Intro to pandas by analyzing shots in the NHL.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Intro To Pandas by analyzing shots in hockey.\n",
    "\n",
    "## What I'm going to talk about today.\n",
    "    1. Setting up our notebook and data.\n",
    "    2. Filtering Data with loc\n",
    "    3. Visualizing our data to see what we've done (with seaborn and matplotlib)\n",
    "    4. NHL Home plate shots (high danger scoring chances) what does that mean?\n",
    "    5. Creating a new Column in our dataset with apply.\n",
    "    6. Grouping our data together to see some shooting percentages\n",
    "    7. Conclusions\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Today we're going to do a short intro to pandas by analyzing shots in the NHL.\n",
    "\n",
    "I've gathered the dataset from the Kaggle. If you'd like to take a look you can take a look here:\n",
    "\n",
    "https://www.kaggle.com/martinellis/nhl-game-data\n",
    "\n",
    "Take a look at the README.md for instructions on installation.\n",
    "\n",
    "Note: Keep your notebook running for this to work:\n",
    "![jupyter_running](images/jupyter_running.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up our notebook\n",
    "\n",
    "First we need to import pandas, the \"as pd\" is an alias so that we can write \"pd\" instead of pandas every time.\n",
    "\n",
    "Note any cell that we run that has variables/packages anything that we assign is available to us later in our notebook (such as pd in this instance in the cell below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second thing we're going to do is that we're going to read in the data (in a csv format) into what's called a dataframe. \n",
    "\n",
    "The csv table that is formatted is the all of the recorded \"plays\" or \"events\" that the NHL records from 2010 to the end of last years' season so we can see just about 10 years worth of data. Note that this is already fairly clean already so we don't have to do a ton of wrangling there.\n",
    "\n",
    "A data frame is just a huge table of datat that is optimized for manipiulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game_plays = pd.read_csv(\"./data/nhl-game-data/game_plays.csv\") # only run once\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can confirm that it's a data frame type by just passing in our variable \"df_game_plays\" to the type function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_game_plays)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the data, so we kind of know what we're dealing with.\n",
    "\n",
    "the .head(n) function on the dataframe gives us the first \"n\" rows of our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011030221_19</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Faceoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>206</td>\n",
       "      <td>994</td>\n",
       "      <td>2012-04-29 19:25:31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Travis Zajac faceoff won against Scott Hartnell</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011030221_20</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stoppage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>235</td>\n",
       "      <td>965</td>\n",
       "      <td>2012-04-29 19:26:26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Icing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011030221_21</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Faceoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>235</td>\n",
       "      <td>965</td>\n",
       "      <td>2012-04-29 19:26:34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Daniel Briere faceoff won against Travis Zajac</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011030221_22</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Penalty</td>\n",
       "      <td>Slashing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>237</td>\n",
       "      <td>963</td>\n",
       "      <td>2012-04-29 19:27:01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Jakub Voracek Slashing against Marek Zidlicky</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011030221_23</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Faceoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>237</td>\n",
       "      <td>963</td>\n",
       "      <td>2012-04-29 19:27:14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Patrik Elias faceoff won against Claude Giroux</td>\n",
       "      <td>69.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2011030221_24</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Takeaway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>254</td>\n",
       "      <td>946</td>\n",
       "      <td>2012-04-29 19:27:39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Takeaway by Zach Parise</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2011030221_25</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Snap Shot</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>274</td>\n",
       "      <td>926</td>\n",
       "      <td>2012-04-29 19:28:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Travis Zajac Snap Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2011030221_26</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Snap Shot</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>282</td>\n",
       "      <td>918</td>\n",
       "      <td>2012-04-29 19:28:08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Travis Zajac Snap Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2011030221_27</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Giveaway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>305</td>\n",
       "      <td>895</td>\n",
       "      <td>2012-04-29 19:28:36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Giveaway by Ilya Bryzgalov</td>\n",
       "      <td>-87.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2011030221_28</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Wrist Shot</td>\n",
       "      <td>84.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>306</td>\n",
       "      <td>894</td>\n",
       "      <td>2012-04-29 19:28:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>David Clarkson Wrist Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>84.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2011030221_29</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>328</td>\n",
       "      <td>872</td>\n",
       "      <td>2012-04-29 19:28:53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>David Clarkson hit Nicklas Grossmann</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2011030221_30</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Takeaway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>339</td>\n",
       "      <td>861</td>\n",
       "      <td>2012-04-29 19:29:07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Takeaway by David Clarkson</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          play_id     game_id  play_num  team_id_for  team_id_against  \\\n",
       "0    2011030221_1  2011030221         1          NaN              NaN   \n",
       "1    2011030221_2  2011030221         2          NaN              NaN   \n",
       "2    2011030221_3  2011030221         3          NaN              NaN   \n",
       "3    2011030221_4  2011030221         4          4.0              1.0   \n",
       "4    2011030221_5  2011030221         5          4.0              1.0   \n",
       "5    2011030221_6  2011030221         6          4.0              1.0   \n",
       "6    2011030221_7  2011030221         7          1.0              4.0   \n",
       "7    2011030221_8  2011030221         8          1.0              4.0   \n",
       "8    2011030221_9  2011030221         9          4.0              1.0   \n",
       "9   2011030221_10  2011030221        10          NaN              NaN   \n",
       "10  2011030221_11  2011030221        11          4.0              1.0   \n",
       "11  2011030221_12  2011030221        12          1.0              4.0   \n",
       "12  2011030221_13  2011030221        13          1.0              4.0   \n",
       "13  2011030221_14  2011030221        14          4.0              1.0   \n",
       "14  2011030221_15  2011030221        15          4.0              1.0   \n",
       "15  2011030221_16  2011030221        16          1.0              4.0   \n",
       "16  2011030221_17  2011030221        17          4.0              1.0   \n",
       "17  2011030221_18  2011030221        18          NaN              NaN   \n",
       "18  2011030221_19  2011030221        19          1.0              4.0   \n",
       "19  2011030221_20  2011030221        20          NaN              NaN   \n",
       "20  2011030221_21  2011030221        21          4.0              1.0   \n",
       "21  2011030221_22  2011030221        22          4.0              1.0   \n",
       "22  2011030221_23  2011030221        23          1.0              4.0   \n",
       "23  2011030221_24  2011030221        24          1.0              4.0   \n",
       "24  2011030221_25  2011030221        25          1.0              4.0   \n",
       "25  2011030221_26  2011030221        26          1.0              4.0   \n",
       "26  2011030221_27  2011030221        27          4.0              1.0   \n",
       "27  2011030221_28  2011030221        28          1.0              4.0   \n",
       "28  2011030221_29  2011030221        29          1.0              4.0   \n",
       "29  2011030221_30  2011030221        30          1.0              4.0   \n",
       "\n",
       "             event secondaryType     x     y  period periodType  periodTime  \\\n",
       "0   Game Scheduled           NaN   NaN   NaN       1    REGULAR           0   \n",
       "1     Period Ready           NaN   NaN   NaN       1    REGULAR           0   \n",
       "2     Period Start           NaN   NaN   NaN       1    REGULAR           0   \n",
       "3          Faceoff           NaN   0.0   0.0       1    REGULAR           0   \n",
       "4         Giveaway           NaN  28.0  24.0       1    REGULAR          21   \n",
       "5     Blocked Shot           NaN  52.0  28.0       1    REGULAR          26   \n",
       "6             Shot    Wrist Shot  80.0  -3.0       1    REGULAR          30   \n",
       "7              Hit           NaN -30.0 -38.0       1    REGULAR          60   \n",
       "8         Giveaway           NaN -60.0  39.0       1    REGULAR          94   \n",
       "9         Stoppage           NaN   NaN   NaN       1    REGULAR         118   \n",
       "10         Faceoff           NaN   0.0   0.0       1    REGULAR         118   \n",
       "11            Shot   Wrap-around  88.0  -4.0       1    REGULAR         139   \n",
       "12            Shot    Wrist Shot  81.0 -27.0       1    REGULAR         154   \n",
       "13             Hit           NaN -64.0 -35.0       1    REGULAR         193   \n",
       "14        Giveaway           NaN  97.0  -9.0       1    REGULAR         194   \n",
       "15            Goal    Wrist Shot  73.0  11.0       1    REGULAR         196   \n",
       "16         Faceoff           NaN   0.0   0.0       1    REGULAR         196   \n",
       "17        Stoppage           NaN   NaN   NaN       1    REGULAR         206   \n",
       "18         Faceoff           NaN -69.0  22.0       1    REGULAR         206   \n",
       "19        Stoppage           NaN   NaN   NaN       1    REGULAR         235   \n",
       "20         Faceoff           NaN -69.0  22.0       1    REGULAR         235   \n",
       "21         Penalty      Slashing   NaN   NaN       1    REGULAR         237   \n",
       "22         Faceoff           NaN  69.0  22.0       1    REGULAR         237   \n",
       "23        Takeaway           NaN  14.0 -32.0       1    REGULAR         254   \n",
       "24            Shot     Snap Shot  63.0 -19.0       1    REGULAR         274   \n",
       "25            Shot     Snap Shot  68.0   1.0       1    REGULAR         282   \n",
       "26        Giveaway           NaN  87.0   6.0       1    REGULAR         305   \n",
       "27            Shot    Wrist Shot  84.0  10.0       1    REGULAR         306   \n",
       "28             Hit           NaN  90.0  37.0       1    REGULAR         328   \n",
       "29        Takeaway           NaN  29.0 -31.0       1    REGULAR         339   \n",
       "\n",
       "    periodTimeRemaining             dateTime  goals_away  goals_home  \\\n",
       "0                  1200  2012-04-29 18:16:06           0           0   \n",
       "1                  1200  2012-04-29 19:19:49           0           0   \n",
       "2                  1200  2012-04-29 19:20:13           0           0   \n",
       "3                  1200  2012-04-29 19:20:13           0           0   \n",
       "4                  1179  2012-04-29 19:21:02           0           0   \n",
       "5                  1174  2012-04-29 19:21:07           0           0   \n",
       "6                  1170  2012-04-29 19:21:12           0           0   \n",
       "7                  1140  2012-04-29 19:21:40           0           0   \n",
       "8                  1106  2012-04-29 19:22:14           0           0   \n",
       "9                  1082  2012-04-29 19:22:42           0           0   \n",
       "10                 1082  2012-04-29 19:22:59           0           0   \n",
       "11                 1061  2012-04-29 19:23:29           0           0   \n",
       "12                 1046  2012-04-29 19:23:44           0           0   \n",
       "13                 1007  2012-04-29 19:24:23           0           0   \n",
       "14                 1006  2012-04-29 19:24:23           0           0   \n",
       "15                 1004  2012-04-29 19:24:26           1           0   \n",
       "16                 1004  2012-04-29 19:25:07           1           0   \n",
       "17                  994  2012-04-29 19:25:21           1           0   \n",
       "18                  994  2012-04-29 19:25:31           1           0   \n",
       "19                  965  2012-04-29 19:26:26           1           0   \n",
       "20                  965  2012-04-29 19:26:34           1           0   \n",
       "21                  963  2012-04-29 19:27:01           1           0   \n",
       "22                  963  2012-04-29 19:27:14           1           0   \n",
       "23                  946  2012-04-29 19:27:39           1           0   \n",
       "24                  926  2012-04-29 19:28:00           1           0   \n",
       "25                  918  2012-04-29 19:28:08           1           0   \n",
       "26                  895  2012-04-29 19:28:36           1           0   \n",
       "27                  894  2012-04-29 19:28:32           1           0   \n",
       "28                  872  2012-04-29 19:28:53           1           0   \n",
       "29                  861  2012-04-29 19:29:07           1           0   \n",
       "\n",
       "                                          description  st_x  st_y rink_side  \n",
       "0                                      Game Scheduled   NaN   NaN       NaN  \n",
       "1                                        Period Ready   NaN   NaN       NaN  \n",
       "2                                        Period Start   NaN   NaN       NaN  \n",
       "3     Sean Couturier faceoff won against Patrik Elias   0.0   0.0     right  \n",
       "4                           Giveaway by Kimmo Timonen -28.0 -24.0     right  \n",
       "5           Max Talbot blocked shot from Patrik Elias -52.0 -28.0     right  \n",
       "6     Patrik Elias Wrist Shot saved by Ilya Bryzgalov  80.0  -3.0      left  \n",
       "7                     Marek Zidlicky hit Jaromir Jagr -30.0 -38.0      left  \n",
       "8                          Giveaway by Scott Hartnell  60.0 -39.0     right  \n",
       "9                                     Puck in Benches   NaN   NaN       NaN  \n",
       "10   Daniel Briere faceoff won against Stephen Gionta   0.0   0.0     right  \n",
       "11    Ryan Carter Wrap-around saved by Ilya Bryzgalov  88.0  -4.0      left  \n",
       "12  Stephen Gionta Wrist Shot saved by Ilya Bryzgalov  81.0 -27.0      left  \n",
       "13                Brayden Schenn hit Anton Volchenkov  64.0  35.0     right  \n",
       "14                              Giveaway by Matt Read -97.0   9.0     right  \n",
       "15  Zach Parise (3) Wrist Shot, assists: Patrik El...  73.0  11.0      left  \n",
       "16     Claude Giroux faceoff won against Travis Zajac   0.0   0.0     right  \n",
       "17                                      Puck in Crowd   NaN   NaN       NaN  \n",
       "18    Travis Zajac faceoff won against Scott Hartnell -69.0  22.0      left  \n",
       "19                                              Icing   NaN   NaN       NaN  \n",
       "20     Daniel Briere faceoff won against Travis Zajac  69.0 -22.0     right  \n",
       "21      Jakub Voracek Slashing against Marek Zidlicky   NaN   NaN     right  \n",
       "22     Patrik Elias faceoff won against Claude Giroux  69.0  22.0      left  \n",
       "23                            Takeaway by Zach Parise  14.0 -32.0      left  \n",
       "24     Travis Zajac Snap Shot saved by Ilya Bryzgalov  63.0 -19.0      left  \n",
       "25     Travis Zajac Snap Shot saved by Ilya Bryzgalov  68.0   1.0      left  \n",
       "26                         Giveaway by Ilya Bryzgalov -87.0  -6.0     right  \n",
       "27  David Clarkson Wrist Shot saved by Ilya Bryzgalov  84.0  10.0      left  \n",
       "28               David Clarkson hit Nicklas Grossmann  90.0  37.0      left  \n",
       "29                         Takeaway by David Clarkson  29.0 -31.0      left  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_plays.head(30) # just some data to play around with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So looking at the data we see a couple of interesting columns that are relevant to analyzing shots\n",
    "- \"event\" column, which gives us a variety of events. We're going to take a look at \"Shot\" and \"Goal\" events. \n",
    "- \"secondaryType\" is going to give us the type of shot that is taken, we'll take a look at this later.\n",
    "- \"x\", \"y\" and the \"st_x\" and \"st_y\" which gives us the locations of where events happened. Note that from looking at the dataset st_x and st_y are the same as x and y except they are adjusted so that the event is always attacking from left to right (this is super important because in an NHL game teams change sides every period)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you were curious about how large just this dataframe is you can take a look at look at the \".shape\" attribute which will return (rows, columns) of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3601092, 20)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_plays.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column has an optimized datatype, You can see these by call the \".dtypes\" on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "play_id                 object\n",
       "game_id                  int64\n",
       "play_num                 int64\n",
       "team_id_for            float64\n",
       "team_id_against        float64\n",
       "event                   object\n",
       "secondaryType           object\n",
       "x                      float64\n",
       "y                      float64\n",
       "period                   int64\n",
       "periodType              object\n",
       "periodTime               int64\n",
       "periodTimeRemaining      int64\n",
       "dateTime                object\n",
       "goals_away               int64\n",
       "goals_home               int64\n",
       "description             object\n",
       "st_x                   float64\n",
       "st_y                   float64\n",
       "rink_side               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_plays.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering with Pandas\n",
    "\n",
    "We're going to use .loc to filter the row. The basic of loc is as follows\n",
    "\n",
    "df_game_plays.loc[[row selected],[columns selected]]\n",
    "    \n",
    "we're going to use .head just to show what we're returned as well.\n",
    "\n",
    "In the [rows_selected] section as we'll we're going to match strings to filter so that we can just get the goal and shot events. You can do this by using \"str.match\" on the column df_game_plays.event to get the values.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is an example of filtering just for shots and goals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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>event</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>st_x</th>\n",
       "      <th>st_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shot</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shot</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shot</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Goal</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shot</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event     x     y  st_x  st_y\n",
       "6   Shot  80.0  -3.0  80.0  -3.0\n",
       "11  Shot  88.0  -4.0  88.0  -4.0\n",
       "12  Shot  81.0 -27.0  81.0 -27.0\n",
       "15  Goal  73.0  11.0  73.0  11.0\n",
       "24  Shot  63.0 -19.0  63.0 -19.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is an example using iloc which (iloc uses the integer position of the index.)\n",
    "# this is why we're not going to use it. below shows the first three rows.\n",
    "# df_game_plays.iloc[0:3, :].head()\n",
    "\n",
    "# below is the filtered dataframe with all columns\n",
    "# df_game_plays.loc[df_game_plays.event.str.match('Shot|Goal'), :].head()\n",
    "\n",
    "# Below is the filtered dataframe with only a few columns (loc uses labels)\n",
    "df_game_plays.loc[df_game_plays.event.str.match('Shot|Goal'), ['event', 'x', 'y', 'st_x', 'st_y']].head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing our data to see what we've done.\n",
    "\n",
    "A large part of doing some type of analysis is to actually show what's going on in a graph.\n",
    "\n",
    "We're going to use matplotlib and seaborn to do this. Note: seaborn uses matplotlib under the hood.\n",
    "\n",
    "The following cell is going to import the matplotlib library and execute it inline, and the seaborn library to simplify our plotting. \n",
    "\n",
    "Note: executing plots inline basically means the plots will immediately our cells that call seaborn or matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I've just simplified the \"preferred shot conditions\" into a variable so that we can modify it as we see fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>play_id</th>\n",
       "      <th>game_id</th>\n",
       "      <th>play_num</th>\n",
       "      <th>team_id_for</th>\n",
       "      <th>team_id_against</th>\n",
       "      <th>event</th>\n",
       "      <th>secondaryType</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>period</th>\n",
       "      <th>periodType</th>\n",
       "      <th>periodTime</th>\n",
       "      <th>periodTimeRemaining</th>\n",
       "      <th>dateTime</th>\n",
       "      <th>goals_away</th>\n",
       "      <th>goals_home</th>\n",
       "      <th>description</th>\n",
       "      <th>st_x</th>\n",
       "      <th>st_y</th>\n",
       "      <th>rink_side</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011030221_7</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Wrist Shot</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>30</td>\n",
       "      <td>1170</td>\n",
       "      <td>2012-04-29 19:21:12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Patrik Elias Wrist Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011030221_12</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Wrap-around</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>139</td>\n",
       "      <td>1061</td>\n",
       "      <td>2012-04-29 19:23:29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ryan Carter Wrap-around saved by Ilya Bryzgalov</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011030221_13</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Wrist Shot</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>154</td>\n",
       "      <td>1046</td>\n",
       "      <td>2012-04-29 19:23:44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stephen Gionta Wrist Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011030221_16</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Goal</td>\n",
       "      <td>Wrist Shot</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>196</td>\n",
       "      <td>1004</td>\n",
       "      <td>2012-04-29 19:24:26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zach Parise (3) Wrist Shot, assists: Patrik El...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2011030221_25</td>\n",
       "      <td>2011030221</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot</td>\n",
       "      <td>Snap Shot</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>274</td>\n",
       "      <td>926</td>\n",
       "      <td>2012-04-29 19:28:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Travis Zajac Snap Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          play_id     game_id  play_num  team_id_for  team_id_against event  \\\n",
       "6    2011030221_7  2011030221         7          1.0              4.0  Shot   \n",
       "11  2011030221_12  2011030221        12          1.0              4.0  Shot   \n",
       "12  2011030221_13  2011030221        13          1.0              4.0  Shot   \n",
       "15  2011030221_16  2011030221        16          1.0              4.0  Goal   \n",
       "24  2011030221_25  2011030221        25          1.0              4.0  Shot   \n",
       "\n",
       "   secondaryType     x     y  period periodType  periodTime  \\\n",
       "6     Wrist Shot  80.0  -3.0       1    REGULAR          30   \n",
       "11   Wrap-around  88.0  -4.0       1    REGULAR         139   \n",
       "12    Wrist Shot  81.0 -27.0       1    REGULAR         154   \n",
       "15    Wrist Shot  73.0  11.0       1    REGULAR         196   \n",
       "24     Snap Shot  63.0 -19.0       1    REGULAR         274   \n",
       "\n",
       "    periodTimeRemaining             dateTime  goals_away  goals_home  \\\n",
       "6                  1170  2012-04-29 19:21:12           0           0   \n",
       "11                 1061  2012-04-29 19:23:29           0           0   \n",
       "12                 1046  2012-04-29 19:23:44           0           0   \n",
       "15                 1004  2012-04-29 19:24:26           1           0   \n",
       "24                  926  2012-04-29 19:28:00           1           0   \n",
       "\n",
       "                                          description  st_x  st_y rink_side  \n",
       "6     Patrik Elias Wrist Shot saved by Ilya Bryzgalov  80.0  -3.0      left  \n",
       "11    Ryan Carter Wrap-around saved by Ilya Bryzgalov  88.0  -4.0      left  \n",
       "12  Stephen Gionta Wrist Shot saved by Ilya Bryzgalov  81.0 -27.0      left  \n",
       "15  Zach Parise (3) Wrist Shot, assists: Patrik El...  73.0  11.0      left  \n",
       "24     Travis Zajac Snap Shot saved by Ilya Bryzgalov  63.0 -19.0      left  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preferred conditions we want events that are shots and goals (not faceoffs or period starts etc....)\n",
    "preferred_shot_conditions = ((df_game_plays.event.str.match('Shot|Goal'))\n",
    "                             & (df_game_plays.st_x >= 0)\n",
    "                            )\n",
    "\n",
    "# note I didn't include the columns relevant, just the row matching.\n",
    "df_game_plays.loc[preferred_shot_conditions].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below let's take a look at a scatter plot of all of our data, this is going to show where every single shot has been take from (past the red line/center ice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvwdwG4D2hmGohJBBAJ40DOO25tr638QcnCCeNY0Ks1YAIBBR0SZZZhaqk6W8rqQQL207wqCKAiEIRVW4JROy9/cjlbildwcAzlb7mboQ3JKIDukei0XMQxedvAjazorJ/dkDyF+PtzLt7uWt2NH+zpixusxyP/Wq8jqkMsstvi3qteS28zFPg8ZBtnx0CgVd21qselkUIJCm2IJLIghGdIgCoOmAKBBbK3HF5P4WDybe0zNgQNUMEEJs4YrrSwpxMRBmFq4U2wpQdYNZsi5RiLOCTchkepKLeR0CIUhPkpAkm3EYgRD8v30VWPRX0wu5NS8bv7ijN1QH2OT6kkKcrG6EWxLRMd3ToidA1yV+Tf/y8BDbdV9fUojyc5eYpczr3tJJfuw7Ud2sNb6upBD/1YKXuGF6IWv3q3pV60oKMdFGl5qz2FXNwPGL9lBb3it44a/m8+hxCba6eM/gbpadArrWPEy3uXEAwP6T1ZBdrjhP1QoRdoK8ftuw2O+s52AYxk8B/BQAYp7DY4ZhFBFCNgMYA2ADgHsBvPGP7tvnFrCk2G/Z/1xcVIDfl1Xghm5tEvYydxyqxAPDcplFs3BMnwRL1iPBFqr4u53H2T75r/74GaZ9r7uj1T739U+wcEwfhBQ1wQqMhy76PPYWjSQSR0uHXi/dfhQLRvdJaP+RTQeYBZ2eZFpDdB5uqcnjeXFCP9bWtvILWFxUkLBPvGLyDbaWYppXajG2wO+lL590g+18dMM8b2FnLeuGOdfm4IqqriMc1S0Wbvy+P28FO+23n7wYQFKndEzj9tuXFPsBAJ+crccDw3LZSzF+rgtG90FdUGHrHlRUti5OnkCqR2qKhckCk30gYu+ZXAxEmGcSb3F3z0xChzQPpqz80NED1A2jRS9R1Vr2rHkvh79uGPb3OrVzvj6MMaW7m4UPLy4qgCwS9jw66aJLJLYez+DubZlcS7cfTXjeF47pgwfX7bfEU+y82xe3fYF3yyttIa/f9cOm3wkoq015AmZw+ksAbQG88o/uIBDR8eK2LzBvZB42Ti/EvJF5eOm9I/jh9R0SYG5zthzED6/vwD4Y+0/X4em3D2P+qOvw10eGYN7IPLy47QsoGpqFHlIYXksQwDlbDsLtkthLgR8HD11sk+SybUd2gHHSwCQA7D9dh2feMeewc85QzB91HZ5+u+nF+cTWg0jxuCyyiag6G1NlQ4S1NTyvHV567wjmjczDs+PymfzcLslWHpIg2MqYh+nyMMYUr2Q7n2S3BNklJMjpia0HIRDSIlxRdBjHV4GEztlyEINzs2xhjz/252DObT1tIa/PjsvHvJF5eG3XcZyNbVfN2XIQAMFru45j3sg8ZCTLtnNrkyxj5ZQBWLP7JBojOpN9utdeHyobIuxeUbDqBr9GTnrpEggbh2MdsUm3nOpInP7x1wmxv9epHQobpTL791tyE9Zl9tp9iGpNUFYnXYxqhi1Ml9fpuSN6AgCeGZtv+6zMWlOGzm2SHWG39P/xkNfmINrfhfKto5VoMQxjO4Dtsd/HAAz4OvtTdQPvlldagq4AMHdEL1trRRSs1vj+03WYsvJDbJxeyLZefnZ7HgZ3b5sAJ5UlAUsn+bGt/AKuyfYhEFGZVRJv1dL+tMvYUyUECdZ/abEfLpGgtNifcBiPEGBDSSHqQ2bAnAYcAeCtT87h6TF9LOOuDkQwftkejIu95FTdwLyReSjdfhSl249i0dh8PLr5ANK9LibLjdML2bid5sC3Q7cEZg7tgZ7tU7Dz8Zvxxr4KGIaBzTMHoaohAoEAL03sh9rGKNt7z0h2QSBAVLO3Rp2sVPoCpYey+Dp0HLIk4Km7rocoELRL9bRoyWpxfdF2tBis18lLnP9mOV6eWACBmNs7pduPQhIJfjYyD9EY9Nhpbh6XgAeGXw3DMFjwHQBbEye9CkaiFlnqRvPe5NJJpt5k+dyYNzIP2SlurJo6gAW3aR8ukeClif3wwLr9tpY2rbNySn+cjsGOg4qGzm28EImBTTMKcaomBLckYH3JQJypCyNZFpt9TuicFNU5fng5uvj8+L7ISnHDALD2voGoaoigTbLM5MrraIc0DxRNv2x965GVjI3TCxlclo9HOB3g/K7ELL4zH4dvurgc4H2abnyl63WhKPstS8QWTloXVLC17DQeGJbL9mtvzcvGqqnm9+9kdZBtd9C29Ni/8f1FY4qZk+FFY0RjVmbHdC9SPRL++0/lDJGy9r6BiEQ1uF0iu07HRAPmz43Lh8cl2I5bFgnG+XMS/kaD3jRY2j7Nw8ZKrT36wNjNQVF1ts1hFzxfUuyHbug4cdG0DBeO6QNJIBa46nPj8qHGsPZ2fQjEfn07ZXixcsoALNtxFMPz2rE6dsH9hWP64MKlsO3c+DZ5qGh8Oysm93ccR/xLduGYPiAEuBRSm4WWUihr/PYUvyZd2ibhlI1e1QajEEiTLP/6yE2sD96bpOg3VdOhG4kQ3MVFBfj5yDx8fq4Br+06jvmjrkOy2wzid27jRW1jFE/ddT1DNL226zim3NjNdh2XfVThCHmmegwAAiGY/+ZnbD4tPSsRVWfXDYc6p2uCbEvP6XS7nY7yW5S8V5TYfghTVn7I1tcjN23W0AOc8ffIUvOkmN9U+dahrP+o8lUD0hcbwjhXH8Yszip5blw+NnOKyr8k4hWHwgHrQ1HUBaNon+ZGituFu20Ceq9NHYAvKwO2Qa2n7roehBCL4j03Lp8djquJWXgCMQ9MaYaBU9VBZCS7sG7PKUws7ILaxii6ZSajqiGSADGMD0DSfldM7o/lO4+xYJ0dHNQM6tpDRXnI5fqSgUiSRdQ0RpHpk2EAtvQPM77fFZMGd0NU02MHoASouuHIz0OBAS5RsA2Urp5mflwFQiwfv9JiP7JTZJy/FElAurT1yYhETVhnVYOCn4/shX/f8DHmjcxrcX2cYg7t09wIRHT2Qo8Pzsffs3SSHxlJLhw+H2ABVZ5ziQbDk2MH987UhS3Q0l/+oRzDrs3CvxXkQDesvEQUtvzjgk6QBIKHNzV5EaXFfqQnuSzyph//eBoLnyzgTF0YbklEp3QPnrQBR7RJlnGqJohOGR743C4GDe/XOR2/uDOPeScGgJx0D1QdKH7lA4tsHhqei26ZySy4PHNoD0c4LgFwMRDB/ev2W+TfLTMJl8IqztSGmUeS6ZOxevdJ9nx0bZsEQsyDbLphIKhoaJPsQlDR8PTbh9k4zl8KM5ABnWf7NI+t/q0vKURENTm2PJKQoG+/vbsvlNiBO/6ZooFnAN96zOE7G5D+Ngt1DXmYYFqSCz/5QS4kgWDT9EJEdQOna4IAgNF+80F86q7r0S2G9b6HO0BWWuy3uOi0VNSGTDSLw5aESxTw1FuHMG9kHnKzfThZHURakgs+t4TTNSHMe+NT9lKiD1ZOhgkxBIBIVLfUiYcYOo1JlgQWrONhqfy9daEodAdob7K7CVLrc0sIKhqzCG/Ny8bqqQMgxpBHq6YOgM8t4kKDwl5M9MFO98q242sIq2iMmBxDTkHQ6oDCApOlxX788s7eUDTD4j2tmjoAjYqGZFm0WOkvTyxAepIERTUwf9R1uKptUovr07N9CggB1pUMRHVAMS3TdA80A9j++TmsKym0BFcBsPjU6mkDUHkpgqCiQdV03L92vwU6e1PPbExesbfFw4WLxuZjQv8c9OqUbjlwFs9LlOaVUBuMYv6o65CZ4mZeZTwYgpL+rS8pRFTToRuApmt4ZNNBBny4FI7agiMe22yCF54f3xdel2hpl+olX9/nliwfBjtoacc0t62+BcJR3LeqDIvG5mPhmD7shfv024fxq1G9EY5qlv6WFBWgc4bXdhzU26IwXTuP8am3DrH501hgvG7UhUzupSVFBXhp90nUhRSsmjoA9aEoIqqOFI+EqSutfGU1MVgx/Qhc2y4Fv59943eSxO+K9Rzioaz8oTFRIAiEo/A5eAJOkDyn6xumF0I3gLW7j6Oga1uLRfTTEb1wKRxFiscFgZjMm4GIirbJcouw0/UlhfiyMtAsrPVyDvX95eEhlgNMvHcAoEVYrBOEcsP0QpyrCyOiaujcJskCD6VWWYd0j63nsHF6If7y2TkMy+sAw7BCS+MhnU7yp2t6dbbPYvnxUNuW4Io8vDg71Q3dMGLMpSZjKj1YRy32kiHdbWVBD0nxkMvz9WFbeDKFlrpiTLySSBjlw3vl53BL7w62sMyiQd0w5On3mezvtllfu8OIT47siVt6d4Aao7R4r/wcOmQkM7gsDzW1m1N8HScILu9tNgcPtdOHdSWFbG7xsrw62+cIZ7Z7HuPHvXa3Pdy6pfnzY+IP3M0bmQcAjh5QSNGw5aNTuG/I1WibLLdCWb9rReMsPKf95lf/dsIWwqY4BLcAJATQ4iFvPLRtcVEBPjxejZ4d0yxeyMIxfSBzcFQnr+NsnelZxB+O4+u4RMIguPz2iouHuxLYWmsgQMDh4FYjF1QPOwQEaxsVqLpusYJ5z2bhmD74fczSjR9fkluAv1smJi7fY3svpUTg++ODgvya8p6H0/WWoL08jHhxUQEOna1HT44ckY5p/8kaxyCqk57RdheM7oOcDA+DltpZu0uK/Uhx2x8m80iEyYIPxtoduqRtjspvD3+3TItHt6TYj1SPyNrSmwnu09/1oSiT31VtvI76RGVzucF9fgz0d/tUj2W7zsnzdfKa+XHLHHMrL0uvq+kAnWAD/DAPyjWNiZ6Roe07HQClHpBJa9IKZf1OFpGDz/HMo0ATRG54XjsGWd3BQdji4YBALGhtAGt2n8TKKQMs9XnIGw9tm712HwqvzrKFOvJ9tAR9fWLrQfjc9nBPHs7H9xvVDFbfLYm2kEm3JFogq3y7x6uDbK5OkFqPS2wRBvrD6zswyCAPmw3G9vCd7p29dh+G57Wz9MdDJfk15eXndJ0PxtqtXTzUdnBu4rrNXrsPN+ZmoX2aB2vvG4j3Hr0JG6cX4rVdx7H/dJ2jntF2KdS0OTbeWWvKEIjojmtqJwsDxLbfFyf0ww96d7CF4YqC0NSWaA9B5cEYZ+tCTH4pHpejPtG1zk5x27bpBHelL8ucDC8yfTJenNiP6YTHZa+/Bloet6bbw8/dLonVAcCAH1RHX9t1HDqaxiSKgqV9pzGJgsD6CCn6dxrKesV+HCgMNCfD62jFpHtdDLIKgLn9AjFQWuxnikOtjUhUxZHKAI5WBWDA3I7hkRW81UL/H79HTa8HIiobH4UG8v29PLEAHdM8WDrJjyyfG+GoZqlDUR5OEDtVN7D2voG4NS8bmu4MBWyu7wkDO0MgpixfnljA6jw5sqeZtEYSMG9kHvp1Tk+Qcb/O6Zg3Mg8uSWCQwfHL9qB0+1GM9ne+LLoSGtSjwT/VMCxrmuVzY+kkPzqmedj4+Laot0DHXRWIQJYE1DQqza5dcxauqhuA0cQSCgBP3pmHFZP7Izfb56hnVB5O7fJ9O61pVDPYumu6gY3TC/GHB250XN/aUNSxLU03MOP7XbG+pBACQYK+PzcuH6XbjzIvpHT7USY/5zZ1vFteiRmry/DIpgMW2VPLWhRgez0YibLfBqwwX6fDf/WhKBaNzbe0RcdKfztBoTVNt4yp5PvdMf/Ncoxftgfz3yzHQ8OvQSSqWp59GvvK65ACl2QPYQ5EVMd1pDoQVFQzcK5/u1v+V+y2kmE0WQOdMrzIyWgepgogAWK3ZtpARtGw5aNTGNqzHds2cIIh0jbp/50YMxsjKha9+wWDFQoE2DS9kME34yGQbZJluESCjdMLQQhQ2xhF0e8+aJZpc+7rn6C02M8OzcXXkQSCuSN6QjcMPDM2Hx3SPNBifVc1KBZ2WBr8TfNKOFsfsWxTUBef9nE5nFBP3XU9q+8EIU1PkrFxeiGCioZUrwvJLrFpTdM9Ceu1auoAyBzbLe8t9MhKxtGqRjzzzmE8eus1LeqDwsEk+TpVDeYLMh79s+doFRStre09UU1n8nCCvvJ9O8EmXSLBvw+/BkW/awIuLBzTh/09vj5F5di2JRCMzM/BhNi23pN35iWAN14u6odTNaaBM3dETwZZ/cUdvW3b5PWMlz2lvKD30jWke/Wv7TqOn47oxSz22UOvRlufm7XVGFFt+zNh3NX3pusAACAASURBVE3cVAaATuke/PbuvjhVE4QkCOzwXoLuiwLr7z/v6I2MZNnKcRWj2VgxuT+2fHQKxYO6Ye19AxkYwmkdKR9T/DraPRPf9hbTFRuQbgiHcaI6gllryvC7e/yIaoYF1kr3ZLNSZMwd0QuAeR4hnheHQhcXjukDTTfYvrjdYi8pKsCL7x2xxBw6pHnwZWUgASJrADhVHUR2qhs///2nyM32YfpNPZDkEjDOIehd1WDyBLkEgQXS7cZBKb5PVQfhlkTkZHhQ3Ri1HJpbUlQAgRDc/uLfcGteNn52ex4IgKNV9pBDGmDmuYH48dFDZZQSuyVOqPUlJra9JSrveAZaSSBoCEfhEkWcqklcry0zB+H4xcaEtnpk+eCSwNK1ArCgnnjYcuc2XiS7RVQHrDJbNDYf7dM87OVMy6152fj5yN6oDkSQkSTbwinpeMb5czBp0FW2ukhjDl3auHGq2grDXlJUgHZpHty1eFeCXDfPHIRLIZPhVyBgiKR0rwxJIjhbF0mg0W6T5GoRELHuvoGoDSoMbm0A6NLGC5EAlYHELGpdMtwoPxewXceqQARLigrweVwsJ172Xdp64ZFElk/iqbc+x5QbuzFd4dvtmpmMQ+caEnSA0WIU+9G5jRunauLmX1SA1Rzdd3sHua4vKcSE5XtQWuxHW5+MlX87ZmGcVTUN014rSxjTyeogurQxz+JUNyqYsToRAk37+LoZXpsLSF+xH4fz9SHohg7dIBAIUB1QcDGgWHDZIATVAQX3r0sMLlJl2zlnKAghOF8fRmaKjJuf2cH6oKcqr8n24UR1EB3S3bjYoCDZLSHN68JTb32Oad/rzhLodEzzQDdg6a+02I9zHN/96qkDcPOiHQnz+eMDNyIQUTFnixloHb9sj+04DCDB66AfwZ/dnhdL4AOouoZUjwuabqA+pFpeggtG90F2iozKBiUhyNkp3YshNglj3n/0Jpy/FGbJgZySytDrG6cXOiYWEgjBQ+v3sw+DXZCXPxzGr9d7j95km4xn6T0FCXkvSov9yPLJuNBgxa/T8zBFhV3Yi5GeQUjzuCzr09L4FhcVICPJhe8/vZ3V/9Wo3hZd7NLGG0ssBRiGDq8sIhBRIXEve1U38ynbyXXvfwzDhUuRhI9JVoqMitowdn95EaMKcpgnEVSi8HBtURRYfNk192acrglZTmTTuQ3omo5Jg7tB003iwzf2VeDOfp3wk1iyo+wUN9oky6i81PShzMnwIBzV4XYJSJYl5gHPiK0JZZDl1+jliQVok+zCQ+s/TlhTSncerwM75gwFYH78fzqiJ0TRTIZFZSlLxEwyFdVZwiw7nd45Zyh0A/j1n8sx57ZrEYrqVo8x9sE+XtXIxrRoXD7ueXWv5bn7+e15MADbtfv7EzejU0ZSwvV/VGnu43DFxhw03cC7n55HbIsYs9buw5SVH2L8sj14vawCYdX8aNIXNeDEz2Myho5duhsqF+QFzCDn/DfLIYkCFE3Hs+9+gUZFQ2VDBE+99TlG+zsjO8WNrBRz79wri6hpVJDlc7P+ZsZShv7pwBmsmNwfgkCwYnJ/9OuczvrJybAGf+MD2HQcqm7gnlf3MsqQitqmYOe75ZUo+t0H+OJCALc8uwP3vVZmvnQ0IyH16RNbnbmfRIeAoksUkOlz43++qEwIOvbrnI4Vk/sDAJtbXSiKqkAEM1aXYfyyPZiw/APMff0TSDGrbOqNXbDz8Zvx4sR+LQZ5nx2Xj6WT/LH4imFpd8bqMlQFIoiqRkJgduaaMqh6YkD/4U0HUDKkO+5ft5/pzJSVH+KBdfshxvFatRSEnr12H4CmNZ05tIdFF+9etgfjlu5h6zJl5UcACBpCGs7Xh1DVEMHRqgCeffeLBNlTufJeMWBSYVwMKIioBrJTPUjxiPj83CWcrw/jZHUjLoU0yxo5ASIMA+zDED+3pf9zAncv2wNF1fHv6/fjupx0SAJhsq9siKDodx9g7NLdTH5TVn4En1tCdUDB6doQdAPswwDAktKU9nf/OlN+T4zoiVRP0y55VopsAWzMHNrDomdRzUC6V4ZLEjFx+Qe45dkdGLZoB255dgcmLv8A5eca8IPndmLXsWpHnRYFguJXPsC75ZW23E2z1u6DouoWPdN0IxYj1PH83X0x57aeWLvnRLO8U99WuWI/Dh65CSrJB4boidHJK/biTG3IMThI3VMeEhqOarbBtLqggvlvluPewd2Qk+FlbJvz3yzHku1H8cCwXMx/sxy3PLsT8974FI/ddi17+VfUmlA6Cre7aeF2zHvjUzz+Q7MO7YMPysUHWqlFF47ac7nwwU7+d3VAYaRt8fc4cdUAiQHFhWP64MH15ot0ZN8cuCTC6lBI4rw3PrXMbVv5hYRA+OKiAgQVFWt3H0fXrFRMXL6n2TWivymP0QPDchnUNL7d5gL3dtfjubbodcPQsYQL3rZNtj/kx4+PQpIf/+G16JjmabE+DxGmAdJ7B3cDgcH65uXKcw9RT4bKe+LyPfB3y8S+E9UYv2wP5r7+CVRdh6rrTE52oISFY/o46gY/VkkgbByvl1Ww8TmBDGjA9rHNBxLWtjlI92ObDwAAnnrrEOa/WY4Hh1+DL85dYnU6pnksejZ5xV4UD7oKAoHjHOizJRD7ILkgNN3ryN3EIcgWFxXg7U/OWeQ/ZeWHuD2/EzwuwT5A/y0iWq/Yj0NYaYJK8qyQfOJyJ4spO8XNIJcK5y2crQ/bQt4o6+YTWw8i3etCelIT2+bwvHYJFke8d6IbiXC7OVsO4sWJ/TB/1HV45p3DFsgpH+zb/thQPBM7TZ2eJNvOhw928r+TZNGZYdOB+dVAU6B/+2P2cN6oarA6PCSRn9uDw3OR7JawauoAvPfoTVgxuT/+dOAMjl0MYswNXdjaNQfz5X9TK73fVW1sobOXwxjKX6dcWwlyEQQL22+aA1Oq3fjmbDEZV1uqz3uJVGYUuvnmxxVYMbm/Ra4qN1Y7T2bWmjKMuaGLRf6KajBvddG4fHRM9+KlCf0sa+oEc+bH6pKamG+v6ZDKZOMEZW3OA74cNmMe8vvD6zuwOhnJsi0c2jBg2yZ9xl/bdRy64QBl1ZvuVR30wSUS7JgzlLHoXtMhNUH+s9fuQziq2/YhCN/eK/qK/TiouoHx/hzsfPxmGIaBNdNMWCdvEdpZ4AtGmwejZqwuw7vlldB0A6/cewNWTO6P7BQ35o7oha1lpy0WHWXMpJbRxUCTxdUcjDYnw4ulxX5n6KKqw+MSUBWIJFh3FFa4+P0vkeaVEI6a1uCqqQNwa142mw+F9t2al43V0wYgO8WNFZP746WJ/RCOagkyoPXqQlFsmD4QG6YXYuP0QqyY3B8vT+wHUQDzinTDsIWEarqBn93eC3kdUh2hmxcDETRGVNzz6l4MW7QDC985hPEDrkJ2ihsel8i23uzW6Ld392VwxefH97XIX5YEVDU04ciTZRFTbuwGl0hsPYo39lXYekLLdx5LsKZLi/3QDIPBNccv24PHtxy0vZ+Ob8HoPpbxaYaBFVP6Y8Xk/ha58vWdoJuabuAHvTtAlgS2fbF0kh+arjMoqpO+iRwihurpkGvbYcrKDzFs0Q6s23MCmSluEAJc086HhWP7oGOaB0viZMbLfklRAUKKisHd2+IvDw/Bte1TMOe2nthWfsEWylpa7IfMQUDj13Zr2emENYqXH++10O2gxUUFFnlsnF7IIOBR3QpLp/BdAMjrkIIn7zSTNP3ijt7okOYBALRJkjFvpHl94/RCvHi3qQ/xY1sS83QvhaKoDkSw61h1s7ku/vOO3syD23eiGv95R28EFRVn60JQVSsT7DdRrlgoa4pHxNBe7dixeKqcHgeoI2WpjGe5rGmMQNUMC38LZaw8WplY3ytLFkifE0wzO8WN+aOug6rrjoywkihAEgSsmWaiRsJRDc+MzUfHNA+DZU7onwNNh2V8L08swCM/uAYel4Sqhgh+Ncok2OOT3jw3Lh8h3bDCPbOTUReMOia+WVrsh8DlI3CCyPo8Is7URTBrzV5HqG2614WLmoH1JYUQBeBiQEk4RU49Ejo+SuCW6nXh6THXo6I2jFSvhNxsH2PPFAhskU9Hqxqx/dAFrJjcHy5JgMrl3e7XOZ3pQFVDBNmpbtw/7GrIkoCXJvRDRNURVMz0r/HQ0P2n6/DaruNYX1IIVdMhSyYlxvN397XVp1PVQSS7Jct6lRb78dsJfZk+OUFteYbRq9om2bKbOkGnKcSS/r+ywTQ45o+6Dr07piTwYtFT4n2vyrBAPFO9Liwu7ofGiA6XRJDmEW0Zf9fsPmmBERswA8T3DOpq+/z1iNHarIrpFqUBiZcf77W4JRPGahgGZEmwXfckWURU0zF/1HXIyfAgrBoMvmtXnxJwxvNafS83C2FVZ0zLFBEWiKgIR3UkySLmj7qOeUzx8ldU3Uy/W+zHrJt74HStFQ5eWuxHz3YpkKRvzp6/Yj2HELetBJhBuqqGCDviz1vgHpcAryyaqIjhuRaLLs0rM+ZLoMlNPFUdNK3UQAQAmBKt3X0cj2w6wKxOu/1c6p1MWfkh7l+3H/UhBSsm32CxJldM6Y9wVEVNUMGv/1yOyoYIth+6wLZ2rs72YUDXdAzOzUoIKN+/bh9EQUDxK2ZA8Gx92DboSoNh1As5VR1k9exOLc9YUwZNNzDlRtNzqG6MWGQ54/tdsWF6IYKc7O0s/0Vj8/HIpgP49Z8/x5eVAcdttYeG57LxtfXJWPz+l2Zgc8WH8LgkKJqOZ945jJIh3dk5B90AJMHM17BxeiGeuut6SIKA7BQPxg+4CgvfOYSHN3yMqoYwdh2rZu1nprgRjqpYtvOoCaPUDBw614Bf/tE8GPXWJ+cAmKlG49dzyo3dTLSVYeBsXRhfVgYQUlRkpbgt+rFobD5EgeDB9fstc525psyiT6JNHwtjuTiobCKqblkfCjjwSIIlJkL1cstHp9j/X5zQjx1qy/TJZkA77lmpDigY2CMTZ2rDeGHbEYxftgcvbDuCipoQFNWAWxIQVfWE09z03tk3X40nRvTEte1NBN2xqkZUNSgJc6PPnywJEASCW6/rgPlvlmPO5oPwuUWL/PiDeYuLChCKanhh2xHUNEZh6Ibt9qWq6vj7F5Xoke2DLIm42BBBls/teKKfTwRFr89aU4agouPQuQAe3XSABbanvVaG9CQZmm7g5fe/hKLpqAtFE7z3xUUFWL7zGGur0SYR2QvbvkBlbK7fVLliPQcnHh5qZVF2xcqGCJ5++zBemNAX4TiGx0Vj85HqtQ9oxbN50rzCBV3bYliv9ozhtUubJBBiZpnKTnHjSGXAYg1V1IbgdYmIqEaCNblq1wnsOlaNBaP7oGd7Hzqme60cOUV+x20b3n13cnU7pHnw3qM3sTHNHdGzxXuiuoGn3z6MeSPzkOp1Ycn7R7Ficn+keERUBaK4e9keC6cRtQ55Vlq3S0Buto8diHNiZe2RlYwdc4ZCUU3IIWUYragNmdZgLGdEmlfC/Tfn4p5X9zpyPFGG0ZcnmhBPwzCwYXqhBYr5bwWdzJwcnIcVz6q6aGw+mz+FVT799mH87PZe+O8/fW7pm9ezumAUbpcAw2hZnzqme/GTGM043wfNWFZRG3Js52hVI977/DybmyQQEAEo7JGFYb3aI6ho8HkkLL2nAJdCGmSJNPusUBk45eXgARuXAzv2uET86o/lCXOjENBXJ9+AZ8bmo1O6B6GoZvFaMpJl9pwu33kMU77XjfW39r6BtvIAAQOm8PNx4ipzAiKousF4zvhUtIQQXNU2KYFn6eWJBXjyzt5ojGgJukscuM4IvtljB1es5+DEwwMAVQ0KTlYHkRKDxmWlyCCEJMD2Ht18AC6HwGx2ipsdFJMEgvJzl7CxrILtRf9+3xm0TzOt/C8rG7HgrUM4Esv5EJ/MJMntSrD+Z64pY+lHn9h6EKGo1dp4cUJf+DxiszA8et0p0CcKJktst8wkvDChHztJ3tw9PFxR1QzUhRQcu9gIVYdjMJJCbY9UBhgktGRId7Y95RS8FAXTUp+y8kP2cNG/0Y/iE1sPIqoZDJIcbxFSWOHCsX0wb2QeXn7/CHQD+OJCIx5ctx83LdyOIU+/j41lFQhHEzmNnth6EKMKciwgBjuobHWjktD3u+WVuOfVvahsiOCFbUdQ2xhFps9+rnWhKHKzfeiR5YPIyZjvoy4UZXBNehLcrp29J+pw5EKAXX/yjc8Yhb2i6Vj49iGEowaDdTb3rFAZlAzpbhvoFgUBt+ZlY+kkP54dl98i7Njnlmzn5oqdWH7mncOoD0URjuqYuvIjC5R48ooPUX6uAY9vOYgR13dAkmweynxxQl9HwIFhIAEe2xxXmRMQQRQIsnxuKKqpS0sn+THj+13hEgh0A2iX6sHg7m1ZH/ev2wfdAEJRDTOH9sB7j96Evzw8BDO+3xWGAVsZ6wa+UVqNK/bj4PM0udfZKe4E62beG5/ilmd3MghkbdCe0722UbF18R/ZdIBB6k7XNGJr2WnWHw+XpfDNx24z4Zt2wUsnbhwaRKyoNeGuNBD81FuHEI7qmPTKXjy4br8tJBSG0ezW1uKiAvzqj59h2Y5jCEd1TFi+x9KW03aYxyVwW0kGg+kasST1898st2yr8ffygUXKJOpUf0mxH7/842f47V+P2AaSl+88xtqKcFBO3uOxW+t7B3dDbaPSIlyYXweeH8tum2xxUQG2lV+w6Bl/f8d0LxvHQ+sT12vhmD744twlpjMv2Mx54Zg+2FZ+gcE17dZ9wWizDg+l1Ll14UEUdGu7ojaEJFloEYLqZFFrepMOXA70NZ4jjM7tofX72dg6pnkcOcmuauO1QFYnvbIX4aiO12Psv/E65ORZO43j7U/O2bZTUdNo0aWtZacxsm8Oxi/bY4HOjvPnsD5qGxUL6IJCvZ28vppgFD9e/HccvtDwjXwgrthtpUBYx4mqS9gwvRAETdwzdpbR7LX7HLlSztaHWeCOBq1poBQwrZL1JYUY7e+MsuMXsa6kECKBhWKCWgbUMqJtnY7xvzgFdnmeFt7amDcyj1moFbUhxizbLTMZhy804KX3juAXd/Rme+/t0zyWtI5tkmUsfOcQ3i2vtORq4NvqkZWMC5ciCakgf3FHbwYVTZIlTFlpzpNnBo1v56hNYFYg9vW7ZyWDEIL/evMzS/7vlVMGwCUSlleBehK8tUct++bWmvIbUat2w/RCHLlgbqvNHNrDdh14jpx4EMOh86a8R/s7M2s0/v40r4tZr/FzBQCBAJ3b5LAtw+F57ZiMKXWJJAh46JZcVie+HRr8jp+zHWPrE1vNedPxBZWmZ4Vei5+DW7LXUVEgCdDw+Dp8EDkjWUY4qjO9PF0TsjxPdH28sn2KTZ/HhftWWT0BStlCWYQlkSCq6tjy0SncEzt7FN9ORrKM9CQX1kwbCD3GIhGIqPi3ghzMf/Mzy7bXi9u+sOSqAOwP7NH3yKayCuRkmLDdWfEH59aUYeP0Qvt3TZ25riWrPvraaTWAK9hzUHUD/3OkBiFFuyyG1nBUw0sT+1mCwi/FIIY8c6sddFM3DPTI8uFH+R2harojNDXd62LB35CioviVvRi7dDcEAQmQwSUx65hahLwFFD8HOj562LKqQYGqG9ANkwtqzuaDSPWKaJ/mRVYMKnpLz2z85eEhFmZVvi0DQFufjFW7TzCL8/6bc6HqBoOK8vOsj8umRdtxiSQhcL9wTB/UBO3r1zQqOFcXsnwYNpVV4JZnd+BSKGoJJNM9fYoiuTUv2+LxtHQQq6LWzCPQMd2Dh4bnIjvFnRBMXFLsTzgkxa/hjNVlSPfKyOuQiiS3aGGvpXvPogCLjIGmTIUCMU/za7phYbjl4bJUT+KtYCqzS2EVbX0yqgKRhDnHrwudd0NYtTC89u6UgV/98TPUNiq28G6XSGwD3bJkhYbzLKnx8OklRQWobVQwduluFL+yF9WBRHbcLJ8bLlGAEMcETMcRdTiMlu51MT25UB/G41sOoqBrW3hc9gF6t0RQG4yi+JUPMGzRDtzz6l40hE0YOi97CmmP95yaYyHePGMQ1kwbaObTjlt3GnOIZ5OlTMjrSwbiqbuu/0aYW69YzyFZboLYzRuZh61lpy372/Ff7UBEtSRmz8loStVJ6zixZdKE8DTZz5zbetrW4w/e0LwPORleKDEqDwsrpEvApEFXYXheO7y267ilTScLjU9M75YExljZta0XAUXHrNeaGFYfHH6NBX4Yn1CdQu9enliAeSPzENWaSNAoBJBnVq0PKg5y1SwsnNQKpuc84utXNjR9RBKsRrd5nmPNtIFwSwRVcfDXxUUFyPLJUHWDAQBa6kPVDVwKRR2hyh6JwODguzyT6Gh/Z7aFSKGPzaUujQ/s2sEp6QvQbtyqbg95PlsXwrbyC1g5ZQA8LquF77QuaV4JD8UxvL48sQCpXhde5LwW3mNskyxhfUkh9BhHk6prOFUdsrRPdY7mGueD+0uKCizsvfHzpKe+abrceOBIc4ywvHfCs+BW1IYYxTwFH9Bsex3TPGYWR91g3q2T9+iJ85z4Z5APxNul/I1/tgACT0xOV7U1eZV4JmQeTPF1MrdescR7fJpQu8WLxzfzjKu05GQ0sbIuLfYj0+dCPZfonFIEX4zRUDSl3jQAkNh+rsjYOZ/8Q3kCAZ4kAhcbFPxk48e2fW8tO425I3pBUTVEVDPw6vRS4RVw4/RCrIqlmkySRcs2lxMLJ50rxalT9/i1qQNwb+wlvGbaAKzafQKj/Z3Rq0MK6oJRzF67j1FHx4/pjf1nMPX7XRmJnBTjCwhGVChcoiL6UiYAGhUNHdI9OFbZaCG9++Ufytn81pUMxH+9Wd5s6sjLIcXTdB0Prk+U/ZppA3H4QgO2lp3G/FHX4fylMCPhEwhBVoobtUEFmT431nDpYeUY1TPPnAtY09QaMfK3qGaw9KE8+2yXtklIkgWEFNO7oJj6cFRHIKKx+cz4flcUD+oGVdOhxdr0SAIqGxQGcNg8YxBkiVgIBNsku5Cd6sEv3vg0QX5TbuwGSSAMvk0t7XSvhPmcvClLb3aqGwIBztSFkemTUdsYRUTV0Cndi0kxneHl+trUAQgrKiobFHauhurAisn92Ufa6RnMSpFR1aAwTiZeby4GlAQWXKfnSRIIYyB+/u6+qIuxz9KPWrxeUsgvz+T74LBczFq7z8K4ejnPVqZPxuHzZvrfR2+9hr13nO79v2wxtaYJtSn8lgcPp+zZPgWSQLB62gAABLJo0uryydFpqagNITfbxw6rLXr3CMbekOOY0HzBaDPF6M9//yl+PrKXpd7SSX4snVSAygbFYq0tKfajXap9IDOvQwraxyCavCUlCeZWDbWGDp1vSIDHarrB+JpWTR3QrEtM7+nZPgUrJvdPgN5JnEvdLsYbFQ8LFgXC4Je52T4Gj83N9iGqGrhvbeLBuuas7CXFfqzfe5L9n/fiKmpDIHCAA3JcOvHrLgomG+fzd/dl8NgJA7vYyqI2xpe1YHQfaIbBYM52luHiogK89N4RZvlNemWvY+pS/p41MdroeI9icPe2KB50VQIDaJIs4sk/lMe2KtJwoUGxHtYqKsBLu0+iLqRg7X0Dze2mZBdO14QSPGIKIIiXX9fMJDSEVYsX65EI3A6pSF9+70v8uKCThXp97uufOMKTXQJBCE0eOu8dpHjsn8Fr2qVg1dQBTD/odhhlWaUv7JwM86Bmlzb2z9P1nVLRPjXX4m0uGN0HbolYYOy35mVjdUyn43OrlBb78cgPrsHFgAKXRJj177Tl6zQH2ncqN2enexVVw9dRrtiYQzy0jTGoCgTjl+3Bzc/sgKqZjIp3vvR3HKkMWOoD5lebwi/vj8Ev4w/ExUP1ZFHAzKE98O8bPrbUm7G6DFEbBtRZa8rYqdv4vgVCLKyxFBopCgRjSnfjewvehyQQW3gsHyiMh+c1B221g43ysvTGpUekYyKkCX7JQ3ZLhnRnQTknqKdHEhLYZONTrj66+YCFj8op0BrPpUPXXRQIvrfgfYxftscCj3WCNFJjgbZJYc5OKU1H+ztb/uaUupS/h4cq81BRnv+LySOW2pLOJ/7gGq1TMqS75UCcbsOs+ujmA45wyqiGBAjplJUfIaoatjBV/tCY0/zjdYx/Bni479GqRsd7eP2g8xMIQdHvPrDozYw1Zey++HYMw56FWdOtMnq3vBKTYs9ZvF7OjMF3GxUN971WhikrP7S8O5qbd3xbT2w9CI9LbPFeWRLxdZQr1nNwiQSvTr6BbQEFFQ2d23hBiBkcLN1+NIFn6fnxfdn2DrWwnnrrEAB7ON84fw5KhnSHSxLwl4eHYPnOYwhE1Gb5Veyu04B5c1Yw0JS3QY0FL0u3H3W8l2ejpLww9IVDYbez4lxzTdexpKiA5RoIKhoyfWYGOtqHEzQwGIk2JVmJBYUpxbeTZUTn45RyMT7lKs+Wq9ukYKSysbPQBGIGSP/jR3ksxebpWhNS+/LEAvbSoHUIAf7+xFAYMK1H6lHR9KT8nvy28gsslwZdFwp5pWSMdvPjocq8nBxho5rOEjM1xybL/1ad0mQ63G8YhmV+UU1nFjSdG90Co2tHr/Oyoalb43OXEAKWGEoghOVBSPe68MrfrHpK104SgMHd26JkSHeIAoGmG1i+81izKVBfufcGnK0LW7Ylvyozb3Mybpss286V133LswzndMG0vt29y++5gaXL/UeXK/bjYBhAQ1i1uNPPjcvHnM2H2BaQQJoCn7nZPqR6pYSgMC2mtdpUnwYi4zllZJEwNkteGZoLaPMpTfkg4M9HNgXfnE6uisQ+WMoH7qgnQOGgokCw/2Q1Vkzuzx62LR+dwg+v7wBJECwyW1JUAHB9OM3B45KQlkQY94wkEqwrGWjhInIK4l1uytVOGV6snjYAv/nz5xaOHjvZvDyxAA8Oy2VMuk/eeR0eGn4NK4N8wwAAIABJREFUil/5wHF7SzMMRKK6bR26vkmymHDvA8Ny2f46H/+hkFenNLU8VJmHijpxbcmSwILIl5Om0iMJFp3l6zlxMAH23FQ/2fAxe27sTkvTMzB2cqU5Ovjtn4Vj+uCpt8xnkXJF/eKO3oxbidflJ+/sbfuseR3gtadqgnBLQsKzTxxk4ZiW1eG6WxLQKcNrO1cAuHApjGfG5iPTJ1vSo9q1RRMi0VzeXlnE67MHI6rqkCURbZPlry2N6BUbkD5XF8LYpbsTFmPeyDzMWF2GnAwv1pcMRFBRIQoiPJI1gMjXpwgg+tGYs+WgJeUlX399SSEeWr/flrSuTbLLTA9aY+6Z0yxgkkgsWcqodRiMaPDKIqoaTA4cuwDfhpJCKJqekP0tJ8OLumCUbenwwUsDgCgSFC235iJul+ph+7GANYgqx/ZlRYHAiNvnfW5cPtQ4bptx/hxMv6kHUj0iLjSYc+NfuHwQz+7lvmJKf1QHFBCAeX0pbgmjS8015dOMOqVgjA9A1oWi8LmlGNIm8bzEupJCRrMQHxzkZcEHkVsKQC4c0wc9spNxoT4xU9vqWHzgpz8yx1cbC846xRzap3nw6ZlLeGHbEeRm+2zrrN59EhlJEooHmXxPbklAdcAawDUZS2VcCqs4XROyWteaYQFH0HnTPNAUdGGn+zxwwaKjsbMk8YH3zm3MnNyEEHRI88AlEDREojhbF7Fm3/O6GOqJb3fT9EI0RjVUcHPomO5Bsixi9e4TLKUnNX4mf687KmqClmD7c+Py0S0zGUcqE9Obds9MRlVAsWRJLC32o22yC7oB/PKPnyUE9O8Z1BVzX/8kIUjeKd1r2wcATFj+AXIyvp6Uoa0BaZvihIfOTmnKwuaWBNQFDcxau9cxgJab7cO8kXl4+u3DeHZcPoKKhqfuut7CXcTX12PJ2AVCsGbaQIhCU9CMBiztcuwO6JqO9SWFIDBQF1ITksjXOeDVz9SZe8i8pfz024fxm7uuA4AENkr2Iin24//NHoxGRYNhGDEqDVheCs0hvBYXmf1dDChI9bosXDX8CfEsnxtPjb7OPJwkEMgSYTBTWj8+cAyYaV0f22x9iN2czHmOntxsn61srmnnS+BcikehAOY5CrqlYrcF5uS1PfPOYcctI15vXpjQl60FC/K6BJQM6YZQ7KQ7b33Wh6IwDAOvxazuqGZA1TWcrw9b+H3W7D7JqDSimgFRAO4f1gOXQpplrVdM6Y9nxuazD62q6XjmnS8SwBWLxuYjy5fIJhA/7yS3PS+R5LAddqY2xMZtB+Xl13lJUUEiEMGB3wwEUFQ9wUPwuAQGxuDXWiSAK8bkStfBJQkIRTVbvqy5I3rilb8dw9r7BoLa2L/+s2kU/emh79kG9LtlJmH11AH4DWesLRqb32wfdD5fV+DZqVyxAWknzqGsFDd2zBmK/zd7MBQuvWJdKMo4Yigf/K152ThSGUDp9qN4aHguYzE1qbSdk8eMX7YHv/7z5zh+sRGEC5o5MUE+Oy4fBV3bYv6bnyEU1ROC1nO2OHPB1IWiyPK5UdOowCuLSJZF/MePesErS7gYUPDCtiMASGLwck0ZIqoOw2jimhKJPceOUxD2bL3J2Hm2LgxNb0oBWjKkOzvhu3BsH5yti2DO5gOIajrGLTVTY8YDAGigVSAEiqrjoQ1W5tKHNx2Agabx8Rw9TmACtRnOJTqHB4fnsrV2Sp3pdNJ65tAejkHEeL3hU4PSIK9Xtqae5IOzo17ehXtf3YtD5xtYWlePS7T0vamsAlNWfohD5xswZ/MBnLgYBEBwMRCxpKKdsuJD1IeirO/71+3Hg8NzE8AVj24+ALdLaHHeGpcEh5+z0zPBp/PkA+8zh/bAir9bE+C8GDttzo9JFKw8UpRfSjeAGavLEvREFISEgP7stfsQ1Qw8YJP6lYDgP37UC8lyU+CXpiGlwe+oprOUoQAgi4IjIGISF3jO8rkRVDToBvDQ8FyUbj+awJdF5fR1BZ6dyhX7ceBPRQNNJywD4SjO1oXwwPr9ljSF28ovMI4YeiL4weHXoL4xwrhcHt74MQAzxaEd58+SYj/e2Fdh4fTh+3CyMmmay3sHd4Mk2ltfTilKeS6dW57dydJA/mTDx4zTyeXQpqYbeHjjxywl5YMc7w8/VqdxZ6e4Wd9Dn2lKAeqVm3iT+NSo/NycEi3VBRXHU71hRWWnXXl5OKW55NOmNpeCkvJr/f1IFVvTbeUX2O/mEjbZJaiJ50FySnXKp/eMb5fKIz6RlF2deD4lu1S08cF9J8+6siHS4gnz+lDUll9K1XXbNeXnwAfb+XS6PO/TVW28lv4o2CEnw5oe1UmuTsF2p+u1wcS0rA8My8W28gusTvxOQdhh7TQjkeE2PkUu5fPik0KVFvuRwa3RN1Gu2I8DH+Tl0/KJgmALt7NL5zlrTRkG52bZQvU2lVUwLpcdc4Zi4/RClB2/iEV/PWKxuPg+WkqD+MTWgwlWEq1ztj7MApw75gxl6UOH57VrkQnTKeWnKBDLnPafrmN8PZfD0OpzS7Z9EweYKT83nqPor48MsaRcrW5UbPvzyBJLk5meJLP1pa75M2PzsXOOfdrUlmQ/e+0+pCW72Zo+dEsu836cWGOzU9wY7e+MPx04g3UlZsa858f3RacMLx66JbdFWKcTA2jHdC9bX56Pij/Z3SnD26wOxKeijQ/uO/VNKeznj7oOndK99rpYF0pYu5feOwKFSw9L043Gz4EG3gFza9Bu3Elul6U/n8eFrBQZG6YXWtKjNgcb/SrX7dKyzl67D8Pz2rE68V6RR3J4phy8b9ouTf9LdXTuiJ4sn0Mtt0bfRLliPw4ukeDBOE/g3sHdLNYkb706WUnNcRpRLhdabszNworJ/dl+c7/O6SjdfhTL7/FjxeT+6JjmaTENYjASxXPjrLwri4tM3pWHhuciJ8ODP+w/g1SvxLh0aJpGSgs8uHtbCxMmYNhadAaA3GwfFo7pgzXTBmDj9ELMHNoDL2w7AsMwGN/T5VjmTjIDmnhnBFhTNtJEL3M2H2SB4+wUN5JlMYFranGRyVFU0LUtHt9yEA+t349p3+vO1nfOloOQBAJBAF4vq0Cmzw2dSw/rxDIbn4KSrqmmN6UDtUt5yTPzDuvVHgIxN6VTPRLcomCRgZOXtHznMVsLvDESxVVtk5CVIlv641OJyiJhnFBOMZee7VPw10duwhv3D0Jeh1RsnzMU//P4zVg9rb9t31QelLNJEGBZL2rh5nVIwcKx+eiamYSQYu6TVzUoCERUPHLrNeiR5YMkANmpTcmO6ME13TCw9j5zTZpjweWTXkkCIEsCCEweKqqvFEIavy5R1d7LFoRETqNFY/Mdx5HudbE4EIVIU94tRbP3kggBVk/rj78+chOubZ9iy62k6wbuXrYHE5Z/YOFvUlQNum6gqiGCM7XBVm6lr6uIIkGKV2LwzWM23Cm89eoEN3SCYvJ1Dp03aRYeHH6NJTi2YHQfBCNRSxpP+pAYAI7bsJWmeFwQBPPkZWaKG6keyYIMKi32Y+KgLqhpjMZOZ3ptYX4UpZaT4QVx4AaaMOAqvLDtiG1ynJpGk1yPBu8EYkJTYQBn68MwDMORhVTiWGbtEi2tvW8gCMy4UHWjghcm9EVtMGo5ubqkqADPjesLSSRI87psOYooj09TCksJSXJi2srSYj8kAagNRhPSrDqloORhj/HpLOn8547oiahm5vm+e5kVQECtbrv7+b6PVAYwf9R16NI2CQSwzHNxUQF+eWdvnKwJsf7o2v3yzt6ME8oJCnzofAPO1TbC3y3TcqZlSbEfnTO8jmOi95+tC2PZzqNsveLhqBQdtetYdQzNJ0DhTsM/eWeehWeJB1mY6D3ZdtyBsIrxy/YgJ8PMWb3/RA16dUq3IN7iEypJAmHrIomirb7PG9mbcRrxcHVVs4cOd8rwMkADHTcFfsiiYNvHk3deh0BEt8g7ntrG5cDC7JEFHL7QgJJVH7F7W7mVLqP8X7iVds29GRcDCmavdeYl2neiBkN7ZifADSkvDaUHePLOPAtPDeX8mTm0hy2kceP0QguvEX/9VE3QFj5H4Zo8nTZ/74bphYy6edujN9nCBymscOGYPrg624eaRsVyKIhyPTmN2wmWuL5kIHOPs3xu/MePelqggUsn+ZGZLOPYxcZmYaYbSgqxerc99xOts2JyfxACnK4JMRgk/7eF7xyy5VaygxjOua0nFr5zCD+7Pc/2RffcuHz8+s+HMKBrOooHdYNLJDgemwO/Pt0yTap1OmcnPqDNMwehkuNjojLvmO7FOS5tK7NqCSxbG7SdTTMKme7yH/9MnxvjYlDt5tBUv53Qj8Fz43XoyIUAMpJdaJ/qscz11rxszB3RC6JAmOHDc1bFr9EPntsZ041CrImtqUsUGFx4eF47Wx3YOnMQ0xNexl0zkzHoN+/ZPkNO0OHFRQUIKRoe3XwAm2cOwgmbdvlni7937X0DcbYuZLvW/2nDP/XTEb1wMWACQOJhrtk+GXfZ9MFDm7tnJtvPu22yLfy+lVvpH1z4040RVbfA/gDg+fF90SZZZhxAADC0Z7bFqgAARW1Ki9kxzWOegrVhbv2qp6JVLt0mD217/u6+rL5TcJo/HUxgD/OjdMFPv30YvxrVG+Go9pXGLTi0qxtg4+7dIQXBuFSOqqYjFNVZnWvbpzi008T99OKEfrZ16kNRjCndnWB9VdSaMGQnbiW76z63mMBCWlrsx9wRvXCyOohUrwvL7inA+XqTr8gpHehv7+5rgUO29dnz+OgcHxMv86hm1UXKi8WnaP3/7H15eBbV2f59ZubdkjcrSdiCshiWIFuiELUqgtXaUm1lJ1FZTAQUW7VUv7a0tn5tUcQFZNeism/2s5+trRUEWhWXgGAFBEGEsCSBrG+Sd5uZ3x/znpMzM+ck2Er9rh+d6/K6wuuZM+c8c2bmec5zP/fN9xPTTdaerw6+d8RlrL0TCsxzbRkS0Rw9IXs5f+wgGKZ9jRsmXPxDzmp9ts5Uhf3t04gQQhrwiGHf4bghfQb4djIxJ+d8aCQZbaNf0bkAhO0XFQ8RrqVQJI4HNn6ElQlJUwoR9nsUaaK6b2KLiY7jy4zvQkFcL9qPA1/1qCoK3jl6DhvLK2ze+LI7CrGl/ASmD++F3jlB/Pb1Axhd2A1JUBHVDSzcdhizb+7LIJNOr6Wi1oLazRmVL91yklVf8lKQQGuxEWBBQhdsPcwSuPRc2sbk2siqaQkhrNhPJDoiGze9hqIQrC8rgpmgZzZMk0F57x+ZhwVbD+PZiUPw5F8+tdls0Vuf4Rff7Y+h3dPRMysZGiHSOZwLRZEd9Am3p+65tjtyUi2Na1rE9NS4QThZ1wKfZjHiRuMGw+VX1FrJzA1lRcIk54ayIvxvIpnNF0bdcXUPTHnxA+ahzlhjeYK8HKhz3Kl+DwiAcMyAaZq4KT+HQRdpu7huojmq28b30Ka92FBWxKRV+3dJZetJdh8A4K6ruyPo1xCJG9BUBfmdg9AUgpvyc5hXy98fr9qaalSIeP0RQmxjovmNoE+zFVvy9uOvR71or0rYcxTjWHbpuTPX7MbaUrG4jeZ4Bvhng7cBvw0je86CPo1FkgDwk2/3Q4R7qWaneKE6bEbnIBuHjH+KikVNefFDVlRLz5EJ+agKQc+sZAztng5VIewZ4qNh2bviQkFcL9qEdJKvVeSjOdIKvaM8Q7kZARt8NRSJC2F1umG0C+2TQRotj8sUio0cOFUvhOfxkDddb02sydrIZA1f3V3RrtfXvUOSLVHrvMaPNu0FAGxJVBH/aNNe27U1BUKbJXkVjBqciykvfmCDx4rm8KOb+0B3QCDvubY7Rg3OxaQVu5i84ncGdWVwQydUl4dsyuCKfKTC9+lVW7mIYpIksnPclGhwS6LO4L4RebgpP0c6P+f4Zo3szaRVRXBcZz/3rC7HsbNNeGD9R5i0YhcKe2QhLaCwfij/l/P+DOmWjm37TwvX36u7K2y2oX3JpD4VBawND/X2aIT9LYuSDcMUAhpk0p6Gadps8D+7K9gcZOAIoFUO9YfrP3JBU2eN7I2KmiYXXP2+EXkwTff4nAJb/HyckGL+/5mAENo8K1FfMWpwLn5fXiGUqfVpClbceYXt3AvJrfS15hwIId0AvAygI6xt3uWmaT5LCMkEsAFAdwDHAIwzTbO2rb7+mZwD3Xvu3zkVv3qtdR/a71GQ4vfAqyls7162v7+hrAin6sJtctSvSqAZNn94nPH6U69kdGE3HDrdgO8V5DKRlEhch09T8VhiTL1zgsJ+N5QVsTnI2qyaOhQN4RhS/B6oCoFKrCrklpjBaB6emzREmvegLweS8DAnSPaVZbaR9cv/Tj3AvI5Baf98/qCrZBxrS4tw3RNvsT1cGhnxf5/vmPjfr3n8Ldb/JIEGQ1vjpvvtG8qKYALCdrLx/fWB62zzpveha3qAVTifTz+yffj1ZUUwTOBsQzNy0pKgG9b6+5/dFZj/5mGhbWR9yezH35O2bEyfISoNSr1mSrPiUQkT4hmR3xkEYDaQ2alTqh8Vtc3waSo6p/uZ7WVzkK3jtaVF+MG6PS6aEFmeRbT2+L6e23qYzYmnWhGtm1VTh+JQVQhbyk/g198fiA7JXpxriiIa178SbqW2cg5fd+QQB/CQaZr5AIoA3EsIyQfwCICtpmnmAdia+PdXevBQxFAkZvNw7127B6frw7a9exmcTTdMxA2DtRGJ3YcicZxrimL0Fd3YNXho5hU9MvHFuSbohonPqkIwTKtPKrdpQuzZx/T2xdurGiNsPh6VYMHWw6hvicMwTFyWE8TQ7unSgkBCgOwUH7KCPrxSXiH1lJwsoRSaSpk6nVA9kQcZ1Q2bFCbfPhzTMeWa1vsjK9AyTZPJKOblBBMcQT4GOXRCJfm50j5ENqZtXt1t92SrQxF0CHqldvFqChsDlfqURZZ0rRAC3H9DL/z94Rvg1RT84rv9cbq2CeOX78LKtz9HTooPcd0Q2omHJ/M2lusAGDhwugGP/fFTnKxtQUNLDMdrmrGB45Oi64DCoXvnBLGutAj3XNudtVmcUHATXaO+JdYqkakSodytRyVMctYpDUqhw2fqw5i0YhfyOqWhMRzD2VDrelcVYpPunLjiPUxYvguGaeKzygZ0TvdDNyw22WV3FNqg5PxYZey/pmnix9+yikUVQuBVFcy+uY+l4eKAvj49bhCDFC8psbbT2FxLChGJxXG4KoQj1SEAwIKtVt0TZV3IDvpszLlVjRGcrm3CL77bH83ROE7XtyDdr6FrRhKyU3wXjHQP+JpzDqZpngZwOvF3IyHkAICuAG4DMDzR7CUA2wE8/FVem9+n/KKmhcmE8rCzR27px9rImFSP1zRDVYgLOicSqHl2wmBsmn4VTNPE2YSEpRN6RxeYV1NsJHSiaxumyYqynPKPtA0vPfrYbZe7YJxLSgqlrK8Th17K9tsXFxdIIXZ8XqMtZAyF6vF7p+fTPujTEIrE8eTYQeic5pfuvdLDCbu9tEMA943Ic0ElH721Pz49YwEOnkhsG7j2vVViK9Y6UdvC4M8x3ULb/ODGPOG5J2qaGQLFqykwJPkfWrBmmib8HgX9uqYzL5feoz1zRuJEbdiVCObtxENteRvJ9uGprsa8MQNhmCYicUMCv3Szni4pKcS0a3uiviWOzR8ex51X9xBeIxLT2f2dN8YSr+EBCgoBWmIG7vzd+1hcXCCVh+XX8eyb+9ryULK8ml9TUNgjCxOW78K8MQPbVUeMxcXyqyccz3huhgVWMEzdBZfOSPZicfEQRHUThAATh16Kad/oyUSRDNNk9lhz9zAhlxr9OORmBJDXMRlJPo1FKNT2fXOC8HguLJ3G/xkoKyGkO4CdAC4HcNw0zfTE7wRALf237Piy20pVDWFUN4ZR1RhFepIHXTP8iMRM6KYJlRAQYoXYtU1RVDVGhfKAIogh3WrokZWMz8+6Q8YNZUUgAMa1E+KuKy3CZ1WhBPcRXLUAj9zSDwSWQHzHND903UA4bthYNCkctToUscrvkzzCMPh/77saFXURG/aaEv7xY98y/SpUhaKudr/ffZKpfbXFgEqV03plJ6Gq0YJftteeJx684+oeiOsmfB6Cs6GYS28iGjeEcqoy2O2GsiLUNscwfXW5kOl0cXEBdhyswrV9slHLQU5zM/wI+jU0JiRh+3dOwWkBLLU5qjNGzXWlRVL4a2rAg+8s+Ps/teXmtBO91x1TfThV14KaphjSkzzCWhD+xbiutAiqAhcs9vHRA9El3S9kPX1p6lA8/voBBv91qqJR2OnYBHRz9bShQqndtaXDcLK2hbGvqgpQURtmKJ+cFC9+9j+foDoUYTrsU67pgVS/hqrGKHIz/IjETTdsNMWLT041uuQ2RfZbWlKIDkEPzoVitn7aghGvKy0Sbu/R7br/fu0TFxChZ3YyqhoiiMR1dEkP4Itzza53xNrSYThS1YRLOyTBpynSrbiuGUn4V4//81BWQkgQwBYAPzRNs4GiKgDANE2TECL8ghFCygCUAcAll1zyJa9qwjAt2On4wlwM79fRVQjUMcXD2tCX8qppQ6ESggMCiGF7XnBFbQuiuill96RHRa3F6cMzbFIoYn7nFNQ02QvC6AvayaK5rKQQ88dZkNS5rx/AT76dL7xWXXMcB07WMVlRVSGYtXaPTT2uotaCFvLtNIWgKRrHvSMuQ01ThMmBiq5BoXpP/uVTPDV+kA2uKWu/oawIqkKw7I4CVDZGbd70mrutiu24YTJJz9GFucK+ZHDeqsYIlmz/jDGdqoSwudE+D1eFMLRnB5tdl5YUYs+n1eiRHcScV/+BDfcUCWGpQZ/GrhXTDcR1MSSSZ97kt4P49dQWK/Az4wcjO8WHZycMZvUZs0bkuca0uLgAj97aHx+fbHDJxlY2hDFm6bss8iUEOFRpRVXzx4mvHfAorojMyf7Ln5sskdqtb7ESuD/atFdYZ7S0pBBPjBmAitow/BrBjOG90CU9gKqGCJNm/e/v9bd58AQm7lm1m2lM8KzAznW25u5hNu3nNXcPA4B2YcSyrbSTtRbK6/HRA9nWsJPBmI9CnO+I03UWu+7SkkLp8xS/gJXR9Pi6cw4ghHhgfRjWmKb5SuLnSkJI58T/7wygSnSuaZrLTdO8wjTNK7Kzs7/UdWMc4+ptBblCVtKYDhvE8439VbjjBUsekMpcni87J2B98T0qYdszwPnxKU0f3ouxkoZjhkvKkMoxOlk071ldDq+mYMP7X2D2zX1BCPCH+67BtoeuZ1Qa91zbHaqqYMFbR/CNx9/C9fO245NTDYzWgB+TphBkpwYwa+0eXD9vO5598zAUYi2huuY4Htq4lyWuneeqCkGv7CDycoLwqBZ0+JtP75S21xSCX/7vJ9hbUW+x4zruT/Hz7wOATbr0y3IUZQV9mHJND1Q1RpDi19AQjtmucbgqhOnDe7lkNKcnOLVCEQuKysuE0jYPbdoLfyLsvyk/B5qqQElAFNti3uQ5emQcXPwcCCz6+dP1YZgAjlY3oboxCr9HdY1p5prdMEwIZWPPJSreKfNrXDcZjNsj4d4yTbigqfeutdh46dx0w2RsxhlJXsbMy/eTFvAI+cl4ewPWfd784QnkpPgRjhkIeDVc3bMDpg/vhT98dBK5GRarcm5GAH/46CSeGjeIbdNkBcX8V5pilxKlLKuaQtj8ZdxZ58My+8SYgZg3diCDVYvm9/CWfVgwcQh7Hun501eXS6+hXcBcAz2+1o9DYsvoBQAHTNN8ivtffwBwV+LvuwC8+lVfm08OmhIPQAa9awjHhdA5XoOAb88zZEZiOhZubWVsPV9OHxohyBLjMq58HqL5w/UfoSkSx52/e5/BNUcNzoWm2LmVdh87J4Q3vsJB7B66MY9pMvDwyLqmsOtcCtWbvPJ9lFx1KbwqYfNXJLxOCmmFwcrYSU/XhzFrbSsUVgZjFMF5FxcX4H92t0JwZ2/ax/7m59MlzS/1DikUtVlyT0KROG7Kz8Gskb0xacWudpk3540ZiBM1Tcx+fFQp4l+aN2YgliTWCT/uH93cR1psFdfFsEy63mg7VSEMmsqvV349yJ4Pfr3/+ePTDDTBM/PyEE2FtB9Je1QF4wpz8Z1BXTF+uWVLup76d04RwpAtKIdlG9EcFieS4aLrqQrYuB/cuNdls8XFBXilvKLdZ7e+JWZjHpa9I07VtbDn8dDphtb7ZRjCZ7FD4MLAV/nj695WugbAHQA+JoR8lPjtJwDmAthICJkG4AsA477qC/MyiDJJRFni83hNMw6dbsC60iIYpgmvpmBxcQE6SLhguib2Nqm0p1OWkxCrIjsr6MPnZ90cNjkpPsy9fQA0RWGekfMaqiRZrBLCvLs5o/JdXsuM1eVYW1rEeHS6ZVoe4by/HLRtfyzcegijC7uxSMW5N87//tdPTmNtaRFgmjhS3WTLXcxcsxsbyorwx70nE/QXchlT6jXLEo7nmqI2ptie2cnMlrphIqYb0BQF3y/Ixa9e+8R2jecSBYw0ySqyzezN+7BOUqDFe4cySc7sFB9+Nqq/Df7K9xvTDYQicTw9fjAqapuhKQo6pvrg8xAGfaX98vxLNJ/1xJ8/lXqisjF5EnUbVKbSEkX6xBVJ+DSFbV3y61VTCWJxA5s/PI7iq8RJaJ41dvrwXq7oYvbmfQyi6ZS7lRaLqgSl1/W0QU1pNLShrEhYXLe+rIjZZmR+R8aiy68BmTxnVG+NiipqW9gao1DW5xK6EvR3Gf8Ujcjauy90Pc1YXY6Vk6/E/DcPIzcjgINnQrZ3DYXzZiZ50NV/YV/fX2vkYJrm303TJKZpDjRNc3Divz+ZpnnONM2RpmnmmaZ5o2maNV/1tRUOvhmL60Kvwu+xoIj87/PHWlC1+W8exrVPvAXA0oQ9F4pKIaEKsUL5mTdcBoUAG8qKcOvgLqhuDAOwtKx32T0RAAAgAElEQVQ7pfnh1QiyU1qZKuk4QpE4NFVBpzSfbdz8NSKxuMuLoYLtlKVSBuHTdQPVIUt6cfamfahpitqggZQVUgaV5PuKGyZe3XsGB043wARssETahhDguj4dMeXFD6CpxFV4dP/I3iAEbKx8YSI/Z97bjeoWnPj4uWbEEn83RXX85k8HENMN4Xz4/Vw5vYnhYvZ0eofhmI5Fk4Zg5eQrGVvoi1OuhEKsdUaFdfh+T9W14M7fvQ+FEIRjcaT4NWQmQA+hiIGzoYhrPVWHIvBqChrDMQaBdDKuUtio36MI17SiWHmP+pYYFEJAAMwakcfGvr6sCOtKh7nuL4WUVtaH8c2nd2LZ347Zikd52yzcehheTWGswCK78holCjHbjaQVYiEMRXN1jpX+zkOj0wMe4RqIGyZWTrnSNv+N9xRBN+yRF2WiPReKYun2Ixhd2A15OUHGUqypBFlBr+3ZFUVk4ZjebrRBnVX6O33X6IaJA6cb8OreM/+WnMPXHTl8bYfJMZFqqoo/7j3uok4Y3rcjkryqi6WRHjS6iCWkCNeWFgm94J9/t3+CKtiwJVXnjRmIRds+w/cLujLFKsbKagImTPz2T3b0ByCGnf7XLf3g9ygsYU4Tk1R6VJYAy80IMC4gOjeZ90ZfurkZcnH1AAfBlXlJhtE6h5husihCUwkICJNa5MdKIbselbB8BNUbdoIAnOL0vAfOj4OPSGRzPlRpFSBRZk+Rd5id4sO5UNQlRzl7U+sYnMiv3IwAS4SmB7wouepShqiiL/KqhjCzU6/sZJyosXQSfvytPkKYs4zh1jBNxHVrTY8feqmrzawRebaxzxszUBqt8fmRL2pasHT7kTbZZHn2Wb4fHpo6urAbtpSfYGu3siGCubcPgEdV2PoeXdgNvbKDwrm2ZQO6hmK6GKZKAITC8fOef0w3hOstpptYuO0wAzek+D144s8HXBFZRpIX4ZiBubcPQLfMJMYG7dS0EGld8NBjv3bh/fr/M1DWf/X4slDWc6Ewjte0YNa6Pbi6ZwfccdWlNsbVx0cPtGiQ24C/zRszEH6PgiUJT6J3TjKaooYNCrfsjkKkBTSYJhHC3r5sdfHc2wcg6NcYtNKnKeiY5kc4qkM3Aa9GABC2ldGewP3SkkJkJnvw8ckGVrG9pfwE7h/Z2zaPhROHIBzTsfLtz/HT7+TDpylCWOalmUl4NFG13SU9IKQUz0n1obYpAlVRoSnWttqv/7gfowu7tTlWi23UC0LAoKxtQWFplerCiYMR100bO+zSkkKkBzSoqoLKRDLXCfecP3YQ5r5+kH1EN02/ysr5cHDh3MwAkjxi1lh+DI/ddjmrGaGMniv/fhRjrriEaTwv33GEbeHkZgTw8tShOF0fZigXynA79/YBePndYxhd2A05KT5kJHlR3RhBVtArrJJfV1qU4F0CapuibL2mBzzITPZi3l8OsoQsrUjWVKuafvW7n2PZ346xDxaBBXftlhlAwKvCMABVtcRtzoaiOFkbtjES/+3TalzfN8cGj+Vht/TjD4BBPVWFYNU7rdelbfJyggxuzN9354eCXw98hTXgphTvEPTi0zMhF5zUqv2A7SOwuLgAmUkeTFjxnsvGz4wfzEgg54zKx9LtR4S66gGvCq+q4Nd/3I97b7gMhNv2pW06p1kJd2p7Cl2vb4mhqjGCLeUn8N/fG4CcVP/5vezaOP7PQ1m/joMQIJgoyMnNtDwIS/8giTGxyiBsFP5W1RBBVtDHmBlFkLq4bmDW2o/w0+/0kyS+5FA10e9pAQs/7/R06MO2pLgAIO1vl/TtlILV04a5vPRLOwRQcMnlqGuJ2+YR9Gvo3iGAHySYS2WspCKmyqUlhfjlrf0RiZv4zZ/2Y/bNfRCNm5ix5n1bmxS/GOrYOyeIl6cOZS/um/Jz8NNR+SySEJ2Tk9KqkZyV0Ol1amDQqMr5AM8akYezoagtSqTbb1EHPHTZHYXwqmI4Lr8N1zM7OUGqZ9lpcckQIUMpYG3h0O03CmHmGW67ZyW5bNwWWOFUnYWesnQa3OdSyCV9+U5eyetmWMVuLTHDRWFOI6MlJYXoku5DQ0vcBefd9mk1TtRa1OU+TQEIQWV9GPPHDbIxHju9cVpkt+dEPfOg6cubQpjpXJ2ss/yHwTmfpSWFePCbvXG6PgLDNHHvmj22jxSFk1q5mP0Wc3DHFObATPtGT6GNfQlkGr3vNBe2vqwI1Y0Rl+OxLLHen9v2mYtRd+KwSzBr3R4sKSlE2fW9UCEofvx3OPVfO5T16zpaogY2f3AcuRkBeFWF8bnQ0I3CVCkMj5a335SfA1WxqmM3fXgC4VhrVem8sQNR1RjFgq2HbWLt04f3kkpbymCWbUkWOiGKvOznjDW7kZZAiwByqGw0buA3f7K8dV4mFSCIxk1MWfmBXfB+5Qdojhq4Z3UrKyllVs1O8TFGSZH86oKth6CbYALsPo/mYoGdvrrcNedxhbl488HrGUXAvTdchg1lRZh9c1/8+rX9uPGpHVIobNCnMaF53QBO1YWxYOth+DWFQRdFAvbPbTuMoE9DVDew+K3PMH14LzYOEMLmT8d9zyo53JDfgonGDRt8VTfAEqRUPjQ9yYN7R1zWCjFWFAZhNkDw5F8+tXIrpltmdfbmffB7VOk4aLIzEjekcOvS63q6pXDXlCOmmza4Z0VtCx7YuBfTh/dCdtCHs40RhMI6GsNxll+pqLU+SAsmDkHpdT2x+cPjACH479c+QU1z1AJyJNhhRRDwGavLEdVN7D52Dk+MGcig1xlJlj/rXCvUTgq3HkTzsdaZYns2r+7ZAR1T/Xh6/GBm+6BPY31qKkFVQwR3XtVdCmvNSPKw9wO97zT/UNUYwdwEo/OGsiLMGzMQjZE4TAC3DOiMH2/ehxHzd+CbT+/EO0fPIZioB5mxulwIXX94y77/5Bwu5OFR3dzyVEHs8dED8fCWfYyV1Rn2LXjzMN45eo5B4USeGO+FpAc8mPv6QdYv7+2t2HkU88YMdG3PLHjzsPB3GUSR91JJAh768JZ9LMHn9I4pY6pz3LVNURs/Pn8N3ls7dLqBMavy3p4i6Zf2AcDGWcX3T5PtD2/ZxyqWea9vcXEBtpRX4J2j55i3G0lIPjqvpykQ0iX4Oe0AKmAv4uOn1dy5GX50TvNj8sr3pYVoigIsKSkUVphTu7x92Hqx0msQuO1kiwBLCqGglddJISZrL9O3CEXiQltQ77ytiLRDsvdLR7Fd0vztFn3yUUvA435WrK1ZcYGaVyXCNaabJn6w7iPh87Fwa+tzI5sPz13UMysJnZ3RUkkhvBqxPYvfL+iKR175GNlBn+u61JOnW5+r3/2C/d4S023rjBbB/WjTXuF9f3y0Ja/bnu31i6EI7us6RNzyD2/Zh5H5HRlk8P4b84QQuZH5HdnfHlU5L/H2PSfq8NI7n2NDWRHefPA6JiA+ujAXgAUt3DnbEl1/4s+fYmN5BYPJ7Zw9nLVP56ICeji91HDMYHN4ZsJgaIqCubcPsHnHXk0s3u73qNIoh/eQvzWgs7Bw0DDEHPeGCXaupoo9bT7q+IHE9qXX9bTbWBCpvPTO5/BqblH4h7fsg4nWa8sE7OnL6uEt+6ApChuHLAozDGDh1kOYMyrfdm+pOPzCrYcwIr8ztv9oOEs0mnDbyRYBri4HCGHzIWiNFiivkHMcVY0Rdt/pWnImNWVRTlrAIy12k0VnMvs51z6LBOKmcM4ybzwmKH6ckSiI4yHMsufGq4nnQ1+suRkB+DyaO1paXQ5NUVifI/M7srXEX3fH7OHs/oRjBlujM4b3Yr8frW6y2UkEPZ69eR+eGjeInXOqPtzu/dLUC//qvmgjh7YKeKiwfbWE6TQ94MGQbumYPrxXu4VAvAd534g8KIolYjNh+S7XmHbMHo4pL37A/k3hcztmD8fjrx9MPHQmFk0qYKGmyEsN+lU8O9FSy9J1E5G4zrwlwBJ7l407FImzgivnHrDfo+C5SUNQ2xSTMljGE+yXfC5i6fYjqG+J4dkJg/GD9R9BVYCVk69ARSJ5aQK4JDOAuG7iJ9/OR3VjRDo+3uvrkOxFOKZj5g2XobbJ+jh6VQUzb7hMen59SwyLiwswc81u6R49z8fP9yOyy/yxg0CIVVn7xv4qbEhItPLHkG7pCelRiwl3cUmBtLCPjwB1w9p6mf2tvtC5Qs1wzB0t0TWQneJFz+xkmAB6ZiczkR7+Hr445UobB1duZgDz/nwQ6QEvsw2778WFME3DtubuubY7Sq7q4aKPoM9EXk4Q68uK0DXdj7OhKJbdUYil249I1wYALC0pdHEjSb3mBGqOfz5Ez81fH7jWPZ+SQgAmVk6+EpnJHugOhl/+ue6VE8SIPtlID3hc416w9TCeHj8YmUle/GxUPupb4lhXOgy6YcJMrMNf3tYfLVEDqkKwcvKVWLHzaLvQXnof77m2u8UlZphMXpUm6C250X9OFvTLHBftx0HGMNo1kSzKTFAxi9rwcDYZY2rXjACemzgEGclezB83iMFj77q6B7pI4H1RCSskYN8iofw39S0x6IaJnFQfnpkwmKE83j9WJ2R7tME7TfHcmiJxW5QTT3D8mzBwvKYZcd1sU7Tez0FZ+WtnBb3Wds1tl0NTFITjpjSpTiG7ov55ry8n1Q9NAatWpn09PW4QTFN8fmaSB5pC8PLUodI1UNXYilVXOaU0vhCtZ3ZywitUGK03H13QPqkoDUWq0bnKrs3DhaMJiPSzEwbb2p+qDzMWYR6t9Kvb+sMEXFKnc0blIxI3oRs6Hn/9UxcH16JJBZhyTQ/kpFqot5cSsF2FQyvRNRfwKjgbimHiil3tQkid9zTJqwrXxvGaZqx8+3MXm/Hsm/uKnweOOSI3Qw6rrmyI4A8fnWLABcWBvlpSXACfprQ5hyUlhQj6xGu6viWKuGFg0or3hJxJlCSQJqEpOaRorF3SA3hy7CB0ywhgSckQVIdiLmbeMVd0Yw7VhaTqpsdFC2Wta47geE2LzatYWlKIDskexA0TJ+vCSE54tc6cQ2aSB796bT+DEjqRCBS9UVHT4mLqzEn1oaohgoBXZbA/E0C3jABMmKhrjrtYIX2aImQbnTMqH1vKT+Cn38kHACYcMn14LyG8kxcOmTMqn4W0/IIHLAbKJcUFWPXuF9hYXsEYJXt3DDLI5pBu6fjFrfl2ttLMAIJeTSiCzkNz//7jGzChHbGaZ8YPRmpAs9nokswAzjXFcLYxgtzMADZ/YMl4ithK15UOE86ve1YyDp5uZGy3v7qtP86GokImW6t9Eqobo641kBX04uq5b7Gx0kSvkzhu5eQr2YuYH59I5H5xcQHSkzyorI+gY5oP9S1xRGI6clJ98Kqt0GH+Gjx0UwZb5u87XxXOt3nstsvhT6CzZm/eh/WJCMht11Ym0rYgpKJ7KoNnr542DJ9WNrLxUeGeSzMDiBuwPQ9LElDPUFSHL0GDbmmqmDY02yO39GPAEa9GoBHCmJD5a78y/SocSdhVNgeZLdaXFrF13BZknBf7WXP3MJyqa3Hd99XvfoHDVaE2GXjXlRZh/+kGJvyTnfKvRw//gbIKjqaIzphB+eIrJ7yRekwKITBNE799/QBmjchzJdYWTbIYL5ujBnRDR0tUFzJ16rrliTthf/y2wJq7h4EQ4MDptlkh+3VKYdBS/mMiY6CkoStNEIugqPPHDcJjt10OADhcFbK9AJyJ0IhjfktLCuFT209m6xIuK35LxacpUhtVhyJYMGEI7riqu1RAJ6abUgZUyna7+5hVeO9kLv3Vbf1trKJ0nfBwQ7qvTrd/KJQyLyeIyoYw5t4+AF3SA6yNc3wtUZ2Nj3cw6PqbtOI927yDPs02H8M0WSEV7b+9LYvHRw+EJoH+dssMQCEED23cy6qLRe34rSQZhFR2T2XbRJUNYTa+oF+1PVs35edg9TTredANE6pK8L3F7wjZWxdNKsDsm/ugOWrYoJ+0nkF4H+IGs6uz2py/v8I1xlVRtyURzP8bAPwehTEY8+y/9Dl7eepQYV+n6lqYnYyEwNiFPC7ahDQhBBOu7GqFaMTi2a9ujLrgjaMLu2Hu6wegKAS/TcDR0pO8rsTavWt3I24ANz61A9NeKgcBcUFOX/j7URBCkJ7ktUUC1MOdPrwXY4VUiZj5lR65GQFE4oYLWvnwln3ShCVflWqiVTR9/PJd2Lq/EvPGDmJQwNf2nsT04b1sMEO+XxlbqQyCyyfWzgf66feoUhtV1Lbg/vV7YIBI+9INk+230yM7xWvjRLqtINcFqZ25xs0qShlkebihbpgMKtsh6MVDN/VGZpIXhmmiIRzHy+8ew+GqkBSqrBsmsz8PdXxq3CBbwjI76EM4ZiA14MG8sYOwdX8lUzt75JWPbcnithh+s4M+ROMGPIoihGd7NRUeVWFjIoQI2zntzeCe53FPz4fFVHcAGt7YX4WSF95DNG7gcFUIxYmPpiixe+/a3fB7NBf0c0aCjVbECKsprc/B4aqQcHw0Z+A8V1XOj13Z2dev/vcAvjjXDACIxg3cM7wXnpkwmDG3ytaMzU7/hg2fi/bjkOIn8Hg8jOGRsib2TBQY8Vw/d13dAyrHEspLFNLD8jAM7m93su6uq3tg/PJdOFXX0q6XpRticXmgNbmoErEXqCWglXz7eWMG4sGNe9l8IvFWjhe+WGj4k9sZq2Wv7GSbR0QTofRDI7o2IW4B9cXFBTBN0/a7iGGUZyeVJYt5G9U2RRHTDWFfPBsoLxa/dX8lO1/Gx8+DCSjU2DmfP398mgnc3/jUTjzyyseIGwZmb9pnu5ZXI6578fhoq186bgp1dMq90qhtzqv/sLGQjivMZePj+Y1kvERb91eyfhZsPeyyy6yRvbFt/2ncv66V4fbT0/WMlZW3n4w/LBp3cwbx99SKVk2hLXheoZguB0rwa1HKhSWRkKV5KZ4RlkKv6Xy27q8Urt0Fbx4WsslGOE42ke0t6LVdJlRJQKznvPoPLNr2GXTTxF2/e9/2DhKxCDvt9O9IB1y020oNLYYLJvfwln1YW1qEh7d86Pp9fVkR82hkkqFxvTVZqjiSZCJufuf5vJelKsTGq1PbFLPxzSzcekjKKOnVVCx8/SCr7qQMnhTSSOcDgCXBnAyrlO2S58bhE6GUH995bcOEkP1y9s19GT8SYOeHouypNKmuEDDoa1s2shJzis1OZ0NRmKaJ7xXk2uhK6JzmjMrHxvIK5GbIE5ld0gOMBbfs+p5oDMewatpQEGJtK4WjOr5fmGvbF6aRDd1jptciRMHCrQdc/Eg8/1BmspdFMPzaEBWHUbbRW892gaYoCPo9eOqvh2zbTU+OHYQuaX7GdcT3MzK/oxC6uXLylXj0tYMMpsnnl5xrQsTtNbqwm41n6VR92KKfuaWvjWPstY8qmMiTiFfIq4rvCQ8SaOsZkrETU6+bZ4RduPUQfv7d/mw+vXOC+O3rB1xrd3RhN2wsr3Cxyc6+ua9trVPbZwW9OFLdhD/uPYnZN/fFtG/0ZM/sz7/bn0U8IpZZmr/hucSEdtIurEQocBF/HNqCycn2HSlnytLtRzB/7CC2rUL321fsPMq+8rph2OB5HZJb9zxFkMinxw3Cb/50kO2dUlbSk3VhKISgptlig+SJvB69tT+WFBfYEqq5GX7EDZNBK38/82obzI+fT1bQhxU7j+LeEZeJvbAEMmh9aRFWvfu5jS9mSXGBsOAKZuu1+WPG8MuYsPqQbmmYNbK3S3kvGtcTVB0eBBKMuJSQkM858LmVuGHBX3/zJ4vagMJItz10vTQquCk/Bz/9Tj4UAhess2uGHwu3HmYcRzSJSv9+aNNeLCsplO5D5+UEGXQzPeCBbhhID3jRMysZPk1Bt8wA7h+ZZ0uA1zTHhGtD5h3H4gZKXrDEjt555AbMGtEbM9bYJVM9GhEyo8r69GoKo/dYsPUwnpkwWNjOhIlZI/JcPGT05TXlxQ/w9x8Ph26YrrVhmiaW/e0Ylv3tGIuKnCymzdE4nr+zEKfrIy6QQHaKlxUbioo7KTtxe4WAikLQr3MqBnRJY7ohVG2PX7s8NJfeUwpTvfeGyxCO6cK1vqGsCI+9th/PTRqCyoYwPKqCVL+GOxM5svVlRahvkcPB83KCuDQzgIBHQdwwkdcxiNsGdWIcXyvuvAIdkv//13P42g6ZVoNXE3seB880smTQq3tOuoTFfR4Fpdf1wMj8jnjpnc/xq9v6I+oxWZv0pFatBx4S2S0zgBM1LUgNePDb2y+HV1Nticm2BNE9iUIYPqG6pLjABntsisSF8zlR04xHXvk4US0ttkVMN3HjUzvYy3vC0EtwpiGCJ8cOQnqSFwsdEQL1pkR9NUXiNpjgPdd2x7pSCyqrKRbEsLY5zthJs4M+PHprPrMRjQh4T3Ti0EsZmR3VyKbXlsGQL+mQxKQtKZkdb79nxg/G4aoQa89HKlSnIG4YOFHTLOyfZ87smu6H16Oi5CprnKLrLSkusI2bXxtdM8SQZ/rRtuyq47W9FZYtdQPxBGR6zBWXMI84h9MAkXncR6ubbMSAsqiKgCCQYCqma9fp1bZwlDL82nj4ln7CefbMTsbBM4146Z3PMXf0AJyqi7jkbp8YOxAEBH8/VMkij6oEe2unND8bx/ThvVh0e1lOEMfPNbvGR9c+rfsQ2UkGza1vsdh3lxQX2NrzNuqWaaHRKhLXEcFc542x2AhE51c2WEVwd79sd57KfzYShknQIdn7Hyjrlzm+LJS1vjmM47URl/eam+FDRU3Y5hktmlQAhQChSByEEHRK9ePzs00uJsf1ZUUgsJLdMd1g0FJKLf3orfk2aGtGsge//MN+9rJfOflKBuOjW0mRuA6PqkBVCHJSfTAMK+rxJOB5v0wwoNKHkD4Ydc0xzFizG/PGDISmEBsj6dPjBiFuWGie+0fmoW+nIM41xVyQQQplpfNbW1qEbftP48b+naEQoLYpxhLidMFTLWvR9UQi7SsnX8leZgGPKoQGtoWh5+1PBXTmvn4A6QGvi2l3SYnFkEuRQO0x1jpx+j2yktEYjjM6d4/aCjEWfbxXTxsGTSFSuCOFCPfpFMTZUNTFXNqnUxA1TTFbZNMtM4DlO44y+pbViXvEj5s6MLcN6epidBWhfOj9UQhh64y+Fnht5Z9+Jx8EwJHEugaAn36nn80Gz04YjA3vn8D3C7q6YMR0DjwSkEJOAcCnKdANUwh3XV9ahGPnmpDi13BvQv2PrmvqSNQ0xZCe5EFW0IuTdWF0SfejrjnmsisATFzxHnIzLHhtTbPF8EuJM6sao+jeIUnKcFtR2wxCCDqn+aFzEFraf8+sZMS5ecjW2abpV+FcKOqCrsvYoDcktoJzgj54PCoMw8S5piiicR1eTf2nPhr/gbIKjroWHQdO1jFBeU0h2P3FOfz8f44zj7V7VhJMEzZPnudEcfLIVDdGEI7pQm8fgBDaSo+K2hb4PIqLg8XpbfDsq9kpXiE3kJagJ37stsuRm2GpUfFRjkdTkO5Vpdz/ugHEDZ150HR8mgIU9shie+18MV4kbuCSzAB++M08NIbtjK6pAQ8CEv6coF9lHFc8dxG//cFDJvNygiAEeHDDXpeIEOXxWVxcgIBHgUdT8OKUoSyHoRs66lviwmvw/fTtlIK5tw8AAMYeSmGwtP/MJA9jSeWZfOmYKmotiGY2l7jnryfSXqC2rGqMWAy3JUNca2ZpSSH+69t9cDYUw4qdR9nHm457zqh8W05jzqh8DOiaarsnCiFYPW0YTJhQE1uWD6zdI1xnS0sK8dhtl6M6FHVBpncfq0FqQHPd6wduykNMt7ZPjMRXprI+ghS/tT00Z1Q+uqT5YZiwQU7njRmIzmkB4T05WdeCR175GPPHDrJU8kwTZxPrOitobbHMefUfro+fyK6P3NKX9Rs3TJR/fhbry4rgVQmqGq3IQMajdTaxDca/BxZNsph8Kfz56QmDcZYDFsjW2Zn6sCUEdp5s0HHDIkBcUlKIPtnJOHKuGaUvf8jGseLOK9CnY8pXFlVctGglTSFY8NYRfOPxt3DwTCPGL9+FWestqOS9a/dgyosfIK6buPN37zMGTxFXD88jE/Rp0jYi6OdDm/bazldJK3+OjIOFZ191wv7o9XQDmLFmN6a8+AHCMQP3JeZDGVbvW7sHPo/mggwWP/8eDlWGcONTO3D3S+VsbHR8pglbEp+K0Vc1RjBh+S6MW7YLpglMe+lD2/WmvfShFOKqGxByFzmhgRQyebgqhEOVIbZXzfdFk44z1+wGQFDy/Pu48akdGDF/B4MYnw9jLSEEJS+8j9mb9+FwVYjBWvn+CSGY+qI1T57J1zkeHpbIX8+ZbOZtSa9ncgVg9P5OX12O5qiBKS9+wD4M/Lj5cVCbxQ37PRm77F2UvPCeBaAgBPclPgyidTZ9dTlihukaB4UCUxvw7L2hsI7rntiOCct3QSUEk1a8h7HL3sXhqhCDjZ6qD7sgp7M375PyblHbP7RpL3QTMEDYuj5V3xrpO58bkV2d8NpHXzuICct3IRxvnadsbYie8XvX2uHPHoXY+Mna6ou+a5xs0KL2lEhwxupyVDdF2YeBjqP05Q+ZLOlXcVy0kYNPU7Dm7qGI6RYRnIj7hIdTtlXkQj2pcEyXtpExRPLnm8B5XY/+LUuq83BAGSRUlnjnk2806UW3ZJzFa6KEnWxMhGNc5T3FGDeOpduP4Pm7CqEpKlQCF6cMH4XRdgoBi5TOhaLYPP0qVjlK7ynvXbfHWLtoUgEUjnvn0T/sZ1xCcd3AXx+4Dit2HrXxBOWk+LCudBhO1oVBANuWYV5OkPH7LN1+BE+PG4QHNu4V8vXQJDada1tSrLwt6ZZPUzju0r145JZ+MAwxf5CmWIWdsnGwtSIZhylZDx5NweppVpFX3LCK9VSFwO9RmZ1yUnxiniXTbDehDNPiL6L9dkz1n/dzSqMiajPdMPhfoZYAACAASURBVPH3H98AVQWi8bZ5tNp7xuluACFAql9joBVZ8pzvi7+eqD1lim3vOYvGdXxVx0X7cfBoQEODbss5LC4ugG60cp/wkFVZIq9rhl1MXdZGBs3kC9P4ZO75wF1pAZSzDZ9glsFuZYl3PqGamxHAtoeuh2HCQr+obfPQzBszEAFJv3ySsk/HFNQ1R5GR7LWNNS8niFjcxN1r7PTJk7/RAwdPt27bjCvMZe1Ee+j8tggvoJObEbDpb/AQ4WSfJhRkef6uQpyqj9i4kRYXFyDF38oTJBoD3TLcWF6BjCQN60qLAJiIxI2EfKZfOO6u6X62HmRQZVUhLJnbKycZdc0xF5/Sf3/vclQ1RnHn797H3NsHSO/bpnuuEo7DUhR0ryfZOpOthx+u/4jlbNa9d5zlIuaNGSi8rgnY7o8o4X3EkTyvbAi3+9zkpPgYPHlx8RCca7LbbHEbwADKo9XWM56T4mMUJF+ca8bszfuwaNIQrC8rQjims3WWm5GEz8+6++Kv1ys7Gc3RONvyVhWCNz85jUdfO8jm7Jc8Z18lxPWiTUifrG0WJr5enjoU50IRV/JO9hLq0ymIk3URltBytllaUgiPCsx/45ALvslTZvz0O/kwTUBRrP1ZynMzY41YXnFpSSGSvQpihmmTZsxJ8eKld75gSWE+WccnNVP9Gqoa3ckwPqG6vqwI0bgBE0A4qqND0Mv4fWQ8NBvKinC8ptm25/uTb1uwUcp/kx7w4u7reuBkbRg9s5NBiDXnTml+oZTq+rIiwARWJaKINx+8nvHvL7ujkMmb8kn50YXdGKcPTfQ/cks/G7FbdWOUJWrbmo9onfC/yxLNFM3TJd2H6sYouqYHWJJz9bShwqTj3NsH4JFXPrYKAcMxdEpPcoEmuqT7cLSqGdkpVjXt8ZpmFzhiQ1kRAyv065zCErPOecrGsb6sCIcrQ8jNDKBDsgefn23GD9Z/ZFvXHZKtiDgUsVh/eQAG7YfnVuIBF307pbCXM3/d1dOGoboxLE2ei4AIvHRuVtDr4kNzPmeqQoTcRaunDUVjxO4wLispRJJXQWNEx8w1u4VjEgERKFfUT7+Tj4aWGHweBSdrw+ic5oOWEBdLT/K4HBKa0I7qJla/+zkKuneQJrNrmqI2qPc/k3P4T0JacMjCMk0hSA14WJKoviWKl6dahVuqQvDM+MHITPayJOXT4wcjyauwghWPSvDk2EFse8GnEaQFPPjZqP7Y9Vk11pUWMWbR6sYIfnv75YgbdibNeWMGYtW7J1BcdAlL9lFyvmcnDIYJMJlLJzRyaUkhHv52H9Q2xfDYbZejT6cgzoWiLtbS5TuOsv5lCdUz9WGmi7u4uADVoWi7PDQRjqtmQNdU1DTFUPJC69wWTSpAkldxJVrnjRkorTw/WdsqGEMlK2k7mWBPql9j53s1BfeNyLMlP5eVFCI7xYuGRKL20g5Jwmu3tbUj2soQMnsWF+Dld4/ZJCY9EmnRbplJWFdahDP1YaQGvEgLaIwdlwdN/PhbfWx2dYIjorrh4ih6eepQENi5npJ9YmnW6kYLTrpw4hAENAUpCUld/oWWHvAyTWfZOPjtKT8HuJAKJxHAoymu5DlgUZ6LgAhO6VwKruCdLZ6xVnZtgMCvEbvMr2HgwY3/QHaKF6umDUVdcwxx3WQJ/UOVYiACRY3FdQM/f/UT9pyK4MxUuvSLmhYb8GFJcSFSA+L7U9ccw7NvHsLau4dBVcg/jVZq67ioE9KypM/8Nyw5RsO0WFIf2rgXI+bvwA/Xf4TGcByGaSLZq+In3+4HQgDTJFi+4wgIIXjp7c/RIdmL7BQfLumQhGjcREvMQCSmozkaByGWfvXnZ5vwmz8dgKoormTf7M37UHpdT5asGr98FyYs34Xxy3dBUYhN5pJCRum501eXoyVqINmnoVdOMgwTLv6gBzbutfUvS6imBTw2gaC0hNaFiIeG8gypCmEcQIYJLEiI4NB+Fr11GABxjWn25vY5oRZuPYSYbtqS220J9tDzNYXYqoKzgz5UNUbQEjNs8rCia2tK+xxDbSWaK2ot8MDowm422VmZwI2mEExcYSWNxy/fhR+s+wiHKkMwTeBQZQi/e/u4K+lKeZPmjR3ISdkqNhEkyhEWd/D2yGxOpSpnrdtjS75XNUYYSEMkwykS+6F/m5xgUVsSvDyAYtOHJxA3TBBifVCHdm/lN6JrLj3JawN7UHBFTDdYMvxUfbhd0SZNJZjiSLBTKdE39lfhjhfeR1bQh/QkD9a9dwxx3ZQCEfh1+FQiz1RR2yJ9ZlVFsSXMs4M+howS8Tql+DW8sb8Kk55/D15NRXaK7yuvfbhoIwdFkCC15BjF8o2/331SiN2me6oWfNItPbq4uACLtn2Gd46ew5KSQvzqfz9hIeTjowdKPUhZAptPNsuSbzykVuYl8f2Lkm+Liwsw7y8HHWOFMGkmCrUXFxfAq4klVL2SylDAdImzLC4uwJLtR5g0qGGaWP3uMVYp25ZgDz0/Gm9N/Ik8e6c8LP+736sIq7nfPlzN5s/boq2E6JbyCiY7K5KbnDdmIGqaou2OlWfdFbVZXGwl1cW2JzYbRxPcVKLEKx27LEqSMbzSDx8vmWkVkLVWgsskeFWlNbLhOb942wOwaZbI1ji/zi7JaI0MZesdaJ8tmIdL+z3KecmyhmPts7dSlBZdi21pY9D3FD33q0xC88dFGzkYEnlJwxTLN/7gxrw2oaUz1+xGNO6WHuWlLWesLsfowm7s/z28RQ7dkzEzetT2WTh5uJ2sDd8/nwzb/qPhWF9WhOe2HbYJyj+8ZR9iOli7Zzn50QUTh7hsM3PNbsQEspCW9KZ4zgBhXDV8xEJlWanM57K/HcNrH1VgbWkR43hy9pWd4sOLU4Zi9btfIG6AtRF59k552DcfvI6th6aIm4NrxupypCX7GA8RbwtZRFDXErPxGslkLk/Vh9sdq5MdV8S/ZHBeOn+uqig2G3tVRfgc8FKVqiRK4llJ+bnSSO+5bYcxa2Qek00NelXWXsTxNHPNbkT11nslikxmrC7HnVf3sK05aSTAjTsloAnX+47Zw/HS1KFQiUXbL7t3/N90rAppBQbIZFlVhdiuLRurV1XY+SPzOwrfQbyUKE0V52ZcOJ6li/bj4NMUzBKwdgJWmM6Hu9lBH+KGiat7dsBfH7gO2x66Hn994Dpc3bMDkwydMyrftT9Nf/doCuvHxe9umnhm/GC2YKgXoxsGXp46FDfl57Dfnxk/GKoCzB87yNqrTEAj+XPnjx0khMjxbURso9WhCLyaAlUBYnGLD8g51/qWGLJTvLi0QxJMADXNUcx/45ANjsrPTbZfrxBgwYQhrjlrKsEb+6sYjfg9q8rxxv4qm+dWn3hQl/3tGE7XteD+dXuYPWhfSxIssEeqQzhcFbKxyco8t7ycIO4fmYfMZA9jVp1yTQ+bt+ts/9BNveH3KDZbPLhxr8veSxLsnM5rUy4iE8CUFz/AnhN1tvvlbE/Xk9+jMNZO2XycMFP6eyxuoLqxFQtf2RDGzBsusz0HU67pwdhUlxQXgABYc/cw3JSfY2Mf5Rlh6VwpJJtyCcV1A51S/dAUBaFIvN1xUxlX+mKVrSsjAcGlW3T8s0LzK3HDxNrSYdiZ4HraeE8R1pcVYUNZEe4fmYesoBce1ULhZQW9UBVgSbGbzZhnll26/YjteZ83dhA6p/usfhJcVvTc5yYNAYGlQ7F62jBsuucqdEnzCxlXFdIq69qeLse0b/Rk515InqWLdlvJowGpSR5WQQsAQZ8Kw4QQIZHib+XI4UNRn0aEkqEyaJ/BocOsSEBhVabpSR6kJ3nw2z+1ohcWF1vVl2dDUaQGNKiEwJ/gdaLIByfHU4jjU3LyOJ2pD0NTFNw74jLGTUOZXim7Zo+sJOFcO6f6cO8N9sQuXdj0era5SSCQqqIgRTDn0YXdhO15z+1UXYvrdyfPlWGauH/dRywEjxuGkD+HvwbNuyy7oxC/vf1yhCKWApsqgQvzkF+6tUi3FKi9e2Ql4/OzTUhN8uDRWy+HIZFm5SGhNm4ljhFXVFG9aupQaBIWUpkcpaYSIX3G5ulXIaobAAgq68N45Ja+zJb3rd3DEHKZyR4ohGDN3cOgEGJjhKVraMo1PQDAVd3/81cPMDErVbI2TtW1YOv+Srw4ZSh8ErhmTDdR3RgWbmX+8rb+qGqIuiqvRdvCS4oLoKkKxi17h22BUdhxXLdQeqFIHM9OGMwYbgEIAQceLgKjDK2xuMEQbU4brJo6FHWJqm0KWy5+/j0sLi5ghZrOefNwWU0h+P3May4oz9JFDWX96yenMSK/M0swR+MWcZkIkieDNFLIH+WaaU828cmxgzBh+S62WLplJjFYXVuwzKXbj+D+kXnomZ2M0wk65NyMJCH0kxYHiSCArbBZE0aCGoTPKzz5l0+xYOIQYb8byopwKDFXO6eRWJKzZ1YyqjnuGFp4RLWux1xxCaNudtpPtNfK/71y8hXQVAUKkUMoeZtRuKymEsa94yxYA8Da+zQFpgnEEmR2IjtR6CIP1+SlWxvCcZyqa8GW8hP4xXf7Q1MIjtc0u3inemYnW8ysDnbYv3x8BkW9OrQJtd14TxFqHLxYTm4l/uXZIdkrheaagA3iyUNyj1Q3JXi7+uNMfRiRuI60RETnZGiVcQPxdlo40dqKm+HIORBYNDM5qVYhIyHWx8owTWaX53d+jlsHdxFe46WpQ/E4FeXiniGZPOr6siKcqGlmnGkVtc3waSoykj04UdOCzGQPuqYHcLrBgqu3BXk+19TKjyWTh3Wulcde288+uk0RnfF2BbyqTYLXycO2afpV6Jxm3576Z47/QFkFh9+joLBHFiat2IWre3ZoF5Inr0g0mOwk9Rrbkk3MCnoZNfITf/7URo0sg2XmpHiFL00Z9LNruuUhPjl2EHJSfKhpiiI7xYcFEwejviVug81SedOPTzaw+cpkPOOGaZsrtY1hiiVHH7mlL174+1GsuXsYvCpBTaLwaOXkK1yJe6f98nKCaGiJoWOaH89MGIwTNZZy1iO39IVXIwjHTcx48X3p/eqS5hfabNOHbg9y/thByMsJ2ojqRBKUc0blY39CupWHLvLbXgO6pqJrulu6lRCgqjFig2lSnquobqChJWZnay0pxLcGdIIJMJ4v8TZMHL4E/LJ7hyQc4iDJlFupb6cURhDHw2n5fk7Xh22V0LJkeDgWR9ww2IuZQWQJGKxTxg3E2wlo5f/qkWV9vCk8+8ff6uOCdlOngLLmyoAcAQ4uy49b1r622Yo+ea6keWMGYvamfagORTB/7CBEdQNBn8KK0mTPBi8nq0iEuHgb9O2UgtXThuE3f9pv2ylIC2g4Ux9pk4ctFv+PTOgFO8IxAwsTMMuZN1yGc6EosoOWt1JR64bktSVHybenfDYyLiE+UMtO8dqgkil+D5MK5Mfh5EGiXroMhqipCk7WWdEFAbB85xEcPNOISNxAdWPE1v+9a3fDNIHTtU14duIQbJ89nG0HOfslhAhhk7zUIs0VUEgehRWGIjpjcPV5NCkEkudQ+t7idzB26bsghODld4+hIRwHAHQI+oVCTQsmDsFfH7gO91zbXQhxnb15H2aNdAMLHtq0F/ePzEPHVD+u7tlBKkEZjhlS6CJ/f0U8RKYJpPg9CMcMVuAY1Q0sfusz6AawbMcRlhSee/sAhKM6PKqKk7VhLNh6GHFdDFBI8WuooG0MN7TSm6B1r2qMYMo1PaQJ87SABx2CXgablCXDfR47txDlLorrJltDndL8ePPB6zGuMFdqp6ygD2dDUSzYehiKQvDrP+7H6MJumDd2IMIx+zPAAz+W7zyCJ8YMRIegF5vuuQqrpw21QWFFgJK2gB8ZSZ42gSYPbdoL0wTueOEDxsMmfOYUgrqWKI6ebUJ1YwQx3RC2420QjRsoeeE9G/Bj5prdUBWlfR62fwNl90UbORABZFVUwMM8v4T0ppNuY8XOowDcPEmxhIQgH40sKSm0wUNpzsIJ6XOOQ8aDRBOtTu/4/nV72DbMa3tPtdu/YZosiqLeoHOuS0oKseuzaiFskiZIRdegY+U9N9l8qP3o/i/9XVNMG5x08/SrhOdTmOGSkkLEJXKTsuT5SQ6iSCReXygSF8JPqQDRvDEDbcI9/LlnQ1Hctuht1m7u6wdZnoJADJ9+YENrLkM33JDTeWMGYlYiH0BlOGkbWXXxEgFvjxO2PG/MQPglLLqyexfgInG+XwB45+g5l53oGn189ECoRAy7dT6LVGqXamM4WYvptpRofJoCIUyah87y7XNSWj9OumEKYcv8s+H3KLbnTPQMOW0g42mSRSf8+0X7N3wcLtrIwZR4GPzXmec9iutgEofbZw9nMEle78DW3oANMrhy8pVYuPWQy0uICOCvznFQHiT+yM0I4FR92AbJo3BI+kDN3rwP3xrQud3+FUJcbKsLtx7ChrIi7Jg9HOtKi7D9QCXSkn1C2GQ4ZrC5bv+RGNLHQ3CdhVhO+1H4Kv09btjHxzNe8udTmOGM1eVIDYijqvbE22eu2W1jbuXbZKf40CMrGWvuHoYds4dj1bSh8HsUzBs7kNneL7lXSV6V2Yz3TC14rhg+zbfhi9refPA6171+eMs+AK3Ssk+NGyT0iEfmd2QQ2h2zh2ODALY8e/M+aYQhu3dwrCFqy/tvzGOcRs9OGCwctymB3TqL6fhoRhTdzVyzG7oJyX2HECYd08Xtgz6N/U0Tvk+OHYRnJwxGsk/Dy1OHYttD12Pl5Cvx2kcVCMcM23PmfIY2lBUhO8WHZycMZuuGCoA5ry3bpeDfL/q/IVV80X4czkdc/sGNe/HYa/tx/8jeUBVgeN+OqGwIo7ElhpZoHO8cPQcALLFI20/7Rk+EYzoGdElDv86p6JjmR8Cj2iCE9HqU3dMJneW9BL9HwVKHMPuzEwZj6fYjGNo9HZflBAG0wiH5/s+HDVaUT3ljfxXihomGlhhqmqLYUF7BmERFMF+aNG8MW/w294/Mw4YyCzq4vnSYTZh+xc6jWOKA8y2aVIBQxNo2qm6MIi8niDceuJZx9/PjE8FzRQLsojYrdh5t91wC0yUWT1FZhmEyUrobntyB7y9+F+dCUWZ7HjbLn0uLynj7jyvMxcrJV7bpKdK/wzEd995wGbyJJLzoXocicfzwm73hVRUbw6+zTwqhBQA9IevqbEcIXGuOFoo5oZjzxgyELlnHumElpwNeNVGB3DpuCgltz1OmcFJeavd8oLB0fJRRWASTrm+JuaDQFA5OaTh0w0R2ih/pSRrCMR23Pvc2Rsy3qOC/+fROLPvbMWGejj5DZ+rDVpU7gKBfRVVDBLPWfYSFWw8Lx3qipkkIP6fvl7uu7gH1wgcOF++2kkcCAeyaYSme8XA+n0fB24eq0a1DMpP9o4JANLGYluTBr27rj7OhKHweBUG/iuH9OtrCbBFpGCCGzvLsnA9+szdLOvKiKouKB+NcUxwTV+yyMW/y85FBBjtz/T8qYf88XR/GhOW7WNIx4FWFYz1V18K4ZAJeFS0czw1tA7QWHV6aGWAQ3Es7JAGAi3wsbhgIxwxMfXGXDSIMWPUBL73zOV6cMhSaQhjLpVOqkme5PMKJtB+uCgl/b70nBJqiYPW0YahtjjK44Z1XdWekeLQt3aqif5+qDzM1Ph7eSYsf6Xl+j8Lgws750Ta2PfoUH2oSHFmy9p1S/TjX1HYbvs+DZxptTLt8u49PNmBL+QmsuXsYYroBj6rYVOHW3D0MzVEdTZE4clJ98KmKcG34Egl4ApPlTSpq7dBc2Vg7pvoTRHwRPHJLX5vUrox9NZh4PnhG01XvfI5re+cI20diugsK7fco6JTmx/0j7cCCJcUFMEwxRFiB+DmjzxBzgMIx2/XSkzw27qz9p+pRumo3hnRLZ8n6Mw1hl0Tur78/EBf6aBfKSgiZD+B3pml+csFH8y8cXxbKWlnfYoMVimQQnRKgv0qwXFIqYV4ClEL+mqM6slN8MCRyh4/ddjmjG358tPXwUJlFvt2au4fhaHUTMpM9yEz22aCl9Ho9s5MR000s33EE3y/oCgCuh/OynCA+qwq5fu+SHsCv/7gfj9zSDwGPgmqHTOXT4wYhJ9WPM/VhBgf8ybf7oeQFt3TimruH4eCZRgbZFM2bkqdVN9rZV2WsoBvLijAu0Y8IObNo0hA0huNIS/Ii1a+xFxfbA9YIKmrDyM3wIyPJIkp02qBf5yBOOKRindKbq6YOxbFzzchMtsjdqLzk3NsHgCTEmejHQkYlsqS4AOGYweCYmckeBP0e3JXA4otqGP7r2/1Q1xxDXXMMmckeZAV9zK6yGpoeWckYs/Td8+qTh++2xzLKM7zy8NCfjeqPSSt2uSDZ/H1cXFyAW5+zci2bp1+FxkgcFTUt6N4hCb9NQE67pPldTKrzxw6CYdqlZYd0S2+XJn3u6weRlxNE2fW94FGJ7flI8qo2duLMZA/Sk7xCdtj1pUVM3pX//aWpQ1HVEHatpe5ZSTh2ttn1O2BJktLzX546lNVg8P3+4b5rUNkQsSm7yWR3v0rFt38VynoAwHJCiAZgJYB1pmnW/8uj+poP3TQZrPDSDklojuou+KEtaWuIk2YiPHlbcoc9s5Ox7aHr25UEPFMfZnKF/BaYjEvHqxL81yv/cMFJn54wWAgzXVQ8xFbQxsspNkXi8GiKyx4yeN6Z+lYWStkWQWVDGA9t2ot5YwairrmVP0gGMYxx/fAyoX07pcCrEhyvabElI5eWFOLRW/vDMIFV77QKBC0pLkBUN4Q2eGbCYGw/UIm1pdbWVTRuuMSBqhLspPPHDmL70BW1VoJ97usHLcnL9AB+uP4jWwHU3NsHoEu6Fbk1tMTwwMaPbPdL47b7+Pnld05BbXMMd7zQCtOlAjKi9nk5QcYQzMOiz6dP2u6JP3+KVdOGQiEEB8+4obpxydrXFDA5Vdl9b4nq3N9xRBJsvEuKC2ySuM5I3Oex6kz4Pvmx1jVb25c2BmSPghF9snF93xwbH9Pi4gKk+FQmAcrbQMYPJYNzexIvZL54lNpeBufmz5dt87ZEdfTpmILfz7wG0bgFcdYUgodu6o3GiF1216f9e7IB7X4cTNN8HsDzhJA+AKYA2EcIeRvACtM037rQA7xQh2kCDc0R9O6YChOwfc15uCYt/lEVcdJs5eQrbcU1FbVWQm9DWZEwzKQog9z0AJ4cN0ga1lNmxuaoxZV/U36OLWrJDlraxBW1ViJufVkRg5M6r8f/TqOOmG5a9Q+Jft7YX4X9pxsxZ1Q+ANi8eTpX2ZzoWKNxA4pkG4smfGdvtmzW3vaAqhA8OqovK1JUCMG2/adxeZdUGCZcUL/pq8uxoazI5r1W1FqMqBsktrG0CGKIxHR4VAWE2D0x532wtjmGsiKp6cN7YUv5Cfz8u/2F/c8ZlY+eWckuBtqZiTE5t8oee20/Vk6+0gUgeGjTXmwoK2JrgPfe/+uWftaaTcCieZuphCAS1wEQYZ+rpg7FoaoQtpSfwKHKEHpmJQsLvDSF2Kp/6dbGL77bHy+/ewyjC7uhm6SSvK4lhvWlQ9EtMxlxw0R6kopN04sQjZs4m4CPTx/eC4ve+gyjC7shCSqD+PLiV/SoDkVwqNLSNp+1rlXelK7rW4d0xdHqJtfzsaGsSAgPXVsqtqtM4IgvLnX+LloDdBuP/lu2ne3VVCgKYSqGhmHiXFMUccPE5JXu4r3fz7yGtb1Qx3l9ggghKoC+if/OAtgL4EFCyPoLOLYLegT9CjKCAYxfvgsnE4sIaPXM57z6D9z41E489tp+zBrZGx6JhyHzBAxJQrSuJYZF2z6Dbpq463fv4/51e1zJzyUlhahvirBxLHjzMO5L8EDd+NROzHn1H/jRzX0YjW9FrcXE6uxn3piB+H15BUt60bB8zqv/wPXztgv7yUnx2RJ//JxqmqLCRO3W/ZVsrLPWuufjTPiGInFmG56rh7ZfXFyAmlCYQSOHz9uOSSt2obBHFk7UNLVZiCT7fYkjubqkpBDNkRhGDc7FlBc/wPAnt2PyyvdRctWlGFeYK5zb8Ce345FXPkbcMBj/0qyRveFViTTJLWONBSDk2JG1j8QNzBrZ28UFtmT7ETaOmK7bbDZxxS60xAyE42LIJOXquW9EHrbur8SKnUddY1pcXABFsaC2/LXvuroHAJOty1mSddynYzKS/VZV9vXzLF3p6sYoFm37jK2/nllJwv4pg6zIrjkpPuEzK1vXkbgEDh6Nu+w6a2RvKaDBMMVr6cCpeuH8t5SfYP9ececVyAn6sOLOK2ztnPxIhmHi08pGfH/x2+wD5xz3hWJi5Y92IwdCyNMARgHYBuA3pmm+n/hfjxNCPr1QAyOEfAvAswBUAM+bpjn3q+w/FG5l2+S9VyEff8IrFX3xZZ4AL0fpTEyWXteTRRsVtS0MWtgjKxmfVjZi4dZDbD+3orZFyGBJE3m0FL+qMYKl24/YEq00+X2itgUvTrHgc07P2tlPVtAHExDO6VR9mF3DKZ1IbeacjyhZ7BxrXUsML0+1NIfpHvF9I/PY/Pn7sLa0CKqEy0nm7RFCUP75WbZ9pCQ86oBHxVSHPWgUdktlZ9fcaBuqhHfPqnK2NmTSljKZ1uaojj/uPYmVk69kFCJtyVB6NcUW3dKxzhmVj43lFWwcM1Z/4LLZ+jYiPr4f6vVS4Sp6L0qv6ymMmjeUFbF1yd93SrexcOsh/OK7/V1jmrlmN1ZOvhIbyyvw8JZ9WFtahIe3fOjqn7ID02coM9mLeX85iD0n6lgBqOyZda5rCmF22iDZp7nWAF1nsmiJQtpVhUA3TGz+8DjuuLoHPArBxrIiRA2LV+ntw1V49NbL8YvvmjYxHn77SCTSc64pynIPssj6QjGx8sf/Y+/NA6uo7v7/95mZu+XekI2ELSiLIAQhkLBE2yJCa2tFfZSdRAsii2tdMWoENgAAIABJREFUirWPpbW1T4uiggubVEENq9h+bWnVKoq2BUQDiJUtiCBhSUJIQm5y15nz+2PuOTkzcybBFvo8z89n/rq5mTtz5pwzM+ecz/vzep/LzGEPgMGU0tnCi4FtwwkhA853oVIzlcUArgFQAGAKIaTgfJ5DHGW2RcIEWhNTnps6BCunDeN5C89NHYJnN1e6SiZlo6FlWz6XzjbiugFCgD55Idw+qjcopZg3tgBDume26RHAYgV56T785qbLcEleCBRA77wQRl+aCwCorAnj89qwKzW2T14I62aVYO3MEanlCDikpuLo3wS0ARfnpEkpkn3yQuaLUzOnySKpkskSRbpryKdh/hv7sPjdQ2hJSYTdpMaUUossViyfphBHuZeVFUMhFJd2ycCXdc041RjF0Tozi9VtppFIGpyw2Vbds89Jg+LRGy5DQZcOUAlBn04h/OKGAVg/qwTZaV6HJJRRcUf164Tpqz7Cvet2w+8xzycj7S6aNNgSc7CXw40KLPZde30tmjTYMptjI9esNI2vaXtUgh9971J4NQUrpw2zZDzLZmpMIqukludqm+KuZVIVwsvtRpDVBYn0pOe348GNe3D/1X3xzv1XIs2ntnvPinJtmYS5rXqllOLOq6zU5h+O6QtCgKIeOXhw4x6rlNWgeOPTEzDRhYDPoyLNq0Em+GHLR92y0qQmPfGk7kqclc00LtR2LjGHlW38r5EQ8h6AovNaKmA4gEOU0sMAkFq+ugHA3vN1AjcSZleBhMm2/CwzESaRNBwBLRZYbksyaYLfCH616TOTXWTIJX3suHbSpyiVFMt0cU4at79kfKjbBOns0rJidOrgQ7fsIH782p42jebZeRnDZmlpEZ6dMhjpfg8fCQNOMN7iqUXICbZSJJlBC5sZMdkjAXAiBQz85Q1m4FgkZy6eWmTmEVCKl28d7kp0JYTgwKkw1u446hjVPZRaf7fbWTJej50S6jbTMNDqW9EtS94fREnosTMtrqDD2nAMK6cP44FTCuCi7ADuuOoSEAI8N2UIQn4NtU0xPDGhEBdnB9Ac122yZQ2UymdLCd3gbeI2Q1CVVt8B5mvdIaChT16IK5IyAh5s+8lVOB1OcCUZW1Yq33YUWw/X8YxnpuQiLjM4kVjb1tp9e1LW/aeauNDhibcOoE9eCIkkxW2rd3DAYVuk3W5ZAUtCpng/Hq5tbrNemfru5VuHQ1MJKIVFEWdXdPk1M0N88vPW++/tz07ikU37+UP9XFRGMsm4SZy9DJqiXFASq7j9y1RWQsguSumQ81QedszxAL5HKb0t9ffNAEZQSu9y+81XlbI2RqL48oxVxihTH11dkIeHrukPTSH84aYQYiGmxhMJFF2cw7XKkYSOunCcUyQjcR1/3nMCYwfn4/byCgvoz43yKFJFL+0ccpA3l5YWISPNg6krTEXR2/eNdKVO/lIwmo8mdByvj6JHTho3u7efl03FV00fjrmvfsI7Krsh7cG7Wy7vwR+O9gA9O+5rcy5HTTjeJtmSfb9g/CAcq2tG/26ZDoTHln3VePdAreXmmf2tHrj5ip7QDcqXQsYUdOJl7Z0bxOlw3NF2v7xhAGqa4g6kAqUUi987hJ98vz88CkF9JCnFIDAwm0xyaaeZjivuzpdt8rMCvJ5EWu5Pvt8fHlWRSkJ/N+dyVKfqTywH0Cpf/tM938TZSMLxkuoQ8GD2KxUWpAmTQiuEwOchOFzbgvzMgFS+uXLaMHxn4Qf884K39uPhawvgUQlO2yTQ9ofmpruvkMqF40mDS7jdIH928m3v3BAef9OUv+al+zhd16cpDtT905MHo3vqJX68PspftPnZAWT4NTRGktj48ZeYMbI3TgkWorJzr5tVgtNNMWQFvdANypeSJg+/GEfqWtA9O4CgV8OE5dscdbdmZglGPv4e//tcAsk1TVHctGSrsw/ccQXy0v1t/varbheayvrfxvwmhMwCMAsALrrooq/021iCIiOg8fVVUcLHaJYFXdJxpjnhYMOLXJz+XUI43hCzcNuXlhVjx+E6rK+owqJJg9Gpgw8lvXORE/LwtfWGljheunW4RdLItqr6CLpmBhw6dSY1bWhJwKAUjZHW4KVbYFwXZIiiubmbtaK4XOJVCRZNHgwAWDRpMDpn+BDyOSWNPXLS8OWZFsy/aaCrBeiRuha8u+8U1s4scV0y6pMXwhMTCnFRdgDds83RJdufEILXd1bhyXdMtPfjbx7Aulkl8KoE1U1xy4iNkS1lvCKx7ZK6laSpGxQrPjiMu8dcgrtG98HNL+xwyCwpgG6ZfvzsugI0tJgJTT61bdvOx8YNQge/ZrlWr6bgiQmF6Jbpx29uugxxneLmF3bg2SlDpHXTnNBR8cVpS3KXQalF7hmOJl0lu/PGFrj7EZQV48W/HcZD1/R3XQISyy0mh3EqK2AhwrL9D5xqRiyRsCR6eVSCiGCdaZcqy+S0/TqnQ1OIpS/LRtfM+yTdr4EQ4GwkaZntPz15MB5MEVeXlBYBtLUPeDVFeu7j9REuw2aDgiWlRUga5krCsrJieFV5wFscfFfVn1sgOeESPP93kFjF7X8qPuM4gO7C3/mp7ywbpfR5SulQSunQ3Nzcr3SChE4xdcWH+PZT7/PpK+sQTFYYTRi4c401EGxn3kTichvJG4ryUVUfwb3rd4Ok1qEPnToL3TBHr3eu2YUxT77PTVnEjU3zxSAbo1/WNMW48bnIAHJjBmmCBFc0N3ezKxSXS5DSvf/Xn/airjkO4sLAAYCOIR/+erDGlQvTEElg+V+PYMqK7SDEug8zilcVgotz0qAbgG4AVWfM+AAAnGiIYGjP7FY0Q7q55hpNUinTR1UUB+HW3nYgBFsP1+E7Cz/g68dbD9dZWFFPTSzEnYLh/eTnt2PKig9R0xTDG5+eRMjnQZpP5RRSt+BoRpqJal87cwTWzSoBADRGEnh0014EfR5+vqw0r9RQXlUUPLJpPyY/b9bf5Oe3Y+TjWyz9pyGSkNJxVYXw/u0muBhX3N21D+kGFcpBLGRfTmWVEGFZu9+y0iyPphBMen47hv/6XUcfYPecQoj0OIQQC4PKja10ojGK6as+wq2rPkY8SS0JplX1Efxw3W6LtW9Cp7wP2J8D4jXY+88dq3fC59HMspRXuFKYRXk0CyQbBkVtUwzH61tQ2xSDYVjH115NlR7r3xGEFrfz8XKIt7/LV94+AtCHENKTEOIFMBnAH87nCdwC0kBr0NBNVmgPRrY1YmAjj8nPb0dWKID9Jxus8lWX4KrSRgCSfVZIa6BRJkNktEh2HDFwt3lvtZSRI1oitsQS3Aj+0U17UdMk949IGhTTV32EsYPz4VHd5Ydsf7+HcDmgKK+9d91uHDndjCkrtuPedbu5b8CVC7bgR69+AgCY/8Z+LjcMeBXXWcjx+ohU0mhpO13OUNINg4sJ3K65a2YAZZdfjGkrd+DKBa0y2F4uvgs1Z6OY/8Z+AKZ3wJULtnCRgqa2SkVHPWFKMR/8nllusS3EvtV6jlbWkUwW/Ni4QYAgv2wreOsmZV3xwWHeP+5es0tar9GE3q6EWTday0rgDJIvGD8Iz26ulEpCX99ZZZEwn6tQQLaPSFxNCsF6t/oTr0E8vniPEwKpxPXdvSf53ytuGYqsgIfLVL/x2Hu4ccnfcaC6yfKCyAl625W7/ju2c5GybqaUjnH7jlJacr4LRSlNEkLuAvAWTCnrecd3uAWku2cH0NCSQF4HH5Q2EnvY57bkk+L+bIS2blYJdq3bw2V/aR7VVTLX3rkNak1OoqlgrqoQqArBrqN1yE7L4ccRZXFjCjpZZIIJ3YCmKDzxj8luRRmtmyyTIa6ZnFIm0RSlrIkk5V4affNCPPYxb2wBHw2KnwG5hHTdrJI224iN2sU4ilh/bLnkiQmF6JLh52V9fHwrGttNSpgR8DiktkwGK9u/rjkuHe2yQLJMLsuS1EQ2k7PPEUs7GpTiiQmF6Bjy8nYU5ZcBr+paXywznC21AmZw9M7Rl+CagV0sXDB7vWYFvYgmjJToIU0qYVYUAq9qLhUqBA75Lzu+GDjWFAKPRnBtYVfLdbu1y7ncmyJx1Z7gx+qva4Zfyt2yH599phTYsq+aL4NqCkHHNC86d/DjOwO6cMmqKFNlbT3z5Y8tsYhzkbv+OzbXlwMhxA8gDUBHQkgWmHUT0AFAtwtdMErpnwH8+UIdPyOgWHjrteEYvJqCua+ayyRzRvVGQZd0BwPezmTfe6JR6n3w+k5T0cF4L0Dr6OndB67k03NCgDuv6sOXr9hoxZNKrLIHxhsjCaycNgzdsvxQXDj4Hfwabl+9E8vLiuFRCVZNH4ZjZyLITDNjHvPf2IfMgAd/2VvjIHKun1WCRzftxdKyYjy7+aDFOYzNsOzr1a/vbMVNEAJcM6grquoj6JjuQ++8IP7z+/3N9XEA+Vl+M1M24EWvjkEQhfDguzgaPJeRoW5QGIbhqH+7l4RI92Rtt6S0CM9uruQPxDd/+E10CGioDccQjiWRGzLxyF0z/Fg8tcjSPgvGDwKBuwzWzvt/buoQhKNJdM1sFRqIa9puSX0sSU0st+nbADw3dQjqmxPQFMLlnuJyCGtH058C2HGkAUU9ctArN4hlZcVWcUOqrQHTe2HS8O5Yv+MYZ/qU3zaCU1wBcDOgPnkhvDr7cnTO8EM3KDLTPFjxgZn0ddvInrhnTB+keVWEfBoy0jzQDYqQ3wO/pqAloeOR6wfgyzMRTpllG5NCA4BBgRc+OIzlfz2CzfeP5P1P5qvA/BxWThuG/OwAlNSs3H5/+D0K38ejEvxsbAGqGqIATFvSi7IDSOoUvXKDfPnS3n+Yu19+VspDWiO4sSgfaT4FTVEdhBCoqoLcdOsjNu6SkHiuSW0sc/rf8dJoa+YwG8C9ALoCqAC4Eq8JwLMXpDT/xi2hAx1DHv6mP+xiIC4GgmNJAxdlm0EtE7cAxJIGOgRUS8AtktAxvFcOLsvPhM/TunLH1m8f2PCJJZgmnkM0HGcjGgYmEwPjCycWoi4cl846Hr62gL8EapviPAgt3kRuJuZdMv1YM3MEKIApwy+2yATFGRZTuvw/IUjMZhF2ya/dBzo76LHIXdnNJkp228JqsM8JneLbT32A2d/qwQO1splKXrqPB7qfnjwY8ZSiSfTiyE7zQlVNhk1euk/aPoDpqHZRdgCGiwTSoynolmX6aid0Co9KcKoxapHR2tUwLG5gP1bXTNNz3E7k/NV/DHTUsf2Y3bJMlY1PU2AY1HE9TF6sKgSKYrb1jG/24sTfH19zKepbEnj0hssssmIx4M6Cwnbp6/v7a3ggmCnz7IKB9/fXYGjPbD4bZMe3S6HZy2vGt3ohrlNLf/dqBK/MGA41FY8QpaZPTx4MlcixH+OKu+PRTXvx9OTBWPH+YYdtrNhfl5UV4xc3DMDRuoijHR65/jJzlqWZM6Kblmzjirr1FVVYVlaMfp3SoQksJBZPsLe1GE9gGdIihG/FLUPRJzeEytqw4/vzBeGzb+dCZf0ZgEWU0rOEkHkwcxoepZTuPO+l+Re2ryplPV7fwhVGogSwLZklm0K/dOtwbNhxFOOHXgRNJfCqCo7VR/jShMzsno3iKKU4dTbWppRzWVkxcoIexHXK7RNl+4tkULFjd80McDXJ8puLpb+1yyDzswLc6JxS4OUUvE6mvllaWoSQT0VTTHfIQDuGvJi43CmHFOtv7cwSKWW2d14QDS0J3LF6p6uTGbu2ZWXFyAhoSOimcqe+OY6soBeUwuLJK2rkGalTIQTl28zrYzReALztnpxYKCVnzr9pIMpe2IH8rAA2zC5xyDiXlhbD7yE4fLoFr1Ucw8PXFkBVCJcSi/Jf1qZPTijEaxVVDngjgyl6NdWBM3cjoIpS4F4p1POZ5gQu7RyStsmamSOgEmKh6IrEX92gaIomkBP0cjmveH+49a01M0v4kpubxFrcR+xjblLo9bNKoCgEZ5rjmP1K66xn0aTB6Jrh5wRf+2/qW6wScIdMdWYJoknDMVgQ+ytjc9WniK4KMZPYFGIuAYWjCQS8Gvya6aMRSxpQFYLDNWfRO68DAMDvVRBPUMR1A5pCUNccRySuoyWuo3t2AOk+DR5NQdKgiCZ0VDfGEEuazK+EbsCnqcjr4JM+X/4VztK/KmUdTyn9JSHkmwBGA3gCwFIAI/6p0vwP2cRgVTzZSu3skxdqdznDpxFcW9jNYlfohjyuqjdleCunDcOKDw5jXHG+65JJv87pWH3bCMsIaFlZccon2Ll/0Kfhl3/c66BzPjmxVabqdq6umeaU+okJhQj6NAf2eklpEepbkthQUWWRGSb0lNkNBcq3HeYqIwogHE0g4WLPaVkOaoMye3VBHl65dTgUheBEQ0RKv3z0hstMJv6mVkN6+/LCI9cPwKfHz/IXAwsei6PR20b2QvXZuIM+68bR8qT8mKvqI0joFH6PwmeM4guHHedsJIHsoFe69HdRdoBfB0uknDe2AH07hUCp1d9i8dQizBtbgKN1LQDcCah98kKYN7YAj795AE9PHoxoioDqJls+2RBFXgcno8j+knpm8yEUdAnxa22vb4kBczeJtbiPKGV1k0LHkgbmv7EPc7/Xz5EgmHCpj6r6CF7422GUzzDNpqQyVcEeFjAT/Nj9wfZJGpTTZNn9XvZCa59ZMH4QXvzbEccMZGlZMX7xx8+kfVScnSycWIjnhRmMzALVnhgrPl8uFGfpXNRK7MzXwiSx/gnAvzdsfgE2LUU6XX5zMTqGfHxtkQD8e9G4XAxEEYFy6RZkFC0O40mDyyQbIgk0RBLScxDSarTOrAyf2XzQVSYX8mlcAlhZE+bSRd2g/Phudo+qYmYaP/bGfvhTeG67henMkb0AmGvAvXNNtzlKKVZvO2LmagiG6odqwlj83qE2pazssyLIGO3Syr/srcHNL+4AIWZSXdkLOyyyTE0hiOsGTtY34+fXDcCzU4dIJY1JA1yWOHNkLweb6vbyCsSTlI8qxbazyyxl16AqBD/e+CkOVoehKQRTVmzH8r8esRwnK6UuEa+P0WsNCvTpFIJfM0UAjPCa1ClueXGHpS3uXLMTAEFuuhf5WWmu/aGyJswTJykAhRDMv2kgckJyeWxDJAFVUXhfWTBhELyqgvk3DbRYac4c2Quvf3IKB6vDlnO7yaHFfdzksfZrYP3Yrf8wlMb0lR9xWfE/qhoQ8JjjW/H6mDQ6J+TFLZf3QG2TiW/plO7DosmD8e4DV+Lt+0Zi9rd6WPhSrL/nZ5mCg4nF+Xjn/isBAF0zA/jtLWYdRROtEunckM+UvI++BN2z03DPVb0tfeynYwfgmSnOPjp34x48NbEQ88YWYMVfD2PmyF58H9kzxS7DttoIXxiJ67nMHI4TQpYD+A5M2J4P/3PzI855C/kVblp/Ra8cbg4ufhZHT+XbjvI3eFwwWRfpkGyrqo/wh7IoBWSBv4FdM/i5xVFGwEOko0y3wFo0oTsCZQvGD8Kbn57k15Ab8jkCd3ZjelHuKl6DqhC+Bmzn4we8irSeiMTI3V6+13dW8X3c6i+W1KWG8E+/U4mth+v4qEwMmIu/j8aTPFDtNnp1kzrWtySkdSYGs71aK/7BLXGttimGbIFw2y6ypKwYcGP9gCKuAzNWbHdt09/vPO4ww7GPPtm52Ohz0eRC3hdlv3ls3CCE/KolzsDOLQsKi4TSuRv3cHmsvR3jCUPapzUFDoEBu4fGFefzunng230wqn8nS7yD1YEshsBG3fb63rKv2tLfWTkCHiLt9y/+7TC2Hq5z9XJZWlaMx2+6DA/+zowH1TfHEU20TcV9bNwgpHlbEynPRYzBni8XUuJ6LjGHNADfA/AppbSSENIFwEBK6V8uSIn+ye1fiTmIa6du66jrZpWgsjqMZzZX4vHxg/i6qNua6vpZ5lpmNK4j6NNwoLqJYzYASN3S1s8qcf1e5sT1s+tM5qFCgGTKnyEr6IVCiKtznGzNUlz/tZ/3q5Z15bRh2Pjxlxg/9CJ4NQXhaBLZIS8SScOCH/F7FKT7PfBpivQ462aVIJFSZCQFLIZ9XRiA67r3rzaZdVbQpYOlPsRzyNbuV04bhgc37rGsvYdjSWQEPIgnDWz8+EuUXt6T15lbn5k3tgD9u3Q4p/3Y+nZbbSGLDfTODUKnFA0tcWQGvFwWfC79eNexBkv/bes3kyXn7p4dQFM0gcw0s8+RlOfGlZd2wqmzURBC0CXDjzSviljS4AmgzbEEgj4PHt3k7NM/v24AAl4FkbjhaHexfB88eNU51ZOsjsXPIhpk7cwS7D15tk1HQztKxA1Z883H3uP7HD7d3G67i+U+l36yflbJeVErtRVzaHcGQCltoZT+jlJamfr75P+0F8M/syUFQ3S2VtsWATWhUzyzuRJzRvWGbhictOlmKE8I8IMXd8CgFEveO4TZr1Tg7nV7EEsabfoOyL534+nXN8dx95pd+PT4Wfg9ChSFoPS3H+JEg5UBz2iZFJAa0+sGxZqZI/Dq7Ms5cXbl9GHwaARxl1R+N8KrphBMGdGDIz4yAhqO1rWAwswIfvzNA5j0/HY88gdzKYwQSGm34WgCcd1AJGEiAz6vDaOyJmwpQ2bAg2VbPncYxC+cWAiCVkN5mZH7ktIi6IbhaLvFU4vg9yiYM6o3ntlciVONUYx+8n1c/9zfcaoxigVv7cfUkh7QDVOyWj7DJOKWz7DW3ws/GIquGX5E4olzSkBjnymljrI+Nm6QKwGVArjqifdx45JtloS9zICH92+eVR7yQTcoLskLYXgPcwmmJa5bfuPW92XnrgvHsXTL51CICabbd/IsXv/kFGqaYpi7cQ8CHhUKAXZ+2YC71+wCANyzdheOnokAoNI+TQhQ35LE6XAMQZ+KSELH1sN1AIDXKo7xunRLfmyLPMzqQCTZelLE2RenDcWzmyu5gdK50GQ9msKfG+I+ukGxctow/PYHxQh4VfTtFEL5jBEWsuriqUXomuFvbRdKeT+WJePZE1S1FNn1QuY+nA+20v/Kza85DdGZmUd+llNqZt+fyQFVheBZIQlJTGJbNX04xOVAtnbq0+QeEKpLQpdhQCrJu+XyHhay5c4jdW0mOiVcJJMHq5ssenom4dN9FEkXDr6YYOS2XLL0vc8dU3z7dPzZKYOR1KlFlrlwYiG6ZvpRfTaO21/aYfmtqDRhMQC7QXxW0Gtx2LMnd9nhfIzsySiuojLIoK3oiB45aZwr1N6yzcKJhfj1n02O0yszzBefm0+GGMtgNNBXbh2OBpu0ub12UIXPBqXSIOiJBnP9emlZMcYP7Y6At9UXwdU7QJX3y25ZAQtniZ0jPyvApdRiXYr3kEhVlclMGZxP9OEghOBI7Vk+o5WVSUyKdOuXfo8i5UvNHNkTkYSBm1/c4UqKBZxsKnu/ZG149+g+mPxS67IXk8VG4oakblT4hX6sEILyGSOgUxO1YZfR/teNgyRPtfO7/ctU1v8p21ddVqpujGLcMif5cP5NA6X45V4dgxi3zEldfHXO5Thto2UuLStGB7+KY2ci8GkqctN9+OJ0M/KzA9j40ZeYUnIxmiJJbh/J5JQE5ozGKV0MoL45gdPhOH8A5qV7cTaaxIGTjRhd0AWUmpLOdz47iaaojlH9O7VLnJV1bPvUtSWetPCL2PV1zfTh4Klwm7TWeWMHoHzbFxg/9CKLMcr4oRfh75U1GF3QBQqBdGmnreUBMUlvXHF36bl/dt0A6AblCixGbk3qZi5KYyTBE/PaouPOv2kgXt52hMtSJ3+Fqb/4edGkwUj3q4jrcCW8ytqCPSg7Z/jMl6WtHfJCXsR1ilONUeRnB3DkdDPmbjTx7KLVKytf+YwROFBtPrzmfrcf/B4FNU0x/HDdblf58CV5IXx5pgU/XNfqg71wYiGShpxG2zM3iFMSgvEj11+GR/7wD4wr7o6uGX4YFI7kT/H6188qQSSh49iZCO/3vfKC0AiBT1NwwkZTXVZWjLx0L2qa4pjTBv33pVuH4wcSqfK6WSU42RBFLKkjI+BxlG9ZWTGy0jzSJV72UmMSawoT0c+EBeLSrmyJdMOsErQknJJahj1nTLT8rABW3DwUl3Y+P7kNF5rK+r9ySxjy5ZLu2WmuEkrZ/qcao/jkyzMWWuY7AsN9wfhBuG+9GQxbXlaMbxd0gUpMJMCq6cOR5lVwpjlhGX0tnmrSJU80RvH4mwfw1CSTjCqOrpeVFaOgSwghfyvGgT0wumf50JIweIKfjDjrRr8UlzhONkYxftk2zP5WD6ydWYKkbkCngEcFmqM6l/8WdElHSBJI96jgkl/2vZkL4eF2lm4yS7dpfb/O6Vg3qwT7TjTiZ9cNgEIgJcXWN8dx++qdWFJahIev7Y+zUd2RiFW+7SgaInGsvm0EP76sP7DRsVjWtoQIss+6QRFPUoT8Km8XhjlZNHmwtC365oUsI3Ax2U9VCF7Z2iqdXTDeFCiwNulmG/WyY1afjfIgqN+jwKMSpPs1y4h1zcwRaIwkcaLBRFosv7nIsg+bnYlEYbdRukjBJbBm9LPkQk0h2Ce5/oROUd8ct/R7MXP/tQoTCaKpBASE57ewWb1bm7qRkE0DKIO/VMXysSS7u0f3kYpGeuSkOWZLj40bhNomEz0nkwgDrdLZWNLAtJU7HN9fnJOGFpu/h0cjMAx6wXEaX9uXA4F8WqopcgNxTSGY/a0efBTs0wgAgqRB8e0BXZA0DIASVFaH8fonpwC0ytwYr+gPu6swfuhFAAie+stBHiy1yynvXLPTMur0prj5omn6HG4L6STC2oPnr1Ucw5xRvZEZ8EBVCPI6+EAp4FWtISf7Ekdds9mxl//1CP70j2ppYtDsVyrw9n0j27WRZN8zs3eZRau9vmXfE0JgUOAXm/ahqj7ieu6V04bx862bVYLby50Wm+tmmiPTNduP4JYrelral81yNIXw9hHLKtpUiuUTuT3cv4aEAAAgAElEQVTi54ZUzsPNL8jFC7IRLgMasu93HGlASe8weuUGUVkdxo4jrQ/SuRtNu02xTdyWsFgdmS8a4NZVHzv2WzuzBCcaIshN9yKhU+k+a2a2cqTc7FRFPtTPrxvgkC3vPdnkev0etXXELPbveWML0BBJYOvhOkeg2n7cr9K3Qj4NS7ccsix1zX9jH3523QA+sJj73X58xm/v6/PfML0mZnyzF1/+YZJTe92IVqn5WQEev3ju3UrM/W4/VNaEcc+YPqkXZIKzrjwqSf0dQZpXu6D4jP/1ktR/dnOzmqQuBuIejXAz+gc2fIIvz0QspulNUR1/3H2cEysnFudz0/NvP/UBHt20F9cWdsOCt/ajOZbgwTh78Biw8oCYfFNGwnQbXdc2xfj+Qb9qMVC/d/1uHK+P4N51ux30TzHotaS0CJv3VjvKxD6L9FU3eu25BN5lRNwlpUX4e2WtlDKrKRTbD9XyNnI7dziW5J/dksaON0QwfdVHuLawG9L9Cm/f0U++zymzaV5FWlY3IQKTF4ufl5QW4bWKY65lbYompAFIUQLJRubzXv8HrlywRdofWmIJXmdu5RMJo/UtCdc2OtEQ4fRbt308Kvj53ILZTK75g5QZk2wfkdYq9gHqwq9iQoT2bEJlpNQlpUX4XUWVnF7rEiQXCclubahT+W/zswKus0wtFctZmrKyZb8J+lQLqZjNZiY9vx0P/e5TJA0D967bLSW6ns/ta/tyAFq5Kyzh56WtXyCSMDjB8t0HrsTKacOwaXcVEsK6uyxJRfRw+PFre6Sm7Hes3olxxd2hKoqD+iluLNi3avpwlG87yqeY9uQXt4ShkE/j+1MDjtmFmFAzd6Op039iQiEA4KFr+vERzJiCTpbjirOKhE4tNFBZOdySycRyi7ymd+4fyc895OJsfnzWPs9uPoikQZAR9PFAZa5Lkl9NU0x6Pvv1sHZpisp9OVrihrSsmWleaf8J+TT+OSvNi3ljC/CnT47jp2MHuNbT0RSR9NEbLsP7c0fh5VuHI+jTkJnm5fu7+USI/eHomQjKtx3FqunDkRVsLd8794+02GWy/U80RNpM+GN14FZ/CR28jdySLcWZiuJyHBDCSb7snvvTJ8cRicu9ThoiCUtbMCtX+36UgveT9+eO4vfTk+9U8vre8qNRvG7g4lfCrEQBcDKx/Vwqkf82I+BBx5C8bjyqYunX4vnONSFu5ssf8xn++d6+tstKeSEf7hnT18FdiSZ0LP/rEZ7tyrapJT14A7WFDWDESo8LBiAz4LGMPmSk0yWlpp/yt596X/r7/CyT9ipLjls8tQhpPhVv3zcSKz447Drqs+MsJj+/3bLPkO6Z6NsplApKmwHwn/6/z3g9JQ3Kqa5Dumc6ymFPdhOvrS4cdSXisofXw9cWYO53+2HFB4exoaKK1yulFAO6dkBjcwz7Tp5F1wy/9ByMzpkV9MCjEcc+zLN6+c3FWLbl8zZnOSLFtDYcQ5pXxbObKx1rz0tKixCOJeFVFdxx1SWgoDx4vnrbF9hxpAHLy4pQ0xQX1u49+MUf9nJ56LsPXMkznZ+ZMhgv/GAoTjREcXGO3CciM+CxEHuvGdgFBjUQiRuWRMgHv3cpasOtL0wW/B19aW67VFuFWBMbmYBCNwxOhAXkiZpPvHWAt11Spw48zJLSIry+swqj+nVygBhXfHDYSQEuLcKz75qgx9x0Ly7OSYNC4CDNLisrBiFAl6wgfrh2Fx66ph8mCX2c1fcf7/oGsoMe3DOmDzQXbErSoO0m/iV0eQyzviUBUCqtm3A0gWVbPuf9unyGidtPGtRCKmaEYLbUxb5n57hQ+Iyv7ctBUQi3CfWqhLPb54zqLV2PFGWCbuvkCmnfNL0hYoLM2P/spFOWZHVLakpq/33XTJPJ4/co0GmrxFUmxVxSWmQhatrLIbs2ANyA5+YXdlhutqcmDsLBGtMjYO53+0mv4eIcE+9QczaGG4Z0g9+r8AAsU2lU1oTx5MRBXFoqI6kyV64lpUXonhVAUY9sh/Tw2c0HeQDy5RSPiVJq8RJeOLEQkbjBR6a+FNzMrghzqydNIRaJYUtct/CQWLsZkvN6VFND//fKGtx8RU/c+k2C2rA1wPpkasbGzldZE+ay4jPNccRT9FW3/tQ5w4+7RvdxEHt//ef9yE33YvVtI3A2mkQ4msATEwrRuYPf4rWw61gD0v2tVGFZW0QSrfUX9KkOAQV7CYj14UY5Zn3pkesHIJow0BRNYHivHO6jYC8f83bonh3AsTMReDWCWy7vgZ9fNwB1zXF+3SJp1k5obUuinpvuQ10q6O1Wxx6FQFMULlJhZe2S4YeqEGytrEXXrDTpbxtb4miO61LZ7qyRvaVBfJGh5Pc4Jfd2ibVHuzALQF9bKWttUww3Lvk7quojePOH30Rcp23SQEXDe9k+S8uK0bmDFzcuMeWubhCz596txMPXFnC9uXiO/KwAHt20Fw9d0x8Br4qaFFWTPZSyg6YZyS//uA/3jOmDS/KCaIwkpbI9UToHWG8Wez6Dz6PAryk43hAFAZAT8uHxN/dZvB7ys1qza7tm+qEQoCVuOHwOdINytcd7P7oSv/mzGaTrmhlAp3QvwjGdvzzcJINsVH+iMYrXKo7hp2MHSLNhWYCc/e0mf100aTASuoG5G/dg0aTBFutIto+bJLlbpg/XP7fVUa9MXpzXwVQksYeleMw1t43A57XNyA56kNfBD8Og0vK9cutwHKlrscwi8rNMyeVjqSCnTPq5eGoRsoIefF7jzHpngWAmKT4bSSAj4EHSMODVFHx5JgICcCpoLGkgJ+hFtUufa4kb+PFre/DyrcOlxFqRCOv3KFAIwZnmBHrkpPGsbUtfmlkCQsDppAGviuygl5tOidRcNiuqaYrhtYpjePB7/eHVCKaucNa5W9by/JsGIt2vWa4tP8sPn0flx3G7ZzuGUkZGqQEFmzkldMPkV72xD9O/0RMAHPe0pij49Z/3tUkYduvX7J6TSb2ZVHnB+EG4tHM6soP/PVTW/19uoulGJGFg7YdfcvcyUcqa0M0M3RG9OyKaSPJRcEBTsGFWCRIpD4e3PzuJ/l0z+TFF0iQjppZvO8r19zIj+KcnD8adV5mjwKWlRZyqKY4yMwKag2Jql+3JOvmysmL84voBSBoABcVTkwqhpEB/Mmokk+GJ0kI2ihVHpsx57tgZkxian926/OERDOEZ1198ATskgypBUqcOOaDVFhO8PGxqzf52WxrKTfeh5myMt51sn0TSwJZ91ZaEq9d3VuH6Id2k9cquwS5xFY95sjHKqaiGQV3LV5MSEIiziKp6k/4rlX661JNI6hS5PT6NmEuHKS6Tva2XlRXjmc0HcffoPtI+l+ZV8cRb+zBvbIErp6pf53TMv2kgAKBTBz+OnYm0SYRlNFSRh2THn8+6sjeq6qOWWdFj4wYhltSRSMrL4UZ17ZGThsaUx4T48g/6Wo8j3rN980KgcNJxf/Ufl6G2yUryZaj7O1fvctzTiyYPxpMTC1F9NuqQxy90kceLS0ZulOP8rDRO4H1u6hAgiPO+fW0D0qKJd8incZPxeNKw0EDLXtiBuRtNUueMlyrwrcffw/5TTbhp2TZc8Zj5edLz2/HIpv2O4LKdmLr1cB0icR2aQlyN4NnI0O9R8cCrVhnfA69+gjSfxyEHLP3th5agoSx4Oae8AgYFpqwwjekPVoc5ibU9sizQmt1dVR/BfRs+wZxRvbm5vKYQPPS7TzFlxYc8CxsAIATpRDKq/XzsOCohDiLpj1/b0y7plf3dlsn7hOXbMGXFdtdjqQrB+ooqjHz8PVy5YAtGPv4e1ldUudareA1uogIWjH0g5X/dXmD8gVc/sQSY7QHS9upJ/K0YCE6kZsWyumd9Y1xxd9c+5/eovC+71bGmEH6vUAp+nPbqZu7GPRYiKTvv7asrTNrpGqds1KsqSA9oruWQfW+AYLZEcCAGm4HWezZpyOm4cZ06jjMndRzZPU0IQWVNWEoYttwrtrphnz2q/HoUhfDjXCgq69f25SCaeEcTOuf7+D2KlLejENOf+eqCPFc7S5ksc+HEQi4PXTltKBojCVcZrUKAK3rl4O37RrqOgHRb8h7jvJBUUC4/y13aZ+dJya5B3J/RHs2RnEnGZP/LDHjwwLf7YM1Mc636j3ddgb/9+Cp4NdPj4A93fQOGcD4xQN8Ww0fGA1KEa+PlKSvGaxXH+N9mEJ869luWsjEV62nxVCe7SJF8v3hqETwqkdZrW+3OPCny0n0WppFHJVgq4SbZDezZDEE3KObfNBBrZ46w1Edb/CDGphKPeS4eDL1Tfs2y+tcUgnWzSrB25ggATtkpm/HwZMl2pMpPTii0lO+rUnPDsaTrPeRRCRZNGuxoR7fZp25Qx3HsMmL7/m5lXWxjhC2/uRgtsYS0DsSAu/3cIkNJVeTXSWDGUC4klfVru6wkmngbhoEvz7TwoFRbvB22BsmCT2JwWgzMsgBaVtCLZ6YMxqnGGKJJinvX7+ZIBnuA6jc3DuQWie7BsVZmkMwop3zGCEeAufW3xMK2kV2DuH9mmperlQBw8F1+VgA5QS9G9e+EqSu2456revN4jLimSgW+z8ppw9o9HyGQrs0eqWvByr9/gfIZI0BBkdQp/l5Zg5+OHYDbR12CzIAHv0lZov7XTZdZgseEAMfqI67Wr4xdxDAcL986HM1xHUGvalljLp8xguvS22p35mZ3s7AUsmD8IHg0BZSaBN1Hb7gMF+Wk4cu6Fkfwt0dOGn44pq9lKUXMNGZxHbc+ID6sxXZvr+6PnYm4Bj9PnY1i7sY9PFbFlhMbIwk0tCTg9ygIeBWMLczHlBXbMf+mgW3eEzLrXFmZRE6S+H1NUww5IZ+UN/bIdQPQIaBxcQQAnpwmOxZsVqIs5pEliEbE/d0S6PyaAkKIg2JAFDhEJ2LQnwXcL85J4xa0v/yPy3CiIcKFH7Lr/Pl1A/D7O75xQZPgvrYBaQBIJg3UhGNI6AZOplgwIZ+WijtEzUBdwoDfq4JVfziWRGbAwxUv9vV6MYCmG5RnI2sKwctbv0BRjxz075LO7TDFm/Ci7DQesJQ9+P/z+wUwaUDggd6vYgGaG/JyPpRs/VzmXyCzO2WsqXmvm5ycAV07SINmIsNmYnE+brmiB+akAvq/+o8BFkln9+wAAh5VGrCdf9NAPPmXgzwQfOxMBL3ygqgPxxHya2iJ6wj5NNOaUYIkXzuzxIKHZraLWUFP6gVuDQTbg5osCF3QJR01qaC1mwMgpVTKNNqQsrn8WarOLskLgQCOIKfIbxJ/LwYpn50yGLoB3Lt+d5u2tqytencM4vMUcyk35MN/fr+fhdXz9OTB+NWmfXjg6r7Ssq++bQSHyf3kmv480D2uuLslY54xh2T9285NYkH47KAHq7d/KXVR65rhw8nGmEOi2iGgwe9R0BhJokrgLuVnBxD0qnjkD2Y5Lsk1mVDPbDalr7Jg8/v7a6RKuE4dfPg8tRwk3hMsfniuweX1qbikSgiSho6gzyM9btfMAK5csMXS/2T+FKyfKATwe/71DOn/C0hLtmTSwP7qJkvHWzB+EF7ZdhQThuZbzNF/YBvF/fz1z1AbjnFVTTiWxPybBqJXbhC1TXHHqI8pg5haSZRfstGrnd8kBscGduuA0+G4xZpwSWkRstLkSwSdOvh5AKxbZoAH1kRjHHvAnO3fNTMAAuD+DZ840N6iDeXi0iE8WOoWdCSwsm18GsGjN1yG/Cw/YkkriXVpaRGobX92nKBPk1I0l2w5xF/Obg+iqvoICKGOnIQF4wdh7qt7UBuOOQLB4pKe/SXNlk50SuFN2azmpfu4tPWha/pJr6EplkTIpznKsXhqEX5yTT80x402g9tikFJTVQR9hI84ZfuLbbVwUiEXQHTN8MOrWaW56X4NffJC8KjypcxTja08pnAsyT938Gt8HxBIg+cEwMFU3YjiBhaEX15WjFu/2QNNUR1rZo6AblDoBqAbOm57qYLLcQkBVELQktAxdcWHuKJXDm65oodjpJ7uU6XsIya1ZVyxeNKw5NCw7xmzanT/zlLRyJMTC6Xfu7HXGJ+M9dkOfrgeV+x/LLg/Z1RvNEbiePnW4fB7nBy2FbcMxaWdzg+Ez759bWMONeGYg2nEgmNsVCWzlxSzE+9cs5PD8RI6tUgNZfuzDGmgNbhY0xSzWGCKwScWHDMopIyiaEIe0GJe0gndhHMZFMgMeB32pMyasrImjCkrPsRDv/sUlTVhHKwJ84Qp+3E3763GggmFMGgrL8Yt6ChaRM4c2QvTV32M6as+AkAcdX/76p1I86jS44R8miPAfnsqiDpnVG+s/LszU3nOqN7cLpJIMl/FdrEHglWFcNtJe3B/+V+P8MD2xOXbMfn57Tw3YdexBte6CPk0JA2KeLLVYpL1IVVVHfwm++/FIGVGwINbU3VZWRN27QO8X6kKD5aeaIxixksfc6vN6as+whNvHcA93+6Dzhl+VzvRqnozGOz3qJbPbB8q5Nysn1WCccXdMf+NfUgalNeN/Zi5IR9qmmLwezScaYnjV5v24mB1GN9+6n3MeKmCix5Kf/shFEKgU2D6yo/4vWnvQ3PKK5A04KhjFqxn95NCCKav+ojTT0WL0snPm3avDZGENMCsG1T6vVsgnGUvsz4bTRjckphtuele6EZr3kI8afDgfmVNGDcu2YZbXtyB5pjuuOYLmSH9tX05uGU0isExt0CZOIrrlRvkHJTj9e6cJPtn8W82knULsiVdyhqOJaUBrc17q3mZrlywBdNW7kDZ5RejsTmGu0b3sfBf7hrdB5v3VluCYaKpinjcgyfPcutE8VplAbcF482AG8vfOJd6jetGu4whe911zfBLmTYXZwd4HYgmOG7twvAPzEKVcafcuDi6S9BVZtTCLE3dmEigbQdwxSDlgvFmMPJc92fMIDcO0ZDumfjBFT0xOcUJs/O27AFzkVkVjiX5LDapG9J2AKiUY7R5bzVnRY16Ygvfv2uGX9o+CZ1agspu2czH6yPSOhZZZdsOObldS0qL0BRNtFmvbkHkJaVFUBU4jinWHSsHIbCwzhi/6s1PT/K+wj7b292N6/R/GdLneXMLLInBMbdAmTiKS/OoDkljW/vb5ZfdssyM58ffPIDHxw+SBp/EbGTxtzVNMSzb8rkZCM0NQkn5FNwzpg8mC0ljbKZht6Bk36+dac4iwrGkaTKvqVi9zRmku6k4n8cE3AKyLFu4oSWOccX5PJtUNDhyq9ekTjnzplduEPUp21O3ADsjnbpRMm97uYIvYbTXLrnpPt4ObJQ7d+MeV7KnWCZLQDo3iOqzMa5pzw56seCt/Q7ZqRhDcDtW9+wATjVGoSkKnp48mNusAu7nZvuIxjBiUJO9BNn1uNFUWcKjPWAuMqs6prAOz71biZ+OHeA4DmsHTUmmgtRpPPvZjRW1ctowaft4U1RSVnZVkRtmibMcsY7z0lvLOve7/bDgrf2W+4x971avn0uCyD07BnGguon/lmWRqwqBV1Pwyz9+5pgxUepknTGS8vcGdsHGj79E2eU9MeTiLGiKggUTBvE+kBvySa+ZkAsTkP7avhw8KsGGOSXQdUCnZiKbphBEEwbnv7iZo4vcHrts78kJhVzjfXVBHn7y/f5oaEmYap3sABa8uR+A2ajLy4qhEOCinDQ8cn0BAMrN3tn52Ah88dQhjszVR/6wl/NlKACVgAfLV04bJuUSyUYeJxoinDuzfpYJs6tvSaJXxyDUFMs+HE1IJYrsBq8Nx5Ad9EIhwJULrEwoANg5bwxn+MjqdVlZMXTDwMPX9kduygeBxSaShpPJw5IE3UaQspG9Pebw+JsH+AiPUppa8jI3zgMyqJTTH0lRT9kxh/fIRJ+8EHRK0SnDj9d3VuHJdyqxflaJJdOclY+NZJmlbPltw5HUTTWTQQFNBZoiSSR0is4ZJmq9McXV6ZMXstRfbTiGjuk++DQFXTL8FoOjeWMHQDcM/Oo/LkM4pgMEWDNzBKobYzAoRU5IPjMyDAqvplh4TGKdLSktwrObK/nSzE++39+1HQJeFfdt2M1jeLXhmKukVlMIXp19eaoPxrFuVgkuyg5wk6aV04aiqj4KBXJekciEEutYjKE9fG0Bpn+jJ1/KAYDapjgn2Yp9OjfdB49K0Cs3yJeDasMx5IS8WPLeIX79t4+6BCMvbeVDzf5WD9w9pi/2nmziz4KHry2wcJOs3hUGHkwtdeqUokPAY2aMJw10zghAU4Hf76xycJ0eGzcI6gWydfjavhz8HoKjZ+KurlzLyoqR5lUR8qnc8EOX8HMUAsvbnFlWdgx5QQEHn+gXN1yGn15rOPgvS0qL8Oanp3D1ZZ2wavpwKMSc3fg1Bfd+pw/qWxKW4NuS0iI8Pn4gWuIGbnlxh1Q9Y+cSucljWRZ4fpZJt/RqCpfUisdi+8hGVky95SYZjCYMzrJSFYKQT0X5DDPQCDjxHiKuRKy/+7/TFz6PipZYEvdt2O16TYrLaFyc2Yij60cEG862Msz3nwrzkS+TPPfNCyIcN/hsjS0PAObypax8bCT70tYv8KsbB6IuHOczIPbye2XbUWw9XIeFEwsx98+tpjlPvHWA01c9KnEY/ywrK8bPxhagPpLElBXbLdnp9r5+z5g+0vJpilXiGfCajoaLJg+WOpn5NLk1LRuVM+6RRyN4+dbhFhtXcf+WhI6kYVjEF6KE9u4xfR2S8965QRw7E3HMchiHTEajBeCweA3HkpZ+Yu+XjAnVEnc6tomzeCZX7RjSsHZmCVQFrjwqpt6iFA71oCVjvKwY3xvYGafDcTwxoRAdQ158Xtt8QS1Dv7ZS1uP1La42lCLXhJnmLJxUiLIXnIyY8hkjUH02igde/cRil9k3LyRlyphqnQC3yxSNZUov7yllCLkxg0TJpZttpchmcXPrAoADJxvxnQFdkBRwII9s2m851u/uuAKnbNaMS0qL4Pco3AxmSPdM/Pz6AtSnZjmMmcOSwAwK6AaFQgDdMH01ZPLTtq65+myU39hu13RJXgiHbJJBN6vUBeMHoXduCElqQNfNMsnkpOtmlYBSmNN8laBjyIvjDVHkZwYsy3ji/galqG6MWqSjdgVbdtArPd/KacPwnYUfOPolk6ky20pZPYnt/vZ9I6W8ITaClcmYe3Y07SxPNUZhpOw+c9N9oClrVTu4sFfHIL4802K5zoUTC5GfnYbLf/Ouo03fnztKyhfrnp2GX7rYcALgfbw9KbaMiSTOEt0sVL84bbKwMtO8FmmqaIHKhEHJlAiFzfJOh+PoluVHJK7jhsVbcXVBHh78Xn+oBNJnAbe8LS1C0jCwdMvnbT47Vk4bxpedmCujTyPolpn2T6uV/k/KKtnOBWXNgltmUE++fAG0zhb6dgpxy0o3SWKv3CACHkVqn+nX2l8iEb/3nkPWcTzZGsyW8Z4ef/MAnpkyGCF/60OGjVQe+HYfPPlOJT9WJK6jfNtRzqBicsBxxfmWc8dSfB5xBCTewG45AqL81K19VIVYJJdu17Ro8mAuGbRbotqtUpkVZl04gdvLK9x5QPWtPKBXP27Vn7vtH0+as7rnpg7hMQhVIXwEzurvztGXuF6rrF/2yQvh0Rsug8+juLJ3xD7TlrBi17EGPP7mAbxy63DUNMW4tPKha/o52EfiaN5uZbu4dAg8NomsmfhH+fnENnXji4kSabFv5AQ9Zl6DS7tz6XZqFG5nIs0bW4C9J5valBtXn41yea1Hbd8Clb3gxVneokmD4dMUHuiftnKHa/9g7ej3KAj5ve1Kw30e05BqijBDXVZWjC4dLoxl6NdWrdQer8ce3NJcGCeaStDB70F20Lx57YE/+/6qQiysG6A1MBzXKd65/0pMLM53/EZ6buH7c5GTAk7eE+O/ROM65t80kMtBN+2uwo3F+Vg/qwTlM4bj1dmXAwCuGdgFD27cg/2nmrDx4y8xc2QviwRyzqjePOYicnzcPrPrt7OB3K5ZN6grw4rdHrnppr+xKBkULVH5/oRg2ZbPcc+YPogkDJxuiiE35HOtS7YUtPLvX1h4QG3VfVV9BHet2YXmuI5Jz2/H+GXbUPrbD7H/VBO+s/ADbD1c59oXRXmj2C8ra8KYvuojTF/5kYO9w+S7APDmD7+Jv/34KqgC/oJhMa4uyOPHrA3HcLAmbJFm2q91y/5qbsaz+rYRoKl18f5dOuCGws6gFFjy3iHEU0uUcd3AkvcOgS3r2/urmySUUqel5o9f24M0n6dNdhmTYid1ORMpmjDalRuz+312ikPG7kW3oD2TQt++eifuHtMH88YW4PkPPkdmmhePjx+EDw5Ut/ks4O246mMk9Pal4SohjmD2nPIK1Nhk5+dr+9q+HBQJs8QuARQlfKIcUNz/7jW7cMuLO0AIgaKAy/nu3/CJVNLYFIlBdwkMH6+PcNnpxOL81rV+F4lrSzzJzyGTUJ4Lv2XB+EF45p1KixUhszT95R8/c1gTMqljY3OM22qKEsiuma3ryKIM1I1LJF6/GECEi13rm5+exGsVxxyywQXjzaAjkwbWhaNcMugm0dR13SL5ZRLIzXurpXXGjv+DK3oi4FX5NbhZnYosKpFTZe9nRCKDZL9vr1/6PQqvJ+bDwewl61sSXKL6oxT8b/4b+x0SZmZj6natmWkan+k+sOETnGiI4K61u3Dlgi2YumI7int2BHWx2Iyn4HILxg/C3hONvKxMlGC/ZrcZo24YrhJrsW7aso1t714R61W8F8U+be+v7DOzVf3BFT3R0BLHgrf249rCbq7PAtGGt6reFAC01Z/aohMnUy/k8719rWMOv/ijE6nQqYMPn7usgbP9L+2Uji9Ot79OztYpe+cGsS+FH/jZdQNAgHbjHWtnlmDvybP8N27rsGzk27NjEGejCSlGQiwHkzrmpvtw7EwEz2yuxJxRvV0RDKJfglg+t6BZYfkAACAASURBVJiA21q3GBNxi4+IHgRMbmi/ZlZ/DEXCgpH2trDLdmXn+Pl1A1zbgdUrkzGea1v3yg1KA7brZ5VANyivf4UQfk0/v26AySjyqqbwwaBoiiYQTRjt9st1s0p43xDXqt3q2B5T++zEWew8UodbUh7P7V2r23Hd+sP6WSU4mIrbPT5+kKVN/R4F6X4Pj7s1RRPICfmk8Zf1s0oAAG9/dhJdsoKu96xbX147swSnGqOIJXVu2EMI4eZC9ms+l75ujwOxzyunDcPh082u/ioMRWLHj8j6U/fsAD6vbeb3hCx2tGH25eiaaZ1pnOvWVszh6ztzILCMdB763afwago0laBnxyD+8/v9OV1x1fRh8KiE72+kZI92vIR91MOsCAGY0+YmM5PRLdlNHLkYlCIv3YuHrukPhQD32BJnfnBFT75/XDfAVk1ueXEH7l23G35PqwyRSe/8XgW6QTF34x7UheP8Gtoaydv/ZqgBNzKoSDcVabd56T5OtZWN3MTR6vRv9EQ4ljTlmMKyw1/21kA3KFSF4DsDuuDRTXst1wEAE4vzsXLaMFBqJZqunTkC828aCKIQ9M4N4f6r+7YZdxreIxN9OplMf1lb69RKJ2VWp4vfPYTqs1FsPVzHr+3JCYVoiiagqqarXDRhjvSYnSghQE7Ii6ZIApRSjH7yfdyweCsmPb8dT/7lIM60xEEB9O0Uwg2FnQGA+3gkdcrrSUz2a69NWVyid24IU0su5g5q7fXrtoi/brEP9tD2aAof0Ex6fjtWb/+SL515NQW7jp5BJJ6Uziha4knctWYXBl+czZcIEzrlnKXsoNdUGdr6FsN4NEQS6Jzhg5b6bTRhwO9RsOS9Qw7Jrv1eFInHYn91m80x/prbs2BZKvAsUnQ9KsFy4Ry14RhCPhVzX93D73fZKsCysmLkhf45o5/2tq9tQNpox2JTDDgtnFiI2hS9c97YAlcZnluylqIQPuWf/Pz2c5LhJQ2KaMLA3WutNogAXC0YRV5TUqecVZPUTTXUzVf0lCZDtZe8J14fO59I3pRBAhkLhgX1xQBaTtCDqvqoxVBJUxQsmjwYCiGgoCCQ12U8aTKIXpw2FIsmDUaucB0Ti/MdEtwF4wfhtYoq3FjUzSJdXFpahOw0ufyyZ24QY335vK1k+8QSBp57txKv3DocFMBRgbBqt7b0exTUtySQkWbq1sX6WJgygokmTB+RtuqVLa1VzBuDkw0xlP72Q1e67rkkZMaTBha8tT+VW7PD9VrFhFE3aa5bUqlHdZJeHxs3CDuPnMGV/fIsbbW0rBhZaV5s/OBznkzGlHxFPXIAQFp/D732KSfFNsd1ZAY0PDGhEF0z/WhoSbhKvZ+cUGixe7Unu7FrSOgUumHwYDsFcFF2AIsmD5baqoqJgvb68GmKo00XTiyETimSwjlMf3EvFpcOwYFTYUff6p0bhKYqyAv5oP2fTWjb21ddVqpviaLqTBS3r97parEpTun+8/umF0DN2Ri6Z/tR0xR3SDr3n2iUavPz0r2I61QqKc0N+fDI9QWWBDcmh7tzzS5H59o453LUNccx+5UKi3TWvtwkTndfqziGh68tAAHg9ygIx0yUNZ9eA9xuVLxRmUczu76coBfH6iOglKJbVoCTN+31N7E4H7Ou7A2/R3FdIqhvSVjOt6TUZO4vfu+Qqy3msrJiaApw9EwEO4/U4eYreqbsSnUcOxPBJXkhruQQz+dmHblhVgmON0QcMtO8Dn5s2HEU44deBE0l3DFPTMB7ZdtRbKioQn6WSY0NeFX8cN1uywOQ3dCM1EsIcOR0i3TZ5uWtX2D80IsQ9KkIx5I4Xh9Fj5w0rEuVQ3xQ3nxFT76UxEB6Jxqi6BjyghDCybdupF0moS3fdhRjCjpJ5aFiu6T7VTRFddyxeqeD+MuuzS0PqHt2GsanSMBi3bvZX66fVYIkpUgkqSMhMODVsO9EPQovyuHy2nf3mstM4vIqWyYSKbNtLWU2RhLolOFHLJVUKeaMLCktQjxpSK1lV982Ag0tCYdVLsvJ+NF3L8Xx+qjlvk7zanKZ9MwSqRx67cwSaCpBdWMUsaTBCcbpPg2KovwflfVCbPGE+VK0ky1lN8hj4wYhmkjitpcrsGC8aZ/JJJ1iBvGofnk4G01a/QRAMfuVnXj42tYMUjtxtb7ZmuC2aNJghHyqpaMA5pT1bCQBj2oSOS/JCyIkkf2JtMxB+R3QLbMvSn/7YZvJUIx+Sakp8w36FNwx6hLcPuoS6azqh2t3W2xCxRcD4y+5SfKq6iN44W+H+fkAIM2ncj6PzBbTMIBf/3kvJ9reNboPJj+/nSOo27KkdJNyJgxqkV9SAN0y/fBpTqkxS8A72WiOCpm3hfn/NCR0g/elSoFCyiSNdqtLUbab0Ck/n3g9K6cNlUqevYJ9qLg/G2iw6wl4VfNhm8pd8SjEIqHdUFFlkSGL/bJf53RoCoGqALEkRfm2w1zCfO+63ZzwalBYrm1JqSlxPR2Ow6MpiCXlXDC3ZaikQXE2mrR6eadexgVdQiju2ZHHktggJifopPY+Nm4Q0gTRgNtyWE1TjEt2uTS1rBgzvtULjZGkVKrNfnumOY6lWw7h5VuHQ1EITjVG0S3Tj59dV4BoQsdZmy3pwomFiCXk9eEmUjkdjiGa0C0v+YUTCzlR+EJSWb+2LwedgmekLr+5mE8B3Zgva2aWIDfkQzRhoEuGh0s67SPAaSvlyUZ1zXFcXZDnCIDrBvD05oNc721QExcR8GpYOW2YY5QZ9Gk8cPX2fSPb5dMYBvDH3VVYOW0YAh7VwVxixj+zX6nA3pNNlsQrFlhbusUczc/4Zi80RBJY+XeTesp+I/KHTPrqR20ubeSl+zCuuDv+6097MW/sADTHElAVAoO2Ukur6iOobYrjaF0LeuWao0EWsxlX3J2/4OaNLeAjf7fzubGcNIXgLmF2xmaMfTuFHFLjOeUVfAbC2pSNVgkB/B4V/6hqQFw3LCNUt/4kBjA1lViuZ8VfD2Pe2AIEvBqmr3KysNbPKuHLg2IQet7YAulsc13qBVHbFENW0Iu65jhuLOqGccX5yA56MftbPVDUI8cy+xSD9R88eBW2Hq7DhooqvH3fSAzvkYleHYPwagoO1zbz9mLle+XW4TAo5cFm6VKri5GPphA8K9wPDZEEnk2xi3weFb/a9Jn1f5sP4ufXWblOuSEf4kkDF2WnndMym/0+uL28AutnlfB+PKagk/S3IZ+Gv+ytwd6TTXwmxGYjSZ9mma1W1ZszVNH0SjyWWz2FfBruXrvLcRxW1pkvf4zf3/ENjpw5n9vXNiAtGs2L0jG3EYZhUAtF0i6NXFpW7Jqslpfuw+a91RYiKpOHRhNJHuie/4aZkfyjVz9xlV8S0pp815Zsj+3vUQkffR5vaJ8aKyZeqQpxpZ6K9MwzzXEpfdWNGCrKJJO6jrhOMWXFdoucdGJxPq9vO81UlMiKn8+FDsu+Z0FO+4xx3uv/QLwNYq/YpqIMdsqK7RjVvxMam2OWMuQEva51zvpNmrc1qe/i7ACv73gbo262z7kEoeub4xb5qShrnb7qI4wdnI/XKo5ZSL1iEpgiyLiPnA47JMx2Amo0aYCkfK7vXrNLKhvdfqhWKlVO8yrS/ub3KNAUSP8n3hNiO96zdtdXkq+K90HSaBUcyH67pLQIbKm/qj7CA+tsNtIYSbjem7I++sw7ldLytUUkZp8vFJX1v+3lQAhZQAjZTwjZQwj5PSEkU/jfTwghhwghBwgh370g5wd4Q4jsnW5ZAf492/KzzKCyLBHmmSlDMG9sAZ7dfNA1cSvk0zCmoJPUG8LnafUqkCWHzd24B09NLOQcHtEQndFG7efLDHj4/mLC3bl4BYiJV7pB4feo0pGvyPJnWbKP3nAZp6/a6/X9uaMs1FN2HI+mOurlx6+Zvhqy884Z1RshX6u5vPhZPN8Hc0fhiZSJz7jifPg9Cl6ZMRzvzx2FVdOHo3zbURw+3cJ/K47wkzqV1pNYN3npPkdb3V5egZJLci3XnJF6CdiPxZLMnt18EJF4qy9Hms/Dy2FPYGS/1YS+KLapW/v6BXKw2K94ElfKG4P9jyVksmMZaOUs9e+a4UjEsicwpvs13qYsA5u1CWMdZQR92JSa0b77wJVYOW0YNu2uQkvckLY7BXFNkBPvCbEdxXM/PXkwNMU00Xnn/pFS5pJ4H6gKwXPvVmLe2AI8dE0/AMATEwqx5UejON1VVVvvAUZGZcepa45L26KmKWbpo+ye2FBRJa2nE43Rdu9Zr6biQmz/nctKbwP4CaU0SQh5DMBPAPyYEFIAYDKAAQC6AniHENKXUnpeX4+EAIsmDeaBptx0L3p0TONm9vZgaUvMNCdhU1qvRpAR8MKgFL06BpEZ8IIQ4LmpQzhXyFQceBBN6K4jSF03+HGZe5ed2EgBdMnw4+fXDUDSoNgwqwRnowkkdCqlm2qqKdf8qU1yKqOTLpxYiF//eT+/TpZ4Zcp3zbGDrEzhWNJiiTpnVO+Uln2ggxjq9yhoiekW6im//jYwGWJ9N6SIpJkBD+K6wevZ71Ec50vzqvB7FOSEvNxGsqEliaBPg2FQfO/pvwEw4wasPsRRtxuNl9WNfQYHtFJcKaVYMKEQlBrwqASEKI7+JKOEMuqsbhiWcrw4baglqJmfHUCSUl43TCI8/419fIRrVwa5zTD75IWw/OZiLNvyOXrnBrF+Vgmv56ROUT5jBGqbYlAIxT1j+mJOeQXKbxvR5kxoSanpjijuw2Sc788dhaygB/eM6YOLc9LQwa+h+mwUHlVBQySBHUcaMLXEXRIL23HZ/5IGxdqZJSjf9oV09sSytrtl+fHlGRMDkxPycvmq2I8ZPdmrEsz9bj8em2Hb7++4guf+PHxtAd65/0p4NYJYUjcl2x18+Onv/wEAFkKzeK+xDHRNJZzIHE0YaIgk8MzmSjw9eTCXosvaVKTjrrhlKE+wPN/bf9vLgVL6F+HP7QDGpz7fAGAdpTQG4AtCyCEAwwFsO5/n92kKNyK3E1SZbJTA5NLEU4kzdjYQk5m2TjOJVGrXOTsNSTcTdYW0yRnKzwqgMRJHNGG0GUhmm0VVU1aM7DQPP6/M7D0r6MWzUwYjYVD4PQruGH0J7h5zCRojSR4/kZUpN92Hu0b3cViipvs9yAjADNIRAo9KUr7X8jVV4rL2nOZVpWqbbpl+GACqG6PSIGxLXIc/ZWB/NmIN9C8tK0ZWQJPWR9fMAP+ePQwY9ZQlaI0rzseYgk7c4J3t7yY5NWgCM16qwMrpw7CktAiZAY9UKkkpUPbCh1hWVmyRSVfWhB1BzWVlxQhoTnkos409G01iXSrhjsksH762v7SOmYPdgvGDUN+cwKTUmvmC8YNQ3xLD3Wt3pygApvigfMYIeFzWxhkBlVKKo3UtrvEEdn+we8hORnWLDx2sbkLv3JD0f/tPNaUAdsXo1MErbRe7euvqgjyUzxiBaMLEoov9+OnJg/FgKuDLaMRMmSbKVFvP28pWWlZWjNGX5uLdA7XwexSHNHVJ2RDEk9RBZH6togpbD9dhwfhB8GkKumcH8MSEQhAACiFYO7MElFJ4VAVeD8FzU4fAq6n/slqpre1/hJSVEPJHAOsppeWEkOcAbKeUlqf+9wKANyilG9s6xr+SId03L4TfpHDT9qAc26df53ROaXSTxa2dWSKVUq6bVQJNITjeELHIHduiajIJ6kPX9IeiEHwhyVxtK9uXBVcHdE1HdZMVTS5y75n8r6Eljk4ZfkRTwDQ3k/tHN+3FktIi5Kb78P+xd+bhWVR33/+embm35A7ZwxaULYhBAiQKQS1SsLVWlEdZhaggson41FrUt63V1i4K4i6LVEFlE6WtrW3dUNSKLAYEK6IRBA1bQsh+rzNz3j/mPidnZs4k+Lwuz3vRuS6vK97MPXO2ueec8/v+Pt8JHhJFUMCvWQwpVqZ1M60XmPPH/v3PT+KSgV1cs+N0v4qJy91lWD1jGAxKcV9KPXWqGbtihqqmENS1JhBNGJw2ank8qzYp5tKppchK80FTCaIJa/YZ9CkAhWUan0Jll/bM9RwP31v4Jn5YXIC7Lh8ATSE4cKJVSkD9rKYFAU1F9+wgTrZaMl+nRLijuvXKT8fnta3okRNCTroPRxosK9w1NwxDTZOdDLt4wiDc+899/GV/71UDUfHkdn6PZ64filGL3+L9zoLnL847H0ca47bxxGSxorQ3HNRsK+ju2UHkpvkxdsmWdp+hR68ejIRObTNu5qGim1Y+DBOSiDP+muZ4KndoAKrrIzzA3FFWvtcz68x4XvTKPn6vhkgS2ek+/Pqve3n7iUIORu/1+i1gpGfxWRaJCHdfMUA69v984wXITQkKErr1nGoKQTRh/I9fFN+ZlJUQ8jqALpJ/+gWl9MXUOb8AoANY8z+4/iwAswDgjDPO+ErfZRnSt2/cg6VTS6UkSMZKun3jHjx69RDeWZ5Baw85Wm1zHPPX7eKJW52CGgI+Fb/7+17MuLC39DvFXTPQ5ftF7Uoga5rj3OxdFpRj9Zn9vZ5YN9PCRzM6KQBP0uTdVwyQlqlf5wy+heFVbkYuXVpRhq6d2rbSgj4Vv/nbXpu7HABcWdYdjY7Z8cOTB8PvYXbP8Oj3jbMkxV42nmJQ2YuqKaONilJMk1L8ZedhXNS/wEWWFVcImUHNczwwKeukJ7Zi9Q3DPM3l7/jTh1g0vgTz1uyyrQa9tlHaq9uylJcEowVrKnERUwM+xXZNn2r/f/FvMfgZ1010Cqp8PImyWHZOXtjv6lNLnt3WTl7PEEAQ8BFbWSmluOuvezkBdcOscoBYBj3O50NVwNu4qCDc4f287ILFOgc0BfMdq+TFqXgWO0cUKxyu95ZPi6Rn8VlmbCaLAC3vd9M08cnxZsx85n3XM/tNyFq/0YA0pfRiSuk5kv/Yi2EagDEAptK2JcxhAD2EyxSmPpNd/wlK6bmU0nPz8/O/UtnEDOmsNL800GWabQEwMfDpFfjzomuyh+L+Vz5Bl8wgAj4VB2otaabXtWJJkyfXiGUSA38iNZZSa+90+TVlWDShxGayvvydg7h6xVYk9DYyZXukyTS/Ki2TqhBOvOyIbDl3dSXiOuVlygsHbJRUVSEghMAwgeaYBUVjRNjlb+1vl5pbXR/F01s+x8LxJchO8+Pv8y/Eplsvwhu3XoTXbhmB2d/ryW0kAW8rTJGsyj6/cc1OHGmMYfqqHZi3dheuLCvk23kywcDc1ZVQFcLrKVJPCSG2eyuE2EikTAqrEMIJqHNG9kFtcwIHals920D8XFa3OasrEUmYqE4xpwwTuGntLkxftQOTntiK6at24Ka1u/hY+mFxAXLS/bayGyblhNfcsJ9/Tikw9Y/b8b2Fb4IQgumrdtj25AuzQ0gL+PjMn5XpJ899YAsce42fcEBzlXVeqqzV9RYBVaeAbkL6fJgUWDRhEDbtPY6qmpYO7+cV9C/ICPA6qwpxWdHe+vxu27PoJOh6BaTFZ7a9Z1laVgr+YhDHMWubmc+8j7rWBL6u47tUK/0IwG0ArqCURoR/+iuAyYSQACGkF4AiANu/7vuLbKUTLXIDejFYyiwEC7O9ZXE+jUi5MJoiN3JvjwDqFUQUyaWiDC9pmJxCevEDb8slhkId2mPviOeJ9evI2N5ZJt2kXL77l53VvHxOya6TGHrd+b0823LT3uO8Laev2oElb34Gg1Jc99R2jFr8FpdmhgNttFJPS0qP1QmDmDln6V7X8alEahq/9bNamxDhxZ3VUoLqyPs383r3zkvjUsx3q+Ryz7qWGG8brzKxGerPLjkLrTHvsfTD4gLMH92P/xizsu86dJKX7+IH3uYS1ybBUyEST0rHgBhUF+9n0jZJ7Ka9x6X96/VdcSbfGE16SkVFmuqnR5v+x9RikfCrteOHwcotihWWbd7vWT+RxOr1LBummwC94tpzPams35Ss9btUKz0GIADgtZQMbCuldA6l9CNCyAYAe2FtN837upVKgLVyYDMuLwN6MTHlSGPMxkOqb01yNhCzmrzr8gFc/iYal/9yzIB2k6FYULRXXjqq6yPtmol7WR8qioK5q7dLr8/2To80xriVIZPsOq/fEE3ymItYD2cQ1mm9KWPMaEpbctePBnblSUF3jimWSitZWZkxvawtx5X1wOjizrwtxaQ7di2WxMSSqUSOlFhX5oXg/Dwz9bAVZtuTtbwSqZIGlZrGP3v9UJsQgRknrZtZ7nKbY/21dmY5bt9ozQ5FuaeTM7Rp73Gsmm7xq7z6kV3TK/GqW1YIvxwzwEavFdtv0hPuBDzxWodORjscJ+L9FEJsyXt/+OfHrv69c4z8u6J0k9l8tldvlixoUIqHJg1GVpqPP7NdMoM2nhnjFTH0BpNbA8Dc1ZVYO7Nceq/u2SE8ff1QxBIG5o3qi0sHduXXnDOyj+f4ZbGZ7tnyZ1lViI33FPJrPNbQUdt8nbLW71Kt1Ledf/sdgN99o/dH276eU+IpmoE/N6sc0aSBaMLAgh/1R/XJKJpjOnLDfhtSYllFGQgBskJ+9M5Lh6oQ5KT7kRXywzDNdpOh8jP86J2fDkopzshNg0KsbOENs8vxxckoCCx+UE66D49uqsKVpd1dFMn2ZlzOvcm8jAAUQl1SO1EBddOoIps66vEppZwcOTv1Q8hkqtv2n8DZ3bNsZVpaUQaigEsufafgWidKKw3Too0y0xZ2MEbR+b1zMbq4M3yapV1XFcIx2Ms2709lBFtL7Ja4jsenlLoYOGrK08MZayKg/O8Xd1ZzWatMVmjNduUzOgqgrjWBVdPP48qvD480YsygblA8ZqOGsAeeFfJh+TsHsfydg7bz3hh6JnLS/JZUFnDVbVlFGTJDGl67ZQRWvH0AsaRhKzcL5J5oiSMj6E1ZlUmY2ary9o17pBajSyvK8G5Vraudlqeej9+MPQfHGmOgACe0ihTY+aOKpGVlMtMeOSGphJn1F4unsTo0RpP4yXMf8AD8nJF90BzTcWZuGt/mZETd+khCKrdujCZd9VlaUYYtVbV45E3rt8OvERvdNTfdLx2/My7sjcJsK67258pqXDKwC24eXWQL3OuU8jIFNAXZIR+ONcWgGyaXPDsBoWx18XXKWv9XqJW+juOrqpWON8YwbtkW/mAwxUffgnQphK41lkA46Ld9vqyiDGl+Fapigdnmfb8vCCEufXzXzCDiuilVAK29YRiaYroLliYLfi6vKIOqAPWRJAgh6JoZ5L4NC8eXSOFyTDmhqQQJ3YRCCHTT4r5QgCtKGGkyadhVOE5iLXtxWjEviycT9Fmy4KQBDkvzawRpPhVVKR/n1TOGcg/uF+YMl4LMmLqEeRKPk6ihmIrL+fISX36Lxpdwy1AZII55KhRmp+Gel9w+GXddPoB7EOz6soGDBH0qQWPU8swAYAPhtafuWlZRhmhKYksB3Lhmpy3HRvzOhtnDMXG5VW8vhY0X/E4hBITAJZPsmhkEpRadF8Tqc/aCXzntPB44lpXdKWEWsSE9ckKI6yYyQz4YKX5TNGmgriXBVWDRpIE0vyW+qG1OdCjbXj+rHKpigeYYt8yZx/NICgjJ6u30L2F1EIGG4aCGgw6l2JKppcgP+7HvWEu7XhBMbWialKvUTEpBKWzeDKxdeudbk0PZmHj2+qE4WBdBYU4IOWk+l/Lr4cmD0TUziA8PW8ql+8YNxOGU6kxsg/ywH0Qh36ha6bTFZwAUD08ezPf12Js6YVDeEUDbMrtHTrrr8zmrK+HXFEz94za8urcG4aBPav/ZEtfxyOtVLib84gmDQAF+3Y6sNGevrkR6wAdNUdClUxCEgM+AZM5aSyvKENQU3PPSR9hT3YhjjTHsPdqEP/xjH8JBn83WsTGaxN1//Qi6SVHXEucuVjXNcZvt4qt7azD1j9ugEIKrV2zFhOXv4UhjDFP/uB0XP/AWRi1+Cxc/8BamrNiGuG7yOqgK4Xu++RkBT5QB22IyKfB4ygviuVnleH72cKyeMczKTL6kvzTbfM7IPpx/FRPuzcp97VPb0ZowQGD9sD66qQrzBaTJPS/txfxRRfBrii1JqiGagELAjeWvFeIbI/t3BgFttz5zVlfiZCSBI40xXm5ZGywaXwKFAOtmDsP6WeU2DwzxusyTg9WNMagIAY42xjD9gl48uP/YG1VI6CZ++Zd/48MjTYgnTf5iAIBHNrmxDY9PKUW3zCDPsp8zsg//Me2WGcTNo4s4OTgSN2CmJpg+TYFPVZCfEUD3bAuvkuZXkTQoskL+du1h2fUVAuw72oxf/22vrb3EZ27BJf3x3KxyjCvrgXv/+TEaIgnkCf3Fxn6aX+Goj/3CREF8NnUTroQzZ1vUtyawu7oRd//tIxBi/egfro9CIeD+I8s27+cr6Z+s/wC/+dtHrngRS35kFq+xpOnajvzv9R9wS9PpF/RCJGFKf3cMChRkBJGTHkD37DTkZwS+9nyH0xa8RwiQEbSS4HrmpuHTFElz8UQ52bM9MxP2OYFcgsYWZwGNuOSEovz1VKw0dcOEbpous3drxk7x9PVDUzRNa/Y/ZlB3qUw3I6BKPw9oBCeSJjZWWnvd4naQWA6RtuklJxXbLGm0GcoT2OWGIsWUfTdpmIgl7QlTrM4vzBkuvV9BRoDLOr0IrbGkwSWeA7plIKqbWDV9KF/x+DTL9uaxN6qw5oZh8KsEtS1tkknRM6OmOY6FL3+ChyYP7rA+BSkwGiuTaVKprPWBiYNwrCnmmuHeOaYYe49aMuTf/Nc5thWvTKZ77z+tTFzG1/Iyr2eIiXUzy6GbJiiFbbv0vnEl6JWX5trOeHDiIDz/vuWTMWWFnfgro/8umVoKQuRbaf0KwlwiLd63k8cz0BhtS9hj9FWDUls/UmoimjSx+ePjWJtKIPN6nsR+MFMmUT1y0mCY1FUmTbGIAazf758wCIVZQTw8eTAIsVbSd1za2FyZBgAAIABJREFUH5GEgU7BNiouk5CLY8LrN0VVCL/Hg5MHe5b7mz5O25VD0qC4ftX7mL5qB3STcolnezybjj73+q5PVXDT6CIseuUTrgp5ZFMV6luT8AmSy1Ph5Kiq4poBzVtryS/f/qSWu2QBwBVDCpER9NlkrYxYGU2a0s8NE8hJ9+N7RTkI+FTPeot1DQc0m5Rz9YyheH72cADAymnnYUiPLBgmxdhBXXB2105QlTZJJ8vSFfedWVCOxUOcM04vmWA40Map8mq//LC1TZaVpiFpUExdsc224pm6YhsSOsVvx54DlRAkDIqMoIZHrx6M52aVY973rVBZdpoffQvC+O1/nQPASiZctnm/Z33CAc0mh1ZVxSZrnf1sJWpb4tL+vXHNTsSSlofynJF9kJ3WxmvykumKPK6kQdttl9qWeCqHneBQXYTHatjMXjetrapxZT34imTFOwdsUuCZI3rzl4H4t1gHL86Upiq21Sm7r9f5TK7Jzgv5NTzw6qfYX9uC2uY49te2YNErn4BS4MqyQgDUcxz7NMUmsW6K6XjmvYM22bZ4L0rB+4oQgj9VViOmWwwAhQBdM63nKWGYWLv1kNXXCpGOCUYHcJaJEMLvoXico37NqwTZcdq+HMS3tijd9NqeaY0npebmDws0xZc/PCqVHv5lZzWmrNiK687vhSE9smzkyPke5EgvEmQ8KZcl9s5Px8izO3O66eQntqIpmsQtz33QIenU+fn0VTvQM78TfvvSR55UTbsEkEqJsz9Z/wEny0YTSc7ip6cgadQ9VlSAXEZrYQ7avuN13Uc2VeGaJ7cjljTbXQ3uP9GKyam2ZOdvP1DHaaMj79+M657aDoNSPP7GZ7yem/Yel7aXSaltnMWTcjtMr/4VZcFi+7XnU8DJtx2Y1y+tKMNvX/qI04adEmhKqZSGGhJosqLc0ysBTLb9tmh8CRoiCc/z25NLi20jK18kaWDyE1vRGNWl0mhrG49KZchBjxVzNGnwNssMKty75KJFmzHpia2oj+o4dMKiFlw2qDsmPbFV+gwtmVqKF3dWS+sX1w3+NxNEOL8b+Ibc38TjtA1IH66P2EzTRUc10fhcIQSRRBJpfh8PXjpN7Vkwu6ggjN9IApxOZzYAnubjqmLN4HSTIqgpiBvWUp+Z1ovOXexgwbeOTNC9HNG8PmffZeVjNoquencOewZkZQbqr90yghvNe6FLRImlLDD7w+ICLLikP062JgTp7QCOLHD2qawvvNrM6/O1M8ttsk+x/Rg64dnrh+JYUwyEEOSF/dxJ8K7LB4AC3MGtX0GYu7z5NAV6B/0r3tvZfszP4VTaXhxvPXIsrtOiV/bZVDUdfZ+dI7q5vX3b923lk40nJi2taYojK83HnyGvQPBzs8o5viYr5ENOul9aVi9XubUzyzFi4Zuua4nP78NXD5H2aXtjI5Iw8ML7X+DaVOa7rF0+OtLk+YyzZ3lDZXUb+iQvHZ8cb25XEKGpFpvqhfe/wA0j+n4tHg7/cYKTHEGflSQ1d3WlVJJ337gS9M5LQ01zHOGgtRfJFAl3XNrfJnkTqZNe8jXA2rrp1zkMSt2k04RhwqQUX55I+ShkhfBBdSOWbd6P34wdAAC4dGBX5IX9WHPDUJsyiFLvWbDTq0EknaoK4QNM9t2zOmfgjVsvgmFSrHj7AG4a1ReFOSGb9C473Yf6iDwhycnHZ+dY17LURosnDJK22f/58dmcvJoX9rtki9ed38tmtgQAc0f2tVFWvfqCEVR1k9r2upn6xSnjFImrMnmnmvIInzOyDyisrQk2Tu55aS8XHjzyehVu+1F/fHnS2t6ZPOxM/OEfH3MVz5YDdaiqacGDEwfZOEhLppZCVcDBdDHdxC8uK+YKIBmJVZR0KsQud2XiiwXP78Edl/Z3tRHru8LsNom21wqLtR9bCd24Zqcn1danElzw+zcBgJvpAHJa8NKpVkB5/qgiG0tp/uh+2Hu02VZXccVoL59pG39su0whhAsxDA98hkV6HYbDDTEOv8vPCEBPZULXR/R2V5598tNdsR32G9EST+LSgV0xrqyQP0PNsSTueWkvHpo0GJpC0CUzyAPsWw7U4crS7jZMxjdFYhWP0/blkNApKj8/gbUzywFKcbwpLk1qI4TgOge/xaRycmRCl5uvN0STPCOWkV/5snHXYYwd0t0VUPzJ+g94QNGvESR0ijtf/DcmlRVi5Nmd7TaKFWXIDKme92Z/e5FO2b87v3usKcbdrZZMLUU4qKKpwc7MWTxhEFRF3h7ivUUDepF66pXERandTP6HxQV4doZFemWBQucebk1zvMMkv6RhugK4j08pxYJLzkIkYbpYPbL+ccovVYVIg8Jn5KbhnrHnIOhT4FMIbr/0LBxtjNvaj0HlYkkTa2eWw6cAzXHLajYrzcdlxOwFIlJMl1WUISfdB91sS/QSk7tYnfekZJHPpMQKIhm2Pbe+e8aeA8M0caC21XO8T1+1A8srypDm1/DC+5/zhL2gT8HqGcMAAj7bnTq8F7+OeF9nQqVCCFa/9zlG9u+MTinRSJpfhUhSZlTh+1/5BAtTWzbO8qV+x1GYHUJQQrK9b1wJTI+xD7TFb5yCCNZv6X75M8dMmmT/pnqQm/NTya2dQhoCPgVndc7An2+8wAbY+zZIrOJx2m4r1TTFcLQxipOtSQzoloFDdRHXbC033S9dNt571UDkZ/ihKiqfveumgafeOYhxZYXSxLKbRxdh3fZDfIlsUgpCCLp0CnJDdPGBFpf162eV42hDDHHdQI+cNKmme8OscpyMJG16aFH7f9+4EmQENfx992GXYf33+lkcIOePGwBcvWIbv4fXUvv5OcNR2xznM0Uxcamgkx+gVoLQUUGayNq4S2YA+2vc+vO8sN9GphSX5X5NwREJ4TYzpCGuUwAmskJ+HG6w+petcnLSfYgkDFvAV1zWi/3g9bnYP4xQ62VA/9CkwRi/7D3M/l5PXJva+5f13Z1jirFp73HMuqgPgr42Iq64Nea1ffT87OE40WpRd2W5MbI8gpOtCVtfMa8Gr+8sGl9iIwqw9tYcOTeEAMcb4zAp5e19twDM27yvBqU9c6QIbTbJyQv7kDQonwRoKS1/a8KAX6AFiyotmTpq6dRS5Gf4cbAuijNyQkjoFE0xd47KjBF9UNNkzyNYOrUUcaFPvfJNNswqR10kKSXUVtW0SOunECug7bzWM9cPxa0bdvMxnpbKiv6mXwL/2VaSHIbZJpVcOf08Tq0UZ2te5NF+ncMWBvvp7bYB1SM7xGWSomH9ry4vRl7YbzOFv+1HZ+FnwkvESWkUt2TqWxPQTdM2a3Ser5vUde8zckJ4aPJgLqNbOH6g1LA+5FNw2wsf2qSYC1/+hDtgsXJ4LaO/qIvgyX8dwDPXD4VfJTgZSeLap7bj5u/3gaZmYe7qSiyeMIjLY9mLacXbBzBvVF8uJezXOQxQ4A+OtpfJNRnhNuBT0SmouXwsctPB+1esqzibk133VFYL1fVR9O+SgaevH4qWWBI5XkZOJsXEskIemPS6VresEA9silJTZtHanjT3i5MRfHykgXs4hHwK/1smnzxcH8WT/zqA1TOGoT6S4Fm494w9B2fmpklluAohWPjyPtw5phj9u2SAUuD3/2hLavuZZDJU2xK3EtauHoy4TqGbBt74pBZvfFLLx1ldSwJrZw4DKEBSq4U++WGc3T1L+oPLAHNAm+sfK5NfI3jm+qEA2iZrtc0J/Oz53TYCr3NyEk3oIKA2iTkABHwqv5dX0D+um3jpg2re3k5C7cKXP8GzM4aipilu+YxoxDObnhA3Jfnrpqx+1eO0VSuZFHyGH/KpnAQpJn15ElN1CUtnzU6MLS20ESUnP7EVV6/YhprmOCjaZubtJQOxe4hbMjKbR+f5qkIwfeUO270nLt9qk9EFfJpUYhjwadj1ZQPueWkvqmpauIyOlYHdoz1SKksyM9GWIX5+UT5vp4ZoElsO1OEHD76NUYvfwg8efBtbUuofJunUDYprJG0vk2tev+p9ZIZ8CApJiLwvUlRSJxmUSULbu25HFqViW1z31HaMfXyLjf7pbBeZrNN5rcxQW/KkWG/RorU9Cu7dL+3D5Ccs6u5VS9/Dhfe9yY1onFtvrK8qnrTGZUbQh2mpceMlw22IJvn40BSCiiet9vaytWU/4nNWV2Lv0WZc/MBbuOHpSswZ2cc2ziYsfw9TVmyDqlgJlcvfOWgbM2K/zRzR2yUVZ9cihGDCsq0YtbhNknzD05UIpn7g2yPwqoqC2at32iiwcx2y2/Zk5cvfOYjJT2yFprgJtbUtcXx6vIVfd/qq95EWkMtzVUVxjbmvm7L6VY/TeOXQ9gYX/84K+aQWjCLLRLT2FC0snYk2E8sKMXNEb/g1BWhHmgm4KY8EwPOzhyM/I9AujVEMGsrKZJiUByy9gm+GYdpmfeya0YTB1Rnds4MI+hQpi0gMfhpmW9sYQmBXFnS8b1wJ4kmdfy6SaJdt3s/ZT7L2yg8HEPRZyU+yILHXKqclrvP28OoHLymmSNEULWFlAXPWll5JlSLziqDN9rNbZpAHj53t4Ww/lvy4esZQqAqxJSyK7cfOZ3whNi59mgKVAJPKCtGvayd0ywzi2RlDYZhtYgfDNHDHxn/zcRlPmqc0jlmAvqggzMvXuVMQK6edx41yRGEA60PDpNJxrCoEyzbvx+obhkJPiTFUhSCgKYjrFnOotjkOw6T8Oy1xvcM+NUyKSWWFGFtaCJNa21kv7qyGpqBdptbSlOxYHG9PXncujjQIplXZQaiKYhN1GKbpYkf94rJiUEqxctp5Lm+MaEJHbTO+tTiDeJy2LwdNsGMUyZsmpVILxl9fMcBSBoHCp7iDW4vGlyCotV1zYlkhKoafybdwRJqlVxCwS+rhFBUsFU9uw51jiqXnd8uynNF+/w8r1d6rTMxy0KfJg79+TcGaG4ZBIcDiiZaJSUI3cMuGtj39hycPRmbQx6maTA7oDH6m+zXPfW8ZxfXWH/bDM+8dxJ1jipHnINGybTLRwhMAD+4z2apsn9ynyuvauVMAJ1oSeOb6oZ7nBDzaqVtWiCeW3XX5AGnAnFLgSEMUmqJg0YQS+Nu51v0TBuGMnBBUQlwWls9cP9T2XTFo2yc/Hboje3fR+BIcbYhK20/MyB91Vj4u6l9g21pcWlGGlz6oxqaIjmuGn8nVQezfll9Tipa4geZYEoS0BXCThlyA4RWgv3XDbtS2xLF4wiAUFYSlW3eywLEFSbR+GJujuq184paR8zuxpMHL5GU/qikEI8/uzOWsrM5x3TIZYlvNeWE/t+2MJAwoBDjRkuDXAey2tExZxUjJ7HckI6AhLS8da24YBlUBGiI6d5hk5wBttqQfp1aA38UW02kbkG6IxJEwTCR0CoVYSXEJnSKgKZ72fqpi7WMHNEVq4/f87OGoaY7hZGsSvfLSUdscRzzlP21SK7h2y4bd0mDcgxMHoTAnjVtvikGwIT2ycNcVxTbbRRZcXfnu5xhX1gNndc7Aum0HXcHmaRf0xiv/PoJRxV0R1IjLMnRpRRk6Z/iRNCiHpuWGA5i20h38XD+rHAoBvjhpuWOJQL7CbAsMmJ3mw0QPTf2XJ6MozAkhM6TBMCl0w0J++BQCJQUzq48kMftZu0Ume9GymbkXLE6EmnUKaFJLzh45IZxsTXJnt5//uD8XIrBZnAXQcAPsVr93CA3RRGqmB2mg+t6rBnJXt4Uvf4Jfjx2A1rjufmn7FFy55D0+biYsd0MGn5pmxQltFqrZQWQGffjli/925XD84rJixJIGjjTE0CMnhBMtCdDUbFiWPyLea+W08wDAMz8hmjTw5cko91u/cc1OLBpvoSucQf+sND//wROvI7PedNbh7ivO4eBB8bvW5MWKS5T2zLV9Z8El/aXlFvvizzsPY2r5Gbayds8OolPQh19J2vJXlw+w5aTIxADs+kunliEzTcOUFW11bs++duehOgw5MxcApPkZLO+IrQyZXcDvriz5WnIbxOM/AWnJoSkUX9ZbP5Tn987lMyavwF9zTEfSMLk2X3aOjAckBpEfmzIE62eVQzcoVAW2mYiVCOXNK4pLgqu98tJ4wHLltHOlwWZVBc9Mzg8HcO+4c2wMGsM0MPvZnZaipKIM2w/UYXjfPPmWTCyJ+kjStar66Q/6wa+puPefH+MXlxXbXgzOGeTDkwdj18GTrqCj+ACz4Ci7Dltmr5x2HvyaAkrluvuaZksm+vDkwQhqipRd9NDkwaApBlXIpyCSMHDP2HP4j544i1tWUYZfXV4M3QDSAyp+8oMi1LcmbefIgsv3XjUQAPDQ5MEwTYq7XvzIVY5fXV7Myx1NGtL6xJMm4g7Z47KKMkCwrxXLkdAN2+yVtavIWYLHFqWmEs921U2K+taEbVbMxAeHG2IuabOmyrdwRL5UugfbK67L2+JYo2UPu2RqKR57o8rGOwr65NnMPXLSsHqGtd10zfAzAUJsZV1+TZnNLlgsh/i5aBEsXv/M3DSsm1mO1e99jlFnd7Gd47XlZlCKnvmdMPmJrZ6/I35NkbKmTPOb5ymJx2kbkG6MmtwM5sbv98WJlgTywwHP4FNGUOPmHcw8xnmOkwe08l1rC4bxaJa8+RlMCigKwdUrtmHyE1ttnCWTtrGIRA7PnJF9pMFVQ7Ax9Qo2J3SKrJAPk8oKMWdkHyx2MGgWv/opHpg4CPdeNRCxhIErhnRHZsiHu8f0x2u3jLBZb6YFfNKgXnrAh2jCwO2Xng2grQ6ygO9/r/9AGnRkQcN5qYC+M8hbVdPCX8BsO8PZ/szo5b/Xf2ALdIvsooRuYs3WL0Cp9XJkwVgvAmg0YWLk/Ztx5ZItaI0beDg1ZlifMmopK0NVTQsqntyOBS/sASHEk6HEJJXiloezPkGfapPIsjIldSoNmAd8Gl8Fie3KAsS3b9zDDazcY1dplyvWEjc4h4tRYEGIa1ze+vxuGCak1xHrTAWbXrEtRXtXWd/euGYnxpX1sNWbwpt7VvHkNk4Odo652c9W2rhTtmtSuUWweH2SQqRPHnYGCjICNr5YTrqf03Rt7UyITaTh9TtiUssbRiyT8S1v8py2Lwci2ISKTBmZbac4k7jnpb346YbdUlaKmEnKZIhO3ktSN2BKDOJF3tFtPzoLQMf8HDHo6hVsPlwfxdUrtmLk2Z0xoGuGtEwtcd3GQ1r0yj6U9crD9FU7bNabXoHxhmiSW3WKdeiWGZTPntqhUbLPRQaQaKl58QNvY9Er+1y8GafVYn0kKWVkUWpy2Whtc5s9rKeVqGAcH/Ip0vbrlhnks/Rlm/fz8RBP6p58oFiyjZ/jZVXpZRWb0L2FBbLPRVm0QuBp7Snjii2ZWoqHX6+ScrjE9hPv1xp3c6OWVpTBrxHp8yS2pWHILWrFvmX1Yf/PzHic9yMKOuzfpCEfi0kP9prYP/PX7sLkJ7aiJW7ieFPUxhebvmoH5o/u58Kti78RMs4Vuy6zOp1YVsjL9G2HAE7bbSVxZgC0vZ1F284eOSHsr23F01s+x68uH8DPr66PYuHLbcHVRCoD9Nrz2zJAgz7VZUrObCAVIajnRdVcP6scT2+xtkWcAVmgbYbBPtc9Am5sxsWsH2V1XjntPH5fxn6S2V6unyW3S8wM+VxWkwte2IN1HvaKYra0+LkYNBQDsEUFYUwWrs9kq8+l9OX7JRalRxqi3EpTZNJce34vXP/0VtuMUJzFudo4RbktzA4BQqKg2H7PzSrHY1cPQXa6H4snDuLxnorhvaCQthmyaKX5q8sH2CwiRavKY02WGVG+h72pV1+3NwbY3yaVl+euywdgXFkh/CrBupnlMCm1MYAA8LHCti697HU1heDB1+wWmY9u+hR3CXV+dMoQz7EoBt5lfeuUWGen+dApqOGxq4cgHNSwv7YVj276FAsu6d9h/3pZxfrasQj+8mTUZSUqs1Wdu7oSK6edhxkX9u7QapfVVbwus2Vlwemv0wL0VI7TduVgtiMPZaYdC57fw003ZPvCjIdUXR/FhPPOgE8l3NBHNuvLDwegEHAmzQ+LCzxnNPWRJKZfYM2sGiIJ6WwqmjTw1LRzsXLaeTBM02Um5JxxtSfvFOvvVSZQuakN8WDb6KbpotQ+nNrzXyrMLGd/ryfWzSyHphKsn1WOv950AZ6bVY6f//hs9ClIhyHpq1f31iChm9BUi3njtE1dttn6Ubv4gbdwvDGGHzz4Npa/c9CTxiuj4DK5Iiuf14pHNylywwH4NQWKQhD0qZg87Aw0RKyA8M0O6ufNo/sh5CPIE8pd2xJHTrofqmIZLy18+RP4VUgNorxWGivePiClnrLVDJM8y2bshACTntiK//Onf2Pv0SarTM0xXDG4G8ewLxpvqa/uHFOMIT2yPGe+saTBOUbsqG1OQDcpwgEVtS1xNEflq6KWuI7aljjS/CoUBTbrTbaS2Vj5pa2v61sTmPrHbWhNGFjw/B5s2nscCy7pD79mJQT+ff6F6JYZlJth+YiUpKybJj9/2eb9/Fmsa7GsRMVckPaeLeY9wWivPpVgzQ3D+IqC2fYC4BDC52aVY/k1ZchPjanXf3oR1s0c9q3wlMTjtF05qEQ+YyjICHCJ4aIJJQj5VCgp9Q87XxZofXDiICx4fg/yM/ypYJ1dwsi2RiYLmbKPTylFbrpPWo6skA+ZIQ1rZw4DpcCjDrPyp7d8jrkj+8AwYQsUrrlhGAggnXERjzrXNLc9fOIs03keBYGmWMwcQsDlqIsnDpKe/+nxFs70aYwm0RBJolPQh1BARWtCx6rpQ5EeUHCiJWmTpYryxCkrvKW8B+simL5qR4f19mI8iTPCrJAPfo3g2RlDQQiBblAYpoHMkA9jBhXi6hVbPcvBEs7E7GC2jfJlfRSZaT6bpFRRgKQBdM8K8NWPU5p637gS1Ecsr2SnHJVlMbPV7bHGGDRFwbxRfVGTYoSlBzTkpPtR0xTHry4v5sqycWU9bHVmY+mXYwbYxrVMUOFkfony5J55aSAgqG22fuy8ZNW8LQJyLlF2mp/zqBRCbKucnHQ/Xnj/C4wr62GbjYursKKCsE1Czl4oTGm25gbrebIAeiZueHonxg7qwrOcmSJq+TsH+bgCrLwKS34qf4a8VsNdM4N4a8FIbr61/J2D/EXN5PE7D9WhpEeOJ/ds2srtWFZRhq6d6H+krP+T46tKWWubY/jiZMTG5xETuhjjxKQUDRFL3hlLGjhcH0PP3DSptO3Z64fi0xrrB/HXY8+xedZ6yS/vvWogVIW4FEAEbRK2X1xWjCMNUdfA6dwpyEFx4jWXX1MGk8KFIHhLYNuI1xG9lxe+/AnyM/wpjbYbYcCWuI9PGcL9sh+9ejBiSVOa2yB6D4sI6Nc+OopRxV2hkPblfOxlLJPy3v3XvbaXwDPXD0VzLOmSVnK+T0UZ8sM+HG+K42RrkuvXGXmTETJ/nbpuYXYI62aW8xeXrBzO88V6rptZjsMNUY6XAOyMKKAt2awlpiPoV0EA27aUTFYtem2LfdczL52POVEK7JRFOyc2SyvKUPn5CXTNTpd+x3lvET/PytEzLx13pSShRQVhHKqLuPlfgj/2upnDuLRbnGDpKYc8hogXX5ptuQN2KfbHhxtwVtdOXELuxchiSOwFl/THbS/s4RLrY40xBDQVBZ0Cnl7U0aQJTbV8K+ojuq0MyyrKkBv2IZowXbknvfPSkTSpdIyvn1WOqhSa+xeXnd0un4u1X7csewD7//X4j5RVclBQbhMqMmUAN+Nk0fgSPLbpM0w4txB3vvhvT2kbM1ixZIV2+8HcsJy/0y0rhONNMdx71UB0zwqBwm3TSKncUvJhDwvBjKDVraumD4VPJTbmy5AeWZxtU9McR/esIB6YNAjHm+KcA9UQSSI3XeMWh05mTHV9NOVMVs3lpT9Z/0G7NpliUDToU7i81kvO58xodUp5F08Y5PpO0Kegttkt+X186hDLAlS18lSccmPxpSZet7o+6tp+bK8cznqalCJP6HcvS8/3Pz+Ji/oX2Oi/DNMta5v+XTK4XPaOS/vbZLoip4p9V9wmdDKJVIVAVYAz8zJs8uH2sp/Z30UFYW5n+fjUIVJJqCjzTQoB83BAg0HtTCOfpiDLr0qpufNHFeFIYwwfH27g45J5rVzYLx8nWhK2fmmPkRUOuu8hWxWxctc0xzF/3S4snVqKoE/h1qOg1JUPs3RqW1lZn5geW04sifK+cSXtZnCzv78Na1DxOG1jDkmd4v5XPuFxg8yQD7f96Cw8MHGQNEA8f3QRXt97DCunnYecdD9e/+lFXEkA2IO/t2/cA02xSyn3p7DH4lGYHeKQu6RhUVprmuPcIP7eqwbCryrQVAVDe2ahd1468jMC6J2XjqE9s7xliYRYpNPnd2PfsWYsemUfRhd3xnOzyjFnZB9srPwSBNbe9j0v7YVuUBsHavqqHbj7r3v5NVWFYGr5GXhuVjnWzRyG9bPKkRv2Y3RxZyx6ZZ/L9pPZWbK90x8WF3Bs+cpp59mM1RuiSZsEkJ0vyiplUt5bn9/NJaSs3lTgZbHzblyzE61xAxfe9ybGLd0KVVE87UfzwwFEEgYWTSjh9QTs0tz2ysHGAKsnAPg1le8ve4kPriwrlMqQKZVLQjWF4Jn3DqIppvPP8zP80BTCLS8JrPjNupnDXNJrkUn00ZEmTFi21SUfdsosWZ1yw37eRyKHy/QQeDwwcRA/X1UIb8ugwDNj6p6b1u5CwKe5JK6Pv1mFrDQ/euelY39tCwwKHGuMYe/RJix+9VNQCim/youRJUrAxX4QrVXFPg0HNFTXW/y0gE/D4terMGXFVhBC3FyvNTu5ZNciN1tyZlk/+jSFZ38zOoDzHNuWqPrt/lyftisHIkl+WTS+hP/Ai0d1fRR+lUiTzADLjMPJGDrZajdh2XmwjpsLOZfEsr1Glri0aHwJtlTVYszgQhfywK+60NC/AAAgAElEQVQRKa/oZCph6b5xJdh58CQ31hHLvXTzfl5u0RSFcXGuE1yu2He2H6jDub1ybBTOJVNLuTzy9o1WMFB2v7f21fB6rrlhGG9jr/Nf/vBohwwkFqBj7eElMhAD7k6OlmxWL6PmLhpfgnCwfSYWi5XIUM3O+4nfT3oxr0wqNc1J8yvS7ZX0gOJqy0XjS7BUxgaqKMOR+lb0EPbJmbWqkyckW2GxbUZ2LS95LVtNL5laikder8KWA3VW0NrjfNUjKa0hksDcNTtTbUkxKeUz0l6/e83GdQ/5akNqsnTfuBJ0Cmr83gzDUV1v55B5/Vaw6yytKIOqANGYLu3HP1dW8+cpPxxw9ZGTdVYQ/nqzozs6TtuVg0zKuuCFPZ4JL7pJpbO7my8uskkS2flsWXnP2HPw1oKRuPb8Xjzpjs2IHt30KS4oym+XbLnghT0o7+tOGpu7uhJxnUoTiY40xvgs6UcDu0rLPbq4Mz9HVdrkmg3RpHSGe2OKOitLghMTmuaO7CO935Vlbd8VVzyjizvLzy8thKYouPeqgZ5Jh5khn60tvczYxYC7SJb1Ir960UZz0uREze7ZFjqkZ146br64yPXduasr8csxA9pJnpTPLFWF4O+7D2PltPPwxq0XYeW08/D33YcRSZjS8dAaN11tueCFPRhd3Nk2Fll7Feak2+ijo4s780RPhmu/f8IgPHL1EGm/zx3Zh1+L8aicdRCT19iYa68tAfkKhBFW566uBNCWezJ3daVnv3uVSVPl57MVwu0b9yArzW97ntg5/hTefOHLn3CWmtd15q6uhGECB05EeD9uXjASq6YPxer3DqFf1068XXd92cD76O0FI/GnueejT34YD08ejA2zh6N/5wxo34JvtHicti8HpyyR7cVrKuEyU8DyKl49Y5hLqsbON0yKvgVhDO1pGbKz2QajhDIrRN2keHVvjS1T9tW9Nadk7+k1M0oa1JZ4w2SJyzbv5+UTSZ1e12+J63x2Utw1A2d1zpB+xysJTiHg5ajxSIwSZ5ZJvU2CW1QQlrdrSuf/zHsHpUmH940rwW0v7LG1JSGwSWTZDK1bZhDrZ5Vjw+xybplZmG2Xr4qzeq8ZPvFIIFMIcNmj/8LwP7zhOYPWDRM/3bAbD00a7Pp+JJ6UXpcQYMyg7qiuj6K2OY5o0uCmQfdeNRDrZg6zyR69xmhRQRhzRvbBI5uqYFKKZZv3Y1xZD76f/cfrynhfiGOUZfF7rWxqmuO2tl88YZCtDosnDJImr1XXR0FBpX3lJRe2rf6EvXfWL49MHmK71iOTh8CnEqnUNprQPRMTWZvphgm/qmDe9/tyKfDSijIoCriUVZYc51ppmFZ7j+zfGdNX7cDxxhgufuAtbKisdo0z9ntBgZQHeQBn5KajW1boW38xAKfxtpIoSZMFCpdMLcVv/+sc1DQnUPHkNhtV1UvxMeN7vfHRkWbXKiKhm0jzsBT0ksaJe41esluTUqx+7xAPPDNpKdAWVPeSX4rXL8gIYM0Nw3hgTayr+B0vG9SkCT4zSveQKIqk02NNlofxnS/+21Y+WbsyK8zD9TFu49o1K4TfvvSRy3eA/WYwkQEhwB/+YVePMDUWk9fGkgbunzAI3YVEQ6+EKdpOAhk734v+aZgURQVhqc0liwM5r3vnGMs7XAyeO7eMxO1HkeLqFfwOaIp77E4txbrth3Dt8J4d9l17Y1Qmu/U6XwFBZpqPJymaJkVzLOn5PLSmXg5sFS/+m6YoyAhptntnhDSoil0KmzRMaIqCNL/GE0yd0nBZ2/xm7ACcaEkgqFkSZ1Y+pxSaXYfhPdhKdfFEize1duYwEJAOx9m+75DEKh6nrZS1vjWG6oY45q62E0DZUZhtSdh+nSIzdssKccexcWU9pOczqF5tc5tVYmF2ECve/hw/+1E/HG9KuCRwGSENTZGkC0PMJIpLppaia2YARxvjrj1LSikef/Mz/PzHxSCp8eMsn9eLj8BCDuek+9C5UxB1LQkupQSAaELH7NVt93to0mBs2PGlVP2x8+BJ/GhgF1TXx9A7P517PIs/yn0Kwjh0ohW3bNjNaZbO8nn1w/pZ5dYSl1hqI1WxDOKnr7THYPLCPhyqs1Y4XTND3PNXvJZTirnzYB2uOb8X9BS4RnQ4c+7/9ikI43hTXEq1jSRM/P4fe5EV8uOGEb3sJNWcEF7Y8QXGn3uGTZ7rjHE479c7Lx3jJJRer/owrX57Y/TZGUO5j7nzOp1SKjdnOc7MTcOhuogNKc5sYBsiSfTIsVDtMrIsI+WKkuJF40vQK8+SqTZGk0joJrpkBhBNWH2rEHB5sUIIOmcGLE9xg8KvEax57yDPF1h+TRmyQ3YSsPj8fnEy4qrPGTkhfHHy1KXhzjZuSNnxevWb6IRHiCVT9msEWWl+KCke0/HGODSVuMjGTgn4n2+84GsnsYrHf6SskiOmmwgHFI4K8NpGcAbHllWUISOoeW6dXPvUdlcyFABE4gaXwLEX8u/+bv0Q3X1Fscve8+HJg7lMbsaFvaUWmz/5QRFuHt3PZnzOZtqsfE7pomlS/EEYjIsnDEJcNzgbSZwxvTBnOA7VRRBJGOgUsoaKeC1mQ1lUEEZcpzYpoSg/XPjyJ3hw0mBuxdojJ82zfLJ2PVxvqYKcD97GOcMR001XgtGi8SWojyQ63E4b0iMT3bJCtsRE1n6H6iJ8pcKkondc2h9vfHyMjxkxYYq9dEM+BTFHWyyrKMPFxV3hF7b4nPU+0hB13e8hQap8KtJS1k7OMSCerxBvIyMAuPef+1yS6QcFiWy3zCAowH9E2VhJ86vS6zJS7pLUDJyNhzsu7c/7lEl5GUad/eCyvxkKm72Mpw7viVFnd0EkYUA3TCTaCeh7kXm9PpddR6TJMptVlnQZ0BQ8dd25qI8kbc+uqliY9NnPtr1E2EuZjdHf/6NtAnj3FQPw4eEmlwQ8oRv4ro7TNuZgmsD6bV/w/UEmsQPaZHsyed6c1ZWeElKfaqEFVr5rSeHO752LkE/FvFF94VMVZARVfHy0CaZJuQRuzsg+nETqtPdkM8CCjIDUYpNScGN0sXxOCaQoXfxDKkuWBXKf/NcBqIriCmTOXbMTummpQv754VEoxMrAXTi+BDsP1kFTCHYerMPC8SW4eXSRqxzz1u7EkcYYlzpqKuHSRVn7+VXFs11ZUNNpQamnDOh/87ePUNozl9dp5bufIxzQpBJZUWqaNNx2r3NWV8Kk1uy54sntNpJqQzSJ5e8cxNUpGSOztmTfvXHNTgBEes2TkYTLZpX1C0uCdN5P9QieO9vG2U6yMcDOkVm9/rC4AF2zQujcKYgHJw3GwO6dbJLppG5yqbKMXju3HdmtGJAWx4PYp2NLC3kgfNGEEsSSlpugTBgwd3Ul1NQyOehTEE2aIITgLzeej7cWjMQbt16EtxaMxF9uPB8UVjLrss37be3qlJmzz4lHYFukyTKb1al/3AZFIWiOG/jjO59j0hNbcd8/9+GTYy2gsPxA/rqr2uqPDuxUmahDZtH6bfOUxOO0fTn4tDZpqkgSnVhWyAmgh+uj0pmESamUx/LeZ7U8KNy/S5jTPy9atBmTntiKsl552Fj5pS1o6zUjTBhmuxTY+8aVeAazmQTSeb5hGFKujiKQIsXr6AblRjusHozQmhlSuLz2cIO8nWy2mmi7h0gGFYmr89fuktbTK6jJclRkdfJrxBWsv2lUET492sTvF/cIHicNOUNKLIdXYlN7XgZ+Tc7x+fhIo5SGuqWq1sb3aY+b5CxfTXNcev7Dr1fZPmdZx1NWbMXI+zej4sltONGSsHhCqb7edegk7y+nzwgfK6acPuvsO9nnqtLGe7r4gbc5AdaL6mtlTe8DAPzs+d24aNFm3LRuF440RLFs834caYjipnW7bM/1kB5ZvF1lZNpF472FAYZp8u9u2nucl+NwfZTTU2+9uMhGV578xFZcNqg7JpYVntKqjwJYce25tnuvuPbcb52nJB6n7bZSUndLUxkNlW0zeAWMFEK4LFWkTi64pD+q6/+N2zda13HNsFLxDfG6njRQwXC8ur6NAiuSKh+dMkRePoXwQLVfJfz8heNLpDJBL9qqohDMHNHb5rLF6vHcrHIXl975/YKMAJcD/koI2opkUNFly6ueXpwkyzTJbczO6LcyiayIw2jPOhKwZJzdMoPScmgeNE8mS3V+Hg5oiCapdNyMK+thExaINNSJZYU8aKum+D4AcLTRIrcumlAitWutaY5j2eb9nPT6+Yk24qdIgFUU4iLqijRQRha97YU9uGfsOVyOK2uzjixku2eHpLJvL0KylzBCU4jnbFzEroifM7TNY29U4a7LB0gD1eGgDw+89qlUcHDnmGI89kYVxpX14AgZcVW0dma5ZzseONEqrYctQE8IzuqcgT/feAESugG/pn4nvtHicdquHNqbdbPPZdRJJjGUyVKZzy27Tn44YNvWyA8HkBXyYdnm/XhsyhCsnHaeixbJXLZM0+T0SwA2WezZXTNw1+WWnaVI7WSgMMOkuHRgV7TGk9i6/wQKc0K4eXSRa1bbJtujNlIkmyX5VHhKYfUO2mnR+BL8dMNuTrUV/SkM08T8FKnUKX0V6+kkcjr9Ela8fQCNHolIXt4GoizTy7/g0U1VXMapqcRWDtY/ukFtkueOZp8Jw0RjakvCOW6yQj5sOVCH400xmKaJaELHlgN1AIDsNA0BTQE1KeI6RTRlas8y2hc8v8fVTo9PseS7onzVSRJNGCYIsX4A8h3JVdZsntj+zs/wcx6Us82Y+xub/S94fg+nr7JzlqVWSHlhv+3zBycOchFa2bj0a1ZMcPb3etr6R1UIskI+6fPllfjGXpbjynpANyl+cVkxctKsWXmnkA/dsoJQCFwE3XnfL+Jij9rmhOfqp71EvFNZ9anEMgHLzwige3Ya8jMC3+mLATiNVw5eFEWRXCry1p3eDrLvMt04GwQyJYOZCkaLjB72o+5TCU60JGzBPqd64XCDdU+nGX1CN5AwqM3CcnlFGUaclY+61uQpy0bvHFOMWNLyPijvk4/CbLmXhNh+znY60ZLgnCYCoEtmEIbZlrBnmkDl5ydc3hbi9b1kiA9NHgxCCNa89zk2VFZjdHFn6ff9HvJLcbXAWFGyGTs7X5SvdssMwqTuYOz8UUWgAPIzAjBMKp19/vzHxaBUvlIRV1h3XzEAumkxh/p3DbuItUunliIrpEjb3gmqY2MuJMiovSSuoo9AYbbF+GJ/p/tV/Pfofpj6x23IDwdsAopIwkDQp9hWg90ygzxZTJS1lhRmoSmWtH2emeZD0Nd++ZZWlGHahb3QEjPwwvuW6sukVPp8eUlhVYVI683ov2yVyZ7FSMJAQFNcbXlmbpp09ePzuK+mKvjV5cXID/u5LTAF+PNR0xzH01s+x++uLOn4R+tbPk5bKeuJ5hiONsZsEtLHp5SiU1BFJGFitoNIymiZnTODMEwL3vblyajNrPyPb3+O7DQNFcN7waAUuuTH5v4JgzjTiA0kRo7sWxD2pHDuPFhnXTeFd3ZeV9wuEb8rLndPRTbqlB4yBLIoo11aUYbskIaTkaTtcyfVlr1QLeP4AWiJ6zhcH7OZ1E8qK8TIszu75KHZaRpOtiY95bsMEyGTE1qKMhWNUd320N9x6dloTRhI96s20ucvLisGpRZSRYSoPThxELLT/ahtjrtIp87+OVrfiosHWJTZSMKwjY2cdB+y0vz4yfoPXFTX7tlBxJMmFGLJGg1KORn0/omDuIJMvJ8oWWVlXTK1FHnhAO7+q0VGzQr5YFKL11WQEYBCUjjtsF9KFL5n7DmYvmoHb7/MkIbPalpRmB1EekDDxOXWGPKS1K6fVY76SJJvnW6s/JKXoyGaTOUDDMCxxhjiusFVWYw6XNMUwy0bdnu2MdsuWjn9PLTGdGQEffjipJv8+tCkwUgapuul0S0rhLVbD2L8uWdwxR970YjbULLnRvx8xbXnci9523gLaWiO6lyYwcbxSx9Uo7RnrvR5Z7kuZ+amoWdu+neyUviPlFVyJFIZU4xFT6lFQxWlpVlpPpcOmc02GKFVnNHdfulZONIQt830GH9pQ6WlXCjICNiCeuIPthehtKR7J3TLDLV7Xa8sVjGr+VRko0x6yGij7AXE6KuEELy4sxpDe+faqKxM1gq4qbb3jSuBplgzc+dqSVUsZPWq6UM5vtqvkZRM9JBLvjtnZB++R89+JH0q4bMya8ZH8Pgb+9EQTeDZ64fCrymojyRdM/4Fl5yFaNK0rbaWpFYCJ1oSyE73I82vSkmnYpud3SWDS2LzwwH8/Mf9XeRW9tw7qa4PThyErfvrcFH/Au52x9rMSyggSlbZSo9Jm5n02osP5WU92isvHW/+7CIQEPz+H20vnWUVZQj45Dwq8fu1zXG8/OERrJ9VDpUA4YCbj2SCQjdNm0fEfeNK4NcIlzn3zk+XXt+vKVg7cxiaYzpuWrdLurKuro/ynICHJg2GYVIuU11+TamUjeZT5dumXjSANL8Kn6rh2RlDoaT2m9hLmtEUFAJoqoK8NB9yL+hteza9Vuym+e16NZzKcdrGHCgF5q7ZiemrdkA3KK59artLWlrTHOefA23BrZkjeruM3Oeu2WnRRtfYZYw3rtmJ+aOLbLRRkX4pMn285IomhfS6M0f05rJMnwf50Us+KZM0ilscT/7rACdTbqisxvRVO6ClgpeLX69CQzTJ5bUJ3eQyPBmX6faNe5A0KGY/21YHJgckIJiyYhsufuAtjFr8Fi5+4C1MWbENJoVUvstIoFsO1MGkFmZg4nJLAszontNXvY+ZI3rj1b01uOap7VY7O1lEKdmpLGgdDmhIGCYWvrwPuglOOtXaYSCxlc+ckX1cY+PW53eDEIIHJg5yUV1v2bBbSmW9feMezz4VJat7jzbbpM0d8aGCPlUq8U0aJgyT4vMTrdzFjd1DJR1LasMBDfURHbGk6Ulo1QjBynfdLDDDBJc5e8lJVYXg46PNtjHErisSVAmxJOB1rW1OdPkZfiQNORvNi7Pk9XwkdBMPvVbFs/EP1La116t7a1Dx5DaoCkG3rBD8fs1CXwjXkj0fc1ZXoiYVh/nfdJy2LwfRelKcTZ0KY8cr6OUtjTS5lBKgtgCueA+vALhX8FxTCZdl3rxOLgOV8WXuG1cCnbrlrmIQ+bqUH7a9HKZUWikGdr3azKsOXm0WSxou2SeTErLyRBN6u4FA9rcX70hrJ3jJ2iCoER4892pj0TTeq/51LYlT4k7Zy+cO/joDoUw95BwnXuUwTNMl8Z0/uh9eeP8Lm4yUCSGq69sIw85+F8ukKeCSZ696GqZcdizKnCnc43LJ1FL4FOIpo2VB4iVTS6Epbt7YTaOKbH0kfpcQdxsvrShD0K9Inxu/RmzSbll7OTfqA5rS8fPxLXs1nMrxnb8cCCG3EkIoISQv9f+EEPIIIeQzQsgeQkjpN3FfcTbEjNIB+8zIa5Ykeg2c6udspgIQWxCRMX0A2D5//acj+MzKaxbjUxUp1XHzz0by7yYNObl139FmLp98e8FITppky/PbN+6xUU+f3vI5EjqVUjtv/H5fBH2WsqR7dkhaVq8EN682C/pUF8X2sTeqOAn06S1Wopuvnevye3vMwL0+F305xEQ5sY3F/jGFBDCvMXOsKfaVx5MuMKveSvWRMxCanxHgn3sRZ+39IEl4XF2J0p65/P+ds3GRMPzgpMGcliuODVVV+XXF58l+byJdUbB/BywfChmJNmlST2Jyt6wQHx+6KV8NeiXpgYKPaZHw2xo3+bPISLbWs+huP2d7se0mdlChTl7Px7ft1XAqx3daIkJIDwA/BPCF8PGlAIpS/80CsPSbuLdCLBrl6z+9CP06h7mUU5wZyWZJ1l6/PMlMZvzOJJeANZAiCSsju7YljtywH0nDsMlRa1viCPoULHh+Dw/WaQpx3W9ZRRlAqVTuWh9J4J6XLOyGTyWYP6pNnpdIJnHX5QPQv0sGbr64CDsP1oECLqljdX0UTSlz9I2VX+KOS8+GphCMK+vBM06Z3NOkFD988B18b+Gb+PhIo3TGryltNNSO2mzxhEGgVE6xFWf1JqXwSRLLWJuze8STbgrn41MsqW5HM3Pnioe1cV1Lgst0t1TV8jLIxgyjk3qtDJ1t0CaXNTFp6JlY9Mo+NEWTUsmqQoDe+ekpYxlIV3bs/PZiDiwhS/x/sT1Yvf0aQciv4o4/fchn5tMv6AXDbFv9yOq5NMUZkq4oKOWUXlUBflxixQZGLX4L01ftwIizOgOUImGY0vZriCT4+HDGCrhc25TLtQkB3xZiR21zAo3RJGpbLD/sbftP8GdAN+3PnLO9Fk8YBNUROshN9+O/Sntg+qodeOT1Kumz/G17NZzK8V0HpB8EcBuAF4XPxgJ4hloyqq2EkCxCSFdK6dGv88Z+TUFSp7hhTVuQcllFGXLTfTApuBVhUFMsE3hKQak9+CQztWcJRr3z06XSyLywH28vGAkA+K3zWsQCjZmU4pGrB6MxqmPqH7dxtRIL/gJ2VY1T7poV8nGT9v21rdi87zjWzSxHOKCguiFuM/FZWlHGufTiQ8Wus+WO73corxXloa/trcFNo/ti1fShUBWrPmKbPXP9UKhKG0HW2WYHalsR9ClIGh3LPq8eahnJPzq5hPeXplh5CTeN6otLB3Z1UU8LMgI2kYFXP7L7eUmeu2dbCh1fan85I6jaxszyijK0xHUu9QTgkp3Kxk2fgnTUt7qD5/kZATyamuE668DGriGsNCz7T4LHrh6CnHQ/CCHQVOKZ+McSssT6yVYqHx5uwsbKL7HmhmGpIDvBscYY/KpcXts7P53beRqmXLasEILGSNImVmDUXCb1vOvyAcgO+fDw626ZsEhBFcnBpyLXvv7C3lJJbEHYj+dmlQOE4sJ++ejfLbPdZ6BbVog/cz4HXltR7Alu6QEVG2YPh26Y0FQFBeHAd4Lk7uj4zkpECBkL4DCldLfjn7oD+FL4/+rUZ1/rEUua/GFjy8lHNn0KkwJ1rQlMemIrLlq0GeOWvYcvTkZAKWyWgDygSoBcIbGHzTZ8KhARkpn43qlK8PGxZkyRXCueNDH5ia0YsXAz9h5t5kHUZZv3Y8RZFg/+y5MRlzUhW9ayQfvTDbsxfdUOzFu7C4ZJOQ/IyyTGMN24iEXjS0BT7eS1jGbnrXj7AP/+zBG9eYD50+Mtrja79qntME0TeRkBV5tt23+Cl7sxmpCWSUyse2RTFQBg/nrL18FIsaA+q2lB0KdantItVvLT9At68aQ7UWTA2l5TiCtBa+nUUmgqpCuTR16vsqxHl72HfceaUXrPJkxKraLGLXsPlz36Lx4gn7d2F24eXcTrmhv2w6Cma9wEfQq+qItg3lo3uyhhmDbPDKdQYs7qSgQ0hStyRi1+i/N/Qn4VV6/YiuF/eAPz1+6Srm43Vn5pa+c0v2LrI/aDXZARwLiyHvjd3/dCSfGlJix/DzXNMVt/sfpYK5UIZjxdiUc3yWfNCoEtiM/GCVsl3jSqCCda4tAp5f0orlpYMtniCYNsqzCv4O/eo82YvmoHLhvUHQal0sB9NGng13/7CAmdImlQ1LUkeLKg7BlQFUsB+fibn9mQ4uwQE9yy0gLolhX6Tr0aTuX4RlcOhJDXAXSR/NMvAPwc1pbS/8v1Z8HaesIZZ5zxFb8rtyM0qbWvzmYuhkm5PE62JDYpkOZXbTPX1z46irtf2sdnpWYq52HF2wcwb1Rfl8ENu5YY6PYyhReJpuJ3+3fJwJ1jil1UR19qL7O9oHDSg1754OTBUDyCtv27ZGD1jGGobY5jXFkhFGLx6intODirKAoKMtraTFUItlTV4rY//Zufk5MesJFKk6mA3UMp4uX8tbtc22CqQrjhvZO8GQ5qXPrq1QYAbAlaAGCYgF8FJ7EmdEs2ylaD4pZMdb039bRXXjreuPUiLsmdN6qvrQ00haCuNYFOHm1GKbi01++Rtd6aMKTy33mj+krHUv8uGVAVgkdet7AQMy7s7SLQrp9lJSrWNrtXj+I2UUKXj6HFEwehKZrEvVcNRI+cNIQDVp0Nk9qow15jbOW087iE2TTB71GQEUB2mpWDcsel/XmiXVVNS4dybfG6YtuI5xBisblEkqpTNltUEObj7Y5L+3OLUSp5Ofz/eHyjLwdK6cWyzwkhAwH0ArCbWMGbQgA7CSFDARwG0EM4vTD1mez6TwB4ArCS4L5a2eRyu7Uzy/Hq3hrsPdrs4rjLlsSEEExL+Qo4E4Rk17lpdBGqapql1xKX/E5eEZOgPudRDlUh0uQhm0G5VxanQKkUP0/qJv9b9h3mdZEV8uFkJIEV7+y3Gd94MZcIIRi31Nraeu2WEdIkJEYqdX6+dmY5ogmDz2jFf2OBwPwMv2edvPpRVQhPiBQ/f25WOWY8XSntX1kbexkifXK8mZelMDuE0cWd+ZhYO7Ocb/W9dssIz/JtOVCHDZXVnudowjni5/99cZF0LN05phi989Kl32EE2r//+ziem1XuWs3cvnGPbSw2pPbone3NpMesLcc+vgXV9VG8fdv3MTWV1zGurIe0PvuONfPvji7ujN556fwey68pw/xUvoP4Hfas3fPSXqydKe9r8bo3jS7ybG/Zill8lsW6iSKGDbOHAwBMk6KuNXHKrKSvev43fXwn6xlK6YeU0gJKaU9KaU9YW0ellNJjAP4K4NqUaqkcQOPXHW8A4CmBbEw96GKQyUnIBNqW4y/urJbKUsVritdJ6IZnsFDcnvE6510h+Mk+X1pRhi1Vte3yW5g8T/bdkMfnL7z/hZQ/xOwSZbJE8VpedXhxZzW/phffyDDlwcdIPIkX3v9CutXz6KYqLs3MDfml53gFzKNJQx4sNWmH9WFt7OxDsb3EbRtn0NvogPfE+pe1h9c5ScMt/2Wfy2TLyzbvl7alq3weqy2TtvGyTuusPxMAAAx9SURBVIUfpChtW3StsbbAuBebS2xXZ1k7etac48zrurJzmOiko+t7EXEppTBNik+ON+PKJe/igvvexJVL3sUnx5theqwqvur538bxvwKfQQg5COBcSukJYi0lHgPwIwARANMppR1yMb4qPuNIQxQTJc5VztUCAZDmV9AcN9EcTSLoV3m2Lws2s3T4os5hTnQVr7luZjliSYsLc90FvUEpRcivQjcpkroVlAKliOkmgpoCEIK4biKhGwj5rPMYUmHXlw249eIi/FdpITeciSSSaIjoHJeQF/YjkjCQnxFAQrfQDH/ZWY1+XTshkUxiyJm5MFLbObsO1aH0zFyEgwpaYibf4shL86MpYSCaNBBL6gj5NH6/eKpckyR1fW5WOXJDftRFEzw4q5sUcd1MZY4SGCaQ7lcR1637hfwqErrJyxTQFMR1E78RViYMtfCryweAADjeGEFepzRQSqWB/z/feAGyghpqWuLQTavcrfEkApqK9dsPcYwCBRBLMX5kfbd+VjlCfgWxRKqsPhW6YSJpUvgUAk1VEE0a8GsKFFgmUuI5mkKwv6YJXbPS4dcUWz+yezhJohPLCjHroj7wqYT33eLXq/g4652fjnBQ5WUSt+VWXFOK4m6ZvB87hRScbDUQSgX5dZMinjTQKeRDPGWURKkBhbjHmdinXn0trh5FtzNGjVUIsfVdZkhBU9RakYrXZHXrk58Og1I0RBJI6NbYYONYbMugT5XiYtbNLIdhmnychQMqYkmrnXwKgaIQxHQToJSf0xLTuQtiR1iN9bPKAQAtsSTS/Br2S9rrzzdeAAC4csm7ru97ObvVNse/0vlf1/G/Hp+RWj2wvymAed/0PQvCASyrKLOxUBiArDDbYqh0ywzxZV0kEcFlj/4LALjHAQs217bEkZ8RQOdwACuuPRczn3nfds2b1+1CbUscK649F106BU9pqchmEtNWbsWjVw/hpFIAWPx6FRa/XoU/33g+YknDpbRY8Pwefr+i/DCqalvwXGU1RyqIzmcrrj0XXVP1zLTLr5Ef0Hg5RHTHimvPhV/1ptoGgxq6BzXouol9x5ttbbysogz9O2fwIBy7vthm940rwYu7/m97dx8jR13Hcfz92bvdu+ud9HgqSIulSoEQtVCUUCGKxSggAQ0iEioErRKiCEZjwH8Iif5BfAa0gfAgBoMllWhTE1RKE5sgVR5EkFbAAkJpuWvxKL3edZ++/jGz29md3ete77Z7nfm+kuZuZp9+v/lO7zvf38z+ZkvsnFAlJgA7x4vBt7UvWcSldz4ea0e+WCKbDU4ARtvxo0sWccf6V6o36al4+Lqz+MXli2NzOd36yIs8tnlHzV3o6u/t+8bIbjYPjzaczO704war972obP/6mWYr1UYljo9t3sFnF8+tTgq3Mkx6w7v2cPTsXo4e6OGF7aNcc/+T1bmpbl0XHLnevGZjde6rM29ZV7O/Ruei+tJ9T9T08/ZHNzHYl2PZkvk17atUu7dc/MHY+TkJrj3nhNi8WFYu0d/TXbO+flse8a6emvcc3hXcV7xYNkbzJa594B8177lxywiPbBri60sXVrdldJvV/1+LTqrX7DaeK5adxraR3Rw1u6/mLoiNYlLpc/Rc14Ybl3L07N6a7VW5B8PWtxvf46TZnd3yxcaVayfvBDcjKofpMNnKAaBYLAdHlqUy2a4MuawYz5cbjvfVZ/bo0V02cjladNww252hOyPG8vs3hhh9r2ZHbr25DPmCUZigD9H3iVYsrbap0Vjotp3jDSuvB69ewjGDe7NMdBs3u2yv8v5jhRL/GdpVPRKrHk3OGaAvW9vWfW2b+iOuSjvKZs0rhGyGPUWrtnVWLsPonn3H8Y2RsZoJ7wqlMj3dXcw5pIdsOMfO9t0FiqUyvbkuiqW98erpFuOFMr3hZxdKZXJdGTKiWklKCtZHPjufLzI8GlRnA71djOWDyisj0ZMV+WJtP6P769DOPTWT3z31yg6u+MgCimWrOYqOVlXNqrhD+jK8M7a34kTGWL7MQE8XpTLV9dnuDGP5EoJqEmj2nn3ZTPVovysjCqUSQzvzNW2tzJtUuUsbBDMdzO7LUiobu/YUGZyVoz/XeLtGK7LotolWgJVtP14s1+yX0X3s8P5cw/MEk60EZmLlkOrkMBmNjnDrjyCT+NnNtFIVTNb+9HOyr2nHtmzHtmhVo/7U35c52qYT5wzw0vbRhv0HGm6bSvU50TaLtqPRTLn177O/z1mx7DRuW/tCtcqpr/Tu/+urPLZ5x4QxLRRKbBraFat4TpozQDYbvy3nwbpftsKTwzTp5NUEM+1KBmitKpis/ennTLgqpB3bolXNqtX+nmD6+fo2TdT/Zo+1ss1aqVAn+5xoZdzdleHI/hwj48Xql8mi/atUeq3EtFAoVc9HdWfEnIGehomhUd8Opv1yXzw5OOeci5koOczMr+Y555zrKE8OzjnnYjw5OOeci/Hk4JxzLsaTg3POuZjEXK0kaRh4dT9ffgSwfRqbczBIY58hnf1OY58hnf2ebJ/nm9mRjR5ITHKYCklPNLucK6nS2GdIZ7/T2GdIZ7+ns88+rOSccy7Gk4NzzrkYTw6BOzvdgA5IY58hnf1OY58hnf2etj77OQfnnHMxXjk455yL8eTgnHMuJtXJQdK5kv4t6SVJN3S6Pe0i6VhJ6yQ9L+lfkq4L1x8m6c+SXgx/Htrptk43SV2Snpa0JlxeIGlDGPOVknKdbuN0kzQoaZWkTZI2SlqS9FhL+ma4bz8n6QFJvUmMtaR7JA1Jei6yrmFsFbg17P8/JS2ezGelNjlI6gJ+DpwHnAxcJunkzraqbYrAt8zsZOAM4GthX28A1prZQmBtuJw01wEbI8u3AD8xs+OB/wFf7kir2utnwMNmdhKwiKD/iY21pLnANwjuQ/9+oAv4AsmM9S+Bc+vWNYvtecDC8N9XgRWT+aDUJgfgdOAlM9tsZnngN8BFHW5TW5jZVjN7Kvz9HYI/FnMJ+ntf+LT7gM90poXtIWke8GngrnBZwFJgVfiUJPZ5NvBR4G4AM8ub2QgJjzXQDfRJ6gZmAVtJYKzN7C/AW3Wrm8X2IuBXFngcGJT07lY/K83JYS7wWmT59XBdokk6DjgV2AAcZWZbw4e2AUd1qFnt8lPgO0A5XD4cGDGzYricxJgvAIaBe8PhtLsk9ZPgWJvZFuCHwH8JksLbwJMkP9YVzWI7pb9xaU4OqSNpAPgtcL2Z7Yw+ZsE1zYm5rlnSBcCQmT3Z6bYcYN3AYmCFmZ0KjFI3hJTAWB9KcJS8ADgG6Cc+9JIK0xnbNCeHLcCxkeV54bpEkpQlSAy/NrOHwtVvVsrM8OdQp9rXBmcCF0p6hWDIcCnBWPxgOPQAyYz568DrZrYhXF5FkCySHOtPAC+b2bCZFYCHCOKf9FhXNIvtlP7GpTk5/B1YGF7RkCM4gbW6w21qi3Cs/W5go5n9OPLQauDK8Pcrgd8f6La1i5ndaGbzzOw4gtg+amaXA+uAz4VPS1SfAcxsG/CapBPDVecAz5PgWBMMJ50haVa4r1f6nOhYRzSL7WrgivCqpTOAtyPDT/uU6m9ISzqfYFy6C7jHzL7f4Sa1haSzgPXAs+wdf/8uwXmHB4H3EEx3/nkzqz/ZddCTdDbwbTO7QNJ7CSqJw4CngWVmtqeT7Ztukk4hOAmfAzYDVxEcCCY21pJuBi4luDLvaWA5wfh6omIt6QHgbIKpud8EbgJ+R4PYhonydoIhtt3AVWb2RMuflebk4JxzrrE0Dys555xrwpODc865GE8OzjnnYjw5OOeci/Hk4Nw0k3S9pFmdbodzU+FXKzk3zcIv3n3IzLZ3ui3O7S+vHJybAkn9kv4g6ZlwuuibCKZwWCdpXZPXzA+nVz5CUkbSekmfPLAtd25iXjk4NwWSLgbONbOvhMuzgWfYR+UgaTnwKeBvwPFmdvWBaK9zrfLk4NwUSDoB+BOwElhjZutbHVaS9EfgeOCUcCp152aM7n0/xTnXjJm9EN5h63zge5LWtvK68IT1vHBxAPDk4GYUrxycmwJJxwBvmdl4OE34cuB9wIVm9vIEr7uN4N4DrwKXmdkFB6TBzrXIKwfnpuYDwA8klYECcA2wBHhY0htm9vH6F0j6GPBh4EwzK0m6WNJVZnbvAW25cxPwysE551yMX8rqnHMuxoeVnGsjSRuAnrrVXzSzZzvRHuda5cNKzjnnYnxYyTnnXIwnB+ecczGeHJxzzsV4cnDOORfjycE551yMJwfnnHMx/wdpjpE6akqVRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib version of plots. \n",
    "# df_game_plays[preferred_shot_conditions].plot.scatter(x='st_x', y='st_y')\n",
    "\n",
    "# Seaborn Version of the plots.\n",
    "sns.scatterplot(x=df_game_plays[preferred_shot_conditions]['st_x'],\n",
    "                y=df_game_plays[preferred_shot_conditions]['st_y'])\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Note: Only run one of matplotlib or seabron versions of the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that this is basically the shape of half of an NHL rink (rounded corners).\n",
    "\n",
    "We also see that we can't really take shots from inside the net which explains that little white area in the middle near 100 st_x and 0 st_y.\n",
    "\n",
    "Let's take a look at hexbin (in matplotlib) plot to see the frequency where shots are taken, because this is just a big blob blue dots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib hexplot\n",
    "# df_game_plays[preferred_shot_conditions].plot.hexbin(x='st_x', y='st_y', gridsize=25, )\n",
    "\n",
    "# Seaborn hexplot\n",
    "sns.jointplot(df_game_plays[preferred_shot_conditions].st_x, df_game_plays[preferred_shot_conditions].st_y, kind=\"hex\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows the range of how long it is on the x axis\n",
    "# This is going to be half of the rink (offensive side.)\n",
    "df_game_plays.st_x.min()\n",
    "df_game_plays.st_x.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-42.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the range on the y axis\n",
    "# This is going to be the width of the rink (on each side of zero.)\n",
    "df_game_plays.st_y.max()\n",
    "df_game_plays.st_y.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NHL Home plate shots (high danger scoring chances)\n",
    "\n",
    "Below we're going to take the knowledge that we just learned (what we're about to learn) to take a look at what is the percentage of shots that go in the home plate area vs outside the home plate area.\n",
    "\n",
    "Below we're creating a function to check to see if a given point is in the home plate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Example of what our highdanger chance looks like\n",
    "\n",
    "What does the home plate area look like? The homeplate area is the trapezoid in front of the net.\n",
    "\n",
    "![scoring area](images/scoring-chance-zone-rotate.jpg)\n",
    "Source: https://edmontonjournal.com/sports/hockey/nhl/cult-of-hockey/how-scoring-chances-should-be-counted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NHL Rink Dimensions\n",
    "\n",
    "##### Rink Dimensions\n",
    "https://www.researchgate.net/figure/Standard-ice-rink-in-North-America-with-dimensions-in-feet-imperial-unit_fig8_309175794\n",
    "\n",
    "##### Goal Crease Dimensions\n",
    "https://www.researchgate.net/figure/Standard-ice-hockey-goal-and-crease-with-dimensions-used-for-the-rehabilitation-exercises_fig1_235604306\n",
    "\n",
    "##### My calculations\n",
    "\n",
    "Here's below is the calculation for my home plate. \n",
    "\n",
    "I'm hypothesizing that there's a \"jam area\" where the sides would score, this would only go to the trapezoid.\n",
    "\n",
    "Below is the length dimensions (back board is zero)\n",
    "- 11 feet from the back is the goal.\n",
    "- 11+20+15 = 46 feet is the top of the circle (top of home plate)\n",
    "- 11+20 = 31 feet is the bottom of the circle (bottom of the plate)\n",
    "\n",
    "Below is the width dimensions of the goal crease\n",
    "- 3 feet from center (on each side) as center of goal is zero\n",
    "- 22 feet is the width from the center of the ice to the faceoff dot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_plate_max_length = 46 # this is the topmost part of the home plate\n",
    "home_plate_mid_length = 31 # this is the start of the line to the end \n",
    "home_plate_min_length = 11 # this where the goal is.\n",
    "home_plate_min_width = 3 # plus or minus since the goal is 6 feet.\n",
    "home_plate_max_width = 22 # this is to the dots.\n",
    "half_length_of_rink = 100\n",
    "# points will be in x and y\n",
    "triangle_points_length = [\n",
    "    {\"x\": half_length_of_rink - home_plate_mid_length,\n",
    "    \"y\": home_plate_min_width},\n",
    "    {\"x\": half_length_of_rink - home_plate_mid_length,\n",
    "    \"y\": home_plate_max_width},\n",
    "    {\"x\": half_length_of_rink - home_plate_min_length,\n",
    "    \"y\": home_plate_min_width} \n",
    "]\n",
    "\n",
    "\n",
    "# a function which checks to see if a point is in a triangle.\n",
    "def is_in_triangle(x_test, y_test, x_one, y_one, x_two, y_two, x_three, y_three):\n",
    "    as_x = x_test-x_one\n",
    "    as_y = y_test-y_test\n",
    "\n",
    "    c1 = (x_two-x_one)*(y_test-y_one)-(y_two-y_one)*(x_test-x_one)\n",
    "    c2 = (x_three-x_two)*(y_test-y_two)-(y_three-y_two)*(x_test-x_two)\n",
    "    c3 = (x_one-x_three)*(y_test-y_three)-(y_one-y_three)*(x_test-x_three)\n",
    "    if (c1<0 and c2<0 and c3<0) or (c1>0 and c2>0 and c3>0):\n",
    "        return True\n",
    "    return False\n",
    "    \n",
    "def is_in_home_plate(row):\n",
    "    x = row['st_x']\n",
    "    y = row['st_y']\n",
    "    \n",
    "    # check if in top rectangle (top of circle to dots)\n",
    "    if ((y<= home_plate_max_width and y>= -home_plate_max_width)\n",
    "        and (x >= (half_length_of_rink - home_plate_max_length)\n",
    "             and x <=  (half_length_of_rink - home_plate_mid_length))):\n",
    "        return True\n",
    "    \n",
    "    # check if bottom rectangle (net to dots)\n",
    "    if ((y<= home_plate_min_width and y>= -home_plate_min_width)\n",
    "        and (x >= (half_length_of_rink - home_plate_mid_length)\n",
    "             and x <=  (half_length_of_rink - home_plate_min_length))):\n",
    "        return True\n",
    "    \n",
    "    # check to see if is in the trangle above zero\n",
    "    if (is_in_triangle(x, y,\n",
    "                       triangle_points_length[2][\"x\"], triangle_points_length[2][\"y\"],\n",
    "                       triangle_points_length[1][\"x\"], triangle_points_length[1][\"y\"],\n",
    "                       triangle_points_length[0][\"x\"], triangle_points_length[0][\"y\"])):\n",
    "            return True\n",
    "    \n",
    "    # check to see if is in the trangle below zero\n",
    "    if (is_in_triangle(x, y,\n",
    "                       triangle_points_length[2][\"x\"], -triangle_points_length[2][\"y\"],\n",
    "                       triangle_points_length[1][\"x\"], -triangle_points_length[1][\"y\"],\n",
    "                       triangle_points_length[0][\"x\"], -triangle_points_length[0][\"y\"])):\n",
    "            return True\n",
    "             \n",
    "    \n",
    "    return False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new column on our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this function created what we can do with pandas is create a column (true or false) to tell us if a shot is in the home plate or not.\n",
    "\n",
    "The \"apply\" function on a data frame allows us to apply a function along an axis. In this case we're going to create a column called is_in_homeplate. \n",
    "\n",
    "We don't really care about the rows that aren't our preferred_shot_locations currently so we're just going to apply our data rows that apply.\n",
    "\n",
    "Note: axis=1 as an argument specifies that we're going to apply this to each row. If it were zero we'd apply to each column, which we're not interested in right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game_plays['is_in_homeplate'] = df_game_plays[preferred_shot_conditions].apply(\n",
    "    lambda row: is_in_home_plate(row), axis=1\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>event</th>\n",
       "      <th>st_x</th>\n",
       "      <th>st_y</th>\n",
       "      <th>description</th>\n",
       "      <th>is_in_homeplate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shot</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>Patrik Elias Wrist Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shot</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>Ryan Carter Wrap-around saved by Ilya Bryzgalov</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shot</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>Stephen Gionta Wrist Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Goal</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Zach Parise (3) Wrist Shot, assists: Patrik El...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shot</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>Travis Zajac Snap Shot saved by Ilya Bryzgalov</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event  st_x  st_y                                        description  \\\n",
       "6   Shot  80.0  -3.0    Patrik Elias Wrist Shot saved by Ilya Bryzgalov   \n",
       "11  Shot  88.0  -4.0    Ryan Carter Wrap-around saved by Ilya Bryzgalov   \n",
       "12  Shot  81.0 -27.0  Stephen Gionta Wrist Shot saved by Ilya Bryzgalov   \n",
       "15  Goal  73.0  11.0  Zach Parise (3) Wrist Shot, assists: Patrik El...   \n",
       "24  Shot  63.0 -19.0     Travis Zajac Snap Shot saved by Ilya Bryzgalov   \n",
       "\n",
       "   is_in_homeplate  \n",
       "6             True  \n",
       "11           False  \n",
       "12           False  \n",
       "15            True  \n",
       "24            True  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_plays.loc[preferred_shot_conditions, ['event', 'st_x', 'st_y', 'description', 'is_in_homeplate']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our \"home_plate\" defined and we have a column for it we can see if there's a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7febc4e93f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the \"is_in_homeplate\" to our preferred_shot_conditions.\n",
    "preferred_shot_conditions = ((df_game_plays.event.str.match('Shot|Goal'))\n",
    "                             & (df_game_plays.st_x >= 0)\n",
    "                             & (df_game_plays.is_in_homeplate == False) # invert this to show home plate\n",
    "                            )\n",
    "\n",
    "# this is going to show the scatter plot of shots.\n",
    "\n",
    "# Matplotlib version\n",
    "# df_game_plays[preferred_shot_conditions].plot.scatter(x='st_x', y='st_y', style='o')\n",
    "\n",
    "# Seaborn Version of the plots.\n",
    "sns.scatterplot(x=df_game_plays[preferred_shot_conditions]['st_x'],\n",
    "                y=df_game_plays[preferred_shot_conditions]['st_y'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib heatmap plot.\n",
    "# you can also shot the hexbin to see that there are a lot of shots from the point and beside the net.\n",
    "# df_game_plays[preferred_shot_conditions].plot.hexbin(x='st_x', y='st_y', gridsize=25, )\n",
    "\n",
    "# Seaborn Version of the plots.\n",
    "sns.jointplot(x=df_game_plays[preferred_shot_conditions]['st_x'],\n",
    "              y=df_game_plays[preferred_shot_conditions]['st_y'],\n",
    "              kind=\"hex\")\n",
    "\n",
    "# Takes a super long time to create\n",
    "# sns.jointplot('st_x',\n",
    "#               'st_y',\n",
    "#               data=df_game_plays[preferred_shot_conditions],\n",
    "#               kind=\"kde\",color='green')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above shows that our filtering on the homeplate area is working as we intended. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping Data together to see some shooting percentages\n",
    "\n",
    "Let's get some shooting percentages.\n",
    "\n",
    "We have our preferred_shot_conditions filter to just filter shots and goals.\n",
    "\n",
    "We can get the the \"value_count\" of goals that happen (or don't) inside and outside the home plate area.\n",
    "\n",
    "Below we're going to use the \"group_by\" function to group the events \"Shot\" or \"Goal\" and get the sum of the values using the value_counts() function on the \"is_in_homeplate\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basically reset the conditions to normal.\n",
    "preferred_shot_conditions = ((df_game_plays.event.str.match('Shot|Goal'))\n",
    "                             & (df_game_plays.st_x >= 0)\n",
    "                            )\n",
    "# Use groupby to get the value counts.\n",
    "home_plate_values = df_game_plays[preferred_shot_conditions].groupby(\n",
    "    df_game_plays.event\n",
    ").is_in_homeplate.value_counts()\n",
    "\n",
    "\n",
    "# Matplotlib\n",
    "home_plate_values.unstack().plot(kind=\"bar\", stacked=True)\n",
    "plt.plot()\n",
    "\n",
    "# Seaborn\n",
    "# Will be adding this soon. A little bit convoluted.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a handy graph, but I think it would be handier if we should values \"flipped\" where we change the columns and rows and inverted the table. You can do this with \"transpose\"\n",
    "\n",
    "Below shows the values that we've filtered and grouped together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>is_in_homeplate</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goal</th>\n",
       "      <td>15229</td>\n",
       "      <td>40940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shot</th>\n",
       "      <td>327876</td>\n",
       "      <td>226652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_in_homeplate   False   True \n",
       "event                          \n",
       "Goal              15229   40940\n",
       "Shot             327876  226652"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shows the \n",
    "\n",
    "home_plate_values.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now by using transpose we can see that we \"flip\" the columns and rows. so we can get a better graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>event</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Shot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_in_homeplate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15229</td>\n",
       "      <td>327876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40940</td>\n",
       "      <td>226652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "event             Goal    Shot\n",
       "is_in_homeplate               \n",
       "False            15229  327876\n",
       "True             40940  226652"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_plate_values.unstack().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now by graphing this below, we can already see that that this is tells us a bit more.\n",
    "\n",
    "- A lot of shots happen in this area even though it's a fairly small piece of ice.\n",
    "- A higher percentage of goals go in when you're in the home plate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7febc4d12588>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Matplot lib chart\n",
    "home_plate_values.unstack().transpose().plot(kind=\"bar\", stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the shooting percentage\n",
    "\n",
    "Now by getting the values transposed, we can calculate the percentage of shots that go in.\n",
    "\n",
    "Below our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>event</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Shot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_in_homeplate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15229</td>\n",
       "      <td>327876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40940</td>\n",
       "      <td>226652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "event             Goal    Shot\n",
       "is_in_homeplate               \n",
       "False            15229  327876\n",
       "True             40940  226652"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test_home_plate_data = home_plate_values.unstack().transpose()\n",
    "\n",
    "test_home_plate_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create another column called \"shooting_percentage\" which will calculate the percentage with the following formula:\n",
    "\n",
    "shooting_percentage = goals/(shots + goals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>event</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Shot</th>\n",
       "      <th>shooting_percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_in_homeplate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15229</td>\n",
       "      <td>327876</td>\n",
       "      <td>4.438583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40940</td>\n",
       "      <td>226652</td>\n",
       "      <td>15.299411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "event             Goal    Shot  shooting_percentage\n",
       "is_in_homeplate                                    \n",
       "False            15229  327876             4.438583\n",
       "True             40940  226652            15.299411"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_home_plate_data['shooting_percentage'] = (test_home_plate_data['Goal'] / (test_home_plate_data['Goal'] + test_home_plate_data['Shot']))*100 \n",
    "\n",
    "test_home_plate_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see it in a bar plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7febc4d127b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the plot\n",
    "\n",
    "# Matplotlib\n",
    "# test_home_plate_data['shooting_percentage'].plot(kind=\"bar\", color=['red', 'green'])\n",
    "\n",
    "# Seaborn (not stacked bar plots are pretty easy to create)\n",
    "sns.barplot(x=test_home_plate_data.index, y=test_home_plate_data['shooting_percentage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last piece here we can do another groupby to take a look at the different shot types to see how effective they are in a game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7febc4ab9cf8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j8l8jCIIgmCidtAB2AJbYvsn2g8DpwN5t5+wNnJK3zwJeNtYbvaRNgHVsX+60KPGpwD4TLn0QBEEwaToxAJsCt1T2l+a0Ec+xvRy4F9ggH9tK0tWSLpb0osr5S8fRDIIgCGqk7lhAtwNb2L5T0vOAH0h6+kQEJB0GHAawxRZb1FDEIAiCwaSTFsCtwOaV/c1y2ojnSJoBrAvcafsB23cC2F4A/AbYNp+/2Tia5OtOsD1ke2jmzJkdFDcIgiDohE4MwHxglqStJK0O7A/MaTtnDnBw3t4PuNC2Jc3MnchIejKps/cm27cDf5a0U+4rOAj4YYHvEwRBEHTIuC4g28slHQ7MBaYD37S9WNJRwLDtOcCJwLckLQHuIhkJgF2AoyQ9BDwMvMP2XfnYu4CTgbWAc/MnCIIg6BFyg+JfDw0NeXh4uPcZ1xxTvPaQ5U2Oid7kskPUnamVj/rziIwW2B5qT++LBWGa/hs3mabf+6aXPwi6IUJBBEEQDChhAIIgCAaUMABBEAQDSl/0AQRBEEwFTe9DihZAEATBgBIGIAiCYEAJAxAEQTCghAEIgiAYUMIABEEQDChhAIIgCAaUMABBEAQDShiAIAiCASUMQBAEwYASBiAIgmBACQMQBEEwoIQBCIIgGFA6MgCS9pB0o6Qlko4Y4fgaks7Ix6+QtGVO313SAknX5r8vrVwzL2suzJ+NSn2pIAiCYHzGjQaaF3U/FtgdWArMlzTH9vWV0w4F7ra9jaT9gWOANwJ/Al5j+zZJzyCtK7xp5boDbU/BGo9BEARBJy2AHYAltm+y/SBwOrB32zl7A6fk7bOAl0mS7att35bTFwNrSVqjRMGDIAiC7ujEAGwK3FLZX8rKb/ErnWN7OXAvsEHbOa8DrrL9QCXtpOz++Q9popG1gyAIgm7oSSewpKeT3EL/XEk+0PYzgRflz1tGufYwScOShpctW1Z/YYMgCAaETgzArcDmlf3NctqI50iaAawL3Jn3NwPOBg6y/ZvWBbZvzX/vA75DcjU9Ctsn2B6yPTRz5sxOvlMQBEHQAZ0YgPnALElbSVod2B+Y03bOHODgvL0fcKFtS3o8cA5whO1LWydLmiFpw7y9GvBq4LruvkoQBEEwEcY1ANmnfzhpBM8NwJm2F0s6StJe+bQTgQ0kLQHeC7SGih4ObAN8tG245xrAXEmLgIWkFsTXS36xIAiCYGzkVW2V4jEYGhry8PCjR43WvjBzzRk0ufxNLnsP5KP8Uysf5X9ERgtsD7Wnx0zgIAiCASUMQBAEwYASBiAIgmBACQMQBEEwoIQBCIIgGFDCAARBEAwoYQCCIAgGlDAAQRAEA0oYgCAIggElDEAQBMGAEgYgCIJgQAkDEARBMKCEAQiCIBhQwgAEQRAMKGEAgiAIBpQwAEEQBANKGIAgCIIBpSMDIGkPSTdKWiLpiBGOryHpjHz8CklbVo4dmdNvlPSKTjWDIAiCehnXAEiaDhwL7AlsBxwgabu20w4F7ra9DfAF4Jh87XakReSfDuwBHCdpeoeaQRAEQY100gLYAVhi+ybbDwKnA3u3nbM3cErePgt4mSTl9NNtP2D7t8CSrNeJZhAEQVAjMzo4Z1Pglsr+UmDH0c6xvVzSvcAGOf3ytms3zdvjaQIg6TDgsLz7F0k3dlDmFhsCf3q05gQUJqFfMIMml7/JZR9VP8o/tn6Uf2z9KSz/k0ZK7MQATCm2TwBOmMy1koZtDxUuUuhPsXboh37ol9HvxAV0K7B5ZX+znDbiOZJmAOsCd45xbSeaQRAEQY10YgDmA7MkbSVpdVKn7py2c+YAB+ft/YALbTun759HCW0FzAKu7FAzCIIgqJFxXUDZp384MBeYDnzT9mJJRwHDtucAJwLfkrQEuIv0QCefdyZwPbAc+Bfb/wAYSbP815uc6yj0V3nt0A/90C+A0ot6EARBMGjETOAgCIIBJQxAEATBgBIGIAiCYEBZ5ecBdIqkzUidzy8Cngj8HbgOOAc41/bDBfIQ8IyK/mLbd3arm7XXBF7NCOUv2UEuaR1gE+Dvtn9fUHcj4AWsXPbhQvd9e+DNpHuzCSv/tt+xfV+X+s8fQ//btu/tRj/nsQGwMyvfn6tdqBNO0jTg2VV923cU0t4EeCMj/2+d1+136IO639j60xedwJJOIs0w/jEwDNwBrAlsC+wKPA84wvYlk9TfEvgAKZ7Rb4FlWX8WcA/wP6QfelI3U9LHSf8A84AFI5R/TeB9thdNUv9xwDuBNwGPJc0gXJM0W/vnwHG2fzZJ7V2BI4D1gavbyr41KTTI52z/eZL65+Ty/pCRf9tXAZ+2/eNJ6p8L3DaG/muAz+fRbpPRfxFwJPAEYGGb/pNIYVC+YPsvk9TfGvggsBvwa1bUzW2BvwHHA6dM1hBL+gawFelhNtL9eTbwAds/n6R+Y+t+1m90/cF24z/AM8Y5vjqwTRf6Z+Yfc9oIxzYB3g8c0oX+q8Y5vhEw1IX+BcBbgQ3a0qeRQnB8FfinSWp/BthilGMzgH2A13VR9o07OGejLvQ3LHHOGNd+HnjyKMdWJ82b2a8L/e8Cu5Bf5kaoN+8BDu5C/9njHF8T2LYL/cbW/X6oP33RAqgiaS3SA2kiMYNWKSStbftvU12OVY3s5ptl+yJJawAzbP+1oP6Tsv4FuR7NcJfupX4iT9rcwvaSGvNobN1vYv3pq05gSXuRmkk/yfuzJRWbYSxprby+wf/k/W0k7VlQf2dJ1wO/zPvPlnRcKf2sub+kD+ftzSU9r5DuxpJOlNS699tJOrSEdtb7J9Js8W/kpCeRmt2l9N9Oclcdn5M2A35QUH+mpOOzS6t1fw4pqL+2pP+Q9PW8P0vSqwvqvxq4Fjg/78+WdHZB/cbW/azXzPoz2abDqvgh+RDXJXWOtNKuLaj/XeBDpA42gLWreRXQv4IUI6la/usK6n+VVEFvyPvrA/MLaZ8LvAG4Ju/PKHzvF5KavNV7s6hm/ZLlP4fkh27dn9UK659B6qeq1s2FBfUXAI+v8f40tu43uf70VQsAeMiP7nEv6eOaZfuTwEMATk3VcgFek+YtbUn/KCi/s+1/Bu7Ped1FqrQl2ND2mcDDWXs5Zct+v9PaEcAjCxWVvPcPtOnPoGzd2cj2d1hxfx5qbRdia9ufpr66+ZDte9rSivqPG1z3oaH1p98MwGJJbwKm5ybwV4DLCuo/mIesGSAHuHtw7EsmxC2SdgYsaTVJ7wduKKj/UB4u2Cr/BpR7CP0167W0dwK6Hv5W4VJJHwDWzCOPziCN+irFxZI+BKwlaXfge8CPCur/VdL6rLg/2wOTGhk1Cg9mv3NLf2vggYL6N0h6AzAtB3H8Aiuv9dEtTa770NT6U6qJsip8SM3e/yJFGx3O22sW1N+DNFztDtIKaDcDLyuovyFwGvDHnMe3aRu90KX+QcDZpMV4Pk76B9u/kPZzgUtJD/1LgV8BzypY9umk4Xxnk3yr72SEUVld6E8D3k76xz0rbz9qZE0X+kP5vtwDXExaHW92Qf3ds+6yXIduBl5SUP8xpKVerya5O44B1i6o39i63+T603ejgFpkF8FjPMnx52PoziRNyBBwmQtNtukVkp5OGjMu4ALb1xXUngE8JWvf6NRMbRz5TWszT3Ls+Ri6qwNPI92f611xGRTS3wDYKetfbvvRK0YNMHXW/bZ8GlN/+soASPoO8A6S73A+sA7wJdufKaS/80jptou4mSR9GjiaNNPvJ8CzgH+3/e1C+k8cKd32bQW0Xw/8xPZ9kj5CahEcbfuqbrWz/q8Zwadqe9tC+vOAvUid160JSZfZ/vdC+m8aKd3Jr1tC/wWkTt+/Snoz6f5/yfbvCumfz8j3/+WF9Btb97P+PBpYf/rNACy0PVvSgaR/gCOABbafVUj/3MrumqQZxlfbfnEh/Vb59yXNjnwvcIntZxfSv4EV/8RrkUZd/Mb2UwpoL7L9LEkvBD4BfBb4qO0R13qehP7Gld01gdcD69r+j0L6V9t+jqS3AZvb/ljrOxXS/1pld03gpaS6+dpC+otIs3KfBZxEWqPjDQXrZvV3XBN4Hanj8/8V0m9s3c/6jaw/fRMLKLOapNVIs0+/avshScUsnO2VxvwrhYgo0rrItH6PVwHfs32vCq4ibftp1X1JOwBvKyTfGrHxKuDrts+RdHQhbWz/sS3ps5KGgSIGAJihFPPmDcCHC2k+gu13VvclrQcUefvPLLdtSXsDx9o+seQ8DNtXtCVdLKk9rRuaXPehofWn3wzA8aTOr2uAS5Rm5hXtA6hi++bsVyzFjyX9ktQMfmfub7i/oP5K2L5SKdZLCW6VdDypM/IYpZm6xUaZSaq+SU0jdYqtUUofOIq0Qt3Pbc+X9GRSbJ26uA94ckk9SUeSgpLtkke8rFZKXCmQWotppNbveqX0aXbdh4bWn75yAY2EpBlOY9JLaH2BFc3IacBzgNtsH1BCP+exPnCv7X9IWhtYx/YfCmn/W2W39U/8BNu7F9BemzRK6lrbv85vQ8+0fV632lm/GrBrOSko32dtX19Cv26UZs1W687TgR8UdKE8gTRRaL7tn0nagjQK6NRC+reQyi9W3P+P2764hH7Oo5F1vxfUVX/6wgBIeu9Yx21/vlA+1Sb1cuDmEv8Aksb049n+327zyPl8orK7nNRa+p67iL2S/2lHxWnCTVfkEV372j6rW60RtL/CGBN2bP/baMcmmM/LKrvLgd/ZvrmEdt3k1sQOtkuO+29pN7buZ91G159+cQE9ru4M8kPoxbYPqkH+NWMcM9D1P0Eu/2q2j+hWq40FrHgzbP1tYQo0U/Mb4ZGk8dWlGa5BcyXyvT+ijrdNSfcx8gNIgG2vM8KxCWH7YaX4V7O71RqBJtd9aHr96YcWQK+Q9HNg1waPb7/c9k5TXY7JIOm/SZOEzgAeiQBaep5HXUi6ENinKeVtJ7s/59kuFoCvlzS57kN99aevDIBSmIZDSf6xNVvptv+pkP4ppIlOP2Tlh9CXS+jnPF7Fo8t/VCHt40gLS3yPlctfJGJqHpkwi5XLPqlFeEbQrsaJeaTFYXuLQvozSQurbMfK5X9pIf2zSW/Q57HyvR/TfTmJfDZi5fIXWflK0t2kQIsPkDpqW/d/TBfgBPNoct1vZP3pFxdQi2+Rwsm+gtQrfyBl44n8Pn/Wzp+i5Gb22qTFZ75BWuzhyoJZPI5UeV5ZSTMpzHJX5PHP7yaFwV1ImpH6C9J45W50d7J9ue3Nuy3jOJxGal28ijSZ8GBSWIVS/JiysYtWQikU+udISwbeQQqXfQPpgdqN7hbZiGzYdSHHzqexdT/TzPrjQrEqVoUPORQrOUwwaRjc5QV0P9mj8i9q+/tY4GcFdA/vQdmvJb35LMz7TwX+t4DuVT269wuq9z5vdx0uGDi5R+W/hrTMYet/YFfgxAbd/8bW/ZxPI+tPv0UDbfnm75H0DFKTdaMCunsU0OiEv+e/f8tT1x8iLTnZLUVcYONwv+37ASStYfuXJHdZU2jVndslvUrSc0gx47ulyEzQDnjI9p2kaJ3TbF9EmivRLUXDnY9Bk+s+NLT+9JsL6ITsh/4IqWn3WMrMFJ2edUf8Z3CBoY6ZH0t6PGl28VWkJurXC2nXzdJc9h8A52efcYk4NE/WGKu62d6rQB4AR0taF3gf8BVSHKkScVzWzg+D0epOkVhJpJeexwKXAKdJuoOKr7gLNpU0ah+XCw1zpNl1Hxpaf/qqE7guJD0A3MrIP4Jtl5zR2cpzDVIo665j6ktaDow03rnYUMG2/F5Man39xF1GLFQKAjfqlH0XnIhUB3mY5nxGrzulOgkfQ5o5K1Lf17rAablV0I3u74CPjnbc9ind6I+SZ2Prfmlqrz+98I/1wP92cmX74Br0iy37OIr+Jyvbuzep/MBrK9vr1aBfqw8aOK+yfWST7n3W36lm/brvf2PrftZvdP3plz6AasTAd09ZKSZPtY/hmCkrxeT4SGX7pzXo31yDZpWZle3X15xXHTyycLqkX9SgX3TNghFoct2HhteffjEAdfuxvlSzft18r0ZtjbJdBBcKlzxWFjXrf7Bm/eo9X3PUsyaJGzx5KlNn3YeG159+6QTeLHdUqbL9CO6yo8r2yd1c3+z4MpgAAB6BSURBVAEb5XhGqmxX8+8qlpHTQvZ1sVbupJpGWq93pQ4rl+vkrItWJ7MYocPZXXYyu1AwvDGYlgcoTKtsV+9/qQEKddHkug8Nrz990Qks6eCxjruGjqqSSPrYWMdtf7xXZZkoki4a47BdqJOzLnKH9ah41e9kvpm0uHnPBiiUpMl1H/qg/vSDAQgGjxxu+i7bD0x1WYKgqfRLH8CUIOldkt6otBh645C0t1Ze6q9JfAv4paTPTnVBJoOkT0r6oNJC7o1D0g35c/hUl2UyNLzuF6s/YQC6Q8ALKRCydorYEfiIVl7ruBHY3o0UavqkqS7LJLmSFNf9C1NdkMngtMTii0gLwzSRxtb9TJH6Ey6goBFI+pbtt4yXtqoi6QW2Lx0vLQhGoq7609cGQNK7gDuB77uLZSHbRya00+1IhTHy3Rv4gx+9IPdEdXqy6lJbnkV99JKusv3cyv4MUuCt7Uroj5DfJ4F7gW+4y9m0WW+l8o+WtqqS69AxpNhaouaZtE2u+znfRtSfRvquJ0DLRXMg0M1wrNaKY08BtmdFCNnXUDZkbTs7As9UWtd4zy50WqsubQTsDFyY93cFLqMeF9a3gK0lfd/2+ycrorQS2IdIw03/zIrRLg8CJ3RfzFG5Etia1MSe9Cpwkp5Puucz214k1gGmd1XCsfNthUE/1vZXC0h+GniN7ZLh1ceiyXUfGlJ/+roFUBpJlwCvsn1f3n8ccI7tXaa2ZJ0h6TxSqIzb8/4mpDAar6gpPwHb2V5cQOu/bR9ZoFij6dfTxE7DBF9CihH/P5VD9wE/sv3rbvTHyXtDYEfb5xTQutT2CwoUa0qou+43tf70hQHolYtG0o3As1pujRy0apHtrsIe96qZKumG3HnX2p8GLK6mdaFdu49eadGTlrGdZ7vYAhl1u2gkPcn273LETmz/pYRur5D0JdKKWj8grQoGdF83+6HuZ71G1p9+cQH1ykVzKnCl0vJsAPsAJSaZ9aqZ+lNJc4Hv5v03AhcU0l5p5anso39eIe3WmsA7kFZeAni3pJ1tf6hL3V65aB4n6WpyjHhJfyK9kV5XQrwHPvp1SFE1X15JK7Foe6PrftPrT1+0AFr0wkUj6XmkfgWAS2xfXVC7dheNpH1Z8RZ9ie2zxzq/A71HfPSkB8RKPvpSbhtJi4DZth/O+9NJkRK7WjCjVy4aSZcBH3ZaqAVJLyFFwty5kP4SeuujL0qP6v5rSUNXoUDdz5qNrj/9ZgBqcdG05TEd2JhK68nlFt6utZmaNZ8EzLJ9gaS1gektg9mlbt0++kXAS5xj20han+QGKrJiUt0uGknX2H72eGld6Nfio5f0FcYIeOZCC8L0ou7XSVPrT7+4gFrU5aIBQNK/Ah8D/gj8g9zMptyybXW6aJD0duAwUjNya2BT0lvLy7rVtn1knT564L+Bq5ViDynnc0RB/VpdNMBNkv6DNDoK4M3ATYW0AYYlnUFhHz0w3OX1nVJ33a/bRdbI+tNXLQCo3UWzhDSqoutxvWPkUdRF06a9kORHv8L2c3LatbafWUC73Ud/AGlR7K589G15bELq4wG40vYfCmrX7aJZD/g4K+rmz4D/tH13If2RZkTbdvE1cSU9oeS9r+gWd9FUtGt1kTW1/vSjAajTRXMRadWiSU8q6yCPWlw0WfsK2ztKutr2c3JH7VUl3Ch1+ejb8tgUeBIr/7aXFNKu1UXTT5Qc3dIr6nKRVfQbWX/6ygXUAxfNTcA8SeewcjO71DDT2lw0mYsltSZV7Q68C/hRIW2AxwOt+PPrFtRF0jEkt8BiUvhjSL9tEQNAzS4aSdsC7we2ZGUD1lW47F756NuzLS5Yv4umLhdZi0bWn74yAKTlIJ9So4vm9/mzev6U5l/ILhoA27+WtFFB/SOAQ4FrgX8G/s/21wtp1+2j34f029YV/vmfSE3s1gPhZzmtFN8jGfNvkF5OStErH32VUnWmSt0zjesaxtqikfWnr1xAvXDR1EmdLpqs/27bXxovrQv9On305wKvLz26oldIWmC72LyIcfKqy0f/XJIP2sClLrjaW90umqZTV/3pNwNwImkyWF0umpnAB0iTnh5Zf7XbZlhF/9PAPaTYIf9KctFcb/vDhfRHmq14datDuIB+cR99xcWxKfBs0sLz1d+21DDEulw06+fNfwPuAM5m5fIXX7KxDh+9pI+SFj1vveHuA3zP9tGF9GuZaVzRP4kRXGWlOsmbWn/6zQCMuLycCy0rlyernEH6od8BHAwss11k4eY89vlQUjNVwNwSLhpJBwBvIr29/axy6HHAw7a77mMYzUfvLtdEVY+W+5R0DamJvYBKE9v2gi51f0t68PRsycaSRr2ieSPwbNv35/21gIWl5tjUPYpJ0usqu2sC+wK3FXyBaGT96SsDUDetZpikRS23jKT5trcf79oO9Wtx0eSRRVuR/PRVv/x9pIlyXbvM1DYJry4krQY8A7jV9h0FdXvmoqkbSe+yfVxhzYuAfW3fk/cfD/xvqdZvr8kvWz8vOEyzkfWnrzqB63bRAA/lv7dLehVwG3niRyEOBtof9oeMkDYhbP8O+J2kA0lvPdW3uM2Am7vRz9wErEaleVoCSf8DfMX2YknrAr8gvWGtL+n9tr87tsK4+q3f70dK60eUbWJL2wO3tHzykg4CXgf8jjSOu5gLqOqjl/Tckj56Umz7xZLOJ72R7k6adPll6N4VV7eLZgRmkUYcdUXT609ftQB64KJ5NcmFsjnwFdLIgo/bnjPmhePr1u6iyfkMAzvbfjDvr07qzJt0C6ZuH72kxbafnrffQwoHsY+kJwDnduvqqL2JLV0F7Gb7Lkm7AKeT+ndmA0+zvV83+pV86vbR1+qK64GL5j5W/M4G/gAcafv7Xeo2uv70mwGo1UVTF71w0eR8Ftqe3ZbW1WSVHjwYHvFn5/kX37N9cvuxVZXq/ZV0LOmF5D/z/qN+jy7yqdVH32tKu2iaSt31p69cQNTsoskuprfz6J7+rpqpPXLRACyTtFerxaK07N6fuhFsf8DX4KO/J7e8bgVeQOokJw+RXatb8R64aKYrrWq1nDSh77DKsZL/f7eR3pzvz/trkO5ZV0g60/YbJF3LyC6aYjO92yjioqmiGmJVNb3+9JsBODr7id/HChfNvxfU/yHJRXMBZSfztDiTFFu8xT9IE0BKtWDeAZyW3yQMLKWL5eqgfh89acLal0lDBN9TGd/+MtJw3245HtgNIDexP8WKJvYJQLcumu+SZmD/Cfg72cUnaRuSX70Udfno353/vrr7Io7OKC6aIq7brP8p0v9R0fUkaHj96SsXUN2UbLJ3qt+ti2aUfIqFrK3bR183vXDRSNoJ2AQ4z/Zfc9q2wGNLddTW5YqTdJ7tl49/5qqN6ltPotH1p69aAHW5aCr8WNIrbf9fIb12irtoqkjaGPgk8ETbe0raDni+7RO7kH2wsr07qcWC7T9IxUPG1EHtLhrbl4+Q9qsS2hW9YmHP25hZk+6jqMNF00YdsaoaXX/6ygBQk4umrXn6IUkPkPobSgesKu6iaeNk4CSgNbP4V6RRU90YgFp99D2gVy6aWuiBj35djbFur8vN1K3LRdOirlhVza4//eQCqttFM0qecuGbWNJF06Y73/b2bSNrurpnuSna8tF/sTJC5xXAy22/r0DRa6UXLpq6kLSJ7dvzSLJHkQcYdKN/J+nFarRhjqVm6tYWTlypKboZsJwaYlU1uf5gu28+wNHAK2vUP6ptfxpwWkH9jUlv4+fm/e2AQwvqzwM2IAWYA9gJuHiqf7cOy74BqWP/KtJ0+y8BG0x1uSb4HZ4A7EVaCP0JhTTPq7nMV/Xo3iwC1q/sr08aAl1K/9qp/v27LP8xnaRN9DOtsD2ZEiTdJ+nPpBELP5b0d0l/rqSXYnOlRdBRWm/4bKDIos+Zk4G5wBPz/q+A9xTUfy8wB9ha0qWkJTT/taB+nZxOCob1OtLIimUk91UjkPQ24ErgtaTyXy6pxNtz3T76XnXktFw0J0s6hWTk/6ug/lV5yGZT2X2EtD27Fe0rF9BIlHTR5KbkaaR4+ruS4ul/sYR21i/uohkhjxmkiKkCbrT90DiXrBJIus72M9rSiixn2QuUJmrt7LxWhaQNgMvc5UQtSTeRZr6PiLv00Ut6pu1rxzmnq/+xul00OY9fAtuQxuf/lRX9d3XNYyiCpHeSogI/GfhN5dDjSLP439yNfl91Aks6yvZHK/vTSCv0HNilbjW07pdIY38vBS5R2Zgrf80PBud8d6LEWN/RO/G2ldT1Q6JHnCdpf9JcCUhv0XNLZpCHru5Auv/zSz6AgDtJM7tb3JfTumVd0hj9EX30dL/gyVckfR/4oStLqyqFEXkhKdzKRaTW66SwbUn/l415V2FVxuAVNekCIOkYt4WcGSltEnwHOJcRogS4QBypvmoBKAWU+pXt/84umjNJHUn/2aXuRWMctsutB/Bckp/7GcB1pOb9frYXdak7UqjdFnaBjrxsuP6TNBLIwM9JfSZFVmfLI7Eew4pQ09NIb3JQYCRWdtF8FLiQ9DB9Man83+xGt6J/KvBMUoeqgb1Jfu9FMPk1K1Tz+ryS1iStbHUgKVzJPaQZx9OB84DjbF9dIJ9TgK/ant+t1jj5bMTKgSJLrRc+0lobi0q1MCRtDSy1/YDSgvPPAk51js46ad0+MwC1umh6QR0uGuWQ0pJeaPvnXRdy5DzOJ63P++2cdCBpUthudeRXmrpcNBX9EdeqaOFJrlmhHsZDUgrzsSHw924fPCNo1+qiyXMMPkfqX7uDtHDRDc6TGLvQrdVFU8lnITBEmuP0f6QXiafbfmVXuv1gANpcNKuxwkVzIkApF42kTwKf9oqY6OsB77P9kS51Rx1nDUX8uAttz67zbbEXPvp8v2ex8htckUXhJV1GMljVSKnzvIoHI+uFj74X1DWMtaJ/DfBS4AKn5VZ3Bd5s+9AuddcF1qMmF00ln6tsP1fSB0gG+CsljH+/GIBeuWgedcNLPFTrdtFI+i7p7eGJrPyWUuwtS9LnSaNcqj76HWyP2kE5Qf23kUZ5bQYsJA1h/UXB37YWF01Fv5a1KiTNA8b10TvPz1jVqdFFM2x7KBuC59h+WAXDrNTloqnoXwF8kTSJ8zW2fzvSS9dE6YtOYNu79iir6ZLWcF71Sila5xoFdBfW6aKxfUDu4JxLGodeB28nDVltuYCmkTq1/5kys6XfTRohcrntXSU9lRTWohS/YWXj+MP893GF9E8jDVt9NZW1Kgro7kHy0X9X0kg++i+W8NHXzWguGpLBLME9ShMsLyHNtr+DFX1IJfg+MKQ0A/gEUv35DtCVi6bCW0n15r/yw38r0gCX7mifGNDkD+mB8PjK/nrA0QX1P0jq3Dw0f34OfKCA7sL8t/ZJN6TwDE+Z6t9qEuWe37pXwBp5e/FUl2sC5V+Q/y6qpM0vnMdqpBmpjy+p26P7cw1pst/VeX9X4MSC+o8hvZTMIBnff6PgREJWTK78APCvefvqqb6v4336ogVQYU9XYofYvlvSK4GufPQVvWOUpqy3Vuj6hO0SQxFvkPRr4IlZv0XpjrDXAJ8FVge2kjSbNNKlSKugTh89sFRpHdofAOdLupvUYViEulw0FepeThSnAQO3l9TsIQ/ZvlPSNEnTbF8kqdgADucQDcDDku50+eB5Dymt7HcQaaY3JIPcFao51lO/GYC6XDSPYPtc0rjckpq9cNFAGqa5AykkBLYX5qZk14zmoyd1vHWN7X3z5n/mPp91gZ+U0M7U5aJpUfdaFU2nbhdNlaOA0pFG63HR1LweQ190AreQ9EGS9W11qr4VmGP704X0dyL98z6N9BY9Hfiry0UDbRmtLWzfWEqzon257Z208kzjImOV8xtKy0c/u+Wjtz3mCKcOdMd8S3ahkRZq6HKi/YKkx5CiaU4jDSFelxRnq8g8kra8VvmlRKsoBca7wDX0dfZVC6BGF02LrwL7k2LeD5Gae9uWEq/bRUNaMepNpJbSLJIf9LJC2vfbvl8SuRX2S0klxtAvYIxFt0njr0vQyOVE+4UeuGiq/HMpobpdNFnjH5IelrSu7aIhpvuqBVA3laFk1bfEYm8TkhaQXCbzKm/oxcbSS1qbNIzs5aQH6lySkbx/zAs70z6b1OJ6D+k73A2s5i4nqvQKpTUNfgZszgoXzcedF+cpoH9Z1l9AZa0K298vod9P1DFfRWlG87tIQ2NbM9W/1m3dV83huCv5/BB4DnA+FdeYJ7/UZ9LtJwNQt4tG0iWk9T+/QVqz9HbgEJcbS1ybi6aXSHox2UfvPLGqC60nAfe03nzyBJ59gJuBY7vV7xWagrUqmkodLhpJZ5LiL7WGKb+JNFrq9QW0a3PRVPIYccnPbltKfeUComYXDfAWko/ycFIH3uak8MSlqM1FkyvQu0lhJiCNsf6y7VO71B3JTdKamfpYVizBN1nOBPYF7s0use+RZl3OBo4D3talPtATF03dy4n2E8VcNBWeYXu7yv5Fkq4vIVyri0bahxSSpBaXWL+1AGp10dRNXS6a/PB/D2k9gKuy9nOBz5AmCk16tIKk3zKGj952Vz76tt/ys8DDtj+gFOl1YcEhsrW6aLQimN0DpLDHWb7cAIImU5eLpqL/bVKwucvz/o7Av9gusuRqbS4a6Szg+cDfSC+Dl5IMwnXd6D6i32cGoBYXTX4b/zDpbfbzwNeBF5Fmjr7NNUcw7BZJlwP72765LX1L4HTbO01BsTqi2gci6SrgyFbHfkn3WLhoppY6XTRZ/wZS67cVWmIL4EaSMe56rk1dLpqK/pbAzvnzfFL553fbx9ZvLqC6XDQnkVbPWge4gvQ2vS/JCHwV2LHbDOpy0WTWaX/4A9i+WVK3YZTr9tFfmB8Ot5Nmdl+Y89kEKOn/r8VF0y99GD2gNhdNZo+CWo9Qt4umRf5fXZM0k38t0mTFtbrV7asWQF1U3w4lLbG9zUjHutCvzUWT9RfYft5Ej3WofQWwr+3bso/+ApKP/lmk2Z1d+eglCXgjKcTBmbZvzenPATYqNcy3LhdN3fenX6jbRVPJp2iwudpdNNKHsv5MUovl8vxZZPsfY13bkX4/GIC6XTTVYWntQ9RKDFmr20Uj6W/AkpEOAU+2/ZgutGv10UvjhzLu5Jypold9GE2nBy6aWtYDqOhvSQ0uGqV1Ev4K/IhkYK4o2dHcLy6gul00T1WaYCbSguqteD2izESk2lw0macV0BiNaufvS4EjAZzC7ZbQv0g1LknYAxdN3fenX6jFRVPhE6TwJCutB1BKvC4Xje2n5pF2OwMvAY5QCplxDamlMVYo+XHplxZA3S6aESd5tOh2skedLpqsUdtbtKQvkdwzt5PiGG1r+6Hso/+R7aFJFXqFfq1LEvbAhVXr/ek3SrtoKrq1rAdQt4umLa8ZwPOAXUhDZbeyPb0bzX5pATxc2f7zGMcmy+9rdkM8TStHAX1EljItjDrfot/DCh/9C71iCcsnkNxyXZGHAR4HHKd6liRcy/ZtefvNwDdtf67loimgX+v96RdGc9Gw6q8HcBA1umjyfdmZtNb204HFpH6G91FgjlC/tABaPm4BW7PC3921jzvrz6PGVZd60MKo7S26D3z0tQ4zbfr96RWqacnGin5tweYqLpqdSW6mci4a6X/JHcukNSWKjhrrFwPQ2Ado1u/ZQ6L0W3TdxrFueuDCmkeD70+vqMtFk7VrD9WQ8ynuoqn72dAvBqCxD9CsOY+GPiTqNo51I9U7zLTp96dXSLqA1Pn+36T/rzuA7W3vXEj/p8BrS7pnsu5oLppfkFoAXa0pUbv3oU8MwDwa+gCF/nlI1OSjr5Wmvzz0C3W6aLJ+XaEa6nXR1O196BMD0BcPUIiHRK9p+stDP9ALF41qCtXQ9BeIvjAAVeIBGkyEfnp5aDI1umhaoRruKKlb0Z9Hg18g+s4ABMFkiZeHqaNGF03doRoa/QIRBiAIgimnLhdNRX9LagjV0JZH414g+mUiWBAEDUQNj6bZlsdDpOHEjSFaAEEQTBk9cNH0LFRDEwkDEATBlFOXi0Y1R9NsOuECCoJgyqnLReOao2k2nWgBBEEwZTQ9mmbTCQMQBMGUUbeLpu5QDU0nDEAQBFNKk6NpNp0wAEEQrBI0MZpm05k21QUIgmBwkbSXpE9J+hkpAuhngQ1IC548oUAWF0n6V0lbtOW7uqSXSjqFFK5hIIkWQBAEU0bTo2k2nTAAQRBMGU2Pptl0wgUUBMFU0jMXje2HbN8eD/8VRAsgCIIpI1w0U0sYgCAIVgnCRdN7wgAEQRAMKNEHEARBMKCEAQiCIBhQwgAEQRAMKGEAglUGSS+R9OMCOq+QtDB//iLpxrx9aolyjpLnXEmPm+A1J0l6yiTy+nZeSWtKkbSNpIXjnPNkSfv3qkzBxIj1AILGImmG7eXt6bbnAnPzOfOA99serrMstl8xiWveWkdZVjGeDOwPnD7VBQkeTbQAgkeQ9BhJ50i6RtJ1kt4o6XmSLpa0IL/lbpLP3UbSBfncqyRtrcRn8rXXSnpjPvclkuZJOkvSLyWdJkn52B457SrgtZWy7CDpF5KulnRZ601Z0iGS5ki6EPippFOrb8NZe+8xvuNlkp5R2b9c0tMlHS3plLz/a0n/VDnnCElXSlok6aOj6C6V9HhJj5N0buUe7jdGWX4uabakGZLuyTFxrsnfe6Nxfq5d83e5SdK+WW+apM9X7v9+OX03SRfl+3ZT/q4HSZqfv9OW+byNJf2vpOH8fXfK6aPem8p32VrSz/LvtUDSjvnQp3JZF0r6t/xdP1+5n28b53sGdWI7PvHBNsDrgK9X9tclxWiZmfffCHwzb18B7Ju31wTWztefT5rEszHwe2AT0kpM9wKbkV46fgG8MF93CzALEHAm8OOsuQ4wI2/vBnw/bx8CLAXWz/svBn5QKe9vW9fltHnAUGX/UOCzeXs7Uvx5gKOBq3KZNsp5bAy8Ejgul28a8BNg5xHu3VLg8fkefa16D8e43z8HZpNa4gb2zOmfB44Y47pvA9/NZXoW8MvK73Nuvv9PyPd2o3z/7srfZ03gD8BH8zXvq9yPM4Cd8vaWwHXj3JttgIX5nLWBNfP2Uyv3dbfW75P339X6bsAawNXAFlNd9wf1Ey6goMq1wOckHQP8GLgbeAZwfn5hnw7cruTr3tT22QC27weQ9ELgu04rOf1R0sXA9sCfgSttL83nLSQ9YP4C/Nb2r3P6t4HDclnWBU6RNIv0cFytUs7zbd+V875Y0nGSZpIM0Pc9gluowhnA1ZKOIM1Arcab/0H+LvdLuiSXfTdgT9KDClKs+m1JhnEkFgGfkvQp4Ee2Lx2jLFX+bvvcvL0AeNE45//A6Sm6SNKmOa16//8g6efAEPAg6YH8RwBJN5FdZKTf/Pl5ezfgKfm3BlhPUmtZxpHuzS8r5VkD+KqkZwPLga1HKffLgadpRb/AuqQXgN+P832DGggDEDyC7V9Jei7prfdo4EJgse3nV8/TBDs7Mw9Utv/B+HXvE8BFtvfNLop5lWN/bTv3VODNJF/zmH51239R6hfYi2QwZlcPt59Oess+2vaJ45S3pX+DpCHSPfyUpHNtf7KDS6tRMDu5P9X7qVHPGvn8hyv7D1fyErCD2yJyZoMw0r2p8j5Si+PNJGP9l1HKIeBdtn/aQZmDmok+gOARJD0R+JvtbwOfAXYEZkp6fj6+mqSn274PWNryvUtaQ9LawM+AN0qant/IdwGuHCPLXwJbSmq9LR5QObYucGvePmScop8MvAfA9vUdfNVvAF8lrThVXX5wn/xdZpLewIdJb8qHSnoMgKTNJG2Yt+dJ2rgqnN/G/2L7W8DngOd2UJ5S/AzYP/cFbExaBnEind8XAP/S2pFUNY4j3Zsq6wK351bJwawwSvcB1ReGucC7lBZ/QdJTKq2MoMdECyCo8kzgM5IeBh4C3klqzn9Z0rqk+vJF0rqqbwGOl3RUPvf1wNkkd8I1pDfED9j+g6SnjpSZ7fslHQacI+lvpAdY62HxaZIL6CPAOWMV2vYfJd0A/KCTL2n7ipxf+3KD1wEXkxYk+Vh2mfxfLv/l+U34PuBNku5mRfCyKs8mvfk/THqrf0cnZSrEWaQlFReR7v97bd9RcemMx78AX5P0VtJvfRErDMKj7k1bS/CrwFm5g/gcVrQwrgamS7oGOBE4FtgCWJjLdQcwaqd9UC8RCyhoPLn1cS3wXHewoLikzUmd1U/Lb6xIOhr4k+0vdpjnbOBNtj8w+ZI3g4nem6A5hAsoaDSSdgNuAL7S4cP/raQO3A+5i7cf2wsH4eEf9DfRAgiCmpE0h+T2qPJ+2xeMc91HqcyNyJxu+1MlyxcMLmEAgiAIBpRwAQVBEAwoYQCCIAgGlDAAQRAEA0oYgCAIggElDEAQBMGA8v8B3NMJY7sIr2QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preferred_shot_conditions = ((df_game_plays.event.str.match('Shot|Goal'))\n",
    "                             & (df_game_plays.st_x >= 0)\n",
    "                            )\n",
    "df_shot_types = df_game_plays[preferred_shot_conditions].groupby(\n",
    "    [df_game_plays.secondaryType, df_game_plays.is_in_homeplate]\n",
    ").event.value_counts().unstack()\n",
    "\n",
    "df_shot_types['shooting_percentage'] = df_shot_types['Goal'] / (df_shot_types['Goal'] + df_shot_types['Shot'])\n",
    "\n",
    "\n",
    "# Matplot lib.\n",
    "df_shot_types.shooting_percentage.plot(kind=\"bar\", color=['blue', 'red'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "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>event</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Shot</th>\n",
       "      <th>shooting_percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondaryType</th>\n",
       "      <th>is_in_homeplate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Backhand</th>\n",
       "      <th>False</th>\n",
       "      <td>737</td>\n",
       "      <td>15698</td>\n",
       "      <td>0.044843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>4930</td>\n",
       "      <td>29479</td>\n",
       "      <td>0.143276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Deflected</th>\n",
       "      <th>False</th>\n",
       "      <td>204</td>\n",
       "      <td>1038</td>\n",
       "      <td>0.164251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1526</td>\n",
       "      <td>6062</td>\n",
       "      <td>0.201107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Slap Shot</th>\n",
       "      <th>False</th>\n",
       "      <td>4467</td>\n",
       "      <td>93861</td>\n",
       "      <td>0.045430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>2230</td>\n",
       "      <td>13748</td>\n",
       "      <td>0.139567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Snap Shot</th>\n",
       "      <th>False</th>\n",
       "      <td>2214</td>\n",
       "      <td>46744</td>\n",
       "      <td>0.045222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>5997</td>\n",
       "      <td>33073</td>\n",
       "      <td>0.153494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tip-In</th>\n",
       "      <th>False</th>\n",
       "      <td>304</td>\n",
       "      <td>1925</td>\n",
       "      <td>0.136384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>5033</td>\n",
       "      <td>21632</td>\n",
       "      <td>0.188749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wrap-around</th>\n",
       "      <th>False</th>\n",
       "      <td>249</td>\n",
       "      <td>3660</td>\n",
       "      <td>0.063699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>200</td>\n",
       "      <td>3036</td>\n",
       "      <td>0.061805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wrist Shot</th>\n",
       "      <th>False</th>\n",
       "      <td>7041</td>\n",
       "      <td>164943</td>\n",
       "      <td>0.040940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>21003</td>\n",
       "      <td>119616</td>\n",
       "      <td>0.149361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "event                           Goal    Shot  shooting_percentage\n",
       "secondaryType is_in_homeplate                                    \n",
       "Backhand      False              737   15698             0.044843\n",
       "              True              4930   29479             0.143276\n",
       "Deflected     False              204    1038             0.164251\n",
       "              True              1526    6062             0.201107\n",
       "Slap Shot     False             4467   93861             0.045430\n",
       "              True              2230   13748             0.139567\n",
       "Snap Shot     False             2214   46744             0.045222\n",
       "              True              5997   33073             0.153494\n",
       "Tip-In        False              304    1925             0.136384\n",
       "              True              5033   21632             0.188749\n",
       "Wrap-around   False              249    3660             0.063699\n",
       "              True               200    3036             0.061805\n",
       "Wrist Shot    False             7041  164943             0.040940\n",
       "              True             21003  119616             0.149361"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shot_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "### Analysis\n",
    "Here are a few insights that we might have learned \n",
    "1. We see that shots are not only a bit more effective, but way more effective when you're shooting from the home plate area. So perhaps on a line you'd like some grinder that will just slash at the goalie so that he can score.\n",
    "2. NHL 95 lied to us, wrap arounds really aren't that effective.\n",
    "3. Wrist shots and snapshots saw the biggest drop off of shooting percentage when taking those types of shots outside of the homeplate area.\n",
    "\n",
    "There's a lot more that we can do from this such as taking this data and separating it out season by season, and there's a lot more I want to do with this, feedback is appreciated.\n",
    "\n",
    "\n",
    "### To do next or ideas if you want to go further.\n",
    "\n",
    "- take a look at season by season shot analysis view.\n",
    "- take a look at the type of game situation (is the game tied or is it a blow out)\n",
    "- take a look at the situation of the game is it a powerplay? is it a penalty kill?\n",
    "\n",
    "### What you might have learned\n",
    "Hopefully you've learned a little bit of the following from this presentation.\n",
    "- running jupyter notebooks\n",
    "- importing data with pandas from csv files\n",
    "- filtering rows and columns in a dataset using loc\n",
    "- creating a new column in our dataframe using apply.\n",
    "- using groupby to group common values together.\n",
    "- visualizing with matplotlib.\n",
    "\n",
    "Thanks!\n"
   ]
  }
 ],
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