matrixmodel_zinser_freedelta_onegamma_limitrho_m20_newpriors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zinser data (regridded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc4\n",
    "import numpy as np\n",
    "\n",
    "# load data\n",
    "datafile = 'data/Zinser_SizeDist_regrid-20-7.nc'\n",
    "\n",
    "data_gridded = {}\n",
    "with nc4.Dataset(datafile) as nc:\n",
    "    for var in nc.variables:\n",
    "        data_gridded[var] = nc.variables[var][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_min = data_gridded['v_min']\n",
    "delta_v = 1.0/data_gridded['delta_v_inv']\n",
    "v = v_min * 2**(np.arange(data_gridded['m'])*delta_v) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "def add_colorbar(ax, **cbarargs):\n",
    "    axins_cbar = inset_axes(ax, width='3%', height='90%', loc=5, bbox_to_anchor=(0.05,0.0,1,1), bbox_transform=ax.transAxes)\n",
    "    mpl.colorbar.ColorbarBase(axins_cbar, orientation='vertical', **cbarargs)\n",
    "\n",
    "fig,axs = plt.subplots(nrows=2, sharex=True, figsize=(12,10))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(data_gridded['time'], data_gridded['PAR'], color='gold')\n",
    "ax.set(ylabel='PAR')\n",
    "\n",
    "ax = axs[1]\n",
    "pc = ax.pcolormesh(data_gridded['time'],v,data_gridded['w_obs'])\n",
    "ax.set(ylabel='size ($\\mu$m$^3$)', xlabel='time (minutes)')\n",
    "add_colorbar(ax, norm=pc.norm, cmap=pc.cmap, label='size class proportion')\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The model\n",
    "\n",
    "modified from *Sosik et al. (2003), Growth rates of coastal phytoplankton from time-series measurements with a submersible flow cytometer, Limnol. Oceanogr.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_code = '''data {\n",
    "    // size variables\n",
    "    int<lower=0> m;         // number of size classes\n",
    "    int<lower=0> nt;        // number of timesteps\n",
    "    int<lower=0> nt_obs;    // number of timesteps with observations\n",
    "    // model parameters and input\n",
    "    int<lower=0> dt;        // delta t in minutes\n",
    "    real<lower=0> E[nt];    // vector of incident radiation values\n",
    "    real<lower=0> v_min;    // size in smallest size class in um^-3\n",
    "    int<lower=0> delta_v_inv;   // inverse of delta_v \n",
    "    simplex[m] w_ini;       // initial conditions \n",
    "    // observations\n",
    "    real<lower=0,upper=nt*dt>  t_obs[nt_obs]; // the time of each observation\n",
    "    matrix[m, nt_obs] obs; //observations\n",
    "}\n",
    "transformed data {\n",
    "    int j;\n",
    "    real<lower=0> delta_v;\n",
    "    real<lower=0> dt_days;  // dt in units of days\n",
    "    real<lower=0> v[m];     // vector of (minimum) sizes for each size class\n",
    "    int<lower=0> t[nt];     // vector of times in minutes since start \n",
    "    int<lower=1, upper=nt> it_obs[nt_obs]; // the time index of each observation\n",
    "\n",
    "    j = 1 + delta_v_inv; \n",
    "    delta_v = 1.0/delta_v_inv;\n",
    "    dt_days = dt/1440.0;\n",
    "    for (i in 1:m){\n",
    "        v[i] = v_min*2^((i-1)*delta_v);\n",
    "    }\n",
    "    // populate time vector\n",
    "    t[1] = 0;\n",
    "    for (i in 2:nt){\n",
    "        t[i] = (t[i-1] + dt);\n",
    "    }\n",
    "    // populate index vector it_obs\n",
    "    for (k in 1:nt_obs){\n",
    "        for (i in 1:nt){\n",
    "            if (t_obs[k]>=t[i] && t_obs[k]<t[i]+dt){\n",
    "                it_obs[k] = i;\n",
    "                break;\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "parameters {\n",
    "    real<lower=0> delta_mu; \n",
    "    real<lower=0> delta_sigma; \n",
    "    real<lower=0, upper=1.0/dt_days> delta_max[m-j+1]; \n",
    "    real<lower=0> gamma_mu; \n",
    "    real<lower=0> gamma_sigma; \n",
    "    real<lower=0, upper=1.0/dt_days> gamma_max;\n",
    "    real<lower=0, upper=5000> E_star; \n",
    "    real<lower=1e-10> sigma;\n",
    "    real<lower=0> rho_mu;\n",
    "    real<lower=0> rho_sigma;\n",
    "    real<lower=0, upper=0.3*gamma_max> rho;\n",
    "}\n",
    "transformed parameters {\n",
    "    real divrate;\n",
    "    matrix[m,nt_obs] mod_obspos;\n",
    "    {\n",
    "        // helper variables\n",
    "        vector[m] w_curr;\n",
    "        vector[m] w_next;\n",
    "        vector[m] u_curr;\n",
    "        vector[m] u_next;\n",
    "        real delta_i = 0.0;\n",
    "        real gamma;\n",
    "        real a;\n",
    "        //vector[m] tmp;\n",
    "        int ito = 1;\n",
    "        int resp;\n",
    "        \n",
    "        w_curr = w_ini;\n",
    "        u_curr = w_ini;\n",
    "\n",
    "        for (it in 1:nt){ // time-stepping loop\n",
    "            // record current solution \n",
    "            if (it == it_obs[ito]){\n",
    "                mod_obspos[,ito] = w_curr;\n",
    "                ito += 1;\n",
    "                if (ito > nt_obs){\n",
    "                    break;\n",
    "                }\n",
    "            }\n",
    "            \n",
    "            /*tmp = obs[,ito];\n",
    "            w_curr = tmp ./ sum(tmp);*/\n",
    "            w_next = rep_vector(0.0, m);\n",
    "            u_next = rep_vector(0.0, m);\n",
    "            for (i in 1:m){ // size-class loop\n",
    "                // compute gamma\n",
    "                if (i == 1){\n",
    "                //if (i < m){\n",
    "                    gamma = gamma_max * dt_days * (1.0 - exp(-E[it]/E_star));\n",
    "                } else if (i < m){\n",
    "                    gamma = gamma_max * dt_days * (1.0 - exp(-E[it]/E_star)) - rho * dt_days;\n",
    "                    if (gamma < 0){\n",
    "                        resp = 1;\n",
    "                        gamma = -gamma;\n",
    "                    } else{\n",
    "                        resp = 0;\n",
    "                    }\n",
    "                } else if (i == m){\n",
    "                    gamma = rho * dt_days;\n",
    "                    resp = 1;\n",
    "                }\n",
    "                \n",
    "                // compute delta_i\n",
    "                if (i >= j){\n",
    "                    delta_i = delta_max[i-j+1] * dt_days;\n",
    "                }\n",
    "                \n",
    "                // fill subdiagonal (growth)\n",
    "                if (i < j && resp == 0){\n",
    "                //if(i < j){\n",
    "                    //A[i+1,i] = gamma;\n",
    "                    a = gamma;\n",
    "                    w_next[i+1] += a * w_curr[i];\n",
    "                    u_next[i+1] += a * u_curr[i];\n",
    "                } else if (i < m && resp == 0){\n",
    "                //} else if (i < m){\n",
    "                    //A[i+1,i] = gamma * (1.0-delta_i);\n",
    "                    a = gamma * (1.0-delta_i);\n",
    "                    w_next[i+1] += a * w_curr[i];\n",
    "                    u_next[i+1] += a * u_curr[i];\n",
    "                }\n",
    "                \n",
    "                // fill the 1st superdiagonal (respiration)\n",
    "                if (i < j && i > 1 && resp == 1){\n",
    "                    a = gamma;\n",
    "                    w_next[i-1] += a*w_curr[i];\n",
    "                    u_next[i-1] += a*u_curr[i];\n",
    "                } else if (i >= j && resp == 1){\n",
    "                    a = gamma * (1.0 - delta_i);\n",
    "                    w_next[i-1] += a*w_curr[i];\n",
    "                    u_next[i-1] += a*u_curr[i];\n",
    "                }\n",
    "                // fill (j-1)th superdiagonal (division)\n",
    "                if (i >= j){\n",
    "                    //A[i+1-j,i] = 2.0*delta_i;\n",
    "                    a = 2.0*delta_i;\n",
    "                    w_next[i+1-j] += a * w_curr[i];\n",
    "                    u_next[i+1-j] += a * u_curr[i];\n",
    "                }\n",
    "                // fill diagonal (stasis)\n",
    "                if (i < j){\n",
    "                    //A[i,i] = (1.0-gamma);\n",
    "                    a = (1.0-gamma);\n",
    "                    w_next[i] += a * w_curr[i];\n",
    "                    u_next[i] += a * u_curr[i];\n",
    "                } else if (i == m){\n",
    "                    //A[i,i] = (1.0-delta_i);\n",
    "                    a = (1.0-delta_i);\n",
    "                    w_next[i] += a * w_curr[i];\n",
    "                    u_next[i] += a * u_curr[i];\n",
    "                } else {\n",
    "                    //A[i,i] = (1.0-gamma) * (1.0-delta_i);\n",
    "                    a = (1.0-gamma) * (1.0-delta_i);\n",
    "                    w_next[i] += a * w_curr[i];\n",
    "                    u_next[i] += a * u_curr[i];\n",
    "                }\n",
    "            }\n",
    "            w_curr = w_next ./ sum(w_next);\n",
    "            u_curr = u_next;\n",
    "        }\n",
    "        divrate = log(sum(u_curr))*60*24/(nt*dt); // daily division rate\n",
    "    }\n",
    "}\n",
    "model {\n",
    "    real diff;\n",
    "    \n",
    "    // priors\n",
    "    /*delta_mu ~ normal(66.0, 5.0);\n",
    "    delta_sigma ~ exponential(1.0);\n",
    "    delta_max ~ normal(delta_mu, delta_sigma);\n",
    "    gamma_mu ~ normal(55.0, 5.0);\n",
    "    gamma_sigma ~ exponential(1.0);\n",
    "    gamma_max ~ normal(gamma_mu, gamma_sigma);\n",
    "    E_star ~ normal(300.0,100.0);\n",
    "    rho_mu ~ normal(16.5, 5.0);\n",
    "    rho_sigma ~ exponential(1.0);\n",
    "    rho ~ normal(rho_mu, rho_sigma);\n",
    "    \n",
    "    sigma ~ exponential(1000.0);*/\n",
    "    \n",
    "    delta_mu ~ lognormal(log(66.0), 5.0);\n",
    "    delta_sigma ~ exponential(0.1);\n",
    "    delta_max ~ lognormal(delta_mu, delta_sigma);\n",
    "    gamma_mu ~ lognormal(log(55.0), 5.0);\n",
    "    gamma_sigma ~ exponential(0.1);\n",
    "    gamma_max ~ lognormal(gamma_mu, gamma_sigma);\n",
    "    E_star ~ lognormal(log(300.0), 10.0);\n",
    "    rho_mu ~ lognormal(log(16.5), 5.0);\n",
    "    rho_sigma ~ exponential(0.1);\n",
    "    rho ~ lognormal(rho_mu, rho_sigma);\n",
    "    \n",
    "    sigma ~ exponential(1000.0);\n",
    "\n",
    "    // fitting observations\n",
    "    for (it in 1:nt_obs){\n",
    "        diff = 0.0;\n",
    "        for (iv in 1:m){\n",
    "            diff += fabs(mod_obspos[iv,it] - obs[iv,it]);\n",
    "        }\n",
    "        diff = diff/sigma;\n",
    "        diff ~ normal(0.0, 1.0) T[0,];\n",
    "    }\n",
    "}\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data for Stan model\n",
    "\n",
    "dt = 20 # in units of minutes\n",
    "\n",
    "data = {'dt':dt}\n",
    "for k in ('m','v_min','delta_v_inv'):\n",
    "    data[k] = data_gridded[k]\n",
    "\n",
    "data['obs'] = data_gridded['w_obs']\n",
    "data['t_obs'] = data_gridded['time']\n",
    "data['E'] = data_gridded['PAR']\n",
    "# use first measurements as initial conditions\n",
    "data['w_ini'] = data_gridded['w_obs'][:,0]\n",
    "\n",
    "# limit the data\n",
    "\n",
    "limit_days = 1\n",
    "stride_t_obs = 1 # keep everything\n",
    "\n",
    "if limit_days > 0:\n",
    "    limit_minutes = limit_days*1440 # The plus 500 term is for the Zinser data\n",
    "    \n",
    "    ind_obs = data['t_obs'] < limit_minutes\n",
    "    data['t_obs'] = data['t_obs'][ind_obs]\n",
    "    data['obs'] = data['obs'][:,ind_obs]\n",
    "\n",
    "    data['nt'] = int(limit_minutes//data['dt'])\n",
    "    \n",
    "if stride_t_obs > 0:\n",
    "    data['t_obs'] = data['t_obs'][::stride_t_obs]\n",
    "    data['obs'] = data['obs'][:,::stride_t_obs]\n",
    "\n",
    "data['nt_obs'] = data['obs'].shape[1]\n",
    "\n",
    "# finally, add light data\n",
    "t = np.arange(data['nt'])*data['dt']\n",
    "data['E'] = np.interp(t, xp=data_gridded['time'], fp=data_gridded['PAR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_4e62b3b227a99b8e13319d32f8c4a5d4 NOW.\n"
     ]
    }
   ],
   "source": [
    "import pystan\n",
    "\n",
    "model = pystan.StanModel(model_code=stan_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n",
      "WARNING:pystan:16 of 4000 iterations ended with a divergence (0.4 %).\n",
      "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_4e62b3b227a99b8e13319d32f8c4a5d4.\n",
      "4 chains, each with iter=2000; warmup=1000; thin=1; \n",
      "post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
      "\n",
      "                    mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "delta_mu            0.25  8.6e-3   0.49 1.3e-4   0.01   0.07   0.27   1.62   3232    1.0\n",
      "delta_sigma         8.92    0.22   3.97   3.38   5.92   8.26  11.11  18.87    330   1.01\n",
      "delta_max[1]        0.06  2.4e-3   0.154.5e-11 4.3e-5 3.7e-3   0.05   0.51   3703    1.0\n",
      "delta_max[2]        0.03  1.2e-3   0.067.5e-11 2.3e-5 1.9e-3   0.03   0.22   2565    1.0\n",
      "delta_max[3]        0.02  9.4e-4   0.055.7e-11 2.3e-5 1.1e-3   0.02   0.17   2526    1.0\n",
      "delta_max[4]        0.02  7.6e-4   0.047.2e-11 2.0e-5 1.1e-3   0.01   0.13   2362    1.0\n",
      "delta_max[5]        0.02  8.6e-4   0.044.1e-11 1.7e-5 9.9e-4   0.01   0.15   2309    1.0\n",
      "delta_max[6]        0.02  9.8e-4   0.053.9e-11 1.4e-5 1.1e-3   0.02   0.17   2668    1.0\n",
      "delta_max[7]        0.02  1.3e-3   0.061.2e-10 3.0e-5 1.5e-3   0.02   0.21   2024    1.0\n",
      "delta_max[8]        0.05  2.4e-3   0.111.7e-10 2.9e-5 2.3e-3   0.03   0.38   2257    1.0\n",
      "delta_max[9]        0.11  4.3e-3   0.248.3e-11 4.9e-5 5.4e-3   0.08   0.87   3032    1.0\n",
      "delta_max[10]       0.26    0.01   0.532.1e-10 1.4e-4   0.01   0.26   1.82   2383    1.0\n",
      "delta_max[11]       1.52    0.05   1.69 2.2e-9   0.01   0.94   2.58   5.58   1107    1.0\n",
      "delta_max[12]        4.1    0.11   4.38 6.6e-9   0.73   3.34    6.1  13.81   1659    1.0\n",
      "delta_max[13]      59.35    0.18   9.99  34.92  53.61  61.75  67.33  71.58   3152    1.0\n",
      "gamma_mu             3.1    0.09   4.47 1.0e-3   0.27   2.07   4.22  13.64   2494    1.0\n",
      "gamma_sigma         6.76    0.15   6.92   0.33   2.37   4.57   8.77  24.92   2074    1.0\n",
      "gamma_max          56.34    0.29    8.5  41.42  49.34  55.96  63.46  71.05    832   1.01\n",
      "E_star            301.11    2.37  67.28 185.81  246.7 299.08 353.36 425.73    804   1.01\n",
      "sigma               0.08  1.1e-4 5.2e-3   0.07   0.07   0.08   0.08   0.09   2430    1.0\n",
      "rho_mu              1.98    0.06   3.43 4.4e-4    0.1   0.85   2.56  10.81   3197    1.0\n",
      "rho_sigma           5.92    0.13    6.0   0.33   2.01   3.83   7.84  23.03   2274    1.0\n",
      "rho                12.93    0.02   0.93  10.98  12.31  12.97  13.55  14.64   1832    1.0\n",
      "divrate             0.23  2.5e-3   0.07   0.13   0.18   0.21   0.26   0.42    924    1.0\n",
      "lp__              -152.2    0.32   5.67 -163.5 -156.0 -152.1 -148.2 -141.6    306   1.01\n",
      "\n",
      "Samples were drawn using NUTS at Tue Mar 10 12:44:15 2020.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "mcmc = model.sampling(data=data, iter=2000)\n",
    "print('\\n'.join(x for x in mcmc.__str__().split('\\n') if 'mod_obspos' not in x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "paramnames = [v for v in mcmc.flatnames if not v.startswith('mod_obspos') and not v.startswith('delta_max') and not '[' in v]\n",
    "num_params = len(paramnames)\n",
    "\n",
    "fig,axs = plt.subplots(num_params-1,num_params-1,sharex='col',sharey='row',figsize=(12,12), squeeze=False)\n",
    "for ax in axs.flat:\n",
    "    ax.set_visible(False)\n",
    "for irow in range(1,num_params):\n",
    "    for icol in range(irow):\n",
    "        ax = axs[irow-1,icol]\n",
    "        ax.set_visible(True)\n",
    "        xname = paramnames[icol]\n",
    "        yname = paramnames[irow]\n",
    "        ax.hist2d(mcmc[xname],mcmc[yname],bins=50,cmap=mpl.cm.gray_r)\n",
    "        if icol == 0:\n",
    "            ax.set_ylabel(yname)\n",
    "        if irow == num_params-1:\n",
    "            ax.set_xlabel(xname)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = data_gridded['delta_v_inv'] + 1\n",
    "m = data_gridded['m']\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(mcmc['delta_max'])\n",
    "xlabels = [str(x) for x in range(j,m+1)]\n",
    "xlabels[0] = 'j='+xlabels[0]\n",
    "xlabels[-1] = 'm='+xlabels[-1]\n",
    "ax.set_xticklabels(xlabels)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = data_gridded['delta_v_inv'] + 1\n",
    "m = data_gridded['m']\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, sharex=True, figsize=(12,9))\n",
    "ax = axs\n",
    "ax.boxplot(mcmc['delta_max'], positions=range(j,m+1))\n",
    "ax.set(ylabel='delta_i')\n",
    "ax.yaxis.grid(True)\n",
    "ax.title.set_text('Division Proportion vs. Size Class')\n",
    "\n",
    "# ax = axs[1]\n",
    "# ax.boxplot(mcmc['gamma_max'], positions=range(1,m))\n",
    "# ax.set(ylabel='gamma_max')\n",
    "# ax.yaxis.grid(True)\n",
    "\n",
    "# ax = axs[2]\n",
    "# ax.boxplot(mcmc['rho'], positions=range(1,m))\n",
    "# ax.set(ylabel='rho')\n",
    "# ax.yaxis.grid(True)\n",
    "\n",
    "xlabels = [str(x) for x in range(1,m+1)]\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vTvb8/WTfp7VOsyHKNbA97fvY5NZ/lpz5uuT8z7ROA7B9Ta8kJz4umdyW3PXd3RKqM0y5BravO96U7Hif5MQnJct/3joNwPZ08tXJ1d9LDn1/Mj/704qVa2D7miwld/9EkrXk+Ecldbl1IoDt5eJ/6uZa7/+sZM+Ht07TC+Ua2N4WX9qNllz5reTRL2idBmD7WD3VrQ6y+O7Jwa9tnaY3yjXALf8o2f+5yWPfmjz+b1unARi/WpOHj3YF+/APJ5OdrRP1RrkGSJI7vjZZer/k4U9Nlt/aOg3AuJ37vuTCTyUH35AsvXfrNL1SrgGSpCwmd/94krnkoVd1V68D0L/lP0ke+Zxk14clt/2L1ml6p1wDXLPw4uTwDyRX/1fy6Oe2TgMwPnUlOf7x3e6Lh96clPFV0fE9IoCN2PP3ktu+KDn7Xcm5H26dBmBcTr0uufI/kru+K1m4p3WaQSjXAE918HXJzg9KHv6M5OoftU4DMA6XfiM5/bpk7ycmez+qdZrBKNcAT1Xmk8P/JpnsSh76x8n0UutEALNt7fHkxMd30+/u/LbWaQalXAM8nYW7u+Whlv8geeT/aZ0GYLY98tnJytuTQz+YzO1tnWZQyjXAM9n9yuT2L0vO/evk7Pe3TgMwmx7/ieTxNye3vybZ9YGt0wxOuQZ4Nge+vFsu6pHPTK78Tus0ALNl5cHu+pWl9+t+n24DyjXAsylzyeEfSSa3Jsdflaydb50IYDbUabe9eV1ODv9Qt/zeNqBcAzyX+TuTu3+027nx4aPdtr0APLszb0ou/VJy5zcni+/cOs2mUa4Bno9dH9It0Xf+R5Oz39k6DcDWduV/J6dek+z5+8m+f9o6zaZSrgGer9u+KNn9Ed3ujVd+u3UagK1pejk5/rHJ5Lbkru9JSmmdaFMp1wDPV5kkh34gmbsjeehVydrZ1okAtp6Tr+6WMT30r5P5A63TbDrlGuCFmD+QHP6xZOUdyYlPNf8a4EYXfj557FuT/Z+d7Pk7rdM0oVwDvFC7PiC542uTCz+VPPYtrdMAbA2rJ7vVQRZfnhz8mtZpmlGuAW7G/s/rLtR59AuSy7/ZOg1AW7UmD396Mj3T7W472dk6UTPKNcDNKCU59P3Jwr3JQx+VrJ1unQignXPfk1z4d8mBNyRL79U6TVPKNcDNmtufHP7xZO2R5PgndhsmAGw3y3+SPPK5ya6/ldz2ea3TNKdcA2zEziPJHd+UXPzZ5MzXtU4DsLnqSnL845Kyo1sdpKiWzgDARt36mcktH5Wc/JLk0q+0TgOweU59VXLlt5K7jiUL97ROsyUo1wAbVUpy13cnCy9Njv+TZPWR1okAhnfp15LTb0j2fXKy9x+3TrNlKNcAfZjbm9z9E8naY91LpHWtdSKA4aydS058QrJwX3LHt7ZOs6X0Wq5LKUdLKfeXUu4/efJkn4cG2PqW3iu589uTS7/UvVQKMFaPfFa3mdahH0zmbmmdZkvptVzXWo/VWo/UWo8cPHiwz0MDzIZ9n5rs/cTk9FcnF3+hdRqA/j3+Y8njP5jc/qXdplo8iWkhAH0qJbnrO5LFd+umh6wcb50IoD8rf5E8/M+SpfdPDnxZ6zRbknIN0LfJ7uTun0ymF5PjH5PU1daJADauTpMTn9Qtv3f4h5Iy3zrRlqRcAwxhx7sld31XcvlXk5NGd4AROPONyaVfTu781mTxnVqn2bKUa4Ch7Pv4ZN+nJ2e+JrnwH1qnAbh5V/5nt5b/nn+U7PuU1mm2NOUaYEh3fkuy47267dFX3tE6DcALN73cXUMyfzA5dKy7toRnpFwDDGmys1v/OivJQx+d1OXWiQBemEe/MFn+w25787nbW6fZ8pRrgKEtvnNy1/cmV34zefTVrdMAPH8Xfi45++3J/s9Ndr+ydZqZoFwDbIa9r0r2f1by2JuS8z/VOg3Ac1t9NDnxKcmO90gOvrF1mpmhXANsloNfnyy9b/dktfynrdMAPLNak4c/LZmeTQ79cDJZap1oZijXAJtlsiM5/ONJSvLQq5LpldaJAJ7e2WPJhZ9JDn5NsvQerdPMFOUaYDMt3pccenNy9beTR/9F6zQAf9nVtySPfl6y65XJ/s9unWbmKNcAm+2W/yu57QuSs/8qefxHW6cBeEJdTk58XFJ2dquDFFXxhXLGAFo4+Ppk5wcmD396N0oEsBWcem1y5YHk0HcnC4dbp5lJyjVAC2UhOfyjSVlKjr8qmV5qnQjY7i79anL6jcm+T01u+Uet08ws5RqglYV7kkM/mFz9veQR8xqBhtbOJcc/IVl4SbezLDdNuQZoac+HJ7d/SXLue5Nzb26dBthuak2W39ZNUVt9MDn8Q8lkT+tUM22+dQCAbe/AVyaXfz15+P9Oll6R7PirrRMBY1RrsvK2bk719bff7tayTpIDX53s/OttM46Acg3QWplLDv9I8mfv3a1/fd9vGTkCNqZOn6FIn+vuL4vJjvdM9n5090f90vsmS+/dNvNIKNcAW8H8Xcnhf5P8xd9OHv6M5NAPJaW0TgXMgjpNVt76NEX68e7+60X6Y5KlI+uvkL28u53eKdcAW8XuD00OvDY59WXJrg9Jbj3aOhGw1dRpsvwnTy7SV//nDUV6x3qR/tj1EWlFerMp1wBbye2vSS7/Wrd6yNL7tk4DtPSkIn3/DUX6fHd/2ZHseK9k78c9pUgvtM29zSnXAFtJmXTL8/35+yQPfVRS/2FS/KqG0avTZPmPn2ZE+lqRXlov0p9wQ5F+d0V6C/IbG2CrmT+YHP6x5B0fkiy/pRuJAsajrj1Dkb7Q3V+Wkh3vnez9xKcUabVtFvgqAWxFuz4wOfjGZO2nksu/kfzpy5O5/cncbcnktic+ntu//u8bbpvsT+Zu9UQMW0Fd6/5IftLFhv8zqRe7+8vObpWOvZ98Q5F+Nz+/M8xXDmCruu3zk4Xf7Z6EdxxI1s4kK3+RrP3vZPrYEy8XP5PJ3hvK9lPL97MU88keK5XAzahryfIfPaVI/68bivSurkjf+qlPFOnFd1WkR8ZXE2CrKpNui/QkufsNf/n+upKsPda9Tc+sf3yme5te+3j9/fRMcvXBJ/77rDzLJ55/hjL+HEV9br8VCdg+6uozFOlL3f1lV7L0Psmt//QpRXqubW4Gp1wDzKqykMzf0b29ELV2I2lrNxTwG8v5U4v56sPJ8h+u33fuOTLtfkr5fq4pLLclc3uTGClnK6vJ6vEbSvT960X6cnf39SL9aYo0yjXAtlNKUvZ00z8W7n1h/9+62hXstacr40+97XQ3snft/np1mMcDm6nsXi/SR28o0u+iSHNdr+W6lHI0ydEkedGLXtTnoQHYCsp8Mnd79/ZCTS8/fRm/tvkFbGVzt68X6Zcp0jyrXst1rfVYkmNJcuTIkdrnsQGYcZOdyeTuJHe3TgIwmEnrAAAAMBbKNQAA9ES5BgCAnijXAADQk1LrMNcdllLOJ3nLIAfnuRxIcqp1iG3IeW/DeW/DeW/HuW/DeW/j+Z73F9daDw4d5vkYcp3rt9Rajwx4fJ5BKeV+537zOe9tOO9tOO/tOPdtOO9tzOJ5Ny0EAAB6olwDAEBPhizXxwY8Ns/OuW/DeW/DeW/DeW/HuW/DeW9j5s77YBc0AgDAdmNaCAAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfzQx14seyoS9k91OGbK7unSZJ60d8nM6uU1gkGV7bBY8z8XOsEgyo7VpIkdWVH4yQDWpu2TjCstbXWCQZXpyP/Gm4TL3vFS1pHuGkPPPDAqVrrwdY5kgHL9VJ25/0nrxzq8M0tvtfVJMnyb474CW/kyty4S1mSTJbG//1Zbt/fOsKg5t7pRJJk9eHZfdJ7LuXshdYRBjV97GzrCIOrV6+2jjC4Oq2tIwzuF+7/sdYRblop5e2tM1xj2BUAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0ZLCl+AAAYCv4Ox+6u54+09+a8w/8ztXfT3LlhpuO1VqPJco1AAAjd+rMWv77z9/T2/EWDr3tSq31yNPdp1wDADBqNTUrdXN2S1WuAQAYNeUaAAB6UpOsZLopn0u5BgBg1GqSlapcAwDAhtXUrKRuyudSrgEAGLVak5XN6dbKNQAA41ZTslLLpnwu5RoAgFGrSZY3aWPyQct1mWzOXwgtXHtkY36MKZvzTdjKZO+e1hEGN32n/hbM36rOvsu4v477L5xMkpx+0e2Nkwzn1j/Z1TrCoBbKiJ8n1k3PPNY6wvCWV1onYIOmRq4BAGDjpilZztymfC7lGgCAUeuW4hvBtBAAAGitu6Bxc2qvcg0AwKjVWrJcTQsBAIAN67Y/V64BAGDDTAsBAICe1JgWAgAAvehWCzFyDQAAG9ZNC5nBketSytEkR5NkKePecQsAgNlQa7I2i+tc11qPJTmWJHvLbbXPYwMAwM2Y2ZFrAADYapRrAADoiQsaAQCgJ+MYuS4lKZszcbyJUrp3c5vzhWphcsstrSMM6tJff2nrCIN7+P3G//fz8l+50jrCoG75+e79Yy9vm2NIC5eXWkcY1L5z4/5dmiSTq1dbRxjctF5sHYENqHUM5RoAALaAblqIcg0AABtWU7IyVa4BAGDDxjHnGgAAtoBaTQsBAIDeTGvZlM+jXAMAMGqmhQAAQE9qSlZd0AgAABvXzbnenP1XlGsAAEZtgJHrA6WU+2/497Fa67FEuQYAYORqktV+R65P1VqPPN0dyjUAAONWzbkGAIBeDDBy/YwGK9clSZnbnL8Qmlh/bJNbbmkcZDiXj7ykdYRB/fk/qK0jDO4j3vu3W0cY3MHFC60jDOo3fmVXkmRy76XGSYZz8S92t44wqF3Hd7aOMLjFx3a0jjC4cuVq6whsQE2yOp3xcg0AAFtBTZn9kWsAANgSqh0aAQCgF6aFAABAT2pK1pRrAADYuFpHsFoIAABsDUauAQCgFzVRrgEAoBc1WbNaCAAAbJwLGgEAoEfTqZFrAADYsFqTOovTQkopR5McTZKl7O7z0AAAcNPWZnHkutZ6LMmxJNm3eGed3L6/z8NvKXVPTZIsv8eLGycZzoMfNu4XNj7sPX+3dYTBfei+P2odYXBLk5XWEQb12wsvS5Lcd/B04yTDecdt4x6MuXJwR+sIg1s4s6d1hMGVlXH/rhm7mpKpOdcAANCDmkxncVoIAABsRXUWp4UAAMBWU2O1EAAA6Ec1cg0AAD0pyjUAAPTCyDUAAPTIaiEAANCTujmfRrkGAGDcahLTQgAAoB91ujmfZ7ByPd25kEvvec9Qh29uYd/ZJMmD77/UOMlw7nqfE60jDGrP3HLrCIN74OJ9rSMMbs/c1dYRBjVfumeDpcXxPs4rd497W+nHzy20jjC4ycq+1hEGt3PXYusIbFAxcg0AAD2oxbQQAADozaxPCwEAgC3BBY0AANCfYuQaAAD6Ufpd5/pAKeX+G/59rNZ6LFGuAQAYu/6nhZyqtR55ujuUawAAxs8OjQAA0A9zrgEAoAeljqBcr+wpOfEB492V6sXvWE2SzL3nucZJhrN/x+XWEQb11gsHWkcY3INnb20dYXBXro7390ySvGTt95Mkp87tb5xkOHccOts6wqAezfh/Dpf3jvvnMEl2n7ildQQ2ylJ8AADQj5kfuQYAgC1hDNNCAABgq1CuAQCgL5biAwCAHpgWAgAAPTJyDQAAG1cyoyPXpZSjSY4myfy+8a7JCgDADJnVaSG11mNJjiXJ0kvvrvVdLvZ5+C1l4aG1JMmBW8b7GM8tL7WOMKiHTm6DPwAfHPfXMEl2PrI5mwK0snK5+zV95q23NU4ynLvf7ZHWEQZ1z72nW0cY3Olbd7eOMLgzB3e1jsAGzWS5BgCALacmUa4BAKAfRq4BAKAnyjUAAPSg1O5tMyjXAACMnpFrAADoi3INAAA9MC0EAAD6M/PTQubnpjmw78JQh29urXYbV1xeXmicZDhnHh/3pgCTP9vZOsLg9ibI/1MAACAASURBVL11k/5Mb2j3iZXWEQa1sP5tevv/Gu9mOQ/uOdA6wqDue9GjrSMM7o5951tHGNzZhdXWEdigmS/XAACwJdhEBgAA+lFi5BoAAPpRkzLdnKmSyjUAAKNn5BoAAHqiXAMAQB/qCMr12rTk3KWloQ7f3K7VuSTJmXN7GicZTnnHuJeq2/e28S9Tt+9Pr7aOMLiFR8e75GeSzL24ezbYd/pK4yTDuXTnuH/X/PncwdYRBrd3/8XWEQY3P7dJzYxBdBc0mnMNAAC9sEMjAAD0oSZlbXM+lXINAMDozfycawAA2BL6X+f6QCnl/hv+fazWeixRrgEAGLkBdmg8VWs98nR3KNcAAIxbrbO/Wsh0bZJL50a8vNLVSZKk/MWIlxs80TrBsPY8uNI6wuAWH368dYThPT7upfjKoe4KnMVHzjdOMpy9b9/ROsKgVvYuto4wuMfXSusIg5vbNf7njLEz5xoAAPpQk6zN+Mg1AABsFTM/LQQAALYKm8gAAEAPSv9L8T0j5RoAgNErszjnupRyNMnRJJnfvz9zpxb6PPyWMlnuroxeenS8V0jf8uAmXVbbyNJD22AljbPjf4z1ytXWEYa1tr5f7/mLbXMMaM+Dt7SOMKjlvSNeOWvdhbXxPt9fs7J3rnUENqLWZBZHrtd3pjmWJDvuvXeTZrYAAMCzMy0EAAD6UGd0WggAAGxFRq4BAKAPNpEBAIB+lNSU6eYs1DBYuZ4sJ7seGu9KGnPL3V8/ux8e73Wbu05caR1hUOXshdYRBjf6lTSS1OXl1hGGtf5kUC9dbhxkOAsnx/2zuOf4+FfSmM6Pf6xu+fKkdQQ2wsg1AAD0p1TlGgAAelCvvxI4NOUaAIBxMy0EAAD6M/sXNK4mu06N92K/6xc0Hh/vxVTzp8d9kVG9PN4LxK4Z/cV+yRPbg4/VtTmCY36cI97aPUmWTiy1jjC4tcXdrSMMbrLigsaZVmuyNuPlGgAAtoxZH7kGAIAtodZNewVQuQYAYNxqTAsBAIDezPq0kMlqzc6Tq0Mdvrmy/tfPwpkRXxT3+NgvaBz3DpRJUlfG+zN4Xd2cX5bNrF/QWFdH/LUc+cXFc4+N+3dpkuxcmmsdYXCT1R2tI7ARpoUAAECP7NAIAAB9sBQfAAD0oyZ11qeFTFan2XFqxPPo9nZfoMn5S42DDKdeGvHXLxn3phzXjH0+8nZw7VXMMX+/Lq+0TjCs8+Ofc72wMP6xuuLX6Wwz5xoAAPoz8yPXAACwJdj+HAAA+lEzhpHrtWkmZy8Odvjmdq9/gS6M9zHWlXHPg6zTzVmSh2GN/+u4vs71mB/n2Ndj3wZr6pfz430uvGZuftI6AhtR6wjKNQAAbAHn89jP/+L0xw/0eMilUsr9N/z7WK31WKJcAwAwcrXWD9+szzVcuZ5OkzEv5bb+Em29utw4yIDGvN1ysi2WqRv1VILt4tqXcMzfr7W0TjCoUW9df83It7BPklLG/X1Kf0wgAgCAnvQ6cl1KOZrkaJIsze3p89AAALDl9Vqu1ydyH0uSffMHax3zFdLrL9HW5fFOCxn7lIKxP75tY8zTJXLjrJARf7+WET+2JCUj3l1zXb1ytXWEwZXixX6eH98pAADQk1LrMCMGpZTzSd4yyMF5LgeSnGodYhty3ttw3ttw3ttx7ttw3tt4vuf9xbXWg0OHeT6GXIrvLbXWIwMen2dQSrnfud98znsbznsbzns7zn0bznsbs3jeTQsBAICeKNcAANCTIcv1sQGPzbNz7ttw3ttw3ttw3ttx7ttw3tuYufM+2AWNAACw3ZgWAgAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6Mn8UAdeLDvqUnYPdfjmyu5pkqReHO/fJ2Uy3seWJFlcaJ1gcNPFudYRBlfnSusIg5pbu5QkqWtLjZMMp1xZbh1hUHVtrXUEeF5e9oqXtI5w0x544IFTtdaDrXMkA5brpezO+09eOdThm1t8r6tJkuXf3NE4yXAmO8f7ZJ4kk3sPt44wuMv37W8dYXDLt4z7D4j95387SbL8+Ls1TjKcuT98e+sIg5qeP986wuD8ATEOv3D/T7SOcNNKKVvmF8nIhyYBAGDzKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0ZH7Ig5dJGfLwTZXSPbbJwqCnsKnJ/ltbRxjUlReN+/ElyeP3LbSOMLiV3a0TDOuWP+vGQB67e1fjJMO5/erdrSMMau7tD7eOMLjp+fOtIwxuurLaOgIzwsg1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0pNftBUspR5McTZKljHc3MQAAeDq9jlzXWo/VWo/UWo8sZEefhwYAgC3PtBAAAOiJcg0AAD1RrgEAoCe9XtD4JKUkZcTdfdI9trJzZ+Mgw1m750DrCIM695LF1hEGd+Hu1gmGt7ZUW0cY1NqJkiQ5/+LSOMlw5q/sbR1hUPtaB9gEk4fmWkcY3uPnWydgRoy4/QIAwOZSrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnswPdeCSpMzNDXX49hYXkiTl4O2Ngwzn3Dvvbh1hUBfubZ1geMu3rbWOMLy52jrBoKbz3eNb3j9tnGQ4j79o3OM8k+VbWkcY3C2ltI4wuMnDg1UmRmbcv9EAAGATKdcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHoy3+fBSilHkxxNkqXs7vPQAACw5fVarmutx5IcS5J9i3fWye37+zz8ljLd9ViS5OK7HmicZDhn37m0jjCoq3ettI4wuLml1dYRBjeZn7aOMKz52r2/dbltjgFdWd3ROsKgzi+P/0XiMh3/gNqe1gGYGeP/iQcAgE2iXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE963f78RtOdC7n0nvcMdfjmFnafTZKcfKfBTmFzV+8d73bLSbK4e/zbny/tGP9j3L/7UusIg1pY6LawP3D7+cZJhnNyWlpHGNSl1cXWEQY3WZ1rHWFw22GLd/ph5BoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ8o1AAD0RLkGAICeKNcAANAT5RoAAHoy2PaCK3tKTnzAwlCHb+6eP5smSS6/7GrjJMPZuWe8jy1JFubXWkcY3N37zrWOMLgPPfDHrSMM6veXut0L//5L/lvjJMP50cUjrSMM6h25vXWEwV1a3dE6wvCm49+Fkn4YuQYAgJ4o1wAA0BPlGgAAeqJcAwBAT5RrAADoiXINAAA96bVcl1KOllLuL6Xcv3bxYp+HBgCALa/Xcl1rPVZrPVJrPTK3e3efhwYAgC1vsE1ksjRNfZfxjl7PvaPbROaWfZcbJxnOpcuLrSMM6vKJ8f8BeOV397eOMLg/XbuvdYRB3XPh/iTJr/7232icZDjvce/x1hEGdeHAhdYRBndmbfyzTC+vjXdjPPo1/p8GAADYJMo1AAD0RLkGAICeKNcAANAT5RoAAHqiXAMAQE+UawAA6IlyDQAAPRlsE5n5uWkO7Bvvwvl1/f3qiBfOXzs37k1kdpyaax1hcLseaZ1geIuP1+f+j2bY5Gr3fbr7t3Y2TjKcP1y8s3WEQd2x73zrCIO7sv9S6wiDu7g2/o3H6Md4myEAAGwy5RoAAHqiXAMAQE+UawAA6IlyDQAAPVGuAQCgJ4Mtxbc2LTl3aWmowze3c7X7u+Ty4+N9jIunBvv22BJ2Pdw6wfBueWi1dYTB7Ti93DrCoBb3TZMkB3/3auMkwzmxe0/rCIM6/u7jX/Zz9+7xfn9es7B3/I+Rfhi5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD0ZbK216dokl87tHOrwzZWVkiSZO7XQOMlwlk61TjCsXSenrSMMbsep8S8dNXf2cusIgyq71pIki49caJxkOLf//riX/Ty5MN7nwmvO3zf+5QYXdq60jsCMMHINAAA9Ua4BAKAnyjUAAPREuQYAgJ70Wq5LKUdLKfeXUu5fO3+xz0MDAMCW1+sl2rXWY0mOJcmOe++tY15JY7LcrRay85HSOMlwdp6srSMMaun0cusIgxv7ShpJUh4f7yoaSZI7u9VCytnxPs7d7xj3aiEru/e2jjC4s2WxdYTBXTnkxX6eH98pAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD0Z7BLtyXKy66HxrqQxWelW0hjziho7T660jjCohTNW0hiDevFS6wjDWutWC6mXxvs4J2fGvVrILW8f78pZ16wt7mwdYROM+/uU/hi5BgCAnijXAADQE+UaAAB6olwDAEBPhrugcTXZdWq8F/vNrV/rt+vUWtsgA9px+krrCIMq51zsNwb16tXWEYY17X6P1ivjfZxl7mLrCINaeGT8F8Lt3THXOsLgpvM7WkdgRhi5BgCAnijXAADQE+UaAAB6olwDAEBPBrygsWbnydWhDt/cpE6TJDvOjfeiv8nZcV9klAvb4GK/y+P9/rymro33ouIkyfrvmro83h1T62Tc36dlfvwXNC5ug4s2b1k0Hsnz4zsFAAB6olwDAEBPlGsAAOjJgHOup9lx6vJQh29vXzfPc+7MiOclnx/3JivTS9tgzvXqeOfpXlOn492sKknq+sMb89zyurzcOsKwLo34uXBdmRv/JjJLi+OfV04/jFwDAEBPlGsAAOiJcg0AAD0ZbgLR2nTc6yTvXp//+Pj5tjkGNB35nOvRz/PMuOfpbh/XJl1P28YY0Oi/T7fB75psg2tYJmfMueb5MXINAAA9Ua4BAKAnw73GMZ2Oe/mhtW5r9+mYp4Vcudo6wrBG/DI7zJKxL6eYldXWCYZXRv58kaTMj3/qC/0wcg0AAD3pdeS6lHI0ydEkWZrb0+ehAQBgy+u1XNdajyU5liT75g/WevlKn4ffUq69jDnqqROmTQDwPJSFhdYRhrdrV+sEzAjTQgAAoCel1mEuJCmlnE/ylkEOznM5kORU6xDbkPPehvPehvPejnPfhvPexvM97y+utR4cOszzMeSK6G+ptR4Z8Pg8g1LK/c795nPe23De23De23Hu23De25jF825aCAAA9ES5BgCAngxZro8NeGyenXPfhvPehvPehvPejnPfhvPexsyd98EuaAQAgO3GtBAAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBPlGsAAOiJcg0AAD1RrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6olwDAEBP5oc68IEDB+qLX/yioQ7f3PHjJ5Ikhw8fapxkOLVOW0cY1FpWWkcY3JW1860jDK6ktI4wqLOPXk2SHDp0Z+Mkw5nUudYRhjXub9FtZPxfyDKZ3cf4wAMPnKq1HmydIxmwXL/4xS/Kr//6rw91+Oa+8itfu/7+KxonGc5qvdw6wqAu1IdaRxjcW87+SusIg5svi60jDOrffctbkiSv+YrPb5xkOLuWb20dYVB1bnYLy/O2DV4Hr2X8X8elnUutI9y0UsrbW2e4Zhv8OAAAwOZQrgEAoCfKNQAA9ES5BgCAnijXAADQE+UaAAB6MthSfAAAsBXsvneprl3pb/+Oq6dWfj/JlRtuOlZrPZYo1wAAjNz06jQv/cf97THzB995/Eqt9cjT3adcAwAwaiXJ3CZtBqtcAwAwbmXztndXrgEAGL25TWq9yjUAAKNWSjI3Z+QaAAA2rBQj1wAA0Bsj1wAA0INSSubmlWsAANg400K2vpqaJJlmrXGS4VzNudYRBvXghd9pHWFwk2zSop4NvfO+D2odYVCTvC1JsqPc2jgJN6tMa+sIg6ubtMQZ3CwXNAIAQE9KYloIAAD0oiSTyeZ8KuUaAIBR67Y/N3INAAAbV0wLAQCAXpRSMj+3OfNClGsAAEatJJmf25wVtJRrAABGrZSS+Xkj1wAAsGGlGLkGAIB+zOqc61LK0SRHk+Tee+/t89AAAHBTSpL5+Rkcua61HktyLEle8Yq/Nv79XgEA2PJKKZmbxZFrAADYiiYT61wDAMCGdetcz+C0EAAA2Gq61UJMC9nipt3/1uXGOYZzae1U6wiDunPXy1pHGNyB+Ze3jjC4HSeuto4wqLLWvZ+/OG0bZEAru1snGNp2uARpc15ub2mSzRn1ZBglRq4BAKAfJTaRAQCAPphzDQAAPemmhSxsyudSrgEAGLVSShbmFjflcynXAACMmpFrAADoiZFrAADoTcmkWC0EAAA2zMg1AAD0ZIA51wdKKfff8O9jtdZjiXINAMDIDTByfarWeuTp7lCuAQAYtW4TGauFAADAhlmKbwbUTJMkK7nYOMlwds7tbx1hULedPNA6wuBO/cLvto4wuMcuXG4dYVhrNUkyd3G1cZDhXN51tXWEQV2pZ1tHGNykjL9OLGZP6wiDW8rO1hEGU0rJwsQFjQAAsGFGrgEAoCeW4gMAgN7YRAYAAHpRUjJvzjUAAPSglMwp1wAAsHElJfNFuQYAgA0rmRi5BgCAPpRSMl8sxQcAAD1wQSMAAPSipGRuFudcl1KOJjmaJPfee2+fhwYAgJtSyoyOXNdajyU5liSveMVfq30eGwAAbo4LGmdASZJMsjmT41vYfeW21hEGdeH3HmodYXBXTzzWOsLw5krrBMOaduMUaxdXGgcZztrB5dYRBvX4yonWEQa3Vsf9NUySWxfvaR1hcHtzZ+sIgylJJpnblM+lXAMAMGrdtBCrhQAAQA9m9IJGAADYakoms3lBIwAAbEXFnGsAANi40m2AvimfS7kGAGD0jFwDAEAvSkpRrgEAoAemhQAAQC9KTAsBAICedJc0bgbl+iZNavfXz9Lq3sZJhjN59FLrCIOa37fUOsLgDn74e7SOMLi53Zuzbmkz3/ufkyTltvF+vy6Uzdk1rZU9C7e3jjC4CyunW0cYXM20dQQ2yMg1AAD0oijXAADQh5K4oBEAAHpRLMUHAAA9MS0EAAB6McC0kAOllPtv+PexWuuxRLkGAGDsaune+nOq1nrk6e5QrgEAGLmaMq2b8pmUawAAxm9zurVyDQDAyNUYud7qyvpGTZOr492xabJr3LumLb50f+sIgzu775HWEQb3qw9+R+sIgzpXH06S/PramxsnGc4r8vGtIwzqlsmLWkcY3J7Fu1tHGNxmLePGgJRrAADoiWkhAADQA9NCAACgR8o1AAD0oD5xvdzQlGsAAEauGrkGAIA+lCRlFi9oLKUcTXI0Se69994+Dw0AADenZtNGrid9HqzWeqzWeqTWeuTgwQN9HhoAAG5aqf29PRvTQjaq1z9PtpZaWicYWN2k14caumX1cOsIg/ub9/yz1hEG9evz35gkebdb/1bjJMOZ1qutIwxqaeWW1hEGN7k84ifDdWVt/M8ZGftThjnXAADQg1pT1zZnuRDlGgCAUas1qatGrgEAoBdGrgEAoA+mhQAAQH/qJl2UqlwDADBuNamrRq4BAGDDqmkhAADQH9NCtrhrG6zUyXh3WqmL494UYLo01zrC4K7OX2wdYXBrWW4dYVCldD+H82Vn4yTDWc24N5Gp83tbRxhcnRvvc+E1kytrrSOwEZu4/blyDQDA6JkWAgAAfajVJjIAANCLauQaAAB6UaNcAwBAP2pNXdmci1KVawAAxq3WTJXrGVHGu/zQ2u5xf3usTC63jjC4aVZaRxjcJAutIwxqsv5rekfZ3zjJcErGvSzmShn3UoNJMr9nqXUEejDmBXhrjZFrAADoRa2ZrqxuyqdSrgEAGLeaZOqCRgAA2DhzrgEAoB+11tR+p4UcKKXcf8O/j9VajyXKNQAAY1fT98j1qVrrkae7Q7kGAGDcxrHOdVl/G6n19WrWdrSNMaQRf/WSJKvVUnxjMPal+K79JJYRL5I1f3W8j62z2DrA4FZ3bM4qDC1N97ROMLwx/zatVgsBAICeWOcaAAB6YuQaAAB6Mo451wAA0F6tNdPlGRy5LqUcTXI0Se69994+Dw0AADenJnVtBndoXF88+1iSvOIVr6h9HpvNN/bVNFZzpXWEwc1tg1UKJtvmBbgRr99Tx/10UdbG/fiSZG5hrnWEwV0tj7eOsAlGvCRKrZlencGRawAA2GrqtGZ6RbkGAICNq8n0qgsaAQBgw+rUtBAAAOiHOdcAANCTccy5rutv41Svv9+c+TstrGW5dYRBTTL+q9u3w2ohc2WpdYRBlevvJ01zDGrEDy1JyvLmLP/VUrm6DR7jkvHIWVaNXAMAQE+mUa4BAKAP3ci11UIAAGDjalJXZ3CHRgAA2HLMuZ4F3V8/a3XcF/2N2VzGfSFcMv6L/ZJkkoXWEQbWXdJY6niv+quT8V78niR1fsRb168rmzQi2NJC2d06AhtgnWsAAOhNzdqqcg0AABtWa7IydUEjAABsWE3NqnINAAAbV2vN6tS0kC1u/SKjMt5d/kod9wUq22GHxrItHuPILxZbv9avTEd80d/Yv4Rz470Y9ZqxP18kSVkZ/2Mc83X+NTFyDQAAfaip5lwDAEAvTAsBAIB+1CTTujnT65Trm9ZNEhzzvN1Sxj0RcvybjyQl45/rmRFPRX6SMf84jnwq64j3/3nCZMzfoJ0y8u/TseumhRi5BgCADavVBY0AANCLbp1rI9cAALBhtVotZMsr19+Pd8716Ockb5e5urDVjX267sivX0mS7XB5RzbpYjiGY1oIAAD04C/y2M9/7vQnD/R4yKX/v737Do+qTts4/p303isphBZCSKihKiLSsaOggIKCIrrurrqrrq67usW1rbrrq7uKKIqKYtdVEQGxU0Ug9F4CpJNO6pz3jxOS0FRkkjOZ3J/rmiuVyZNDMrnnd57z/Gw225omb882DGM2KFyLiIiIiIszDGNMS30thetfyqgfxVfnwqf7bK59CsxoC6dqRVoDV/9VdO2HUgAMd1f/T6RN/D+KY7SFLikRERERkRbh0HBts9lm2my2NTabbU1eXr4j71pERERExOk5tC2kvpF7NkDfvn1c/ARK/bdnd+Fvsy2c5pPWz9V/TOu/P1duY7K5+BSGttAyYadl5gdbqdYot7qEZueNn9UluAS1hYiIiIiIOIjNaKYVA5vNVgpsa5Y7l58SAagvp+XpuFtDx90aOu7W0bG3ho67NX7ucW9vGEZkcxfzczTntJBthmFkNOP9y2nYbLY1OvYtT8fdGjru1tBxt46OvTV03K3RGo+72kJERERERBxE4VpERERExEGaM1zPbsb7lh+nY28NHXdr6LhbQ8fdOjr21tBxt0arO+7NdkGjiIiIiEhbo7YQEREREREHUbgWEREREXEQhWsREREREQdRuBYRERERcRCFaxERERERB1G4FhERERFxEIVrEREREREHUbgWEREREXEQhWsREREREQdRuBYRERERcRCFaxERERERB1G4FhERERFxEIVrEREREREHUbgWEREREXEQhWsREREREQdRuBYRERERcRCFaxERERERB1G4FhERERFxEIVrEREREREHUbgWEREREXEQhWsREREREQdRuBYRERERcRCFaxERERERB1G4FhERERFxEI/muuOIiAgjKSmpue7ecgcPHgQgLi7O4kpExJXpsUZE5Kd9//33+YZhRFpdBzRjuE5KSmLNmjXNdfeWu/feewH4xz/+YXElIuLK9FgjIvLTbDbbPqtrOEZtISIiIiIiDqJwLSIiIiLiIArXIiIiIiIO0mw91yIiIiLSetXU1JCVlUVlZaXVpTTw8fEhPj4eT09Pq0s5LYVrEWl1CsqqyDxYTGZWMbEhvlzZN97qkkREXE5WVhaBgYEkJSVhs9msLgfDMCgoKCArK4sOHTpYXc5pKVyLiFMrrqgh82AxGw4WkZlVzIasYg4WHT3ucwrKqrhpaCeLKhQRcU2VlZVOE6wBbDYb4eHh5OXlWV3Kj1K4FhGnUVpZw8aDJWQeLGJDVjGZB4vZV1DR8PH24X70Tgxh2uD2pMeFkBobxH0fbOShhVsJ9fdiYkaChdWLiLgeZwnWxzhbPaeicC0iliivqmXz4RIzRGcVseFgMbvzyhs+HhfiS4/4YK7ql0CPuBDS4oII8fM66X4en9CToopq/vDOBkL9vBiZGt2S34aIiMhxFK5FpNlV1tSx+XBJQ1tH5sEiduaWYTfMj8cE+ZAeH8zlveJIjw8mPS6Y8ADvn3XfXh5uPHtNXybPWcmt89cyb3p/BnQMb8bvRkRE5PQUrkXEoapq69iWXVq/Il3MhoPFbM8ppa4+SUcEeNEjPoSxabH0qA/SUUE+Z/U1/b09mHtdPyY8+x03zFvDgpmDSG0X5IhvR0RE5Iw4NFzbbLaZwEyAxMRER961iDihmjo727JLzQsO61ekt2WXUlNnBulQP0/S40MYnhJFenwwPeKDiQnyaZaeuTB/L+bNGMCV//2OaXNX8c6swSSG+zn864iItEV/+d8mNh8qceh9prYL4v6Luzv0Pp2BQ8O1YRizgdkAGRkZhiPvW0SsVVtnZ2de2XEr0lsOl1BdawcgyMeD9PhgZpzbsWFFOj7Ut0UvPokL8eWVGf258tnlXPviGUhkoAAAIABJREFUSt6aNYiowLNbFRcRETkTagsRkZPU2Q325JtB+tjUjk2HiqmsMYN0gLcHaXFBXDc4ifQ4c0U6MczPKa7i7hwVyNzr+jH5+ZVc9+Jq3rhpIEE+zrvZgIhIa+CKK8zNReFapI2z2w32FVawIauoYUV608FiyqvrAPD1dCctLojJ/dubK9LxwXQI98fNzfogfTq9E0N59tq+zHhpNTe+vIaXp/fHx9Pd6rJERKQNULgWaUMMwyDryFFzRbp+U5bMg8WUVtYC4O3hRmq7IK7sG096fAg94oPpFBmAuxMH6dMZmhzJ4xN7ctuCdfzm9R/4z5Q+eLi7WV2WiIi4OIVrERdlGAaHiysbLjQ81t5RVFEDgKe7jW6xQVzSs119j3QIXaID8HShAHpprziOlFfzwP8288f3NvLwFelO0boiIiKuS+FaxEUUVVSzPquYDQeKWJ9VxLoDxeSXVQHg4WYjOTqQMd1jzKkdcSEkxwTg7eH6rRLXndOBwvJqnvp8J+EBXtw1JsXqkkRExIUpXIu0QpU1dWw6VMy6A8VsyCpi/YEi9jbZJrxTpD/nJUfQs761o1tsUJvuOb59ZDL55dX854tdhPl7ccOQjlaXJCIiLkrhWsTJ1dkNduSWsv5AUUOY3prduClLbLAPPeKDmdgvgV7xIaTFB2s6xglsNht/uzSNoopq/v7xFsL8vRjfJ97qskRExAUpXIs4kWMXHK6vX41ef6CYjYeKqaif3BHo40GvhBBmDe1Iz/gQeiaEEH2Wuxu2Fe5uNp68qhdFFau58+0NhPh5ckFKtNVliYiIi1G4FrFQYXl1kyBdxPqsYgrLqwHw8nCje7sgJmYk0DMhmJ7xISQ5+Qg8Z+ft4c7sqRlMmr2CW15by6szBpCRFGZ1WSIi4kIUrkVaSEV1LRsPltSHaPN2oPAoADYbdIkKYHhKFD0TQugZH0LXmEC8PFxncoezCPD2YO71/Zj47HKmv7Sat2YNpmtMoNVliYiIi1C4FmkGNXV2tueUsv5AcUOY3p5TSn2bNHEhvvRMCOaaAe3pmRBCWlwwAd76dWwpEQHevDy9P1c++x1TX1zJ27MGkxDmZ3VZIiLOa+EfIDvTsfcZkw5jH3bsfToB/TUXOUuGYbC/sIJ19T3S67OKjtsqPMTPkx7xIYxKjaZnQgg94kOIDPS2uGpJCPNj3vQBTHj2O6a+uIq3Zg0iIkD/LyIicnYUrkXOUF5pVcP4u3VZ5vSOYxuzeHu4kR4XzOT+7emZEEyvhBASw/y0cYmT6hoTyNzr+zFlzkqum7uK128cSKAmrYiInMwFV5ibi8K1yI8oq6olM8tcjTYDdTEHi8w+aTcbDRuz9IgPoWdCMMnRgS61w2Fb0Ld9GP+d0pcb5q3hple+Z+71/drE5joiItI8FK5F6lXX2tmWXcq6+lXpDVlF7Mgtw6jvk04I86V3YgjXn5NEj/gQ0uKC8PPSr5ArGJYSxWNX9uCON9dz+4J1/N+kPrhrKouIiPwCSgbSZuWWVPLtrnzWHyhm3YEiNh8uobrW7JMO8/eiZ3ww49JjG3Y5DFc/rksb3yeewnJzk5kQv408eFma2nlEROSMKVxLm2O3G7y2aj8PfbKFiuo6fD3dSY8LZtqg9g1j8OJDfRWs2qAbhnSksH6b9Ah/L+4Y1dXqkkREpJVRuJY25UBhBXe9vYHluws4t3MEfxibQkpMIB7qk5Z6d47uSkFZNU99vpMwfy+uO6eD1SWJiEgronAtbULT1Wob8I/L05nUP0Gr03ISm83Gg5encaSimgf+t5lQfy8u7RVndVkiIpYwDMOp/lYaxy6EcmIK1+LyTlytfviKdOJDtWGInJ6HuxtPTerNtBdX8bs31xPs68n5XaOsLktEpEX5+PhQUFBAeHi4UwRswzAoKCjAx8fH6lJ+lEPDtc1mmwnMBEhMTHTkXYucMa1Wy9nw8XTn+WkZXP3cCm5+dS3zbxxA78RQq8sSEWkx8fHxZGVlkZeXZ3UpDXx8fIiPj7e6jB/l0HBtGMZsYDZARkaG86/bi8tqulo9pEsED43XarWcuSAfz4Zt0q9/aTVvzxpE56hAq8sSEWkRnp6edOig607OlK7iEpditxu8smIfo//1FZkHi3lofDrzpvdXsJZfLDLQm1emD8DT3Y1rX1jVsImQiIjIqShci8s4UFjBlDkr+dP7G+nbPpRFt5/HpP6JagORs5YY7se86f0pq6pl6gsrKSyvtrokERFxUgrX0uqdbrU6LsTX6tLEhXSLDeKFaf3IOnKU619aTXlVrdUliYiIE1K4llZNq9XSkvp3COPpyX3YeLCYWa9+37Cjp4iIyDEK19IqabVarDIyNZqHx6fz9Y587nhzHXa7rt0WEZFGmnMtrc6Jk0AevqKHQrW0qAkZCRSWV/PQwq2E+3vxwCXddbZEREQAhWtpRex2g9dW7uOhhVtxs9l4aHw6V/fT3Gqxxk1DO1FQXs3sr3YT5u/Nb0d0sbokERFxAgrX0ipotVqc0T1jUygoq+bJJdsJC/Di2oHtrS5JREQspnAtTk2r1eLMbDYbj1yRTlFFNX/+YCNhfl5c2CPW6rJERMRCCtfitLRaLa2Bh7sbT0/uw9QXV3Lbgh8I9vXk3C4RVpclIiIW0bQQcTp2u8Ery/dqEoi0Gr5e7syZ1o9OkQHc9MoaNmQVWV2SiIhYROFanErD3OoPNmlutbQqwb6ezJven1B/L66bu5pdeWVWlyQiIhZQuBanoNVqOSNVZXBwLaybD3u/sbqaBlFBPrwyYwBuNpj6wiqyiyutLklERFqYeq7FcuqtltOqLoe8bZC3FXK31L++BYr2N/kkG1w9H1LGWVZmUx0i/Hnp+v5cPXsF176wkrdmDSLEz8vqskREpIUoXItlNAlEGjQN0XlbIXfrySHa3QvCu0B8P+h9LUSmQHhn+OBX8M4MuH4htOtl3ffQRFpcMM9PzWDai6uY/tJqXr1hAH5eergVEWkL9GgvltBqdRvVEKLrV6Bz68N00b7GzzlViI7qBqEdwP0UD1mT3oA5w+H1q+HGzyGoXct9Pz9iUKdwnprUi1teW8str63l+akZeLqrE09ExNUpXEuLOnG1+uHx6Vyl1WrXU10O+dsbV6AbQvR+wDA/51iIjusLva8xQ3RkCoR1PHWIPp3AaJi8AF4YDfOvMlewvQOa5ds6U2PSYnnw8nTueTeTu97ewOMTeuLmpp91ERFXpnAtLUar1S6ougLytzWG6LxtZm900xDt5gkRTUN0V4jsduYh+sdEd4cJL8H8CfDODXD1a+Dm7pj7PkuT+idSWF7NY4u2EernxZ8u6qYnkyIiLkzhWpqdVqtdwHEhemvjBYanC9G9pkBUiuND9I/pMgLGPgqf/B4++xOM+Ufzf82f6ZbzO1FQVs2L3+4hPMCLXw3rbHVJIiLSTBSupVlptbqVORaij61AHwvSR/bx0yG6A7h7Wlo+/W+Egl2w4hkI7wj9brC2nno2m437LuxGYXkVjy3aRri/F1f3T7S6LBERaQYODdc2m20mMBMgMVF/ONoyu93g1ZX7eFir1c6pusLsiT5xxN2pQnS73tBzcn2IPtYTbXGI/jGjH4TC3fDJXRCaBJ1HWF0RAG5uNh6b0JOiozXc+14mIX6ejEmLtbosERFxMIeGa8MwZgOzATIyMgxH3re0HgcKK7jz7fWs2F2o1WqrNQ3RTUfcnRiiwzs3hujIruZ0DmcP0afj5g5XvgAvjoW3rofpiyA61eqqAPB0d+M/U/pwzZyV/Ob1dbw03ZPBnSKsLktERBxIbSHiMFqtdiJVpfDVY7Div1BXbb7vuBA9qXHEXWsN0T/GO9CcIPL8BeYEkRuXQkCU1VUB4OflwYvX9WPCs8uZOe973pg5kLS4YKvLEhERB1G4FofQarWTsNthwwJYcj+U5ZghOnmM64boHxMcB5PfgLnj4PVJcN1H4OkcP5Mhfl7Mm9GfK/+7nOvmruKtWYPpEOFvdVkiIuIA2tFAzordbjBv+V5G/+srNh0s4ZEr0pk3vb+CtRUOfg8vjIT3Z0FwPNywFC5/FrpfZrZ6tKVgfUy73jD+efPYvDfLfPLhJGKDfZk3oz92A659YSU5JZVWlyQiIg6gcC2/2IHCCibPWcGfP9hERlIYi24/j6v6JaoNpKWV5cL7vzJbIIr2w2X/hRlLID7D6sqcQ7eLYNTfYPP7sOzvVldznE6RAbx0fT+OlFcz7cVVFB+tsbokERE5S2oLkTPWtLfa3WbjkSvSmZih3uoWV1sNq56DLx+FmqMw+Ddw3p3gE2R1Zc5n0K1QsBO+fhzCOkHvKVZX1KBHfAjPXZvB9S+t4oaXV/PKjAH4eDrHBjgiInLmFK7ljDTtrT4vOZKHx6fTTi0gLW/HEvj0D1CwA7qMgtEPQYQ2Jjktmw3G/ROO7IX//RZCEqHDEKuranBulwj+dVVvbn19LbfOX8uz1/TFw10nFkWk+VXW1PHl9jwWZh5m6uAk+iSGWl1Sq6dwLT+LVqudRMEuWHQvbP/UXIGd/CYkj7a6qtbB3RMmvAwvjIIF15g96U70hOTCHrEUVqTxp/c3cvc7mfxzQg/9folIsyivqmXZtlwWbsxm2dZcKqrrCPHzZFhKlMK1Ayhcy0/SarUTqCqFr/4JK/4D7l4w8q8w4Gbw8LK6stbFNwSmvAnPD4f5E8zedP9wq6tqcO3A9hSWVfPkku1EBHhxz7huVpckIi6ipLKGz7fksnDjYb7YlkdVrZ2IAC8u6x3HuLRYBnQMw1NnzBxC4VpOS6vVTsBuh8w3YfH9UJZtbvIy4n4IjLG6stYrNAkmvQ4vXWSuYE99Hzy8ra6qwW+Gd6awvIrnvtpNmL+ePInIL1dUUc3izTks3JjNNzvyqa6zEx3kzaT+iYxJi6FfUhjubvqb7mgK13JK+wrKufudDVqtttLBtbDwbshaBe36wNWvaQKIoyT0h8v/C29Phw9/Y44sdJInjTabjfsv7k5hRQ0PLdzKiPIqogKdJ/yLiHPLL6vis005LNx4mOW7Cqi1G8SF+DJ1UHvGpsfSOyEENwXqZqVwLcepqbPz/Ne7+feSHXi5u2m12gplubD0L/DDa+AfCZc+Y65Yu+l0nUOlXQEFu83xfOGdYeidVlfUwM3NxuMTelJUUc2exWXU1tmprKnTFBEROaWckkoWbcpmYWY2K/cUYDcgKdyPG4Z0ZFx6DOlxwfo73oIUrqXBD/uPcM+7mWzNLmVM9xgeuKQ7McE+VpfVdtRWw6rZ8OUjUFMBg2+F8+7SaL3mdN7vzRF9y/4OYR0g/UqrK2rg5eHGs9f05dpvX2V/YQXDH/+S20Z0YXyfeJ3GFREOFh3l043ZLMw8zPf7j2AY0DkqgFuHdWZMWizdYgMVqC2icC2UVdXyz0XbeHn5XqIDfZh9bV9GdVdPb4vauQQ+vQfyt0PnETDmYYjoYnVVrs9mg0ueMjffef8Wc0RfQn+rq2rg7+1BSkwgxUdrOBDgxZ1vb+D5r3dz1+gUhneL0h9OkTZmX0E5Czdms3BjNusPFAHQLTaI20ckMzYthi7RgRZXKKBw3eYt3pzDnz/YSHZJJVMHtuf3o7sS6NMGt8m2SsEuWPRH2L4QwjrCpAXmaD2Fppbj4W32s88ZDq9PghuXmhc9OpFgX0+e/tU5fJKZzWOLtnLDvDVktA/lD2NTyEgKs7o8EWlGu/LKWJh5mIUbs9l0qASAHvHB3DWmK2PTYukQ4W9xhXIihes2Kqekkgc+3MTCjdl0jQ7kmSl9NNuyJVWVwdf/hOXPmKP1RvwFBt7sVFMr2hS/MJj8lhmwX5sIMz4zx/Y5EZvNxoU9YhnVPZoFqw/w76U7uPLZ5YzoFs1dY7qSrBUrEZdgGAbbc8r4JPMwCzceZntOGQB9EkO478JujO4eQ0KYn8VVyo9RuG5j7HaD+av288jCrVTX2blrTFduHNJRsy1bimHAhjdhyf1Qehh6ToLh90NQrNWVSURncwV73mXw1jSY8ra58YyT8XR345qB7RnfJ44Xv9nDc1/uZsy/vuKKPvHcPjJZU31EWiHDMNh0qISFGw+zMDOb3fnl2GzQPymMBy5OZUxarK6BakUUrtuQ7Tml3PNuJt/vO8I5ncN58LJ0knQ6qeUcN1qvN0x8BRL6WV2VNJV0Llz8b/jgFvj4d+brTtqi4+flwa0XdGHKgPY8s2wn85bv44P1h5g2qD23nN+ZUM3IFnFqhmGw7kBRfQ/1YQ4UHsXdzcagjuFMP7cDo7pHExWoQN0aKVy3AZU1dTyzbCfPfrmLAG8PHp/Qk/F94nQxVEspy6sfrfcq+EfAJU9Drykareesek+Bwl3w9ePmRaWDf211RT8q1N+L+y5K5bpzknhy8Q7mfLOHN1YfYNbQTkw/pwO+XhrfJ+Is7HaD7/cf4ZPMw3y6MZvDxZV4uts4p3MEvx7WhRGp0do8ygUoXLu45bsK+ON7mezOL2d87zj+eGE3wgPU19si6mrM0XpfPAI15TDoVzD0LvAJtroy+SnD7jMvNv3sTxDaAbpdZHVFPyk+1I/HJ/Zk5nkdeWzRVh5btI2Xv9vLb0d0YWJGglq/5BeprbNTUF5NXmkVeWVV5De8rG54O7+siuKjNYT5exEV5EN0oDfRQT5EB3mbb9e/HhHg3SZ/Dmvr7KzaU8jCjdl8uimbvNIqvDzcGJocyZ2juzK8WzTBvs7Xgia/nMK1iyqqqOYfn2zhzTVZJIb58cqM/gzpEml1WW3HzqX1o/W2Qafh5mi9yGSrq5Kfy83N3LWxOAvevRGu/8Rs5WkFusYEMmdaP1bvLeThhVv543sbeeHrPfx+dFfGpsXojJVQZzcorA/M+WVVJ79sEp6PVFRjGCffh7+XOxGB3kQGeNMpMoAgXw+OVNSQW1LJ9uxS8sqqqLMf/w9tNgj39yY6qEn4DmwM39FBPkQFeRPu793qZ7nX1Nn5blcBCzMP89nmHArLq/H1dGdYSiRj02IZlhJFgLcimKty6P+szWabCcwESExMdORdy89kGAYfrj/E3z7azJGKGm4+vxO/uaCLTg23lMLdsOg+2PaxueI56Q1IHuO0fbvyIzx9YdLr8PxwmH813Pg5BMdZXdXP1i8pjLdnDWLJllwe/XQrt7y2lp7xwdw9NoXBnSKsLk8czG43OFJRTX7ZqUNzXsPb1RSWV2E/RWD28XQjsj4wtw/3IyMplIgAbyIDvRteRgZ4ExHohZ/Xj8eHOrtBQXkVuSVV5JRUklP/Mre08fUNWcUUlFedFN7d3WxEBng3Wfn2Jro+hEc1BHMfQv08nerJYmVNHd/syGfhxmwWb86mpLKWAG8PLkiJYlx6DEOTo/S3uI1waLg2DGM2MBsgIyPjFL+60pwOFFZw3/sb+XJ7Hj3jg5k3fQCp7bS7X4uoKoNvnoDv/g/cPM0JIIN+pdF6rV1AFEx5E14YBfOvgukLwbv1jLyz2WyMTI3mgpQo3lmbxb8Wb2fy8ys5LzmSu8d0pXs7tSg5M8MwKKqoOWVAPjE8F5RXn7RSDOZOn2Yg9iY+1I/eiSENb5/40t/L3WFh1d3NRlSgD1GBPqTFnf7nrKbOTn5ZVWP4bhLEc0qrOFBYwZq9hRypqDn5e3M3nwxENwncUScG8UAfgnw9mi2EH62u48vtuXySmc3nW3Mpq6olyMeDkakxjE2L4dwuEfh4KlC3NTon4QJq6+zM/XYvTyzejpsN7r84lamDklr9abVWwTAg8y1Y/GdztF6Pq8yZ1Rqt5zqiusGEueb867dnmKvZbq3rj6W7m42JGQlc0rMdryzfx9PLdnLhU99waa92/G5kVxLDNTO3pRiGQUll7elbMurDsxmYq6ipOzkwe7rbGlaSo4N8SGsXTESg18mhOdCbQO/mC5aO4OnuRmywL7HBPz5CsrKmjrzSquNWvnNKqswwXlrJjtwyvtmZT2ll7Un/1tvD7fge8MDj21COBfOf26ZRVlXLsq25LNx4mGVb8zhaU0eYvxcX9YhlbHosgzqG4+XR9nrLpZHCdSuXmVXMH97dwKZDJYzoFsVfL03TnNuWcmidOVrvwAqI7QUT5znV1tniQJ1HwLhHzfF8i/4IYx+2uqJfxMfTnRvP68jEfgk89+UuXvx2D59kHmZy/0R+PbwLEbrY2aFKK2v4cnseS7fksjuvrCE0V9fZT/pcdzcbEQFeDaG5a0zgce0YEQFeRNW/HezrXO0QLcHH052EML+f3Dylorq2oRUlt/T4lzkllWw5VMKyklwqqutO+rf+Xu4nBe6owMbXDxZV8ElmNl9uz6O61k5koDdX9I1jXFos/TuE4dEGL9aUU1O4bqXKq2p5YvF25n67h4gAb/47pQ9jdLFSyyjPh6V/hbXzwC8cLvk/6HWNRuu5un43QMFuWPEMhHeC/jdaXdEvFuzryV1jUpg2OIl/LdnBqyv38/b3WdwwpCM3ntdRF1qdhYNFR1m6JYfFm3NYsbuAmjqDMH8vurcLolNUAJEn9DAfexni64mbzjaeNT8vD5IiPH5yD4eyqtqGwH1cX3ip2Zryw/4ickoqqao9/olQbLAPUwYkMjYtlr7tQ3WGWE5Jj6Ct0LKtudz3/kYOFh1lyoBE7hqTojE+LaGuBlY9D188bI7WG3iLOVrPybbJlmY06m/mRasL7zIvWO0ywuqKzkp0kA8PjU/nhiEdePyzbfx76Q5eXbGPWy/ozOQBiXh7tK72Fysc21lv8WYzUG8+XAJAx0h/pp/TgZGp0fROVAhzNgHeHgREBtApMuC0n2MYBiVHa8kprSS7uJJgX0/S44L1JEh+ksJ1K5JXWsVfP9rM/9YfonNUAG/NGkS/pDCry2obdn0OC/9QP1rvgvrRel2trkpamps7XDEH5o6Bt66DGYsgurvVVZ21TpEB/GdKX9YdKOKRhVv5y/828+K3e/jdyK5c0rOdwsQJqmrrWLG7kMWbs1m6JZfDxZW42aBv+1DuHZfC8G7RPxrapHWw2WwE+3kS7OdJcnTruZBZrKdw3QrY7QZvrjnAPz7ZQmWNnTtGJnPT0I5aVWoJhXvgs/tg60cQmgRXvw5dx2q0XlvmHQCTFsCc4eYEkRuWQmC01VU5RK+EEObfOICvduTzyMKt3LZgHc99tZu7x3RlaHJkm247K6qoZtm2XBZvzuHLbXmUV9fh6+nOeckR/G5UV4Z1jdQGXSICKFw7vZ25Zdz7Xiar9hTSv0MYD41P14pIS2gYrfc0uHnA8D/DwF+Bp4/VlYkzCI6DyQvgxTHw+tVw3cfg5RoTN2w2G0OTIxnSOYL/bTjEPz/bxnVzVzOwYxh3j0mhd2Ko1SW2mH0F5Q3tHmv2HaHObhAV6M0lveIYlRrNoE7hGrMmIidRuHZSVbV1PPvFbp5ZthMfTzceuSKdCX0TdHq2uRkGZL5dP1rvEKRPhJF/gaB2Vlcmzia2J1zxArwxGd67CSa87FIXtbq52bi0Vxxj02J5fdV+nlq6g8v/8x1jusdw55iuLvkk3243WJdVxOLNOSzZnMOO3DIAUmICuXloJ0amRqvnVkR+ksK1E1q9t5B73s1kZ24Zl/Rsx58uSiUyUKcbm93h9eZovf3LzeA0YS4kDrS6KnFmKeNg9IOw6F74/K8w4gGrK3I4Lw83pg1O4oq+8cz5ejfPf7WbxVtymJgRz2+HJxMT3LrP5hytruPbnfks2ZLDki255JdV4e5mY0CHMCb1T2RkavRPjn8TEWlK4dqJFB+t4ZFPtzJ/5X7iQnyZe30/hnWNsros11eeD5//Db5/2Rytd/FT0PuaVrdRiFhk4C1QsBO+eRLCOkGfa62uqFkEeHtw24hkrhnYnqc/38lrK/fx3g8Huf6cDswa2qlVTSzKK61i2dZcPtucwzc786issRPo7cHQrpGMTI3m/OQogv1az/cjIs5F4doJGIbBwo3Z3P/hJgrKqrhxSAduH5mMn5f+e5pVXQ2sfgG++IfZYz3wZhh6t0bryZmx2WDso3BkL3x0G4S2hw7nWV1Vs4kI8OaBS7oz/ZwOPLF4G89+uYv5K/dzy/mdmDY4ySl7kA3DYFdeGYs357J4czY/HCjCMCAuxJerMhIYmRpD/w5h2lVPRBxC6c1ih4qO8ucPNrJkSy5pcUG8OK0f6fHBVpfl2gwDtnwIn/8d8rdDx2HmaL2oFKsrk9bK3RMmvAQvjIYF15gTRCK6WF1Vs0oM9+NfV/fmxvM68uin23ho4VZe+m4vt49IZnyfOMt3q6uts/P9viMsqd/QZW9BBQDpccHcNjyZkanRdIsNbNMTUESkeShcW6TObvDyd3t5/LNt2A2478JuXDc4yfI/SC5v1zJzd8VDayGiK1w9H7qO02g9OXs+weYEkTnD4bUJZsD2D7e6qmbXvV0wL0/vz/JdBTz86VbuemcDs7/ezV2juzIyNbpFw2tZVS1fb89j8eYcPt+WS1FFDV7ubgzqFM6MIR0Z0S2K2GDfFqtHRNomhWsLbDpUzL3vZrI+q5jzu0byt0vTdMFMc8v6HpY+AHu+guAEuPQZ6HE1uOtXQBwotL05C/3li8wpItM+BI+2cTHyoE7hvH/LYBZtyubRT7cx85Xv6ds+lLvHpNC/Q/NtdpVdXNmwOr18VwHVdXZC/Dy5oGsUI1OjGZIcqe3cRaRF6RGnBR2truNfS7cz5+s9hPp58tSk3lzcI1anJZtT7lbzYsWtH5kXK455GDKmt5nAIxZI6AeX/Rfevh4+uBXGz24zZ0ZsNhtj0mIZ0S2at77P4l9LtjPxueVckBLFXWO6khITdNZfwzAMthwubQjUmQeLAWgf7sfUQe0ZmRpN3/ahOgsoIpZRuG4hX23P44/vZ3Kg8ChXZSRwz7gUQvy8rC7LdRXthy8ehvWvg6c/nH8vDLoFvLWFrbSAtPFQuNt8YhfeGc6/2+pA+1XjAAAVcUlEQVSKWpSHuxuT+idyWa84XvpuL//5Yidj//01l/eO446RycSHntmZuupaO6v2FDYE6oNFR7HZoHdCCHeN6cqoVHO7cS1UiIgzULhuZgVlVfz94y2898NBOkb488bMgQzs6Pp9mJYpy4OvH4c1LwA2c0zauXe0id5XcTJDfgcFu8xpNGEdoccEqytqcb5e7tx8ficm9U/gv1/sYu53e/lo/WGuHdSeXw3rTJj/6RcYio/W8MW2XJZsyeWLbbmUVtbi4+nGuZ0j+e3wLgxLidL8fxFxSgrXzcQwDN5Ze5C/f7yZ8qpafnNBZ24Z1tkpx1S5hMoSWP40LH8Gaiqg1xQ4/w8QHG91ZdJW2Wxw8b/Nsygf3AIhCW12U6IQPy/uGdeNaYOT+NeS7cz9dg9vrj7AzPM6MmNIh4axowcKK+o3c8lh5e5Cau0GEQFejEuLZWRqNOd0jsDXS4+hIuLcFK6bwd78cu59L5PvdhWQ0T6Uh8an0yVa7QjNoqYSVs8xV6uPFkLqpTDsPohMtroyEfDwgqtegTkjzAscb1gKYR2srsoy7UJ8efTKntw4pCOPLdrG44u3M2/FPsalxbByTyFbs0sB6BIVwI3ndWRkajS94kO03biItCoK1w5UU2dn9le7eWrpDrzc3Xjw8jQm9UvUH4bmUFcL6+ebfdUlB81Z1cP/DHF9rK5M5Hh+YTDlLXNE3/yJMOMz8A21uipLdYkOZPbUDL7fV8gjC7fxyop99EsK474LuzGiWzRJEf5Wlygi8os5NFzbbLaZwEyAxMRER96101u7/wj3vJPJtpxSxqXHcP/F3YkO8rG6LNdjGLD5A3MDmIIdENfXnMzQcajVlYmcXngnuOo1mHcpvDkVrnnX3HimjevbPow3Zw2ips6Op6Z7iIiLcGi4NgxjNjAbICMjw3DkfTur0soaHltkrrzEBPnw/NQMRqZGW12W6zEM2PW5uQHM4XUQmWKGlZQL28yYM2nlks6BS/4P3p8FH91uvq6fXQAFaxFxKWoLOQuF5dWMfOIrckormTYoid+P7qrNCppD1hpY8gDs/RqCE82V6h5XgZsubJJWptckKNwFXz1mbo9+zm+trkhERBxMSfAXyC+rYntOKYXl1YR08OTZa/vSKyHE6rJcT+4Ws/1j60fgFwFjHoGM67UBjLRu599rjuhbfD+EdoDUS6yuSEREHEjh+gwt3ZLD3e9sIKSihoQwP+b8+lyd0nS0I/vgi4dg/Rvmpi/D/ggDb9YGMOIa3Nzgsv9A8QF4d6Y5LlIX4oqIuAyF65+pvKqWv3+8hddX7SclJpCUuGD8vNwVrB2pLA++/iesfgFsbjDoV9oARlyTpy9c/TrMuQBev9oc0ReSYHVVIiLiAEqGP8Pa/Ue48KmveWP1fm4a2pEPbj0HP21k4DiVxfD5g/DvnrDqebMv9TdrYfSDCtbiugIiYfJbUHMU5l9lboQkIiKtnlauf0RNnZ3/+3wnzyzbSUyQD6/fqK3LHarmqBmmv3kCjh6B1MvggvvMC71E2oKoFJj4Mrx6Jbw9HSa9Ae56WBYRac30KH4au/LKuGPBOtZnFTO+TxwPXNKdIB/NpXWIulpY9yp88QiUHoJOw2H4n6Bdb6srE2l5nS6ACx+Hj26DRffCuEetrkhERM6CwvUJDMPg1RX7ePCTLfh4uvOfKX0Ylx5rdVmuwW6Hze/DsgehYCfE94Pxs6HDEKsrE7FWxvXm78Typ80NZwbcZHVFIiLyCylcN5FbUsmdb2/gy+15DE2O5LErexClXRbPnmHArqWw5C+QvQEiu8HV86HrOG2iIXLMyL9C4R749A/miL7kUVZXJCIiv4DCdb1PNx7mnnczOVpTx18v7c61A9tjU/A7ewdWmaF63zcQkgiXPwfpE7QBjMiJ3Nzhiudh7lh4+3qYvghi0qyuSkREzlCbD9ellTU88OFm3lmbRY/4YJ6Y2IvOUQFWl9X65WyGz/8G2z4B/0gY+xj0naYNYER+jJc/TFoAz19gThC5canVFYmIyBlq0+F61Z5Cbl+wjsPFR/nNBZ359fAumlt9to7shWUPwYYF5qYvF9wHA24Gbz1hEflZgmJh8gJ4cYwZsO0DdaZHRKQVaZPhuqq2jicX7+C5r3aRGObHW7MG07d9qNVltW5lufDVY7BmrhkEBv8azr0d/MKsrkyk9YntAVe+CG9MgvwwiEyxuiIRcXVVpeYGbl7+VlfS6rW5cL0tu5TbFqxjy+ESJvVP4L4LU/H3bnOHwXEqi+Hbp2DFf6G2EvpcC0PvhqB2Vlcm0rp1HQOj/wFPvQ6H15kz4dOvBF8tBIiIAxRnwf4V5u3ACsjZBJf+x9zITc5Km0mVdrvBi9/u4dFF2wjy8WDO1AxGpEZbXVbrVXMUVs2Gr5+AyiLoPh6G/REiOltdmYjrGDALItZAyWH45Pfw2X3Q7RLzSWz7c8FNbWwi8jPY68zwfCxI718JJVnmxzz9IT4DzrsT2vWytk4X0SbC9aGio/z+rfV8t6uAEd2iefiKdCICdGHdL1JXAz+8Cl8+AqWHofMIGP5niO1pdWUirsdmg4Bo83bTQ7D2FdjwJmS+aY7r63Mt9Jxs9mmLiBxTVQYH1zSuTGetgepS82OB7SBxACT8GhIHQnSadoZ1MJc/mh+sO8if3t9Ird3g4fHpXNUvQSP2fgm7HTa9a24AU7gb4vvDFXMg6VyrKxNpG2J7woU9YdTfYPOH8MMrsPSv8PmD0GUU9JlqvtQfSZG2p+TQ8S0e2RvBqANsEN0dekyExEFmqA5O0B4TzcxlH4WLK2q474ON/G/9IfokhvDkVb1oH64m/TNmGLBzCSz9C2RnQlQqTHoDksfol1PECp6+0PMq81awyzyTtO412L7QXOHuNRl6X2vu9CgirsdeB7lb6ts76ls8ivebH/P0g7i+MOQOc1U6vh/4BFtbbxvkkuH6mx35/P6t9eSXVfH7UcnMGtoJD43YO3P7V5qhet+3ENIeLp9tXlClsWAiziG8E4y437zeYcdnsHaeeYHxN0+aPdl9pkLqJWYgF5HWqbocDn7fpMVjNVSVmB8LiDFXowfdAgkDICYd3D2trVdcK1xX1tTxyKdbmfvtXjpG+vPu1MH0iA+xuqzWo67G/MXdsQi2fwb528A/Csb9E/pMAw8vqysUkVNx94CUceat5DCsn2/2Z783Ez65E3pMMIO2ro0QcX4lhxsvOjywAg5vaGzxiOoGaVc0tniEtNdZZCfk0HBts9lmAjMBEhMTHXnXP2njwWJuX7COHbllXDc4ibvHpODrpRXWn1SWBzsXw/ZFsGsZVBWDmycknQP9ZkDvazTzUqQ1CYqFIb+Dc243zzqtnWe2jqyeY4br3tdC+gTw1cKDiOXsdsjbCvuXw4GV5gJX0T7zYx6+ZovHubeZYTq+n35vWwmHhmvDMGYDswEyMjIMR9736dTZDZ77ahdPLt5OqJ8XL0/vz9DkyJb40q2T3Q7Z682V6R2L4OBawDBPLaVeAsmjoeP55u6KItJ6ublBhyHm7eijkPk2rH25caRf6qXmanb7c7TyJT9fbbU5fvXokSa3IqguM3t7/cLALxz8IsyXXn5WV+xcqivMFo9jK9NZq8z9IsA8U5w4APrPNMN0TLrOGLdSrbot5EBhBXe8uY7Ve49wYXosf78sjVB//SCepKrUXJXesQh2LIayHMBmPiMedq85YSCmh2bmirgq31Dof6N5O7TOXM3OfAs2LICwTo0j/QI1+79NMAzz78JxIfmEwNzwsRNe1pSf2dfy8DVDtn94fegObxK+wxrf9q8P475hrjXxpjTnhBaP9WCvNT8WmQKplzW2eIR20BNdF9Eqf4INw+Ct77P4y4ebcLPZeGJiTy7vHacRe03l76zvnV4E+74Dew14B0PnC6DLaHM+dYBW+EXanHa9zNuov8OWD82gveQBWPo3cwpQn2uh80jXCjiuqq7GXPU8cRX5pIB8wscqixoD3qm4e5tPyI7dQhLNliLfULMtwSekycfrX/cKgMoSqMiHioLGW3k+VBQ2vl2423z72AV5p+IT3Ljy3RC+TwznTW4+wc4RSu1281ql/SsaWzyO7DE/5uED7frA4F83tnj4hVlbrzSbVvfoWVBWxb3vZbJoUw4DOoTx+MSexIfqtBO1VWZ/5bF2j8Ld5vsjU2DgzWa7R8IAXUUsIiYvP+h5tXnL32nOzV43H7Z9bLaJ9Z5iXnMR1tHqSl2bYZjTIH7WKvKxW32gPrYpyOn4BB8fhIPjjw/NpwrJvqG/fLpMQBTwM3fpra2Go4X14bvg9LeSLHO1tyIf6qpPfV9uHuaK97HV7xNbU469z7/J246YoFNz1GytbFiZXmn+X4H5tRMHQsZ0M0zH9lSLRxvSqsL1sq253Pn2BkqO1nDvuBRmnNsRdzcneLZqlZLD5vitHZ/B7i/MnjcPH0gaAgNvMds9QttbXaWIOLuIzjDyL3DBfebZrh9eMcf5ff24+XjSZxp0uxg8fayutPWw283Zw7lbzQvWynJOv8Jsrzn9/bh5Hh+Ig+LNHfV+KiT7BDv32FQPLwiMMW8/x7EnIRUF9avjhSesjh8L5IXmDOhjr3Oay788/U7TmhLGye0r4eYxPXqkcbb0gZVmi9Wx/7uIZPN3JHGQGarDOjrHarpYolWE64rqWh78eAuvrdxPSkwgr8zoT7fYIKvLann2OvNZ8rF2j+wN5vuD4s3dl7qMhg7n6QISEfll3D2h20XmreSQuTnND6/CuzeYYa3HVeZFkDHpVlfqPOx2KD4Aedsgb0t9mN4CeduP70/2DqoPwfXhN6rb8avFxwXkJiHZ008hDcxj4B1g3n7uopG9zmyb+anV8YoCKNhphvGfOhsA4O5ltngMuqW+xaO/2bYiUs/pw/UP+49wx5vr2VtQzszzOnLHyGR8PJ342bijHS2CXUvNdo+di80HAZub2eIx/H6z3SMqVQ++IuJYQe3gvDvh3N/B3q/N1ezvX4ZVsyG2lxmy069sO7u/GQYUZ5mr0LlbmrzcdnyIDoiBqBTz+ESlQGQ3iOyqEWpWcHOvX4k+g97m2qr6VfETVsPL882Fq4SB5jULHt7NV7e0ek4brmvq7Dz9+U6eXraT6EBv5t8wkEGd2sAzQ8MwH7S3LzLbPfavMIfH+4aaFxklj4ZOF+hCCBFpGW5u0HGoeRtbaE4ZWTsPPr4DFv0Rul9mBsnEQa7xJN8woORgYztHw2r0tuNXNQOizdDc+5rGEB2VYj5WS+vl4W3Oig+KtboSacWcMlzvzivj9jfXs/5AEZf3juOBS7oT7OvCF+LVHIU9XzfujFi833x/dLo5PL7LaIjPcO7+ORFxfX5hMOAmcw7voR/qR/q9Detfh/DO5gY1vSbXX9jm5AwDSg+fsApdH6KbTrLwjzQvDO81yXwZ1c18qQUOETkNpwrXhmHw2sr9PPjxFrw83Hh6cm8u6tHO6rKaR9GBxjC95yuoPWr21nU8H4bcYV6MGBxndZUiIiez2SCuj3kb/SBs/qB+pN/98PmxkX5TodNw60f6GQaUZp/QD73NfL2quPHz/MLN1eceE5uE6G7qpRWRM+Y04Tq3tJK7397Asm15DOkSwWNX9iQm2IWuTK+rNXdiOtbukbvZfH9okvlHKHkUtD9XV+OLSOvi5W+uVveabF7E98Mr5kr21o8gsJ35/t7XQFiH5q3DMKAs9/gQfexlZZMQ7RtmBuf0KxtXoaO6mZMiREQcwCnC9acbs7nn3Q1UVNfxwMWpTB2UhJsrjNgrL4CdS8wV6p1LzfmXbh5mb+Kov5vtHhFdXKNPUUQkMhlG/Q2G/xm2f2quZn/zBHz9T+gw1FxISLno7BYRDAPK807RzrHVHJV2jG+oufLcffwJITpSj7ki0qwsDdellTX89X+beev7LNLignhyYi+6RAdaWdLZMQzIzmxs98haDRjmg3nKhWarR6dhbefqehFpm9w9zZm/3S6G4oPm5jQ/zIN3Zpgj53pebfZnx6T9+P2U5TUG56Zh+mhh4+f4BJshOvXSxosKI7uZfd8K0SJiAcvC9eq9hdy+YB2Hio5y67DO/GZ4F7w83Kwq55erKoM9X9a3eyyG0kPm+9v1hqF3m+0esb3NK+5FRNqa4DgYeicM+Z35WPnDK7DmRVj5rDkr+Nh260X7mgTo+naOioLG+/EONoNzt4uOD9GBMQrRIuJUWjxcV9faeXLJdp79chcJoX68edMgMpKc/Kpre535IF+WY/b0leWamwZUFsOjHcwtWb0CzVXp5NHmH4rAaKurFhFxHm5u5mNkp2Hm3OANC8y2kY9uP/7zvALN4Nx13PHtHIGxCtEi0iq0aLjenlPKbW+sY/PhEq7KSOBPF6cS4G3R4rndbvbnlefWh+a8+pc5Zj9f0/dV5INhP/7fH+kJnr7mSKouo8w+ag8va74XEZHWxC8MBt4MA2aZu84eXANhncxQHRSnEC0irVqLJFu73eCl7/by8KdbCfD24Llr+zK6e4zjv5BhmKvJZbmnCM0nvK88F+y1J9+Hu5e5OYB/JATHm6OmAqIa3xcQbb792HPmxYmjH3T89yEi0hbYbBDf17yJiLiIZg/Xh4uPcudbG/hmZz4XpETxyBU9iAw8g21DDQOqyxrbMcrrXzZt0Wh4Xy7UVZ18H24e4B8FAfXhODq9PjBHNQnO9a/7BP+8VRM3pxi0IiIiIiJOpFkT4ofrD3Hfe5nU1Bn84/J0JvVPwHYsuFZX1IfiU7VjNAnQ5XlQU3HyndvcwC+ifiU5EiKSzXDsH9X4vmOh2TdUFxSKiIiISLNrtnCdl5/Lt28+wd3BlVzcyYOgffNhU5PwXF166n/oF97YgpEwoHF12b/JKnNAlPl52g5cRERERJyIQ8O1zWabCcwE6BvrxiOez0MFsCOkMRi369UkKJ/Qy+wfYc5HFRERERFphRwarg3DmA3MBkhL7Wpw+1IzOHucQY+1iIiIiEgr1WxtIT5+gea0DRERERGRNkJX+YmIiIiIOIjCtYiIiIiIgyhci4iIiIg4iMK1iIiIiIiD2AzDaJ47ttlKgW3NcufyUyKAfKuLaIN03K2h424NHXfr6NhbQ8fdGj/3uLc3DCOyuYv5OZpzh8ZthmFkNOP9y2nYbLY1OvYtT8fdGjru1tBxt46OvTV03K3RGo+72kJERERERBxE4VpERERExEGaM1zPbsb7lh+nY28NHXdr6LhbQ8fdOjr21tBxt0arO+7NdkGjiIiIiEhbo7YQEREREREHUbgWEREREXEQhWsREREREQdRuBYRERERcRCFaxERERERB/l/uJboW2UwFzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x1152 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = {'model':np.mean(mcmc['mod_obspos'], axis=0), 'obs':data['obs']}\n",
    "colors = {'model':'darkred', 'obs':'0.1'}\n",
    "\n",
    "diff = res['model']-res['obs']\n",
    "\n",
    "t = data['dt']*np.arange(data['nt'])\n",
    "slice_indices = [1,6]\n",
    "v_ext = data['v_min'] * 2**(np.arange(data['m']+1)*delta_v) \n",
    "v_width = v_ext[1:] - v_ext[:-1]\n",
    "\n",
    "norm = mpl.colors.Normalize(0.0,0.2)\n",
    "norm_diff = mpl.colors.Normalize(-0.1,0.1)\n",
    "\n",
    "fig,axs = plt.subplots(5,1,sharex=True,figsize=(12,16))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(t, data['E'], color='gold')\n",
    "ax.set(xlim=(data['t_obs'][0],t[-1]), ylabel='E ($\\mu$mol photons/m$^{2}$/s)')\n",
    "\n",
    "ax = axs[1]\n",
    "pc = ax.pcolormesh(data['t_obs'],v,res['obs'],norm=norm)\n",
    "ax.set(ylabel='size ($\\mu$m$^3$)')\n",
    "add_colorbar(ax, norm=pc.norm, cmap=pc.cmap, label='size class proportion')\n",
    "\n",
    "ax = axs[2]\n",
    "ax.set(ylabel='size ($\\mu$m$^3$)')\n",
    "pc = ax.pcolormesh(data['t_obs'],v,res['model'],norm=norm)\n",
    "\n",
    "ax = axs[3]\n",
    "pc = ax.pcolormesh(data['t_obs'],v,diff,norm=norm_diff, cmap='PiYG')\n",
    "ax.set(ylabel='size ($\\mu$m$^3$)')\n",
    "add_colorbar(ax, norm=pc.norm, cmap=pc.cmap, label='difference in size class proportion')\n",
    "\n",
    "ax = axs[4]\n",
    "prop = np.mean(np.abs(diff),axis=0)\n",
    "ax.plot(data['t_obs'], prop, label='mean(abs(diff)), avg: {:.6f}'.format(np.mean(prop)))\n",
    "prop = np.sum(diff**2,axis=0)\n",
    "ax.plot(data['t_obs'], prop, label='sum(diff$^2$), avg: {:.6f}'.format(np.mean(prop)))\n",
    "ax.set(xlabel='time (days)', ylabel='model-observation misfit')\n",
    "ax.legend()\n",
    "\n",
    "for ax in axs.flat:\n",
    "    for i in slice_indices:\n",
    "        ax.axvline(data['t_obs'][i], color='.3')\n",
    "\n",
    "# plot slices\n",
    "\n",
    "plotdata_y = np.zeros(2*(data['m']+1))\n",
    "\n",
    "fig,axs = plt.subplots(nrows=len(slice_indices), sharex=True, figsize=(12,len(slice_indices)*3))\n",
    "for i,ax in zip(slice_indices,axs.flat):\n",
    "    for name in ('obs','model'):\n",
    "        color = colors[name]\n",
    "        cdata = res[name][:,i]\n",
    "        plotdata_y[1:-1] = np.repeat(cdata/v_width,2)\n",
    "        ax.plot(np.repeat(v_ext,2), plotdata_y, color=color, alpha=0.5, label='{} pdf'.format(name))\n",
    "        ax.text(0.01, 0.95, 'minute {}'.format(data['t_obs'][i]), transform=ax.transAxes, va='top', ha='left')\n",
    "        ax.set_ylabel('density')\n",
    "        ax.set_ylim(bottom=0.0)\n",
    "axs[0].legend()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}