{ "metadata": { "name": "", "signature": "sha256:4537ac8a5b3077ac3b863528c8bd028376249c1f266e0467dc0789359828f04f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Notebook to compare Tofino water levels to previous years." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "from salishsea_tools.nowcast import figures, residuals\n", "import numpy as np\n", "import datetime\n", "from dateutil import tz\n", "\n", "from salishsea_tools import stormtools\n", "import arrow\n", "import pandas as pd\n", "import requests\n", "from cStringIO import StringIO\n", "import matplotlib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tofino" ] }, { "cell_type": "code", "collapsed": false, "input": [ "start = '01-Jan-2014'; end = '31-Dec-2014'\n", "\n", "wlev_2014 = figures.load_archived_observations('Tofino',start,end )\n", "mean_2014 = wlev_2014.wlev.mean()\n", "\n", "start = '01-Jan-2005'; end = '31-Dec-2014'\n", "\n", "wlev_10yr = figures.load_archived_observations('Tofino',start,end )\n", "\n", "mean_10yr = wlev_10yr.wlev.mean()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "fig,ax=plt.subplots(1,1,figsize=(10,5))\n", "\n", "\n", "s=wlev_2014['time'][wlev_2014.index[0]]\n", "e=wlev_2014['time'][wlev_2014.index[-1]]\n", "\n", "ax.plot(wlev_2014.time,wlev_2014.wlev,'g', label='2014 water levels')\n", "ax.plot([s,e],[mean_2014,mean_2014],'-b',lw=2, label='2014 mean')\n", "ax.plot([s,e],[mean_10yr,mean_10yr],'--k',lw=2,label='10yr mean')\n", "ax.set_xlabel('metres')\n", "plt.legend()\n", "\n", "print 'mean 2014', mean_2014\n", "print 'mean 10yr', mean_10yr" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 2014 2.12706077601\n", "mean 10yr 2.08611675446\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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CPB1OI0aMkOuKiYnBjz/+iBdffFG1vJspQJ3ccwRBEARhkiFDhuDAgQNYunQpSpUq5fJd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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2014 mean is about 4 cm higher than 10 yr mean.\n", "\n", "Other years" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means_ann={}\n", "means_winter={}\n", "means_summer={}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "means=[]\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 1,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,12,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " mean = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]['wlev'].mean()\n", " means.append(mean)\n", " print '{} Mean: {}'.format(yr, mean)\n", "print 'Cummulative mean:', np.mean(np.mean(means))\n", "means_ann['Tofino']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2005 Mean: 2.10024250086\n", "2006 Mean: 2.10803021632" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2007 Mean: 2.0597378963" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2008 Mean: 2.0437769661" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2009 Mean: 2.0755643315" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2010 Mean: 2.13724504979" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2011 Mean: 2.07849358974" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012 Mean: 2.11923744292" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2013 Mean: 2.00888221709" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2014 Mean: 2.12601236122" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean: 2.08572225719\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Winter months (Nov-Jan)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = [] \n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 11,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr+1,1,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " year = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]\n", " \n", " winter_mean = year['wlev'].mean()\n", " means.append(winter_mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),winter_mean)\n", " \n", "print 'Cummulative Mean:', np.mean(np.array(means))\n", "\n", "means_winter['Tofino']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Nov-2005 to 31-Jan-2006 Mean: 2.23816849817\n", "01-Nov-2006 to 31-Jan-2007 Mean: 2.20092906178" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2007 to 31-Jan-2008 Mean: 2.16491075515" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2008 to 31-Jan-2009 Mean: 2.11303113553" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2009 to 31-Jan-2010 Mean: 2.31651258581" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2010 to 31-Jan-2011 Mean: 2.23461327231" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2011 to 31-Jan-2012 Mean: 2.10429487179" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2012 to 31-Jan-2013 Mean: 2.23285583524" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2013 to 31-Jan-2014 Mean: 2.01351487414" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2014 to 31-Jan-2015 Mean: 2.29385783299" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative Mean: 2.19126887229\n" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Summer months (June-Augys)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = [] \n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 6,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,8,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " year = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]\n", " \n", " summer_mean = year['wlev'].mean()\n", " means.append(summer_mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),summer_mean)\n", "print '10yr Mean:', np.mean(np.array(means))\n", "\n", "means_summer['Tofino']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Jun-2005 to 31-Aug-2005 Mean: 2.00887395737\n", "01-Jun-2006 to 31-Aug-2006 Mean: 1.9906819222" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2007 to 31-Aug-2007 Mean: 2.01298855835" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2008 to 31-Aug-2008 Mean: 2.00680091533" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2009 to 31-Aug-2009 Mean: 2.02556064073" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2010 to 31-Aug-2010 Mean: 1.96096567506" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2011 to 31-Aug-2011 Mean: 1.99928604119" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2012 to 31-Aug-2012 Mean: 2.018003663" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2013 to 31-Aug-2013 Mean: 1.99447139588" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2014 to 31-Aug-2014 Mean: 2.010201373" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10yr Mean: 2.00278341421\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Neah Bay" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_NOAA(station_no, start_date, end_date, product):\n", " \"\"\"Retrieves recent NOAA water levels from a station in a given date range.\n", "\n", " NOAA water levels are at 6 minute intervals and are relative to\n", " mean sea level.\n", " See: http://tidesandcurrents.noaa.gov/stations.html?type=Water+Levels.\n", "\n", " :arg station_no: NOAA station number.\n", " :type station_no: int\n", "\n", " :arg start_date: The start of the date range; e.g. 01-Jan-2014.\n", " :type start_date: str\n", "\n", " :arg end_date: The end of the date range; e.g. 02-Jan-2014.\n", " :type end_date: str\n", "\n", " :returns: DataFrame object (obs) with time and wlev columns,\n", " among others that are irrelevant.\n", " \"\"\"\n", "\n", " # Time range\n", " st_ar = arrow.Arrow.strptime(start_date, '%d-%b-%Y')\n", " end_ar = arrow.Arrow.strptime(end_date, '%d-%b-%Y')\n", "\n", " base_url = (\n", " 'http://tidesandcurrents.noaa.gov/api/datagetter?')\n", " params = {\n", " 'product': product,\n", " 'application': 'NOS.COOPS.TAC.WL',\n", " 'begin_date': st_ar.format('YYYYMMDD'),\n", " 'end_date': end_ar.format('YYYYMMDD'),\n", " 'datum': 'STND',\n", " 'station': str(station_no),\n", " 'time_zone': 'GMT',\n", " 'units': 'metric',\n", " 'format': 'csv',\n", " }\n", " response = requests.get(base_url, params=params)\n", "\n", " fakefile = StringIO(response.content)\n", " try:\n", " obs = pd.read_csv(\n", " fakefile, parse_dates=[0], date_parser=figures.dateparse_NOAA)\n", " except ValueError:\n", " data = {'Date Time': st_ar.datetime, ' Water Level': float('NaN')}\n", " obs = pd.DataFrame(data=data, index=[0])\n", " obs = obs.rename(columns={'Date Time': 'time', ' Water Level': 'wlev'})\n", " return obs" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 1,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,12,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " wlev_NB =get_NOAA(figures.SITES['Neah Bay']['stn_no'],st.strftime('%d-%b-%Y'),ed.strftime('%d-%b-%Y'),'hourly_height')\n", " mean = wlev_NB.wlev.mean()\n", " means.append(mean)\n", " print '{} Mean: {}'.format(yr, mean)\n", "\n", "print '10yr mean', np.mean(np.array(means))\n", "\n", "means_ann['Neah Bay']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2005 Mean: 1.90511152968\n", "2006 Mean: 1.91054315068" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2007 Mean: 1.84638929498" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2008 Mean: 1.84311372951" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2009 Mean: 1.85891335616" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2010 Mean: 1.93104486301" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2011 Mean: 1.86976210046" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012 Mean: 1.91248258197" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2013 Mean: 1.79901723744" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2014 Mean: 1.91986472603" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10yr mean 1.87962425699\n" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Winter months (Nov-Jan)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 11,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr+1,1,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " wlev_NB =get_NOAA(figures.SITES['Neah Bay']['stn_no'],st.strftime('%d-%b-%Y'),ed.strftime('%d-%b-%Y'),'hourly_height')\n", " mean = wlev_NB.wlev.mean()\n", " means.append(mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),mean)\n", "print 'Cummulative mean', np.mean(np.array(means))\n", "\n", "means_winter['Neah Bay'] = np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Nov-2005 to 31-Jan-2006 Mean: 2.02779302536\n", "01-Nov-2006 to 31-Jan-2007 Mean: 2.00450724638" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2007 to 31-Jan-2008 Mean: 1.95591802536" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2008 to 31-Jan-2009 Mean: 1.91240217391" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2009 to 31-Jan-2010 Mean: 2.08869791667" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2010 to 31-Jan-2011 Mean: 2.02478985507" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2011 to 31-Jan-2012 Mean: 1.89351494565" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2012 to 31-Jan-2013 Mean: 2.02927853261" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2013 to 31-Jan-2014 Mean: 1.81285869565" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2014 to 31-Jan-2015 Mean: 2.03355978261" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean 1.97833201993\n" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Summer months(Jun-Aug)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 6,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,8,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " wlev_NB =get_NOAA(figures.SITES['Neah Bay']['stn_no'],st.strftime('%d-%b-%Y'),ed.strftime('%d-%b-%Y'),'hourly_height')\n", " mean = wlev_NB.wlev.mean()\n", " means.append(mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),mean)\n", "print 'Cummulative mean', np.mean(np.array(means))\n", "\n", "means_summer['Neah Bay']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Jun-2005 to 31-Aug-2005 Mean: 1.80663360507\n", "01-Jun-2006 to 31-Aug-2006 Mean: 1.7936861413" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2007 to 31-Aug-2007 Mean: 1.79255932971" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2008 to 31-Aug-2008 Mean: 1.80103577899" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2009 to 31-Aug-2009 Mean: 1.79492798913" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2010 to 31-Aug-2010 Mean: 1.76126721014" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2011 to 31-Aug-2011 Mean: 1.78460643116" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2012 to 31-Aug-2012 Mean: 1.79932971014" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2013 to 31-Aug-2013 Mean: 1.78000543478" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2014 to 31-Aug-2014 Mean: 1.79429393116" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean 1.79083455616\n" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Plotting" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig,axs=plt.subplots(3,1,figsize=(10,10))\n", "\n", "\n", "yrs = np.arange(2005,2015,1)\n", "for key, c in zip(['Tofino','Neah Bay'], ['b','g']):\n", " \n", " ax=axs[0]\n", " ax.plot(yrs,means_ann[key], c=c,label =key)\n", " ax.plot([yrs[0],yrs[-1]],[np.mean(means_ann[key]), np.mean(means_ann[key])], '--',c=c)\n", " ax.set_title('Annual Mean Water Level')\n", " \n", " ax=axs[1]\n", " ax.plot(yrs,means_winter[key], c=c,label =key)\n", " ax.plot([yrs[0],yrs[-1]],[np.mean(means_winter[key]), np.mean(means_winter[key])], '--',c=c)\n", " ax.set_title('Nov-Jan Mean water level')\n", "\n", " ax=axs[2]\n", " ax.plot(yrs,means_summer[key], c=c,label =key)\n", " ax.plot([yrs[0],yrs[-1]],[np.mean(means_summer[key]), np.mean(means_summer[key])], '--',c=c)\n", " ax.set_title('Jun-Aug Mean water level')\n", "\n", "\n", "x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n", "\n", "for ax in axs:\n", " ax.xaxis.set_major_formatter(x_formatter) \n", " ax.set_ylim([1.7,2.4])\n", " ax.legend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CXn311bzzzjsF4SsnJ4eJEydy7bXXVui9qYgyX4haREREEsvui8uyTvjogxtuMzPuv/9+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\n+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CkVqlLr9VX1JfH8MTOFLRFZGA4MHsgIYju7d7KrZ9fYtuqK6kkBbP3S9axftp51Teuo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\nkcKWiIiISIwUtkRERERi9P8BiVUyhO2Hb7MAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Residuals at Neah Bay" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from salishsea_tools.nowcast import residuals\n", "\n", "t_orig=datetime.datetime(2014,1,1)\n", "t_final = datetime.datetime(2014,12,31)\n", "\n", "\n", "start_date = t_orig.strftime('%d-%b-%Y')\n", "end_date = t_final.strftime('%d-%b-%Y')\n", "stn_no = figures.SITES['Neah Bay']['stn_no']\n", "obs = get_NOAA(stn_no, start_date, end_date,'hourly_height')\n", "tides = get_NOAA(stn_no, start_date, end_date, 'predictions')\n", "res_obs_NB = residuals.calculate_residual(obs.wlev, obs.time, tides[' Prediction'], tides.time)\n", "\n", "fig,ax = plt.subplots(1,1,figsize=(20,5))\n", "ax.plot(obs.time,res_obs_NB)\n", "mean = res_obs_NB.mean()\n", "\n", "#ax.plot([obs.time.index[0], obs.time.index[-1]], [mean,mean],'--b')\n", "ax.set_title('Time series for forcing and observed residuals')\n", "print 'Annual mean residual', mean" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Annual mean residual -0.00449372146119\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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EkAaEohAhhBBCCCGEEEJIA0JRiBBCCCGEEEIIIaQBoShECCGEEEIIIYQQ0oBQFCKEEEII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\ntHYI+vcHHn1UVFNt0LVjpBWsKwolGSgMHep1UnP59heDVoi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Hj47fH7a26qDsK1dS38D1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\nnzsX+OST4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vvTpWkpdMMNwB13FP79MFFo551zX9tmG2DaNPfnv/gC+POfaUL94ouFn5OJ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\n+zpefxnAy4X+nkn//mkcRRAEQ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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If 2014 is a high water level year, then why is the mean residual negative?" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "2013" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from salishsea_tools.nowcast import residuals\n", "\n", "t_orig=datetime.datetime(2013,1,1)\n", "t_final = datetime.datetime(2013,12,31)\n", "\n", "\n", "start_date = t_orig.strftime('%d-%b-%Y')\n", "end_date = t_final.strftime('%d-%b-%Y')\n", "stn_no = figures.SITES['Neah Bay']['stn_no']\n", "obs = get_NOAA(stn_no, start_date, end_date,'hourly_height')\n", "tides = get_NOAA(stn_no, start_date, end_date, 'predictions')\n", "res_obs_NB = residuals.calculate_residual(obs.wlev, obs.time, tides[' Prediction'], tides.time)\n", "\n", "fig,ax = plt.subplots(1,1,figsize=(20,5))\n", "ax.plot(obs.time,res_obs_NB)\n", "mean = res_obs_NB.mean()\n", "#ax.plot([obs.time.index[0], obs.time.index[-1]], [mean,mean],'--b')\n", "ax.set_title('Time series for forcing and observed residuals')\n", "print 'Annual mean residual', mean" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Annual mean residual -0.126170547945\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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CCCGEEEIIIYQQHxSFCCGEEEIIIYQQQpoQikKEEEIIIYQQQgghTQhFIUIIIYQQQgghhJAm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vi5UDa/xx6LVk5Oa6u+TlHdl4YMQVP0q67C60/CWblilAG4V0kFYUVmwYLgAJtCOnbYQQeXFYRy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\nurGJQh1tTltX58+g1tbm/7+IQ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N+3YEs2ChGRG4AO4O7eVEZVV6vqu7ClHj+plr8FOaqq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\nRK5CYII=\n", "text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2013 was a low water level year. Mean residual is negative" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Means - residuals" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res={}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Annual Means" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 1,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,12,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " \n", " start_date = st.strftime('%d-%b-%Y')\n", " end_date = ed.strftime('%d-%b-%Y')\n", " stn_no = figures.SITES['Neah Bay']['stn_no']\n", " obs = get_NOAA(stn_no, start_date, end_date,'hourly_height')\n", " tides = get_NOAA(stn_no, start_date, end_date, 'predictions')\n", " res_obs_NB = residuals.calculate_residual(obs.wlev, obs.time, tides[' Prediction'], tides.time)\n", "\n", " mean = np.mean(res_obs_NB)\n", " means.append(mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),mean)\n", "print 'Cummulative mean', np.mean(np.array(means))\n", "\n", "res['annual'] = means" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Jan-2005 to 31-Dec-2005 Mean: -0.0203574200913\n", "01-Jan-2006 to 31-Dec-2006 Mean: -0.0138042237443" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2007 to 31-Dec-2007 Mean: -0.0747854279651" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2008 to 31-Dec-2008 Mean: -0.0826446948998" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2009 to 31-Dec-2009 Mean: -0.0664906392694" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2010 to 31-Dec-2010 Mean: 0.00672659817352" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2011 to 31-Dec-2011 Mean: -0.054851369863" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2012 to 31-Dec-2012 Mean: -0.0132654826958" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2013 to 31-Dec-2013 Mean: -0.126170547945" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jan-2014 to 31-Dec-2014 Mean: -0.00449372146119" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean -0.0450136929762\n" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Winter (Nov-Jan)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 11,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr+1,1,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " \n", " start_date = st.strftime('%d-%b-%Y')\n", " end_date = ed.strftime('%d-%b-%Y')\n", " stn_no = figures.SITES['Neah Bay']['stn_no']\n", " obs = get_NOAA(stn_no, start_date, end_date,'hourly_height')\n", " tides = get_NOAA(stn_no, start_date, end_date, 'predictions')\n", " res_obs_NB = residuals.calculate_residual(obs.wlev, obs.time, tides[' Prediction'], tides.time)\n", "\n", " mean = np.mean(res_obs_NB)\n", " means.append(mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),mean)\n", "print 'Cummulative mean', np.mean(np.array(means))\n", "\n", "res['winter'] = means" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Nov-2005 to 31-Jan-2006 Mean: -0.00138224637681\n", "01-Nov-2006 to 31-Jan-2007 Mean: -0.0257413949275" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2007 to 31-Jan-2008 Mean: -0.0701576086957" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2008 to 31-Jan-2009 Mean: -0.113500905797" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2009 to 31-Jan-2010 Mean: 0.0591557971014" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2010 to 31-Jan-2011 Mean: -0.0050303442029" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2011 to 31-Jan-2012 Mean: -0.132290307971" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2012 to 31-Jan-2013 Mean: 0.00279528985507" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2013 to 31-Jan-2014 Mean: -0.217135869565" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Nov-2014 to 31-Jan-2015 Mean: 0.00438677536232" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean -0.0498900815217\n" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Summmer (Jun-Aug)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = []\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 6,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,8,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " \n", " start_date = st.strftime('%d-%b-%Y')\n", " end_date = ed.strftime('%d-%b-%Y')\n", " stn_no = figures.SITES['Neah Bay']['stn_no']\n", " obs = get_NOAA(stn_no, start_date, end_date,'hourly_height')\n", " tides = get_NOAA(stn_no, start_date, end_date, 'predictions')\n", " res_obs_NB = residuals.calculate_residual(obs.wlev, obs.time, tides[' Prediction'], tides.time)\n", "\n", " mean = np.mean(res_obs_NB)\n", " means.append(mean)\n", " print '{} to {} Mean: {}'.format(st.strftime('%d-%b-%Y'), ed.strftime('%d-%b-%Y'),mean)\n", "print 'Cummulative mean', np.mean(np.array(means))\n", "\n", "res['summer'] = means" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "01-Jun-2005 to 31-Aug-2005 Mean: -0.00722644927536\n", "01-Jun-2006 to 31-Aug-2006 Mean: -0.0194234601449" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2007 to 31-Aug-2007 Mean: -0.0209270833333" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2008 to 31-Aug-2008 Mean: -0.0134035326087" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2009 to 31-Aug-2009 Mean: -0.0190851449275" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2010 to 31-Aug-2010 Mean: -0.0519796195652" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2011 to 31-Aug-2011 Mean: -0.0291055253623" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2012 to 31-Aug-2012 Mean: -0.0150751811594" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2013 to 31-Aug-2013 Mean: -0.0337423007246" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "01-Jun-2014 to 31-Aug-2014 Mean: -0.0187527173913" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean -0.0228721014493\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "fig,axs=plt.subplots(3,1,figsize=(10,10))\n", "\n", "\n", "yrs = np.arange(2005,2015,1)\n", "key='Neah Bay'\n", "\n", "ax=axs[0]\n", "ax.plot(yrs,means_ann[key], c='b',label ='mean wlev')\n", "ax.plot(yrs,res['annual'], c='g',label ='residual')\n", "ax.set_title('Annual Means')\n", " \n", "ax=axs[1]\n", "ax.plot(yrs,means_winter[key], c='b',label ='mean wlev')\n", "ax.plot(yrs,res['winter'], c='g',label ='residual')\n", "ax.set_title('Winter Means')\n", "\n", "ax=axs[2]\n", "ax.plot(yrs,means_summer[key], c='b',label ='mean wlev')\n", "ax.plot(yrs,res['summer'], c='g',label ='residual')\n", "ax.set_title('Summer Means')\n", "\n", "x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n", "\n", "for ax in axs:\n", " ax.xaxis.set_major_formatter(x_formatter) \n", " #ax.set_ylim([1.7,2.4])\n", " ax.legend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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PamjrpoL9R/ZTOKSwWThreh2dO5pdh3YlDVq7q3czbsi4Vnu1+vr1bQpiIiIi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\n0R7pL8AdeQwY0Pq66+qgtvb4o6bm+HB1NRw6lPQnQTrqu99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kREFMREREJCEKYiIiIiIJ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\n", "text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Comparing to 10 yr mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Centre both residuals and wlev about their 10 yr mean." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig,axs=plt.subplots(3,1,figsize=(10,10))\n", "\n", "\n", "yrs = np.arange(2005,2015,1)\n", "key='Neah Bay'\n", "\n", "ax=axs[0]\n", "ax.plot(yrs,means_ann[key] - np.mean(means_ann[key]), c='b',label ='mean wlev')\n", "ax.plot(yrs,res['annual']-np.mean(res['annual']), c='g',label ='residual')\n", "ax.set_title('Annual Means')\n", " \n", "ax=axs[1]\n", "ax.plot(yrs,means_winter[key] - np.mean(means_winter[key]), c='b',label ='mean wlev')\n", "ax.plot(yrs,res['winter']- np.mean(res['winter']), c='g',label ='residual')\n", "ax.set_title('Winter Means')\n", "\n", "ax=axs[2]\n", "ax.plot(yrs,means_summer[key]- np.mean(means_summer[key]), c='b',label ='mean wlev')\n", "ax.plot(yrs,res['summer'] - np.mean(res['summer']), c='g',label ='residual')\n", "ax.set_title('Summer Means')\n", "\n", "x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n", "\n", "for ax in axs:\n", " ax.xaxis.set_major_formatter(x_formatter) \n", " #ax.set_ylim([1.7,2.4])\n", " ax.legend(loc=0)\n", " ax.grid()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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WEBZ344am9sh+lCnqybLuI40OJ13r1rw8s+qsY9hf/Rj9/Y9Gh5OiO3eg1Zu7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gJh2EZ07bJ/VQNSAr9j+8S8EvCLsM+n2DoUHwaq2tclxjbCbw+2/jrDsY9+dMcvZ+Vt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\nH/S3ZEoOT+QetGoom7adZkjsZNq29Y7pdHylva2mu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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The curves are practically aligned. So I think we are doing the right thing. \n", "\n", "The 10 year mean water level at NB is 1.85cm. 2014 is 1.92cm\n", "\n", "The 10 year mean residual is -4.5cm. 2014 is -0.04cm.\n", "\n", "\n" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Point Atkinson" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "start = '01-Jan-2005'; end = '31-Dec-2014'\n", "\n", "wlev_10yr = figures.load_archived_observations('Point Atkinson',start,end )\n", "\n", "tides,msl = stormtools.load_tidal_predictions('Point Atkinson_atide_compare8_01-Jan-2005_31-Dec-2018.csv')\n", "\n", "mean_10yr = wlev_10yr.wlev.mean()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "means=[]\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 1,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,12,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " mean = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]['wlev'].mean()\n", " means.append(mean)\n", " print '{} Mean: {}'.format(yr, mean)\n", "print 'Cummulative mean:', np.mean(np.mean(means))\n", "means_ann['Point Atkinson']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2005 Mean: 3.12569015056\n", "2006 Mean: 3.14218138097" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2007 Mean: 3.09581797367" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2008 Mean: 3.07026255708" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2009 Mean: 3.08191598947" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2010 Mean: 3.15423371867" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2011 Mean: 3.11629235348" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012 Mean: 3.15143297909" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2013 Mean: 3.05776696807" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2014 Mean: 3.15729541032" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean: 3.11528894814\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "means=[]\n", "means_res={}\n", "msl=figures.SITES['Point Atkinson']['msl']\n", "for yr in np.arange(2005,2015,1):\n", " \n", " st = datetime.datetime(yr, 1,1); st=st.replace(tzinfo=tz.tzutc())\n", " ed = datetime.datetime(yr,12,31); ed=ed.replace(tzinfo=tz.tzutc())\n", " \n", " wlev = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]['wlev']\n", " time = wlev_10yr[(wlev_10yr.time <= ed) & (wlev_10yr.time >= st)]['time']\n", " \n", " residual = residuals.calculate_residual(np.array(wlev),np.array(time), np.array(tides.pred_all)+msl, np.array(tides.time))\n", " mean = np.mean(residual)\n", "\n", " means.append(mean)\n", " print '{} Mean: {}'.format(yr, mean)\n", "print 'Cummulative mean:', np.mean(np.mean(means))\n", "means_res['Point Atkinson']=np.array(means)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2005 Mean: 0.0348834322492\n", "2006 Mean: 0.0514537538074" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2007 Mean: 0.00610295626789" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2008 Mean: -0.0200130471461" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2009 Mean: -0.0086933961314" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2010 Mean: 0.0645384469498" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2011 Mean: 0.0270743274954" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012 Mean: 0.061097693635" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2013 Mean: -0.0325515863569" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2014 Mean: 0.06773889905" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Cummulative mean: 0.025163147982\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "fig,axs=plt.subplots(1,1,figsize=(10,5))\n", "\n", "\n", "yrs = np.arange(2005,2015,1)\n", "key='Point Atkinson'\n", "\n", "ax=axs\n", "ax.plot(yrs,means_ann[key] - np.mean(means_ann[key]), c='b',label ='mean wlev PA')\n", "ax.plot(yrs,means_res[key]-np.mean(means_res[key]), c='g',label ='residual PA')\n", "\n", "key = 'Neah Bay'\n", "ax.plot(yrs,means_ann[key] - np.mean(means_ann[key]), '--r',label ='mean wlev NB')\n", "ax.plot(yrs,res['annual']-np.mean(res['annual']), '--k',label ='residual NB')\n", "ax.set_title('Annual Means')\n", " \n", "\n", "x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n", "ax.xaxis.set_major_formatter(x_formatter) \n", " #ax.set_ylim([1.7,2.4])\n", "ax.legend(loc=0)\n", "ax.grid()\n", "ax.set_ylabel('[m]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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RcOHhTVybjuOCwzIa5mpAr2K9yJw4c5TP6RfoR7FZP3BpU0V2Dx5M0YL+ECNG\nxAdqmmYycw9spt2KATz9/TSJE1tPgvfejh3QoM8+HBrWZ+Q/uZh68CQnnz/HyUmvjR+S2/GNtFzb\nhrqBa1k2ssQXvVUyykTA1RXWrYO8eXEdVJPsqiabhrU2Q+NaVLR1G86qLY95uWQqPj7exIkTB4cw\nn93/x2bWwVNKVVZKXVZKXVNK9f1MncnB+88opfIHl6VVSu1VSl1QSp1XSoW/TLyVCwiA5cuhYEH4\n5bcz+FRujH/LwpQqkpBLnS4ys/rML0ruAJwdndnYbBWxSsyiRs+tPPfSyZ2mmcK7kItPhkNEGLBt\nGDVcBlllcgdQsSLM/bU0rFvE74XPkvjtW2b3DfNHtN2afXgFLdf8TCPZbL7kDnj1+jWl/fwIXLsW\ngC4lW7Hz+XwCA83TvhY5IsLqy27U/7YxSkG/fv2YPHmy+YOwxAdwBK4DGYAYwGkge6g6VYEtwX8u\nAvwb/OeUQL7gP8cDroQ+VqLBGDwfH5GpU0UyZDRIrh/3SIHxleSbcd/I6H9Gi9c7ry8+b1hjFw7c\nOSBxhiSXkjVufH52VzRnK2M2okr32zr88MMPsn379gjruR3eKQ5dvpXHTwK+qB1z9nvePJGkJVdL\nncrxJLmTo7x4+tRsbYdmTdd78v754tQ3lbQZcObzY+6MJKx+53Z1lX/q1hUREb8AP3Hqn1zmrLti\n2kDMzJqu95c4cveEOHTPKNevB60rmSFDBjl37lyEx2EjY/AKA9dF5LaI+APLgZqh6tQAFgKIyBHA\nRSmVQkQei8jp4HJv4BLwjflC/zovX8KwYZAho4GFR9cSp3NR/Cq255fS9bjZ5Sa9i/cmQcwERm2z\nRLoSDKs4gJNZ6jJk+BsgKLn38/MzajuaZo8OTZ/OzStXKFu2bIR1e28cTpW4A0iR3NEMkX2dVq1g\nUN26HAycSvakil69o/XDEqMYtWc6PTb9ys8x9jB7eJ7P37kLCMDb29skMdRu0oT1GTMCEMMxBqVc\nmjJ29wKTtKV9mZGbl/LNi8a4uiou7d+P4elTcubMad4gjJUpRvUD1ANmh9huCkwJVWcjUCzE9i6g\nYKg6GYA7QLww2ogwWzanO3dEunYVcUn6Top3niMZxmWVwrMLy5qLayQg8Mt+m48Kg8EgtZc0ltiN\nmsv27QaZOG6cdOzQweTtappNCwyUKvHiyczOnSOsuub4fnHolkkePPI3Q2DG89tvIimrjJJv+qWR\n+173LR2ucANMAAAgAElEQVSOxQzeNkZi9MooXYbcDP/O3d69ci1fPkmdOrU8fPjQ6HGcPHlSXF1d\nP6yk8M+1c6J6ppanz0z/74gWsYDAAIk98Bv5dVLQOoej69eXX1xdI3UsNnIHL7KzH0L/fvThOKVU\nPGA10FWC7uRZpXPnoHlzyFfkFafijCFmn0zEK7yKebVn8m+bf6mTvQ6ODqb/bV4pxeL6f5GqwCnq\nj5pJnS17WOnmxrlz50zetqbZqhNjxnDW15eWo0dHWLf7uuGUi9mfb1JGr8kKgwfDT9n64HSnMz8s\nLM9Tn6eWDsmsRIRem4YycsccOsfbz6TfMn7+zt2NG7xs0IBqz58zePBgUqVKZfR48uXLR0BAABcu\nXACgeOZcuDil4rfFO43elhZ1Wy/tw9cjOV0aB61zuHnvXqrVqGH2OCz5U+YBEHLF4bTA/QjqpAku\nQykVA1gDLBGR9Z9rpGXLlmTIkAEAFxcX8uXLR5kyZQBwd3cHMMm2CEye7M6yZXDzaXZytp5EQKxp\nOMYsyNamm8ifKj/u7u7su7PP6O2/L/vc/u2t15LPpxgdl9anEQfp0rkze/buZd++fSb7+zDH9sSJ\nE812fa1p+32ZtcRjV9fbYGDyH3/Qp2VLDv/7b7j1R82dwd1rp9g/buNXtf++zJz9VQp+/NGdy2MK\nc+XMayouqsRw12HEc45n89e7dOnSdFzfl9luq6mfcCzjxqX5fH1vb4r17k0dFxfy5c9P1qxZec+Y\n11spxXfffcfs2bOZNGlSUJwUZ9HuUUzrXtmsfz+2dr2NsT1muxtpbhfl/Hl3ShYuTEwPDxwLF8bd\n3T3M6+vu7s7t27cxOmPdCozqh6Dk8gZBj1idiXiSRVH+m2ShgEXAhAjaiNQtUWMKDBRZu1akaFGR\n9PmuS6nR7SXRyETSYVMHufHyhlliiMzg1PWXNkis/qnlTNpSkvubb2TVqlWmD8zEovug3C+l+205\nhrVrZViqVOLj7R1hXdeBNaRU7ylf3aYl++3vL1KnrkFcO3aVIrOLymvfz79yydgs0e9AQ6C0WNFB\nYnb+Tgb98Tz8yv7+YqhUSVpkyya1atWSgADjPC79XL/9/Pw+2n72+qWoAQnkwPEXRmnX0qzh/+8v\n8db/rTgNTCTTFwcPZdi5MyghiCSM+IjWYgleUD+oQtAM2OtA/+CydkC7EHWmBu8/AxQILisBGIKT\nwlPBn8phnD/Sf6lf6907kTlzRLJlE8lR7oQUG99AkoxKIgN3D5Qn3k/MFkdU9Nw8SL6rV1C2xXWR\n9GnT2uwbLjTNZEqWFFm3LsJqey6eFtUrlVy99cYMQZnWu3ciDUs/kJpNa8oPC3748A5sWxMQGCD1\n3VpKrA7FZegoz4gP2LBBbhcvLhUrVBDvSCT8RnHvnsikSR82cwz+Scr2/fpfIrQvN+fgGnFqU1Y+\n/HPau7fIsGGRPt6YCZ5e6PgreXnBrFkwcZKQuvgepPgoHgdepHvR7rQt2Jb4MeObtP2vEWgIpMTM\navwy6j5ky8pPm5bj7Oxs6bA0Lfp48QISJSLsF4/+J/vgBiR6U4RDY3uaKTDTert2C83r/8S5X8qQ\npZiwtuFaYjjazhqb/oH+1HVrxq6DL/g163r694wbyQPNvJC8hwdkyAAPH0LcuMw/sIO2y/vjM+EE\n+ke5ZeQdUZe4j6pwaOrPQQUBAeDrC3Ej9x2ymYWOo7NHj6BfP8joGsjGG6tI1KcQ3qU70bFUI252\nvUnPYj0tltyFfLYfHkcHRza1dGN0Qy8OPstIQED0/okQ2X7bGt1vC0qSJMLk7vC1S1zx3cfcX9oZ\npUlr6HfsOlVJkyYpCec/4upVRbN1zQg0mHalXXP1+13AO6osqMuu/T4M/XZj5JM7MElyF26/EyWC\nwoWDXj0CNC9eDocET5m2+qzR4zA3a/ieR5XnO08uvN1F72r1/it0cop0cmdsOsGLoqtX4X//gxx5\n3nE08C8SDviWgMLjGVFpEBc6XKBV/lY4O0afRClJnCQs7PY3C39cRKMuF7HhG7qaZhFtl/xBQf+u\nZHeNZ+lQjGrImpXceXsC57ltOXn5OW03tsUgBkuH9VXe+L+hwrwaHD4Qiz/yrKV392jw3u7376Yl\n6Jf2islbMOWf+RYOyj5N27MGp/vlqFHRxdKhBDHWs15r/GDEMXhHjojUqSOSJLWnlBsyUlKMTiVV\nllSRfbf3fViLKDqb+e98ce6ZVSbP+vI3aGiavbh06VKk/r8/cfO6qL5J5PTlSIzhiob+qlBBvk+a\nQlJnfCWZ/ygmXbZ0ibY/D73eeUmh6SUlbpMWMnlq5NYpNNZEiq9xxd1djidIEDQDRkRO370mqk8y\nuXPf18KR2Z8MQ36Q6r1Xf9geOXKkvHz5MkrnwEbWwbN6IrB1K5QtC3WaP8KrUF+kcyZS5jnHjubb\n2NJkC6XSl0KZ6yWEJtSuSEtq5ytLr39acuKEvo2naZ9z//59ihUrhoeHR4R1/7doJHl8O5A3W0Iz\nRGZ+rZcv563XS/o0W8yrmZvZcHY/v+791dJhRdnLty8pObs8F/flYkyJeXTuGMEKYoGB+LVuTeXS\npdmzZ495gvyMY/fvM1QpOHAAgLxpM5PcITu/Lt5k0bjszX2vB9z1O8WA+tUAePToESNHjiRePMvd\nudcJXhj8/cHNDfLlg27DrqFqtMWnVQ6y53nDiXYnWFJnCXlS5LF0mJ/1pWMXFv40iXQ5H1K28/+Y\nPXupcYMyg+g4ZsMYdL/NSIQx1avTukEDEidOHG7V8/fucurtWmb/3NWoIVjT9XZMnJjJmzZhSOTH\ntvUuvJ62g0XH1jDqn1FGb8tU/X7q85Rif5Xl5t5SjP9hGr+0j/ifRenTh1+2bSNOokSULl3aJHG9\nF1G/q1WrhntAAN4h1txrnb8Va2/Oj9ZDbqzpex4Zo7YsJ/6DWhT9Luix/rbFiylfogQxzDnpJhSd\n4IXg4wOTJ0OWLDB+xXEStKnPy9rFKJkvFVc7XWVK1SlkcMlg6TBNJqZTTPa2XcGPDsvp2LkjL168\ntHRImmZVnqxZw+Jz5+g1aFCEdX+eP5rs736mUM4kZojMckpWrEi3bt3Inx/+XpYM7xm7mHRoFtOO\nTrN0aBF68OoBRWeV4uHu2kysOoa2bSPxNGb+fEYtXMipZMlwW7YMR0fLvlPYxcWFosWKsT14oW2A\n/jXr8SbpP2w98NiCkdmX5RfcqJ25yYc3nGyePp1qCS18595Yz3qt8UMkx+A9eyYyZIhI0mQGKd58\nu3w3+QdJOz6tTDg8wawLeVoFg0GeF80vOdPGkALft7B0NJpmVXqnSSOdypePsN7Vhw9F9UskB049\nNkNU1mXbNpHEmW5KipFpZMGpBZYO57NuvrwpacdkkvhVRsmCyIa5f7+sTJBA0qRMKffvW887eadP\nny5Nmzb9qKzA0FZSpMdoC0VkX848vCgOvVPJ1WtBYzJ9376VhErJ4yNHonwubGWhY1N/Ikrwbt0S\n6dRJxCWxv/zQZbnkmJRfckzLIQtPLxS/AL9wj7VpJ0/KrYTxRMVykpmzj1k6Gk2zCs///lsSOTjI\n3Zs3I6xbcnhPydqlixmisk6rVokkzX5Rko1MKasuWN9bci4/uywpR6WVBOWnyqJFkTzIy0t8U6aU\nPBkzyqlTp0waX1Tdv39fEiVK9NHbLVYd3S8OnbOLj0/0nPQSnTScNUhStez+YXv3tGlSKFasLzqX\nMRM8u3xEe+YMNGkCBQq/5WqCmSQcmI13eSczstJQzv1yjuZ5m0frRTu/euxC/vykr1mHJhmS0GFg\nDR49ih4DOaLbmA1j0f02jzgTJrC6Z0/SZswYbr3bz57xj/c8pjftbZI4rP56+/tTrx6M6pUdx+Vb\naL+xA1uubfnq0xqr3+eenKPk3LK82TKUGa060qxZJA9MkADngwc5cfUq+fLlM0oskRGZfqdOnZoJ\nEybg5+f3oazudyWIFcefscuPmjA607H673kwEWHTnaW0yN/kQ1meW7eYWb++BaMKYjcJngjs3QuV\nK0PlWp68zPkHMXpnxDnXZpbUW8jB1gf5MduPOCi7+SsJlxo+nHlPfImX8BUlO0wkIMDSEWmaBT19\nSmxfX34YMSLCqj/PmUjGNw0oVyiNGQKzMmvXsrdcOQwGA61bQ5/m+Ymz4W+arW2B+213S0fH8YfH\nKTu/An4bxzOrfSsaN47iCTJlwskpghm2FtKiRQviOjqCtzcQ9EaEH9O24q+j8ywcmW3bffVf3nrH\noEejAh/Kku7dS4E2bSwYVRCbf1VZQICwbh2MHg0v/B+QpelEjgbMpXrW6vQp3odcyXNZOkzrNXAg\njx18SScLaWzYyoI/vrN0RJpmOSIQwZJIDz08SDM6M5t+PE7VYuHf6bNF8uYNxRMn5n9dutBq9GgA\nhgyBJQf34FWxIZsbb6JImiIWie3g3YP86FYbw9+zmd2zJlZwg8X4OnQAV1foGfRKvOtP75N1Qh4u\n/nyfb13jWDg421RhQmfuXUrO5b+ClwcyGKBPH/jzzy96s4kxX1Vm8wlelixCnHSXSVpzDCffrKN5\n3ub0+L4H6RKms3R41i8gABwdmXdkLW3X9GBhsRM0qZ3U0lFpmtWqOup3zj+4xd3J9vsmgWOjRlFz\n0CAuP3lCgsSJEYFu3WDnnU08L96GXc13mn2Zqd03d1NvxU+wxo05/StSt65ZmzefLVuCEovgNfEA\n0g2oTOGYzVg9pEk4B2pfwj/Qn3iD0zD624N0bZbZKOfU76KNgpRd6/CwUilK5k7Htc7XmFh5os0n\nd0Ybu+DkBErRumhdfsr1E602N+L6DdO+b/JrRJcxG8am+20dnnm9ZrvHFCbW7W/Sdqyt36EV6tOH\nSkmTMuKnn4Cgm54TJkBhl+qkOjWZyksqc+X5lSif90v7vfnqZuqvaAQr1jBvYBSSu4MHGdupEzNn\nzvyido0lSv0uVw7OnYOnTz8UtS/ais0P52OIZm+Rs/bvOcDKE7sIfJGR/9U1TnJnbDaf4NX7rgy3\nut5iSJkhJIlj2+tRmdKCZiPIkNFAicG/8u6dpaPRNPN4+/Yty5Yti1TdtrNnkPJNeeqUzhpxZVum\nFH+6uTF31y6uHTsGgIMDzJkDru8a8s3l4VRYXIHbnrdNHsqqC6totqY1LN3Ewt9LUbt2JA+8fZt1\n1aszcflyqlWrZtIYjSpmTKRiRdiw4UNR9yo18U9ympU7blsuLhs1YZcbBZ2bECf46XdAQAAB1jRg\n3VjTca3xgxHfRauJXH94Q2K2TykVu6yzdCiaZhZTJk+WmjVrRljPw9tHHPqkkCU7z5ohquhhVLNm\n8nOLFh+VvXsnUqGCSJEuk8R1kqs8fPXQZO0vPL1QkvyRUhJ9e1o2bozCga9eydFMmSRp3Lhy/Phx\nk8VnKsWyZpVLpUt/VFZ8REfJ2+U3ywRko7x9vcVxYEJZsenJh7K///5bqlev/lXnRS+TolnCqX1H\nSeMen12x/sfouVctHY6mmZTvkSOM6t2bgQMHRli3/V+zSfq2GE3K5zZDZNFDtzlzmDxjxkdlMWPC\nunXgcKwLKR62ovzi8jx/89zobc88PpPeWwdiWLCHJePyUr16JA8MDORu7drUevyYOUuWULBgQaPH\nZmr5S5dmnadn0KSgYL/VbMVZxwV4ekWz57RWbKb7BpwefU/dysk/lG3evJkyZcpYLqhQdIJng0wy\nduHJE+oOGEDqJMmp/KoiA87U5uhpb+O38xWiw5gNU9D9No1Fv/xCzowZKVSoULj1vN/6svrRGP6s\nGnEiaAzR5Xo7OzsTO3bsT8rjxoXNm+H11gEkfvYjlZZUwuudV4Tni2y/xx8ez++7RxEwx51lk7NT\ntWoUgh44kA6nTtFzyBBq1qoVhQNNJ6rXu3bDhqxzdv5oxne5HAWI7xyf4Uv2GTk607H27/msQ26U\nSdqY92+qE4OBLYsXU614ccsGFoJO8LTISZECVawYk3Lm5PjaXRRKlo9yk9vw6pXtzsLW7FfAyZP8\nefYsg6ZMibBux9kLcPHNQ+vK0e9uj6UkSgQ7dygeu/1JnBffU21pNXz8fL7qnCLCsH3DmHBgJr6z\n9rN8hiuVKkXxJGXL4nb8ON17m2aRanMoVaoUN27c4P79+x/KlFLUy9yKRWftd3a3MT3zec51/wMM\nrPvfLwHnNm0ihp8f2QoXtmBkH7P5ZVJsuX9md+cOFCjAL9WqoRLEY1XCI6R+2YRT03tEtDyYpkUr\nq77/nunPnrH3+vVw67319SfBoKxMKrmUDjW+N1N0tuPOHShR0kD6Lq2Jk+IhGxptIJZTrCifR0To\nv7s/K09v5tXUnayYm5Jy5UwQcDTRvHlzihQpQseOHT+UPfR8RprRWTjW+C4FcyWwYHTRX++VM5i1\nbT9ec5d9+Lfvz2rVeHjnDlPOn/+qc+tlUjTLSJ8e/vc/hgUGsnrlalbVncIFl9F0HR99bvtrWoQu\nXKDOjRss37Ejwqpd57oRzy+TTu4icvIkgXPnflKcPj3s3OHA9fFzeP08AT+t/gn/QP8ondogBrpu\n68q6M7t4NcmdVfPtO7kDqFOnDleufLwUzTcuyXB1+IFBy1dYKCrbseSMGzUzNvnoxsbL8+ep2aCB\n5YIKi7Fma1jjBzudRbt3717TndzTUyR5cnmyZ4+IiCw4sF0ceqeSv/fcN12bkWTSflsx3W8j279f\nZN68CKv5+gVIjB5ZZOzqPaaJ4zOi4/U2XL8uRZyc5NyuXWHuP3lSJGkKXyk0sYo0XtNYAgIDPqkT\nVr8DAgOk9frWkmNcMUmS2lPc3Y0dueUZ83pP3bFBYrT/Xvz9jXZKk7HW7/nVp7dE9U0ql6/5/lfo\n6SkSL56It/dXnx89i1azmIQJYcECkqdPD0CLEhVplLkj9VfX4+ETvwgO1rRooGRJaNUqwmq95q8i\nVmByutcuY/qYojnl6krTH36gW9Om73/5/kj+/LB2lTM3/1zDpfsP+GXzL2HWC8k/0J9m65px8uZt\nno7bzrplCSldOgpBGQycdHNjyJAhUexNNPH2LbRtS8gVjtuVqwIut5j792ULBha9DVu/lBTP65Et\ns/N/hXv2QIkSQTOIrIgeg6d9NYMYyDakNr5P03Jr2tQPs4o0zVYFBBqI1zsPvxYZy8CGlS0dTrQQ\n4OlJvmTJGDF8ODX79g2zzrZt0OznV6ToXZ5K35ZkbMWxqDAG+PoG+NJwdUMePvbn1qjVrF8dm6hO\nXrzfoQNF581j0pIl1K1X70u6ZP2yZ4dFiyDETPAKY3pz744jl6eOtGBg0ZOI4DIwFz+nmMW4riX+\n22EwgKcnJE781W3oMXiaVXFQDvzbZxHPE+6g/vDFlg5H00yu/8K/cSQW/etHdZqm/XJycWFiz570\nGDIE37dvw6xTuTJMn5CA5xO3sfnyTobuG/pJnTf+b6i5vCbPnjhxa+Q6NqyNenL3etYsqs+dS5e+\nfW03uQOoVSto4cEQhtVpxdXYi3jyzIreuBBNHL51ltfvfOjXpNjHOxwcjJLcGZtO8GyQJdYPShw3\nAZubr+Xvtz2Yse602dsH6183yVR0v413viVLlkRYLzBQmHZ2ON0LDMLBwfzTx6Pz9S7/55/kTpyY\nib/99tk69evDiEGJ8Zmxk0WnljLu0DggqN+vfV9Txa0Kvi+Tc/WP5Wz625nvozi/JWD/fhp16UKh\nH3+kdzhxWIuvut61a8P69R8VFXXNQWLHdAxZtP3rAjMxa/yej9jghuubRiRLGj1Sp+gRpWbVtm7d\nSvPmzSmbMxcDC0yh84G6nLv+0tJhaVqkyfPnDBowAMdIjC/4zW0r4ujP0MY1zBCZjVGKcQcOkCeC\n1f7btIHubVOgFu9i0r9T+OvEX7z2fU35xeWJ5Z2dCyMWsGWTE0WKRLH9O3cYVa0avjlyMH3ZsjAf\n/9qSeylSsPTRIwg1o7ZprlasuKrXxIsKgxjY/XQZ7Yo1+ah8/fr1nDt3zkJRRcBYszW+5ANUBi4D\n14C+n6kzOXj/GSB/FI/9qtksWiTcvi0+S5ZI2rRpZd++fSIiUnxYN3HpWEXevgu0cHCaFjl7q1aV\nLEmSSEDAp7M3QwoMNEicLkWl57zlZorMvv36q0j24tck1ZhvJNOkTFJ9SndJltwgX/yK2IMH5cXo\n0eLh4WHUOK3VnTt3JEmsWOL/xx8flb/08RTVP6Hs+feZhSKLftaccBeHjnnEx+fj8nz58smBAweM\n1g62MItWKeUITA1O1HIAjZRS2UPVqQpkFpEsQFtgRmSP1cxEhDhduzJm4EC6dOlCYGAgu/uOxiGW\nN2WG/G7p6DQtYrdvM3zHDgb89luEd/D+XL6XwBge/NnMhsdtWZGhQ6F8gcyk3LmTkg69OTp8HNu3\nKb74FbHFipG4d29cXFyMGqe1SpcuHemzZuVA3rwflSeKk5CcMarx25qlFoos+hmzzY38jk2IE+e/\nsgcXL3L39m2KFi1qucDCYclHtIWB6yJyW0T8geVAzVB1agALAUTkCOCilEoZyWPtllnHLmTIAK1a\n0eDECRImTMicOXOIGSMGB7ut5LhhDv3nbTZbKNY4ZsMcdL+/zuFu3bgeNy5N2rULt54IjPp3GP/7\ndgAxnCw3VdyerrdSMHEi5E6Zg3UDv2XHdkX+/JaOyry+9nrXrl+f9ds/HW/Xp2JrDr6Zj5+Vrm5l\nTd9z3wBfjvmsoUfFnz4q3zpqFBUTJMDJyclCkYXPkgleauBeiO37wWWRqfNNJI7VzGXAANT69Uzq\n0oXBgwfj4eHBt2lSMrviSkZdacXO4zcsHaGmhe3ePdZv306/QYOIESNGuFXHr/6HdzHvMLZFIzMF\nZwf8/ODmzXCrODjA/PmwZAmEuhGlRUKtWrVYv379J+sKNilWFqd4HkxaecpCkUUfs9234vg8Fw0r\np/uofPPOnVSrUsVCUUXMkmlnZBeo+6pRsC1btiRDhgwAuLi4kC9fPsoED/B9/xuC3jbC9oABeI4Z\nQ6tWrQgICJp+n9HJjzLejajhVpe7mQ9x4fRRk8bzvswq/j70tsm335d91fkmTmRk585Ijx7h1heB\nIUt7UCFjbWIGJ4KW7r8tbMvRoxwdO5ZOV69y7PTpz9Z3cID48aN+vd++fMnJS5fo27cv//zzj8X7\na4nt0qVL4+zszF9//UW2bNk+7N+/bz/feZdm+qH59G6a32riNer/30bann7AjVyvv+PAgf/i2bF5\nMzsePeKvrl2/6vzv/3z79m2MzWILHSuligK/iUjl4O3+gEFERoWoMxNwF5HlwduXgdJAxoiODS4X\nS/XP7vj5QcGCsGEDZMz4odhgEDL3aQYoro9eZJFlJTTts3btgjx5IHnycKtNX3+crodq4zXsOnFi\nxjRTcHZAhMbffEOWPHkYGsZjxK8ReOsWdXPkIFH16sxbudLmZ8yG59ChQ2TNmpWkSZN+VH7hwS1y\nTy7MjU73yZhWf6/D4vXuFYmGpWX3j7coW/S/te58N29mb69eVL50yajt2cpCx8eBLEqpDEopZ6Ah\nsCFUnQ1Ac/iQEHqKyJNIHmu3Qv5mYDbOznDq1EfJHYCDg+LIoL94FHiWJpOmmTQEi/TbCuh+f4Xy\n5SNM7gAG7xpOo/R9rCK5s6nrrRSj3NyYtnMnd06eDLdqlPrt40OfIkV4lTo1s9zconVyZ4zrXaxY\nMZI6O8Pdux+V50ydkZQOufh18cavbsPYrOV7PnrTWuI+K0OZIh8vZBxzxw4qN21qoagix2IJnogE\nAJ2A7cBFYIWIXFJKtVNKtQuuswW4qZS6DswCOoR3rAW6oYX0mYGmyVzisKHJWlY8/p15Ow+ZOShN\n+zrzNp/FM+4RprX+2dKh2KS0P/xAlyJF6FO/vnFOaDAws0QJNvv5seboUZydnY1z3uhuxQro0+eT\n4jYFW7P+9nz0w66wLTrlRrW0Tfjkd4SMGYMWkrZi+l20mtn0m7eJsZfbc77Lcb5Nk9LS4WhapKTo\n+BOlsxZkZdfelg7FZr15+JDsadOyeMECSjVr9lXn2tesGQ1XrODg6dO45shhpAhtwJMnkC1b0H9D\n3In28XtDgqGpWVf+PDXK6rmKId31eESG0Tm40Poh2bPENkubtvKIVrNxV69eZenSpR+2R7auTkGH\nNhSf2ABff38LRqbZu86dO7Nr164I6y3dcZnn8fcw6+dfzBCV/YrzzTeMnjSJzV/7RgAPDwpcvcr2\n7dt1chdaihSQKxfs2fNRcVznOBSIVY/hG/V7xEMbtnYFyV7WNFtyZ2w6wbNBVjF2QYQYInTu3Jn7\n9+9/KN43dAiGd/EoP6qv0Zu0in5bgO531NxeuJClbm589913Edbt/fef/Ji8C4nixvuitkzBVq93\ng44dGTV69Gf3R6rfiRIR/99/yVu2rPECszBjXu/AGjXwXbPmk/JB1VpxwjAfHx/reeJlDd/zNdfc\naJC98Sfl71eKsHY6wdNMY8YMMk6cSIcOHegTYtxHrJgO7O+2hMMe6xm8crkFA9Ts0rNnjGzblvZN\nmkT4NoO1e2/yOMFm/vq5k5mCs29GmwgRjSdUmFr7EydYsHIlGAwfldfI/z2xYylGLT1socisz8m7\nV/E03GdQkx8+Kn/9+jVp0qTB19fXQpFFnh6Dp5nGy5eQLRs+27aRvXZt3NzcKFmy5Ifd09edovOR\niuxqtpeyOXNZMFDNntzv1Ik8c+Zw5d49kiVLFm7d9B3bkTNDcrb0Hmam6DTNtFauXMn8fv3YevIk\nhPoFp8mMUew9c52HM2dbKDrrUmvSEM5e8eLm9Ikfla9bt47p06ezc+dOk7Srx+Bp1i9xYujXj7hD\nhzJ69OgP76l9r0Pt/NSIPY5qi+rwwtvLgoFqduPlS8bOmUOrpk0jTO42/3OPewlWMfvnrmYKTvtS\ny5Yt4+3bt5YOI1qoUqUKB58/xyuMu5zD6zfjcaI1XLjqY4HIrIuIsOPRUn4u0uSTfZuHD6daruhx\nUzVM0RYAACAASURBVEIneDbIGsYuANCpE5w/T8PkyUmTJg0XL178aPeaX5uT9FUFvh/dAoMYPnOS\nyLOafpuZ7nfkyPjxeKZLR69hEd+R67xsDGUTtiF1oqQR1jU3u7jeBw7w8KeP3/sZVr9nV6/OkO7d\nefPmjZkCMz9jXu/48eNTqlQptm7d+sm+jEm/IZ3D9wxa+ukYPUuw5Pd869lj+L5TdKv/8ThdCQxk\ny+nTVPvxRwtFFjU6wdNMJ2ZM+PNPVO/ebFi/nty5c3+028EB/v1tAndePKHF7JEWClKzCz4+qJkz\nWbBtG6lSpQq36u4jj7mdYAlz2vQ0U3BaaFKgAJXWrmVrOJMudg4YwKCtW9m8fj1JkiQxY3TRW61a\ntVi3bl2Y+zp834qtj+eHHqJnd0ZucSO3akycOB/f6Ty9dClxnZzI8sMPnznSuugxeJppicC8edC8\nOXzmZe4b3B9Qe0sh5tVYQIsSFc0coGY37t6FdOkirJatU2+Sf+PLgQGTzRCU9jmb+val16RJnPP0\nJEasWB/tu7B8OWUbN2b13LmUatXKQhFGT0+fPqV9+/asXbv2k33v/H2JNyQNC4ofoWm1TBaIzvIC\nDAHEHpiG2cUO0PLHLB/tW1WvHmcePGD4YdNNRjHmGDyd4GlWocv4fcx41pDz3f4lW4oMlg5Hs1MH\nTjyn9OqsXOpyhmyp0lo6HLsmBgNVkialcvnydFu58kP509OnKfLddwzt1InmEyeGcwbtS5T+syse\nj1w4O3mopUOxiLnuO/hl1SDeTj6Ko2OonQULwvjxULq0ydrXkyy0cEXHMTqTupcmp2cfSkyux7uA\nd190jujYb2PQ/Tae9gsmUShuPatO7uzleisHBybMmcOI1at5du3ah37HHzSI0Q0b2k1yZ7Lr3bw5\nhFij9L3farXifIwFeHha9jmtpb7nk/a6UTxBk0+Tu8eP4eZNKFbMInF9CZ3gaWYnIh8tfgxBS1ft\nH9Ud30euVJrUEX3nVTMWH5/IzQo8etaTS3FnMKdlPxNHpEVW9jp1aFK6NL8NH/6hLPbixdRfssSC\nUdmQv//+pKhs9nwkiJGY3xfvCeMA2+bj94YL/hvo/2PDT3emSAEXL352qJE10o9oNbM7fPgwTZo0\n4eLFi8QKNbbm2Blvvp9bhIHluzK0RlsLRajZCm9vb7Jly8bJkydJkSJFuHXzdRmOQ9JrnBy80EzR\naZHh6emJl5cX6dOnt3QotmXtWpgxA8JYz63tvMmsPXKE57PcLBCY5fy+dgWjd87l9fQdFlsvWz+i\n1aKv9ev5PkkS8uXLx7hx4z7ZXShvPP7Iu5YRhwey9+pRCwSo2Qxv7/+zd99hTZ3tA8e/h6GCgiAq\nigvFiYrUjVZx4cAF4h5vHe2rrdu2at22ddS6auvbarW4tW5UxDqjVsWqdVtXFbfgAEQRGXl+f4D8\nUAJECUlIns915WpOzp1z7puk+HDOM1jk60vDBg2ybNydufScczYLWPyfr/SUnKQtBwcH2bjLCa1a\nwfHjEBmZbtfXnXvy1CmY42ejDJCY4QSeWEMrl14msxiKbOCZIKPuo3P1KowZw5w5c5g3b166W7UA\nowdUonnsYtot70LE80daH9qo685Bsm7NXv74I3NOnGDCxIlZHuuTRb/gbtuM2q6VdZRdzpGft3nJ\nqbpjFYXRRYsiduxIt6+YfWEqWvow8fffc+Tc2tD35x3+7Cm3FBWTu/qn23fp0iWNcwcaO9nAk/Rr\n2DA4fZqyd+4waNAgxowZozEs6Dt/7G715MMfupOozh0LO0tGJDaWpTNmUKd+fTw8PDIN/efaS07l\nncMvPcfpKTlJMjwbGxs2Rkdzfu1ajftHNu2HKiqQRDP59fv1pg0UimyFRyX7dPtWrFjBkSNHDJBV\n9sg+eJL+rVkD8+fzYt8+Kru7s3HjRurVq5cu7Pq/SbjPaE23RrVY+ZGcCFnSXvzs2ZSfNImNKhV1\n69bNNLbByB95Yr+PK1O36ik7STIOo4YNo6CDA5O//jrdvkR1IrbjyzC/5h4+6+JugOz0q+iYxgQU\n/5yfR3R8c4cQeFSqxKJly/DSwwha2QdPyt26dwchyB8czL59+6hVq5bGsPJulgS2XcvaC2sJDE0/\nKackafTyJbGzZ/PFkCFZNu6u3njFcctZLOw2QU/JSZLx8O/Sha3bt2vcZ2VhRTOnPsw/GKjnrPTv\n4t3bPFYuMalnm3T7bqtUPPj33yx/lxgj2cAzQUbfV8XCAmbPhmXLqFixIlZWVhmG9vIvTG/rjQzc\nMZCL4ZczPazR151DZN1vOXoUh4YNGZbJMlev/Xfhclxtq9HCvXaWscZCft7mJSfrbtCgAffu3ePm\nzZsa938b0I/rtqt4EJ6QYzlkRJ+f99TNayn9PIDiRfOk27fzp59oXa4clpn8O2WsZANPMgxvbwgO\n1ip0ydd1KHtjBk1+9ifmVUwOJybles2bw4YNWYbdvJ3AYTGTHzrLq3eSebK0tKRDhw5s3aq5e0Lt\nspUobFGOSSt36Tkz/Qq5u5q+tXpq3Lfz4EHa+vnpOSPdkH3wpFzh4UNwG/EJNRtGcWjIehRTGccu\nGYzPFyu4YhPI7W8OGDoVSTKYO3fuYGtri5OTk8b9n69ZwhLVTqIWbTaZ6UPS2n/xPC1+a0vMN2Hk\nt33rmld0NLuKF6f+tWs4lCihl3xkHzzJ7BQrBlsH/kjo5TAm7Ew/f54kvYu795LYnzCduX5ZT6Ei\nSaasVKlSOFlawl+a5x2d6N+VmCL72RcaoefM9GPatjVUFT3SN+4Adu+mtbe33hp3uiYbeCYot/ZV\nuXTpEr17985wmTKfpvn4svRGvj8ym93X0l91ya11Z5esO5larebcuXNavXfQgo042xcioGbTHMgs\nZ8nP27zope5bt6BHD9Dwu9fBxp7q1h2Yulm/q1roo261UPNn1BqGevfSHJAvH3z8cY7nkVNkA08y\nDq9eUaFCBU6fPk2QhvURX5s2ugy176zCf1VP7j5LP0myZL527NhB3759s1zH+GG4mpDYb/mu7QR5\nq1+SADw8QK2GCxc07h7Tqh/H4gKJizOtLk+rDx9BHWdH/7bVNQe0bw8BAfpNSodkHzzJ8IQALy+Y\nM4e9L18ycOBALl68mG6d2teiosDto5kUarCVC58fJK9VXj0nLBkb8dtv1Js0ibELFtCpU6dMYzuN\nC+JPi68J/+akbOBJ0msjR4KjI0yalG6XWqjJ/1V5prhvYMx/NE9rlRvVnDSI/AllODzDeJYo1Fsf\nPEVRtmvxkCtzS9mjKDB4MHz+OS2aN8fDw4O5c+dmGO7gAHsmj+H2xeJ8tG6EHhOVjFJiIrvHj+eF\ntTV+WYx2e/RIsC3qG6a1klfvJCmtV76+hGcw+txCscDXpS//O2Y6c+K9SoznbMJGxrbTPHpWrVbr\nOSPdy+oWbWVgNjBHw+P167lnAikzkSv7qvTqBfHxsGEDc+bMYe7cuRrXqX2tZk2F2R8uZ8uZ/Sw6\nvgzIpXXrgLnXLVav5pvYWMZPm4aFRea/0ob+8AcFC71iwIcdM40zZub+eZsbfdW97No1Rl27ltwf\nT4NpXT7iTsG1/HsrTi/55HTdPwT/Qd6YKvg2KKNxv6+vL0ePHs3RHHJaVg28CUKIg0IIlYbHQSGE\nCki/xokWFEUppCjKHkVRriqKsltRFIcM4lorinJZUZRriqKMSfP694qi/KMoyllFUTYrilLwffKQ\njMTryY/HjqVciRJMnz6dqKioTN8y5BN7WkdvZnjwl/x9/7SeEpWMSlISB8ePJ8Lenm7dumUa+uSJ\nYNOjb5jSfDwWiux+LElptffzI8TKivg4zQ24ysXLUFz5gAkrM+4jnZv8GroaH+eeGqd+iY6O5ujR\no9SoUUP/iemQwfrgKYoyC3gshJiV0nBzFEKMfSvGErgCtADuASeAHkKIfxRF8QH2CSHUiqLMBNDw\nftkHL7dp3x6aNoVRo7QKj42FygHriW04hiujTuJkq3kuJ8lErVnD0wULCFu4kJoZLHn32n8mHyAo\naSBPv/4HSwtLPSUoSbmHl5cXU6dOpWXLlhr3T9m8mu//WMnzX3bl6jnxnsTEUGRGSU7+519qVi6c\nbv/GL79k6e7dhJw9q/fc9D4PnqIodRRF2aIoymlFUc6nPLSbjyBjHYDX/feWA5o6z9QFrgshwoQQ\nCcA6oCOAEGKPEOL1TfLjQMls5iMZg9mzoWZNrcNtbWHvD115cbITzZZ04H7M/RxMTjI6kZEUmj49\ny8ZddDSsvfct4xuPk407ScqAv78/W7ZsyXD/6HadiHM6wdb9uXsGg283bcHhWWONjTuA4PXr8c3l\nV+9A+2lSVgOBQADQPuXRIZvndhZChKc8DwecNcSUAO6k2b6b8trb+gM7s5mPycjVfVUqVYImTd7p\nLRUrwoo+s7i6uhJVF9Rk5zXz+irk6s87G1QqVfLgnGbNsoz9csFR8hW7wcjmGcx3lYuY9edthvRZ\nt7+/P0FBQRkOMLDNY0Md2y5M27Eix3PJybrXXVyDf3nNvwvUL18ScucObYcNy7Hz64u2q+c+EkJs\ne9eDK4qyByimYdf4tBtCCKEoiqZ7qVneX1UUZTwQL4RYo2l/3759cXV1BcDBwQFPT0+apDQgXn+B\nTG37NWPJRx/bXQIsOXLYgxWra9Dt1SD61etC+3ytsba0Nor8cnL7NWPJR1/bZ86c0Sq+Vq0mLLsx\njZ4V/Tly+IjR5C8/73fb1vbzNrXt1/R1Pn9/f6KiolInDX97/8T2/egQ2JuQEC9sbJRc93mXrFSF\nh1ahtCw9HJVKlW6/6+3bVChQgNvPn3Nbw/6c+HxVKhVhYWHomlZ98BRFaQl0A/YC8SkvCyHE5vc+\nsaJcBpoIIR4qilIcOCCEqPxWTH1gihCidcr2V4BaCPFdynZf4BOguRAiXc9Q2QfPNERFRbF///4s\n5zcDePYMBgx5wq68/Sld7T7b+qzDrZCbHrKUjNXQ6adYGtuRyCn/yjkTJSmbhBDYf1WVoWUXMX1g\nI0On8856/7iAg9dOcGfBSs0Bw4dD0aIwfrzm/TnMEGvRfgTUAFoD7VIe7bN57m0px319/K0aYk4C\nFRRFcVUUJQ/JjcxtkDy6FvgS6KipcSeZjoSEBAYOHMg///yTZay9PWxY4cT/Gm/l1rY+1PipPqvP\nrdVDllKOio+HVatg0iQSEhJYsWIF+/bty/Jtz5/Dr1emMbzOl7JxJ0na+uIL2L5d4y5FUfB37Ufg\n6dw5J972W6vp45lJV43gYGjXTn8J5SBtG3i1gTpCiI+EEP1eP7J57pmAj6IoV4FmKdsoiuKiKEow\ngBAiERgC/AFcAn4XQrz+V/5HoACwJ2Xwx/+ymY/JePvSfq718iX88ANFChdm/PjxjBgxItNlqNLW\n3aePwrlfh1H2zz/475rJ9Fw3gBfxL/SQtP6ZzOetydOnMGMGlC1LzJIlzHv4kPLly/Pbb79x6dKl\nLN8+5X8XoNRRJvp+oodk9cOkP+9MyLr1qEwZ2JzxDbpvu/Yh3HEL5688z7EUcqLuI/9cJ8YijK+6\ntcg4aP/+5KXbTIC2DbyjgLsuTyyEeCqEaCGEqCiEaCmEiEp5/b4Qom2auBAhRCUhRHkhxIw0r1cQ\nQpQRQnyQ8vhMl/lJRiBPHggMhM2bGTx4MHfu3GF7Bn9ValKuHPwdXJPB1qfYsi2BKvPqcD78fA4m\nLOnU6NHg5sbzixcZ07IlZS9c4PizZ2zcuBGVSkX16hmsH5kiNhYWnpvGp56jsLW21VPSkmQCOnZM\nvoKXmKhxd+lCxXC1aMSENRv1nFj2fLN1DZWTumGXP5PhB6VLk6vngElLCJHlA7gMJABXgfMpj3Pa\nvNeQj+TypFxtzx4hypcX4tUrsXv3blGuXDnx8uXLdz6MSiVEoabLhc2kwmLB0Z+FWq3OgWQlndq+\nXYj790VCQoKYMGGCuHnz5ju9/as5l0We8UXEs7hnOZOfJJmymjWFOHAgw91zd24WeQc2FomJ+ksp\nO9Rqtcj7RUWxYPMxQ6eSqZR2i07aQNpewWsNVABaortpUiQpay1aQPny8Msv+Pj4UKNGDfbs2fPO\nh/H2hmsb/0Oja38yev0iWi3tQuTLyBxIWNKZdu2geHGsrKz45ptvUkfDayMuDuafnMmAakOxy2uX\nczlKkokaYm3NnZUZDEQABrdsS1Khf1gZfF2PWb2/jUdPkZiUxKft62ncn5CQwK+//pppN6DcJtMG\nnqIofwOI5ImG0z3SxkjGw+T6qnz/PXz7LURFsX79etq31zy+J6u6CxWCXasrMdf9GId3ulB+9gcc\nvX0sBxLWr1z7eb/uX9ezJwkJCaxZs4b169dr/fbM6p61OIxEt21M9xuqg0SNS679vLNJ1q1fMc7O\nBO3dm+H+PJZ5+NC+F7P3LsuR8+u67tl/rKFOvp5YWWm+/XrkyBEWLVqEYiq3Z8m6D16VNCtXaHwA\nmqeCliRdqVYN/Pxgxw6srLSdulEzRYFPP8nHmRkLKHjsB5ou9mPiHzNRC80Te0o54OrV5AmKy5fn\n2fnzzHVxwc3NjV9//ZUiRYpk+/CvXsGsozPpVWkQDvk0LnEtSVIW/Pv2ZYtb5lNMfe3fn0t5lvPk\naZKesno/iUlJnHq1ji9b98wwJnj1atq2aaPHrHJepvPgKYriqsUxEoUQRrluiZwHz4QkJYGlbpeY\nevUKhk28w7KYnlSrnI/gASspVkDTvNySznzyCQQFEf/xx0yIimLp77/TsmVLPv/8c2rXrq2TU8z8\n310mPfDg/ldXKWwr//6UpPcRGxtLsWLFuHnzJk5OGa/xXWhsbXoXn86C4S31mN27mb9tL1/tG0Ps\n/FOax08kJeGeNy/LNm6krp+mVVP1R2/z4GV0a/ath1E27iQTo+PGHUDevLBoVimC/A9wdZ8XFWbX\nJPjybp2fR0rj008hLAzradMo7OrKqVOnWLt2rc4ad/HxMF01m85u/WXjTpKywdbWlubNmxMcHJxp\nXPfK/Vh9ybjnxPvl6GqaFu6V4eDYm0FBPAFqdzCtoQXaDrKQchFz6qvy7Nmz1OfvW3frllbcWPo1\n1a6uxn9Zf/67YSwJSQk6yjDnGeXnncFaltSsCba2KIrC6NGj32ngxNs01f3TsnBeVlzBnIDP3/u4\nxs4oP289kHXrn5+fH9u2Zb5K6dTOPYh0CuHoad0OWtNV3dEvXnJV2crkgO4ZxgQvXkybKlWwsDCt\nJpFpVSOZlbt371KlShWioqKyfawiReDo6qZ8XeI0y3aeo8qsxtx4elMHWZqZ1/3rGjcmISGBVatW\n8csvv+jl1ImJ8PWeubR37Ulxu+J6OackmbKuXbsSGJj51bkiBQpRyaoVk9Yb54pBMzYFY/+iFvXc\nXTKMaRAWxvARI/SYlX5otRZtbiX74JmwuDjIl49PPvkEOzs75s6dq7NDnz2nptXk+URVm8miDv/j\nozqddXZskyQEqFQwbx6EhvKsb19+tbHhh8BA3NzcGDduHD4+Pjmexv8CnzD834r8++VpShcsnePn\nkySz8OIF7NwJXbpkGLL04B8MWj+B2PknsLbWY25aKPm5P81KtmfFyP6aA+7fh+rVITwcsjmITxcM\nsRatJBmPS5egVi1ISGDatGmsXLlSq3VqtVXDw4Kba0bR4XkwH68bg9+SQbxMeKmz45ucHj3gs89Q\n+/oypndvyi5dysmrV9m8eTMHDhzQS+MuKQkmBv9Ay1KdZONOknTJ0jJ5cFRERIYhfRu1wML+Ib9s\nvqDHxLJ2KyKSe3n3M7VbQMZBT5/C558bReNO12QDzwSZfF+VKlWgRAlYvJiiRYsybtw4Ro4cyYED\nB3R2ChsbWD+vDqsbn2aXKhrXaXU59yDrtU8NweCf95w5cPEiFoMGUa5SJZ0PnMhI2roD10TzrNL/\n+KHz2Bw9pzEw+OdtILJuA8mXD1q2hB07MgyxtLCkRZH/8MMh3Q220EXdU37fRPFYH8q6FMw4qFo1\nGDcu2+cyRrKBJ+U+ipI8+fHXX0N0NEOGDOHWrVscO6b7SYu7drTnxqw1FL4+glo/eTNz9xKTmun8\nncTEaH69RAlI6Zw8cODAbA2ceB9JSTBu60KalPClvFPm83ZJkvQe/Pxg69ZMQ77t3Jcb+Vdx74Hx\nDFALurGaXtV7GToNg5F98KTcq39/cHaGGTM4d+4cxYsX18lEuZqo1TBmziXm3elGXdeqhHy2iIL5\nMvmr0FSk7V936xYJJ07w+/r13L59m3FG8lfvinUvGHCuHGdHqHAvWsXQ6UiSyYm9f59rFSpQIzwc\nChTIMM55bCN8HT8ncIxh55IDOHn1LnUDPXgy7j6Odvk0xgghjG7lCtkHT5IAvvkGFi+G27fx8PDQ\n2LiLiYmhfv36DB8+nHXr1hEWFvZeV+AsLOD7L9051OcvLv3tSMlva6K6dkIXVRin+HhYuTJ5WpPP\nPuNZ8+bM6d6dcm5uBAYGUqtWLUNnCKQ0vNf/Qv1i3rJxJ0k5JCwqivZqNWLXrkzj+nr2Z9ONQIzh\nusrUzWspn9Apw8YdwOzZs5k1a5Yes9Iv2cAzQQbvs6EvJUpAYGByHxE0150vXz6+//57SpQowfr1\n6/Hy8sLFxYVBgwa91ykb1LHh3qKfqffsO5r/1pbhv88x+DJnOfJ59+wJy5fDtGlMDAig7Ndfc/Lc\nObZu3cq+ffto1aqV7s/5jlQqFb9vfsmTSnP4qesEQ6ejN2bz//dbZN2GU6VKFWyKFOFURvNbphjv\n14UXTofYfTQ82+fMbt37ItYw0Cvz27Pbt2+nWrVq2TqPMTO9YSOSecli5nFra2saNWpEo0aNgORL\n8rdv3+buXc0LsDx58oTo6GjKli2b4aX7/Plh74LO/G9NbYarerDryj5UI5ZR3L5o9moxJitXJo80\nAdyjozl16pTe+9ZlRQj4cs1vfNC4DjWKeRg6HUkyWYqi4NejB1vPnaN2164ZxtnnK4BHXj++3rKK\nVg0NN9n49tBLvLJ8xNAOjTOMiTxxgjPHj9O0aVM9ZqZfsg+eJKWxa9cuBgwYQGJiIvXr18fLywsv\nLy9q165N/vz508XfuJVAkymTiCi+kuV+K+hWt5kBsn5PQsCNG5DFguLGanNQPN2Plufw0I3UK1nX\n0OlIkkkLDQ1lwIABXLx4MdO49ccP0XP1p8R8dwEbG8P0b2s4aTyvkl5xctrsDGN+792blYcPs+PW\nLT1mljXZB0+Sckjr1q25d+8eJ0+epHfv3kRERPDVV19lOJFyuTLW3Fwyg+42v9FzY2+6/DyRRHWi\nnrN+R2n713XrRsKrV6xatYpRo0YZOjOtCQGjlq/AvWgV2biTJD2oW7cukZGRXL16NdO4LnUbkcf2\nFXPWGaaPslot+Ct2DZ/7ZH57NnjvXtq2baunrAxDNvBMkDH02TAElUoFixZB0aLJM5P7+ECfPvDF\nF5BF5+C3lSpVii5dujB37lyOHj3KxIkTNcYtWbKEmTOn0aeBJeuaHmLHmeOUmtiEKw9v66Ai7Wj9\neT95AtOnQ9mysGIF0ePGMbtbN8qVL09gYKBeJiTOrpgYWLsWOvoncs96EgsCNH8upsys//82Q8ZS\nt4WFBd9++y1JSUmZximKQruS/Vj0V/bmxHvfuheFHMVCbUO3xp4ZB0VFcfHRI9oOH/5+yeUSsg+e\nZFo+/jh5zqaHD5OXnnn4MPmRUefgRYvgl1+Sp1spViz54ewMjRqBFhP1urm5cfnyZSZPnsyZM2dw\nc3XjIflwf/IBszsuZWQbw08XkGr06OSfQ0gI327bxrxBg2jdujVBQUHUrFnT0NllKCoKtm+HDRsF\n+/85RbGmW3hRZxMeMSVp7PqhodOTJLPRv3/Kcl9qdercl5pM6/IfKt315NrNuVQoa6On7JItPLiG\nxo69sLDI5C7n7t2cbNUKpVIl/SVmALIPnmTeIiPh5s03G4Th4eDtDf7+6eOXLIEtW/6/Ifi6UVi7\nNvElS3LmzBlCQ0O5GVeGBeEjaODUjj+++B7bPMkjfePi4siXL+Nh+zlKiORJooHg4GCqVatGmTJl\nDJNLFp4+haAg2LAxiYNhf1KsyWaiXbbiUCAvnat2olOVTtR2qY2FIm9CSJJeLViQvGzZt99mGlZq\nXCu8bPqyfmIPPSUGL14mYDelBKreoTSuXi7jwI8+gnr14LPP9JabtnTZB0828CTpXdy8CRcuvNkY\nfPgQunZNtxj3mctRLP7MhzpPbtC8YXdKuVehzNSp2NvZ4dWwIfWbNMHLy4vKlStjkclfw+8kPh6O\nHUtuoOYyERHJk+Vv2PyKIw/34uy9hadFtlHWqSSdq3bCv7I/7kXcjW5iUkkyK6GhMGAAZDHY4tut\n65i+aykvft6Dvv6XnbQymPl/T+PZvKOZBz56BHnyQEHjm6xeNvC0ZK4NPJVKRZMmTQydht4ZY90J\noSdZ9M08HkdvxSdPPera2XHu5k1Ca9fmWHw8x44dIzY2lrt372JpaQnr18M//7x5u7hYMSheHPLm\n1XgOlUpFk+rVk283//QTeHiQsHUr6zZsYM+ePSxfvtxoG0UPHiRfEF23JYaT0SEUabSZxw678HTx\noLO7P/5V/HF1cNX4XmP8vPVB1m1ejK5utRpKlkxe4aZixQzDXibEYTe5BOuanqazT+l3Ps371F1m\nVE8alGzI2lGD3/l8xkKXDTzZB0+ScpB1/doMCV5NYPB5muzpRnnbEhxacYRaBe14/SsoKioquXEH\nYG8PiYlw8iRP7txh0+XLeL16hfucOVj27Jn+BDt2JK/mcfgw+PkRvWEDvx47xg8VKlCxYkW++OIL\nvdWqrbt3YfNmWBv0mLNx2yjUcAtPGx/kwzIN6VK1Ex0q/YBzAWdDpylJkiYWFtCxY3Ifii+/zDDM\nxjofdfN3Z3rwcjr75PxgqPuPn3M7XzB7uv2Q4+fKLeQVPEnSkzvhL2g8fTj38xxktd/vdG6YneoZ\nMgAAIABJREFU+cCGsLAwJk+ezLFjxwgPD6dOnTp4eXnh4+ND48YpE3guXw537sDHHzN71SpmzJhB\n69at+fzzz41q4MStW7BpE6zecYfLYiv29TbzrMDftC7fkoCq/rSt0NY81vaVJBMwvV8/Kh49Sucr\nVzKN23X+JG1/60bk19ewt8vZ/rIDf1rN1hurCZ+7M9O4oKAgGjdujKOjY47m877kLVotyQaeZGyE\ngIE/rmPJvWEEFB3P7yOHZT7aK8Xjx485fvw4x44dw9HRkc8/Tz9L/KFDh3B1daV06Xe/HZIT/v0X\nNm6EVbuucCPPFvLX3swr2xt0rNKOzlU74VPOBxtr/Y6wkyQp+35bvJiQMWPYcP9+6oo3mgghKPiV\nB5+V+YmZn+Zsv+DCw33pWb0XCz7OeP67+Ph4ihQpwrVr1yha1DhXHtJlAw8hhN4fQCFgD3AV2A04\nZBDXGrgMXAPGaNj/OaAGCmXwfmGODhw4YOgUDCI31b37xHVhO7y2cB7RXly58zhbxzKmui9fFuKb\nb9Siovcpkb/9eOE4wV04TXcRn24fLPb+u1fEJ8br7FzGVLc+ybrNizHWHRERIezt7cXLly+zjO27\naI4oOvA/73yOd6n77PVwwVcFRURUTKZxe3//XdStU+edc9GnlHaLTtpahppjYCywRwhREdiXsv0G\nRVEsgZ9IbuS5Az0URamSZn8pwAcwrnVGJEkLPrXdeDj9CKVsK+L+wwfM23zI0Cm9FyGSB9NNnppE\nuaaHqDV+JLPiy/LCtxufDkkgZOBvRIy9w//a/UTzcs2xtrQ2dMqSJGVTkSJF8PT0ZO/evVnGftOl\nN48KBXH6UkyO5TNlwwbKJbalSMECmcYFf/UVbY3kDoc+GOQWraIolwFvIUS4oijFAJUQovJbMV7A\nZCFE65TtsQBCiJkp2xuAb4AgoJYQ4qmG8whD1CdJ72La+p1MOtWfBtafsXvCeGzyWRo6pUwJAWfP\nwrqNr1h1ZD9RxTcjKmyjREEXetfsRCd3f6oWqWq0I3clScq++fPnc+HCBZYsWZJlbPkJ/lRS2hH8\nzYAcyaXA8AaM+3AC47r4ZhwUF0clW1vW7NlDrebNcyQPXTCFtWidhRDhKc/DAU1D5koAd9Js3015\nDUVROgJ3hRDncjRLSdKD8V19OfPp3/zzUkXR0c05fPaeoVNKRwg4eRJGjX1O8eYb+HBeT+ZbFaOw\n/3SmDqnChRGhXB11mklNJlKtaDXZuJMkE+fn54dKpUKbiyjDGvVj7+NAsljl7L388dcNXtpcZ1TH\nzJdavL52Lc+srPigaVPdJ2GkcmyaFEVR9gDFNOwan3ZDCCEURdH0DdH4rVEUxQYYR/Lt2dSXM8qj\nb9++uLq6AuDg4ICnp2fq3Dqv17ozte3XrxlLPvranj9/fq79fKu7uvC773gmbFiD9+paDDuylI5V\n8qMohvu89+9XcfkyXLxRnd/PbONF3l9JLHKGOp0b81HdThR51JlCNoVo0sAwP7/c/HlnZ/v1a8aS\nj/y8c3b79WvGks/r7bCwMH7++efUP+Yyi/+0RRtGLu/L2Bkr+X5CH62Or+3nPX3fn1S37ELo0SOZ\nHu/0+vV85u2dOqm8oX9+aT9flUpFWFgYOqerznzv8iB54ESxlOfFgcsaYuoDu9JsfwWMAaqRfNXv\nZsojAQgDimo4xvv1cszljLFTrj6YSt2B+w8J69GlRMWhI8XDR3FZxuuy7sREIQ4dEqL/iDvCoeWP\nwvbTpiLvZHvRYnGAWHl2lYh8Gamzc2WXqXze70rWbV6Mum61WojZs4WIy/r3VLPvPhdVhnyl9aG1\nqTsxUS2sR1QWgfuOZB6oVgtRtqwQZ89qfX5DQYeDLAzVB28W8EQI8V1K3zoHIcTYt2KsgCtAc+A+\n8BfQQwjxz1txN5F98CQTc+/pUxrN7s+9mLssb7+O7i3L59i5EhOT50lesuUq265tRl1xC6LQdXzK\ntKN//U74uPlga22bY+eXJCkXa9gQJk2CVq0yDTt67SIfLm5JxNjbFHbSTT/j30JOM2h/J+K+u5H5\ndFPPn8OQIRAYiN7WTXtPuX4ePEVRCgHrgdIkX33rKoSIUhTFBfhVCNE2Ja4NMB+wBJYKIWZoONYN\noLZs4EmmRgjBp4ELWXx1Kn55f2D9xJ5Y6ahTRUICHDggWBR0hl23NqOutAVr+6e0r+BPv/r+eJfx\nliNeJUnK2vffw40b8PPPWYY6ja1Hd+cpLBzZRien9hz9BY52eTkwcZpOjmcMcv0gCyHEUyFECyFE\nRSFESyFEVMrr91837lK2Q4QQlYQQ5TU17lJiymlq3JmztPf2zYmp1a0oCr/0H8Ifvfbwx6uplPis\nP5euvUgXp23d8fGwY2cSbT87jH2XUbTfV5ZDzl3p3fcVB0YuIWrSXVb3XEiLci1yRePO1D5vbcm6\nzYvR1+3nl7xsmVqdZWgv936svRyo1WGzqvtlXBLnxFrGd8h4YmNzZ5AGniRJ2vOp7smDKacoXUaN\nx0+1+W7ZWa3fGxcHm7bG02LQLux6/pdOf7pwpvhQPuvvwMlR24mYeJVFnWdRv2R9LBT560CSpHcT\nX6YMWywt4a+/soydHNCdKKfd/Hkq+9dk5mw6hK0oSosa7tk+lqmSS5VJUi7yTdBKpoaOotbzqeyZ\n9in29umv5MfGwpbg5/y8Zxd/xWxBuO2ktE1VetXyp289f8o5ljNA5pIkmaKkpCRc7O0J7dOHsr/8\nkmV81Uk9cY5vwP6ZQ7J13nIjPuaD0pXZNOqLTOPOnj3LpEmTCAoKytb59EWXt2hzbJoUSZJ0b2LH\nPrSvWY8Wv3THZeRetvZfSouGjjx/Duu3P+WX/ds5HbcZ4aqiUtn6TKvfiT515lCsgKYZiyRJkrLH\n0tKSDh06sMXOjlFaxH/Zoh+frB9DQsIQrN+zJ0jE0zhu2mwmqHPWU+EGBwenTpVmbuQ9GRNk9H02\ncoi51O1ZqiL3ph6jsWcpWm3xpETr/+IwrAUDL5UlqcI2FgzqwqNxYVz86g++bDrQZBt35vJ5v03W\nbV5yQ93+ffqwJTRUq9g+HzbDssATftqYeVeTzOr+et1OCifUoHrpkpmfLCmJ4AULaNuypVa5mRrZ\nwJOkXCivVV52Dv2Bxf4/4lz+Pss+G0L0pAecGr2JTxv2xtHG0dApSpJkJpo1a8b58+eJiIjIMtbS\nwpKWzh/x42HtBltosv6fNQRUzHpwxZO9e7nw6BGNjXhpspwk++BJkiRJkpQt3bp1w8fHh48//jjL\n2PN3b1Djx/qEDbtL6RJ53uk8l8OiqbK4NPe+DMPFMfM/ZFf7+bH+4kWCrl17p3MYUq6fJkWSJEmS\nJNMxatQoPDw8tIqtXrIcRS3cmbhyxzufZ/K6TZROapZl4w7g5NGjtPXze+dzmArZwDNBuaHPRk6Q\ndZsXWbd5kXUbt3r16lG3bt3kYfxa6O/Zj803fyOjm2wZ1b3z7mr61tRi7rt795iXlMTH00xnEuR3\nJRt4kiRJkiRlX0hI8sTHWhjn15lYpyOEHH6g9eFVp+7zwu40o/3aZR28cye0aoVFnne7BWxKZB88\nSZIkSZKy78ULKF4cbt8GB4csw2t//TFWzyoSOnu0VodvMXEu4eIC57/9LevgZ88gOhpKldLq2MZC\n9sGTJEmSJMm45M8PTZokXz3TwjjffpxICCQ2NusLMWo1HI5azeDGPbXLxd4+1zXudE028ExQbumz\noWuybvMi6zYvsu5cws8PsWWLVqH+tRqQz0bN92uPp9v3dt1r91xGnf8BHzdvqosszYJs4EmSJEmS\npBPrEhMZtm1b8kLYWVAUhfal+vLryaxvuc7bu4Z6+btjZWmZZeypU6e4fv26VvmaMtkHT5IkSZIk\nnbh27RqNa9Tg3unTWFSqlGX8v4/uUWFudf755C6VytlqjHn1SmD7VXmCeq2nXa1aWR6zQ4cOdO/e\nnZ49tbyda0RkH7xsUhRFPuTD0F9DSZIkk1OhQgWcypUj9MkTreLdipSglFKf8as2ZxizYPNx8lpZ\n0bZmzSyPFxcRgUqlonXr1lrnbKrMsoEHIISQDzN+mJJc10dHR2Td5kXWnXv4+/uzdetWreM/9epH\n8P1A1Or/fy1t3YuPraaFcy+t/jA/OG4cHvnzU6hQoXdJ2SSZbQNPkiRJkiTd8/PzY8uWLVr/MT2i\ndQcSCp1j/e6wdPseP03g33zrmeSv3e3Wnbt20bZly3dJ12SZZR88RVFM7iqO9G7kd0CSJClnCCGo\nU6cO27dvp3jx4lq958PpQ4mJcOLs/ClvvD7qf7tYFjaFp7NCsz5vZCQVnJzYdPQoNerXf5/UDS7l\n3ybZB0+SJEmSJOOiKAonT57UunEHMLVjf85bLCP6mfqN19deWI2/mxZLkwFJu3YxpHJlPOrVe6d8\nTZVs4Ek65+rqyr59+wydhtnIjX10dEHWbV5k3bnQjz/ClStahTav+gF2eRz4ZqUKSK77WtgLwh22\nM6lzV62OYRUSwoihQ+UguhSygSfpXE6MUg0LC8PCwgI7Ozvs7OwoW7Ys33333Rsxffv2xdramocP\nH+r03JIkSdJ7uH4dNmzQOrxr+X6sOBeYuj113TZc1PUp4+Ss3QHs7aFt23fN0mTJPniSzpUtW5al\nS5fSrFkznR0zLCyMcuXKkZiYiIWFBaGhoTRv3pzNmzfTqlUrXrx4QbFixShXrhx9+vThiy++yPR4\n8jsgSZKUw1Qq+OILOHlSq/AH0Y8p8V15/upxi9rVC1Lw0/Z81rgrM3r0ydk8jYjsg2fiXF1dmT17\nNh4eHtjZ2TFgwADCw8Np06YNBQsWxMfHh6ioqNT40NBQGjRogKOjI56enhw8eDB1X2BgIO7u7tjb\n2+Pm5sbixYtT96lUKkqWLMncuXNxdnbGxcWFZcuWaczpwIEDeHh4pG77+PhQt27d1O1GjRqxbdu2\ndO8TQjBz5kzKly9P4cKF6datG5GRkQC0adOGhQsXvhFfo0YNrYbX169fn6pVq3Lx4kUANm3aRNmy\nZRk9ejTLly/P8v2SJElSDvvwQwgLg9u3tQovXrAw5S2aM/H33zly+jExhQ4xpqNfzuZowmQDzwgp\nisLmzZvZt28fV65cYceOHbRp04aZM2cSERGBWq1mwYIFANy7d4927doxadIkIiMjmT17NgEBATxJ\nmWTS2dmZ4OBgnj17RmBgICNHjuT06dOp5woPD+fZs2fcv3+fpUuXMnjwYKKjo9PlVL9+fa5du8bT\np09JSEjg3LlzPHjwgBcvXvDy5UtOnTpFo0aN0r1vwYIFbNu2jUOHDvHgwQMcHR0ZPHgwAD179mTt\n2rWpsZcuXeL27du0zeQS++t57I4cOcLFixf54IMPAFi+fDndunWjQ4cOXL9+nb///vs9fvK5U67u\no5MNsm7zIuvOhaysCKxUiSdr1mj9lhFN+rPvaSAj5kyjotIGB1u7HEzQtMkGnpEaOnQoRYoUwcXF\nhUaNGuHl5UWNGjXImzcv/v7+qY20VatW4evrmzprd4sWLahduzbBwcEA+Pr6UrZsWQAaN25My5Yt\nOXz4cOp5rK2tmTRpEpaWlrRp04YCBQpwRUOnWBsbG+rUqcPBgwc5deoUnp6eNGzYkD///JPQ0FAq\nVKiAo6NjuvctWrSIb7/9FhcXF6ytrZk8eTIbN25ErVbj5+fHmTNnuHPnDgCrV68mICAAa2vrDH8u\nhQsXxsnJiU8++YTvvvuOpk2bcvv2bVQqFV26dMHOzo5WrVqxYsWK9/zJS5IkSbqyQ61m+zv8Pv5v\ns1ZQ8BYn49bx6YfajZ5Vq9U0bNgw9e6QlMwgDTxFUQopirJHUZSriqLsVhTFIYO41oqiXFYU5Zqi\nKGPe2jdUUZR/FEW5oCjKd5ren70cs//IDmfn/+9UamNj88Z2vnz5eP78OQC3bt1iw4YNODo6pj6O\nHDmSOtAgJCSE+vXr4+TkhKOjIzt37ky9ugfg5OSEhcX/fw1sbW1Tj/02b29vVCoVhw8fxtvbG29v\nbw4ePMihQ4do0qSJxveEhYXh7++fmpu7uztWVlaEh4djZ2dH27ZtU6/irVu3jl69Mv8f+smTJzx9\n+pRLly4xZMgQAFauXEm1atWoWLEiAF26dGHNmjUkJiZmeixTkdHP3tTJus2LrDt38vv4Y7a6uGgd\nb2VhRVOnPlhWjGdQi1Zavef06dM8fvxY40UGc2aoK3hjgT1CiIrAvpTtNyiKYgn8BLQG3IEeiqJU\nSdnXFOgAeAghqgGzdZ2gENl/6DYfzQcsXbo0ffr0ITIyMvURExPD6NGjefXqFQEBAYwePZqIiAgi\nIyPx9fV978EF3t7eHDhwILVB97rBd/DgQby9vTPMb9euXW/kFxsbmzo/Uo8ePVi7di3Hjh0jLi6O\npk2bvnNeK1as4Nq1axQvXpzixYszYsQIHj9+zM6dO9+rTkmSJEk32nXqxIETJ3jx4oXW7wn870iW\ndQokr1UereKDx46lbcOG75uiyTJUA68D8Lon/HJAUy/KusB1IUSYECIBWAd0TNn3KTAj5XWEEI9y\nOF+j1bt3b7Zv387u3btJSkoiLi4OlUrFvXv3iI+PJz4+nsKFC2NhYUFISAi7d+9+73M1aNCAK1eu\ncOLECerWrYu7uzu3bt3i+PHjNG7cWON7Bg0axLhx47id0sn20aNHbwzG8PX15datW0yePJnu3bu/\nc07Hjh3jxo0bnDhxgrNnz3L27FkuXLhAz549zeY2ba7uo5MNsm7zIuvOnRwdHalbt+47/dvjYl+M\nks/ttQuOiyN4/37atm//nhmaLkM18JyFEOEpz8MBTZPclADupNm+m/IaQAWgsaIooYqiqBRFqZ1z\nqRqHtPPKpZ1nrmTJkgQFBTF9+nSKFi1K6dKlmTNnDkII7OzsWLBgAV27dqVQoUKsXbuWjh07Znjc\nrNja2lKrVi2qVq2KlZUVkNzoc3V1pXDhwhrfM3z4cDp06EDLli2xt7fHy8uLv/76K3V/njx56NSp\nE/v27aNnz8zXGtSU64oVK/Dz86Nq1aoULVqUokWL4uzszPDhwwkODn5jtLEkSZKkf6/Xps0JEVu3\ncllRaCTnv0snx+bBUxRlD1BMw67xwHIhhGOa2KdCiEJvvT8AaC2E+CRluzdQTwgxVFGU88B+IcRw\nRVHqAL8LIcppyEHOgydpJL8DkiRJ+vHo0SOuXr1Kwxy4jRrUti0rbt1i04ULOj+2IehyHjwrXRxE\nEyGET0b7FEUJVxSlmBDioaIoxYEIDWH3gFJptkuRfBWPlP9uTjnPCUVR1IqiOAkhnrx1DPr27Yur\nqysADg4OeHp6vlc9kul5fevjdSdmuS235bbcltu63y5SpAgXL1xAtWEDTbp00d3xhaDjpUv4btpk\nVPW+y/br52FhYeiaQVayUBRlFvBECPGdoihjAQchxNi3YqyAK0Bz4D7wF9BDCPGPoigDARchxGRF\nUSoCe4UQpTWcR17BkzQype+ASqVK/aVhTmTd5kXWncvduQM1a8KDB2CV9bUlreq+dAlat4Zbt7I/\ndYWRMIWVLGYCPoqiXAWapWyjKIqLoijBAEKIRGAI8AdwieTbsP+kvP83oFzKrdq1wH/0nL8kSZIk\nSdoqVQpKl4YjR3R3zHLl4I8/TKZxp2tyLVrJLMnvgCRJkp598w08fQrz5hk6E6NlClfwJEmSJEky\nJ35+xG3erPuJYiWNZANPknK5tJ11zYms27zIunO/i4pCrQcP4Ny5LGOzqjsiIoLjx4/rKDPTJBt4\nkiRJkiTluCru7kTnz8/l8+ezfayNGzeycOFCHWRlumQfPMksye+AJEmS/g0ePJhSpUoxdmy6FUq1\nl5RE2zZt+M+AAXTr1k13yRkB2QdP0srq1atp1SrjxZqbNGnC0qVLs30elUpFqVKlsg6UJEmSzJq/\nvz9bt27N1jFiDx3i8L59mf77JskGnknr1asXf/zxR4b70y55lpMsLCwoUKAAdnZ2lCxZks8//xy1\nWp26f8qUKVhYWLyxhJmkPVPqo/MuZN3mRdZtGry9vbl69Sr37t3LNC6zulWLFvFBiRI4ODjoODvT\nIht4Ri4xMdHQKejEuXPniImJYd++faxZs4Zff/0VACEEK1asoHr16qxYscLAWUqSJEk5ydramo8/\n/pgbN2689zGC9+6lrVx7NkuygWeEXF1dmTVrFh4eHtjZ2aFWqwkNDaVBgwY4Ojri6enJwYMHU+OX\nLVuGm5sb9vb2lCtXjjVr1qS+3qhRo9S4PXv2ULlyZRwcHBg6dOgbfdCmTJlCnz59UrfDwsKwsLBI\nvdIWGBiIu7s79vb2uLm5sXjx4veqrVKlSjRq1IiLFy8CcPjwYZ49e8YPP/zAunXrSEhIeK/jmjOT\nmOX+Pci6zYus23TMmjXrjX+bNMmw7rt38YqLo9Pw4bpPzMTIBp6RWrduHSEhIURFRfHgwQPatWvH\npEmTiIyMZPbs2QQEBPDkyRNevHjB8OHD2bVrF8+ePePYsWMa19t9/PgxAQEBTJ8+nSdPnuDm5saR\nNDOKZ3Wr1tnZmeDgYJ49e0ZgYCAjR47k9OnTWtfzujF56dIlDh8+zAcffADA8uXL8ff3p0mTJtjY\n2LB9+3atjylJkiTlUitWwJYt7/6+4GB6d+xI+cqVdZ+TiZENPCOkKArDhg2jRIkS5M2bl1WrVuHr\n60vr1q0BaNGiBbVr1yY4OBhFUbCwsOD8+fO8fPkSZ2dn3N3d0x1z586dVKtWjU6dOmFpacmIESMo\nVqxY6v6sRpT6+vpStmxZABo3bkzLli05fPiw1jXVrFmTQoUK0aFDBz755BP69etHbGwsGzdupEvK\n4tMBAQHyNu17MLU+OtqSdZsXWbeJsbSEwMAMd2dYd2IimNjI2ZyS9Yq/ZkqZmv3BB2Ly+0/DkXZU\n6q1bt9iwYcMbV7cSExNp1qwZtra2/P7778yePZsBAwbQsGFD5syZQ6VKld443v379ylZsmSG58hK\nSEgIU6dO5dq1a6jVamJjY/Hw8ND6/adPn6ZcuXJvvLZlyxasra1p3rw5AF26dKFZs2Y8fvyYwoUL\na31sSZIkKZfx9YVPP4Xnz6FAAe3fN3hwzuVkYmQDLwPZaZzpQtpbpqVLl6ZPnz4Z9ntr2bIlLVu2\n5NWrV4wfP55PPvmEQ4cOvRHj4uJCUFBQ6rYQgjt37qRuFyhQgNjY2NTthw8fpj5/9eoVAQEBrFq1\nio4dO2JpaYm/v3+255Fbvnw5MTExqQ1PIQQJCQmsWbOGYcOGZevY5sQU++hoQ9ZtXmTdJsbREerV\ng927oVOndLtNtm49krdoc4HevXuzfft2du/eTVJSEnFxcahUKu7du0dERARBQUG8ePECa2tr8ufP\nj6WlZbpj+Pr6cvHiRbZs2UJiYiILFix4oxHn6enJoUOHuHPnDtHR0cyYMSN1X3x8PPHx8RQuXBgL\nCwtCQkLYvXt3tmq6d+8e+/fvJzg4mLNnz6Y+xowZI2/TSpIkmYENZcpw5D0H7ElZkw28XKBkyZIE\nBQUxffp0ihYtSunSpZkzZw5CCNRqNfPmzaNEiRI4OTlx+PBhfv75Z+DNee4KFy7Mhg0bGDt2LIUL\nF+b69et8+OGHqedo0aIF3bp1w8PDgzp16tC+ffvU99rZ2bFgwQK6du1KoUKFWLt2LR07dnwjx8wG\naWjat3LlSj744ANatGhB0aJFKVq0KM7OzgwbNozz589z6dKlbP/czIXJ9tHJgqzbvMi6Tc/9UqX4\nTaWCpKR0+zTV/eWXX7J///6cT8xEyKXKJLNkSt8BlUpllrczZN3mRdZtesLCwqhbuzb3Hz7EyurN\nHmNv161WqylevDjHjx/H1dVVv4nqkS6XKpMNPMksye+AJEmS4dWsWZN58+bh7e2dadxfM2bQb9Ei\nLoaF6ScxA5Fr0UqSJEmSlOv5+/uzRYv58IJ/+YW2tWvrISPTIRt4kpTLmXIfnczIus2LrNs0+fn5\nsXXr1nR3VN6oOzKS4Lt3afvf/+o3uVxONvAkSZIkSTKIatWqsXnz5kxjIjdt4paVFQ2aNtVTVqZB\n9sGTzJL8DkiSJBkJtRr+/hsyugXbpw8va9fGxgzWn5WDLLQkG3hSRuR3QJIkyUio1VCiBBw+DOXL\nv7kvKQmKFYNTp6B0acPkp0dykIUkSalMvY9ORmTd5kXWbcIsLKBDB9i6NfWl1LoVBYKDzaJxp2uy\ngSdJkiRJkmH5+7/RwEtlYQF16+o/HxMgb9FKOufq6srSpUtp3ry5oVPJkPwOSJIkGZFXr7hfpAgu\n166Bs7OhszEYeYtWMmppl0jTlbCwMCwsLGjbtu0br/fu3ZupU6cCyZf0LSwssLOzw87OjpIlSzJl\nyhSd5iFJkiTpXryiUDUujvBVq954XaVSkaRhKTMpa7KBJ+Uqf/31F8eOHUvdfrsxWaJECWJiYoiJ\nieHPP/9k6dKlBAUFGSJVvTGLPjoayLrNi6zbtOXJk4dWDRuy7eZNILnuGzdu0L17d51fMDAXBmng\nKYpSSFGUPYqiXFUUZbeiKA4ZxLVWFOWyoijXFEUZk+b1uoqi/KUoymlFUU4oilJHf9nnPFdXV2bP\nno2Hhwd2dnYMGDCA8PBw2rRpQ8GCBfHx8SEqKio1PjQ0lAYNGuDo6IinpycHDx5M3RcYGIi7uzv2\n9va4ubmxePHi1H0qlYqSJUsyd+5cnJ2dcXFxYdmyZRpzOnDgAB4eHqnbPj4+1E3TL6JRo0Zs27Yt\n3fuEEMycOZPy5ctTuHBhunXrRmRkJABt2rRh4cKFb8TXqFGDrZr6YaQYPXo048ePz3B/Wq6urjRo\n0IB//vlHq3hJkiTJcPwHDWLLjRup28EbN9KmTRssLOS1qPcihND7A5gFjE55PgaYqSHGErgOuALW\nwBmgSso+FdAq5Xkb4EAG5xGaZPS6sXB1dRVeXl4iIiJC3Lt3TxQtWlR88MEH4syZMyLaCsH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