mtg_booster_box/MTG sampling.ipynb

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   "source": [
    "import requests\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from multiprocessing import Pool\n",
    "import seaborn\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
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    "# MTG: Best booster box to buy\n",
    "In this notebook we are going to show a statistical test that calculates the expected revenue obtained from a given box of booster for different sets of *Magic the Gathering*. Here is the problem:\n",
    "Suppose that we buy a box of booster to sell the cards separately, afterwards. However, we only have one shot, I mean, we can only buy one box of booster, and there are several sets of expansions that we can choose to buy the box. That means that we need to pick the one with highest ROI, we need to *choose wisely*:\n",
    "![alt text](download.jpg \"Choose wisely\")\n",
    "\n",
    "Therefore, we are going to calculate the expected value obtained from a box of boosters. Then we will compare all the sets of expansions, and pick the one with highest expected revenue per card.\n",
    "\n",
    "Well, first of all we need to gather all the information about the cards. For that purpose, we are going to use [Scryfall API](https://scryfall.com/docs/api), which gives us a thorough description of every card in each set. In particular, we are going to focus on the card's name, the rarity, the set and, of course, the price. Finally, the only sets we are going to focus on are the last 6 sets of *Stardard* version, since they are the ones that can be best sold in the future (they will still be Standard next year as well)."
   ]
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    "sets = \"m19 xln hou akh rix dom\".split()\n",
    "cards = list()\n",
    "\n",
    "for s in sets:\n",
    "    has_more = True\n",
    "    next_page = 'https://api.scryfall.com/cards/search?q=set%3A'+s\n",
    "    while has_more:\n",
    "        try:\n",
    "            res = requests.get(next_page)\n",
    "            result = res.json()\n",
    "        \n",
    "            card_batch = [(card['name'], card['eur'] if 'eur' in card else None, card['rarity'], card['set']) for card in result['data']]\n",
    "            cards.extend(card_batch)\n",
    "            has_more = result['has_more']\n",
    "            if has_more:\n",
    "                next_page = result['next_page']\n",
    "            \n",
    "            time.sleep(1)\n",
    "        except Exception as e:\n",
    "            has_more = False"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "After gathering all the information, we can create a *pandas* DataFrame, so that it is easier to handle all the data."
   ]
  },
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>rarity</th>\n",
       "      <th>set</th>\n",
       "      <th>rarity_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abnormal Endurance</td>\n",
       "      <td>0.05</td>\n",
       "      <td>common</td>\n",
       "      <td>m19</td>\n",
       "      <td>common</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Act of Treason</td>\n",
       "      <td>0.03</td>\n",
       "      <td>common</td>\n",
       "      <td>m19</td>\n",
       "      <td>common</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aegis of the Heavens</td>\n",
       "      <td>0.06</td>\n",
       "      <td>uncommon</td>\n",
       "      <td>m19</td>\n",
       "      <td>uncommon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aerial Engineer</td>\n",
       "      <td>0.05</td>\n",
       "      <td>uncommon</td>\n",
       "      <td>m19</td>\n",
       "      <td>uncommon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aethershield Artificer</td>\n",
       "      <td>0.05</td>\n",
       "      <td>uncommon</td>\n",
       "      <td>m19</td>\n",
       "      <td>uncommon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name price    rarity  set rarity_norm\n",
       "0      Abnormal Endurance  0.05    common  m19      common\n",
       "1          Act of Treason  0.03    common  m19      common\n",
       "2    Aegis of the Heavens  0.06  uncommon  m19    uncommon\n",
       "3         Aerial Engineer  0.05  uncommon  m19    uncommon\n",
       "4  Aethershield Artificer  0.05  uncommon  m19    uncommon"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cards_df = pd.DataFrame(cards, columns=['name', 'price', 'rarity', 'set'])\n",
    "cards_df['rarity_norm'] = cards_df['rarity'].apply(lambda r: 'rare' if r=='mythic' else r)\n",
    "\n",
    "cards_df.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Seems perfect so far. Now we are going to move to the part where we calculate the expected revenue obtained by selling each card separately. \n",
    "\n",
    "### Sampling\n",
    "In this section, we are going to sample several booster boxes for each expansion set, and then we will aggregate them all. \n",
    "\n",
    "In the first place, we are going to sample a booster. There are 11 common cards, 3 uncommon and 1 rare / mythic card per booster (there is also a land card, which we will not consider for the analysis since the price is minute). Besides, there might be repeated cards within a single booster. Therefore, we need to take 11 common cards from the pool of common cards, with replacement, then we will take 3 uncommon cards from the pool of uncommon cards, and the same with the rare card. **Note:** We will consider every card has the same probability of being picked in a booster.\n",
    "\n",
    "That process is repeated 36 times, to get a sample of a booster box. We will repeat the sampling procedure 1000 times per expansion (that is the number of samples we have specified), which can be considered enough to get a sense of the actual revenue we can get from a selling cards. \n",
    "\n",
    "In particular, for the code, a multiprocessing library is going to be used, to boost the sampling times. Also, for the sampling part, *Numpy* library is going to be used, and then the results are going to be stored in a DataFrame."
   ]
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   "cell_type": "code",
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   "source": [
    "n_samples = 1000 # Total number of samples to be run\n",
    "n_cards = {'common':11, 'uncommon':3, 'rare':1} # Total number of cards per rarity in each booster\n",
    "n_boosters = 36 # Boosters per box\n",
    "\n",
    "def sample_booster_price(set_df, rarity, n):\n",
    "    rarity_df = set_df[set_df['rarity']==rarity]\n",
    "    return rarity_df.iloc[np.random.randint(0, rarity_df.shape[0], size=n)]['price'].astype(float).sum()\n",
    "\n",
    "def sample_set(set_):\n",
    "    # First we obtain the dataframe for the analyzed set\n",
    "    set_df = cards_df[cards_df['set']==set_]\n",
    "    \n",
    "    # Now we sample an entire box a given number of times\n",
    "    quants = [pd.DataFrame([{rarity: sample_booster_price(set_df, rarity, size)\n",
    "                              for rarity, size in n_cards.iteritems()}\n",
    "                            for _ in range(n_boosters)]).sum().to_dict()\n",
    "              for _ in range(n_samples)]\n",
    "        \n",
    "    return pd.DataFrame(quants)\n",
    "\n",
    "# We are going to parallelize the processing\n",
    "p = Pool(6)\n",
    "prices_per_set = p.map(sample_set, sets)\n",
    "p.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
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   "source": [
    "Once we are done with the sampling we can take a look at the results obtained. First of all let's see some numbers by using the *describe* method provided by *Pandas*, which returns the most important univariate descriptive stats for every set."
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m19</th>\n",
       "      <th>xln</th>\n",
       "      <th>hou</th>\n",
       "      <th>akh</th>\n",
       "      <th>rix</th>\n",
       "      <th>dom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.159</td>\n",
       "      <td>78.893</td>\n",
       "      <td>49.669</td>\n",
       "      <td>51.373</td>\n",
       "      <td>61.812</td>\n",
       "      <td>53.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.520</td>\n",
       "      <td>14.608</td>\n",
       "      <td>4.300</td>\n",
       "      <td>3.951</td>\n",
       "      <td>7.365</td>\n",
       "      <td>5.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>37.160</td>\n",
       "      <td>48.690</td>\n",
       "      <td>37.550</td>\n",
       "      <td>41.870</td>\n",
       "      <td>43.280</td>\n",
       "      <td>35.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.040</td>\n",
       "      <td>68.430</td>\n",
       "      <td>46.675</td>\n",
       "      <td>48.612</td>\n",
       "      <td>56.460</td>\n",
       "      <td>49.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.985</td>\n",
       "      <td>77.295</td>\n",
       "      <td>49.400</td>\n",
       "      <td>51.200</td>\n",
       "      <td>61.590</td>\n",
       "      <td>52.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.105</td>\n",
       "      <td>88.078</td>\n",
       "      <td>52.662</td>\n",
       "      <td>53.930</td>\n",
       "      <td>66.463</td>\n",
       "      <td>56.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.470</td>\n",
       "      <td>139.850</td>\n",
       "      <td>63.090</td>\n",
       "      <td>66.220</td>\n",
       "      <td>85.190</td>\n",
       "      <td>74.320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         m19      xln     hou     akh     rix     dom\n",
       "mean  50.159   78.893  49.669  51.373  61.812  53.030\n",
       "std    4.520   14.608   4.300   3.951   7.365   5.624\n",
       "min   37.160   48.690  37.550  41.870  43.280  35.620\n",
       "25%   47.040   68.430  46.675  48.612  56.460  49.275\n",
       "50%   49.985   77.295  49.400  51.200  61.590  52.780\n",
       "75%   53.105   88.078  52.662  53.930  66.463  56.802\n",
       "max   67.470  139.850  63.090  66.220  85.190  74.320"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for quants in prices_per_set:\n",
    "    quants['total'] = quants[['common', 'uncommon', 'rare']].sum(1)\n",
    "    \n",
    "stats = pd.concat([q.describe()['total'].apply(lambda v: round(v, 3)).drop('count') for q in prices_per_set], 1)\n",
    "stats.columns = sets\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
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   },
   "source": [
    "Each column in the table represents a set, and each row represents the univariate stats used to compare each set. The *mean* tells us the *expected revenue* obtained from selling the cards separately, or, in other words, is the average revenue obtained from selling cards separately for a box of boosters. Focusing in this number, it is clear that the set that might give us the greatest revenue is *xln*. \n",
    "\n",
    "The other important number to look at is the median, which separates the sample in two sets with the exact number of data points. That means, that at least 50% of the samples for *dom* might generate \\$52.78 or more. In that sense, again *xln* is the most convenient set, since the median is by far the largest among all sets. \n",
    "\n",
    "In addition, it is important to see what are the minimum and maximum revenues which might be obtained from each set. If we check out the prices for the boxes to be bought, the only box which would give us some profit is *xln*, since its maximum sampled revenue is greater than the price to buy such box ([approximately $80](https://www.amazon.com/Magic-Gathering-Ixalan-Booster-Display/dp/B073RZLF2M/ref=sr_1_1?ie=UTF8&qid=1533273599&sr=8-1&keywords=magic+the+gathering+ixalan+booster+box)).\n",
    "\n",
    "We can visualize all these descriptive statistics using a *Box-Plot*, which points out the three most used quantiles and also the outliers."
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
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s4+dAYh2wjHVAYh18nhaHzTPPPJN99tkn3bt3T5KcdNJJufrqq9OnT580NDQ0366+vj7d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\nBgAAKN7/A0WsqyHE3TgeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f446533f210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs = list()\n",
    "\n",
    "for s, quants in zip(sets, prices_per_set):\n",
    "    d = quants\n",
    "    d[s] = d['total']\n",
    "    dfs.append( d[s] )\n",
    "    \n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=12)\n",
    "pd.concat(dfs, 1).boxplot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this chart the red line represents the median, the line delimiting the box from below is the Q1 or 25% quantile, the line delimiting the box from above is the Q3 or 75% quantile. The lines below and above the box are set to 1.5\\*IQR, where IQR is the so-called *Inter-Quantil Range*, and shows how much the data extends to both sides of the half. Graphically, it is really easy to depict the difference between *xln* and the rest of the sets, with a median that is almost as large as the maximum values of *rix*.\n",
    "\n",
    "Suppose now that we want to claim that there is sufficient evidence to say that *xln* has greater revenue than the rest of the expansions. Staticians don't like these kind of claims by simply doing a visual comparisons, they ask the statistics what is the set that returns the greatest revenue. Do not get me wrong, visual assesment is a crucial part of the procedure when comparing two groups. However, it is not enough, we need some statistics proofs. The way to get that is by implementing **statistical tests**.\n",
    "\n",
    "### Distribution\n",
    "Many statistical tests work assuming that the data follows a particular distribution, the most used is the *Normal Distribution*. Let's now see what are the distributions of the samples obtained for each set. First of all, we are going to use the *Histogram chart* which shows an approximation of the real distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+SVLthrlH81PlD1jy+oxk0AcAAACAPETQR0HY/XK/DEOqbak65GuVlns1Ojyl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23u3bUSQgghhBBHmwT6DcS2bTz5JCndT2fIzahtMbYS6CUqJeLlrQPKqNNAW6qNEg92PCq2Np0v\nka+aHAt61ub8x8slEpUSEd0g6lw/yl+pJCiVhqhUdj8/ej9VzSVK1fvo7g5c/ljdzuOMGuiR2men\nRwyc0W2yJZrbIdiCJ3GPFzpbAbg7tLDh83c1ebEqJvcW0ozZNmO2ve0136mQodPudjKSLlA2N19m\nz7ZNSqWhtcdqUHvUmJbJWDrOWDrOcGqIRG4Gk34se5nRiR8xHppA6XlUB6OSKFGON2bHmRBCCCGE\neP4k0G8g5eUUhlki6w7hUGtBRJ+icEzViOjbp91rikJpsYDhcuDzPbot7q3M+V5N214V0Q1ihgvt\nSwX/nM4BdD0CgK5HcDoH9uR9PU/58lXAJtj+xqbFDPeKoikYMdfaQ9G2PpeiKCj9Z6CY42RmkWM9\nQcYm0rRUlHWfvytUu0bLszn6FIW+OrT/RNBL1bYZzRQ2fb1cHlnr5KlUEpTLz17Q7jCKp0eYyk6t\ne67KSWxUtNQdIqFeBs4Prl3/1U4fIYQQQggh9oIE+g1k5u4wABV/6473rZSrLCcLhNt86wLce6kc\nqgLHA09eUk1RNAxjcO2hKM9Y6v05MytZCpXbaGoId+jkfjdnHWXgHAD2w+u8dLIN24ZrD9ZX33c1\n+QBIz9Zznn7tPri3Tfq+rkcwjMG1Tp+jqsvXRV8gSiw0SCw0SF/gNB79PEo1S7tVeOal+4QQQggh\nhNiKVN1vIItDI/gAR1P7zvedrwVu4bZHI/fpcpXJfIkBvxuXQwOzsldNPZAy81cBE4/zJRTlgPWB\nRc+A5sAe/oIXf+OX+YMfD3H1zizfuNSztomrqXbt0rNZiNRnrfuIz41LU7mXyvGX+uy6Zj00Iq/x\nCoXqddIz7+JtPrdn91nZrJCv5slXCuSrBUZSCUzbJF0uosDKdVJQV67XTG4ah6rh1Dy4HW48Djcu\nh4F60O57IYQQQgixKxLoN5DS+AQ+INDWueN9l1YD/VYfkAUeVds/FfJutVvDsMwy2YVrKIobt352\nv5sDpolrcgQNG8wYiuGuLbM3egtfJceJvhD3xlIsXL9L5+IkViSCpjtw+lyk53LYtrsuQbimKpwM\nefliMcNUvkS39+gsubgXNDWAL3yJ7MKn5JZu4guff+I+lm2RKi0zm5+vPXLzxNMJcpUcVcskVy1Q\ntarP3DYFBZfDhcfhQlNU3A4Xnb5OmowgQSNIyAgQMkKEjAC23didPKZlEk9vP+0kGhiQrAwhhBBC\nHFgS6DcQdX6aiqLR0rLz1P3FuVpw39L+KNC/uzI//+QRCPRzS9exzAJe56soir7fzcE1OYIxlUC1\nbVyKQrFvEOXYBezRW+Ru3uCNcye4N5bi/etT/PrrEczoAIxUcTX5SI8vkM+aeP31+fE+vRLo30nm\nJNDfhUD762QXPyM9+y7e5hfWjeqblkkiM8HD5AgT2am14L5ibcymcWkGPqePbiOER6+Nynt0Dx6H\nm0I1h6ZotLhbsVf/sx99XSjMU7VM3A4P+WotCyBfKVBY+Xe6nGGusEgiM7npe9AUFb/TR6e3gxZ3\nmBZ3M63uMC3uMGFXMy7H4a45EE+PkEgniAQ2n36SSNfqUMRCg8+zWUIIIYQQT00C/QZhVav4c0sk\njRBR3QHsbKmuhbkcigJNYQ+mBVULHqbztLh0wi5nfRp9QNi2RWb+Y1A03M5L+92cNaWuCFafDWO1\n75XYeewf/WdyN77g8t99k//suMe7SY1fHTi2kpJdxdXkJT2+wHKqUrdA/3jQi0NRuJPK8lZPuC7n\naGQOZxBf80Wyi9fILH7BvKOZu8v3GS7GiY+PUf5SUK+rOm2eFjo8bbR5WmlfeeQqyzg155aB5nBq\nCNg6EH2a1ytmhSZ3K8ulZVKlNKkvfZ3NzZIuZ7m79GDT/f1OH23uFjq8bbR72ujwttHhaaPJFTo0\n0wMigYgE8kIIIYQ4tCTQbxCzw2M4bJOiv2XH+9q2zdJ8jmCzB4euYZYgnoOKZR+JtP1S9SHV0hLe\n8EW0ysF9v0pTG7R0kb97Bx2Tl3s9vDOa424iyZloM/ClgnzJCl297rq0w6mpHAt6uJfKsVgsN3xH\n0F5bKia5XtWIAvHEn/JvlnOsLlbY4W1nMDTAYGiAaKCPJldw08B4OLX5qgd7Sdf0tY6FjeevdRR0\n+3pZKCyxUFhkvrDIQmGRhcIS84VFRpYTDC/H1x9TfXTMLl8H3b5OurydNLtCDT0VQAghhBDieZNA\nv0HM3n2IB1CCbTvet5QtUy5V6Yk2rT13P137eiJ4cAPfvZIvfwJAoO0K1uaZygeGcuwC9kd/Rv7e\nHd6MenlnNMf7N6YfBfohLyiwnKxv4cQzIS/3UjnuJHO82SmB/pNkylmuZq7xee4mo4laisY33U5e\ndDn59bYTeK0LDLgitBzfeUfdfnI5XPT4u+jxd214rWJWmCssMJufZyY3u/J1jtn8PBPZKa7NXV/b\n1u1w0eXtoNvXRfdqB4CvE0OTe0sIIYQQYjck0G8Q2XgCD+Bp6dj5vou10cHm1lpQb9swlAaXphLx\n12dU+KCoVKeomJO4AsfQXa2UKO53k7Y167tEG3/G9Dtf4D79OmGXg0/vzfPb36wF9qpDw9vsJp0q\nYts7m76xEydCXhTgTirLm51NT9z+KCrZZT5MXOXG7F2GiqNYWCgoDIYGeLH9Ai+E+kkP/VuOkybs\nOYGiPPp1bJs25Xhp7Xtn1EDRlLXXrFGzdo7AxvvVGd3/+fGmZTKWiQMQdLoJOqOcaIoCtQyidDmL\npjiZzs8ymZ1mMjuzIQNAQSHsaqLD20qHp5V2byst7mY0Rd12/vxhIMX+hBBCCFFvEug3CHtmCoDm\nto0ja0+SWcgDEF4J9OeKsFyBc80etAZPp82VPwUg0PbqPrfkyaJRG9uKYX3gxx76HE69xsUWLz+e\nWOYH78/R2l8bDQ62e8ktFshmqtQrH8OnO4j43SQyBTKVKn5dfpWsiqfH+P7oX/BwOY6JCUXoc3Zz\n0fsCL524TJMnuLat1foSmbkPKahf4DFeXHu+HC9RSZTQIwaVRC3gN2KutdfsCRulZ+PP5uq27HPp\nhO2K2SmKQqq0RCQQ4Rfavrn2fNmsMJObZSI7zZ3FO4xlJkmVllkoLnFr8T4AmqLR5gnT7mnFpzcR\ndi8TMoIbznHQSbE/IYQQQtSb/HXeINzL8+Qdblr9/h3vm10J9FdH9B9kas+fCvn2rH0HUbWSoVR9\ngENtwfBF97s5T6RpcGxQYebyRdLvvkOrY4JfudDFTybTfDE6xVurgX6nn6k7C6QWK5sG+nYlgbXy\nFefuA4kzIS/xTIF7qRwvtR6+YGsvWbbF9fnb/MX4O4ws14K0sCvEa12vcLntAq2ezSPvQPvrZBeu\nkSt/hNt5Dni0ioEeMdaC+8cpPQpqv4YROrirHuy0mJ1T0+kL9NAX6KHdEwJqo9oz+Tni6TES6QnG\n0uNM5maYzs3xxfxtAFrdYQZDAxwLDTDYNECz63BkmEixPyGEEELUkwT6DWBpPkWgkmUhvLtU1uxi\nAc2hEgjV0vTvLYMKDAY9e9jKgye78Clg4XZePlSFwHwXL5F+9x0yDx7Q/o1eBjvDPJhaYDGZJdzk\nI9RV6+xJLVbofmxfkyiKXkvpV/QITufArttxusnH98cXuJPMHtlAv1gt8uH0p/x0/D0Wi0sAnA2f\n5GTzAL2+Lo41Hd92f83hwd/2CumZd8mXP8PF155Dqw8PTdXo9nXS7evk9a5XgNrI/0R2kuFUnKHU\nCMOpOB9Mf8IH07VaG82uJjo8LfT5u2nzdOJ3NnaHpRBCCCHEZiTQbwCTt4cwAKW9c8f72pZNbjFP\nc4sXVVVIlipMFWDABx5H484Pte0q2YXPUHDh1k/vd3N2xHPqNIrhInP/Pm1f/wYXB7p5MLXA0OgM\n4aZj+JrdOBwKycXyxp0VDdUZe/Stsvtr3GTodHoMHqYLFE0Tl9a498vjksUUP514j/cnr1I0i+iq\ngze6XuHrvW/S4W1bq0r/NAKtr5KZuUqudJVQ9Qqq4+CO0h8ETk1nIBhlIBjlrcjXsGyLiewUD5Mj\nDKVGeZga4c7SEHeWhvjzxNtEAr283HeOqKufXn/3oVneTwghhBDiWUig3wBSD0doB3z9Ox/Rzy8X\nsUx7LW3/djILwKkGH6AtVu5jVXN4nC+jKPp+N2dHVN2J94VzZD+9Sml+noGO4wTcBqPj81w+14+i\nqgSbdRbnypQLFTDqFzieafIxnV/kbjLHxZZA3c5zUGQrOX4Y/ylvT3xA1a7icbh5rfMy51tP43a4\nyFWWGU4t76hYnOpw4TWukC29TXrufUJd33zyTntgdR74Vq8dlmJ3qqLS5++hz9/DN/q+gmVbXJ2+\nSjw9wUx+geHlOPFbtZUO/LqP0+ETnGo+jld3blnV/zC9fyGEEEKIzUig3wCqE7U/YtvPnKAyaT1h\n6/Uen59/O5lFAU42cMxm2zb58jVAweO8uN/N2RXfxUtkP71K5sF91LY3uDDQxTu3R4mPzxMbaKep\nxcniXJnUVAZCO6/b8LTONfv48eQiN5YyDR3ol80yPx1/jx+N/YxCtYjf6WMw1McbXVdwbFIZPRKI\nEA08/bQIj/MS+fI1MnMf42t5CajvsnJPattO23+QqIpKqydMqydMLDRIvlJg2pzgg9HPub14j49n\nrvHxzDVUFPqDfRxvGiAW7MP5paD/ML9/IYQQQgiQQL8huBenqSgOWmIRpidHd7RvdqG2tF641Uu6\nXGUsW6TPC74tBrkTlVpV74i+/0t47VbFnKZqzeAOnkCzD2fqgveFcyiaRub+PXgDzke7ePf2KEOj\ns3zzq+2EmmsXcGwiRVusGQDT3vuR/RaXk26PwVA6T75q0mjJ+6Zl8tH0p3x/9Ecsl9N4HR5+/div\n0ONrxaE69qyYmqLo+Iw3SBf/G8szb+PjrT057lY0VTsyheA8upsrXZeIuQbX0vzfmXifodQow8sJ\nhpcTOFQHZ8InudR2jrPhU7gch/f3mxBCCCEESKB/6GWWc4QKSVLBDtRdzJHOLq6M6Ld4uZnMYrN1\n2n7UaWz678OmUL4GgL/1ZZjb58bskubx4IlGyQ0PoyZnCTS109XRxORMkpm5HG3NOgpgzdY6chKV\nEopdn4KD55r9TE6UuJ3Mcq5BBvVt2+ZhKs7v3ftjZvPz6KrOz0e+wVuRr+J2uHc0B/9pufSz5PmU\n3OIXuLyXcGj7vEZeA1pN83+18xKvdl7Cqwf4bO4G1+ZucH3+Ftfnb6GrDs6ET/FS+wXOtpzCocr/\nJoUQQghx+MhfMIfc+M37OLCxOnp2tX9mIY/D0PD6DW5PLQBbB/qaohCr43zv58GsZChW76OtLKlX\nnivtd5N2LXDiJLnhYez7n6Jc+WUG+9uZnEly/fYsvxhTCQd1krM5oo5aSvJYndrxQrOPH0wscH0x\n0xCB/mR2mm8/+FOmcrOoisobXa/wi/3fqvt67YqiEur8Jguj3yZbeoeQ56/U9XyN7ss1CJKqj6VU\ndt1rkUCEDm87v9T/Fr/U/xZT2Rk+m7vBZ3PX+WL+Jl/M38Sne3mp/SJXOl+kx9+1H29DCCGEEGJX\nJNA/5JYeDNMGePv7d7xvtWqSTxUJdfrJVU1GMwV6vS4CenHvG3pAZBc/Byw8zouHakm9zfgGj4Py\nZ9j3PoUrv0x3RzNul86tu/N8K9pGR4uTheUcC7NZCNdvznfI0In4XIxmCmQq4D9ctQ3XlMwyPxj9\nMT8ZfwfLtjgWjPI7p36Ddk/rc2uDO3gcp7eHUm6IcnUSg9iTdxIb7KYGQZevgy5fB7/c/xaT2Wk+\nnrnG1ZnP+OnEe/x04j16/d1c6XyRl9oPZ10PIYQQQhwtEugfcpXx2qhV+5nt1+veTGqxADb4wm7u\npnLYwNkmH9CYgb5tWytL6um4tllSr5JYP8rvjBoo2sHrFHB4PHgiEfLxEez0IqrPSyzSzq37E9yb\nLHCmxcmt4RyTiSTBcHtd23Ku2U8iW+TOMrzSUtdT1cXNhTv84YPvslRMEnY18Wb3ywwE+55rkA+g\nKAqhrm8xN/T/ki39FJ8tBeF24/EaBK1hP/NW5qn2VRSFHn8XPf4ufi32i9xevMeH059ye/Eef/Tg\nuyFCzhcAACAASURBVHxn6HsMBPs423KS/mBMlusTQgghxIEkgf4h51qYpqJotA30og0PYUyNUeqq\nLQulTk3VNurp3XTfpYUcAL6wh1tLtbTWM80+YKHu7d4PheUhzEoat34BVdm8xoAzuv751aDfiO1+\nyoIxVeuMUe0EVuTJS3apiQTGtLJ2HVf3J9oPmgamiRYfQU0kCJw4ST4ex75/DS5/hVi0jVv3J/g8\nnufnr9Qi7slEiuCl+gb6Z5t9fG9sntupwxXoJ4sp/mjoT7g+fwtVUfm5yNf5xeg3Gc9svfRcvbl8\nfRiO45SqD8in7uCQUf1941AdnG89y/nWsyyXMnwy+xkfTn/Kg9QoD1KjvDN5lTe6XuHVzpfwOb37\n3VwhhBBCiDUS6B9i2UyeUGGJ5WA7+sQYaiJBsauXQpdFvsumalYAqEYiYG5cdm810Hc2uxhOZ+jy\nGDQbOqXDO219W9mFTwFwb7OknqIpzxTUP86MDlAaq2UDWJEIZnT7EdrV10uKQrF7ADNqUxpTMKYS\naHEbMza4FuRbkQieC5fgz/8c+/4ncPkrBHxuIr0BEuNpMlWL5lYvMxPLnKxaUMea+H7dwUDAzXC6\nQKoM7Sv9JZXKo4DZ6RxAUQ5GXX7TMnl74n2+N/pDSmaZWLCf3zrxV+jydex30wDwub5KKTtMaurH\nhI0IiiK/qvdb0PDzrb6v8s3er/D+1AfcWLjLg+QIfzz8Z3xv9IdcbD3HV3qu0B+IHPppQUIIIYQ4\n/OSvx0PANCEef/SHYzRqo2kwfuM+GjZWew9jYwooUYZVlU5lFNQ+rM4e7EqCspkANo7qL83XAv15\np4Jl19ZEb1RVK0kxO4zh7UXXnk86diKhAg4S9gkiEQszZj95J03DjA1SZOV6azbFvtUU5Ef7W5EI\nZmyw9gPcMwjjQ6i5NJY3wIUz7STG07wzmqO7L8TSfI7UTBaobzG5c81+htMFbqWg3V8L7FetBvyG\nsf9Luk3nZvmPd/6AscwkXt3Dbwz+Gq90Xj5QKdgOtQmP8xL58ifklU/xGlc2bPPlYnOrrLQJwLg+\nQSTw5OwRsXOKotDpbaPT28bfPv07fDRzjfcmP+KT2c/4ZPYzun2dvNF1hZc7LuJyHMzipaZlEk+P\nbLtNNDCAptanY26/zy+EEEIcBQfnL1uxpXhcWQkaa8HjatC/9OAhANVwP5OTtde7uix6e/swjEFU\nZwxF3/qP/eRCDt3tYCxfG8I/1+yv59vYV4XydQB8LS8+l/NFozaRSC2LIhKxiEafIsjfJeXEZcDG\nNfQFACcGm3HpCu/F83T1hQBYGluu2/lXnW3yoSlwI1lbnk5RNAxjEMMYRN/mPnxeLNvix2Nv8y8+\n+b8Yy0zycscl/ukr/4hXu146UEH+Kq/xKqrDQ670EaaVW/daj69320B+s2JzYu95dA/f6H2T//WV\nf8jfv/A/cLH1BaZzs3z7wXf4J+//M/7wwXeZzR28NTzj6ZFNO4pWJdKJJwbih/n8QgghxFEgI/qH\nRCRiEYvZwKMU/MpY7Q8lX/QE7apFX59N/ilX2auUTdKpIt5okJFciajfTcg4pOXSn8C2qxQqN1Ed\nHjyhU5ST1bqfU9NYuV71p5x4Efsn/x/u+5+Tv/AVdIfG6R43n43myQCKAkvjaXa5AuNTczs0Tgbg\n9jJM5kr0+A7OaOZ8fpH/dPcPGV4exa/7+O0zf5XzrWf2u1nbUhUXwY6vkpz4AbnSe3j4tUevPVZs\nblUpUCukaYQOzmd/FCiKwonmY5xoPsZyKc0HU1d5d/Ij3p54n7cn3udU83G+2vMaZ8InD0ynUiQQ\n2fQeOirnF0IIIRqdBPqHmHt+grLqwN87AJM7G/1ILtZGCPMdbgAuNPBofrFyH9su4G9+DUV1APUP\n9J8nJdgCHVGMsXsoxRzg4VxfLdC/+mCe1g4/8zNZ2qomqqO+qbAXmmuB/rXF9IEI9G3b5r2pj/iv\nD79P2SxzsfUFfuvErx+awmm+lsukp65SqNygnH8Fp+dg1BAQWwsaAX6x/1v8XOTrfDF/i7cnPuDu\n0gPuLj2gxdXMmz2v0ukJ43JsXhBUCCGEEGIvHIyhBbFj2aVlmooploOdqNrOg7elhXztq8eBqtSq\npjeqQqWW0u5rubzPLakf5cSLKJaF6+ENAHrCTlq9Gp89mKe9J4ht2eTn03Vvx4APfA64sZihYm0s\nAPk8Zco5/vX1f8sf3P8OmqLxt0//Nr979m8emiAfQFFU/K5vADbJiR9g288nS0Q8O03VuNx+nv/p\n8t/jf3npH/Ba58sslzN85+H3+X9u/h4/SrzLZHZ6v5sphBBCiAYlI/qH1MTnt1EBq3t3c5+X5nOU\nvQ6yik2f34WnziO9+6VcmKNiTuLU+nEYTfvdnLpRTryI/fZ/wf3gc/ilKyiKwusRL398J01eq9V0\nyM6m8HXW9zNQFTjXBB/MW9xL5XhhnzJF7i495C/G36dkljndfIK/ceqvETLqW4ywXgxHP4ZjkFJu\niHzyFg4aI935SXO0D3oxwe3aD+uLyfX6u/gbp/4af/nYL/Hh9Cf8ZOxtbi7e4+biPWLBfr7W+zrn\nW85I8TkhhBBC7BkJ9A+p5Xv3aQK8x3f3R39yIUe+wwNALHR4Rjh3Krv4GQBu5/l9bkl9KeFOKuFO\nXKN3sIu1edqvRTz88Z00N6eXCRga2ekl7PPRurflQhN8MA+fLaSfe6BfrBb5wwff5eOZa+iqg98+\n8eu83vXKoV/uzO/6BuXcKMnJHxF296Eqhzvt+0mFAg96McEntW21E+DxOehe3cO3+r5KxN/B6PI4\nD1Jx7i49YHh5lKAzwJvdV3it65W6tVsIIYQQR4cE+oeUPT4KQPeFM2QLO99/cSFH4VwTuqrQF9j/\nudQ7ZZs25Xhpy9edUQNbqZJbuoGqeDEcsV2fq5IorX3VIwc3wCocv0jgwz+Du3cg3Ey7TyfWHeDO\nWIqfH2hmcThFKZ0HPHVtR6sLerwGD5bzpMtVAs7n82tmLD3Bv7/9+8wVFmj3tPJL0a/zYsfzWWWh\n3jQ1SKD9dZZn3iZX+gC/6+tbbrt6v65yRg0U7WB1dGhbFBM8LJ61/aqiEgtF+Lnot5jNzfHO5Id8\nNP0p3xv9IT+I/4TBUJTzracZCB479J1UQgghhNgfMkf/ELItC9/SNEtGkLbO8I73L5eqLCk2VUMj\nEnTjUA/fbVCOlzYENKsqiRLleIl88g62WcStv4Ci7C4l1hk11oJ7PWLgjB7cQL94/AIA9vXra8+9\ndrYT24a8o3aNs1PJ59KWSy0BbOCLxUzdz7W6bN7/ee1fM1dY4K2+r/Fbx3+VJtfhTNXfir/9NTRn\niHz5GlVzYdNtvny/wqOfBXFwtXvb+I3jv8Y/e/2f8NeP/2Va3WHuJYf59oM/5Z9f/Ze8M/EhhWpx\nv5sphBBCiENGRvQPoeLUKE6rQjbcs6vRnqWFHLnOw5+2r0cMjNjW2Qi5tbT9c7s+h6Ip257jIKm0\n91H1N+G4fRP7q18BVePFE6383g8f8HApT7cC2ekk0F33tpxv9vP9sQWuLaR5syNUt/Okyxn+050/\n5M7SffxOH3/r1G9xKnyc4dRQ3c65X1RVp6nn51kY+TaZ4o/x2H9rw8//YbpfxXouh4uv9LzGm92v\n8rOJt7kxf5eHywm+/eA7fGf4+7zUfpE3u1+l19+1300VQgghxCEggf4hlHl4hzCgRfqfavtKJYFl\nlwGwnSeZm8+Sb3fjQaHLd3BHqJ9F1VyglBvH5Y+hKfULNA8URaE4eB7fZz/DnhiH/hh+j5PT0SZu\njS7R3xSgmMxSypUxvM4dHbpYHKVUyq5973QObJsl4XZonGnycmMpSzxbpMtZuw+/bKtj2LZJuTzy\nxO3uLj7gP9z9AzLlLKebT/Dfnf7r+J17t3rEQSwW5w4cx+kYoFwdIZ+8ibd5951Y4mBSFIU+fzd9\n/m5a3B18OH2V9yY/5v2p2iMa6OON7itcbjuHU9vZz/HzYlom8fTWS74ehmKLQgghxGEngf4hZE7U\nRiubTp984raKHkVXnFAuY1cSlMtO7iyD7VE56/egNuj8z0Kllr7ua7kEi/vcmOeoOHihFugPDUF/\nrS7BS6fauDW6RMHpQAPmR1L0vNC2bj/TtomXayneUaeBtnJfmLbNuN1F0NZIlWvFIOxKgl4b3K7t\n5yi/0hbixlKWq3PL/ObA+uJlq0G/YWw8Rrk8QqWSQNcjm25nWiZ/OvLn/GjsZ2iKxl899it8rfcN\nVGXvpqAc1GJxiqIQcL3FQvbfkZz8Ia7AMTRHfWsuiP0TNPz8QvSb/Fzk69xevMd7kx9xe/E+8fQY\n/+XBn/BSx0Ve63yJXn/3gZrLH0+PbBvMH/Rii0IIIUQjkED/EPLMxymqOpEzTy4wpygahjGISpHV\nVc1HFRPQuNLVRK6uLd0ftl2hUL6N6vDiDh6nvFjZ7yY9N6Xe4+D2YA0NYb/18wBcOt7Kf/hv9xnP\nF4gCsw8WNwT68XKJROXRXO6Y4Vp7fqxa5aJ7ELVQu1smqmWUSpnBJ2SIR30uWl1ObiWz/Eq1Fe8m\nQf1WdD2yaSfAUjHJv7v1+4ymE7S5W/g7Z3+HPn/PUx/3aR3kYnGaGsRnvE629DNSkz8mHPlL+90k\nUWeqovJCy2leaDnNYiHJB1Mf8+H0p7w7+SHvTn5It6+T17pe5uX2i3j0g9HxEwlEDuzPkBBCCHEU\nHL4qbEectbyAr7jMjK+TpoB7x/svlSDj1vCkK3TsYv/DoFh5gE0RX/jCrovwHVqahnL2LKTTlGZn\nAfC6dAYiIeYzBfSgh8XxZUq58oZdI7pBRN84lSOiGxz3eIgZLmKGiy7H06ULK4rCy60BTNvms4X0\ns70v4ObCHf6Pq/+K0XSCF9sv8D+/9PfrEuQfBh7ni+juDnJLX1DMxPe7OeI5Crub+NXYL/C/vfaP\n+Xvn/g7nW84wnZvljx58l3/8/v/Ov7/9+9xfeohlW08+mBBCCCEalozoHzLFhzcxgGLX083Pf9wn\ncxag0lOy97RdB8lq2r43fHGfW7I/lHPnsT+5ysStq1hnOgBo77N5OApJZxWfDXevD/HClSdP/dit\n1Tm6TTpoCrw/u0Cna4HV7GKrkqDH1/uUx7L4r0Pf4yfj7+BQHfzOib/KKx2XSWRGt9yn0ecAK4pK\nc+8vM/vg37I0/n06T/5dFFV+nR8lmqpxtuUUZ1tOkS5n+Hj6Gh9MX+XT2S/4dPYLwq4mXu64xMsd\nl2nztOx3c4UQQgjxnMlfhodMafgWBuDp6EQbHsKMDvC0l9Gy4UZKQalYDHoaswhfpbhAxZzAqUXQ\njeb9bs7+OHUaW1UpDMcxTBPX2AQxh8qHKkzmKpxQYGEozeTZcY676xPsf3mO7oAPhjIwXYCulazi\nqdwUAIPuE9seZ6mY4T89+AsSmTnaPC387pm/SY+/i+HU0JGfA2x4u/G1vkx2/irLs+8S6vz6fjdJ\n7JOA089bka/xrb6vMrwc58OpT/h8/gY/iP+EH8R/wkAwwisdl7nUdh6P3piZXEIIIYRYTwL9Q8Y5\nNURBddLj0VATq1XBjz/VvlM5hbyt4J3N0TrYWb9G7qPsHiypd9gpbjeVrnacE9P0j+cwklDVoDfk\nIrFUxN8VJDOVobxchdb6tWN1jq7uKDB0d4LxvI83u2r3nVXZuqL9qhvzt/mPd79DoVrixfYL/PaJ\nX8fleFQYQOYAQ6jz6xRS90nPvI8neBKnpzF/rsXTURSFY6F+joX6+U3zL/PF3E0+nrnGg+QwI8sJ\n/mjoTzjfcoY+fyeRQP2X2RRCCCHE/pFA/xCpLMzjKizzwNvLt071YTl3VmJhaLk2X907laf5NW89\nmrivbMskt3QDRXFjOI52AFiO9uCcmCb78CH6xUtYlkn/QoHEUpGip/ZjvzxcgGP1b0uv10WH28md\nVJZMpYpf3/7XTtUy+X7iE96euolD1fjN2Jt8pe9XDlRV8YNC1Qya+36F+eHfY3HsT+g4/t+jqEes\nLoXYlKE5eaXzMq90XiZZTPHxzGd8PPMp1+auc23uOl6Hm1c6X+Tljsv0+Drl50sIIYRoMFKM7xDJ\n3bsLwLy/C+8Og/x8xSSRUXAXqxjZCqHwwajMvJcKy/exqnnc+hkU5Wj3YZX7a0XqskNDa8/1NblQ\nVYXhZB5VV1h+UMCy6l+rQVEUXm4LYtlwdW55220XC0n+75vf4+2pm7R5WvgH536NKx0nJQjZhjsQ\nwxu+RKUwS3r2vf1ujjiAmlwhfiH6Df7pK/+If3j5f+R8yymqtsVfjL/Lv/jkX/HPr/5LfpT4GanS\n9j+fQgghhDg8jnY0dMgkb9wCwGzt2/G+D5Zy2Ch4J7KEmtw4HI036reWtq+f3+eW7D8r4EdpDpJL\nxLEqFdBUnA6VzrYQkzNJTg54yI8USQwv0j9Y/0JdF8MBfjixyNX5Zb7auXnthJsLd/iPd75Nvlrg\nUkuMv3H6b6GY43VvWyNo6n6LYvohyzPv4g6ewOnpqPs5E+mtp180ejHEp/Gsn892+wNEAwNoO8ze\nUBSF/mAf3+x7g6/2vErBrHJ15jNuLdzlj4f/jO8O/4ATTceIBro4FtpdwVchhBBCHAwS6B8Stm1T\nfHCfkmrQ1LqzebiWbXNvKYtDsTEm8jQfC9eplfunWkpSzIxgeHtxaI33/nbDEe2h8tltcvFRiMUA\niHSHmZxJknYpOIDbn089l0Df0FRebAnw3myKm0sZfF96zbRMvjvyA34yVquq/xuxN7nSfgKXw0XJ\nrHvTGsKjFP7fX0nh/926pvA/qdDhUSiGuJ1n/XyetP9qJ8Cz1KhwqBoXmk9yofUs2UqOz2ZvcHXm\nM+4lh7iXHEIff5/LbRd4pfMyx0L9qIokAAohhBCHiQT6h8XSLFp2mTFvhB7/zirm31/OkauYRLQq\nlmnT1NJ48/Ozi58D4A1fgtQ+N+aA0FYC/ezQ0Fqg39PVjPq5wthcmTNtOuMjSywnC+Cpf2r8lfYQ\n78+m+GA2xVutoCiQLKb4d7d/j5HlBG3uFn737N+k1Zmre1sakTtwDG/zBXL/P3v3Hd3YfR54/3vv\nxUUjGnsnwD69j0YjjcYqVpetbluKYsd26tlNdk/ek3iT991sNps9STbZ3eRN3k1iJ45lR7ZiS1a1\nep+u6TMcDjsJdrCBAEh03Pv+AQ05nCE5nBkWcPT7nMNDErfgAXEB4vmV5zd2isDgx7hK7lyy+1Jk\n5XNfCHE+1/v3We6/r03NYm/ZbvaW7WYoPMLbXe/RONbC4cFjHB48Rq45m11F6fn+eRbRkCoIgiAI\nq4FI9FcJveMMAF22UjZbjVd17JHP5kUXTEQYBPIKbJfto+sp4vEOABIJL6q6eobd6rrG5NhpJMWE\nNXsdiXHRDQwgF+aiWKxMtLeBrgMSJqOKu8xBZ3cAyy4HkaEEjaf6Kbjl6ipw6wkvsdjl1+F8106O\nSWWNK4vz45MMx4xMxvv4XtNfM5kIs71gM0+veTzdix9rnfV44cqyy+4lOtFF0Lcfs6MaicKVDklY\nZQqsedxSsp3dxdvQUDgycJwTw2d4o+s93uh6jxpXJbuKdrCtYOOMVTAEQRAEQcgsItFfJbS200jA\nZHE9BlkCFlZELRRP0hoIU2A1Qk8cgJz8y3v04/GOqSRNVd0Yjatn2G0k2EoqEcKWtwNZVgGR6ANI\nskxWVRXBcw0YhnxAesrHmtpcOrsDjGg6VqtK46kBcnYUYTAubKi3pHrm3DZ17UQ6Zt1+S6GL8+OT\nfDwk0Rc4iUE28LX6x9hTsksU3FsEsmIiz/0IvtZnGfW+TI7pV5ClqxsBJAiQns9f56qmLruaJ5MP\nc2r4LEcGjtMy3k7beCcvtL7CjsKt7CnZRYWjbKXDFQRBEAThEiLRXwX0RBx6mhkyusguLrmqY5tG\nJ9CBNbk2hkeTGAwSDpdl1n1V1Y3JtPqG406MpIvw2XK3rXAkmcdeU0vwXANaWztUpRP9+poc3vqg\ng25vlCe3lXN0fxd9DUO4ty2s9oMkKWgGD72R/tl3iHTMWWzMpUZRCBFKFeA05vJbm5+h3C7W815M\nJlsFjqI9BAf3EdLfxWl5aKVDEpZZSkvRFZy9sQ2urRhgvsXJQ1V3EojtpHGshaaxdg70H+FA/xHK\n7aXcWrKLHYVbsIhefkEQBEHICKK6zmrQfR4plaDDWkpprnPBh6U0nZaxSawGGbfNzKQ/hSvPgCzf\nOD2nyXiAaLANo7V0WSqNrzZZVVUgSejt7dO3WY0UFBoZHkpQVp+HQZXxnhhAS2kLPm/fRM+8VcFn\nKzZ2Zvgcf370bwhFTyJJMuvz7hBJ/hJxFu3FaC0hmmgkmji/0uEIy6wr2HHVr8+LeRxVczYEOE12\nSrLy+Ma6J/itTd9kU956+iYGeL755/zhgT/lufMvMDg5jK4v/dKdgiAIgiDMTfTorwJ6x1kAOqyl\nfDnXCSPBBR3XGQgTTWncVuQiFoiha5CTpy5lqMsuXYRPx5YnevNno5jNWMvKCfd0I0XDU7dXVFrw\nDcY51z3O2k3FnD3ex2DLKCVr8xd8brfDvaCCYUktycvtb/Bhz35U2cBjtRv4cDBF66SDWErDpIj2\nxsUmSQq57kcZPP9dgpG3scbcqKbZlzUUbkwLfX3OZqHFADfk1bMhby3jsQCHB45xoP9TDg58ysEB\nKLTmcZ/ni2wr2IRBFh81BEEQBGG5iU/Yq4Defoa4rBLKK8VhWfh828aRCQB2FbiYGEknedk3UKKv\n6xqTo6eQZCNW1/qVDidj2WpqADD2TffqV7jTw2uPNw+xaWcZkgRdR/sXvRduJDLK/zz+f/iwZz+F\n1gJ+b8dvs7d0F2sdIeKawrHhwKLenzBNNediN9+NTpyRzhfRteRKhyTcoFwmJ/d57uK/7v4O/27z\nt6l2uvGFR3i28Xn+6OCf8Wbn+4TiEysdpiAIgiB8rohm9gwXHxoCv4+urHIqy3IWXLCsZyLKcCRO\nhcNCjkmdSvRvpB79eLKDVCKYLsKnzKwAn/DGZvysuk2zbp9t243GVlPD0IcfYOydTvStWQr5BSrN\nPeOgKhTV5zHQNMJwu5+CmsXp+T0xdIbnzr9ANBVlV9F2vlr/KKbPnqe19hBnAw4O+Ma5ucCFcgNN\nJ8kkFuMG4qkeopGz+PveJaf8/pUOSbiByZLMutx6TIrMeCxIV7Cfg/1Heb3zbd7yvs+Owi3cUbaH\nMvvV1ZoRBEEQBOHqiUQ/w02eOQVAu7WUuhIHAH19MjA9n9qry9Rfkicd9KUXk1+fm15KLzTVo59+\nylO6Tlc8hjcRw62uzkQ3nDgNXF6Ez+iZ+XhUt2nGbRf/fOm2TOL1pp9nr/fy5/dqGHPzwOlE7e+A\nVIoLA3kqPBaGhxKcaB2m8qZSBppGaD/SR7jCCoDHeG1/l3gqwYttr7G/7zBGWeWX136Fm4t3zNjH\nrGjU2SY4H3Jw1h9iS2762k7pOt3xOJqWQgq3ICW7AZAvaZ9aSDExIc1h/iIpxcfEyFFMtgqyssXo\nlxvBfHPwM+H14TI5eLx2Ow9W3s3hgeN81LufwwPHODxwjFpXFV+s+ALrc9eI1TYEQRAEYYmIRD/D\nTZw6CUBbVhn3lTjwlOqYu2cWTXO7NUpT00OuQwlo8IdwmQwU29LJWmg4jClLxmxJL6F2cZLvMZpI\nxpfpAS2SlBYknuzAaC25rAifpEiYqueu/Hyl7ZnA49G50Jhz6fN7tSRJQq6uhhMnMPa1QWUdABUe\nM8c/DXK8aYhH1q6lsC4HX8sYNb0RvOXX9uG7f2KQ7597joFJHyVZRXx7wy9RlDX7Wu7rHSGaQg72\nDfjZnGNHkiS64jH6k3GGw0NEEyq7Ll8JErhyMTFhmiSp5FU+wWDz9xjrfg2jpQjVnLvSYQnX4UrX\nfia9PswGM7eX38rest00jjbzYc9+mvyttI53UGor5l73nWwt2IgsiZmEgiAIgrCYRKKfwVITE0Ra\nmhnOKiSsZuEpcqAoUFExM+lLVeso06OyOT4KKR3W56WTp1g0QWwiTm7FzG5Rt2qi2pROeFfb7N1I\n4izpInzbVzqUJaEoUF09/Txf/PxeC6m6Bk6cwNx2FvakE32b3YCnyM557zj3RBJU7SrD1zJG39F+\nNleuu6rz67rO/v4jvNj6Kgktyd7S3Txa8xBGZe6pInY1yYYcG2fHJmgLhql1prP6EoMRSYZUVgkV\njnTD1Gpc9jGTqOY8csofYtT7EiOdP6Ww7lvISmaOZBGubKHF8jKJLMlsyFvLhry19E0M8I73Q477\nTvP9c89R0JHH3e47uKloqyjcJwiCIAiLRDShZ7DJM6dB02g0l1JiVbH2dKC0tyJ7p4dsyl7vjNuS\nGhwbA7MiU52dHoI9OjQJgC33xvgApesakfgZJD5fRfgufa6vhlRRga4YsDQfBy01dfuONQVouk5L\n+xj2PCuVdXkM9YcY7b6kSF4qhdLeitLeOuN4gHAizD81/CvPN/8cVVb59Y1f56v1j86b5F+wtygb\ngE8G/Vf9mISrk5WzEVv+TSSiw4x6XxbLnwkrptRWzDfXP80f3fx73FpyE6NRP881/Yz/cugv+LBn\nPwlROFIQBEEQrtuNkfndoCZOngCgOauc9dJ0cblURRmRkvSwbkUDNC/GijI0TxWNwx1MJuG2Igeq\nnG7HGRlKVzu25RpIJNJJohaPI6me5XswiygSaEHTQ1jUrZcV4btRpTzTw3A1t3vG7wuheyqJl1Ri\n6mlFOnsGKtMJ9o41BbzwUTvnW0bZsqGQ7be46WwZoeNQL7kVzun5s+3tUw0MspYCV/rmtvFOfnDu\nJ/hj49S4KvmVdU+RbXYtOK7SLDPVDgvtwQg9E1G4cWpFZqTs0rtJRIaIBJoJDH6Mq/j2lQ5J+Bwr\nsObx9JonuN/zRT7o2cf+vsO80PoqFoOZamc5d6QSqHM0GHocVSiysswRC4IgCMLqIXr0M5QWrymp\newAAIABJREFUjzN57iwxZy5jRidlxfmkqmtJVdcSKVdIpHpAlklVuIkWQ6RcQZdlDo+ARHpJvQtG\nfOlE31HiRlXTBZr0hBc90bUCj+z6TYwcA8Bq3LrCkSwjRZl6/lPVtemx/VdDVgivvxkArb1t6uYC\nl4XKYged3eNMhuPkF9nx1OQyPjDBWE9wxik0txvN/dn1o2scGjjBX5/4B8ZjAR6svJv/sPU3rirJ\nv+CO4nSV/w/6R6/6WOHqSJJCXuUTKEYXwcFPCI83rXRIgkC22cXjtV/iv93yh9zvuQt0aBht5Z/P\n/RsnhhpIXtLD7w166Qp2rFC0giAIgrA6iEQ/Q4Ubz6HH4/RkVwLgtl9SSV51YzLVYjLVTiXvXRNR\nBiKwxgE5pulekBFfCNkgY8vOmjpGUldnxfJEdJRoqANVKceg5K10OKtKtGoDAHrbzAn/u9YWoOvQ\n1DoGwI49HgA6DvfOep5gcoKPO9/i0MBxXCYn/3Hbb/JA5d3XXEyrymHFY7fQHAgzHF5lVSFXIcVg\nJb/qq0iyyqj3JeLhwZUOSRAAsBmzeKjqXv7klj/gfs9dpPQUH/Ue4ofnf07/5AhuRyXVrtoVX1FA\nEARBEFYDkehnqIkTxwE4TiF5ZgNZ6pV7cA98Ns/55vzp27Skhn8kjD3finQDrFX+uezNXySa3UUy\npwi9pxspOj0VZOfadFX8xuYRAPKL7ORVuvD3hRjrndmrf2yynWcHX2ZkcpAal4c/uOk/UuOqvO7Y\n7ipJ9+qf8gWusKewGIyWQnLdj6BrCYbbf0wyLv7uQuawqpZ0wr/7D/hixReYTIR5vvkl/uTwX3Jo\n4Biarl35JIIgCILwOScS/Qykp1JMnDoBDhedSs5lvfmzGYvB+fFJSixQbp2+fWI0jKbp2PPnWKds\nFdFScSbGTiEbbJgMq6vidKaIl9WApmFumR72mm03UVHqoLsvyFgwCkDVTaUAdB7tAyCcivGDkY/4\np5EP0NDYXrqHL1V+kSzVevmdXIMquwW3zUx3KMpIdPU3SK0GVtdaXKX3kEpOMNz+Y7RkdKVDEoQZ\nbMYsHq15kP+6+z9xe9mtBGJB/vX8T3m28QWa/e2ioKQgCIIgzEMk+hlosrMTLRwm6F4LkrSgRP/I\nCOjAzXkgXZQnBYfCADgKFichWyl6SifYcgo9FcMibyTZLaoyX4tYebqBxNzYOuP2dfXpaRDHmoYA\ncJXYyS6zM9oVoL2jne80PcfhyVbcxjy+XvgwlTl104X6FoEkSdz5Wa/+6ZHPZ4GthDdGrD1KrD1K\nwhu78gHXQE/pU/cRa49iy7lpqhL/cOdP0S9aUeHSffWUSKqEleE02Xmy7mH+ePd32FOyi0AsyC86\nP+B/HPtbWvxtVz6BIAiCIHwOiUQ/AwWbzgPQbPcAl8/Pv1Q0Baf84FQNrLukFlpoOL203mrv0Y91\nRpmcOA5IWIxbUN0mjB6xDvjVSuUUgsOBubkdUtNJ3ZraHCQJjpwfmrrNs6MYgBP7zjAUD3CfYwu/\nX/Qw2apzSWKrcVgpsBrpnpAJxj8fqylcYPSYUN3T1/NSXd/xrthUI0LCGyPhjZNdeg8WZz2xiS5G\nu1+Z6iW9dN9419I0PgjCQmWbXTy15nG+se5J6rOr6A718jcnv8v/d+qf6Q31r3R4giAIgpBRRKKf\nYfRUilBrCwZXNscmrThtRrJN86+CeGIM4hrsLnSiXNLJGhyaRJYlbHmru0c/mRogZRjG4qzHWpuP\nqdqMdOmDFa5MkpBrapCjMUydPVM3Z1mNVFa46BwIMjgWJhD1837wRSLWAI6xIn634HEeyd6Jco0F\n9xYWmsTWwnQjQkswZ8nuJxNJioSp2jzja6mub9VtwlRtnmpYkCSZXM9jGLPKCPsb8Pe+MZXsX7qv\nIGSCbLOTByvv4vd3/DZ12TU0jjXz50f/hh+ce57RyNhKhycIgiAIGUEk+hlmsqsTLRpF3riFwGSC\n2jLXvEOkU5rOpyOgSrAzf2ZPq67phIbDZOdaUQyr+6kOJ04AYM/bscKRrH5SbR1w+fD9jevyAZ2f\nnf6IH536P/RP9KDWJZCQGO1YnkSv1Gai2KoxErUyFlmWuxQAWVYpqHoK1VLIxMhxxvvfFfOfhYzn\ndpTzO1t+jX+/+VcptRVz1HeCPzn8l7zQ+ioT8cmVDk8QBEEQVtTqzv5uQMHz6WH7g0X1ANSVzT9M\nusE/QSABW3LAYpg5t3nSH0VLauQV2pYm2GWSjAeJJppQ5DxM9uuv8P55J5WXo5lNmM+3zkjmKtxm\nLHWnadI/AuC+2se4d+8DmB1GTrcEiMaXvtK1JEnsKEhPKWj1y4hcc/nIBgsF1c9gMOURGjrMZPzg\nSockCFckSRJrc+v4zs7f4RvrvobT5ODDnv388eG/4B3vh8RTiZUOURAEQRBWxPxjwoVlpSWThFqa\nkSx2Do7YgTB1ZdlwfvZ1rnVdZ9+gHwnYPcuS8hfm5+cV2pcu6GUwMXIU0Mgy7ljUAnA3Eq9Xpl+R\nKC25cmYsKQrR+mqspxuJ9XSjlro50tzKsbGXwDVBKpjN3ppHWVfgAaBiSzEtn3g51zHJ9jWLdy31\nT/YjqzNHFXQnUijJHlxGB+PxPM4FYINrjhMIi05RsyioeQZf6w+YjB1AQsHE7SsdlpCBvEHvvNuW\ne617WZK5qWgbWws2sa/vEG91vs8r7W/ySe8hvlR1LzuLtiIv4dQjQRAEQcg04r9eBpnsaEeLxYjk\nrqdz2I9ZNRKfSPfG66SIxVqJxVpJJNIfsDpCEfrDMdY6IXuWkdVB32eJftHsPfp6wnvZOTONlooz\nMXIcSbJiVtetdDgZyePRcbs1Skt0SssW1gUeWZ8evh849in/fPwlDoz8iLgWYbPxLuJNN9HaNL3U\nWumGfIyqzJnWCVLa4nSxl9nKKckqAUDXU2gJL1rCi5L0oqT62ZQTRpbgg0FIiSWzl5XB6KCw5peR\nJTsTsU8YH/hIDOMXZvA4quZN5N0ONx5H1TJGNE2VDdxZfht/vPs73F1xO6HEBD88/2/8+dG/4fxo\ny4rEJAiCIAgrQfToZ5BA4zkA9Np6JnvirK8onOrB1pReEgkdVXWjqm6Mxir2e9M9/bvz5zjf4ASS\nBPmFdrr1mcMXJdUz4/cL58w0k2On0VJRsoy3IEnicp2NokB1tQ6xhSdjsfoqNINCz4F3Of2AE4da\nyE25T7DR5KE3/wgtHX7CkQRWi4pqMrB1jZMjZ/209URQyq9/vr4sy1Q43JhMtZ81NKWvQeJxoIxC\n1cNIIkjj6ATHxmBv6XXfpXAVDKZscrKexj/5bwQHPyFpjGEz7V3psIQMocgK1a7alQ5jXlbVwiM1\nD7C3bDevd7zDp4Mn+LvT/8TanDoeqX6AMnvJSocoCIIgCEtK9OhnCC0eZ6K1FcmeQ5uUnpfvKZhZ\neVxV04mRyVTLUDRJcyCM22ambJaC+qmURnBoElueFdV4+brkkqQgG6unzmcy1SJJmbV+ua7rhIaP\ngKRgMW5d6XBuGCld45z/HO0lBhyBOLclNnNX0W/iMhYjSRJ7NpWgaToN54enjrnps/HzZ9omliSm\nC9e2bKxGNlYjSQpbCx2YZPjIBxOJ5JLcrzA3RXaSnfUUBlMu4fgRJmIfiJ59YdXJMWfz9XVf5Ts7\n/wNrsms5P9bCnx/9G55tFBX6BUEQhBubSPQzRKilBT2ZxOBZT3swvV51ZUH2nPvvHxwH4Lai2ffx\nj0yiJTUclxTiS+k67bEo3kTmr4kdCbaQjI2Rlb0RRc5a6XBWBW8iNu9zG9D9/Nj3Go1DJ+mqTDco\n1fQZ8TE9Pv7m9YXIssSRM4N0xdND+LMdRtzFJnxjCQL++YtbXbjG2mNRUteRGJoNCrcXQTQFb/eO\nXvN5hGunyHYKa7+BIucRjh9n1PsyupZa6bAE4aqV20v47a3pCv0ltiI+HUxX6H+x9TVRoV8QBEG4\nIYmx0Bki+Nmwfcm9ns7WCAUuC84sy+z7xpOcGg2Ra1JZ48oiEb98n6GBEADOS+bnd8XTiaBbNeEx\nLv2SaXpKJ941M/E0ekwLWiM8NHQYAHvBLvT+JQlv1UppKbqCHTNv03WkZDphlyZ6wTY9NFXXNVpC\nh2lIvYuWSuF21VBbsw0OfRe16Rglt32ZUil9PTisRnatK+RQwyDJvgieeieEYWO1De9AjO6OMPbK\nuVeDuHCNXVBtMl/z47wpF06NwfGRIDvyHLjts78mhGuT8M7dKJTwxlDdJhTVRo71KcbGf0bYf5Zk\nMITT+jCyNPP9Y6Gv60vfExZ6nCBcbL5igPMxKhJfqX2QkUiQN7re5YOefRzsP8rd7tu5o3wPJsW4\nyJEKgiAIwsoQPfrLIZVCaW+FlhZIpaZ+v/CVCowz0dmBqbCQQcVOLKWzzu3C3N2KubsVU//MDzSH\nfOOkdJ09RS7kOarQ+/qDADiLLu8Jd6smqk1mlGWoYB/vis1IJhLe2GWJ/6zHhfuJTXgx26swWgqX\nMsRVqSvYcdkHXUWSqFCV9JethIoxDdnrZSQR5KcN/8KZ8bdQUXlU3soeqQbVZEXasBGLf4jykeEZ\n18O9O8sBaDg1OHW7u8iEI0thoDtCKjZ/hTy3asKtLsJcfgke/Gx+/sveoUUrBiikE2zVPfdzpLpN\nGD3p7eYqF7nZT2E0VBNPdeGffJ6UNt0LutDXNcx8T7ia4wThgisVA7yS7lA3+VYn//nm3+OJ2i+j\nyDKvdbzFHx/6C/b1HSYlRq0IgiAINwDRo78MlK4OZK8XAlYUZ3qOs+z1orndyF4vE8ePgabh3LCR\n/YH0UOn1ljimvn5iJe70V2E6sYomUxweDmAzKGzLc8x5n0MDIRRVxpY7ywT+Zaa6TZiqr65XNzC4\nDwBHwS1LEdINwe1wz10Qa1MVemc7H08084LvI2KpOCWWtWxzPki1OsZo+0kApK3b0E8cRz9/BKmw\nYurwikI7aypcnOvy0zs0Qb6aXq96Q3UWB88ECbREYJu6HA+T8izYkefg2EiQg0Pjc05XEa6OpEgL\nfl1KioS5xk6h/hRjPb9gcvQk/vhz5Fd9DaOl4Krv+1reEwThgsUqBqjKBu4o38PNxdt5r/sTPuj+\nhOebf84HPZ/wUOU9bC3YJJbkEwRBEFYt8R9smWhuN1RWzvg9VV2L5nYTPNcAkoRj7To6gulEf22B\niViJm2hFLdGKWpDTT9WnwwFiKY1bCl2o8uxPXyKuMTY8iaMgC0lefUNi4xEfkUAzRmspJnvllQ8Q\nLhNITvJ3k/t5bmwfsiRzX+1j7M59CrPqRKvwEC/8bJTEho1oRjN6w0F0bWYv/T0704n/O8d6pm5b\n67EiyzDeHF7Wwmz3ledhNci83zeKPzZ/jQBh6UiSTE75QziLvkAqPo6v5ftEAq0rHZYgXBeLwcKX\nqu7lj3d/h9tKdzMSGeP7537Mn33615wabhBFKAVBEIRVSfTor7D42BiRvj6yPJVolix6QuMUWVXs\nJgX/JfsmNTjgG8cky+wqmHuO9MhQOhFyXDI/f7UIftab7yzaO7W8oLAwuq5z1HeSn7W8QjgZYU12\nLc+sfZIxyUx39+V/S8loJLJ2B1mn94P3PFSun9q2qSaXwmwLh8/5GN+SvpYsJoWiMjP93VH8/XFY\nps51q0HhgfJ8Xuj08UKnj2/Xl845bUVYWpIk4Sz+AgZzLmPeVxnueB676Q4sxu0rHZogLNhcc/x3\nFm6gxlnO4cETNI21872zP6TAksctJdupdJRP/U/yOKpQ5GtbqWa2GiuXup7zC4IgCAKIHv0VFzjX\nAIBjwwYafFGSOtS5Zi84dtoPoUSKXQVOLIa5PwCMDKar811aiG81SESHCY83YrSWYHbUrHQ4q0og\nFuIfzz7Ls43Pk9RTfLXuEf79ll8l2+ya97jwht0A6Gf3zbhdliTu3llOMqXx0unpqvfllenpIL2N\ny1upemuunXWuLDpDkalVJ4SVk5W9gYLaryMbsgjFPiAYfQtdE8sgCpnvSnP8s81O7vfcwTfWPUF9\ndhVDkRFebn+b51texRvspSvQdcVEfT6z1Vi5mDfova7zC4IgCAKIHv0Vpes6gXMNSKqKo34Np06n\n5++vyb480dd0nYPD6YJrtxTOn7j5+ldvoh8Y3A+Ao/A20Zu/QLquc8x3ip+1vMJkMkytq4pn1n6F\nPEvOgo6Pl1ZDdiF683H0WASYnjt926Zi3jjs5fUGP7eVm8ixGHDlqhhdCr7OCJFwHIt1eapUS5LE\no55Ces55ebdvhBqnlRLr0q8cIczNlFVGUf23GTr/PNHEWXyto+RVfgWDce76IYKw0hY6x78a2Fm0\nk76JAX7R+S6nhxt4se1NSm1FyJJKlbPmmv9PzVtjRRAEQRAWgejRX0HRjnYSfj/22jpQVU4ORLCp\nMqVZ6cRJJ4WmtaJprXgTw4zFYVueHYdx7vYZXdcZ6o9jc5iwOFZXEpSIjhL2N6CaC7E461Y6nFUh\nGA/xvbM/5AeNPyGhJfhK3SP8ztZfX3CSD4AkIW3cA8k4etOnMzapBoUv31pJPKXzamPws90lXPVW\ndA2azg4u5sO5oixV4TFPISkdftoxSEKbv/o/gK6niMVa5/zSdVFh+3oYjE5ysp7GrK4nHu5nsPl7\nRENdKx2WICyaUlsxv77x63xn5++wIXcNfRODvND2Bn957O84PdyApl/5fUgQBEEQlptI9FdQ8OAB\nAJwbNtIxFicU06h3WabmHmtKL7ruRdd1zsbykeCKFccDY0miEY3i8rnn8GeqoO8AoOMsEr35V6Lr\nOk1jbfzp4f/J6ZFz1Lqq+L93/S5fKLvlmqpESxtuBST0059ctu3WjUWUOo180jmJbyI9NNtZa0FW\n4PypgWUvVFXvymJXgZOhSJw3ukeuuH883kEiMfsw2UTCSzwuhsheL0lScZgfILvsPrRkhKG2HxH0\nHRRFzIQbSoW9jN/a/C2ern+EGpcHb6iH7579If/90//NkYHjYlk+QRAEIaOIofuLLJWCrq7pJNXj\n0ZltNr2WTBI6egSDzUZWZSUnz6V7S+svGbY/MFCJj2JGrb1szLGRZ55/mPRgX3rYfnHZ/MP7M008\nMsTk2GlUcz4W19qVDiejBWJBXut4j7ZAF0ZZ5cnah9lbtntBCX5ff/raDAxLaBYfsWAX/REJDAq5\n7irM3jb2v7ePREHx1DGlpTp3r9P5wSH43qF+1lbJdLqgoCiLQF+YY4cD5BSkG6A8npmJXUqD9vZL\nXg+LUF/q/rI8vKEIR4YDlGaZ2JE/f8OWqroxmcQw2aUkSRL2/JtQLYWMdL7IeP97xCZ7yK14GNkg\nltITbhxFWfl8uepuslQH73g/4qjvJD88/2+83vkOd1Xs5ZbimzAqy7P86LUQxQAFQRA+H0SP/iLr\n6pLwetN/Vq9XnpH0X2yitQUtHMa5YSOSLHOyP4IqS1Q7pj8QFxbqFBen6LOney3vKL7ycGxfXwxg\n1fXoj/e/B+i4Su4Svflz0HWdg/1H+W9H/oq2QBeltiL+8Kbf5fbyWxeU5JeW6ZSWpBNxLVCMHCmc\nsd185w4Ask4fnbqtr1+mr09iS5lMnlmndVxidEKnwFxCbW06ce7t6Admv977ehf2erhaRkXmmZoS\nLIrMK95huicii3Je4fqZbW6K1/w6JpuHSKCZwebvEQ8PrHRYgrDoirIK+fq6r/LHN3+HL5TdSig+\nwc9aXuGPDv4Zv+h8l0AstNIhzkoUAxQEQfh8ED36S8Dt1qiu1oG55+2NnzkDgHPjRoYmkvQFk2wp\nNmNUphM2RYZkNgwHoqxzZVG0gMJjg31xTGaZ7Fwr/njsuh/LcogE24kG2zDZKjE7RK/rbEYio/y4\n6UWa/W2YFRNfLN/Dxrw15FtzF3wORYaKinSibxqQgBIKHW4Yl6iQzVRtqKLrjY+wtTay8dd/FYPd\nQXtWOjGvzsnikaox/qnRR8uggW/fV06txUJHg5Wh/mFKimPA7NfnQl4P1yLHrPJUdTH/0tLHc20D\n/Lt1FfPWr1jNEt7YjJ9Vd+bV37g4RqMni4KaZwgMfEzQt4/Blu+TXXo3tryd8x6XPtaEpFzeIKSn\ndOJdC9tXEJZTriWbr9Q9zP2eu/ioZz8f9x3ijc53ebvrA7YVbOaO8ltxO8pXOswZRDFAQRCEG5/o\n0V8BiYkQk50dmDyVmPLyOTWQ7o3cWjJz2L6uwye+9M93lly5Nz8UiDIZSlFYalw1veK6rjHe9x4A\n2aV3r5q4l4uma3zQ/Qn//cj/otnfxobcNfw/u/4vNuWvXfS/lSTLuO74InoySeCTjy/b7rab2Flg\nYziY5PDxfiRJYt3mErSUTvNZ36LGslA1Tiv3l+cRSqR4rm2AeOrGK4pl9JhmJPaq24TRk1mJ/sUx\nJrwx4l0xJEnGVXIH+dVPIysm/L1vMdL5UzQ9MutxFx87m3hX7LIGj7n2FYSVYDfa+FL1ffzpLX/I\n1+ofJd+Sy1HfCf7Hsb/lr479HccGT5IUS1AKgiAIy+TG7P7KcMGGBtB1nLfuAeBobxgJ2FJsITg8\nvV9X0kR3GNY4syjJuvIc14HeAABFpcuz3NlimBw7QyLqIytnM0Zr0UqHk1H6JwZ5rukFuoLd2NQs\nfmnNE2wv3IIkSYxFh698gmvguHUPIy+9SOCjD8i5935g5jzTu8tdNAbC7D/cy30bSqjbUMjhj9pp\nPNXPTXeVr0hDza2FLgbCMU6Ohvhx+wC/XFOCIt84DUaSImGqzuw57vPFaHHUULTmNxjteolIoJmY\nNIDT8hAmaq76saluU8b/LQTBbDBxW+lu9pTcTLO/jY9699Mw0sS/NP4ER9vrrM+tY2Nu/UqHKQiC\nINzgRKK/zHRdZ/z0KSRFwb5zF8MdPbSMxFlXYMJlUQhetO9HsfQ8+zsW0JsPMNAzDkBhaWb19s1F\nS8UJDHyIJBlwFt+x0uEsquspdpRIJXjb+yHveD8kpafYUbiFJ2q/jN1oW6pwpygWC87b9jL+3jvp\nVSFKb5+x3WKQuWezk5c+9fP9N5r4/ae2Ur22gJYGH2NDfnILr2JZv0UiSRKPegqZTKZoCYR5odPH\nk1WFU6tXCCvPoNopqHmGoG8/gYGP8Yd/gjawF0fRbUjXsEqEIKwGkiSxJqeWNTm1DIdH+aTvIAf7\nj3Jo4DiHB06wPvcEu0tuYmPuWlH4ThAEQVh0ItFfZpGeHuJjYzjWb0Cx2TjonQTgFnfWjP06EtCZ\ntFBtg3Lbwnqw+rzjqEaJ3ILMrfZ7saDvAKlECEfhbRiMjpUOZ1FdKHbkdrhn3X6hENKlcySbxlp5\nvvnnDEdGcZmcfK3+UTbmrVvyeC+Wc9/9BD76gLE3Xkf/1h4kZebbxPoyM41jOTS3jfH6oS52bSul\npcFHT3vfiiT6AAZZ4unqYr7f3MfpsRBWg8xDFfliKkgGkSQZZ9Fe5EAxgcjrBAY/JhrqINfzGAbj\n6ioeKghXK9+ay+O1X+LBynt4o/MtGkabaRhtomG0Cbtq46bibdxSfBNFWQUrHaogCIJwgxCJ/jIb\nP30KANfmLei6zgFvGKMisaN0en6+js7bn01jvX0Bo9k1HVpGIgT8EcqrzCBJtMeieBMx3Gpm9u7H\nIz6CvgMoqgNH4S0rHc6SuJpiR8F4iBdbX+OY7xQSEneW38aDlXdjXsJlyfoUjWgihsTM+zC4snHu\n/QLjH7yPdO4Q0qbbZmyXJIkH765m2DfJK/s7sRdZsRdkMdQ3wuRkhJlNVsvHqMh8va6E7zb1cmgo\ngFGWuads4cUKheVhNJSTa/smk8p7hMcbGWj6R3LKHyQre/1KhyYIS85sMLE5fx2b89dhNtg41H+U\nTwdP8H73J7zf/QlVTjc1Tje12ZUrHaogCIKwyolEfxklwxGCTedRs7OxVlTQOhDEN5Hk5nIrFnV6\n+KpXga4krDGEKbNar3je3kScDm8YgKxyA72JOD1SOsn3GDMv0dd1DX/3a4BGTvmDyErmxbhcNF3j\nQP8RXml/k0gyittRzlP1j1FuL13S+y1Lpa+3QtVESrr8759934MEPvkY/cCr6Bsub4ixmFUevK+W\nf32hgVfeauWh7aWEhiZp6uhhe/3KVZe2GhS+VVfK95p6+XjQjwbckQ+iYz+zyJKZXM/jmMeq8fe+\nxWjXi0QCrWTpdyDPcj0Kwo2o1FbME3Vf5uGaBzgzfI5DA0dpGmulI+Dl/Z4DbMxby521uylXPRiV\n1TFSTxAEQcgcItGfTSqF0pWeX53yVIGyOHPnhk+eQk8mcW1OF1Q71DAIwM6KCWLaBAAa5XwqJZF0\nlbss48CVE32AVM8EYCC7eBiowK2aqDZlZtGqcPwY8Vg/1uyNWJyf3+V9hsOjvNT2Np3BbsyKma/W\nPcKe0puRl2HOsoJEXU8vhaUy3T0S0dIqSOlT170CODduYvzkCQwfvQDby4CZQ0oryhzctquUTw73\ncbqxD5siMd45hJYsRTZc+TEkEt6p76o6+xSHi/dbKBPwjSr4YafKvkE/iSTcU3xVp7iMrqeIx6dr\nLhiNVUiSmFN7PSRJwpa7FVNWBaPelwj7zxCVvDgtD2KiZqXDE4Rlo8oGthduZnvhZkYjft7qeodm\nfzunhhs4NdyASTGyKW8DOwo3szanTsznFwRBEBZEJPqzULo6kL3TyUWq+vqTUV3XGTz8Kcgyro2b\nSGo6n54fwmHSqcnvA0qJ63005lcwHDGyzQh57oVVodflCsb6RrA4JLJyK5BUz3XHu1SSmp+J2H5k\ng5XssntXOpwVEU5E+Kj3ECeHzqGjs71gM4/XfgmnabpOwZWK+c03/38hoqVVn/2kY+pPX+uKoiN7\nvWju9Hnzbt3D+JmzJE5/TPKh3yNqq8DcNzOmvSUaQZfOqaE420wSSizBQPMopevz571/o7Fq6mdV\ndc/4fa79roYZL9+odPPDTiOHR+KkdHi4Ur/mAn3xeMdUg8SFhgeT6fPbSLWYVHMuhXXOixeHAAAg\nAElEQVTfJDDwMUHfAfzhn5DqvxVn0ReQREIjrELX8/6da8nm5uJt7CraikW10xhqZF/npxz1neCo\n7wRZBiub8zdQnJVLma14zqR/rmKvgiAIwueHSPTncCHZWSzhnm7Cg4M41q7DYLNxrDfMRCTBXTVg\nUUoxyW5SKTgRk5CBO9d4gK4FnTvoi5BK6OStK0A2Zu68Pl3XCUbeBpJklz2MYljYaIUbhaZrHBo4\nyqvtbzGRmMRpcvDMmidZN8syS1cq5ud2uPE4ri0JBkBRiFbUkqrWiXVPJ7+a2z3VsCUD0i0Poe97\nib7TZ6D28mtLkiR+4/Yy/mSfn4bRMJuR8J4YoGRd3ryF8CRJWVCivND9ZqMCv7amlH9q6uToKCR0\nH49XFl7z0nuq6hbJ/RKRJAVXyZ0ooQoCkV8Q9O0nEmwnz/MIqnn+RiNByDSL8f4tSRKltmK2VNbx\nxaI76Qp2c9x3muNDpzk48CkAJsVIpaOcGpcHj6MMo5JeWneuYq+CIAjC54tI9JeJ//gxALK37wDg\nrZYQALdV6lP7nIjbCGmw1uQkx6QSiy3s3KPeAAC57syuXB0aOkgi1Y3JUIPVdXkleT2lE++a/UEn\nvDFU9+qdu9s+3sXPWl+hJ9SHUTGyp2Qn2wo2Up8z91rKV1PMb6lIu+5HP/URfa+8hvRrt866j1WV\n+Z3HN/Ff/vkIYykdRsKMdgfIc7uWOdrL2VQDv1IFP+6CU2MhIqkUT1UXY1Q+P0u6JbyxGT8vx+vo\n4vucbdtcMRgNZTjHniJScIBo5CwD57+Hq+RO7AW7risePaUTa4/OvC+PCUkRxRuEpbGY79+SJFHp\ndFPpdPNY7UN80ruPtnEv3lAfTf52mvztGCSF+pxaNuetJ88iGscEQRAEkegvi0QwSKi5maziYixl\nZbSNxmgdjbOpOpcSxzAAEQ3ej7owAFssV1cpfKRrHEmWyCnL3CXqoqEuxvs/QJZsOMz3ztrbG++K\nzZkEqG4TRs/qS/T90XFebn+DY770ags7C7fxSM39jEaGVjiyhZFUE9IXnkR//bvo7/0Ydt4/636F\nOVbuqs/hk8ZRcpBoO9ybEYk+gMUAv1wFL/ZYaQ6E+ZeWPr5eW4LFcOMPa730NbMcr6MrnX++GNK3\nOzByPyZDNcHJtxjvf4dIsAU796HI1/YeF2mPzHhvudAQYarOzDomgjAXWZIpt5dQbi+hyllD78QA\nZ4YbOD1yjnOjTZwbbQKg0JrHtoLNrMtdg8dRviy1XwRBEITMIhL9RZBKQVdXOnH1emXcbm3Gdv/J\nE6DrFO/ZjSRJU735d++ooL9nBIBDCoSNCrVxCatsAHQWYiIYJeibJKfcgcGUmU9nSgsx1vUiIOG0\nPIwsz70Am+o2rYoP31eag9kx3sFwNMDRwdPEtQQV9lKerHuEKmd6KGcmJPperwxojPbJlJZqc+7X\nY78VT+U+ki3HSRZVQVn1rPuVusxsq3Yx1B6A/gmGugPA/Mn+xa8dgFQJLEVnu1GGZ2pKeKFzkDNj\nE/xjUy/frCsh86+06yMp0rK/nq7nPi8+1sQm1NYSgtG3iU20E+f72M1fxKhvn3dayFxWy3uLsDpc\nGB4/17brqaGyUJIkTSX9D1bdw0hkjDPDDRwZPE7/xCBvdr3Pm13vk2Wwsja3jnU59bNOFZvNfI/v\neub/X+l/5/WeXxAEQZiWmZnhKtPVJU0l+G63hscznaTriTjjp04hm83kbdlM62CYY70R3C4VUyob\nn09GLZI5o4JDS7A7X51x/JV0tKQbCgpqcxb9cS0GXU8RiLyKlpoku+w+jMGlXTZuucw1B1PTNc6P\ntXGw/xihxCR21caTdY9wc/H2jOpRSV9j6eS+tFSjtHT2ay69n4S99Dfx/6/fJXXyLdj2q3Oet74w\ni6TBQLJ5jMOvt7Dhvu1gnDuOi187Xq+MpElUVCz8+r8aBlniK1VF2NQRDvrG+YfGXp6uhAKR+2Us\nRbbhsjxGMrsJf/fbBKNvkOhsI6f8IcS/L2GlXGl+/XXXULlGeZYc7qzYi9tRTCwVJ57SODfaTONY\nM8d8p6ZGlhVa86iwl5LQoMp5+dJ988V+vfP/r1S/QNQXEARBWDzik9Iicbs1qqsvT1D0M/vQwpPk\n7r4FxWjknVYfOnB/vR1Jkigo1HjPlouehIdsfirdBVe1ml9Hc3rof2F15iX6uq4Tin1IItWH1bUe\nW95O4sEFFh5YBS6eg6nrOg2j53m1/S36JwcxyAbuqtjL/Z67sBgsKxzp5RSFqetVmWf0yIX98vPL\nON/0IGOvv4q87xOk2rl7hW6/r4a3u09gjiQ5vL8Rz+3bgbl7YKdfOxrdcw8sWBSyJPFgeR4O1cBb\nvSP8Szt8zQ11q29WyOfGhWX45LESApE3iARaGJj4e+zGuzGra1Y6POFzSJGVjE9ETYqRdbm1bCnY\niK7rDEz6ODfaRONoM22BTnzhEY76TmOQDVQ53NTn1FCfXUOFvWzJH18m1J8RBEH4PBCJ/hLStRT6\nkTeQDAZydu5kdDLBx52T5FgUdpZZ6QC6ZAvtSQu1Bqg3RLiaPCc8GWegJ4CrxI7JNk+36QoJ+g4Q\niZ9AkXPJqfjSNQ23XQ06Al5ebnuD9kAnEhI3F+/gocp7yDZnxhz1xZLz4ENMHDxA/Phx5D17kepm\nT/ZlWWbnAzWceLEJdTTEWydbqKmpY75kfzlJksTe4mzsqsKLnT5+1AlPSiE25dhXOjRhHorsJNv6\nNRLOs4z3vUcg8irRRDN5iYdQ1LmnAwnC550kSZTYiiixFXG3+3bOjzbSNzFIMB6mxd9G63gHLePt\nvMbbmBUTNa4qarOrqHZWUmEvFcPoBUEQVimR6C8hvfEwBEZwbduOIcvGs58OE0/pPLzOgUGWSOga\nB9VsZHS+lCVxtXlw52fD9gszcNh+aPgYgYEPkCUH2dYnkZXMa4i4XqMRP+93P8vpkXMAbMxbx5er\n7qPEVrTCkS0NWTVS8tCX6PrRs2g/ehb5D//znPvmVbjILndAT5Dmjj5eO2BkffHyD2Wdz9Y8ByZ8\n/NQLz7cPEogn2VPoumEbpG4EkiRhz78Js72akZaXiSWbGWjqIafsAazZl6/kIQjC5YyKSqWzfKpX\nfSIxSYu/nWZ/Gy1jbTSMnqdh9Hx6X1ml0umm2lVJjbMSnSSqLD46CoIgrAbi3XqJ6JqGfugXIMnk\n7LqZjrEYH7QEqHCp7K1M9z4dC48Qkg3cagpQqDiJXeWw5QvD9gtqMivRnxw7i7/3DWRDFtmmr1xz\npexM5QsP81bXR5wfa0NHp8rp5uHqB6hxXb7O/I3GUlqKfPNutEMH0V/8GezdO+e+a77g5tBzZ6lE\n5tX9ncS2GdlWXbaM0V5ZtR2+WQ0/6VJ4s2eE8ViCByvykUWyn9FUcy7Z1qcIx48zGd/HSNcLWMbX\nklP2gOjdF24IF+aq+2UbY+MTl21bzGJ/NjWLbQWb2FawKX2f0XHaxztpC3TRPt5Js7+NZn8bADIS\nBdZ81uXW43GU43G4yTFnTgOpKPYnCIIwTST6S2TixHEY6UNafwuq08lzH6arrD+92YWERlN8mMYY\nOPUId5oDgPOqzh+eiNHn9ZNfZMfiyJwJxuHx84x6X0ZSTBRU/xL6wI0zfH1g0sdbXe9z3HcaHZ1c\ns4vHax9mU966jPmQsxzkW29F6+lBP3QQrbAA1q6fdT97fhY5NcWMtQ7gNii8daIZq8lIdfXyrPGc\nSHinvqvq3B+Kcw1evlXt5sddcGgowHg8yVerijAusPy/rqeIx6c/WBqNVUjS8n+IzJQ4loskyWSZ\ndmKvWsdY96tExs8zEOoiu+xerNkbP1evSeHGstLF/rLNLnYUbWVH0VYg3ePfMd5FW6CTcyNNDIWH\nGQxPrxzjMNqpdFTgcVTgcVZQYS/DbFiZzyWi2J8gCMI0kegvAT2ZZOSlF0BWkPY8zJGeQdpG4+yu\ntLO2wEwo1c1L4QIk4M5YgCz56ivRNzf40HWo31i4+A/gGoWGj+LvfRNJVimoegqjtYgY0ZUO67r1\nhvp5q+t9Tg03oKNTaitma/46al2V1GTXrXR4y05SFORvfgvtL/6M1JtvkigpY66UOG9dOeE+P/mR\nKKOKzMtHGqhyb2atZ2lHoRiN0x+CVdU94/fZ9nMCz1R081J/BefHJ/leUy9fryvBrl75LTIe75hq\nTLjQuGAyLf+HyEyJY7mp5lwKar9BaPgogYEPGPW+zKS/gZzyBzEYr64BVRAywcXF8PJz7QxroRWN\nx6ZmsSl/PZvy17M5r56ElsQgm+kKdtMV6KYz2M3pkXNT09gkJAqzCii3lVJhL6HcXkqZvRSLYXmW\nOBHF/gRBENJEor8EAvs+JuHzIW27i5DFxY/2nUeV4Zu7CiAV592Ii3HNzEZzNiUTEpJ0dcuJ6brO\n+TMDKIpE3fpCekldd8x6SifeNbMivtFjQlKkebddiCfQ/z7BoYPIhixcpsfAV0DMFyXhjaG6M2fE\nwdXwBnt4s+t9zo40AlBhL+N+z11szFtHR6BthaNbWVJRMdKTX0V/7kf0vfoyZZu3zrqfbFBYt72O\nE/vOsF41cjwW4//9+Vl+/6mtVBYv3ZQOSVIWlOReut8veeCNQTsnRkL8fWMP36grodBy5etXVd0Z\nkVRnShyLKeGNTX2/9L3kwjYAE5spqqvF3/cLosE2Bs7/Pa7iO7Dl7wQuH9lw8bEXv58JgjA/VTak\n5+xfNF1tPBaYSvq7gt30hvoZnPRx1Hdiap8CSx7ZZgcFljyiKY1SWxFOo0OMvhEEQVgiItFfZFo0\nwuirryCZzOi3PsyrRxqZiGt8fVs2xU4jR3xxjsQd5MkJtllygfGrvo/B3gCBsQi16wowmVWIXX+i\nH++KzfggfeFDsKnaPO82XUsy2v0aYf9ZDKYcnOoT6D0W+GzUnOo2YfSsnkRf0zUaR5v5sGc/Tf5W\nACodbu6vvIt1OfXiA8lFpN23wPFjRJrOM/LzF6Bwy6z75RXlsv0WN8cPernJaeNAIMT//ulp/tMv\nbQNsyxv0FSgyPO4pJNuo8n7/GP9wvpenq4uoWJ6OKOESF793XPpecun7SsIbQ8VKfvUzTI6dZrzv\nHfx9bzPpP4vB+WXAMeuxF7+fCYJwbVwmJ1sKNrKlYCOQ/l86EhmlJ9RHd6iPns++hiIjNPs72Nf/\nKQBWgyW9IkBWcbqQrZ4kz5K9kg9FEAThhiES/UU2+vprpEJBch9+lF/0+PEO+9lWYuHOqixGExo/\nnkivW/6YdYSUtLA5wJc6f3oAgDWbihczdFS3ac4Pu7NtS0RHGOl8kUTUhzGrjPyqr5H0yuC+/g/N\ny11QJ5aKc2TgGB/27mconF7NoM5Vzb2eO6nPrhEJ/iwkSUK+7z7kIR/+t9/E+AULcffsS+7tvM1D\nd9ckw/0j3FXs5P2BAH/1/Emevm0H2TbLMkc+P0mSuKs0l1yzyoudQzzb0s/9pbAjd6Uj+/yRFGnO\n95K5tkmShC13CxZHLf6+dwj7z9IZ/kesxu2oqbuQFdO85xWEG8GFuehzbVvMYn6zkSWZAms+BdZ8\ntn/WCKzrOieHTjIUHiGFRP/EIP0TA7SPd9E23jnjeJfJSZG1gKKsz76sBRRmFWBVLHhDnbPd5bI9\ntkwmihEKgnAxkegvomhXJ/6330TNz2eo/iY+fqkJu8XEt3dmk0Tie4NRwjp8yTJGmSHO3P+GZ5fS\ndVpDk7Q2DWNxmogWmUnpM4f9exPp3im3urS96JF4A6Hmd9G1BLa87bhK70GWVZKLNCd/uQrqjEX9\nfNJ7iP39R4gkIxgkhZuLdnBH+R7K7CXXde5Ms5jXxoVzVZpMlD32BJ0//AG2g6/TkpNDNFF02X1I\nksTGm9ZyfN9pAgNB9mRbOeAP8+zHx/nG7duBy5OuC/fhMZpQJImUrtMVn3nbtVrIubbkOsg2qvxr\n2wC/6EsxEoOHPFc3zUZYOYqaRZ7nUSI5mxj3vkk4foxoYxPZpV8UxfqEG9pKF/ObiyRJOE12nCb7\njP/d8VScgUkf/RODNI6dZyTiJxifoMnfOjWy7gKTYsKmWiiyFpBtduIyOT/77kCVDSv22DKFKEYo\nCMLFRKJ/NVIplK7pltKUpwqUdKuoHo/h+9Hfg64j33wHf/tqEzI6j+xcS5Y6yguT4I1rbDfCDuPM\npXJk72cpf/n8d98Vj3H2RB9aUqNsYyHdyf+/vTuPkqK8Fz7+raquru7pnn2fgVkYFkFE2QT3KJgI\naFQucYV4TTjJzX716HU5Gg8ecxNzc0zuUUPUmLzmjVcTDRFeQwyQG9eIoqiIrAOz7/v09N5V9f7R\nM80MDMPiwCz8Pue0XV1dT/XT48PT9at6nl9FBhysljiNQZeHkxkL0BXcSCi6C0U1yCz5Fzzpg2dd\n/7xOVUId27Yp7zzIm3Xv8nHLTizbwqt7WFqymIsLLyDVSB72zxxpw9k2ympqAbAmTKRUUdGys8m9\n7Ws0PvMrpmz5E/qCqZQ1NlFhW2AV0Zepz6E7mHfRORzY9iGVDSHmGDr7AmF+/+Z2Jhefh6u6HkMz\nUbEpKp0MHAr2ywwXlZFw4nXfupN1vPsqTnbzrRkTeW5vJe+1QlukjuUTwC0955jhTikjo+Db1Db9\ng0DkPdqqXqGndTvpE67CmZQ30tUTYtj1T+Y3Fjg1J8UpEylOmUhOUvxOPWVpUwjGQvEM//5mGgPN\nNPqbqfHV0h7qoi105LTHdCONnKQsspM+JcedRZY7k2x3JlnuTJyafrq/1oiRZIRCiD5yuHoCtMqD\nqFVVWMXFieDcLIt3ptqm3xNubsY4exa/qEgiHLO4JdNmaridrWH4IAJFhsr1SRZWv4uCocJJWFp8\nhVlgDfn5ZtSk65MWnIbGpfOLqCE2sH6K8rmCn6HYto2/fQcddZuwYgEcah7ZZ61AN05t9vTh1BX2\n8V7jB7xbv43mYHx4fqE3n8snXMy83PPQx/GBwHC1DbNkEhowtaoKS9Wg90RTyoKFtL2zA3b9E9fa\nJ9EWXojq1DEUBQrPSpT3NFVx9YQeNioaFQ0m01Bp7wnz8/+zja+mx6BkAmp9PU5Vo6zsyAOV4Ryp\ncrz7yjB0vj4Z1lXDvu4gz5TDzSUwYeyknjjjqaqO17iI1LK5dNT9jWDXXhr3Po0n41zc1oVo6vg7\nuSfEWOd2uBInAPoc6NyPaZmkGhk0B1tpDrTSHGiJPwdb2dtRzt6OI5PlphmpZLszMTSdNCOFrkiQ\nLHcG2e5M3I7RNX1MCCGGiwT6J8gqLk4E933s8k+IfvomqjeZZ/U5dPVY3LJ4Cmf5wux32GwOgEeB\nb+W5UH0Bwv1H/2raof2FBw5RO1zdZy1EgzHmXFiE03BAODbk9sMlZrbjC20h4qtEUXW8xhdIcs5D\nN5JOy+d/HqZlsqt9L/+s38bOtt1YtoWuOpifO5sLC85nStokGcJ7Ivq318Po512O5WsjULWX+pRU\nzCVLoHGQDUuKmVpkktcZZfv2EBkdfgjarA9rTMx1kVsyvLknhoOhwU0l8GZrOq83dPDrcviXUh/n\nZEiAOJY4jDSyJ91IsPsAnXVb8Ld/gp/P8Djno5uXoGoyb1+I0U5TNXI98Tn7hwubEVoCrbQE22gN\nttESbKUl0EZLsI39nYdGZL5dvy2x7HEkkeXOTAT+fctZ7kxSjRTUk8ynJIQQI00CfcA0obLyULBX\nZiZG5B9TpKkRa8OvsFWNl3O/QG2PxZcvKmHxvIm89eZ+Nhug2/CvXsjSVdpPuo4WlR/UozpUZs2b\ncJJ7OTGWFaC99nV6/B8AFq7kMjImLsWsHf1nv+t9DWyu/gefte/DHw0AkOPOZGbWNM5Kn4zLYVCS\nUnLUIP9YCW3O9IQ/g1EUhbaS68nwP0fXpzsImC6aJiwmt/DQNnV1KmDRoCmAE8e0qUzQOmj+aB8E\nLFo/quDvnypU5SZzfnYEjnLV3LTgwIH4/7uSEvu4/72eqL6+wbLin7VoUhZZegfra+GFA41U94S4\nPAfkzmxjizulDFdyKf72HXTW/B1/5F2Cn31Ecs4FJGefj6rJcA0hjmaoRH8wssneDM3JhOSCQXPs\nRM0oHzV/xL7OcgzNRWe4m86wj65wN7U9dVT5ao4ooykqKc54XoFUZwppRjIpRjJpzhRSjGQMzXlE\nGUl2N35JskMx1kigT/xAvqpKpbjYoqpKxaUoFBUdO+lWrKeHut8+C+EAW/IvZp+SzlcuL2PJgmL2\ndfnZ5AbNhoXVKmTa8Dmmg3a2BQj5IhTNzsOddOQPy3CyrCj+8Hv4w1uxe8JoShpe12WklM1CURTM\nYUq4N9xaAm182PwJ25s/oa4nfmcCQ3NybtYMZmZNIzcpK7HtsRLSHCuhzZme8GcwhYU24MDOuZng\n5udI2vU+JU4XJSVTgXhA7qoeOD1lQiEUTkjj2vw8/ri9k08P+smOwd66bvb/aitFs3MpPb/wiIC/\nrlbBrum7ymJRVnZqkuT19Q0TJ0J9vYqqKpw9AXJc8FK1zjtNnVT7YEURZEtsOKYoioo38zwcHZMI\nRD4iEHufroZ/4GveSnLOQrxZ89BkSK8QAxzrd280J3vTNZ25uXPJHOT2fZZt0RMN0BnupivcTVfY\nR1ckfhKgK+Kjo7tr0H26NCNxEiDVSCZmRZiUWsvMrJmku9LRVTnMHk8k2aEYa6QH6lVc3BcsWFB9\n7O2jPh/V//M80fY23s+YyXbPJP51bjqXLihmR5uPlyuaUICvpYCe+fmDkIxsD/NWTCct/9QNFbbt\nKN3NH9Hd9E+smB9FcZFW8EWc3eegKNqoHOLeFuxge29wX+2rA0BTNCalFjEtvYwvFi8+6SQ8ktDm\nxGgavSfI3ETzbqH8/z6H5+M36fh/aWReez2adugEWqQ3UYXV+1pRFG6cm06sOJst/6ggT1Eo1jUq\nP2ygdmczscsm4ZqRMaANFhcPndNiuBQXW0yY0Ns39Mp2wbdnFLGuoolPO3pYux+uK/ExzXNaqiSG\nkaI48RgLSJu2EF/L+/ia36Wr4R90N72NJ+M8knMWjKlcJEKcSmMt0d/hTrb+wViItmA7raF2WoNt\nieW2YDttoQ6aem/LC/BRy27+VP5XFBRSjRQyXOlk9j4y3OlkujLIcKXJiYAxSo4NxVgy7noY2zaJ\nRA4Nq3E6J6EowzeExrZNeqrepfn3/0Os08fWtLP5Z/Ycbppic2mJmy11bfxvfTuGqrLSa1OmQxUm\nllZLIOYmbAWI2HU4lcJjf1g/iqKQMTH1xOpq2kQq45nFrUh4wHthJzhL4veTtswQ/vB7BCLbsHwB\nFNWJx3kBevMsjORUotVh9OKBf8No1cD99V+vFx/fpc3+9TvesrZt0+Bv4v3GjznQVUWDvxmI37N3\nRsY05uSey7lZZ9Pgj2eGP5My7Y4mekoKjltuIfbii7S/ugErGCT7xpuPWW7+efl4/J1s2NZBUzjC\nxZMziNR089am/aR96uXsL5XhST91V1mj0UNDUp3OSRyti4xGq9CB6ydAodHG35syefFAI7PSYMmR\nAxCGzeH92xF10osHvO5zPP3gYPse7v7zVDtav6R1aTBE9xkvZ+BiPs6kWUS9u/C1vk9P6zZ6Wrfh\nSpmMN3M2Ls8UotUDc6P09aNwZJ92LH1lByvXf79CiJHldriOOiXAsi26Iz5ag+3sbttNV6Qb21Zp\nC7XTHuqgsruag12Vg+43xZlMhiuddFcaGa60xEmBNCONdFcqHkfSqLzIIoQYG8ZdoB+JHEwc8PYd\n6BrG8J15a//sbZr/thUiMd5JP4fdpXP56oIcckLb+R9/Jjs720k3HHx1SgEF9fGhAZZWi6XVAfFb\nhjmVQpzKqZ9nH6kMHzV4jlaFMa1uwu6P6Wnbjm1FUHCSknsJyTkLURUXEVf8wFMvNnCWHNpH/+XD\nHb7tidTvaGUjZpR9HeV81raHnW17aA91AKCgMDV9MvNyzuXcnJl49RO7nDrUPMPTMQd/pD//VFNS\n03DccivqK6/Q+ffNRFuaKVy0GM01dMKzmRPdZPh0XjjQwRvlbcydkU1ZTKFiXyvv/v5Tzrq8BDv5\nyCRMn1c8sI87FCRPHXI7RYEFOZlMz5rASxUt7OgMU9EDS4t8zMrwDvsBWv/+7XC6Xpyo22Df5Vj9\n4OH7PhX956k0VL/jKnURSxk8eenh5cxqMIpnk3z2AgKdu/A1byXUXU6ouxxFScIInkVS5tk41Dxi\n1REAjLJ4mx6qzz1c30kJo8x1RLn+7wkx1oz337bDqYpKmpFKmpGK0ns3pP5XfE3LpDPcRVuog5ZA\nKwe7KuiO+PBFeuiO9FDtq6Wye/ChpA5FI9npJdnpIVn34nV6KE0pIcOdTpqRSqqRcsInA0ZzjoVj\nkTnyQpyYcRfoQ/yAd7gPTqMdHTS+8me6d+8iojrYmHsprqln8cMb5vNxVYyXY9Ad1Snxurh1cgEe\nfWAno5qFJDlKCak9w1qvY9GLDYwyF2rfxSLbRgnU4I99RLhnH/TYaA4vbn0hbud5uAvSEmWPdpCp\naMqwHYD21e9wMctkV9s+NlW9zd6OcqJWFIifVZ+TM4scdzolKRM5J3vWSX3useYZnuo5+CP9+aeL\nkpzMxP+4j4an1+Lf8QmVtbUUXn89ZGUNWa4kxcXti+fz0vsf8+GuFlrzkllwWTE1W2vZtfkgyYVd\nFMyfga4PXxemKNpx9RuDbZcNfHP6RP5eU85bzfCHg4283+LmmqJs8pKG9/r+8fRvx/tdTmbfo9VQ\n/VJSdhL+Ft8JlVMUFU/6TDzpM4kEm/C3fUxP6yeEXNsJ+bejOVMxcqdgx6bitCeh9GbmPlqfdiwn\nW06I0eRM+W07EZqqkenOINOdgaZYhE0fF6bMTbxv2Rb+aDAR+HdHfPiifnoifnyRHnxRPx2+QzkC\n3mv8aMD+ddVBqpFKmpGCQ1Hx6h6quhtINVJIcaaQasQfhuYc0zkWQObIC3Gixi0xsSUAABleSURB\nVGWgP5zMUIi2/7ee9tc2YofD1BtZvJZ/EV+cV8zFZV42NnXwvq8LRXFwkdHFl6aV4VBH4a1YzCBq\n1y7U9o9RIm2EAYeaTcqEC+MHshXRka7hADtad/N67bsA5HlymZl5FjMzz2JSagmaqnGgc+hbER7L\nSM8zHOnPP500r5fCH9xJ67qX6Pjba1T89jeoF12MunDhkOXSPG5uu/Ec/vZ6BTs+a6a+zc8V50/A\nquiks66NrZ3bmLXwbFIzUk7TNxmapihclguz0mBTk4c9nX6e+KyamRleLs3PoGCYA35x+jjduTgn\nfAl36CLCsQqirnKCXfsIWB8QiHxA5w4DV3IpjshEnFoxTjtfhtuKM9KZ9Nt2sk5mjnfEjNIZ7uSz\nts/wRfw4NTdd4a7eOwd00hnu5kBnJTbxvDcfNO84Yh/xxIEppDiTE49kp/fQs5FMhisL0z49+W9O\nlsyRF+L4SaB/FFFfNx3bt9Px4YdY4RBBzeAf2RfQXVzG7cvyae1y8rMu6LG7SNecfMFfzez08KgK\n8m3bIhKrwFexG0fXXhTbxEbFSplOZmw2ulaIK7NvvvPoCvRnZEwhyeFiYf4CMt2SCGusUzSN7K/c\nhDcllYa//oXYW29i7d6FesNNMOPso5ZzOjWu+eJkLpyczW9f28Pf3qlmYr6XspIc/JXNvP+/25k0\no5jS0iLUUfJvL92Ar04pYE+nn021rexo72FHew9TUpKYn53ClFQPhvb56mrbNiHToidqEjYtQpZF\nxLTQFAWHquBQFNwOjTSndPHDSVEcuPQppJacg23F8O3bQzh2kKhSRbBrD7AHgI6dSRieIgzPRJye\nApzufNRBbsMlhBDHw6np5CRl44vE8zsNFuialsmO1h30RPwkO1PpjHTTHfbRFYnfSaC79y4CTYGW\nY36e2+EmWffgdXrw6l68vcvxdV48ehJJjiQ8uhuP7sHtcKEqo+M3WAhxiBwFHsa2LMJbX6W8YgdY\nFiHNYGvmbD7LnMGSczMxCmK83OrEH4sn3FpcmMGEQCaunvKRrvoROoPriMQOQhAwMjFTZ2KlzQSH\nB2ftSNduaC6HwVkZkyXIH2e8kyczafU32P+/f8fe8QnWL5+AqdPomT0bT+nRhxSePz0XPdvFptcr\n2FveTq3iZ05+Fq4OHwc+q6S7rZVLvjiV3ILRcXUf4Kw0D9NSk9jfHeDNhg72dwfY3x1AUxTKUtxM\nTkki2+Uky6WTZuho/a4AxywbfyyGL2rSHYnRHo7SHo7SFoTOCHRHDxK2ju+qi1uDNCfkJzWSl2SQ\nl2RQmGTgdsgcxs9DUR0Y+uT4o8xFNNxOz8G9RGO1RJU6gl17eoN/AAXdlY0zKR/dnYczKQ/bTkNV\nZJSHEGJ4aKpGitNLitM75BVv0zLxRXvoDvvojvjojvTgi8SXG/wN+KNBTNuiJ+KnJdiWGCUwFAUF\nt8OVOAHgdrhI9XhRTQduhwuXZsSfHW7cDhdOzYmhOTE0A0PTe5+dODWnnDAQYhhJoH+YSDiMXbOf\nbmcq7ydPY29qKTMXlDA738M7viBmCJwqXOGCS1xgFGRy4MDoHKLp1IrQ1DRSSmZTq2XEM4eNI2da\nwp/xQnO7cSxZgj13LrH33oNdn1Gzby9Gdg7WFy5HKcgHvEeUS0k2WHHNWew/2M5ft1TyYUMLTkVl\nVrKblsYe1v1uO9POyWPOBUWkZSSd/i82CEVRmJrqYWqqh/pAmM86etjT0cO+rgD7ugKHtgNURaHv\nX2jMPvqBlUuDdMNBmlPHq2u4NBWXQ8Wpqli2TdSye08UmHRFYnSEA7SEoCHog7ZD89SzXDoTPS6K\nvC6KvG7SVFDHVxdxWulGBknO2eCcjVHmIhbpJNxTSyRQRyRQTyTYSDTUDHySKKMpqTgP5qGGMnBo\nOaihQhxyKz9xhhvrv+2nuv6fd/+aqiWSBx6ub1pk34kCy7YIxIL0RPz0RP10h7up6q4iZIYJxsKE\nzBChWJiQGSYUCxOIBWkPdWLaJnSc3PfTVQe6qscfmo4zsewgakVxKBppxvs4VEe/h4au6nSHO+mO\ndJPVvBOHoqGpGpqioioaDlVF613nUBxoiopD1dBUR2Jbh6KhKupRp16NhfY3mo10MsXj/fzxRAJ9\n4sNg99a18LcdjXxc3gbTb8Kd4SKnMIlsj04tCviC5GkwS+/gvEnpZDbGy5ojW/UheYzzATA8LgiH\nRrg2w0sS/ox9Sk4O2ne+h11djXfDK3Tv3QMv/YGYrqNf+a9QNPgViSmTMtC/mE5nTSdv76rgA58f\nLzBNd7D300b2ftpI6ZQsJs/IobgsA32UDF8vSDIoSDK4sjCTjnCU6p4QbeEIbaEoHeEopk3iyomu\nqiTrGsm6g2RdI93QyTB0vEoNLg0M4/gPNMLh/Vg29NjFNATDNATC1PlD1PSE+Sjk46Pe4N+pwoQk\nKE5uY6LXIEcHz+j4041JDmcajow0PBkzgfhUqliojUiwkUiwkXBbPVGrhWDX3kSZrt3x6QGakoFL\nn47BZSNVfSFGxFj/bT/V9T/dfx+1N7lf312NDnTuJ9VIYlbK9CHLxSwTT4pOY2sHEStC2Iw/6nsa\nSHamkOJMJWxGiJgRwma49/1wYruoFSVqRomaEfzRAFErSszqf+eUoe8cAJ8vj5Pe7ySCrsRPJDhU\nB7rqYF9HLW/VfZAYgdA3GsHQDLrC7eiqTtSKj0x1aQYuhyuxzZk+WmGkkyke7+fn5c45JZ8/Es64\nwzjbtolYNoFYfEhsRyTKm3XtHOzsQE9xknlpAUrvZS0/kK3ClOw0ZmcmM7GhhojdjelIH9kvISTh\nzziiFBVReN315Pb0UF5TDZ/uAH3oIc2qqnJeaQHnFOexp7aZXfV1fFjTSTqQj0LF/lYq9reiqAqZ\nOR5y81NISXOTnGrgTnLi0yx0Q8NhOND00//Dm27opBv6CZcLH/8t2gdQFch2Ocl2O5mVkQyAZds0\nByPU+ENU94So8nVzsAcO9rQnyl2SA0vk4sWwUBQV3Z2N7s7GwzmEQ/GTr46iGP6DNcTMFmxPO5Fg\nM9FgC6HobpBAX5xhxvpv+6mu/2j4+xxvMrzs7GRaHAPvdnL4iIETYdkW+zr2ErNiTEwuJmrFiPU9\n7BhRM/4cX2cOeO/Qa5OYFSXau13UjPXuJ5rYX8SKJk40RKwoETOCPxYgYkZ7T8bXnXDd+xiaE5fm\nwuVwJaY0uBwuXI7eqQ1a73Lvs6vfukPPxpi+feBIJ1Mc6c8/3cZdoP9xO9QGwFIaCUYhYkHUriFk\nWoRNi0DMPHJYbBIkJXnRgDTNICcU5uw0mOyAdA3Momygd+T7sacqCSFOgsPrRb3yS6jTpqHWHl/X\npKkqZxfl8eXLc6lv9fPB3mY+2ttCRXMP6SikWTZmo4/WxqPf1tIoTKZyeg6mZRONWURiFqFwJzHT\nprNrN5Zl497Zhd8Ps0oyKSsbHyf6VEVJzNmfn51KONxNMAbNsQJqekLU+NrJkPxxp5ymezEcpfFH\ncfz2eqHyACDzKIQQoo+qqL3D+h2kGqc/H49t28Rsk8gRIxEOLdf6aoiYUbzOZEJmmHC/aQ0hM0Q4\nFiZohvFH/bQF24jZJzcu2KE6+uU4cOLSjAF5DpyajlN1Hpr60PvaqekDRitoyqGRCo7EFAZHYrqD\npqhoanxZVdTe9Wf2qISxZlwF+rZt8/dG6IkBHDqLqCkhDE3D0BTykpwkOTQ8Dg2v7iDdcBBs00nW\ndGZP1qmsUHF17afIkIheiLGkIMvDl7NK+fJFpXT4wpTXdVFe20VVYzedbQEiwSgG4EDBAWi9j/a6\nLt6u6zrKXgeeINAdTSxmfAT6g3E7YKonnlMgHG4/dgFxSihyICWEEKOKoijoSjwo9uiD5wFKN+JT\nHI73inHUihGKhQjGQr35DkIEY2HCZphQLJ7/IGiGel/H1wXNMBEzkjiR0B320WK2nvRJg5OhKioK\nCoqiJJ7V3ud4asbe/yp9r+LPZm8dHerA8PNQfqLe95Wjh6eH509Q+p0UV5T+n0aifn31iFpRFMDQ\nXAPq3vccMcNMz5jCwikydH9UUhSFb00Ffww8rhKIVaKYVbiNYg7Npo8dUa66K94sYlEby1Kob63B\n1OIZrZ3NdUTqqhPLoXyIUkJSLRj1VYSrFdrqVIpcdUTybMzwoXlBmlWFUl+P1tVMfXguFV4I7Y7v\nK1xQTJWtUlw8MHN2NFo1YFnXhx43WxWNj+UtPsZQ58G2jVYdGgccrQqjF5+eDNB9n3s6P1MMTa2K\ntzur+PjHaQ9W5vB1NU2NA8vU12MVFFBt25Q0NABQbds4GhqwCgqoioYp7d22qkpFsRTq7TDVikK9\nHaGgN0v5x00RyI1vV1evUG0NcmJOSyWvOJW83upFozGCoSjhSJSGcJA024Fl2+TaNjm9c+ccWvyh\nKXVoaiPtbRPIz7PJzamluWUCaZ6zjvl36f9veLgdT59wtHKnat/jUf++8fD1h/dZn6cflb5QCDHa\njGQyweMpO9qT4X2e76cp4NWdeHUnkHxCZU3L7J120G9Kg2UmXkf7TWMwLRPT7n1Y1hHLlm1jY2Ha\nFrZt9z5bWLaFhY1t29jY2IncQoOtg8RS7zrbshJJFhP6Hb4pVoSoFTtq7nC7b4/9RmZb/XZg2/H/\nDPj0fq9jVgxVUQnFIv2+h5Wou2Vb5CRlndDffbRTbHuI9M5CCCGEEEIIIYQYU2R8oBBCCCGEEEII\nMY5IoC+EEEIIIYQQQowjEugLIYQQQgghhBDjiAT6QgghhBBCCCHEOCKBvhBCCCGEEEIIMY5IoC+E\nEEIIIYQQQowjEugLIYQQQgghhBDjiAT6p9iWLVuYM2dO4vWvfvUrrrrqKq688koef/xxbNsewdoJ\ncaS9e/eyatUqrrvuOpYvX87OnTsBabtibNi8eTPXXHMN1157LatWraK6uhrTNHnkkUcS7feFF14Y\n6WoKAYBt29x77708++yzAEO21crKSm655RaWLl3KihUrOHDgwEhVW4gj2m4oFOK+++7jmmuuYdmy\nZdx3332EQiEA2tvbWb16NUuXLuXqq69m+/btI1l1IY5ov/1997vf5eGHH068HsvtVwL9U6iyspJH\nH300ERC98cYbvPbaa6xbt45XX32V9957j7/+9a8jXEshDgkGg3z9619n9erVvPLKK3z729/mrrvu\nkrYrxoRQKMTdd9/NE088wfr161m0aBGPPPIIL774IlVVVbz66qu8/PLLPPfcc+zYsWOkqyvOcAcO\nHOC2224b0JcO1Vbvuusubr75ZjZu3Mj3vvc9vv/978sJVzEiBmu7a9euxTRN1q9fz4YNGwiHwzz1\n1FMArFmzhnnz5rFx40b+67/+ix/84AcEg8GRqr44ww3Wfvs888wzfPDBBwPWjeX2K4H+KRIMBrn7\n7ru59957E+s2b97M1VdfTVJSEoZhsHz5cjZs2DCCtRRioHfeeYeJEydy2WWXAbBo0SJ+8YtfSNsV\nY4Jpmti2jc/nA8Dv92MYBlu2bGH58uU4HA5SU1NZtmyZtF8x4p5//nmWL1/OkiVLEuuO1labmpo4\nePAgy5YtA+Cyyy4jGAyya9eukaq+OIMN1nbnz5/Pt771LVRVRdM0pk+fTn19PbFYjNdff50bbrgB\ngOnTp1NSUsJbb701UtUXZ7jB2i/A1q1beeutt7jpppsS68Z6+5VA/xT54Q9/yI033si0adMS6xoa\nGsjPz0+8zsvLo6mpaSSqJ8SgKioqyM7O5v7772f58uXcfvvtmKYpbVeMCR6PhzVr1nDTTTdx8cUX\n8/zzz3PXXXcN2n4bGxtHsKZCxI8TrrvuugHrjtZWGxoayMnJQVUPHbbl5uZKOxYjYrC2e/HFF1Na\nWgpAXV0dzz33HFdddRUdHR1YlkVGRkZiW2m7YiQN1n6bmpr40Y9+xM9+9jM0TUusH+vtVwL9U+D5\n55/H4XCwYsWKAesHG2LX/0dbiJEWi8V44403uPHGG1m3bh0rV67kG9/4BpFI5Ihtpe2K0Wbv3r08\n+eSTbNy4kbfffpt/+7d/43vf+x6WZR2xrbRfMRod7ThhsDYMDDggFWI02LlzJ7feeisrV67k8ssv\nl7YrRr1oNMqdd97J/fffT05OzoD3xnr7dYx0BcajP//5z4RCIa699lqi0WhiecaMGbS0tCS2a2pq\nIi8vbwRrKsRAOTk5TJo0iXPPPReAxYsX88ADD6CqqrRdMeq9/fbbzJkzh6KiIgBuvfVWfvzjH7Ng\nwQJpv2JMyM/PH7StFhQU0Nraim3bKIoy4D0hRou//OUvrFmzhgcffJBrrrkGgMzMTAC6urpITU0F\n4m03Nzd3xOopRH87d+6ktraWn/zkJwC0trZimibhcJg1a9YAY7f9yiWNU+Dll1/m1VdfZf369Tz9\n9NO4XC7Wr1/PlVdeyYYNGwgEAkQiEdatW8fixYtHurpCJFx66aXU1dUlMu1v27YNRVG47bbbpO2K\nUW/GjBls27aN1tZWID7fecKECSxatIg//elPxGIxuru7+ctf/iLtV4xKR2ureXl5FBUVsXHjRgDe\neustVFVl6tSpI1xjIeJee+01HnnkEZ599tlEkA/gcDj4whe+wB/+8AcA9uzZw4EDB1iwYMFIVVWI\nAWbPns0bb7zB+vXrWb9+PTfddBNLly7lRz/60Zhvv3JF/zS64oor2LdvH1/5yleIRqMsWrToiDki\nQoyk7OxsnnzySdasWUMwGMTpdPL4448zb948abti1Lvgggv4+te/zqpVq9B1ndTUVH75y19SWlpK\ndXV1YpTVjTfeyPnnnz/S1RXiCDfffPNR2+pjjz3Ggw8+yNq1a3E6nfz3f/+3TEERo8Zjjz2Gbds8\n8MADiXVz5szhoYce4qGHHuKBBx7g6quvRlEUfvrTn5KcnDyCtRXi+I3l9qvYcm8WIYQQQgghhBBi\n3JBTwUIIIYQQQgghxDgigb4QQgghhBBCCDGOSKAvhBBCCCGEEEKMIxLoCyGEEEIIIYQQ44gE+kII\nIYQQQgghxDgit9cTQgghRtgVV1xBXV1d4rWiKKSkpDB37lx++MMfkp+fP4K1E0IIIcRYI7fXE0II\nIUbYFVdcwapVq7j66qsBsCyL8vJyHnroIQoKCvjd7343wjUUQgghxFgiV/SFEEKIUcDr9ZKdnZ14\nnZuby/e//33uvvtufD4fycnJI1g7IYQQQowlMkdfCCGEGKWcTicAqqri8/m45557mDt3LhdddBEP\nPvggPT09ANxwww38/Oc/H1B29erVPProowDs37+fr371q8yaNYsrr7yS3/zmN/QN6Hv88ce54447\nePjhh5k7dy4LFy7kqaeeSuxn1apVA/ZdW1vLtGnTqKqqAhiyXkIIIYQYGRLoCyGEEKNQTU0NTz/9\nNJdccgkej4f777+fjo4Onn/+eZ566ikqKiq47777AFi2bBmbNm1KlO3q6mLr1q0sXbqUUCjE6tWr\nOe+889iwYQMPPPAAzz33HL///e8T22/evBlN01i3bh2rV6/mscceo7y8/LjqOVS9hBBCCDEyZOi+\nEEIIMQo8/PDD/Od//icAsVgMXddZtGgR999/P9XV1WzevJmtW7eSlpYGwKOPPsoVV1xBQ0MDS5Ys\n4Sc/+Qnl5eVMnjyZLVu2UFBQwDnnnMNLL71Eamoqd955JwAlJSX8+7//O08++SSrVq0CIDk5mXvv\nvRdN01i9ejXPPPMMO3fuZPLkyUPW+Vj1kiSCQgghxMiQQF8IIYQYBb773e9y1VVXEQgEeOKJJ6ip\nqeGOO+4gPT2djz/+GNu2ufzyy48oV1lZyQUXXMD8+fPZtGkTkydP5rXXXmPJkiUAHDx4kPLycmbP\nnp0oY1kWkUiESCQCQGFhIZqmJd73eDxEo9Fj1vnAgQND1ksCfSGEEGJkSKAvhBBCjAIZGRkUFxcD\n8POf/5wVK1bwne98hz/+8Y+YpklSUhKvvPLKEeX6EvgtW7aMF154gZUrV/Luu+9y9913A/HRAeef\nfz5r1qw5oqzDET8M0HX9uOtpmuaA5WPVSwghhBCnn8zRF0IIIUYZp9PJI488wp49e/jtb39LaWkp\ngUAA0zQpLi5OnBD48Y9/nEh896UvfYny8nJefPFFSkpKmDp1KgClpaVUVlZSWFiYKLt7926eeeYZ\nVPXYhwFOpxO/3594XVNTk1g+nnoJIYQQ4vSTQF8IIYQYhWbNmsWKFStYu3YtXq+XSy65hP/4j//g\nk08+Yc+ePdxzzz20tbWRk5MDQFpaGhdeeCFr165l6dKlif18+ctfJhKJ8MADD3DgwAHeeecdHn74\nYVJTU4+rHjNnzmTTpk3s2LGDTz/9lMcffxxFUQAoKys7Zr2EEEIIcfpJoC+EEEKMUnfccQe6rvPo\no4/y05/+lOLiYr72ta+xcuVKcnJy+OUvfzlg+2XLlhEIBFi2bFlindfr5de//jV1dXVcf/313HPP\nPVx//fXccccdx1WH22+/nbPPPpuVK1dy55138s1vfnPASIDjqZcQQgghTi/F7ruRrhBCCCGEEEII\nIcY8uaIvhBBCCCGEEEKMIxLoCyGEEEIIIYQQ44gE+kIIIYQQQgghxDgigb4QQgghhBBCCDGOSKAv\nhBBCCCGEEEKMIxLoCyGEEEIIIYQQ44gE+kIIIYQQQgghxDgigb4QQgghhBBCCDGO/H8ZRlgbnk4A\nRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44653bff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "colors = \"b g r c m y\".split()\n",
    "\n",
    "quants_per_set = dict(zip(sets, prices_per_set))\n",
    "\n",
    "plt.figure(figsize=(18,12))\n",
    "for s, color in zip(sets, colors):\n",
    "    noise = pd.DataFrame(quants_per_set[s])['total'].values\n",
    "    density = stats.gaussian_kde(noise)\n",
    "\n",
    "    n, x, _ = plt.hist(noise, bins=100, color=color,\n",
    "                       histtype=u'step', normed=True)  \n",
    "    plt.plot(x, density(x))\n",
    "    \n",
    "plt.legend(sets, fontsize=16)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.xlabel(\"Revenue\", fontsize=14)\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Visually, they all seem Normal distributions, with the bell-shape form, even though there might be some skews on them. Again, it is possible to see that *xln* is a lot more profitable than the rest, since its mean is about \\$78, and is larger than the maximum revenued obtained from the rest of the sets. Besides, we can see that the minimum revenue obtained from that set is close to the average revenue of the rest of the sets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named gofplots",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-a4d7d74b773e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshapiro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices_per_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgofplots\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mqqplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices_per_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named gofplots"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "\n",
    "t, p = shapiro(prices_per_set[2]['total'].values)\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "qqplot(prices_per_set[0]['total'].values)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "editable": true
   },
   "outputs": [],
   "source": []
  }
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