ml-finance-python

python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb

(220410B)
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      9     "# Sentdex\n",
     10     "\n",
     11     "In this notebook, we'll take a look at Sentdex's *Sentiment* dataset, available through the [Quantopian partner program](https://www.quantopian.com/data). This dataset spans 2012 through the current day, and documents the mood of traders based on their messages.\n",
     12     "\n",
     13     "## Notebook Contents\n",
     14     "\n",
     15     "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
     16     "\n",
     17     "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
     18     "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
     19     "\n",
     20     "### Free samples and limits\n",
     21     "One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
     22     "\n",
     23     "There is a *free* version of this dataset as well as a paid one. The free sample includes data until 2 months prior to the current date.\n",
     24     "\n",
     25     "To access the most up-to-date values for this data set for trading a live algorithm (as with other partner sets), you need to purchase acess to the full set.\n",
     26     "\n",
     27     "With preamble in place, let's get started:\n",
     28     "\n",
     29     "<a id='interactive'></a>\n",
     30     "#Interactive Overview\n",
     31     "### Accessing the data with Blaze and Interactive on Research\n",
     32     "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
     33     "\n",
     34     "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     35     "\n",
     36     "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
     37     "\n",
     38     "Helpful links:\n",
     39     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     40     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     41     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     42     "\n",
     43     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     44     "> `from odo import odo`  \n",
     45     "> `odo(expr, pandas.DataFrame)`\n",
     46     "\n",
     47     "\n",
     48     "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
     49    ]
     50   },
     51   {
     52    "cell_type": "code",
     53    "execution_count": 1,
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     58    "source": [
     59     "# import the free sample of the dataset\n",
     60     "from quantopian.interactive.data.sentdex import sentiment_free as dataset\n",
     61     "\n",
     62     "# or if you want to import the full dataset, use:\n",
     63     "# from quantopian.interactive.data.sentdex import sentiment\n",
     64     "\n",
     65     "# import data operations\n",
     66     "from odo import odo\n",
     67     "# import other libraries we will use\n",
     68     "import pandas as pd\n",
     69     "import matplotlib.pyplot as plt"
     70    ]
     71   },
     72   {
     73    "cell_type": "code",
     74    "execution_count": 2,
     75    "metadata": {
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     79     {
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     81       "text/plain": [
     82        "dshape(\"\"\"var * {\n",
     83        "  symbol: string,\n",
     84        "  sentiment_signal: float64,\n",
     85        "  sid: int64,\n",
     86        "  asof_date: datetime,\n",
     87        "  timestamp: datetime\n",
     88        "  }\"\"\")"
     89       ]
     90      },
     91      "execution_count": 2,
     92      "metadata": {},
     93      "output_type": "execute_result"
     94     }
     95    ],
     96    "source": [
     97     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     98     "dataset.dshape"
     99    ]
    100   },
    101   {
    102    "cell_type": "code",
    103    "execution_count": 2,
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    122    "source": [
    123     "# And how many rows are there?\n",
    124     "# N.B. we're using a Blaze function to do this, not len()\n",
    125     "dataset.count()"
    126    ]
    127   },
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    136      "data": {
    137       "text/html": [
    138        "<table border=\"1\" class=\"dataframe\">\n",
    139        "  <thead>\n",
    140        "    <tr style=\"text-align: right;\">\n",
    141        "      <th></th>\n",
    142        "      <th>symbol</th>\n",
    143        "      <th>sentiment_signal</th>\n",
    144        "      <th>sid</th>\n",
    145        "      <th>asof_date</th>\n",
    146        "      <th>timestamp</th>\n",
    147        "    </tr>\n",
    148        "  </thead>\n",
    149        "  <tbody>\n",
    150        "    <tr>\n",
    151        "      <th>0</th>\n",
    152        "      <td>AAPL</td>\n",
    153        "      <td>6</td>\n",
    154        "      <td>24</td>\n",
    155        "      <td>2012-10-15</td>\n",
    156        "      <td>2012-10-16</td>\n",
    157        "    </tr>\n",
    158        "    <tr>\n",
    159        "      <th>1</th>\n",
    160        "      <td>AAPL</td>\n",
    161        "      <td>2</td>\n",
    162        "      <td>24</td>\n",
    163        "      <td>2012-10-16</td>\n",
    164        "      <td>2012-10-17</td>\n",
    165        "    </tr>\n",
    166        "    <tr>\n",
    167        "      <th>2</th>\n",
    168        "      <td>AAPL</td>\n",
    169        "      <td>6</td>\n",
    170        "      <td>24</td>\n",
    171        "      <td>2012-10-17</td>\n",
    172        "      <td>2012-10-18</td>\n",
    173        "    </tr>\n",
    174        "  </tbody>\n",
    175        "</table>"
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    177       "text/plain": [
    178        "  symbol  sentiment_signal  sid  asof_date  timestamp\n",
    179        "0   AAPL                 6   24 2012-10-15 2012-10-16\n",
    180        "1   AAPL                 2   24 2012-10-16 2012-10-17\n",
    181        "2   AAPL                 6   24 2012-10-17 2012-10-18"
    182       ]
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    184      "execution_count": 3,
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    189    "source": [
    190     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    191     "dataset[:3]"
    192    ]
    193   },
    194   {
    195    "cell_type": "markdown",
    196    "metadata": {},
    197    "source": [
    198     "The Sentdex Sentiment data feed is elegant and simple. Just a few fields:\n",
    199     "\n",
    200     "Let's go over the columns:\n",
    201     "- **asof_date**: The date to which this data applies.\n",
    202     "- **symbol**: stock ticker symbol of the affected company.\n",
    203     "- **timestamp**: the datetime at which the data is available to the Quantopian system. For historical data loaded, we have simulated a lag. For data we have loaded since the advent of loading this data set, the timestamp is an actual recorded value.\n",
    204     "- **sentiment_signal**: A standalone sentiment score from -3 to 6 for stocks\n",
    205     "- **sid**: the equity's unique identifier. Use this instead of the symbol.\n",
    206     "\n",
    207     "From the [Sentdex documentation](http://sentdex.com/blog/back-testing-sentdex-sentiment-analysis-signals-for-stocks):\n",
    208     "\n",
    209     "```\n",
    210     "The signals currently vary from -3 to a positive 6, where -3 is as equally strongly negative of sentiment as a 6 is strongly positive sentiment.\n",
    211     "\n",
    212     "Sentiment signals:\n",
    213     "\n",
    214     "6 - Strongest positive sentiment.\n",
    215     "5 - Extremely strong, positive, sentiment.\n",
    216     "4 - Very strong, positive, sentiment.\n",
    217     "3 - Strong, positive sentiment.\n",
    218     "2 - Substantially positive sentiment.\n",
    219     "1 - Barely positive sentiment.\n",
    220     "0 - Neutral sentiment\n",
    221     "-1 - Sentiment trending into negatives.\n",
    222     "-2 - Weak negative sentiment.\n",
    223     "-3 - Strongest negative sentiment.\n",
    224     "```\n",
    225     "\n",
    226     "We've done much of the data processing for you. Fields like `timestamp` and `sid` are standardized across all our Store Datasets, so the datasets are easy to combine. We have standardized the `sid` across all our equity databases.\n",
    227     "\n",
    228     "We can select columns and rows with ease. Below, we'll fetch all rows for Apple (sid 24) and explore the scores a bit with a chart."
    229    ]
    230   },
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    238     {
    239      "data": {
    240       "text/plain": [
    241        "(734791.0, 736078.0, -4, 7.5)"
    242       ]
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Lw8qVK5Gbm4vVq1dDo9Hgl19+wcyZM7F27Vp89dVXiIuLw+23345Jkybhuuuuk8rWaDSo\nrq7G66+/jpEjRyI/P196vHnzZhQXF+PEiRP473//i7feeguvvvoqrFYrNBoN6uvrsWLFCkyaNAn5\n+fm4//77odPpVCVBvvZLHl9paSmWLVuGOXPmYM2aNaiursY777yD//3vf5g7dy6+//57l3WUYvZ1\nbx+NRoPs7GwcOHAAlZWVeOaZZ/D2229j8ODBWL9+PW655RZs2LABALB371707NkzpEkQwB4hIiIi\nIiL861//woEDB/DNN9/g9ddfx7vvvou3334bO3fuxJw5cwAADQ0N6Nu3LwDg7LPPRkZGBgAgMzMT\nRqPRY9n9+vWTlouJcfZDdOrUCZWVlfjpp5+wY8cOTJw4EYCzF6WsrAwAcOmllwIAunTpgh07dgR1\nv+QGDx4sbcdoNOLIkSO48MILER8fj4yMDCl+kaeYe/To4TUWs9mM2NhY6PV6vPDCC7BYLCgrK8OY\nMWNwwQUXwGQyoaysDBs3bsTNN98c0P76g4kQEREREbUtRUVBHRoHAPX19ejVqxd69eqFiRMnYtSo\nUThx4gSSk5Ol4WSikpISaLVal+fEYWBK5Mu6rxcXF4c//vGP+POf/+x1PW+WLFmC77//Hr1798ZT\nTz2lar88bUfcDzFhA5pfG+QtZjn5ejabDfv27cP555+Phx9+GFOnTsWVV16JN954A7W1tQCAMWPG\noKCgAEVFRXjggQdU7XtLcGgcEREREbU9RUVBS4Lef/99PPnkk1ISUFNTA0EQkJGRgT59+uDrr78G\nAGzYsMHrbHIajQY2m83j6/JkSXzcv39/bN68GYIgwGq1Yv78+V5jVUq4pk+fjlWrVjVLgrztlzfd\nu3fH77//DpvNhoqKCuzevdvldbUxy2NdsmQJhg4dio4dO6KqqgpZWVmor6/Hl19+iYaGBgDORGjN\nmjXo0aMHEhISvMYYDOwRIiIiIqKodscdd+DgwYMYN24ckpOTYbPZMGfOHCQkJGD27Nl45plnsHz5\nciQmJuKFF15ATU1Ns14SjUaDgQMH4m9/+xvS09MVZ1iTPyc+HjhwIK644grceeedEAQBEyZMaLaM\nfL2LLroI48aNw//+978W7ZcnGo0GGRkZGDNmDMaOHYvzzjsPffv2dekh8haz3AsvvIAVK1aguroa\nAwYMkIYYTpw4EQ899BC6d++Ou+++G/Pnz8eNN96I3r17o0uXLhgzZozPfQsGjeCtH68VMBgM0rjF\n9ri9tor1pA7rSR3Wk2+sI3VYT+qxrnxjHanT0noS7y2TmJgYrJAoCNatW4ebbroJWq0WN998M1as\nWIEuXbqEdJsVFRW4//778eGHH3pcxlN7CaQdskeIiIiIiIhclJeXY9y4cYiPj8fNN98c8iRo48aN\nWLx4sdRrFA5MhIiIiIiIyMWf//xnn5MhBNPw4cMxfPjwsG0P4GQJREREREQUhZgIERERERFR1GEi\nREREREREUYeJEBERERERRR0mQkREREQU1erq6vDII49g4sSJGDduHL788ksAwMmTJzFx4kRMmDAB\nM2bMQH19vct6JSUlGDhwICZOnIiJEydi8uTJXm+4qsasWbMwZswYqczx48fDYDB4XL6kpAR33HEH\nAOC6665DbW0tli1bhp9//rlFcZSWliI7OxubNm1qUTmtGWeNIyIiIqKotmXLFvTr1w/33XcfTpw4\ngXvvvRdDhw7F4sWLcffdd2PEiBFYuHAh1q5di7vuustl3fPOOw+rVq0CABw7dgwPPvggXnzxRfTu\n3TugWDQaDR5//HFcc801Upn3338/CgsLVa8fjNneNmzYgJEjR+LTTz/FsGHDWlxea8REiIiIiIii\n2o033ig9PnHiBLp16wYA+P777zFv3jwAwLXXXos33nijWSIkl5WVhQcffBCrV6/GvHnz8Nxzz2HH\njh2w2Wy48847MWLECIwdO1ZKaj766CP89ttvmDVrltcyzWYzBEFAaWkpZs+ejYaGBsTExOCf//xn\ns+UFQcCsWbMwcuRIVFZWori4GJWVlTh06BDuu+8+/PGPf0R+fj5WrFiBbt26IT09HVdccQVuu+02\nl3I2bNiAhQsXYvLkybBYLKirq8P48eOl2NetW4e9e/diypQpmDNnDhoaGqDVajF//nx069YNN9xw\nAy655BLk5OSge/fueOmllxAfH48OHTpg0aJFAIAnnngCJ0+exKBBg1BQUIAvv/wS+/fvR15eHjQa\nDVJSUvD8889Dr9erOYx+YyJERERERK3GG+t/wbc7jge1zD/0744pYy72udz48eNRWlqK1157DYBz\nyFxcXBwAID09HWVlZT7LuPjii/Hee++hvr4ePXr0wJNPPgmLxYLhw4dj7Nix6N27N4qLi3HppZfi\niy++wNSpU5uVIQiC9PiHH35AZmYmNBoNXnrpJYwdOxajRo1CYWEhXn75ZUyfPr3Z+hqNRnq8b98+\nrFmzBocOHcJjjz2GO+64AwsXLsS6deuQlJSEm266CUOGDHFZ/+DBgxAEAT179sSQIUPwxRdfYPTo\n0ejWrRv279+P888/H5s3b8aUKVOwaNEiTJkyBTk5Ofjqq6+wdOlS5OXloaSkBK+++ip69eqFwsJC\nLFiwAFlZWZg1axa++eYbCIKA+vp6rFmzBlu2bMHKlSsBAHl5ecjLy0PPnj2xevVqrF69Gg8++KDP\neg8EEyEiIiIiIgDvvfce9uzZg8cffxwff/yxy2vy5MQbs9mMmJgYxMfHo6qqCuPHj0dcXBwqKysB\nALfeeivWr1+Pvn37oqSkBBdf7JqgCYKAF154AStWrEBVVRWSk5OxYMECAMAvv/yCJ554AgBw+eWX\n45VXXvEZz4ABA6DRaNClSxcYjUZUVlZCp9MhPT0dAJCTk9Ns3z755BNcd911AIBhw4bh/fffx+jR\nozF8+HBs3rwZWVlZ2LdvHwYOHIjZs2fj8OHDWLp0KRwOBzIyMgAASUlJ6NWrFwAgLS0NTz/9NOx2\nO44dO4YrrrgCZ86cwaBBgwAA11xzDbRaLQBg586dmDNnDgCgoaEBffv2VVXvgWAiREREREStxpQx\nF6vqvQmm3bt3IyMjA926dUOfPn1gt9tRUVGB5ORk1NfXIz4+HqWlpcjMzFRV1sUXX4wffvgB27dv\nx+rVq6HVajFw4EAAwFVXXYUFCxbgq6++wvXXX99sffk1Qnv27MFTTz2F8847T3rN4XAAgDQ8zhcx\nwQCcSZYgCD7X27BhA2JiYrBp0ybY7XaUlJTAZDJh+PDhmDFjBi688EJcddVVAID4+HgsXrwYnTp1\ncilD7EkDgNmzZ2P58uU477zzkJeXJ8UixqbRaKRerOTkZOmaq1DjrHFEREREFNWKi4vx5ptvAgBO\nnz6N2tpadOzYEbm5ufjss88AAJ9//jmuvvpqr+UcPXoUK1euxOTJk1FZWYmuXbtCq9Xiiy++gN1u\nR0NDA+Li4pCbm4sXXngBN998s2I5Yg9Nnz59kJ2djdWrVwMA+vbti+3btwNwDpkLpLckLS0NlZWV\nqKmpgcViwQ8//OAylG7nzp1ISUlBQUEB8vPzsX79eowaNQqfffaZNETvk08+wYgRIwAA/fv3x8aN\nGwEA27ZtwyeffNJsmyaTCd26dUNNTQ2+++47NDQ0oGfPnti9ezcA4JtvvoHdbpf2+euvvwbgTMha\nOgufN0yEiIiIiCiq3XXXXThz5gwmTJiAqVOn4u9//zs0Gg2mT5+O/Px8TJgwATU1Nc0mFACAQ4cO\nSdNcz5w5E3PnzkXXrl2Rm5uLI0eO4O6778bhw4dx7bXX4h//+AcAYOTIkUhLS0NWVpZiPPLEZMaM\nGVixYgUqKirw8MMPIz8/H/fccw/y8/Mxffp0CIIgLS9fT6ksjUYDrVaLadOmYcKECXj88cdxySWX\nuCyzYcMGaTpu0e23346CggIAzim6xWucAOAvf/kLNm3ahLvvvhtLly6Ver7kZU6YMAF33XUXnnrq\nKdx///1YtmwZBg0aBJPJhD/96U8wGAxIS0sD4Ow9eu211zBx4kTk5+c3GzoYTBpB7YDHCDEYDBg8\neHC73V5bxXpSh/WkDuvJN9aROqwn9VhXvrGO1GlpPVksFgBAYmJisEJq9RYuXIhzzjlHMbEKh8LC\nQgwZMgSpqam47777MH36dAwYMCCsMVRXV2P79u244YYbUFpaismTJ0vJljee2ksg7ZDXCBERERER\nhcl9992HlJQUzJgxI2Ix1NXV4Z577kFSUhKys7PDngQBkIbfrVixAg6HA7Nnzw57DEyEiIiIiIjC\nZMWKFZEOAbfeeituvfXWiMYQGxuLhQsXRjQGXiNERERERERRhz1CRERERBRRVqs10iFQG2G1WpGQ\nkBCUstgjREREREQREx8fH7Qvtq3ZL7/8EukQ2gRf9ZSQkID4+PigbIs9QkREREQUMTExMVEzY1y0\n7GdLhaue2CNERERERERRh4kQERERERFFHSZCREREREQUdZgIERERERFR1GEiREREREREUYeJEBER\nERERRR0mQkREREREFHWYCBERERERUdRhIkRERERERFGHiRAREREREUUdJkJERERERBR1mAgRERER\nEVHUYSJERERERERRh4kQERERERFFnYgkQh9//DFuueUW3H777fjqq68iEQIREREREUWxsCdClZWV\neOWVV/Duu+/itddewxdffBHuEIiIiIiIKMrFhnuD27ZtQ25uLpKTk5GcnIx58+aFOwQiIiIiIopy\nYU+Ejh8/DovFgv/7v/9DTU0N/vKXvyAnJ8f7Slqt8/8rrgCKipSXyc11/buoqOk5T+sorNfbbAZS\nUpqvIy/LW7l6vfP/vn2BXbuaHhcVNb1mNCqX621fPMWxa1dT+f5w366vv930njLFtZ6U9s19e0qx\nuu8r0LSc/LG83j1xj91TmfJllWLzVv/eKLWRJUvUL+urnYoxytuV0nqe6knNfniqC6V1/dkHH3Xa\ne8oU4OBB1/YRyLbVxuHpPaWGn+8Vl/W8tUPA9X3kVm6z95yaOH2dH3ydN32V4Wnf/T0O/tapXg/U\n1jZ9JrTkuKptP2JdAL7PSf6ej5W2o1Tnvs5N3vZFKf6WxKgUh7dztKfzivux9DcGtcdNadvucQCe\nP8Pct+u+T0rbBNTXtZpzWiBt2ttz/sTk/hzguo/iZ523c4JSfXn6XqTmM1l8fteupjbkvozSPrnH\n4e19pdQ+AzlPiNv09J1ADfeyleJVG4vI22e9ms8scTn5a0rvEfGxnJdtK37eBfrZ64sQZq+99prw\n4IMPCna7XTh69KgwdOhQr8sXFxcLAtD0Lyen+UI5Oa7LAIKg03lfx9N6SuvIl/NWrvw1938xMa5l\nuJcrlqUUk9Jr7tvytI++9jsnx/ffvtaXxyLum7d69ravnv55q1tPsftaVikGpe2oqVsPx8bYr5/q\nZQNqp97aqr/74a0u3Nf1Zx+8tWml1321QU/b9icOpePv73FW817xVgfu68jjl58v/NmOtzpVE5On\n+vR1HDwdTzXHwd86dY/L7bgXFxcHdlzUtnc15yR/zsfetuPrva32c8rDusXFxS2P0ds2lP55auf+\n1Fugx81T+1b6fG7kUkdq3se+jpM/++PP+U2prEDPc2rKcftn7NdP3TlB/prSdwe1n8n+fC56i8Pb\n8VI6B6r9bPIQv+J3An+PRyD76qkuPbUtNW3dfTl/zgNqt+3pOQ/HIZBzWth7hDp16oSBAwciJiYG\nWVlZSElJQUVFBdLT01WtbzKbsddgcHmut9kMndtydocDWi/reFpPaTvy5byVO0D2mjsBgEZWxs8G\ng0u5YllKMSm9Znfblqd9VOK+XQBe//ZW3yazGUmyWMR987S8r331xH1/lbjH7mtZpRiUtqOmbr0d\nG4OX+mhpO3VfT22b9rUPvmLzZx+8tWml1321QU/b9icOpePv73FW815RWk9Ovo78/CE/X/izHU/b\nC+S86esc46nufNVpS88/7udZ9+Mu/u/vcVHb3pW2686f87GneNzL8ed9pFQnSusCzc9P/sbobRtK\n7B7auViOmnoL9Lh5at9Kn89yBh/nV3+Okz/748/5TamsQM9zaspR4ul956ldD1D47qD2Mxnw3t7U\nnPO8vTd6FpDSAAAgAElEQVTc41NaPtDzhL/vOU9lu8fra3lP5yyltqWmrbtvx5/zgNpte3pOvp1A\nz7cijSAIQsBrB6C0tBRPPvkkVqxYgaqqKtxxxx3YvHmzx+UNBgMGX365848wDI0zmc3QcWicz7oz\n9e/vWk8cGqfYRgxLlmDw4MGqlo3moXGm/v2h49A4r0Pjmr3n1MQZhUPjDAaD8z3HoXGuyyjEL9VV\nIDEqxdEOh8Y1qyMOjWt6LNtH6bOOQ+O8Do3z+J1AjSgaGqf4eaficyKQc1rYEyEAWLNmDT744AMA\nwLRp03Dttdd6XDbgE3WAwr29tor1pA7rSR3Wk2+sI3VYT+qxrnxjHanDelKH9aROoPUUyHphHxoH\nAHfeeSfuvPPOSGyaiIiIiIgoMjdUJSIiIiIiiiQmQkREREREFHWYCBERERERUdRhIkRERERERFGH\niRAREREREUUdJkJERERERBR1mAgREREREVHUYSJERERERERRh4kQERERERFFHSZCREREREQUdZgI\nERERERFR1GEiREREREREUYeJEBERERERRR0mQkREREREFHWYCBERERERUdRhIkRERERERFGHiRAR\nEREREUUdJkJERERERBR1mAgREREREVHUYSJERERERERRh4kQERERERFFHSZCREREREQUdZgIERER\nERFR1GEiREREREREUYeJEBERERERRR0mQkREREREFHWYCBERERERUdRhIkRERERERFGHiRARERER\nEUUdJkJERERERBR1mAgREREREVHUYSJERERERERRJzbSAQRdbq7z/6KiyG87krFEQkv219O63sps\nLfWrJo7WEmuwBWnfe0+ZAqSk+Fc/wazTUB+fULVjpXUDLS/c63kqa9cuoG9f9eXJt5+bi95ms7Mt\nqYnJ23nH3zhaqq2cIzy1OX/qK5jt1h/iNkStva71euf/RmPTc6H4nA03b+2ltR4jNXG1lvptiyJ1\nTgAAoZUrLi5Wv3BOjiAAzn85OaHfnrdtByGW1qxZPbVkfz2t663M1lK/PuIoLi5uPbEGm5r9CtYy\nwVgnHGX5W74f21b1ngt0X8K9nq+y1JYnX0enc13fVxlqzjvhes9G6Bzh9+edrzanJv5gtttAY/dj\nWwF/J2gpeXvW6ZzPheJzNkhU15O39hLgMQo5NXGprN+ItafWTKHujP36BdQGAqlfDo0jIiIiIqKo\noxEEQYh0EN4YDAYMHjxY/Qot7Erze3vett2Ou0kV64lD45q9JNVTa4k12II0NM7Uvz90HBrntQjV\n77koHxpnMpudbUlNTFE+NC6gz7soGxrXou8ELdWGhsb5VU9RPDQuou2pNXOrO4PBgMHTp7s8p0Yg\n9dv+EqE2tr22ivWkDutJHdaTb6wjdVhP6rGufGMdqcN6Uof1pE6g9RTIehwaR0REREREUYeJEBER\nERERRR0mQkREREREFHWYCBERERERUdRhIkRERERERFGHiRAREREREUUdJkJERERERBR1mAgRERER\nEVHUYSJERERERERRh4kQERERERFFHSZCREREREQUdZgIERERERFR1GEiREREREREUYeJEBERERER\nRR0mQkREREREFHWYCBERERERUdRhIkRERERERFGHiRAREREREUUdJkJERERERBR1mAgREREREVHU\nYSJERERERERRh4kQERERERFFHSZCREREREQUdZgIERERERFR1GEiREREREREUYeJEBERERERRR0m\nQkREREREFHWYCBERERERUdRhIkRERERERFGHiRAREREREUUdJkJERERERBR1mAgREREREVHUiVgi\nZLFYMGzYMKxbty5SIRARERERUZSKWCL06quvIi0tDRqNJlIhEBERERFRlIpIInTgwAEcPHgQQ4cO\nhSAIgReUm+v85+t5T8sprNd7yhT12wmXSG9fTm3d+nMMQrV/oay3QMr2t10GI341ZQS7ngItL5Lt\nPNxtJRjHJVztI5TbD6ZgxROp/fJ3u5Gu/9xcQK/3rx2r/bzw9/VA1ot0/QWLv/uh1zv/+bsNNcfa\nVxme2oGn1wL9LhdofGLd+Gozatp0exKKfQzkO2Ko6lqIgKlTpwrHjx8XlixZInz44Ydely0uLlZ+\nISdHEADnv5wcz897Wi7Q8sIt0tv3EIuxX7/gHINQ7V8o682PsqX262+7DEb8asoIdj0FWJ6xX7/I\ntfNwt5UAj4vLuTBc7SMU64aC+7kpCOWEdb/83W6Q4vT4+erP9tW2Y50usM/YQPc1SJ87AddROPhb\nN/JjoNP5vw0v2/FaT97agafXvC0XyvO0mjbjq0170arbk5JQ1LuK70OK351UxBFI/cYGP7XyLj8/\nH5deeinOOuss1b1BBoOh2XO9zWboGh+bzGbsbVzG/XkAissFWp6n9UMl0tv3FIsYT0uPQaj2L5T1\n5m/ZBoPB73YZjPjVlBHsegq0vN6yx+Fu5+FuKy05LoYgnpdaUkZrOi+5xwMof2b4W04498vf7QYz\nzkDqSumzwFc7tjsc0Cos72tfAj6neFkvkHN4a+TvfgyQHQO7w4GfVeyX2mMNeK4nb+0AgOJr3pYL\n5Xla5K3NiDy1aV9aa3tSEopzotrvQ+7fnaCwTDBoBLXZSJA8+uijOHbsGLRaLU6dOoX4+HjMmzcP\nOTk5issbDAYMHjxYuTCxi6yoyPvznpZTKM9kNkO3Y4e67YRLpLcv1xiLYckS53EJxjEI1f6Fst5U\nlu3Sfv1tl8GIX00Zwa6nAMozGAwYPH16cOPwR7jbSgDHpdm5MFztIxTrhoL7uamF5YR9v/zdbhDi\n9Pr5qmb7u3YBffuqb8dqPy+8leFvjJ7WC+Qc3hr5WzfisDij0b9t+DjWPuvJWzvw9Fqg3+UCIe4j\n4Hk/3YdleWvTHrT69qQkFPXu49gqfncSeYkjkPoNeyIk9/LLL6NHjx649dZbPS4T7kbTJhtpBLCe\n1GE9qcN68o11pA7rST3WlW+sI3VYT+qwntQJtJ4CWY/3ESIiIiIioqgT9muE5P7yl79EcvNERERE\nRBSl2CNERERERERRh4kQERERERFFHSZCREREREQUdZgIERERERFR1GEiREREREREUYeJEBERERER\nRR0mQkREREREFHWYCBEREVFEFRQdwoL/GnDqjDnSoRBRFInoDVWJiIgoupRV1sJc14Bzz0oFADgc\nApau3QkA6N45BXeN6BPJ8IgoirBHiIiIiMLm6f8U4eEXvsTpqjoAkP4HgHLZYyKiUGMiRERERGFz\n4rRz+NunRYcAACVlJum18komQkQUPhwaR0RERGETq9XAZhewY185thiO4cV3fpReK6+qjWBkRBRt\n2CNEREREYdFgs8NmFwAAB0qqXZKg+DgtyivrIAhCpMIjoijDRIiIiIjCotpULz22O1wTnsF9MlFv\nc6DKaA13WEQUpZgIERERUVhUmZxJzuXZXV2eT4zXokemDgBQUm5qth4RUSjwGiEiIiIKi5rGHqEL\nz07DtZf2wOGTNdAlxeOy7C7Ye6QCgHPyhL69OkUyTGqFPt56ALv2n8aT91yOmBhNpMOhdoKJEBER\nEYWF2COUpkvAlf2748r+3aXXzHUNAICSMmNEYqPWbXn+bgDAkVM10j2oiFqKQ+OIiIgoLKobE6FU\nXUKz17p3bhwaV8ahceTZb4crIh0CtSNMhIiIiCgsqmU9Qu5SkuKQ3iGBiRB5te9oVaRDoHaEiRAR\nERGFRZWXHiEA6JGpR3llLSz1tnCGRW2Iqa7e90JEKjERIiIiorAQp89O1cUrvt49UwdBAE6eNocz\nLGrlrA126XGdlUkyBQ8TISIiIgqLapMVcbExSEpQnqtJmkK7lMPjqImptqkXqNbCRIiCh4kQERER\nhUW1yYpUXQI0GuXpj3tk6gFw5jhyZWqcURCIfI+Q3e5Ag80R0RgoeJgIERERUcgJgoAqoxVpHobF\nAbIeIU6YQDKm2taTCM35TxFu/9t62B1CROOg4GAiRERERCF3ptqCepsDXTJSPC7TKTUJCfFaHGOP\nEMmYZT1CkR4a98vBMwAin5BRcDARIiIiopATh7uJvT5KYmI06JyWhDPVlnCFRW3AkVM10mNLvQ2C\nEPnemDpeq9QuMBEiIiKikBOHu4nXAXmiS4qDqa6hVXzZpchrsNmx4dtDAIBePVIhCICl3u5jrdCQ\nD4erszZ4WbKJtcGOXftPu0z4QK2H8rQtREREREF0XEqEPPcIAYAuOR4Oh4A6qw3JiXHhCI1asQ+3\n7MeZagvSOySieycdDpRUo9bS4HHmwVCqMVulx7Uqh8a9W7gHa7fsR7/zO+H2yxNDFRoFiD1CRERE\nFHJij1D3zj4SoSRn8iOfKYyi1/FyZ7u5/5ZLkJToTH4idX1OlbEpEVI7NO7IKeeQ0APHq0MSE7UM\nEyEiIiIKuZIyIzqlJfn8JV9MhMxMhAhAZWPycfnFXaW2E6lEqLJGlgipjOF0VZ20vIMzzbU6TISI\niIgopGotDThdbUGWj2FxAJCS3NgjVMtEiJy9MCmJsUiI00pDJWvrIpMIVcuHxqnsESqvrAUAOBwC\njJbIXNtEnjERIiIiopA6dcb5ZdDXsDgA0CU57zNkquPF5QRUGi1I0zuvrYn0sEl5L5CaHiFzXQPM\nsoSp2sxEqLVpv4lQbq7zn7fH/pYTauHcVqS3G+pttuSYh2Od3FxAr29ZHUSqvfjattq4glEH3sqO\nVN1EWqD77m/7DWb9iuVF6rjp9YBWG9kY5FoaQyjrU+371m3bFTXO6bDTUxMVX5cTv+wa1fYIhaPN\nh4LS9tvCd44wxmj7w5WoMVrRsUMCAEAn9hb+Pa/lMfj7PSE3F3UvviT9KSVCXtY9fdMdLn+3q0So\nvXxnFVq54uJi/1fKyREEwPlPp1N+nJPje3vycjwsHzTh3FYQthvQcWnhNgMqX8UxD0p8HtZRrCf5\nsoHWQaTai69tq43LrQ6M/fqFJ742TNV7LtB992e9YNev+/uhheX6fW6SnyOCFEOLtLR+/ahPv+tK\n7blLYR8+/+6wcNNj+cLG7Ud87uO2XSeEmx7LF9Zu3udfTCFo8y36rPN3+23hO4c/n3VBiPF0Skfh\npsfyhX9Nek4QBEH4/peTwk2P5QvvX3Zby+rJ3+8Jjcu/nfsn4abH8oWbHssX3ly/2+fn4Q/nDhZu\neixfeHz888JNj+ULi97eEli8rU2I26rH9uRju4G0w/bbI0REREStgnjBu/jLvjfpHZy9Rmeq60Ia\nE7V+NUmpAIDUBufQSmnYZILvIZahUBef1PRYxdC4cn0nAED28d8AANVm3oS1tWmf9xEqKmrqNvP2\n2N9yQimc24r0dkO9zZYc83CsIy67axfQt29gdRCp9uJr22rjcquDvUuWYHA44mvvAt33QNqvv9tQ\nU578uXAxGp1DvWprgSuuiEwMci2t31DWp9pzl8I+VBqdQ+M66hN97qN4HZE43baqmDyUFfT1gkVp\n+23hO0eYYzSNvBMAkDLpLgCyoXFn90K15jokb/4cAd1pyt/vCY3L1PU4W3rKXGfz+XlYfucsAEC2\n3oEPAVTXtpOhce3oO2v7TIQA1wry9NjfckItkh+87W2bLTnm4VgnWF8gI8VXkuNvGQZDy+IJNIb2\nKNB9D3f7DWV5/jIaI7t9dy2tj1DWZyDvb8h6hPQJPstJSYpDeocElJSpPC7haPOhoLT9tvCdI4wx\nGv+9EHjrB+iTnT1BYiK0f/A1uLvLAIz6aDem3dE/sML9/Z5QVITat74Hdp4EAFSZLD7XLRszFvip\nBOes+g+SFmxuX9cItZPvrBwaR0RERCFVZbQiRgN00PkeGgcAPTL1KKusgyVC94uh1kGcHU6cQEMc\nGnegxHlz0oKiw2GNR7yJanycVkruvSmvqkWMBshITUSntGRUmW2w815CrQoTISIiIgqpGrMVKUnx\n0MZoVC2f0Ti7XLWZU2hHM/FeUmIiFBfr+rX17K76sMZTZ7VBG6NBZsckl5ur2uwOvPv53mbXtZVX\n1SG9QyJitTHIPjcd1gYB3+0+GdaYyTsmQkRERBRSptoGaViTGuKNM9VckE7tl3gvKV3j0DgA6HN2\nR3RJT0an1MSwt486qw1JCbFI75AIY209GmwOAMCnRYfwTuEezF3+Hc5U16GsohZ2uwNnqi3o3DEZ\nADAy5xwAwI97ysIaM3nHRIiIiIhCRhAEGGsboPcjEUpKcF7CXGdhIhTN3IfGAcDzf7kKr/7tenRI\nSYCxtnmPocMhwGZ3hCSeWqsNyYmxSGu81q3a5OwVEnuuDp+sweR5n+O+f25ERY0VDoeAzmnOmea6\npCe7rEOtAxMhIiIiChlrgx02u0O6vkMNMRGqtaq8qSq1S2ZxaJwsidbGaBAXGwNdchzqrHb8t+A3\nTJz7GSxW5/U3D/2/zbhz9gbsP1YV9HjqLM4eoY5659BN8UbB7kP2AOBEuXPWw84dnYlQSmIcNBqg\nhsM9WxUmQkRERBQyZoVf9X1JTmzsEeLQuKgm9QglN0+i9SnO59Zs+h1VRisOn6rB4RPVKCkzod7m\nwIHj1UGNxe4QYLY0QJccL/XunDpjBgDUKvRc/nPldgCQeoRiYjRITohBjZk9Qq1J+50+m4iIiCJO\nHDaUwqFx5KcqoxWJ8VokxGmbvaZ3S45qTPX48Mv90t8mhWFzLWE010MQgFRdPHpkut7rSqmXp85q\nR2K8FhedmyE9l5wQg2oTe4RaE/YIERERUcgoXefhi9gjVMseoahWXlUrDS1z537N2RfFR/HLwTPS\n30rXD7VEdWNPToeUBPTIdM5W15QINfXy3DnsQunxmn+OxnndU6W/UxK0MNU1wB6ia5jIf0yEiIiI\nKGTEX+YDuUaIQ+OiV53VBmNtAzqnJSu+7t4jVNR4o9OrB3YH0JSAB0tNY09Oako8MlITkRCvxXG3\nHqFX/3Ydzu7aAYDz2qAYt+nikxOcX7trgpykUeA4NI6IiIhCRvxC6s+scdL02RwaF7VOVznvyeO5\nR0g5sb7rht74+qfjQe8REpOdDrp4xMRo0FGfgKrGGeCqTdbGIXN6dOukw6SKi3Dt4KxmZSQnNiZC\npnppwgWKLPYIERERUcg0XfDu/zVCHBoXvcorGxOhNHVD4zJSE3H9ZVnolpECoOnatGARh8alpjin\nztYlxcFU14D9JVU4Xm7GuWc5h8BpYzQYe/2F6KQQt9Qj1EZmjnM4BHy89QAqG2fHa4/YI0RtXn2D\nHXuOVOCcbqnokKJ+6AUREYWesSVD49gjFLVKK5wzsnnsEZJ93k+68SKMvb7p2pzkxNigJxviJAep\nOud2dUnxqG+w46sfSwAAY648z2cZKQnOSR+q28jMcZt+OIrl+buxpfgYFj46NNLhhAR7hKjNe6dw\nD+a8WoTn3/oh0qEQEZEbpXvB+CJOlmC28D5C0UqciECcmMCdfGice6+RLjk+6LPG1TQOg0vVOXuE\nxFkQ9xyuAABc0DPNZxltrUdI7JXbXxLcqchbEyZC1Obt2FcOACitrI1wJERE5E4cGpfix6xxSQmx\nSErQ4kx1XajColauKRHSKb7ukgh1THZ7LQ7GIE+WUC1eI9TYEyVuf8+RSqQkxSGtMUHyRrxGiFNo\ntx5MhKhNq7PacPBEDQDAZuN0lNR+/XK0Fp9tOxzpMIj8Fsj02RqNBp3SkqVfpCn6HCszIr1DojRx\nhjt5D6N7j5A+KR7WejvqG+xBi6fa1DR9NuDanntk6qDRaBTXk0sRe4RMbWNonKW+/Q9NZSJEbdrx\nchMcDgEAp1ml9u39byrwygc7YGE7pzbGVFsPbYxGuu5Hrc4dk2Cqa0Ath8dFnWqTFeWVdejZVXlY\nHADEapu+wqanus7AJiZJwZxCu8Zcj5TEWMTFOrcrT4QGXNBZVRnJjdcItZWhcZU1bSNhawkmQtSm\nmWWzwljqbVJSRNRe7T1aGekQiPxiqmuALjlO1S/mcuKv/OI0yhQ9fv7dOeQ9+9wMVcvLkyKgadha\nMKfQrjZZ0UE2/C02tmmbd4+6SFUZ4jVCbWWyhEpj02xx9nb6/YqJELVp8l97BCE6unEp+ghC0weQ\neGEuUVthqmvwa1icSEyEypkIRZXKGgsWrDYAALLPSfe67PLZw7B89rBmz0s9QkGaQlsQBNSY611m\nphXPy1ldPPdauYvVOntG20yPkLEpYVu85qcIRhI6nD6b2jT3X3vqrDaP44mJ2ir5sE/5BxNRaycI\nAky1DejidjG7GmmNN5ysbiPXU1BwlJQ7J0nQaIBLennvEeraeM8gd8HuETLXNcDuEKR7CAHAyJxz\nUFFjxa3X9PKrrFRdfJuZLMEs+7H5WKkxgpGEDnuEqE0Te4QyGscH8zohao/kPZ+cTpjaktKKWtjs\nDnTycC8Yb8Rf39vKr+cUHOLxvv+WS6DVBvY1VS/1CAWn7YgxifcQAoDE+FhMGXMx0jskelpNUWpK\nAmrMVpee/taqztqA885KRWbHpIj9CPf9L6dw4rQpZOUzEaI2TTzJiUMoannzPWqH5L/K1daxjVPb\n8cvBMwCA7HO9D3FSwkQoOtVI01T7no7aE53UIxScH47EHpxg3LRdnxIPm11o9T/cOhwC6qx2JCXG\noqM+EVVGS9iTt7LKWuS9sR1Tn/siZNtgIkRtmvhLuXgPgdZ+YiEKhHycO3uEqC3Ze8Q5uUf2Oeou\nepdjIhSdasziNNWBJx3BHhonTm6QquJeQb6IvUqtfXiceM11UkIs0vQJsNmFoM7Cp0ZVGHqheI0Q\ntVlHTtag8LsjAIDMxmEXTISoPamsseDJpd/ieHnTsABOJUxtyakzZgBAdw83xfSGiVB0knqEkgNP\nhII9WYKYtMiHxgVK7OmqMVvRrZPyNU6tgfh9KjkhFsmNk51U1lhcbmQbasGc9c8Tnz1CCxYsaPbc\nnDlzQhIMkSd2uwPvFu5BWUWt9NyXP5ZIjztxaBy1Q78drnBJggDAzDZObUh5VR30yXF+30MIcA5v\n0miYCLVX9Q12vP7RbqzZuNdlyFXT0LjW0yPU1EsVhB6hxv2qbuXtWvw+5Rwa59zvcF8nFI77GHk8\nM23cuBGff/45ioqKUFpaKj1vs9nwww8/hDwwIrmiXSfxzud78cm3h7B63igAQGLjjckAIK3xTWqq\na90nFiJ/KE0bXBvmoQlEgRIEAeVVdejeyf/eIADQxmigS4qXvoRS+2CsrUdljQUnT5vx0dcHAADX\nDOohzQAXjERInK69dfYINfZ0tvLZEMUeoaSEWGlCqvLKWm+rBE2DXcDvRytd7mMUKh4Toauuugrp\n6enYtWsXcnNzpWw9JiYG06dPD3lgRHJi+5P/MiheNP78Q1dKz4VjPClRuJRXuiZCPbvqUVJmgiAI\nft+ckijcjLUNsNbb0TmAGeNEafp4nKm2wOEQEBPDNt8evLDaAMOeMlw9oLv0XHllnZQIGc31iIuN\nQUK81lMRPsXHaZEQr4UxSD+OStcIBaNHSCcOjWvdP9zWWZqGxnXv7Pwxo6QsdLO3yX25sxrfrvk6\nKJNT+OIxEUpMTMTgwYPx0UcfISEhAYIgtImp/qh9kl/7I96cT7xovENKPLSNH5BMhKg9Ka9y/fUt\ns2Myjp4ywlJvD2ioEVE4lTX+eizO6hmI83uk4VhpCUrKjOjZtUOwQqMIMuwpAwB8/fNx6bnZr36L\n//fwVehzdjpqLTakJMa1+McefVJc0GaNqxFnjQtGj1AbmSyhVuwRSoxDj0znTWNDlQgt+d/P+OXg\nabz6t+uh0Wjw00HnuUOeLL77+V6s33oAK566Iaiffz6vEVq1ahUuvfRSXHTRRcjOzkZ2djYuvvji\noAVApIb8ZFZR7fyVXBy/mpwYKw2N480mqT0pr6xDXGwM/jSiD/50TQaSE50nf06Y0Lr8uLcM//lw\nJxwO/lgod7LcOVFCSy4Izz7XOdvcr4cqghITRV7PrnrF5/+9qhiA8941SYkt/6KrS44P2n2Eqs1W\nxMdpkRjf8rjayiQg8qFxqbp46JLiml2zGiyfbz+C4+Vm1FpsaLA5UG9zNFvmncI9MNY2YH9JVVC3\n7fOIfvDBB/j4449x1llnBXXDRP6Qn8zE6RvFHqGUxDgkxDu7wStqQj+elCgcBEHAqTNmZHZMwl03\n9IbBYILJ4fwAPVNtQUZq4L+yt1e/HarA6ZrwJol7jlTg78u2AQBG/+FcZHVR/pIXjUrKnHeiF39N\nDoR4/6FfD53ByJxzghEWRVhtXQMy05NxbrcOOHXGjCOnnO3E2JgY1FltSNP7d5NSJfrkeBw+WQOb\n3YHYAG/MKqo21Qfl+iCgaXhddSu/9q2u8TtWckIsNBoNumQkh3xoXKXRgpgYDWx2z8uYg3ydrM+W\ncc455zAJooiTz10vXvxYW9eAmBgNEuK10Gg06KhPQFUYLqwjCocacz2MtQ0uXyJ7ZIZ3nLYadocg\n3W8ikuqsNvz15a14+ZNS3wsH0Yvv/Cg9rmrlFz+Hm9hOewQwdbaoR6YeuqQ49gi1I+Lw9qemXIEF\nj1wtPW9tsMMu3sQzCEOfxCm0g/HFucZcL8321lLJibGI1Wqk4XatVU3jd62Uxokn9EnxsNbb0aDQ\nW9MSNntTeZU1Vuk7XteMZMXlT542o77BS6bkJ5+JUO/evTFz5kysWbMG77//Pt5//3188MEHQQuA\nSA2XRKjx4kezxYaUxFhpHLHzzsdWDk+hdkHpS2TTOG1jRGJS8tq6nRj75AacqW4+w104/d54485w\nstTbpPvkAEBVGKZ6bUtKyk2Ij9NKtzcIREyMBhedm47SitqwzCBFoWWzO2Cpt0uzuiXGx2L57GG4\n6Jx0CAJQ3fhjQnIQhsaJU2i3dAiaxWpDfYMdHYJwM1UA0Gg06JAS3+qHxp1oHAZ3Vmfn0NYU8d5M\nQZ6dt1r2A1Kl0SIlQsMu64lFj16DMVed57L8G+t/wcyXvg7a9n0mQqWlpYiLi8PPP/+MH3/8ET/+\n+K9ieokAACAASURBVCMMBkPQAiBXgiBg4bs/YuP2I5EOpVUx1yr0CFkakJwYJz2vT46HQ+BNVal9\nUBpWJCZFx0pbTyJUUHQYQORj+vXQGelxg7dxFUF08rQZggCkNX5Bev3jXVj+0a6wbLstqKi2oHNa\nYotne+vS0fnLMCfDafvEz2/5TTm7ZqRIybL4g0oweoTEa3FaOoW2eL+fYPUIAc77EbX2oXElZUbE\nxcagc+P7L9hTkovk13ZXGq1SoqVLjkevHmnSduUOn6wJ2nneZ0t7/vnnYbfbcebMGWRmZgZlo//+\n97/x448/wmazYerUqRg+fHhQym0Pasz12Fx8DJuLj2H4FWdHOpxWoaDoEA6fqpH+FnuHai0N6JbR\n9Gt5SpKzOZvrGqSuXKK2qqxx6uwusuEBafoExGpjWuW1cBpEdmrjQyebzhFnqi3SVLyhVFLq/MX0\nkl4Z+GbHCVTUWPHx1wcx5aaLoW3hNQmh9MOvp0I+66Dd7kC12YruLRgWJ5J+iQ7yFzC5/ceqcKrC\njCv7d/e9MAVMvMGpOGxNJPYAnam2NP7d8s/wDtKNS1uWcIg9FqlB6hECnLE5v8w7EBfb+s4VgiCg\npMyE7p110qy8oUqE5D9wVNZYpPoQtyfezNXd6SpLiyZiEfms/W3btmH48OGYOHEiAODZZ5/Fli1b\nAt7gd999h/379+O9997D66+/jmeffTbgstoaU12Dz+Ej8rGsnK7c+evQ0rU7Xd4oproGNNgcqLPa\nXU6mYvJj5oxa1A5UNiY76R2aLhrWaDTQJceF9AuhP+TXBtVGuCdWPlywvLIOlTWWoM0Y5Wubfc/v\n5PL8mVaYqIrsDgHzVmzHk0u/Del2qs31EATPX2L8oUtyfqE1hvB4PrroK/zr7WLOyBhi4ncc91/5\nxcRcTISC2SPU0iFowbjBq7umewm1zl6hGnM9LPV2l0RD19iLZ6qrx9FTNUH7jnpCNhNdpbHpGiHx\n+518VMRVA7rjzuEXAmh+e4lA+UyEXnzxRaxZs0bqDXrwwQexdOnSgDd42WWXYdGiRQAAvV6P2tpa\n35WZm+v7dfky7n/7sUzvKVNcXxMf5+YCer3vWMR15Mvq9YBej/97/gtMnvc57B3c7oXQ+DrgeqKv\nuHZEszIC2nf3dX2to1SGp/1Uqme19eROHmdj2eIvriNP/YSXH78WgHMGOfEXmrTvvpa2JSZC8uuJ\nVLUF9+fd/6kpR2kdNdSsJ39NrCNv6/kTt6+45NuUvxboMfZVhq/YlNqqezlqypCv4+8xUxOX0vNK\nsWu1rsdTFl9l4WYAQMfbb3JZR1d6wncbl5Wj6li7tylv+yb7+9SZpg+iOquXmAI5LynFqVSGVgvb\nH67EyZPV0tMHjldj0j8KMekfhZ7LUyrXz/dOyTvrAAB9n37YZTH3G+G6rO+t3Xs6z3uJodlrSm1K\n9np5j17SnxdOmeJ9W2rIz0myx+KPVx1liXyztqb03lWoH32ywrndnT/HUSn+RmdG3+5SZm/3OlJb\ndkvOk57qSf4vkLID/ZxyX9c9Nr0egy6/vNnnt9LjE7dPAAB0WLnc5fmk/74FAFJvt3SNkKfztYrP\nAsVEyK2dKnL7nBUnYUpd8qLnuhHXkdeN+F6ULSO2pxYlaf4cR/f9VWpHCuccY5++zqrY9Jn0nJi8\nvrXkEzz0/7Zg6833+47DW5ttfP7Xw00ToVQZrdL7XNye/DpZ3TtvofNTfwUAlI+7J7D3lxufKXdy\ncjI6d+4s/Z2eno74+MCzYq1Wi+Rk51CPDz74AEOHDvV906xt25w7W1TU/LXcXOfr4mNxefHvoiK/\nlnHpxNfrAZPb7EzeYnGPZ9s25xvB4UCDNlaaUahKiEOGXg8Yja7b0Othuupm4KJxAICSg6XIOLZT\nKkOKyWhUv+9u5aNvX+/rKJWhoPeUKcDOnYp1qKqe3MnjlO1vycQ/A1dOxsU/bkHncV8Clz8KY22D\ndNFsxyP7pG3pnnsbgKxXzb1+lNqCGJ/8eQAnU7viibueR3VyGh684jaM/t5LOUrtRM2+u21TcT35\nMvJ24Gk9T3H52n9Pcbm3PXn78fcYK5UvlrFkie/YvLV3sRz5c77K2LZNuY78paZNuW9HHqfJpLhc\nlRWIb7Ai6ZuvnF/IzGZg507ozr4FJ5I6QhAEaP7wB9/bVnv+lLcpledOY4cs4OI/AQDq/vkvYN0r\nwTkv+Tr2bmWU/noItiFaXHqwGDt79sXaD74D4nW+ZzfydIzUvHcAlPQai4QGC7pv3oDEvpNg0To/\nG8urFBIhT8dE6TwgP8/7qgel1+Rtym07JecMklZx7DsceLsX4xTrSb4P27ah8oFpwEXjmnqEPLU1\npfeuW5v1OSTHn+PoKf5G5QdKkCWLRydf19f5VSkef8+T3urJnT9lq/m88Wddt781gLM+3D8zZMfi\nyx6DsPDGBxHjsGPIF2uAqlPSMsmDOgNZV7peI+TtfC2PTR6PrE6aJRvu+6FUfwpt+visecDAm9HN\n8I33Y61UN251otu5E8jNRerf33DG5u/Mcf4cR6X99UR+ztHrYdKf5Yz36EGpfPG6riPJzpxgV108\nrlb63PAWq8L7/bfBe5HeqRPqrDZUGi3ISHX+eCL2QKXJepV1xip0Pl0CAPjm7EF4K7MX8q6/GWd/\n8bHnffPBZyKUlJSE7du3QxAEVFVV4dNPP0VCQsu7ujdt2oS1a9fijTfeULW8yWzGXoVJGnqbzVLy\nYjI7Z+/Rua3j7zIiu8MBrR+xuMcDAAKcJ4j9mU2/wpXrOyOt+hh+NhgwQLYNu8OB07ampLA6uYNL\nGeIyPzduW81+uZdf52MdpTIU91OhPtz33dv67uRxyvf3aGo3AECPiuOwx6dAowFKyyvxw0+/AAA6\nmqukbZWXngAA/PLbPsRZTzSrH6W2sFehLgFgzRVjUZ2cBgD4z5WTMfr7fI/lKLUTedmeJhdRanPu\ndSZfRl4v7rzF9fklwxCXnIEOXvbfU1zuba+uBcdYqXyxDPF/b7F5a+9Kz/kqQ9wnrZfl/d0fT23K\nfTvusSvFU5mSho7mSmhk6wCAzmKCQxODbduL0U9le1ZzrKGwvK9zzGnZ7KZVdg0MKs+3vs5Lvo69\nexmVKc73aq+ygxA0GhjOHSyt621yH0/HSKlduMfggAYlHbujR0UJYiBg/vb/YNZVj8BmF/Dz7n3Q\nC6Uet+WpXJH8PO+rHjyV72k7JelN18CU6ztDG2C7B1zP2+5OOZxfL6rPnILBYPQYnxgj0Pz9LMZ1\nvMz5I+L+Q0dh6FDtvrpfx9FX/OUdOsNkLnWJx9d539ex8Ofc4q2elKgtW83nTTBicv/MEOuoXgBW\n/cHZGzT+uzU4q+qUyzJJDc4E6NCxMgBA2anjLp8J7uXJ9wFQbjsVRudw3UNHT8BgsKiqA6U2cSxN\n/B5SAlP62ared3Lun90msxlVFc429tOuPbAZj/oooYk/x9Gf4yY/5wxwOGBKcK6ps5hgsjrLP3HK\ndchvutl1pk5/zkmA85jVxiWiIl6PXskABA3KKkxI1Dp/8Djw+684ddR5NIYPSMWpDUXI3VeEOLvz\n9eLzLgMAvNk9F2NaMImbz0TomWeewdy5c7Fr1y7ccMMNGDRoEObNmxfwBgFg69atWLZsGV5//XXo\ndCoOU04OdEVFGKz02o4dUnapk/8a2vh3f7sDP71biAEPjEWcYFdcZrCsHJPZDF2Kc0ykVvwFSLRr\nF9C3r+dY5PE0Lqtp/OVyf89saZH/z953x8lR1v+/Z3u/2+uXu8vl0gsppBByELpSRJAugihNFAFF\nBP2hCCog1SAqtq8IqAgC0iSUUExCEpKQXAqk18vl7nJ177bX+f0x+8w+MzszO7s7u7cHeb9eeWVv\nd3bm2SnP8ynvz/vTW92IqZ07uH34/XxasntfFw58uA/4cD8AIDD1GKAizu8DAPReb+rYGX67eP96\nrzcV3ZL5juQ+JLDhyScx7+abJc+hqvMkBjVOxuvlx7D/y5dDt78PjZMaYPlwORx3vQnoTaisaQLQ\nD3d9JX9/zNjSidfWrkd1bQPmzZuQdn4EYxT/Pup9ALBUlQvHt2iR7H7S7hNquw0bNmDePJmzIDpm\n2phE29D3AWbOlP6exLh+u+gmAMDr8+bJ/34VxxTcP7lcY/H+qX1gwwY4Mo1N7n6n90Pey7SP5Hfo\na5fTb5EYl9Q9JT4OP861awGbjb+eZLvE1k/gsbsxyd/N398bNmzAvJtvhiPZaHDi5Bny50ztsyja\njkDueeHHnfw7vq4deL4NABD/9o2YN2+6JvNSpmsv2EcgAN9s7hsONgq3UagkNHfuXHnWgcw1krwv\nRGPweMOI3PMW6vz9wKJFmLL6Pfy2x4vvPPg+4npX+nMvd03oeWDrVv6cqFnvJOcK0T0lPs4RZxP/\nlUMz52Piu8/ldt8DguuImTP58WPmTPh+eCfw5g4cO2sK5k2tlb7XpJ5diXu2qmsYf3v3AzjKKjFv\n3mzF85LxOsqNP4meacfC8fyD/Hh8fj8cmzcrzvuy1yKXeVLhPAmQ7b7VrDdqvyseGwA2EABjswnW\nb/ocHXjuDfT8ZgXO2LkclzOH+DWVbGOt5O5LX5ir2Jg5fVJqfpOYr+nfAEDyfAdCUTz++lIYLU7u\neaT3B0ifP/E9DeDwwlNh7+xD+ezpYJSuNQE5N8lnkT8nW7fCN348HJs345hNh7H0449RVduAefOE\n8tCKyOY6SvzeNEjNOX4/fHO+yO17TDUcr7yIeeCew2feT2kE+MZNBJhF8uOQuW8cq1cjGotj+QXf\nwl5HLQBg4rg6dPT4sH1/P/xRAwx64ITj5/OiM/PmAfj6KYDzVxg2C/0Gyxmn8/NtLqrWGR2hMWPG\n4M9//nPWO5aD1+vFQw89hKeffhouca2MHDKlbsWfU39v2tWLX/x1LW759T+EKmxS+1y9Gjs3bMDc\nuXPBsskCqlwoA+LveL04+MIm4CNOErv9wd+lfQ4A377tVcHbwe/fBpwyUbBNxmNJjVf83UzfUfub\nZc5hzqDHuXo1ItE4dv9kKcY3uWH59XIA4AvFu/q41HX5w/cBU7j6NV4sgeaRZzNG6n3z658C/9vD\n/82uWpVuTNH7yfV3q/kevY3cfSCzffzDVcDtr8l+ntMx87nGmfaRx7Ou6T6yhZrfo/a4q1fD548g\n/rM3Ub5oHvDnGwWfOV7eAny4H75gFDVqj53L2FWMmxYmCYZiktvIHiPb+0phH76kQ+Z47GGwO/YB\nm1MKcqFIhuaMctcow71FJF6dX7sEuOQ+AEBDtQNOm0kg5Z3xNyi9n+22Ks7h4NPrgS1c9nzbNd/G\nqeqPLA2ZOan3hU0AgGq6h1Au1xhUY0wlkZBsriMNrxfxBMvPk31fuUzw3Z0bNgiNO7X7zmde0WJO\n0nq/Gb67kQ76SZyjbcu59XT2L34AzG1M27dt+xHg/z7i1TL5upAs5k0xrGYD9DpGWIej5hxQ9zTL\nsuj+0etoaakD85s8nt/kNuR+KnNwtK+hXJqqajVfKMD/uz8CL2+F456f8u+JFdp8510IXH5vTsdf\nuakTSyafx/9dXW6FLxhFggXau71orjFJK296vXAkWBh+/DpicU5fwJinQmdGR2jVqlV49tln4fV6\neVEDhmHwzDPP5HTApUuXwuPx4Hvf+x7/3kMPPYT6+vqc9pcJhCNKS6tmwpOvf4pXlu/Fv355Ns9R\nzAevLN+Ltz9K9QV6btlOnL6gSSDvGpdoAvp574ezp8ODWDyBaS2V/HsOqxFdfX689AE3qdKKRLxY\nggaqP7QaFgB4A1FNFWOKhRB1D7Esm7ke7yhKAkS5SkpClihoad3ULhfQQYeRVI0jxbV2qwllNuGy\n5g9GCyIVLS7oBbi1cXpLBdZ+2o3+oSAqy3JvJFooeHy0Aqe2HeJpkDqpfJqpEjg0aowpB3qeLPUm\nl6MVOw5wNKrp4yokP6efUb2O0UT+nmEY2CyGvGypYDiGWJzVVDob4PoIAflLexcKXon5zWQUkgb9\nSuIlGSBuAVHttgrmprHV8udbp2NQ7jCjL6kwaCi0I3TPPffgxhtvRG1tLf9ePsbUZZddhssuuyzz\nhhqBRBUO9/gybJnCK8v3AuA6u0+VeWizwdNvbBOMp6PHh7ZdvTh7Efegr9rSiXfXpXNEA6HPtyPU\n3s1FZSY0lPHv0U3Y7FYjmutSWUWHVEYoR3hFkceewcCodIToeygcjcNiKmzvkKPQBmThtkkY8I4i\n9FRRC/pZG8nAjY/qTVJmT1+stTDG049JnC+hs0oMuMHhcEk6QoOUAeILFa7xbO9gEA6rUZN+MGaj\nHrUVNuw97EEiwebdoFUMep4sVTnj0Y6B4RB0OgbVbulngs4c1lfZ8zZuCazm/BwhsZSzVijTSNq7\nUFDzuxVVHDPuX/i7HVYTxtWn7DklRwgAyl0W3hEKRvJbezLeaS0tLbjgggvQ2trK/1u0aFGmr5UM\nbBYjKlwWbNzZg+5+f8btw9FUhEyrhb0iqYBxyemT8P++wRV30dSJB55ej4+3pwprrWa9pscfrehI\nOq8C6UTK6PjOhbMEC6IkNS6JVVs6s+p8L+5XUYoNLNWAljTWwkE8iuKAGGZWi4QjJCUTP0KgxxAc\nwcANnZ2pdRvRVOtI+0zzYybnCKeINUAMh0L2vMkHg94w6pPOWqEyQizLos8TkDV6c8H0lgp4A1E+\nUKkl6HmyVA3T0Q5fMAKH1SgbSKeDFc31KssmVMBmMeYVVJbK/GoBJ3GEcqHGFQE89Vc0v/3ga3Mx\npdkNg57Jq08bOa9f++IUTGwqxzETKjGdYv80VSkHnmk2kCJlVgUyOkKXXHIJfvKTn+DFF1/EK6+8\ngpdffhmvvPJKXgctNsZUc5P+i+/vVtyOZVl8uC1lLGsRcY1E4+gbDGDG+Epcdc50NNY4YbMYsLcj\nXfkGAL59wUz88cdnADjqCJFmhQ1UMy07FZ0QL7Ikeu4PCs/bsrUH8cDT6/HLv65FNBbnexApwRuI\nwGzS49bLjwXARXdHI+h7aNOu3sxywkdREiDXTYrSVaju3rmAdq61oKTmCj56aTXBYtThiTtOx9Xn\ncgI1hWqwzBtIoohpKTmqYoTCMQTDMdRX2WE16wuWEfIHowiG46gut2XeWCXmTOZqQZ99Z4dm+yQI\nyFDjAqGoIDh6FLnDG4gqOhN0UHOaBkwcApIRyrX5J3EICCVZKxj0OtitxpLNQJI+YGImzKnzmvDI\nLSehssyKg91exOO5PR9kzj6rdRyWfP9k2CxGPuhdVWaBxaTsnjRU08Gu/JzJjI7Qn/70J7S3t2Pt\n2rVYtWoVVq9ejVWrCtuRWmvcejnXN4FcWDmsaDuMlZ+mHKFhDSJ6XX1+JNhUVkOnY1BZZuEbdIlR\nU2HjDfrPe4frzl4/yp1mweRJvxYvsnq9DlazQWCc7TnkweP/5op2u/r9ePgfG3Dl3W9lzPB4A1E4\nrUaUJxW65K5XqYOOhD32XBv+9t9PR3A0R6EWJLsiTY0roRqh5BzFzWkjt6DzxgrllChliLU5pnSk\nuJQdIcLBL3eaUe60FMwRIvVBWmaETpnbiDKHCeFIHJGotuOms5mBUIwPGF32k6V45D9dmh7r8wiW\nZeELRNOyC2KQ/jETG8sVt8sGVrMBiQSLSI5BQEKTd2pMjQM4etxQiWYgD/f6UO40y1JbyVz73LJd\nOe1fysFkGAb/+PlZ+O3tp2X8/lXnTMPDNy+G3WLIe67NWDBgNBrx97//Pa+DjDSqyqxgmMwLU1eS\nOldfaUdXvz+vtB+BFL3L7bTg0BEforG4IE181TnTMGdyDQx6Bjrm85cRWvdpN373wiYsPrYB158/\nE0P+MGorhM4O/dBUuNI5pHarEfs6h/DM0m246pzpONybqg2rLLNgzVZuUTvQOYwKuuO5CF5/BHWV\nNj79OjiCRp4c3l3XjhVtHbjr2uNhNEjHNMT30K72QcntjqK0QCLUVimxhBKrEbKaDagqs2LvYc+I\nCXL4glHodQwsplR9UMEdISoLRYN3VEuQGkeyHU6bCW6nGd39fsQTLPQa19z0JpW/qjWszdLpGMyc\nUIUPN3fCF4yiwijXwSh7iOdJbyDCO7TROHtUaCZPhKNxxOIJAaNDCg9890Rs2z+AGeMrFbfLBlYq\nsGzO4Z4pVI0QwGVbOvv8uPa+ZQhHYrjirGk4e9E4zY+TLSLROI4MBBSvw9fPnoZ7/vIRdh3Kzabw\nBqKwmPRptotaUQqjQY+p4ypQVW5F/1B+geqMGaFTTz0Va9asQSQSQSKR4P+NJuh0DGwWY8aFidzw\nF57KSVZrkREi9K5Git5FuuR6vBGepnXSnAZccvpkGA06MAwDS54FfqMRq7d2YtAbxmsr9iGeYBEI\nxdKMjHlTazCpqRxfXjxeUlqRTHovvMfRIGmaA02Je3d9Ox5/vk2ybigWTyAYjiWNBc5ZGizBjNBv\nnm9D265e7O+UplkC6Qt8R48vZ4rAURQPaqhx2w8M4PllOxHLkZqgBfzBKOxWI8qdZsTi7IhlQYZ9\nEbjsJoGxarcUOiOUnoUCtBVt0Rrk+jhtRjRUO8CyQHu3ekVVtegdDADQNiMEFM7JJNfKnHSkh/0R\ndPalaoo/b2ux1vCrrLOpq7TjtPlNittkC1uyzjLXa+gvEDUOSBn9PQMBDPkiWPtJaWQfO/v8YFmh\n3SrGvKm1qHCZ+WB/tvAFlamSauGwmeAPRZGQUF5Wi4wZoT/84Q8IBoOC9xiGwfbt23M+6EjAYTVm\nXKRJcSvRStci4iqXEQI441qXXLjLRdkNm9mAQW8YOw4MaKJcNxpAP1C0ChSN5noXfv39k2X3QVMm\nWJblHSHx9V+56TAALmp87XnHCPbhpYqgnXYTdDqmpGuElIp7xUWi/mAUHl+YvwePojQRJPLZCtS4\nA13DONA1DIZhcOkZk4s6PgJ/MIpqt43Prg4OhzLSX7QGy7IY8IYEnHGAktMvkEPS3R8Aw6RHMPmM\nXQk6Qv5AyiCtbKnAsnXt2LavHy1jyjJ8Mzvw1DgNa4SAwtEOyf6aap3Yc8iDZWsPClTLlq4+gC8c\nN1ZzCeXPC3wBdY5QIUCCSbmKuaSk+QuTEaJRKsyTVADfobhdY40TW/f2IRSJZa1I6w9EUO3Of35w\nWI1gWS7jl2u7m4wZoba2NuzYsUPwb7Q5QUCyEWeGyZNELYiijhbqMV19fhj0jOCCu/mMUBj9Q8Hk\ne0LDtMxphscbxh2/W8lv81kGy7ICR4jQFLOdOGnD3xeM8oWIcio0UmoydCpcr2NQZjcJ9O1LAXRW\nhxgdUiBRsLuuWYiLkpnOzt7M6olHMbJIUePSFxezUUgneG99uvR+MZBIsAiEY7BbjSNKIQ2GYwhH\n4gIVIaCwmZlgOIbd7YNoqS9Ly9qRyHEpqsaRLJbdZuIDbLsOeTQ/Dsmm1FRonBEqsCM0ZawbAPDa\nyn34D9VQ++k3tuHPL2/V9JifJ3j5wGbxW1Dw1LgcM0JyoihagCjlOaxG1FfaS6YW+dCR9AC+FMYk\ns8pH+gNZ7T8eT8AfimniXGoR8JJ14V588UVcfPHFeOyxxwR0A8KVpRuijgY4rEaEI3FEYwnZegpy\nIivLrdDr8pMGJBj2c5QNmoPtTmZ/+odDfJSioVrYPOzWy+fiydc/xcYdPegZKM3GfFrC4wsLDJaO\nJGUt2weFlkEdHA7xzuzYOic+3Zfe7V0qXU6+Q6I1Noux5GgudJ8jQkORAhHccNpMfDSzFA20oxBC\niRoHcPMZcTq6+/0IhKKa9GvJBpwSE0dBK6cyQsUGEWkQB5P4GiGNRWdWtHXg0X9uQIIFprWkZ+tL\nqYZLDFrggWTxtJaLZlkWOw8OoMJlVqzDzAWFOrdkrZ/a7MYbq/YDAC46dSI+2dePnQe5Goj+UdpC\nYaTx5ur9eOKlLQBGJiOULzUuJVyj/djPWzwelWVWTGl244kXN2Nne6AgNXvZQqqkQwpljqQEeJY2\nBRGIEAevcoFgTsixtEw2I6TX6/n/pf6NNqhRWvIFIrAYGeh1DJw2kyYGoy8YgV3ELR1by2Un9nZ4\nZG+45joX5k3h5EJLsT5Fa/R7hL/xN89zSm/ZRmFi8VSmZNAb5hf5SSIVmvnTuAbBUsp8PC0ved2s\nFkPJKfjRzo9SRujIQIqrX0qyy0ehDH7xlcgIAcLnIsGCN9aKCT9PGTHwoiZdfcXLNoYiMTz+fBva\ndvYASAWYCGwFqhFataUTCRZYML0WZ7eOS/vcoNdxQgQD2UVJiwEfpYBlNRvAMNoHRrr7AxgYDmNa\nS6XmAgOFUkwkDuLkZjf/3uVnTsXCGXX830cDSNlhYDiER/6xgXeCgNFJjQtkmIvzgcNmwpnHN2Nc\nvQtulxmJBAvvCKvI7enwYEXbYZgMuoxiJ8RGytamIAEztwaBEn4MecwJso7QBRdcAABwOp246aab\n+H8333wz4vHCdaMuFNQYgb5glNcud9iMgqh7LkgkWPglCsJaxrhgNevx9kcH0barFzodw3cjp5Gq\nJSotWlYhQJw9EmEgyLZA8UdXzedfv7n6AA73+mC3GATUuMVzGvCzaxfKKvORBc9l566bzWxAJJbI\nWS+/EKDvCaLQJIWOHh8sJj0qyywlXbtwFEIMeENgmPRmdgRVogzxoR71zYK1Asm02K2p/g+5Fs7m\ngrfWHMCyde34Y5KyJM4IGQ06mE16TR0hlmWxbf8AKsssuOuahWiuk6bcNtY40TsYQCjPjudaI0Xz\n4YQlrCad5oER4gy3aNgUk6BQwRxyXiqcFiyaWY/TFzTBbNTj5LmNcNn0BTnmZx1/fe0TLG/rELxX\nCHpZJuRLVSU2gkUmO68VSkWYaWkyIzp1XIWgt5MUyPP41poDWfUofHPNAQDaZIScGtg1slf2o48+\nwkcffYTXXnsNQ0NDPCUuGo3iP//5z6ikxgHy0cFEgkX/UAj1bm47p82Ezl4fEgk2480gh0A4/2Wt\nZgAAIABJREFUhgSb/vDr9TpMbHRj694+9HmCqKu0SdL1iIDCSD8YxQAx7OdOqcEHG1KTZ7ZylyfO\nboDxah3u/ds6rNrSCYBzPGmuK+lubTUbJGuEvIGUsQBQEaVwbEQ4zlKgaZty1Lh4gkVnrw9j65xg\nGEaTyEkhwbIsguFY0SlepYiOHh9q3DaYZO7/plon2nb18n+PRA8fuoi4qswKs0lfVEdILGAiRcOy\nW4xpDZbzOqY3DI83jEUz6xWzHY21Dmzd24euPr/mQgT5INW7g3vGrCad5hmzQLhwxeUFqxEKRGDQ\nc47znd88jn+/xm3DD75Sj3+s8KL9SPHu7dGOIV8YHRLnayTWT2JH5TpHBsNRmE36gtPVyqk6y5aC\nHolTxu0ZCKC+yp42j23bPwCr2YBf3NCacT/Ett24swdPv7EN151/TIZvAFv39uHtjw4C0Igap0Fw\nRDYjNH78eIwfPx5Aih6n0+lgtVqxZMmSnA84Ukil1KVP1lNvbAMAmJMZIafNhASbe4EdkDJWpaK6\nt1w2JzU2mQWDFlX4rIM4eyfPbcSSW0/mBSv0+uwnnzGUetRp85tw93XHC4xrUvtjlZEop3ttAKmC\ndSmnaaRA38d9QyHEJaQj+zxBRGIJNFRztMtSrl0AgLc/OojLfrIUW/f2jfRQRhS+QAQeb1ixUHVy\nsqibrM0joWpIS+LqdAwaqhzo6C2ePPuQqCP7mOr0rLpdhVpoNiC0lfIMCzifISsx45k8+8RJsZh0\n8AUjml4zQkGSq2/LB4WSz/YFonDYjLLOrcNqQiQaRzQ2+tgwxUb/UBDf/MXb2CfR1mEkqHH5CrkE\nQrGC3MtilCXtEq1r9qTwwNPrccMD7+Hf7wmboXoDERzu9WFqs1uV40dfz0/2qVu3aYpiuQbqtZls\nezWQvbo1NTU477zzMHfuXDQ2NuZ8gFIBuWBy6VFSq3PCNM5odCZpUV5/JOeHV67zOCBsNCcXASep\n0oHPQZGmJ2nIVbgsaBlThgdvOhHvrm/HSXMast4XkT8HuFogIjTx06uPw+Y9ffjCwmYAXLPKIQk1\nOELtqKvk6h5s5vyKLQuBVD8QrpZtcDjEK9AQELVB0ssjVz5vsfD8sp0AgGVrD2LmhKoRHs3I4VdP\nrwegXKi6eE4DPL4wjhlfie8vWT4iWWPeqE7OX9VuK/Z1DnF04CJEfg9T2SeGEQZACBxWIw4d8WJF\nWwdOOjb/dYxXv8rUD6WCm4OOKAiZjAQ8vjCcNiMvDW01MYjFWYQjcc2oP5mEPvJBIVXjxFLGguNS\nQSS3a/TVSBcTHT0+xOIsJo8tx+xJ1diwvYd3igrRiycT8qWcBcMxyTYGWsNl5xy2YX/hg1rb9g8A\nAPZ2CJ3V7qRab1OtskgCQS7zPE2h06SPEJ8RKkCNEEFbWxvOP/98nHLKKTj55JNx8skn45RTTsn5\ngCMFpWj4pl09WL/tCHQ6BhPruZuRZAPIwrerfRCrNnNUqz0dHqxsO5zxmH4F7Xy6GajcgmGzGGCz\nGPiC988ySLSGRFrdLgsuOX2yZNPUTKD7P9BR9YXH1ONbX5nJF3bbZKhxHT1eWM16nmpjLUVHKHlv\ntYzhePhSdUKDvJoWd07JM7C8raMks4yGJD2UFrz4vGHHwQFs2cNF1hbNrJfdTqdjcP5JEzC+oQxm\nk35E6gjbk8qOtcmAQXmRJbRpFa8yh1mSRntsUnCG9A3LF3QAQgkk+NCnIGQyEhgcDguisKQmVkvH\ngrAoClFcbjUboGO0C+bsODiAlW2H4Q1EFHsEaRF1/ryAPP9nLBiLq86ZjtuumMt/NhI1QjaLASaD\nLuO8dLBrGO+uO4jNu3uxfls3/34wHJNsY6A1XEXMCJFsj1hoidgRavv70LatXFJ52B/BK8v38DXW\ntLLv+Ib8acNanLeMV/e3v/0t7rvvPtTXyy/KowFykaREgsVdf1oDgItsktS42BG67TcrAAD/efBc\n3LpkOQDg2CnVih4xz6HP8PDLOUIMw6CxxoF9h4cRjydycgpGCzr7fDAZdHxUJF/MnVqDjTt6JKPE\nBFazAbF4QiCpHk+wONzrx7gxLv5e4KlxpeQIJbn+4+pd2LKnD/3D6QaXhyizJA0fiyllKL7w/i5c\nf/7MIoxUPYgDGyshUYpiY92n3AL8s2sXYsb4zFqgDMPA7TSPSP+Jbfv7odcxmNzE0fToyKvaiGI+\noI1huXP11S9Mxovv71ZUVszlmJkimSTjryRkUmxEYwl4AxE+eAJwNUIAt1aJM8q5opDUOJ2OSdId\ntTEWb398Jf+6xi3/+0tZcTMUiWFwOCxgQowkyFxEFMFoG4leg4oFhmFQ7rLw66Ecvr/kf4Ig3OuP\nno94gkUoEi8KNa5YjhDLsrxt2jcYhC8YRSAURY3blmqErPAs0KDnQTm21b1PrsX2AwPQ63T48uLx\nfPD5x99YIFsDmw20CMBlvLrjxo3DggULcj5AqUBOdvNwb4peQXv9RInC6xfypz1eqkjdE1R0hLwi\nGWY5KEUbGmuc2NXuwZHBAMZUKTe3Gq3wB6M42DWMaS2VmhUk/vTqhYjG4opiC1aqv4DRwF2jPk8Q\nsXgCDdS5Jv0DcpXfLATIgkzUBqUWaD7LliwWpfnvCYmaopEGESX5PMvUklqfhgyN7Gi4nRbsPDiA\nWDwhyIYWEvEEi70dHrQ0lPGUKiJfXYx6pXg8gWA4hhnjK3HjRbNQLzM3MgyD6nKrZg6J2uaKdqsR\nVrMevZ7SyeYTGjBd38RnhDR85gIFpMYB3FpeCIdEKQrOU+tLUGjmN8+14cPNnXjijtOKEoDIhIFh\nEROBMpa1llNXiwqnGbsPeRR79IiZCLSISCF6CIlRLEcoHI3zwUaPL4yv3bUULAu88tCXUxkhlUER\nM+XY9nuC8HjDafWT2w9wNDyyrhNmjVZ0Q6vZkGRF5B4MzLhqHnvssfj1r3+NDz/8EGvWrOH/jTbI\nRXQIVxKAQB6ZXEyPLyyo0aFPdqYoo8cnnBDkoHRDNCQzGgc6hxX3MZqx8+AgEiwwXaI5Ya4wGnQZ\n1cf4jtNUjyAy+bkoGe8UNa50ooG+YBQMA57mJ0XZEFPjAODGi2YVZ4A5gBSif7K3H7c8+gGW/Gsj\nfv5/H43wqIqDe/6yBpfe+QZWbuYoXGIpaCXUV9mRYFFUCq0/GEUszqKqLDXOfIuSswG53112E8bW\nuWSbZANcdHPYH9FEyjqluqYc3GIYBlXlNvSUUEZoQJQhBoQZIa2Q6oFVGOPRobEABoGS8VeKQjPb\n9w/g2nvfwYdJyj6h1I40iI1E7jMtov75or7KjniCRU8Wc2SvJ1jQejcxnElHqNB9hMQqkSTOv3Vv\nH15dsReAekeIYRg8eNOJOLt1HBJsShZbCkSghT+nGtENCStib8cQrr33nZz2kXEkq1evBsDVCtFY\ntGhRTgccKZDMjfgmIAXlgLCIi6d5DAtlID0q+7dw31XXNMpskr8McyZX4+9vbsebaw6gddYYxf2M\nVhzo4py8CaKmp4UGrcpHMivEKaInPvLAPvXGNpy+YOyIRbVo+INR2CxGnsIpFdEVL0hAqmailOqd\nAC5DRQtX7O8cxv6k8987GFSdqh+NCIVj2LCjh//bYtJntfCmFMq8fOCk0JBSxCT3WTFoekpCNGKQ\nRf2+v62D2aiH02bCty6YmZNxw1PjVNQ6uJ1mHDriLWqmTgkeicCItQAZoUIbjw6rEdFYAuGocsY/\nE8RKeUpzTCm1HojG4njqv9uwbF27YB7ffWgQKLjwcmYQ4SM6M3D1udMVxSgKDSI809HjlaQQhiTW\nw97BAPQ6btti1AgZ9DrYLYaCZ4TIHNZc54TTbsIne/sBpJyYiU3lGVUxaUxvqUR9pR1vrj6Ag13y\nAft4MuMWKAB11u20oLs/kHPgKeNI/v73vwMA30dotMKW7KItjiTRE4nAEXKl+O7dA6lu6bTjJNe/\nhUAqIi8FJTbY5LFuTGgsw5Y9fZ/ZOiGi2NeUBR1IC9A8/qnjuPek0rbNddwkOuSLoNcTRI3KQsJC\nYtgfhstmUmySOuyPwKBnBEXLpSj8AAChaAJybL0te3qh1zGIxRNYML1Osah5NIKm5wLZZYMAepH3\nYaEG4+kZCKBtVy9cdiOOP0a6Xw7dQ4iAZFGLUexLDHc1vWpmjK/EsnXt2ET1XVo0qx7HTa/L4bjq\ne+SQbfzBaEncsyTQQI+lMGIJ3L4K1YCSltA2l+UeIBHPgUq0MjLPEgGk7n4/QpE4xhWgaWwmrNzU\niddW7kt7/9CR4jdVlsKQP8wJFFBO6oWnThrBEUHQ8HnB9PTPCbvniwubMXNiFR795wYsW9eOC0+d\nCEA7GlcmuF0W9HqCBbW3CUXtuBl1uOqc6fj3u7vw9ze3Y/WWLgDAPdcdn/WxXQ4zdEy6Mh9tL5Pn\nLWVjaZcxzsZxk0JGq3r79u248MILcdZZZwEAfv/732Pz5s15HXQkoNMxcFiNaYu0nCNEF2DRVI8O\nymhRosYlEiw83jB0OiajwlCmao2GagcSCZbn3o5WsCyLRIL7R0fjOnp80OmYohd7Ek54D+XQSqVt\nG2ucOO8krqdWIYuf4ypFAuIJFh5fBOVOs6IjFAzHYDULe2PYSrAnEgCEo9z9IDWhPfn6p3j02Y34\nzfOb0voefBYgbkKa7aROL/Ja4A//2YLfvbAJ9z+1HrvaBzEwHBLQRwHpzAhPNS2Ck61WvQ0ATl8w\nFv++/0v4173n8NTQXOuY/CH1mahCST3nCq/UNeMzQhpS48IxWArYgJKMP18KJv2bG6rtigEu8bW8\n/v53cfMjH4Bl2aL1zSKQq+/0+kvnPhuJfkFKIJnyzj6/5OdE3bGqzILG5LZrtnahvZtzLouldtdQ\n7YA/GMWQr3DBJPHcSQfq66vsOQVt9DoGZQ5z2jP5l1c/4V+TAInW1DggVSudKzI6Qr/4xS9w//33\no6aGo9Scc845+NWvfpXXQUcK5U5zmmwwXQBfX5WaCM1GPewWAzzeME9xA4A9hzz8azmjuKPHi/Nv\nfw3bDwyg3GHmi8BzBZ+5KKHC22wRT7C44mdv4pZHP8D5t7+G8374Gl//cbjXh9oKG4yG4nKJq/jz\nmrqOcmlbMpFqpT4lxsYdPfjKHa/jExXNRL3+CBIJFm6Xmads+CUMmUAoXfbTaNDDoGdKLiMUjnJO\n4AmzxuDBm07EA989ETddwjUdpoMX/Z7PXk8tcSQ3W0eovsoOvY7hM6v5oqsv5VA9/I8N+MbP38bX\n7noT7d0p2oNUrQyfbSyCk50NRQ3gxuawGlGbXDBzpe8FQlxtnhpahxJtdSRArplToOLFJD/TMCNU\n4AaU7qShduuS5XnJopPfPKmpHEtuPUVxW1o+m6bwXnLnG7inyHWMND3PZTfhjq/PR22FrSj9Z9TA\nH4wUpY9YNiDGvdw5IvOJ027ChMYyngVC5uZi/Z5UUKtw2T2fqBcaXbpRmwfbxe20pM2r9LPy2op9\neHP1/oJQ4y47YzK++SWJVJ9KZHSEDAYDpk6dyv/d0tICg6E4aUKt4XZa4A1EBJkfEr08/6QJuOc6\nYd1TudOCjh4vlq4+AIAr+qPFFeSM4rfWHEwd05XZqMnkJpHMRSlJsWYLfzAKbyCKg92pB/zj7UcQ\nicYx7I8oSpcWCoQT/r+NHbjl0Q/w8N8/5rvVi1PhvNNUoAaJf3l1KwDgH2/tyLgtXftjNOhgNukl\nuetyjeCsMv2TRhKRGBfltFkMmN5SiRnjK/HFhWP5Inh70qErBu2q2Nh5cFDwd7Yyxga9DnWVdnT0\n+PKOTrMsK5hniABDPMHyNVuAdI2O2aiHTlccJzubGiEa+Qo6cFlWgyrqiFK2diQgdc60zgjF4wn0\nDgYKSh8upwy3h/7+cc77IXPm/Gm1GY0yWmyJqGABQDgSx0aqvq8YIMHcukobfnjFPCye04DaChv8\nodiItx6IxRMIhuMllxFySohdDA6HcOcTq/DwPz7GMOUcMAyDE2ZzjdxJg9Fi/R6a5lwodPdzc3pN\nhbD/G5BSmM0F5S4zguG4YP4XzytPvLQFW/f2waxxxthuNeKsReNy/n5GR8hoNOLQoUP838uXLy96\nKlgrkAtOe6nBcAwMA1x73gz+xiA4/pg66KgF76pzpgk+HxgKptGZVrYdRtuu1MRYpcBhvuuahaiv\ntOMLC5sVx00M9kJlI4oBuahoSlkvu7oILeCwGjFncjWC4Rj2dw5jxabDeH89d6+LMynVEtkjLUGc\nczXKVmmNUq1GnvZCwLIsb7SJYbUYSzAjxM0p9HiJ9DEAHDOhCnarUTKi5wtEsGztwZKUBM+EeDyB\nHQcHBPUJuQQFGmscyWh1fo7isD+CSCwh2e+Ddh74CCoVKWUYBlazoUiOkDr1NjHy7TKfTbaDl1wu\nEaUxv0QWLVUjpE2A4chAALE4m5X8e7YQ19yqpRSL4VXZEwpINXL1BiK8cUxDTB0tJMgz/vNvLeLF\nb4gQwR9e2oJ/vLkdL7y3K00YqhggxxyJxqlK0Ot1sImECD7Z14+te/uwou0wtu3jBANI5ofcE8Rp\nKJojVKstzVkKZN8k+0QrxOVjh9HiUwS+QATlElQ7Wm1UK5BnNBdkdITuuOMO3HjjjWhra8O8efPw\n6KOP4qc//WluRxthSC2CwVAMFpN0hO+b587Az649nv/7zIXNAgMhwQq7m4fCMTzyz495XimQutmk\ncNyMOvz5zjNQkUFVrrrA2YhiQC4qSqLP+Ra75QKGYfDLG1px6rxG/j3imIkL+UiUvhDUrGA4xkfe\n6XtHDiT9TDrEO22mtExJOBpHIsFK8nBtRTBWA6Goqkj4oDeEaCzOU+PEGSwSBJjeUgmXPf13AsC9\nf1uHx/+9Ce9/fCjts1JHV7LoevLYlGJidXn20XRSX0cLu+QC8jxKKTjStAe5fjpWs6E4NUJZUuMI\nnHYTdDoGXX1+7Dg4gIPdCipHCTYtCy8XXJBCirZaGllMKYELk4GBXsfAF4yidzCIHQcHsKeD67eS\nC8RGViEgXi/9OWa3yVziVKFmxjVyNcGXZDaI0dmb33OXDcgaRRuYxBF6Z+1BPP/uLjyzdDveWXtQ\n8vuFQizOpqhkWQYoigGnzSQIyNJrybptXCNrMp+Q/48k59NiOXakPqmQ1LiOHi+sZj3/HNFqfpmE\nvZRAgv5dVB2WLxhFhYTTc8fXte9NSpot5/RduQ+8Xi+efPJJTJ06Fa+//jq+/e1vo6ysDM3Nzaiu\nrs55sCMJKVpEIBwVqGqJMbnZzb+2mA340/87A0u+fzIuSqqJ0NGh4UAECRaYm4zSANBEzpanxo3q\njBC3eCyaWS94f3/nEICRyQgR0IYnz18V3RN2izGpOqi9UUOrhkVjCUSiccXtSaE3oV1WlVsRDMcE\nEUAlCVur2YBgKFrQzO419y7D5T9dqrhNZ68PV93zNv7yyic8NU583kkR5IzxFbwjJB73p8lo3mh8\nPkh0lzbucpEKd8q0B8gW5ByObyhL+4yeN0mvC3Gk1Gbh7q1Cw58jNU6vY1DhNGN/5zBuf3wlbnr4\nA6z9pEty29+/sAnX3PsOOqnnMxiOKa4XNOwlRo3zBiIw6HUCyWmGYeC0m3C4x4fvPPQebn98JW5d\nshyvS6iSqQGZywrpCImDZrne83yBvEoqqtNmhDcQkWQ3dPQWLoIvxrA/DINeJ5jb6Rnxp1cfByA1\nLxYLb6wfxP97YhWA4mVQsoHTZsQw5cTSjlAowq25ZNxkPg2GyfvFcewcNhPKneaCZYRYlkVnnx8N\n1Q4++E8nAZx5OHxTkrYyoY7G4gmEItI0yXwcLiXkep1kHaGf/exn6OvjCrf379+Pp59+Gvfeey9O\nPPFE3HfffbmNcoSR6nwewh9e2ozzb38N3f0BmBSK9K1mA+6+7nj8+vsnAeAMlolN5ZjYxEVMaX4/\nMfbHVKcULDL1EFIDu8UAq9kwqmuEiAMxe5LQid53OOkI5cFNzRdV5enXSOxA6HQMbBblRn6JBIv7\n/rYWz76duc6HhnjS82cwJMXUOGI4C9TvQuky4ARWiwEJluO3FwK0UybnbHX3+3HDA+8B4PoXkIyQ\n+Lxfevpk3Hr5sZg81g2nzYR4guWVaLr7/bjp4ff5bU0KTTVLFWQxdtlT939lDrQBUkMVCOaXjSHj\noaXsSbaJFo0hsqji6HyxqHHeLOSzxbj5smNx0akT8ZWTJ4BhuIzix9uPYGA4hB88thwffdIFbyCC\nZevaAYDv8RSNJRCNJbKmxpWKI+QLRuG0GdPYDy31LviCUYQjcRwzoRIA0LYzt7qXoSJQncVGVK6O\nEGFYqG0eWVthg8cblgy40M9GoeHxRVDuMAmuI5nvpza7sfCYelS7rdi2f6CoZQxt+1LrT6lR4wDO\nuYlE4wgnA41SNGtiSIuN93wchGzRWONAz2CAH6eWCIZjiMYSPJtEjHxul2njKgAAzy3biX+9sxPX\n37cMgPS94CpQOwF7jtdJ1nLo6OjAHXfcAQB4++23cfbZZ6O1tRWXXXYZ7yCNNpDMSnd/AEtXH+Br\nCrokOL805k+rxaQmt+C96S3cgvHM0u18t2Kat373dcfjpGMb0gz/XMAwDKrd1lEZ8SagC3V/8a2U\nKEUqIzRyjpCUWp2UA+G0GWWLiuMJFr98ci0++qQb/3pnZ1bHJ2lwUhuSSchA3Ci1RiJjGFCQqKxM\nGq+Fup92UcGBkIyzRZq4AUCFy8zXCIkpiTUVNpw2n2tiS2hMJFr94eZOgfhGqdU9ZcL7Hx/CirYO\nABw94bYr5uHs1nEZqbJSsJGeNSGO4vSvt3fkVDvhT85hlWVW3rEsd5hhsxgEGaFeTxBOmymtV4zN\nbEAsziIaK4yTTZCrWALAZey/ee4MXHveMTgh2aR6/bZuvPy/Pdh9yIO1n3Rjb0dKHZQEu7JtFOqQ\nKNAeSfgCUUmjZPr4Sv71ZWdMRkO1HdsPDOREj5Oi32kNo0GPr5w8gQ8Y5OwIeYJgGO5eV4PGZB3f\njgODaZ/lK+WdDYZ8YZSJ1ssrzpqKuVNrcPuV8wEA48eUwRuIjFh9WmlmhDgn56OtXXhz9X4+6FPm\nSGURyPNB378MA9gsxfs9NW4bWLYwzrUcpfihmxbj+GPqsHhOQ877dtg4xT0AePbtHegb4sbvsJpw\n9blCRbdCSet/8bixOX1P1hGyWlOTw9q1a7FwoRat+kYWJF1PS2DnigqXhTfef//iZqzcdJjfr8Nm\nxPxptbj9yvm86lW+qC63wh+MFrUoU0vQD+CxU2rwo6u4CXtPB+cIjWTDwVkTqwR/11TYYJYoFndY\n5TNCezs8+Hj7kayP3ecJ8o0eibOdaWEnxYjknNGNYQmUjDat+86Isb9rKG0cYtBOmMcbRiiSzAgp\n0I4uPGWi4G/xQjGaFOUi0TiW/GsjPtzcCYBzhE6Z24gbL5qdUyM9e3KhDoSi+PHvV+LZd3bivRxq\npujaH3ItrBYDKsss6B0M8H1Tej1BSQqftUh9qnwBjtKcb4Pp7yYl2vd3DuOtNQcAcG0K6GeDOOB8\nI0CVRhEvn10AOm22YFkW/lBUkjoyf2otGIYb75TmCkxqciMYjuVUk5pr7Va2uPa8Y3BRsklnrhm3\n3sEgr7ypBmTeJNlIui1GsTJCoXAM4Ug8bb2sq7Tj59cv4gWfyJqwektnUcblpeiCDAOMrSt+o9lM\nILVgj/xzA554aQsfhCWZDIBT4ASE968rWVdYLJCanUKsZ3IBpGktFfjJ1QvzboL87Qtnpb3nsBpx\n4amTeMpmIXF2a0tO35P91YlEAn19fQgEAti0aROWLFkCAPD5fAgGR2dmotxhht1qxCf7hBmtusrc\npD4funkxrr//XWzc2YONFJWgENEQuk6oua70oi2ZIG7iNbW5QvB5IftOZEJVuRWvPPTlZKPSMMoc\nZklj1GHlUuuRaFzQNRsQ0tIASG4jhat/+Q4AblElymGZnN1BbwhOm4lfwHlq3EBqDMSZkurenJLo\n9AKoT/s8X9AOWSAUlcxwECNrYmMZ9nQMoWeIG6/SfXDuiePx3w/3wZOsqxE7cqPJEeoSNfZzqSjY\nVgKpW/GHYuhJnv9oNI54soGxWmPPRylp2S1GDPkiMBv1aKxx4tARHzy+MHQMg3AkLkkp4nsJhWMF\nDW74gto0bSS0Y1oSuXcwKJAKJ88j+V/tXEUcplKgxgXDMSQSrGSmZmJTOf75i7Nh1OtgMRv4TMvg\ncDjrRoW5qvnlAnse1MNEgkX/UBATGtJFQeTQUJWii5Y7zfjjj05HLJ7AlXe/lbMKYbaQEkqQArEX\nfv/iZoxvKMPksW7F7fPF4eRcfE7rOFxx1rS857NCQOycHzrig9Wsx6QmNz76pFuYGaLuXy3qvLNB\nIR0hb6Cwz6fUnEzEEqYmHc5SvDdkZ/Trr78eX/rSlxAMBnHzzTejvLwcwWAQX/va13DppZcWc4ya\ngWEYNNY4eKrD2DonvvWVmTkXdtZWSDtQarqdZ4sKIk04HEZznea7LzjETbyqyq2ocVt5o20kHSGA\nk9fU66HY/4Iufq4QOTm9/O/QIxiO46If/xdXnTMN4xXWWZq6ZNAxPA9ZTgXp0BEvvr9kOSLROMbW\npeSWyURNiy7c97d1ACBZ2F2IjFDbzh7c/9Q6PHjTYkG2J1NGaFpLJfZ0DGFfd1h2vDRcdjO6+vxg\nWRYdPV64nWbcdsU8/PSPq0eVIyQ+9/kuDsQoDFBGoV6vw/X3L8OQL4LHbztF1YKeygiZ+H0GwzFM\nStZEdvT48Mwb2wBIizrY+MxUYTNC/mAE9ZX5GyiEdtze7YXDaoTbZcahIz6+A319lR3DSeMzlRFS\nN1fpdQzsFoOAGre3w4NbH1uOe65bhLlTaxS+rS0yZWroNYvUD3h82Rv3vmAUZpNeMyaEEshakgs1\nzh+KIhZns1IrpeloDquRfz4cVmPRqHGkBivTfEE/m72eYMEdIULvHt9QXpKGLgC4JOxpmYZDAAAg\nAElEQVQyp92Mc09sQbnTjImUUqbRoINOxyCRYLMOBuQLUi9ayIxQoWqeaAdrxvhKnD6/CSfM5ujH\nZQ4zHrllMSpcxe8ZmQmys9XJJ5+MlStXYtWqVbj++usBcHS522+/HVdeeWXRBqg1JlBqSGcubMbs\nSdWqOcJi0FkDOnVaCH40aSJXrMiT1pDijpMibEC9cTGSSDXVS5+gSEaIriV7Zul2xf0doTI4kVgi\nZUTKLOwv/28PryhXQRU7uuwmOG1GfjGiDYMF02vT9lNbYYNBz/BRPC3wl1e3IhSJ4+k3tgkoNbKO\n0GAQLrsJ9dQiU+40Z6yP4YUeonEMDIdR7bZi9qRq2K1G7OkYxO9e2DQq+pyJ5VHzzwilaoQIuvr8\n6B0MIhKN40CnvEw0DTpgQUfcifP84DPrsS+5ry8cl97/jESqBwpIFeKbNmq0mJPgx7knjuezpQBw\nwuwxqHBZEEhmU7KtEQIAu0iy91/v7ATLAn9+ZasmY1cLb0Ba5U8K+TSd9QW0ydSpgd2afs+rBXEo\nssla0r+Lfu12mXklz0KDqExmygjRn9MN5AuBrj4/fvP8JgCFVQvMFw6RIzR/Wi2+esZk2CxGfHFh\nc5pSZnkyQ1TsJrWFygiFIjE88PR6AIWjrtL7XTCtFl9Y2CygEk9prshJFbXQUAzbmEwmOJ1OwXuL\nFy8u6IAKDSJyAGijbHPVOdMwsbEM86emDM5C3GQkI/TJvn5s219cWUwt4JNoXkdHD6QEC0oNKc5/\n+sJLMkLiyFtEYRESZwXs1hS9KRNqKTonl+l0oqs/gGgswWdbzm4dJ+nk6/U61Fc50NHj1cxpqHSl\nGs4KRBuo3xKNxfHm6v146f3dONzrQ02FTaAW+O0LZmWsjyFG6MBwCLF4go+ezZlUjWA4jrc/Ooi+\nAvR60hq0EwzknxElqnH04knTNb0qe9l4g1GYDDqYjPpUxD0QxYzxXB3dkC+CSDSOE2aNkZTY1rL5\nc1efH1v3pgvz+DUuyJ83tQZNtQ6ce2KLIFD2g8vnwmE1gmU58RFSeJ7NccV1hcRoL3YxuZiarISU\numoOjlAwWhBGhBTseWSEiENB06Eygb5mdO+hCpcF3kAkY9sDLeBR6cARVVtAvpm5Vnjpg93861J2\nhOgsyC2XzsHd1x2v2Mz+R1ctQIXLgktPn1yM4fFIOULaOtfLN3bwrwtFjTNQNZsjqQScLUaX3mxr\nK/dP7WcS7x3zg2v413xGQrxdFse55PTJWHLrKYKLLpWCVdynEpLfIzLcb390ED/63YdpBmxAi74w\nmcZIPm9txZRrrpHfTgK+YARWc7K4ObkPWYdRzbmit8l2e6XPFe4FJTncvqEgTAYdmuuFRaI9Hmpb\n0b5ptcIvHNlM0YokFvbWVvS9s5z/ky7wBLgFKJHg6GK8LOy/npL9uY01DvhDsVQX6FzOIfV3eEMb\nAM74JkYGQGWEWlvx0bwz8cRLW/BUklrVVOMQBCMkaSqiYxI1P9Lx27X8XaC1FT/+xgJ8qXsDgDyK\n01XOIZLvt7YCTqfqc0hH2y88vBbMCSdkNy4RrMl7h3au6VottXUU/kAUjsAQ0NrKUxoWH9uA2nPP\nwG27X+e3a6xxSI6L1A1t29evzkBVOOff+tW7uPOJVWkR7UzZjSnXXJPVXHvuiePxxB2no8xhxlmL\nxgEAJo8th8moFxjbpKGs+757pPfvdKbuAWrOCEXiXFS5tRWBLdy9b93Spnp8ksjmfnM64bvoMn48\nmcA3Hn/8D9L7kzlmPMHCH4xy54weXzZrXxbb2i+/BEBujpDnltsAUJkTFcelBXQqPnib3570oevz\nBLP/rWquIQWSac1E6bOaDbw6q+yzr9F1oY3frDPb2a7j2e6TAu28CijwMttPb6nE03efmbam54tM\n85PqjJCczSKD9iMpFkJaMIfej9Q9mYN9UH7XjzOOSXYfra2Yc9JJyr9Pq/sFCjVCJYfWVmDNmtTr\n1auVP5N5r3LNGjz+6S6Ej52PiU3np203xe8HtmxRf5wk6AUmLVqjNHaVv7n8qq8C827kPwpF4nwU\neePOHtz95zX4/lePxekLcpMPzDhG+nMAjix/iy+YlG6l9uMY+zzQcHx24xBv43QCPp/67VX8Nv49\nQPA9x6+f5X6LhCTp4HAYbpcFDT+6BZj1Df79UFIWWmoMhJ5x07Lf47RtH+DgkTZg1jfTF67kdzun\npfY7VeQITR1XgWXr2rmi74cfBsafiepPN8qeE75OqNcH91mnZXfOxefG6UTvVzkxFXFvouC9vwKO\nbALWrEH3gosEnzXUOOC+7irg2G8BANzXfR14n2rCKnHOiCoZyai42vdx2zidcMw6D6ibB9813wLe\nej79NyhB7RyS6VysWaPqHHpmboKpvAEPbfgrxr/3avoxJL4z5ZprgM2bJYev1zGwmg2CTFOvJ/Va\nbVTYe6QPFcMDwJo1OPE7l6DpP2+h8cKzgTVrMN25B5j0ZQBA4x8ekTw3pEj7fxs7sGl3L5762Zny\nUqkK5zxsMAFJlf3AaV9A2Yr3Ur9FKbvR2gqH3PytAmUOM/7vJ1+AJWn00o4Q38i47SOga5dw//Q8\nRM8ZNzzGjfnUL6J8zRp4ZnI080AwktP4+N+V5f3mY7nf43jkAeCkvyju3j19IvDNP6PTUc39Lq83\n/bgSx+Sb3H68Rjg+ejxKvzebdbK1FfaP24B5N8L33v+AK+Yp/ibxd4f8ZcAUoOzh+4Efb1d1XDpb\nXb5/J3/uq+9+EgDQ+/XrMSaL8au6hiIQOrOazIuifHuW51ppW7KOXX9mTXaKl9mu49nuU7Qfer7g\n6Vm52ma5QsX8pMoRkrNZFMa/k5J9t9CquFL7ou/JbG2y5Lzt/lj9fS01Dj0ZB/253Bqc53UbXRkh\njdDSdxBTfdrKStL800JILbqjQioNXXvxXLJvzdLV+zU/bq7462uf4DsPvsd3t/YFonCK0rGOWOlT\nmGiQdLI445BIqs25nWY0BoW0xUhUnhpHJrrpHdtgjMdQE+bkPMWKYgReK0dTveXSOWmF7zOSvUC2\n7RtAr5mLYFUP98oemzjrWtAm4owOAw6hY1bl5ShNAX0qKNDrEsqUO6wmlEdSv9UdUe7nBaQoZEeS\n2TRXMFX74ghxC6lPX7hmjlph0OiA22XBhEAPtJotxokilwMUtUlNRmjYH4HPYEXtUEoBs7nOBX2y\nb32Ntxff3vcOLjp1Io4f2CO5D7oZrMcbRjBHuf9ddZP410G9cN4otERzbYWNfz6ILLk/GE317/Kr\nb7/AzxkGC+KMDh4bR70btKtXK9MCfgs3XzjimedcRySAaYe3YWvTTLSXj1F9jK4+7vmrDQ1l2FIb\nWKMh6BJx+HN43oeS16E8mr1EOCC8B/j2BebCS0Z39HhhNOj4gIMS5NYrrTHoDYNhgDp3aavZ0o5Q\npcomuiMBh80EhtG2RohlWbQfGYZBr8MNF8yUpDVrDVewOPOAFhg9GSHimZLXmT7L8b2dGzZg3s03\nqz9OEhaJvjOqxq4E6nvGVSth+vF/eR4ykSXuGQjw0q/N+Wj3Zxoj/TkAn98Ph2i7Dzcfxu52D75y\n8gS8snwvAGDVlk5Mbeb6UjhsRsF+7Ld9D3hRFOFWc67E22S7fYbfJtiO+p4j2e9HHGHzBiJIJFi4\nXRZYP1wO3PYq/1k4xsqOgUx0rmOmALGxcK14H2V3v5kuYrB6NdjWVgRNNswYXynJax5TZYdOx6Bn\nMADdeZcAGzpQObkZ+OBNyVNio2SOcz6Hyb+H334fiXveFmxezYbQByB47bfAnvVrvHTJD/Bm8ykA\ngDu/eRxeXbEXJx3bAOvKD4AfvgYA3LnLcEwiB95NMkJjqoFFi7jrc/4NAAD/3b+Q/g1KUDtfKJ2L\nrVuBmTMznsMEAI/VhUlOc1b35s7f/hZKce9p4yoEMtA01DhCfLTZmuDPqXgMX3r599x75y6XHLdB\nr8NZi8bxPXkC4VhakbLUbxMfa1tDquly8J/PSf4WSZrX6tXwzZ4Nh92uSXSXFowgdMbymVOAxATh\n/r1eLqoNcPdAcizO/37Kff/JZzD8jcuQ0HHrxKCjAuyqVbk5weS8qbzfsHUrfE4uUGH/7WOZ9+/1\n4tTjzsf2hunY8/K74DkGGe5VQstsvOU64OD7qfHR31fzu1Ruy7S2wh6PwD95WubfJPqu54q7AQBl\nf/sTUO/Ken0ub6wBbNwzUr2LCxz03ngr0LtV9fhVXUMKLMvicK8PDdUOVQ0pFTNCWZ5rpW093hDK\n7Obsm2Rmu47nsk8KNB3MTFRftTpuFuPLND/pdQwcVpOyI6Rks0hgYDiEYJir7Tz3xPHK+xLfk1na\nBzddMhufPPEs3LOm5mzvAkB882boZ88Wfi61rQbXbfQ4QoDyD5YzInJ5L9vjgKOqKSLXi0V9z2E1\nYCDpCJGM0NtrD/Kf552JUjMhJrFzwwaBURaNJfDgMx9z46QitR1HvOmKccn9GNe15zYO8TbZbp/N\n5/T5t0nXCPEGUpK3PampHLuTzXXDdEZIdIxhfwQMAzj+9y6QvHaNNU5s39+f1oco9MEKsHe+IVtU\nzzAcNSoYjqXG8/brktsCEo0vczmHyb99R7xpm1afvBDb2w4jGI7hQNcwnk46QQCwaGY9Fs1M9S+y\nWQyIx2WeH9Ex06hxv7wLmMlFrR333QP8bV3ufVvUzhdS76t9vlevhtcXRvzut/i6P9X35oYNiptN\nHScvketX0WGeKNk1fv8GYOH90mPI9B6A7148GzoGWLr6AIKZhD9k9rvtL2uAHZyBKZbi9mfohbHz\nyScxb14WVCkFOIiASTAKjzcMu8UA84crpDf2pj8HtCNlevZF4Nf/AwDEdAb4g1F5JzETZM797kOD\nMBn0aK53cYIkK1bCaNDjyD8+BtoOqy5grnzqT8Bf1/J1UZmOC1COUI1Dk/VOzbb2+5flJpbwxXOB\nLZ0psYQsx2t95CEgKX9OBGn6h0PZG34KYFkWB7u9aK5zgmEYDPsjCIbjArVVJdgsRjCMQhBEo7EO\nesOKbSdU71crR0TBwQCAGrFqWTEcIApq5ieX3QRvpoxQFuM+lFyjG2tlKJW52kcS25wJ4Mzj7wRw\np+rxSR1n04YNyudJw+v2uaTGFQK1yYlg1sSqDFvmDjqiQYwDOmqQ0egoIPYeTlEF2rtTBkFHry/F\nHRdFcPX64nVr1gLEaBHTyUhXcVJk/OBNJ+LOby4AAESi8gIWw/4IHFaTIJLWUO1AggW6+4U0MTXS\nvTaLAYEQZ7DZLAZYTArbmlM9YvIFub5uqoCXLIyBUFRQtyJFIf/7PWfhhxeqo+AQdTSSvaDpDuT+\n8qow+kcSRKAikwRutlDq5q6GHkN652jRQJBurJoLdren5hPxPvjASoGocTRoieZBbyirvjOAcM4Q\ntz7QuvfMvsND+MFjK3DTIx9gzdYufPuB9/D7ZMZ9+4GBNLl6JeQioU36mDUWsQGl3WrMzRHyc3Qu\nSWEjBRDHqbI8RccjrR+0mEtpbN3bh5sf+QBPvMTVlGSj/AeQPlbGgvZXC0ViCIRigrm/lPHiA+fi\nDz86faSHkREuuwnDSaaJFtBybv8sYnRlhEoYJ84ZAzDzMXdK4Zrk1VXacegIt9iQSZeWxQ1oPBFn\ng+37U5QcWp2kdzCIv7z6CYB0HX9FOmEJQk41jhgLZDEwGvS8U8RT4yTg9UfgsguNOWJoiRcvNc0c\nrWYDBodDCEfjGRcmYqhq0fiSnI+mWid/LkgxajAc49XLvnRCC848Pp3WZzLqYVTpFLeMKeP3CwiD\nA+T++ve7u3DGgrGqI6fFBnlmnRo3HpRr8Kxj1DmHZFxaNETkM445zEksywoct2Aohmgsju/9+n84\n3OvnjYNiSFDzGZ1AFN5AVLUjQUDPGbE4N+7KMgv6h0IY9IbQVOtU+npWODKQCp68+P4uAMB76w/h\nyrOmoXcwiOOPqVNdzM4rx2Uhoe3xhqHTMVn15skXdktKlY9WL8uEIV+YC0Jl8R0A+M0PTsH+zmEB\nDT1fp18O+5P9ut5acwBrtnbihgtmAcjuvq+rsuNg1zDiCTZ76poKkKAOl90u/f5tZuPosDlcdhMS\nCRaBUB5ZYwrEntA6+PZZwdGMkEZgGAaL5zQUpJkqwc2XzOFfk0mXprxoPRFngz6qb0hH0hGaPJYr\nCP54+xEAQJnIwDpueh3OXjQOD988OnpTWc0G6Jh0zrVPwrAli6OcWEIiwWI4EOH74BCQaJ+47wuR\n1FbKCFnNBngDUQz7I3x3eNltNYxikt9PS3qTAuJgKMb3lDllXiPvyOSKxhqHICJKnw/aQFi/vTuv\n4xQSheruTRuC5y0ej2njKnDm8c1ornep6klBnGIt5rB8Mo6hSBx0J4BAOIaewSAOHfEJIqSFEkug\nQcQS+oeCSCTYrI0Sh6DGiMsIkWdA6yac9Ly0i8qokUxNNjLAxJnJpoH3sD8Ml81UELEgOeTaS8jj\njaDcmb2BWVlmxfxpwibVJPOu9fpLy8YP+SLYsINbR7O57xtrHFxvucHcRCEywSMKAh6FNtC6qWqh\nBWZGO446QqMIbpcFt1/JcSaJ0eILRmAy6rn6kBGkxtGGO2kieu6J4wVZHzE/Xa/X4caLZ6dJQZcq\ndDoGdlGDRCDVANVOZWuIoxGWocb5glEkEmxa9J0Yx+IIvipqXPIzls28MNk0jGKS8zFuTMrQcjst\n0OsYBMKxVF8jDZR6GIYRNL2kzwdNW/IHR+5ZyASp5sJa4YsLm1FVbsU1X56Bh25ejJsumQO3y4Jg\nOJ7xWpNmn0pZR7Xgo+Q5qMaRcZoMOv5vQj+loUVD7Ewghjahd2Z7zeiCdVJDM7WZq+XSmhonVwtC\nMrLZ1HEYDTo4baasHCGPLwJXFg1KtYCDoi6qRTyegDcQ0SxzpdMxsJr1mmTXaRAj+JLTOQVFkiHK\n5h7k2ySIBXhyBMuy2LijhxdtIvdHpsDbUWQHzR2hoHJd5ecdRx2hUQZxGp7r5G3kC+VHClKLsNtp\nFkzan4XJ0mU3Y2A4JIhMB3gDMvVbiaMRjklnhHgpXpfwnJCskrhQkji5itQ4i7RToLStZPPWLEGr\neDVUc9ShMoeZvyf7h0LQ6RjNrj/tPNLnw6DX4Yk7TgOAgkVAtUBKPET7RenmS+fgb3d9UUD5IU6x\nJ4PhHQhGodcxmtBH8qHGkXuyIinFHQylevjQ84lSUEArkON15+oIWVMZ3m37++GwGnHMBK6ONE2I\nIE/IOULdScpctoEIt8usOmsViyfgD0aLTr0hjuqwL4K4ynoKYlxqSeGzmo2S6y/LsojH5VsoKIFk\ncSc0cMyKA13EEVI/bzTWcNRLIoSSL5Zv7MDdf1mD376wCUA6LfwotAFhihzNCBUHRx2hUQZibBNj\nwReIwmHlHKFAeOSKxH2BKHQMUEFlfdxOi4BK8lmYLCc1lcMfjPJ0EyBFy7BLGGlyYgmeYekFxGmV\nocapyAjRn2UqqNWS157KcJjw6PdOxpJbT0a12wqbhXOEhnxhuOwmzTjqdJG80SA02kltUi9F1Sw1\nECphsRYlvt4jg+HtD8WSSlP5X6d8Mo7kO0SNKxCO8WMnWccmOfUjjWFNzrc9xBHKkhpHMryHjnjR\n3R/AtJYKvteS5hmh5H0lrmMijRSrxWpZGeB2muELRhGNZVBEBdUKQOO6t0wgjtePfrcS3/rVu6qc\nIXLexVTtfCAXiPxgwyF85Y7Xsb8z+54qQz7unE5o5DLgJPiWjUiI1hkh4oytbDsMIEXvVKtGeBTq\nkMoIaTNHkCAJHaw9ihSOOkKjDLQBm0iw8CeL6WyWkaXG+YJR2K1GgaFQ7jQLjL1iUFkKjektHI1v\n2/5U49SARLZGr9fBZNQL5bMp8BkhsSNklxZkUKcaRwsHKE94Br0OJoMubzrHS+/vxqsr9vLHtFuN\nmNhYzo81EIph2B/R1EBSispbTAY4bSbecC1FKPbBKQDUKoAFQlHYrdpkWfgatBzuL3JPViazpbQk\n/Fe/MAUXnzYJ91y/SPb7WkKvYwTPdbbOK5EwJnL601sq+SCFFtHef729Ay+8xwkjkICEOBu8eQ/X\n/6wq24yQU73DNuRLOhdFzgiRjHqC5ZxVKQqlGORaiBsQ5wOrxSA5lxLFt9dX7st6n15/BAY9g9oK\nm6CVQjbzxpgqO3SM0BE62D2Me59cmxZsUwNLcv2JJ1gM+yN4bhnXzP2zsLaXEgjFVLuMUAR2i6Eg\nghmfBRx1hEYZbFT/l0A4BpYFnxGKxBKI5ZiGzxf+ICcFTSZpHcNlJehJW2uVrJHA9JZKAMA2SiWP\n8NPtomhLudOM4aB0NHVQoLaTgpyRlKLGyS+CQuGAzOfaapGOYrIsi/fWt2NAhVHx1Bvb+Nfi32Kz\ncNK2vmBUY0dIeV+Txpajs8+P3YcGNTumlqAzaMUAMVI8Ga6nPxjVLGLIqxLmkRGqS6r+LVt7EBt3\ncj2Fatw2fONL03PvW5IDBMqEWTqvOh0joItNb6mAzWKATsfkJPtMIxSO4dl3duKZpdsRjSV4B/uK\ns6aiqdbJ15awLBdhVpLTl0K5SkolwFHTAG2zLGogDiSReiglkCAWmcu1gM1sQCQaT6PBkXlv2bp2\nVeeRBgkgMQwjUITMxhk3GvSorbQLqHH3/20d1n7ajeeX7cpqPADAUhm3dZ928a/rKov3PH4eoH2N\nkDbqc59VHHWERhlSfSmifB2Jw2bkJ8fOXh/2dHg0059XC18gCrvNyC+eCZYzAmhj77MQjWiqdcJu\nNeL9jw8hFI6hzxOELxDhMiyi2orGGgd8wYSkwSPHreZVpkRiCSR6l0k+m9+PisXSZjZK1gh9uq8f\njz3XhluXLFf8vvgeE9eW0OPR0hHKpLb2pRNaAABrtnbJbtMzGABLSZPFE6xA+bCQ4AtXi0SNI7U2\nSnTBeDyBUCSe5sznCnK9+z3Z18EQR6jGbYXTZkSC5XrkWM16/rcUE/Q5ySWLR+o0AI5ayzBcfxc1\nvZ2U8OHmTv718o0d6BsKwqDXYdbEKjxxx2k4YVaqN1e2tDiAltDOfA09yYyQa4QyQgS9nsyZ4AOd\nwzCb9JpKl/NMDaqxOsuygozos+/sQDgaR0ePFzsODGSkyw35w3ytCKG4AdlLII+psmPIF+Hnel5x\nNgdHnGYqtO3iMo2P33ZKGkX5KPJDYRyho7Q4ORztIzTKYDMbwDCcYZyiV1lQkczyf/fhDwAA37lo\nFs5pbSnKmMLROCKxBJxWIyY3ubF6S8oAJYt9jUyPk9EGnY7B9JYKrN92BDc/+gG6+7mFt0xCLamx\nxoGNO3pwuNeHyWPdgs+IcSEWENDrdbBbDGm0hb6kQalEbxFQeFQYbHabEf3d6UYOOXamjBDtRM2Z\nXJ32udAR0s5AyiTv3JQ0POXGv2pzJx54Zj2+e/FsnLVoHACuV8cf/7MFd35zARbNVNfcNVcM+cIw\n6Jmi9dEiTfToujYxSOZGK2qc22lBjduK7QcGwLJsVnVHhGJEstwAsGB6Lb5z4ewR6QNCO/G5UL9I\nTZDVbOANRofNmBbsyAYebxi/eb6N/5u8riyz8Oeabp6Yi2IjqftQRY3zE2pc6WeE/KEonFajpjLf\ntPgM3djZF4yi2m1F72AQb64+gDVbuwSZoTu+Ph+L5zSk7Y80KiWBxTFUT7Rss7bknh32R2CzGGFI\nqjHmwh6hHaEVbYdhsxgUGzkfRW7QUiwhEo0jHIkXjYo9GnE0IzTKoNORaGJUkFWgI0YA0Nnrl/p6\nQZAq/jbx9SEERMZ4UlN52vdGK64+dwYA8E4QIL04KSn29HqCYBigwpUe4XbaTWmOUK8nAB2TqpuQ\ngoA+oWLSc1iNiETjvBQqASnSBZRpMfQkffuV89M+px0zLTNCRoPytFWeoSbmnbUHAQC/f3Ez3lvf\nDgB4/2Pu/z/+Z6tWw5REOBrHga5hjBtTpokogRqUOUywW42yBdMPPrMef0r+bi2Laae3VMIbiODB\nZz7O6nt0PRxxpiePdeeU1dACV587AxefNgnXnjcDk0QBDTUg92sDNUc7JGT4s8Gn+zh6V43biivP\nnoqLT5uEi0+bhFsuPZbfxkIFIsSZEzXga8tUZIR4alyRM0JiUZgeFWqRwXBMoLCpBXhxECoDROqm\njp2carJO5tMTZo2BTsfgxfd3S+5P3J+HXMtc5lFxdoH0G8tGchwANu/qxfsfHxK8N7W54jPB9Cg1\n2JP0WS0coa4+zhasy7IZ9OcJRzNCoxAOmxH+YITn/LudFtSKOLrt3cN4Zuk2XPaFKQWPotLF38dM\nqMRp85uwcEYdAOCGC2fimTe244YLZhZ0DMVEU60Tp81vEiwKUue4Jmm49Q+lGxK9niDcToukUe+w\nmdCeVOch6BkMoqLMqtgJnabgqOED080eK6jx0w5ErycgK8VNJumLTp0ouUDTxpeWjpBep+wIcQa0\nnlfmE4OmxD32XBtOXzCWP19ayIkrYXf7IGJxFjM0rE/IBIZh0FjjwJ5DHsTiCUHj1WF/RECx0jJg\ncebxzfjfxg6s/bQbkWg8jToqB1qO/iffPA4vvr8b5544XrNxZYuJTeWYmMd5ufyLU9E/HMINX0nN\ngQ6rEdFYAuFoPKf5mdS5/OBr8zBjvPy9VO40w+MN55R9IkGafhWO0JB/ZGqEdDqGr4V64b3daf3X\npBAMxzSvaSHnqtcT5BvXEuqZFJX3+q8cg/6hIHYd8iCeYNOcCUI1JPTEc1pbsPPgIK48a2rWYxM7\nQtFk4CtbKvCS5zbyr3UMR3+vHAGq6ucBDMPAZTdpohpHAmC0fXAUQhzNCI1COGwm+AKpjFC5yyyg\nQQAcf/eF93Zj6ar9BR8PrVGv1+tw6+Vz0ZrkpzfXuXDXtQs/c6oyxyUdPQIicUpDjucbT7AYGArK\nRrhdNhMiSSMJ4Oo3BoaCGekt9OeqMkJ8vZlwfLTMspJhkUkyl85SarlgknNNauzmOX8AACAASURB\nVIGkUO60yMpFD4mux5qtXehKZlBjcVbgKGkNIrJB1AeLhbG1TsQTLNq7hdnJw6Is0RkLxmp2zGMm\nVOHLi8cjFk/g9ZX7+Ah5JtAqjFPHVeCn1ywc1bSOarcVP79+EcZQc7Tcs6cW+zqHwDDI6KB958JZ\nAJSfFTlUJwUp1NDNRko1DgCuOmc6Lj19MoDMgYxoLIFoLAGbWdv7KZX9Tz1PPqqtwnmLU478hMYy\nVJZZUVFmQSLBpvWMA9JlqV12E+6+7nhMaMzeIadpVizL8k5Wto4QPc9///K5AHK7r45CHdxOM/qH\nQnmvR4SRImYNHUUKRzNCoxAOqxGRWAJHkjQAt9OcsW6ikPAXWQ64FHDCrDH4x8/Pgs1igDcg3UhQ\njufr8YYQi7Oyjk2qG30E5jIrBr1hJNjM8rc0511N9J3OCAnHlzJY/YqOULJAWsYRaqIiUNPGaWf4\nlznMePXh86DELKtwWbB9fz/8wSisZgN/bhIJNq1W5v6n1vGvY3HOAc1WYUstSCR/WpEdoektFVi2\nrh1b9vRiXL2LPx80bfOsReMEdCotMHNCJV5fuQ9PvbEN7Ue8uDVpQClhpHrSFBP0s0f6JWWD3kEu\no5wpm9Q6awxefujLgiygWljNBjhtRlUCBEO+MBgm+z5LWsFs0kPHZO5bxdMuNabGpfr1pJ4nQm92\n2ky4+LRJuPrLMzhRoeS1p/t7ibPupNmuFk2o6YCcLxhFLM4mxxdFNJbISDUmoGmzp85rwomzG1R/\n9yiyR0O1A/s7h9E/FMpa+p5GKiN01BGSw9G7eBSCLKIdR7hJV2my/P/t3Xd8FHX+P/DX7maT7GZT\nSQihIwpIR5QuAoegCBZEQSGKiBXQU78CIsrpeXbuPMAuHihwongigojlZ0OaBJUmSidASE/Y1E02\n8/tjM5PZyWxLtiXzej4ePiS7Uz7z2fnMzHs+zdvZthvDKusjpCXxligYIwxIiotW7XjrqkZIHL3L\n1cVNOYS2mL/ePBi+vWA0ljwywqv0exMIuRvZSkyfq8lb2zjVCPm3f4der3PbxyYhNgo1AjBl4ef4\nvyU/SG/V8orLUWmzo1uHRDx992DcNu7ieus2dlhjVwRBwKETBUhLjgl6DWn32uZTyzccwLxlP0qf\ni0Hh9Vd0Dkjz1QE90vDI1P6IMOjq1T654inAbg7qXnb4fq7ZawTku6lRVmpIECRKSTAjt7Dc41vp\n4hIbYs3+mzTZVzqdzuWkpnLezMfWEGkq8/XIW0rodDpEGPRIiI2Sggex/88Di7+rV1uaXVDmtExj\nyCfnVPb38raWVr7sa3NHAfDcV5Max10fY1+czrEiMkIv1fBSfTyTmyAx4DiX7+hAH+Pm7VZDm174\nItgTRDYV0ZEGGPT1Z4cWOxa7usnFyoZIl//fm+EvW7WIQafW9ZvpqXH1MCZPb3GpDa99/JvUOVtO\n7PvkKsgxRUXgzmt74LHbL/MqPf50zdBOGNijFZITTDicWSQ17xEfVC7p2hL9urbEdcM711u3MaN5\nuVNhs6O0otppBKhgSWsRgxtHXojk+GgcOlkoNYsRRyO89vLOjXpgdsWg12HEJW2RnGDyqmYBcATY\n8hHWmiPx7brag3tVtR1L1v6CI6eLVNf1VKPsTymJJlTY7B4HdjhfWhn0EeOUxAmc3ZH6n/k5EIo0\nGpCaFOMU7Hu6L8pfYGYcypb+XVVtxze7M2GOjvBLnz35CznlADKumg+rKbRWokOrWL8OO06uiTU4\nmdnevUBSIwgCTueUoHWKhYNauMFAqAkSL2wltc1+xDfji2YOqlfL0JiRiVypqq7B3hNl0vCbvjyo\na4lOp4M5ylCvRkisZYlxMaGm2Ln2fG0QK8074+cJOMXtKWt95OfMdxmZ2LztBOa/urXe+mLNlrs3\n09dfcaHUXyyYenVOxsIZA3HdcEfbfLFJWl17acfNXN6E8MLavkcNmXHdG1LNaZAmUpXT6XSYPr6H\nNOjA77V9lQqlJjiBTVNKghkF5ytRVa0+wbCctXYiyebMLBtuWWnrb2fx1a5TLufx+jbjNAAE5Q2v\nOAiAu6H07fYaWMuqQtI/SM7VBNFygWoaBwBtUy0oKqmUyrmn67Z8VM3DmXVBb1ZeKYqslRjSq7Vf\nRnFUC4TEEUYLXQwoo2SrsqO0vKrZ9fUNZ+IAWI2Z3y6/uAIVNjubxXnAQKgJktckyKv4L704Ff95\nYozTnC6BCITWf38E/9tWgLfW76vdR+ge8MKdOUpfPxAqcz2aEFDX4VgcFVAKNP1c4yYGrtbSunNE\nEASUlFVJtVJn3AzDnltYhsgIfVg/tHZt7+iLc/SMY/JCccjztJS6WhkxAOrfLRVAYMoM4H4UqWDp\n2sEx/PPRM44Hr0JrJWLNxoDXvqS4GEGxorLaaT4TQRBwXgOBkDQBp8qDu7vJsAVBwKrNvwMA2gXh\n4cabJnznpRHjQhwIeVEjFKimcUDdy5U/TxUC8PyCUP5weuxM3eSq4gA14kAJjWUxR0KnA/KLy5FT\n2+TugjaOa563NUJiufVXmsgzV03XfZGV77h/p4WgFUJTwkCoCZK/lVF7s/XY7ZdJ7XgD0TRObL8s\nDh/NpnGumaP0KKuoRlV13cOelF8ubpCtax/Sz9SO/+9p+YYSA+oiWTvxCpsd9hoBacme3zbnFjn6\nKQRrPpyGEDshu+tv9cy9Q/H2gtHSW9JANY0TtxsTwkBIzA/xYavIWuGXDtmeiM245COQVVXbcdOC\nTXhuxc/SZ5U2x+TMWgmE1B7c3T34FJyvgL1GQHKCCaMubRew9InEa7q7fnPiKIxxIW4aZ44yotpe\n43StVZJGJAxAICQGpn97ewdqaoS6QYRc9KHs1Doer80dBVOUwakW2t+DDxn0OlhMkfjzVBHerw2i\nxebTBV7WCIl9CduwZiFoXLXY8EWZdC417+tpY3HUuCZIPsKM2jCg5mgjzNFGREUaAvJ2W3xIqbTZ\n8X///kEaKIBN4+qLjnS8ayirqMK2fVn45Nsj0rwcri5O4lDom7cdx5mcErRNdfztalCChhLn+ZF3\noBUf1lOTYvDnKec+CnZ7jTSPUWWVHcUlNnRK864/UqiItS/iw4Va7VqMyYgYkxHHzzreyv577S8Y\n0b+t3/vMhEPNqfzmWlVth7Wsyus+ZY0RK2vOKzqS6cjvXQfPSZ8Va2DEOKCuWZRajZC8PBaXVDo1\nOTtd21/gL5e1czunmL/EePEwJg2dHeoaIVmeGiPUzx+xKWIgmsYN7dMaSz78FQBQYatGSXkVdDr3\nQVe71FgkxEY7vXzx1HS6ISxmo1OwJfY92rEvCz/8chrdO7XAnJv7ulxf2aSYAq8xA6qIxOuLOQDn\ne3PCGqEmSF497a6K32IyBuTttvxh5o9Thfj9RAFizcaANDdo6iIjHEFieWU1PvvxGLLyS2GrnR/I\n1Rs/c7QRLeKjUW0XsOePHHz+0wm3yzeUxWREhEGnOkpcfExkvSHZ5W8P87zoHxQOzNFG6HR152xp\neRX0ep3qudq1Q92Q1q46qjdGoJo4+kJ+cy2yOn7rpLjA1wjVNQVz5IHdXoPFazLqLXeydiLh5j4L\nurumcfIO7fK+IwCQGeQHUq+axtUO/hIOgyUArucSOn62GG+t3w8A6BiAFzjmaCOG92sDwPG7Wsts\niIk2qo4oKmcxGZ3uqYHoc6scPVZsInvsbDFO55Tgy50n8eo69UFxAA7BHAoRBj1MUYZGPcOVBbAp\naHPCQKgJ8tQ0ThQfE4XsgjKczW34qCNqxLll5t12qfRZ904twrqJVKhEGR1FLLeovN4wmO5udPIJ\ncsU+FP6ucdPpdPUmHpU331KOapd/vq5ZU27tHFbhPiSnXq+DOdooNRG1ltlgMRlVz9WkuGg8Oq0/\nAODgsQKf9lNaXiW1v3clUE0cfSHdXMurgjoJpvTgX9s0ae+RPKmJLQBpeGZxUItgTzgbbO6axslf\nTIj5IRLzLK1FcMqdWKPqbmLl4tLwqBEyuwkuAeD9zb/DVmVH3y4pUh8Zv6ehdnCDsopqlJRVeVXW\nLSYjqmon0BYEAQdqf3N/vjCx2ZwHKYk1R9brq/jF9hP4z2cHVNc/dqYYEQZdSEa81LIYU2SjmsaJ\n19tA1IA2JwyEmiB5dO8u0hfb897z/DeNGnlESXyg69ulpfRZj9p5SshZlNHxwL33cB6UU3G4++3U\naloCMWluYmwUCq2V0oOovPmW8gFZ/mZK7OsRjCF8G0v+xrWkvMrtA0b3To7zWPkA6smcxd/izn98\nhcoq16Oi1Y0aF9ompI6ba5Vs/q/Ap0caJa32ITVT8VKgtPaGfTizCDod0KV9YsDTFEruhs8WX0zo\ndMAfJwudvpMGWglS00Fv+imIfQxD3UfIFO06uKypEfD78QJEGPR4YsbAwKVBFox5utaILNJ0CTZs\n/fUstu3Ncnzux+tEZVX9PFF7GVRWWT/graisxtEzxbiwbUKzHtI+HClrC30lNY1jjZBbDISaKLG2\n3d3DtLwaOyvP9ehfviopt8Ggd8xf9Mw9Q3DX9T0xdlAHv22/ORGbxokPfvLmI+5q0FISnN/4mqIi\nEB3p/4tZYmw0qqprVPvQ6BXpk1+QvRk6O1xYzI6biTginrsH/+QEE1ommnDweIHLSSQFQcATb2zD\n8g2OZjb2GkEKDJUTFsqVeOg8HSyOJrM2KT2xQQjMlE3BxKY24oh9Yu3U+VKb1MexOVNrxmWvETBv\n2Y84fvY8WifHIDE2yqnWDAh+PzNvRq4Sa7D8MflnYyRY6g/+Inp2xS6UlFfh8r6tnYbM9zfxdz1f\naoOtyu5VWZfn8f5jeXWf+/E6Id9WWm2zU7GlQZf2dXMVqTXD+jOzEDU1gvSSiIIn1hyJsorqek0b\nvRXIURKbEwZCTVRS7SSW7kbzkdcg5BX7sUaorAqmSD10Oh36dEnBtZd3bvYPLg0lNo3Lqh2G+vor\nLkSfi5Jx+zXd3a6nnA8qIUAPGdIcE9LcF66bb5XKOttKNUJNIRAyGVFZO5lptb3G40Nk904tYC2z\nYcXGg6rfZxeU4dfDuVj//VEAwKlz56XviqyuR2EqDYM+QoDjty2rqJZG0osJQmCmbBp3JqcEOh1w\nce3DlRgIlVZUuZ0gurmIjjRAp3OuEcrOL8XB2vmdEuOikZJgRn5xudNDkNhELRC1w2q86SMknvPB\n6GvmjthkvEjlZYQ4EMq4oZ0Cmgax5lNsOuxdjVBdHsfJmhf68zqxaOYgDO6VhsG90vDkTEeN2Lz0\ny3D14I54+u4hGHVpO6e+lHLZtVMOcCLV4LMoBvvxlTRKIp/P3GIg1ES1qL3pKOflkBsqm8hSPmxt\nY1nLqqTR0Mg9sWnc2TzHG/B+XVLwzL1DMWnURW7XizE5PwwG6m2r8kFHqiVQeTh2rhFy3ByT45tC\nIOQ4lk9rAxdPDxiDe6UBAL7adUr1e/FhVXTyXF0zL3fzcoRDHyGg7vjF2oZgBGZS34naB//conIk\nWKLQsraPmRgIlZVXaeKmrdM5BuyQB0KnZX05E2KjkJxoQrVdQJHsnCotdwSKwZol3hQVgUijAQVu\n7jOF1gpEGPRBC85cSagdRKhQ5WVEeWU12reKRbcOge17Jgb8Yo25dzVCdU3j5CO7+XOkrw6t4rBg\n+gAsmD5AGmjjkm4tcf+kPogxGfHQLZeg70UpUl8lucIwCXS1SKzlzG/gi2zWCHmHT7NN1BWXtAUA\n9L4o2eUyyQkmLHt0JADH6DD+YK8RUFpRBVMUTx1vREY48qmitrNqopc3E+Xbt0DN6C0FQlLTuLp+\nLJd1d0wwOqR3mtMygCOwToiNCmgzE38R+y588NUfAOrXtikN6d0andvGo7Tcpjq55VHFiHLyB1W1\nhzBRSbkNEQY9okKcZ9JkubXN04IRmCmbxpVXVsMcHYGE2t+mqMSR12WV1SF/oA4Wi8no1D9PHBob\nAGKijXVzL9U+VFfba1BSZgtKDZ5Ip9OhTUoMzuSVuJzotdBaicS4qJAPliM+NCrLoCAIKK+sDsrD\noLiPnILaQMiXGqHyKqmWduWisUHPTzFoKy6pdGoWLDb35WSqwSf28z7TwAGvpEBIA7XsjcGn2SZq\n/LBOeGH2MNzkoWZBvJn+9NtZ7D+a53ZZbxSer0BNjYB4c/g/AIcDsUYIcHR+jveyk3OHVnGYO61u\nVL6A1QjVvo0Um23Jh269/orOeGH2MKkZn/i2UhAEx2SqTWCgBAC4qG1dG/j7J/XBzaO7eFwnKS4a\nNYJ6Z3bxYQVwzKdUKBtWvNDNBIVi/6RQPzC2rh2R8M9Tjo74wagRUjaNK6uohinaKA3IUVxSifLK\nagiCdua8uLBdAoqslVLNnHxUyWp7jdTsNDu/DPuO5OGGuZ8hr7gi6E0r27aMRaXNrtq8WhAEFJ6v\nDHn/IEA2L5qiVraqugbVdiEoHcalQMiXpnGyPkJizai/54zzhpiOO5/5Ch9/e0T6XAwsA9U8m1wT\n+3mLfSp9VVZRBYNeJ72QJXXMnSZKp9Ohe6cWHifVM0cbMayPo4nczwezG71fsYkdAyHviH2EAMfw\nsr5Mgtj9grpmHAkBehtXdxN27iMUUzvEdPdOLaSbshgkFZfYUFVd0yT6BwHOIxpeNaiDVzUO7jqJ\nO8/5YXN68HLXNM5a5t0oUoEm3lzFTuXB6HhvjNDDGKFHeWU17PYa2KrsMEdFSA9XxSWVKK0dOCBG\nA03jgLoRCp94cxsee20r9h+tG6mwRhBwQe1Et4czi7B6yyHpu+AHQrUPY9n1H8bWf38U1faagNVY\n+yImOgLGCH29AUuC+VZcDOKz8h19Qr0Zml7ePNkxWIjjOIJNXjO8clNd/8hCawX0Ojj1X6LgEJsx\nZmZbPSypTqwJDfXLt3CnjVdvGvfg5H7Yti/L5yGB1Yh9Q+JjeOp4Qxw1DvC9aYG8r0RagCaYtCjm\nCVFrviWmQwwAxHNAObJduEpLjsHAHq1wQZt4r28I8iFtU5Ocj7NE1o6/pLzKqRZIOcqXSBAcTUrb\npIR+Hg7lpIjB6rNkiopAWWUVymubiZqiIqQHxY1bj0uDAmilRmhgj1bY8MNRFJyvxLn8uvOmY1oc\npo7thsS4aEQYdNi+Pws1tSN8AcHvYybOHZNdUH/k0S07TgCA1Iw2lHQ6HeItUU41tkBw+0mI+5BG\n0vPimi++aDp+thgnss6jVZDmiFJSBtjvfe4IhjKzSxBviQpavzSqIzbjdtcX3J3SimqYw+DlW7jT\nxh1H46KjInBBm3gcOV2Eyio7oowGFForYDFF+vzmiTVCvpH3pWrp4+Sj0ZF1eRyooUvFm9+R2n4v\nas23DHodYs1GqS+M+NDWsonUCOl0Oiz0ce4Q72uEqlBorUBMdARMUREumzCUV1ajpkYI+dDZANCq\nRQxizUZYy6qQnGBChA+1lI0RazaiuMQmDRltio6QRi0EgM3bTgAI3ohoodaqRQyWLxyDXQfO4e/v\n7gTgCI7k52qPC1rgt8POTZoD9VLEFXHS5BzFgDtF1kqcyS3FJd1aYuygjkFNkytx5khk5TuXwWCO\nnKXchzc1ZeL5vvPAOae/gy1NMVnqR98clv7dq7PrvsgUOAa9DtGRBpeTBHtSWm5DWrLF84IaF5JA\n6Nlnn8XevXsBAI8//jh69eoVimRoSvdOSTiSWYTDpwqRnGDCXc9+jWF9WmPebZf5tB1xYta4GAZC\n3oiRBUK+9qmRByOeOvg3lPhgvn1fFrbvy0JJeZXTw6modYoFRzKLUG2vkTpuitX2zZEUCKkMG6xs\nGldUUomE2CikJJjx6+Fc1Y7ZJWEydDYARBj0WPp/I5FdUIbWQbxJpiVbcOb3bOlliikqQjUI00rT\nOJG8iWmbFOffY8H0ATiR5RiePTXJjLyicnSW9XkLSvrEQRsUgdAfJx2jJ3bvGNiR2HxhMRtRftaO\nquoa6SVfMGuElOXbmxoh5ToP33KJX9PkrUE907DkkRGIMRlReL7Cadj2Dq3iQpImctSQNyQQqrbX\noLzSHhb3nHAX9EBo165dOHXqFD744AMcPXoUjz/+OD744INgJ0NzundqgQ0/HMPB4wVoXds8Z+tv\nZzHPxfJbdpzAq+t+c3rDFR1pkG4ucSYGQt6QBzNJ8b63o3/mniEBbdsuv0g+u2IXgLqmMHLtWsbi\nj5OFOJdfKvUVUDaxak6kpnHlzs1sxElZRdayKljLqpDWIgZtWlrw6+FcnMkpwYXtnB9Wi0vF/jjh\ncVNqEW9CiyAPfd62pQW7f8/G4UzHIA2uOq9rrSlHiqymuEv7RKfvzNFGp9rgYP9mgOO6pdfVNYkV\nic1Aw+mFSGxMXZNWcfAEqQYySE3j9DqgRoDX/WpMURHQ63XSqHwpPrYc8BedTodOtf3SfG29QIFj\niopAaYXvgZB84CNyL+g98nbs2IHRo0cDADp37ozi4mKUltZve0z+1THN8UbnXH4p7HbPsxQv++g3\nCIJj3oqkuCi0iI9GfnEFzuWXQa/XcfjsBnA1/Kw7fbqk1Hs48qe4mEiMGdjB6bMR/dvVW04+ek1m\njhWRRkPAaqnCgauJJCur7NKM7ABw8tx5qcmb2JdIbXStbzNOAwC6dAjcbxnuxAfmw6cczTDFB9N7\nb+iFEbXTAQDOo/xpgXwC2Ys7hU/tiijCoEdSXDRyi8pRUmbDio0HcPB4Pt7+dD+A8JpUWexvI5+P\nR3ybHoy+Z3q9Tmra5m2/Gp1OJ70gEZtCEYmU8415S3yJF4zBcJq6oD/N5uXlITGx7mEgKSkJubm5\nwU6G5ojDmxZaK2GvqXH67vjZYhw7U+xy0tWlj4zEsv8biU6tHcFUrNkIPUch8dq9Nziafg6RTXAb\nLnQ6Hebc3NdpgtdrVGZfF9uPZ+WV4kxuCdqkxEDfjDvPinOSFNSOQFVSZsPW387g292ZAIButQHN\noROO5kEWs9GpjCntOZSDmOgIXN63TcDTHq46pDkCof3HHH1exJrOa4ZdgIdvvQRtUmLQr0tKvdq0\n5k6n06HHBS2Q1iImbCetTEu2IK+oHOv+32F8/O0RzFu2VfouvAIh58FfgOBPKik+ePoykp6Y7lhz\nJEf4IifmaCMqbXbY7TWq3x84lo8ffjmNc/l1FQrnS204cMwxOFa4tEIIZyEfLEEQBBb8IDBFRSAq\n0jFIQrmsmvVcfikeWPyd9Pdni69zmkwt1hwpDfncuU0Cjp89j0qb88zT5N64oZ1w9ZBOYR04dK99\nEz2wRyvV78UHtCOZRai02cOqOUwgiH01Ttf2h1qx6SC27Dgpfd+1QxKOnT2PQyfr5uIRH3yKztcf\n4ed8qQ1J8dFBG5ggHHVuEw9jhB4FtaPsyZvG6XQ6vDr3L4Dge61pc/Dc/UPRgArjoOnWMRH7jubh\nlz/qv7SMD6NhldVqhMSgKNh9z3wJEB3pLuXEl1SPNAebzQ6Lyfn+kZVXivmvOl5KtG8Vi1cfHQUA\nePz1n6S+hWwa51nQS13Lli2Rl1c3Ck5OTg5SUlLcrpORkRHoZIV0f8FijgRy8q3440jdA90P2391\nWiYjIwOlFY5AxxKtx8wxSVJ+2MqKAQAVtYFQc80nf9uzZ0+ok+CRHsC0Eclol6JX/V0LSxzB855D\nWY7lq8/7/fcPt/PJYtLjWGY+MjIy8POBc4gy6jC6bzz0Oh26JJdhp0WPc4WOsmAtysPZTEefiT+P\nnUZGRt3buZoaAdYyGxJjGn+M4ZZHvkpLjMCpXMdDataZU8jIaPwkz2qaej4Fkzd5FWl3tBY4dra4\n3ne//BI+17f8XEe523/wMIyVZwEAew85am3zs08go+JMg7bry/l0vtSRV7YK76+RRp3j5cD5kvIm\nfe425bQHky/5VFbqCGh2/byn3rQle47W3WdOnbPi+592ISZKLwVBAJCfm4WMjIbNQxRqwTqfgh4I\nDR06FEuXLsXkyZNx4MABpKamwmx23zGvf//+QUqdI+ODub9garXtR/xxqhCxCckAHAUlJa09gLqH\nkZ69++K1db8BAEZd1hGjLq8b0e+U9Qh+OHBA+ru55pM/NaXzyV0yK2zV+PeGTSipcFTPD+jbBf37\ntXW9go/CMZ8u2FWBvUfy8NGOchRYq9G/W0vcO2Ww9P3OY7txrtDxYHVR5w4Y2r8dXvt8MyKiY52O\nxTFb/Bm0adWiUccYjnnkK70lB5t+Og5TdARuGNsrIMOJN4d8ChZv86pr9yqs+f7zehV2c9MvRf8w\nau5ZHZWFT3fsQlJKa/TvfyEAYO22H6HXl2P08MtgjPC9/42v51PVR46XRe3atEL//t6NiLv/3EHs\nO3EYpRU1TfbcZbnzjq/5tPPEb9h34gQu7HIx2itG79t6+BcAhRjapzV++u0s1m0vRZSij1mPrhei\nf7/wKaPeauj51JDgKeiBUL9+/dCjRw9MmTIFBoMBTz75ZLCToFkJsVGoqRFwNq/uLYKyX1Dh+Qps\n3+e4kPe5yLmmbtSl7fDF9hOYdvXFgP1cwNNL4SM60vlSEah5jcJJWnIM9h7Jw4Fj+Ygw6DBcEfi1\nTa1rHmgxGWExGRFh0NXrIyRO8Kg2LLnW9OvaEv26tgx1MshHFpMRHdPicPzseafPw63PmzRqnGy0\nx9M5JWiVZG5QENQQtqq6CYO9lRAbPs0LKbyITYjLVAZMyMy2IsKgw+TRXfDrHzk4ec5RPnU6Ryvj\nWLMRndvGBzW9TVFIGqQ+8sgjodit5rWo7echrzbNKXQeEvXYmWKUV1ZjSO80DFD0F4m3ROHNxxwj\n/mVkMBDSqvm3XdasR4wTydv4v/LQCHRIc34bJx8+3GIyQq/XIS4mSppVXsRAiJqDPhel4PjZ80iK\ni0bB+QoM6K7enzCUxD5CYpkrLa+CtcyGrkEcrXHsoA7YsuMk+l7kvsm/XO8LHROWDusTfgPqUGhJ\nfYRUhtAutFYgITYanVrH44N/XBPspDUb7JmnIa1rO4CLk6ICzkERAOw7874CeAAAIABJREFU6mgm\n166Zd4anhgvHIX4DISWhrsmuWsfndrIaIXG+kBiTEUVW58ES6gIhvvWlpmvqVd3Qr2tLdG4Tj9KK\nqrAc4U4MhMRh78VBE4L5EuKeG3rhhhEX1psc151OrePx5vy/oIUGXjCRb8QBNEornKdyEAQBhdZK\naWoUajgGQhqiNgHmHycLERmhR/u0OBzJLMK+I3kulyVtW/zgcJzNKw3LB6BAkAc/ZpURpzq0isX0\na7rDVmWX5geymIw4k1viNBoma4SoOYiOjMAltc0a4y3hGdRbpOGzHWWupLx2UskgDiFsjDD4FASJ\nWjdgHWr+Umsntz2X79x6p7SiGlXVNT4N007qGAhpiKshj4f1bYNrhnbCI//+ASfPOUYX6dia7UrJ\nWZf2iQGd3DXcpHh4O6vT6XCjbP4lwDFpY02NgAqbXWrS4BgsAYi3MBAiCqQIgx7m6Ii6QKhMnFSS\nQwhT0yT2RT2d4zzyW2HtNA2JceH5UqIp0e6kFhrUIj4aalPZXNotFRe0iUek0dGZNCY6Au1T2TSO\ntK1FvCMQ6nNRstfriHOVlMmaMRTWNpXTSk0aUShZzJGwlipqhAIwOiFRMKQmmRFh0OF0TonT52Jf\nVA600XisEdIQvV6HGJMR1rIqXNGvLW4Z2xUGvQ6pSWbodDp065CIvUfycHGnFmE9+SdRMBgj9Pjo\nuWt8mgTVXPvmubS8SgqkCnnDIgqaOLMRmbUPjWJfIU4qSU1VhEGPtOQYnM5xbnItvmBLDNNmqk0J\nAyGNEQOhantNvXbM90/qg10HzmFQz7QQpY4ovCiHDfckprZja1lFNfKLyzF32VbkFJRBr+NgCUTB\nYDFHotJmR2WVPSR9hIj8rW3LWGRml6CopFLqE2StDfJj2fe00RgIaUyM7I21UpsUC24YcWGwk0TU\nbIiDKpRWVGF/Rj5yChwdXOMsUTCwlpUo4MS+fTkFZbI+QnxYpKZLHLzqdE6JFAiJc2Xx3G489hHS\nmJnX9gQA3Dq2W4hTQtT8SDVC5dUol02Al8DmC0RBIQ4K9Ml3R2R9hFgjRE2XPBASsdmn/7BGSGN6\ndk7GZ4uvC3UyiJolqY9QRRXOyG5aqUlmV6sQkR+1TXU8NH6165T0YoIPi9SUid0Y5PeUUjb79BsG\nQkREfiI2PbWW2XAq2zHc6bzbLkWPC1qEMllEmiGfDLy0opr986jJa1n7Ii23qG4uIXGIeI6I2Hhs\nGkdE5CfisPN7j+ThdI4VvTonY1ifNpz0jihIWrUwo2fnuhcP8eyfR01cfEwUjBF65BaWS5+JzT5j\nWCPUaAyEiIj8JDXJjMTYKPz6Zy4EAejeKSnUSSLSFJ1Ohzsn9JT+5ksIaur0eh2SE0zILZIFQmVV\nMEdHMMj3AwZCRER+otPp0K9rS+lv+b+JKDgS4+qawiXEsVkcNX0pCSYUWSthq7LjbF4Jiksq2T/I\nT9hHiIjIj+6f1AdXD+4IU3QEOrSKC3VyiDQnXjZKYyInMqZmICXRMSz8oZMFeOKNbagRgIvaJYQ4\nVc0DAyEiIj+KMhrQrSObxBGFSoShrrELm8ZRc5CS4BgwYc+hHNQIQO8Lk5F+9cUhTlXzwKZxRERE\n1KzcOqYrurRPwOBeaaFOClGjiTVCB48XAAD+clk7vnDzE9YIERERUbNyy9huuIUTh1MzkZLgCIR+\nP1FQ+zfnpvMX1ggREREREYUpsUbI1d/UcAyEiIiIiIjCVHJCXeCj0wEt4tn3zV/YNI6IiIiIKExF\nR0Zg2tXd8OfJInTrmAhjhCHUSWo2GAgREREREYWxyaO7hjoJzRKbxhERERERkeYwECIiIiIiIs1h\nIERERERERJrDQIiIiIiIiDSHgRAREREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzGAgR\nEREREZHmMBAiIiIiIiLNYSBERERERESa07QCoSFDgNhYx//Ff4t/i9+L/5avI19euZ6rdWNjAYPB\n+TNX29Lp6m/Pl2NxlXa1z5V5IE+PwVD3ncFQ95/yuOX7Ff+WH7dyeYNB/RiVaVCmT8wbeT4q0yxP\nizd5pbZP+TLutufp/PGU3/I8Vm7HxTa6zphR/3dWS6Pa7yn+Lu7OK1e/v3I9+TmhPHZXx6KWF+I+\n1NIA1D+f1I7X07Epy574t/JcVstDtXNbfuze/P7y815cR/5vtbKvXFd5jrg7xzzlS236+w4f7pwf\naseqzH/Z+lJZVLsOqKVFbR/i7+Lqd1Mes3Jfyjxzlx/ufgvlMaqt6+5arZYOd9tSO0/k6XBX1pR/\ny5f3Jh1qZVV5nGr3K7X8UOg7fLhzHqvdU5X3koZwdw1TW1b5O6udC67KlqvvXV031PYvu+dJ13C1\nNKmd52r7kK8rv58qj81TmVQrN67yRv63q3NMuZw8HcrrpTd8yRN363v6ztW/RWrH4e567+l67C69\n3h5bra4zZri/HinPFeW/3d335Mu5WtbVtdbdfVG+nNq9VHm9cHefdrVvxfF0nTGjftrcnRvisTeE\nEOZ2797t+MfgwYIAuP7PYqn79+DB3q2jsq61d2/nbYnb83Zb8v27otyWWtqVy/maBm/+0+ud06A8\nbjf/VZvN7n8DT7+Rt3nm6Zhd5Ytye75upyH5rVxHebxq55WntMl/I1+OSb4vH35Xl+ek2jmrTIPy\nfFKuM3iw83Zly1t793ZsT5lW+TbdnTfKfXlaz9vzyNv/vNmf2jkmcpEvXv12avtW+3085aFaWjwd\nn7huQ88xV/nhTbrFY1Sw9u7tezpcUTuH3Z1r3uSDq2XU0uFqWXfnq7vrhKtzrrHpdMfV/r3J74aW\nLU/H0tD7ja9p8pTPnsqV2m+ldm1tyLnekPubG16VO3fbcHeeuLqvqj07eVMG1c4XV2XD1fnubn1v\njrEx//nyjKu2rLfXWl+uyY19vnP1nOFpW4p1pJjBB02rRoiIiIiIiMgffA6dgswpuhPfKIuRo1iL\nIY941aJF+fLK9RTrSvuzWBxva5QRsdq25G9pvHkroDwWV2lX+1yZB/L06PV13+n1df8pj1u+X/Fv\nkfx78d/iWyvZfnfv3l0/Dcr0iXkjz0dlmuVp8Sav1PYpX8bd9jydP57yW57Hyu242IZUw6iWP2q/\nszJtamlSW0/5+yvXk58TymN3dSxqeSHuQy0NglD/fFI7XpVjcyrnyrIn/q08l9XyUO3clh+7N7+/\n/LwX15H/W63sK9dVniPuzjE3+SLP92qz2Tk/1I5Vmf/y300si2rXAbW0qO1D/F3UjkHtmJX7UuaZ\nu/xw91u4qZWod21S27daOlxxVcbl6XBX1pR/y5f3Jh1qZVWZd2r3K+UxqHxXbTY757HaPVV5L2kI\nd9cwtWWVv7PaueCqbLn63tV1Q23/snuedA1XS5Paea62D/m68vup8tg8lUm1cuMqb+R/uzrHlMvJ\n06G8XnqgWu68OS5lerw5h139W5lXyvPa1fXe0/XYXXq9PbZa1t693V+PlOeK8t/u7nvy5Vwt6+pa\n6+6+KF9O7V6qvF64u0+72rfieKRWIvJ9uDs3ao+9ITVCOkEQhFAHY+5kZGSgf//+zXZ/TRXzyTvM\nJ+8wnzxjHnmH+eQ95pVnzCPvMJ+8w3zyTkPzqSHrsWkcERERERFpDgMhIiIiIiLSHAZCRERERESk\nOQyEiIiIiIhIcxgIERERERGR5jAQIiIiIiIizWEgREREREREmsNAiIiIiIiINIeBEBERERERaQ4D\nISIiIiIi0hwGQkREREREpDkMhIiIiIiISHMYCBERERERkeYwECIiIiIiIs1hIERERERERJrDQIiI\niIiIiDSHgRAREREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzGAgREREREZHmMBAiIiIi\nIiLNYSBERERERESaw0CIiIiIiIg0h4EQERERERFpDgMhIiIiIiLSHAZCRERERESkOQyEiIiIiIhI\ncxgIERERERGR5jAQIiIiIiIizWEgREREREREmsNAiIiIiIiINCcimDurrq7G448/jszMTNjtdsyd\nOxf9+/cPZhKIiIiIiIiCGwht2LABJpMJa9aswZEjR/DYY4/ho48+CmYSiIiIiIiIghsITZgwAePG\njQMAJCYmoqioKJi7JyIiIiIiAhDkQMhoNMJoNAIAVq5ciQkTJgRz90RERERERAAAnSAIQiA2/NFH\nH2HdunVOnz3wwAMYOnQoVq9eje+++w5vvPEGDAaD2+1kZGQEInlERERERNSM+Dr2QMACIVc++ugj\nfPnll3j11VcRGRnpcfmMjIygDqgQ7P01Vcwn7zCfvMN88ox55B3mk/eYV54xj7zDfPIO88k7Dc2n\nhqwX1KZxmZmZWLt2LVatWuVVEERERERERBQIQQ2E1q1bh6KiItx1113SZ++++67Ub4iIiIiIiCgY\nghoIPfTQQ3jooYeCuUsiIiIiIqJ69KFOABERERERUbAxECIiIiIiIs1hIERERERERJrDQIiIiIiI\niDSHgRAREREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzGAgREREREZHmMBAiIiIiIiLN\nYSBERERERESaw0CIiIiIiIg0h4EQERERERFpDgMhIiIiIiLSHAZCRERERESkOQyEiIiIiIhIcxgI\nERERERGR5jAQIiIiIiIizWEgREREREREmsNAiIiIiIiINIeBEBERERERaQ4DISIiIiIi0hwGQkRE\nREREpDkMhIiIiIiISHMYCBERERERkeYwECIiIiIiIs1hIERERERERJrDQIiIiIiIiDSHgRARERER\nEWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzIkKdAJ8NGQLs2+f4d69ewLZtjs8Ax78bsi1x\nO41Z15c0uDsG+efK7YnLiMvJ0y3fv6u0KJdxtw21v2W6zpgBxMT4duzy9HtaXm2bnn4v+ffu9uEu\nH71Jj7d5vW8f+tbUAHq9d+eJ8rzwNn3e5KtampXbd7WMt9tWLhMb6/i/1aqeDnfcHZOrc0P+mTe/\nsa/nmC9p9+Y8bAh3xy7flzfnWUOve2rbVV6/rFbvziFPedrQa7t8/cbcL9TW94VaGZBv1902Y2OB\nsjJg4EDH366Wd7UPT4YMQdfSUsd13JWGnif+oswDwHV5VX7vw/3MaXuKZbrOmAEcO+Y+H9zdEzxd\nnxuTTqXGHKs31xFfy6uv5dfXdHu6zsmX8/e53NByp5Y25b9FvlyvlOnx5j7Z0DT7+r2788LXZ1e1\nbTf2txXC3O7du+v+GDxYEADn/yyWun8PHuz9hpXbql3XaX/erutLGjwdg/I/cXtq64nfy79zlRZX\ny6htQ+1vV8fg7bGrpd/V8mr7dvF7ud1+Y5bz5ni9yWtvzxNX6/iSV77+Xq7S5s356Gn/yu25+l1r\neSznaueBq8+8+Y19PcfclQdvfkdfrk0uWHv39u664Mt51tBrpqfzXa/3fA55ylNv81xBOpcae7/w\n5ZqlRq0MqG1XbZvelkFX+/DE0/UmAOevz1zlga/3RE/3M1FD7jvKZZTnl6dzqLHp9ObYvTlWb8qK\nh+3Wuz75Wn59Tbe31zm1553Gami5ExT55Om+6+31Srkdb+6TvmjMtdrduebmeUr1Ou7F/cyrZ3gF\nNo0jIiIiIiLN0QmCIIQ6Ee5kZGSgf//+dR8EuGlcvf15u67GmsaV9OkDC5vGeWwaZ6+pgYFN49TT\nIaNazl3tV6NN4zIyMtB/zhz1NMtpvGmc07nEpnGuDRmCktJSx3XcFTaNc9zr2DTO43ZVr09sGldP\nvXxi0zjVf9e7jrvbtuy39foZXqbpBULNbH9NFfPJO8wn7zCfPGMeeYf55D3mlWfMI+8wn7zDfPJO\nQ/OpIeuxaRwREREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzGAgREREREZHmMBAiIiIi\nIiLNYSBERERERESaw0CIiIiIiIg0h4EQERERERFpDgMhIiIiIiLSHAZCRERERESkOQyEiIiIiIhI\ncxgIERERERGR5jAQIiIiIiIizWEgREREREREmsNAiIiIiIiINIeBEBERERERaQ4DISIiIiIi0hwG\nQkREREREpDkMhIiIiIiISHMYCBERERERkeYwECIiIiIiIs1hIERERERERJrDQIiIiIiIiDSHgRAR\nEREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiIiIiEhzGAgREREREZHmMBAiIiIiIiLNYSBERERE\nRESaw0CIiIiIiIg0JySBUF5eHi677DL8/PPPodg9ERERERFpXEgCoRdffBHt27cPxa6JiIiIiIiC\nHwht374dsbGx6NKlCwRBCPbuiYiIiIiIghsI2Ww2vP7663jooYcAADqdLpi7JyIiIiIiAgDohABV\ny3z00UdYt26d02eXX345OnfujKuvvhqPPfYYbrjhBgwYMMDtdjIyMgKRPCIiIiIiakb69+/v0/IB\nC4TU3HLLLaipqQEAnDp1CklJSViyZAk6d+4crCQQEREREREFNxCSe+yxxzBx4kRcdtllodg9ERER\nERFpGOcRIiIiIiIizQlZjRAREREREVGosEaIiIiIiIg0h4EQERERERFpDgMhIiIiIiLSnCYdCJ0+\nfRr9+vVDeno60tPTMX36dGzfvr1R28zKysL06dORnp6OO+64A3l5eQCADRs2YNKkSbj55pud5kfa\nuXMnhgwZgu+++0767NChQ5g6dSrS09Mxa9YsVFRUNCpN/rRx40b07NkThYWFDd7GypUrcdNNN2HS\npElYs2YNAMBqteLuu+/GrbfeipkzZ6K4uBgAUFlZiblz5+LGG2+U1i8vL8eDDz6I9PR03HzzzU55\nFw78kUeiHTt2YPLkybjllluwYMECiF3ynn32WUyZMgVTpkzBvn37pOVXrlyJnj17ory8XPps2bJl\nmDJlCiZPnozXX3+90WlqrHAtd9988w2mTJmC9PR0PPjgg7DZbI1Kk7+EQ5kTVVRUYPTo0fjkk08a\nnJZACLcyJ3r44Yfx2GOPNTpNjRWuZU70wQcfYNSoUY1Kj7+FQ7nbuXMnBg0aJP1uzzzzTOMOys/C\nrdz16NFDyqv09HRpupVQCddyl56ejkmTJknpOnDgQKPS5E/hUO4AR35ed911mDhxIr7//nv3OxSa\nsMzMTGHixInS36dOnRLGjRsnHDp0qMHbnDdvnrBp0yZBEARh1apVwosvviiUlZUJY8eOFaxWq1BR\nUSGMHz9eKCoqEk6ePCnMmjVLmDNnjvDtt99K25g2bZrw22+/CYIgCC+88IKwevXqBqfH3+655x7h\n4YcfFv773/82aP1Tp04J1157rWC32wWbzSaMHDlSsFqtwtKlS4Xly5cLgiAIa9euFV566SVBEATh\n73//u/D+++87/U6bNm0S3nnnHUEQBOHMmTPCmDFjGnlU/tXYPJK78sorhXPnzgmCIAgPPPCA8N13\n3wk7d+4U7rnnHkEQBOHIkSPC5MmTBUEQhE8++URYsmSJMHLkSKGsrEwQBMc5/sADDwiCIAh2u10Y\nM2aMkJOT0+h0NUa4lrvbb79dsFqtgiAIwvz584XPPvuswenxp3Aoc6J//vOfwo033ih88sknDT+g\nAAinMifaunWrMGnSJGH+/PmNTlNjhWuZEwRByMvLE2bMmCGMGjWqwWkJhHAodzt27JCu3+Eo3Mrd\nwIEDG50OfwrXcjdt2jTh8OHDDU5DIIVDuSsoKBDGjBkjlJaWCjk5OcITTzzhdp9NukZIqV27drj3\n3nuxevVqAMDq1atxyy23YOrUqfjPf/4DADh//jzuvvtuTJ06Fffeey/KysqctvHkk09i7NixAIDE\nxEQUFRXht99+Q69evWCxWBAVFYV+/fphz549SE1NxdKlSxETE+O0jddffx29e/eWtiFGrqFWVFSE\n48eP46677sKmTZukz9PT0/HSSy/htttuw+TJk3H27Fns3LkT9957L9LT053e4rRt2xZr1qyBXq+H\n0WiEyWRCSUkJduzYgSuvvBIAMHLkSGzbtg2A443qyJEjndIxbtw43HnnnQCAs2fPIi0tLdCH7jV3\neXTkyBEAwKpVq7Bs2TJUV1fjr3/9KyZPnowXXngBI0aMqLe9//3vf0hNTQUAJCUloaioCDt27MDo\n0aMBAJ07d0ZxcTFKSkowZswYzJkzx2n9tm3b4t///reUNp1OB4vFEohDb7BwKXcrVqyAxWJBdXU1\n8vLy0KpVqyAcvXvhUuYA4OjRozh27BhGjBghva0NB+FW5gDAZrPhjTfewH333ReAI268cClzAPDy\nyy/jwQcfbDLnVLDLXTjli1w4lrtwF07lLhzPq3Apd9u3b8eQIUNgNpuRkpKCp59+2m26m1UgBDiq\nVo8ePYrTp09jy5Yt+O9//4tVq1Zhy5YtyMrKwvLlyzF8+HCsXr0agwYNkjJTZDabYTAYYLfb8d//\n/hcTJkxAXl4ekpKSpGVatGiB3NxcREVFQafT1UuD+KBaVlaGDRs2SCd9qH3xxRcYMWIEunXrhuzs\nbOTk5EjfJSQk4L333sOECROwcuVK6HQ6/Pnnn3j33XfRq1cvaTmdTicVyq1btyIpKQmtWrVCbm4u\nEhMTATgugrm5uQAc+emqwE6ZMgWPPvpoWDQ9EbnLI5H4m//www+w2WxYu3YtBg4ciOzs7HrLiudC\nTk4OfvrpJ1xxxRXIy8uT8gpw5FdeXh7MZrPLdD3zzDOYMGECZs2aBZPJ1NjD9LtwKHeA42Z85ZVX\nokOHDrj00ksDeszeCKcy99JLL4VVWROFY5l78803MW3atLB76SAXDmVu586diImJkV78hYtwKXc6\nnQ5Hjx7Ffffdh1tvvbXebxBK4VjuKisr8cgjj+CWW27BihUr/HCU/hcO5Q4AlixZgmnTpuHJJ59E\nZWVlQI/ZW+FS7s6cOYOKigrcd999mDp1qsfmjM0uECotLYVer8fevXtx8uRJpKen47bbbkNZWRnO\nnDmD33//HZdccgkAYPr06dLbCjm73Y65c+di0KBBGDRoUL3vvYnEy8rKcN999+HOO+/EBRdc0PgD\n84ONGzdKxztq1Ch8/vnn0ndDhgwBAPTt2xfHjx8HAHTt2hVGo1F1W7/++itefPFFvPzyy/W+8/ZN\nxQcffIDXX38djz76qE/HEUju8kjp2LFj0rk0fPhwGAwG1eXy8/Nx33334W9/+xsSEhLqfS8IgsuL\nnWjhwoXYvHkz3nnnHZw+fdrbwwmacCl3EydOxNdff42ioiJs3Lix8QfWSOFS5tavX49LL70UrVu3\nDrs3ieFW5k6cOIE///wTY8eODbu8kgt1mbPZbHj11Vfx17/+1X8H5SfhUu46dOiA2bNn4/XXX8cL\nL7yAxx9/HNXV1Q06Jn8Lt3IHAPPnz8czzzyDd999Fxs2bMD+/ft9OaSgCHW5A4Dbb78dc+fOxapV\nq6DX66UaqlALl3InCAKKiorw6quv4vnnn8eCBQvcLh/h9tsmaP/+/ejRowciIyNxxRVX1KsSe+ed\nd2C3291u47HHHkOnTp0wa9YsAEDLli2lDm0AkJ2djX79+jmtIy/c1dXVmDVrFq699lpcf/31jT0k\nvzh37hz27t2LZ555BjqdDuXl5YiLi8P06dMBQMqTmpoa6VhcnaCHDh3CE088gTfffFOqCm/ZsiVy\nc3NhsViQnZ2Nli1bSssrL3z79+9HixYtkJaWhm7dusFut6OgoMDpjUgouMsj+TFUVVUBcBQ28Yag\n0+lUL/AlJSW466678PDDD0sXAuX5lJOTg5SUFJdpys3NRa9evRAXF4dLLrkE+/btQ9u2bf123P4Q\n6nJns9mwfft2XHHFFTAYDPjLX/6CXbt2Yfz48f44vAYJpzL3/fffIzMzE1999RXOnTuHyMhItGrV\nCoMHD/b3YfskHMvc999/j5MnT2Ly5MkoKSlBQUEBli9fLjXnDRehLnO///47cnJypHzJzc3FI488\ngsWLFzf62BojnMpdamoqrr76agCOZlXJycnIzs5GmzZt/HrMvgrHcgcAkydPlv49ePBg/Pnnn+jZ\ns2fjDtbPQl3uADgFVyNHjsTmzZsbfDz+Ek7lLjk5Gf369YNer0e7du0QExPj9hmzWdUInTp1CitW\nrMD06dPRvXt37Ny5ExUVFRAEAf/4xz9QWVmJXr16YceOHQAcNRLr16932saGDRsQGRmJ2bNnS5/1\n7t0b+/btg9VqRWlpKfbs2YP+/ftL3wuC4BShvv322xgwYIDqqE2hsnHjRkydOhWffvop1q9fjy1b\ntqC4uBiZmZkAgIyMDACOKPzCCy90uR273Y4FCxZg6dKlaN26tfT5sGHD8MUXXwAAvvzySwwfPlz6\nThm97969W2pPm5eXh7KyspAHQYD7PLJYLFI17549ewAA7du3l95Ybd26VfXi9/zzz2P69OkYNmyY\n9NnQoUOxZcsWAMCBAweQmppar6mAmGf5+fl46qmnYLfbYbfbceDAAXTq1Mn/B98I4VDu9Ho9Fi1a\nJP1Gv/32W8hrYsOpzP3rX//CunXrsHbtWtx0002YNWtWyIMgIDzL3O23344NGzZg7dq1WLRoEUaM\nGBF2QVA4lLk+ffrgiy++wNq1a7F27VqkpKSEPAgCwqvcffbZZ1i2bBkAx7U8Pz9ferALpXAsd8eO\nHcP999+Pmpoa2O12/PLLL7jooov8f/CNEA7lThAEpKenS4HTzz//jC5dugT60D0Kp3I3dOhQ7Nix\nA4IgoLCw0OMzpk4I57p/D06fPo1rr70WPXr0QFVVFex2Ox5++GHpBr9mzRp8/PHHMBgMGD16NO6+\n+26UlJRg7ty5sFqtsFgsWLx4sVPBnDJlCmw2m9RG8aKLLsKTTz6JLVu2YPny5dDpdEhPT8f48ePx\n5ZdfYunSpcjOzobFYkFiYiI+/vhjXH755WjTpo0U7Q4aNEiK/EOtnuGZAAAKkElEQVRl4sSJePHF\nF51OwNdeew16vR4//fQTunXrhuPHj6OkpARLlizBiRMnsHr1aqmjvmjr1q145JFHnAre3Llz0blz\nZzz66KMoKipCXFwcXnrpJVgsFtxxxx3IyspCVlYW2rdvj+nTp2P8+PFYsGABzp07h4qKCsyZM0e1\n82Wwucujiy++GM8++yw6duyIdu3aISEhAXfffTdmz56NkpISDBgwAB9++KFTe+Dy8nIMGDAAffv2\nlT679tprcdNNN2Hx4sX4+eefYTAY8OSTT6Jr16745z//iW+//RYnTpxA+/btcdlll+Fvf/sb3nrr\nLXz99deoqanByJEjQ34uhWu5++GHH7Bs2TIYjUYkJyfjxRdfRFRUVEjyCAivMid/KbNs2TK0bds2\nLGqrw7XMiXbt2oVPPvkEzz33XFDyw5VwLXNyf/nLX/DNN98EL1NcCKdyd9VVV+GRRx5BcXExampq\nMGvWLKcHuFAJ13L38ssvY9u2bTAajRg1ahTuueeeoOaLUriWu82bN+Ott96CxWJBy5Yt8eyzz4b0\nXgeEV7m78cYbsXbtWmkY8vvvv191IBNRkw6EyD/S09OxaNEit1E61VdcXIydO3dizJgxyM7OxvTp\n08OiiprCH8tcw7DMUWOw3DUMyx01RriXu2bXR4goWGJiYrB582YsX74cNTU1HjvkEVHjsMwRBR/L\nHTVnrBEiIiIiIiLNaVaDJRAREREREXmDgRAREREREWkOAyEiIiIiItIcBkJERERERKQ5DISIiJqx\nnJwc9OzZE2+99ZbP6548eRKzZ8/Gddddh0mTJmHatGnYvn279P2WLVswevToenPKzJ8/H1dddRXS\n09Mxbdo0zJw5E7t37/a4v6NHj+LgwYM+pTEnJwdTpkxBcXGxT+u588ILL2DChAk4cOCAV8tv27YN\n6enpbpfJycmRJlp05YEHHsBPP/3kdTqJiKhxGAgRETVj69evx4QJE/DJJ5/4tF5lZSVmzpyJ66+/\nHp9++inWrVuHJ598EgsWLMDRo0cBAN9//z3uvPNOpwlbAUCn02HmzJl4//33sWrVKjz88MN49NFH\nsW/fPrf7/PLLL70OPkQLFy7EnDlzEB8f79N67nz99df497//jR49evhtmzt27PAYCD399NN46qmn\nUFZW5rf9EhGRa5xHiIioGfv444/x2muv4dFHH8Uvv/yCfv36AQBefvll7Ny5E5GRkUhNTcXzzz+P\nyMhIab3169ejV69eGD16tPRZly5dMGPGDLzxxhsYPXo0fvjhB+zZswcGgwE333yz037lMzN0794d\n999/P5YvX45XXnkFX331Fd555x1ER0fDbrfjhRdeQE5ODlavXg2LxQKz2Yxhw4Zh0aJFKCwshNVq\nxYwZMzB+/HinfRw8eBBZWVkYOnQoAKhut02bNli5ciU+++wzmEwmREdH46WXXkJCQgJee+01fP/9\n94iIiMBFF12EhQsXSjO5z58/HwsXLkTv3r1V8/Xrr7/GK6+8gtTUVHTs2FH6fPfu3Xj55ZcRFRWF\niooKLFq0CHFxcXjllVcAAAkJCZg6dSqeeuopnDp1CqWlpRg/fjzuuOMOJCQkYMSIEfjoo49w++23\nN+DXJiIiX7BGiIiomfr5559hMpnQuXNnjBs3Dv/73/8AOGaKX7NmDT788EOsXr0ao0ePRn5+vtO6\nv//+u2oQ0KdPHxw8eBBjx47F5ZdfjpkzZ9YLgtT06dMHf/75JwCgtLQUixcvxsqVK3H55Zdj1apV\n6Nevn7S9a665Bq+88gqGDx+OlStXYtWqVViyZAkKCgqctvnjjz9i+PDh0t9q2wWApUuX4q233sL7\n77+P2267DdnZ2fjll1/w1VdfYc2aNVi9ejUKCgqwceNGPPTQQ0hOTsbixYtdBkEA8Pe//x1LlizB\n8uXLodPppM+Li4uxaNEirFy5Eunp6XjjjTfQtm1bTJw4Eddddx2mT5+OlStXIjU1Fe+99x4+/PBD\nbNq0CX/88QcAYOjQofjxxx895icRETUea4SIiJqpdevWYdy4cQCAcePG4brrrsPChQsRHx+PYcOG\nYerUqbjyyisxbtw4pKamOq1rMplgt9tVt6vX171D83ZObqvVCoPBAABITEzEggULIAgCcnNzpVoq\nuZ07d2L//v1Skz6j0YgzZ84gKSlJWubcuXO44IILpL9dbXfSpEm48847MXbsWFx11VXo2LEjVqxY\ngQEDBkhpGjhwIPbt24frr7/e47EUFhaioqJC2vegQYOkQKZFixZ4+eWXUVlZCavVKjXZEwRByqud\nO3ciOzsbu3btAgDYbDZkZmaia9euSEtLw5kzZ7zKUyIiahwGQkREzVBJSQm+/PJLtG7dGp9//jkA\nwG6344svvsB1112HJUuW4Pjx4/juu+8wbdo0LF26FN26dZPW79q1K7755pt62923b5/bmhKRvJYE\nAPbs2YOePXuiuroaf/3rX/Hpp5+iffv2WL16Nfbv319v/aioKPztb3/zup9OVVWVy+3Onz8fWVlZ\n+O677zBr1izMmzcPer3eKYirqampl2ZXBEFwCgblAePcuXPx97//HQMHDsS3336Ld999t16eREVF\nYfbs2RgzZoxX+yMiosBg0zgiomZo48aNGDhwIDZt2oT169dj/fr1ePrpp/G///0PmZmZWLFiBTp1\n6oQ77rgDV155JQ4dOuS0/jXXXIPDhw9j06ZN0mdHjx7FypUrcd9993ncvzzI2LdvH9577z3ccccd\nKCkpgcFgQOvWrVFZWYmvvvoKNpsNgCNQqKqqAgD0799fCuAqKirw1FNP1auhSktLQ1ZWFgBHszj5\ndr/++mvYbDacP38eS5cuRatWrXDLLbfg1ltvxd69e9G3b1/s3LkT1dXVAByDGfTt29ervE1MTITB\nYMDJkycBOEaNE4Oc/Px8XHjhhbDb7di8ebN0PHq9XvXYampq8Nxzz0mj3p09exZt2rTxKh1ERNQ4\nrBEiImqGPv74Y8yePdvpszFjxuD555+H3W7H77//jptuugkxMTGIj4/HnDlznJY1Go1Ys2YNnnnm\nGbz99tswGo0wmUx47rnn0LZtW2k5V7Uoy5cvx4YNG1BaWgqTyYR//etf6NKlCwBg/PjxmDRpElq1\naoWZM2di3rx52LJlCwYNGoQXX3wRADB79mwsXLgQt956K2w2GyZPniw1YxNdfvnlmDdvHubOnYuE\nhASn7d55552YN28etm3bhrKyMtx4442Ij4+H0WjEP/7xD6SkpOCaa67B1KlTodfr0aNHj3qDMbii\n0+mwYMECzJo1C23btnUaLOGuu+7C7bffjtTUVMycORPz58/He++9h0svvRQPPfQQIiMjce+99+Lw\n4cOYMmUK7HY7Ro4cKTWh27Ztm1O/JyIiChyd4G0DbyIiojBzzz334LbbbpNGjmvKCgsLMXnyZKxf\nvx5msznUySEiavYYCBERUZOVm5uLOXPm4M033/TrXEIAcP/998Nqtdb7fOLEibjhhhv8ui/AMaHq\nlClTMGTIEL9vm4iI6mMgREREREREmsPBEoiIiIiISHMYCBERERERkeYwECIiIiIiIs1hIERERERE\nRJrDQIiIiIiIiDTn/wNqBArLO/l7swAAAABJRU5ErkJggg==\n",
    251       "text/plain": [
    252        "<matplotlib.figure.Figure at 0x7fb064feee10>"
    253       ]
    254      },
    255      "metadata": {},
    256      "output_type": "display_data"
    257     }
    258    ],
    259    "source": [
    260     "# Filtering for AAPL\n",
    261     "aapl = dataset[dataset.sid == 24]\n",
    262     "aapl_df = odo(aapl.sort('asof_date'), pd.DataFrame)\n",
    263     "plt.plot(aapl_df.asof_date, aapl_df.sentiment_signal, marker='.', linestyle='None', color='r')\n",
    264     "plt.plot(aapl_df.asof_date, pd.rolling_mean(aapl_df.sentiment_signal, 30))\n",
    265     "plt.xlabel(\"As Of Date (asof_date)\")\n",
    266     "plt.ylabel(\"Sentiment\")\n",
    267     "plt.title(\"Sentdex Sentiment for AAPL\")\n",
    268     "plt.legend([\"Sentiment - Single Day\", \"30 Day Rolling Average\"], loc=1)\n",
    269     "x1,x2,y1,y2 = plt.axis()\n",
    270     "plt.axis((x1,x2,-4,7.5))"
    271    ]
    272   },
    273   {
    274    "cell_type": "markdown",
    275    "metadata": {},
    276    "source": [
    277     "Let's check out Comcast's sentiment for fun"
    278    ]
    279   },
    280   {
    281    "cell_type": "code",
    282    "execution_count": 4,
    283    "metadata": {
    284     "collapsed": false
    285    },
    286    "outputs": [
    287     {
    288      "data": {
    289       "text/plain": [
    290        "(734843.0, 736039.0, -4, 7.5)"
    291       ]
    292      },
    293      "execution_count": 4,
    294      "metadata": {},
    295      "output_type": "execute_result"
    296     },
    297     {
    298      "data": {
    299       "image/png": 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w/GrPvuyMRiMAIDg42JdZalDLli3DTTfd5DCAagjbt29H//79ER4ejscffxxz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AAAAg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\nAAAAAElFTkSuQmCC\n",
    300       "text/plain": [
    301        "<matplotlib.figure.Figure at 0x7fb064703810>"
    302       ]
    303      },
    304      "metadata": {},
    305      "output_type": "display_data"
    306     }
    307    ],
    308    "source": [
    309     "comcast = dataset[dataset.sid == 1637]\n",
    310     "comcast_df = odo(comcast.sort('asof_date'), pd.DataFrame)\n",
    311     "plt.plot(comcast_df.asof_date, comcast_df.sentiment_signal, marker='.', linestyle='None', color='r')\n",
    312     "plt.plot(comcast_df.asof_date, pd.rolling_mean(comcast_df.sentiment_signal, 30))\n",
    313     "plt.xlabel(\"As Of Date (asof_date)\")\n",
    314     "plt.ylabel(\"Sentiment\")\n",
    315     "plt.title(\"Sentdex Sentiment for Comcast\")\n",
    316     "plt.legend([\"Sentiment - Single Day\", \"30 Day Rolling Average\"], loc=1)\n",
    317     "x1,x2,y1,y2 = plt.axis()\n",
    318     "plt.axis((x1,x2,-4,7.5))"
    319    ]
    320   },
    321   {
    322    "cell_type": "markdown",
    323    "metadata": {},
    324    "source": [
    325     "<a id='pipeline'></a>\n",
    326     "\n",
    327     "#Pipeline Overview\n",
    328     "\n",
    329     "### Accessing the data in your algorithms & research\n",
    330     "The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this PsychSignal data, you can add this data to your pipeline as follows:\n",
    331     "\n",
    332     "Import the data set\n",
    333     "> `from quantopian.pipeline.data.sentdex import sentiment`\n",
    334     "\n",
    335     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    336     "> `pipe.add(sentiment.sentiment_signal.latest, 'sentdex_sentiment')`"
    337    ]
    338   },
    339   {
    340    "cell_type": "code",
    341    "execution_count": 6,
    342    "metadata": {
    343     "collapsed": true
    344    },
    345    "outputs": [],
    346    "source": [
    347     "# Import necessary Pipeline modules\n",
    348     "from quantopian.pipeline import Pipeline\n",
    349     "from quantopian.research import run_pipeline\n",
    350     "from quantopian.pipeline.factors import AverageDollarVolume"
    351    ]
    352   },
    353   {
    354    "cell_type": "code",
    355    "execution_count": 5,
    356    "metadata": {
    357     "collapsed": false
    358    },
    359    "outputs": [],
    360    "source": [
    361     "# For use in your algorithms\n",
    362     "# Using the full paid dataset in your pipeline algo\n",
    363     "# from quantopian.pipeline.data.sentdex import sentiment\n",
    364     "\n",
    365     "# Using the free sample in your pipeline algo\n",
    366     "from quantopian.pipeline.data.sentdex import sentiment_free"
    367    ]
    368   },
    369   {
    370    "cell_type": "markdown",
    371    "metadata": {},
    372    "source": [
    373     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    374     "\n",
    375     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    376    ]
    377   },
    378   {
    379    "cell_type": "code",
    380    "execution_count": 7,
    381    "metadata": {
    382     "collapsed": false
    383    },
    384    "outputs": [
    385     {
    386      "name": "stdout",
    387      "output_type": "stream",
    388      "text": [
    389       "Here are the list of available fields per dataset:\n",
    390       "---------------------------------------------------\n",
    391       "\n",
    392       "Dataset: sentiment_free\n",
    393       "\n",
    394       "Fields:\n",
    395       "sentiment_signal - float64\n",
    396       "\n",
    397       "\n",
    398       "---------------------------------------------------\n",
    399       "\n"
    400      ]
    401     }
    402    ],
    403    "source": [
    404     "print \"Here are the list of available fields per dataset:\"\n",
    405     "print \"---------------------------------------------------\\n\"\n",
    406     "\n",
    407     "def _print_fields(dataset):\n",
    408     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    409     "    print \"Fields:\"\n",
    410     "    for field in list(dataset.columns):\n",
    411     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    412     "    print \"\\n\"\n",
    413     "\n",
    414     "for data in (sentiment_free,):\n",
    415     "    _print_fields(data)\n",
    416     "\n",
    417     "\n",
    418     "print \"---------------------------------------------------\\n\""
    419    ]
    420   },
    421   {
    422    "cell_type": "markdown",
    423    "metadata": {},
    424    "source": [
    425     "Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
    426     "\n",
    427     "\n",
    428     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    429     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    430    ]
    431   },
    432   {
    433    "cell_type": "code",
    434    "execution_count": 28,
    435    "metadata": {
    436     "collapsed": false
    437    },
    438    "outputs": [],
    439    "source": [
    440     "# Let's see what this data looks like when we run it through Pipeline\n",
    441     "# This is constructed the same way as you would in the backtester. For more information\n",
    442     "# on using Pipeline in Research view this thread:\n",
    443     "# https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters\n",
    444     "pipe = Pipeline()\n",
    445     "       \n",
    446     "pipe.add(sentiment_free.sentiment_signal.latest, 'sentiment_signal')"
    447    ]
    448   },
    449   {
    450    "cell_type": "code",
    451    "execution_count": 29,
    452    "metadata": {
    453     "collapsed": false
    454    },
    455    "outputs": [],
    456    "source": [
    457     "# Setting some basic liquidity strings (just for good habit)\n",
    458     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    459     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    460     "\n",
    461     "pipe.set_screen(top_1000_most_liquid & sentiment_free.sentiment_signal.latest.notnan())"
    462    ]
    463   },
    464   {
    465    "cell_type": "code",
    466    "execution_count": 30,
    467    "metadata": {
    468     "collapsed": false
    469    },
    470    "outputs": [
    471     {
    472      "data": {
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n1aFOcavtXt455FegkCrVrKs2nQNVoWotw9azet/FR7YbKeE25syVR4s2H7Zo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\nCP4PvLXu7YeHtHMAAAAASUVORK5CYII=\n",
    474       "text/plain": [
    475        "<IPython.core.display.Image object>"
    476       ]
    477      },
    478      "execution_count": 30,
    479      "metadata": {},
    480      "output_type": "execute_result"
    481     }
    482    ],
    483    "source": [
    484     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    485     "pipe.show_graph(format='png')"
    486    ]
    487   },
    488   {
    489    "cell_type": "code",
    490    "execution_count": 31,
    491    "metadata": {
    492     "collapsed": false,
    493     "scrolled": true
    494    },
    495    "outputs": [
    496     {
    497      "data": {
    498       "text/html": [
    499        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    500        "<table border=\"1\" class=\"dataframe\">\n",
    501        "  <thead>\n",
    502        "    <tr style=\"text-align: right;\">\n",
    503        "      <th></th>\n",
    504        "      <th></th>\n",
    505        "      <th>sentiment_signal</th>\n",
    506        "    </tr>\n",
    507        "  </thead>\n",
    508        "  <tbody>\n",
    509        "    <tr>\n",
    510        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    511        "      <th>Equity(2 [AA])</th>\n",
    512        "      <td>-1</td>\n",
    513        "    </tr>\n",
    514        "    <tr>\n",
    515        "      <th>Equity(24 [AAPL])</th>\n",
    516        "      <td>3</td>\n",
    517        "    </tr>\n",
    518        "    <tr>\n",
    519        "      <th>Equity(62 [ABT])</th>\n",
    520        "      <td>1</td>\n",
    521        "    </tr>\n",
    522        "    <tr>\n",
    523        "      <th>Equity(67 [ADSK])</th>\n",
    524        "      <td>2</td>\n",
    525        "    </tr>\n",
    526        "    <tr>\n",
    527        "      <th>Equity(76 [TAP])</th>\n",
    528        "      <td>5</td>\n",
    529        "    </tr>\n",
    530        "    <tr>\n",
    531        "      <th>Equity(88 [ACI])</th>\n",
    532        "      <td>-3</td>\n",
    533        "    </tr>\n",
    534        "    <tr>\n",
    535        "      <th>Equity(114 [ADBE])</th>\n",
    536        "      <td>-3</td>\n",
    537        "    </tr>\n",
    538        "    <tr>\n",
    539        "      <th>Equity(122 [ADI])</th>\n",
    540        "      <td>2</td>\n",
    541        "    </tr>\n",
    542        "    <tr>\n",
    543        "      <th>Equity(128 [ADM])</th>\n",
    544        "      <td>1</td>\n",
    545        "    </tr>\n",
    546        "    <tr>\n",
    547        "      <th>Equity(161 [AEP])</th>\n",
    548        "      <td>-1</td>\n",
    549        "    </tr>\n",
    550        "    <tr>\n",
    551        "      <th>Equity(166 [AES])</th>\n",
    552        "      <td>5</td>\n",
    553        "    </tr>\n",
    554        "    <tr>\n",
    555        "      <th>Equity(168 [AET])</th>\n",
    556        "      <td>6</td>\n",
    557        "    </tr>\n",
    558        "    <tr>\n",
    559        "      <th>Equity(185 [AFL])</th>\n",
    560        "      <td>5</td>\n",
    561        "    </tr>\n",
    562        "    <tr>\n",
    563        "      <th>Equity(216 [HES])</th>\n",
    564        "      <td>4</td>\n",
    565        "    </tr>\n",
    566        "    <tr>\n",
    567        "      <th>Equity(239 [AIG])</th>\n",
    568        "      <td>-1</td>\n",
    569        "    </tr>\n",
    570        "    <tr>\n",
    571        "      <th>Equity(328 [ALTR])</th>\n",
    572        "      <td>2</td>\n",
    573        "    </tr>\n",
    574        "    <tr>\n",
    575        "      <th>Equity(337 [AMAT])</th>\n",
    576        "      <td>-1</td>\n",
    577        "    </tr>\n",
    578        "    <tr>\n",
    579        "      <th>Equity(338 [BEAM])</th>\n",
    580        "      <td>3</td>\n",
    581        "    </tr>\n",
    582        "    <tr>\n",
    583        "      <th>Equity(351 [AMD])</th>\n",
    584        "      <td>3</td>\n",
    585        "    </tr>\n",
    586        "    <tr>\n",
    587        "      <th>Equity(357 [TWX])</th>\n",
    588        "      <td>-1</td>\n",
    589        "    </tr>\n",
    590        "    <tr>\n",
    591        "      <th>Equity(368 [AMGN])</th>\n",
    592        "      <td>3</td>\n",
    593        "    </tr>\n",
    594        "    <tr>\n",
    595        "      <th>Equity(410 [AN])</th>\n",
    596        "      <td>1</td>\n",
    597        "    </tr>\n",
    598        "    <tr>\n",
    599        "      <th>Equity(438 [AON])</th>\n",
    600        "      <td>6</td>\n",
    601        "    </tr>\n",
    602        "    <tr>\n",
    603        "      <th>Equity(448 [APA])</th>\n",
    604        "      <td>-1</td>\n",
    605        "    </tr>\n",
    606        "    <tr>\n",
    607        "      <th>Equity(455 [APC])</th>\n",
    608        "      <td>-1</td>\n",
    609        "    </tr>\n",
    610        "    <tr>\n",
    611        "      <th>Equity(460 [APD])</th>\n",
    612        "      <td>6</td>\n",
    613        "    </tr>\n",
    614        "    <tr>\n",
    615        "      <th>Equity(465 [APH])</th>\n",
    616        "      <td>5</td>\n",
    617        "    </tr>\n",
    618        "    <tr>\n",
    619        "      <th>Equity(510 [ARG])</th>\n",
    620        "      <td>6</td>\n",
    621        "    </tr>\n",
    622        "    <tr>\n",
    623        "      <th>Equity(630 [ADP])</th>\n",
    624        "      <td>-3</td>\n",
    625        "    </tr>\n",
    626        "    <tr>\n",
    627        "      <th>Equity(660 [AVP])</th>\n",
    628        "      <td>1</td>\n",
    629        "    </tr>\n",
    630        "    <tr>\n",
    631        "      <th>...</th>\n",
    632        "      <th>...</th>\n",
    633        "      <td>...</td>\n",
    634        "    </tr>\n",
    635        "    <tr>\n",
    636        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    637        "      <th>Equity(36346 [LO])</th>\n",
    638        "      <td>2</td>\n",
    639        "    </tr>\n",
    640        "    <tr>\n",
    641        "      <th>Equity(36372 [SNI])</th>\n",
    642        "      <td>3</td>\n",
    643        "    </tr>\n",
    644        "    <tr>\n",
    645        "      <th>Equity(36930 [DISC_A])</th>\n",
    646        "      <td>2</td>\n",
    647        "    </tr>\n",
    648        "    <tr>\n",
    649        "      <th>Equity(38084 [MJN])</th>\n",
    650        "      <td>-1</td>\n",
    651        "    </tr>\n",
    652        "    <tr>\n",
    653        "      <th>Equity(38691 [CFN])</th>\n",
    654        "      <td>6</td>\n",
    655        "    </tr>\n",
    656        "    <tr>\n",
    657        "      <th>Equity(38936 [DG])</th>\n",
    658        "      <td>6</td>\n",
    659        "    </tr>\n",
    660        "    <tr>\n",
    661        "      <th>Equity(39546 [LYB])</th>\n",
    662        "      <td>-1</td>\n",
    663        "    </tr>\n",
    664        "    <tr>\n",
    665        "      <th>Equity(39778 [QEP])</th>\n",
    666        "      <td>4</td>\n",
    667        "    </tr>\n",
    668        "    <tr>\n",
    669        "      <th>Equity(39840 [TSLA])</th>\n",
    670        "      <td>4</td>\n",
    671        "    </tr>\n",
    672        "    <tr>\n",
    673        "      <th>Equity(40430 [GM])</th>\n",
    674        "      <td>2</td>\n",
    675        "    </tr>\n",
    676        "    <tr>\n",
    677        "      <th>Equity(40852 [KMI])</th>\n",
    678        "      <td>-1</td>\n",
    679        "    </tr>\n",
    680        "    <tr>\n",
    681        "      <th>Equity(41451 [LNKD])</th>\n",
    682        "      <td>-1</td>\n",
    683        "    </tr>\n",
    684        "    <tr>\n",
    685        "      <th>Equity(41462 [MOS])</th>\n",
    686        "      <td>-1</td>\n",
    687        "    </tr>\n",
    688        "    <tr>\n",
    689        "      <th>Equity(41579 [P])</th>\n",
    690        "      <td>2</td>\n",
    691        "    </tr>\n",
    692        "    <tr>\n",
    693        "      <th>Equity(41636 [MPC])</th>\n",
    694        "      <td>-1</td>\n",
    695        "    </tr>\n",
    696        "    <tr>\n",
    697        "      <th>Equity(42023 [XYL])</th>\n",
    698        "      <td>-1</td>\n",
    699        "    </tr>\n",
    700        "    <tr>\n",
    701        "      <th>Equity(42118 [GRPN])</th>\n",
    702        "      <td>2</td>\n",
    703        "    </tr>\n",
    704        "    <tr>\n",
    705        "      <th>Equity(42173 [DLPH])</th>\n",
    706        "      <td>3</td>\n",
    707        "    </tr>\n",
    708        "    <tr>\n",
    709        "      <th>Equity(42230 [TRIP])</th>\n",
    710        "      <td>6</td>\n",
    711        "    </tr>\n",
    712        "    <tr>\n",
    713        "      <th>Equity(42251 [WPX])</th>\n",
    714        "      <td>-1</td>\n",
    715        "    </tr>\n",
    716        "    <tr>\n",
    717        "      <th>Equity(42270 [KORS])</th>\n",
    718        "      <td>2</td>\n",
    719        "    </tr>\n",
    720        "    <tr>\n",
    721        "      <th>Equity(42277 [ZNGA])</th>\n",
    722        "      <td>6</td>\n",
    723        "    </tr>\n",
    724        "    <tr>\n",
    725        "      <th>Equity(42788 [PSX])</th>\n",
    726        "      <td>5</td>\n",
    727        "    </tr>\n",
    728        "    <tr>\n",
    729        "      <th>Equity(42950 [FB])</th>\n",
    730        "      <td>5</td>\n",
    731        "    </tr>\n",
    732        "    <tr>\n",
    733        "      <th>Equity(43399 [ADT])</th>\n",
    734        "      <td>6</td>\n",
    735        "    </tr>\n",
    736        "    <tr>\n",
    737        "      <th>Equity(43405 [KRFT])</th>\n",
    738        "      <td>3</td>\n",
    739        "    </tr>\n",
    740        "    <tr>\n",
    741        "      <th>Equity(43694 [ABBV])</th>\n",
    742        "      <td>3</td>\n",
    743        "    </tr>\n",
    744        "    <tr>\n",
    745        "      <th>Equity(43721 [SCTY])</th>\n",
    746        "      <td>2</td>\n",
    747        "    </tr>\n",
    748        "    <tr>\n",
    749        "      <th>Equity(44931 [NWSA])</th>\n",
    750        "      <td>2</td>\n",
    751        "    </tr>\n",
    752        "    <tr>\n",
    753        "      <th>Equity(45815 [TWTR])</th>\n",
    754        "      <td>-1</td>\n",
    755        "    </tr>\n",
    756        "  </tbody>\n",
    757        "</table>\n",
    758        "<p>8214 rows × 1 columns</p>\n",
    759        "</div>"
    760       ],
    761       "text/plain": [
    762        "                                                  sentiment_signal\n",
    763        "2013-11-01 00:00:00+00:00 Equity(2 [AA])                        -1\n",
    764        "                          Equity(24 [AAPL])                      3\n",
    765        "                          Equity(62 [ABT])                       1\n",
    766        "                          Equity(67 [ADSK])                      2\n",
    767        "                          Equity(76 [TAP])                       5\n",
    768        "                          Equity(88 [ACI])                      -3\n",
    769        "                          Equity(114 [ADBE])                    -3\n",
    770        "                          Equity(122 [ADI])                      2\n",
    771        "                          Equity(128 [ADM])                      1\n",
    772        "                          Equity(161 [AEP])                     -1\n",
    773        "                          Equity(166 [AES])                      5\n",
    774        "                          Equity(168 [AET])                      6\n",
    775        "                          Equity(185 [AFL])                      5\n",
    776        "                          Equity(216 [HES])                      4\n",
    777        "                          Equity(239 [AIG])                     -1\n",
    778        "                          Equity(328 [ALTR])                     2\n",
    779        "                          Equity(337 [AMAT])                    -1\n",
    780        "                          Equity(338 [BEAM])                     3\n",
    781        "                          Equity(351 [AMD])                      3\n",
    782        "                          Equity(357 [TWX])                     -1\n",
    783        "                          Equity(368 [AMGN])                     3\n",
    784        "                          Equity(410 [AN])                       1\n",
    785        "                          Equity(438 [AON])                      6\n",
    786        "                          Equity(448 [APA])                     -1\n",
    787        "                          Equity(455 [APC])                     -1\n",
    788        "                          Equity(460 [APD])                      6\n",
    789        "                          Equity(465 [APH])                      5\n",
    790        "                          Equity(510 [ARG])                      6\n",
    791        "                          Equity(630 [ADP])                     -3\n",
    792        "                          Equity(660 [AVP])                      1\n",
    793        "...                                                            ...\n",
    794        "2013-11-25 00:00:00+00:00 Equity(36346 [LO])                     2\n",
    795        "                          Equity(36372 [SNI])                    3\n",
    796        "                          Equity(36930 [DISC_A])                 2\n",
    797        "                          Equity(38084 [MJN])                   -1\n",
    798        "                          Equity(38691 [CFN])                    6\n",
    799        "                          Equity(38936 [DG])                     6\n",
    800        "                          Equity(39546 [LYB])                   -1\n",
    801        "                          Equity(39778 [QEP])                    4\n",
    802        "                          Equity(39840 [TSLA])                   4\n",
    803        "                          Equity(40430 [GM])                     2\n",
    804        "                          Equity(40852 [KMI])                   -1\n",
    805        "                          Equity(41451 [LNKD])                  -1\n",
    806        "                          Equity(41462 [MOS])                   -1\n",
    807        "                          Equity(41579 [P])                      2\n",
    808        "                          Equity(41636 [MPC])                   -1\n",
    809        "                          Equity(42023 [XYL])                   -1\n",
    810        "                          Equity(42118 [GRPN])                   2\n",
    811        "                          Equity(42173 [DLPH])                   3\n",
    812        "                          Equity(42230 [TRIP])                   6\n",
    813        "                          Equity(42251 [WPX])                   -1\n",
    814        "                          Equity(42270 [KORS])                   2\n",
    815        "                          Equity(42277 [ZNGA])                   6\n",
    816        "                          Equity(42788 [PSX])                    5\n",
    817        "                          Equity(42950 [FB])                     5\n",
    818        "                          Equity(43399 [ADT])                    6\n",
    819        "                          Equity(43405 [KRFT])                   3\n",
    820        "                          Equity(43694 [ABBV])                   3\n",
    821        "                          Equity(43721 [SCTY])                   2\n",
    822        "                          Equity(44931 [NWSA])                   2\n",
    823        "                          Equity(45815 [TWTR])                  -1\n",
    824        "\n",
    825        "[8214 rows x 1 columns]"
    826       ]
    827      },
    828      "execution_count": 31,
    829      "metadata": {},
    830      "output_type": "execute_result"
    831     }
    832    ],
    833    "source": [
    834     "# run_pipeline will show the output of your pipeline\n",
    835     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    836     "pipe_output"
    837    ]
    838   },
    839   {
    840    "cell_type": "markdown",
    841    "metadata": {},
    842    "source": [
    843     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    844    ]
    845   },
    846   {
    847    "cell_type": "code",
    848    "execution_count": 11,
    849    "metadata": {
    850     "collapsed": false
    851    },
    852    "outputs": [],
    853    "source": [
    854     "# This section is only importable in the backtester\n",
    855     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    856     "\n",
    857     "# General pipeline imports\n",
    858     "from quantopian.pipeline import Pipeline\n",
    859     "from quantopian.pipeline.factors import AverageDollarVolume\n",
    860     "\n",
    861     "# Import the datasets available\n",
    862     "# For use in your algorithms\n",
    863     "# Using the full paid dataset in your pipeline algo\n",
    864     "# from quantopian.pipeline.data.sentdex import sentiment\n",
    865     "\n",
    866     "# Using the free sample in your pipeline algo\n",
    867     "from quantopian.pipeline.data.sentdex import sentiment_free\n",
    868     "\n",
    869     "def make_pipeline():\n",
    870     "    # Create our pipeline\n",
    871     "    pipe = Pipeline()\n",
    872     "    \n",
    873     "    # Screen out penny stocks and low liquidity securities.\n",
    874     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    875     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    876     "    \n",
    877     "    # Create the mask that we will use for our percentile methods.\n",
    878     "    base_universe = (is_liquid)\n",
    879     "\n",
    880     "    # Add pipeline factors\n",
    881     "    pipe.add(sentiment_free.sentiment_signal.latest, 'sentiment_signal')\n",
    882     "\n",
    883     "    # Set our pipeline screens\n",
    884     "    pipe.set_screen(is_liquid)\n",
    885     "    return pipe\n",
    886     "\n",
    887     "def initialize(context):\n",
    888     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    889     "    \n",
    890     "def before_trading_start(context, data):\n",
    891     "    results = pipeline_output('pipeline')"
    892    ]
    893   },
    894   {
    895    "cell_type": "markdown",
    896    "metadata": {},
    897    "source": [
    898     "Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
    899    ]
    900   }
    901  ],
    902  "metadata": {
    903   "kernelspec": {
    904    "display_name": "Python 2",
    905    "language": "python",
    906    "name": "python2"
    907   },
    908   "language_info": {
    909    "codemirror_mode": {
    910     "name": "ipython",
    911     "version": 2
    912    },
    913    "file_extension": ".py",
    914    "mimetype": "text/x-python",
    915    "name": "python",
    916    "nbconvert_exporter": "python",
    917    "pygments_lexer": "ipython2",
    918    "version": "2.7.11"
    919   }
    920  },
    921  "nbformat": 4,
    922  "nbformat_minor": 0
    923 }