AFML 4.1.ipynb

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    "# Sample Weights\n",
    "\n",
    "This notebook will cover exercise answer.\n",
    "\n",
    "* Exercise 4.1\n",
    "* Exercise 4.2\n",
    "* Exercise 4.3\n",
    "* Exercise 4.4\n",
    "\n",
    "As we go along, there will be some explanations.\n",
    "\n",
    "More importantly, this method can be applied not just within mean-reversion strategy but also other strategies as well. Most of the functions below can be found under research/Sampling.\n",
    "\n",
    "Contact: boyboi86@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Num of CPU core:  4\n",
      "Machine info:  Windows-10-10.0.18362-SP0\n",
      "Python 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]\n",
      "Numpy 1.17.3\n",
      "Pandas 1.0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import research as rs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dollar = pd.read_csv('./research/Sample_data/dollar_bars.txt', \n",
    "                 sep=',', \n",
    "                 header=0, \n",
    "                 parse_dates = True, \n",
    "                 index_col=['date_time'])\n",
    "\n",
    "close = dollar['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Labels\\triple_barrier_method.py:75: UserWarning: Data and events index shape must be same, reindex data to fit events\n",
      "  warnings.warn('Data and events index shape must be same, reindex data to fit events')\n",
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Labels\\triple_barrier_method.py:112: UserWarning: Not Recommended: No side prediction provided\n",
      "  warnings.warn('Not Recommended: No side prediction provided')\n"
     ]
    },
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      "[                                             t1                      sl  \\\n",
      "2015-09-16 19:59:40.048 2015-09-17 19:59:49.542                     NaT   \n",
      "2015-09-16 20:00:13.782 2015-09-17 20:03:58.960                     NaT   \n",
      "2015-09-16 20:13:49.208 2015-09-17 20:49:02.616 2015-09-17 19:55:09.135   \n",
      "2015-09-17 02:31:29.158 2015-09-18 05:53:05.346 2015-09-17 20:03:58.960   \n",
      "2015-09-17 07:50:29.399 2015-09-18 08:36:51.929                     NaT   \n",
      "...                                         ...                     ...   \n",
      "2016-04-28 01:20:23.379 2016-04-29 04:43:02.149 2016-04-28 03:08:23.517   \n",
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      "2016-04-28 07:22:07.437 2016-04-29 08:08:00.258 2016-04-28 08:58:32.457   \n",
      "\n",
      "                                             pt  \n",
      "2015-09-16 19:59:40.048 2015-09-17 18:13:04.358  \n",
      "2015-09-16 20:00:13.782 2015-09-17 18:13:04.358  \n",
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      "...                                         ...  \n",
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      "\n",
      "[7408 rows x 3 columns]] this out\n"
     ]
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     "text": [
      "2020-05-24 13:16:25.929578 33.33% _pt_sl_t1 done after 0.47 minutes. Remaining 0.94 minutes.\r"
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      "[                                             t1                      sl  \\\n",
      "2015-09-16 19:59:40.048 2015-09-17 19:59:49.542                     NaT   \n",
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      "...                                         ...                     ...   \n",
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      "2016-04-28 07:22:07.437 2016-04-29 08:08:00.258 2016-04-28 08:58:32.457   \n",
      "\n",
      "                                             pt  \n",
      "2015-09-16 19:59:40.048 2015-09-17 18:13:04.358  \n",
      "2015-09-16 20:00:13.782 2015-09-17 18:13:04.358  \n",
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      "...                                         ...  \n",
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      "[7408 rows x 3 columns],                                              t1                      sl  \\\n",
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      "\n",
      "                                             pt  \n",
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      "...                                         ...  \n",
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      "[                                             t1                      sl  \\\n",
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      "2015-09-17 07:50:29.399 2015-09-18 08:36:51.929                     NaT   \n",
      "...                                         ...                     ...   \n",
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      "\n",
      "                                             pt  \n",
      "2015-09-16 19:59:40.048 2015-09-17 18:13:04.358  \n",
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      "...                                         ...  \n",
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      "[7408 rows x 3 columns],                                              t1                      sl  \\\n",
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      "\n",
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      "[7408 rows x 3 columns],                                              t1                      sl  \\\n",
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      "...                                         ...                     ...   \n",
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     "text": [
      "2020-05-24 13:16:26.159833 66.67% _pt_sl_t1 done after 0.47 minutes. Remaining 0.24 minutes.\r",
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   ],
   "source": [
    "day_vol = rs.vol(close, span0 = 50)\n",
    "\n",
    "events = rs.cs_filter(close, \n",
    "                    limit = day_vol.mean())\n",
    "\n",
    "vb = rs.vert_barrier(data = close, \n",
    "                 events = events, \n",
    "                 period = 'days', \n",
    "                 freq = 1)\n",
    "\n",
    "tb = rs.tri_barrier(data = close, \n",
    "                    events = events, \n",
    "                    trgt = day_vol, \n",
    "                    min_req = 0.002, \n",
    "                    num_threads = 3, \n",
    "                    ptSl = [1,1],\n",
    "                    t1 = vb, \n",
    "                    side = None)"
   ]
  },
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      "[date_time\n",
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     "text": [
      "2020-05-24 13:16:40.730994 33.33% _num_co_events done after 0.23 minutes. Remaining 0.46 minutes.\r",
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   "source": [
    "concurrent_event = rs.num_co_events(data = close, \n",
    "                                    events = tb, \n",
    "                                    num_threads = 3)"
   ]
  },
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df0 = pd.DataFrame(index = concurrent_event.index).assign(volatility = day_vol, \n",
    "                                                          concurrent_event = concurrent_event )\n",
    "df0[['volatility', 'concurrent_event']].plot(secondary_y='volatility', figsize=(10,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "df1 = df0.groupby(concurrent_event, axis = 0).median() # Try piecewise estimate median\n",
    "df2 = df0.groupby(concurrent_event, axis = 0).mean() # Try piecewise estimate mean\n",
    "plt.plot(df0['concurrent_event'], df0['volatility'], '.',\n",
    "         df1['concurrent_event'], df1['volatility'], '-',\n",
    "         df2['concurrent_event'], df2['volatility'], '-.')\n",
    "plt.axhline(y = 0.015, c='r', ls='--')\n",
    "plt.axvline(x = 52, c='g', ls='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on the multiple line plot\n",
    "\n",
    "Where there is an increase in number of concurrent events, there are evidence of high volatility. However, volatility might not lead to high concurrent events.\n",
    "\n",
    "### Based on Scatter plot\n",
    "\n",
    "Before the vertical green dotted line (where the num of concurrent events is before 52), there is an obvious gradual increase which obviously meant a positive relationship.\n",
    "\n",
    "After the vertical green dotted line, the median num of concurrent events display high fluctuations, as a result the regression line might experienced a kinked plot, if we were to plot them seperately. \n",
    "\n",
    "This is most likely due to rare occurance of concurrent events that experience more than 52 times.\n",
    "\n",
    "Below the red line, most frequent and average volatility is actually below 0.015 (Maximum volatility was around 0.03).\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "* An increase in concurrent events may be caused by volatility (weak positive relationship for most cases).\n",
    "* An increase in volatility might not cause an increase in concurrent events.\n",
    "* High number of concurrent events that occurs beyond a certain point is rare. Does not require high volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[date_time\n",
      "2015-01-05 14:54:26.286     1.0\n",
      "2015-01-05 14:57:13.616     2.0\n",
      "2015-01-05 15:01:57.494     3.0\n",
      "2015-01-05 15:07:29.012     4.0\n",
      "2015-01-05 15:13:09.655     5.0\n",
      "                           ... \n",
      "2015-09-17 18:03:09.283    24.0\n",
      "2015-09-17 18:05:03.370    25.0\n",
      "2015-09-17 18:07:26.866    19.0\n",
      "2015-09-17 18:10:32.478    20.0\n",
      "2015-09-17 18:13:04.358    20.0\n",
      "Length: 7953, dtype: float64] this out\n",
      "[date_time\n",
      "2015-01-05 14:54:26.286     1.0\n",
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       "                               tW\n",
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       "\n",
       "[22207 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercise 4.2\n",
    "av_uniqueness_by_coevent = rs.wght_by_coevents(data = close, \n",
    "                        events = tb, \n",
    "                        num_threads = 3)\n",
    "\n",
    "av_uniqueness_by_coevent\n",
    "\n",
    "# not sure why but mine was never hitting 1 in the first column, \n",
    "# but seems to match the data freq from the book"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lagrange Multiplier tests for autocorrelation.\n",
    "\n",
    "H0: the sequence was produced in a random manner.\n",
    "\n",
    "H1: the sequence was not produced in a random manner.\n",
    "\n",
    "If pval < 0.05 (Random: Accept H0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue for LM test: 0.0\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.sandbox.stats import diagnostic\n",
    "\n",
    "pval = diagnostic.acorr_lm(av_uniqueness_by_coevent.squeeze(), maxlag = 1, autolag = None)[1]\n",
    "print(\"pvalue for LM test: {0}\".format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "**Statistical significance is the likelihood that a relationship between two or more variables is caused by something other than chance**\n",
    "\n",
    "It is statistically significant, since it is lesser than 0.05.\n",
    "\n",
    "In the earlier exercise, when we try to explore a potential relationship between volatility and number of concurrent events, it was concluded that they display a weak positive correlation.\n",
    "\n",
    "The volatility that we were using was actually daily percentage change in price (daily returns) and in financial theory context financial returns are seen as \"random\" (considered sub-martingale).\n",
    "\n",
    "Therefore it is only natural that average uniqueness of concurrent events would also display similar randomness characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise 4.3\n",
    "# We will start using ML models from sklearn\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bollinger Band results:\n",
      "\n",
      "Num of times upper limit touched: 10195\n",
      "Num of times lower limit touched: 8987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Labels\\triple_barrier_method.py:75: UserWarning: Data and events index shape must be same, reindex data to fit events\n",
      "  warnings.warn('Data and events index shape must be same, reindex data to fit events')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                             t1  sl  pt\n",
      "2016-04-13 18:29:02.408 2016-04-14 18:54:50.907 NaT NaT\n",
      "2016-04-13 18:48:48.529 2016-04-14 18:54:50.907 NaT NaT\n",
      "2016-04-13 19:07:58.695 2016-04-14 19:32:17.351 NaT NaT\n",
      "2016-04-13 19:19:12.560 2016-04-14 19:32:17.351 NaT NaT\n",
      "2016-04-13 19:31:57.511 2016-04-14 19:32:17.351 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-12-30 20:59:58.848                     NaT NaT NaT\n",
      "2016-12-30 21:00:00.352                     NaT NaT NaT\n",
      "2016-12-30 21:00:24.294                     NaT NaT NaT\n",
      "2016-12-30 21:03:03.027                     NaT NaT NaT\n",
      "2016-12-30 21:13:31.990                     NaT NaT NaT\n",
      "\n",
      "[6015 rows x 3 columns]] this out\n",
      "[                                             t1  sl  pt\n",
      "2016-04-13 18:29:02.408 2016-04-14 18:54:50.907 NaT NaT\n",
      "2016-04-13 18:48:48.529 2016-04-14 18:54:50.907 NaT NaT\n",
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      "2016-04-13 19:31:57.511 2016-04-14 19:32:17.351 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-12-30 20:59:58.848                     NaT NaT NaT\n",
      "2016-12-30 21:00:00.352                     NaT NaT NaT\n",
      "2016-12-30 21:00:24.294                     NaT NaT NaT\n",
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      "\n",
      "[6015 rows x 3 columns],                                              t1                      sl  pt\n",
      "2015-01-05 14:54:26.286 2015-01-06 15:01:01.702 2015-01-05 16:15:59.512 NaT\n",
      "2015-01-05 14:57:13.616 2015-01-06 15:01:01.702 2015-01-05 16:21:16.062 NaT\n",
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      "2015-01-05 15:13:09.655 2015-01-06 15:13:19.811 2015-01-05 17:32:22.888 NaT\n",
      "...                                         ...                     ...  ..\n",
      "2015-09-10 10:27:02.613 2015-09-11 12:38:05.265                     NaT NaT\n",
      "2015-09-10 11:43:14.539 2015-09-11 12:38:05.265                     NaT NaT\n",
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      "2015-09-10 13:01:38.292 2015-09-11 13:30:09.634                     NaT NaT\n",
      "\n",
      "[6016 rows x 3 columns]] this out\n",
      "[                                             t1  sl  pt\n",
      "2016-04-13 18:29:02.408 2016-04-14 18:54:50.907 NaT NaT\n",
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      "2016-04-13 19:31:57.511 2016-04-14 19:32:17.351 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-12-30 20:59:58.848                     NaT NaT NaT\n",
      "2016-12-30 21:00:00.352                     NaT NaT NaT\n",
      "2016-12-30 21:00:24.294                     NaT NaT NaT\n",
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      "\n",
      "[6015 rows x 3 columns],                                              t1                      sl  pt\n",
      "2015-01-05 14:54:26.286 2015-01-06 15:01:01.702 2015-01-05 16:15:59.512 NaT\n",
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      "2015-01-05 15:13:09.655 2015-01-06 15:13:19.811 2015-01-05 17:32:22.888 NaT\n",
      "...                                         ...                     ...  ..\n",
      "2015-09-10 10:27:02.613 2015-09-11 12:38:05.265                     NaT NaT\n",
      "2015-09-10 11:43:14.539 2015-09-11 12:38:05.265                     NaT NaT\n",
      "2015-09-10 12:19:25.229 2015-09-11 12:38:05.265                     NaT NaT\n",
      "2015-09-10 12:36:39.559 2015-09-11 12:38:05.265                     NaT NaT\n",
      "2015-09-10 13:01:38.292 2015-09-11 13:30:09.634                     NaT NaT\n",
      "\n",
      "[6016 rows x 3 columns],                                              t1  sl  pt\n",
      "2015-09-10 13:30:20.359 2015-09-11 13:38:02.648 NaT NaT\n",
      "2015-09-10 13:35:08.110 2015-09-11 13:38:02.648 NaT NaT\n",
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      "2015-09-10 13:51:06.807 2015-09-11 14:03:06.126 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-04-13 16:50:16.392 2016-04-14 17:06:00.165 NaT NaT\n",
      "2016-04-13 17:09:16.157 2016-04-14 17:21:06.881 NaT NaT\n",
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      "2016-04-13 18:15:45.578 2016-04-14 18:23:13.530 NaT NaT\n",
      "\n",
      "[6016 rows x 3 columns]] this out\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-24 13:17:36.254202 33.33% _pt_sl_t1 done after 0.4 minutes. Remaining 0.79 minutes.\r",
      "2020-05-24 13:17:36.394793 66.67% _pt_sl_t1 done after 0.4 minutes. Remaining 0.2 minutes.\r",
      "2020-05-24 13:17:36.410409 100.0% _pt_sl_t1 done after 0.4 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "dollar = rs.bband_as_side(data = dollar, \n",
    "                          window = 50, \n",
    "                          width = 0.001)\n",
    "\n",
    "dollar['volatility'] = rs.vol(dollar['close'], span0 = 50)\n",
    "\n",
    "events = rs.cs_filter(dollar['close'], \n",
    "                    limit = dollar['volatility'].mean())\n",
    "\n",
    "vb = rs.vert_barrier(data = dollar['close'], \n",
    "                 events = events, \n",
    "                 period = 'days', \n",
    "                 freq = 1)\n",
    "\n",
    "tb = rs.tri_barrier(data = dollar['close'], \n",
    "                    events = events, \n",
    "                    trgt = dollar['volatility'], \n",
    "                    min_req = 0.002, \n",
    "                    num_threads = 3, \n",
    "                    ptSl = [0,2],\n",
    "                    t1 = vb, \n",
    "                    side = dollar['side'])\n",
    "\n",
    "mlabel = rs.meta_label(data = dollar['close'], \n",
    "                       events = tb, \n",
    "                       drop = False) # when you have a side binary, you won't have rare labels usually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Features\\fractional_diff.py:93: UserWarning: thres val <= 1.e-2 may not suit non-trend series, may take up longer than expected to calculate val i.e. 1e-5\n",
      "  warnings.warn('thres val <= 1.e-2 may not suit non-trend series, may take up longer than expected to calculate val i.e. 1e-5')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reject ADF Null hypothesis: Weak evidence that series is stationary\n",
      "Reject KPSS Null hypothesis: Series contains unit root\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1685: FutureWarning: The behavior of using lags=None will change in the next release. Currently lags=None is the same as lags='legacy', and so a sample-size lag length is used. After the next release, the default will change to be the same as lags='auto' which uses an automatic lag length selection method. To silence this warning, either use 'auto' or 'legacy'\n",
      "  warn(msg, FutureWarning)\n",
      "C:\\Users\\Wei_X\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1708: InterpolationWarning: p-value is smaller than the indicated p-value\n",
      "  warn(\"p-value is smaller than the indicated p-value\", InterpolationWarning)\n"
     ]
    }
   ],
   "source": [
    "dollar['st_series'] = rs.fracDiff_FFD(data = dollar['close'].to_frame(),\n",
    "                                      d = 0.2,\n",
    "                                      thres = 1e-2)\n",
    "\n",
    "rs.unit_root(dollar['st_series'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Features\\fractional_diff.py:93: UserWarning: thres val <= 1.e-2 may not suit non-trend series, may take up longer than expected to calculate val i.e. 1e-5\n",
      "  warnings.warn('thres val <= 1.e-2 may not suit non-trend series, may take up longer than expected to calculate val i.e. 1e-5')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reject ADF Null hypothesis: Weak evidence that series is stationary\n",
      "Reject KPSS Null hypothesis: Series contains unit root\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# use crossing averages (primary model indicators), volatility and stationary series only\n",
    "\n",
    "dollar = dollar.reindex(mlabel.index)\n",
    "\n",
    "#only generate stationarity series when data is continueous\n",
    "\n",
    "dollar['st_series_cs'] = rs.fracDiff_FFD(data = dollar['close'].cumsum().to_frame(),\n",
    "                                      d = 1.99999889,\n",
    "                                      thres = 1e-5)\n",
    "\n",
    "rs.unit_root(dollar['st_series_cs'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reject ADF Null hypothesis: Weak evidence that series is stationary\n",
      "Reject KPSS Null hypothesis: Series contains unit root\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1708: InterpolationWarning: p-value is smaller than the indicated p-value\n",
      "  warn(\"p-value is smaller than the indicated p-value\", InterpolationWarning)\n"
     ]
    }
   ],
   "source": [
    "log_price = dollar['close'].apply(np.log)\n",
    "\n",
    "dollar['st_series_log'] = rs.fracDiff_FFD(data = log_price.to_frame(),\n",
    "                                          d = 0.2,\n",
    "                                          thres = 1e-2)\n",
    "\n",
    "rs.unit_root(dollar['st_series_log'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reject ADF Null hypothesis: Weak evidence that series is stationary\n",
      "Reject KPSS Null hypothesis: Series contains unit root\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dollar['st_series_cs_log'] = rs.fracDiff_FFD(data = log_price.cumsum().to_frame(),\n",
    "                                              d = 1.99999889,\n",
    "                                              thres = 1e-5)\n",
    "\n",
    "rs.unit_root(dollar['st_series_cs_log'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-bag Accuracy (OOB Score): 0.678175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = dollar.drop(['open', 'high', 'low', 'close','cum_vol', 'cum_dollar', 'cum_ticks'], axis = 1)\n",
    "X.dropna(inplace = True) # we lost quite abit of data\n",
    "\n",
    "mlabel = mlabel.reindex(X.index)\n",
    "y = mlabel['bin']\n",
    "\n",
    "n_estimators, max_depth, c_random_state = 500, 7, 42\n",
    "\n",
    "# Random Forest Model\n",
    "rf = RandomForestClassifier(max_depth = max_depth, \n",
    "                            n_estimators = n_estimators,\n",
    "                            criterion = 'entropy',\n",
    "                            #bootstrap=True,\n",
    "                            #max_samples = av_uniqueness_by_coevent['tW'].mean()\n",
    "                            oob_score = True,\n",
    "                            class_weight = None, #This will be covered later\n",
    "                            random_state = c_random_state)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
    "\n",
    "rf.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print(\"Out-of-bag Accuracy (OOB Score): {:.6f}\".format(rf.oob_score_))\n",
    "\n",
    "rs.feat_imp(rf, X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our OOB score is: 0.678175\n",
    "\n",
    "In sklearn version 0.22 they start to include max_samples, which was previously not covered.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "\n",
    ">\"A second and better solution is to utilise the average uniqueness..\n",
    ">\n",
    "> Accordingly, we could sample only a fraction out['tW'].mean() of the observations or accept a small multiple of that\"\n",
    ">\n",
    ">   Advances in Financial Machine Learning, page 63, last paragraph.\n",
    "\n",
    "For more information regarding Out-of-Bag Score vs Random Forest Score:\n",
    "\n",
    "https://datascience.stackexchange.com/questions/13151/randomforestclassifier-oob-scoring-method\n",
    "\n",
    "Optional\n",
    "\n",
    "You may wish to run func feat_imp from rs. I included 4 different stationary series.\n",
    "\n",
    "At this point, you should be familiar with stationarity concept and what it means to a mean-reversion strategy.\n",
    "\n",
    "If by some chance stationarity is not considered as a key feature by random forest model, you might wish to revisit the previous steps before meta-labeling.\n",
    "\n",
    "In the above case, it seems that using non-log stationary series has the highest feature importance rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean KFold accuracy: 0.539802\n"
     ]
    }
   ],
   "source": [
    "# Exercise 4.3b\n",
    "\n",
    "# k-fold\n",
    "no_of_folds = 5\n",
    "kfold = KFold(shuffle = False, \n",
    "              random_state = 1, \n",
    "              n_splits = no_of_folds)\n",
    "\n",
    "accuracy_array = np.zeros(no_of_folds)\n",
    "i = 0\n",
    "for train_index, test_index in kfold.split(X):\n",
    "    # print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    rf.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "    y_pred_rf = rf.predict_proba(X_test)[:, 1] #True positive only\n",
    "    y_pred = rf.predict(X_test)\n",
    "    accuracy_array[i] = accuracy_score(y_test, y_pred)\n",
    "    i += 1\n",
    "    \n",
    "print(\"Mean KFold accuracy: {:.6f}\".format(np.mean(accuracy_array)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean KFold score: 0.539802\n",
    "\n",
    "For more details onsklearn KFold:\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html\n",
    "\n",
    "https://machinelearningmastery.com/k-fold-cross-validation/\n",
    "\n",
    "It is obvious why OOB score is much higher than KFold score. \n",
    "\n",
    "KFold only splits data to sample, but it does not replace or repick them. \n",
    "\n",
    "Random Forest OOB actually picks and replace samples (Bootstrap = True by default) and when uniqueness of dataset is not high due to concurrent events; this will make your OOB score very much the same as In-the-Bag sample and redundant to each other.\n",
    "\n",
    "**KFold will reflect a less bias/ more correct outcome.**\n",
    "\n",
    "For more details:\n",
    "\n",
    "Refer to Advances in Financial Machine Learning, page 62 - 63, section 4.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[date_time\n",
      "2015-01-05 14:54:26.286     1.0\n",
      "2015-01-05 14:57:13.616     2.0\n",
      "2015-01-05 15:01:57.494     3.0\n",
      "2015-01-05 15:07:29.012     4.0\n",
      "2015-01-05 15:13:09.655     5.0\n",
      "                           ... \n",
      "2015-09-10 19:47:56.574    41.0\n",
      "2015-09-10 19:56:15.133    40.0\n",
      "2015-09-10 19:59:57.063    40.0\n",
      "2015-09-10 20:02:40.549    40.0\n",
      "2015-09-11 00:03:59.027    41.0\n",
      "Length: 7803, dtype: float64] this out\n",
      "[date_time\n",
      "2015-01-05 14:54:26.286     1.0\n",
      "2015-01-05 14:57:13.616     2.0\n",
      "2015-01-05 15:01:57.494     3.0\n",
      "2015-01-05 15:07:29.012     4.0\n",
      "2015-01-05 15:13:09.655     5.0\n",
      "                           ... \n",
      "2015-09-10 19:47:56.574    41.0\n",
      "2015-09-10 19:56:15.133    40.0\n",
      "2015-09-10 19:59:57.063    40.0\n",
      "2015-09-10 20:02:40.549    40.0\n",
      "2015-09-11 00:03:59.027    41.0\n",
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    }
   ],
   "source": [
    "# Exercise 4.4\n",
    "# This func can be found under research/Sampling/sample_unique\n",
    "\n",
    "wght_td = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = 1.0)\n",
    "wght_td0 = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = 0.75)\n",
    "wght_td1 = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = 0.5)\n",
    "wght_td2 = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = 0.0)\n",
    "wght_td3 = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = -0.25)\n",
    "wght_td4 = rs.wght_by_td(data = close, events = tb, num_threads = 3, td = -0.5)\n",
    "\n",
    "# index is according to earlier func, which is part of the func wght_by_td\n",
    "td_df = pd.DataFrame(index= av_uniqueness_by_coevent.index).assign(wght_td = wght_td,\n",
    "                                                                   wght_td0 = wght_td0,\n",
    "                                                                   wght_td1 = wght_td1,\n",
    "                                                                   wght_td2 = wght_td2,\n",
    "                                                                   wght_td3 = wght_td3,\n",
    "                                                                   wght_td4 = wght_td4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1512c6f7a48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B0WDvZLmfTodeMXAo/gAJGQk0rd6Urou7arrMfGAm8anxdAjsgIu9C1mXLpG6dSsXX36ZpCqBaiIV0KAKRzeZEqnG9z9ENf+AXKezNkmmhBBCiPLMaIT9v8LCp+5uv46TTQPIxx8AVz/Q3ZonIWKwxe7p+nSMihEXexc+3/U5M/fNVNs+bf+puv14g8d5PvJ5dDrzvAuHQuur29eqNuC/cPPoIZ+a5zi66VZIT46+u5+hhEgyJYQQQpR1RqPpllrcclgwEJRbqxg4ukHGHcY3AQS0gIc/MSVLDpVNt+luaz0+31MnpCew7OQypmybAsCKnis0idQn7T4hxjeGfY/vQ1EUTRIFkPSneYJPuwYN+a/6KAAUYxI2yi8c2WKar69B2/vyjcVaJJkSQgghypL0RLB1hLg/4LcnzPW1YuHkem3fnIlUZW944QgkX4Yq3tyrXZd38fifj1ts863ky7ut3wXg4eCHNclTzkTKmJLC+WefRQGut+rP1bAucCThVuimGc19Q+oy7NMZVPaods/xFjdJpoQQQojSyGiEjR9D9EiYGgE3L4NbICTmseRT9QbmZKpxf9PTc5FDwZAFtvbavveYSGUZs4iaH4XeqM/V9mLzF+lVtxe2NrZ0rd3Vwt5mmadPY+/vz+Fmpjkw17b7wtRwJAF7J1ta9nBl5a1ZEhp16IyHr18eRyodJJkSQgghrE1RIPUaXPwP1rwFF3ab29a8Zd6+nUi5BUDVYFPyNPh309QDTq7w4JTcx86ZSN0Fg9HAs+ueZd3ZdRbbP273MffXvB+9UY+dTf4phaLXE9ewkaYuwdW8vE10t2DC2lZj+hPmWZlCInMvk1PaSDIlhBBClBSjEW6chE2fmZ6ic/GEde/eeR+v+hB/yLQ98TQ4uxdriJkG02LJh68fpv/y/pq2B4MeJM2QxrCGw2ha3bxeZIESKYNBk0jV+OhDMBqJT6kL687jVt2ZyIeCWDfHvGDxs/MXFfbHKRGSTAkhhBDFIe0GJJyBGW1NA7n9m8NP/e+8T7+fTGOi3PzBpxE4uZVMrMDVtKu0/znveaW29t+Ki71Lnu35iQvTLu/r1qULe9eeZd+yowAMfDOGrIx0di5bbCq/9ym2dvd+Va0kSTIlhBBC3AuDHmztTFeblj8PO2ab2169BO8HmcsbPwFXf+3+4X0gdqJpGZaAqBJNnG47mXiSuOtxrDq9ilWnV2naOgZ2ZPWZ1ewYuANHW8d7Or4xNZW0PXtwCg9X67xffRWXHn0wGhX+WWBKpALDqpKVmcHBDWvVfl5Bte7pnNYgyZQQQghxJ4piulpkyIRL+3KPabIkPQmaDIA9t5ZK6f8LBDQ3TY5ZqbpmZnCq1S6+2C0wGA18ve9rpu+ZbrE92ieaGffPwNbGtlDnyT53VOCc7/F9911s3VzZfNKboy/8o+kbOyCAzwc9qpZ7THwdm0KevyRJMiWEEELcpiimmcHn9YQWo+DXYfnv02Qg7Jln2g5qAz2/Nj0t13266b9SIE2fRtT8qDzbl3ZfipujGx5OHkVyvtNDhqrbHoMH4RwWRqWoKI7vvsLRX/YD0PSBQNy9XQht4cMn/bup/Yd99jUePjWKJI6SIsmUEEKIisegN01S+c9HsPYd0/Io22dq+/y6XVv2rAvtXwEnd7hyyHSbrtKtuY+6f1Eycd+FhPQE3BzdGPP3GP45/0+u9mjfaD5u9zGuDq5Fcr7k1atJ+G0hvu++Q+qWLQDU/GE+LhERACTGp/HnjP1q/5Y9QwBIunpFresx8fUyl0iBJFNCCCHKO6MRjvwJP9163N4nHC7t1fbJmUiBaTD4j33h6d3gUcu8nApA7btYALgEKYrCzH0zmbp7qsX2N1q+QY+QHrkmzyysy++9x/Xv5wBgW6kS1YYPx65aVTWRApg3abO6PXq6+fXbv3a1uh0c0bxI4yopkkwJIYQo+7LSTLfobOzgh95wYp2pPjAGzmzW9s2ZSAGM2wUJp02L9zpUMtdPzmcpllJg/9X97Lq8i/YB7Xlo0UOatjoedTh64yjfd/6eCO+IPI5QONnHRgFgb0/158zL0ChGBSVbc//J0ehszMmc0WCaAPS5n5YWS3wlQZIpIYQQZUvqddNg8HPbTevQ3cntmbpt7MHWAWJGQ7tXICsVHCtr+5bwQPDC2nJxC0/9ZV68+P92/J+67e3izR89/sDJzqlYzq2Pj+dom7aaOu9XXqHq4EFqOTUpk98/30PKjQx6PB9BuwH1cPNyxsPHnKxmpqWyddHPQO6lZsoSSaaEEEKUTooCyZcg8ZzpqlH2dejyEvsSrJ8Cjq4wdjtU8bHcL2ciVUZcuHmBTr91QocO7fUeWN9nPScTT9KgWgOc7ZyLLQZFUXIlUoAmkVKMCt++uFEt29jqCGuTe0mYqUMeA8A3pJ7FRZDLCkmmhBBCWJ8+A+IPg2KEy/thyZj896nZGk5vhLE7TEur3H6Uvv3LxRtrCTIqRmx0NvRf1p99V/ep9dkTqRU9V+BfxTSHVVWnqsUaj6IoxNVvoJaDfvkF50YNc/X7/pV/1e1B78TgWk2b3CmKgtFgUMsPjXuhzCZSIMmUEEKIkqQoptt0O7+FxLPQqDd81yX//dpOgP0LofkTpoV/y9AcRPdiz5U9DFoxyGLbJ+0+oYVvCyo7lNzVtawLF0jbu5ekZcvVunp7/8PGwUEtXzyWwNm4GzTpGEBKQgYAT33aFgcnbaqRFH+FmWOH4exqmqQ0dtATuPv4lsBPUXwkmRJCCFE8jEbTYO+vY03l5k/C9lnaPju/05bbvwruNU239Zo/CS7ZrrTc91qxhmttyZnJnEw8yYDlA/LsU9glXe5FypatnBkyRC3bBwYS8MU0TSKVnpLFwg93ARDRKZC60d54+FTKlUgBzBxrmrvLxdWNqEd60fiBh3L1KWskmRJCCHHvjAbTVaJzO2BWB3O9pafociZSAK9cNPULagN2DrnbK4DEjES+2f8N3+7/NlfbU42eYmzTsdjobCzsWfzOPPEkKZs2qeXaq1dh7+eX65bcN8+b57GytbPh/qFhFo93cMMadXvQ+5+VmbX38iPJlBBCiPwpCty8DM4esO1r+Cufq0RnTJM24uQO6QnQ42to3Md0HEXRLqcS0sHyMcopRVFYcXIFyZnJHLx+kIVHF6ptA+sPJNI7kibVm1DNuZoVo4T0Q4c0iVTooYMWxzV9MdKcILUbUC/PsU9Go4EVX3yslstLIgWSTAkhhMhOuTWwedUk09ikHbNNs4TfjVcuaOdqyk6n005+WcG8uOFFVpxcYbFtUbdFhHiElHBEuZ2f8CJJS81zPlVq04aAr2fkSpK+nbiR1MRMtdzjhQhqhLjnedxNP81Vt5+ZuzDPfmWRJFNCCFFRZaZA3DLYPA3qdzMt4JvdvxZm0XZ0g4xE6DYNvELBL6LcDwYvjAxDBpHzIrm/5v0EVgnMlUit6LkCZztnq1+Fuu1Ej55kHDqkqfP/YlquRCr71ajb7pRIAWxb8isA0T36YOdQvm7pSjIlhBDlmaKYJq48vgZ2fAtHVkD712Dt29p+F//LvW+nd+HsNqjVFpr0B/vim7uovMgwZGCns8PWxpbRq0era+KtOr2KVn6t1H57Bu3BthQloYpeT1zDRpq60IMHQKfLlUhlpuvV7Wr+len7Wt4LKKvHV8xTObTua/kpxbJMkikhhChPrp+E6TGgT8u7T85ECmD4OlNCVSsW3APNV5tiCjDfk2Dd2XWMWzNOLa/vs16zuPDtNfHS9Gkl/jTenRjT00nbs4fzEyaodQ41axL85wqLY58URWHmsxsAqNmwGg+PbXzH4ytGI+ePHMI3pB5t+g/BO9j6tzGLgyRTQghRlhiNkHLFNCbpxin4qvXd7d/2RYgabrrKlHMW8BpNiyzM8i4hPYEn/3qS+tXq4+3izYy9MzTtVZ2qsrnfZlKyUvCu5K3Wl6ZEKnXXLk73107D4PZoT2q8806e++xaeVrdzi+RSktOYt7Lz5IUf4U+r08h6pFehQu4FJNkSgghSqubV+DAIji9CWq2ghUv3t3+IzaAdyPtk3OiUOJT43l40cOk6lMBOHzjMHY25l+lk1pM4rF6piVSKjtULtGJNe/GjR9/5NIbb6rl2iv/xL5GDXT2lp+wmz3hH9KSs0wFHQyY3OKOx1cUhelP9gfAw9cP75A6RRN4KSXJlBBCWJtBb5oRfPkLENzedMXo0FJtn4NLtOU6nUwDyE9vhM7vQ/2Hwc2/5GKuIFKzUpm1bxYz98202D6pxSRiasTgW8lXk1SVRkpmJsa0NM4MHUb6wYNqfa0lS3CoWTPP/b4YtYbbq9dEPxJMaAtfKns43vFcCZcuqNtdnp6AvcOd+5d1pftfXgghypusNPjnY9jwgeX2E2tz1zUZCF0/g58HQ88Z4FileGOs4LIMWSw+vhhFUXhri/YJx6caPYWnsydRPlHUdq9dZtaTU4xG4sK1t+Vs3d0JWbsGG+e8Hyz4duJGNZFqPyiUBq1qFOh8s58dAUCHYaPK7Tip7CSZEkKIomQ0mpZC8QgyzQr+Tce727/3dxDWA9KTTElT9l/W/X4oykhFDnvj97L42GJ+OfKLxfZtA7bhbFf2nmjMunyZY7Ht1LJrly64dulC5di26GzzfqJQURTNPFL5JVJpyUlkZWRQpap5mocmnQqw7mI5IMmUEELcK30GJF8CW3u4etR0m+7qkYLv3+8n0+06xQi2Ob6OnVyLNlZh0d+n/+aTXZ9Qzakau67s0rSNaTKGL/Z8wca+G3FzdLNShIV3esBAddvz6XF4jR59x/5Go4KNjY7po0xXSYPCPXloVKM77gNwYN1q1s+bzZjZP+Hu7UutppGFC7wMkWRKCCEKwmiEY6vBzQ+WjIULu/Lf57ZR/5oW78359JxKBoiXlCxDFva29vRY0oNjCcfU+tNJ5qfU/nr0L3wr+wIwsvHIEo+xKCSvWcu50aNxqF2brHPnAKg5fx4uzZrlu++Xo7W3mmO65387MyM1hfXzZgPg6FKJJz6fiWI03mP0ZY8kU0IIkZM+A3bNgarBMK9n/v0feNuULBmzoP4joLORJ+hKmYT0BNosaJNn++5Bu0nVp+LqUPavCCatWsX5cU8DkHn8OAABs2YVKJH66e1t6vZjrzRHURSq1shjaaBsFkx+Sd1WFCM6nS26CvQZkGRKCFFxGY1w5YBprqZmQ01P1OWnUnVo+wKc+gce/QbsyvdTSmWVoiik6dM4f/M8z659ljPJZ3L1ub/m/bzX5j0cbU3/huUhkToUWl9TrrP5X2zd3Qs0UP6nt7Zy7XwKAI4udngFFuxBB0VRiD99EoD2Q4ZjU4pmdi8pkkwJISoGoxEOLITfnjCVg9trn5yzlEg1egwMGVCtDsROBLts64lFjyjeeMU9MSpG3t/2Pj/EWR6s/1OXnwjzDCvhqIpf4u+/Y+umHddVb9dObFwKNknomYPX1ETKzt6GJz7M+ypeTh/37QpAVb8AIh7sVuD9yhNJpoQQ5Y/RCEdXwo998+6TcwqCLh9DjSaQfBmCY00zjIsywagYaTzH9Nh/tG80Wy9uVdveaPkG3i7etPBtUarWwitKt69G2XmbZ1oPPXigQLfZLp9MYuvSE5w9eF2tGzG1XYHOm5qUSOKVS2p58AcWFsauICSZEkKUbSnX4PAy+H0chD4MqdfhzL933qdWLAxabHqKzsZWO/2AKDMu3LxAp986aeoup1xWt3cO3ImDrUPO3cqN1F27Od2/v1oOmPEVNpWrYFfVo0CJ1NmD1/n98z0AuHu7EBhWlTaP1S3QubMy0vnyKe1SNLZ2FTelqLg/uRCibEhPhKQLkHgO5t9a26tma8hKgQu7tX3j/si9/2NzoX5XSLkKlb1yNFacAbLlhaIohM8Jt9g258E5NK1e/tcXVAwGDImJmkQqeMVyHGvVKvAxEuPT1EQKYMAbd14eJqfPB5vX2evzxvv41WtwV/uXN5JMCSFKB6MRtn4Fzh6meZtuj22yJO06uNYAz3pw9TAExoC9C/T/Ofd8TbflSqREWaAoCjqdjp2XdzLkzyEW+2ztv7VULSBcnNL2H+BUL+2CwXU2/4udh0eBj5FwOZX5r29Ry31ei1xI0SQAACAASURBVLqrGAx6vbrde9K7+IeWvzFod0uSKSFEydNnwt4F8PtYaP4kbJ+V/z595sGCgTByI/jkP4GgKNviU+O575f78mz/teuv1KtarwQjsr7MM2c0iZT/l9Op3K5dgZe0uXb+JntWnyFus3mc02OvNsfT/+4WYz6w/m/T+es3JLCh5auEFY0kU0KI4qEokHkTTm00DwRvOhB2z9P2s5RIPbUWts6AblNNV6lu/7KYnFi8MQuru5F+g9+P/86HOz7U1Ae7BXPh5gWmdZhGtG+0laKznisffcS1mabPSsCMr6jUsiU6e/sC768oCj+9tU1T1+P5CLwC7m6dR0VRWPW1aaB59xf/d1f7lmeSTAkhCsegNw3kTjwLS58xzb+Ul5yJFMC4XWDIBM+6psHgt/WcUfSxilIpOTOZlj+2tNgW6x/LR+0+UueCqkgUg4HDTZriP22qmkjZuLhQOTb27o6jKMybtFktdxkTTmBYNWxs7v7Bi72r/1S3HQs47UJFIMmUEKLgUq5BfBy4B8Cnd3mr7aEPoW4nMBqgktcdllYRFcGZpDMkZyaz/+p+3t76tqZtUotJuDm60SGwA3Y2FfPXlGI0EhfWEICzI8xL2tTdtjWvXfK0+68zJF1NB6BhWz+CGnne9TEMej0Xjx1m/9q/AHjis5l3fYzyrGK+S4UQd6bPhJQrsPETqPcQ/DIEMpLy7m9jB8Zbg1IHLjRNPZDXQHBRof19+m+eXfesxbZWfq34quNXJRxR6aMoCnENzIO6HWrVwu+jD3GsX7/A46NuM2QZ2bzItKSMW3VnYvvf2zizTwd0V7djevXH3cf3no5TXsm3nRAVmaKAPh2O/gV7f4aW42C2dt4ei2Oa7JxM+9VsBY//IevQiTwdvn6YXktNg6b9K/tz7uY5TfvewXs5mXSSYLdga4RXqigGA/pr1zjW1nwbz2PgQHxee/Wuj3X20HV+/2yPpm7gmzH3FFf8mVPq9pNTv8GtunfenSsoSaaEqEiSL5muMqXdAFc/OP63tj0lPvc+vWbDiXXQ+jlwD9SOaxIiB0VRiLseR6o+NddUBh5OHhgUAxdTLvJL118IrRoKIInULedGj+Hm+vVque6O7dhWvvvb4WnJmbkSqcffa3VPMRkNBuZMGAuY1t2TRMoySaaEKK+WvQDbb41r6P4lLB6lbY+P05ajRkDnKXDtKFQL0SZNDR8t3lhFmXc9/TqxC/IeGP1lxy9p7de6BCMqO5SsLJL+XMnN9eux9/fH+6WJuERF3VsidTOT2RM2quUh77eiktu9D97/pP8j6nbj+x+65+OUd5JMCVHWGbLgrXwGlB5foy03fxI6/A8u7gX/SLB3Nrd5Vay5e8S9+/Pkn0zYMIGB9Qcy75D2Sc35D83niz1f8Gr0qwS6BlopwtJPMRiIa2Seq6nG/32AS9N7m8VdMSrMfsGcSA18K6ZQiVR2Ic1jKvRyMfmRV0aIssBoMF0pOrrKvKRKQQ1dATVbQo8ZuW/R1Sr4yvBC3Lb/6n76LeunlrMnUk62TmwbsA2dTseM+2V6i7xknDiBMTmZU33Mi3EH/fYrzmH3Npv44S0XycowqOUuo8Nx83K+wx75u3n9GgChrWLp8vSEQh2rvJNkSojSyGiEm5cgbhksf6Fg+wTGQKtnoN6DlttlrJO4RwajgbYL2pKUafmJzhU9V1DNuRrOdoX75V1RJK9bx7mR2tvuNd6fcs+J1BcjTVeefWu7ARDRKZCg8Luf/iCnGaMeB8DDt0ahj1XeSTIlhDWlXjct5Gtrb3pqbuMnefd1dNVOT/DKRXCQSfNE8TibdJYA1wAmbZrE4mOLc7UPDx/OU42ewsnOyQrRlV3XvpnNlf/7P7Uc9MvPODVseNdTHtz2399n1e3obsG4+7jg6Fy4X+0/TprAhSOH1HJk156FOl5FIMmUECVBUeDSXji/C64fh3+nFnzfwb9DFV/wqlt88QmB6Um8OQfn5FrKJbu9g/fe8y/+ii51125NIlXvvz3YON77mKYln+7mXNwNAMLb++NXr+CLHeclKzNDTaT6vD4Fo9GIg5NcccyPJFNCFKfpMXDlYP79Hv4UXKrBjVPQoBt4BBV3ZEIAkGnI5MUNL9I5qDMTNmjHxbTwbcHVtKt8FPsRwe4yfcG9MCQlcSSmJRiNVGpjfpqx7rathUqkjAajmkgBtOlTNH9sffec+fajX/0wSZwLSJIpIYpaZir8ORF2zbHcHvqwaSD5C4fBufB/SQpxLzIMGUTOi1TLf5/Rzjm29rG1eDoXftxNRabo9RyJMi/K7NK8Oc4NG+L19NP3fMysTANfP71eUzdyWrt7Pl526Sk3SYq/AsCjL78hidRdkGRKiMIwGmD1ZEi9Zhr3tPM7bXsVX0i+CK9dAbuKt1CrKF36/tGXA9cOUL9qfQ5dP6Rpm/PgHPRGPaFVQ6niUMVKEZYvcQ3N61f6vP4/PPr1u0PvgvnpTfPafA88EUZIs+ro7mHBYku+GJbtycImzYrkmBWFJFNCFETGTdj3C2z/Bi7vg9iJsP79O+8zbCUEtiiZ+ISwYOWpldjqbBm/brym3qCYH6Ff/MhiarvXLunQyiXFYODME0+SceQIIWvNc7sF/fIzzo3ucmFwC45su0TS1XRs7Wzo93p0oac+yG7v6j/V7fE/Limy41YUkkwJkVPqdbhyCL67NduvfxSc26btYymRGr7OtOxK1Ah5yk5YTZYhi493fpxrEs3bXo56mX6h/eQWThFTjEbiwhqqZZ2DA6GHDqJkZGDjVPgnHveuPcc/C44A8OTHbbBzKJqpThSjkYvHDrN61nQAons8ho1Mo3LXJJkSFZuiQMpV+DAEdLbQ/hVY85a2T85EqtkQeOhDQAe2OT5CNe5t5mIhCuNq2lUm/zsZnU7HurPrcrVPiJxAiHsI0b7R2MovyiJnSEjgSAvzIsKVO3RQk1VdIRMpg96IrZ2Nmki5eTkXWSIFsPj/3uLEru0ABISF07rv4CI7dkUiyZSoOBQFjqyEH/vk0W7QzuNU90EIjoXokaaFgZ09QP6aF6VIcmYyj//5OEdvHLXYvrnfZio73P36biJ/iqJwbuw4lLQ0FL1erQ/66UecmzQpknOc2BPPiq/2aeoGvFl0QwcObVqvJlJDPv4SDx+ZnPNeSTIlyg+DHrJSwaEy/D0ZNn12d/s3Gwod34D7/pf7ipNL1SILU4h7dSnlEvf/en+e7b3q9uL1mNdRFEVu4xUjxWDQ3NJzrBOCzsWFwFkziyyRunkjQ5NIRXWtReMOAUX677r8c/OcV9X8AorsuBWRJFOibEpPgoTTkHgO1n8AF3YVbL+WT5sSrlbPgnuAadkWGxttn5yJlBBWdDbpLNP/m86llEvsuLzDYp8nGz3JMxHPqGVJpIqP/sYNjsa0VMvVJ07EvUd3bNzciux1NxqMfP/yJrU8clo7bO1s7rDH3dv++2/q9oB3Pi7SY1dEBfqtodPpOgOfAbbALEVRpuRoDwS+B9xv9XlJUZTlRRyrqGgUBfTppqfodnwL147dmtjy5J33aznOPMN47Q4w4Je816XLmUgJUQooisLU3VOZuW+mxfZ3W79L19pdSziqis1w8ybXv/ue5FWr1Dqv556j2tAhRX6uL8esU7eHfx5b5IlU/JlTbJj/LQAN29+PT4isrlBY+SZTOp3OFvgCuB84B2zX6XS/K4qSfVrn14CfFUX5UqfTNQCWA0HFEK8oz9IS4MRa2PAhuPnDkT9z98nIsdBqx8mgswFXP2j4qHlM0wNvF3e0QhSp25NohriHcCzhmKatjkcdFnZbyKWUS/hU8rFShBXXzX/+4exTwwGwdXenUps2+Ex6DYfAwCI9z+EtFzm2K14tP/lJW+yLcLD5bXMmjFW3O4185g49RUEV5MpUFHBMUZQTADqd7ifgESB7MqUArre23YALRRmkKCeMRkiJB8fK8OsTcGSFuc0tABLNC3ZyeX/u/fvMB99wcPU3JU1yK0OUcfGp8bg5utFsnnmCxGMJx3CwcSDTmAnA8h7LCXA1jWeRRKrkXZv9LVc++EAt192yudjOte2PkyRdTcfd24UW3YMLvWBxTjPHPkFS/GW1LIlU0SnIv5QfkO23HOeA6Bx9JgN/6XS6cUAloGORRCfKJqMBrh4BJ3eo5AWnNsDcHnfeJ/W6efvxP6BmK7kFJ8qtFSdX8OKGFy22davdjXdav1PCEYnsFEUBReHChBdJWrZMra/3355iO+ftRKpZ55q06F48k6hmT6QemTCJ2hHNi+U8FVFBkilLf/4rOcr9gO8URflIp9PFAHN1Ol1DRVGMmgPpdMOB4QCBRXx5VFiBosDlA6YrSj/2hTHb4YscH877XoM1OW651YqFk7fWlur7A9TpZBr0rShytUmUW/2X9WdMkzGMXD1SU98+oD1rz65lXNNxDA8fbqXoRHaHm0agpKdr6kL370NnVzwPp8wYtw59lunXZWiMb7Gc49xB89X+Z+YuxM7BoVjOU1EV5J1xDsj+zKQ/uW/jPQF0BlAUZbNOp3MCPIEr2TspivI18DVAZGRkzoRMlHb7foXFo6BuJ8hMgeNrtO05EymAOg+Ybsu5B4BfpGl9urwSJkmkRDmTkJ7A02ufZveV3QC5Eqm/e/9NdZfq1ghNWGC4mULyyj/VRMqhZk1sq1Yl4Ksviy2ROn3gmppIdR3XGHfvol89ITUxgQVvvARAjXoNJJEqBgV5d2wH6uh0ulrAeaAv0D9HnzNAB+A7nU5XH3AC4hFlj6KYbtPN6Qanbz2aW6stnNxg7nNoae79gttBvwWm6Qoqe4Ozu7nNt3FxRixEqdJ7aW/irsfhU8mHSymXNG2dgjrR2KsxfUP7Ym9jb6UIhSWKXs+RyEi17Pvee7j36F5s51s79xCXTiZx/UIKAJXcHQloUDzz2e1Ytljd7jt5yh16inuVbzKlKIpep9ONBVZimvZgtqIoB3Q63ZvADkVRfgeeB2bqdLrxmG4BDlEURa48lWaZqaarS7Z28H7QnfumXoeQ++HYrUeCx+4EzxDQZ4KtvfaKkle9YgtZiNLsXPI5Hlz4oFrOnkgt7b6UILcgK0Ql8pNx/Dhnh48g6/x5ta7OPxuw8/IqtnOe3HuVg5suAuDi6kBA/ap0GFK/2OYHO7JlIwBPffEtOhmLWiwKdN3y1pxRy3PU/S/b9kGgVdGGJopE4nnT1AGJZ01TDfzz0d3tP3AhhHSw3GYnl4pFxZWalUr0D6ZncTwcPbiRcUPTvu6xdTjZOVHJvpI1whMFkHX5Mie6PKypq7Xwt2JNpFISM1g+fS9g+jt06Aeti+1cANfOnyXxsimxd/Usvp+ropOpnsuL5Esw71HTlAKdp8CFPbD3pzvvE/sSGLPg5D+msU1RT2lvzwkhcjEqRlr/1JrkzGS1rl7VejTybESYZxgdAvP440OUKuefe56k5eZrBHU2/4udh0exnlNRFL6baJ7ZfMTUdsV2rhO7trPo/TfUcpenJxTbuYQkU2XT4RVw5SD8/aapXPs+7WDwP1/KvU//n8HOCXbNgW5Twd5ZBnwLUUDZr0Ll9HrM6/Sq26uEIxL3QsnM5PTQYbj37qVJpEIP7EdnW/STY95myDJiY6dj18rTat2jE5sV+czm2WVPpJxd3QiJanmH3qKwJJkqjYxG00BujyDY9Cmsnnzn/vY5nv4Yf8D0f50tVPHRJk3BsUUZqRDlVpYhiwPXDjBoxaBcbcPDh9MpqBMh7iHY6GQMSlkRF256GCZt504A7Hx8CFm7pljXMtRnGvj6mfUoCgQ1qgZAr4mReNdyzWfPe3di13Z1e/D/TcMrMKjYziVMJJmytvREyEqDhDPwTd6rwVs0Zjt41DRNNyCEKBLbL21n2MphFts6Bnbko3YfSQJVxhiSkjgSZb6y6PX8c7g99BD2fn7Fel5FUZjx9Hq1HBrjS+2I6sWaSKUmJapXpR4c85wkUiVEkqmSoiimq01VaoBihHe889+n03uw5i3ISoU+86C+LGwqRFHTG/U0nduUgfUHMu/QvFztL0e9TKo+lScbPWmF6ERhKXq9JpGq/dfKIl9TLy8bfz6qbrfsGULtiOKfU+zLpwao2/Vbtyv28wkTSaaKQ8pV2PgJpN0AdLAnxxf08HW59+n3E6x5B7p8CP5R5qVUYkYXc7BCVEyKohA+J1wt50yk3mz5Jj3q5LMMkii10vbt4/zzL5B15oxaV9xjo3Lau/YcADE9atP0geJP4FISzE+UPjjmOZkGoQRJMnUvjEZQDJB6Df6dCpunmeo960KD7rDhg7z3rdUWajSFjpMhuD34hJsTp3oP5r2fEKLQ0vXpNJ9veT2y8c3GM6D+AIyKEWc75xKOTBSlxKV/cGGC6ek1l5gWuHXthlvXh0skkVo6dQ9nDlxn5LR2NLk/EK+AytSJLMCdiEJQFIWDG9aQcPmiWle/TftiPafQkmQqP1lppmkHLu6BP1+B5Jwr6WRz9UjuSSvHbAebWwPBHbLNN9N6fPHEK4TIJSUrhRY/tLDY5uXsxZrH1lhsE2VP+sGDaiIFEDh7drEOMM/ui5Hm95GtnQ2tHg0pkfNuXfQzmxbMpUHb+wDoOv6lEvuZhYkkU9kpClzYBYnn4OfBBd/PyR2e3g0ut5YCaCSPSQthbWn6NKLmR1ls61OvD6+1eK2EIxLFRVEU4uo3yFVfP+5QicXw/cvm+aM8AyqX2HkBNi2YC0BQk2Z0fGI0NnYldytTmFTMZMqQBbu+h+TLUDMG5t7FuIjh68E7DGzsZJ4mIUqhLGMWEXMjLLZ1DurMlDZTsLWRXzblSfZEyu2RbngMHIRzo4Yldv4LxxK4eSMDAFdPJx57xfKt5OLw7y/z1e260a2wLaYFmcWdlf9XPT0Jzm2HeT3hvkmmp+Py03Qg7J4HTQeZJriUpEmIUu3PU38yYf0EglyDOJV0StO2qNsiQjxK5naLKFlKZqY6dxRArSWLcapXMuuDKorCypkHSL6ejoOTOTkf9HbJTY55/cJ5Nv/6IwDDPp0hiZQVld9XXp8Bb+d4DNVSIjV0BVw5ZFpGJaynOXF65Ivij1EIcdf0Rj0Hrx0kMSORYwnH+Hjnx2rbqaRTtPFrwz/n/6GZdzO+7fStjB0phwzJyWAwcKRFjFoXvGI5jrVqlVgMv07ZwZXTpiWFvGu50jDWj9h+JbvQ+7fjR6jbHr7FO2eWuLPym0zt/y133ciNcGYLNOqtXYOupkyzL0RplpyZzInEEwxcPjDPPrH+sXzW/jO5hVfOKYrCkebasXDBfywt0URqw4IjaiLVonsw4e0DsHcs2fed0WhQt/tMnlKi5xa5ld9kqnE/03Isvo21T9H5NLJaSEKIu3PsxjF6/J57TOMzEc8Q6R2Jm6MbeqOe2u61ZVbyCkDJyuLUAHNCXePDD6ncrh22lSvdYa+idfXcTfbdmj8KIKJTzRK9+qkoCtOGPkZWhmmM1oNjn8e/fsmNDxOWld9kSqeTK05ClDGKonA04SgHrh7gf//+T9P2avSrvLP1HX546AcaeckfRRVNxtGjnOjaTS3X2bQRu2rVSuz86Tez2LbsJCd2XQHAr5473cdbftChOMVtXEdmWhoAzbp0p05UTD57iJJQfpMpIUSZcTzhON2XdLfY5uHowaJHFlHNuRp9Q/uWcGTC2m789BOg49LkyWpd4Pffl2gipc8y8M0L/wDg4eNC/VY1iO4WXGLnz27jrWkQ2g4YSvNuj1olBpGbJFNCCKvYF7+P/sv7E+sfy/pz6zVt33b6lrPJZ3ko+CEcbWUh74rqcEQzjKmpmrqQDeuxr178a9zdlpKQwXcvmeeQ6j/Z8uSvJeHU3t0kxV+haeeukkiVMpJMCSFK1F+n/uL59c+r5eyJ1JCwITzX7Dl0Oh2RPpHWCE+UEodC62vKtZYsxrFOnRJdb04xKqydF6eWS3L+KEsOrFuNrZ0drfrk/SCGsA5JpoQQxSohPYH+y/tzNvksNSrV4EKKdkmmFT1X4FfZDwVFBpELABIWLlK3XVq0oOZ331oljh/f3MqNS6YrY4+/14rKHta7SvrfquXEbTL94eHoUnID7kXBSDIlhChSWcYsZu2dRRWHKry//X1NW/ZEau6Dc2lSvYla1iHzQVVkiqKQvGIF8V9MJ/P4cQAcgoKskkht+OkI+9ad09RZM5ECWD1rOgB1Y9pYNQ5hmSRTQogiYTAaGLpyKLuv7LbY/m2nb2lSvQm2OluZSFNo5Fxbz+3Rnjg3bozHY4+VfCxGRZNI+dZ2o8uY8BKPA0yvy6qZ06gbZX4yveuzE60Si7gzSaaEEPfMqBh5deOrrDy1kixjlqbtw9gPqeNehzR9GmGeYVaKUJR2SlYWR1q2Usver75K1UHWGRN08r94ln+5Ty2P+eo+q8Rx2741f7Hv75Xs+3sl6HT0fu1tq8Yj8ibJlBDirqVmpRL9Q7TFtmn3TSM2ILaEIxJljTEzE52NDWeGDsOYbJpNvPJ99+ExcIBV4jFkGdVEytXLmb6TovLZo/it+nqquj1i+ne4uLvfobewJkmmhBAFMm33NGbsnWGxbVmPZQS6BpZwRKKs0l+9ytHWprE/zk2bAlBvz25snJxKPBZFUYjbfImdf55S6wa+2cLqt6KXff5/6vbzC/6wYiSiICSZEkJYlGHI4ErKFRIyTE/jWTKu6TiGhw8v4chEWZayZQtnhgxVy0E//oCi16Ozs86vo+mj1mrKPZ6PsHoilZWerj65J0vFlA2STAkhNOJT47nvl7zHioxqPIrRTUaXYESirFP0evRXrpBx/ARnn3pKrQ9eZrriYq1E6sA/59Xtni9EUL2WK7a21p+eY+kn76nbsohx2SDJlBCCw9cPM3v/bDoEdtBMqAnwSvQrnEg4wfhm43Gxd7FShKKsyjx7luP3P5Cr3lq39W5bNfsAR7ZdBqBVrxB8Q0rPeKSTe3YCMGrmfCtHIgpKkikhKrAf437k3a3vquXULPPSHR0CO/Bp+0+tEZYoJ5TMTE0iFTDjK5ybNMHWzc1qMSVdTcPB2U5NpLwCq9CkY+kY77f51x85+M8aAOzsHXBxtd7rJO6OJFNCVCCKorDpwiaeW/ccafo0TZunsydTO0xFURSrjxkRZd/N9es5O2KkWg787jsqtbD8BGhJ+WHyFnVG89t6v1x6li3av241NrY2RHXvTXT33tYOR9wFSaaEKOdSs1JxsXfh1Y2v8vvx3y32eSX6FfqF9gOQREoUyoWJE0lcon2fhR46aPX3laIomkTqvsH1CY3xsXpc2aUkXCfiwW606fe4tUMRd0mSKSHKqUPXDvHYH6YZpMM9w9l7da/aFu0bzawHZlkrNFFOZV+c2Pt/k8g8fgKPgQOsnrCkJWcye8JGtTz881jsHWytGFFuf3z2AYasLCpXrWbtUMQ9kGRKiHLkWto1UrJS6LKoi6b+6YinmblvJkkZSczvMh97G3srRSjKq+yJFPb2VO1veToNa8ieSA39oHWpS6S2LFzA4X83ABDaSia8LYskmRKijFMUhQ6/dCA+Ld5i+8wHZhLtG020r3XHq4jyJ+vCBU50ewSHmjXVOscG9QleuNCKUZkd33WFi8cT1fKIqbHY2ZeuREoxGtm0YC4A7YeMkEHnZZQkU0KUQYqisPH8RoyKkbFrxmrawr3CGdN4DGGeYbg5yhezKB6GpCSO3dcBgPQDBwDw/2IaVTp0sGZYqnXz4zjwzwUAPHxc6DyiUalLpMA8DQJAw3al47UTd0+SKSHKkNSsVJ5Z+wxbLm7J1da7bm+eiXhGEihR7PQ3bnA0pqVaDpg1i8qtW91hj5J1ZPslNZHq81pzPP2rWDkiy1ITE1j0/hsA1G/dDgdnmcetrJJkSogy4K3Nb5GiT2HZiWWa+teiX8PGxoYutbrIhJqi2CmKgv5KPMdizeN6aq9ejYO/nxWj0kpJzGDVNwcBCGtTo9QmUh/1eVjd9gyoSefR460YjSgsSaaEKKUUReFGxg1iF+QekBrsFsyv3X6VgeSixJwdMZKb69dr6urHHbJSNLkd3noJe0db9qw+o9a1GxBqxYjydmy7+cpyn8lTqFGvPjY2pe8WpCg4SaaEKEVOJ51m7N9jifWP5fuD32vaVvVaRSX7SjjZOUkSJUqMotcT17CRps7/i2lUbtfOOgFZkHYzk9XfHlTLjWL9aN2nrhUjyltWRjpLPnwbgOgej8lCxuWEJFNCWJmiKAC8teUtfjnyCwCnDp5S21+IfIHHw2QSP2EdR1rEaMqhBw+gs7H+YsDZzX7BPPXBoLdjcPV0tmI0d7Z39Z/qdnSPx6wYiShKkkwJYQVZxize2vwWi44tAsDL2UsztcGI8BEMbTgUOxs7HG0drRWmqKAURSGufgNNnUf/fvj8739WiihvmxcfV7dHTW+PjU3pmdHcknVzTJPlhsV2wN7Regs9i6IlyZQQJSTLmIVRMfL+tvfVK1C3NfNuRnOf5kRUjyDEI8RKEQoBGSdOcOIh7aSv/tOmUqVjRytFlLd5kzaTGG9aY3LQ2zGlPpHa+7f5qlTLxwZYMRJR1CSZEqKY6Y16ms5tmmf7vsf3lWA0QuQtYdFiLr78sloujbf0AM4cvEZKQqaaSIW19SvVt/YunzhGVkY6Fw6bBuwP/WQGrp7VrRyVKEqSTAlRjBp93yhXXbRPNFsvbZUkSpQa5597DvfevTWJVL1dO0tlIqUYFZZ+/p9ajnwoiOhuwVaMKH/zXn4WAP8GDalRtz5Va5SeqSRE0ZBkSohikGXMYuflnZq6SS0m0btub6sv+irEbYabKRyJjAQgafkKtb60XpEC2LP6rLo99IPWuLg6WDGa/C2Y/JK63XPiZNJTb1oxGlFcJJkSohhEzI3QlOUqlChtFINBTaQAvF97jSod7sPO27vUJlIHN13g34XHABjwZotSn0jps7I4FSjrdwAAIABJREFUd2g/AE06dcHeyQl7Jxl0Xh5JMiVEEUrJSqHFDy3U8oqeK/Cv4m/FiITQyjxzBls3N45Em9+ngd9/T6XoKCtGlb/ty06ybelJAAIaVMW9eumf8X/he68D4FTFlQ7DRlk5GlGcJJkSopCO3jjK7iu7WXV6lWbNvLkPzpVESpQqh0Lr56oLWb8Oe29vK0RTMCf2mKYMuZ1IATw0MvdYxNLo7IG9ADwwfGw+PUVZJ8mUEPdo0/lNjFw90mLb0LChNKnepIQjEiJv5198Ud32nfIeAK6dO2NTim877Vl9hk2/HlPLrp5ODHq75R32KF1qhjclsGFj6kSVnZjFvZFkSogCSs1KZeruqcw7NI9Wfq3YdH6Tpn3JI0u4mnaV5j7NZZC5KBWyrlzBztOTuAZhal31iRNx797dilEVzM0bGZpEqu+kKKrWqGTFiApmzovjiD99kvE/LKHXq29ZOxxRQiSZEuIOFEUh05hJ5LxITX32RGrv4L1q8hTsXrof0RYVR8Kvv3LxtUmaukqxbak2dIh1ArpL379s/oyN+eo+K0ZScNuW/Er8adPtSBtbWbi4IpFkSggLUrNSif4h2mLbfQH38dl9n5VwREIU3IWJL5G4ZIla9nr2Wdy6P4K9j48Voyq43avOqNvDP4u1YiR3558fvrN2CMJKJJkS4paE9ATcHN0InxNusX1A/QE8H/k89jb2JRyZEAWXc5B5/bhDVork7l06kciulac5+d9VAB4aHY69Y9m4wvPtePP4yVEz51sxEmENkkyJCi81K5Wv937NN/u/oa5HXbU+0juSFyJfIMwz7A57C2F9iqKQtHQpyatWaerr7dxhpYju3pmD19SZzf3quhMU7kmtcE8rR1UwSVfjuX7hHAB9Jk/BxdXNyhGJkibJlKiQ9EY9BsXAC+teYN25dWr9uKbjOHrjKMMaDsPWpmz8RSxEXP0GuerK0hWpjDS9ZomY7s9F3KF36bN3tXn2eP/6Da0YibAWSaZEhbLx/EZGrbY8eV5glUDa+LWhXUC7kg1KiHuk6PVcnvK+WtY5OBC697877FE6zRq/Qd0e9mFrK0ZScIrRyLKpH3LxaByuXqZFi0fOmGvlqIS1SDIlyrV0fTobz2/katpVZu+fzcWUi7n6vNXqLbqHlP5HxYUAUwKVumMHNs7OnOrTV633fuVlqg4ebMXI7s3KmfvV7S5jwnGuXLqXiLkt/swpDv9rSgK9a4XQbvBTVHL3sHJUwlokmRLl0pmkM3RZ1CVXfZBrEDE1Yng56mWZC0qUSXENc8/+7dSgQZlLpHasOMX5wzdIupYOwFOftsXBqez8Spo78WkAGj/QhQ7DRsr3SQVXdt65QtzB/EPzmbJtCgDtA9qz9uxaTfubLd/E0daRB4IewM5G3vai7DEkJmrW06s+4QWcm0Zg71ejVC8HY8nhrZfYuuQEYLqtd/N6RplKpH7/+F11u02/wZJICUmmRNl1KeUSjraOtF3QVlP/X7x5zMjzzZ5nSMMhJRyZEEUnbc8ekv5axfXZs9U6r+eeo9oTT1gxqnsXfzaZ1d8eVMvOlR3KzK09AINez9Gt/wIQ3aMPji6lf1Z2UfwkmRJlSpYhi00XNjFuzTiL7at6rcKnUtmYmFCI/Pw/e3ceF1W9/3H8dVgEF0RxQRERTFRwAVFxBcVUKvfUXFo0yzIty7rlcvu51r1KZptbVtrqzbTMJdfcENMUA00Fd1RUVBQFFGSGOb8/yAOjqCjLmRk+z8fjPh7f73dm8A1d4M2Zc74nKzGRhIGDzNbchjxH1ZeG65SocLINJn56f482f/Gj0Hs82zId3LYJgApuVWg/8Fmd0whLIWVKWLy0rDRe3PAihy4fuuOxTrU70dCtIZ28OtHArYEO6YQoHknT3iPlh9zNH61pq4PbnYi9xNr5f2Nnn/t22Mh5YVb19lhq8iU2fTWXE3/llMHB73+ocyJhSaRMCYu17Mgypuycku9jHT078lHYR3L+k7A5qsFARmysWZGq/+cuHRMVzp7fTrJ7Vc796jwbuuHfriY+gdWsqkiZTNns+PFbTvy1h1oN/WnQNhQXN+vYUFSUjAL9JlIU5THgE8Ae+FJV1en5POcpYDKgAvtUVR1chDlFKXH4ymH6repHecfyXDdcN3tsdqfZtPVoi6IoUqKEzVFVNd/NNxvsjcauvHWel5NtNGlFCqDHawE6pnl4iYcOcGh7zkUtAybPsKoiKErGfX8jKYpiD8wBugCJwB5FUVaqqnooz3N8gfFAO1VVUxRFqV5cgYVtSbiWwLbEbXSv252OP3XU1q8brtOsejNiLsawtMdSGro11C+kECUgb5GqPnYs9pUrUam39e5/diM1i58jcm5nU/MRV3q90UznRA/nRuo1lk77NwB1mjaTIiXyVZA/74OBY6qqngBQFOVHoBeQ9wSW4cAcVVVTAFRVvVjUQYXtUFWVqLNRjNw0UlubGT1TG6/qvYraLrXldi6iVFCNRrO9o+zd3Kjy/FD9AhXS9Ws3ORR1jsO7kkhNztlD6sm3m+uc6uFFfr9IGz85brJ+QYRFK0iZqgWcyTNPBFrd9pz6AIqi7CDnrcDJqqquK5KEwiaoqkrwD8FkZmfm+/iP3X9kxbEVTGg1oYSTCaEPw7lzHOv0qNlaudatqfP1oru8wjp8PXYHAHYOOUdw2vatp2ecQkm7nMzBbb8D8Pr3y7Gzlz/wRP4KUqbyO6ap5vNxfIGOgCewXVGUxqqqXjX7QIryEvASgJeX1wOHFdbptxO/MW77uHwf+6n7T/hV8QOgUZVGJRlLCN1knTnD8S5dzdaqvfEGVUe8rFOiojFnxGZtPPS/7SjrYj37R+VnwcihekcQVqIgZSoRqJ1n7gmcy+c5u1RVNQAnFUU5TE652pP3SaqqLgAWALRo0eL2QiZsQHpWOoN+G0RCagIA77Z6l/f+fM/sOTHPxpCtZuNk76RDQiH0lZ2eblak6u/+E7uyZVEcHXVMVXiLp/ypjf3a1bTqInX6wH6WTjM/Su5g5f99RPEqSJnaA/gqiuIDnAUGArdfqfcrMAj4WlGUquS87XeiKIMKy6SqKpczLxP2UxjLeiyj36p+Zo97VPDg+cbP082nG/Uq1dPOg3KQXTlEKZOVeBZM2RzvGq6t1Y+Oxr6CdV6pl1fq5QxSzudcfVvFswKdnvXTOVHh5C1SFau58+yMT3RMI6zBfX+jqapqVBTlVWA9OedDLVRV9aCiKFOBaFVVV/7zWFdFUQ4B2cDbqqpeLs7gQn99V/blSMoRbZ63SLWp2YZ5nedhb2dPiGeIHvGEsBgJg58m46+/zNYa/r3f6o9G5d1D6paB7wbrlKZoZBsN2vjVRUvkdjGiQAp0eEBV1TXAmtvWJuYZq8Cb//xP2Khz6eeYtmsaR1OOcuHGBbPHmlZryhddvsBgMuDq5KpTQiEsz+1Fqvbn8ynXqpXVFynDzWyzItX1hUbUbVZNx0RFY8+KnwGoG9RSipQoMHmvRdzTDcMNTqWeYl3COhYeyL3R6uM+j7P25FpauLfgi65fyCaaQuQjrmHu2131tm7BsYZt3DfSlG1iwevbtPnzEe0pV9F6z5G65dMh/TFkZmBnb0/Pt+TKYlFw8htQ5GvqzqksPbI038dW9V6Ft6s3EaERJZxKCMt34YMPuPLVQrO12gs+t5kiBfDrrBht/Nx/2tpEkcrKuIEhMwOAwe99iL2DdR85FCVLypQAICs7iyk7p+BSxoUf4n4we2xq26nUqViHhm4NKedYTqeEQlg+Q1KSWZEq17o1tWZ9iIObm46pis7Vizc4deAy549fA+CVOR2xs7fTOVXhZRuNHNuTe/9D97rWuzeW0IeUqVJKVVUURcGQbSDo+6C7Pm9jv43UKG87f1ELUVxuHj3KiR49tXnDgwdQbGiTx5WfxnLm0BVtHvZsQ5soUtevpjD/5We1efiI13VMI6yVlKlSRlVV9ifv55k1z9CyRkv2JJltBcbYlmN5ou4TlHUoS1mHsjqlFMJ6ZJ05w/Udf5A0eTIAbkOG4D4+/01qrdUfvxzLLVIKPPd+W1zcnPUNVURuFanKHp74te9A47AuOicS1kjKVCmgqipJ15OYtmsa289u19YPJB/Az80P9/LuDGwwkHa12umYUgjrk/cEczsXFzxmzMClU5iOiYpewv5kYjac1uaj5nXSMU3Runw2905pvd9+FzcPTx3TCGsmZcqGHbx8kIGrB+b7mL1iT+SASJwdbOOvSyFKkqqqxPv5a/M633+Hc+PG2Dnb1veTqqr8Nne/Nh8133aKFMA3/xoFQLPHekiREoUiZcpGqKrKuzveZeXxlQxqOIitZ7Zy/vp57fHRzUZT17UuDnYOtPVoi6O9XKkixMPKW6Rqf7GAci1a6Jim6KmqytxXtpitjZxnW0fcfpoyHtVkAqDT89Z9T0ShPylTVuxoylGysrOoUrYKXZblvs//v/j/mT1vYfhCWtZoWdLxhLApanY21379let/7NTW7KtWpUKI7e3wn7dI+QRUJbhHXRQlv3veW6dso5Ezh/4GoPsbtnV+m9CHlCkrpKoqs/bO4uuDXwNgp5hfUbP3mb2UsS/DzeybcjNhIYqAKSODw81yr3ot37491d96E2c/674H3e2ys03MH7VVm9vK1ge3mLKzWfbeu5SvnLtVRYM27XVMJGyFlCkrkZyRzMHkgyw/tpxNpzeZPTbn0Tl4lPegdsXaONrlvn0nRUqIwjNlZpoVKeeApnh9+YWOiYrPwn9FaeMeowNsqkgBfPvOa1xOzD2Z/rmIz3RMI2yJlCkL992h74jYk/9O41EDo+Q+eEIUE9Vkwnj+PMce7aytVR83lipDh+oXqphs+PIADdvWJCvDCEDYMw3x8q+ic6qid6tI1fZvQr//ew87O9vZB0zoS8qUhUm6nsQ7ke8QczGG4U2G88Xf5n8Br+q9iiuZVwhyv/tGm0KIwjFcvMix0A5ma/V37cS+UiWdEhUP1aTyx/LjHI2+yNHoiwBUr+OCf3sPnZMVveQzp7Rxn/GTpUiJIiVlygIYTUbOpJ2h5689zdY3ntqojVf0XkFd17oAeLt6l2Q8IUoVY0qKWZGqNetDHGt72VyRAohccoQD284CEBTuRbOudXAqa3u/FlKTL2rbIPQdPwXHMnIKhChatvddY0X2JO1h2Pph+T42MmAkIwJGYFSNZudBCSGKT1biWY53zn1b75F1aynj7a1foGJ06UyaVqQcnexp08d270cXvXo5AN4BQXgHNtc5jbBFUqZ0cDP7Ji2+N9+XZniT4ew8t5MPOnyAp0vu5nGOihQpIYqTqqqkrV9P6tp1pK1fr617zp1rk0XqRmoWZZzt+en93FtJDf84VMdExS9m7SoAnnjtXzonEbZKylQJuG64ztcHv2b+vvkAfP/E92aPv9/+fXo+0pPRQaP1iCdEqZZ3A85bfP/YgYObWz7Ptm6/zd1Pwv5kszVb29U8r9gNazgZk1MaA7o8TlmXijonErZKylQxuXTjEgsPLORoylH+TPrT7LGAagEs6LKABm4NcHO2vR/YQliLuEaNtbGjlxf1Nqy/x7Ot243ULK1ItetXD4cy9jSywRPNb0m7nMymr+ZSvrIbDdqE0H7gEL0jCRsmZaqIxF+Jp07FOjjYORD0Xf5X2v3Y7Ufqu9UHoI1Hm5KMJ4S4TcpPP0F2NgBuzz+P+9h3dE5UfFSTyqJ3cveQCuzspWOakrFg5FAAHh02At/gtvqGETZPylQhGE1GEq4l0GdlH23Nv4r5Wwbr+q7Do7yHTd2KQQhrpWZnE5/naNQttlqkVJOKYqcwd2Tu7WFs+W29W26dcA5Qr0VrHZOI0kLK1ANSVZUev/bA2d6ZwymHzR57JeAVXm76MmO2jmF6yHTKOZbTKaUQIj95i5Rrr55UHjyYsgEBOiYqPqmXM/ju3zvN1rq+0EinNCXnRuo1tn33FQAjPv8Oxc62dnEXlknKVAGdST3D+3++z45zO+54TEEh9rlY7R55n3b6tKTjCSHuwXTjBoeDci+Jr/n++1Tq+6SOiYpf3iIV0Lk2TTt6UrFqWR0TlYy05EvauHylyjomEaWJlKl7SM5IJuynsHwf61+/PxNaTcBoMuLs4FzCyYQQBaFmZXHzxAlO9s59K94lPNzmi9ScEZu18YjZHbF3sP2jMzdSr5Fy/hy//HcSip0dwz5eoHckUYpImbqNSTURfyWeAasH5Pv4rsG7KO9YXps72MmXUAhLZMrM5HBgM7O18u3b4/nJxzolKhl5i1TTTp6lokgBzBv+tDZ+9IWRVHKvoWMaUdpIEyDnPKjlx5Yz6Y9J+T7+etDrvNjkxRJOJYQojLxFqvbn83Gs7YVTXR8dExWvjLQsTh24rM0fHepHw9Y1dUxUcv5au1IbD5+zkIpVq+uYRpRGpbZMGbIN3DDe4MKNC/Rd2dfssdoutTmTdobPOn1Gx9od9QkohHgoqqqabcRZd81vONWtq2Oi4rd/SyLblxwxWystRQpgy9c5b+k9OW6yFCmhi1JXpk6nnqbb8m53fXzP03vkHCghrJCqqpjS0rg480NtrdKggTZfpFSTalakhn8cShnn0vOjPfl0gjb2adbi7k8UohiViu+4E1dP0GtFL7rU6cLGUxvNHvugwwfUr1QfN2c3Kjnb3l3hhSgNUjds4Ozo183Wqo8bS5WhQ/UJVII2fxunjYfOaFeqipSqqnzz9qsAPP/R5zqnEaWZTX/Xzdo7i0UHFmnzvEVqiP8Q/tVSbnophDVTVZXMAwe0ImXv6or7hPGUa9UKxxq2fwJy3pPNn/tPW8q7OumYpuTNGthDG7t51NIxiSjtbLZMXc28alakAFb1XsV1w3X8q/jLjuRCWClVVcncv5+bJ06Stn496Vu3AuDUoAF1V/yqb7gSkBh/hRUfx5qtKQq4uJXe0xP6/9/7ekcQpZzNlqmoc7n3oYp5Nka2MBDCBmSnpnJu7DjSt2wxW3cfPw63IbZ/I9tso8msSNVrXh0UCH/xzlvk2LIPB3TXxvVbt8ersW3uYi+sh802jMe8H8Onog8N3Rpib2evdxwhRCFlJSZyvHMXAMq1bk2NSRNxcHPDzsWlVNwy5GaGkS/HRGrz0nCPvfxkG43auHFYV9oPfFbHNELksNky5WDnQKOqtn8fKiFsnfHyZc4Mf4nMQ4e0tTpfL7rHK2yLISsbVMyKVN+xze/xCtt2ZFfuuw7hI0brmESIXDZbpoQQ1u/GXzGcGjwYgIo9epARE4PH9P/qnKpk/Tj1T1KTM7V5oxAPavi46phIX3t/yzkvrt+77+mcRIhcUqaEEBbpyjffcOG/07V5rQ8idEyjj01fH9KK1CtzwzAZTTiUKb2nLez97VcunDgGIOdJCYsiZUoIYVFuHj1K2patXJo1S1trGHfoHq+wTVE/HSV+VxKQc6K5nZ2CXSkuUlmZGWz99ksAmj3eQ67IFhZFypQQwiKYrl/ncPPbdrB2dMTv7/36BNJRVoaRfZvPaPOuL8r5n58N6a+N2z0lJ50LyyJlSgihO9VoNCtStWZ9iEONmjg3bKBjqpKXlWlk98qTZkVqxJyOpfooTErSObJu3NDmr3+/HAdHRx0TCXEnKVNCCN3FN26ijcu1bEnFJ57QMY0+so0mvngj0mxtxJyO2Nvb/rYPd5NtNLDw9Ze0ecte/aRICYskZUoIoQvDhQsc69AR+ypVtDXXXr3wmDH9Hq+yXfNf3aqNR84LK9VHo275+Ok+2njw+x9S45H6OqYR4u6kTAkhdHGsQ0cAsi9fxr5KFaqNHk3lAU/pG0onv83Zp43b9HlEihSQnnJFG7/2zVLKOJfVMY0Q9yZlSghRokw3b3I4IFCbe33zDeVbBeuYSD8mk8qxvRdI+PsyAO4+FQkKr6NzKsvw+YjntLEUKWHppEwJIUrEzRMncXCrzJHWbbS1qqNGldoiFbPxNH/8fMxsre/bpXdn87xUk0kbj/5mmY5JhCgYKVNCiGJlyswkefZsLn/5ldl6jcmTqTxwgE6p9JV53aAVqaq1K1C/ZQ0Cu9SWt/f+sWnhPG3s6OysYxIhCkbKlBCi2Ny+d5SjhwceMz9AzcqifOvWOibTj2pS+eqt7dr8qQktpUT9IzM9nRN/7WbfxrUAvPnjKp0TCVEwUqaEEEXu2ooVmG7cIGnKVG3N+8f/UTYw8B6vKh3m5blqb8RnpXsPqdvt+uVH7d57fu3layOsh5QpIUSROvfuu1xb9rPZmu+OKBzybIFQ2qiqyspPYqnuXRHVpALw7HttsHcsvXtI3e7Ynl1akRr97TIcneTtPWE9pEwJIYrM4VatMV27BkDVka9Q+dlnsa9YEcW+9N5TDmDuK1sASIxPAaBxh1pUrCpXqOW1YuZ7AJSvVFmKlLA6UqaEEIWmGo0ce7SzVqS8vl5Uas+Jut2+Tbm3hqlZz5WeowNxKMU3LM7P/k3rtfGgaR/omESIhyNlSghRKIdbBmNKS9Pmtb/6UorUP07EXiJq6VEABk1shZtHeZ0TWaaNCz7LGSgKrtVr6BtGiIcgZUoI8VBUo9HsnnqVn32Wsk0aU6FdOx1TWQ5VVVk7/28AavtVliJ1F38sXQyAd0AQvf71rs5phHg4UqaEEA/MePkyR9u11+b1tm3D0b26joksx75NZ7SjUbc8MbKpTmks36HITQCEj3gdhzJldE4jxMORMiWEKDDDxYugwqnBg7W1yk8/LUXqH8asbLMi1STMk3Z962HvIFft3W77/77h6O6dtHpyAKf/3kcFt9J7taewflKmhBAFkpWQwPHHHjdbq/3ll1RoL2/rQc5mnL9/HafNR84Lk32S7sKUnc3uX5cC0LhjF5qEddU5kRCFI2VKCHFfxuRksyJVZfhwTBkZUqTyiF6bwPG/LgIwan4nndNYto8G99I7ghBFSsqUEOKeDGfPcuzRztq8zg/fU6653JD3lstn0zl18DK7V50Ecq7aE3eXknROG/cdP0WO3gmbIGVKCJGvw8GtMKWmmq01PHig1G/AmdecEZu1cbmKZej7TnPZjPM+Fr7+kjb2DpRSLmyDlCkhhBnVZCLev5E2LxsQgGPt2tSaKZsp5pV4OEUbd381AE+/ytjby4nmd2PKzuZY9C5tPuZ/K3RMI0TRkjIlhDCTt0i5/9+7uD39tI5pLFPmdQMrPorR5nUay5Vo9xKzfjWbF84HwLW6O93fGIednRzhFLZDypQQgsxDh7i8cBGONdy1tXKtWkmRuouv3tqujUfOC9MxieXLSEvVilSHZ1+gcVgXnMtX0DmVEEVLypQQpZxqMnHyyb4AVB39GoqjIz4rV+Dk46NzMstz7dIN7WbFAGHPNJQTqO9j7ou5e5K16N5HxyRCFB8pU0KUYplxcZzs86Q2rzZyJNVGjtQxkeVaPWcfp/6+DICjkz09RgdS8xFXnVNZtmyjURv3fmeijkmEKF5SpoQopa7v+pPTQ4dqc69vvtEvjIU7ezhFK1IAw2a2x8FRzvm5n9j1vwHw6LBXeKR5sM5phCg+Bbr0RFGUxxRFOawoyjFFUcbd43n9FEVRFUVpUXQRhRBF6frOncQ19DMrUrU/n0/5VvLL7m5+zXOy+YsfhUqRKoC47VvY+u0XAASGd9M5jRDF675HphRFsQfmAF2ARGCPoigrVVU9dNvzXIDRwJ/FEVQIUXhXvvueC++/b7ZWd80anOrK+VF3k3cvqef+0xansnJA/16uX03h6O6d/PnrT3pHEaLEFOSnQjBwTFXVEwCKovwI9AIO3fa8aUAE8K8iTSiEKBLHu3cn69hxbe4XH3ePZ4uE/cn8vS1Rmz8f0Z5yFcvomMg67PrlR2LX/0bdoJbUDWxB26fkilBh+wpSpmoBZ/LMEwGz+yUoitIMqK2q6mpFUe5aphRFeQl4CcDLy+vB0wohHojx0iWOd+tOjX9P0IqUXYUK1N+zW+dklm37kiPs35JbpFyrlZUiVQCGrJvaeVJhQ1+mknsNnRMJUTIKUqbyu+5X1R5UFDvgI2Do/T6QqqoLgAUALVq0UO/zdCFEIWSnpnI0JBSA1HXrsXd1xbF2bXyWLdU5mWW7ecNgVqTkpsUF9+mzfbWxFClRmhSkTCUCtfPMPYFzeeYuQGNg6z/7rdQAViqK0lNV1eiiCiqEKLjMw4c52au3Nq/14UzsypXTMZF1UFWVL9/MsyHnXNmQs6DSriRr42GfLNAxiRAlryBX8+0BfBVF8VEUpQwwEFh560FVVa+pqlpVVVVvVVW9gV2AFCkhdKKqqlmRqj5urBSpApr7yhZt3Om5hih2siFnQS14ZSgAZSu6UrmGh75hhChh9z0ypaqqUVGUV4H1gD2wUFXVg4qiTAWiVVVdee+PIIQoCTdiYjg1aLDZWsO/96M4OuqUyDpkZRj5etwODDeztbWW3bzxayuFoKCuXkjSxq8s+F7HJELoo0DX+KqqugZYc9tavtvZqqrasfCxhBAP4tqq1Zx7+21t7v7vf1N50EAUB7mM/36+GBOpjYPCvXD3dqVus2o6JrIeN29cZ8XM9zlzcD8ALXv1k9vriFJJftIKYeXiGvqZzR/5/XfKeNbSKY11Obb3ojZu39+XgEdr3+PZ4naznx+gjR99YSRNHw3XMY0Q+pEyJYSVyjp1iqzERLM12Tuq4AxZ2az/4gAATTp6SpF6QMasLG3cpFNXArs+oWMaIfQlZUoIK5Ty4xKSJk/W5uVatKDO99/pF8iKZGUacShjz4/TcvfaCh1YX8dE1umTZ3NvkN315dE6JhFCf1KmhLAyx5/oRtaJEwC4dOlMxe49KN+unc6prMcXb0SazZ99r41OSazX5cTT2lhOOBdCypQQVuX286M8P/tMpyTW6au3cveQ6jCoPvVb1aCMs/wYfFDr5n4EwKBpH1DOtZLOaYTQX0H2mRJC6Oz0sGFcfgPpAAAgAElEQVQc7Zi7gaRrr15yftQDSr2cQeZ1AwAtnvCmcQdPKVIPIT3lCknHjwJQ07ehzmmEsAzyk0QIC5fwzDNkRO81W/OYMV2nNNYpJek6iyf/qc2Du/vomMY6GbOy+PK1F8jOztmPq9njPWQbBCH+IWVKCAtmyszUilSdxT9QLihI50TWRVVVrl3KMCtSI+eGyc7mD2HBqOfJSL0GQEDXboQ+PUznREJYDilTQlio1DVrOPvmW9pcitSD+/LN7WRlGLV537HNpUg9hKtJ57UiFfr087Ts2fc+rxCidJEyJYSFufDBB6DClYULAbCvVpX627ff51Xidkd2J2lF6vERTajm5YKLm7POqayPqqp89fpwbS5FSog7SZkSwkKoqkq8n/8d6z5Ll+qQxrqZTCobFx4CoGmYJ3UD5fYwD2v3imXa+M0fV+mYRAjLJWVKCAuRt0hV7NYNjw8iUOzkgtsHZco2MW/UVm0eMkA25CyMXb/8CIBT+fJywrkQdyFlSggdpW3eQva1a9w8fFhbq9S/HzWnTdMxlXWL/f2MNn5mWmsdk1i3lPNnKVO2HMabNwEY9dWPOicSwnJJmRJCJ5cXLuJiRIQ2d+nalZrvTcO+YkUdU1mvLT/Ec/O6gXPHck6UfuylxrhWK6dzKuukmkwsfONlbe4fEiZHpYS4BylTQugg7y1hALyXLsW5cSP5hfWQvnhjG1mZOfsfefhWou2Tj/BIUHWdU1mv37+cC4BLlWr0n/g+FSq76ZxICMsmZUqIEnZ+0mSzItUw7pCUqEJIPJyiFakn/xVEzXpye5PCuJF6jf2b1gHw/MfzcSzjpHMiISyflCkhSoBqMHD88ScwJCZqa+7jx1H5ueekSD0EVVXJyjCSsD+Z37/Oua3Oo0P9pEgVUrbRyLzhT2tzKVJCFIyUKSFKQHyTpnesuQ0ZokMS65cYf4XN38aTdiVTWwvoXJuGrWvqmMo27Nu4VhuP/nbZPZ4phMhLypQQxSyuoZ82LteqFXW++Vq/MFYuMf4KKz6OBaBFN28qVS9HGWd7vBpV0TmZbdjy9ecAhAweiqOTbHAqREFJmRKiGOUtUmUDA6VIFcLVize0IvX4iCayEWcRWvPZTOo2D9bmwb366ZhGCOsjZUqIElBr1odUfOIJvWNYpWyjiTXz9nP64BVtTYpU0bly7ixxUVuJi9oKQEAX+f+pEA9KypQQxUA1mcg8eAinBg3ITk2VIlUI81/dCkDlGuXIysym64uN9A1kQ27euM6iMTn7STXp1JUuL70GqqpzKiGsj5QpIYrBpc8+4/K8+QBUGzNG5zTW69eP/tLGgya1kisfi9jGL+Zo464vj84ZyNdYiAcmZUqIYnCrSHn/tATnxo11TmOdEv5O5uzhqwDUbVZNilQRu3njBof/iATgmf9+rHMaIayblCkhipjpn3uZAZRteueWCOL+rl+7yW9z9gPQsps3wT3q6pzI9mz99gttXN1bvr5CFIaUKSGK2OGAQADc//1vnZNYH9WksixiLxcTUrU1KVJFS/3nnKgDWzYCMPq7n1Hs7PSMJITVkzIlRBHKuxWCa6+eOiaxPhdPpbL0v9EAeDasTEaagfDhcrJ5UToZu5df/jtJm7t5eMou50IUASlTQhSSajCQHhVF4isjtTWXxx7DvmJFHVNZF9WkakUKoPurAdg7yNGSonarSDUO60ol9xq07NVX50RC2AYpU0IUgvHKFY62bWe25vX115Rv3UqnRNZp7sgt2rjXG4FSpIpByvmz2jh8xGgdkwhhe6RMCfGQUjds4Ozo17X5I+vX4ejpiWJvr2Mq63Ll/HUunso9P6pKrQp4NnTTMZHt+mPpYgBa9x2kcxIhbI+UKSEeQnZqqlmR8t0RhUMVuT/cgzj+10XWLThgtjbw/4Lv8mxRGMf3/kn8jm0AtHvqaZ3TCGF7pEwJ8QDOjHiF9K1bzdb84uP0CWPl8hapFz8Kxams/DgqLr9GTNM7ghA2TX56CVEAqtHIkXbtMV27BoBDzZpUGf4iboMH65zMOv06K3dn8xc+DJEiVUw+HNDdbP7WktU6JRHCtslPMCHuQzWZiG/cxGzNd8tmndJYvzkjcr92QeFeOJd31DGN7dr+v2+0sXdgc9r1l7f3hCguUqaEuIvsa9c4NfR5MBq0tWpvvkmVF1/QMZX12rb4MLX9c08ut3ewo02fejomsl2qqrL716UANHu8B52GvqxzIiFsm5QpIfKhGgwcadXabK3y4MFUfWm4TomsV7bBxJ41JzkQeZYDkbmX54+Y3VG/UDZuyeRx2jhsyEs6JhGidJAyJcRtsk6f5njXcG3us2IFduXLUcbTU8dU1mv+a1u1sX+IBx0HNyDbaNIvUClwNv4gAJ7+jeUG0UKUAClTQuShGgxakbIrX54633+Hc4P6OqeyXis/idHGPUcHam/zOTjKXlzFJe9J50+Om6xfECFKESlTQgBqVhanhgzFqX5ucaq/ayeKo5wc/aBUVWXJe3u4fDZdW3v85SZm50uJ4jf0w3k4OjnrHUOIUkHu2SAEcOWHxWTExIACLl060yA2RorUQ/p1VoxZkXL3qUjdZtV0TFQ6xKxbxdzhuVfsVfGsrWMaIUoXOTIlSr24hn7auFKfPpQNCNAxjXU7EHmWc0evAtB/fAuq15GbPZeE6NXL2fbdVwC0H/gcjcO66JxIiNJFypQo1RJfM7/hq5Of312eKe4nK9PItsWHAejxWoAUqRJ0q0gBtOrzlI5JhCidpEyJUkc1GIhv0hT7alXJvpQMQI2pU6j8lPwSehhGQzYXT6WxfGbOruau1cvi1UjuU1hS0i4na+PnP5qvYxIhSi8pU6JUufbbb5x7618AWpGqMvxFKVKF8Plr28zmz0xto1OS0inyh0VAzuacbh6yfYcQepAyJUqNrDNntCIF8MjGDZSpLSfpFsaFk6naeOD/BeNWs7yOaUqfmPWrid+RU2Zll3Mh9CNlSti8zMNHuHnkMOfefgeA8iEheH2xQOdU1i9hfzK/zd0PwDPT2uBarazOiUoPkymbm9evs3mhvK0nhCWQMiVslmoyEe/fyGzNfcJ43J57TqdEtuNCQqpWpAApUiUoZt0qNi/63GztzR9X6ZRGCAFSpoSNUlXVrEhVGzOGcs2DKNusmY6prJ/JpIKqsm7B39raqPmddExU+twqUq7V3Wnb/2lqN24qt4wRQmdSpoTNUA0GsM+5TUneIuU+8f9wGzxYr1g2IzvbxPxRW83WRs4L0ydMKZV46IA2fuHTL6VECWEhpEwJm2A4f55jYXceIXGfMF6KVBHJW6Qah9YCBfllXoKMBgNLpowDoG7zYPnaC2FBpEwJq2e6edOsSFUZ8TIOVavh0ikMRw8PHZPZjkNR57RxhcpOdBjcQMc0pdN3Y3M3mA0f8bqOSYQQt5MyJayWajSiODhwOCBQW/PdEYVDFdkwsihdvXCDLd/HAxDQuTbt+tbTOVHpkhh3gJOxe7ly9gwA3gFBlKvoqnMqYakMBgOJiYlkZmbqHcVqOTs74+npieMD3J9VypSwStlpaRxpGWy25v3TEilSReiv9afYufy4Nq/m5UL7fr46JiqdlkzOeWvvidFvc+1CktwuRtxTYmIiLi4ueHt7y1vBD0FVVS5fvkxiYiI+Pj4Ffp2UKWFVVFUlOzmZoyGh2pprr564vfACzvXr65jMtiQnppsVKXefivQb20LHRKXTxYQT2rhh21D55SjuKzMzU4pUISiKQpUqVbh06dIDvU7KlLAKqqqiKAonHn+CrIQEbd0vPk6/UDYq87qBJe/t1uavzOmInb2djolKr1vnSVXx9JJfjqLA5P8rhfMwXz8pU8LiqapKvJ+/2Zpz48bU+eZrfQLZuK/e2q6NR84Lkx/MOolc/LU27jN2kn5BhBD3JX9uCouXt0hVe+tNPD74AJ9lS7ErL/eBKyqGm9lcOXedfZvPaGt932kuRUonqqqyZ8Uybe5a3V3HNEIUv6FDh7Js2bI71hMSEli8ePE9XxsbG8uaNWvu+ri3tzfJycmFzngvUqaExbq6bBlxDf20ed01v1F1+HBce3TXMZVt+vWjGP439U9Szl8HYODEYGrUlSvG9HL4j0htLLeKEaVZUZSpkiBv8wmLY7hwkdTVq7n4wQfamtfCr3CqW1fHVLbr+rWbXExIBaDDoAZ0fLqhzolKrxup11g//xNO7M05Z633OxPl6KB4aFNWHeTQudQi/Zj+HhWZ1KPRXR+PiIjA2dmZ0aNHM2bMGPbt28fmzZvZtGkTixYtIiwsjBkzZuDh4YGvry9OTk7Mnj0bgMjISGbNmkVSUhIRERH069ePcePGERcXR2BgIEOGDGHMmDFm/15WVhYTJ04kIyODqKgoxo8fT+fOnRk0aBCXLl0iODgYVVWL9GuQHzkyJSxK+vbtHOvQwaxI+e6Ionzbtjqmsl3pKZl8PXYHAF6N3FDs5Be3nuYNf5oTe3dTo159woa+RN2glnpHEuKBhIaGsn17znmX0dHRpKenYzAYiIqKwtfXl2nTprFr1y42btxIfHy82WvPnz9PVFQUq1evZty4nC1Bpk+fTkhICLGxsXcUKYAyZcowdepUBgwYQGxsLAMGDGDKlCm0b9+emJgYevbsyenTp4v985YjU8JiJL33Pinffw9ApaeeosaUyfJXeTFKu5LJtxP+0OZtn5TNOPV04cQxbTx42kwUO/lbVxTOvY4gFZfmzZuzd+9e0tLScHJyIigoiOjoaLZv306nTp3o0KEDbm5uAPTv358jR45or+3duzd2dnb4+/tz4cKFh84QGRnJL7/8AkC3bt2oXLly4T6pApDvVqEr1WhENZm4efSoVqQ858+j5tQpUqSKSXa2ifPHrpoVqd5jmlGlVgUdU5Vuqqry/fg3tLkUKWGtHB0d8fb2ZtGiRbRt25aQkBC2bNnC8ePH8fPzu+drnZyctHFh35or6d8fBfqOVRTlMUVRDiuKckxRlHH5PP6moiiHFEXZryjKJkVR6hR9VGFrVJOJ+MZNiPdvxIkePQFwG/IcLh076hvMxm388iC/zPxLmzd/vA61GhT/X27i7v7evEEbj/zqfzomEaLwQkNDmTlzJqGhoYSEhDB//nwCAwMJDg5m27ZtpKSkYDQa+fnnn+/7sVxcXEhLS3ug54SGhvLDDz8AsHbtWlJSUgr3CRXAfcuUoij2wBzgccAfGKQoiv9tT4sBWqiq2hRYBkQUdVBhW26eOEG8f+4haI+ZM3Fu2hT38eN1TGX79vx2kuMxOTv79hrTjJc/7UDrXo/onKp0U1WVjQs+A8DRyZmyFVx0TiRE4YSEhHD+/HnatGmDu7s7zs7OhISEUKtWLSZMmECrVq3o3Lkz/v7+uLre+6rhpk2b4uDgQEBAAB999FG+zwkLC+PQoUMEBgayZMkSJk2aRGRkJEFBQWzYsAEvL6/i+DTNKPc7lKYoShtgsqqq4f/MxwOoqvrfuzy/GTBbVdV29/q4LVq0UKOjox8qtLBuaZu3kDhypDZ/ZP06ytSRg5nF7Wj0BTZ8eVCbj5rfScc04pbfv5zDvo1rAXjjh+XYOxT85qpC3C4uLu6+b6fpKT09nQoVKmA0GunTpw/Dhg2jT58+ese6Q35fR0VR9qqqmu99tQpyAnot4EyeeSLQ6h7PfwFYm98DiqK8BLwElEhTFJblaGgHjBcvmq357vwDhxI4ObC0uvXHkmpSpUhZmFP7Y1n2/rva3KtJoBQpYfMmT57M77//TmZmJl27dqV37956RyoSBSlT+Z3Fle/hLEVRngFaAB3ye1xV1QXAAsg5MlXAjMLKmbKySPq/iVqRKhsQQMa+fdSPjsa+guxiXpxO7ktm7fy/8fCtpK1JkbIMeYtU0BO96PjcizqmEaJkzJw586Ffu379esaOHWu25uPjw/Llywsbq9AKUqYSgdp55p7AudufpChKZ+DfQAdVVW8WTTxhCy5/voBrK1Zo8zo//k+u1CsBxqxs1s7/GwD/9h50GNyAilWcdU4lAG7euK6Nh89ZSMWq1XVMI4R1CA8PJzw8XO8Y+SpImdoD+CqK4gOcBQYCg/M+4Z/zpD4HHlNV9eKdH0KUNhn79mFIuoB95UqkLFkCQNXRr1Etz7lSoviYTCqfj96mzRu0qqFjGnG72c8P0MZSpISwfvctU6qqGhVFeRVYD9gDC1VVPagoylQgWlXVlcAHQAVg6T9HHE6rqtqzGHMLC6WqKuf+9Tapv/0GQPV/vYVib49HxAxce8r/JUrKvJFbtHGbPnK1niVJSco9sP/WktU6JhFCFJUC7YCuquoaYM1taxPzjDsXcS5hpeL9zHfNcBs6FLdhw2QTwhJiyjaRfjX3XfYuL/hTv6UclbIkC19/CYAuL72mcxIhRFGR28mIQss6dYrj4Y/hPmGCtlauVSu8vl4k50aVoKQT1/g5Yq82bxrmKUXKwty4dlUb+4V01C+IEKJIyeECUSgpS5dyPPwxAC7OmkW5Vq3wWf4Ldb75WopUCbqRmmVWpHq/2YyQAfV1TCTyunQ6gQ8HdGfeS88AUK2OD45lnO7zKiFKj6FDh7Js2bI71hMSEli8ePE9XxsbG8uaNWvu+ri3tzfJyckArFu3jgYNGlCvXj2mT59euNB5yJEp8dCSpr1Hyj9b9tuVK4dv1HbsypXTOVXpk5yYzpL3dmvzp6e2plJ1+e9gSb59+1Vt3OWl12jSqauOaYSwHrfK1ODBg+/6nNjYWKKjo3niiSfu+bGys7MZNWoUGzduxNPTk5YtW9KzZ0/8/W+/qcuDkzIlHtiN6GjSt23TihRA/b3RciRKJ3mL1Mh5YfLfwcJcv5p7X7Bnpn+Cu49cECBKyNpxkPR30X7MGk3g8bsf0YmIiMDZ2ZnRo0czZswY9u3bx+bNm9m0aROLFi0iLCyMGTNm4OHhga+vL05OTsyePRuAyMhIZs2aRVJSEhEREfTr149x48YRFxdHYGAgQ4YMYcyYMWb/XlZWFhMnTiQjI4OoqCjGjx9P586dGTRoEJcuXSI4OFjbvHj37t3Uq1ePunXrAjBw4EBWrFhRJGVK3uYTBZadfp0zL4/g1DPPcvmLL7V1v/g4+QWug4y0LM7EX9Hm3UY1lf8OFmj+y88C8OS4yVKkhM0LDQ1l+/btAERHR5Oeno7BYCAqKgpfX1+mTZvGrl272LhxI/Hx8WavPX/+PFFRUaxevZpx48YBMH36dEJCQoiNjb2jSAGUKVOGqVOnMmDAAGJjYxkwYABTpkyhffv2xMTE0LNnT06fPg3A2bNnqV07d9tMT09Pzp49WySftxyZEgWiZmdzpEXuLYnqbdmMY82aOiYq3bINJha+HaXNm3SohXeTqjomEvlJPHRAG/s0y/eWXkIUn3scQSouzZs3Z+/evaSlpeHk5ERQUBDR0dFs376dTp060aFDB9zc3ADo378/R44c0V7bu3dv7Ozs8Pf358KFCw+dITIykl9++QWAbt26UfmfW5bldy/iovoDVI5MifsyZWRwonsPbe69bJkUKR3dzDAy/7WtAFRyL0fP0YG0lr2kLNKSKTl/Xbfq85TOSYQoGY6Ojnh7e7No0SLatm1LSEgIW7Zs4fjx4/e9AbOTU+5FGfkVnweRX0ny9PTkzJncWw0nJibi4eFRqH/nFilT4q7Stm4lrqEfh5sFkXXyJJBzY+KyjRvpnKx0+3JMpDZ+ekpravu7UcZZDjJbClVVSTx0gCvnErW1Ft2f1DGRECUrNDSUmTNnEhoaSkhICPPnzycwMJDg4GC2bdtGSkoKRqORn3/++b4fy8XFhbS0tAd6TmhoKD/8c07v2rVrSUnJOW+xZcuWHD16lJMnT5KVlcWPP/5IzyLaTFrKlMjXpU8/JXHEKwBU7N4djxnTabAvFod/DpcKfcwZsVkbD5rUSsck4m4ObtvEkinjWDRmBCgKg9/7EOcKFfSOJUSJCQkJ4fz587Rp0wZ3d3ecnZ0JCQmhVq1aTJgwgVatWtG5c2f8/f1xdXW958dq2rQpDg4OBAQE8NFHH+X7nLCwMA4dOkRgYCBLlixh0qRJREZGEhQUxIYNG/Dy8gLAwcGB2bNnEx4ejp+fH0899RSNGhXNwQGlsIfSHlaLFi3U6OhoXf5tcW8ZsbEkDBykzRvGHZITm3V2Jv4KScevsXtVzhHCoTPaUd5V9imyRB8O6A5AhcpuPPHav6jdqKnOiURpEhcXd9+30/SUnp5OhQoVMBqN9OnTh2HDhtGnTx+9Y90hv6+joih7VVXN9+RHeW9AADknmF+aPRs7J2cuffyxtu4XH6djKgFguJnNyo9jzdakSFmmvCecvzTvG/kjRIjbTJ48md9//53MzEy6du1K79699Y5UJKRMCVSTifhGjc3WytSpwyPr1+mUSOS14PVtADQOrUXooPqgz8FkcR+qqrJ7xVIAhs9ZKEVKiHzMnDnzoV+7fv16xo4da7bm4+PD8uXLCxur0KRMlXKqyUS8f+57xpUGDaTG+PEoZcromErcYriZrY1DBvjm/IKW39EWKSF2Lydj91LOtRIVq1bXO44QNic8PJzw8HC9Y+RLylQpdfPkSRzc3DibZxM0t2HDcH/nbR1TibxUk8rKT3Le3nP3qYidvVwvYqlUVWXjF3MACH/ldZ3TCCFKmpSpUuj6n7s5PWSINndu0oSa772HcwO5Ma4lmTtyizbu+05zHZOIe7mYcIKju3eSdvkSLXv1o26zlnpHEkKUMClTpczZN98i9Z+7a1fo/CgVH3+cil27ojg66pxMQM4RjrmvbDFbe3xEEzn/xkKd+juWZe+9C0BVL2+Ce/bTOZEQQg9SpkqR4926k3X8uDavOXUqDv9s6y8sQ94i1aRDLQK7eFGxalkdE4l7uVWkur48miaduuqcRgihFzkJw8apqsqFDz7gSLv2ZkXKLz5OipSFWf7hX9r4mWmtCR3UQIqUBTsZu1cbS5ESonCGDh3KsmXL7lhPSEhg8eLF93xtbGwsa/55xyU/3t7eJCcnAzBs2DCqV69O48aN7/r8hyFlyoZd37mTeD9/rny1EDUzk6qjRlFn8Q+yd5QFun7tJueOXgXgkaBquFYrp3MicS+qqvLLfycBUK9la53TCGG7iqJM5TV06FDWrSv6bX/kbT4bZUxJ4fTzw7R5/d1/otjb65hI5OfHaX9y+ex1nMs7Yu9oR/dXA/BsILfssXQ7lnynjXu8OV7HJELc3YzdM4i/El+kH7OhW0PGBo+96+MRERE4OzszevRoxowZw759+9i8eTObNm1i0aJFhIWFMWPGDDw8PPD19cXJyYnZs2cDEBkZyaxZs0hKSiIiIoJ+/foxbtw44uLiCAwMZMiQIYzJcwU6QFZWFhMnTiQjI4OoqCjGjx9P586dGTRoEJcuXSI4ONjspsmhoaEkJCQU6dcE5MiUTUnduDHnxsTNW3C0TVtt3WflCilSFmjHz8e4fPY6AI5O9nQf1VSKlJX4c/lP2tjOTr63hLglNDSU7du3AxAdHU16ejoGg4GoqCh8fX2ZNm0au3btYuPGjcTHmxe98+fPExUVxerVqxk3bhwA06dPJyQkhNjY2DuKFECZMmWYOnUqAwYMIDY2lgEDBjBlyhTat29PTEwMPXv25PTp08X+ecuRKRtx9eefOf/vnJNhTdevg6Lg4O6O79Yt93ml0MPpg5eJ3ZjzDV6ncRW6vxqgcyJRUHn/yn3zfyt1TCLEvd3rCFJxad68OXv37iUtLQ0nJyeCgoKIjo5m+/btdOrUiQ4dOuD2z/m6/fv358iRI9pre/fujZ2dHf7+/ly4cOGhM0RGRvLLL78A0K1bNypXLv4/UqVMWTFVVVEUhZvHjmlFCqBBzF/YlZUTly3Zqs/2aeNuo+RGuNZix5LviN2Qc25G+CtvoNjJwX0h8nJ0dMTb25tFixbRtm1bmjZtypYtWzh+/DijRo3i8OHDd32tk1PuPUfz/tHyMEp6Oxn5SWCljJcuEe/nT1xDP04NfV5b94uPkyJl4eaM2KyNX5wVIntIWYmVH/6HXb8sITM9jS7DX8U/NEzvSEJYpNDQUGbOnEloaCghISHMnz+fwMBAgoOD2bZtGykpKRiNRn7++ef7fiwXFxfS0tIe6DmhoaH88MMPAKxdu5aUlJTCfUIFIGXKSh0NCdXG1V59Fa9FC+UqPQumqirfjN9hVqSGfxSKUznZLNUaqKrK0d1/aPOmnR+Tc6WEuIuQkBDOnz9PmzZtcHd3x9nZmZCQEGrVqsWECRNo1aoVnTt3xt/fH1dX13t+rKZNm+Lg4EBAQAAfffRRvs8JCwvj0KFDBAYGsmTJEiZNmkRkZCRBQUFs2LABLy8v7bmDBg2iTZs2HD58GE9PT7766qsi+ZyVwh5Ke1gtWrRQo6Ojdfm3rZlqMHCsS1eMSUkA1Nu2FUd3d51TiftZ9dk+Th+8DICHbyWq1q5AyFNy+x5r8eGA7tr4zf+tlLf3hMWKi4vDz89P7xh3lZ6eToUKFTAajfTp04dhw4bRp08fvWPdIb+vo6Ioe1VVbZHf8+WcKSuhGgykbdnC2dG5N1H1WblCipQVuJCQqhWpal4u9HkrSOdE4mHV9m8iRUqIQpg8eTK///47mZmZdO3ald69e+sdqUhImbICxitXONq2ndla5cGDca4vRzYsnTErm2XTc4/APjVBboJrbdbOmaWN+4ydpGMSIazfzJkzH/q169evZ+xY8ysUfXx8WL58eWFjFZqUKQuX/PkCLuV5n9h3eyR25ctjV052yLYGn4/epo2fj2ivYxLxsA5F5pznNvTDeTg6O+ucRojSKzw8nPDwcL1j5EvKlAVSVRXD6dPYV65sVqQeWbcWh2rVdEwmHtbIuWEodnLVnjWJWbeKP5bm3saiimdtHdMIISyZlCkLdOzRRzGeO2+2Vn/XTuwrVdIpkXhYdUSolPkAACAASURBVBpXIfVyphQpK5NtNBK9+lcy09MIfWYY9Vu1vf+LhBCllpQpC6KqKvF+/trcc85ssk6epMqLL+qYSjyMG6lZ/L01kfPHr+ETUFXvOOIBffx07kmxLXs8qWMSIYQ1kDJlIUyZmRwObKbNa332KRU6dZINHa3UXxtOse/3M7i4OVM/WK64tCY3b9zQxgOnROiYRAhhLeQaX52pBgOJY8aYFSmvRQup2KWLFCkrtu/3MwA8M601Xv5VdE4jHsTs55/SxrUa+t/jmUKIojJ06FCWLVt2x3pCQgKLFy/O5xW5YmNjWbNmzV0f9/b2Jjk5mTNnzhAWFoafnx+NGjXik08+KXTuW6RM6chw/jzxTZqStnYd5Vq2xMnPD++fllC+TRu9o4kiYmcv32LWJO9RqWaP9dAxiRACiqZM3eLg4MCHH35IXFwcu3btYs6cORw6dKhIcsrbfDoxJidzLKyTNveYMR1HDw8dE4mikno5A0WBR4Kq6x1FPKC8R6U6Pf+yjkmEKLyk//yHm3HxRfoxnfwaUmPChLs+HhERgbOzM6NHj2bMmDHs27ePzZs3s2nTJhYtWkRYWBgzZszAw8MDX19fnJycmD17NgCRkZHMmjWLpKQkIiIi6NevH+PGjSMuLo7AwECGDBnCmDFjzP69rKwsJk6cSEZGBlFRUYwfP57OnTszaNAgLl26RHBwsHbT5Jo1a1KzZk0g535+fn5+nD17Fn//wh+Blj+bdaCaTBxtH6LNa332qRQpG/Ldv3eiqtAkzFPvKKKA9m1cy9dvjdTmby1ZrWMaIaxXaGgo27dvByA6Opr09HQMBgNRUVH4+voybdo0du3axcaNG4mPNy9658+fJyoqitWrVzNu3DgApk+fTkhICLGxsXcUKYAyZcowdepUBgwYQGxsLAMGDGDKlCm0b9+emJgYevbsyenTp+94XUJCAjExMbRq1apIPm85MlWC0rdtQylTBvvKlbU12fLAtuxcfkwbV/WsoGMSUVC7flnCjiXfAeDVuCkte/XXOZEQReNeR5CKS/Pmzdm7dy9paWk4OTkRFBREdHQ027dvp1OnTnTo0AE3NzcA+vfvz5EjR7TX9u7dGzs7O/z9/blw4cJDZ4iMjOSXX34BoFu3blTO8zsXcu4P2LdvXz7++GMqVqz40P9OXlKmSoCqqlxdsoSkyVMA8IuPo0FsDGRnY1e+vM7pRFE5eySFv9bn/AVU268yZZzl28sa3CpSAP3/7z86JhHC+jk6OuLt7c2iRYto27YtTZs2ZcuWLRw/fpxRo0Zx+PDhu77WyclJG996a+5h3e0CLoPBQN++fXn66ad58smi2/ZE3uYrAfF+/lqRcmrYEAA7Z2cpUjYi9XIG6788wK+zYgCo+YgrPV9vdp9XCUtwI/WaNn4u4jMdkwhhO0JDQ5k5cyahoaGEhIQwf/58AgMDCQ4OZtu2baSkpGA0Gvn555/v+7FcXFxIS0t7oOeEhobyww8/ALB27VpSUlKAnIL2wgsv4Ofnx5tvvlmIz/BOUqaK0cm+/TjcqrU2rzF5MnV/1f+GjKLoXL96k+/+vZNj0Re1tT5vBemYSDyIecOfBsAnsDnV6vjonEYI2xASEsL58+dp06YN7u7uODs7ExISQq1atZgwYQKtWrWic+fO+Pv74+rqes+P1bRpUxwcHAgICOCjPLdXyyssLIxDhw4RGBjIkiVLmDRpEpGRkQQFBbFhwwa8vLwA2LFjB9999x2bN28mMDCQwMDAAl0FWBBKYQ+lPawWLVqo0dHRuvzbxS09agdn8uxaXsbbmyovvkClfv10TCWKWlaGkS/GRGrzkfPCZG8wK/PhgO4AvPLFD5SreO8f6kJYg7i4OPz8/PSOcVfp6elUqFABo9FInz59GDZsGH369NE71h3y+zoqirJXVdUW+T1fTuooIqqqoigK2VevmhWpetu24egul8jborxF6vmI9lKkrMzqT3J3N5ciJUTJmDx5Mr///juZmZl07dqV3r173/9FVkDKVBFQDQbimzRFcXSkbFDuWzx1V6+SImVjVFPOkdzkxHRtbfjHoXKyuRXZuex/xG74jRvXrgIwfM5CnRMJUXrMnDnzoV+7fv16xo4da7bm4+PD8uX6nz4jvwEKKevUKY6HPwbk7B9Ve+4cLsyIoNobr+Pwz+WfwnYs+c8eLucpUn3eCpIiZWX+WPqDNn5s5BgqVpU/eISwBuHh4YSHh+sdI1/yW6AQLn36Gclz52rzhn/vR7Gzo+bUKTqmEsXlyJ4krUjVbVaNgE6eePjKHmHW5K81K7Tx6G+X4ejkrGMaIYStkDL1EI60ag12dmT/c7klQMO4Q3LOjA3b8fMxYjfm7qL7+MtNdEwjHtaWb74AwKdZCylSQogiI2XqAahZWcQ3DbhjveHf+6VI2bDMdINZkRr+caiOacTDyrun1GOvvKFjEiGErZEyVUCqqhLfPPeKSJfwcDw/+VjHRKK43UjNYvO3cZw+eFlbk5PNrdetPaUAyrnK27NCiKIjm3beh6qqGK9cId7PHwwGAKqNGSNFqhRY9E4Upw5c5tZWbO361ZMiZaX+WLpYG8tNjIWwPEOHDmXZsmV3rCckJLB48eJ8XpErNjb2nptvent7k5ycTGZmJsHBwQQEBNCoUSMmTZpU6Ny3yG+GezBeusTREPO3dBrsjZbbwJQCf648oY37vNWMyjXKU9aljI6JxMPKvJ7OzmU5P4y9GjfVOY0Q4kHcKlODBw++63NiY2OJjo7miSeeuOfHcnJyYvPmzVSoUAGDwUD79u15/PHHad269T1fVxBSpu5CNRjMilT1d97B2a+hFKlSYOfyY9oNiwM718bDt/J9XiEs1bq5H3Nw2+/avPuY8TqmEaJkbf/pCMln0u//xAdQtXYFQp6qf9fHIyIicHZ2ZvTo0YwZM4Z9+/axefNmNm3axKJFiwgLC2PGjBl4eHjg6+uLk5MTs2fPBiAyMpJZs2aRlJREREQE/fr1Y9y4ccTFxREYGMiQIUMYM2aM2b+XlZXFxIkTycjIICoqivHjx9O5c2cGDRrEpUuXCA4O1m6arCgKFSpUAHJueGwwGIrsfGd5my8fWQkJxDfJ/QvWZ+UKqgx7nvJt2uiYSpQEY1a2VqQef7kJ7fr56pxIFMatIhX+yhu8suB7ylZw0TmRELYtNDSU7du3AxAdHU16ejoGg4GoqCh8fX2ZNm0au3btYuPGjcTHx5u99vz580RFRbF69WrGjRsHwPTp0wkJCSE2NvaOIgVQpkwZpk6dyoABA4iNjWXAgAFMmTKF9u3bExMTQ8+ePTl9OvcCouzsbAIDA6levTpdunT5//bOPL6q6ur735WBhCGEIRAgCoRJJkMIkDCYQBgMCmJigzhAoWitVktFfR6FVkV9WpSXV21fn9a2j+JcsQLVhzIp81CsQeIEiKBgI6MImCAIgfX+cU6SGwhTbpKbe8/6fj58cs8++5y7fvucde5i733WJi0trUp0W8+UD0Vr1lK0ciUHX365tKz5ffcS3ensUbgRGpw6eYo1b3xO3YbOUF5ss7q069kswFYZ/rD6tRdKP3cfNDRwhhhGgDhXD1J10atXLzZs2EBhYSFRUVGkpKSQl5fH6tWrGTx4MAMHDqSJm9B69OjRbN26tfTY7OxswsLC6Nq1K3v37q20DatWrWLu3LkAjBgxgsaNy0YXwsPDyc/P59ChQ+Tk5PDJJ5/QvXv3Sn9XCdYz5fL9Bx/w71tvLRdItX7pRZr6rLNnhC5HDh/n45Vfs/+rQpom1Gf0lArXsjSCiH+95UxmHTh2YoAtMQzvEBkZSdu2bZk1axb9+/cnPT2d5cuXs3379vMuwBwVFVX6uWRorrKcb/iuUaNGDBo0iEWLFvn1PSVYMAUcmjuPnTeVvTZ92cYP6LJlM/VTUwNolVGTLPrTxwB0S0/ghgfTiKoXGWCLDH/Y+8W20s+9Rta+FekNI5TJyMhg5syZZGRkkJ6ezrPPPktycjKpqamsXLmSgwcPUlxczJw5c857rpiYGAoLCy+qTkZGBq++6iwbtXDhQg66Cbb379/PoUPOmpxHjx7l3XffpXPnzpWVWQ7PB1Mn9u1j99SppdudP/2EsLp1A2iRUZOoKv99+zL27XQcsWGcZcUOdk4WF/PKlLKknJZQ1zBqlvT0dHbv3k2/fv2Ij48nOjqa9PR0EhISmDp1KmlpaQwdOpSuXbsSGxt7znMlJSURERFBjx49eOqppyqsk5mZyaZNm0hOTmb27Nk8/PDDrFq1ipSUFJYsWULr1q0BZ05WZmYmSUlJ9OnTh2HDhjFy5Mgq0Sz+dqVVlt69e2teXl5AvltVnbxRAOHhcPIkAB1WriAyPj4gNhmBYdOaXSx/xZkEGdM0mh//pn+ALTL85YMFb5UuG3PP6/9rwZThKTZv3nze4bRAUlRURIMGDSguLiYnJ4eJEyeSk1P7eo8rakcR2aCqFc4B8dwEdC0uZkv3snXVGo3OpdHo0dTt1i2AVhk1zYnjJ/lu/9HSQKpfTntSstoE2CqjKgiPdF4iGHXfryyQMoxaxrRp03j33Xc5duwYV155JdnZ2YE2qUrwVDB1sqiIrb37lCtrOW1aYIwxAsa3u4/w10feK1dmgVRwo6osfOb/sm/HFxwo+IpG8S3p0Nv/RHyGYVQtM2fOrPSxixcv5v777y9XlpiYyLx58/w1y288EUwd27KFHaOvh4gyua1mPEHsqFEBtMoIBJvX7WLZS2W5TXLuTaFF+3OP2Ru1n39/+jGb16wAoM+1uXTLGGy9UoYRYmRlZZGVlRVoMyok5IOpU8eP82W2Ox7rrq1ngZQ3OXVKSwOpHoMvZcDoDvaDGwKoKn97zHmJZNhtvyBpSO182BqGEbpc0Nt8IjJcRD4TkW0i8kAF+6NEZLa7/z0RaVvVhlaGvY8/wWdJPUq3O6xYzmUf5lsg5VGWv7QZgAZNorji+o4WSIUIW9atKv1sgZRhGIHgvD1TIhIO/DcwDCgA3heRt1V1k0+1W4CDqtpBRG4AngDGVIfBF4qq8u0LL5Rud9myOXDGGAHlnVmfcuDrIxwoKKJ1t6aMvMsWuw0VDu3ZzYLf/x8AJr/2VoCtMQzDq1zIMF8qsE1VvwAQkdeBawHfYOpaYJr7+U3gGRERDVTeBWDPmvdYmHw5qt8jYXVZMGYknfrlsvWfb5arN+xnD/POnx4p3W7Y/HLqNWzMnm2rytWrU7cVx4/uKt2u3+QyTvxwhONHCoAw4tqk0imtL606JTLnt1PQU98TFh5Dr2vG8f5b/wN6nAZNu9AtYwipOYP5y533cKywACimaes0umUMZN0bz1N8/BsAJKw+gydOZv2cNzhy0Em3n9BlKPViY/l8/Rzqxrbn6OHtRETFceXP7mbZrP/hWOEOAHqPuoMTPxxj6/qVHD38BQBDf/oge77YySdLXwKJJCysLhnj7iQ8PIylz/0WcC5Vm+QRhEdE8kXe3wGIiGpO5oQ7OHLoMOvnvsCpE4foceVPiLu0FUufnwn6AwAxcd1IvTabTavXsnvrCgAaNO1M9n33UPDZl6yf89dS+9r1zi49fwkDx/0nGxcvQMIiaNY6keaJbdi6fj3f7FxfWqdFhwzi27Vny9p3OFl8HNWTdM0YyeF9+/jqo4WOvdHxZI6/nRUv/okTx/aUHhtZLwvVo8Q0asPc6U6iuPa9+7I9bz27tm7m+NGjdBs0lF5XX8tHSxfz9Web+OFIEd/t38eA68ey9o1XSs8V2zyeobf8nK3/WsfHSxcD0Ci+JT2Hj2TtG69w/OhRAC7rl86hvXvY+8Xnpcc2SbiUmKZx7PxoY2lZp7QBpIzIZv/OL1n63B8AZ95P0bcH+Py9dVza7XK+3JjHiEn/wT/cwKHkXH2uuY68+fM4UFC29tSQiXdQdPAA7817A4D4dh3Jun0SK15+jq8+zgcgsWdvWnXqwtrZZRn/k4YOp881P2Lp839kx4cfOIUiXH3XvezI38Cm1ctL6/bLvYl/vvkaAC07daZbxmAO79vL1vfWEp/YgQMFX9Fz+Eh2b9vKpyuc9fEiIuuQdccvy2kAZ928xX98unS7ZafO9B99M3N+8yCxzeM5vG8v3TOH0SapJ//43Qwat0xgwJhxFB7Yz/o5r5dey7DwcAzDMALBefNMiUguMFxVb3W3xwFpqnqXT51P3DoF7vZ2t843ZztvdeeZWjr9D+TnL6i285+Nxgm9OPj1hnPWueX3L/HcpB9X2XeGRcRyqvhwlZ2vKmnfJ5vt7//9/BVrCS07XMbubZ8F2owq50J1JQ0dzkfvVs3yCjXJbX94gZimcYE2wzACTm3PM3U2JkyYwMiRI8nNzS1XvmPHDtatW8dNN9101mPz8/PZtWsXV199dYX727ZtS15eHnFxzjPi5MmT9O7dm4SEBObPn1/hMRebZ+pC5kxVNLHk9AjsQuogIreJSJ6I5O3fv/8Cvrry9Ljx2jPKErqeudhp+tj7ym1H1m1Jw2YVLXpYvhMvvE4TkLJ1hOo17kjy8FvIuLH86Gb3wePKzhAVR/chP6ZeowZIWP2yY2Pb0yf752d8Y/8bJhMeWbZAY6NWPUnoMgSAsIiGpeWZE35R7nzdBt1Mx74/Iiw8prTsipvupV3v8vk8+uZOov+Yu8uVNWvbnxYdMsqVpY+9j+SrytYo7JB63RnHRUQ1p/+Yu2kYXzaEFl6nKX2uGUbf3ElIeJl9LToOPENras6dREa3oE69BFp2GkTyVbdSr1HHcnVimnWjQ+p1brs7t1z7PtnEtfF9BV7ca1q+l2LguFvoNTKHtJzriW0eT2x8C/qPvpnY5mVJWi/tejmZE26jfe80wn3e/Oxx5Ygz7B159/20SepZriwt5/py2/HtOhLhs9ZUCQ2bNS+33axtO3409VH65ZY9LLqkZ9LqMiexbKMWLQEYPPH2M86VcfNPzijrl3sj3QYOKd2OjIomc8JtxMSVLdzcuGUrkrPK62qb3IuUq0bRsFn5xLWZE35WaksJl/vMTYqu34D+199M5wHOdW2e2B6A/tffzKXdyg+pDpl4x5kaTls7L6p+fa65Z0q5sku7JTHkFsdHRMIYO/1pfv7cX7l39nzunT3fAinDCFF27NjBa6+9ds46+fn5LFhw4Z0nv/vd76o84LyQnql+wDRVzXK3pwCo6nSfOovdOv8UkQhgD9DsXMN8gcyAbhiGYRihiG+PyvIX/sy+nV9U6fmbt2lH5oTbzrp/xowZREdHM2nSJCZPnsyHH37IsmXLWLp0KbNmzSIzM5MnnniCVq1a0bFjR6KionjmmWeYMGECDRs2JC8vjz179jBjxgxyc3Pp27cvmzdvJjExkfHjxzN58uRy33f8+HE6dOjA0aNHSUhIYMqUKQwdOpQbb7yR/fv3k5qayqJFi9iwYQNxcXEUFBQwfvx4fvWrX/Hkk0/WaM/U+0BHEUkUkTrADcDbp9V5Gxjvfs4FlgVyvpRhGIZhGDVPRkYGq1evBiAvL4+ioiJOnDjBmjVr6NixI4899hjr16/nnXfeYcuWLeWO3b17N2vWrGH+/Pk88ICTOODxxx8nPT2d/Pz8MwIpgDp16vDoo48yZswY8vPzGTNmDI888ghXXHEFGzduZNSoUXz1Vdmc0rvvvpsZM2YQFla1SxOfdwK6qhaLyF3AYpyxk+dV9VMReRTIU9W3geeAl0VkG/AtTsBlGIZhGEaAOFcPUnXRq1cvNmzYQGFhIVFRUaSkpJCXl8fq1asZPHgwAwcOpEmTJgCMHj2arVu3lh6bnZ1NWFgYXbt2Ze/evZW2YdWqVcydOxeAESNG0LixM11m/vz5NG/enF69erFixYrKi6yAC0raqaoLgAWnlT3k8/kYMLpKLTMMwzAMI6iIjIykbdu2zJo1i/79+5OUlMTy5cvZvn07d955J599dvYXYaJ85pj6O7hVUR7BtWvX8vbbb7NgwQKOHTvGd999x9ixY3nllVcqOMPFUbX9XIZhGIZheJqMjAxmzpxJRkYG6enpPPvssyQnJ5OamsrKlSs5ePAgxcXFzJkz57zniomJobCw8KLqZGRk8OqrrwKwcOFCDh48CMD06dMpKChgx44dvP766wwePLhKAimwYMowDMMwjCokPT2d3bt3069fP+Lj44mOjiY9PZ2EhASmTp1KWloaQ4cOpWvXrsTGnntt1KSkJCIiIujRowdPPfVUhXUyMzPZtGkTycnJzJ49m4cffphVq1aRkpLCkiVLaN26dXXILMd53+arLuxtPsMwDMOoWmp7nqmioiIaNGhAcXExOTk5TJw4kZycnECbdQbV8TafYRiGYRiG30ybNo3k5GS6d+9OYmIi2dnZ5z8oCLigCeiGYRiGYRj+MnPmzEofu3jxYu6///5yZYmJicybN89fs/zGginDMAzDCCFUtcK32YKdrKwssrKyzl/RTyoz/cmG+QzDMAwjRIiOjubAgQN+pxbwKqrKgQMHiI6OvqjjrGfKMAzDMEKESy65hIKCAqp7/dtQJjo6mksuueSijrFgyjAMwzBChMjISBITEwNthuewYT7DMAzDMAw/sGDKMAzDMAzDDyyYMgzDMAzD8IOAZUAXkf3Azmo4dRzwTTWcNxjwsnYw/V7XD9YGXtbvZe0leL0Nqlt/G1VtVtGOgAVT1YWI5J0t3Xuo42XtYPq9rh+sDbys38vaS/B6GwRSvw3zGYZhGIZh+IEFU4ZhGIZhGH4QisHUnwNtQADxsnYw/V7XD9YGXtbvZe0leL0NAqY/5OZMGYZhGIZh1CSh2DNlGIZhGIZRY1gwZRiGYRiG4QcWTAUZIiKBtsEIHHb9vY1df29j17/2YsFU8NEIQEQ8uUi1iNwkIj3cz158sESXfPCofq9j/m/+D3hWf60lqIIpEckWkccCbUcgEJFYEVkCLAJQ1eIAm1SjiMhQEVkNPA30BFAPvT0hIleKyDrgGRG5GbylH8z/zf/N/zH/r7X+X+v/d+NG32HAT4AHgDYiskRVVwfWshrnGHAQGCAio1X1byISrqonA21YdeFe+2jgRaA58F/AtUA9d39I6y9BRJoBjwKPA4XAL0WktapOF5EwVT0VWAurD/P/Usz/zf/N/2ux/9f6nil1OAlsw/kfyc+BWhudVgciEo7Tvb8eGAP8PwBVPRnKXb3utT8KvKqqg1R1MbAOGOfu98KDVIB44ENV/buqLsV5qNwnInGqesoD94D5v/m/+b/5f632/1obTInIJBH5i4jc6hatVNVCVf0LUF9EbnHr1VoNlcVH+0QREfdm+g4YoarzgY9E5CER6a6qGmrO5KP/pwCq+pZbHg58CXwqIpcG0sbqRETGi8gwKO3KLwL6i0gTt2wT8DfcH9VQxPzf/N/83/w/qPxfVWvdP2ACzv/ChgMrgSlAe5/9VwGfAo0DbWsNaJ8KtMft5nbrTASKgTx3OzLQdlez/nY++y8H3gdiAm1rNWhvDLwJ7AY+AsJ99r0EvHxa3feAxEDbXQP3gPm/+X/JfvN/Nf+vjf5fe6K68gwBnlDVRcC9OOPmN5fsVNWFwGbgNhGJEZHRgTGzWqhI+2jgKHCVOwl1ErAM2OkeE0qTUU/XXwcYW7JTVT/GaYsbAmNe9aGqB4ElQBdgA/CQz+67gOEi0sfdPgJ8CByvUSNrBvN/83/zf/P/oPL/WhVM+XTZbQRGAqhqHvBPoJWIDPCpfj8wHfgcaFGTdlYH59C+DkgErgDeAf6lqsmqeiUwSEQS1Q3Xg5lz6F+Pz7V3hzSWANGhNLzho+UlVT0E/AG4TkTaAKjqd8AjwIMiMh74NdAdZwggJDD/B8z/zf/N/4PS/wMaTIlIC/dvGICWvZWwFggTkQx3+xOcrs9Wbv0OODfb34EUVQ26seOL0P4p8DUQAzykqr/2OU1rVf2yhkyuUip77d0fjubAkWD+EalAv7p/j7l/3wcWAr8pOUZVn8F5NbwX0AbIVdXDNWt51SEi3USkNG+Ox/z/QrWHqv9X6tqHkP+frt+L/j9ARNqXbAe7/wckmBKRniKyFHdWfkkj+kSmn+M8RMaI8/prAU702dbdfxi4S1WvU9VdNWq8n1RC+79xbqI2qnpcRMJ9foCP1LwC/6iCaw9wn6o+X3NWVx3n0C8VTKZ8BujgPnjjRaSDqi4DJqvq+GC790sQkSQRWYPzqntTn3Iv+P/Fag81//f32kNw+//Z9HvJ/1Pc4eplQKxPeVD7f40GU+4N8xTOZLoXVfWnPvt882UUAqtxxstnikgkzoS7AwCqul9VP69J2/3FT+2NKNN+UoMwr0hVXXsAVQ26eQIXoF/Vec25rog0AFDVr4B5wMc4EzEbuuXB/kr4r4E3VTVHVb+G0pxBIev/PlRGe9D7vw9+XXsITv/34Wz6Q97/RSRSRP4E/Bn4PbAYGOTuC3r/r9Fgyu3KjAE2qupLACLS3vfHVJwMp6/hRJ8P4TTianf7xZq0tyrxsnYw/Reo/xHgVaCdu30jTl6VmcDlqvpBQIyvIkQkzO3WL1LVp92yYSLSCBB3+78IwXvAy9rB9F+g/scIYf8HooBVQLo6KT7mAl1EJKIkQHSfgUF5D0h1DzuLSF/gW1Xd6m43xHm19VUgC9iLM4nuaffvNJy5Advc+mFAfVUtrFZDqwEvawfTXwX6+wJ7g3VeDFTYBjHAB8Bk4FagLk47bAb+CvyWELkHvKwdTH8V6A8p/xcRUZ+AQ5xcUX1U9XYREZy0Fw8AD6rqdrdO8NwDWn25IhoB/8Dpsvs1ToOU7JsE5AMZONHqEzgz85v51AmrLtuq+5+XtZv+KtEfXtM213AbTMX5URnlbmcAbwH9QuEe8LJ2018l+kPW/3F64cLczx1wgsnGJfuC+R6ozmG++jhjor9wP5fMzEdVfw9kquoqVf0BZ1Z+b+B7OGMOTTDiZe1g+v3VH5RzIk7j29qv5wAAA41JREFUrG0AzMeZTNrE3c4D9uCsPxcK94CXtYPp91d/yPq/Opxye5x2uHUGluyD4L0HqjSYEpEfi8hAEWmozuS6PwNv4NwoaSLSqqSuOgnKSugF/Bs46e4Luob0snYw/V7XDxfUBgkAqvoR8B/AnSISh5OU8XLKJpgGXRt4WTuYfq/rhwt/BrrDfadwknFCWSApELxt4PecKbcBWuBMGjsFbMeJRH+pqt+4dQYA1wPvq+orblkU0A9nct1u4F51x5aDBS9rB9Pvdf1w0W2Qp6ov+xx7D85k2444r3tvqmHz/cLL2sH0e10/+PUMDFdnoe6Xge2qOi0Q9lclfvVMuQ1S8pbS16o6BOftg29xolIAVHUtTpdeZxGJFZG67hDHcZz1pq4Jth8TL2sH0+91/VCpNrjMbYMYt/xJnB+SrGD7MfGydjD9XtcPfj0D6/kMZU4MhUAKKtkzJSIRwKNAOLAAJ/9FrqqOd/cLsAu4QVVXumUNcBKVDQBaAz21FiXculC8rB1Mv9f1g99t0B8ne3NQtoGXtYPp97p+sDY4GxfdMyUiA3EWYWwMbMPJ5HwCyBSRVCidSPYozqveJYzAiVrzcXJmBF1Delk7mH6v64cqaYMPCdI28LJ2MP1e1w/WBuciohLHnAJmloz/ikhPIBEnwdYfgV7izNSfh9PAbVV1B84ks6GquqpKLA8MXtYOpt/r+sHbbeBl7WD6va4frA3OSmXmTG0A3hCRcHd7Lc6Cmy8A4SLyC3Vm418CnHQbElV9KwQa0svawfR7XT94uw28rB1Mv9f1g7XBWbnoYEpVv1fVH7RsAtkwYL/7+Sc46eHn42R0/QDKXnkMdrysHUy/1/WDt9vAy9rB9HtdP1gbnIvKDPMBzkx+QIF44G23uBAnw2t34Et1F3J0x1BDBi9rB9Pvdf3g7TbwsnYw/V7XD9YGFeFPaoRTQCTwDZDkRqMPAqdUdU1JQ4YoXtYOpt/r+sHbbeBl7WD6va4frA3OwK+kneIsYrjO/TdLVZ+rKsNqO17WDqbf6/rB223gZe1g+r2uH6wNTsffYOoSYBzwpDqJCD2Dl7WD6fe6fvB2G3hZO5h+r+sHa4PT8Xs5GcMwDMMwDC9TpQsdG4ZhGIZheA0LpgzDMAzDMPzAginDMAzDMAw/sGDKMAzDMAzDDyyYMgzDMAzD8AMLpgzDMAzDMPzAginDMAzDMAw/+P9Ea2+F09dq1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "td_df[['wght_td',\n",
    "       'wght_td0',\n",
    "       'wght_td1',\n",
    "       'wght_td2',\n",
    "       'wght_td3',\n",
    "       'wght_td4']].plot(figsize=(10,8)) #looks correct, seems identical to the book example\n",
    "\n",
    "#Once you go negative for time-decay factor, data samples will start to be omitted"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}