generate_dv_PT_nonlimber.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "185d081b",
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 'Y1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "baeb8a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import sacc\n",
    "import pyccl as ccl\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.special import erf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830c1795",
   "metadata": {},
   "source": [
    "# Fiducial cosmology\n",
    "* Planck 2018 TT,TE,EE+lowE+lensing\n",
    "* https://arxiv.org/abs/1807.06209 Table 2 second-to-last column\n",
    "* for our fiducial cosmology we have 3 degenerate massive neutrinos with equal mass, and a total mass of 0.1 eV (to include the effect of massive neutrinos without having to do calculations for multiple masses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b672d502",
   "metadata": {},
   "outputs": [],
   "source": [
    "fiducial_cosmology = {'name': 'desy3',\n",
    "                      'h': 0.6736,\n",
    "                      'Omega_m': 0.3153,\n",
    "                      'Omega_b': 0.0493,\n",
    "                      'A_s': 2.092e-9,\n",
    "                      'n_s': 0.9656,\n",
    "                      'w0': -1,\n",
    "                      'wa': 0,\n",
    "                      #'tau': 0.0536,\n",
    "                      'sigma8': 0.8091,\n",
    "                      'S8': 0.828\n",
    "                     }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2df1177",
   "metadata": {},
   "source": [
    "# $\\ell$ binning choices\n",
    "\n",
    "* decide whether to make bins smaller\n",
    "* look at hsc, des, kids choices?\n",
    "* I don't think there is any point in going to 15 000 so even if we want the same width we should be able to just make fewer bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3390434",
   "metadata": {},
   "outputs": [],
   "source": [
    "does_pt = False\n",
    "does_TATT = False\n",
    "auto_galaxy_clustering_only = False\n",
    "\n",
    "nbin_ell = 20\n",
    "lmin_bins = 20\n",
    "lmax_bins = 2000\n",
    "\n",
    "IA_model = 'NLA'\n",
    "if does_TATT: IA_model = 'TATT'\n",
    "    \n",
    "gbias_model = 'lin_gbias'\n",
    "if does_pt: gbias_model = 'nl_gbias'\n",
    "\n",
    "twopoint_data = '3x2pt'\n",
    "    \n",
    "lbinning_str = str(nbin_ell)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5230c556",
   "metadata": {},
   "source": [
    "# Galaxy clustering systematics and analysis choices\n",
    "\n",
    "* nonlinear bias with Eulerian perturbation theory, redshift evolution (see bias challenge paper https://arxiv.org/abs/2307.03226, figure 7 of https://arxiv.org/pdf/2111.00501.pdf)\n",
    "* kmax 0.1-0.2 h/Mpc\n",
    "* redshift: shift and stretch?\n",
    "* magnification\n",
    "* redshift space distortions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4e72300",
   "metadata": {},
   "outputs": [],
   "source": [
    "if year=='Y1' or year==1 or year=='y1':\n",
    "    # galaxy clustering fiducial systematic parameters\n",
    "    linear_bias_lens = np.array([1.23852, 1.36452, 1.50445, 1.6498, 1.79915]) #add nonlinear bias\n",
    "    kmax = 0.2*fiducial_cosmology['h']\n",
    "    magnification_bias_lens = np.array([0,0,0,0,0]) #figure out fiducial values\n",
    "\n",
    "    # galaxy clustering redshift bins\n",
    "    nbin_z_lens = 5\n",
    "    z0_lens = 0.26\n",
    "    alpha_lens = 0.94\n",
    "    sig_z_lens = 0.03\n",
    "    z_min_lens = 0.2\n",
    "    z_max_lens = 1.2\n",
    "    \n",
    "    # survey-informed parameters --TODO: update these\n",
    "    Ngal_lens = 18 # Normalisation, galaxies/arcmin^2\n",
    "    sky_area = 11808.28 #square degrees, this is still srd values for now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4030c06c",
   "metadata": {},
   "source": [
    "# Weak Lensing systematics and analysis choices\n",
    "\n",
    "* TATT for intrinsic alignments\n",
    "* Mean redshift shift and stretch\n",
    "* Multiplicative Shear Bias\n",
    "* using DESC SRD lmax cutoff, ell binning and z binning for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2d43260",
   "metadata": {},
   "outputs": [],
   "source": [
    "if year=='Y1' or year==1 or year=='y1':\n",
    "    # weak lensing shear (source sample)\n",
    "    lmax_src = 2000\n",
    "    \n",
    "    z0_src = 0.13\n",
    "    alpha_src = 0.78\n",
    "    sig_z_src = 0.05  #(0.05(1+z))\n",
    "    sig_e_src = 0.26\n",
    "    nbin_z_src = 5\n",
    "    \n",
    "    Ngal_src = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de8cbb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_PT_biases(b1):\n",
    "    b2 = 0.30 - 0.79*b1 + 0.20*(b1**2) + 0.12*(b1**3)\n",
    "    bk2 =  0.66 - 0.57*b1\n",
    "    bs2 = -4/7*(b1-1)\n",
    "    b3nl = b1-1\n",
    "    none_arr = [None, None, None, None, None]\n",
    "    if not does_pt: return none_arr, none_arr, none_arr, none_arr\n",
    "    return b2, bs2, bk2, b3nl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee72ae49",
   "metadata": {},
   "source": [
    "# CCL cosmology object\n",
    "* Done: HMCode 2020 with baryons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28c2f539",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmo = ccl.Cosmology(\n",
    "    Omega_c=fiducial_cosmology['Omega_m']-fiducial_cosmology['Omega_b'],\n",
    "    Omega_b=fiducial_cosmology['Omega_b'],\n",
    "    w0=-1.0,\n",
    "    wa=0.0,\n",
    "    A_s = fiducial_cosmology['A_s'],\n",
    "    n_s=fiducial_cosmology['n_s'],\n",
    "    h=fiducial_cosmology['h'],\n",
    "    Omega_k=0.0,\n",
    "    m_nu = 0.1,\n",
    "    mass_split = 'equal',\n",
    "    matter_power_spectrum = \"camb\",\n",
    "    extra_parameters = {\"camb\": {\"halofit_version\": \"mead2020_feedback\",\n",
    "                                     \"HMCode_logT_AGN\": 7.8}}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "729ba705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 367.6158588   579.69089554  767.44594092  933.54261404 1080.83381786]\n",
      "[1.231994   1.36683885 1.50929764 1.65731706 1.80939185]\n",
      "[None, None, None, None, None]\n",
      "[None, None, None, None, None]\n",
      "[None, None, None, None, None]\n",
      "[None, None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "if year=='Y1' or year==1 or year=='y1':\n",
    "\n",
    "    #All other Systematic Parameters\n",
    "\n",
    "    #TATT IA\n",
    "    # Bare CCL setup\n",
    "    a_1 = 1.0\n",
    "    a_2 = 1.0\n",
    "    b_ta = 0.5\n",
    "    a_d = b_ta * a_1\n",
    "    eta_1 = -1.0\n",
    "    eta_2 = -1.5\n",
    "    z_pivot = 0.62\n",
    "    if not does_TATT:\n",
    "        a_2 = 0.0\n",
    "        a_d = 0.0\n",
    "        eta_2 = 0.0\n",
    "    #Shear Calibration\n",
    "    s_cal = np.array([0.0, 0.0, 0.0, 0.0, 0.0])\n",
    "\n",
    "    k_max = 0.2 / fiducial_cosmology['h'] #in Mpc^-1\n",
    "    if not does_pt: kmax = 0.1/ fiducial_cosmology['h']\n",
    "    \n",
    "    #THIS IS JUST APPROXIMATE\n",
    "    #need the n(z) distributions first to calculate true <z>\n",
    "    z_mean_lens = np.array([0.3, 0.5, 0.7, 0.9, 1.1])\n",
    "    a_mean_lens = 1/(1+z_mean_lens)\n",
    "    l_max_lens =  k_max * cosmo.comoving_radial_distance(a_mean_lens) + 0.5\n",
    "    print(l_max_lens)\n",
    "    \n",
    "    #1-Loop Galaxy Bias\n",
    "    b_1 = 1.05/cosmo.growth_factor(a_mean_lens)\n",
    "    b_2, b_s, b_k, b_3 = get_PT_biases(b_1)\n",
    "\n",
    "    print(b_1)\n",
    "    print(b_2)\n",
    "    print(b_s)\n",
    "    print(b_k)\n",
    "    print(b_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ba88f8",
   "metadata": {},
   "source": [
    "# $\\ell$ binning\n",
    "\n",
    "* currently matches DESC SRD\n",
    "* consider changing binning (smaller lmax, narrower bins?)\n",
    "* should we use a more realistic ell binning function based on mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed41aa5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.3318288023596634, 3.1697863849222268, 4.308869380063767, 5.857289129250473, 7.962143411069944, 10.823390530929272, 14.712845089192829, 19.999999999999996, 27.187127817570516, 36.956995948445815, 50.2377286301916, 68.29097747667205, 92.8317766722556, 126.19146889603866, 171.53917971817881, 233.18288023596645, 316.9786384922228, 430.8869380063769, 585.7289129250474, 796.2143411069947, 1082.3390530929275, 1471.2845089192829, 1999.9999999999998, 2718.7127817570513, 3695.699594844581, 5023.77286301916, 6829.0977476672015, 9283.177667225555, 12619.14688960386, 17153.917971817875]\n"
     ]
    }
   ],
   "source": [
    "ell_edges = np.geomspace(lmin_bins, lmax_bins, nbin_ell+1)\n",
    "ell_centers = [lmin_bins**(1-(i+0.5)/nbin_ell)*lmax_bins**((i+0.5)/nbin_ell) for i in range(nbin_ell)]\n",
    "print(ell_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bf874e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tophat window functions in ell (use more realistic window fns?)\n",
    "ell_unbinned = np.arange(0, lmax_bins+1)\n",
    "n_ell_unbinned = len(ell_unbinned)\n",
    "ell_windows = np.zeros([len(ell_unbinned), nbin_ell])\n",
    "for i in range(nbin_ell):\n",
    "    ell_windows[:,i] = (ell_unbinned>=ell_edges[i]) & (ell_unbinned<=ell_edges[i+1])\n",
    "    ell_windows[:,i]/=np.sum(ell_windows[:,i])\n",
    "    plt.plot(ell_unbinned, ell_windows[:,i], label=r'$\\ell$ window '+str(i+1))\n",
    "plt.xlim(10, lmax_bins)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('$\\ell$')\n",
    "plt.title('window functions in $\\ell$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a7c0f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a SACC bandpower window object\n",
    "sacc_ell_windows = sacc.BandpowerWindow(ell_unbinned, ell_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2621bfbe",
   "metadata": {},
   "source": [
    "# Galaxy clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa312d2",
   "metadata": {},
   "source": [
    "## z binning\n",
    "\n",
    "* same as DESC SRD (5 equally spaced bins from z=0.2-1.2 for LSST y1)\n",
    "* no cross spectra between bins (look into whether crossing neighbouring bins adds signal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "399abdf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_redshift_distribution(z, z0, alpha): # shape, not normalized\n",
    "    return (z / z0)**2. * np.exp(-(z / z0)**alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f90746cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_z_int(z, splined):\n",
    "    return z * splined(z)\n",
    "\n",
    "def nz_int(z, sig_e, z_max, z_min, splined):\n",
    "    sig_e *= (1+z)\n",
    "    upper_lim = (z-z_max)/(sig_e*np.sqrt(2))\n",
    "    lower_lim = (z-z_min)/(sig_e*np.sqrt(2))\n",
    "    return  0.5*(erf(lower_lim)-erf(upper_lim))*splined(z)\n",
    "\n",
    "def get_nz_bins_from_zmid(z_arr, ngal, nz, sig_z, z_lower_bounds, z_upper_bounds, zmids=None, title=''):\n",
    "    nz_bins = []\n",
    "    z_avgs = []\n",
    "    nz_avgs = []\n",
    "    if zmids is None:\n",
    "        zmids = (z_lower_bounds + z_upper_bounds)/2\n",
    "    fig, ax = plt.subplots(1,2, figsize=(10,5))\n",
    "    ax[0].plot(z_arr, nz, label='total nz')\n",
    "    splined = interp1d(z_arr, nz)\n",
    "    for i, zmin in enumerate(z_lower_bounds):\n",
    "        zmax = z_upper_bounds[i]\n",
    "        zmid = zmids[i]        \n",
    "        nz_bin_i = np.zeros_like(z_arr)\n",
    "        nz_bin_i[(z_arr>zmin) & (z_arr<zmax)] = nz[(z_arr>zmin) & (z_arr<zmax)]\n",
    "        ax[0].plot(z_arr, nz_bin_i, label='z bin %d'%(i+1))\n",
    "        ax[0].set_xlabel('$z$')\n",
    "        ax[0].set_ylabel('$p(z)$')\n",
    "        ax[0].set_title('before gaussian binning')\n",
    "        ax[0].set_xlim(0,2)\n",
    "\n",
    "        ax[0].vlines(zmid, 0, np.amax(nz), color='k', linestyle=':')\n",
    "        norm, e = scipy.integrate.quad(nz_int, z_arr[0], z_arr[-1], args=(sig_z, zmax, zmin, splined))\n",
    "        nz_bin_i = nz_int(z_arr, sig_z, zmax, zmin, splined)/norm\n",
    "        nz_bins.append(nz_bin_i)\n",
    "        splined_2 = interp1d(z_arr, nz_bin_i)\n",
    "        z_avgs.append((scipy.integrate.quad(avg_z_int, zmin, zmax, args=(splined_2))[0])/(scipy.integrate.quad(splined_2, zmin, zmax)[0]))\n",
    "        nz_avgs.append(norm*ngal)\n",
    "        ax[1].plot(z_arr, nz_bin_i, label='z bin %d'%(i+1))\n",
    "\n",
    "        ax[1].set_xlabel('$z$')\n",
    "        ax[1].set_title('after gaussian binning')\n",
    "        ax[1].set_xlim(0,2)\n",
    "        ax[1].vlines(z_avgs[-1], 0, np.amax(nz_bin_i), color='k', linestyle=':')\n",
    "\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.suptitle(title, fontsize=15)\n",
    "    plt.show()\n",
    "    return z_avgs, nz_avgs, nz_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d11feb85",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_arr = np.linspace(0., 5., 5001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8bbf44f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_199237/4171376416.py:35: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  z_avgs.append((scipy.integrate.quad(avg_z_int, zmin, zmax, args=(splined_2))[0])/(scipy.integrate.quad(splined_2, zmin, zmax)[0]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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mTAQGBmL58uUGZaQcx+G1116DSqXS9acC2t+ZFStWQCqVYvjw4VZr5TH2d9qjRw/4+/vjxo0bNv1eW/u5x8bGQiAQYPny5Vi6dKnR/kKCIMofCsIIohJhyjmQN7EYNmyYnkkC/xUcHAwAePjwoW6bBw8eAABq1apldJ/GDBW6dOmCd999Fw8ePMCQIUMQEBCAVq1a4eOPPzZ5AxcWFma0r8Hb2xsAjJpzVBT2nANTfPDBB4iLi8Phw4fRuXNn+Pv7o1u3bli4cCFycnLs0ueIFbWl98S/94qGP66pc8svv3//vsFrNWrUMLqNt7c3lEqlw9oyMjKgVCoRFBQET09Pm/U5yzqc/3u+fv260b9njuN0phll/6Z5jJ0nU39vK1euRHBwML744gtER0cjMjISI0aMwI4dO6zWy9/gR0REGH3d1HnZv38/oqKiMHToUMybNw8///wzVq5ciZUrV+rGE2se9DDG8N5776F+/fqYMGECvvvuOyxfvhwrV67UTUFgaj9RUVGYPXs2CgsLUVJSgh9++AHVqlUzuu7IkSMhlUrxyy+/gDEGQNt/l5qaiv79+yMwMNCi1rL4+/vrPpfH4f9+bfk7tfZzr1evHubNmweFQoHXXnsNISEhaNq0KSZOnIj//vvPlrdAEIQDkDEHQVQiZDKZ0eUajQaA9gmqsUZ9HmfMKfX111/j9ddfxx9//IG9e/fi8OHDOH78OObNm4d169YZPPUWCJ6MZz0+Pj7Yv38/Dh8+jD///BMJCQnYv38/9uzZg7lz5+LQoUOoW7euTfs09XlXNPzvV0VgzsjCHX6XzOlz1ufFn++wsDCLBgnGgm1bztOzzz6LxMRE/PXXX9i5cycSEhLw66+/4tdff8WAAQOwadMm28RbSX5+PgYNGoTMzEx88sknGDx4MGrVqgUPDw9wHIehQ4di3bp1umDHHL/99hu+/vprREREYMGCBWjTpg2Cg4MhFouhVCohlUpN7kej0ei9x5MnT2LQoEFG1w0KCsKAAQOwdu1a7Nu3D127dsXPP/8MABgzZowdZ8G52PK5v/feexg0aBC2bt2KPXv24NChQ1iwYAG++eYbLFiwAO+88045KiUIAqAgjCCqBPwT0NGjR5st/SlLeHg4rl69itu3b6NRo0YGr9++fdvktvXr18eHH36IDz/8EEVFRfjuu+/wwQcf4M0337T6+O5AeHg4kpKScPv2bTRs2NDgdXPnwBgcx6F9+/Y6p720tDRMmDAB69atw9SpU7Fhwwan6LYGU9r55WWf9kskEgDaG+PHUavVZu36baVatWq4deuWyd87Pgv0+GTdFUFgYCAkEgkePnyIgoICo9mwitDH/z0HBQWZnDbAmfj4+GDo0KEYOnQoAODYsWMYOHAgfv/9d2zfvh29evUyuz0/d9Xdu3eNvm5s+aFDh5CRkYEXX3wRM2fONHjdltLILVu2AAB+/PFHA+dCS/v54osvcOjQIcTFxSExMRHz589Hr169TE5x8cYbb2Dt2rVYunQp6tWrh127dqFu3bpGS64tkZWVhby8PKPZsDt37gCAyaycM4iIiMD48eMxfvx4qFQqrF+/HiNHjsSHH36I4cOHW+3EShCEfbj+sSJBEA7z3HPPAXh0M2INfJ+YscAgMzNT12dmCZlMhvfffx/h4eFIT09HWlqa1RqchVgstqsHy9w5uHLlitG+H1sICQnRzY104cIF3XI+6HFG35gpjL0nlUqF33//HYC+JTt/E21sbqYDBw6gpKTEYLm974E/5+vWrTN4LT09Hbt27dL1PFU0YrEYrVu3BgCsX7/e4PULFy7g3Llz8PLyssqK315q1KiBBg0a4NKlSzbPl+UMWrdujVdeeQWA/u+tKfjPiv/dKotarcbmzZsNlmdlZQEwXkKXmJiI06dPW63X3L7MPfg4deoUpk+fjoCAAKxZswYrVqwAYwzDhw83Ob9bhw4d0LhxY2zduhXz5s2DRqPB6NGjrdZqjb7du3cjMzMTUVFRFTY5s0gkwssvv4xnnnkGSqXSaA8dQRDOhYIwgqgCDBgwAI0aNcKaNWvw6aefGvR9MMZw+PBhPbMDvr9hzZo12Lt3r255SUkJ3n33XRQUFBgcZ+vWrTrjgrKcOnUKqamp8PLygp+fn/PemJVUq1YNqampNk+M+8YbbwAAvvnmG5w7d063vKCgAOPHj7eqFIpn8eLFuHXrlsHy7du3A9Dvl+Gfbl+9etUmvbbwzz//YNmyZXrLpk+fjjt37iAmJkYXDAFAx44dAQCrV6/WZXoA4NatW3j77beN7t/e9zB27FgIBAJ8++23OHnypG65UqnE+PHjUVhYiP79+5vsLypvxo8fD0A7QXTZLEpeXh7GjRsHxhhef/31ci8VnTZtGjQaDQYMGGD0YUBGRoZubip7uXPnDlasWAGFQqG3vKioCAcOHABgus+rLAMHDkRAQAD27NljELzOnj3b6N8FbxixefNmpKen65ZnZ2fj1VdfNRr4m4Lf15IlS/T+Zg8dOoQvv/zS6DaFhYV4+eWXUVJSgiVLlqBatWro0qULJkyYgLt37+LNN980ebzXX38dSqUS33//PcRiscFE37Ywc+ZMvb+5hw8f4oMPPgAAq+dos5UDBw5g7969BmXGt27dwuXLl8FxnMn+S4IgnIirbBkJgrAe3rK8rL3041y7do3Vrl2bAWAhISGsa9eubOjQoaxbt24sJCTE6Lwz3333nW5eobi4ODZ48GAWGRnJfH19dXOFlbUtf+eddxgAVr16dfb888+zoUOHsri4OCYUChkANn/+fL39w4x1uCmbcHss6sePH88AsNq1a7Nhw4axV199lc2bN8/kuSrL+++/zwAwsVjMunfvzgYNGsRCQ0NZzZo1WZ8+fay2qOctxRs1asQGDBjAXnrpJd0ymUzG/vnnH731Y2JidHP1xMfHs1dffZX98ccfjDHrPm9LFvVvvvkm4ziOtWzZkg0ZMoQ1btyYAWA+Pj56c6Lx8Nbxvr6+rE+fPqxLly7M09OTDRw40KQtuLn3wJjpz3/OnDkMABOJRKxr165s8ODBLCIiggFgdevWZSkpKXrr22Ohbw5zFvWMMfbaa68xAMzDw4P17t2bDRw4kAUHBzMArHXr1qygoEBvfXO/m5YwZ/X/0Ucf6f4+n376aTZw4ED24osvsmbNmjGhUMh8fX1t0vH453HmzBkGgMnlctaxY0c2dOhQ9sILL+jea4sWLVhRUZFV7+P333/XjQNt2rRhQ4YMYU2aNGESiUR3PufMmaO3zXPPPccAMD8/P9avXz/Wr18/5ufnx+rUqcNeeOEFq//2rl69yjw9PXV/f4MHD2YdOnRgHMfp/r4f/z188803GQA2cuRIveVFRUW6ORdXrVpl9L1mZ2frpvB48cUXrTo/jwOA1axZkz3//PNMLpezPn36sP79++vmT+zcuTMrKSnR28aSRb0xjP1+LViwgAFgwcHBrEePHmzYsGGsW7duTCqVMgBs/Pjxdr0ngiBsg4IwgqgEWHNTzpj25mD27Nns6aefZl5eXkwmk7HIyEjWvXt39v333+vNKcSzZcsW1qpVK+bh4cH8/f3ZCy+8wC5fvmz0xvfMmTPsvffeY8888wwLCQlhUqmU1apVi/Xp04ft3bvXYN8VFYTl5+ezcePGsYiICCYSiaw6V2VZunQpi4mJYVKplIWEhLCXX36Z3b9/36abnv/9739s1KhRrHHjxszPz4/J5XJWr149Nnr0aL0JVHmuX7/O+vXrxwIDA5lAINA7F84Iwg4cOMD+/PNP1qZNGyaXy5mvry974YUX2MWLF43ur7i4mE2ePJlFREQwiUTCoqOj2ezZs5lKpTJ5k2fuPTBm/vP/66+/WJcuXZivry+TSCSsTp067MMPP2SZmZkG61Z0EMYYY7/++itr27at7u+ocePGbM6cOQYTAjNWfkEYY4z9/fffbODAgaxatWpMLBazwMBAFhMTw8aNG8f+/vtvm3Q8/nnk5uay+fPns169erHIyEgmk8lYYGAga9GiBVuwYIFBsGnNe4mLi2Oenp7Mx8eHdevWjf3777+6+bcWL16st75CoWBTp05ldevWZVKplEVERLA33niDPXz40OaA4/Lly6xPnz4sJCSEyeVy1qxZM90cXI+/723btjEALCoqiuXl5Rns67///mNSqZT5+PgYjEM8/MTdu3btsukc8fCaioqK2EcffcQiIyOZRCJhtWrVYlOnTrXp98zWIOz69evs448/Zu3atWPh4eFMIpGw6tWrsy5durDff//dYI40giDKB44xG+ptCIIgCIIgbKBHjx7YtWsXjh07hlatWrlajsPcvXsXtWvXRkREBG7evGnWMZMgCMIU1BNGEARBEIRD3L9/H6mpqXrLNBoNFixYgF27dqFevXpo2bKli9Q5l88//xxqtRpjx46lAIwgCLshi3qCIAiCIBzi0KFDePnll9GsWTPUqlULxcXFuHDhApKSkiCXy/Hzzz9X6oDl6tWr+PLLL3Hr1i3s378fNWrU0Bn7EARB2ANlwgiCIAiCcIjmzZvrrN13796NXbt2Qa1W45VXXsGJEyf03DgrI8nJyfjll19w9OhRdOzYEdu2bYOXl5erZREEUYmhnjCCIAiCIAiCIIgKhDJhBEEQBEEQBEEQFQgFYQRBEARBEARBEBUIBWEEQRAEQRAEQRAVCAVhBEEQBEEQBEEQFQgFYQRBEARBEARBEBUIBWEEQRAEQRAEQRAVCAVhBEEQBEEQBEEQFQgFYYTdzJgxAxzH4eHDh07d76pVq9CgQQOIxWL4+fk5dd9PMhzHYcaMGRV+3Pj4eKsnNa0IjStWrADHcUhKSirX4xAEUXm5fv06unXrBl9fX3Ach61bt7paUqUkKSkJHMdhxYoVFX7suLg4NGnSxOJ6FaWRv2ciCB6RqwUQRFmuXLmC+Ph49OjRA5MnT4ZcLne1JIIgCOIJY8SIEbh16xbmzJkDPz8/tGjRAmvXrkVaWhomTJjgankEQVQBKAgj3IqEhARoNBosXLgQderUcbWcKkVhYSFEIvf+k68Ija+88goGDx4MqVRarschCKJyUlhYiKNHj2Lq1KkYN26cbvnatWtx4cIFCsJsoFatWigsLIRYLHa1FJNUlMaPP/4YkydPLtdjEJUL974jI5440tLSAMCpZYgKhYIyagBkMpmrJVikIjQKhUIIhcJyPw5BEJWT9PR0AM69DplCo9FAqVRWivHZHjiOc/v3VlEaRSKR2z8IJSoW6gkjHObhw4cYNGgQfHx8EBgYiHfeeQdFRUUG661evRrNmzeHh4cHAgICMHjwYNy9e1f3emRkJKZPnw4ACA4ONugP+uGHH9C4cWNIpVJUq1YNY8eORXZ2tt4x+BrwU6dOoWPHjpDL5fjoo48AAMXFxZg+fTrq1KkDqVSKiIgIfPjhhyguLrbqfX7//feIioqCh4cHWrZsiUOHDiEuLg5xcXG6dZRKJT755BM0b94cvr6+8PT0RIcOHXDgwAG9fSUkJIDjOCQkJOgtN1abnpKSgpEjR6JGjRqQSqUIDw/HCy+8oNfTdPLkSXTv3h1BQUHw8PBA7dq1MWrUKL19P34+b9++jbfeegv169eHh4cHAgMDMXDgQINeKb6H6vDhw5g4cSKCg4Ph6emJ//u//9PdrFjDzZs30b17d3h6eqJatWqYNWsWGGNmNfI19ImJiYiPj4efnx98fX0xcuRIKBQKg23HjRuHrVu3okmTJpBKpWjcuDF27txp9P2UfZ+RkZF4/vnn8c8//6Bly5aQyWSIiorCr7/+avA+/vvvP3Tq1AkeHh6oUaMGZs+ejeXLl1OfGUG4OdaMeTNmzECtWrUAAB988AE4jkNkZCTi4uKwbds23L59GxzH6ZbzWHt94cepNWvW6K5nj49RZdFoNJgxYwaqVasGuVyOzp0749KlS4iMjER8fLxuvczMTLz//vt46qmn4OXlBR8fH/Ts2RPnzp3T25+pnlhj16Tr169jwIABCAsLg0wmQ40aNTB48GDk5OTo1tmzZw/at28PPz8/eHl5oX79+rprLmD8mvbff/8hPj4eUVFRkMlkCAsLw6hRo5CRkaGnyZbx3xynTp1C27ZtddfGxYsX671uTCPfy3z//n3069cPXl5eCA4Oxvvvvw+1Wm2w7VdffYUlS5YgOjoaUqkUzzzzDE6cOGH0/ZTF2usWoP2MWrRoAZlMhujoaPz000/UZ1bJoZCccJhBgwYhMjISc+fOxbFjx/Dtt98iKytL7wZ2zpw5mDZtGgYNGoTRo0cjPT0dixYtQseOHXHmzBn4+fnhm2++wa+//ootW7bgxx9/hJeXF2JiYgBoB6+ZM2eia9euePPNN3H16lX8+OOPOHHiBA4fPqxXRpCRkYGePXti8ODBePnllxEaGgqNRoO+ffvin3/+wWuvvYaGDRvi/PnzWLBgAa5du2ax6frHH3/EuHHj0KFDB7z77rtISkpCv3794O/vjxo1aujWy83Nxc8//4whQ4ZgzJgxyMvLwy+//ILu3bvj+PHjiI2Ntfn8DhgwABcvXsT48eMRGRmJtLQ07NmzB3fu3NH93K1bNwQHB2Py5Mnw8/NDUlISNm/ebHa/J06cwJEjRzB48GDUqFEDSUlJ+PHHHxEXF4dLly4ZZA/Hjx8Pf39/TJ8+HUlJSfjmm28wbtw4/Pbbbxbfg1qtRo8ePdC6dWvMmzcPO3fuxPTp06FSqTBr1iyL2w8aNAi1a9fG3Llzcfr0afz8888ICQnBF198obfeP//8g82bN+Ott96Ct7c3vv32WwwYMAB37txBYGCg2WMkJibixRdfxKuvvooRI0Zg2bJliI+PR/PmzdG4cWMAwP3799G5c2dwHIcpU6bA09MTP//8M5U2EkQlwJoxr3///vDz88O7776LIUOGoFevXvDy8oKnpydycnJw7949LFiwAAB0hkO2Xl/279+PDRs2YNy4cQgKCtIL5h5nypQpmDdvHvr06YPu3bvj3Llz6N69u8GDzps3b2Lr1q0YOHAgateujdTUVPz000/o1KkTLl26hGrVqtl0rpRKJbp3747i4mKMHz8eYWFhuH//Pv766y9kZ2fD19cXFy9exPPPP4+YmBjMmjULUqkUiYmJOHz4sNl979mzBzdv3sTIkSMRFhaGixcvYsmSJbh48SKOHTtmEFRYO/4bIysrC7169cKgQYMwZMgQbNiwAW+++SYkEonBg8rHUavV6N69O1q1aoWvvvoKe/fuxfz58xEdHY0333xTb921a9ciLy8Pr7/+OjiOw7x589C/f3/cvHnTYpmjNdetM2fOoEePHggPD8fMmTOhVqsxa9YsBAcHWzwHhBvDCMJOpk+fzgCwvn376i1/6623GAB27tw5xhhjSUlJTCgUsjlz5uitd/78eSYSifSW8/tMT0/XLUtLS2MSiYR169aNqdVq3fLvvvuOAWDLli3TLevUqRMDwBYvXqx3rFWrVjGBQMAOHTqkt3zx4sUMADt8+LDJ91lcXMwCAwPZM888w0pKSnTLV6xYwQCwTp066ZapVCpWXFyst31WVhYLDQ1lo0aN0i07cOAAA8AOHDigt+6tW7cYALZ8+XLdtgDYl19+aVLfli1bGAB24sQJk+swxhgANn36dN3PCoXCYJ2jR48yAOzXX3/VLVu+fDkDwLp27co0Go1u+bvvvsuEQiHLzs42e9wRI0YwAGz8+PG6ZRqNhvXu3ZtJJBK9z/pxjfzvQ9lzxxhj//d//8cCAwMN3p9EImGJiYm6ZefOnWMA2KJFiwzez61bt3TLatWqxQCwgwcP6palpaUxqVTK3nvvPd2y8ePHM47j2JkzZ3TLMjIyWEBAgME+CYJwL6wd8/hx+PFxt3fv3qxWrVoG+7Dl+gKACQQCdvHiRYt6U1JSmEgkYv369dNbPmPGDAaAjRgxQresqKhI7/rIvw+pVMpmzZqlW2Zs/GPM8Jp05swZBoBt3LjRpL4FCxYYXK8f5/FrGmPGP4d169YZjMG2jP/G4O8H5s+fr1tWXFzMYmNjWUhICFMqlSY18tetsueOMcaaNWvGmjdvbvD+AgMDWWZmpm75H3/8wQCwP//80+D9lMXa61afPn2YXC5n9+/f1y27fv06E4lEBvskKg9Ujkg4zNixY/V+Hj9+PABg+/btAIDNmzdDo9Fg0KBBePjwoe4rLCwMdevWNSjVe5y9e/dCqVRiwoQJEAge/cqOGTMGPj4+2LZtm976UqkUI0eO1Fu2ceNGNGzYEA0aNNDT8OyzzwKAWQ0nT55ERkYGxowZo1fPPWzYMPj7++utKxQKIZFIAGifjmZmZkKlUqFFixY4ffq02fdpDA8PD0gkEiQkJCArK8voOnzfwl9//YWSkhKb9s1TUlKCjIwM1KlTB35+fka1vvbaa3pPKDt06AC1Wo3bt29bdbyyDe58CYZSqcTevXstbvvGG2/o/dyhQwdkZGQgNzdXb3nXrl0RHR2t+zkmJgY+Pj64efOmxWM0atQIHTp00P0cHByM+vXr6227c+dOtGnTRi+jGRAQgGHDhlncP0EQrsXWMc9abL2+dOrUCY0aNbK433379kGlUuGtt97SW85fY8silUp110e1Wo2MjAxdeaA9783X1xcAsGvXLpOlf/y1548//oBGo7F632U/h6KiIjx8+BCtW7cGAKNarR3/jSESifD666/rfpZIJHj99deRlpaGU6dOWdze2LGNXU9eeuklvfsB/lpizbXH0nVLrVZj79696Nevn15Gs06dOujZs6fF/RPuCwVhhMPUrVtX7+fo6GgIBAJdzfn169fBGEPdunURHBys93X58mWdGYcp+Jv8+vXr6y2XSCSIiooyCAKqV6+uC4R4rl+/josXLxocv169egBgVgO//8fdGkUikdEykpUrVyImJgYymQyBgYEIDg7Gtm3b9OrorUUqleKLL77Ajh07EBoaio4dO2LevHlISUnRrdOpUycMGDAAM2fORFBQEF544QUsX77cYq9bYWEhPvnkE0REREAqlSIoKAjBwcHIzs42qrVmzZp6P/MXHFPBYVkEAgGioqL0lvHn3po+KmuP/fh6/LrWaLRm29u3bxt17SQnT4Jwf2wd86zF1utL7dq1rdqvqWtPQECAwQNAjUaDBQsWoG7dunrv7b///rPrvdWuXRsTJ07Ezz//jKCgIHTv3h3ff/+93r5eeukltGvXDqNHj0ZoaCgGDx6MDRs2WAzIMjMz8c477yA0NBQeHh4IDg7WnRNnX3uqVasGT09PvWXWXntkMplBuZ+p64kjGi1de9LS0lBYWEjXnioI9YQRTufxem6NRgOO47Bjxw6jrnTWTuRrLWWfspXV8NRTT+Hrr782uk1ERIRTjr169WrEx8ejX79++OCDDxASEgKhUIi5c+fixo0buvVMNdKWbfjlmTBhAvr06YOtW7di165dmDZtGubOnYv9+/ejWbNm4DgOmzZtwrFjx/Dnn39i165dGDVqFObPn49jx46ZPL/jx4/H8uXLMWHCBLRp00Y3KengwYONXkRNOQqyx8w1ygNrj+2IRle+P4Igyh9bxzxrsfX6Yuwa5SifffYZpk2bhlGjRuHTTz9FQEAABAIBJkyYoPfebLn2zJ8/H/Hx8fjjjz+we/duvP3227re7xo1asDDwwMHDx7EgQMHsG3bNuzcuRO//fYbnn32WezevdvkmDpo0CAcOXIEH3zwAWJjY+Hl5QWNRoMePXq41bXHFhdduvYQ9kBBGOEw169f13uyl5iYCI1Go8sSRUdHgzGG2rVr655A2QLvVHX16lW9bIpSqcStW7fQtWtXi/uIjo7GuXPn0KVLF5udhPjjJyYmonPnzrrlKpUKSUlJOvMQANi0aROioqKwefNmvePwro88/FOyx90dTZX2RUdH47333sN7772H69evIzY2FvPnz8fq1at167Ru3RqtW7fGnDlzsHbtWgwbNgzr16/H6NGjje5z06ZNGDFiBObPn69bVlRUZKDJGWg0Gty8eVPv87927RoAmG1Kdzdq1aqFxMREg+XGlhEE4V44OuaZunY4cn0xR9lrT9lrbEZGhkGGZdOmTejcuTN++eUXveXZ2dkICgrS/Wzrteepp57CU089hY8//hhHjhxBu3btsHjxYsyePRuAtsqhS5cu6NKlC77++mt89tlnmDp1Kg4cOGD02pyVlYV9+/Zh5syZ+OSTT3TLr1+/bul02MWDBw9QUFCglw2rbNeekJAQyGQyuvZUQagckXCY77//Xu/nRYsWAYCuVrl///4QCoWYOXOmwZMdxpiBLe3jdO3aFRKJBN9++63e9r/88gtycnLQu3dvixoHDRqE+/fvY+nSpQavFRYWoqCgwOS2LVq0QGBgIJYuXQqVSqVbvmbNGoMLIf9Eq6zOf//9F0ePHtVbr1atWhAKhTh48KDe8h9++EHvZ4VCYeCCFR0dDW9vb125YVZWlsF55XuWzJUkCoVCg+0WLVpk9ImoM/juu+903zPG8N1330EsFqNLly7lcrzyoHv37jh69CjOnj2rW5aZmYk1a9a4ThRBEFbh6JjHOyQ+jiPXF3N06dIFIpEIP/74o97ysmMpj7H3tnHjRty/f19vGd97VPbao1arsWTJEr31cnNz9a53gDYgEwgEuutKZmamgQ5L1x5j10gA+Oabb4yu7ygqlQo//fST7melUomffvoJwcHBaN68ebkc09kIhUJ07doVW7duxYMHD3TLExMTsWPHDhcqIxyFMmGEw9y6dQt9+/ZFjx49cPToUaxevRpDhw5F06ZNAWgH/dmzZ2PKlCk6a3dvb2/cunULW7ZswWuvvYb333/f5P6Dg4MxZcoUzJw5Ez169EDfvn1x9epV/PDDD3jmmWfw8ssvW9T4yiuvYMOGDXjjjTdw4MABtGvXDmq1GleuXMGGDRuwa9cutGjRwui2EokEM2bMwPjx4/Hss89i0KBBSEpKwooVKxAdHa335PP555/H5s2b8X//93/o3bs3bt26hcWLF6NRo0bIz8/Xrefr64uBAwdi0aJF4DgO0dHR+Ouvvwx6B65du4YuXbpg0KBBaNSoEUQiEbZs2YLU1FQMHjwYgLYH7YcffsD//d//ITo6Gnl5eVi6dCl8fHzQq1cvk+fk+eefx6pVq+Dr64tGjRrh6NGj2Lt3r0Urd3uQyWTYuXMnRowYgVatWmHHjh3Ytm0bPvroo0plsfvhhx9i9erVeO655zB+/HidRX3NmjWRmZlJ87UQhBvj6JjXvHlz/Pbbb5g4cSKeeeYZeHl5oU+fPg5dX8wRGhqKd955B/Pnz9ddY8+dO4cdO3YgKCjI4Noza9YsjBw5Em3btsX58+exZs0ag17cxo0bo3Xr1pgyZQoyMzMREBCA9evXGwRc+/fvx7hx4zBw4EDUq1cPKpUKq1atglAoxIABAwAAs2bNwsGDB9G7d2/UqlULaWlp+OGHH1CjRg20b9/e6Hvy8fHR9TaXlJSgevXq2L17N27dumXz+bGGatWq4YsvvkBSUhLq1auH3377DWfPnsWSJUssWse7EzNmzMDu3bvRrl07vPnmm1Cr1fjuu+/QpEkTvYeCROWCgjDCYX777Td88sknmDx5MkQiEcaNG4cvv/xSb53JkyejXr16WLBgAWbOnAlAWyffrVs39O3b1+IxZsyYgeDgYHz33Xd49913ERAQgNdeew2fffaZVQOpQCDA1q1bsWDBAt1cZHK5HFFRUXjnnXcslkmOGzcOjDHMnz8f77//Ppo2bYr//e9/ePvttyGTyXTrxcfHIyUlBT/99BN27dqFRo0aYfXq1di4caPBxMyLFi1CSUkJFi9eDKlUikGDBuHLL79EkyZNdOtERERgyJAh2LdvH1atWgWRSIQGDRpgw4YNugthp06dcPz4caxfvx6pqanw9fVFy5YtsWbNGrMN4AsXLoRQKMSaNWtQVFSEdu3aYe/evejevbvF82krQqEQO3fuxJtvvokPPvgA3t7emD59ul45SmUgIiICBw4cwNtvv43PPvsMwcHBGDt2LDw9PQ1+FwiCcC8cHfPeeustnD17FsuXL8eCBQtQq1Yt9OnTx+Hrizm++OILyOVyLF26FHv37kWbNm2we/dutG/fXm+8+eijj1BQUIC1a9fit99+w9NPP41t27Zh8uTJBvtcs2YNXn/9dXz++efw8/PDq6++is6dO+O5557TrdO0aVN0794df/75J+7fvw+5XI6mTZtix44dOifDvn37IikpCcuWLcPDhw8RFBSETp06YebMmTp3RWOsXbsW48ePx/fffw/GGLp164YdO3bYPJeZNfj7+2PlypUYP348li5ditDQUHz33XcYM2aM049VnjRv3hw7duzA+++/j2nTpiEiIgKzZs3C5cuXceXKFVfLI+yEY9T5RxB2odFoEBwcjP79+xstQyGeHCZMmICffvoJ+fn5NjVzEwRB2Ep2djb8/f0xe/ZsTJ061dVyCBfSr18/XLx4sdx66ojyhXrCCMIKioqKDGrYf/31V2RmZiIuLs41ogiXUFhYqPdzRkYGVq1ahfbt21MARhCEU3l8vAEe9U/RtefJ4vHfhevXr2P79u30e1CJoUwYQVhBQkIC3n33XQwcOBCBgYE4ffo0fvnlFzRs2BCnTp0ymJeMqLrExsYiLi4ODRs2RGpqKn755Rc8ePAA+/btQ8eOHV0tjyCIKsSKFSuwYsUK9OrVC15eXvjnn3+wbt06dOvWDbt27XK1PKICCQ8PR3x8vG5+1B9//BHFxcU4c+aMwXytROWAesIIwgoiIyMRERGBb7/9VtfMPHz4cHz++ecUgD1h9OrVC5s2bcKSJUvAcRyefvpp/PLLLxSAEQThdGJiYiASiTBv3jzk5ubqzDp4i3jiyaFHjx5Yt24dUlJSIJVK0aZNG3z22WcUgFViKBNGEARBEARBEARRgVBPGEEQBEEQBEEQRAVCQRhBEARBEARBEEQF8sT3hGk0Gjx48ADe3t400SpBEEQFwxhDXl4eqlWrBoGAngvy0LWJIAjCNVTUdemJD8IePHiAiIgIV8sgCIJ4orl79y5q1KjhahluA12bCIIgXEt5X5ee+CDM29sbgPZE+/j4uFgNQRDEk0Vubi4iIiJ0YzGhha5NBEEQrqGirktPfBDGl3n4+PjQhY4gCMJFUMmdPnRtIgiCcC3lfV2iAnyCIAiCIAiCIIgKhIIwgiAIgiAIgiCICoSCMIIgCIIgCIIgiArkie8JIwiCIAiCIAhXwBiDSqWCWq12tZQnCrFYDKFQ6FINFIQRBEEQBEEQRAWjVCqRnJwMhULhailPHBzHoUaNGvDy8nKZBgrCCIIgCIIgCKIC0Wg0uHXrFoRCIapVqwaJREIusRUEYwzp6em4d+8e6tat67KMGAVhBEEQBEEQBFGBKJVKaDQaREREQC6Xu1rOE0dwcDCSkpJQUlLisiCMjDkIgiAIgiAIwgUIBHQr7grcIetInzxBEARBEARBEEQFQkEYQRAEQRAEQRBEBUJBGEEQBEEQBEEQTofjOGzdutXk60lJSeA4DmfPnq0wTe4CBWEEQRAEQRAEQVQ4ERERSE5ORpMmTRzaz5w5c9C2bVvI5XL4+fk5R1w5Q0EYQRAEQRAEQRAVjlAoRFhYGEQixwzblUolBg4ciDfffNNJysofsqgn3ILk3GysP78P/z44h2RFEhTqPGhQDAEk8BB6o36BL7qqI9Hp+SEIq13d1XIJgiAIB1GpNfhy91WcTMrCkJY18WLzGq6WRBAugzGGwhK1S47tIRZa5RaYlJSE2rVrGyzv1KkTEhISTG6XnJyMnj17IiEhAeHh4Zg3bx5efPFFvX2eOXMGsbGxSEhIQOfOnbF3715MmjQJly5dQmxsLJYvX4769eubPMbMmTMBACtWrLD4PtwFCsKqGAqFAnXr1gUAXL9+3aVzT1jSotFo8NOJnVh3ZR0yNefBCcoMPmWmbCgC0H+9CpFpQNb3v+B8SCRU3Xuj9egh8A8NdJqeioS0EATxpPH4WLPw79v46e+bAIBTt7NQ3c8DbaKtH9MJoipRWKJGo092ueTYl2Z1h1xiOSTgSwd5UlJS0LVrV3Ts2NHsdtOmTcPnn3+OhQsXYtWqVRg8eDDOnz+Phg0bmtxm6tSpmD9/PoKDg/HGG29g1KhROHz4sPVvqhJAQVgVgzGGBw8e6L53Vy3LT+3Fd+e+hlJ4FwDACQChKhjVZI1Rx68eaviEwFMsR0FJEe7k3oVf/lLdtjXSkoBV3yNp3VIc7NALrae8jdCa4Q7pqWhIC0EQTxplx5p7mQVY/k8SAKB2kCduPSzAt/uuUxBGEG4MXzoIAEVFRejXrx/atGmDGTNmmN1u4MCBGD16NADg008/xZ49e7Bo0SL88MMPJreZM2cOOnXqBACYPHkyevfujaKiIshkMue8GTeAgrAqhkwmw5kzZ3Tfu5uWu9kZePWvj5CsPgIIAaaRoK7Hs3irxTB0jW5qNB2eWZSJq9O0QZjP94tx7uBpyHb/hfCsB6h3YCseHNqOUy+8jC4fvw2ph9QmPa6CtBAE8aRRdqzZ+l86lGoNWkcF4OtBsWj/xX4cvZmBu5kKRARQNp548vAQC3FpVneXHdtWRo0ahby8POzZs8fihNNt2rQx+NmSG2JMTIzu+/Bw7YP2tLQ01KxZ02at7goFYVUMoVCI2NhYV8sAYKjl9wuHMfPfKWCiLDDGIUraBQu6TUZ0YKjV+wyMCEevme9C/ck7OLrmDxQu/Qk10m+j9u/L8M/+nQj59FM81bWtVXpcCWkhCOJJgx9rNBqGt3bsBwC83LoWqvl5oEWtABxPysSBq2kY3ibStUIJwgVwHGdVSaA7MHv2bOzatQvHjx+Ht7d3uRxDLBbrvucf0Gs0mnI5lqsgd0SiQpi5fzWmnxwLJsqCQB2E6c1/xP+GLLApACuLUChA++H/h2cPbMO9Me8hT+qJalkPgPFj8Ofkz6FSuaa5lSAIgjDPxQe5eJBTBE+JEF0baq8BcQ2CAQD/XH/oSmkEQVjg999/x6xZs7BhwwZER0dbtc2xY8cMfjbXD/akUDlCbsJqSkpKsGbNGgDAsGHD9J4kuErLqrO7kPrUeXAiDv5ojg0DFyDM298pxxCKhHjuvdHIGNwXh8d9gLqXj6PO1pXYffYk2ixfDP/wIAM9gPucG9JCEMSTAj/W7L2UCsYaoF2dUMhKy6Ba1AoAAJy9mw3GmFVObQRBVCwXLlzA8OHDMWnSJDRu3BgpKSkAAIlEgoCAAJPbbdy4ES1atED79u2xZs0aHD9+HL/88otTtd25cweZmZm4c+cO1Gq1rtyxTp068PLycuqxnAVlwqoYSqUSI0eOxMiRI6FUKt1Cy/6F68HUDHVlPbH/5V+cFoCVJbB6CJ7/fTkejJmIIqEEtZMu4twLA3Dt5AUDPe50bkgLQRBPCvxYs+bLyWBqFeLqh+hee6q6L4QCDml5xUjOKXKhSoIgTHHy5EkoFArMnj0b4eHhuq/+/fub3W7mzJlYv349YmJi8Ouvv2LdunVo1KiRU7V98sknaNasGaZPn478/Hw0a9YMzZo1w8mTJ516HGdCmbAqhlAoRK9evXTfu5LpB1bCK0b79KGp9wCsGTSzXJ9uCgQCdHlvDC62ehoZ74xHaG4ackYOx6k5X6J53y5udW5IC0EQTxpCoRDde/TEoevp4AQCdKj7qFLBQyJE/VBvXErOxX/3slHNz8OFSgmCMEZ8fDzi4+Nt2oZ3XX7rrbeMvh4ZGannzBwXF2fg1BwbG2vRvXnFihWVao4wgIKwKodMJsO2bdtcLQPfH/sTOx8uRuTESDSS98WaFx0LwHRbWuGg3rh9c6Rv3oTT8a+hZvINFE+egMO5s9Hu5Rfc4twA7vM5Ae6lhSCIqotMJsPM71fhpSXHEOojRQ1//UCrYbgPLiXn4lpqPno0cZFIgiCICoLKEQmn8/fNi/jx0ixwHEM1YUesG/Bphdf3B9eqhvb/24DEOs0g1ajgM+cjHFi8tkI1EARBEPqcupMFAGhey9/gulA3VFs5kZiWX+G6CIIgKhoKwginkpafjXcS3gYnLIKHJhqbB35tcf4IS3Dgyv5gNZ7ecnTftByJTdpAxDQI/mY2Di5Z55AWgiAIwn5O39YGYU/XNOwNrhOsDcKuUxBGEMQTAAVhVQyFQoG6deuibt26UCgUFXpsxhiGbZkMtTANnMoPS56dj9gmTVyihUcik6LXuiW42qwTlBo1XhkXj4iwai7Tw+PKz8mdtRAEUXUpKCjA6on9cH/JGDQMkRq8zmfCbqTnQ62xovacIAiiEkM9YVUMxhgSExN131cknyasRYrmMBjjMKXFbNQNCnOZlrIIxSL0XrkImweOwp3r14DUZJz4Yy86DenrMk2u/JzcWQtBEFWXOxkFKM58AABoGGY4wWsNfznEQg5KlQYpuUWoTuYcBEFUYSgIq2LIZDL8888/uu8rivMpSdh46xtACDT3GYihTTtBrVa7RIsxxBIx+q79CV90S0X1u1fhMXsartSsgQbtnnaJHld9Tu6uhSCIqsuNTCVCh81DdLAnfL08DV4XCjhU8/PA7QwF7mUqKAgjCKJKQ0FYFUMoFKJdu3YVekyNRoM3d34MCIsgVdXG4j6TXKbFHB6ecozbtRUJ/YbC+95VZIwfiwcbfkO1OjUrXIs7nRt30kIQRNXlWloBZDUaoV2LGianw4jwl+N2hgJ3swrRqoL1EQRBVCTUE0Y4zBeHNiGHOwfGhJgXNwceYomrJZlE7iVH69U/I8UvDAGKbFyMH42czBxXyyIIgqjyXErOA6C1ojcFb1t/L4v6UwmCqNpQEFbFUKlU2LhxIzZu3AiVSlXux0vJy8K6G98CAJ726Y9no58qFy1caauSIx1LvJ59hw6g5o/fI1fqhRoP7+LQ8DegLin/c2VMS0V9TpVFC0EQVZdL97JQcOUfPDhzwORYExEgBwDczSysSGkEQZQTHMdh69atJl9PSkoCx3E4e/ZshWlyFygIq2IUFxdj0KBBGDRoEIqLi8v9eON2fA4mzIFAFYxFvd4vFy2cLb70ZiirJ6xeLci+/AbFAhGiE89i+wefOuUY9mipiM+psmghiKrCjBkzwHGc3leDBg1cLctl5BSW4F5GLh7+8Tk+Hv+qybGGz4TdpUwYQTwRREREIDk5GU2a2D9De1JSEl599VXUrl0bHh4eiI6OxvTp06FUKp2o1PlQT1gVQyAQoFOnTrrvy5ODty7jimInOA54vcn78JXJy1+LA/HY43qadmuH3a9NRMTieaizcwMOrohBx/gBztFpoxZX4k5aCKIq0bhxY+zdu1f3s0j05F5yryTnAhwHn9pN0aymn8mxpoa/9jpyL5OCMIJ4EhAKhQgLC3NoH1euXIFGo8FPP/2EOnXq4MKFCxgzZgwKCgrw1VdfOUmp86E7riqGh4cHEhISkJCQAA+P8nWW+vjgF+A4DfwQg7da9XKpFmswpqfbhJG41q4nAEA+fzaunbzgMi2uwp20EERVQiQSISwsTPcVFBTkakku43JyLgRiKQZOX2p2rOEdEVPziqGhucIIwq3gSwcf/4qLizO7XXJyMnr27AkPDw9ERUVh06ZNBvvkyxETEhLAcRz27duHFi1aQC6Xo23btrh69arJ/ffo0QPLly9Ht27dEBUVhb59++L999/H5s2bnfG2yw0Kwgi7WHcuAVk4BcY4zOowydVyHKLnd5/hdvW68Cwpwr3xb6MgO8/VkgiCqAJcv34d1apVQ1RUFIYNG4Y7d+64WpLLuJqaDwBoEGbalAMAgrwk4DhArWHIKHDvUiKCcCqMAcoC13xZOUcoXzrIf505cwaBgYHo2LGj2e2mTZuGAQMG4Ny5cxg2bBgGDx6My5cvm91m6tSpmD9/Pk6ePAmRSIRRo0ZZfSoBICcnBwEBATZtU9E8ubURhN1oNBosOL0AEAC1JJ3ROSrG1ZIcQuIhQ7Off8CN/gMQnpWM/W+8jz7rf3K1LIIgKjGtWrXCihUrUL9+fSQnJ2PmzJno0KEDLly4AG9vw4mKi4uL9fqkcnNzK1JuuXMjXRuE1QnxMrueSChAkJcU6XnFSM0tQrC3tCLkEYTrKVEAn1VzzbE/egBIDOfue5yypYNFRUXo168f2rRpgxkzZpjdbuDAgRg9ejQA4NNPP8WePXuwaNEi/PDDDya3mTNnjq5VYvLkyejduzeKioqsms80MTERixYtcutSRIAyYVWOwsJCxMbGIjY2FoWF5eMu9ePxbSgU3ATTiDGvy/sm16sILbZgTk9o7RoQTf8ManCoc/Yg9n+70mVaKhp30kIQVYWePXti4MCBiImJQffu3bF9+3ZkZ2djw4YNRtefO3cufH19dV8REREVrLh8uZleAE1JMSa/3MPiWBPqow280vKKKkoeQRA2MmrUKOTl5WHt2rUW+8nbtGlj8LOlTFhMzKMH/OHh4QCAtLQ0i7ru37+PHj16YODAgRgzZozF9V0JZcKqGBqNBufOndN9Xx77X3ZxCSACGnp2R+NQ0zcK5aHFyoy5XXpa9uuC//09GHV3rIPfkgW40aElops1tP+ADmipSNxJC0FUVfz8/FCvXj0kJiYafX3KlCmYOHGi7ufc3NwqE4jlKErwML8YYAxXL2n7bs2NNaHeMlxALlJzy9GtNekf4Mh3gFAMdPwACK/cFR1EFUAs12akXHVsG5g9ezZ27dqF48ePG83sO0WSWKz7nuO0rmyW7lEePHiAzp07o23btliyZEm56HImFIRVMWQyGXbv3q373tn8dHInlKIkMI0Yc7u87VIttmKNnl7zPsKe/04h8v41JL79Lqrv3gqZh/O1u9O5cSctBFFVyc/Px40bN/DKK68YfV0qlUIqrZqldzceaksRwwK88IsVY02Ij/a11NzyyYSpr+3B/jkDAI0az9YWQnjjADB6DxBSPg/dCMIqOM6qkkBX8/vvv2PWrFnYsWMHoqOjrdrm2LFjGD58uN7PzZo1c6qu+/fvo3PnzmjevDmWL19eKdyeKQirYgiFQjz33HPlsm/GGJZf/AkQAPXl3VAnMLxCtPBPQBzFGj0isQixi7/F3QH9UTP9Nna/Ox19F3/hlOPbqqWicCctBFFVeP/999GnTx/UqlULDx48wPTp0yEUCjFkyBBXS6twbqYXAADqhPriuedaW1yfL0csl0xYcT6Ef72D52pzQL3ngcIs4O6/wLb3gPht2hthgiCMcuHCBQwfPhyTJk1C48aNkZKSAgCQSCRmTTA2btyIFi1aoH379lizZg2OHz+OX375xWm67t+/j7i4ONSqVQtfffUV0tPTda85an9fnrh/mEi4DavO7ivtBRPh07hxrhFRAdfH8Lq1UDTxIwBAdMKfOPXHvvI/KEEQVYp79+5hyJAhqF+/PgYNGoTAwEAcO3YMwcHBrpZW4fCmHNHB5k05eEJLM2Fp5ZEJO7MayL0P+NUEXlwODPgFEHkAtw8Dt484/3gEUYU4efIkFAoFZs+ejfDwcN1X//79zW43c+ZMrF+/HjExMfj111+xbt06NGrUyGm69uzZg8TEROzbtw81atTQ0+bOUCasiqFSqbBr1y4AQPfu3Z06OeiSc8sBDoiUxqFRaA2XarEHW/R0jB+ALXsPoMHJfVB8Oh25HVrAJ8DXJVrKG3fSQhBVhfXr17tagttwszQIq+UvxbZt2wCYH2tCSh0RU51tzKFRA8d+gErDsIs9C+w5oNURMwg4vRI48TMQ2c65xySIKkR8fDzi4+Nt2oaVNvO/9dZbRl+PjIzUrQMAcXFxej8DQGxsrMEyR3W5A3S3VcUoLi7G888/D0Dbg+CsG+q9ieeRw/0Hxjh81O51l2qxF1v1PLtwNs51P4vg/Azsf3sq+q3+zmVayhN30kIQRNXjRmk5YoSvGD3iLI81QV7aICwj38nzhN05BmTfRrHQB89P/BbAt1odzeO1QdjVHYBSAUhsMykgCIKwB7rbqmIIBAK0aNFC972z+PrfnwEAQYJmaFurgUu12IutenwD/SCfNgOYNB71T+7DoVV/oMMrL7hES3niTloIgqhaqNQa3M7QBmHRoT5WjTUBnhIAQEaBEowxp/UF4+IW7bEb9EKLFmce6ajWDPCNAHLuAjcTgAa9nHM8giAIM1AQVsXw8PDAiRMnnLrPW5mpuKM8CE4AvBYT71ItjmCPnhYvdMWf255HnYN/Qfj1Z8js2hYB4Y73dLjTuXEnLQRBVC3uZRWiRM0gEwsQFepv1VgT6KUNwpQqDQqUanhJnXCrwhhw+U8AgEezF3HixGP21fV7AseXAIl7KAgjCKJCoMfehEU+PfgLOIEKElVNDI7p5BINnAPzgzlK169nINU3FP6FuTj8zhTXCSEIgqhk3M5UAABqBsghEFiX0ZJLRPAQCwEAmc4qSUy/AuSnaE04ooxcx2p31P5P5hwEQVQQFIQRZikqUeJk5nYAQO9aLz2R5WoeXp7wnTUHanCo899hHFvzh6slEQRBVAru6IIw2+Y/4ksSHxY4yab+1iHt/zVbASIj87HVbKv9P/0KUPDQOce0gFqjxqF7h5BwNwEl6pIKOSZBEO6D291Rf//994iMjIRMJkOrVq1w/Phxs+t/8803qF+/Pjw8PBAREYF3330XRUXlM8FjZaCwsBDt2rVDu3btUFhY6PD+vvv3TzBhDqD2xPvtB7pUCwCHLOod0dOseztc79QHAKCe/znyMrLtF+KgFmfjTloIgqha3C2TCbNlrOFLEp2WCbv1t/b/2h2N6/AMBIJLJ2u+c9Q5xzRDiaYEb+17C2/tewvj949H/M54KEoU5X5cgiDcB7fqCfvtt98wceJELF68GK1atcI333yD7t274+rVqwgJCTFYf+3atZg8eTKWLVuGtm3b4tq1a4iPjwfHcfj6669d8A5cj0ajwZEjR3TfO8rv1zcBAqCxT1f4yDxcqsVRHNXT9YuPcfq5IwjJe4iE96ajz4qFLtPiTNxJC0EQVQvelKNmgIdNYw2fCcsscEIQptFo5wEDgMiOpnVEtATSLwP3TwMN+zh+XDMs+W8Jjjw4AmGJEOfGnMMFXEDtfbUx+9nZ5XpcgiDcB7cKwr7++muMGTMGI0eOBAAsXrwY27Ztw7JlyzB58mSD9Y8cOYJ27dph6NChALRzDQwZMgT//vtvhep2J6RSKbZs2aL73hH+vXsdedxFcAAmtnrFpVqcgaN6vP28IZ00FSmL1iJd0xp/H7yJTh2jXKLFmbiTFoIgqhZ3MrWZplqBnjaNNYGe2tedUo6YeRMozAJEMqBaLKSMM64jvKn2/5T/HD+mGbKLsrHy4koAwOxOs3F52WV8d+Y7/Hn7T4zJHYNaPrXK9fgEQbgHbhOEKZVKnDp1ClOmPDI+EAgE6Nq1K44eNV4a0LZtW6xevRrHjx9Hy5YtcfPmTWzfvh2vvGI6YCguLkZx8aNBPTc313lvwg0QiUTo16+fU/a18N9V4DgGb00jtIyo71ItzsAZelr26YyN6y6hUB6Koxtv4pmWEZDLxC7R4izcSQtBEFUHxpiuHDEiQG7TWOPUcsQHWjt6hMUAQjFEgHEdfBD24KzWTdFZ1viPsSVxCwpVhWgQ0AC96/TG83Wfx82Im/jn/j9Yf2U9JrWcVC7HJQjCvXCbnrCHDx9CrVYjNDRUb3loaChSUlKMbjN06FDMmjUL7du3h1gsRnR0NOLi4vDRRx+ZPM7cuXPh6+ur+4qIiHDq+6gqFKmUuJC7FwDQN3qAi9W4J94lAiz7pXyfmBIEQVRWshQlyC9WAQBq+NtWzu7UckQ+CKvWzPx6oY0BTggoHgJ5yY4f1wQ7bu0AAAysN1A3B9qQBkMAANtubiOTDqJKwXEctm7davL1pKQkcByHs2fPVpgmd8FtgjB7SEhIwGeffYYffvgBp0+fxubNm7Ft2zZ8+umnJreZMmUKcnJydF93796tQMXlj1qtRkJCAhISEqBWq+3ez77Df4IJ88AxIca37utSLc6iPPSozmfjyvVMt9BiL+6khSCIqgPfDxbmI4NMLLRprAnUuSM6PwgzqUPsAQTV036fcsHx4xrhTu4dXM68DCEnxHO1ntNpKblWAn+JP7KKs3A67XS5HJsg3JGIiAgkJyejSZMmDu2nb9++qFmzJmQyGcLDw/HKK6/gwYMHTlJZPrhNOWJQUBCEQiFSU1P1lqempiIsLMzoNtOmTcMrr7yC0aNHAwCeeuopFBQU4LXXXsPUqVON2qlLpdIq3fdSVFSEzp07AwDy8/Ph6WmbLTBPzKHXgIhq8GBqeEpkLtUCPDJFdGS6MGfqAQANADE4/P7LeUz5rINN9v3O1uII7qSFIIiqg86ePlAOwLaxRleO6GhPmEYNJJ/Tfl8ahJnVEVxfa87x8BpQr5tjxzbCoftaq/xnwp6Bv8wfBQUFOi3v734fOx/sxMF7B9EqvJXTj00Q7ohQKDR5n28LnTt3xkcffYTw8HDcv38f77//Pl588UWdCY874jaZMIlEgubNm2Pfvn26ZRqNBvv27UObNm2MbqNQKAxufIVC7QSPjLlwdl8XwnEcGjVqhEaNGunKHGwlI/Ve2R26VIszcbae2s8EQg0Gv2w1Nv953aVaHMGdtBAEUXUoa08P2DbWBJQaczjcE5aVBJQUaCdpDqprWQefCXt4zbHjmuDfZK1xWJtqbQy0tK/RHgBw8N7Bcjk2QTgKXzr4+FdcXJzZ7ZKTk9GzZ094eHggKioKmzZtMtgnX46YkJAAjuOwb98+tGjRAnK5HG3btsXVq1fNHuPdd99F69atUatWLbRt2xaTJ0/GsWPHUFLivuW9bpMJA4CJEydixIgRaNGiBVq2bIlvvvkGBQUFOrfE4cOHo3r16pg7dy4AoE+fPvj666/RrFkztGrVComJiZg2bRr69OmjC8aeNORyOS5evOjQPhL3r0C10u85zv443RlatBrKXCQdiBGcpYenVi0fJOYqIbyah5u77yE7rhb8fK3LGjpbiyO4kxaCIKoOdx4LwmwZa/w8tIZHOYUO3kCll964BdUFBELLOnRBmG0P1qxBrVHjZMpJAECrsFYGWvKUeRCcECApNwlpijSEyA2n5iGqLowxFKpcM1enh8jDqoewfOkgT0pKCrp27YqOHTua3W7atGn4/PPPsXDhQqxatQqDBw/G+fPn0bBhQ5PbTJ06FfPnz0dwcDDeeOMNjBo1CocPH7bq/WRmZmLNmjVo27YtxGLbzdMqCrcKwl566SWkp6fjk08+QUpKCmJjY7Fz506dWcedO3f0Ml8ff/wxOI7Dxx9/jPv37yM4OBh9+vTBnDlzXPUWqgQBN/5wtYRKAsPo15ri+0n/wEvFYdlP5zDxQyohIQiCAIC7pfb0fBBmC76lQViBUo0StQZioZ0PBNOvaP8PbmDd+qXZsvLIhF3Nuoq8kjx4i73RIMBQj7fEG/X86+FK5hWcSTuD7pHdna7BGKrMTKTN+xLKW7fg80Jf+A8ZQlURLqBQVYhWa11zD/Hv0H8hF1v+Oy1bOlhUVIR+/fqhTZs2mDFjhtntBg4cqGsd+vTTT7Fnzx4sWrQIP/zwg8lt5syZg06dOgEAJk+ejN69e6OoqAgymemH3ZMmTcJ3330HhUKB1q1b46+//rL4nlyJ25Qj8owbNw63b99GcXEx/v33X7Rq9egXMiEhAStWrND9LBKJMH36dCQmJqKwsBB37tzB999/Dz8/v4oXXkW4f+MC6qrKpwyjSsBxeiWaXp4SNHpeO6eL+GY+Tp4z7uRJEATxpPEgRxuEVfOzzRkRAHw8Hj29digbxmfCgq2cZiWwjvZ/xUNAYbvpkjkuZmgzXk2CmkAoMF6t0yxE27d2Ju2MU49tClZSgrujxyBn61YUnjuH1FmfInPFygo5NlG5GTVqFPLy8rB27VqLPfGPtxW1adMGly9fNrtNTEyM7vvw8HAAQFpamtltPvjgA5w5cwa7d++GUCjE8OHD3bo9ya0yYYTjFBYWom9frZvh//73P3h42Hbxu3twFaq7iRZnU156+vSIxmeH7sM3Q4Wdq67g6adCLA5I7nRu3EkLQRBVA42GITm7CABQzU/75NqWsUYo4OAjEyG3SIVsRQmCvOw01HpoGISZ1SH1AnxqALn3tCWJNZ2Xmbj4UBuENQpsZFLL0yFPY92VdTidWjEOiZmr16Do0iUIfX0h7vYchn39NfD2eOx8tjN8atGk0RWJh8gD/w7912XHtoXZs2dj165dOH78OLy9vctFU9kyQj4zq9FozG4TFBSEoKAg1KtXDw0bNkRERASOHTtm0lvC1VAQVsXQaDTYu3ev7ntbYBoNwu9qU7f58moW1i5fLeVBeep58dWnsGPeafjna7Bx6zW81N986Ys7nRt30kIQRNXgYUExlGoNBBwQ6qMNwmwda3zlYuQWqZBTaKc5h0YDpJdWdpQpR7SoIzBaG4RlJDo1CLuUcQkA0DiosUktTwU/BQC4nn0dJeoSiIXl18/CVCpkrtRmvYInToS4dy8cnT0bAPDw11XwmfZxuR2bMITjOKtKAl3N77//jlmzZmHHjh2Ijo62aptjx45h+PDhej83a2Zh3j4H4f+2i4sddFgtRygIq2JIpVKsXr1a970t3LhwDHU091DMxJDUfRbI3O+ID4ZDWsqD8tRTL8ofuxr6ApdzcXvffeR2qw0fM09u3encuJMWgiCqBnwWLMRbpuvnsnWs8fOQ4C4K7S9HzL2ndUYUiAH/2rrFFnX41wJuAci+bd9xjaBUK3E9W2v2UTYT9riWasJq8JH4IFeZi8TsRDQMNG1c4Cj5hw5BlZICYUAAfPu9AI1QiKUffYTM5ctRtG0b2KQPwUkk5XZ8ovJx4cIFDB8+HJMmTULjxo2RkqJtwZBIJAgICDC53caNG9GiRQu0b98ea9aswfHjx/HLL784Tde///6LEydOoH379vD398eNGzcwbdo0REdHu20WDKAgrMohEokwbNgwu7ZNP7oGdQBc8mqNQLkP4GA5vCNaTOJAaW+56CnDyNEx+OHDf+Cp5rD85//wzoRnXKbFFtxJC0EQVYMH2Xw/2KMmelvHGj+5NguUrbAzCOOzYIF1AOGj2x2LOvxKy/Cy79h3XCNcz74OlUYFX6kvqnk+qjQxpqVBQAMcTzmOK5lXyjUIyyvNwPn07AmBVAoBgFdnzkTiwUNQpacj//BheJfOYUYQAHDy5EkoFArMnj0bs0uzpgDQqVMnJCQkmNxu5syZWL9+Pd566y2Eh4dj3bp1aNSokcn1bUUul2Pz5s2YPn06CgoKEB4ejh49euDjjz9264fLFIQRAACmUaN28k7tD08NBJDoUj1l4RzKx1UcXp4S1H6uOtJ23geu5OJKYiYa1DH9ZIggCKKqcj/bflMOHt4h0e4gLOuW9v9A60qmdPhHlm7vvExYYpb2mlrfv75F58H6AfV1QVh5wVQq5O8/AADwfq6rbjknEsH7ua7IWrsO+Ql/UxBG6BEfH4/4+HibtuGNMd566y2jr0dGRuqZZ8TFxRmYacTGxpo12Hjqqaewf/9+m3S5A27njkg4hlqtxokTJ3DixAmo1Wqrt7t+OgFheIh85oGGHQe4VItZHLDNdZ4e0xoG9K2LbG8BROCwefmFCtDiOO6khSCIqkFyDm/K8SgIs3Ws0QVh9pYjZiVp/+eDKmt16DJhzgvCbuTcAABE+UZZ1NIwQJv9Ks8grOjiRaizsiDw8YG8RQs9LVdDQ6FmDPl//+3WznIEUdmhTFgVo6ioCC1btgQA5Ofnw9PT06rtsk79DgC47NMWz8i9XKqlvHCGHkshoEAgQPeXG+Lojxe0bon7bqFHl9oG67nTuXEnLQRBVA105YhlJrC3dazhyxFznRyEWdThXxqE5T4AVMWAyPFyplvZ2qxclJ9+EGZMS/0ArZPjtaxrYIyVy5xdipOnAADyZ54BJxIZaDnV5CkIU1JQfP06ZPXqOf34BEFQEFbl4DgOtUptZa0duJlGgxop2tpwQaM+LtVSnlSUnhZNQ3Gg1k3Ibxfi7B+30LlDBKQS/T81dzo37qSFIIiqwQMj5Yi2jjV+HlpTiGyFne6IJoIwizo8gwGRB6AqBHLu2V7OaISbOTcBGGbCjGmJ9ImEgBMgvyQfDwsfIlge7PDxH0dx8iQA6LJgj2uRN20KdvIkCk+foSCMIMoJKkesYsjlciQlJSEpKQlyuXVWp0mXT6A6S0URE6NB+/8D4JD/hUNaypOK1DPitaYoFDB4K4FVay66VIsl3EkLQRBVgwdGyhFtHWscKkdkzGQQZlEHxwF+NbXfO6EksVhdjHv59wAYBmHGtEiEEtTwqgEAuJVzy+HjPw7TaKA4VZoJKxOEldUS2FJrLKU4fcrpx7dEQU4xigocmKCbICoJFIQRSDm2EQBw2fMZeHr7lS7VPpGjxIh9hATK4d9S+/Qy5/hDpGUoXKyIIAiiYihWqZGep52bxyFjjtJyRLss6hUZgDIfAAf4Rti+vb/zHBKTcpKgYRp4S7wR5BFk1Ta1fbVl7OURhJXcuQNNbi44qRSyhsbntPR4ujkAoPD0Gacf3xQaDcP+Xy9jxaTDWP7BPzi9y3k9eQThjlAQRiD0/h4AgKpebxcrMQ1XmppjTsnROQdL8enwYY2RJwFkjMOvv5yvEE0EQRCuJqU0CyYTC+Avt3+yYb/STFiOPe6IfBbMpxoglpld1Sg+1bX/5z6wfdvHSMrVaqntW9vqkm9dEJbr/CCs6PJlAIC0fn1dP9jjeMQ2BQQClNy7h5LUNKdrMMbpXbdx+UgyAG1AdnTLDST997BCjk0QroCCsCpGUVER+vXrh379+qGoqMji+ndvXESUJgkqJkC9DgNdqqW8qWg9YrEQTXpHar+/mY9L1zJcpsUc7qSFIIjKz4PSiZqr+XroBR22jjV8JsyuckQTpYhW6/ApncvLCUHYvTxtKWJN75pWaynPTFjRJW0QJmugnwUrq6VEJIK0Th3t8ouGJfXOpjBPiVM7tZmvZ4c3QIP2IViyaxoGDOwPhaKw3I9PEK6AjDmqGGq1Gn/88Yfue0vcPbwBEQCuyJqiSWCoS7WYwlnzhDlLjw4rrHt7d6uNz/bdhV+uGlt/vYRGszuUjxYHcCctBEFUflJytTfNYb76GShbxxremCOnsMR2l0B+jjAjQZhVOpwZhJX2g1X3qm61lnINwkozYbJG+hNBP65F1rAhiq9dQ9HlS/B+tnznC7tyNAWqYjWCa3qjQZtwhDfwwn+vHAEA3DyXiiZtIsv1+AThCigIq2JIJBIsWbJE970lAu7uAgAoonu6XItVONCk5hQ9Nh6f4zj0GNYAR368AN+HJdj/zx08275m+ZwbO3EnLQRBVH5Sc7X9YKE++kGYrWMNb1Gv1jDkF6vgLbOhtDH7rvZ/I/1gVunwDtf+n5ds/TFNcD/vPgCghncNq7XU9tEGYckFyVCUKCAXO8c0iTH2KAhrqB+EPa5F2rAB8McfKL5SfvOV8ZouH9EGu407VAPHcfD09sCkN2fjzsVMJJ54SEEYUSWhIKyKIRaLMWbMGKvWzUq7j3rKKwAH1GqnX4rojN4rW7RUBK7S07xpKPbWuAGve0U49vsNdGpdw63OjTtpIQii8pNWGoSF+OjPr2XrWCMTCyERCqBUa5BTWGJbEMZnsHwNs09W6dBlwu5bf0wT8Jkw3vHQGi1+Mj/4Sf2QXZyNu3l3dXOHOYr64UOoMzIAgQDSx6znH9cia6AN0vjyxfIi80EBslIUEIoFqNsiVKflw48nYN2sf5F8LQ/FhSpIPeiWtTLCcRy2bNmCfv36GX09KSkJtWvXxpkzZxAbG1uh2lwN9YQ9wdw4sgUCjuG6MBqh1aOMr+Q+PhiVmiGjmkDJMfgWMmzYctXVcgiCIMqN1Dxtb1Ootx2GGI/hLdPeeOcVqWzbkA/C+GDKVvjtinIApf3utiqNCsn52myasUyYOfigjQ/inEHxTW15o7hGDQg8zDtX8s6JJffvQ52b6zQNj3P7orZfuno9f0jKBFoB1TzhHyaHRsXIoKMKExERgeTkZDRp0sQp+ysuLkZsbCw4jsPZs2edss/ygoKwKoZGo8HFixdx8eJFaDQas+sKEncDAB6GxRl5tbTszoHyP1u0VASu1FOzmg9Ejf0AAHcTkpGbV+Q258bdPieCICo3abnaIOzxTJg9Y41PqUOi7UFYaeDiY5gJs0qH1AcQe2q/d6AkMVWRChVTQSwQI0QeYpOW6t5a7byxhzNQ3tJOGi2pHWlRi9DXF+Jq2mC0qBxLEu+UBmG1mgQYaGEBWdAwDe5cyjC1OVHJEQqFCAsLg8iEU6etfPjhh6hWzc6HLxUMBWFVjMLCQjRp0gRNmjRBYaFpR6ESZTHq5h8HAPg1e96lWioKp+uxMT6Nj38K+UIGTzXwy9JTbnNu3O1zIgiicpOWZ7wnzJ6x5lEmzAaHxOJ8bQYLMBqEWaWD45xSksj3g1X3qg4BZ3jLZU4Lnwm7n+94SSSP8pY2EyaNrG2VFkldrUOi8uZNp2koi6pEjeRE7WdVs1GggZaX3+mFElUx7l/JArPCDIsoX5KSksBxnMFXXFyc2e2Sk5PRs2dPeHh4ICoqCps2bTLYJ5+1SkhIAMdx2LdvH1q0aAG5XI62bdvi6lXLVUQ7duzA7t278dVXXznyNisMKrCtggQFWZ4M8trJfWiMQmTBB/ViO7pUizU8inccG4SdpccevL0kqN4xHDkHUlB8KQsBAYEQCNxjNmxXnheCIKoOjDGk5pouR7R1rOGDsFxbgjA+cyXxBmQ+RlexSodPOJBxHci1PxOmc0b0NgwGLWkpj0xYcWkQJqltGIQZ0yKNikbB3wdRXE5BWPqdfGjUDB4+EviG6JdH8lqEIgEKcpTITlXAP8yzXHS4A4wxMBc9COU8PKxyH+VLB3lSUlLQtWtXdOxo/j5y2rRp+Pzzz7Fw4UKsWrUKgwcPxvnz59HwMXOYskydOhXz589HcHAw3njjDYwaNQqHDx82uX5qairGjBmDrVu3Qi53jpFNeUNBWBXD09MT6enpFtfL/e8vAECib2s846QUsL1aKgpn6WF8SGhHPDhkQAN8fiwVfoVyTHrvT3z4URuH9TiKu31OBEFUXnKLVCgq0ZbVPV6OaM9Y4y21oxyRz1yZ6AezWoe345kwPoAyZsphSUu5ZMJKe8IkUYZBmDEt/HrKG+UThKXe0mbBwmr76AUBZbVs/fo07l/Lxv1r2VU7CCssxNWnm7vk2PVPnwJnReDClw4Cj+aVa9OmDWbMmGF2u4EDB2L06NEAgE8//RR79uzBokWL8MMPP5jcZs6cOejUqRMAYPLkyejduzeKioogkxk+3GGMIT4+Hm+88QZatGiBpKQki+/FHaByxCeU8LSDAABB/e4uVmIZ/aczLs4cOdAjB2if6D3TV2uCIrujwH9XqNmYIIiqQ3qpKYePTASZWOjw/uwy5sgxH4RZjRPmCkspSAEAVPOyXUvZIMwZpXia4mKU3NeeG6mJTNjjSKOjAQDFt8onCEu5qQ3CQmsbz1gCQFiULwAgLan8zEEI2xk1ahTy8vKwdu1aCATmw4k2bdoY/Hz5snnXzZiYGN334eHaKSPS0tKMrrto0SLk5eVhypQp1kh3GygT9gRy79YVRGruQsUEqNPmBaPrUOV1+dGtcy2c3JUE32w1/lp9GTGlEzgTBEFUdkzNEWYvvC29TeWIZuzpbTt46Vxh+al27yJVod3WmCmHJcK8wiDgBChWF+Nh4UMEy4Pt1gEAytu3AcYg8PKC0MqyUL5sUfUgGZqCAgg8nZuJSr2lDaz4QMsYIZHaAC21igdhnIcH6p8+5bJj28Ls2bOxa9cuHD9+HN7e3uWiSSx+NCUF/zDelJHO/v37cfToUUil+tn3Fi1aYNiwYVi5cmW5aHQUyoRVMYqKijBs2DAMGzYMRUVFRte5++9WAMB1aSP4+js2qDuqpSJxJz1xAyKxYt8cbPltJnb/nehSLe50XgiCqNzo+sGMBGH2jDV2ZcJ05YjGgzCrdXiVXh8L7C/X5oOwUHmozVrEAjHC5NrSL2eUJCpvJQHQBlbG+n+MaRH5+0Po7w8AKHZyiZciV4n8rGKAA4Jr6t/Il9XiG66dxDozuQBKW10yKxEcx0Egl7vky5p+MJ7ff/8ds2bNwoYNGxBdmim1xLFjxwx+NtcPZivffvstzp07h7Nnz+Ls2bPYvn07AOC3337DnDlznHYcZ0NBWBVDrVZj7dq1WLt2LdRqtdF1PG/vAwDk1HjWzJ64Mv+Wn5aKxJ30NG0UhJOJ+3EycR8Ob77uUmt4dzovBEFUbvhMWIi31OA1e8Ya+4Iw83OEWa3DszR7lW+8BMoSjDGkKbTb8sGUrVp05hxOmCus5J52H5KICJu0SKK1JfR8P5mzyHiQDwDwDfaARKZfmFVWi8xLBC9/KcCA9Dt5TtVA2MaFCxcwfPhwTJo0CY0bN0ZKSgpSUlKQmZlpdruNGzdi2bJluHbtGqZPn47jx49j3LhxTtNVs2ZNnbNnkyZNUK90IvLo6GjUqGHb/HwVCZUjVjEkEgkWLFig+/5xlEWFqKs4C3BAULPeLtVS0ThbjyPtYRKJBB9N+wyX9t5FoEKIv3bdRN+edRzWZK8Wd/qcCIKovKTl8XOEGWbC7BlrHs0TZos7YmkQ5m08CLNah5djQViuMheFKq3bXYin8XJES1rCPbUlkXxvmSPw/WDi6sYzhKa0SGtHofDkKd0cY84i834BACCwmpdFLSGRPsjPSkdaUh6q1/N3qg7Cek6ePAmFQoHZs2dj9uzZuuWdOnVCQkKCye1mzpyJ9evX46233kJ4eDjWrVuHRo0aVYBi94aCsCqGWCzGhAkTTL6eeHo/GnFKPIQfohq1dKmWisad9IjFYsyZNQVf+/wLYWIBLu64g57PRUEsqvjktDudF4IgKjdpup4ww0yYPWONjz2ZMD5o8jZeAmi1Ds/SckRlHlBSCIht65vhSxH9pf6QCg3PhzVawjy1GTSnBmEmMgOmtEhq1QQAKO86zyofADLuazNhAdUN+8we1xJUwws3z6QjszR7RriG+Ph4xMfH27QNbyrz1ltvGX09MjJSz3gmLi7OwIgmNjbWJnOax/fprlA54hNG7sXdAIBbPs9AIKxEHz/j/3ODPyo+A+aEP/CX459CEcfgowTWbzTvFEQQBOHu8D1hIUbmCLMH3pjD6kyYRg0UlLrOmsg+WY3MF+CDJzuyYakFpf1gnsaDQWvge8n4fTmC8r42iDKVCTOFuIa2fLHk7l2HNZQl44HpTNjj8Ovw2xBEVaAS3YUT1qDRaJCUlISkpCSjfUYBqUcAAKx2nMu1VDRO0eOgRf3jWhT5aZA9pXWFSv4nBfkFSqfs3x4t7vI5EQRReUnLK+0JM5IJs2essbknTJEJMDUADvA07gBotQ6Oe1SSaIc5hyVTDmu06DJhCscyYYwxlNzXlmlKahgPwkxpEUdoM2fKe87LhDENQ2ZyaRBmJBP2uJaAatp1MpMLoNG4wcNYgnACVI5YxSgsLETtUkvZ/Px8eJaxk83JSEWdkusAB0S2NN8P5oyMkzkttsDBOfOEOUuPMyirJSUtCysvnoKnmsOqVRfw5htPu0yLq88LQRCVm4f5po057BlrHmXCrAzCeDt5eSAgFBtdxSYdnsFAzl37MmFWBGGWtDgrE6bOzAQr1PaniaoZ75UzpYU38lA/fAiNQgGBFZP6WiI3oxCqYjWEIgF8gw3LPB/X4hMsh0gsgKpEg5w0RZWetJl4cqBMWBVELpdDbmSQTDy+HQKOIUkQgZDq1k3UWF5aXIU76eG1eMnFCGunfdpZeC4LaekKl2khCIKwF4VSBYVS66oX6GW8B8rWsYbPhOUXq6C2JgPCB2Fe5ksArdahy4TZX45oaY4wc1r4TFhWcRaKVPZPIcL3g4lCQiAwY0ZiTIvQxwcCX23FhrOyYZnJ2uucX5jcZGtEWS0CAfcoG0YliUQVgYKwKoanpycKCgpQUFBg8ERNdX0/ACAlqI2xTfXhHLeoN6fFFbiTnse1DBnUEDkSQMo4rFlx3qVaCIIg7CEjX1tOLRUJ4CkRGrxuz1jjXca6PL/YimwYn7HyMh342KSDN+fId6Ac0UxPmCUtPhIfeIg89PZnD5ZMOSxpkZRu56y+sJy00iAsxHjwaUxLQPXSvrD7ZM5BVA0oCHtCYIyhRta/AAB5g64uVkM8jlgkQOOeWgcq4Y18JN7Kdq0ggiAIG+FLEYO8pDZN/moOqUgISalrrFXmHHzGykImzGp0NvW2B0A6Yw4z5YiW4DjOKSWJfAbLVlMOHnFNbUmi0llBWLq2NNI3xHrHyUDKhBFVDArCnhDu3byM6iwVJUyIOi27u1pO1cBJNxk8fbpHIctLACE4/P7rRafumyAIorx5WJoJC/Jy7nyDPqV9YbmFtmTCgp1zcD6Ys6cc0YqeMGvgM2mOmHOUPNCacohN9INZQqJzSHROOeKjTJj1QZhfqDZrlp1W8SX7BFEeUBBWxSguLsaYMWMwZswYFBcX65bfP7UNAJAobQi5l59LtbgK5+nRBl+OzEFhTItAIECHAdoJm+XJRTh93r4JQp2hhSAIwlYySjNhpvrB7B1rHs0VZkUmzIqeMJt02FmOWKQqQn6JtmwuWG46ILRGS5jc8bnCVKna64k4PMwuLY8cEp2cCQs2Xo5oTAsfhOWkFYKRQyJRBaAgrIqhUqnw888/4+eff4ZK9eipoeTOQQBATnh7l2uxB93UXA64NjpFj5OyX6a0dGpTAzkBIgjAYce6K045lr1aCIIgbCGjdIqNQE/jmTB7xxqbbOqtCMJs0mGnMUdGUQYAQCKQwEtseh4sa7Tw5hyOlCOqUrQBnCjEvvPCOySW3HE8CFOrNMjL0JqMmCpHNKbFJ1AGgYCDqkSD/Gx6YEhUfsiivoohFosxe/Zs3fcAwDRqROWfBgD4Nn7OpVpciTvpMael19CGOPTdf/DLVCHhyF3EtY1wmRaCIAhr0fWEGbGnB+wfa3Q29cXWZMIsG3PYpIOf8NnGTNjDQu2E0YEegWb746zR4pRyxLTSTFiY6SDMnBbe0KPkwQMwxhzq+cvLKAJjgEgigNzHeMBuTItAKIBPsAeyUxXITlPAO8A5E4IT5QvHcdiyZQv69etn9PWkpCTUrl0bZ86cQWxsbIVqczUUhFUxJBIJpk6dqrcs6fIp1EY+CpgU0U07uFSLwzgw8JeLHjsxpyW2STB2hcvglVyMw5tvoGPr6hAIyi9p7U7nhSCIygvfE2YqE2bvWGNbJsyyMYdNOvjesuIcoKQIEFt3459RqM2EBcoCza5njRZHjTmYUgl1hlaPKNS+88Jvx4qLoc7Ohsjf3y4twKOeLt9guclgzpQWvxBtEJaTVoiIBnZLINyIiIgIJCcnIyjI+OTq1hIZGYnbt2/rLZs7dy4mT57s0H7LEypHfAJIO78XAHDDowkkUuNPKA1x3KKesJ8BwxtDBQa/fA127EtytRyCIAiLZJRxR3QmVgdhKiVQmKn93lnuiDI/gCu12+f3bQV8OWKQh2M3lgAQ7KENBPnsmq2o0tMBxsCJxRDaGTwJJBIIS2+SeZMPe8lJ0/aD2WLKwePLm3OkkjlHVUEoFCIsLAwikeN5oVmzZiE5OVn3NX78eCcoLD8oCKtiMMaQnp6O9PR0nXGE+O5RAEBBeGuXa3El7qTHkpY6tf2gqq214z277TbUao3LtBAEQVgDP09YoAl3RHvHGl05oqUgrKC0ZFAg1gZPJrBJB8cB8tJsliLDSsX65YjmsEYLb+yRWZQJlcb2vt2SUlMOUUgIODNVFZa0iMPDATzqL7MXa+zpTWnh5xUjh0TXkJSUBI7jDL7i4uLMbpecnIyePXvCw8MDUVFR2LRpk8E+z549CwBISEgAx3HYt28fWrRoAblcjrZt2+Lq1asW9Xl7eyMsLEz35e5zn1IQVsVQKBQICQlBSEgIFAoFNGo1ahecAQD4N+zsUi2uxtl6HPHosEbL4OGNUcIx+BUx/P6/6/YfzAlaCIKwn88//xwcx2HChAmullKuPLSQCbN3rPGUap+QF1iarJk3z/AMAswEGzbrsCMI48sRA2QBZtezRou/1B8CTgAGhswi67NxPKrUUlOOMNPOiNZoEZduX5LsWBCW+1AbhPkEmQ7CTGnhs2d8Nq0qwRhDSbHaJV/WPhThSwf5rzNnziAwMBAdO3Y0u920adMwYMAAnDt3DsOGDcPgwYNx+fJls9tMnToV8+fPx8mTJyESiTBq1CiL+j7//HMEBgaiWbNm+PLLL93ebIx6wqo4SVdPIQp5UDApoiqwH6xqUzFFmtXDvSFo4ANczsPN/feh7B0NiYT+ZAmiMnHixAn89NNPiImJcbWUckWtYchUmM+E2YuXVFsOaDEI44MkueMlgHrYEYTxwZIzyhGFAiECZYFIL0xHemE6QuSmTUeMUZKq7SUTh9q23eOIwvkgzLFyxLxMrTOid6Dtxhq8TX1ueiHUag2EwqqTS1ApNVjyzt8uOfZrCztBXPp3Zg6+dBAAioqK0K9fP7Rp0wYzZswwu93AgQMxevRoAMCnn36KPXv2YNGiRfjhhx9MbjNnzhx06tQJADB58mT07t0bRUVFkMmM/968/fbbePrppxEQEIAjR45gypQpSE5Oxtdff23xfbmKqvPbSwAAPD09wRgDYwyenp5I+28fAOCmrBEk0op1EnpciyNwpQ9pHCmXc4oeJ1nUW6tl2IgmKBIweJcA6zaWj2W9Mz8ngiAekZ+fj2HDhmHp0qXwd8DIoDKQpVCCMe0QGSA3HoTZO9Z4SXl3REtBWGmWSG4++2SzDn5/CuuzUNaWI1qrhQ/m+AybLahStEGYKNR8JsySFnG4dqJnlQOZMMaYzp7enLuhKS2evlIIxQJoNAz5mWRT70pGjRqFvLw8rF271qJ5WJs2bQx+tpQJK/vgKry0FDYtzfRUERMnTkRcXBxiYmLwxhtvYP78+Vi0aJFbz39Kj9WrOOK7RwAA+eFtLKypjyPzcRHOI9DPA56xAVCfzkLqkVTk/199eMnJRp4gKgNjx45F79690bVrV53dtimKi4v1bhZyc3PLW55T4fvB/OUSiJycnfC0NRPmWV6ZMBuMOax0R7QWPghLV9hmlQ8AqjQ+CHMsE8ZP9FziQE+YslCFkmI1AMDLDot5TsDBJ1CGrBQFcjMK4Rtsu7mHuyKSCPDawk4uO7YtzJ49G7t27cLx48fh7e1dLprKTpPAu2hqNNb3x7dq1QoqlQpJSUmoX7++0/U5A8qEVWE0ag1qF5wFAPg1sq8fzB3cER2Zj6Qq8PLLTVAgZPBUc1i9+oKr5RAEYQXr16/H6dOnMXfuXKvWnzt3Lnx9fXVfERHlOz+gs+H7wUzZ0zuCl9U9YaXugXLnBD467OkJc6I7IvDInCO90PYgrCSFL0d0zDGSN+YoSU62ex95pdkrmZcYYonl8jdj8GWMfEatqsBxHMRSoUu+bLnP+v333zFr1ixs2LAB0dHRVm1z7Ngxg58bNmxo0/mxlbNnz0IgECAkxLGHD+UJBWFVjOLiYkyYMAETJkzAlfPHEIBcFDKJ7fODOSHwKavFaelgB3SVi54K0OIlFyOktXYQyT+biYxs5zYku9N5IYiqwN27d/HOO+9gzZo1JvsXHmfKlCnIycnRfd29e7ecVToXXRBmph/M3rGGD8IslyPyPWHmgzCbdejKEa0LwgpVhSgoKQBguRzRWi18MGePTb0q1bpyREtaRGGl7ohpaWB2Gh7o+sEsZMHMafEJ1Ga/eIMPouK4cOEChg8fjkmTJqFx48ZISUlBSkoKMjPNZ4k3btyIZcuW4dq1a5g+fTqOHz+OcePGOU3X0aNH8c033+DcuXO4efMm1qxZg3fffRcvv/yyW5eCUxBWxVCpVFi4cCEWLlyI5LOP+sHE0opP2ZfV4g4ONe6kx1YtQ19qiDwx4KHhsGalc7Nh7nReCKIqcOrUKaSlpeHpp5+GSCSCSCTC33//jW+//RYikQhqtdpgG6lUCh8fH72vysQje3rTc4TZO9ZY7Y5oZRBmsw4bM2F8KaJEIIGX2MspWuydK4wxBlVpH40lYw5LWkRBgYBIBKjV2rnH7CDfyiDMnJaqmgmrDJw8eRIKhQKzZ89GeHi47qt///5mt5s5cybWr1+PmJgY/Prrr1i3bh0aNWrkNF1SqRTr169Hp06d0LhxY8yZMwfvvvsulixZ4rRjlAfUE1bFEIvF+OijjwAAninHAQB5YRU7P5gxLWVre12FO+mxVYtEIkLtztXwcPcDqK/k4n5KPqqHmb+4l5cWgiDM06VLF5w/f15v2ciRI9GgQQNMmjQJQqF9ZVjuTGZBaRBmphzR3rHmUTmiYfCqh5XGHDbrsDUIKy1FDPQItFjmZa0WXU+YjeWImtxcsJISANBNtmyvFk4ohDg0FCX376MkOUVXnmgLfCbMK8D8hN7mtPDW9rkPKQiraOLj4xEfH2/TNryh2ltvvWX09cjISD3Ttbi4OAMTttjYWLPGbE8//bRByWNlgIKwKoZEIsGcOXPANBpkzqoNAPBuEOdSLe6Cs/Q4w7LEHi0v9q2HLw4mw7cIWL/yAt6b5Jzg2t0+J4Ko7Hh7e6NJkyZ6yzw9PREYGGiwvKrA29P7m3BGBOwfa7xk2luV/GIVNBoGgcBEYGNlJsxmHTa6I/KZMGv6wazVoitHVNiWCVM91K4v8PGBQGo+8LFGiyg8DCX370OVkgygmU1aAOvLEc1peZQJo3JEonJD5YhVlJSkSwhENpRMhKim5ifRI6zHlSYhQpEAMb1qAgBEtwqQmJTtMi0EQRBlydYFYc7PpvOZMABQlJjJhlkZhNmMjZkwnT29k5wRgUfGHA8LH9o0VYsqXatFZCELZi28Tb295hzWliOawydIu21BjhIqc78PBOHmUBBWxWCMoaCgAInHd4IxhhuSuvCQ2z73kwPTcRloKSgocGh+L2fhTnrs1dKra21kewkgAoffV150qRaCIKwnISEB33zzjatllBtZBdqSN38z5Yj2jjVSkQDC0uyXyb4wxqyerNlmHXwQpioElAqLq5ctR7SEtVr4TJhSo0Su0vrpC1QZpUFYoHO08H1lKjPzNZmDd0e0ZE9vTovMUwxR6bQF1BdGVGYoCKtiKBQKeHl5oeOQD6AoAbIDn3Zof5wDJvW8Fi8vLygUli9c5Y076bFXi0AgQLv+dQAA8uQinLtoX3O0M7QQBEHwZFlRjmjvWMNxHDxL7czzikwEYUXZACvNiljoCbNZh8QLEJa+LyuyYZmF2rLFAJl5HbZokQql8JZo52OyxZxDXVqOKAq2nAmzRouo1O67xI4gTK3SoCBHG4RZyoSZ08Jx2rnCAArCiMoNBWFVHGlUW1dLcJiygeCTPmcYAMS1rYFsfxEE4LB97RVXyyEIgrAqCHMEb5m2zNFkJozv15J4AyLzvU82w3E2lSTmFOcAAPxlzrXG5h0SbTHnUD3U6hUGOqcckQ/CVGm2PwAsyCkGGCAQcfDwdqxslQ/CcikIIyoxFIRVMeRyOe7fvoH8Kd6Qi4HIWPsmaXaWlvz8fOTn50Mul7tMhzvqcVRL9yH1wcDgk1GCIyceuFQLQRBPNowxZCm05Yh+ZnrCHBlrPEvLz0wHYXwpouXsk106PEr3W2jZnCOrOAsA4Cf1c6oWXRCmsCUIs74nzBotj4Iw2zNhihxtoO7pK7X4QNWSFu9Sh8SqYM5BbQCuwR3OO7kjVjE4jkPa9eOoJuFwl6uGiJDqLtXi6Wl7P1p54Ww9juTkHNXSIiYUe0KvwydViYSN19H2mWou00IQxJONQqmGUqUBAASY6QlzZKzxtDRhsw2mHHbpsMEhMbs4G4B1mTBbtASUBoKZRda5NAK2BWHWaCkbhDHGbKpOKcjWliJ6+lrOVFrSosuEVWKbet56X6FQwMOj4udyfdJRKrUPBVw5ZQgFYVWQwhuHAQApvrGIcLGWKonuouPapyj9XmmE3V/+C8+0/7BzdwB6dHvKpXoIgngy4UsRJUIB5JLyuaHxsjRhc3k5I/LYUI6YVaTNhPlLnVuOyLst8vu3Bp0xR5BzzosoWJuNY8XF0OTmQujra/W2fD+Yp5/jJate/togLD+r2OF9uQqhUAg/Pz+klWYV5XI5tVxUEBqNBunp6ZDL5RCJXBcKURBWxVAqlVixdgv+Uheh97jmLtcyc+ZMAMD06dMhkZRPr0Bl1OMMLQ3rBGAHdx2s8BBOrktEt64/QSCwvcLYnc4LQRCVj2wF74woNnsT6chY48wgzC4dVgZhjDFdJsxP5udULbzRhy2ZMHWpRb2liZqt1SKQSiHw9YUmJweq9HTbgrDsR+WIjmrx8tfuIz+r8mbCACAsLAwAdIEYUXEIBALUrFnTpYEvBWFVjPz8XCz5+z4AYMR37ezejzNyPCUlJfjss88AAB999JHLb+7dSY+ztHhJ1MgD4KFIxpatuzCgf0+XaSEI4skks8A6Uw5HxhqL5YgFpY6BVgRhdung91tg3pmwUFWIYrU2O2NNJswWLbYGYUyjgSpTu64oKNhpWsQhwSjOyYEqLQ3SOnWs0gKUzYRZDsIsaeEzYQU5SvMTeLs5HMchPDwcISEhKCkpcbWcJwqJRGLXg2tnQkFYFePe5RN4p5UExRCjZr1Yl2oRiUR45513dN87AlcaFTIHwkNn6nEUZ2kRlHmCc+F/6/FC3+ds3p87nReCICoffDmiOVMOwLGxxnImrDQw8bQchNmlg+8JKzRfCshnwSQCCTxElvt8bNHC95hZG4Sps7MBtda2XxRgOSC0VosoOATF1xNttql/1BNmOei1pEXuKwEn4MA0DIocpS4zVlkRCoUu7U0iXAPdcVUx8m/+i296yHBO3gYyRxo9nZCelUqlbjU5qTvpKQ8tXoUZWLt2K4YPf9HlWgiCeHLgyxHNmXIAjo01j4IwtfEVbChHtEsHX1pYlG12NZ0zoszPqjInW7TYmglT8aWI/v7gxJYt4a3VYq9NfUGpO6LcikyYJS0CAQdPXwnys4qRn1VU6YMw4smELOqrGLLkEwCA4vBnXKzEeehdyKhp1TgC7RO023t+R2Fh5W1UJgii8sGXI/qV0xxhQJlyRFOTNfNBmIdli3q78CjNJFnKhJUGac425QAeGXNYnQnLsN4Z0RbstalX5FjvjmgNVcGcg3iyoSCsCsE0GtRSnAcA+NRv72I1REVSrf7TUIg8IVfm4dfl61wthyCIJ4hs3UTNjk3Aaw4vmYVyRD5D5eH84Ee7Xz/t/4XZZlcrmwlzNrxFfaGqEIoShcX1eXt6oZOcEXnsCcJKlGoUK7SfnTU9YdZQVcw5iCcXCsKqECm3r0CmzAY3MxcNW/VAQUGBS/UUFBSA4zhwHOdyLeWixwH3EqdpKU0MisVShHbqAwB4+M82ZOfkVbwWgiCeSPiJmi0Zczgy1njxkzUrTQRhfIbKiiDMLh02ZsKsmajZVi1ykRxSoTbw4IM9c/DliNaYctiihbeptyUI47NgIokAEpnl3idrtOiCsGzKhBGVEwrCqhDJFw+5WsITgnuWRL4yfBDypX6QqQuxcslKV8shCOIJIUthnTuiI3hKtJmwfGOZMMYeZaj4jJWz4YOwohxAozG5mi4TZmUQZgscxz3qCyu0XJKoytCWaIoCnZ0JKw3C0q3vCStrT+8sS3BdOWImBWFE5YSCsCpEye0TkIuB7XOHIi0tDXK53O59OcOiXi6XIy0tzWEtzsKd9JSHFg+ZBNE9tKYcitP7kJJq3kq5PLUQBPHkoAvCPM2XIzoy1vDliPnGesKUBYCm1N7bijJAu3To9suA4hyTq+l6wmTWlUXaqsUWh0R1qT29MNC6PjlrtYjLlCMyZt3dgi329NZqoXJEorJDQVgVwj/rHDiOQ1DjDggODnbK0yZH9sBxHIKDg52mxVHcSU95aRkyqA9yPUMg0ZTg15+WuVQLQRBPBlkF1pUjOjLWmLWo5/vBBGJA4mlxX3bpEEkAcem+zZQk2poJs1WLLQ6JqqzSOcL8rQsIrdUiLC1HZCUlWht8K7DFnt5aLbq5wqgckaikUBBWRSguUiCy5CYAILxRBxercT78EGztU7cnD+15EYqEaPZ/Q7VLLh3GjVv3XCmKIIgngAopR5SaKUcsW4pYng+SrDDnyCnNkpWHOyLwKAjLKMqwuK46KxsAIAxwrmOkQCKBsDSws9am3hZ7emvxCtDuqyC7GBq16RJRgnBXKAirIty++C8knAqpai+sWLcFc+bMgVKpdKkmpVKJOXPmOKyFc1IPlrP0uJcWPih9dI769n4WuX41IWJqrP/p5wrUQhDEk0axSg2FUjt3l6UgzJGxxrtMEGbwMM4GUw6HdFhhzmGrO6KtWmyxqdeVI1qZCbNFi63mHI8yYdYFYdZo8fCWQCDgwBigyKVrF1H5cLsg7Pvvv0dkZCRkMhlatWqF48ePm10/OzsbY8eORXh4OKRSKerVq4ft27dXkFr3IevaEQDALUl9TJs2DR9//DFKSkpcqqmkpAQff/yxc7U48JTT6XociA3L5dyUIhAI0HHoCACA5NYpnDt/1WVaCIKo2uSUOiMKOMC7tG/LFI6MNXwmTMOAopLHsh58OaKVgY/dOqyYsNnWecJs1cJnwrKKLLsj8kGYteWItmgRldreqzKs6z3WzRHmZ1221BotAgGn6zGjucKIyoj5EbOC+e233zBx4kQsXrwYrVq1wjfffIPu3bvj6tWrCCltBC2LUqnEc889h5CQEGzatAnVq1fH7du34efnV/HiXYww+TQAQBkSi9GjowEAIpFrP16RSITRo0e7hRZegzP06J7BOlAaWd7npkunVji0qT68065i67JlaLrgC5dpIQii6pJTqL1B9vEQQyAw/2TKkbFGLhGC47TDbn6xCh6SMjbnukyYn1X7sluHrhzReADEGNNlwqw15rBVi7XGHJriYmgU2rnErC1HtEWLsHQCaHWG5bJI4FE5oqePdZkwa7V4+UuRl1lEQRhRKXGrO66vv/4aY8aMwciRIwEAixcvxrZt27Bs2TJMnjzZYP1ly5YhMzMTR44cgVisdWWKjIysSMluQ1jeRQCAf4N2WPr6ABer0SKVSrF06VJXy9DhND1OqI6siHPTa+QoHPxiErweXMThY2fQrnUzl2khCKJqogvCZJYnanZkrOE4Dl4SEfKKVcgvViHYu8zNvK4nzLrAx24dFnrCCkoKoNJoe9Z8pb7losVaYw51VmmgKBJB4O3tdC2iQG0QpnpoXRDGlwt6+FiXCbNWCzkkEpUZtylHVCqVOHXqFLp27apbJhAI0LVrVxw9etToNv/73//Qpk0bjB07FqGhoWjSpAk+++wzqNVqk8cpLi5Gbm6u3ldlJzM9GTVYMgCgZkxHp+yT7C8qPy2fbgxFRFMAwJ5fl7tYDUEQVRE+CPP1sByEOYqnKYdEG8sR7cZCTxifBfMQecBD5FEuEgI8rJsn7FE/mF+5uN7aUo6oLtFAWaj9zORWBmHWwht98Jk2gqhMuE0Q9vDhQ6jVaoSGhuotDw0NRUpKitFtbt68iU2bNkGtVmP79u2YNm0a5s+fj9mzZ5s8zty5c+Hr66v7ioiIcOr7cAV3zmsnab4rqA5f/2Cn7psMyysJJi6yA8eMgRoCeGfcxI7dBytYFEEQVZ3coooMwrQliHmPzxVmozGH3eiCsGyjL/P9YOUxUTNPWWMOc27BqtJMmMjfuc6IPMLSCaDVVmTCFHnaAEkg4CCVO7cAizf6IJt6ojLiNkGYPWg0GoSEhGDJkiVo3rw5XnrpJUydOhWLFy82uc2UKVOQk5Oj+7p7924FKi4fFDf/BQCkeTdBQUEBPD094enpiYKCAvt36oQnZ07TAuhSc8yBHJ1T9ThIRWlpWD8SqnqtAADHNqyCRmNo4+tO54UgiMoFb8xhTRDm6FjDzxWmUD4ehGVr/7eyJ8xuHRaMOWydI8weLXxPmIqpkKs0XcmjztRqsdYZ0VYtj8oRLWfCCkuDMA9vsdVZOWu18POOKXIpCCMqH24ThAUFBUEoFCI1NVVveWpqKsLCwoxuEx4ejnr16kEofNSg27BhQ6SkpJi0NJVKpfDx8dH7qux4pp8FAGiqNwcAKBQKKEobcl2NO2kB3EtPRWkZ9vqrKOHE8MlLxu9bdrpUC0EQVYuc0jIzHyszYY6MNSbnCrMjE2aXDgvliHxQ5CO17b7CFi1SoRSepZNGm+sLU5dO1CwMsC07aK2WR+WIVmTCbOwHs0XLo0wYlSMSlQ+3MeaQSCRo3rw59u3bh379+gHQZrr27duHcePGGd2mXbt2WLt2LTQaDQQCbTx57do1hIeHQyIpv0kj3QmNWoNaRVcADgio3wYeHh64desWAMDDo3xq0q3FWVqcNU+Ys8+NI8nCivycatUIgyimE3BuLy797zeUvNAN4jJuU+70O0MQROXikTui5dsJR8eaRz1hj/V929gTZrcOC8YcucWlQZjE+iDMHi1+Uj8UlBToJoY2hj3liLZoEfHuiFlZYGo1uDIPwx+Hz4TZ0g9mrRZPXU8YZcKIyofbZMIAYOLEiVi6dClWrlyJy5cv480330RBQYHOLXH48OGYMmWKbv0333wTmZmZeOedd3Dt2jVs27YNn332GcaOHeuqt1Dh3Lt5AX5cPoqZGLUatoRAIEBkZCQiIyN1gamrKBctDkQ+ztOj1eCAQ32Ff04jXo9HsVAGr6IMrFm92aVaCIKoOthizOHoWONlypjDxnJEu3VYyITlKfMA2BaE2aOFn4PM3Fxh9pQj2qJF6O+vvR5rNI+cGE2gy4R5Wx+EWatFXlqOWFKkhvLxXkGCcHPc6o7rpZdewldffYVPPvkEsbGxOHv2LHbu3Kkz67hz5w6Sk5N160dERGDXrl04ceIEYmJi8Pbbb+Odd94xamdfVUm7fBgAcEtSByKJzMVqCHclONAP3i27AwDu7t0CRSHZ+RIE4TgV646ozbY4oxzRLiz0hOnKEW0IwuzBr1RHdrFxHUAZd0QbyxGthROJdAGepZLEwjzt74jchiDMWiQyEcSlvxcKckgkKhluU47IM27cOJPlhwkJCQbL2rRpg2PHjpWzKvdFc/ckACDLPwaAdpb577//HgAwduxY3fxptuO4Sb3ztDgHd9LjbC3W5AdHjhmGBSf3QV6ShxW/rMNb40aWixaCIJ4cbHFHdHSsMWpRr9EARaVleVaWI9qtgw/yShSAqhgQ6U88zGfCvCXWzctlrxY+E2Y2COPLEa2cqNkeLaLAQKgzM7XmHPXrm1zPnp4wW7R4+kmRnapAQU4x/ELlVh+DIFyNW2XCCNvxyz4PABDWeBqAdr61d999F++++65JcxJbcKQby9laHMWd9DhPi/XBspenHOFxfQEAWUe2IzM7z8laCIJ40si1IRPm6FjjKSkNwsq6IxbnQDcOWlmOaLcOqQ/Ald42GekLsycTZo8WfiJo3o3RGHxPmC3liLZqEZaac6gtZsJKe8K8rQ+6bdHC95pRXxhR2XC7TBhhPWpVCWoqbwIcENqgDQBAKBRi6NChuu/tx3EzDOdpcQ7upMdVWoYPH4i5B3fAqzgLK5esxLsfjnOr80IQROXClnJER8eaR+6IZYw5+GBILDfITDldh0AAyHy15Y+FWYC3/rym9mTC7NHC29RnmyiLBMpO1mx9JsxWLTqb+nTzNvU6i3obMmG2aOHNOagckahsUBBWibl77TQiuRLkMQ/UiG4CAJDJZFizZo2LlWlxphY+JHRknrCqem5sQSIRo26vgUjesgRFZ/bhfvJLqB4e7DbnhSCIyoXOHVFmOQhzdNzzKu390StHtNEZ0WEdHv6PgrDHsMei3h4t/DxkpsoRmVoNdY62RFNkQ0+YrVp4h0RLPWF8OaIt7oi2aOHnCqMJm4nKBpUjVmIeXtVO0nxbWpcyGC7ACfNZu4SXXuyNPM9QSDQlWPXTMlfLIQiiklKi1kCh1GalKsaYw0hPWEWZcvCYMeewJxNmD5aCMHVurrZXDoDQz6/cdIh05YimM2EaDUNRvjZQt8Ud0Rbk/FxhlAkjKhkUhFViNPfPAADy/Ju4WEk5wxn91oWUqnDcu8Rp2CJFKBKi+YBhAADuyhFcu3mnfEQRBFGl4bNggPWTNTuCLggr2xNmoz29w5ixqbdnnjB74MsRTVnU86WIAh8fcOVotCTkyxEfms6EFeWXaKdz4QAPr/LR4umnDe4U1BNGVDIoCKvE+GVfAACII57WLSsoKEBwcDCCg4NRUFDgKmlup8WpepwQCTr/3NgmqnfPOOT61YKIqbF60Y9u9TkRBFE54IMwb6kIQoHlMcjRcc/L2GTNdmTCHNJhYsJmDdMgvyQfgG2ZMHu08JkwU5M180GYyAZTDnu08Jkwc+WIfD+YzFMMgdD6W05btHhSJoyopFBPWCWlRFmMWiW3Sk05Wuu99vCh+SZZa3BkIuKyOEOLM3EnPa7UIhAI0PnleJz6biakd8661XkhCKJywDsj2pIFc2Ss4d0R8x3sCXNIh4lMWJ4yT9ezbGsmzFYtfCYsR5kDtUYNoUC/HcEeZ0R7tIgC+SDM9Db2TNRsqxZdEEY9YUQlg4KwSsqdK6cRzZUgF3JUr91It9zDwwMXLlzQfe8ojiR9nK3FUdxJjztoievwDP7eWB/ylCt4c1g8xk553+XnhSCIykOOjUGYo+Oel9GesOzSnftZvR+HdJjoCeP7wWRCGSRC6wMOe7T4SrQW9RqmQZ4yTzd5M486szQIs2GOMHu08OWI6swsMLUanJHedJ09vY9tpYi2aJGXGnOUFKuhLFJBIqNbW6JyQL+plZSM6/8iGsBdSV00LjPwCQQCNG7c2PEDOMF1wmlanIQ76XEXLc+PehX7505CdEk60jIK0FhAFcoEQVjHI3t6624lHB335KXuiAqlGhoNg0DAAaV9WLDBkdAhHTJtAKSbILoUe+YIs1eLWCiGl9gL+SX5yCrOMgzCsvlMmJ/hxk7UIgrw194rqNVQZ2frMmNlsTcTZosWiUwEsUyIkiI1FDlKCsKISgPdcVVS2INSU47AKm7KQZQrzWMbobh2cwBAwupl0JQ6ahEEQVjClomanQGfCQPKmHMUlQZhsvI1w9DBZ9weC8IqyhmRx1xfmDorG4DtPWG2wonFOvdFU+YcjyZqLh9nRB4qSSQqIxSEVVL8cy4BACQRzfWWl5SUYOnSpVi6dClKSkqMbVphOFMLV9qj5kirmtPPjQPJQmdrcSRxOfDVeBy5eQ+XTh/Bhk3bHNZCEMSTgS0TNQOOj3tSkUBnAKIz57AjE+aQDguZMFuDMHu1mHNI5OcIE/j6lrsWSzb1irxSe3ob5gizR4turjBySCQqEZSzrYQUFxfqTDnCHzPlUCqVeO211wAAQ4cOhbgc7Wkt4U5anKvH8VJNZ2lxhmV/jWrB2HzyHACg3ta16N+vByQS135WBEG4P7YGYY6OexzHwVMiRG6R6pE5hx2ZMId0mAjC+EyYLRM1O6LFV6rVYWyuMHW2dpmtc4TZo0UYGARcTzTpkFiYa18mzFYtNFcYURmhIKwScufyadTlSpAHOcIiG+q9JhQK8cILL+i+txfmhEmwnKWFc9LsYM7SUxW19OzZC0n/nYW3MhsrV27AmDHDXKqJIAj3J7dQGwhZG4Q5Y9zzkoqQW6SCgi9HtCMT5pAOC0GYrZkwe7X4S0szYcWmM2FCGzNh9mgRlZp/8Lb4j6PrCbMxE2arFsqEEZURCsIqIRmJ/6IugDvSegZGCjKZDFu3bnWJrsdxJy2Ae+lxNy3bt2/Djz+sgOLvTUhN+APZg/rCz7diehsIgqic2OqO6Ixxj5+w2ZFMmEM6TARhfG+WrcYc9mrhzTjMZsJ8/cpdC+/AqMowHoTZ2xNmqxa5jzYTpqBMGFGJoJ6wysiDswCA/MCnyvEgzsk+EZWH+FFDUCD1g4dKgeU/rXS1HIIg3BxbyxGdgefjEzbbkQlzCN6JUJkPqB9Z5Ve0MQefCct+zCofKJMJs7Ec0R5EgaWZsCzDIIwxhkK+J8y7fH9HeJt6PugjiMoABWGVkACdKcfTLlZCuAvOmFzbQyZFnV6DAADFp/fhzr1Ux3dKEESVxdZMmDPQmytMo9YGQ8CjDFV5UzbY4wNA2G9Rby98T9jj5YiMsTJBWPmfE6F/aSYs07AssqRYDbVK67hrz2TNtiAvLXfkyx8JojJAQVglo0RZhEjVLQBAaP3WBq8rFApERkYiMjISCoWiouW5rRZ30+NsLZwD9ohltfR9/lnkeoXj/9s77wA56vL/v2d7ud29fpdLLr2RDiGJoQhIICAiwS+ICAiRoggqPwQVCwERgoAoCoIgEHqXIiCdgECAJCSQ3ntyvWyvM78/ZmduL7d3tzPzmZ25y/P6fuNt9mZ335ldPp959nme92MXUnj8vgc06yIIYvCiNBPGYt3zOMT+oHAi3S0IUpIJ06TDagMcJeLtnCyUbMyhMAhTq0VyRzzYop4Ph4GMmCVU2hOmRou1XNSRyWPMEQ+Lnw+r3QKbQ9nlplItchBG5YjEAIJ6wgYYezauxGgujSC8GDJyYo/fC4KAXbt2ybeNxExaJA1m0cNKCwsDlVwtHMfhqHMvwtoHFsO+5TN8uXYzpk8Zr/k1CIIYfCidE8Zi3euWCZP6wWwuwFZ4pkWzDldAzMDl9IWpzYSp1SLNCTvYol7qB+NcLlhcLt21SAOa03nKEWPZIMxdYlf8RaFSLVIQFo+kkMnwsFopx0CYHwrCBhhtWz7HaAC7HOMw1dJzkXG5XPj888/l20ZiJi2SBhZ65O1AQ/BkpnNzsJb5847GspfGw9e8GS8+8C9Mv+s2Q/URBGE+MryAUNYcw+8q3JhD67rnzQ3CEtkMicJ+MM06XAEguK9bEKa2J0ytFikIO9iYI9Ohvh9MjRapHDGTpxxRyoS5SpSXqyrV4vLawVk4CLyAWDCFkjKn4tckiGJDQdgAgz/wFQAgVDYp7++tVitmzZql+XVY5IlYaWFpUc9Gj3m0sCCfltMvuQTvLf4VfA3r8d6Hn+EbX59jkDqCIMyI7E4IwOcq7FKCxbrX5Y6YUeWMyESH1H8W65DvkjNhCgNCtVpyyxEzfAZWi1imqdaeXq0WW7YckQ+FwCeTsDi6MpKxcNaeXkUQplQLZ+Hg8dkR6UwiGkxQEEYMCChfO8Dwd24EANjqphusxCA09D4RhTNzxiQkRs0EAHzw2MPged5gRQRBmIlQXMxyOKwWuOzFm3VY4hRfK5pMF98ZUSKPTX2x3RElYw4BgvzagPpBzWqx+P1Ado5Xpr17NqwrE6avKYeEm8w5iAEGBWEDCD6dxvDkNgBA5bgj8x6TTqfxxBNP4IknnkA6nc57TEFw3X6ogpkWRphJz0DQct7llyLN2eAP7sULL71poEKCIMxGKC6uFYVmwQA26163OWEqM2GadRwUhCUyCSQy4pBgpT1harXYLXb47GLAl+uQmOnsAKAuE6ZGC2exdJlzHDSwOaahHFGNFnlWGAVhxACByhEHEAd2rsdQLoG4YEf9uGl5j0kkEjj//PMBAAsWLIDNZtxbbCYtZtPDSguLxGBvWkaNGArrtOOBL9/B+pefQuJb8+B0FM+KmiAI86ImCGOx7nXvCVOXCdOs46AgTMpEceDgtXuLpiXgDCCUCnXrC+sa1Kw8CFOrxVZWjkxzS4+BzfEcY45iaJFmhVEQRgwUKAgbQDRuWYGhAHbZR2GCPX9632KxYN68efJtIzGTFkmDWfQMFC0LL1+I+674GCXxNjyy5Blcdtn5RkgkCMJkhBPiBbavQFMOgM26V5I7rDmhLhOmWYc0sDkbhAWzOnwOHyycsufToqXMVYa94b3dHBK1DGpWq8Vann9gs5YgTI0WT3YWWYyCMGKAQEHYACK5ZxUAoMPf05pewu124+233y6WpD4xkxaAvR4tWSgznZu+tFSUBVBxzGmIfPA8mj94GW3f/TbKS4vcf0EQhOlQkwljse7lLUd0Ksv6aNZxUCZMMuVQ0w+mRYvkkJg7K6yrJ0x5JkytFpsUhPUoRxSDITU9YWq00MBmYqBhfHqCKBhP2wYAgFCbvxRxMMNl7RqNnu8FAIIUfRkvpShc+MPvI+wqgysdw0P3/MtoOQRBmIBgNgiTMlPFwpsd1hzpZsxRHDMMmV6CMKX9YFqRZ4Ul2GTC1CJlwnorR1TTE6YGKkckBhoUhA0g6uJbAAClo47Q/bUOkfiCKAC3y4HJZ5wHAMis+QCbtu4yWBFBEEYjuSMqKUdkgTffsGaF5Yia6aUnrNhBmOSQyKonTC2yMUd7fmMONeWIaqBMGDHQoCBsgNDSsBuV6AAvcBh+WH5nRACIRqOYPHkyJk+ejGg0WkSF+mnhGNnSD8Zz04X6c1SIlu8sOBnBshGwCRk8c+99ql+LIA4V7r33XkybNg1+vx9+vx9z587Ff//7X6NlMUNNOSKLda8ktxxRpTGHZh29ZcJUWOVr0ZI7K0yC1zCsWa0WW3kFACCdM7CZ5wUkIuozYWq0UBBGDDSoJ2yAsG/D56gEsMc6FCNKev+GSxAErF+/Xr6tHi7nf9XBTksOGgIyXfQYrYXBP6MQLRaLBfN/eBk++fNv4d27Bkv/txzHH2uOYdMEYUaGDRuGW2+9FePGjYMgCHjkkUdwxhlnYNWqVZg8ebLR8jQjZcL8CoIwFuuelAmLp3gI8U5xj1KYCdOso5dMmJqeMC1a5HLEfMYcKjJharXks6hPRtOQnkJNEKZGixSEJWNppJMZ2BzFm19HEGqgIGyAEN0tmnK0eMdjRB/HuVwuvP/++/JtIzGTFkmDWfQMRC1HzZ6Od0fOhGfnSrz/6L/w9aNnGu7sSBBm5fTTT+/295tvvhn33nsvPv3000ERhIXlTFjhF9gs1j2vs+vCmo8HYQUUZ8I06zg4Eya5I9qVB2FatEjliFImTOB5ZIKiFjWZMLVaJGOOdFurfJ9kyuFw22C1Kt8n1GhxuG2w2izIpHlEg0n4K92KX5cgigkFYQMER/NaAECqakqfx1mtVhx//PFFUNQ/ZtICmEvPQNVy/hU/wjO/vAL+4D48/ex/8P3vnaGvOIIYBGQyGTz33HOIRCKYO3du3mMSiQQSiYT892D2YtqsqClHZLHuOW1W2K0cUhlBdU+YZh1SEJaKAJkUQqlsT5iKckQtWqRMmNQTxodCAM+Lz6siE6ZWizVbjpjJKUfU2g+mRgvHcXD77Qi3JRANURBGmB/6GnuAUBXZDAAoGam/KQdRIGxa1QYUo4bXwTlTnN2y9bVnEI7EDFZEEOZlzZo1KCkpgdPpxI9//GO8+OKLmDRpUt5jFy9ejEAgIP+pr68vslplhFRkwlghlSR29YQVz4RCfL2cYCse7DYnrJgcHIRJphwWjwecQ7ktvFps2XJEPhSCkBQzYMV2RpTw+J0AgGgn9YUR5oeCsAFAKNiOYfwBAMCwibP7PDadTuOll17CSy+9hHQ6XQx5A0ILWz2SRb36hiwznRulWi7+8UJE7SXwJoN46P5Hi6CQIAYmEyZMwOrVq/HZZ5/h8ssvx4UXXij3uRzMddddh87OTvnPnj17iqxWGcFsT1iJgkwYq3XP67CBAw9LMizeoTATplmH1QZIAVe8Q5M7ohYtuXPCBEGQ+8EsKmaEadFi8fsBq1gmmm4Xs2FaBjVr0ULmHMRAgsoRBwB7NqzAJE5AM8pRVT20z2MTiQTOPPNMAEA4HIbNpvItZmBcwUwLupJOggYnCpZ6tMJMC3fQzyJo8fs8GH7yWWh5bQkin7+B3fvOxPCh1eoFEMQgxeFwYOzYsQCAmTNnYvny5bjrrrvwz3/+s8exTqcTTqez2BJVo6YckdW653VaUYI4OGk/UFgGyESHKwAkQ0C8Q9OwZi1aSl2lAICMkEEoFYJFtqcvVaxDixbOYoG1vAyZ5hZk2tpgr6nJGdSsLghTq0UKwmIhCsII80NB2ACgc8dKAMAB9zhU9XOsxWLBUUcdJd82EjNpkTSYRQ87LdqDZTVazv/+mbjpgzfgCzfgsXvuw29vuV6zDoIY7PA8363vayATTohBmBJ3RFbrntdpgw9Z23KrA7ArM7RgosMVAIJ7gXinpmHNWrQ4rU64bW7E0jF0xjvhlwc1q8uEadFiKytHprkF6axDYkwuR1RXFqlWi5wJo3JEYgBAQdgAgGsUTTliFfl7CXJxu934+OOPGbyoZFGvPsXCSks3DRos6pmdGwYMdC1WmxVHf/+H+Or+W+DYthzLv1iPWUf0//kkiEOF6667DqeeeiqGDx+OUCiEJ598EkuXLsWbb75ptDTNCIIgB2FKesJYrXslThuiXDYIU2GGwURHjkOiliBMq5aAM4BYOoaORAe87R0A1GfCtGixZh0SJZt6reWIarVQOSIxkDA+PUH0S3lwIwDAMWyGsUIIIoeTTjwK4SGHwQIBr/7rn4bPXiMIM9HU1IQf/OAHmDBhAk488UQsX74cb775Jk466SSjpWkmmswgw4v/vSspR2SF15GTCVPYD8YMdykAgI91IJztTVPjjqiVXHOOjMZMmBZsB80KM8yYI0BBGDFwoEyYyUmnkhiR3glwQM04Go5LmIuzfvxjvLbo/8Hfug0vv/ouFpw+z2hJBGEKHnzwQaMl6IbUD2a1cHDbiz8Q1+u0IcFlnVkNCHwAyJmwcLRZ7lUutjsi0EsQpjITpgXJpj6dtanXalGvFo9PCsIGR9kvMbihTJjJ2bf1Kzi5FMKCG7UjJ/Z7fCwWw6xZszBr1izEYsbah5tJix56tDjUs9eiXo0WLZMnjoEw6RgAwJfPP4p4gr59JIjBTijrjOhz2cApKBFnte6VOK2aMmFMdGSDsFBMHFDstDrhtCo3VtGqpVsQJhlzqBjUrFWLVc6EiecjLhtzqOsJU6uFMmHEQIIyYSanZetyjACw2zEak6z9f+PI8zxWrFgh3zYSM2mRNDDRo6EvjbkWBmjVcvFPL8M/r1yOkngbHnzgcVxx5Q9ZSyQIwkQEs5mwEqeySwhW657XaUNGQ08YEx1SEBYXy+/UZsG0aglkZ6R1z4SpK0fUosWW7QljlQlTq8WdzYSlkzyS8TQcBpTLEkSh0KfT5KT3i6YcQf+Ego53Op149dVX5dtqYdHdw0oLK8ykZzBpqSwvxZATv4P2t55A58evYc93Tkd9XX8+ngRBDFTUmHIA7NY9r9MGAdnsiEt5wMFER/Z1g/EOAOpMOVhoyZ0VpjUTpkVLrjFHJsUjFc8AUN8TplaLw2WDzWlFOpFBNJikIIwwNZo+nalUCg0NDYhGo6iqqkJ59j9Cgh2edtGUA7VTCjreZrPhtNNOY6hAfTjGVIsg/TCJHo0MNi0/uPBs/PHjt+GLNOHBh5/BDb+9kpE6giDMRm45ohJYrXtehxWchkwYEx1SJiw7qFltJkyrFpbGHFq02HKCsHhE/HxwFg5Ot7rLTC1aPH4Hgs0xRINJlFZ7VD0HQRQDxT1hoVAI9957L4477jj4/X6MHDkShx12GKqqqjBixAhceumlWL58uR5aD0lq49sAAIERMwx5fS29RsShg81mw8Tp0wEAG3Y348s9HcYKIghCNyRjDiUzwljSbU6YUe6IUiYslXVGVJkJ00q3ckSNmTAtWOVyxLauGWFeGzhL8a8hvDQrjBggKArC7rzzTowcORIPP/ww5s2bh5deegmrV6/G5s2bsWzZMixatAjpdBonn3wyTjnlFGzZskUv3YcEnS0HUAWxvnrYxCMKekwmk8Hbb7+Nt99+G5lMRk95RdPSrfFbQz/WYDw3Elrc4VlpqfJ1lYzc8J914HmyrCeIwUhXJkxZqRmrtabEaYNPQyaMiQ4pCEuLOtRmwrRqKXOJhhid0XbwQXFemdqeMC1apEwYHwoh1iGeE6k/q9ha3NkgLBaiIIwwN4q+xlq+fDk+/PBDTJ48Oe/vZ8+ejR/+8Ie477778PDDD+N///sfxo0bx0ToocjeTSsRALCXq8Uwf1lBj4nH4zj55JMBAOFwGF6vV0eFA0eL2fQw16Lhy0bWWuxWCz7b3YEXV+3D/80cpum5CIIwH1ImTGk5Iqu1xuu0wSH3hCkPwpjokIKwTByAVXUmTKsWqRwx2dEm32f1F1+Lxe8HrFYgk0GkUSyLdHnV29Nr0SINbI50kk09YW4UraBPPfWUfPtvf/sbzjrrLNTV1fU4zul04sc//rF2dYc44d2rAQDN7jEo9FLWYrFgerYszGIxdgKBmbRIGljq0ZLnYadFe7aJ9XmZPbIMH7UBt76xEfOn1Cp2UCMIwtyEVLojslprtLojMtEh9YTxSQBu1ZkwrVqkckS+U8yCWbxecHZ1wY8WLZzFAmtZGTItLYg2i0GYlhlhWrRIQViMbOoJk6P66uiqq67CXXfdhaVLl6K+vl6+P5lMYs2aNZg5cyYTgYcylsb1AIB4+WEFP8btdmP16tU6KVKGmbQA5tIzmLVMry/FSM6Dna1R/P29Lbju1MI/vwRBmJ+uTJiyi2xWa02J0wZeQyaMiQ4pEwbRPl0KhoqtRcqE2SJi1kdLP5hWLbbycjEIa4sAUO+MqFWLFIRFQynVr08QxUDT197z5s3Dcccdhz179sj3tbe3Y/bs2ZqFEUBpeDMAwDlsatFfW4sL4eCHzEryIfXu2Swcrj99EgDgoY92YHtz2EhZBEEwRq07Iiu8TmtOT5i64Ecz2QxcKJulUZsJ00qJvQQ2zoaSuLhnWwIGGZWgy5wj3hkHoK0nTAvS69LAZsLsqA7COI7DTTfdhPPOO69HICZocQkgAACZdBrDUrsAAJVjCjPl0AV6LwkVfGNiDU6YUIVURsAfX9tgtByCIBiitieMFSU57ohpe4khGmCxAk4/ggYHYRzHIeAMoCSbGFRrysECW7nYux4Li8GPlp4wLXgCUhBGPWGEudHcAHLTTTfh/PPP7xaIcRoc7AiRfTvWwc0lERWcGDpqUsGPi8ViOP7443H88ccjFoupF5B9D7W8l8y0IOeDqiEoZKlHK6y1aBklwFqL9Bb9/luTYLdyeG9jE97b2Kj5eQmCMAehhJgJ8yssR2S11njsVpRkyxGjnPI5UMzWPFcAoawFu1pjDhZaSp2l8IrJJ1gDpaqeg4UWa1k2ExYTSzS1lCNq0eLxST1hKUoKEKZG9ddYuR/sP/zhD+A4DscddxyefvppJsIOdZq3foHhAPbaR2C8rfC3ied5fPDBB/JtIzGTFkmDWfSw06L9Cw+9zsvoqhL88OhR+OeH2/GH/6zH0WMr4bRZmT0/QRDGoDYTxmqtcfBRgBOvQcKcB0rDH2ZrnqsUQUsrAPWZMBZaAs4ASqQgTKUzIgst1goxCEskxH1JizGHFi1ST1gmzSMZS8PpMSYjRxD9oToIu/nmm7tZht54440AgNNPP127KgLJfWsAAJ0+ZRb/TqcTzz77rHzbSHTRoiEzNzjPjfZv+ZhpyfPWXPmNsXhx1T7sbI3i/g+246cn0sgKghjohFUaczBba+KiE2BKsCKcUX6BzUyHK4BQdpan2kwYCy2lzlKUxMS9QEs5olYt0qywREb8sk1LT5gWLTaHFQ6XFcl4BtFgkoIwwrSoDsKuu+66HvfdeOONsNvtuOOOOzSJIgBn20YAAF+dfyZbb9hsNpx99tl6SFKMmbQA7PVwGgIgM50bPbX4XHb89rTD8POnV+Pu97diweFDUV+uvHyIIAjzIFvUK8yEMVtrEmIQFoIb4aTyAcesdCSdPsRT2nrCWGgpdZV2ZcJK1QdhWrVYy8ohAEgKdoDTVo6oVYsn4EQyHkU0mERZrbEzSgmiN5gPb/rd736Hjo4O1k97yFET2woA8I2YYawQogdUYa6Mb0+vw1FjKpBI87jhlXVGyyEIQgPxVAbJjFgeZpQxh5QJCwkeRBJpYzQACDrFi3sOxhlzANlMWDYIs2goR9SKrbwMGasTPCd+LrQEYVpx+8TXJodEwswoCsJ2796t6Mn37dun6HhCJNjZhqGCaGQwdIKyeWuZTAYff/wxPv74Y2Qyyr8hlGDRy8pKCyuY6WHgO8P63Gh5u9i/T93VcByHP5wxBXYrh3c3NuHt9WTSQRADFSkLxnFAiUNZEMZsrZEzYeqCMFY6gg43AKCEs8PCqftOm4WWUmcpvAzKEbVqsVZUIJV1q7TaLbA71PcAa9Xi8YsljBSEEWZG0Qo6a9YsLFiwAJdccglmzZqV95jOzk48++yzuOuuu3DZZZfhZz/7GROhhxJ7N32BSQCaUY6qilpFj43H4zjmmGMAAOFwuFvfXrExkxaz6WGtRUtcyEpLXw6NY6tLcOmxo/GPpdtwwyvrcMzYSrg1bNAEQRiDNCOsxGGDxaJs5WG27sU7RS2CB5GE8gt0VjpCdvFC38+pX8tYaMnNhGlxR9SqxVpWhpRdfIwWUw4WWiRzjhgFYYSJURSErV+/HjfffDNOOukkuFwuzJw5E3V1dXC5XGhvb8f69euxbt06HHHEEbjtttvwzW9+Uy/dg5rgzlUAgAb3GFQpfCzHcRg7dqx820j00KJliPRgPzdm13LlN8bi5dX7sa8jhrvf34Jr50/U7bUIgtAHLTPCmK01OT1hkaTyTBgrHcGsc7FPQ2cHCy0BZwBuOQhTX46oVYs1EEAqO8Ta6dS2l2jVIgVhlAkjzIyiVbSiogJ33nknbr75Zrz22mv46KOPsGvXLsRiMVRWVuK8887D/PnzMWXKFL30HhIIjesBANFS5RepHo8HW7ZsYS1JFWbSAphLz6GoxeOwYdHpk3DZYytx/4fbcebhwzC22qBBqwRBqCKcUOeMCDBca+Jd5YhhFeWIrHSErOIllJ9X/+UgCy2ljgDsDIY1a9XCWSzIBKoBAE6HtnEnWrXIQViIgjDCvKjqqnW73TjrrLNw1llnyX1fQ4cOZSrsUMYf3AQAsNdRMEsMPPoajnnSpBp8Y2I13tvYhEWvrMXjF88xPBNIEEThyOWIRplyAHImLGi0MUe2HNNn8NzJgOBCMitBSxDGgoyvAgDgtBj3vgCAm8oRiQGA6hz6xx9/jFGjRmH48OEYPnw4ampq8Ktf/QrBYJClvkMOPsOjPrkDAFAx5nCD1RCEAgoIpjiOww2nT4bTZsHHW1vxn68OFEEYQRCsCGooR2RGPKccUUVPGCtC2fJ4fzplmAYA8CfES7m0Bcg4jZ2JlfaKs8LsQsJQHVSOSAwEVAdhP/rRj3DYYYdh+fLl2LRpE26//Xa88847OOKII8gVUQP792yBn4siJVgxdOx0xY+Px+M47bTTcNpppyEej+ugcGBqYatHe+bGTOem2FqGV3hwxQlirf8fX10vf7NOEIT5Cakc1AwwXGsSXRb1asoRWekIQgwAfWn1F/ostHiiYhos7AaCSfVfhLPQknKJPWGOTEy1DhZacoOwvqozCMJIVH+VtW3bNvz73//G+PHjAQBjx47FBRdcgO9+97u46qqr8NxzzzETeSjRuGUVhgHYa6vHKIdL8eMzmQxef/11+bZahB43lMNKCyvMpIeVFjkc1FDSZ8R5uezro/Hiqn3Y0RLBnW9vxqLTlQ0lJwjCGKQvTdRkwpitNXHtFvUsdIQE8bX9SfWBHBMtwTAAIOwCOhOdqHBXGKYl5SgBYoAtGVL1eFZaPD4xCOMzAhLRNFxeYzOEBJEP1UHYYYcdhqamJjkIA7LzgP7wB8yePZuJuEORxL6vAABtJeMwSsXjHQ4HHn74Yfm2VrTkfFhr0YqZ9LDSosUtkrUWmQK+dXTZrfjDGZNxwYOf45FPdmLBjKGYXl+q/bUJgtAVLe6IzNYajZkwVjqCGbHkzpdOAqkYYHcboiUTFC37wy6gI9Gh6jlYaUlZxHNgz44RMEqL1W6B02NDIppGNJikIIwwJaqDsIsuugg//elP8corr6C+vl6+v7OzE34DJ7YPdBytGwAAqYrDVD3ebrfjoosu0i6EgVkCMy2MMJOewahF6Ufm2HFVWDCjDi+t3o9f/3sNXrnyaNit6q2eCYLQn3A2CPOrKEdktu516wlTHoSx0hFMiyV3fp4XZ5epCMJYaMl0igFPxM2hPdGu+nlYaElCDJis4TZNz8NCi8fvkIOw8iHGzikliHyoDsKuuuoqAMC4cePwne98BzNmzEAmk8Hjjz+O2267jZW+Q47KiGjJ6q6fZrAS80D13IOX331rEpZubsaGA0E89NEO/Oi4MUZLIgiiD0KJrDui00h3xK5hzbGkcaXlUv+VHIT5ag3RwXd2ZcI6E9oyUFpJZMTPhS3YYqgOAHD7HGhviJJDImFaVK+iBw4cwOrVq/Hll19i9erVWLJkCbZs2QKO43Dbbbfhv//9L6ZNm4Zp06bhlFNOYal50JKIRzEssw/ggJpxM1U9RyaTwZo1awAAU6dOhdVqZSlxwGoxmx7mWjT27hl1XipLnPjtNw/Dtc9/hb+8sxnfnDoE9eWeor0+QRDK0FKOyGytyWbCgvAgobInjIWOULbvyc/zQKxD1XOw0JLpFM9H2AU4NJQjatUiCAKSKbEkwtreoFoHCy0A4AmQQyJhblQHYTU1NZg/fz7mz58v3xePx7FmzRo5OHvllVdwyy23oKOjg4XWQc/+rV9iFMejQyhBTd1IVc8Rj8dx+OGitX04HIbXa1wKXhctGsok2etRH/kw16KhepS1FqVn5ayZw/DvL/Zh2fZW/PaltXhk4SyaHUYQJkXqwVKTCWOy1ggCkBCDn5DgQUqlOyKLNU/KhPmkTJgKWGiRyhHDbg4WDUGYVi2pRAaSh4alTdv4ERbnRTLnoCCMMCtMGzBcLhdmzZqFSy+9FHfffTc++ugjVQHYPffcg5EjR8LlcmHOnDn4/PPPC3rc008/DY7jsGDBAsWvaQZad64FAOx3jABnUffWcByHuro61NXVGX4hayYtbPVo/7cw08LgtBp9XjiOw81nToHDZsGHm5vxypf7NWggCEJPpJ4wNcOamaw1yQggiFf6aueEsdDBCzzCKdGV0K8hCGOhJcOoHFGrlnhELFW1ZJJAewuElPrxIyzOizSwORqiIIwwJwYWdefnmWeewdVXX4377rsPc+bMwV//+lfMnz8fmzZtQnV1da+P27lzJ6655hoce+yxRVTLlsYO8ds9m1N9JsLj8TCZ08bCdY+VFlaYSQ8zLQza5cxwXkZXleBn3xiLO97ajD/8Zz2+Pq4KZV7jHTUJguhOSIMxB5O1JuuMKHBWxOAEMjySaR4OW+FfXLLQEU1FwQvifC4fLwDxDlXPw0KL5I4YcQEplTpYaImHxaDLno6AA5Bub4e9j+s2PbUAObPCOikII8yJ6azI7rzzTlx66aVYuHAhJk2ahPvuuw8ejwcPPfRQr4/JZDI477zzcOONN2L06NFFVMuWhk7R7tZrZMPzQRifvyIOFS77+hiMrylBaySJW17fYLQcgiDyoKUckQnZUkS4/JB2KDUOiVqRShHt4OAU1AdhLOgqR9RmUa8VOQjjxblpmXb1To0skIKwGGXCCJNiqiAsmUxi5cqVmDdvnnyfxWLBvHnzsGzZsl4f94c//AHV1dW4+OKL+32NRCKBYDDY7Y9ZaOgU7W49TmMNLEQo/CJUotLN0mGzYPF3pgIAnlu5F59sNd5diyCILnhe6ArCVJQjMiFrysE5/XBms19qZoVpRTblsDjE3VLjXCwt8B1SOSJnbBCWLUd0cuLPTJs2m3qtdGXCEobqIIjeMFUQ1tLSgkwmg5qamm7319TUoKEhv9PORx99hAcffBAPPPBAQa+xePFiBAIB+U/ujDMjiSbTaItkM2EO9ZtbPB7H2WefjbPPPhvxeJyVvAGvxWx6BqUWBn1/M0eU4/yvDQcA/ObFNYinjLOfJgiiO5FkV7Cj1phD81oj9Ty5/LKGXF3F0iGbclid4h0q3RFZaMlkv0yOaBzWrFVLLJsJc9jEdTvdqj4IY3FeujJhKQg8jbohzIepgjClhEIhXHDBBXjggQdQWVlZ0GOuu+46dHZ2yn/27Nmjs8rC2NoUlhMIDpv6TFgmk8Hzzz+P559/HpmMsRewzLTkZFa0zAwblOdGRn0AZKbzAgC/PGUiavxO7GyN4i/vbDZaDkEQWaSMk93KyVkoJTBZa7KZMDgDcum+0nJEFjrkGWG27EgNlZkwrVqEVAp8WDQICbuBYCKoep/UqkXOhGXjUi2ZMBbvkTvrjsjzAuJR9SYhBKEX5mk+AlBZWQmr1YrGxsZu9zc2NqK2tucQxG3btmHnzp04/fTT5ft4XmyUtdls2LRpE8aM6T781el0wimtECZiU0OIyfM4HA7cfffd8m0jMZMWSQNLPVryPmY6N2bSAogN/39cMBWXProCD3y4HadOGYIZ9aVGyyKIQ56uGWF2VY51TNaarDEHXH54HOIXlkodElnoCGZ1+GxZIy2VQZhWLZlQ17VDxAXwQhrhVBg+h6/oWqSeMJdbvLRMt6sPwli8R1abBU6vDYlIGtFgEu4S4/c3gsjFVEGYw+HAzJkz8e6778o28zzP491338WVV17Z4/iJEyfKw/wkfve73yEUCuGuu+4yTalhIWxuDDHpwrLb7bjiiisYPJN2dNGioeSNmR4Gb9Rgfp+0ZCslTppUgzNm1OHl1ftx7XNf4tWfHQOnhgwxQRDakYIwtaYcTNYaORPm6ypHVJgJY6FD7glz+LO6OlQ9j1YtkimHxeeD0wHE0jF0xDtUBWFatchBmE90zsxoKEdktS95/E45CKuo0/x0BMEUUwVhAHD11VfjwgsvxJFHHonZs2fjr3/9KyKRCBYuXAgA+MEPfoChQ4di8eLFcLlcmDJlSrfHl5aWAkCP+83OpsYwquS/kSmGuaH3pxjccPpkfLy1BVuawvj7u1txzfwJRksiiEMaw50Rga5MmNMvlyMaYcwhlyM6A+IdBhlz8NkgzOr3I+DkxCAs0YF6FP9LaKkc0R1wAQAyGjJhrPD47Wg/AMRoYDNhQkwXhJ1zzjlobm7G9ddfj4aGBsyYMQNvvPGGbNaxe/duWFQOMjYzmxtCOUGYeniex7Zt2wAAY8aM0XyutIQbrLVoxUx6BqMW1gO5y7wO3HTGFFz+xBe494NtOGVKLaYMDTB9DYIgCkfLoGaA0VoT7ypHVJsJY6FDzoS5y7O61AVhWrVImTBrIIBSpwUNkQbV5hxatUjGHJ4yLwQA6Tb1FvWs9iVPti8sSkEYYUJMF4QBwJVXXpm3/BAAli5d2udjlyxZwl6QznRGU2gIxpnYpMRiMYwfPx4AEA6H4fWqHPzM4HqamRZGmEkPaSmMU6cOwWlTh+C1NQdwzXNf4pUrj1E0lJUgCHaE4uJFtl9lEMZkremWCcv2hCWV9YSx0CG7I7orxDvinQDPA0oDF41a5CCsNICAU3xttUGYVi0JKRNW5UcU2ow5WO1LHr/oAUBBGGFGTBmEHWpsbhK/USvz2AAGVRWBgHmyBWbSAphLDwstrJJPZjovB3PjGZPxybYWbGwI4d6l2/DzeeOMlkQQhyQsyhE1rzU5mTAt5YhadciZME+2hkXggWQ4O0S6eFoyneL5sPgDKM0GpZ0J9aWRWrRImTBvTRmiANIa54Sx2JfcfrE/jcoRCTNCQZgJkJwRawMuoBWarqy9Xi86OjrYCNOIHloEqDd9GIznhoUJhpnOSz4qS5y44duT8fOnV+Pu97dg/pQaTKxVfqFDEIQ2QhrLEZmsNTmZMLXliCx0dGXCygGrA8gkxWyYwiBMq5bu5Yji+WhPqCsD1KIllcwgkxLdqb11FWiG2K8mpFLg7PaiasmFMmGEmaG6HhOwuTEbhPldBisxKQwCDV0wq65ByLen1+GkSTVIZQRc+9xXSGd4oyURxCFHVyZM+UU1M6TeK1fAUGMOORPmDACu0qy2jqLr6B6EiTq0ZMLUIjkjWmwcnFVlcllmxuAv+KSBzdEQBWGE+aAgzATImTAKwggiLxzH4eYFUxBw27FmXyfu+2Cb0ZII4pBD6gnzqcyEMSG3J0yeE2aAO6I0J8zhA1zGOSTyQSkI88tBmNqeMC1IQZjba4fFZoM161StxZyDBXIQ1klBGGE+KAgzGEEQujJhASkIU1+OmEgkcNFFF+Giiy5CIpHQoEv1Q5lr6YaGUk1d9JCWHrAokcxHtd+FRadPAgD89Z0tWLvPGEtogjhUkTJOaoMwJmtNnp6wqEJjDhY6QqmcOWFSEBbrUPw8WrVkOroyYYGsXb7aIEyLFnlGWHYgsrW8TNTX1lp0LblIQVgsnALPU/UKYS4oCDOY5nAC7dEUOA6oydYuayGdTuORRx7BI488gnRa+7eDWnwfWGvRipn0sNei/p0y03npjzMPH4pTJtcizQu4+tnViKeUXXwRBKEercOaNa81gsBkTphWHalMCrF0DEA2CHOXir9QkQnTqkUe1sygHFGLllhEzDS5SsT3xFYmWverNedgtS+5fHaAAwRekANFgjALZMxhMJsbwgCAkRVe2K3a0/Z2ux233XabfFs92m332Glhg5n0DEYtrOeE9fYaN585BSt2tWNzYxh3vr0Zv/nmYbq/LkEQ2t0RNa81qRjAZy/IXX6UOMUMidJyRK06JFMOACixl2gqR9SqJRMUtVj9AZS5PACA9ri6awktWuJh8T1webOZsArRuj+jshyR1b5ktVrg8toRD6cQCyXlzBhBmAEKwgxmU7YUcXxNCZMaQIfDgWuvvVbz87DATFoAc+khLeqpKHHi1u9MxSWPrsAD/9uOb0ysxtdGVxgtiyAGPVrdETWvNVIWjLMAjhJ4nWImPJJQlhHXqkMKwkrsJbBarDlBWIfi59KqpfucMDEIU5sJ06IlHpYyYWLAZJPKEdvVZcJY7ksevwPxcArRziQqhjJ5SoJgApUjGszmrCnHhBpf151FyCgQhG4UwTVy3qQanHNkPQQBuOa5L2XDAIIg9COcDcJ8RrkjSv1gTh/AcSjJzsUqtjui7IzoyNrRy+6Ixe1TFQQhrztiPBNHPB0vqhbZmCMbhFmlcsRWbbPCWEAOiYRZoSDMYORMWK2vnyMLg+d57Nu3D/v27QPPG2vjzUxLzkW9FtMH9ufGTFrUYyYtSvj96ZMwrMyNve0x3PTqeqPlEMSgR6sxh+a1Ru4HEzNP3pw5YUr2Bq065Blhjuy+raEcUYsWIRoFsv1SVr8fJfYS2DjxnKgx59CiJR7JGnN4s0GYbMyhLghjuS+5fdkgjGaFESaDgjADEQQBWxpzM2HaMwixWAzDhg3DsGHDEIvF1GszkRZWsDs32jOVZjo3zLQUOYNb4rThzu/OAMcBz67Yi7fXNxb19QniUILnha6eMJVBmOa1Rp4RJmagpCAszQtIpAu/UNeqQ8qE9QjCVLgjatEiZcE4ux2c2w2O4+B3iudGTUmiFi0x2R0xW46Y7QlLqyxHZLlHegIUhBHmhIIwA9nXEUMkmYHdymFkpZfZ89psNths5mj3Y65F44U+Ez2MYg0WWqTToTX+MdNnRgmzR5XjsmNHAwCu+/dXaA0ba/dPEIOVcLKr5E+tMQegca3JcUYEAI/dKv9KqTmHFh3SjDC5HFGDO6IWLbIzYmlANkYqc4oZqPaEOkMMtVrkTNhB5YhqjTm0aDkYTzYTFqMgjDAZA++qaxAhzQcbU1UCuzU3HlZ/Re31epFKMeiPyS7oWq7tmWlhhJn0MNPCoP/KTOdFDf/vpPFYuqkZmxpD+NULa/DAD2YWxamRIA4lpH4wu5WD06bu+1vNa03OjDAAsFktcNktiKd4RJMZFGrPo1WHNCOMRTmiFi2Zzi5nRAkts8K0aJHnhHkPMuZoVTcnjOW+1JUJoy/pCHNBmTAD2ZS1px9fw6YfjCDMQXEHYrrsVvzlnBlwWC14Z0MjHv9sd1FfnyAOBbr6wezGfclxUCYM6MrKFdOcQ86ESTo0uCNqIdeUQ0KeFVZkkxApEyYbc5RnM2GdnRAMnj/pkXvCBu6XjcTghIIwA5EyYRMkU44iuMoRhF4YmXuaVOfHr06dCAD446vrsSnrOkoQBBu0DmpmwkGZMKC7OUex6GnMUSr+LHLgk+nsAHBQEJbVoiYTppZUMoN0UuzJk405SkvlippMR/G05IMyYYRZoSDMQKQLxR6ZMA3fMiYSCVxxxRW44oorkEgYu+CYSYvZ9DDXoiF+N9N50cLCo0biuPFVSKR5/OypVYinlM0OIgiWLF68GLNmzYLP50N1dTUWLFiATZs2GS1LNVoHNQMM1po8mTCvQ3kmTKsOKQjrYVGfDAMZZcGgFi28PKi563xoKUdUq0UqRbRYOdhdYp8eZ7WKgRjU2dSz3Jckd8R4OAWepy+7CfNAQZhBpDM8tjaL5YgTGJYjptNp/OMf/8A//vEPpA0uATCTFrZ6skGyhrWc+bnRELib7X1Si8XC4Y6zp6OyxIlNjSEsfn2D0ZKIQ5gPPvgAV1xxBT799FO8/fbbSKVSOPnkkxGJRIyWpgppFp9aZ0SAwVqTJxNWImfCCv/SRauOnnPCuvQozYZp0ZI7qFlCMuZQE4Sp1ZJrypFbqiqXJKpwSGS5L7lL7AAnFhvFaFYYYSLImMMgdrVFkUzzcNutGFbmzt6r/Rsau92ORYsWybfVwuK7IlZaus0J06CMmR4JDYEPMy0MejNYnxcjq2qrfE7ccfY0XPTwcjyybBeOHVeFeZNqjBNEHLK88cYb3f6+ZMkSVFdXY+XKlfj6179ukCr1dA1qVn/ZoHmtkTJhrq6gw5sd2KykHFGrjh6ZMKsdcJSImbB4B+At1CJEm5ZMR9YdMU9PmJogTK2Wg005JGxlZUhC3awwlvuSxWqBu8SOWCiFWCgJb8Cp6fkIghUUhBnE5mwp4riaElgs7LppHA4HbrjhBmbPpwUzaQHMpYedFu0RDzMtJnEkPH5CNS4+ZhQe/GgHrn3+S7xx1ddR43cZLYs4xOnMZi3Ks9mBg0kkEt3KroLZUjOzoHVQM8BgrZGyTM6ePWFKyhG16ugxJwwQA8NkWHEmTIuWTLB3d0Q1c8LUajnYlEPCKs0KU2FTz3q/9vgdiIVSiHYmgWHMnpYgNEHliAaxqbGXfjAAxlocmByTXOgT5uaXp0zA5Do/2qMpXP3sauoDIAyF53lcddVVOProozFlypS8xyxevBiBQED+U19fX2SVfSMbc2gIwjQjD2vOyYQ5im/M0aMcMVdTER0S+3JHLKYxR2+ZMKtkU9+mzqaeJR5/1pyDyhEJE0FBmEHIzoiM7ekFQUBHRwc6OjogGOy2aCYtZtNDWvTFabPib+ceDrfdio+3tuK+D7cZLYk4hLniiiuwdu1aPP30070ec91116Gzs1P+s2fPniIq7J8ud0QNZe5a15p8xhxSJixZeBCmRYcgCL1nwgDFmTAtWvL1hGkJwtRqiYW7D2qWsGUHNqdVlCOy3pfcUhBGA5sJE0FBmEHIzoi1OYs4g4UmGo2irKwMZWVliEajmp9vsGgxmx7WWrR8cpifF5MEcmOqSnDDtycBAP781mYs36n8QoAgtHLllVfi1Vdfxfvvv49hw3qvg3I6nfD7/d3+mIlwQrzQ1lKOqHmtyWvMIfaERRUYc2jREUlFkBHE1/I7czNhpVmNyoIwLVr4fJmwrI5QMoQ0ryw7qFZLrjFHLrIxh4pyRNb7kscv9oFREEaYCQrCDCCeymBnq7io5M2EUckdoRAzfGIMG+DaB989sh4LZtQhwwu48skv0BoeuBb8xMBCEARceeWVePHFF/Hee+9h1KhRRkvSBIueME0IQi/GHMUtR5RMOewWO1zWnF5TSVOsoyg6gJxMWE7AnlsiqaYvTA29GnNUSEGY8V+ASQObYxSEESaCjDkMYHtzBBlegN9lQ42frUuPx+NBMikuMjabsW+vmbQAeuhRn/Ex07kxkxbWcByHm8+cijX7OrGtOYKrnlmNRxbOZmqGQxD5uOKKK/Dkk0/i5Zdfhs/nQ0NDAwAgEAjA7Xb382jzwWJYs6a1JhUFpMyORmMOLTpynRG7ffGkshxRrRYhlQKfHXeQ645os9jgc/gQSobQmehEhbtwp0a1Wno15tBQjsh6X+oa2ExBGGEeKBNmAHI/WK2PefaA4zjY7XbY7XZNz82ioIyVFlaw1qPlGZhpYfBGme19Yo3XacM/zpsJl92C/21pwT3vbzVaEnEIcO+996KzsxPHH388hgwZIv955plnjJamChZBmKa1RipF5KyAwyvfLc8JU9ATpkVHMJuN61aKCADu0qxOZUGYWi2ZUEi+bT2odFVtX5haLVImzNmrMYfyIIz1viRlwigII8wEBWEG0Lszojl6aUyHSXqMiMLQMstNLybU+vDHBVMBAH95ZzM+2dZisCJisCMIQt4/F110kdHSVCFlmgxzR5RNOXzdSva7MmGF94RpoceMMIkiuyPKM8J8PnBWa7ffFdshMRYWAxt3iaPb/basRX2msxNCpjjvT29QJowwIxSEGYA0I2xCLVtnRABIJpO49tprce2118qpfHVwOf9rtBY2mEkPaSk+Z80chrNnDgMvAD97ajWaQnGjJRHEgKFrWLN6d0RNa028Zz8YoG5YsxYd/QdhyjJharVkOjoAdDflkFAbhKnVEo+I595V0j1At5aWigGzIMh69dbSG+5sJiweSSGT4TU/H0GwgIIwA+h7Rpg2UqkU7rjjDtxxxx1IpVLMn99oLVqCwsF9btSfGXZazF/K+IczpmBCjQ8t4QR+/tRqZGh+GEEUBAtjDk1rjWQy4eoe/JSoMObQoqPXckSV7ohqtchBWGlpj9+pDcLUaEmnMkhns5CugzJhnNUqB4npVmWzwljvka4Su5hAFYB4yNj9nyAkBlcX/gAgnEhjb3sMQJ4gjEHZnd1uxzXXXCPfNhIzaZE0mEUPMy0M4h4znRe9cTusuOe8I/Dtuz/Csu2t+Mvbm3HN/AlGyyIIU5PhBSbliJrWGim4cXbP/Hgcyo05tOjoNxOm0B1RrZa+grBA9hwpDcLUaIllAxqLlYPDZe3xe2t5OTIdHYpt6lnvSxYLB7fPgWgwiWgwCW8pW1M0glADBWFFZks2C1blc6Lc68h/kIYmVIfDgdtvv13141liJi2AufQMai0mTy6NrS7B4u9Mxc+fXo2739+KqcMCmD+51mhZBGFack0vtBhzaFpr8swIy9UTSaQhCEJBJg5adLDuCVOrpZBMmFKLejVaJFMOd0l+Aw1beTmS27cj067MnEOPPdLtzwZhocFbdk8MLKgcschsaQwDAMbXlBishFCPtNGYIdowg4aBxxkzhmLh0SMBAL949ktsbQobK4ggTIzUD2a3cnDaDLpskI05ugc/Uk8YLwCJtP69PnI54sFBmEe0Y0esoyhmUgWVIxbBJEQKaFy+/F8qSwOb1djUs8brz5pzdFIQRpgDCsKKjNQPNqHG38+R6hAEAalUCqlUCoKGjYCFwx0rLawwk57BqGWgudv/5puHYc6ocoQTafzosRUIxalPgCDy0dUPpnH0iZa1pjdjDkdXZq7QkkQtOuRMWA+LetGOHXwKSBb+pY5aLX0GYdn+NKXliGq0xEOSM2L+kkGrNLBZYU+YHnukOxuExSgTRpgECsKKzGbZlCNfJkz7QhONRuFwOOBwOBCNRjU/nxm0CN1uqz9HzM4Ng2BjML5PAw271YK7v38Eav0ubGuO4JrnvgRPRh0E0QPpCwotpYiAxrUmkb8c0WLh4HEoc0jUoqPXckS7B7Bms0Gxwvuf1GrRw5hDjZaYVI7YSybMVlkJAEi3KAvC9NiXPNkgLNKRYPJ8BKEVCsKKzKasPf34Pu3pzZBS0G5RTxyaGJ3ZU0KVz4n7LpgJh9WCN9c14t4PthktiSBMB4tBzZqRjTl6VpF0zQor3JxDLb0GYRwHuKWSRGUmFGrQIwhTg2TM0VsmzFYhBWHGz2aUzDgiVI5ImAQy5igiHdEkmkLiNzDjqvXpCfN4PGhvb5dvG4mZtEgazKKHnRbtYbKZzosRzKgvxR/OmIxf/3sN7nhrEybV+XHChGqjZRGEaWA1qFnTWtOLMQcgBofNoQQiBQ5s1qKjV4t6QCxJDDcA0cL7n9RqKcQdMZgIFmxWolaLPKjZ10sQVqUuCNNjX/IGxCAs2kmZMMIcUBBWRDZnTTmGlrrhc+VZsBhkEDiOQ2meRdkI9NGiPugYlOfGTJ+ZgdYUlsP3Zg/Hl3s78dTnu/Hzp1bhPz89BiMqvEbLIghT0DWoWdslg6a1phdjDkD5wGa1OgRB6D0TBnT1hSnIhKnVUkgmLC2kEU6F4XMUNpNUjRYpE3bwjDAJqRwxozAI02O/7sqEURBGmAMqRywisilHn6WIBEEYwQ3fnoQZ9aUIxtO45JEVCJJRB0EAYDOoWTO9GHMA6maFqSGajiIjiNm2vEGYpzjliIIg9BmEuWwuuKwuAPo7JMb7yYRZc8oRjS5V9waknrCk4VoIAqAgrKhszvaDjevPnl5DRiGZTOKGG27ADTfcgGTS2LpnM2lhqUeQsnEa1nAznRv2Wgbm5ua0WfHPC2aixu/ElqYwfvbUKmTIqIMgEIyzKUfUtNZIPWF5grDcWWF66pBKEW0WG9w2d88D3KXiz1jh5YhqtAixGITssfmCMECdQ6IaLV09Yb1lwipEzckk+FBIVy39IZUjZtI8EhH9+wcJoj8oCCsiXfb0+mXCUqkUbrzxRtx4441IpbR8k6/94pOdFjaw1qOl+s5M58ZMWoymxu/CAz84Ei67BUs3NeOW1zcYLYkgDCcsG3Pkz3YUiqa1JsHOmEOtjtxSxLx9VrIxR0fBz6lGi5QFg90Oizd/v5Qacw41WrrcEfN/NiwuFyw+8ZpHiUOiHvuS1W6BK2sgQiWJhBmgnrAiIQgCtsj29PoFYTabDT/5yU/k20ZiJi2SBrPoYaaF63HDOC2DxEtz2rBS/PnsGbjiyS/w4Ec7MK66BN+bPdxoWQRhGOGEeBGstRxR9VojCEAim0XJa8wh9oRFk4UZc6jV0Wc/GKCqJ0yNlq5SxECvphuSOYeSIEyplkyaRzImBr69ZcIAwFZRgWQohHRLM5yjR+mipVC8ASfi4RQiHQlUDNXHII0gCsX4K+NDhOZwAu3RFCwcMLZfZ0T1F7NOpxP33HOP6sd3SdBuUc9MS07ttpY5Ycz0MIC0mJvTpg3B1qbx+Ms7m/G7l9ZiZKUXXxtdYbQsgjAEVj1hqteaZBgQ+OyT5MmEOZSVI6rV0aczIqAqCFOjRQrCbH0YV0iZsE4pg6iDlng2C8ZZODg9vX82bJWVSO7cqcicQ699yVvqQOs+yoQR5oDKEYvE5gbRGXFEhRcuu9VgNQShH4Ol3/lnJ47Ft6YNQZoXcPnjK7G79dAZZE0QuRg+J0wy5bDYAXvPXqxizQnrNxMmGXMosKhXg5wJC5T2ekwxZoVJ9vSuEjs4S+9f2VpV2tTrgdQXFukwvk+dICgIKxKb5FLEPrJgg+XqVU8GsA06MbDgOA53nD0d04YF0B5N4eJHlpNjInFIYnwQJply+PPuAVKGTtKpF3qUI6ohLQVhZaW9HlOUIKyfQc0SXQObC+8J0wvZpr6DMmGE8VAQViS2FMGUAwAikQjsdjvsdjsikYiurzWQtJhNz2DUMhjjY5fdigd+cKTsmHj54yuRTPNGyyKIohLKfvmg1R1R9VrTx4wwAPC7xSCg0C9J1OqQSvtYBmFqtPRlTy+hJghTqqW/Qc0S0qwwJZkwvfZI2aaeyhEJE0BBWJGQM2GFzAjTeDWbTqeRTpvDftVMWgDGejRmLs10bsykxWzU+F148MJZ8Dqs+HhrK379wlc0Y4Y4pJAyTH6XNndEQOVaI88I6yUIy+oKxgrPVKvREUqK+3jvPWGSO2Kbov1BqZZCgjA1xhxKtfRnTy9hk8sRm3XTUiiUCSPMBBlzFAFBEOQZYXpnwtxuN/bu3SvfVguLS0xWWljBXI+GWNlM58ZMWszKlKEB3HPeEbj4kRX496p9GFrmxi9OnmC0LIIoClKGKeDWFoSpXmukTFieGWEA4HeLlzLBAssR1eoouByRT4tmIs7+93s1WpRkwpQYcyjVIhlz9FeOaK0QTY0yCsoR9dqX5CCsk3rCCOOhIKwI7OuIIZLMwG7lMLLS28eR2kMfi8WCoUOHan4eFphJC2AuPYNayyDNEh0/oRq3nDkFv3phDf7+3lYMLXWTdT0x6EmmecRTYgmuVndE1WtNvEP82Vs5osJMmFod/QZhdjdgdQKZhFiSWEAQpkaLXuWISrXEQlljDl8/mbDKKgDKyhH12iMlY45oMAmeF2Dpw1CEIPSGyhGLwJZG0RlxdGUJ7NZCTrl5FgXzKCHyY6aAZ/B/Ws6ZNRw/+8ZYAMBvX1qL9zc1GayIIPQllNNnZbg7Yi+ZsIDCnjC1yEFYb+WIHFcUh8RMh5jd6jMIc4m/65ACWB2IFZgJk8sRW1sh8Mb21Lp9dnAcIPCCHEQShFFQEFYEFPWDaSSZTOL222/H7bffjmTS2AWGmRZGc8IG5bkZZFoGAv/vpPH4zhFDkeEFXPHEF1i7r/ByH4IYaEglfl6HFbaCvkTsHdVrTX/GHNlMWDzFI5Huf2CzWh3ynLDeMmGAYnMONVoKyYSVOUUd8Uwc0VRh4zWUapGCGHd/mbDybGCaySDTWdh6qde+ZLFa4PZnzTmoL4wwGArCikBXP1g/Q5oZlHGlUin88pe/xC9/+UukUsbaaZtJi9n0kJaBC8dxuPU703D02ApEkxksXLKcZogRgxYpE+bX2A8GaFhr+jHmyHVtLMSmXq2OfssRAcVBmBothQRhXrsXTqtYetcWLywrp1RLoRb1nN0ua003F2bOoee+VEJ9YYRJoJ6wIiBlwsbpbMoBADabDRdeeKF820h00aLBOXJQnxtTnRczlUjqg8Nmwb3nz8R371uGjQ0hXPDQZ3jux3NR7XMZLY0gmBKMsXNGVL3WSCV12RK7g7FaOPicNoQSaQRjKVSWOJnrEARBDsIk58G8KAzClGoR0mnwQVFHX0EYx3GocFVgf2Q/WuOtGOYbxlyLPKy5H4t6QCxJzHR0INPSAowfz1yLEjwBJ4AQZcIIw6EgTGcyvICtTWJPWMHOiBouqJ1OJ5YsWaL68SwxkxaAoZ7s+6PFptxM54aVFm4wDgrrA7/Ljkd/OBv/d98n2NUaxQ8e/BzP/GiuZgc5gjATUp+VVlMOQMNaE+sQf7pLez3E77aLQVgBmTA1OmLpGNJ8NiAtKBNWWPZJqZZMNgADAGugj2AQQLmrXAzCYoW5EirRwmd4JCLi+ejPoh4ArBWVwJatSLey16KULodECsIIY6FyRJ3Z3RZFIs3DZbegvtxjtJyCGaQGd0zhDgEjCqJvqv0uPH7xHFSWOLGxIYSLlyxHLNl/TwpBDBRYliOqpp9MGNAVJCqZFaYEKQtm42xw2/qwTJeMOaTAkTFSKaLF5wPXT4aowi1aw7fGC7eGL5R4NgADB7j6KUcEcgY2NxfukKgX0sDmKGXCCIOhIExnNmX7wcZV+2Dt1wqVIh+CGGiMqPDisYtnw+eyYcWudvzkiZVIZYx1ACMIVkjliCwyYaopMBMG6OeQKM3b8jv9fWf9FZYjKqWQfjAJOQgrMBOmBNme3mMvyOZdDsIU2NTrBc0KI8wCBWE6s1lyRlTUD6Y+wxKJRFBaWorS0lJEIhHVz6OlJJK5FkaYSQ9zLRrid9ZatJRpDlQOG+LHQxfNgstuwfubmnHtc1+C5w+980AMPuRMGIOeMNVrTQGZsK5ZYf2XI6rRIc3bkuZv9YoUhBVoUa9Ui6IgzKUsCFOiRbanL6AfDABslaKWdEthxhx67tfSrLAwZcIIg6GeMJ3pCsL6cUZkSGeBFrDFwExaAHPpYalFa8xspvMyUJk1shz3njcTlz66Ai+t3o+A244bvj35kOuVIwYXUo8Vq0yY4rVGEArMhGXLEQvMhCnVUXgQJpUjFp4JU6Il0yYGd9bysn6PVVOOWKiWQu3pJazZTFhGQSZMr31JyoRFqSeMMBgKwnRms5IZYQwyCG63G5s3b5ZvGwkzLblzwjSco0F5bsykhWINnDCxGn/+7nRc9cxqPLJsF5x2K647dSIFYsSAJciwJ0zVWpMMA0K2z7KgTFj/QZgaHVI5Yp/OiIBiYw6lWtKt4vPaysr7PVZpJkyJlmhQWRBmq6oCULhFvZ57pLdU1BwLpZBJ8bDaqSiMMAYKwnQkmeaxvVlMoxfsjKgRi8WCcePGFeW1+sNMWgBz6SEtg5czZgxFJJHBb15cg/s/3A67lcM1J0+gQIwYkLC0qFe11khZMKsDsPd+Ma6kJ0yNjoIzYR4x8EGksIyPUi1yJqyigCAsmwkrdE6YEi1SEOYJFBaE2WtqAACpxibmWpTi8tphtVuQSfEId8QRqBo4pmnE4ILCfx3Z0RJBmhfgc9owJKBgfhBdrPWOKU6NKURkMWHfkQklFZvvzxmOG789GQBwz/vb8Ld3txqsiCDUwdKiXhW5/WB97I1+2R2x/54wNRQchHnFsjvE2gGevVNquj2bCStnnwlTQkwKwgrNhFVXAwD4YBB8LMZcjxI4joOvXLwmC7dRSSJhHJQJ05HN8pDmkqJ9C55KpXD//fcDAC677DLY7eq+vRQYXEmz0sIKM+khLYOfC48aiVSGxx9f24C/vLMZdhuHnxw/1mhZBKGIULYnjEU5oqq1poB+MEBZJkyNjsLLEaXgSBDNOUqqmGrJtIm9ZtZCyhGzmbBQKoRkJgmHte+ASYmWaLYnzOMvLAiz+Hzg3G4IsRjSTU1wjBjBTIsaSsqc6GiMItweZ/q8BKEECsJ0RArCJhTSDwaARQohmUziyiuvBABcdNFFhl5Qm0mL2fSw1qKlnZCVFpqb1pNLjh2NRJrH7W9uwm1vbILDasElx442WhZBFIzUY8UiE6ZqrSnAGRFQ1hOmRocUhPWbCbPaxL6wWDsQbe03CFOqJd0mZrUKMebwO/ywWWxI82m0xdtQ661lpiXaqSwI4zgO9upqJHftQqqxsd8gTO/9uqRMNOcIUSaMMBAKwnREmhGmzJ5eG1arFWeddZZ8Wz1czv8arYUNZtLDXIuGN8pM52UwcsUJY5HK8PjrO1vwx9c2wGrhsPDoUUbLIoiCYGlRr2qtKTgTJrkj9l+OqEZHweWIAOCpzAZh/feFKdUiZcIKKUfkOA7lrnI0RZvQGmvtNwhTokV2RywwCAPEksTkrl1IN/VvzqH3vlQilSNSJowwEArCdETOhCkOwtRfUbtcLjz33HOqH88SM2kBzKVnMGthUco62Pj5ieOQTPP4x9JtuPE/65HOCLj065QRI8wNzwsIJSRjDu2XC6rWGh0yYWp0FFyOCIh9Ya1bCjLnUKJFEIQci/qKgh5T4aoQg7ACbOoL1SIIQpcxh5IgLGvOkW5sZKZFLb4yKQijTBhhHGTMoRPxVAa72qIACrSnJ3on16KeLvCJAQjHcbh2/gRceYLYE3bz6xtw93tbDFZFEH0TTqbl5ZdFT5gqCsyEBRT0hKlBWSYsGyAVkAlTAh+JQkiKwY+tgHJEIGdWGENzjkQ0DT4jfjAKNeYAAFuNaM6Rbuo/CNObrnJEyoQRxkFBmE5sbQpDEIByrwOVJc7CHsRgThhBGAa5evYJx3G4Zv4EXH3SeADAHW9txp1vbdI0+44g9EQy5XBYLXDaDLpckDNhfWegpExYPMUjkWbrSsgLPILJIACgtJ+MHIAuh8QIW1fCTNYZkXO5YPEUZqte7hLLFpUMbO4PKQvm9NgUzdiyZx0SU02F2dTrSVc5ImXCCOOgIEwnpH6wcdUlyh+s4WI2Go1i6NChGDp0KKLRqOrnYYGZtLDUI+S5ZZQWFphJy6HAz04ch1+fOhEA8Lf3tuLWNzZSIEaYklxTDhYOv6rWGikT1k/wU5JTLhnqpy9MqY5QMgRe4AEAAUcB5YiebBBWQCZMiZZMa+GmHBJKMmGFalE6qFmiqxyx/yBM731JyoQlY2kkdRprQBD9QT1hOrEp2w922BB/UV9XEATs379fvq36eRiU/bHS0g0NFwLM9Wi4JtHl3JhFCwUU/fLj48bAabPgxv+sxz8/2I5Eisei0yfRQGfCVLC0pwdUrjVSJqyfckSrhYPPZUMonkYwluqzAkWpDqkU0Wv3wm4t4FwoGNisREtaMuUowJ5eQsmssEK1xFT0gwGArTobhBWQCdN7j3S4bHB6bEhE0wi1x1HhVvGFOUFohIIwndhwQCxdKNyeHmBhUe9yubBq1Sr5tpGYSYukwSx6WGvRYg9vpvNyKLHw6FFw2Cz47YtrseSTnYglM7j5zCmwWalAgTAHLO3pAZVrTYGZMEAsSQzF0+jox5xDqQ4pCCsoCwZ0lSMWkAlTokUqR7RWKAjCpExYgcYchWhRY8oBdA1sTjc1QRCEPr90Ksa+VFLmQiIaRrg9gYo6CsKI4mPKIOyee+7B7bffjoaGBkyfPh1///vfMXv27LzHPvDAA3j00Uexdu1aAMDMmTNxyy239Hp8sdiYLUecWGRTDqvVihkzZjB4JjYW9Wy0sMFMegajFsriKOe8OSPgsFrwqxe+wjMr9qA9msTfzj0cLjuNCiCMJ5RgZ08PqFxrCsyEAUCZ1459HTF0RvsOwpTqUOSMCORkwvoPfJRoSWedEfXKhBWqRW0QZq8WZ6YJySQyHR2wlfVeVlmMPbKk3InWfWGEyZyDMAjTfeX6zDPP4Oqrr8aiRYvwxRdfYPr06Zg/fz6aeklfL126FOeeey7ef/99LFu2DPX19Tj55JOxb9++IivvojWcQHMoAY5TOyOMLmaJQqHSv4HO2UfW4x/nzYTDZsFb6xtx0cOfy7OZCMJIgjGpHNHA72sVZMJK3WJQ0B5NMpVQ8KBmCQWZMCVkWiV7en0yYYUSVTEjDAA4h0PWXkhJot6UkE09YTCmC8LuvPNOXHrppVi4cCEmTZqE++67Dx6PBw899FDe45944gn85Cc/wYwZMzBx4kT861//As/zePfdd4usvAvJlGN4uQdeZ3E3r1QqhSVLlmDJkiVIpYy9kDOTFrPpGcxaqCVMOadMqcUjC2ejxGnDp9vb8L37P0VziC4MCGORyxGdbDJhitcaQVCUCSv1iDrb+8mEKdWhyJ4eyDHmaO13QVSiRS5HVGDMUeUWs08diQ4kM30Hp4VqUdsTBuSUJPYzK6wYe6SvXOwbpEwYYRSmCsKSySRWrlyJefPmyfdZLBbMmzcPy5YtK+g5otEoUqkUynv5piiRSCAYDHb7w5oNaksRGVy9JpNJLFy4EAsXLkQyyfbbQMO05J4XDadoUJ6bLFo+OWY6L4cyc8dU4OnLvoYKrwPr9gdx9n2fYE8buVUSxiEPamaUCVO81iQjAJ91risgE1bmEYOCjn4yYUp1yD1hhZYjSpkwPg3EO5lpkY05FGTCSp2lsFvE4LQl1ndmrlAtassRga5ZYal+grBi7EtSJizUTkEYYQym6glraWlBJpNBTdbGVKKmpgYbN24s6Dl+9atfoa6urlsgl8vixYtx4403atbaFxuzphwTa1U6I2rorbFarfjmN78p3zYSM2mRNDDRI70/GoJm9udmcHxmDnWmDA3g+cuPwgUPfoadrVH8372fYMnC2ZhUV1yXVYIAIPdWsewJU7TWSAGMxQY4vP0eXiZnwvq+aFeqQy5HLGRGGADYnIDDByRDYjasjyyeEi2ZNuXliBzHodpTjX3hfWiKNqGupE6zFi1BmD3HnKMvirEvSTb14TaqOiCMwVRBmFZuvfVWPP3001i6dGmvbjrXXXcdrr76avnvwWAQ9fX1THVI9vTFNuUARBeh1157TfPzsKgoY6WlO9pcAFnq0dK5p8+5UYeZtBDAqEovXrj8KPzgwc+xqTGEs+/7BHefdwROmFBttDTiEKMjJl5sS2V+WlG81sTasw8sLejLyVKP1BPWvzuiEh2KyxEBwFshBmGRFqBiDBMt6Ww5opJMGCCWJEpBWF8UokXgBbkcUemcMCDHpr6fWWHF2Jd8OQOb+3NrJAg9MFU5YmVlJaxWKxoPSlM3Njaitra2z8fecccduPXWW/HWW29h2rRpvR7ndDrh9/u7/WFJhhfknrCJimeEUTMNMRigz7FWavwuPPvjuThqTAUiyQwueWQFnvxst9GyiEOMjmwwE/Aov9hmQkwMOmS3wX4o84rBYn/liErpyPalKQrCJM2MzDkEQVBlzAEAVR6xL6w51qxZRyKaBs+La7xHTRAmlyM2aNaiFW+pE+CATJpHLGR83zpx6GGqIMzhcGDmzJndTDUkk425c+f2+rjbbrsNN910E9544w0ceeSRxZDaKztbI0ikebjsFgwv9xiqhQX0vZDJoXhn0BJw27Fk4Wz83xHDkOEF/ObFNVj83w3yBRBB6E1n1pijlNGwZsVEs45+nsKCDjkTFmF7QS05C5a7FAQ/kjlHAQObC0GIRiEkxLI5qwKLegCo8YjZp/4yYYUQ7hA1uErssNqVX0Lah4jlkOkDxgdhVpsF3oBYkhhsjRmshjgUMVUQBgBXX301HnjgATzyyCPYsGEDLr/8ckQiESxcuBAA8IMf/ADXXXedfPyf/vQn/P73v8dDDz2EkSNHoqGhAQ0NDQiHw4bol7JgE2p8sFrUhjDqQ59oNIpx48Zh3LhxiEaNbeo3kxaz6RmUWqiUgzkOmwV3nD0N/2/eeADAPz/Yjp8+tQrxVMZgZcShgByEMSpHVLzWRBVmwgo05lCqoz0ulkUqCsJKxOwTIn0HPoVqSbeKgSDndsPiVfYFr5wJi/adCStES6RTDMKk4EUp9rohAIDUgQOatbDAX5k152ghcw6i+JiuJ+ycc85Bc3Mzrr/+ejQ0NGDGjBl44403ZLOO3bt3w2Lpih3vvfdeJJNJnHXWWd2eZ9GiRbjhhhuKKR0AA1MOjQiCgK1bt8q3jcRMWiQNZtFDWohC4TgOP583DvXlbvzqha/w2poDONAZwz8vOBJVPnUXQgRRCFI5ojR/SyuK1xqpHNFdmCW7ZMzREes7E6ZEBy/wck+YsiAsazAW7jsIK1RLulkMoGyVlYp7lySb+v4yYYVoiWQzYd5SlUHYEDEI40MhZEIhWH35e+eLtS/5K9w4sLWTMmGEIZguCAOAK6+8EldeeWXe3y1durTb33fu3Km/IAVI9vQT1JhyMFhoXC4XPvroI/m2kZhJi6TBLHqYaeG6/TBWC6Er3zliGIYE3PjRYyvwxe4OnHH3R7j/B0diytACbbMJQgHxVAaxbMY1wNCYQ9FaozATJpUjRpMZJNIZOG35XfWU6AgmgsgI4nko2B0RAEqyfezhvq3YC9WSbhbLGm2VlYVryCKXI8b6N8PoT0tUyoSVqgvMLR4PrKWlyHR0ILX/AKwT8l8rFWtfkjJhwWYKwojiY8ogbCDTZcqhwRlRo0X90Ucfrf61GcJKS/cxYdps4dmcG8miXv0zDMb3SYKyafoxd0wFXrziaFz66Apsb47grPs+we1nTcfp03u3nSYINUiDmjkO8DnZXCooXmvkIKywDJTPaYOFA3hBzOLV+PMHYUp0tMVFDX6HX563VRAlWTfTUN9BWKFa0i3ZIKyqqnANWQotRyxES7hDLPVUW44IALa6IWIQdmA/XBPGq9bCAn+lGwAQbKVyRKL4mK4nbCATTqSxOztc1ahyRKKIUAtUN8jetziMqSrBiz85GsdPqEI8xeOnT63C7W9uJMMOk/Hhhx/i9NNPR11dHTiOw0svvWS0JEVIJX0Btx0W1f3NGpGMOdyFBWEWC5djU8/GIVGVKQeQU47YdxBWKOmWrnJEpVR7xIAwnAojmtLWX6W1HBHoMudI7d+vSQsL5ExYC2XCiOJDQRhDpCxYtc+Jcq+aVL32i6h0Oo3nnnsOzz33HNLptObn07L1stYiClKvSBc9pIUwgIDbjgcvnIUfHTcaAHDP+9tw2WMrEIqTzbJZiEQimD59Ou655x6jpahCD2dExWuNQot6oMtEpC+HRCU6VJlyAICvsJ6wQrXIPWFVyoMwr90Lj0008+irL6wQLV3liBqCsDrJIbF3c45i7UtSJizclqAvsoiiQ+WIDFE/H4wdiUQC3/3udwEA4XAYNpvKt5hBVoOZFkaYSQ9pIbRitXC47tTDMLHWh1+9sAbvbGjCmf/4BPedfwTGVhd/UDzRnVNPPRWnnnqq0TJUo8eMMMVrjUKLekBySIz06ZCoRIdUjqg4CPNmyxFTESARBpwlmrRoKUcExGzYzuBONMeaMTIwUrUWyaLeG1D/uZDMOVL7ew/CirUveQNOWGwc+LSAcHsc/gq3Lq9DEPmgqy2GbGwQnREPU2PK0Q31AZDFYsFxxx0n3zYSM2mRNJhFz6DWQl8mFpUzDx+G0ZUl+NFjK7G1KYxv3/0xFn9nKs6YMdRoaYQCEokEEtk5UAAQDAYNVNNl884yE6Z4rYmKWSglmTDJIbE92nsmTIkOKRNW5irMoVHGWQI4SoBkWCxJ7CUIK1RLJmvMYVVRjgh0BWF9ZcL608JneMSC2Z4wTZkwKQjrvRyxWHskZ+HgK3ehsymGYAsFYURxoSCMIRsPaHBGZITb7e7hIGkUZtICmEsPaSFYMr2+FK/+7Bj87KlV+GRbK37+9Gqs3NWO3552WK8OcYS5WLx4MW688UajZciwnhEGKFxrMikg0Zl9YOFZqEJ6wpToUN0TBojmHG3ZIKxijCYtciasUl0mTDLn6CsI609LLJSCIIiBi9unIROWLUfsa1ZYMfclf6U7G4TFgAkKg22C0IDx6YlBgiAIciZMtSkHucoRBKGSyhInHrt4Dq48YSwA4NFlu/Ddf36Kve3GD0on+ue6665DZ2en/GfPnj2G6umaEcYuCFNELJsFAwe4Swt+mDwrjJExh1SOqDgTBhRsU98fQiYjD2vWUo4I9D8rrC+kUkSP36HJrEUqR0w3NUFIGd/HKvWFhcghkSgyFIQxYn9nHMF4GlYLhzHVXm1PZgKTOS1W8IcMpgiazaCBMAtWC4dr5k/AQxcdiYDbji/3dOBbf/8I729Sf+FFFAen0wm/39/tj5F0xMQgJmBUECbZ07tLAUvh2dyyrClWWx/GHEqQyhErXIWXRMpINvX9mHP0R6ajA8hkAI6DrVxdpqbarT0IY+GMCADWigpwdjvA80g3Gb82+SvIIZEwBgrCGLF+v5gFG1ddYmj5TywWw4wZMzBjxgzEYsYuKOy0CDm31AcdrM+NFu8S1lq0hGLsPzMUGBrNNybW4NWfHoOpQwPoiKaw8OHl+OOr65FIZ4yWRgwQOmOiIx1LYw5Fa41syqEs+Kn0igFCayTR6zFKdGjLhGUdEkMNmrRIpYjWsjIxeFFBjVfU0hBVr0V2RtRgygEAnMUCWz99YcW8lpFnhVEQRhQZ6gljxLr9Yu36pDot315qv3jleR5ffvmlfFsrWpJyrLVoxUx6SAuhN/XlHjz347m45fUNeHTZLvzrox34dEcr7vre4RhTld8kgGBHOBzG1q1b5b/v2LEDq1evRnl5OYYPH26gssLQw5hD0Voj2dMr6AcDgIoSMUBoDfdejqhEh2qLeqCgTFghWtLNUj+YOlMOAKjzin1YDeHeg7D+tEQ6tZtySNiH1CG1a3evfWHF3JekWWGdzRSEEcWFgjBGSJmwSQba0wOAy+XCW2+9Jd8edFo0RIWD+dxwGk4MKy00rNl8uOxW/OGMKThmbCV++cJXWLsviG/97SPc+O3JOPvIYfSe6ciKFStwwgknyH+/+uqrAQAXXnghlixZYpCqwtHDmEPRWiOVIyqwpwfE3kgAaAn3ngkrVEeGz6Aj0QFAZSbM139PWCFa5EHNKvvBAKDWK2ppjjUjlUnBbu35vvanpcuenkEQJplz7NuX9/fF3K9Lq8UZarFQColYGk43XRoTxYE+aYxYlw3CJtcFGDyb+gsjq9WKk046iYEG7ZhJC2AuPaSFKCYnT67FtGGluPrZ1fhkWyt++cJX+GBLM245c6pxPT+DnOOPPx6CKfpG1SEbczAMwhStNSrLEXMzYYIg5P2ioVAdHYkOCBDAgUOps1SRDgBd5Yh9ZJ8K0SIPataQCSt3lcNldSGeiaMh0oB6f71iLeE20biipFx7EOaoHwYASO7Ob0BTzH3J4bbB43cgGkyisymK6hHGfplOHDpQTxgDOqMp7OsQ09jayhEJYuAzkC88BzO1ARceu3gOfnnKBNgsHF776gBO/euH+GhLi9HSCBMilSMG3Ox6whQhlyMqy0BJmbBkhkcwntYkoSUm/rdR5iqDzaLiO2tWxhzyoGb1QRjHcXI27ECkd2v4vgi3i5kwX5n2zJS9XizJTe7Zrfm5WFBaI2bDOhrJTZYoHhSEMWDdAbEfbFiZW9u3ygwuXtPpNF577TW89tprSKe1bUCDSYvZ9JAWwgisFg4/OX4snr/8KIyo8GB/ZxznP/gZfvfSGkQS9N4TIumcAIZlJkzRWhPJfjngVRZ4uOxWlDjFgKm1l5LEQnU0x8QMVKVbZfAjWdRHmoFM/tcpRIucCdNQjggAQ7yiGcb+SH4zjL60CIKAkJwJ0x6ESZmwVC+ZsGLvS6XVojkHBWFEMaFyRAasl0sRjc+CJRIJfOtb3wIgNobbbOreYhbJDFZaWMHs3GTLRbVkfMx0bphpof6iAcOM+lL89+fH4tb/bsSjy3bh8U9348PNLbjj7OmYPUqFAQExqGjPliJyHFDG0B1R0VojZY+81Ypfp7LEgXAijZZwEqPzxC2F6miOisFPlVtl8OOtBCw2gE+LfWGBoaq0pBrEnjJbTa06HVnqSsQ+rAPh/JmwvrTEwylkUjzAASVlDHrCsuY06aYm8PE4LAf1fRV7jwxImbAmMucgigcFYQzoMuVg0Q8GTRezFosFRx55pHxbMxqCMWZacoIdLYEP83OjoXePvRb1mEkLUTw8Dhv+cMYUzJ9ci18+/xV2t0Vxzv3L8MOjR+Ha+RPgshs3aoMwlrZIlzOiVcNQ3oNRtNZEskFYifIgrKLEiZ2t0V4zYYXqkMoRVWfCLFbAVwd07gaC+/IGYYVoSTeIPWX22hp1OrJImbDeyhH70iJlwbx+B6w27fuEtbQUlpIS8OEwUnv2wDluXMFa9EAy56BMGFFMKAhjwPoDrDJh2tNPbrcby5cv1/w8LAZGM9PCiEF9bjQE7sy1UE/YgOLosZV446pj8cdXN+CZFXvw4Ec78P7GJiz+zlTMGa1iQC0x4JFmbJV72faDKVpr5HJE5Vmoyqw5R28OiYXqkIKwKo+GMsDAUDEI69wL1M9WrEXIZJDKDjS21WrLhA0p6bscsS8tLEsRAbFHzT68Hon1G5Dcs7dHEFbs6we5J6wp2quhC0Gwhr721kg8lcGWpjAAYPJQ48sRCYIglOJz2fGns6bh4YtmodrnxPaWCM65/1P8+oWv0JktTSMOHaRMWIVXe9mZKnhe7KMCVGfCAKClj1lhhaC5JwwA/GIJIIL5rdj7I93aCqTTgNXKrCest3LEvgi3ZU05GAVhAODImnOkTGDOEah0g+OAVDyDaFDb54YgCoWCMI1sbgwhwwso89hR62e1ONE3ML1C306ZFnpnBj4nTKzG21cfh+/PES+Onl6+Byfe+QH+8+V+cr08hJCCMNaZsIKJd4h9VADgUR4AFTIrrBDkTJjanjAA8GdLEDtVBmGN2X6wqipwVm0lwnJPWOQAeEHZAGTWmTAAcAwXbfJ7s6kvJla7Bb6K7NDmJipJJIoDBWEaWZ8zH8wM6etYLIajjz4aRx99NGIxYxtMzaTFbHrYadF+YWym80IYT8Btxy1nTsVzP56LsdUlaAkn8NOnVuGHS5ZjbztdnBwKtGYzSOUlbIOwgtcayZTDVQrYlGuozJkVpkWHbMyhqRxRdAHsLRPWn5aU1A9Wo60fDACqPdWwcBak+BTa4m2KtEgzwnwMZoRJ2OuzQVieTJgR+1KXTT3tg0RxoJ4wjUhDmpnMB2PwRTPP8/jkk0/k20ZiJi2SBrPoYa1FS5KCuRbNz0CYgVkjy/Haz47BfUu34573t+L9Tc046c4PceU3xuKSY0fBaSPjjsFKVzki2yCs4LVGgykH0H8mrBAdgiB0GXO4tJQjZjNhvQRh/WlJS86IGvvBAMBusaPKXYXGaCP2h/f3KLPsS4ucCWMwI0zCkXVIzGdTb8R+XVrtwe51bWgncw6iSFAQphF2phxscDqdePHFF+XbamFxIc1KCyvY6dGe8WStRUsS1mzvE2EenDYrfj5vHE6bNgS/eXENPt/Rhtvf3ITnVuzBotMn44SJ6i6SCXOjlzFHwWuNBnt6oCsIa+4lCCtERzgVRjwjBh6VKkoiZaSesF7KEfvTkmoQ+7e0OiNKDC0ZisZoI/aG9mJa1bSCtUhBmFSyxwKHlAnbtw9CJtOt3NKIfUnOhDVEivJ6BEFBmAYyvIANegRhGq6obTYbFixYwE6LBsykBWCvR0soZqZzw0yLCcpxCX0YW12CZy77Gl5avQ+3vL4RO1ujWLhkOeYdVo3ff2sSRlR4jZZIMEQq45MMLlhR8FojOSOWqCsDrPGLuhs643md7grRIZlylNhL4La5VekA0FWOGG4E0ske5ZX9aenKhA1RryGHel89vmj6AntCPbNPvWlJpzKIhUSDHh/DTJitthacwwEhmURq3z45M9aXFj2pGCquY637KQgjigP1hGlgR0sE0WQGLrsFoypLjJbDGC7nfw0md04YFbsRhCFwHIczDx+G935xHC77+mjYLBze2dCEk/7yIe54cxOiybTREglG6FWOWDARbZmwmqxJViLNozOmzt2zJapxRpiEpxKwOgAIQEi5K2Gqkc2MMInhfjHQ2R0q3JEw2CJmwewuK5xedt/dc1YrHCNHAgAS27Yxe161lA8Rr+NCrXEk47SeEfpDQZgGvtrbAQCYUhdgNNBSe4CRyWSwdOlSLF26FJlMhoGmwaHFbHqYaWHgWMf8vJCL3qDG57LjN988DG9c9XUcO64SyTSPu9/fiuNvX4qnP9+NDE/v/0BHL3fEgtcauRxRXSbMZbeizGMHADQE46p0SJkwTaYcAGCx9GlT358WORNWo70nDACG+8QgLF8mrDctwWbRqCJQ5WZuQOYYMxoAkNy+oyAteuIqscMTED/zbQcoG0boD5UjauCrvZ0AgKnDAoyfWf0iF4/HccIJJwAAwuEwvF7jyoR00aJhAxj052YQaCEGDmOrS/DoD2fjzXWNuPn19djTFsOv/70GD328A9edehiOn1BlCsdYQhk8L6A9qk8mrOC1Rp4Rpj4AqvG70B5NoaEzjom13dsFCtHREBEzULUeBsGPfxjQvjNvX1hfWgSeRyprUc8qE1bvF/uwdgd7ZsJ609IpBWGVGsoye8E5egxCABLbu2fCjNqXKuq8iHYm0bY/gtpRrK/tCKI7FIRpQMqETR9WaqiOXDiOw6RJk+TbpKULM+kZjFqM/ncQxYfjOJwypRYnTKzCY8t24e/vbcXmxjAWLlmOo8ZU4DffPAxThtKFzECiI5aClMwsYxyEFbzWaDTmAIDagAsbG0JozJMJK0THgYhYOljrZRCESX1hnT0Dn760pFtagFQKsFg0D2qWqPeJQVhrvBWRVARee1dw05uWzhYxCPNX6RCESZmwbdu73W/UHlleV4I9G9rRto8yYYT+UBCmklSGl+3pp7HKhDEo4/J4PFi3bh0DMdoxkxbAXHqYaWGwN5npvBADE6fNikuOHY2zZ9bjnqVbseTjnfhkWyu+9fePcMaMOlw1bzxGVVKGdSDQlnVG9LtssFvZdiwUvNaExCwUfOoDoNpsX1hDZ0+HxEJ0NEbEDBSTIKxspPizfaciLam9YubMXlsLzm7XrgOA3+FHmbMM7Yl27AntwcTyif1qkcoR/TpkwhxjxgAAEtu3dzNRMWpfKq+TzDnCRX9t4tCDesJUsqUxjESah89pw8hB6AxGBhgFQL1PeRHovByyBDxiv9i7vzgOZ8wQ+2BeXr0f8+78AL98/kvsaaP5O2anJeuMWMnYGbFg+IzoJAh09VKpQDLnaAiqG7zbEM2WI+ochPVFat9eAIB92DDtGnKQzTnylCTmozOnJ4w1jhEjAI4DHwwi09LC/PmVUlEnmnO0kUMiUQQoCFOJVIo4dVgAFiamHDlQWdfAgN4mgshLfbkHd33vcLz602PwjYnVyPACnl2xF9/481L89sU1ONCp7sKY0B+pfK/KZ1AQFm4ChAzAWVUbcwBiOSIg2tSrQe4JYxGElY8SfyoMwpJ7RPMM5kGYr3CHRIEXEGzVLwizuFzyvy9xkDmHEZQNEWeFRYNJxLJfSBCEXlAQppIvdTPl0EYsFsNJJ52Ek046CbGYlgsd7Rb17LSwwUx6BqcWikqJ7kwZGsBDF83CC5cfhWPGViKVEfDEZ7tx3O1LccMr69CUp1+HMJbmkFi+V+1nNw9KoqC1Jrhf/OmrBSzW/McUgFyOGOxZjtifjlg6ho5Eh/g8LDNhnXuBTHfLJyduEAAAMoZJREFU/L60SOWIjnq2QZhkznGwQ2I+LeGOBPi0AIuFQ0mZPoG5c7TkkNhlzmHUHulw2eCvFD87rXupJJHQF+oJU8mafR0AWJtyaC/j4nke77zzjnzbSJhpYTQnbFCemyxaPjlmOi/E4GTmiDI8fskcfLq9FXe+tRmf72zDkk924snPd+PMyWVGyyNyaMoGYTU6ZMIKWmtCUhCmbTixlAnLZ8zRnw4pC+a1e+Gz+zTpAACU1AA2F5COA517gPLRBWlJ7dWnHHGEbwQAYEdn98xTPi1SP5ivwgUL4x5BCceYMcAHHyCRY85h5L5UVe9DsCWOpt0hDJtYXtTXJg4tKAhTQTyVwcYDIQAMTTm6oT6j4HQ68fjjj8u3jcRMWiQNZtHDWgs3SD4zxODma6Mr8MyPvoaPtrbgr+9swcpd7Xjq857zigjjkIKWaj/7taCgtSaYHWjs1xiEZTNhbZEk4qkMXPaurFp/OnLt6Zk483GcmA1r3iiWJOYEYX1pkYOwoWyDsDGlohnG1o6t3cww8mmRnBH1KEWUcI4dCwBIbN7cdZ+B+1LVCB+2rWpG8+5QUV+XOPSgIEwFGw4EkeYFlHsdGFqq38KkBpvNhvPOO89oGQD00qJ+Qxz850YdZtJCDH44jsOx46pwzNhKLNveijtf/RIUhpmHpmz5XrWPfTliQWuNnAlTb8oBAKUeO9x2K2KpDA50xru5c/ang2k/mETZKDEIa9sBjOm6uzctQiqFVIOowz5sKDsdAEYGRsLCWRBKhtASa5EHUufT0tEomunoGYS5DhMdGuMbN8pBoZH7UvVwca5c0y4Kwgh9oZ4wFazZJ/aDTRsWYDu/glzliAEM+ckQSuA4DkeNqcRDC2cZLYXIoSmkXyasIBhlwjiOQ325GDjsVujKqU8QNlL8WaA5R6qhAeB5cE4nsxlhEk6rUzbn2Nqxtc9j2xvEc1c2RD8XaOeYMYDdDj4YRHr/ft1ep1CqhoslqMHmGBLRVD9HE4R6KAhTwerdHQCAaSYa0iyRyWSwfPlyLF++HJlMRsMzCQf9NFILG9jpkaKNwXFuzKSFIAhjkXrC9MiEFbTWMMqEAcDwctHp7uDRCP3p2B8RNTANwiSHxLbuQ4l705KSnBGHDtVlWPHogFgSua2jywwjn5b2A6JVe2mth7kGCc7hkEsS4xs39qqlWLhK7PBViJ9/Kkkk9ITKEVWwcnc7AOCI4aX6vICGBTcej2P27NkAgHA4DK/XuBlmZtJiNj3stAjdfhirhSCIgUwsmUEongagTyasoLVGckfUmAkDgGFl+YOw/nRIroH1vnrNGmQqx4k/WzZ3u7s3LYmdOwFk52jpwJjSMXhvz3vdMmEHa3E53Ahme8LKa/XdF1wTJyKxYQPiGzbCd+KJhu9L1SN8CLWSOQehLxSEKaQ5lMCuVnFBP3y4+Vy9OI7DiOyire3bM8miXv1zsNPCBjPpGcxaaFgzQQxMpFJEt90Kn5P95UG/a40giDbuABDQHgDJmbD27kFYfzr0CcLGiz/btos29VZ7n1qS2ZlZjtGj2GnIYWypmHna3tmVmTtYS0dTFIIAOFxWeAIOXXRIuA6biM4XgfjGDXm1FJuq4T5s+4LMOQh9oSBMIV9ks2Dja0oQcNsZP7v2i1ePx4Od2W/QjMZMWgBz6WGuRcMeZabzQhCEcciliH6nLhe+/a414SbRxp2zAAHtjoD12SDs4J6wvnTE03E0RZvEx7MMwvxDAUcJkAyLgVjVhD61JHeIQZhzlD5BWD6HxIO17N8gnoeyIV7dAyHnRNGcI7FBLEc0el+qHimaczRuDxqmgRj8UE+YQqQgbOYI82XBBi25mRVKspgXE2Q7CYJQz/4OsfSsVodBzQXRsUv86R8qZ4q00NUTVviw331hcUCy1+5FqbNUswYZjusqSWze1O/hiR1ihsqhUxA2MjASVs6KUDKExmhj3mPaG8R+sDId+8EkXNkgLLVvH9Lt7bq/Xn/UjPSDs3AItcURbqeh8oQ+UBCmkC92Sf1gegZhdDFLEARBFJe97WKwMrTMoNErHbvFn6XDmTzdsOy/ozOWQmesMJe73FJE5tmfSjH7hZa+gzA+FkN6v+gS6Rg9us9j1eK0OjEqIAZ4G1o35D1GMuUo07kfDACsfj8cI0cCAOJffaX76/WHw2VD5bASAMCBbZ0GqyEGKxSEKSCZ5vHlXvE/Rl0yYQx6aeLxOBYsWIAFCxYgHjf22xtdtGjYEwfluTHjZ4Z6wghiQLIvmwkbptP8y37XGikTxigI8zptqCwRe5lyzTn60qFLP5hEVbYvrLnLnCOfluQu8TxYAwHYyvT7wndyxWQAwPq29Xm1tOwNAwDK64pjiuGeMQMAEF292hT79ZCxAQDAga0UhBH6QD1hCli3vxPJNI8yj73b4Eczkclk8PLLL8u31SLkuWWUFlYw06Pdod5U58ZMWgiCMI59OmfC+l1r2qUgjJ0jYH25By3hJHa1RjFlaKBfHXtDojHIMJ/2nrQeSOYcOZmwfFqS2/UtRZSYVDEJL297Geta1vXQkogl5UHN0twsvXHPmI7Ol15C/Msv4TXBvjRkTCm+em8vDmzrMOT1icEPBWEKWJlTiqhrk6qG53Y4HLj//vvl20ZiJi2SBpZ6tHwEzHRu2GmhMlqCGMhImbChpfr0APW71jAuRwSAMVUlWLW7A1ubwgXp2BncCQAY4dPBGr56kvizeROQSQNWW14tCckZUecgbHJlNhPWuh6CIHTTEm5JQxAAt98Bb6A4g7ulTFjsy69gt1oN3yOlTFjr3jASsTScbrpkJthCnygFyEGYiU057HY7Lr30Uu1PxHX7YawWRrDToz3YYPc+SVrUazLb+0QQRPERBEH3TFi/a41UjljGLgAaVy329Wxp6rIa70uHNLxYcg9kStkowOEDkiFxXljNpLxaEpvETJlz3Dj2GnKYUDYBVs6K1ngrmqJNqPHWyFrWfigalFRl+6KKgXPsWHAeD/hIBPyu3YbvS96AE4EqNzqbYziwpQMjp1UaqocYfFBPWIHwvIDPdrQBAOaM0mtwH/XSEAMf+hQTxMCjPZpCLCWWfQ0JGOCOmEl1ZcLKRjJ72rHZICw3E9Yb4WRYdgqUTCuYYrEAtVPF2we+7PWweDYIc02cwF5DDi6bSw4217Wu6/Y7qR+ssr54QRhns8E9VTw/sVWriva6fVF/mHi9t3t9m8FKiMEIBWEFsqUpjLZIEm67FdOGlRotp1d4nse6deuwbt068Dw/KLTkDv8VNFziD8ZzM9i0EARhDFIWrMrnhMtu1eU1+lxr2ncCfBqwewFfHbPXHFct9jNtb4kgwwt96pAGF1e5qxBwBphp6MaQ6eLPbBB2sJZMOILUbjEYlWZn6YlkzrG2ZW03Lc27xflYlfXF6QeT8MycCQAIf/apKfal+kliELZnAwVhBHuoHLFAPt3eCgA4cmQZHDa9Y1f1pWWxWAxTpkwBAITDYXi9xhmImEmL2fSw0yIc9NM4LTQmjCAGLvs6RBOGoTo5IwL9rDUtWcfAyrFixogRQ8vccNosSKR57GmLYmSlt1cdUhA2ulQfW3gAXUFYg2jDfrAWbrN4HmxVVbCV61V108WM6hl4ceuL+KLpC1mLxWLFPT95BwBkm/Zi4T1qLlr+8Q+0frIMh//1rwCM3a+HTSgDZ+HQ0RhFsCUGf6VB4xuIQQkFYQUiBWFfG12h34swsvaurDRP3bKZtADm0kNaCIIwC9tbxJlQIyv0Hczb61ojB2Hjmb6e1cJhTFUJ1h8IYnNjCCOzzsb5dGzvyAZhgSIEYQe+ArIZnlwtiU0bARQnCwYAM2vEzNOa5jVIZBKorKxEXdkYZFI8nF4bSqv1H9Sci3vaNHAeDzLt7agsKwOs+mRlC8XhtqF2tB8HtnZi9/o2TPn6UEP1EIMLKkcsAJ4XcoIw/b+Z0oLX60VzczOam5s1fXPEIhxkpaUbGtItzM+NhqCZ/bkx/rzI0Jwwghhw7GgWg7DRVfplPvpca1q2iD8ZB2EAMLFWLKlbfyDYp44tHaKGMQEdTDkkKscDNrdoztG6pYeW+HpxcLLe/WASw33DUemuRJJPYnt0O5qbm/Hovc8BAGpHB8BZilviwDkc8Mw6Eh6LBRvv+DPb6weVDM+WJO5a02KoDmLwQUFYAWxuCqE9moLbbsXUoaX6vyDVdREEQRBFZEc2E2bYDEw5E8beEXDqMLG/a83e3ofuCoKA9a3i0OKJFTpmoaw2YNiR4u1dn/T4dexLsVfMNW2afhpy4DgOR9aIelY0rgAAHNgmnqchY3Tqi+sH79y5AIDIJz3PjxGMmlEFANi9oQ3JWNpgNcRggoKwAvhkazH7wcwFhYNEwdCXBwQxYNluZBAmCLqVIwLA1OyQ5jX7eg/CGqONaIu3wcpZMaFM5yzUiKPEn7uXdbs7E44gsUXMxrmnT9dXQw5SSeLyhuUQBAEN2eHEtaONCcJKjj4aABD9/HPwkYghGnIpH+JFaY0HfFrATsqGEQw5tCIKlXywuRkAcMxYvftmtJdxxeNxnHfeeTjvvPMQj8cZaBocWsymh7QQBGEWOqJJtEWSAPQNwnpdazr3APFOwGIHKsYyf91JdX5YOKAplEBjMJ5Xh5QFG1M6Bi6bzhb9w8VMD3Z90k1L8IuVgCDAXlcHe3W1vhpymFsn6lmxZwUuPO8SRDqT4CxA9Uh/0TTk4hg7Fvywobhm505871vfMnxf4jgOY44Qs2Hbvmg2VAsxuKAgrB/iqYzcD3b8hOItimrJZDJ48skn8eSTTyKTyZAWk+phpoVB+xXz80I9YQQxoNjWLM6EqvW74HXq59fV61ojzcyqngjYnMxf1+OwyVb1a/Z25tUhBWGTKiYxf/0e1M8GLDagcw8yrTtlLbHVqwEA7hnFy4IBwAj/CIz0j0SaT2PrqgYA4nwwu8MYUwyO4+A+4QS8FgriuaVLDd+vAWDMEeL13651rUhQSSLBCHJH7IdPt7cikeYxJODC+JpiWbWqL+tyOBz4y1/+It82Ej20CBou8Af7uRkMWgiCKD7rD4QAABOH6DsTqte15oBo1y47B+rA9PoANjWGsGJXO44bV95DhzSsuChBmMMLDJkB7FsBx77PZC3J1WIw6p5xuP4aDuLYYcdiR9sOHH/8aYDQZUZhFJUnn4xf3/U3cE4HbCb4Yq9yWAnKhnjRfiCCLZ83YMpxw4yWRAwCKAjrh6WbxNTz8ROqwOnd88JgobHb7bjqqqu0a2GAmbQA5tLDTAvX44ZhWjjqICSIAcn6/aJr4KQh+paf9brWSJmwWv2CsDmjKvDsir1Ytr0VdvvEbjrSfBqrm1YDAKZXFSkLNfZEYN8K2He+i6uuehR8IoHNs+cAALxzv1YcDTkcN+w4PL7ucVRZRwBpYOTUqqJryMU3cyZ+OHky0gcOIPG//8F56qmG6uE4DpOPqcNHz23Buo/2UxBGMIHKEfvhw2w/2HHjjV2Qio0JvnjKjynMHyQNZj1JBEEQhSNZt0+qM6AHSBCA/V+It3XMhH1tjDjjc+2+ToTiqW6/29S2CeFUGD6HT39TDonx88Wf294H0knEVq2CkEjAVlUFx1j2fXH9cUTNERiTmAJH2g2rE6geoW9WtD84iwWBBWcAADpe+LehWiQmzKmFxcahZU8YTbuCRsshBgEUhPXB1qYQtrdEYLdyOEp3U44cNAQaPM9j586d2LlzJ/jsIEijMJMWs+kZzFoECk4JYsCQzvDYeKA4mbC8a03rViDSDFidQN0M3V57aKkbw8s9yPACPt/e2k3H8oblAESXQKulSH1QQw4HvNXg453Y+cm/sf/1/wIAvEfN1b/qJg92ix1Hx8VsU2vpLsOtkXmeR8eRR2JfKonQRx8hdeCAsYIAuErsGHO42Bu2+p09BqshBgMUhPXBf9eIDapHj62E32U3WE1hxGIxjBo1CqNGjUIsFlP/RAw2AWZaGGEmPcy0MEhZmum8EARRXDY1hpBI8yhx2jCiQl97+rxrjTQra9iRuphy5HJUNhv2zprd3XR83vA5AGBWzSxdX78bFgsw4RTEUsCo487FxsceAwB4s/bsxUbgBfj3iiV2zz76IFqDrYbokIjFYphwzDE4aft2xHke7U8+aageicNPGg4A2LqiER1NUYPVEAMdCsL64L9rxSDsm1OGFOkV2WQQPB4PPB4Pk+fSipm0AObSM+i0mKJUlCAIJSzf0QYAOGJEGawW/f8b7rHWSLOyJNt2HTlpUg0A4J0NTbKOeDouZ8LmDJmju4ZuTP0uAGCKx4mxTidgs6Hk+OOLqyFLw44gUiEB8VQUGxtX4LXtrxmiIxePxwOPSxwX0P7U08iEwwYrAqqG+zBiSgUEAfjizV1GyyEGOBSE9cLOlgjWHwjCauHkhXsg4PV6EYlEEIlE4PUaMHTTpFrMpoe0EARhBpbvbAcAzB5Zpvtr9VhreB7Y+q74y1HH6v76x4yrRInThuY4h/9t2ItIJILVnasRz8QxtGQoxpexHxTdJyOOhrdqON47RWx3KDnmGFj9xszmWv+/fQAA/zQO4+8dh+d2PocMb5w1vPRZCUciKB03Dnw4jPYnzJENm3nKCADAxk8OoGWv8YEhMXChIKwXXv1qPwDga6PLUeYttm03ZRSIAjHjR4VawghiQMDzAj7LZsJmjTTAknz/F0CkCXD6geFH6f5yTpsV35go9vS8slrc49/Z9Q4A4IT6E4rfi2WxQJh6Djp3ugEAvvnzi/v6WeKRFLasbAIAnHTqkfA5fNgd2o339rxniJ5cOIsFlZddCgBofeABpFuNLZMEgCFjSzHmiCoIAvC/ZzZrGp1DHNpQEJYHnhfw7Iq9AIAzDycbUsPJWeDI9IEgCIINX+3rREs4Aa/Diun1pcUXsDFb8jb2RMBWnC87/2+muKe/sHIvGkPtchA2f6QxAVA4PQ2piA0WOw//pFJDNKz9YC8yKR4Vw0owYlw1zp14LgDgri/uQiqT6ufR+uM//XS4Jk0CHw6j+a93GS0HAHDU/42FzW7B/i0dWP/RfqPlEAMUCsLy8PnONuxui8LrsOKbU2uL98IMvk1JJBK49NJLcemllyKRSKiXwiDYYaWFFcz1aDhFZjo3rLRQSxhBDCzeWif2PR8/sRouu/6ugN3WmlgU+OoZ8ReHna77a0scO7YSwwM27HzpTpzx++8hnoljdGB08eaDHUTr0y8BAD4XgkgtXVz0+TCJaEp2+pt2Yh0uu+wyrLx7JUqtpdgV3IUnNxpTApj7WUmmUqi57tcAgI7nnkP4fx8ZoikXf4Ubs789GgDwv2e3oHU/lSUSyqEgLA9PfrYbAHD69Dp4HANrnnU6nca//vUv/Otf/0I6nR50WrSUiwzKc8NgvzbTeSEIojjwvIDX1oi23ycXqe85d60Rtr4DBPcB7jJg4reK8voAYLFw+P6sYQivfQttdTsAAGeNP8sQW/jIp58h9umnSAkCrtvcivSuZcC64s7EWv7qTiSiaZQN8aJ+Sin+9a9/4ZGHHsGPp/wYAPD3VX/H1vatRdUE9NyXPLNmoez88wEA+39znSks62ecWI/hk8qRSfF47Z6vEOkw/stmYmAxsCKMIrC3PSpvTOfNGWGMCA2bgd1uxx//+Ef5tgYROf9rtBY2MNPDHfTTSC0MMJMWgiCKw4dbmrGrNQqf04Z5hxUnCJPXGkGA47O7xTunn6u7Nf3BfH/uaNxx2elw1uyAk/Ph/8b9X1FfHwCEZBKNf/oTAGD3xIm4/NgRsFuXAa/9Aqj/GhAYqruGhu2d+Op9MQt2zFlj4XQ65L3grMPOwv9a/oeP932MX3zwCzx66qMIOAO6a5LIty9V/+JqRD/7DIktW7Dnsh9h+KOPwFamv6FMb3AWDvMWTsILt61EZ3MMr/xtNU7/6QyUlBX380wMXCgIO4gHPtyODC/g6LEVmDqseAuOiPa0hsPhwG9/+1sGWrRjJi2AufSw16I+ImSthZqUCcLcCIKAf36wHQBw9pH18DqLcykgrzWb/gs8dbs4oPmonxbltXNJWiIoP7oFsQwQavw61u+L48iRxR0X0vy3vyGxYQOsgQBOeehBfCvgAx46Gdi/CnjqHODC/4hZQp2IdCTwxj/XQBCA8bNrMHyyOEMtdy/449F/xDn/OQfbO7fj8ncuxz0n3oMyV3GCnnz7ksXtRv0/78OOc85BYssW7Pr+eah/4H44hhnXu+/2OXD6z2bg33esRNv+CJ7/0wrMv3QKhowp9vUjMRAxZTniPffcg5EjR8LlcmHOnDn4/PPP+zz+ueeew8SJE+FyuTB16lS8/vrrql53a1MYT2RLEX9y/FhVz0EQBEEQgPK9rFj856sDWLa9FQ6bBT88ZmRxXzzUCPzn5+LtOT8C/HVFffloKoqrl16NWCaEEm44Eq1H4eJHVmDtvs6iaWh79DG0/utBAEDtTX+AraJCNCY562HAWw00rAEeOR1o36nL63c2R/Hin79ApDOJsiFeHHfuhLzHVbor8c+T/omAM4A1LWvw/de+j9VNq3XRVCj2ujqMWLIEttpaJHfswI4zFqD92WchZIyz0w9UufF/185EWa0HkY4E/n3HSnz4zGZEg0nDNBEDA9MFYc888wyuvvpqLFq0CF988QWmT5+O+fPno6mpKe/xn3zyCc4991xcfPHFWLVqFRYsWIAFCxZg7dq1il43wwv4/UtrkeYFnDixGkePrWTxzyk6giCgubkZzc3NhmckzKTFbHoGpxZy5iAICaV7WbFYuasd173wFQDgx8eNwbCy4mWAhNbtSD98GhBuhFA5HjjhN0V7bQDYE9yDS9+6FKuaVsFr8+K2OYswo74cnbEU/u/eT/Cv/21HPKXfxXy6rQ37f/tbNN5yCwCg8ieXw3fSSV3rb9lI4AcvAZ5KMRC79xjgw9uBaBub109l8NX7e/DMH5ejszkGX4UL37piGhxuMROaby8YWzYWS+YvwdCSodgb3osL/nsBfvnBL/FV81e67l197UvOMWMw8umn4J4xA3wkgobrF2H7t05H2xNPIN3G5lwpxV/pxv/96khM/FotIABr3t+Lx373Cd57bAP2bmpHJs0booswN5xg9BXgQcyZMwezZs3C3XeL9eI8z6O+vh4//elP8etf/7rH8eeccw4ikQheffVV+b6vfe1rmDFjBu67775+Xy8YDCIQCOCXTy7DM1+2wuOw4rWfHYtRlQYMrX37euDju4C5VwLzb1b1FJFIBCUlJQCAcDisevjue5/chp9veQzTBTsev+gLQ7Wk29qw5aijAQC2Za9gXNk4Q/U8fPHziNrL8c0zfBh16ixDtTz0/xahff9KjJl1JhZcc7GhWr58+79451/3YOysuTjjGnOUfRLmR1qDOzs74TdoUK0eKN3LDobleYkm09jUEMIrX+7HE5/uRjLDY+7oCjx28WzYrDp+FysIQKQZaFoPbHwdwqpHwaVi2B/iUfr/PoVn2GT9XhtAPB1HU7QJ61rX4YO9H+CtnW8hxafgt/uxatEqxLbGsL+lHde9shlLNzUDAMq9DsyfXItZI8swodaHGr8L5R4HLBZlXzIJggA+GESqsRHx9esRXbYMwbffgRCNAhyHyp9eicrLL0c0Gu25/nbuBZ7/IbDnM/HJrA5g9AnAiLlA9WSgdDjgqwWcPsCS39WSz/CIR9KIdCTQsjeMA1s7sP3LZiQiovnSkLEBnHzxlG79S33tBZ2JTvx5xZ/x4tYX5ftqPDX42pCvYWL5RIwOjEaVpwqV7kr4HX5Ye9FVKIXsS0ImI2YV77sPmc5sJtNigXPiBHhmHA7HmNFwDB8OW3UNrKUBWP1+cC6X7iYse9a34dOXt6FpV0i+z2a3oGZ0ABVDvSir8cBf6Ybb54CrxA6X1w6b3QJO4WeM0I9i7UumCsKSySQ8Hg+ef/55LFiwQL7/wgsvREdHB15++eUejxk+fDiuvvpqXHXVVfJ9ixYtwksvvYQvv/yyx/GJRKKbDXcwGER9fT1u+c7ZcJEpQV60LAvSp0vzmpd9npRNm55MSnwiq139s1h4G8BxcCSawQnqyg0EAdgRFT+HozxO1ecnYfdAQAYOxyy4raPVPUcqgSufFIdh3v39B+C0q2sqTqS3Ip5aCcAGC2fAlxjEgCSeSuE3/35uUAVhavay3vamv537T7gd7pwj8y0WPe8T8vxGyPkbd9Bx/T1f/sMK03IwAvqyfur939Lv6xx8l4AeGjkAHCzI8GJmwpoNQgWB6zGaRSjYoCrfEflV8xYrEnYXMlKQIghIRsQAwuENyHo5ADYhBScSsAp8Nz35NAJczitySAku5Ct2clnaMdb3NoZ7PgHHddcYTfKYfPN6AMC6306Cx9Hz8dvswMt+4GMPkOwjfrcJgCP7xy6I/x6LdD2Qo0y67+CnSid4vPlTUcv8v0+Czdn7izkTAmatE3DEeh71jb1rAoC0BUhbAd4KZCzZP1aAt/R8x4Q8b2vB9wGIesYiWDobIf90ZGwlfQsDwPEpcEJa/gNBAIdsBk0QIL7b4k9RbM7fCabEkjH87Kkf6b4vmcqYo6WlBZlMBjU13Z2aampqsHHjxryPaWhoyHt8Q0ND3uMXL16MG2+8scf9vNAJXqAgTDcYrREWjQ7qNmmxZOAKn7BxANS7INU5xMdqM7UVS2cEew0SDpVuWi7g7h+9K/9VrZ5MKgWkVgJIgxeK119BDGx4wfhhsKxRs5f1tjclXENgcdCXGnphxFAOmwDY5KpHDk53qXizR8WaHSnYof6/EB5uSwjltt2otO3ASOdy1DnWw8LxyHvtbgOERdIF5948eoCRCeDEZiDOcVjucuJLpxNbHHbsstvQarWiwyoGl2lO/BNVK91hwZQlUwAA+/o9lsPmr3F44msWlIUETNwrYOx+AbXtQE2HgNIw4I0DVgGw8eIfDSdVAVuB3Vsh4ClEvLXo9I9C1FOLqLsaCVcZkvYSpOwlECzZclCLHQLoOtQMJCyRoryOqYKwYnDdddfh6quvlv8ufds4fMJklLhcBirLYrEBLnO46nDJKCosdlhN0DqY4tNo9QkQnA6jpQDpDJzBFjgcxs8EEQQglSqF0x0Cx6krG2VJLHIk0ilqRiYKJxpPAHjLaBmG09veVFW/HB6XGxYOAMdlEyVd2Q8ue7+Fy94r3iHf5jjAYbPCYbWKmY/s7+VcFNeVlwLHdd2Uf2+R7ug6Rn5+6a7cuR1c9v85wGIBnH5wuaVpXM7vu+6Uf+R7Xi53D8op2ZJv5dXNwWv3wm1zi/dxuQ/o/poH3eimIcMLCCXSSKUFpHgeaV5AKs2LGahsJkmAmEmD/DdAcDjB+/yA3Z43g5U3u6agIoLjk7Cmo7CkY7BlYoCQETMnQgbgM7A7eDjsmey5HwVgFNrxDbQX/hL9IKAUwHHZPxJpIY0on0BKSCHJp5EUUkgJ6WzMJ54bHnw2mSP9X9fvNH9hWwsgp2OhLfsHggBLIgVrLAkuw4t/0hlwfPZ2Rnrh/Dq4g4vGBHV6OcThxW54sVt+GkGwQuAt4k/BIt8WD+g+E0fodl/2fkqEaaTnCRT3Jf0xVRBWWVkJq9WKxsbu+eTGxkbU1tbmfUxtba2i451OJ5zOntmLM667YdCUwhAEQQwUgsEgcOudRstgipq9rLe96cxfXEN7E0EQRBEJBoO46KabdH8d41McOTgcDsycORPvvttVGsXzPN59913MnTs372Pmzp3b7XgAePvtt3s9niAIgiD0RM1eRhAEQRxamCoTBgBXX301LrzwQhx55JGYPXs2/vrXvyISiWDhwoUAgB/84AcYOnQoFi9eDAD4+c9/juOOOw5//vOfcdppp+Hpp5/GihUrcP/99xv5zyAIgiAOYfrbywiCIIhDG9MFYeeccw6am5tx/fXXo6GhATNmzMAbb7whNzjv3r0bFktXAu+oo47Ck08+id/97nf4zW9+g3HjxuGll17ClClTjPonEARBEIc4/e1lBEEQxKGNqSzqjWCwzqghCIIYCNAanB86LwRBEMZQrPXXVD1hBEEQBEEQBEEQgx0KwgiCIAiCIAiCIIoIBWEEQRAEQRAEQRBFhIIwgiAIgiAIgiCIIkJBGEEQBEEQBEEQRBGhIIwgCIIgCIIgCKKIUBBGEARBEARBEARRRCgIIwiCIAiCIAiCKCIUhBEEQRAEQRAEQRQRCsIIgiAIgiAIgiCKiM1oAUYjCAIAIBgMGqyEIAji0ENae6W1mBChvYkgCMIYirUvHfJBWGtrKwCgvr7eYCUEQRCHLq2trQgEAkbLMA20NxEEQRiL3vvSIR+ElZeXAwB27949IC4AgsEg6uvrsWfPHvj9fqPl9Avp1RfSqy8DTS8w8DR3dnZi+PDh8lpMiNDepC+kV19Ir76QXn0p1r50yAdhFovYFhcIBAbEB0PC7/eTXh0hvfpCevVnoGmW1mJChPam4kB69YX06gvp1Re99yXa9QiCIAiCIAiCIIoIBWEEQRAEQRAEQRBF5JAPwpxOJxYtWgSn02m0lIIgvfpCevWF9OrPQNM80PQWi4F2XkivvpBefSG9+kJ688MJ5AtMEARBEARBEARRNA75TBhBEARBEARBEEQxoSCMIAiCIAiCIAiiiFAQRhAEQRAEQRAEUUQoCCMIgiAIgiAIgigigzIIu+eeezBy5Ei4XC7MmTMHn3/+eZ/HP/fcc5g4cSJcLhemTp2K119/vdvvBUHA9ddfjyFDhsDtdmPevHnYsmWLIXofeOABHHvssSgrK0NZWRnmzZvX4/iLLroIHMd1+3PKKacYonfJkiU9tLhcrm7HmOn8Hn/88T30chyH0047TT5Gz/P74Ycf4vTTT0ddXR04jsNLL73U72OWLl2KI444Ak6nE2PHjsWSJUt6HKP0vwm99P773//GSSedhKqqKvj9fsydOxdvvvlmt2NuuOGGHud34sSJhuhdunRp3s9DQ0NDt+PMcn7zfTY5jsPkyZPlY/Q6v4sXL8asWbPg8/lQXV2NBQsWYNOmTf0+zuj1t5jQ3kR7k1q9Ru5NtC/RvqRFr5H7EmDuvWnQBWHPPPMMrr76aixatAhffPEFpk+fjvnz56OpqSnv8Z988gnOPfdcXHzxxVi1ahUWLFiABQsWYO3atfIxt912G/72t7/hvvvuw2effQav14v58+cjHo8XXe/SpUtx7rnn4v3338eyZctQX1+Pk08+Gfv27et23CmnnIIDBw7If5566inNWtXoBcQJ6bladu3a1e33Zjq///73v7tpXbt2LaxWK84+++xux+l1fiORCKZPn4577rmnoON37NiB0047DSeccAJWr16Nq666Cpdcckm3DUTNe6aX3g8//BAnnXQSXn/9daxcuRInnHACTj/9dKxatarbcZMnT+52fj/66CPNWtXoldi0aVM3PdXV1fLvzHR+77rrrm469+zZg/Ly8h6fXz3O7wcffIArrrgCn376Kd5++22kUimcfPLJiEQivT7G6PW3mNDeJEJ7kzq9Ru5NtC+J0L6kTq+R+xJg8r1JGGTMnj1buOKKK+S/ZzIZoa6uTli8eHHe47/73e8Kp512Wrf75syZI/zoRz8SBEEQeJ4Xamtrhdtvv13+fUdHh+B0OoWnnnqq6HoPJp1OCz6fT3jkkUfk+y688ELhjDPO0KwtH0r1Pvzww0IgEOj1+cx+fv/yl78IPp9PCIfD8n16nt9cAAgvvvhin8f88pe/FCZPntztvnPOOUeYP3++/Het54Cl3nxMmjRJuPHGG+W/L1q0SJg+fTo7Yb1QiN73339fACC0t7f3eoyZz++LL74ocBwn7Ny5U76vWOe3qalJACB88MEHvR5j9PpbTGhvor1Ji96DMWpvon1JX2hf0h8z7U2DKhOWTCaxcuVKzJs3T77PYrFg3rx5WLZsWd7HLFu2rNvxADB//nz5+B07dqChoaHbMYFAAHPmzOn1OfXUezDRaBSpVArl5eXd7l+6dCmqq6sxYcIEXH755WhtbdWkVYvecDiMESNGoL6+HmeccQbWrVsn/87s5/fBBx/E9773PXi93m7363F+1dDf55fFOdATnucRCoV6fH63bNmCuro6jB49Gueddx52795tkEKRGTNmYMiQITjppJPw8ccfy/eb/fw++OCDmDdvHkaMGNHt/mKc387OTgDo8d7mYuT6W0xob+qC9qbBvzfRvlQcaF9Sh5n2pkEVhLW0tCCTyaCmpqbb/TU1NT1qZSUaGhr6PF76qeQ59dR7ML/61a9QV1fX7YNwyimn4NFHH8W7776LP/3pT/jggw9w6qmnIpPJFF3vhAkT8NBDD+Hll1/G448/Dp7ncdRRR2Hv3r0AzH1+P//8c6xduxaXXHJJt/v1Or9q6O3zGwwGEYvFmHzG9OSOO+5AOBzGd7/7Xfm+OXPmYMmSJXjjjTdw7733YseOHTj22GMRCoWKrm/IkCG477778MILL+CFF15AfX09jj/+eHzxxRcA2Pw3rBf79+/Hf//73x6f32KcX57ncdVVV+Hoo4/GlClTej3OyPW3mNDeJEJ7k3q9uZh9b6J9SV9oX1KP2fYmW8FHEqbj1ltvxdNPP42lS5d2ayj+3ve+J9+eOnUqpk2bhjFjxmDp0qU48cQTi6px7ty5mDt3rvz3o446Cocddhj++c9/4qabbiqqFqU8+OCDmDp1KmbPnt3tfjOd34HMk08+iRtvvBEvv/xyt1r2U089Vb49bdo0zJkzByNGjMCzzz6Liy++uKgaJ0yYgAkTJsh/P+qoo7Bt2zb85S9/wWOPPVZULUp55JFHUFpaigULFnS7vxjn94orrsDatWuZ1fQTAwvam/SF9ib9oH1JX4zclwDz7U2DKhNWWVkJq9WKxsbGbvc3NjaitrY272Nqa2v7PF76qeQ59dQrcccdd+DWW2/FW2+9hWnTpvV57OjRo1FZWYmtW7caplfCbrfj8MMPl7WY9fxGIhE8/fTTBf3Hz+r8qqG3z6/f74fb7WbynunB008/jUsuuQTPPvtsj5T/wZSWlmL8+PGGnN98zJ49W9Zi1vMrCAIeeughXHDBBXA4HH0ey/r8XnnllXj11Vfx/vvvY9iwYX0ea+T6W0xob8oP7U2Dc2+ifan40L7UP2bcmwZVEOZwODBz5ky8++678n08z+Pdd9/t9o1XLnPnzu12PAC8/fbb8vGjRo1CbW1tt2OCwSA+++yzXp9TT72A6Mhy00034Y033sCRRx7Z7+vs3bsXra2tGDJkiCF6c8lkMlizZo2sxYznFxCtSROJBM4///x+X4fV+VVDf59fFu8Za5566iksXLgQTz31VDd75d4Ih8PYtm2bIec3H6tXr5a1mPH8AqIb1NatWwu6UGN1fgVBwJVXXokXX3wR7733HkaNGtXvY4xcf4sJ7U35ob1pcO5NtC8VH9qXesfUe5MCQ5EBwdNPPy04nU5hyZIlwvr164XLLrtMKC0tFRoaGgRBEIQLLrhA+PWvfy0f//HHHws2m0244447hA0bNgiLFi0S7Ha7sGbNGvmYW2+9VSgtLRVefvll4auvvhLOOOMMYdSoUUIsFiu63ltvvVVwOBzC888/Lxw4cED+EwqFBEEQhFAoJFxzzTXCsmXLhB07dgjvvPOOcMQRRwjjxo0T4vF40fXeeOONwptvvils27ZNWLlypfC9731PcLlcwrp167r9m8xyfiWOOeYY4Zxzzulxv97nNxQKCatWrRJWrVolABDuvPNOYdWqVcKuXbsEQRCEX//618IFF1wgH799+3bB4/EI1157rbBhwwbhnnvuEaxWq/DGG28UfA6KqfeJJ54QbDabcM8993T7/HZ0dMjH/OIXvxCWLl0q7NixQ/j444+FefPmCZWVlUJTU1PR9f7lL38RXnrpJWHLli3CmjVrhJ///OeCxWIR3nnnHfkYM51fifPPP1+YM2dO3ufU6/xefvnlQiAQEJYuXdrtvY1Go/IxZlt/iwntTbQ3adErYcTeRPsS7Uta9EoYsS8Jgrn3pkEXhAmCIPz9738Xhg8fLjgcDmH27NnCp59+Kv/uuOOOEy688MJuxz/77LPC+PHjBYfDIUyePFl47bXXuv2e53nh97//vVBTUyM4nU7hxBNPFDZt2mSI3hEjRggAevxZtGiRIAiCEI1GhZNPPlmoqqoS7Ha7MGLECOHSSy9l8h+eGr1XXXWVfGxNTY3wzW9+U/jiiy+6PZ+Zzq8gCMLGjRsFAMJbb73V47n0Pr+S9ezBfySNF154oXDcccf1eMyMGTMEh8MhjB49Wnj44Yd7PG9f56CYeo877rg+jxcE0cp4yJAhgsPhEIYOHSqcc845wtatWw3R+6c//UkYM2aM4HK5hPLycuH4448X3nvvvR7Pa5bzKwiiTa7b7Rbuv//+vM+p1/nNpxNAt8+jGdffYkJ7E+1NavUKgnF7E+1LtC9p0SsIxu1LgmDuvYnLCiQIgiAIgiAIgiCKwKDqCSMIgiAIgiAIgjA7FIQRBEEQBEEQBEEUEQrCCIIgCIIgCIIgiggFYQRBEARBEARBEEWEgjCCIAiCIAiCIIgiQkEYQRAEQRAEQRBEEaEgjCAIgiAIgiAIoohQEEYQBEEQBEEQBFFEKAgjCIIgCIIgCIIoIhSEEQRBEARBEARBFBEKwghigHLLLbeA47gef/76178aLY0gCII4BKF9iSAKhxMEQTBaBEEQygmFQohEIvLfr7/+erz11lv46KOPMGzYMAOVEQRBEIcitC8RROHYjBZAEIQ6fD4ffD4fAOD3v/893nrrLSxdupQ2OoIgCMIQaF8iiMKhckSCGOBcf/31eOyxx7B06VKMHDnSaDkEQRDEIQ7tSwTRPxSEEcQAZtGiRXj00UdpoyMIgiBMAe1LBFEYFIQRxABl0aJFeOSRR2ijIwiCIEwB7UsEUTjUE0YQA5A//vGPuPfee/HKK6/A5XKhoaEBAFBWVgan02mwOoIgCOJQg/YlglAGuSMSxABDEASUlpYiGAz2+N3nn3+OWbNmGaCKIAiCOFShfYkglENBGEEQBEEQBEEQRBGhnjCCIAiCIAiCIIgiQkEYQRAEQRAEQRBEEaEgjCAIgiAIgiAIoohQEEYQBEEQBEEQBFFEKAgjCIIgCIIgCIIoIhSEEQRBEARBEARBFBEKwgiCIAiCIAiCIIoIBWEEQRAEQRAEQRBFhIIwgiAIgiAIgiCIIkJBGEEQBEEQBEEQRBGhIIwgCIIgCIIgCKKIUBBGEARBEARBEARRRP4/Zv8gcHst3EoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3083232685515537, 0.5011373846158427, 0.6981257392184467, 0.8964730913147888, 1.0954122947279203]\n",
      "[2.2504192424711285, 3.098089914346935, 3.070682456098286, 2.594693076250891, 1.997667876075201]\n",
      "[0.2 0.4 0.6 0.8 1. ]\n",
      "[187.72731737 289.66256783 382.14571619 464.64237246 538.07279115]\n"
     ]
    }
   ],
   "source": [
    "# z binning galaxy clustering (lens sample): evenly spaced redshift bins\n",
    "bin_edges_lens = np.linspace(z_min_lens, z_max_lens, nbin_z_lens+1)\n",
    "z_bounds_low_lens = bin_edges_lens[:-1]\n",
    "z_bounds_high_lens = bin_edges_lens[1:]\n",
    "\n",
    "pz_lens = get_redshift_distribution(z_arr, z0_lens, alpha_lens)\n",
    "norm, e = scipy.integrate.quad(get_redshift_distribution, 0, 10, args=(z0_lens, alpha_lens))\n",
    "pz_lens = pz_lens/norm  \n",
    "z_avgs_lens, nz_avgs_lens, pz_bins_lens = get_nz_bins_from_zmid(z_arr, Ngal_lens, pz_lens, sig_z_lens, z_bounds_low_lens, z_bounds_high_lens, title='redshift distribution for lens galaxy bins')\n",
    "\n",
    "chis = ccl.comoving_radial_distance(cosmo, 1/(1+np.array(z_avgs_lens))) # Mpc\n",
    "lmax_lens_zbins = kmax*chis-0.5\n",
    "print(z_avgs_lens)\n",
    "print(nz_avgs_lens)\n",
    "print(z_bounds_low_lens)\n",
    "print(lmax_lens_zbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63255d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = sacc.Sacc()\n",
    "tracers_lens=[]\n",
    "tracers_lens_nobias = []\n",
    "\n",
    "for i in range(nbin_z_lens):\n",
    "    # galaxy clustering CCL tracer for computing mock datavector\n",
    "    tracers_lens.append(ccl.NumberCountsTracer(cosmo, \n",
    "                                          has_rsd=False, \n",
    "                                          dndz=(z_arr, pz_bins_lens[i]), \n",
    "                                          bias=(z_arr,np.ones(len(z_arr))), \n",
    "                                          mag_bias=None\n",
    "                                        ))\n",
    "                                        \n",
    "    tracers_lens_nobias.append(ccl.NumberCountsTracer(cosmo, \n",
    "                                          has_rsd=False, \n",
    "                                          dndz=(z_arr, pz_bins_lens[i]), \n",
    "                                          bias=None, \n",
    "                                          mag_bias=(z_arr, magnification_bias_lens[i]*np.ones(len(z_arr)))\n",
    "                                        ))    \n",
    "    # galaxy clustering SACC tracer for saving to file later\n",
    "    s.add_tracer('NZ', \"lens%d\"%i,  \n",
    "                 quantity='galaxy_density', \n",
    "                 z=z_arr,  \n",
    "                 nz=pz_bins_lens[i]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008ce739",
   "metadata": {},
   "source": [
    "# Weak lensing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbcba65",
   "metadata": {},
   "source": [
    "## source z bins\n",
    "equal number binning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de2dfec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fraction_in_redshift_bin(zmin, zmax, z0, alpha):\n",
    "    i, e1 = scipy.integrate.quad(get_redshift_distribution, zmin, zmax, args=(z0, alpha))\n",
    "    i_all_z, e2 = scipy.integrate.quad(get_redshift_distribution, 0, 10, args=(z0, alpha))\n",
    "    fraction_in_bin = i/i_all_z # what fraction of the total galaxies are between redshifts zmin and zmax\n",
    "    return fraction_in_bin\n",
    "\n",
    "#need to get equal for GAUSSIAN bins\n",
    "def get_equal_number_binning_bounds(nbin_z, ngal, splined, z_arr, sig_e, z0, alpha):\n",
    "    z_bounds = [0]\n",
    "    i_all_z, e2 = scipy.integrate.quad(get_redshift_distribution, 0, 10, args=(z0, alpha))\n",
    "\n",
    "    for i in range(nbin_z-1):\n",
    "        # use cumulative redshift distribution to solve for zmax \n",
    "        # such that 1/nbin_z of the galaxies are in each bin\n",
    "        sol = scipy.optimize.root_scalar(lambda zmax, zmin, bin_number: \n",
    "            get_fraction_in_redshift_bin(zmin, zmax, z0, alpha) - (bin_number+1)/nbin_z,\n",
    "            args = (0,i), method='bisect', bracket=[0., 5.])\n",
    "        assert(sol.converged)\n",
    "        z_bounds.append(sol.root)\n",
    "\n",
    "    z_bounds.append(5) #technically infinity since there is no upper bound but this is a pain numerically, and redshift distribution falls before then\n",
    "\n",
    "    return z_bounds\n",
    "\n",
    "def nz_integrated(z, sig_e, zmax, zmin, splined):\n",
    "    return scipy.integrate.quad(nz_int, z[0], z[-1], args=(sig_e, zmax, zmin, splined))[0]\n",
    "\n",
    "def get_nz_bins_eq(z_arr, ngal, nz, sig_z, z_lower_bounds, z_upper_bounds, title=''):\n",
    "    nz_bins = []\n",
    "    z_avgs = []\n",
    "    nz_avgs = []\n",
    "    fig, ax = plt.subplots(1,2, figsize=(10,5))\n",
    "    splined = interp1d(z_arr, nz)\n",
    "\n",
    "    for i, zmin in enumerate(z_lower_bounds):\n",
    "        zmax = z_upper_bounds[i]\n",
    "        \n",
    "        nz_bin_i = np.zeros_like(z_arr)\n",
    "        nz_bin_i[(z_arr>zmin) & (z_arr<zmax)] = nz[(z_arr>zmin) & (z_arr<zmax)]\n",
    "        dz = z_arr[1]-z_arr[0]\n",
    "        ax[0].plot(z_arr, nz_bin_i, label='z bin %d'%(i+1))\n",
    "        ax[0].set_xlabel('$z$')\n",
    "        ax[0].set_ylabel('$p(z)$')\n",
    "        ax[0].set_title('before gaussian binning')\n",
    "        ax[0].set_xlim(0,2)\n",
    "        \n",
    "        norm, e = scipy.integrate.quad(nz_int, z_arr[0], z_arr[-1], args=(sig_z, zmax, zmin, splined))\n",
    "        nz_bin_i = nz_int(z_arr, sig_z, zmax, zmin, splined)/norm\n",
    "        nz_bins.append(nz_bin_i)\n",
    "\n",
    "        splined_2 = interp1d(z_arr, nz_bin_i)\n",
    "        z_avgs.append((scipy.integrate.quad(avg_z_int, zmin, zmax, args=(splined_2))[0])/(scipy.integrate.quad(splined_2, zmin, zmax)[0]))\n",
    "        nz_avgs.append(norm*ngal)\n",
    "        ax[1].plot(z_arr, nz_bin_i, label='z bin %d'%(i+1))\n",
    "        ax[1].set_xlabel('$z$')\n",
    "        ax[1].set_title('after gaussian binning')\n",
    "        ax[1].set_xlim(0,2)\n",
    "        ax[1].vlines(z_avgs[-1], 0, np.amax(nz_bin_i), color='k', linestyle=':')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.suptitle(title, fontsize=15)\n",
    "    plt.show()\n",
    "\n",
    "    return z_avgs, nz_avgs, nz_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4c50df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_199237/3117838376.py:47: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  norm, e = scipy.integrate.quad(nz_int, z_arr[0], z_arr[-1], args=(sig_z, zmax, zmin, splined))\n",
      "/tmp/ipykernel_199237/3117838376.py:52: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  z_avgs.append((scipy.integrate.quad(avg_z_int, zmin, zmax, args=(splined_2))[0])/(scipy.integrate.quad(splined_2, zmin, zmax)[0]))\n",
      "/tmp/ipykernel_199237/3117838376.py:52: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  z_avgs.append((scipy.integrate.quad(avg_z_int, zmin, zmax, args=(splined_2))[0])/(scipy.integrate.quad(splined_2, zmin, zmax)[0]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2253273944137912, 0.4490567958326107, 0.6545664037512012, 0.9258332934738261, 1.5905474641826118]\n",
      "[0, 0.35249940071821584, 0.5471735370940678, 0.7715892988028372, 1.1100640283086705, 5]\n"
     ]
    }
   ],
   "source": [
    "# z binning weak lensing source sample: source galaxies use equal number binning\n",
    "pz_src = get_redshift_distribution(z_arr, z0_src, alpha_src)\n",
    "norm, e = scipy.integrate.quad(get_redshift_distribution, 0, 10, args=(z0_src, alpha_src))\n",
    "pz_src = pz_src/norm \n",
    "splined_src = interp1d(z_arr, pz_src)\n",
    "\n",
    "#get_equal_gaussian_bins(nbin_z_src, Ngal_src, splined_src, z_arr, sig_z_src, z0_src, alpha_src)\n",
    "bin_edges_src = get_equal_number_binning_bounds(nbin_z_src, Ngal_src, splined_src, z_arr, sig_z_src, z0_src, alpha_src)\n",
    "z_bounds_low_src = np.array(bin_edges_src[:-1])\n",
    "z_bounds_high_src = np.array(bin_edges_src[1:])\n",
    "\n",
    "\n",
    "z_avgs_src, nz_avgs_src, pz_bins_src = get_nz_bins_eq(z_arr, Ngal_src, pz_src, sig_z_src,\n",
    "                                     z_bounds_low_src, z_bounds_high_src,\n",
    "                                     title='redshift distribution for source galaxy bins')\n",
    "print(z_avgs_src)\n",
    "print(bin_edges_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819a8e9b",
   "metadata": {},
   "source": [
    "## weak lensing ccl and sacc tracers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76d12dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tracers_src=[]\n",
    "tracers_lensing_kernel = []\n",
    "for i in range(nbin_z_src):\n",
    "    # weak lensing CCL tracer for computing mock datavector\n",
    "    tracers_src.append(ccl.WeakLensingTracer(cosmo,\n",
    "                                             has_shear = False,\n",
    "                                             dndz=(z_arr, pz_bins_src[i]),\n",
    "                                             ia_bias = (z_arr, np.ones_like(z_arr)),\n",
    "                                             use_A_ia=False\n",
    "                                             ))\n",
    "    tracers_lensing_kernel.append(ccl.WeakLensingTracer(cosmo,\n",
    "                                                        has_shear=True,\n",
    "                                                        dndz=(z_arr, pz_bins_src[i]),\n",
    "                                                        ia_bias=None\n",
    "                                                        ))\n",
    "    # weak lensing SACC tracer for saving to file later\n",
    "    s.add_tracer(\"NZ\", \"src%d\"%i, \n",
    "                 quantity='galaxy_shear',\n",
    "                 z=z_arr,\n",
    "                 nz=pz_bins_src[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359c394f",
   "metadata": {},
   "source": [
    "# Calling Eulerian PT Calculator\n",
    "\n",
    "We will now define and call the PT Calculator to find the higher-order biased P(k)s\n",
    "\n",
    "Divergences may exist at extremely small ell i.e. O(1). You can use linear theory in this regime, though"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "59408da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_1, c_d, c_2 = ccl.nl_pt.translate_IA_norm(\n",
    "    cosmo, z=z_arr, a1=a_1, a1delta=a_d, a2=a_2, Om_m2_for_c2=False\n",
    ")\n",
    "\n",
    "# Code that creates a Pk2D object:\n",
    "ptc = ccl.nl_pt.EulerianPTCalculator(\n",
    "    with_NC=True,\n",
    "    with_IA=True,\n",
    "    log10k_min=-4,\n",
    "    log10k_max=4,\n",
    "    nk_per_decade=200,\n",
    "    cosmo=cosmo,\n",
    ")\n",
    "\n",
    "ptc_lin = ccl.nl_pt.EulerianPTCalculator(\n",
    "    with_NC=True,\n",
    "    with_IA=True,\n",
    "    log10k_min=-4,\n",
    "    log10k_max=4,\n",
    "    nk_per_decade=200,\n",
    "    cosmo=cosmo,\n",
    "    b1_pk_kind = 'linear'\n",
    ")\n",
    "\n",
    "\n",
    "a_1_z = ((1+z_arr)/(1+z_pivot))**eta_1\n",
    "a_2_z = ((1+z_arr)/(1+z_pivot))**eta_2\n",
    "ptt_i = ccl.nl_pt.PTIntrinsicAlignmentTracer(\n",
    "    c1=(z_arr, c_1*a_1_z), c2=(z_arr, c_2*a_2_z), cdelta=(z_arr, c_d*a_1_z)\n",
    ")\n",
    "ptt_m = ccl.nl_pt.PTMatterTracer()\n",
    "pk_mm = ptc.get_biased_pk2d(tracer1=ptt_m, tracer2=ptt_m)\n",
    "pk_im = ptc.get_biased_pk2d(tracer1=ptt_i, tracer2=ptt_m)\n",
    "pk_ii = ptc.get_biased_pk2d(tracer1=ptt_i, tracer2=ptt_i)\n",
    "\n",
    "ptt_i_lin = ccl.nl_pt.PTIntrinsicAlignmentTracer(\n",
    "    c1=(z_arr, c_1*a_1_z), c2=(z_arr, c_2*a_2_z), cdelta=(z_arr, c_d*a_1_z)\n",
    ")\n",
    "pk_mm_lin = ptc_lin.get_biased_pk2d(tracer1=ptt_m, tracer2=ptt_m)\n",
    "pk_im_lin = ptc_lin.get_biased_pk2d(tracer1=ptt_i_lin, tracer2=ptt_m)\n",
    "pk_ii_lin = ptc_lin.get_biased_pk2d(tracer1=ptt_i_lin, tracer2=ptt_i_lin)\n",
    "\n",
    "\n",
    "\n",
    "ptt_g = []\n",
    "pk_gi = []\n",
    "pk_gm = []\n",
    "pk_gg = []\n",
    "\n",
    "ptt_g_lin = []\n",
    "pk_gi_lin = []\n",
    "pk_gm_lin = []\n",
    "pk_gg_lin = []\n",
    "for i in range(nbin_z_lens):\n",
    "    ptt_g.append(ccl.nl_pt.PTNumberCountsTracer(b1=b_1[i], b2=b_2[i], bs=b_s[i], bk2 = b_k[i], b3nl=b_3[i]))\n",
    "    ptt_g_lin.append(ccl.nl_pt.PTNumberCountsTracer(b1=b_1[i], b2=b_2[i], bs=b_s[i], bk2 = b_k[i], b3nl=b_3[i]))\n",
    "\n",
    "for i in range(nbin_z_lens):\n",
    "    pk_gi.append(ptc.get_biased_pk2d(tracer1=ptt_g[i], tracer2=ptt_i))\n",
    "    pk_gm.append(ptc.get_biased_pk2d(tracer1=ptt_g[i], tracer2=ptt_m))\n",
    "    pk_gi_lin.append(ptc_lin.get_biased_pk2d(tracer1=ptt_g_lin[i], tracer2=ptt_i_lin))\n",
    "    pk_gm_lin.append(ptc_lin.get_biased_pk2d(tracer1=ptt_g_lin[i], tracer2=ptt_m))\n",
    "    temp = []\n",
    "    temp_lin = []\n",
    "    for j in range(nbin_z_lens):\n",
    "        temp.append(ptc.get_biased_pk2d(tracer1=ptt_g[i], tracer2=ptt_g[j]))\n",
    "        temp_lin.append(ptc_lin.get_biased_pk2d(tracer1=ptt_g_lin[i], tracer2=ptt_g_lin[j]))\n",
    "    pk_gg.append(temp)\n",
    "    pk_gg_lin.append(temp_lin)\n",
    "\n",
    "print(type(pk_gg[0]), type(pk_gg_lin[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5ce13ebf",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "angular_cl() got an unexpected keyword argument 'p_of_k_a_lin'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m temp \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(nbin_z_src):\n\u001b[0;32m---> 25\u001b[0m     cl_GI\u001b[38;5;241m.\u001b[39mappend(\u001b[43mccl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mangular_cl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcosmo\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtracers_lensing_kernel\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracers_src\u001b[49m\u001b[43m[\u001b[49m\u001b[43mj\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mells\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp_of_k_a\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpk_im\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ml_limber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp_of_k_a_lin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpk_im_lin\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     26\u001b[0m     cl_II\u001b[38;5;241m.\u001b[39mappend(ccl\u001b[38;5;241m.\u001b[39mangular_cl(cosmo,tracers_src[i], tracers_src[j], ells, p_of_k_a\u001b[38;5;241m=\u001b[39mpk_ii,l_limber\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m, p_of_k_a_lin\u001b[38;5;241m=\u001b[39mpk_ii_lin))\n\u001b[1;32m     27\u001b[0m     cl_GG\u001b[38;5;241m.\u001b[39mappend(ccl\u001b[38;5;241m.\u001b[39mangular_cl(cosmo,tracers_lensing_kernel[i], tracers_lensing_kernel[j], ells, p_of_k_a\u001b[38;5;241m=\u001b[39mpk_mm))\n",
      "\u001b[0;31mTypeError\u001b[0m: angular_cl() got an unexpected keyword argument 'p_of_k_a_lin'"
     ]
    }
   ],
   "source": [
    "ells = []\n",
    "for ell in ell_centers:\n",
    "    ells.append(int(ell))\n",
    "cl_GI = []\n",
    "cl_II = []\n",
    "# The weak gravitational lensing power spectrum\n",
    "cl_GG = []\n",
    "\n",
    "# Galaxies\n",
    "cl_gG = []\n",
    "cl_gI = []\n",
    "cl_gg = []\n",
    "# Magnification\n",
    "cl_mI = []\n",
    "cl_gm = []\n",
    "cl_mm = []  \n",
    "cl_mg = []\n",
    "cl_mG = []\n",
    "\n",
    "\n",
    "cl_gG_limber = []\n",
    "cl_gI_limber = []\n",
    "cl_gg_limber = []\n",
    "# Magnification\n",
    "cl_mI_limber = []\n",
    "cl_gm_limber = []\n",
    "cl_mm_limber = []   \n",
    "cl_mg_limber = []\n",
    "cl_mG_limber = []\n",
    "\n",
    "\n",
    "cl_ss_theory = []\n",
    "cl_gg_theory = []\n",
    "cl_gs_theory = []\n",
    "cl_gs_theory_limber = []\n",
    "cl_gg_theory_limber = []\n",
    "for i in range(nbin_z_src):\n",
    "    temp = []\n",
    "    for j in range(nbin_z_src):\n",
    "        cl_GI.append(ccl.angular_cl(cosmo,tracers_lensing_kernel[i], tracers_src[j], ells, p_of_k_a=pk_im))#, l_limber=200, p_of_k_a_lin=pk_im_lin))\n",
    "        cl_II.append(ccl.angular_cl(cosmo,tracers_src[i], tracers_src[j], ells, p_of_k_a=pk_ii))#,l_limber=200, p_of_k_a_lin=pk_ii_lin))\n",
    "        cl_GG.append(ccl.angular_cl(cosmo,tracers_lensing_kernel[i], tracers_lensing_kernel[j], ells, p_of_k_a=pk_mm))#, l_limber=200, non_limber_integration_method=\"FKEM\",))\n",
    "        temp.append((cl_GG[-1] + cl_GI[-1] + ccl.angular_cl(cosmo,tracers_lensing_kernel[j], tracers_src[i], ells, p_of_k_a=pk_im) + cl_II[-1])*(1+s_cal[i])*(1+s_cal[j]))\n",
    "    cl_ss_theory.append(temp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679e8ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(nbin_z_lens):\n",
    "    temp = []\n",
    "    for j in range(nbin_z_src):\n",
    "        cl_mG.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lensing_kernel[j], ells, l_limber=np.max(ells), non_limber_integration_method=\"FKEM\"))\n",
    "        cl_mI.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_src[j], ells, p_of_k_a=pk_im, l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_im_lin))\n",
    "        cl_gI.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_src[j], ells, p_of_k_a=pk_gi[i], l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_gi_lin[i]))\n",
    "        cl_gG.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lensing_kernel[j], ells, p_of_k_a=pk_gm[i], l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_gm_lin[i]))\n",
    "        temp.append((cl_gI[-1] + cl_gG[-1] + cl_mI[-1] + cl_mG[-1])*(1+s_cal[j]))\n",
    "    cl_gs_theory.append(temp)\n",
    "\n",
    "for i in range(nbin_z_lens):\n",
    "    temp = []\n",
    "    for j in range(nbin_z_src):\n",
    "        cl_mG_limber.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lensing_kernel[j], ells))\n",
    "        cl_mI_limber.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_src[j], ells, p_of_k_a=pk_im))\n",
    "        cl_gI_limber.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_src[j], ells, p_of_k_a=pk_gi[i]))\n",
    "        cl_gG_limber.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lensing_kernel[j], ells, p_of_k_a=pk_gm[i]))\n",
    "        temp.append((cl_gI_limber[-1] + cl_gG_limber[-1] + cl_mI_limber[-1] + cl_mG_limber[-1])*(1+s_cal[j]))\n",
    "    cl_gs_theory_limber.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e11d71dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(nbin_z_lens):\n",
    "    temp = []\n",
    "    for j in range(nbin_z_lens):\n",
    "        cl_gm.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lens_nobias[j], ells, p_of_k_a=pk_gm[i], l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_gm_lin[i]))\n",
    "        cl_mm.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lens_nobias[j], ells, l_limber=np.max(ells), non_limber_integration_method=\"FKEM\"))\n",
    "        cl_mg.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lens[j], ells, p_of_k_a=pk_gm[j], l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_gm_lin[j]))\n",
    "        cl_gg.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lens[j], ells, p_of_k_a=pk_gg[i][j], l_limber=np.max(ells), non_limber_integration_method=\"FKEM\",p_of_k_a_lin=pk_gg_lin[i][j]))\n",
    "        temp.append(cl_gg[-1] + cl_gm[-1] + cl_mg[-1] + cl_mm[-1])\n",
    "    cl_gg_theory.append(temp)\n",
    "\n",
    "\n",
    "for i in range(nbin_z_lens):\n",
    "    temp = []\n",
    "    for j in range(nbin_z_lens):\n",
    "        cl_gm_limber.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lens_nobias[j], ells, p_of_k_a=pk_gm[i]))\n",
    "        cl_mm_limber.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lens_nobias[j], ells))\n",
    "        cl_mg_limber.append(ccl.angular_cl(cosmo, tracers_lens_nobias[i], tracers_lens[j], ells, p_of_k_a=pk_gm[j]))\n",
    "        cl_gg_limber.append(ccl.angular_cl(cosmo, tracers_lens[i], tracers_lens[j], ells, p_of_k_a=pk_gg[i][j]))\n",
    "        temp.append(cl_gg_limber[-1] + cl_gm_limber[-1] + cl_mg_limber[-1] + cl_mm_limber[-1])\n",
    "    cl_gg_theory_limber.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2325ead8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(cl_ss_theory), len(cl_ss_theory[0]), len(cl_ss_theory[0][0]))\n",
    "print(len(cl_gg_theory), len(cl_gg_theory[0]), len(cl_gg_theory[0][0]))\n",
    "print(len(cl_gg_theory_limber), len(cl_gg_theory_limber[0]), len(cl_gg_theory_limber[0][0]))\n",
    "print(len(cl_gs_theory_limber), len(cl_gs_theory_limber[0]), len(cl_gs_theory_limber[0][0]))\n",
    "print(len(cl_gs_theory), len(cl_gs_theory[0]), len(cl_gs_theory[0][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa06077",
   "metadata": {},
   "source": [
    "# Angular power spectra\n",
    "bin combos from DESC SRD\n",
    "* 5 density-density\n",
    "* 7 density-shear spectra\n",
    "* 15 shear-shear spectra (src-src)\n",
    "\n",
    "\n",
    "NOTE: if using SRD cosmolike covmat the order is shear-shear, shear-density, density-density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7605b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for plots\n",
    "colors = ['#e41a1c', '#ff7f00', '#4daf4a', '#377eb8', '#984ea3', '#a65628', '#f781bf', '#999999', '#dede00'] \n",
    "linestyles = ['-', '--', '-.', ':', ':']\n",
    "markerstyles = ['o', '^', 'v', '+', 'x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb7c784",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "ell_centers = np.array(ell_centers)\n",
    "for i in range(nbin_z_lens):\n",
    "    \n",
    "    #plt.vlines(l_max_lens[i], -1.e-4, 1.e-4, color = 'k', linestyle=linestyles[i])\n",
    "    if not auto_galaxy_clustering_only:\n",
    "        for j in range(i, nbin_z_lens):\n",
    "            cl_binned = cl_gg_theory[i][j]\n",
    "            s.add_ell_cl('galaxy_density_cl',  \n",
    "             \"lens%d\" % (i),  \n",
    "             \"lens%d\" % (j), \n",
    "             ell_centers,  \n",
    "             cl_binned,  \n",
    "             window=sacc_ell_windows\n",
    "            )\n",
    "\n",
    "            plt.plot(ells, cl_gg_theory[i][j], color=colors[i], linestyle='-', marker = markerstyles[j], label='lens bins %d-%d'%(i+1,j+1))\n",
    "            plt.plot(ells, cl_gg_theory_limber[i][j], color=colors[i], linestyle=':', marker = markerstyles[j], label='limber lens bins %d-%d'%(i+1,j+1))\n",
    "            #plt.plot(ells, (cl_gg_theory[i][j]-cl_gg_theory_limber[i][j])/cl_gg_theory[i][j], color=colors[i], linestyle=linestyles[j], label='lens bins %d-%d'%(i+1,j+1))\n",
    "\n",
    "    else:\n",
    "        cl_binned = cl_gg_theory[i][i]\n",
    "        s.add_ell_cl('galaxy_density_cl',  \n",
    "         \"lens%d\" % (i),  \n",
    "         \"lens%d\" % (i), \n",
    "         ell_centers,  \n",
    "         cl_binned,  \n",
    "         window=sacc_ell_windows\n",
    "        )\n",
    "\n",
    "        plt.plot(ells, cl_gg_theory[i][i], color=colors[i], linestyle=linestyles[0], label='lens bins %d-%d'%(i+1,i+1))\n",
    "        plt.plot(ells, cl_gg_theory_limber[i][i], color=colors[i], linestyle=linestyles[1], label='limber lens bins %d-%d'%(i+1,i+1))\n",
    "        #plt.plot(ells, (cl_gg_theory[i][i]-cl_gg_theory_limber[i][i])/cl_gg_theory[i][i], color=colors[i], linestyle=linestyles[i], label='lens bins %d-%d'%(i+1,i+1))\n",
    "       \n",
    "if not auto_galaxy_clustering_only:\n",
    "    plt.yscale('symlog', linthresh=1.e-10)\n",
    "    plt.ylim(-1.e-6, 1.e-4)\n",
    "else:\n",
    "    plt.yscale('log')\n",
    "    plt.ylim(1.e-8, 1.e-4)\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='lower center', ncols=3)\n",
    "plt.title('lens-lens', fontsize=20)\n",
    "plt.ylabel(r'$C_{g,g}(l)$', fontsize=20)\n",
    "plt.xlabel('ell', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc428312",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "tracer_combos = [[0, 2], [0, 3], [0, 4], [1, 3], [1, 4], [2, 4], [3, 4]]\n",
    "for inds in tracer_combos:\n",
    "    i = inds[0]\n",
    "    j = inds[1]\n",
    "    plt.loglog(ell_centers, cl_gs_theory[i][j], color = colors[i], marker=markerstyles[j], label='lens bin %d and source bin %d'%(i+1,j+1))\n",
    "    plt.loglog(ell_centers, cl_gs_theory_limber[i][j], linestyle=':', color = colors[i], marker=markerstyles[j], label='limber lens bin %d and source bin %d'%(i+1,j+1))\n",
    "    #plt.vlines(l_max_lens[i], 10**-10, 10**-6, color = 'k', linestyle=linestyles[j],)\n",
    "    cl_binned = cl_gs_theory[i][j]\n",
    "    s.add_ell_cl('galaxy_shearDensity_cl_e',  \n",
    "                 \"lens%d\" % (i),  \n",
    "                 \"src%d\" % (j), \n",
    "                 ell_centers,  \n",
    "                 cl_binned,  \n",
    "                 window=sacc_ell_windows\n",
    "                )  \n",
    "plt.yscale('symlog', linthresh=1.e-7)\n",
    "plt.ylim(-1.e-5, 1.e-5)\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='lower center', ncols=3)\n",
    "plt.title('lens-source', fontsize=20)\n",
    "plt.ylabel(r'$C_{g,s}(l)$', fontsize=20)\n",
    "plt.xlabel('ell', fontsize=20)\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84dce0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "for i in range(nbin_z_src):\n",
    "    for j in range(i, nbin_z_src):\n",
    "        plt.loglog(ell_centers, cl_ss_theory[i][j], color=colors[i], marker=markerstyles[j], label='z bins %d and %d'%(i+1,j+1))\n",
    "        cl_binned = cl_ss_theory[i][j]\n",
    "        s.add_ell_cl(\"galaxy_shear_cl_ee\", \n",
    "                     \"src%d\" % (i), \n",
    "                     \"src%d\" % (j), \n",
    "                     ell_centers,  \n",
    "                     cl_binned,  \n",
    "                     window=sacc_ell_windows\n",
    "                     )\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.ylim(1.e-11, 1.e-7)\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='lower center', ncols=3)\n",
    "plt.title('source-source', fontsize=20)\n",
    "plt.ylabel(r'$C_{s,s}(l)$', fontsize=20)\n",
    "plt.xlabel('ell', fontsize=20)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "928c6a03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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