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@fonnesbeck
Created September 13, 2012 14:29
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Working manatee survey model
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{
"metadata": {
"name": "Manatee Survey"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import some fake data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pandas import read_csv\n",
"import numpy as np\n",
"\n",
"#survey_data = read_csv('fake_data.txt', sep='[\\s]*')\n",
"survey_data = read_csv('fake_data.csv')\n",
"\n",
"\n",
"year = np.array(survey_data.YR)\n",
"DDN = np.array(survey_data.DD)\n",
"DDN = (DDN - DDN.mean())/DDN.std()\n",
"\n",
"obs_north = np.array(survey_data.xnorth)\n",
"obs_south = np.array(survey_data.xsouth)\n",
"\n",
"water_north = np.array(survey_data.WTN)\n",
"water_south = np.array(survey_data.WTS)\n",
"\n",
"# Difference in water temperature\n",
"delta_W = water_north - water_south"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"survey_data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 17,
"text": [
" ID YR DD WTN WTS xnorth xsouth tnorth tsouth\n",
"0 1 1 34 20 22.5 43 73 490 534\n",
"1 2 1 87 17 22.0 88 82 272 752\n",
"2 3 1 137 14 22.0 37 191 209 815\n",
"3 4 2 126 16 22.0 127 52 470 579\n",
"4 5 2 170 13 21.0 40 191 242 807"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import array, mean, unique, ones, exp, round, log, inf\n",
"\n",
"# Overdispersion parameter, used in initial distributions an movement probabilities\n",
"overdisp = 0.075\n",
"\n",
"# List of survey years\n",
"years = unique(year)\n",
"\n",
"# Y = max(year) - min(year)\n",
"Y = len(years)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a triangular prior for the growth rate."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def growth_prior(value, lower, median, upper):\n",
" \"\"\"Prior for growth rate. A mixture of triangular distributions\"\"\"\n",
"\n",
" value = array(value)\n",
"\n",
" if (np.any(value <= lower)) or (np.any(value >= upper)):\n",
" return -inf\n",
"\n",
" a = lambda x: log(x - lower) - 2*log(median - lower)\n",
" b = lambda x: log(upper - x) - 2*log(upper - median)\n",
"\n",
" return np.sum([a(x) for x in value[value<=median]]) + np.sum([b(x) for x in value[value>median]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Population size process. The populations each year are given discrete uniform priors, and the growth from year to year is constrained by the prior on the growth rate."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pymc as pm\n",
"# Initial population\n",
"N = pm.DiscreteUniform('N', lower=0, upper=3000, value=[1000*(1.05**t) for t in range(Y)])\n",
"# Growth rates\n",
"c = pm.Lambda('c', lambda N=N: [1.*N[i+1]/N[i] for i in range(len(N)-1)])\n",
"\n",
"@pm.potential\n",
"def constrain_growth(growth=c):\n",
" \"\"\"constrain_growth = uniform_like(N, c)\"\"\"\n",
" return growth_prior(growth, 0.8, 1.0, 1.1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Movement process"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Parameters for north-to-north and south-to-north movement probabilities\n",
"delta = [pm.Normal('delta_1', mu=0., tau=0.01, value=0.), \n",
" pm.Normal('delta_2', mu=0., tau=0.01, value=1.)]\n",
"pi_nn = pm.Lambda('pi_nn', lambda d=delta: exp(d[0]*water_north)/(exp(d[0]*water_north) + d[1]*exp(d[0]*water_south)))\n",
"pi_ss = pm.Lambda('pi_ss', lambda d=delta: exp(d[0]*water_south)/(exp(d[0]*water_north) + exp(d[0]*water_south)))\n",
"\n",
"\n",
"# Initialize arrays\n",
"N_north = []\n",
"N_south = []\n",
"N_north_init = []\n",
"N_south_init = []\n",
"ns_y = []\n",
"nn_y = []\n",
"sn_y = []\n",
"ss_y = []\n",
"\n",
"# Initial proportions in each region\n",
"p_init = pm.Beta('p_init', alpha=0.5/overdisp, beta=0.5/overdisp, value=[0.3]*Y)\n",
"\n",
"# Loop over years\n",
"for i,y in enumerate(years):\n",
"\n",
" # Initial probability of being in the north in year i\n",
" p = pm.Lambda('p_%i' % i, lambda p=p_init: p[i])\n",
"\n",
" # Initial distribution\n",
" N_north_t = pm.Binomial('N_north_%i_0' % y, n=N[i], p=p)\n",
" N_south_t = pm.Lambda('N_south_%i_0' % y, lambda N=N, n=N_north_t, i=i: N[i] - n)\n",
" N_north_init.append(N_north_t)\n",
" N_south_init.append(N_south_t)\n",
"\n",
" # Loop over times within year\n",
" for t in range(sum(year==y)):\n",
"\n",
" # Index movement rates\n",
" p_nn_t = pm.Lambda('p_nn_%i_%i' % (y,t), lambda p=pi_nn: p[year==y][t])\n",
" p_ss_t = pm.Lambda('p_ss_%i_%i' % (y,t), lambda p=pi_ss: p[year==y][t])\n",
"\n",
" # Movement, sampled from a binomial\n",
" nn_y_t = pm.Binomial('nn_%i_%i' % (y,t), n=N_north_t, p=p_nn_t)\n",
" ns_y_t = N_north_t - nn_y_t\n",
" ss_y_t = pm.Binomial('ss_%i_%i' % (y,t), n=N_south_t, p=p_ss_t)\n",
" sn_y_t = N_south_t - ss_y_t\n",
"\n",
" ns_y.append(ns_y_t)\n",
" sn_y.append(sn_y_t)\n",
" nn_y.append(nn_y_t)\n",
" ss_y.append(ss_y_t)\n",
"\n",
" # North and south totals\n",
" N_north_t = pm.Lambda('N_north_%i_%i' % (y,t+1), lambda sn=sn_y_t, nn=nn_y_t: sn+nn)\n",
" N_south_t = pm.Lambda('N_south_%i_%i' % (y,t+1), lambda ns=ns_y_t, ss=ss_y_t: ns+ss)\n",
"\n",
" N_north.append(N_north_t)\n",
" N_south.append(N_south_t)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observation process"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Logit-linear model parameters\n",
"theta_0 = pm.Normal('theta_0', mu=0.0, tau=0.001, value=0.0)\n",
"# Temperature effect\n",
"theta_1 = pm.Exponential('theta_1', beta=0.01, value=0.1)\n",
"# Regional effect (south=0, north=1)\n",
"theta_2 = pm.Normal('theta_2', mu=0.0, tau=1/0.1225, value=0.0)\n",
"# Effect of year<1987\n",
"#theta_3 = Normal('theta_3', mu=0.0, tau=1/0.1225, value=0.0)\n",
"\n",
"@pm.deterministic\n",
"def mu_N(b0=theta_0, b1=theta_1, b2=theta_2): #, b3=theta_3):\n",
" \"\"\"Mean sighting probability for north\"\"\"\n",
" return pm.invlogit(b0 + b1*DDN + b2) # + b3*(year<1988))\n",
"\n",
"alpha_N = pm.Lambda('alpha_N', lambda mu=mu_N: mu/overdisp)\n",
"beta_N = pm.Lambda('beta_N', lambda mu=mu_N: (1.-mu)/overdisp)\n",
"\n",
"p_N = pm.Beta('p_N', alpha=alpha_N, beta=beta_N, value=[0.5]*len(year))\n",
"\n",
"@pm.deterministic\n",
"def mu_S(b0=theta_0, b1=theta_1):\n",
" \"\"\"Mean sighting probability for south\"\"\"\n",
" return pm.invlogit(b0 + b1*DDN)\n",
"\n",
"alpha_S = pm.Lambda('alpha_S', lambda mu=mu_S: mu/overdisp)\n",
"beta_S = pm.Lambda('beta_S', lambda mu=mu_S: (1.-mu)/overdisp)\n",
"\n",
"p_S = pm.Beta('p_S', alpha=alpha_S, beta=beta_S, value=[0.5]*len(year))\n",
"\n",
"dev_S = pm.Lambda('dev_S', lambda p=p_S, mu=mu_S: p - mu)\n",
"dev_N = pm.Lambda('dev_N', lambda p=p_N, mu=mu_N: p - mu)\n",
"\n",
"# Observation likelihoods\n",
"count_n = pm.Binomial('count_n', n=N_north, p=p_N, value=obs_north, observed=True)\n",
"count_s = pm.Binomial('count_s', n=N_south, p=p_S, value=obs_south, observed=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run MCMC model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iterations = 10000\n",
"burn = 5000\n",
"thin = 5\n",
"\n",
"M = pm.MCMC(locals())\n",
"M.sample(iterations, burn, thin=thin)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \r",
"[****************100%******************] 10000 of 10000 complete"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function will plot the time series:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_ts(M):\n",
" import numpy as np\n",
" import matplotlib.pylab as plt\n",
" # xy = np.array([n.stats()['95% HPD interval'] for n in M.N]).T\n",
" xy = (M.N.stats()['95% HPD interval']).T\n",
" plt.plot(M.years, xy[0], 'b-')\n",
" plt.plot(M.years, xy[1], 'b-')\n",
" med = (M.N.stats()['quantiles'][50])\n",
" plt.plot(M.years, med, 'b--')\n",
" # Add true values\n",
" sims = read_csv('simfinal.dat', sep='[\\s]*', header=None)\n",
" n_sim = sims[['X.8','X.9']].sum(1).unique()\n",
" plt.plot(M.years, n_sim, 'r--')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot observations\n",
"plt.plot(year, obs_north+obs_south, 'go')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 26,
"text": [
"[<matplotlib.lines.Line2D at 0x10d2bfa90>]"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10d43d9d0>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot of population time series\n",
"plot_ts(M)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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WLlzIoUOHMJlM3HjjjUH/W4fATcQhQ4ZUjJoKpvKEv337\ndgoKCrBardx8881Bj+3111/nyJEj5ObmkpaWFrRVDarKl1dccQXffPMN+/fvZ/fu3QwfPhyDwVDt\n88hEMCGEiFLBv9YTQggRFFIAhBAiSkkBEEKIKCUFQAghopQUACGEiFJSAIQQIkpJARBCiCj1/9Az\n5Lbl/v2UAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d2f32d0>"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot of southern detection probabilities\n",
"pm.Matplot.summary_plot([M.p_S])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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HjUYDrVaLkSNH1ikOPFjD9vT0RL9+/eocy93dHRMmTAAA3Lp1CzqdDm5ubgCA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\nmJgYAMDp06exe/duuLq6orS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"text": [
"<matplotlib.figure.Figure at 0x10d2a6550>"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot of northern detection probabilities\n",
"pm.Matplot.summary_plot([M.p_N])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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BYIC7uzsAYMeOHRBCsL5NNbCCTqRsVq9v16ax4XHw4EFs3boV8+fPh5OTEwDW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\nxAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d2a4e50>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Temperature effect\n",
"pm.Matplot.plot(M.theta_1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting theta_1\n"
]
},
{
"output_type": "display_data",
"png": 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yfAXrImSLTwuC5a97TknaCIvsT7Yq785sbHfi4JlWLPXWOlRiL7Nz0WLhxJ1r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\nIMsAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1082d8290>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# North effect\n",
"pm.Matplot.plot(M.theta_2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting theta_2\n"
]
},
{
"output_type": "display_data",
"png": 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QJKMmhvDJV8lyLVotoHoH9I6AVhhc/tLn2H7Y/E1ekiI73jgZKx4/M5eFkHXH\nK9X4qK7VMl+h/jmbCc6v7z6OhjYrjYyxfHdNtuxys/nF+ssLjSzGVVaaI9J+fB1gu2AZJzqClnPE\njB/8fS/abYKfdtl8afKFyVbNHdwLia7aupNdN9d0l/LyclRUVOCWW25Bc3MzampqsHr1avV8bW0t\nhg4dCkARuGpqlBhuoVAI9fX1mDx5cq/3OdlQHCTCDpofySPlzIVBFmCIcxqXZODvu0/EvNYqaa4v\nGEamS0QgFNtcCCgClllILLbwtPpDuvKsTsXZnWklREEwOO+/WaOMwx9SBD99N/RLG9MGWGl7djW2\n466/78U/bj3Lsoz+aLpTRGfYfIHVxydj/eRp5UJh7Gr0onhwBlwOMRK7KTqmiqPtuOyvn8bMxViQ\nnYZmXwgefwg57uiUrG7qwMeHPKp2U088mqzfvXcIV08twG3njzRer9dk2fYyNgJXiVlgWbv62Qif\n+O9hfPuCUTHbknQ+WWxemfZLJ9mwOWXnhO6K4Yj/730n0REM47JJgwFo76U+b+e2g0qIiyZvwDAX\nv/NqDe6bZttUUtm/fz9+97vfoaioCPfffz/8fj8uvfRSZGVl4U9/+hMKCwvh9/uxfPlyAEBxcTGm\nTp2KsrIyeL1eLFu2rN87vQPkc0PYQ/MjeaSckMV8c0RwWgELicghKEKZQxQ0Udr1pf0hxQ8pLEdD\nK+g1TP/HaWdEKAsmv1j85aN6NcK7pzOM/EwnmjuYoKIsHbe9XI2wHBWI+HhZUOtS/voimqy/bj+C\nSQWZePaTo0pN3Cr01p5m/O9/apU+WayH3oimKSzLcFh4FevXUrdLRKuFAkGGdrcf6ycPnyblu6/W\n4Fvnj8TCqQVKH7jwFy3++NLEjB3oRnVTBw61+jHVrfUBEwWd47uJPxPDSgvE55bsDEmq0GZWmq/j\n0W11CEsy7rpoTFzj4Os07Qs7ZPKY2KkjnoBl3e8fbMHgLBdKCrIMIRzsBCb9GaatNBv/58e8kOXY\nmqw//fcwWv2hqJAVqa32pC+6u1D3P/bVsipDPT3hg2jH+PHj8eyzz3bpmiuvvLKHekMQxKlOypkL\n2SIvcEKTlWnPIQq4fM1nqoDCSsmy1uXaF1QWVuU33+iYDAA//ue+6JdI+2wV9wclbKpsUk+3dYZQ\nkJUWLR5ZKPQ7VTrDWr+WsCSjxReEKCjpdQCgoS2Ajw958PZeY7oR5ksGWC+iHRGBx1Yzp1tm0x32\nIRyc3MpSbX1oAAAgAElEQVT3vdeMqV70ghdzsNZrV6zw+EPwm/ikmY8hKmXJsqyaWYH4fbKYVqa6\nyYsr/vZZ9IQM3PxCFRrbAuoB/r68tacZbyaYksZMyLLTZLFzXxiVY1nm/rcP4PH/Hlbr59M88Yon\n/QYR/SNh2mKzPv74n/vws7f2G+K58ew74VPNm3q+/tJuVDYo5vQeDkZPEASR8qSckAUoi6LAaVOs\nNFlsQX9nnyKgqLvadMVDkox0p5JTT9VkcYX0TuhMe2K19d7jD6GAC3FgtVAoQUmjVwfDEpwRs9pT\nH5vnm7MKV2BlCmRCh14zp6lTd6nLIai+aWY4Y6gXfDonfF9QQliSIQCmscH0LHpup6qhA2C7GUHg\nNFkbK46hkvOH02sJreYJG0802n1Uk3PEE1A3VMiy9lnzypz5q3fg4X8ftB2XJJvvLmSaNHbM9O7K\nQH6GM6bJ8vNjHVi8bic+P+bVOL7zn49a+qApMG2x2XPyBsLoDEsa7Z9Weyjjtpd3I4PLXCBxGmIA\n+FvkpUeSZJwbERqtNkukiiP86Qb53BB20PxIHilnLgQUR2j+p9fKeZoJHg1tARw86Yvm5TMpmx4J\nW2Dmk7XvhE9TVoCgyV9o5kifnxlbyFKujX7mk/palbFCv5OMEdVkxe9RxKe+MYPXRp072qhZ0Wuh\nOoJhNaWOwDsm2dDC7VqM3mdjObbTE4DB4d6oyVLMuheOy4MvGFY1bsyxnvnKsTRL7J51hqObEPhn\nLegc87buPYkfXjwWANT6+XnAB63gn8eYPDf2nvCZ9JjrO2TLxOR6TvpCBh9F/qpYVdhpsgBF08lr\nshraAvjf/9TiBxeNUdvhNyssePpT03rCsowRuelw1guW/8O9bS4kFMjnhrCD5kfySFjIam9vx/r1\n61FYWIiGhgYsWbLEsLV57969ePXVVzFy5Ej4/X4UFxfjvPPOi1m3ICgLvT8kocUXtDQ78JoGbyDM\nabKMF7idIsISt71cVvylGtsDhtAQTMYw00oASmqfhVMLuAusx6LJYWdRlK/e6s3eajFiAo+duVB/\nrcth3DlpVj47zYFAyFjOrzMXSrIiCCtCr70Ax+AXcf6Z2GHYPacr7gtKeHHnMWS4RLxZ04zGiLlV\nr0ny6nZMBkJRoUP7LKz78vC/a/FBbaulU7+kSRCt1Ryxak9wu+5kWYnDlujuRrM0Pozthzy4YEwu\nMlyKiY89U6um3C5Ro8kCFNPpDy4aYwg0at8n5YXFKVoLWaTIIgjiVCZhc2FZWRlKS0uxcOFCzJw5\nE2vXrjWUaWlpwZe+9CVcd911uPbaa/HMM88gELB27GUIUH5812w/guvXVVo7vnPSQ3l9G2cKNJZN\n1wXglCQZ/6w+gWXP7zIs7qpPWOS7vrpWfwhnDsvmyls7nGs1WeZJfZmfDWAtr1ktRuze2AlNep8s\np2jvk8X6mOESseNIG77/Wo3mvD6KsigoQhbTZMUjJ/A7Bq3MvIAybtbahWPzNOf0z5nt+gyGZQwZ\nEPWZY+ZCJjQz8zBrr5Mzn/F1Mk3YCa82VhigDUvAowoh/FyLaDDVI5HHsYQliY6g5MxMTMoKmwh1\njHf2ncQb3O7DE7q8iXrSnaKleZrVzYSmoNk2Sg5RUIRcq+jxInllEQRxCpOwkFVeXo6JEycCAEpK\nSlBeXm4oM2PGDEybpuzPFkURgUAAohi7SUFQxAK2O63iaLupJoc3odU0deDeN/YZC0VI1+8ulIHq\n4+bJklVzoY1mLDvd3PFXj0bDIMd+c9fEhLLZSccIc+OxrlT71Ska0/3wsHvNct/tbNDGBdP7ZImC\noObS0xrYrNEIWZG/kixrnP31BfTaFb1AsnJLJJ6RpHVg1+8mZNo/9p1psph2U8/NG3cZjllGoY/8\n/Uf1CfyYi/QeK3G2slvQ/rnYwc8zJvdohyKo5e57c1/kvLW50Kqz7JJQ5MPlaz4zL8haFZT5tueE\nMZbbCW9QTZRN9C7kc0PYQfMjediaC3/5y1+itbXVcPz666+Hx+OB260kWM7IyIDX64UkSZZC1Ouv\nv44lS5bA6YzPQikKUU3Cr/9TiwyXaNjVxjd1kAtqaKbVyUl36ByT5WiKG93LuGouhPYvj5WPlB5+\n0bTSZMWD1dorxaXJ0uIU7TUmrI9WSYv1z0EAp8myrFULHyJA9aWTgCc/qjf0hd1Dq3hZehTtitF8\nxm5RVMjS+mQ1d4RM7wq/MWLP8Q4UD7aLk6TU8PEhT6RNZfODg/O3MrtLym7BxCPOhyUZF43Lwwe1\nraamOSY4v7jzGAJhGQVZLs3c/POHh/FhnfK/rmgPzTvC7kQMBZaKAEU4vucfxhegw57eD0RKKJDP\nDWEHzY/kYSvx3HvvvZbncnJy4Pf7kZmZCZ/Ph6ysLEsBa9u2bQCASy65JK5OKeZCQeNgneZQhCw+\nhpNDEDCpIBOj89zYuje61d5soZowOBN7mjo0AUsl2VxAEaDswLrnH3sxb0I+Li4aaKiPN6fYOe/y\nVVv5ZOnbjlUPT0gVsuKv0+mIocmKPEYrk5HefCsKUB3flc5a1632SaOmU/5IkA1aMgHRBV8fVsDq\nngR1miy9IOrXmQv5efZBrfGlgufbm6uxdvFUU0HJrE+SzJkLbe4LE8C7khR9btFAdWetJAOjB7rx\nYV1rNAm5yTWrI7tald270RI76ts0MbqsumEVA8sKQRDgtPhdiCcbAEEQRH8mYXPh9OnTUV1dDQDY\nvXs3zjnnHADKj/Dx48fVclu3bkVbWxuuueYa1NXV4ejRozHr5rftM5jmg1/4BUFJRzKxIFPjHK8X\nmu754hg4IiataMoP6+jvoiCgMyRh7wkfyus9pqsVH+bATn+j8QOTY++m4oUPvuiJjqDpAhy2EBSt\n6gSUnWF2azkbj1UoB70SQxCEqON7nD5ZfM2q4CuZCHaccJLu1J57uaoJZngDYU0f9c9XH22fj3If\niENFI0NWB8A0VtFzWoKSrOZ0ZJjd1XAC5sIffXEsd70ipImioApADW1RoUnfpj4Bu95lymp+sP4F\npfhUWYpG2jynoj67AEEQxKlGwkLWkiVLUFFRgU2bNmH79u1YunQpACXv10MPPQQA2L59O9auXYvt\n27fj/vvvxx/+8AecPGkMuqlHgDFMAFvweTOdYv4DRuRoM6K3B8I42BwNy/DFonxVAxbVXtnE4eJ3\nLQYlU9NJIuZCWZYTjgv0wNYDagBTnnA8mixTnyzrCxwxNFn626FxfIfRV+qkL4hLV+9QQydY1SdB\nNgh2vI+XXYBM3l3rP/tbsIuLp8XG+u4BJb1LUHV0j2hluHvhisNnkPULAF7aqc2nqb+tLDabQxBw\noNmHb72827S+sCTDEaeAapbrUYoIuQ7B3MmczbszhmYB0Eb1Z+1r6ovRk7jNhYKA6SMGmJ4jTVbf\nQT43hB00P5JHwiEcsrOzsWLFCsPxsWPH4pFHHgEAzJw5E3/729+6XrlgFBrY4ssvwgPSHWp8IZ41\n24/igC7xrCPyhs+qlSTZ0lzIltlMl6gJDaGtj+tupPk8txM3nTNMPS7Ligbq7T3NuKQ4Py5Nlh3t\nJkJK1PHd2Mnj3gCy052m5kL75S2GJsuwG1OImAsjWjBd5R6/4uvkDYTVSOGs5vpWP455A+qibypk\nRdpLs/HJcooCwhaaETaXXowIREwIYcdZsNrOkBSXJisYlrmk4PZ38rg3gJO+ILLTHfjsaDv2nvCZ\nOs1LMiDGEH4Zt8wchksnDtIce77iGBaXFkIUzLVxrM2SgiycNzoXr+8+oRGGeYFHlu00WcqJWLsK\nGaIADNLEEotCQlbfQT43hB00P5JHSkZ8N9ujxlKIMO3KTy8Zp5o8BEERuBhmKVtEAbrdhVFzof4t\nnr31D0h34lCL33RBMTP/ZKc7UDpMm3tvf7MPv45EN49rdyGANf93BHe+WmMQhEQImL96ByqORjVa\ndpqsr5RV4bH3DxmOK8FIrRc4ds5MYwIYg7Qyx3crc6FZlH3GLRs/R3VTh6Jdk4yaLHChNNJt8ulZ\nat0A/F+91qTHnmdNk7LjjfmTXTJhoGmuRj1BSTZsjgBYxHjtGL/x0m4EwnJMUyBLLs5uUSAk4Vh7\nwDRUhEsUMSDd+H7UFghDFAQ1NyEPuzvhyHPSa7L4Z1NzvMNgBuXHCMSXMoe1ayWsk5BFEMSpTooK\nWSZ+UpG/LBaSI+JXxLRDD315gmoyMnPKZYscH2CUmUT0xdkinJfhRHaaA8+WN5jWp/aXa1fvM8Yj\nQ7b132LXvFXTjKpGr6FfrL5j7dGFN1acrFZ/yKA9i73gR8rF0GTxaYc05kLZvrwyGF2fIiEOzMyw\nsgxMKsjEnPHGDQgMu1RAH9XphKxIR17brfgOhiVFwEl3ipb+Qzy8L5F+rIbvkb/KLknrm852IEqy\nDI8/hP/522e4cUMV7ny1xlDWaqjBsLJD0cxcyLS9IUlph88NCsR2ZGfR8tXAsV1wfLeaRyRkEQRx\nqpOaQpbJTqxOnTaJLUjMz6l4cCYmFihb60MmixkzabEzwbBkGjjy8kmD1C36bqeIOy8cjf3NPujR\nLhyCWg8vJJjtNIvp+A6osYP02iZWtcuhjGX+6h1q372BMNojiZr19en9wMzuL98eW0itBBc5Iqy+\nFUmeLMmyLq2OFrbm22nPHKrJUVsBC+L5peJ8S7MTADWx+BdNdoLqYUJSTkQbFJKU+zQy1x0zbyOg\nOH2zexrvbkBHDFNgWFK0tTK0QpKZiVi06GMwojEz07yqLwIyFzSW98mKMQyXQ0B7ZzTERbwCkq0m\nixzf+wzyuSHsoPmRPFIydyFgNLuMycvQbDHPcInYe8KH8fluVVJ0cG/retgixxaW5yuOYfpwxSGX\nX5O+M3u09jpBME2+zC8cbOdjWNL6avGEpahAaEvk9OTCTKMmK3LS5RCiuwojff/+a3uQ63bi+a+e\nYV8/rLUqvI8SK2eGJAMdQQl/eP8QctIdnEYxInjoy0fq49dUfc2qJsvEJ0uK4761dSrCiNWC/q99\n0RAfTJPFhyMQBAHXnlGIa88oxPzVO2zb4n2y9GO1EhscXMocc58sWTXhxfK9t9p0EZQkiIJgKpgJ\nUMZ5oNmHSQVZEKHdJRpLaApJMq5ZuxNlX5lm3zkdomCtETUzaxK9A/ncEHbQ/Ege/UaTNSwnTfN9\ndJ4bQ7LT0NAWUBdg9mNuZspQYjkpi9mCkkEoyHLhhI+lF5FRPDjDtB+CYJ5Tj60bP5k3Vv1sZy4M\nhqX4HN8jTbmdDmP8rsi1FUfbsTZiwmRb6SVZSRzs8eu0WYKxTaPHW6T/qlOz/eLHAmy6nSJunD5M\n3bXJQm8c8XSa1suP5++7T+Bwa3RzgkOI7pAzNhh/jjsrP7IH/1WrfmaanjAnVHZlP8JHdR6c9IVY\n1wx9NcPBCbb66TQg3aGJkxVL9rAzFzrEaCwsPVWN7fj8mOKH1lVzYbyO/obrBMFS8I3X5EgQBNFf\nSU0hC8bdb3qtiigAI3LT0RGU1AWYBQ01d3yPaLKgDHpghkvNSReWZcybkI8fzx1r6IcgCKbb1XnN\nihoKgjMXXjutENdOK1TLBMKypeP7VVOiyaZZXsCQZIzJztaqTZVNeP6zRgBGgWjRczsBAP/lgmq6\ndbvyYmmy2O40u9QxbCyK9oXdVwFsOb6sJLr7jdX7m3frsLMhmuR5/4moGdYhMp857W40QRAgwaj5\nssJp4xzPMGiyIlqkeHmp8hjqWvyaOpS+KvdmYIZRQcxHrudvfUGWK5KWCJGo8EZB5rfb6vDrfx/U\n1GVGYXaapc+fIAiqU39IimrNGLE0WR2Ra7sqGAk2miw+zAZBEMSpSEoKWYDxbd+46UxAlksbDuDy\nSYMxKNOFWi58w5eK8wEo4Rg+qW/D7987BEFQzHHMzCfJiiBysc6xWoCiYbFaWH61oAiZLocqDPGa\nrBXnjcCF46IJjUOSrISbMFkE+TWTLYSBsGQwU5rFf7QyuTzzyRF1DHw6mtsvGGnpk8XaC+gEN70m\nggkCohDdB8qELjaWwVlRQYkJDdVNHXh3f4tpf1VzLmSNkKI40sevaYonfpkaJyvyPSQlHr9Mf/sV\nIcvoO+YQoveBv4TdQ0lSnNYl2fic/1l9Am/vPcldY+zHdaWFuO28EdaCsSzjvjf3A2Dj1QpzpiZ2\nk7oCIWsh69zROYZjdubCbQfN5wLR85DPDWEHzY/kkZJClqIR0JvKjL46zHeFf7O/7bwRmnI/uGgM\nAGDq0GhoBVEQ8O0LRmF9xL/E0kxlYy4EgBkjc5RFTdVkaRcmrbnQWpOlJ9ftVMyLuuNm8Y86TbR2\ngHbxZ02KAnDllAKDFiN6jfk49YmZpcjTERDVijFTKCvp5JzTeCH1/w5Hd/rxrUV3f2q1Vux+xSsE\nxVNMNRcyXzEpfk3WrTOGab4bbpksm/rliaKgCk+89ivNIUTCi0Q0WZA1z4G/d1MKs9S69GSnOeBy\niJbj5zWeoYjvVizLn5mZz86P6vbzR2G4zqwvQLD8kUlMrCWSwcqVK8nvhrCE5kfySEkhCzAuXvq3\nakGIai34hcUuXpIeflu7lQmG7UrUtK37rIaCkGTNAsgLBkEpTp8sAFdNGayaF3k8nUaH5uqmDgwd\nkGY4zq493NqpydfI+hWUJM24WnxB1STEj+2mc4ZhXL7WX02OaFtEIeo/x5z61R2Q3ED525fhik45\nfngsCr2kl7Ii18eraIonRTVzXOc1WbHIjgRRzXA5NMf1LwMyzIUThyiY5hRUBCOBC+EASyErLZJW\niK/+2cVTAHCCtMX4eeEoLBkjvpth9r9kFYT0+3NGozDbZUgqbvfcuvK/ShAE0R9JSSHLzDGbF1iW\nf2E43E5R/ZHmf6vj2YLPF3EIygJk5crDUsZo+8L3NaoR0Idw4OtUNFnmZikBwI8uHqN+nz0uD8Gw\nbNAsnfQZA1MCii+OHib4HW7tNGyVFwAcaunEz97erx5buqEKv3uvzlDPWcOzDQ+DxRgTBCHq66aT\njfjnwAsKl08erH7m+1Xb4scTH9ZH6uEEVd3fWMQjjOl9ksI2QjaDzTVeSAR0CcAjmjg29kevKI5e\nb+H4nu4U1ATbSjBSWbMLkxeOmNmXn2NDBygppdR5ZTGMEGeDZObRWOEnTIUsC8lsSmEWBEHA5Ii2\njaEI4tp6xg50q+cIgiBOZVJTyBKM8YdEIfqjfH3pEE2QQ/5HPB4hiy8vigLqTvrNYw/JUJ2SGfmZ\nTtXPi/UVgMZkZtaOJMuQYH3D+XVoQJpT8cnSrWdsR5seM58x/va9XNWEfN7PKdJWdWSnGQB0hmXD\nrkCAjV+nrYkIVeyZSLIih/Fj4B3Q//jBYfVzBucftuuY0fHZLPl0PCEcouVjl+kMS5q4VWEugrtl\nvZG/mTpNVpM3KvhKYPG6lNIjc92qpsntFNAZYpos3lwoRvyjlLn47/0tms0B/KNlWiIzgTCqyTKH\ntyqHJdkQwsEMM/82ZrK+dGK+5jib93fMGoXXbj4T90Y2kQgQcO7oHJREYtgBwPIvKCZ90mT1HeRz\nQ9hB8yN5pKSQBRj9RURBQFF+hiYZNPuN5gcRzw83XyQYllHb4kfxoExDOUVbA40D+o8uHovvz4lq\nndgCqfok8QIcV1eYM6mZwR9PdwoRc6H2Jlj5XzHNgKbvuvt31vBokl4rk5IZbPcgoIuWzxzfI+bU\nP/33UMSJW6mbNxc2tvPxzaJCynFv9DhDlo0CjyzHr/XQT+h7vjgGU4dotSsdAUnNGABENVt2sD6N\nzE1XfaOAaAYC1k8gKugLiGoZ3U6H+vz4Z5PmECFAUONktfpD+P17xlRIQNQ3zrjvFNwLh3n/Q5yZ\nr3RYNiobvfj+a3sQCEn4yvpK02vMXlg6QxJG5aZr/geUdgW1H2lOUe2rICgbAfhci6pAmOBmA6L7\nkM8NYQfNj+SRkkKWmbnQIQr4zRUT8cTVJdFjJj5Z8Wiy9HypOB8jctMNx+WIM7LdFn+27OljZAHa\nRURJSG29CPKH0xyiqSbLKnmx3kSj9F17cUlBJlYvmqy01YVbpAk9EDmmmgcjmqwWX0hN9cPqthJ2\nWTiJn8wba+o/pReuzeaCHWyxf+yqiQCUcBA5ujx/HcGwxnynZAiwvykClHyZYwa68a0LRmrOsbyZ\nioYvGrVeyREo4B+3ngW3S0Szzty7ctYofO/C0ZBkGSd9oZhCh1MUkOESMThTax5eNW8cFkRCZlgJ\n8SFJhsshYPOyUo3A3dAWUDMM6DF7hj9764BpG8ZYbMyUbzTpeyOpi+KItkEQBNGvSU0hS9AutheM\nycXCqQVwO0W4OU1IouZCfjF75aZSfO/C0ZZlmaaKCQdmoSRYSAO9eYX/GtX+WLfDcDkEhCI+WTnp\nDtXp2ipIqFmyYLO8h6PztL4wLf6QbdwjRYgSsO+ED41tAY3vGb+bsKlD0UjxdVkFBWVhM9IdosYM\n+T+TByM/0wm9uCPDqMkak+fGT+aNtewzAOS5lTAK6U7jjruOQFiNy6X0W1v/178wHACw5KwhXMXA\nrLF5EATB4OOW5mB5/aCJWs+qdIgC0hyCGpWetTt7bC4GZblUk2OsqetyiHjlpjPV9FGM2ePykBWZ\nI1b/0EFJRpbLgcw0rbmT1zLqsfpfMpPj9AJidFeo8byHyz9KEARxKpOaQha02oszhmYbAmoCUU2W\nxpE98uV2nbYBAM6LxPHh14MMl8Pyx17xM1LORdvXLSZQfFtiarJUPyaLhYt39hYEOB0COkMSbpkx\nXJNv0Qx9Tj91l55N/Ywv//VT9bNei3TG0Gz13vpCYS4eWDTZtSgIaIn4igU53yaroKAlhVn44cVj\n1F2bUyKmvIEZTmS5HGrkeJ5/7z+Jdm5nZdGgDMwZZ56jkF3KBAR+txvrkjcY1jxFvU/WdaWKcMU/\nK97EOmGQdrdlulNrxjObl3ysMlmWccmEgcjTxdOKFeMrnhhgZgq5nHQHQpHk0Xp+vvWAZVWW4rfJ\nCX3X2NiZsM3fizmR+HFkLuw7yOeGsIPmR/JISSELgrnfiR62aPACBFtcJxVk4bVbztSUvzOisYp3\n0LIc3XXodllosgBANsbI0pc1c4y3Kgsoi1NnSNF8eSO56PRBQhkTCzJx90VRH5mQZAz/wK9nVn3Q\n137ZpMGqsOaKOIoLUPrD/JhEIRphP8ylp7HSgjhFAfMm5Ku5/PhnETTpN4PXuNhp39jCzWQrXpPF\nNjf4IlkCWBeDkmTqp6bdxBD9nKYT+NM5TZbSDrsmelG67hpTk1sMzY4+XpkZbPyPXD4BAHDJhIG4\nckoBXq5qMhXSrPz8gGh6pHNGDNAcr23xG8rq7x/7n0wzCRrGNK9WeT6Jnod8bgg7aH4kj5T8mePD\nIthhpjFwcm/O+h949lYd7041xe9IqxHRXykISgBJM02W3lx48KRPo5Ex4/VbzwKgCALbDrZo+mrl\nkwVoQwscaw/YpiXS+0LxiZIZd81RBFI1nYoMQFY0VFlpDrT4QpG4WIJqxlRyAGp9caxgcaN4k6Fi\nItX2j/WNv28hm8mhatIizyLNGV3+WZ+CYa3mKhSWTTVAvFBiNxwmdOlDOPDTgZ+LknlzMX2UumJe\nGzNQ0bYJgoAbzjJq5uJFgOKLFrOclSbLYT0fSJNFEMSpTsoJWffOHYs7LxwVM1AiYL6jinc61sMW\nunh/2yVEFwvmy6IPo8B8tvQxsgDtIhKWZfzxg8M4cNKoBWC+T+DaY0LT5MKo/42VJgvQCgQHm/0G\ncyH/XT/+VW8p8bKavEF1oc+PmCCHRHbHSZKs5hDMy3DipC+o0QYB2nhisW6xKAioOBpNWDypIEvR\nZHHi4fh8N7IjTuW8eGkXPJS1y55/mkNQBVXWV14YZN/1/wi3zhyOS7hQHXpN1KalZxjOsRyOUSEr\n2kauW+s3F49fk56cdIfteaWOSP2I/jUz2f1xYYnqsG9HWsQPcuWsUTHL8rChuEz+51g/Gtqs/cEI\ngiBOBVJOyLpo/EBMHZJtmmRXj0NdUKK/4GYBShnsrTruHYhy1AwiCkrKntJh2ZoirCZzn6zoZ7O8\ng2awS8ZGoqwzjQRg7fgOQONv0+IPGYRBHxfNXT/8j+qiqW70AklhdhrG52coGqdICIqsNAfaO8MQ\noV08w5yKhj9utpjzQuEfF5ZgypAsBMMS+I1+ZwwdoGo0b+HS2egdz3lY/3kNpF6TxcbHvimaLe1N\nueHMIZogr/pI5rxmKp2FVpCZidko/PN+c7/dVmcqKOqFLJeo1TlOHaKde2awK3jHc8GkPxMHZ2q0\nn49cHg2cysPG9j9cEFkzzEKuANGNDpo+CoIm1hzR+5DPDWEHzY/kEVuSsaC9vR3r169HYWEhGhoa\nsGTJEuTm5pqWbW1txd13342rr74aCxYsiKv+H148Bh1BCd/ctNtS4DLTZOW5nfhi0UAMzjJGQRcE\nAWuum2yawNcMGbLGv+bqaYUmdSoLTFgy+pjoHd/jwc6UaeX4rm/r7T3NhvP8YmfXhtluMIeojI/5\nlGW6RHgDYSXiOycGBDmfLHb92IFu/Hz+eCx7fpemHf5eDRuQBqcoqL5kqhZGUPzEXI5oMvCfzB2L\n0SZxwRhq7DQB2HJTqc5cqB+r8vCCccTJ0muyeIGamQuPdwTR5A2qmyT4KscOdGNUXjoOtSh+Ti0m\ngWV5QdkpCnj0yom4fXN19FgcPln656d91tbX6yPZM/TjtkKy8KG02mVqdZzoHcjfhrCD5kfySFiT\nVVZWhtLSUixcuBAzZ87E2rVrTctJkoQNGzZgwoQJXap/6IB0jM/PwMYbz8AXi8x3kpnF4Elzirjn\ni2PVLe16RuS6DdvYreD9g6yWBAECfMGwqbmQF2ximT8NCbFNytiZC/l70BmWNDvgfvzFsbhiSoFp\nWUM9rH2uDIsVxpRMmS4H2gNhNXchwyxyuoxo6pezhmuTdANK5PAB6U5VyJIga0xdzM+J1Ttn/ECM\nHb6ZP7UAACAASURBVKjd3cfz5Ui8KIcgRMN9mAiOgqC9x1a35M3lZwMwarI0Owe5c2/taY6a7Lj2\nhuWk4+lFU9TvZk+S17CeNTwbEwdnatqJRzAx3Zhh8jnWdb+4dDwAYHiOMX6cGQN0/1NRnyzzn5h4\nBEaCIIj+TsJCVnl5OSZOVAI+lpSUoLy83LTcli1bMG/ePGRlZcXtcM6T63ZaXufgtEw9hWiyQPPk\nup041NqJ3ce8ht1h/ALz2udNtu3oFV1mi3C8mqxAWNIs2IOyXJrvtoutiT+RQ1TCLbCI9Zlpjogm\nK+q7BWh3F6rNcQPR1wlE7xH7HuIENfaXRZePxcAMp5r2xmDGhNaxXIBWyoo1h+x2B+YY/K1i99Xs\nWc4am6ea7aIvENG64jNza1884v3P0PfZ7RTxwlen4f4vjY957biBbk38Or4+qx2RiQQNJgiC6G/Y\nmgt/+ctforW11XD8+uuvh8fjgdutmG0yMjLg9XohSRJEzuZRWVmJtLQ0TJgwAW+88UbMhLRdxWx3\nYTLh41pZNTFkQBqy0xx45F1jcmV+gdl+uA0A8ItLi9ARDONX7xzscn88NjsT+XsQCMnIc1vfFDuB\nRV3cuWMszx9zucp0iTjREYQoCCgeHHXM558uW2T5Y3ysM3ZrHDohQu93JkPWaLJiYWbu/GJRPgqy\n0rDtQAs3Tp2WJ0b9Zr5FjHED3Xjuhql4dVcTnq84Ftd8NNNKClDS9rD+6fsVXwiHaF3aDzGu0313\nOx2GOF5WmDrxR/6yPhfla7WPZC7sW5i/DZmFCDNofiQPWyHr3nvvtTyXk5MDv9+PzMxM+Hw+ZGVl\naQQsAPjkk0+Qm5uLzZs349ChQ+jo6IDb7cbFF1+clM5PLszCRePzLE2D3UWGUatixgPzx+PO1/YY\njpsJM6Pz0g1JhgF7c+ILX52GJz+qx9a9Jy3L8G0FwxJc3LPQ98JuLKrmjlsEHYIApngRBMVcWBfw\nq/XOGpuL9w+2akJeRBVZysD+cs0kjdZLNNF0KEKWpF7NQnnETnoDFA/OwMhcN5yigB9drM2rd+G4\nPFw4Lg/vHYwKWWkOMW5TmjJu6xIOUUBhdpqqjTMLnMtg98osPpUky8hxO3HF5MFqkFblPin3sCva\nH8FEE2b33PXn7Magx+zdSc1lyHzz8jMwPj8D+5t9AAAnBcnqU1Jp8WzxBeMK2UP0Hqk0P/o7CTu+\nT58+HdXV1Tj//POxe/dunHPOOQCU3VUnTpzA4MGDcdNNN6nljxw5gvHjxydNwAKASYVZuHfuuKTV\np4flLgTstWVTh2Zj2IA0HI1jS7qSVNmkLZvgq3kZLtVcleESNTsFo+Y53lwo2/q8xKPJ4ks4RJZK\nRzEHZqYpju+s7E8vGY/5q3eYh4mIHBqr02QwgYQXHFwOEYGwjFljcuAQo+ExBMQ2wf1p4ST189wJ\n5jvXNKY3hxDppGw4Z0Y8mjRVyHI5VF8uPffNG4cFT39qGvNMisTYuoMLl8A/xq6kjFJfDmJ3W3Md\nw23hCA8A15psADHWp/zlNwjw8+OCMbmoafKqGl7i9OW58gZ8UGu0mHQXliOTIPqShIWsJUuWYN26\ndTh69CgaGxuxdOlSAEBtbS3++Mc/4pFHHlHLvvPOO6itrUV7ezuGDh2Ks846q/s97wWG5aRzmqzk\nmDccgllaZMTMgnzmsGy8vvsE3E6dkBWpLMetaMcuKxmEt/c225qW7EbCLjNqsuRIewIyIo7v+sTL\nmp2Baupsq3bMNVmBkITxgzJxw1lD8dTH9aZpdhLFodu9xxOrDbvTTEBhdcYqe+lExXypRzJRZ3Zw\nz9rKiVzTT51wZTcuXnugD7NhpcnKdIlYcd6IuPvBm4OvnFKgRpEvHpyJXy6YgPmrd8Ssizi18XSG\nLJOUE0R/J2EhKzs7GytWrDAcHzt2rEbAAoC5c+di7ty5iTbVJ7y8rBQuUUBH5G3ILgBmVxBFc4Et\nVu1sl5d+8WPdGpnrxpvLz8bTH9crmiwbrYdd5HDVzMT3WRDwr30n1VQ6WS4HvJ1h5Okcvvlt/KwJ\nq9vGrJlaTZaAzrCs8SsKS/E5vceD3olc0JxLvF5VoIhDyAKA788ZY3rcZvMogPg0WXrNJn+FXRiR\nvAwX7pw9Co++dwiAvSYrHtjd5eXCWLG2iN6DfG4IO2h+JI+EhaxTHebnxfLyfS+S97C7iBaaLP3y\nN2xAGnY1etXvzFzIC0hjB7oNwVHVXXs2uwntFmuz8AMOEXhn30m8s0/xCWPCkF5Y5B3Uo6fMF3aH\nTvvD+uwPSZoQDlIXnN5jwd87p6g32ybeiGoa62ZHY+XrjM9cqPyNdiV6TWfIvn5R9yy6g9kGBDMe\n/vIEyI1Gf0aiZ6HFk7CD5kfyIO/TGLA1YnCW/U4rq7Xk2cVTNN/1saVUdOvfnbNH4yUudcvgTJdh\n4btv3jjcfoE23Uk0nYz1o7WNk6U6LHN1ch3+zuxRcIgCQmHJIJZIZuZCi3U96mMWNVM5RQEBPq+g\noOxqtJukw3PSsGz6UJsSUX4xvwiPLywBoKQGsouCr8feaVw5qeZ+TFA+iZUVIL7chVoNFn/JrLHm\nwYK1V0Y+d1NgNPPJMuNsXfJpgiCIUwkSsmIwIN2Jv10/JWGTFQvEyXAIgmEBEyAYtBhpThEDOJ+n\n7HQn/n7rWRqhZVSeMfI5E4jMzqnt6drn18Fo7DH+WPTLoAwXHIIQiZKu12RFtVvslJXuJCvNgbKv\nTFODhwKKM3owLGlMXWHZfsF3iAJunD7M8jzPkAFpmDA4Ey/eeAbumjMGF43PU88l4pO1cKoS4JX9\nE7UFuudoO2SA0U8LAAqz4wulAPCaRO1zAOJL2p0IZs9YMBHWCYIgTjdIyIqDeKNeW8E7FYui0Vy4\n5KwhSdvCHA3yKWBKoRIGYHiuff/5xdUsCGYa5wfmdolwiOZmPH4IrEq7cQ3KdGnaTncoTv18uiQl\nEKl1HRbbCGzJcTuR7hQ1jvuJ1MOSZzOBgjmu+4NxJqrkOGNoluU8C8Qw89nBjyumti7y94WvTku4\nPbUt9pfiYaUklJuOsIPmR/Ign6ykYb2YPHZVCW5+QcndZ7bm5LidMR3fGRMLMlEf2aFlBtNE8Sa+\nWLkaHYKAoC6UgSZVEaeOyHCJqp+afixagarri+vdF4/BCW8QkyLCoQDFhGaba7HLrUTJ5Jy7Y/lD\nmfVBH/iThWVoT0ijZT2SYDc2XfBW41iCJBOI7IKQxtsTs92FROpAPjeEHTQ/kgdpsnoBfpmxDOEQ\nJ3dfNAZXcXkI9bAgpKIQW3BgOEWjtkPj+M59znA64FTNedqRsDhafD3x9gFQTKtTh2YbYiv1lDKk\nIDtqnktEjtGb5FgU99E2ptpEsEunFAteIynG+G/v6o/Bo/9THEf7XayUIAjiFIKErF5Ab0Yz842J\n11zoEAXMHDXAciFnya8dotClOqP9M2qy2PlV88ZhRG66+t2gyYI2TtOYPDdKBmfF1wkTxIjju+06\n3Y1F/LJJg/HcDVMBmMeoitVMVKBUPrEdqckOVcBHyo+F/plrnm2Ma7vqdzh1aLblOdaPnswrShAE\nkeqQuTBJ2C0l+l1bgmqai2pQuqJI+cKoXHxhlPlOMWYC64oTs5kmS5PMOfJx9jjFUVwUtRochmZ3\noQD8aWFJt3xyBCG243t3lnBnJB0OACSiK9KHq7j5nGG4epq1ljFRLp04CNckWC9/+2M9i3jkITPB\n3Sy0ROK6N6I3oDhIhB00P5IHCVm9wNABach0iWr0blEQ8Obys3H/2/tVJ+lkJc9m2hTFXBgf/CJp\njHMFlBRkYdxAt6G8QZDjAmUJgqBxmE8USeo5c6G+HTvMBBC9aTTNKaLAab5DsDuIgjbUhR36fmrM\nhRZS1BURzVui/lM5buPPSLKTwRPJhRZPwg6aH8mDhKxeQBAEnDs6F//ap03w/NNLxie9LdVcKAiY\nNyEf+TGc3oHYztGzx+WpWiylbuVvui4WVzS1c/c0TNG+KDkTbWNUJaEdIDGfrGioiZ6VArticrM1\nF1pUw3IlJmrZ00f+N+sHQRDE6QgJWUliVF66bf6t5hi5ubrid2NHUX4GVs0bh+kjBiAzzaHGcrLD\nYaLJiqe8PsUPn7swGdqnkCRjZ4MXooUQs3LWKAzPSY7myC7lDGAh6Kim1aR0wZLu6AP5vsXqZiL+\nU3fNGY1pJr5ZqWoubGhoQFlZGYYPHw5JkjBo0CDMnz8f7e3tWL9+PQoLC9HQ0IAlS5YgN1cxyW/Z\nsgU+nw9erxelpaWYMWNGH4+CIIj+AglZSeInc8fZ5jesbuqwvX7GyBxsXlba7X44REGjdbJi5axR\n+MP7Sp46szhMdsstMxfqzYG8oJIMh+ete0+isT2AAoto+8l0ME9IkxX5mwzfbvuI8vHX0+oPab47\n4jAXRsvGrl9/m+ZPHGReLkVVWV6vFzNnzsTs2bMhyzLuuOMOzJgxAy+99BJKS0tx3nnn4ZNPPsHa\ntWtx++23Y8+ePaiqqsI999wDSZJw5513YsqUKcjMzOzroXQL8rkh7KD5kTxIyEoSaU4RdjoVf0hS\nU8lYwUx9vcHlkwZh3oSBuOqZCjXuVbywxZofz48uHqMc18WO6g5MaE1Wgmg7YuUFNN1d2Avmwl9c\nOh7Thljv4tNTc1wrzPMWXb0P3eAsF05wGlY+pEV3GZyZfN+0ZFBUVISioiIAyvMLh5WYZuXl5bj2\n2msBACUlJfjTn/6kHi8pUVIxiaKIESNGYNeuXf1em0WLJ2EHzY/kQUJWL3HrjGEYmCSTYDIQBEEV\nXjqCXQueyRbrdE71MXdCPoBoTKdkyEUsxlZPy1i/v3Iixg60j21l1gVjMubkY7WLNF7YM374yxMw\nuVCrffnVggmaGFyj89x4c/nZlnUNznRpshfYMSjLZVtXKrBt2zbMmTMH+fn58Hg8cLuVOZCRkQGv\n1wtJkuDxeDBixAj1moyMDHg8nr7qMpECHPV0Gl5mkoEoAKNz3UnZMESkDiRk9RI3nBVfEuPehAlL\nv/7yBIRlGXf9fS8EAGcPH2C6Y0y9jttNpyep2p2IxamnfZ4mF8YRy8ukD2YxxVINZi40S8Ss7ESN\nX3v6l2snnTJxryorK7F3717ccsstAICcnBz4/X5kZmbC5/MhKysLoigiJycHPp9Pvc7n86m+WsTp\nyXM7GvHcjsak1zsmz43fXVFMQtYpBj3N0xinqISSmDo0G6XDlEV4QLoDD182AWkO66mR63Zi5axR\nmGPi+8XHyeouzKunp3fvxYNZD6LJtPu+f1YkUwDMTneqIUL6M+Xl5aioqMAtt9yC5uZm1NTUYPr0\n6aiurgYA7N69G+eccw4AYPr06aipqQEAhEIh1NfXY/LkyX3W92RBuekIO2h+JA/SZBEa7PLWMQRB\niOl0nozFXe4lTVaiMC1Rd99URuam4+zhRk1TdxEAFA3u3w7ayWb//v343e9+h6KiItx///3w+/1Y\nsGABlixZgnXr1uHo0aNobGzE0qVLAQDFxcWYOnUqysrK4PV6sWzZsn7v9A6Qzw1hD82P5EFCFqFy\n7qgczJ+Y3606oml1ui8Zjc/PQPmRtuQFw+oG5gmiBctzXeGv103p1vV6mH/ZGynuE9UXjB8/Hs8+\n+6zpuRUrVpgev/LKK3uySwRBnMKQuZBQeeDSIpw5LDkalWRY0H5+qRKstRv5kZOGubkw9Xyy5ozL\nw2WTzMMqEARBEL1Lwposu+B9PDU1NXjrrbcwZMgQVFVV4fbbb8egQbQInKok0yeL+YUd8XR2v7Ie\nwMZtrc/4ybxxfd0Foh9AcZAIO2h+JI+EhayysjLT4H08HR0d2LBhA1atWgUAmD17NrKz44/5Q/Q/\norsLk8Mz10/BK7uaklRbconuLkwhVRZBxAEtnoQdND+SR8Lv4uXl5Zg4cSIAJXhfeXm5ocyOHTuQ\nlZWFF154AS+++CJaW1uRnp6eeG+J045hOen45nkj+7obppo5h+qT1cudIQiCIPoFtpqsX/7yl2ht\nbTUcv/766y2D94liVG5ramrCp59+ittuuw1paWm499578f3vfx+FhYVJHgaRaqRyWINEsAvhkEo+\nWQRBEETqYCtk3XvvvZbnrIL38WRmZmLcuHHqlufJkyfjww8/pN06RD/EKEk5krS7kCB6G/K5Ieyg\n+ZE8EvbJYsH7zj//fE3wPlmWceLECQwePBhTp07FCy+8oF5z5MgRFBcXd7/XRMpzOogdopicOFkE\n0dvQ4knYQfMjeSS8PixZsgQVFRXYtGkTtm/frgbvq62txUMPPQQAGDFiBK6//nr8/ve/x8aNGzFt\n2jTMmjUrOT0nUppTTblDPlkEQRBEV0lYk5WdnW0avG/s2LF45JFH1O/z58/H/PnzE22GIFIC27Q6\np4XejiAIgugqZOkgeoRTTewwC9MgkiaL6KdQbjrCDpofyYPS6hA9gvMU2nL36P8UY2x+huG4mrvw\n1BkqcZpAPjeEHTQ/kgcJWUTSeXlZKdwuR193I2lMHWoeQFc1F5IqiyAIgjCBzIVE0slKO3UELDuc\nkZAlDhKyCIIgCBNIyCKIBCnMduGPV5WgMNvV110hiC5BPjeEHTQ/kgeZCwkiQQRBwMSCzL7uBkF0\nGfK5Ieyg+ZE8SJNFEARBEATRA5CQRRAEQRAE0QOknLmwvLy8r7tAEARxSkO56Qg7aH4kj5QSsubN\nm9fXXSAIgjjlocWTsIPmR/IgcyFBEARBEEQPQEIWQRAEQRBED0BCFkEQxGkGxUEi7KD5kTxSyieL\nIAiC6HnI54awg+ZH8iBNFkEQBEEQRA+QMpqsiooKbN++HTk5ORAEAYsWLerrLlnS0NCAsrIyDB8+\nHJIkYdCgQZg/fz7a29uxfv16FBYWoqGhAUuWLEFubi4AYMuWLfD5fPB6vSgtLcWMGTP6eBRaAoEA\nfvzjH+PMM8/E0qVL++1Yjhw5gpdffhlDhgxBdXU1Fi9ejKFDh/a7sbzzzjs4cuQI3G43/H4/brzx\nxn7zTFpaWrBhwwbU1tbiwQcfBICE+n7w4EG88cYbKCwshMfjwdKlSyGK9F5IEET/ISV+sTo7O7F6\n9WrcdNNNuO6661BbW4vKysq+7pYlXq8XM2fOxOLFi3HDDTdgy5YtaG5uRllZGUpLS7Fw4ULMnDkT\na9euBQDs2bMHVVVVWLx4MW6++WasXbsWHR0dfTwKLRs2bMC4ceMgRJId98exSJKEJ598EitWrMCi\nRYtw2223obCwsN+NxefzYcOGDVi8eDEWLVqEffv2YdeuXf1mHLt378bMmTM1x7rSd5/PB1mW8dhj\nj+GGG27A1VdfDVEU8e9//7sPRnNqQj43hB00P5JHSghZNTU1KCgogNOpKNZKSkpSOihpUVERZs+e\nDUDJXxcOhwEogVQnTpwIQDuG8vJylJSUAABEUcSIESOwa9euPui5Oe+++y4mTZqEwsJC9Vh/HMve\nvXshyzJeeeUVvPjii6irq8OAAQP63VhcLhcEQYDX64Usy2hra0NWVla/Gcd5550Ht9utOdaVvldV\nVaGxsRGBQEDVdpWUlGDHjh29OIpTm5UrV5LfDWEJzY/kkRJCVmtrq+ZHOTMzE62trX3Yo/jZtm0b\n5syZg/z8fHg8HnUcGRkZ8Hq9kCRJc5yd83g8fdVlDYcPH0Z9fT2+8IUvAABkWQaAfjmW48ePo7q6\nGrNmzcK1116LN954A1VVVf1uLE6nE9/5znfw0EMP4Ve/+hXmzp2LMWPG9Ltx8HS17x6PBxkZGepx\nt9vdb34TCIIgGCnhk5WXlwe/369+7+joQF5eXh/2KD4qKyuxd+9e3HLLLQCAnJwc+P1+ZGZmwufz\nISsrC6IoIicnBz6fT73O5/Opb+h9zccffwyXy4XNmzdj9+7dCIVCeP3119U+96exZGRkID8/H//f\n3r3HRV3n+wN/cZHLQAPELa9oiggiK6gbXuq0mZdV1EmxIjV3N1vW8uS6Hts81uo5Xdxt1dpaj25p\nZISQt0jKkyZmIptKDkZAMoQy3BEFZmQuDOPw+4Mfc5wFRnC+w8wwr+fjUfL9zne+3/d3+DDzns/3\n/f187rvvPgBAdHQ0zp8/73Dncv36dezcuRPvvPMO3NzcsGvXLmRnZzvcedyur38bfn5+dn9ORER3\nYhc9WeHh4WhoaIBerwcAlJSUIDY21sZRmSeVSlFQUIBf//rXaGxshEwmQ1xcHEpKSgB01KVMmjQJ\nABAXFweZTAYA0Ov1qK6uRmRkpM1iv93ixYuRmJgIiUSCcePGYcyYMZg3b55JzI5yLuHh4VCr1cZ6\npJqaGtx7772YNGmSQ52LUqmEj48P3NzcAAABAQFobm52uPO4XV//NkJCQuDh4YHm5mYAHe8JcXFx\ntgl+AGLNDZnD9iEcl/bO60M2VlBQYOx1cHNzs+u7C69cuYItW7Zg9OjRAACtVou5c+di0qRJSEtL\nQ3BwMOrr67Fs2TKIxWIAHXdQqVQqqFQqxMbGGj9k7MX58+dx/Phx3Lp1C7Nnz8bPfvYzhzyXCxcu\nICcnB2FhYQCARYsWobW11eHO5eDBg7h16xbc3d3R3NyMpKQktLe3O8R5FBcX48yZM/j+++8xa9Ys\nJCQkQKfT9Tn28vJyfPnllwgKCoJKpbL7uwuzs7OZCN6FN05dxekrzf16zO9e7Jgnd/Kb2f16XHPC\n/L3w9oJw+HjaxQUm6iWpVGp23mW7SbKIiBwZk6y7wySrA5Msx3SnJMt+vxYSEREROTAmWUREToY1\nN2QO24dw2C9JRA7r0KFDSEhI6DIuF5nHMZDIHLYP4TDJIiKHNXr0aFy4cAFarRZBQUGIiYkxDmpM\nRGRrfDciIofl6ekJrVaL77//HmVlZWhsbISXl5dxRgYiIltikkVEDis9PR1z587Fb3/7W3h6ehrX\nkXmd9Ta8LETdYfsQDpMsInJYixYtwqhRo6BSqXDz5k0EBQUhKSnJ1mHZPX54kjlsH8Lh3YVE5LDK\nysoQEBAAPz8/nDt3ztbhEBGZYE8WETksHx8fqFQqcExlIrJHTLKIyGH97Gc/w4EDBwAAc+bMsXE0\njoM1N2QO24dwmGQRkcMaPnw4EhMTodPpUFpaimHDhtk6JIfAD08yh+1DOEyyiMhh7dq1C76+vvDy\n8sLVq1cxbdo0W4dERGTEJIuIHNbgwYMhkUgAABUVFTaOhojIFJMsInJYFy5cQGlpKXx8fFBbW4tX\nX33V1iE5BNbckDlsH8JhkkVEDmv16tUYPnw4AOD69es2jsZx8MOTzGH7EA6TLCJyWDKZDDk5OZg8\neTKam5sRFBRk65CIiIyYZBGRw/L09MSQIUMwduxYfP311716TnNzMzIyMiCXy7F161YAwMGDB1Fc\nXGzc5rHHHkNMTAwA4OjRo9BoNFCpVIiJicHkyZOFPxEiGpCYZBGRw1IqlSgvL0dDQwMMBkOvnnP5\n8mVMmTIFcrncZP3mzZu7bFtaWoqioiJs3LgRBoMB69atQ1RUFEQikSDx2wprbsgctg/hMMkiIoc1\nb948/PTTTwCAMWPG9Oo58fHxKCoq6rL+yJEjcHV1RWhoKCZPnoxBgwZBKpUiIiICAODq6oqhQ4ei\nuLjY4Xuz+OFJ5rB9CIdzFxKRwzp9+jSqqqpQVVWF99577673M3XqVCQkJEAikaCqqgp79+4F0NFT\n5uXlZdzO29sbSqXS4riJyDkwySIih6VUKhESEgKRSARvb++73s+wYcPg4eEBAIiLi0NhYSEAQCwW\nQ6PRGLfTaDTw8/OzLGgichpMsojIYU2dOhXBwcEYNmwY3N3vvvphz549xp/lcjnuu+8+AB0Jl0wm\nAwDo9XpUV1cjMjLSsqDtwDvvvGOsuyH6V2wfwmFNFhE5rJSUFIwaNQoeHh544IEHevWc4uJi5OTk\noLm5GUeOHEFCQgJ8fHywc+dOhISEQKvVYtWqVQCA8PBwjB8/Hunp6VCpVHj66acdvugdYM0Nmcf2\nIRwmWUTksBYsWNClZ6mkpMRYrN6dqKgoREVFmaxLSkrqcfuFCxdaFiQROS0mWUTksPbt2wd/f38A\ngEqlwuDBgzm9DhHZDSZZROSw4uPjjRNEZ2VlYcGCBWhubrZxVPaP4yCROWwfwmGSRUQO6+bNm/js\ns8/g4uIChUIBAMaeLeoZPzzJHLYP4TDJIiKHtXz5cpSWlgLoKFInIrInHMKBiBzW+fPn8cMPPwCA\n8V8iInvBJIuIHFZraysCAwMxduxYNDY22joch8FxkMgctg/h8HIhETkstVqN69ev4/Tp06itrbV1\nOA6DNTdkDtuHcJhkEZHDeuCBB3D58mUAwGOPPWbjaIiITDHJIiKHtXfvXqxfvx6urqx8ICL7w3cm\nInJYQUFBuHDhAoqLi7F//35bh+MwWHND5rB9CIc9WUTkkP73f/8XTU1N0Gq10Gq1UKlUtg7JYbDm\nhsxh+xAOkywickienp6IiopCZWUlVqxYgYcfftjWIRERmeDlQiJyWHPnzkVgYCAAoKyszMbREBGZ\nYk8WETmkzz//HLm5ubhx4wYuXryIpqYm7Nixw9ZhOQTOTUfmsH0Ih0kWETmkZ555BuPHjzcuFxUV\n2TAax8IPTzKH7UM4vFxIRA7p9gSru2UiIltjkkVERERkBUyyiIicDMdBInPYPoTDmiwiIifDmhsy\nh+1DOOzJIiIiIrICJllEREREVsDLhURETqav4yDVKlvx0cVaq8TyXdVNq+yX7h7HyRIOkywiIifT\n1w9PQzuQXdZkpWjI3jC5Eg4vFxIRERFZAZMsIiIiIitgkkVE5GQ4DhKZw/YhHNZkERE5GdbckDls\nH8JhTxYRERGRFdhVT1Z2dratQyAiG5g5c6atQyAiEpxdJVkAEBcXZ+sQiKgfSaVSW4fgdDgOEpnD\n9iEcu0uyiIjIuvjhSeawfQiHNVlEREREVsAki4iIiMgKmGQRETkZjoNE5rB9CIc1WUREToY1Gehc\nvgAAIABJREFUN2QO24dw2JNFREREZAUW92Q1NzcjIyMDcrkcW7du7fK4TqdDamoqAgMDUVtbC4lE\ngsGDB1t6WCIiIiK7ZnFP1uXLlzFlypQeHz927BiCg4MhkUgwf/587N6929JDAgCKiorwwAMPCLIv\nIiJnwpobMoftQzgW92TFx8ejqKiox8fz8/ORlJQEABgxYgTKy8uh1Wrh5eXV52OdPXsWubm5+OMf\n/4jx48cjNDS0V897/vnn8dJLL2H48OG92j47Oxu5ubkYPXo0CgoK8Nprr2HQoEF9jpeIyB6x5obM\nYfsQjtUL3xUKBby9vY3LIpEICoXirpKs3NxcFBQU4NChQ0hMTAQA/OMf/0BJSQkeeughSCQSfP31\n16ivr4ebmxsaGxuxYMECXLlyBZ9++ilmzJgBpVKJH374ASEhIfDy8sKiRYu6HCcyMhIPP/ww3Nzc\nkJ+fj/Pnz2PGjBnGx4uLi3H06FGEh4dDrVZjxYoVqKysxMGDBxEUFISTJ0/io48+wqefform5mYA\nQEBAAH7+858jOTkZEyZMgJubG+655x6cPXsW0dHRcHNzQ0BAAP7whz/0+XUhIiIi+2P1JMvPzw8a\njca4rFar4efnd1f76kx0OhMsAFi8eDG8vLzw7LPPQiKR4K9//Suefvpp6PV6yGQyhIaGYvTo0Vi8\neDGGDRsGuVwOmUyGW7duYd++fd0mWUOGDAEAyOVyuLu7Y/r06SaPh4aGIjAwEFqtFqmpqVixYgX+\n8Y9/YN68eZg2bRqmTp2KtrY27Nq1CydOnAAAzJ49GwkJCZgxYwbCw8OxePFiaLVatLe3myw7qwUL\nFmDz5s2YPHmyrUMhIiIShFXuLmxpaTEmVrGxsZDJZACAiooKjBw58q56sQDAxcUFAGAwGNDe3g4A\nCA4Oxj333AO1Wg2go9B+3rx5SEpKwsKFC+Hm5gYXFxe0t7dDr9dj27ZtiI2NxVNPPQWVStXjsfLz\n8/Hll1/itddeQ3V1tclje/fuRWBgIJYtW2YSW2dMHh4eMBgMPe678zJn5+vwr8vOqLW11fj6EZF1\nseaGzGH7EI7FPVnFxcXIyclBc3Mzjhw5goSEBHz22Wfw8fGBRCLBvHnzkJqaiiNHjqCurg6rV6++\n62NFRUXh448/xhtvvIHFixejvr4eOTk5AIBr166hpKQEf/rTn7B7926EhYXBzc0NADB9+nQcOXIE\n99xzD375y18iIyMDUqkU165dQ35+PmJjY02Ok52djZdffhnTp0/Hiy++iLFjx+J3v/ud8fFf/OIX\n+PDDD9HU1ISamhrk5OQgOTkZn3zyCaqqqnDjxg0899xzeO655/Dhhx8CAJ577jmoVCoUFBRAo9Fg\n4sSJ0Ov1Jss+Pj53/doQEfUWa27IHLYP4bi021H3QXZ2NuLi4mwdBtnA7Nmz8frrr5u9U5UGJqlU\nipkzZ9o6DIsN5PevakUrfn2w2NZhCOa7Fzva2+Q3s20cyf8J8/fC2wvC4ePJMcIdyZ3ev5z+t9nU\n1IS8vDzjsru7Ox555BEbRkREREQDgdMnWQEBAZg9e7atwyAi6jed9Ta8LETdYfsQjtMnWUREzoYf\nnmQO24dwmGQRkVPpbiqwlpYW7N+/HyEhIairq0NSUpJxqJmjR49Co9FApVIhJiaGw4wQUa9xgmgi\ncirdTQWWnp6OmJgYSCQSTJkyBampqQCA0tJSFBUV4YknnsCvfvUrpKamGoeLISK6EyZZRORU4uPj\nu4xJJ5VKMXbsWABAREQEpFKpcX1ERAQAwNXVFUOHDkVxsePfZcdxkMgctg/h8HIhETk9pVJpTLy8\nvb2hUqlgMBigVCoxdOhQ43be3t5QKpW2ClMwrLmxUy62DqAD24dwmGQRkdMTi8XQarUQiUTQaDTw\n8fGBq6srxGKxybRgGo3mrqcFIzLnmkqHY5dvwNUKidbPh/thuL/zzihiS0yyiMjpxcXFoaSkBFOn\nTsXly5cxadIk4/pDhw4BAPR6PaqrqxEZGWnLUGmA0rQZ8P6FGqvse3yor1X2S3fGJIuInEp3U4El\nJSUhLS0NtbW1qK+vx4oVKwAA4eHhGD9+PNLT06FSqfD0009DJBLZ+Awsx3GQyBy2D+EwySIipxIV\nFYWoqCiTdR4eHkhOTu52+4ULF/ZHWP2KH55kDtuHcHh3IREREZEVMMkiIiIisgImWURETobjIJE5\nbB/CYU0WEZGTYc0NmcP2IRyLk6yCggLk5eVBLBbDxcUFiYmJJo8rFAocPHgQ9957L5qbmzFjxgzj\nyMpEREREA5VFlwtbW1uxZ88erFy5EkuXLoVcLkdhYaHJNidOnIC/vz8WL16M+Ph4pKenWxQwERER\nkSOwKMmSyWQIDg6Gu3tHh9jtc3518vHxgUKhANDRq+Xry0HRiIhsiTU3ZA7bh3AsulyoUChMJloV\niUS4evWqyTazZs3C7t278fbbb6OxsRFr16615JBERGQh1tyQOWwfwrEoyfL394dWqzUuq9Vq+Pv7\nm2zzwQcfICIiArNnz8a1a9ewefNmvPvuu3BxsZOZMImIiIiswKLLheHh4WhoaIBerwcAlJSUIDY2\nFi0tLcZJVZVKpTHx8vX1hU6nQ2trq4VhExEREdk3i3qyPD09sWrVKqSkpEAsFiMsLAzR0dFIS0uD\nj48PJBIJnnjiCZw4cQKVlZVoaWnBihUrTC4xEhFR/+LcdGQO24dwLB7CISYmBjExMSbrli1bZvx5\nxIgRWLVqlaWHISIigfDDk8xh+xAOR3wnIiIisgImWURERERWwCSLiMjJcBwkMoftQzicu5CIyMmw\n5obMYfsQDnuyiIiIiKyASRYRERGRFTDJIiJyMqy5IXPYPoTDmiwiIifDmhsyh+1DOOzJIiIiIrIC\nJllEREREVsAki4jIybDmhsxh+xAOa7KIiJwMa27IHLYP4bAni4iIiMgKmGQRERERWYHFlwsLCgqQ\nl5cHsVgMFxcXJCYmdtkmMzMTCoUCrq6uUCqVeP755y09LBER3aXOehteFqLusH0Ix6Ikq7W1FXv2\n7MGOHTvg7u6O7du3o7CwENHR0cZtzpw5Aw8PD6xcuRIAUF5eblHARERkGX54kjlsH8Kx6HKhTCZD\ncHAw3N07crWIiAhIpVKTbXJycqDVanHw4EF8/vnn8Pf3t+SQRERERA7BoiRLoVDAy8vLuCwSiaBQ\nKEy2uX79OmpqarB06VJERkbi7bfftuSQRERERA7BoiTL398fWq3WuKxWq7v0VIlEIkRFRQEARo8e\nDblcDqVSaclhiYjIAhwHicxh+xCORTVZ4eHhaGhogF6vh7u7O0pKSjBnzhy0tLTAzc0N3t7eiI6O\nRlVVFQCgubkZ7e3tEIlEggRPRER9x5obMoftQzgWJVmenp5YtWoVUlJSIBaLERYWhujoaKSlpcHH\nxwcSiQSLFi1CSkoKUlNT4erqig0bNhhruIiIiIgGKouznZiYGMTExJisW7ZsmfFnkUjEIRuIiIjI\n6XAwUiIiJ8OaGzKH7UM4vG5HNqXX63Hq1CkAQGVlJfz9/REeHm7jqIgGNtbckDlsH8JhTxbZlFar\nxTPPPAMAOHXqFDIzM20cERERkTCYZBERERFZAZMsIiInw5obMoftQzisySIicjKsuSFz2D6Ew54s\nIiIiIitgkkVERERkBUyyiIicDGtuyBy2D+GwJouIyMmw5obMYfsQDnuyiIiIiKyASRYRERGRFTDJ\nIiJyMqy5IXPYPoTDmiwiIifDmhsyh+1DOBYnWQUFBcjLy4NYLIaLiwsSExO73S4nJwd///vf8dFH\nH8HT09PSwxIRCW7Tpk3w8PAAALi6uuKVV15BS0sL9u/fj5CQENTV1SEpKQl+fn42jpSIHIFFSVZr\nayv27NmDHTt2wN3dHdu3b0dhYSGio6NNtquqqkJ1dbVFgRIRWdvEiROxdOlSk3Xp6emIiYlBfHw8\nLl68iNTUVKxZs8ZGERKRI7GoJksmkyE4OBju7h25WkREBKRSqck2ra2tyMrK6rGHi4jIXlRUVCAz\nMxOZmZm4cuUKAEAqlWLs2LEAun+Pc0SsuSFz2D6EY1FPlkKhgJeXl3FZJBLh6tWrJttkZGQgMTHR\nmIi1t7dbckgiIqtZtGgRxowZA51Oh5deegnPPvsslEql8X3O29sbKpUKBoMBrq6Oe98Qa27IHLYP\n4ViUZPn7+0Or1RqX1Wo1/P39jcs3btyASqVCbm6ucd0XX3yB2NhY3H///ZYcmohIcGPGjAEAeHh4\nIDo6GkVFRRCLxdBoNBCJRNBoNPDx8XHoBIuI+o9F7xTh4eFoaGiAXq8HAJSUlCA2NhYtLS3QaDQI\nDAzEc889B4lEAolEAgBISEhggkVEdqempgZffPGFcVkulyM0NBRxcXGQyWQAgMuXL2PSpEm2CtFm\nXGwdAJGDsqgny9PTE6tWrUJKSgrEYjHCwsIQHR2NtLQ0+Pj4GBMrpVKJr776CgBw9OhRzJw5E/fe\ne6/l0RMRCcTb2xvFxcVobGyEu7s7YmNj8eCDDyI2NhZpaWmora1FfX09VqxYYetQe1TeqEFRveqO\n25V9tR8AMHrWU73a781WvUVxkWPprMfiZUPLWTyEQ0xMDGJiYkzWLVu2zGRZLBZjyZIlWLJkiaWH\nIyKyioCAAGzYsKHLel9fXyQnJ9sgor6rUbbib7mVd95Q9GDHv73ZlpwOkyvhsLCAiIiIyAqYZBER\nERFZAZMsIiInk6DOQYI6x9ZhkJ3iOFnC4dyFRERO5vPOmiyibrAmSzjsySKb0el0aGlpMVl369Yt\nKJVKG0VEREQkHCZZZDMnTpzAunXrTNZVV1cjKSnJRhEREREJh0kWEZGTYU0WmcOaLOGwJouIyMmw\nJovMYU2WcNiTRURERGQFTLKIiIiIrIBJFhGRk2FNFpnDmizhsCaLiMjJsCaLzGFNlnDYk0VERERk\nBUyyiIiIiKyAlwuJiJxMZz0WLxs6Bw83V7TdMvR6+107/w4AWP38GrPbuQBwd2NfjTkWJ1kFBQXI\ny8uDWCyGi4sLEhMTTR7PzMxEbW0tQkNDoVAosGDBAgQFBVl6WCIiuktMrpzL66euwtfTrfdPGDkH\nAPAfX5Sa3ezJn92HqWF+loQ24FmUZLW2tmLPnj3YsWMH3N3dsX37dhQWFiI6Otq4TUtLC5KTk+Hq\n6oqTJ08iMzMTq1atsjhwIiIiurNKRatV9tusabPKfgcSi/r5ZDIZgoOD4e7ekatFRERAKpWabLN8\n+XK4uv7fYQyG3ndZEhERETkqi5IshUIBLy8v47JIJIJCoeh2W61WiwsXLmDx4sWWHJKIiCzEcbLI\nHLYP4Vh0udDf3x9arda4rFar4e/v32U7vV6PlJQUPPXUU6zHIiKyMdZkkTlsH8KxqCcrPDwcDQ0N\n0Ov1AICSkhLExsaipaUFGo0GQEfd1nvvvYcFCxZg5MiROHfunOVRExEREdk5i3qyPD09sWrVKqSk\npEAsFiMsLAzR0dFIS0uDr68vFi1ahHfffRdVVVXYu3cvgI6kKz4+XpDgyXHl5+ejrq6ux8c//fRT\nLFy4EG5ufbgjhoiIyI5YPIRDTEwMYmJiTNYtW7bM+PN//Md/WHoIGoD279+Ptrae70xZvXo15s2b\nxySLyAo4ThaZw/YhHA5GSkTkZPjhSeawfQiHQ7USERERWQGTLCIiIiIrYJJFRORkOA4SmcP2IRy7\nq8lSKpUQi8W2DoOsqL6+vlcj/9+4cQMBAQHw9vbuh6iInAdrbsgctg/h2F1P1pEjR2wdAllZQkIC\nbt68ecft1q5di5wcfpsiIiLHZHdJlrOqqanBzp07MWnSJNy4ccPW4VA/WbduHXbs2IHDhw/bOhQi\nIhLYgEiyNm/ejBMnTtg6DIvcuHEDn3zyCZRKJdrb220djlVoNBp8++23fXpOQ0MDSktLrRSR7Wk0\nGlRUVCA7O9vWoVgsISGBXxAcBGtuyBy2D+HYXZJVXFyM3//+92a3yc3NxfHjxwEAv/71ryGXy3H+\n/Hm89957/RGi1b3//vvIzc21eD/19fW4deuWABFZ5tq1a2hpacH169eRnJzcp+fm5eUhJSUFAFBZ\nWWmcwsmWmpqajNNGWSI5ORk6nU6AiGyroqIC//mf/4nS0lLs2rULp0+fhlKpxLZt28w+TyqVYvny\n5f0UJd3uc9GDrLuhHrF9CMfukqy2tjY0Nzeb3SY/Px9Hjx7FE088gdzcXBgMBjQ2NqKgoEDweD76\n6CNcu3ZN8P2aU1xcjLq6Ouh0Oly+fLlXz/nwww+NCUhVVRXWrVuHX/ziF3jnnXfwz3/+E+3t7Whr\na8OKFSvw008/WTN8AB3TJ9XW1gIANm7cKEhP4+zZs3H9+nVjwtYfXnjhBZw7d86YrK5duxZr167F\nX//6V+zevdu4XW5uLn788cc77u/69euoqakBAHzzzTe9ugFASAcPHoRcLhd0nzdv3sSZM2cAAKWl\npfjyyy+xZcsWY3Lck7a2tn77PRIR2YLdJVm9ZTAYjB9WndRqNR58UNjs+7333kNDQ4NF+9BoNNi5\nc2efnlNTU4OsrCwkJiaarNfr9cjMzOyy/caNG6HX66HVatHQ0GCciLugoAByuRzXrl3DhAkTcOXK\nlX7pPSkpKcGTTz6JjIwMGAwGVFdX9/lSYSetVouMjAwAwIULF/DBBx/g5ZdfFjLcHlVWViI9PR2v\nvvoqACArKwvt7e1oampCYWEhFAoFgI65Fv/5z3+aPLeoqKhLknzgwAFs374dhw4d6lMc3377rSC9\nm2lpaSgvL7d4P50++OAD7Nmzx2Rda2srmpqaBDsGEdknAwBt2y2r/GcYIGUzdjeEg6Wamppw6dIl\n1NTUYPDgwYiNjbV1SGhtbcW2bdsgFovx6KOPYvDgwSaPq9XqLpefrly5gvz8/G73tWbNGkgkki6P\nvfzyy3BzczN+8Hf67rvvcPLkSYvPQy6Xw8fHB0FBQQCA1157DStWrEBYWJjJdqdPn0ZxcTEAYNOm\nTZg2bVqP59MbarUaf/vb3wB03H0aEBAAnU6H119/HZs2beqy/ZtvvgmJRIKxY8cCAC5duoQJEyZY\nNA/itWvXMHv27C5xPfTQQxg/fny3w45kZmbC09MT48aNM1mvUqnw1ltvmawzGAxoampCQEBAl/3s\n378fxcXF8PHxwfTp0+/6HIT01ltvYc6cOWhsbBTk0in1L85NR+b0tn3svVCDL34Uvjc6xNcD6x8c\ngXu8HD9FscszaG9vh1qthkgk6vJYW1vbHQvDk5OTMXHiREyaNEmQJCsrKwtlZWVYuHChyXqdTgel\nUonW1lYMHjwYrq5dOwYbGxtx9epVAEBKSgqio6O7JFl79+41JiXduXjxIiIjI7t9PW7evIm6ujoA\nHbUxQ4YM6bKNwWAQpJZpx44dmDRpEpYsWYKtW7ciNzcXCQkJJknWV199hfPnz3fpZRTarVu38PHH\nH2PChAl48MEHTZKTb775Bp6enigrK8Mvf/lLLFiwAD/++CN8fX0tOmZPPYAymQxxcXFQq9W4evUq\nRo0a1e12xcXFUKvV3T7W2tqK6dOnd9sOPv74Y4wePRqDBg1CcXExoqKiut1HY2Mjbt26hUGDBsHf\n39/kMYPBgCeffNLc6fXJG2+8gdzcXAwbNszsdjqdDoMGDYKLi4vJ+ra2NrMThJN1Mbkic3rbPlp0\nt/DTDeG/ZGna+reMwprs8nKhXq9HZGSkcTk/Px8bNmzAxYsXkZSU1OuaotvrgjrV1dXh888/71M8\ncrkclZWVJut++OEHHD58GE8++SSmTZuGlpYWk8fz8/Oh1Wpx5swZ7Nixo0/H+1erV69GdXU1jh07\nht/85jcAOmpfWlpaUFhYiLVr11q0/77S6XTYv38/gI5ess5LkwCwa9cuiy+v9sWf//xnY5Kp1+vx\n5ptvAgDKysruuufsbpWVlWHz5s0AOhIvnU4HuVyOhIQEAMCWLVtQVVV11/tvaGgw3hRSUlKCxsZG\nk8e3bNmC119/HevWrcPBgwe7PL+vdzBevXq1y3PUajWqq6t7vY9/+7d/wzfffIMvvvgCr7zyinH9\njh07jJeAiYgGKot7sgoKCpCXlwexWAwXF5cuNUQ6nQ6pqakIDAxEbW0tJBJJl56cO7l58yZkMhnm\nzJmDhx9+uNfPKysrw/r167F//35kZGTgwQcfRGVlJXbu3Gn84Out0tJSPPnkk7j//vsxf/58ZGdn\no76+3iRGT09PeHp6Aui46/Gzzz67437//Oc/d7m8152vvvrK5LLMmjVrjHVC/en777/H119/bVyW\nSqWorKxEaWkptm3bhvvvv7/fY0pJSUFISAhCQkKwfft2TJ48GQBQW1vbpfexvzz//POIiIgw9uD8\n8Y9/7NXQHBs2bMDatWvv2EP06quvIikpCfPnzwfQcZncYDDA1dUVer0eGzduRHBwMK5du4ba2lqc\nPXu2z+fQ+UVi5syZSEtLwyOPPIKysjL85S9/6dN+0tPT4e3tbfL3QkTkDCzqyWptbcWePXuwcuVK\nLF26FHK5HIWFhSbbHDt2DMHBwZBIJJg/f77JHVn9ob29HTdu3MCHH37Yp16EhoYGY01P537a2trw\n/vvv4y9/+UuX3poNGzYYh5Uw55tvvjFu9+ijjyIvLw8qleqOz7t06VKX3rT6+vq7uvOxrKwMZWVl\nxuUXXnih170+BoOhy2Wz5uZmHDlypEt8/UWpVKKyshJZWVkm69vb26HVanu9n2eeeQYlJSXG5b72\neAIdr09eXl6X9Xe6067T2bNn8f777+Pw4cNoa2vrdS/lQw891KU2avPmzfj6669x5coVk6E8Dh8+\njP/6r//q1X47paSkoKysDK2trX16HtknjoNE5rB9CMeiJEsmkyE4OBju7h0dYhEREZBKpSbb5Ofn\nG5OVESNGoLy8vNcffP/93/9trGe6WwaDATExMQA67uzq7jJKfyotLUVhYSFaW1stvpx1/Pjxuypo\nP3bsGL744gvjHWBXr17F0aNHsW/fvh6fM3/+/B7riRzBxo0bcerUqW4fUyqVePzxx1FWVgatVmu8\nO+7pp5/u83EMBgOWLFliUax1dXWor6+HRqPBJ598YtG+LNXW1mb8+0lPT+9V7+ydbN26lYOW2hjH\nQSJz2D6E49JuwfDiZ8+exbfffosNGzYAAE6dOoWioiL8+7//u3Gb3//+91i3bp2xOHr16tXYsmUL\nQkNDu+wvOzsbjz46827DISIHdPJkNmbOdPy/++zsbMTFxdk0hn+WN2PLScu+mDqD717saG+T33T8\nmRYGoqFiT7yzcKxD3F0olUrNvn9ZdAb+/v4mvVJqtbrLXU1+fn4mlzHUajX8/Px63GdjY/+Or3P4\n8GFUV1fjhRdeEGR/nZcm169fj7NnzyIoKKjLLfz26rvvvsPGjRshk8kwbdo0hIaGQqVSQSqV4uLF\ni7YOz2J79+7FuXPncOnSJbS2tmLKlCkQi8VQKpXYu3evrcPrtX379uGpp57CxYsXce3aNcHqzmpq\navCHP/yh3wvS/6Xzm4howLAoyQoPD0dDQwP0ej3c3d1RUlKCOXPmoKWlBW5ubvD29kZsbCxkMhnG\njRuHiooKjBw5El5eXkLFbzFLL+38q8DAQKxfvx4AMGPGDEH3bQseHh54/PHHbR2GIKKioqDT6XDp\n0iVbh2KRlStXAgDi4+MF3e+QIUN4x18/UWhsO3wFx8kic9g+hGNRkuXp6YlVq1YhJSUFYrEYYWFh\niI6ORlpaGnx8fCCRSDBv3jykpqbiyJEjqKurw+rVq4WKnaxs9OjRiIqKGhCXcgBg6tSpGDduHLy9\nvS0eVoPIEinf1eK7aqXg+21p7d1cpfzwJHPYPoRj8QXPmJgYY2Fsp2XLlhl/9vDwwDPPPGPpYagf\nuLq6GoegAICRI0cOmASrU0BAAH71q18ZkywXFxeTcybqD83aNlxr4WCsRAOdXQ5GSrYRFxd3V8MW\nOLLY2Fj8z//8j63DICKiAYhJFhGRk+E4SGQO24dw7P/+SLKJ4cOHm70L1NGNGzeu27kmiZwBa27I\nHLYP4TDJom69/PLLEIvFtg7Dag4cOCDYsB008N1p+jAiou4wyaIuSkpKnKIYfPv27ca5BYl60jl9\n2I4dO+Du7o7t27ejsLAQ0dHRtg6NaEDStN1CfYsOVUrhp/EKFA1CiK+H4PvtCZMs6sKexjGzpkGD\nBtk6BHIAPU0f5shJFsdBInNs3T4aNXo8l1ly5w3vwtsLxjp3kvWvcx8SEdmSQqEw+eIhEol6nFO1\nt+9fCwM7/rOdGf///3c9q5pjM8756qTnf0cDt31oq0sgre6/49lVkjXQxmQiIsfXm+nDAL5/EVFX\nvL2KiMiM26cPAzpqFmNjY20cFRE5Apf29vaB1x9IRCSggoICnD9/HmKxGG5ubry7kIh6hUkWERER\nkRXwciERERGRFdis8L29vR0nT57EgQMHsHnzZgwbNsz42NGjR6HRaKBSqRATE4PJkycDAMrLy3H8\n+HGEhIRAqVRixYoVDj9q98GDB1FcXGxcfuyxx4wTbvf0Ogw0zjbQ46ZNm+Dh0XELsaurK1555RW0\ntLRg//79CAkJQV1dHZKSkgbEiPvNzc3IyMiAXC7H1q1bAcDsuTpLmyci52CzJEsulyM8PNz4YdOp\ntLQURUVF2LhxIwwGA9atW4fx48fDy8sL7777Lv70pz/Bz88PqampOH36NB555BEbnYFwNm/e3GVd\nd69DVFQURCKRDSK0Hmcc6HHixIlYunSpybr09HTExMQgPj4eFy9eRGpqKtasWWOjCIVz+fJlTJky\nBXK53Liup3N1ljbfV71NwGUyGb766iuEhoaiqKgIa9asQWCgTceJ6KIvXyYUCgVefPFFPPbYY5g7\nd24/R3pnvTmXn376CVlZWRg2bBi0Wi3Cw8MRHx9vo4hN3enLrU6nQ2pqKgIDA1FbWwsECrk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"text": [
"<matplotlib.figure.Figure at 0x10d242d90>"
]
}
],
"prompt_number": 31
}
],
"metadata": {}
}
]
}
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