CCL_round_trip.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "if sys.version=='2.7.9 (v2.7.9:648dcafa7e5f, Dec 10 2014, 10:10:46) \\n[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]':\n",
    "    pass\n",
    "else:\n",
    "    raise Warning('Python version may not be compatible with Galsim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/myhome/py2_kernel/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['ndim']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "sys.path=[]\n",
    "\n",
    "for i in ['/Users/myhome/Library/Python/2.7/lib/python/site-packages/','/usr/local/lib/','/usr/local/lib/wxPython-3.0.0.0/lib/python2.7/site-packages', '/usr/local/lib/wxPython-3.0.0.0/lib/python2.7/site-packages/wx-3.0-osx_cocoa', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python27.zip', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/FontTools', '/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/VPython-6.11-py2.7-macosx-10.6-intel.egg', '/usr/local/lib/wxPython-3.0.0.0/lib/python2.7', '/Library/Python/2.7/site-packages', '/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python', '/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/PyObjC']:\n",
    "    sys.path.append(i)\n",
    "import galsim, os, math\n",
    "from astropy.io import fits\n",
    "import emcee\n",
    "import pandas as pd\n",
    "%pylab inline\n",
    "import seaborn as sns; sns.set_style('darkgrid')\n",
    "import pyccl as ccl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = np.linspace(0., 3., 200)\n",
    "i_lim = 26. # Limiting i-band magnitude\n",
    "z0 = 0.0417*i_lim - 0.744\n",
    "\n",
    "ell = np.arange(2, 3000)\n",
    "\n",
    "Ngal = 46. * 100.31 * (i_lim - 25.) # Normalisation, galaxies/arcmin^2\n",
    "pz = 1./(2.*z0) * (z / z0)**2. * np.exp(-z/z0) # Redshift distribution, p(z)\n",
    "dNdz = Ngal * pz # Number density distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying cold dark matter density $\\Omega_c$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kb22OBZN6wFdhh+Px2dhy8DJ0OvEVAUuAiETL1soM/3qyOzp52uG3CznYdEB8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\nYv8fCrBpJV1PqPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c62a610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for Omega_c in [1]:\n",
    "    p = ccl.Parameters(Omega_c=Omega_c*0.27, Omega_b=0.045, h=0.67, A_s=2.1e-9, n_s=0.96)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=dNdz,z=z)\n",
    "\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}, $\\Omega_c$ = {}'.format(ccl.sigma8(cosmo),Omega_c*0.27) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying the hubble parameter $h$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-eb1f5f145203>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtracer1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mccl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClTracer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtracer_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lensing'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdNdz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mtracer2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mccl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClTracer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtracer_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lensing'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdNdz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mccl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangular_cl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtracer1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtracer2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr'$\\sigma_8$={:.4f}, $h$ = {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mccl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma8\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyccl/cls.pyc\u001b[0m in \u001b[0;36mangular_cl\u001b[0;34m(cosmo, cltracer1, cltracer2, ell)\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m# Use vectorised function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m         \u001b[0mcl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangular_cl_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;31m# Use vectorised function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyccl/ccllib.pyc\u001b[0m in \u001b[0;36mangular_cl_vec\u001b[0;34m(cosmo, clt1, clt2, ell, output, status)\u001b[0m\n\u001b[1;32m    766\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mangular_cl_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m     \u001b[0;34m\"\"\"angular_cl_vec(cosmology cosmo, CCL_ClTracer clt1, CCL_ClTracer clt2, double * ell, double * output, int * status)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_ccllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangular_cl_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosmo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclt2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    769\u001b[0m \u001b[0mK_PIVOT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ccllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mK_PIVOT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m \u001b[0mRHO_CRITICAL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ccllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRHO_CRITICAL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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sYPLwYG4eH05phYEXN8eQll1av8xOreXOfrcyyHcAJ0pSeS1mHWWG8ma2JsTF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\nhPR2cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c658d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for h in [0.9,1,1.1]:\n",
    "    p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=h*0.7, A_s=2.1e-9, n_s=0.96)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}, $h$ = {}'.format(ccl.sigma8(cosmo),h) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying Power Spectrum Normalisation $A_s$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for A_s in [0.9,1,1.1]:\n",
    "    p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.68, A_s=2.1e-9*A_s, n_s=0.96)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}, $A_s$ = {}'.format(ccl.sigma8(cosmo),2.1e-9*A_s) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying spectral index $n_s$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n_s in [0.9,1,1.1]:\n",
    "    p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.68, A_s=2.1e-9, n_s=0.96*n_s)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}, $n_s$ = {}'.format(ccl.sigma8(cosmo),0.96*n_s) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying baryon density parameter $\\Omega_b$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for b in [0.9,1,1.1]:\n",
    "    p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045*b, h=0.68, A_s=2.1e-9, n_s=0.96)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}, $\\Omega_b$ = {}'.format(ccl.sigma8(cosmo),0.045*b) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying $\\sigma_8$ instead of $A_s$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sigma8 in [0.9,1,1.1]:\n",
    "    p = ccl.Parameters(Omega_c=Omega_c*0.27, Omega_b=0.045, h=0.67, n_s=0.96, sigma8=sigma8)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=dNdz,z=z)\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}'.format(sigma8) )\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weak lensing is mostly sensitive to a combination of $\\Omega_m$ and $\\sigma_8$ where  $\\sigma_8 \\Omega_m^\\alpha \\approx Constant$; where $\\alpha \\approx 0.6$ Since $\\Omega_c$ provides the larger contribution to  $\\Omega_m$; I'll use  $\\sigma_8 \\Omega_c^\\alpha$. \n",
    "\n",
    "Using standard values, $\\Omega_c = 0.27$, and $\\sigma_8 = 0.834$, we can solve  $\\sigma_8 \\Omega_m^\\alpha \\approx 0.380$ and use values for both around the standard values to keep this expression constant. We would expect the shear power spectrum to remain approximately constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.29731235737e-08\n",
      "2.66394918788e-08\n",
      "2.00173934812e-08\n"
     ]
    },
    {
     "data": {
      "image/png": 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W/4P8zv3T+pp50Qu5OGPDEZcZHR3l/vvv4bHHfkVcXBq///3vcLlcXHfdjYcse7jrYV9w\nwUUALFiwkPLyskPWS0pKwePx4PV6sdvt6HQ61qxZy8knnwpAR0c7QUGf9YKffPK/+OpXL+FPf3oe\ngKKiAlasWHXE9zhWEtjAkGuYst5KSnrKKe+tmhjFrVE0zLGlTAxoSgyOl170DKLVaJkXnsW88Cy+\nnn0R5b1V7OssorCrmA8aN/FB4ybSbWmcEn8SedELMWgPPSfbbNRx7soUTs9L4L1djby3u4kX/1XF\n+7saueDUNE5ZECejyoU4Qfbs2UVW1lwWLVpEV9cQ6emZ7Ny5nY6ODn7960cJDg4hKSmFq6++ZtLr\nYT/88H9gtX42XbFGo8HtdqPTfRaFZrOZ9vZWrrzyUgYG+nnkkScA0Ol0PPjg/WzevIkHH/wlAO+8\n8xahoaGsWLFqIrDtdvtR3+NYzcrA9ng91A82UdpbQWlPOY1DLRPP2QwhnBy3nHkR2cwNywj4QWKq\nqqKOjuJxOvE6HHidDjwOB+rYGHi9qF4PqtsDXg+qx4Pq9YLHAxoNik6HotWiaHXj/9ZpQatDYzKh\nNVvQWMxozBY0JhOKjy8pqtPoWBA5jwWR8xjzXExRdwnbW3dT3ldFzUAdr1T+nWWxuZwSdxLJIYmH\nrG826vjq6jmcsTSRd3Y0sHFfC8+/U86He5q54qxMspPDfPBbCeEbF2dsOGpv+Hiora0hPf2zY8eV\nleVkZc2ltraatWvPZP36c/nZz+4BDt/Ddjg+O3NHVdVDgvSVV17gpJNWcdNNt9LR0c4dd3yX//u/\nlzAajdx338/p6enmhhuu4c9/fpW3334TRVHYs2cX1dWVPPjgz0hLSz/qexyrWRPYI+5R9nUWUdpb\nQXlvFU73+JSkWkVLVmj6eC86Ipt4a+yMm5Dk81SvF8/QIO6+vgO3Xtz9/Yz19eLp78djt08Es9fp\nBK/3+DZIUdCYTDSEBKMEhaCz2dDabOhCbOhsoejCw9FHRqGLiECjP/4Du/RaPUtjclkak0u3s5cd\nbbv5pG0PW1s+YWvLJ6SFJHN60qnkRi085Fz4EIuBy8/M5OzlSfx1cy3bi9v55Qv5LM2O4rLTM4gO\nlYGGQhwvNpuNvXt3A9DY2MDHH2/k6aefA+C+++7m7bffZP36cwEm7WFv2vQh27Zt4cwz11FcvP+g\ngWOfCg4OQasdj8aQEBtut5uNGz+gu7uLq6++FpPJhEajQaNR+O1vfz+x3q233sAPf3gvdXU1R32P\nYzVrRom/VPE3trTsAMYnKvl0N3d2WPqXnv7SFzx2O6621vFbextjPT2fBfRA/3gv+DAUowntp71f\nsxmtxTLxb43FgtZsRjEYxnvFWh2KVoOi0YJWg6LVgqIBVR3vcbvdqB43uN2obg+qewzv6Oj4F4KJ\nXrsTr9OB6rDj6h84fNsUBV1oKPrIKPRR0Rji4jHExWGIi0cfFXVce+le1UtpTwVbWj6hpKccFZVQ\no43TEk7m5ISTDnuufF3bIC/+q4rqlgF0WoWzlydz3qoUzMZZ8114WskocXEkDoeDBx74CV1d7Vit\nwdx2211kZ8/lhRf+RE7OfHJzl3DffT/iwQcfmXT9T0eJ19RUo6oq9957PykpqQwODvDwww/y0EOP\n4nA4+MUv/h89Pd2MjY1x2WVXsHr1aTz00M/p7e3B7XZz1VXfYvXqtQe99qeB/eko8c+/x1TJaV1A\nu72Tqv4aMkPTibFEzYhetKqqeAb6cbW1MdragqutbSKkPYOTzDGu0aALDUUXGoYuLAxdWPiBn2Ho\nQsPQh4WjDQ09Ib3YyURFBdPZMYDHPoxnYAD3wADu/n7cvT2MdXcz1t3FWHcX7t5e+FxZKjodhvgE\njMnJGJNTMCWlYExKQmOa/i9bnY4uNjVv55O23Yx6XOg1elbELuGs5LVEWSIOWV5VVXaVdfLqpmp6\nB0cJsRq4ZM0cTlkUJxcZ+YIksMVUfL5Oamuree65Z7HZQjGbLdx6650+bN2XI4E9A3hHRxmpr2Ok\nrhZXayuu9lZcbW3ju6w/RxcRgSEuAeOB3ud4DzQSbYjN58eKj2SqH8aq281YdxeutlZGWw/sRWht\nxdXagup2f7agomBMSsackYEpIxNzRhb68PBpa6/T7WRH6242NW+nZ6QXBYWlMYs5O+V0EoLiDll+\ndMzDe7saeeeTBlxjXjISbHxzfTaJ0XJN7qmSwBZTEch1IoHtZ1RVZay7i5Gaapw1NYzUVDPa3HTw\nsWStFkN0zMQu4YmfsXFojL6/7Oex+LIbmerx4GpvY7SxkdHGhokvOP8e4rrwCMwZmZgzMjBnZWNI\nSPzSe1O8qpf8ziLea/iIluE2ABZG5rA+5QzSbMmHLN87OMJLG6vZU96JVqNw9klJXHBKGka9THV6\nNIH8QSymTyDXiQS2j3lHRxlpqD8Q0NWM1NTgGfpsl7ai02FMScU8Jx1TejrGhET0UdEo0zSy0F8c\nj43MOzbGaGMDzqpKnNVVjFRX4xn+7D20oaFYFyzEumARlpwctJZjP3deVVVKesp5t34jdYMNACyI\nmMuGOV8hKTj+kOWLarr503uV9AyOEGkzcdXZ2SxKP3SXuvhMIH8Qi+kTyHUigX0CqaqKu7sbZ231\nRA96tLnpoIFWurBwTOnpmOdkjAd0corPjiufSCdiI1NVlbGODpzVlTjKynCUFH8W4BoNpjnp4wG+\naDHGpMPPeHa096jqr+Xtuvep7q8DYGn0Ys5LW0eMNfqgZUddHt7cNj5TmldVWTY3mm+clYktaGbu\nJTneAvmDWEyfQK4TCezjbKy3F3thAfbSYkZqqg8aEKbodOODpNIzMKenY5qTMa3HWWcSX2xkqtfL\naEM99uL92Iv3M1JbMzGgTR8VRdDS5QQvW44xJfULh7eqqpT3VvFm7T9pHGpBQWFl3DLOST2LCPPB\n52U3dQ7zx3fLqWkdxGrSceW6LFbmxMyIwY8nUiB/EIvpE8h1IoE9zVRVZbShgeHCfOyFBYw2Nkw8\npwsLwzQnHXN6Bqb0DIzJyWj0h86gNRv5w0bmGR7GUVrCcEE+w4UFqKMjAOgiIwleuoygpcsxpc35\nQkGqqiqFXcW8Vfc+7fYOdIqWUxNWck7qWQdNX+tVVT7a18Krm6pxjXnJy4zkm+uzpbf9b/yhRoT/\nC+Q6kcCeBl6XC0d5KfbCAoYLC/D0H7iUplaLZe48rItzCVq4GH3U5HNTC//byLxjLhzFxQzt2Y29\nMB/vyHh462NiCFl1CiGrTkEfMfVjzl7Vy+72fN6u+4CekV7MOjPnpa1jTcKqgyZg6ex38r/vlFHe\n2C+97c/xtxoR/imQ60QC+xi5BwawF40HtKO0BNXlAkATFIR14SKCFudhmb8ArVlmt5oKf97IvGNj\nOEqKGdq1k+H8veNTsyoKlrnzCDn5VIKWLJ3y6Hy3183mlh28U/cvnG4nMZYoLs7YwPyIuROh/Glv\n+7VNNYyOecjLjORbX5lLiHV2743x5xoR/iOQ60QCe4pUVcXV0sxwYQH2wnxG6uomjncaYuPGe9G5\neZjSM/z6fGd/NVM2Mo/DwfCe3Qzu2IazqhIAjdlMyKqTsa09A2N8wpReZ9hl5+2699nS8gkqKvPC\ns7gk83zirDETy/x7bzvEauDb586b1SPJZ0qNCN8K5DqRwD4C1e3GUVE+vqu7qAB3d/f4ExoN5oxM\ngnLzsC7OxRATe9zbEuhm4kbm6uhgcMdWBrZumTgMYs7KJvT0MwnKWzKlU+9ah9t5veotyvuq0Cga\nTks8mQ1pZ09MietVVT7Y3cTrH9fg9qicuSSRy05PxzALz9ueiTUiTrxArhMJ7M/xDA9j31/EcGE+\njpLiidnENGbz+Ck/uXlY5y9EGyQzVE2nmbyRqW43w4UFDGzaiKOsFACtzUboGWcRuvYMtNYjn9/9\n6Tncr1W9SZezB5shhEuzLiAvauHEbvLGjiGefauU1m478ZFWbjg/h+SYw2+8gWgm14g4cY61Tj6d\nS7y6ugq9Xs+Pf/xTEhOTDlrmnXfe4p133gLA5XJRXV3J3//+HnfccROWA/M4xMcncO+990/6evHx\nCUd9j6P9boczawLbY7czsHUz9sKC8d2cn57aExmFNTeXoMV5mDOzAm6yEn8SKB/GrvY2+jd9xOC2\nLXidThSjEdvqNYSddTb6yCMPOhzzjPF+4yber9+IW/WQE57N17K+OjFHuWvMw6sf1fDhvmZ0WoVL\nTktn3fKkWTMneaDUiDh+qqoq+e1vH6e7u4eGhnpUVeWaa67n+utvOuq6H3+8ka1bN/OTnzxAcfF+\n/vzn53n44ccPu/xjj/2SjIxMvvKV87jppmt5/vkXjvp655yz4Qu9x+cdKbBnTTp1//VVBj7eBIqC\naU46QYtzsS7OwxAfL6NzxRdiiI0j+vIribjwIgY2b6L/Xx+M3zZ+SPCy5YStPwfTYa7Oo9fqOS9t\nHctjcnm54g1Keyv4z12PsT7lTNalnIZBr+MbZ2exMD2c594u4+WN1RTX9nD9+fOxzfIBacJ/dL36\nEkN7dk/rawYvW07UZZcfcZnR0VHuv/8eHnvsV8TFpfH73/8Ol8vFddfdeMiyh7se9gUXXATAggUL\nKS8vO+x7lZeXUldXw/e/fzclJcWMjIzwve/dgsfj4YYbbmHBgoUUFRWwYsWqg14vISHxkMemy6wJ\n7PBzzsOSPQ/z3HnoQkJ83RwRALRmM+HrzyHszHUM7d5J73vvMrRrJ0O7dmKeO4+I8y/Ekj130nWj\nLVHcmns9+zoLea3qLf5R9x75XUVcNfcykkMSWZQeyf+7bgXPvVNGUU0PDzy3i5sunE92ctikryfE\nbLBnzy6ysuayaNEiurqGSE/PZOfO7XR0dPDrXz9KcHAISUkpXH31NZNeD/vhh/8Dq/WzQ50ajQa3\n241ukj2rf/zj83z72zcAYDKZuOKKqzn//K/S1NTID35wOy+88Dp2u/2Q15vsscO9xxc1awJbHxl1\n1N2VQhwLRacjZNUpBK88GUdpCX3v/hNHWQnN5WXjwX3BV7FkZR+6nqKwNCaXnIhs/lb9Nttad/Ho\n3ic5K/k0zk09ixCrgTsuXcR7u5p4bVMNj7yYz0Wr53DuqpRZs4tc+Keoyy4/am/4eKitrSE9PWPi\nfmVlOVlZc6mtrWbt2jNZv/5cfvaze4DD97AdDsfEfVVVJw3SoaEhGhsbWLJkGQBJSckkJo5fSCg5\nOQWbzUZPTzdWq/WQ15vssekIa5hFgS3E8aYoCtb5C7DOX4CztoaeN9/AUbyf5vIyLPNyiLjgIsyZ\nmYesZ9aZuXLupSyJXswL5a/xfsNHFHWVcNW8y0izpfCVFclkJNj43d+L+evmWiqb+rn+/BxCLLKL\nXMwuNpuNvXvHd8U3Njbw8ccbefrp5wC47767efvtN1m//lyASXvYmzZ9yLZtWzjzzHUUF+9nzpwM\nJlNYuI9ly5ZP3H/77TepqanmBz/4Md3dXdjtdiIiIlm4cPEhrzfZY9Nl1gw6E743GwcUOWuq6fn7\n33CUlgBgyZlP5MWXYkpNm3T5Efcob9b+k4+bt6OgcEbSajbMORuD1sCQw8X//KOM/bU9hAUbufGC\n+WQlhZ7IX+e4m401IqbO4XDwwAM/oaurHas1mNtuu4vs7Lm88MKfyMmZT27uEu6770c8+OAjk67/\n6ajumppqVFXl3nvvJyUllcHBAR5++EEeeuhRAF544Y/odDq+9rUrARgbG+M///MBOjraURSF7373\nNhYuXDzp6yUlJU/6HlMlo8SFX5jNH8bOqip63vzbxClhwSetIOKiSzBERU+6fFVfLX8pf5UuZw/R\n5ki+Nf9yUkOS8aoq//ykgb9urkVB4ZLT5rB+RXLA7CKfzTUipu7zdVJbW81zzz2LzRaK2Wzh1lvv\n9GHrvhwJbOEX5MMYHOVldL32CqP1daDVEnr6mURsuGDSc/5dHhdv1b7HR01bURSFc1LPZH3KGWg1\nWioa+3j6zRIGhl3kZUZy/YYczMaZf4RLakRMRSDXyZECW/vAAw88cOKa8sU4HC5fN0FMI6vVOOv/\npvrIKGynrsEQF8dofT2O4v0MfPwRKArG1DQU7Wezm2k1WnIisskITaOit5qi7lLKe6vICJ1DSmQE\nJ8+PpaE7VJNrAAAgAElEQVRjiP21veyr7CInNYzgGX5cW2pETEUg14nVevhrFkhgixMmkDeyL0JR\nFIwJidhOOx1tUBDOqkrshQUM7dqJPioKQ+zB0+BGmMNZGbeMvtF+Snsr2NG2m2C9lYzwZFbOj8E1\n5qGwuoftxe3ERViJizjyrGv+TGpETEUg14kEtvALgbyRHQtFq8WcnoHttLWoY24cpcUM7dzBSF0t\nprS0g3aT67V68qIXEmOJorSngvyuIpqGW8mJyGJJRiyx4RbyK7vYUdKB16uSnRw6IycEkhoRUxHI\ndSKBLfxCIG9kX4ZGb8C6YCFBS5bham/DUVrCwOZNeEdHMc9JP2i63PigWJbH5NE83EZZbwV7OgpI\nCUliUVIii9IjKK7rpaC6m/r2IRalR6DXzawLiEiNiKkI5DqRwBZ+IZA3sumgCwkhZNUpGBMScVZX\n49hfyMD2rehsoRgSEiZ6zGadiZNi89BrdOzvKeOTtj0oaFiSkMXJC+Jo7BymuLaXPRVd5KSGz6jj\n2lIjYioCuU4ksIVfCOSNbLooioIxPgHbmrWg0eAsLWF4zy5GamswZ2ROXBVMURQyQtPIDsugrLeS\nou4SagfqWRQ9j9MWJuP2qBRUd7OjpJ3kmGBiwiy+/cWmSGpETEUg14kEtvALgbyRTTdFp8Mydx7B\nK1aO7yYvKWZgy8coOh2mtDkoGg0A4aZQVsQtpcPRSWlvJbva95EYHM9pOZnEhJnZWzEe2haTjjlx\nIX5/XFtqRExFINeJBLbwC4G8kR0vWquV4JWrMMTE4Cwvw56/D3thAabUVHSh4xcCMWgNLI3Oxaw3\ns7+7jJ3te3F73ZyeuYgFcyIoqO5hb0UXA3YXC9LC0Wj8N7SlRsRUBHKdSGALvxDIG9nxpCgKxsQk\nbKeuwTM0NH7u9pbNeByOiWu4K4pCmi2FnIhsKnqr2d9TRs1AA6emLmb1gkTKG/ooqumhqrmfxRmR\nGPT+ORhNakRMRSDXiQS28AuBvJGdCBqDgaC8JZgzsw4MSitiaM9uTMmp6CMiAAg12lgRu5R2R+fE\nKPL50RmcuzSbth4H+2t72VvRxfw0/xyMJjUipiKQ60QCW/iFQN7ITiR9VBS21aehjo1h31/E4LYt\neEdGxnvbWi16rZ4l0YvQa3QUdZeys30vNlMQFy/PQ1VV8qu62VHSQUpsENF+NhhNakRMRSDXiQS2\n8AuBvJGdaIpWi3X+AizzcnBWVmIvKmR47x6MqWnow8InRpHPsaVS3FNGflcRvSN9XJy3griIIPZW\ndLG9uJ1gi560uBBf/zoTpEbEVARynUhgC78QyBuZr+gjIrCdugbv6Cj2/YUMbt2C6nZjyshE0WqJ\nNEewNGYxtf0NlPSWU9xTxrrsJSzPTKCgqpvd5V2MujzMSw3zixHkUiNiKgK5TiSwhV8I5I3MlxSd\nDuvCRZizsnFUlmMvLGC4IB9zVha6kBDMOjMnxS1l2DVMSU85O9v3sjB+DufkzaO4dnxmtNYeB7kZ\nEWgPnC7mK1IjYioCuU4ksIVfCOSNzB98eiUwj30Yx4Fj2xqLBVNqGlqNloWROYSbwijqLmFX+z7C\nrBYuX3kSta2D7K/tobyhn9zMSIw+HEEuNSKmIpDrRAJb+IVA3sj8haLTEbQ4F2NyCvbiIux79zDa\nUI8lZz4ao5Gk4HjmhWdS3F1GQVcxg+5+vr16DT0DLvbX9pBf2cXC9AiCzHqftF9qRExFINeJBLbw\nC4G8kfkbQ2wcIStXMdrUjKNkP4OfbMeYmIQhOppQo42lMbnUDNRT2lNBZX8131y1Gr1iIL+qm52l\nHWQmhRIeYjrh7ZYaEVMRyHUigS38QiBvZP5IYzITvHIVGpNp/Hrb27fhcToxZ8/FbLBwUswSekf7\nKe2pYG9HAefnLiElIoq9FV3sKGknPsJCfOSJvba21IiYikCuEwls4RcCeSPzV4qiYM7IxLpoMY6K\nchxFhdiLCrDMzcEQYmNx5HyMOiOFXSXsbN/HkpRkTs3MZm9FF5+UdGA26khPsJ2w9kqNiKkI5DqR\nwBZ+IZA3Mn+nCw3Fdupq3EODOPbvZ2DbVgzR0RgTEpljSyUlJJmi7hL2dhYSZtNx6dIVFNR0s7ei\nC5fbQ07KiTntS2pETEUg14kEtvALgbyRzQSKTkdQbh76mJjxXeQ7P8EzPIR5bg4xQdEsjpxPWV8l\n+7vLsCu9XHvKGkrqBiio6qZncITFGRFojnNoS42IqQjkOpHAFn4hkDeymcSYmETwkqU4KsqxFxVi\nLynGmjOfkNBoTorJo36wkdLeChrt9Vy/Zi11zeNzkDe2D5GXFYVOe/zO1ZYaEVMRyHUigS38QiBv\nZDONNjiYkJNPxd3fN37O9vZtGOLisSYksywml/6RgfGZ0XpLuGb1qfT0quyv7aWisZ+8rKjjdrUv\nqRExFYFcJxLYwi8E8kY2Eyk6HUF5S9GFh2MvyGfok+14R0cJmpvDougF6DQ6CruL2ddVwCUn5cGo\nlaLaHgpresjNiMRs1E17m6RGxFQEcp34RWC3trZy9913s3XrVmpra1m6dOlR1wnUP8hsFcgb2Uxm\nSk4haHEejvJS7IUFOCvKsS5cTFbsPGItUeR3FbOns4A1C1KJNsRRWN3NnopOFqRFTPslOqVGxFQE\ncp0cKbCndDDqnnvuYdWqVWzYsOGgxzdv3sz69etZt24dzz777BFfo7KykvXr1/OLX/yC0tLSqbyt\nEOIEMSYlkXzfAwQtW46zqpLGBx9gpK6WpTG53JF3IxadmVcq30CfXMYlp6XROzjKL/68l5qWAV83\nXYhZQ1FVVT3aQrt378ZisXD33Xfzj3/8AwCPx8P69et5/vnniYmJ4dJLL+Xxxx/H4/Hw+OOPH7T+\nQw89hEaj4fbbb0dRFC688EIuueSSozauq2voGH8t4Y+iooLlb+rnVFWl79136P7rayhaLdFXfwvb\nKavpdvbwu8LnaXd0khu1kEzvafz53Rp0WoWbL1rIovSIaXl/qRExFYFcJ1FRwYd9bkqBDdDc3MxN\nN900Edj5+fk8+eST/OEPfwDgmWeeAeDGG2+cdP0//OEPLFq0iOXLl3P77bfz3//930d9T7fbg07n\nuwsRCDFb9e3Lp+JXT+Cx24k77xxSv30NTq+LX217hpLOSuZFZXBm1MX8+i8leDxevv+NpazOTfB1\ns4UIaMc8aqSjo4PY2NiJ+zExMRQVFR12+dWrV/Pkk0/y1ltvkZAwtQ27r89xrM0TfiiQvxUHnKQM\nku79Ga2//W/a3v4n/ZU1xN10C9/JuYY/8hL7Oovoc/wf37noMv7wRh2P/nkPXd3DrF4c/6XeVmpE\nTEUg18mRetjTP8zzMLKysqbUqxZC+AdDTAzJ9/6U9uf/h+G9e2h88OfE33wr186/EpshhI+at/JX\n95+45qKv8+c323j+n+U4XR7OXp7k66YLEZCOeQaEmJgY2tvbJ+53dHQQExMzLY0SQvgHjclE3E23\nEHnxpbj7+2j65UMM7/yESzLP56KM8+gfHeCVpj9y+YXh2IIMvPRhFW9uq2OKR9qEEF/AMQf2woUL\nqa+vp6mpCZfLxdtvv80ZZ5wxnW0TQvgBRVEIP3cDCbd/D0Wvp/1/nqXn73/lzMTVfCvnclyeMV6u\n+zMXnGci0mbijS11vPJRtYS2ENNsSoF91113cfnll1NXV8eaNWt49dVX0el0/OxnP+P666/n3HPP\n5ZxzziEzM/N4t1cI4SPWhYtIuuc+9FFR9P7jLdqeeYplYfO5efG30Wl0vF73KmvXuYiLsPDerib+\n790KvF4JbSGmy5RHiftCoA4qmK0CeaDIbOIZGqL1qd/grKrEmJpGwq130KZ18FThHxh0DXFa/GpK\ndkTT1GHnpHnRXL8hZ8rzj0uNiKkI5Do50qCz4zeLvxAiIGmDg0m464eEnHwqo/V1NP7n/yOqb4wf\nLL2FaEskH7duIf2kRtITg9lV1slTfytmzO3xdbOFmPEksIUQX5hGryfm2uuIvORr44PRHv5PjOV1\n3LXkZpKC4tnZsZuoReXkpNkoqO7mv1/fj2tMQluIL0MCWwhxTBRFIfycc4m/+VYAWp96krEPN3N7\n3g1khKZR2L0fY9Y+FqbbKKnr5devFTHqktAW4lhJYAshvpSgvKUk/fgn6EJD6X79FYZefo2bF36b\nBRFzKe+rwpP2CYuygilr6OOJVwtxjrp93WQhZiQJbCHEl2ZKTiHp3p9hSExiYNNGup95muuzLmdZ\nTC51gw0MJ2xh8dwgKpv6eeIVCW0hjoUEthBiWujDwki6+14s8+ZjL8in7YlfcVXSuaxJWEWbvZ3e\n6I/Im2+humWAx14uwDEy5usmCzGjSGALIaaN1mwm4Y7vEbLqFEZqa2n+xUN81baSr6SeSfdID21h\n/yJvgYna1kEefamAYaeEthBTJYEthJhWik5HzLevJ3zD+Yx1ddL88EOcpaZzScYGBlyDNNs+YMli\nAw3tQzz6Yj5DDpevmyzEjCCBLYSYdoqiEPnVS4i++ho8DjvNjz3CST1BXDn3EuxjDhqsH7AsT09T\n5zCPvJjPoF1CW4ijkcAWQhw3oaetJf7W2wFofeo3zK9ycvW8r+F0j1Bjep/lS3S0dNkltIWYAgls\nIcRxFbQol6Qf3YPWGkTnn/6XzPw2rs25Apd3jErj+yxfpqG1286jL+UzMDzq6+YK4bcksIUQx50p\nNY2kH9+LLjyC7r++RtLHZVyf8w08Xg8VuvdZuhRauuzc9/R2GYgmxGFIYAshTghDbBxJP/4Jhrh4\n+j94j+h/7OCGnG+gApW6D8hd4qG+bZBfvZgvoS3EJCSwhRAnjD48nKS778U0Zw5DO7YT9vIHfHfe\nVSiKhir9hyxZ4aGxc5jHXi7ALudpC3EQCWwhxAmlDQoi8a4fYZm/AHtRIdb/fYObs76BXqOjgg/J\nyXXS0D7E4y8X4BiRGdGE+JQEthDihNOYTCTcdifBy08av67271/k1jmXY9IZqTdsZt5iB3VtQzzx\nSoFMYyrEARLYQgifUHQ6Yr9zE7a1ZzDa1ITy1B+5Z/5VmHRGGoxbmLvIQU3roMw9LsQBEthCCJ9R\nNBqiv3H1xKxog488wy0JF2HUGmk0bSF7kYPqlgH+69VCRlwS2mJ2k8AWQvjUp7OiRV5yGa7ubrxP\n/S+3xF6AUWuk2bSVrAUOqpoH+M3r+3GNyfW0xewlgS2E8Avh55xH2neuwzMwgPq7/+OWyHMxaPW0\nWLaSnmOnrKGPp94oxu3x+rqpQviEBLYQwm/EbziXmG9ei8c+jPeZP3Jz2FcwaPW0B20jNXuYopoe\nnn2rFI9XQlvMPhLYQgi/YltzGrHXfQfvyAjeZ/7EzcFnotfq6ArdTmLmEHvKO3n+nXK8qurrpgpx\nQklgCyH8TsjKk4m78buo7jG8//MCN5vWotfo6AvfQdycQbYXt/OX9ytRJbTFLCKBLYTwS8FLlxN/\ny+3g9aI+/xLf1Z2KTqNjMOoTolMG+Si/hVc/qpHQFrOGBLYQwm8FLVpMwh13gUaD+sdXuVl7CjqN\nDkfsTiISB3l3VyNvbqv3dTOFOCEksIUQfs0yL4eEO7+PotOh/uk1vqusRKMouBJ2ERY7xN+31vHu\nzkZfN1OI404CWwjh9yxZ2STe+X0UnR7+8jduUFaiouJJ3Y0t2s4rH1Xz0b5mXzdTiONKAlsIMSOY\nM7NIvPMuFJ0e3Qt/5zssx6O6UebsIjjczp/er2Tb/jZfN1OI40YCWwgxY4yH9nhP2/jS21znXYrL\n60KXvQeLzcFz75Sxp7zT180U4riQwBZCzCjmzMyJ0La88i7XevIY8Tgx5+zFEOTk2bdKKKnr9XUz\nhZh2EthCiBnn30M7+NX3+aZ7EQ6PneAF+8Dg5Mm/7qemZcDXzRRiWklgCyFmJHNmJonf+z4ag4Gw\n1zbyjbEF2D1DhOXmM6bY+a9XC2nuHPZ1M4WYNhLYQogZy5yRScKd46Ed+fomvj46l2HPAJFLC7F7\n7Dz2cgGdfQ5fN1OIaSGBLYSY0cwZmSR87wcoegOxb2zjYlcWQ54+opcWMjA6zK9eKqBvaNTXzRTi\nS5PAFkLMeOb0DBLu+B6KVkvyG59w/lgmQ95eYpbup3tomMdfLmDYOebrZgrxpUhgCyECgiUrm4Tb\n7gRFIf2NXawfS2NQ7SJmaTEtPYM88UohIy63r5spxDGTwBZCBAzLvBzib7kNVJV5f8/ntLFkBpV2\nYpaUUdfez29e38+Y2+PrZgpxTCSwhRABxbpgEXE33YLq8ZD3VjErXXEMapuJXlxBWUMvT/+9BI/X\n6+tmCvGFSWALIQJOUG4ecd+5CdXlYuXbleS5ohgyNBC1oJr8qi7+95/leOWynGKGkcAWQgSk4GXL\nib3uO6gjI6z9Zz3zXWEMW2qInFvPtv1tvPxhtVxLW8woEthCiIAVsmIVMd/6Nl67nbPfbSFzNAR7\nSAVhGc18sKeJt7bX+7qJQkyZBLYQIqDZTl1N9FXfxDs8zIYPOkkZtTISXoItpZU3ttTx4V65LKeY\nGSSwhRABL3TtGURdfiXewUEu+rCXuFEjrpgiguI7+MsHlewobvd1E4U4KglsIcSsEHbW2URe8jXU\n/gG+/rGdcJceb2IB5qhu/vB2GQVV3b5uohBHJIEthJg1ws85l/ANF6B293L11jGCxzRo5+SjC+3h\nqTeKKW/o83UThTgsCWwhxKwSceFFhJ61Dto7uWa7imHMizErH8XSx69fL6KubdDXTRRiUhLYQohZ\nRVEUor52BSGnrkZpaee6XXo0bjeWnHzGdP08/nIBLd12XzdTiENIYAshZh1FoyHmm9cStOwktPXN\nXL/XiMftxLYwH4c6yGMv5dPV7/R1M4U4iAS2EGJWUjQa4q6/AeuixeirG7muyMqox054bj79o4M8\n9lIB/cNyWU7hPySwhRCzlqLTEXfTLZjnzsNcVs+1JcE4vINE5hXSOTwgl+UUfkUCWwgxq2kMBhJu\nvR3TnDkE76/lqvIQ7GovkXlFNPcM8OtX5bKcwj9IYAshZj2NyUzCHd/HkJhERH4NX6sJwa50E5m3\nn5q2vgOX5ZQrfAnfksAWQghAa7WS+L0foI+JJW5XDRfUB2PXdhCZW0JZYw/PvCmX5RS+JYEthBAH\n6Gw2Er//Q3ThEaRtr2F9UxB2fSsRC8vZV9kpl+UUPiWBLYQQ/0YfHkHi93+E1mZj7pZaTmuz4jA1\nEp5TJZflFD4lgS2EEJ9jiIkh8a4forFayd1Uz8pOC86gWsKy6vhgTyNvbav3dRPFLCSBLYQQkzAm\nJJJ45/fRGI2s2NhIbo+JkdBKQtKaeGNrHR/sbvJ1E8UsI4EthBCHYUqbQ/zt30PR6ThtYxtz+wyM\nRZUSlNjCix+O7yIX4kSRwBZCiCOwZGUTf/Ot4PWyfmMXaYM6PPH7scS28/w75eyr7PJ1E8UsIYEt\nhBBHYV2wiLjv3AQuFxds7CN+SAvJhejDu3j678WU1vf6uoliFpDAFkKIKQhetpyYa74NDieXbRoi\nwg76jHyUoB5+8/p+aloHfN1EEeAksIUQYopsp6wm6vJvwNAwV24eIcjhwTw3H7exl/96pZDmzmFf\nN1EEMAlsIYT4AsLOWkfEVy9G6Rvgm1vd6J0ugubn41D6eOzlAjr7HL5uoghQEthCCPEFhZ93PmHr\nv4Kmq5drdwCjDkIXFTDo7udXLxXQNySX5RTTTwJbCCG+IEVRiLz069jWrEXX1sW3d2pwjw0Ttjif\nbkc/j8llOcVxIIEthBDHQFEUoq/6JsErVmJs6uTa3VpcnkEicgto7e/j8ZcLcIzIZTnF9JHAFkKI\nY6RoNMReez3WxblY6tq5Ol+PU+0jIreI+s4+/uu1QkZdHl83UwQICWwhhPgSFJ2OuJtuxjx3HrbK\nVq4sMuBQuojM3U91Sy+/+WsRY24JbfHlSWALIcSXpNEbSLj1Dkxz5hBZ2sJlJQbs2nYiF5dS2tDD\n794owe2Ra2mLL0cCWwghpoHGZCLh9rswJCYRX9TC+ZVG7IYWIhdWUFDdxe/fKsXrlctyimMngS2E\nENNEGxRE4vd+gD4mhjl7mzm7xoDd1EDk/Gp2l3fw/DtleOVa2uIYSWALIcQ00tlsJN71I3Th4czb\n2cyaBgN2aw2RcxvYVtzOXz6oRJXQFsdAAlsIIaaZPiKCxLt+hDY4hLxtzZzUosMeUk5ERjMf7Wvh\n1U01EtriC5PAFkKI48AQG0viXT9EY7Gwaks7i9q1OMKLCU9r592djby5rd7XTRQzjAS2EEIcJ8ak\nJBLuuAtFr+f0jzvJ6tIwElVIaGIXf99ax7s7G33dRDGDSGALIcRxZE7PIOHWO1AUhXM+7iGlF1zx\n+YTE9fHKR9Vs3Nfs6yaKGUICWwghjjPLvBzibroF3B4u3NRPXL8Hb/IegqIG+PP7lWzb3+brJooZ\nQAJbCCFOgKDcPGKvuwFGXVz68TBhA2406XuwhA/y3Dtl7Crr8HUThZ+TwBZCiBMkZMVKoq/6Ford\nyZWbnViGRtFn7cVkG+L3b5VSUNXt6yYKPyaBLYQQJ1DoaWuJvOzrKIPDfHPLGHq7E9PcfWitQzz1\nxn5K6nt93UThpySwhRDiBAtffw7hG85H0zvANdu84LQTtGAfGO385rUiyhv6fN1E4YcksIUQwgci\nLryY0DPXoevs5dodCp6RYUIX78Ojs/Pr14qobOr3dROFn5HAFkIIH1AUhaivX0HIyadiaO3i2p1a\nRkaHCM8rwK2x88SrhVS3DPi6mcKPSGALIYSPKBoNMd+6lqClyzA1dnDtXj0OVz+RSwoZU5088UoB\ndW2Dvm6m8BMS2EII4UOKVkvcd27CsmAh1ppWvlloZGisl9jlRYx4nDz2UgEN7UO+bqbwAxLYQgjh\nY4pOR/x3b8WcmYWtvJlvlFjoG+si/qQSnG4nv3opn8YOCe3ZTgJbCCH8gMZoJP62OzGmpBKxv4Gv\nVwTRO9ZO4opSHK4RfvVSAc1dw75upvAhCWwhhPATWouFxDu/jyE+nth9dVxUF0y3u43klRUMj4zw\nqxfzae22+7qZwkcksIUQwo9og4NJvOuH6KOiSP6klvMag+l0N5G2qopB5yiPvphPe6/D180UPiCB\nLYQQfkYXGkbiXT9CGxpKxtYa1rXZaHfXk76qhgH7CI++mE9nn4T2bCOBLYQQfkgfFUXiXT9EExRE\nzqZq1nSF0OquIWNVA31D46Hd3e/0dTPFCSSBLYQQfsoYn0DinT9AYzKR92EtK/pstHgqyFzVSM/g\nCI+8mE/PwIivmylOEAlsIYTwY6bUVBJu/x6KTsfKD+pYMhhCs6eMzJVNdA84efTFfHoHJbRnAwls\nIYTwc+bMLOJvvg1UlTUfNLHQYaPZW0rmimY6+x088oKE9mwggS2EEDOAdcFC4m64CdXl4sz3W5g7\naqNZLSFrRauE9iwhgS2EEDNE8NLlxFxzHarDwTkftJPpDqVJ3U/2inY6+x388oV9ckw7gElgCyHE\nDGI75VSirvgG3sEhNvyrizRvKI1qIfNWdtLV75TQDmAS2EIIMcOEnbmOiK9ejLe3j4s29pGMjXpv\nPjkru+keGOGXL+yje0BO+Qo0EthCCDEDhZ93PmHnnIens4tLNw6SgI06714WrOqhe2CER17Il9AO\nMBLYQggxAymKQuTFlxJ61tl42jv4+qZh4rBR49nNolW9n4W2TK4SMCSwhRBihlIUhaivX4Ht9DPw\ntLZxxRYnMUoIVZ5dLF7Vf2D3uIR2oJDAFkKIGUxRFKKvuIqQ1WvwNDXzjW0uopVgKj2fkLtqkJ7B\n8WPaXRLaM94JC+zq6mruuOMO7r//ft59990T9bZCCBHwFI2GmKuvIXjVyXjqG7lqh5dITTAVnu0s\nOXmYnsFRHnlhH50S2jPalAL7nnvuYdWqVWzYsOGgxzdv3sz69etZt24dzz777BFfY/PmzVx99dX8\n/Oc/54033jj2FgshhDiEotEQe811BC8/CU9tHd/cCeEaK2XurSw7xfFZaMtVvmYsRVVV9WgL7d69\nG4vFwt13380//vEPADweD+vXr+f5558nJiaGSy+9lMcffxyPx8Pjjz9+0PoPPfQQAE8++SQmk4n8\n/Hxeeumlozauq2voWH4n4aeiooLlbyqOSGrky1Pdbtqe+R3D+XvRzs3iuWUu+r0O5uvXsGebhbBg\nI3dfmUd0mMXXTT1mgVwnUVHBh31ON5UXWL58Oc3NzQc9VlRUREpKCklJSQCcd955fPjhh9x44408\n88wzk77O/fffj8fj4dZbb51q24UQQnwBik5H3I3fpfWp32AvKuTbmrn8YalCydhmlp2yhj3b4Jcv\n5POjK/KICZ+5oT0bTSmwJ9PR0UFsbOzE/ZiYGIqKig67fHNzM8888wwOh4PrrrtuSu8RFmZBp9Me\naxOFHzrSt0chQGpkukT+9MeU/efD9BcU8l3LYp5eoKHEtZlTzjidbRvh0ZfyefCmU0iKmZn/37Ox\nTo45sL+oxMRE/uM//uMLrdMnx1oCSiDvxhLTQ2pkekXecAuj//0Ezj2FXOdZzP8sVNk3/BHLV69m\n9xb48ZNb+MEVeSRGBfm6qV9IINfJkb6IHPMo8ZiYGNrb2yfud3R0EBMTc6wvJ4QQYpppDAYSbrsT\nc2YW7vxCvlMWSqguiOLR/9/enUZXWR94HP8+Nzf7cm/23bDLvu+7LOPasUWtCkq1bNpRWxCxfTE9\np5yO50zBau201kqn49gKimsBNxIIiSxhy0KAQBKyk4Ts201ys82LWEZbEUGSJzf5fd4lL/L8XvxP\nvufm5j5PMjPmN1HvaONXb6RSWN4/49ffXHewx40bR35+PkVFRTidTvbs2cOiRYtu5DYREfmWLJ6e\nRD21Hq8hQ2g7dpLV2SHY3P3JaElm1kIHTc1tbNmeSl5pvdlT5Sq+UbA3bNjAAw88QF5eHvPnz2fn\nzvJihK4AABIiSURBVJ1YrVZ+/vOfs3r1au644w5uv/12hg8f3tN7RUTkGrl5exP9k6fxjBtE2+Gj\nrM0Nw+buT5ojiVm3OHC0trN1Ryq5JXVmT5Wv8Y0+1mWW/voexUDVn993khtDZ6RndTQ2Uvz8f9Ja\nVITHvNm8OvQStc56JvvP4/A+P9zdLay/bwIjYu1mT/1a/fmc9Mh72CIi4lrc/PyI2bAJj+gYnMmH\nWHMhHLtHACcbkpm9qJH29k5+/VYaWQU1Zk+Vr6Bgi4gMIG7+/sRs/DzaSQdZeyGCQA8bJxqSmbOo\niY6OLl7cmc7pvGqzp8o/ULBFRAYYq3/A5Wi3Jn3GmrzuaB+rT2LOkkY6u+A3b2eQkVtp9lT5AgVb\nRGQAsvoHEPP0Jjyiomk9kMya/AiCPO0cq01mzuIGLAb89p1TpJ6vMHuqfE7BFhEZoKwBAcRsfLY7\n2onJrMnrjvbR2mRmL27A6mbh9+9ncizrktlTBQVbRGRA+/9oR9GSmMSa/EiCPe2k1CQza3E97lYL\nf/ggkyOny67+w6RHKdgiIgOcNSCAmKc/j/b+A6zOjyLYM5CU6mRmLq7Dy8ONV3ed4eCpUrOnDmgK\ntoiIYLXZuqMdGUXL/kTWFEQS4hlIStVnzFxci7eXG/+95ywH0krMnjpgKdgiIgJ8Hu2Nm/CIiKR5\nXyKrC6MI9QriSOVBpt9SjY+3ldc+PkfCieKr/zC54RRsERG5zGqzE7PxWdwjImhO2M/qwijCvINJ\nqTrMtFsq8fd15697z/Pp0UKzpw44CraIiHyJ1W4nduNPcY+IwBG/j1WFUYR7h5JSeYTJCy4R4OfO\njn05fHikwOypA4qCLSIi/+RytMMjcOxNYFVRJBE+YRytTGHSgnICAzx4OzGXXQfzzJ46YCjYIiLy\nlax2O7HPPIt7eARNn8bzw8JIonzCOVpxlHFzSwkK8OS95DzeS7pAH36OVL+hYIuIyBVZ7YFfiPZe\nHskPI9o3gmOVxxgzt4QQuye7DuXzzgFFu6cp2CIi8rW6o/1TPCIiaYpP4Ae5wcT4RnK88jgjZxcT\nFujFh0cKeHNfjqLdgxRsERG5KqvdTswz3TdXadq3n5XZgcT6RXGi8gTDZxUSEezNp8eKeCM+W9Hu\nIQq2iIh8I90f+fopHtExNCUm8nCWPzf5RXOy8iRDZuQRFepDwoliXv/0PJ2K9g2nYIuIyDfWfe/x\nTXjExNKUlMxDZ3wZ5B9LamUacdNyiQ3zITG1hP/5KIvOTkX7RlKwRUTkmlj9A4jd+CyesTfR9Nln\nLM/0ZLD/TaRVphM9JYebInz5LKOUP+05q2jfQAq2iIhcMzc/P2Ke3oRn3CCaDh7iwQx3hvjHkV6V\nQeSkcwyO8uPw6TJe3X2Gjs5Os+f2Cwq2iIhcl+5oP4PnoME0HT7M/WkWhvoPIqMqk9AJZxka7U/K\nmXJe+eA07R2K9relYIuIyHVz8/ElZsMzeA0ZiiMlhe+fhBEBgzlVdZqgcacZHuvP8XMVvPx+Jm3t\niva3oWCLiMi34ubjQ/T6jXgNHYbj2FHuOdbBzQFDyKw+g21MJiPjAkjNruR3752irb3D7LkuS8EW\nEZFvzc3bm5j1T+M9fASOE8dZdtTJqIChnK4+i++odEYNtpGRW8VL75zC2aZoXw8FW0REbgiLlzfR\nP96A94ibcZw8yXePNDPaNowz1efwGpHK2KF2TudV85u3M2h1KtrXSsEWEZEbxuLl1R3tkaNwpKVx\n98EmxtpHkFVzHuuwE0wYbudsQQ0v7EynubXd7LkuRcEWEZEbyuLpSfSTP8Fn9BgcGenclVTHeNvN\nnKvJhsHHmXxzIOeLannhLUX7WijYIiJyw1k8PYl64sf4jBlLc+Yp7kiqYaJ9JOdrc2iPS2Ha6CBy\nSurYuiMNR0ub2XNdgoItIiI9wuLhQdQTT+E7bjzNpzO5bX8lk+yjyK69QEv0YaaPCSavtJ4tO9Jo\nbFa0r0bBFhGRHmNx9yDyR0/iO2EizWfPcOu+cqbYR5NTl0dT1EFmjw+hoKyBLdtTaXA4zZ7bpynY\nIiLSoyzu7kQ9/gS+kybTnJXFkvhSptnHcKEun9qwZOZOCqXoUiO/2p5KXZOifSUKtoiI9DjDaiVq\n3Y/wmzKVlvPnWLS3mBmBY8mrL6Aq+AALpoRRUtHEr944qWhfgYItIiK9wrBaiVzzGH5Tp9OSnc3C\nTwqZGTSe/PpCyu37WTQtjNIqB1v0SvsrKdgiItJruqO9Dv/pM2jJyWHBR/nMCZxAQUMRxQEJLJwa\nysXKJrZuT6Ve0f4SBVtERHqV4eZGxKq1+M+YSUtuDnM+usC8oIkUNZRQEpDAgikhlFQ2sWVHKvX6\nR7TLFGwREel1l6M9cxatF3KZtSeb+UGTKW68SIktgflTQiip+PyVtqINKNgiImISw2Ih4odrCJg1\nh9a8PGbuPsfC4KmUNJZSEhDPvMlBFFc0sXV7mj7yhYItIiImMiwWwh9dRcDsubTm5zH9b2dYFDKV\ni01llNgSmDM5iOKKRrbq5ioKtoiImMuwWAh/5IcEzJ1Ha0E+U98/zZLg6ZQ2lXMxIJ45kwIputTI\n1u2pAzraCraIiJjOsFgIX/koAfPm01pYwOT3M1gaMp0yxyWKbfHMnmin8FIjW3cM3DuiKdgiItIn\nGBYL4Q8/gm3+QlqLCpn4bjq3hczikqOCYls8MyfYKCxv5N9fOTQgX2kr2CIi0mcYFgthD63EtnAR\nzuIixr1zgttDZ1HRXEmJPZ4ZE/zJLa7j+TfTaBpgT/lSsEVEpE8xLBbCVjyM7ZbFOEuKGfv2ce4M\nnUNlcxUXbQnMmx5EQVkDzw+wR3Mq2CIi0ucYhkHY8oewL1qCs6SE0TuP8p3QuVS2VFPg8wnTxvuR\nX9bACzvTaXG2mz23VyjYIiLSJxmGQeiDK7AvWYrzYgkj3zrC3aHzqHBUUWKLZ9IYH3JL6nnp7Qxa\n2zrMntvjFGwREemzDMMg9P7l2JfeirP0IiPePMTyuCXUtNZSHpTAuJFeZBXW8rt3T9HW3mn23B6l\nYIuISJ9mGAah33+AwH+5DWdZKTf9byL3hC2kprWOipB9jBzuTmZeNX/4IJP2jv4bbQVbRET6PMMw\nCLnvfgJvu4PmkosM3n6Ae8MWUuespzb8AMOGuJGaXcm23Wfo7Owye26PULBFRMQlGIZByD33EX3P\n92grL2fQGwf4fvgi6pz11EcmMSjO4OjZS/z5w7N0dvW/aCvYIiLiMgzDIO7hFQTd+R3aLpVz01/2\ncX/YYhraGmiJPUhsjMHBzDL+8ul5uvpZtBVsERFxKYZhEPzdZQTd9a+0VVwi9q8J3Bd2C3XOejoG\nHyYq0kJiagk7EnL6VbQVbBERcTmGYRDyhWjHbU/ku6HzqGmtxTI8hfAwg73Hi3gv+YLZU28YBVtE\nRFxW8N3fI/D2O2krL2f4m4e4M2Q2VS1VeI48TkiIhd2HCth9KN/smTeEgi0iIi7LMAxClt17+SNf\no98+xtLg6VS0VOA35gRBdoN3ky6w72Sx2VO/NQVbRERc2t8/8vX3O6JNfC+dhUFTKG8uxz4hHX8/\n+Oun5zlyuszsqd+Kgi0iIi7v73dEs93S/ZSv6X87zZzACZQ2XyR8SiZe3vCnPWdJz6k0e+p1U7BF\nRKRfMAyDsAcfwjZ/Aa2FhczZnc0M2xhKmouJmX4WN7dOfv9+JueLas2eel0UbBER6Te6n6f9AwLm\nzKO1IJ8FHxcwOWAkxc0FxM06T2dXO795O52Csgazp14zBVtERPoVw2Ih/AeP4j9rNq15F1iy9yLj\n/YdT3JLH0Nm5tDjb+PVbaZRWNZk99Zoo2CIi0u8YFgsRj67Gf/pMWnNzuH1fBaP9BlPUmsuIOQU0\nOJw8/2Ya1fUtZk/9xhRsERHplwyLhYhVa/CbOo2W7PPcdaCWYd7RFDrPMXJWCdX1LWzdkUa9w2n2\n1G9EwRYRkX7LcHMjcvU6/CZNofXcOZYdaibGM4yCjkxunn6JsmoHL7yZjqOl3eypV6Vgi4hIv2ZY\nrUSuexzfCRNpPZvF8uMGoZ5BFJLK8ElVFJQ38F/vZtDW3refpa1gi4hIv2dYrUQ+9iN8Ro2h9dQp\nHsnwxm71p9j9GEPG1pFVWNv9LO0+/LAQBVtERAYEi7sHUU88hdew4ThPpLI6OwhfN2/KfI4QPaye\nY1mX2BGf3Wef8KVgi4jIgGHx9CT6qfV43hRH2+FjrMuPwsNipS44hbDYRuJPFPNxSqHZM7+Sgi0i\nIgOKm48PMes34hEVRUfyYR4rHYxhGLREp2ALa2JnYi6HMkvNnvlPFGwRERlw3Pz9idmwCffQMEj4\njLWVw+jo6sAy5BjeNgd//jCLzAtVZs/8EgVbREQGJKvdTszGTViDgnD/OIlVdTfT0tmCz+gTWLwc\n/O69TPJK682eeZmCLSIiA5Z7cAgxGzbhFhCAz+4DrGwahaOjiaCJ6Thx8OLOdMprHGbPBBRsEREZ\n4DwiIojZ8AwWX18C/5bEvY5h1LfXEjn1NA0tzfz6zTTqmsy/G5qCLSIiA55nTCwxP3m6+7/Id6Vw\nW3McNR2XiJmWRUVd9yvtFqe5d0NTsEVERACvwUOIemo9hpsbI3enM6c1kqquYqIn51JQVs8rH5ym\no9O8u6Ep2CIiIp/zGXEzkY/9G12dHUz/KJsJbSFUu+USObaQ9Nwq3jDxxioKtoiIyBf4jZ9AxKOr\n6HQ4WPRJMUPabdT6nCV46EX2nyzhk6NFpuxSsEVERP5BwMzZhD6wgs76eu7eV0NEuzeO4AwCoit4\na38Ox7Iu9fomBVtEROQrBC5ZStBd36GzspIHP3Ni6/SgIyYVr6BqXt11huzi2l7do2CLiIhcQfDd\ny7AtWEjnxVIeSbHg3g4ew9Po8qrlt++cory69z6jrWCLiIhcgWEYhK1Yid/UaZBXyOoMXzo6nASM\nTaOpo47XPs7qtS0KtoiIyNcwLBYiVq3FZ9QYrGdzWXXWRktnE6FTTjFzfFCv7VCwRURErsLi7k7U\nvz2J56DB+KRnsyLXTkN7NVldSb23odeuJCIi4sIsXl7E/HgDHhGRhBzN5v7SCGL9onvv+r12JRER\nERfn5u9P9IaNWAODiEg8xZwy7167toItIiJyDdyDgole/zQWH19qE/f12nWtvXYlERGRfsIzKppB\nm/+jV6+pYIuIiFwHq93eq9fTn8RFRERcgIItIiLiAhRsERERF6Bgi4iIuAAFW0RExAUo2CIiIi5A\nwRYREXEBCraIiIgLULBFRERcgIItIiLiAhRsERERF2B0dXV1mT1CREREvp5eYYuIiLgABVtERMQF\nKNgiIiIuQMEWERFxAQq2iIiIC1CwRUREXICCLSIi4gIUbBERERdgNXuADFxFRUW8/PLLNDY28tJL\nL5k9R/qg+Ph4EhMTaWxs5N5772Xu3LlmT5I+Jjc3l9dee43a2lpmzpzJ8uXLzZ7UY/QKW26on/3s\nZ8yaNYu77rrrS99PSkri1ltvZenSpfzxj38EIDY2lueee86MmWKiazkjS5Ys4Ze//CW/+MUv+PDD\nD82YKya4ljMydOhQNm/ezIsvvsjJkyfNmNtrFGy5oZYtW8a2bdu+9L2Ojg42b97Mtm3b2LNnD7t3\n7yYnJ8ekhWK26zkjL7/8MitWrOjtqWKSaz0jCQkJrF27lgULFpgxt9co2HJDTZs2DZvN9qXvZWRk\nEBcXR2xsLB4eHtx5550kJCSYtFDMdi1npKuriy1btjB//nzGjBlj0mLpbdf6e2Tx4sVs27aNXbt2\nmTG31+g9bOlx5eXlREREXP46PDycjIwMampqeOGFFzhz5gyvvPIK69atM3GlmOlKZ+T111/n8OHD\nNDQ0UFBQwIMPPmjiSjHTlc5ISkoKe/fuxel09vtX2Aq2mCYwMJDNmzebPUP6sJUrV7Jy5UqzZ0gf\nNmPGDGbMmGH2jF6hP4lLjwsPD6esrOzy1+Xl5YSHh5u4SPoanRG5Gp0RBVt6wbhx48jPz6eoqAin\n08mePXtYtGiR2bOkD9EZkavRGQGjq6ury+wR0n9s2LCBo0ePUlNTQ3BwME8++ST33XcfBw4c4Lnn\nnqOjo4N77rmHxx9/3OypYhKdEbkanZGvpmCLiIi4AP1JXERExAUo2CIiIi5AwRYREXEBCraIiIgL\nULBFRERcgIItIiLiAhRsERERF6Bgi4iIuAAFW0RExAX8HwL3u1AVtTJ5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1042043d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sigma8,Omega_c in zip([0.9,0.834,0.75],[0.238,0.27,0.322]):\n",
    "    p = ccl.Parameters(Omega_c=Omega_c, Omega_b=0.045, h=0.67, n_s=0.96, sigma8=sigma8)\n",
    "    cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "    tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "    tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=dNdz,z=z)\n",
    "    cls = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "    plt.plot(ell, cls, label=r'$\\sigma_8$={:.4f}'.format(sigma8))\n",
    "    plt.legend()\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    print cls[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import emcee\n",
    "data_path = '../../PittAstro/cov_LSST_data_Cl 2/'\n",
    "cov_file = np.loadtxt(data_path+'cov_LSST_lmax500')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "117977.010295\n",
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    },
    {
     "data": {
      "text/plain": [
       "(array([ 137.,   23.,    8.,    5.,    6.,    8.,    1.,    0.,   10.,    2.]),\n",
       " array([ 0.26998034,  0.27031216,  0.27064399,  0.27097581,  0.27130764,\n",
       "         0.27163946,  0.27197129,  0.27230311,  0.27263494,  0.27296677,\n",
       "         0.27329859]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c95b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, define the probability distribution that you would like to sample.\n",
    "cov = reshape(cov_file[:,2],(1810,1810))\n",
    "z = np.linspace(0., 3., 200)\n",
    "i_lim = 26. # Limiting i-band magnitude\n",
    "z0 = 0.0417*i_lim - 0.744\n",
    "\n",
    "ell = np.arange(2, 1812)\n",
    "\n",
    "Ngal = 46. * 100.31 * (i_lim - 25.) # Normalisation, galaxies/arcmin^2\n",
    "pz = 1./(2.*z0) * (z / z0)**2. * np.exp(-z/z0) # Redshift distribution, p(z)\n",
    "dNdz = Ngal * pz # Number density distribution\n",
    "p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, n_s=0.96,  A_s=2.1e-9)\n",
    "cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=dNdz,z=z)\n",
    "data    = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "icov = np.linalg.inv(cov)\n",
    "def lnprob(theta):\n",
    "    \n",
    "    z = np.linspace(0., 3., 200)\n",
    "    i_lim = 26. # Limiting i-band magnitude\n",
    "    z0 = 0.0417*i_lim - 0.744\n",
    "\n",
    "    ell = np.arange(2, 1812)\n",
    "\n",
    "    Ngal = 46. * 100.31 * (i_lim - 25.) # Normalisation, galaxies/arcmin^2\n",
    "    pz = 1./(2.*z0) * (z / z0)**2. * np.exp(-z/z0) # Redshift distribution, p(z)\n",
    "    dNdz = Ngal * pz # Number density distribution\n",
    "    for i in range(len(theta)):\n",
    "        p = ccl.Parameters(Omega_c=theta[0], Omega_b=0.045, h=0.67, n_s=0.96,  A_s=theta[1])\n",
    "        cosmo = ccl.Cosmology(p, transfer_function='boltzmann')\n",
    "\n",
    "        tracer1 = ccl.ClTracer(cosmo,tracer_type='lensing',n=[z,dNdz])\n",
    "        tracer2 = ccl.ClTracer(cosmo,tracer_type='lensing',n=dNdz,z=z)\n",
    "        theory = ccl.angular_cl(cosmo, tracer1, tracer2, ell)\n",
    "        diff = data-theory\n",
    "    print np.dot(diff,np.dot(icov,diff))/2.0\n",
    "    return -np.dot(diff,np.dot(icov,diff))/2.0\n",
    "\n",
    "\n",
    "\n",
    "# We'll sample a 10-dimensional Gaussian...\n",
    "ndim = 2\n",
    "\n",
    "nwalkers = 8\n",
    "\n",
    "# Choose an initial set of positions for the walkers.\n",
    "# p0 = [np.random.rand(ndim) for i in xrange(nwalkers)]\n",
    "# p0 = emcee.utils.sample_ball(np.ones(ndim)*0.27,np.ones(ndim)*0.01,size=nwalkers)\n",
    "# p1 = emcee.utils.sample_ball(np.ones(ndim)*2.1e-9,np.ones(ndim)*1.E-10,size=nwalkers)\n",
    "# p00 = (p0,p1)\n",
    "p00 = [np.array([0.27,2.1e-9])*np.ones(ndim)+np.array([0.02,1.e-10]) * numpy.random.rand() for i in range(nwalkers)] \n",
    "# Initialize the sampler with the chosen specs.\n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)\n",
    "\n",
    "# Run 5000 steps as a burn-in.\n",
    "pos, prob, state = sampler.run_mcmc(p00, 25)\n",
    "\n",
    "# Reset the chain to remove the burn-in samples.\n",
    "sampler.reset()\n",
    "\n",
    "# Starting from the final position in the burn-in chain, sample for 100000\n",
    "# steps.\n",
    "sampler.run_mcmc(pos, 25, rstate0=state)\n",
    "\n",
    "# Print out the mean acceptance fraction. In general, acceptance_fraction\n",
    "# has an entry for each walker so, in this case, it is a 50-dimensional\n",
    "# vector.\n",
    "#print \"Mean acceptance fraction:\", np.mean(sampler.acceptance_fraction)\n",
    "\n",
    "# Estimate the integrated autocorrelation time for the time series in each\n",
    "# parameter.\n",
    "#print \"Autocorrelation time:\", sampler.get_autocorr_time()\n",
    "\n",
    "plt.hist(sampler.flatchain[:,0], 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.27184423945\n",
      "theta [  2.71844239e-01   2.10922120e-09]\n",
      "0.27184423945 2.10922119725e-09\n",
      "theta [  2.73048713e-01   2.11524357e-09]\n",
      "0.273048713338 2.11524356669e-09\n",
      "theta [  2.85554854e-01   2.17777427e-09]\n",
      "0.285554853606 2.17777426803e-09\n",
      "theta [  2.75594721e-01   2.12797361e-09]\n",
      "0.275594721488 2.12797360744e-09\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-5717fc05bdf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memcee\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEnsembleSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnwalkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlnprob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mp00\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_mcmc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp00\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/emcee/sampler.pyc\u001b[0m in \u001b[0;36mrun_mcmc\u001b[0;34m(self, pos0, N, rstate0, lnprob0, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m         for results in self.sample(pos0, lnprob0, rstate0, iterations=N,\n\u001b[0;32m--> 172\u001b[0;31m                                    **kwargs):\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/emcee/ensemble.pyc\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, p0, lnprob0, rstate0, blobs0, iterations, thin, storechain, mh_proposal)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mblobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblobs0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlnprob\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m             \u001b[0mlnprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lnprob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;31m# Check to make sure that the probability function didn't return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/emcee/ensemble.pyc\u001b[0m in \u001b[0;36m_get_lnprob\u001b[0;34m(self, pos)\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0mblob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0mlnprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m             \u001b[0mblob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number"
     ]
    }
   ],
   "source": [
    "def lnprob(theta):\n",
    "#     for i in range(len(theta)):\n",
    "#         print i,theta[i]\n",
    "\n",
    "    print 'theta',theta\n",
    "\n",
    "    a = theta[0]\n",
    "    b = theta[1]\n",
    "    print a,b\n",
    "\n",
    "\n",
    "ndim = 2\n",
    "\n",
    "nwalkers = 4\n",
    "\n",
    "p0 = emcee.utils.sample_ball(np.ones(ndim)*0.27,np.ones(ndim)*0.01,size=nwalkers)\n",
    "p1 = emcee.utils.sample_ball(np.ones(ndim)*2.1e-9,np.ones(ndim)*1.E-10,size=nwalkers)\n",
    "p00 = [np.array([0.27,2.1e-9])*np.ones(ndim)+np.array([0.02,1.e-10]) * numpy.random.rand() for i in range(nwalkers)] \n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)\n",
    "print p00[0][0]\n",
    "pos, prob, state = sampler.run_mcmc(p00, 20)\n",
    "\n",
    "sampler.reset()\n",
    "\n",
    "sampler.run_mcmc(pos, 20, rstate0=state)\n",
    "\n",
    "plt.hist(sampler.flatchain[:,0], 10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = sampler.chain[:, 5:, :].reshape((-1, ndim))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Too few points to create valid contours\n"
     ]
    },
    {
     "data": {
      "image/png": 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REmQDnJ2dERQUZFfhSmQrOvxbU1FRgaqqKmzatAmSJGHUqFF3tEdERCAhIYHHzBBRl9Xh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb99090>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REmQDnJ2dERQUZFfhSmQrOvxbU1FRgaqqKmzatAmSJGHUqFF3tEdERCAhIYHHzBBRl9Xh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb99090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import corner\n",
    "corner.corner(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.71429967e-01,   2.10714984e-09],\n",
       "       [  2.70810367e-01,   2.10405184e-09],\n",
       "       [  2.70737254e-01,   2.10368627e-09],\n",
       "       [  2.70735398e-01,   2.10367699e-09],\n",
       "       [  2.70735398e-01,   2.10367699e-09],\n",
       "       [  2.70564475e-01,   2.10282238e-09],\n",
       "       [  2.70564475e-01,   2.10282238e-09],\n",
       "       [  2.70564475e-01,   2.10282238e-09],\n",
       "       [  2.70099139e-01,   2.10049570e-09],\n",
       "       [  2.70049403e-01,   2.10024702e-09],\n",
       "       [  2.70049403e-01,   2.10024702e-09],\n",
       "       [  2.70049403e-01,   2.10024702e-09],\n",
       "       [  2.70018293e-01,   2.10009146e-09],\n",
       "       [  2.70016356e-01,   2.10008178e-09],\n",
       "       [  2.70016094e-01,   2.10008047e-09],\n",
       "       [  2.70139566e-01,   2.10069783e-09],\n",
       "       [  2.70139566e-01,   2.10069783e-09],\n",
       "       [  2.70139566e-01,   2.10069783e-09],\n",
       "       [  2.70139566e-01,   2.10069783e-09],\n",
       "       [  2.70109072e-01,   2.10054536e-09],\n",
       "       [  2.70088400e-01,   2.10044200e-09],\n",
       "       [  2.70088400e-01,   2.10044200e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70079585e-01,   2.10039793e-09],\n",
       "       [  2.70044736e-01,   2.10022368e-09],\n",
       "       [  2.70035804e-01,   2.10017902e-09],\n",
       "       [  2.70028752e-01,   2.10014376e-09],\n",
       "       [  2.70028752e-01,   2.10014376e-09],\n",
       "       [  2.70019054e-01,   2.10009527e-09],\n",
       "       [  2.70019054e-01,   2.10009527e-09],\n",
       "       [  2.70007161e-01,   2.10003580e-09],\n",
       "       [  2.70007161e-01,   2.10003580e-09],\n",
       "       [  2.70007161e-01,   2.10003580e-09],\n",
       "       [  2.70007161e-01,   2.10003580e-09],\n",
       "       [  2.70007161e-01,   2.10003580e-09],\n",
       "       [  2.70008897e-01,   2.10004449e-09],\n",
       "       [  2.70009686e-01,   2.10004843e-09],\n",
       "       [  2.70004362e-01,   2.10002181e-09],\n",
       "       [  2.70004362e-01,   2.10002181e-09],\n",
       "       [  2.70004362e-01,   2.10002181e-09],\n",
       "       [  2.70004595e-01,   2.10002298e-09],\n",
       "       [  2.70004595e-01,   2.10002298e-09],\n",
       "       [  2.70004155e-01,   2.10002078e-09],\n",
       "       [  2.70004577e-01,   2.10002289e-09],\n",
       "       [  2.69999205e-01,   2.09999602e-09],\n",
       "       [  2.69999205e-01,   2.09999602e-09],\n",
       "       [  2.69999443e-01,   2.09999721e-09],\n",
       "       [  2.69999443e-01,   2.09999721e-09],\n",
       "       [  2.69999443e-01,   2.09999721e-09],\n",
       "       [  2.69998782e-01,   2.09999391e-09],\n",
       "       [  2.70356461e-01,   2.10178230e-09],\n",
       "       [  2.70356461e-01,   2.10178230e-09],\n",
       "       [  2.70356461e-01,   2.10178230e-09],\n",
       "       [  2.70356461e-01,   2.10178230e-09],\n",
       "       [  2.70356461e-01,   2.10178230e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70321939e-01,   2.10160970e-09],\n",
       "       [  2.70192934e-01,   2.10096467e-09],\n",
       "       [  2.70192934e-01,   2.10096467e-09],\n",
       "       [  2.70100027e-01,   2.10050014e-09],\n",
       "       [  2.70062922e-01,   2.10031461e-09],\n",
       "       [  2.70062922e-01,   2.10031461e-09],\n",
       "       [  2.70005227e-01,   2.10002613e-09],\n",
       "       [  2.70005227e-01,   2.10002613e-09],\n",
       "       [  2.70005227e-01,   2.10002613e-09],\n",
       "       [  2.70005227e-01,   2.10002613e-09],\n",
       "       [  2.70005227e-01,   2.10002613e-09],\n",
       "       [  2.70005491e-01,   2.10002746e-09],\n",
       "       [  2.70005491e-01,   2.10002746e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005652e-01,   2.10002826e-09],\n",
       "       [  2.70005355e-01,   2.10002677e-09],\n",
       "       [  2.70005355e-01,   2.10002677e-09],\n",
       "       [  2.70005355e-01,   2.10002677e-09],\n",
       "       [  2.70005355e-01,   2.10002677e-09],\n",
       "       [  2.70003808e-01,   2.10001904e-09],\n",
       "       [  2.69999042e-01,   2.09999521e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72765086e-01,   2.11382543e-09],\n",
       "       [  2.72010353e-01,   2.11005177e-09],\n",
       "       [  2.71886678e-01,   2.10943339e-09],\n",
       "       [  2.71886678e-01,   2.10943339e-09],\n",
       "       [  2.71776379e-01,   2.10888189e-09],\n",
       "       [  2.71210616e-01,   2.10605308e-09],\n",
       "       [  2.71201940e-01,   2.10600970e-09],\n",
       "       [  2.71201940e-01,   2.10600970e-09],\n",
       "       [  2.71201940e-01,   2.10600970e-09],\n",
       "       [  2.71019105e-01,   2.10509553e-09],\n",
       "       [  2.70854109e-01,   2.10427055e-09],\n",
       "       [  2.70854109e-01,   2.10427055e-09],\n",
       "       [  2.70854109e-01,   2.10427055e-09],\n",
       "       [  2.70677256e-01,   2.10338628e-09],\n",
       "       [  2.70054521e-01,   2.10027260e-09],\n",
       "       [  2.70054521e-01,   2.10027260e-09],\n",
       "       [  2.70054521e-01,   2.10027260e-09],\n",
       "       [  2.70054521e-01,   2.10027260e-09],\n",
       "       [  2.70033858e-01,   2.10016929e-09],\n",
       "       [  2.69980336e-01,   2.09990168e-09],\n",
       "       [  2.69980336e-01,   2.09990168e-09],\n",
       "       [  2.69987324e-01,   2.09993662e-09],\n",
       "       [  2.69987324e-01,   2.09993662e-09],\n",
       "       [  2.69987610e-01,   2.09993805e-09],\n",
       "       [  2.69993454e-01,   2.09996727e-09],\n",
       "       [  2.69993454e-01,   2.09996727e-09],\n",
       "       [  2.69993454e-01,   2.09996727e-09],\n",
       "       [  2.69993454e-01,   2.09996727e-09],\n",
       "       [  2.69993966e-01,   2.09996983e-09],\n",
       "       [  2.69993966e-01,   2.09996983e-09],\n",
       "       [  2.69993966e-01,   2.09996983e-09],\n",
       "       [  2.69993966e-01,   2.09996983e-09],\n",
       "       [  2.69993966e-01,   2.09996983e-09],\n",
       "       [  2.69993098e-01,   2.09996549e-09],\n",
       "       [  2.70015782e-01,   2.10007891e-09],\n",
       "       [  2.70015782e-01,   2.10007891e-09],\n",
       "       [  2.70015782e-01,   2.10007891e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69984109e-01,   2.09992055e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09],\n",
       "       [  2.69985265e-01,   2.09992633e-09]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.2700239030373921, 0.0027411829263394027, 3.9793579315106076e-05),\n",
       " (2.1001195151869606e-09, 1.3705914631697055e-11, 1.9896789657505737e-13)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),\n",
    "                             zip(*np.percentile(samples, [2.5, 50, 97.5],\n",
    "                                                axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 2)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = [0.27,2.1e-9]\n",
    "stds  = [[0.00001,0],[0,1.0e-23]]\n",
    "randar = numpy.random.multivariate_normal(means, stds,500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X7REsWrSA9PR0KlWqxOeff06nTp2UYjTZSUtLY+7cuYSEhGQK3pfyvb3mYiXm\n99doNBIcHEx6ejqTJ0+mcePGeHh40KNHD7p27cru3bstHm8eVtDr9YwdOzbL3nzOzs58/fXXdO3a\nlR49enDo0CGbFw1Hjx7N5MmTAShb1o9Jk75Wjt27l8q6dauVMd7KlSvbfG0jRowAULZI2rp191N3\nDUE8mx47fI8cOYLRaGTs2LFARi+tS5cufPzxx3Tv3l2ZdvThhx8WmmLsyclJ/PTTEqXg+OjRowkM\nDLQavGlpafz4449MmDCByMhIPD09WbVq/f0eb8YwS0pKCnv27ESlUuHt7Z2v7U9ISOD06dN4enry\nxhtvABnT3QICAjh+/Dh79uwB9zeV+0dHR/P333/z2muvUb9+/WzP+frrr9O1a1eWLl1KYGAgGzdu\ntBrAmzdvVv68a9eOLMe9vLzYvn07r776KvHx8Tm+nv/3//4fs2bNIjIykrCwE9Sq9absnCwKvcf+\n6q1UqRLu7u7K1KMBAwbQtGlTli9fjo+PjzLGW1iCF8DNzZ09ew7w3nvvk5KSwgcffMAbb7zB+vXr\nLQp4p6WlsXDhQgICAvj000+5efMmvXr14cCBP7IMNUycOI4LFy7Qr1+/fF8F5OHhQf/+/bl79y6j\nR4/m3r17fPzxxxw/fpwGDRowYMAAi/t7e3vj4uJCXFyczfMOGTKEdu3acfDgQQIDA63O8jhw4ADn\nzp1TbmfOnLH4e0RERI4zJ8xu3LhBo0aNiImJISioP2+8UVtq+YqnwiP1fE0mE2FhYdSoUQOdToeP\njw9//fUXHh4elC9fnt69exMTE0NycnKh3Vq8fPkKfPvtXHr16sOMGVPYunUznTp1IiAggCFDhhAf\nH8/kyZOJjIzEycmJXr360LfvALy9S5CSkmIxt/no0T+YO3cOFStWZPz48Y/UHlvbyGdnwIABbN++\nnZ9++okVK1ZgMBho2LAh8+bNUxZMmKnVaqpWrcqJEycs6vw+yHzxD2Dt2rVWe8DOzs5UqvTfRPxH\nnecdFRVFYGDg/aI9/Rk1aozyPkgtX1HYPVL4hoWF0b9/f8aOHUvdunVp164dCxYsYOfOnZQrVw6T\nycTff/9NWlpaoQ1fs2rVApg163sGDx7GrFnTWbduNd26dQPAycmJHj0+JSioH6VKlQYgKSlJ6RG6\nuLgQG3uH4OA+AMyaNQuDwaDUU0hOTra5Qi02NpaUlBSuXLlC165dee+99wgKClKOP1he0twrT09P\nR6PRMHVmSm6PAAAgAElEQVTqVJo1a4bBYKBBgwaEhoaiUqmUqWaAUtDd39+fv/76izNnzljtmZuH\nCL766iuSkpLYvn07TZs2JTQ0FFdXV6uPS0xMtBro1t6DqKgoWrVqxcWLF5XgNc+E+O9rKvuZM0aj\nkcTERKkfIQq0Rwpf81LSefPmYTQaqVevHr169eLAgQMcPXqUmJgYpk6dmm8rsPJP1p6lWq3BxcWV\nKlVe4Lvv5hMSMoQjRw6TlJRIYGBbiwUDADqdMyZTxv9BxZQp3/Dvv//Su3dv6tSpY3FfFxcX3Nys\nT9Y2124YPHgw4eHhTJo0iTp16ijTzR7sTZr/XrJkSdRqNQ0bNuT06dPExsZSvXp1nJycSEtLU6aa\nwX/DQdWqVQMgLi6OMmXKZNueq1evorpfD2LKlCkAbN++nU8//ZS5c+fa7JlbO5bde3Djxg1at259\nP3gHKMGbWykpKRgMJglfUaA9Uvg6OzuzZMkSoqKiWLBgAZBxQadDhw5ARjgX9h6vNRUqVLK5tl2t\nVivf9EeP/sGCBaGUL1+eUaNGPdLzLV26lN9//52AgABOnTpFcHAwu3btyjJ0YE3FihVzdb8XXngB\ngPPnz9O8efMc7+/g4GARwL169WLHjh2PHXjmMd7MQw0POz1Rp9Ph4iIF+0TB9khfoeXKlSM1NRV/\nf3+Sk5NZvHgxer2eBg0aKFu8POsyFpdkDBHMmTPnkQrgXLlyhTFjxuDp6cmyZcuYPXs2CxcuZOLE\nicp2P3nFPCvlwoULuX7MgwHcunVrNm3a9MjFfvIieMHyB+CzytZ+alL6sWDIMXyNRiP79+9Hr9fT\nuHFjIKNgilmzZs1IS0tjzZo11K5dO1+qbBVGf/11lAsXLuDn55frOgyZGY1GQkJCSE5OZtKkSZQs\nWZLhw4eze/duQkNDOXv2LN9//z1VqlTJcf600Wjk7t27xMTEcPPmTeLj40lM0mA0GBgwYAD79+/n\nzJmMzT3v3LnzUO00B7DBYGDXrl2sX7+ezp07P/TrzavgFRnM+6llF7JS+rFgsBm+JpOJoKAgfHx8\nOHbsGPv377eobmW+ih0YGEijRo0keDOpXbsOrVq1YdOm9bz33nusWvVwBcRDQ0M5dOgQTZs2VWo7\nODs7s2rVKoYNG8bevXuVLd9HjBihnDs2NpajR49y/Phxjh8/zrFjx4iIiLCYIgdQd2DGhpib5s5F\np9Px1ltv8dxzz/Hcc8/Rv39/zp07R3R0NL1796ZHjx42g9DBwYF33nmHXbt2kZSU9LBvlUXw9unT\nT4I3j8h+agWbzfBdvHgxnp6efPnll6SnpzN48GASEhKUCyTx8fF4enoCyFDDA7RaLd9/vwCDQc/W\nrZsfKoAvX77M0KFD8fT0ZNKkSRZBVLZsWZYtW8b27dsZPXo033zzDcuXL6dWrVrZFjAqWrQotWvX\nplixYhQrVgwPDw90Oh0nHNzQGwzUrFmTyMhIDh48aPE4R0dHHBwc+PLLLzl58qSyYi2vZTedTIJX\nPAtshm/p0qW5e/cu9+7dIyEhgdu3byuLJiIiIjh69CiBgYE4Ojo+M98w9+7dY9iwz1CpVPTtG0z5\n8hWs3tfBwYGZM+dgMpnYtm1LrgLYvNQ3KSmJWbNmZbtbsUqlolmzZnTo0IGJEycyefJk1qxZQ9Gi\nRWncuDE1a9bklVde4aWXXqJ06dKcPn2aw4cPc+TIEX799VciIiKUnu+JEyeoUKEC9erVw8/Pj2rV\nqlGlShXKlSvHrVu3+OSTT1i7di3nz59n/PjxWZYeP46oqChatmyZZTqZEM8Cm+H78ssvU61aNZyc\nnEhISECv1+Pu7s7WrVs5deoUn376KY6Ojk+qrXkgNxXVsv/mT09PIy4ulr59e7N3b0ZthJ9/Xkb7\n9u8xYMAgKlbMvkaBg4OWWbO+Q6VSZekBZ7cN0IIFC5Q6uq+++ir/93//l+15o6OjKVasGIGBgdSt\nW5eUlBRKlChBREQEBw4cYPny5YwePZpLly4pc3khYyZAxYoVcXJyQqPVMHDgQOUzdHBwIDk5mWPH\njnHs2DEAOnfujKOjI4cPH6Zz586MHTs220I45rnLBoPB4vkgY+cOc/0Js6zzeEeTkbsPfkYSxuLp\nZDN8ixYtqvxZp9MREBDA3r17WbVqFaNGjcrTXlBBFxcXS8+eH3Pw4H7eeecdunTpwtdff83KlT+z\nZs0qOnToRHBwSJaesE7ngk6nYt68H+nd+xM2bVrP+++/z9atW7MM1URERPDll1/i6enJjBkzSElJ\nsbqS6//+7/84f/4858+f5+zZs5w7d45///2X1NRU5T7mrXsqVapE1apVlT3YXF1duQrcS7nH9OnT\n0Wg0FClSBLVajbu7O0WKFMHR0RFnZ2fq1q3Lu+++i7e3N1u3buWzzz5jyJAhdO/e3aKXah6KMp8r\nMwcHBxwcHJS/W87jfZwerwSzKLxyPdUsMTGRpUuXcvLkSSZOnEi5cvmzvUtBlJycTO/ePZXgXbly\nJU5OTrRr147169czbtw4Vqz4idWrV9C+fUdCQoZk2f7GwcGB77/PmBO9adN6mjdvzs8//6wMQRiN\nRvr27UtSUhLz5s3Dx8eHy5cvK4+/evUqR48e5fTp05w6dYqzZ89a9DAz9taqgL+/Px4eHrzyyiv4\n+fkpQWgymTh58iRHjx7l2rVr1O6XseCjWLFiAEpB9ZiYGIvznj59mnbt2vH6669TtmxZVqxYwYQJ\nEzhz5gxjxozJ9Xxjs8wX1xo2bEyrVq05ceK/LYk8PDwpV06uxIunX67D18PDg+bNm9OvX7/H2rK8\nMFq4cB6//fYrAQEBSvBCxnzSdu3a0bp1axYvXkxQUBArVy7n4MHfOH78TJbzmAM4JSWZXbt2MHv2\nbIYNGwbA/v37OXDgAG+//TbvvfeexeOSk5Np3ry50qvVarWULVtWGZ+tUqUKFStWVIYPTp06pczb\nNZlM/Pnnn/z000/8+++/QMZUwSKOjjg4aGnfvr3yPHfu3KFGjRoYjUbS0tK4cOECq1evZsWKFQwZ\nMoRy5cqxbt06+vbty8aNGylbtix9+/ZV3gtzW235/PPPOX/+PAB79+5WhnDMVCoVW7bs5LXXXs/p\nYxGiUMt1+Op0OiZMmFCoqpPllWbNmjN37mxOnTrF0qVL6dGjh3Ls7t27fPvtt8yePRuj0Ujx4sXp\n02eA1WIyer2eyMjLqFQqi/KNAQEBODs7c/HiRVJTUy16lE5OTpQtW5aLFy8ydepUmjRpwpUrVyhe\nvLjNdhsMBiZOnMihQ4dQqVS89dZbFClShEqVKnHVxueoVquVpcgmk4nSpUsrQyQlS5akVKlSnDx5\nkoCAAOUx1atXB2DHjh0EBwdbPXdQUJBFPd7MBYMSExOZN28ekydPYNWq9TZfmxCF3UOtcCvswWs0\nGpVx1IepslWxYiVWr95Ahw6tlZ5ehw4dmDNnDrNmzeLu3bsUL16cL74YTceOH2AymUhJScl2+p25\ntOSAAQOoXbu28u/FixenR48ezJw5kyVLltCzZ0/lmFqtpk+fPgwcOJDff/+dFi1a5KrdCxYs4NCh\nQ1SrVo0+ffrg5+fHkiVLlLDTGwysX79eWaSh1+s5cOCA8vjo6GicnJzo2LGj8n5duHCB7du3ExAQ\nQL169ZT7li5dmtdee43ffvuN6OhoqzWLX3/9dV5//b9e7YM/pMLDw9mzZw9//HGYgIAa6HTO+VK3\n92nfvTivdguW3Yvzrw0qUwHdVC0/dilNSkpUql1Zn5ec/UUcg0HP+fNn6dChNdHR0cq/Fy9enE8/\nDeL//b+euLq6Wgl4FSaTiU2b1tOz50dUqFBBKd+YWUxMDNWrV8fDw4N//vmHGzduKBfcjEYjLVu2\n5PLly/zyyy+kpaVZ7fmeOnWKP//8k9DQUHx9fZk8ebLyepcsWYKXlxcR3g1ITkriwPSPlYpjJpPJ\nYlaCo6MjrVu3Vnab0Ol07Nq1i+3btxMaGmrRczeZTKxfv55hw4YxZ84cPvnkE4tjmS+4ZfZg+B44\ncID69etTt249vvtuPi4uro+0VDinb5Ccvr4Kwk65j9OO8PCLAI+9yCJ42R9oNGqmvv/aY50nL9pT\nED6TvNy9+JmqPqLTOWX6v7WfOdavoPv7V2X16g18//0s/v77bzp16sKHH/4/XFyc0Wi0gAm1WoWL\ni3mlnwm9Pp3du3czY8Zkjh07hlarZd68eWi1WhISEixKLXp4ePDxxx8ze/ZsFi1aRJ06dSwufn30\n0UeMGDGCWbNm0aZNG6vjq8uWLWPfvn04ODjg4+PDnDlzlGPm2hup9+5hMpl44403lIpmd+7coXz5\nrBe7YmNjAbh+/Trbt2+natWq1KxZ06L4usFg4J133mHYsGGsWrWKTp06KceSk5OtVm/T6/UW4Vqr\nVi0aNGjAr7/+yvnzZ3jrrfrZfxhCFHLPVM/3ceb5ZjmTRXHzrOc1mUzs2rWDadMmcezYMVQqFe3a\ntWPEiBFKBTFzecfMoqOjqVy5Mp6envz2228WwWQ0GmnQoAHh4eEsXLiQKlWqZHneCxcuKPWGs/P6\n66/z0UcfsfxCxnSyMa3K4+fnh0ql4tSpUzZLXH7++efs27ePpUuXKnU+zFJSUihWrBgNGzbkn3/+\n4cKFC0rP3FaVuwfDF+DgwYM0atSI+vUbsmrVBqvtsUV6vtLzzQ952fOVTbAekbV5qSaTiZ07t/Pu\nu43o0qUjx48fp3379vz999/89NNPSvBa4+3tTe/evbl+/TorVqywOKZWqwkJCcFgMLB8+fIsj711\n6xYhISE2z5+ens7w4cNRqVSoNRrefPNNateuzfjx47lw4QLWfhaHh4ezb98+XnzxRRo1amT1/K1b\nt8ZgMFjs4/aw3nrrLRo0aMC+fXv588+cd0sWojB65sLXaDSSlJSUpdBMXhkxYihdunTk2LFjdOjQ\nQZnmZf7VPid3795VChTNnz8/y/EWLVpQuXJldu/ezbp16yzCcsyYMRbj0dm5du0asbGxuLq5kZKc\nTMmSJYmJieHbb78lKCiISZMmZfu4H374AYDPPvvM5oII88XA7du3Z3t86NChvPXWW0RG2t4GfuTI\nkQDMnj3d5v2EKKyeufBNSUnJ1w0Yz57NmN97/PhxVqxYkevQjY6OpkePHvj6+jJ27Fi0Wm2WX+0h\no/c7YcIEnJ2d+eabb+jXrx83btwAyNW+ZuaLfOapbNWrV6dNmzbKubNbPGOegeDv72+z1wtw9OhR\ngGx3wzhz5gxz584lLCyMFi1a2Azgt956iwoVKvDnn39Y7Y0LUZg9c+Gr0+lwdXXN9w0YM8+BzUl0\ndDTvvPMOS5YsoVSpUowfP55Lly4xevTobO//5ptvMn/+fOrUqcNff/1F586d2b17t8WCCWvMY8xq\nlQqNVsPu3btZtmwZFSpUYPr06XTs2DHLY8y93g8//NBmr1ev1/PNN9/g4OCQZYdkQOlVt2jRgitX\nrij/t+aFF17g9u3bxMTE5Pi6hChsnrnwNV/tz4+5o8BD99LMwWsuVHT69Gk+++wzvL29iY6OJiws\njD179rBq1Spu3rypPK548eJMmTKFL774AoCJEycSEBCQ4+pDc/gaDAa8vIpSv359evfuza5du7Id\njzb3eqtWrUqtWrVsnnvNmjWEh4fTtWvXLD3fM2fOsGHDBl566SWWLVvGyJEjuXLlCq1atSIiIiLb\n81WtWhWA8+fP2nzeZ5XBYCA8/GK2t5yGdYT9PVNTzXKWEZzZz9U1YX0mhFE5Zh5LNhqNyk7B1oJ+\nx44dDB48mMuXL9OoUSPOnTtHzZo1uX37NrGxsVl2qKhSpQrffvstWq2W6OhoHBwcaNSoEVeuXGHx\n4sVs2bKFNm3aMH269XFSc/impqaic9YpF+hOnz5NVFSUsizZ7PvvvwfgvffeIzo62upsiLS0NKXX\nGxwcbDGmfvPmTb766isAPvnkE65du0anTp2Ii4tj9uzZNG7cmE2bNlG2bFmLc5qnvZ0/f5a33nrb\n6mt6VsluFYXbMxa+uZtGZh4XhtwWiVcp5zYHrVqtVnb6zS58o6OjCQkJITIykgYNGnDixAliYmLQ\narUUK1aMSpUq4erqip+fH97e3pw4cYJDhw6xbt06unXrRrly5ZSe6nPPPcfy5cvZtm0be/bsYc6c\nOWi1Wjp37pyllOOlUi2oAOiBhDSY/V9NG3pU9aRSpf+mAl24cIGDBw8SEBDA+++/z82bN62G78aN\nGwkPD6dXr15ZxrmvXr3Kjh07CAgIoFGjRsrQhXm14OzZswkMDGTLli0WQWI+z/nz53J4/59dsltF\n4fWMhW/umMeDH2VcODelEc1DDebgDQsLIyYmhg8//JD27dsr54iOjlbm8sbFxfHBBx8wf/586tat\nazG84OPjQ5s2bVi1ahV//fUXlStX5tSpU1y6dMkiTB+WeXFGv379chzr/e6773BwcODzzz/Pcvy7\n776zep5evXrh4eHBuHHjaNGihUUAV6pUCbVaLeErnkr5tsjCZDKxf/9+ypUrh7e390MHWefpu3O+\nk53Ext4h9YGC4WZ6fTomowlfP19ABdm8vTdu3CAtLQ2dTkdqWhpGgwFXV1ecH+hlGw0Gi2W59+7d\nIzY2Fq2DA16enhbvaWpaGjejotA561Cr1CQlJaHRaLLsq6fXWt9nz9XBSBGHjGGJtLS0jKGNIkUo\ncb9Og8FgQKPRYDAaiLsbh7OzM05OTiSnpBB75w5ubm4UvV+i0iw9PZ3r//d/OBRxoIR3iSzPacKE\nVqPlbtxd4uPi0Wq1PPfcc0pIX79xA6PRwPOlSlttd3Z+Gph1pkhmT8Mii7xaSGGLLLLIvg0Fdnmx\n0Wjk008/pVixYsTGxvLSSy/x2muvZbsDQmFzL/UeiYmJGeGQbW9QhZPOeo1bgzFjpwdHJyd0zjpS\nUlIyCpfnYnjDyckJZ2dnkpOTiY6Jwd3NDXcPD9QqFemZfhio1Gq0Wi16vR69Xq8sYTYYDLn6xNPS\n04m5fRtU4JFpp2pz+2OiY9Dr9aSlpVGyZEmMxoyxaYfsCi+ZTKBSoU/Xk5qWhuMD9zEaTcQlxpGY\nkDHMY8JksXrQ0bEISYlJpKWnKT8YhHga5Ev4/vHHH/j4+DBmzBjOnj3L33//ze7du3FycspxhZfZ\njC5v5EfTcmDrlwATJhO0bPkOR48e4c8//1R+mFgrHwlkueCWnJyMl1dTmjRpQpeePXnvvU8ICAhg\n4MSJWR4bHR2bZQmxyeTF/v37mT59OlFRUTz//PP079+fr77+CkdHRw4dOsT06dNJSEhg1apVuLi4\n0LFjR06dOsUff/xBk9HWV569XzEVlSqNjz76iKSkJL755htatnxLOX769GlCQkK4fPkyfn5+RERE\n8MUXX1C/fn0aNWpP48aNWbdjR5bzhoaGMmDAALRaLfPnz+e1114jPj6eJUuW8OOPP5KQkECxYsXo\n378/n3zyicVy4927d9Ou3cf07z+IkSO/stp2IQqbfJlvpVaruXTpEpAxXejNN9/E09OT06dPAw8/\nHaug+O23Xzl69AgtW7Z85F68eQZFYmIijo6OlChRwuo+bdlRqVTUq1ePn3/+mcGDBxMdHc3QoUNJ\nSUlh1qxZPP/88wB4enoSEBBAQkICCxYs4MiRIzkO/YSHhz8QvC2VY7du3SI4OJjLly/z8ccfs3z5\ncnQ6HaGhoXh7e1OjRg1+/fXXbOfkNmzYkJkzZ6LX6+nSpQvdu3enYcOGzJ49G61Wy+jRozl58iTB\nwcFZ6jw0a9YMFxcXtm7dWGi/boTITp6Fr9FoZMuWLfz++++88sor1KhRg/79+wMZU4ZeeeUVDhw4\nQEpKih13qDVZvRmNBpvLjvX6dCZN+hqA4cOHk56ertzi4uJISEjI9hYXF6ds0ZOWlkZ6ejpubm4k\nJCRw8+ZNihcvzp07d7h8+TI3btywuN25c4eYmJhsb+fPn+ell15iwoQJ1KpVi7Zt26LVatm6dSu3\nb98mISEBf39/ZcjBx8eHd955x+a7M3r0aJKSkhg+fDivvfYaUVFRREVFcfr0aTp37syVK1d45513\nqF27NmfPnqVevXrcunWLiRMnUr16dQwGA2vWrLF4b9LT09Hr9UoAAxw5cgStVstnn33Gxo0b6dGj\nByqViqSkJIub+QdUixYtCA8P58SJ4yQlJd4f5jCRMcXP2mcqRMGWJ8MOJpOJ4OBgPDw8cHV1Zdq0\nacyYMYM1a9bQq1cvQkNDiY2NxWg0ZqlhW1CYp5eZTGRbP/bAgQP8/feftGjRItter7UfKA4ODlkq\nl7m5uZGYmMgLL7xA1apVOX36NK6urlmGGGJjY7Mt8Qhw8eJFihQpgpeXl1J4/d69e0BGT/PVV18F\nYMCAAcr8XY1Go5zzy80Z+8ONblmOsLAwPvroI1JTU7Pt8YaEhHDlyhUaNGhAhw4dlNf6zjvvsHfv\nXrZt28bIkSP56aefWLdunUUtX4CKFSvi4OBApUqVaNOmDREREbzwwgu4uroSHx9vdcjG3NNt3749\nK1euZP36NfTpk/EDPXdTAIUouPKk53vkyBGMRiNjx45l6NCh1K9fn/79+9OtWzfKly9PSEgIixYt\nol+/fjZLFtqTednxg7MDICMEpkzJWBprLvjyOFxdXZV5xOZpVbaW2T4OrVZL6dKls8z3NTMHr7nH\n+2DwduvWTRlqaNmypcUPGXd3dxo1asTdu3cJCwvjtddeY9++fTaXA5coUYLXX3/9oQqkm4cedu78\nBRcXl3xfGi7Ek5An4VupUiXc3d05fjxjxn6/fv2oV68eQ4cOZejQoYwdO5Z58+bh7++fF0+XL2wt\nO96/fx9//vkHLVq04KWXXnrs53JzcyM+Ph69Xq+s6goPD89yP4PBwLlz51izZg2TJk1i//79j/3c\nmaXr0y3GeJs0aaIci4+PtwjewYMHZ9u7b9q0KUWKFGHTpk20aNECw/2tiazZt2+fst9dbul0OmXo\n4dKlf/NtabgQT9IjDzuYTCbCwsKoUaMGOp0OHx8f/vrrLzw8PChfvjzBwcF89dVXNgtpFxbm2gKx\nsbHKNkSPo0qVKvz5558MHDiQwYMH4+joyLJly3BxcaFo0aKcO3eOs2fPcvHiRYudLH744QcaNWrE\n8OHDH+v5zVJT00hISKBSpUo0bNiQhIT/5lD+/vvvXL58mfbt21sNXsjo/TZu3Jht27Zx9OhRtFot\no0aNokGDBlSsWDHL/b/++mv27NnD2bNn+fbbb3PVzhs3bvDPP/8AEBUVRY0aj/BihShgHjl8w8LC\n6N+/P2PHjqVu3bq0a9eOBQsWsHPnTsqVK4fJZOKff/4hLS2t0IfvRx/14I8/DrN580Zat27Nhg0b\nHiuAp02bRlxcHJs2bWLgwIGEhIQwd+5cZSUYgEajwdfXl5dffplq1arx/PPPM3/+fPbs2cPBgwdp\n2LAhrVu3tro3Wm64uDjzzjvvsH37dnr16sWYMWOUY+aeaUBAQI4XSAMDA/n333/ZunUrLVu2ZPPm\nzbRt25YDBw7g4eFhcV9zvYrQ0FAgI4xt9WRv3LhBo0aNOH/+PEFB/WnSpOkjvVZRcBkMBiIiLtm8\nz9NYp+KRwzcpKYmUlBTmzZuH0WikXr169OrViwMHDnD06FFiYmKYOnUqXl5eedleu3BwcGDOnHkA\neRLA7u7urFy5kn79+rFw4UKmTp1KUFAQcXFxFCtWjCpVqlChQgWSk5MtLri9/fbbbNmyhUmTJvHL\nL7/w999/88EHH1DjEbuCKlRMmTIFyCh+/vnnn7No0aKH/mHp4ODAoEGDmDBhAps3b6ZZs2b88ssv\ndOnShfXr12c73vziiy8SGhpKWloa06ZNyzaAo6KiCAwMVIJ31Kgx2f4geJRdqZ+G3YvzakdgW57E\n7sUXLlwgLi4621rSAJcvXyY+3hUfH88C8ZnkVRseOXydnZ1ZsmQJUVFRLFiwAIB69erRoUMHwPa+\nXYXRgwFcsWJFqlWrRtWqVSlfvrzyZ29vb4uAMBqNVsc3u3fvjr+/P1999RXTpk2jY8eOfPjhh8r0\nsNu3b1tsUgkZAfzyyy8zatQoDh06xIwZM3jxxRd57733lD3TYmNjOXcu+3oICQkJJCYmkpbuCMDZ\ns2fp3r07cXFxHD58mI8++oiJEydy9+5dIGPsNyoqCsi4AJd5aCIzlUrF8OHDCQ4OZu/evVSrVo2d\nO3fSpUsXBgwYQJ06dYD/ZjCsWLGOTp3asmjRIgCmTJliEZxRUVG0atWKixcv2gxeeJRCSBAbm/3m\no2YFYSkrQNGizvz554lsj5krmuVnOw0GIxqN+rGf486dRCIjI7lzJzHLMfPr8PJ6LptHZjzW/Dh7\nfyYFYnlxuXLlSE1Nxd/fn+TkZBYvXoxer6dBgwbKxauC59HnF2s0WjQaLXPn/kCpUqPYu3cnhw4d\n4uDBgxb3K1q0KK+++irt2rWjTZs2FHug1kFmtWrV4s0336RFixa0bduWlStXEhYWxqxZs2jQoAFR\nUVFWe3JLlizh5s2bDB48mIMHDxIREcHChQtp0qQJe/fuzTK9zSwhIQFnZ2dUqowQNM/u+Pzzz5k4\ncSKHDx9m1KhRBAYGAhm9dB8fHyCjuHnRokWzPe+lS5coXbo0Q4YMYcyYMVy/fp1SpUqxfv16ypYt\nS926dYH/puQVLVqMlSs30LFjaxYtWkSRIkX49ttvUavV3Lhxg9atW98P3gE2gjfj33Q6Z0ymjP8/\nzmdcEIWHhz8VZSNttbMwvY68lKvwNRqN7N+/H71er2xt455pzX+zZs1IS0tjzZo11K5dO9vpWk8L\nBwcHxo6dwNixE0hJSeHffy9y4cI5zp/PuP3773l27tzJzp076dOnD/Xr16d9+/a0adMG7/sFah5U\nuXJlDh06xNChQ5k/fz6NGzemY8eODB48WFmxlp0XXniBLVu2sGzZMkJCQujQoQMjR458pBkZWq2W\n/rMxUVkAACAASURBVP374+Hhwfbt2632nHNi3h35hx9+wMPDAzc3N7799ltatWpF/fr1Le5btGhR\nVq5cT6dObZUx4JEjR9K4ceMchxoyU6vVj30RtCB7GspGajSaQv8a8lqO4WsymQgKCsLHx4djx46x\nf//+LBdm1Go1gYGBNGrU6KkO3gfpdDqqV69B9eqWY65Xr15h8+aNbN68nj179rBnzx769u1L/fr1\nadasmTJP1XL7+Yztgbp27cqgQYNYuXIlW7Zs4bPPPqNXr142A6hs2bKEhoYycuRIxo4dS61atRg8\neLCyT1tuabVaizHgR9W2bVuuXr3Krl27qFGjBqdOnaJDhw4cOXJEuc+PPy5UXlOzZi34559/CA0N\nVUI4t8ErRGGVY/guXrwYT09PvvzyS9LT0xk8eDAJCQnKYon4+Hg8PT0BWXVkVqZMWfr06UefPv24\nejXyfhBvUILYlh9//JFDhw7xww8/8PnnnzN69GicnZ3p1q2bxf1MJhM7duxgwoQJHD9+nDfeeIN9\n+/bx4YcfcvDgQWbNmmVzipg1Dg4OFgH8KItiVCoVQUFBXLhwgZMnT9K9e3cWL15MUFCQcr6hQ61v\nce/nV06CVzz1cgzf0qVLc/fuXe7du0dCQgK3b99WLppERERw9OhRAgMDcXR0lG+WbFgG8RWOHfsL\no9GIyWQiLS2NIkWKoFKp+PPPoyxYEEpSUhJqtZpPPvmEl19+mcaNGzNmzBgaNGhAmTJlMJlM7Nmz\nh2+++YawsDDlPU9JScHb25sNGzZQp04dfvvtN2rUqJFjPYfsODg4MHXqVLp06ULNmjUf6XWrVCpi\nY2Px8vKiY8eOXLt2jV27drFhwwY6deqk3C9z7//mzZsMGjQId3e3LL8VPM1sTbWKi4vGwyP74SpR\nuOUYvuZ5pk5OTiQkJKDX63F3d2fr1q3Kpo+Ojo5Poq2FXpkyZSlTJmNFm7l4jIuLi/Ibw4IFoRb3\nL1WqFGPGjGHAgAGEhITQo0cPpk6dyokTJ1CpVLRp04YhQ4bwv//9T3mMg4MDISEhDBo0iLlz51K5\ncuVs60MYjUbS09LYuHEnzZs3V2ZYmGm1Wl577dELaIeFhREfH0+LFi3QaDR88cUXyhZHmYc0HizH\neezYMZYtW8a2bVto0aLVIz9/YWJrL7Zy5crh7p61CL0o/HKcFFm0aFFlU0WdTkdAQAB79+5l1apV\ntG/fPsskepE7Op0uV3UKOnToQOPGjTlw4ADdunXj5MmTtGrVim3btvHjjz9mWx/Z29ubQYMGkZ6e\nzsSJE0lOTiY1NZXjx4+zceNG+vfvT0x0NHFxccybN4/vv//+oco1xsfHM27cOBYvXpxlKpzZgQMH\nAJRZDnXr1qVRo0bs2rWLQ4cOWT33iBEjUKvVzJgx+aGWIBd25otqD94qV65stS6HKNweaqpZYmIi\nS5cu5eTJk0ycONHqpOjCymg05DBZP+9+Dc6Yjqe7f87/gs9kMimhk56ejkajYcKECVy/fp3y5csz\nYMAAqlSpQlxcHElJSRaPMf89ISGBgIAAZbVZUFAQsbGxpKenAxm9Wt8iRShSpAjlypVj+/btlClT\nhhdeeIH4+Hirbb59+zY6nY6JEydy4sQJDh8+zKZNm2jevDnVqlVTxv71ej2///47Hh4e+Pj4cPPm\nTW7dukXfvn3Zs2cPQ4YMYfny5Tg5OWEwGJT5yZDR0+vUqRPLly9ny5aNtGrV1sa7KKUjReH1UBVK\nPDw8aN68OZMmTXrqghf+m6yfkpKSh2dV2bipM/35/r1VKtRqNWq1mpIlS1KiRAlq1KjB8ePHWbt2\nLW+//TYlSpTAx8dHGbIwP8b89zp16lC3bl3mzJnD66+/zq1bt/D396d3794sW7aM6OhofHx8cHJ0\npEGDBpQoUYKFCxcSGRmJSqWyeitdujT79+/nxIkTNG3alEmTJuHh4cHatWuZMmUKBw8epGLFihiN\nRhITE2nZsiW1atVSeue1atWiYcOGHD58mCZNmnDs2DHlNWe+DRs2DLVazcyZU5+p3q94tjxU+Op0\nOiZMmGCxc+7TxFxW8mkpWejg8P/bO/PwGK/2j3+SyIpsiFBLgsZascTa19LwCr9aorWEUrXVmkji\nTYmt9tLWmvAiVG0pUUrE0iKEIrVWURWhREgsCZF9kpnz+yOdeYVsIpNM4nyua67WzLN855mZb85z\nn/vctyE7d+7k+vXrHD58mFmzZlGtWjWmTp3K/eho4uLi+O6773BwcMDQ0JC1a9fy4MGDXI935swZ\nVq5cSb169ViwYAGjR4/m8uXLLFq0CDMzM9auXUuXLl346quvAHKc7AsICGDkyJFERkbSq1cv5s+f\nr6lDrMbBwYFBgwZx5coVDhwIKdqLIpHoCK9dmy+3lVNlgbzKSuoqFy5cICUlJdcCO0ZGRppwwJMn\nT3BxcWHt2rXo6ethYWlB586d+fXXX2nXrh2pqaksW7Ysx3q84eHhrFmzhgoVKtCjRw9atWpFr169\nUCqVjBs3jpCQEKZNm0b58uWJjIykSpUqORadNzMzY/78+ezevRtra2vWrFmDn5/fK9tNmjQJgB07\ntr3J5ZFIdJbS4zKSV7hw4QJ9+/ZFCKFp2ZQX6hrCffr04Z13amBpYcn27dtxdHTk+PHj2NvbExcX\nx+LFizUGrFKp2L17N+vWraNcuXK0adMGPz8/DAwMOHXqFAMGDCApKQkTExOGDRvGkSNHmD9/PitW\nrMhzoujBgwfEx8djbm5Ojx49Xnn98OHDADRtWrhUN4lE15HmW0q5ePEiffv2JTExkXXr1uHq6lrg\nfStVqqSJMltZWXHw4EEcHR01XYnVBnz//n3++9//EhISQuXKlalatSpHjx6lWbNmXLp0CVdXV40B\np6RkFaoxMTGhf//+tGzZMtfz79q1Cw8PDypUqMAPP/xAkyZNsr2elJTE0qVLsbS05JNPPs3lKBJJ\n6UaabzFy9eoVFiyYw+3br3ateB3Onz9Pv379NMarriRXWCpVqsTBgwepWbMmd+7c0RjwzJkzuXDh\nAnZ2dqhUKu7cuYOrqysHDx6kVq1arF+/XmPA7u7ummyL3FAoFAQEBGiMd8eOHTg6Or6y3Zo1a4iL\ni+Ozz0Zia5tzpau8UE/4SSS6TJE00JTkjlKZ1TD0998v4ub2MQkJCfj7L+fjjwfg6elFvXoO2bZ/\nMdUsMTHxlVv3ixcv8vHHH5OUlMSsWbNo3Lgx27dvR6FQ0KxZM8qVK8ezZ89yLOLz8OFDzTnUqM9l\nZWWFp6cny5cv586dO9jb21O9enUSEhKIiIhAoVDg6uqKv78/QgiN0a5YsQKlUsm+ffsYPXo0fn5+\nr9T3SEpKYvPmzfj5+REdHY2FhQXbtm2jSZMmZGZmZstoeHHUO3bshELF31NTU1EqRZkutiMp/Ujz\nzYZ2lrOqjTcxMREfHx/2799PUNAP7NoVRL9+A/Hy+s//FPyTNgZZ2Qovms+FCxfo168fSUlJTJs2\nTdOFYu7cuahUKiwtLencuTOtW7emT58+r6xaU6+k09fXh3+W7r44gTp+/HgGDRpEjx49uHz5MjVq\n1ODatWuaNkfdu3fPcWJv48aNDB8+nH379uHj40NQUBAVKlRAoVAQGBjIt99+y7179zAxMcHd3Z3J\nkydrFu4IIbIdc/369cTFxfHFF9Owts69HOc/VyvHZ01NzShfXi5MKEvcvXsXa+sKOdYDtrOrUyoX\nokjz1TIvGu93333HoEGDmDt3Lj/99BPz589nx45AfvxxBw0avLpS7UXUk2vqUEO9evU0xmtmZoaz\nszO//vore/bsYc+ePXzzzTd069aN7t2707p161eMODfUIYgePXpw8uRJqlWrxo8//oiTk1OuhdQN\nDQ3x9/fHwMCAPXv2MGDAAPr168eyZcuIiorK0XRzIikpiW+//RZLS0tGjx5TIL05UdZLTL5t5FXr\n9+7duwClslylNF8tkpyczODB/UlISGDjxo0MGjQIyDKHjz/+GFdXV4KDg5k3bx7Xrl3J9TiJiYl8\n9NFH2WK8O3bs0Bivn58fDRs2RKlU8vvvv3Pw4EHCw8PZsWMHO3bsoGHDhuzZs0dzvPyWEleqVIlD\nhw6xadMmBgwYkGdNYTWGhoaajiZ79uzh1KlTmJiYMH78eCZMmJBjM82XCQwMJC4ujsmTp2BhYZnv\n9pK3A3UtYF3pLlJUSPPVIiYmJtjZ2fH06VNOnTqFm5tbtjCCvr4+/fv35+OPP2bXrl3s3LkzW5Ec\nNcbGxhgbG2NmZkaXLl00+6pUKlq1akXDhg2BrC9py5YtqVOnDvPnz6d///7cvHmTmjVrAmi6aty7\nd4+qr85zZcPa2hovL6/Xer9qA65RowYA7u7u2NrakpmZWaD91cuM79+Pfq3zSiSlEZntoEUMDAzY\nunUnjo6OrF+/Hnd39xyXy6pNOCgoKMcKZEZGRnh5eZGUlKRpt163bl1q1qzJmTNnXlkhplAomDRp\nEjdv3qRTp06a+rwVK1bE1taW69eva+HdZmFoaMiCBQtYsGCBpv1QQXF1daVRo0bs3Ln9jTNCJBJd\nR458tYy1tTWBgbsYPPhjzW25n5/fa8/if/bZZyxbtoy1a9cyceJE9PT0cHZ2ZtOmTZw+fRpnZ2cA\n0tPTmT9/PufOnaNTp074+fllK/lZr149fv3111dKOb5Meno6v/zyS7F2J9HX12fmzJkMGjSI5cuX\nsHLlaq2dS5e6F+fXhbgkO/YWVffiouJlHcXRwTk/DYVFa+arUqkICAigU6dO1KlTpxDLkvOrWKVL\nhbbz0ipeMWA9PT1WrlypqTKWG5mZmZqJMmNjYzw9PfH19WXlypV06tSJli1bsmnTJg4cOEDTpk1R\nKBQsXLiQ8+fP07ZtW6ZPn87Tp0+zHVMdv81QKDA2Ns6m4dGjRxgYGJCWlsbo0aM5duwYbdq0YfPm\nzZQvX57U1FSsrKxy1armyZMnGBkZafr8ZWRkoFAoctxPoVBk64DSp08fGjZsyM6d2/Hw8KRevfp5\nXqPCokvdi/PqzFvScc6i6l5cFOR0LYq7q3FRdi/WSthBpVIxfvx4nj17xo0bNwoc8yuLqLseV6li\nw44de2jWrBkBAQF4eXlhaGiYZxWxihUrYm5urnmoY6gBAQFUrVqVbt26YWdnx4ULF7CxsWHFihUa\n4123bh21a9fG1tY22+O9994D0Jjui+fT19cnIyODzz//nGPHjmFtbc1vv/3GsGHDSEtLw9TUlIoV\nK+b4qFq1KhUqVGDjxo00adKE5s2bc+jQIczNzbGyssLonxKWOT1e1GBgYMCMGTNQKpWsWLG0JD86\niUSraMV8z507h4ODA1OmTOH06dMsWLCArVu38ueff2rjdKUGa+tKbN/+E82aNWPNmjV4eHi8VslE\nU1NTpkyZQlJSEhs3bkRPT4/u3buTmppK69atCQsLo1OnTsydOzfX7iJ169YFQJHDqDstLY2RI0cS\nGhqKs7MzZ8+epVevXoSHhzNkyJA8V7BFRkbSuXNnPD09MTU1JTk5mYEDB9K/f38ePXpU4PcIWQ04\nGzZsyI8/BsnYr6TMohXzzczM5NKlSyxZsoQOHTrQs2dPEhMTOXPmjKZ/WVnlu+8C8PZ2JzMzk+Tk\n5FfM9UUDDggIYNq0afkeMyYmhn79+rF582ZGjx6Nra0tW7du5d69e9mK0uQU430ZdbnMnEIeX3zx\nBaGhoXTu3JkNGzZQvnx5Vq9eTc+ePQkPD2f8+PE5HvPo0aM4OTlx8uRJPvywNydOnOXYsVO0bt2W\nH3/8kebNm2s6WxQEfX19zeh31aqVBd5PIilNFJn5CiE4fvw4V69epVWrVnTu3Jnjx4/TsWNH2rVr\nR4cOHbh48SJpaWkFaoyYk3HpOrGxMXz55XS2bt3M3r0/5VqYXW3A9erVY+XKlURGRuZ6zJiYGLp0\n6cJPP/3E8OHD2bx5M3PmzCElJYVhw4ZRvnx5du7cia+vb57Gm5aWhr+/PwMGDADAMIdFF+qWQBER\nEZo27+fPn+fq1asAuXa5OHfuHKmpqXh7f8F3323BxsaGOnXqsXfvQebN+4qEhAR69+79WgasLoOZ\nkpJ3vQiJpLRSJOarUqmYPHkyoaGh/PLLLwwfPhxHR0dq1qyJr68vkFVXIDMzM99JJjVF31FC+/j5\nLSc9PR2A//53JWZmZrkWZre2roSPjy9KpZLFixfnuI3aeG/cuMHgwUOoUqUK48ePR6lU4unpyf37\n9xk2bBjW1tZ89tlnuRrvsWPH+PDDD/Hz88PCwoItW7ZgbW39ynYBAQF4e3vz6NEjBg0aRLVq1ejb\nty9RUVGMGzeOwMDAHI/foEEDACpUqJjtD6uBgQFjxkwgIOB7MjIyCmzAQggWLFgAwLhxE/PdXvJ2\nc/fuXW7dupnjQ6lUlrS8XCkS8w0ODkahUDB37ly8vb2pXbs2S5YswcvLCysrK8aOHcv69euZPHly\ngRtulraOErGxMWzevBE7OzsGDBjAH3/8QVjYsTzTuXr2zJrZ37Zt2yuj35iYGFxcXLhx4wYTJniw\nbJk/u3bt0xiwpaWlxoAHDhxIUFAQe/bsYd26daxYsQIPDw/c3NxwdnZm7NixxMbGMmLECA4ePKgZ\n/QohiIqK0pzTyMgIHx8fDhw4QKNGjTAxMaFu3brs3buXWbNm5fp5qM03IuKvHF93cenBunXfaQz4\nxIkTeV7L0NBQTp8+jYtLD1nPV5IndnZ1cuz6DFmmfOfO7WJWVHD0RBEEYH///XdCQkJwd3fHwsKC\nY8eOERISgo2NDVOmTOHp06cYGhq+1nr7x49zb+SYhe6kmimVGcyY4cuGDetYvXo1//rXv2jWrBmN\nGzfm0KFQDA1zHpEqlRkEB+9hzJiRDB06lHXr1gFZxtu9e3ciIiKYMMGDWbPmakaUf/11nY8/7sXj\nx4+ZPXs2z549Y/ny5Tke38DAgMqVK9OoUSMmT57Mu+++S2xsLFevXmXvAyvSUtM4sXQEXbt2ZcaM\nGdjb22tS2xQKhWbUoDbdlJSUHEfMmZmZ1KxZk8aNm/Dzz8dzeJ9Z2S4//3yQ0aM/Q6VSMXjwYKZO\nnUrNmjWzFdYRQtClSxfOnDnDzz+H0ry5U0E+glfILxUov9Sk4kzxunXrJpBzfYKSTjXz3PobBgb6\nLBnUqsQ0qHnda5HXdX1TDSWaaiaE4NChQ4SFhWFtbU10dDSbN29mzZo1fP/997i5uWnM1srKqhCF\nTvJqPKk7xgsQGxvL1q2bqF27NkOHDqV+/fq4ublx5coVDh48kOt+Bgbl6NPnIxo1akRgYCBRUVHE\nxcXRo0ePF4x3Hnp6/2u02aBBI3btCqFKlSrMnj0bBwcH7t+/z5w5c1i7di379u3j/PnzxMbGkp6e\nTkxMDN988w3nzp1j8ODBdOrUiQkTJpCakko5w3K0adOOI0eO0LlzZ0aNGsXt27extbWlVq1a2Nvb\nY29vr0lTq1y5MuXKlXvlYWJiwrvvvktExI0c4/TqdLv/+79e/PDDLho0aMCWLVto2rQpEydOJCoq\nSpN2dvLkSc6cOUP37v9H8+a5F2SXSEo7hTJfIQTDhg3j0qVLbNq0iaNHj9K+fXtq1qyJtbU17u7u\nQNaIOCkpqUxnNwD4+68gPT2dqVOnahaTTJ069Z8OvN/mOXGor6+Pl5cPSqUSHx8fTYz35RHvizRo\n0DBbCGLv3r3MmDGDkSNH4uDgQEREBEuWLKFbt25YW1vj5OTEnDlzuHz5Mh06dGbevK+wta2GbdVq\n7Nv3M8HBh+jQoTOHDx+mQ4cOdO/endOnT7/WNahfvz7Jycn51mXo2LEzR4+eYv36TTRo0IDNmzfT\nqFEjRowYQWRkJHPnzgXgP/+Z8lrnl0hKG4Va4Xb79m1q1KiBr68vaWlpbN++ncTERBwdHTE2NiY8\nPJzdu3ezdOnSMl/a7+HD/416hwwZonlePfoNDAzkwIEQevbsneP+KpUKZ+d/06hRI4KDgwHyNF41\nDRo0ZOfOvfTv34fx48ezbds2rl27xrNnz7Jt5+DgQLduPejWrTudOztjbp4Vc/fc+ptmm7Zt27Nr\nVzDh4af45ptFHD58mMOHD/Pvf/+btWvX5hpTexF1cZ8bN65Ts2atPLfV19end+++9OzZh5CQPSxb\n9g2bNm1iy5YtqFQqunf/PxnrlZR5CjXyNTIyIjw8nPPnz2NiYkK/fv3Q19fnyJEjODg40KtXLwIC\nAgpURrC0c+fOHdLT01GpVNnarqelpfH48WMgy5ByIzU1lZSUFLy8vqBcuXJMnDgpX+NVU7u2HevW\nfU+1atU4deoUNjY2fPRRP2bPns+ePQe4dSuaX389j7//Wnr37qsx3txQm3Bw8CE6dswaCX/wwQfc\nuXMnz/2EEFy+fBngtdID1SZ89OivrF//PQ0bNsTY2Bgfn6kFPoZEUlop1Mi3Zs2aeHh4sG3bNoyM\njGjatCljx45l1KhR3L9/X1PC8G2gTZu2eHn9h2XLvqVbt2788ssv2NraMmDAAA4fPoyzc1cmTJiU\n6/7qyaxevfrQtWu3bHUO8sPU1JRmzZpz9uxlFIp0KlY0f+HVwsfF27Ztx48/7mXp0m9YtGg+zs7O\nhIaG5jjZBlkNMUNCQmjX7n26dnV57fO9OBJOTk566X1IJGWTApmvSqXixIkTZGZm0rVrVwA6duyI\nQqFg9erVDBkyBIVCgUKh0BRTeZv4z3+yRmrLln1Lp06dsLS05K+//sLZuSsbNmzGxMQk13319fU1\nhvs6xvvyvnmtaiss3t4+ABoDDg4Oxs7OLts2jx49wsfHBzMzM5Yt8y9UzzU1+vr60nglbw35mq8Q\nggkTJmBra8vFixcJCwtj3rx5WFtb07t3bywsLPjhhx8wMzNj+vTpBc7j1U0KV0lNX1+fL76YhqGh\nEV9/vZDY2NgXjNc4j+Oqcj3m//TkZmb57Vs0GSEvGnDPnj0JCQmhVq2smK4QAi8vL+Lj4/niC19s\nbKrmcpSspqCpqamYmpq+ZNCiyLRKJKWJfM1306ZNWFpa8uWXX5KRkYGPjw/Pnz/H3NwcExMTXFxc\ncHFxISMjI8fmim8DBgZZl/E//5nK++93wNjYmEaNmvwz4s3L0HNPm8vdrLLvm/92r0POWry9vwCy\nDLh3796EhoZiZ2fH9u3bCQkJoW3b9owZMyHPur+pqamadu7ZR/i6lzookRQH+ZpvjRo1ePbsGWlp\naSQmJhIXF6d57fbt25w7dw5XV1et3PaWRtq1e79IjpOamqqpIpZXOCJ3UytaXjRgZ2dnfvjhBzw8\nPDAzM2PFilVUrFiRvExUHdsuTasWJRJtkq/5tmjRgsaNG2NiYkJiYiKZmZmYm5uzf/9+rl69ytix\nY8uc8RbtaLJwvGxWP/98kJiYB69sJ4QgIyODDh060bBh3h2Q3wSVSsWYMeMRQrB48QLat28PwMKF\nX2Nvn3t3WTUvxqclEkkBzPfFGW5TU1OaNGlCaGgoQUFBzJo1q5THeHOmuEaTeZFlVmaAHmFhxxg6\n1C3P7atXr054+KU8J/fehJSUFJKTkxg7dgJ6enosWjSf7t0/ZMSI0Vo5n0RS1nmtVLOkpCS2bNnC\nH3/8waJFi7C3t9eWrhJFl26RhRB8880iIKv3m/qPoRBCkwu8f/9+AgMD2bZtMyNHfq4VHWZmZujp\nZV0Tb28fJk3yxsAg7x5oEokkd17LfC0sLPjwww9xd3d/JeWoLKFLt8gnThzn7NlwevXqla2Y+YsN\nMLt06cKePXvw81vGJ598qpXR78vXRBqvRPJmvJb5mpqa8tVXXxWiGabkZQoSV86q9ZtV13bWrFm5\nHqtKlSpMmDCBb775RqujXzW6EBN/U2T34oKh692L80JbnY1LrHuxNN6ioSDZDEeO/Mz58+fo2bMn\n7733XrZC9Onp6dkmOj08PFi1ahV+fstwcxtM+fKF+YLkl+eclVucXbs6vSyvnGTdy+WV3YsLhq53\nL84LbXQ2LsqSklprHV860YZB5HxMU1MzhMj6b07bCCFYuXIFADNmzHil1oO+vn6252xsbBg3bhxL\nliwhMHAro0ePK7q3oCErJzd7TLwg1+ztzuVVKpV5FvW+e/dugYoXScoWpfOesQygr69PhQoVcr1t\nP3HiOOfO/UbPnj1p3rx5gY7p5eWFmZkZq1atIC0trSjlZkMd/80r5KBSqUplHz5tcOfObe7evZvr\n67Vr18bOLv90PUnZQo58dZQdO7L6pXl4eBR4n8qVK9OuXTuOHj3K4cM/06tXn0KduyhiurqQrqdL\n1K5du0g7KkjejPzuRuzs6mh9UlmOfHWUZs2yRrtHjx4t0PZCCKZNm8bRo0dxcHCgfft/FfrcauN8\nkwampqampa4Pn+TtIa+7keLq/SZHvjrK0KHD8fdfzqpVq5g0aRKVKlXKdVu18S5duhQHBweCgvbk\nuX1+FEWesy6l6xUV+Y2WckPGdEuOvAy2pO9G5MhXRzE1NWX8eA+SkpJYsWJFrts9e/aMSZMmZTPe\nqlVt3+jcBYnpvo3kF7vNDRnTLRny6mysC5+JHPnqMEOGfMrq1StZtWoV7u7u2UazT58+JSAggJUr\nV5KQkED9+vXZseOnNzTe/6WaZcV90zA1NXnBhHUvZay4KenRkqTgGBgY6PRnJYc2OkyFChWZONGT\npKQkVq1ahZGRESkpKSxatIj33nuPefPmYWhoyMyZczl48BjVq9fQdAouHP/rDp2amvZP3Dfthef1\ns22T/ZHXa2+3YUskOSFHvjrO0KGf4e+/nK+++orExES2bNlCQkIClStXZsqU6YwZM0ErTUp1qb6F\nRFIWkear45iamjJxoiczZkzF39+fypUrM2PGbNzcPqFy5Spai8uWxQkziaSg5Bbbf/q0AvHxSVSp\n0uKNzyHNtxTw6afD6d3blZ9+2sXQoZ+9MNKVt/MSSVGT30Tc3bt3adtWh81XCMHu3bt59913l/4v\nSgAAEoZJREFUsbW1xcbGRlunKvOYmJhga1uNceMmlrQUiaTMk9dEXZUqFTU1I94UrdyzqlQqxo0b\nx2+//caGDRsICwvTPC+RSCQSLZnvoUOHsLe35+uvv8bNzY2DBw+SnJws80Z1AkH26mXipYdEIikO\ntOKGCQkJ3Lt3D4AGDRpQoUIFzeSNNgu+lD3ySt3SVgW24j6nRPJ2UmQxXyEEx48fp0qVKri6uvLh\nhx8CWb2/4uPjATQjYFdXV8qVk3N9Et1DvYRYPav9InKZsKQoKZKRr0qlYvLkyRw7dozDhw8zatQo\nTeFvlUpFgwYNOHToEJs2bcLJyUkar0RnyWsJsS4sSZWUHYrEBYODg1EoFMydOxcAX19fJk6cyOrV\nqzEwMGD37t3cu3ePRYsWleneb5LSw61bN3N8Xj26dXBw0InuDZKyS5GMfO3s7LC1tSUhIQEAFxcX\nqlevzpo1azA3N6dNmzZMnjxZGq9EZ5CjW0lJU+iRrxCCn3/+GVNTU+zt7YmOjmbz5s0YGhpy5swZ\n3N3dOXfuHBUqVGDlypUYGhoWpW6J5I2QBXIkJU2hzFcIwbBhw2jYsCE3b96kQ4cOtG/fHnNzcxQK\nBe7u7gghuHDhAklJSXKZqo4ihMjWgl4ikRQfekKI107uvHXrFhs2bGDhwoWkpaWxfft2kpOT+de/\n/oWxsTHh4eHs3r2bpUuXUq9ePW3olrwhSUlJPH/+HHNzc60U5pFIJHlTKPO9d+8ew4YN4+uvv8bJ\nyYmkpCS2bNlCSkoKXl5ePH36lMzMTKpWraoNzRKJRFLqKdT9Zs2aNfHw8GDbtm388ccfVKhQgbFj\nx/Lnn39y//59KlWqJI1XIpFI8qBAMV+VSsWJEyfIzMyka9euAHTs2BGFQsHq1asZMmQICoUChUKB\nubm5VgVLJBJJWSDfsIMQgvHjx2Nra8vFixdp2rQp8+bNA7KWCoeFhREcHIyZmRkjR46kQYMGxSJc\nIpFISjP5mu/333/PjRs3+Oqrr8jIyMDHx4e5c+e+MsLNyMiQ6WQSiURSQPKN+daoUYOqVauSlpbG\ns2fPiIuL07x2+/ZtduzYQXp6ujReiUQieQ3yjfm2aNGCxo0bY2JiQmJiIpmZmZibm7N//36uXr3K\n2LFjMTY2Lg6tEolEUmbId+RrbW1NtWrVgKx+Yk2aNCE0NJSgoCD69euHhYWF1kWWZZKTk0taQjYK\nkXmoNXRJi0Q3Kc0NGl4r1Uydz7t27Vpmz55N3bp1taWryBFCEBYWRlRUFKmpqSUtB5VKxfTp07l5\nM6vAS0kajRCCa9eu8fjxY/T0Sr5mr0qlYtasWdy+fRsoeRMWQrBr1y7++OMPHj16VCIaVCoVa9eu\n5a+//kKhUJSIBtCd39F3331HfHw8+vr6JWLAQghCQ0O5fft2oc9vMHv27NkF3tjAgOjoaKZPn469\nvX2hTlgSqFQqxo4dy4MHDzh8+DCPHj1CX19fM6IvCT3u7u44Ojri4uKCQqEgPT0dIyOjEtEyevRo\nbt26RWBgIPr6+piYmGBlZVXsWtR6PD09qV+/Pl26dEGlUqGnp1difxRUKhXjx4/nyZMnnDt3DiEE\njRs31ugqTg2mpqbo6elhb29fInMsuvI7unLlCv7+/sTExGiaNRQnKpWKzz//nOjoaHbv3k2lSpWo\nU+f1izG9lvkaGhrywQcfUKlSpdc+UUly5swZYmNjWbBgAXXr1uXJkydcvnwZKysrqlSpUux6du3a\nRVRUFKNGjWLOnDkcPnyYy5cvo1Kpir3y26FDh0hNTWXu3LlUr16dq1ev8ujRI6pWrUrFihWLVQtA\nQEAAGRkZfP7558ybN4+QkBCio6MpV65ciSzcOXjwIAqFgjlz5mBpacmOHTvo2rVrsc5znD17lpSU\nFHx8fNi4cSOnTp0iJiaGcuXKFev3Nzw8XCd+R0ZGRly7do1atWpx/PhxLCwsSElJwdrauljOHxYW\nRmxsLIsXL8bS0pK9e/dSv359njx58lre+Nor3EpidPamlCtXjr///huAhg0b0r59eywtLbl27RpQ\n/Le1TZo0oWrVqsybN4/27dvj4eFB06ZNOX36NM+fPy8WPSqViqSkJMzMzIiMjASgffv2fPDBB0RH\nRxMREaF1DS/rSUlJ4b333iMtLY358+fTunVrPvnkEyDLgJRKZbF/VrrQEiszM5NLly6xZMkSOnTo\nQM+ePUlMTOTMmTOoVKpiuyb6+vqaUFBJ/o7U35WPPvqI1NRUvL29NeGg4tCgp6dHdHQ00dHRHDt2\njOfPnxMYGMjGjRu5d+9egTWU2XJW6hjZwYMHad26Ne+88w5eXl4A1KlTh5YtW3Ly5ElSU1OL5fZR\npVKxZs0aDhw4QIMGDahduzYGBgb06NGD2rVr4+TkpKmHrG096qp0ixYtolOnTlSrVo2lS5cC0Lx5\nc1q3bs2uXbuKLbao1rNgwQLatWtHpUqVePr0Kb169cLJyYk2bdrw559/kpGRUSyflRCCY8eOcfXq\nVVxdXVm4cCHwakuskJAQMjMztabh+PHjXL16lVatWtG5c2eOHz9Ox44dadeuHR06dODixYukpaVp\n9ZqoVCpCQkI4deoULVu2pGnTpnh4eADF9ztSqVTs37+fsLAwnj59SqVKlXjvvfeIiYnh3r17tGnT\nhr179/LkyROtalBfh/fff5+PPvqI9evXc+XKFTZt2oSnpyempqaUK1euwBrKpPkKIfDw8CAjIwMD\nAwNSU1Px9fWlVq1ajBkzBoCnT5+iUqm09uPJSU9mZiZGRkakpKQwdOhQfH19NQYXERHB8+fPta5H\npVLh4+ODUqnUjOBGjRpFcnIy8+fPB7LuFExMTIplIuNFPerY3RdffMGECRM0Ezrqu4Hi+Kx0oSWW\nWkNoaCi//PILw4cPx9HRkZo1a+Lr6wvAw4cPyczM1GjTBkIIPD09+e233/j1118ZOHAggwYNwt7e\nvth+R2oN4eHhnDt3jk8//ZSkpCQSExPx9vZm/PjxzJgxgzp16qBUKrWq4cXr0LlzZz766CPN53/2\n7FkiIyNfrzyrKIOEh4eLqVOniszMTDF27FixcOFC4ebmJh4/fiwWL14svL29xeDBg8Vff/1VInrm\nz58vBg4cKJ48eSKCgoJE//79hZubm7h586bWtcycOVP4+/sLIYTw9vYWUVFRQqFQiKioKOHt7S3G\njRsnBg4cKK5fv651LTnpiYyM1Lzm5+cn+vXrJwYOHChu3LhRLHp++uknMWHCBM2/p06dKtzc3ER8\nfLy4f/++aN68ufj888/F33//XWwafH19hZubm4iIiBDTp08XY8aMEW5ublq/JqdPn86mY+XKlcLV\n1VU8efJELFq0qFh+Ry9rWL58uejTp48IDg4WDx480Dyfnp5ebBpWrFgh+vTpIx4/fix8fHzEhAkT\nhJubW7bvbkEok+YbExMjZs6cKfz8/MTOnTuFEEIsXbpUDBgwQKhUKpGSkiKSkpJKVM+yZctEv379\nhBBCPHv2rFj0KBQKcfz4cSGEEEqlUkyePFmcPn062zaPHj0Sz58/17qW3PScOXMm2zZRUVHi2bNn\nxaJHCCEuXbok5s2bpznnsWPHhLe3t1i4cKFITEwUY8eO1brpvawhNDRUeHt7i0WLFgkhhIiPjxeJ\niYla1SCEEI8fPxa+vr7i4sWLmueWLVsmRo4cKYQQIjk5Wevf25w0rF69Wri5uQmVSiVUKpVWz5+b\nhmXLlokRI0YIpVIpYmNjRUJCwmsf97WyHXQZlUpFYGAgf//9N9bW1vz+++/cunULJycn6tSpQ7t2\n7bh06RItWrTA3Nxc6xOH+elp27Ytv//+Oy1atMDS0lKrelQqFVu3biUmJgYnJydNylJGRgbbtm2j\nbdu2mlv+8uXLa30m/3X0WFhYYGJiolU94p+WWPfv36d69ers37+fmJgYLl26xL59+xg5ciTx8fG8\n//77uLi4YGNjU6waQkJCGDFiBI8fP6Z169aYmppq7fsihODKlStUrVoVIQQ3b97k8ePHWFlZYWVl\nRdu2bbl48SKtWrWifPnyWtGRn4ZWrVpx/fp1nJycSvQ6XLp0iTZt2mBlZVWo30yZMF8hBKNHjwbg\n/PnzPH/+nKFDh3Lq1CkyMjJQKBT89ddfHDhwAFdXV0xNTXVGT58+fbSqR61FT0+Pc+fOERERQceO\nHYGsxqdPnjzh6dOn2NnZaSV+WRr0DBs2jMzMTEJDQ1EqldSuXRsbGxuMjIzo3r07Qgj27duHs7Mz\nJiYmRT6pUxANAPv378fZ2RlDQ0OtTSxduXIFDw8P6tatS7169ahRowZhYWE8fPiQuLg4IiMjCQkJ\noVevXlr73hZEw759+0pcQ0hICD179iy0Bu1/u4uBU6dOYWNjw4wZM1AqlXz22WcMGDAAHx8fQkJC\nOHz4MPHx8SxdurRYcgF1Sc/LWkaOHElERAQODg4YGRlRpUoVbty4gYuLi1Z16Kqe27dvU6NGDXx9\nfTUtsRITE3F0dHylJZa2kvl1QYOa5ORkUlNTWbduHSqVik6dOjFmzBhOnjzJ2bNnefLkCUuWLNHq\nIpy3RUOZMF8TExNiYmJ4/vy5ZnWWUqnEzs6OTz75BCsrK5KSkoptJYwu6XlZi4WFhaYeR0ZGBn37\n9iUhIUHrdwO6qsfIyIjw8HDOnz+Pk5MT/fr1Y8uWLRw5cgQvLy+qVKlCjx49tLrAQxc0qDEzM2Pz\n5s3Exsayfv16ADp16kT//v2BLFPSdkPct0bDG0WidQSVSiXi4uKEEEIkJiaKTz/9VKSlpYn9+/cL\nf39/kZGR8dbq0SUtuqhHiKzsAk9PT3H58mWNxhEjRoioqKi3SoMQQiQkJIhHjx4JIYQ4cOCAGD58\nuDhy5IhQKpVSQxFrKJUxXyEEERERxMfHU6lSJfT09DA2NkZPT49y5cppisTs3r2bUaNGUbly5bdG\njy5p0UU9KpWKsLAwIiMjNevxbWxsyMjIICgoCEtLS27dusWFCxfo27evVib7dEFDbjqMjY01I7p3\n330XgODgYLp06aKVehJvs4ZCdS8uSYQQjBs3DisrK+Lj43nnnXeYNWsWAAqFgqSkJLp06YK9vT3L\nly+nVq1ab40eXdKiq3pKuiWWLmjISYejoyNz587VvK5SqTQLBrQVInvrNRTZGLqY2L59u5g2bZoQ\nQoi0tDQxcOBA8eWXX2peT0lJEWvXrn3thOeyoEeXtOiino0bN4qpU6cKIbJyjCdNmpRjfqZCoSjT\nGnLT8WJ+99OnTzX/r61c2rddQ6lbXly3bl309PR4+PAhxsbGbN68mevXr+Pv78/Nmzc5ceIEQ4YM\nKbZaw7qkR5e06KIeXWiJpQsactMh/rkJvnPnDr/88gvp6emA9mqNvO0aSqX5mpqacvnyZeLj4zEy\nMmLlypUkJyeTlpZGixYtMDMzeyv16JIWXdTTokULBg4cqImhvtgSa+fOnXTv3l3rC0x0QUN+Onbs\n2IGLi0uJXou3QUOpm3AzNTWlevXq7NmzB6VSScWKFbl06RInT55k+PDhxV6DVpf06JIWXdWjPqcQ\ngr///pvMzEwCAwMZN25csRQF1wUNuqLjbddQ6ibc1Pz999+EhIQQERFBWloaPj4+ODg4SD06pkUX\n9QDExsbSuXNnHB0dWbRoUYl0ZtEFDbqi463UUKQR5GImIyNDxMXFafJGSxpd0qNLWoTQPT0pKSnC\n29tbq9XJSoMGXdHxNmootSNfieRNUSgUJd6ZRRc06IqOt02DNF+JRCIpAUpdtoNEIpGUBaT5SiQS\nSQkgzVcikUhKAGm+EomkTHP58mWGDh362vspFAomT57MgAEDGDFiBHfu3ClSXWWinq9EIpHkREBA\nAMHBwYWqDx0UFISZmRlBQUHcvn2befPmsWHDhiLTJke+EomkzFKrVi38/Pw0/75x4wZDhw5l6NCh\nuLu7k5iYmOu+kZGRmhZXderU4datW0WqTY58JRJJmcXFxYXo6GjNv2fOnMnChQupV68eO3fuZP36\n9bRs2ZKvv/46236enp40bNiQY8eO0bVrVy5fvszDhw9RKpUYGBgUiTZpvhKJ5K3h1q1bzJkzB8hq\nW2VnZ0fHjh01I9wXyczM5NatWwwePJgWLVrQuHHjIjNekOYrkUjeIuzt7Vm8eDHVq1fnwoULPH78\nONdtr1y5Qrt27Zg2bRpXrlzhwYMHRapFmq9EInlrmD17NlOmTCEzMxM9PT0WLFiQ67a1a9dmxYoV\nrFmzhooVK+a5bWGQy4slEomkBJDZDhKJRFICSPOVSCSSEkCar0QikZQA0nwlEomkBJDmK5FIJCWA\nNF+JRCIpAaT5SiQSSQnw/wJw53x3MVEmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f946e10>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X7REsWrSA9PR0KlWqxOeff06nTp2UYjTZSUtLY+7cuYSEhGQK3pfyvb3mYiXm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\nNF+JRCIpAaT5SiQSSQnw/wJw53x3MVEmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f946e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corner.corner(randar, truths = [0.27,2.1e-9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samplar = samples.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samplar1 = np.append(samplar[:,1],samplar[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 320)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape([samplar1,samplar2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "samplar = np.array(samplar).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         2.69993966e-01,   2.09996983e-09,   2.69993966e-01,\n",
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       "         2.09992055e-09,   2.69984109e-01,   2.09992055e-09,\n",
       "         2.69984109e-01,   2.09992055e-09,   2.69984109e-01,\n",
       "         2.09992055e-09,   2.69984109e-01,   2.09992055e-09,\n",
       "         2.69984109e-01,   2.09992055e-09,   2.69984109e-01,\n",
       "         2.09992055e-09,   2.69985265e-01,   2.09992633e-09,\n",
       "         2.69985265e-01,   2.09992633e-09,   2.69985265e-01,\n",
       "         2.09992633e-09,   2.69985265e-01,   2.09992633e-09,\n",
       "         2.69985265e-01,   2.09992633e-09,   2.69985265e-01,\n",
       "         2.09992633e-09])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samplar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13571498257839534"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(samples[0]-2.1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples1cop = samples[:,0]-0.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.10214984e-09,   2.10214984e-09,   2.10214984e-09,\n",
       "         2.10214984e-09,   2.10214984e-09,   2.10214984e-09,\n",
       "         2.09905184e-09,   2.09868627e-09,   2.09867699e-09,\n",
       "         2.09867699e-09,   2.09782238e-09,   2.09782238e-09,\n",
       "         2.09782238e-09,   2.09549570e-09,   2.09524702e-09,\n",
       "         2.09524702e-09,   2.09524702e-09,   2.09509146e-09,\n",
       "         2.09508178e-09,   2.09508047e-09,   2.09569783e-09,\n",
       "         2.09569783e-09,   2.09569783e-09,   2.09569783e-09,\n",
       "         2.09554536e-09,   2.09544200e-09,   2.09544200e-09,\n",
       "         2.09539793e-09,   2.09539793e-09,   2.09539793e-09,\n",
       "         2.09539793e-09,   2.09539793e-09,   2.09539793e-09,\n",
       "         2.09539793e-09,   2.09522368e-09,   2.09517902e-09,\n",
       "         2.09514376e-09,   2.09514376e-09,   2.09509527e-09,\n",
       "         2.09509527e-09,   2.09503580e-09,   2.09503580e-09,\n",
       "         2.09503580e-09,   2.09503580e-09,   2.09503580e-09,\n",
       "         2.09504449e-09,   2.09504843e-09,   2.09502181e-09,\n",
       "         2.09502181e-09,   2.09502181e-09,   2.09502298e-09,\n",
       "         2.09502298e-09,   2.09502078e-09,   2.09502289e-09,\n",
       "         2.09499602e-09,   2.09499602e-09,   2.09499721e-09,\n",
       "         2.09499721e-09,   2.09499721e-09,   2.09499391e-09,\n",
       "         2.09678230e-09,   2.09678230e-09,   2.09678230e-09,\n",
       "         2.09678230e-09,   2.09678230e-09,   2.09660970e-09,\n",
       "         2.09660970e-09,   2.09660970e-09,   2.09660970e-09,\n",
       "         2.09660970e-09,   2.09660970e-09,   2.09660970e-09,\n",
       "         2.09660970e-09,   2.09660970e-09,   2.09660970e-09,\n",
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       "         2.09502826e-09,   2.09502826e-09,   2.09502826e-09,\n",
       "         2.09502826e-09,   2.09502826e-09,   2.09502826e-09,\n",
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       "         2.09502677e-09,   2.09502677e-09,   2.09501904e-09,\n",
       "         2.09499521e-09,   2.10882543e-09,   2.10882543e-09,\n",
       "         2.10882543e-09,   2.10882543e-09,   2.10882543e-09,\n",
       "         2.10882543e-09,   2.10882543e-09,   2.10505177e-09,\n",
       "         2.10443339e-09,   2.10443339e-09,   2.10388189e-09,\n",
       "         2.10105308e-09,   2.10100970e-09,   2.10100970e-09,\n",
       "         2.10100970e-09,   2.10009553e-09,   2.09927055e-09,\n",
       "         2.09927055e-09,   2.09927055e-09,   2.09838628e-09,\n",
       "         2.09527260e-09,   2.09527260e-09,   2.09527260e-09,\n",
       "         2.09527260e-09,   2.09516929e-09,   2.09490168e-09,\n",
       "         2.09490168e-09,   2.09493662e-09,   2.09493662e-09,\n",
       "         2.09493805e-09,   2.09496727e-09,   2.09496727e-09,\n",
       "         2.09496727e-09,   2.09496727e-09,   2.09496983e-09,\n",
       "         2.09496983e-09,   2.09496983e-09,   2.09496983e-09,\n",
       "         2.09496983e-09,   2.09496549e-09,   2.09507891e-09,\n",
       "         2.09507891e-09,   2.09507891e-09,   2.09492055e-09,\n",
       "         2.09492055e-09,   2.09492055e-09,   2.09492055e-09,\n",
       "         2.09492055e-09,   2.09492055e-09,   2.09492055e-09,\n",
       "         2.09492055e-09,   2.09492055e-09,   2.09492055e-09,\n",
       "         2.09492055e-09,   2.09492633e-09,   2.09492633e-09,\n",
       "         2.09492633e-09,   2.09492633e-09,   2.09492633e-09,\n",
       "         2.09492633e-09])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples2cop = samples[:,1]-0.005e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samplesss = np.append(samples,randar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.71429967e-01,   2.10714984e-09,   2.71429967e-01, ...,\n",
       "         2.10412813e-09,   2.69562705e-01,   2.09790636e-09])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samplesss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples0 = samplesss[0:-1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0.26915393,  0.26568217,  0.27040257,  0.27106794,  0.26816921,\n",
       "        0.26986106,  0.2680868 ,  0.27489863,  0.2725764 ,  0.26787139,\n",
       "        0.27072432,  0.27037016,  0.26607839,  0.26936747,  0.26976008,\n",
       "        0.27191169,  0.2734063 ,  0.26762504,  0.26623674,  0.26956271])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples1 = samplesss[1:-1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         2.09844301e-09,   2.10017544e-09,   2.10628413e-09,\n",
       "         2.10099505e-09,   2.09956004e-09,   2.10138828e-09,\n",
       "         2.09942552e-09,   2.10234529e-09,   2.10208706e-09,\n",
       "         2.09826960e-09,   2.09922369e-09,   2.10351893e-09,\n",
       "         2.09704050e-09,   2.10077768e-09,   2.09586960e-09,\n",
       "         2.09819293e-09,   2.09963130e-09,   2.09729279e-09,\n",
       "         2.09559361e-09,   2.09674438e-09,   2.10369675e-09,\n",
       "         2.09844983e-09,   2.09768608e-09,   2.09897987e-09,\n",
       "         2.10487998e-09,   2.10302226e-09,   2.09994957e-09,\n",
       "         2.09769348e-09,   2.10370755e-09,   2.09647134e-09,\n",
       "         2.10054761e-09,   2.10412813e-09])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x111955510>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIi0xCnXoC677DJuv/12AAzDwGq1Nrnd5XKxcOFC8vPzW3wfERGRljjlGlRi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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112082790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(samples0,samples1,ylim=(2.05e-9,2.15e-9),kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/myhome/py2_kernel/lib/python2.7/site-packages/ipykernel/__main__.py:1: DeprecationWarning: using a non-integer array as obj in delete will result in an error in the future\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "samples0 = np.delete(samples0,samples0[len(samples0)-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "659"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(samples0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.10714984e-09,   2.10714984e-09,   2.10714984e-09,\n",
       "         2.10714984e-09,   2.10714984e-09,   2.10714984e-09,\n",
       "         2.10405184e-09,   2.10368627e-09,   2.10367699e-09,\n",
       "         2.10367699e-09,   2.10282238e-09,   2.10282238e-09,\n",
       "         2.10282238e-09,   2.10049570e-09,   2.10024702e-09,\n",
       "         2.10024702e-09,   2.10024702e-09,   2.10009146e-09,\n",
       "         2.10008178e-09,   2.10008047e-09,   2.10069783e-09,\n",
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       "         2.09769348e-09,   2.10370755e-09,   2.09647134e-09,\n",
       "         2.10054761e-09,   2.10412813e-09])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.jointplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
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