5cuatCggX3ciBEjlC9fPk2dOlVr1qxRvXr1tGLFCrVp0ybd8d59910tXbpUK1askM1mU2BgoEaMGKEPP/zQcNbMDBkyRMePH9fYsWOVnJysRo0aqWnTpg49JwA4K0vanfPLAAAAAPAIuBQKAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIY99u+xSE1N1alTp+Tp6SmLxWJ2HAAAAMBlpKWlKTk5WaVKlVKePJnPSTz2xeLUqVPy9/c3OwYAAADgsuLj4+Xn55fpmMe+WHh6ekr6+w/Dy8vL5DQAAACA60hKSpK/v7/9Z+rMPPbF4vblT15eXhQLAAAA4BFk5ZYCbt4GAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGFOXyxKly4ti8Vyz9K3b1+zowEAAAD4P07/HoutW7cqJSXFvh4bG6vmzZurQ4cOJqYCAAAAcCenLxa+vr7p1kePHq1y5cqpUaNGJiUCAAAAcDenLxZ3unHjhubPn6+wsLAM3/5ns9lks9ns60lJSTkVDwAAAMi1XKpYfP/997p8+bLeeOONDMdERERo2LBhORcKALJZ6Y9/MjsCHOzY6DZmRwCAbOf0N2/fadasWWrVqpVKlSqV4Zjw8HAlJibal/j4+BxMCAAAAOROLjNjcfz4ca1atUrfffddpuOsVqusVmsOpQIAAAAgudCMxZw5c1SsWDG1acP0MQAAAOBsXKJYpKamas6cOerWrZvc3V1mkgUAAADINVyiWKxatUonTpzQm2++aXYUAAAAAPfhEr/+b9GihdLS0syOAQAAACADLjFjAQAAAMC5USwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGOb0xeLPP/9Uly5dVKRIEeXPn19PPfWUtm3bZnYsAAAAAHdwNztAZi5duqQGDRqoSZMm+vnnn+Xr66tDhw6pcOHCZkcDAAAAcAenLhZjxoyRv7+/5syZY99WpkwZExMBAAAAuB+nvhRq6dKlqlOnjjp06KBixYrp6aef1owZM8yOBQAAAOAuTl0s/vjjD02ZMkUVKlTQ8uXL9fbbb+vdd9/V3LlzM/yMzWZTUlJSugUAAACAYzn1pVCpqamqU6eORo0aJUl6+umnFRsbq6lTp6pbt273/UxERISGDRuWkzEBAACAXM+pZyxKliypoKCgdNuqVKmiEydOZPiZ8PBwJSYm2pf4+HhHxwQAAAByPaeesWjQoIEOHjyYbltcXJwCAwMz/IzVapXVanV0NAAAAAB3cOoZiwEDBmjz5s0aNWqUDh8+rIULF2r69Onq27ev2dEAAAAA3MGpi0XdunW1ZMkSLVq0SNWqVdPw4cMVGRmpzp07mx0NAAAAwB2c+lIoSWrbtq3atm1rdgwAAAAAmXDqGQsAAAAAroFiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAw5y6WHzyySeyWCzplsqVK5sdCwAAAMBd3M0O8CBVq1bVqlWr7Ovu7k4fGQAAAMh1nP6ndHd3d5UoUcLsGAAAAAAy4dSXQknSoUOHVKpUKZUtW1adO3fWiRMnMh1vs9mUlJSUbgEAAADgWE5dLOrVq6fo6GgtW7ZMU6ZM0dGjRxUSEqLk5OQMPxMRESFvb2/74u/vn4OJAQAAgNzJkpaWlmZ2iKy6fPmyAgMDNX78ePXo0eO+Y2w2m2w2m309KSlJ/v7+SkxMlJeXV05FBYBHVvrjn8yOAAc7NrqN2REAIEuSkpLk7e2dpZ+lnf4eizs98cQTqlixog4fPpzhGKvVKqvVmoOpAAAAADj1pVB3u3Llio4cOaKSJUuaHQUAAADAHZy6WHzwwQdat26djh07pk2bNunll1+Wm5ubQkNDzY4GAAAA4A5OfSnUyZMnFRoaqgsXLsjX11fPPfecNm/eLF9fX7OjAQAAALiDUxeLmJgYsyMAAAAAyAKnvhQKAAAAgGugWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMMwhxSI+Pl4nT560r2/ZskX9+/fX9OnTHXE6AAAAACZzSLHo1KmT1qxZI0k6ffq0mjdvri1btmjQoEH69NNPHXFKAAAAACZySLGIjY1VcHCwJOmbb75RtWrVtGnTJi1YsEDR0dGOOCUAAAAAEzmkWNy8eVNWq1WStGrVKr344ouSpMqVKyshIcERpwQAAABgIocUi6pVq2rq1KnasGGDVq5cqZYtW0qSTp06pSJFijzycUePHi2LxaL+/ftnU1IAAAAA2cEhxWLMmDGaNm2aGjdurNDQUNWoUUOStHTpUvslUg9r69atmjZtmqpXr56dUQEAAABkA3dHHLRx48Y6f/68kpKSVLhwYfv2t956SwUKFHjo4125ckWdO3fWjBkzNGLEiOyMCgAAACAbOOw9Fmlpadq+fbumTZum5ORkSZKHh8cjFYu+ffuqTZs2atas2QPH2mw2JSUlpVsAAAAAOJZDZiyOHz+uli1b6sSJE7LZbGrevLk8PT01ZswY2Ww2TZ06NcvHiomJ0Y4dO7R169YsjY+IiNCwYcMeNToAAACAR+CQGYv33ntPderU0aVLl5Q/f3779pdfflmrV6/O8nHi4+P13nvvacGCBcqXL1+WPhMeHq7ExET7Eh8f/9D5AQAAADwch8xYbNiwQZs2bZKHh0e67aVLl9aff/6Z5eNs375dZ8+eVa1atezbUlJStH79ekVFRclms8nNzS3dZ6xWq/1RtwAAAAByhkOKRWpqqlJSUu7ZfvLkSXl6emb5OM8//7z27t2bblv37t1VuXJlDRw48J5SAQAAAMAcDikWLVq0UGRkpKZPny5JslgsunLlioYOHarWrVtn+Tienp6qVq1aum0FCxZUkSJF7tkOAAAAwDwOKRbjxo3TCy+8oKCgIF2/fl2dOnXSoUOHVLRoUS1atMgRpwQAAABgIocUCz8/P+3evVsxMTHas2ePrly5oh49eqhz587pbuZ+FGvXrs2ekAAAAACyjUOKhSS5u7urS5cujjo8AAAAACfikGLx1VdfZbq/a9eujjgtAAAAAJM4pFi899576dZv3rypv/76y/7mbYoFAAAA8HhxyAvyLl26lG65cuWKDh48qOeee46btwEAAIDHkEOKxf1UqFBBo0ePvmc2AwAAAIDry7FiIf19Q/epU6dy8pQAAAAAcoBD7rFYunRpuvW0tDQlJCQoKipKDRo0cMQpAQAAAJjIIcWiffv26dYtFot8fX3VtGlTjRs3zhGnBAAAAGAihxSL1NRURxwWAAAAgJPK0XssAAAAADyesm3GIiwsLMtjx48fn12nBQAAAOAEsq1Y7Ny5M0vjLBZLdp0SAAAAgJPItmKxZs2a7DoUAAAAABfDPRYAAAAADHPIU6Ekadu2bfrmm2904sQJ3bhxI92+7777zlGnBQAAAGACh8xYxMTE6Nlnn9X+/fu1ZMkS3bx5U7///rt++eUXeXt7O+KUAAAAAEzkkGIxatQoffHFF/r3v/8tDw8PTZgwQQcOHFDHjh0VEBDgiFMCAAAAMJFDisWRI0fUpk0bSZKHh4euXr0qi8WiAQMGaPr06Y44JQAAAAATOaRYFC5cWMnJyZKkJ598UrGxsZKky5cv66+//nLEKQEAAACYKFuLxe0C0bBhQ61cuVKS1KFDB7333nvq1auXQkND9fzzz2fnKQEAAAA4gWx9KlT16tVVt25dtW/fXh06dJAkDRo0SHnz5tWmTZv06quv6l//+ld2nhIAAACAE8jWYrFu3TrNmTNHERERGjlypF599VX17NlTH3/8cXaeBgAAAICTydZLoUJCQjR79mwlJCToyy+/1LFjx9SoUSNVrFhRY8aM0enTp7PzdAAAAACchENu3i5YsKC6d++udevWKS4uTh06dNCkSZMUEBCgF1980RGnBAAAAGAihxSLO5UvX17//Oc/9a9//Uuenp766aefHH1KAAAAADksW++xuNv69es1e/Zs/e///q/y5Mmjjh07qkePHo48JQAAAAATZPuMxalTpzRq1ChVrFhRjRs31uHDhzVx4kSdOnVKM2bM0DPPPJPlY02ZMkXVq1eXl5eXvLy8VL9+ff3888/ZHRkAAACAQdk6Y9GqVSutWrVKRYsWVdeuXfXmm2+qUqVKj3w8Pz8/jR49WhUqVFBaWprmzp2rl156STt37lTVqlWzMTkAAAAAI7K1WOTNm1fffvut2rZtKzc3N8PHa9euXbr1kSNHasqUKdq8eTPFAgAAAHAi2Vosli5dmp2HSyclJUWLFy/W1atXVb9+fYedBwAAAMDDc+jN29lh7969ql+/vq5fv65ChQppyZIlCgoKynC8zWaTzWazryclJeVETAAAACBXc/jjZo2qVKmSdu3apf/+9796++231a1bN+3bty/D8REREfL29rYv/v7+OZgWAAAAyJ0saWlpaWaHeBjNmjVTuXLlNG3atPvuv9+Mhb+/vxITE+Xl5ZVTMQHgkZX+mPf9PO6OjW5jdgQAyJKkpCR5e3tn6Wdpp78U6m6pqanpisPdrFarrFZrDiYCAAAA4NTFIjw8XK1atVJAQICSk5O1cOFCrV27VsuXLzc7GgAAAIA7OHWxOHv2rLp27aqEhAR5e3urevXqWr58uZo3b252NAAAAAB3cOpiMWvWLLMjAAAAAMgCp38qFAAAAADnR7EAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhFAsAAAAAhlEsAAAAABhGsQAAAABgGMUCAAAAgGEUCwAAAACGUSwAAAAAGEaxAAAAAGAYxQIAAACAYRQLAAAAAIZRLAAAAAAYRrEAAAAAYBjFAgAAAIBhTl0sIiIiVLduXXl6eqpYsWJq3769Dh48aHYsAAAAAHdx6mKxbt069e3bV5s3b9bKlSt18+ZNtWjRQlevXjU7GgAAAIA7uJsdIDPLli1Ltx4dHa1ixYpp+/btatiwoUmpAAAAANzNqYvF3RITEyVJPj4+GY6x2Wyy2Wz29aSkJIfnAgAAAHI7p74U6k6pqanq37+/GjRooGrVqmU4LiIiQt7e3vbF398/B1MCAAAAuZPLFIu+ffsqNjZWMTExmY4LDw9XYmKifYmPj8+hhAAAAEDu5RKXQvXr108//vij1q9fLz8/v0zHWq1WWa3WHEoGAAAAQHLyYpGWlqZ//OMfWrJkidauXasyZcqYHQkAAADAfTh1sejbt68WLlyoH374QZ6enjp9+rQkydvbW/nz5zc5HQAAAIDbnPoeiylTpigxMVGNGzdWyZIl7cvXX39tdjQAAAAAd3DqGYu0tDSzIwAAAADIAqeesQAAAADgGigWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAxz+mKxfv16tWvXTqVKlZLFYtH3339vdiQAAAAAd3H6YnH16lXVqFFDkyZNMjsKAAAAgAy4mx3gQVq1aqVWrVqZHQMAAABAJpx+xgIAAACA83P6GYuHZbPZZLPZ7OtJSUkmpgEAAAByh8duxiIiIkLe3t72xd/f3+xIAAAAwGPvsSsW4eHhSkxMtC/x8fFmRwIAAAAee4/dpVBWq1VWq9XsGAAAAECu4vTF4sqVKzp8+LB9/ejRo9q1a5d8fHwUEBBgYjIAAAAAtzl9sdi2bZuaNGliXw8LC5MkdevWTdHR0SalAgAAAHAnpy8WjRs3VlpamtkxAAAAAGTisbt5GwAAAEDOo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMMwlisWkSZNUunRp5cuXT/Xq1dOWLVvMjgQAAADgDk5fLL7++muFhYVp6NCh2rFjh2rUqKEXXnhBZ8+eNTsaAAAAgP/j9MVi/Pjx6tWrl7p3766goCBNnTpVBQoU0OzZs82OBgAAAOD/OHWxuHHjhrZv365mzZrZt+XJk0fNmjXTb7/9ZmIyAAAAAHdyNztAZs6fP6+UlBQVL1483fbixYvrwIED9/2MzWaTzWazrycmJkqSkpKSHBcUALJRqu0vsyPAwfh/EgBXcfvfq7S0tAeOdepi8SgiIiI0bNiwe7b7+/ubkAYAgHt5R5qdAAAeTnJysry9vTMd49TFomjRonJzc9OZM2fSbT9z5oxKlChx38+Eh4crLCzMvp6amqqLFy+qSJEislgsDs2LnJeUlCR/f3/Fx8fLy8vL7DgAHhLfw4Br43v48ZeWlqbk5GSVKlXqgWOdulh4eHiodu3aWr16tdq3by/p76KwevVq9evX776fsVqtslqt6bY98cQTDk4Ks3l5efEPGuDC+B4GXBvfw4+3B81U3ObUxUKSwsLC1K1bN9WpU0fBwcGKjIzU1atX1b17d7OjAQAAAPg/Tl8sXn/9dZ07d05DhgzR6dOnVbNmTS1btuyeG7oBAAAAmMfpi4Uk9evXL8NLn5C7Wa1WDR069J7L3wC4Br6HAdfG9zDuZEnLyrOjAAAAACATTv2CPAAAAACugWIBAAAAwDCKBQAAAADDKBYAAAAADKNYwGVMnjzZ7AgAAADIAE+Fgsvw8fFR3bp1NWfOnCy9Vh6A89q6dasWLVqkuLg4SVLFihXVqVMn1alTx+RkAB5VQkKCRo4cqaioKLOjwCTMWMBlxMbGyt3dXdWqVdP8+fPNjgPgEX300UeqV6+eZs6cqZMnT+rkyZOaMWOG6tWrp4EDB5odD0Amfv/9d0VFRWn69Om6fPmyJOn8+fMaMGCAypYtqzVr1pgbEKZixgIuJzo6WmFhYWrSpIkGDRokd/f073msXr26SckAPMjcuXPVp08fffbZZ+rdu7fy5s0rSbp586amTJmigQMHatq0aeratavJSQHcbenSpXrttdd069YtSVLZsmU1Y8YMdezYUbVr11b//v3VsmVLk1PCTBQLuKRVq1apZcuWSktLU1pamiwWi/2/KSkpZscDkIHg4GCFhoZqwIAB990/fvx4xcTEaMuWLTmcDMCDBAcHq0GDBho+fLhmzpypsLAwVa1aVbNnz1bdunXNjgcnQLGAyxk/frwGDx6sDh06aPDgwffMWAQGBpqUDMCDFCxYUHv37lXZsmXvu/+PP/7QU089patXr+ZwMgAP4u3tre3bt6t8+fJKSUmR1WrVsmXL1KxZM7OjwUm4P3gI4Bz++OMPdevWTYcOHdLChQv10ksvmR0JwENyc3PTjRs3Mtx/8+ZNubm55WAiAFmVnJwsLy8vSX9/L+fPnz/DXxIgd6JYwGVUr15dLVu21JIlS1S0aFGz4wB4BLVq1dKCBQs0fPjw++6fN2+eatWqlcOpAGTV8uXL5e3tLUlKTU3V6tWrFRsbm27Miy++aEY0OAEuhYLLmD9/vrp06WJ2DAAG/Pjjj2rfvr3CwsL0/vvvq3jx4pKk06dPa9y4cYqMjNSSJUvUtm1bk5MCuFuePA9+mCj3OuZuPG4WLsNms2W6Pzk5WT179syhNAAeRdu2bfXFF19owoQJKlWqlHx8fOTj46Mnn3xSEydO1Oeff06pAJxUamrqAxdKRe7GjAVchre3t0JCQjRz5kyVKFEi3b7ly5erV69eKly4sHbv3m1SQgBZdfLkSS1evFiHDh2S9PcL8l599VX5+/ubnAzAo0pNTdV//vMffjmQi1Es4DKOHTum7t27a8+ePYqKilJoaKiSk5PVv39/zZs3Tx988IGGDRtmfy4+AABwvMOHD2v27NmKjo7WuXPndPPmTbMjwSRcCgWXUbp0aa1Zs0aDBw9Wr1691LZtW1WrVk2bN2/Wxo0bNWrUKEoF4OTi4uLueUfF6tWr1aRJEwUHB2vUqFEmJQPwMK5du6avvvpKDRs2VKVKlbRp0yYNGTJEJ0+eNDsaTESxgMvpjfH9gQAADSNJREFU3bu3WrRoof/85z+6ePGiYmJieDEP4CIGDhyoH3/80b5+9OhRtWvXTh4eHqpfv74iIiIUGRlpXkAAmdq6dat69+6tEiVKKDIyUi+99JIsFosmT56sPn362B/IgNyJYgGXsnHjRtWoUUMHDhzQsmXL1KpVK9WvX18TJkwwOxqALNi2bZtatWplX1+wYIEqVqyo5cuXa8KECYqMjFR0dLR5AQFkqHr16urQoYOKFCmiTZs2aceOHXr//fdlsVjMjgYnQbGAy3j//ffVtGlTtWvXTjt27FCLFi30zTffaNasWRoxYoQaN26so0ePmh0TQCbOnz8vPz8/+/qaNWvUrl07+3rjxo117NgxE5IBeJCDBw+qYcOGatKkiYKCgsyOAydEsYDL+OGHH7Rq1SqNGzdO+fLls29//fXXFRsbK29vb1WvXt3EhAAexMfHRwkJCZL+foLMtm3b9Mwzz9j337hxQzxTBHBOf/zxhypVqqS3335bfn5++uCDD7Rz505mLGBHsYDL2LNnj0JCQu67r3jx4vrhhx80efLkHE4F4GE0btxYw4cPV3x8vCIjI5WSkqLGjRvb9+/bt0+lS5c2LR+AjD355JMaNGiQDh8+rHnz5un06dNq0KCBbt26pejoaMXFxZkdESajWMBlFChQINP9+/fv5x0WgJMbOXKk9u/fr8DAQH300UcaO3asChYsaN8/b948NW3a1MSEADKyfv163bp1S5LUtGlTzZ8/XwkJCYqKitIvv/yiypUrc+VALsd7LODSrl69qpiYGM2aNUubN29WUFCQYmNjzY4FIBO3bt3S77//Ll9fX3l4eMjDw0NeXl6SpN27d8vPz09FihQxOSWAu7m5uSkhIUHFihW77/5du3Zp9uzZmjhxYg4ng7NgxgIuaePGjXrzzTdVvHhxvfXWW3r22We1b98+SgXgAq5cuaLp06erevXqKl68uAoXLqwSJUooPDxcFSpUoFQATupBv4uuWbMmpSKXY8YCLuPs2bOKjo7W7NmzlZiYqNDQUHXq1En169fX7t27eUIF4AIuXryo+vXr688//1Tnzp1VpUoVSX/fW7Fw4UJVrlxZv/76q/bs2aPNmzfr3XffNTkxgNvy5MmjM2fOyNfX1+wocFLuZgcAsiowMFCvvfaaJkyYoObNmytPHibcAFfz6aefysPDQ0eOHLnnRVqffvqpWrRoof/5n//RihUr+M0n4ITeeOMNWa3WTMd89913OZQGzoZiAZcRGBioX3/9VQEBAQoMDFTlypXNjgTgIX3//feaNm3afd/OW6JECY0dO1atW7fW0KFD1a1bNxMSAsiMp6en8ufPb3YMOCmKBVzGgQMHtHHjRs2aNUt169ZVxYoV1aVLF0niGdqAi0hISFDVqlUz3F+tWjXlyZNHQ4cOzcFUALJq4sSJGd68DXAtCVxKgwYNNHv2bCUkJKhPnz5avHixUlJS9M4772jGjBk6d+6c2REBZKJo0aKZvln76NGj/NACOCl+iYcH4eZtuLz9+/dr1qxZmjdvni5evKibN2+aHQlABt58800dOXJEK1eulIeHR7p9NptNL7zwgsqWLavZs2eblBBARvLkyaPTp09T/pEhigUeG7du3dLSpUv1yiuvmB0FQAZOnjypOnXqyGq1qm/fvqpcubLS0tK0f/9+TZ48WTabTVu3blVAQIDZUQHcZdSoUapevbratm1r3/bVV19p6NChunr1qtq3b68vv/zygTd34/FFscBj48CBA3rxxRcVFxdndhQAmTh69KjeeecdrVixwv5cfIvFoubNmysqKkrly5c3OSGA+2nZsqWaNGmigQMHSpL27t2rWrVq6Y033lCVKlX02WefqXfv3vrkk0/MDQrTUCzw2Ni9e7dq1aqllJQUs6MAyIJLly7p0KFDkqTy5cvLx8fH5EQAMlOyZEn9+9//Vp06dSRJgwYN0rp16/Trr79KkhYvXqyhQ4dq3759ZsaEiXgqFADAFIULF1ZwcLDZMQBk0aVLl9I9KnrdunVq1aqVfb1u3bqKj483IxqcBE+FAgAAwAMVL15cR48elSTduHFDO3bs0DPPPGPfn5ycrLx585oVD06AYgEAAIAHat26tT7++GNt2LBB4eHhKlCggEJCQuz79+zZo3LlypmYEGbjUii4jMKFC2f6DO1bt27lYBoAAHKX4cOH65VXXlGjRo1UqFAhzZ07N91jo2fPnq0WLVqYmBBm4+ZtuIy5c+dmaVy3bt0cnAQAgNwrMTFRhQoVkpubW7rtFy9eVKFChe55Rw1yD4oFHispKSn3/EMHAAAAx+MeCzwW4uLiNHDgQPn5+ZkdBQAAIFeiWMBl/fXXX5ozZ45CQkIUFBSkdevWKSwszOxYAAAAuRI3b8PlbN68WTNnztTixYsVEBCg/fv3a82aNemeTAEAAICcxYwFXMa4ceNUtWpVvfbaaypcuLDWr1+vvXv3ymKxqEiRImbHAwAAyNW4eRsuw93dXQMHDtSnn36a7gbtvHnzavfu3QoKCjIxHQAAQO7GjAVcxvDhw7V48WKVKVNGAwcOVGxsrNmRAAAA8H8oFnAZ4eHhiouL07x583T69GnVq1dPNWrUUFpami5dumR2PAAAgFyNS6HgspKSkrRo0SLNmjVL27dvV7169fTaa6/xZCgAAAATUCzwWIiNjdWsWbO0YMECnT171uw4AAAAuQ6Pm4XLuHbtmlavXq22bdtK+vvSKJvNZt/v7u6uI0eOmBUPAAAgV6NYwGXMnTtXP/30k71YREVFqWrVqsqfP78k6eDBgypVqpQGDBhgZkwAAIBciUuh4DJCQkL00UcfqV27dpIkT09P7d69W2XLlpUkzZ8/X5MmTdJvv/1mZkwAAIBciadCwWUcPnxYTz31lH09X758ypPn//8VDg4O1r59+8yIBgAAkOtxKRRcxuXLl9PdU3Hu3Ll0+1NTU9PtBwAAQM5hxgIuw8/PL9OX4u3Zs0d+fn45mAgAAAC3USzgMlq3bq0hQ4bo+vXr9+y7du2ahg0bpjZt2piQDAAAANy8DZdx5swZ1axZUx4eHurXr58qVqwo6e+nQUVFRenWrVvauXOnihcvbnJSAACA3IdiAZdy9OhRvf3221q5cqVu/9W1WCxq3ry5Jk+ebH9CFAAAAHIWxQIu6eLFizp8+LAkqXz58vLx8TE5EQAAQO5GsQAAAABgGDdvAwAAADCMYgEAAADAMIoFAAAAAMMoFgAAAAAMo1gAABzm2LFjslgs2rVrl9lRAAAORrEAAAAAYBjFAgAAAIBhFAsAgGGpqakaO3asypcvL6vVqoCAAI0cOfKecSkpKerRo4fKlCmj/Pnzq1KlSpowYUK6MWvXrlVwcLAKFiyoJ554Qg0aNNDx48clSbt371aTJk3k6ekpLy8v1a5dW9u2bcuRrxEAkDl3swMAAFxfeHi4ZsyYoS+++ELPPfecEhISdODAgXvGpaamys/PT4sXL1aRIkW0adMmvfXWWypZsqQ6duyoW7duqX379urVq5cWLVqkGzduaMuWLbJYLJKkzp076+mnn9aUKVPk5uamXbt2KW/evDn95QIA7oM3bwMADElOTpavr6+ioqLUs2fPdPuOHTumMmXKaOfOnapZs+Z9P9+vXz+dPn1a3377rS5evKgiRYpo7dq1atSo0T1jvby89OWXX6pbt26O+FIAAAZwKRQAwJD9+/fLZrPp+eefz9L4SZMmqXbt2vL19VWhQoU0ffp0nThxQpLk4+OjN954Qy+88ILatWunCRMmKCEhwf7ZsLAw9ezZU82aNdPo0aN15MgRh3xNAICHR7EAABiSP3/+LI+NiYnRBx98oB49emjFihXatWuXunfvrhs3btjHzJkzR7/99pueffZZff3116pYsaI2b94sSfrkk0/0+++/q02bNvrll18UFBSkJUuWZPvXBAB4eFwKBQAw5Pr16/Lx8dHEiRMfeCnUP/7xD+3bt0+rV6+2j2nWrJnOnz+f4bsu6tevr7p162rixIn37AsNDdXVq1e1dOnSbP2aAAAPjxkLAIAh+fLl08CBA/XRRx/pq6++0pEjR7R582bNmjXrnrEVKlTQtm3btHz5csXFxWnw4MHaunWrff/Ro0cVHh6u3377TcePH9eKFSt06NAhValSRdeuXVO/fv20du1aHT9+XBs3btTWrVtVpUqVnPxyAQAZ4KlQAADDBg8eLHd3dw0ZMkSnTp1SyZIl1adPn3vG9e7dWzt37tTrr78ui8Wi0NBQvfPOO/r5558lSQUKFNCBAwc0d+5cXbhwQSVLllTfvn3Vu3dv3bp1SxcuXFDXrl115swZFS1aVK+88oqGDRuW018uAOA+uBQKAAAAgGFcCgUAAADAMIoFAAAAAMMoFgAAAAAMo1gAAAAAMIxiAQAAAMAwigUAAAAAwygWAAAAAAyjWAAAAAAwjGIBAAAAwDCKBQAAAADDKBYAAAAADKNYAAAAADDs/wE/b83znoB4iwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(mode_df), figsize=(8, 4 * len(mode_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(mode_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row {index}')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43ace51e-3a22-4aed-8be4-4d2acd81b50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(normalized_mode_df), figsize=(8, 4 * len(normalized_mode_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(normalized_mode_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row {index}')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "561223b6-2992-4c19-8686-5139e11ff073",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class    GALAXY       QSO      STAR\n",
      "u      22.84177  21.50324  21.01065\n",
      "g      21.58220  21.05629  19.54283\n",
      "r      20.10459  20.77272  18.95513\n",
      "i      19.22366  20.58211  18.59239\n",
      "z      18.76663  20.37397  18.31923\n",
      "\n",
      "\n",
      "class    GALAXY       QSO      STAR\n",
      "u      22.84177  21.50324  21.01065\n",
      "g      21.58220  21.05629  19.54283\n",
      "r      20.10459  20.77272  18.95513\n",
      "i      19.22366  20.58211  18.59239\n",
      "z      18.76663  20.37397  18.31923\n"
     ]
    }
   ],
   "source": [
    "median_df = og_stars.groupby('class').median()\n",
    "median_df = median_df[['u','g','r','i','z','redshift']].T\n",
    "\n",
    "normalized_median_df = stars.groupby('class').median()\n",
    "normalized_median_df = normalized_median_df[['u','g','r','i','z','redshift']].T\n",
    "\n",
    "\n",
    "print(median_df.head())\n",
    "print(\"\\n\")\n",
    "print(normalized_median_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d215aaa-163c-45cc-9bbb-ec1527c2b64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(median_df), figsize=(8, 4 * len(median_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(median_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row {index}')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "da611f14-9a0c-47ce-89db-078b09364610",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(normalized_median_df), figsize=(8, 4 * len(normalized_median_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(normalized_median_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row {index}')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}