{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Use ODM2API to connect to an ODM2 database, create a dropdownlist to pick a time series and visualize them

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1) First load standard python libraries

" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import sys\n", "import os\n", "import pprint\n", "import numpy\n", "import getpass\n", "import matplotlib.pyplot as plt\n", "from matplotlib.dates import DateFormatter\n", "from IPython.display import display, HTML\n", "import ipywidgets as widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

2) Next load odm2api components we will use

" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from odm2api.ODMconnection import dbconnection\n", "import odm2api.ODM2.services.readService as odm2\n", "from odm2api.ODM2.models import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

3) now connect to the database and instantiate the read service

" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#print(\"Enter your ODM2 username\") \n", "container = widgets.Box() # would be nice If I could get a container to hold the \n", "# user name and password prompt, getpass doesn't seem to play well with the other \n", "# widgets though\n", "username_text = widgets.Text(\n", " value='', placeholder='Enter username',\n", " description='', disabled=False)\n", "username_output_text = widgets.Text(\n", " value='', placeholder='Enter username',\n", " description='Username',disabled=False)\n", "database_address_text = widgets.Text(\n", " value='', placeholder='Enter database address',\n", " description='',disabled=False)\n", "database_address_output_text = widgets.Text(\n", " value='',placeholder='Enter database address',\n", " description='database address',disabled=False)\n", "database_text = widgets.Text(\n", " value='', placeholder='Enter database name',\n", " description='', disabled=False)\n", "database_output_text = widgets.Text(\n", " value='', placeholder='Enter database name',\n", " description='database name', disabled=False)\n", "def bind_username_to_output(sender):\n", " username_output_text.value = username_text.value\n", "def bind_database_address_to_output(sender):\n", " database_address_output_text.value = database_address_text.value\n", "def bind_database_to_output(sender):\n", " database_output_text.value = database_text.value \n", " \n", "def login(sender):\n", " #print('Database address : %s, Username: %s, database name: %s' % (\n", " # database_address_text.value, username_text.value, database_text.value))\n", " container.close() \n", " \n", "username_text.on_submit(bind_username_to_output)\n", "login_btn = widgets.Button(description=\"Login\")\n", "login_btn.on_click(login)\n", "container.children = [username_text,database_address_text, database_text, login_btn]\n", "container" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "enter your password: \n", "········\n" ] } ], "source": [ "print(\"enter your password: \")\n", "p = getpass.getpass()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "session_factory = dbconnection.createConnection('postgresql', database_address_text.value, database_text.value, \n", " username_text.value, p) \n", "\n", "read = odm2.ReadODM2(session_factory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

4) Now get some time series results based on the action related to them

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    a) In this case the result contains a featureaction and featureactions contain an action

\n", "

5) Next loop through the results and create a string based representation of a result.

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6) Create a dropdown list in order to pick a time series result

" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16553\n" ] } ], "source": [ "#featureaction = 1700\n", "results = read.getResults(actionid=30)\n", "resultids = []\n", "resultnames = {}\n", "\n", "def print_result_name(result):\n", " print result\n", "def on_change(change):\n", " print(change['new'])\n", " \n", "for r in results:\n", " #print(r.ResultID)\n", " resultids.append(str(r.ResultID))\n", " detailr = read.getDetailedResultInfo(resultTypeCV = 'Time series coverage',resultID=r.ResultID)\n", " for detail in detailr:\n", " namestr = str(detail.samplingFeatureCode + \"- \" + detail.methodCode + \"- \"+ detail.variableCode + \"- \" + detail.unitsName)\n", " resultnames[namestr]= detail.resultID\n", " #print(detailr.Methods)\n", "print(resultids)\n", "resultWidget = widgets.Dropdown(options=resultnames)\n", "rwidget = widgets.interactive(print_result_name,result=resultWidget)\n", "rwidget.observe(on_change)\n", "display(rwidget)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

7) Use the selected value to retrieve the time series values

" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16525\n" ] } ], "source": [ "print(resultWidget.value)\n", "selectedResult = read.getDetailedResultInfo(resultTypeCV = 'Time series coverage',resultID=resultWidget.value)\n", "SUNAResultValues = read.getResultValues(resultid=resultWidget.value, starttime='2016-8-1', \n", " endtime= '2016-8-30')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

8) Format a plot and plot the selected time series

" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eBCYAHYB7AETkXuB9Vb3K+z0J+ClwBvCuiOzl1dOoqhu977cAU0TkP8Bq4Frg\nfVzmHMMwDMNoU+LRTpVUmo9CBhqD7ASgfPYjlzgfU4BZInKqiHQTkc7BJZdOeK6vl+EEileA/jiN\nxsdekX1JNBS9COfd8hCwJrBcFqjzBuA24C5gKbALcHIGdiGGYRiGURRGjx5MRcWi0G2FDjQWjUa5\n5JLpfPjh2xQ70moumg8/HNpcEnvrG3NW5tIRVb0DuCPFthOTfvfIsM7pwPRc+mMYhmEYhWbmzIk8\n9dQ4VqxQz+bCvUorKh7zAo09XJB2fSPTt966ENXnca/2Uc3KFUoAykX4OCHvvTAMwzCMHRA/2qkL\n9DU7KdBX4dxsJ0+e5QkedwIXAnNwkyFBAWghvXrdUhABKGvhQ1WfznsvDMMwDGMHJSzQV6GZN2+J\nZ8j6Y5zAcRIuy8lsnMnlJ/TrF+Hvfy+MAJRTbhcR+aqI3Cciz4lId2/d2SJS2Cw4hmEYhrEdUwzB\nI25k+hzge7lEcFYKjwN/AZ5lw4atBdO8ZK35EJFxwO+A3wMDgPbepi64mBun5K13hmEYhmHkFRGh\nXbtGoBNuiiUKzAKW4LKVbAQGs2XLTgXTxOTq7XKhqv4/oCmwfglOGDEMwzAMo4RxRqSfAA3AOFza\ns8W4aBSLgUF88sm/aWxsLEj7uQgfhwLPhKzfAOzauu4YhmEYxo5HsTPMz5w5ka5dG4GLidt9+BoO\nAU7miy9mM2XKTQVpPxfh40PgyyHrhwD/17ruGIZhGMaOQTQapbZ2Gj16DGO//U6jR49h1NZOIxqN\nFrztSCTCG28spKLiJeJ2H4mojipIdFPIzdX2V8AcETkfF9djHxGpwU0Y/TSfnTMMwzCM7ZFSyGbb\nrVs39trrID74oLjRTSE3zcfPgfuBJ3HWKs8A/wPcpaq357FvxnZAsVWJhmEY5UBiNtv4dIfLZjuh\nYNMdQRLDu4dRuPDuWQsf6pgJdAX6AoOAPVT16nx3zihP2lKVuKNiQp5hlBdtkcwtmWg0SmPjp8DC\n0O0VFQsLFt49a+FDRO4WkYiqfq6qb6nqi6raKCIdReTuQnTSKB+i0ShHH306t912FKtXL6a+/hFW\nr17M7bcfxdFHn24CSB4xIc8wypNSyWY7efIs1q2bhsvDupC4BkSB+ey661XMmHFZyv+3hlymXc7F\nJWlLZhfgnNZ1xyh3Lr/8OlasmIDLERBXJaqOYsWKS5k06Wdt2LvtB3++uK6uJkHIq6uroaZmnAkg\nhlHCpJ7FRf07AAAgAElEQVTuiL/8C53NFvwop6cDD+Pyrw4HxnqfL9Kp024FszvJWPjwstZ2wb1R\nIknZbHfDBRf7b0F6aZQN99+/mNRx5kbx+98/XszubLeUwnyxYRi5E89mGwWmAcOA07zP8xg58siC\ntp+offGjmy7GRTddDFzD1q2dC6Z9yUbzsR5YixPN/gWsCyyfAHcDdfnuoFE+qCqbNrUnnSpx06b2\nZp+QB0phvtgwjNyZOXMihxxyA07LMIh4gK9FwDd5+umXCqrBTK198Z/fhdW+ZCN8nAAMxfXs68CJ\ngWUIsL9niGrs0ERJZzntthutQVXZsmUX0gl5W7bsbEKeYZQwkUiEY445Ahc0/DBckK++wHHAFaxY\nsZFLL51W0D6MGHEkIgtCt1VUPFYwY1PIIs6Hn81WRHoA76o92YwkRIQOHSAafQw4OaTEQjp0KE7i\npO0ZESEaXYMT5sKOpRKNrrHjbBglTDQa5Xe/W4SLLno8zujzZPx4H7CQu+/+IddeO5F99tmnIO0/\n/fRSVP+GH9E03vZ8Dj30FmbM+HPe2/XJSPMhIv1FxC/bBejnrWu2FKynRllw5pmn4GLNJVtOLwSu\n5ayzRrVV17YrPvtsPanc42CBt90wjFLlqqtupKmpO86H4xacrVwwvPkpQB0jRpxbkPYnT57Fv/41\nCZfF9kUSjU0f4rjjjixokDPJRIEhIjFgb1X9r/c95ZBLVSvz3MeiICIDgGXLli1jwADLj5cra9as\noW/fEaxbdwTwAdAB2ATsQ8+e7/Hii48UPGrf9o6qUlHREzgAmIBT1/o8BtwMvEMsttK0H4ZRovTo\nMYzVqwHWAG+SSosp0pdY7M0Ctb+YoI2HQ4AY1dUjWLVqccb1LV++nIEDBwIMVNXlLZXPdNqlB/Bx\n4LthNCMajTJ8+HmsX38N8BpO+FBgM127vsqTTy40wSNv7AbcA3wXuByX03E90N1b/7W26phhGC0Q\n9zTpDzxBOvst1c7EYjEqKnKJjNFS+424zChLgPbAe8BOQDfee+8jamunMnPm5QV5bme0N6r6jm/j\n4X1PueS9h0bZ4Lt/qn4NuIa49fazrF//c66//pdt28Htig3AeTjNxxvAs97nBG/9hrbqmGEYLRD3\nNLkAN2hIZ6S/Pu8aTBGhsnIDMA6oAf4ENAHXAy8B89i69VXq6o4pWNygTG0+xmS65L2HRtkQ7v7p\nbhpz/8wfIkK7dk3ApTRPgz0SuIR27ZpsysUwSpjRowfjPF22kM5+q6qqMPfyrru2J/4MuQln+DoE\nF+9jGHA6sdgs3nyzG5MmXZf39jOddvlLhuUUKEubD6N1ZBMu2F6KrWfnnbvQ2BjmUQRwCrvsUlgX\nPcMwWsfMmRO5884amprGALW416dvdKrAAuASzj67MGP69eubiHslLgEuw2lCfowTQPx+PMavf30J\nN9xwVV6nXzKddqnIcDHBYwelLbMj7mioKpHIPqQT9Dp16mZxPgyjhOnUqRO7794DuBbYB5iJswEZ\n4n3OpKJCufnm6XlvW1XZujVCXMDYhbj2I1mbejJNTbOZMmVWXvuQPwsWY4cnHi64OYUOWLMjISK0\nb7+ZdIJe+/abTdAzjBImfh93AubjXFz3xhmT7w2cxL777kfnzp0L0nZ8sNgIrMJpP8KjJsMo5s59\nLq99MOHDyBszZ06kV6/ZVFQkxvioqFhIr143Fyw74o6ICXqGUf7E7+NgbpW5wGIqKgZx2mnHF6Ht\nWcBAnMVE8bLsmvBh5I1IJMLzzz/M+PFLqa4eTvfuY6muHs748Ut5/vmHzc02j5igZxjlT/h9TFHu\nY79t5+p7G1BPMafNMw6vbhiZEIlEmDNnOnPmYMalBcQX9KZMuYm5c2fT1NSBqqpNjBkzmBkzTNAz\njHIgEonw+OP3cPLJ3+WttyYRi3WhomIDvXvvw8KF9xT0Po5EIjz33EPss89YNm7sDIzGGbkmR6F2\ng5p8a1OzEj5EpB1wJrBIVT/Ka0+M7Q4TPAqLCXqGUd74gRlXrPjxtjAFsRj84x+LGD78vIJrjDt3\n7swee1SycaMCV+G8XSqAwTgD1CVAJZWV9WzZMppoNJq3/mQ17aKqXwB3AjvnpXXDMPKCCR6GUX5c\nfvl1vPnmJcRivoeJW2KxkaxYMYEpU24qeB8S7U7uwdmAHA4chbNBWURT0xv86lfH5TXgWC42Hy96\nPTMMwzAMIwfWrFnDr371CC62R3OKFZjRt/0QeRg4B5eioQ43/RJ3uc23QJSL8HEHMFtExotIjWW1\nNQzDMIzMiUaj9O07klhsP4rpYRKGb3ey227TcekZ1pCYrDJOPgWiXAxO/+B93hpY52e5tQinhmEY\nhpGGyZNnsW5dBNhKmiTxRQnM6NudrF1biYt4+ssU/YF8RqrORfiwrLaGYRiGkSNz5z4L7I6zYFhE\nXNMQFETmM2bM4IL3ZfLkWbz11gRgBm4yxA8+VliBKOtpl0JltRWRH4nIKhHZLCIviMiRacr2FpGH\nvPIxEakNKVMhIteKyP+JyCYR+Y+ITMm1f4ZhGIbRWlSVL77ohHvJXwbcAJyLS+Z2mvd5Lu3aXcqM\nGRML3p9585agOgKI4oSOwTiBqDn5dLnNKciYiJwtIktEZI2IHOCtu1RExuZY37dwfj3TgCOA14BF\nIrJ7ir90AN4GrgA+SFHmSly+4h8CPYFJwCQRGZ9LHw3DMAyjtcRDmx+DC/BVAXwL51nyiPf5TSKR\njgXvSzwhqC8KPAZMBGbjMu3GAxjCPHr1uiVvgc+yFj5E5CKvZwuAXYnbeKzH5efNhQnAXap6r6qu\nBC4ENgHnhxVW1ZdV9QpVfRD4PEWdNcAjqvqYqr6rqn8CHsf5DxnGdkNbJpCz5HWGkT2jRw9G5HDg\nGlwyNz+bLd7nKDZs+FnBXW0Tc7ycAvwUeBZ4CFiKyzczAuhL37635jXuSC6aj4uB/6eqM3HWMj4v\nA/2yrUxEqnCB5Z/016l7oj2BEyBy5TlgqIgc7LVzGE6ftKAVdRpZYi+nwhCNRqmtnUaPHsPYb7/T\n6NFjGLW10/Lmg59N29XVQ4vWtmFsD8ycOZHeve/EKfFPDimhxGInF9TV1r+PP/30v7jX4mRcdts/\n4oKNveKV3JOePffiuef+lNeAZ7kIHz0CvQqyBchFT7Q7TnuSHDH1I1xqv1z5Oe4orhSRz4FlwC2q\n+of0fzNai72cCks0GqWmZhx1dTWsXr2Y+vpHWL16MXV1NXkNApSu7dtvP4zVq4+hvn4j77zTidtu\ne4Lq6iGsWbMGMKHTaHtK+Rr0Q5t37LgzcY1HFGd54Nt+nMTHH39KQ0ND3tsPPkOi0WeBOcDfgb8A\n1V6pzVRVfciFF+7Hiy8+kvdIq7kIH6sIDzI2EljRuu4k4Lvu5sq3cKHgv42zIzkXuFxEzs5D34wU\nZPpyMnJn8uRZXjhmPyoiFCsqorOMvxDVO4Cjic9TP8vatVPo0eM4DjjgxKJrYwwD2lYjmC1+aHP3\nmovitA01BG0/Nm68lmOO+Xre+5/4DOkMPIyLHzoOeJ5IZB0XX1zDp58+xy9+cV1hQryralYL8H3g\nfdzLvRH3cp/sf8+hviqgCRiTtP4e4M8Z/H8VUBuy/l3gwqR1k4G3UtQzANBjjz1WR48enbDcf//9\namTGxRdPVZGHFU5SWKgQU1Dvc7527XqYNjQ0tHU3y5rq6qGB46rNvldXDytw21coPBpoUxUavHM+\nP+GcV1Qs1D59TrJzbhSchoYG7dPnJK2oWFg21+DFF0/1+jvVe15qs6WiYoHW1k7La7vNnyHBZWuL\nz5D777+/2Xvy2GOPVZwkNUAzefdnUqjZn+As4N9AzFveA76XS11efS8AcwK/xavz8gz+m0r4+AS4\nIGndT4CVKeoZAOiyZcuyOolGIu6ivjrljQTz8n4j7UjEYjHt3n2M97KfqjBUYbT3ebVCg3bvPkZj\nsVgB2x4Y8uAq7sPTMJKJv8jL5xr0BSY4Jo0wkN8BRfw+DmvLLd27j9GtW7dmVe+yZcuyEj5ycrVV\n1d+r6sFAJ2BvVd1PVX+dS10es4EfiMg5ItITl7yuA077gYjcKyLX+YVFpEpEDhNnLrwT0N37fVCg\nznnAZBE5RUQOEJHTcV41f2pFP400qPpuW8/hLKTDGFWUfAXbKyJCZeUGYCzwDvAFThH5IfA3oIYN\nG96msbGxIG23a9cItKd5AKIlpDrnxcpRYezYzJu3ZFtm2GRK9RqM237sROI9FbQ4yG+Y9UQPl2Si\nwFQ+/PBt9t//9IJOW+UkfACIyJ44L5VDRGSP1nRCncvsZTg/n1eA/sAIVf3YK7Ivican+3jllnnr\nJwLLgV8FyozH+QvVAW/hIrn8Apjamr4aqYm/nDrS1vkKtmcikUpgMzAGJ3tfB7wBPAO8QWPj9QUz\nPB09ejCwgcQHl2Ln3GhL4gOf8rsG47YfDSQanA7zfjfkPcx6PJNtEN/uZBBbt75RcEP2XOJ8RETk\nd7jsM0/jnnhrROQ+EemSa0dU9Q5VrVbVXVS1RlVfDmw7UVXPD/x+R1UrVLUyaTkxUGajqv5YVXuo\nakdVPVhVp6nqF7n20WgZF/3uE1LbChcnX8H2zDvv/BcnQ7+GixHgG54qfoyAQhmeXnfd5YhsxgUj\n8hHiIZnDsHNuFJb0o3ko9WvwhBP64zSHQYPTx73fIxg5MmXA75zwM9lWVAQDid2IC9WVGHOkUIbs\nuWg+/gdn5j4KF2SsC3Aq8BXgrvx1zShHZs6cSNeujbjoeM3JZ3jeHRFVZfNmwQkcS3BREpuPlmKx\nwQVRM0ciEc47byxOSRl8cNWQ+pw/ZufcKDjho3lHKV+D0WiUP/95MTAFF4pqOu4+Ph0nEBzM55+n\niqWZG5FIhOeff5jx45dSXT2c7t3HUln5J8JjjhRo2ioTw5DgghviDAlZ/1VgY7b1lcqCGZzmjfr6\neu3a9TCFeUlW5/NL1uq8XIjFYlpZ6RunnZLCq2ihwknardspBTE8bWho0J49T1D4hsJBCj0VjlKo\nVpibdM4X2Dk3ikLc22VBWV2DF1881TM43eDdzw97xuNDFcYoHKMi1VpfX1+wPmzdujUjI9R0z5Ni\nGJx+ipv0TWYDsC6H+oztjH322YfVq/9Obe3L26Tq6urhjB//Yl7D8+6IiAgdOmzxfr2Hs6FOjPfh\nfl9KY+N7BVEzRyIRHnnkTioqXgZuxZlULcVNAz0E9GbvvU/1zvlSO+dGq9EMbDXCRvPlcA3GM9ze\nhMsscidOoxmPoaNaR79+pxQsXklFRUWKaSvd9pnvaat2OfxnBjBbRM5R1Q8ARGRvnH7o2rz1zChb\notEokyfPYt68JTQ1daRdu0ZGjx7CjBmXlewDoJz4xjeO4+675+OMTUemKHUyhbKtdoHkvk4sdjtu\nftinM/BbYD577nkrr722uCDtGzsGyc+RqqqNjB49mJkzJ6Z8jkQiEebMmc4tt7iXZqnaePioBjPc\nLsG97H07Lh8BTmHt2hhTptzEnDnTC9KX0aMHU1e3iFhsMDDL609Hr2/d8253ksv0xCs4s9jPgf94\ny+feuuXBJdu623LBpl3yQjkG+ik3LrjgCm+a46S0atJu3U7N+7RLfMqlZ9q4BO3a9c1ru8aORS7P\nkYaGBr344qlaXT1Uu3cfo9XVQ/Xii6eW/DPHxUaa4t3P6YJ/FTaAYPzeHqSQOHUFj2qvXkPTHsts\np11y0Xz8pbUCj7H9khi218e3mNaCSu47CosWvYxTyZ5I3MMlGaV9+815H/lNnjyLlSv3xaVySu3W\nGIt1IRaLUVGRsze/UQaoFkbDkO1zxE/r4P4zHd/7q65uEU89Na6kp11Gjx7MbbcdAjwI9CQTd+FC\nTaced9zRrFw5hETDU+dB989/Sn6f35lIKDvCgmk+8kL6sL2Fldx3BBKjE4ZFFY1tM+4tRERHd36H\nKvRJe54rK/vkvW2jNGhoaNALLrhCI5F+WlnZTysrj9FIZKBecMGVedMyZPscSRfdVKQw90K+aGho\n0K5d+yucoy1HOh1a0L7sv/8JIe3HNSAHHHBiyv8WQ/NhGKGoZh7op9TnYkuVxHgGE3FBgTbikkL5\nrq8RYB2Njc5ALV8jPlXl88874M5vd6+9U0JKLqBv3+55adMoLaLRKEcdNZaVKzcD1+MbO0ejyl13\nLeCZZ05n6dI/t+qay+U54qKbTg/2FN9uQbUjdXVvo6pp7UXaikgkQqdOX2Lt2ttwTqPh95XIgoK6\nC6sqn3zia1Ljxy9u9zGYjz/+PG/Pb9OJGnmj3AP9lAvxeAYRXAaCKbhsAj8DXgeeIxZ7i7vvPpGj\njz49bxbyIsJOO23CPYjuxgUkmk/QIt79/hELFvwmL20apYGfLbZ798HetNs0nGo+6GU1ipUrL90W\njEq1ZQ+VMLJ9jjQXVppniN269Q3q6gYVLPJva0js/4m44NzzYFu226nAMVRWXsdf/vJMwcKdNzY2\nsmnTh7hIq80z7MIgNm16P2+pG0z4MPJKuQb6KScSoxPe6K2dhYv7l/gyWLHi0rxGJnTh1ffFhXP/\nG3AbLhvCEO9zBuef/zX22WefvLVptC2+PUVdXQ3R6J644NbhOVRUj+U3v/lTq1PaZ/McaS6szCIx\n8i84e5GTCxb5tzWICNHo+7gX/gjg78DtQB/gCFxMzyV88cUS3n33yYKFO7/qqhtxHnS1hB0/J2ze\nypQps/LTYCZzMzvCgtl85IVyDfRTbjQ0NGht7TSFHhnME+fPzibRIn5+oN0vFOa1aBFvlB9xe4qY\nugzKqYJRNXgeG49m7KGSimyfI4k2H+Vnd1ZVdZB3P/nHcKG6QGMLQvcjmyy9mXq8OTub41q050p1\n/IqS1TaIiFSKyOEisltr6zLKn3IN9FNuRCIRbrllGrAzLkBRcRJqRSIRnnzyPvr06YBILdAPGERV\n1eF873t/a/V8v1FaRKNR7rlnvpctVgB/2i3sepqFC3qXqIHLJTdIts+RmTMncuihs3DTFeWVYE5V\n2bp1F5xmIai1eY5UcXxaCnfuT5NlqoHSbVM/x+Lythb++GVtcCoitwBvqOqvRaQSl1zuGGCTiJyq\nqn9rda+MssYP9DNnDmZcWkDcA6Az8ZdBuMttPu1sotEoQ4d+h5Ur9/fa6wBspKmpJ0uWLM9LG0Zp\nEI1GGTToa0SjXyJuhFgJ7AUsovmLcQkuL0lzYrERzJ07mzlzMm8/m+dINBrlww8/Ah4A/kWx7od8\nIdIZ11//GDYAW8nFeD8Xt+P41NVE3Ou88McvF83H13FxlAFGAz1wzsk3AzNb3SPDMDLCxdBYj3tY\nBOfHg6OSR/NqZ3P55TM9T4dv44zQ5gJPAN9m5crNTJp0Xd7aMtqWyZNvZOXKy3AvQd8I8QLgfeAa\nYAHxay2Ge50EX0pR4kkPT+e99z6itnZqi7YKYaPq4MsueXs0GqVfv5NZt24aLqP20SRmXY5TUfGY\nZ7eUHWF9ypf2JJ4yIYbT2jTiXrOfk4vxfmKMlMw1UM7O5jnca31BaJm82u1lMjcTXIDPgH29778E\nbvG+9wAasq2vVBbM5iNvlGukwXKjoaFBXTK3BxVO8OIE+MmohiqcrbBfXo97JNIv5Tw0zNdIpN+2\nsoVIamfkRqbnInjvVlb28+b+pyqcq/GYMg0KVyr085ZB2q5dL62oCEa9DdoutGz/ke6ZEYvF0m6P\nJ2a72mvPbztoL7JB4Wytquqj3bqdmtEzKazNCy64Qi+44Mq8P9suuOBKz1bmhMB+hMXxadnmIzxG\nSvwcpLLZ8O1sRB4KOX4tJwbN1uYjl5f0O8BwnP7tXeBUb30fYF229ZXKYsJHfggPi7zVwqsXgPHj\nr/YeVsepC7eeaOgH87Sy8mDdsGFDXtpzGXX7pX2wVVT01osvvnrbw/mAA040wbONyHYQkHjvbtW4\nYWlDGiPEmMJWTygNCiiZvzjDnxkbFM7Vqqo+utdeI7Wq6uBm17f/TNlvv+MDAndQ+JmmMExd9ufU\n/08Vqj28T4Oyqiebc9Wr11CFozVuQB4mRLn7OlV7iUEIG7zzEByQTE2b7do3ZN9//+O1Y8cjtF27\nvtqx4/F6wAEnam3ttLyGV8/lJT0dp+td4Qki7b315wPPZ1tfqSwmfOSHuNV52IV/jl544ZVt3cXt\nhni00R97D8TmD3qYl7fojk74OCbNg+0KhYO8kVNiSvCuXfsXNCW4kUhqb5HUL8rmUUL9l3lM4dQU\n15dbnFDqp4RfoOEeJ+Gj7+btJmtNpmoqbZvIfO3Y8XiFEzW1F072XiPhEVNz00Rkc846djzMEz6C\nx8IXosYoDNOOHY9IO6BwzwX/XCRqnmCBVlUdnJGg5AsomWrNCi58qHtRfx1n1rxvYN25wNhc6iuF\nxYSP/JCvC99IT3yEc4VC75AHffyBn0/XwkhkYJrze466KaCwbfO1a9fD7NwXgYaGBu3ff7imEkhT\nvSibq+uDL9vkbcHvW5OE0ivUaQj83+lH3+nbbVAYmPb6rqzs4wkYyS7nftvZu46GT10U1oU3fk+H\nhTj3j3nLIdbdNNS5mkpQEsnfgCRIUVxtVfUhVb1ZVd8PrPutqj6SS33G9oGq7651E6mC1DQ1zc5f\nkJodmMTASvtSLNfCM888CReBMez81uOCj4VtO4W1a2eUXICn7Q3f0+H11xsJD30f7qYZv3eD19FE\nYDYu3PcxwJ+JG5Ce5n1OQ+TPnsGkf40txxmfpo6U+fHH/+Sii66iunoo7777WVK7S3DBtqLA14Av\nke76bt9+J0QOwxlqLvTWr8GFKj8SZ46Y+v9btuyccH+EHwul0C688Xt6CIkG5PE2YH6LBp8zZ06k\nquplUgeCG5XWTbdY5CR8iMiRIjJJRGaJyOzgku8OGuVD/ObxHx5hjGLu3OeK2KvtF2e1/yzOGyHV\nQy+/roU33ngVVVXLaX5+fUv950K2+ZTGQ297ZvLkWbz11gRSx35Rwl6U4SHNI8DDwFLgrzih8mgS\nBYl+iEwkFvsM5yHhx6kYRupImUP44os9uOuur/LOO08Qi+0caFdxsWvEq8v3tkm+vuOeNJ99thPt\n2v3Ea+sq4LfAccB1wKnAByH/jx+PxsYPEu6P8GMhpI5v4urJx302YsSRuNg5vtAXPC7z6dp1CjNm\nXJa2jk6dOrH77skCV+K+lEKsk6yFDxG5Cnc1fhf4Ci7+q78cntfeGWXHqaceg7NFLp8gP+XKjBmX\n0a4dwGDCR0rgRkrZuxamIvHBluhKCf+h3AI8bW/Mm7cE1ZEkviiD58lpLBoaVjXL0TF69DEhIc0j\nOAHgC6AOp00J5lD5JbFYHRs3PgfMwbldj8BpTVKNvmfh8pX4dQWv30bgba/v/iAm+fpOzN0Siz1P\nU9PLwF+prNzs1b07ToMwDVhHKtdb94L/vNna8PDuqe+zfLigRqNRnn56KU7w+AHwAs63YywwhN12\n+wlvvLGgxSB+IkL79ptxmqfmmipoKI1YJ5nMzQQX4CPgvGz/V+oLZvORFxoaGrSqqqU51sKmhd6R\ncCmwg4Z+iXYWhbCxidv1DNVEy/+rvbl+O/dtQaKng283Ua9wmCZ7aIg8qn36nKT19fXbPGK6dTtF\nq6oOVpF5gbIbFI5U6K9xDwzfhuMITbQr2aBwfOD6S2Wkmmw7EfTquFqhl7pQ42OStvvh/KcqPKRh\ntiRwpkKNwuBAnaekuD8WKJwU6v0RbrDre7vMS6gnX6kjEo31p2ncyPREhXOzMtb/7ncneH1Ntr1a\nqDCoIIb/xbD5iOFEUsNoRiQS4fzzRyOSHKTGjcIsuVx+GTv2q15gIF897o+UhgMP8r3vjct7uHM3\n3XMRzUNpXwh8SHzePZGKioV27gtI4nTBROAGYChu+iEx5LnqKN566wf063cKdXU1rF69mA8+mE9T\n08uoPkRVVX+6dRtNp041OAX5XjitxOnAUbgpl6447YWvWfka8Cn+1A5sprn2ZSguVFRw1O1P7zwD\nPIiz0bgF+C9u9D4Lp52YgpuS+CNwF81tSWqAV4HdcDYfE3Ahy7cAD9H8/lgKPET79luaaQGC4d33\n3/9EOnYcQGXlMXTo0I5OnX5CJHIk3bqNbnXqCA1oAefNW+KFsfe1TcfgNFidgPf5/e/nZ5RMLhqN\n8uc/L/aOV/KU10hgMqm1k0UkEwkluACT8AKLbU8LpvnIG4nBaszlspDU19frbrv1V5gbGOF8oSKF\nS/LW0NCQFFDKX/wRadgo81Ht2vWwvMUcMcJJdBG9UtMnHbxa03nEXHzxVO3Uqa93Tvuo82J5NHBO\nT9X0brH+9wZN1JKFeY00qNOaDPK2v69woPf7Pq+NPuri2Ryq4Z4cMYXhCn0VenqaCuf+7bQVwXLx\n/Uzn+ZHOZbl372E53V9h8VfGj79au3XzNUXhAdrg0Yw0LPGga+k8ZhI9c/IRELAYcT4qcEObt3FZ\nfP4UXLKtr1QWEz7yS319vXbt2lzda9lt80c8w+yR6tTNfsTJYxT666679imIoJcY7yO4+C+VoNr4\nVO+lMlwrK/tYxNsCk/iyTBX7Ip0LanyKoUOHQ9RNgVztXV/9A9vrA9uCgsA/FQ5Q+LYnJPjfHw20\n208ThZ4GT2g427t2r/C+n6lwp7rMzcEplzDX8gavL33UTQcdpc5ldZCmEohbitipmireR2aCS/rz\n0zzya3y6unXxRA44IPm8J7s7n6gdOx6h77//fl4jURdD+LgdpzdbCNwD/Ca4ZFtfqSwmfOSXfN+0\n2xv5GGnE/fkf1mLG1giPdBoLPPDqvZdJH3URG6u1EFEhjebEYjGtr6/X/v1P8o59OvuKVJEwj1Wn\nOTjQq+M4dQLk0YHy/dXFdQmOsBs8weArCgPURRUdp3CIJsaG8b/PD3w/xmt/kjphurfX7pe9cn79\nAzRuV+Kvu8Jr60F1Au8Qr81zNB5u/QpNFM4P0759W9ZchMf7iF/z2cb2SPdchHNU5NGQc5Z5m3G7\nnwF3P/MAACAASURBVOBA4CTvGRHUQh+tFRU9sgpC1xLFsPk4Fxinqier6nmq+t3gkkN9xnZIfP7S\nR7d9aykd9PZKtmmuW2LevCW4JF+vUszYGi4RFiR6EPiuiPXA8cAluJgfI4A7yEeadSOc4HXVvfup\nVFcfz+uvX4KzFUhOOuinvT8Zd778WByHEbcxaAC6ALcCG7zfk4h70Mzy6r7Vq9P3fDrd236w93tX\n3PX5JRJj/3TG2Xi8iPNIqfXKdMR51UzFSxUG7OT11Y/54bsQK3Gvlw+BnwG/wtkifeL1r97bp3G4\na/I14HXg78DP+Oc/30l7XFXD4n0Eyd57q/lzMchtVFZeSmu8BeN2P/55n4WzxbrTW+fbyAwnFrud\nWOxk2uq+zEX4WIubcjF2UFq62VSVLVt2wRmoTaO5q1fjDudy6Qd/8o376usfYfXqxdTV1VBTMy5r\nAURV+fzzDrRVbI0zzzwF+CmJsQhqcMGPbyHuRrmE5qnXHTuqEJpPkq+rDz74Ck1Nc3DC3mBc9IPZ\nuJf9VO/TPx+DcS/+5JdTJe7e/SrOWHMLccH2MeBpnBDQBWhPXID5r/fff+CuiV64+z1K89g/EVym\njj29vm7CCTd/C/z+DGc8mhzzY4jXDz+myPs4weJCnBFpDGcMGxbwUHGvvdQBD/3nUni8j+D3WFYu\nqy0LM53ZY49DiER8o91mNUAG8URGjx6MiH/en6D54ERxz4yTQ/9frPsyF+FjOnCNiHTIc1+MEiab\nUXtjYyMff/wfwqMb1gDjqKzc0PZ+5kUk1zTXqRARdtppE+4lsQvpRkrJERzzwY03TqZnz11ws60H\n4140j+NiKvgPNcXifuSPsOPkrqsJgevq78SFiwtwAuJZwLU4L5WDiJ8PPxZH8OUEUIXTTtwEDMRp\nEQQ3eJiKEwr8l/JXcQLMBNx12NFbvxNO8zDYKxs2mg9eH4OB7jhhphEnyHwJl0ZMSYz5cThwM+7F\nOtyr4xmcAPURTmD5wGt3CU6oChsEHbst4GGq59uIEUciEozseirQHxfiaiSffro+Y+1luDCTeDza\nt9/CeeeNCsQYSY7RMoQuXSrTtjdz5kR6974TFytkJ5ygETwGY3GeSG18X2YyNxNcgFdwom4Up1dd\nHlyyra9UFszmIyXpjKTC5gedLcKgwFxt8vKoHnbYiDbam7Yh33PHqkGbj8PS1h2JDCzAHrk5XpFq\nhf/15pO/qvGcHv7S0n5b3I90pMpM68fncHlNgnEofENgP77Hb9XZZ/gGn8E8Kb7HytCkdf3U2Xz4\nyQKP9uquUdhfnSFpMH19H4X13n8O9a6Boz3bAj/2Rq8U14Hfdr1XT2+NG7geos5T5VF1sTpU4zYM\n/6tx248T1RmZLvTqu1qdwenZ3rYwe6iF6sf42LBhQ8rn20EH1WhFhW/wGh5PJxs7iXCbD1eXbwvX\nPLV9cr9aNtr3s9NWVPTWeJyTYD35vy+LYXA6Ld2SbX2lspjwkZpsjUfdizZVciR3cR9wwIltszNt\nQGLwp/Cle/cxWRuhxr1dfIPO5g80mKedOvXJ+z41NDRo+/Zf9l4CvkHbSdrcE6GwmUC3Z1K5eYo8\nrO3bH6wiwUBc/rE+xntJ9g+cG3/dSepe6sFrJcwz4mB1L/3hgZf5GeoEi7PUCRL++f5fr/6r1QkL\nfb22feHFD0h3kIYPRqZ6dZyg8A3vWj7G+32CwtfVecwcEjgGvjdV0Dukt8JWb18avPZPUGc0G57R\nFuZrJNIvzfOtwdtn/3i1/lqOewH+UZPDEOy2Wx89//wfBwK+HaTZJgdMxg1QeoUcg6lJ6zJ3P05F\nUbLabo+LCR+pyWbUHovFdO+9R2nq9NZuyeVlW860fAxz0wBs2LBBKyr8l8WDzR5ocIDuvHPPvB9r\n91DzR6kLAw/m4UkvmaB3hblcZ0r6zLRTA8c4eF35gsK5GhcIFqjTbkxVJzB8VePaBP/F7XusNHgv\n7CO9Mgdr/GV+oDqhYqi6F/tJ6lxYh6vTcpyoTkDwvWQOVfiOt129dsPivzykTpsywPv/QK++4711\nQ9V533w58LIMugrP03jkUf8YrPf2qd7rR6r7bqtGIgPT3JtTNdG9OHftZUNDg15wwRVaVXWwOm1U\nchiC9d4+BNelH8Bloi2tr08+Bv6xO847pmdrYpTYc7RnzxNK1tvF2IFQzc7iW0TYuPFDipGEqZwI\nzxXhaE3U186dOxOLbcWZYs0kMfHXs8AdfPbZ5mZ5PFrLI4/8HTe37hu7+nPyvwEuBebjzn8EF13y\nD0Bv9t771FZHhdzeaTkz7RLidjWDcdlmp+Lm8S8HXsIZhD6Hs+P4wPvPq8CBuMinL+HsJV7GRQNd\niLt+NgNX4s7dbjjPkY7E7Tk6Avvj7AneAN71yrTzliNwxp4xr/33vO+7ER6F9w2gA24WvyvOdmQ9\nzuajvbd8hLP/uBm4D2dn0hdnrDoTd537yekG4zytAH7h9TX4nEnMR9TY+Bkff7yV8Ofbs8TtXZRc\n7ZcaGhqoqRnHXXd96BkDvw38HDgWd98OA07ARST1vcIacIa+qZ+RLdlyRaNRTjrpXGA/4h5Jvh3e\nXOAA4FsEbfJEvolIkcSCTCSU4IKzHJqI85P6EOf9sm3Jtr5AvT8CVuGu/heAI9OU7Y17oq3CXdm1\nKcrtA/wOd3dswplEh0plmOYjJalHBn60vMRReyTST+M+9mFS+9wdTt2eOlJi6zQAsVhMRfpo82BP\nycd7at72JT6N1NsbMfm2A3579Qoj1KngByv01crKg/X999/fobRd2eLbd7j7Z56Gaw9jSevr1Wko\n5mviqD84ndJH41MoyfdyTOE9byTeU+MRSXt75+4Ary816mxBTlAXffQgdZqNXuriehzsfR+gzt7i\nVI0HHAuLXRHUfhyo8amBqeqmePp5/e4TaPt9jcf98DVt/hRMT6+fft+v1njskOCoPzkfUUyb5yPy\nA5b57fqRUsOi+sY07DkYtNXp2NHPgeP3JZiPybfDSD4+V2qi1iXYL6epEOmVMihYoubMr/tKzecU\nUjLF0HxMw5lG/xHnazUbF900hhPjskZEvoUzrZ6GE51fAxaJyO4p/tIBJz5egRPrw+rcFSfub8EN\nyXrhfLXW5dLHHZnEUXt662tVpVOn/XAjnmtwabbV+68Cj9Ku3USuvfbHxdyFNieYK6K6ejjdu4+l\nuno4P/rRC63WAFRUdCG9u+2p26z680Hcar87Tq6HeNryKC7+wxrcaGsrsBMiSufOnXcobReQdmQa\nJOgyG4367qeptIfRwPq7cNlkT8F5YJyEGwcOxp2b93GPS//TH72vwV0v/YBve21V4DQlM3Hjtiiw\nB+5+X+u1txl4Hnfuv038HHfGaQkqcaP2zTgtxWyvf91pHhfG3wdfazIS561zI+6xvTlQnwJX4bQd\nQ3CatWAelC1ev//i9WOiV/eRuGcQuJwmyfmIorhx6cLA73E475AO3r4NA57yti/0ylyJ83o5zOvP\n4XTq5M5jsvvzxo1dvTJbvf9uIdEFGBK1KlFc8HD1jpnf3kCCmk3VN0Nd9Ztrznzt2DzimrRk1+c4\nRXOBz0RCCS64l/4o73sUOMj7Xgvcn2193n9fAOYEfgvubpmUwX9XEaL5wOm1ns6iD6b5SIE/aoff\naViGzOTRezzr6ZWaGFVwoMKVuv//Z+/L46sqz/y/JwtLkosCogJKWERIQoKK7EvYFRSsYqfVX5Xa\nTktmFBQFQWWxVu2MopVaZ7Sd6dTWti4wOrK5tDqtoHVrR22rTGeKLQTrqEhuAsqS+/z++D5P3vec\ne+7NTUgUlffzOZ/ce3OWdzvv+yzf5/v0qf54G/QxF2pFyw+J1tjXrKiRfbQYG2I+7hMCBDfoGK8R\nR2kd9e0z18xnIbdLpgiV3Gi8fcuGr92bn75KgBO1z6PRK5cJ8RLlOjZ9hDiKUaq5myVghzhriWE9\npup5Fr1ymtAi8WOhteECcZTp5boGWHRNhdDKtVF/qxCHO7FIlsk6V6JAy9FCS4mBYkeIA8bOEcew\nOlDn+Uyt63SvX8rF5XIpFwd6Hi8O43KvOOxDNDPvvXr/9RKO4pkuxL9cpO25T9s5PMMcXy9lZVNk\n3rylHoA1JS7axHA4QyXd0uF/X65tnKn1L9M6xANn/SiZeMtZUvh+Tvfq1PbrxUcR7bIHQB/9/JY9\nCHQm1rXifoUADgCYHfn9hwAezuH6bRmEj9+D4uWDoNPwNwD+Nst9jggfWUptba1GN8SHz+blbWgy\n1aWjx800eSTCoS1y3qSHPjeXQKztQ1qdQPo94eY0WRzldtSca4v9aCkunvipzu3S0rB0K8616QsU\nBgAdJU7wv0AILJ4k3Jine31skR2DdSzOEwoCFvli4xMNgzchp0JIb27J2QbpNSYYTBBuvLbRT9LP\n1bq5WXurxSWJ8+m9LxfmaYkCLcuFws7fCwUUc/1U6/0nCoWWkfqcjfo/c1tUaB2Ten+bg9P181q9\nbrRXH9/dsVzPWan3MpfIWv0+yXvehUJBID5cNgjWSSLhC4Ti1dnAvUO8cYuOgQkipwkFj0pxLrXM\n73efPhO9eWfn+tdMknRw8scbatsat8sOAD318/+CqCGA9q19rbjfMaBt7e3I728DOL4V97PSH+Ta\n3QrW8W4A3wmC4EuHcM/PbPmHf7gH+/YdizArnjR9SqVmNJnqbrppEcrKbkdenrFf0pyYl7cJZWXf\nxo03XvWR1ftwKvX19aisnIFdu9JTnKdSM/D661fkRDaWTlh2LUiDvTHDFRtw5pkj2qAFrpgbqabm\nTygpMUKp7vrXN+f6ILct2LPn6UNidj3cS2vI5EQM1N0A9tUQ0NyeAM3sV4GuiGUgPfj5+vsK0JQv\n+v/OoBtFQJfBn0Bv+NUAbgTN/L8BAZ3+e/yMXrdHn3sQBCkCdAccA7pV1oNe666gXtdTr/tA62rt\nrQYwH8B+rZcxqO4GcBfc3Le5kQLJz9brc8wVYff7PwCf1/4p1P93BIHNZ4DuojoA/6h9cCfo7kmC\n8/Fc7acGrU/U3fGsnrNS62EukakgsuBDvdeV4LZkLquoC3oqRJ7H3r0dEAaKdtDnXaVj00frJd45\ni7TOG7VOH4JbbUKvzQ52fe+9OiWbOwOOdHAsSLEueh/7jsjncDkUAHxLSmuEj4cBTNHPdwL4ZhAE\nfwTwIwA/aKuKIewUbE3JA/CyiCwXkVdE5Htw5P9HSgvLo49uBheh5inTHb7h1yF8w2c9wuG661Zh\n164iZKY1npGTrzU9P0QCRMvfiDDdeQoWxbB/f2v0gubLM8+8jL17bwM3hwKkL5JGgd02zK6He8mW\nuyOTL91haGxjvBOM7NgERqW8CuIYXgGxE4H+PgNuE3kG3Ci/qn9T4EZ9EMC9oKDyJ3BzPxpuLCyq\n4mJQ1+sM4hPq9JwecNiThF7fAGIGfg/gDwDe0d9s3i0ChY0hAH4Bx6C6A2Gq/ZtBDMY52rbOcNEp\nq0AB5oC2B9qebvr//XDvUUL75SmvjmtAAcUYV7vqub+AE44DrbfNWctPZKyoZ4PGfXvedDjcjAmK\nUQbnMWhs3OX1hYBCWqD16qT9fTbCykICLt/NH7Wtx4Jrbh6aix788MP9KvA2gM4AgRNoHtPrvw5i\nR9aDgpAJO66urVUQRVq+VbdY+BCRpSJys35+AIx7+mcA54vI0hbXgKioRgDHRX4/FunWkJaUtwC8\nHvntdXB2ZywLFy7E7NmzQ8fPfvazQ6jGJ7+ICA4eLAEXpOyU6Q0NDViwYCWqqs7F2rX/BUBw7rlD\n8eqr/4477lj5mRU8AOCRR/4TfOXiaKaBXGiNnZYcvcdz4Cb0KxAEVwW+mssBVOPBB59CW5frrluF\nP/yhBqnUc+Dm9xekL5KHAbDtIyoiltOo5VT3s2aNhdsYbSP6NQiG3AJuQs+BS2UKbsNcBGr8H4Kb\nmlmedoOCRT648Z8HAsCnwlGWA0zIBnC5TILjdxWo1R+j3y1JmYBAVNE6fAgKBifod9tME2DI9XZQ\n+NkMR4Pu982TIABygX7vBlpRRNv8W72mDrQ2NOg960BhINB6vwfgX7UOJgR1gdu0A23zmXqOX4dx\n4BZk/TFG++xmUKhKAJisdcuDy0HjW1BMIZsGCl0BKPzYb9u8+48HLYSngUBcX1koATAchYUfat+a\n4GfjGG/ZDIKN6NSpB5zQdjo4XjaPfgUKnueDQuiL4DpeCM6fchQVTWyRgvizn/0MM2fORP/+g1BU\n1ANFRT0xZsyUrNeklVx8M3ZobX8AoF9LrsvhvnGA0+0AFudw7TbEYz5+ggjgFFQnNme4zxHMR5ZC\nn7SlwY7zE66XIUOmxPi76wSYK4WFFdKz59mfan9/tpJKpaSo6FQJEzoZ6M0IfpbnBMZND31OifN5\nZ6KSHtDmYM8+faojz1sqxCSs954dBbaF/cyfNrK5kpLTsvrSS0pOi72urq5OCgrGRM6vFWIezhYX\nOmvYAH8OWFjmFeLItU4WAhUtPNQn5vLZRocJcJ0Qy1EmLuTVnm14iElC/IThQkqFmJBZWi+jR/+S\nEB9xshCbMkgc5XuUxt3m7FSdR2XiiNHsnagWYlkGCzEkF4sDs9bpNcZCOk6IH7GQW8O4rBWHe/Hp\n6MU7b4P33QC0o7Xvk951S4WYD/89jr5zFopslOwnRe7fX9uyRsdkqjhSwMFy1FGDtS3DtK9HCcGu\nBoq15zQKsF46dhwoJ5wwURzOw6eBjzLb+uvOLP17kVxyycIWzfMwtqlO7zlc0F6YDxE5AIrQbV1u\nB/D1IAguDoJgMCg+FoGgUwRB8KMgCG62k4MgKAyCYGjA1H0dAPTW7wO8e34bwKggCK4JgmBAEAQX\nAvhbAN9th/p/6gs1s3pkchkAM/Hmm29H/N31oLT9RRw48Breemvdp9rfn60EQYAPPvgQ9Fg+jHgL\n0kg0NOxutl/iCcvqEdbGnIuD37+N5cvbzsUhInjvPfOD2/Pmg2bjG+G0NNPcVsK56qbo9+Snjmxu\n374GhENK/bJJ/59eunTpgl69OsJpwfaujQD1sLEg9sHM5X7o6otgP78NWgFWgwRSHUDLhLkIRoFQ\nuAEgXmI96Jq4Wu+dArX7G0ArSy/QdfJdUOP/G3D5H6n37gpnCSgB3TYXgORZq8HstubuEKTjDGzO\nXqXP7gVa7L4NWh2KQfzIKaDlwfAcL4EWhHlwIbbfBl003wAxG5eChGvPaPsWgdioKqS7Ozbp/x7U\n+hwELUw9QEtMCYBZcC6K1+EsKL5b0SwgM+DCn28DLR136HNKtB2PgwRrz4Jb3R7QSvJz1NUJqOfv\nB11VywD8NxjAuRlhy+ZK7Ns3HEcfnY8geEz7rAscods4kHTtbT3fX3ce1b8X4L771rdoPXbYprHg\nGj8adIC0oOQiofgH6EBc2NLrcrjv3wN4Ey6Q/HTvf08B+IH3vRQc/cbI8VTknjNBh+le0EH5lSzP\nP2L5yFLiNbPwkZ/vI95FjuT1cIVkYIb+j2pavgVkjAwdOr3ZpFHphGUWznxodMwtKeGkZjbepjVP\nV23RQj3XSjr1+2D5yldapnEdziWVSkle3kiJpxHfKMA0ycsbmdHSwyixjV5fmnZvIa+WxG+JUMPv\nJ8CjqsEO0qNSHA26JYXzCeiMaGyH0JpiCd/6Cq0E94mzgDwYozUvEWryI4URGUbnPjfyDNPCJ+k1\nDwotM1bnVGTOWuj2QD13utf+cgHOElohjNBuod7L5mBS6+NHDFmCOrPEbdXz+wqtI/74/FiCoFQY\nTtxPaPWwSJhN4hL1jRYXTuxHlPgWEN/CYxarpNBqYhacOGugWYGm63MnR54TJSZzc6ugYICUlU2R\n9DVgiricN5nXY2Bdi4gInfXVv2f7R7v8EcCKIAjWqFVhgX+04n4AABH5JxHpKyKdRWS0iLzk/W+y\niHzF+/5nEckTkfzIMTlyz40iUiUiRSJSISJtCYj9TJV0zSxaUggj3gFqHZ8Nf38uJQjqQc2nO6gd\nGdp/FHxK9FdeWZDVMhRHWFZcfBDp/uzQ09s0TbaIeH5mI0F6ENRuvwwCCV8DNeUrQWPmGISp32/D\nww//4lNlAQuCvaC/P0oj/jyANfr/+LJ06TwUFl4OEmgZwDsB4GRQI78B1NAngFaFV0ArwP+A49AH\ntHR0BsdhBKh3+SDLsSBG43kQIAxQIy8CrQr/qs+8FQQRrwUDBk1rngjqgrtBTb0SxPq8qM8QUPuG\n3rMzqN0vAb3rN4CWlEoQ12A4jX2g1eI6AP8CWntqwTWkP0imvVPvuwfAf+r9j9K2l2i77HNvfe7b\noA5aD+Ay0FKxWdtapf1Rga5dvwWRY0H9t6fWZwxodblF274MtBQZnmQTHI7FLCBneL+J1sVwUM9r\n3/qYG8C9s3aPRtBS81cAffV/nREmJvMtmzNw8OAdqK4+HUOHlsBZdmpBXd6wL5nxV8BZORMRivi4\ns2z3zF5aI3x8Fey9YSB8dqF3XNGqWhwpn4hyzjnjs+QneRxFRfvgXqq43AThF64tN8PDvTQ0NCCV\nMiZFQ8ufD5pEZyC8mJzVbNhtIpHA6tXXY9u2J7F9+yN4663nUVjoM19GS9vm0wmCAN27Ww6Kc8DN\noA/SF8gXQSHE/03070y8//5Nn5qIlyAIUFQEcEG+HhS0HtG/1wPYjM6dJeMYMJx9JWiy36NHPeiC\n6A4ab78NbqYBuJGtBPtVwE2rK1xkww1wm1kAmu4fBPBz0BXRALoTbgA31AAOEG1MmLaR27iOBbeA\n/SC7wj1gpIxF0DSAmx5AwWc7gH6g8LIYDIo0wfRlcCuZAwpK5+pz3tLnNoLuk/f0eR+AZNpvg26J\no8A1xubTHu9zA8KMrjeD79pYAJdoHV4FBZHf4/33BSTBXgS6lKaDAtLdoDCyDHxf80GBqTfoSjGw\nqm3C0XoYYHcB+H6cpHU2RlXAvbNbwLEcC8dYvA2cA9uQTZkDzsJjj72MZ55Zg4qK1WBWkUne/ccg\nHuxuJTMYOu3MpugsH/jc8tKaaJd+WY7+rarFkfKJKOn8HQCQagrPuvDCaZ5wchs4OaP+fgvNTaKg\noOFT5e/PVq67bhW4YF4PGg/ngAtkXOKwllmGgiBAIpHAF784GdSa00t7xO6fc854EOfRB2zXAYQ1\nIdOCnwUXv7h5MOFTZQG78MKZ4Gbuc9wkAcwFsBgix6Bfv6lYsGBlmsXn4YefBr3aV4KbbyXYlxXg\n3Pk5OF+MZ2II2K8PgfiEEeBGnYKLbHgD3OC3gpaKBLiZ3gFaVpaBm/wu0Nrxd3Bhp0A4/PQMUDtf\nCAYj3gMmJjM4XhKc14NA7XssuMXU6vECKACYEGp1+Tq4yU8ChYGZWue74cKMjWvkBtBikdQ6T4XT\n9H1MyTjtC8McGbV4NPTb2lkA4M/apv0ArgHwT6Dl7jdw7+lYUKDcDlq4SsB3zt+E/XoMB8fxJdBC\n9DttwwJQaPNxUKasWYjs26COv0Db/AGyCQ8HDhShpKQEzz23Ft27rwLHeKrWZbHWObNy0tDwVs7r\n8RlnDEcQbEL2EOBmSi6+mc/CgSOYj5xKMpmUmpqlkkhUSn5+peTnj5FEYpjMm7dUamtrPSxCtfor\nR0V8lIx+Aco/9WyXfiktnSykSy4XUkivl/RIkDD+Iz+/UubPX55T39TW1kqHDgPEsVf6PuFHZdCg\niW3ex3V1dVJYWOH5oy2Bmd8ma3emKJxp0rPnzE9NxEsymZTBgyfpHDeK7GiUQjrjaV1dnQRBmec/\nXyqMHjCsRbm4SBafGn2FEH8xWBzjqM0vw47M1d8vEBfV4uMISvV/Rv9t9OQ2hsu9cTVf/0RJT/Q2\nV1xkzCghdmOUEJMyU8L4jBXCxHWDJExpPk3v41OJ+5TvxtTZXxzb6xQhhsMwERZlUqXnfCmm/lHM\nxWgh1fomrdta/a1UwiyyVwhp6y2qy+rsYy0MH/KAOCbaqeKYYOu0/6IJ7vx7GG7F3qsl0jyma0rT\nHORc8rEoG8VR4Mddv0ESicoWznGLPrJ7tjPmIwiC2zMctwVBcFMQBJcEQdCtdaLQkfJJKM888zL2\n7LkFjY2voLFxC+rrX8T3v1+N6dO/jCee+CEuvfTXIHp+KKhZpUe/AL/71LNdWhER7N9viPaU/jU/\ntOhZPhMocRGNja/grrvG5NQ3M2Zcgv37vwPgCVDDnA5gNqjtrkFhYX6bcKzU19djwYKV6NdvKsrK\nvoSDBxOg1ncbON47tE3G/vgOaDJeiPgonCvQ0LD9U2MBSyQSeOGF/0BNTU8kEm+DFodvI53RNkyy\ntmzZbRAJQOuCuUfq4TTuRtAa8GVQozXXy2ZQUz8a1Jh/AWr6fgKx74Bj8hqAwaAVIQC18stAPME/\ne+d3RDgqpQbUmn0z+wS4RG8z9NkvgYRiV4Pz8DcgI+peUOvvBhd5M1TrWaptMKtKHBlZAnQP7YUj\nsusMWkbuAS0nUe6KETjmmB6gq+I3cPwocTwsRiK2X+sxD8SoCGj9OABabsaAVrzjQDzGN7T/9oPz\nfAM4Zl/W35eB1owntB/e1rbcpvcPzwlaKcwdY+6uBOi2egm0jsS7vYENmD17HHbu3Ik+fcZCpCuc\ndWktGMNRDEdeZ+uO6Pc7UFJyYk5ul+uuW4X//m8b416gNWu9d88cSy4Sin8AeBpkeWkAnXa/AXt8\nN4goeh+0h5W39N4f54Ejlo+cShiR72sO4fwujISIahmfnegXX5NPJpNqIZgqLsFTNOrl0PrGRZ5Y\nEq/+Qg12pBjCvzWWj2g7KiqmSRBY5Eq1UHOd4h0W2WCWjjpJT0Ue/pxIDGtxvQ7nEo5Gai6HBiOQ\nTjxxomrDtcLEcfcLNW8RYL5Q0y6TsFXCeFTK9NqU9nd15P8HVUu1d9Lyl4zT8bPnTBRaKAZLZp4I\nP7qjXJz1bofONb+9Zg2ZK7QEmAa/XpzFZoKEeUAaJT7pWZ03lyd71ySFOVmMK2OKACslL2+EM4Iu\nggAAIABJREFUNDY2ar+eLc6aEY3Qsn66WvvD8ulUeudOFmc5Wie0kKzVZ/nJ+aaJs8ZUaTvPFmc9\nGizxSeXssHtYJM4U7/mzJWzF8Lk+NkpBwcmyY8cO6dbN1hQbYz+Srlx/8/trqn6vyzmfS58+kyL1\nN0vWCEF7Wj5AqPrPAfQSkWEichoooj0J4Gf6+VegiHWkfMrKww//Snk80vMapFK/xsMPPw0RQceO\nxyAdjPTpZrv0rQInnvi5Jt/+4sU348CBYaC8ngI1qZtAzcji/+P6RgA03zepVAoiR4NAvQmghvhd\n0L/8HKjx3onTT5+dk3UpWzv+8IcaiFjkygRQg+0J+vaLwWiFGwB8TZ89C45rIjxfjJa/pKRnThrX\nJ6HU19dj3Ljz8fvfX67vSfacHAcOFKGurg5vvWWMnV8CtfPfgzreVlDb/T9QUzZgaD2Is3kD1JIB\nB2I+gDDgMQ/UB4tBEGYHcHyWgADI9/X8CWDkDEBrgs8TcTe4pPcC+UVKwCgUw1TMhJsD1l6zuvwZ\npBXPh8NebNbvnfRcs6pkohLvonU/W+tQp+ckEAb3PgFgJYLgA4gIUqkuIFbiKtBSMUz70yK0qsA5\n/Evt4xv1f53hQLR/Ba02W3UM9oMWnkUIg39/qL+/pv0zE3wnRc8VkEMkH/FzglaK4uIV6Nt3OoqL\nd4F4FmNhLYFjLA2zGHfqVIhvfvO7XvqG48D1xCyp94FWo81efz0MA0Pn5W3OCROWTCaxc2c0kCCh\nfdv+PB+1iLFqgKioWnFWhHdbeu+P88ARy0ezhX5pP1Nlug8/CE6Suro6ZdyLMhp+tGnfP8oSn83U\n8C2Dhdk7R6l2VSHO727cCRb7H8d8uqJZXEQQmLZqjI7p9wAeaNaCki0rK603PmdEtbZluGpVhiGw\nuqzVv8MzzhdgWk6srp+E4jL9+r75ODZa97lPn2qpqjJOi+VCjokR4jgy+urcMKZNyyhr/Wvsn8ZG\napgFGyPjXinVc4w1dJA4Dg3Tli0r7BpxGrZv6ZgktIAMFGrnxoMx16u/td1/342Do1xo9UsJLSyD\n9J5W7zjuCL/fVgh5TiaJy+ArEuaVGan/O1mOP/4stQja/SeJy8Y7XPvVtyIs0TpdLA5H06j1nuW1\nLbq2+VaL6UJLhVk8hohjMu0n5GuJWgLD7TQLRF1dnVoa10TGKO5dWicFBeVaNxFay9Z79zXLjzHV\n+mvDRdK165CcLKPz5xv/TJzlox0ZTrUcBTr6oqUHKJ4CdMF0aMW9j5TDuNAvnUQ2Jk2R27F8+e3o\n1q0DaAR73Pt/9uRIn2S2y/RspoZv+RuQp2EPqGHuBjWW7nB99xtQ47NogSjz6Si8++4f0dAQz44J\nAAUFKVAv+DOopcbl3/kXPPLIL1vYDoAYhTNw4IBxJxifQxJcDh7W+vcCo186gPPjFRAv8D4YhR+H\n+bgcXbua9nt4F2nGOsNcNwvhMBUAIx8eRrzV5ydoaNiNV19Ngv78gWDf1YFWgFNBDTwJ4mYMBzAf\n7N8XwaV4v15nfB4WLbEJ1Pi/DWrue8AxKgW1eotIu1SPX+pzz4PLLWMRFqtALMdPQG/7i6CloFI/\nHwNGVDToc/33PQFaLApBqwzAeRrob4sRZm21+q8FI3as3x5DXt7VYKisgBa278NZZ54Ft6DvAngD\nf/3rejQ2nge+b7fps7qADK1lcGykNlaXwEXnWAK+TVp/C3u24lt4DK+1R8eiBLR4pPTzNfrMzmBW\n39OQKU+LH5XWpUsXPPfcWsyf/xp69SoCeTjnwUUMmTVxPICFir96B5wvT8HhfupBPMpUbd8XEF4b\nvoBk8oPY+kTLunVbEMae+Fi19rd8/ATMUnMuyOrSWz//L4Af6zlfBPBSS+/9cR44YvlottB/eqpq\nDNn92Mz7YYjoH6m03j8ijbvjk475COdbSaoGZG01LXaQOPZJ8yeblrdCwmyM4T4NgnUZ+yeVSkmP\nHtO1rw2xHzc2G6S4+NSsFpT0vDH+Yb5n+14uLpLFtKpyCUdTLG92vpSWTj5sLV7JZFLmz18hfftO\nkd69Z2eNznJ95/fhVqHGG41A2iB5eX0lCDaotpoU4gyGi8uHMkkYEeJHMW3V/6d0rGcLrQo+dsLm\n4EpxETcDdGyqhVEUZqkyK9X39PkjI+MT1yb/GZaXxf5vkR7rJGwlM8zGECEmokKf7UfAGGvpo0IM\nyVCJRoQEwRrp2PEkvcd4YeSJWQT8dy5qkXhQHAOq4ZOi7ZkmtDrN1r63aB6zKlneG1/7t+eu036u\n0nZWCi0e/cRZV2ZKuvUobL3wI6D8Mm/eUgHulbDFdJo4VtgNwvfzVKFlw4/QMdbUqEXJt7JOl0Ri\nWNbIw1QqJb17R7En/hi3P8PpPFDEvh8UX/+in38BqjkAHZF/24p7HymHaamtrcX27Sb9C7L5sffv\n74zGxqNAyXoYmFn1CjBL5Wq0VRrnw6WI+Ix/pgn4EQdjQc01AWo/5nd/HA7rMQ/UKH1tZSVM6xO5\nAz/4wdpYzEYQBCgubtRnGmIfMfe5B3v2fIDLL78+9j7hdsSVEaBmZWvMsWAUxEJQW/4xaAmxFOjF\n2r5jM9yTuIUdO94JYUsOl6in+vp6jB49B3fdNRpvvvkkamsfyRidFe674eAcrwdZQr8Lhw0AjMQr\nleoFkRlwfVqo19g8KgS5Ko4CI1BuBzE1J+j/O4Daby+QeTQF914lQKvHGDDi4mRQC18HRkBZttfu\noIWhFsQJJL17AJy7ljMkOoYJkPRrF1zm215ghMlL+tciIcx6MBi0dCRAfphiuAiP34EWlpdAi8RN\niEaEiMzBvn3HglbFsdqmccjMm2PRHi+DuImNcMyr0fbs0PY36HW9QWvQPtDCY3lvqvSctd5zzwK3\nwsXalz1AHERHOCvF2yAuZaDee4XeaxKAqUgkro3NKFtfX48f/GAdyA9kLK6rwO32H+Gin8aBc+Jl\nMLpH9LyrkM7BE42uexz19S9mjTx05GKGPXkeHP+PiOFURBpE5GvgrD0VtBh0F5Gvi8gePee/ROS/\nWlWjI+WwK/X19aisnAG+QMeAgCnJcLagQ4e93iR9AwxXmwmX8MhCQc9AYWFVzmmcD9fiXkp72ech\nzCZ4JdzCVQQCQruApmADn90NmsN9AWY0+HKfAgBoaOiF7t1Ho6bmmrTFgYn/GkHzetQc7Ltg/oC7\n7hoVu8CE2xEt9eBi8z6cWd3M5gZAvsQ7F+BC7tNLW0l5dRuFxsZXUFv7H4dd2DXdKPOQSj0Hbtif\nAzANqdRz+MMf5oWYWcN9JyBwsQZsf1wyRgHfJYBzYDrITtpJr7kKLiV9N3CT3guCGd8HN+Z6cAPe\npecbgy6QPva26ZYA+BaYXG4n6AYsgQMhzkbYJbAIdNv4aef9kgDf7Uo4V4/d7ykA30THjgtRWjoV\nQdAIJmUzts0faFvuAENTTYj9BiiwxhHwWb8F4HzsCr5HUXdXtI4dAXwTFN7+inCa+3rQpWSMqb29\n9vYCXae3gYJFHeh23gO+15drHRrAsfotCMB+Gxw3+98ckBTO2G+fBoWEVwE8hby8RbjkkvNi18BF\ni27CgQO9QAD3e1qvp8E14yDc/FoEzoFecERnW8D+fhdhoGuUbA2ICwGPFpfUMqHX90BmZaWZkot5\n5LNw4IjbJWOhyW+MOJKfK9Vk6Ezn7vOjUlNzjQKT7pPs4KpGKSgY8nE3r00KQ5ANNOcD76x/LETS\nDz9cIzSBR03BZh6NB5cFwfo082wymZT8/EESDiVsefiua0f0mhVq2vXN6su0fiIkxbIQyn5CYGCV\n9sMSocnYQIFj5XB2wZmrJS/PXBKZgLITQ9e5vjOT/WDJDrK28T5J+2eQ0C0yWOiK+Zy4EFlzyUwX\n59oycKiBETO5POyYLi60u1boshjp/WameCPt8oHTIyX8zrv3PgjWSLduQxUYuUL8FPHdulVJbW2t\n1NXVSX7+aK23JaRbo+01Qq1cwel+SK4f0p/NZThenJuiVOiaiBKbGQD2dP3sJ3+cJMDnhQDg9RJ2\nJ1YIQ3XHCxMpVgtwqf7fEtz5z1oj4SSLo5v6KW4uFhQMFhfWXCl8t8q0fhYmHSVuq5OwuyUaZpxb\nCHhcfRwINgqubpnbJdeN+d8BdPE+Zzxyud/heBwRPjKXkpLThGjvCeKYCodLOmr6YgGGS03NEqmt\ntcUteybcvLyx0tjY+HE38ZBLMpmU8nLf/218BraYmi/dFrLThJuHLerGRdAc6j9+g06lUpp1eKmE\nsSYtW2AMYR/OmJuSMG7D8AQTvYXO990PFG6ItkgOFS74Pu6hdYtfexe3uG6U7PiZjWn4mWQyKYMG\nTdS+MlbQbO1crmPlRypdrJ9rxUWyLNH+HCwu0+np4iJhvqd9/qg4Hoc4PotqCfNEXKbzcICEsRV1\nWo9BcvzxZ0nfvlOlpmaplJVNkSB4SKLZibt1q5KtW7fKggUrpW/fqdK792wpLZ0iCxasbBKQqYyM\nFmIqfKbWURLm9rBNNK7+0X6r1robviHT+2IRWIatGiXp/CXV4qJR+gkFwjJxQlitODbXpDi8jeg4\nVelf4wSpFid4x7GfRhlvN8biPS67bLk4fMxo4fo7RCiM7JZw9JPx6lR69Rwmbg4u9Z7b+sjDZDIp\nQ4cavuZKcetNy4SPAuRWLKjaPh8pn5EiIvjgg06g6fA90MRboscX4NDiApprb8fGjc+jsPAe0D9p\nUyfONCcIgt3Iy2sN9OjwKolEAk8+eS9OOGEKmOLoBdBfzKyTxGIIaK78D5D/ogDhPA4/Ac2zZh41\nE216IffH7Vi9mt+DIMDBg7tAZP35en3zHBMigoaGBlx33SqsW7cFBw4UIz9/P4YMuQN1datw8GAJ\nCgr2oLY2DwcPmkvoJjjTvICREr3hMoqeDJp/p2v9O8G53uya3Or2UUc/WbSPyFgw0sP3Z/vz+Ezs\n23d1qH6JRAITJ47C1q2/AiMvAIeFODPmHkNw9NHXYPfurqALZA3o2nkbNKkfB2Z+HQ6XxXU4yH7a\nCLomCkG3yE0gVuI7IJ6hC8L9K6Bb4T7QhXA76H6wOXY56FY4CnSX9ULv3t2xffu6pjbu3LkTlZUz\nsWvXTaBrhO/97t2P4bzzLsNzz63F6tXXx46bi5JYC87vu0H3zw0gpsT4Qs4HzfkS6TdXgmAouna9\nDrt2dQbxTDfp+YvA90cQTmJ4GejmOQN0mRyr/fNDEAv1Y5Bzpy+Y+G44mHdlgvbVanDtOxbEVcwG\n3Th7dByOAV2JhWAEUAcdw8+DfBzPwLlTL4HDsjS1CKnUDLz+OiMKV6++vuk/69c/C87DW3ScUnoc\nC47hPrjop7Haf8tBV5atPS/o52u1f3wulfh1OVvkYSKRQF1dI+gOtiijAJyvLSi5SCifhQNHLB+x\nJZVKqXRtjH8nS7pGGEZNB0GZlJScKtTmfVNv9FgvQ4dO/7ib2GaF2t2F4lDxfsTBKRI2Ww/xzmGs\nvTPNDpPMTI/x2kljY6M+d5M43oPm+QSy8XpUVEyTuro6ETEGVeNI8PkRloiLnLDrLSrGogLitNjm\nLB+5sS1mK62JoHERK8uFZvR43hUgKcXFE9OeweuXSdjcnZlb4eWXXxZaAMaK04yHaB/PFmrQlrOl\nXGjeLxdaLE7T/o++X3EuiDjXxsSY6xqbvkfbl9kll91VFo6S6C+0IBgviHHBmNVnk1ffKJunsxDU\n1taqRdbypKzzrrN3jpYZx39hGr8xdC4UxzZsHCh1kX6yw5/Tc736b9LxKBXnZhkvtGhaG3xLS3P5\nWaaG+u34488S9/4sFJdrxnLc/L0466M9yyyQK8VZJ6NWy0pprdvTjaet7XbP9mc4PVI+Q4XcEnvh\nYvhLEY6oSEdNi/wODQ09wFj3H4CRLhvgjGei3y/Fxo3/9hG1pH3Lzp07cffda0FrTwrUvHz2xV+C\n2sh60BqUAjWVOaBm811Qk7Gsnk+gJbwoeXl5yMsTUEOaAzJUnojm+AQy83oQeLZ8+e0QEXTq1AEu\nLfhKOIvXiyDTZTSj6Lvalm5wTJF+8c+Pr1trSiZ21lwArCJ+xIpZLuJ4V0YDmIOuXRtD2qG7/mow\n4uEqcFz3gqBF/x5/g+7dj8a0aV8GQYnvg5r/l+HyiuwBte+eYKTRflCLPgoEFU7X71FAawAXpQK4\nd3SI91sAF2XjX2dbguDDD98JtW/dui1IpVrOUByOkjgHjLxIgX18LmgN6QUCSO3+FqXyvLbzHBQU\nEJz+xBM/xLe+dTf27i0ENf35oMXHB7s+AeBvUV5ehB49+sPNP3unvgeuQY3gO9ND+2wLnBXPisAB\np7eAUTFTQM3/FvD9zQPf655g5FE9XFRIFzhAbSZQLOBb/KzfGhreAsc7APmAirXOw0ELUgkc6Pbr\ncEB3W3ueBq1ptg7Y71tAi054Xc4l8tCNZy049+ye7cDzAUJ4f5PLkcv9DscDRywfsYU+R9/vPEEc\nG6dIZj/rFJWuNwq5CaqEmrmBzgZLUdHAj7t5OZXmNGhmlD1JNR7L4pqu1TgNob/2wwrtH2OvXKJ9\nVi70ZV8kmbJQxmknZMq03BIGrjMAoq89bmjyL2fn9XCaGHlbKsRpjSIuj8gUcbwMll3Uctj4nCb+\nvf0MpM37vnMpzVlxcrkn81aY1allVrtkMimJhGnNk/Ra3ycebf8oHevLhflcLhZnCagSZxkwq9Mg\nveY07dfdkhlT5ecJiQIeN+q1FZIrpsVpu3Hn8siGE3BWEwOvlknYspcSxwoaPxd7954dwSTZ+mLt\nXSm+xaOwcIA3x5eL4+joK0Av7Uuzzhn7qW+d8K1elUKr0zQJ51n5GwF+qO3xrRzG72F1s3egZRa/\nRKJS50Kj0HpqFrXPi7M4ThLHFBtnWbG6ht81AoWrpLR0snLYTA3hdLKVr31tsaSzMrcPw+kjoMie\ny3GkfIoKfbVdQH6AE+FyMYj+zZSvZQyoTdwKYC6AfwDzVWzRv7di//4AyWSy/Sp/CKUlGjQzyppf\nuBNIfyMIc2ycDWpW60B/8T6wL6rAnBFzQI3pFZBZcg6o9f09whkjM2snmzb9G6gDvAaG+94Lcgm8\nCNMegXEoLLwcTzzxQ5SUlDTD6+E0sTPPHAWOp+V1MAbX3SCXy/nes+aAGuCfwXmQALVSP5/GOFBT\nvBx5eRXo2XMW+vadjssuex7PPrumVWHXzVlxMoUP+qVLlzzQOlAPauhRq4KNw0zs3n2w6dedO3ei\ntHQM6uvzADwEWv1Wabt97hZr/2mgD74ryG/xMDje/wWHeRgKapO9tD3HgnPsQ/3tWXAuWZ38kgCw\nBonEtcjPNy4Gsyb8CtScS+DCY9384vdvo3v3Lk2Wj+xh2LwuG07gppsWoazsduTlbQGxK70QDt8N\nQAtCpvsD+fl1GD/+85o3ZwaIrTBMSDTHy2Z06nQ0EokEZs0aiyAYCmJjjK+jE2iRulKvHw++j2u1\nX9YgbPWaCVoRauGsJxXg+3oRaN37of7/CXBcbtC+LAYz05pFKpPFb1PI4iciKCrqCb4nm8D52Aha\nOF4F15R80Cp2AJwPcZlvbdzvRyIxHL17n4O+fadj/vzX8Oabm/Hmm7/A9u2PYNu2J7F69fU5vXt5\nefmgpc5nZW5nhtNP64Ejlo+04rSdgeJQ9iZhmy82kzZk0r7vk476z0fL0KHTW6XltmfJVYNOJpMy\nb57lg2gUYmHKhJEID0kYgW4aZ6PQD7vE69NoWJ8xhlrm2FMFqJAgGCknnjgpo3aSSqWkqKhawvky\nwpoV/zq21OYtH8SFDBw4XqgxmmZlFq9KIc7lwcjYTtZ2GhNlP9WM4qKkLpIBA8bIvHlLQ0yil122\nXJLJZM7YjVytONnGnWGNxkbrs0Sm4z4s304ymZSuXSu89g0S5vBZom23e/h4GaurhVCmhBYzCyM1\n5lgLx/bzn1ygc2KAfs7su58/f0WMxcLGzjApvsWAWU6DYE2aZa21mA+/fxcsWKna/EgJZ3X265V+\nfwvnDeeOyWYpEenZ82xJpVJSW1srHTsO1Lm4TBx+ZqCOhWFH/Ey+0bpZpMhSAb6o43ixN0eMPXWc\nuEivsXqeRadY1Ey6NTITuyn76sfC92yc9pE917AqNl8zZb5NiWW+rauraxM2YVoIDavTOobTj33T\nP1yOI8JHuBjXAYGGp+nkv8ib9LYwniaZF/wlQveC/9KFQ8yAdM6Kj7vkssgmk0kZPNgSVJnpe7wu\naBZqaouXuVfsPmZG76vn+WnSbUPwBRDb9EZJx44nxfIBWMnLM3BdbhtxLm0lkHauOFeAbYK2Scbx\nYdSJC+N8UOtki3aUN+M+cfwJlga8Wiy8tKhoovTpMylH6ufMm1FziQvpYpwu3GAe8sYp2jamMS8s\nHNj0nrAPzG1ymjgAo0+jP9e7j22cU3T+WJK32d7vFjZbKW7TNaDhEmHaAgOnRunbH5WysikZXGt+\nsjh/o7IjfiN0Qnk8CDTXd7iurk7DwqMbsdUnPQy1W7cqpaKfHdOOTHN8StMcDwITssYKgZEjhALQ\nJCEANgoQH+Ld2wfrJvV/68W5PMydsk4coLVOf7tH53E/ce5QS/zoXESZ3mvnxrPkfBbWbs+dpHU5\nRcLuopUSFihXtFkCx1QqJcXFE3X8fDBr+/B8vA/GWDZ75HK/w/E4Iny4Etb8zc85TOjXt9wCtnBk\nxiUAD3lZcKOahL/JbThs8rqkUimV6rNv3C6yZb24TapSF6bREsZG+J9TulA8KIwcGiUu0sEWOT8K\nICqAZF6ouCiY9SW3jTiXDYX9MVmAsyScK0LE5ZOICjBLdW7Yol+hR3Su+HPDt/qkk3s1t8nlYsXJ\nVJLJpEZP+GRv1gbDS5j1Y5aYxaamZqmceOIEcXlTjOulv/61vvE3KrMK1gk3QcN7zBUXreBbAerE\nbSyzxFlffCxHdLNh3USiAmbUWpl+bXHxqU1RTnH95PN5tAQn4OfJycuzzT36/IkCTJf8/IrQ/d07\n6Y9xZkuJb4kJzwuLAjtZx2yyHlEhzOcd8ee7aB1N8La5W611syilFQJ8XyhUbxCunf0k3UqVkkxr\nYCqVkp49fevOCr3XmeKsKzaPBkv8Gptu7WyLQqW0Ubgm2LPaR/iYm+uRy/0Ox+OI8OFKeLFaItzM\nzpQwKY+ZIePNfEFA7emEEyZKZiBUeENvq9JSs6K/KB5//AzhhpB54+7Z82xd0MzqM12c1jtIKIDM\nFvfyR0May/W8keI25TpvEbSNJX4TBjZIt25DmxZ8v73cCMty6O8pofZHN5SamqUyb55trCPEkcvZ\nhmEa2QoJW25swTbmyTqhAHtKzHm2oEY33Nw2lWiJt+Kkmr2utrZWunatEpq1zfohQgG7TFyI8VyJ\nkurl5/eXzp1PkXBSv4ne2I4Xbl6TxFnIzAoyV6+7R1y4pE9k5b9X/uY72auD358p8eeJvVPpAmYm\nIS2VNjeylZa8Z+muTJ8ML32jrKm5JgPY1Z8b2cNxzWUXtohVCEHAJ4tL+GeWipU6VjPEhanb86z/\nTRAUccnjTDixJHNjtI99oXq8hK0Eua2B6Qkrp2nd1ogDdZsrJh5gDqwPrReHWuje9YXqdrR8fBaO\nI8KHK+EJbyZen7tikjgLiL0U9uJSe0okKj2T9Ahpzj/bnEm8uZJL9tG4+6cviit00ci8SBQXnyq9\nes3yFiHLNLpbXEZLf/OPbrrDhBtUpVBDHiEuSqVc+8rXbKOLswjwgAwdOj2tvaTCHy6t1YJ8awhN\n1Yb+HyJh/64tyha14T9nuV5nuIW5Qg1wtKT350RxwlkuNNm5UD/Hs3Du2LEjbS4kk0np1s36yoRA\n+2tWjCUS7y7aJMAoCQKjDK8Qp+H6VOkPCTc733VizzEN0t4PowE37IG9V1XCTcUEl9HioqTMIhPm\nIfHfKV/ApHXso6W2TxcMl4hj9/X7dJ0AI5usNlbcmhQVOKz9Y6SgYIyUlk5Os8S4axuFVkbDSpjA\nZ1YEc/edLM6iO0XCGJAV3jj6LhH7XCuOddUXYEZK2HqS2xqY3m9J777mgpsozj2YTnEfBKVZXbUt\nLQ4b5SujIu1l+ejif8525HK/w/E4InywhDUFMz/6E17E5S3wXwjfJD25yXT7xhtv6IuZjS750Eil\nsgFEBw+eJPPmLckolLiX29owWCgQbAzVz1/kgBGKragUt0lNFSe4XK0Lm2EY+osTBFJCU+XZwg29\nQogRGSTcMD8vzm1iC190c1mif6O+cba3a9dKoTY9UID7JZpHokOH7LgRCjDmnx7jtScKyrPwvYkS\nHltzXVgo5BRxOT3880yLtA3YgJctx26Y8HnCCWMlCCxvjI0bLW/5+SOlpKRCEolhTdYrhiePEQcY\nXqv1NiCn3464+twrFB5NQKkSJ0wO8vrR8DJrxG1eM8S53Px8JXFuFBvzB4Sbjs2ZbLlnqmPHd8eO\nHQrezByC3dYl3SVWLc6a5LdzrgCTsuTNsfXG758xMnToGRldReFrbR3aIRQIVkg4Lb3vyormRrH3\nwQD3vsvBP+c+oQBjwrZhf5pz56avgelWq5Q4HN1scTlm4vpligArJT9/ZJuATP3+5Dh9XyhoGebo\nJWkP4aMRwLH6OaXfo0cKQGMu9zscjyPChyvhhcJ4D87yJnh/CYOb4pIlUdssLzcria81R49HD0nb\nije31wkX7GjeinDUCttqZvXzvUWjWqj9TBFq8AO875bgydq1VlwOhSHaF2VCC8QocZuEb0I/Tdym\nXq7nVev1F4jL4+BvLnbMlWya60UXXSpBUKrPKEtrP7C+CYwYLbW1tZKXVyZugZ0tZFacJmEuEotY\nOFkPP4/NbKFZ3fgJzhZuslFT+wptZ9SEm32R7tNnUqjOW7du1WgGS/gVNcuv0b62c/y+GC1O2KnW\nOv9IwsJWJsHZ3Etlws3MooEqhFYws3jt0N8N3+Jrzj4gNWrGlsjnOkkkKqVPn4mSn98/08X+AAAg\nAElEQVRf0pPC+cd6GTr0jLTxTSaTUlY2RVx0ktvAu3atbFMN2Uq66yMl6Tghv53xeXPSsUmNEgTN\nC0zha6d5fWYYH5vrJij4Fl0/N4qxqRoLrY2vvRd9vXsvFSoyhg2Ji+7xj3Uyf/6KjPU3qxUtrvZc\nW7vi3Dn+HB/cpuPJNXOH197WMZzmujFXAyjwPmc8crnf4Xg0J3y0peTY2vJR1SG8mRvRkk87XSEO\nkzBdnHaXjk3gC2gobUOE++c8KkHQ75C0rbBJNs50Gr9JuzDEJeJCFy0E7yRt13JxwkE0msPXPi8Q\nbpoj9ftIve86cZk7V+ri1Ve4Ya3Rvpyq308VZ2kY7J0TtXzE0T+7RaekpELbnlngC4J010symZSj\njx4i4YRo47WO1TFjfFDMH15QcJK3MVQL8ROGnZkkzpXhzwFb+K3PLhZnKcltU6Ww5C/6/lzw5+bc\nyFxwFpFwIr9R2v+Gv1kjLilg9FghRpjH8T9ZnKCVEidw+MRVfhh0pTit0c6zPoifs2EQZXYhrbR0\nctq7QqtWdqxFe5R0y0d2S2hctutkMik1NUslkaiU/PxKyc8fI4nEMJk3b2mz64dt4CecME5cZFV0\nrsdZ3XwMkIhzi12oY36vtuUiCbtXk0IqdAOF9te5FBcGu16A0pzWwFQqJSUllpphhc4dS6sQ15cb\nBShrs+SdTpC0d8Ufw5a5XXIiGRORX4rIQe9zxiOX+31SyqFQNX+S6+AIgTaBpFJ3gJTDj4FEPEcB\nqAEJkxpAUqsr4ZI5Qf+eCZIo9dTvKZC8ZzIo61UCuBUiHbB48U2tapOIYN++zloPI7uZoHXeibjE\nVACQSp2JdeueVeKk9SBh0bdAgqED+vkuAG+CpE4rvPbVg3TGPfRuVwLoDpJq5YOU8gFIGnUWHNXy\n9Vq3W0Ev5T1gYqa/gNTZH4L0yy8C+DmAg3qOT+/9hD4rEzFYAxoaDoIU0LVIJ4AT7bez0uiwFy++\nGbt3d9HnFoN9WguOfUeQJn+stmMqgPNAIq0TMHBgL1x22fPo23c6Cgp2gpTrBSAJ0Xsg0dFmkEzr\nJQDTtL2LQXKpr4PEU/O17ncgnvxqNd5/f19TnWfMuASpVC+QDEy8es9BeG7ugJsLO0FSqZFgErYx\n4Nz+uv7/FyD1+EK9jyUFjJbN2kaAxHDHalt262/7wLG+EiRiW6/9UANSqPcAk5EtAIm/1mgfXI5s\nxHIigv37i/RemQniDh4sMcWqqfz0p0aYFT6X5Sz85CdPZLjfoZVZs8YiL8/IrwRse+a6d+x4TFrd\nAeCZZ17Gnj23oLHxFTQ2bkF9/Yv4/verMXr0nKzrRyKRwOrV12P79mewY8dmVFV9ByQa2wGX5DBA\nekqDxeA7IOB7/Bb4HrwNjve14Dico99tfVilbbSx7wi+L2HKeP59AQUFRTmRewVBgBNP7A4SmFWB\n70kK2cji8vKkzZJ3OrK5zWD/HULix1wklOihTx0BUqzN9o/W3O9wOBCxfDga39ZTNR9qaQu66EN5\nNnk+THtbKrQGTBcXDmpx99lQ98YTEkfx7NoUBK3j+0gmk1JY6FtilogzdzaPHbjkkoV6foVQ4zVN\n9gpxhFDRmH/fAuCHulk75wrN+T4qPmrWnSSOkvwCoRVlpFDjrtZzTsmg0WQDZC4Tat7VXvuzk2RZ\nKS4+RcIJ4aw/jL45jvMiJT7nhYgREJmGP1fHZbyku8CsHRa2bXgGixxYKVHyqyiQktibKPeD7zLy\n6dKtL6IcLPeJ812P1LYbPmWrUIONsxbM8s43/I754DcI35kxArwhQB/v/hZOPEz7dKE+v0ycm25I\nWrhpOoiyZaHFqVRK3+fM70R+/ph2sbCmu01a5loTOXSSs2hhyoAhEnYhW6p4//5mLTJt3+ZThXBN\nNIuG8bX478gEcUD09Hba56Ki6mb73ZEaDhSHlzFL6o+07kOE779Zde6Ndb8dSnF8OFHrVTtHu4Dq\nw/8BTbl9/eMTj/n4whe+Jn37TvlYEOHR0tYvW2tKeqjXMHFI9ZROfgPJ+XiAMXquYSOizJht0yYH\nfjLCoNPFZSPN7p444YSxUljYX4jNGKt1/ZJQcDhZnICUKZeNASbNdWDRB7YJ+T5f8zX7IYNzxQlk\nxnRqLhcTaPy+NwEi89x0mAM/1DUONxIWGHbsMACeXVOhz58s4ZC+TObddbJgwQpJpVLqlzbfuEVF\nJSV9k+0vdMEkhcJeP3F5NjLhHtym2tjYKHl5YyWd+8Hm2gRxAqDdc4W4uWv9WiUUAA1waJwdtcKF\n/SGJdxmO1n4qF4dd8QWtB/T5fshlhfc5utHVicNNTZdEYlgTy2vmeZ+7W43CR6Vkeyfy8yvbzb2b\ne8RNPO/PoTLYRgvBxg/p/DZGYgON+sqRRXQZh4cI3wtjSvXzxvhj4rtxsruZ8vMrmu27igpz70aF\n80lCAdmI5uzYIEHQV7Zu3dqifmmuMNqlXNJxO+0vfPwRtEcf19JrD+fDhI8guFNykczbkpciU2nr\nl601JV0AWiGU9EeIW7ht4/dTrdvL90OhFmmbQNu2iX20W7ihXShOuyQJVLr/PCUm6HTvXqULYLke\nQ4TWCCMgsnr6YcVRbX2IOJp0i1g4W4DXdXEyjgALm/S5MvwFyTa6C8RthiO9/w0Vh0Gp1jqGN8Mg\n2CD5+aP13CrtDyP6Srd8AA9ITc1SmT9/hRQU9BcnNBnjqEX0GDYlt7HjmAwWF6qafi77KSn5+SeJ\no6reIGHiprjnrJP585c3jT/JjnyQqc/DYJEsfqjkBEmP1DpNwgRg/bXtvjbrC9ajxQERB4uLcqnW\na34sTsAaLE6ItKgxw77cK46Xwaxg6eyemdhGybAbx266LiOgmHTdmSJ3NkgiUdmKVSL3YhbVPn2q\npaAgfQ5nam9bMNhGCy10deLo//0UByvFB+NWVExWVk8f+2aA8ih5nP+O2JoQR8RnRzrANlocS2vc\ne+gDZsNHexE4EjtkVjxb89sB8xEpxwG4XUTebsW1h30RGQP67CyBVlwJpz5un3oYliFzHfbt69Su\ndQCi+A8BsAiUPetAf/ZKMEHUiQinWof+nat/LY189ja1pF9FLIX5UWCyqNcAXArnr78LTCa1FsRs\nTAXTS4/B0Ucvxe7djaDv/0PQb1sMpkAvgfOnC+hlfFw/G6ZgBoBl4FwpAH39vUFMxgegH18A3Azi\nRjaDWIk5IL6gBC7ZFEBczLFgwqiuoF84ADET9qzvg/iEp0HcxIsAxiEIRqO0dCrmz38BvXt3AjEr\nH+g9ngdwN+LTwt+Nf/mXNbjrrtE4eLAziG0YqvW8RfugA4jPEBDPEk01bsWN3dlnjwFxIwvBMY+O\np6VuL0anToWgX/x32s7tOlZRzEcdgIsBLMKaNb9twj6VlR0P+r7N5211Fm3PFB2LleBc2K19Kjp2\n54HzZx84rktBv38KxAMcDSbNmwgmEXsVHMsb9RrDC+wC0AfEc/wYfDeeAz3TR+s5t+rnb4GJ/+7T\ntm4GE+3dDGKE/KR4M/D661ekJcVLJBJ44YX/QE3NRCQSS5GfPxT5+WORSAxHTc2zeP75h2MxBBde\nOBMu4Zn1rej3b+L//b+z0q5pq1JfX4/Ro+fgrrtG4y9/eRoHD9ocHo+CgrEoLZ2Kyy57Hs89tzat\n7oea2C7tbBE0NiZA7Na1APrCJRFMT1K3Z08eevTIB9+/x8F1cA9I/i3gmEXfaYAYqSdAjFduSfzi\nyrp1WyDyW3CNiSanexaZsW0z0rBdbVFuvfVadOtmWC3DsCxs2U1ykVD8A0TTfbWl1x3uB9TyQenN\nN9362pqv5bWelyLX4nj946TllCQSw9rluXEcCj4DZlFRmThXiuWkyKYVV4kLSY0zPzpTYUv7lVp2\nozhrgOVVsWdslfgQy/Xi0toPF2q/g/TvFRIOX6v2JHzDFIwR4itO1/ZXidOELtbfJmmdHvSu9/kx\n/MiM5frMsyQcUXSx3jMuUZwdLlSZ5viheu8HxVlfovM4pfdeL458yaJ6LNS2SmjFMd4Pw7fEWVHq\nmsYumUx6/Rd11fjuo8k6NhPFWSMsMmqJ1meA3is+ZHrgwPHSoYMlDVshzgf+oLhoE9/aNFicFmp1\ns7Dh8UKqc4t2Gi2ZOT6S4kzxS/R51frdXzss5NJclOUSxkCJxK834fejOYtgKpXKSet3FpO5EuXX\nGDx4UrviyLK5kYOgeQ29LdzQPhkhXVA+v0pc3/Po3Xu2zJ+/XK0Pk/TdMYJA30oaXQf9KKY1kmsS\nP784q49FOEUTVbatRSjXUltbG+GLaQeej9AFzIe8AcwffBWoVjQdLb3f4XI44eNr4ih4H5D4hfZ+\n+epXrzrEoWu+MGQyk29/nZSUZPcTtqTU1dU1yxAqwheBG/4gcXiHac28AOXiTN8Xi8M5RDEiVTJk\nSLy5OK4kk0kZMsR88X4InZnd/SRQmeo1SbhpTNQ2jRZHCvSQUBA5UefCUnHAxNl6Xbk4DMwkIUnY\niXrOleIEId+UO1Pb7Asmm4SLWZUA2/V+lrgp90RxLmnXJK27mflXiJ8hl/f0BcFyoZuijzi31RIJ\ngy8XSjamT2OlJP2ysZb6AGNbNH+sf/sIN3oT/AyY5/ve7W9m/NVXvrJQ50E/veeD2o8+z4iIc8mc\nov1jYzNMn2FuEus/ExSiSkjSO99PHWBz2nfrGM32g8K51V/cHLNrTpOPcgOJ8ka0JD/LoZS2yDp8\nKInt0gH8fhht8wRgTnAbqfPxan2fBkk6869/vQH2B4jbqFMtqntpqVHqG/WBv55kx/G0p6Lsz6Ue\nPSZIewsfXwXtuvWgPXmbd/yppffz7nup3uMDMG5teJZzy8G4tG2gfTSr0APgGj3v9iznqPBxi3Bh\nnqCTJepP3SDAACkrm9CWY5hW6urqJC/P/MnRrJMbBRglRUVVbUJJ7vyvmcm4RLipNDY2qhQ+XRdV\nk8YzLSymVdsLboRewzO0LUyAlal9DoD1OR0nS2Vu5E6WBC8un4gvfIzUl7pWqO0au+go4WZWKi4d\n/CihQDJGXMp4i983cirLMmrZZU379oXYSfq9VsJAsXH6rIu96+1euW9OJ55oIOCBwo0uLlmbD4ar\nFQpMZjny03OXiWNqjOOIsCPMERFOzmcLZaUQ5zBAiGnpq8cgcRiVweLAez6rZKYxbJQ+fSZ6+Afj\nHjD6erNW7dCxsTkxQu+dElqbJmg9ysRl6h0gjlzNH78KIZZplN53lHdOtTiMkPXxVnECqVkNDZxs\nLJoti1xpq/JRcQe1FWbjUBLbxVOVmzUvt8SXxDr42BATUuMsm2EBafDgSVJTs7TVded7UC3pgNjl\nMXV32LaPKmnnSy+1v+Xjr6CTLK+l12a55xdAx/vFAAaD5Aa7AByT4fzTAfwjgL8Bg7AzCh8AhgP4\nE4Df5iZ8jBSHxM8MzAqCAW03apGSTCZl4EBLRLRDXFjTWP07XYAdsUQ8LXmG0wIym/SDYI0MGTI5\nROzDXBY7hBvnZHEWjUz9VS5h8+BScW6YOMvSD0N5S0pLJzdZYWxxcgAs2/xtk1whFCL66ZEtn4K5\nGaYIsFi44Zi5vUK4QRmgb764jdfCSM1yMkqozRsLqpnyx2m94rPTst98944lJLON72L9buRjuW1O\nXKhGiwPR2mYeHWPb8KYL2V37ar0rxLnUjOwsJblaX6wO1FCjz7P+MgHL2HJHCAWfwd6z/JBZ/z5R\nYcD6/cfiIlmqxUUZnSsu5f3F2ifmRkoJ3VQjxIFHLUzxZZ0PvtBmrhsDWk8TCqa+gDdX0kOrbbxN\n6DK6dWM1zeZWa9uMpB9XOZSsw3GlpYJT/PP97MvxBGB+Urb0e5jS8pCkWzZpUUskhqUJGa1Jfsm0\nCXFh6BN1DsfnNGoP1tq48vLL7R/tsgvAgJZe18w9fw1gtfc9ANFeV+dw7bZMwgeIANoKslo9nZvw\ncauEqXPjXxSgos1Y46KFm8dIcVprfO6GoqJxrdZcwjlNMmFLTLIfIWGryHL9bonR1mhdSyUeW3GC\nhDEO5uqIa9ta4SZkkR1Gb14meXmD5fjjz5K+facoHmaZuLwgFmJqFOdflMwRGqaVms/3YuEGO1K4\nMViEQpm4ZGFGt71Cf7tfzzGOCMvlYc8bq99LxYXxRdu6XsIZdE2gOVUc3bqF4GaPAPE3p2QyKV26\nmFXH3AZx/WAbXoW4iJ0xQqHJBMpR4vAWuScHjM9JMVucUG9WHbM0DRAnsJk1wp8vvhUl2pdTtL8t\noZtRTk8RcmycKI7J1ASqJeLyt/jMtsbXUq3P9dlT7dlGkT5NXKimJR2LctmYAGVhuJOE74OfosC3\n8mTf/D7J5eOkDshseYligvxNnfmaLM9M/D3ixs5Zcbt2rWqzsXOpD6JuQBOCco+Wao/SUuGjNdEu\n96qlok1KEASFIAXcL+w3ERGQ4nH0Id7+LgDrROSp3C/ZCiKdo+yB4n0msjlXZHVLy7p1W0CvVj2I\nII4yh54B4Ark5e1udR3WrduCVGoMiPaPY8wUEJ3fA0Tk+yj8xQBWg9EEHfQenUBWvxcRZu97EcAy\nBMGlIHvjWtCzlkQ6K2oDaPS6CS6y417QuHUrUqk/4K9/XY8333wC9fXdATwDRio0gFEMxqR6EDR0\nlcAhw+vBiIepWrepYKTC22C0QQBGdfTT/kjA5Uu8ROt+PjglpwH4BzCl0b0gW+ULYLRDsbalGxjZ\n0xmMkIhjgD0LjCCxubUYNAB+qP2eBKNPRoDMn1cAWOednwKwAd26LcONN17VNHKJRALnnDMZROJb\n1IBFZfj98DKAy7SNnfVvvX5eBEZr5Gk/3gYaEP33wC/haINEIoHnnlvbxHrau/fnEAR/BOfJGdrG\nXeD47QGZPlNaZ4A6hUUWvQdGBQCcY1fCMa1OAefT/8ExTNaAY9MbwJcAnACOaWdtXzHIIHocgG+C\n7JMddCxO1XM7ghFcx4FREMaKugecX8doG/5Jv88A2X/P0D43JstnwHHbB+BcMBKmSK+H1ncRGJE0\nD9TD7P0Zh27drsVrr23Mif3ycC/pkXMAIuyt7VUyR8v4ESrRKJcnAXwDjY1dICIZ7rEFHFefufRz\nMObSRKJbm41dr1698LWvnYMg2Oi3TP/eg8zRUgvToqXao3Dbzr20RvjIB3B1EAS/DILgziAIbveP\nVtzvGL1nNHT3bQDHt+J+AIAgCL4I4BQQ79GC8gq4mTaAi78t1J8DF7qVAJIoLGxoc+HDJEKG2HYB\nF0QLoYpunrdj3759raYkZ4jqbSBmuBFoCj1cCoYuDgFwP7hxRumYEyDkJgAX6AaQdngO3Mv7sP69\nHsDXEQQdUFb2F3AhzgMFF5/6ux7plNhj9dnRlyoPXMwP6N+btf5zQRgSwCn1gbbvZtBTtw3cKHaA\nm8l7+v0WsL+vBjexB8DQzjpwM9yp1yzUOj2m9R2tz7wL3KRS4OYEAHu1n7qCoXBRmnMrUwHYYpIA\niYK7aD/s12d/S+u4HAxBHQigAhTOrsT06eky+iOPPAcKTO/oL3vA+WwU9E+CuPHfgKHS27Qvoef9\nABQIDoKb5UjtQz/Ez5Ug2IDZs8eFfjNK623bnsT27Y/g0kvP1zbu0XYN034aCYagHgPO+cfBjf1d\nuHBuC73dou22dvwcFBTywTEJQMEzADf+JChMJAH8DzifGkDB5Ff6rC5ere/Wuh0Ax9yo+2eAQnF3\nUMhp0ON48B0AwmHktpE9DQqmR+v5X4YTburAeWvCymvgXCkCsAeJRD3efHMzevXqFdvnn7SSLpCe\ng759p2cMr23rEqZ4txKA60R047RxDAvV6TTxJtRnF1zaqtx667UoL1+dJsDxPZgRe00qdWa7hNsC\n4fQfM2Zc2aJrWyN8VIJvdwrcoU71jlNacb9MxUgWWn5hEJwArlZfEpEDLbvaFpBx4IYxFFzs9oBS\n8s8BjMScOeMy3qElJZq7pX//aWhoeAtOaw3AxXA0uEEA9mIcODAKp58+q8UCiJPgTVMbCwoL5wD4\nJbjY9wK11K5It4oAXLDzwc3pm6Cm3wAnIJ2rf1cCaIBIV4jk63WT9Xz/vreCm3s3uM16Fdjn0Zdq\nJ5hHZReoja4DMAsUAlJ6TqNe9wi4iXQDBcgOIGfGid7zz4PLvzIdtHB0AzfGD+B4RM7UenYAN487\nQaHgJnDjHAtq2Y/r5/6gRaYY8X0IAItRUHAl8vI2ghtkR3Ds88G+3w/ge6AA8m9apztBXoxfA3gD\n999/FkaOPLdpHogI9u7tCPJINGp9hoNWDt8CI+CY7dF2DgK1wIP6vxScZeJFfW4cV8FGFBRcmVVz\nDYIA11xTo227BXzP7tb6naK/B+D8sLwYDaBB1DTLX2s9b0PY+vGmXtcVFKCOArlXRupvb4FjdjQ4\nxsXaj4XguO6Gs2Z0AedAgZ77DjjmJeBcq9V6jtV7vKptqAffz+iSVa99tlvvcxU4pp1A4esxPS+6\neV2BSy4571Nh8fBLVCDdtu1JrF59/UfSzkyWFwqiG2Ovyct7LCRUh+8BpOeCATIJLm1R4gS40tJp\nKC7ugMxrTPvwUvm8LW+++STeeefbLbtBLr6Z9jzAFeAAInlhwFDeh3O4fhsimA9wF20E3/IDeqS8\n34KY+yjmI6H+uyIhFuIYoX84HPHSFr68TLlbHBjPQlSNLTE+xPErX7myxc92/PzmLzxJXBis+TEH\nSeYwLovOsFDZwZINn0Lf+TohDsFHmRtwsFIcZ4hFn0SjPAxoaNTVlk3UQFjVQn/96fqcrUL/epmE\nQZdL9frR4jAXFtZsER21AvTWfrG8KyL02Q+ScASPYSoskseiYow3IjtYtE+faqmpWSqFhYZ1Md4J\nozifpHMwc5Zen07b0Wgb9mGSuHBWv89n6Lj0ExcqbJiXi7T+k8QBMG0Mon7xlWl5YuIK81KcLi5y\nyObeEGGkjQFR1+qYbZdwJInNkyhtvEWqVAjxGOXiqOHL9fdRQpzJLAHO1DlkXCqfF4d5sTEs199O\n1Xlypv5uc+4BrbPRzs+VdLpzH5/ih+QuFRcpk4mDpvQjAwp+lkpctExNzVIpK5uScwhv7jTx7R9p\nYu9cW4N5mys//elPpV+/k3XdmKXHBEELMB8fu/Ah3PjjAKfbASzO4dptMcJHMRiO6x8vgA76sgz3\nUeGjv7hY//ZFoGcGYPlhn3P1c+bIm8LCk5t9VhxxGBOy2SJuUR8G8LtanHCR6dmDhCGHIyQ7IHK9\nnntQHJDX54ywSBhLCne6uCgDnw/BFnIDiFlCOJ/y3DbSQeLykpgA5QMObYPx869MEW40Bhg7Q+fD\naXrubnHhnAZ09XO1GNhwqTgw7r0SBi1GFyiG8bm5sEII4LXEbgOFm3WFtCSFOmm0L9b5YzTrlvBt\nrrgcMQb6tNDPMm3vFKEwN0v7JhM5XPbFzQ/n5piYoGBRLZb/pb+OuR8yu0E4B/1oIROErL8NyNpH\nx32qhEOoB3rfz9b7Wyr7IeKErjJxAt4mIUfLRcK5bbl+LhZG1PQVJ8z5uXB88icDktq7UycuZNny\n6JiQny7MAQ/IggUrDml9OVKyF39NbG0Ir0tAGi+41NXVtVv9fcK0oqKPXghKF3jaH3DaHuV2AF8P\nguDiIAgGg/bYItD6gSAIfhQEwc12chAEhUEQDA2C4BTQntpbvw8AABHZIyJ/8A/QPvaeiLyevSqn\ngqbaY5DdV5+ekrylhaDP6P3rQb/wqaCh5kWEsR9WRP/OwIEDhSZAhe8Ucen06zcVNTVLUVNzDYYM\nmYVUah9oil+IMCBvL4gHKARxLpnomAH60ZOgmyLe58jfj4bDYTwGuheWabvy9LkbQdrqd0A3w044\nOuNVIJBwC5w7qpceYwH8BBwrowMPQBO5UaUDlElX6Tk9QbN3F23LPtC03xEuvfZB0HX0nvbJ34Gu\nngZ97mNwZtdF4DTeDGJMXgVdGBdp/aNgUQGwHmVld+DGG6/y5sIW0A30IYDrQKPdLq2vIB0EbW6m\nAO++m2qaB6TRfgnAd0Ac03YQRvUBgC+C5v2jQPdGF9A1UQTO/Z7g2L+vbd8H0oRHTdNWl3S8BxCl\n0x4BzgGjGu+t/T0DdAl9F8DJIP37RH1uDegy+zq4JIzR/qmFcxkaBX0vkAXgL+D47wbH5izto47g\nfCrQ2h2jba8DsT69QEzLb/Q5vwXwhvbBLnCObgOxMB21HV3BubcZHOsuCAMPH4Z7bwM4Ku7v6XcD\nK16PMFbgegCfx6OPPpvWp0dK2xXfHdJad1CXLl3S3CB9+kxGZeUdqK8/iPLyi5pSAbQGn5ep1NfX\nY8SIc3DnnX/Gm28Ce/f2BNfx9fDXmPYC84o0l/6j+VLQ/CntX0TkwSAIjgF3ueMA/BeAM0TE0HIn\ngDuBlV7g6mC9vEiPX4KAgtjH5FabF0EfdAO4GDefW6U1Pj0RA31GrzU0/y9BXIMB9CxSYRW4aBXr\n72MBdE6rx86dO1FZORO7dt0ELmZcpO+55wy9/8tglMYqcKP/Eyh8rAIX7lJQALJAofvBzbUI3IiP\nBxdgWyCz+xxZd9sEbgCa8p5A//eO/jZN6/ESaIw6RZ+bBKMvrgTBrklwStSCWIQp4DRJgFEjD4Cb\nQ73WDdpfm0HP2yta97NBct6x+t2iTwJwM3oEGjQFbgzQc2YB+HsAE0AhZJzWd4keJd59Ttb+vg0U\nBtiHxcXv4dln/xMlJSU6FwCHDzkAN86N4Aa4Dw4EvQ4UEI4GN9Be2Lu3tmkO3HLLtfjXf30GBw8m\ntF/qtL9WgBtiUvthLzjX88AcFHXa/38B58UNAE4CI25Ww2FfDDOyUaNtnkG0XHfdKrz++pVIpc4E\nsQ7Qex8N4Efa58frGJ6p/VMCRvZsAkG1W8G5cLmeUw8KDnlw82qX9ukccB69pc+wPBibQcGjUNtX\noueUaJ3OAKOX7gGXlttAgWQsCAB+C5xLu0Aw9l5QkBNQMJoECjICh90QEGNk73pi5LUAACAASURB\nVMQq/fwYOK5TQWOs/86EP5ufvr2i6o6U+NLS/jbBZfVqIJlMYsyY8/HaawtVmeB7ctddj+Opp+a0\nGbB28eKb8MYbpkj4QvgCAEtQXNwDPXoUYvbssbjxxrYH8zY0NODdd7cBTbixVXAKaW7lcLF8QET+\nSUT6ikhnERktIi95/5ssIl/xvv9ZRPJEJD9yZBI87B45wHG7gwvaWFBbzCSzCBoa3mr1wpA59Ms0\nuhfBhfqvcMmw/EgFSxA2CsC72LNnT9Md6uvrUVk5A7t2RaNEbgM3rt+BURrfArW1mSCOeK/eswDc\n8A7o+b8ANTt49T2gdSoBwXO1MW3xy4egJgg9H3Cbx7lgJEcJCMo7CpTgbfP7KjjBr9S+OaB/D2r7\n5mo7VsJtAIV6jQEpN4LCxH5w2vfWa+4HN+enQWHCLBpWv+7aP7eAQkx3fcZt4AvXG9wspwOoRjj5\nWJ7XJ1FA4RPo0aMbunTp4s0FwCU1OxEUuo6DEzD2gxvdf4DzszcYedELQG+IfIja2loA1Mh69eqo\n99yp1/sWtNv0OWNAr2QP7fu9et980AKzCbQG3AputOFQ0K5dr8kYCuqsOQIKTqfreNSD1pXe2scm\ncBnweQYomP6Pnrdex6Ben7sXFI5MgF2mv/0atKC8ClogngGFiPfAOWXKRFLr00H7F/r/fwcFDD9k\nNg/AAP29EYxISer5HUG+xX8A5240BNJ/v806crv27d+BykXm9aWtwYpHSvuXZctu8wRuP+T1zDYN\nef3pTzfCKRL2nC6gs+BWBMF77Qrmve66VThw4HS4QIU/t/geh43wcfiUOrBbqsBFJh4FDWyAyIeH\n9CQXtmVhtFPATRrgQnkUqGEnkB6pAP07A8CdWLbstiY3S+/eY7FrV1yUiC2qW8DIiXHgBt0RNCQV\ngYthF3CjeAxc9P2N04Sek/We7+o9i5FZ8t2Ibt06g5vECG1LHZxA9S6Az4MbwhYw3PU4kF9jMWjq\nPggn4ReCLoluoEb9PigwnQma8DeCGvVeUND5q9b/KXAjMstCEnRxXQNuZq+Cm918cMN7AdSyLZSu\nGzhWM8GNcw4oIH1e2+Zn9M0Dtdu4PgnSUPRuLlgWTHMDWbhvgdY5T/vmAoTH4wIAvXHmmXOb7nnO\nOeP1noVeG6x+5r6q0v5/S8fmG+Cmbe6RXqBFaCIoCD6s/boV8+ZNwJ//vCU2FDTdsmfRQXWgALJJ\n7+Nv0ov0+SVwQtL/aR0CUNjrA4aDHwCFhXVgdNKDoHBg4c29QaF2lN4vpf2wB7RQHQsKZWYKtzp0\ng8veK3rtPq3rUfp7PjiPPgAtWueC1rjV4NwzgWIMnLvS7rNW+/tu0O0Xv77EhS4fKYd/iXels7RV\nyKuIYO9eIFMmW2AGPvggiHXFt1UhF9V3QAHILDD/3KJ7HBE+0koKNKneA27GNyIe73ATUqn8Qxrg\nm25ahJNPvgXUJEeBpuj39L97wQXxGnDjeR7Z8CePPPJL9QH+D+rr98NtmlYEjgCrGFx4a8HNuR5c\nuDuBVhbjHzghcg94358FBaNdoKCSRHoadOur1Sgq6orCwnxwA7pT/78A9FMWg5vfLnBTKQLN5/NA\njPAVoFDQAJemfQLog/8nOFIw2zBu1L7rAC7692l/rgA3wW7gRjQfYYEuAbL2/xbAAwgCGwNzn9hG\n9Vykf19EOhcKwM30DuTih7UQviCw9PC9QQFhJpyLobP2UVTjCfT7cvz+92+G7jl48G3aPwfhMAdJ\nuMCv74FplXqBvBdfArX03Qhr5R3ADbsvOE8O4NZbr82oVaVb9rqA/TsA1JSuBxctc5c9DifgWd+W\ngHN/p95nC4gB+a3WY4Ve06BtMKuGzd9XwHe5VNs6DRQcvqu/TwbnxWPgHD4DxJmYFc8Eo9HgHH0f\nTkDfCwq6JhgZV8cLcJahZxAE8zUscw8ojPxcx+IX4GK9Gq0JXT5SDr+S2ZVupS1DXqMkmOHnOJxb\n2xfXzi76rOh6lFs5InyklWI9/h3c5NaAG0gVaCmo0u9rsH9/6pDMoolEAtXVI0GXwzhw0eLi4zgj\nngU1pFKkCxNWArz77i688UYDgP/Vc6MuHVtIAW6iZobuBVpbOmsdjHBsMygIxL0oJsgsA831y0E3\ngQ+2M4bT5wGsRSrVFd279wUn7Gi9x0ugu2c3aIG4EdR0+4Kb3SugcDBD6zUH3IBKQbdBAnSXvK9t\nsHv+//bOPEyussr/n9PpLCTdnRBQTAJJ2LIvkgiENYEssqPiPAriAjNDMgIRZAk7KInzE0FB1Blc\nEWQQBFFiCLgyQJRgwqhsMqMyYpIZdFi6AwlJSJ/fH+d9+96qrqpeU3Vv53yep57uqrp167z33rrv\ned/3nO95MLTnF1hnfXqw+0hs9PkKyai1lEPXCNxGXd1GkoDXjSTLTOnRejwWpc6NdUhDhlzVoahS\nzN8/99ynGD16G4MH/wbrUKdg12EDNhsxiEojHtVBtLa2tu3z8ce/T0PDyySzed/DllOiHsatmAOy\nLhzjONsxGusUo/hbXO67H+tAv8ghh5xSMYgumc0RrPOHZBZqE+ZkXITNOn0KC3JuIrlOo/bH9PDe\noHCsV4XX68JjGeaULsBiQ6LTOzgct03YdXph+HwT5jj/EjtHX8CWWK8Ix+ZEkhmJw7C4o6HYNf5y\nsHUw5sRExwjaL639nL322otzznmCxsa/Ytf6p8L3QPsA1fibeYLdd9+fhoYYk+JUi544BuWX0tv2\n3itLaSLC4MFbKn7P4MFbdtiSXdLOVpKBT9dx56MdM7HOMN7wz8BubL/DpqN/i43Ez2DAgGE99mJX\nrlyNdfonYSOiJqwTnoqJJ12O3cxaaa+4Og/zOlvYtOl1YCJ289uGdZoPUkiUGj88tPF1bJQXFVXf\nwEaG87HOKS1rnUawmYkGrFN4D3bzbqA4rsGeN1Bf/zpvvPG/2A/mV6Gtw7Hp/M3BjqewS/IVbNkk\nZhttCI+zsM7wLawDioGkrVg9whVY5zI0tCMK5O4ZHjdgHfgEEvGw8qOHQYMGIDIdm4k4B+twjiWJ\nlYgda1QPLT43FoPytrcN71QUfQxc+/Off8Hrr/+GlpbfsnjxMzQ01GPno5mORzwN7aL4P/ax4zHH\n4m9Yx3c+Fp/yEub0nQ88F45dvJ6/hV3nZ1Na4v/4DtewCwWZ5mMd+lbs3L0dm31qIMlwuRT4z3Bs\n7wh72YTNii3DrrHXg42Hhr9zSZYHL8SczxMx4buXsXNzIOacNmIzKYrFakThwHuwwNY4e3UZyYzE\nBdgs6GZsFq4ufOdwEiem1NKJUFe3kve8Zw433XQN69evYvLkr2HX0PuxGbh0gGo62+VqBg7ccZ2H\nU0iprMDuZqeUVlE1ipdae8Jpp8XfUylW8KEPLeiV7ynHiScehkicrezmddqZfNyd4UGbzseH1DQH\nPqJJee8WNQ2AqBlhFSrr6/fuMA+8kvBSc3Oz9usXdQbGqQkfxe+7JDyPQkZLNNHFSAtFHa0mZDVR\nE82BWN69WMAo6lR8V00XIVbyjIJW+4X3Lwmv36Plil3B6KKqpQvUdBmKK47OVfiwTpkyN2hPrFDT\nl/hOsPv9oZ2zwrF9l9JWXTWKje0T2nd5sHWqFlZ0jAJQsRhf1FhYqabxEKupzlUTzoql1TsW/5o8\neb6K3KOJJkZLOL6xWNwDanoc5UXgFi26pOI10hEtLS06bFjUpCguLFVoc//+k0t+3vRPbtekiu6S\n8FpafyQKrMXn67W0vkfyfelKtuVsX7z4ah09eo7267e/2vU6Lxy7tA5GfFwZ7BwTroUJ4ThGDZco\nwPea2vXbrIkAXKsmeiVRL+SDajopE9R+E1FzZbuaUFy0oVTl3Ks1qVI6WuH+8P9VmujVpIvIpc/9\n/e0Eqpqbm3XIkDlhm3h9tj+u6RLuzo6lnNBjXd3KbhVka19QMe6v9wq8tbS06MKFS7S+fr9wTSbf\nI7JcJ06cu8MLycV22r0wfv8OrmrbVx+J8zFeTdhospqQUFq1svgGU/pEm/jLlW0l4ceOndtWEj6N\nVa+dqCZmdHDRDW1u6AiiiNaS8DwtthVtG6+JqNIlasJW+6p1kJeE/cbKnTPCjf0d4bV3qTk4kzUR\n3ZqrSTXU0pUehwyZWvQjm13hOP1I+/XbV/fY45hge1Ru3V+TqrhRTXW+Jp3EwWqd1elqzs3csH3s\nRGMp8lj+PTpsy4Pd88K5PCns56iwbVT87FhELgr59Ot3qBYKY01XU7mMVW5LC/zAcl206NIOf8wd\nsX79eh06dIKaYmv575o06ah2n123bp2aWm9UTtVw7OakzlO6w16hhZVoS32XPdKVbDuiubk5VCNe\np3bdF4tyqSYicNNS10p0lo4O53N8OG8T1ZyIKalrdWbq/0s0qbS8XgsrDD+ghcJglRzR7Tp69Gyd\nODGK0M0Nn0lXu03/Rg7V3XYrrYCcCDOVc1qWV60KqbNjKu12V7Css/tOnKXm1HW3QPv3n6KLFl1a\ntWunpaVFp0yZq3a/deejF5yPqeGmfHz4G6WTS12grQWy1tEjNZns4tLG7T1puxHNUFN9nKDJyDoq\nLr6lyehslhaO6DVlW1TcnB32c0S4Qa8LN+boEMQb7e1qI7n54XMnhZv6QWqd+/ywr/btjf8PGTJb\nm5ubdfHiq3XMmLlaV3eYth85F3aM1vG8ptYBzg22xs7liHBjP0qtQ3mX2sxIVCM9Wm3UOV7NaZig\niRR3Wl013tRXhPbG8ugTQtsOUVPtPEHLdwAPaH39uAJ1wjFjjtZkFF7c4ZSToLf9pWcHOttRl6Kl\npUWHDJkWjt1yLe60YG5b+e/0Z4YPn6aJvHhUdD1RCx2LtEJruiPtaOaja7LN5nB/VJPfVCnnNs4u\nHqnJbyIqvEaJ9rjdyvA3ztJ9VAtnU9KqsMWl0/fVZLZugXakENnS0qITJ84O267XZGYxPRM5X2Gc\nnnnmJ0t2AgsXpn8j7Z2WKVPmueNRRTqWJa88s9cRPfm9l6K8s9RaFTn3Yuw3MVdFlius0a44Hx7z\n0Y6NWAGpV7E18lVYDvO7U+9fTVzbV72Rb33r+2zYsIFDDjmFW275X7Ztu4n2pY0L87xVla1bB2NZ\nHM3Ymv4bmJ7CniTFfl/Fgi4J+3s0ZcujWMZHzIZ4k6Ri7ChsDXtGsPdYkoJc3wzf8VcsFmACFlPx\nGhaYtwFLB9SiYxPbo7z55v/R1NTETTddw3//908ZPXoQFjhXKusD4HhUt1BX9yts3f6CsP+4Ztga\nvjM+fxWL2WgK2x2OCaG9LZyTfbAYgM9ha/2XYVklDSRBfMcishlbrx+NxXtExdLNRdumA/4eZ+TI\nETQ1JdVOTzzxsPBfLKwW41sews5hJTG6ASxefFWP15QbGhoYNmwslu66psjmNcB97apoXn759bzy\nyuDQ7pgN9GOSgnKKXdMrSLQtriGJQTiF9rFDRnfWsJctu5D+/aPyalSEvZqkEvJ5WAxSC0mA7bUk\nGVAPY9f3Qiy26fNYUO4V4bM3YwGkK7FrKn19xfTh2MZHwmcewQJxi1NlCzOTNm7cyB//uA4Lzh0R\nbPkCFpB6UGjDj4Hfc+ut88sE5CpJBl28hn4c2tPK4Ye/q88Vk8sqqjs+O6W343bKp/LKDq1eW47G\nxkZWr76Pc89dw4gRH+/ahzvjoewMD9pmPkaFUdTkMKI6QpPiY8Uj5fj4kQ4fPi0sQXTekzav+yhN\nipeN1aTGShyl7RdGZ5PVZg0O1WR6+hBNCl8dp8mySbT90CJ75qrNhowLI604yxFnYPZXm0W4OPwt\nX09myJADCrz6s85aojZ7Ump7ewwefLhOmBCXibYHu+P0+wSFhZrEbIxXG53HAnvrwmux2FyMbzlf\nbQS7f9iH1eXp12+yLl58ta5bty7UsIkzJzFmIxbRKzw/UFikLe3hi8QlouKR+kwtf86btX///Xtt\nTbn9SK3w/+KZiL32OjJcBzHuIxaaS88c2bRtafvjNb+8yP7urWG3trbqiBEnpPadPpZzgh0TUtfv\nleEaGadJDMi9mvxu7lGbcRufsq/crFSpWZz1ajV8pigcqCL7av/+U/Qd7zi+3XT5tGkxrik9e1Ep\ndqP9SNTOX3q6PF3PpbnHI22na1S7IFtPaG1t1VGjem8ZtLdZuzaftV0yxC6Y1sMu2AjrBWyU+Dzm\nn/wjpqQ4H8tqmA88wSuvDA4eaSlPWsPfQk/6iCMmYqP3ftiMxUASCfIB2Ki6Hza66ocpcMb93RD+\nbsVGta9ieg5N2AivPjyP9mj4/8PYrEqUj98e/r4Ni+BvJVGi/AQ2Io72a3h+HrvuOrjNq9+4cSOP\nPPIEpWdLkmOwZcurHHnkQdgsRB1JSfOPh+8ehmW6pEXCtmIzOu8J7w8M3xM1E57GUh6fxzI2ngUu\nY+jQepYuvYCRI0ey++57YxkPf8RGm1G181MUjnQBfsSECTe201iwrJGTMaGrg7HRaszomUz5yPNz\n2bbtxl5TPGwfTZ9ca8UzEarKK6/EkXdM1X0LG7Vfi117l2PZLDHFOk2U898KXEx9/TSGDDmKMWPm\nlUwX7gwiwsCBm0lmva7BZgxux66H8zDRtqnYtTCV5PqNabRxFmc0lolyBLB/6lik9/sekpmbd9A+\neysq2v4/pk4dRmvrH9i69Sk2bFjeLjPpmWfWYzOI6WyYKNzXnuKRqGpaHyHal67n0rRDSp875alW\ndkpvUK1U3mrhzkc7GrGO9xpMubA/dmN7N6ac+DVKS5xHzYH0VPbVJKmXc4Gr6NevGRFhw4YN3HHH\ncqyjj6qJo7Hp5j9jN6jrMQfi+2Gfa8N+HsJueg1Yp31R+L6Xw99Xw+eLRbLewNIWm7HOuxFbzngZ\nW2rph92Il2Id1WcwAa309P6vgWXstluiQXD55dfz/PMXYQ5b6R8yPMigQQN48MEnsA5QsSn1aZho\nVDO2dPAzrEPcgqU9K+YQTcSckCbMcZoWjm+s+VGYBvraa5/hiituSHV2UR30wdDu40lqIUwN53QS\nixb9ktWr7yvZqd5006eYMGEXrG7MAuy8LgDeYsCA86iraz9l37//WsoV3OvONGlh+mrhdxULl4kI\nb765FbtmVmJLcQOx0kkLsKUCgn0xDTuS1vf4BfAsb731WzZtupiGhn4sXXpBt5cHSt/w45Lgsdj1\nHAvdRSGxgViq69vDthdh5/oebPnxDxTelOPvLy6t3IU5UYsp5VAPGHA+Dz54a9uni2/gzc3NbN8e\nRZWioNjj2L2ic9P2pTuPQn2YPHUefYGu/J6yQJ6cpY5w56MdG7FO/ThM42AXLNbgcyT1RUpJnINd\nvLE+xSmYONKhJHoCP+Oll9azYcMGjj32DFpbR2FxF0MxYa31WCe5CRPS+jjmSDRgo7bR2E33BsxR\niBVPlWTW4k1shuMlzLEZSSKSJeG7tgTbXsViL14P783FtDYeIxH1uob2I7T389pr29qO2PLlq1A9\nBnMMYkxE+ua+EvgCw4btwoYNWzCHLlaS/Z/wuZGhTaOwWYRN2Mj8QKwT2hBsGoMpk14SnpeOMWlt\nPbatY7d4jZ9gIlGxDsuBoX1jsfgRgDe54oqzy3aqjY2NPPHED1m8eCxjx8LIkcrYsbB48f688MK/\nc845TxSIiZ199uNh1qV315QPP3wGQ4YsoV+/6fTrdxiNjQdy1lmPtpuJUFUGDXobptp5HnatHI9d\nH+/BZoJihxqr8sZzdz02I3IYds7nAe9F9QaeeWYEF1/cVmS6y5S+4adnEBoxxz6K7r1CUnm3X2rb\nw7DZr7XYbFSc4Sh2nNZgcU5LMGfkZhLRwKnAMk4//YSSMvGRK6/8PIlyarTxU9j9ofMj0b7UefQF\norhfuiptOSHALJA3Z6kinVmb2RketMV8fFaTuIor1TIzJoT/Y2ZGqfW2JZqkeKZ1IIq1H1borrtO\nCfs8Pqz5zlCLYThfLX7gQE3SHmPqY0wPjdH6M8JnP6mWqvuAWszFAQojwv5PC/uarhYXcWtow/7h\n8XfB5nWhnelMkc6tLRauQ16lth7ffj1b5Hs6ffqCcGxnaxJnENsSYzpiRs69mmQ4HBSO1eFqMTj7\nquk3lItTKLTRUk2jPshpFc/N8OHTOx3HUG5tNf16b64pl9Yk2F4xfmT06KPCtXtvOF53q8UWzQjH\nMZ1iWhwr0VzmOD2g/fvv36OsjHQ64siRJ4ZU5lLH6KpwzqaoZX7NT533+FuLGSsxHqtUCnX6PLTX\nomlsnFqxPXYe0+m1afs6H/NRDR0Ip/vUKl6iK+zIVN6e0NWYj5p3+ll5JM7HGk3S82I66CFqAYtz\nSt5kEt2CiWraD5U0JKIWwazwN2p4fDjcZMeF74y6FeM1SYvdJ3Xzu0qtQ41ptTENd7wWCof9Xi1l\nNQbK7RM6nPXhu8eqCdXENscOqPMpluW1C+LDtAusI7xCC52OVk0C+KIjFXPYZ4XOYXzYdrImeidz\ntePgXrPxnHOu1ES3Ja2ZUupz7YNNe0Jv6giU3ldrxX1Zamt0MOL5iWJy39PSKaZR36N8x2rH6ape\nOUatra0VnLQYJD1azXGerYUpszO0vfNUfO2m9UrSOjmFWjTlOv/EwU6n16Z1SWZpsdhTJWciq52H\nkz+y5Cy589HNR+J8rNVC1cNPhs7vBG0vynRVuBmODjeg76iNxKIgVvoGGLefqtbBTlIbzX0kfC6K\ngE3SRIUxfjbOCozTpHNvVnMkPqhJlsnz4eY4Va2TnxfsmBW2jzfvKJZVbFe6E4rHoOOOrrBTbK9d\nMH36u7W5uTncwKNmQ6smqqhL1Jylu1LvXaU2YzNXk0yWaWod6XbtSudoHdsUTdQqe6baWUylG0Bv\njnQLnbzCkTtc2U7jQ9WEverr07MK6zTJioozTKW0TqKYVnU0EMo7VvE8f01NYO3ocK1+T0tn6ZQT\nRott6dpMRftjn86OOSz8PVqHDJnYLWciS52H4/QEdz567Hys0WR0FJUzR2uiuJie4l2pSSrgA5qM\n4GcX3QDTMwJxxB7VJmPa493hc8epKTHuq4UjrIlh+/XhBjxCzRmJMwLN4e88TWY2xquNcg9VOE/N\nCZmthTMoaRvnpb4z2nx3aGPs6A7V4cOn6fr169suunIdrMiKgg42uYGnj9P81HH4iFqnFzuL6KjN\nCdvEtsT3Sylk2vJJXBZIRq1LNHH4OrdcU4mofNqRim3ctqcj3aQd5UbuK9sJo0Vsxime03mayLSX\ndxhttqznx6mzx239+vVF11CLJkudUcn2djXHPKbC362l05xLOU3R6eieQ1XaOdquxU6LOxNOraj1\ntefORw+dD5GbUzfkOCW9VhO57/mhk4yzAnNChxY7/2ZNapW0Fu2nVZM4jwM0iXE4QJNlnih7vU6T\nEdZBoeONGg1T1PRI5ql11pPVlmwmaaF0+AfCZw5Vmz2IeiCHhPYUqzMerTBWGxpm6IgRJ+ieex6u\nAweWUmttP2rvTAeb3MBjJ7ggddzijX2uJgqcx4X2xHbF+ibpEWwphcyPFtRTSbQVpqg5kT2Lw+hJ\nPYie3CCsHR1LwheTHPd4zGZq4exXYfvtMS7oo/SeBkJzc3PF47Z+/fq2OjD19ePUnIt4ncRzdkD4\nbeyvpRVN045G+rX0Ndd1h8pjNZws0pVB0I7GnY8eOh/77HNw0egrCiwdqIlYUxxtRaGvWWoj6wVq\nQaP7qXXu3wud5vTUDWumWic/Rm0G4nuajOavUnNsim+QcQQ4Vq0Q2yg1p+IAtdmB8WoCUkeHDvbU\nsM+LQydzRfh8epYmxnzsraWk4CdNmqcLF17SrXiFch1s4Q28OXUc051ceoQanYa5avVdjtSkVkep\n+JLtKtK+NkbS+cbA057FMuyIehCdweI3olNbziFoP3JPikDF2I9YpLB8J1xff6guXLhERSpLjndE\n+uY4ZMgB2pGEeWynFfOLy0JRWK5VbfkzFrSKzmep+jCztFgYTeQerasr53TF41feofJYDSdL9HZR\nvJ7izkcPnY9HHnmk4AYzevQcnTDhyNBpx7ojUaExBvPtr0ltlTGaVISdrEkdk3iDm6rmYIwL703R\nwoDAONJP3yBjsOsEtVmVMeHmum/qtahqeqqaEuQstc76IIW/aJLNkr75Vl7/tlosvbvmn76BW2e0\nXAsDeWNn8hFNVEjjcdkntC/Wqlmn5qhNDsdwgg4fPrVgSSh+Z+L0HKWdWa6pxI6uB1GOJGunvNNQ\nbuReWFG1pcQ1VtiGPfec0+PRfvubY+eOW/sZnktSv5Go/pt2WEvNfu2tixZd2s5R6K5DXUytp7gd\np1aDoHK489HNR3Q+1q5d23YwW1tbU4W5xqvFH9ybuvHNUZtV2EeTsu37aGHV1tvVHI04Oj9OE+dg\nprbPjIkVbWOlwBhANzvcgGNg6jhNAgf3C51SzJ6JSzlxZuRKTSTNj09dpJU6g+0V0h8rd3SdJU7D\ntx/Nx4qkcfkqFomLFX5jJlD7JaFyWQvR6WlsjIG1HS/XlKKWEseFmSvlOvDyI/dCp6l8EcB0Sfee\njPYLb47lAkELj9v27dvD8S1OjY0ptVeozTaWyzyzzwwZMrvtHKTPhS+fOH2FWg2CyuHy6r2IiITC\nXA2YCNbvMfXNRkxZ8U3gnzDho3WYENZATPDrFEwi/VskyqGvY0JiL2NF3OaG7S/CRJ1iYbT7sMJb\nK4IlbwBHYmJL9WGbLZhg2FxMBXUrJkQW5ZtPDNscF/a7GRPvehO7PpTSUvCROkysScu833M1xqam\nJn71q3uZPr2JQnnyRuCfMeXTvenf/3zq6g7D2h3bNwA7RoUF/OB4nnvuvHay5Y2Njdx00zWsX7+K\nyZNvoq6usKBZXd0nmTx5A9ddd1lFm2spcbx8+SoShdtSrKgoUlUocJWWCI9tiWJFibx8PG4vvPAT\n/vKXH7STHO/I3qQIVlTYrXzc6urqqK9/ncJrszHYeRkmCvYqpk5bal8meEuhYQAAHlBJREFUxS6y\nse0cpM9F3kSlHKcUqju+KN6Oxp2PDrj//sewWihgTkE/zGFYit0A/yW8tgW7GQ7FHIuFWGe5Htgb\nU0idg9UlibVb3kWimtiKOTQLgNMx9dJlwEQGD441Lt4KdjQDe2A34UewC3BBeB5v8BdgnfRlJNVi\nl5LUsui4Mxg8mB2uxtjY2Mijj94THIJiefLHmDz5/3j55TW89dbvGDu2IWVzpZoax5aVLS/d+by7\nS51PLVQqk5vNRZRTka2vv4Brr/1kuV0UqSPGir6PA4dTX39YhzVbuuJQlb45Fku4J6SPm/0trhM0\nEpNRfxZztDdTrtourERkW5n3euZQOU4W6BN1XjozPbIzPCiz7GJTwLEi6hy13P5YgfOAMA0eK24e\nrLaMMkMttXV/TWI+zlNbIokiX8+rBZDG6qLptbv0VNr9unDhkrA8cYsmmS/T1IJbJ6stq9yjSTxJ\n3NeRYV/jNcnEicsVy7VS5kRd3QO6aNElVZui7my2TJLd0DtLH91ZHqnV1H2hzsfVWrhkdJWOHj27\nU7aXOs6lUnR7z954XkoFh7Y/bslSZ3E6eHx8W22JsVTczgMK83XEiOM8LsPp03jMRx95lHI+VNPB\nb+PUMl4mBWfkpNC5z9JEgn2SWoZJVJEcqUmWSVRKnaRJzMjzmsSIVF67i1kDtp9xwaG4Swtl0W9X\nC2CdpXBbsOH+8Lw4qDPqXuytlcql1yLCv1K2zIQJUXa++7EPvUEtjkt3FE4rsaM759L2RsfpUB0y\nZE7Z47Z+/XodPnx6iWvTtGNGjTpSK5emz04pdMfZEWQtfsmdj152Pizt797QSX9EbeT90dABXhkc\nkijmtbfaDMTE4FSMDX9vDa9Fgae5mgT7NWuSalv6kR7FNzTEujDr1GY/pmnhaHhOcG720qSGyQRN\n5NNjBxCFqtI38AXav/8UXbTo0rIy07WmpaVFFy26RPv331eToNzCR7W9/modl6zdbDqiI3s7mm2p\n5OC1d2xaa3b+HadWZCn9252PXnY+WlpadNKkeVqYaXFUcDRuUpN8nqWmvxH/nxEckihnPl4LU2M/\nroUKpkdpZ0fxZ511sdrSS3Qi0pkx8XGlFqbvjtFCYbRyKbatublxdzQyzlpH3Ftk6WbTGXrL3mIH\nL2+OmOPsaGo9OOyq8yFqHe9Oj4jMANauXbuWGTNmFLzX0tLC0KELsCDPH2JZLXOwzJGRWODebOy4\nb8dKxg/Hgu0GY4Gi/4vF947CSoNfi5WvX4Vlv1yLZW4UUle3knPOWc1NN10DwMaNGxk69CBUnw37\n3wi8FyuZHjM/5mIBhT8Me5kVvnMkcCUWsPgTSkdKK2PHLuCFF37SmcNWEzZu3Mghh5zCs88uQvW3\nwC+x4/wyw4dv5KmnVlYsj95XUNVsB5QV0dv2bty4kSuuuIH771/Ftm2D6d9/EyeddBhLl17gwaOO\nU2WefPJJZs6cCTBTVZ/saPv6HW9S/mlqaqKx8S02bnwDy3D5KJaxUoc5JKOwrJPTgP8C9sEclH7A\n27E03C3AnsBtWGbK+7B0XMI+3x/2dwwxXbCu7kEmTvwCS5fe22ZLY2MjZ5xxHN/85grgBCyL5T7g\nBix1EkQ2oWr7MPYI3/s+4OzwvOMUrax2bJdffj3PPfdJVI/B2gTWVuG111by2c9+tc1Z68tk9fyU\no7ftjVkrN92UP0fMcXZ2PNW2k5x22nxs5mAxlsa6FZvdiFoYI4GHgfFYmiCp/7dgMxHNwAgsHTd9\no2zCZk9WY47JIRW1B2688RomTrwRkR+F724ErkbkE0yY0Mpee+0CHIqlNcaU2v2Bp4EXgi3lZryy\nn6JVqB8RMXtbW48pm2br9F2yfL06jtMedz46yec+dxnjxr0I/Bo4BHMgXsUmj1aGraIjMAA4Gevs\nXwF2Da+/A9Mm2Er7zr8RuAb4MQ0NWytqDzQ2NrJ69X2ce+6aAqGkc89dwxNP/ICTTz4CkXeS6EFE\nRyTSOb2FLKKaf3Edx3GcnR1fdukkjY2N/PrX9zNy5Mm88cYN2IzHNmzJ5EasMzwGUzEdAVyBKZTe\ni6mN1mOxF+dgMyZx2aSYFZx+emnxrGJ7yk05L1t2IT//+Sk8++xCVB/HxJnuwpZmjgcuxJZ8tmMK\nqOWXebJGobhO6ZiVrM/cOI7j7Oz4zEcXaGpq4m1v64cFiQIcgHWC6SWTV7HZjiGYsum/YY5HK6Ym\nOQo77Esx2fM4QldgOePH38B1113aJbuKO9qo4nnuuU8xduwvGTWqib32GsH06V9izJh5jBp1OqNH\nb2P69JvD83xJTNdCYdRxHMfpPXzmo4uccMKhfOlLj2GzBi9jMyAN2JIJ2EzIEdjyymbgMcwJ+SMm\nuz4L+CC29BGDRAcDm4ARHHXUwb3S+VeaGenoedaJMzvPPae0tlYO0HUcx3Gyh898dJFLL10E/AnL\ndvknLHNkRWqLJpIiWCNI6r4cT1KA7t0kMR4/AX4Q/n6bBx9c2+s2FzsWHT3POl4czHEcJ99kxvkQ\nkbNF5AUR2Swij4vIgRW2nSQi94TtW0VkcYltLhWRJ0SkRUReEpH7RGRcT+389KdvxoJNdwO+jGWS\nLMOqssYllBHApQwb9iTwIhbw+SvgbZSuJJtUZfVgyc7hxcEcx3HySyacDxH5ALYGcTUWSPFb4CER\n2b3MRwZj6xhLMPWsUhwB3AwcDMzDlL9+LCK79MTWO+74MTAaExUbRcxQgSewmI+Tw98VfPCDJ9HQ\n0B+YhjkYdXSmrHjeZiJqjR8vx3GcfJGVmI/zgVtU9TYAEVmErVOcCVxXvLGqrgHWhG0/W2qHqnpc\n+rmIfAz4KzATC8ToMqrK5s0DsViOC7AZje9gjsU1cStiDMLKlfNpbBzN669/FQs4PRz4M5bmeky7\n/Yus8GBJx3Ecp89T85kPEemPOQQ/i6+prTv8FBPU6C2GYZ7BKz3ZSWvra5jTsQoLJC2/hPLWW0MY\nOHALcA8WCzIV+AvwKQqXaSzTZcKEG1m69IKemOc4juM4mafmzgewOxaJ+VLR6y9hqlw9Rmxe/kbg\nMbWiKN3dD6pbgOnYKtHLdLSEYmmhv8QckK8BH8eWbC4J+zmEurrJ/P3f/zurV9/nMQuO4zhOnycL\nzkc50sVJespXgElYjmu3UVUGDx6JBZq2YOY9UHLbqDexbNmFTJz4eerqHsMckKeBtcAI6utbWbhw\nDq+9tpqvf/16dzwcx3GcnYIsxHz8HyaasUfR62+n/WxIlxGRL2Eynkeoarng1DbOP/98hg4dWvDa\nqaeeyqmnnoqIsPvu/XnxxYOAI7GY1lMwP+lYor8ksoKJE7/I0qX3tqWFWvXNz4fqm3DiibNYtuxC\ndzgcx3GcXHHnnXdy5513FrzW3NzcpX1IFtI6ReRxYLWqfiI8FyxH9Yuq+rkOPvsC8AVV/WKJ976E\npZ/MVtU/dbCfGcDatWvXMmPGjLLbLV58NTff/FMsZjWWtL8BiwExsbDGxpdYv35VSccib4JejuM4\njtMRTz75JDNnzgSYqapPdrR9VpZdPg+cJSIfEZEJwL9iPfmtACJym4h8Jm4sIv1FZLpY9bQBwKjw\nfN/UNl8BPoTVuX9DRPYIj0E9MXTp0guor4cksLS9WFhT0940NDSU/Lw7Ho7jOM7OThaWXVDVu4Om\nx6ex5ZffAO9W1b+FTfYE3kp9ZCTwHyQxIReGx78DR4fXFoX3Hy76ujOA27pra1NTEyNHDuTFF0sV\nNrNlF9fqcBzHcZzyZML5AFDVr2CBoaXeO7ro+Z/pYNZGVXfYrM7JJx/Bl7/8UKgrUogXNnMcx3Gc\nymRl2SVXJBksK0lrddTVrQyFzVyrw3Ecx3HK4c5HN/DCZo7jOI7TfTKz7JI3KpWsdxzHcRynPD7z\n0Qu44+E4juM4ncedD8dxHMdxqoo7H47jOI7jVBV3PhzHcRzHqSrufDiO4ziOU1Xc+XAcx3Ecp6q4\n8+E4juM4TlVx58NxHMdxnKrizofjOI7jOFXFnQ/HcRzHcaqKOx+O4ziO41QVdz4cx3Ecx6kq7nw4\njuM4jlNV3PlwHMdxHKequPPhOI7jOE5VcefDcRzHcZyq4s6H4ziO4zhVxZ0Px3Ecx3GqijsfjuM4\njuNUFXc+HMdxHMepKu58OI7jOI5TVdz5cBzHcRynqrjz4TiO4zhOVXHnw3Ecx3GcquLOh+M4juM4\nVcWdD8dxHMdxqoo7H47jOI7jVBV3PhzHcRzHqSrufDiO4ziOU1Xc+XAcx3Ecp6pkxvkQkbNF5AUR\n2Swij4vIgRW2nSQi94TtW0VkcU/32VvceeedO/ordih5tx/y34a82w/5b0Pe7Yf8tyHv9kP+27Aj\n7c+E8yEiHwBuAK4GDgB+CzwkIruX+chg4I/AEuB/emmfvYJfbLUn723Iu/2Q/zbk3X7Ifxvybj/k\nvw193vkAzgduUdXbVPX3wCJgE3BmqY1VdY2qLlHVu4GtvbFPx3Ecx3GqQ82dDxHpD8wEfhZfU1UF\nfgockpV9Oo7jOI7TO9Tc+QB2B/oBLxW9/hLwjgzt03Ecx3GcXqC+1gZUQACt4j4HATz33HM9+oLm\n5maefPLJHu2jluTdfsh/G/JuP+S/DXm3H/LfhrzbD/lvQ1fsT/WdgzqzvdhqRO0ISySbgFNU9f7U\n67cCQ1X1vR18/gXgC6r6xZ7sU0ROA+7oWWscx3EcZ6fmQ6r6bx1tVPOZD1XdJiJrgbnA/QAiIuH5\nFyt9tpf3+RDwIeC/gTe7872O4ziOs5MyCBiL9aUdUnPnI/B54NvBYXgCy1QZDNwKICK3AetU9bLw\nvD8wCVtGGQCMEpHpwOuq+sfO7LMYVX0Z6NBbcxzHcRynJL/s7IaZcD5U9e6gv/FpYA/gN8C7VfVv\nYZM9gbdSHxkJ/AdJ/MaF4fHvwNGd3KfjOI7jODWg5jEfjuM4juPsXGQh1dZxHMdxnJ0Idz4cx3Ec\nx6kq7nw4fRYR8eu7RoTsMicj5P185N3+vkBvnwOP+XD6HCJSp6qttbZjZyc4f6Kq22ttS1cRkUnA\nm6r6p1rb0puEc6LqN/6qICJDgFZV3Zx6TaCt5MdOizsfvUi4qMZh2TWvqOrTIiJ5ushEZBHwfVX9\na61t6Q4iMg44HTgI+A5wR/r45+mHH2du8uZIicgxwH+o6ktFrw8GNufk2P8K2A8TKnyk1vZ0BxEZ\nBEwNDwGWp3/XWb83iUgjMBvYH3hUVdcUvZ/5QYaIXA6sx479yyXez3QbRGQgVhX+IGA71o4XU+93\n+xpy56OXEJFdgIuBy4C/AP8FXKiqz9TUsC4gIrsBfwPGquqLoaPeB3gn8LKqPlxL+zpCRJqAHwMD\ngWeBKVjq9QTsBvYLVf1z7SzsGBEZgP3QV6vqttTruRixisiuwMvAZmAN8A3ge6q6WUQuAh4Ffo21\nJZM33dCGvwJPYQOJC1X1zvBepjvsSBhxfwH4GNaOYcDewFrgRlXNtJpzsP8W4CTgGaAZeB/22x6v\nqo/X0LxOEa6jdZjEw2MiMhqrqn4Q8BjwjWIHPUuEc3A9cCqwGivWOhzTzbpFVb/Vk/37mnjvsRB4\nD/BhTCm1DvhB6BDbEJG9amBbZ1kErAmOxyhgKeZEfQq4WUTuEpGRNbWwMh8HtgELgHOwm9Y3gW8B\nnwH+KCLLMh4LcibwQ+DfROQiEXkn2OxH7PREZHcRObiWRlZgMKbB83Xs+F8H/EVEvg98Ftikqtuz\n6ngEzgV+qaozsHPxGRE5A/IxYxY4FzgYc77fC5yA3Z+eBz4vIj/K+L3obGzAcATWliEkWk53iMh/\niciJNbSvM5wJPBMcj6lY+Y4PYs75PwAvisiZtTSwA84BZmDn4HTgPGxw+jLwORG5vUf3UlX1Ry88\ngKeBf0g9b8RGHFekXjsW+EGtba3QhheAj4T//wXzzk/Hpt0WAi8C/1JrOyvY/zhwXur5NzCPfX54\nvgT4T2CvWttaoQ2PAKuwG9Vj4f/bgX/EZqQAzsLWkWtub5k2fDKci3Hh5vUx4DmsbMELwL9io9ea\n21rG/j8B/xj+HwF8DWjFZhJ2C6/3q7WdHbThceCyEq83hvvQU8C3gbpa21rG/meK7qd3BZsvBA4D\nfgT8NKv2B5uvx2Y3AO7EBkF7hucDMRXux4DGWttaxv61wD8VvXYbcAE2I7UOuLi7+8/yCDA3iMie\nQH/sx4GI9FPVjdho+xQReUfYdAl2wjKHiOwHjAFeDS8dBSxT1e+o6n+o6i3AzcDEoBybKcLa5H9i\ntQXi8sUpwPWq+pOw2V1YwcGDamFjR4jI27FyAd/EnL2rsBtsE/D3wLdE5GbsOrqhVnZ2hKp+Hlta\n+SdVfVJVb8V+H3dgbTsGOLl2FpZHRKZg19B3AVT1f1T1H7HzcSx27NEMB9GKSD/M+ZhZ/J6qblTV\nlcC1wDTMQcwUIjIGc5JWp15eAPyzql6vqqswB3YIMLkGJnaWHwMHhv+3Ag+r6rrQP2zBlpV2AWbV\nysByhPisP2P3njTzgD+oFWy9Ezg8lDvpMu589A5NwO+AXcPzODX7EBboNU9EGoBDsNFTFhkM/AKb\n7t+O/bCjMxWvk19gDkq/mlhYgfBj/jlwloj8DlgOtGBr95FNmP1r2u8hE7wBfBV4TVVfV9Wfq+rV\nWMd3HbaccQC2dn9z7cwsT+j4AG4C3ikiB4jIHsBobBnvc9gNrFtFI6vAdCwmYqMY/ULs0+3Al4Gz\nReRhEZlWWzPLExyjB4FjROSrIvKu4JynWY0ta7xedQM7ZjC2bLcNQET2BZ4E7k/di36Pld1oF8SZ\nIX4DbAnBy8OAE6HAcX0F2BdbCssMIa5pEzbr+nEReb+IzBORS4EGYGX4TTwITASGdut7wlSK0wPC\nKPtkLF7ihXRwYAiyex9wH3CWqu5XS1vLES6mRqxzPha7yL6uhZHNi4EzVfWdtbGyMiJSjwVHTcfW\nhhdgI6OzsGnzRcDRqvqumhnZBcIIaXvRa18GDsvqOUgTrpejsEHOEFWdV2OTOkREhmL3xdfKvH8A\ncDfwkKqeU1XjuoiInA4sxhyMVdgA6Q/Y4Og8YJpaXEumCA7s24FXVbVkhXER+QTw96qaWScQQETG\nYjFzh2DZUz/AsvC2YDFqQ1X18FrZV4kQ93cdNoM2AAv6vVFVvx3eX4wtT07t1v7d+dixiMgwrCOc\nCpyrql+usUkdEpynRqBFwwUSUli/h61hZnXUWkBYDvsm9sM5AFtH/rSqPlhTw7pITA/G2vEU5hRe\nV0OTOo2IfBULrvuYqt5Wa3u6SzpFW0ROAN7K6nWUzsgRkVnAGVjK6hasmOhE4PvAF8ISRmYJ96I6\nVX0r9do4rAL5rar6pZoZ10lEZAJwHDAfW9LbHZsJu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dateFmt = DateFormatter('%m-%d')\n", "fig, ax = plt.subplots()\n", "ax.plot_date(SUNAResultValues.ValueDateTime, SUNAResultValues.DataValue)\n", "ax.xaxis.set_major_formatter(dateFmt)\n", "plt.xticks(rotation=70)\n", "for result in selectedResult:\n", " plt.title(str(result.variableCode) + ' September 2016')\n", " plt.ylabel(result.unitsName)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

9) Use the numpy libaray to calculate some statistics about the time series

\n", "

10) Dynamically create histogram bins based on the time series standard deviation and graph the histogram

" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maximum of values\n", "15.61105\n", "minimum of values\n", "8.478889\n", "mean value\n", "10.5034952838\n", "standard deviation of values\n", "0.952486090455\n", "standard deviation of values\n", "[7.6460370124299901, 8.1222800576575125, 8.5985231028850357, 9.074766148112559, 9.5510091933400822, 10.027252238567606, 10.503495283795129, 10.979738329022652, 11.455981374250175, 11.932224419477699, 12.408467464705222, 12.884710509932745]\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"maximum of values\")\n", "print(numpy.amax(SUNAResultValues.DataValue))\n", "print(\"minimum of values\")\n", "print(numpy.amin(SUNAResultValues.DataValue)) \n", "print(\"mean value\")\n", "varmean = numpy.mean(SUNAResultValues.DataValue)\n", "print(numpy.mean(SUNAResultValues.DataValue))\n", "print(\"standard deviation of values\")\n", "varstd = numpy.std(SUNAResultValues.DataValue)\n", "print(numpy.std(SUNAResultValues.DataValue)) \n", "print(\"standard deviation of values\")\n", "varbins = []\n", "for i in range(-6,6):\n", " varbins.append(varmean + varstd*i*.5)\n", "\n", "# histogram = numpy.histogram(wellResultValues.DataValue) #, bins=[101,102,103,104,105,106,107,108,109]\n", "print(varbins)\n", "plt.hist(SUNAResultValues.DataValue, bins=varbins) #[101,102,103,104,105,106,107,108,109]\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": { "8b89aca155614ebf8519401b2270f808": { "views": [ { "cell_index": 9 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }