diff --git a/lan_nle_comparison/LAN-NLE-Figures.ipynb b/lan_nle_comparison/LAN-NLE-Figures.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "004c8e82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed 223174\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import sbibm\n",
+    "import torch\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append(\"../../results/benchmarking_sbi/\")\n",
+    "from utils import compile_df\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from sbibm.utils.io import get_tensor_from_csv, get_ndarray_from_csv\n",
+    "import pandas as pd\n",
+    "from sbi.analysis import pairplot\n",
+    "\n",
+    "# Plotting settings\n",
+    "mpl.rcParams['font.sans-serif'] = \"Arial\"\n",
+    "mpl.rcParams['font.family'] = \"sans-serif\"\n",
+    "mpl.rcParams[\"axes.spines.top\"] = False\n",
+    "mpl.rcParams[\"axes.spines.right\"] = False\n",
+    "mpl.rcParams[\"axes.labelsize\"] = \"medium\"\n",
+    "mpl.rcParams[\"xtick.labelsize\"] = \"small\"\n",
+    "mpl.rcParams[\"ytick.labelsize\"] = \"small\"\n",
+    "mpl.rcParams[\"legend.frameon\"] = False\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 18\n",
+    "mpl.rcParams[\"font.size\"] = 20\n",
+    "\n",
+    "# Colorblind color palette\n",
+    "colors = ['#377eb8', '#ff7f00', '#4daf4a',\n",
+    "                  '#f781bf', '#a65628', '#984ea3',\n",
+    "                  '#999999', '#e41a1c', '#dede00']\n",
+    "\n",
+    "# Fix seed for figures.\n",
+    "seed = 223174 #torch.randint(1000000, (1,))\n",
+    "torch.manual_seed(seed)\n",
+    "print(f\"seed {seed}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce1d2a31",
+   "metadata": {},
+   "source": [
+    "## Load DDM task from `sbibm`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5279e900",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task = sbibm.get_task(\"ddm\")\n",
+    "prior = task.get_prior_dist()\n",
+    "simulator = task.get_simulator(seed=seed) # Passing the seed to Julia."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4229e528",
+   "metadata": {},
+   "source": [
+    "### Load pretrained LANs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "868009d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# network trained on KDE likelihood for 4-param ddm\n",
+    "lan_kde_path = \"../sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5\"\n",
+    "lan_ana_path = \"../sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5\"\n",
+    "lan_kde = keras.models.load_model(lan_kde_path, compile=False)\n",
+    "lan_ana = keras.models.load_model(lan_ana_path, compile=False)\n",
+    "\n",
+    "def lan_likelihood(theta, data, net, ll_lower_bound):\n",
+    "    \"\"\"Return log likelihood summed over all trials in data, \n",
+    "        given a batch of parameters theta.\n",
+    "    \n",
+    "    Args\n",
+    "        theta: batch of parameters\n",
+    "        data: batch of iid reaction times and choices encoded\n",
+    "            as negative and positive reaction times. \n",
+    "        net: lan keras model\n",
+    "        ll_lower_bound: lower bound of single trial log likelihood.\n",
+    "        \n",
+    "    Returns\n",
+    "        llsum: batch of log likelihoods over trials. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Convert to positive rts.\n",
+    "    rts = abs(data)\n",
+    "    num_trials = rts.numel()\n",
+    "    num_parameters = theta.shape[0]\n",
+    "    assert rts.shape == torch.Size([num_trials, 1])\n",
+    "    theta = torch.tensor(theta, dtype=torch.float32)\n",
+    "    # Convert DDM boundary seperation to symmetric boundary size.\n",
+    "    theta[:, 1] *= 0.5\n",
+    "\n",
+    "    # Code down -1 up +1.\n",
+    "    cs = torch.ones_like(rts)\n",
+    "    cs[data < 0] *= -1\n",
+    "\n",
+    "    # Repeat theta trial times\n",
+    "    theta_repeated = theta.repeat(num_trials, 1)\n",
+    "    # repeat trial data theta times.\n",
+    "    rts_repeated = torch.repeat_interleave(rts, num_parameters, dim=0)\n",
+    "    cs_repeated = torch.repeat_interleave(cs, num_parameters, dim=0)\n",
+    "\n",
+    "    # stack everything for the LAN net.\n",
+    "    theta_x_stack = torch.cat((theta_repeated, rts_repeated, cs_repeated), dim=1)\n",
+    "    ll_each_trial = torch.tensor(\n",
+    "        net.predict_on_batch(theta_x_stack.numpy()),\n",
+    "        dtype=torch.float32,\n",
+    "    ).reshape(num_trials, num_parameters)\n",
+    "\n",
+    "    # Lower bound on each trial log likelihood.\n",
+    "    # Sum across trials.\n",
+    "    llsum = torch.where(\n",
+    "        torch.logical_and(\n",
+    "            rts.repeat(1, num_parameters) > theta[:, -1], \n",
+    "            ll_each_trial > ll_lower_bound,\n",
+    "        ),\n",
+    "        ll_each_trial,\n",
+    "        ll_lower_bound * torch.ones_like(ll_each_trial),\n",
+    "    ).sum(0)\n",
+    "    \n",
+    "    return llsum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b76dd7ea",
+   "metadata": {},
+   "source": [
+    "## Likelihood comparison for single example\n",
+    "\n",
+    "##### Sample example parameter from prior\n",
+    "\n",
+    "For creating a figure showing the likelihood over the entire data space given a fixed parameter combination we sample a single parameter combination from the prior and evaluate the synthetic likelihoods for a large range of reaction times and both choices, while holding the parameters fixed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "de62e5e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.1089,  1.0866,  0.6434,  1.3179]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample one parameter from the prior for a likelihood example.\n",
+    "theta_o = prior.sample((1,))\n",
+    "l_lower_bound = 1e-7\n",
+    "theta_o"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "70e92c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load pretrained NLE model\n",
+    "with open(\"../sbibm/algorithms/lan/nle_pretrained/mm_315_2.p\", \"rb\") as fh: \n",
+    "    nle = pickle.load(fh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "62a28671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# construct rts and choices for nle in [-test_tmax, test_tmax]\n",
+    "\n",
+    "# RT range\n",
+    "test_tmax = 5\n",
+    "# Number of test points\n",
+    "ntest = 1000\n",
+    "rs = torch.cat((torch.linspace(test_tmax, 1e-7, ntest//2), \n",
+    "           torch.linspace(1e-7, test_tmax, ntest//2))).reshape(-1, 1)\n",
+    "cs = torch.cat((torch.zeros(ntest//2), \n",
+    "           torch.ones(ntest//2))).reshape(-1, 1)\n",
+    "\n",
+    "test_rts = torch.linspace(-test_tmax, test_tmax, ntest)\n",
+    "\n",
+    "# get NLE synthetic likelihood for each data point with fixed theta.\n",
+    "lps_nle = torch.tensor([nle.log_prob(r.reshape(-1, 1), c.reshape(-1, 1), theta_o) for r, c in zip(rs, cs)])\n",
+    "\n",
+    "# from analytical likelihood\n",
+    "lps_true = torch.tensor([task.get_log_likelihood(theta_o, d.reshape(-1, 1), l_lower_bound=l_lower_bound) \n",
+    "                     for d in test_rts])\n",
+    "\n",
+    "# and from both LANs.\n",
+    "lps_lanana = torch.tensor([lan_likelihood(theta_o, \n",
+    "                                   d.reshape(-1, 1), \n",
+    "                                   net=lan_ana, \n",
+    "                                   ll_lower_bound=np.log(l_lower_bound)) \n",
+    "                    for d in test_rts])\n",
+    "lps_lankde = torch.tensor([lan_likelihood(theta_o, \n",
+    "                                   d.reshape(-1, 1), \n",
+    "                                   net=lan_kde, \n",
+    "                                   ll_lower_bound=np.log(l_lower_bound)) \n",
+    "                    for d in test_rts])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c5ab0003",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(1.0000e-07, dtype=torch.float64)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lps_true.min().exp()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7248dbf0",
+   "metadata": {},
+   "source": [
+    "## Systematic Likelihood comparison\n",
+    "\n",
+    "Next we do a systematic comparison based on the Huber loss (the LAN training loss) and the mean squared error (MSE) between analytical and synthetic log likelihoods of LAN and NLE.\n",
+    "\n",
+    "To mimick the inference setup we sample an observation from the simulator via parameters from the prior and obtain the likelihood of this observation given a large batch of parameters, e.g., `1000`, sampled from the prior. The Huber loss and MSE is then calculated across this batch, giving a single number. This procedure we repeat for many observation, e.g., `100` and then show boxplots over the resulting 100 numbers. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d7b0567e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define losses.\n",
+    "def huberloss(y, yhat):\n",
+    "    diff = abs(y-yhat)\n",
+    "    \n",
+    "    err = np.zeros(y.numel())\n",
+    "    err[diff <= 1.0] = 0.5 * diff[diff <= 1.0]**2\n",
+    "    err[diff > 1.0] = 0.5 + diff[diff > 1.0]\n",
+    "    return err.mean()\n",
+    "\n",
+    "def mse(y, yhat):\n",
+    "    return torch.mean((y - yhat)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3c62f219",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# mimick the MCMC setting: xo is fixed, thetas are scored with loss\n",
+    "num_observations = 100\n",
+    "num_thetas_per_observation = 1000\n",
+    "\n",
+    "# first sample observations xo\n",
+    "xos = simulator(prior.sample((num_observations,)))\n",
+    "\n",
+    "labels = [\n",
+    "#     \"LAN-ANA\", \n",
+    "    \"LAN\", \n",
+    "    \"MNLE\",\n",
+    "]\n",
+    "errors = []\n",
+    "for xoi in xos:\n",
+    "    \n",
+    "    # Sample test thetas from prior.\n",
+    "    test_thetas = prior.sample((num_thetas_per_observation,))\n",
+    "    xoi = xoi.reshape(-1, 1)\n",
+    "    # Extract positive RTs and choices for mixed model.\n",
+    "    rsi = abs(xoi)\n",
+    "    csi = torch.ones_like(rsi)\n",
+    "    csi[xoi < 0] = 0\n",
+    "\n",
+    "    # Evaluate\n",
+    "    lps_nle_i = nle.log_prob(rsi, csi, test_thetas).squeeze()    \n",
+    "    lps_true_i = task.get_log_likelihood(test_thetas, data=xoi.reshape(1, -1), l_lower_bound=l_lower_bound)\n",
+    "    lps_lanana_i = lan_likelihood(test_thetas, xoi, lan_ana, np.log(l_lower_bound))\n",
+    "    lps_lankde_i = lan_likelihood(test_thetas, xoi, lan_kde, np.log(l_lower_bound))\n",
+    "    \n",
+    "\n",
+    "    # Score\n",
+    "    errors.append([\n",
+    "            [\n",
+    "#                 huberloss(lps_lanana_i, lps_true_i),\n",
+    "                huberloss(lps_lankde_i, lps_true_i),\n",
+    "                huberloss(lps_nle_i, lps_true_i),\n",
+    "            ],\n",
+    "            [\n",
+    "#                 huberloss(lps_lanana_i.exp(), lps_true_i.exp()),\n",
+    "                huberloss(lps_lankde_i.exp(), lps_true_i.exp()),\n",
+    "                huberloss(lps_nle_i.exp(), lps_true_i.exp()),\n",
+    "            ],            \n",
+    "            [\n",
+    "#                 mse(lps_lanana_i, lps_true_i),\n",
+    "                mse(lps_lankde_i, lps_true_i),\n",
+    "                mse(lps_nle_i, lps_true_i),\n",
+    "            ], \n",
+    "            [\n",
+    "#                 mse(lps_lanana_i.exp(), lps_true_i.exp()),\n",
+    "                mse(lps_lankde_i.exp(), lps_true_i.exp()),\n",
+    "                mse(lps_nle_i.exp(), lps_true_i.exp()),\n",
+    "            ],         \n",
+    "        ])\n",
+    "errors = np.array(errors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "80b6621e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract separate dataframes for huber and mse.\n",
+    "dfhuber_log = pd.DataFrame(data=np.array(errors)[:, 0, :], columns=labels)\n",
+    "dfhuber = pd.DataFrame(data=np.array(errors)[:, 1, :], columns=labels)\n",
+    "dfmse_log = pd.DataFrame(data=np.array(errors)[:, 2, :], columns=labels)\n",
+    "dfmse = pd.DataFrame(data=np.array(errors)[:, 3, :], columns=labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cae9bc7",
+   "metadata": {},
+   "source": [
+    "## Estimate evaluation times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2e39964e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "# Vary size of theta (number of MCMC chains in parallel)\n",
+    "num_chains = [10]\n",
+    "# Vary size of data (number of trials)\n",
+    "num_trials = [100]\n",
+    "reps = 100\n",
+    "\n",
+    "lan_rts = np.zeros((len(num_chains), len(num_trials), reps))\n",
+    "nle_rts = np.zeros_like(lan_rts)\n",
+    "\n",
+    "thetas = prior.sample((num_chains[-1],))\n",
+    "xs = simulator(prior.sample((1,)).repeat(num_trials[-1], 1))\n",
+    "\n",
+    "for ii in range(reps):\n",
+    "    for jj, nc in enumerate(num_chains): \n",
+    "        for kk, nt in enumerate(num_trials):\n",
+    "            # LAN timing\n",
+    "            tic = time.time()\n",
+    "            lan_likelihood(thetas[:nc,], xs[:nt,], net=lan_kde, ll_lower_bound=np.log(l_lower_bound))\n",
+    "            lan_rts[jj, kk, ii] = time.time() - tic\n",
+    "\n",
+    "            # NLE timing\n",
+    "            rts = abs(xs[:nt])\n",
+    "            cs = torch.ones_like(rts)\n",
+    "            cs[xs[:nt] < 0] = 0\n",
+    "            tic = time.time()\n",
+    "            nle.log_prob(rts, cs, thetas[:nc])\n",
+    "            nle_rts[jj, kk, ii] = time.time() - tic\n",
+    "\n",
+    "# convert ot ms\n",
+    "lan_rts *= 1000\n",
+    "nle_rts *= 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1e01f4e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4.782371520996094, 0.18311150697348427)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nle_rts.mean(), nle_rts.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f694b2f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5.552349090576172, 0.25677282964301373)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lan_rts.mean(), lan_rts.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "2d27d31a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rt = pd.DataFrame(data={\"LAN-KDE\":lan_rts.reshape(-1), \"NLE\": nle_rts.reshape(-1)}, index=range(reps))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63d94251",
+   "metadata": {},
+   "source": [
+    "## Results figure 1\n",
+    "\n",
+    "- likelihood examples\n",
+    "\n",
+    "- likelihood accuracy\n",
+    "\n",
+    "- number of simulations\n",
+    "\n",
+    "- evaluation time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "aa2d8483",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:104: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 3, sharex=False, figsize=(18, 8),  \n",
+    "                       gridspec_kw=dict(wspace=0.27, hspace=0.2, width_ratios=[0.6, .2, .2]))\n",
+    "\n",
+    "grid = False\n",
+    "showfliers = True\n",
+    "\n",
+    "labels = [\"Analytical\", \n",
+    "#           \"LAN-ANA\", \n",
+    "          \"LAN\", \n",
+    "          \"MNLE\", \n",
+    "         ]\n",
+    "\n",
+    "plt.sca(ax[0, 0])\n",
+    "plt.plot(test_rts, lps_true.exp(), label=\"Analytical L\", c=colors[0]);\n",
+    "# plt.plot(test_rts, lps_lanana.exp(), label=\"LAN-ANA\");\n",
+    "plt.plot(test_rts, lps_lankde.exp(), label=\"LAN-KDE\", ls=\"-\", c=colors[1]);\n",
+    "plt.plot(test_rts, lps_nle.exp(), label=\"MNLE\", ls=\"-\", c=colors[2]);\n",
+    "plt.ylabel(r\"$L(x | \\theta)$\");\n",
+    "plt.legend(labels)\n",
+    "plt.xticks([-4, -2, 0, 2, 4], [])\n",
+    "# plt.yticks([0, .4, .8, 1.2], [0, .4, .8, 1.2])\n",
+    "plt.axvline(0, color=\"k\", lw=1)\n",
+    "y = max(lps_true.exp())+.1\n",
+    "plt.arrow(0, y, 0.3, 0., width=0.03, color=\"k\", alpha=0.5)\n",
+    "plt.text(0.1, 1.05 * y, s=\"up\")\n",
+    "plt.arrow(0, y, -0.3, 0., width=0.03, color=\"k\", alpha=0.5)\n",
+    "plt.text(-1.25, 1.05*y, s=\"down\")\n",
+    "# plt.yticks(np.linspace(0, 4, 5), np.linspace(0, 4, 5))\n",
+    "# plt.suptitle(fr\"v={theta_o[0, 0]:.2f}, a={theta_o[0, 1]:.2f}, w={theta_o[0, 2]:.2f}, $\\tau$={theta_o[0, 3]:.2f}\");\n",
+    "\n",
+    "plt.sca(ax[1, 0])\n",
+    "plt.plot(test_rts, lps_true, c=colors[0])\n",
+    "# plt.plot(test_rts, lps_lanana)\n",
+    "plt.plot(test_rts, lps_lankde, ls=\"-\", c=colors[1])\n",
+    "plt.plot(test_rts, lps_nle, ls=\"-\", c=colors[2])\n",
+    "# plt.legend(labels)\n",
+    "plt.xlabel(\"$x$: reaction time [s]\")\n",
+    "plt.ylabel(r\"$\\log L(x | \\theta)$\");\n",
+    "plt.xticks([-4, -2, 0, 2, 4], [4, 2, 0, 2, 4])\n",
+    "plt.axvline(0, color=\"k\", lw=1)\n",
+    "\n",
+    "\n",
+    "plt.sca(ax[0, 1])\n",
+    "box_widths = [0.3] * 2\n",
+    "bdict = dfhuber_log.boxplot(ax=ax[0, 1], patch_artist=True, return_type=\"dict\", \n",
+    "                        medianprops={\"color\": \"k\"}, grid=grid, \n",
+    "                           notch=True, \n",
+    "                           widths=box_widths, \n",
+    "                           showfliers=showfliers,\n",
+    "                           )\n",
+    "plt.ylabel(\"Huber loss\");\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i+1])\n",
+    "plt.yticks(np.linspace(0, 0.4, 3));\n",
+    "plt.ylim(0, .4)\n",
+    "\n",
+    "plt.sca(ax[0, 2])\n",
+    "bdict = dfmse_log.boxplot(ax=ax[0, 2], patch_artist=True, return_type=\"dict\", \n",
+    "                          medianprops={\"color\": \"k\"}, grid=grid, \n",
+    "                          notch=True, \n",
+    "                          widths=box_widths,\n",
+    "                          showfliers=showfliers,\n",
+    "                         )\n",
+    "plt.ylabel(r\"MSE\");\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i+1])\n",
+    "plt.yticks(np.linspace(0, 2.5, 3))\n",
+    "plt.ylim(0, 2.5);\n",
+    "\n",
+    "\n",
+    "ddd = pd.DataFrame({'method': [\"LAN\", \"MNLE\"], 'training budget': [1e11, 1e5]})\n",
+    "plt.sca(ax[1, 2])\n",
+    "ddd.plot.bar(x=\"method\", y=\"training budget\", color=colors[1:3], ax=ax[1, 2], \n",
+    "             rot=0, width=box_widths[0])\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel(\"training budget\")\n",
+    "plt.legend(\"\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylim(5e4, 1e11)\n",
+    "plt.yticks(np.logspace(0, 12, 3), [r\"$10^0$\", \n",
+    "                                   r\"$10^6$\", \n",
+    "#                                    r\"$10^6$\",r\"$10^7$\",r\"$10^8$\",r\"$10^9$\", \n",
+    "                                   r\"$10^{12}$\"])\n",
+    "\n",
+    "ddd = pd.DataFrame({'method': [\"LAN\", \"MNLE\"], 'rt': [lan_rts.mean(), nle_rts.mean()]})\n",
+    "plt.sca(ax[1, 1])\n",
+    "ddd.plot.bar(x=\"method\", y=\"rt\", color=colors[1:3], ax=ax[1, 1], \n",
+    "             rot=0, width=box_widths[0], \n",
+    "            yerr=[lan_rts.std()/np.sqrt(reps), nle_rts.std()/np.sqrt(reps)])\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel(\"eval. time [ms]\")\n",
+    "plt.legend(\"\")\n",
+    "plt.yticks(np.linspace(0, 8, 3), np.linspace(0, 8, 3))\n",
+    "\n",
+    "weight = \"regular\"\n",
+    "fontsize = 23\n",
+    "y1 = 0.9\n",
+    "x1 = 0.07\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.46, y1, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.64, y1, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.46, y1 - 0.42, \"D\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.64, y1 - 0.42, \"E\", fontsize=fontsize, fontweight=weight);\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"LAN-NLE-likelihood-comparison.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0b25758",
+   "metadata": {},
+   "source": [
+    "## Figure 2: posterior space\n",
+    "\n",
+    "- posterior example\n",
+    "- posterior metrics, timings should be according to likelihood evaluation timings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "id": "fc34f5b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af076afa15cf4803ae4b3e32feca6149",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e72abe5b079421d89745409198a4620",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# df = pd.concat([\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-01/09-59-40/\"),  # LAN 1-10\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-01/18-35-25/\"),  # NLE 1-10\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-09-29/21-43-22/\"),  # LAN 11-20\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-06/07-58-18/\"),  # NLE 11-20\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-09-29/22-30-49/\"),  # LAN 21-30\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-09-30/09-16-33/\"),  # NLE 21-30\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-09-30/09-49-49/\"),  # NLE 31-40\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-09-30/11-48-19/\"),  # LaN 31-40\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/11-42-01/\"),  # NLE 41-50\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/12-35-48/\"),  # LaN 41-50\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/14-59-22/\"),  # NLE 51-60\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/15-25-27/\"),  # NLE 61-70\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/16-40-14/\"),  # NLE 71-80\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/17-05-47/\"),  # NLE 81-90\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/17-31-34/\"),  # NLE 91-100\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/17-57-20/\"),  # LAN 51-60\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/18-43-08/\"),  # LAN 61-70\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/19-29-10/\"),  # LAN 71-80\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/20-14-40/\"),  # LAN 81-90\n",
+    "#     compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-05/20-59-55/\"),  # LAN 91-100\n",
+    "# ]\n",
+    "# );\n",
+    "\n",
+    "df = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-25/11-01-18/\"), # NLE 100, 1000\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-24/04-03-04/\"), # LAN 100, 1000\n",
+    "])\n",
+    "\n",
+    "df.num_observation = df.num_observation.astype(int)\n",
+    "df = df[df.num_observation <=300]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aa89f68",
+   "metadata": {},
+   "source": [
+    "### Calculate mean and variance erros post hoc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "id": "a8aa7ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get posterior sample mean and variance\n",
+    "N = 100\n",
+    "nle_samples = np.zeros((N, 10000, 4))\n",
+    "lan_samples = np.zeros((N, 10000, 4))\n",
+    "true_samples = np.zeros((N, 10000, 4))\n",
+    "true_params = np.zeros((N, 4))\n",
+    "\n",
+    "for ii in range(N):\n",
+    "    obsi = ii + 1 + 200\n",
+    "    dd = df[df.num_observation==obsi]\n",
+    "\n",
+    "    nle_samples[ii,] = get_ndarray_from_csv(dd[dd.algorithm==\"NLE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "    lan_samples[ii,] = get_ndarray_from_csv(dd[dd.algorithm==\"LAN-KDE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "    true_samples[ii,] = task.get_reference_posterior_samples(obsi).numpy()\n",
+    "    # get tho\n",
+    "    true_params[ii,] = task.get_true_parameters(obsi).numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "id": "f919de91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meanerr = np.zeros((N, 2))\n",
+    "varerr = np.zeros_like(meanerr)\n",
+    "\n",
+    "meanerr[:, 1] = np.mean(abs(nle_samples.mean(-2) -true_samples.mean(-2)) / true_samples.std(-2), -1)\n",
+    "meanerr[:, 0] = np.mean(abs(lan_samples.mean(-2) -true_samples.mean(-2)) / true_samples.std(-2), -1)\n",
+    "\n",
+    "varerr[:, 1] = np.mean(abs(nle_samples.var(-2)) / true_samples.var(-2), -1)\n",
+    "varerr[:, 0] = np.mean(abs(lan_samples.var(-2)) / true_samples.var(-2), -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "id": "d3fe3b2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.sort_values(by=[\"num_observation\", \"algorithm\"])\n",
+    "df.loc[:, \"MEANERR\"] = meanerr.reshape(-1)\n",
+    "df.loc[:, \"VARERR\"] = varerr.reshape(-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef9c1fc3",
+   "metadata": {},
+   "source": [
+    "#### Load posterior samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "id": "795b3288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-0.1677,  1.2243,  0.4250,  1.1840]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "obs = 211\n",
+    "labels = [\"LAN-KDE\", \"NLE\"]\n",
+    "cols = [\"MEANERR\", \"VARERR\", \"C2ST\", \"RT\"]\n",
+    "paths = []\n",
+    "\n",
+    "# df = df100\n",
+    "for alg in labels:\n",
+    "    idx = df[df.num_observation==obs].algorithm == alg\n",
+    "    p = df[df.num_observation==obs].loc[idx, \"path\"].values\n",
+    "    [paths.append(pi) for pi in p]\n",
+    "\n",
+    "ss = [\n",
+    "    sbibm.get_task(\"ddm\").get_reference_posterior_samples(obs)\n",
+    "     ] + [get_ndarray_from_csv(path+\"/posterior_samples.csv.bz2\") for path in paths]\n",
+    "\n",
+    "print(sbibm.get_task(\"ddm\").get_true_parameters(obs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "id": "62ce203a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:79: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x576 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(18, 8))\n",
+    "outer_grid = fig.add_gridspec(1, 2, wspace=0.2, hspace=0, width_ratios=[.6, .4])\n",
+    "# Plotting settings\n",
+    "notch = True\n",
+    "\n",
+    "# posterior samples\n",
+    "num_plots = 4\n",
+    "inner_grid = outer_grid[0, 0].subgridspec(num_plots, num_plots, wspace=0.09, hspace=0)\n",
+    "ax1 = inner_grid.subplots()  # Create all subplots for the inner grid.\n",
+    "pairplot(ss, \n",
+    "         points=sbibm.get_task(\"ddm\").get_true_parameters(obs), \n",
+    "         limits=[[-2, 2], [0.5, 2.0], [.3, .7], [.2, 1.8]], \n",
+    "         ticks = [[-2, 2], [0.5, 2.0], [.3, .7], [.2, 1.8]], \n",
+    "         samples_colors=colors[:3], \n",
+    "         diag=\"kde\",\n",
+    "         upper=\"contour\",\n",
+    "         kde_offdiag=dict(bw_method=\"scott\", bins=50),\n",
+    "         contour_upper=dict(levels=[0.1], percentile=False),\n",
+    "         points_offdiag=dict(marker=\"+\", markersize=10), \n",
+    "         points_colors=[\"k\"], \n",
+    "         fig=fig, \n",
+    "         axes=ax1,\n",
+    "         labels=[r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"],\n",
+    "        );\n",
+    "plt.sca(ax1[0, 0])\n",
+    "plt.legend([\"Analytical\", \"LAN\", \"MNLE\", r\"Ground truth $\\theta$\"], \n",
+    "           bbox_to_anchor=(-.1, -2.2), \n",
+    "           loc=2)\n",
+    "\n",
+    "\n",
+    "# posterior metrics\n",
+    "inner_grid = outer_grid[0, 1].subgridspec(2, 2, wspace=.7, hspace=.3, )\n",
+    "ax2 = inner_grid.subplots()  # Create all subplots for the inner grid.\n",
+    "\n",
+    "bdict = df.boxplot(ax=ax2, column=cols, by=[\"algorithm\"], rot=0, \n",
+    "                grid=grid, \n",
+    "                patch_artist=True,\n",
+    "                widths=box_widths,\n",
+    "                return_type=\"both\", medianprops={\"color\": \"k\"}, \n",
+    "                notch=notch, \n",
+    "                showfliers=showfliers,);\n",
+    "\n",
+    "ticks = [\n",
+    "    np.linspace(0, 1.5, 3), \n",
+    "    np.linspace(0.4, 1.6, 3), \n",
+    "    np.linspace(0.5, 0.9, 3),\n",
+    "    np.linspace(20, 80, 3),\n",
+    "]\n",
+    "\n",
+    "for a, t in zip(ax2.reshape(-1), ticks):\n",
+    "    a.set_yticks(t)\n",
+    "    a.set_ylim(t[0], t[-1])\n",
+    "\n",
+    "\n",
+    "for row_key, (axi,row) in bdict.iteritems():\n",
+    "    for i,box in enumerate(row['boxes']):\n",
+    "        box.set_color(colors[i+1])\n",
+    "        \n",
+    "col_labels = [\"mean error\", \"dispersion\", \"C2ST\", \"inference time [min]\"]\n",
+    "\n",
+    "for i, a in enumerate(ax2.reshape(-1)):\n",
+    "    a.set_ylabel(col_labels[i])\n",
+    "    a.set_title(\"\")\n",
+    "    a.set_ylabel(col_labels[i])\n",
+    "    a.set_xlabel(\"\")\n",
+    "    a.set_xticklabels([\"LAN\", \"MNLE\"], fontsize=mpl.rcParams[\"font.size\"])\n",
+    "plt.suptitle(\"\")\n",
+    "\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.9\n",
+    "x1 = 0.09\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.46, y1, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.64, y1, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.46, y1 - 0.42, \"D\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1 + 0.64, y1 - 0.42, \"E\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"LAN-NLE-posterior-comparison.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea820d95",
+   "metadata": {},
+   "source": [
+    "## Simulation-based calibration figure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "824f10a0",
+   "metadata": {},
+   "source": [
+    "## Compare posterior means: True vs LAN vs NLE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc22957",
+   "metadata": {},
+   "source": [
+    "## Post-hoc SBC based on posterior samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "id": "ee9eddcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L = 1000\n",
+    "B = 5\n",
+    "\n",
+    "ranks = np.zeros((N, 4, 3)) # 4 params, 3 methods\n",
+    "\n",
+    "for obsi in range(N):\n",
+    "    # rank tho under samples\n",
+    "    ranks[obsi, :, 0] = (true_samples[obsi, :L, ] < true_params[obsi,]).sum(0)\n",
+    "    ranks[obsi, :, 1] = (lan_samples[obsi, :L, ] < true_params[obsi,]).sum(0)\n",
+    "    ranks[obsi, :, 2] = (nle_samples[obsi, :L, ] < true_params[obsi,]).sum(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "id": "3cf4ed41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:81: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BuBwJ0Y5WCBElVXOQLcqCUWbw7cOTiVgifDBxHK7yPLAemDJaIUQdVDUdZ/vy+apqE78XygqYkZTG05FOfPvVAlKPakTWNe2INQyCNgc53j/JDJbKuYUQDV3VuAE8/N9CvpiyhRcuSGVQehhPURHuoB+XETrglWqSPBFC7JrfS+70+xnbtBV/fJ3H2p820OHStrTs2ZQImpSIwZ3tLiM940Q5uRGigfMEvGjAGoGvH/kvvk0++j15JpaZg8nTGnckgqv3KGh/VrRDFUIcSDUudADoMxpfqxPwFK/BPfUOXCgIFDEzKY2RMYcz/YVfiUuLI+uOoyhx2kmsKMOhwW+x4Ql45fxCiIasspm0z2LnrXllPDFlC9k9kxj26BjUT4/g8gdBKegz+oDfoJHkiRBil3z588hJcrNyzmYWT1xG8xOa0e6S9oQAC/B/xVvo0OI4kBMbIRo8tzMFAiX88PIvrJ2/gTNu7khyK4MXSUMphdIG2dPvJyu9h1SgCFFfVV7ooKxgc0I4QO70+8nJ6IqOVKBSm5Nd7CXTaueN5PbMfnguWmuOuT8LmyuGEOCzxRAb3wxlscp0YCEautJ8cmNieXKzna/eWUSzI1K4/qqWqLTOkD2r9gq3A0QmHwshdm75ZDyTh7JuTSk/v7yQ5LZJdB3SBaUUALd4CznhlCfkIkgIAYBr+RQS3vofi75bz9HntaTdKU1R2sCGJlZrLChykhvh866MdqhCiAOlNN98tjkB8NljyUlKwVK8htjSjVjCQXISk1hji+XHMYvx5ZfT/a5uJDSPN49TCo8jjrBdpgMLIcDnTOSlcDyTn52Pyx1D39u78G6TVHzORPMaJLXjQbsWkeSJ2G0jRoyovmjemenTp6OUYvr06QcsjgkTJvDWW2/Vul0pxerVq6u3jRgxgmnTph2wWOq1yjtHxcURvn92AfY4G8fe2w1rjBWARtY42p83QcrvhRAmv5epY4Yw6stSzjnCxufH+xiWt5AYI4zDMABwaAMNeOzO6MYqhDhwqnoOhAMAeIwg2ojgQIHFisNiRRsR7v/OYO08D12yj6Bxl0ZopUApmjib0Ci2McO63U5WWvcofhAhRF2wrszHN88vIlwR4by7upCUaEfHp+HBOOixSPJE7Lbrr7+e2bNnRzuMnSZPzjnnHGbPnk2zZlsbBT366KOSPNlbpflUlJVy3bgVBEuCHHNfFjFuJwqwaANLsMRcDkwIIYAlc2dyybuFdE5VfHiRldbhCtIrylDKSlAp0BGCKFR8Gu6kVtEOVwhxoMSmmL0HdASCPtzBMpTFSlCZlx1BZeHvHzfw9fdb6HhWS9r1bWP2KwCaOJsQa4/FYbGTnnDglhsVQtRxfi9sWoxRtoV7br4Hz+oiej/Qi8ZHdiWY0hbliI/KlD7peSJ2W3p6OunpdfcfsiZNmtCkSZNoh3Hoq+yOr8MV3PBhPrlrDHoPO4LmLR0UV941VigGejce8OXAhBCHBo/HQ79rbyPGpvh6UDNcMVsgEsKlFNmt+5FTsgh/JIyy2sg+8kYpwxeivmt/FqT3gNJ8XPZYsj+9iJzkRvhR5C/azMyc5aQf1Yhe17RDB/2UJjTHW1EECioiFVzb6ToZJ4RoqLZbWefrKVsY/uhwAj3K8AMKojalTypPxG7bftpOYWEhV1xxBYmJiSQnJ3PNNddQVFRU67Gff/45xx13HHFxcSQnJzNgwADy8vK22ad169ZcddVVfPjhhxxxxBHEx8fTvXt3ZsyYUb3Pqaeeyk8//cTMmTPN5oNKceqppwI7TtupivWJJ56o3nfEiBGMGjWKmJgYCgsLt/n7WmvatGnDZZddto//Sx3Clk+GnOPh44t4bvAZvDvf4K4zYri2fQi7NmgS9NM4VMHtaxdyQqDigC8HJoSo+0KhkDmmr13HF+OfpVHTJPLcbfAlZ8CFE8nqMZxnTxnNfcc/xrOnjJYyfCEaiqpeBM5kso4ezLMb13LlouX88vyftGrk4LThRxK02bCiibPYSIpJAhRWZeU/f79PbsGcaH8CIcTBVmNlnZf/DFavrDPqzgd59uTnue/YB3j25Oejdi4hlSdir1144YX8+eefPPnkk7Rv356PPvqIW2+9dYf9xo4dy0033cR1113Hww8/TGlpKSNGjOCUU05hwYIFJCRszRr+8ssvLF26lJEjR+J0OnnooYc499xzWb16NcnJybz22mtcddVVRCIRxo0bB0BiYu2lD7Nnz6Znz54MHDiQwYMHA2b1TFxcHA8++CBvv/02d999d/X+U6dOZdWqVbVOCaqvfMFSPAEvbmcKrki4ujv+14sC3Ps/Lz2zXCy/6QSWK4UC+mxew5WbVuOKawT9XpZGsUI0cFprhg4dyrRp03j33XeJOfUI7l6wGF1VZdK4NVmAy5Egd5GFaIhq3EE2/CEGv+MnjJVjH+iO1eVgo1IkGuBUYFEWYm1OHFYHwUiQnIXjyUzJlLFDiAbEl/cLvzhsvLE5lkk1V9bxbcSV2jHq44EkTw6yYcOGMX/+/KjG0LVrV1588cV9eo/vvvuOGTNm8MEHH1RXavTp04e+ffuybt266v18Ph/33HMP11133TZJiWOPPZbMzExycnIYNmxY9faSkhLmz59PSop5Ud60aVOOOeYYvvnmG6644go6duxIYmIi4XCY4447bpcxVr3eokWLHfa99NJLeeONN7jrrruqK1TGjRtHhw4dqitZ6rvcgjnkLBxfOQ0Hslv2IQtYsNHgirdWcWRGHI3v6IlFKSyAAUxJbUP/057Hld5TEidCCF599VXGjh3LPffcwwWXns/dP9+JxWLHYY+Xix8hGqrK6b/YY6tvykQsMVz+7jqW5vk4e+RpuJtacURCxGIhFN+YG466hfEL38BhdQDgsDrwhwN4Al4ZP4RoIHK/u4PxW+awGjfTn/uVuBSHubJOkoPOzkRc0Q4QmbYj9tLs2bOxWq1cdNFF22zffsrL7NmzKSkp4corryQcDlc/WrZsSYcOHfj555+32b9nz57ViROALl26AOwwxWdf3XzzzaxcuZIffvgBgPz8fCZNmsQNN9ywX/9OXeULlpKzcDwWZcFhsRMxIoxf81/+KTXo9+oKkpxWHh/SDmuMFYstBmwxWGxOtLKyxtVIEidCNEC+YCl5JXn4gqUAfPXFRG677TbOPrsXTz75JJ6AFw3bXPxowBPwRi9oIcTBVWP6L++cARU+sDm5+/N1fLPIx2OXNKdFtwwcjTMhpR3OxpnYYhKJs8WhgGAkCJXPCqLSEFIIcfD5Ni0iZ0suwYDBr0/PI1IRocd9WbgSbVFbWac2UnlykO1rxUddkZ+fT0pKCna7fZvtaWlp2/y+adMmAM4888xa36dmogTA7XZv83tMTAwAgUBgn+Ld3rHHHktWVhZjx47lzDPP5M0338Rms3Httdfu179TV1Vd5IQiIfID+QCEg2HO/aCCQl+YX27LoEWcwVsWG4YGi8WCYRgooFViRlRjF0IcfNtXqrXb5OC+QWNJTo+j2aUh5s0fT2bnK6ovfqrK7uXiR4h6oqqaJKHZzm+gVPYqQFnB5gQUlK4nJzfMC99vYsjJboaemMzdVhsBI1R5bhFCAekJ6WR3GUTOwvH4w4GoNoQUQhx8nvw5GIbmp9f+pnhNKcff243EDBd+RxK2KK2sUxtJnoi90qxZM7xeL6FQaJsESkFBwTb7NWrUCDCbuXbq1GmH96nZ7+Rgu/nmmxk8eDDr16/nzTffZMCAATskb+ojX7CU8lAZESPM5sBmFAo0zHvtT9YuXMcbOS/S5PQjcbnbc+mGX/ho6YeEjQgKuDTzMprGS5NYIRqSbSrVrA6KNnt4+I73sdgUF9x9JE6njZyVn/Jsu3Pk4keI+qhG3xLAXIa4/Vlbf69KrFQUgxExM6yhMrDG8PPGJG76eCO9O8Qx+sIm2HqP4pSIl4+WflidjL008zJcjgSy0rqTmZK5tRebjB1CNBjuZt2Z/dgI/vljC52uzaRR9zQiWmOxxdapcwlJnoi90rNnTyKRCJ999tk2U3U+/PDDbfY7/vjjSUhIYMWKFfutqiMmJobS0tLd2tfhcOD3+2t97fLLL+fOO+/kiiuuIC8vjxtvvHG/xFeX5RbMIWfBWHQ4SCDsxzDCWC12ln2xnLU/rqPrVUfxe/qfzF2xuPrC57Uzx7KmJI9WiRmSOBGiAao5HScSjjD1iR/wb/bT/8EjSXRbIBLAb3Xg8Swnq/VpcvEjRH3i98K3Q0FriHFBJGQmUtJ7mK8v+hhmjTJ/DhRB+WaoTIv8U2ThwgnmSoYffT0RW3omPquNn36+k6ZxzVAWhTY0P62bzjltzq1uLC3jhhANz8eTf2XB56tpdXoLDu/XCq0NEixORpz0VJ26/pDkidgrvXr14sQTT2Tw4MFs3ry5erWdv/76a5v9EhMTee6557jlllsoLCykb9++JCUlsX79en766SdOPfVUrrjiij362x07duS1117jo48+om3btiQkJJCZmbnTff/3v/9x1llnkZKSQvPmzWnevDkAsbGxDBw4kNGjR9OlSxeOP/74vfsf4xDhC5aSk/siFt8GHOEgSlnwxcRTMmsVf727mHantKX1RRnYLXacNmd1s8dnT36eHs16RDt8IUSUuJ0pKKAiXMGMMbNYPz+fbkO60DQzCbQmiEIZEdxxqYCsrCNEvbLoYyhaAxYLlAKJLUFZtiZNitaAjpjbjFD1YSUVmnP+EyGMhQ8/mkBye7Nxv6ckDw3E2s1p2ViRxrBCNHC//fYbN99wC826NOXcO/qgqcBmjyOoFMFI6N/f4CCShrFir33++eecffbZ3HfffVx66aWEw2FeeeWVHfYbPHgwX3/9NUuXLuXqq6/m7LPPZsSIEYTDYbp27brHf/eee+7hjDPO4Prrr+eYY46pXoa4Nq+88grx8fH069ePY445hjfeeGOb1wcMGFAdY33nKV6D9m3EETYHIac2iKzcwk9jFtGkTSIn3n4cSTHJOG1OQJo9CiFMLkcC2V0GseDLhSz4eiFHD+jM3V1tgMKvLBhKke3ZiAsV7VCFEPuT32smSKq+2hooWg2RCpg1Cp9S5Dld+CzbJk4iBpz9pZ1lHsXJdxzF2II3+XbVt/iCpdXJWGkMK0QD5/fCpsWsW/4X559/Ps2aNaPPI70hxkFMXCMiFmudHBuU1jraMRySunfvrufMmRPtMMQ+euCBB3jppZfYsGEDiYmJ0Q7ngPJtmMPdM+7GEgnj0AaekjCfPTSf+GCQSXek0/KysTyW91V1X4NgJIihDZ49+fl9uRskV1PIeCEOTb5gafX0m1nTZ9O3b19OOPMEPnx/HM0/uhAfFjw2O+5wCBcGZM/al5W4ZKyoJOOFqDM2LTZXzYmEoXjN1gSJ001uUio5TVqiw0EUmuz1i8kq3YzPauPWqVYmzKjglP9rR+ZZrSh0xAOQGpfKoCPNm1U1G1BndxlEVlr3PYlMxotKMl6IQ9LyyWyccjvzDQvDnl/BBo9i9m9zCDT27+vYUJv9Ol7ItB3RIM2bN4+lS5fy0ksvccMNN9T7xAmAK7YR2esWM755Jh7tYMpLi/CXhDjrka5YYtbSdNIgsk97mBzPXGn2KEQDV3N1naK8Ir4e9l+SWyXRfnBLHp8/iuzjh5E5++XKcn2g92hZwlyI+iahmdkAtqJ0m8oSn8NFTkoaFkPjUBaCRpicFh3x5y/loSWJTJ+xnDZnZ3B47xZstjuxKAsajda6ejrwsyc/L72RhGiI/F4++vl+/tM0gz9GzSd/fQWn3H0UAd9UsjoNr/N90yR5IhqkCy64gIKCAvr06cOjjz4a7XAOqOq7x/4t4EzBpyz88uYyPEuKOPaOriS2TSJHJ5K58g+yZowic+A0PBh1dtASQhxYNVfXMcoMvn14CmEV5uw7upIUyifotfBKoISY9sehjAjKaiM7sTFZ0Q5cCLF/rfvNXDWnvGolRQUWG56wD62sOHQElMKhrJQpxdPFTfk5ZwEtuyTTeWAHCq0Ki7KiMavcnTYnQSOEJ+AlIzFDzjGEaIA2rpvNRymp/P3BCvJ/LaDjwA4kHtuccWv+xwtHDMCVmF6nxwZJnogGafXq1dEO4YCqSpjkla7hP3+/jwa0ESKQ2pL5X+ex7sf1ZF7ajrQTmlGkDRqHKvDYnbiMEK5ACa7UjtH+CEKIKKlaXceqrXw94r+Ubirl+BHHkOC2QSSEFSgOldLYHovLmVzdXDozJbNOn/AIIfaA32uuqmOLBavDXGUHwGLDHQqglCKY3BqHshEEijds5vsXZpKQ5uSou7qhrIqIxYrWBhZloYmzCREdqZM9DIQQB88afyFrf8ln+acryTgjnbb9WgMQUgqPZzmuxPToBvgvJHkiRD1TtRyxEQmxqcJLSoybJGcSZcEIS+Z6WfT+Upqf0Iz2l7RDAQYKQyncoQA4G5llukKIBsvtTAGt+eHlH1k7bx3dhh5FyhFuNmqDJsFA5V6a2OK1gAWHM1FWyxCivinNN58dLqoqTjDCYIRxKUV2q3PIKV2CHwiVBfn9yXmA4tgHj8OeEAdKoTS4HC5ibbFYLFYMbch0YCEaOE9BHHNf/Qt3xxSOvKETKLMliR1wu9tHN7jdIMkTIeqRrcsRF5hjkdVGUYUHl8OFL6+M3NHzSG6bRLdbu6IsCq01Chi4YRmuWDf0fVn6FgjRwLkcCST8kcyi/y6m/QXtaHVaSxKD5ZTY7BQ6nDQO+kkOB4kohbVkLUFrW5SSu8lC1CtVN1KMsLk8cUmemUBJaA69nyer08VkBkspLNvMjVfcSN7KPG597RZWpq4gjAEaLMqCzWLj9qw7ibPHy3RgIRq4tWvX8n/XDKVx4yS63XU0ym4u/JsQCTO47cV1vuoEJHkiRL3iKV6DLivAgSaCBQXoSIjSzcVMevC/OOId9HrkDMLOMFprEhwubmhxJicc8zA07iCJEyEE3333HS88/AKn9TmNwwY2IS6wBcMIk1ARJGCxcXven5TZnOS0PBI/GhWpIPvoIXJRJER9EpsCfUabU3csVkhqBcffAZ0uqT5XcDkSGHH/o0ydPJUXx4xmUeuFUL71LQxtUFxRTLIzmabxUtUqRENWVlZG//79KSsrY/bs2TRqamNp3s8k2GM5POPUQyJxApI8EaJecYfMVXKCyoIDSDYMNkfgm0cnU+718/bXb3HBaRewrnQdAOkJdbspkxDi4FqyZAkDBgygY8eOTMx5jQdmD2W9zY7CjkaTHA6SHgriimlCZuFGPBZw93n7kDnpEULsgfZnQXoPKM3H50w0m8lbbbgqX87JyWHUqFEMGTKEC665kDkzft/hLeLt8QQjoR22CyEaDsMwuPbaa5k/fz6TJk2iU6dOADRtlBnlyPacJE+EqEdcKW3JLtpCTnIj/MqCIxIm+PxsCv72887/teDqNqngSKBDoyOiHaoQoo7xeDz069cPh8PB119/jYqsJQRozNWIQaGx4EtohsfuxG1EyDhjFEjiRIj6ye+F0nxyA5vI+fMFdCRsrq515I2ULS3npptuonfv3owePZo1vtX4Qj4AFKp6hZ1YW6xM6ROigRsxYgSfffYZo0aN4pxzzol2OPtEkidC1CexKWSd+iSZU+/Ao+Cdr5czdk6IEec245puyWb5bXoPmZ4jhNhGKBRiwIAB5OXlMW3aNLbEbmbsvPH4LFYsaJJDQVyREMV2Jw8dfgI2LLJEsRD12fLJMGU4PmVhfJNmhAF7ZRp19H9HMvnembRp04aPPvqIP7fMZ+yfr1UnTKqeLcrChe0vkgpXIRqgqpU/f/jqe0aOHEl2djbDhw+Pdlj7TJInQhzCqgambZqwtT8LV3oPpr39PI9MfZJLu6fw8DnNzG7WQZ/ZQV+SJ0KIGm677TamTZvGhAkTOOqYI7n7u8HYywqw2OwAFNkd2I0IJVYbTa0OnPZ4WaJYiPqqapliZWVGfCIbbXaMyhUxjLIQM0b8AGErkyZNwhZnJWfOeOwWO1ZlxYLZADLF6cZusXFii5Oi+UmEEFGQWzCH8fNeIX/xev537zSOPu5oXnvtNVTlOHIos0Q7AHHomDBhAkopVqxY8a/7Tpw4EaUURx99dK2vr169GqUUSimmTZu2w+snnngip5566r6GXK/lFszh7p/v5Knfn+Dun+8kt2BO9WsLlq/lynteJqulk7euaGoOVuGAmUCRpYiFEJV8wVJGjhrJ66+/zt133821116L568PMXwbUEbYXMIcc0nzClsMSUYEJ1YAHFYHGvAEvFH8BEKIveb3wqbF5nPNbWt+ASOCzx7L5/Hx1YkTIpo/XvyTkg3lvPnao7Rv3x5PwIsGnDYnjZ2NUUqhAYtSDDpysCRWhWhgfMFSXpn9OGvWrmbKyJ+JSYkh86Y0glREO7T9QpIn4oB45513AJg/fz4LFy7c5b4PPPDAwQipXvEFS8lZOB6LshBrc2JRFnIWjscXLGXTpk3069ePxMREvpr4JnF2bVac6IjZOV+qToQQmAnYy0dfxiN3P0Kr4zK46LYLwe8l748xbLLHku+IxWN3khSuoHGoggc3rSNWa4KVxwcjQRSyRLEQh6TlkyHnePj4IvN5+eSt26beCcVr8FQUY6Cwao0CFr+3lE25hRyT3YFjzzwTML//CnM8iHfEkxaXRuPYRow84Qmy0rpH9SMKIQ6+dRt+Z0t5GbOfmkukIsLx93WjwmVurw8keSL2u/Xr1/PDDz/Qt29fYGsipTa9e/fm119/ZdKkSQcrvHqh6k6Pw+oAI4LDMNBGhPzijVx44YUUFhby1Vdf0fzEKyF7Flz6ufnc/qxohy6EqAN8wVKe/+Y5po78nkat3fS+/0wmLH6LjYV/8R93GimhCnOpc6DIFsPA/CW0jYTJbnsxhgJ/OIChDbK7DJI7y0IcampMy8Eebz5/O9R8KCvEJIKrGe6S9di0gQLW/bCWlV+vok3fDI4+vyvupFaAuVzxlR2uoiJSQVmoDIAbj7pZliYWoiHyezHWzCB3zEKKV5fSY/hRJGVUrs/l+Se6se0n0vNE7HfvvfcehmHw6KOPUlJSwsSJE3nmmWewWq077HvxxRezefNmHnzwQc4999x6MRfuYKi+01PuwerLx68USmtG3DCYmTNn8tFHH9G9e+Udn9gUqTYRQmzjnw2r+PahKVjtVs57oh+uBBf+cIA1OojWmqRIEFckSERZCClFS2xw9RSyUg4js7ZeS0KIuqNylRwSmtX+739pvvlsc259DpaCEQFHvLktrjEuq51BbS7goR+/Yt64RaQe1ZiTbj6RQVnDq7/7uQVzmLjkfSzKiqEjXN3xWqk4EaIhWj4Z39Q7eOV/m9jw62Y6X5tJWlYTDCA5HCQ9/fhoR7hfSOXJoaa2+al1zDvvvMMRRxzBMcccwzXXXMPGjRuZOnVqrfsqpXj88cdZsGABH3300UGO9NDlciSQffjllJcXsNZmZ7PNztxJefznyx8Z8cA9XHLJJdEOUQhRR4VCIYZeN5TSTaX0HdGbpKaJ1VNwWjXuiIprRFBZsaIAhdVix21zQcgPmONPRmKGJE6EqItqm46zvareZ2GzpxHlm82EStkmKFwMFSUQDuCzOvDTlt+eyaVVq1Z8/dkkXuk9tjo5UnMKcbw9jhhrDP/5+318wdKD9GGFEHWC30vu9Pu5YLmF17/dTPvTmtGtbzPcoQpSQwFuSemKK7VTtKPcLyR5cijZnX8Qo+z3339nyZIlXH311QBccsklOJ3OXU7d6du3LyeeeCKPPPII4XD4YIV6yMu0uXBoTZNImNCv65kzcTlte6Zx+00XRTs0IUQddtttt/HT9J94+PmHSOuUts0UnKbxzcg+8iYMmwO/PQbDFkN2SREutDSbFqKuq206zpThO95wi00xe6DpCFQUgy8fXM2gcioORWvItdsYltaZ8wZcQVmwjBfeG0WPtsdtkzTdZgox0kRaiIbK513Jk5tt/PjaYlockcwZgzoSq6zc0fx0nj/tVbJ6jYp2iPuNTNs5VNT8B9HmNO8WTBkO6T3q1JSMd955B4vFwlVXXQVAcnIy/fv356uvvqK4uJikpKRaj3vyySc5+eSTmTBhAtdff/3BDLnu20n5rcfuRGmD0lVFTHl5EamHuTj1pg54HbHI/WAhxPZ8wVJGj3mxemWdR4aOqHW586yMU8ksL8Iz/WHc4bCZOJFm00LUfbVOx/GZ27f//rY/yzyHXPMLTL0TnzMJj8WCO8YFFSWMb96Z7x//haK8Ivo9fQ7flU2hV7DXNsmTms1iHVaHNJEWooFaVFjE5Of+JD7Zwbl3HEWsFfxKEdfhAlxN6kfFSRWpPDlU1PYPotZbt9cBwWCQDz/8kJ49e5KQkEBRURFFRUVccMEFBAIBPv74450ee9JJJ3HWWWfx2GOPUVFRP5ay2i92UW3kjkmmqCTMl6MWY42z0e2+LAynHXdMcvTiFULUSbWurAO4ImEyAj5ckW2r/lwdzifj2p9wXfKpNJsW4lCx/XSccACU2nnVWGwKtDqJ3FgXd6ck81RyMnc3asyMhEb8+s6frPktj9NuO5W2x7RBA+tK15FXklc9LcflSCC7yyAMbUgTaSEaqLKyMm687i5CAYOz7+5KXIKVIAoVn1bdWLo+kcqTQ0XNfxCrKk929Q9iFEyaNAmPx8PMmTNJSdnxrsM777zDoEGDdnr8E088Qffu3Rk7duyBDPPQ8S/VRhVb8pj2/AIqioOcPLIHse5YtBEB30ZITI929EKIOmJnK+t0LMnH9f19W3fsM3rbJIk0mxbi0FI1HWfKcLPiRKl/rRrzWW3ktOiIxVeAQ4cJYuHpPxV/fvoXXfp3pvO5nfCFygiE/Tw35xlsFjtWZSG7yyCy0rqTldadzJRMaSItRANkGAbXXnstf87/k9HvvsDStL/wR8Ioq43sI2+sl+OBJE8OFXvxD+LB9s477xAfH89XX321w8o677zzDhMmTGDlypW0bdu21uO7devGRRddxFNPPUWzZs12OsWnwdhF+a12JnPj0DvZtLSIs27NpG1LO9ZQBUGLFY/diSt6UQsh6piaK+v0r1pZJ1iGZ/rDuOr4VFAhxB6qmo6zq9V2avAEvGhHPI7GmRAJsWlRIb+8No2uJ3TlqEGdWVuah6ENDAysyopFWUh2JJOzcDyZKZm4HAnVDyFEwzJixAg+++wznn/+eW67alitU4HrG0meHEr28B/EA2Xy5Mk0bdp0m22RSIRvv/2Wq666ijPOOGOHY5o2bcqECRN49913efTRR3f63iNHjqRz584UFBRwyimn7PfYDynbVRv5IkE8dgcOq5WXR9zJp/+dTfeLD6Pt8Wk4DIOgAcrVtF6WyAkh9k4oFOK2/7uN0k2lnP/suSQ2chAMBVBGGHckArZYc8dd9UYQQhxa9qBqrLpviY7g3+Rn0ohvSGiawAcf/ocX/34Bu83OlsAWM4GiDSxYKAoW0djaBE/AW28vkIQQtfB7oXAJKPjwx0WMHDmS6667jttvvx2gQSRSJXlyqKkDZdS33nrrDtvi4+MJh8P83//9X63HdOjQgeOPP553332XESNG7PS9O3TowNVXX82ECRP2U7SHsBrVRrkqQk6TNPyxbpa8cA2/PvU7bXumcUe/Fvxsi8GvQWGQ3e7iej9oCSF237Bhw5j+43QeeeQaNrXYgL/oHxSQnXE2Lv1bnZ4KKoT4F95VULAQ0rpAymF79RZVfUte/+U5vrjvf+hwhLcGNyfOOw+lLMTYbCilUFqh0SilKhMpEWkMK0RDsnwyvv/eyLpIGQvyQlw3toQTszrx+uuvo5SKdnQHjSRPxG4bOHAgAwcO3OvjZ86cWf1z69at0VrXut/bb7/N22+/vdd/p15J74HvnFfJWTIBLDbWLlnFnBdySW6TxGk3deRnp5WHvUUEjRDucBBXxmnRjlgIEWVVZbOfvv0Jr732GncNv5URjb/DV2jH44jFHfTjKngDThsJPz5UZ6eCCiF24afH4eeRoAEFnPwQnPLgHr1F1VjRztGEdSO+oWR9GV/ddjjnpFjw/fwE6vCeGJX7WrAQIYKhzS3XdrpObtYI0VD4veT+cBcvt2hDQbHBT+//ii05hkcuhxijHIiJdoQHjSRPhKirlk+GKcPx2OxodxMCxUFmPzEHW6yNnvd1J8adSqS8kGDYT0YkLBc+QghyC+aQs3A8ebnr+Ob+bzmp90k8ddcg+Ow7XFYHrkgErA6IBKFJR3MlnShPBRVC7CHvKjNxAmC1gRE2fz/yyt2uQKkaKzQwe+xM/lzk47XLMzgn00yIuII+sjPOJmftFBLsiZQEi3E73NitdgZ2vI4TWpx0gD6cEKKu8S2cyEvuJmw27Pz69K+E/GF6PtWDT1vEcpx3Ja7Y7tEO8aCR5IkQdVGNlXYcWlERDDF51AICxUFOHnkssSk2jLJNqPhU3P2fgpS2cuEjRAPnC5aSs3A8JetL+P7xH0jJSOHwW9rij08xm0jXNkWnDkwFFULsoYKFZsWJtfI03mKDSNjcnnLYvzZtrBorLMrC8qkr+POzRXTuk87VJ1Y26q8cI7IyTiPzsF54Al4cVjvBSKheN4IUQtTC7+X7P3Pwupsy/8X5FK8q4dj7snC1SiIUDjW4hSokeSJEXVS50k6uzcqbiUn8Mm45G5eWcMKwzqS0SyYhHAQU2RuW4DpdEidCCHPVjEBpBd88NBmLzcL5T52HPc6BBwNXHV+tTQixB9K6mFN1jLCZODHC5u9pXbapKFFQvaRwTZ6AFw1sWlTI9y9MIyOrJcffciKe9X/h2m6McIEkS4RoqPxefLNH8XViMss+XkH+7I10vLYDad1TzdedSQ1uoQpJnggRZbXeIUpohk9rcpJSmD8pjxU/beToizJo37MJt6+cQ5I2cCdm4Ar5ZXUMIQQACVYXU0d+T3F+CRe/cCGxTZwY2jCbOtaR1dqEEPtBymFmj5OfR5oVJwroeSc+/xZyFo3FYrHjsJora+XMe4XM4x/HlZhefbjbmUJpfgmTHv4fSc0S6fXgmVhj7biv+AYCJTJGCCFg0af4vruLr50OFi5zsuzjFbQ8vQVtzmsNgAXNdZ2ub3DJVUmeCBFFO71DFJuCp/v1LPvyXX79YBXNezalxeUd8Sp4OaMTN27OJyMSktUxhBDVHrz7IdbPW89pd55CcockKiIVDOxYo6mjTNERov445UGzx0nBQijfArOexbNiErpRGo6EdAgFcJSsxa8Unv+cjeuMZ80kKmAENLOf+B0jYtDnsd44XHayuwwyEyyJUf5cQojo++sTcr8byvhmh7MkL8jcV3/HfUQyXQZ3QimFRWuGpPfixDZnRTvSg06SJ0JESc05xw6rg2AkSM7C8WSmZOJyJLAyfDg/vbyY5DaJHDX0SJRFYQBWrclp1IzMwo24znpRLoaEELz66qvmyjp33cV5t/XjnUVvY1FWJi55H6c9doeyfSFEPZByGDiTIed40OCOaBQQLFmLQ2uCyoJSFtwG5rS99B5EHIlcccUVrFmxhi8mfc5RJ3SVPiZCiK38XnzT7mN880y83iC/PT0PZ3IM3e/uRozNgtaaa9texJlHXR/tSKPCEu0AhGioquYcO6wOIpEghCuIREJ4Al42bdrEZZcPwhpro/u9WdhirOZBWmO1xqAtNjx9tt5FEkI0XN999x233XYb5557Lg88ej//WfwOMRrirTFYlIWchePxBUujHaYQ4kAozYcKHxStwuVZTva6RRjawK8UhsVCdmkpLqsDtIbSfO69917+97//MWbMGPr1PY+MxAxJnAghtirNZ0ZsAhsMB1OfW2SurHPf0bgS7SRHwqQ5UzjziEujHWXUSOWJEFHidqaggOLSjRSFStCV21f+9Sn33/UJWwq3cPzjPYhtHGue9AAoRURrLErhbtotWqELIeqIZcuWcckll5DZIZNnxj5D/rIv0EVrcFSOGY7ElvhtDjwBr1wgCVEf2WOhfJP5s8VGlm8LmSt+xZPSCjd2M3FSuXrO219M4/nnn+eWW27hpptuim7cQog6yZc/l89cieSOWUDRqhKOu/do4lolYSgLDlcLBh11U4M+n5DkiRBR4nIkcEWbC3hh/ksAKKVICoW46YHnWTorn9PuO4Wk9gkYOlKdWLGgiSgY1PbibZq/CSEaHq/XS79+/cACR99zJGMWv4AuWkNQWbCicWiDYMk6VFJL3L5NYE+QaX5C1DchP8SnQvlmMCKgLLhiG+E6djj8OhoiQVCKX9zZDB54O2eeeSYvvvhirW/1b0scCyHqOb+XGbkvM2Oyhw2zC+h4TSZNu6eBsnBx5uX0b3tegx8bJHkiRBRlWGJIjYSxorAB8yatYen0fG4ecjmRU51s8uVjA3Tlwx0JM/KEJ2na/JjoBi6EiCpvmYfzLuzPqlWrOOfZviQ3T8ZhGAQ1VFgUYSCirCgjTPaqP3Atv9o8sM9ome4nRH1ijwWLHRJbgtUB2gA0tO0FqeaSxqt8sVx4ci8OO+wwPv74Y2y2HU//d2eJYyFE/ebzruT5hX6WfLKSVqe3IPO81migSUyKJE4qSc8TsdsmTJiAUgqlFMuWLdvh9Z9++qn69e+//x6AESNGoJSibdu2hEKhbfZfsWIFSikmTJiww99YsWLFTuOoes/aHl27dt0vn/VgcbvbYwGUNlg9ZxO/TFxOm+NSeWrk0wxqdS5JRgStFChFitbc7N1MU1t8tMMWQkTRzHW/cMLlJzBj+gx6Du1BUmYSDqsDrHYcaJyGwbDiYu4rzOfZ5b+SFY6APR6U1Wwa6fdG+yMIIfaH5ZNh4tkQCUHxGqgoBjR0uRImno1v0vUs/ux6zu3fn3A4zKRJk0hJ2bH6bJsG9hY7ESPC+AXjpFeSEA3Mj3+tYPqri0nLTKLrDebKOgq4oG1/SZxUksoTsccSEhJ47733GDly5Dbb33nnHRISEigt3fEf23/++YecnBxuvPHG/RbHjBkzsFqt22yLjz+0Eguugr/ILlzHs754vn35Lxq3TuDd5x8kMTmDrJgExnz/EOvsMWB1kF5RjssIydLEQjRgM9b/wpCRt/D3pCW0P78dbc9sQ1FFEXH2WJw2J0FXM1TZRtL9JbjCAYh1gz3OPNjmhKDPbDAp03eEOLT5vWYyVFnBlQYOF0Qq4KIP4LPLyXXGMz7BzeRn55G3cguvvPcchx9+eK1vVdXAPhQJkR/IB0BrzS/rZ9D3sL4H8UMJIaJl/fr13DBwOHHuWM658yhcKkwgbEHFN+Gk1n2iHV6dIZUnYo9deOGFvP/+++iqJqaA3+/n008/5aKLLqr1mN69e/P4448TCAT2Wxw9evTguOOO2+bRpUuX/fb+B1zliU9Giea3ZxeSEqOYeV0TTuh2lfl6bAqu3qPoEPTTobzITJz0GS0XPUI0UL5gKSMmPMKCN/8irXsqmVe1x1vhJTEmkVAkgD9QjGGPIbvHA7j658A5r4Mt1mwWCdVNIyUBK0Q9UGomObA5zWdHvDltpzgPn7KQk9yI3yYuY83czZx83eH85V6400oStzMFrQ0KA4UAmPea4asVn0v1iRANQHl5Of3798dX6mP8h28S06o9wcSWWFNaMaj77VJ1UoNUnog9dvXVV/Puu+8yY8YMTjrpJAC++OILDMPgoosu2mYaTpUnnniCY489lldffZU77rjjIEdcR5XmUxEyuDBnAwUlIX6+I5PDk/W2d4XbnwXpPcxtCc0kcSJEA/bLvBn8/NQMXOnxdLu9KxarhYiOYA2HGJm3iKBF4Y5EcDlaw8KJ5kEhP4T9YITNxIkkYIWoH6qSoOEA2Jz4IkE8dgdud1s8Vit//7iO+ZPWcFSfdLr1Tsdvse101S2XI4Hz213AGwvGgQKNpklcKkpZZKUuIeojv7f62qKkopQrrryGuXPn8vXXX3PuqedyTvAcaR69E5I8OcTUhU7orVq14uSTT+a9996rTp68++67XHDBBbhcrlqP6dq1KwMGDODpp5/mhhtuICFh32OPRCI7bLNYLFgsh0ZBVWlMAtmfFDJjhY8Psg/jmHQr6MiOd4VjU+RiR4gGzuv1ctMVN6FsiuMe6IE91o6uXIerX+EqmmoNKgaMMvh5JKS0NafrKKu52sb5b0PjDjKWCFFfxKbgO/MpPNMfJs9i8B93Gjo+DbXgZTrbTufncS+T3sXNqde2N6fzWay4nTv//p/Y4iS+XPEFBppYaywRHcbQxi6PEUIcenxLvsQz/WHckQhLLQY3Tgsw55vV9LyqPc2arwfMhKokTWp3aFxlCsDshH73z3fy1O9PcPfPd5JbMCdqsVxzzTV88sknBAIB8vPz+f7777nmmmt2eczIkSPxer2MHj16v8TgdDqx2+3bPIYOHbpf3vtAyy2YQ5+7B/DJbC9HX9yGlj1SzMSJ3BUWosHzBUvJK8mrLpcPhUIMGDCADWs3cOaDp+FKi8eqrFix4rYncma5b2vpvrKYS3Opyn/ebU7z55gkGVuEqEdyC+Zw97pvebLNMbyQmk6FqxmxcY3wbfRxx53jaN6yOb0f6k1FozYYjjiyuwza5cWQy5HAoCMHY1NWgkYQQxv/eowQ4tCSmzeduxe+yhPuVG5LbcHwpVbmfLqajqc04+hzM8hZ+Sm+knXRDrNOk8qTQ8Q2ndCtDoKRIDkLx5OZkhmVf9gGDBjAkCFDmDRpEmvWrKFp06acccYZ/Pzzzzs95vDDD+faa69l1KhRDBkyZJ9j+PXXX3doGJuamrrP73ug+IKleIrX4PB7eeidkcwe/xstjm9Oq8s7Msqq0F1u5sQ2soSoEA1ZbcuFvvXY2/zwww+8/fbbdOnbmfELxpnTdZSVQZlX4lp1U3XpPtowD9SG+YbS50SIeqfmOaGyOgBFUagEe9jJNw9PwYgYvPfZ+3TvnLVH1cpZad3JTMmMeoWzEGL/8wVLyfnzVSqMCEV2B1tWlDDz9b9Jy0zkjEEdsFks+NF4PMtxJaZHO9w6S5Inh4iqTugOqwMqn/3hQNTmoiYkJHD++efz3nvvsXr1aq688srdmi7zyCOPMHHiRJ555hkGDRq0TzFkZWVhsx0a/wnnFswhJ/dFdFkBm/J8fPfkHyS3SaT7kE7YCBPRNt5Z8TFd00+QkxUhGqjakuTDnxjGL6/N5M4772TgwIEAZPZoisezHLe7vXmC02e0uepG0GcmSk5+yOx5UvW7VLQJceio0YtgZ9/bmueEEcNAKYURNpjy3FQ8azyc+1RfunfO2qvSeynXF6J+8vz1EaGyTXgdsQQ2+/nt6bnEJMfQ7Z7uKLuFoNYoBW53+2iHWqcdGleeArczBQUEI8Hqk2pVuT1arrnmGs455xwMw+CDDz7YrWMyMjIYPHgwr7zyCv369TvAER5kNU94oPpnn9VGzoKxWHwFhEsq+O6Z+dhibRx3z9HYY6wYgDLCWDTSmE2IBmz7JHn+/I3MeHUWp/c5naefftrcaflkXFOGU91dqs/o2htLH3uLNJoW4lCzfLKZCK1S9f3ezvbnhMkxKfz89i+s/nUNJw45nkeufRSAvJI8qSARQoDfy9o5L7O5cQsqggaznp1P2B/m5IeOIybJQVkkjAPIbnuxVJ38C0meHCJcjgSyuwwiZ+F4/OFAdTl3NP9B7NWrF5dccgnJycl06tRpt4974IEHyMnJ4fHHHz+A0R1kNU94Knxm2bzDvLzxnPIQOhLGEoowadRCyouC9BnRDUejWCJoFJAcCmINlUljNiEasJoXRGUbyvjfiG9IyUjmvfffNacoVi5vjrKaU3TCAfP39B47NpaWRtNCHFr+7ftdw/bnhMumLmPFlys5ol8HOp3XkRXe5TtM/8tK6x6VjyWEiD6fdyUTU9JICgaYOmYJxf+UcOx9WWSkxxITl8awdpeSntpFEie7QZInh5C6NhfVarXudsVJTampqdx22208+eSTO91n8uTJNG3adJttSUlJ9OrVq/r33377bYeeJ1arlWOOOWaPY9onNU94LDYo/8fcHtcYIiHcPz8BbY/luzeXsGFJEb2HdqZVm3guWruAD5sejkVrrED2hqW4IuGDG7sQos6ouiB6bdYrfHH/1yirhbc/epvmjVuYO5Tmm89VzWFtTnNqTs3lzWtRF1ZpE0L8iz38fmeldaeFqwWfTP6EN15+k/SjW9Br6BmEdJiPln5I0/imxNqcUe+RJ4SIPo/diQaWfrqa/Fkb6XT14TTPaoLNkcigo4fQQZKru02SJ4eY+jIX9a677uL111/H6/XW+vqtt966w7ZOnTrx119/Vf9+4okn7rBPfHw8Pp9v/wW6O2qe8IT8mPd5gEgIbE5cQR/OSRtYMj2frAtb0bZnI7ILVpNVvIkevmI89hjcsY1xof71IkgIUb8d6T6KVa/kUV5Qzn+n/Jfex/be+mLVlMBwwEzUBn3mSjq7aAZbWwNauQMtRB1U8/tdVXmyi2bPuQVzGD11FJ/d+gVxqbGc8cBpWG1WwpEIGrBUrrgV7R55Qojocye1YmWun18/W03HU5py0jktCDsSGXnaizSNl4bye0KWKha7beDAgWitadeu3U73OfXUU9Fac+aZZwIwYsQItNY7NHZNTk7G4/Ggta5ugljzb9T2qEqcVL1nbY+DnjiBbU94rHbMdUK1+XM4wH8XlvDS61M5/+hEPu3dmGcL88nSVkjMwOVqSkZCS1wWu6yIIYRg+PDh/DjtR8aNG0fv03pv+2JsitkDwe+FwkVQnAdhP6z7rdb3qtmANtbmxKIs5CwcX70EshCiDqn6fuuImRjVkZ02e/YFS3n9t1f59uGp6Agc90APSuwlRIwI2jCnAxuVK27VhR55Qojo+vvPJfz80hyadk7jhNvPRLnbcOMxd0niZC9I5YkQ+6rqhGfKcAiHIS7VvMUb8vNXfpDLJ3rolu5k4nVtiQNwJJgnRj1vh19Hb727JCtiCNGgvfbaa7z66qvccccdDLjy4tqbPVaUQnkhoMyqE2XdaV+EurZKmxDiX9TW/LkWhWWb+f6paXjzvFzwbH8at29MYfkmysLlOCx2Ls28jJ/WTa8zPfKEENGzfv16+vfvT7OmzZg2eRrWBKtM490HkjwRYn/Y/oQHKFy1iH5nX0lCYgpf3RBPnCUI1CjF7TTAfMiKGEI0eN9//z1Dhw7l3HPP5ZLbB3D3z3cS0QaGjnBtp+s4scVJZsXJ9/eY44fFBtqAsgJITK91yl9dXKVNCPEvdqPZ80uPv8SaX/M4+dYTaZWVQTASJDUulWHdbic9IR2XI4Fz2pwrvY6EaODKy8vp378/paWlTJ06lcNaHBbtkA55kjwRYn+pccKzpXQz51x/OxsLNvHzzz/TInmLeXc46NuxykSSJkI0aMuWLWPAgAG0y2zH6PEv8MJfz1MRrqAoWITWmhfmPI/ScIKjkZk0QZmJE2UxeysZkVqn/NXFVdqEEPtmwoQJvPTCSwwYeDGNz3NXf7cHHTmYDo2OqN6vvvTIE0LsHcMwGDhwIHPnzuWrr76ic+fO0Q6pXpDkiRD72ZyNf3D1wKtYMnsZZ95/OpYMBWm7V4orhGhYvF4vvc7uRUAHyLqvG88seIpgJIQvZPYlsVqsRIwIExa/zVHHPYbLYoX4NLPiRIfNZGyvZ3c6ptS1VdqEEHtvxowZ3HDDDZxxxhlMfOM/VOiAfLeFELV67LHH+OSTT3juuefo169ftMOpNyR5Ig59fm9UkxJVy4A6rHaKAkXc+sgQlkxZRo9rjiXz9MO3LhG4G6W4QoiGIxQKcdHFF7FuzTr6P9ePtPQmlAf9FFcUopTCpixoI4JSCouy4MHAVdVfydrSbCh55jPQ6eJd/h25Ay3EoW/16tVccMEFtG7dmk8++QS73Y4du3y3hRA7XAt9/PHHPProowwcOJA77rgj2tHVK5I8EYe25ZPNC4kqfUab/UcOkqplQP3hAMUVRXjnFfHr+N857KTD6HltD5RFbdOgsSrRIneJhBBVK+ucescptO7airJQOZsDhRgYYJgriFnRJIcjWG0us1fJbjaUFELUH6WlpfTr149wOMykSZNISZHvvRCiUtW1kBHBp8NMj+vNtUMncOKJJzJ27FiUUtGOsF6R5Ik4dPm95mChrGCrbMS6k1UnDoSqZUANQ1NcUUTJ6hJmPDOT5MOS6DzkCAw0kUioukFjVaJFQ3Xvgay07gc8TiFE3fP666/z6quvMnT4UEJnBQiEKtjsLwStsRsGCaEKiu0xuEMV2LUme91fuCJh82CpYhOiwYhEIlx55ZX8/fffTJ48mczMzGiHJISoKyqvhXyRCn6wwQeWNCbd8zb2BAsPP3IeMTEx0Y6w3rFEOwAh9lppvvlsc2591nrr9gPME/BSHvKzKVBAWVEZs5/8DWusjT4jemGLsVEeLsPQBtldBgGQs3A8FmUh1ubEoizkLByPL1h6UGIVQtQdP/zwA7feeivnnHMOLzz3AtldBhHSQQytAWgcDuGOhEgN+rl+4wqeXTmHrPLigza2CSHqjvvuu49Jkybx0ksvceaZZ+7wui9YSl5JnpxPCNEQleaT64hhSLPDGNeoPZNHLabCH+GMuzvzhXcqvpJ10Y6w3pHKE3HoqlpdIhzYWnmiVK2rThwIDqudkmAxkVCEOc/MJVBUwfEjjyO5aTLANksG5pXkoQGH1VF5rGOb6TxCiPrPFyxlzl+5DBhwMR06dOA///kPVquVrLTujDzhCR6a+QD2oB9n2ENQKaxoOvk8uIwwWOwHbWwTQhwAe9Gf7Z133uG5557jpptu4pZbbtnhdaloFaJh86EZn9yYIouV+S8toPifEo65txuqTSMikTAez3JcienRDrNekcoTceiKTTF7nOiIuQSwjmy7BPCe8Hth02LzeTcFIyESHUn8OXYhnr+9dB1yJI0y3UR0pHrJwKrEiNuZggKCkWDlscHq6TxCiPovt2AOt/3vVs7vfz5+I8CTbz1BYmJi9etNLU5ubHY6+DfjtzkwlCJ7/WJcoXKIawx9X5apOkIcqpZPxvfWieR9fhm+t040exT8ixkzZjBo0CDOOOMMXnrppR1er5o6LBWtQjRQyyez7osrqVCKpZ+sJH/WRjpenUmzY9LQgIHC7W4f7SjrHak8EYe2/dE8cS+bzrqdKSz7bBFrfsjj6Ita061HY7C5GHnCEzSN3/YOscuRQHaXQdXNZavuEEnViRD1my9YyrrSdYyd+zpTH/+e0o2l9H+uH5NLvuH04OnmGFA5BmWFK8gs24gnKR231YnL3ggS4+DiDyGjZ7Q/ihBib/i95E6/n5zU5mhlQWmD7On3k1VLf7aqpvKlG0u48MILt1lZZ3uegFcqWoVoqCrHlfHuJiz6tZAlH6+k5anNadP/MMwJwIqBh50nVScHgCRPxKFvX5on7kPT2Z/++w2/vjWX5j3TyLjscLZYFJdu+IumFmet+2eldSczJVNW2xGigagqqQ8aIX585UfWzV1P77vPpHXXVtUXOQSK8fxwN26LHRdBXCE/rs3LwRoDrqbgiIMmHaL9UYQQe8nnXUlOciMsKBxaE0SRk9yITO9KXLFbp9jkFswhZ8FY/CVlfHXnZELBML9M+mWnK+vUrGh1WB1S0SpEA+LzriQnKYXNS7cwd8xC3B2S6XJTZ2wWKy5LLIM7Z3NCm4O3+mhDIskT0bDV1nQ26DO37+SOkNuZwupla7js6oE0au3i7EFtiQn5MYCfEtyck/cLrszzav1zLkeCJE2EaABqltSv/OYf/vlmNe36t6VDnw7VFzlrS9bw9F856EZpKKXIXreILIsdjDBoA3z5cOFEma4jxCHMY3eaFSLaAGXBoQ38SuGxO3FV7uMLlpKT+yL+0gImP/8nRXnFHP/QsWyO30Qmta+uIxWtQjRcnrUzKfQE+N8Li4lJdnDc3V1JVmEGdcime5u+Mg4cQJI8EQ3bbjadrdmULVDkZ+rtPxAXF0PvOzqQEKPMCx3Ar6x4StdXnxAJIRqmqpL6jfML+GnMz2T0aEnnazpSHi7DbrFzxRFXMfHv97FYrDiMCEGlyGnanszyElxhBSmHgRGBJh2j/VGEEPvAndQKFZ9G0FeAQ0cIYkHFp+FOalW9j6d4DZGyAmZ9uJKNcwrpev0RpB6ZwoRFORyV2nWnF0JS0SpEA+T3suCPN/nuxTxC/jAnPdUTR1IMfhQd4prJOHCASfJENGxVTWenDDcrTpTa2nS2sjO+z5lo3kHWYA1F+OrR79hUUMikz99kUunbBLXGoQ2CymKWzGacHO1PJYSIMrczheJ1Rfz30W9wZ6TQ5/5eWGIt1atweQJedLAMh28joHGEQ/gtFjw2O674pmCxHdTVw4QQB4bLkUB21jBzSk4kjLLayD7yxm0ucNyhAEumb2DZl6to06clbfu2QgMWbfxrDxOpaBWigfB78eXPY+2mBQz9TyHF/5Rw7L3dSGiVQARwR8IEpVL1gJPkiWgQak652eEko7amszWayHpsdnTz9tj9RXw37m/y/9rIGXedQKeTTif1t4XkbJmDX9lQaLIbdceV2ikKn1AIUZeEysLMfPxXlEXR57He2OJsZHcZRIdGR5g7BIpRZQUEUTgsdoLKhiKC29nI7MG0L6uHCSHqlKy07mSeMnqn5yF/Lilk9ht/06RLI7pkd0QDyZEwVqtDepgIIWD5ZGb+cAdvudPI/WQ1q2YX0OnqTNKzmqC0gQHYnSnbVLSJA0OSJ6LeqznlpmpOcFZa9213qtl0drsmsu5wAFW+hT++2cCiH/PpfuFhHH5MLG4sZPQaReamRXjy5+BuJokTIQSEw2EuHnAR+XkbePGDF+l0TEcyG3XYZhUuV6CE7KIt5KQ0wQ9mzxPPZlwXvAsxSXu/epgQok7aWYXI6tWrueCya2md3pyXshvxOQaWiMYan7ZDhYoQogHye/n+x3sYk5rB+tkb+fPTVaSf2oI2/VuDUhgAFhsDuw6R8eIgkOSJqNdqNm10WB0EQhWM/fO1bZcTrpyeU32xUqOJrE8pPDY7h/3wF6+/v4U2PVLpfmk7sr2FuAIlkAiu1E6SNBFCVLty8JVM++FHut5yJJOt3zB17rckxyRza6fryXKmmmNNQjOyKvxkbsrH44jFHfTjMkLQuIMkTYRoIEpLSznvvPMIBoNM+uUXMjNSOcm7Eo/diTuplVwICdHQ+b3M/PlhXm3cHO8/Jcx7eQEpHVI48qZOYLGQYInD7ozn2s7Xc0KLk6IdbYMgyRNRr1U1bXRYHZSFytnsL8TQmodmPsCNR91MVsnm6uk5gFkmn94DgFwr5CS72ZJXwhcTizi8ZSxfXd2S9M0bzYsc6UUghNjOS6+8yMdvfUyb/ofR8sx00BoDjSfgYczvT/BKwQZc2jDHmj6jcU0Zjssf3LbfkhCi3otEIlx11VUsXryYb7/9lsxMc1UdV2x3aTovhIDlk9k4+TbeTEunvDjCH0/lEpMUwzF3H43VbsWqNQMz+tC902WSaD2ILNEOQIgDye1MQQGBUAWb/YWgwaIUdoudnAVj8U29w5yeY483nysTKb4znyInKYWKIj+Tn5mP3Wmnx31ZpOtyM3EiFzlCiO388MMP3DHsTtK6p9Lx6g6g9TavF1tsrIupMdak94DsWXDp5+Zz+7OiFLkQ4mC7//77+frrr3nxxRfp1atXtMMRQtQlfi+53w3ngdRmFEYs/PbMfELlYY69rxsxyTEAJEXCdG8ryxIfbJI8EfWay5FAdpdBhHQQQ2tQ0NjZGKfNiY6E8VjNviaA+aw1lObjad6NcHw6U15aTllRiP5PnEd8m0w8574qFzlCiB0sW7aMAQMGcFj7w+h+exbKqmrf0QhvM9YQmwKpHSUZK0QD8s477/Dss89y0003MWTIkGiHI4SoY3z583ilcXM8thjmvfIXxf8U0234USS2TgTMhtK3tr0YV2J6lCNteCR5Iuq9rLTujDzhCRrHNiItLo14RzzBSBBlteGORCAcMHcMB6qXBk2JSebnl2ex/q+NnHnX6bgPd6MsVtzNu8tFjhBiG16vl379+mGxWnjhnVEkJyZgMYxt9rFqTXK4gvRIeJuxRgjRsMycOZMbbriB008/nZdeeina4Qgh6qB1FVsotjlY+slKNszayBFXZ9L0mDSSIiH+L6kTr/V+i6xug6MdZoMkyRPRIDSNb8aNR90MgD8cwNCG2cW+9yhzSdCgb5ulQR9++hGWTllG5iWHE3dMLGXBMrK7DJLSOCHENsLhMBddfBH/rPqHY+7O4u38HHyhcjQQGw6htCYpVEFasJwhaxfiChTLMsRC1Ed+L2xabD7vxOrVq7ngggvIyMjgk08+wW63H8QAhRCHjORWrJuZz98fraTVqc3pcF4rrFoz/OhhnH/6c1JxEkXSMFY0GFlp3clMycQT8OJ2ppiJkDTMvgNVq+0An777Ii8++iJtT2pD7+tPIxAux2K1k5mSGd0PIISIGl+wdNuxo9KVg6/kx2k/cvSQo7C3t6G1xqqsaMK4jBBPrvgVOwp32I9LKzj/JWjfVxInQtQnyyfv2Hx+u+m926ysM2kSbrf7IAcphDgU+ErW8devs5n7ykLcHZLpemNnUBZSHIkc3vLkaIfX4EnyRDQoLkfCDtUjPqsNj9OFe81P/P3+HVw7ajWNWyfQ99ajsHlX4AL8SuFZOglXlyuiE7gQImpyC+aQs3A8GlBAdpdBZKV158Uxo/n4rY/pclFnWvdqRdgIY2BgVVaw2lAhsAMZFT6wWCE+TRInQtQ3fq+ZOFGVPdTCga0NoSu/6zVX1vnmm2/o0KFDlIMWQtRFuXPH8dIf7/PZfX/gTHJw5v0nkJiYgtXqYNBRN0kFfB0gyRPRMPi9W6tLaly45BbMYfyCcYSNIKXr1/DN+Hy000b3+7Io116SsBJUFhQa989PQDu58BGiIfEFS8lZOB6LsuCwOghGguQsHE/+vHzuHH4XLY9N56QbTmBjYCMAGo2hDUBhi0nG7UwBpxusMdD3ZRk/hKhvSvPN55rN54O+rQ2h2bqyzpgxY+jdu3eUAhVC1EmV1yg+NGMXf8SUZxcQ8oe5aOQxJDkrGNb1VtIbHSGJkzpCkiei/ttJOa0vWMorc1+mOFSMDkb4edQCyr0VXDyiO65kO16rFVskglUpskt9uIzINidDQoj6zxPwogGH1QGVz/mrC7h82BUkpidy1LAu5PvzSYxJpDhQTIQIWodJDgYYtG4xLt8W6D4YTntMxg4h6qOqxs/hwNbKkxoNoatW1rnxxhu55ZZbohioEKLOWT4Z39Q78FitlEVC/DjRw6ZVJZx3V1eatUrAjyauokwSJ3WIJE9E/bZ9OW2oHP53M1z7A+uMAMXBYgDmj1uIZ7GXbsOPwt0+iVhtYAsG+b/SEjppK66QX1bHEKIBcjtTUEAwEsRhdVBaXMrkhyYTJsz5I88jrlEshYFCSipKaBLXhLObHk+HqfeSXlGOC8xxI/cN6Hm7JE+EqI9iU8ybMlOGmxUnSlU3hJ41a1b1yjovv/wySu1kCXMhRMPj95I7/X7GpzYnDMz75B9Wzt5Ezyva0bZ7E4JaoxS43e2jHamoQZInon6rWU5bUQIla8Ew4J0z4LSHAFjx9UpWf59H+wHtaHFScwiFCKKwxjWi05ZCs+KkxsmQEKLhcDkSyO4yiJyF4ymrKGfKyO/wFZRxzjN9adKyMQBOm5OycDm3dxlMh7zfobwYrJWraFhsEAlDwUJIOSyKn0QIccC0P2vb5vOxKaxZs0ZW1hFC7JTPu5JXkhtRbLWyftZG5n7yD4ed0owu52Xgx0ycZLe9WFbWqWMkeSLqt6pKkVC5mTjRhpkIscaQPuN5SjelsHDCIpofl8YRl7bFoiwYCc1QNoe5lHGvtrX2ShFC1G8by/JZU5JHq8SM6pW6bh06lLW563h13Kv81eZPgqEADiACOMJB0j+/DkIBMEKgAZsdjLDZZTatS3Q/kBDiwPF78XlX4rE7cVtt6NJS+vXrR0VFhaysI4So1bpgMcVWG0UrisgdsxB3ZjJdburC4COvp3FsY9zu9pI4qYMkeSLqt6py2v/dbFacKAWJLcERz5rVm5j94hIat0qg1y2dcBoGV3vzadn/adxJrbbOL5SkiRANykdLPuCjpR9Wr65zaeZleKcXM2HcBO644w5uvuFmcueOI2flp/gBpTXZhWtwGTZzvAg2gfJCM6uiFJz8kFSdCFFfLZ9M7vT7yUluhAa0swl/v7qexYsX8+2338rKOkKI2kUq8HsCzH5mHjGJDnre0xWr3UKcI56M1qdFOzqxE5I8EfWOL1iKJ+DF7UwxEyDtz4JrfzCn6lhj8DliWVFcygXj80lwKKYPPozYgA93uYGrogy0BaQxkxAN0sayfD5a+iEKhdViwTAMxnw8hl8f+52zzz6bZ555Bvxesma9SKbFjocIbu8qsy+SNQYSWkB8Ktjj4Pg74fBzJHEiRH3l9+Kbegc5qc2xoHBog+lvzmLelDU8P/o5evXqFe0IhRB1lDu+PXOenkuoPMypTxyHI8lJshEhvcmR0Q5N7IIkT0S9klswh5yF46vvGF9xxFVkJLTCHd8Y1zmvkfv9cN6IdzHpxX/YVGKQM6wlma4IRCI7dMgXQjQ8a0ry0IDVYgGgbGM5vz83h1btWvHBBx9gtVqreym5lAXX5mWVRyowIlC0yuxzohTEp0niRIj6rDQfj9WKVhYchsGin/KZ9/UaOp59OBdfNyDa0Qkh6iitNbfe9iBb/iml911H0rJlHNZImEHS46TOk+SJqDd8wVJyFo7Hoiw4rA6KA8W8MOd5UuNSsSgLV7a9gPcbteCXsYvJX1JMr6Gd+a17KhcVrMe1XYd8IUTD1CoxAwVEjAjhsjAzR85GKcV/Pp1IYmKiuVNVgjXoM5+VMhMmRhiz2QkQ3xSmPwxtz5QxRYj6KqEZ7kgEZYRZ8/cWfnjjb1p0SubEG46mUaz0ORFCbOUrWce6dbNBwXvv/crHH3/MM888w803XoHHs1x6nBwiJHki6g1PwIsGHFYHESNCUbAIAKvFhgImLJnIX//L4+8f8+lx4WF0PrEZfjSePs/iSm4nTWGFEKz3rSfWFktxoJjfn5tDWUEZD7/1ID06H7d1p6peSt8OBa0BbY4fpflmIsXd3lzhK+gzt8m4IkT9FJuC65QRnP3RLQx4uYj4xk7OvP1Ibti0DFckHO3ohBB1RO7ccbyy8lOKrTbWz9rI76Pmc845x3HXXXehlJKkySHEEu0AhNhf3M4UFBCMBInoCFprlFLYlA2H1cG6P/L57b3ltO2RSs9L2prrpwPupt0gtaNc4AjRwG0sy2fsn6+RFJNE3vvrKZy/mRNvPYE7L71rx53bnwWDfoOzx0BSK7A6KqfqNDUTJzINUIgGoTSuNbe/XYIRNuh1V2ecccpc4a9yep8QomHzlaxj/MpPKbJa8a4oInfMAtyZyTS7Io6y0vXRDk/sIUmeiHrD5Uggu8sgDG0QMsw7PskxKVgtFvL//ocfnp5O63Qnp9x4OAFlYMj66UKISrkFc3ho5gNs9m9h9me/svDLhXQbcDSdzu6IJ+AFvxc2LTafq8SmQPcbzCTKZV/BBe+DI86sONERmQYoRD3mC5ayumg1A266kxX5FfQZegQZzZzYtCbH3RRf1RQ+IUSD5StZx6JF/8EHlHmD/PrMPByJDo65uyvYLXg8y6MdothDMm1HHPJ8Jeuq5wpmpXUnMyUTT8DL2pI1TPz7HTwb8/nviB+wxVg54f5jscc7OK94Myed9z6u1E7RDl8IEWVV/ZLsysHmhZv5842FpGWlkvV/R5vVaRvmwvf3bT2gz2iz8qRKbIr5SO1o9jgpzZdpgELUY1XN6We/+Rvzp/7JsdcdTrsjk0FrHErjtzjwlG/ChZxjCNFQ5c4dR86KjwkaEbzE8MfTfxAqD3Pik8fhSIlFhYO43e2jHabYQ5I8EYe03LnjyFn5afXqOtltLybriEtwBXxkFBdwxOJZ9HllDX5vgPMfyaJJoxiCwKSkxpwU9kc5eiFEXVDVLylQ4Of3Z+YQ3yKeo28/isKKQi5rcQGu7+8DZd06HWfKcEjvUXtypCqRIoSoN3zBUjwBL26n+d3OWTieZd8tZ/5Hf9LxnA40P6cVgUgEJ5ogymx9JBdFQjRYvpJ15Kz4hApt4LHHMH/0QopWFnPMPd1IbJ2IRWsubNxdqt8PQZI8EYcsX8k6clZ+ikWDQymCWpOz4mMyf34aFxZ0yVru/iaGOSvKOWPIEWQc5jTvCqHxK4XH7sQV7Q8hhIg6tzOFoK+CL+//ChQc/0APYuNjSY1J5qd1P3KOsuCyOc2dpRGsEA1KVZVJ1U2a89r2J3/RRqaP/omWR6dz5rAzKAkUEvJ70KjqGzlyUSREw+X5ZSRBHcZjd7Lk4xVsmJnPEVdn0uKYJrhDQexKcWLWkGiHKfaCJE/EIcvjWY4GrEpRAdgwiBhhPBVFuCrKeHFWhJzffNx9dhqlJ7cgGA7i0BGCyoqKT8Od1CraH0EIUQc4LbEsfmkpJfmlHP9oT1xN42gcLCeuogy/Ao9RgSuspBFsHaSUaq+1lknj4oComtJnURYcVgfBSJD3Zr7LlBFTcaW6OHfE2URUhFhnMg/3fIxg6QZZblSIhs67iulrf2JTWhs2zMpnyUcrST+1OW3PPwwAi1IMkgTrIatBN4xVSsUopZ5RSm1QSvmVUr8ppXpFOy6xe9zu9lQoxVqrlY02K2ttdvwWK+U2B5+tsHDnVIMLj1A8dVYjsou9GDYH/sR0jJRWZGcNw+VIiPZHEPtIKeVSSj2qlJqslPIopbRSauBuHnuGUuotpdQypVS5UuofpdSbSqkdroqVUtMr33v7x+T9/qHEQXf77bfz6/Rfef7l5+iQ1Y60Cj/xWhNUFhQKdzgMkaA0gq2bliilPldK9dyfb7q35wdKqRE7GSsC+zM+cXBUTelzWB3mhiBMeeQ7LBErfR87Cx0HhjbI7jKIpo0yyWh9mlwQ1VFKKccBet+9Pg+pPD5ZKfWGUqpQKVWmlPpRKdXtQMQqDo6NK6fyZWprSlYUMe/lBaRkJnPkTZ1JDgdpbHcx8vRXyOo2ONphir10UCtPlFK/AsO01r8ezL+7CxOAi4EXgeXAQOAbpdRpWusZ0QtL7BZnEjomGUKloEGjKLI5eMjI4KtP59G+uY13LwSLDpIVMMg88UE8zbvhdqbskDipOZ9ZkiqHlMbAw0Ae8Cdw6h4c+wzgBj7B/P63AYYA5yqlumqtN263/zrgvu22bdiLmEUdMm7cOMaMGcPtt9/O8JtuJ3fp5+QseB0/FhSa7JISc8pO/7fBmSSNYOueD4ABQP/Kc4zntdZf7If3ncC+nR/cBPhq/B7ZDzGJg8ztTEEBwUgQu7LzzeOT8az28sXXn3NGrzO26YOSV5In5xB1W7FSarjWeux+ft+9Pg9RSlmA/wFHAc8Bm4GbgelKqSypqjsELZ/M0rmvU25J4ben5xKT6OCYe7phtVuxOVK4sfudNG2UGe0oxT442NN2jgZmKqU+Au7VWucd5L9fTSl1LHAZcJfW+vnKbe8CfwHPAsdHKzaxG/xePAXzcToTSYxrRDBYRoG/kGBJiB+f/wt7jJVj7zsa3eM6yDgJEprhik2ptcfJ9vOZs7sMIiut+0H+QPWfUuou4DrMhMU6zJOMOUAuMF9rHdyLt80HmmmtNyqlugN/7MGxtwMztNZGjRgnAz9hJlEe3G7/Yq31+3sRo6ijpk2bxi233ELfvn159tlnAcjKOI3Mb27HEyrBHQriMkIQlwpNOkjSpA7SWl9VObYMAQYBnyqlVgIvABO01ntc8bGfzg8+1Vpv3tO/LeoWlyOB7C6DyFk4nh/H/8SqWau57bHb6H/O+dWvyznEISMGSD0A77sv5yEXY44nA7TWnwIopT4GlgGPAlfs72DFAeT3kjv9ft6Jb8RvI3IJlYc59YkexCfZ0crCgyc8TtvkdtGOUuyjAzJtRynVSin1Yy0vdQA+xzwpWaKUekIpFa2enRdj3gl6o2pD5UlWDtBTKdUySnGJ7fm9sGmx+QywfDLkHI/7f0PQRWsIBIrBHosRsfD7c/Mo8wY5747OxLc+DM/hfc3lQ3dy0VNzPnOszYlFWchZOB5fsPQgfsD6Y82aNZx22mk7bK8sYX0GSAeKgG6YiZRXgNlAqVJqnlLqjR0O3gWtdUUtFSK7e+zPNRMnVdsAD3BEbccopWxRHLPEfrR8+XIuvvhiMjMz+fDDD7FareYYU7gEV6SCjIAPlxE2d1bRjVXs8rwCrXW+1voBoCVwIxAEXgPylFKPKKUa7+Gf2x/nB0oplaiUkv96DnFZad1x/5nAnx8t4IizM6k4rpzcgjnAdj1RLHYiRoTxC8bJOUQU7WqsOBD25TwEc6wpwLw2qnq/QuBjzGq6mP0QojgY/F58S77kzcRkZry2iKKVxXQbdhRxhyWBxc5lHa6UxEk9sd+TJ0qpQcBCYM32r2mtV2mtBwAnVe5zH7BcKZUdhROMo4FlWuuS7bb/Xvnc9eCGI2pVmSjh44vM50WfmsuEKitLnS6CCgorvOSXrmf+GwvZvNhL7ztOpVGP7qiYxOpy2p3Zfj6zw+pAV24Xe2b8+PF06dKFVq1qbcR7M2Y5anutdYfKbe8Aj2NOfbEDXYDsgxHrzlQmRlyYsW7vcKAMM9GzUSk1UillP6gBiv2iqKiIfv36YbFYmDRpEomJiVvHmo8uAN9GzLmAGhLTwR5vrq4jomJX5xU1VV7EjNdadwb6YN4BfhhYo5R6fQ/+5P44P/gHKMYcL95XSqXtwd8Xdcj3HzzNU/e+QIvOKfS6sjnWkL/6JkvVOUQoEmKdbx2bA5vZVL6JX9bLzO9o2N2xYhfHX6aUem7/RrVLRwNzt7+RgznWxGGed4i6bvlkfK8fxZyfHuL3z1axfFYBJ17WlmOPdtMoEubBrDu5tMPl0Y5S7Cf7NXmilBoGvATcobUeuLP9tNYztdY9gKuACsy7O/OUUqfvz3j+RTPMUrvtVW1rfhBjEbXxe6sTJT6Hizy7E98P94IRwWeEGOeKhUiYphVlFHzxN2u+X8vRl3ai1RntMJRZOvtvc49rzmem8llVbhe778UXX+S2225j1KhRTJgwobZd2gFfaa0LamxbpbV+BDNpMhX4DMg64MHu2jDAAXy03faVwBPA5cA1wG+Y03pkGs8hJhwOc+mll/LPP//w+eef06ZNm61jDQrKt5g7GmEwQlCUB9qQ1XWi5N/OK5RSGUqprkqp05RSFymlBiml7gbOANYDi4BY4IY9+LP7cn7gxayoG4x5V/lN4FLgF6VU4h7EIOqANUsXcNkND+Jq7OS824/CarPg8OWjjUh1vxOtDQoDhQCVCxXDVys+l+qTg2w3r0HOVUrdrpQ6XSnVqJbXjwGGH6AQayPXIoc6v5fcqcO4Jb0dd61rzNxPVtPylOYc0a8FoImNS+XwNOn/W5/s754nqvKxW43RtNb/UUp9hjlQ3Qt8p5T6L+Y842X7ObbtxWImbrYXqPH6NpRSN1B5ApaRkXHgIhOmyju9uXGJ5CQkmPOJdSOyt+STa4WNjjgACnI38cf7/3DYsY15/yRwHXUr7qRWu9W0reZ8Zn84UD1fWRq+7RmtNVprc+pD7WzAphq/G5Xb0FoXKaWuBJYAXwPzD2CoO6WUOhl4BPhYaz2t5mta6+0rYt6rnGI0SCk1urYm2DJe1E233347U6dO5Zkxz9DtuKPNjVVVJVqbq+lU06DD0P0m6XcSPf92XrFqu32rRIASzOqP+ZXPu2uPzw+qaK1f2m7TZ0qp34GJmBV4T9d2nIwXdY/P5+O8CwcQjmjOvrcbFpdZaBjUBsoIVzeHPb/dBbyxYBwo0GiaxKWilAVPwCvnEgfX7lyDdK98aACl1AbM/mt/YY4X12BOozlY9mqskfGi7vDlz2NMk+asyqtg7piFpGQm0+XmzhTaLKQ5GzFIVvesd/Zr8kRrPVopVQ68qJQ6Xmt9/W4cUwE8rZTKwWyONAiz3Na5P2OrhR+zedT2nDVe34bW+g0q50B3795dH7jQBAAJzfApCzmueCxa49AGQRTjmnWgMFIOQGleKXNfmE9C60ROufkIMorW49IW2IOBKiutO5kpmbLazj4YPnw4cXFxDBs2jFmzZvHmm29uv0s+2zZqKwGq7/porbcopb7FXKFi4gEPeDtKqQ7AF5gnUP86blUahTlenQnskDyR8aLuGTt2LGPGjOHIi7qwqsNK7v75TrO5Y2Jbc4dIVc9iBWiwOMyESsvjohVyg7cb5xWV/2fxJWYz13WYzZ197L09Pj/YlcobRaMwx4pakycyXtQthmFw1VVX8deSFXxzY3MaxwfIUfH4tUYpRXbHgbgiYdi0mBMbHcmXcakYaGKtsUR0GEMbUsF6kO3mNcgEYCbm1LujgCOBsysfVR44wKHWtFdjjYwXdYDfC6X5rCvNY2OJ5o+ncrdZWScxEmbY0UPpIM2j6539vtqO1nqcUuo74O1/21cp1RzoBHSufHQCwphl8wdaPtCilu1VtdmyBGm0xabgOfkBIkvexqINgoB2pRIwQhCBSFGA35/KxRpj49j7utHLn49LWfeqvN7lSJCkyT4aPHgwvXr14rrrrqvt5T+AjjV+X4rZNLamfKD/AQpvpyqbP07FvDN9ttZ6d2ut11Y+uw9IYGK/mjZtGkOGDCHj2JaccENPnDYnwUiQnIXjyTz5eVx9RsO3Q8FiNafsWOzmZbkrDRp3+Nf3FwfOv5xXHIdZudofOA0Yi1m6vy/JkwNxfrAWGSuir/KC59+WHH/ggQf46quvePnll+lzVnuYMpzMjeV4bDbcpz6GqyJg9kjCbJI16Phh5HjmEjSCUsEaRbtxDbJaa51Tc4NS6jDMBS0SMXsdzTvAYdaUz9ZxpSa5FqnLlk82zxeMMAFi+O3VfELlYU588jicyTFowOZMIr1RrWsPiEPcAVmqWGv9j1Lq1O23K6VuYWuSpBOQXPVS5XMEc3muPw9EXNuZD5ymlErcrilcjxqviyhbm9KSQkcsWmsiGFgCHgCMkMHvz88n4KngpMeOwdUohtPzAnDOWCmvj6I2bdowffr02l76BnhHKZWqtd5U+fsIpdRJWutflFJWzLuye7ys6L6onPM8FfPOzxla6z3pCtqm8rlwvwcm9htfsJTcRXO5+OKLaNO+DSfdfwJOh3lTz2F14A8HzPL69mdB+m+w6BOY+YzZ58Rih74vy5hSB+zsvEJr/TtwoVLqcOBOzGnAw5VSE4HntdZL9uLPzWc/nh9UNsRvDRzMizKxveWTK3sbVeozGtqftcNu7733Hk8//TSDBw9myJAhoBSk98BVuASXAlxNYeLZoKxgc0I4QNasF8m85js8GFLBGmU7Gyt2sf8qtp3+dzDNB05SSlm2axrbAyjHvCYSdYivZB2eyUMI+T0UxsTy+NvrKFoZ4th7upHYOhENWJWF644eKuNAPXVAkicAWuvaysjG1PjZA/yEmShZUPm8qHIaz8HwKeaJ1g3A8wCVS4JdB/ymtV67i2PFQeALljJxyfskx6SwObAZtMZQCncoxIw3FrN5sZesYUfiPjyFS72baHrhB5DRM9phN3i1LZxVWbb+NZXzjDHHgluAKUqp6ZiJiPbAewcopmZAErBSax2q3BaPmcRpAZymtV6+k2MTgYqaY1PlxdCDlb9OORAxi32XWzCH12a/wudDvyQQCTDyzcf4yf8jwUgQh9WxY4Po2BTofgN0GrBbd6fFwbWT84qq15YBNyilHgJuw1yyeKBS6hvgWa31nix/slvnB0qpDCCuZoJGKdWkcqnRmm4CmgCT9yAGUYuqFW5qJihq27aDGg3oqxIeTBkO6T22+Y7PmjWL66+/ntNOO40xY8Zs/fds3W9bEy/hCrOZdHzlAko2JwR9uAIluFI7IqJvJ2NFVKe31HYegjnWXAxcWPkzlUurDwAmHcRrIrEbcgvmkJP7IiVNW1NiO5xlHy1n6YIQ51zUjPSTMgmhwGLj/zpfzwktTop2uOIAOWDJk524n8pkidZ6/UH+29vQWv+mlPoEeEoplQqsAK7FvDsU1eVSG7qqE6HyUBkaiLXHYQ9YUDqEASz97ypWTttA9hlxDOrkoFXBBppGgtBESuvrspo9CCqbxPbGTJZU3fqbBtyxp++rlBqCWcVW1ZW+n1IqvfLnMVrrYuApzO/3YcDqytcmAscCbwFHKKVq1lf6tNZfVv7cDfhAKfUB5jgRC1wAnAC8obWeu6cxiwPPFyxl/PxxfPf4D5RsKOW8Z8/lJ/+PXNnhKiYueX/XDaJjUyRpcoiqXNHrfqXUE5jJj2HAT0qp37XWu5Vd34Pzg3eBU9i2Ue0apdRHmMulBoATgcsw7zCP2/tPJnIL5pCzcLzZPB7zuwvssC2rth4DVU2hbc6tz0Gfub3yu75m6QIu6H8eGS3T+eSTT7DbK1ei3z7xooHS9WZvNXucmYhRSlbkqvtSMWdZ7Xf7cB7yKWbPtLeVUh2BzZiNpa2YDexFHeELlpKT+yKGbwMldicbZuWz9KMVtDy1OfbLu3DXYRdjb9FdKs8agIOaPNFa19ooLYquAUYCVwMpmBUw52qtf45qVA2UL1jKjPW/8OWKL1DKgtYGgVA5dq3RRhgDxYZ5m/l94kqa90jl7P7xdPKX4FIWs/xWLnYOKVrrBcBRlScYAa315r18qzuBVjV+v7DyAeZSwjtbZaNr5fP/VT5qWoPZgLLq518wEyZNMVcK+hvzzvYbexmzOEB8wVLWla6joGwjM8bOYm3uOk6//TQO69YafzhAy8RWPHvy89Igup5QSo0AEnbxcGFeVx+7h2+9t+cHE4HjgYswmz6uwWxk+4TWunwPYxCVfMFSchaOx6Is1VVj4xeYuSibxVa9LWfheDJTMnf8XlclNsKBrZUnSuFzJuIpycOxfCbnnXcdFWUhJl3vpNHGH6HRxeYx2ydeHPEQ18R8D22YiRM5B6nztNZbgC0H6O336jxEax1RSp0NPAcMxbw58wcwUGu99ADFKvaCp3gN2rcRv1IUrSxm3ssLSMlMpusNHUEpNscl0SNRVj5qCA525UmdorUOAHdVPkQU5RbMYfyCcWwq3wQKmjibEK4opShcgtLmjZ7idT7mvjCfpFaJdBt6JO/HxTA1xs2gTv9HVsap0f4IYi9prdft4/Gtd2OfgcDAPT2ucr9VwCV7Hpk4kGor1Z+x/hfG/fk6vlAZq6asYtGXi2nb7zCST0mkOFBMjC2men9JmtQbD2/3u4F5oeKtfKyq8fNu253zA631qbVsG7Qnf0fsHk/Ai8bsU0TlcyBSgQLirHHV26p7GNVWTdZntFlBEvSBUuQeP4zxvz1KOBTkh0e+JS+/gm+uttMhrhA+v9JcaavzgNoTLzEJcOU3EPLL9D6x1+chldu9mKv87e5KfyIK3KEAWsGmEmOblXVw2kEpWjWWKXsNRYNOnoi6oeqOkmFEzNpnrSj0F6IiQSxAWtBPcWmI756ch92u6HlvV2KcNrTFirY6yFn2AZlNs+RiSIgGorby/UDIz6g5zxEhwuaFW1jwxl+kdmvCEdd2wDAMvBVebu9ypzlO7OaKG+KQcCZbkyPeyvJ4Uc+4nSko2KZfkU1ZYbtt2/Qw2l77s8weJ6X5+JyJvDrrAYoqilj03mJWzynk2IHtadLFxxIM0v2luL6/B9qeWWvihT6jIeWwg/b5hRDR5Uppy1mbPfzfC/9Ur6wTk2yuMt23ZW+axsu0vYZCkici6jwBLzpYRqxvI9jsKEBbrBiABbCGwvz0wl8EPBWc/UhXHI1j0RYboMzlRo1Q7XeahBD1zs7K90OG2X+vbEM5c56dS3zzeLJuPxqbzUYTRzLKaiMjodVur7ghDg1a62nRjkEceC5HAtldBpGzcHx1v6JBRw4G2Gbbvy0R7LPa8DhdbC4voKiiiLU/rmXZZyto3bslqf3aMhIzIZsUDjLEu5msqp4oNRIvknQVouHRzmTenxSPd2UxvW/vRNumFgKhELb4xlzRZfuZ36I+k+SJiDo3FlRZAREUjSMRCq1WdGUVSlI4zE9vrWDDkhJOvrUjzdqnsBkFykITZ2MiOrLrO01CiHqltvL9ioi5IEGoLMzvT/4BCo69vzu2eBuWSAhbaT4ocK/6GX554l9X3BBC1D1Zad3JTMncYbpebdtqU7NiLRgJsunvQua+Op/GXRrR6fqOKKVQ2kBpKLY5GO9OJdOZuLXDqDSSFqJhqKU6deTIkXz27QyG3HsDkZP9aMBpc5B95I1y87aBkeSJiDpXoITszesY707D0IrGRpALNq0m0eLk7t8j/DW9gG7nZ/BChwiZhfnM6H4dX5YsRSkLhjb+9U6TEKL+qK1836qsKEMx/4U/KdtYznEjjiW+aRwWrUkwDLBYyC7aguvHh8Fig5h4881qWXFDCFF31davaHd6GG1fsVa4Np8/ns7F2TiWrDu7YrFZALCgUMpsnBOOa4wH48AszyKEqJtqqU79ZH4pjzzyCFdffTUvPzmWspBPms43YJI8EdFnj4VAEehUtAKr1qQGy9m0PMSv722gT6/j+Pj1N0i0WCGhGWfFpnBiLc0ihRD1387K959/8HnWz93AycNPpN0xbTkvtQc9Zo4haHPgRuFSVrPixAjvsOKGLDEqRP3lC5ayaMtiDG3gtDnxl/n57rHpGCGDY0dm4UhwVO9rWGzman+AzeGSqlYhGpLKZcl9FjseRyzuoJ9lb97EtWMK6NmzJ2+88QZKKWk638BJ8kREnc+/hZwWHbFFwsQZYYLKwnM6g2/fmceRzWP4bNwY4pt22eYYGbiEaLi2L9//z4QP+PDNj7jl1pu5e8Q9ZlJ1+RTwrqw8QkF8Gjji4LSn4MeHtm38KFUnQtRLVVN1ItqgsHwTyY4Upj/5EyV5pfR4sDvuFnGgDSIoNKAtCoAkRxKDjhws5xlCNCSl+eTGxJKT0gQNlHtjmfzGEpo0aswXX3yB0+mMdoSiDpDkiYg6j92JVhYcFitEDCIlQb59bhExDgtfD04nvmnbaIcohKhjqhKoP/74I7fccgt9+/blxRdewmazmXePfnwIXM2grACMCJRthN7vQ6eLoc0Z0vhRiHqu5lQdp81JOCaFGW/OZNWs1Rx5fWfaH92ccqMCjdmc/ub4dqR3vxmA9IR0SZwI0cBstFoZm9wIu2FgDxlMfmYeHr/Bz/+dQFpa2v+zd9/hUVXpA8e/Z1rapJKEhF6EIE0pigV7Q8WObd1ddRF7Q13bWtfee8XY+8+CYgHXgl2QJr3XQAowkzLJTKbc8/vjTioBElImybyf58kzmTv33jmTyZy5973nvG+kmyfaCQmeiIhLS+6NiknCV7ENI6SZ9tRyyt1+pl7Ti57nPCsnN0KIBq1evZoJEyYwYMAA3nvvPWyBMnDlQ2W4Wm18utl/hAIQqoTMIeZySfwoRIflaeS03frJpbf8nM/Kj1dz6t9P5fo7JvPu8jeJD1RgGEEuGHA2B+ec0UavQAjR3swtnMOLC55lmz0GFQqy8KlFFK0t5bj/HEr3YYMi3TzRjkjwREROOJu10x7HYa7NvJ+YwvzcJWxZVsKVk/pyxK3fQWrfSLdSCNEOFRcXc9JJJwEwbdo0kot+r0nyZoQg6K2pqkPATBQruU2E6NBqV8ypKk08quvoBtetnVx62/LtfPPIt3TbJ5tXX8wldcts9l35Oy6lSAv6cPY9qy1fhhCiHfH4y8id8zj28iIsNjvLPlrDmt8KGfOv/eh32CDJfSTqkOCJiAjP8qnk/XgXoEnxe/mxa0+Kpq1jw7d5jDy9L6Fj+uPxbscpwRMhRD3BYJBzzjmH1atX8+2339K/WxrkTjaDJVY7eEsgWAkWr5kgVnKbCNHh1a+Y4w/5yV00hZzUnB1GoFSNTvnb3n/npR9f5PPbviAxw8mb771JqkXBjMk4A16c5YWgDfjkPNAahp4ZoVcnhIgIrxvXn89geLZg1ZqSn/NZ9v5qeh7WjaFnDpKKnmIHEjwRbW7uxpk8t/h5ijOyQSkSQiHy5xby+5sr2Wv/TA49sy8+zFwoUiJQCFF/mP4NN9zAjBkzmDJlCocddhgULTVXDPlh+0ozYII2gyZj74EhZ0rgRIgOrv40HIfVgTfow+Vz1zm5mVs4hykLXyKkQxg+g9/vnYVd2/nu6+8Ztfcos78wQmY+JDBHpRlB+PYm6H+09BVCRItV0+Hrq9kYKqeo9zDca0r4+fnlZAxM4qhJOdw7eBJZOxnZJqKXBE9Em/L4y5iy9DWKLVYsgNaa/LxyfnxqMV16Oxl3xd4ELBZUQlfSkntHurlCiAirP0w/ZX4XnnrqKSZPnsxFF11krpSYbZ4MFa+vCZygzKmBvz1qBk+EEB1a7Wk4VSNPVHh5FY+/jOfmP0NxZTFo+OOB2RSsKOTTzz9h1PBR5kqJ2WY/oQ0zcKINQJkj18ryJXgiRDSoKkusFO9mDyCmqJQ/HpyPI8nB6JtGclH5NrIyhka6laIdskS6ASK6uHxuQkEfCo3SmkBJJX88OA+bw8rRdx9LMLMPRmpvJo66VobJCRHlag/Tj7PFsuWvfB665UGOPe4YHnnkkZoV41LhoBtqbanA6gBlASNgnhAJITo0pyORicMmYWgDb9CHoY06Q+o9/jLmFszBXenGoiwsfXs5+bMLGPavIeQckFOzo7hUOOZh83cjaE7XSegKVsmLJETUCB8X5MUlUxEw+OGRRYTKA5x8/WB6xmt6jr5aAqmiQTLyRLSpNCxYvS601UYoGOL3RxfgdVVy8v1H8sQZz+IPBXabQV8IER1qD9PfvsnF9Lu/Ial7Mo9NeRyr1Vp35SFnwi8PQekmM2iiVPjKsl1OiIToJEZ1HU1Oas4O1XaqRqhVhioJ6RCbfshjxccr6XNcb/qd2EDutCETzKDJtzeFcyXZJC+SENEkMZu5MXG8nJTMd08vpnCth2OvG0J6vxSMhK6kDT070i0U7ZQET0SbcvpKmVS8nWdTM/jhlWVsW+rm0KuHcttJl5GVsGcnOI0tWyiE6FiqhukXbi/i81u/wNAG+90yimKLe8eV41LhxOfg80lQXmSeGDkz4fin5YRIiE7E6Uis811fPUJNg1PZWbW8hPnP/UX60C7sc/EwUmJS6JHYY8cdDT3TzHFSlm8GWKWfECI6eN0U5P3Oi137svCdBeT9WsDg8waScEgfgvGZTNrncjmfEDslwRPRthKzGVXpZdD7i3j923wuOb4rjw40cPY6Yo9215SyhUKIjsXpSGT/jDHceONNlOeXc8Bd+5PZM4O3l75Jemw6PRJ71D3AGTAOLl8IW5ebHUL6IDkhEqKTc/ncaH85Dk8BpVu9zHlwDnHpsRx1+1GkJCYxKec8nMWbGg6QxKVKHyFENFk1nV++u57ctK4s+dPNnx+uZa/De3HUBUfgNfxcO+rfDOqyd6RbKdoxCZ6I1uV1172qE5fK17F/49ZP7uC04U6eH5eC5djH9ujgpSllC4UQHY/HX8ajdz3G1vnbGHnFvmQPy6I8WE55oJzH5j6C3WLfMWAalwq9Doxco4UQbSoNC6q8EI8vxGePLCTkD3HSnaO4a9gEevgrcX5+Wc3Kxz1hBlmFENHH6+bXH27h8cxeFK8tYe7TC0nNSWHIJTkEdRC7xd7wKDUhapHgiWg9q6bDjMk19497gqWBXpxz/aMMHzaMt6a+iiWr/x5f9Wls2UIhRMf07AvPsWTqUvY6qR99j+0DQMgIYcVKvDWGULCS3IUvknPYE/KZFyJKOX2lXOjaxtm5W9i+0cOxN+/LVWoDg768Biq2gTMbEjIg6DOPSXqMkdEmQkQhj3sNr6am43X7+OOBecQkOdjvppHgsBIIVnLpyGvkWELsllTbES3C4y9jY+lGPP4yc0G4BBjKCvYEUFa2fXwVJ40/kbi4OD7/4ksS+o5u1gFM7bKFhG/rly0UQnRMP/zwA7ffcDs99+vJ2EvHojEIGgEA0u0JWLevwlGyEV28AdeKaRFurRAiYhKz+fTjTaz7s4gx5+fQZ59k3s3ai7kWbVbb8uSbVXVssWYuJKm+JUT08br5Zel7bA0q/nhwPv6KIAfeMoKEZDupoSD37H+bTPsXjSLBE9FscwvncOOPk3ngtzu48cfJzC2cU3NwYosFwI+DM3M3s3nLFqZOnUrPnj2b/by7K1sohOiY1qxZw4QJExgwYABvvfMmyvBhBP0oHcJiGBgVWwHwK4sZMP3pPjNgK4To3LxuKFpa5/P+9sdf8sD/tjP46O6MHNeN+FAQi4bcbjl4rHYzgBL0mT9KSfUtIaLNqukU5B7Mh9vmMv/5RRSvKWHENfuQ0DcFUEzsezJZXXJ2txchAJm2I5rJ4y8jd+6TWDyFOND4UeTOfZKcsQ/iBAj60NYYrnx3PTNXe3n71Zc44IADWuz5d1a2UAjRMZWUlDB+/HgApk2bRteMGN5b4CJTQ6xSeNC4rTZsoRBWBRPLynEaITNgK0Pxhei86k8FPuIefl/vZ+LEiRxwyAEMu25vYnQAitfjwMCrrLgcCTi9xRCoAEeClCMWItp43cydeSsvdu3OH59uIO+XAvY+bwA9989Ea80/ex7L2NFXRbqVogOR4IloFlfJBnS5GThBWXBog/LyQpa4VzDk6AfwfH87D/1QwJTf3Nx66dmcd+HFLd6G+mULhRAdUzAY5Oyzz2b16tW8PfVtErLiWbLpFwAS0KAhWYewhQL8K38lQ7xlOOO6gD1OriYL0ZnVngpsi4WKbWx89W+cNsWgZ5KF9x+6jIfKf8Wv7TgsNvzaQGlIC/rB2Q0mfAAZUn1LiGjjca8hN6ULG/8oZNl7q+h5aDaDTutLl2AQq4Kjc86MdBNFByPBE9EsaQGfmXdEWXAAJRYrbouF15a9SVmwknX58fz+0WKyD8hi6FUnRbq5Qoh2yOMvw+Vz89B/HmTGjBkcNvkQplu/4oNv3yVR2Smz2kgN+kgOBvErhVUphlSU4qzKZ3D6O3JSJERnVnsqsA7hcRVw8rtBvAHFD9f0pvfcB5h48gvkrnwPb1J3VHkhE7fn40zsDsc/LRW4hIhSLnssRWtL+f7ZJWQOSGbkZUPQygLKYFL/CTiTpLqOaBoJnohmcab2Z2LxdnJTulCuLLgtFlJDQWy2WLasWs+fj88juXci+101lP9b+T5H9D6SrAS5QiyEMP2a9zOvL32NlV+v4qdnf2HwqYMYNG4QRd5C0BpPqJzkoB+3LQabobGizZOitL0gFICQHzIGR/plCCFaU9XIsqAPw9D842M/iwo1X17eh717JIPfw6jYTHIOfdScxosFp6/U3E4Cq0JELX9FDNMfWURsop1TbxiG1aIJ2J3cc9i9kudE7BEJnojmiUtl1OH3k/PN9SxxOMhNSccRl0bxdjez75+D1WHhwJtH4oi1ETSCbNi2VIInQggAvlv/P5796xm2LdrOr8/8TrdR2fT/Rz8KvQWEdAgFKCAhFMRhhPhXwWqGlBXi1Aoyh5jJH602mbIjRGcXl2rmK5kxmds+L2TqMs2Tp6QwblhanUSwdabxJkW2yUKIyPGU5rFly2LO+/stGF4Y/9jxWHolg9XGpcMvlcCJ2GMSPBHNN2Aczh5jCP75JNu2zcYoL+aXe/7Et93HQf/dn4T0OAzMk6DeyhHp1goh2oFfNv/MswueoTS/lFkP/UlCdgIjr9/XrAGnzf5Ca41WiiAaK5iBE0MDCnzF5hB+SQApRKdVNaUvLTYV54BxvP3zBh749lImnXEUVx+WB36PGTiRfkAIAWZJ4t8f4PWtc5n59BLWzC3k0Ucv5ZJ/PSzFJUSLkOCJ2GO1D2oIlPNO4W+kaM0Pr6xg+xI3I64ZRpecFEIolNacXeomK2NopJsthIgwj7+MN5a8RqA8wOz75wIw5tbR2OJsaKVRGizaIITCojUhi4VJeUtwhkKgLJDcE854G9IlAaQQndXcwjnkLppCOFzK/v4DuPTyazj8oP14NvcdlMNh5kKpNzXHU5qHy7WKtLQBks9AiGiyajpffHc9r2T2YuVnG1nzWyGj/rYXa3qtBl8JvZJ6RbqFohOQ4InYI9UHNTqECgU5OWM/tNas/WIt67/NY+QpPRl9QBqXrJ1HIDGb3sEQWcc8Iic6QghcPjcYijmPz6M8v5wD7xpDfHY8KEixxBLvdaOMACEgpCzcs3Y+WZWlYLEDCsbeCD0lAaQQnZXHX0buoilYlAWH1cH2Ldu5/IrL6ZZo8NHJxTjeOtwcbTJgXJ3t5s57idw1H1UHXCb2n8CokZdE4iUIIdqSex1vzryJj7r2ZsvvBSx7bzU9DutGr9P7ETJCuFyrJJgqWoQl0g0QHU/1QY2/gjj3eiylm5m6diobFmzjp7fXsdd+XTjgrD5YgYG2RMYc+l+yLvxph4McIUR0SotNZfaU2RTOK2LfS4eTOTQDK1auHPwvripYA0phoLAAl+YtISvgBWuMOeIkpTcMOSvSL0EI0YpcPjcacCgr/tIyvr59OqHKAFOu6E+XlCSzZPGMyWYJ4zBPaR65az7CoiEOhUVD7pqP8JTmRe6FCCFa36rprHn7GD7pkk3xmhLmP/UXqQNTGH7ZULRSGFqRljYg0q0UnYSMPBFN5vK50TqEw5NPCEApXHkefnxiEWm9Ejjs8r3RFgsT3VtxWu3Q+xAZcSKEqPb+mx+w8JPFDDttKPudsh+GDnH+kAsZa+8CZVvJ2boKl9VGmr8cpxEyT5QSMsDqkNwGQkSBtNhUlL+cytICZjy+kG3rtnPCzfswppsdtDbzHfk95rSdcH/gcq0KB1wUhG+9aLniLERn5nUz95trebprbyrcfv58YC6OJAf73TwSq8OK1przex4jfYBoMRI8EU2WFpuKCgUpURaKrVZ8ZX5+fGgBsQ4LPz92FQkFn5JWpnEqi5zoCCHq5Eea89tcLrvsMsaNG8d7b79LabCsJoGbex2UF+EEnDqcZlpZ4Z/fQny6lB0VopOr7iuwMHHzUiZ95WbNn1s5+IKB3J1RiNOfCrYYqPRUV9ipkpY2AAX4tYEDhR9zFbniLETn5Zn7Ms9kdMMVsvDnQ38SKA9y8P0HEJMSA8BFaSMZe+CNEW6l6EwkeCL2yLG9j+HNZW9jBAxmP7IAn6uSY+8cRc9jrsBpv7HBJG5CiOhTO+lj6ZZSvrz2awYMGMD7779PcnwyKV6geJPZXwS8EJ8JFVvBCAHKHHESnw6ZgyP8SoQQralOgthgJdl/uJg/dQN/OzSDFw9IJNFTDl6XOVVHYfYVebOqpwQ7CxczcVseuald8QLKYmVi/7PkirMQnZXXzbfL32d7Whbzn/2L4lUljL5xBMl9k7Bozempwxl/+H2RbqXoZCR4IppkbuEcche+iD/gw1BWlk1ZxLYlLsZdOZiuIwbiwsAZlypBEyFEnaSPwfIQX90+HV/Qx/sfv0dycjKsmm7mLTBCYAThsDsgxgkOp1lVRxvmSVKtq8tCiM6nfoLYjUsKefmtTRwyIIHXz+6JPegDe7zZVzjCfYQRNPuPHmPMncyYzChlJaekFFeokrRQCOdJkh9JiM7K417DtOR0Vn28hs0/5zPobwPJPiALi9b8e8hFHJxzRqSbKDohSRgrGs3jLyN37pNY3BtI8BSwdtoaVn63mdFn70P/k8eiHAlm2WIhhKAm6WNlwM/Uuz+jZHMJ+904ii2xm82rxzMmg78CSjZC2Wb44hIYdJoZMNEh81am/gnR6VUniLU6KC0s4+u7vyEhPZ5nLu6FPVRu9gcH3QA2B8SmgMWGxx7HRqsNj3uNOdoVwBaLEwu9rHE4oWa5EKLTcdlj2fhHISveXUX3w7ox4Ix+ACTa4tin77ERbp3orGTkiWg0V8kGdHkhDjTr/3Kx+PXlZO+fyT7/3AdDwcRhk8y8BUIIATisdvyhSn58/icK5xUx4op9yBieztTVnzI2JhNn0A/lBYACiw1CAVj8Ppz/nTmFR6b+CREV0mJTUYDHU87n/5lGsDLEyY+Mp/+Zj4OvtGb02R9PQNDH3Pgkcp0J5hSfJS8yMefvjAII+sxkskHfDjlRhBCdy6Z1xcx8bikZA5IZdekQc0SAsnDpyMlyPiJajQRPxG5VJXBzeN0ooGBzBV8+sYi0Xk6Ov2Jvbugznh57jZOOSghRrSp/wdKvlrFm2jr6je9L32P7kB6bjlIWXAXzcZZuAiOAOcTEDhar+RPwSo4TITq42omid3d84HQkcuGQiZx95tlmZZ17x3HD+BvNfCVJtfZ59APk/XgXU5wJ2DQ4knrgt9jJXfkeOUc/gPPbW8wqPErJqDUhOrH8/HzOOeNcuqSnM+6eI4lJsmFYFOcPvYiDux8S6eaJTkyCJ2KX6iRw0yFGFrq4/qFlWB0Wjr9hCJdvW8Ggb/8D9sTqpG1CiOhTHWS12in2FTNl4UsULixk9nNzyByRwdALB5OdYF4FNowAab8/CQldzek6aHPUSVIPcwSKXC0WokOrc+yAOTJ1VNfRDa5b1Xd8+NT/se7X9dz54J3cMPn6HQIucwvnkJv3NYHe+7K90kVGbDqOmEQcgDfow9VtJM6Jv0nCeiE6Oa/Xy6mnnorL5eLXX39lr8H9Gx2oFaK5JHgidqp+Ajevz8udL+fh2+bl/UlOjilZhDOxO1gdNUnb5GBFiKhTdaJUEfBS6i/B6XBSsKGQn+7+hZTuyZxwx/GUqGJ8oUocFjsTe52Ac8Wv4OwKFjt4NoPWZuBErhYL0aHVP3bwh/zkLppCTmpOgwGRKQtfYvl3K/nxoZ847e+ncueNd6KU2uk+4x2JbK90s7VyO7H2eEI6hMKc+oMjUfoPIToxrTUTJ05k9uzZfPrpp+y7774AEjQRbUaCJ2Knaidw01rz69M/smVREXde0J1jerhwJmRDTHg8rd9jXu2RgxYhokrVSQ1AWaAUhaK4uJg/7puFgcH4+04kITGeGMPBtSOvo0diD5yhoLlx0AfxaWCLgVClmesktW8EX40QorlqHzsQvvUGfbh87jonOB5/Gc/Oe5p1i9fx8+O/kT60C84z4ykPeHY4Eaq/z4z4TLZWFFEerDADspJzTYiocP/99/Pee+9x//33c+qpp0a6OSIKSfBE7FRVAjd/yM+STxez6OuVDDy9HwUn9ufGoJ+J+SsYZSSa5QIlMZsQUcfjL2PJ9qWEtIHdYn6daEMz+7E5ePLLOfiuA7Fn2jGMAJN6ncCg+CzzyjCYI0xmTK7JT3Di8xI4EaITqH3sUDXypHpkCDXTdLZVbCV/cz5/3P8ncV3iGHPT/ni0h7yyPAZ12XuX+7RbbGTGZ9YEZCVwIkSn9/HHH3Pbbbfx97//nZtvvjnSzRFRSoInYqecjkQmDpvEna/ewY8v/Er2/pkcfHZ/4jX4LTZys3PIKdyE0+qQofZCRJmqqTqGNthaUUSyIxlDa/56bSGFc4vY9/J96D+6HzdkHkKPXx/FuewnM9B6zMMwZIKZI6nHGMlPIEQnU3XskLtoCt6grzrnidORWCcXSnl5OX/cP5uQP8Rh940lNikGQxuN3uek4ZfsEGQRQnQenqIluPLnkJY9mlWb/fzzn//kgAMOYMqUKTtM7ROirUjwROxSvCuBnx/8hb0G9OCQK/uTjAFYzARtVhuucU/g7HWInPgIEUVq5x+ItcWSGpuGy7edtTPWsW7aevqN70ufY3tCKGgGTvzlUF4I2oBPzjPzmww90+w3pO8QotMZ1XU0Oak5dZI41u437MrOjIe/oWRDKQf+ZwzOHk4MbZASk0KPxB6N3qcQonOa+7/rmeKaSwhF5R8v8c1ti+jSpQuffvopsbGxkW6eiGISPBE7tX37dk4adxSxuoKp53Xl2RgLfgwcWuPHgkroSlrfI2qG4QshokL9/AM2i42ti7ax6OUlZI7IYPA/B6BCQUpDxfxss3C8u9Dc0GIzR598exP0P1oCJ0J0Yk5HYp0AR1W/YVU2fsr9hXW/rWe/i0ez98GDsCgLVmVl0vBLdhkUqb9PIUTn4ylawnPuBRTbYzEqQ/z02HzKyir48YPXycrKinTzRJST4IloUCAQYMLpp7JpSwEzr+7D4MxkJpaWkJuciteZjbI5mDj8UjmIESIK1c4/YFU21q1Zx+yH55CQncDI6/ZB2awordHAZyldOGTbGpwoc+QJCpRVEkwLEWXSYlOpDPqY880c5r07n97H9GLIqXtz39gH8IcCMppECAFAXt5vFNscKG0w74UluFeVMObGEaSluCLdNCEkeCJ2pLXmyiuvZOZPv/DmP7I5cGAaAKNCkFO4Gdd+t5LWbbQc5AgRpWrnHygrdfPHfbOwYGHcnUdQmWBW0tFKkREKobDissXg9FcAChK6gtUmCaaFiEJFy4qY9+wC0od2Yd9LhgPgtDtxJsjxhBAiLK0frIflH69l08/5DD53AN3GdDWXCxFhEjwRO3jmmWd4+eWXufmGa/lH7+lmOVFbLAR9ONE4u+4rU3WEiHKjuo6mf2J/Tj75ZMrzyznpofF069udTSXr0UC3UBA0GEqRduidMOtpc8SJ1SYJpoWIQotXL+H7e37Eme7k5LvH40xx4jcCO5QwFkJEKa8byvLpkZZD8aztLH1vNT0PzWbgGX1JtsTQo9v+kW6hEBI8EXXNmDGDyZMnc8opp3DfQ4/BmuPqlhOVkx4hRNjd//kvP3/3C7c+cgvufbbjrywjJRREa4OQEUKhmeguwln0klllJ2OwVNYRIgqVl5dzyTkXEaoMcurD40lOS96hhLEQIoqtmo5n+rW4LLBpg4/fn8knc1AGR157ELFxTiaNuFKCrKJdkOCJqLZs2TLOPvtshg4dyttvv43FYpFyokKIBr3yyis8+eSTXH311dx3w/14SvNwvXsCaeG0Jq7STaQF/TjTBoAKwg+3w8TfpA8RIsoYhsE/TjuWxYuX8MSV/Vie7MZb4UA5EqpLGAshopjXza/fXc+rmdlUlFQyLXceybEGv34+HXvXdMmHJNoVCZ4IIFxZ56STiImJYdq0aTidzpoHpZyoEKKWH3/8kcsuu4zjjjuOxx57DACnrxRnMAD2BHOKX8BnliQOBcAeZ45ekySxQkSd22/+N5/+7zeeOCOLq4ek4incgkttIu3Mj3FmDol084QQEfbLik95NLMngYDmt0fn4ysPcuR/R9LVWowzaWSkmydEHZZIN0BEXiAQYMKECWzatImpU6fSq1evSDdJCNFOrVmzhjPOOIO99tqLDz74AJstHIOvSgAb9IHVHq6so83fgz5z2p8kiRUiquS+8Qr3P/I4FxyUwjVHdwNfCU73GnptX4fz3ZNg1XQzz0HRUvNWCBFVPP4yXlv/KSEUfz23iOJVJYy4ZjjWvbqQV7k90s0TYgcy8iTKVVfWmTmTt956iwMPPDDSTRJCtEMefxnrCzcwYfwEtNZMmzaN5OTkuisdMBl+fQgC5RCfDlhqAieSL0mITs3jL8Plc1cPsX/9q9e4ZNKlZA/riuPKIcwrczOqdJM5Ik0BFhtMmwS2OLBYzZ0c94Q5XVgIERVcRYuo9Few+rM8Nv+cz6C/DST7gCyzn0jpHenmCbEDCZ5EuerKOjffzN///vdIN0cI0Q7NLZzDlAUv8dXtM9i8Ko/nP3yBvfbaq2aFVdNrEkt7CiE2DWKT4Mj7IHOI5EsSopObWziH3EVT0JhxkaMSj+Hy8y4nIc3BKdfkYLVqpqSkklAcT49KD04NlGwEHYKkXmB1mKPVZkw286xJfyFEVHC41rJqrpvl766k+6Hd2OsMsxxxktVBjy57R7h1QuxIpu1EsTqVde67L9LNEUK0Qx5/GbmLpvDHy7PY9OcmDr3qEP5KmofHX2au4HWbJzyGAeVF5rLKYrMs8cw7JHAiRCdX1UdYlIU4WywhX4hLzruEYGWAU28cTlxSDAFlo8jm4NHew7lxwEHMTco0gyVGEErzwL0GitfV5EYSQkSFeVuCzHvqL1IHJLPP5UNRSmHRmnP2miBJYkW7JMGTKLV8+XLOPvtshg0bVlNZRwgh6nH53Cz9ajl/fbKIfU/fhxGn7IsOLwfME51KD5SsN5PDGkEwQqAs5rBbORESolNz+dxowGF1oA3N9w/PxLXOzZHXDiOxZxIhYGs4N1KCBovW5GbthcdqD+9Bh/uLcADWHheplyKEaEMFBQVcPOk2YhNjOPm6vemlfXT1V5BljeOQAadGunlCNEjOmKPQ9u3bGT9+PDExMXz22Wd1K+sIIUQtS2cv4eenf6bnqB4cdvkh+EN+FJAWm2qOOinNg/LCWltocyh+KCBJYoWIAmmxqSjAH/Lz22t/sPrnNRx48f7c1s+CgaZCKdCajGAQq7LisNrRVjuu5B6A1QycGCFAQXwGBLwRfkVCiNbm8/k49dRTcbvcvPjeFOL77g1J3bGn9GPSAbfKqBPRbknOkyhTu7LOzJkz6d1bkjEJIXbk8Zcxf+kCzjvn7/Tq15sjbzuMSm0GTiYOm4Rzw6/mdJ1gJWbABDMBpBEMX0UOwnFPy5QdITo5pyORicMmcdOTNzL77T/Z+/hBPHXnM4wu286+31xPntXGk10ysTmzwGLDX5pnBmC1gqRssDtrRp4oJOAqRGfldUNZPtqZxUUXX82sWbP45JNPOO3o0zjFf3KdhNNCtFcSPIkiWmuuuuoqZs6cyZtvvimVdYQQ1aoqZTisduYXLeDDv97ns2s+p8LvJffNVzh65NE1BzahIPzfP8y8JrHJULbZ3ElKP3PUiRGE87+D1L6RfVFCiDYRXB/ilyd+44CxB/Dlh1+Q5uwCWeDsMYZBZflM8hUxZcU7+HQIW0pPJvUej3P8EZA3ywzC6pBU5RKiM1s1Hc/0a3FZIHf6dt75Yhv33Xcfp512GmAGYSVoIjoCCZ50cPVLA+7Ks88+y0svvcRNN93EP/7xjzZqoRCivauqlFERqKDYX4wRNJh9/xyKN5dw8kPj+cYznaM5ml5JvcwNipaat7ZY8za5NxRvMMsS22Ph+KclcCJElNi0aROnnHIK3btlM+3lR0mz1poRHpdq/hRWAObAEpQVUnqZyweMM6vrlOVLcmkhOiuvm7nfTualzGxWz97GD19s4/j9krjl2ksj3TIhmkyCJx1Y/dKAE4dNYlTX0Q2u+80333DttddyyimncP/997dpO4UQ7VdVpQxv0Ier0gXAkjeWUjiviOGXDqX3iN74DT+ukg04tcU8wakaVh/0mQEUqwNSesOpb0DGIDkBEiJKlJeXc/LJJ+MtL+P7ifGkf3eR+cBxT5iBEWr6GBsW4pUFP5C7aAo5qTnmRZ+qAAtUD+tvUiBlT7YRQrQZT8F8nkjvzqaNXn59bhkpA5JJvmYE5YULcPY9MtLNE6JJJHjSQdUuDeiwOvCH/HUPRmpZvnw5Z511FkOHDpXKOkKIOlw+N4Y2KKksBmDD/zaxdtp6+p7Yh97H9cLl204iFtI+PNvMSQDmidFxT5jD7f0ec7j98U9DL5kKKES0MAyDf/zjHyxcuJAvLu7G4G7xZjA16DP7hh5jIC7VrMbjL8fhKQDAAXidWbh87rrHK6umm9tVqRWA2ak92UYI0aZWuldTVKaZ/cA8HIl29r95FOXxMax0r2akBE9EByNn0R1U7dKAhG/rlA8F8LpxrfiNk8afSExMDNOmTZPKOkKIOtJiUwnpEFprti3ezqKXFpMxIp3BFw4CwBPw8Lcty3CiwJ5gDrmvOjGa+Buc/Yl5KycsQkSVO+64g08//ZT/3HoJhwxNqpnGZ4utKVPudZO2fTWqvBA/CpQFPwpVXkha7UNQr9vsV5S1bj/jdTf85Hu6jRCizW0LBPnzwbkEPAH2v3kkMakxAJRaVIRbJkTTSfCkg6pdGpDwbXX5UIBV09n08kEce8qxbNywjqnP3EyvXr0i1l4hRPvkdCRyweALKS+oYM7D80jIimfU9SOwWC1YlZV0RzK9goGGT4ziUiFzsAyVFyLKvPvuu9x3330MGpdD0SGV3JjZjbnW8INBnzkarWgJ5B6E87OLmJi3GAONVykMi4WJ7m04faU1OyzLN28b6md2Zk+2EUK0GU9pHhvWfc9zD06leFUJI64ZTnK/ZAAsWjOo56ERbqEQTSfTdjqoqtKAVbkKqsuHOhLB6+b9H2/hlq/KWb+inFFXDWNj2Vsc6L1ATnKEiHINJZkemjiclY+vRmnF/v8ZjT3BjlVZSYvtgt1iJS0UAnw1Q/KVknKiQkSpWbNm8a9//YvsYdkcfs2hxDni8CdkkQvkFG7GiYYj7oEfbsdjseNKcJJTtJSHV/2OK3Nv0gJ+nEagbh9SP49SY/qZPdlGCNF6auUfmrvsQ3LXfMTcT9Yxe+oajv/7vsSM6YrWGgtwTtcDyeqSE+kWC9FkEjzpwEZ1HU1OXBYu1yrS0gbgTOoBQMGm33ngj0rWf7OJnNP60vvwbnyA4rBNv5M18IQIt1oIESkNJZneN30E5557LpvXbeazL6fi71nCtLVfYLNYsFqsTBx+Kc4+E+rmN5FyokJEpU2bNnHqqaeSmZXJkXceQVxsHACO+DS8thhc+92Ks+u+UJbP3Jg4clMzzP4m5SAmblrMKM92syJX/T4kLnXHPEq762f2ZBshROuolX9ojYJnM7qzZfZWZr+/hgEHZ9H7pC7cMPoWtlUU0jtjuARORIclwZMOyuMvw7ViGmk/3UcvbYARgoOuhyFn8cH337PwtWV02y+ToX8bgAKCwIbKrWRFuuFCiIioSjINYFUWDG2YSaen2fn666958cUXOaGvFT6/iaN9Lly2GNJikszAiZQTFSIqVY1UcwS8FBeu4exzbqS8vJzvvv6W17bl4g/5q5PWK4uVtG6jwZGIJ1BObkoXLIaBQyn8GnJ7DiVn1C04s0c03IfsST8jfZMQkVcr/9AHCQm8n5SGa30Zvz67hPQByYy7bDABpbAbIcYMOjPSrRWiWSR40gHNLZxD7sIX0cUbUJndmOgqImfbalzf3cLWqQ9yx1OFJPdKZPQ1w1BKYWBeZe7d7YBIN10IESEun5uKgJeyQE2egYLvC/nt6T+46qqruOSfZ8GUMVBRhFNZcPq9UOmBr66Ai+fULScqhOj0qkaqeX3FFPtKmPfofDYtLuLhJy5hv333x1JoaXjqMODCQMdnmBV2tMKhFN6ELFzpe+Hc3WiSpvYz0jcJEVnhPENrHLG8n5SKz+3jzwfmYnfaGXHzKPx2CxYgLW1AZNspRAuQ4EkHU12i2DBwaI0fxXOp6TiSU6n0BPj0yfnYVIC7H/wX39vXEMQMnJwtcwuFiGoOq51SfwkKhcVioWhREb8/N4vDjjqMxx9/HLb8aQ59NzSoEBhBc8OSjbDkQxh9SWRfgBCizVSPVNMhSv2lLP1gNRtnFTH0/Bx+67mBX9ZOZ2y/ceSk5uyQQwkgbcs8VMVW/MqCQxv4E7JQjoSapPZCiM4jMZu5MXE8m5qGP6CZ9dB8/GUBxt43htjUGCoDAa7sP6E6vYAQHZkETzqY6hLF9ngArBgU2xx08Zbz3ZNLKNvm45S7RnHBIRcwISaRDVsXytxCIaKcx1/GctcKEuwJlAfKKcsv448H/yQxK5FnXn0G27pv4fOJ4AlXqdC1NlYW+O0xGHKWXN0VIkpUHWtYDINNP29h5Udr6HVUDwac1AeAN5a/zb49DsYZCuL0ecBeEzjB68b57S1MjE0gN6ULXm1BVRTVGZkihOg8CgwfL3bLweop4q/nl+NeVcLoG0eQ2jcRtOa2vf9J/2H/iHQzhWgREjzpYKpLFOsQjqSeeEvz0Foz67VVbFpSwnFXDKbrwBRc9lh6dcmRoIkQUe7XvJ95ceELeAIeQjpEqCLErPv/RKE48Z7j6ZuSCq+fAeVbG96Bs7sZQKkqTSyE6PSqjjUKVm5n7nOLSRucyvCLh4BSKMBideBaMQ3nj/fUbHTcE2YOkvAQ/lEhyHG5cFkspPnKcMZmRuS1CCFaz9zCOTw//2lcAQ+rvtjC5p+2kPO3AWQfkAVac467kP57jY90M4VoMZZIN0A0jsdfxsbSjXgCHk7qfypBI4jX5sCS0ov8Gfks+q6A0af0Yq9Du6ESupKW3DvSTRZCRNgvm3/m0TmPUOIvIaRD6JBmzmPzKMkr5bBbDuH642/A6Ss1p+gohTnJT9XswGIHi0XKfwoRTbxunMWbONF5FNPv+h/xaXGMvnEkVpsZOEmxJ2G1WEj76T5QVrAnmLczJpuJI2uVEHZqTa/KcrN8sfQhQnQqntI8nv3jPlwVW8n7fTNL3l5Oj0O7MfbkXnQJBXg0fxNnH3q/XHgRnYqMPOkAqpK2VQS8lPpLSI5JIcbq4JT+p1C51M+U3Nfpc2AvRl18OIbdbpYWlaGxQkQ1j7+MN5a8VmfZ0jeWUTRvK8MuHUr3Ed3ISc2BUBAsNtBVc3V03R0pi5T/FCJahMuNllca3P7kBqyVFj75aiqkevhg9UdYrA6sVjsTex6Hc8WvZuAEwBZr5kwqy4fMwVJCWIjObtV0Vs64Cne3/pSsLWH+UwtJGZDM8MuHopMyuLLf6fQ/8Qj53ItOR4In7VxV0jbD0JQGSgAo9ZfQNS6LN354g6+vm8HQoUOZ8cV0/LbADknbhBDRyeVzY1FWDAwANn67ibXT1tP3xN70Oa4XSilcPjfOpF5w/NMwbRKUFYAOAlZI7AqH3QlDzpSDHyGiQbjcqKEt/POdAv7aXMm0Sd04ulsCpI/igD5H1SSHDYUTSgd9ZuAk6Ks7Qk1KCAvReXndzP3map7o2puK4gB/PLQAu9POmJtG4LArrugxjpE5p0e6lUK0CgmetHMunxtv0EdJZTFBHTQrZWChsrSCr26fjsPh4PPPPycrTYbDCiFqpMWm4va60Gi2L3Gx8KXFZIxIZ/CFe2PBgt1ir6l8MWAcXLYQti0HXynEJkH6IDnhESKahHOV3DLdzSfzi7n/1AxO6L4NPj4PbDE4j3sC54BxNevvbnSJlBAWolPyzJ3CUxk9cBtW5jw0m0BZgIPvG4MjLY7kgI+Bzm6RbqIQrUaCJ+2cw2qnpLIYtJmJQGuDQCDI9P9+gafIw4xvZ9C7t+Q3EULUVVhRiM/wUV5QwZ8PzSW+azyjrtsXq4K0YCWTuoyuO0otLhV6Hhi5BgshIisxm/sWeHn4q3wGHdmNjWftxdyClYyKTYZQAL6+GmLegIxwYFVGlwgRXbxu2Lqcb5e9g7tLN/56aiHulcWMvnEEyf2SsWjNOcVbcWaNiHRLhWg1Ejxp5/yhAEmOZMoCpaigOQB/Se5Stix2c9f53Tl6eP9IN1EI0Q4t276cQEWAP++fAxrG3DIKe4Kdkz1lnF1chHPLY7C3lB8WQph+mDufO9/cRLe9Uzj2XwMIocntMZQcdzFOfzkUb4BPzFEo1ZV1ZHSJENFh1XQ831xPntJMTc9m9Sdr61bWAVJCAcYeeo/0CaJTk2o77VxabCrx9ji62pPoEfTj/nId62ds4spDY7iz31Z44ygzwZsQQtQyMGUA8x5fgGdLOaP/PZKEbmZix8O3LMVZvB7c62HJhxFtoxAiwrxuKFpK3qrFnD3hHBLSEzj5vtMhvR9Y7YRQuBRQssEc/hqTVLeyjhCi8/O6mTvzVm7M7MajGT1YNtfN8ndW0v2QbAacUXMR99Scc3EOOjVy7RSiDUjwpJ1zOhKZmDYSyrawbsFWfnt9BWOHxvLUkYZZQtQWKwcxQogdPHPPsxTN3crQiwaTPrwLAEe48ujvdZlD8HUAZv5X+g4hotWq6ZB7EOVvncbJR47G5/Fw/H+Pwx8XIq/SxRZHHFutVjZpn7l+Ui+zMpct1qzOFc6RIoTo3DzuNeSmdMGConxdKXPDlXX2uXwYSikAUqwJHD3orAi3VIjWJ9N22juvm1E/3MuKbX5yn95Oao8Ecq7bh/nF6xhldYI9vqY8oAyTE0IAtz9xO88+9SxDT8ph/6OyGbU9n2OLVtG/0hNeQ4PFDhXbYOty6CW5ToSIKg1V1rmkB0lHTuTxRS8AoJSFlNguvNMzg30qAzgt4UPG+pV1hBCdmsseiwYCbi9fPLyAWKfNrKwTawcUyY4krho1Wap9iqggwZP2butyNrq2c/nrYLVZOO26QcTGWsntPpic4hKcAa8cxAghqk3/bjr333g/PUf14KhzuxHUBosSEvlH0FdvTQvVmaiFENElPGrkzukus7LOhB4MHZ5ChTbItCdgLd+KTWusleV4nVm4Dr8D54/37LyyjhCic/K6SQv4CNnS+PzRGfg8AU6/ezSpWdlcPPp64m3x9EjsIYETETUkeNLOBYJBzv0gQNm2EGf+ZyhJGbGgDbzawBXy4dQhOYgRQgCwdu1a/nbW30jKTmT8f47CUrkBR9CP12LFZYvFGfLUrKxDkJhlliQWQkSXxGzenVPCvV8VcMohmWw8M4cHlEKv+hB/xTbiNViVBb/WqPJC0nofChN/k8o6QkSTcJLY7RYL69/YTOGqEo697XC6jOzPxOGXMrLrqEi3UIg2J8GTCPP4y3D53KTFpu4QtdVac/UDr/HbmiBHXpZDek4KaAO/sqCUhbRxT0HWCDmIEUJQWlrKSSedhNaaY+4+CuIVeINmfwGk1R95ktQdxr8o/YcQUWjWwpX8671tHLxXPF0vG4oVhSOxB36lqASCFgshQCnFRFcRTl8pJPWQ/kKIaBFOEpub2Y25U9cze5abi07J5varnyEtubeMNBFRS4InETS3cA65i6agMUfOTxw2iVFdR1c//txzz/HiK69x4z+P5ay9/iJXZeFVFpTVzkT3NpwJWXIgI4QgFApx7rnnsmLlCk64fxwJ2U4KfNtJssUQbwSYuHkpzlCwZoOhf4MTn5X+Q4gotGnTJk499VS6de/Bs9Pe4KX1uTgcTrBYcRghQlpzjXsb8RY7aX4vTiMgU4OFiDIFeb/zYkoX8mYXMfvd1Qw4OAvb3waTFvBJ4ERENQmeRIjHX0buoilYlAWH1YE/5Cd30RRyUnNwOhL55ptvuOaaazj55JN54PlXseQeSM7WfFyxSaQFKuVgRghR7cYbb+Srr77i0KvH0mvf7jiAuNh0Av6N3LFmNlmBqlEnFkjqKYETIaJUeXk5p5w0nnJPGd9+8TFdevYluNGCzwgQa7Hi1yF0fAa4tpLmK8OJlqnBQkSZufNe4sU1H7FmUwU/PbuEjL2SGHfpIAJK4bLH4ox0A4WIIAmeRIjL50YDDqsDwrdefzmuLXPIK47nrLPOYsiQIbz99ttYEhLh+KdxzpiM01cmydqEENVeffVVHn/8cc6/+HzijgvicK8BIFYbaG3gt9Tr5o97VPoOIaKQYRicf8ZxLFi4kGkX98A370r+WziYoMXKdu92khzJKEBZ7TzVbzQqFGTi4AsY1evwSDddCNFGPKV55K75iIC7kj8emIcjwc7om0cSdFhRCV1JS+4d6SYKEVGWSDcgWqXFpqIAf8gPgL/ChS7ZwJZPrmL8sYfisMK0adNITAwPjRswzkzWdvYn5u2AcZFrvBAiIjz+MjaWbsTjLwPgp59+4tJLL+XYY4/lyQfuQpUX4keB1viNEApt5jpRdrM0sbKauU6EEFHnrv/cxMczfuXRU7ty2MgspqR0IeQpJMkeT1Z8NlaLBbvVTrw9njh7AhZ7HLkr36vub4QQnZ/LtYqAP8Q3jy7EX+rngJtHEJMaS8DZlYmjrpUpOyLqyciTtuZ1w9blOBVMHHguuSvfw+svx1dRiA7BhNfzyXcHePm63vTOTKq7bVyqXDEWIkrVz5F0XNLxTDz9Ivr168cHH3xAin8LE4u3k5uajjcYMhM9bl4SznWiwOIANOjIvg4hRBvyuqEsn/e+/o17HnyUf41JYvIRaUx32Cmy2VFag2cL6bFdUKEg2gIOmxUCPhxWu1nZz+eWEyYhokRq6l789NIy8leVMP664fTslUAgFOSeMXeQ1SUn0s0TIuIkeNKWVk2HzydBRRFoGOXMJOeEZ8izxfDEvMf49dUVbF7k4qjLhzB3dDZnu9fgjBu9+/0KITq1+jmSPKUeLrzoX2Bopk2bRkpKCng1oyq95ORvwOXbRpq/EmegvGYn2oCErpAhpYmFiAqrpsOMycxe7+XCpzZyyLCevHBcAeWlG/gsMxO0RgFaG2wtzyc9FMSiNX4MHFjwo1DOrqTFykUbIaLFs8+/zcqfC9j/7H70OCATgEv7T5DAiRBhEjxpK143fH01VBThsTpw2R2keV04v7mR+NNfZ+mMPJb8L4/RJ/dh+GFZeEGSMgkhgLo5koyQwbf3f09JXjFvf/oOAwYMMFeKS4XjnsD59dU4vR5zeEpCNni3maNNknrCic/J6DUhooHXDTMmk1dscMqUzXRLtvHJCQU4krMoCJWjtCbD72WbIw6MAKA4vbycDPc6crNz8NpsKK13rNQlhOh0PKV5uFyr+PW3Ndx6662ce+65vPTCQ7jdq0lLG4AzqUekmyhEuyHBk7ZSlg9GkLnOLuR2G2gOvdcwsXgreV99xa+vr6TP6HQOPrevebVHkjIJIcJq50j67cXfWTdrPYdcfTAnHTe+7ooDxkGPWbDkQ/jtMVAWiHHCQdfDkLMkcCJEJ+fxl+HyuUnzFKEqDU6Zkke53+DbK/uQHpOHJzaFCns6WinshqZH0I9Xh7AAYz3FOD1uctbMwZU+gDRlw1lZYR6/SN8hRKf065xneH39NNzrSvns9j8ZMqQPubm5xMXFkZjcM9LNE6LdkeBJW0nMxmN1kJvRC4vWOLSBH8Wj5fF8ffsD9M+O4bAbD6cyMQFltTFx+KUyx1gIAYDTkcjEYZO4+oGrmP/RX/Q7sS97Hd+fFe4VjOpab2pfXCqMvsQMlpTlmyXN5cRHiE7N4y/j582/8NnqT1DKAqEgK98tYsGmCqZdvhdDsmzMDaaRm9EVDVRqA78VYpQVmxFiYv4KnNZkQOM0Ajg1EPSa1f0SsyP86oQQreGXtdN5fONX+EqCzHz4L2KcdkZd3ZNQYDvEyWgTIRoiwZO2EpeK6/A70MtycQTNCjuGp5KvHl2C3Wbhfxf3JL1oKa7DPyAtubcEToQQ1Tz+MqZ/N53fn5tF5r4ZDJ84FKUs5C6aQk5qTsP9hSSYFiIqzC2cw5SFL1FUUQRARnwm895awJx5xdx/WldOyLHisVjI7X8QFl8JVm0QwgBl45rSUnp4inB6XJDkhPhMc8pfIBw4Oe4J6UeE6IQ8/jLeWJJLyB9k1iPz8Zf6OfS+McSmxuByrZKpOkLshARP2orXTVqXgajk3vi1gdUfYNoDX+PZ5mP65IH06eoEvwentoAEToQQYXML5/DYjEf4+MpPie8az/7/3g+r1UpxpZv0uHSphCFEFKtKJq21RimFQjF/xnzmvDOPQeNy+NuL/wdYcSkD/eeDBFQp+bYYwEyFtJ4Qg6wxcPo7kDG4ZpSJjFoTolNzlWxAVbiY/8IyXCuKOeCGfUjpl4QRCpGWNiDSzROi3ZLgSVsIZ7x3AufFxvNa1gB+fWk+eYvd/OfC3hzT3wn+cgj5wR4X6dYKIdoJj7+MF/54jul3fIPWsP+to1Hx5mNaawxtSCUMIaJYVTLpWFssANtXuJj79Hyyhnbl0KvH0iWlDzgSSSvNQ5cXstVqw0JNxfKp2TmMHfvgjleZJWgiRKeWFvCx8PONbPopn8Hn7kX2gVmA4oKuB8moEyF2wRLpBnR64Yz3KCtzE1J5JzmNhZ8uYPlXKxlycm9cx/ZhbqULiteaGe/fOcEMtgghot7W8m387/7vKN5Uwpgb9yOpmznCJKRDAFww+EIZdSJElCooz2dd8RpCRpBQKEDstgC/3z+L2NQYjr02h4u77lfdPzh9pZxSVgxKVQdOMkJBlMWGCyNir0EIERnf/rycWe+uZq+Du3LwqX3oEgxx3fYCDt7v2kg3TYh2TUaetLayfAAKbA5edCaQ/9d25ry+kuz9Mhn4z70hFCA3syc5KganIwGCPjPY0mOMXPkRIgpVV8uITeWJ/z7BxtmbOOyaQ8gZ2YOtlW4UkBbyc2Hfkzm4xyGRbq4QIgI+WP4eH6x4H41G6xA2j4+Z987GqAxy95U9OL9oAVk/rIW9w1W2ErM5xFvBZ4kBNIpYbRBCYVhtMnpNiCjz119/8feJl7LfsIF8eYYF7/YC0gwD57GPybmHELshwZPWlpjNXAu8mJLMugIfPz25iMQeCex/zXCURaEMhYEizxFLvNVKmorDWVkmpQGFiEJzC+eY+QuA5dOXM/PJnzh74tmknehEF28gEzi1rISxFeU4C5+HIX+XfkKIKFNQnm8GTnQIQxsYhmb2c0vYvt7DgbeMZPmwZP5rGEzcsoKc/Pm40vciLTYV57GP8Y8f/8PryV3wKYU1oatU9hMiyhQWFnLyySeTkpLCZzNmkpESKzmOhGgCCZ60Mk+gnNz0bgRKvfzxwF8oq4X9bh2FJc7802sNlRYrT6alo5QFpQ0mFocYJaUBhYgqVYkfLcpC0eKt/PTUL/QY2Z0Xn34BW/EaXB+dQ1pZAc5QANAQny5BViGi0IbSjWjMnEcAKz5YRf4fhQy+YBBZozIoBeL8Xp7rMRjH8ldRjgQUcFiPw/mx9wgsQT+GRfGPoRftWOpcCNFp+Xw+TjvtNLZu3covv/xCdnb4XEOOI4RoNMl50ko8/jI2lm4kb+tCPIbiq6dX4N3qZb+bRhKfEYcGkkJBDAXakYhNQ5wRxKIht/tgPFaJawkRTaoSP3oKypl255ckZydxzO1HUxoswxnXhV4lm3AGfWZuJCMAnnzYPDvSzRZCtLHeSb1AazSw+ZctrPq/NfQ8qgf9TupTfVCngWKbA211EGeLBa34YMX7oKwkxKUSY4vn3WVv4/GXRfCVCCHaitaaSZMm8fvvv/PWW28xcuTISDdJiA5JztBbQe2h98Ggj59fX8nWRS5GXzmMzEEpaAX/3WcyyRY7FTEJPLUkF4fFDqEADqsdrxGQ8qNCRJm02FRKS0qYdstXhEIhRt86Eh0TrqZTuBhikqFiG+ZpkQJlhR//C4NOkatGQkSRrIRsTuszninfvMH8ZxaRNjiV4ZMGo5TC0BqFImhxgMVGrN0sz6UsZqJYizLDKw6rA2/QJ8caQkSJBx98kLfffpt77rmHM844I9LNEaLDkuBJC/P4y5iy8CW01sTaYlk4bRHrZ2xiwKl96XVEdwCS7Ukkpw+iV1IvPP4yFODXIRz2WPwhv5kQUhK4CRFVQqEQPz30C57NHg6+6wCc2U6UUrD2O/jmRvAVAxqsDgifAGGxytQdIaJIVULpMcVWJt/3J/EpDo6aPISg3UKssuKz2EiyO7Ha4kgOBQjpEFasaEOjoHqqjxxrCBE9pk6dyq233sq5557Lf/7zn0g3R4gOTYInLeznzb9QVFGIAgoXbOX3F2fRdXQmg/82EA2k2J3ExqZUH7A4HYlMHDaJ3EVT8AZ9KGDisElyJUiIKPPvG28k78/NHD5xIINz4rBVevDb4nHNvAOn1QGJPaBkHYT8YI0BZxZYbGaSNyFEp1c1qtVf4eOzaz+lsjLE9+fbydq2EEdhEH9qXxwo/IfcQlrOSaxwr6g5tlBwds45/Jg3U441hIgSBVv+5NsfPuKSi59hv/32Izc317woI4TYYxI8aUEefxmfLXsLQgE8mz3MfngOzp5ORl07HKvVHDJbHPBw/dCJdQ5YRnUdTU5qTnV5UjmYESK65ObmMuXZKQw9rjtDjsrCEfTjVxaUp5C0gB8cTrDFmiuXbQJnV7DHwXFPyKgTIaJAVUJpFdL8+PBMtq0v44QbhjI4ZQNOX6m5UjAIVhv8eA/sdXyDxxYn9hsvxxpCdHZeNx98cwVvbM9n5k1/oGM1F149kLi4uEi3TIgOT4InLchVsgHlKSKxzMv/HliAslnY/5ZRdLWHSAxBCEUATU8Vs8O2TkeiHMgIEYV+/vlnLrvsMo4eux/3Hr2RN5TCa7GiNEzcvBQnMeYIE1ssOOIhpS+c9gakD5LAiRBRwuVzo/3lzHv1V1b/upFD/9aHXiO74Nq0Bac/fCVZYfYTfk/1dL76xxZyrCFEJ7dqOmtmTOad9J7MengB/lI/h943hv/Fb+e0LX+S1W2/SLdQiA5NgictKC3gwwgafP/kUiq2ejnh9n2xZ8QQW1mB1WohBFgVpKUNiHRThRDtwLp16zj99NPp27cvHz5/D6mfnsyQtXNxOWJJ8/twBv1w9IMw72XzhEgpOP5p6HlgpJsuhGhDaVhY/c1fzPp4PUMOz2boiT3QaNL8PkDXBFiDPrOfkOl8QkQfr5tff7iFFzK7M+/5JbhWFDPmhn3o0i+JILBh8+8SPBGimSR40oKcqf1xvbyQvMXFHHFpDj0HJnFY4Rp+TO2GV5kXhSb2n4AzqUekmyqEiLDS0lJOOukkgsEg06ZNI7VnBjgzcZYX4fQGAA3OTBhxgflTlm+eEMloEyGiztLffuHn55eQvXcKB108BG3VTMxbjDM2FWJTAEtN4ESm8wkRlX6Z/QSPp2ex4tM1bP5xCznnDKDrQd0IaQMF9O4uF16EaC4JnrSg5159l49+3M41RyRxXZ8i0tZswhmbxokHTcKV3J20tAESOBEiynn8ZWwt38blf7+c5cuXM2PGDAYOHGg+eNIU+PpqMAJgsZujTKpOguRkSIiolJeXx6kXTqZbko3vzs/GWl5Kmt+L05ZUM4UPJMAqRBQr2PInuYW/s2Xedpa+s4ruB2cx4Mz+aECjOCeul4w6EaIFSPCkhfzvf//jmmuuYfz48Tz23mtY3atAAxmDcMal4ox0A4UQEffL2um8tvQ1Zr+6gMVfreTmB2/iqKOOqllhwDjoMUtOgoQQAJSXl3PKKafgKS/nf+8/Q9+1T4K3pOEpfNJfCBGV5s57iRfXfMT6vArmPLWQ1P5J7H/5UDQap7Jx16h/07/XoZFuphCdgiXSDYgUpdRRSqlXlVIrlVIVSqm1SqlXlFJNnii8YsUKzjrrLPbee2/effddrM5084Cm14FyMCNEK1NKxSilHlJKbVFKeZVSs5RSxzRiu7uUUrqBH99O1p+olFqmlPIppVYppa5qSjt/nfMMjy54irnTl7H405X0HdeTgpzlePxldVeMS4XMwdJ3CBFBzehXTlNKzQhvV6mUylNKfaSUGtrUNhiGwQUXXMD8+fN57733GHLCJTDxNzj7E/N2wLg9e3FCiBbTjL7idKXUB+Hzjwql1Aql1GNKqZSmPL8R8pO75iMC7kpmPTAPe7yN0TePglgbCsWl+1whgRMhWlA0jzx5CEgD/g9YBfQDrgTGK6X21VoXNGYnbrebk046CbvdzrRp00hMlCz2QrSx14EJwJOYn+ULgK+UUkdorX9pxPaXAZ5a90P1V1BKXQK8CHwMPA4cAjytlIrXWj+0uycwQn5eXfc5W1eWsfClxaQP78LgiYMpNirJ2zKbQX2O2t0uhBBt63X2rF8ZBriBp4BtQBbwL2C2UupArfVfjW3AXXfdxUcffcSjjz7KiSeeaC6MS5XAqhDty+vsWV/xMrAFeBvYiNl3XAmcoJQaqbX2NubJg6FKAv4Q3zy2sLqyTnxqDMnBIBP7nczBfSXIKkRLiubgyXXAL1pro2qBUmo68CNm53Xb7nYQCAQ488wzWb9+Pd9//z19+vRptcYKIXaklNofOAf4t9b60fCyN4HFwMPAQY3YzUda6227eI444D7gS631hPDiKUopC3C7UuplrbV7V08QDFVSVlTBnIfnEZ8Zz+gbRmCxWkAb4FoLEjwRot1oTr+itf5vA/t7BcjDDNRe2pg2vP/++9xzzz1ceOGFXHfddU1/EUKIVtfMY5AJWuuZ9fY3F3gDOA94pTFtsFlj+OnlZeSvLGH8dcPp2SuBQCjIPWPvlxwnQrSCqJ22o7X+qXbgpGoZ4AL2bsw+rr32Wr777jtefvllxo4d2xrNFELs2gTMkSIvVy3QWvuAXOBApVTPRuxDKaWSlFJqJ48fAXQBnq+3/DkgAThxt0+Ane8e+gsd0ux/6yjsTjsAyUE/PXo0Jr4jhGhDLdGv1FYEVAApjVm5vLycCy+8kLFjx/LCCy+w865JCBFhe9xX1A+chH0avm3UeQhA0VYXK38qYL+z+tHjgExQcGn/CRI4EaKVRPPIkx0opZyAE3Oo7S4VFRXx/PPP8+9//5sLLrig1dsmhGjQCGCl1rq03vLZ4dt9gU272cdazM99uVJqKnC91rqw3nMAzKm33VzACD/+9q6eYMPGPDweL0ffsg9x2XGgNclBP1eljsCZOWQ3zRNCtLFm9yvhvAV2zGk71wJJwHeNefI1a9bQvXt3PvnkE2JiYhrfaiFEW2uJY5DassK3uz0PqbJ582bOOeccXn7xYdzu1VLZU4hWJsGTuq4FHMAHu1tx06ZNjB8/ngceeKDVGyWE2KlsIL+B5VXLuu1iWzfwLPA7UImZx+QKYH+l1OhaB0PZQEhrXVR7Y621Xym1fTfPAUBJSQnPPfcc//z7yeSt/w58xfToc7QEToRon5rTr1T5A8gJ/+4B7sW8Gr1boVCIadOmkZGR0ZjVhRCR0xJ9RW03YY5k+aixG8THx/Pqq68SFxdHYnJTB8UJIZqqUwRPwrkHHI1cvVJrrRvYx6HAncCHWuvvd/I8FwMXV+3niy++WGyzdYo/YWeRThOi9aJNLNZaN7nKRBPEYQY+6vPVerxBWuun6i36WCk1G3gHuBx4sNY+/DvZjW9nz1G/v7jiiisWX3HFFTtrjmh70l+0L63dVzTFHvcrtVyIOdqkX/j3OMCKOVptB/X7i2HDhi1uSoNFq5P+on1pL/1FS/QVACil/gZMBB7WWq/azbp1+ov4+HjpL9oP6SvanxbtLzrLmf+hwA+NXHdvYHntBUqpQZjzDBcDF+1sQ631y4TnNSql5mitR+9Ra0WrkPek/VFK1Z/q0tK8QEPj2mNrPd5oWut3lVKPAUdTEzzxsvPgbOzOnkP6i/ZN3pP2pQ36iqZodr+itf696nel1PvAsvDdG3ayvvQX7Zi8J+1LO+ovWuQYRCl1CObItBnAf3a3vvQX7Ze8H+1PS/cXnSV4shzzyk5j1BleF07m9A1QApygtS5r4bYJIVpPPtC9geXZ4dste7DPTZhlzGs/h1UplVl76o5SyoGZSHZPnkMI0X61aL+itXYrpb7HrKDRYPBECNEhNbuvUErtA3yOeQF3gtY62HLNE0K0tE4RPNFaF2DWWW8SpVQXzMBJDHCU1rqheYtCiPZrAXCEUiqpXsK2MbUeb7RwxZ0+wPx6zwEwGviq1vLRmBXLmvQcQoh2bwEt2K+ExQHJzWyXEKJ9WUAz+gqlVH9gOmZFrhO01p7WaKQQouVEbalipVQC5olQd8wOa5fzCxvw8u5XEW1M3pP2p7Xfk48w8whUzf1FKRWDORJtltZ6U3hZr/D0PGqt11A2xsuADMyDmSrfY5Ywv6yBdSuALxvRTvnfbH/kPWlf2tP70Zx+JbP+zpRSfYCj2LFi1860p7+FMMl70r60l/ejOX1FFuYFXAM4Tmu9dQ/b0F7+FsIk70f706LviWogd2pUCJckPQV4lR3zpXi01lPbuk1CiKZTSn0InAY8AawGzgf2xxxN9lN4nZnAYVprVWu7CszKWoswk7uNBc4B/gIO1lpX1Fr3cuA5zAOlGZiVef4J/EdrfX8rv0QhRBtrRr9SiFmSeAFmRa8BmEkg48Pb/tZ2r0II0dqa0VcsAPYBHsY8DqmtUGv9v1ZvvBCiyaI5eLIe6L2Thzdorfu0XWuEEHtKKRUL3AP8HUgFFgK3a61n1FpnJjseuEwBDgJ6YiZ32wB8DNzXUO4jpdQk4HqgL2ZelGeBpxqq3iWE6Nia0a/cBZwI9AcSMYfj/wTcr7Wuf4IkhOjgmtFX7OrY4Uet9eGt0mAhRLNEbfBECCGEEEIIIYQQojGiNudJS1JKHaWUelUptVIpVaGUWquUekUplb37rUVzKKVilFIPKaW2KKW8SqlZSqljIt2uaKWU2k8p9axSaolSqlwptVEp9aFSamCk29Yce/p/ppS6SymlG/jx7WT9iUqpZUopn1JqlVLqqpZ/NR2fUsqplLpbKTVdKeUK/00vaOS2je6vlVIzd/L+TW9o39GquZ97pVSKUuplpdTW8PY/KKVG7mTdk5VS88KfkY3h/4N2n/y+GX3IaUqpGeHtKpVSeUqpj5RSQ9ui3Z1ZW/XronGa8X6s38n7oZVSTc1n2C40429xulLqg/D3WoVSaoVS6jGlVEobNLvTas4xR3j7Rn/HicZpgfdklFLqC6VUgVLKo5RaqJS6Will3d227f6Ao4N4CLO06f8Bq4B+wJXAeKXUvuFqQKJ1vA5MAJ7E/NtfAHyllDpCa/1L5JoVtW4CDsb8LCwEsjA/C/OUUgdorRdHsnHN8DrN+z+7DKidRT9UfwWl1CXAi5hThx7HzKvytFIqXmv9UHMa3wmlA3cAGzFz1BzehG2b2l/nAbfUWyblqeva48+9UsqCmXR5H+ARYBtwOTBTKTWqdjJ3pdTxwFRgJnAVMAy4Dchkx4TO7c3r7FkfMgwzd8pTmH+bLOBfwGyl1IFa679asc2d3eu0cr8umuR19uz9uBZw1lvWG7gXMyFrR/Q6e/a3eBnz++ltzO/HYZh98QlKqZFaa28rtrkz2+NjjqZ8x4kmac57Mgr4DfOz9RBm8YfjMb9n+wPX7HIHWmv5aeYPcChgaWCZBu6NdPs66w9mQi4N3FBrWSxmwq7fIt2+aPzBzCHiqLdsAGZC1rcj3b49fE17/H8G3BXeNn0368VhfqF+UW/525gH56mR/ju0px/M8vJZ4d9Hh//GFzRy20b315gn6Ysj/Xrb+09zPvfAWeG//YRayzIwAwbv1lt3CWYiVlutZfdiVqsYFOm/wy5eY4t+VwFdgQDwYqRfW0f9aYt+XX7a5v3Yyf5uC+/voEi/trb8WwCHN7Dsn+H9XRTp19ZRf5p5zNHo7zj5abP35GWgEkirt/xHoGR328u0nRagtf5Ja23UX4ZZ3nTvyLQqKkzAvNJTXYJKa+0DcoEDlVI9I9WwaKW1/k1r7a+3bBXmSU9H/Sy0xP+ZUkolKaXUTh4/AugCPF9v+XNAAmYCShGmta7Ueziib0/6a6WUTSlV/8qmCGvm534CUAh8UmvbrcCHwCnKLPuJUmowMBh4WWsdrLX984AK76e9aunvqiLMK2UpLdXAKNQW/bpovJb+jPwNWKc7ZnWrPf5baK1nNrD40/BtRz0Gi7jmHHPQyO840TTNfE+SMC/uFNdbng/sdnSWBE9aSfhA24l5NVm0jhHASq11ab3ls8O3+7Ztc0RDwgeWXem4n4WW+D9bC5QAZUqpt5VSXRt4DoA59ZbPxbyqPgLRanbTXw8EyjHfuwKl1D1KKXubNrADasLnfgQwr35AC/PzFY/5969aD+p9RrTWWzCnVrXnz0iz+5DwnPkMpdQw4BXMg7/vWrSV0aUt+nXReC12PKeUGoEZKHi3ZZrW5lr62DYrfNtRj8E6usZ+x4m2MxPzO/QlpdTeSqneSqlLgdOBB3a3seQ8aT3XAg7ggwi3ozPLxowS1le1rFsbtkXs3HlAd8y5iR1Rc/7P3JgljX/HHCJ4CHAFsL9SanStg6NsIKS1Lqq9sdbar5TavpvnEM13LQ3312uAH4BFmCOAJmAOBx8InN2G7euIGvu5z8Ys5Vtf7c/XovB6tZfXX7c9f0Za4rvqDyAn/LsHc7pSbvObFrXaol8XjdeSx3PnhW/faVaLIqelj21vwhzJ8lFzGiX2WGO/40TbmQIMAS4BLgovCwFXaq1f3N3GEjypJ5zYx9HI1St1eJJUvX0cCtwJfKi1/r4l2yfqiMM8cKnPV+txEUFKqUGYU09+B96IcHP21B7/n2mtn6q36GOl1GzMg7rLgQdr7cNPw3y7eg7RPLvqr7XWE+ut/pZS6mVgklLqCa31H23Vzo6kiZ/7xn6+qm53tm5SE5vZllriu+pCzNfYL/x7HGDFHJkmmq4t+nXReC1yPBc+hj8HmK+1XtZCbWtrLXZsq5T6GzAReFhLYtJIkXOVdkZrHVJKrQFmYCa69wHnAs8opQq01lN3tb1M29nRoZjznRrzk1N/4/BB46fAYmqiWaJ1eDETBtUXW+txESFKqSzMDOMlmImyOmolghb9P9NavwsUAEfXe46dBW1jm/oconH2sL9+LHx79C7XilJ78Llv7Oer6nZn67bnz0iz+xCt9e9a6xla6xeA44C/04jhxWKn2qJfF43XUu/HYZgj3jrqqBNoob+FUuoQzNFpM4D/tEzTxB6Qc5V2Ril1M+aIrHO11m9qrT/UWp8G/AI8p5Ta5eASGXmyo+WYV3Uao86wunASp28wDxpP0FqXtXDbRF35mF+S9VUN75ZyohGilEoGvsZMaHhIOC9BR9Ua/2ebMMvl1n4Oq1Iqs/bUHaWUAzORbEf++7VLzeivN4Vv03a5VhTaw899PjWfpdrqf77yay3f1MC6s2m/WrQP0Vq7lVLfY05PuKGZbYtWbdGvi8ZrqffjPMzRWO+1RKMipNl/C6XUPsDnmBcGJtRLsi3aVmO/40TbuRz4Xmvtqbf8c+BxoA9mdasGSfCknnDm3tebup1SqgvmgXgMcJTWuqH5iqJlLQCOUEol1ZtjPKbW46KNKaVigWmYeSGO1lovjXCTmmsBLfh/Fk6k2QeYX+85wCy39lWt5aMxRwg26TnErjWzv+4Xvt3a4g3rwJrxuV8AHKKUstRLqDcGs6LMylrrgfmZqA6UKKW6AT2oVZmiHVpAy39XxQHJzWxXNFtA6/frovEW0Mz3I1y15AxgZge/YLOAZvwtlFL9gemYVblOaOAEUbStBTTuO060na6Y017rqyoGsMv4iEzbaQFKqQTME57umB2VzCtsGx9h/vNfXLUg/OV5ITBLa13/6qRoZUopK2bSzQOBM7XWv0e4SS2hUf9nSqle4Wkg1Fovo4H9XQZkYB7cVPkes1TuZQ2sW4E5DUI0kVIqWyk1qHZ1nMb21+ESpDH1linMhLFgDoUWNP5z39D7gfn56oqZ5b5qvXTgTGCa1roSQGu9BHNk6MXh56tyGaBp38kQm9OHZNbfmVKqD3AUO1bnEo3XFv26aLw9fj9qOQFz1FtHnrIDzfvfzMK8MGAAx4VL4oo20pzvONE6dvKerASOCV9Iq1rPCpwFlGEWC9gpGXnSMt4B9gdeBfZWStWupe7ZXeIZsWe01rOUUv8HPBA+wFwNnI959ad+okfRNh4DTsa8Ap2mlPp77Qe11m9HpFXN0IT/szcx51urWss2KKU+wMyk7gPGYiazWwC8VOs5vEqp2zHnWv4f5on5IZh5Df6jtXa1zqvruJRSV2IeKFdVHjhJKdUj/PszWusSzJwQ5wN9gfXhxxrbX48E3lNKvYf5nscBpwEHAy9rree1wsvqqBr7uW/o/fgIs5LMa0qpwZjlNC/HPHm4s97z/BtzWO03Sqn3gaHAlcAr7Tk5ZDP7kEVKqe8w+ww3MCC8jR24udUb30m1Rb8uGq+Z70eV8zATc37cuq1tXc38W0zHHB35MDBWKTW21mOFWuv/tWbbO7NmHHM05TtONEEz3pMHgbeBWcosAuDFTBg7CrhNax3Y5RNrreWnmT/hN0Pv5Gd9pNvXmX8wEy49gjmn0Ic5nPu4SLcrWn8wa6fv7LOgI92+Zryu3f6fVb32esumAEuAUsxqOqvCnXbiTp5nEubV9UrMA6ZrARXp198ef3bT7/YJr/N67fuN2G59rfX6Ah8C68JfrOWYV/ovkfdkh/eiUZ/7ht6P8PJU4BXMg8ry8P5G7+S5TsWcGuHDzDFxD2CP9N+gEX+jPe1D7gL+xByZFgA2Y+ZzGBbp19TRf9qqX5ef1n0/wsuTwv30x5F+HZH8W+yqH8aczhTx19ZRf3Zz7NAnvE6zv+Pkp83ek+PC78NWzGPuhcAljXleFd6BEEIIIYQQQgghhGiA5DwRQgghhBBCCCGE2AUJngghhBBCCCGEEELsggRPhBBCCCGEEEIIIXZBgidCCCGEEEIIIYQQuyDBEyGEEEIIIYQQQohdkOCJEEIIIYQQQgghxC5I8EQIIYQQQgghhBBiFyR4IoQQQgghhBBCCLELEjwRQgghhBBCCCGE2AUJngghhBBCCCGEEELsggRPhBBCCCGEEEIIIXZBgieiQ1JK3aKU0kqpq3byeD+lVKVSarZSSrV1+4QQ7Y9S6m9KqXeUUiuVUmVKKbdS6k+l1IWRbpsQInKUUkeFjyn+W2/5mPByrZTqU++xt5VShlIqp00bK4RoF5RS6eE+QO/mp1IpFRvp9oqWYYt0A4TYQ3+Fb4fu5PGHAAcwWWut26ZJQoj2SimVCLwJ/An8DBQBmcApwKtKqUyt9UMRbKIQInLc4dvEestvqvV7GrAeQCnVDTgLmKa1XtHqrRNCtEdOoHbAtQ9wPjAX+KLW8q1aa18btku0IgmeiI5qYfh2SP0HlFIHAxOAD7XWv7Zpq4QQ7ZUGemitC2ovVErdBqwELsQMugohos8OwROl1EDM4OpU4FQgtdb6VwB24JG2aZ4Qor3RWq8H7qq6r5S6CDN48r7W+tEINUu0Mpm2IzokrXUe4KJe8CQ8RedxoJK6V4yEEFFMa+2pHzgJL88HtmBeVRZCRKeGRp7cAJQBD4fvpwGEh99fDMzSWv/SZi0UQrR3+4ZvF0SwDaKVSfBEdGQLgRSlVI9ay84F9geeDEeEhRACpVSqUup2pdQf4Vwnoar5yMBAIC/SbRRCREwJYBAOniilugL/BF4CNoXXqQqw/gNIB+TKshCitn3Ct3/tci3RoTVq2o5Saj3Qu95ijRmRXw58ADynta5s0dYJsWt/AYdjjj7JC18Nuh8zl8H9EWyXEKIdUUoNB74BugKzgfeB7UAQ6It5kiQHO0JEKa21VkqVUDPy5BpAAU9hHutCzbSda4A1wCdt2kghRLsVHvk+HNiitd4a6faI1tPUnCczgKphzzagJ3AQ5pX+M5VSR0hCHNGGqvKeDMX837wWM8h3ida6NFKNEkK0O28BKcARWuuZtR+oVV1jThu3SQjRvriBRKWUE7gMeFdrvQVAKRUC0pRSx2JesLlSa21ErqlCiHamL5AESK7FTq6pwZMHGzjwHIj5j3IAcAlmlF6ItlBdcUcplQncAiwGciPXJCFEe6KU6ol5NWhGA99fKZiJYsHMji+EiF5uzNElFwPJ1E0GW4o5bedazFFrr7V144QQ7dqg8O3iiLZCtLpm5zzRWq/EnBMK5hQKIdrKEiCEeRXobsyI73Va61BEWyWEaE+qRkP2U0rZqxYqpbpgTjntgTl9Z0HbN00I0Y5UBU+uBb7SWi+t9VgJMAYYBzyvta5o++YJIdqxpPCtjHzv5FqqVHHVVB77LtcSogVprX1KqZWYV5VHAl9qrf8X4WYJIdoRrfVWpdT3wJHALKXUt0A2cDzwA2aSyKUy5VSIqFcVPEnFTApbWzFmJQ0f8GybtkoI0RGsDN9eq5RKA/7UWr8XyQaJ1tFS1Xb2D98ua6H9CdFYfwExmAmMb4hwW4QQ7dM5mHlPemLmMugPXIeZWNqC5DsRQtSUK/5Ta/1jvcdKwrdvaa2L2rBNQogOQGs9D7gNCGCOXhsd0QaJVqO01rtfqabaTnWyPaWUDXO48z+BuzC/WEZqrde1UluFEEIIIYQQQggh2lxTp+38YFZi2sF04BoJnAghhBBCCCGEEKKzaerIk9qligEygH2A7sA3wD+11oUt30whhBBCCCGEEEKIyNjjaTu1HrMD9wI3YuafGCXVToQQQgghhBBCCNFZtESp4gBwC7ANcxTKuObuUwghhBBCCCGEEKK9aJFqO1prA1gfvrt3S+xTCCGEEEIIIYQQoj1okeCJUsoC9Anf9bTEPoUQQgghhBBCCCHag2YHT8Ilix8A0jFrW09v7j6FEEIIIYQQQggh2oumliq+WSl1Qa376cC+mNV2DMxyxetbpGVCCCGEEEIIIYQQ7UBTq+3UVwlsBn4BntZaz23R1gkhhBBCCCGEEEJEWKOCJ0IIIYQQQgghhBDRqkUSxkaKUsqplLpbKTVdKeVSSul604p2t32KUuplpdRWpVS5UuoHpdTIVmyyEEIIIYQQQgghOpgOHTzBzLlyB2Z55L+asmG4QtCXwN+AZ4EbgUxgplJqQAu3UwghhBBCCCGEEB1UUxPGtjf5QLbWukApNRr4swnbTgAOAs7UWn8EoJT6EFgJ3I0ZVBFCCCGEEEIIIUSU69AjT7TWlVrrgj3cfAJQCHxSa39bgQ+BU5RSMS3QRCGEEEIIIYQQQnRwHTp40kwjgHlaa6Pe8tlAPDCw7ZskhBBCCCGEEEKI9qajT9tpjmzgpwaW54dvuwGLaj+glLoYuBhg8ODBo5YsWdKqDRSiPfH7/axbt46dVeh69c+rKbeGsGlVvSxt9SEn33nnndPaqo3tifQXIpqVlJSwefNmLJYdr9HkFSzhky3PYwUs4f4imvsKkP5CRLfNmzdTUlLSYH/xw/wXWKQXY5dji2rSX4hoFQqFWLt2LaFQqMHH3591A1vt3lY9F4nm4EkcUNnAcl+tx+vQWr8MvAwwevRoqfEsokogEMDtdpOQkNDg4yGl6RXI5JJTXqhe9tRTT/3YVu1rb6S/ENHM6/VSWlraYH/hC5hfvQfEH86Jh18FwFNPPbWwTRvYzkh/IaKZy+UiGAxis+14WhIy/KBg8tgXSUlOB+Cpp56a29ZtbE+kvxDRKhQK4XK5iI+PB0AZAayVrpoVdCVpgUSuP+X16kVPPfXULy3ZhmgOnniBhvKaxNZ6XIioZRgGZWVl1ffLy8ux2Ww4HI4G1w8pjUXZSElJqV525513lrZ2O4UQkef3+/F6a742PR4PDoejwf5CWcwrQrExMdX9xZ133rmhTRoqhIi4iooKAoFA9f1gMIjD4WgweKK1AQrSu2TidCYBcOedd25ps8YKISJGa01ZWVn1qPfKykosFkv1sUXGvMeI3zqnen1bUhoBlVr/XMTdkm2K5uBJPubUnfqqlknHLKKa1+tlyZIldU5+GjqwqRJSGquK5i5FiOi1fft21qxZQ2ysef1Ba01c3A4DOAEIGeZBkE1Fc9o1IaLX6tWr8Xq9WK1WYNf9haHN4fkWqxxfCBFt/H4/y5Ytq+4rgDq/2yoKqUzai7JexwOQV/AHhqWh0/uWE8090QLgEKWUpV7S2DFABWbJYiGiltYah8NBYmIiAMmrPySmeEX14z/ZKtlgqZlzaAAWCZ4IEZUMwyA+Pn6n0/pqC4VPhqwW627WFEJ0RlprEhMT65wEVdlS4ueH1WWEY6w4KgOoeIVFgq1CRB215AP6LPsSh93e4ON2bwHl2YdQ3v1wAIq3rsCpGg7EtpSoONNRSmUDycAarXXVOMGPMMsVnx7+HaVUOnAmME1r3VA+FCE6La/Xy/LlyzGMmlhi7eSwiRu/BqUIxmUCMNsRwABSw0mZkipjiInfp03bLISIjKKiIjZt2lR93zAM7Ds5uKkvFO5XLA2cOAkhOp8VK1bg8Xiq71dWVpKamtrguiu3VrJyayXdk+0oIMFiYFMNriqE6GQCgQBLliypTgjb78/XiA14IKlHg+v7E/tSkTmm+r6B0eqB1g4fPFFKXQmkYFbHAThJKVX1F35Ga10CPACcD/QF1ocf+wj4A3hNKTUY2AZcDliBO9uk8UK0I8FgkGAwWD3SpL5AMEhe6ljW9D4fgG0lr9DDMZCcuEMBeHfudnpnJLVZe4UQkeP3+wHqjDRRaudnOBvclXgDZtCkqMy8NmFDzoiEiAZer5fY2FisVisq6CW9Yh1q29oG180sKWfvgJcL+5vJYX/N97Fdy6gTITqF4g3mz04Yfj8xBWuqE8LGhMrxdD8U95CLG1w/v7yE5cVbYPMyAEL4sdK6F2Y6fPAEuAHoXev+6eEfgLeBkoY20lqHlFInAI8AV2NW1/kTuEBrvaKhbYToTAKBAC6Xq3p0SVWyx52dAG0t9TG3opKvt5tZrX1dfWyr8LOkLJzlWkO8Qw5whOiMysvL6ySQrioruquASZXt5UFenbW9+n6atQLSINbeGQ5BhBD1uVyu6gArmBdnYmJiUEqRvPFLUla/v9NtD/KG2DdgkDnfHMlmTUxCJSQ3qq8RQrRzX10FZTVpRTUQDNYkj8bQ9AoG6kzpC8Wl73R3r6+ZRrGxuc7+4qwybWeXtNZ9GrHOBcAFDSx3AxeFf4SIKl6vl1WrVlUneATq/F6b1hpthOiWEsslI9LRWvPuNsWwrskMTzA7tWAgQM/03ec7EEJ0PC6Xiw0bNlT3EUqpnSZ4rM8XNKcCjhuURO9UByUlTn7YrnDGdvhDECFEA9atW4dhGNUBD4vFgsViXlxRQS/aYqdg//sa3PaH1aWs2ebnogPMY4uSsnkEKjc3uK4QooOpLIW9joPhfzfv+nwsW7aszrRfu91eXaxCK0XA2bvBXQEEdCVOlc2p3Y8EzCnEfZ1dWvEFdILgiRCicbTWBIPB6pEmfr+f2NjYnU7T+WC+i7zimmjw0K7JrE3Nw1HyOgAOq6JLfAzdks0OrrJSY5ErQ0J0CoZhEAwGq+/7/X6cTmejAya1hcJplDISbHRLdmAJWMGFJIAUopMIBoN18qUBOJ3O6oBJbUqH0MqGP7k/ACuLfHy5tKQ6QWyZxU2w6xxeKDFPpnxGJRbVuHxKQoh2LuQnFJdBKPz5r3R4CaYGiU+qO+3f39C2DdCEiCWJYelmxo6G+qKWJsETIaKEx+Nh+fLl1VeCDMPYZenh1dsqSY6z0iPZAVqzLhBHss1BRpwZAbYoKwMSBrRJ24UQbWvLli3k5eVV9xGhUKh6DnJTGVUJYi1m31MVwJXgiRCdw9KlS6msrKw+vggEAjuvvKUNqPXZ31Tsp8QXYt/uZv+Sr9wU2330iturep0U3XByWSFEB2IYGMFK8gq2snXhwvAio8Ega6N3SRCratvk8xI8ESJKVA2h3dlIk/r89jyc6RV0z4oDHSJ/g2KoLY3hGcftdBuZkyxE5xAKhYiNjd2jkSY77Ct8EcgaPj4yMBdYkOCJEJ1BMBjc6UiT+pQ20LWCJ+UhDzpxCd17JgPgr9iOJRDPMbWONWrnWxJCdCAbf4Xtq8zfdQitNYayN/pcpL4tvi1s9uVV3w8pH5Y2DmdI8EQI0aDK5F/YpIOUuK2gNTFApqV1kzAJIdqH2mXKm6uqNLFVVY08MYMnSkaeCNEpaK0bf/FEh+qMPCk0luN3zuM3d83UnJ6xvVq6iUKISPj+djPPSRWl8Mdl7/HuZm7/nq3+our7WgWJMVLqrNPaF3IleCJElGjKyZDWGk2Qnmpv/tHjUCxBHz1WXUZZchdKd7+5EKKDa+6c4WBIVwdNfAEDTQiDAH5D49dmLiWZtiNE59CU4wulDUJYqAwnkvYbQZSycEWfq6vXaWhUmoxsFaIDCvrM5LD7XQ6Ar6KCspWraey4E0MbBHVN/kVvyEtOwt4cnXEMAM/9vJWMpIaLXbQWCZ4I0UkZhkFFRUX1/fLy8kYf4GgNXY3NjChYRL8Nr9Ust9prraOprKys3mdlZWWLDPEXQrQ9v99fp7Ro7fwFTVXsDfLMz1sJhjNAaksFlV2n8sFWsG0HFarEoXWbz1MWQrQMr9dLKBSqvt+UkSebiysJbQvw8LcFAAQSK7EkWrGpmlOSQCBAZbCy+n7t5NVCiA7CMNAhP5WGhaDPPL7wVPiatIsviqaxrmJNnWXx1vha/YUFIxTE6/UCZl9RVamntUjwROyRSZMm8corr3DttdfyxBNPtOpzvf7661x44YWsW7eOPn36NHq7mTNnMnPmTO64444683DXr19P3759ee2117jgggtavL1KKe68807uuuuuFt93U5SVlbFkyZI6nUhjO5RgKIgVP+Ux3XD1OdhcqKyUZx9SvU4gECAYDJKaWpPILTk5uWUaL4RoU1u2bCE/P7+6XKDWeo8TxJb4QgQNzeie8XSJt1Fq+JjrNxieNIyshGxs5VvILFhOT2fXlnwJQog2snz58jrBk6bw+gM4LFaOyzGra6wM2im21D02qaioIDk5ubo/Sk1NxWqVYKsQHYoRwDAM8gq2UhyzvHrxropV1OcOuMhwZDLIuTcAChiQMLD68ZChMYwQSbWq9ew0WXULkeCJaDKv18uHH34IwLvvvssjjzzSpA9CW5k5cyZ33303t912W53gSXZ2Nr///jv9+/ePYOtan2EY2O2NT8qUtG4q9rKNAPhDZoS4IrYnZX1O2uk2TqeTfv36Nb+xQoiIMgyD+Ph4YmObP/w1GD6nGp4dR++0GDb7ilmaZ+HAXgexX5/9oeAvWPYOxMpINSE6Gq01wWCwzsnKrmwu8TNrQ3n1/X3L/WRbrBzU1wmAd5sNf0XdY0iLxUKfPn2IiYlpuYYLIVre/NfBvbbhxwxzuo2yxe1xgtiQDpEd042RyaMafgoNDrutTc9F2t8Zr2j3pk6dSmlpKSeccAJfffUV06dPZ/z48ZFuVqPFxMRwwAEHRLoZLa68vJzVq1dXD50NhUJNSOBmkLLyHQxbPIY9AcPQBJMc+GJ2ntSpSQnihBDtSkFBAQUFBdWB5crKyhY7UQmEp+vYrGb/ENRBQGNT4Wl/VdMHpf8QokNYvnx5nWl9gUBgF2vXtWiLl4VbvKTEmSNHRqBxxlipGrdi6BBWqbwlRMejNfz5HDicEGMGU0MhA0+5p3oVw5JGRUIf9nQiTVAH60zp27EJus0PJaS3Ek32xhtvkJqayuuvv05cXBxvvPFGncfvuusulFKsWrWKE088EafTSe/evfnvf/9bJwmhz+dj8uTJDB06FKfTSVZWFieddBLLly+v/5R1nHTSSYwYMWKH5evWrcNisfDiiy9y1113cffddwNgt9tRSlWf6K9fvx6lFK+//nqd7X/88UeOOeYYkpOTSUhIYJ999iE3N7f68ffff58jjzySjIwMnE4nI0aM2OG1R5Lf78fn82G1WrFarTgcDpxO507Xt3q3EuNeQYx7BbGuxbiB37KO4YtBNzFt4GSKLN3wxXRruxcghGgz5eXlGIZR3V8kJCTscfBEa02RJ8BGt5+Nbj9FZQEMWzHuYAFbfFvYWrkVALu16gCoKveSBE+EaO+01pSVlVX3FVartc503d1uHwqwl1rPTcOLuWl4MaO7+qlMjWWLbwtbfFsoD5VjkfxHQnQ82jADKMPOg3M/g3M/w3vqe6wY8RB5Y58lb+yzbBn7FLbuO56z7UxFqKK6b9ji20LACNTJj+YNGNXHGhvdfgKhmkp+bUVGnogm2bJlC99++y2TJk0iIyODU089lU8++QS3273Dl+lpp53GhRdeyOTJk5k2bRp33nknPXv25MILLwTMK51lZWXcdtttZGdn43K5eP755znwwANZtmwZWVlZDbbhsssu48QTT2T27Nnsv//+1ctffvllEhISOO+88ygpKSEvL4/c3Fx++eWX3c6V/eyzzzjjjDM4+OCDeemll0hPT2fJkiVs2LChep21a9cyYcIEbr75ZiwWCz/99BMXXXQRXq+XSy+9dE//pC3KarU2egpVt9+uwxKsSSh7e1oGef6VeLdsrl4Wb931yZSMPBGi42pKf7ErhWVBXvhta/V9w1aMP30a3xTbsVaX51LEWcM5VGTkiRAdilJqj/uKIa7PGVA6lazZ5siz7xMSmJGcQSD//ep1MhyZLdJOIUQbMsKJnOsVk7BYLHvcX3xWOJWiyoI6y+KsNVN8P5jvZp2rJpm01poYW9uOBZHgSRt759f1bNxevvsVW1GvLgmcd3CfPdr27bffJhQK8c9//hOA888/n/fee48PPvhghwDC9ddfXx0oOfroo/n+++957733qpclJyfzyiuvVK8fCoU47rjj6Nq1K++99x6TJ09usA3jxo2jX79+vPTSS9XBk0AgwGuvvcZ5551HYmIiiYmJ9OjRA4AxY8bs8kOsteaaa65h33335Ycffqgexn700UfXWe/WW2+t/t0wDA4//HDy8/N54YUX2kXwpCmlAtEGlmAFnm6HVSeB3bhpGqm2wRzdZTgAdouVERm9W6OpQogIMwyjxYKf3nDJ0WMGJpGVaKMw4OG3ChtHpB9Gqr0LACFvkNSY+lerJXgiRHvXpGOLBsQFXFRYEikcdQMAhRVLIVjIKV1Pr14nzZG2w3ZycUaIdi4Unr5nse96vSbwhbz0iO3FqOTR5q6VIrvWKHhvwKBnioPD+5sj60NGiO5JbRvOkOCJaJI33niDAQMGcOCBBwJmgKFbt2688cYbOwQQTjzxxDr3hw4dyvz58+ss+/DDD3nsscdYsWIFJSUl1ctXrFix0zZYLBYuueQS7r77bh5//HGSk5OZOnUqhYWFXHLJJU1+TStWrGDDhg3VI0p2ZtWqVdxxxx389NNPFBQUVE9BimRCs9rToEKhUKMPclQ4WhxI6IkvfQSGYVCZ9w1pMV0Y223gbrY2Sc4TIToOrXWd/qF239Fc3qCXyrRvWKYUG/1WKo1KHFZFr7g+dHGYwZPSYGlNf6HDzy39hxDt0p4eWwDEFc4iZc1HVE3Pi/VsociSgjXdHLrv3bYdW0UZfeL7tGSThRBtLZwQ1lAWCPcZe1qFq4pGk2RL3Gn/EDI0XRKs7JVhJrcPBoPNDvA2lQRP2tiejvhoD+bMmcPSpUu56aabKC4url5++umn8+yzz7Jy5UoGDqw58U5Lq3slISYmBp+vpr73tGnTOPvsszn//PO58847SU9Px2KxcMIJJ9RZryETJ07kjjvu4K233uLKK6/kxRdfZP/9928wF8rubN++HaB6pEpDPB4PxxxzDPHx8Tz44IP0798fh8PBCy+8wKuvvtrk52wJJSUlrFy5svq+1rrxw+TCHZ62mOsb4Y7H0sQrwRI8EaJj2LBhA0VFRdWf2WAwuMfZ7+srCW1HOwqxqR44bU6cJNIjtgcp9pTqder2FZLzRIj2SmvNokWL6iSIDQaDjd4+btt87OWb8HbZB4CCWCcLGMrh4cdDu0kAKYToIIwgIcNg/YZNuHxzAbP/aE5ZcUMbWNTOL2SHtK6T4yQSF3Kl9xKNVpUc9aGHHuKhhx7a4fE333yTe++9t9H7e//999lrr73qJG4NBAK4XK7dbtulSxfOOussXnrpJY477jh++OGHOlOAmiI9PR2AzZs373Sd33//nQ0bNvDzzz8zduzY6uVNOaBoaVXR3caWC6xN6XBkOBw8CYavBO+qwxJCdFx+v5/Y2NhWGSkXCF9x2td5MCPS+zS4Tp0rQ218lUgI0XiGYeD3+/fo2AJAhfyEYlLZOvIWAL5asIVNlWtJL1sEwPaAq04CyJ3uRy7OCNH+bP4TSsPnS5UlgMZQ1j3uL+ozMFC7qGcTMsBqiWzfIMET0Sh+v5/33nuPMWPG8OCDD+7w+OTJk3nrrbe45557Gr3PioqKHUZKvPXWW40e8nX55Zdz4IEHctFFF5GcnMw555xT5/GqkwSv17vLK6wDBw6kT58+vPLKK1x88cUNfmFXVJiJVe32mnl9brebzz77rFFtbQler5dt27ZVn4SUl+957pyqaTs6fAATMMy/+S6jvaEQ5eXl1X8fv9/fYleuhRAty+Vy4fHUlAssLy9vkeSwDTHCwVh7rRMin89Xp5xpZWXljn2rBGuFiDitNfn5+dUXg0KhULOG3ivDj7bUFCbdZvmL0vhFfLetpv/pGVc3n1pV9a/abRJCtDOhAMaXVxD0+6qvgWhtEHCk0VL1ssyRJzsPjgQNTcBvFhwB8yJ2SwVuGkuCJ6JRvvzyS7Zv385jjz3G4YcfvsPjl1xyCZdddhkzZ85s9D7HjRvH1KlTmTx5MuPHj2fOnDk888wzpKSkNGr7Aw44gBEjRvDTTz9x1VVXER8fX+fxwYMHA/DYY49x/PHHY7VaGT169A77UUrx5JNPcvrpp3PkkUdy6aWXkpGRwbJlyygqKuLuu+/moIMOIikpiSuuuIK7776b8vJy7r33XtLT0+vkamlNXq+XDRs2kJCQUL2s9u9NUm/aTtXIE+suor3BYJC4uDh69epVvSyS+V6EEDu3bds23G43DkfNSUzt4G9LCoaDJzZLzeFTIBAgMzOzTn9e019JtR0h2gvDMMjLy6vTP9Q/nmoKZQTQtRJIBvBiNeKZ2PP86mXVlbfCgsEg/fr1q+6vmlPdRwjRSirL0KEAGzJPxNfLLKqhlQ1LQpeWDZ7sYm8hQ2O3WeukiWitY5udkZ5JNMobb7xBYmIiZ555ZoOPn3vuuVx33XW88cYb9OnTp1H7nDRpEps2beLVV1/lpZdeYr/99mPatGmcdtppjW7XmWeeyfz58xtMFDt+/Hguv/xynn/+ef773//ukDCxtlNOOYX//e9/3HPPPUycOBGA/v37c+211wKQkZHBp59+yvXXX8+ECRPo1q0b11xzDS6Xi7vvvrvR7W0OrTWxsbF7dlCjDbJ/ux57+Za6i8MHOKFGjDwBiI2NbfMIrxCi6bTWxMfH1wmetJZQ+Ipx7eCJ1hqn09lwf1GVMFZyngjRLlgslj0OmCSun0bqqneqp+P9aLPxaXImhQseCa9hkKDScdp2PlLVYrGQlJTUJv2VEKKRjBD839lQFp6mE/6M68TuOFJ3nieyWU9J3ZEnnyx0szi/Jg9m0DBw2GwRPReR4IlolKlTp+7y8eTk5OqpLQB33XXXDuvUzm0C5pflvffeu0OelPXr19e5f8EFF3DBBRc0+LxffPEFY8eOZciQITs8ZrVaee6553juuefqLO/Tp0+DQZQjjzySI488ssHnqXq8frUg2PG1ttZw010Ff3ZHGUEcno34UodQmWJGa7XFji+c/T5QffKz8+CJVNcRouNoy2HvVSNPrLWCr0qpnfcXMiRfiHajuX2Fo3Qd2mKnrOdxAMwp3UiZ0gyM3bd6nb2T+zbrOYQQEeArgeL10PNA6JIDQKU/hMc+nD0c975b9UeebCkNkBpvZVCmWV1HGyGGd4ttpWdvHAmeiA6nsrKSefPm8e233/Lbb7+1ad6RSGpW8CI8Tacicz/K+pwEwNR1syla/QsAlSEzq/6uhsoJITqO1gx2lgcq+XDtLwTC/cp2v1mxzFYreNKoEzIJxgrR4RXqChYnd6Gki3klenWgnGClhYmDjmnSfuTijBBtTGv46y2o2Nbw434zrwgDToQB48xFxcUYq1e3WBNWlq8k31czKl5j1OkLDAO6Jds5JsccaRIIBCLeV0jwRHQ4+fn5HHTQQaSkpHDrrbdy8sknR7pJraKsrIxNmzZVdxKVlZV7vK8dquuEDH4v+QGFBRUOmFiJpaczY9f7kYMbIdql/Px83G539WfU4/EQFxfXKs/117ZNLPfOQWFDhafexJBMWqyzzno77y+qcp5Iwlgh2prWmtWrV9dJEFs7uXNTzVYeFsRYUJ7FAAQIEhPq3yJtFUK0ovIimPU0WB1gbThvSCgmlXXFFgLLlgHUKWHeEn5x/UR50IMtfH4SY4klw1FzLmJoTf3iOpE+F5HgiehwdjbtprOprKykpKQEp9M8IbFarXucoFUZQQwNpZXgKg9SET5Q2jdxDOf0P7RR+4iGv7kQHVVxcTE+n686cVpcXFyrJ4g9t/tZ7JPRs8F1dnlwIzlPhIiYUCiEy+Wqk+OkSfkDjBA2b1H1XR2qJNVq4dzeVwLw4QIXhb5gk9sV6RMiIaKCt7hmREnxBvP2yHuhX8NpCzwlJWxbtoyEcLDVYrHsebGKBgSMAEMTh3FE+lENPm5odqi+E+m+QoInQrRTWmvsdnuLJFDTRoDCsgCfLytn3poitApAV4jbRTZ7wzDwer3V930+H8nJyc1uixCidbRUf7E7wXCp89oJYoPBYJ3RcT6fT3KeCNFOWa3WPe4rUle8QdLGL2sWJKdgSUivvhsywFr/UnE9Pp+vTjnk5pRGFkI0kq8U3h4HRr3gZlzaTjfRWmOz2Vrt2CKkg9WjThp83NCEggHKy8sBc9pOYuLOk0+3BQmeCNFOGYax+5UaKRQMYNHQIy2ePn1T8Gsf31VY6Z6085Esfr8fq9VKerp5UKS1jniHJYRoWFsmdA6G+ya7tWbaTUVFBcnJyXWuSMXG7iapm1xpFqLNNXcUqc1bSDAuk+K9zgGgzLuYkK1mlFvI0Fh389H2+XxkZWVVlyO2WCxYrZJzTYhW5XObgZPBE6DrcHOZPb7m951ozZHnQR3EuotwRMgwp+107dq1ui0tOfJlT0jwRIh2qlmdldakLX2pemitEfBSDHRxxtCnezwVIc1vGy04rLvuApxOJ927d9/zdggh2kQkquvYVN2TnYyMDFJTUxvYoBJm3g2VJeZ9rzv8gARPhGhrTe0r4gv+wJn3TfV9R8kaAkl9KO92GACF67aSV1LKm3+aiaO3lAZIT9j1sYXVaqVbt26tNrVQCNGAYHh0aPf9dzpNp77mXpj5zfUrBf6Cne0cjd7lyBOtNXabrV2di0jwRIh2wuVysXbt2upOyjCMPb4SYwmUkpj3P4JxmYQcqYQ0rLXmUJZgJnEzwjkHLLtJ2BjpeYVCiIatXbsWl8tV/Rn1+/1tNq3u/9n773BZrurOG//sqk6nu0+ONyfdq3uVUSKJHIZoBhOMhyRs4+zB7zCM33ntn8FgY2ObcRicZmwjCbBNssDY5CBAAkkgFK9uzvHk0LnC3r8/qrv69LkndJ+u6pP253nuc7qqq3ftI51avfbaa31XNXhSZ2vi6bNw4uvQuR0SXRBJwLbneMcajSZUXNfl4MGDviisUqohgdjUpe+TmDyE1e61G3ZSW8htqmqljeUssiWwXC8o05eKcP2mcMSqNRpNE7jl4Elk4azQTCbD0aNH/eNmN2Z+MvMIbUaC9sj8ukqbE1vYnljYF3DV0mWArUYHTzSaVUJFwXp2OlojwYvY9DHMkreja5amOB+JcnbnSyn23kjekny6NMlzolmSuePk3bx33SKtibVArEazeikUCiQSCT/tPZVKhRbsdKTLw8On/Jbm53OXAIgukbnmU7Elz/yvsOuFwU9Qo9EsiJSSUqm0/LJb5WCnNnP5WR8GoOAWuFi8ADmvXWmJPHEzyi/c1rfYKFegN2c0mhZw6bFq1ufECe9nZOGSfcdxkFLW2IvFntXR0ggzzsy87ykUrnK4vuNGbu96Zl3THc85jGarmiyOVBg6eKLRaMBb/ExPT/vHU1NTi+/eLoJw8gw9+P8h8DJKLCH46MAgpdIR1MgZHKmwux0eL5kcG6nuFifMxXeHDEO3EtVoVgOVbjoVSqUS8Xi8JQuQHw2f5ovDn59z1iAdrXXAFhaIlZULgp+cRqOpQSnFyMiIvwFiWVZTqfdCOihRXS78YPJ+nso86R8XcYmhs8g0mlXHzEX4t1+48vwsgdhSqcTk5KR/nMlkGlqLfPbSZ7DV4u2L2yP1B27/5dEJRmYFT5SCVGx1rUV08ESjWSGmp6c5duxYTbvAtrblpboaTgGBZGr3m8gP3s60m6M48WWe2fM8dievYqrg8IlTEzzv6g72DXjpehFh0h1dWGFbo9GsHs6dO0c+n/czTYCWCSzOWF6m2sv7XsVQWy8APYkU7bElBGEr6NbEGk3LsG2bU6dO1eiJxOML7zQvhVAuyqiOZUmL9kgHrxn4KQC++OQU0k4vf8IajSYc8mPez+f+Dxi60XsdTUHn1uol+fwVa5Elxd7LuMrFVhY3tN/Ite3Xz3uNIQx6GlhrZEuSa4faeN5uz6bYts3WntVVBqiDJ5q6ueuuu3jXu97FsWPHuOqqqxa99lOf+hRve9vbuOmmm3j00UeveP/06dPs2uXVz37rW9/ixS+uFS664447iEQi3HfffYHNf7WhlCKZTJJON+50OMrhs5c+Q87JAiDcIon+ASxO407N4CqXsbzg25cj3G8ppDIwnB56ot0MxOc3QkqpmnaBjuPotFqNZpUghCCdTtcET8JiNGvzTz+ZwC6bg1xkBtphf/cmtpRbkkopcZxZqbX12IslNJY0Gk0wRKPRZfkW8yIdmCXoeGGmxOkZl3uOe5kteSvNtu7F25i6rltTCiyl1P6FRhM0xRkv08TKeMflUlv6D0Df1fN+RClFIpFYlr1wlecDdEQ7GYgPLGvK9z4xyYnxauZK3pZ0JQT9yYqe2+rbdtHBE00o3H333QA89thjPPnkk1x//fwRSYDf/u3f5oc//GGrprYuKLoFRkqX2ZzYQne0B7M0RXvpMXLdA9jJbViO5EymSF9kC4Od3o5T1BDs7Fl496lUKmHbth9xjsViNZFojUazcrSyFfHFaZuJvMt1Q23EIoJxTC5GTNpj1QVSLpfDNE1/d3vRwI4u29FoWoZSKlDNMiFtpFn1HaYLDkoJ9vZXz10zuPhO9fT0NKlUyi8F7ujo0GXBGk3QTJ2GyZOeIHuqHMyIt0Pf/lBu55SDJxGx/HDC0dESqbjBti7Pv1BSclWn9G1YJBJZvl5TSOjgiSZwLly4wLe+9S1e+cpX8pWvfIW7776bP/3TP5332pe//OV8/etf50tf+hKv3imu9gAAsX5JREFUfe1rWzzTlaWZnRdXSez8NJvH4+xxHNrsSXZNTPLDtr0My5twbEl0Jsezb+jm+s31pbsppejr62Pnzp3LmpNGowmPMIMnJUfyyLk8jvSclbNTeZzkIYa2JolHDERpmEzeuKKd4O7du+vr8KPLdjSaNUN05hRtoz/xjyPFcUrxLv/YVZKYafK667qu/PBCY0ajXHPNNS3JnNNo1i3Shac/B1Zu/vdnznk/b3k3DC68aT2bRn2Lo9kjTDlTAFjS697TSPDk2GiRSzPVbl8FW3LrtiQv2ed147Esi2g0yv794QR8gkBbMU3gfOITn0BKye/93u8xMzPDpz71KT7ykY/MW5//xje+kbGxMX7nd36H17zmNes6jXN6eprR0VH/d8xms8seKzczRrQ4ysDIEbZmPT2CSSJ840KSUdNL14sYgv60fsQ1mrXI5cuXyWazvr0olUqhLTyOjZb42pGqWr4bP4fqfYRHZiJ+skjaTBM3lq+bAOiyHY0mBFzX5ezZs0jpBSlt264pwW2UruOfJjn6o5pzdmqL/1oqibGMZ3k9+3caTUsYfgIe+JPFr4mloH3Lgm/n83kuXrzoP4/5fL7uTDWpJF8d/TKK6vUCg65oV12fB7j3ySlylvSPBTDQHq25ZrXbCr2y0gTO3XffzYEDB7jtttt4xzvewS/90i/x9a9/nVe+8pVXXCuE4Pd///d51atexac//Wne8pa3rMCMW0M+n2d0dHTZArGOq7DLO8OZghd1nt7+Wor731S+wuCXjWqAyoCG2nsppXQarUazShgeHsZ1XT/oHI/HQxOIrWSc/Pod/XQnIxzNzfD1sQhv3fw2emKeQKxAXLFgqtvB8ct2tH3RaILGcRyGh4dr/IlUKrXs8YRbotS5j8u3fxDwfIOijIDtPcfuMoMnGo2mDuwiSHv+97KXvZ+vvxt6981/jTBhEV++WCwyMjJSYyPqtReuclEont39XG7pvNW73Ty+Qc1npMJyq8GWvCW5Y1eaF+1tL38ezFlrlVaWKC8XHTxpNT/4KIwdWdk59F0Nz3lvKEM//PDDHD58mA9/+MMAvPnNb+Y973kPd99997zBE4BXvvKV3HHHHbz//e/njW9847pN65RSEo/H61axnk3Blvyv+4Z9A9QjxqAPotEEkcjiQm2LzadUKvnHxWJx1RssjWajIIQgkUi0pKOOWw6exCMGEUMghEQAUSOGKbz7O45Dya7aC8tavDVhDUoufY1Go1k2kUhkWb7FfAhpocw4lDvsfO3wND88XS0TKPU4dLUtbpcsy6rJflkLCyKNZsUZOwr3vt0rz1mMZB+Y0cWvWQClFLFYbFn2QuJ9l0dFxPcNluLvfjjGcKY2GJSOe75GZT7FYrGmtXpQtiws1ucqVbNi3H333RiGwdve9jYAurq6eN3rXscXv/hFpqenF6yP//CHP8zzn/987rrrLn7hF+bpSb4OaEbALW9JLFdx4+Y2NnVEUfkIj5YEPanlt++qBE66uroA6OzspKOjY9njaTSa4AhS8HEpKptClc0ftxzsmL2blM/nSafT/u52V1dXA+1PyzfQu9UaTeAEbSuEtJGRaobsSMahK2HyrJ3e7vQjlkl3YvFnP5vNMjAw4Ad/TdPUwRONZikyF73AyY1vh2T//Nck+yA92NRtlmszXOUFdYw6AydKKUYyNnt6477AtCEEN8zSYrRtG8dx6O3t9T+z2tciOnjSakLK+FgNWJbFv/zLv/DsZz+b9vZ2pqamAHj961/Ppz/9aT7zmc/w7ne/e97PPu95z+MVr3gFH/zgB3n729/ewlm3jmYcHFe6vKr4WW7P2vQT4aI9yeMmmObysk4qdHd3s2PHjqbG0Gg0wVPRL2gFlcyTSuqsLDtIJlUHSQjB5s2b6xOInYvSwRONJiyaDZ60jfyI1OUH/GNn6gJHzXa+/fgkAGcLZ4h3n2I66S14oiJLKrZ4mn80GmXnzp0tyZzTaNYNldbCV/8UdO8K5RbN+BYV38Bg4e/y+09muVzONFHK2zrZ0xfn2TsXboWcTqfXVLMKHTzRBMaXvvQlJiYmeOCBB+ju7r7i/bvvvnvB4AnAH/zBH3Drrbfyt3/7t2FOs2WMjY1x/vx5X0fEsqxllyRFc5d4rvUt2jO9xNwUhilQ7XFkcmjZ89NptBrN6uHkyZNkMhn/mSwWiy1LXXXLvpRZNgeynHkyNy132fZCl+1oNIFh2zaHDx/2gyau6zYlENt+5sskpg7hJPoAOGeneNDZx4VpbwHkpo7hJC5wudQFQNyIs6NNb7poNIHjFL2fZpPi7LOYmZnh5MmT/lrEcZxl6xu6C/gGs/nO8QwRQ5CMeffoT0XY3r3wRu9aXIvo4IkmMO6++25SqRRf/OIXr9htuPvuu7nrrrs4ceIEe/bsmffzN998M294wxv4wz/8QzZt2rS8Hc5VRKFQwHEcXyA2EoksugsTzZ7DsKt1xePODEXlOS/ZyQtYiRjju36Gru3XM2qNYo9/GxVb2/+NNBqNx/T0NNFo1HdqOjs7AxNwlkpxacb2gyRzmSjYyMg4w5bCdGDS8XacgxOF1JknGk1QOI5DoVAgnfZ2cpvVOxHSotS1n+HbPgDAp340TsmRvOfZXtnAl4YTzDj9vHVL/VnBa3FBpNG0HKU8HUy3rCc2edL7GQkueFIqlSiVSrS3ewKtkUhkUd9iyp4k7+bnfS/jeF1CF/INpFI4UnHH7jQvuqq9yZmvXnTwRNMwX/3qVxkaqs14cF2Xr3zlK7ztbW/jJS95yRWfGRoa4q677uKee+7h937v9xYc+0Mf+hDXXXcdw8PDvOAFLwh87mFSKBTI56sGJ5vNYppmXWmrkdwlNj/wm/7xtGHwif4B/1gB9pYBbPUo0UtP++fjxvLLdrRzo9GsHDMzM9h2VURNSolpmqF0vDp4qcjnnphc8H03fh6n/zt8fjhKxSKY8wjCLT/zRAdPNJrlIqVkamrKzzSpiLsHVRIzV+NEKlXT/UIqiaCxZ1/7FhpNHVx4GP7j12rPGRGIJue/vg5KpRLZbNY/np6ernstYkuLT5y/2xeGXYiEMX+wtrJBE22g0yesPXuhgyeahvmN3/iNK86lUikcx+Hnfu7n5v3M/v37ec5znsM999zDBz7wgQXH3r9/P29/+9u56667Appt6xgbG+PMmTM1O0Cz2xIvhlkaB2By3zux2ncw4kxjZ77PcxP72BLpZjRr850zDnds2c/mTi9gEhVR+mMDiw27JGvNYGk064Xjx49j27bv0AghQmsVnrO8lP6fuambeOTKe5wujfBoIcJ/6n8FKdPTMkiZ6brV9JfEb1Ws7Y1G0yilUonDhw8Ti1U3S6LR5XXamA/hWqhZGzGunNM6FFW3QKT/Gb05o9EsTXbY+/miD0LSE0wl2QfR5TeDmJmZ4dChQzXrj3oz0yxpIZHc1HEzu5Lza66YIsKm+KZ533PK+mnzuBkLshZthQ6eaOrmzjvv5M4771z25x94oCpItnPnzgVFzj7+8Y/z8Y9/fNn3WSmUUqRSqboCJoadpf/Rj2A45UwVK89YzuHuM1sYjW6mZEYYTUX53sRVJJytlBzJpOOyOd7H9rblZ5vMFoqSUq45g6XRrBeEEHR0dIQWMJmNU37sr+qPEzOvvF9uRhC1BNsTO0hFvOCJUipAe1Gx9dreaDTLIRaL+Wn3zdJ++kukL97nH0fzl7A6qgslRyrikdrME2OJZ7eVAtcazZph4iR89/fAted/vzjl/dzxPIgH83wrpUgmk8uyF45yABiIDbC9Tl2jzz8+yUjW+5xbXtdFzPrtRSs7CwaFDp5oNAHRSPQ0mjlNYvJpil0HkNE0WUPyoNjBTHwzXXGTnFBMGoKOWIxk1ARMtnfHGGpf/iNbKBSwbdsXrRVC1OxkaTSa1tFKh8H2d4Pmt09u2WGanWmSyWQwDMMP7kSj0eWXCfiZJ7psR6NplKBtReryA5ilSUqd+wBwEv3kNlfLpKXy2on6x8gl9Y8mJiZoa2vzfaBKS3ONZkNz+VEYOQjbngPmPNli7Ztgz8sgtnAnmkZpxl44lU57dWaaOVLxxKUC/ekIvUlvbTGQjrCnd2HNFsuyyOfz/vpDSrnm7IUOnmg0AbGowVKS5OUHMJwCAJHMaR5PJLi48wW4bYOM5my+Vcrwwu0XGWqPMm6PkZsxefVVvQzFewKb3+DgINu3bw9kPI1Gs3zCTFUtOZKDl4vIsk26MGVhCuEviGxpcyx3FLfsKF0oXgAgYlRdAiEEe/fupaOjo/kJ+ZonOvNEo2mUZoMn0ew54pOH/ONIYZRiz3WM3fjfABjPOZyaKME5T7A+Z7n0JKuLJ6kkEbH4ciEej3PTTTe1JJNOo1m1WHk49a1qpsn5h7yfL/+TQEVgF6NR3+Jk/iS5shBsxskAtb7AXE6MlZgseBsuluPZptu3p7h9++Lty2fPr7u7m3379tU9x9VGaMETIcR2YEopNRPWPTSalWR6epqpqSnfSE1PTy94bWz6OP1P/Ll/fD4S5Z/7+rByj0PeoOQonE6Hx/IRYpY3nsDw9QeCYC3WFWo064XLly9TKpX8Z7CZ1qJL8cTFAv/+dK09quwKAZzIH+cbY1+reT9ppjAJSCD2CnTZjkZTL47jcOnSpRqB2GbKYnqe/j8kJp+uOWcnN/uvv3JommNjpZr3OxOzgid1ZJ6sxdR7jSZwTn0L7pvTFCM1EGrgJJfLMTY25n9fZzKZuj9bcAt8afgLc84K0ub8JT+OVHzix+PMfdpn+xf1sNbXImFmnpwCPgB8qHJCCPEC4AVKqQ+GeF+NpiVMTU1x8eLFGiGmhUSZjHIbstEb30epax+niuewJr7BTw39NP2xAY6NFbn31BSvv7WHoY6yIKwRJW4Ea3DXusHSaNYqFy5cQAjhP4PxeDzEzBPPtXnP8wf8Up22aHXxY0tvV+wtm9/qB2jjRnjz0WU7Gk39WJbFuXPnalLZ6xWfnw/hlij03MD49WWxfyFwY13++zlLsqsnzk/fUD2XjlefVamWDp54w2r/QrPBscpdbt70maqGSSzclr3ZbJZz586RSlU3W+stg6mU7N7R83yuTu0HICIiJMz51zJFW6KAl+xt56Ytnk0yDUjF6i/pXQ8buWEGTwRXbjO9EPhdQAdPNGsepRRtbW11GalpJ8PfDgySzX4HWXgQVypGsi53nbQw3BmUAkGSzlg76UhwKvpzWesGS6NZyySTyZY8gxWNk642s0a7oEKlDWF7JE1ykey2wOaqy3Y0mrpRShGLxZoKmMxGSAcVSeAmvBLgLz89zY/OXfLfL6UfIZ46wl2X5l8SKCQdkc7F76GfbY0GXMv72b4ZovV1uGmWylpkOfaiUtqbMBKkI1fqrkip+Nj9o36ZTuWrvDsZoSOx/G58a91eaM0TjWaZNJJGO+3mKAjB1fHtpFI7mSg4jF1wuLZvwE93a4sa9KbCeyR1Wq1Gs3K08vlzXFWjcTIXWdY6MQioFfGS6LIdjWalENJGzdIsuThj05EwuWGTt/HzpJwkEe/khs5rFhxjT+qqRe+h/QuNBnDK5W9m65oxNPPsVTZSDObPLMvbkvG8w96+OJs6vI1d0xDs628uK14HTzSaDUy9BqCSGndTci/9PbdzaqLEj/Lj3HZtil2LqFIHjRZz02haj1Iq1FTVqYLDg2dylBNOODNhEVkkLuKWy2iWUtRf9nxdB37y99UU5umz5QG1/dFoliLwQIRyUUY1ozXLBeg6j9nr7VS3ZXNsSWzlOT3PXd7wZdu21hdEGk3DZEfgyX8C6fn4DD/hddVpoa/djB6SKm9szH52HziVZbrobbCUbG/sGzcnuX5zMB1x1kOgVQdPNJo6GR0dZWRkxA9A5PN5otH6SmxU2bAa5cVKxdaZC7QODYJSqUSxWPTna1mWdm40mhZx8uRJSqWqCKPjOKHd6+DlIj88nSMRqVbL7uheOCgrKWeezApmZLNZXNf17UVTwZ7xo17wJNoGFdX+jq2Q6l/eeBrNOsayLE6cOFFz3MyCaC5COmBUA6XT8ceQkVEOZ6uLoa2JrQ2NOTU1hWEYCCFQStXtC2k064rT98ETn4RYqro5sOnmUG85NTXFhQsX/O/qYrG47O9qWd5IEeXMk2zJ5etHZogYwtdLa4+bDHYsP1xg2zbZbJZIJOIfd3d3L3u81YAOnmg0dZLJZMjn877GSTwe943BUrjKE2g0hedguOUtYjPE4LTjOAwMDLBp0yb/nHZwNJrWMDExUSMKG0jL3wWwXM+e/NZLhjDKi5kZZ4ZJe3Le63NuHqhN1ZVSsnv3btrbq+J2sdgyU48ru3Av+xPY9qzljaHRbBBs22Z6epp02tMcME3Tf70chGthFseqJ1yLnC0Yz3nPpaMcutQWfnnHzy77HoZhcP311/v2TWe1ajYkFY2Tt34FYsFoFC1FoVAgk8n4NiIajda9FpmLQuIqyJYk48JhMu/ZiP98XVdgmSau69LV1cWuXbv8c8ud72oh7NnfKYR44azjnQBCiG8vcL1SSr0k5DlpNMsmFostKwBRie4a5dRZt5y2ZoaYCVLZDYrHW1cWpNFoPCqij61grsbJ0dwRvjr65UU/ExWxmt0qIQSJRCIYe1EJnhit0lTRaNYuSikikUhgmxv9j/4RbeOP+8fjOZf7Mi5fGR4BwO5ziBrNu/9hdgzTaNYE5c51BPA81X1LKYlGo4HYi6ItGc7Y3HtmGrM04p9PxYMNhsZisXW1Fgk7VLwTr8NO5d9OvJziFy7yT7NKueuuu/y61qNHj17x/ne/+13//W9+85sAfOADH0AIwZ49e7Btu+b648ePI4TgrrvuuuIex48fX3AelTHn+3fTTTcF8rvOR1OiTGXNE1EWbXPLGblhb9bo3SCNpvW0uhWfI1WNxknGyQDw0r6X85/6Xznvv5/e9MYrxglszpXgyRKaKhqNJnjM0hSljj2MXv+bjF7/m3yh/Rc41vdq3nBDF2+4oYvNnQY7upe/q6w1TjSaMm7rgycQ3Hd1xrJBwYHBpG8ffvYZPezsCW7jZz20Jp5LmP+3XxTi2JoVpL29nU984hN86EMfqjl/9913097eTiaTueIzJ0+e5B/+4R/45V/+5cDmcf/992Oatc757D7nQbOYAcg5Wb41/i2cchRaSIvExEEod7XIyCKOhH9/Oo9ljpMpeefDzDyBta9ordGsRSoCsWFxYdriW0czfg+bsazj1ycDuGW7cyB9TY2uScso37/VDqVGsxYJ2lZM5CzOul18NeJ1zzmReIrO7gc5LrwFkREtkIw0t2utfQuNBm+jQBgtFYhtxl64UnHvk1PkLG8Hd9qdgQRs74pzw+bwyo7Wm70IzbNRSn03rLE1K8tP//RP88lPfpIPfvCD/gNRKBT43Oc+xxve8IaaTJIKL3/5y/n93/997rzzThKJYHqfP/OZzwy1bu7cuXMMDw/7x47jLBicuVy6zKn8Cfpi/URFFLM0iSqOIqNplDCJyAhduQQT9JJA0RYxODCYoLMtuJ3ZQqGAZVk1go9rva5Qo1krPPHEE1iW5R/Pfh00h0eKnBgvsb3LWwx1tZnsmLVT5CgHgbFo4GRmZgaoOjWmaV4RjF42UgdPNJqFKBQKHD58GNd1/XNBBlCm8xYzQuCUtZCiHScQ8RyO8gSbB2ODS7Yensvo6KjvuymlAvPjNJo1jXRC/54bGRnh7Nmz1VtKueyS4Jmiy5OXCvQmI6RiBqahiEcE/eng9BAtyyKXy/nrj1aWMLcK7dloGubtb38799xzD/fffz/Pe97zALj33nuRUi4YPPmDP/gDbr/9dv7qr/6K9773vS2e8fKwLOsKzZCFoqdOeaf1lf2voifWS9vwgwwc+zYXn/0/sTt2cXK8xN0/Guddt/eysyecuj/Xddm6dWuNQOx6i/ZqNKsRpRSlUqlGbLUZwce5lBzJqXHLbyt4ecamLWrw88/qA8CWNueKZzme84K94/Y4EbH01/uBAwdqAsKBl+1ozRON5gpc18VxnBp7EeR3taFc2pMx3z588nyEzugOXjv4U8sarxIsueWWWwKbo0azJrGLcOHhanbl1CmvNXGYt7RthBCBfFe7CmR0hAM7o+zqjTFhZ/nhZIRkNLjvatd1GRgYqBGIXW9rkZYET4QQceDFwO1AP6CAUeAh4DtKqfC26DSBs2PHDp7//OfziU98wg+e3HPPPbz+9a9fcMFw00038aY3vYk/+qM/4hd/8RdrnIblMnvXpoJhGIHpfDRS1+uWNU3M8oJFlMt3lBkrj1WeX8gGpNI6UKPRtJ6wnr2HzuT41rHacsih9qrDdjD7FN8d/07N+52RrkXHnK0VFTgVx1Jrnmg0VxC2ZojArXn2HOXUFUxddEytcaLRwJF/gwf+uPZcR2NtvhslSHuRtbNYvV/jkaLJ0yPVtVKbEUxnnQrrfS0SevBECPEzwP8Chiqnyj8rOYoXhRD/j1Lqc2HPZTVw77F/5UL2/IrOYUt6K6/f+9NNjfGOd7yD9773vfzlX/4lk5OTfPOb3+QrX/nKop/50Ic+xOc//3n+7M/+jN/93d9t6v7AvGmjv/Zrv8bHPvaxpseGxtJonXLwpOKgiLKIlDK84IksjxWmLVmPokwazVog7GcvU5IkIoJ33d7nn5td8pdzcggEP7v5rf65dGTpAHVoc9ZlOxrNgoSphwQglETNKtlzlUOkiUCm9i00mjL5MU/j5Kc/WT2XHgz1llLKwJ6/glsE4NrEc3jWoFe6FzNidEa7Ahm/wnq3F6F6NkKIdwL/iBcwOQN8H7hQPt4MPB/YDvyLEOIdSql/CnM+muB405vexK//+q/zpS99iTNnzjA0NMRLXvISvve97y34mX379vHOd76Tj370o/z6r/9603N48MEHr6jRHxgYWPZ4ruvWZLPM7Q40m3FrnH8b/oIfNHGcHNHsWXbc/19JKBBuCQBVbk0s/cyTZU/vCpRSOI7jHzuOs+4NlkazWnAcByk90TXXdf3XQXDwcoGvHJrxF1lFR9EeNxjq8OzJ6fwp/uPSN5DlPQhLWsSNBP3xhe2flLLGvrmuG5y9yI3Cl34J7Jx37Hj2T5ftaDTed/VsfyJMPSSA+7vjPNU1Rvzs3wGQd/MNZ57MtmlhC2BrNKuWsz+A7/1+NZvSykG8A/r2hXZLKeUVvn1Q2OXfo8PsWtRfaASlFK7r+jZisbXTeiG04IkQogf4C6AE/Cpwt5pjfYXnub0L+BjwMSHEl5VSU2HNaTXQbMbHaqG9vZ3//J//M5/4xCc4ffo0b33rW+sql3n/+9/Ppz71KT7ykY/w7ne/u6k53HLLLYEKop45c4aRkRF/zLk1ybMZs8aYcabZm9pH3EgQzZ5n28xjiM7ryce8z7iJPmSsE6hmngRZtpPL5TAMw+/1nkqltIibRtMClFI8+eSToQk+npu0yFsuz9haVb+frZV0uXSZrJvluvYb/HOb4ptYjEwmQywW8+1be3t7cPZz5jxMn4Xtd0Cq7JAluqB9SzDjazRrmFwux6FDh/xgpVIqOHHmebjQFsHEYHdyj3/u2vbr6/68Uorp6Wnf/xFC0N3dHfg8NZpVz8hTkBuBA6/HL5wYuC7UW16+fJmzZ8/WrEWC6iRqlbPiIwFmhZZKJRzH8fUhE4lEqJ1PVwNhZp78LNAB/KJS6q75LigHU/5RCGECf1f+zN+EOCdNgLzjHe/g1a9+NVJK/vmf/7muz2zfvp1f+qVf4mMf+xivfe1rQ55hY7iuSzKZrBGIXfDacsbJc7ufR2e0k6TzQ/pzX+LijW/Gbt9xxfWVdVXQeSGbNm1iaGho6Qs1Gk1gVHZagtBumg9HKhJRg9de2zX/+8rGFBFe0vfSusc0DIO9e/eG49SoctbNDW+DLbcGP75Gs4aplL2EZS/i40+SmDpSvR+SHjfWkH2YTaU7xnXXhbtI1GhWPW7JE4R9/m+37pauSzweJ5lsvHWwVIofnc1TdKpZY5fVIWxVAGDSmgYgFmDwRClFb28vO3fuDGzM1U6YwZOXApfwynaW4h+BDwAvQwdP1gwve9nLePOb30xXVxfXXntt3Z/77d/+bf7hH/6B3//93w9xdo3TjMZJpbuEWsAguZWynQBbwes6ZI1mZQg7hd2WEFmkxs9RLuYyNAxCsxeV4MkirZE1mo1K2Pai5/DHiWXP+MeyqxfHaC5Qo30LjQavBNVsbZvdZuzFWM7hy4em/WNpzmD131dzjSDCULJr2feYj41mL8IMnlwPfF8ptWQhuFLKFUJ8H9B90NYQpmnWnXEym4GBAd7znvfw4Q9/eMFrvvrVr16RUdHZ2cnLXvYy//ihhx66IvXVNE1uu+22hucEVwYjLGkhF/jzLUpPdKmS+ibKwRS1QF2xCqFsBzaewdJo1isFu2prLEdeETwploXeAEqy1HT3jEDRwRONZkFCF4h1S0z3P4dL1/1XAA4/9bdsbdve3Jjat9BsFEqZanr4XKwsmEtnowdJMwKxdnmn9i3P6Obq/gSjlsk/X4zyqv5XszvpCcQiIBKgHtlG3MgN0/vqA841cP1Z4BUhzUWzynjf+97H3/zN3zA5OTnv+7/xG79xxblrr72Wp556yj++4447rrgmlUqRzWbrmkOpVKoRbiuVSr4BOFs4w72X/5VqU6grEYhqd51y5slC3SXCKtvZaAZLo1kpcrlcaAKxPzyd5auHZ2rObe6otiJ+cuYJvj3+zZr3u6KNaxDozBONJnyklOTzeT9oks1mQw2gXJgq8tR4ic+NjALgDqhlZaZpNBuOg5+F+z+y+DWd20KdgmVZlEol/7hYLC5y9eK45a/iqCEwDIGLixAQM2NEQtRZ2miEGTxJA5kGrs8B61thZo1z5513cueddy56zQtf+MIaJ+EDH/gAH/jAB664rquri4mJiWXdY6ExG+XMmTOMj4/7gqtKKV8PYNqeBhTP7n4OMTF/1Lkj2uk7KNXMk/mNkxtStx2NRhM+ruty+PBh/5kLWvBxMu8SNQUv3VtNtd/WXU0VnrAnMEWEO7qf558bCEgpPxD86LAOnmg02WyWp556iljMe4aVUr6fEQauY5Nqi/HK/R0AfK8k2NG5fPF47VtoNgyZi96m57N+c+Fr+g+EOoXLly9z/vz5GnuxHL0TmNWcorzYcOdKDISAzjwJluV4Udrz0rQMKSXpdNo3WLOpaJrc0H4TCfNKJ8QoTdF97FN+S+Jo7iJQ1Twp2JKvH57BKkdNJvLeeLpsR6NZe1RadYYl+OgqRcwUPGtnGoDh0jCPTn+PJ0Y8+3G5dJmkmeSmzmc0dZ/wMk/KXYd08ESj8QVXw7IXPz6X49R4NWv26qTBsb4cyeR3AUgol7Zoc+59Pd0TNZo1j3QhEofr37JyU5CSZDJJW1tb02NZroPd8RAP50yOuCY5NweEGzyBjbcWCbto+iYhxDvqvTbMiWg0Z8+eZXx83HcKisUi6XR63muvEISdQ2LyMOkL38ZJ9KMMb0ep0HsTKuJFi89NWvzkQp7OhOlrF+zojpGKLd8hyefzWJbl73hLKbWDo9GEgJSSQ4cO4TiOf2zbdnj3U7WB1cPZQxzNHaEz2gWAIQx2te1uaMyZmRmUUr6NMAwjxOCJzjzRbFxyuRzHjh3zny/XdUPN3vj+ySx5S9Ie93yBYncbMwmLtDUCQFe0i62JrXWPp5RiYmLC30iSUq77VqMaDeA1e2hxidvw8DAXLlzwfXnLsurq8lkPk84YbvIoY04XJct7ngfjQ8sq812IUqlEPp/3Wym7rhtq6/XVSNjBk9eV/9WDYDGBCY2mSXK5HIZhEI1GkUqSi2XJOVlwrrx2yva0WIyFFgPl4MrILb+Dnd6KUopLMzb2pLfAOj/t7Qq987ZeelPBPGau67J9+3b6+/v9cxvNYGk0rUBKSS6XqwmuJhLLT4NfCkc5uNFhLhQ9+zFpT5COtPPOre9a9phCCPbv3+/vZgkhwrMXOvNEs4FxHIdSqeRnmkSj0VA3NuL2FM/umuIFe7z73XMKrjE7eNEy7YVSikQiwQ033OCf0xszmg2BdBbUKgyLih5SpZQvGo029d18ePISWcvTSTmR8bLgn9/xCm7o29H8ZOfBdV0GBwfZtq2qBbPR1iJh/sXcHeLYGs2yME0T0zQ5nDnEN8e+vui1cSOxYPBElBcLqvz+6UmLux4er70Gmso0mYtSikgk4kd7NRpNOFQyNlrlEFzmUaY7HuVzl6rP9qb45qbHbZm90Jknmg1MRQOpVfbiLTP/m93Z83ROlLNQ+/sxmminWrF32rfQbDiU2/LgScWXD8JenJge5eNn7rnifFds/qz6IKgEfjayvQjtN1dKLX/LTKMJgdndMSqthl87+LoFS3M6Ih0LD+YHTzzjN1Pwjn/6+i7S5VTadNwgEQ12MaF3gzSa9YdFDlMleP1QNVGzJ9rb9Lgtq0PW3XY0G5iKJlKriMscwx3XU3zGmwDIT30dp705UcuNplmg0QCe5kmAbXvrIUhbMV3KA3BD6tnsSnsbLp2xNrZ3NO8/LMZGtxcbN2ykWfcopWoCJrM1QmTZ2d/etmNZQkrZgo3KOnz8R1NkTUHe9ozhVf1xUrFgDPFchyzI1qgajaYWKaX/vDmOE+pi6FjuKA9PPYQqV6rOGBMYbprtbctPs53PXujgiUYTDq7r1rwO81n7yqFpTo1XW5m+SzkUo73Ee70yGzt7f8OZJ3N9I41mQ9ICzZO5a5HZtqNZ3PJG7p72zTxr6KrAxp2Lthe1hBo8EULcBHQAP1RKzau2J4SIAc8CppVSj4c5H83G4tixY0xNTfkBE8uy6OrqAqoGx1hmg6fJXImoq0jEo8TiEXqArkSCZICZJtlsFiGEP/9oNBpqu0ONZqPiui5PPPGE79TMDqSEwan8SabsKXaUgyVJN4Gw6hd4nI+pqakarYVEItG6OmQdPNFsIGZmZjhy5IgfMJFShprC/tSlAhFTsKnD+/7vjElUR4KKUy2VbMiXkVIyNTXl6zgppejs7Ax62hrN6keFn3ly7tw5Ll++7H8327YdWCcuW3o+SyTE36FQKGDbtm/jhBCBCdyuVUKz9kKIXcCDwBeUUt9b6DqllCWE+BXgPwsh9iulzoQ1J83GwnEcksnkvAEHqSQCsbAg7BKossH6Twe66OzsaWqeC95DKXbt2kVvb7jpdxrNRkdKieu6obUWzTgZTuVP+scXCyNIO82g8RIAzszk/K5cyyUSiXDttdeGKmy7IH7wZGOn8mo2BpWd17DsxVwcCdcOJXjVNV6Aw5qOcDJRJDvj7Tc6ysFswJdRSpFMJrnxxhtDma9Gs2rJj8Pp7+L3J5k6E7rmiW3bxOPxUL6bnfJ3bzTEVsRSSjZt2sTWrc1t8KwnwvyL+XnAAH6rjmt/C/hp4N3A74Q4J80GYrHUMomL0USqnqxEe83wHqGNXlOo0bSKsPUKHp56iKcyT/jHUwWX4sw2/n1q2j93YLB5x2rFbIYfPNlYivuajYlSqqXPmiMVEbN6v890JBlhFHf8W/659sU02uZB+xeaDckTn4THP1F7bttzVmYuAeBIr/NnmJknoO3FXMIMnrwM+EE9mSRKqbNCiAeA/4QOnmgCZKEH3lWyoZ2auaiywTJDVpvWBkujWftYskRHpJM3b3oLAF98apJRK8rPv6jPvybIkr+WozNPNBuIVorDKqVwpCI6KzMtJwQ3Gt3cvO2XAO+xS5qphsbUvoVmQ2LlINEFb/p09Vw83JK1MPXHnPImcdQMz3+odOPSVAlz5bcPuKuB6x8H7gxlJpoNgWVZNUJMtm0Ti3kiaidyx/ny6H+gyk6+QtFmJusee/LcQboffD9mWbpncznlL8jMEykltl2VBrIsSzs4Gk1IFAoF/3WpVAp0QfS9Exm+fSxTHb9rGhVx+ZODXqaJwmB7l+l35loOjuO0VLSyhqmzcO/bwS7/N6wET1rc8lGjaQVSSkqlqmBroVAINYBy18PjnJ6o3m9P+59xKJvnyGPesRKCDpEgFak/YGJZVo0g9kZuM6rZwLgliLZBMrxyeNu2cRzHP7YsK7Cxz2XG+esT9yB9xSPvmY4F+N0731pEU0uY1jMJ5Bq4Plf+jEazLI4fP04mk/FFEl3X9WsMJ+wJpHK5tfM2RDnjZCA2UPfY9uQ5IsriwsDLUVHvz1S0b2Yw2pjC/WLkcjlisZg/50QiseFFmTSaMLBtm4MHD/rHQe/EDmcc2qIGt273bMXTroFDnBs60/41V/U192xns1na29v9RVAqlWqdQGzmgreDd/VrIVW2o8k+759Gs86Ynp7m0KFD/maMUipU8fZLMzabOqJc1e/ZiJOZAptIsKttOwBCGOzd/Jq6x3Ndl0Kh4AvmA6TT6YU/oNGsV5wSmOH61efOnWN4eNi3Ea7rBva8ncuNI7HYHNlPR9QbM20m2Z4OTnuxUChgGAbJpOe/JBIJ/7XGI8zgyRSwuYHrNwOT4UxFsxGQUpJOp+fdUal013l293OXJxIrvShs6qafId3V39Q8F0IpxZYtW7RArEYTMpUd2LAEH7MM43YexejxnJtkbobOaBcv2dSYLsFimKbJnj17VibA6pZ3pa55Ewxc0/r7azQtpBIsaZVA7CbjP+iPXiJte76MYyh2te3gpqv/67LGU0rR1tbGvn37gpymRrP6GTsKR74IlUyx0achFm7g0HVdUqlUIN/NtrR5eOpBrHLW+5nCOAAvG3om1/QNNT3+fCil6O/vZ8uWLaGMvx4IM3jyFPASIYShKrUSCyCEMIGXAAcXu06jWYzFdo+b7a4jXC+F1ogEl2lyxT2E0GU6Gk0LCFuzYNx4mnz8OEeyVSdtc2IdOSJlzSdM3TpdowkSpRT59kc5aypGLM9fSWEw0H71Cs9Mo1mDHPpXePpzEJ+1cbHj+aHeMkj/4nLpEj+e/hFREcMQBjlHIpxOOqLBbcTMh9Y4WZwwgydfAj4K/DfgT5e49j3AVuB/hTgfzTpn0eBJo911lMIsjCDKGSfxkhftFZHwdnlbKUKn0Wxkwn7WpHKJ0sUv7/jFUO+zYsHWSvBEa5xoNgBBl/UV3SJ5N189diR5y8uOdSXYBlwnt/KSm/9nYPfUGzOaDYlreaWlb/tyy24ZpL1wlYsr4WXdr6M/NsTR0RJfH5uh7UB4G7mapQnT8/k/wH8H/lAI0Qn8iVJqZvYFQoh24H3A/wQuAP83xPlo1hm5XK5G1GgxEbRGu+u0jf6YgUf/yD/uLEkmiGCEuNOqM080mnBQSjEzM+MHTYrFYqgBFInEYB0/y+WgMobOPNGsP2zbJperSvZlMplA7cWnLtxD1s36x8MZB1dWx9+MIimCWxzpjRnNhkU6oQf5C4VCjaB0sVgMLHNjJGcxnLH551NTGE51AzgeCde/0GuRxQntL0oplRdCvBH4GvD/Af9NCPEIcL58yRbgViABZIA3KqXy8w6m0czDiRMnKBQKvkjiYu20pHIxqD/zxLS8rhgTB34BN9rOk5cKfPtSknfpVDaNZs1hWRaHDh2qCa6G2W1CKuULU69LdNmOZh2TyWQ4ePAgbW1t/rmKkHuzKKXIuln2JPeyN7UPheKTZybY2R1lR7eX2XrzsY+wZfMAQfa40IshzYZE2qF/T507d47x8XFfIFYpFZhA7EzRswJ37Opkc7IbgHTMaKpT31LoVuZLE2o4Tin1oBDiduBjeJomd8xz2TeB/6qUOhzmXDTrk/b29nk7TFwuXuIHkw+gym28JuyJhvROMgUbcg7/eGEvebOLyYLLlOkSZuxEGyyNJjwikUhogo9PXirwyLnqTnUOh1Qi/OBJaPYiOwzf/7DXmWA+ciPeT122o1mHVARWw7AXLi7TRZdjk0nGnF6kArPYwfWdHTx7exqUZMehIlPRmA6eaDTNIp3QMySVUoEJxGZLLl98ahrb9aRCR2UOErB/oI0d7W1LfFrTKkL3fJRSR4CXCSF24gVPNpXfugTcr5Q6HfYcNOuTxVJRT+ZPcq54js0Jr+FTV7SLLYmtdY89kikSdRQlaSIN6EyYXNUXxwjQAclms0hZ1VI2DKN1rUY1mg1E2GnrT1zMc2Haay8KkExAdzJYh21ycrLGPsRisfBE3S4/BmcfgP5rYD6dp2Qv9B+ARHc499doVpiwbIYjHXIliWkZdJbdid29cXb3lp+zclaXEst3z13XZWpqqqadcne3flY1GxDXbkmQP6jg5IVpm6OjRYbao8QjglhUkYgadMbDCwDl83kcx6k5F2Zm7nqgZf91ykGS0626n2b9MzvwYMkSZwtn/eMRa5iEkeBNm36mrrEcV3F8vIQs1x2LbIktwFtv6cdIhNPWTErJnj17apwavTuk0QRP0AuhmaLL+anqvvBk3mVrV4x33ua1Gf/cpeDF3CKRCDfccEPNgigwe+E6cP5BT1wP4OIj3s9X/Dkke4K5h0azRpjtWwTBcGmYjONJ/hWcIgC7e9r42X19AEQzp4nmjkEOhCw/g00s+JRSdHR0cO211/rntG+h2RAUpuDST6rHuZH5NwACJEh74UoXN36BW/al6UlGOFvI8WTGJGKEt7EqpWTr1q0MDVVbH2t7sTg6tKRZM0xMTNSIMrmu6z/gj808xg8nH6i5vj82UPfYT10ucO+TU/7x80pFbhJgRkzC2rOuCMRqI6XRBIvruoyOjvpBk1KpFGgA5SuHpnl6uFhzbmtXNWAilcRsYud4IUKzF2fvh6//99pz0STEwylz0mhWE8VikcnJSf94ZmYmsOdMKslnL30aVznlY+98m5Hyrxn88Qd9nbUKbqxz2feslABr30Kz4Xjk/8DBz9SeC7g18dTUFIVCwT8uFos1mxrNMGxfxO7+Nt+bjhDNes+vIUxiRridPg3D0PaiAXTwRLNmOH36NK7r+qnqhmH4r0uyiCkivGXzz/rXt0fq74NecjyP5l2399IWNRg8l6T/TJTzjbQ3XgbaWGk0wWPbNidPnqypQQ6iHrlCpiTZ2hnlp67r8s/1JqtfpxJFbC0JxtplvZZX/gWkBr3XbT1aEFazIcjlchw7doxUqhrQmC0W2wyWtHCVwy2dt7I/fYC8Lfm/JyYZ6qqWERt2juyWFzOz4zUAKGHipLY0dV/tW2g2JHYOUv3wyv9dPddRf8l+PVy6dInp6Wk/YCKECKzMxZLepsyLul/NjnYvk7XNaCNmhNuaWNuLxtDBE82awTAMEonEvLogjnSIiih9sf5lje2Ut4M2dUSJRww64wLTINBaSSklrutW7+k42mBpNCGglCIWiwWmeP+d4xl+dLYqCJu3JAcGEwy2e87TI9M/5osXfuy/X3QL7Gjb2dQ9XdetSQeWUoZnL9xy6+GeqyA9GM49NJpVipSStra2QOyFKyV/+OQnyKtKJolECZvvXDT4QclFKTDcDmKRcnBVSYRycBJ92O07ln1fx3H87DrbtgMNFms0awbpQCQBvVeFdgshBKlUilgs+ICGrbw1Ql+sd9nrmaVQStVonOi1SOPo4IlmzbBYNxpHOUSayBKRZafDrIyvKkGOYAVio9GoH61Op9OBpfppNJrwOD1RwhBw9UC1XemNm5P+6wvFCyil2Jva65/bm7q6qXtOT0+TTqf97LrOzs7wBGIrrYdD7kqg0axGgux0l7NtMuoy7WKQ7oinaWJgMti1hyie/YgYgn39XnBDSC9wqZrYWXYch1wu52fORKNROjuXX/aj0axZpAshZ4yHuZHhlL+LYyGU/VbI5XIYhuEHf9LptA62NogOnmjWDLM1C6btaY7kDvnnRqyRpjQGTCvLC0pfpedUCoEgMXEQJUwI2EDu3LmTrq6uQMfUaDS1BC0Q60gYSEd57bVd877vKoeuaBcv7ntpYPeMRqPs37+/NQHW8gJOtx7WbEQatRc/OZ9npujO+17WzgNwbcd+Xr/rWfNek7z8INFznsB9RSBWmcsPniilSKfTXHPNNcseQ6NZFyg39O+xIP2LnFXi387+CLv8HXypeBmAWIgls0optmzZwsBA/bqQmlq0p6RZtZw/f558Pu9HeG3bJpn0dnufzDzBI9M/qrl+T3LvFWPUy6bph9lX+gLdJ6oGy2rfuezx5kOnxWk04eA4DmfOnPHLXCzLClQB33EV6djCWR9e5ls4ArEtwdXBE83GIZvNcvHiRf/5KhQKdT9rBVvyxaemFnxfGTkYhJ62hYMhfU/+RbWrDp7Gid2ExkmQmTMazZpGuhBwZ5rh4WGmp6f9Zyyfz/trkWZ5aPQ4j2Vrm11ESZGOJRb4RPNoe9E82lPSrFqGh4cxTdNPVU8mk/4D7yqXqIjxyzt+1b9eLFFiU7ClX54zF1VuH3juxXcjIxWjGLxx0QZLowke27YZHR2tcWhmiz82ilSKgj2rFbqriBjVZ1cpRVFWu+3Y0iZpBuNMhUYpWy3PmYuV9X5qgVjNBiCfzzM2NlajcVLvYqjkSBQOr7gmWVO6V2HGEXz6UpSB9AKLHyUR0mJqz5uY3vPm6vkmBaa1b6HR4H3HBVy2MzY2VtNRJx6PNyUQa8kSrvL8i5ybAeAXd7ybHeVSOwMRXoluGW0vmiOU4IkQYvtyP6uUOhvkXDRrFyEE8Xh8XiMikRjCwKjT4XjyYoHPPTG54PsvLGXZL0AasaadmAqO42Dbtn9cKpW0wdJoQkApRSQSCaxu91+fmOLJS4Waczt7qjvJ941/mycyj9e8vze1r6l7lkql8ARizz4AX3nP4tcYZui14hrNaqAiKL0ce1FyHUqDn+e7RZcfnZvfVxBATMyfeVLVOIkv29dQSlEsVoO3lmVp/TSNBrzgScAZlJW1SBDP2IXieT536bOAt5HrbdIIUtE2IgFnzFRwXRfLqma66bVI84SVeXKayl9GYyh0NoymzGKpZUrJugMnAFNFb8f1Ffs7MOYZc99lk+6xKMMBCibm83nS6bTf8rCrq0uLMmk0a4DRrMNgOsIt26rZKxWBR4AZZ4a02c6tXbf557a3Lb9TBnj2or+/3+8mNjvrrmkyl7yft/0qxBboKNKxFULe7dJoVgPNpK3n7AIIi63R/dzQO/8zb4rIgt22hFvWOGnC13AcB9d16e3t9c+1t7cvezyNZt0QguZJkBsZM/YMoHhm17NpM9s4M1Hi4QuCWIhi7ZUyo0p2XVdXV2Ct2DcqYQUq7mF5wRONxmcxUSZXSQzqd/TLnYh55vYUhiGI5C7SeeKziIqytXWaSCwWqECsEILNmzdr1XuNJmSaFXB74mKewyMl/3gi73BgMMEzd8xf+qNQpCJpbuy4qan7ziYSibBjx46m0oEXpNI97Jo3QELbI83GphF7IZXia4dnyJS8rLAZZwoiMBTb4T3/StF17FNE8sNzPvm1eccLSiA2lUqxc+fOZY+h0awLzj8Mh++tHk+cgN7l6x+GjaO8Ncd17deRjrTjzuR5pDSFEWIiiFKKgYEB+vr6wrvJBiOU4IlS6s4wxtWsb44cOVIj3GZZ1sKZJzSWeVLJhq8M1zb6COlL38NObvZOCkFu6NlNzX8+dGqcRhM8pVKJw4cP+8eu6zYlEPvQmRzDWYfOhJf10Zkw2du/sGCbVBJjLWkiVbROdFmOZgMyOTnJqVOn/Kwux3HqftamCy4PnsnRHjeIRwwcwyYaF/S1lVsNO3k6T92LG+tERhfI6pqD1b6TUmdzCzztW2g0wJEvwqnveJmTAIku2PbcpoY8ffo0k5OTfuZnoVCgo6OjyYl6VIInEeFlmlR0GOfLiA8KIYS2FwGjS2Q0q4ZsNksikfAf8u7u7gWvlUo1mHmiEFQdjkrq7MXnfBSa2AHSaDStx3VdSqWSL/gYjUabKnFxFezqifPWW3rqul42GLyth1AV8GU58ySkmmqNZjVTKpVwHIdEwguIRqNRP5AyHyNZm7zlBWMn8i4yMsUz98XY1ZNg0i7y7fEInQnPb6hkkkzteTPZ7a8I+Tfx0N0yNJoyrg2d2+HNnwlsyEwmg2mafhZoLBZbtn8hlWS4dNkXiJ2wJwAwyxsZlaz4sDNPtL0IFh080awalFKYplnXQy5xGzIGrgJzlnUypIVCQIB1hqVSiVKpmvpfLBa1wdJoQkAphWEYiy6AGkGqxpwXRWPB2/nI5XK4rtvUGHVTyTzRrYg1G5CKb1GPvciWXP76/lG/7lyJEtbgv/NgweQnl6tGImF4mgFVDZPwNmGklORyOb/cyLIsPxCk0WxoQhKIDcq/OJk/wX+MfKnmXFRE/eCJW46emAFGT2zbplCoCt7Pfq0JhpZ6UkKI24D/BGwB5lPOVEqpn2/lnDSri3qDDcvJPKmxTdL2AicBBjdKpRIDAwM1LVK1KJNGEzzNapzMRSqF2UAsRCmFaFJc1XEcduzYEY5A7Fwqmie6bEezAWnEXhRsiQJesCfNrp44OXeGr8yYPKv7NnYmdwEQM6IMxAYBMCrdc0LMYHUch1gsxubNm/1zOnii0RBK8CTITI2C6wUuXjnwGtrKAdd0JO1nroaReVIsFunt7a0pNZrdll3TPC0Jngjvr/Au4G14XdxU+WcFNeu8Dp5sEKSUvlOjlFrUwTmRO84PJ3+AKu8HZZwMndGFhQ8T40/SffjjUE6Ve+WMzbOLLpvu9xwOszTptSUOECEE3d3dgdVGajSaKrOzNFzXDTSAImVjNceS5jVPhBD09fUFlj2zKNLxAsW6m45mA6CUqtFAakTjxHY9u7K5I8au3jiTdoRYTtAX72Nb2zaQLgOP/iFmYQyY3Xo4vG4ZSikSiYQWfNRo5iJtMJt79mavRcDzL4L6Xq5onGxPbCdhJhjN2nzm0SlsOQLglwcGmaSulKKrq4uenvrKkDWN06rMk18H3o7XhecvgR8Dfw58Bngh8P8CXwb+Z4vmo1kFPPXUU36/caUUtm0veO3Z4lkm7Ql2JfcA0B3tYVd5F2g+EuOPE82eozBwOwDTxSLjtotTzgpxUlsode0L8Lfx0GU6Gk3wFAoFnn76ad/BCdK5AXDnZqYtgWywVfp8tFTETQbfvlGjWa2MjIxw+vTpGoHYerNAnXLMJVJ+vGV5A8bEG8uwZ2gbexSrfRdOm5d9Uuq6mlL3/gB/gyvRvoVGMw/Sabr8/ujRo0xPT/v2wrIs4vH5iiMapxI8qZTpnJuyuThjs7cvTtQUkIKeZIRYI6mvS6BtRfi0ypt6J3Ck0oWn/D92Sin1IPCgEOJrwIPAN4CPt2hOmhXGsiza29vretCVUsTNBK8ZfO287xulKZIjD0F5cWWOH2FKJvlm768C8GSpwKTpsPsZQ8H9AhqNpiVUdoba29tDGV+pxjJPFAoRQLed0JycUhZOfqOqdTL6tC7Z0WwYpJREIpG6UtWVUhy8XCRve0GSsVy5G4bpPZuyXPJWWfxUNE5mtr+K3NYXBz73heaoF0QaDTB9Hs7/sHqcHYauHU0Nads26XTaF4htBktaHM0dwS3bjfOFC+QtyU/OFRHC4tS4p4v4xhu7SUTDywTV9iJcWhU82Q/cvdC9lVKPCiH+HfhVdPBkQ1DZQa73AVfIRRcr7ee+RteJqtp2piR52t7Ffzw97Z/b2hlsWm2hUMC2bf93mP1ao9EER9AaJ3NpVPNEKoloUDA2k8nULIJCtRXHvgwP/HHtua6d4d1Po1lFSCnrfr4mCy6ffXyy5pwhoKPcttxFls95z3srNE5c1yWXy/m/Q2WjSaPZ8Pz4b+D412rPbX1WU0MGGZw8ljvKt8a+4R/nbclUNsGXT2X8c+1xg1gkuO//YrGIZVn+71DJ6NeERyvzeKdnvc4Bc4uxjgEvb910NCtNIwsiqRbXGDCcAsqIcf75fwPA909m+OZpeN+LBv1rgo7y2rbNli1bapyaZDIZ6D00Gk0rgieNZ540cj14v8O+fft8UVjDMMJzcIpT3s+3fw1fXiymBeM0G4NG7IXleNe+7rou9vV7qfpRUxAv1+24czJPkJXuOuFpnFTKjHbsqO6ox2LhBWs0mjWDXYDu3fCav62eS3Q1NWSQ/oVVtg/v2HoncSPOT87n+frJIr/63H5SMc+mJCJGw/7DYti2zdDQEF1dXf45vRYJl1YFTy7gddipcBK4Zc41e/GCKpp1SrFY9EXcGjVWEoVYRGNASBtpxpHxLgDyhoEwcqTj4aWqCyFob2/XO0IaTcAopSgWi76dyOfzTTk4n350gqeHiwu+b3f+gEfEKY6fqt9eDMYHl75oDu3t7eF01Dn9PfjG+zxtkwrxDkj2Bn8vjWaV4TgOlmX5x8Vise7ApFu2K8mYQTpuUpIl7j7/cQpu3rtAKaLZs2w99VvsmKXLpsxwu93E43HtW2g0c5EORBKQXL4YqmVZOI7jHzuOQzQaTDC0EmxNm+1EjSimUghsOhImbSGV6SilSKVS2l60kFYFTx6mNljyFeB9Qoj/H/CveKKxrwP+vUXz0bQYpRSHDh2q6ZjR6OcXK9sR0kLN6p7jysbS8DUazeohl8tx8OBBX8BNKdVUPfLljMNgOsKBwflFIx9zs6QTvVzXWb/o497U3obmEKpA7MRxL3DyjJ8DoxwA6r06nHtpNKuMikBsJTuj0p2mHtyKQGz50cw4MxTcPHtT++iO9mCWphi88Di93Tcz1b7dG99MUOw+EPjvUUFrnGg0CxBAd50jR45QKBT8jQwpZWCbGnMFYivdu6JB9iKeQ0vF5zVA64InnwduFULsUkqdAv4YeDPwe8AH8PKKJ/C67tSNECIOfBCvk0838ATwO0qpbyzxuQ8A75/nrZJSKtzthA2M4zjLbuOrqO1uEc2eo/3s16Dcujg+ebimBtlVYIZorCpog6XRBI9SCtM0A9tJcVzFzu44L9o7/3jnzwt6YwM8u/s5gdyv5biW1+vwtl8JtuehRrMGkFKSSCRIlbvpNYIrXez0YzxeiHBuzCTn5jBLkzyncJLdXMKwpknlc4zsfymF/ptDmP38aN9Co5mHALrrSClJp9OBdOzLOhkemX7EF5Y+l7/ITBG+csjTODk/5WXE6c3c9UVLgidKqS8AX5h1PCGEeAbwbmAPcBq4Ryl1qcGh7wLeiNf2+BhwJ/BlIcSLlFL31/H5XwGys46XlxahWZJmd1IkEmOWQGP6/DdpP/cV3Gg1GJMffGb1+gZbj2o0mvWLI5XfPWPe95VT1TQIiVB3k90SmDEdONFsSJop6Zt0JnHTT3LWamNMRRHSoj83yo7p0yTLLrLTNoCV3hbUdOtCB080mnlwbYg110Y4SI2T47njPDbzE+JGAoEgU3LJz/TzZLbg58rv7o2H+jzrTLXW00rB2BqUUtPAny7380KI24G3AO9TSv1p+dw9wFN4mS31bCF+Tik1ttw5aOpHKdWUwVJKYjgFotlzAJj5EfKRXg7e/le1F2a9muRsSRLRmScazZqkWeem5Eimi9VYuOWqmrRZW1rMOFX1e1vZRER4ApCBOzdKwfTZqsZJbhTM5hxKjWat0ujzdSk3TcYuAHA+NwzA8zpeza0De4hmzrL56PcYvfG9nB9ao5loGs1axbFg5vzC79s5aOtu6hZBfh9XynR+ftu7iRpRfng6y1cnZ3jPSwZC0ziZi16HtJ4VC54EwBvxMkX+T+WEUqoohPgH4MNCiG1KqXNLjCGEEB1ARoXdzmEDMjMz4wvEuq7b1IIoOnWEVOYEm494FVleK+Jt/M39owt+pj8d7J+3ZVk1onSlUinQ8TWajYrrumQy1WBGpa3vcrnr4XEuztg15xLRqoPxpeF/41zxbM37cSPYbhaFQmHZGk9L8vTn4f4/qj3XsWX+azWadUapVKJQKPjHs9v6LsW5zAQfO/F/rzjfGfNKfoTfTad13W2klBQKBd/mlUqlZZc4azRrmu/+3pWtiOfSoJ5XNputEYitrEuCYCU0Tmzbrll/FIsLi+FrwqElwRMhxC8D7wOep5S6OM/7W4DvAR9WSv1DncM+AziqlJqZc/7h8s+bgKWCJyeBNJATQnwBeK9SarjO+2sWQUrJ0aNHaxZAzQg+KreIECajN/w3AB4+m+P7kwO86caFI9CD7cH+eReLRXp6emhr80QnhRDE43q3V6Nplnw+z8GDB2vacTbTmjNnSXb2xLhtm7cgMgTs6as+q3mZZyA+xC0dZR1zIdie2L7s+82HZVls2bIFIQRKqcDU/AEoTHg/X/LhaqlO9+7gxtdoVjEjIyOcOXOm5vu33tackyWvqePexDPYntwEQDqaYE/HAADCLQdPzNYFTyzLwjRN+vv7vXsrpTtnaDYmhUno3O7pdy3E0E0NDXns2DEsy/JFYYMUWHWUgyFMX5OxIkAdpsZJsViko6ODdDrtn9OtiVtLqzJP/gtwab7ACYBS6oIQ4jzwNqDe4MkmYD6NlMq5zYt8dhL4GPBDoAQ8D/g14HYhxK3zBGQ0y0Aptezdk9P5U/x4+kf+8bQq0i8i5Dc9F4CT41Nkc0Wu2zR/94wwEEIwODhYY7A0Gk3zKKUCbc3pSkVfKrKgfVBK0RntYF86vI40Qgg2b94cTkqttL2uOle9PPixNZpVTqU153IWDE5513l/x07u2LwPXIv+J/4C45zn9hm2F1xpZeYJeG3MN29ezG3VaDYAyoVkL+x5WaDDdnR0BNJRZ6Q0wv0T30Pi2ZGR4iRjOcU/PuQpQEwVXCJGuN1vlFIMDAzQ1dUV2j00i9Mq/d+rgceXuOYJoP4+kdCGF/iYS3HW+/OilPoLpdRvKKX+SSn1eaXUbwLvBPYCv7rQ54QQvyiE+LEQ4sejowuXi2iaryk8nj/OpVI1NjaoDG5yquMtJQAZFrq2UFMv2l6sHFKBscizOleAOmgq9i80exFAxwHN6kLbi/ppxr9wy10xouWW3tHcBZIjD2LYXu8AGU2R77+1pQKxumpc0yjr1l5IFwIWbw/y+TpbOMO54llUudOnstPY01dRuUVXm8lt28PNAtHrkJWnVZknncDUEtfM4LUbrpcCMF/NRGLW+3WjlPonIcRHgZcCf7TANf+HssbKrbfeqr/tFqFZY6WcPB2OxTviXh1/avpH5IqChy95/1vH826oNYUaTbNoe1E/QS8eHKlYLLYqlUSIcPcOQnVwXBuMtSxZppmLthf104y9sF2XuCrSM/MUSXWZaM4Tp5w48POUeq4LaooNE8SuuGbjsG7thZIQ8HdzGAKxbxh6E4Yw+F4+w0gmwztf1tvSDV0dQFlZWuV9XQJuWOKaG4BGwqeXgPkU8jaVf85bIrQE54CeZXxuw6OUYmRkxBdIdBynKbHEyORB2vJn6T/xIADTBZen5AE++/ikf82unnD1RgqFArZdFZ20bVsbLI0mACzLYnx83F8E5XK5QMeXCsxFgqtSuRgE+yzPFrlVSjWl8bQk0gZTZ55oNgaZTIaZmRn/+3dmZvmV1WZxmF45zO7T/0S/qopIuon+pudZL67r1ti8UqlEd3dzHUQ0mnWBcpveGBgdHcWyLN9eBC0QuxIaJ7pZxeqiVcGT7wBvF0LcoZS6f+6bQojnAa8EPtnAmI8BLxJCdMzRKHnmrPfrRnhP2U7g0UY+p/GQUnLmzBlMs5pul0gkFvnEUuNZGEaUi8/9cwC+emiapzId/NrtVQenKxFsat9cbNtm8+bNpFIp/1wzv5NGo/EoFoucPHmy5nkK8tlypVrUmZEojABTgytBk7179/rnZtvCwNFlO5oNxMTEBOfPn/dthBDCF25vFOV6QYuR7W8isdVzF2UkiZvoDWaydWDbNslkki1bqvt/2rfQaCiX7TQXiTh79mxNtkkkEgk08yQiqktnW6rQNU5s22ZwcLBGQ3L2ukTTeloVPPkI8DPAN4UQfw18FbiAlznySuBX8PRLPtLAmJ8D/jvwi8CfAggh4sC7gIcqbYqFENuBpFLqcOWDQoh+pdTcLJdfAfrLc9MsA8MwAhNTzZRschb8/kPeAqHk9DDYHmUg3boFgxCCrq4ubaQ0moCpCMQGZS8+9/gkJ8aquzGKpTJPpL9zFBQVexEKp+6D73/YS2kGsHOQbN1OuUazklQEYpcTMBnNZ/jLo5/Exdu5jakM3YCbHMRuoa7JbCq/jxZ81GjmEEDZjhCCZDIZSCncqfwpvjH2NX+DJGOVmMwZfOT4ZQAsVxFtQblOZ2cnnZ2dod9HUx8tCZ4opY4IId4M/BPwm8B7Zr0t8PRO/otS6lADYz4khPgs8IdCiAHgOJ7o607g52ddeg/wgvJ9KpwRQnwaeBJPYPYO4C142Sp/18jvpvEIWrOg5LigjJpuGVf1tb4tsC7T0WhWP6cnSqRiBrt6PRshgOsX6cSlAhaMDbKmel5GD0JxEq55Y/Xc0DPCu59Gs044mx3HYoZeYyftkTTt1mWuzxxhx65+ll9Y3Dzat9Bo5iGAsp0gv4+HS5cpuHmub78RAZwuWUxNtXNgMOFv0Gzq0FmgG42WKc4ppf5DCLEbuBOvtKYLT0T2QeBupdT4MoZ9B/Ah4O14YrNPAK9RSn1vic99CngO8AY8gdkzwB8Df6CUyi9jHpoGGbVGOZ475h8XZ8awRo/7x1lyxEWUV1+zspFW7eBoNKsfqWBHT2xBe3G2cIYLxQv+sS3tRbvxLIdQbYV0wIzDHb8V3j00mnVIybFIqRnewjTXxxRRa4pUKc/FSHzFgiehB1s1mrVKAGU7QW7mVjROXtz3EgC+N53hdDHDqw50aoHYDUxL5frLAZKPBjheEXhf+d9C17xwnnPvDmoOmuXx46mHOZo74h/bM8NERaZ6gQn9tDYtPZfL4bqub6S0g6PRhEPQmWpSgbnIs/rd8fuYsKvxeYGgJ7p8jQOlFNPT076uiVKKWCy27PGWRHfX0WxgmhF8jOTP0Ckn2Tp6nK6yQKwbbcdN9AU1vSWxbZt8Pu+XEViWpbvraDTzoSQY4eoZNsJcjRNZdl3CfHzz+TyO4/jrj9mvNasD7Y1pAmGpxZBSioKsJvVknRx9kU28buDN3vHhP6TLmiLxupWrmnJdl127dpFMej3ahRDE460vFdJoNgJBBlBcqVisc7mtbA6kr+Hl/a8I5H6VbjoHDhzwz4UrEKu762g2No0sHrL5SUq2JwxbKgwDcO7Af8PY9szFPhYajuPQ1dXF1q1b/XOhBls1mrWClFCcmnVsQ5Ni7ottfHprkQKeMtrSlGQRc9Z8pFIICDxzdTau67Jt27YagdjlCmRrwiGU4IkQ4vnllw8rpYqzjpekjpIbzSohm836CyDbthfdHfruxH08PlNtZDRddCnMbOboQc+xeXuhSFd0ZZ0JIcSyRek0Gs3CuK5LPj8reJrNBjq+VGAsEj1xlIMpgv26MwzDD7SGju6uo9lAWJZV046zUCjU/dlzlw9y74W/umJpFE90BTO5ZaCUIpFItM5eaDRrhfveD8e+Untu67MaGiKfz+O6XhHeUpsyP5l5hPsnGltmdka6/NeuZNGNmqBIJpPaXqxiwso8uQ8vrHcAODrruB5WT76WZkEcx+HwYb+Bkb8TuxAzzgxpM82tXbcD8INTWWaKA7yorFFw1QnoiqaYCnXWGo1mJchmszz11FN+JlfQZS5SKRYrP3bnpN42S9BlR0uiy3Y0G4iLFy9y4cKFGntR70JiPHMOBexzt5GMeaU5bbFOhvqvDmu6daHT7jWaechchK4dcN1bque239HQEEeOHPFLW5b6bp5xZoiIKHf0PK/u8QdiA/5rpVrzLGt7sboJyxv7IF6wZGzOsWad0d7eXtd1UrmkImlu7LgJgMmZ77I3+8/cNO45RHF1ATu+Paxp1o02WBpN8FRaE9drLxodW6raNNrHZx7jXOGcf2xLm0iTqcBzCVWzYORpePyeamvikacgkgjvfhrNKkJKWXcWaNEt8r2J+7CkDcCYewqAawZfxI4dje1gh4XWT9NoFsC1oX0LXPum5Q/hurS3t9f1jEkliRkxfy3SKFIpTC1XtOEJJXiilPrAYseatU+jO6+uqm0Nui97P7tLTxDJbfPej3WS77810DkuxfT0NEopX6vAMAwt4qbRhERY2RqVUWc/uo9M/xhLlkiZaQB6Y31sbdu2/Hsoxfj4uL8TXlnchcbJb8Kpb0HXLu84loYd9e+UaTRrnXqDDSPWMIeyT9MR6SQiItjKZkehRGck+EBtvZRKJfL5vJ+N67puuJpIGs1aRTpNZVUqpRoKTrrKrVmLNIqrgtc7yWazOI7j2wghhF6LrHJakgcshPhd4JRS6hOtuJ8mfBpdCFnSoWjBqXGvjjni2kxHByne8echzK4+DMNg//79Nbtb2sHRaIIn6J3XqYLDZN6rcXbLtmh2HbKjHPamruYlfS8N5H5KKdra2rjxxhv9c+G2JrYhmoI3fya8e2g0q5RG/AtHOliu4obky+iJDFK6fD/XXfwuM/tWLlPLdV2GhobYtq0asNWLIY1mHgIQQ2/ku1gqt0YAdikcqTg/ZVExSdMFN3DNEykl+/btqxGI1WuR1U2riqh/B/jzFt1LEwJKKSYmJuoWiJ3Luakik1mT00e8dqHvsCxEYuUFECORiDZSGk3AWJbFzMyMf5zJZALNPPn4w+NMFdyac4lIdXEShsaJaZqtsxWu7q6j2TjkcrkaUdhcLlf3guhyxmIs6/CF0zMYToQb7CzXA7EVFKCvaMBp30KjWYJliKFPTk7WCMQ24ltIZEPBkx+fzfGVwzM15/pSwS6dhRDaXqwxWhU8uQB0LHmVZtXiOA7Hjh2rebij0foNnuW6JGNx3n57LwAHno6SMtp8URyNRrN+yOfzHDp0iESiuvs7+3XT41uSa4fauH27p5lkCMGWzqo9cpQbuMZJS5G27q6j2TBcvnyZy5cv+yLSlc539ZC1Pa2Tl+/rYlu6l77RJP0nIozEozihzXhptMaJRlMHDYqhK6U4fvx4zblGgg6ucjFE/VlgOdvbJL7z9l4qT3RPMvils7YXa4tWBU/uBX5KCNGmlKq/55xm1VDZSalX8PFzJx7kiczj/rHFDL1mOzt7PM2AjpgCWrs4kFLWRKi1iJtGEw6VMpcwBGLBS6XtTZq+Pfn22Le472JVINZLzW3u6222vXBdt7W2osk6cI1mrZFOp+sKsE7mHT7z2CSW6z2bFpfoT13iBWc+wpAQGHYO0xSoFgdPKzvhc19rNJpZPPL3cPyr1ePcSMPfdUKIun2Lo9kjPDT1IKqsjpZ1MnRHe+q+l+tCxBDsKvsaQaCUqsncbySLX7M6aJV39n7gecAXhBDvVUo91aL7alaI47kT2BToM7eWz/Rye+/1/vtCOkgzOGNUDzMzM0SjUT9KHY/HdZqcRhMCjabSNoKUXncdc1bh8dHcEdrMNvpj/QD0xwa4KrW3iXtIpqenfT0kKSWdnZ3NTbwRdGtizQZCSll3cHI463BxxmZPb5xE1MCwp7ksbCJtPViRLgDceDduojfEGddSKBRwHMfPxjVNM9BMO41m3XD2+1Cagc23eMe9+2Dfq+v+eKO+xenCKTLODDuTnvh6X6yPXcnddX/eloqoGezGyczMTE2ZTjwe98WlNWuDVv3fehyIATcDjwshisAIV7YvVkqpPS2ak6YBGu+u45ISPbz3+jcCEMldpG38cTh7EgCzNIFMb11siMAxTZNrrrlGOzUazRrj5HiJ0ayXhO9KhYwNM8JZHp/xnmVLlrih40ae0/3cQO43n0BsqORG4dR3qsdTp7XmiWbD0JBArOvgtB1nx/YkXW0RRiZmGJ2C3J6fweq+LrxJLoKUkk2bNrFly5YVub9Gs2ZQEvoPwEv/MJThZ+wZThVO+sej1iid0S5eNfCauj6fs1yevlxElk3SpRmbSMAKsYZhsG/fPtLpdKDjalpHq4InBmADZ+ecn/sXqWsoVglSSi5cuOCnn9q23VAqqsLBpBqk6D76KZIjD9ZcU+i/OZjJNoAu09FogqdQKDA8POwf5/P5QJ+1zzw2ScGuprZaA9/npLS4NF6tXe5pIBV3KVpe0vfkP8Hjc5rR6dbEmnXKxMREjaB0NputW0NtxL6A0/lDfpyNEMl7ZTrtShEzE1fsxrWKsLLsNJp1h3ShgZI6pRQXLlzAcbzNEynlomuRH00/zFOZJ2rO7UtdXff9fnQ2z3eOZ2rO7eheOfFpzeqkJcETpdTOVtxHExyO43DhwoWaLI1kMrnoZ6SqLm5cXKIYXpQZMOwZSp1XM3Lz/1u9PhqOHoJGo2ktxWKR8+fP1+ykLGUvGqHkSJ69I8Xz9njj/+MFwbXtz+DZ3c8BQCBImMFmlLW0tahdgEQnvPnz1XNxbR8165Px8XEmJiaIx73SXcMw6g6eWMoC4JW9b2RX+wBtl3/I5lN/xejeBHZoM14a3YpYo6kD5YLRWPDk0qVLNfZhMd/CkiU6Ip28ZfN/8c/FjfolAmxXYQp474sG/XOzO/kFhd7IXdvoIivNvFQEYis1/0tx3/i3eXzmMf84rka4LTPMjq9/1j+XH3gWMta6pkuO49REqB3H0QZLowkBpRSJRKJue9EIFY2TRNQgFfOcLoVDwkjQZgZ3v1Kp5L+erV/QEqQDZgzaulp3T41mhaiUxVWCJ41gS28Huj2Sps1sIyVMooBqoUaQlBLbroZqLMvSvoVGUw9KQgPdbirU61s4yiVqRJftG7hKYRrC9zWCYO5aZLbt0KxNdPBEEwhj1hjtkQ6uTV8LQObM57ixEGFq78/41+QHbmvpnLLZLB0dHb4QUzqd1gKxGk0IhJm2bpeLjyuiba5yUSgiAS6WXNelVCrR1dUFeL9PR0frAr26NbFmI9GMvXCUFzyJlZ9/UQ6mqCa7azVCoVDANE1/BzyRSASaaafRrFuWUbbTSGDSVQ6RJmyBK2vF6IMgl8uRSqX8VuyzX2vWJqF82wgh3lF+ea9SKjPreEmUUveEMSdNYyzl3LSN/oS2kR/5x657kZit2Hk8B0Dv+CVm2q9m+qo3hzrPxTBNk927dy9rd0uj0TRGkAGUh8/mGM54uzOOBKftKMfdPKWxGG65FNAMsBVpJXNm797ld+hpCt1dR7PBqHdBNFnK89mT3/MzTqbtYTrVJFtOfJIOI0I0dx5obeaJUoqBgQE2bdrUsntqNOuCZZTtLMbF4kUOZ5/2j8esMbqiXcudHVIpAo6dYBgGO3fuJJVKBTuwZsUI69vmLrxOOg8CmVnHiyHK1+jgyQrgOA7Hjh3z+407juMLNM1Hx6l7iU8dRUY9Y6DaErQ5kt6Jssq1MLD7bgh93kuhU2k1muDJZrOcPn3af76CTFuXSvHlp6eJmIJYOdtE9f6YcaKU8l4gNG2mGYwNLjZMw6yorZCODp5o1i0XL15kYmLCf8by+XzdafiPjJzkROlxDOIIDJIyw/7SOAP5SxjlRVipY4/vi7QK7VtoNMtAyUUzT5RSHDt2zC9tUUphWdaC1z8x8zhHc4dJzCrT2ZJYfidPV4Kpn23NEoTlrf0cXiDkUvn4XSHdRxMQrusyMzPjR0ZN01w0bV1Ih2LPtYzc+rsAHH3874mRJnLHW/xrgl3aLA/t4Gg0wVMqlchkMrS3e6Km0Wi0Kb2TnOWSLXmB25KjkKLI7btNbt6WxFWSf7lo8JzuZ3Nb1+2BzH8uLe+uI12vHXFlV604pVsTa9YtMzMzWJblp6onk0m/nHYpbOlpBbx711vZ3dlP8vIP6H/8o1x87p9jp7eFNufFaLm90GjWC9JdVPNESsnU1FRNGVylnHY+FJLOaBfv3Lq8ZaZUirGc438V5ywXMwTtZ20v1hehBE+UUnfNOb47jPtogkMphWma9Yskqtq6RYkMNI1+ORSLxZpsmcUyZzQaTXNEIpFARFWVUvzv74/WtCIuDfw7j7gWhy5UvZikGZymgFKKfD7vpwTbtu0HglrCj/8OHv3H2nObWt+6XaNpBUIIotHosuxFJXgSK+uVCdfbhVZG6zQDHMehWCz6x4VCQS+GNJrloNwlsywb6b7lKonB8qMdD5zK8s2jta2Jh9qb82tKpVKNKGypVNL2Yp2h84Q1QON6BUK5qFnRY6lczBVu1VcqlRgaGvKNrmEYde9uaTSa+qmU9wWBUlCwJdcNtXHtkNdu+N9nHA507ONA+wEADGGwvW1HYPesKN9v3rzZPze7LXvoFCYg3gHP/53qub79rbu/RtNCmhKIrQRPyiU6QlaCJ63L1CoWi6RSKTo7O/1zLQ22ajTrhSW67TRqKxTNZYFNFVwSEcHrruvyzw0GEDzp6+vzfQohhBaIXWe0dGUphEgDrweeAXQC08CjeMKy2VbORdOcQ/Nt1+JhZ5rzj3sSNRa5Fc88MQyDoaEhbaQ0mhCYbS+CTFt3yuNu6ohyzVAbUkm+UYCBeD9XpcIRcFVKEY/HV07wUdoQS8PuF6/M/TWakJltL6SUdduL7PgF7If+CkN6O7cDbS593bDjyT+mQxiYxQlvfLN13/NKKbq6uhgaGmrZPTWadcETn4LT91WPi9NXBE/m+haNoFANZZ6cGCvx3RPVTJOxnEM6bnLN0PLLjucihGBwcFB34FrHtCx4IoR4E/C3QBeeOGwFBfy5EOKXlFKfa9V8NjqWZXHw4MGa0pZGdpN/EoMJQyLK/ys7xBA3dV8d+DwbRafGaTTBMzk5yfHjx2vOBdX2u2J2KnXGEu+EEXIwdkVthe6uo1nHnDx5kvHxcf/YcZy6W3/nzj/BluxTjLddhTKiCBMihiQiTBACt62PUs81yEhrBWKNFc6s1WjWJEf/A3LD0FPeCNl8C+x8of+2lJInn3yyRhS2kZJ7qVzEIpksc3l6uMD5KZvt3V52yUA6wr7+4LNO9VpkfdMS700I8TLgnwGJ103nPuAyMAS8CPgvwD8LIaaUUt9sxZw2OlJKXNet26GZsicZLo34xzlTsNWJ8aZb3x7WFJeFNlgaTfBUylzqtRcNjV3eaTLKz65Ubvk4vMXKigs+6u46mnWMZVkkEom6skBdq8TEyYdQrrdgUqNPcDzVRu7ZbycSS5LJn8TIHWJix6+TaaHOyWxW3F5oNGsV6cCmW+Dlfzz/21Ji2/ayfQuF8jdx5yNnuZwcrwZmhjMOXW0md97et6z7aTTQusyT3wVKwPOUUj+Z897dQoiPAd8rX6eDJy2g0dS4b4x9nYvFC9XPm7C1tLJlOrlcriZbppkyJI1GszBhPlvunMwTV3kngiwDlFKSy+X8Y9u26e7uDmz8xifk6O46mnVLI/Zi/NgDbD745/7xsVQbX9zUD1PfoRKviBkxTNG6YKNlWZRKJf+4WCzq4IlGsxzqEIhtanilMBYJnnz/RJYfnsnVnNvTGw90Dvl83t9gAi9zRtuL9U2rvo2eAXx6nsAJAEqpHwshPgO8sUXz0TRISZbYltjOC3u9Gn311HvJx7ev6Jwcx2HXrl1aIFajaQFhOQOu9BZa5pzME5Pggieu6xKJRNi5c6d/bkW1kaQNLRS81GhaTb32Qlme3N3w7R8imuxkzDqJKj3CGze9kZSZBqDNbAs1E20upVKJnp4eenp6/HOpVGvLhDSadYF0wFj4u7zZjRmJXNQ2lBxFKmbwrtt7/XOdbcFu/DqOw9atW2lr83RThBDE48EGaDSri1atNEvApSWuuVi+TtMCljJYPQf/juTlH/jH0e52Bh2HG57+EgDj1hRHEysbqDAMg+7ubh0w0WhCJsjME6kU//DQOOM5xx+71PM1vpXP8tAZE0WljCe4xVJFILarqyuwMRvi4OfgR39dPbZzMHjDysxFowmZRuxFpfVwcmAXbckOYjNTGOMmvbFekubKBSza29tXzl5oNOsF6UKI+mVKqUX10VyliJqC/nR4mxVCCLq6uvzgiWb906pV5/eB5y5xzXPxSnc0q4DExJPIWAfF3hsBKHEWRRv5dD8APynlON/xArat5CQ1Gk1LCDJ44ko4P2WxrSvGpo4oEsmjYoxNyS1sbvO6WUSEya7krsDuCSushzTypLcDt+811XM7nrdy89FoQqSh4Em59bBR7p7jljPPIi0s05mL1jjRaAIi5LIdiVy0pM+V1azWMNH2YmPRqm+n3wJ+KIT4I+BDSim/AE0IkQLeD1wHPKdF89HMITH2KInJw/7xKTfD4YFryW06AEBmuoCdvoaJvpcA8M3xEQbjK592rg2WRhM+QS4mZHlhdWAwwXN3pbGkxckzJvvb93FL562B3GM+VlwgNtkHd/yPlZuDRtMiFgueGNYM7ee+hpBe5lkx8zgPdndQyPwIM29wqXgRoKUaJ3MRQmjfQqMJAukGWrYzUhrheP6Yf5xxMvTFFhZ/lUqhG2VpgqaVwZMngPcBvyiE+AkwDAwCNwOdeFknvzXnC0sppX6+RXNc19i2zZkzZ3yBVdu2a8RWew79I9H8RVS5X/pdvb2cMwuoqYf9a/pi/f5rqRRGi32LmZkZoLoIMgxDOzgaTQhkMhkuXapWWhYKhcDGLkuc+PbDUTYQ7E6z67pkMhm/nbLjOCubUqtbE2vWMZcvX/a/n8GzF8lkct5rk5d/QNfxf0EhAMGTZoz7evuJZn7syz72RHsxaN2Kp1gsYlmW70/Mfq3RaJpAOjVlO1JKTp8+7bcjdl23odbED089xIn8sZoOO32x/Qte70iIBPwsZ7NZpJS+jdCZahuPVnlzd8563QW8eJ5rXlD+NxsF6OBJADiOw9jYWNWhkTYdERujNAl4u0GZba9g4pp3AzBz/i72xPp41cBr5h1PKloePFFKceDAAV/jRAiBoUPKGk3gFAoFxsbGSKc9wUYhxIKLoUaRSqFwsCmQcwRZ1xOMDDp4kkwmueqqq/xzK6qNpLvraNYx4+PjFItFX7w9kUjUPG+GlQHlLZAixVGkhEMv/BQYEb5z8keMlr7L72z9Bdqj7Ssyf8uy2LRpE729VVFJLfio0SwD14HSdPVYOjUbB67rMjIyUuNPtLcv/NxLJSm41c2bvJtjS2Irb9z05nmvV0qRs6obw5Yr/U5+QSGl5KqrriKRSPjnVlSAXtNyWuVNBlu8rmkYpRSRSMR3CAYf+iCJqcM117ixap91V7kYi3S7kLL1afCGYZBMJv3dZI1GEw5KKWKxWCgLCMeVlAbu5f6SzaPnql5NzAjW+YjFYqtHwE06uruOZl0Tj8f94MlskpcfpP/xP/GPi7bidCHKn3x3HAAnmUd0QmQFM7OEELS1ta0ee6HRrFW++h44/1DtuUii9nDWWmQpvjP+bZ7KPFFzbk9y78LXH8/w3RPZmnO7eoL1Yyr2YnbwRLOxaMm3lVLqTCvuo1mYuXWFkcIIxa795DY/v3zGID/4TP99qSTmIt0uFK0v2wlStFKj0SxMmGmolrTBKDJo7uW23j2Al3Wyq213YPdYdWm0UpftaNYviz1vZnEUgImr70SZMY6NlvjqxQ5efnUHMVNwxklw0o0s6m+EjfYtNJqAyFyC/mtg/+vKJwTsfKH/dqPP2owzTUeks0YPbXvbjgWvnyq4JKMGL95bzWbZ0RN8Vsiq8i80LUd7cxsU4VrY7bvIbvtP877v4ta0/3rsQp7DI0X/OG+1PniiRdw0mtYQ5mLCKgtF9kW2cEPHjaHdZ0VtxbkfwqF/rR6PH4XefSs3H40mRBYLnoiyLclufSkq0sYJO8vpkRneuj1JzDQQU1EuTIpFM13DRvsWGk1ASAe6d8E1b5j37UZ9C0c5tEfa6/YVXAltUYPbtofb5lzbi41NS4MnQggD2AJsBebNYVZK6XbFAVAqlTh06JBvqKSUNQKxhrSQ5sLRWKkkxqydoB+ezjFZcOhKeA5OXzrCnt5wa4InJiaIRCK+kYpGo9pgaTQhMDExwalTp3wNIdd1QyuPc0JoReo4DtPT037dsZSSjo6OJT4VIke+BGfvh87yDlmyH7br1sSa9cHJkyeZmpryv49LpdLCz1v5eafsTzhlxehIefel0prYaGHmSS6Xw3Ec394ppbR+mkYTBHOyLF3X5eDBg7iu95xLKRsSiHWUS5tRf3lMGN11pqc9DZeKjTBNU69FNjgtC54IId4H/Hdg4Z5SHlrQIgAcx8GyLF+IKVIYJWpfRoyXH3hpo2ZpDGSdDBP2hH9ccm0mc4oTqgRApuRyYCDB62/obtnvEIlEuOmmm3yDpXeHNJpwsCwLKWWNiFuQi4ljU5cZLXh1yOMlrytH0AKxPT097N1brYVe0cWQW4KuXfDGf1q5OWg0IZHL5YhGo74obCKRWPB5U9Kl5ChOjNsoA0ZzFsRGOF/0umxNOZMIREuDJ67rsmvXLnp6evxzWktNowkA167R93Jdl1Kp5IvPw+JZGwW3wKg14h8X3QLtZnrB6+cSRncdIQTXXnutr9Oim1VoWhI8EUJ8APhdYBy4G7gA1B961CwLwzD8B3zoJ79PNH+x5n0Zq9YEfmn43xixhv3jsZzDyJTD47lx/1xHorXOhRAC0zS1kdJoQqay8xrGs1awLf7+9CcAWXO+PRpM954KpmmungWQU4KI7tahWb/Uay/OTRbpyznc88gUShi4iZO4fT/g3stV9zNhtFaoteJbrBp7odGsF+Z0lquU9NXrW3xr7BucyB+vObe9bXv9t1cq8O462l5o5tKqzJOfB04Ctyilppe6WNM8c+sKDWuG/MCzmNnxau+EMCl17vHfL8gCO9p2clvX7QB8+tFJuuIDvOTarvL1sLmjta24Vp3oo0azTpFShvasZZw8INkTv5mbe7zWwTEzwjU9mwO7x6qzFa4Fi5RFajRrmUZ0C4q2l65/5+19CCE4VDjNwaLJ64fegFnWVUubrW1RvOrshUazXmhSHL0gC/TF+nlh74v8c/2xgbo/70qFEYIgo7YXmtm0KnjSC/ytDpyEy+w6QsdxahwcQ1rYyUFKPdfM/1nl0B7pYEtiKwCmHaMjGWVHwC2+FkNKqVXvNZoWoJTya5CBmtfN4iqXL16+l4ybAcByPbu0OTHErYPBda0Pa/7L4uDn4MlZJTrZy7D51oWv12jWEHM101zX9Ut25nJmosSXnp6mcvnVhSm+uX0It/DPgJeWHzMjbEtsb9mCRClVM//ZrzUaTRP84KNw9oHqsVNCChNZXo/Ytt2QX+8ol5SZ8tciS/GT83nuP1ltTTxddNnW1dzGxdy1SJibS5q1SauCJ8eB1ollbEDy+fwVArF+iplSCGnVaJzMxVVOjQaBKxVmi23F5OQkyWTSN1LpdFobLI0mBEZGRjh9+rRvIxzHoa0tmNT5olvgXPEsA/EhuiKdFB3JqUI3fb1bAhkfPI2WYrHoC8QKIUilwlXXX5Qz34PSNGwpt3vv2w97X7ly89FoAuTIkSNkMhk/9d6yLBKJ+UUcz03ZjGYdrh1KIBDIaImpeIzdsX4E3vd5f2ygpd/t2WwWwzB8exePx4lG5+1ZoNFoGuH0dwEFA9cD4PZezRFrK/nHHgMaDzy40iFi1r80PTFWIlNy2dfv2aNNHVGu21S/wOx8VMTnK/YimUxq+QBNDa0Knvw18CEhxJBS6nKL7rmhUEqhlPIFYuPjTxKbOuupzChvl0XNEnG6WLzASKkqymRLx0+hhXLwpMW9iGOxGDfeeKMOmGg0ISOlJBKJ1Ii4BYWjvB2nG9tv5Jr2axnPOTw2PULb9uYcmtlIKenp6WHPnj1LXxwGk6fh/IPV4+kz0Hs1vPTDKzMfjSZEHMchlUotmG0ym4JbwEke4apdnRgILlx0iJXglf2vXrHvdqUUu3btortb7+FpNIGipJdl+aIPAOCUSuSffNJfi8xlpDTCxeKFBYfLyzy9Yqm+IlWkUnS2mbzppuCe7UgkwvXXX68DrJoFaUnwRCn1t0KIfcADQogPAj8B5i3hUUqdbcWc1htz0+L6n/gzTKv2P7GTHPRff230q8w4te93Rbv811IRevAkk8n4r5VSNW2JNRpNeISZhlppRWyWM9lk2TY1U4fsOA75fN6fs2VZdHZ2NjnTJvjRX8Opb9ee062INeuURtLuL8ujOB0P8/0Jb+FhkmOL29q092KxiGVZNfZC7xxrNCGgXDDqF1K9b/zbXCpdXPSa2WuRpQiiu042m73CJ9JrEc1itKxVMfA4cCfwj4tco2jtnNYtwrXIbH05U/veCnjBEBlN+lkoBbfA9e038JzuOwDPOYobCaT0nCSnBWU7Sin279/vH2sla41mbSJVVUPAVjYKMDGRUmG7nk1pxp5Udr63bdvmn6u0DVwRnKJXmvPqv66ei7dW9FKjaRWNCKy6ykEAv7DlFzCFSfexT9I1dZ5L4U6xBsuy2LRpU02AdXYbdo1GExDShVlZ60sFWm1ls6NtJ6/of9WC18SN+r/bg+iu47ouV111lZ9pYhhGXVl2mo1Lq1oV/wLwd3jtie8DLqJbFQfKXIMllIOMtCGjXlr+5y99lvPFczXXpMw0CdNLpf+nRyY4MjpZ836YmScVZ2yh1D6NRhMeQQoz29Li4+f/kYKbL48Nw1mbu05NY1rVJVOkyWhsIpFYPfZCOl43nUTHSs9Eo1lVdBROsUmdZf933kVlTePGWpslJoQgnU6vHnuh0axXlARRf/TCUQ5xI+6vPZoliO46hmHQ3t6uy3Q0ddOq0Np/B0aA5yilTrXonhsb6cAsjZMxa4zB+BC7k7sBEBhck6523rk0Y7O5I8r+wUT5fbhhczACkhqNZnURZPAk7xYouHn2JPcyEO+nYCu+fj7Pga7tbO307EnUEOxqonOXUmp1pd0rt6l2jBrNWqKR7jQxZxrDUMzsebO/I2217wxpZguj0+41mhYwp2xnKd/CkY5f0hsErgxmo1fbC00jtMr72wH8vQ6chEeNwVISgULNcu6nSxYy28PwZLVM5jIOMAVAznK5dlOCF+xpzU7NqlsMaTQbiEbS8JeiIhC7N7WPq9NXM5F3+HZ+hGv3pLhpyzpNlZeul3mi0WwQFrIX4zmHH57OUvFAsiUb0QbTu9/YkBaCRqNZg8wp25nL8dwxzhTO+McFWSCy2PVjRQ4NF+u+/XjeYXNH8xkjOniiaYRWBU8uADofKkCKxSInTpzwgyaze6kL6S1mVNlAKaWYLJTI5CTZ4vxGKRUz2dkdrobAxMSEnxanlPLbjGo0mnAZHx/n4sWLvoNQKpUC0xiqBE8qDlFF4yTSxG6QZVnk8/maVsqrShNJOhDVmXma9cmpU6fIZrM19mKh1sRPXS7wo3N5UjEDAWyKup5j2UAqf7Pkcjlc1/XnK6XUmzMaTQtQ0mV4dIyxp54CvO9q13X99x+eeohxe5yE4dmPuBFnU2LzguP94FSOUxMlktH6nl8BbO9ubC0xNTWFYRi+vTBNUwdPNA3RquDJPcAvCCHalVKZJa/WLInjOGQyGb/VqCzlcPMTTGeHMWSJaQnHSgXGxy+ikEil2NOT5M79QysyX6UU0WiUG264wT+nnRuNpjUUCgUKhYIvmphIJJoKRmScDEW3AMC4NYYrYboAl12b0Ww5mNKExonruvT09LBjxw7/3KoScFOL77ZpNGuZqampmgVFe3v7gt/XrlQI4H+82PMtHj4S5ck80MLFiJSSnTt31gjEav0CjSZ8lHQoFkv+cSQSqRFzl0qyq203rxl8bV3jOVKxrSvGzz2z/nbFjSKE4Nprr/V9CiGEXo9oGqJV3uiHgRuAbwohfgt4RAdRmqPS2tfP5PjOf6fLqQq+/sX2IU4XD5M/d94/l44EI9C0HCplAjrbRKNpPXPtRTPk3TwfP/f3qHKivlQwnLG593Qewx71r2uLNrd4ikajq9deSK15olnfRCKRugKsczUHlJKYtH4XNxaLrV57odGsV5SLMBf2LVxcjAYCqVJBNORWn4ZhEIvFVteGjGZN0aq/nEpYUgDfggXry5RSSv8110GNxol0STmTXOh6FmL7HSilGON77Ipfw9aYp3EihMHtA7tXaLaU56DT4jSalSBIjZOCW0ChuLnzFjbFNzOZd/ni6Rx3bN7B9h5v8RIzDbZ1LX8hE+R8Q0E6Ws9Bs66puzWxVMyu0JNKYqxA8GRV2wuNZj2iFCjpSwTMf4nCoP6sDlcqEpHwn2VtLzTN0KpAxfeB4No7bFBmB0xiD/0vdpx91Iv2SpfP9XVxvNcg1XERgEgpxTXd27m169qVmm7NfFf9YkijWWeE9fyVXJuJvMtjY2mOut2UXIVpdXBVf4I9fc111Jn9elXZi0P3wtF/rx7PnIeeq1ZuPhpNgMztkNFId51pNUyh+wd85qKX2Zoj0xKBu7n2QqPRtICH/xp18cflA4X36C38XS2RGA3oH7kqmO45s5lrH1adf6FZc7QkeKKUemEr7rOeyeVyHD582DMCyuXAU58mmhhAxbfgCMmDXf1EY210lluA7Wjbyc7krhWbr1KKiYmJmjTait6CRqMJl0uXLnH+/HnfQXBdl7a2YAROp4oWRVtiuQYdpiBiCvYPJNjcufwlU7FYpFQq1dQdr6oU/KP/AZOnoK/crWzoJtjz8hWdkkYTFE8//TT5fL5GIDaVStX12Wl1Hjc6jCn2IIABFWVv0Q5xtpDJ1FZ9m6apU/A1mhYgD32BbC5HsW0TACpxFZnuG1jo21opiWgw88QMWH5kfHy8RoclHo/r4ImmKfS3zRrh3KEHmTz6OLF4DFOWOGHGeLL3mYx23YIlHcamv8UtsVv46U13rPRUAS94kkgkuPnmm1d6KhrNhsO2bSKRSGABy9P50xSlJxB7tuDpmty2tZ3nb+0NZHwpJYODgzUCsSvKzAUYfrJ6nL0Mm2+Bl//Jys1JowkJy7JIp9N+8LK9vX3Bawu25PhYyd/NzRfztJl53q4Gvc/mniJadDi/4AjBsG/fPjo6OkK+i0ajmY2SDjO9N5O76df9c4ttc7hLZJ5M5B3OT1n+ccGWmAEHNuLxOM94xjNWV8c+zZqm5cETIUQK2AeklVLfb/X91yLKdYh87T3sci0QAgX8791bGJZnKU6O+df1xLpWbI7zoSO7Gs3KEGRa6pQ9xReH/9U/tsqtiNORdCDjV1hV9uL7fwjnH6w9t/slKzMXjSZkGil7eehMju8cr2Z+bGs7TCw9Sv+Tf+6fs9p3Bjg7jUazanBtFPUHIaRaPHjy7wenOTFeqjmXjgebeqLLdDRB07LgiRBiK/AXwGsBE08DJVJ+7w7g/wC/qpS6r1VzWiu4xRkM5XBs4LX0XvdKbGVTnP4sz2l/JnvbPE2TiDDYmu5e4ZlWUUrp1l8azQoRpAaAJT3H5kW9L2Fb23bOTVl87mSG7m3B2ZtVZy+sDGy6GZ7/O9VzHVtXbj4aTYg0srjIWZK2qMG7n+W1Ej102uSoBRfu+Esq2gduPHxfRC+GNJoVQNqoBjrNKeSigrFFR7KjO8brruvyz3UndYaIZnXTkuCJEGIT8BAwCPwbMAA8e9YlD5XP/QxwXyvmtKqREvWFO1FTZ/zjf9rSz7n0ZZL5f0OhMCNRtqT62N3ev7JznYXjOP6izXXdhkTnNBrN8nFdF8dx/GPLsha5ujFyts1wxuGLZ10idgmpFEImiTTRTlApVTNf2w5XI6FhnBKkBqBr+0rPRKMJHNu2a76fFwu2PnmxwH8cmqZyie1Kutoi9KY89zESkZgWOKktoc1XSonruv6x4zg6eKLRtAClVI0/YbrWot11vjH6NU7kj/vHlrQwF8k8cVxFe9z07UkQzF6LVND2QhMkrco8eT9ecORlSqnvCCHez6zgiVLKFkJ8H3hui+azunEt5PBTjEe2UOrYjSUVRzlHu7mZ/ek9AESEye7kyrYeno2UkkwmQ2dnJ+D1UU+ng03r12g083Px4kXOnTvni6y6rlu34ONSTBRKuFKxqT3O5rLobCIi2NTRnECslNIXsU2n04HNNxBcC8xVJFir0QTI008/jWVZ/oJisWDEhRkLy5Hcur36fO7srj4brpINJPEvj0wmQzwe9+1bZ2en12lQo9GESrFY5KmnnvLsg5IcKBTAWPjZu1C8QJuZZEfbTgAEgmvSC3f9dCREAu6uMz09TVdXl2/Turu7dfBEEyitCp68Cvg3pdR3FrnmLPC8Fs1ndaMkSimy/bdhH/gZMqUSM4f+nD3x/bywd3UIws5FKUU8HufAgQMrPRWNZsPhui7JZDKQjjqOdPnMiR9ScosATDpTANy6rYOb+jubHh+8YGt/fz/btm0LZLymufBjuPBw9bgwDuby2y5rNKsZ13VrBGIXQ0qIRQxedcB79mfsGQ5mH+UHE97O7kVVwFThLkwMw2D37t2LCtlqNJrgEae/w8CZ75JIJEC5mBGTWFuS4gLXu8phe2IHL+x90bzvn5oocXKsqnGStVyiAW9UxGIx9u/fv7pKgTXrilYFTwaBY0tcYwOraOtxBVHl9NRyqpuLd7xY3eBqQBsqjWbtc2TyEo/nHqCiXwBgkmBLqivQ+6wqe/HQX8Lo02BU9tAF9F29olPSaMKiEY0TVylmbww/nT3Iw1MP+u1HI5S4ZlZJTVjonWONpvVEH/wofVOXMCJetokyotjphctZHeUSEQsvLb95NMP5Kcu3KQIYag9+KarthSZMWhU8mQCW2mLcB1xuwVxWP36tnvfw27IcPFmkbnCl0WrWGs3apegWcZSnQTJpTwHw+qE38qyh1VMaGCiuA8XJ6nFxEva+El78oZWbk0bTIhoRlHbnpNW7ysEUEX59538FoOfp/0syczH01sQajWYFUA6Tm15K7hm/Nu/bjnIousWaY3OR4IntSPYPJPjZm3sCn2qFIAXzNZr5aFXw5AHgp4QQQ0qpKwIkQoi9wCuAT7ZoPqubcuaJqmSelIXdzEVEmlaCQqFQIxAbibS887VGo8Erg1lu8HLKnuKe8x9H4T3LJcf7mTKDSwSUUlIsVh2sQqGwspknX/tvcO4Hted2zp9mrNFsZKRSmLMeVYnCmJWVhnL9LNmgsG27RkR6tj6LRqNpIUqiFnn27r38eS4WL9ScixkLl+GEoXFS0VCrIITQ9kITKq1a7f4J8Drgu0KI3wSSAEKIFPB84M8ACXy0RfNZ3SjPCKhK279yMGUxxepWU+nuMTAwAHiR3mQyucKz0mg0jZJ1sigUN3XcTE+0hwvTFvdfcOje2RXYPQqFAtFo1BeU7unpWVlB6ekzMHg97HutdywEbNeSW5qNQSOZoo4EY9a1UknELF9EKLeh1qX1UCgU6Ozs9HQW8BZDFbFYjUbTQpRcNDg6Y0+zKb6ZA+lrAO9Z3ZO8asHrbVcRbaJT33wUi0WGhoZ8m6bFpDVh05LgiVLqISHELwF/A/z7rLdmyj8d4OeUUgdbMZ/VjnIdvpRKcVKdQg1/kazttQkzVlnmSSwWY/t23cpTo1lpmklTLTgWk3mX41ODJNUgM0UXs2gTNYMN1vb09LBlS3jtTBfl4Gfh/IPV49wIbHsuXPPTKzMfjWaFWCpwcnbS4oFTWf/4wrRFMlb1PYzCKPHMOfp/8kcAxDKnAs88EUKwefNm3bFPo1lppItaRG/RUS79sX6u77hh3vcfOZfj6GhVIDZnSSIB7wMbhsH27dt1tommZbSszkIp9Y/ldsS/CjwL6AWmgQeBjymljrRqLquRbDbLsWOepq4qXOa+RIIYedqcGSxXIew+us2BFZ5lFa1xotGsHJcuXeLixYt+6Ytt2/4ubaOM5EoUbMlkXmGVxal39cTpbgsuWKuUWtkynSc+CaUMpIe84+7dsP25KzcfjaaFPP3005RK1QWMZVkLXvvUpQJHRooMtnu7t6mYyf7Bqm0xsqeJWtNEil5ZjYymKfTeGPictX+h0bSeYrHI4cOH/Q2ZvYU8qmvhZ9FdQuPk4bN5JgsO3W3eNf3pCFf1Lc9XmY/KWkTbC00raalIhVLqGPD/tPKeawXHcbBtm/b2dhzXW7QckFsYir+ZaeVyenyK9i1dKzvJOWhjpdGsDIVCASGEHzBJJBILBieUUpybsrFd5R9PuMM4yltAHZu5CMCr9/dwoLe/BbNvAZnLXmlOheIU7H013PE/VmxKGs1Kkc/nSSaT/nf2YoFWVymSMYNfea5nC4RbIj51BMY8+2GUpjCMCJeeo6usNZr1huu62LbtZ32ZAuKzWhNP29NMO1P+9Y5yFu2u4yrFnt44P/OM8ARi9VpE02q0wucqwjAMDMPAkS6Oqzg0bPH1s+P++23Rldu5VUqRzVZTeR3HIZXSnaU1mpXCNM26sjnOTdn8w0Nj/rGMjGP1ffmK6/ragnueHcehUCj4x/l8vrUOztf+Hxg/VnsuPdi6+2s0qwzDMOp6Bud21+k49UW6Tny6Ok5nJyLZHejcisVijUBsqVTSCyKNZgWoZHJUfAuBhFmSAV8cvpdJe6LmM23mwsFYpWo1k4Igl8v5ArErntWq2ZDo4MkqopIm50gHBWxuT/Dym/oAiBgw1LFyIkiu62KaZo3GiRZw02hWPyXHczJee20nA+kol6wc35uJcEv6hXSavQB0xpL0J9sDu2exWKSzs5OenupuU0v1C4pTsOP5cNM7vWNhQN/+1t1fo1klKKUaKrOd213HsDNIs42RW34HgJnMj7BVPtA52rbN0NAQbW1tgLeTHI/HA72HRqNpkIqW2izbUXAL7Elexc2dt3hvIRiIL7wxIZUiyOY6SimklOzatcs/pzt9alqN/otbJcx2bhzXASAVi7C9e+UCFK7r+q8dxyEWi9Hb27ti89FoNB6NCMSWq3XY3BFlc2cMOyeI5QXX9Wxd1OlpdD6zWwW6rktHR8fK2Qun5OmbDAWvxaDRrDUayeKY213nqJzmO73d5LNea++8zNEe6WhqPlLKGhsmpaSnp0d37NNoVhPlzp+zBaFd5dAR6WRzoj7xd1fSdPBktr2QUiKE0GsRzYqigyerENv10ldXsruO67rMzMz4zoxSivb24HamNRrN8mkkeCJlWaugEpwttz5frE65UfL5PEopv0VgLBZb2Z1jtwSR4ETpNJq1TEPBVqkwZ612TqkcU6bJ9viQf25bW3Nd9jKZDNFoFNP0fJy2tja9e6zRrAJqbEXZV6h021FKLalxMhfZZNmOUorJyUk/c1UpRUdHc8FbjaZZ9LfVKuHwkX/i3OWjxKIxpktTAERWMHgipaS9vZ1rr712xeag0Wjmp5E0fEe6OG3HOFw4zyVlMlwaBlhUIb9RpJRs27aNwcEV0hW5+BMYO1w9di0wdVmhRrNU4CQ3eYnMiQegfNnQVJZYW4ZDx70SmkvOJO1C8IqBVwU2J8MwuPrqq3WmiUazihEojsdinJBjFKYfQQEKRcSof20ilaIZSRKlFIlEghtumL8VskazEujgyQpRKBS4fPkyQghsa5pPn7kXBYiS58MIIBYdWmKUcNGCbRrN6mBiYoLp6Wn/mcxms/6u7VKMORdxOh/kkWyESN77fMyI02a2BTrHFbUX3/4dyI3UnuvctjJz0WhWmPPnz+M4XvmvlLKmBHcu2cc+w9bRb/nHx/q6uJhsZ6SqD89eoyvwOWr/QqNZeSzL4uLFi/5xsVisBlyV5F86u5hyLyAncv41nZH6BaObzTwBbSs0qw8dPFkhisUiFy9eJJVKUSxcQAHPT97Anm1vZDzn8PEfzxDbvHXF5tfIzrZGowmXiYkJxsfH/VIYwzDqFmy2lVcG+MqeN7C7wwvIRoxIoGU70GIHR0o/pRjwBGKvewvc+kvlyZgQ07vamo2HUopLly75JXTAFRkejmP5ryPWDFOxIYyX/TEAU1PfIe2M8F+G3uhfE412hjxrjUazEliWxaVLl3yxZikd4nED1y0i3AI5w+CZkSFu2P5zABjCIGYs7HsopXydNSiXATbhGjRScqjRtAodPFlBYrEYbW1tWBY4UvHwOcFnT5bK76aINmNxGkQphWVZvqGybVvXIGs0q4hEIkEi0biOh6O8Heg2s43EIi0FG0FKiWVVF2Atby36b78Aw0/Unkv1Q1zrMmk0gL8YmsvlBz/F1rPV1sMHu9J8a8cQ5sg/+ucG4kPE48EJMjqO42fCVI715oxGs/JUtMoq9uILj7+XS6pYc027kajbd/iXRyc5PFL7ebNBxdhSqVQjEKsDKJrVhl4drxCzMztKpTxKQW8yyVUDnvMfNQW7e1pXs+84DpZl0d3d7c9PizJpNKuDZjLBKsGTmBFcq/NcLkcsFiOVSgGQSqUWXKyFwuQJGLwBtt/hHRsR2Pea1t1fo1mlLGUrjJmzWGaakS2vBuB0/BKRpMuzup/rX7M1EWzJWy6Xo7293c+Wa29v15szGs0qZFwV2SpS7GzbCYBhmOze9Oq6Pz+StRlqj3LtkBdsEQKu31S/byClpFgs+t10lFKt9S00mjrQ314rgJKS7//4DxiZuogZiZJ3CgD0p1M8Z8/K7ZymUin27NmzYvfXaDTzM3vnJVty+e6JLI6cfzfGocRl8QgSL2gy7UwCEDODNfebNm2iv78/0DHrxrVg081w88+tzP01mlXK3F3aM5MlHrtQ8I9vLJYoRbsYetZbAUiOfo1U4Qy3dz0ztDkZhsHOnTv1IkijWWXUdtdRSBSbE5u5ft+vLms8x4Ud3VGe38RaJhqN/v/bu/MwR6763OPfn6Ru9b5Nz0z3bB7bs3jBG/GOiW0IO1kghiQswSSES7gJCUlILll4CHlCEiA3hBu4CTcBG8hlCZvJxYTF4A0TL4ADXsbG+9izT69qrVV17h9VUqt7pG5Nt9RSd7+f59HT06Wq0lFp9Hbpp1Pn6LOItDQVT1ZIKpXi8ccfx8zIZQ7xnfH/ot0ZyUIM56Dbi9Pef1rT2qcxTkRax6FDhzhy5AixaJj6TCZTumTnseM57npqhu72WMWB2PJt+5nuu49YkIRoisEe20R/HccAaWpeBAH4Bc2mI0L4Xnz44YcpFAql38svqbvryTT3H8rQkwwHmP4pl6ers6M4uQ4Oh9kypsOokc4vRJovm83yyCOPlH4vFAqzBZSggI9hsaV/NCwEbllDDjjnSuc9Iq1KxZMVks/nS11Xveh6wqsGLuP0U1/L8RmPD912hIu2DTS1jTq5EWkNU1NTeJ5XGiC2q6ur1M292OHkNy4dZrDrxAh/ZGaKrx5J8Jotv8LG5KaGtM/MGpsX6TFIH618nxeNC5Woz/gtIquZc46pqanSoLBmxsDAQOn+wMtzTsdBfvnZ4SW5G+73cfFuDhfvd44YKp6IrAeFQoGZmRl6enoA6CRDWzCBTR3DCmkcELPaL/HNFAImMrODtxd8R2KZcaKskFan4skKSiQS0QegMGgS0RgEflT1jZ3koErLEQQB6XS69Hs+ny9dYygizWVmZXkxlx9VT6pN/xe4ILq/tqmMa+F5Htns7CBw6XS6sSc4n/9lyIwtvI4GhxUBZvOikovHPsPZ099m4/dm709vvLD0b0dQ9/dyNpudM0BssVeMiDSXc454PF7Ki223/SHx/AQQfTLZPAInMbD89Xcf5+DU3Pd3Z9vJVU9mZmZKvV983695JkGRZlHxZIU452bDIQi/OS0WT4Lws86yq7Unw/M84vE4IyMjpWW6HlmkNSw0unyx50m1WmtAsXhSvw9E6XSagYEBentnCxbFwWIbIjsBp/0M7H5J5ftjCdhyUeMeX2SVKD+3qKTDmySV2IC74C2lZfm+XaV/BwR173mSy+XYvHlzqeecmc2ZOllEWoBzxPMTzIw8h5nRKyg4n8LUTXj9e2rexWTGZ/dwkgu3R+cDBqeexGQXvu/jnGPr1q2lZcXcEGlVKp6sAC/w+Oxjn+bQ+CHaUm0U8mN4Adz5ZJ6xZ46R9+r/YaeS8hOsIAjo6uqaUzwRkdaw0JgipeJJlc87xff5cj8Qzf9AtmHDBoaGhpa1z5oEAbgAhk6HnVc2/vFEVrnyrPivZ9LcvX+2V+kLchlybX3ENl1ccdt6jF80PyvMjJGREX0IEmllQdhjJN97CplNF5MPcrj097D4bKHz6/sm2T9RvedYphAw2tfGGZtr761SnhfOOdrb2/VZRFYVFU9WwHh2jH2TD9LluumMdREL2jk9lcYr9NCeNNrjcQa7Emztb9w3M4VCgVQqVfr2xzm3Mh+ERKSuArfIZTtRz5PlDAKZSqXmfKiKxWIrN7VoEHX3r+PUyiLrxQOHsxyeLrB9IPz2d6A9oK+rk2offwIcxvKKJxMTEyQSiVJetLe3E4/X77JBEamP8sKFBeHA0i4WZkVQ4YuXe/an6WqLsaG78t//XcPJkyqcBEHA+Ph4qbDqnKO/v//knoRIk6l40iD7xh4klU8BMJYdg0Kaq71hzm4fppDNERw6zqPPGuKUs1ZmnJEgCBgaGmLPntq744lIcyzUDd+PLvOretlOccyTZfQ8CYKA008/vTkF1ujbMJYx4r/IejE/K9pzY/x0/EFevLUPgP5sGq9zI0eqbh8se3ykRCLBOeeco54mIi3u6f3f5sDB79M+lsT8LMc6Oph2k2RSD5AP8uR9x/5xj1gq7L2W9x3PObWLq3bVZ4yxIAjo7e3lnHPOqcv+RJpBZ6cNMJ4d5x/v/Ug0YJqB80hM7Wf3ke8z7AI833EY8NoGVrRdGsFapDVNTEyQSqVK79FMJlO1p0fx26H4Ij1PGn0ZYMP4UfEkrp4nIvM55zh06BBBNFia53n4/uxsF5dMfI69mTsZ/vFsfuQG9lbfXx16njR16nIRqSqfz3P0aDhzXRB4fPz+j1DAYcUr+/oHKBSewB0Ny6tjMx53jQd8PztR2sdgZ317kSkrZLVT8aQB8n6eIAg4J3Yee/r2ksgeZfe+9+Dteg3PjF7OsbTHP9+d4kU9O1esTZo7XaR1HTp0iImJiTmjzFcbYHGxMU9me54s/YSn4VMRL0SX7YhUFQQB+/fvn1Nc7eiY7TYf97NMtG0hd8W7Ssu8zo3V9+cCYsssnujDkEhrymazPPHEE3R2dpLPHaeA49LkLnZveSkALtZG0DFYWv9Dtx7jvE0bueK0cCrjeAz6O+pXPFmoV63IaqHiSQP4LvwWaLBjiC39W2iLBwwYHO3ejNc9StYvMB072tDZdZxzeJ5XCipNFSjSusyM7u5u2tvbeWIsx2d/OI7vpiqu6/lgUPq4czR/lC8d+gK+C4sOfmmq4toDJgiCE6YWbdgHoiCAL78BJp+qfH/x5Eo9T0RO8MW793PDndPEE5U/0Lwmn8dPduB1jwLw/cl7uGv/F6rurxAU2NKxter9lfi+P6e3i+/7KqCItJjA9/joN1/PkcIMZoYjPG8Y6NxCb3+V3mhehq726mOcLEWhUCh9FvE8T1MRy6qn4kkDBC4ohRQA0YcaLDzc/iIDPtZDPp8nn8+XphONx+NzphkVkdb0zGSBdCHg4h3dxKtExHDP7OCMY/njZPw0Z/acRTIWfgPdn+inPVb7CUoqlaK9vb10UjMwMNC4E5z8NBx9ELZcCBuqjMEUb4MdVzTm8UVWsUePpGiLG+dv7ap4/2gQY0NnB6no92eyzxC3BHu7z6i6z1O7Tj2pNkxNTdHT01MaFHZwcFADxIq0GL+Q5hk/zRY6GE1uBiBuCbZufl7VbQJXvVfrUnieRzqdLn3+iMfjGiBWVj0VTxogcAG4sgEb/QKpXMDdT2c5MDnFdC78xibe4J4nAwMD7Nq1q3EPIiLLNpnOc9PDUxQ8n3g8y1PjeQx46Zl9NX2b60XF2UsGLqO/bWknJWbGjh07GBwcXHzl+ZyD+z4L6WO1rR8NpM3en4U9Lzv5xxNZx/zAMdgZ48VnVn6veym4ow2mx24D4Fj+KENtQ1y54aq6tSGRSLBnzx59gyzSwnwvA8Apnbs574y3LLp+4ByO6uOpLYVzjq6uLs4888y67VOk2VQ8aYDiZTvFEexT2Twu63PvoQKPHp8BoLMtxmBnYw+/utGKtL67HjvOrY+mMHOlS212DLbX/P4tFk/aljE7zbKuQ545Cnd8ACwGsRq/fW7vgSEVdkVOlh+4qj3SAG5N5HkwYQRTPygtW6jXyVLp/EKktXmFsHgSs9ougS2Np1bnt7bGW5S1RsWTBgicTxD4eOlJ0pNHyE4eoxt4/t4hXrt7dEXaoNHvRVYHz3c4HO+4cpieztqm+kz7aYKoSJv2w2Hz47b0OF/WALFeNvx59Z/D7pcsuQ0isjjne/S6CeLZsNdH4BwzQbp0fwqfna6Tl+38nYa2Q+cXIi0ony717vSnnwHAajw3CILGDCmgrJC1RsWTOile1wdw+NAzuLFH2Pj0d+nL5umL1mlLdjbs8YMgIJvNln7PZrO6rlCkRWUymdIgzjPp9EkVO5/KPMmXDs0dANIwEidRPPE8j3w+X/o9m80u/QTHz4U/47UVfkTk5ExPT5d6h11x8CPsTd3G8C3ht8lf6Ovn7s6ycwuDM6yv0m6WLJfLnTBArIi0GL+A+9eXEmQnAcjHYrjBIYjV9re5Hj1PnHNks9k5k1X09PQsfYciLUjFkzqZnJzkwQcfpKOjg8cO7wPnON5/IbEdzwpXaOtm47Yqo1vXQT6fx8wYGhoqLVPxRKQ1Pfnkk0xMTJBIJDh4MDzRaKtxwMVpbxqA5ww+l454OEBsX6KPuNU+YGNxALeurnDQyeHh4TnTnZ4ULyqeJFQ8Eak33/fZt29fqbjZndnPWNs27OxXAnAwex89LstliW2lbUY2XFrXNmQyGTZu3FiaHjmRSGiAWJFWU0jjZyY52H0OuQ3nMu6y5IP7cYNVBmafp1g8iS+jeuJ5Hr7vs3Hj7PToxYkrRNYKFU/qIJ3P8Z7vXs9UeoJYPAH+UZJAx+iz2Lzn5SvShuIAsTt27FiRxxOR5SlOTdyehHi8QKLGEaSLY5yc1XsWXfGlnZSYGaOjo0srsAY+3PZeyIyHv2cnwp9xDR4pUnc/uZHtD3yG9vawp8mYf4hDXeeT3PYzAGQPHKfLYuwafVXDmhCPx9mxY0epeCIirccVsny5u5snBzoJ+gLygYNsH8mO6j0/bvrJFIenwnMKr3TZzjLa4Bzd3d36LCJrmv4S1sFDR5/hyez9JOiiPeigw/nszOXYMbKEmStqlM/nmZmZKX3745zTyPciq0T5AK3Ff9Z61UxxQOqTGeMklUrh+37p22szW/o3x1PPwL4boHcUktHlAaPPhsHTl7Y/EanK9n2J7skHcb1bARiPb+Kp7mdT/F7Xw6fL6vu3f3x8nFgsVsqL9vZ2Dfoo0uIy+Sm+29lJG1k6ox6qo8ktbGrfVHH9wDluezRFd3uM3mR4PrCtv43tA7Xnied5TE5O0tYWFneLxRORtUzFkzoo+GHV9jl9z+Glp19AcvwhRu66lcPJLrKLbLtUvu+zadMmdu7cWVqmQZlEVo/i+9ULPIL2gzyZyS+yRehYPpwS+GTGOAmCgN27d8/pabLkD0PFMU4ufTuc9ryl7UNEapIvFDjcfioP7X4XAJ/PHWS0L8WG9BMAZP0MvfHeuj5mPB7nvPPOm9PTRMUTkRaVn4H938ObeByAixKncO7W1wJwJFXg2ITPsQqfRgpBODXxFaf1cNnOpY1LEgQBAwMDnHHG7Ixe+iwia52KJ3XgBWHxJE50chF1q3fLmP2iFvF4XCc0IqtQec+Tw+5h8kM3c8Ph2qYTBGiPtROj9ve+mRGLxeqTFxrjRGTFPHJwkqdScT75/TEA8oN3kE8c4IbDsz3HTuk8pa6PWeyZpvMLkVVg8im48bcpxGLQ34/Fw7HMvMDxT3ccK12OU01fx/LGL1JWyHqj4kkdlIon0YCNFv1OrHGHV1MRi6wNnsuCwS+OvIpYjYO+9iR6Tvr9X7e88KMeMhrjRKThfK9Ad3uSX79kGIBvTAT0tG/jpzdcWVpnY/twXR+zvLgrIi1uYCf8/McoZMcIHvoUXlRMzRYCvMDxnFN7OGNT5QHhEzEY6av9i5v59FlE1iMVT+rAy6fY6B/kpx7/EFueThCLurW7OhdPfN8vndSUj18gIqtL+YcTHx8DtnRsJWb1+fbGOXfC1KI150V2Cr76VshPV77fi7r/ampikYZ4eizNO/7vDwDY1enzUH+e9tQnAUgzzba2vWzp2FK3xwuCgCAI5izT+YXI6vB46jivuf16nPkUHHxtf4Fb9h0uzZ4z2tvGjsH6fdkx/7OIyHqj4kk9ZI7STg6/bYDcQDhdoEt0ke+pX1faTCaD53mlQWETicTSpxYVkaaaUzxxHubidSucAExPT8+ZTrSzs7P2mTKmnoZj+2DLhdBdeaA52ntguHFTr4usZ8m2OLtHwnFMjgVxYgmbUyx5Vu85dX28sbExenpme7OV/1tEWltbrJ3RrnB2m8A3ejpG6UpGnxVixqkb6lc4KRQKpNPp0ucPM9MAsbLuqHiyRNP5aW7e/x0AHko9DMCRoSsYftbPNeTxgiBg8+bNbN++vSH7F5GVcWhqnC8+fhcBjljMOFI4BCzvmuP5zIxdu3bR27uEgSSLPUsu+DXYdnFd2yUii9vYm+TNz9sNwN9/Pk7StfGijS9p2OMlk0nOOeccjVsgsgpt69/E3770d4CwEProo4/S19fXkMcKgoDBwUF2797dkP2LrAYqnizRRG6cf3vgswAUchk6AkdHrKtu+y8UCmQymdK3P7lcbulTi4pIU3mex2OPPQbAp/bdwvfTtwFG8bvdbttYddtapNPpOd1nPc9b+jfHxTFNNCCsSFPk8/lSXuQKBRLxzrruf3p67iV55dMSi8jqksvlSnlR/rmhHnzfZ2ZmprTPQqFAT8/SZuYRWStUPFmizR0jXNP9S3R3dTP+1P3sfPyvOfbswbrtP5/PMzAwwOjoaGlZMqkPMyKrUaFQ4MixwyTa2pjKTYCL8ydn/Rax6Bync5mDr3qex7Zt22Z7mgQeXckEeLVNfzy3sTPhT41pItIUhXye6YnjAAQuIBGv76mac469e/eWepqoeCKyehUKBaampkq/d3XV74tcz/Po6urilFNmhyEoDh8gsl6peLJEMYvR29FLb3cvhfY2Eg5iiaWPWF1JZ2enKrwia8DR/BGuP/5xzIxJ84m7DvqT9fs22czo6ekJ8+LYQ/Dla8EvLG+nbfX9tltEatM58yR7b38jAF/bMEyszoPPmxm9vb0qmIisAbFYjM7Oxvy9ds7R0dGhzyIiZVQ8qYcGTU2sExuRtaE70cOl/ZcTj8f50cEMB6brfyJSyovxx8PCybmvg46Bpe2sox/6d9StbSJSu0JykPHdrwUgV9hHsue0uu5fUxGLiIgsjYonSxSbOcTow/+HZDJJcuwQPvWdmtg5p8HbRNaIQr6DA0/vIRaLk57I0+EHi29Uo1h+ipFHPkn7eB+0tcPEE+Ed5/0qdA3V7XFEZGX47QNMnfZKAHJPfxza6ldsdc5hZvpyRkQWVcwLEZml4skSWSFNz/h9xBMJ/LzPo7GteB1LH/Qxm82SzWZLg8J6nqfiicgakfMCHj2eL01HvGvj8qYZT6VS+L5PLBaj78h/MnjwW8SzWyARXYs8esHSe52ISMtwzmEs71xgfHy8NFW5c05jFohIRYVCgVQqVcoLz/MYGtKXMCLlVDxZokzPTm4762/p7u7m8bE833hoit9cxrdDnucxMjIyZ4DYtrb6jqEiIs2xubeN333ucOX3tAtoSz2FBf6J91XRnkqx45QddHYmieV9Yp2dxF79OfU0EVljHI7YMr75dc6RSCQ499xzS8v0xYyIVOL7Phs2bJgzQGyxkCIiIb0jlujwVJ7rf5giHs+UlrUnaj/B8TyPTGZ220wmQzKZ1DdCIutM94GbGb7vwwuuEwRuzjgFG4OAzoe6iBc/BCWSkOxtZDNFpAkCF5x0z5OZmRmCYPbSQDPTuYWInCAIAtLpdOn8IpfL0d/fr7wQWYCKJ0s03NPGL57dTWc0JVhnmzHUVfvhzGaz9Pb20t/fX1pW/m8RWR/iuUkAjp73B1XHTZqZmWFwcLDUc8XM6Ozvh1h4mR89IxBXTzWRtcbhMGr/YsY5h+/7bN++vbRMvVhFpJJcLkcymWR4eLi0rLdXX8SILGRVF0/MLAm8B3g9MAj8CPhT59w3a9h2K/B3wAuBGPAd4O3OucdqeezO9ji7NrTR21v72AXl3xwHQcDAwACbN2+ueXsRWXssyAOQ3nwJRGOizJ8NI9UxzSlnn01XVKwVkbVrwhvnMwc+DUDaT5fGSqpm/rlFLBZjZGSkoW0UkdVpfl709vYqL0ROwqoungDXAdcAHwR+AlwL3GhmVzvnbq+2kZn1EBZL+oH3AgXg7cAtZna+c+54vRta7EZbHLU6FoupW5yIYEEeZ4lS4QRgcnJyznXGbW1tuu5YZJ0wjI5YEoAdnTvY3b276rrOOcbGxkgmk6VlKrKKSCXZbJZcLleanAKYkx0isrhVezZuZhcDvwy8wzn3gWjZJ4D7gPcBly+w+VuB3cDFzrm7o22/Fm37+8Af17u9QRCwY8cONm3aVO9di0iLixWm6Tl4K4myE5ai9qnHcbG53eoTiQRnnXUWnZ2dK9VEEWkR/YkBfmHklTWt65yjs7OTCy64oMGtEpHVLggCRkZG2LFjR7ObIrJqrdriCWGPEx/4aHGBcy5rZv8CvNfMtjvn9i+w7d3Fwkm07T4zuwl4NXUonuTzeXK5XOn3TCYzp9IrIutHInOEzQ98uNTzbL5Mxxamp6dLv3ueV3VdEVm/nHOkUqnS70EQqBeriFTkeR7pdLp0PpHL5fQlrsgyrebiyQXAw865qXnL74p+ng+cUDwxsxhwLvCxCvu8C3ihmfU656Yr3F+zbDbL8PDwnPnR1ZVWZH0q9Ozgycs/RFui8sCN4zlj+/btdHSEYyiZmbrSisgJipf/7tq1q7RMl/SJSCXF2XPKx1dUj1aR5VnNf3FHgYMVlheXbamy3RCQrGHbhxZrQKFQIJ/PV7zP8zz6+vo0g46I4GJtpOODVYsnXm6GgYEBFUxEBOdc1XML3/eJx+M6txARYOG8KBQKdHd3Ky9E6mg1F086gVyF5dmy+6ttx1K2NbM3A2+Ofs0///nPr3ZZUPgAuVwqCAJvoXWkroaBY81uhMxxn3PuWc1uRDPMz4srrriial4451w2m50CXLV1pO6UF61l3WYFnJAXhUsuueSpauv6vl/I5/MzK9MyiSgvWovyIsoLM1swL/L5fNr3/crVFWkEZUXrqWterObiSYawB8l8HWX3V9uOpWzrnPso0RgrZnZPOp2+sLamykows3ucc3pNWoiZ3dPsNjSL8qK1KS9ay3rOClBetDrlRWtRXigvWpWyovXUOy9ii6/Ssg4SXrozX3HZgSrbjRH2OlnKtiIiIiIiIiKyzqzm4sm9wB4z65u3/JKy+0/gnAuAHwOVqoKXAI8td7BYEREREREREVk7VnPx5PNAnNlrhDGzJPBG4M7iNMVmtsPMzqiw7UVmdmHZtnuB5wH/VuPjf3TxVWSF6TVpPXpNQjoOrUevSWvR6zFLx6L16DVpLXo9ZulYtBa9Hq2nrq+JObd6xyc0s88BrwD+DngEeANwMfB859yt0To3A1c656xsu17gh0Av8AGgAPweYTHmfOfc0RV8GiIiIiIiIiLSwlbzgLEAvwr8BfB6YBD4EfDyYuGkGufctJldRVh0+VPCHjg3A29X4UREREREREREyq3qniciIiIiIiIiIo22msc8WXFmljSzvzGzA2aWMbM7zewFzW7XemBmF5nZP5jZ/WY2Y2ZPmdnnzGzPvPWuMzNX4bavWW1fq8zsqirH2pnZpfPWvdzMbjeztJkdMrMPmVlPs9q+EpQXzaO8aC3KisUpL5pDWdF6lBcLU1Y0j/Ki9TQjL1b7ZTsr7TrgGuCDwE+Aa4Ebzexq59ztzWvWuvBHwHMIB/T9ETAC/BbwAzO71Dl3X9m6OeBN87afXJFWrk8fAu6et+yR4j/M7HzgJuBBwrGFtgF/AOwGXrIyTWyK61BeNIvyojUpK6q7DuVFMygrWpfyorLrUFY0i/Kida1cXjjndKvhRjgQrQP+oGxZR/TC3NHs9q31G3A50D5v2W4gC3yqbNl1QKrZ7V0PN+Cq6D1xzSLr3QgcAPrKlr0p2vaFzX4eDTo2yovmHn/lRQvdlBWLHh/lRfOOvbKixW7KiwWfs7KiucdfedFit2bkhS7bqd01gE/ZdEfOuSzwL8BlZra9WQ1bD5xzdzjn8vOW/QS4Hzhz/vpmFjezvpVq33pnZr1mdkJPtug1eAHhH5Wpsrs+AaSAV69QE1ea8qKJlBetS1lRkfKiSZQVrU15cQJlRRMpL1rbSuWFiie1uwB4eN5BB7gr+nn+yjZHzMyAzcCxeXd1AVPApJmNmdmH1/o1sE32ccLjnTWz75jZhWX3nUN4eeA95RtEf3zuJXxfrUXKixajvGgJyorKlBctRFnRMpQXJ1JWtBjlRctYsbzQmCe1GwUOVlheXLZlBdsiodcCW4F3lS07CLwP+AFhcfDFwFuB88zsKuect+KtXLvywBcIu8IdA84ivH7wNjO73Dn3Q8L3DVR/7zx3JRraBMqL1qO8aB5lxcKUF61FWdFcyovqlBWtR3nRXCueFyqe1K6TcPCf+bJl98sKMbMzgA8D3wOuLy53zr1z3qqfMbOHgb8k7O74mRVr5BrnnLsDuKNs0VfM7POEg2j9FeEfi+L7otp7Z62+b5QXLUR50VzKikUpL1qEsqL5lBcLUla0EOVF8zUjL3TZTu0yQLLC8o6y+2UFmNkI8FXCUauvcc75i2zyd0AA/Eyj27beOeceAW4ArjazOLPvi2rvnbX6vlFetAjlRWtSVsyhvGgByorWpbwoUVa0COVF62p0XqjnSe0OEnbLmq/YFejACrZl3TKzfuBrwADwXOfcosfdOZcxs+PAUIObJ6H9QDvQzWwXudEK642ydt83yosWoLxoecqKkPKiyZQVq4LyQlnREpQXq0LD8kI9T2p3L7CnwqjJl5TdLw1kZh3AvwN7gJc75x6ocbteYBg42sDmyazTCLvBpYD7AA8oH7gJM2snHNjs3hVu20q5F+VFUykvVgVlRehelBdNo6xYNZQXyoqmU16sGg3LCxVPavd5IA68ubjAzJLAG4E7nXP7m9Ww9SDqdvVZ4DLgVc6571VYpyMKp/n+DDDgPxrbyvXFzDZWWHYe8HPAN5xzgXNuEvgW8Lp5r83rgR7g31aksStPedFEyovWoqxYlPKiSZQVrUd5sSBlRRMpL1pPM/LCnHPLaPL6YmafA15BeN3aI8AbgIuB5zvnbm1m29Y6M/sg8DuE1d7Pzb/fOfcpM9sJ/BD4NLAvuutFwEsJw+plzrlgJdq7HpjZtwmvE7wDOEI4wvWbgQJwmXPuwWi9Z0frPAB8FNgG/D5wq3PuRU1o+opQXjSP8qK1KCsWp7xoDmVF61FeLExZ0TzKi9bTlLxwzulW441wUJn3E147lSWcV/1FzW7XergBNwOu2i1aZwD4JPATYCZ6je4D3gm0Nfs5rLUb8DbgTuB4FFIHouO/q8K6VwDfjQLuCPAPQG+zn0ODj4/yonnHXnnRQjdlRU3HSHnRnOOurGixm/Ji0eOjrGjesVdetNitGXmhniciIiIiIiIiIgvQmCciIiIiIiIiIgtQ8UREREREREREZAEqnoiIiIiIiIiILEDFExERERERERGRBah4IiIiIiIiIiKyABVPREREREREREQWoOKJiIiIiIiIiMgCVDyRVcfMdpqZM7Prmt2WZtJxEFmc3ichHQeRxel9EtJxEFmY3iOh9XgcVDyRZTOzm83MNbsdItL6lBciUivlhYjUQlkhKyXR7AaILMEzwJnAZLMbIiItT3khIrVSXohILZQV65SKJ7LqOOcKwL5mt0NEWp/yQkRqpbwQkVooK9YvXbazCpVfX2ZmZ5jZl81szMxmzOx2M3thle2SZvY/zOzHZpY2sykzu83MXl1l/Z8zs5vM7KCZ5czsgJndYmZvLW8HcGX0uyu73TxvX9vM7B/M7LFoX8fN7CtmdlGFx313tI+rzOw1ZnanmaXM7In5z7/CtqNm9mEze8LM8mZ21My+aGY/VWHda6P9XGtmL466/E3W0u0vOvbOzE4zs982sx+ZWab4vM2s3cx+y8xuNLMno+c8ZmbfMrOXVNnnE9Gt28zeb2ZPRds9YmZ/ZGa2WLui/cTM7O+j9n3RzDpr2U7WJuWF8mKRtikvpER5obxYpG3KCwGUFcqKRdu2ZrNCPU9Wt1OB7wE/Bv4JGAV+Cfiamb3GOffZ4opm1g58nTBc9gEfBrqAa4DPmtn5zrk/Llv/zdE+DwH/DhwDNgHnAm8EPgJMAH8OXAucEv276ImyfT0b+AYwFLXhi8Aw8AvA7Wb2CufcjRWe3+8DL4ge/ztA/0IHw8xOBW4HtgDfBj4NbAdeBbzMzH7ROff/Kmx6DfBi4GvAP0bPpVZ/DzwX+CpwI+BHy4ei++4AvgkcJXx9fha40cx+wzn3zxX210Z4jLZE7fEIj9NfAx3MPcYnMLMO4F+BVxK+xm9zzgUn8Xxk7VJelFFeKC9kQcqLMsoL5YVUpawoo6xYB1nhnNNtld2AnYCLbu+fd9+FQAEYB/rKlr8zWv9GIFG2fBNhuDjg8rLl3wdywKYKjz887/ebw/9KFduaAB4BssCV8+7bQnjN4EEgWbb83VF7ZoALFnj+181b/vVo+Z/MW3454Rv/ONBTtvzaaP0AePFJvgbXRds+A5xa4f4ksK3C8n7gPmAM6Jx3X/F1uLH8vug1mohubdWOA2FI3h49nz9q9v9T3VrjprxQXlQ6DsoL3SrdlBfKi0rHQXmh2/ybskJZUek4rIes0GU7q9sk8J7yBc65ewirfQPAK8ru+jXC/9y/55zzytY/AvxF9Oub5u3fIwy/OZxzx06ijS8DTgf+l3Pulnn7OQC8DxgBnl9h2486535Yy4OY2TbghcBT0T7LH+cOwsrvEGEVdL4bnHP/UcvjVPA+59zj8xc653LOuacrLJ8EPgYMAid0E4y8zTmXKdvmCHADYdjtrbSBmZ0CfBe4GHi9c+5vTvaJyJqnvIgoL5QXsijlRUR5obyQBSkrIsqK9ZEVumxndfuBc266wvKbgTcAFwDXm1kvsAt4xjlXaXCjb0c/Lyhb9q/A3wIPmNlngFuA7zrnjp5kGy+Lfp5iZu+ucP/u6OeZhFXOcnedxOMU236bCwdxmu/bwOui9T6xjMeZr+q2ZnY28A7gpwm7yXXMW2Vrhc0mnXOPVFi+P/o5WOG+vYRdJruBlzjnblqs0bIuKS9mKS+UF7Iw5cUs5YXyQqpTVsxSVqyDrFDxZHU7XGX5oehn/7yfB6usX1w+UFzgnPufZnYMeCvwNuB3AWdmtwDviKrKtdgQ/XzVIuv1VFh2qMKyak76OS7xcWra1swuJQzJBHAT8BVgirAb2/nAzxN2p5tvosrjFCv08Qr37SGsZN8L/KCmVst6pLyYpbxQXsjClBezlBfKC6lOWTFLWbEOskLFk9Vtc5XlI9HPyXk/RyqsC2Elsnw9AJxznwA+YWYDhNfqvYKwy93XzeyMGiu/xX3+vHPuKzWsP6cJJ7Hukp7jEh6n1m3/FOgErnbO3Vx+h5m9kzCw6uXfgYeA9wI3mdkLnHPH67h/WRuUFyc+jvJCeSGVKS9OfBzlhfJCTqSsOPFxlBVrOCs05snq9uyoG9x8V0U/fwgQdad7FNhqZrsrrH919LNipdA5N+Gcu9E59xuEgxMNEXb/KvIBzKxSJfI/o5/Prf406qJ4PeIVZlapKLjgc2yAXcDY/LCKXFnvB3PO/RXwdsKugDebWbU/ZrJ+KS9mKS+UF7Iw5cUs5YXyQqpTVsxSVqyDrFDxZHXrB95VvsDMLgReS1jV/FLZXR8DDHh/ebCY2TDwZ2XrFJdfbVZxLu9N0c902bJiZXFHhfVvIAzL/25mL630JMzsMjPrqnRfraIBkb5JOOrz787b/yXAawhH/f7S/G0b5AlgyMzOndeWXwde1IgHdM59EPhN4GzgFjPb0ojHkVVLeRFRXigvZFHKi4jyQnkhC1JWRJQV6yMrdNnO6nYr8KboDfldZudWjwH/zTk3VbbuB4CXEHbR+i8zu5FwbvVXEYbQ+5xzt5et/yUgZWb/SfjmM8KK7UWEU4d9q2zdm6L9fDHabwZ40jn3SedcwcxeSTh111fN7A7C6+HShPOeXwScFrW9PASX4i3RcXi/mb0QuIfZudUD4I1VBrVqhA8SBtPtZvY5wj8gFwJXAJ8nnM+97pxz/2hmWeBfgFvN7HnOuaca8Viy6igv5lJeKC+kOuXFXMoL5YVUpqyYS1mx1rPCtcB8ybqd3I2yObUJR4a+gbCSmSZ8w76oynYdwB8Tzu2dAaYJ5+L+lQrrvoUwtB6L9jtG2B3tD4HeeevGCa9xe4xwOjEH3DxvnU3AX0ePnQZSwE8I37yvY+587++O9nHVYs+/wn1bgf8NPAnkgWPAl4GLKqx7bbSfa5fwGlwXbbtzgXVeTthVcJpw8KVvEHYxrPi4hH8YnqiyrxOOySLH4Vei1+IJ4LRm/5/VrXk35YXyoobjoLzQrfh/QXmhvFBe6LboTVmhrKjhOKzJrLDoyckqYmY7gceB651z1za3NSLSypQXIlIr5YWI1EJZIeuVxjwREREREREREVmAiiciIiIiIiIiIgtQ8UREREREREREZAEa80REREREREREZAHqeSIiIiIiIiIisgAVT0REREREREREFqDiiYiIiIiIiIjIAlQ8ERERERERERFZgIonIiIiIiIiIiILUPFERERERERERGQB/x9ooIZyXDSwwQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1296x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 4, figsize=(18, 10), \n",
+    "                      gridspec_kw=dict(wspace=0.33, hspace=0.25))\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 16\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "\n",
+    "for ii in range(4):\n",
+    "    truth = true_samples.mean(1)[:, ii]\n",
+    "#     truth = true_params[:, ii]\n",
+    "    plt.sca(ax[0, ii])\n",
+    "#     plt.scatter(truth, true_samples.mean(1)[:, ii], s=s, color=\"C0\", label=\"True\")\n",
+    "    plt.scatter(truth, lan_samples.mean(1)[:, ii], s=s, color=colors[1], \n",
+    "#                 label=fr\"LAN, $R^2={np.corrcoef(truth, lan_samples.mean(1)[:, ii])[0, 1]**2:.2f}$\",\n",
+    "                label=fr\"LAN\",            \n",
+    "                alpha=alpha,\n",
+    "               )\n",
+    "    plt.scatter(truth, nle_samples.mean(1)[:, ii], s=s, color=colors[2], \n",
+    "#                 label=fr\"NLE, $R^2={np.corrcoef(truth, nle_samples.mean(1)[:, ii])[0, 1]**2:.2f}$\",\n",
+    "                label=fr\"MNLE\",\n",
+    "                alpha=alpha,                \n",
+    "               )\n",
+    "    plt.plot(np.linspace(limits[ii][0], limits[ii][1], 100), \n",
+    "             np.linspace(limits[ii][0], limits[ii][1], 100), color=\"k\", label=\"identity\")\n",
+    "    plt.ylim(limits[ii])\n",
+    "    plt.yticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "\n",
+    "    plt.xlim(limits[ii])\n",
+    "    plt.xticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "    plt.xlabel([r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][ii])\n",
+    "    plt.ylabel([r\"$\\hat{v}$\", r\"$\\hat{a}$\", r\"$\\hat{w}$\", r\"$\\hat{\\tau}$\"][ii])\n",
+    "    if ii==0:\n",
+    "        plt.legend(loc=2);\n",
+    "\n",
+    "bins = B\n",
+    "repeats = 50\n",
+    "nbins = 50\n",
+    "from scipy.stats import binom\n",
+    "\n",
+    "for ii in range(3):\n",
+    "    for jj in range(4):\n",
+    "        plt.sca(ax[1, jj])\n",
+    "        hist, *_ = np.histogram(ranks[:, jj, ii], bins=nbins, density=False)\n",
+    "        histcs = hist.cumsum()\n",
+    "        plt.plot(np.linspace(0, nbins, repeats*nbins), \n",
+    "                 np.repeat(histcs / histcs.max(), repeats), \n",
+    "        #          label=labels[i], \n",
+    "                 color=colors[ii], \n",
+    "                 alpha=0.8)\n",
+    "        if jj==0:\n",
+    "            plt.yticks(np.linspace(0, 1, 3))\n",
+    "            plt.ylabel(\"empirical CDF\")\n",
+    "            plt.legend([\"Analytical\", \"LAN\", \"MNLE\"], loc=2)\n",
+    "        else:\n",
+    "            plt.yticks(np.linspace(0, 1, 3), [])\n",
+    "        plt.ylim(0, 1)\n",
+    "        plt.xlim(0, nbins)\n",
+    "        plt.xticks(np.linspace(0, nbins, 3))\n",
+    "        plt.xlabel(\"posterior rank\")\n",
+    "        if ii==0:\n",
+    "            # Construct uniform histogram.\n",
+    "            hb = binom(N, p=1 / nbins).ppf(0.5) * np.ones(nbins)\n",
+    "            hbb = hb.cumsum() / hb.sum()\n",
+    "\n",
+    "            lower = [binom(N, p=p).ppf(0.005) for p in hbb]\n",
+    "            upper = [binom(N, p=p).ppf(0.995) for p in hbb]\n",
+    "\n",
+    "            # Plot grey area with expected ECDF.\n",
+    "            plt.fill_between(x=np.linspace(0, nbins, repeats*nbins), \n",
+    "                         y1=np.repeat(lower / np.max(lower), repeats), \n",
+    "                         y2=np.repeat(upper / np.max(lower), repeats), \n",
+    "                         color=\"grey\", \n",
+    "                         alpha=0.3)\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.9\n",
+    "x1 = 0.07\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1, y1 - 0.43, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"LAN-NLE-calibration.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa18c68a",
+   "metadata": {},
+   "source": [
+    "## Supplementary figure: synthetic data from NLE.\n",
+    "\n",
+    "check out this commit or later:\n",
+    "a078720b50c275cc1c22cd9a5fab907b30a326c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 519,
+   "id": "f2fb2913",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([10000, 1])"
+      ]
+     },
+     "execution_count": 519,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 527,
+   "id": "5ebebfc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'RT')"
+      ]
+     },
+     "execution_count": 527,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qAEObJElSBRjaJEmSKsDQJkmSVAGGNkmSpAowtEmSJFWAoU2SJKkCDG2SJEkVYGiTJEmqAEObJElSBRjaJEmSKsDQJkmSVAGGNkmSpAowtEmSJFWAoU2SJKkCWh7aImLjiPheRPwlIp6JiD9GxDERMa6ubuuIuLasuT8iTouI8Q32NyYiToiI+yLi2YiYHxE7trrfkiRJ3WzFVu4sItYHFgCPA2cAjwBbAccCbwN2Kes2B64E/gB8DlgP+DywMfBPdbs9D9gNOBW4HdgfuCwits/Ma1vZf0mSpG7V0tAG7AOsDrwzM28p22ZFxArAvhGxRmY+ChwHPApMy8wnACLiLuCsiNgpMy8v27YAPgoclpknl22zgZuBE4GtW9x/SZKkrtTq06OrlssH6tp7gZeA5yNiVWBH4Py+wFaaDTwF7F7TthvwIjCrryEzFwPnAFuVI3uSJEnDXqtD27xyeU5EbB4R60fEHsCngNMy82ngzRQjfDfWbpiZzwOLgMk1zZOB2+rCHRSnYAE2b2nvJUmSulRLQ1tmzgG+SDGS9hvgHuB7wOmZeWhZ1lMuexvsohdYp+Z9zyvUUVcrSZI0bLX6mjaAu4BrgP8GHgb+GTgqIu7PzDOAlcu65xpsu7hmPeXXA9VRV7tERBwEHASwwQYbLGP3JUmSuk+r7x79KMX1Z5My8y9l8w/KGxFOiIjvAs+W7WMa7GJszXrKrweqo652icycVfaDqVOn5jJ9CEmSpC7U6mvaDgZ+UxPY+vwYGEdxjVrfqc0eltYD3FfzvvcV6qirlSRJGrZaHdpeD4xq0D66XK5I8biOvwNTawsiYiWKGwsW1TQvAiaVd5zW2rJmvSRJ0rDX6mvabgN2iohJmXlbTfvHKB758bvMfDwirgD2joivZuaTZc0+wHjg4prtLqF46O5BQN9z2sYABwDzM/PeFve/cmZcsLCpulP2mtLmnkiSpHZqdWg7iWJGg19GxBkUNyK8v2w7OzP7Tmd+AbgOuDoiZlHMiDADuLy8AxWAzJwfERcDx0fEWsCfgP2AicDHW9x3SZKkrtXqR35cQzFLwa8prm87FXgjRUj7VE3dQmAHihsJZlKMpJ1D8TDdevuW+9kHOI3iVOv7y+8lSZI0IrT8kR+ZuQB4XxN11wLbNFG3GDisfEmSJI1Irb4RQZIkSW1gaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVYChTZIkqQIMbZIkSRVgaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVYChTZIkqQIMbZIkSRVgaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVcCKne6AhsaMCxY2XXvKXlPa2BNJkjQYjrRJkiRVgKFNkiSpAtoS2iJiSkT8OCIeiYhnIuLmiPh0Xc3WEXFtuf7+iDgtIsY32NeYiDghIu6LiGcjYn5E7NiOfkuSJHWrll/TFhE7AT8BfgN8FXgKeCOwXk3N5sCVwB+Az5XrPg9sDPxT3S7PA3YDTgVuB/YHLouI7TPz2lb3X5IkqRu1NLRFxKrAbOCnwG6Z+dIApccBjwLTMvOJctu7gLMiYqfMvLxs2wL4KHBYZp5cts0GbgZOBLZuZf8lSZK6VatPj+4JvB74Qma+FBGrRES/71EGux2B8/sCW2k2xajc7jVtuwEvArP6GjJzMXAOsFVErN/i/kuSJHWlVoe2HYAngHUj4laKEPZERHwnIsaWNW+mGOG7sXbDzHweWARMrmmeDNxWF+4AFpTLzVvae0mSpC7V6tC2MUUg+xHwc+DDwH8AnwTOLWt6ymVvg+17gXVq3ve8Qh11tZIkScNWq29EGA+MA87MzL67RX8QESsBn4iIY4CVy/bnGmy/uGY95dcD1VFXu0REHAQcBLDBBhss0weQJEnqRq0eaXu2XH63rv3CcrlVTc2YBtuPrVnft7+B6qirXSIzZ2Xm1MycOmHChFfttCRJUrdrdWi7r1w+UNf+t3K5Bi+f2uxhaT01+6CsHaiOulpJkqRhq9Wh7dflct269r5rzx6keFzH34GptQXlKdTNKW5G6LMImFTecVpry5r1kiRJw16rQ9v3y+XH69r/lSKozcvMx4ErgL0j4jU1NftQXBN3cU3bJcAoyuvToJghATgAmJ+Z97a2+5IkSd2ppTciZOZvIuI/gH+JiBWBq4FpwEeA4zOz73TmF4DrgKsjYhbFjAgzgMszc07N/uZHxMXA8RGxFvAnYD9gIksHQ0mSpGGr5dNYUTze4x6K0bAPAncDh2bmqX0FmbkwInYATgBmAk9SPDD3/zXY374U02HtQ3FN3O+A92fmNW3ouyRJUldqeWjLzBeAY8vXK9VdC2zTxP4WA4eVL0mSpBGp1de0SZIkqQ0MbZIkSRVgaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVYChTZIkqQIMbZIkSRVgaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVcCKne6Aus+MCxY2VXfKXlPa3BNJktTHkTZJkqQKMLRJkiRVgKFNkiSpAgxtkiRJFWBokyRJqgBDmyRJUgUY2iRJkirA0CZJklQBhjZJkqQKcEYEDZozJ0iSNHTaPtIWEV+IiIyImxus2zoiro2IZyLi/og4LSLGN6gbExEnRMR9EfFsRMyPiB3b3XdJkqRu0dbQFhHrAUcBTzdYtzlwJTAO+BxwNnAQcHGDXZ1X1lwAfAZ4EbgsIt7Zjn5LkiR1m3afHj0ZuAEYBaxZt+444FFgWmY+ARARdwFnRcROmXl52bYF8FHgsMw8uWybDdwMnAhs3ebPIEmS1HFtG2mLiG2B3YDPNli3KrAjcH5fYCvNBp4Cdq9p241iZG1WX0NmLgbOAbaKiPVb3nlJkqQu05bQFhGjgNOBszPzpgYlb6YY5buxtjEznwcWAZNrmicDt9WFO4AF5XLzFnRZkiSpq7Xr9OgngQ2BHQZY31Muexus6wXeVVc7UB3AOvUrIuIgiuvj2GCDDZroriRJUndr+UhbRLwO+Arw1cx8cICylcvlcw3WLa5Z31c7UB11tQBk5qzMnJqZUydMmNBcxyVJkrpYO06Pfg14hOL06ECeLZdjGqwbW7O+r3agOupqJUmShqWWnh6NiI0pTkt+FlgnIvpWjQVGR8RE4AlePrXZw9J6gPtq3vcC6w5QR12tJEnSsNTqkbZ1y32eBtxZ89oSmFR+fQzF4zr+Dkyt3TgiVqK4sWBRTfMiYFJ5x2mtLWvWS5IkDWutDm03Ax9s8LoFuKf8+pzMfBy4Atg7Il5Ts/0+wHj6P2D3EornvB3U1xARY4ADgPmZeW+LP4MkSVLXaenp0cx8CLi0vj0iPluur133BeA64OqImAWsB8wALs/MOTX7nB8RFwPHR8RawJ+A/YCJwMdb2X9JkqRu1fa5RweSmQspHgnyLDCTYiTtHIqH6dbbFziVYiTuNGA08P7MvGZIOitJktRh7Z7GCoDMnDZA+7XANk1svxg4rHxJkiSNOB0baZMkSVLzhmSkTSPbjAsWNlV3yl5T2twTSZKqy5E2SZKkCjC0SZIkVYChTZIkqQIMbZIkSRVgaJMkSaoAQ5skSVIFGNokSZIqwNAmSZJUAYY2SZKkCjC0SZIkVYDTWHW5f7nvs69a8x/rnNr2fkiSpM4ytHVIM2FMkiSpj6dHJUmSKsCRtmHAU6iSJA1/jrRJkiRVgCNt6hozLljYVN0pe01pc08kSeo+jrRJkiRVgKFNkiSpAgxtkiRJFWBokyRJqgBDmyRJUgUY2iRJkirA0CZJklQBhjZJkqQKaOnDdSPi7cB+wPbAROBh4Abg6My8ra52U2Am8E7geeCnwOcy88G6uhWAzwOfAnqA24DjM/O7rez7cNfKCeqdEkuSpKHX6hkRjgC2AS4GfgesDRwCLIyId2TmzQARsR5wDfA4cBQwniKYvTkitsjM52v2+XXgSOAs4FfALsCFEZGZ+b0W91+SJKkrtTq0/RuwZ23oioiLgJsogtfeZfNRwCrA2zLznrJuAfALYH9gVtm2LjAD+FZmHlK2nQ1cDZwUERdn5ost/gySJEldp6XXtGXmdXWjZGTm7cAtwKY1zR8G/qcvsJV1V1Cc+ty9pm4XYDTw7Zq6BL4DrAds1cr+S5Ikdau234gQEQG8HniofL8usBZwY4PyBcDkmveTgaeBPzSoo65WkiRp2BqKu0f3AtYFLirf95TL3ga1vcBrI2JMTe0D5ehafR3AOq3sqCRJUrdq9TVt/UTEm4BvAdcD/1k2r1wun2uwyeKamudqlq9U1+j7HgQcBLDBBhssc7/V3WZcsLCpulP2mtLmnkiSNHTaNtIWEWtTPMbjcWC3mhsGni2XYxpsNrau5tkm6/rJzFmZOTUzp06YMGGZ+y5JktRt2hLaImI14GfA6sB7M/O+mtV9pzZ76rcr2x7JzOdqatcur4urrwO4D0mSpBGg5aEtIsYCPwEmAe/PzN/Xrs/MvwIPAlMbbL4FsKjm/SJgHP3vPAXYsma9JEnSsNfqGRFGUdxwsBWwS2ZeP0DpfwP7RcT6mXlvue27KYLezJq6H5XvD6Z4SG/f3aifBP4KXNfK/mt48do3SdJw0uobEU4BPkAx0vbaiNi7dmVmnl9+eRzwEeCqiPgmxYwIh1E8hPfcmvq/RMSpwGERMZpiRoRdgXcBe/lg3c5oZkosp7qSJKm1Wh3aNi+XO5eveucDZOa9EbEdxQwK3+DluUdn1FzP1udI4FHgExSzJdwO7J2ZF7a475IkSV2rpaEtM6ctQ+0twHuaqHsJOL58SZIkjUhtfU6bRq5mTqGCp1ElSWrWUMyIIEmSpOVkaJMkSaoAQ5skSVIFeE2bRrxmn+cGPtNNktQ5hjZpGfjAXklSpxja1FE+qFeSpOZ4TZskSVIFGNokSZIqwNOj6npVPIXqtW+SpFZzpE2SJKkCDG2SJEkVYGiTJEmqAEObJElSBRjaJEmSKsC7RzWiVPFOVEmSwNDWFs0EA7VWVf/NfTSIJKlZnh6VJEmqAEObJElSBXh6VKrTjde9eRpVkuRImyRJUgU40iYNI47ISdLwZWiTBqEbT6FKkoY3Q5s0AjkiJ0nVY2iTOsxRO0lSMwxtkgbU7IgcOConSe1WidAWEWOArwD7AGsAvwOOzsxfdLRj0iuo6iwNg+UpV0lqr0qENuA8YDfgVOB2YH/gsojYPjOv7Vy3pKHRygDoqVZJqqauD20RsQXwUeCwzDy5bJsN3AycCGzdwe5JldPpa+iW5ZRrKznCJ6nquj60UYywvQjM6mvIzMURcQ5wXESsn5n3dqx30ghVtdE/T99KqroqhLbJwG2Z+URd+4JyuTlgaJNaaKivx+umANiOkUCDoKRWqEJo6wF6G7T3ta0zhH2R1OW68QaQGRec+qo1Q93v2nC73KHywp37vb3lr4+/4vdrp2Y/S6vD+SnxpVcv2vMnLf2eGnkiMzvdh1cUEX8Gbs3M99W1bwT8GTg0M0+tW3cQcFD5dhPg1jZ1b03goTbtW+3n8as+j2G1efyqz2PYeg9l5nsbrajCSNuzwJgG7WNr1veTmbOouQauXSLixsyc2u7vo/bw+FWfx7DaPH7V5zEcWit0ugNN6KU4RVqvr+2+IeyLJElSR1QhtC0CJkXEqnXtW9aslyRJGtaqENouAUbx8jVqfTMkHADM7/DjPtp+ClZt5fGrPo9htXn8qs9jOIS6/kYEgIj4PvBBYCbwJ2A/YAvg3Zl5TSf7JkmSNBSqEtrGAl8F9ubluUe/mJk/72jHJEmShkglQpskSdJIV4Vr2oZURIyJiBMi4r6IeDYi5kfEjk1s96GIuCgi7oiIZyLi1og4JSJWH4JuqzTY49dgP7+IiIyIM9rRTw1seY9hROwREddHxNMR8VhEXBcR09vZZ71seY5fROwQEVdFxEPlsVsQEfu0u8/qLyLGR8SxETEnIh4p/1+4/zJsv3pEzIqIB8ufw6siwmlBWsDQtrTzgM8BFwCfoZj39LKIeOerbDcL2BQ4H/g0MAc4BLg+IlZuW29V7zwGd/yWiIgPAVu1pXdqxnkM8hhGxJeB71JMbfc54GiKyynWbVNftbTzGMTxi4gPAJcDKwFfBr5A8RzO2RFxaBv7q6WtCRxD8Tvtt8uyYUSsAPwU2BM4AzgcWAuYFxEbt7ifI09m+ipfFDc3JPD5mraxFDc/XPcq205r0LZvub9/7fRnGwmv5Tl+dfV3Al8s93VGpz/XSHot58/gO4CXKGZJ6fhnGYmv5Tx+lwN/BcbUtK1YbvvbTn+2kfSieKD92uXXU8tjun+T2+5e1u9W0zYBeBS4sNOfreovR9r6243ir8IltzBn5mLgHGCriFh/oA0zc16D5h+Wy01b2EcNbNDHr8bhFCPQJ7elh3o1y3MMPwvcD3wzCuPb2VE1tDzHb1Xg0cx8rmbbv1NMkbTUzDdqn8x8LjPvH+TmuwEPAD+o2d+DwPeBXcpHdmmQDG39TQZuy8wn6toXlMvNl3F/a5dL52UbGst1/CJiA+BI4IjM9JdEZyzPMXw38CuKyxMeBJ6MiN6IOKTlvdRAluf4zQM2i4ivRsQ/RMQbI+KLFCM9J7a8p2qXycDCzHyprn0BMA6YNPRdGj6qMPfoUOqhmDarXl/bOsu4vyMo/uq8ZHk6paYt7/E7BfhNZn6vpb3SshjUMYyINSiuw9kGmA4cC9xD8RDu0yPihcz899Z3V3WW52fwq8AbKK5lO7psewb4cGb+qGU9VLv1AI2en1r738BNQ9ed4cXQ1t/KwHMN2hfXrG9KROwJfBw4MTNvb0Hf9OoGffwiYnvgw7w8PZo6Y7DHsO9U6OuAj2bmRQARcQnFL4ijAUNb+y3P/0OfA26j+CP3B7w8E875EbFjZt7Qyo6qbVr2e1RLM7T19yzFBZj1xtasf1UR8S6Kazh+TvFXo4bGoI5fRKwInAb8V2b+qk19U3MG+zPY1/4CNSPbmflSRFwEHBsRG2TmPS3rqRpZnv+HnkFxM8mUvlNr5Ww4twDfxD+oqqIlv0fVmNe09ddLMbRbr6/tvlfbQUS8FfgxcDPF3TN/b1339CoGe/z2BTYB/j0iJva9ynWvKd+Pa21XNYDBHsNHKP6SfzgzX6xb97dyucbyd0+vYlDHLyJWojgz8dPaa6Ey8wXgZ8DUskbdb7l/j2pghrb+FgGTImLVuvYta9YPKCLeSPF8tr8B78vMp1rdQb2iRQzu+G0AjAb+l+JxH30vKALdncBOreyoBrSIQRzD8hf9ImBCg1/ufddRPdiaLuoVLGJwP4OvozjzM6rButEUv6sarVP3WQRMKZ/XVmtLimsUbxvyHg0jhrb+LuHl6yiA4uneFBczz8/Me8u2DSLiTbUbRsTaFM8Zegl4T3mLs4bWYI/f94APNngBXFZ+Pb/tvRcsx88gcFG57X41244F9gJ+n5n+hd9+gz1+fwMeAz5YG7rLx7bsDPzRO7q7T0T0RMSbImJ0TfMlwOuBD9XUrQl8BPhJ7SNdtOy8pq1GZs6PiIuB4yNiLYqHOu4HTKQYuu8zG9gOiJq2OcBGFLemv7Pu6d8PZOYv2tl3Df74ZeYfgT/W7y8iAO7MzEvb2nEtsZw/g/8O/CvwrYiYRHH36D7AhhS/+NVmy/Ez+GJEnAx8DbghImZThL+PA+sBew/ZhxAA5aNyVuflkeqdI2K98uvTM/Nx4HiK4/sG4K5y3SXADcC5EfF/KB55dTDF8fzSkHR+OOv003277UVxseRJFOflF1M8W+Y9dTXzin+6fm35Cq95nf5cI+U12OM3wL6cEaFix5BiupzzgIfLbW+o39ZXVx+/PSlGtR+lOJV2A8UjPzr+uUbaiyKEDfQ7bWJZc17t+5pt1wDOpghsT5fHe2qnP9NweEX5DyxJkqQu5jVtkiRJFWBokyRJqgBDmyRJUgUY2iRJkirA0CZJklQBhjZJkqQKMLRJkiRVgKFNkiSpAgxtklQjIrLu9WJEPBIR8yJi/yjnN4uI8xrUvtJrXoc/mqSKc+5RSWrs2HI5GvgH4IMU82VOBQ4BLuXl+Rb7TCtrrqaYuqdWfa0kLROnsZKkGhFRTDybGXXt2wDXUExy/sbMvLPBtl+mmBT72Mz8cts7K2lE8fSoJDUhM/8X+CNFaHtbh7sjaQQytEnSsnuh0x2QNPIY2iSpCRGxLfAm4HlgQYe7I2kE8kYESWqgvD4N+t+IEMDnM7O3U/2SNHIZ2iSpsS/VvU/g45l5bic6I0meHpWkBjIzyjtIxwM7AvcCZ0bE9M72TNJIZWiTpFeQmU9n5hXAzsAo4D8jYlyHuyVpBDK0SVITMvN3wFnAesChHe6OpBHI0CZJzfsa8Bzw+YhYo9OdkTSyGNokqUmZ+VfgTGB14PDO9kbSSGNok6RlczzwDPDpiHh9pzsjaeRw7lFJkqQKcKRNkiSpAgxtkiRJFWBokyRJqgBDmyRJUgUY2iRJkirA0CZJklQBhjZJkqQKMLRJkiRVgKFNkiSpAgxtkiRJFfD/AZDC4CDSy9tAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.hist(xo.numpy()[xo>0], bins=50, alpha=alpha, label=\"simulator\", color=colors[0]);\n",
+    "plt.hist(-xo.numpy()[xo<0], bins=50, alpha=alpha, label=\"simulator\", color=colors[1]);\n",
+    "plt.legend([\"up\", \"down\"])\n",
+    "plt.xlabel(\"RT\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 529,
+   "id": "ec11e35d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, '\"RT\"')"
+      ]
+     },
+     "execution_count": 529,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.hist(xo.numpy()[xo>0], bins=50, alpha=alpha, label=\"simulator\", color=colors[0]);\n",
+    "plt.hist(xo.numpy()[xo<0], bins=50, alpha=alpha, label=\"simulator\", color=colors[1]);\n",
+    "plt.legend([\"up\", \"down\"])\n",
+    "plt.xlabel('\"RT\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 587,
+   "id": "edf671c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed 219793\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_examples = 3\n",
+    "fig, ax = plt.subplots(1, num_examples, figsize=(14, 4), sharex=False, sharey=False)\n",
+    "seed = 219793 #torch.randint(1000000, (1,))\n",
+    "torch.manual_seed(seed)\n",
+    "print(f\"seed {seed}\")\n",
+    "alpha = 0.9\n",
+    "show_likelihood = True\n",
+    "\n",
+    "nbins = 150\n",
+    "num_samples = 10000\n",
+    "for i in range(num_examples):\n",
+    "    # Simulate\n",
+    "    tho = prior.sample((1,))\n",
+    "    tho[0, -1] = 0.2\n",
+    "    xo = simulator(tho.repeat(num_samples, 1))\n",
+    "    rt, cs = nle.sample(tho, num_samples)\n",
+    "    x = rt\n",
+    "    x[cs==0] *= -1\n",
+    "    \n",
+    "    # Likelihoods\n",
+    "    if show_likelihood:\n",
+    "        test_tmax = 2.\n",
+    "        # Number of test points\n",
+    "        ntest = 1000\n",
+    "        rs = torch.cat((torch.linspace(test_tmax, 1e-7, ntest//2), \n",
+    "                   torch.linspace(1e-7, test_tmax, ntest//2))).reshape(-1, 1)\n",
+    "        cs = torch.cat((torch.zeros(ntest//2), \n",
+    "                   torch.ones(ntest//2))).reshape(-1, 1)\n",
+    "\n",
+    "        test_rts = torch.linspace(-test_tmax, test_tmax, ntest)\n",
+    "\n",
+    "        # get NLE synthetic likelihood for each data point with fixed theta.\n",
+    "        lps_nle = torch.tensor([nle.log_prob(r.reshape(-1, 1), c.reshape(-1, 1), tho) for r, c in zip(rs, cs)])\n",
+    "\n",
+    "        # from analytical likelihood\n",
+    "        lps_true = torch.tensor([task.get_log_likelihood(tho, d.reshape(-1, 1), l_lower_bound=l_lower_bound) \n",
+    "                             for d in test_rts])\n",
+    "\n",
+    "    # Plotting\n",
+    "    plt.sca(ax if num_examples==1 else ax[i])\n",
+    "    c1, bins, _ = plt.hist(xo.numpy(), bins=nbins, alpha=alpha, label=\"simulator\", \n",
+    "                           color=colors[3], density=show_likelihood, histtype=\"step\", linewidth=2)\n",
+    "    c2, _, _ = plt.hist(x.numpy(), bins=bins, alpha=alpha, label=\"MNLE\", \n",
+    "                        color=colors[2], density=show_likelihood, histtype=\"step\", linewidth=2)\n",
+    "    if show_likelihood:\n",
+    "        plt.plot(test_rts, lps_true.exp(), color=\"k\", label=r\"$L(x | \\theta)$\")\n",
+    "\n",
+    "    if i == 0:\n",
+    "        plt.legend()\n",
+    "        plt.ylabel(\"density\" if show_likelihood else \"count\")\n",
+    "    \n",
+    "    # fix yticks.\n",
+    "    maxcount = round(max(c1.max(), c2.max()), 1)\n",
+    "    if maxcount % 2: \n",
+    "        maxcount += 1 \n",
+    "    plt.ylim(0, maxcount)\n",
+    "    plt.yticks(np.linspace(0, maxcount, 3))\n",
+    "    \n",
+    "    # xticks\n",
+    "    plt.xlabel(\"$x$: reaction time [s]\")\n",
+    "    ticks = []\n",
+    "    bound = 2\n",
+    "    plt.xticks([-2, -1, 0, 1, 2], [2, 1, 0, 1, 2])\n",
+    "    plt.xlim(-bound, bound)\n",
+    "\n",
+    "y1 = 0.95\n",
+    "x1 = 0.01\n",
+    "# fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "# fig.text(x1 + .34, y1, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "# fig.text(x1 + .65, y1, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"mnle-synthetic-data.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6891961",
+   "metadata": {},
+   "source": [
+    "## Supplementary figure: posterior accuracy for different number of trials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d34f1ccf",
+   "metadata": {},
+   "source": [
+    "### Post hoc SBC for all number of trials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "id": "228ab1b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd25c53783894cc4a46b0cb42e670532",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b3e67ee3ee94d30b7182853ed0fab34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5079b32af4c14789a8e5a809ce2d9343",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38d31d4b72c3488c9b8ad0520f64f059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-22/17-54-20/\"), # NLE 1, 10\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-22/21-43-48/\"), # LAN 1, 10\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-23/10-17-26/\"), # NLE 100, 1000. SEED 2\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-24/04-03-04/\"), # LAN 100, 1000    \n",
+    "])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "id": "966358cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:81: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 4, figsize=(18, 10), \n",
+    "                      gridspec_kw=dict(wspace=0.33, hspace=0.25))\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 16\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "\n",
+    "for ii in range(4):\n",
+    "    truth = true_samples.mean(1)[:, ii]\n",
+    "#     truth = true_params[:, ii]\n",
+    "    plt.sca(ax[0, ii])\n",
+    "#     plt.scatter(truth, true_samples.mean(1)[:, ii], s=s, color=\"C0\", label=\"True\")\n",
+    "    plt.scatter(truth, lan_samples.mean(1)[:, ii], s=s, color=colors[1], \n",
+    "#                 label=fr\"LAN, $R^2={np.corrcoef(truth, lan_samples.mean(1)[:, ii])[0, 1]**2:.2f}$\",\n",
+    "                label=fr\"LAN\",            \n",
+    "                alpha=alpha,\n",
+    "               )\n",
+    "    plt.scatter(truth, nle_samples.mean(1)[:, ii], s=s, color=colors[2], \n",
+    "#                 label=fr\"NLE, $R^2={np.corrcoef(truth, nle_samples.mean(1)[:, ii])[0, 1]**2:.2f}$\",\n",
+    "                label=fr\"MNLE\",\n",
+    "                alpha=alpha,                \n",
+    "               )\n",
+    "    plt.plot(np.linspace(limits[ii][0], limits[ii][1], 100), \n",
+    "             np.linspace(limits[ii][0], limits[ii][1], 100), color=\"k\", label=\"identity\")\n",
+    "    plt.ylim(limits[ii])\n",
+    "    plt.yticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "\n",
+    "    plt.xlim(limits[ii])\n",
+    "    plt.xticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "    plt.xlabel([r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][ii])\n",
+    "    plt.ylabel([r\"$\\hat{v}$\", r\"$\\hat{a}$\", r\"$\\hat{w}$\", r\"$\\hat{\\tau}$\"][ii])\n",
+    "    if ii==0:\n",
+    "        plt.legend(loc=2);\n",
+    "\n",
+    "bins = B\n",
+    "repeats = 50\n",
+    "nbins = 50\n",
+    "from scipy.stats import binom\n",
+    "\n",
+    "for ii in range(3):\n",
+    "    for jj in range(4):\n",
+    "        plt.sca(ax[1, jj])\n",
+    "        hist, *_ = np.histogram(ranks[:, jj, ii], bins=nbins, density=False)\n",
+    "        histcs = hist.cumsum()\n",
+    "        plt.plot(np.linspace(0, nbins, repeats*nbins), \n",
+    "                 np.repeat(histcs / histcs.max(), repeats), \n",
+    "        #          label=labels[i], \n",
+    "                 color=colors[ii], \n",
+    "                 alpha=0.8)\n",
+    "        if jj==0:\n",
+    "            plt.yticks(np.linspace(0, 1, 3))\n",
+    "            plt.ylabel(\"empirical CDF\")\n",
+    "            plt.legend([\"Analytical\", \"LAN\", \"MNLE\"], loc=2)\n",
+    "        else:\n",
+    "            plt.yticks(np.linspace(0, 1, 3), [])\n",
+    "        plt.ylim(0, 1)\n",
+    "        plt.xlim(0, nbins)\n",
+    "        plt.xticks(np.linspace(0, nbins, 3))\n",
+    "        plt.xlabel(\"posterior rank\")\n",
+    "        if ii==0:\n",
+    "            # Construct uniform histogram.\n",
+    "            hb = binom(N, p=1 / nbins).ppf(0.5) * np.ones(nbins)\n",
+    "            hbb = hb.cumsum() / hb.sum()\n",
+    "\n",
+    "            lower = [binom(N, p=p).ppf(0.005) for p in hbb]\n",
+    "            upper = [binom(N, p=p).ppf(0.995) for p in hbb]\n",
+    "\n",
+    "            # Plot grey area with expected ECDF.\n",
+    "            plt.fill_between(x=np.linspace(0, nbins, repeats*nbins), \n",
+    "                         y1=np.repeat(lower / np.max(lower), repeats), \n",
+    "                         y2=np.repeat(upper / np.max(lower), repeats), \n",
+    "                         color=\"grey\", \n",
+    "                         alpha=0.3)\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.9\n",
+    "x1 = 0.07\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1, y1 - 0.43, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"LAN-NLE-calibration.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 419,
+   "id": "9c30b9a8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# get posterior sample mean and variance\n",
+    "task = sbibm.get_task(\"ddm\")\n",
+    "N = 99\n",
+    "num_trials_list = [1, 10, 100, 1000]\n",
+    "invalid_seeds = [75]\n",
+    "nle_samples = np.zeros((len(num_trials_list), N, 10000, 4))\n",
+    "lan_samples = np.zeros((len(num_trials_list), N, 10000, 4))\n",
+    "true_samples = np.zeros((len(num_trials_list), N, 10000, 4))\n",
+    "true_params = np.zeros((len(num_trials_list), N, 4))\n",
+    "meanerr = np.zeros((len(num_trials_list), N, 2))\n",
+    "varerr = np.zeros_like(meanerr)\n",
+    "idx = 0\n",
+    "\n",
+    "for seed in range(N+len(invalid_seeds)):\n",
+    "    \n",
+    "    if seed not in invalid_seeds:\n",
+    "        for ii in range(len(num_trials_list)):\n",
+    "\n",
+    "            obsi = seed+1+ii*100\n",
+    "            dd = df[df.num_observation==obsi]\n",
+    "            nle_samples[ii, idx,] = get_ndarray_from_csv(dd[dd.algorithm==\"NLE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "            lan_samples[ii, idx,] = get_ndarray_from_csv(dd[dd.algorithm==\"LAN-KDE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "            true_samples[ii, idx,] = task.get_reference_posterior_samples(obsi).numpy()\n",
+    "            # get tho\n",
+    "            true_params[ii, idx,] = task.get_true_parameters(obsi).numpy()\n",
+    "\n",
+    "        idx += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
+   "id": "d5478f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meanerr[:, :, 0] = np.mean((nle_samples.mean(-2) -true_samples.mean(-2)) / true_samples.std(-2), -1)\n",
+    "meanerr[:, :, 1] = np.mean((lan_samples.mean(-2) -true_samples.mean(-2)) / true_samples.std(-2), -1)\n",
+    "\n",
+    "varerr[:, :, 0] = np.mean(abs(nle_samples.var(-2) / true_samples.var(-2)), -1)\n",
+    "varerr[:, :, 1] = np.mean(abs(lan_samples.var(-2) / true_samples.var(-2)), -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 421,
+   "id": "0aa99a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 99, 4)"
+      ]
+     },
+     "execution_count": 421,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "id": "e7458387",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Post-hoc SBC based on posterior samples\n",
+    "L = 1000\n",
+    "B = 5\n",
+    "\n",
+    "ranks = np.zeros((len(num_trials_list), N, 4, 3)) # different trial settings, N, 4 params, 3 methods\n",
+    "\n",
+    "thos = prior.sample((N,))\n",
+    "\n",
+    "for obsi in range(N):\n",
+    "    for ti in range(len(num_trials_list)):\n",
+    "        # rank tho under samples\n",
+    "        ranks[ti, obsi, :, 0] = (true_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "        ranks[ti, obsi, :, 1] = (lan_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "        ranks[ti, obsi, :, 2] = (nle_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "\n",
+    "# Correlation over posterior means.\n",
+    "corrs = np.zeros((4, 3, 4))\n",
+    "corrs[:, 2, ] = [[np.corrcoef(nle_samples[jj, :, :, ii].mean(1), \n",
+    "                           true_params[jj, :, ii])[0, 1] for ii in range(4)] for jj in range(4)]\n",
+    "corrs[:, 1, ] = [[np.corrcoef(lan_samples[jj, :, :, ii].mean(1), \n",
+    "                           true_params[jj, :, ii])[0, 1] for ii in range(4)] for jj in range(4)]\n",
+    "corrs[:, 0, ] = [[np.corrcoef(true_samples[jj, :, :, ii].mean(1), \n",
+    "                           true_params[jj, :, ii])[0, 1] for ii in range(4)] for jj in range(4)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "id": "d758f824",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.49581974, 0.87134411, 0.97864535, 0.99776185])"
+      ]
+     },
+     "execution_count": 423,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corrs.mean(-1)[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "id": "bb4fc0ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"num_trials\"] = 1\n",
+    "df = df[np.logical_not(df.algorithm == \"LAN-ANA\")]\n",
+    "df.num_observation = df.num_observation.astype(int)\n",
+    "df.algorithm[df.algorithm==\"LAN-KDE\"] = \"LAN\"\n",
+    "df.loc[df.num_observation <= 100, \"num_trials\"] = 1\n",
+    "df.loc[df.num_observation > 100, \"num_trials\"] = 10\n",
+    "df.loc[df.num_observation > 200, \"num_trials\"] = 100\n",
+    "df.loc[df.num_observation > 300, \"num_trials\"] = 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "id": "75feef38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.sort_values(by=\"num_observation\")\n",
+    "mask = np.logical_or(\n",
+    "    np.logical_or(np.logical_or(df.num_observation == 76, df.num_observation == 176), \n",
+    "                  df.num_observation == 276), \n",
+    "    df.num_observation == 376)\n",
+    "\n",
+    "# mask = df.num_observation > 300\n",
+    "df = df[~mask]\n",
+    "\n",
+    "df.loc[:, \"MEANERR\"] = meanerr.reshape(-1)\n",
+    "df.loc[:, \"VARERR\"] = varerr.reshape(-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "013f4470",
+   "metadata": {},
+   "source": [
+    "## Parameter recovery plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c208c2b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-325c8a2c255f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m fig, ax = plt.subplots(4, 4, figsize=(18, 11), \n\u001b[0m\u001b[1;32m      2\u001b[0m                       gridspec_kw=dict(wspace=0.33, hspace=0.25), sharex=\"col\")\n\u001b[1;32m      3\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"legend.fontsize\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 4, figsize=(18, 11), \n",
+    "                      gridspec_kw=dict(wspace=0.33, hspace=0.25), sharex=\"col\")\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 16\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "\n",
+    "for jj in range(4):\n",
+    "    for ii in range(4):\n",
+    "#         truth = true_samples.mean(2)[jj, :, ii]\n",
+    "        truth = true_params[jj, :, ii]\n",
+    "        plt.sca(ax[jj, ii])\n",
+    "        plt.scatter(truth, true_samples.mean(2)[jj, :, ii], s=s, color=colors[0], \n",
+    "                    label=fr\"Analytical\",            \n",
+    "                    alpha=alpha,\n",
+    "                   )\n",
+    "        plt.scatter(truth, lan_samples.mean(2)[jj, :, ii], s=s, color=colors[1], \n",
+    "                    label=fr\"LAN\",            \n",
+    "                    alpha=alpha,\n",
+    "                   )\n",
+    "        plt.scatter(truth, nle_samples.mean(2)[jj, :, ii], s=s, color=colors[2], \n",
+    "                    label=fr\"MNLE\",\n",
+    "                    alpha=alpha,                \n",
+    "                   )\n",
+    "        plt.plot(np.linspace(limits[ii][0], limits[ii][1], 100), \n",
+    "                 np.linspace(limits[ii][0], limits[ii][1], 100), color=\"k\", lw=1)\n",
+    "        plt.ylim(limits[ii])\n",
+    "        plt.yticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "\n",
+    "        plt.xlim(limits[ii])\n",
+    "        plt.xticks(np.linspace(limits[ii][0], limits[ii][1], 3))\n",
+    "        if jj==3:\n",
+    "            plt.xlabel(\"true \" + [r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][ii])\n",
+    "        \n",
+    "        if ii==0:\n",
+    "            plt.ylabel(f\"{[1, 10, 100, 1000][jj]} trial{'s' if jj>0 else ''} \\n posterior mean\")\n",
+    "            \n",
+    "        if ii==3 and jj==1:\n",
+    "            plt.legend(loc=2, markerscale=1.2, handlelength=0.3, frameon=False);\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"parameter-recovery-trials.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "id": "c32b1d0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = [\"Analytical\", \"LAN\", \"MNLE\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 430,
+   "id": "406b51fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:47: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x792 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 4, figsize=(18, 11), \n",
+    "                      gridspec_kw=dict(wspace=0.33, hspace=0.25), sharex=\"col\", sharey=True)\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 16\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "\n",
+    "for kk in range(len(num_trials_list)):\n",
+    "    for ii in range(3):\n",
+    "        for jj in range(4):\n",
+    "            plt.sca(ax[kk, jj])\n",
+    "            hist, *_ = np.histogram(ranks[kk, :, jj, ii], bins=nbins, density=False)\n",
+    "            histcs = hist.cumsum()\n",
+    "            plt.plot(np.linspace(0, nbins, repeats*nbins), \n",
+    "                     np.repeat(histcs / histcs.max(), repeats), \n",
+    "                     label=labels[ii], \n",
+    "                     color=colors[:3][ii], \n",
+    "                     alpha=1., \n",
+    "                     lw=.75)\n",
+    "            if jj==0:\n",
+    "                plt.yticks(np.linspace(0, 1, 3))\n",
+    "                plt.ylabel(f\"empirical CDF \\n {[1, 10, 100, 1000][kk]} trial{'s' if kk>0 else ''}\")\n",
+    "                if kk == 0:\n",
+    "                    plt.legend(loc=2, markerscale=1.0, handlelength=0.7, bbox_to_anchor=(-.05, 1.1))\n",
+    "            else:\n",
+    "                plt.yticks(np.linspace(0, 1, 3), np.linspace(0, 1, 3))\n",
+    "            plt.ylim(0, 1)\n",
+    "            plt.xlim(0, nbins)\n",
+    "            plt.xticks(np.linspace(0, nbins, 3))\n",
+    "            if kk == 3:\n",
+    "                plt.xlabel(\"rank \" + [r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][jj])\n",
+    "            if ii==0:\n",
+    "                # Construct uniform histogram.\n",
+    "                hb = binom(N, p=1 / nbins).ppf(0.5) * np.ones(nbins)\n",
+    "                hbb = hb.cumsum() / hb.sum()\n",
+    "\n",
+    "                lower = [binom(N, p=p).ppf(0.005) for p in hbb]\n",
+    "                upper = [binom(N, p=p).ppf(0.995) for p in hbb]\n",
+    "\n",
+    "                # Plot grey area with expected ECDF.\n",
+    "                plt.fill_between(x=np.linspace(0, nbins, repeats*nbins), \n",
+    "                             y1=np.repeat(lower / np.max(lower), repeats), \n",
+    "                             y2=np.repeat(upper / np.max(lower), repeats), \n",
+    "                             color=\"grey\", \n",
+    "                             alpha=0.2)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"sbc-over-trials.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b7920de",
+   "metadata": {},
+   "source": [
+    "## SBC with NLE ensembles over 5 seeds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d9e207df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffe26572a7bf4aa98bbb70577ae50ee4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fba586d0a1d4396be5f6d490c11938e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c863e0b28c941a586e170d0ddcb973f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1927a28b15ff43ec9c8df974ce02e64f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4300d8cc3ada43f49371a5a3c43e4528",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4de81f5b0e874705b67e16b2d23b44d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f32287dd7e743048fd1804bbb4ba576",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0f862bab3484752849b1a31c4f4a6c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "660a9b119cf04ebe8c298a9c1c8e6f6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29570ad9affe474382afc693bed573ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dflan = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-22/21-43-48/\"), # LAN 1, 10\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-24/04-03-04/\"), # LAN 100, 1000    \n",
+    "])\n",
+    "\n",
+    "dfnle0 = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-27/19-12-02/\"), # NLE all but 6. SEED 0\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-28/18-43-19/\"), # NLE rest. SEED 0\n",
+    "])\n",
+    "dfnle3 = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-11-02/14-37-00/\"), # NLE all. SEED 3\n",
+    "])\n",
+    "dfnle2 = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-22/17-54-20/\"), # NLE 1, 10\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-23/10-17-26/\"), # NLE 100, 1000. SEED 2\n",
+    "])\n",
+    "dfnle1 = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-11-03/23-38-29/\"), # NLE all. SEED 1\n",
+    "])\n",
+    "dfnle4 = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-29/00-40-46/\"), # NLE 1, 10. SEED 4\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-25/11-01-18/\"), # NLE 100, 1000. SEED 4\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "06847006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get posterior sample mean and variance\n",
+    "N = 99\n",
+    "M = 10000\n",
+    "ensembles = [\n",
+    "             dfnle0, \n",
+    "             dfnle1, \n",
+    "             dfnle2, \n",
+    "             dfnle3, \n",
+    "             dfnle4\n",
+    "            ]\n",
+    "num_ensembles = len(ensembles)\n",
+    "\n",
+    "num_trials_list = [1, 10, 100, 1000]\n",
+    "# Exclude observations, e.g., when for one number of trials a run failed. \n",
+    "invalid_seeds = [75]\n",
+    "nle_samples = np.zeros((len(num_trials_list), N, M, 4))\n",
+    "nlee_samples = np.zeros_like(nle_samples)\n",
+    "lan_samples = np.zeros_like(nle_samples)\n",
+    "true_samples = np.zeros_like(nle_samples)\n",
+    "true_params = np.zeros((len(num_trials_list), N, 4))\n",
+    "meanerr = np.zeros((len(num_trials_list), N, 3))\n",
+    "varerr = np.zeros_like(meanerr)\n",
+    "idx = 0\n",
+    "\n",
+    "for seed in range(N+len(invalid_seeds)):\n",
+    "    \n",
+    "    if seed not in invalid_seeds:\n",
+    "        for ii in range(len(num_trials_list)):\n",
+    "            obsi = seed+1+ii*100\n",
+    "            \n",
+    "            # get samples from nle ensembles.\n",
+    "            nle_ensemble = np.zeros((M, 4, num_ensembles))\n",
+    "            for jj, df_ens in enumerate(ensembles):\n",
+    "                dd = df_ens[df_ens.num_observation==obsi]\n",
+    "                nle_ensemble[:, :, jj] = get_ndarray_from_csv(dd.path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "            # select samples from ensembles with equal weights.\n",
+    "            nlee_samples[ii, idx,] = nle_ensemble[torch.arange(M), :, torch.randint(num_ensembles, (M,))]\n",
+    "            \n",
+    "            # get nle samples\n",
+    "            dd = dfnle4[dfnle4.num_observation==obsi]\n",
+    "            nle_samples[ii, idx,] = get_ndarray_from_csv(dd[dd.algorithm==\"NLE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "\n",
+    "            # get lan samples\n",
+    "            dd = dflan[dflan.num_observation==obsi]\n",
+    "            lan_samples[ii, idx,] = get_ndarray_from_csv(dd[dd.algorithm==\"LAN-KDE\"].path.values[0]+\"/posterior_samples.csv.bz2\")\n",
+    "            true_samples[ii, idx,] = task.get_reference_posterior_samples(obsi).numpy()\n",
+    "            # get tho\n",
+    "            true_params[ii, idx,] = task.get_true_parameters(obsi).numpy()\n",
+    "        # Increase array index only for valid observations.\n",
+    "        idx += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "41c11284",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meanerr = np.zeros((4, N, 3))\n",
+    "meanerrabs = np.zeros_like(meanerr)\n",
+    "varerr = np.zeros_like(meanerr)\n",
+    "varerrabs = np.zeros_like(meanerr)\n",
+    "dispersion = np.zeros_like(meanerr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "9a1a5348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meanerr[:, :, 0] = np.mean(abs(lan_samples.mean(-2) - true_samples.mean(-2)) / true_samples.std(-2), -1)\n",
+    "meanerr[:, :, 1] = np.mean(abs(nle_samples.mean(-2) - true_samples.mean(-2)) \n",
+    "                           / true_samples.std(-2), -1)\n",
+    "meanerr[:, :, 2] = np.mean(abs(nlee_samples.mean(-2) - true_samples.mean(-2)) \n",
+    "                           / true_samples.std(-2)\n",
+    "                           , -1)\n",
+    "meanerrabs[:, :, 0] = np.mean(abs(lan_samples.mean(-2) - true_samples.mean(-2)), \n",
+    "                           -1)\n",
+    "meanerrabs[:, :, 1] = np.mean(abs(nle_samples.mean(-2) - true_samples.mean(-2)) \n",
+    "#                            / true_samples.std(-2)\n",
+    "                           , -1)\n",
+    "meanerrabs[:, :, 2] = np.mean(abs(nlee_samples.mean(-2) - true_samples.mean(-2)) \n",
+    "#                            / true_samples.std(-2)\n",
+    "                           , -1)\n",
+    "\n",
+    "varerr[:, :, 0] = np.mean(abs(lan_samples.var(-2) - true_samples.var(-2)) / true_samples.var(-2), -1)\n",
+    "varerr[:, :, 1] = np.mean(abs(nle_samples.var(-2) - true_samples.var(-2)) / true_samples.var(-2), -1)\n",
+    "varerr[:, :, 2] = np.mean(abs(nlee_samples.var(-2) - true_samples.var(-2)) / true_samples.var(-2), -1)\n",
+    "\n",
+    "varerrabs[:, :, 0] = np.mean(abs(lan_samples.var(-2) - true_samples.var(-2)), -1)\n",
+    "varerrabs[:, :, 1] = np.mean(abs(nle_samples.var(-2) - true_samples.var(-2)), -1)\n",
+    "varerrabs[:, :, 2] = np.mean(abs(nlee_samples.var(-2) - true_samples.var(-2)), -1)\n",
+    "\n",
+    "dispersion[:, :, 0] = np.mean(abs(lan_samples.var(-2) / true_samples.var(-2)), -1)\n",
+    "dispersion[:, :, 1] = np.mean(abs(nle_samples.var(-2) / true_samples.var(-2)), -1)\n",
+    "dispersion[:, :, 2] = np.mean(abs(nlee_samples.var(-2) / true_samples.var(-2)), -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "09239d4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sbibm.metrics import c2st\n",
+    "# from joblib import Parallel, delayed\n",
+    "\n",
+    "# num_workers = 24\n",
+    "\n",
+    "# accs = np.zeros((4, N))\n",
+    "# for jj in range(4):\n",
+    "#     out = Parallel(n_jobs=num_workers)(\n",
+    "#         delayed(c2st)(torch.tensor(true_samples[jj, ii,]), \n",
+    "#                       torch.tensor(nlee_samples[jj, ii,])) for ii in range(N)\n",
+    "#     )\n",
+    "#     accs[jj,:] = torch.stack(out).numpy().reshape(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3c04c64f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Post-hoc SBC based on posterior samples\n",
+    "L = 10000 # number of posterior samples\n",
+    "nbins = 100\n",
+    "B = nbins\n",
+    "\n",
+    "ranks = np.zeros((len(num_trials_list), N, 4, 4)) # different trial settings, N, 4 params, 4 methods\n",
+    "\n",
+    "thos = prior.sample((N,))\n",
+    "\n",
+    "for obsi in range(N):\n",
+    "    for ti in range(len(num_trials_list)):\n",
+    "        # rank tho under samples\n",
+    "        ranks[ti, obsi, :, 0] = (true_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "        ranks[ti, obsi, :, 1] = (lan_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "        ranks[ti, obsi, :, 2] = (nle_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "        ranks[ti, obsi, :, 3] = (nlee_samples[ti, obsi, :L, ] < true_params[ti, obsi,]).sum(0)\n",
+    "\n",
+    "# Correlation over posterior means.\n",
+    "corrs = np.zeros((len(num_trials_list), 3))\n",
+    "corrs[:, 0] = np.array([np.corrcoef(lan_samples[ii, :, :, ii].mean(1), \n",
+    "                           true_samples[ii, :, :, ii].mean(1))[0, 1] for ii in range(len(num_trials_list))])\n",
+    "corrs[:, 1] = np.array([np.corrcoef(nle_samples[ii, :, :, ii].mean(1), \n",
+    "                           true_samples[ii, :, :, ii].mean(1))[0, 1] for ii in range(len(num_trials_list))])\n",
+    "corrs[:, 2] = np.array([np.corrcoef(nlee_samples[ii, :, :, ii].mean(1), \n",
+    "                           true_samples[ii, :, :, ii].mean(1))[0, 1] for ii in range(len(num_trials_list))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "5892ed86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c20c6f499e0404e8e08aad554c338c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69cb98abdf5749c8b652f747927bf400",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d10b16d9a5d49dd83e859748bde99f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "394c04cd765a4145ae918395a83f662c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4ed4d595c024b43969fc8e5bbc18334",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e676ffd8f714e61aeb6534ad1853481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load another df with lan and nle to save ensemble metrics.\n",
+    "df = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-22/21-43-48/\"), # LAN 1, 10\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-24/04-03-04/\"), # LAN 100, 1000\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-29/00-40-46/\"), # NLE 1, 10. SEED 4\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-25/11-01-18/\"), # NLE 100, 1000. SEED 4\n",
+    "])\n",
+    "\n",
+    "dfens = pd.concat([\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-29/00-40-46/\"), # NLE 1, 10. SEED 4\n",
+    "    compile_df(\"../../results/benchmarking_sbi/multirun/2021-10-25/11-01-18/\"), # NLE 100, 1000. SEED 4\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "6df7692c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  import sys\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.algorithm[df.algorithm==\"LAN-KDE\"] = \"LAN\"\n",
+    "\n",
+    "# pseudo duplicate NLE results to then put in ensemble metrics. \n",
+    "df.algorithm[df.algorithm==\"NLE\"] = \"MNLE*\"  # the first df has the ensemble mnles\n",
+    "df = df.append(dfens)\n",
+    "# the second the mnle \n",
+    "df.algorithm[df.algorithm==\"NLE\"] = \"MNLE\"\n",
+    "\n",
+    "df[\"num_trials\"] = 1\n",
+    "df.num_observation = df.num_observation.astype(int)\n",
+    "\n",
+    "df.loc[df.num_observation <= 100, \"num_trials\"] = 1\n",
+    "df.loc[df.num_observation > 100, \"num_trials\"] = 10\n",
+    "df.loc[df.num_observation > 200, \"num_trials\"] = 100\n",
+    "df.loc[df.num_observation > 300, \"num_trials\"] = 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "2d5bae56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort to match order in post-hoc metrics\n",
+    "df = df.sort_values(by=[\"num_observation\", \"algorithm\"])\n",
+    "# Exclude failed seeds.\n",
+    "mask = np.logical_or(\n",
+    "    np.logical_or(np.logical_or(df.num_observation == 76, df.num_observation == 176), \n",
+    "                  df.num_observation == 276), \n",
+    "    df.num_observation == 376)\n",
+    "\n",
+    "df = df[~mask]\n",
+    "\n",
+    "# Put in post-hoc metrics\n",
+    "df.loc[:, \"MEANERR\"] = meanerr.reshape(-1)\n",
+    "df.loc[:, \"ABSMEANERR\"] = meanerrabs.reshape(-1)\n",
+    "df.loc[:, \"VARERR\"] = varerr.reshape(-1)\n",
+    "df.loc[:, \"ABSVARERR\"] = varerrabs.reshape(-1)\n",
+    "df.loc[:, \"DISP\"] = dispersion.reshape(-1)\n",
+    "\n",
+    "df.C2ST.values.reshape(-1, 3)[:, -1] = accs.reshape(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "db193928",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:116: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4fD6WL1/OHXfcMe7Y8vLyjG36fD6uuOIKLrroonHbmpubh/PqBAKBUcdkcx6/f3wRBK1TaXK8Xu+4bWP3ufPOOwkEAqPycqSXfzmWhXnrSj7BxRTN81PR0MBVV13VP2GjoiBFo1Hs1lfQT/8Cd+ISBpRJlfYQLfOy37J3jdtfKUVjYyNXXXVVOA/dFdshEokQj8cpLi6e8PPvRCP8V8t5FB+whDjQ1NTEVVddFduxPRXbQ2tNOBwmkUgQDAaHf2cPmN106bdoVH70mPnISimam5u56qqrZq4cohBCCCFEgZMlWmKUyy+/nKOPPppPfepTtLe3s3z5cjZu3EhVVRVLlixhyZIl1NbWcvnll/Paa69lbGP58uWsW7dueP8lS5bw2muv8dWvfnU40JLpmFzPM9Zee+3FypUrRy0D+93vfsdBBx1EVVUVAL29vSxevJhFixaxaNEirrvuOh647z5W33Uj6/55CzF3hOIPnkDfIeWjgkCi8GmtcRyHtrY2BntaaXXO5o0qKP/gyZR86ASWnf3BjMdorWWs5xitNZZl0dbWhtvtxuVyZR5D26Bk2xO0+TuJ71MBIGM9xziOg2VZtLe34/V6h8e6P27R3tNBcxKObnsNXO8E+NL/zshYCyGEEGJXI3c/YhSXy8V1111HPB7nv//7v7nooouorq7m/e9/Py+++CKvv/46H/jAB3j22WdZvnx5xjauvPJKbr75Zr73ve+xbt067rnnHj7xiU8QCAQyzsABpnWesT796U/T1tbGZz7zGdasWcODDz7I17/+dY444ggaGxs5++yz+dKXvsTDDz/Mpk2b+N73vsdf/vIXqiuCNIbrsUt8xPauIBaL4fF4Ri31EoVNa83mzZvZsGEDyWSSni4ThZvmg/aa9LhoNIrH45EHwTnEtm02bdrExo0b0VqPmgk4ljvSije8jainhEgkMhwgEHODZVnDYw1QVFQ0vG1dR4yQ00OIQRILTyG+21nD28LhMD6fj9EFSIQQQgghdn5ypyvG2XPPPfnKV77C3//+dx566CEefPBBiouLWbFiBUcddRSWZfHwww9TW1ub8fjTTjuNv/zlL9x0003svffefOITn+Diiy8elcx4rEAgkPN5xmpqauL+++9n5cqV7L///lxyySWce+65fOlLX6KsrIwf/ehHrFixgssvv5wTTzyRRx55hJ/9+Hu8J7yU1tJ2lp1yHov2PRqlFA0NDcPLAERh01rT09ODYRgEg0Gqol6WtSxiW2gLjXWTXzsul4vGxsZJl/eJwuE4Dr29vZimOZwUfjL9Zh83lpVhu8pwu900NTWNSsAsCpfjOPT09GBZFiUlJePGOuloSnwuVFEpiYWn4RS/81n3eDw0NTVJkF4IIYQQuxw10ZIZsWMdfPDB+sUXX8x3N3YapmmyefNmvF7vhIGaWGKQOx69ihP7jqfhwncDqYeKaDTKggUL0t8Wz/hXwDLWM+vZ514k+uyrBJxBFIqKZDkvlbzGQFWc9xzz1QmPs22bWCzGokWL0teIjHWBi8fjbNmyhaKioqyCcj1bn4SnB+j1LaP+pGp222239HEy1gVMa00sFmPLli0T5ld6bOU69l7TglmzjYaT3ll+mf5cL1myJB3gkWk8eSCfh52CfHaGyPU858m1PIJcz3PelNezfJUpdjqO44wqmTyR7r6tlFllNNfugz30XiwWIxAIyDe/c4Rt27S+uZYKVyfBmiiRyn3pAeoDB3NY076THhuPx8cl3BaFy7IsWlpacLvdWc+4csVsbK14plzxwWBQxnqOSCaTtLa24vV6M4510nYwe7vpKxpkr+o4yRHbotEopaWlsjxLCCGEELukOR3gUUqdDbyotW7Nd19E/mmtaW9vx7IsTNPMWDJ5pORglHd1n4prRe1wgMflclFXVydLdgqc4zi0t7dj2zYlsTZK2ET9gR/GDi3Iug2Xy0V9fb0s2Slwtm3T0dGBZVnYtj3lsqyRBmMJkjrKHvNKqK+vl8BtgbMsi87OTpLJJFprgsFgxv1Wbo3QMxhn92ILr9c7KsDjdrupr6+XYJ4QQgghdklz/Q7oTuD4kW8opXxKqYuVUnX56ZLIl2g0yuDgII7jTPhgkJaIRah+2WRj8QaS80uA1MNFMpmc9DiRf1prIpEIg4ODvPXE3ew1uBuR2gNzCu4kk0kZ6zkgPdbhcBitNcXFxVkf64p3U7H+H+CGZXWZk7uLwuE4DuFwmMHBQYBJx7rD2Iq3+G1KvC5wvROgNU1zwkqNQgghhBC7grn+1XWmOdilwP8BJwMdO7Y7Il/SJZM9Hs+o2TeJ2CBtr9xPPALody4XV9KN36vw6JeBVP4GwzCoqKiQGR0FLj3WVk8XdS02bzZvYZ9jzsupDcMwqKyslLEucKZpDpfHzmlWne3gequNLnUY/d4q9qqslBkdBc40TTo6OvD7/VOOdSzew6GJZSy212C63gnmG4ZBdXW1jLUQQgghdlk7612QLL7fhdi2zbZt27Bte1zJ5J7Vj2FteZ2q3nJU3Bx+aStOvLgd3/4XAqnZPy6Xi6qqKlnGUcDSeVgA/C++guXS2Ls15dRGJBLB4/FQVVUlD4IFLJ2HRSk1qjx2Ntx9JvZaiy0eA//uTVRKgKegGYZBa2srbrcbv3/q2VZN/V4qjCQuvQqz8SgABgcH8fv9VFZWSv4dIYQQQuyy5OtrMadprRkYGMA0zXG5ObpXPUTVm+WUuk5hcL8Qe+19yoTtKKVobm6W3DsFzHGc4bHueeUflCQ6eajZw/F1h+XUTnqsZfZO4bJtm4GBASzLmnK55Vjt8Xb+/ebf2MdVQn9FD+efcIwEbQtYeqxt2856CZ7Ldmgp6aBp/n7Ypc1AKvfOvHnzJLgjhBBCiF2aPOGIOcu2bZLJJJ2dnfj9/lE39u6uFirejPFCsJXwXu/i5L0qJ2xHa43jOPJgUMBs28Y0TTo7OwkEAsQH+gh5DuaE+fU0Fjdn3Y7jOAAy1gXMtm0SiQSdnZ0UFxfnNFbR/i42PnsdJ/cto1xpdj/oAhnrAmbbNvF4nO7uboLBYFZj1dseY16Hn4EKC+1OzfZJf65llpYQQgghdnUS4BFzUjKZZMuWLdi2nXFav2/dWjpdJi9Ul/CR3combSsSieD3++XhoEAZhjG8BM/r9eLxuPEkaukqG2C3+Ufk1FY0GqWoqEjGukAlEgm2bduG4zj4fD58Pl92BzoOOjpIx+rHCEW8eHwVhPdppmJM4FcUjlgsRktLC1rrKfPuOFqTMG0MK07vq23EimOomg60OxXcDYfDlJSU7KiuCyGEEEIUrJ0hwHOMUmrk3yN9l3eKUirjV/ta6xtmv1titti2TXd3N47jTHhT/2Z/F6uCXey/aAUl/omXZyQSCZRSNDU1yZKdAmRZFj09PaPGeuuTz1Jj1NOzII7Xm2UAAIjH47hcLpqammTJTgFKJpP09PSgtc75YT3w2vOol0z8gWJK3IcSW15F7bx5zJs3T4J5Bcg0TXp7e1FKZbUE7z9r+rhn86Mca7dxZs/e/KvhXk7p68BasIJoNIrP56O5uVmCeUIIIYTY5e0MT7QfH3qlpe/wrgDG1ktVQ+9JgGeO0loTi8Xo7++f8MFgwytPUNtTzeJazT7z9pq0Pdu2aWhokOBOAdJaE41GGRgYGB5rT0uU+duCvFzyKHXBo3Jqz3EcGhsbZawLUHqsBwcHcw7udHU+x7qtD5KobGb9ono+vt8KIpEIjY2NEtwpQI7jEIlEcpp1Y3dv5tNRP/tE9sS1t5f3HfR9AJKAisUkuCOEEEIIMWSuP+l8JN8dmMsWLlzIRz/6Ua688spJ99tnn31YvXo1W7ZsobGxcdS2Sy65hJtuuomXXnqJffbZZ9S2b3zjG/z1r39l/fr1M9Zn0zRpa2vD7/dnfFAPGwnat27ACfrY4/hzpmwvXaFHHg4KTyKRoL29fdRYe9vjPLdgK3brWlyeE3JqbzrVmMSOEY/HaW9vp6ioKOfZVW3dr+DRVVT7D+eo5UuA1FhnU41J7HixWIyOjg4CgUBWY903OEhDZx+u4j7UQhexA84ft4+MtRBCCCFEypwO8Git/5zvPuzsXnzxRdauXcu8efP44x//yNe+9rVx+ySTSS699FKeffbZWV36kkwmaW9vn/RB/Za3HmOhk6SxvAafP5Bxn7R0aXQJ7hQe0zTp6OjA5XKNHmsHOvtfow5Nad3irNtLj7UoPIZh0NnZicfjmdaDenv/IMVGLcubavB7vITDYbxer3yuC1A8Hqerqwuv15t1fqV1T66kynLRWDaAU7MHuN75NyYcDmefp0kIIYQQYhew0z3xKKU8SqlapVTNmNw8Yhquv/56DjvsMM455xz++Mc/DlcrGWnevHm88sor/PCHP5y1fjiOQzgcJpFIEAhkDty89MIrvPs1P+/u2YPqqupJ2zNNE8dxJPdOAbJtm3A4jGEY48Z649YX2OLppeSgTxAsm3yM0wzDQGvNvHnzJPdOgRk51tOZXeXteJFFres5NrYv5VVFGIaBy+Vi/vz5EuApMJZlEYlEMn6ux3rukWfovPFJum58lAO7/NR5/86CrTfjFFUN75NIJPB4PMyfP3+2uy6EEEIIMWfsFAEepVSzUupHSqnXAQNoA9qBmFJqlVLqaqVUXX57OfeYpsnNN9/MSSedxLnnnsvmzZt54IEHxu23dOlSrrjiCq6++mreeuutWemLYRjDy3XGzsRoXf8qb97zN6o2tBIveR77hAjx/Q6btD3TNKmqqpKp/QXIMAw6OjrGjfVbm56h3+rhxIrTWLrw8KzbM02T6urqSav0iPyIx+N0dnbmXMXOFesgvPJvrH36abTzLp6bZ5BcUEIymaS2tlaCtgUoPdaBQGDK4Ju/P0HvnrBH1fUETvNS8t5f03n+kyRrDwBSOZscx6GmpkaCtkIIIYQQI8z5AI9S6gLgLeByUhW07gNuAv4OPAY0AF8D1iml3puvfs5F//znP+nt7eXcc8/lyCOPpLm5md///vcZ9/3617/ObrvtxqWXXppxls90JRIJNmzYQEtLy4TT+sOvvUog6iIW7Kd+XgNGw+SlswcHB/H5fFRWVsq3/AUkHo+zYcMGWltbM5bIfnLDTZTqIPPn7Zd1mwMDA/j9fioqKmSsC0g0GmX9+vXDQdtcl9kUbbyPzk19OMlFDJQHaV6+mP7+fvx+P2VlZbPUazEd4XCY9evX09HRQVFRUVaBVo8DNX3PkaxahlW1bNQ2rfXw7/BQKDRb3RZCCCGEmJPm9NecSqkjgL+SCvB8XGv91AT7rQB+AtyslDpca71yB3ZzZvz9Aoi0T7y9pB7O+9uMnvL6669n2bJlLF++HID3v//9/OIXv6CtrY2GhoZR+/r9fv70pz9x5JFH8otf/ILPfe5z231+y7Lo7+/HcRyKi4szPqAXr7mJoNFPT/Ny9js6lXR3bOm0sdxut5RPLjDJZHLSsX7hnsc4r/d91BXVkCgNTTnGaV6vV5brFBjTNBkYGEBrPeHneiL/evIHrLJf48DBPVkRPpl1B4U4ZPmeQCp5ryzXKSyGYTA4OAiQ1cydW1/qoHzjaxwVD1BuP0H0iGvRntHLubTWFBUVyVgLIYQQQmQwpwM8wP8A24AjtNaDE+2ktX5YKXUs8OrQMRftoP7NnBkO3kylvb2dBx54gK9+9avD751//vn85Cc/4Y9//GPGyluHHXYYn/vc5/jqV7/K2WefvV3nT5dD7+npobS0dMIHg46OPoL2fnSUVGXcPpbjOGidbXhA7AjpEtm9vb0TjnVpLIrX/3fMU69GF2f3ays91hLcKRxaayKRCH19fYRCoZzGZkt0M51GjP8X+wiL3EtJnFDBPvOCQCqXDyBjXUC01oTDYQYGBib9HT7Sptg6zvAl8Fe9Ant/EydYP24f+R0uhBBCCDGxuT6F4Qjgz5MFd9KG9vkrcMys92on8Ne//hXLsrjmmmvweDx4PB6OPPJIgAmTLQN861vforGxkY9+9KPbdRMei8Vob28nEAhMmE/DSsboGgiyIThAoKYpq3bD4TCBQEBm7xSQaDQ6XDY501jHwv2ELIvIotOxS5uzbjcSiVBcXDyTXRXbaXBwkK6uLoqLi3PKnWIlY7zQ8igndy8l6C8hfmAVycbUzI50MFjGurD09/fT09OT1VgblkNnJEaP8zK7uRZQlexDe8ePp+M4xONxgsHgbHVbCCGEEGJOm+szeKqALTns/zYgyZaz8Oc//5lDDjmEP/3pT6Pev+WWW/jWt77Fv//9b0477bRxxwUCAf74xz9y/PHHs3nz5ml9o24YBt3d3ZOWQwd44tXvUWHN522nmpPKp364i8Vi+Hw+mpqa5Jv+ApFIJOjp6cHtdk841h33/ZtiXUzR7itAZReYi0ajeL1eGhsbZawLRCwWo7+/f1rl0B9b9W3mb21iYXQJg0fUY9W/E9yJRCL4fD4aGxtno9tiGqLRKAMDA1mXQ/+/p9tYr+/mwu7dqbe6cKs27OLR/1SnZ/r5/X7q6uSfcSGEEEKITOZ6gMcLxHPY32Du/51n1Lp167j//vtHvVdRUcHrr7/OX/7yF/bee+9R2+bNm8fPfvYzfv/732cM8AAce+yxfPKTn+Q3v/kNixcvzqk/juMQiUSIxWKUlpZOum9vPEZVbD7HH7onlcHJE3cmk0kcx2H+/Pkye6dApEtkTzTWz3Q9Tf+qFk6LLaLvhCJKy7Mri55MJlFKMW/ePAnuFIj0WMfjcUpKSrI+7rmNA8RfuZclxiE0xnaj9dgF1M9/J4myaZp4PB6amrKbwSdmn2VZDAwMYBhG1mO9R6/JOezN7rEQfvfl9Bx/A9o3+neCYRh4vV4ZayGEEEKISUiwYxd3ww03cMMNN4x6b/ny5dTU1HDeeeeN27+srIxLL72U3/zmN7S1tU3Y7ve//33uueeenPsTi8WGl+tMFYhx4m6WFJVTWlmEPUW7iUSCyspKKYteQCKRyPBynbFj3Rk2eWvjKt7bchDu0j9RXXM12dRm01pjGAaVlZU5V2YSs2dgYICenh6CwWDWAVZ3TxuuN1ayzAiS3M2Lsfse1JW9E8h1HAfLsqiurpaxLiD9/f0MDAwQDAazDrAuiif49+LXaYxtIb7s+zjB0Un8bdvGcRyqq6uzqsIlhBBCCLGr2hkCPHsOJVDOxl6z2pM5ZtOmTdM67uc//zk///nPgVSlrUxKSkpybj8Wi9HZ2YnP58sqELP3wHKUV+GEJr/hD4fD+Hw+qqurZUZHgYhEInR3d09YIvvPz7ZxYU8DPu9GAgv2IFpUOWWb6fLJgUCA6ursZvuI2RcOh+nr68u6RHZa6SMvUGclGAhYLFx2Gt6Sd34nOI5DOBymuLiYioqK2ei2mIZ0cKeoqGjC3GmZaNVFqKKWYOQ1ooHaUdvSYx0KhSgvL5/hHgshhBBC7Fx2hgDPV4de2VBMXUVb5IFlWYTDYZLJZNbT+j3aQ1tjlN18kyfwdLvdUiq7gCSTScLhMLZtZ0yWevMLHRzcZtBEPca7m4iWzcu6bZ/PJ+WTC4hpmgwODg6Xts7W6n/cyB7R3fjlwrv57AFfwFsyPsBXVFTEvHnZXxtidhmGQTgcBshppuSLdz/K3kaQJYPtFHWuJOwvG7XdcRyCwaDkWBJCCCGEyMJcD/Bcne8OiO2XTpTa3d09ZTnd/lWvEN+4gT6XQYNRTb938ks4Xckrl4o9YvakSydPVhI90b6Rkzx+vtt8HZ8P/CSntgHJsVQgtNYMDAzkVCY7LZgoYtshbn647NsTtq2UkrEuEFpr+vr6iEQiU+ZOS/O0x1m/YSN79BXB8ZqKl++m++x/jMu9A6nPtIy1EEIIIcTU5nSAR2stAZ45bmBggM7OToBJS6KnhVZtYHNpDMsLbVV9LNr9iEn3HxwclPLJBaKvr4/u7m601hQXF0841sdFDDaXPMmZ1Qfj8WT3sJgOEkr55MLQ3d1NX18fjuPkXBIdwKPduIpDGbc5jkMsFsspWbOYPZ2dnQwMDAyPdbaBmKJVvbycXEXdwkWc+faNxPa4ACcwemmlbdvE43HKysomaEUIIYTInVLq4RloRmutT5yBdoSYUXM6wDOSUsqvtTYyvL8H0Ku17spDt8QkEokEg4ODuFwuAoFAVsd0eOGphjYuOfgyij2TB27SZdEXLFgwE90V2yEWixEOhycthw7w0/9s4WLHYJ7rJWJ73ZhVWfR0+WSfzydLdgpALBYjEolkXSJ7JOfPt2FRTpGnmKKq+nHb02NdVFQk1ZQKQDQaHf7s5Zr8uD3cwuN1G/n+4e9HPfpHEovOgBGzvBzHIRqNUlJSQn39+GtBCCGE2A4utj9th+R+EAVpzgd4lFJe4HvAh5RS87XWiTG7fBc4Qyn1O+DLWutcyqqLWeI4DoODg8PJM7Ox+uE7WWI28N7G5VMGdyzLQiklD4EFwHEcBgYGiEajky7feGxdH0dteYEa1Uz41OtxZxHcgVROH4/HIzk6CoDjOPT29pJIJHKeYfPgc7/hYHcNv9/jaf77gP8hkOEzbpomPp9PHvgLgG3bdHd3k0wms54594dXHyDR+wQHDdRxSPRQLlzfRo37Grzdr+N4R4+3aZoEAgFqampmo/tCCCF2YVrr4/PdByFmy5xe1K6U8gH3AZ8HuoHaDLs9BKwBPgPcq5SSZCwFoL+/n56enqyn9CszQklnkrXNndTN22PSfbXWxONxSkpKckrsKmZHT08P/f39U5bIfvbNLZzo8vLnPZ/C5clu5ofjOJimSWlpqYx1Aejs7BxeFplrzpRNg69S5l3Mh/a4NGNwx7IsLMsiFArllMRXzI729nZisRiBQGDS/ErhZJje/g5aWlpo6V7FRX2dHJU4js37xNj9uA8TX3ouvadeD553ZnEmk0kcx5GxFkIIIYTI0ZwO8ACfA1YAV2itl2mtt4zdQWv9G+AA4NvAccBlO7SHYpxIJMLAwMCEJbIzCT37TVzaR9keB036MJHOxeL3+6mrq5upLotpGhwcHC5dPlV+pY/2f5NWXwm++sasEvI6jkMkEqGoqEi+5S8A6VlawWAwpxLZaad3n4K3upLG0vGz7tLLdYqLi6mqqpqJ7ort0NfXRzweJxgMTplf6Zdv/Zzgv7fR++RbXNR3FIujx+Nf2MDiA0+gcreDSVbvg1X5TtDetu3hHEsVFRWz/VcRQgghxBymlJr0tSua6wGei4B/aa1/NNlOOuVrwOPAxTukZyIjy7IYGBggmUzm9M3sK11NNBnzKQmVT7qfYRh4PB5ZmlUAksnkcPLVifJz3Ph8O9+6bxPP3rKBInUJwWQZ5y+8YMq2tdYkEgn8fr8szSoAyWSS/v5+tNbTCu4EX7uOIsdD+LDaUXlY4J0ZeYFAQIK2BcA0Tfr7+3G5XFOO9S2bb+bk9ftixkNc75nHknOPQpXdQfywzAHZkbMvJWgrhBBCiKlorUe9Fnzp7lE/58NUQafZDjzN9QDPUuDfOex/N7DnLPVFZKGvr4/BwUGKioqyvrjdAxvxm/VsODJAeWjiHC6O4+A4DlVVVTkn/BQzr6enZ3iGzdix3tAV56lX78G79V4uLXuVY51BfrfwLuwL98mqbdu2UUpRWVk5rYCCmFkdHR3EYrGcPtcjdW58E59TQ1HR+KCvZVm43W6qqqpkrAtAe3v7cC6kyWyMvE1sWx9L+vcgeHKcnx20Bf+Wh1FWbMJjkskkXq+XioqKnCuvCSGEENlQSr1PKfUzpdSlSinPmG335KtfYucxVdBptgNPcz3AEwVyuQuMAeMqbe2qFi5ciFKK3/72txm3n3766Sil+Otf/8r111+PUoprr7123H6PPvooSim2bdsGwDe+8Q2WLFkybr++vj76+/tZtWoVCxYsoKmpKeNr7DlKX/4ZNkFKyiZfmqG1xuv1Ul5enuX/ATFbenp6CIfDE5bIvvaJFp7ov4Vz4kvR/THWVK3i6PI98Xqzr7rk8/mkfHIB6OzsJBaLTZljaTLRyArWVCVweTL/Oi8qKpo0QbfYMTo6OoaXZk011n/deAPnrj+avj3KqH3uKwT61+DtXU102YcnPS4QCOScoFsIIYTIhlLqMuBXQDHwReAppVTliF2OyUvHhJhBc/3r0DXAUcAvstz/GGDTrPVmDvJ6vdx222188pOfHPV+b28vDz/88Lj9r7jiCs4888ycy1EbhsHg4CBut3v4W/gHHniA2trxebHHPsht6Y6QpJiywMRLutJT+2XmTv4lEgkikQgejyfjePzsP5fwOeMcyrZeTEXNIrynHg3Ablm2r7UeDiiI/IrH40SjUfx+/3bNrvE6XpafcNi49x3HIR6Py8ydAhCLxYhGowQCgUmDOz9d/SMcNJe8dhKD3mIOCn8Nu7iGyP6fnrR9x3GGkzYLIYQQs+Qy4FSt9aqh2Tu/Bh5WSq3QWvcipc/FTmCu3zX/GfitUuoErfUjk+2olDoOOBf4xo7o2Fxx4okn8uCDD9Ld3U11dfXw+3fccQeHHXYYTzzxxPB7LpeLyspKPv7xj3PfffdlfQ6tNT09PcRisVHBm8rKyowBnrE6nXrm2SWoSZYE2LaNx+ORHB15prWmq6trOI/GWI+v68d0OewZOpDwmfOITOMcyWSSoqKiUder2PG01nR0dGCa5nbNuOh/7DFCdgiVoQpaMpkkEAhIYuU801rT3t6OZVlT5k7bfWsDx0SqKU24KD34Ydwb2ug+5+4pz5Guhiez8oQoXEqp8d/85U5rrU+cgXaEmI4GrfUqAK21BXxCKfUT4BGl1AogP0lbhJhBc32J1vXAi8DdSqmvKqXGRQuUUrVKqa8A/yI1e+c3O7SHBe7II4+kvr6eO++8c9T7t956K+eff/6o95RSXHfdddx///1cf/31WZ+js7OTcDg87SUcRc6BtOxu4SvK/M2ubdvDywaKi8eXVxY7Tltb23AlpbFjnTQM7nmlBb/LNe1/Pi3LwjRNgsGgfNOfZy0tLcOfu2kni9MO9esHuLP5JVxqzPWSTJJMJikpKaEoQ/BH7Dhbt27FMIxJx9rRDgnT4NiOJTRFHyC+Zxg7tICBo66Zsn3TNLFtW8ZaiMLnIjXDYXtec/3ZQ8xt3UqpRSPf0FpfDjwy9Jrrkx+EmNsXsdY6qZR6N/A34Brgm0qpzUAbqdw8tcACUv+gPAtcoLXuz1N3C5JSinPPPZfbb7+dj33sYwB0d3fz+OOP85e//IXLLhtdVf7kk0/m0ksv5fLLL+fUU0+loaFh0vYjkQjRaJSioqJpL7NwaxflC5ozbktP6y8uLpbZO3kWDoeJx+MT5t3Rt7zEl91hilsvxlqU+0NceqxDoZBU2MmzgYEBDMOgpKRk2nl3ACof+Agb/R+iZ97o3w3pUtmVlZUyeyfP+vv7h2fXTBTc0Vrz1ZVf5ppXL+T16jiL9GaCR55KIov2LcsiFotRU1MjZdGFKHBa6+Pz3QchttN/gEuAq0a+qbX+nFLqF8De+eiUEDNpTgd4ALTWHcAJSqn3AhcABwL7ATbQDtwA3KG1/lf+elnYzjvvPFasWEFfXx8VFRXcfvvtHHXUURMGTH7yk59w//3386lPfWrczJ+RbNump6dnwmn9xx57bMYHhlWrVg3PxHlifT9LtMI9QW4d0zTx+/0S3Mkzy7Lo7u7GcZyMwZ11t91ElbuKyotOBSCeY/vpsuiBQECWZuVZeqyBaVU6eu7+x6ke7MOjTdqck6i0y/j47v9veHt6rEtLS6msrJykJTHbLMuip6cHl8s1aSDP0hbnbj6Obk8JJ9T9Gzqzy4WWHuvy8nIJ7gghhNgRLmOC51+t9X8rpX60g/sjxIyb8wGeNK31HcAd+e7HbPnhC9+nL9E34faKogq+eMiXptX2UUcdRU1NDXfddReXXHJJxuVZI5WVlXHttddy9tln87e//Y36+vqM+7W3txOLxSgpKckYyLnpppsyzsQYufRm1dpNLHO58Pgyl092HIeKioopS/aK2dXa2jo8o2PsWPd0r6Qh3kDs+MyzsLKRTCZRSlFeXi5jnWfbtm0jmUxOq6pVpGU1S7tdFBX9k46l/0Xc7ae9rIEFI64Z0zRxuVyEQiFJmp5nW7duxbKsCXMs9XW00B02iCXizI9VUHacwv/SE/Seen1W7RuGgcfjIRQKSSJtIYQQs05rbQLm2PeVUo8DN2mtx5cLFgBZLcef7fLfIjtyRzVHTDd4k430Mq3bbruNM888k6eeeopbbrll0mPOOussPvCBD/CZz3yG3/xmfFqjdPWbYDA44bf8zc3NNDY2Tnqe9/T8FOX9AG736Ac927aJRqPyzW8B6O7uHs7PMfZbfkc7/HLNz7lc/xe183afVvuWZRGPx6mqqpIErHnW2dk5YSAvG+qRDtq86zh0yUEE9j1y3PZkMkkikaCmpoZQKDQTXRbTNDKBdqaxTsQihP7dxjpfPwGvm43NAc568kOED/o8TmDqWXamaWIYBvX19btUWfShJLXf1lr/J999EWJ7KaV+DLw89FqjxzzdKaWagF6tda4Td4XY0Y4mlZ/nelLpPSJa65b8dqmwjA3eLPzyPWz63pl56o2YjAR4BJBapnXyySdzww03cPzxx2e1DOYXv/gFy5Yt48tf/vKo99Mza1wu13Z9K3vHK10caCYgEAT3O+2MLJOdTRUuMXsMw2BwcBCv15sxkLf1b0/wP9ZFDHgamM7j+si8O5KLJb8SiQThcJiioqKc8u603HwnQTv1AK9w8WffCSzbf8m4/dJjXVFRIUuz8iwejxMOhwkEAhMG8mLxGJ3eMK807MmXo/+LGoiSrNmf+NL3T9l+eqxramp2xaDt8cAf8t0JIWbI53mnbEJcKfUqqWDPyqHXBcAJwCH56Z4QOTkO6AN8AEqpduAW4Ida67Z8dkyIXEiARwCpZVqVlZVcffXV/PznP8/qmKqqKn71q1/x/vePvqGPRqPEYjGeeuqpcQ+Chx9++PB/9/b2ZgwA+f1+ysrKWNMe5ZLqZnqiRTAieJBMJvH5fFRWVk4rB4iYOa2trViWRTAYHLctmTTxahcLyn9Hz1m3Tav9dJns8vJyGes8a2lpwXGcnJZNrXnsLurtEso/ePLwe9+fYF/TNCkuLiYUCm1X4maxfbTWtLa2AkwYoNeOQ9/ajSTdmk8cWQUPanrOzn6FtGEYlJaWUlpaKmMtxNxWSSr35cjXJ0kVN0kHfmL56ZoQOSsH1gOPAn7gUOBzwEeUUudqrR/OW8+EyIEEeAQALpeLc889l9/97necc845WR933nnnDVfhglSZ7GQySVdXFxdffPG4/Z988snh/z711FMztnniiSdyww03YDsa9+CxPN+0kcNd+wOp2UGGYVBZWblLTesvRC0tLZMu14nFE5guG+2eXs4c0zQxTZPq6uqMASSx42zZsgXTNHNeNrVoczUbmjspn2I/wzCwLIuKiorhBOtix9NaD4/1ZDNrouF+6jdq7qgoZb9kDO0JTLjvWIlEAtu2KSsrk5LoQsxxQ5VpHx56AaCUKgXOAL4BVAOfyUffhJiGR4FTtdZW+g2l1KGkZl3+Qyl1oNZ6fb46J0S2dliARynl8E40P1taay1BqFmyadOmUT//8pe/5Je//OWo90aut7zkkksytnPbbanZGdFolI6ODq644gq+8pWvTHjeRYsW0dIy9bLWc/osuinl8ZqnOVK5cRyHaDRKRUWFVFLKs0gkQiKRmLBMtuPYFP1zDe2BJLhy/winy2RXV1fLcp08C4fDw8GdXPLuRP7yCN3+LpatmHzJTrpMdn19PeXl5dvZWzFdWmsGBwcxDGPKQJ5lGoRLTcJF86n613tJ7HZ2VudI59Nqamra1XMsvVcpNX6d4sS01vqaWeuNEDNIax0GblFK3QO8TirII0Shc0hVXbZGvqm1fl4pdRywCvgKcGk+OidELnZk8OQGcg/wiDnCcRza29txHCdjSfRcFG16gK0btrK3uS/XLLmeryz/E0qp4YSfFRUV00rwKmaG4zh0dHSgtZ5weYWRNBjwmPic59Gu3GfwJJNJQqEQ5eXlMtZ5ZNs27e3tuN3uqcdBa4pe7wfL4Y2N/6HcVcrC86fOx5JMJqmsrKS0tFTGOo9s26ajowOPxzPpOFjJJOGVG+hP2hQrAzu0iPDBX8jqHDIjb9g5wHtz2F8DEuARc4rWOqKUuoFUnp5f5bs/Qkyhl9SSw3G01n1KqT8BH9uxXRJienZYgEdrfclMt6mUent6XdGLZ7ovu7qtW7dO61v+cWyD4lW/xZ34HHeF1nLx/PNwj5gB4vf7tzuAJLbPli1bpiyTHU5ESDgOK1yPMHjY76d1nqKiIimJnkfp5Tq2bWf3QG5r3Ot66dgTXi56nUP3OINs02LLWOeX4zhs2bIFrfWUy6ZiA30UDyhu8Vdy6fJS9Cu5jVtxcXFOeZx2Ut8BHsp3J4TYAbqAhnx3QogsPA98TCn1c631YIbtPZD1bY0QeVWQy5+UUi6gFFihtb5zkl23ILOC8q6vrw/TNGfkG/iq+z/M85FGSpSHzTXH8u4F84HU1P70kiCRPz09PVPmYkk6SX679hdcwtm4gjXYZbvldI70WO+C1XUKhtaa7u7unPLuxCMDbDFXc0fbbTQH57Ng+eFTHpNMJjEMQ2bu5FF6rKcK2qY5ySThYotASRXVAY12ZxdwT+fUEgCs1lo/lu9OCLG9lFJdpKplpUulv5zOUaJSv9jPBF7MXw+FyNoPgf8ATyqlPqa1fi69QSlVBHwAkEpaYk7IW4BHKVUG/A44DSghlXE/kwlL52itj5/5nolcJJPJ4WpYM1EN5Z4iN10lJ3P6YBH/vSIV3NFaDz/wV1RUbPc5xPSYpklfX9+UMy1Mx2Sefz4eJwRZPvylaa2JxWJUVlZKgCePDMOgr68v6yS4ZjLBLc9/ndP0yXzuxOuzOsZxHOLxONXV1VkFFsTsiMfj2Y+1aVO5Ms6ahElJmU3Zs9/F8U0dAEyPdW1trSTRFmLn8hSwP3DS0M9aKRUG1gA1Q68PK6W8WutkfrooxNS01o8ppf4L+C3wtFJqPam8Oyap8umNpGZfClHw8jmD5/vA+4FngUHgFOBGoA44FrCBi2b6pEqpA7TWK2e63V3Vtm3bsG17u6qhdCY66duwDhWO8rY9nzP69sQ4t2H44jQMA6/XSygUmrBsr5hdWmu2bt2K4zhTBngsx6Jp0Eux7s+5glYikaCoqIjS0lIZ6zxxHIeWlhaUUlkvpekf7KDSrGZe9b5kewefSCQIBAKUlpbidk8YxxezyLIsWltbcbvdU4+11ng2R9hqhbmvZhvXNLthrcXgEVdNeZ5EIkFxcbGMtRA7Ga31ewCUUuWkAj0HDP25PzCP1HPGbYCllFoLvAas0lp/f4d3VogpaK3/rJR6HLgcOAt439CmBPALUpXhhCh4+XyCehdwp9b6XKVUFal1ur/QWr+glNofeBzYM5cGlVI+4Ju8Myto5JQSD6llXyEmmRUkstfW1jac+Hh7llj8ccN1fOHFw9ha3MMRHEJPfQmLS8uBd6b119bWyvKsPNFa09raOpz4eCpOuJfTNiylw/MC8d2zzyNqmibJZFKSsOaR4zg5jXWaMdjHu3pOgROyK5aSLpVdV1dHIJB9iW0xc2zbprW1Fdu2s5pB5e4z4ZVObi17nONq96HqhW8xePjX0d7JP6uGYWDbNg0NDZI/LeVq4NV8d0KImTRULv3RoRcwfE++N+8EfA4gtWTrfFJf8gpRcLTWG4HPAJ8ZClyWAu0yA03MJfkM8NQC/wbQWvcopVqAw4AXtNavKKX+SGq94/dyaPMa4IvANqAP2Ad4AqgHFgJx4LMz9RfYlcXjcaLRKMXFxdudP+PC1XtRnjTxnXfhqPcdxxkulS3lk/MnGo0SjUanDLrETZuf3PMG/x3pZq2/k8fnnct/LWzM6hy2bRONRqmrq9vVyyfnjdZ6eKyzCabGe7Zh3f8WtnYo0242FXey2/wjpjzOtm3i8TiNjY0StM0TrTWRSIR4PD7l53rTlhaCT2zC57h5MrSOPa2nef/qB0ksPA1j3vGTHjuyLLoszUrRWl891T5KqQogrrVO7IAuCTErtNYm7+TmGaaUkkInYk4YClz257kbQuQsnwGeQUbPsNlAKiCT9hpwaY5tnkfq24OTSAV1tgGf1lq/rpQ6A7iT1FpKsR3SMzqA7Z5u37p2NdWJCj5TNW/c1zmGYVBaWkooFJqR/D4id47j0NbWhtvtzjjWscQgj754HQsGGrAcONtxiBUF2f28C9g9h/MYhkF5ebmUys4j27Zpa2vD6/VO/XmzTZy37qbPW8N9DYfziWOayDYsZxgGlZWVBINBGes8sSyL9vb2rMa6t7eXvpIkB7zrYM5/9XlciWV0Z7EsC1JjXVFRMSNfBOxMlFJeUvc3h2utPzLi/WOAa0nNXtZKqQeBy7TWG/LTUyFmnlzPQggxu/L51Pw8cL5SKv3U+AZwjHrnLnAPwMixzSbgDq21o7VuBTqBIwG01vcCfwY+tt0934WlSycbhjEj38iWvTRAp+t+zj+obtT7iUQCx3EIhULbld9HTF+6dLJlWRMuo1mz6Tl6I60sHpiHFygJevHuU5PTeeLxOAChUEiWcOSJbdts3bo1qzLZAJ7+dVidb9FDMactz75qaHqsy8rKpCx6nliWxbZt2wCyGmvTTKLdLjx96/C1PUtsr+xS48XjcVwuF+Xl5VIWfQSllB94mFQizwuVUp6h93cHHiAV3Lkf+Amp+6CnlVJ1EzQnhBBCCDFKPgM8PwKOBtYOTUe+ntSNzb+VUr8htZTqkRzbjDN6hs56Rs8Keg6QqaHTpLVmYGBgeLbF9rrsyavBGqTINcCB89/JAZGe1l9dXS2VlPJEa01/fz+GYUw4Bqs6t3B362Nc2n4hoYP3ZdlZH2DRaedTuefRWZ8nmUySSCSoq6uTSkp54jgO/f39WZfJdg9sxHrwqxQPXIhRFWJBZZaVtkyTRCJBfX29LNfJk/RYW5Y15VhvjmziKyuvZGX381S4FZUPfYL47udilS+Z8jymaWIYBg0NDZJjabzPkvri6QqgUmttDb3/DaAIuFlrfabW+grgYMACvpKPjgohhBBi7slbgEdr/TBwBvAW0K+1foHUDc8RwP8DVpLKYp6LV4DTR/y8Zqi9tGZAT7PLO52FCxeilOK3v/1txu2nn346Sin++te/cv311+Nyufj5z38+7tvYp59+mqampuFlWz/+8Y856qijJjxvev87z/89S7/8EQ75wr9oamoafv3qV7+iurqakpIS2traWLp06cz9pUVWTNOkq6sr4yyLyGAfm/79KK7Hn+DTHQcSqG7HXJp7IE5rTSKRoLa2Vh4C88gwjAnHeiRXohf7yR+w/oHH2cxHeK0iyuEnTvw5H0lrjWEYklQ5zxKJBJ2dnVnNqGmJ9FLauz8fip3IAYm1xJaeR3z3c6c8znGc4bGWGXkZvR+4TWv9I611DIaT0Z5N6v7kR+kdtda9pL78elce+ilETpRS5Uqp3fLdDyGE2NXlNbGJ1vrfWusztNZ66OcfAVWkvtU6QmvdkmOTvwberZR6QikVAv4GHKiU+j+l1BXA50ktDcuaUsqtlPquUqpNKRVRSt022XRppdStSik95vVQjn+PHcbr9XLbbbeNe7+3t5eHH354+GfHcQD44Q9/SHd393adM55MtfWPz/2A1+69k5UrVw6/nn76aT784Q9TUlLCxz/+cW677TZ8Ph/f/va3+e53v7td5xXZSS/hUEqNf0BzHIz1GyAaxetsILhXPeFjDp/WeeLxOD6fj5KSEimJnifJZHK4TPakAR6t8W59grf7TdANDOzeQPOR+2V9nlgshs/nkzLZeWQYBq2trfh8vimDeY5j09HVxz5GBTWlCShdQ3TZh7M6Tzwex+/3y1hPbHdSxR9GOgIIAm1a61fGbFsPZJetntzuWZRSj2a4X0m/jh3a54wJtjdn/TcWu4rPAutmqrFp3H8frJR6SikVU0qtU0pdPGa7XMsiK7MRrJzp63nEfkopdZ9S6sqZ7K+Y2wouc63W2hjKWj6dY/8OfJxUkCiqtX6IVNDnw6SqcfWT+6ygbwwdfzFwLKlZQLdPsv8+wJeBhhGv83I85w5z4okn8thjj40L2txxxx0cdthhQOrb93A4jMvloqKigi996Uvbdc7fP5GK26ml5VTudyi1tbXU1tZSXl5OWVkZ8+fPJxgMcv755/Pwww/zxhtvEA6HOfXUU7frvGJqjuPQ1dWFZVkZKxwFXn+JplfD9Piep6/uYIr2PxKnLLvS2COlS6LX1NTIjI48sW2brq4ubNuespKSr+1Ztr7+Ox72+PFUN7LvQYdSW12Z1XnSZbJlRkf+WJZFV1cXjuNktTyufe0aTn41yGGJemgMY1U2oYsqpjwunTutrq5OcixNzE1q2dVIJw79menLoDIglkP73yD7e5b3MvpepYnU7OnHgKeH9tln6L2GMa/WHPokxHR8gyyvZaVUDakcVi8DBwK/AP6olDplxG5yLYtszWiwcsg3mNnrOT378w/AaTPcVzHH7bAAj1LKVkp9YMTPztB7k73G3gRNSWv9B631Mq21PfTzZ0iVSD8QWKq1Xp1Dn32kPuRf0Vo/qLV+GbgAOEopdWSG/f3AEuB5rXX7iFdfrn+PHeXII4+kvr6eO++8c9T7t956K+effz6QehiPxWIopfjhD3/II488wi233DLtc57HegAaDzlg+L10SfS6urrhh81ly5bx0ksv8e1vf5tHH32UZcuWTfucYmpaa2KxGAMDAxmDLt1bIwysVNxRvZobvfvSX3XYtM6TLpNdV1cnuVjyJF0SfXBwcMpEu+6Btwm98F3aq97FBX0nsqSuKevz2LY9nHdHkqXnR3qsw+FwVsHUNWvfJvDiAPcWxXl7MRS//QO0b+oaabZtYxiGjPXU1gP7j3nvHFLLs+7OsP+pQ8dMKdd7Fq1178h7FeBDwG7ABSNyA+0NvDbmnqZda+1k0ychpiPXaxn4KDAAfFZrvUZr/Uvgr8D/jNhHrmWRF7NxPSulDiSVW/YEpJS7GGNHzuC5gVQp9JE/T/X6S64nUUodpJT6m1KqdsTb/w1cSerGJRf7A6WkSq8DoLXeBGwCjsmw/56kSs9nHUTKN6UU5557Lrff/k4Qubu7m8cff5z3ve99APT39w+X0j322GO54IIL+OY3v0lHR8e0zllmj8/XkkgkKC0tpaSkZPhcFRUV/PrXv+YrX/kKn/3sZ+Ub4VlmWRatra14vd7xS6aSDmptH6vLNuGu6+TrR5/FKcuym8ExViKRoKSkZNRYix3LNM3hkuhTLY/z9K9noO44YpG92FIax1hWnvV50p/rYDAoY50nhmHQ3t6O3++fdMlUXzzKQ2tfIbZmM+uqDN6zv5sTnEcxm44muizjzPBR4vE4oVCIYDAoJdEn9zfgYqXUu5VSxUqpzwPLSVX9/OfIHZVSFwGnAHdl2fb+5HbPMvJc9aTuk74yFOxJ25s5dE8jdhr7k9u1fAzw+JhgzaOkHqDTv5DkWhb5sj8zfz2fDDw+1PbAzHZXzHU7LPGF1vojY976IfBmOv/OTFBKHQ08SKqaVjWpGyaANuCDwAtKqaO01q9m2WR6Xe7YXECtwLwM++9NqorX1Uqp04f68XfgW1rrRNZ/kQzM299AR8wJt6sSH75zl0+r7fPOO48VK1bQ19dHRUUFt99+O0cddRTV1amlN47jjAquXHXVVTz66KN85Stf4Y9//GNO59Jao0n9vjr22GNHPQik/7ujo4NgMEhpaSlnnXUWABdeeOG0/m4iO+ngDpDxW35vS4yivgGeqV7NhYF6yoqnF2yLx+NoramqqpKAXZ4kk0na2tpQSmU1o0Ml46werGJev4fwwQ3gyu7hPV0Svbq6Wspk54lpmrS3t+NyuaacVfOPDc9QvLmTZYnFuJs7qV9zE8m6g4nvdha4Jr9ViMViuFwuqqurJZ/W1H5CalbOnaRm7ShS9w2Xaq1NAKXUOcBlwPGkClH8NMu2c71nGelLpO6Zfpd+QynlJvXF1UFKqVVADfACcIXW+q0s+yTEdOR6LTeTWn41dt9ioEop1YdcyyJ/ZvR6Brq11t9Pb5AvVcRY+bwT+w+p6hBfnsE2ryF1M7RiqPoEAFrrHyul/kQq+vk9UtW7slEMOFrr5Jj3DVLlTMdaTupmbQ3wK1LrfX9C6sM7LkOlUurjpHIGMX/+/Ek7Mt3gTTaOOuooampquOuuu7jkkku49dZbef/73084HAbA7/dj2/bw/qFQiO9+97t85CMf4a677qKmpibrc1n2O6vubrrpJiorK4lGo9TX11NSUoJSaqdctpPLWO9ojuMQDodJJBIT5mLx9G4gZt/P3pGXqFv4/5hOtDJdEn3+/Pk7dd6duTDWhmFkzLE0VvdrjxBYFaJOV/PmAg+HLc1uEmQymcQwDObPn79TL9cp5LG2bZvBwUFM05wyxxJAXa+LM/v2w71iEZ74QyR8pxDbe+z3MuOZpolpmixcuFByLGVBa20qpU4CzieVXDkM/FVr/eaI3Q4CjiI1Jf9yrXU8y+ZzvWcBQClVClxK6mHXHrFp8dBxfuBjgI/ULJ8nlFJ7a607x7RTsJ8HMefkei0Xw7hbE2PozyJyvJZBrmcxo2b6es6ZXM+7lnwGeILAxhlu8wDgf0cGd9K01n1KqeuAq3NoLw64lFKeEevRIfUPRDTD/lcCPxpx/teUUjbwN6XU5VrrnjF9+j3we4CDDz44b+Xb08u0brvtNs4880yeeuop/vKXvwwvwcoUGT7llFM455xzuPLKK/nOd76T9bkeeObn+EjN3GhubqasrIxQKERVVdVO/c1voYx1Julv+QOBwITLaMxIF2tLqlg971McvfCUjPtkc550UuWd+duGQh5rwzDo6OigqKhowjEY6FvN6jX3U9ZVSygeYHMwRtfS0zlteVVW59BajxrrnVkhj3UikRieDZlprNsGDF5ft5lQXyceO87y/iTrql5m3/6X8HW+jNFwxJTnSI91bW3tTh3Im2lDQZSbhl6ZfAf4+jRyg+R6z5L2blL3g38d08+1SqkqoD/dF6XUe4EtpPL1/HjM/gX7eRBzTq7XcnxoG2P2hVTRlW25XMsg17OYUTN6PU+nA3I971rymRThZ8DlSqmDZ7DNJKmlWRMJkdvfeevQnw1j3m9k/DQ7tNZOhuDSa0N/TjU9Oq/OO+88HnroIW644QaOO+44EonElA/h3/zmN3G5XFmXL7fsJKus1wl6Uzl44vE4LpeLUCi0Uwd3CplpmrS0tODxeCZdMtVpDpJ0QbN3r2mdJxqN4na7KSsrk7HOk3SZbI/HM+lMi7Vb/omny09pvIqBkEly0QEcvmjqJLtpI8daymTnRzwep729naKiogk/18+8PUCwvY2QYRO027ADG2luLsbxlZFoPgFj/okZjxspEong9XopKyuTHEszSGsdm2bi15zuWUZ4N3C31nrcg8NQImZnxM8x4G0K/J5GzHm5XstbJ9g3wlB+ErmWRR7N+PUsxGTyeUd2MKmL9TmlVEQptVkp9faY14apGhnjEeAzSqlFYzcopZpIrWl/NIf2VpGaPn3ciHYWkqrK9XiGc9yqlLpzzNsHk5pWl1UVjHw56qijqKys5Oqrr+aMM86YsEz2SJWVlXzrW99i8+bN47bF43EeeeSR4ddD/3mQx77+Bz658UKKGlP5rzs7O1FKMTAwQHt7+/Crv79/Nv6KYgzbtunr68NxnEmXcPT952EattTSavhZWJ57YmXTNLEsi4aGBlnCkSeWZdHb24vWetKxDqz/B1XrwuzbcyTuA49g2ekXcPj+yykvzi6HjmEYOI5DU1OT5FjKk2QySX9/P47jTDqDKmE5VPhcVFd2ckz0Tyw95kSK9jmXxJJ3k1jybpzAZN+VpGYIATQ1NUmOpRwppQ5WSj2glAorpfqUUncP5RDMtO9FQzOBs5HTPcsIxwAPZzj3e4b6WDPivVJgKfBGln0SYjpyvZafBI5Vo7+ZPAF4SmvtyLUs8mxGr+fZ66bYWeTzq/QA8OIMt/k14HlSS6PuBdaRSmK4mFTeHQ18JdvGtNaGUuo3wI+UUt2kEhD+BnhMa/3sUNm7SqB3KDnibQwtxyJV9eIA4Eeklm1FZuovORtcLhfvfe97+f3vf8/hhx+e9YP4WWedxT//+U/uvffeUe93dHTwwQ9+cNz+Tz75JImhZL7pKl1jnXnmmdx9d6ZqsWKmpEui9/b2TprzaFtfgt6OLl5uGGDpoQewV9XUuTxGchwHwzCoq6uT4E6epMe6v79/0uBO19ZNxF+DUuMkXt3T4eDdx1e7m4zjOJimSX19vQR38iRdEr2vr2/SAH3Lm73svq6NZjzYxYOED/1frMo9sz6P4zgkk0nq6+sluJMjpdRhwGOkptk/SGrW8enAaUqp72qtvzbdtqdxz4JSqgGo453ZxiM9BgwCf1FKXUHqnvE7QDfTqHIqRLamcS3/EbgCuFYp9TPgJOADwGlDTcq1LPJmFq5nISaVtwCP1vr4WWjzLaXUQcC3Sd0wpSMIceDfpPLzrMmx2SsBL6m16V7gfuDTQ9uOJDVr6ATgUa31rUqpIuCLQ33oBH4OZLeGaQfbtGnTqJ9//OMf89nPfhaXyzV8097S8s7MwfPPPz9jO9ddd92on7/whS/whS98AYDN21pJ9K7n+dZ/cUnyo9iLFlFbW8umTZtoaGiQB8E8MQyDtrY2fD5fxge0ZNJkoLuNN9/ayGK/jTsQZGnF/jmfJxaLUVRURGlpqSzhyJN4PE5bWxt+v3/c8rjoQC89ramJkvbGAUyni446m30POjLn88RiMQKBwHCydLHjxWIxOjo6CAQCEy6FNI04zludDNS28HZzGUf1bsX07J7TeaLRKMXFxTLW0/MtUlPyj0gndlVK7QvcAHxFKVWntf74drSf9T3L0HvpZQAT5S48CfjB0P4eUkGpFdtbGVTslNTQa6bkcv/doZQ6DfgFqepDm4GLtdYPg1zLoiDM2PUsxFTyFuBRSj0MfFtr/Z8Jtp8FfFdrvXcu7Wqt1wPnD01rqwLcpMrJZTvFeWx7FvCFodfYbY8y5h8zrfUNpG7U5hTLsujs7ERrPWOJUbv7+2n6Txtdvq2c6TkF9/4NROJxHMehtrZWgjt5kkwmh5fHTTTWrS+vpPTtGMvoxO0pomr+fLyu3L6pT5fJlm/588c0Tbq6uiYsk93y0ANUGCHirgRuNF61ht33vgx/jp/NeDyOUor6+nrJsZQniUSC7u5u3G73pLPl2p95GWVabKzu4XhzC77+dcT2zT6eEIvFcLvdNDQ0SI6l6Tkc+MbIqj1a61eVUkcB/wL+Syllaa0/NZ3Gp3HP8vLY98ZsXw2cNZ2+iF3OT4H/m6nGpnEtPwscOkl7ci2LbM10sHLGr+cx+y7c/h6KnckOuxNXShUzOgHy8cCdSql1GXZ3kZqBk11N3gy01prU1EsxBa01kUiEaDSaVTndbISNBL9c+Qcu8+7Gnk2rGDj2B8QsCzMapampSSqu5InjOEQiEWKx2IRLOFYPvEl79xr2sV7Gtf+7adhnRc7nsSwL0zRpbm6WQF6epEuix+PxjGO97bVVLIksYcOyXnY/5NyhdzMvm5xMMpkkmUwyb948CeTliW3bRCKRCcd6JFdS82xNKe9119L4xrfoO/E3OCWNWZ0nmUxi2zbz58+XQN70uQBr7Jta66hS6kxSswo+oZSKaq2/uMN7J8Q0aa0HkASwYucwo8FKIXa0HXmHFgReAdKJHTSpSlo/m2B/RepGZ0JKqbeBz2mt/zni56lorfXiLPbbZRiGQXt7O36/P6dvZM1YPz2vPE5/wiI1nO8YtEyOS1Swu0vTf/R3h89TVlZGcXGxTOvPk0QiMVxdJ9OSqb5Yktc2vM7pvWUkm86kcRrBHUiNdUVFhYx1HsXj8eHlOpnGWndGWFv6FKHS4zIcnb30WAcCARnrPEkvzQoGg6PGum3AoHXAwA734OvdhD/ZS1VvBQHvZhoSj9N/zPdI1uyX9XkSiQQVFRUSoN8+LwOXKqWu1VobIzdoreNDQZ5HSVUZdQGv5qGPQgixy5JgpZjrdliAR2vdpZS6iNR0MwV8HbiTzDcvNtAF/G2KZjeTSlSYtoWxkQYxqXQulqmm9WfSv/ppijb5MT0D+D2jHyDLgL3cZST2XQQuN7FYDJfLRVVVlXzzmyfp4M5kZbIfeauPPTYm8bjcePbZf1rniUajw2MtSzjyIx3c8fl8E461cjRKGyjv9B/WI5EIHo+HqqoqybGUJ9FolM7OTvx+/7jZcre+1El1iZdDt23Bbbdiu5L0+uLs4W9F1e+HWbN/1ucJh8P4fD6qq6slkLd9vgXcC6xRSt0C/Exr3Z7eqLUeUEqdQipv4OeA/nx0UgghhBBz0w590tZa3wfcB6CUWgBcq7V+bjvaO2HMW+/VWo9LFCgys22bcDhMMpmc1tKsSCRBvztG/KCz2G9xecZ9EqTygJimyaJFi6SSUp5YlkU4HMayrAnH+umup6BzPcuiexA/aIBQTW3O5zEMY3gJhyzXyY9sxhrAFeulLrGGmP/iaZ0nkUigtaa5uVmCtnmSTCYJh8PYtp1xaVYi6fDxjueJRqrQJbfiOvPPaH85AMa4vSeWSCRQSjFv3jwJ5G0nrfUDSqkVpGYv/w9wXYZ9upRSx5CqsvJB5IsrIYQQQmQpb3dqWuuPbE9wZwKvKKWunOE2d1rxeJzOzk58Pl/O38hG+jqwOtZSVOTmqAmCO5DK75MunSzBnfxJj7Xf78841ht74mx5cy2nty+krOhWqqqrcj6H1ppkMkldXZ0Ed/IoGo3S1dVFUVHRpJ9rnbS423ceTrAu53NorbEsS8Y6j9K507q7uzMumXqjNcJufc+R6A3x0+WPEj3rr8PBnVzPY1kW9fX1MiNvhmitH9NaH0AqL+HGsduVUrVAXGt9MbAcmFbCZSGEEELsena2r+KqgY58d2IuiMfjw3l3ppMEt3Ptc7jw4atomHS/SCSCz+cjFArJN795Eo1Gh/PujH0Y146DlYzx2Oo2Du4qwijrxDzoVJJVORWvA2SsC0EkEpmyTDaAY1k4FiysL6ehLPfAazgcxu/3EwqFZLlOngwODtLV1UUwGBw31o7W/OOVTo6jmC0LOzl50VkEPZMnX57sPH6/n9LSUhnrGaSUugx4gsz3YT8DWpVSn9dar9Za/26Hdk6ILCmlfqyUukgptZfK8AtCKdWklJqZ0qxCCCGysrPNq78J+KhS6p9aawn0TCCZTNLb24vWmuLi4pyPf2VbmIE1cU5Wx/FyVWLC/dJlshsbG2UJR56YpklfX9+EJdHvfvEKmrftxfuiu5F0V+A7dilWbe7F69Jlspubm+Vb/jwxDIP+/v4JS6KP9PY//k6NVcvypYtxu3J7aI/H43g8HpqbmyWQlyfxeJyBgYEJc6f99D9beW9kM17tofbAY2kqrZ/WeWKxGD6fj/nz50twZ4YMPQT/mdTSqz5gAbBhzG5vAycAP1JKHaq1vnDH9lKIzJRS5Vrr/hFvfZ53lhDGlVKvkkokvnLodQGpa/mQHdlPIYTYle1sT90OsAzYppRaD3SSStg8ktZan7jDe1ZATNNkYGCA0tLSnI9196+nfuWf2d19FA/WvYyndq+M+9m2TTKZpLGxUZZw5JFhGAwODmYc65Y3XmPx1sPYfWBPwmcsprI+92AfpMbasiyampokuJNHiUSCwcFBQqHQpPu1P3MXC2IL2HZYkEV183I6h2VZ2LYtgbw8i8ViRKPRjJ/rh9/qo753HXsmygnV/oaI/8hpJXCxrFQl76amJgnuzKyPkgru/Ab4H631uG9JtNZXKqW+BVwLfEgpdZ/W+oYd3E8hRlFKfRj4HjBy6nYlcOCY1ydJFVNJ/+qJ7cBuCjElpdT7gKNJFfq5QWttjdh2j9b6zLx1TogZsLMFeE4Guof+uwiYn8e+FDSfz5dbSXQzQW/bNtT6++hP7klPhWLPw06hsqQx4/6xWIzS0lKCwaA8HORZprFWCZvA2igD9ho6DlpGU+30KymlHzSlJHr++Xy+SWfVhBMWzrYkj5ZsZb/Gc3JuPxaLUV5ennE2mNix/H5/xrF+ekM/H3f76TsuQM0r3YRduS/BhdQsofLycimJPvM+Cjyutb5ssp201gml1KXAvsAnAAnwiLxQSi0Ffg8sBkZl5R+azfPw0Cu9fylwBvANUqkTPrODuirElIaWx14J/BP4IvAJpdTpI4r0HJO3zomsXHzfRfQb/RNu3/dwOPsfv8m4rdxfzg2n3zhLPSscO1WAR2u9KN99KHSGYdDd3Y3WuX2n2/HaGwTWDhCjAsdbg3eP3akLLci4bzQaxePxUFdXJ9/y51E8HqenpyfjtsALXbTZUfx6M42LylHTXGoTjUbxer3U19fLcp08ikaj9Pb2Thlg+8tz7Zxtu4hW1FEeyO3XfzrHUl1dnQTy8igSiTAwMDDhdssBn6NY8NbPMGv2A3duOZbSyZvTYy1m3HJSDxdT0lo7SqnbgP+d3S4JMakLgf2B/bXWm6baWWsdBm5RSt0DvE4qyCNEXvT391NeXj7yrcuAU7XWq5RSHuDXwMNKqRVDQR65wSlwkwV3ZvPYuUSeyHYh6bLo8Xg852/gBwdNXlVvE9jNxbL3ncLSJZljaaZp4jgOTU1NkncnjyzLIhKJkEgkxo31f9ofYl3Hy9xV/Q/2S76C2z+95KumaQ6XyZZAXv6ky6KbpjnpbIt7X+9mv9aHmJ9YwOmH7I7Pk/2vf8MwhstkS3Anf5LJJAMDA1iWNWFVwuMSbxOK+SjqepnBw6+CHMfLMAw8Hg/z5uW2fE9kzSK3KvXdpJafC5EvN5MK1DyulFqR7UFa6wipmWefn62OCTGZP//5z+y117hUEg1a61UAWmtLa/0JUjPQHlFKVcG0VjULUVDm9BO4Uuph4Nta6/+M+Hkqu2wOnng8TkdHB8FgMKfZFuGeBMWdmt0CPcyra2bitMqph/6qqiopiZ5n0WiUzs5OSkpKRj2Qb+5N0LJuDe8O74mXS2g796eE/NP7NWAYBtXV1dOqwiZmTjgcpre3d9xYj9Qaa2Ftx8O8x11C9JRyAmW5Jd1NJpPU1NRIPq086+/vZ2BgYMKx3hzdzFLTTVXNr+g8+TFw5RZ4dRwHx3Gorq6WAP3sWQccnMP+hwBbZqkvQkxJa70WOFop9VFSwZ5cpvZ1MTpnjxCzbu3atXz84x9nw4YN3HDDuNWt3UqpRVrrjek3tNaXK6V+BjzCHH82nnE/rIdo5rpFm4pILcScSLAOvtg+G70SU9hhF3GWwZexpgrGLAKCI37eDYm8ZhSLxejo6JhWWfTuV7px617s0m7MuonvS8PhMD6fj8rKSvmWP4/C4TDd3d0Zy6L//aVOLuhpoq00wn577k9J0fRm3gwODlJUVCRjnWeDg4P09PRQVFQ06QP5fW330mA8Q73rA/jLmnI6R39/P8FgkIqKiu3trtgOfX199Pf3U1xcPOFY377l73zIPgmXHUV7cpulqbUmHA4TDAbHTmcXM+tvwLeVUj/RWr8x2Y5KqeXARcAvd0jPhJiE1voPSqk7R76nlOoiVS3r5fRLa71+aJsCzgRe3NF9Fbu2m2++mVdeeYVXXnmFhQsXjt38H+AS4KqRb2qtP6eU+gWw9w7p5FwxQXBn1o+dgxI/fgqiyYzb1hQHSXzz0cwHBr0UfeGoGe3LjoxSrieVXFADraSmKW+XsTl3tNYLt7fNnVEymWRwcBDbtikpyW05jm9DmMUtnfyu8VHetccZOIHMS6kTiQRKKebPny+5WPLINE3C4TBaa4qLx1fFCunv4+Y0Ss44iVOKp5coN10mW0on51e6QppSatKlWaGnv84x2xazb/g8vJVLCPuzD/DGYjH8fj/z50u++nxKJBJEIhHcbveEAfo/bfgDp72+L/XJbtAOqOx/D2uth8dalmbNut8BHwceVUp9Dvib1npUtU+llItUeekfAWHgZzu4j0JkpLUem9jvKVL5eU5K76KUCgNrgJqh14eVUl6tdeYnHyFm2IUXXsiDDz7Isccey/XXX8+KFaNWFl7GBM+/Wuv/Vkr9aId0Uux8JgjuzNpxk9hhAR6t9ceVUs+RysT/kNb6I7N9zqG1lPZQlv9dUjphZnd395TlkzMJD/bw79on2CO0Ow1NmZdeO46DZVk0NDRILpY80lozODhIX19fxrH2bo1wyOD+LGVPwv7pVcZxHAetNQ0NDRLIyyOtNf39/QwODmYslT1SYpuL+fHldF2wF6XB4KT7jmTbNkopGhoaJJCXR1pr+vr6iEQiE471c48/x+79fvYdKKei9H/oOuuxnM5h2zZut1vGegfQWkeUUmcDd5HKT/IbpdRLQBvgBmqBg4ASUkuzztFat+Wrv0JMRmv9HgClVDmpQM8BQ3/uD8wj9ZxxG2AppdYCrwGrtNbf3+GdFbuMpUuX8uSTT/KHP/yBCy+8kI6Od2aSaK1NwBx7jFLqceAmrfW1O7CrQsyKHfqEprX+I3A1cLFS6tzZOIdSanel1I1KqX6gE+hRSnUqpX6rlNrlSoKEw2E6OzsnndY/mc5oOxWmwwJP86TnKCoqynl2kJhZ/f399PT0EAwGMwbavG8O0O7rZOCIELin9xAXiUQIBAIEcwgUiJnX09MzvHRqsqBqMmlC5BDuWLSS4kD2ebHSMzoCgUDGmWBix+nq6mJgYGDC3GlJ02C3rRb7Ga/Tt3ec/hN+llNiZcdxiMfjBIPBnJPvi+nRWr8F7EeqRO9bwNGkKhW9HziS1FKXzwF7aq1fyU8vhcie1rpfa/2o1vqnWusPa633IxWkPBj4GKmZa/2klmx9J389FbuSj370o7z55pvZ7n40cIpSqkgptYdSKrf17EIUkHwkkvoWcC7wA6XUnVrrGasOoZTaD3gMKAbuI5XM0A3sTmp52FlKqSO01ltn6pyFLB6P09fXh9vtnnQJx2QS3Vtoim0jVH9Wxu3RaBSfzyfVdfIsFosxODiI1+udcAnH1q44axve5pTm8mmdIz3WTU3yb14+RaPR4XxXUyU97r/7eToCCaINDi6V3ey6kWWyGxsbZ6LLYpoikQiRSGTSHEvxO19moy/B6UUb6Dn8OHKZ6Os4DpFIhOLiYimJvoNprQ3gJ0MvlFLVpGYc9+W1Y0LMkKGZEuncPMOUUovz0yOxK6qqqspl9+OAPsAHoJRqB24BfigzKcVcssMDPFprRyl1KFA0k8GdIT8A4sBhQ9+QDVNK7U0qO/r3gQ/M8HkLTrokeiwWm3IJx+QNOcQbjmJR45JxmywrlUapublZgjt5ZNs2AwMDxOPxSWdR+Z02uvwWbnfuFc5M08TlctHcPPFMLjH7bNumv78f0zSnnDHX8u9baBhchNf8Fh9d8nLWn1HTNPF4PBLIyzPLsujt7cWyrAlnzG15/GnKLcXf/PM5fRrnME0Tv99PfX1uVdXEzNNad+e7D0LsCFrrDfnugxATKCeVM/ZRwA8cSmo25UeUUudqradTMEiIHS4vpeCGvrkyZqHpI0iVTX9r7Aat9etKqZ8Dn5+F8xacwcFBuru7cy6JnhYdNOnZtJWipA/bm3lGSDwep7KyUkqi51lfXx99fX2UlJRMONauSBI3Dp/b8ws5t+84DqZpUllZKSXR86ynp4fBwUGCweCkAZvwltco76zjl8VRPnLWvYSyDO6k82nJWOdfV1cX0Wh0wpLoAKE2+LM/yvcP86BeSuTUvmVZwyXRZayFENmYZkXcsaaqkCtEvjwKnKq1Hi4ENDQp4Q/AP5RSB6arxAlRyHa2LKlhYLI1CwZgT7J9pxAOh+nt7c1YJjtb7Y+34X/9LaLuCFUL98p4Dp/PR01NzfZ2V2yHgYEBBgYGCAQCk+ZYKn6ygzeCb1FZuSyn9tOlk/1+P9XVmSuoiR0jnVR5qrEGcJ5sY51vK81LlhOqbMiqfcdxhvNp5TilWcywnp4eIpHIlDmWEpbD55M/prL138QXvzvr9m3bJhqNUlxcLCXRhRC5cAFqO18727OH2Dk4wB0jgzsAWuvnSS3d6ge+kod+CZGzvMzgmUW/Av5HKfUvrfWqkRuUUvOBzwC/yEvPdiDTNLFte1qJcN+8+2809VezyCrh2vkP8L5l51NTP3q5dCKRGC6TLfLLMAy01pN+A3/7lttY0VPJ3c1PcLDr4zm1H4/H8fl8sjSrAMTjcZRSk471tr9/Fts6iGp7Pn/2Hs5PD6jNqu10UuWioiLJu1MAYrEYbrd7wkDeT/+zhU2eO/mGXk5ZSTE9h30167bTY11cXCxLs4QQOdFaH5/vPggxS3qBykwbtNZ9Sqk/kUoYLkTB29kCPC5Ss3heVEo9AKwmVQpvMXA2oIHFQx/SNK21/q8d3tNZ5vF4cs6L4+ozmNdfTeuemrKDD+dTHD5uH8dxcByH2tpaKYleINxud8ax3vZaD7HWF/E5nSyIV3DVMX/Oqd10mWwZ68Ix2TisGVxN0vGxWO9B8YcO46c5tGvbNh6Ph5qaGhnrAqCUmjC488KzL7F/73r2LStiseXQc9btObVtWRY+n4/q6uppLd8VQgghdkLPAx9TSv1caz2YYXsPINObxZywswV4rhnx32cMvcb68JifNbDTBHj6+vro6emZ1tKsojUDrCragrds94zb08t1gsEgoVBoe7sqtlN3dzf9/f0TzugofqmH9VVvsKdqYut8h7IcHuYcxyEajVJaWrp9SbrFjOjq6hpenjWRu7beyRfjp9B2aDG5FDa3bZt4PE5ZWdmUiZvF7Gtvbx9ePpXJHuuSRIrWE/QsJ7F/bp9Ny7JIJBJUVFRMa4anEEIIsZP6IfAf4Eml1Me01s+lNyilikgV6JFKWmJO2KkCPFrrXfrryEQiQTgcxuPxTCvx8YYtz/JU5SrOrTpp3DatNdFoFL/fL0uzCkAsFhsuWz42wFN63zY2d8XpLt7GxrJVHH/s5Tm1nR7rQCAglZQKQDQaJRqNZiyVXX3Xu1kfvgyAT+uD8eoITcv2ybrtdCCvpKRElusUgEgkQiwWIxAIZJxJ9aknv8Y17rPYp2glPSu+SiyHth3HIRaLUVZWRm1tdkv3hBBCiF2B1voxpdR/Ab8FnlZKrQdWkVoJchzQCHwnj10UIms7VYBnV+Y4Dn19fcOzLnIVePU56uMVnDDvImoqxwdwLMvC4/FIfo4CkB7ricqitw32cFXjizRUvMFPN7bQlWP7yWQSn89HXV3dzHRYTJvjOPT09GAYRsax1i4PCRWg/qKjgdwzyJumSSAQkGTpBcBxHLq6urAsK2OAvujVXj7SsReLlUlq4mluDMOguLiYysqMKQaEEEKIXZrW+s9KqceBy4GzgPcNbUqQyuH6jTx1TYic7HQBHqXUh4BTgAYyZ+rfKcsz9vT0MDAwQHFx8bTyKnjWtPOv6jb2qDpr3DbHcTAMg/LycoqKimaiu2I7pJfrBIPBcWMdj0aIWWE+tHQ/9n31X/Sd+Juc2rYsi2QySUVFhYx1AUgv15moVPaAhuk87EMqkGfbNuXl5dOa8SdmVktLC4lEgtLS0oxj7d4wwMbAq8T2X4pd8+Oc2jZNE601oVBIxloIIYSYgNZ6I6miPJ9RSpUDpUC71jqZ144JkYOdKsCjlPo28L+kptN1sguURIdUyfJ0eePplkVfZzusLo5xQmh0/jDHcYhEIhQXF8u0/gIwMDAwPB4Zk7De9QZv+g3et/IHGA1HEK5YmnXb6eU6oVBISqIXgP7+fuLx+KSlsr9aXsrnY7nnUrFtm1gsRkVFhczoKAB9fX0kEglKSkoyB+i1Zou5lVLXBszFB+fUtmVZxONxqqqqpCS6EEIIkSWtdT+p8uiigPzrnHu3r4HpfS86pcBVJ0z7WP31me3UThXgIZVA+QHgXK11LukJ5izLsujv78eyrGklzdzy4ot4N/TSYMzjv5YfSKk/NWtDa00ymRxeLtDQ0DDTXRc5SiaT9Pf3o7UeF9wpfqaTLRtfxOP42T36ByhNEj70f7NqNz3WyWSSQCAgS7MKQDKZpK+vD2DCakp/23QT57evYF5JDeEs202PtWmalJSUSCCvAJimSV9fH263e8JAnnY0Lpebo3qz/2fNcRwsy8IwDEKhkATyhBBCiCFKKYcZeNTXWu/UpUfV1dt3vP7GjHRjlLPuzFRDKf/iVz+S7y4M29kCPCHg9l0luAOpSkqRSGTCJRxT8bTE6bP/g+uoc6mrWzD8vm3b2LZNaWnpds0MEjOns7NzOO/O2LGOtfZzY91dfHifr1Eb+BU9RRNXWxormUziOA6hUIhAIDBhQEHsOG1tbZimOWHQ1t2dQG+NcdBgM+H3Zp8I2zRNAMrKyggGgzLWBaClpYVkMjlpBbOOzRtwaRPlyn68TNPE5XJRXl5OSUmJjLUQQgjxjkuZtbkcOw99Vb57IKZjZ7vjux9YAfwh3x3ZUbTWE1ZcyUYy7lDr7qWqcdHwe7ZtE41GKS8vl9kcBURrPWGOpbZEG8fE+yivbU7tm2Wb6bLJlZWVsgSvgEw21gDBR9qo9VRA4CnwnJZVm8lkEsMwqK6ulpk7BSYYDE4aoK9+aoB2179I7Hkx5Vm0Z5omyWSS2tpaKioqZqyfQgghxM5Aa319vvsgxGzZ2QI8nwEeUkrdCPyDVB6ecc+6WuvHd3C/ZoVhGMPfyE/HuttuYoGxiHr/enr9qcpbI8smS3CncCQSCSzLyvjAX3L/Ngx3DycWVdKXQ5vpsS4rK5MqSgUkHo9j2/akydK36XbWBu/CHyohnkWb6bGuqqqiqqpq6gPEDhGPx3EcZ8LtjtZ89a71fNaVoMZZi/+wn0zZZjpAX1tbKzl3hBBCCCF2MTtbgGc+UAZcCFyQYbsiFfCZ8+sltdZ0d3dPWCp7Ktve6GVhdD6xdzXSW/XOmkHLsggEAlRXV0+rGpeYeVprOjs7MQwj45KdzsE+bq37BwtPuCGndpPJ5HDZ5Oks7xMzT2tNe3v7hKWy02zL4IOtPfRfkt2YG4ZBaWkp5eXlMtYFQmtNa2srjuNkHBOtNQ+u7qWsCIqSLhqLiujJot10xcNQKCRjLYQQQgixi9nZAjy/BsqBHwJrASuvvZlFHR0dw9WUphOI8b7cw6PVT3B86WXD76WTr1ZWVhIIZJ/DRcyutrY2YrFYxrLoAAPJPj4UyWYexztM08SyLKqqqqQcegHZtm3bpKWyAbTjoByT6BFfozSLNg3DwHEcSktLpUR2AdmyZQumaU4YiDEtzb/f7OUTR1agn+lEe6b+nCYSCQAphy6EEEIIsYva2QI8ewPf0Fr/IN8dmU2RSIRYLDbthLju/7sdv7eItWXrOMmTmhEysmyy5OcoHOFweMpS2QGdYF7dgVlXUrJtm3g8TnV1tVTWKSCDg4OYpklpaemEQVutNf9982quVg71exw+ZZvpEtl1dXWyXKeA9Pf3TxrcATCMOL8cOJ+vv30YXzROx1x40KRtJpNJ4vE4zc3NlJZmE/oTQgghhBA7m50twLMVmDihwU7Atm26u7unXMIxkcDa24gqDz9d9gxXHfAHlHKhtcYwDEpKSuSBv4DYtk1XVxda6wkf+B3HQaNyKokej8cJhULywF9ALMuiq6sLYNIZeTc+38GnvKvALp+yzfRYV1ZWUlZWNlNdFdvJsiy6u7vxeDwZgzuGabL63icJGZ10l/wPHxpooLJuDyIHnT1hm+mxrqmpmbDymhBCCCGE2PntbElWfgB8Tim1LN8dmS1tbW3E43GKi4unlV/BXPM3Or0VfHLPT+FSqeFPJpMopQiFQvh8vpnuspimbdu2YRgGRUVFE471+r52VA5VHg3DwOPxEAqF8Hq9M9VVsZ22bduGZVkZl8u5wkn6W6NsWr0Ka9tK5pn1+E/Yfco2E4kEPp+P0tJSKZFdILTWbN26Fdu2JwzQb1vbSUVCUe17Gu/+y6k7eF9iR0xe4S4ej1NUVCRjLYQQQgixi9vZ7gT3J5VE+VWl1Aagg/F5eLTW+sQd3bGZorWeMBdLNn5YEuCSaDGVxY3AO0s4ampq5Fv+AqO1nnS5DsDPV/+MXzjHZ9VeMpkkkUhQX18vSzgKjG3blJSUjAvkqYRNyT1bWRmP4ytZw8naw2BjhMb6FZO2Z5ompmnS2NgoMzoKjGVZE37+wj0J5j0f5uGF7VyApn+P/YAMpSBHMAyDZDJJXV2d5E4TQgghhNjF7WwBnneRCuhsBXzAvPx2Z2alE+NOlIslk9Cz1/Da5maCVj0AF7ouZJG7mj7HIZlMDldcqaiomK1ui2kwDAPbtif9Nt7RDp9pOZkaFaV3kracobFOJBJUVVXJ0qwCYxjG+PcGO4ne8yJu28tqXwcPLXqQJl8xFxz520nbsm17OGhbW1srgbwCk2msR4pEY2wObcZ23YfjnT/pviPHur6+XgJ5QgghhBBi5wrwaK0X5bsPs6m9vR3TNHO6kfe0dhJKnoTv7D0ACLq8DARKME0Dt9tNVVXVpAl8RX60trZi2/aky/De7HuTxmQRVP4ZeM+E+yUSCfx+P9XV1ZSUlEx79peYHdu2bRsulW0acdY/dT/eWJwit5f7avbmA4cfxKc978Lnnfpzn0gkCAQClJSUTDn7S+xYWmtaWloyV8yyLe7d8DL1m0zKSXChLmfwiKsnbc8wjOGxls+1EEIIIYSAnS8HzzClVINS6jClVJlSyqeUmtN/1/b29uFqSjndyA++h/sq1xIMVRIMVRIoKcWwTJLJJKFQiJqaGoqLi2ev4yJnLS0tw4G8CUtla83tm2+hKtlLdJ9LJ2wrkUjgOA5lZWXU1NTIEo4Cs3XrVpLJ5HDQdstrz9DYWoJjarZUBjl+nwWUlFRSVFSOyz15zqREIoHWmoqKCmpqaqRMdgFJ594xTTPj79s1Pa28vvVx9u0qokE/SHzZxeCaOOgej8dHjbXkThNCCCGEELATBniUUkcppV4CtgFPAwcBxwNblFLvz2ffpisWiw2XRc8luOP901286YvxQqhj+D3LsohGo1RXV8tSnQIUjUZJJBKTBndsyyF8/Vo++eZp+OjGmJ85H4tlWcRiMVmqU6Aikchw9TqArWteYv6bRbSGutjjvRdx1ElnsLQuu+BreqxlqU7h0VoTDodJJBKEQqGM+1jROF9qOZF5+l4a99iHZN3BE7Y3MpeWBOeFEKKwKaUmfQkhxEzbqQI8SqlDgIeAUuBnIzb1AkngJqXU6Xno2nZpa2vLKffO4N33svGOm0m4gvzffq/y06OvGN5mGAZVVVWUlpbKPywFqL29fdKy6Nq2id15N6ZvE/9cegfBhscy76c1iURiuGyyjHVh0VrT1taGUgqXy0Wip4eyFzeyIbiRZWd/IOe2EokEtbW18sBfgBzHob29feKy6LbB5u5XaPWvJ7nYIbrv/5uwLa01hmFQW1srs/GEEGIO0FqPei340t2jfhZCiJm2UwV4gG8BG4H9gO8CCkBr/eLQe6uBr+Std9PkcrmyKotuGnHi0TCN7V5eCnXx+l5xLljwzsNiPB7H5XJRWloqU/oLlNZ6wgc324wTbt9G7YCfOxe8wDmVRzB46Fcz7ptIJHC73ZSWlko59AKllCIQCGAlk8ReexW3drNpj9w/l7FYDI/HQygUkhLZBUopRVFRUcZtXUYXdqyXJjNMdJ+PTdqOjLUQQgghdmXl/vK8HDuX7Gx3iEcA12it40qpUV9la60HlVK/B76Zn65NT39/P7ZtZ/WQ7v7bKsKefh6v6sKzRzP7zTtreJtpmiQSCZqbm4eXhIjC0tfXh+M4GbfFe7ZR/K92TE8399VvY/GSI6iqPxErw76maWIYBvPnz5cZHQVIa01vby+2bdP+9jqqn+wHV5x/N7aypPmYnNoyzVQ+rcbGxgkDCCJ/0mM90ecaILp+AxduOIDupkE8JU0T7mcYBpZl0dzcLPmVhBBCCDH7gnUQ7Zh6v4mOnQU3nH7jpNsXfvkeNn3vzFk591yxswV4ACarQ1vEHJq1ZFkWPT09uFyuUUt2kvE4rr/fQ6sPHFLTOxWKgKeYmovOoAo4cGhfx3GwbZt4PE5dXZ0EdwpUMpmkp6cHn883aqaWSsbwPnUNrW2H0h5Icl/9kXzquNMytpEe60QiQV1dnSzhKFCmadLT04Pf76ezq52obxXuKs05J34m6zZGjnV9fb2MdYEyDIPe3t4JAzKRvg5cb97NW6WHstup52XcJz3WhmFQX18vwR0hhBA7palWKsiStjz4YvuEmySQUrjmTLAjS88BGRNYKKWCwEeBF3Zoj7bD1q1bsSxr3A196/OPYmsfjyxqhX3mwT7z0Ps0o05YNq6NRCIBQCgUoqSkRMqhF6B0hR3HccYvnYu0sbEvSMJVivuoI/jw4Q0TthOPxwEoLS2VsS5QjuOwbds2lFJox8Jq24bpC9B0zH/l1E56rNOfaymRXXhs26alpQW32z3qc23Zmrfb+9jw8qNseeEeLM8iPJUTf65jsRhKKRlrIYQQM2KqxM/5yts4Wa4iCe6IghecZkqM6R43iZ1tBs/XgUeVUo8BdwEaOEwptTfw38ACYOIMlgXGcRxKSkpG/aLt3LSW+s0hnmx+i/2WHsuCiv0nPN4wDGzbpqGhQSrrFDjbtjPOrtrW1kll+ATe2LOYg5vnTXh8uhy6lL0vfFprgsEgbz10B3WRanqWefEWZT9m6XLodXV1siyrgKVvSMd+Hl9vi7D5mQdZYVTT7Qui/E34FmWexhyPx1FKUVdXJzN3hBBCzIixwRKZiSHE9iv6wlETbtvRn7GdKsCjtX5GKfUu4FrgR0Nvf3vozzbgAq31I3npXI7SM29GUoZN8/MWqwIbWHHipZMeb1kW8XicxsZGeeAvcOnZGGNFBvsoe0WzNbCegw/98ITHW5aFYRg0NjbKUp0CF4vFUjO11r/BfltreLxuM4ce+MGsj0+PteRhKWxaa+LxeMZvHBOmQ3Opi8HiDhadeeGEbSSTSUzTZP78+ZIUXwghhBBCZGWnCvAAaK0fVEotAQ4AFgNuYBPwotY6U07agtTS0oLWenj2jmnEaX3wQTzK4anKZ9mLiyc93jAMysvLpUR2gdNa09raOm7ZRSyRYMMzrxL0deB3r520DdM0ZaznANu2aWtrw+12Y7a28ExFK2/Oa+LQHNowDIOKioqsquqJ/LEsa3isR4oaNqu39XBM3A/F5qRtGIZBVVUVRUVFMtZCCCGEECIrO12AB0CnvjZ9eeg152zevBnTNCkrKxt+r23tq1T0B3m59jUObjp70uPT0/orKiqkRHYB01qzZcsWksnkqLEG2LRpKw3dCrf3EZwDJ15VGI1GcblcVFRUSNnkAuY4znBOrbKyMt4O9zHoMjm+eb+s20iPdWVlpeRXKmDp3Dta63Ez6t5oixIYbKMsWUxpZfeEbUQiETweDxUVFTLWQggxl/ywftKqQ5uKgG9MsDFYN2lSWyGEyIY8ERYYrTXJZJJQKDTqfcuI0e3r5oRTPjfhsemSyaZpsnDhQlmuU+C01pimOW6sAVSkk6TrLRbsexzx3cYHAdIlky3LYuHChZKLpcA5jjP8uQ6seo5lnU1sna9ZXFE75bGJRALbtrEsi0WLFslynQKXHutMObUMy2GJvxc7tpay2n3HlXxMf6611jQ3N0uAXggh5prplpTe3mOFEGKIBHgKjOM4o5ZmxcN9dD3yO+oHlrAtYE94XDpYUFVVhd/vlwf+OcC2U+OZafmFMsLEvcXE97xg3Lb0WNfU1ODz+SQXyxzgOA6QGuu+ri4eq2qlq35pVsdZlkVNTQ1er1eCO3OAbdsZq5B0R0xee2s9ZxqrscsXYiw4edR2x3EwTZO6ujo8Ho8Ed4QQYg5SV2/f8fobM9KNcS6+7yL6jf6M2/Y9HM7+x28mPLbcX84Np984431K/PgpiCYzbltTHCTxzUcnPjjonTSprRC7MgnwFJD0kh3LNIkZfRhJh96NL1EWNqG4kbV7bGaPCY6NRqP4fD5KS0vlIXAOSC/ZST/4D9MaZTo4sSjOBGk3otEofr+f0tJSeQicA2zbZtu2bWitcewkLZFBvB4PZ+52yJTHRqNRioqKCIVCslRnDkgmk8PLs9Isy2Swp51Xt0U4VL+Ix1eJU9Y87thIJEIgECAUCkkpdCGEmKP0VfnuQWYTBXdm+9hJTRDcmfVjhdjJSYCnwNi2jbHhWYreLMXw9FGCB8t7JE81bWVB07KMx6TLJtfX10twZ47QWmPbNqWlpaPed3cl8Dy4gVK3l0jt+Ie89Fg3NjZKcGeO0FrjOA4lJSW0PXob9eFGenezqQgEJz0uHo/jcrlobGyU4M4ckS6NHgy+M7ZrH7iDBb31LHeHcbua0d4gTv3osuixWAyPx0Nzc7MEd4QQQgghxLRJgKeArHz8XuwXN1Fil/JyYCMDy87gtOVVANRn2N+2bWzbxjRNGhoaZKnOHGKaJlprPL1v0ffMnxkcTM3mKLVLeLVkNS3l7Zx//HeG97csC8dxhktkS3Bn7jAMg00vPkxZS5Jyq5rH/a1UNb1rwv3TY21ZFk1NTZI8ew4xDGN49k7fs88SWNvKAtXAlqZe9lpxzrj902Nt2zbz5s2TQJ4QQsx1wbrp59IJ1k29zzT965x7t68BPfUuQojCIE8OBcK2kgyuWo3td1D77c5ulYdRFRqfpHOkeDxOcXGxlMieg9asW0/Lxrep632aSGJfoqVB6vdqJALM8zdwUPXCUfsnEgmCwSCBQEBKZM8hWms2vfkmFS0xusss1B67c1D1oVSHJk6AHo/HKS0tlbGeY7TWw6XR+zu34Vm/jZdrIjA/wKF7nJnxmHg8TigUori4WJLiCyHEzmCKKlgLv3wPm76X+d+E2XTWnWfs8HNOJXDVCdt1vP66RJ2EyEQCPAUiNtBHhVFO/MAaGhfvPfX+sRhut5u6ujqZzTEHbVj5Jgu7EiTdIQxfBaHle9C4aEHGfaPR6PBYy2yOucf/5LNo24de2jjlZzsSieD1eqmvr5elOnOQy+UiEAiw+b7HKHUH6Z7n47jdj8DjGb90NhwO4/P5qK+vlyCeEEKIXU786kfy3QUhdkrytFgg/EkvHfUJ9t5r8sSrpmkOl0xesGCBBHfmINu2KY734HY/y8JjzsJszFwFwDAMHMfBcRzmz58vwZ05SA/2UxWt5/tLHuU9DR+acL/0WAOSh2WOchyHjjffoGJtnCXJ3di8X5Kz937PuP3SebRcLhfz5s2T4I4QQohZV+4vn3ay5HJ/+Yz2ZVjQO/1kyUF5/hEpGasRf/+d/x5Z+GJXIU+MU1BKuYFvAZcApcD9wKe11hkX2CqlDgZ+DhwAtADXaK1vmOo8uqKYxUeePmV/TNOksrISt9stOXfmqFg8TmXUT3/1ftRNENyBVEWeqqoqPB6PJM+eoywzyZ/qVrJs/ruYH5w/4X7JZJLq6mrcbrcEbeeoZDKJ7uynRz1I4vBzWLzHseP2SSdXr66uxuVySdBWzLhp3LPcCpw35u3/aK1PGtpeDPwMeC+pe8a/A5/XWkdmo/9CpM30/feufi1PVuY8X8vGJitznq8+zZad4XqeKpAC+Qmm7IoBnKnI18RT+wbwYeD/s3ff4XFc18GHf3f7ovdCgEWiRFGd6r1akSXLJW5xkXuNE/cWJ/Fny3GNW2wncRy3uFe5y5Il2WoWZfViURJFUWzodRfYPjsz5/tjsRA6dkEAuyDP+zz7QJi5O7jQnQVnztx7zmuAC4FO4BdzNTTGNAM3Ag8CpwJfAb5ljLm8kB+0WA6G8fFx/H4/9fX11NfX61P+NcpOW1i+FBvWzZ9jaWxsjEAgQH19PbW1tfqUf42yRAj6QpzRtmXeNtFolGAwqGO9xhljMPEoVkULDRtPmrVfRBgfH5/2uVZqBVxDgdcsE04EPgS0T3lNDfj8L3A+8FzgecDFE9tWjDFm0ZfKOcT/X13D8l5/r/q5rNQU17DGz+d8pdCFXipn5t/h/f/+3FX926yPDxdgjAkA7wLeKSI3T2x7ObDXGHOuiNw14y1vAsaAd4mIC+w0xpwKvB+4aan9SKVSuK47OaVfK62sbWFbWOe5keq2d2HP2Jcfa5/Px4YNGzSIt9YJbK3uoDY0O3ibTCYBCAaDbNiwYa1fiB/2rLEIbelWxs86iUDomTLpIjI51oFAgI0b5861pdTBKvaaxRgTBI4C7hWRWZlhjTGdwCuBZ4nI3RPb3gTcaoz5oIj0rMTvMfMmoVye5C/2N7ocnlyXy/+rg7Xc19+lOpcnfs7sbYf58pHDzaF0PqvClPpzrXePC9tGbhrdbfkNIrIP2AdcMEf7C4A7Jj6MebcB55kl3L3lSyU7jkNbWxudnZ0a3DkEmPoqKl78v9iNx01uy+dVcl2X9vZ2Ojo6NLhzCPCJn0AgNG1bfqyNMbS3t7Nu3ToN7hwCAm6AgVMCtG48ZnKb4zhks1k8Hg/r1q1j3bp1JeyhOgxso7hrlq3kHvQ9Mc/xzgVcYPuUbdsBh9yT42Wz0AyUuZ58lmKWyswn1Rv/6Tp9cr1ytrG819+rdi7PpDMeFIfQ+azWBp3Bs7DOia8zI6G9wPp52j80R9sKoBEYLuaHx+NxKioqqKyspKqqSm8CD1H5J/xTx1odGqygy8YTz5383nVdkskk4XCYyspKKisrF3i3WkusoMuGY0+f/N5xHFKpFKFQaPKzrdQKK/aa5QTAAj5mjLkSSJHL4/AJEUlPHG9QRCazoIqIbYwZnOd4SyafbYXEnKkoFlfZupxdmfSaG65eMCntSWfD83/91Tn31QXrFsx5slTpL2xfMCntzopK0v9229w7K/0L5jwpM8t9/b1q5/JaUK65VGb2qxz6tEz0fFarSgM8C6sA3KkfoAkZIDRP+/QcbZmrvTHmLcBbJr7Nbt68ed+MJpJOp2NF9bg8NFFkMKuM7RCRxevWL2KxsRYRyWQyOtaltSJjvfW44/ZN3S8ibiaTWYtJHXWsZ5g61saY7NHHHLNv6m7XdW3LshIH+3NKQMd6bSr2muV4wAA7gf8il4/ni+RuEF7L3Nc08x5vxt++uDHmySX8DnNZ5HwcgA+W5AHYgv36Pj9axa5MWvj/1fuLOtYfROSKg+zPUi339XdR5zKU8nwuiVl9KoOHysvZp1Key3Dons/leC5DefZrOfu06PmsAZ6FpQCPMcYnIlPTpQSBuS7aUxP7mNGWudqLyNeBrwMYY+5PpVKnz2yzFhlj7heRQ+Z3WY7j6FiXPx3rhelYz6ZjXf6Wa6zXiGKvWT4MfF5ERie+f9QY4wA/Mca8l7mvaeY93tTPw3Iq1/OxHPtVjn1aouW+/i7qXIbD63zWPq24Q/J8LtcxKsd+rXafNMnHwromvrbP2L6O2dPs8u3nahsnlyxLKaWUUmolFHXNIiLulOBO3qMTX9dPHK/F5Mr7AmCM8QEtcx1PqWW03Nffei6rUtLzWa0qDfAs7BEgBlyU32CM2QRsAu6Yo/2dwIUzEipfAmyfkShLKaWUUmo5FXXNYoz5mTHmVzM2n05umv9uckk7fcA5U/afT+7acTtKrZzlvv7Wc1mVkp7PalXpEq0FiEjGGPNV4PPGmGFgEPgqcLuI3D1R9q4BGBURC/gW8EHga8aYLwGXkStjV8i6z2WfBlpC+rus/jFLRX+X1T9mqejvsvrHLBX9XdagJVyzXMszy7F+A5wCfJ7csq04uTwNPwO+ZYx5A7l8Pd8Avr/KZXjLdQzLsV/l2KeiLff1t4j0lMm5DOU5RtqnFXQIn8/lOkbl2K9V7ZNZwxnJV8XElLd/J5dw0A/8AfhHERk2xlwM3ApcIiK3TbQ/G/gKcBKwH/ioiPxk9XuulFJKqcPJEq5ZXgN8ADiK3E3H14FP52cdG2OqgP8EXgTY5IJC7xaR1Or9VupwtNzX33ouq1LS81mtJg3wKKWUUkoppZRSSq1xmoNHKaWUUkoppZRSao0rKMBjjHmdMUbmeDnGmGFjzJ3GmL83xmjASCmllFJKKaWUUmqVFZtkeRC4ecr3QaCRXNWF/wEuNca8THTdl1JKKaWUUkoppdSqKTbA84SIvGrmRmNMI/Bn4KXAj4GZZTeVUkoppZRSSiml1ApZliVVIjICfGHi2xcuxzGVUkoppZRSSimlVGGWM2dOz8TX6mU8plJKKaWUUkoppZRaxHIGeE6b+Hr3Mh5TKaWUUkoppZRSSi2i2Bw80xhjvOSSLD8f+FdgD/C/y9AvpZRSSimllFJKKVWgYgM8Fxlj5quQ1Q88S0SiB9clpZRSSimllFJKKVWMgy2T7gFqgZOBDuAuY8wLROS+ZeqfUkoppZRSSimllFrEcpVJ9wD/BHwK+L0x5kgRiS9HB5VSSimllFJKKaXUwparTLorIp8GHgWayeXkUUoppZRSSimllFKrYDmraAE8PvF1/TIfVymllFJKKaWUUkrNY7kDPEdPfO1a5uMqpZRSSimllFJKqXksW4DHGPMPwKlAFPj9ch1XKaWUUkoppZRSSi2s2CTLxxpjfjBjWwA4CTgGcIC3isjYcnROKaWUUkoppZRSSi2u2Bk8LcDVM17PBQzwbeB0EfnZsvawjBhjvmaM+eYibU43xmw3xiSNMU8ZY16zWv1TSimllILluWYxxlQYY75ujBk2xkSNMd8wxlStbM+VAmOM1xjzaWNMnzEmboy51hjTWsD7Nk+075yxXc9lVTLFns8F/G0+yhjz64nzecgY83NjzIaV/03UWlBQgEdEviMiZp5XhYgcIyJvFJGHV7i/JWFy/g146yLtmoEbgQfJLVf7CvAtY8zlK99LpZRSSh3ulvma5X+B88k9zHsecPHENqVW2jXAa4HXABcCncAvFnqDMWYLcBNQOcduPZdVKV1DgefzYn+bjTGVE/u9wKXAs4Em4AZjTHBFfwu1JhgRKXUfypox5kjgW8AJQBK4WUTeNE/bfwbeDBwlIu7Etv8DOkREgzxKKaWUWjHLec0yMQNiP/AsEbltYv9FwK3AehHpWenfRx2ejDEBYBh4p4h8Z2LbJmAvcJ6I3DXHe94FfBx4itxN8XoR6Z7Yp+eyKpliz+cC/ja/EPgZ0Cgi4xP71wMHgItE5I5V+cVU2VruKlqHonPJVQU7kdwHcSEXAHfkP4wTbgPOM8aYlemeUkoppRSwvNcs5wIusH3K/u3k8i2ev1wdVmoO24BqcucjACKyD9hH7rydywuAtwDvm2OfnsuqlLZR3Pm82N/me4Hn5IM7E/Jt65epz2oNKzbJ8mFHRH4A/ACggBhNJ/DQjG29QAXQSC56q5RSSim17Jb5mqUTGBSR7JTj28aYQWD9cvVZqTnk8+fMnFnTyzznnohcCmCMuXie4+m5rEql2PN5wb/NEzPOZh7rQ0AC+PPBdVUdCnQGz/KqANIztmUmvoZmNjbGvMUYc78x5v7jjz9eAH2V6JVMJiUSiUg0Gp18PfnvP5CPfexjp80ct6XQsS6fVyKRmDXWez7zMx3rQ/AVj8enjXVfT5fs//QvdKwPwVcsFps21k8//pAcWMaxPkQtds0y1/58G72mOfRfpVQBuFMDMhPmPPcKPF7B5zLo+XyIvUqt2PO52PvJtwFvBz4kIqNzdUDP50PqtSgN8CyvFDAzuVX++8TMxiLydRE5XUROD4fDK945NZvruti2zcjICAMDAwwPDzM8PMwvd16LY5xl+zk61qWXH+uhoSEGBwcnx3qwv5+0J7VsP0fHuvTyYz04OMjQ0NDkWHfve4oR//JNpNSxLr2pY50f55GBHqy7/4feYF+pu1fuFrtmmWt/vo1e06iVlAI8xpiZKw3mPPcKPF7B5zLo+ayWVbHnc8H3k8aYfwW+CnxaRP5rvg7o+Xx40SVay6sLaJ+xbR0QB8ZWvztqMf39/cTjcUSEyspKvF4vAI+P/pXnWidBbn2sOgT09PSQTqdxHIeqqio8nlx8O5kYwzE2wO6SdlAtm66uLizLmjbWA/ExEpEBKkwW4MlS91EtjwMHDmDbNrZtU11djTcTwTe+m/6sw5i3Chh5vNR9LGOLXbN0AS3GGK+IOAATNygtzF4eoNRy6pr42j7lvyF3fi7l3NNzWZVSsefzoveTxhgPucDOW4F/EpHPLmeH1dqmM3iW153AhTMSKl8CbJ+RKEuVWDQapb+/n1QqRSgUyt0YTAR3XHF5z47LseVRKI+pneogRCIR+vv7yWQyhEIhampqJoM7AFYyvqyztVTpDA8PT451OByeHOueWJTPPvgpjn0kRLR6+WZrqdIQEYaGhibHuqKigpqaGvzRp2j6zfPJPPJTaiPPZ68+pVzMYtcs28k9CDxnyv7zyV07Tk1Wq9RyewSIARflN0xUHdoELKVCkJ7LqpSKPZ8LuZ/8L+BNwOs1uKNm0gDPQTDGBIwxbRPl7yBXmrQZ+Jox5lhjzDuAVwL6wSsj2WyW0dFREokEXq93MrCT98O9P2DYkybuvb1EPVTLxbIsRkdHSSaT+Hy+WWPt2jaJP96LnZvBo9Ywy7KIRqOk02mCweBkEC+ddfnBnU/yL13nU13dz5GXvrDEPVUHK5PJTI51KBTCE++l/o9vo3b7vzJ8xMsYjb+Iva1peqo2lLqrZaXYa5aJRJ4/A75ljDnPGHM+8A3g+1pWWq0kEcmQm53weWPMFcaYU4GfALeLyN1znMuLHU/PZVUySzifF/zbbIy5Cngb8AngDxPvzb+WkqNKHWI0wHNwzgX6Jr4iIgPAFcAp5LKfvx14jYjcUrIeqll6e3vJZrOEQiECgcCsSiOewSR2tpGO2oKuG1QZ6+npwbZtgsEggcDs8bz3r7+g0qnCf8npJeidWk7d3d2ICIFAAL/fP7k9Es9wYnYU43OIPvfyEvZQLQfXdenpyd2PBQIB/B4hvOc67JqNRC79KoMdz8UKuhxYfwxez6JVpA43S7lmeRNwF3A98BvgFnI3FkqttA8DPyRXFe5WYD/wkol9087lAum5rEqp4PO5gL/NV098/ejE+6a+8sdUhzHNwVMEEbl4xve3AWbGtruBM1evV6pQ+en86XSaysrKOUvIuuLyyt1n8Yugy7EeL4AurVuD8kE8y7Koqqqac6zHYkPcPvZHTvK/jkBNA+hYrzkiQm9vL7ZtY1kWNTU1s9qMjQ5ywXgtTvOdJN3L8+eCjvUa47rutLGura0FINB3N6EDNzN27r/hVjTjDI2T8sF1j0V49RnN3P/k4TvWy3HNIiJx4PUTL6VWjYjYwPsmXjP33caMc3mxfXouq1Iq9nxe6G+ziLyS3IwepeakAR51WEilUiQSCfx+P5WVldNysExl2xbJYIpLEx/D37mZj77to9HV7ak6WMlkkmQySTAYnBbIG0/ZfPH6vbw77pL2DRKUFO+Uq6nZdAQV7e189KMfjZW466pIiUSCRCJBOBymqqpq2r54dAS54Uk67QyRwA6ObQ4wIEJHRwcf/ehHkyXqsloCESEej5NIJKioqKC6uhoA38jj1N71/0hs/SAVf64k69lDZWyQ+/wWZ22s4rLTjuaqcz+aWeTwSimllFKHDA3wqEOaSC5Hcm9vL67r4vMtfMr3P7KDoJsghIv9vG/jX7C1KidTx9oYMy3fTtZxueHRHk6vvA7jOZ7b1t/C38dSRC/5MmPJJDWLnBeqvIgIruvS19c3Zx4tgFhkhJSvF1/wPja86N+JAyaZnLOtKl8iguM49PX14ff7p42fN9FHasvfEW84j8HIHn5U+wQpS1jvPouXnFCrY62UUkqpw47e1ahD2oEDB7AsC9u251y+MVXWytC8y2Hc+SHjp79/3lk+qvyICPv37yebzWLb9uTyjbzuSIa+ob+yLtiLz3cil1cdS/zIs4jH4/h8Ph3rNcR1Xfbv3z9ZGnvmWOfFBrvxO1GqznlD7vtYjEAgoGO9hti2TVdXF7adS4IeCk3PHWkcC/EGGIr3sT/dy6hbyzntx3JMdQCfzzfn0kyllFJKqUOZBnjUIWtsbIxMJjNr6cZc7GwW7092cEtojEsYo+3CvyUYDK5CL9XBEhEikQiZTGZy6cZUO4a6+emOb/Kxkefh8z0X32nbsI65DBvwJJN0dHRMS8qrypeIMDo6SjabnfdzvX8kzff++Aj/nK5hf71FQ+cxAHi9Xjo7OxedxafKg+u6RCKRBce6ZyhO7c6TCQYiPOyp5LlnnMrZR9SSTCbp7OzUGTxKKaWUOuzola46JGWzWQYHBwu6cXedLIO//w0SCLLZ+SONdQvP9FHlJZvNMjQ0NGdALjGWwb1thDeZy2is2Im94QCxYy4CcjeQjuPoU/41JJPJMDIyMmusPfEefA99g8eGXMgey5ukmsGaAU7uzBDnmbFWa0c6nWZ4eJhwODzn/ujoYzw8uosTWwMMnPAs6voSnH1ELa7r4rqufq6VUkopdVjSAI865IgIXV1duK5b0CwcKxGhOlHHtYEEf3vy3zHWupFWffK7JjiOQ1dXF8aYOcugj/UmqBCbriN62Tb8F+InfXxyXzKZJBQK6ZKdNcK2bXp6evB4PLPG2ju0g8FoiqjvEpp8BrPJT3PDiSSac7N38omYdazXBsuy6O3tJRAIzPm5xnXoPXADjd5a2ptO59GYRcifG9t4PD5v5TyllFJKqUPdmg7wGGOeD9wvIr2l7osqPRFhcHBwwZLJc7EyCbLeFEO12wh2NtPU2alLdsqc67oMDQ1N5tyZa2kWQCQ1wFB4N52tNThWAxKqn9zn8Xhob2/XJTtlznGcRce6e3CU7shWzghu4IETBzntuIsBkIn9Xq+XdevW6ZKdMmfbNsPDw2SzWVzXnXdpVmj/jTB4L3XVb2Iw4+P6PSO8+bx1QG6s29vbNZinlFJKqcPSWr8C+hVw8dQNxpiAMeY1xpjW0nRJlUoymSQajZLNZqmoqCjoPVk3yy+7fkfWtanwG2zb1ie/ZU5ESCQSRCIRbNuedwnH2Mgg63dG8fvaODbQgnifmc2VT9Crylt+rKPRKI7jzPm59o3u5Iinv0FdQw3b63eQapoexMlms7o8aw3Il0KPRCI4jjPn59o/8AANf3gto3/9T+6qOIkjDnRy695xrjiuka1tlZOBIaWUUkqpw9Vaf3Q91514NfB/wN8AA6vbHVUqtm3T29uLz+ebe0r/HESEh0YepGO0hbaMzatOq6emplpndJQ527YnSybPN9aWlSa6YzdDNRbDLZsJDFwPUwI86XSauro6ndFR5izLWnSsfcN7GKp+Pa7ZTHP1OEc1nzdtf36sdUZHectkMgwMDBAMBmeNtSc9in/or4T230Rq8wt4qrKV5z7pp7+9Hqn08KytuZl56XSahoYGHWullFJKHbYO1asgnYJxGHEch56eHlzXnXc2x1wi1ijb99/OeV3HYmp2EfT7aGpq0pv+MpbPwyIiC4710P49VA4IdzlVnNswSnjPdaQ2XQHkcnR4vV4d6zKXzWbp6+vDGLPgWHtHA9TGNmN5baItgsc8889aLBbD7/fT1NSkN/1lLB/I83g8s0qhA4R3XUt4969wKtvJdJxP/WCY+kSI7voAVSEfHmOIxWIEAgEaGxt1FqZSSimlDlt6xavWNBEhFouRTqcLKocOMJrI8tFfPIL350/yjh2n0GBuIbjOS0dHR8Gzf9Tqc12XWCxGJpNZcKz3x/dxV++fGW0MsW79BtZVe7BaT8NuOgEAY4yWyy5zrusyPj6OZVlUVlYu2DY6nuZet5vvNf+SQEPttH0ej4f169drIK+MOY7D+Pj4vEtrQ0//lsonvseT3tfT8/CzGfx5lPpHvPzQHeBHu0ZprsrlS8uPtQbylFJKKXU40zsctWaJCJZlMTAwgN/vL+ipbSZl033rPq6Wp0hWGTY03kf0/E+TTCap0huDsuW6LpZl0d/fTygUmnes4+MRxh7ayeXJM3nCqiPU4gHHQjy5m0CRXNpdvQksX67rkk6nGRwcJBwOL/i5Hh/qJti3k6qmTVzecSnb6rdN7tOxLn/5sR4YGKCysnLaWHsjGXw9SQLdwlDT57G7Ajy8pQd7s8PTsd2c2nAab2g8GtCxVkoppZTK0wCPWpOy2SwHDhzAcRyMMXNO659LtD9JR2SMhxp3c976Y4lv/Hvi8TjBYFBvDsqUZVl0dXXhOA4+n49gMDhv27ED3TTFKxlqauSxcXjR5lrMmA0TAZ5YLKal0ctYOp2mp6dncqwXm1E3/sTN1Htr8Ta1E/BMT6Q8Pj5ORUWFLtcpU8lkkt7eXlzXJRQKPTPWIoAQeHoc8RrEG2Wv1cCO5h6ybeNsChxBQ+OZHFOzdfJYOtZKKaWUUjmHQoDnAmPM1N8jv3bjcmNM51xvEJHvrXy31EpxHIfR0dEFy+jOJxDtpc69k1inED7xBSTSachm6ejo0CU7Zci27eLG2nYZqvLwv3tSvO3CDuor/BDJzeBJpVJ4vV46Ozt1yU4ZymazRCKRgsc6tPcGjt1zH0nv3/MT509cHH7e5L5UKoXf79clO2Uqm80SjUYRkVljXX3/5wh2346bejGE/8pNNcP8rKGTqmA9b1n3WtrCbdPaJ5NJAoEAGzZs0ACPUkoppQ57h8Id7VsmXnn5K7wPAjKjrZnYpgGeNUpESKVSjI6OFlwKPWpFuW3vjZy+dx3hmPBQTS2+hiOAXLCovb1dgztlSERIJpNEIpFF87AAeMYsWp72crcvzYtOaebEjio8qRGqHv0WVtsZuK5Le3u7BnfK0NSxLjRoG40M82TgInqbH+PELeeyufqoyX2O49DZ2anBnTKUL4cejUaprq6eti944E+E9l7P2KnfxfNghl9uqeWe7GPYXRfxd2ecTlt47r8DHR0dGtxRSimllGLtB3heX+oOqNWVr7YSCATw+/0FvWcgOcDGkTrWVazjSd92POl9/E37KwAmK/TozUH5SafTk2NdSADOHUzwWGCUvhab5x1ZjS+yC//QwzhVHSSOfx0mq2NdrlKp1GR+pUKDrf2RGKHQOtbXN7C14bRp+4pZtqlWVyKRYGBggHA4PCvYGtp/M+NnfwTvQAX3bNpNQ8s6NndvpfXIVo5umT+gr2OtlFJKKZWzpgM8IvLdUvdBrZ5sNsvAwABAUeXQK4ZttvS0M37EGEc+9W1GzrqGkDdEMpnEGKM3/GXIsiwGBwfxeDwFj/UDPU+zzxzgvE3nUzXyIDV3f4xs88kkj34xcQs8Hh3ncpTJZBgaGpq3RPZ8kolKWjN1mA1H4fM8809ZIpHA6/Xq57oMpdNpRkZG5s2lZewkdv0WAv0wGkpyZMVR7LVDdLaG8M7x+c2XRldKKaWUUjlrOsAzl4l8PA3klmJFRMQucZfUMnBdl3g8nqt2VWTenXjMYjsDnPHIZ+hb/0I2nXAxlmXhOA4bNmzQ5VllJj/WqVSq4LF+ciDJ3qEYm1qP5JTWTZjuA6Q3Xk781HdjWRZiWZp7pww5jkMsFit4rHt3PkbwkSiOnWWLvYWnNg1zbMu6yf2WZQHQ2dmpAZ4ykx/rZDI5a2kWQMUTPyTYs52xc6vYObSHe8OPcnNXB8PRCs49snZW+0wmg8/nY/369avRfaWUUkqpNeGQuLOdSKb8buAK4NgpuxxjzBPAr4GvisjA6vdOLYdMJjO5hKPQvBrp1BC7d/8W754mWnyjNJzyIgLb3j55vIaGhgUrMqnSyJdNDofDBY31eMrmz493c0nAQ6BuYraPmwVP7sl+JpOhqalJn/SXoVQqxeDgIBUVFYuO9Xh0GPPEEDuC3ZxTeQ+hY5/DsZufN62NZVk61mUqmUwyNDQ071gHBu5n+HnXMkSWiDXIFZtewgnH5cqge2YE60QE27Zpbm7WAL1SSiml1BRr/srIGPNy4FtAGDgA3ABEAD/QCJwM/D/gPcaY14nIL0vVV1W8dDpNb28vIrJoieyZdu//Nf1dg5yS2oDZOEB683MxHs/ktP6GhgZNwlpGUqkUvb29APj9/oJv0u/bP05bvI9KvFRt2ACAcbOIx8fY2BjhcJiGhgad0VFGkskkfX19AASDwYLGevSvT4I9xib3Pio2nojTcda0/WNjY1RUVNDQ0LAifVZLE4vFGBwcBBYea2OnEH8Vdwzezla3mapw06zADuSCO/my6HV1dSvZdaWUUkqpNWdNB3iMMecAPwCeBN4iItvnaXcp8EXgx8aYs0XkoVXsploi27YZHx/HcZyik+Pu3ftLHjiwl1f1v5SukzrYdPrf4Ezs83g8rF+/XpfrlJFsNsv4+Diu6xY11g9HHuKm2K+5OHA0oY6zqKprzO1ws4g3MLmEQ4M75SM/1o7jFDRz547B29g+tJ3LE8fR4O/Ft+kq7G2Xz2rn9/vZMBHgU+XBsixisRgisuDsy+r7P4cvuhs3UI0rLiG3gZBv7iT6IkIwGNSxVkoppZSaw1qfvvB+oBs4Z77gDoCI3AJcCPRNvEetAalUiqGhIQKBAB6Pp6Cb9Ejv0zxyw8e5c/dtXDn0LPYd2cqm01sm97uui4joDX8ZyZfIHh4eJhgMFjzWnqhF9cMZnrP/Ci5PXUBzdXtue3KI8J7rEZML4OksrfKRL5E9PDxc8HJLz1CKdw1fxkWj7bRHHsUbmF1NyXVdAP1cl5H8TJtIJDL5uZ6Pd2wvI1d+H3whgv1+mhIevL6524sIoGOtlFJKKTWXtX7ncw7wXREZX6zhRJsfABeseK/UQUsmk5M5dwoph+6KkMo6DD1+O0MyxjHOOYQbN9J+WtO0dvF4nGAwqDcHZWRq2eRC82m4roN5epSRZIp0gxfPyQ3YzYLJJgj2bscN1THYdKGWTy4z+eU6FRUVBY21Y9t09IXxju/mqdAuHj/iSpqOml4SPR80Kqaynlp50WiUkZERKisrF54t6dp4rBjirwRXOKqrnr0bDPWts8fTdV2SyaR+rpVSSiml5rGml2iRy7FzoIj2e4DWFeqLWiaWZTE6OgoUXg79licj/OGxEf4+XcnlvByrJkTwnE6c6mfyPSSTSfx+P+vXr9dZHWUik8kQiUSKLpHddff91O5zuL7lUV564osIj99K9fVfwanKVVQa2voWfNUtWk2pjKRSKSKRCD6fr+Cx7r7+E5jEBnp4nOuqr+ZVZ5yAP/BMwDc/+ysQCNDR0bFSXVdFSiaTjI+PF5RLq+6OD4IxiK+S8P3DNGXC9B8TxuOd/jdaREgkEgQCAdatWzfP0ZRSSimlDm9rPcDjB1JFtM+w9n/nQ1q+RPb4+PicpXTnE884XHayRfzpFDd476f9tHM4qf6Zm0jbtnFdV4M7ZcR1XWKxGLFYjJqamoLf98ADj3DM08K3KuBlJ7yRE81eqh79OuOnf5DMpsuxbRvLslivwZ2ysViJ7Pl4M2M4VeupPuVS3tm5adb+bDaLx+PR4E4ZcRyHsbExUqkUVVVVi7Y31hiRi78MHi+Doxm+Xvs4LwnPHs9MJoPf79exVkoppZRagAY7VFlJJpOTy3UKTYKctBy6x2JU8ADnJao5oaMTT92J09qkUinq6up0an8Zmbpcp9Cg22BinER3D7s2tvDyk0+guSZIYOcjpDa/gMyGZyEipFIpGhoaiqq4plbW+Pg4w8PDVFZWFjzWicggFakI4g0hvtl/C1zXxbIsHesyE4lEiEajBY21JzmANzmI+HN5lUZiFps2BaivmD7rx3EcHMehsbGx4Op6SimllFKHo0MhwLPVGHNhgW2PXdGeqIOST6rs9/uLumG76YlR+uQBnt/r0J4Kkt50DLZ55sYiXxa9paVFZ3SUiUQiwejoaMElsvO+/fjvudCt5ZgNm6iszQXrTDaBU7MBMZ7JHEvNzc0r1XVVpFgsRiQSKTifVl7kj59mnTybgQB4Kqb/UyUixGIxKioqaGpqmucIarWNjY0xNjZGKBQqKMdS7V0fwWo7CzwTfwNcweeXaeXR87M6q6uraWhoWKmuK6WUUkodEg6FAM+/TrwKYQBZwb6oJbJtm1gsRiaTKWoJx917xxja+RjvdZvZlGrDOqcKu3X9tDZer1dLZZeRbDZLLBYjm81SWVlZ1HszTppjqk+hpqISBwh230HlY98hesmXASbLoqvykC+Jbtt2wWP9+0eHCe34DlfFPUj4JL7b+nPeGjphWpt82e3Ozs6V6LZaAsuyGB8fR0QKDtqabILY6e8DY7hhxwgdSRu8gmdK/QfXdQmHw7S3t69U15VSSimlDhlrPcDzsVJ3QC2PRCLB0NAQVVVVBQdihtJDHOj6M88zVfhq78W64AIynafM2bbQ6kxq5cXjcUZGRqiuri4q6PboUBfe5Bj1CS94PXjivYSf/Clj530Cq+0MxHUxxhS8tE+tvGg0ytjYWFGf61j3Di6svIvhxn/mN43bed1xf099oH5aGxHB4/HoWJeRSCRCPB4veKz9Q4/gjfci3txMvN1DKS5r83KXPYIx05d2eb1eHWullFJKqQKs6bteEdEAzxo3Pj7O4OAgkKuYVcwSju1Dd3LiUAafJ0v9hk1YLafOapPPBaFKLxKJMDIygogUXCZ7ql88fTMvjJ1KoL0Gu9ZP5RPXI8FarLbTJ5fs6FiXh5GRESKRCK7rFj3W547+iN9sOZrnDhuObTqB5uD0JViu65JIJIqa6adWztDQEGNjY5NjXWggpvq+fye+7e0wEQzK2C5Ra5Bj646jxp9Luu44DqlUitra2hXrv1JKqcOPMeaWZTiMiMizluE4Si2rNR3gmcoYExSRzBzbjwFGRWSoBN1SC8hkMoyPj2OMIRQKFTWb40/9f6R2Z4qTY5sZOCGFe8LFs9okk0mCwSAbNmxYxl6rpUin08TjcTweD8FgsOjlcvf//nbeETmGjop2Mic0gs+DsZOkNz0b1189Oda6PKv0kskk8Xgcr9dLOBwueKwr/jLIyJ5xGp2XctWeEJ2hJuraN8CURL35UtmhUEhLZZeBRCJBIpHA5/Ph9/sLHuu6W9+JEYfU0S8C4Pv39HN6dBeV2WrObj8fr/FOBvKqqqpoa2tbyV9DKaXU4cfDwaft0NwPqiyt+QCPMcYPfAZ4tTFmg4ikZzT5NPAcY8z/Ah8SkWLKqqsVIiKMj49PlkMv5obf159i8+MBNkVP46/mVzTVXDCrjeM4GGNYt26d5t4pMdd1iUajk4lSix0PX2+SYyPV7D6risajj8xtG3mMYNetWO3nYNs2Xq+X9vZ2HesSc12XSCQyWSK7mPHwxLL8ov5P7K3ZzvtP/QLxitmJsrPZLIFAgLa2Nh3rEnNdl5GRETKZTFFjHXr6twQGH2LwZX+e3LYv+QSnBTJ4LjmNYChXUSubzRIOhzVhulJKqWUnIheXug9KrZTC6tWWKWNMALgBeA8wDLTM0eyPwE7gHcD1xhhdyF8G8st1ipnSD5B1szhPDTDgGeGGmhE66gZoqaua1kZESCaTVFZWEg6Hl7vrqkgjIyNEo9GiyqFPsl2CT45xfdM+nKpnzpNQ122kNz2bdNNJpNNpqqqqdKzLwNDQEOPj48WPte3ipm0Gg/fz0uZnE54juGPbNpZlUV1dTSgUWsZeq6Xo7+8nkUhQUVFRcHDHZBNU7fgmo8/6n8ltMSvNiO9+jggfQUUgt8Qym81i2zZVVVVFVVRUSimllDrcrekAD/Bu4FLggyJynIgcmNlARL4KnAJ8ErgIePuq9lDNkkgkGB8fJxAIFFUiG+BXXb9kV/8D7E/eznGjX6M9nIbqjsn9IkI8Hp98yq9KKx6PE4vFii6RnVdxzxAm6/LXyiGC/mfOFWMnyTSeSCJlEQqFaGmZK7arVtP4+DjxeHxJ+ZUqb+/nofQeQpLl6A1Xztrvuu5k0FbLopdeNBollUpRWVlZVIC+4cbXYzVvw256piraJ+/7Jl6PlwZ/HXgMjuOQTCapqqqisbFxBXqvlFJKKXXoWutLtK4Gficin1+okYgI8P+MMRcArwG+vBqdU7PZts3Y2NjktP5i9N3+C84cCHPyeCN1p7yT5qOOJD6jTSaTwefzafnkMmDbNpFIpKgS2TMZy+W2dSEG+1OE/X6CB/5Exa6f4R95nGj7pQQCAc3FUgay2SyRSAQRWVLFuqFohp/XX89nBncQD05PniwipNNpDeSVifxYA8VXthKX8XP/bdomSyzef/Lr8NyXwTVMLu/TpVlKKaWUUsVb6zN4tgA3FdH+OmDrCvVFFSASiTA2NlZU8lVPPIuvL0l1bw2+LWH+vbkSt2V2AMd1XRzHoaGhYUmzRdTyGh4eJh6PFzXWeaOZEfb37yIZGeemrsc4sjlJ6/hjVOz6OYmtV9P/nGux6o+lvr5ex7oMDA0NkUwmi06WPpge5IG9t5KKDxAORoi/4HfgmT6etm1jjNGxLhP9/f1YlrWkZOkzDcYsLCdLVTCIESHr2Ph8Purq6pYUKFRKKaUWY4x5iTHmS8aYNxhjfDP2/b5U/VJquaz1AE8CKOYRYhKYVWlLrY5oNDoZ3Cnm4j187zD+Awm6Krr402gFvqYWGipnv19ECAQC1NfXL2e31RKMjo4Si8WKzrGUd+2Bn1P54Cj304dbd4BTkyO07Lkeu+5osq2n4YYbCQSD1NXVLX/nVVHygbzKysqicyz9cO/3ePqJbxDyJTmtogMq2+dsFw6HtVR2GRgcHJxcKld0Pq05fPuuPmrCEPL6wRXwQEVFBdXV1Yu/WSmllCqSMebtwH8BFcAHgO3GmIYpTWZXblFqjSnpIzKTe/y3SUT2Tny/BXgzYAP/JyK7FjnETuA84CsF/sgLgH1L6606GFNLoheSdyd8zxC+oXTuCW/a4Xst32R/Qz+JnhP52N+24PdOv7nIL+PQp76ll8lkiMVieL3eJc24+G33r+lJ9eBLXs7Ps1muOf1CWu95gsSJr8JuOBYRITWR60WVVjqdJhaLEQgEivrs/XDv9+lP9xMffpp/6OulqfVoLj790lntXNcllUrp57oMpFIp4vE4oVBoScGd2js+wNSKtD+4p5+zxnZzcuRcGgf78SQdUlaGkClu6a5SSilVhLcDzxaRRyZm7/w3cIsx5lIRGUVLn6tDQMmumo0xncCN5GbUnGqMaQXuAfKPad9ujLlQRB5a4DDfBf7HGHOJiNy6yM+7CHgxcM1Bd14VbXR0lEQiUfCTWd9wmthzOvneH77PCVv7GE0M8M/bPk3FBR1ztnccB6/Xq3kbSkxEGBoamsyjUYynxndh9YwQ6h3nXVVvJzA+xkdediQhvwdcC/HkAoO2bRMIBGhoaFjkiGoliQgDAwNYllX0WO8ffpy3DG6jrS+K9/xf49kRnbOdbduEQiGdlVdiIkJfXx+2bS+pqlWw61b8w48y/KI/PHPM3gQX2PVkXryFWLCKTCZD2OPRmVpKlTFjzC3LcBgRkWctw3GUWop2EXkEQERs4K3GmC8CtxpjLmXqkwil1qhSLtH6FLAeyNdLfTO54M7fAUcAXcDHFjnGd4D7geuMMf9qjJmVgdMY02KM+Rfgd+Rm73x1OTqvCjc4OMj4+Hhx0/oF8BgSwVvxGw9XNp5HuGLu5RuO45BKpQiFQkXfaKrlNTAwQCKRWNISjt/3Xsexu2o4qekU7u1J0LO1hnA4t7zLOBnwBrFtm3Q6TUVFxZITN6vl0dvbO1lJqdhcLL54D+3RzUQ2PhvxeUieMbsyVjabnQwe6Wyt0urq6iKTyRRVEn2q6gf/g/Gz/nXatktiLndseAq/z49lWWSzWaqrqwmFQsvVbaXU8vOQm+FwMK+1nh5CrW3Dxpgjpm4QkfcCt068dMqwWvNKeRJfDnxJRL4x8f3zgS4RuRbAGPMN4P8tdAARyRpjXgD8BPg48G/GmP1AH7ncPC3ARnL/oNwNvFxEoivwu6h5JBIJEokEoVCo4GUWVX/sRYIePrj9K7QYlzOO/0eMf+4bvHz55IqKCq2mVGLxeJxkMkk4HC46707VzT28YuhM+uwAX9vvxRswXH5OK8YYqu/9NL7oHmx/NclkkurqalpbW1fot1CFiMVipNPpJQXyvrzzP6jPOoRqW6n+m/VYc7TJl8qur6/XsuglNj4+jmVZVFdXLznvjnj8WB25tAafvWk/42mHdxnYUd/DBeIhkUzQ3Nyss/KUKnMicnGp+6DUQfoT8Drgo1M3isi7jTFfAU4oRaeUWk6lDPDUAvncOy3AacA3p+xPUED/RGQAuMQY8yLg5cCpwMmAA/QD3wN+KSK/W9beq0U5jsPIyEjRSzhGB/bxg9YfIWLxmf6dDPvC87bNV3PR8sml5TgOQ0NDRS/hCO6M4olmiQ1G+ejG3/G18/+dj0/df+AWQvtuZOS5PyMtAcJhny7DK7H8WIvIkhJo22Lzsp4ozDOMIkImk6Gqqkpv+EssP9Yej2dZkioDDPEwZ23LUH3fUQxYg2TSGWprazVhulJKqdXwdua5vxSRdxpjPr/K/VFq2ZUywLMfOHHiv18+8XVqEOYKJgJAhRCRXwK/XJ6uqeWQX65TVVVV8LT+gfgYAUlwyrorOdcXIHr8+2Ge99q2jeM41NbWLikvhFo+fX19pNNpqqurCx7rZGyM4OMj7DopwHeSN/C2Lf84q014z3WMn/0R0r46xLapra0tKEm3Wjnd3d2TMzqKMZayiQ3to3Lc4JfaXNWkOViWhTGGmpoaHesS6+rqwrbtg1r66kmPgusQS9sMp2KY4A7OC15NrSfLm4/4e3w+HzU1NUtKyK6UUkoVQ0QsmD152BhzB/AjEfna6vdKqeVVygDPj4CPGGOOAi4FDgB/MMZsBv4DuAp4bwn7pw5CNBqdLKdbzFP+Tzz8n3zOPZNTjrsKyJVTm4vjOCQSCWpra2lsbFyGHqulGhkZmUyqXMxTfuf3j3NDYJSbBvZRVdnKyS0bZrUxdpJU3VZSqRQNDQ36lL/EhoeHJ2fXFJuL5ePX7+WaZDcv8h5BKHAC9rrZyy7zOZaam5s12W6JDQ0NLXmsp2q4/mrSR1zJZ27cT7rlJ3yo73I2EcJuDVFHiLq6usOqLPpEktpPisifSt0XpQ6WMeYLwIMTr50iIjP2dwCjIpIqRf+UKsL55PLzfIdceo+4iPSUtktKLU3JAjwi8m/GGAd4JbAdeL+I2MaYGuBC4BMi8uVS9U8tnWVZRCIRPB5PUeWNq+/7LK8daGOdHWB0gXYiMpncVZdmlZZlWYyNjeHz+QoO5H1zey8D4xZvch2uly18/ryr5mxX8cQPCfTfS9LxUlNXo7lYSiyTyTA2NkYgECg4kFd7+/vwpoYg8mw+bNeCqaDllS8GYObVfj6fVl1dnVbNKrF0Os3Y2BjhcHjJwZ2v/7mHC7s+x0ZL+GL/80nbWdbVBThypI2xC1qIxWI0NTYejoG8i5m+HF2ptew9PFN1KGWM+Su5YM9DE6+XA5cAZ5Sme0oV5SIgAgQAjDH9wE+Bz4lIXyk7plQxSpopXEQ+CXxyxuZHgGYRyZagS2oZ9Pf3Tz75LZRveAcD+3dzVOp1jL7ylAXbZrNZfD4fDQ0NRQWQ1PLr6ekhm80WPNZPDSZ5eijFP24zhO/28um/3TxnO+/YXsK7fk7PVb8mUFlPXV3dkvK9qOXT09OD67oFVznqffzPBAd3s33TR9k2FuATG3/BZ858/7ztLcsiHA5TW1urY11CIkJPTw8isuS/r7sGkuwbTfNPlfvpfdGveK83QChq8bunWjC2O1mRq7q6WsdaqbWtgVzuy6mvt5ErbpIP/CRL0zWlilYH7AZuA4LAmcC7gdcbY14sIreUrGdKFaHsShWKiKvBnbVrYGBgcnZNMct16u78Z9Lpq9nZAYTmT6qcX8JRVVWlJdFLrLe3F8uyiiqT/dXbe3j+SU3U/2WQsa1B/N65z5Gaez9FsvNiUt46qqqqtCR6ifX09GBZVsFlskWEtrs+wF+aXs1xT2a5ubGXU5svwReaexzzZbJramq0JHoJiQhdXV2Tn+ul+u/bu3n+SU14PF7CoTBhv5eavwzRnqgldlQltm1TV1dHODz/33qlVPkTkaiI3CIinxeRV4rIVnI3ya8AdgGjwJtL2UelinAbcLyIvFVEXicixwFnA13AryfSiihV9lZt+oMxxuWZaH6hRER0isYakUwmSSQSRZXJrr/pTfT09xPJvpZkBZx96bnztnVddzLvjlZSKq1kMkkqlaKioqLgQF7Pj/7MP2MI3D9EIuyyftvZs9rU3PVR/CM7MJlx+k77DI319ZpjqcQSicRkjqVCgzv/8qun+Ir5KJuH6hirs7jyssvnbZ/Pp9XS0qI5lkosHo9PJktfig//5mmMMRzVXMFzdr4dp6KFyj/14kk5dNu9bO/cw+bWC+loaaGmpmaZe7+mvKjIGwURkY8v3kyp0hORGPBTY8zvgR2Arq9Wa4FLrurytPSfInKvMeYicitM/gV4Qyk6p1QxVjN48j2KD/CoNUJE6O/vL7hMdmD3ON0je+kaO5EdFa/gSmnhe8f+mn/honnfk81mqayspKGh4aCSfqqDIyL09fXhum5Bgbw/7Rwl2P0ER4mh5erzJ7eH9v0Bb6xrWttg920MvfQ20pkMNYEAdXV1OtYl5LoufX25ZeeFBvJufHyUK+zrCfqP45sn3sn7jv3Agu0ty6K+vp7a2lod6xJyHIe+vj68Xu+SSqKLCMYYPr/1ATyZKGZvguiV36f6ui7+fN4wv++5mfdsfN/k7MvDfKxfCLyoiPYCaIBHrSkiEjfGfI9cnp7/KnV/lFrEKLklh7OISMQY8210NppaI1YtwCMir1vuYxpj9iytKzJ34g+1ZF1dXWQymYLLZIcejXB34wMcnx2i46g69jS4/F3rKxZ/XyhUcA4QtTIOHDiAZVmLPoF3RXBc4bodQ3wi42XfqQ20Tdlf9cj/ED/5bdPeM3beJ2Hi/AmFQgUFC9XKEBEOHDhANpstOBGuiPCHx4b5P/cGsnVXYPhrQe8Lh8NaEr2EXNflwIEDiMiSl01lbCHgM1Q9+nVip72P8XOugYmCOjf13cjLNub+vldWVmpJdPgU8MdSd0KpVTAEtJe6E0oV4F7gzcaYL4vI+Bz7RwCdUq7WhLJc/mSM8QDVwKUi8qsFmh5AZwWVXDQaxbIsqqurC37yK34P+7mVjW4LJ5952qLtHcchnU5rLpYSi0QiZDKZRW/4HVd4yw930lEXgPXfxN9zNccfu2VaG/H4SW+6YtZ783mWDsPqOmVDRBgdHS1orKeyXaElLHiHP851tQ9xTM3WBdtns1kymczBdlcdhPxY5/+GL4Urwjt/tosrjmvA7a6d/FzX/Ho/dlsYr/GyIbiBRCKxnF1fy54QkdtL3QmlDpYxZohctax8qfQHRWT3xD4DXAXcX7oeKlWwzwF/Au40xrxZRO7J7zDGhMhVfdZKWmpNKFmAxxhTC/wvcAVQRS7j/lzmXQMiIhcvf89UMWzbZmRkBI/HU3Bwp+vW7TRYASzx8NPqf+DkRdrny6LX1NRo+eQSymazjIyMFDTTwnaEIxpDvNbfjTN0JQ2VzcQL+Bn5sc4v2VGlYVkWIyMjRc+gsl3hfSOPciBcwcNHDvKOjvln5eXHuqmpacmBBXXw0un0ksY6T0T49l19HNUc5rXWNxBfbgZQ6KERIk6Un7b9GStukU6naW1t1STaSh1atgPbgMsmvhdjTAzYCTRPvF5rjPFrARVVzkTkdmPMG4H/Ae4yxuwml3fHIlc+fR252ZdKlb1SzuD5d+DvgLuBceBy4IdAK3Ah4ABXL/cPNcacIiIPLfdxD1ddXV3Ytl1wRStPLEtdv6HnpBQvf2CE8Is3LPoey7Lw+XzU1NTo1P4SyVfXcV23oACP5bi0SISaiJfgJceTqJq+nMsT7wXXnvW+TCZDIBCgpqZmySWa1cFxXZfu7m6MMUUvmxoc7+Zot4YvHfNXXnfk3y/YNpVKEQqFqK6u1rEuEdu26e3txev1LnmJ3L6RNN2RNB860yFw51OMnfN5vJEMgQMJvrj5Fl7Y+nKozy3Dq6qq0rLoSh1CRORvAYwxdeQCPadMfN0GrCd3n3EtYBtjdgGPAo+IyL+vemeVWoSIfNcYcwfwXuB5wEsmdqWBrwDXlKhrShWllFfVzwV+JSIvNsY0klun+xURuc8Ysw24A1h4fv8MxpgA8G88Myto6pQSH7llXzUsMCtIFW5gYADLsopKmFl9fTd/rPgzWx7vJdvxXDoqFw7YZLNZ0uk0LS0t+pS/RPIJtAvJu5OXSmd4w7BL37Yw65sbZq2jbLj5TaQ2/+20bZZlkclkaGtr06V4JZJPqpwvWV4Mx7X57tNf5+3Bszl9/YVU++f/vGYyGWzbprW1VUtll4jjOJOJ8Zf6tzVlOXz25gO8+oxW1t98OVn5NoE9AFEyW2pIYdPgaSBjMjQ2Nmr+tJyPQYHJqZRaI0QkSq7E9G35bRPX5CfwTMDnFHJLtl5G7iGvUmVHRPYC7wDeMRG4rAb6dQaaWktKGeBpAW4CEJERY0wPcBZwn4g8bIz5Frn1jp8p4pgfBz4AdAMR4ETgz0AbsAlIAe9arl/gcJZOp4nFYoTD4aKqoXR7Bnm88lEufcEPFm3rui7JZJKGhgZdmlVCqVSKWCxWcNAlY1nY1z/Irkqb406+ZM424qsgcdJbJr/Pj3Vzc7MuzSoRESGZTBKPx5cUYEtbKd6x60LqnThnNp01bzvHcUgmk7S3t2sgr0REhEQiseSxzmRdPnPTftJZl20dlVy+exzH+QQ7ayP8qOEPk+0MhlQqxbp163SsJ4jIxxZrY4ypB1Iikl6FLim1IkTE4pncPJOMMVroRK0JE4HLaIm7oVTRShngGWf6DJunyQVk8h4F3lDkMV9K7unBZeSCOt3AP4rIDmPMc4BfkVtLqQ6CiNDT0wNQ1NKKxyI7WO9avLI7VlD7TCZDZWUlNTU1Syrbqw6e67qTSzgKWVrhOg4Pb3+Aep/D1hdeOGt/oO9uPOlRjDv9QUg6naampkbHuoQcx6G3txefz1f0MhoR4YGnejnBuMir/nbehGqQ+1zX1dVRVVWlY10itm3T19dHIBBY0pKpWMamNujlk8c+RU9vivFEA7s6f86N7cfxnq3vm2yXSCSoqqqisrLycC+LPo0xxk/u+uZsEXn9lO0XAF8jN3tZjDE3A28XkadL01Ollp+ez0optbJKeXV9L/AyY0z+6vIx4ALzzFXgMUCx5VU6gF+KiCsivcAgcC6AiFwPfBd480H3/DCWL51sWVZRyTJFhGv3/5SmdJSbqxdPrZTJZHAch9raWl3CUSKu69LV1UU2my14DIZGRtnSLaS2tuCZ48ax9q6P4E30ETv5Hye3pdNpXNeltrZWy6KXiOM4dHd347rukj5vScvlrid6EY+LWSBgkEqlEBHq6uq0LHqJ2LY9GaBf6pKpdNalzhvH+8B/U7WvmZ0tjzHQeTZXdFyJMQZjcjN3PB4P9fX1mjttCmNMELiFXCLPVxhjfBPbjwZuJBfc+QPwRXLXQXcZY1pL1F2llFJKrTGlDPB8Hjgf2DUxHfk75C5sbjLGfJXcUqpbizxmiukzdHYzfVbQPYBODT0I4+PjZDKZovNzZF2LjkgXaTtEvO38Bdvatk0ymaSpqanon6OWh4gwNjY2ObOmEOlknPqb9vFodYaTTzpuzjZuoIbECW8ks+lyIDfWqVSKtrY2zbFUIvmxzmQySx6DRHKcQPPPCfjnD+7k82lpjqXScV2XsbGxydxpxRpNZPmXn+6i8dcHeFW/oS/7LobWw3EX/D0XbXkjx9Tk0uZZloVlWbS3t2uAfrZ3kXvw9EGgQUTy2eavAULAj0XkKhH5IHA6YAP/UoqOKqWUUmrtKdkSLRG5ZWLZ1LuB6ERy5Q+Su8h5FrlgzHuLPOzDwJXA1ye+3wmcM2V/J8zK96oKZNs2AwMD+Hy+gqfbi+PQe9PPcRyXczIX0hfcxBvPWzd/exHS6TSNjY1UV1frtP4SyWazDA4O4vf7Cx6DZDzOcCjBE41bmL04a7b8WDc3N2vp5BKyLIvBwUGCwWDxnzc3S8UjXye6r4vL3DNpa95GYo5mIkImk6GlpUVv+Esok8kwMDBARUVF0WOdzrr88uEhzmwLkk4N8sf6n5Bw0rz83P+e1k5EsCyLlpYWTao8t78DrhWRz+c3TCSjfT6565PJ7SIyaoz5DvByNH+gKnMTCWkbRGRPqfuilFKHs5ImQBCRm0TkOSIiE99/Hmgk9w/EOSLSU+Qh/xt4gTHmz8aYGuAnwKnGmP+bCB69h9zSsIIZY7zGmE8bY/qMMXFjzLULTZc2xvzMGCMzXn8s8vcoO/nlOiJS1EW7HR2herQJb2M1T9gbCFzUvmD7VCqF1+ulqqpKp/WXSH65jjGm4LEeS6foGxkjKS4v3NY8u4GdwljxaZtSqRQ+n0/LZJdQNpulu7sbr9e7pOVxntQwnv238rB7IpuzW0md2jhnOx3r0rMsi97eXgKBQNHL41JZhz3DKUaSCU5pTTBiD7FtfJArtr5tVttkMonf76e6ulpLos/taHLFH6Y6B6gE+kTk4Rn7dgPzPxWZoZhrFmPMbXNcr+RfF060ec48+zsL/o3V4eJdwFPLdbAlXH+fbozZboxJGmOeMsa8ZsZ+PZdVQYwxdcaYI5f5mMt6Pk9pZ4wxNxhjPryc/V1L8kvD53uVY59Wul9ll+FSRDITWcuX8t6fA28hFyRKiMgfyQV9XkuuGleU4mcFXTPx/tcAF5KbBfSLBdqfCHwIaJ/yemmRP7OsiAjDw8NYllXcEg7LoeEPUR4O7+C70RMJH7+N5vXzLwvIT+tvaWlZ0vIBdfBc12V4eJhsNlvwGOwa7eVT93yS4+5JkWmpoKV69s1jy7WXUX/LP5LpyC3Py2Qyk2OtMzpKw3EchoeHcRxnyUumEokkd4/U0Lz5LOKVNm717KBsJpMhm83S1tamMzpKxLZthoeHcV236Nly+0ZSvPfa3fz8rzuh4occeXeMWvdRtmx6IXUNJ0xrm8+d1tbWpvm05uclt+xqqmdNfJ3rYVAtkCzi+NdQ+DXLi5h+rdIBPATcDtw10ebEiW3tM169RfRJqaW4hgLPZWNMM7kcVg8CpwJfAb5ljLl8SjM9l1WhljVYOeEalvd8zs/+/CZwxTL3dU0RkWmvjf903bTvy7FPK92vVXuUaoxxgFeLyI8mvndZfLmUiEhRfRSRb5I72fPfv8MY8zmgAXh8omxjoX0OkPuQv1NEbp7Y9nJgrzHmXBG5a0b7IHAUcK+I9BfT73KWTqeJRCIF35xlsi7fv30Pb+5+nEdDKR6veZx/uezdC77HdV1SqRQtLS26XKdERIRUKkUkEil4DBJ2gt/1/ZyN7klUHtXOWefP/TDCqWxn9IrvArmxTqfTtLa26liXSL4kejQaPah8OJaVpqKymuP292EHZj+NyI+1BndKJz/WY2NjRY91xIpw656f8a+mGcYNTSNnUtUwQPPz/4nUjLaO45DJZHSsF7cb2DZj2wvJXQ9dN0f7Z0+8Z1HFXrOIyOiM9/8TcCSwdUpuoBOARw+laxpV/oo9l4E3AWPAu0TEBXYaY04F3g/cNNFGz2VVEitxPk98/y1yDwGiq/KLqDVjNefKf49cKfSp3y97+MoYcxrwAXIfosGJze8ENgEfJpeXp1DbgGpypdcBEJF9xph9wAU884Qrbyu5/6dPFN/z8pSvuOLxeBZfMiWCZyjF0FM7OCcyRNgP1x2zn1cf9clFf046naaiooKqqiqd1l8itm3T29uL3+9fdBmNIw7diS56Uj1Ueuo4MX06hOeZbjgjSp1KpSbHWstkl4ZlWZNlspe8ZEoE//BjpHwbqASaLjl1VpNUKkVlZaWOdQllMpklj/VAvIdTBsM0ui7O+VsJeSoZa2iZs20qlaKqqoqqqirNnbawnwAfMcbcANwMvBU4HhgAfju1oTHmauBy4P8VeOxtFHfNMvVntZG7RvqnGTfAJwA/LfDnK7VctlHcuXwBcMfEzXDebcBXjTFmIhWEnsuqVLax/Ofz3wB3kPv34a8r1XG1Nq1agEdEXj9j0+fIzahZtiCPMeZ8chdMKaCJXJl0gD7gVcB9xpjzRKTQD0J+Xe7MXEC9wPo52p9ArorXx4wxV0704+fAJ0QkXfAvUiYcx6G3txfHcQpamuWNWARv62bz+N1IzXrGttZRVdNEbfUcOVmmyE/rb29v1ye/JZIP7riuW9BT/r3xvfyy61o2V23mdE5n24iLtW2eJV1uFjy5PzX5kujNzc26hKNEstks/f25+7eDWR7nHd9HeNfP2Zx5H6Mn1RIMTZ+NlS+J3tLSovm0SsSyLAYGBvB4PEsa6+o9+9gy0s5j6yo4sfOEedulUimMMTQ3N2uOpcV9kdysnF+Re8hlyF03vCE/w9gY80Lg7cDFwJPAfxR47GKvWab6J3LXTP+b32CM8ZJ7cHWaMeYRoBm4D/igiDxZYJ+UWopiz+VOcsuvZratABqNMRH0XFals6znMzAsIv+e36EPVdRMpbwS+xO50ugfWsZjfpzcxdClU6cei8gXjDHfJhf9/AzwnAKPVwG4IpKdsT1DrpzpTMeTu1jbCfwXufW+XyT34X3tzMbGmLeQyxnEhg0bCuzS6hAR4vE4yWSyoFws0cEU/uv38+f6+3j4mMfxN2Ww3SxbAscs+L58mex169Yd0st11sJYp9PpgoI7/al+frTvB1xV/Tec/1An/aMZ9rcH2dw+Y/xEaLj+lRgnjVPViW3bpNNpOjs7D+m8O+U81q7rEovFCh7r+XhSw9Te/FZ+5TyL1so0W7ecPW1/NpvFsiw6OzsP6UDeoTbW/oEHCPz5Y/TFHMZ962iW17E9+DRW53PnfU82myWTybBx48ZDeqyXi4hYxpjLgJeRS64cA34gIo9PaXYacB7wA+C9IjJzRdx8ir1mAcAYUw28gdzNrjNl1+aJ9wWBNwMBcrN8/myMOWHKLOn8ccr286DWnGLP5Qpg5oPUzMTXEEWey6Dns1pWy30+F03P58NLKQM8lcDeZT7mKcA/z1xXDiAiEWPMN4CPFXG8FOAxxvimrEeH3D8Qc1UD/jDw+Sk//9GJ3EM/Mca8V0RGZvTp60yUdD/99NPLqny7bdv09/cTCAQWXlqRdTF7YkSeHCZUuZfkukf5pLuB2LEfKOjnZDIZ6uvrqaysPKQj0OU81pZl0d/fTzAYXHQZTcpJsb3nTi4dvoj1vRt51Otwz/E1XH1mG4hL6OnfYSY+KsbJgDfIyFU/BiCTSNDQ0LCkEs1rSbmP9cDAAOFw+KCWTLnxAZ7wnog/cCZVodn/jFiWpWNdYul0erIkeiFjHU3ZjD25g8HAs0md8TJOHx6kJxDhgnPnLNwxybIsmpqadPZlESaCKD+aeM3lU8BHZkzPL0Sx1yx5LyB3PfiDGf3cZYxpBKL5vhhjXgQcAF4NfGFG+7L9PKg1p9hzOTWxjxltIVd0pbuYcxn0fFbLalnP56V0QM/nw0spkyJ8CXivMeb0ZTxmltzSrPnUUNzv3DXxdWZt73XMnmaHiLhzBJcenfi62PTosmHbNt3d3QCLXrT7BtO4u8aIJvvwha8n4K8mtaWwomHJZBKPx0NdXZ0u4SgRy7ImcywV8vT9qfFdSL/F5t42YgEPg+squGRLPZCb1VH16NcxTiYX3AFip74TgEQiMTnWuoSjNDKZDD09Pfh8vqLLZM80HInSlaljaypA6ISN0/blx7q+vl7zaZVIKpWazLtT6Fjft2+cwcgY9bW1nO334U0Lg+tmFnuaLh6P4/V6dayXmYgklxDcgSKvWaZ4AXCdiMy6cRCR0al9EZEksIc1dE2j1qRiz+WuedrGySWr1XNZldKyn89KLaSUAZ7TyZ2s9xhj4saY/caYPTNeTy92kBluBd5hjDli5g5jTAe5Ne23FXG8R8hNn75oynE2kUvYfMccP+Nnxphfzdh8OrlpdQVVwSg113WJRqPYtr1g3p3eJyIkv72L8Zt2Exj4DJv8n+M/2zw0d/4Ndv3Ri/4cy7ImK64cyst1ypnjOIyNjeE4TkHL8J6K7eLHe3/KaG8H/s5aNl+yjnNPbWZdXS4wZJwMdt1RJLe+cvKVbTmVTCaDbdusW7dOn/KXiG3bRKPRgnMsLST21C423WFzpHsB8c0BmtY9c22cH+vOzs6DDiKppclms4yNjRVVEv0ve8YI3vtnLhw9iYa+9WQe3sl1NXdTXdc473symQwiQmdnpwboi2SMOd0Yc6MxJmaMiRhjrpvIIThX26snZgIXoqhrlikuAG6Z42f/7UQfm6dsqwa2AI8V2CellqLYc/lO4EIzfcroJcB2EXH1XFYltqzn88p1Ux0qSvkoPQzcv8zH/H/AveSWRl0PPEUuieFmcnl3BPiXQg8mIhljzFeBzxtjhsklIPwqcLuI3D1R9q4BGJ1IjngtE8uxgN+QWzL2eXLLtuLL9UuupHQ6zdDQ0IJBF088S+vOIayqu6lqvIvf1rXQX3caZ4VaOKvp7HnflyciWJZFa2ur3vCXUCqVYmhoqKAb/tHMCPfsv4M3Db6QJm8TLc0Vk4uBJzkZxDt9FpDrupNjrfk5SkNESKVSjIyMHFRwJ56NcUvP72l6eic9dcey86wAf7v+4sn9+bFua2vTG/4SyZdEHxkZKSgxPsCB0TQP//Vh3ut/kt0dW9h3XBPnNZ/Ni5n/b/nUz7WOdXGMMWcBt5ObZn8zuVnHVwJXGGM+LSKFVsyaZQnXLBhj2oFWnpltPNXtwDjwfWPMB8ldM34KGAa+v9R+KrWYJZzL3wI+CHzNGPMl4DLglcAVE4fUc1mVzAqcz0otqGQBHhG5eAWO+eREmfRPkrtgesnErhRwE7n8PMWUSYdcXh0/ubXpfuAPwD9O7DuX3KyhS4DbRORnxpgQuTLtnyT3Af4y8Okl/1KrKJPJTJbJXujpu3c4jeP0kgruon/zlQxm+7mg5ULaQjNnE84tmUwSCASoqanRaf0lkk6n6evrIxgMFnSDtjexly2pDqrtJnavC1N31OybR29qGPFOP29SqRTBYJDq6motk10i+eU6hY71NOLiSQ0jIuwa30Fi7GnOSlUT8R/Js9q2TmuaTCYJhUI61iWUTCbp7+8nHA4XtBTScYW/PB3liuBuvN5jiFbVEPAsPvMqkUhMjvWhnGNphXyC3JT8c/KJXY0xJwHfA/7FGNMqIm85iOMXfM0ysS3/D/d8uQsvAz470d5HLih16VqsDKpWnJl4LZdirr8HjDFXAF8hV31oP/AaEbkF9FxWZWHZzmelFlOyAI8x5hbgkyLyp3n2Pw/4tIjMX5t1DiKyG3jZxLS2RsBLrpxcoVOcZx7PBt438Zq57zZm/GMmIt8jd6G2pti2zdDQECKy6FN+/+AjBBPbeXDrswnU1dKWDHBU9eLLsuCZMtmtra26hKNEstksw8PDQOFlsjNOhkCqjnvGs2w9sxUJTA/MeRL91N32bsbPeubhc36s29vb9Sl/iViWxcjICMaYJS2FDHbdQvX9n+fx+k1cH3J400gLzaPPJX1aB9X+Z4J8qVSuyE97e7vmWCqRTCbD8PAwXq+34JmRNz0+StXTMU5yTyRSYfEXeYDnhF+y4HuSySRer5eOjg4N0C/N2cA1U6v2iMhfjTHnAb8D3miMsUXkH5Zy8CVcszw4c9uM/U8Az1tKX9Rh5z+A/1uugy3hXL4bOHOB4+m5rAq13MHKZT+fZ7TddPA9VIeSVbsSN8ZUMD0B8sXAr4wxT83R3ENuBs6RS/15IiLkpl6qRYgIiUSCWCy2YC4W3/AOqh/6CkOjLdxas5l7Aw/h60lwTvM5Bf0cx3HIZDKai6WEXNclHo8Tj8cXDOTtjj3FE7vu46yuXCnFDidNXbyF+s3VbG6ZndfDY8XIdF5E+sirgFzAMJPJ0NHRoYG8Epk61oXkWJpqz3CKu+75Cy8b+gy3V72O9v4TuNqk8Wca2NMZ5qhtz/wpt22bbDaruVhKKD/WyWSy4KVZAAnL4cqGKMHhn/DA8SdxfN0ZrKvomLe9bds4jsP69es1kLd0HmBW9moRSRhjriI3q+CtxpiEiBRWjlKpMiAiY2gCWHVoWNZgpVKrbTWv0CqBh4Haie+FXCWtL83T3pC70JmXMWYP8G4R+e2U7xcjIrK5gHaHjUwmM1kme74nsiknxeDAnZiWY9nvOZqgXcnbjz8NjzFU+grL65FOp6mpqTnkS6KXs3zp5IXGOmEnuGf4bi5zT6L56DaSG4I8uT/GjqSPK09vnf0GcfEPPID4ngn8ZDIZamtrdaxLKJVKTZZEL2amRSoeZd9Dd3KlPEDFkW/lnOBFDGQHeLhtnHMaT+GI6ulBnHQ6TW1t7SFfEr2cJRKJokqiA8TSNqMjQzSM7SS5+SrSNR4qPQsH6FKpFLW1tZoY/+A8CLzBGPM1EZmWykxEUhNBntvIVRn1AH8tQR+VUuqwpcFKtdatWoBHRIaMMVeTm25mgI8Av2LuixcHGAJ+sshh95NLVJh3gFzgSBXIsiz6+/vxeDwLzqq5f+Q+dqX3si7QRCVBGgMd1AZqCv45yWQSYwxNTU365LdEMpkMAwMDeL3eBRMebx+6E8u1aPO14KuqxFRXYflcwrVmzptH7/h+Kh//DmPnfhx4pkx2c3OzLuEokVQqxeDg4KL5tObSd/8vObn/Jjqanos/fgp2pYeRtiz+kI/q+unnTb5MdnNzs+bdKZFkMsnQ0FBRJdEB/vRkhNPHf0uQdaSbjyMrO/B55v/bHI/H8fv9tLS0aCDv4HwCuB7YaYz5KfAlEenP7xSRMWPM5eTyBr4biJaik0oppZRam1b1TltEbgBuADDGbAS+JiL3HMTxLpmx6UUiMitRoJqb4zjEYjEymcyCSzh27vo+twzfwjuG+skc9U9s723nqKNr520/k2VZWJbFpk2btJJSiTiOw/j4OJZlLTjW94/cx58H7+AD4bcQfCLJf/jj9D7iJZaxed3ZcyfRNtkEVtuZZNvOwLKsySUculynNGzbJh6PLzrWM923b5xfPDTIRfEhLtrwWoKDW3n6FJdvZb5ONpblyurnTms/tUy2Bm1LI5vNEovFsG27qApp4d2/4m93fJ0h/0mMZS7nxyO/5alED6/a9Jo526fTuRyk69ev10DeQRKRG40xl5Kbvfx+4BtztBkyxlxArsrKq9AHV0oppZQqUCmraL1+BQ77sDHm6yLyiRU49iEnP6NjvqUVQ+lBnhh/gl3jj/J3gUsZrb6Ugacsrqz1UN8cxirgZ+RLore0tGjenRLKz+hYbMnUQLqfNzS/isZHvPy4yuWMk5s5feMCM7XsNOE9v0N8FZNj3dzcrHl3SiiZTDI4OEhVVVVRMy1i3Y/yBv8BamoqMdFKHjhqkLs8T3B+y4Wc13z+tLY61uUhkUgwPDxcdDUrX+QpHmh+L0fGG+jaFuSVW946b1sRwbZtWlpaNJC3TETkduAUY0wdudLN0xhjWoAREXmNMebTwIWr3EWllFJKrVGH2qO4JmCg1J1YC6aWTp7vBu3ekXuJWKOcaapo6FuPJ2LR3hwmdHQt2Y7CnhYnEgn8fj+1tbX65LdE8qWTQ6HQojfjGTdDS5+PA65LoiHAce2LVFSL7MI/upPk1pcTj8cJBALU1dXpWJdIPB5nYGCAUChU9AyqlqG9VGS8SF07w+uDxNoMx9Yey0l1J89qG4vFCAaD1NXV6XKdEhkfH2doaKjgkuhTGTtJa6yOwaokVtPC58nY2BjBYJDa2lod62VkjHk78Gfmvg77EtBrjHmPiDwhIv+7qp1TqkDGmC8YY642xhxr5vgDYYzpMMZo0i6llFpFh9rjuB8BbzLG/FZENNAzj2w2SzQaxXXdeaf1/6n/jzwUeYDnDTdxyu5xMp5muLiR1iNzszkKmS+eTqcRETo6OnS5TolYlkU0GgUWL4n+x/6beWLscV7I2TxWaThuQw0Vgflz6Hiju6m/+c2Mn/WvxP2tGMehs7NT8+6USCaTYWxsrOiS6P/10G95LHEPV3M07e0tNJ57OQDr52mfSqXwer26XKeE0uk0Y2Nji+ZOm0vNjb8n3ncRAX+K3216iJdWvnLetslkkkAgwIYNGzS4s0wmboK/S27pVQTYCDw9o9ke4BLg88aYM0XkFavbS6XmZoypE5HolE3v4ZlLwpQx5q/kEok/NPF6Oblz+YzV7KdSSh3ODrUAjwscB3QbY3YDg+QSNk8lIvKsVe9ZGbEsi0gkMi0/h787QfeOvYzE9xNy4zR5Xd6ZPo7G1Aie6tdizt9Ea4GzdiBXtjebzdLW1qbBnRLKZDJEIhFqahZOiP1UbBf3Dt/Na498A+kHfDw2GOOsY+oWfI8nHSF11N+S2HQVdjLJunXrNLhTQul0mmg0uuhY5z01vou+vbs4d1+Wt2RPpS1ZQ/KYRtILvMdxHBzHoaOjQ8e6hBKJBLFYrOCxnqovluL/1j3GGSe8iLetm3/lj+Pk/uns6OjQ4M7yehO54M5XgfeLyKyPnIh82BjzCeBrwKuNMTeIyPdWuZ9KTWOMeS3wGWBqQr4G4NQZr7eRK6aSD/wkV7GbSi3KGPMS4HxyhX6+JyL2lH2/F5GrStY5pZbBoRbg+RtgeOK/Q8CGEvalrAUCgclp/Zl0Es9jA9zMQ5wRegpv3fFUBluwPXUMezZTt+4IpLlikSNOl0gkCIfDRecBUctvoZLoeY9FH+PSpktpTTfSN9jPiRurOaZ1njEXF2+sG9/4PsRXQSKRoKqqSkuil4FCxhogk7R5bP9jXNh3BNHs/UhLFfHOdVibtiz4vkQiQXV1NRUVxf09UMsvFAoVPYMqnXUZcByODx/JaW1HLNg2mUxqSfSV8SbgDhF5+0KNRCRtjHkDcBLwVkADPKokjDFbgK8Dm4FpmdgnZvPcMvHKt68GngNcQy51wjtWqatKLWpieeyHgd8CHwDeaoy5ckqRngtK1jmllklJAzwT/whUkovyj4tI6mCOJyILX7EqMpkMw8PDiDyzyGr0lgeJJSxidfdxskmSOfODSOCZ2T3Flu9IJpN4vV6d0VFi6XSa0dHCisrZYnPKvjasrj7SlsPRm2vxeuYO1gT676XmL9dgNxzHyIbn4/f7aW9v1+U6JZRMJolEIgUH2Lpu7OaUdAsRrx8PB/Bsfi7W+uMXfE++TPa6des0kFdCiUSC8fFZeXkL8rMHBrhQDFsb2/F55v/bnM+n1dbWttRuqvkdT+7mYlEi4hpjrgX+eWW7pNSCXgFsA7aJyL7FGotIDPipMeb3wA5yQR6lSiIajVJXVzd109uBZ4vII8YYH/DfwC3GmEsngjx6gaPWvFUN8Ex8kF5Lbk3uaUDtjP1R4H7gx8APRSS7mv071DmOQzweJ5lMUl1dDUBwZ5SOoRr+MZDhW/4Q1rZ3TgvuFCubzU6WydaKK6Uz11gvpLO/ksa9Lj/vrGTD5hpqm2Y/tfdFdlFz98fxpIZJHvtqoptfimVZbNDlOiVl2zaxWIx0Or1gWfSbnxjlnn3jJAKP8uKsj7s6/8I7kntpHX2K0erXYM/7ztyyTmMM69ev1+BOCdm2zdjYGJZlLTjWM926K0Lmrz/nDGuATdYVxGrmn4GVyWTwer10dnYuR5fVbDaQKaL9MLnl50qVyo/JzZC/wxjzOhG5ZbE3AIhI3BjzPXJ5ev5rJTuo1Fy++93v8qEPfYi+vr6pm9tF5BGAiaVZbzXGfBG41RhzKcU/11aq7KzaHbgxpgm4idxTgCeBG4A+ID9rJwysA04Bvg28zRjzHBEZWeCYtwCfFJE/Tfl+MYdtDp5UKjVZFt3j8TA62EvzE+P826bf8uGmkwjv3Ufad3DT8dPpNPX19Tqtv8Ty1ZQqKysXnFkzkhnhydEDVAzCX7Z4eHQky9H+2e2NFSf81C/IrL+Y5DGvQHxhMvE4DQ0NBIPBlfxV1CJisRgjIyOLjvXjfQnecmKWe/r2cE7kfC4ei5I5830MNRyDLPK5tyyLpqYmLYleYtFolLGxsaKWQ3rH9xN44iEuoovRwGUMnVdLc+fcS/Fc18W2bZqamjR32sp5Cji9iPZnAAdWqC9KLUpEdgHnG2PeRC7Y01rE24eYnrNHqRW3a9cu3vKWt/D000/zve/NWt06bIw5QkT25jeIyHuNMV8CbuXQS1+ybAq57pi6QkSVzmqexF8AjgD+Jh+QmY8x5jLg58BngTcu0PQIcku88o5EI69zSiaTDAwMEAgEJm/SrLuf5ukGw7Gx/XT4DekNl+JWtCz5Z8RiMQKBAE1NTfqUv4Ti8TjDw8MFlUW/se8P7Oq3eFnmGHoyAba0hOionR2wCfbeiX90J2PHvgrxVzA+Pk4wGNSxLrHx8XFGR0cLKouedYRNO/+L7PjFmNAA0nE82Yat4Fu4CtPY2BjhcJiGhobl7LoqUjQaJRqNEgqFipodWfXQf3JG9Hy6a08h0+Cyad2mOduJCLFYjIqKCurr65ep12oOPwE+aYz5oog8tlBDY8zxwNXAf65Kz5RagIh80xjzq6nbjDFD5KplPZh/icjuiX0GuIrczHylVs2Pf/xjHn74YR5++GE2bdo0c/efgNcBH526UUTebYz5CnDCqnRyDZoZvNn0od+z7zOaj7ocrWaA57nAZxcL7gCIyB+NMZ8D3rlIuyNmfL/poHp4iMpms8TjcRzHmVYW3eOAE72dFww9TuqyL2GHCq+SNVM6nZ5cwqG5WErHsixisRiu604b66l+1/0bdo7vxGM89MfHuWD3yzkjUMFx29qRinn+JNgZ0hsuxaleP1kmW0snl1Z+rEWkoBlzl41+k6c9m2nOdBC78gSC4cUTJU8tk61KJ5PJEIvFMMYUPGPuh/f283hfgreNDRD113Hrunt5w6n/iN83O+grIpNjvX79+uXuvpruf4G3ALcZY94N/EREplX7NMZ4yC1l/zwQA760yn1Uak5zzKrfTm5m/mX5JsaYGLATaJ54vdYY49e0C2q1vOIVr+Dmm2/mwgsv5Dvf+Q6XXnrp1N1vZ577XxF5pzHm86vSSaVW0GpPQyvmj3sKOKhSLcaYRsCZyPJ/2EokEgwODlJTUzN5Q/70nmsJZ2pYH/8T2ZddR/gggjsigm3btLW1aS6WEso/gR8ZGVmwfHJfqo+3Hv026iN+7t01wLkVkHzWuvmDO4Bx0ogniIggIrS3t+tYl5CITM7oKKRUds+jt9FiDVLrfQ7W84+mqoDgjuu6GGNoa2vToG0JiQiRSKTosuhPxXZx0ZYn6d4fpzVl8bqj30ilb+6/847j4PV6daxXwURekucDvyFXGeurxpgHyC1Z9wIt5HIUVpFbmvVCEemb73hKlZKI/C2AMaaOXKDnlImv24D15O4zrgVsY8wu4FHgERH591XvrDpsbNmyhTvvvJNvfvObvOIVr2BgYGByn4hYgDXzPcaYO4AficjXVrGrSq2I1bySux1490S5xQVNtHkfcEexP8QYc7Qx5ocTCZsHgRFjzKAx5n+MMcWsGz4kjI+PMzg4SEVFxbRp/bf1/IFaq5KuE99HuPrgpuPnl+tUV1frjI4SikajDA8PU1lZOe8SDldcMm6GoCdI8Mkx0q4QOb4Ot2aO5T0i4Nrg2hgnDb7g5DK8YhK8quU3OjpKJBKhsrJy0UCb7TrII98lG34+ka1BKqtqF2wPuaBCPB4nFArNOxNMrY7h4eHJvDuLBl/ExXUssnaaSOAuOvrupWbDc9gQWk91cO7gkOu6pFIpKioqqKg4qGcqqkAi8iRwMrkSvU8C55OrVPR3wLnklrq8G9gqIg+XppdKFU5EoiJym4j8h4i8VkROJhekPB14M7mZa1FyS7Y+VbqeqsPJm970Jh5//PFCm58PXG6MCRljjjHGdKxg15RaUas5g+e9wG3ADmPMbcADQA+QJJc3J0wuEdup5KZ6Rshl3i+YMeZkcoGkCnJJnJ8i90TsaOBNwPOMMeeISNfB/zrlL51OMzY2hsfjIRSanmfjir4riZtG1p169kH9jKlLODS4UzrJZJLx8XH8fv+CSzi+u+f/APB7/OzpS/IXv3D6kTUwx9iFd/+Sqoe/iluRq3A6cOJ78Pv9ulynxBKJxGSgrZCkx//6l69S12l4bTRE+zHHYRYJEuSDO4FAgI4Ovb4ppXg8TjweJxgMFpT0uOHG1/OFCsOwI2x2hfMHr8JOnIP4PcSDswOBIkIikSAcDmtJ9FUmIhngixOvfCEKR0QiJe2YUstkYqZEPjfPJGPM5tL0SB2OGhsbi2l+Ebn7zwCAMaYf+CnwOZ1JqdaSVQvwiMg+Y8yp5GbmvIxn1uvOtBf4Mrl8PcNF/pjPklvaddbEE7JJxpgTyGVH/3fglUUed81xXZfx8fE5p/Xvu+M7VNoNfMK4/Idv6ZO4bNtGROjo6NDgTgnlx3qxkuh3D/+Fp2JP8fGTP8n+e4fojGX5+5dsIjhH1Sz/0CNUPvYdxs/5CJnOi8hms9i2rTf8JeY4DtFodNGS6FNlJME1acEf3kzC61s0C302m8Xn8+lYl5jjOIyOjpLNZhedRfX0H3sIDT9Mn1zGtkQNbuwYzj+qDteTInZlJ8zzdz6TyRAIBDS4UwaWcL2j1JokIk+Xug9KzaMO2E1uQkIQOJPcbMrXG2NeLCKFVGtWK+w1N1xNNBOdd/9JZ8Pzf/3VOffVBev43pU/XKGelY9VzcEjIqPAvwL/OrFetwOoBgwQBw6IyNhB/IhzyJVNf3LmDhHZYYz5MkXOClqrxsbGGBkZmSyJPpWn6/eI7x/58HM24zmIwEwqlaKurk5LopfY6Ogoo6Ojiy7heHD0Af5xyztIpAV39zjD57bQPqNilskm8Ma6CO/+NcljXk5m3XmICOl0moaGhlkzwdTqGhkZYXx8fNGxHrOixO0EsYxN0HYIWXW4BsS38OfddV2y2Sz19fUFJ/NVK2NwcJBEIkFVVdWCAfToUIqG7nF+s/4WLmx7JbWhVhpCdWQqA2RONvMGd2zbxnVdGhoaCpoJppRSxpjluMEVEXnWMhxHqeV2G/BsEbHzG4wxZwLfBH5tjDk1XyVOlc5CwZ2VfO9astpJlidNJD6OLvNhY8BC89gzgLPA/kNCPB4nEokQDAbnvHD/TVuIN0eqCdUu/aI+v0SktfWwS2tUVmKx2GQZ68WWcNhulpZQC/91ay+v8npo3Dh7tk/F498j2HcPdnUn6Q3PQox3colIc3PzSv0aqgBjY2PEYjHC4fCiZbK/vedbtIXauf/AOO8ZfBZe+2msI4PgnT9Q4LousViMyspKmpqalrv7qgiRSIREIlFQjiX391080vwYR9DPkcef9sz2hd7juiQSCWpqamhoaFimXiulDgMeWHQi6GJ0yrcqRy7wy6nBHQARudcYcxHwCPAvwBtK0TmlilGyAM8K+S/g/caY34nII1N3GGM2AO8AvlKSnq2iTCaDbdtzTusPPTjCq/teTYuvjvElzt5Jp9P4fD4tp1sGUqkUrusWNLOmL5biY9cd4PTRLEf5/YwFZj/Z92TjxE96K9a6cwBIp1L4fD46OzuXve+qOMlkEhFZNJD3q65fEsuOsy7+Kp69N8KJxsJnvkjqrFfOmWsJcrlYUqkUoVBIl2aVgXg8jsfjWTSQ9+N9P+IS/wbuabqFL3QNMFTAsfMl0SsqKnRpllKqKCJycan7oNQKGQXmfOIhIhFjzLfJJQxXquwdagEeD7lZPPcbY24EniBXCm8z8HxyTx02T3xI80RE3rjqPV1hPp9vzmn9gQNDfHvd93jPJd9f0iMU13VxXZempqZFbz7U6phvrKfqShwglo3zpbPX0fCXQcbmyMvhjffgG3mc9MbLgdxYAzQ3N+tYl4lCxnpP/GmubvsQ99+4nZcGW/H6PsDgC/8yb3AHcvlePB4PTU1Ni84YUSvPGLPoZ2537ClMf4w6q5rP9+5j6CV/LOjYtm3j8/lobGzUsVZKKaVy7gXebIz5soiMz7F/BCgqY7NSpbJqd21LXLdb7Drdj0/57+dMvGZ67cyfARwyAZ5oNMro6Oi8Nwe+8QOM1DctKSmyiEwu4airqzvInqqDNTo6ytjYWEHVdX7RdS3h+JnUPjxK5uhamCOxcsUTP8CpPQK77qjJ5TrV1dWzknSr1Tc8PEwsFis4B9J3/tLHl7MWbsc+EuveAN75l2M6jkMymaSmpmbBJN1qdQwMDExWtlrIzw/8lA/3vpisfJv4yf9Q0LFt2yadTlNXV1dwkm6llFLqMPA54E/AncaYN4vIPfkdxpgQuQI9WklLrQmr+Vh+N7lS5QL0AvbCzYsnIksvCXUIyGQyxGIxvF7vvAlSuw2Me4oPQOen9QcCAV2aVQbS6TSxWAyfz7dggtRvP/1NIlaEvqjFawePx1vtkDi2dtpsjsq//i/hPb8D12H08m/i+ipITtxg6nKd0kulUpNlyxeb1fG5m/dzIJzmP4cfwuerYPSS5y3Y3nVdkskklZWVtLe3L2e31RIkk0mSySShUGjRse6LCoMxL2HfE3iO/Nyix87n3amtraWlpWW5uqyUUmoBiz1QFTnYlEZqOYjI7caYNwL/A9xljNlNLu+ORa58+jrgUyXsoipz6S9sh0R2zn07KypJ/9ttc7+x0k/ofecta19Ws0z6W4wx9wBfB/4oIq9frZ99OBARIpHInGXR8/beO0CnW8lXzv9w0cd3HAev10t7e7uWRC8xEWF0dHTRsugPjN5PT7Kbf6t5P7c93M95AS/jz18/Lbjj77+P8NO/JfKs/8ap2QRA1rLw+/20trbqWJeYiDA0NFRQWfT9d/6QC0fHoWIDDTQw+ppLFj1+NpslFArR3NysY11irusyMDBQUFl0gMtGm9jsj+ILuxRSX9uyLCoqKmhsbNSxVkqpVTIzgLPpQ79n32euKlFvcgr5N+BwDDyJyHeNMXcA7wWeB7xkYleaXA7Xa0rUNbUWzBPcWbH3LWC1y6R/yxjTAXzUGHOdiPxiuX+GMebVwOVAO7mcPHN049ArzzgyMsLY2NiC5ZNrnojwy7breJkpLkrouq6WRC8jg4ODC5bKTqTSZDIWfz5wKy9f92oCfxlljw+SF7Y9E9xxsxg7ReXOH5I44Q041RuA3BIOy7J0rMtEX1/fZKnsmWNtuRaOlSWbSuDaWWqe+in1wQ9Ce5LElsVn6WWzWWzbpqGhoeClX2rl9Pb2kk6nqa6uXvTiOzE+yrOiW8mcZhNv+eyix7YsC9d1qa+vn3d2p1JKqcNDOQadyoWI7CVXlOcdxpg6oBroF5HlvwtXaoWUInPqJ4AXA581xvxKRBaq5loUY8wngX8mN51ukMOgJDrkKq7EYjGCweCC+Vi83i481XPlDZuf67rE43EqKiq0JHoZiMVik/k55lvCkf7FA8R8Ni+U8wjscHnQazG+LoTd+kzApvauj+Ab24PrryF9xHPAeCaXcFRXV+sSjjIwNjZGKpWat1T2p3Z8gg/veAFx6cE1Hgi9jWwjdJ5+OZlFjp3Pu1NXV0djo+YMLLVoNDo5S2u+AP1Unl8/RndFH5XHvXLRtrZtk0qlaGxspL6+fjm6q5RSSh3yRCQKREvcDaWKtuoBHhFxjTFnAqHlDO5MeC1wI/BiEUku87HLkm3bRCKRRaf1776xi41OBS89978LOq6IYNs2tm0TDAa1nG4ZsG2b0dFRHMeZ9yn877p/yxmmnk0vv2hyWztwzIx2JjNG5OIv41a2ISJkLQvbtgmHwxrIKwP5sRaRWcGdrmvfS0f8NF4b3kZFepS0+R+qXn9DQccVEbLZLNlslqqqKpqamlai+6oI2WyW0dFRPB7PglWtfrb/p2x+qJ7mdIoaTwVHv2Th4E5+rC3LoqamhoaGOau/KqWUUocdY4xLLi/sQRERLUepyk5Jah+LSAYWfci8FDXALw6X4A7kquvE43GqqqoWnNbf3D/KNzZ8n3+gsOVZjuNg2zYVFRWEw+EFE/mq1TE4OEgqlZpzrLsjaSJDfQwPdhOwt8z5fk+8F29yAABvcgDxVwC5G0zHcaisrKSioqKgqlxqZfX19ZHJZGaN9d6RFJ3jw1TVnslN639LY/sL8dX+oODjWpaFiFBVVUVlZaWOdRno6emZDLjN5B3JMJTopz+eJBWJsCV6DBXVX2Tskk8setxMJoMxhurqaqqrqxdN2qyUUkodRt7AMgR4lCpHh9oV3x+AS4Fvlrojq0VECIfDCz75BRDvICfUrivomI7jEI/Hqaur08o6ZcR1XSoqKuZcwvG5mw/wKSsCVacSWV/DXM/q6+78Z+zaI8D4sNrOQvxVk8s3GhoadOZOGXFdd1aOJdsRPv+HffxQ/p7EtlbilkvN+iMLPmY2myWdTtPc3Kwzd8qIiFBZWTk7QO+4VP2xh3sqHiSVrOP04Gl0mO+TPfH5eFs3LHhMy7KwLIuWlhaduaOUUkrNICLfKXUf1oTPtUFiYM5d+0IsnHa6shU+0L8SvVKLONQCPO8A/miM+SHwa3J5eGZFZ0XkjlXu14rIZDJYlrVou70//RmNNHLhqR9ZtG2+HHpVVZUuyyojmUwG27ZnBXc+ecM+0lmXd0ovqQofF9RdizfWDb+bfQxvcoDRZ39nMtFyvkR2TU0Nzc3Nq/BbqEKk02kcx5k21q4I11y3h0/jZUd1Hz+1tlPjn7ta3lzyY11fX683/GUknU7junOvVHYzLo9aFn9u+i1vi0boqAjgdjQxvuUlc7affN/EWDc3N1NXV7cCvVZKKaXUYWGe4M6Kv1cdlEMtwLMBqAVeAbx8jv2GXMDnkFgvOTIyMrlkZyH12XpqXlVY4bBsNkswGKSxsbGgZJ9qdQwODpLJZKblWbrnzvs4LpPgtA3V1OwOUPeic/H+7kuMPO/ago6ZzWYJh8PU19frWJeR/v7+ydxXeZmMzZXZe6kMruOH7dfzvmO/VdQxLcuisrKS2tpaHesy0tPTg+u6s2bviOuy754BxD9GfcBL6NW/Y6TAY2YyGWpqaqipqdGxVkoppZQ6zBxqAZ7/BuqAzwG7ALukvVlBAwMDxGKxeZfsLEU2myWTydDQ0LBgwma1unp7e0kmk9PGOms5nLDHpeKIELguqVMbqSvimJZlkc1maWxspKKiYkX6rYrX09MzWU1p6k1/PDbCqakOnt78FM/pfGFRx8zP/qqvryccDi/+BrUqDhw4MJkAeWaAJz6WpXFfjJ8f/RdemCn8eUR+RlBNTY2WQ1dKKaWUOgwdagGeE4BrROSzpe7ISkokEiSTSUKh0IKJMxt//3L2D8UQ/3sXPebUJRy6XKd8xONx0un0ZJ6lrOPynp89xX+k4ZHGAc694Pkz3rF4vjjHcUilUjQ1NWmJ7DISi8Wmlco2GYeqX+1lr7MXvxi6KgfYcu4bizpmPsdSa2urlsguI+Pj4/MGd9LJLKFf7mF70y1sydzLxvoziRdwzPxYd3Z2UlNT+PI9pZRSqhwsVCwGcmkklFKLO9QCPF3AcpdeLyuu6zI8PLxoWXSASDzFh9veyEfHFn+Sm6/Yo/k5yofrugwODk6WRXdc4cf3DXBsU5hsYphHj+3h3CntKx77DsZdeNKaiJBOp6mpqaG2tnZlfwFVMMdxGBwcRETweDxkrQwjtz3EqD/G9qaf8ObLvlXUDC14Zqzr6+v1hr+M5Mfa4/HMupjNuln+tO96Tqkw9DXv4BXn/qig4I6ITAZtdfalUkqptWhqAGfTh37Pvs9cVcLeqHL1uxdef3AHOAzihIdagOezwMeMMdeLyOOl7sxK6OvrI5FIzPnkd6betEPjsU/QZp28YE36fKLm6upqLYdeRrq7u8lkMlRXV2OMIZmxeWowxXtPb2L07t28dMPfTWtfsetaIpd+ZcFjZjIZPB4PNTU1OtZlpKurKzejo7oak7QZ7R3EjVvcGxjl1Z7qJR0znU7j8/morq7WcuhlQkQ4cOAAjuPMuTSyO9lNOjpMYzbIVce9p+DjplIpAoGAlkNXSqlSW6DqECxSeUirDim1qOf96jml7kLZO9QyMG4jF5f7qzHmSWPMHcaYW2a8/lTiPh4U13Unl3As5hubGtnWcCIBz/w38vlp/TU1NVpxpcy4rkt1dfXkWKezLq2VPjbeNsj+pjEagtOXV4kviFM7f9nsfJns+vp6qquXFjRQK8NxHGpqavDGbCp+e4DR7RG2h3t5V+QL1G28pOjjWZZFJpOhsbFx0STsanXZtj3n528kM8LPdn6fVz59NrZngJq6LQUdL18SvampSfNpqUOCMWbRl1JlS6sOKaVK7FB71PdccomVu4AAsL603Vle2WwW27bxeudPutn3y2sxmQAIvN68iK3Z0zF2dlY713WxbZt0Ok1tba0uzSozlmXhOM7k0/iM7fL1O7uh6kZ2h4/nqY4Iz7n9fXis8WfeZOYO+k0d68bGRl2aVWbyM+gAUjGLh90EN2/6MieIjRx1JenNLyj4WPmxTqVSNDc3a3CnzEwd67zx7Dg/2f8jknaSU0In4mQfZrS2j8UWWk0d69bWVl2apQ4ZM/Ns6FINpZRSqnCHVIBHRI4odR9WUl9f32S545n8Q4+wP7GfqlQ93wiHeO1Z7VSFqklUNiDe2U+7LMvC4/FQX19PVVXVgkEjtfp6enqwbZtQKISI8MC+cY52szSYBtbXHsHVm87Gv+MljD77O5PvEd/cFZLS6TR+v39y5o6OdXnp7u7GcRyMgN2bRIIDbPAF+JtTP8N4oLhgXCqVIhQK0dDQoGNdZkSE7u7uadsc1+ah0Qep89XxxsSJJLrGyHrStD3vE4seL51OEwwGaWhoKHhWp1JKKaWUWn7hjxY/4z5PPrK8iYEOqQDPVMaYdmADsBNIAbaIrNkEzAMDA6RSKSorK+e8kK/70z/w4SO28Z7s1Zx99nE0ddQBc+eRypfIbmlp0co6Zai/vx/LsiZLZQ/GLG58aJBP2V7+GqjHOa4BPyC+CtzQwjOv0uk0juPQ0tKiiXbLUE9Pz2Q1Je9wmqoDUbY3PMhLvA14wsVVs8tkMriuOxm0VeUjH9yxLGvaDLqeVA/3jNzN6wMX0fRggojEGdu6kbpAaMHjpVIpXNelsbFRl2UppZRSSpVY6mO3lroLkw65AI8x5jzgK+Ty8QD8Dbnf89vGmPeKyM9K1belSqVSJBIJwuHw3E9p3Sy77I/ytqdqiIaaOW9z3bzHchyHRCJBa2ur5twpQ8lkkkQiQUVFBcYYYmmbT/9hP8/eUIPE0txzRDfHdlRirDjiXThJsm3bJJNJOjo6NOdOGUokEqRSqclgjLFdhmsStGfu5oiWq0gUcaz8WHd2dupSnTIUj8dJpVKzPodZN8uW6i10pqvIVA3wy7bzeMu5HQseK59La/369YTDc8/aU0oppaZ6zQ1XE81E59x30tnw/F9/dd731gXr+N6VP1yhnimlltshFeAxxpwB/JFcDp4vAe+e2DUKZIEfGWNiInJDSTq4RH19fZOlsqcS16XroYdI9j5Fo6mh5eqzFz1WJpOZLJusiQrLT19fHyIyubQmkXE4qjnMFVvrGPrLDnye3Ec2vPuXiCe40KHIZDKTZZN1rMuLiNDX14cxBo/HQyadJPL4k/Tb3aQC60ic8Maijpcf63xgUJUPx3Ho6+vD5/NNC9CnLIcHd/VxdG8CK/4UaVcI+RdfZmVZFs3NzTrWSimlCjZfcGel36vWNvOxg3u/XLMs3VBFOqQCPMAngL3AaUAl8B4AEbnfGHMysB34F2BNBXiMMYTD4VkX85lMisZdFq7sYOyYS1lsQUcqlQK0HHo5E5FpT+VtV/B5DfH0GMP2CJe2XQauQ+Vj3yFyyfwl0VOplJZDXwPyYz3W04OMxxlpj3HicHFL6ZLJ5ORYazn08mSMIRSavuxq91CKUM8AR8Vridb56W3u5G+2LLzkMpFITI61lkNXSqnyozfE6lAiHy11D9RSHGpXiOcAHxeRlDFmWmICERk3xnwd+LfSdG1pxsfHcRxn1o1bNjFG8Oe7eDp4D766Go46/dIFj5Of1t/Z2anLdcpUNBqdVT3EdoWQm6DqxjjBYxtYF16HsVM4Veuwm06Y8zj5EtkbNmzQ/BxlSESIRCK4bi4lWOShB9n0YJo7am/ilMfvZ3Tr6wo+Vr5E9saNG3W5ThkSEaLR6ORY5+0eSvL7Pz/KB6yNRNY9TNNlb2axdNr53GlHHHHErGCRUkqp8qA3xEqpUjvUAjwAmQX2hYA1U2rEcRyGhoYml3FMcoWKe4fZHd7P75pexHuetWHeY4gIjuOQSqVoaWnR/BxlyrZthoeH8fl8GGNI2An+eOAGjt/ZzLlJobvNx4Yzzp9onEa8s5dnua6L67pkMhlaWlr0hr9MZbNZhoeHJ4O2ydgodzX0M3bORVS3fZxCwq9Tx7q1tVVv+MuUZVkMDw/PWl6bjlq8xROip+Yejm1mwXxLU8e6ra1t1rGUOiR8rg0SA3Pu2hcCrlngvZWt8IH+lejVNIstiZz5gEYptbD0F7ZDIjvnvp0VlaT/7bb531zpJ/S+81amY0qtcWsm2FGge4BXzrXDGFMJvAm4b1V7dBC6uromS2VPZSwXGU5wU93tvP7cdQseI19tpbKykqqqKp3WX4ZEhK6uA7D8FwABAABJREFUrsk8S67rcs/AYzhRm2arlR1NdTSedzIAnngPvrE94Jm97Co/1hUVFVRVVWmJ7DLkuu5kqexgMIjjCkOpMbwYzms+v+DjTP1cazn08uQ4Dt3d3Xg8nmnLJB1XsAbiZEw/3U1Pkzz2VQseJ5lMThtrLYeuDknzBHdW/L1FEJFpr43/dN2070vBGLPoS6myNU9wZ8Xfq9Qh7lC72/8IcJsx5nbgN+SqhJ9ljDkBeCewEfj7EvavKI7jTJbKnio9NkrV+INY6+upC88/hJlMBtu2aW9v15k7Zc627cmlc3/p3cXP9/2GCxLPo88OsGlzB+HKakxmjKbfvYTMunNJb7p82vvT6TSu69LS0qLLssqc67qTlbMe70swMJZgSzhAyFvYLJx8cKetrU1n7pSx/E3fzL+9j/UmGOi5l1Soj/UbLkYC85e0z+dNa29v15k7SqlZZgaWNn3o9+z7zFUl6o0CcjPKlhp0rGxd3r5M8bsXXn9wB9AJakqtGYdUgEdE/mKMeS7wNeDzE5s/OfG1D3i5iJRPkfoFZDLzrzRLx6P0VFXR2LZ13jaO45BOp2lra9Mb/jI3dayHM8Pc03MdH+t5EUeFmrGOqiLTaqj/w+sw2TiZ9Zcwdv6npr3ftm0ymQzt7e26LKvM5QNxeeOjD5Kp7KLCf1xB77dtG8uy6Ojo0Bv+MiYipNPpOZ/qm2iGK6yN7KuPsWHD/Ddi+bHu7OzUROlKKbVWLLJcsFRBuOf96jmr/jOVWgl1wbolV3arC9Yta1/K1SEV4AEQkZuNMUcBpwCbAS+wD7hfROxS9q0Y3d3duK47ffaOCN6uOP2P/3/27js8kqtK+PDvdm7lMNJII2mSx+Mw43GOOBuDA7Yx0YYlLgsfS4YlLAuLCbvAwrLALix5yTkaGxvnNMbZHsexJwfl2Lm7uqrO90erNcrq1khqaea8z9OPrO5b1bd9qzVVp+495zH8xvCaVddMuX06naayslJLZC9yIkJ7ezsAMSvNH3bczYrUUWTDtWQ27QTjEN7egVPWQPSs/0UmWZqVyWSoqqrSsV7kHMeho6NjZDlVJmnj3dPFpvSRtK0+H6uAfWQyGaqrq7VE9iKXL40+ejnVQCrO5j3PsHa74dnKZ4g0dLFmmn2k02lqa2t1rJVSSimlhv340p9N+7rOZDwEAzwAkrtt+tjwY8nZt28f2WyWqqqx5ZI90SzeB/fT7+unvnUNHjN5Lob8tP76+nq987uIiQh79+7Fsiyqq6u5YcfDPN2/jVeaq1hRHqPiqf8h05arjpY8+nWIb+LsnGQyiTGG+vp6LZG9iOVz79i2PfK93v/MAEcNNpGo68ZunXkJZb4c+rJlyzSX1iLmOA7t7e0jeXPybtr9CGZXB03pVnbUPMH61jdMuY9EIoHX66W+vl7zK6nDgpaWVkoptaSV+2eXG6p87q/f9CphkRERMpnMSI6Ood5OArfswZIoXvHxROUzdFR3cNZZ/zhhW8uyRpbraNnkxU9EsCyLqqoq9iT2cM/QHzjHcxlXdHjxZX9C8pRXkTp68lla+bG2LIvVq1drLpZFznVdLMuisrKSQMffCNz7KcKZ03m0toGWI6twq6cOxGYyGRzHwbIs1qxZo0HbRS4/1vm/4c8MPc2f9v+R5X21vG3gQsJHdbEyvIlEwykTts2PteM4rFy5UoO26rChpaWVOvyEP3XBQW0v/6qJgebdIs0ptRhNV9VtoWcVaYBnkXHjGXh0Ly/0PQpAyC7HCkQIBnaw6qrPcA4XTrpdPlhQV1dHMBjU4M4SkM/F4kk5BLZEuKz3YjZ52si2RXC9dVMGd/JjnZ+hpblYFr98LhZjDKmB/dzhv5I23ybWNYdYvf60abezLIuGhgb8fr+O9RKQX1qbX1aV7t7D2/asp7qvnlTNU1TH7sGqnHgSICJks1kaGhrw+XwayFOqxN540+unzfOw6Qy48o/fnPS1mmDNjMsIlDrcpT69JNKiHt6mySmlS6EWLw3wLCIiQs8TO8l2dbAvuJ+W8BEksfGXlVOz/h+m3TaZTOL3+6mqqtILgyXAdV327duXC/J0xrAHbAYDLpXranB8HXj7px7DRCIxMtZ6h3/xcxznwFgDXQMxykwtJgSNxxwz7bbxeJxgMEhVVZUuy1oCstnsSP40AHEcGnZH8SV87K3KsH59NanwlVhNE2fvxGIxwuEw1dXVWgpdqUVgtkk8D3bb6aT/c/O0SwC2lpWT/sxdk79Y7p/2DrM6tGliWqUOH3rFsMj4up/Dse+jo7Wei898VUHb5Kf1t7W1aXBniRARbNvm2ae3smmb8PvmR9l45Nn4V1fDvgzinXymRr4yT0tLiwZ3lggRwXVdKisrAaiMVtIaW8tTG7KsLK+ccrtUKoUxhpaWFg3uLBH50uj55Vmd9/yexp5WfhYc5MJzL0Dqw0xWHzGVSuH1emltbdXgjlJqarPJ7zAX26olb7oZZToTQ6lDi141LCLtu7eT2mNzX0UddsX6Gds7joPrumQyGZqamnT5xhKSzWZ5omcPuweeIdS6gQ+eegUVj38d9oI33k6m7fwx7fNjbVkWzc3NGtxZQizLQkToa9+D9fh26gZr+GPTg7Qum3xpVn6sbdtmxYoVGtxZQrLZLCLCli3PEHphHy3pZdwWbueE0y5jTf3EZbO2bY8Ee1tbWzWhsjp8LcI8D3+++i8HtwNND6KUUqoE9MphkRDX5fm/3YvXP0Bt0OXFR5874zapVIpQKERVVRUVFRVaSncJeWHXDu7sfpRzAqs4sfkIfAOP4garSR71WgDcipYx7ZPJJGVlZVRXV+tYLyEiQkdHB8YYYvva8bodPF89RLjtxRxfc+Sk2yQSCSoqKqiqqqK8vFzHeonIj3U8kcK7vQuffxeJFeUcf9TVrKirmHSbZDJJZWUl1dXVlJWVLXCPlVpEFmGehyv+cNmCv+dMNCnt0jPZv+Hmiwf+O5+j77A026pD+W2VUpPSAM8ikY71E+h8HMtfyTFHXUCZf/rZOPmSyTqbY2n63Y4H6LH3clzNufgCPoydxKlsxalZN6FtIpHA5/PR3NysszmWqLKyMpI9eylL7OT55Zdz7oq1+DwTxzKfX6m5uVlncyxBxhj2PfQsTVaG1sqn8Jx4HW7Z5Mvw4vE4gUCAFStWaBBPKVUQTUq79BzWAZwZLKaqQ0odSvRqcZGwbYeV2QvpOaGcZas2TtnOsqyR5RtaRndpiqcsOvc/y/vdQdYNtkPlt/A6m4md+uEx7fJj7boubW1tGtxZglzXRUR4LvIsXSbOqvqNvObCiQl282MtIrpUZ4mKJC22/v6vnGm10FvxTXxnfhK7rHFCu0wmg4hgjKGtrU2DO0otUosyKa3OeFBKKTUDvWKcgTHGC3wOeDNQCdwMvEtEJl0sbow5BfgacCLQDnxWRH480/t4bMPzy3az8ZjXTtvOsixqamrw+XyEQqGiPotaHNLpLFf6amhxTiJ+3nFYa8+atF0mk6Gurg6v16v5lZaobDaL1+ulY88LnD5UT7atZtJ2lmWNjLUmSl+akhmb40Muv1/zKMef8EVqyldNaJPPt1NXV4fH49EAvZpzszhn+TXw6nFP3y4iLx5+vQz4KvAKcueMvwE+ICLx+ej/YjJTmfNSzDCYqQrWoTTrYa7Pvw/nY1mVnh7PaiFpuY6ZXQe8CXgjcC7QCvxusobGmAbgr8BjwEnA14HvG2NeMtObOB6hYeOJ07aJxWL4/X7q6+tHLhDU0hNwhLZkE6mKDNnmiYlXAaLRKMFgcGSs9S7/0mSMIRQK0fRCHMfjENqwYUKbSCRCIBCgvr6e2tpaHeslSoBe41IVqmR5qGni6yITxlqpeXAdBZ6zDDsO+BjQPOoxOuDzbeBs4GXAFcD5w88pNd+uY27Pv/VYVqV0HXo8qwWiM3imYYwJAO8D3isitw4/dw2wyxhzlojcP26TtwER4H0i4gJbjTEnAf8E3DLdewWralm+cvLKWel0Gtd1AWhra9PlG0tcwM5yU/XtvHj9y6gJj/0KplIpRASv10tbW5sG8ZY4iUQx/3c7K32t9B7nZW3DgSU7+bH2+/2sWrVKAztLXL2VoTLVwMktKwh6D8y4E5GRv+HBYJCVK1eWsJfqUFbsOYsxJgisAx4SkQlZjo0xrcDrgItE5IHh594G3GmM+YiItM/T55j43BfH/q55TQ5tc33+XapjebHS79jCOlSOZz1ulg4N8EzvBHLT6O7KPyEiu40xu4FzgPFfyHOAe4a/jHl3Ad80xhgp8qjP5+SwbZumpiY8Ho8Gdw4BAW8P/3DMVWRazht5Lj/WruvqWB9CjGv42RFPc+qJl3JkZS6Am8+rBIyMtQZ3lr7eYB/Jl4RpaDwQqHddF8dxAGhubtaxVvPtBIo7Zzma3Hngc1Ps7yzABTaPem4z4JC7c/yrOejzBHqBULhD+ILrBObw/JsSHcuL1WI9JsYfz4fIsQyHyPG8WP//a6W6iTTAM73W4Z/jI6EdQNsU7R+fpG0ZUA/0FfPmsViMcDhMOBymsrJSLwwOEU54GZmm00d+FxHi8TihUIiysjIqKyevuqOWnozf5YRjL2BN+Vogd8GfSCQIBoOEw2EqKiYvn62WnhpfNfXLTh753XEckskkwWCQiooKHWu1EIo9Z9kIWMCnjTGXAilyeRw+JyLp4f31iMhIVl8RsY0xPVPs75C2GC8iDuELl7k+/9ZjeQnQ43lMez2eC7QYj5tSB981wDO9MsAd/QUalgEmy3BcBqQnactk7Y0xbwfePvxr9ogjjtg9romk0+lYUT1eHJZRZDBrEXtaRKYua1agCWN91Ibdo18XEclkMjrWpTUvY33F6VfvHv2yiDiZTGYpJsHTsR5n9FgbY7JHHnnU7lGvGcdxbMuyEgf7PiWgY700FXvOsgEwwFbgf8jl4/kKuQuENzH5Oc2U+xv3ty9ujHl+Fp9hMov1eBzTr0VyE24u/1/dLCKXzNG+ijXX599FHctw2B3Ph3qfSnksw6F7PC/G4wYWZ78m9Okg/s2Y8XjWAM/0UoDHGOMTEXvU80FgspP21PBrjGvLZO1F5DvAdwCMMY+kUqmJ9ZOXIGPMIyJyyHyWudiPjvXip2M9PR3riXSsF7+5Guslothzlk8AXxaRgeHfnzLGOMAvjTEfZPJzmin3N/r7MJcW6/G4GPu1GPs0S3N9/l3UsQyH1/GsfZp3h+TxvFjHaDH2a6H7pBlcp7dv+GfzuOdXMHGaXb79ZG3j5JJlKaWUUkrNh6LOWUTEHRXcyXtq+Gfb8P4ah8v7AmCM8QGNk+1PqTk01+ffeiyrUtLjWS0oDfBMbwsQA0ay4RpjVgOrgXsmaX8fcK4ZO+fqAmDzuERZSimllFJzqahzFmPMr40xfxj39CnkpvlvJ5e00wecOer1s8mdO25Gqfkz1+ffeiyrUtLjWS0oXaI1DRHJGGO+CXzZGNMH9ADfBO4WkQeGy97VAQMiYgHfBz4CfMsY81XgxeTK2BWy7nPOp4GWkH6Whd9nqehnWfh9lop+loXfZ6noZ1mCZnHO8lsOLMf6E3Ai8GVyy7bi5PI0/Br4vjHmreTy9XwX+MkCl5VerGO4GPu1GPtUtLk+/xaR9kVyLMPiHCPt0zw6hI/nxTpGi7FfC9onsxgzTy8mw1Pevkgu4aAfuBl4l4j0GWPOB+4ELhCRu4bbnwF8HdgE7AE+JSK/XPieK6WUUupwMotzljcCHwbWkbvo+A7w+fysY2NMBfDfwCsAm1xQ6P0iklq4T6UOR3N9/q3HsiolPZ7VQtIAj1JKKaWUUkoppdQSpzl4lFJKKaWUUkoppZY4DfAopZRSSimllFJKLXEa4FFKKaWUUkoppZRa4jTAo5RSSimllFJKKbXEaYBHKaWUUkoppZRSaokrKsBjjAkZY15vjLnJGLPbGJMxxvQbY+4wxrzNGOOdr44qpZRSSimllFJKqckVXCbdGHMc8GvgaCAOPAV0AC3AKYAPuB+4RERi89JbpZRSSimllFJKKTVBQQEeY8yRwGNABfBl4N9FZHDU6+uAnwOnAvcC50mhkSOllFJKKaWUUkopdVBmDPAYYwzwILngzWdE5FNTtFsGPAM0Ai8XkT/NcV+VUkoppZRSSiml1CQKycFzNrngTjvw71M1EpE+crN7bgfK5qR3SimllFJKKaWUUmpGvgLavHb45x9EJDNdQxH5EvClg+6VUkoppZRSSimllCpYITN4jh7++fB8dkQppZRSSimllFJKzU4hAZ7m4Z/d89kRpZRSSimllFJKKTU7hQR47OGf/vnsiFJKKaWUUkoppZSanUICPJ3DPxvmsyNKKaWUUkoppZRSanYKCfA8OvzztJkaGmNWGmM+Y4y54OC6pZRSSimllFJKKaUKVUiA50/DPy83xgRmaPt3wCeBrx1UrxYpY8y3jDHfm6HNKcaYzcaYpDFmmzHmjQvVP6WUUkopmJtzFmNMmTHmO8aYPmPMkDHmu8aYivntuVJgjPEaYz5vjOk0xsSNMb81xiwvYLsjhtu3jntej2VVMsUezwX8bV5njPnj8PHca4z5jTFm5fx/ErUUzBjgEZGHgHuBNuBjU7Ub/kP63uFfvzknvVskTM5ngHfM0K4B+CvwGHAS8HXg+8aYl8x/L5VSSil1uJvjc5ZvA2cDLwOuAM4ffk6p+XYd8CbgjcC5QCvwu+k2MMasB24Byid5WY9lVUrXUeDxPNPfZmNM+fDrXuBC4KXAMuAmY0xwXj+FWhKMiMzcyJjjgIeAEPBF4PMiEhn1+rHAL4BNwAPAOSJiT7avpcYYsxb4PrARSAK3isjbpmj7z8A/AOtExB1+7v+AFhHRII9SSiml5s1cnrMM37jbA1wkIncNv34ecCfQJiLt8/151OFpeMVAH/BeEfnh8HOrgV3Ai0Tk/km2eR/wWWAbuYviNhHZP/yaHsuqZIo9ngv423w18GugXkSiw6+3AXuB80TkngX5YGrRKmSJFiLyFPBiYBD4KNBujLnLGPMrY8yDwNPkgjv3A1ccKsGdYWcB+4DjyH0Rp3MOcE/+yzjsLuBFxhgzP91TSimllALm9pzlLMAFNo96fTPgkJsJodR8OQGoJHc8AiAiu4Hd5I7byVwFvB340CSv6bGsSukEijueZ/rb/BBwWT64MyzftnaO+qyWMF+hDUVkszHmGODdwCXAieSmQA4BtwM/AX467mBc8kTkp8BPAQqI0bQCj497rgMoA+rJRW+VUkoppebcHJ+ztAI9IpIdtX/bGNNDbtm+UvMlnz9n/MyaDqY49kTkQgBjzPlT7E+PZVUqxR7P0/5tHp5xNn5fHwMS5NKqqMNcQTN48kSkW0Q+KSKniki1iPhEZJmIXCwiPz7UgjuzUAakxz2XGf4ZGt/YGPN2Y8wjxphHNmzYIIA+SvRIp9MSjUbHPJ79jx/Jpz/96ZPHj9ts6FgvnkcqlRo71pGIbPviz3WsD8FHMpkcM9Y9ne2y4wu/0rE+BB+JRGLMWO964SnZmRvrkwoYysPVTOcsk72eb6PnNIf+o5TKAHd0QGbYpMdegfsr+FgGPZ4PsUepFXs8F3s9+U5yEzA+JiIDk3VAj+dD6jGjgmfwqIKkgPHJrfK/J8Y3FpHvAN8BOOWUUxbDH6DDjojgui59fX3EYjH8fj/3D9zHmmd6qaNhLt9Hx7rE8mPd3d1NOp3G58v9+St7LkLcG5/L99GxLjHXdRERurq6yGazeL1ecAXfY530+XsPnCYdJB3r0hs91o7j4HUtqp//CZmBKIOBDZBBl0dPbaZzlslez7fRcxo1n1KAxxjjG5f2YdJjr8D9FXwsgx7Pak4VezwXfD1pjPkX4HPk8uP+z1Qd0OP58KIBnrm1D2ge99wKIA5EJjZXpdbZ2Uk8Hsd1XcpDPvxewwMDm3lZ5/l8tzIGKXaXuo9qbrS3t5NKpbBtm8rKSjweDybjEN6b4rba2yCxYXup+6jmxr59+7AsC8dxqKiowOPx4AxmCPY53F17L8SPfb7UfVQHSQSyKfbt3YNt22SzWaqqqvAP7KK8/1l2VV5BLOYD+p8tdVcXsZnOWfYBjcYYr4g4AMYYH9DIxOUBSs2lfcM/m0f9N+SOz9kce3osq1Iq9nie8XrSGOMhV7X6HcBHReQ/5rLDamnTAM/cug94izHGyIHyZBcAm3X52uISiUSwLItUKkUoFMLngeU/P41M/QaqK/3EHB++1pNh4C8a5V7ihoaGyGazpNNpQqFQbjYHgCtU/243v6i/h7pMB0k2lLaj6qANDg5i2zaWZREOh/F4cquQkxGL0O92c1vVAKng3M3WUqUhIsSf+B3he/6FZaEmvL5RpzJ2Dc7Qe8l2uXS07syVhlBTmfacxRizmdx54pnDbSGXkNbD2GS1Ss21LUAMOI8DOaVWA6uB2VQI0mNZlVKxx3Mh15P/A7wNeEu+MpdSeRrgOQjDZe/qgAERsciVJv0I8C1jzFfJVR57Hbmk1GqRsG2b/v5+XNfF6/Xi9XqRTIQB+wNsj57BxfHd9AWauOq4Wn72ZKl7qw5GNpulv78fYGSsRzhCti7A7VWdfPqFJD8vUR/V3MiPtcfjwe/352ZoWTGq7v83qjrOZdAXYd+KO6gyfqIz704tNh2PwkPfAMB1HXydzzKw4f+RXHc1gUAAgFQiRuqWZ3i2Yi9/WfEsxzesmLmO1GGk2HMWEWk3xvwa+L4x5q2AAb4L/ETLSqv5JCIZY8w3gS8bY/qAHnKzFe4WkQcmOZZn2p8ey6pkZnE8T/u32RhzOfBO4NPAzcaYplFvNyQik+WbUoeRopIsqwnOAjqHfyIi3RyoMPY4uYRXbxSRO0rWQzVBe3s7lmURCoUIBoPsHcww+NjTWLKMoVMsHjhiN1VXrSp1N9Uc2L9/P7Ztj4z1aMYV4lmHrG1oqp5sab5aSvbt24fruoRCoZELfm+iCzKGoYo2vtcWJFF3FC85+qMl7qkqWiYOT/0c2s7EPefj7F/7ZrrO/AL20a8YGWtcwd7VR8Sf4eHKFv5xw9/xkga9tzLObM5Z3gbcD/wF+BNwB7kLC6Xm2yeAn5Gb8XAnsAd41fBrY47lAumxrEqp4OO5gL/Nrx/++anh7UY/8vtUhzGdwVMEETl/3O93wdgEjiLyAHDawvVKFaqnp4dMJkMmkxnJyxHPOHz7pq18IyV8a9Xz+HxRNq46AW/AxbZdKDBbuVpcurq6RpbqVFRUTFouOGs57OhL0tDixzvoAR3rJUdE6OzsxHEcstkslZWVY143dpKhwDKeSu+iPTzIVfXnEww3gI710vL4D5CBHXSvfR3ZZJBUqJmqqqoxTbz9GepfyLK5NU69u55aU4HHe3h/r+finEVE4sBbhh9KLZjhZLQfGn6Mf+0umDyB+lSv6bGsSqnY43m6v80i8jpyM3qUmpQGeNRhIZ1OE4vF8Pl8hEKhkdwc6azDsbUuz/kHkSMbePWq1wKQSCRobm4GGCpZp9WspFIp4vE4gUCAsrKySYM7AJblEgx6qA8HCITCkFsfrZaQZDJJPB4nFApRVlY25rVE+z6W37QPy3sWfVWDHOt/GUdXVLBixQo+9alPzaYKi1pIT/8qtyTLeJDofpKX/4CIU0FYhPLy8jFNPVGL0N3t/Kn6Yf4y4OWi5typTUtLC5/61Kd0qrpSSimlDhsa4FGHhfb2dlzXxefz5S74XYfgzhvp3TvEppTBYyp43erXj7Q3xuDz+fjUpz512N79XYpEhI6ODoCRMuhjuIJ/dxxcoXtPFNsXw2s8eDxeHeslxnVdOjo68Hq9Y8ba2Cl82/+K9wmH/rBw1wVDxOw416xcTjKZnPy4UIvLwA7Y8hO49GvQfCKObdO+cyc+n3fS8XOjKZ6r76K7tYxNPadz1fENOtZKKaWUOizp2Y86pO3Zs2dkqU5VVdXIbA5vopPQlu+wN3sxL3JOwjrBGdkmkUjg8XhGZvmoxU9E2Lt378hYV1dXT9rOE88SemaQoVUVPN0Tp6P1Ps6vOx86nljQ/qrZc12XvXv34jgOtm1PGGt/7xY8T/8NT+oK2k/x8Gzkfl6z6hri8Tg+n0+/10vB4z+A5cdh1x7Jvl27cF0XESEcDk/avDfRQ6/Tz1HlJ/O0xxCLxQgEAlPO3lNKKaWUOlRpgEcdsqLRKJZlUVZWRigUGnOy39vXSyr+CU4JOAys8bJywzkjrxljWLFixYSkvGpxEhEikQjpdJqKigpCodCk7SL9acJ/3MvN5R7u3jXI8UcHccJ+zlp7Gp5nv73AvVazISIMDQ1hWRbl5eVjxtrf8wQ1d3+AtKwhEX0fO46q4Ofu97lk2aW0lLWSTCZpbW3VWR1LgZ3G3fBaIokMtm1PWH6X5+1JUX5vNz7j0LW6ioCnHp8nhtfrpa2tbWzVPKWUUkqpw4Ce6apDkm3bdHd34/V6J72LG0/ESQb7ab3m5dSPet51XRzH0Tu/S0h+rKe7Y5/OutzyRC9nVfu56JUrsTtuIpaN4c/4MQgYndWxFFiWRW9v74Sx9iS6qHjyW/Qf+Qb+0H4hJ5R7KDtJ2NR3PC9qOAfXdXFdV7/XS4WdwXI99PT0EA6Hx451LEvwhQgA7kCCh5v3cG/j86ypWEuTK3gMOtZKKaWUOmxpgEcdckRkpGTy+GScACY9gKSHcCcprpJKpQgGg3rnd4lwHId9+/ZhjJl2xlVf3CKezLKysYy9qU62x7dzXs35NDc04zFogGcJsG2b/fv3TzrW/t4ncYN1PF/2UqqcLKtWhHkq20nQm2uXSCTGJFdXi4ydgUx05FcnOUBPfwR/Zc2BMugAIvj3xEHAWhHmufKdPOPv4tyG81gRbuHpfQ6ObU2YsamUUkopdbhY0gEeY8yVwCMi0lHqvqjSExH6+vpG8rCML5kM4BvcRt0tb6Gv7CyGvOdNeN0YQ1NTE36/fyG6rGbJdd2RsZ6sPPZ4WUdoDPvBZ/ONF/6bq9teyZHh9axcuRJfNgIeDegtVo7j0N/fTzabxXEcKioqJrTx2Ekc32kc+1iGRr9hsD7Jb/f+mtesvAYAr9dLS0uLBm4XqxvfBfEuXF8Iy7JwBSx/zYSlWYGdMYLbo8TPa2KLeYHrB27mda1/x5qKtezuT/GTh9p5w8l1tLS0aDBPKaWUUoelJR3gAf4AvAH4ef4JY0wAuAb4q4h0l6pjauGlUikGBgYIBoOT5mHxJHupvv+TdKy5mh9EQ1xuV415PZ+0Ve/8Lm4iMjLWoVBoysSro7kph3P7LKw1Po6pPpYTq08imUzmXrz5A+DRgN5iJCIkk0n6+/sJh8MTvte+/mepfPBnZPqPp8e3gpvKH+W51i7K0hnOajibDTUbsW0bx3GmeAdVco//EHbcgrzrWWJph46ODsLhMIFJAjQm65LeUMv+YB9/2fVnLm56KWsq1gKQtFzOXF3JhqbJc3AppZRSSh0OlnqAZ7Ir8Urg/4CLAQ3wHCYcxxkpmTxmSv8o3vg+0lVr+EloAxWxPo5vXEd21OupVIrKykq9y7/I2bZNR0cHfr9/yrEezXGFrs44QX+KZ1ZFCGSCpFIpqqqq8BpgaDe84ZZ577cqXjabpbOzk2AwOGGsTTZB4IU/0GdOpr1pBTdW7KXSdyL/uKEBrzFU+HLLM/NjrTM6FqltN8IbbyUjPrq62icd6xGOkPXbPDzwEKfUn8Yp9aeSdVy296bY3pvEg0NNTY2OtVJKKaUOW4fqWZBOwTiMuK5Le3v7tNVWAEw2yfOeMh6ObuasxpPGVNNJJpN4PB4aGhq0ys4ilg/uuK5b0MwdgP2DGR7Zt58+/0667R6ODR+Lz+ejsbERLy4EKiAw9XGjSiMf3AEmnZEX2nkD8c6tPBBbzS7HIeENc/66NuqCNVQHqvF6fCQSiZGx1ov+RSqbxAo10NXVhcfjmbIKHoBxhF2p3exL7OP42uPxGA/Pdib5xcPd9EdTnNRWSUNDg87CVEoppdRhS69k1ZImIsRiMVKp1KS5OfK80d1U3/9JIqtewTqrjnO2lWGt847ZT0tLS0EzQlRp5Mc6nU5Pmjx7KpbjsqrGS63UckLrOSQSCVpaWnKBvEwKvLo8a7FxXXfKsQ7uvY3Kx7+OlYhwg/0RXukt5ydlD/CSI19Ea+3E4ICWy16EbvkwtD8EGCTWSSztkMlkpvxe+/fECW0ZwGO5xDdmOLX+NJYFG9jTn+Z7mzt4zcmNnNjkY+3atRrIU0oppdRhTQM8askSEWzbpquri0AgMO2JvXeok0TlO4gn6jgvGiDbUk56U93I68YYnbmziIkIlmXR3d1NMBgs/CIu61Kzc4jV8QimKhfIMcYcuOB3LPBqUG8xyY91V1cXZWVlI2Ptje4m2HE/wb13Ejv5Q9zWv4EL9iTYtXKQpuZj2FR7/KT70+DOIrP7bth2E/zjUwi5JXTdu3ePGes8T8TC35nCvy9B6uRl9DVYPLD3Fk72nAJAfyLL+etrOGddzcgsTKWUUkqpw5le0aolKZvNjpRCB6YtkQ3gHXCw4k0M+bupDh9P5ujqkddisdiMASJVOpZlsW/fPkQEj8cz41iP5h3MUNkZY3dZB6s3XkAsFhsbIHIsTbC8iKTTadrb2xGRCTmWwjv+jCcTIb3mUqzGk2l8fgj8HjoaEgQ9E4+JaDSq5bIXozv/FV78BVLpNB0dHYgIgUBg0tmTwe3RXEn0NRXYjSGeHHyICl8F66uOAnL5tXwew9DQEOXl5TrWSimllDrsHQoBnnOMMaM/R36dzkuMMa2TbSAiP57/bqn54jgOg4OD2LY97bIsAH/3I9Tc/lXs9Lv4fXgft5DmVUc04VbnLibS6TQAra2tOoNnEcqPteu6RS3LyusdyPBYMsL+dYZgRQ2O49Da2pqb1eHY8L0z4dR/nIeeq2LZts3Q0NCUY23sJOlVL4bEsXh/10OtFeOn624nHsnyirZXjmmbSqXwer2sXLlSA7eLxU8vg2wSyhvJrn0pQ319U451aMsAgV0xhtwo/7vqr6SzAtvBFZdrV7+O8uEE2rYruHaWYLCClStXaoBHKaWUUoe9Q+GK9u3Dj7z8Gd5HABnX1gw/pwGeJSydTtPX1zflBb83sovEcz/kVhMjHLPZZN7E4/VDPNDSzSUtR/Oi5pqRto7j0NTUpMGdRShfIntgYGDa5NmTifSl6L13H75MH/7aXo6or8J1XZqbm0ctz8pAw7Fw9kfmofeqGKPHerKgrb/7UYL77ya95jK8KYfH2sI8XLONk5rO4vRlZ0xo7zgOLS0tGtwptQf/GyJ7c/8d74T/9zgiQiISYXBwcNKx9nUkCeyM8dzpWX4y+GeuXf16VpWvmnT3jiv4vIYVK1ZocEcppZRSiqUf4HlLqTugFlYmk6Gjo4NgMIjfP8nSGtfGv+smHvG4VAQ3cURsDUOBaqo2Bbm2dj2rKprHNDfGUFZWphcHi1Amk6Gzs5NAIDD5WE/CcYX+RJbu7RHKnBSPrdnHxjUn01q3AizGjrWT1fw7i0QqlaKzs5NQKDRpsDW4/x6SR16LEzgSXyJGSlzSEiHobZx0f8aYgqusqXmQ6M3N1nnyp3DZ/+SeOzl3HyaZTNLd3U04HJ4w1lY6he+5Hno2Brg7cy/nL7+AtrK2kddtRxhIZkd+H0zaeHWslVJKKaVGLOkAj4j8qNR9UAvHtm16e3sRkSlP6MPb/8jjPfcQCbyYV7afTb8/wJMtAc5c0zShbTKZBNDgziKUzWbp6ekp+kL9sb0xfvt4D+f7fWxYBuVtVaxvXEcymZw4zo6lFbQWAcuy6O3tnbZEtnEymKFNlO/rwy3z8WzgWbrs7awInz+hbSKRwOPx6Pe6lH5wDrSeDse+ClpOHXk6P/vS6/VOmkur555HCSSEW2QHjgjH1WzCYw7Mwrp3+xC3bx2gsSoXmM1mbS7bUDv/n0cppZRSaolY0gGeyQzn46kjtxRrUETsEndJzQERIR6PE4/Hp1ya1fHMPVhPfotbGi/g7/eewW8qPNyVyXBFXeWEttlsFsdxaGtr0+VZi4zrusTjcRKJBJWVE8duMjt6U/zq0W7641muPqGBk904j+x9nrBv+chYr1q1amxFJTerCZZLLF8OPZVKTZtPyzNUj7c/yGMnlPPLPRH2J/t46ZHn0xgaO4PHsiwgl1NLAzwLwLHhd68DOz32+ZrVcPXY+y+O4xCPx0kmk1N+rz1ZwX/2Ov6u4Zwxzz/dEef6J/vojlq85axmTmitxLIsHMdh5cqVc/mJlFJKKaWWtEPiynY4mfL7gUuAY0a95BhjngP+CHxTRLoXvndqLqTTabq6uggGgxPKHkdTNvs6Oqh55tc8tepSLvW9iIoTmrnoqGouMoawf2IejnQ6TU1NzZQzBlTp5Mc6FAoVnENlz0CaE9sqOe/IGrxuhr6/7aO5vIW1DWeSiCeoq6ubOGNg72ZdolVio5frTCiRHc/iGbLwZAbx9DbQt8nlxq44564P8YKbZm39xIBQJpOhvr6+qEprqkiuC7vvBNeB9BCIC6/46dg2vol/V5PJJD09PWPKoXsiFp5YbslVLBulLOHF6899JweTWfb05wJHm3dEuOioWo5rqSDs9yAiZLNZ6uvrC16+qZRSSil1OFjyAR5jzDXA94EwsBe4CRgE/EA9cDzwSeADxpg3i8jvS9VXVbx8zh0RmXJa/13bBmna/jvaPGmCzUdS2RfEX+6nLDj54R2LxfD7/SxbtkyTsC4i6XSazs7OkRLZxVykZ7IuVWEvZQEvnc/uIjjgUHb2GhLxBIFAgGXLlo2d0ZGJ58o1v+x/5+GTqJnkAzv5EtmTjXVoywB4Df6+R5CKJLekcvmTEsFnkJTNqorVY9pHIhHC4TDLli1boE9xmBraBbd9DI6+Ovf7ae+GUNWUzePxOL29vbiuO6EcetmDvTj1QcRr2D3wNP6mIGsqqwH467MDDCSyNFYGaKoOcGxzOWUBLyJCNBqlrKyMurq6ef2oSimllFJLzZIO8BhjzgR+CjwPvF1ENk/R7kLgK8AvjDFniMjjC9hNNUuO4xCNRslms9MmQk5nXU4KNlKfeDFljwthx4/bNvWh7fF4aGtrmzATSJWObdsFjfVkHtsb44an+3j3+a14e1KsftLLlmMMRy9vIZFITF4+2c1CwzGw5oI5/iRqJtlslng8Pu1Yh54cwN+RJHrlSnyPPk5y/asY2hXmgvXl7DdZTlt2OlX+sUEFv9+vy3XmW6IXfnEVnPgWOOtDMza3LItYLIZt24TD4VxS+83deAdzS+lM1iV+8Qpu7LiRhz0P8c8b/gWvN/e3O5N1ufiYOo5sHFtBLx8UbGtrm/B+SimllFKHuyUd4AH+CdgPnCki0akaicgdxphzgSeHt3n9AvVPHYRUKkVPTw/l5eUjM238vU8S6H54pM1QMkvTrijVboi+I4f4btUWzmk8j+NqJi+rLSIAOnNnkUkmk/T29lJRUVHQ2HQMZXh8XwyAZzoTvOmMZtpCaYb+tpetre1YK2qmH2vXBs9S//O3NCUSCXp7e6msrJxyrH1dXdhrHif1xF8xex7hHt8VbO1OsK4lzQvR51ldvmZMe9d1Af1ez7tED6w6t6DgjogQi8UYGBgYM9beQYvYyw4EZ7bHtvPYwKN8+NiPEvTmZnLt7k+xrSfJBUdNTKAsIhhjdKyVUkoppSax1M+QzgR+NF1wJ2+4zU+Bc2Zqq0ovmUyO5GEZmdIvQtlzP0Xw4pQ1kw0t54mhSirqW5Flp/BCwKU53MyRlUdOud9oNEogENAErItIIpGgu7ubUChUUD4NEeG+HUPEMw715X7OXVfDcc1lDOzcTcQkCK1bwfE1x4+M9aQcTbBcCtFolJ6enklLZCOSy+/iuvgGduF6PTwyUMETzW/GX7eGSzbU0eM+zeryNawpXz1qs1wCds27M09EDjysOAQmT3I/3tDQEP39/ZSVlR0Y6+Gg64FdC7d13cJlLZdT5i0bee6O5wc5fU01K6rHjqnruiSTSR1rpZRSSqkpLPVb2PXk8u4UaiewfJ76ouaIZVkMDQ0BjCmRXfXAp/ENbcMKfYDAdou96X5a3UpCPh8dRni4/hkuWXY1Ie/kiZNTqRR+v5+2tja9+7tIZDIZBgcHAQouh37DU/08uDvKx16yiobKAIEdUcwft5MkgXNmK8c1rSWZTOIN+CZfngU6g6cE0uk0kUhkynLo5fd0E9i/DWPHyDjCR3ZswoTCfPjMVZQHvdzRdTtP9D3KO458J4HhmR4iQjKZHPleqznW/jD85rVQs+rAc6f8vxk3SyaTRKNRfD7fmGBMxV/bcSsOfO9+uPMHWE6GE2tPGvme/uKRbp7vTvLqkxrxeQ98d0WERCKXU6ulpWUOPpxSSiml1KFnqV/h+IFUEe0zLP3PfEjLn8QPDQ2NlNIN7byRYMdm9nRmSFS8j6Zt3Xyt9Um2eZ/nrWv/nlNXrAXgzbxoyv06joPjOLS2tmpwZ5HIl0OPRqMFl0N/piPOXS8M8u7zW2lKuwSf7Mbpi/Gb1gdZeewmTqhdi23bALS0tEw9U8vNgldn8CyUfDn0WCxGVdXEhLyB7VF8nUlo+ClD5/w733goxUXremnPPsH1nX8DYFd8J3+3+o0sCzaMbGfbNh6PRy/450NkP9z1aTjj/XDGewvezHVdotHopOXQjS3Ez2/mb733szuxi+2xbVxa+VG+d1/nSJunOxJ84rLVVIbG/lOdzWbx+/2sWLHioD6WUkoppdShTIMdalEZvVwnnwQ5tPtmkmteAz0GOaKGX4ccjm46nQtrzmVTQ2FJVZPJJFVVVQXPElHzLxaL0dvbSzgcLjjh9TN7Yrx+XS1rvR6Cz0ew6nzcUbeNiuoWjqs+DhEhlUpRU1Mz6SyREYO7dAbPAopEIvT19Y0pkT1aYEeM+HkV1Dzci/jLsJwk25NPc9KyjTQGGwE4b/kFNIWaRrZxXZdMJkNtbe30Y62K59jw/PXQuBFOeltRmw4MDDA4ODjlWAM80P83Xr3ytby46SX86N4Yl26opzKU+xtw2cZ6GirGBl8dxyGbzVJXV6fLs5RSSimlpnEoXOEcPZxAuRDHzGtP1EFJp9P09fXh8/nGXrCJg393K4nAXipWtjDUmeDc5Q201BR2oh+Px/H7/TQ1NWnunUUikUgwODhYdDn0Td1pjvHbhOMOGLi//DkeT27l9fVvwGO8xONxAoEAy5dPsxIzNQR/fjtc8l8H/0HUjOLxOIODgwSDwSlzIhnbpfLZz2LXHol4Q2QdwRGLlnArDaGGCe3zeXdCoRANDRNfVwdp1+3w5E/g8m9CYPKE9ZOJRqNEIpEZ82m54tJa1gpA2t7N2oYQYf/kQd78TL+Kigrq6+uL+xxKKaWUUoeZQyHA8y/Dj0IYQGZspRZcviR6Op2moqJizGs3+7K0DeziRy13csru9Ty6N8a1pxaeSilfFl2DO4uDbdvEYjEymcyEsZ5KYFuU1JZ+jhyyGLi8kdrmXKLXaOcWzlt+AcuCyxARfD7fzLlY7DS0nAZHX3WwH0XNwLZtotEotm1TXj55ct7Q4/14Ylm6k0N8NfD3JG/YRbs8Qn3iOcLe1066Tb5Udmtr63x2//CVicJxr4PmEwveJJvNEo1GcV130hlV4Qd7MVmXP+//ExFriN8/3sOW/XHahzIEvFMvm3Vdl3A4THNz86w+ilp6Cvm3WkRP5ZRSSqnJLPUAz6dL3QE1N+LxOH19fWNKorcn2+nN9PC8W8uLZSX/fPyn+M5D3Xzy8jXUlBWXP6WQ6kxqYcRiMfr7+6msrCw46Ga1J/iRXzj1ZZU4wRfYk8vLzJ7kHlqGZwLkyydPqM40nmOBd4rqWmpODQ0NMTQ0REVFxaRj7e1PE9gV58mLmwlfn+B9rz2CARL8Yvce3nLEP1PhnzwAKCJ4vd6Zx1oVLx2FvZuh6fiiNhsYGCAWi0061t6eFOk9A/zouD6e6HmMK2v+iV8/1scXrz4Cr8fgmeHvgI714WV88Gb1x25k9xcuL1FvlFJKqaVlSZ8xiYgGeJa4WCxGT08PAMFgcEwg5vf7fstRVUdzbdeLsI+uwAS8pLMuYX/hSZIjkQhlZYUvMVDzJ182WUQmL5M9jf29KRqbQrxg/Q0nY1MfzC3VWF2+hpXlqxARYrFYYWOtCZbn3eDgIAMDA7iuO7ZM9jhlD/aR3ljDzx/u5mNlggmFuW337zi6+hhqArWTbjN6yY6aB9tvhlgHnPnBgpr39fURiURwHIeysrJJ82mVP9DLN8o3M5iqY6U5m964w+Ub6/FPM3MHcjM78/nTlFJKqblijLljDnYjInLRHOxHqTm1pAM8oxljgiKSmeT5o4ABEektQbfUNCzLIhaLISKEQqGRmTs/3vlDejO9uOJy1Z6TCFvPMLCxht883suu/hSVwcIS8iaTSQKBACtXFpaIWc2fTCZDPB4HGDPW0/n5Q1083ZngkqRwcibBC5W/JRXN8I/r303tqIv/0eWTCxprncEzr9LpNNFoFGMM4XB4+rF2hXD3v/OerqdYVm3o9/jIuBnObjgHj5m4XX6sw+GwLs+aa9tuhts/Dh4vvPiLUDPzdymVSpFIJPB6vQSDwSnHem8kwyNr2/naSW8tuDuu65JIJCgvL9flWUoppeaah4NP26G5H9SitOQDPMYYP/AF4A3GmJUikh7X5PPAZcaYbwMfE5FiyqqreSIiRCIRBgcHqaqqwuPx8NjAo/Ske9id2MW/Hvdp/PsTBJ/YgzfwrzzTcwN/2xnhusvXEA7MHOBxXRdjjCZWXgREhKGhIaLR6MhYz+Sp9jgP7o7y71cdQcNDvfy27gleu+b1tJRNLIftOA4+n6+wsXay8Nj3waMzeOaDiDAwMDBSInu6sfbvieONRXAjm/lo4Hu850Jhd8dNtCfbCUwRgLNteySJtn6v50DvVtj6x9x/7/8bnPsJOPYVBW0qIvT19ZFKpaioqJh2rG1XWFlXXKUz27YJh8M0NDToWCullJpTInJ+qfug1HwpfK3LPDA5a0b9vt4Y8yVjzOeNMesL2D4A3AR8AOgDGidpdhuwFXgP8BdjTGHTP9S8yi/hKC8vx+v1Yrs2t3XdyspQG29sewNkXUJb+vGU/ZzoOZ/l9hcivOKEBqrDM8ck83f5Q6HQlIld1cLp7+9naGhoTH6l6Tiu8Ocn+3jjGU2EfSAZm5hJEpzkot91XVKpFGVlZYUtz+p/AXqfhbM+NJuPombQ09NDNBotaKxDTw3iCf6A/w1/mHee38q9vfdQH6zn2tWvI+wNT2jvOA6ZTIby8nLC4Ymvq1nYdiM4GWg6AU55J6x7acGbdnZ2jsywmTa4k81iirxJats2lmVRXl4+acJmpZRSSik1uZIFeIwxrcDTwO+Gf18OPAh8CPgo8KgxZqYSHu8HLgQ+IiLHisje8Q1E5JvAicC/AecB756rz6BmJ5FIEIvF8Pv9I2WT/2vrf7KybCWnP1jDxgfDVNzRiS/6GFLeQ6blXNJZl+NaJk/UOtro5TotLRNne6iFFY/HicViE/IrTeerd+zD6zGc1FZJ161/Y//QboY8cSp8lWPajQ7kNTU1zbzjob3ws8vg+DdAzarZfBw1jVgsNrJ8qtD8Ssa/l62+49jQXE7WzbKheiNHVR09oV1+uU5ZWRmNjZPF8dWsWAlYeTYceUnuESgsIB6JREYCq5Pl3Bkt9btHeKFiG21lM1S3G+Y4DolEgsrKSpYtW1bQNkoppZRSKqeUS7T+HWgjF9AB+AegGngN8DC5mTmfBq6cZh+vB/4sIl+e7o0kV5Lhk8aYc4A3Al87uK6r2cqXQ89P6we4qeNGsm6Wa1a/Dp7eh7vyNgK9W/DzN3pe/Dfu2R5he2+KUAHJlS3Lwuv1anBnEXAch6GhISzLKigh7l+f7eeF7hTPdyf51uuOIrgzRmtfJQNXreAdlWdPaJ/JZAgEAoXl50hH4Ob35Uo/H3ftbD6OmoZt2wwMDGDbNsFgcNq2oScH8A5a2P09pNwOBkJZjDHYYuOfZOmciJDJZAiFQixfvny+PsLhZecd8ORPoONROPLSojbNj3W+ktlkfvZQFwMJmx7/3bzbXckfzNH8x6p1M+5bREin05SXl2twRymllFJqFkq5ROslwFdF5LvDv18J7BOR34rIHuC7wMSrurHWA7cU8Z43ABNvD6sFMzAwwNDQEGVlZXg8HnrTPWwZ3MJ7jnrfSJvwzhuInfR++q76EwNJm9u2DvCvl60m6Jv+cHVdF9u2qampGZkZpEqnt7d3pLJVITk0/rYzysXH+XnPS/3sS+7F2tnDL456CF9o4nIc27ZxXbfwsY51gL8Mzv3kbD6KmkFPTw/JZJJwODztWHtiWQK74mTWW0Scz9J9+W/48GV17EnsIWHH8U6ygta2bQBqamoKngWmppAcyAV1nvwJHHUlXHs9NJ9c1C46OztJp9OEQqEpx7pnX5w3rqlmnS9Ka7iFf7l0dUH7zmazeDweHWullFLzxhjzKmPMV40xbzXG+Ma9dmOp+qXUXCnlDJ5qYBeAMaYROBn43qjXE8zcvwRQTE6dJDCh0pZaGJFIhGg0OmYJx3e3f5uzG8+h0j92+Y1TlVtC871b9rCuIUxLzfSzAiB39zcQCFBfXz/3nVdFGRoaIh6PF7SEI89xhVv7f8nyUBOepKE2uYaKIxoo802eWycYDFJbO3kp7QnsNJQ1QKCAPD2qKP39/SO5WGYa64o7Osisq6L64b/nrvCpnN3Yws+2/hdN4RVsrD5u0spZAOFwmJqamnno/WHm/i9BZC9UroA1F0KouqjNC02q/K6oS3VXmlMHVuE5uq6g3Gl55eXlWhZdKaXUvDDGvBv4BHA98GHgHcaYS0VkYLjJOSXrnFJzpJQBnj3AccP/fc3wzz+Pev0ShgNA09gKvAj4eoHveQ6wu8C2ag5ZlkUkEkFExtyZDXpDnNt4PgBV9/8rvv6z8FREAPj1o93s6kvxTxevLCj3TiaTKTiYoOZPJpMhEong8XiKugv/UmsXxzxxIqvLc3nXPaksl686BcaNfX4ZR1GJdu0M+DRZ61zLZDJEo1F8Pt+MeXfK7+5CfB7+eyDJNf0Z/lT1Wnr3/Z4Ba4D3Hv2BSbfJj3WhOX3UDDIxOPtjsPy4mduOk06niUajBAKBaf/OVtzWwZOhTv67+k8EagMce9RlBe3fdd3iv9dKKaVUcd4NvFREtgzP3vkGcIcx5sLhII+WbVRLXinPmn8O/KsxZh25RMl7gZuNMUcA/wVcDnxwhn38CPhfY8wFInLndA2NMecBrwSuO9iOq+Ll7/JXVFTwTORpknYSAFdcfB1JPEkb334/dvUxPHDmn+jaNsTfdkb5n2uOwlPA8p58WXTN21B6o+/yF+r57iRHZALUXnsWMc/0Qbp8WfTCZ+9kYPvN4Jt5FpgqTnd390i1o6n49ycwaQdPxOJvp9Sx7YEuMs0VvPqUfv7a+RTXbfrslNtms1mCwaDO3pkLfc9Dz9Pgn11lwc7OTmzbnrEyYf/gIP/Vdjf/fcy/FrX/bDZLOBymsrJy5sZKqZIwxtwxB7sREbloDvaj1Gw0i8gWABGxyc3g+QpwpzHmQiiy7KNSi1DJcvCIyGeATwFHAJuBK4e/aFXAucDnRGSmZMg/BB4BbjDG/MvwUq8xjDGNxpiPk5sdtBv45px9CFWQ3t5eotHoSN6d3+37DRk3Q8bNcHnLyyh7qBdjuyABUifU85MHu0hnXV55YgNeT2HBnXw1Jb04KK3u7u6RpVmFlETP+8mDXQS8HjwzBHds2x6p3lNwAKnvedhzD2zU5MpzqaOjg1QqNWOZ7PDw9zt1Uu67/dqTG/lpRdnI938q2Wx2pCz6TEEFVYBHvgWrz4fqwqpZjbZ//34ymUxB+bQGrX4uXzFdbYSJLMsaScZeVqbLKJVaxDzkZjgczKOU+T+V6jPGrBn9hIh8ELhz+KFThtWSV9KDWET+jVz58tG2AA0iki1g+6wx5irgl8Bngc8YY/YAneRy8zQCq8j9g/IAcI2IDM3dJ1AzSSaTI2XL/33rZ6nwVbKheiPnNJ5L5Q37AHBq/Xxq1xDvTt7Dl558NWG/h5ccW1fQ/vOlssvKylixYsV8fhQ1g0QiMZJot5glNR/a/J+8I308+KdfQuW6LslkksrKysLKoufZaWg+CRo0v/pcicfjI4G26YI7VX/cg91Uxke3DSAC9vLfcv/+7dQYH+c0njvldvmxrq2tpaGhYT4+wuHHTsNJbwNvccmLY7EYmUxmxrw7AOK4iDFcsvaEgvfvOA7JZJKGhgbNn6bUIici55e6D0odpNuBN5ObZDBCRN5vjPk6sLEUnVJqLi26KKWIuIBbRPtu4AJjzCvI5fI5CTgecIAu4MfA70Xkz1PvRc0H13Xp7+8nnU5zb+xuGoKN/OP6dxN8ehDPo30AxF6Wu5ucvH4na5aF+ewVa4t6D8uyCAaDNDY2FlSpSc0P13Xp6ekhm80WVcHswb4HOClSxSmedURfuXratpZlEQ6Hi1+G52j+nbmUH2vXdae84A89OYCxXCTgIXlWI3J9nEvObOeeHodP7xyi/4rfTvse+YBCXV1hgV5VgFnkocqPNVDQjLwdt+wnYJyC95/PnVZdXU11dXEJn5VSSqlZeDdTXP+KyHuNMV9e4P4oNecWbJqkMcY1xjhFPuxC9y8ivxeR14jIOhGpEJFqETlKRN6iwZ3S6OrqIh6PEwwHeXJoC9c0vwaTtAlui5JdUUbinOUApCyHgJMAKfzCAHLLdWzbpqKiglBIL+BLqaOjg3Q6XXBJdIBMOsmD+zZzae9ZJC5onrZtNpvFdV2qqqoIBovIpSMCiR7NvzOH9u3bN/1yHRECO2JkW8pInL185On7eu7jLW3XzPg9tywLgMrKyqKChWoK2TTEeyATBW9x34O9e/eSzWZn/Psazzi0RyMs705wy5HPFbz/TCaDMYaqqioda6WUUvNORCwRSY5/3hhzjzHm/4nI3lL0S6m5tJAzeH6MJq46bEQikZH8HBky1PhrWXNDHLvRJnNkFXbzgTwL1924iy8kPoJ1ZOGVCfN5d6qrqzWxcokNDg6STqcLWsIxWvaPW7hUzmCoqoy6+qkvPPNLOGprawtPrJzX+Tjc8Qm4tNBCe2o6/f39WJZFZWXl1IE8R3DD3jHfcQCfx8fG299HpuXsKfefz7G0bNkyTaw8V25+H0TbIVgF4cJnRPX19c081kDWcfngb58nvOb/+ET4ata2ri9o/9lslnQ6TVNT02GVO204Se2/icjtpe6LUgfLGPOfwGPDj60iIuNebwEGRCRViv4pVYSzyeXn+SG59B5xEWkvbZeUmp0FC/CIyJsX6r1UaWWzWQYGBnDE4Rd7fkbaSVNhynFrAiQumpgn59XJH9BQbug7/eMF7V9ERvJ/NDZOyKutFlA2m2VwcBCPx1NwifqfPNjFcXsTrLLhW+FG/vPqNVO2zY91VVVV8YG8yD645UNw0j/AERcXt62awLIshoaG8Pv9uUCe5VC+uQfcsXF74wpPRnfxzft+M/Jctkaoi+0FA7HTPjbp/vNjXV1dXXwgT03uiR/BtpvgXc9AsPAgSn6sg8HgtMEdy7H5l/v/l/cHjqGh5+WsqV9NQ9PMudBc1yWVSlFfX384Ls06H/heqTuh1Bz5AAdu3qaMMU+SC/Y8Pvy4BrgAOLU03VOqKOcBg0AAwBjTBfwK+JKIdJayY0oVY9Hl4AEwxniASuBCEflDqfujitPV1ZWbeh8yJOwEb1r7Fip7QHyxMe18A1vxZCJssB6m/9W/L3j/2WwWj8dDbW1tUcl81dxrb28nm80WVNFqd3+KpOXybGeCt/hc/mP9fXz2mA9Ou41lWfj9fqqrq4sbazsDz/wG2l4Ep7+38O3UlPbv34/jOLnlOiIE9iQQnyF18tgkyLsTu/mfZ+/lA5veMfJcRWInrQ/dSf9lv5xy/5lMhmAwWPxYq6ltuwne8Neigjsiwr59+xAR/P7JEzKLCC/0pHh26GmMWJwYOoLEhU3EC1w+mclkRkqiFxoYVkotSnXkcl+OfryTXHGTfOBnwnIYpRapGmA7cBcQBE4D3g+8xRjzShG5o2Q9U6oIJTuLNsZUA98GLgEqyP1jMBk9+1tCenp6RpZmxZ0YYW+YKlNB9eY9JM8YeyFYe+d7SK+8mLvDV3FJoLALkPwSjvr6eqqqqubjI6gC5QN55eXlBeXd+cpt+zhjbRWXHFtH7xPPcPqKMykLTP31zpfJXrZsWeEl0fP23APbboRLvgpeDRYcrI6OjpFAnjEG70CG0LODJM9oRMoO/P/Nulm+u+0HhLPn01Z1oCJSwy2vJb7pHUhg8nG0LItsNktjY6OWRJ9L2QRUFl5dUERGxnq6v6/RtMP/3PMC/lV/5JJlr8RvBfGVhwt6j0wmg23bLFu2TEuiK7XEDVemvWP4AYAxphK4DLgOWAa8pxR9U2oW7gJeKiIjOWCNMaeRm3X5R2PMSSKyvVSdU6pQpbzy+SLwGnLly6PAS4CfAcuBc8lVwXp9yXqnipZKpXJJlYNBvF4vWTtLQPxU3bifbGs52ZVjL+7cQBWfjlxDqtrlkgL2r3l3Fo9UKkUikSAcDheUd+czN+7iXxwP67qz0J1lh9flRQ3Tl8lOJBLU19cXVzq541G48V25ktDn/DMsP67wbdWkkskkyWRyJJBnElnK7+wkc1Q19vIDF/WOa/OV575MrH8lxwY3UPeX12OcDAB21WpS61896f5Hl8nWvDtz5I5/hd13QaIb/IUHzBKJBIlEYsaAaiKbwtP6Oy5oOo1XPd6AvbywU4n8WDc3Nx/uAfpXGGPWFdFeROSz89YbpeaQiMSAXxljbgSeJhfkUWqxc8lVXR5T4EdEHjLGnAdsAT4OvLUUnVOqGKUM8LwM+IOIvNIYUw/0Al8XkYeNMScA9wBHl7B/qggiQmdnJ7ZtEwwGccXl0b5HOKFzBU6ln+RZY3PlPLArwlmJLL1Ols+//IiC3iObzVJWVkZtbW1RyXzV3Mrf5Xddd8qlNCLCPduGsBxBBFwR1lUFufdF/cSyUf7Wdz8fYeoATz65a3V1dcFVuQCI7IWN18CZ7y/yU6nJ5MfaGDPynfMkHbIt5WQ21mI5Nj9+5nZcccmSIWNXsCJ7Ke+4YAXmhjT9V/xuxvewLIuqqiqqqqqKG2s1ta4n4HV/hlDh+W0cx6GzsxOv1zvt31dHHO7vv5tyZwWvip2HU5UmeUZhudAymQy1tbUjM8EOY1cDryiivQAa4FFLiojEjTE/Jpen539K3R+lZjBAbsnhBCIyaIz5AfAPC9slpWanlAGeRuAWABHpN8a0A6cDD4vIE8aY7wOvA74w1Q6MMTtn8b4iIoVFFFTB9u/fTyaTGam4ErWibO95jqsGriR9zsS/l796pIezjeHvTmsq6n3C4TDhcGFLAdT82Lt378hF+VTSWZc/P9XHFZtyN+5ef+pyeHiAG9uv58VNL+HlrVfP+D7hcHjG8swT2BnwFbmNmpSIjJTJHp0I12Rd8OcuzrcPdLEl+hCn1JxFkAAtgYs4/uRGcC3EU3jZ6/LycoIF5m9RBXCK+x64rsu+ffuwbXvGpMeD1iAvxJ+mLftSgtujJM4u7m94ZWWllkSHfwduK3UnlFoAvUBzqTuhVAEeAv7BGPM1EYlO8no/UMSUcqVKp5QBnigw+jbhDmD0eoqnmHka3F609HrJRaPRkeBO/s6vnUrxvmdejJxYjzNcAvtdv3ieuvJc0s4T2yqo7vWxYUVhywccxyGdTmt+jhKLRCJYljXtRWDWcXnfb7Zx2bF1XPnYEG557s/MPZ5HOarqaE5fdsa072HbNul0enbVdew0+DRQcLBEhMHBQTKZzJhx8AxmCN3dxX87ER7OfAGPU8mmqtN508aLRm9M46/OJrVu5iBefqzVHLMz4C0siDLVWE/FcjLU+5uplmU41b6Rv+8zyefUUgA8JyJ3l7oTSh0sY0wvuWpZ+VLpj+VzlJjcNL3LgUdK10OlCvYl4HbgPmPMP4jIg/kXjDEhcpMOtJKWWhJKGeB5CHitMeY7IuIAzwAXGGOMiAhwFDDt2aCInD//3VTTcRyH3t7eMUs4slaG9OZt7F1mWLEhV+7YFaG+ws9nrlh7YOM/F/YeIkI6naayslJzdJSQbdv09vbOWOEoZbmsawjz8k3LcLssYpe2AnDHc7/kQys/PO22+bGura0tLkdH3wvw6Heg+0k4UZdHH6xsNktfX9+YKkomZRN+rJ+ulWWkPTHObj6B163+O3CzVD70eYybzTV0bbK1RxE75Z+mfY98WfRly5ZRWVl4lSc1g6d/BdF9UOASKMuy6OvrK2gGVcbJcHPHX+noM1w6mEWWFx5ESqfTNDY26gxMpQ4tm4ETgBcP/y7GmBiwFWgYfrzJGOMXkWxpuqjUzETkbmPM3wP/C9xvjNlOLu+ORa58+gpysy+VWvRKGeD5MrklWi8YY04BfkiutOItxpht5Gbv/Gmu39QYc6KIPD7X+z1c5af1V1RU5O4EWwNEBnqpSXsJXHzsSDvbEXye3AWHJ9mLcdIghU2+siwLj8dDdXW1Tu0vkXzpZMdxpq180xfP0hu3CPk9eKJZxJcL+kWsCGbKQnkHZDIZfD4flZWVU5ZonsDJwnO/h3AtXPTv0LihsO3UpFzXZf/+/QDD+bSEvphF1Z4EeA1P1QcIxAQvXjzxdnzxDvwDW4mdfKDkvVO2fMb3SaVSBAIBKioqtCz6XHrih/Dq3xTU1LZt9u/fj8fjmfFvq4iwPbaNwVSao4YuYH1ZgNTJhc1WT6VSBINBHWulDjEi8nIAY0wNuUDPicM/TwDayF1n/BawjTEvkJudv0VEvrjgnVVqBiLyI2PMPcAHgSuAVw2/lAa+Tq4ynFKLXsnOtETkDmPMZcD7gaHh5MofIffluQh4kNwXrGDGmADwGQ6UXh+9BMwHVAJVaOn1OdHb20smkxlJmNmV6uRb2/6XEzxHc2nVCQQra0baZh3B781d4C+7/uVYTaeSXnPZjO+RzWZH7vzqXf7SEBF6enpGEh9PJZqy+cT1O9jQXM6pLRVU/rWd1Mm5HDxffu6LXNR08bTvY1kW6XSa5ubm4sqiP/kz2H4TXPFdWLa+8O3UBCJCd3c32Wx2ZKy37I/z0P1dvCUDv2/ws7/T5pjVAaJ2imU3XoO1/GSSR11DtuH4gt9Hy6LPIzsDyzfN2Mx1Xbq7u7Ftu6C/rV3pLn6x+1ew6zz+JRnAeVENEp75FCKTyZDNZmlqatLZOzmfBp4sdSeUmkvD5dLvGn4AI+fkGzkQ8DmR3JKt15KrpKvUoiMiu4D3AO8ZDlxWAl06A00tJSW9lSYitzCcaHn49y8bY/4bCA//Y1GszwIfBvYDg+Ry+twLNAGrgRTwvoPrtYLcSXskEiEcDueSKmej/HTXjzmt/jSuCF9MMDJEcrituC6x37yFd5Km7uYQds0RDJ3/1Rnfw3VdUqkUdXV1ujSrhNLp9MhYTydju2xcXsbHHC9mRwK7uQzryCr+b8f3aQ6v4PzlF0y5bX6sly1bVlzunT33wv1fgku+psGdOZBMJolGoyNjvW8wzc8e6uItq6uoCvi4+sTcjI2d/Y+x7ck7SK29gtipHynqPVzXJZlM0tTUVFwgTxVGXPBO/0+7iJBIJIjFYtPOyMvbF+njy09/F096PS9ddiwe4yW9euaxy+dOa2pqKuh9Dgci8umZ2hhjaoGUiGiCKjWtQqrRSYGzpeeaiFgcyM0zwhijhU7UkjB8LTpU4m4oVbRFN1daRDLMkHtnGq8md/fgxeSCOvuBd4nI08Ozhf5Abi2lOggiwv79+xGRken2A5l+2spXcknzpfiejyHeA5OntuwZ4AhniLLX/IxBr0EKTP6ZyWQIh8NUVlbi9eqkq1JwXZf29nY8Hs+MSyuyjlA5vAwrdtEK8OX+uz/Tx/uP/tC026bTaSoqKqiqqpq2RPMYiT7Y8hO44DOw9qKZ26tp2bZNR0cHPp9vZKz3D2Y4+4gajq8Lw6CFnU2ya8+f2B/bgQk3EDt5+nGdTD6fVnl5eeFjrWbW8wxE90NmsuIfY9m2TWdnJ4FAYMbvte3aPND9EKcnjuO1R15IKOFCvLAbmTrWkzPG+MktQz9DRN4y6vlzgG8BR5PLZXIr8G4R2VGanqrFbnzwZvXHbmT3Fy4vUW8Kc7gdzzMF4UoVgFNKHboW7IzLGOMYY1436nd3+LnpHnaRb9MC/F5EXBHpAHqAswBE5C/Aj4B/mKvPdDjK52KxLGvM0oqMa1HmLcMXdQi+EMVam7u7m7QcfvvgLqpr6vCGK5FARUHVXTKZDLZtU1NTo3d+SyRfOjmbzRY0BllHKDMgAQ8EPOAxuOLiNV58nqkvItPpNI7jUF1dXVxZ9C0/AjcLq84rOKGsmpzjOLS3t+O67piZWmnbpSLkxQiIx9DeeQc3d/0VcTJsbHsZeIoLvKbTaVzXpa6uTsuiz7Ub/xE6H4OT3z5tM9u2aW9vR0QK+r7tTuwi1befK/qOpjzi4LWFzNqZl3SlUikA6urqNHfaKMaYIHAHuUSe1xpjfMPPHwn8lVxw52bgK+SKTdxvjJk5qZVSalESkTGPVR+9YczvSik11xZyBs+PyZVCH/37XP9lSzF2hs52xpZef5ADCbPULMTj8ZGS6Hm96R5uev4PfGj3FZQHusgcUUm8JsBnf/sUn4q+j88EQ5Q3vYhYge/hOM7IEo6iKimpOSMixGKxkTvwM4mmbL55216+aHtwjj0QDLLdLD7P1MmSbdsmmUzS1tZWWI6lR78Hj38fvEFIDcArfw7lywr6TGpy+bHO59MCeKo9zo8f7MJ1hUtOsvnDjntZFk9xb91jXFhzIidvfH/R72PbNqlUira2Ns27M9f++BYwXjjnn6dt5rou0WgUy7IKWh7XkWznr8/+gXfvvpzH6sqpPKGuoO7kc6etXLlSA/QTvY/cjaePAN8UkfyNrOuAEPBzEfk7AGPMF8glpf04urxcKaWUUgVYsADP6GnIw74EPCtzG75+ArgU+M7w71uBM0e93srcB5UOG47j0NnZidfrHTPdPpaNcUpoE4GVjURPb8DOWuy57Ru80fSzfO0mhs7/asHBHcjd5a+rq6OysrKg9eVq7tm2TVdXF36/f8alFVnH5c9P9XHS8jLKxUvixAOVde7vux+fmfrPTCaTYdmyZZSVlc081l1PwpM/hZd9C5oKT+irppfNZunq6iIQCIyMQU/M4rIN9Vy4spK+J54g1N9EU62XM86e3TWmiIwZazWHtl4PXVvg/z02Y1PLsuju7iYUCs34fUslYgw88gzXZi5ksLWOF8o8nFhAd0QEy7JoaGjQpMqTew3wWxH5cv6J4WS0V5I7Pxl5XkQGjDE/BK5BAzxqkRtOSFsnIjtL3RellDqclXJR/O3A5+d4n98ArjLG3GuMqQJ+CZxkjPm/4QpdHwAeKmaHxhivMebzxphOY0zcGPPb6aZLG2N+bYyRcY/bDupTLQKu67J3794JSzgAspIl6AZGcq5EunbQ1HMHq44+mdgJ7ynqfVKpFB6Ph8rKSp3WXyKO47Bv3z6AaZdwuCK4IuwfyrC7P82Lj6xGvOCKiysulpNhc+99XNl61aTb58e6qqqqsJLoT/8C1l8ODcfO6nOpibLZLPv378fr9Y6MtYiQzroEfR68HQnCfTF6K3aQPPnUWb9Pfqyrq6u1TPZcEoH7vgCXfn3GppZl0d7ejs/nm3F5nCsuA7t3UxcLU7G2jf3NYbyewoLtyWQSr9erYz21I8kVfxjtTKAc6BSRJ8a9th1YUejOizlnMcbcNcn5Sv5x7nCby6Z4vbXgT6wOF+8Dts3VzmZx/n2KMWazMSZpjNlmjHnjuNf1WFYFMcbUGGPWzvE+5/R4HtXOGGNuMsZ8Yi77q5a2Up59lQO75nKHIvIbY0w1ufLqCRG5zRjzDeBdw032UmTpdXLTpt8EvBHoB74J/A44e4r2xwEfI5fvJ2+2SaMXBRFhYGAAy7ImXTIVGHQ4+6lm7HNCxHr20HTztWw76v2s3nAlThHvY1kWmUyGtrY2ra5TIq7r0t/fP2Pp5F19Kb54yx4aK3NBuLOP8vKzvf/NpdGTuWHrUyPtTqs/nZayiedO+bEuagmHnYE1F4G3gGCQmpHjOAwMDGDb9pjv2+YdEW55boC3r0uSesbirw03ceLRZ+HUNs7qffIl0VetWlVcjiU1sx+ck5vNtmqqf45y8mPtuu6My+OeGXqax5+6k2t3v4hvBIW+5xJAgiuOm3kpZD53Wmtrq+ZYmpoXGJ9fMJ8lfrKbQdUwUpSyENdR+DnLK4DRd1I8wA1AFLh/+LnjgMeBy8Zt21NEn2b2pSZIdE/60u4QuU81lfLl8OGuOe3OZDRZ7oK7jgKPZWNMA7kcVj8H/h64GPi+MaZruGIvLNSxrA4F7wP+ldzf67lyHXN7POdnf/4vcAmweQ77qpa4UgZ4vgp80BjzqIg8Mlc7FZHvAd8b9ft7jDFfAurILQkruIrW8BfnfcB7ReTW4eeuAXYZY84SkfvHtQ8C64CHRGT+zzYWSCaTob+/f9IT9ogVYUvPo5Q1r2P9vs/jiXTzXN2lrD33zUW9R75MdkNDgy7hKKF0Os3AwMC0SysGEll+8XA3l26o56rjG0g7aX61/Wf8Xf9FtCxby5HHvHTa93Bdl3Q6TWNjY3FLOOw0+PSicS6ICKlUioGBgTHft119Kf7yTD/v3LiMo5/by/6q23jd2tNIrX75rN4nP9bLly/X4M588HjhZf87bRMRIZlMMjg4OGlwx9uXJvh8BICknSQQ6+B1zoV0HVHPyjIv7z2hoaCuuK5LJpOhqalJgzvT2w6cMO65q8ktz7phkvYvHd5mRsWes4jIwLjtPwqsBY4elRtoI/DUvJ/TTBHcmfdti7AUK1YtVcUey8DbgAjwPhFxga3GmJOAfwLyF8QLcywvAYWkP9CA5dyZj+N5+Pfvk7sJMLQgH0QtGaVconUKuWnHDw5PVdtjjNk57lF0KUVjzMnGmF8aY0bfbn4v8AlyJy7FOAGoJFd6HQAR2Q3sBs6ZpP3R5IJmzxX5PotWvuKKx+OZdMlUT6abWk81q10H3CyPtL2Th5reWvT7pNNpysrKtCR6CWWz2ZEy2dMtmdo/mKGhMsAlx9YjImyNbqUyG6IlsIL0iTMnYU2n04TD4eLGOh3JncT7NEgwFyzLGimTPXqsX+hJcmZbJcenXHqr9tO7PEXqiCtm/T6pVIpwOExFRYWWyZ5rkf3gzjxHMpPJ0NnZSTAYHLtkKuviGcwQ2BHDqQsSP7qCzU3b6T3Sg7xoNY+XeQn6Cx8zHeuC/RJ4ozHmKmNMmTHmA8AGcrMIrh/d0BjzeuAlwJ8K3PcJFHfOMvq9msidJ3183AXwRg6hcxq1ZJxAccfyOcA9wxfDeXcBLzIHohl6LA+bqbKXBnfm3AnM/fF8MXDP8L4jc9tdtdSV8iwsDDxC7uB8GNgJ7Bn32FvMDo0xZwP3kTshGj2fvJPcFLiHjTGbithlfm1J+7jnO4C2SdpvJFfF69PGmL3GmOeNMZ8zxizJq1LXdenu7sa27Smn9Wddm5qsTVnPQ+xrvpLvba3k2FXNRb1Pflp/fX293uUvEcdx6OrqwnGcGWdQpW2Xxko/Qb+HjlQHN7T/iVOqTsJUBJHg9AGbfEn0hoaG4u7y3/XpXHCnoqnwbdSkbNumu7sbEZkwg8pxhROSLoGeNJ017XjK63IVy2YhlUrhui6NjY2aT2s+/OJKOPaV0zbJJ1U2xkz42xraGqHsoT6M7WKtLOch2cLD8hRHrDqOxxMOm/dEOXp5YbMp8yXRm5qaCsundXj7CrmKnn8AYsB/kjtveGt+hrEx5mpjzO3kqo0+D/xXgfsu9pxltI+SCzJ9O/+EMcZL7sbVycaYLcaYDmPMn4wxRxXYH6Vmq9hjuXWKtmVAvR7LqsTm9HgGEJEvisj7RCQ6lx1Vh4aSLdESkfPnYbefJXcydOHoqcci8p/GmB+Qi35+gYnrb6dSBrgikh33fIZcOdPxNgCGXPWu/yG33vcr5L68bxrf2BjzduDtACtXriywSwtDRIjH48RisSmDO/79Ozj+zr2IuwyrtYyempM4oXWITa2F589xHIdUKkVzc/MhXTp5KYx1MpmcMbizfzDNzx/u4spNDQxaA/xo5/9xduVZbHy8HKtt+uCObduk02laWlqKW5r1+P/BM7+GdzwKgcV/jCzmsXZdl1gsRjKZnDTP1bIhi3V7Uuw8Q/jd4DO83ju7hNa2bWNZFi0tLYd00LZkY339P0CwEs78wJRNXNclHo+TSqUmz2lmu6Q31GC3lrMt9gJ/ab+R82pfxTduTTCUjPCakxtZs2zm72k2m8WyLNra2jSQVwARsYwxLwZeSy65cgz4qYg8O6rZycCLgJ8CHxSRVIG7L/acBQBjTCXwVuAjIjJ6WtgRw9sFgX8gl6/nE8C9xpiNItIzbj+L9m+fWnKKPZbLgPQkbRluX9SxDHo8qzk118dz0fR4PryUbAaPMeYOY8xF07x+hTHm6SJ3eyLw7fHrygFEZBD4LnB6EftLAR5jJtR5DgKJSdp/AmgSkf8SkadE5Ofk1ly+0RhTP76xiHxHRE4RkVMaGgrLc7BQbNums7MTv98/6TKaTDrJ4DP72Frfy60vyTJw7sVs2R8n5C9ueVU6naampoaKiopDuiT6Yh5ry7JGSqJPt2QqZTnc+cIQlxxZy0V+2P/0k1yeOYuXRk7GXh6acXlWJpOhtra2sJLoo+37G7zxVqiYstjAorLYx7q7u5tgMDhmGU00so0XnvwRa9uf54nlj3JH4te8Ou1hU6hlVu8z67FeYko21v0vwJvvmrZJOp2mq6uLUCg06ZIp4wh4c2PTkezgitYrCVhrOGttNV99zZGcva6moK5YlkVdXR3hcPiQHuu5JCKOiPxcRN4jIh8fF9wB+HegTETeJCL9Rey62HOWvKvI3fD76bh+vkDubvHLReQhEbmPXGJmD/CGST7Xov3bp5acYo/l1PBr49tCruhKUccy6PGs5tScHs+z6YAez4eXBQvwDK81X5l/AOcDx45+btRjNXApxefMyTJ2adZ4VRT3mfcN/xy/5mgFE6fOISLuJMGlfEmhmaZHLxr5vDvAlDMt+nbtIh1ziNR0s6F2E7sHLZ5qj3PWEdUFv08qlcIYQ01NjU7rL5F83h2PxzPjTIst7XE6IxnOrgzgebaXxOAgx3iOAI8hc2Q1THNxl0wm8Xg81NbWFj/W2SQEpq7opQqTyWTo6OjA6/VOWB738Pafwh6bLHH2B7fT4rgcXXUUVtNpRb9PIpEYGWstkz1fzLTft3xwJxAITD2rxhFkOMCTcTMEPSEs2yXg8+ApMFATj8fxeDzU1dVp7rQ5JCLJcbkXClXUOcsoVwE3iMiECwcRGRjdFxFJkltSv2TOadSSVOyxvG+KtnGG85PosaxKaM6PZ6Wms5AzeMqBJ8iVRt9FrmLEV0f9PvqxA/h/wL1FvsedwHuMMWvGv2CMaQHezagEVwXYQm769Hmj9rMaWE0ud9D49/i1MeYP454+hdy0uoKqYJSa67pEIhEsy5qyVPmznQnufKaHbdJLZI/Lf98S5/ubOzhvfS2r6gqbOWhZFul0mqamJq2aVSKO4xCJRLBte8YxeK4zwU8e7OIlx9RR6/cy2OAwcLSfwImryGysxamfOk+LZVlYlkVzc3Pxy3Xu/ix0Pwnh2uK2U2Pkx3qyHEube+8l0VPHut6T+YU5ivXHfpCzT/wEvk3vxC0rbtZUJpMhm83S0tKilZTmy48ugrojpnzZtu2RsZ4qQB/cOkRgbwIJ+3h04FE2995HbaAW2xX83sKCO+l0Gtd1aW1t1QB9kYwxpxhj/mqMiRljBo0xNwznEJys7euNMTNn084p6pxllHOAOyZ575cP97Fh1HOVwHrgmQL7pNRsFHss3wecOyoBLcAFwGYRcfVYViU2p8fz/HVTHSoW7PaqiPQOV4Q4jVyemn8ll2TwyUmaO0AvuWoTxfgk8BDwlDHmL8A2coGkI8jl3RHg40X0OWOM+SbwZWNMH7kEhN8E7haRB4bL3tUBA8PJEX8L/NIY80FyVS9OBL4MfFlE4kV+lpLIZDL09PRMmG7fm+7l7s4H6OjpZXn/bi5yzsAT7mHNxhVccExxE61EBMuyWL58eXG5WNScSqfT9PT0FLSMZjBpc/nGek5cFsbzt052eHcTKCD57uixntUFf/82uPb6JZF7ZzFLpVL09fVRXl6OMYZd8V08H80VE3m27yH+30AtA+uyfPDc9bN+DxEhm83S1NSkuVjmk+vAVd+f8uVkMklfX9+ky169vWn87Un8+xPEL2ii3dfH5o57ee2qawm6TTy6p4MLj5o5mJof6+XLl+tYF8kYczpwN7lp9reSm3V8KXCJMebzIvLJ2e57FucsGGOageUcmG082t1AFPiJMeYj5M4Z/x3oA34y234qNZNZHMvfBz4CfMsY81XgxcDrgEuGd6nHsiqZeTielZrWgs6fF5GbgJsAjDGrgG+JyINzuP/njTEnA/9G7oTpVcMvpYBbgH8Wka1F7vYTgJ/c2nQ/cDPwruHXziI3a+gC4C4R+fVwxawPD/ehB/ga8PlZf6gFZFkWHR0d+P3+kZN2V1ws12LL4ON0RBO0RiOcbi+jrrwK/9HHkmo9uej3SSaT+P1+LYleQul0eqRMdiEXaJbtEMbg70wRJ8lzy7u5on76Cj6QW64TCARmN9aODZkoBApP2q0mSqVSI2Wy8zMtHh14hPpAHY3BBo5O3Y/rW0u66uBmqcfjcYLBIFVVVVomu0QSiQRdXV2Ew+GRsXbEIetmwRWqtw5i1flJbaogUy082vMw6yvX0xpay73boqysC3F8AUny4/E4oVCIqqoqzbtTvM+Rm5J/Zj6x63B1zx8DHzfGLBeRtx/E/gs+Zxl+Lr8MYNLchcMJof9juL2PXFDqQhEZnwBUKTP8mCvFnH93G2MuAb4OPE6uEu8bReQO0GNZLQpzdjwrNZNSVtF6yzztdzvw2uFpbfWAF+gbVxmimP3ZwIeGH+Nfu4tx/5iJyI/JnagtKbZt09fXh+u6Y6pZbe69j3t77qbaX02z5yWcHkxwVGgV2YbjyKyrRop8n0wmg+M4tLa26hKOEslms/T35/J2Fro8blmfxfF7EgTqQ+w4wqa2rIFK//R5cdLpNCJCc3Pz7O7y/+Vd4GQgVHheJzWWZVkjYz16tpwjNkdWreforb8n2P0Mjwdej+cgZmLk82mtWLFC8+6USCaTYXBwcEI+rV/t+QXd6W4u3b2B5lg1v6l7nEzKzmWeMIbW7GV89m/7qSnz8YbTmwgHpg/EplIpPB4PLS0tGqCfnTOA60ZX7RGRJ40xLwL+DPy9McYWkX+czc5ncc7y2Pjnxr3+HHDFbPqiDjv/BfzfXO1sFsfyA+RWCUy1Pz2WVaHmOlg558fzuLarD76H6lByyJ6Ji4iQm3qpZiAiJJNJIpHIhFLlKSfFy1qupNxez4133sOR9r14/aditRR/h95xHDKZDE1NTRrcKRERIZFIEI1GCy5L37M7SvO2KDuOqmb1mcsZGOzFl54+54bjOCN5d2a9hCM1CC//IXg1v8dsuK5LIpEgFotNyKdVuSdEcPNuSFXyXPg6lqW9RGa4sJ+K4zgjeXc0F8s8u/Wj4Jn4z/ZUY/1w/4M8M/Q0n6v8MDXRKNGXtfG2sjNHXt/aleB7mzt4y5nNbFgx88wd27axbVuDOwfHA9jjnxSRhDHmcnKzCt5hjEmIyIcXvHdKzZKIRNAEsOrQMKfBSqUW2pIO8BhjdgLvF5HrR/0+ExGRqTNUHoYymczIcp3xd9/TWYsey6G9Y4jLGjvxOFeQqVuBtbr4ZTPpdJry8nLKy8t1CUeJpFKpkTLZhcy0yKRsYk8O4DQH8R2bYE98N52pToIz5N9JpVJUVFSM5HwpWqwT4p3g19w7s5Uf61AoNOZiPJKyae4pozx0L/3HvQj/snWkjaGpscgE2MOSySSVlZWHfEn0kuvdCnvuhdffOOGlRCIx6VhvGdzCu9veSeUjaeLnNyFlB77ziYzD3duGuHJTA8c0F/Y9S6VSVFVV6VgfnMeAtxpjviUimdEviEhqOMhzF/BBY4yHyfMUKqWUmicarFRL3ZIO8JBbkzi6rOdeKHrV0GHNsiy6u7vxeDyTJjze1hcnOhBjTXkza2s8EK3FrQiAp7iT+1QqBUBjY6Pe5S+RfALtQkqi5+1/qIfySJbdpw/yYOdmWspaAdhYs3HKbfIl0ZcvXz775To3vx9azwC/VlibjXQ6TW9vLz6fb8JsuV8+0s1LnSTBqiS+o08leBD/jxOJBD6fj+XLl+uMjvn2iyvh/E9NqCiXT6Dt9/snjLXlWqx+xodb7sNZNvY7f+vWASIpm+NbKwoqi54f68bGRg3QH5zPAX8BthpjfgV8VUS68i+KSMQY8xJyeQPfDwyVopNKKaWUWpqWdIBHRC4Y99QrRGRCokA1Odd1icViIzNrxnti8HH22Y9z+ZqTuPiIZsrvL8e3r5H02uIu5LLZLJZlsXLlSl2aVSKO44yM9fjlOlPZu6Wf1u0xnjvR5qb09bxq5Ws4rmbTtNtYloVt26xcuXJ2gbw7r4Pdd0JkH7zy56AXkkXLj3Umk5kw1ttu289V7T0spxVr4xF4DyK4Y1kWruvS1tameXfmm+tCVQtsev2Yp23bJhqNYlnWhLG+ueMv7E/uxyse0sfXTQjKpy2XyzbWUx0uYCZfJoOI0NbWpoG8gyQifzXGXAh8Ffgn4LuTtOk1xpxDrsrK33EY3Lgynz647eW6OemGUqqEZpoZmsu+oZSaSUnPyo0xlUA5uZOXqIikDnKXTxhjviMinzv43h368mWyw+HwhDuyz/d3cMPeu6mJXsGaBj/hZ64nsNdDZt0Qdmtxy2Ysy6K+vr7gWSNq7qXTabq7uwtaHtcXt3i4vYPA7na613vZWvs8L6u6YsbgDszBWO+7H95wC/g0EDhbyWSSnp6eCWWyd/enyHameGDDY1zSfTPlVV9jVpnnh+XHWoO2C2DrH8A3cYZlIpGgv79/zFjvju+iO93NIwMPc92mz2Du7kfGfeW7oxY7+lKcvGr6ROl5lmXR0NCgsy/niIjcDZxojKkhV7p5DGNMI9AvIm80xnweOHeBu7jg5FOl7oFSqtRGB3BWf+xGdn/h8hL2RqnZK2QZ+3wGLBf09rgxxmeM+XtjzK3GmAFyU4/bgQ4gbozpN8b81RjzZmPMbM4klwHdc9jlQ1a+TPbokuij/WHnXTiZOk5fcSTHRm4juHMQp7yB9FFHF/U+8Xgcr9dLbW2t3vktkWQySVdXF8FgsKCEx/dsG+KJgScoD8QoqxRWl6/h6KpjZ9wuFovh9/upra2d/RIOxwLv7Ks5He5G52IZfzF+/ZN9NFf4keR2TNPpOGXLZ/0+0WiUQCBAXV2d5mKZbyJwxyfgnI+PeToajdLb20soFBozg+qG9j+TdJK8pOkSAp4AuDJh9s7tzw+wqi7EytqZA7GRSIRgMEhtba2O9RwyxrwbuJfJz8O+CnQYYz4gIs+JyLcXtHNKFcgY85/GmNcbY44xk/yBMMa0GGMmRqeVUuoQJiJjHqs+esOE5+bTgs3gMcYsI7em/ATgeeAmoBPIz9oJAyuAE4EfAO80xlwmIv1FvM3PgbcZY64XEQ30TMG2bYaGhiaURAe4rfMWHh54iH7X4b2miZO2vhnJnowtbyRx8SrcmsJnZuSn9be2tuqd3xLJZrNEIpFJx3oytz43wN3bhnhLWx2nRxuxT1+DUzfzDI10Og1Aa2vr7Jbr/OYaiHflZu7oReSsWJZFJBJBRCgrm7j0Kp11wdvLfk8G75rLJ63GVIh8mey2tjbNxbIQ7AxUr4RVZ488lclkiEZzEz/Gz5bLSpZzG87FOzy+ZlyA58an+nhod5RPXLqGoH/68UulUvj9flauXKnBnTkyfBH8I3JLrwaBVcCOcc12AhcAXzbGnCYi1y5sL5WanDGmRkSGRj31AQ4sIUwZY54kl0j88eHHNeSO5VMXsp9KKXU4W8glWv8JrAEuFpHbp2tojHkx8BvgP4C/L+I9XOBYYL8xZjvQAxNWIYiIXFTEPg85mUyG/v5+KisnTs9v73qW8zrj+PoNx5b9hcjZ/4andy1uhQ+nvvDgjuu6WJbF8uXLNbhTQul0moGBgUnHerxtPUnufGGQD5/YyPKnkuw40aG1gOCO67pks1mam5tnF9x56hfQ8xS865nit1UjUqkUg4ODVFVVTXhtz6O9nNaZptYX4O0VhqC/albLs1zXxXEcmpubdUbefBOBez8P6cEJ1eTi8TjRaHTS77UrLsG9Kfx9uQJN3kEL8R4IzvTFs7zrvFaWVUz/d9l1XUSE5uZmDeTNrbeRC+58E/gnEUmPbyAinzDGfA74FvAGY8xNIvLjBe6nUmMYY94EfAFoHvV0HXDSuMc7AcOBwE9yAbup1IyMMa8CziZXpfDHImKPeu1GEdG1YWpJW8gAz8uA/5gpuAMgIrcZY74EvLfI97gY6Bv+7xCwssjtDxsTymQ7GZzUEG7fc3jLN7G6+TjSNVfjVGwgtDeGU1PcsplEIkEoFKKyslLv/JZYISXRs47L3duGuHBdDWsHLJ5sGMTTsKKg/cdiMcrLyyfkfCnYI9+CV/6i+O3UBMFg8EDgJeviSSfJpmJUPDPIsceG+AsPccngEDLLZXCxWIzKysqCE3Wrg2Cn4fk/wUu+DFVtE14eXxIdwEqnCGd8hHcOkTqxHjxgrSzHApLJLADRtEPQN3PAJh6PU1lZWdDMP1WUtwH3iMi7p2skImljzFuBTcA7AA3wqJIwxqwHvgMcAbxx9GvDs3nuGH7k21cClwHXkUud8J4F6qpSMxpeHvsJ4Hrgw8A7jDGXjirSc07JOqfUHFnoJMvZItqmgKJKvIjImuK6c/ixLIuBgYmFxqoe+jzdu57GWVbFJu9FNHZW4A54YGeurXt04ReE+SUcLS0tepe/hPKzdwpZ5/mHJ3rZP5jmbaEAyY4u7l3zNK8smzmpciKRIBAI0NLSchB3+Q00zfxeamrJZJKhoaExz1Xc3oG//1FiGZdGY1M5dDs3lDn4fGW4gYmzfGYyeqw1aLsA7AxUNMGqseeaiUSCWCw26SaJGx7ntYEzcWoD2CsO/PP5o/va6Y5lCfk8eD1QXz797J14PD4y1mrObSB3cTEjEXGNMb8F/nl+u6TUtK4ll17hBBHZPVNjEYkBvzLG3Ag8TS7Io1RJDA0NUVNTM/qpdwMvFZEtxhgf8A3gDmPMhcNBHj3BUUveQgZ47gbeP5wf54XpGg7fLfgQcM+C9Oww4bou8Xh85M7saD39g3zZfQfnVCZp2OsjcUkjbnXxd/lt28a27dnnYlFzwnEcEokEiURi0iU746WzLlc3R5GtEX5wxJ1cvO5q6oL1026TzWYREVasWDH7QN7dn8stQVGzli+LnkqlRmbWhJ4cwNfbj+1+jrc2/BOnHD0I5gR2xnaQPOOzhLzFVTnLZnOx+dbWVg3uLBQnA96xSyQdxyEajZJOpyfMogo9OUBVOoTnlSeSHDVGd78wyMN7YvznK9dRGZr5b7JlWXi9XlpbW+fmc6jxbCBTRPs+csvPlSqVX5CbIX+PMebNInLHTBsAiEjcGPNjcnl6/mc+O6jUZH70ox/xsY99jM7OztFPN4vIFoDhpVnvMMZ8BbjTGHMhB5YWKrVkLeQV+AeBu4CnjTF3AY+Sq6CVJPdlCpNb13sS8GJyyQc/MN0OjTF3AP+WX/Y1/PtMDtscPPlqSpOVRd+fTnLeqYblO0N0n15O+SyCO5CbvVNdXU04rEUTSikej9Pd3U1ZWVlBM2uyjhCOxth/Qg3XHvk2Kvwz5+xJp9PU1tbOviQ6wI5b4C33zn57RTQapb+/n/Ly8pGxll0x0qH/4GN8m43r7uPlbdcQ8obwGm/RwR3IjbWWRF9g+x/MJR0fZXBwkKGhoTFjDeAZsgjsjPHl4+/m3eakkef74hZ3PD/IdZevKSi4IyJYlkV9fX1BFffUrGwDTimi/anA3nnqi1IzGr4pe7Yx5m3kgj3FlGDsZWzOHqXm3QsvvMDb3/52duzYwY9/PGF1a58xZo2I7Mo/ISIfNMZ8FbiThV/dotScW7CDWER2G2NOIjcz57XkgjiT2QV8jVy+nr4p2uStAUYnCFiLRl4nlUql6OnpIRgMTnqRdm+1g2Nv5xjPiYSqZr64n0w8Hsfv99PQ0KBJOUsoHo/T399fcFl0yAV4fI4QrKqgsoDlO7FYjEAgQENDw8HN6DAGQsUvF1I5sViMgYGBCWXRO4Yy1PhjnLSxjR0mS32gbqSqUrGi0SihUIhly3SW/YLJxODWD8Ol/z3yVCQSIRKJTCiLDlB+XzeZ9dVkjD3m+Z882MX65WUsr5r574CIEIvFCIfD1NdPP3tPHZRfAv9mjPmKiEybWd4YswF4PfDf07VTaiGIyPeMMX8Y/ZwxppdctazH8g8R2T78mgEuBx5Z6L6qw9svfvELnnjiCZ544glWr149/uXbgTcDnxr9pIi83xjzdWDjgnRSqXm0oFHK4bWN/wL8izGmBmgBKsmtd4wDe0UkUsT+1oz7ffWcdfYQYts2sViMbDZLRUUFlQ/+G4Gexxi9zNQXgJfHLmT1kJ9oOFR0lCyTyc04b2tr07w7JZTNZonFYti2XXBy1FueHaCm63lWZqpIlM28TTqdHimTfVDBnT+8GTxaYW22LMsiFovhuu6YsujV936Msuy5lFXZOLX3EB2KzDq4ky+T3dY2McmvmkdWHJpOhHUvyf1qWUSjUURkyllUPyz7C3biQJq7Xz7SzY7eFO85vw2vZ+bvqY71gvk28HbgLmPM+4FfisiYonbGGA+58tJfBmLAVxe4j2qRSv/nZkhMnc5ya1k56c/cNfmL5X5CH3rRQb2/iPSPe2ozufw8+Zu2YoyJAVuBhuHHm4wxfhEpJg/n0vWlJkh0T/ny7hC59NOTKV8OH+6aj14dVq699lpuvfVWzj33XH74wx9y4YUXjn753Uxx/Ssi7zXGfHlBOqnmzEzXIoXkIj3UlGwamogMGWMccjNwBIiKSGou38MYUw84w1n+D1uJRILe3l4qKysJdj5AaO9t9L7qdjAedsd3Ee3opHLbdho7fcQubUHCxR0WIkI2m6WxsVHz7pRYLBZjcHCwoIpW3r40Q7tjODsivMxbRe8FdTTXN067jYjgOA7Lly8/uLF+4UbofRbe/tDs93GYi0QiDA0NjalUF9x/N077I+z2vpXIRV/lyT0/5ZMbr5vV/vNlspcvX65B24XkuvDkTyFwINg6MDBAPB6fkHfn6aGnGLQGeVG2ju2xbXzyuNwNya1dCR7YFeErrzoSn3fm4I7jOBhjWL58uc6+nGfDeUmuBP5ErjLWN40xjwKdgBdoBE4GKsgtzbpaRDqn2p+aP2+86fUMZYamfH3TGXDlH7856Ws1wRp+fOnP5r5T0wR35nXbKYjIywGGb9qeAJw4/PMEoI3cdcZvAdsY8wLwFLBFRL44551ZLKYJ7szrtmrE+vXrue+++/je977HtddeS3f3gf+vImIB1vhtjDH3AD8XkW8tYFfVHBgfwFn9sRvZ/YXDu9L9gp7JGWN8xpi/N8bcaowZAIbI5eHpAOLGmH5jzF+NMW82xszq1r4x5khjzM+MMUNAD9BvjOkxxvyvMaaYdcOHhGg0Sk9PD+FwGJ/PR+Vj/0X0tH8Gkxv6W7tu4YidZVTZ64gdVYNbUfz/9mg0SjAYpLq6WhOwltDQ0BB9fX0jYz2T0DNDbG1PsLIxzLOtPUjNzMs4otEogUCgoMTN07r7s3DxfxzcPg5jAwMDDA4OUlZWNib4UvHof/Ftz9tYXhXgz+3Xc3XrK/GY4v/MiwjxeJxgMDghIbuaZ7EOeP56OPODAPT29hKJRCaMNcAN7X8m6AniwfDKla8aef4PT/TyqhMbCRRQDt11XZLJJKFQaEIASc0PEXkeOJ5cid7ngbPJVSp6DXAWuaUu7weOFpEnStNLNV1wZz63XYpEZEhE7hKR/xKRN4nI8eSClKcA/0Bu5toQuSVb/166nqrDydve9jaeffbZQpufDbzEGBMyxhxljNEykmrJWrDpFsaYZcAt5KL6zwM3kbtjlZ+1EwZWkIv+/wB4pzHmskmmg073HseTq9ZVNrz/beTuiB0JvA24whhzpojsm4vPtNhlMhmi0SjGmAOJcMUhsyo37b/jL/dyTWwTyyvqeCJcw0mrKwkVMJV/tGQyid/vZ+XKlRrcKaFUKkUsFsPn8xWUCDf8QC8D7Ql+texe6hr2E/AG+H/Bc6bdJplMEggEWLVq1cF32BeCNecf/H4OQ8lkklgsht/vn5Bjac9glpf4T6EjuJcyXxmbao8vev8iMjLWulxngSUH4PtnwTn/DI0bRirhBYPBkRxL//b0gSpobWVtnLbsdCr9+zi2+sBYZWyXM9ZUz/h2IjKyfy2JvrBEJAN8ZfiRP0dyRETLCqpDwvBMiXxunhHGmCNK0yN1OCoyp9x55Ir8BACMMV3Ar4Av6UxKtZQs5Hqa/ySXFPnifNWrqRhjXgz8BvgP4O+LeI//IBcwOn34DtnofW4klx39i8DritjnkuS6LtFolGg0OnIHPvzCr7nPpHlm769Z01XNCf31PHLWGn7bY7FjdxSft7g7/Y7jICK0tLRocKeEXNclEokQj8cLnm0RjfTxtRWPEK7ZwTvXf5DaQO207W3bxhjDihUrDr7Df/sqOBNmx6oCuK7L0NDQmJLoNzzVx8o9P6PGame1fSI1YYtPrLmN6478zKzew7ZtPB7P3Iy1Ko4Vg9Xnw6nvxHVdBgYGyGQyI/m0/tpxEyFviA8d8+GRTYJbhzDZA1W079s+RHfUKmhplmVZBAIBmpu1yE2pFVBUQqlDgojsKHUflJpCDbCdXNXnIHAaudmUbzHGvFJECqnWrFTJLWSA52XkKmNNG9wBEJHbjDFfAt5b5HucSa5s+vPjXxCRp40xX2OG0uuHikgkQn9/P2VlZfiyETyJOOXP/oSbjziDq+tO5YhnHDrO9nPrc1Gu3NTABetrqAgWl2cjmUxSVVU1JsGrWnijl+sUkj8jnUqQig2SpZVrV55Djb9m2vYiQiqVoqamhnA4PPuOxrshm4SnfwGv+tXs93MY6+vrIxKJjJTJHkrZ3LWjg0/LnXSuex++55fzzTW38s4j/3FW+3ddl0wmQ21t7YFZf2rh2JmR0ujd3d3EYjEqKytHvtePDT7GO9a9c8wmgW0x4hfmAjTxjMONT/fz8UtXz/xWto1t29TV1RU060+peVW+fPb5R8oPu9X3JWOMmYsLXBGRi+ZgP0rNtbuAl4rISElKY8xpwPeAPxpjTspXiTscFXIz/3BMaLwYLXRG3GIyvKXILbUqRgyYLolMBnCmef2QkEgkGBoaIhAIEAgEqP/TW7Cr15JZ8SJcn5fVFWsok338eqdQHfaxYUV5QVVWRsuXRNc7v6UVj8dHyliPLpM9nchtjzMoWcq8q1lfvWraP9ijc7EsX36QJ9E/PB9az4ANr4Xa1Qe3r8NQJBIZKWOdz7F03Q07Caz+HX9OVnH11iqerO+gsWk1y8NNRe/fdV3i8ThlZWU0NDTMdfdVIew0+EIMDQ2RTCYpLy8fybsjIgQ8AeqCdWO38YBbnVuq95Xb9rJ+eRlttdMH5/J5dyorK7UkulocpqkcpAkzFxUPFF1odTyd8q0WIxf4/ejgDoCIPGSMOQ/YAnwceGspOrcYaDLjpWMhAzx3A+83xlwvIi9M19AYsx74EHBPke/xP8A/GWP+LCJbxu1zJfAe4OtF7nPJSafTWJY1UhIdxyJT+S/079/LtXuGqNjbjieaJVXn5f+dtaLo4E4mk8Hr9Wp+jkUgmUwWVRI9+OwQrYNV/GSFj7ee2TrjMo5MJoPP55ub/BzljfDy/zv4/RymkskkruuOmVlTEfRRW1HBP247D7u8geBFJ3P0LPYtIqTTaYLBoC7NKpV0FG56L7SdRSwWA8Dn8/H/2bvv8Miu8vDj3zN9RjPqfSVt8+66e917BRsbm95MMyXGIQktkBCSEDAtkEBI6AQCAUJvhh9gg40Lxr339Xp3vUW9S9NvPb8/RqNVGfWRRtK+n+fRs9bce8+c8bkzuvedc973m3v/G9MxcNHjuXcAIvf14Rm1UPbh5VmWo3nb2bMH3fMz8kKh0NKDtkKII4rW+qJS90GIZTIEVBfaoLUeVkp9m1zCcCFWvZUM8Lyf3NS3p5RSdwAPk6uglSb3bUAYaAJOAV5ILsnVQpdTecjN4nlIKfUHYBe5UnhbgZeOPc/WsTdpntZaLyTPz5rg9/tRShHofZhnT/sSTQ8O8NWj/sD7jn4/SY8fFJi3HMLvW1hwR2uNbdvU1tbOe8aIWF75sZ7Xvp0p/vPEe+k7cC7BOSrsuK6L67pFHGv50m6pJo717t40aE3LcADMNuKva1l0u/ky2dXV1fOqwCaWQbIHKjfBJZ9AdXXj9/txcYlbcf5m+7sB8KvDY+MdMEi8aMN4LcyDQ1myE3LxzMS2bbxeLzU1NTLWQgghRM4DwDuUUl/QWscLbB8EZMqrWBNW7OpOa31AKXUKuZk5ryMXxClkP/AFcvl6Fpp08BMT/vvFYz9TvWVq11hYIudVbXR0lOHh4cPldJWH4L39PLXZ4ZzmC/BNyLVgOy7+eeRsydNaE4/HiUQiVFXNnpRXLL/h4WHi8fiCbtKUpdmTGuHklkrCgZnH3nVdEokE0WiUysrKpXdWa5BE3Is2ODhIMpmcVDXrq3/q4A2nN7DzrpMxdjiwwCTpeY7jkEqlqKiooKJi7spLYpmYSQhX0T84NF623HZtAp7ApJk7k/gPj/k37+riRccV/PJxnG3bZDIZqqqq5p2QXQghhDgCfBa4FbhLKfUOrfX9+Q1KqRC5Aj1SSUusCSv69Z3Wegj4Z+CflVKVwAYgRu6r/SRwSGs9uoT2F3eHs04YhjE+tT8YDFJ18ztwE28mG7S4ObWFvntNbuL58f1TpjPve0Ipnby65Mfa4/FMK5VdyI8P/JATn6lky2gtmco4Vx7fgGeGgEt+rEOhEC0ti58VMu7b54PyQPW2pbd1BMpmsySTSXw+3/hMqo/85nm21Uc46pEnifsHCZ79ikW17bouqVSKsrIyyadVSoke+OWbMHdeOx7I8/l8GLaBzzP9z3T05k7c8OGk+F+6vR2PghcePXOAJz/W5eXl1NfXL8vLEGI9+c0rblxaA5Jr9IikPra04/X1RenGNNfc9EZGjJGC2048C176q6/OeGxlsJLvXfGD5enYKqG1/pNS6i+ArwH3KKX2ksu7Y5Irn94M/GsJuyjEvJVsfrbWegQYKdXzrzda6/EkrNFolNC+/4dnuI5h3wn8Z9uPyR5s4vOvXvwNtuu6eDwempqa5lWpSSwfrTWDg4OkUql5fQv/9MhTuO3DnJ06m3suqIW7KgmfOXPFNNu28fv9NDQ0zHvp14xcFzw+eOvtS2vnCKW1ZmBgYFJZ9Nzj8LKKPnx9HraU/YBBFhfgsW2bUChEXV3d0sdaLF6qD73lBXS1vBLTMIhGo2itubv/Lvxq+vJIZbrcfGw5g0/kJrkeGMryH6+a/fPdsiwikQg1NTUy1kLMw0tuKDQJXIjZ6Y+WugeFzRTcWe5j1xKt9XeVUneSSyvyEuDVY5uy5HK4Xl+irgmxIOtuAb5S6s3AZeTy+RSKRKzL8oyDg4MMDw0SCQVRaMoe+28sz+f4qrbx+xRvPnfxiVMnVlyRkuil19/fTyKRGC+VPZebu37P3w29hNTZ9fzqiX7efGYj/hmmbjmOQzabpbKysjhjbaXAt4TS6ke4np4eksnk5LHWGqU1tbsskuc15r5fWgTbtjFNk+rqasJhGaOSMhMkjVzy41gshlIKy7V4YPA+3r51LKej1pNmBPzqsX5edXKu2tm1c3y+m6Y5XhJ9YpJuIcTaEv7oxUs6Xn9EphUJMRut9X5yRXnePbbaJAb0aK0XUglaiJJaVwEepdSngH8kN52ujyOgJDrkyqInk0m2/O4K3FgLz/WlCep/5sGwjz1bfsSpVVs5pml+VZam0lqTSqUIhUJSXWcVSCaT4+Mx39w7b9x1BqGqcj7+7CAKOH1j4Vk/+SUc0Wi0ONV1tIavHAtnvHvpbR2BEokE6XSaSCRyOKcWUPGT/bzbdHm2oZfLHvhnsq2XLLjt/FhXVlZSW1tbzG6LhRrci/uzq0md/A9Eo9HxQJ6jHar8VTSHc5+7FT87gBvNvedvHs5wzFExztw8d84kx3HIZDLU1NRISXQh1rjMx2Q2rBArRVabiLVqxQI8SqnbFnHYQmfbvAX4A/AqrXV6Ec+35jiOw/DwMLH7P8UPmrZzX+RczjdbKHODPHPa/dSmPLxh0xsX1bZt29i2TSAQoLGxscg9FwvlOA6Dg4NYljWvsuh39N5O1R6To9PVfL7eZW97mq++fse05RlaaxzHwbIsQqFQ8fJz2AbUHQvnfbA47R1BbNtmYGAA13UnBXfQmi6t+VXDlzEqLF5oVpI485/m3W6+Cl7+HJLgTunZ6SGSLZeR2ngF4Qlj7WgHj2fsd61xIz4SL27lZ4/08bhl8sl5lEO3bRvDMIjFYpIYXwghhBijlHIpQqYsrfXMOQ+EKJGVnMGzF7iW3JupC7CX4TnKgV8cKcEdyC3XMTqfwBh8nPs3nYz/wEm8SFfReUmEi8PNhL2LW3qRX74RCoUIh8MEJ1TfEqXR29tLOp0mGo3OK4fG7p4neG//lXyyIsnrjq/hFTsL51nJB/KCwSDRaHReSZvnNLgHUv3gX9zMsSNdd3c32Wx2fLkOQM+oAUMGjukwGhnhr7f+LcMnblhQu5Zl4TgOoVCIWCw2nrRZlE5/dzsBvNOWTjnawTdWFt0zaqF9ufPg4YNx/uFFG+ds1zAMACKRCBUVFTLWQgghxGFvR1Khi3VqJcukX6eUuh/4BvBHrfXbluFpfg9cAvzPMrS9KmnbpO3Pf8kb206jIXUa/5KqQZ1aQ1115aLbnFg2WZZlrR6u61JWVjZ5Rscsrt59OmyrYqTdQ2tV4bwbtm2TTqeprq4uzrIsgGwcvn85HHU5HP+64rR5hHFdd9JyHdvRXP+b/XzD8bJna4wAikjdTtwFtJkf6/r6epm5s5rYBv5QFGNK8NV2bbwq916P/aGTzCm55VU+r4eqyOzBGtM0MQyDhoYGqqtnL50uhBBCHGm01t8pdR+EWC4rXSb9W0qpDcBHlVK/1Vr/oshP8W7gj0qpHwC/IpeHZ1p0Vmt9Z5Gfd+XZJu4Pr6IqOcyDzvG8t/PVnBg9Cl3rIXl05aKb1TqX6LOsrKx4N/xiyfJJUudKqvz1OzvpGjVIRG/hH+xj+GDHKPFM4clyE5NnF+WG30zBj18OZjIX3LnyK0tv8whkGAaO4xwea1cT+0MHn3G9hDdG2XxuA+ruhbWZH+uqqiq54V9FjP59VD/4STI7Xjvpcdu1+d/nv5XLv6M1btSHua2c/7z1ECHf7LP38mNdW1tLZWXlMvZeCCGEEEKsNqVIsvxJ4FXAvyulbtBaL+RL6Lm0ARXA64GrC2xX5AI+a3u9pNbw9E/IeqI8f8F/cdedj3Ol4yfx4pYlN50vkV1dXT3vmSJi+fX29pLNZieVyp5qT/w59mX28vpzGnh2j8u2yEY+dlXbjPvnc+5UVVUtbawdG/b8DuIdULUFrvr64tsSdHd3Y1kWwUAA/8EkKmszYLv8b3OAv72wkaeHH8e/wEnFpmkSDoepqKiYV+U1sTIG9j1CuOk8kse9jaeGH0Xr3MCm7BRVgSqu3vgGcDR4FI93JBlO23zsqs2ztpnPuVNeXi5jLYQQYtXK/sfdkCpcnOrZSBnZj98x88FlfkIfOHd5OibEGrfiAR6ttauUOgMIFTm4A/AVoBL4LPAcy5Pnp/SyI9h3fpr+Uz6K6/Py6niE4XPKYR55WWZj2zbZbJaqqqpZAwliZeVz78xVFv0nh36E6z2aESvImzsvIntK9Yy5eizLwjRNampq5pWweVbDz8Pd/w7HvQ5OvW7J5+GRrKurC8MwcmOddQk/Mkhqa4xvJQ0uP2MDLppftP+cd2TnXyDQNE0sy6KqqopIJLKMvRcL0dHRgZuN4wtXMmAOcEv3zZxafdr49osbLkEphXJdtFfxP3d38RfnNs2afyubzeI4DuXl5VIOXQghxOo2Q3Bn2Y8VYp0rSZl0rbUBGMvQ9PHA9Vrrf1+GtleNTGoUI7aZTyVuYeTgnXwo+EKajtqxpDYnlk2uq6srUk/FUqXTaVKpFOFweNZZNv+x67MMDtbw4d5T2ZQNoiu8JDcXLofuOA7pdLp4ZZOtFGw4A856z9LbOoKlUikymQyRSASPx4OybJyaICPby0l3J9jREOZTT32C0wf2cEzZ0cTn0WZ+rOvr62W5ziqSTCZxe56k6ZFPkTjt7zFck83RzVzSOKFopKMp/8UB9sVNDgQVJ2+KsrOl8HsacgH6TCbDhg0bqKiYu3y6EGJ2lcFKRoyRRR8rhBBClEJJAjzLqB0WlHd0zdFaM9jbDdrDRe2nUJ0+ga21YVJLbNc0TaLRKNXVM8/6ECvLdV16enrGK1zN5M99uZRSL+i5nE2xAImrWmdtN7+Eo7KycmljnY3Dg1+BkQMQkaDgUuTHerwsuuMS2jWC9imiT3ydJn2QWx4O0KgzvNkMMPSCj82r3fyMvPLycnlfrxJuvBvjts9RGT9A+pg3MbL5Rdzd/nOivgnBG60JPT6EVRngayHNR67czOmztKm1JpvNFmdGnhACgO9d8YNZt2/60O848JkrV6g3Y8r8i5+5UCaV9IQQ4kiw3gI8/w58TCl1o9b6mVJ3Zjl0dXUxmhzCr2o5ZriByBm1ZBoWVwo9zzRNXNclFotJOfRVpLOzk2w2O+vNuZG1ubfrLl7X+iaqM1nSL5w9MXY2m0UpRSwWW1o5dCMJnfdD31Nw0lug/vjFtyVob2/HNE3Ky8sBsOIWDBqMnuoneO+tPFu/lXMqj+dMX4zRbcfMq81MJoPH4yEajS5trEXRaK3pf+L3BEcPYh3zaqzaEziUOkjaTnNl81Xj+5kpm7L2FB1n1BB6cmDOdjOZDD6fj/LycimHLsQ6NlfOkZIEncRkZQ2Q6l38sUKINWnROaWWIZ/Uegvw7CSXRPkJpdQ+oJfpeXi01voFUw9cK0aNEb7Q/WM+1fU67qpOcPn2iiVNWcpP65eKK6tPPpfGTHl3tOvi/cE+Xld2NsGnDPpqg8RqZg7Q5XMsNTQ0LG0JR/u98Ku3Qc02OP1v4KjLFt+WAHJjkw/k9SdMvvzb/bzR6+PWBzu5MFtNNLyRbUe9HoD5ZN+xLAvDMNiwYQOx2MzLesTKc7MJaDoZo+VCOtLt/N/+7/LajVdTEagEIGU4fPyGvfyVx88PH+vnlLbZx880TUzTpLW1VXIsCSFEqf19z6ybSxWE+80rblxaAwss7iDEEWexsyuXIZ/UegvwXEUuoNMOBIDZ16qsMclskh8d+D/OSWYx6eFA5UmLbktrPX4TWFFRQVVVVRF7KpbKsiwcx8Hnm/IWdTWRe/pQjubxvj0EIor/d/ITvHvHxTO25brueHCnurp6acGd4f1w24fhzPfAGX+9+HbEOMs6/MGujBGa7vkI7/dG2eQ7hVMDN/LlzRE2Vs1vzPJjnQ/aSrL01cXqfYbK3d+j85jX8NN932bUGuX8+gs5ofLE8X2yWZv3+gIc1Rrln86b+dvciWPd0NAgwR0hhBAzeskNLy51F4QQK2RdBXi01rPXj13jnmt/Dm1luWTE5VBgK285u3HRbRmGMb5Up7y8fHogQZRUZ2cntm1Pq4Tj681gDBvcuz3NTe4DXHvim7i24pRZ28pms/j9fsrLy4nFYgsfayuTW4oFcOAOaDsfTrl2YW2IGbW3t+M4DkopPPEOMolqnqu4lLJIjGd3VJNNPM1b2l47r7YymQyBQICKigrKy8tnTcwtVpbWmqFdf8LfcDaHGk+mbPRpXtLyMsr95Yf3cV1GnhslqiB95ux5rfLv68rKSqLRqIy1EEIIIda9a25646wJ8E88C176q68W3FYZrJwzv9p6sG7v6pVSTUAb8CyQAexlKMu+Yvr7+0llU0RUiAOjZWxoihD2L+6CPj+lv6GhQWburEK9vb3jpbIn5d4xHSJ39vJ5M8OTI9/l7Mar2FAz+3ptwzBwHIf6+vrx/C4LtuuX8MT3D+fZ2flW8ElOl2Lo7u7GsqzxZVQj7Qli8UvxBPt5oGqUbjvFeQ0XE/LOXfI6m83iui41NTUyc2eV0VrT1dWFyibwVm/EQlPuL6cmOLmKXWLYoPnJEfpPqQZ/4aWZcLgcenNzs8zcEUIIIcQRY7HVDZd67Fqy7gI8SqlzgS+Sy8cDcCm51/ltpdT7tdY/LVXfFiubzZJIJPDqFLVdD3IMf42vNkxmEW3lyybX1dVJKd1VKJPJkEwmCYfD03LvZNMGz7KXZ7fdy5kNJ3D1pvNnbSs/1k1NTYvLw/L8rXD7RyEzCFd8EbZeuvA2xIzS6TSpVCpXFj1hEbr5ALHhJHurRtl9gsEbNr183m1NLJEtVZRWn1QqhX3oXpqf/Qbxs/4F27XweaYnQ3ayLkNlXjbtrJ2xrfxyyw0bNhAOLy3BvhBCCCGEWF/WVYBHKXU68EdyOXj+C3jf2KYhwAJ+qJRKaK1vKkkHF6mrqwvDMtg19Bibk+ezN9bAxtNmvgGYjWEYlJeXz5q8V5ROV1cXruuOL6NyXM09z4/iOi4NB4exvYrPnfWRebWVL5EdjUbnXyJ7+AAcuiv333t/D+d9CHZcNeshYuHyMzoATBd2P9PDUcYTdDc8jbrkLfgH7ltQe6ZpUl1dPX3Wlyg5x3Ho7u6mPNVO6oTrGNlwHs92/IrmcPOEnTSB/Qki/RkGvbOPn2EYMtZCCCGEEKKgdRXgAT4J7AdOBcqAvwXQWj+klDoJuBv4J2BNBXiUUsRVnM6hXi4fPZ2+CxcX3Mlms2itKS8vl3Loq9jEJRfdowa37BristYYW4ccfr2ln/kUJM9kMiilqKioWFiJ7Kd+BKk+qDsONl0EbectuP9i/iKRCI91JDAO7ibq9hI75WqGZ5jdMZN0Oo1SisrKSimRvYoFlY32RdiX3EfcGuVFTZePb/OOmASfizNQH+SpKj9HzdBGOp3G4/FQWVkpedOEEEIIsbw+2wip3oKbDoSA62c5tqxhzqpyYnmstyvEs4FPaK0zSqlJiQm01nGl1DeAj5emawt3YPQAn7n/U/icMN0jcd7Z24VTdhoNWxaeSyW/hKOlpWXxuVjEsorH42idq0PZPWrwqZsOUB72cfmWCl78zCi/rL6PyramOduxLItsNktbW9vCl+uYKTjmlbBx9uVfYmnufKqdL95yiMpIgJ3pg1ybDGKeeBxubRX/++x/cmXz/GZNmaaJYRhs2rRJluusQlpr4vE4kY47iD7xdYYv/DyGa7I9tp14Msx//+I5PmBAygN3hzzcYxlcuK2yYFuGYWBZFps2bZqWfF2I9aTQzDT1b5N/z/+tFEKsXeGPzlwBdj70R+RzYNnNENxZ9mPFkqy3AA+AMcu2ELAm1iWNGCP87LmfcFLoeLY98ihNKsFm3+sxWo5fUO4drTWu65LJZKirq5P8HKuU4zj09fXh8XhImS4/f6SPS7ZU8EavDz1i8UxlN33bPbyhceY8OPmxzmaz1NfXL/yGv/NBeP6PcOyrlvhqxGwO9sX58b0HedkJ1Vy6uZrYbQmcyv30n3IZNx76KWfUnMnZdefM2kZ+rA3DoKGhQW74V6GD8QPccfB24n0HiPbcg33sS7HtDg4MdxAa2k5NVxfvDAUoP7acwM5qrgIKhfVc10VrjWma1NfXy+xLse5J8EaII0PmY7eXugtCrEvrLcBzP/AGckmWJ1FKlQHXAg+udKcWSmvN4/2PobOabfEY1VYXFTv/Gqe9juwJC6t6lclk8Hq9hEKhxZXIFstOa82hQ4ewbRvHF2Z3bxIFvKSuDM/+JH3bFDePPMmrml8/azvpdBqfzze/sbYykB2d/Njj38uVP68/YekvShRk2Q6/fWAPrRU+LmitxN+RQqsEI/W97E3swcXlkoZL5mwnP9bhcJhYLCYlslehR3sfIZvIcGqiH3/5djKtV4AnQHR0M/6RKDsqQ3iOrsComz0Qm06n8fv942MtudOEEEIsVGWwctEVhCqDlUXtixBiea23u/2PAHcopf4E/BrQwJlKqeOB9wAbgXeWsH/z8vTgU/z8uZ/ystpXUJ7swohtxl9/InYigQ7Pf8hM08SyLJqammTmzipn2zaHkh6+dfcBWqqCvLwlRu2DA6TPrme0ZoSQE6MiUDnj8fly6Bs2bJhf2eSb3gPJXvBP2Fd54OiXg3e9fSysHrc81cM9+xP8845aam/rRvtH6TF+yH+oKM2DivPqzifqn73iWb4celNTk8zcWcUydpbtw/s5tfshRi76PE7FFvyHkux4so/hoIey06qxG8LMNlchnzetublZZu4IIYRYtO9d8YMZt2360O848JkrV7A3QojltK7u5LTW9yqlrgK+Dnxu7OFPjf3bDVyttV718wENx+C8qhM4+f7PM9jXxXDDlUQe7MdqnMeN+xjHcchkMjQ0NEhujlXONE164hY/eGCQFx9fw6XHVOPrzWBuihJvUvx8709pLWub8XjHcchms7Pf8N/3hcPVsQA67oe/eQaC0SK/GjGTpzpG+MHd+7lmU4y2vQnMrSbRff9M1/bLOaN6M5c3v3jONhzHwTAMueFf5Z588rvcueen/E1PO4kzPgJ2M9Fbu7BHTH6kHWqPq+Kihtk/l23bxjRNmpubF5YoXQghhBBCHLHWVYAHQGt9i1LqKOBkYCvgBQ4AD2mt7VL2bb5s1ybdd4jnrUYOtvwdFzbV4yQcsjur591GNpslGo0SjUZlSv8qprXm+QOHuPdAktM3lXPJjipwNd7+LK5P8djwY1QHa3jJhpfN2EY2myUWi1FWVlZ4rAf35pZfvfEmyCev9PgluLPCnu0c5YUbw5yYzNBZc4jRzJ9xN57Pc5WthD3zC9ZkMhnKy8ulRPZqlhrgub2/4/KynUQu+AhmVQuBPXGshjD3tUZw41kumCGR8kTZbFbGWgghhBBCLMi6C/AA6FyGvkfGftacvoE+kskso8mNXEgEn6ExjioHz/wu8jOZDFprampq5JvfVUxrTUdHB4+2x3l+yOLa8+rwehS+3gyBA0naT1bc0Xcbr2t7PT5P4bdqJpNLuV1bWztziezfXAcnXQPR+uV6KWIOruvSMzDEzoSFPdDFjdW/ojIYwipvQ6M5uuKYOdvIl8iura2VXFqrmPvAV0hbJtWVJxCqbgVA2Rod9pHGIRb245kjYJNKpfB6vdTV1Ul+JSGEWMPmqgonScWFEMUmdwmrjNaa/o7dbO54hlPd9+EcV0Vm2/zKmluWhWVZGIZBW1ubLM1a5bTWZDIZlC/ImZsDNFcE8YyYRP7cS/aEKkYrhtnmbGdLbOu0Y03THF/C0dbWVnhp1m/+EgZ2g52Fs967Aq9IzOTp237ACx/+NKr8cn4XdTht47ls3fKaeR07caw3bdokS7NWq/QAfOtcVHqQ0eNeT2t5A/72FOFHB8FySZ9Tj5mw8XtnDu7kc2k5jkNra+vMQVshhBBrggRwhCiu37zixqU1cAS8JSXAs4ocGOrlhodvo/6AQwuvxD25DXOewR3I3QiWl5dTV1dHOByWaf2rnNaauOHyWEeKnS1RMqkk2Yf30NHssLfqIP1DfQRmWLpjWRYVFRUEg8GZA3kDu+FtdyzfCxDzZvbupmfrX1AXb+XkjTFat5w0r+Py5bGrqqoIBAKSVHk1ywzS8YLv8JidYOCp+8h2WAzH+3m4IUh3VQCGMzzbk2ZnS+GlkVprLMuiuroav98vsy+FWGVkJoYQQpTeS26YO2dlKYQ/evGij9UfKe7fDwnwrCI3PXcvHOznRKOJipok5pbZq+lMlJ/SX1lZKd/wrwGu69Le3s4zXSk8SrGzNcbAoWcJJlMM7AgQ9oZoK2tjU9mWaccmk0n8fj9VVVWzf8MvAb5VwXEcMolBtK8RO6So3TR9RtZMkskkgUCAqqoqWZa12kXquSW+ByNlcVrPqUSI0NcQIlUTIOLL5cY6pS3GiRsKB3gSiQShUIiqqipZliXEKrQaAzhzBZ1gdfZbCADK/JCyFn+sEKtI5mOrp46T3DGsEmkrzfD+B7i2/wKqvJ9n9Jh3YJbNb3gMw8C2bVpbWyW4s0bkv633B0NU1+7h83v+mzPi2zi3+XRObJk5H4thGGit2bBhw+zBne++ACo2LkPPxUKMpEy+9Z8fpjZ4D2n/RjZuOpVw2fySW2ezWQBaWlokuLMGDCYtzvp9DaZTgQcv6tIGqpvLmB6inS6TyeDxeGhtbZXgjhBi3iR4I9ay0AfOnXGblG4XYvHkrmGVcLTDsf7T6C3vI3jUuZjNM3/o5bmui+u6mKZJY2OjTOlfQ6yeOBWPjLAtnqLSGeCq2KvZYNZi1ZZhFNg/P9aGYdDY2Hg4uPOnT0BmePoB2RF4y63L+RLEPDha88JN8JTndbyw+3Q80VrmKuXnOM54ALCpqUmCO2tEdbCCja85n057hLKysnkdkx9r27bZsGGDBHeEEEIIsWqojy3teH19UbohFkjuHFYNjZ22QFlo7/wCNel0mmAwSFlZGbFYTMqhrxGWY3HwkaexHIc7onsIBzUnbNlCxuPHqSg89qlUiqgzTKVyidle1PAQaBee+y1c+bXpB5z57mV+FWI+tOuihg+xQb2Q/laXmqbInMekUikikQjRaJRoNCq5tNYKx6VveABVPv9p46lUavzze75BISGEEEKIlaA/WuoeTFcZrGTEGFn0sUcCCfCsElqDx1X4fQ56hsS6E2UyGZRSNDU1ycydNWbIGOKxvmc44KR4unqAqzdfiac+hjPD/ul0moA1QvOtb0JtvAB2T9h46nXQfMpKdFssQsgaomqkEcI1dGxQ1HjmLo/t9/tlNsdapBRO2EskMncQDw7n0mppaZEgnhBCCCHEPHzvih/Mul2W90mAZ9VQhsn5XRD03IIduHTG/SzLwnVdLMuSMrprVHU6yuld29h4ei1vPrFmxv0sy4LsCI13/i1hj43a8VK4/PMr2FOxZK7D/ooT+WP5/ZwTmznrv2maaK3H8ytJcGftcWN+tH/uWZT5sVZK0draKsEdIYQQQoi1brFJw5chYbgEeOaglPICnwTeCsSA3wN/o7XunWH/04AvACcDncAntNbfm/OJLIcnyg4SP/EfOHNz04y7ZbNZysvLicViUgp9jRr1wd2nVfCCE2tQZoJAz4MF93MzGWLOIN7qTXiu+iL4ZyiHLlYt19UEjABnN53L5ujmGfczDIPKykq8Xq8kSl+jLMuac5lsvux9ZWUlPp9PZl+KolvENctPgddMefhWrfULx7ZHgP8CXknumvFnwN9qrZPL0X8h8op9/S3nsiglOZ/Xv9WUNFyStszteuAtwDXABUAL8ItCOyql6oA/AI8ApwBfBL6llLpsridxXU1KwdYNtTOWt85P6a+vr6e2tlZy7qxRLopYRW4ZR+jQH4ns/hH+oV2Tftzux4gk9hILQvjsv0EFo+CRWR1rTYgQdsBFV80cnBsdHSUYDI6/ryVouzYppQiFQjNu11oTj8fHx7q6unoFeyeOINczz2uWMScAHwKaJvxMDPj8N3AecBXwEuCisceEWG7XU9zrbzmXRSldz1o8n8saSnOsWBKZwTMLpVQAeC/wHq31LWOPXQ3sV0qdo7W+Z8oh1wKjwHu11i7wrFLqFODvgJtney4Ll6byCLXR6d/oZrNZXNdFay1ldNcBjcbnUQQ67iT28OcZOf8z41XTMpkMWmtc12XLli14pILSmuZx/fy47m5eU3bttG35sfb7/bS1tUlgZ53SWpPJZHBdl0AgQGtra6m7JNaphV6zKKWCwFHAA1rrngLttQBvAF6gtb5v7LFrgduVUh/UWncu7ysSR6piX3/LuSxKaU2fz38/7U/DOMl1s3rJFJDZ7SQ3je6O/ANa6wPAAeD8AvufD9w59mbMuwM4V81x91bt+NleOTkfS/5G37ZtamtraW5ulpLJ60DAGOSog9+lbNf/ET/9g5jN546Pteu61NXVyVivEz1lKa487vU0hhvHH8uPNUB9fT2NjY0yG2+dcl0Xx3FQStHY2ChjLZbbThZ2zXI0uS/6ds3Q3jmAC9w94bG7AYfcN8dCLJedFPf6W87lNUApNf5z8N+umvT7Gv8SbCdyPosVJHeQs2sZ+3dqJLQLKPQ1bAvwaIF9I0ANMDDTE7lBL+ljT5r0WH46fzAYpKKiYq1/uIkx/kCQqqatZHzbMJrPQWtNIpGYNNZifYj6YrSWtY3/7rrueKWsUChEeXl5CXsnltPEsQ6HwzLWYiUs9JrleMAEPqaUugLIkMvj8EmtdXasvT6t9XjWSK21rZTqm6E9IYql2Nffci5PUOh+Qv3b5N+11ivUm9I+5wqR8/kIU+r3mAR4ZhcB3IlvoDEGUCjZQgTIFtiXQvsrpa4Drhv71dpy0ikHpu6TzWbjC+nwKlHLLMGsNeYprfXxS21k6lhvetG7DkzZRWez2cRSn6cEZKynmDjWSinr6G1HH5i4WWvtGoYhY11ayzLWW7duPTBhm3IcxzZNM7XU5ykBGeu1aaHXLMcBCngW+DK5fDyfJ3eD8BYKX9PM2N6Uv3NJpdTuRbyGQlbr+bga+1XMPv1ea315kdpaqGJffy/oXIYj7nye1qdV8KXyejmXYf2ez7Xq31bduQyrs1/FfI/NeT5LgGd2GcCjlPJpre0JjweBQhftmbFtTNmXQvtrrb8BfANAKfVQJpM5beldLj2l1ENa63XzWorRjoz16idjPTsZ6+lkrFe/Yo31GrHQa5YPA5/TWg+N/f6kUsoBfqyUej+Fr2lmbG/i+6GYVuv5uBr7tRr7tEjFvv5e0LkMR9b5LH1aduvyfF6tY7Qa+7XSfZJkALNrH/t3at3yZqZPs8vvX2jfJLlkWUIIIYQQy2FB1yxaa3dCcCfvybF/W8faqx8r7wuAUsoH1BdqT4giKvb1t5zLopTkfBYrSgI8s3scSAAX5h9QSm0CNgF3Ftj/LuCCKQmVLwbunpIoSwghhBCimBZ0zaKU+qlS6oYpD59Gbpr/XnJJO33A2RO2n0fu2vFuhFg+xb7+lnNZlJKcz2JFyRKtWWitDaXUV4HPKaUGgD7gq8CftNb3jZW9qwaGtNYm8C3gg8DXlVL/BbyQXBm7+az7LPo00BKS17LybZaKvJaVb7NU5LWsfJulIq9lDVrENcvPObwc69fAycDnyC3bSpLL0/BT4FtKqbeTy9fzTeD/Vris9Godw9XYr9XYpwUr9vW31rpzlZzLsDrHSPq0jNbx+bxax2g19mtF+6TWccbyohib8vZv5BIO+oHfA3+jtR5QSl0E3A5crLW+Y2z/s4AvAicCB4GPaq1/vPI9F0IIIcSRZBHXLNcAfw8cRe6m4xvAp/OzjpVSUeBLwCsBm1xQ6H1a68zKvSpxJCr29becy6KU5HwWK0kCPEIIIYQQQgghhBBrnOTgEUIIIYQQQgghhFjjJMAjhBBCCCGEEEIIscZJgEcIIYQQQgghhBBijZMAjxBCCCGEEEIIIcQaJwEeIYQQQgghhBBCiDVOAjxCCCGEEEIIIYQQa5wEeIQQQgghhBBCCCHWOAnwCCGEEEIIIYQQQqxxEuARQgghhBBCCCGEWOPmFeBRSl2klNIFfmylVFwp9bRS6ktKqaOXu8NCCCGEEEIIIYQQYjKltZ57J6UuAm4HUsCvJmzyARXASUATYAB/qbX+bpH7KYQQQgghhBBCCCFm4Fvg/gNa6zdNfVAppYC/BL4E/I9Sql1rfVsxOiiEEEIIIYQQQgghZleUHDw65+vAP5MLGv3HWNBHCCGEEEIIIYQQQiyzYidZ/jzQCewEzipy20IIIYQQQgghhBCigKIGeLTWNvD7sV9fUMy2hRBCCCGEEEIIIURhy1Em/dmxf3csQ9tCCCGEEEIIIYQQYorlCPCMjP1buwxtl5RS6utKqf+ZY5/TlFJ3K6XSSqk9SqlrVqp/QgghhBBQnGsWpVREKfUNpdSAUmpEKfVNpVR0eXsuBCilvEqpTyulupVSSaXUz5VSDfM4buvY/i1THpdzWZTMQs/neXw2H6WU+tXY+dyvlPqZUqpt+V+JWAuWI8ATGPt37vrra4TK+Ti5SmGz7VcH/AF4BDgF+CLwLaXUZcvfSyGEEEIc6Yp8zfLfwHnAVcBLgIvGHhNiuV0PvAW4BrgAaAF+MdsBSqntwM1AWYHNci6LUrqeeZ7Pc302K6XKxrZ7gUuAF5GbWHGTUiq4rK9CrAkLLZM+H9Vj/w4tQ9srTim1BfgWcDxwaI7drwVGgfdqrV3gWaXUKcDfkfuDI4QQQgixLIp5zTI2A+INwAu01veNtX8tcLtS6oNa687leh3iyKaUCgDvBd6jtb5l7LGrgf1KqXO01vcUOOa9wCeAPQW2ybksSmYR5/Nc95OXAW3AyVrr+Fh715D7zD8TuHMFXpZYxZZjBs/OsX+fWIa2S+EcoB04Adg/x77nA3eOvRnz7gDOlbLxQgghhFhmxbxmOQdwgbsnbL8bcMjNhBBiuewEYuTORwC01geAA+TO20JeBlwHfKDANjmXRSntZGHn81yfzQ8AL84Hd8bk960qUp/FGlbUGTxKqTC5qWIAtxaz7VLRWn8f+D7APGI0LcCjUx7rAiJADTBQ7P4JIYQQQkDRr1lagD6ttTWhfVsp1Qe0FqvPQhSQz58zdWZNFzOce1rrSwCUUhfN0J6cy6JUFno+z/rZPDbjbGpbHwJSwJ+X1lWxHhR7ida7yEUOH9VaP1zktteCCJCd8pgx9m9ohfsihBBCCDGTua5ZCm3P7yPXNGI5RQB3YkBmzGLPPTmXRSkt9Hxe0P2kUuqvyN2Dv1trvS5SpIilKUqARynlAd4KfBKwgfcUo901KANMTW6V/z01dWel1HXkppNy7LHHnvr0008vb+/ENFrncoF3dnTAk/14dW7V4vODWap6dvOzzIHTPvrRjy45WCljXXr5sW5vbyeTyeD1escft264R8Z6HcmP9YEDBzBNE6/Xi6s1tz3WwRkHn+M3qR4Z63UiP9bPP/88ruvmZq04JvYD32akfxN/SAwWZazXqbmuWQptz++zbNc081nRnh93saxKmVogA3iUUj6ttT3h8YLn3jzbm/e5DPL5vs6UOk3GQs/ned9PKqX+mdz996e11l+eqQNyPq8rc57PC83BU6uU+v6Enx8ppX5PbtrYt8hVzrpGa33XIjq7HrQDTVMeawaS5JJlTaK1/obW+jSt9WnhcHgl+iem6OnpYe/evaQSKSr2ZfFVhvFVhLinL8sf6v8IufWxSyZjXXpdXV3s3buXdDpNJBIZ/wkEQrjKBdhbjOeRsS699vZ29u7dSzabpaysjEgkgusJsqc7heO1AHYX43lkrEvv0KFD7Nu3D9M0x9/T5c4AocQh4r4wwDOl7uMqNtc1SztQr5Ty5jcqpXxAPdOXBxTt/aC1nvSz8R9+O+2xUlBKzfojiqp97N9C5+diEiIv6FwG+XwXRbXQ83nO+0mllEcp9XVywZ1/0Fr/02wdkPP5yLLQGTxlwBsn/O6SO9meJ7fm+8tjSaOOVHcBb1NKKX34CuRi4O4pibJEiSUSCUzTJJ1OEwwG8fk96GgCc1OUv/rRbi7ZUUVPupcwR8nXhGtcPB7Hsiyy2SyhUGh85k6eZbk4yNtzPRgZGcFxHEzTJBwO4/F4UGmb8l8fIBTxcl0mzO6KLohLFdG1bnh4GNd1MQyDaDQKgHdkHzU3vh47ciyh0Q+wv24vDJe4o6vbrNcsSqm7yV0nnj22L+QS0nqYnKx26T7bCKnegpsOhMgVGJ5JWQP8fU9Ru1PI1MDSpg/9jgOfuXLZn/cI9TiQAC7kcE6pTcAmFlchaOXOZSGmW+j5PJ/7yS+Tq7b1Nq31d5at52JNmleAR2t9B6Wf3rbqjJW9qwaGtNYmuVlMHwS+rpT6L+CF5MoyXl6yToppHMehv78fx3Hwer25G37T5uCowc/v6qKtOsRrTqnnS0fqPLR1xLZt+vv70Vrj8XimBXe01vzioT4uVU6JeiiKxbZtBgYG8Hg8+GyI3j8AGpThkN0U5XNZk1CFgcf7CPSeXeruiiWwLIuBgQG8Xi9+vx9Pup/oo1/EmxjGKPsEZtkJ3G8P8kBVH22dy1EsdG1a6DWL1rpTKfVT4FtKqbeTuw78JvB/RS8rPUNwZ9mPFauS1tpQSn0V+JxSagDoA74K/ElrfV+Bc3mu9lbuXBZiikWcz7N+NiulrgT+CvgY8HulVOOEpxvRWhfKNyWOIMVOsnykOQe4nVxU9Q6tda9S6nLgi+Synx8kt2TtthL2UUzR2dmJaZpEo1E8nrGcOwe7cKw0px/lJRb20Z3txoNM3lnrOjo6sG2baDRacAp9x4hBqj8LHpnBs9a1t7fjOA6RcJhAZwIcjXFsJQD9aEbv7+G1Z1Tyu2fku4o1w0xDvH3SQ67r0tPZic+xiUQiAAR6HgYjRqbpDRi9ER6oS3OHp5ejaiKYBfOqHrEWc81yLfAl4EZyORZ/DrxvJTstjlgfBvzkZjz4gd8DfzO2bdK5PM/25FwWpTTv83ken8351TQfHfuZ6M1jzyGOYBLgWQCt9UVTfr+DKTObtNb3AWesXK/EfA0MDGAYBtlsdlJwZzSZou2BAW5ta2cgm4As2I7N2eksjyBRnrWot7cXx3HGl28UCu5Yjsunf3eAb2kvN1XvhkSljPUao7Wmr69vfFlWLBbD258l9Pgw6XPqcapzS7EyIwY1ER8eN5n/wJaxXgvu/wK03wOxZrTWpDMZ0JqoZRHw+w/vl6mH+EtIuxX890g3z5b/nI01Wzi+4mQe4dEjdqyLcc2itU4Cbxv7EavMek5IPZaM9gNjP1O33cEMKwtm2ibnsiilhZ7Ps302a63fQG5GjxAFSYBHHBEMw2B0dBSv10soFMLj8WBYLp+95SAeM83V4Tgvuvjl4/unUik2776JR2CkZJ0Wi5LNZkkkEvj9fsLh8IwXwFnD4WN40U0hngk9Q7DrnMQKd1UsUSaTYXR0lGAwSD5poLJczE1R7MYwiazNf97aTsp0OL45iuPaBLw+bEiXuOvr36+vhdGDS2tjaC+84bdQfxypZJLOzk5CoRBKKYJxh8h9/UBuGd7gtij//Pwwxx/l49yak7iy9iW0tLTwkpNfKlN4xLoleYGEEEJMJQEecUTo7OzEcZzxmwO0xt2f4BQbLttaQ8+B/kn7KzTK6+WjH/3o2vzq6wiltaazM7ec3j/xG/4p+zzaniQRNzhOKYbOqkQ9joz1GuO6Ll1dXbk8LHjxd+ZiNqovQ3vaZv+BOL1xkw2VQd5yVhMeD/T29uFRSsZ6uey/HYyxOOngbnhLEVYne/3Ytk3vgQ6igzaBQC5g6+vJYG6KYhxdgWm7/PrJHtpaOqlvTGO4QZRSM34GiNVHfWxpx+vri9INIYQQYs2TAI9Y1w4dOjRp+UZ+NocnblH19Aj1gMdxeLK5l5axY9LpNAoX5fHO2K5YXbTWtLe3Y9s2pmlSUVEx475DaZsfPdjLZRtjlMd8mB4lGeTXENd1x/PtWJZFRUUF/gNJgnvi2I1h+iyHnw6laa3K/Xm7ZEcVPq8ilUqhlItHSdLdZeG68Nu/glOvy/1+7j+Ad2kBFsdxaD9wAMdxCB5MEx3VODW5d6uO+LDaysCjeG4gw+MDByhrvh+f2snm4LG5CmpSunrN0FOzSAghhBBiUSTAI9atZDKZy8GS8VB35xA6MHp4o6vpa4nwcNZk0zFBejtSk47ddMfb8TSdtMI9FouhtSaRSJDNZolEIgSDk0tg9yVMPv37g0SDuYCd7WrO31TOqw6kSbWEqG1uQj0qN4JrgdaaeDxONpulrKyMYDCIrydD5IF+0mfXY7WWsb8jSV1Q8dIT6yYdq5SipqYazwEZax75Njz83+ALF69N7cK2K+Cc9xelOdd1icfjmKZJJBIh7A1gbg5gbYxO2u/gUJav3dnJJTtDBKI7eGHTZaRSKVpbW6dVzRNCCCGEWO8kwCPWJcdx6O7uxmdqIk+PYmyvwDi+any75bj85IFefF7N3f1/xuvJvRW01rju2Oydq75Wqu6LBciPtd/vH0+cnZc2HX71WD9nbi7n6pPrCD09kiudbbrYtUHix8eIKo9UTFsjLMuit7d30lirjE32uEqs1jISWZs/7x2htWpykE9rje3Y3N1/P16ZrwXDz8Nln4ON55e6JzMyTZOenh6ihpfIvhF83WmyNcFp+8UzNuccFSQdvI+op348J8nUzwIhhBBCiCOBXAGJdUdrPb40K5rM3cyZR5WPbzdtl45hg84RgxccE2F3fDeXN10OQCo+TEBnl7y0QKyM/HIdgFAoNGmbYbs815cmZTpcdkw1nqSNvzONUx3AbgwxuD2Uy9Ehn4Jrgm3bdHR0oJSaNNbK1uDNvc9396bRGi7cXjXp2GQyifZpnhzZzatU2Yr2e9VwXbAyuR9jFPyr9/+DZVl0dnbi8/ko6zFzJe+Pq8RumD7jyHY1SdWO7dqcXXcuiUSCQCAgy7OEEEIIcURa0zN4lFIvBR7SWneVui+i9LTWDA4O4roupmlSXl4OgwnsmiA6lJuq3zli8Ikb97OhMsjF26uoKNPUBGqoC9WD67D5N5fiaTwRz1GXl/jViNm4rsvQ0NCk/EoT7epJ8ZU7OmgoD3Dl8bVUl/lRg1mcygBWa26Jh06naW5uRntcifGsYo7jMDw8jG3b2LZNNDp5iQ6uRns9PN2V4jv3dvOX52+gMjz5T5vX66WuqY7arkqalLmCvV9Fbv3HXMnxQBSUB8pb5j5mheXH2rIsXNelrKwMZWWwmiLYzZGCx3Rmn2e3+zveUXsN5f5yUmaKlpYWmcEjhBBCiCPSmg7wADcAbwZ+mH9AKRUArgb+oLXuLVXHxMrLHhyEP+/D5/FQpxReTwpP3MLYnpu9E8/YfPe+bi7ZUcVrT20AoD/bj8/jA60p//OHyNSeTPB1N+ANTl8KIFaPbDbLwMAAwWBw2sydvoTJTx7q5SUn1vKiY2uAXBnl8CODOGNLdxzHwXEcADTIop1VSmtNJpOhv7+fUCg0Kb+Sd8gg9NQwnhGTnq0xfvrsAC87qY4TNkwOAOXH2rayePufAX/jSr+M0tv1K9h1A7z1DihvLnVvCtJak0qlxsc6OuQSebgHb38Wc0renbwBY4CHk39gq+88jqk4Ftu2cV13hXsuhBBCCLF6rPUAT6H7shjwv8ClgAR4jhCO4zC6qxOqAnDU5OUZblluuVVf0qQi5ONlY8lXtdZ0Z7rwKR/KyeIZ3sPoi/6XiCTmXNUsy6KrqwufzzctoTJAx7DBltowl2yrxDuYBQ3eERM35CVzUjUAmUyGaDSK1+vFdm0kBc/qZFkW3d3dBAKBaWPtHcjNyMrsrOaZgQzbGyJctL1yWhvpdJpoNIq2E3htA1787yvU+xJK9ECy+/Dvj34LXvG9VRvcATAMg+7u7lzQFh+R54cxN0ZxdlbjxqYvmbVdm4cHHyHstLIpdDqQG+uKigqZvSOEEEKII9ZaD/DMRL6QP4K4rktXVxdew8bfEMMqDxTcz3I0FWEfQX/u4n/EGuGX7b/gNW2vJRsfIBaqpq51Gz7fen1brH35hMqO40xblpVnOi5VET/hQYPIAwPY9bkZPua2cvB7SKfTeL1e6uvrc1V2XEnDsxrZtk13dzdaa8Lh6blXlOXixPy45QEyvWlqo3783skjmUql8Pl8NDY20j+0C68vAKHyaW2tO7/9S6hog7Hk8dQeA82nlrZPszBNk97eXjweD6FQiNAD/aA1VnMYAoUD7rviu7i392GyfZdw7JnlJJNJAoEADQ0Nkn9HCCGEEEcsuZMVa5rWmmQyibtvmOrnMyQ3Vc6475AxxFPqe/zHrlwAyHRNTq4+mePKd1D161fhazkVf6BwcEiUXr4ceiaToaxs5gSx4VGLK56LE9mTJHt8FeaOimntbNiwIZdgGdAyfWfVcV2XRCIxXg59Kl9XmtDTI6TOb2Bff4afPdLHG89oKNhWS0sLXq8Xx87iXc+hvJs/CJ0P5P473g5X/wrWQKDDdV2SyeT4WAf2xAk8nyBxVeuMwZ09fWm+9+gh0u4mXr99B5trwiSTSVpbW2X2zlpV1gCpRU66Liv83hdCCCGORBLgEWuaNZhi9NHniXWZpM5rwKmf/k1/xnK4/8Agdw/9mRrfVj5wzNWTtqvsEE6oGu8VX1qpbotFyH/LHwgEZryJSydMyvbFaa8L4rt4Q8F9lFKTZmlpLUmWV5t8iexQKDRtrFXaJvjcKOkz6xiuCnDrg728YmcdZ22uKNiWz+fD1S6PDDyB17NOl18eugeevwXe+Wipe7Jg2WyW3t5ewuEwHo8H32CW5AubcaOTl2UNpy2e7koB8HR3iuM2hNlaH+O8+kogVxbdK8tr166/75lx06YP/Y4Dn7lyBTsjhBBCrF0S4BFrkmVZdHR0UPbYMEHDQbdEcepDBffd3ZPm9kO7UJXdvLTttdO2Z0YHiPrLZFr/KmWaJh0dHWitUUoVzLuT1/vUMCHTJXpcdcHt+RLKE4MGMn9n9TAMg87OTrTWM+ZYCuxLoP0e7IYwDx2Kk7VddrZMT8Ibj8cJhUIopRg1Rrm9537+xlN4Wd+ad8sH4YIPl7oXC5LJZOjqyhXA9Pl8BPKzJ53DZe8neuBAnL39GY6qC7OpJoSnwodP5S5hRkdHCYfD8hkuhBBCiCPeegjwnK+Umvg68lf6lymlCtaB1Vp/b/m7JZaL67qMjo4S2jVCpD1L4rJm7LG8O0+OPMGv2m8g4suV1M1YLh3DWeqrvLxs0xWcUN06qS0zOUzLrW/Fc/p1+PzTE3mK0nIch9HRURzHIRIpXCY5r/2JQZqfGaHv9FoqC8zkymazwOElO3lau5K0axWwbXvSWBe6WQ/sjRN6dpTkC5t4qC/Fzx/p490XtVBdNvm9m81m8Xq940t2rMwgrb1Ps3Xrq1bq5Syv71wMekJoMtoIx66d12bbNvF4HNd1p421cjS6QIAna7mcvrGcMzaVszu+m+/v/wNv2nwNmUwGv9/Pxo0bJcAjhBBCiCPeegjwXDf2k5e/wvsg07+cV2OPSYBnjXIPjWDs7sMaGaFswCF1cSN6QlLluDXKpU2XclbtOQA8eCBOpzZ4+Ul1hdvLDEPzqXgv+McV6b9YmHyJ7LKyuWdYWaMmHVtibD2xpuB2x3FoamqalkRbyqSX3sRy6NFodMax9qRs0mfV4VQFGelN8aqT6zm6cXqOHtu2aWpqGg/kWZkh/GX1cPbfLuvrWBEPfh0cC/7irlL3ZFHy5dAHBgaIxWKTxtrXncbbn50W4GkfzvLwoQSvPjkXuB0xh7ms6UXsKN9BOp2mqalJgjtCCCGEEKz9AM/bSt0BsbLMJ7oZDpg4zRGyW0KYFQFSGXt8e8IwiXjLiI89NpKxCflmzrDizw7iC1fIzcEqlM1mx8sm+2eZXeVqTTJj46ZtPGUz76eUKhgoSttpCfCUWCaTobu7m1AoNPNYuxqVddC+3GhZjiYaLPzezo91Xjx+CL9nnSRQf/TbuZLna1Q6naanp4dwOHw42Oq4KEsT3D1K9uQadGTypclj7UlOaI7QWueStBIkrDgx/+FqaLMlXRdCCCGEOJKs6QCP1vq7pe6DWDm2bZNNpMnsCBNurMQGfvNYPw8djFMezp3KI8FB/K7DfVbn+HEzzd4xRrpo+dM7cS/9N0myu8pYlsXAwABAwRLZE/157wh9Dw3wYguS5xVOtJtOpws+7mqXf/rzP/Bay1lah8WimabJ4OAgSqlZxzr4zAi+vizZ4yuBXIDHV2ApTzqdnpRjaXhgN5976r95W8ulRe97SfgjUHd0qXuxKIZhMDQ0hNfrJRQ6nDMtcm8/nqSFDngx28qmVf8ybBc7+iTf2PsglYEqAK7c8BJSqZQkVhZCCCGEmGBNB3gKGcvHU01u5cWw1tqe4xCxBmityTzaga8zReDUah4+FOdPz42wfzDL+1/Qihvs5Pbe2zAy3byq7TUcW7Fx1vZs28bNJlCbLsK7880r9CrEfOSXcCQSCaLR6clzpwoNmrxOefFeWE/Zxun727aN4zi0trZOuxk0HIPNFZu5ovtQ0fov5i9fIjuZTM451spyyZxcza6EyY33dtMxYnDNmY2T9rEsC601LS0t40GebLqfE32VnHfWB5ftdYi55cc6kUhQXl4+aZsyHFIXNI7P3Hly5AnuH7iPkYxNb9wkZTjUqARv3Po6ji4/BsgFBh3HoaWlYKo9IZbsmpveyIgxMuP2E8+Cl/7qqwW3VQYr+d4VP1imngkhhBAzWxcBnrFkyu8DLgeOmbDJUUrtAn4FfFVr3bvyvRPFkO2PYz/aTeLienzRIE8/NcQF2yq5+gyXNB08MPgAJ1aexGvaXjtp6v5MMpkMVZEA3qBUz1ptMpnMeIns2b6dTxkOh4az0J+huyVMQ1vhZRqZTIbKysqCs0OeG95N0BuaNmNArIx0Oj1eInuumRjK0WRdzZ17Rjl1Y4y3nt1E5ZSlPNlslurq6lz1LdeBzgewuh8m4F1Dy7O0hs4HwbVK3ZOiSqVS9Pb2EolEJs2wUikLT9pGT1hK+1x8NyfEzuCujgBXtZazqTZERchLRaASyAWBTdOkurr6cPUtIYpstuDOch4rhBBCLMWaD/Aopa4GvgWEgUPATcAw4AdqgJOAfwH+Vin1Vq31L0vVV7FwhmHQ29tL9M5e7Eo/voZcmWPDdqmLBngyeTvt6XbqQnUcXXEM5fMI7qRSKXw+HzXhMpSvcGl1sfKy2Sy9vb24rovX6521HDrArbuHeK43wxUeRWVduGCQJpFI4Pf7qaurmxbIy9gZvv74V3n7cdfCMzcX9bWI2WUyGfr6+nBdF7/fP7+bdEfzUGeSeNbhpJYYleHJf74SiQSBQIC6urElmQPPwY3vwtp8Af7qbcvwKpZJshd+/XY4+mWFt5/2zpXtzxIlk0kGBwdxHIdAIDBtrCP39WM3RsB/+P1puiZPHFQEKee0libKghOr3mni8TjhcJja2toVex1CCCGEEGvBmg7wKKXOBr4P7Aau01rfPcN+lwCfB36klDpLa/3oCnZTLJLjOLkb9If7CfYb/HJHlLt/8zwepRhKWbzq5HqMjMGF9RexrXz7vNr0pLpp+cO1BCPleOzMzDdRYkXZtk0ikSCbzRKJRGa94b9r7wg3Pj1IIutw/fG1bHl6lFRlkEJrMT0eD21tbZNmDBh2ln+558MAnNZwOqc3nFbslyNmsZCxzgvsjRM4kCTeGuKFx1RNC+4AeL1e2traJjxRlnjrOXzBZ3Fh/fHFfAnFNdoBP3sNeMcCmnYWjnkFXPKJ0varCCzLIpFIYBgG4XB40vsQIPzwAL5Bg9GLm/hj7x95bOgRRjIuQ8YwqvNoPnp5/aTgDuQCPIFAgNbW1pV8KUIIIYQQa8KaDvAAfwd0AGdrreMz7aS1vk0pdQHwxNgxb1yh/oklyGazjOzqoKHDYPSVm3j+7i7efVELjRVBejI97Erez4Hkfk6qOnnOtpSVIvT8b/GN7ifTcDaBV34JJDnnqpHNZunr6yMajc64VKdjOMvu3jTPPTfCBzeUUx8L4O9IkT67HruhcHJepdS09tJ2mtpwLR88/UO5B1wXKZS+clKpFP39/cRisXklyPWMmAT2xjl4Zi0P7hrkVb7ps/S01gCT27MNRrwejqnZzqu2vbpo/S8qMw0PfR22XAqXfLzUvSm6RCLB8PDwpPe1rzuNJ55bfuY/kGT0VZs4lDnEQ4MP8tfb38XPH0zytm1VbD1n5oTbHo9HkisLIYQQQhSw1gM8ZwPfnC24k6e1jiulvg9cs/zdEkuVL6VbvdfAOKEKfIqs5RL0574Bvqv/z4S9Yc6sPZumcNOc7fn7Hyd08I8MN5yPu+VsKiXnyqqRSqXo6emZsxz6zbuGiIV8vCIcoN7W4FFYbVGshsLL7EZGRgqWT7Zce/2UzF5j4vE4fX19k0tkzyG4exS7Icwf+1NsqgmxqWbyjX9+yU4kEpl8oJ0l6/ER8q7iZZjdj0DfU/Ciz5e6J0U3MjLC4ODgtLEOPzyIsSNX7S5zSg14FTd3/4Fz684j5othOQkCvsKfz/lEzVIWXRypsv9xN6Rmzs/1bKSM7MfvKLyxzE/oA+cuT8eEEEKsGms9wFNDLu/OfD0PNCxTX0SRWJbF6OgoWmt8WpHcEuM79/XQnzQpC3i5sfO3PBd/lnfveB8xf2za8cH22ym/93rc8ITy6Nph6Oi3k9pwCVu3bp22VECUhmma42M9203bTx/u5ZnuFB+9cjP1Tw5jtUVnnLUDuQBhMBikra1tWu4d0zHxeyZ+9GlJsrwCDMMgHo+jlJpUIns2oceH8HeluWFjmLsPJPjHyzcS8k9+76bTaQKBABs3boTuxzh0wxv5ZEMbta6LG2vmReWbl+PlLE3ng/DLN0FZPZz1t1C9pdQ9KqpsNksikZiWSyt6cyc65MXcdngW1o8O/IBhc5hzas9BKYXlaPye6e9HrfX4+1qWZ4kj1izBnWU9VgghxJqx1gM8fiCzgP0N1v5rXtfyJbKNx7uoSnvwjpjgVbQbuzjxxH5+0/UYz4w+zTu3/XXB4A6AJztE6oTrSB9zeCWe4zhkMhk2NDdLcGeV0FqTTCYZHh6eVjZ5osc7ktz7fJx/vHwjVaMW/s405paZ93ccB4BobRnffeZ/GVu9My5ujhL2zRwcEsXnui7xeJx4PE4sVvh9W4gnY/PUMeX8/plB3nNxC/WxyTOvbNvG4/HQ3NyceyDVR3LHVZzXdCzXHPuWYr6E4ol3wp8/DWe8G858V6l7U3Su6zIyMjKtHHpg9yiehEX8VZvGH3t06BGeGX2aj5zwMbweH090JNnTl8bvm/4ZbVkWPp+Ppqa5Z2wKIYQQQhypJNghVpVUKkVfezeN+w0yZzeQ3F7JiJFmwPsQLy6/nOpwjNNqTqc2OHP1FOWYaO/kCkypVIpYLDZ9GYcomUQiQX9/P5FIZMZ8GlnL5ZZdQ7zxjAbqon78u+Nkj6nEqSq8xMpxHTpHO4nFYnTE28nYGV7Q9sJp+9VHZCLfShodHWVwcHDWsZ7KsFxU1uH+Q1muOqGGjTWTZ/24rksmk6GyspJwOJzLpTR6CMsbIOCZealfyfU8BuUbYOcqDUAt0dDQ0PjyyHwwXWUdQs+OknxBLhCXsdNknAy3997GtVuvw+/x597rzw7xpjMaqY5MvjRxHAfTNKmqqpr37C8hhBBCiCPRegjwHD2WQHk+jlnWnoglyWazDA4OUn/vCG5DBKc+N8vi0/d8g6AnxAnVxxLwzuOUdQy0//Byn2QySSAQoLm5edpyHVEa6XSa4eHhOcuh/9/93Wg0J7VEUUqhbBenJggzLOF4tO8Rft9/E9tqc2WxX7TpCrZX7Zi9M1ojSZaXTyqVYmRkpGCJ7Nn84MEeLuhKY9T6Oa2tHM+E925+pl8wGKShYSxYd+gueODLWOd/AP9qDvCYKag9GoLzn8m0ViSTSeLx+LR8WrE/dGK1RHArc+P/P/u+SdQXpSncxMboJgC+c283WjP+Xs9zXZdUKkVZWRl1dXUIIYQQQoiZlTTAo3JXcZu01vvHft8OvAOwgf/VWj83j2b+eexnXk8J6Dn3EivOcRyM2/YR7Yrjy7jEz6oH4I7e20l69vOx4z85Y3An0Plnyp769vjvvvhB4md+ePx3j8dDS0uLBHdWCcdxSCQSZDIZotHorPtmLc01Zzbh93rw70/gP5Ake2zlzG3jcOnmS3nF9lcVuddiMWzbZnR0FNM05xzrvNt2D3P//lGae7Oc4vez7bwN6AKlsv1+Pxs2bACgI9HOd/b9FDaeyPDAI7yg7QVFfy1L1vME3P4vMNoO53yg1L0pOtu2GRkZwbbt8Xxakfv6xipmaTKn1vK7zt9yMHWAvmwv7z7pvdz09CCf/v0BAJ4fyPJfr9lGYMryLNd1CQaDsjRLCCGEEGIeShbgUUq1AH8glxfnFKVUA3A/UDG2y7uUUhdorR+dpZmPLXM3xQpJ7e9H7xsm+aJmjFCAnkwPQ+YQ9w7cQ9PoOwj7J8/y8KR78Q/tBiC895ekj34DZtOZ49v1lPw8C5k5IJZXPB5ncHBw0hKOmViOSzjr4IunCO6Nk7ykCbe88FhqrXG0Q2TB+XUkyfJyGRkZYWRkhGg0Ou8A69NdKd58XA3b9QipM+vRkel/prTWeDye8VkiHYkOdgRreHFoI5zx16svx1I2Dk98H466Ak54PQRnziG1Vg0ODpJIJIhGo/gGDJTh4O3Lkrx8A9qj6Ex38vjwY/zt0e/Hq3IBuyc6krz9nGaiQS8eD4T9hZfv+Xy+eVddE2tfoc8K9W+Tf9dTk6sJIYQQAijtDJ5/BVqB/FeZ7yAX3Hkt8CBwE7kAzktnakBrLQGeNS6RSDAwMEDNzd1kdpTjj+byK/x47w/ZHtvOGTVncveBED7v5Au+sif/B4+Vwok0YFdswWw8HR2YftM0Ojqay88hSm50dJShoSFc1yUUCs1aEj3PcjQ1z4zgD3mx68M41YWXc2mtSSQS4NF4PXIjWGojIyMMDw/jOM6CSqID2K5L274kujqEUzk9mJcvlT1xRlDWyRJTXsoCUfCvwhLaz/0WhvbAGe+CUMXc+68hg4ODxONxbNsmHA7j9Xop+1M75tYYxvFV6EAuaPOD5/6PCxsuIuw7nAfNcjUVYd+0ymh5juOQSqWoqFhf/8/E7CR4I9aTub7ckPO9NJRStxWhGa21XoVThsWRrpR3QpcB/6W1/ubY7y8F2rXWPwdQSn0T+Jf5NqaUCmqtjQKP7wCGtNb9ReizKCLTNEkmk1T8qQ9CPtzjalDkyuYaTpb+Q6eypy9N0rTwTwjwRJ7+DuEDv2fgqp/hljXO2H4mk8Hv99PW1rYCr0bMxjRNEokEjuMQCoVmnbnz/ft72LovwQ5L81YXQjFN4qWtEJg5OW++VHZ5uAKvZ35JfMXyMAyDRCIxHshbaNU609b40BjHVk7LtZQvlb3P2MvtvbcRPBAEY5TU8D6uSSTgnA8V8ZUUkZmEY18NlevrsyibzZJMJtFaEw6H8Xg8xG7swKkL8fmyH5ExMrArt2/IE+TcuvMmHW/Z7qTP9onyeZbKyspobJz5c14IIVazqQGcTR/6HQc+c2WJeiMm8LD0tB0y/VusSqUM8FQA+dw79cCpwP9M2J5iHv1TSvmBzwBvVkq1aa2zU3b5NPBipdR/Ax/SWi+krLpYJlpr4vE4xlPdRNMuiZdvRAHP7fkh+0bu593GMdz9/Hd558l1ueSqT981fmx4368ZvPy7swZ3XNcFoKGhQcqil5jWerxscjQanXM8OkYMrq2JYFzYiA55SczRvuM4eDweQlUhHt/3GGc0njnHEdM6iPyNLg6tNUNDQySTSWKx2ILfe53PDHFcv0EgorEKzOpwHIe4O8qjo3dztYpyeqAe+jth2xtXb1Wq0XbY9wc48c2l7klRaa0ZGBgYz6U1PtauJnVhI5ldGT5wzN/PePxTXUkGUxbeAgnTIVcWPRQKUVdXJ5/hQgghikprfVGp+yDEcillgOcgcMLYf1899u9vJmy/nLEA0EyUUgHgRuAS4FmgHjg0Zbc/AluAdwMnKqVeqLV2ltZ1sVQjIyMMDg7S/FyWzDmHS1bf2Hszb6i9iNv3u5x1zFHosjBTByt50l/hxDbO2LbWenwJRyy2/irVrDWDg4MMDw+PL9+Yi2m7eJRG++YOuriuSzqdJhaLsTv1LLFAjFMaTi1Gt8Ui9Pf3E4/H55VfqaDHhjh+Y4xsaxQdnHy84zhkMhn2ZPfQYiY53g1CXTMc/XLYdHFxXsByeO53ULkJNq/iPi5Cb28vyWRy0WP968cHuOaswomTbdseL4suS2yFEKK45pMTT5aOCbF2lTLA80PgI0qpo8gFaA4Bv1dKbQX+E7gSeP8cbbxv7NgPaq0/V2gHrfVXlVJfAz5OrtrWu4AvFOUViEXJfPk+vH5o1BqnKYLdEOY/dn0WgGbH5cf7L8JfpXj56RuZOh1rLvlp/YFAgJaWluJ3XixIKpUimUwSDAbnnej6DcM2nho/zLB0I29iqewD7Oenu3/C+0/9ALGABPVKIZlMkkqlCIVCC06IW3ZHN70dKbyupvHsBuwClZRSqRTRaJRAz5NsO3gf4Zd+D+qPLeZLKL7H/w8e+BK8+ifrKvdOIpEgnU4TiUQmB21dPe/JcIbtcmrb9PdqPmhbUVEhZdGFEGIZyLIxIda3kgV4tNYfV0o5wBuAu4G/01rbSqly4ALgk1rruQIxbwR+M1NwZ8JzaeBflFLnA9cgAZ6ScV0XO6joOr9ifFr/b54YoDtt8Ia+ck4fPshfh0y+9Lrti2rfsiy8Xu94+WRROo7jMDw8TDabnVeJ7Nt2DxPcG+dMwyX5og1zVrYyTZNAIIAVNfnNrv/HW459K9uqFnPeSBWtpXIch6GhISzLGi+RvRAjgwb/qGz+84078BYI7JmmSSgUIh6Ic09iP0ef8o7VH9zpehge/Bpc/gVoOL7UvSkax3EYGBjAcRyCwclJzztuv5dy08fvDvwZNUuk5/bdw/Qnpy/P0lqTzWaJRCLU1NQsS/+FEEIIIdazkpab0Vp/CvjUlIcfB+q01tY8mtgOfGMBT/lbpLR6SQ0ODuKzbSKRCB6PhwMj/fzp4PNUbvRygX0bey75Nv9S2bqotl3XxTRNqqurp914iJU3MDBAIpGYdQnHcNoiZeQW4d38zCCf9QUYuLSZqhnycuSlzTQ9qR4qKyp4rO8xzmo6m9MaTy/6axDz09vbOz7DZr7l0AFGMzbpUYNIwuRDL9s0rVoe5IK2rutSHQvyaOefeIEOcnTFlmJ2f3kcvDNXEn3TRaXuSVF1dXWRzWaJxWKTxlqNmpT3Q+bSZi4IbyDqKxzoG8nY/P6ZQT565eZp2yzLQilFRUXFvGf8CSGEEAuhlHo1cB7wBPA9rbU9YdvvtNYynUmsaauunrDW2gXcee6eAhZSMicNTKu0JVZGPB4nPjJKrdc7voTj35/6Ai3NGzmt7hSCBx+iqXXrotvXWhMMBqmtrS1Wl8UijYyMkEwmiUQisy7X+ewth2irCgFwYkuUSL9NVcvcs33u7L+Dvcm9tNi5ZXiXbrwM32LLo0uS5SUZHh4mnU5TVlY2rxxLE/3nre28N+4yWOlny9h5UEgkEqHy2e+R7b6LLaE6vHWrfPYOgJmCljPBu+r+zC7a4ODg+Iy8SUFbV1N+Uwe3NbVzcc05s7bxhdvaOWlDlMbywgGcsrIyKYsuVoXfvOLGpTUgKUyEWHWUUu8CPgz8P+Dvgb9USl2htR4a2+X8knVOiCJZsStPpZTLwv/caa31bH18FjgX+OI82zsfOLDAPogisCyL0dFRqu4agODhm0APAf7p1HdQfu/HUFZqSc9hGMaCbzBF8ZmmyejoKMCcuVi8SvHOCyYsp/tt+6yxlkeGHuZPfXfQn+njL4/6a87ecXYxuiwWyTRNRkZG8Hg8C86788MHe7hgyGJTyE/Vy3OzOSJPf4fwgZvG99E6tyTo4XCQrwVduitbOfPsD0P5Kl+Cuef38Oi3YNsVpe5J0RiGwejoKD6fD6/Xi+VafH3PV3Fchzc9cwaHwgb7NowyVypp29G84YzpFRBd1yWbzUpSZbFqvOSGF5e6C9OEP7q0ZO36IxJ1Eke8dwEv0lo/rpTyAV8BblNKXTIW5JFv/MSat5JfLX6P4n+f8V3ga0qpi7XWt8+2o1LqQuBVwPVF7oOYh8HBQex9g/hsRfLFzTzTnWJfYi8hN0nowB8I9DzIwBK+LXMcB6UU1dXVRey1WIz+/v7x0smFlus825Mins0tyzKd3GQ972AWT9JGmW7BfDgDRj8d6U7uG7iXlza9jAZ/I00NhSvwiJXT09ODaZoLzrvTvWcUd2+cl0cDjL78cEW8YPd9DF/4n7iReiAXQBo0hvjT0O944/bXsK3mGLyeNRDE7XkUXvwVaF4/Fd26urqwLGs8n1bGyRDwBLh26zuo2N/N6OVNvF0t/uYzXxZ9Pvm6hDhSZT4266XuslNK3VaEZrTW+gVFaEeIxWjSWj8OMLY06y+VUp8HbldKXYLMvRPrwIoFeLTWb12GZr8D/AXwW6XUvwLf1Fr3TdxBKVUPXAt8iNzsna8uQz/ELAYGBoiPjNL8eIr0OQ2gFN+8qwv/pl/wFyO78VYeRfKkd4JaRFllct/8JpNJysvLZWp/ifX19Y0vzZop78437+7iom1VALz0xNxyush9/VhtUbLHVxY85o/dtxD0Btke3U6lrqI8Wk4sVqxqWZJkeTF6enrIZDKzjvVMgnf3cvHGKNmWKEzIt6TsNDpQDh4ftm1jWA4Ppx6lMdbM5qqj1kZwB3LLs4Lrp5pbZ2fneCAvH7Q1HIOQN4TXVeD14F3sEklygTzTNKmpqVlUkm4hxIrxsPQbYPmDK0ppQCm1WWu9P/+A1vr9Sqn/Am5nFaYvEWKhVuVJrJTyADHgEq31DTPtp7W2lFIvA34MfAL4uFLqINBNLjdPPbCR3B+T+4CrtdYjy9x9MUEmkyGVSNJ0ywBWWxR7QwQAu/En7KzZzHk9zzB44nWLbl9rTTKZJBwO09zcXKxui0XIZDIzlsn+x1/twzd2I99WFeIlJ07Jk6Qhe0LV+K+/bP85zyf2ocaCflkny3u2vw834xKLxWhoaFjeFyNmlU6nSafThMPhBS2L/MwfDvK6PpOwT7Hh4mbMCdti938Kb6oH7QuPB22rqqoIWAHOaj6LoG/mHD2rymPfhWd+DjvfWuqeFEUqlRqvbOXxeEjZKf5j12cJeUOcVHkSytHgm/l+7TN/ODieSB3APyWRtuM4pNNpamtrpXKWEKuc1vqiUvdBiCW6FXgr8NGJD2qt36eU+iKwfspeiiNWyQI8SqkK4L+By4EoM0f0Z7170Fr3AhcrpV4JXA2cApwEOEAPuaVhv9Ra/6ZIXRfzpLVmoL+fwGMDuBUBMmfWAXDLriFQmrdseRs8/bslPUd+Wn9DQ8OCqveI4tJa09vbi2maRMrK+M0TA1iuzm8k6PNw/VXTq+YA+A8kUWM3gE+PPEVHup0nhh/nn4//CH6Pf3w/wzDwRDzU1tYuw1jLuTNfruvS09ODbdsLqlZ3554RhtMWJ1aFSFw1vVKeb2QfA1f9DDxezLEqTdXV1TgDDl61Kr+LKCzeAS/+EtQcVeqeLFl+rLXW44G8pJ1kc3Qzb978ltx7+7Eh9CxV75KGwydfOnPVM8Mwxmdfyme4EEKIZfYuZrj/1Vq/Ryn1uRXujxBFV8qr5n8DXktuZk0cuAz4AdAAXEAuQPPG+Tamtf4l8Mvid1MsVk9PD+nRJM3dFqmLc/lSLMfl5qc6adruQ9mZJbVv2zamaVJVVSWJOUuss7OTTCZDLBYjY2nu2jfC6049PMvm9E3l0w9yNbia0JNDpM7P7Xtb762cX38Bb9j0pknBHcuysG2b6upqQqEiz+TQstx6Idrb28lms5SXl8/7htxyXG56op93ntMMDw9N3ujaKNfCYyXQ/gimaeK6uZlawWAQ27UWXyGtFBwTJpy7a1l7ezumaVJeXo7pmqA1KTtJwJML7Km0g78jTeqCwjPqLGf2gpjZbBal1PhYCyGEEMtJa23CpAnEACil7gR+qLX++sr3SojiKuVV81XADVrrVymlaoB+4Ita6weVUjuBO4GjS9g/sQTxeJx0Ok0kEMKNpXErAmitefdPnqOx9bOcPeyh6pbrMBtPX1T7ruuSSqWoqKigrq6uyL0XCzE6OophGOOlkw3boiEW4JS22XOQxH7bjg54cKqDOPW5AJ3lWpxYeRKeCfmY8ks4qqqqZAlHiQ0PD4/f8C8k786HbtjHZwxF1ePDOLWTb+SrbrkO5Ri4oWpsV5HJ5JbrVFZWAuBoB99ayb0D4FjgLVwCfC0ZGhoan13Tk+3hy7u/QGM4F6g/u/YcsFwqfn2QzCk1uBWFX++HfrWPnS2FPwds2yabzdLY2HhE5U4bS1L7Ka31raXuixBLpZT6D+CRsZ9ntZ78jYlSagMwpLVe2jd6Qiy/88jl5/kOufQeSa11Z2m7JMTilDLAUw/cDKC1HlRKdQJnAg9qrR9TSn0LeAPwmRL2USyCbdsMDg6iHZfyh+Lgz90IdvziA/yTTvArj4+Lz/82Q3O0MxOt9XhOCAnulFZ+rAG8Xi+2o/nufd2E/bPf/IceG0S5msTlLQDc0Xs7h1IHGTKHJgV38mMdi8WWMbgjSZbnw7IshoaG8Hq9C06qHAl4qQ75x8d7/PFnvod/6Fn6Xn9PbqzHgrZVVbl8TA/3PsQT/Y/zym2vLtrrWHaOCd61PYMnP9aBQAClFGk7xWk1Z/CK1leO76NSNlZjBOPoyoJt/PjBXoI+D28+c3pJdK016XSa6upqyssLzO5b3y4C/qfUnRCiSP6Ww0mXM0qpJ8gFex4d+7kauBhY3Ld5QqysC4FhIACglOoBfgJ8VmvdXcqOCbEQpQzwxMll48/bB5ww4fcngbevaI9EUXR3d2MYBjF/GE/GIfHCJvq6/kzCeh7fhX+HE797Se1bloVSisrKSvz+tX0jtdZ1dHRgmuZ4RauM5ZDIOrzz0g2zHudvT5F40eF9Hh56iGs2v4WXeV4+aT/TNPF6vVRUVMhYl1hHRwe2bS+4jLVn1KTV0mOXS5OF9/0/Bl6aW1lrGAZ+v5/y8vLxJN1P9D/O24+/lobIGkmqnRmBRNeaXqKltaa9vR3XdQkEArjapTPTSdAzeQB9Q1n0LMmVH+9M8o8v2lhwWzabJRQKEYvFpiVkF0KsKdXkcl9O/Pkrcont8oGfdGm6JsSCVQJ7gTuAIHAG8D7gbUqpV2mtbytZz4RYgMXVpS6OB4DXKaXyc++fBs5Xh5M67ACMkvRMLNrAwACZTIaysjI8KHTQg+t1+dL+/+G39SfyuNPJOfUXLrp927bJZDJEo9Ejalr/atTX1ze+NCv/tjUdTSzkJRyYY0mNUujw5Bu7ulA9FYHK8d8tyxrP9VK8kuhiMXp6ejBNc9JYz1f0j12cnnUxtxUeQ7escbxMdkVFxaQAkumY1EXq107y3Qe/Aq4NlYUDG6ud1pru7u7xsQY4kNzPvf13c0zFseP7qbRN5P4BrM0zvy99HkV5eHrwxjAMLMuiqqpKSqILscZprUe01rdprT+ntX6D1vpocjfJrweeA4aAd5Syj0IswB3AcVrrv9Rav1VrfSxwFtAO/EoptfarJ4gjQim/OvscuSVazymlTgO+Qy7qf7NSag+52Tu/Ll33xEJls1ni8TiBQCBXccW1cBV85sbHqYoogr7reM3GlrkbmoHruqTTacrLy2VpVolls1kSiQThcHjSch3L0fi9c8eNe7O9fG3Xjw+352Qnbc+PdVVVFdXV1cXreCFaI1W0ZpbJZEgmk0QikQUHWv795oP8RdLkxvoAJ09YylN+z0fwDz6DxxgZH+uJeXfyLNealGx71XNtOP2vIFxV6p4sSjqdJplMjgdeerO9fP/A/3FxwyVsjuYqYSnDIXZLF+bGKFbr9ADNv/3hIEnDIRyY/jmQz6fV2Nh4pAdtX7nAGwWttf7EsvVGiCLSWieAnyilfgc8BdSWuEtCzIdLruqyPfFBrfUDSqkLgceBf0JWl4g1oGQBHq31bUqpF5Ob+jYyllz5g8D1wAuA+4H3l6p/YuG6u7uxbZuysjJc7bJr6Ek2mj5q/b8jGArwlnMXH9yB3IyOcDhMVVXVgnOAiOLq7OzEdd3x0skArtY8dDCO3zt7EKD7uV2UORYfOObvZ9zHNE3KysqoqKiQsS4hrTVdXV0Ak8Z6Nv6DSZTtsn8gw44Ri001Yf75ilxZ9EDXvXjTvQR6HmTglTcBYIxVX5s61h2JDjqSHWsswOOAWpvnq+u6dHd34/V68Xq9ZJ0s9/TfxVm1Z3N+/QXgaAIHknhSFnZdiMzpk+/ZHjwQJ2O5pEyHT8xQFt0wDCorK8cTsh/BXgG8cs69DtOABHjEmqK1TiqlvkcuT8+XS90fIeYwRG7J4TRa62Gl1LeR2WhijSjp4net9c2MJVoe+/1zSqkvAWGt9chcxyulnl/c0+qtizhOzKKzs3PScp1hY5j7eu+mSZ/NMcZDbDrur4vyPOFwmEgkUpS2xOIcOnRovJLSxBkdA0mLu/eN8o7zmmc9vuKhOCOnlDNXatVIJEI4HC5Cj+dhrSwBWkFa6/GxnvdySNsl/NAA2WMrefC5Uc7fVkFmQjW18gc+RXr760ie8r5Jh0Wj0Wllsn9/4EZOrD2JmvAaqpymXVBrqOLXGNd1aW9vx7Ks8bHek9hDX7aPV7XlltR64ibBXSOYW2MYR08+H1yt+d79PVx1Qg2vO7V+1ucqLy+Xkujwr8AfS90JIVZAP9BU6k4IMQ8PAO9QSn1Bax0vsH0QWEMXJOJItuqyG2qtDeafe+cQh5O4iRJJJBJks9nxb2WTVoKvPPVf/MOTr+bBIJzqrybSesWSnsNxHDKZjAR3SiwejxcM7sQzNtf/dj9XnVDDltrpQZnyXx1E+zy0p9tJVzo0H33cjM+RH+uVy7EkHyFTaa2Jx+PjZbLnxdVU3HAQY2s5f/1UH6dsi1FzVgMT5zprb4j0sW8e/z1fKrsQ07G4bOP5+Dyr7s/UzPTam8GjtWZ0dHTSWO9N7OVnB3/MazdeTW0wN1NHueBUBzGOqZzWhmlrGmJ+XnTszNe++ZxaAoBdWus/lboTYg0q80PKWvyxRaaU6idXLStfKv0RrfXesW0KuBJ4qOhPLETxfRa4FbhLKfUOrfX9+Q1KqRC5ys5SSUusCSt25ayUcoA3a61/OPa7y9x3VlprPWMftdYXFa+HYjFc16Wvrw+tNR6Ph4SZ5QuP/5Ro+ig83h6u2HQXHrOKkSU+T/7mI18+Waw8x3Ho6+vD4/FMy8WSMBxOaC7jxccXXmqvfR4SV7XyrV0/nnVpVr4selVV1ZGeo6OkbNumt7cXn88377w7ynJxKgKkdlZT3jnKNWdN/tI28uyPUPbhG/z8WNfU1BSszGW5FoG1Vm5cu+BZWzN4LMuir69vvCT6sDnErT23cFnT5RxfOaGwpavRU5Zfulpzw2P9JLMOQf/MgS2tNYZhUF9fv3Kz8oRYh0IfOHfW7Zs+9DsOfObKFeoNAHcDO4EXjv2ulVIJ4FmgbuznLUopv9Z6kZEpIZaf1vpPSqm/AL4G3KOU2ksu745Jrnx6M7nZl0Kseiv51ej3yJVCn/j7in91rpQ6WWv96Eo/73qktebp558mm8kSi8XoHdjPM0OdxLN9XBM8nxrvITKbrsAub1vS8xiGgVKKWCxGIFCg1rJYdvnlOo7jFJxFNZK2Ct7gKdMBS4NnfkECwzDweDwrO9aSZHmS/HIdrTWhUGhexyjDwZOy0X4Pw2mbSk8aT2Zw0j6RXd9n5KL/HP89k8ng8/mIxWL4/ZMDOZZjkbZSayv/Dqy5HDyO49De3o7H4xlfNnUgeYAKfwWn1Zw+aV/l6ml1N7OWy0MHE7zpjAbqYjO/XzOZDH6/X8qiC7HOaK1fDqCUqiQX6Dl57N+dQCu5+4yfA7ZS6jngSeBxrfW/rXhnhZiD1vq7Sqk7yeWAfQnw6rFNWeCL5PLECrHqrdiVltb6bVMe+izwjNa6aEEepVQA+DhwORBl8uWoD4gB5cDa+op1lbrn+bv57p7v0BxpwuwYJdD7MIY3xluzUU7qC2Ie3YjZfPaSniO/hKO+vl7KopeI1pqBgQFM0yw4q6YvYfLlOzp469lTltm7mvJfHMSpD2G2zV0OOb+Eo6mpSWbvlMjEsZ730izToeIXB7EbQmQ3lPFPN+zhx+5b8d51yqTdsm0vxK7anjvENDEMg9bW1oKlsr/z9P9iuRbRwBo7D9ZQDp787Evbtie932xt0xxpJuSdEtxzpwdqs5ZLbdTPcc3TZ2DlmaaJaZq0tbXJ7J2cjwFPlLoTQhTTWN7MO8Z+gPFr8uM5HPA5mdySrdcBEuARq5LWej/wbuDdY4HLGNAjM9DEWlLKr9JuJVca/UNFbPMTwN8DHcAwcALwZ6AR2ARkgPcW8fmOSB2Jdr7/zPfpGu3kovqLuaTiWIJ/eBdPcgYnVb4J5XXIHhskc+bSSplPLJU9tXyyWDmGYTA8PDzjbI6U6XDqxnLO3DwhAKc1ZX/qwW4Ik7okF/j56cGf4J3h5ndiqex5BxZE0WUyGYaHhxeU60qZWZR6Fp/+DV2PpPlXj4O38WSGL/1Gwf3zY93Y2FgwuAOQspJcd+I7CXrXWDLeNZKDR2tNOp1mdHR02ljbro1PHZ45pdI2kfv68WRsrJbceJm2y9fu7CRlOFRFZr6McF2XTCZDY2Oj5E8bo7X+2Fz7KKWqgIzWWpIWiTVLa21yODfPOKWUFDoRa8JY4HKkxN0QYsFKGeApA/YXuc3XkPv24IXkgjodwN9orZ8aK8l+A7m1lGIJDgw9R208yVXhU6gjTOj53zFSfjxx49XogIf0mXXo4NJvckzTJBQKEYvF5l2iWRSX67p0dnbi8XimLaMBcFzN3r4MId+U8bY1noxN4rINtKcOYbgGB1L7ec+O9xV8nmw2SyQSoby8vARjraWKFrnZcl1dXXi93vkvo9GaYPtdWI7NvW1/xx3ZEf7q/A2MlM082y6bzVJWVkY0Gp021gOZAfrSvfRn+gn55rc8bFVZIzl4bNumu7sbv98/PtZZJ0tHup2uTBetkdbxfb1xCx32kjyrDh3MvbZ41sGwXf7qwg1EAjO/3kwmQzQalbLoUyil/MDbgbMmzm5WSp0PfB04mlwuk1uAd2mt9xVuSYi1R85nIYRYXqW84vov4P1KqdOK2OYG4Jdaa1dr3QX0AecAaK1vBL4LvKOIz3fE0VozuOd2GrvuZePQ40Q6/oQn089IxVUcN+JibCtHR3zgXdqpZZrmeMnemb7lF8srH9yxLGvGb987Rwxu3jXE6Zsmz7pRtosOeHG98I29X+fp0ae4pOEF05d9kLvhdxyHqqqqeed8EcXlOA5dXV0z5liaiTfZSeSJb7LHqufJkTLOPXE7vopGtK/wUhzDMHAch9ra2oKlsv/vme9yV+efOaH2BGL+NbY8C8aWaK3uQEY+kOe67qQlU48MPcwt3Tfj83jZHN0y/riyXdywd+xzPRcINWyXWNBLVcRPcGpwd0wmk0FrTU1NjeROm0ApFQRuI5fI8/VKKd/Y49uAP5AL7vwe+Dywg1yyz4YSdfeIVhmsLMmxQgghxFKUcgbPaeQykt+vlMoAg4AzZR+ttV7IVM4Mk2fo7CW3TCvvfg4nzBIL9GT/E3znkS9ijOznlW2XkdhxDQCehMWG37XzSLWP4+uXnmPBcRxSqRQNDQ2yNKtEtNYkk0nS6XTBCkd5WcvlhOYydjSMBQVsl9iNHQDc73uKG599iu3lO3hZyysKHm/bNul0mpaWlhLn3TlyZ/DkxzqTySwomKqyw1Tf/HYGG1+O3V3HG85onHX/iWM9UxApY2f4ixPeQXlgjS7Tc51VnYPHdV0SicT4LCqAJ0ee4KauG8k4ad6w8U1sK98+vr9nxCRyXz+ZEw9XL0waDp//4yHO3DzzGOVzp7W2tkreneneS+6Lpw8CX9Va22OPXw+EgB9qrd8EoJT6DLmktP+ELC9fcd+74gezbi9BxSohhBBiTqUM8ISBh4rc5mPAFUA++cOzwMQsvy2UoHLXWtef7ufRvkd4svt+rhjs54S6K3F2XA1AxnTY/cwQkZBid0OI44vwfIZhUFVVRXl5+bxLNIvichyHnp4e/H5/waUVGcvh7n2jdA4bhCZUz1Kmixv1M3hhFbft3cvf7fjgrM9jmiY1NTVEIpHSjXXx8ryvSbZt09PTQyAQmPcyGstxeeyZfRztP4a7/K/gBE9mzmMMw5h1rJ8b3k1XsqvgLK81wc5C/zOregaPZVn09vYSDAbxeDwMGAPc0383V214CcdWHDe+n8o6BA4k8Q4bGNvLMbdXYNouf947wmDK4pimMl576syTSuYa6yPca4Gfa60/l39gLBntS8ldn4w/rrUeUkp9B7gaCfCIVW4sIW211vr5UvdFCCGOZCW7EtVaX6S1vniunwU2+xXgZUqpPyulyoEfA6copf5XKfVB4G+BBxbSoFLKq5T6tFKqWymVVEr9fLbp0kqpnyql9JSfPy7wdawq93TdzaH4QbYPj3C0E8bdetn4tucHsjzfnSYW9XPe1solP1cmk7tRlJLopZMvnTxbmexDQwYP7I+zqSbEhdsPf7uvLBd8CtM1CXhnH79MJoNSioqKChnrErFtm/b2dpRSBZdMzaRj2OCZQ/1oX4S6Mj8bqmc/Np1O4/F4qKioKJjLCeCnu3/Cq7e/Zu2VRs/LDMHmi6FyU6l7UpBpmuP5tPJj/dDgg9SH6tkSnTxR1teXwdedxq4NYWzNzaxrHza49/lRGmIBrjiuZsbnSaVSeDweqqqqpCR6YdvIFX+Y6GxyeQm7tdaPTdm2l9xs53lZyDWLUuqOAtcr+Z8LxvZ58QzbW+b9isWR4r3AnmI1tojr79OUUncrpdJKqT1KqWumbJdzWcyLUqpSKbVl7j0X1GZRz+cJ+yml1E1KqQ8Xs79ibSvZ1ZdS6jbgU1rrW2fY/hLg01rreU8K0Vr/TClVAbwfSGmt/6iU+grwN2O7HBrbthDXA28BriG3jOyrwC+A82bY/wRylcG+O+ExY4HPuWrceuiP3Lz7p/z9oWeod7ykLvhXrFA1AN7+LKfc0cM2D5SdXIlVvrSb9HyZ7La2NimTXSKu6zI8PIxlWbOOge24bKkNc3FjGbGbOnHLDn+UZDdH+MzT/8pFDTPHZ/Nlstva2lZHdZ0jcJaB67oMDQ3hOM6sy/Amah/O8r833svHs3/Ptmgz/mPfRGV9OaHEMOkZjsmXyd60adOk5Tr/ev8nSZrJ8d+9Hi8Xtly0dmd8aA1bLwPf6gtWOo7D8PDw+Fjf1nMr9w7cQ8AT4K1b3jZt1pSyNHZDGHNbOe3DWT79/56nKuLj8uNqOP+oyhmfJ587bfPmzQsKGB5hvIA95bEXjP1b6MugCpjx7VXI9cz/muWVwMQT1gP8FogD94w9dgLwKPDiKcf2LaBPQizG9czzXFZK1ZHLYfVD4C+AS4FvKaV6tNY3j+0m57KYr/cCHyH3eV0s11Pc8zk/+/NrwOXA3UXsq1jjVizAo5SKALUTHroIuEEpVSja7yG31GrB0VOt9f8A/zPh93crpT4LVAPPjJVtnG+fA+Te5O/RWt8y9tjVwH6l1Dla63um7B8EjgIe0Fr3LLTvq0rH/fDMzxmxBnjLYDfeY99L38aLCQQC9CVMbnpqkE0Jm4Afho+p4MUb53eDOBOtNZlMhvr6esnZUEKGYTAwMDBnsmPT0US0JvzwIOZRMTKnHn5rp+00rc+3clnTiwoe67ouhmGsorE+MpdoZTIZBgcH5xyDwZTFb58YAKA/aXHxRg8R71XEz74eG/AOGzOmMMqPdUNDw7Qb/qSZ5F/P/0wxXsoqoVdl/p38Z+vg4CBlZWXsTezl/sF7uWbzW2gta5u0ryduEtw1infYwNyay7ETz9icf1Qlrz999jy/+bFubGyU4M7s9gI7pzz2CnIfRL8tsP+Lxo6Z00KvWbTWQ1OO/wdy111HT8gNdDzw5Jq/phFrykLPZeBaYBR4r9baBZ5VSp0C/B2QvyGWc1mUxHKcz2O/f4vclwAjK/JCxJqxkku0ysjlyNk/9qPJVdLaX+BnH/BOpk9jnpNS6lSl1I+VUvUTHn4P8GEWHjDaCcTIlV4HQGt9ADgAnF9g/6PJBc12LfB5Vh1zz430xRoZrNpEctNryDaeOb6MZn9/mjLDYWdFkOPaYlw8YYnOYmUyGYLBINFoVKb1l4hlWXR1deHz+WZdMuVqzVDSpC3tgFeRPX7y+NvaxueZeQyz2SzBYJBYLFaCkugzWaOzRhbJNM3xMtlzLY/bP5DBcjXnbK3gZSfVcs7GMNp7+AZepZ0ZZ0Dl39dTxzppJrD11EkM68AqLJFuGAbd3d0Eg0H8fj9PjjzBhfUXsyEyfVWCry8LHkX6lBo6a3PB/N6ERdA/96VCJpMhFAoRi8WkJPrsfgxco5R6mVIqopT6W+A4crMI/t/EHZVSbwQuA349z7Z3srBrlonP1UjuOumfptwAH886uKYRa85OFnYunw/cOXYznHcHcK46PC1UzmVRKjsp/vl8KXDnWNujxe2uWOtW7E5aa90/drFyBrm7qY8ANwBPFNjdAfrJXQjNm1LqPOAWctW0ajk87bIbeBPwoFLqXK11oecsJH8F3Dnl8S6gtcD+x5Or4vUxpdQVY/34GfBJrXV23i9kFfhV+hBP6yjBQAM7q0+jLFY5vq2q3+CMboNos4/ssZU4gaXd1OSn9Tc2Nq6SGR1HHsdx6Ovrm9dynd29aZ58fJB/UD6M8xvRwcnjb7kWPlX4o8UwDGzbprm5efV8y3+EJVm2bZu+vj601vOqmmXYLhsqgmyrzy2lC3RZ4MkFhVTapuyuXtJn1k47LpvN4rouDQ0N04JIn7r/k5xQe8K0Y9a21TeDx7Is+vpyfwbzn62ma9BW1oanQDJoZbs4lQEeMWy+/+ce2qpzM/kuPbp61ufJZrNorWlsbJwxx5IY93lys3JuIPdFlyJ33fD2/AxjpdQrgHeRm+m8G/jPeba90GuWif6B3DXTf+cfUEp5yX1xdapS6nGgDngQ+KDWevc8+yTEYiz0XG4ht/xq6r4RoEYpNYycy6J0ino+AwNa63/Lb1izS9vFslnRqRJa65uAmwCUUhuBr2ut7y/iU3yC3MXQJROnHmut/0Mp9W1y0c/PMH397UwigKu1tqY8bpArZzrVceQu1p4Fvkxuve/nyb153zJ1Z6XUdcB1AG1tbVM3l8Q993+e29pv5ZA2ef/WDwCbpudIsTQHG0JsunD2ssjz4TgOmUyGxsbG1ZGLZZmsxrHO01qTSqVIJBLzuuHPmC6nN0fxRAIYzYfHzHQMvv38t8g4GRpD088Nx3HIZrM0NTWt60Deah/rZDJJIpGYV96dQ0NZfvlYPy89MRfA8aS6KX/w38hszn2EKsvFagpjbZqcr8m2bQzDoLm5ueByP4XizcdO+0hccyaO9bEbYqtqBo/ruvx2z2+4r+tefF7f+AVgR7qDFzReOmlfX2+G0ONDeBIWmdNrSVsOF++o4srjpwfupsqPdUtLiyRLnwettamUeiHwOnLJlRPA97XWz0zY7VTgXOD7wPu11nOXqctZ6DULAEqpGPB2cje7zoRNW8eOCwLvIJev58PAn5VSx2ut+6a0s2o/+8Sas9BzOQJM/SI1n/8yxALPZZDzWRRVsc/nBZPz+chSsrUwWuu3LUOzJwP/OHVd+djzDSulvgl8bAHtZQCPUso3YT065P5ApArs/2HgcxOe/0mllAP8WCn1fq314JQ+fYOxku6nnXbaqphGsKv/MV6+/bW0bbyUnt4kXq930pKpjOkwGDdojhbnQj6bzRKLxSgrK1vX0/pX41jnTVyuM9eSKcN22dUzSpPuoc8qo320d3zbiDlC2BvmLVveRkBN/xY/m81SXl5OWVnZ6vu2oYj9Wc1jbRjGeJnsucY6YzrcuWeES3ZUccGmEMGOP+Eb3IXRdA6pY6/F15nCG7egwPIdwzAKjvXzI/sYNUcx3TWbd36SSWO9rV6vhhLpHYkO+jN9ZLMGt7ffyls3/QUVkYrx7Qo1KamyyjoE9sYxt8YwW8vIoNn7SD8bKuc3w84wDCoqKqQk+gKMBVF+OPZTyL8CH5kyPX8+FnrNkvcycteD35/Sz+eUUjXASL4vSqlXkitY8WbgP6bsv2o/+8Sas9BzOTO2jSn7Qq7oSsdCzmWQ81kUVVHP58V0QM7nI8t6S3ZiMTmR81TlLCzvUPvYv00T/htyJUunTrNj7I/G1ODSk2P/tpLLmr6qZV2LaKSBoWETrfW0mRYPHkxgZxwaWpe+vCZfEr26ulq++S0Ry7Lo7u5GKTWvWTVPdSXZNfocsVAng84WDqZGJm0/r+58wt7p7eRLotfU1KzCJRxHxt+5fCDP4/HMmUQb4KFDCTpHDC47tppQ772UPfENzKYzyRz1cjwpl8hDA5gbo+NltPPyZbJra2un5dP6ymNf5syms7hi85VFfW2rxipYovWtp77JlthW0sk0J5bvpCHaMGvgJfB8AmW6WE0RCHh5Yv8o7cMGlx07cyn0vPxY19XVraJ8Wmuf1nohVbMmWtA1ywQvA36rtZ524zD1CzOtdVop9TxzL/kSYikWei63j+3LlH2TjOUnkXNZlFDRz2chZrPeAjy3A+9WSn1fa71/4gal1AZya9rvWEB7j5ObPn0hY99sKaU2AZvIJbaaRCn1U8CvtX7FhIdPIzetbl5VMFbKd5/+X3YN7cIzJd4Vd9O81AmQdoxpSzie6EjyxAO9/J3yYVcHp9V5XYiJJdHX89Ks1cx1XeLxOKZpzrk067aDT/Lz9p+C6+fYSIzXtF+OPn0Dm5rL53yefEn0jRs3ziuwIIrPcRzi8TiWZc1rGd5TXUl+/FAvHzyun2P/+FegXZI730V2y1UAeAcN7Low2Z2TgwD5fFqbNm2almPp0/d/iupQNa/d8brivbDVROtVsUTLsLNcVv8iRgOj85otp2wXc2sMHcldDmQtlzM2ldNYPnvQ3TAMHMehtbV1FQZtVzel1GnAp4BzyJVMvxv4jNb6rgL7vhH4ntZ6PifXgq5ZJjgf+GiB53458H/AFq11/9hjMWA78M159EeIxVrouXwX8DallNJ6PLHexcDdWmtXzmVRYkU9n5e9t2LNW28Bnn8BHiC3NOpGYA+5r+e3ksu7o4F/mm9jWmtDKfVV4HNKqQFyCQi/CvxJa33fWNm7amBoLDnizxlbjkWu6sXJwOfILdtKFutFFsPB+AE+evbHCPsmzLbofhT3met4foNDoDaAUoruUYNHDiUA6OxI8RflYdxjKrE3zH2TOBOtNYZhUFdXt7R4onUAAGLdSURBVK5zsax2hmHQ19dHOBwueBM4aAzy+PBjADwwvJuTY+dz3Y4XEH5oAOe4wHgZ5dlorbEsi7q6ulUe3FnfS0uy2Sy9vb3zXh6XHOzgH+tu4fiBPaSOuYbM0VdP3sFx0d7J7WitMU2T+vr6aWN9V+efGcoO8dkLp82CX0dKn2T50b5HGcoMM9A/8P/bO8/wOMprAb9ndyWtVr3LtiTbVIMN2AbTO4QWQkI6yQ0kgfRGCKQXCOm9knLTG2mkkBtIqIZQQzNgisHGxk3Vatt3Z+a7P2ZXVll1aYt83ueZR9LMNzNn9pxZzXfmFMrLy8fVtacvTtEuN0jEtzuCVe+nJ5Tkwa0DPNsZYd3Sie/t9H3d2Nio0ZfTRESOAe7CDbO/FTfq+FzgHBH5ojHmUzM99gyeWRCRRUATe6ONh3MXMAj8WkQ+jPvM+AWgB3eyrCjzwgxs+afAh4Efisi3gDOBNwDnpA6ZPVv+ajOEO8fdvM0PXD3OxrImuEq7uC805sGeFWVCcl8wYA5JVcI/Evgn7k3wMVyHzgXAHcCxxpjptkj8JPBbXI/rncCLwKtT247H7dB1fOr8fwTeDLwF2Iib0/tt3I5heYMxhrgdp2RYq2OMwf7vD9jT+jJMZSslJSUYY7h/Sz+DUYu6gI+XlhdTWVtCsnXmzh2ASCSCz+ejqqpKW6LniHg8PmFLdGMMj/dtoD/RR7WvmmZZRVvxofg6o4htSIxKyxmPcDg8pOu8TeFY4F20otEo7e3t+P3+iSfjxgwtTT33UBXbSWzZOcSWnT1mnNgGRjl4QqEQRUVFVFVVDdXTMsZgjOEvz/+Zyw5721xfWv6Rowie9Of8+2d+x8saXo7f7x8bVTNMvyWbg4hlcMp8xFdUYTX6eXJXiN0DcU7Yr4o1rRMX4A6FQhQXF4/QtTJlPocbkn+IMeaVxpiTcV8GbQQ+LiI/nuXxp/zMkiKdBpCxdiHuxCKJG/28HtcxdXqhdQZVsoIwt29LpvP83Yn73L8Gt/vQe4GLjTF3pLZnz5YncO7M675KvjNn9qwok5HT2XUqPLIMN7JmcBqdIsbFGLMZeJ24ry7rAC9uOzl74j3HPZ4FfCi1jN62nlH/zIwxvwJ+NZNzZYsvP/RFSn2BEW1ynVuuxN69gYFT3kVZhfv29pZnepFnBri0qAhvt1v4PXJs45i22NMh3Sa7paUlf9pk72NYlkVvb++EbbLv67mXf+9cz6GbX8Fh0QraPJU0V9r4i/uIrqkbSueYiHg8juM4LF68OP/f8i/Q4rDJZJK+vj6ASaPlApuup+zJn+L4ayCc5KlDPkbF8mNHjPH2xSn/9y6ciiJih1YPrY/F3OfjJUuWjHAsvPv2d1Lrr+HwhiM4uHbFHF1VnmInwF+dk1N/+r5PYtkW5VLOQeUHZdR16SN7KNoZxhR5QCB06qIR93Es6XBIcxnH7lc1Zt/hpOtptbS05K/TNr85Frh6eNceY8wTInIC8A/gUhGxjDHvnsnBZ/DM8ujodaO2PwO8bCayKPsc3wR+PlcHm4EtPwAcPcHx1JbznMkijE32XsjNtbNyzu151Nhls5dQWUhk1cEjIj7cduGvx420qRq1vR94GLge+G2GdnJTJpWz2DNjYRcot714Ky/0b+FHTafDP98DpL4wt9zKtpN/jr98b02NUNzmtJYKkvtVEJnkje5UcByHRCJBc3OzOndyRLolen9//7jOnW2hrdy2ez1212mcs7SFsvpSig6sxMJNIJ4KjuMQj8dpbm7Of+fOAi2yPFzXk7VEL+p8BM+Tv+HXlR9ke2ANW+0Yr6puGDNO4jaJAyqJHrW3lr1t2ySTSRYtWjTCufObp39NfWk9157w+bm7qHym7iAorcn6af9vy40MxPu5Zs3nhiK1MiEJm/DJzdi1I797t3RHuf3ZXrbuifHqtWN1PhzbtrEsi8WLF6tzZ+Z4YGwJO2NMWEReipu29Q4RCRtjrsq6dIoyQ4wxA2gBWGUWDHfgLPvoP9n2pZw1ZJhTZ6WiZJusOXhEpB64BVgNbAJuxg1HS0ftlOJWCF8D/Ax4l4icN7q1+KhjvgBcboy5cdjfk2GMMfvP9DoKlb5YL6FkiH9tuZHPHvIW5JaPwnnfA28RiXic3c0vx1e1eG/KVNKhPGQRiDsY39yE4EejUfx+P+Xl5RrWnyOi0SgdHR2UlJSMmx73XPA56pzVHNmyihZvEXim/xIjEokQCAQmrAOizC+RSITOzk5KS0vH6FoSQbzhvXn+pZv/ykO1r8ZuWMc5La7ffXHVMEeAY/AMJPD2J8Z8H0QiEcrKyigrK6Mz0kHSSWKMYUP3Y3zimBmXE1EmoCfaQ9Rya+is33knHzzsyiFdj+t4sQ3GK0QTNnvCe9+d3Luln7ZaP+esrBup8wyEw2EqKiqmXMtJycijwFtF5IfGmPjwDcaYaMrJsx64QkQ8wBM5kFFRFGWfRZ2VSqGTzQierwPLgZcYY26faKCInAn8CfgKcOkEQ1/EzaFNs52F+jp+lnz5oS/RVrGUtd2baQ7/GtpOgEWrSSaTdOzejVVVQdmwN7/+jX0cvTuGp7YEu2r2ERjRaBRjDM3NzdpxJUfE43G6u7snbZMdt+MU240sqS/F02/h+KY3kYtEIogIzc3NBVRjaWFNVmOxGD09PXi93ozRchWPfB1vuAOnpNpdIcLWyhNoqiqlrXasbfi6ogQe6Maq95M4cG8B3nQ9rebmZpImyafu/QRrGo8E4Nzl51Hjz35Ey77ANfd/hkNqDwVgXcMxSFjw+XwTRkam6yb9dUM3O/vjVJXuvTfXLaukrmzi7+VwOExRURHNzc3qoJ8dnwNuAp4VkT8A3zLGDHlbjTEDInIW7guxy4H+XAipKIqiKEphks3Z1/nAVyZz7gAYY24Tka8C759k3GmjVr3SGDOmUOC+zs83/pRoMsK7V78HHvsTvPaHIILjOASDQaLR6IgUjqKtQeSpPr5tLC49ajGtU6i3MhGWZZFIJGhtbS2AdJ2FieM4hEKhMbq+v/s+Hthz/4ixu0KdhLadzwVlFv5n+gmf2DTl8ySTSSzLKixdL7Aiy7ZtZ9Q1QOCZ31C65UZ8Ay/QfeFNOIHGoW2Dj3Sy2DvOxN0yJJaVj2iLnkgksG2btrY2jMfwufs+y+rGNe73jDJvfPvRb1FdUsW7V79nqJ5Wb2/vuGl4Jc/0U7w1iLc/wW3bg6x/vp9rX7YfTZO0QR9OIpHAGKN1d+YAY8y/ReR04FvAlWRo0WyM6RaRk3C7rPwP+uJKURRFUZQpku3X69OpqRMFAtM8/gYR+bEx5nPT3G/hsvNBntt1H99sPR+e+RvEB4cKyqZbJ5eWlo54I+vbE+ffS0p51UFVtNbMvrV1PB6npqZm3HbcyvwTjUbp7OwkEAgM6bor1sWDex7gjcvehElU8kKPmy15/8AAZx5bz9KgRXRNHdbiqd+Gw3VdUCwgu0ynZpWVlY24r739myl9/i/0nfF9nNLGER2fesNJXuyNcdjizE4Csc2YVL14PE5tbS1+v59gYpBAUYB3H/He+bkoZYiOcDufP/GLgBtV09PTM0bXaTz9CYo3D/LCujpeiCS57dk+Pnnusmk5d8DVdX19feE4bfMcY8xdwBoRqcZt3TwCEWkE9hhjLhaRLwInZ1lERVEURVEKlGzGWd8FXC4iB002MDXmQ8Dd0zxHPaA9Bodz0/sQK45n4EXo3wbHfRBwnTsdHR2Z22Q7hqhj8BfN3jxCoRAej4e6ujp985sj0nV3iouLR+j6X7tv4sCKg6grqeO2Z/t5uj1KT8jmoMZy9m8odWt2TCM9KxQK4fP5qKur0xSOHBEOh+nq6qKkpGTMfV3xyDeJt52JE2ga08777uf7KS/xZUzPAlK2sFenwWCQ4uJi6urqEBFidpxSnzpws4EgeMRDMBikp6cnc0v0FKUb9pBsLeMvz/XxfHeMtW0VLJpmyu3AwAAlJSXU1taqfucQEXkv8B8yP4d9C9gtIh80xjxjjPlRVoVTlCkiIl8XkTeKyCGS4QtCRJaISIG98VEURSlsshnBcwVu4cCNIrIeeATYBURww49LgUXAWuBMoA/44DTP8TvgMhG50Rijjp7fvYwv1jdTXr8Cjrl8aLVlWQwODmLbNoHAyOiM/3uyh+bn+thUBGtLG5kN6TbZS5cu1bo7OSKZTA7pOt016687bmBzcDNJJ8Hrll6EV7zEkg6nHVzDfvV7n8PSNTumQiwWwxhDa2trAdXdwU3P+tWZcFDOOjXMGc71F1I02Mki287oTJVkmP5Tvwkydj4ZsxxO3L+KspKx+3m7ogQe7iFyjNthKd0mu7W1FY/HQ9SKcvV9n+K0tjPm/qKUjCQSCYLBIMaYiaPlbEP8oCpiD0S4cE0tjRXTc+5Eo1F8Ph9tbW3q3JkjUpPgX+KmXvUBS4Eto4a9AJwGfE1EjjbGXJRdKRUlMyJSbYzpH7bqg+xNIYyKyBO4hcQfSy2vx7XlddmUU1EUZV8mazMxY8w2EVmLG5nzOlwnTia2At/Grdcz3TbnDnAosFNENgNdgD1WFLPwZyKP/xrC3QRbT+ULx3xyxKZ0sd3hHY6e7QjzdHuYB7cN8sUlFRxzeC32NCcDwzHGkEgkaGpq0rD+HJIutltRUcHTA0+xI7KDx/s28LGVn6TE6xZkfb4rwqbOCOeurMPbG6doh1u3vGh3BKt+8nb2xhgsyyqwosopHAvKm+DML+Zakunx8I8hOrLcmNO/nedO+iWVlZXT0sOLe2I8tTvM2taKEeslnKRkcxBvb5zYYTUkl5bjOA62bdPc3DzkRAonw+xffQCvOei1s78uZUJu2fZvRNwIqr6+PiorKyccL47h2Z4oW/dE8U+zG6LjOBhjWLRokUbkzS2X4Tp3rgOuNMbERg8wxnxSRD4H/BB4k4jcbIz5VZblVJQRiMglwJdwX8amqcV9MTt8eRdu54K04yeSRTEVZVJE5NXAibhdCn9ljLGGbfunMabw3/op+zRZnY2lCiB/AvhEKvd8CVCB+48gBGxPtaabKS8B0k4hP9A2i2MVNMkHv4t93vcwW2/IuL2kpGQoqsZ2DLc808thi8p445oGyrdHiU0xcmM8QqEQfr+fyspKffObY9It0dd33sHxDSfyhmX/M+TcAbhncz+nH1xDU0URxY/2Yko82DUl2LUlWE2TR1YHg0FKS0upqKgoPF1bMfBO7sTKK5IxePgHcMZIp1S06Tj8Hv+0nWwPbhtgTVsFy+v9bkST5T6TF++I4IlaJA6oxGp0U7eCwSBlZWUUlxYRt90Oz4PxAfy+2dfqUjKTsBM4xsFguGnrP/ngkVdAkolbogO25WASDjc/08tr1zZR4Z9eimwoFKKsrGzc4s3KjLkMuNsYM2HBKmNMTETeChwOvANQB4+SE1JlE34M7A9cPHxbKprnjtSSHl8BnAdcjVs64X1ZElVRJiWVHvtJ4EbgKuAdInLusCY9J+VMOEWZI3L2ut0Y0y8iNlCG6+UfNMZEZ3nM5XMi3ALgytpa6nb8k4NqRpY8SiQS9PX1jVj38/vb6Q1bnN1vUdITxhR7MNOcDAwnGo3i8XhYsmSJ1t3JIfF4nP7+fkyqS1TCSXBEzWo8o1J04pZh1eIyyp7sx9ceIXTGYkzZ1L4a0ikcLS0thfmW34pDITknjIHvrYB174YDzxlaHYvF6O3shOR06ti7xC3DEUsCFHk9FD83gP+pfpxU57zokXXY9e7nEw6H3Wi8anjfne+htaJ16BgnLTlldtelZMR2bN5529tYVrkMgBOWnEiDr5Huvu5J9939p610WA6JhiLWLZue8zXdEr21tXXywcp0WYk7uZgUY4wjIn8GPja/IimFSqb7Wr488m8z+06RFwGrgdXGmG2TDTbGBIE/iMg/gY24Th5FyQn9/f1UV1cPX/Ve4GxjzOMi4gO+D9whIqennDwF9qZSUcaSVQdP6ka6BDcn90igatT2fuBh4Hrgt8aY6c9WFLjjU5QZ+PRx14xYnW6VPTg4SEXF3nSMaMLmqv2rKX2yn8GXtmDKZl4vx7IsLMtS506OSev6X9tvptvpQkToifeMce78d9sgnhdDLEsKxX0JwmcsmrJzx7IsbNsu3NbJjgO3XAlF023Wl2UcG256rxtt5NjQvBpO/PDezY7D4OAgkUhkxH09FZ7YGeKhbYOccmA1AJJwiK6pJbls5HEsy41eLqsv48dP/YCXLD2L1x78+lldljIxjnH4/obvckjtoVy17iMA2LZNV1cX0Wh0/Lboz/bT/fwAiyMWXHIgH55malYymcTj8dDS0jLra1AyYgHxaYzvwU0/V5QxzIHzZipcjxshf7eIvNkYc8dkOwAYY0Ii8ivcOj3fm08Blcm5+OY30h/vz7jt8GPhgr9dN+6+1SXV/Orc386TZPPHL3/5Sz760Y/S3t4+fPUiY8zjAKnUrHeIyDeAO0XkdPamFipKwZI1B4+I1AO34L4F2ATcDLTjtkMHt8jyYmAN8DPgXSJynjFmzwTHvAP4vDHm9mF/T8aCrcGzM7iTmB2FF9fjLB9bzy7dPnl4aL9ELBoiNrXbwoRObZ6Vcyd9jqqqKgKBQOGl6ywgQqEQXV1dPB15ircc8FYAXtFy4Zhxz24b5K01pbCiilBlMU751L8SotEoVVVVhdcSPc2O+yDcBa/+fa4lmZhkBHo2wct/6v7trxmxeWBggJ6eHgKBwLSjqDbuDvGmNQ0ssw3SE8M7kMCuHlszKxQJESoOsaVvM4vKlvDyA8bakjK3PNv7LH3xPq48aq8zr6+vj/7+/nF1LeEkJZsG+b3P8PLzWmiapnPHGEM8Hqe2tpaSkgJLXSwcngeOmsb4dcD2eZJFUSbFGPMccKKIXIbr7Gmaxu7djKzZo+SI8Zw7871vLnjuued4+9vfzpYtW/jVr8Zkt/aIyHJjzNb0CmPMFSLyLeBOcpjdoihzRTaN+OvAcuAlaYfMeIjImcCfgK8Al04wdDluilea/diHPa+fe+Aa1jUfA6WlnL7s7BHbotEoPT09FBUVjXhwDzzUw5GDFtay0qFUjJmSDutvbGwszHSdBUI4HGbPnj0UFRXh8XioL2kYd+yx3XGKa0vdWjvTmAwGg0GKiopoamoqTEdebBD+9mY455vgn7hQbc6xYlBaAzVjM1BDoRB9fX34/f4ZFTOPJh1WdcUp3RHFKSvCeD3YtSMn9oODg2xPvMj67js5sPogTm09dUQNJ2XuCSWCfOfRb/LWVZdRVuT+ixscHGRgYGDCtujld3eSaCujY/cg1Q3Tc7waYwgGg/j9furrNaNiHvk98HkR+YYx5qmJBorISuCNwHezIpmiTIAx5ici8tfh60SkG7db1qPpxRizObVNgJfiRuYrSta4/vrr2bBhAxs2bGDZsmWjN98OvBn4zPCVxpjLReQ7wKqsCKko80g2HTzn43bGmtC5A2CMuU1Evgq8f5Jxy0f9vWxWEhY4NcF2Lt35G/BXwzAHj2VZhEIhYrHYmBSO9p4oP7ItvrCugWLPzCfqiURiqE12QabrLBAsyyIYDJJIJLhpz/+RcEZmAvzlsS4e3REaSjC+MGQRPbWGkmk4d+LxOB6Ph9bW1sJ07gAkQrBkHRz8slxLMjnj1AlKJpMMDg5iWRZlZWUZdhyf4k0DxB7v5bxQgobSIiLntuBkiNzZ3LeZG3b9Cdtr86qDXs1prafP+DKUqRG3Ynz2gatZ13w0Ry86BnC/XwcHBzHGTOzIsw2xNXVYOwcommah/Gg0qnV3ssOPgLcD60XkcuD3xpgR3T5FxIObyv41IAh8K8syKkpGMkTV34sbmZ/ujGtEJAg8CzSklktEpEjLLijZ4qKLLuLWW2/l5JNP5he/+AWnnz7i2eW9jDP/Nca8X0S+lhUhFWUeyXYY2nS+3KPArIpjiEgdYKeq/C9oHOMgtgVvWT9mWzgcpqura0RbdICOzQOUDyb57Gv3p3iaofyjicfjNDQ0jPtmWckOwWCQ3t5eOkw7W0Jb+PiqkbU8n+uK8qmjm6kIuTVVSp7uJxQomlbYWzKZLGxdOw48+1comp5TJCcYA5tuzNjpa2BggP7+/jH39WR4+uKUbB7kD43FHHZaE6GGzF+zxhi6Y12ctOQkXr1SW6DPN7Zjc9/ue+mL99FS3sqlh71taFtfXx/BYHBcXfs6onhCSSTp8HxXhIGohWcaNuE4DiJCY2OjOujnmVRdkguAv+N2xrpORB7BTVn3Ao24NQrLcVOzLjTGtI93PEXJJcaYVwCkOuOuxi2zsDq1tOLOM/4MWCLyHPAk8Lgx5stjDqYoc8RBBx3EPffcw09+8hMuuugiOjs7h7YZYxJAYvQ+InI38DtjzA+zKKqizAvZzKO5C7g81W5xQlJjPgTcPd2TiMiBIvLbVMHmLmCPiHSJyA9EZDp5wwWFZSfJNE0PBoN0d3dnDOtPPthNV1sZpaWze6AfGBigpKSEmpqawo3oWAAMDg7S09OD3+/n77v/ykuXnD9mTCzpULOxD4naiOUQP7gKMw399/f3D+m6YIn1waM/dTtR5TuhDnj8l3D0e0as7uvro6+vj0AgMO226P6N/SRbyugSg38cx64xhsHBQWyPTVV59UylV6ZBd7SLf7xwI8XeYs7fb29kWU9PDwMDAwQCgXGdL6UPdSOWQ3R1Lb9/uJNXrh4/LXM06YLsfr9/2kW6lZlhjNkEHIHboncTcCJup6LXAsfjprpcDqwwxmzIjZSKMnWMMf3GmPXGmG8aYy4xxhyB66Q8CngbbuRaP27K1hdyJ6myL3HZZZfx9NNPT3X4icBZIuIXkYNFZMk8iqYo80o2I3iuANYDG0VkPfAIsAuI4NbNKcUtxLYWN9SzD7fy/pQRkSNwHUkB3CLOz+O+ETsQuAx4mYgcZ4zZMfvLyQ2hRIgr7/ogtf66EeuNcTh4VJ+NRCJBMBgEGFEI9/vrd9I+EOddtsPyE5uRWdTLSYf1t7W1qXMnh8TjcQYHB/nhtuvAA/UlDRxRs3rEmB/ctZMLByx8tpfIymqYZkpeJBKhpKSEtra2uRM8FzgW1B0Ii9fmWpKJiQ3Az05ynTuLj9y7OhYjGAzi9XqnXXen9JEegtuDXFNqiItQ6R/7L8AYQyQSYVtiK3f03MZ7WyfMlFXmiKgVY/+q/Tln2blD6yKRyFB7+nHr7tyyCxPwEV9RDUDiyW5OTnVFmwxjDOFwGL/fz+LFi2d7Cco0MMbEgW+klnQjCtsY05dTwRRljkhFSqRr8wwhIvvnRiJlX6Surm7yQXs5BXf+WQwgIh3AH4CvaiSlUkhkzcFjjNkmImtxI3Nex9583dFsBb6NW6+nZ5qn+QpuatcxqTdkQ4jIKtzq6F8G3jDN4+YNoWSQFbWHcPmRV4zc8J8vgbV3ApB+A9/X10dl5d4isve/MMDT7WF+dPxiyh7oYaBo5s4dx3FwHIfFixdrUeUccuPmv9PR104kGsXC4iOHfGzMmAe2DrBxd5grG8oJnto8beeObduICIsWLSp8R56dBE8BpJdF+6DtBDhur5/bcRz6+voIh8PTirZ4dHuQTZ0RTt8e4Wdehw+cvZy6siK2hbayfucTI8a697VNj+nhohVvZFX9YXN2SUpmYlaMf77wDyqK9+rUcRx6e3snbIkObmv74Plu3Zz7XhigL5Kc8j2aTCYpLi6mubm58O/rAmcGzzuKUpAYY7bkWgZFGYdqYDNuQEIJcDRuNOVbRORVxpipdGtWlJyT1Ro8xphe4BPAJ1L5ukuACkCAELDdGDMwi1Mch9s2fdPoDcaYjSLybaYZFZRv9Mb68A8vuBrtg2QUnrkBXv2HodX9/f3s2bOHsrKyEWH9Nz+1hyvObMP/WC/hk5umPdEfTigUoqKiYtoFXpXZE0qEsIyFMYbbtt3CObXnUVxbzMmlJ48ZG086/OOJHj50Wgueh3sxM2idHIlEqKqqIhCYVVms/MCxwFMAXTBD7WPqBKXTdcrKyqblVL1jUx8nH1hNXXeC16+tozbgw3Is7u+5n5ZAC83+ZsCt5RWLxSgvL6emppYDqg+Y00tSMvN833NErAhvPORNQ+s6OzsJBoNT1nUsdZ9fcebUIuwsyyKRSFBbW4vfP7sOioqi7BuIyFxMcI0x5ow5OE5eItfMbn9z9ZyIocyM9cDZxhgrvUJEjgZ+AvxNRNamu8QpSj6Ts1mOMaZfRGzcNucGGDTGRGd52CAw0av5OGBPsD2v6Y508c1HvsabV77VXZGMwQ+OgJZjYc1boXY/wC2qPDAwQFFR0ZgUDmNg/4ZSxDJua+wZEgqFKC4uZskSTVHNNpZj8f4738PBNSuwLIs1lUdySM2hlJRkbl395VtepK3Wz+FPDeCU+6bl1DPGDOm6ubl5ri4htzhJ8OZ5BE+wHf58EZz11b2rgsGhWinTrbsTtxwOW1JG9eYQRc0BjAg37/onnbEOXrbkAsqLynEcx03XqfWzdOnSub4iZRx2BnfwvQ3f5bLD3kaN361tNTAwQCQSmVaNpS/9exv7N5SyX/3k3+uO4xCJRCgvL9eW6IqiTAcPTKsvQyYWdLig+czkY5S8xAH+Mty5A2CM+a+InAI8DnwceGsuhFOU6ZBVB4+I+IBLcNt/HglUjdreDzwMXA/8dgYtFb8HXCki/zDGPD7q2G3A+4DvzEz63BJKBPn+hu9x4pKTOaHpaLjhfyARhOWnw4W/GDE2FosNvYUfzuZ/7eB/epOU3b4bz0BixtE7iUQCr9dLS0vLTC9HmQZxK8b3NnwXO9VJ13IsVtat4kNHXUVXVxd9fX3j1mL524ZuuoIJPv3S5cjNOwmdNb06G/F4HJ/Pt7AceYUQwRMbgAPOhpWvGVoVCoVm1BL9lqd7Wd4Zo/7uTnxdsaEIrqgd5ZWtr6a8yP2eiMfjFBcXay2WLBNOhjl20XGsaz56aF26JfpEzp3Sh3rwDCYQy+Evj3XRG7a4+vzJdWeMIRaL4ff7aWpasH0HFEWZB4wxp+ZaBkWZJ3qB2kwbjDF9IvIz3ILhipL3ZG2WkyogeAtu68RNuEWQ23Fr5oBbZHkxbovFnwHvEpHzjDF7pnEaD24Uz8Mi8m/gGdxWePsDF+C+ddg/dZOmMcaYS2d6XdnANjYPtj9AY6CJNxzyRgjvcZ07L/sxlFRm3Ke4uHgorH9Xf5x4T4zG9ihFFy4jUuwF78ycO8YYEokEdXV10y7wqsyMwcQgSSfJOw5/19C6QNHeVKni4uIx9TN29sUYjNnc+8IAXztzKb6OCBKzp+XUcxwH27apr69fOLo2BtofzW8Hj+PAzgcytnHPpOtMdAzE6Y24L6Fu39TLNxeVE1tbx25viK7YZohBT7yHEo+rV8tyx9bU1IxbzFeZO5J2kuf63EziFwZeGJl2C4jIpLr2dUQInbEY4xMevXkbX3zF1OqWWpaFiKiuFUVRFGUv/wXeJiLfNsYMZti+B5hWxWZFyRXZnOV8HVgOvMQYc/tEA0XkTOBPuEWTp+N8uXbY7+elltFcMupvM81zZJ1dwZ38c+v/8fbD30WRpwiSYSitg/Kxb18HBgbo7+8fMTH43vqdfCYKu1rL2L+6ZMaxtcYYgsEgpaWl061Kr8yCpJOkvKh8KH0jTX9/P4ODgxnf8n93/U6OaCnn9INraHiwG6uplMT+FTDFQqqO4xAMBlO1WAq4Jfpogu3wny/Ced/LtSTjs2cTPPR9OOdbQ6t6e3uHUuWmwnV372JFUwAEjl1eRXFHnFB5EX/f8Q9KvCWUektpDbRRU1KLbduEw2Gqqqqorq6en2tSRrBxz0ZueO5PrKg9BIBjFh07tK2np4doNDpuyuUQIpiAb+j3spLMLdSHY1kWkUiE2tpaqqqqJh2vKIqiKPsIXwVuB+4RkbcZYx5MbxARP26DHu2kpRQE2XTwnI/bGWtC5w6AMeY2EfkqMK3+vMaYBdnKKeEkWFm3ihWlTfCT49wohAPOGTsu1RbdGDOiaOabQg4N1X78Z84uzSbdEr21tXVWx1GmR8JO4hvV9SndFj39pj/NN27bTl/EorTIyxvWNRO4rxOxHKJHTb3ORrqost/vX1ipWQBWDJasg+Wn5lqSsfS/CH96HRgHVl3kdtDC1XW6LfpUIy6MgTcc7dZM2n7nvQz0lPLt569nwBrg4ys/RYm3JDXOEAqHCAQCC6fGUh6zuf95fvj4D0g6SV514Ks5ueWUEdtjsRjBYJCioqIJ07PKb9+NKXb/3X1//c4pFbRI192prKykoaFhNpehKIqiKAsKY8xdInIp8APgPhHZjFt3J4HbPn0x8IUciqgoUybbeQrTqakTBRZAy57Zk0xP8KO9UH8IvOJnGcelIzoqKioo3h7GE3XTLlotM9RGd6ak22Q3NTVpS/QsYjkW9+2+143cGkZvb++YVtn/3TZIdyjJF1+xP76dYbzP9uPriDH4yukVzLVtG5/PR0NDw8LTtR0H7ySREbkgHoKHroMDz4NTPz1iU3d396Stsofz5K4Q4YRbr6njhedp2l0Erz+MK3xrx4y1LIuSkhLq6+sXnq7zkM5wJ6e2nMr5+1+QcXtXVxeJRGJcXRdtDeKJ20jE4s7Dq+l7ppdtvTG++srJu51ZlkVpaSk1NTWqa0VRFEUZhTHmlyJyN3AF8DLg1alNMdwarlfnSDRFmRbZfMq7C7hcRA6abGBqzIeAu6d7EhF5k4j8WkRuE5E7MiyTRhDlG0kn6U7wrTj4Mrez7enpoa+vj0AggNfrpfSxPRifB+Pz8Lfy2al5qLuO3z/lSaYyN/REe3iy5wnOXrY3Yqu7u5vBwcEx7ZP/8Egn/5OK2gg86uo/evT0OuTYtk00GiUQCCxMXVuxce+hnLL7YejdAkdcPGJ1R0cH4XB4Wm3Rr394rx2UPNpF9+EleH1jI38syyIWixEIBKZduFmZGTE7Rsk49rdr1y4ikQhlZWWZa+8YQ2n6vl7XwB8f6aLYJ7zp6Mkjr5LJJIlEgrKyMgIBfW+iKIqiKJkwxmw1xrzPGLMMt+jyUqDKGPNBY0zBdmJW9i2yGcFzBbAe2Cgi64FHgF1ABLcOTimwCFgLnAn0AR+czglE5PPAx3DD6boo4JboaXaFdvGdR7/Fm1e9NTU5HRt9EIlECIfDlJSUDKXrvNAf59tP9wDQWDfzCa0xZsi5s+DSdQoA29gsKV9CS4XbsSwcDmdslX3VDZs5tLmMlYvLqPjnDuzqYhIHZC7APR7pFI6ysrKFma4zuBt+fyGc8unJx2aLHffD394MZY1wwoehZtnQplAoRCQSobS0FK938voqAJ/951YayotY21ZB59//g9cjtBx2xJhxaadtdXU1jY2Nc3QxykQ81vUYf9z0Bz6w9vIx24LBILFYLLMjzxiq/rQNJ+Aj2VY2dF8X+zyccuDk9bFs2x6qu6Mt0RVFURRlahhj+oH+HIuhKNMmaw4eY8w2EVmLG5nzOlwnTia2At/GrdfTM83TXAL8G3iVMSYyY2HziFAiyAlLTuLE+jXwz3dD5cjW5I7j0NfXN5TC4X+8F4nZeASuvWC/WZ3bsixs26a4uJhFixbN6ljKzLCcvfV3HMdhz549GVM4/EUeLj1hMSVP9SGWIXzy9Bw0lmUNpessyPoc8SDc/nFY+VpY+9ZcSwN9W+G/34c9z8FxV8BR7xix2bZturu7h3QyFX74+E10lezkFYH92HHrdhYN+vG9ad2IMcYYbNseiubQYunZoz/exysPfBWH1q0csT6ta2PMSEeeMZQ+sgdJOtjVxYTOch3sf3+8m76Ihe1MXC4/ret4PE5FRcXCKpauKIqiKLNARByYcd+ZIYwxU3sDpyhZJKs1eIwxvcAngE+ISDWwBKgABAgB240xA7M4RSVww0Jx7gAkHYsijw+23wOOBcd/aMT24ek63phD8dYgHUfV84vdg1wxi/NalkU8HqekpIRAIDDlSaYyt1iOhc/j/u9Ip+uUl5cPpXD0R5Ik7L3/n4q3BAmdNj3nTjKZJJlMUlxcTHl5+cLTtTHw4n/c++fEj+ZaGrAtePbvUFTqRhM1HDJmSHt7O7FYjIqKiknbokvUIhxK0r7nKd61/8tZu0HYcaSXwcOKqR01NplMDjltKysrp9yVS5kdtmPTE+2hsXRstNTOnTuJx+NUVo6MuPP0J/B1Rokc3YCT6pbVH0ly75YB3n7SYs5bNbFzLh6PY4yhpKREda0oipItypog3DnzffchYl+/F8KZy7M+Gygj9tn14+9cVoT/QyfM5vRvZQ4cPIqSj2S7yPIQ8xT29i/gdOAnc3zcnJF0EhRH++HOL8PZ34DAyId6x3EoLS3F5/NR+ZetxFbV8LH7dnHGipm/rR3eNnnx4sWzvAJlNrgOHvc2dRzHdeSl3vIbY/jY37Zw6KIyjtsvNTn0CE7l1Cdy6fSNmpqahZmWBdD5JNz64dT9M9rlkQOe/Rs89Qd42f9C06qMQ2zbpry8fEp1dypu3sXToTgXlR3Bkd5KnFYfjW1jU3EsyyIajVJfX78wo7TymHt2/4eHOx7iPavfN2ab4zhjHXlJh4p/7yJ6VD12w94U20/e+ALnrqzjgIaJ6+gkk0lisRjNzc3U1uaBzSuKouwrXNUx4eZlH/0n27700iwJk+eM49yZ930BY8wvZnUARcljcubgmSfeB9wmIr8F/oZbh2eMd9YYM+3izbmgJ9rDbzf+nFPan4LD/wcOOHvE9nTkRZodCZvvvdhPTaCICw6f2QTOGDNUZFdrc+SOvlgf39/wXQYTg6xtXEsymcSyrBET/m/evoOldaW877SZdUhLt0OvqKhYuBP+wd1uauMRF8MBZ+VaGpf4IBz1znGdO4lEAtu2J3TulN3dgUTdEmPtsSQ/bLmLxU3drFiZ+RrT9ZWqq6t1wp9Fnt7zNDc89ye6op1cfOibaatsG7E9kUjgOM6Y/STpYDX4x9TRqg4U8dLDJq6jk9Z1XV0dVVVVs78IRVEURVEUpWBYaA6eNqAKuAh4fYbtguvwKYh8yZ5oN4eUNvLSatwCrKPo7u4eKor7zO4QDbbDB89oxeedOKVjIizLwufzUVtbO6KIr5JdeqI9NJQ2cMWRV1LiK6F9l5uyU15ezlO7Q0QSDsG4zSfOWQaAtyuKJ+FMK9g0nZZVXV095SK+BUf/Nmg5Bo6/MteSuET7of1RWHbKuEPa29tJJpPjpsr5OqJ4BpMEz1rClp4IP38wSktzL5evuGrcYyYSCfx+P5WVlQtX13nItsGtHLf4eI5ffAKlvtIx23fu3Ilt2yOjd4yhaHcEikY6+J7pCOOZwld7PB4nEAhQUVGhulYURVEURdnHyNoMXkTumMFuxhhzxjTGfx+oBr4KPAdYMzhn3mA5Fn7x4vFXgmfkg3pPTw+hUMhteSsefnTXLr5RWYKneOYP9OkUjpqaGioqKmYrvjILYlaU8uJyAkUBurq6hlplxyzDj+/ZzckHVvPyw+uHnHll/+kksX8lsZXVUzp+MpkkHo9TV1e3MNuhp0mEwF8DU2wxPu88cwNEeqD1+IybOzo6hropjdcqu+w/HUSOaYBiD9+7t53XHNnI/XEPRZ6xrdDBde4kk0kaGhq0HXqWSdpJakpqCBSNTanatWsXyWRyRE0tAIna+J/sI3Ls3qg6YwzfX7+TS46duNh9PB7Hsizq6+u1HbqiKIoyxD8uvGl2B9BqNYpSMGQzRGMzcBnuV8Ru5sf5sgq42hjzlXk4dtZJOhY+Y8A3ss15LBYjFApRXFyMx+vlo3/ZzBcdL+WNpcy0unS6bXJVVZWmZuWYjnA71z3+fV514KuJRqNDbeodhE/euIWjl1XyqjV7dVRx0w7sej+x1VNLvUnruq6ubmF3Uep+Bm5+P5z8yVxL4vL8v+CeL8MrfgGVS8ZsjkQihMNhSktLM6ZnGWMI/+EBnisf4B/hP/PiPTF8LcL98RLKfZmddOl6Wo2NjdpFKQcknQTF3rE1scLhcEZHniRsKm7eSWK/CqxFroPGGMPH//4Chy0pZ92yyjHHSmNZFuFwmCVLlowp2KwoiqLs27zsr+flWgRF2WeYrEEKuM9380U226S/XUQeBH4M3GaMecs8nGYHMLagQSES7cN+5i/4+rdA3eqh1cYYurq6iMVi3Ls9QdQynIuXhrpiQifMvPp+IpEgEAhQW1s7JaNU5o/++ADHLjqWM9rOZNu2bSSTScrKyhiI2SyuKuGNR7vFkIs3DSBJ19zDp0y9QHK6Y091dfXC0nWwHZ76496/u5+BtZfCEf+TO5mGM/AinHYNtI2N3jHG0N7ejm3b+P3+jNvv6riT1b5SDjr/HI587hj6e/u49oL9JjxlPB6npqaGqqqqhaXrAqA93M5Te57iwJqDR6xP6xoY48iThINdV0JsTR3GGG59po9I0qasxMs7ThrrFBxOPB6nvr5+/OgvRVEURVEUZd4Z7bzJdnH1rOYtGGN+ClwDXCwir5qHU3wFuFxEDp2HY2eXnQ+Q3PMcvvoVboHlFO3t7UQiETxFfu54rp+W8iLONUL06JkXyU0Xda2srMw4uVSyS9JOUOQpYufOncRiMQKBACJCLOlQ4ktN3GwH/9P9OOU+N11niqRbJy/Idujb74GujVDV5i4HnA2HvynXUu3FioE382e+fft24vH4uJPzmB1lQ+fD1Jc34hjDjU/08JbjJ07XicViiAgVFRXaIjsHPNTxX5ZVLufgYQ4eYwwvvvgiyWQycwqVbTCptMtwwuH2Tb00VxbzpmMmduBGIhE8Ho/qWlHmEREZsbz45fNH/K0oiqIo+UAuquh+DngV8BUR+asxZi4jblbjpoA9ISJbgE7GpoJNt65P9tl+H5tu/RA/X7aadx14PlTubVVu2zaBQIBIEprKfJz13z3EDqvFrp3ZZH1462RN4cgPkk6SIm8xtm1TUVGBx+MhGLP45I1beO3aJjCG6j9sJbaqhuSyqddKSuu6qamJ6urq+buAXGHFYcnRcMiFuZYkM1Z8TLrl0CbLGjfKJukkufbxq/nGM2/GHF7Du6/fxOkH17Bf/diivUP7JJNEo1FaWlq0nlaOiFlRDqs/DP8onSeTyXG7W4ltwCskLIcP/PE5XrWmgWOXT9wJK5FIEI/HaWtr0xpLijKPzGc4vaIoiqLMFVl38BhjHBE5GvDPsXMH4Hxch84OoBiYWf/oHNMd2sXvl67l5Stez5rGtUPrLcsaap+csA0Bj2DV+4kdNn3HjDEGy7JGpOsouaUn2sOfnvsDXZEuDq87Atu28fl8JCyHn9/fztFLKznr0FpIOli1JcQOn1rNnbSuY7HYULrOgsSOjxshk3N2P+IWWF6ybswmy5qgHJljKL2/k3f3nod3SQ3fD8dpq/Xz2iMzp2M6joNt20SjUS2qnEM29T7LA+33c1j94SPWT6jrpIN/Qy9OmY+kbTiwMcB5q8ZviZ7WdSwWo6mpSYsqK4qiKIqiKLlpk26MiQPxeTju8rk+Zi54MdpJi6+cM5e+ZMT63bt3E41GqaiooH0gwmIbTPHMsuzicffjLy0tpaqqiqKizB14lOxgOzb/7XiQUl8pb1l5KdHuKJZl4ff76Y8miSQcLj3ejeTy9ifGtFCeiFgshtfrJRAIUFlZic+Xk9t+frHibkv0xsNyLclIjANdT8PG38Oat0LbSWOG7NixA8uycIxDV6wLM6xVhTfm0Nwb5pGl7Sw+4ESe+fc2Pn7OsnFPF4vF8Pl8lJWVUVFRsTB1XQBs7HmSs5edy0G1I9OzduzYMTYKwBg8A0m8AwkQiK2pJWkbvJPc4mldl5eXU15eri3RFUVRFEVRlNw4eLKBiCwC2oBngShgzUPE0LwQs2I0+cooGRaN0NPTQzwep7y8nGDc4Zd37eJ7xkfi+KlFcQwnHdLf3NysaVl5whM9T3Dn9jt45xHvpjReSsyOESjbW3unJuCjrMQLlkP57bunXHMpHo+TSCRYvHjxwo3cAXjqT7DjvvyquQMQ7oZbr4LqZXDQ+eAd+ZXb2dlJIpGgoqKCnZEd/HzLz9i/4oCh7RXRYo72LKGt5WBiPqHK76OqNPPXdiwWw7IsmpubKS/P3FVLyQ4xO8bB5YfgFdfpYoyho6ODRCIxpsOVtzdB2V0dWE1+4gdVYoq92OEkPs/4NT3i8Ti2bbNo0SKN0lIURVEURVGGWHAOHhE5AfgObj0egJfgXufPROQKY8wfx9s3H3hxcBvX9zzKa8v3H1oXj8cZHBykqKiIhAPf+Pc2rsGLZ3k5seXTq6/hOA6RSISGhoaFPeEvMKLJCCe3nMIS/xJ27dlF2PbwtZu24RiIJh3Wtrp6Fstg1/lJ7De53tOpOk1NTQu3DsvWO+Gua2FwJ5z3Pag/KNcSjcKBY97vFnweRTQaJRgMUlrq1tKJ2TEOqz6cV7SmagglHSr+tQur0U9DTT3X3rSN2kDmSLt0qo5O+HPPg+0PcOf2Ozhh8d5orUgkQjAYHKMbTyhJ2d0dJA6oGJFyadlmXAdPOt1y0aJFmpalKIqiKIqijGBBOXhEZB1wG24Nnm8Bl6c29QJJ4HciEjTG3JwTAafArr4tnOUUcXL50qF1u3fvJplMUhoo485NfRxQWUJjhZfwcY3TPn4sFqOiomKoeK+SO4KJIBt7ngTgqT1PsbRyKbt27SKWtLj3hSgHNQZ4/Tq31ooAGEPRjjCmaGrdOmKxGJWVlZSXly9MXQc7YMMv4fgPwQHnQj5eozHgHdvVyBjD7t27AXDEZkPfk+wI76DEWwyOq2dP2MKuKuI/TcWEnu/H7/Pw3lMzt8qOx+NUV1dri+wcYjkW9+++j7t2rufKdR9mWdUywHWqt7e34/V6R6ZRWQ7Fzw+SWFpO7LAabMfwyPYgjjH0ha1xHTzxeJyqqirVtaIoiqIoijKGBeXgwe3QtRU4EigDPghgjHlYRI4A7gU+DuStgyfe+Til0V5kvzNHrC8rK6MnbHH7s328f00j7IjANB/uY7EYjuNQVVWl7dDzgMe6HuX+3fdxSN2hNJU1saZxDYPtQTrCwsbdYd5y/CI8w3QscRv/E71ETsxcYHc4sVgMgJqamoXXDj3NE79xu1K1HJefzh0AMjt4wHXyBAIBnhl4mnu7/sOKqkNZWbUSTzCJ//FeEvtV0L1fOb9/sJ1TD6rhgsPrM07oo9EoANXV1doiO4cMJgZ4sucJVtWtYlnl3nJwxpghXQ/H1xXD1xklcmwjiLAnlOCGx7o46YBqAM5YMTb9NhKJICLU1tZq3TRFUZQCINP/bfny3t/35e5spZ85bVb7m0/vu5+dokzEQnPwHAdca4yJisiIp2ljzKCI/Bj4bG5Em5yNPRv5Q/dDvLNpLdQsAyAcDuM4bumgpG1YUV3Cyof3ED94eulV6RbZS5YsGVMDQskNtrFZ07iWs5a56TsbtnTw+Ru34y/2cv5h9bTWjHLCWQ52bQlW0/jtsWFvi+zW1taFl67z0A9gwy/AVwqJIFz4awhMvw5V1jAGPGMn4qFQiK3hF/jrCzfgkyLOXnQOa2rdjnkSjmE3+Ok9sJKP/m0Lpx1cw/mHZe6mlEwmicViLF26VNN1cowBTlxyMofWHbp3nTEEg8ExD/DePTHK7u0kcmQ9TrXrlEtYDsvqSsfVdSKRIJFIsHTpUnXQK4qiFAj54sCpLqmmP94/433ng+g1d87LcRVlX2ehOXhg4u5cfiBfX/WzJ9rDK6pWsNq/CHBD+zs6OjDG4PF4SNoOFSJYjaXTapFtjCEWi2nb5DzDcWy84kn97rB5ZyfHL6/gdUcvzjDY4H+6H+Mb33zTuo7H4zQ0NCy8Cf+uh92onZf9LzQfPvn4fCARGhPB0x5s529P/pWOeDunNp7OiY2pWi0JG/8zA3iCSaxiDzc81sWqxWW8cvXYgtrDdd3Y2DhUx0fJIcbgkZH3p2VZdHZ2joi2kaiF/8k+YodUkxxWSyvpGIq9Y9/0pnWdSCRobGxU546iKIoybX517m/H3bbso/9k25demkVpFEWZT/LW2TFDHgTekGmDiJQBlwEPZVWiaRBMDFJiJ8HnptTs2LGDRCIxNHmLJhzKDRjf1FOzotEo8XicoqIiysvLNaw/j7CNg8fjxXEcntuyjYFwkopA5hQbidn4OmPEVo/v2Buu6wXVDt2xIR6CDT+HIy6BxpW5lmjqVCyGxpU4xiFqRQknwtz27K1ErDAnNp3E2tojh4Z6B5L4umMkl5Wze2kZW/fEMjp3wE3VGa5rbZGdewwMOWzBLXy9Y8cORGSvU8YYinZHMD4PiQNGRlJGEw5FGRw8qmtFURRFURRlqiyQGeAQnwbWi8hdwN9xn7mPEZFVwPuBpcA7cyjfuISTIf793J+4fMdGeOmPAfftb2VlJSJCdzDBz2/fwXeND+v4qaVYpVtkt7W1advkPMQxDh7x8OCWPXz1H9tYVOXnlaszp96J5eBUFuFUZHbQxeNxksnkwkzVueVKaH8M/FVw+ufBU0AT3JJK8BZx27Z/c/PWf1LjryESifDKpa+mJdA6YqhYDnZNMcmWMsK9MRZVFtNQMdbhl26R3dLSopE7ecbwCB5jDI7jjPjuLdoZwf9YL5GTmjD+vXbcMRDnO3fu4JJjF404XrpuWltb28KtpaUoiqIoiqLMGQvKwWOMuV9Ezgd+CHwttfrzqZ/twOuNMXmZ8Gkbh1dUHMj+R54MS9aRTCZHbI8mHY5uLkP8xST2n9zB4zgOsViMpqamhTfhXyDYxsYrXvpDMV5ycBUvX7Mo80DLIfDfHpyyzLer4zjE43Gam5sLJ33j7s/DnuenNnb7f+DS+6F8+l3j8oVwMsRFK97I4TVHsGPHDsoCI1MlJWJRuqGX5JIAkYTN9Q910lI9dkJv23bh6XqfwSDsjcBJJBIjai94+uL4N/QSO6J2qI5WfyTJrx/sZCBmccqBNRy3314Hr23bJBIJFi1apMWzFUVRFEVRlCmxoBw8AMaYW0XkAGANsD/gBbYBDxtjrFzKNhEJO44/1AmVBwBuepZt20PV95OWw5KkgymfWlZdNBolEAhoO/Q8xjY2jg1PvNBOQ9n4USmeqA1A5KjMxVfTui6oduib/+UWSJ4KHl9BO3ciyQjt4XaWVS5n165dGcd4B5PYVUXEDq2mN5igxCe8Zu3Ya45Go5SXl1NeXq4tsvMMA3jEvY8ty2L37t17dWQ5FG8NkVxaRmJ/t+6O7RgeejFIoNjD645aTFXpyH/HkUhEda0oiqIoilIAxL5+L4STGbc9Gygj9tn1mXcsK8L/oRPmVJYF5+ABMO5r00dTS2Fgxdm/+zlY90Ha29tJJpNUVOwtwOkLJjl6T5Lk6smLJKfD+hsaGvTNbx5jOxaPPN/Bps5Gzji1bdxxknRwSr1QNNZ5E41GMcbQ2NhYWPWVxDvUKW6hc/v22+iL9VEcKSGZTGbsYieWgxMoAp+HmOVQEyiiZJS+o9EoIkJDQ8PCqa+0kEgVWbZtm927d2Pb9tB3ePGLIYo6o4RPaASP66zZ1Bnhjk19vPX4RTSOSsWLRCJ4vV6ampq05o6iKIqyMCkrGndCPKV9FSWfmKktz3S/CdBZQp5Q7y2leeVFmNr9CW/ZMiKtqjec5K8PdfL6qhKaF42fbmVZFslkkng8rvU58pwnu5/gpq03cUDyXE49uJbFGdJxJGJRfkc7Yjkklo6soZRMJrEsi0QiQUtLS2HV5/jLxWM6Sy1kEnacM1tfQlG4iED5yPvX2xcncE8Xdszij7bFg9v7iSZtTjygeu/+icRQatbSpUsLS9f7EG4EjwfHcYhGoyM6FkrSIX5AJU6la/c7+2L86D+7uODweg5s3GsTaV1blsXSpUsLy2mrKIqiKNNgoqgF7eylKDNHHTx5gzPUPcsYM/TWNm453PH0Hs4rK2F5cxnRCY4Qj8epqKigpqaGQCCgYf15zO7wbi5Y9hrufqCS/ZZneENvOZRsGsBq9BM9emwnpUQiQWVlJcXFxfmt62f/7rYKH07fC3DpPbmRJwcknSRF3iI8Hs+IFDqJ25RsGiCxXzmP1hQzuCvIteuax+6fTFJVVUVtba3W3clz0kWWvV7vkK4lYuHsivBUwEM7brrlCz0xzlxRyxkrRnbFSyQS1NTU4PP51JGnKIqiKIqiTJsCKdixD2AMxlvM9u3bRxRY3toTZaA9wipHiB84fnHlcDiMx+OhpqaG2tpaTeHIcxJ2go0v7MGxHVYuHtvhzNsbx9cZJX7Q2K5aoVAIr9dLTU0NNTU1+ZvCYcXh9k9AMjJyOfXqXEuWVeJWnL6ePixrZAkw3+4IErVJLCsnbjn4fWO/joPBIF6vl9ra2vzWtQIYHNth586d2LY9tLb4xRA9MYu7QgkSliFhGVqqSzh2+cjv82AwSFFR0ZCuC6aelqIoiqIoipI3qBcgX4j2QlUbiWiCqip3Uv/inhjfuXMn7z24Fq8FierMaS2JRALLsli+fLm+9S0QknaCRBLW7VdDdaq4qrc7RvldHTh+LziG+MpqnFE6j8fjOI7DkiVL8ru+kp2EHxwOqy6CI9+Wa2lyxmBikP92PMjBiw6hsrKSohdDlD7SQwjoGEhwU4WH526PYjmGVxwxsoh2PB4HoLW1VR22BUDCTlDq9RO1okO1d4peCOLf2M+mpaUcXFPCyQdWZ9w3FoshIrS2tqoTT1EURVEURZkxOmvIF+pXYDcfCVu30h+1WL+hm/12R/loZSmHDFrYVWMn847jYIwhHo/T1NSU3xN+ZYiEk+CenQ/S3b2KI+rcyZyELfxP9BI7tJr4odVj9knrOpFI0NzcnFtdh7vhwe/iVh0Zh2QEFq2F067OllR5ieVYnL3ojdz5tNDg7OKk3TE21hVzp2VzwqoG3nRQzZh9RutanTuFQUtFKwFvGb30AW4Unv/ZAXYcVs2/tvRxTuPYmmhpXSeTSRYvXqzOHUVRFEVRFGVW6Mwhj9ixfTtEkuzqT1LXm2RFpR/PiirixR7sDEV4w+EwxcXFlJaWajv0AiKajGIPNrKqcn/W1PmRiEXRrjAm4CNxQEXGfYbretK2yfGg62CZL7beCZFuOOwNE4+rXjZ/MhQIlmPxwNPdLPe0cKLtxSwuo3p5Oa/wCvvVZy6CHgwG8fv9lJWVUVFRkb/1lZQRGGOGWqPbloNsGmCwyc8dkQQrmgMc0TI2FTMYDFJaWkpZWZm2Q1cURVEURSlQSj9z2oz3NZ+e4KX5DFAHTx7h2x6i4akwXi8kLEPZUbVYzaVYGcamWybnfaqOMoZIIklnTw3vba2m7o4O7HL3NowdXospHvsGPxqN4vF4pp6q86sz59+5csz7oW387geKSyxpcdD2cl4esPGUe4iuq6eycvz7Ne3Ia2tr02iOAsQYQyAQYMstO6lqj/DXxhIGfMIb1jVRMqrGUlrXS5cuVceOoiiKoih5zVSeVYyZW0dFIRG95s5cizCEOnjyBGMMkjR0tga4escA56yq47TmsW/4LcvCcRySySQtLS3aRrcAiSQsLmyoY9HmINEj60i2jX2zD3t1bVkWLc31+P5+iVvbZlIEXvOHuRVamRGVTjXnm1ISxzViZ0jRSZNMJjHGYIzRVJ0CxXEcHt8VpvOZTs7uSdJ/YiNvO6h6zLi0rgFaWlrUuaMoiqIoSt4z2nmTL63sJ3uO2hedTprTMwki4hWRL4pIu4iEROTPItI0wfijROReEYmIyPMicvFUzpNIJLAth017Yhy3XxUnH1CdcVw0GqW4uJja2tr8bo+tjEugyLB2sJzYymqSrWXjjouGQ5T3bqAx+iyl228BY+C8702+XHxbFq9GmZCEw/YDy7AbJm5vHovFKCkpoaamRluhFyhWIkl4W4RzPF68pzTTfMDYrofpmml+v5+amhotiq/MOTN4ZvmjiJhRy23DtgdE5Mci0iMi/SLyvyKS+a2Eoswhc/38rbas5BK15/kj/YI0vSz9yP+N+DtrlM0w6GKm+02ARvBMztXAJcDFwB7gOuAG4MTRA0WkAfg38DvgUuAlwE9FpMMYc8tkJ+oYdGgPJThmWSNez1jHTTgcxufz0dTUpG/4C5hyq4RaU0FiSRmM46ALBoMEIi9S98gXkQPOdlce+XYob8yipMps8XmgrrliXD2Dq+vi4mKam5vVYVvAeGMOBw/Y0BSgpLUMM6ommjGGwcFBSktLaW5uzpGUyj7A1UzxmSXFYcBHgV8OWxcf9vuPgCOB84Ei4GepdW+cS6EVJQNXM7fP32rLSi65GrXnBY3/Q+OXrsh2tJM6eCZARIqBDwDvN8bcmlr3emCriBxvjLlv1C6XAQPAB4wxDvCsiKwFrgQmdPB4BpLUPD/IoWvqWFQ18q1uPB7Htm0cx2Hp0qXq3ClwypI+yvu/BHd0jdnmOA4YQzXg99rIERfDse/PuozK3BBwoKSyCCfDtlgshuM4eL1e2tra1LlT4Dgxmx8lkly+phb/qFpa0WgUY8xQfSVFmQ+m+8wiIiXAAcB/jTEdGY7XArwBOMMY80Bq3WXAnSLyYWPMrvm9ImVfZa6fv9WWR5LpeUO+PPLvXKS1jJYrH2SaCwranr/aDOHOjJu2+XHdVuNR1gRXjfnXMmsuvvmN9Mf7x91++LFwwd+uy7ituqSaX5372zmXKd9QB8/ErAYqgPXpFcaYbSKyDTgJGH1DngTcnboZ06wHrhMRMRN8M3V6YcPJTZw4LDUrPTyRSNDQ0IDX69WaOwsBbyehYz5DvHVvtfW0riORCPX19Xi9XqRi4sgPJf9JVhfhVI0sqpwOGXUch/r6ejwejzptFwCmtpjPveHgketSegaoq6vD5/Npt0NlPlnN9J5ZVuA+Bz4zzvGOBxzg3mHr7gVs3LfOWuxNmS9WM4fP36gtjyBfHSX5KtccsJpCtedxnDvzvu8ETOTcmc99Cwl18ExMS+rnaE/obqB1nPGPZRgbAOqAnvFOVFrkGdNGd2BggJKSkqF6DfqGf2FgF1eSaFo39Hc6daO4uJji4mJqampyKJ0ynziOQygUoqioiOLiYqqrq3MtkjJPOI4zlFab/g5XlHlmus8sq4AEcI2InAtEgT8BnzPGxFLH6zLGDFX3N8ZYItI1zvEUZa6Y6+dvtWUllxSsPcs1s9vfXD0nYozgHxfeNLsDZMGPmOsoOXXwTEwAcIbfQCniQKZKqAEglmEsmcaLyNuBt6f+TK5eefC20WNisdjgdATOE+qZwJlVYGw0xqya7UFG63q/Q47YNmqIicViwdmeJweorkcxXNciktx///23Dd9sjHHi8bjqOrfMu65FRBzHseLxeHi258kBquvCZLrPLCsBAZ4Fvodbj+cbuBOES8j8TDPu8Ub9nwuJyKYZXEMm8tUe81GuuZTpX8aYc+boWNNlrp+/p2XLsM/Z80KXKZe2DAvXnifVUY6CEyaUK19kmoUck9qzOngmJgp4RMRnjLGGrS8BMj20R1PbGDWWTOONMT8GfgwgIg9Ho9GjZi9y7hGRh40xC+Za5uI4quv8R3U9Marrsaiu85+50nWBMN1nlk8CXzPG9Kb+flJEbOD3InIFmZ9pxj3e8PthLslXe8xHufJRphky18/f07Jl2LfsWWWadxakPeerjvJRrmzLpMUAJmZH6ueiUesXMzbMLj0+09gQbrEsRVEURVGU+WBazyzGGGeYcyfNk6mfranjNYrIUJEwEfEBjZmOpyhzyFw/f6stK7lE7VnJKurgmZjHgSBwSnqFiCwDlgF3Zxh/D3CyjIy5Og24d1ShLEVRFEVRlLlkWs8sIvJHEfnrqNVH4Yb5b8Yt2ukDjhu2/UTcZ8d7UZT5Y66fv9WWlVyi9qxkFU3RmgBjTFxErgO+JiI9QBdwHXCXMeaBVNu7WqDXGJMAfgp8GPihiHwLOBO3jd1U8j7nPAw0h+i1ZP+YuUKvJfvHzBV6Ldk/Zq7QaylAZvDM8mf2pmP9HVgDfA03bSuEW6fhj8BPReStuPV6/hf4dZbbSuerDvNRrnyUadrM9fO3MWZXntgy5KeOVKZ5ZAHbc77qKB/lyqpMsoBb0s0JqZC3L+MWHCwC/gW8xxjTIyKnAncCpxlj1qfGHwt8BzgceBH4jDHm99mXXFEURVGUfYkZPLNcDFwFHIA76fgx8MV01LGIlAPfBV4JWLhOocuNMdHsXZWyLzLXz99qy0ouUXtWsok6eBRFURRFURRFURRFUQocrcGjKIqiKIqiKIqiKIpS4KiDJ4eIiFdEvigi7SISEpE/i0hTruWaCiJyqIiYDMuJqe1nicgGEYmKyBMicm6uZc6EiPxQRH4yat2EsotIY6o4Zb+IdInIl1OhlxOdR3WdY1TXk6O6Vl2rrhVFURRFUQoXdfDklqtxczEvBk4GWoAbcinQNDgM6MFt4zd8eVBEDgVuBP6EW7Tx78DfRGRljmQdg7h8FnjHqPVTkf0GoBm3Gv6bgbcA10xyyqtRXecE1fW0UF2rrlXXSt4jMqK7jKIULGrLykJC7TlPMMbokoMFKAYGgTcPW7cMMMDxuZZvCvJfi1v9PdO2HwHrR627E/hxruVOybJfSp5u3MJlP5mq7LgtCQ2wfNj2S1K6LFFdq65V16pr1fXC0rUu+bMA7wbOz7UcU5DzYmBdruWYQL56oDnXcuzLi9rynMqo9px7Hag9z418c2LLGsGTO1YDFcD69ApjzDZgG3BSLgSaJquAZ8bZdhLDrivFevLnuo4HduC+wd46attksp8EvGiM2TpqewWuTjOxGtV1rlBdTw/V9cjtquu9rCd/rivbulbyABFZDLwcuE5EXpJrecZDRL6L292mI9eyjEZE3iUi7wRejxt1iIjoXCDLqC3PDWrP+YHa8+yZa1vWmyB3tKR+7hq1fjfQmmVZZsIqYKmIPCAiHSJym4gcndrWQh5flzHmN8aYi40xmW7wyWQfbzuMf32q6xyhup42quuR20F1nSZvrisHulbyAGPMbty0yPuAH4nIWbmVaCwi8k3gDcDJxpgd+ZKuMEyOLuBdwNm4aYoYY5xcybWvorY8O9Se8wu155kzX7asDp7cEQAcY0xy1Po44M+BPFNGREpxQ+SrgKuAC3AfkO8SkUNwry02are8v64Uk8k+ZntKh4bxr091nZ+oroehulZdo7ouhOvbJxERL4Ax5n7gm8AjwA/zaSKRmkC8GTjNGPO4iPhMKuY+1xhjjIg0AIcCm4CjgLNE5LLcSrbvobY8e9Se8we159kxX7asXSNyRxTwpIzMGra+BAjnSKYpYYyJikgNEDfGxAFE5M3Akbg5mFHc6xhO3l9XislkH7NdRIoAYfzrU13nJ6rrYaiuVdeorgvh+vZJjDH2sN8fFJFvAR/EnUi80xhzS86EA0Tk68CbcCcQTwz/XhCRjwFPGGP+mSPZ3gQ8jZuS2QF4cdMb35ILefZ11JZnLZ/acx6h9jwr2ebNltXBkzt2pH4uGvY7wGLGho/nHcaYwVF/OyLyFG6I+w7c6xpOQVwXk8u+Azgvw3YY//pU1/mJ6noUqusx20F1naYgrov50bWSA0Tk48BBwBbgDtyi2s8bl3tFZBC3+9kPROQ9xph/5UjO1+NOaE43xmwQkWJjTCK17aqUjOfnSLbv4hYVPcIYEwH+N+XQ3AjYxpjeXMi1r6G2PGfyqT3nAWrPcyLbvNqypmjljseBIG6bVgBEZBluF5a7cyPS1BCRI0VkUESOHLbOi1ug8ingHoZdV4rTyPPrSjGZ7PcA+4lI66jtQWDDOMdUXecnquthqK5V16iuN8y7dMqUEJEW4DW4D8DXAL/C1c/DIvJHEXk3kASuBx7EnUicnCNxt+C+gf2SiCweNoH4KPBR4LxcvMWWkTUntkmqYGcqJfEvOhnODmrLc4Pac36g9jx7smLLJg9agu2rC/Al3JCsc4C1wAOMau+ajwtu5NcTwMPAMcBK3Bu8F2jEDS9L4N74K4DP4obFH5Jr2TNcy3pGttidUHbcMP77cQuJrQXOxS2MdbXqWnWtus6PRXWtup5rXeuSE92eCtyIO3n4QkpX38at8bATsHGjrsKAA4SAE3Ik6+G4b14fTf19BbAHOCvD2A8Al82zPN8E+oDDU3/7cq3PfXlRW561TGrPebSoPc9KnqzYcs6NZF9ecB+yvw70AAPAH4D6XMs1RdmXAL9NPRiHgVuAVcO2vxT3TXAs9QVwZq5lHuc61jNscjAV2XGrm/81dd0dqS83j+pada26zo9Fda26nmtd65JVnXqG/f4S3InEvcDRqXUC1OFGXr0e+B1we+o+PDCHch+B+7a4N7WckpZ32JhrU3a5ah7lSH8nrU797Ru27WPAS3Ot431lUVueE1nUnvNkUXuetRxZs2VJHVRRFEVRFEVRco6IFJlUhzoRORO4Erfr2+fMOAUxRaTaGNM/z3KJMcaIiMdkaGErIqtx39DuBxxmjBkcts81uF3rTjLGPDJP8qUnVacbY9ZnqDnxeeB8k+PCp/sSasuzklHtOc9Qe56xfFm1Za3BoyiKoiiKouQMEfmsiFwnIi8XEW96AgFgjLkN+BpuWPsnROTsYft50/ULcKPo5pvVKZmcYecdzuO4RT1DwF0i0pCaQHwW+AjzPCEmT2tO7EuoLc8pas85Ru15zsiqLWsEj6IoiqIoipJ1RESAFuBFwMKtY1UNfA63ZsLmYWPPxq2RUAN8JtsTOxE5B/gLcIUx5oepdeO9LT4C923tIG7Np3eSnQkxInJ46twJY8xaEbkC+ARw0ejPTEQ+AISNMT+Zb7kWOmrL8yar2nMOUHueFzmzZsvq4FEURVEURVFyhoh8Bngj8C3gDOBluEU6fwDcaYx5KDXuVNxQ+v2A9xpjbs+ijIcB7wXOBr5gjPlxav3QRCId8p/6/XDgBmB/4ChjzKNZlPUI4PdAU2rVhcaYu0bJdy3uZ3mUMWZjtmRb6Kgtz4u8as85Qu15zmXNii1ripaiKIqiKIqSdVJviQHuAvoBxxjzKuDduG9XrwVuFpHrReTY1LhrcYtob8mSjG9PhdQ/iVvD4SbgUyLydhiZEjDsAb0MeA54JbBsPiYQ6c8uUzqCMeZx4CLctIQg8Niofa8BPoTb2UYnw3OA2vKsZVN7ziPUnmclV85tWSN4FEVRFEVRlJwiIv8CFuF2GEk/jN8PtAGlQADYDrwZN0UglgWZ6oFHgShu55UOETkEeB/um+xr02+Lh+2zCPgR8DxwpZmnB20RWWOMeSz1+5h0hNQk4wjcbnkJ3LbA3amaEx/GnUBkJc1mX0NteUbyqT3nKWrP05Yt57asETyKoiiKoihKThARb+rXT+OG91+eWv8LoBV4DbAG+A7QBfRnYwIBYIzpAc7DfdN6h4gsMsY8A3wX+Afu2+J3DruWFuArwFnA9fM4gTgHuDd9bpOhsKhx2QC8ASgGbhSRb+B2vdHJ8Dygtjwz1J7zE7Xn6ZMvtqwRPMq8ISLrccPfluVYFGWeUV0vfFTH+xaqbyXbpN7I/gGwU8tq4GXGmIeHjSk1xkSzJM/wmgirgN/gPoyfYYxpH/W2+HPAz4CfAxfiPqRvmEfZCqbmxL6I2vK05VN7zmPUnqclW17Ysjp4lHlDJwj7DqrrhY/qeN9C9a3MNSJyGvCiMeaFCcZcAPwN6ASOM8ZsS63P2BFlnuT0GmPsUesEWInbAcXH2InEeUAMWILbkWXDPMn2duD/jDG7RWQF8H5GpSOM/qxSNSds4EBgwBizfT5k25dQW54z+dSe8wC15zmRLa9sWVO0FEVRFEVRlHkjFR7/L+DdItI2wdB7gFuA3UAkvTKLE4gPAt8QkdeKyIkiUioigVRI/UbgtbiThbuGpQR8B1iPW4fixHmcQNQDnwTuFJFmY8yzjExHGCosOmyfRcD1wOeBjToZnj1qy3Mmn9pzHqD2PCey5Z0tq4NHURRFURRFmTeMMTuBM3Efwt8jIkvHGdcL3AGswC3gmTVEZD/cIqHvw21j+0tgG3CriHxeRN4CGOBduBOcv6YmEs8CXwTWGbdDyryQrzUn9jXUlucGtef8QO159uSlLRtjdNnHFtyb4n+Bn+JWH98JNADvBP6bMtAY8CzwEVKpfKP2/yHwP7jt8GK4FcnfM2rcemDbsL/LgQeBEK4ndTz5fop7ozZk2HYAbsXxH+b6cyyEJd91nRpbC3wWeADoxv1yTsvjyfVnmO9LvusY+EDqfj5o2LoqYDC1ftmw9S1AEvhsrj/XfF0KQN9np/Q60fJIrj9HXXKzACfivgH+MrB02HoZ/jvQDvw2i3JdArwaOB/4a+reOgO3JfAfUvdIKPU/qgvYBDjAk0BTFuQb/vmsAjYATwOLUusOAa4DdgDvAIpw61KEcTvf5Fz3C21RW56VjGrPebaoPc9Yvry05ZwblC7ZX3Af8EPAQylj+wxuESoD/AJ4G3BFykgN8O4M+78I9ALX4HpM02PPGzZuPakJAm7xq1tTN+bpk8iXnhCelmHbDbgTw8Zcf46FsOS7rlPjXwc8A3wtdfwPAPelzvHhXH+G+b7ku46Bi1PHOnLYuo/gOmoNsGbY+i/gOhz0/i5cfa/AdR6ll92p8w1fd3yuP0ddcrcwzkQita0N14H5MHBoluS5AHdCcBduzYRzU/fXBqA4NaYe1wF9IfAp4Fe4E4l+YPk8yubNsE5wJxJPkHkisQ3XwRtEJ8PzbTtqy9OTT+05jxe152nJlte2nHNj0iX7S8rAbGBx6u8iYAA3TGz4uErcydaNGfZ3gMOHrWtOrfvtsHXrU2M9wJ+BOMMmEBPIdxruZON9o9Yfn1r/sVx/hoWy5LuuU/uWZVhXBGwB7sn1Z5jvS77rGPef8pDDFtdZsAv4UWr9Gan1pUAP8L+5/kzzecl3fY86V1Fqvz/k+nPTJb8W9k4kvgK0ptYtAn6bspkjsijLYbiRow5ulNr5uKHzG3AnChXj7FcJ1M+jXB8Evo2bOnFi6jsyMGz7CuBR4Dn2TiRW4Dp6t2fzM9yXF7XlKcum9lwAi9rzlOTKe1vOuSHpkv0F96F906h1/tE3CrAfbrjbHRn2fybDcduB24f9vT419se4E7lLpihfXWr8j0atvx/3TbA/159hoSz5rutRx5T0l3JquR94KNefYb4v+a7j1D8/A1yQ+vstuI6HttT6V6fWX4b7T/yQXH+m+bzku75HHfOw1L4fz/Xnpkv+LYx8W7wO9+E3qykY7O0m+07cCcwWYDPwUuAluA/pz5J6EcHet8bzmj6cun8fT30nOim5OoF7cYtyvgU4GDgGd7LzAHsnEgeTpVQbXYb0pbY8sWxqzwW0qD1PKFdB2LIWWd536Rr1dwI4S0R+JSIPikgvrtE2kLkYd3eGdXHAO2rdUtyJG8AJUxHMGLMH94tlZXqdiLwWOBZ3ohCbynGUIfJW1+DqNtWSOYwbidCdWo7Fza1VJiefddyX+lmZamd5JfAb43YMiAM1qe3vB242bmE6ZWLyWd/DWZ36uWEG+yoLHGPMPbhvQN+AGyX2Kua5c89wRERM6qkb2Ir7FvaluPfO93Ej0D6C+wb5YREpM8YkUq16561zjIhcAqwFPgH8HffefDtuSuVO3M/su7hdbf6Rkvto4BYRaTLGbDLGdM6XfMpY1JYnlE3tucBQex5XroKxZXXw7LvY6V9Sk66/4d7Ey3Hrn1wJHIhbFCoTU72BDK739dfAZSIy1UnCE6QcPCJSjFsF/SHgd1PcX9lL3upaRL6CWyQtDHwIN53nJanjADw2xXPv6+StjnFrvQBU4HYZOAT4emrdAFArIqfjRnt8fezuSgbyWd/DOSL1c167sSiFS2oi8QagBLc207x/56da2mKMMSLiS/3+b8ACvmSMORs3Wvi7uCmlH8GtmfBCaiJhZz7ynMh2AfBz3G4xghtBtxH3u/EnxpjXAcfhhvu/PSXjg7gvQ1px2wErOUBtOaN8as8FitrzGNkKy5azESakS34tuGH364f9fTLug/xnR43z4U6810+0/wTHXc/eIp1NuAWvNgJFU5DxyymZWnBzHQ1wUq4/u0Jb8lnXKd2OqPsxbFu6aOwZuf4M833JZx2nxvpT8nwYuBP4x7BtzwNfwn0T8miuP8tCWPJd36OOeTPQl+vPTJf8X4DSLJ3nk8DtwJUZtrUB/wZOSP39EG5KwLm4b4/XA/vPs3x5WXNCl2npUG15rxxqzwW+qD0PyVBQtqwRPAq4NW/Arfg9nLfhehx9sz2BcUPSPo0blXPlFHZ5IvXzJNyb/i/GmP/MVg4lr3TdiusFf3b4ShE5adh+j85Wnn2QfNIxxk2pjAGnA6cCXx22eQA4EvcfpUbvzIy80vcoFjE2nUxRxmCMic73OURkEfBZ3EYOV4vIf0XkEhFZnhrSi1sA/tKUTOtw7fe3QBK3yPiWeZRPjDFP4nbBux63Ft23cNMRrsJ12D4kImWp8cWpnx5jzKAxpme+ZFOmjtrykIxqzwsAtefCtGV18CjghvQPAt8UkY+IyNtF5HrcPMcYbmrFXPB93FD9Tw27accj7eD5Vur8H5kjGfZ18knXG3G/tK8Ukc+IyLtE5Oe4X9i9uNEDfePsq4xPPuk4TR9wNvBfY8zdw9YPAmfi1tz64xzJta+Rj/pO0w4sF5FPisjL5kgORZkRxph23BD6btyUxiBuCsJNIvIG3FpWnwbOTv2NMeZ44BHgBWNMZL5ky9eaE0p+ks+2DGrPyvTIZ3suVFtWB4+Sfjt7Hq6xfhL4Am5xzdcD1wErRaRpDs5jA+/GTdm4bpLhz+Le0I3A940xm2d7fiW/dG2MCeJGbjyD++X4KVydH4c7KdXonRmQTzoeRtpRNzpKZyD18zvGmORsZdoXyVN9p/kEbmTR1eytq6UoOcMY8yBuwdATgLtx6yj8EvdB/S/AK3GjDFeLSCC1z0vm6xkkn2tOKPlNvtkyqD0rMyff7LnQbTndgkxRFEVRFEVRFjwicjJutOjvcN8MHwC8Bngzbr0HB1gxz5PhT+KmJNxsjPnaqG1twP/i1ta6V0Qewu04+D7cl7NXAZfOd5qNkv/kgy2n5FB7VmZNPtjzQrBldfAoiqIoiqIo+xQiciJuOsCvgS8bY3pEpAG3FsQJwCvn8e3wItyaEuCG9j+N+6b6bmPMVhEpB74DYIx5a2qf+3A7tLweuGe+02yUwiGXtpw6v9qzMmfod/PsUQePoiiKoiiKss+Rmkj8CXci8UNjzAsi4gHKUmnE83nuY4AbcbvNteKG/rcB1+JObhpxu7VcZYz5XWqfW4F3adq6Mppc2nLq/GrPypyh382zQx08iqIoiqIoyj5JaiLxW+D/gK8aY7Zl+dw/B36D23jgQNxaDvcCt+CG/C8Grs6Ht8JKfpNLWx52frVnZU7Q7+aZow4eRVEURVEUZZ9FRE7DLR5+ijGmK8vnznnNCWXhkEtbTp1f7VmZM/S7eWaog0dRFEVRFEXZpxGRQK7exOa6hoqysMilLafOr/aszBn63Tx91MGjKIqiKIqiKDkk1zVUFGUuUXtWFgqFaMvq4FEURVEURVGUHJPrGiqKMpeoPSsLhUKzZU+uBVAURVEURVGUfR1jzD249R1Ox23RqygFi9qzslAoNFvWCB5FURRFURRFyRNyXUNFUeYStWdloVAotqwOHkVRFEVRFEVRFEVRlAJHU7QURVEURVEURVEURVEKHHXwKIqiKIqiKIqiKIqiFDjq4FEURVEURVEURVEURSlw1MGjKIqiKIqiKIqiKIpS4KiDR1EURVEURVEURVEUpcBRB4+iKIqiKIqiKIqiKEqBow4eRVEURVEURVEURVGUAuf/AV7PZg2fKFkTAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1296x864 with 28 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpl.rcParams[\"legend.fontsize\"] = 15\n",
+    "mpl.rcParams[\"font.size\"] = 18\n",
+    "num_methods = 4\n",
+    "box_widths = [0.4] * (num_methods - 1)\n",
+    "notch = True\n",
+    "showfliers = False\n",
+    "labels = [\"Reference\", \"LAN\", \"MNLE\", \"MNLE*\"]\n",
+    "\n",
+    "fig = plt.figure(figsize=(18, 12))\n",
+    "outer_grid = fig.add_gridspec(1, 2, wspace=0.2, hspace=0.2, width_ratios=[.5, .5])\n",
+    "# Plotting settings\n",
+    "\n",
+    "# SBC results\n",
+    "inner_grid = outer_grid[0, 0].subgridspec(len(num_trials_list), 4, wspace=0.2, hspace=.2)\n",
+    "ax1 = inner_grid.subplots(sharey=True, sharex=True)  # Create all subplots for the inner grid.\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "repeats = 50\n",
+    "from scipy.stats import binom\n",
+    "\n",
+    "for kk in range(len(num_trials_list)):\n",
+    "    for ii in range(num_methods):\n",
+    "        for jj in range(4):\n",
+    "            plt.sca(ax1[kk, jj])\n",
+    "            hist, *_ = np.histogram(ranks[kk, :, jj, ii], bins=nbins, density=False)\n",
+    "            histcs = hist.cumsum()\n",
+    "            plt.plot(np.linspace(0, nbins, repeats*nbins), \n",
+    "                     np.repeat(histcs / histcs.max(), repeats), \n",
+    "                     label=labels[ii], \n",
+    "                     color=colors[:num_methods][ii], \n",
+    "                     alpha=1., \n",
+    "                     lw=.75)\n",
+    "            if jj==0:\n",
+    "                plt.yticks(np.linspace(0, 1, 3))\n",
+    "                plt.ylabel(f\"{[1, 10, 100, 1000][kk]} trial{'s' if kk>0 else ''} \\n empirical CDF\")\n",
+    "                if kk == 0:\n",
+    "                    plt.legend(loc=2, markerscale=0.5, handlelength=0.5, bbox_to_anchor=(-.05, 1.1))\n",
+    "            else:\n",
+    "                plt.yticks(np.linspace(0, 1, 3), np.linspace(0, 1, 3))\n",
+    "            plt.ylim(0, 1)\n",
+    "            plt.xlim(0, nbins)\n",
+    "            plt.xticks(np.linspace(0, nbins, 3))\n",
+    "            if kk == 3:\n",
+    "                plt.xlabel(\"rank \" + [r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][jj])\n",
+    "            if ii==0:\n",
+    "                # Construct uniform histogram.\n",
+    "                hb = binom(N, p=1 / nbins).ppf(0.5) * np.ones(nbins)\n",
+    "                hbb = hb.cumsum() / hb.sum()\n",
+    "\n",
+    "                lower = [binom(N, p=p).ppf(0.005) for p in hbb]\n",
+    "                upper = [binom(N, p=p).ppf(0.995) for p in hbb]\n",
+    "\n",
+    "                # Plot grey area with expected ECDF.\n",
+    "                plt.fill_between(x=np.linspace(0, nbins, repeats*nbins), \n",
+    "                             y1=np.repeat(lower / np.max(lower), repeats), \n",
+    "                             y2=np.repeat(upper / np.max(lower), repeats), \n",
+    "                             color=\"grey\", \n",
+    "                             alpha=0.2)\n",
+    "\n",
+    "\n",
+    "# posterior metrics\n",
+    "inner_grid = outer_grid[0, 1].subgridspec(len(num_trials_list), 3, wspace=.6, hspace=.2,)\n",
+    "ax2 = inner_grid.subplots(sharex=True, sharey=False)  # Create all subplots for the inner grid.\n",
+    "ylabels = [\"C2ST\", r\"$|\\hat{\\mu} - \\mu|$\", r\"|$\\hat{\\sigma^2} - \\sigma^2|$\"]\n",
+    "\n",
+    "for idx, num_trials in enumerate(num_trials_list):\n",
+    "    d = df[df.num_trials == num_trials]\n",
+    "    \n",
+    "    bdict = d.boxplot(column=[\"C2ST\", \"ABSMEANERR\", \"ABSVARERR\"], \n",
+    "                    ax=ax2[idx, :],\n",
+    "                    by=[\"algorithm\"],\n",
+    "                    grid=False, \n",
+    "                    widths=box_widths,\n",
+    "                    notch=False, \n",
+    "                    patch_artist=True,\n",
+    "                    showfliers=showfliers,\n",
+    "                    return_type=\"both\",\n",
+    "                    medianprops=dict(color=\"k\"),\n",
+    "              );\n",
+    "    ticks = [[0.5, 1.0], [0, .1], [0., .02]] if idx < 3 else [[0.5, 1.0], [0, .1], [0.0, .02]]\n",
+    "    for ii in range(3):\n",
+    "        plt.sca(ax2[idx, ii])\n",
+    "        plt.xlabel(\"\")\n",
+    "        plt.title(\"\")\n",
+    "        plt.ylabel(ylabels[ii])\n",
+    "        \n",
+    "        if ii < 2:\n",
+    "            plt.yticks(np.linspace(ticks[ii][0], ticks[ii][1], 3))\n",
+    "            plt.ylim(*ticks[ii])\n",
+    "        else:\n",
+    "            plt.yticks(np.linspace(ticks[ii][0], ticks[ii][1], 3 if idx < 3 else 3))\n",
+    "            plt.ylim(*ticks[ii])\n",
+    "\n",
+    "        if idx == 3:\n",
+    "            plt.xticks([1, 2, 3], [\"LAN\", \"MNLE\", \"MNLE*\"], rotation=45)\n",
+    "    plt.suptitle(\"\")\n",
+    "    ii = 0\n",
+    "    for row_key, (axi,row) in bdict.iteritems():\n",
+    "        ii += 1\n",
+    "        for i,box in enumerate(row['boxes']):\n",
+    "            box.set_color(colors[i+1])\n",
+    "\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.91\n",
+    "x1 = 0.03\n",
+    "dx = 0.0\n",
+    "dy = 0.2\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1, y1-2*dy, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-dy, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-3*dy, \"D\", fontsize=fontsize, fontweight=weight)\n",
+    "# fig.text(x1, y1-0.53, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"SBC-different-trials-absolute.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fc4bc99",
+   "metadata": {},
+   "source": [
+    "## SBC and metrics in separate figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "fc0464c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots(4, 4, figsize=(14, 10), sharey=True, sharex=True)\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 15\n",
+    "mpl.rcParams[\"font.size\"] = 18\n",
+    "num_methods = 4\n",
+    "box_widths = [0.4] * (num_methods - 1)\n",
+    "notch = True\n",
+    "showfliers = False\n",
+    "labels = [\"Reference\", \"LAN\", \"MNLE\", \"MNLE*\"]\n",
+    "s = 20\n",
+    "alpha=0.8\n",
+    "limits = [[-2, 2], [0.5, 2.], [0.3, 0.7], [0.2, 1.8]]\n",
+    "repeats = 50\n",
+    "from scipy.stats import binom\n",
+    "\n",
+    "for kk in range(len(num_trials_list)):\n",
+    "    for ii in range(num_methods):\n",
+    "        for jj in range(4):\n",
+    "            plt.sca(ax1[kk, jj])\n",
+    "            hist, *_ = np.histogram(ranks[kk, :, jj, ii], bins=nbins, density=False)\n",
+    "            histcs = hist.cumsum()\n",
+    "            plt.plot(np.linspace(0, nbins, repeats*nbins), \n",
+    "                     np.repeat(histcs / histcs.max(), repeats), \n",
+    "                     label=labels[ii], \n",
+    "                     color=colors[:num_methods][ii], \n",
+    "                     alpha=1., \n",
+    "                     lw=.75)\n",
+    "            if jj==0:\n",
+    "                plt.yticks(np.linspace(0, 1, 3))\n",
+    "                plt.ylabel(f\"{[1, 10, 100, 1000][kk]} trial{'s' if kk>0 else ''} \\n empirical CDF\")\n",
+    "                if kk == 0:\n",
+    "                    plt.legend(loc=2, markerscale=1., handlelength=0.75, bbox_to_anchor=(-.05, 1.1))\n",
+    "            else:\n",
+    "                plt.yticks(np.linspace(0, 1, 3), np.linspace(0, 1, 3))\n",
+    "            plt.ylim(0, 1)\n",
+    "            plt.xlim(0, nbins)\n",
+    "            plt.xticks(np.linspace(0, nbins, 3))\n",
+    "            if kk == 3:\n",
+    "                plt.xlabel(\"rank \" + [r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"][jj])\n",
+    "            if ii==0:\n",
+    "                # Construct uniform histogram.\n",
+    "                hb = binom(N, p=1 / nbins).ppf(0.5) * np.ones(nbins)\n",
+    "                hbb = hb.cumsum() / hb.sum()\n",
+    "\n",
+    "                lower = [binom(N, p=p).ppf(0.005) for p in hbb]\n",
+    "                upper = [binom(N, p=p).ppf(0.995) for p in hbb]\n",
+    "\n",
+    "                # Plot grey area with expected ECDF.\n",
+    "                plt.fill_between(x=np.linspace(0, nbins, repeats*nbins), \n",
+    "                             y1=np.repeat(lower / np.max(lower), repeats), \n",
+    "                             y2=np.repeat(upper / np.max(lower), repeats), \n",
+    "                             color=\"grey\", \n",
+    "                             alpha=0.2)\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.96\n",
+    "x1 = 0.005\n",
+    "dx = 0.0\n",
+    "dy = 0.229\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1, y1-2*dy, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-dy, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-3*dy, \"D\", fontsize=fontsize, fontweight=weight)\n",
+    "# fig.text(x1, y1-0.53, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"sbc-over-trials.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "b9e5d8a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py:396: UserWarning: When passing multiple axes, sharex and sharey are ignored. These settings must be specified when creating axes\n",
+      "  **kwds,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4OfBe278dR5wR0T6t5v02lKFH9W2nA5tKepjkfcQgaGvuAw/YfmIH+jV1RMTE0vaiwfZhdafOAH49NJ9hrCTNAT5K2Rl6JbAQON32NcO0PRi4wHaztzGnA6uH2SxmBWXH6ZFi2hg4nPKhYFXNpe2r500FjgQ2pNyN+KmkHWzf12RcEdE5reb9dGD5MG2p2ifvIwZDu3M/YlLoxkToK4CPjacDSf8AXAPMAX5M2RDulcACSR8Zd4SwDJgiqb6ImgosbfC811AKrwtrT9r+HeXbh9favr4qbF5H+ff+x/pOJB0l6QZJN9x///3jeBsR0YJW835Zda2+LcDSVvMekvsRPdLW3B9LAMn9GETdKBpmALePs49TKbcRn2v7dbb3AHYEfgm8X9J54+z/zupxq7rzT2Pd25e1XgN83/Y6vzRsP1Q7/rEaPvVHhrn1afs823Nsz9l8881bDj4ixqTVvL9zhLZLqMYwt5L31fXkfkT3tT33W5Xcj0HUjaLhbODEanjRWL0YOKf29r7tX1BWS7oKeJvKRnFjdTNlRaY9h05ImgXMokxsGsnuwPz6k5JeK+nRapm2oXMbA8+m7IgdEb3Xat5fA+yhtQcs7w0stL06eR8xMNqa+50LM6K/dGPJ1TmUivw6ScuAB4FVdW1se/sGfUyhzGOof9JSSQdQhiwdLWmp7fe0GqDtFVXRcaakBygTm88BrrL9s2p5tk2Ah2r2hdgK2JIyVKreVcBfgK9Kei/l3/k04AHgq63GFxHtN4a8P5+yMeW5ks4G9gPewpo12ZP3EQOgA7kfMSl0407DRsANlOr955Rb9XfUHX8apY8bgcMl1Y8ppNqP4QDgF5Q7GmcxtmLog8DXKPMTrqziekN1bRfgnupxyNCtyrX2bKhiepjyS+WvlGXZFlDGPe5ju34yVUQ0T9XRLk3nve0/Uz4k7EhZSeUYYG61633yPmKwtC33IyaLjt9psL1XG7o5FfgBZUOVi4Cza/dEsP1ItTPj5cAJjGFjlWpTtndVR/21BdR9ULF9Y/25uuu/AQ5sNY6IaOhTwJfa1dkY8v5nwIsa9Je8jxgA7c79urazxh9hRP/p+J0GSfMl7dvg+oGSftmoD9s/AvahFAPvpkyurm9zP2WOwdeAp4wn5ojoT7YfsX1Hr+OIiK5r913GiGhR2+80SJoObFZzai/gEkm/H6b5FMrSqc8crV/bVwE7SppJGTc8XJslwFxJp1EzwSkiIiIGWlvvMkZE6zoxPGkG8D/Ak6ufTVlB6ewR2osykbkpthcP24m0BfCg7VW2bwVubbbPiIiI6F+2H2GMy5tGRHt0Ykfo+6sdmV9EKQg+BFxCmahcbxVwP/DNZvqWdCxwFLBjNR6x1tnAvpJOt/2pMYYfERERERF1OjIR2vZlwGUAkrYDzrV93Vj7q9ZG/grwVuBhYDvgD3XN/khZN/lMSS+y/X/G+noREREREbFGN1ZPOqwN3RxBKRjOAd493PKFtj8o6VTgXOAfJV1m+4I2vHZE9FC1jPKN1XGrbddd35qynvqyXsQXERGdN/eyg1m8YvGI11/wYnj1d0fe53fm1Jlc8MqvdSCyyaMbm7u1wxHA1baPadTI9nJJhwMvAI4GUjREDBhJM+vmLr2TMjcKYJmkX1AKiJuq482Uu4wv7GacERHRPY0Khm48PwanaHgeZSOWUdleLeli4J87G1JEtJukQ4DTWbN5IpSdWXeqO95OmTM1VEw81sUwIyIiJp1BKRpWAitaaP8AsLpDsUREm0l6NnAesD0wt/ZadddhfnUMtd8Y2B+YR1ni+dguhRoRHSDpDcBulEVTLqhd7ETSpbYP6Flwg+SMp8LSP494edE0ym/NkczYEt5zb4MG0QnLz1oIS/864vVbp89g+YcXjNzBjA2Y9q5d2x9YnY5v7tYmvwfmtND+hcCfOhRLRLTf/wFmA7vbHnUJZtuP2r6IkutLWXtvmIgYIJKOAT4LTAfeAyyUtElNk917EtggalAwdOX5MTYNCoauPL9Jg1I0fBM4WNLzRmtYtTkY+EHHo4qIdvkG8Evgakn7NPukakPHCyjzHiJiAFSbtNY6Bni57aOA51P2eppfUzhkJ+iIPjAoRcPngDuABZIOlrRefQNJUyS9hbJR3KOMvJlcRPQZ27+zvRvwYUoB0Yr7WXsORET0qWre0m/qTm9l+2YA2yttH00ZjnilpE1ZM3cpInpoIOY02F4i6dXA9yjfKp4j6b+Be4D1gC2AnYEnUYYlHWT7nl7FGxFjY/sLki6pPSfpfsoqSUPLrt5o+7bqmoADgBu6HWtENK/RvCXgAUnPsH370AnbJ0o6G7iSAfmsEjHRtT0RJc0fvdU6bHvfURr8VtLfA++gLLG4G2vifxz4L+A7wHm2W5k0HRF9xPaDdacWUuY77DfURNKjwK3A5tVxiKQNbHdnYGdEtGpo3tJs24vqrl0BHAqcXHvS9gmSPgPs0IX4ImIUnajeb6Psq2DgbsrKR21RFQOfrA4kbQassv1wu14jIvqL7dfCE+OgZwM7Vo+zgW0pv8cuBlZK+h1wC3Cz7Y93PdiIGMk3gJdS5i0darv2C8ZjGOHziO3jJJ3ZjQAjorG2Fw22j5J0HeU25E/atCP0SK/1QKf6joj+Ui29uqA6AJC0IeVbyNmsKSgOAN4EpGiI6BO2fwfsJukISgGxZc21xykjBtYi6Wrg67bP7VqgETGijowTtH2+pK2BkyV93/a3O/E6ETG5VR82huY6PEHS9r2JKCIaGW7eUgO7UeY7fBnYDlhi+66OBRcRDXVy9aRTKUsofkLSoKzSFBETgO0/9DqGiBjeMPOWGtkTeBj4NfAnSXdJ+qSkrJgW0WUdW5HA9mpJLwKm2c7uzBHRlDEuplBv1MUVImIgzKTMlVwATAVeBJwAHCbp9XVzIyKigzq6jFk1cTkrGUVEK6Yw/nXZsxlUxMSwgLLx2xOLqlRfSH4B+K6knYaWYI6J7T8PasOevdnxY1yy9nFE9BXbe/U6hojoC6uB79QWDAC2r5e0J3Az8H7g8F4E1490yvj78Lzx99EJB16yf69D6JiNTt573H34Q52viFI0RERERD96CNhkuAu2H5b0ReDI7obU33zy6G2i/yw75cpeh9CUTFCOiIiIfnQ9cKSk/zXC9QeBTbsYT8SklqIhIiIi+tEZwNOAayT9Q+0FSdOAtwD39CKwiMkow5MiIiKi79i+StLbgH8DrpV0G2Uew+OUpVifBpzWwxAjJpUUDREREdGXbH+l2hn6ROBA4A3VpeXAZ4B5PQotYtJJ0RARERF9y/btwLHAsZJmAhsD99r+a08Di5hkUjRERETEQLC9GFjc4zAiJqUUDREREdFzklbThu23bK/XhnAiok6KhoiIiOgHh5M9eyP6VoqGiIgJTlJT7ex8Xovesf3lXscQESPLPg0REROc7bWO7U76/jrnUjBEREQjKRoiIiIiJoIZW/b2+R00c+rMnj4/MjwpIiKiLzUzrCx3iGIt77m34eVZ77uURacf0KVg2uuCV36t4fVBfm+DIncaIiJiYEka9RhUzQwri4gJYMYGvX1+k3KnISIiBlb9B+d82xgRg2bau3ZteL1ffq/lTkNERERERDSUOw0RERPNGU+FpX8e8fKiacC8UfqYseWo46MjImLyyJ2GiIiJpkHB0NU+IiJiwsidhoiICUantKcfz2tPP+0097KDWbxi8YjXX/BiePV3zxnx+sypM0ddhaVXlp+1EJb+dcTrt06fwfIPLxi5gxkbjDo2OiJirFI0RERMMD651xF0TqOCoRvP76gGBUNXnh8R0UCKhoiIiWbGluMfXtSnmzz950E/GH8nfbpS6UYn7z3uPvyhPn1zETHwUjREREw0E3iDpwMv2b/XIXTMslOu7HUIEREjykToiqT1JH1M0j2Slki6WNKIX7VJ+pYk1x0/qbk+XdJ5kh6QtFjS5yU9qTvvJiKaMYa8nyNpoaTHJP1e0ty668n7Dps5dWZPn99RA7LB00SQ3B989Zs43vHxV02YjR37Ve40rDEPOASYCzwInAN8G9hthPbPB94HfKXm3Iqa//05YGfgVcAGwBercwe3M+hWNJtA2WU0JpF5NJn3kjYHfgR8HXgb8FLgfEn32r68atZ3eT/RjDaJeZDvogzKBk8TxDwmQe4P93dfH1/750H9mz+ocTejX/9/S9EASNoQOB44zvaPq3NvBm6XtIvta+vaTwWeBVxve51xAJK2Ad4C7Gv7Z9W5I4ArJb3X9l2dfUfDG+4/sPwR6n/NFHsT+Zdnp7Sa98ARwCPA8bZXA7dK2gl4N3B5v+Z9Fce65z6+brv8dxSTwWTK/eT0YOrX/99SNBSzgY2BBUMnbC+StAjYHaj/BfIcyr/db0bobxdgNbCw5txCYBXlW4yL2hDz6EbZ4Ama2ORpQDZ4msgfrOvjTqHXNrNpLe93B66uPjQMWQCco/IfYH/k/TAG9b/9iA6ZzSTJ/Yh2StFQbFM91n8bcDew7TDtdwAeB06R9EpgGfDvwKm2l1f93Wf7ifXvbK+UdN8I/XWE3tuOzZn+jN/Thm7arH6t9mYmR776u2t/0O7X9dqbKYCG+5a4Vj4kNqXVvN8GuGmYttOBTemTvJ9s+vU2fjtM5PfWY8n9iDFI0VBMB1bXJnxlBTBtmPbPAwTcCnyWMr/hk5RfDodU/S0f5nnD9ifpKOCo6sclkn47hvcwVpsBDzRqMKCTiUZ9XwBf5etdCKXtuvn/2Q9tv6JdnfWZVvN+uLwemsc0bYTrjfrrZe43lR8Dap33NqC/w4bTzfeW3F+7fXK//+W9tceIuZ+ioVgGTJG0vu2VNeenAkuHaf9B4EzbD1U/3yJpFfBNSSdW/U0d5nnD9mf7POC88byBsZJ0g+05vXjtTpqo7wsm9nvrslbzfri8Hvp56QjXG/XXs9yfyP8N5b1FE5L7E1DeW+dlydXizupxq7rzT2Pd25fYXl1TMAy5pXrctupvC0nrDV2UtD6wxXD9RURPtJT3Vfvh2i6hTJJM3kcMhuR+xBikaChuBh4F9hw6IWkWMAu4ur6xyh4Nl9SdnkO5FXkbZQLU+sBLaq7vRvn3XkhE9IOW8h64BthDa48H2RtYWE2QTN5HDIbkfsQYZHgSYHuFpHOAMyU9ANxHWbP5Kts/q5Zn2wR4yPbjwMWsGYr0PWBH4EzKkKUllPGJ36Ks43w4Zf7D54Gv9nLptRH0ZFhUF0zU9wUT+711zRjy/nzgvcC5ks4G9qMss/iKqr+7kvd9Ie8tGkruT1h5bx2mrLxQVLcSP06ZyLwB8EPgHbYfkLQXcCWwt+0FVfu5wHso+zXcR/k/9GNDS7JVO0H+C/A6YCWl0DjB9rLuvauIaGQMef9i4DPAC4A7gJNtf7Omv+R9xABI7ke0LkVDREREREQ0lDkNERERERHRUIqGiIiIiIhoKEVDREREREQ0lKIhIiIiIiIaStEQERERERENpWiIiIiIiIiGUjRERERERERDKRoiIiIiIqKhFA0REREREdFQioaIiIiIiGgoRUNERERERDSUoiEiIiIiIhpK0RAREREREQ2laIiIiIiIiIZSNEREREREREMpGiIiIiIioqEUDRERERER0VBTRYOkQyV5mGOVpAckXSPpnySlCImIiIiImGDWb7H9fcCPa36eCmwKzAH+DdhH0ptsu03xRUREREREj7VaNPzG9lvrT0raFPgp8L+BbwCXtCG2iIiIiIjoA20ZTmT7QeCs6seD2tFnRERERET0h3bOQbirety4jX32hKRzJX1hlDZzJC2U9Jik30uaW3d9uqTzqjkfiyV9XtKTOht5RLRC0nqSPibpHklLJF0sacsmnrd91X6buvPJ+4gB0GruN/E3/1mSvlvl/v2S/l3S0zv/TiK6p51Fw87V48/a2GdXqfgwcPQo7TYHfgTcCOwEfAY4X9LLapp9DtgNeBVwILBXdS4i+sc84BBgLrAHsA3w7UZPkPRs4HJgxjCXk/cRg2EeTeb+aH/zJc2orq8H7AO8HNgMuEzS1I6+i4guUjNzliUdCnwJuMr2XjXn16NMhH41JYnuAXa2vbgDsXaUpGcC5wM7AI8BP7Z9xAht/xk4EniW7dXVuS8BW9t+WfXt4x3AvrYXVNf3BK4EtrV913D9RkT3SNoQeAA4zvaXq3OzgNuBXW1fO8xzjgc+Avye8uFhW9v/r7qWvI8YAK3mfhN/8w8CvgVsavsv1fVtgT8Be9q+uitvLKLDWr3TsGftkqvASuDPwOeBRyh/LBe3OcZu2QW4E3g+5RdHI7sDVw/98qgsAHaVpKqv1cDCmusLgVWUbyEjovdmU4ZTLhg6YXsRsIiS48N5DXAU8K5hriXvIwbDbFrL/dH+5l8P7D9UMFSG2j6lTTFH9Nx4l1ydAjwZ+Htga+BaSa+x/fM2xdc1ti8ELgQovwMa2ga4qe7c3cB0yp2XbYD7bP+1pv+Vku4Dtm1XzBExLkPzEervANzNCHlqex8ASXuN0F/yPqL/tZr7Df/mV3cR6/t6H7CUsrJkxITQriVXpwAnAacBl0p6pu0l7QiwT00HltedW1E9Thvh+lCbafUnJR1F+faSv/u7v9v5V7/6VfsijRifUSvoATYdWF37Ib8ybJ422V/TeQ/J/ehryf212zf6m78WSW8HjgGOtf3QcAEk96OPjZj77VpydbXtjwG3AJtT5jhMZMsoG9vVGvp56QjXh9osrT9p+zzbc2zP2WijjdoaaESMaBkwRVL9lyfD5mmT/TWd95Dcj+iRVnN/tL/5T5D0AeAc4GO2PztSAMn9GETtXD0J4NfV40S/FX8nsFXduacBSyhzO+4EtqgmigNQ/XLagnVvYUZEb9xZPQ6Xy2PJ0+R9xGBoNfdH+5uPpCmSzgVOBU6y/f72hRvRH9pdNPxN9Xhnw1aD7xpgD609+WFvYGE1UWohZejXS2qu70b5966dJBkRvXMz8Ciw59CJagWVWcBYVjtJ3kcMhlZzf7S/+QCfBY4ADrP9iQ7EHNFzrc5pGJGk/4+yBOFi4NJ29dsPquXZNgEesv04ZWnW9wLnSjob2A94C/AKANt3SfoWZR3nwynjwz4PfDXLLkb0B9srJJ0DnCnpAcpCD+dQlpb+2TB5P1p/yfuIATCG3G/4N1/SAcDbgVOAH0p6as3LLbY93FyniIHTatHwXEkX1p3bEHgB8LeUpQWPtv1IO4LrI7tQ1lrfG1hg+8+SXkHZm+Imytrsc23Pr3nOEcC/AD+gLE17MXBCN4OOiFF9ENiAsnLaBsAPgXdU19bK+yb7S95HDIamc7+Jv/kHV48nV0etf6xeI2Lgtbq523CWUYYjXQP8i+3/aVdwk9GcOXN8ww039DqMiCETeQWVvpLcjz6T3O+S5H70mRFzv6k7DdWOiV9uUzARERERETFA2j0ROiIiIiIiJpgUDRERERER0VCKhoiIiIiIaChFQ0RERERENJSiISIiIiIiGkrREBERERERDaVoiIiIiIiIhlI0REREREREQykaIiIiIiKioRQNERERERHRUIqGiIiIiIhoKEVDREREREQ0tH6vA4iIiIiQNL8N3dj2vm3oJyLqpGiIiIiIfjAF8Dj7UDsCiYh1pWiIiIiInrO9V69jiIiRZU5DREREREQ0lKIhIiIiIiIaStEQERERERENpWiIiIiInpL0BklnSzpc0vp11y7tVVwRsUZXigYVz6j5+dmSzpD0MUnP7kYMERER0X8kHQN8FpgOvAdYKGmTmia79ySwiFhLx1dPkrQN8CNgBbCTpC2B64AnV02OkbSH7Zs6HUtE9L+s1R4x6RwDvNz2zdVdhn8F5kvax/ZDZBnViL7QjTsNpwHbAv9W/XwkpWB4I/AM4E7glC7EERGDYQrlQ8J4jgy9jBgcW9m+GcD2SttHA/OBKyVtyvj3boiINujGPg0vA862/fnq51cDd9q+GEDS54H/26iD6pvHj9q+oqORRkTPZa32iEnnAUnPsH370AnbJ0o6G7iS7CkV0Re68W3ck4HbASRtAewM/LDm+lJG/4WwF7BlJ4KLiIiInroCOLT+pO0TgAXAtO6GExHD6Ub1fgfw/Op/v7l6/M+a66+gKioiIiJi0jmGET6P2D5O0pldjicihtGNOw1fB46T9B+U+Q1/An4oafvq3GuAL3YhjogYQJLOknSwpOdKWmdCpKStJW3Ui9giYvxsP277sfrzkq6W9E+2/9SLuCJibR2/02D7w5JWAW8BFgLvtr1S0v8C9gBOtf3pJrp6naRntfbS/sgYQo6I/vJO1kyEXCbpF8CNwE3V8WZgb+CFvQkvIjpkN8p8hy8D2wFLbN/V25AiJq+uTC6y/VHgo3WnbwY2t/3XJrs5CHhdKy8LpGiIGHybADvVHW+nrJI0VEys8y1lREwIewIPAxsCSLoXuAg4w/Y9vQwsYrLp2YoEtlcDq1t4ymnATzoUTkT0KduLKcsvPrF/g6SNgf2BecBmwLG9iC0iOm4mcBtlQvRU4EXACcBhkl5vux37ukREE9peNEhaTetrKtv2aLH8xvZVYwwrIiYQ248CF0m6FPglpXCIiIlnAWXjt5VDJyS9CPgC8F1JO9m+rVfBRUwmnbjTcAHZiCUiusD2EkkXUOY9fLbX8UREW60GvlNbMADYvl7SnpRhzu8HDu9FcBGTTduLBtuHtrvPiIgG7ge26nUQEdF2D1HmNK3D9sOSvggc2d2QIiavbiy5OiJJUyQ9WdJBozQ9BfhFN2KKiP4i6X5Jl0s6XdIba1dRq5ZgPQC4oXcRRkSHXA8cWa22OJwHgU27GE/EpNbxidCSngx8jrKJ25MoK54MZ72R+rB9ShOv8xRgme3lY4kzIvrWQmA2sF/1syU9CtwKbF4dh0jaoIXV2CKi/51B2S36GklH2r5u6IKkaZSl3LOCUkSXdONOw8eBNwK/pqx+JMqGb1cAfwWWA68frRNJG0g6WtKX6s7vLulXwAPAEkmXSdq+ze8hInrE9mttz6IMU9gHeDfwPWAjYFvKlxEXU/L/Fklfl3RSr+KNiPaoFj95G/As4FpJv5X0LUkXAr+nrKR0YS9jjJhMurHk6quAS2y/XtKmlPHHn7H9c0mzgauB5zTqQNJUSsGxK/B49Y3DSkl/A/yIsgzbD4FfAW+g/HJ5ge0/d+xdRURXVUuvLqgOACRtCOxAuRMxG9iRMlzpTZQvLCJigNn+iqSrgROBAyl/46F84fgZyrLLEdEF3bjTsAVwOYDtB4G7gH+ofv4f4HzKLcZGjgd2Ad4LbFKzksI8YBrwDdsH2H4vMAdYSVlRISImMNuP277R9hdtH2d7d9tPBp7d69gioj1s32772Jo7jtsBT7b9TturehtdxOTRjaLhL3Wv8wfg+TU/30L5BdDIG4GLbZ9p+zF44hvGV1OWdz1zqKHth4AvU+5wRMQkZPsPvY4hItrP9mLbd2b+UkT3daNouB54k6Shic6/AnavVj0B+FtgxSh9/A3w07pzLwFmAPdUdyxq3QY8bcwRR0RERETEE7pRNJwJ7Ab8rlrh6MuUOQyXSzqHMvToylH6WI8y5KjWvtXjT4Zp/2TgsbEGHBERERERa3S8aLA9H9gf+C2w2PbPKXMTXgL8E3ATZYJTI7dRJjnWOogyNOn7w7R/efWciJhAJM2U9MxexxERETHZdGVzN9uX297ftqufz6RsyLKJ7ZfYvmuULr4JzJX0GknTJb0TeB5wH/AftQ0lHQy8jLIkY9MkrSfpY5LukbRE0sWSthyh7QJJHuHYo2qz/wjXt2klrohYy/GUpRbbopW8r9rPkbRQ0mOSfi9pbt315H3EAGh37te0U7X0+wc7F31Eb/RsR2jbK6olFJvxSeA64BLgUeAs4HHgcNuPA0g6SNIVwAWUuxqfajGkecAhwFxgD2Ab4NsjtH0dsFXNsTXljslVwLVVm+dX57aqO+5uMa6I6Jx5NJn3kjanLPF8I7ATZbnH8yW9rKZZ8j6iAzpwl3Ee7c39oQVavkDZzDZiwmn7Pg2SVgH/aPvr1c+rKcOIGrHtEWOx/bik/Shrr7+EUjhcaPvXNc12puzjcCFwou1lLcS8IeUbzONs/7g692bgdkm72L62tn21QlPt808Cngk8p2Y52B2AW2zf22wcEdE9reY9cATwCHC87dXArZJ2omw2d3nVJnkf0RnHAx+izHEcl07kfvXz+ZQ5lYvHG2OMbs16OiOrBrgMnH59b53Y3O0CyrKqtT+P+51VazF/vTqGcxrwoSqhWzUb2JiaTaNsL5K0CNidNXcP1iHpqcAHgZPqPijsAFw0hlgiojtm01re7w5cXfc7ZgFwjiRVwy+T9xH9bzbtz/2XUjar/b/ALzoVeKxR/6F51vsuZdHpB/Qomvbq1/fW9qLB9mF1p84Afu0Ol0RD+zeM0dB44/q5FXcD247y3JMocys+N3SiWl72OcDOkm4GNgd+DrzX9m/HEWdEtE+reb8NZehRfdvpwKaSHiZ5HzEI2pr7wAO2n9iBvplviSMGUTfmNFwBfGy8nVSTkH4k6VFJD0v6vqTdRmh7cDVMqlnTgdXDbBazgrLj9EgxbQwcDnyiblfK7avnTQWOpGxONxX4qaQtWogrIjqn1byfDiwfpi1V++R9xGBod+5HTArdKBpmALePpwNJ/wBcA8wBfkzZRfqVwAJJHxl3hLAMmCKp/s7LVGBpg+e9hnK35sLak7Z/R/n24bW2r7d9DWXy9BTgH+s7kXSUpBsk3XD//feP421ERAtazftl1bX6tgBLW817SO5H9Ehbc38sAST3YxB1o2g4GzhR0pxx9HEq5Tbic22/zvYewI7AL4H3SzpvnDHeWT1uVXf+aax7+7LWa4Dv217nl4bth2rHP1bDp/7IMLc+bZ9ne47tOZtvvnnLwUfEmLSa93eO0HYJZZJkS3lfXU/uR3Rf23O/Vcn9GETdKBrmUJLrumot5Dsk/bHu+MMofbwYOMf2fUMnbP+CslrSVcDbVHaXHqubKSsy7Tl0QtIsYBZlYtNIdgfm15+U9NpqGNXmNec2Bp4N/GoccUZE+7Sa99cAe2jtAct7Awttr07eRwyMtuZ+58KM6C+dWD2p3kbADePsYwqwsv6k7aWSDqAMWTpa0lLb72m1c9srqqLjTEkPUCY2nwNcZftn1fJsmwAP1ewLsRWwJWWoVL2rgL8AX5X0Xsq/82nAA8BXW40vItpvDHl/PmU3+3MlnQ3sB7yFNWuyJ+8jBkAHcj9iUuh40WB7rzZ0cyNwuKRzba+ovWB7WVU4LKAMg5rC2JY7+yCwAWV+wgbAD4F3VNd2Aa6kfLOwoDo3dKtyrT0bqpgervaV+ETVfn1KYbOP7frJVBHRPFVHuzSd97b/LOkVlI2dbgLuAObang/J+4gB07bcj5gsOl40SJoPfNT2FSNcPxD4mO0dGnRzKvADyoYqFwFn1+6JYPuRamfGy4ETGMPGKtWmbO+qjvprC6j7oGL7xvpzddd/AxzYahwR0dCngC+1q7Mx5P3PgBc16C95HzEA2p37dW1njT/CiP7T9jkNkqZLevrQAewF/F3tuZpjFmUVpIZbw9v+EbAPpRh4N2VFpvo291PmGHwNeEo731NE9Afbj9i+o9dxRETXtfsuY0S0qBN3GmYA/0PZSh3KbtBnV8dwRLmF35Dtq4AdJc2kjBsers0SYK6k06iZ4BQREREDra13GSOidZ3YEfp+SQdTbuMJ+BBwCcPPM1gF3A98s4X+Fw93vto86UHbq2zfCtzaYugRERHRh2w/whiXN42I9ujInAbblwGXAUjaDjjX9nXj7VfSscBRwI7VeMRaZwP7Sjrd9qfG+1oREREREVF0fJ8G24eNt2BQcQHwacqeD9sN0+yPwGrKEmrfGM/rRURERETEGt3Y3K0djgDeSllHeWvb62wGZ/uDwDMo66G/UdLc7oYYEZ0g6SxJB0t6bt3mSkPXt5a0US9ii4iImCy6sblbOxwBXG37mEaNbC+XdDjwAuBo4IJuBBcR7SNpZt3cpXdSFlQAWCbpF5S9W26qjjdT1lN/YTfjjIiImEwGpWh4HmUjllHZXi3pYuCfOxtSRLSbpEOA01mzeSKUnVl3qjveTlloYaiYeKyLYUZEREw6g1I0rARWjNpqjQco8xsiYgBIejZwHrA9sNbQwuquw/zqGGq/MbA/MA/YDDi2S6FGRAdIegOwG2WlxQtqFzuRdKntA3oW3AAbZkTnOmyP2iYCujSnQdLGkp4qacsxjj3+PTCnhfYvBP40hteJiN74P8BsYHfbzezb8qjtiyi5vpRSOETEAJJ0DPBZYDrwHmChpE1qmuzek8AmANtrHdud9P11zkU0qyNFg6T1Jb1N0o8lPUTZyfku4G5giaQHJf1I0qGSNmiiy28CB0t6XhOv/TzgYOAH43gLEdFd3wB+CVwtaZ9mn1Rt6HgBZd5DRAyAapPWWscAL7d9FPB8ygax82sKh+wEHdEH2l40SNoMuB74PLANZb+GTwGnVcengB8BTwe+CFwjadNRuv0ccAewoFpFZb1hXneKpLdQdpd+lJF3oI6IPmP7d7Z3Az5MKSBacT9rz4GIiD5VzVv6Td3prWzfDGB7pe2jKcMRr6w+H+Tr8Ig+0Ik5DWdRlj59qe0rGjWUtB/w78AngLeN1M72EkmvBr5H+VbxHEn/DdwDrAdsAewMPIkyLOkg2/e04b1ERBfZ/oKkS2rPSbqfskrSjUOH7duqawIOAG7odqwR0bxG85aAByQ9w/btQydsnyjpbOBKBmf+ZXTQ3MsOZvGKxSNef8GL4dXfPWfE6zOnzuSCV36tA5FNHp1IxFcBnxitYACw/RNJZwDHNdH2t5L+HngHZYnF3VgT/+PAfwHfAc6z3cqk6YjoI7YfrDu1kDLfYb+hJpIeBW4FNq+OQyRtYPuvXQs0IloxNG9ptu1FddeuAA4FTq49afsESZ8BduhCfNHnGhUM3Xh+dK56b+UP9zLK5KdRVcXAJ6tjaCjUKtsPtxxhRAwE26+FJ8ZBzwZ2rB5nA9tSfo9dDKyU9DvgFuBm2x/verARMZJvAC+lzFs61Pb8mmvHMMLnEdvHSTqzGwFGRGOdKBquAk6Q9B+2f9eoYXW78l3A1WN5IdsPjOV5ETF4qqVXF1QHAJI2pHwLOZs1BcUBwJuAFA0RfaL6PLCbpCMoBcSWNdcep4wYWIukq4Gv2z63a4FGxIg6UTScSPmj/ktJC4D/pqyc9BhlMtNGlEmLO1GGGzxMVj6JiDGoPmwMzXV4gqTtexNRf2pmrXbIeu3RecPNW2pgN8p8hy8D2wFLbN/VseAioqG2Fw22F0naiXIH4U2sGYdc73bg05T5D7ljEBFtY/sPvY6hn9QXA7PedymLTs9eWdEbw8xbamRPypeLGwJIuhe4CDgjC57ERLH8rIWwdOSR/bdOn8HyDy8YuYMZGzDtXbu2P7A6HZnTYPsh4APAB6pxyFsDG1PWWl4C/Mn2I5147YgYbJLmj95qVLa9bxv6iT6XHW8nvJnAbZQRDFOBFwEnAIdJen3d3IiIwdSgYOjK85vU8WXMqnHIizv9OhExYUxh/OuyZzOoSSJ3USa8BZSN31YOnZD0IuALwHcl7TS0BHNEdFbWPo6IvmJ7r17HEBF9YTXwndqCAcD29ZL2BG4G3g8c3ovgIiabtu8IHREREdEGDwGbDHehWmr9i8DLuxpRxCTW9jsNYxyPnPHHERERUet64EhJn7b9l2GuPwhs2uWY+tsZT4Wlfx7x8qJpwLwGz5+xJbzn3nZHFRNEJ4Yn3QYcQRmTfDewsnHziIiIiHWcQdkt+hpJR9q+buiCpGnAW4CsoFSrQcHQlefHhNaJJVePknQdcB7wE9uHtfs1IiIiJrrJvjKU7askvQ34N+BaSbdR5jE8TlmK9WnAaT0MMWJS6dSSq+dL2ho4WdL3bX+7E68TERExUWVlKLD9lWpn6BOBA4E3VJeWA5+h8WCbmED+86AfjL+TiVtjd0UnV086FXg98AlJl9he3cHXioiIIeMd1wwZ2xx9w/btwLHAsdXeTxsD99ruzuL00RcOvGT/Xocw6XWsaLC9ulpLeVoKhv7QzK1umNi3uyMmhXaMS87Y5uhD2fsponc6uk+D7RXAik6+RjRvuGJgMt7ujoiI/iNpNW0YQGJ7vTaEE9E1G52897j78Ic6/4VvNneLiIiIfnA4GXUek9CyU67sdQhNSdEQEREDY+5lB7N4xeIRr7/gxfDq754z4vWZU2dywSu/1oHIYrxsf7nXMUTEyFI0RERMMDqlPf14Xnv6aadGBUM3nh/Rz9qR+/2Y99EfUjREREwwPrnXEURELyT3o5NSNExkoyy7CBNnS/nJvglSRAy+5WcthKUjryJ66/QZLP/wgpE7mLEB0961a/sDi4gApvQ6gOigSbTsou21ju1O+v465waVpFGPiJgAGhQMXXl+REQDudMQ0eeyK2y0bMaW4y/4Z2zZnljaLLvCRkxOM6fOHNecpJlTZ7YtlskqRUNExEQzypDCQS48sytsxOQ02qpng/x7bVCkaJjAJvIKKhERE82gbPAUEW02Y4PxDS+csUH7YmkgRcMENpFXURjvWu2Q9dojor8MygZP0cfGOzSxT4clTnSjLWDQL3dRUjTEQGrHWuv9ul57VlCJGFnGNUc0MIGHJkbvpWiYyCbwZMgJLSuoRIwo45ojInojRcNE1sT+CoP6B7YtK6hAVlGJiIiIaEKKhoqk9YBTgUOBjYEfAu+wPexX9ZK+BfzvutNX2N6vuj4dOBt4HeXf+d+Bd9pe0on4J5uJvIJKJkN2zxjyfg7waWBH4C7gI7YvqLnel3k/3F4e+vi67QZ5P5MJYUAmQ04EkyX3I9opRcMa84BDgLnAg8A5wLeB3UZo/3zgfcBXas6tqPnfnwN2Bl4FbAB8sTp3cDuDnqzGO655qI9+tOzMa/LBoXvm0WTeS9oc+BHwdeBtwEuB8yXda/vyqllf5n2KgcEwKJMhJ4h5TILcj2inFA2ApA2B44HjbP+4Ovdm4HZJu9i+tq79VOBZwPW21xkDJGkb4C3AvrZ/Vp07ArhS0ntt39XZdzTxTeRxzfng0B2t5j1wBPAIcLzt1cCtknYC3g1cnryPGAzJ/YixmdLrAPrEbMrtyQVDJ2wvAhYBuw/T/jmUgus3I/S3C7AaWFhzbiGwipHvXHScpHWOOz7+qnXORUwSs2kt73cHrq4+NAxZAOyqkjh9mfcTXX6nxRjMZpLkfvIj2il3Goptqsf6bwPuBrYdpv0OwOPAKZJeCSyjjF881fbyqr/7bD8xxsT2Skn3jdBfV0zkIQrNjNke1Pc/kd9bj7Wa99sANw3TdjqwKX2a9xPdRP5vP7nfMZMm9yfyfx/Jj+5L0VBMB1bXJnxlBTBtmPbPAwTcCnyWMr/hk5RfDodU/S0f5nkj9RfjNJF/MUzk99Zjreb9cHk9NI9p2gjXG/UX0VByv2OS+xPARM6Pfi2IUjQUy4Apkta3vbLm/FRg6TDtPwicafuh6udbJK0CvinpxKq/qcM8b9j+JB0FHFX9uETSb8f4PsZiM+CBLr5et0zU9wXdfW8/tP2KLr1Wt7Wa98Pl9dDPS0e43qi/XuZ+8mMwJffbI7k/MU2q99bBoWUj5n6KhuLO6nGrmv8N8DTWvX1JNa7xobrTt1SP21Z9bCFpPdurACStD2wxQn/nAeeN5w2MlaQbbM/pxWt30kR9XzCx31uXtZT3VZut6s49DVhCmSTZUt5D73J/Iv83lPcWTUjuT0B5b52XidDFzcCjwJ5DJyTNAmYBV9c3lvQtSZfUnZ5DuRV5G2UC1PrAS2qu70b5915IRPSDlvIeuAbYQ2t/vbM3sLD6IiF5HzEYkvsRY5A7DYDtFZLOAc6U9ABwH2XN5qts/6xanm0T4CHbjwMXs2Yo0vcom72cSRmytIRyq/FblHWcD6fMf/g88NUsvRbRH8aQ9+cD7wXOlXQ2sB9lmcVXVP3dlbyP6H/J/Ygxsp2jTCZZHziLMmbsEeAiYLPq2l6Agb1q2s+lDElaBtwBfACYUnP9ScCXqr4epGzyslGv3+cw7/uoXseQ95X31sN/y1bz/sXA9ZRJj78F3lzXX/I+7y3vbQCO5P7EO/LeOn+oCiYiIiIiImJYmdMQERERERENpWiIiIiIiIiGUjRERERERERDKRoiIiIiIqKhFA0REREREdFQioaIiIiIiGgoRUNERERERDSUoiEiIiIiIhpK0RAREREREQ2laIiIiIiIiIZSNEREREREREMpGiIiIiIioqEUDRERERER0VCKhoiIiIiIaChFQ0RERERENJSiISIiIiIiGkrREBERERERDaVoiIiIiIiIhloqGiRNk3SwpMskLZK0QtKDkuZLOkLSep0KNCIiIiIiekO2m2soPR/4FvAcYAlwC3A3sDUwB1gfuBZ4he1HOxJtRERERER0XVNFg6S/AW4EngScCZxm++Ga688Cvg68EPgpsKebrUYiIiIiIqKvjVo0SBJwHaUg+LDtk0dotxnwK2AL4LW2v9fmWCMiIiIiogeamdOwG6VguAs4baRGth+g3IW4Apjeluh6RNK5kr4wSps5khZKekzS7yXNrbs+XdJ5kh6QtFjS5yU9qbORR0QrJK0n6WOS7pG0RNLFkrZs4nnbV+23qTufvI8YAK3mfhN/858l6btV7t8v6d8lPb3z7ySie5opGt5UPV5ie0WjhrbPsL2f7W+MP7TuU/Fh4OhR2m0O/IgyZGsn4DPA+ZJeVtPsc5SC61XAgcBe1bmI6B/zgEOAucAewDbAtxs9QdKzgcuBGcNcTt5HDIZ5NJn7o/3NlzSjur4esA/wcmAz4DJJUzv6LiK6qJnhST8B9gUOsX1BV6LqAUnPBM4HdgAeA35s+4gR2v4zcCTwLNurq3NfAra2/bLq28c7gH1tL6iu7wlcCWxr+65Ov5+IaEzShsADwHG2v1ydmwXcDuxq+9phnnM88BHg95QPD9va/n/VteR9xABoNfeb+Jt/EGWhmE1t/6W6vi3wJ8ocz6u78sYiOqyZOw1bVY9/7mQgfWAX4E7g+ZRfHI3sDlw99MujsgDYtZoDsguwGlhYc30hsIryLWRE9N5sYGNK7gJgexGwiJLjw3kNcBTwrmGuJe8jBsNsWsv90f7mXw/sP1QwVIbaPqVNMUf03PpNtFlZPW7QyUB6zfaFwIUA5XdAQ9sAN9Wdu5syl2PT6vp9tv9a0/9KSfcB27Yr5ogYl6H5CPV3AO5mhDy1vQ+ApL1G6C95H9H/Ws39hn/zq7uI9X29D1hKWVEyYkJo5k7DPdXj5p0MZMBMB5bXnRua7zFthOtDbabVn5R0lKQbJN3wvOc9z0COHP1yTGTTgdW1H/Irw+Zpk/01nfeQ3M/R18dE1mruj/Y3fy2S3g4cA7zP9kPDBZDcz9HHx4iaKRr+u3p80WgNJT1d0ocl7d1Ev4NsGVA/uWno56UjXB9qs7T+pO3zbM+xPWejjTZqa6ARMaJlwBRJ9Xdch83TJvtrOu8huR/RI63m/mh/858g6QPAOcDHbH92pACS+zGImikahvZbOKCaPNTIW4H/C3x6XFH1vztZM9djyNMoO2U/Ul3fQtJ6QxerX05bsO4tzIjojTurx+FyeSx5mryPGAyt5v5of/ORNEXSucCpwEm239++cCP6w6hFg+3rKWPytqWM0RtWtXLIcdWP57Qluv51DbCH1p78sDewsJootZAyX+QlNdd3o/x7106SjIjeuRl4FNhz6ES1gsosYCyrnSTvIwZDq7k/2t98gM8CRwCH2f5EB2KO6Llm7jQAvIMynu8USadLenLtRUl/B1wKbAn8DGi4MdqgkbShpKfW3Gk5nzLH41xJz5V0LPAW4BMA1aSob1HWcd5V0m7A54GvZtnFiP5Q7TtzDnCmpFdI2gn4JnCV7Z8Nk/ej9Ze8jxgAY8j9hn/zJR0AvJ1yl+GH1XOHjrHMj4roS00VDbZvAfYDHgZOAu6StEDSRZKuA34JvAC4FjjQ9sqRextIu1AmhO8CYPvPwCuAHSkrKhwDzLU9v+Y5R1D+PX5AGeI1n/JLJSL6xweBr1FWTruSss/CG6pra+V9k5L3EYOh6dxv4m/+wdXjydXzao+hPiMG3qibu63VuGyxfgwleZ5N2RF1MSWJvgpcWLeOcbRozpw5vuGGG3odRsSQUdcfjvZI7kefSe53SXI/+syIud/MPg1PqKrt/1sdERERERExCTQ7pyEiIiIiIiapFA0REREREdFQioaIiIiIiGgoRUNERERERDSUoiEiIiIiIhpK0RAREREREQ2laIiIiIiIiIZSNEREREREREMpGiIiIiIioqEUDRERERER0VCKhoiIiIiIaChFQ0RERERENLR+rwOIiIiIkDS/Dd3Y9r5t6Cci6qRoiIiIiH4wBfA4+1A7AomIdXWlaJAkYJbt26ufnw0cCawEvmT7d92IIyIiIvqT7b16HUNEjKzjRYOkbYAfASuAnSRtCVwHPLlqcoykPWzf1OlYIiIiIiKidd2YCH0asC3wb9XPR1IKhjcCzwDuBE7pQhwRERERETEG3SgaXgacbfvz1c+vBu60fbHtO4DPA7t1IY6IiIiIiBiDbhQNTwaG5jJsAewM/LDm+lIyIbsrJDV1REREdJOkN0g6W9Lhktavu3Zpr+KKiDW6UTTcATy/+t9vrh7/s+b6K6iKiugs2+sc2530/XXORUREdIukY4DPAtOB9wALJW1S02T3ngQWEWvpxjf8Xwc+JOlZwD7An4AfStoe+BRwAHBiF+KIiAGQtdojJp1jgJfbvrm6y/CvwHxJ+9h+iCyjGtEXOn6nwfaHgZOB7YGFwKttrwT+F7AHcKrtT3c6jogYGFMoHxLGc2S3+4jBsZXtmwFsr7R9NDAfuFLSpox/74aIaIOuzCWw/VHgo3WnbwY2t/3X0Z5fffP4UdtXdCK+iOgfWas9YtJ5QNIzhvZyArB9oqSzgSvJvMeIvtCzb+Nsr26mYKjsBWzZwXAiIiKiN64ADq0/afsEYAEwrbvhRMRw2l69S1pN67cSbTvfJEREREw+xzDC5xHbx0k6s8vxRMQwOvFB/QIy/jAi2kTSWcCN1XGr65b4krQ18JDtZb2ILyLGx/bjwOP15yVdDXzd9rndjyoi6rW9aLB9aLv7rLyuWoGphVD8kQ7FEhHd807WfBGxTNIvKAXETdXxZmBv4IW9CS8iOmQ3ynyHLwPbAUts39XbkCImr54OCZI0BdgY2Mf2JaM0Pwh4XQvdG0jREDH4NgF2qjveTlklaaiYeKw3oUVEh+0JPAxsCCDpXuAi4Azb9/QysIjJpuNFg6QnA5+jbOL2JEZeb3m9Ubo6DfhJG0OLiAFgezFl+cUn9m+QtDGwPzAP2Aw4thexRUTHzQRuo0yIngq8CDgBOEzS6223Y1+XiGhCN+40fBx4I/Az4C/Ay4CvUVZD2gNYBRzcRD+/sX1Vp4KMiMFh+1HgIkmXAr+kFA4RMfEsoGz8tnLohKQXAV8AvitpJ9u39Sq4iMmkG0uuvgq4xPYurCkOPmP7ZcCLKUXDc7oQR0RMMLaXUBZfeGevY4mItlsNfKe2YACwfT1l2NJi4P09iCtiUupG0bAFcDmA7QeBu4B/qH7+H+B84C1diCMiJqb7ga16HUREtN1DlDlN67D9MPBF4OVdjShiEutG0fCXutf5A/D8mp9voayK0MgpwC/aHFdEDABJ90u6XNLpkt5Yu4qaJAEHADf0LsKI6JDrgSMl/a8Rrj8IbNrFeCImtW7MabgeeJOk82yvAn4F7C1J1XrrfwusaNSB7VNGexFJTwGW2V7ejqAjom8sBGYD+1U/W9KjwK3A5tVxiKQNWthlPiL63xmU3aKvkXSk7euGLkiaRhmlkBWUIrqkG3cazqSstfy76oP9lylzGC6XdA5wPHDlaJ1I2kDS0ZK+VHd+d0m/Ah4Alki6TNL27X4TEdEbtl9rexZlmMI+wLuB7wEbAdtSVmW7mJL/t0j6uqSTehVvRLRHtfjJ24BnAddK+q2kb0m6EPg9ZSWlC3sZY8Rk0vE7DbbnS9qfskTaYts/l/ReylKJ+wLXASc26kPSVMpyq7sCj1ffOKyU9DfAjyjLsP2QchfjDZRfLi+w/ecOva2I6LJq6dUF1QGApA2BHSh3ImYDO1KGK72JsnJbRAww21+pdoY+ETiQ8jceYDnwGcpniYjogq5s7mb7cqrJ0NXPZ0r6F2Cj6oPAaI4HdgHeC5xTs5LCPGAaZZv5twJIOp0yT+L91fMiYoKy/Thld+gba8/nbmPExGH7dspeLMdKmknZFPbeDEeM6K5uDE8alu0VTRYMUPZ5uNj2mbYfgye+YXw1ZUfYM2v6fYgyBOpVbQ04IgaG7T/0OoaIaD/bi23fmYIhovvaXjRIWiXpLTU/r67ONTpWNuoT+Bvgp3XnXgLMAO6plm6tdRvwtPG+l4h+IGnUIyIiIqKTOjE86QLKsqq1P3ucfa4H1BcW+1aPPxmm/ZOBx8b5mhF9oSwytsas913KotMP6FE0MYiaLSzr/1uLiIgY0vaiwfZhdafOAH7t8f01uo0yybHWQZRi5PvDtH959ZyImECq8cyb2P5jr2MZJCk8IyJivLoxp+EK4GPj7OObwFxJr5E0XdI7gecB9wH/UdtQ0sHAyyhLMjZN0nqSPibpHklLJF0sacsR2i6Q5BGOPao2+49wfZux/ANEBFAWN/h9uzprJe+r9nMkLZT0mKTfS5pbdz15HzEA2p37Ne1ULf3+wc5FH9Eb3SgaZgC3j7OPT1KWZr0EeBQ4C3gcOLxaPQVJB0m6gjIc6rfAp1p8jXnAIcBcYA9gG+DbI7R9HbBVzbE1cBNwFXBt1eb51bmt6o67W4wrIjpnHk3mvaTNKUs83wjsRFnu8XxJL6tplryP6ABJMyU9s41dzqO9uT+0QMsXgFe0Mc6IvtGNJVfPBk6U9N+2bxhLB7Yfl7QfZe31l1AKhwtt/7qm2c6UfRwuBE60vazZ/qtEPx44zvaPq3NvBm6XtIvta2vbVys01T7/JOCZwHNqloPdAbjF9r0tvNX2OuOpsLTxVhWLptF4lesZW8J7evcWmtXMmO2M145areY9cATwCHC87dXArZJ2omw2N7SkdO/zPmJiOh74EGWO47h0Ivern8+nzKlcPN4YI/pRN+40zKGsZHRddQvwDkl/rDtGXR7R9irbX7d9rO331xUMAKcB020fYvvBFmOcTVn3eUHN6y0CFgG7N3qipKcCHwTeX/dBYQfgNy3G0V6jFAxd66MLbK91bHfS99c5F1FnNq3l/e7A1dWHhiELgF21pmrtfd5PMlldLMZgNu3P/ZcCV1d9P9LecCP6QzeKho2AGyjJ9HPgj8Addcefxvsith+rS+hWDI03vqvu/N3AtqM89yTK3IrPDZ2QtB7wHGBnSTdLulvS9yT97Rjji4j2azXvtxmh7XRg0+R9b+QLgxiDtuY+gO2P2z7e9l/aGWhEP+n48CTbe7WjH0lzgI9SdoZeCSwETrd9zTBtDwYusN3sbczpwOphNotZQdlxeqSYNgYOB95re1XNpe2r500FjgQ2pNyN+KmkHWzfV9fPUcBRAE9/+tObDDkixqnVvJ8OLB+mLVX7lvIekvsRPdLu3G9Zcj8GUcfvNEiaL2nfBtcPlPTLUfr4B+AaylCnHwO3AK8EFkj6SBvCXAZMkVRfRE0FljZ43msohdeFtSdt/47y7cNrbV9fFTavo/x7/2N9J7bPsz3H9pzNN998HG8jIlrQat4vq67VtwVY2mreQ3I/okfamvtjCSC5H4Oo7XcaJE0HNqs5tRdwiaThlkmcQvnwP9qKCKdSbg2+ZOjbOkkvoKyU9H5JW9o+ahxh31k9blXzv6HMxai/JVnrNcD3ba/zS6N+srTtxyT9kdGHO0VEd7Sa93dWbalru4RqDHPyPmIgtD33IyaDTtxpmAH8D2WZ1dspG7CdXfNz7fEH4J+An47S54uBc2pv79v+BWW1pKuAt0k6Zxwx30xZkWnPoROSZgGzKHMxRrI7ML/+pKTXSnq0WqZt6NzGwLOBX40jzohon1bz/hpgj5qJjwB7Awttr07eRwyMtuZ+58KM6C+d2BH6/mpOwYsAUZZIuwT4xTDNVwH3UzZva2QKZR5D/WstlXQAZcjS0ZKW2n7PGGJeURUdZ0p6gDKx+RzgKts/q5Zn2wR4qGZfiK2ALSlDpepdBfwF+Kqk91L+nU8DHgC+2mp8EdF+Y8j784H3AudKOhvYD3gLa9ZkT95HDIAO5H7EpNCRidC2LwMuA5C0HXCu7evG0eWNwOGSzrW9ovaC7WVV4bCAsh/EFIYvUEbzQWADyvyEDYAfAu+oru0CXEn5ZmFBdW7oVuVawxGqmB6u9pX4RNV+fUphs4/t+slUEdE8VUe7NJ33tv8s6RWUjZ1uoqz8Ntf2fEjeRwyYtuV+xGTRjdWTDmtDN6cCP6BsqHIRcHbtngi2H6l2ZrwcOIExbKxSbcr2ruqov7aAug8qtm+sP1d3/TfAga3GERENfQr4Urs6G0Pe/4xyF3Wk/pL3EQOg3blf13bW+COM6D/d2Kdh3Gz/CNiHUgy8mzJvor7N/ZQ5Bl8DntLN+CKiO2w/YvuOXscREV3X7ruMEdGijt9paBfbVwE7SppJGTc8XJslwFxJp1EzwWmy0int6cfz2tNPREQ0r5ndrCfR5nVtvcsYEa0bmKJhiO3Fw52XtAXwoO1Vtm8Fbu1qYH3IJ/c6goiIGKv6gmDW+y5l0ekH9Cia3rL9CFneNCaJfv3CYCCGJw2RdKykW4bZkAXKsq53S3pnl8OKiIiIiGgL22sd2530/XXO9UJXigZJG0t6qqQtJW00hudL0gXApykbqmw3TLM/AqspS6h9Y3wRR0RERETEkI4UDZLWl/Q2ST+W9BBlAvNdwN3AEkkPSvqRpEMlbdBEl0cAb6Wso7y17T/UN7D9QeAZlPXQ3yhpbrveT0T0jqSzJB0s6bka5p6tpK3H8mVERERENK/tcxokbUZZ+nQ28FvKfg33AMuqJhtR7hbsCHwReLuk/W0/2KDbI4CrbR/T6LVtL5d0OPAC4GjggnG8lYjoAUkz6+YuvZOyszzAMkm/oOzdclN1vJmynvoLuxlnRETEZNKJidBnUb7xf6ntKxo1rDZC+nfKZkhva9D0eZSNWEZle7Wki4F/bi7ciOgXkg4BTmfN5olQdmbdqe54O2X5xaFi4rEuhhkRETHpdKJoeBXwidEKBgDbP5F0BnDcKE1XAitGaVPrAcr8hogYAJKeDZwHbA+sNbSwuuswvzqG2m8M7A/MAzYDju1SqBHRAZLeAOwG/AK4oNp8bejapbYn57JREX2kU0uu/rWFtsuA6aO0+T0wp4U+Xwj8qYX2EX1j+VkLYenIKXTr9Bks//CCkTuYsQHT3rVr+wPrrP9DGdI42/ai0RrbfhS4SNKlwC8phUMMOeOpsPTPI15eNI1SbjUyY0t4z73tjCpiWJKOoYwm+A/gPcDRkl5p+6Gqye49Cy76Vr8uSzqRdaJouAo4QdJ/2P5do4bVt4vvAq4epc9vAh+V9Enbvxqlz+cBBwP/0kLMMWDmXnYwi1csHvH6C14Mr/7uOQ37mDl1Jhe88mttjqwNGhQMXXl+b3wDeClwtaRDbc8f7QlQNnSsVlZ7J/DZTgY4UBoUDF3tI2IYw8xbOgZ4ue2bqyXV/xWYL2mfqnDITtCxjuxj0n2dKBpOBBYAv5S0APhvyspJj1HGH29EGa+8E7Af8DDlD34jnwOOAhZIOgH4pu1VtQ0kTaFMiDwTeJSyb0NMUI0Khm72Ee1RfcGwm6QjKAXEli08/X7WngMREX1qhHlLW9m+GaAalnS0pE8CV0rahzVzlyKih9peNNheJGknyh2EN1EKg+HcTtl34RO2HxilzyWSXg18j7Ii0jmS/puyKtN6wBbAzsCTKMOSDrJ9TzveT0R0j+0vSLqk9pyk+ymrJN04dNi+rbom4ADghm7HGhHNazRvCXhA0jNs3z50wvaJks4GrqRzQ6kjogUdScTqduIHgA9ImglsDWxMucW4BPhTtSV8K33+VtLfA++g3FHYjTXxPw78F/Ad4DzbrUyanrhmbDn+IQYzWvnCN2L8hll+eSFlvsPQFxCW9ChwK7B5dRwiaQPbAzk2K2ISaDRv6QrgUODk2pO2T5D0GWCHLsQ3IWXcf7RTx6t324slrQJmUG4x/sX2slGeNlJfK4BPVsfQnhCrbD/crngnlCYmMWYMYPQ726+FMg6a8qFjx+pxNrAt5ffYxcBKSb8DbgFutv3xrgcbESNpNG/pGEb4PGL7OElndiPAiSjj/qOdOlI0VBOZDqHcEdgZeHLd9cWU4QTfAL421m8HRxvWFBETRzVxckF1ACBpQ8q3kLNZU1AcQBkamaIhok80mrdk+3HKiIG1SLoa+Lrtc7sWaESMaFB2hI6IWEf1YWNorsMTJG3fm4giopHh5i01sBtlvsOXge2AJbbv6lhwEdHQoOwIHbGW/zzoB+3pKEM5JyTbf+h1DL2kU9rTj+e1p592Gu9yy3271PIk0uKXhHtSVlncEEDSvcBFwBlZ8CSiuwZlR+iItRx4yf69DqFjNjp573H34Q8NbjUkqak9GkZh2/u2oZ+B5JNHbzOoxrtUcpZaHjgzgdsowxKnAi8CTgAOk/T6Zvd0iYjxG5QdoSMmjWWnXNnrEHptCuO/B5TNoCImhgWUjd9WDp2Q9CLgC8B3Je00tARzRHTWoOwIHRGThO29eh1DRPSF1cB3agsGANvXS9oTuBl4P3B4L4KLmGwGZUfoiIiImFweAjYZ7oLthyV9ETiyuyFFTF4DsSN0RERETDrXA0dK+rTtvwxz/UFg0y7HFDFpDcyO0BERERPZ8rMWwtKRpwTeOn0Gyz+8YOQOZmzAtHft2v7AeucMym7R10g60vZ1QxckTQPeQlnSPSK6oCs7QgOLO/06MbnMnDpz3KugzJw6sy2xRES0RYOCoSvP7zO2r5L0NuDfgGsl3UaZx/A4ZSnWpwGn9TDEiEml40VDRCeMts76rPddyqLTD+hSNG02Y4Px/fGfsUH7YomI6CHbX6l2hj4ROBB4Q3VpOfAZYF6PQouYdFI0RPSZ0YYXDHRBFN0xY0tY+ufx99GH2rKx4+BuYzIp2b4dOBY4thryvDFwr+2JdWslJq1BGZrY9qJhjBszTeqNmCIi2uo99za8PMiF50Te2DFGlyHPMSENyNDETtxpuA04gvJdzt3AysbNIyIiYrKTtJo23AeyvV4bwok+M/eygxvOZXzBi+HV3z1nxOszp84cdWhzNNaJJVePknQdcB7wE9uHtfs1IiIiYsI5nAweixGMd/GT8T4/Orfk6vmStgZOlvR929/uxOtERETExGD7y72OISJGNqWDfZ8K/BL4hKROvk5ERERERHRQx1ZPsr1a0ouAabZXd+p1onmShj//8bV/tnN3OCKi2zY6ee9x9+EP5ff3pHbGUxuunLZoGo0XqZ2x5agLKcTk1dElV22vAFZ08jWieSkGIiL617JTrux1CDHoxrvU8nifHxNahg1FRMTAGO9O7tkJPiJibLK5W0REDIwJvRt8REQfS9EQETHBDTefqX4uE2QIY0REjCxFQ0TEBJdiICIixitFQ0wIzXyTmg9OERERg+k/D/rB+Dvp048Bg7JyWoqGmBAmckGQgihikpixASz96/ieHzFBHXjJ/r0OoWMGZeW0FA0RfS4FQcTkMO1duza8nkneEdFLKRoiIiIiJgCdMv4+PG/8fXTCzKkzWbxi8bieH+OToqEiaT3gVOBQYGPgh8A7bA+704mkbwH/u+70Fbb3q65PB84GXkf5d/534J22l3Qi/oho3Rjyfg7waWBH4C7gI7YvqLmevI8YABM1931yN1+tu7Lccu9lc7c15gGHAHOBPYBtgG83aP984H3AVjVHbRHxOWA34FXAgcBe1bmI6B/zaDLvJW0O/Ai4EdgJ+AxwvqSX1TRL3kcMhnkk96NfjHc+UpfmM+VOAyBpQ+B44DjbP67OvRm4XdIutq+taz8VeBZwve17h+lvG+AtwL62f1adOwK4UtJ7bd/V2XcUEaNpNe+BI4BHgONtrwZulbQT8G7g8uR9xGCY0Lk/Y0tYOuzNkuafH103KPOZUjQUsym3JxcMnbC9SNIiYHeg/hfIcyj/dr8Zob9dgNXAwppzC4FVlG8iLmpDzBExPrNpLe93B66uPjQMWQCco7LEVfI+YjDMZqLm/nvW+R5zLf3y4TMGU4qGYpvqsf7bgLuBbYdpvwPwOHCKpFcCyyjjF0+1vbzq7z7bT6ydZ3ulpPtG6C8iuq/VvN8GuGmYttOBTUne98REXpJ4Ir+3HkvuR4xBioZiOrC6NuErK4Bpw7R/HiDgVuCzlPkNn6T8cjik6m/5MM8bqb+I6L5W8364vF5RPU4b4Xqj/qINJvKH5on83nps0uT+RC488966/95SNBTLgCmS1re9sub8VGDpMO0/CJxp+6Hq51skrQK+KenEqr+pwzxv2P4kHQUcVf24RNJvx/g+xmIz4IEuvl63TNT3Bd19bz+0/YouvVa3tZr3w+X10M9LR7jeqL9e5n7yYzAl99sjub92LF166Y7Le2uPEXM/RUNxZ/W4Vc3/Bnga696+pBrX+FDd6Vuqx22rPraQtJ7tVQCS1ge2GKG/84DzxvMGxkrSDbbn9OK1O2mivi+Y2O+ty1rK+6rNVnXnngYsoUySbCnvoXe5P5H/G8p7iyYk9yegvLfOy5Krxc3Ao8CeQyckzQJmAVfXN5b0LUmX1J2eQ7kVeRtlAtT6wEtqru9G+fdeSET0g5byHrgG2ENrf72zN7Cw+iIheR8xGJL7EWOQOw2A7RWSzgHOlPQAcB9wDnCV7Z9Vy7NtAjxk+3HgYtYMRfoeZbOXMylDlpZQbjV+i7KO8+GU+Q+fB76aZRcj+sMY8v584L3AuZLOBvajLLP4iqq/u5L3Ef0vuR8xRrZzlMkk6wNnUcaMPUJZIm2z6tpegIG9atrPpQxJWgbcAXwAmFJz/UnAl6q+HqRs8rJRr9/nMO/7qF7HkPeV99bDf8tW8/7FwPWUSY+/Bd5c11/yPu8t720AjuT+xDvy3jp/qAomIiIiIiJiWJnTEBERERERDaVoiIiIiIiIhlI0REREREREQykaIiIiIiKioRQNERERERHRUIqGiIiIiIhoKEVDREREREQ0lKIhIiIiIiIaStEQERERERENpWiIiIiIiIiGUjRERERERERDKRoiIiIiIqKhFA0REREREdFQioaIiIiIiGgoRUNERERERDSUoiEiIiIiIhpK0RAREREREQ01VTRI2kuShzlWSvqLpF9J+hdJz+l0wBERERER0V2yPXojaS/gSmAp8N2aS+sDTwb+HtgKWAEcbfsrbY4zIiIiIiJ6ZP0W2z9g+631JyUJOBr4F+ALku60Pb8dAUZERERERG+1ZU6Di3OBD1AKkbOqQiIiIiIiIgZcuydCfxK4C5gNvLjNfUdERERERA+0tWiwvRL4YfXjvu3su5sknSvpC6O0mSNpoaTHJP1e0ty669MlnSfpAUmLJX1e0pM6G3lEtELSepI+JukeSUskXSxpyyaet33Vfpu688n7iAHQau438Tf/WZK+W+X+/ZL+XdLTO/9OIrqnE0uu3lo9/m0H+u4oFR+mzM9o1G5z4EfAjcBOwGeA8yW9rKbZ54DdgFcBBwJ7Vecion/MAw4B5gJ7ANsA3270BEnPBi4HZgxzOXkfMRjm0WTuj/Y3X9KM6vp6wD7Ay4HNgMskTe3ou4joolYnQjdjcfW4WQf67hhJzwTOB3YA/jRK8yOAR4Djba8GbpW0E/Bu4PLq28e3APva/lnV/xHAlZLea/uuTr2PiGiOpA2B44HjbP+4Ovdm4HZJu9i+dpjnHA98BPj9MNeS9xEDYAy53/BvPvAy4OnAjrb/UvU3l/JZ4h+Aq7vwtiI6rhN3GjasHkdfy7W/7ALcCTwfuH2UtrsDV1e/PIYsAHatJoDvAqwGFtZcXwisonwLGRG9NxvYmJK7ANheBCyi5PhwXgMcBbxrmGvJ+4jBMJvWcn+0v/nXA/sPFQyVobZPaVPMET3XiTsNm1SPD3Wg746xfSFwIUATCz9tA9xUd+5uYDqwaXX9Ptt/rel/paT7gG3bFXNEjMvQfIT6OwB3M0Ke2t4Hnti7Zrj+kvcR/a/V3G/4N7+6i1jf1/soe1v9dHyhRvSPThQNs6vHX3Sg734xHVhed25F9ThthOtDbabVn5R0FOXbS/7u7/5u51/96lfti3Tt12mqXTMb/sWkMZGXTp4OrK79kF8ZNk+b7K/pvIfu5X7EGCT3127f6G/+WiS9HTgGONb2sF+gJvejj42Y+20dniRpI8okIIAr2tl3n1kG1E9uGvp56QjXh9osrT9p+zzbc2zP2WijjdoaaN3rrHNsd9L31zk3iCSNekTUWQZMkVT/5cmwedpkf03nPXQv9yNiLa3m/mh/858g6QPAOcDHbH92pACS+zGI2n2n4RjK+L2bbP93m/vuJ3cCW9WdexqwhDJZ6k5gC0nr2V4FUP1y2oJ1b2F2zhlPhaV/bthk0TTKGhIjmbElvOfedkbVEfXFzqz3Xcqi0w/oUTQxIO6sHreq+d9QcnksedofeR8Ro2k190f7m4+kKZRi4WjgJNufaGfAEf2gLXcaJE2RdDhwKrASOK4d/faxa4A96na93htYWE2UWkgpyF5Sc303yr937STJzhqlYOhaHxH96WbgUWDPoROSZgGzGNtqJ/2R9xExmlZzf7S/+QCfpayydFgKhpioWr3TsJmkC2t+Xo9yZ2E2sCVljN9c29e0J7z+UC3PtgnwkO3HKUuzvhc4V9LZwH6UpRZfAWD7LknfoqzjfDhlfNjnga9m2cVoVTNDqwZ1WFkv2V4h6RzgTEkPAPdRvim8yvbPhsn70fpL3kcMgDHkfsO/+ZIOAN4OnAL8UNJTa15use3h5jpFDJxW7zTMAA6uOd5I+VbtHuAs4Dm2v9HWCPvDLpT3uAuA7T9TflnsSFlR4RhKsTS/5jlHANcCPwC+B8yn/FKJaMlEnYfSJz4IfI2yctqVwB3AG6pra+V9k5L3EYOh6dxv4m/+wdXjydXzao+hPiMGnvKBo7/MmTPHN9xwQ1v6atfk30H8b2Qiz2no8nvLDPIuaWfuR7RBcr9LkvvRZ0bM/U4suRp9wif3OoKIiIiImAg6sSN0RERERERMICkaIiIiIiKioRQNERERERHRUIqGiIiIiIhoKEVDREREREQ0lKJhIpuxZX/0EREREREDLUuuTmTvuXfUJhN5P4OIiIiIaI/caYiIiIiIiIZSNERERERERENdGZ4kScAs27dXPz8bOBJYCXzJ9u+6EUdERERERLSu40WDpG2AHwErgJ0kbQlcBzy5anKMpD1s39TpWGLimHvZwSxesXjE6y94Mbz6u+c07GPm1Jlc8MqvtTmyiIiIiImnG3caTgO2Bd5V/XwkpWB4I/Bz4DLgFODVXYglJohGBUM3+4iIiPaQNL8N3dj2vm3oJyLqdGNOw8uAs21/vvr51cCdti+2fQfweWC3LsQRERER/WsKoHEemasZ0SHduNPwZGBoLsMWwM7AF2quL+1SHBEREdGnbO/V6xgiYmTd+LB+B/D86n+/uXr8z5rrr6AqKiIClp+1EJb+dcTrt06fwfIPLxi5gxkbMO1du7Y/sIiIiJi0ulE0fB34kKRnAfsAfwJ+KGl74FPAAcCJXYgjYjA0KBi68vyIiIiIOh0vGmx/WNIq4C3AQuDdtldK+l/AHsCptj/d6TgiIiIiImJsujKXwPZHgY/Wnb4Z2Nx2vhaNiIiYxCS9gbIoyi+AC2yvrLl2qe0DehZcRAA9XGXA9uoUDN0laZ3jjo+/ap1zERER3SLpGOCzwHTgPcBCSZvUNNm9J4FFxFrafqdB0mrALT7NtrOCUofZrf7fEhER0XHHAC+3fbOk9YF/BeZL2sf2Q5SlVCOixzrxQf0CWi8aIiKAbPDUCc3eQcwXC9EjW9m+GaAalnS0pE8CV0rah3ymiOgLbS8abB/a7j4jYlKZwvg/JOSbyRr1xcCs913KotMzRDz6xgOSnmH7ieXXbZ8o6WzgSrKXU0Rf6GkiSpoCbAzsY/uSBu3mAx+1fUXXgouInsgGT9GKZu6i5A5K37sCOBQ4ufak7RMkfQbYoRdBRcTaOj4RWtKTJX1T0mJJKyWtGjqAvwIPAReP0s1ewJadjjUiIgaL7bWO7U76/jrnou8dA3x8uAu2jwNmdTWaiBhWN1ZP+jjwRuDXwE8owwa+Tvlm4a/AcuD1XYgjIiIi+oztx20/Vn9e0tWS/sn2n3oRV0SsrRvDk14FXGL79ZI2Be4HPmP755JmA1cDz+lCHDGB/OdBP2hPR/kSsu9JOgu4sTpudd1Xx5K2Bh6yvawX8UVEx+xGme/wZWA7YIntu3obUsTk1Y2iYQvgcgDbD0q6C/gH4Oe2/0fS+ZTdok8fpZ/XSXpWC69r2x8ZU8TR9w68ZP9eh9AxG52897j78IcmVDX0TtaUd8sk/YJSQNxUHW8G9gZe2JvwIqKD9gQeBjYEkHQvcBFwhu17ehlYxGTTjaLhL6w9DOoPwPNrfr4FOLyJfg4CXtfC6xpI0RADZ9kpV/Y6hH6zCbBT3fF2ylDHoWJinaENETEhzARuAxYAU4EXAScAh0l6ve12LNEcEU3oRtFwPfAmSefZXgX8CthbkqphBn8LrGiin9MocyIiYhKxvRiYXx0ASNoY2B+YB2wGHNuL2CKi4xZQNn5bOXRC0ouALwDflbST7dt6FVzEZNKNouFMyvCk30maA3yZ8i3h5ZJ+T7nL8L0m+vmN7as6FmVEDAzbjwIXSboU+CWlcIiIiWU18J3aggHA9vWS9gRuBt5Pc6MVImKcOr56UnXrcH/gt8Bi2z8H3gu8BPgnypjkEzsdR0RMPLaXUHahf2evY4mItnuIMjxxHbYfBr4IvLyrEUVMYt1YchXbl9vef2jVE9tnApsCm9h+SVZDiIhxuB/YqtdBRETbXQ8cKel/jXD9QcpniYjogq4UDcOxvaIaq9yMU4BfdDCciOhTku6XdLmk0yW9sXYVNZXtgA8AbuhdhBHRIWcATwOukfQPtRckTaOsvJgVlCK6pO1zGqqdnv/R9tern1cz+mr4tj1iLLZPaeJ1nwIss728lXgjou8tBGYD+1U/W9KjwK3A5tVxiKQNbP+1NyFGRLvZvkrS24B/A66VdBtlHsPjlKVYn0ZZJCUiuqATE6EvoCyrWvvzuBeNl7QBZbLTi20fVnN+d+BcygZxlvRj4Bjbfxi+p4gYJLZfCyBpJqV42LF6nA1sS/k9djGwUtLvKMs432z7410PNiLayvZXJF1Nmft4IPCG6tJy4DOUFdQiogvaXjTUfqCvnAH8un4X11ZImkpZbnVX4HFJR9peKelvgB9R1m7+IWU51zdQvpF4ge0/j/U1I3pmxgawdBxfmM/YoH2x9JFqOOOC6gBA0obADqwpInakDFd6E5CiIWICsH07ZVnlY6svDzYG7s2dxYju6saSq1dQlll93zj6OB7YhbLq0jk1y6/NA6YBX7f9VgBJp1O+aXx/9byIgTLtXbs2vD7rfZey6PQDuhRNf7P9OGV36Btrz0vavjcRRUQnVV8eLO5xGBGTUjcmQs8Abh9nH28ELrZ9pu3H4IlvGF9NGfp05lBD2w9RipRXjfM1I2JAZXhiREREe3WjaDgbOLHa2G2s/gb4ad25l1AKknts/0/dtdsoE6QiIiIiImKcujE8aQ7lA/x1kpZR1lVeVdfGthsNJ1gPWFl3bt/q8SfDtH8y8NgYYo2IiIiIiDrdKBo2YvxrqN9GmeRY6yDK0KTvD9P+5dVzYoKaOXUmi1csHncfMViqSZCb2P5jr2OJiIiYTDpeNNjeqw3dfBP4kKTLgB8DRwPPA/4M/EdtQ0kHAy8D/m8rLyBpPeBU4FDKygw/BN4x3ApMkhZQ1ogezp62r5a0P3DpMNe3tf3/Wokt1nXBK7/W8HomC09YxwMfotx9HLdW8r5qPwf4NGWVpruAj9i+oOZ68j6iA9r9hUG7c7+mnYAfAAttn9qOWCP6RcfnNEiaL2nfBtcPlPTLUbr5JHAdcAnwKHAWZXOXw6vVU5B0kKQrKPtC/Bb4VIuhzgMOAeYCewDbAN8eoe3rgK1qjq2Bm4CrgGurNs+vzm1Vd9zdYlwR0TnzaDLvJW1OWeL5RmAnyhrx50t6WU2z5H1EZxwP/L6N/c2jvbk/tEDLF4BXtDHOiL7RiR2hpwOb1ZzaC7hE0nDJPgV4JfDMRn3aflzSfpS1119CKRwutP3rmmY7U/ZxuBA40fayFmLekPIL6TjbP67OvRm4XdIutq+tbV+t0FT7/JOq9/CcmuVgdwBusX1vs3FERPe0mvfAEcAjwPG2VwO3StoJeDdwedUmeR/R5zqR+9XP51PmVC7uyhuJ6LJO3GmYAfwPZZnV2ynzDs6u+bn2+APwT6y7MtI6bK+y/XXbx9p+f13BAGUr+em2D7H9YIsxz6bcnlxQ83qLgEXA7o2eKOmpwAeB99d9UNgB+E2LcURE98ymtbzfHbi6+tAwZAGwazUkAZL3EYNgNu3P/ZcCV1d9P9LecCP6Qyd2hL6/mlfwIkCU8ceXAL8Ypvkq4H7KnIXxvu54Vkvapnq8q+783cC2ozz3JOA+4HNDJ6qxks8BdpZ0M7A58HPgvbZ/O444I6J9Ws37bShDj+rbTgc2lfQwyfuIQdDW3AcesP3EDvRr6oiIiaUjE6FtXwZcBiBpO+Bc29eNp89qEtJHKTtDrwQWAqfbvmaYtgcDF9hudrLkdGD1MFvSr6DsOD1STBsDh1M+FNQuI7t99bypwJHAhpS7ET+VtIPt++r6OQo4CuDpT396kyFHxDi1mvfTgeXDtKVq31LeQ3I/okfanfstS+7HIOr4RGjbh7WhYPgH4BrKng8/Bm6hzIVYIOkj44+SZcAUSfVF1FRgaYPnvYZSeF1Ye9L27yjfPrzW9vVVYfM6yr/3P9Z3Yvs823Nsz9l8883H8TYiogWt5v2y6lp9W4ClreY9JPcjeqStuT+WAJL7MYi6sSN0O5xKuY34XNuvs70HZdmzXwLvl3TeOPu/s3rcqu7801j39mWt1wDft73OLw3bD9WOf6yGT/2R0Yc7RUR3tJr3d47QdgnVGObkfcRAaHvuR0wG3djcrR1eDMyrvb1v+xeSdgX+E3ibpJW2/78x9n8zZUWmPanuGkiaBcyiTGwaye7AyfUnJb0W+CrwTNv3V+c2Bp4NfH6MMUZEe7Wa99cAh0mSbVfn9qasx766r/L+jKfC0mGXmwdg0TTKgpONzNgS3pNFoGJCamvudzzaiD4xKHcaplDmMayl+ob/AOC/gKMlnTGWzm2vAM4BzpT0imrptG8CV9n+maQNJT21WqYNAElbAVtShkrVuwr4C/BVSS+o+vt34AHKh4qI6LEx5P35lMnN50p6rqRjgbcAn6iu90/eNygYutpHRB/qQO5HTAqDUjTcCBwuqX5MIdV+DAdQVmc6UdJZjO0OygeBr1G+dbgSuAN4Q3VtF+Ce6nHI0K3KtfZsqGJ6GNgP+CtlWbYFlHGP+9iun0wVEc1TdbRL03lf7RT7CsrQyJuAY4C5tudX15P30VaSRj1izNqW+xGTRVeGJ1W36GdQ9mz4Sysbr1VOpWzLfquki4Cza/dEsP1ItTPj5cAJjGFjlWpTtndVR/21BdR9ULF9Y/25uuu/AQ5sNY6IaOhTwJfa1dkY8v5nlOWkR+oveR9ts2YkTDHrfZey6PQDehRNz7X1C4N2535d21njjzCi/3TkToOk9SW9TdKPJT1E+RB/F2Vd4yWSHpT0I0mHStpgtP5s/wjYp+rn3ZQCpL7N/ZQ5Bl8DntK2NxMRfcP2I7bv6HUcEdF1nwKe0esgIiaztt9pkLQZ5Rv/2cBvKfs13ENZsgxgI8qqAzsCXwTeLmn/0XZxtn0VsKOkmZRxw8O1WQLMlXQaZYJTREREDDjbj5CViiJ6qhPDk86ifBvwUttXNGooaT/KRMFPAG9rpnPbi0foawvgQdurbN8K3NpK0BERERERMbxODE96FfCJ0QoGANs/Ac6gTGQelaRjJd0yzIYsAGcDd0t6ZyvBRkREREREY51aPal+a/ZGllG2aB+RiguAT1OGNm03TLM/AqspS6h9o4XXj4iIiIiIBjpRNFwFnCDp2aM1rNq8i8YbqAEcAbyVsq7y1rb/UN/A9gcpw6K+CrxR0txWA4+I/iPpLEkHV+ujr7N6iqStJW3Ui9giIiImi07MaTiRsj75LyUtAP6bsnLSY5QlVzei7HGwE2VN84eB0YYUHQFcbfuYRo1sL5d0OPAC4GjggjG/i4g+Mdxa7Pr42j/XL804yCTNrJu79E7K7w6AZZJ+Qdm75abqeDNld9YXdjPO6I25lx3M4hWLR7z+ghfDq797zojXZ06dyQWv/FoHIouImNjaXjTYXlTtrvgu4E2UwmA4t1OGG33C9gOjdPs8ykYszbz+akkXA//cZMgRfW0iFQSjkXQIcDprNk8E2ITyJUPt8XbKOupD/ziPdTHM6KFGBUM3nh+dIekNwG6UjVovqPZRGLp2qe1Ju0FFRL/oyOZuth8CPgB8oFoidWtgY8of+SXAn6rl05q1EljRQvsHKPMbImIAVEMVzwO2B9YaWljddZhfHUPtNwb2B+YBmwHHdinUiGgzScdQvhj8D+A9wNGSXll9loCyB1NE9FjHd4S2vVjSKsa3I/TvgTkttH8h8KcWXyMieuf/UPZ2mW170WiNbT8KXCTpUuCXlMIhIgbAMEMQjwFebvvmanXEfwXmS9qnKhzathN0RIzdQOwIDXwTOFjS85p47ecBBwM/GMdbiIju+gblw//VkvZp9knVho4XMPq8qIjoA9UQxN/Und7K9s0AtlfaPppyZ/FKSZuyZhhiRPTQoOwI/TngKGCBpBOAb9peVfe6UygTIs8EHqXs2xARA8D274DdJB1BKSC2bOHp97P2HIiI6DONhiACD0h6hu3bh07YPlHS2cCVdGFURESMbiB2hLa9RNKrge9RvlU8R9J/U4qR9YAtgJ2BJ1GGJR1k+542vJeI6CLbX5B0Se05SfdTVkm6ceiwfVt1TZTNIW/odqz9TKe0px/Pa08/ETQegngFcChwcu1J2ydI+gywQxfii4hRdKJoaGlHaElnAMc10fa3kv4eeAfljsJurIn/ceC/gO8A59luZdJ0RPSRYe46LqR82Bhaic2SHgVuBTavjkMkbWC7lY0lJyyfPHqbiC77BvBSyhDEQ23Pr7l2DCN8HrF9nKQzuxFgRDTWqVt+bd0RekhVDHyyOoaGQq2y/XDLEUbEQLD9WiiTJynFw47V42xgW8rvsYuBlZJ+B9wC3Gz74+t0FhE90WgIou3HKV/+rUXS1cDXbZ/btUAjYkSdKBqGdoT+j+qXxIha2BF6WE3s7xARE0S12sqC6gBA0oaUoQuzWVNQHEDZIyZFQ0SfGW4IYgO7UeY7fBnYDlhi+66OBRcRDQ3KjtAREeuovqEcmuvwBEnb9yaiiBjNKAuf1NuT8jlhQwBJ9wIXAWdk7mJEdw3KjtAREU2z/YdexxARbTETuI3yZeRU4EXACcBhkl5fNzciIjpoUHaEjohJQlI7PgTY9r5t6CciemsBZeO3lUMnJL0I+ALwXUk7Da2mFhGd1ZHN3WpV45D/BCwC/gjcloIhIhqYQvmCYTxHx3+3RUTHrQa+U1swANi+njJsaTHw/h7EFTEpdeROQ7UN/CGUpVF3Bp5cd30xZV31bwBfyzKJETHE9l69jmHgzdgSlv55/H1EVy0/ayEsHfnP4a3TZ7D8wwtG7mDGBkx7167tD6x3HgI2Ge6C7YclfRE4srshRUxeg7IjdERENOs99za8POt9l7Lo9AO6FEw0rUHB0JXn95/rgSMlfdr2X4a5/iCwaZdjipi0BmJH6IiIiJh0zqDsFn2NpCNtXzd0QdI04C2ULyUjogsGZkfoiIiI/zzoB+PvxOPvIjrP9lWS3gb8G3CtpNuAmykbwe1JGbVwWg9DjJhUBmpH6IiImNwOvGT/XocQXWT7K9XO0CcCBwJvqC4tBz4DzOtRaBEdI2nUNnb3v/3oxAojQztCP3u0huPdETpiiKS1jjs+/qp1zkVExOCxfbvtY23PokyM3g54su132l7V2+gi2s/2Wsd2J31/nXO9kB2hY0LoVQJFRET3VMu4L+5xGBGTUnaEjoiIiJ6TtJo2zDixvV4bwomIOtkROiIiIvrB4WSaekTf6tRE6CfkVmJERESMxvaXex1DRIysExOhIyIiIiJiAun4nYaIiOit4VYP08fXbZcFBXpro5P3Hncf/lD+P4yIzmh70SBp/hieZtv7tjuWiIiYWMXAzKkzWbxi8bie36+WnXJlr0OIiBhRJ+403AYcQZnMdDewsgOvERERk9AFr/xaw+uz3ncpi04/oEvRRERMHp1YcvUoSdcB5wE/sX1Yu18jIiIiIiK6p1NLrp4vaWvgZEnft/3tTrxORERERMQgW37WQlj61xGv3zp9Bss/vGDkDmZswLR37dr+wOp0cvWkU4Ff/v/t3XvwlNV9x/H3l5tcbIYaUKB4GUcrJtwkaLwCGiLeE53aWjMBkzA0mqkURpAmaYMapyEharRhomkuo4kxSCdpaqLVRpGIGuMNtTGpFzBWVEKpjiKXAN/+cc6G/a37W3aX3X2ePc/nNXPm99tnb+fsbz9nfud5znMe4MtmplWaREREahnSP9vni0g2agwYOvL8OrVt9SR332VmxwAD3X1Xu95HREQkBXvaU6jzNUQkS21dctXdtwHb2vkeIiIiIiLSXpo2JCIiIiIiNWnQICIiIiIiNemK0CIiIiIiGemWq8Fr0BCZWV/Cik8XAX8C3AV8xt1f7+Xxy4HzKzb/3N2nx/sHA9cB5xE+59uBee7+djvqLyKNayL3k4GvAUcBrwBXufvNZfcr9yJdoCjZN7M9PqZbrxifUtu65Wrwmp6022JgFjATmAKMBmpdX2IcsAgYWVbKBxE3AicCZwFnA9PiNhHJj8XUmXszGw78B/A4MAm4HviWmZ1a9jDlXqQ7LKYA2Xf3HuXgy+9417ZulVTbumS5ZR1pAMxsADAXuNTd74nbLgDWmtnx7v5gxeP3AQ4DHnH316q83mjgQuBD7v5w3DYbuM/MFrr7K+1tkYjsSaO5B2YDbwJz4zLSvzGzScBlwN3KvUh3UPYlb7pluWUNGoKJhMOTK0sb3H2dma0DTgIqO5AxhM/u2V5e73hgF7C6bNtqYCdhT8QPW1BnEdk7E2ks9ycBqyquO7MSWGbhOLlyn4FqUxRsSc/bXbXHsUzKbcvYRFLN/ldGwOaqM6wAWDeQcIylN0MOgAXv2heaCzPv/BhvbHuj1/vHHwvn/HhZr/cP3WcoN5/+/TbUrDg0aAhGx5+VewPWAwdWefxYYDtwhZmdDmwhzF/8ortvja+3wd3/eIk+d99hZht6eT0R6bxGcz8aeKLKYwcD70W5z0TK/zSn3LaMJZt9W9j7gKE+r+MLWlKVlrvljFv3+jVu9u4YNOR1h4EGDcFgYFd54KNtwMAqj38/YMBvgH8mnN9wDaFzmBVfb2uV51V9PTObA8yJN982s9820YZmDQM2dvD9OiXVdkFn23aXu5/WoffqtEZzXy3XpYtXDuzl/lqvl2X2lY/upOy3hrJfQz0nGOdUodrWxrb0mn0NGoItQB8z6+fuO8q27wNsrvL4zwNL3X1TvP20me0EbjOz+fH19qnyvKqv5+43ATftTQOaZWaPuvvkLN67nVJtF6Tdtg5rNPfVcl26vbmX+2u9XmbZT/k7pLZJHZT9BKlt7afVk4KX48+RFdtH8e7Dl7j7rrIBQ8nT8eeB8fX2j0u6AWBm/YD9q72eiGSiodzHx1d77NuEkySVe5HuoOyLNEGDhmAN8BYwtbTBzA4BDgFWVT7YzJab2Y8qNk8mHIp8nnACVD/guLL7TyR83qsRkTxoKPfAA8AU63lM+GRgdTxBUrkX6Q7KvkgTND0JcPdtZrYMWGpmG4ENwDLgfnd/OC7Pth+wyd23AyvYPRXp3wgXe1lKmLL0NmF+4nLCOs6fJJz/8E3glhwuvZbJtKgOSLVdkHbbOqaJ3H8LWAh8w8yuA6YTllk8Lb7eK8p9LqhtUpOynyy1rc1MqzME8VDiEsKJzP3ZfXXIjWY2DbgPONndV8bHzwQWEK7XsIHwB/2n0pJsZrYvcAPh6pA7CAONv3P3LZ1rlYjU0kTujyVc2Gk88BLwBXe/rez1lHuRLqDsizROgwYREREREalJ5zSIiIiIiEhNGjSIiIiIiEhNGjRIDxWrQ4hIQSj7IsWk7Eu9NGgQAMzsEjM7ywtykouZzTSzo7OuRzuZ2TAzG5F1PSTfipT9IuQelH2pj7KfnnZnX0uuCmY2CvgIcKSZbXP3e7KuUzuZ2Q3ATGBs1nVpBzO7GHBCvjcAy82sT2llL5GSImU/9dyDsi/1U/bT0qns60iD4O7rgcXAg8CNZnZqtjVqHzO7lrC+9hR3fzmlw7JlbdkAXAzMAEZAuIp5VvWS/CpK9lPOPSj70jhlPw2dzr4GDQVXuuy9uz8EXAs8RriATXIdSOw8LiKsvb3GzPqldFjW3d3MhgPvA35LuEr5qWY2O9uaSR4VJfup5x6UfWmMsp+OTmdfg4aCc/edZb//ErgOeJzEOhAz+yrwcULn8VTsPHbE+/7ezM7MtoZ7x8w+bmYfADYDrwG/Bt4APkG4arlID0XIfuq5B2VfGqfsK/tNv2digy6pg5l9Fvhz4AXgXuD3wHOlEbiZjQOuACYQrpB5V1Z1bQUzuwC4FTjF3Vea2QB33x7vWwBcDZzl7ndnWc9mlc3XnODu6+K2/oT5qve6+6YMqyc5UqTsp557UPalfsq+st+S99WgoVjMbDTw74SOAWAtMBJ4ltCZrCR0KOOAc4HjgFnuvqrjlW2RuGLCd4G3gPPiXE7MbBGwAPgrd//P7GrYvHj4dSahc1xTfuKTToCUckXLfsq5B2Vf6qfsK/ste28NGorHzKYB84GDgJ8BvwBOA04EDiB0Jq8BQ4FBwDvADHdf3fnatoaZjSfsedju7pPMbD7wOeCvK/c2mNlcYLO7/0sGVa1b2XzNqZWHX0WqKVr2U8w9KPvSOGVf2W/J+2vQUBwVo9EPA38LvBeY5+6PxLPw9wPGEzqRc+LPCcBx7v5cNjVvDTObANxGaBPAue5+v5lZ2SHaqwh7Iia7+zMZVXWP4nzNWcB0d3+ycr4m8JS7/zTTSkpuFDn7KeUelH1pjLKv7LeUu6sUqAD9y36fDtwFPAScWeM5Q7OudwPtKw2E+/Ry/0TgPuAl4D0Vz7mCsHflA1m3Yw9tvADYBUyLtweU3bcA2A6cmnU9VfJVUs5+EXIf66rsqzRclH1lv1VFqyclzsyuNLNlZvYRM+vr7n8o3edhTt9S4P+Az5nZjLLn9TWz0vfjzc7Weq9MhLA+cVn9y60B5gFvA/eb2XB3dzO7ErgcOMndH+tYbZvzAmEu6pfMbJTvPsFrEbAIOMO7+AQvaY2CZX8iJJ97UPalDsp+D8p+K2U9elJpTwEMOJAwMt0OrAKeAv4SOKzisTMIcxwfoov3UhHmZ74DfLpsW297HyYA/xXbfA1dsrehrP7jgWeAx+Pt+cD/Vvv7AXOB2VnXWaVj341CZb9IuY9tUPZVevtuKPvKfumxbcl+5h+CSnsL8AXgv4FLgH+NHclaYCFwdNnjpgE/JYxkP5R1vZts6zjgRmAdMKdse5+y363s9/HAc7GDnZR1/Zto74T499oUy9QqbbwK2AqMzbq+Kh3/fhQi+0XLfWyDsq9S6/uh7O/+Xdlv5ftn/QGotKewe87eNOAR4kgcmE1YUWAbsBH4AXAsYQ/FscAK4JCs699gW+cAo+LvY4BlwMu9dSLx9hBgYOx0Dsq6DXX8HZOer6nSlu9M0tlPOfcVf0dlX6XR74yyr+y3JfuZfzgq7S+Ek57W0HMk+hDwCmGkupWwV+J4YGDW9W2wbcOA3xEunz4ibjuyWidS9pyRwE+Ar5Z/JnkswFFlv7+rA4md/kTCYdcngOFx+5Xx76p/GgpcUs1+6rmP9VX2VZouyn6P5yj7rapb1h+OSvsK0Df+PIZwkZN58fZ3gf+JncXBwJeBB4D3ZV3nJts5FniUcAn1kXFbeSdSPt9xNHBLDNbkrOu+h3YVar6mSutKEbKfau5jfZV9laaKsq/st7V+WX9AKu0vhJH5z4G7gTuBVyvDAwzKup5NtKt8D8pY4MkancjfAP2B7wGbgYlZ17+O9hVuvqZKa0uK2U8997ENyr7KXhVlX9lvR9HF3RJgZicDL7n7izUecw7wY+B1wgVb1sXtbb3keDvEJeR2Vmwz4P2EeZv9CCd1vWpmRxIuZnMGYU/DnxGWWHuys7Wun5nNAe5w9/VmNga4FDgbuMrdb4qP6fF3M7MhwE7gcOBNd/9dBlWXDitS9lPPPSj7Uj9lX9nPIvsaNHQ5MxtNWL/3BuD63r40ZrYfIVzDgdPdfUPnatk6ZjYPOARYDawHHiN8j9+J948htHNfwqoCr8Zti4BTgLPdfU0Wda+HmQ0DHge2EOr/Wlkn2KMDKXvOSMKeieeAy1yhLoQiZT/13IOyL/VT9pX9zLKf9aEYlb0vwEmEE4OWAAfXeNxCwmG63M/r66X+hxJO7NoVywuEPSirgauBTwBHAB8kHLZ8mN2HLY8ADsi6DXW2M9n5miot/64kn/2i5D7WV9lXqfe7ouwr+x3Pvo40JMLMTgSWE75Iy9z9pbjdPP6R4+G89cC97v6xzCrbBDObRej4tgKfIpwsdBahY5gKTCKskLAFcMLVLg8nnCQ03d1fz6DaDan4W40lzMUcwLsPu54NfBH4NvAd4FzgBM/54Vdpj5SzX4Tcg7IvzVH2lf1OZ1+DhoT01oHE+w4C/gE4Cpjp7r/OppaNK5uX+QtgKbCDsLRYf+AYd98eD+8NBI4mjNgPJ+x9OICwfNnaDKpelyLM15T2SjH7qecelH3Ze8q+st9JGjQkpqwD+R5wg7u/HOe+LQX+ghC4XM/vq2Rm44BfEjqIXxGudridsGTcIMLhubeqPO89wAB339jB6jakCPM1pTNSy37KuQdlX1pH2f/j85T9dtddg4b0VOx5WAF8BjifLjyMXTp0Z2afJszhPJZwKHIuoRNZAgwmrE282cwGxL0QuV8dwswOBX5EWGINYC2hk3geWBl/PggMJZzwtBU4N3YgRwBvdMshWOmMVLKfcu5B2ZfWU/aV/U7QoCFRsQP5AeHkof2AKe7+RLa1akzFXL8ZhHWXPwt8jTCv8RLgD4ROpLT3YXO1w355U5T5mtJ53Z79lHMPyr60j7KfbylkX4OGhJnZScDtwIfd/ems61MvMxtWOrxoZv3cfUf8fTnh0ONHzex+wgoClxL2PlxNuMrloe6+OaOq16UI8zUlW92Y/dRzD8q+tJ+yn0+pZF+DhsSZ2SB335J1PeplZp8HTgbudPelFfcdBHwTuNLdV5vZr4A/JZwg1AdYAHzK3V/ocLUbkvp8TcmHbsp+EXIPyr50hrKfP6lkX4MGyY144tYr8eY7hPWKvw6scve1ZrYvcD2Au38yPudBYAxwAfBA6USivEp9vqZIo4qQe1D2RSop+92X/T5ZV0CkxN1fBY4Dfk84kest4ELgZ2Z2ISFc/wjMiLdx9+MJKw+8mPfOo3y+JuHkp0HAmYQL1nydcJjyckLn+aiZDYkdR9+8dRwirZJ67kHZF6lG2e++7OtIg+ROPJnrO4Tl454hzOu7nLA82d2Ewe4oYHGXdBrJz9cU2Vup5R6UfZF6KPvdk30daZDccfcHCCsLzAaOAa4FTiRcYn0+cE38OSqrOtYrztf8oZldBlDqOKLLgEFmdoK7TwU2EVaJ6AdcATwLjOhwlUUykVLuQdkXqZey3z3Z15EGya2492EFYd3pJe6+0cyGE1YcOAE4z92fz7KOtRRlvqZIK3V77kHZF2mGsp//7GvQILkWO5HbCZ3IN9z9RTPrAwypttJA3pjZB4GfAHcCBxKWWTuIsHLCCmB/wooKC9z91vice4CL8945irRLt+celH2RZij7+c6+Bg2Se7ET+T5wB/AVd1+XbY0ak+J8TZF26/bcg7Iv0gxlP780aJCuYGYnA8uAqe6+Iev6NMrMphA6wVsJq0EcBpwPXETYA7ELGJP3vQwindTtuQdlX6QZyn4+adAgXcPMBnfTiLxSCvM1RTqt23MPyr5IM5T9/NGgQaSDUpivKSKNU/ZFiiml7GvQINJhKczXFJHGKfsixZRK9nWdBpEOi2tSXwScQliSTUQKQNkXKaZUsq8jDSIZSWG+pog0TtkXKaZuz74GDSIiIiIiUpOmJ4mIiIiISE0aNIiIiIiISE0aNIiIiIiISE0aNIiIiIiISE0aNIiIiIiISE0aNIiIiIiISE0aNIiIiIiISE3/Dz4+jpyGEXb8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 792x648 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax2 = plt.subplots(4, 3, figsize=(11, 9), sharey=\"col\", sharex=True)\n",
+    "\n",
+    "ylabels = [\"C2ST\", r\"$|\\hat{\\mu} - \\mu|$\", r\"|$\\hat{\\sigma^2} - \\sigma^2|$\"]\n",
+    "box_widths = [0.3] * 3\n",
+    "\n",
+    "for idx, num_trials in enumerate(num_trials_list):\n",
+    "    d = df[df.num_trials == num_trials]\n",
+    "    \n",
+    "    bdict = d.boxplot(column=[\"C2ST\", \"ABSMEANERR\", \"ABSVARERR\"], \n",
+    "                    ax=ax2[idx, :],\n",
+    "                    by=[\"algorithm\"],\n",
+    "                    grid=False, \n",
+    "                    widths=box_widths,\n",
+    "                    notch=False, \n",
+    "                    patch_artist=True,\n",
+    "                    showfliers=showfliers,\n",
+    "                    return_type=\"both\",\n",
+    "                    medianprops=dict(color=\"k\"),\n",
+    "              );\n",
+    "    ticks = [[0.5, 1.0], [0, .1], [0., .02]] if idx < 3 else [[0.5, 1.0], [0, .1], [0.0, .02]]\n",
+    "    for ii in range(3):\n",
+    "        plt.sca(ax2[idx, ii])\n",
+    "        plt.xlabel(\"\")\n",
+    "        plt.title(\"\")\n",
+    "        if ii==0:\n",
+    "            plt.ylabel(f\"{[1, 10, 100, 1000][idx]} trial{'s' if idx>0 else ''} \\n {ylabels[ii]}\")\n",
+    "        else:\n",
+    "            plt.ylabel(ylabels[ii])\n",
+    "        \n",
+    "        if ii < 2:\n",
+    "            plt.yticks(np.linspace(ticks[ii][0], ticks[ii][1], 3))\n",
+    "            plt.ylim(*ticks[ii])\n",
+    "        else:\n",
+    "            plt.yticks(np.linspace(ticks[ii][0], ticks[ii][1], 3 if idx < 3 else 3))\n",
+    "            plt.ylim(*ticks[ii])\n",
+    "\n",
+    "        if idx == 3:\n",
+    "            plt.xticks([1, 2, 3], [\"LAN\", \"MNLE\", \"MNLE*\"], rotation=45)\n",
+    "    plt.suptitle(\"\")\n",
+    "    ii = 0\n",
+    "    for row_key, (axi,row) in bdict.iteritems():\n",
+    "        ii += 1\n",
+    "        for i,box in enumerate(row['boxes']):\n",
+    "            box.set_color(colors[i+1])\n",
+    "\n",
+    "weight = \"regular\"\n",
+    "fontsize = 22\n",
+    "y1 = 0.92\n",
+    "x1 = 0.007\n",
+    "dx = 0.0\n",
+    "dy = 0.218\n",
+    "fig.text(x1, y1, \"A\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1, y1-2*dy, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-dy, \"B\", fontsize=fontsize, fontweight=weight)\n",
+    "fig.text(x1+dx, y1-3*dy, \"D\", fontsize=fontsize, fontweight=weight)\n",
+    "# fig.text(x1, y1-0.53, \"C\", fontsize=fontsize, fontweight=weight)\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "fig.savefig(\"posterior-metrics-over-trials.png\", dpi=300, bbox_inches=\"tight\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "406f7b51",
+   "metadata": {},
+   "source": [
+    "## Plot posterior metrics as lines over trials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "6297697a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_methods = 3\n",
+    "c2sts = df.groupby(by=[\"num_trials\", \"algorithm\"]).C2ST.mean().values.reshape(-1, num_methods)\n",
+    "mers = df.groupby(by=[\"num_trials\", \"algorithm\"]).MEANERR.mean().values.reshape(-1, num_methods)\n",
+    "vrs = df.groupby(by=[\"num_trials\", \"algorithm\"]).VARERR.mean().values.reshape(-1, num_methods)\n",
+    "rts = df.groupby(by=[\"num_trials\", \"algorithm\"]).RT.mean().values.reshape(-1, num_methods)\n",
+    "\n",
+    "vc2sts = df.groupby(by=[\"num_trials\", \"algorithm\"]).C2ST.std().values.reshape(-1, num_methods) / np.sqrt(N)\n",
+    "vmers = df.groupby(by=[\"num_trials\", \"algorithm\"]).MEANERR.std().values.reshape(-1, num_methods) / np.sqrt(N)\n",
+    "vvrs = df.groupby(by=[\"num_trials\", \"algorithm\"]).VARERR.std().values.reshape(-1, num_methods)/ np.sqrt(N)\n",
+    "vrts = df.groupby(by=[\"num_trials\", \"algorithm\"]).RT.std().values.reshape(-1, num_methods)/ np.sqrt(N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "b6a09f8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'RT')"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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lW3mwfg0bprdwyqKzuXbGW5ldOQc3BvoxjCfDLQR+AXzKOXcbcAfwLuB0YB/wu4EHOudeD1wDfHIE4hQRmZh6O8MFfx77Lux7EpKVcO6N4dC/htOjju6oLDCCba30PLkTW9tEPOvoiHfx50nPsW76AWYtPpULZ7yO11Up+UdpuIXANwjv8n9D/3LEGeBt+fn/cc79NfAe4DJgHfCvIxWsiMiE0bgmvPtf/VPIdMCUs+BF34MzXw+piqijOyIzw/Z20r16B/6z+0h1OzJehr9UbWDttEbqT13IBbNexkur5ir5F4nhziyYcc5dBbyasJNgB3CLma0ZcNi5hPMM3AJ80MzSIxWsiMi4lsvAc/8XDv3bdh/EknDaK8POf7NeUNRD/4LmNN2rt9P79C7K2zxwPk9VbmbNon1UnT6H5895Idco+RelYQ0fPK4vdK4M6DGzYES/eARp+KCIFJW2HfD4zfD4D6BrH9TMDZv+l70Nyuujju6IrDND11Pb6V69naqmcITCs+XbeXrKHkpPn87z5l/I3Kp5Sv7FY0SGDx6TmWmUgIjIsfz4UuhpgUnzYf3vw86Ai14Y3v0vvBa8WNQRDsl6c3Q9s4P2VVuo2QNx82guaeKe2bvwzqjn3IUX8ubq+Ur+Y8iJrDWwHPgC4QyDOeAh4Mtm9uAQx74e+A8zK84rWkSkkDobYdOfYNPtsPPP4aI/nXvhwg+HHQAnzYs6wiGZH9D53E5aVm5g0g5IBDFyiXbumL4DO20yZ556Pq+qfqWS/xg13LUGzgfuA7oIRw3UAdcD1znnvmRmGiEgItLHz8LOR2DjinDinz2Ph9vL6qBkUjjj39+sgngq2jiHYGZ0bdrD/pVrqdkcUJpLUBLr5ZH6HfSeWsWpZy7nxTUvUfIfB4bbIvB5YBfwAjNrBHDOnQX8B/Ax59wUM7txhGMUERk7WreFSX/jCthyF/S2g4uFnf2u+DwsuDZc/KcIZ/0zM7p2NrL3L89QtTFLVW8Jkzyfp2t3kj6ljPnLzuGq2uuV/MeZ4RYCzwc+01cEAJjZU865C4HfA293zuXM7O9GMkgRkaKVTcO2+/N3/Sug6blwe9UsOP3VsPA6mHcFlNZEGubRdDY2sevRJylf30NdVzlTgedqmmg/O8Hsc5ZyUf3VSv7j2HALAY/DpxjGzLqcczeQn2TIOddlZh8aiQBFRIqKWZjs+5r7t90HuZ5wlb+5l4XP+hdeB3VLinq4X2drC9v+8gSptZ3MbKtmDjE2VnayZWk305efybnTrlDynyCGWwg8AbzNOfc9M+sduMPM0vli4F7gg845D3hqZMIUEYlQTxtsvitM/ptuh7bt4fa6JeEwvwXXwpxLIFkWbZzH0NnZxubHHie2toW5TZNYQIzdpQGrTz9Aw/JTOX32pUr+E9BwlyG+FrgV2A78N/BNM9s76Jh64E/AWUArUFNsowY0j4CIHFUQwN5V/c39O/4M5ofT+86/KrzjX3gt1BT/ojhdPR2sf/wx7Nn9zG+spSRI0JzsZM/cLLXnLmLuwtOV/CeOIf+HHvaEQs65S4FvAmcCi81s0xDHVAA3AW8ATIWAiBS9vqF9G1eE7937w+3Tzg2T/sLrYObzIZaINs7j0JXpZO2TfyH39B4W7Kml0i+lM97Dnlm9VC6bx+zTzsArws6KMupGZkIhM7sPWOacqwHaDzuLcw3AATN7k3PuS8Alwz2HiMio87PhnX5fc/+eJ8LtZfVh4l9wLSy4Bioaoo3zOHVnu3l6zSP0PLWT+TurOSNbTcabwu7pabrPrmHW2WdSFy+qezIpEic0s6Bz7j2EKw8uAwZPJfxN4Ern3JfN7F+BtScVoYjISGnZGib9jSvCZ/6ZjvzQvgvgii+EBcDUZUU5tG8o3dluVm98lM7VW5m7o4Kze+rxmcneKWn2n1XF9HPOZEmq+FswJFrDnVDIAT8lbPJvAeYAgx8NbAYuB77unHuemb12JAIVERm2bBq23tf/rP/AunB79Ww487X9Q/tKqqONcxi6s92s2voXmldvYNb2Ms7pmgHMY39tmqbnVTBt+RksqCiJOkwZQ4bbWfCdwPcJn///o5n1HOG4EuB7wBuBt5rZf4xArCNGfQRExikz2L+2/65/633g90K8BOZcmu/kdx3ULS7qoX2DdWe7eXznX2h88jmmbU1yVvsc4sRorewlOK2WKeedRqy2POowpfidfGdB59yjQNrMLjuOYz1gZf74C4/7JAWgQkBkHEm3hjP49c3m174j3F53an/v/jmXQKI00jCHK51Ls3L3o+x46lkatngsb1tASZCkqzRLdkk1dcuXEJtaqR7/Mhwj0lnwdOATx3OgmQXOuV8BHx3mOUREjiwIwo59fZ38+ob2parCoX2XfjLs6FczO+pIhy2dS7Nyz1/YtOZJajcZ57cs5Dz/dHoTPr1LyqlYfgq1cyYp+cuIGm4hkAN6j3lUvyYO70woIjI8nfsGDe1rCrdPXw4XfSQ/tO/8MTG0b7B0Ls3KvY+xdv3jVG3I8oKWUzgvcxY5L6BnXgnxcxeSWliHi4+NDowy9gy3ENgALB/G8ecRTj4kInL8/CzseLi/uX/vqnB7eQMsvD5s7p9/9ZgZ2jdQ1s+yrWMrX3/sayS6As5pmsvzmxfxpp5lBBg9s5LEls0jdWoDFakTGtglMizDvcp+AXzBOfcNM3v2aAc6504HXg98+0SDE5EJpGVrf3N/39A+Lx4O7bvyi2Fz/9SlY2ZoH0DGz7CtfSsbWzeyvWkz3bsOUNYUMCddzwfSVzOrpw4PR09DHO/S2aROn0JZRfEtSSzj23A7C1YAjwO1wPuBX5iZP+gYD3gN8HUgBiw1sz0jFfBIUGdBkSKQ6Q4X7OlbvOfg0L45/b37510BJVXRxnmcev1etrZtYVPrRnY0baF3VwtlTcbcdD3z0lOY1luLl++rlS2F7lw3fgymvuMyvNriXqNAxo0Rm2J4MfBbYBHQSVgY7CFM+g3AuUAF4SOBvzaz1Scc8ihRISASATPYv6a/uX/b/f1D++Ze3j+N7+RTin5oX2+uhy3tW9nUupGd+7aQ3d1K+QGYm25gXnoKUzM1/ceWg5tWQemMOrxplXjTKnCVuuuXSIzYFMPrnHNnA+8mvPO/aMD3ZIA/A/8L3Dx4hUIRmWDSrbD5zv5x/e07w+31p8Hz3p1fte/ioh7a15vrYXPbZja1bGT3vm3k9rRSeSDG3HQ9Z6encGX2zIPH9lRBbG4VsZmTw6Q/tYKS8mSE0Ysc27BbBIb8EufqAN/MWk4+pNGnFgGRURIEsOfx/ub+nY/kh/ZVH7pqX/WsqCMdUjqXZkvbZja2bKRxz3Zye9qpao4xN93A/PQUanLhpD2G0VPjiE+rpmRA0nclY2/UgkwoI9MiMBQzaxqJ7xGRMahjb//Qvs135If2OZh+Llz80TD5zzgfYsXVA747233wTr9p9w6CPR1Ut8SZm57CBekGKvzwTj9wRu8kj/icauL55n03tZzSZHH9PSInqqiuZOdcDPg88BagElgBvNvM9h3h+JmEixxdC6SBXxFOfdxdiHhFJqRcJhza19fcv3d1uL18Cix6YX7VvquhvD7SMAcKk/4mNjVvpHnXToI9ndS0JpiXnsLF6QbKgjDp+57RW+uRmD+J+Ky68C5/SjllWrVPxrGiKgSAzwBvBt4EHCBc0+DXhP0QDuGcSwF3EHZUvBCYTLggUgC8pzDhikwQLVv6m/u33AWZzvzQvgvhyi+Fzf1Tzi6KoX1d2S42tW5k84GNtOzcDXu7qG1LMS89hcvS9aQsTPq5WEBvXZzEKZOIz6wLO/HVl1Mei/5vECmkoikEnHNJ4H3Ae83sjvy21wBbnHMXmNnDgz7yOmAacEFf3wTn3KeBvy1g2CLjU6Ybtt7bP67/wPpwe81cOOsNYXP/3MsjH9rXmelgU9smNjdtpGPnXtjbfTDpX9lTR8ImA5CNB2Tq48ROqyUxsw43rZLU5DIqvOIenSBSCEVTCABLCR8H3Nu3wcy2Oue2AhcDgwuBa4E7BnZQNLOfAD8Z7UBFxp2DQ/vyy/VueyA/tK8U5l0O5707P7RvUWRD+zoyHWxq3cjW/Rtp37kPb2+auvZS5qWncE3PZGLUAZBJBmTqE7iZk8OkP7WCVG2p5ucXOYJiKgRm5t93Ddq+Gxiqi/EpwN3Ouc8BbwCMcNjiJ460PLKIDJBuCWfw60v+Hfn/69WfDs97T9jcP/tiSBR+bfv2THuY9Bs30bVjH96+Hurby8Kk31uHR9j/oDcVkG1IwszJJGbV4U2tJFWdUtIXGYZiKgTKgMDMsoO29wJD/ZeoCng7cBvwSmAG8G+Ekxq9aRTjFBmbAh92P97fyW/nI2BBOLRvwdXhHf+Cawo+tK+tt42NrRvYsXcTXTubSOzrob6jgnnpKZyaqYd80k+X+eSmpQhm1pPKD9lLVSSV9EVOUjEVAmnAc87FzSw3YHsK6Bri+CzQDLwxP83xSudcAvilc+4DZnZg4MHOuRuBGwFmzx57y5OKnJCOPQNW7bsD0gcABzPOg4s/nh/a97yCDe1r6Wlhc+tGdu7dQveO/cQbM0zNJ/3Ts1OAKQB0VfgEM0vwZ9VTMrNOE/OIjKJiKgR25N+nDfgZYDqHPy4gv61n0FoHa/LvcwlHHRxkZjcDN0M4odAIxCtSfPqG9vU19+97MtxePgVOeVH/qn3ldaMeSktPMxtbNrB791bSO5tI7s8ytaOK+ekpnJ5rABowjK4qn2BuGf7MhnBynqkVlJRqYh6RQimmQuBJoAO4FLgFwDk3lzCp3z/E8Q8A73TOJQY8TjgD8IGtoxyrSPFo3tzf3L/l7v6hfbMvgqu+HI7rn3LWqA7tO5A+wKaWDezZuZXeXc2kmnJM66hiXnoKZ/rhnX7gjK5qH1tQTjBrCiUza3FTKijVUrsikSqa/weaWa9z7ibg6865JqCRcB6B+8zskfzwwlqg2cwywPeAvwf+wzn3WcLOhl8D/mPwYwGRcSXTlR/al0/+zRvC7TXz4Kw35lftuxxSlSN+ajPjQM8BNjavZ//O7fTuaqFkf47pnTUsTDdwVjAVmIrvBXTWBNjscoJZ08Kk31BOWUIT84gUm6IpBPI+ASQIWwQS5GcWzO+7ALgHuBy418z2OecuAf4VeIJwJcRbgI8WOmiRUWUGjc/2N/dvfwD8DCTKwrH85/99mPxrF47o0D4zoyndxOYDG2jcsZ3s7hZKmgJmdNawJF3PUguTftYL6KoNyM6rwGZPIzUjTPqamEdkbBiRRYfGGi06JEUv3RKu2tc3m1/f0L6GM/K9+68Nm/5HaGifmbE/vZ9NTes5sH0H2d1tlDUZM7smMbunjoSF9wyZmE9XreFNr6R69nSSM2pxdaW4IphRUESOafQWHRKRkxT4sHtlf3P/rkfDoX0lNWHnvoND+2Ye86uOxczY172PLfs30LxtJ/6edsoOwMyuSZzVU0eMacA0ehI5uidD95JqqmdPIzmzllRtKVUaricyrqgQEIlKx54w8W+6PRzil27m4NC+Sz4RJv/p553U0L4w6e9ly74NtGzfhb+nncoDHjO7ajm7txYvn/TTySzddY7O6ZVUz5lBcsYkUtUl1Cjpi4x7KgRECiWXgR0PDRja91S4vWIqLH5x/6p9ZZNP6OvNjD1de9i2dwOt23ZjezqoaPGY3TWZZZlJkE/6nSUZ0vUeHdOrqOlL+pVJJinpi0xIKgRERkMuA937oasRdvy5f2hftgu8RH5o31fyq/adNexOfoEFYdLfvYH2beEKe1XNMWZ317EsW0Vf0m8vy9A7NUbbjComzZlJYkaNJuYRkUOoEBA5HpnufGLff3zvve2Hfn7SfFj65v5V+1IVx33qwAJ2d+xkx67NtG/fg9vbRXVLnNnddZyTqwCmE2C0V2TIzIjTNrM6TPrTa2jQxDwicgwqBGTiMQsT9XEl9abw52z30N/lJaC8Hsrqw/eaeYf+XlYPU84MV+07Dr757O7Yyc4dm+jYvhdvX5qalkR4p++XAdMIMNoqe8nMSdA2o4baubNITaumTBPziMgJ0H85ZOwLgrCj3eDkfaQE390UjsMfSqIsTN5ldWEirz/t8MQ+8D1VdcJj933z2dW2g13bN9G1oxFvX5pJrUnmdNezLEgB08m5gLaqXrrnp7CZtWHSn1qliXlEZMSoEJDi4+eOncwHvqcPhEPthpKq6k/a1bNh+rlHTupl9ZAsG50/KfDZ0bqdPds20r2ziVhjD7WtKWZ319FgSWAaWc+ntTpD26IEuVmTqZ07m9SUSio0MY+IjCIVAjL6sj3HvkMfuK2n5Qhf5KC0tj9p1y2B2Rfnf68bIrHXQTw14n9OYAHpXJrubDfp3i7S3Z30prvJdKfJ9vSQS2fwe3uxnhz0+sRbc0xuK2FWejJTLQFMozeWo7UmQ8vsBOWz6qmdN4tUfQWVmphHRApMhYAMj1m4qM3g5H2090zn0N/lYv1N8GX1MHXp0e/WS2tPekx9j99Dd6aLdLqTnq4uetNpMuk0uXQPuZ5egt4sQU8OenO4TICXNWIZiGcd8ZxHKhcj5ScoDZKU+kkqbXBnvFT+1T/Pf9rL0DY5x/75MSpmNYTN+3UVVGu4nogUARUCE50Z9LQef1LvboJcz9DfFUsdmrxrFx1+hz4wsZfUHNeKeGZGr99LOt0W3n13ddOb7iKb7iHb04ufDhO49YZ34Ick8JxHIuuR9GOU+AlK/SSlQZLyw2baTORf/XLOpzeeIxP3ycWNXMIIyhy5pKMjFaMj6fBKHLFUknhpikRpCanSUpKlZSTLyoiVJCAVI5WKa4y+iBQtFQLjTeBD94GjN70P3hfkhv6uRHl/0q6cFo53P1rHuWTFYR3nMrkM6a4Oerq76OnqItPWTXZfE7n0LvyeDEEmGzahZ3xcxvAyRjwbJvBkziOZi/cncGKUHhZkDOh/rh8Q0BvL5RN4QC4e4JdCLuloTzo6kgGU5PBSCeIlKWIlKZKlJaTKSkmWllNSXkasJAnJOCVxj+Mf5CciMjapECh2ucwwO841A0dYSKqkpj9pT5oPM88fMqlbWR2+V006m8s3n3cd+vy7I4Ptz2KZHPTuxsvsxMuGzeeJnCOZix1M4ClLHGwsrz4kGA8oyb+gx8vSG8uRiefIxgOyiYBMqSOdCGhNZXFJw6XieCVhAo+XpEiWlZIsLSVVVk5pWQXx0hQkPMp09y0ictxUCBTacU1MMyDx97YN/T3Og9LJ/cm77nSYPYUgMYWe5GR6YlX0unIyVkLOT5LzYwSZHEFvFnp96A3wOoxYFuJZSORiJHM+Kb+JlN+GhzvYWH74qvZJIEnO+aRjmXwC98nGA3pKAroSAUEyhyUzuFQcl4oTK0kSK0mSKOm7+y4jVV5OWVklidIUJeokJyISCRUCJ8vPQtv243/GPmhiGsMDyjCvit7SWfSUTKc3eSbZ8lpyVdXkXDk+pWAp8JPgx/FyMeI5j1jOkeyKkWwLn3/HLEymMaA8/zpUgoA4aa+XnliW3niObNwnHQ/oKIEgAUHSQSoGyfDuO5ZKECtNkSwtzb/KKC0P78DLU6VU6O5bRGRMUyFwkoLGTWRufhm9XhUZr5KMqyDnKvC9KoL4LMw7DajAUYpXWkI8lSTuJ0kECVJ+gmTQ/z+BS0NpmiGeg0OPlyHtZfIJPEtXPCCbCvArwvrAEh4uFYNUHC+VIFYSdmBLlpaSKC2lpKyM0rIKSkvLmZQoxXO6AxcRERUCJ21fGia5m0gZpPxD9+UyPt1eL+lYmMDTsRzZZC/ZRA9+wgiSDkvGIBnLN58n8EqSJA4m8DJK+xJ4spyGRCkxpxnlRERk5KgQOEnVU2fy2CXr8s+/S0mUlYTN52XllJaUU5YoozZeRtzTP7WIiBQfZaeTVFZWwcWXvSTqMERERE6IHhSLiIhMYCoEREREJjAVAiIiIhOYMzvCLHTjmHNuP7DtKIdUA0PN5HOk7QB1QNNJhjaajhZ7sXz/iXzHcD5zPMce65ij7T/SvmK/NmB0rw9dG7o2Rvv7R/P6GE/XRpOZXXfYVjPTa9ALuHk42/P7VkYd94n8TcX0/SfyHcP5zPEce6xjjnENHOm6KeprY7SvD10bujbG8vUxEa4NPRoY2u+HuX0sGO3YR+L7T+Q7hvOZ4zn2WMccbb+uj9H7bl0b0Zno/+0Y99fGhHw0MBqccyvNbHnUcUjx0bUhR6JrQ46kkNeGWgRGzs1RByBFS9eGHImuDTmSgl0bahEQERGZwNQiICIiMoGpEBAREZnAVAiIiIhMYCoEREREJjAVAiIiIhOYCgEREZEJTIWAiIjIBKZCQEREZAJTISAiIjKBqRAQERGZwFQIiIiITGAqBERERCYwFQIiIiITmAoBERGRCUyFgIiIyASmQkBERGQCUyEgIiIygakQEBERmcDiUQcQheuuu85WrFgRdRgiIiKF5IbaOCFbBJqamqIOQUREpChMyEJAREREQioEREREJjAVAiIiIhOYCgEREZFi8pPLwleBqBAQERGZwFQIiIiITGATch4BERGRopXpgLYdkOmCZPmon04tAiIiIsWgYy/89u2w5wnoaYX9awtyWrUIiIiIRCnXC498E+7/AuR6oGomVM+BGcsLcnq1CIiIiETBDNb+Br5zGtz5EZh7Gbz7WZi0ALzC3aeP6ULAOTfFOfdT59we51yrc+5259wZUcclIiJyVHufgp9eCf/9MoiXwhv/BK/7HUxeVPBQxuyjAeecB/yGcBGFlwCdwGeAu5xzp5nZgQjDExEROVxnI9zzSXjih1AyCV74HTj3RogNSMdvvbegIY3ZQgA4G3gBcJqZrQVwzr0RaAZuAP4jwthERET65TLwl2/Dff8M2W543t/DZZ+G0klRRzamC4HtwIuAdQO2Bfn36P9lRUREzGD9H+D2f4DmDbDwerj2G1C/JOrIDhqzhUC+6f+Pgza/FygF/lT4iERERAZofBZWfAA23wF1S+D1t8Ki66OO6jBjthAYzDn3YuBLwDf6HhWIiIgUXFcT3PtpWPl9SFXCdd+C8/4WYomoIxvSuCgEnHNvAX4A/AL48BGOuRG4EWD27NkFi01ERCYIPwuP3QT3fgZ6O2D538Dln4WyyVFHdlTOzKKO4aQ45z4OfB74N+C9dhx/0PLly23lypWjHpuIiEwQG26D2z8ITc/B/Kvhun+FhtOjjmowN9TGMd0i4Jz7MGER8Ckz+1zU8YiIyASzf23YEXDjbVC7CF77ezjlBnBD5tyiNGYLAefcWcAXgR8DP3DOTR2wu8PMuqKJTERExr3uZrjvs/CX70CyAq75F3jeeyCejDqyYRuzhQDwGiAGvC3/GuiThC0FIiIiI8fPwePfh3s+FS4MdM474YrPQXl91JGdsDFbCJjZx4CPRR2HiIhMEJvuCIcD7n8W5l4O130Tpp4VdVQnbVQLAefcJSfyOTO7f6RjEREROSFN6+FP/wjrfw+T5sOrfwNLXjKm+gEczWi3CNwLnMiwhNgIxyEiIjI86Va4/3Pw6LchXgJXfQWe/z6Ip6KObESNdiHw1kG/VxM+u98O/BB4jnAFxAWEY/wbgPeMckwiIiJHFvjhokB3fwK6D8Cyt8EVn4fKqcf+7Bg0qoWAmf104O/OuR8BG4ELzSw9aN93gQeAlwG/HM24REREhrT5brj9A7DvKZhzSdgPYNqyqKMaVYXuLPhywjH/6cE7zCznnLuFcJpgERGRwmneFPYDeO7/oGYuvPKXcNrLx00/gKMpdCGQAY7WtnIK0FmgWEREZKLraYcHvgCPfBO8BFzxBXjBByFREnVkBVPoQuCPwPudc382s9/3bXTOecA7gL8FvlvgmEREZKIJfFj973DXx6FrH5z9Zrjyi1A1PerICq7QhcCHgecD/+ec2wdsJlw2eB5QAzwMfLTAMYmIyESy9X5Y8X7YuwpmXQCv+wPMWB51VJEpaCFgZvudc0uBdwLXAXMJhxc+APwv8J9mFhQyJhERmSBatsIdH4I1v4KqWfDyn8MZr54Q/QCOpqCFgHNuNrDfzP6NcLXAwfurnXNna0IhEREZMb2d8OCX4OF/AS8Gl30WLvhHSJZFHVlRKPSjgS3AG4CfH2H/K4BvARUFi0hERManIICn/hPu/Ch07oEzXw9XfRmqZ0YdWVEZ7SmG5wBvHrgJeLlzbtEQh3vAS4DDhhaKiIgMy/aHwn4Au1fCjOfBq/8XZj0/6qiK0mi3CGwHbgDOy/9uhBMGvewIxwdoISERETlRrdvhzn+CZ34BldPhr/8TznwdeF7UkRWt0Z5Z0JxzVwG1hK0Bm4H3A78d4nAfODDUZEMiIiJHlemCh74KD30NMLjkk3DRP0GyPOrIit6o9xEwsw6gA8A5dzmw1swaR/u8IiIyAQQBPP0zuPMj0LELTn81XP0VqJkTdWRjRqGHD97nQvPMbAuAc+4UwuGEPvBjM1tfyJhERGSM2vko3PY+2PUoTDsXXvELmHNR1FGNOYUePjgTuB3oBc5xzk0BHiVclRDg3c65S8xsVSHjEhGRMaR9V9gC8NQtUDEVXvITOPtN6gdwggo9fPCLwCzgH/K/v5OwCHgV8BhwG/BZ4MUFjktERIpdphse/jo89JVwiuCLPgoXfxRSlVFHNqYVuhC4Bvimmf0g//uLgR1m9isA59wPgE8WOCYRESlmZvDMf8MdH4b2HXDqy+Gar8GkeVFHNi4UuhCoJpxUCOdcA3Au8MMB+7siiElERIrVrpXhfAA7HoIpZ8PL/hPmXhp1VONKoZPuNuDM/M+vyb//fsD+68gXCiIiMoF17IG7Pgarfwrl9fBXP4Blbw2nCJYRVehC4GfAp5xzC4ErCCccWuGcWwD8K+HkQx8scEwiIlIssj3w52/AA18EPxOuCXDJJ6CkKurIxq1CDx/8Z+ecD7wOeAj4RzPLOeeqgEuAz5vZtwoZk4iIFAEzWPPrcHXA1q2w+CVwzddh8sKoIxv3Cv483sy+AHxh0OYngXozyxY6HhERidieVbDiA7DtPmg4A950J8y/MuqoJoyi6JhnZgHhOgMiIjJRdO6Duz8BT/wIyibDDd+Fc94BsaJITROG/rVFRKSwcr3w6P+D+z4HuTQ8//1w6aegtCbqyCYkFQIiIlIYZvDcb+FP/wgtm2DRDXDtv0Dd4qgjm9BUCIiIyOjb93Q4H8CWu6H+NHjDClh4bdRRCaNcCDjnZprZztE8h4iIFLGu/XDPp+Dxm6GkBq7/Niz/G/UDKCKj/b/EdufceuDO/OtuM2sf5XOKiEjUchl47Dtw72ch0wnnvRsu+wyU1UYdmQwy2oXAEuCq/OvHQKVz7nH6C4OHNGRQRGQcMYP1f4Q//QMcWA8LroVrvwENp0UdmRzBqBYCZrYeWA/c5JzzgOcBVxIWBv8I5JxzD5IvDMxs9WjGIyIio6hxDdz+Adj0J5i8GF73R1h0PTgXdWRyFM7Mojmxc6XAZfQXBmcCB4C7zOy1o3nu5cuX28qVK0fzFCIiE0f3Abj3M/DYd8MlgS/9NDzv3RBLRB2ZHGrIiiyy3hpmlgZuy79wztUDVxMWBiIiUuz8bJj87/0M9LbBue+Cy/8ZyuuijmxM+9gDHwHgixd/uSDnK5pum2a2n3BRop9FHYuIiBzDhhVw+wehaS3Mvwqu/VeYckbUUckJKJpCQERExoCmdWEBsOFWqF0Ir/ktLP4r9QMYIe29bbT1ttKZ7aQ7201ZomzUzzmuCgHn3PeAuJm9I+pYRETGlXQL3PfP8Jd/g0QZXP01OP/vIZ6KOrIxLRtkee7AWlbvX8WqxlVsat2IYXjOY1fnLhZNWjTqMYyLQsA554DPAu8CfhRxOCIi44efCycDuudTkG4OFwW64vNQ0RB1ZGOSmbGzcwerG1ezqvEJnml6mh6/h5iLsbh2Ca9d8noe2f1nSuOlBSkCYBwUAs65+YTJ/wxge8ThiIiMH5vuDIcDNj4Dcy8L+wFMWxp1VGNOe28bT+5/klWNT7B6/yqa0k0ATC+fzhWzr2JZwzLOrDvr4GOAp/Y/WdD4IikEnHNlZtad/3ky8BrAB/7HzJqH+XUXADuA1wK/GNFARUQmogMbwoWB1v0OaubBq34Np/61+gEcp77m/lX7V7G68Qk2tW7CMMoT5Zxdv5RXL34tS+uXMaV8ypCfL9RogT4FLQScczWEyXoScL5zrgp4HJhFOL7xk865i81s8/F+p5ndAtyS//4Rj1lEZMLoaYP7Pw+PfCt89n/ll8IlghMlUUdW1Pqa+1c1hon/maZnDmnuf92pb2Bp/VIWTlpEzMWiDvcwhW4R+DxwBfCl/O9vA2YDHwJWEib0zwOvK3BcIiITV+DDEz+Cuz8B3U2w7K1hP4DKaVFHVrTae9tYvX81qxpX8eSA5v4ZFTO4cvZVLB3U3F/MCl0IvBj4tpl9Ov/7XwONZvYvAM657wAfHI0TO+duBG4EmD179micQkRk7Nlyb7g88L4nYfZFcN1tMP3cqKMqOlk/y9rmNazev/qQ5v6KRAVn1Z99zOb+YlboQqABeAbAOVcNvIBDn+s3AeWjcWIzuxm4GcIphkfjHCIiY0bzZrjjQ7D2f6F6Nrziv+H0V6ofQN7g5v6nm56m1+89pLl/WcMyFtQsLMrm/uEodCGwC5if//mlQAz4w4D9F6Ce/yIio6e3A+7/Ajzyr+DF4fLPwQX/AInSqCOL3MDm/tWNT3Cg5wAQNvdfNftqljWcwxl1Z46J5v7hKHQh8Hvg/fnWgNcAzcDvnXPTgY8AbwI+V+CYRETGvyCA1f8Od30MuvbB2W+CK78IVTOijiwyfc39fYl/c9vmg839Z9cvZVnDOSxtWEZD2fieM6HQhcCHCZv+3w7sBP7WzNLOuTOBdxN2FizsuAkRkfFu2wNhP4A9T8DMF8Brfwcznxd1VAVnZuzo2MGqxid4cv+qQ5r7l9SeOq6a+4cjsmWIDwnCuSQw2cz2FOJ8WoZYRCaElq1wx4dhzS+haiZc9RU487UTqh9AW28bq/evYnXj6kHN/TNZmr/rH4/N/UdQXMsQD2RmGWAPgHNumZmtijgkEZGxq7cTHvwyPPx1cB5c+mm48EOQHJW+2EVlcHP/prZNABOuuX84Cj2hUBL4Z+A6oALwBsVSCVQRdiIUEZHhCAJ46ha466PQsRvOfB1c9WWonhV1ZKNmYHP/6v2reGZQc/8bTn0jyxrOYX7NgjHT3N/70/BeOPXmZQU5X6FbBD5HOHnQTqAFOBN4AJgKzAXSwPsKHJOIyNi3489hP4Bdf4Hp58GrfgWzXhB1VKOiv7k/XLGveUBz/9VzrmFZwzmcPvmMMdXcb2ZYcxrb24m19EDGxzozuIrkqJ+70IXAK4F7gasIk/9O4N1m9oxz7oXAb4BMgWMSERm72nbAHf8Ez/wcKqbBS38KZ70BPO/Ynx0jsn6WNc1rWN34BKsaV7E539xfmajk7IalLK1fNqaa+80PsP3dBHs7sL2dBHs6sX2dkPH7D0p4WPf4LARmAN8wswDY7ZxrJJw74Bkzu9U591PgneQn/hERkSPIdMNDXw1fFsDFH4eLPgKpiqgjO2lhc//2fHP/ap5ueppMvrn/1NrTxlRzv2V9bF8+2e/tDJN/Yxf4+Y76CQ83pYLY2VNwUyvxplaQuX0Dzjm8hsL8b1noQiDNoXf8GwkfD/R5FHhFQSMSERlLzODpn8Od/wTtO+H0V8HVX4WaOVFHdlKO1tx/zRhp7rd0lmBvPuHvCe/27UA39A3OK43jTa3Ae95MvGkVuKkVuNoynHdoZ/6St5xT0LgLXQisBq6n/47/OcJphvvMpP+fTEREBtr5F1jxPtj5CEw7B17+M5hzcdRRnZCMnwnn7s8n/qGa+5c1LKO+CJv7zQw6MweTfZBP/LT19h9UlQqT/mn1eNPCO32qUkW5Sm6hC4HvAP/tnHsAuIFwnYG3Oed+AqwFPgD8ucAxiYgUt/ZdcOdH4an/hPIp8OIfwdI3g1fczeIDDWzuX9W4imcOPDOouf9N+eb++UXV3G9mWEu6/1n+3g6CvZ3QlT14jKstxZtZhbe8Eje1Am9qBa589J/tj5SCFgJm9sv89MIfBLrM7M78ioPvzh+ynVFafVBEZMzJpuHhf4EHvwRBDi78J7j4Y1BSFXVkx6W1t5UnG1cffNbf19w/s2Im1865lqUNy4qqud/8AGvqPvgsv++5/sFOfJ7D1ZcRWzgZNy2f8KdU4FJFMSXPCSuWmQVnA7XAmvzkQqNKMwuKSFEzg2d/Gc4K2LYNTn0ZXP01qJ1/7M9GqK+5f1XjE6xuXF3Uzf0HO/H1PdPfm++5P6gTnzc1fJbvTavE1Zfj4mN6NEZRzyy4Ha06KCICux8P5wPY/iBMOQteejfMuzzqqIZkZmzv2HbwOX9fc3/cxfOT+RRHc/8hnfjyQ/asaUAnvpIBnfjyid9NPrwT33g1qoWAc24z8H4z+92A34/FzGzBaMYlIlJ0OvaGKwOu/ncoq4MXfR/OeXvR9QNo6Wnhyf2rWd24itX7V9Hc0wwc2tx/Rt2ZlMajWdbYOnoPeZZvezux1p7+AyqTeFMr8ZbU402rwJtaCdXF2YmvUEa7RWAb0DXg9+1oVICISL9sDzzyTXjgC5DrhQv+AS75BJRURx0ZcGhz/6rGVWxpC+/nKhOVLG0IJ/JZWr+M+rL6gsYVduLrCRP+gDH6gzvxuemVxM6ZFo7Rnza2OvEVyqgWAmZ2+aDfLxvN84mIjBlmsPY38Kd/hNYtsPjFcM3XYfKiiMMKm/tXNa5iVeMTPHvg2YPN/adOPpU3nvZmltYvK2hzvwXhTHwDh+rZvk7ozXfic+AayvEW1oZ3+33N+2O8E1+hRPav5JxrIFxfwAc2m1lLVLGIiIwaM+jcC/vXwP61+fc1sONhCLJQfzq88U+w4OrIQuxr7l/VuIonBzT3z6qcVfDmfsv6WGPXIWP0rbELckF4QNzDTSknduaU/qF6DeW4eHE9QhlLCl4IOOeuAL4CnDNo+/3AB8xsdaFjEhE5aWbhvP99ib5pQNLvae0/LlUN9adB2eTw579ZDbHC/qc442dYc+DZcKne/QOa+5NVLK1fWrDmfusZOBNf+Fx/yE58y6eHvfanVuAml+LG0ToKxaDQyxBfA/wRaAO+DWwiHM6wGHg98KBz7hIze6KQcYmIHLfAh9at/Um+706/aS1kOvuPK6sLE/4Zrwnf60+DulOhchoUuGOambGtfdvBpXqfbXqGTJA5pLl/WcMy5lcvwHOjk2Sto/fwnvstAzrxVSTDzntL6vub9mtKJnQnvkIp6DwCzrlHgGrggsGPApxzUwhnFdxiZleOZhyaR0BEjsnPQvOmQQl/DRxYB7mBvdCn9yf5voRffyqUF7bz3GBHa+5fWn8OSxuWjkpzv5lhrT2HzLcf7O2Ezv4pYtykkoML7Lh8z/1CrLInxTGPwNnAR4fqD2Bm+5xzNwGfLXBMIjKRZXvgwPpDm/L3r4EDG8Jn+H2q54RJfv5VYaLvS/6lNZGFPtAhzf2NT7ClfQvQ39y/rOEcljYso660bsTOacGAmfj29N/pH9KJr74cb/6ksIl/ar55v0Sd+IpJof/X2A0c7SqMAQcKFIuITCSZLmh67vBOey2bwmV8AZwHk+aHSX7xi/uTfd2Solve9+jN/aeNeHO/5XxsX1e+eT8/ZG9wJ76GcmJnNPTf7TeU4xLqxFfsCl0IfAH4f865B8zs9oE7nHNLCRcd+kKBYxKR8aSn7dBE33en37q1/xgvDpNPCWfuG/gMf/IpkCiJLPRjaelpZnXfZD6Nq2jpDRtXZ1XO4tq517OsYRmn151x0s391pPrn2+/b1Ke/V39nfhSsXCY3rnT88vpVuLq1IlvrBrtmQXvHmKzAbc6554B1uV/nwucCzQDy0czJhEZJ7qaDm/O378GOnb3HxNLhXfzM18Ay97Wn/BrF0IsEV3sx6nX72XNgTWsbnyC1Y2rRqW53wYtp2t7Ow7vxDe1Au+Uyf0999WJb1wZ1c6CzrmtDH8mQTOzUV1ZQ50FRcaII43B378Guvf3H5co739uP7CH/qR5RTdF79GYGVvbtx6cvndwc/+y/Ex+J9Lcf/yd+Pqf5XvTKnAVqZH+MyU6he8saGZzR/P7RWScGO4Y/L7n932vqpkwRpulj9zcP5vr5l3P0vpwMp+S+PE/sgg78aX759vf00mwrxN6cuEBDlxd2cFOfAcn5ikp/lYSGXnquikihXPcY/DrDx+DX38aVEwt+Bj8k2FmdOe6aelpoaWnmebeZlp7WmjuaeHu7XfR6/fQ44fN8FXJqoMT+Qynud9yfTPxDRijv29AJ76Yw02pIHZaPW6aOvHJ4VQIiMjIG+4Y/GVvGzAOP/ox+Mfim097bxvNPS209rbQ3NNMS09zmPDzv7f2tNDc20LG7z3s83EvjsORjCV50+I3s6zhHOZVzz9mc7/15g6db3+ITnxuagWxc6f3T8pTV4aLjc3WEikMFQIicuJOaAx+3zP8JUUzBr9Pr9978O69L6m39DSHib03vJNv6WmmrbeNgOCwz5cnypmUmsSkkloW1y6hJjWJ2pLw90klk5iUqqW2ZBLliYpjdrazzszBcflh8/6gTnzlibDn/imT80m/MnzGP4ZaTKQ4qBAQkWM77jH4C8I7+oHP8CcvjnQMvpnRme0Mk/vBu/eWfKIfcCff00JXruuwz3t41JTUMCk1idqSWhbULDiY0GtKwm2TUrXUlNSQig2/Y52ZYW09h8y3H+zthI4Bnfhqwk58sbOn9i+nW6lOfDIyVAiISL/hjMGfenakY/BzQY623taDd+n9d++Dmut7W8gFucM+n4qlwjv11CRmV81hacOy/B183917eCdflaoa9nK7ZhY+o09nse4cls6GP6dzB9/9p/aCHwDu8E58cwd14itVJz4ZPSoERCaiYY/Bf3v/8LxRHoOfzqUPe97eckiyD39uz7RjQ4xOrkxWhc3xqVpm1M042CQ/qe/uPZ/8S+Olx9WMbjkfS/di3VlIh0k9TOy5Q94P2dadBf8oI6dj+fPGPGJn1PcP2ZuiTnxSeCoERMar4Y7BH/j8vv40qJk7YmPwAwvoyHQccpc+VLJv7W0hnUsf9vm4i1OTv0tvKGtgycHn7/m793yyrympIeENXaSYH0B3eIfOgQxBuuuQO3QGJXNL58KEnju8L8BBnoOyBK40DqUJ3KRSvOmV4c9928oSUBLHlSZwZfltSvZSRFQIiIx1wxmD33D6iI7Bz/rZQ5J6//C4Qzvbtfa24pt/2OdL46UHn7EvrFmYT+q1B5vla/PJvyJZebBHvfnBoXfj7VlsXxbSPVh6B9lBTfAH79Czx0jopWGypjSOqy7Bm5o4dFtZAkrCZN63jURMnfNkzFMhIDJWDHsM/msPnW3vOMfg9499D3vLD2yO7+tg19zTQmtPCx3ZjsM+73BUp6oPJvM5VXPDZvnUgN7zJbVMStSQysYOvxs/MDCRd0F3G9me/L7uLGQOLygGnPzQu/HKVDhmfqg79LIBiT6phC4TlwoBkWJzImPwB06rWz70RDS++bT1tPVPbjNoSNzBO/ve1iHHvie8xMG79BkVMziz7sx8Qp9EnVdDLVVU+xVU+CV4PX7/3XhbfzO79eQg3YF1N0Ovz+FnyXMcbE6nNI4rT0JdOV5ZPJz9rq85vmRAc3tpIhxHr4QuMiwqBESicrxj8GvmHj4Gv/5UKKkG+sa+5+/WW9fRvO/P/c3zAya3OdLY94pERXiXnqzl7MozaKiqpY5J1Fo1VUEZlX4pZbkUyV6HdWVh/6Be8D05IJ1/7cMHDrlnL4n3342XJqC2FG/AXfnBZD/wvSSuhC5SICoEREbbsMbgnwaLX4LVLaGzdh4t5fU0++lDm+f33U/Ltv872Dx/2Nh3g3IrYXpsCtO9eua5hdQxiUmxSqr9Mir8UspySVKZGImMw6X9MLH35IZYIqw7/wI/FQuTdFkCVxIPE/rBu/IBTe8Dk35JHOcpoYsUszFfCDjnYsDngbcAlcAK4N1mti/KuCQiQQB+Bvze8D3Xe+jvQ20b7u/D/Ux308Hwcl6C1voltEw7g5Yl19NcXkdrqoJmz6Ml055P+Ltp2f4suS05SoIEFX4plbkSKvxSJlklDa6WU5nHpOAsqoJyKvwSyrIJUtkY8V6H1xPgjrbmZ9LhyoBSFzaz15TilSYOJvFD79D7Entca82LjFNjvhAAPgO8GXgTcAC4Cfg1cFGEMY1fZhDkhpEITyLhnkgCDnIYEODIOYfvPHzX//PBbbj89kP3+4cc5/BdjFwsiR9LkIsl8L14+IolyOV/ziVj+CXl+K6KnBcLP+/FCPLf0+kl6CBJLudBj1Hul1DZVUJFeymVvlGRy3GKVTLJ5lMVlFOeS1GaT+xecJS76YSXf1aewJXHDx3GNiCZH7ZN886LyABjuhBwziWB9wHvNbM78tteA2xxzl1gZg9HGuCJCoKC3a2a30sulyHwM+SCXnw/S87P4Ac5/CBLLsjiB7n8u48fZA9JqP0J0yOHw3dxfBfDiOG7OAExAhcncLH+n4lh+d+NGOYSBF4cc0kCF8e5OAEpzJVjxIAYuBhBzIOYBxbD8HB4gAPzwByGw7Nwm2fhzx6O2MD3/M+H7DNHjPw+c3jkt5lHwhwp3ztkm5f//r7PDHw/5Fz5z5T4CeJ2lHHjce9gEh/6bnzgvgHb4kroInLyxnQhACwlfBxwb98GM9vqnNsKXAyMeiGQbdtL64Pfw/dzBLksvu8T+Dn8IHwPAiPwfQILCHwLf7cAC8KfzRyBhe8G4bt5WD6ZGh7kEyf0JcBYPkGG+w59D/eDh7MkjlLCAV0xXH6fM4eXf3eHJS93SELs25a0Q5PbwX3mDkl8xSpwNrBmAOfI1wzhGHIv3Ibn+t89Fz7f9hzOeQd/d5434NV/bN9n3eDvS8YG3aEPHJce1+QyIhKpsV4IzMy/7xq0fTcwqxABHNi9lZrHLyvEqQ7ju4AAw5wRuIHvYAff+5Nf38/kf8YDc4OTIIOSoBf23s5vs5hH4DwCz+F7Hl6sPyl6fe8xDxeL4bkYzvOIxWK4mIcXiw1IlIee6/AkTHicc4cf4zEo6Q7aN/AznlPvcxGRoxjrhUAZEJhZdtD2XuCQ1U+cczcCNwLMnj17xAKomnka61/waJj4Yh6eFwPP4XkxvFgMz/MGvceJxWJ48RieFyPmxYjF4nixGPFYAi8WC/fnk+bgxKfkJiIiI2msFwJpwHPOxc1s4PJiKeCQMVVmdjNwM8Dy5cuP1qd6WMoqqzjr6qtH6utEREQKqngf6h6fHfn3aYO2T+fwxwUiIiIyyFgvBJ4EOoBL+zY45+YCc4H7owlJRERk7BjTjwbMrNc5dxPwdedcE9BIOI/AfWb2SLTRiYiIFD9nNmKPyyPhnIsDXyGcVChB/8yCTUf5zH5g21G+thpoG8Z2gDrgiOcsAkeLvVi+/0S+YzifOZ5jj3XM0fYfaV+xXxswuteHrg1dG6P9/aN5fYyna6PJzK47bKuZ6TXoBdw8nO35fSujjvtE/qZi+v4T+Y7hfOZ4jj3WMce4Bo503RT1tTHa14euDV0bY/n6mAjXxljvIzBafj/M7WPBaMc+Et9/It8xnM8cz7HHOuZo+3V9jN5369qIzkT/b8e4vzbG/KOBYuGcW2lmy6OOQ4qPrg05El0bciSFvDbUIjBybo46AClaujbkSHRtyJEU7NpQi4CIiMgEphYBERGRCUyFgIiIyASmQkBERGQCUyEgIiIygakQEBERmcBUCIiIiExgKgREREQmMBUCIiIiE5gKARERkQlMhYCIiMgEpkJARERkAlMhICIiMoGpEBAREZnAVAiIiIhMYCoEREREJjAVAiIiIhOYCgEREZEJTIWAiIjIBBaPOoAoXHfddbZixYqowxARESkkN9TGCdki0NTUFHUIIiIiRWFCFgIiIiISUiEgIiIygakQEBERmcBUCIiIiBSR3p+uovenqwp2PhUCIiIiE5gKARERkQmsoIWAc+5059yQ4xhFRESk8ArdInAX8KUCn1NERESOoNCFQDmwpcDnFBERGTPMDOvKYGYFOV+hC4FvAh90zi0v8HlFRESKXrCjDfZ0QlMa295WkHMWeq2B5cB04FHnXBo4APiDjjEzW1DguERERCJjPTlyd2/GX7kbYg7qy/Dm1BTk3IUuBEqAlQU+p4iISFEyM4LnmsjetgG6MsTOn4G/uwPnFa5ffUELATO7vJDnExERKVbW1kP2tg0E6w/gplaQeM0ZeNOrCAo4mRAU4TLEzrllZlbYfwUREZECscDw/7KT3D1h3/n41QuInT8D50UztU9BCwHnXBL4Z+A6oIJDOyvGgUqgCogVMi4REZFCCPZ0kP3jemx3B97CWuIvXIRXUxppTIVuEfgc8CFgJ9ACnAk8AEwF5gJp4H0FjklERGRUWcYnd+8W/Ed3QlmCxMtPwzutnmKYY6/QhcArgXuBqwiT/07g3Wb2jHPuhcBvgEyBYxIRERk1/oYDZG9dD229xM6ZRvzK+bjSxBGPT715WQGjK/w8AjOA/zWzwMx2A43ABQBmdivwU+CdBY5JRERkxFlnL5lfP0v250/jEjGSb15K4kWLj1oERKHQLQJpDr3j30j4eKDPo8ArChqRiIjICDIz/FV7yN25GbI+8UvnErtwNi5enOv8FboQWA1cD9yc//054AUD9s8ECjOnooiIyAgL9neR/cN6bEcbbk41iRsW49WVRR3WURW6EPgO8N/OuQeAG4BfAG9zzv0EWAt8APhzgWMSERE5KZbzyT24Hf/B7ZCMEf+rxcSWTi2KzoDHUugJhX7pnKsGPgh0mdmdzrnvAO/OH7I9v09ERGRM8Le2kPvjeuxAGu/MBhLXLMSVJ6MO67i5Qq1udNQgnJsN1AJrzGzURw0sX77cVq7UTMciInLiLJ0ld8cm/NV7cTUlxF94CrGFtVGHdTRDNk8UekKhO4FbCEcOtPdtN7PthK0BIiIiRc3MCJ5pJHv7RkhniV0wi/ilc3GJsTkXXqH7CCwAfgzc5Jz7A/Az4I9mli1wHCIiIsMWtKTJ/XE9weYW3PRKEm84G29qRdRhnZRC9xGY55w7H3g14eRCrwBanXO/An5mZvcWMh4REZHjYX6A/8hOcvdtBc8Rv24hseUzCrpK4Ggp+KJDZvYo4XwBH3TOXUxYFLwMeLtzbjfwczP7cKHjEhERGUqwq53sH9Zh+7rwFteRuH4hrqok6rBGTLF0FjybcB2CFwFmZqP6oEWdBUVE5FisN0fu7i34j+2CyiSJ6xcRW1IfdVgnI/rOggM555YRPh54JTAf6CScYvi/oopJREQEwH9uP9nbNkBHhth5M4hfMQ+XiixljqpCjxpYCryK/uSfBVYAHwN+b2Y9hYxHRERkIGvvIXvbRoJ1TbiGchKvPANvZlXUYY2qQpc3TxBOIfwA8FXgl2bWWuAYREREDmGB4a/cRe7uLRAY8SvnE3v+TFysONcHGEmFLgQ+QtgZcEeBzysiIjKkYF9n2BlwVwfe/EnEbzgFb1Jp1GEVTKGHD361kOcTERE5Esv65O7biv/nHVCaIPHXp+Kd0TAm1gcYSeOz54OIiMhR+Juaw/UBWnuILZ1K/KoFuLJE1GFFQoWAiIhMGNaVIXv7RoJnGnGTS0m86WxicydFHVakVAiIiMi4Z2b4q/eSu2MTZHxil8whftFsXHxsrg8wklQIiIjIuBY0dYfrA2xrxc2qJvGiU/Dqy6MOq2hEUgg45+LAecBs4F6gG4ibWUsU8YiIyPhjuQD/oe3kHtwGiRjxF51CbNm0CdcZ8FgKXgg4514JfAuYkt90NVAC/NI59xkz+1qhYxIRkfEl2NZK9o/rsaZuvNPrSVy7EFeRijqsolTomQWvAX4OPAR8DfiX/K4twNPAl51ze8zslkLGJSIi44Ols+Tu2oz/xB6oTpF47ZnEFk2OOqyiVugWgU8BK4HLgUnkCwEzW+ucuwi4B3g/oEJARESOm5kRrNlPdsUG6M4Se/5M4pfNwyXVGfBYCl0ILAM+ZmbB4Gc0ZpZzzv2McOphERGR4xK0psnduoFgYzNuWgWJ152FN60y6rDGjEIXAhngaDM2TCZciEhEROSoLAjwH91F7t4tAMSvXUjsvBk4T50Bh6PQhcC9wNudc/82eIdzbhrwd4QLEomIiBxRsLud7B/WY3s78RZNJvHCRbjqkqjDGpMKXQh8DPgz8BRwK+FKhC91zr0IeAuQAj59ol/unPse4TDEd5x8qCIiUmwskyN3z1b8v+yE8iSJV5yGd2q9hgSehEIvOrTWOXcx8G3gvfnN78m/rwTea2arh/u9LrwCPgu8C/jRCIQqIiJFxl/XRPa2DdDeS2z5dOJXzMeVaF68k1Xwf0Ezexq4zDlXCywAYsBWM9t7It/nnJtPmPzPALaPWKAiIlIUrKOX7IoNBGubcPVlJN66DG9WddRhjRuRlVJm1gw0j8BXXQDsAF4L/GIEvk9ERIqAmeE/vpvcXZshFxC/fB6xC2bhYl7UoY0rhZ5QaPPxHGdm84/3O/OTD92S//4TjExERIpJ0NgZdgbc2Y43t4b4DafgTS6LOqxxqdAtAtsJOwgOFAOmAguB9cAdo3Fi59yNwI0As2fPHo1TiIjISbKsT+6BbfgP74BUjMRLluCdNUU3eqOo0J0FLzvSPufcucAKwiGGo3Hum4GbAZYvXz64GBERkYj5m1vI3boea07jnTWFxDULcGXJqMMa94qmu6WZPZ6fX+BTwK+jjkdERArDujNk/7SJ4Kl9uNpSEm84m9j8SVGHNWEUTSGQtxc4JeogRERk9JkZwVP7yP5pI/T6xC6aTfziObiE1gcopKIpBJxzU4G/BbZFHYuIiIyu4EA3uVvXE2xpxc2sIvGiU/AaKqIOa0IqllEDKaCBsOPg3xUuIhERKSTzA/yHd5C7fyvEPeIvXETs3OnqDBihYhg1AOATLkH8czP744l++dE6I4qISLSCHW1k/7AO29+Nd2o9iesW4ipTUYc14RW6EHg3sMbM1GtfRGSCsJ4subu24D++G6pSJF59BrHFdVGHJXmFLgTuAn4CfLTA5xURkQIzM4K1+8mu2AhdGWLnzyR++Vxcsmi6pwmFLwTKga0FPqeIiBSYtfWQvXUDwYYDuKkVJF5zBt70qqjDkiEUuhD4JvBB59zjZraywOcWEZFRZoHh/2UnuXu2ABC/egGx82fgPK0PUKwKXQgsB6YDjzrn0sABwo6CA5mZLShwXCIicpKCPR1hZ8A9nXgLa4m/cBFeTWnUYckxFLoQKAXUEiAiMo5Yxid37xb8R3dCWYLEy0/DO61eQwLHiKJZa0BERMYef8MBsreuh7ZeYudMI37lfFxpIuqwZBgK+tDGOXe3c+7Ko+z/K+fcM4WMSUREhs86e8n8+lmyP38al4iRfPNSEi9arCJgDBrVFgHnXBkwcLDoZcBvnHMbhjjcA64H5o9mTCIicuLMDP+JPeTu2gxZn/hlc4ldMBsXV2fAsWq0Hw2UA6uB6vzvRjhy4JtHON4Bd4xyTCIicgKC/V1k/7Ae29GGm1NN4obFeHVlUYclJ2lUCwEz2++cez3wPMIk/yngN8BTQxzuA/uBX4xmTCIiMjyW88k9uB3/we2QjBF/8WJiZ09VZ8BxYtQ7C5rZbcBtAM65OcD3zOzR0T6viIicPH9rC7k/rscOpPHObCBxzUJceTLqsGQEFXrUwFsLeT4RETkx1p0ld+cm/NV7cTUlJF5/FrEFtVGHJaNAEz6LiMhBZkbwTCPZ2zdCOkvsglnEL52LS8SiDk1GiQoBEREBIGhJk/vjeoLNLbjplSTecDbe1Iqow5JRpkJARGSCMz/Af2Qnufu2gueIX7eQ2PIZOE+dAScCFQIiIhNYsLOd7B/XYfu68JbUkbhuIa6qJOqwpIBGe0KhTYTzAtwJ3GVmLaN5PhEROT7WmyN39xb8x3ZBZZLEq04ntqQ+6rAkAqPdIvAV4Crgu8Ak59xqwqLgTuABM+sd5fOLiMgg/nP7yd62AToyxM6bQfyKebiUGognqtGeUOhm4GYXzjpxDmFRcBXwXgDn3MPkCwMz06qEIiKjyNp7yN62kWBdE25KOYlXnoE3syrqsCRizswKf1LnUsDF9BcGS4E24B7gDjP7/mief/ny5bZypeoOEZkYLDD8lbvI3b0FAiN+6Vxiz5+Ji2l9gAlmyN6fkbQF5R8J9D0iwDlXC1xJWBR8CBjVQkBEZKII9nWS/f06bHcH3vxJxG84BW9SadRhSREpiodCZtYM/DL/EhGRk2RZn9x9W/H/vANKEyT++lS8Mxq0PoAcpigKARERGTn+xmZyt67HWnuILZ1K/OoFuNJE1GFJkVIhICIyTlhXhuztGwmeacRNLiXxprOJzZ0UdVhS5FQIiIiMcWaGv3ovuTs2QcYndskc4hfNxsW1PoAcW0ELAedcbb4/gIiIjICgqTucGXBbG252NYkbTsGrL486LBlDCt0isNo59wMz+1yBzysiMq5YLsB/aDu5B7dBIkb8RacQWzZNnQFl2ApdCNQBewt8ThGRcSXY1kr2j+uxpm680+tJXLsQV5GKOiwZowpdCPwMeIdz7ndmtq/A5xYRGdMsnSV312b8J/ZAdYrEa88ktmhy1GHJCAgsoDvbRUe2k688+iV88/nqpV+nND76cz4UuhAIgNOAnc65jUAj4A86xszsygLHJSJStMyM4NlGsrdvhO4ssRfMIn7pXFxSnQGLUcbP0JFppz3TQUemnY5MBx2ZDtoz7XRmO/K/9+0Pf+7MdhJYcMj37OnczfyaBaMeb6ELgauBpvzPJcDsAp9fRGRMCVrT5G7dQLCxGTetgsTrzsKbVhl1WBOCbz5d2a6DybpjQOIOE/vA7e10ZDvpyLTT6x95Pb1kLEVVooLKeBmVsRLmJmuoTNVR5WJU4qgIjOc2/YHKXJbpXmEe9xS0EDCzeYU8n4jIWGVBgP/ITnL3bQUgfu1CYufNwHnqDDhcZkbG7+1P3Nn+O/TDk3z/753ZToyh1+PxcFR4SSq9OJXEqMcxz4xKP0GlD1V+hspMmspMN5WZTqrS7VT0tJHqbYcjfGefg03iXfuhauaI/lsMpejmEXDO1ZvZ/qjjEBGJSrC7newf1mN7O/FOmUzi+kW46pKowyoKfuDTmb/zHtz03pHtoKOnJXz1ttGe7aAj20VHrpusDX4K3a/UoMKMyiCg0s/SkMtQmU33J3I/Q1UuS4WfoTKXpcrPUObnOGzJpkQ5JCsgVQnJyvzPtVA5O/w9ld92cF/loJ/z+371WvBiMG3ZqP5b9il4IeCc+xvgOqACDvl3jAOVwOlAstBxiYhEzXpz5O7dgv+XXVCeJPHK0/GW1I3LIYFmRjrbTUd3Ix1d++hIN9HRc4COdAsdmbYw0eeTeIffQ0eQpcNydBEc8TtjFlCRT9SVuSxT/QyLclkqcxkq/SyVuezBpF4Z5Kj0klTGSkkkyw9NyCWVUDUoOQ+ZxAfsS5SFyXskvOOhkfme41ToCYU+DHwZ6AXaCYcT7gQmA2VAGvh/hYxJRKQY+OuayN62Adp7iS2fTvyK+biSImu0DXzIdEJvB2Q6Dv6c622jI33g4J14R/bQJN4ZZGgPcnQQ0OGMDufR4cXIeUdeBrlsQOKuzGWZFvhUGlS6WPg83UtS6ZVQmSijMlFBZaKSslQVru8u+1h33/ESGIcF1oko9FX2VmA1cClQD2wELge2ATcC/wY8UuCYREQiYx29ZFdsIFjbhGsoJ/Hy0/BmVY/Ml+d680m785DEfejP/fust53uTL45PdtNe9BDp5+hw7K0E9DhHB3xJB2xBB3xJO2xBB3xBOnYkRc0ijuj0gVUeo5Kl2S6i1HlJamMlYQd5hLlVCYqqSyppjI1icqSyVSW1RMvqe5P3MkKiKuheLQUuhCYC3zUzDqADudcC3CxmW0Gvuucuxh4P/CrAsclIlJQFgT4T+whd9dmyAXEL59HbHktLtcFB/YfO3EfI8Fns120O0dnPEFHLEl7/r0jPjCJJ+nsS+r5n/2kg6RH+PS24pCYy/N34lVeiupEGTPj5VQmq6hMVYVJvHQylaV1VJU1UFlSQ2WykpJYybh8tDGeFLoQyAIdA37fAJw14Pd7gC8UNCIRkRFkQQAdPdiBJqypCWtpw9q6oLMX6wqwHg/LpMAvAzw87xni7tt4d22Auw7vTR4A3bE47X1JPJakI1lKe6qSjkQZnckSOlIp2ssm0eHVhXftzug5Ss/0pIuHd+LJKiqTVcxKVYfJPFlFZaKSqmRlfl9l/lVFRaKC2Eg9A5eiUuhCYC1wAfCj/O/rgOUD9k8CNE+miBQXM6ynE/bvCRN8cyvW1ol19EC3j6U9LJPEsmVglRzaD9qBlQEZoA0/3kkm2UtXro2WxH52LeikPX4+HbELwiSO0YFPR5CjI8jQ6fcQHCGpOxwViYqDybo2WcmcZFU+kQ9K5onw96pkJam4RiBIv0IXAj8BbnLOpYB3Ab8Dfumc+zRhkfB+4MkCxyQiE5Gfhe4mrGMfHNiPNbdgrR1YRw/WmYO0wzIJLFuKBVVgNeD6EnwZUIYRYLSTi3fQm+igu7SJjpIMrakszakMjSU97I13sifWTkvQTWeu6wixQIrUwcRdlaykLllFxcG784FJvergHXt5ohzPHbnDncjxKPSEQt9zzs0E3kP4mOB/gT8An84f0g78UyFjEpFxwgx62qCrEboasY5GaG7OJ/durDOHpYHeOJYtwfwqjMlADbgE0JB/gRGQ8zrpSXTTVd5DR7KR1tR2mpJpGhNd7Im3s8sdYH+sjcAdfrceczGqU9VUJquoTlYzNzWDM5NVVKeqqcq/hwm9P6knY+oMJ9FwZkef4WhUTupc3MxyA36/mHAI4cNm1jja51++fLmtXLlytE8jIicr1xvOrta1b0CC3w+tbVh7N9aZxboN64lBtgSzGow6jFrCBH/o3bIR0BPvpiuRpi3RTWsyTVOii33xdvbEWmiOd9Ka6KQt3n1Igi+Nl1KdrKYqVUVVsvpgQq/qS+yD9pXFy9RBTorRkBdloecR+CFwi5ndO3C7mT1QyDhEJCJBAD0tB5M6nQMTfBO0d2KdmXynuhjmlwF1BNQSuDqMyXjMOfxrCehKdNOW6KYl0cX++C6a4mtpiXfSkuikJd5FSz7BO+eoTOWTd7Iqn8AbWJhaSHWy+pB9YcKvJnGU4XEiY12h+wi8Fnirc2438HPg52a2qsAxiMhIynT3J/ZDXmGSt84DYS/6Lj9M7jYJ39WR8erwqQcmE2M2Cas67Kt9F9Ce6KY53klzooPW+FaaE520xrvy7510l+SgLEFlSdWA5F1FVWoKU5OD7txT1XquLjJIoQuBeuCvgFcT9hP4B+fcOuAWwqJgS4HjEZHB/BykDxwhufffyVtnM9aVw3Kl9HgN9HgNZF09gavDUUvMziVlk0gF5f3f7cKXT0B7vGvAHftmWvIJvqc0IFfmsIo4sYoSqkryyTxZT3VqITMGJvtklXrAi5ykSPoIADjnKoGXEhYFVwEJwlkF/8vMbhrNc6uPgEwoZuFkM0dL7APu4rPdHXTFGuiKTaXHa8D36ggIk3ucOlJBNWV+FWV+6WGnyuHTmggTfGuii9ZENz2lPr2lRlAewyrieBWlJCvLqS4Z+Fw9fK9IVhBzGqsuMkqG7CMQWSFwSBDOzQO+BbwIMDMb1f8SqBCQMS+Xge6mQ5rgh3pZVyPp9IFwethYBV2xBjKuHt/VY24ynqsnYbWUBNWU5SqpzJVTcZQE355I05nK0FPqkykjn9yTxCpLSFVXUFbd1wwfJnnNKidSVKLvLDiQc64OeBnwKuASIAbcB/xXVDGJRMYMelqHbIIf+Lvf1UhHTzPtuTTt8SRt8SSdsXJ6vbBJ3lwdMepJ2CJK/SrKrYKaWAWTcuXM6h0iwTufjmQP3aks3RUBbaVpgoosriJJrLqMVFUFpZOqqayexNRUFTO9IlsER0ROWqFHDUwGXg68knDhoTjwFPBxwj4COwsZj8iIMgM/Ew5583sh1xP+PFSCz9/F93btp623mfZMB22eR3t+zve2eJIuV07g6sCbQpzpJP1TKYtVMCkVJvfabDlTuyuo8A9/Ru67gO6SHL2lAbkaR3t5nM7KBPGqUlI1VZRPqqGkpopUWYIK3bGLTGiFLu/3Es69uR34GmF/gDUn84XOuRjweeAtQCWwAni3me07uVCl6AX+gKR7vO89xzzGcj0Efi+5XC9Zvxffz5DzM2T9DH6QIednyVmWnJ8L3wOfXJAjZz455+VfLnz3PLq8+CEJPh2rxHOTibk5VJScTm2sgppkBbXZCmpy5czqqWBStpzyYIgE7xmZUiNX7kF5AleVIlNVTqqmknhlGa4yiatMQmmCciV4ETkOhS4EfkCY/B8awe/8DPBm4E3AAeAm4NfARSN4DjELp2QdVtLNJ9VcDzm/h2y2B9/vIZfrySfYXnJ+htzB9wy5IBu+/Cw5y4UJti/JWi5MuhaQswDfQTafdP18As72/YxH4OIExAlcjMDFMRKYixEQw4iHLxf+7A5ui4FLEbMyYubwzCNG/uV5xJyHF/OImxfu69uf/zk83hGz2MFtk3IlLOyppDZXSU22nBL/8DHpgQd+uQeVSWJVJSSqyvNJPQUVYXJ3FSkojSvBi8iIKvQUw383kt/nnEsC7wPea2Z35Le9BtjinLvAzB4eyfMVVBCEzcwD7mLDpJoml+0ml0uHr2yaXK4n/NnvIZfrDRNrrodckCGXy5ALesnlsvh+Lv8KCPwAPwhfQWBYYPgGFjgsMAIcgYGZl385zIWJ0oiDi4VJkzhh947w5Sz/TgzwcMTDBIlHzJLErCSfPMMkG7cYnrkhkqlHqR26Lc6A5GuOGP3Jti8he/l9HtGNEzcP8Bw4B57DlcZxNSlcZSqf0Acl+MoUlMTVqU5EIjHWe/4sJXwccG/fBjPb6pzbClwMjHoh0Nm0g7V3/zeBH2BB/uXbweSKgQWAufDn/LsLPMJVyTwwD2cejoHvMcJ0FsPr+/lgQj008cWtHM8qieOROiR59iXYGN7QnUULInCGeRb+E3hgnsMc4Ln+pOm58KGR5+E8BzF38GcX83Ced/Ddi3k4L5b/ve/Y/sRL3/YjvWLuqPud5x12/KH7D/++gz87p4QuImPKWC8EZubfdw3avhuYVYgAuvfv4sznlh/7wAFyzicgIHD9L8PCd2f9L6/vZ8Czg8kTjzDp9SXLmAeeRxDzsFgMPxbHxWK4eBwXS+DFY7hYDC/m4XkxvFgMLx7D82LEYjG8WBwvFsMdkvC8QxNf7GiJc6j9+c87lBhFRIrYWC8EyoDAzLKDtvcCh/S0cs7dCNwIMHv27BELoHb+Urpeu5VYspR4spRYspRYPJFPqoffWSopiohIMRnrhUAa8AavZgikgEMW/jazm4GbIZxQaKQCiKdKqF60ZKS+TkREpKDG+sobO/Lv0wZtn87hjwtERERkkLFeCDwJdBBOTgSAc24uMBe4P5qQRERExo4x/WjAzHqdczcBX3fONQGNhPMI3Gdmj0QbnYiISPErikWHToZzLg58hXBSoQT9Mws2HeUz+4FtR/naaqBtGNsB6oAjnrMIHC32Yvn+E/mO4XzmeI491jFH23+kfcV+bcDoXh+6NnRtjPb3j+b1MZ6ujSYzu+6wrWam16AXcPNwtuf3rYw67hP5m4rp+0/kO4bzmeM59ljHHOMaONJ1U9TXxmhfH7o2dG2M5etjIlwbY72PwGj5/TC3jwWjHftIfP+JfMdwPnM8xx7rmKPt1/Uxet+tayM6E/2/HeP+2hjzjwaKhXNupZkNb2YhmRB0bciR6NqQIynktaEWgZFzc9QBSNHStSFHomtDjqRg14ZaBERERCYwtQiIiIhMYCoEREREJjAVAiIiIhOYCgEREZEJTIWAiIjIBKZCQEREZAJTISAiIjKBqRAQERGZwFQIiIiITGAqBERERCYwFQIiIiITmAoBERGRCUyFgIiIyAQWaSHgnJvpnPulc67ZOdfqnPuFc276gP2vd86tc86lnXOPOOfOG/T5hc65Fc65TufcDufchwr/V4iIiIxdkRUCzjkH/BGYBFwOXApMA36f338V8GPgX4BzgKeBPznn6vP7k8AKoAN4HvBPwGecc+8s7F8iIiIydjkzi+bEzk0Fvgl8xMy25re9BPg/oBb4BbDHzN6S3+cBG4AfmdkXnXOvBX4ATDWzzvwxnwZeb2anFPSPERERGaMiaxEws71m9poBRcBM4F3AY0AbcCFw74DjA+B+4OL8pouBlX1FQN69wCLn3JTRjl9ERGQ8iEcdAIBz7v+AlwAthI8JaoByYNegQ3cDff0EZh5hP8AsYN+RznfdddfZihUrTipmERGRMcYNtbFYRg18EjgfeBC4A6jMb+8ZdFwvUJL/uewI+xlwzJCamppOOFAREZHxpChaBMzsaQDn3GuAHcAb8rtSgw5NAV35n9NH2M+AYw5yzt0I3Agwe/bskw9aRERkHIhy1MCUfOI/yMy6gU3AdMJkPm3Qx6bT/zhgxxH2w+GPDDCzm81suZktr6+vP9nwRURExoUoHw3MAX7unFvet8E5Vw0sBtYADxMOKezb5wGXEHYYhPAxwnLnXNmA77wcWGdmjaMcu4iIyLgQZSGwEngA+KFz7nnOuWXA/wD7gZ8C3wDe7Jx7t3PuVOD7QDXww/znfwM0Az9zzp2RH074IeDLBf47RERExqwohw8GwMuA1cAfgPuAduBSM+s0sxWEz/T/AXgCOA24xsya8p9PA9cBVYRDDr8MfMzM/r2wf4mIiMgI+vGl8N2lBTtdpJ0F80n9LUfZ/xPgJ0fZvw64YuQjExERiUDjGtj3JPS2wbYHYM7Fx/7MSSqW4YMiIiITV28n/OnD8L2zIdsFtYtg1gUFOXVRDB8UERGZkMxgza9gxQegYxcsextc9WUoL9zoNhUCIiIiUWhaB7f+PWy+A6YuhVf9Ema9oOBhqBAQEREppEwX3P8FePjrkCiD678Ny/8GYtGkZBUCIiIihWAGz/0WVrwP2rbDWW+Ea74GFdGuk6dCQEREZLQ1bwofA2y8DRrOgLfcB3MviToqQIWAiIjI6Mmm4cEvw4NfgVgCrv0GPO894c9FQoWAiIjIaFj/R7jtvdCyGc54LVzzdaiafuzPFZgKARERkZHUshVWvB/W/RbqlsCb7oL5xTv3nQoBERGRkZDrDUcC3P8FcA6u+go8//0QT0Yd2VGpEBARETlZG/8Et74HmjfAaa8I+wJUz4o6quOiQkBEROREte0IZwVc++twWuA3rICF10Yd1bCoEBARERmuXAYe+Ve475/BArj8c3DhhyCeijqyYVMhICIiMhyb7w4fAzSthcUvhuu+BZPmRh3VCVMhICIicjzad8Of/gGe+QXUzIPX/h4WvyjqqE6aCgEREZGj8bPw6Lfh3k+HP1/6abjonyBRGnVkI0KFgIiIyJFsvR9ufTc0PgMLr4fr/x9MXhh1VCNKhYCIiMhgnfvgTx+Cp/4TqmfDq38DS14Szg8wzqgQEBER6ePnYOV34e5PhOsEXPyx8JUsjzqyUaNCQEREBGDHn+GPfwd7V8P8q+CF/wZ1i6OOatSpEBARkYmtaz/c+RFY9WOonAGv/J9wdsBx+BhgKCoERERkYgp8ePwHcNfHINMBF3wILv0UpCqijqygVAiIiMjEs+ux8DHA7pUw97LwMUDD6VFHFQkVAiIiMnF0H4C7Pg6P3wwVU+Bl/wVnvnbCPAYYigoBEREZ/4IAVv8E7vgn6GmF578PLvsslFRFHVnkVAiIiMj4tmdV+Bhg5yMw+yJ44Xdg6llRR1U0VAiIiMj4lG6Fez4Jj90EpZPhpf8OZ79pQj8GGIoKARERGV/M4Mn/hDs+BN1NsPxv4YrPQ2lN1JEVJRUCIiIyfux7OnwMsP1BmHE+vP42mH5O1FEVNS/Kkzvnpjjnfuqc2+Oca3XO3e6cO2PA/tc759Y559LOuUecc+cN+vxC59wK51ync26Hc+5Dhf8rREQkcj3tsOKD8L1lsH8tvPiH8PaHVQQch8haBJxzHvAbwAEvATqBzwB3OedOA5YBPwb+HngA+CDwJ+fcKWa23zmXBFYAq4DnAUuBHzjnWs3sBwX+c0REJApm8Mwv4PZ/gM69cO474covQtnkqCMbM6J8NHA28ALgNDNbC+CceyPQDNwAvB74uZndnN/3LuAK4J3AF4GXA1OBt5pZJ7DGObcI+BCgQkBEZLxrXAO3vge23gPTzoXX/B/MfF7UUY05UT4a2A68CFg3YFuQf58EXAjc27fDzALgfuDi/KaLgZX5IqDPvcAi59yU0QlZREQi19sZzgfwvbNh7yq44SZ456MqAk5QZC0CZnYA+OOgze8FSoGVQDmwa9D+3UBfP4GZR9gPMAvYN2LBiohI9Mxgza/h9g9A+05Y+la4+itQXh91ZGNa0YwacM69GPgS8A1gW35zz6DDeoGS/M9lwP4h9jPgmIHffyNwI8Ds2bNHIGIRESmYpvVw29/Dpj/BlLPhFb+A2RdGHdW4UBSFgHPuLYTP9X8BfJjw0QBAatChKaAr/3P6CPsZcMxB+b4GNwMsX77cTjpoEREZfZlueOCL8PDXIF4C130Lzvs7iBVF+hoXIv+XdM59HPg88G/Ae83MnHPNhMl82qDDp9P/OGAHsHiI/XD4IwMRERlLzGDd7+C290HbNjjrDXD116ByatSRjbogCNiy5ikWnLG0IOeLtBBwzn2YsAj4lJl9rm97vhh4GLgU+M/8sR5wCf0jAh4EXu+cKzOz7vy2y4F1ZtZYqL9BRERGWPMmuO29sOFWqD8d3nIfzL0k6qhGXaa3l+ceeICKle3MyNSyxXuKeaeN/poIUc4jcBbhMMAfE47/H1jmdRD2Ffi9c24VcDfhPALVwA/zx/wG+ALwM+fcJ4AzCYcOvrswf4GIiIyobBoe/Ao8+GWIJeCar8P57w1/Hsfa25rZdNdDTH8uxim5MnaXeWx4XsDiRYMbvUdHlC0CrwFiwNvyr4E+aWafz3fw+yTwdeAJ4BozawIws7Rz7jrgu8BjQCPwMTP79wLFLyIiI2X9rWFnwJbNcPqr4dp/gaoZUUc1qvbs2MLeu1cxf3slp1olmyYfoP2CehacfQmeV7jR/c5s4vWbW758ua1cuTLqMEREpGUrrHg/rPst1C2BF/4bzL8y6qhGTRAEbH56NT0Pb2b+/snknM/mWW3UX3o2M+YtHO3TD7nsYuSdBUVEZALK9cLDX4f7vxAuC3zVl+H5H4B4MurIRkU2m2Hdww9T8ngLMzsn0REvZ91pHcy9/PksndwQaWzHLAScc7OB/WaWLkA8IiIy3m38Uzg1cPMGOPXlcO03oGZ8zu/S1dHGhnsepGGNsTBTwb7SGOvOz7D40ktZVlIWdXjA8bUIbAHeCPxslGMREZHxrG1nOCvgml9B7cJwieBF10Ud1ajYv2cnO+9+jLlbylkSlLO15gDtl09m0Xk3MMeLRR3eIY6nEBjymYKIiMhxyWXgkW/Cff8M5sPln4ML/hESh00CO+ZtXfs07Q+sZ8HeSSyiik3TW5l0yaksOeWyqEM7IvUREBGR0bPlHvjju6FpLSx+MVz3TZg0L+qoRlTOz7H+0UeIPdbInLZaqmIVrF/Uzuwrz+fshsHz4hWf4y0EJt7QAhEROXEde+D2f4Bnfg418+C1v4fFL4o6qhHV093FuvsepPbpXub3VNGUSvDcuWlOufQillVURh3ecTveQuCbzrkvDON7zcwWnEhAIiIyhvk5+Mu/wT2fAj8Dl34KLvoIJEqjjmzENDftY9tdjzBrY4rFfgk7KrvYdIHH4udfz8z42GtoP96IHcPrK6B+BSIiE822B+GPfweNT8PC6+D6b8PkUR8bXzA7Nq6j+f5nWLCrhsVWxaaGA5RfNI1FZ1wWdWgn5XgLgfebmUYNiIjI4Tr3wR0fhif/A6pnw6v/F5a8NJwfYIwLgoANj/8F/9FdzG+eTKVXyYZ5bUy//FzOnHF51OGNiLHXhiEiIsUh8OGx78Ldn4BsN1z0Ubjk45Asjzqyk9bb28O6+x+ganU3c9LVtCZKWHtWJ4uuuJBlVZOiDm9EqRAQEZHh2/FI+Bhg7yqYf1U4NXBdYRbJGU3tLQfYdPfDzFiXXwCo3NhwccDii65iaiIVdXijYlQKAefcG8zsltH4bhERiVBXE9z5EVj1I6icDq/4bzj9lWP+McCebZvZd+9q5m+vChcAqjtA2wsKvwBQFI6nEJgHNDvnXgE8n7Aj4BPAL8zMH3igc24O8D3gGkCFgIjIeBH48MQP4c6PQqYjnBDo0k9BauwMkxts4AJAC/fXUe6qDi4AdPq8y6IOr2COpxBIA/cDZ9E/GsCAjzjnLjGzFgDn3PuAzwPlwIOjEKuIiERh18rwMcDux2DOpXDDd6Dh9KijOmHZbIZ1Dz1MyRP9CwCtPa29KBYAisLxFAJfBM4Gvgv8FOgCrgc+Dfw/59zbgf8GXgy0Ah8wsx+OSrQiIlI43c1w98dh5fehvAFedguc+box+xigq6ON9fc8yNQ1sDBTHi4A9PwMiy8pngWAonA8hcBVwP+a2bsHbHvWOdcNfAXIAC8Bfgu8y8waRz5MEREpmCCA1f8Od/4TpJvh/PfC5Z+FkuqoIzshjbt3sOuelczdUs6pQTlbag7QdkUti5YX3wJAUTieQmAKcOcQ21cA/wa8CXivmf3bSAYmIiIR2LM6fAyw888w6wK44SaYenbUUZ2QLWueouPBDSwctADQqUW8AFAUjqcQSAHtQ2xvy79/R0WAiMgYl24NpwV+7DtQOhle8hM4+00wxnrMH1wA6C+NzGmvpTpWwbpT2pl9xdhYACgKIzF88LYR+A4REYmCGTx1C/zpQ9DVCOf9LVzxeSgdW5Pm9HR38dx9DzD5qSzzeyvzCwD1cMqlF46pBYCiMBKFQHYEvkNERApt39PhEsHbH4AZz4PX/xGmnxt1VMPSvH8v2+5+lFkbUyzxS9he2c2mCz0Wv+B6ZsY0Z97xON5/pcnOudmDttXm3xuG2IeZbT+pyEREZHT0tMN9n4VHvhV2APyrm2HZ28fUY4AdG5+j+b5nWbC7hsVWyaYpLVRcNI1TTr8s6tDGHGdmRz/AuYBw3oAhdx9hn5lZ0ZZiy5cvt5UrV0YdhohIYZnBM/8Nt38QOvfCOe+Aq74EZZOjjuy4DF4AqMfLsGVuJzMuP5eGGXOiDm8sGHLc5/Ek65+OcCAiIlJo+9fCre+BLXfDtHPgNb+BmedHHdVxGXIBoLM7WXT5+FsAKArHLATM7K2FCEREREZBpgvu+xz8+RvhqoA33ATn3ghjYPx8W0sTm+9+mBnr4pySK2NXubHhkoDFF47fBYCiULTN9yIichLMYO3/wooPQPsOWPoWuOorUFH8U+geugBQVX4BoIYJsQBQFFQIiIiMNwc2wK1/D5tuhylnwSt+DrMvjDqqowqCgM1PraLn4a0sbJp8cAGghsvO5vS5l0Ud3rimQkBEZLzIdMODX4KHvgrxErjuW3De30ERD6PLZjM899BDlD3Rml8AqIy1p7Uz74oXsLS2PurwJoTivTpEROT4Pfc7WPE+aN0KZ70Brv4qVBbvTHqd7a1suPchpq6BRZly9pV6rHt+dsIvABSFoikEnHPfA+Jm9o4B264BvgosBjYA/2Rmtw3Y30C43sE1hIsf/QT4uJnlChm7iEhkmjeHBcD6P0D96fCWe2HupVFHdUSNu7ez6+7Hmbs1vwDQpL4FgF6kBYAiEnkh4JxzwGeBdwE/GrD9NOB3wOeAXwOvB/7POXeOmT2bP+zXhPMYXArMAP4dyAEfL1T8IiKRyPaEjwAe/BJ4cbjm6+EqgbFE1JENKVwAaD0L99YOWADoNC0AVASOOaHQqJ7cufmEyf8MoBu4o69FwDn3fWCxmV024Ph7gA1mdqNz7gXAw8B8M9uS3/9m4NtAvZn1Hum8mlBIRMa0DbeFnQFbNsHpr4Zr/wWqZkQd1WFyfo71j/yZ+GP7md1eS1esh+0LephzxfnUagGgKJzwhEKj6QJgB/Ba4BeD9l0M/M+gbfcCrxmwf1tfETBgfyWwFHh0ZEMVEYlY6zZY8X547v9g8mJ44x2w4KqoozpMuruTdfc9eNgCQIsvv4hlZRVRhyeDRFoImNktwC0A4ROCQ8wEdg3athuYdYz95I9RISAi40OuFx7+F7j/8+AcXPkleMEHIZ6MOrJDNO/fy9a7H2H2xpJwAaCqbjZfGOMULQBU1Ir5f5kyoGfQtl6g5Ej7zSzrnLMBx4iIjG2b7oRb3w0H1sOpL4Nr/xVqDlvnLVIDFwBaYlVsnNJMpRYAGjOKuRBIA4PnkEwBXUfa75xLED4D6Rr0OZxzNwI3AsyeXVz/JxIROUzbznBxoDW/hEkL4PW3waLroo7qID/w2bDyL9iju5nXMplKr5IN89r5/+3da4xc5X3H8e8zM2due7GNjcFeG4yNvYIScEJagoXxJaraJkFVXrRq+yJNq0oRbdT2RaPmBa0K7ZtKVYuUKkQoeVNFifKCF03UixJUDA6YRBCF0IC5uMYY2xgMxnuZmTPn8vTFObs7szNnb7Oz5+zO7yONPHNu+1/77/0/z3POPs/YsU9w59jxtMOTZchyQ+A8MP9pkp3M3Q44D3ymy37ovGWAtfZx4HGIHhZcvTBFRFZR4MHzj8KJh8EGcOwROPQVcLIx0Nm6ANCe+iauOmVevWs6XgBoc9rhyQpkuSHwY6JfC/z7lm3HgGda9v+jMWa3tfZ8y/5J4OdrFaSIyKo5eyK6DfD+K3Dgc9HMgNftTTsqYOEFgHZoAaB1LcsNga8BLxpjHga+C/wBcA/wYLz/FPA88D1jzJeBG4gmH/pna20zhXhFRFZm8hL88K/g5e/A5j3w+9+H8QfSjgqAS+fOxAsAbZpbAOjQdvbdqQWANorMNgSstS8bYz5PVNz/GjgNPGCtfTXeb+P9jwEniUYCvgk8klLIIiLLE/jw03+Fp/4WAhfu/xu476tQTHeK3TAMOfOLn9F87hz7rmxlyGzSAkAbWKoTCqVFEwqJyJoLvKjnP3kBJi7Ak1+FqXfBm4Z9vwGf+Rps3Z9qiLMLAL14jbHpzUwU6lwY97jl2L1s1gJAG0EmJxQSEVn/3MmouE+8M1foJ+PPM++nLhPNiN4iX4LffQJu+3w0P0BKpiY+4o2n4gWAvCHerRhev9fjwOEjbNcCQBueGgIiIknCEKbfSy7uM8W/Odl5buU6GBmLpv7d8fG59yNjMLorel+5LtUGQNcFgO6JFgDKawGggaGGgIgMJq8Bkxe7FPeW95MXIZy3mKnJR8v7ju6C62+Hfb/eXtxnCr5TSef7WoKzr/yCyZOvc+vleAGgsXgBoP1H0w5NUqCGgIhsLNZC/erivfj6B53nOkNzBf3mI/OKe/x+aDusw96yH/i8fuoUhReiBYBG88O8dmCCm4/fw11aAGigqSEgIutH4EcP2CUV95n3fr3z3KHtUUHfdBPsvrd7L740mupQfT/Ua1O8fuIkW1/2ZxcAeu2TdQ4cPawFgARQQ0BEssKdWrwXP30ZbNh+Xr7Yci/+bhj/7fbiProLhndkboGefvvwvUuc+5+fcNOZMuNBhbdHP+T/7stz4FNaAEjaKRtEpL/CEGpXFu/Fu9c6zy1vnuu53/Cx9uI+8766bcP14nvx9huvcvWZV9h3YTPjxAsAHd7JgduPph2aZJQaAiKycr4bPVCXVNwn3okfuPPazzO5qJc+OgZbx+GW4+3FfXQXjOyE4lA639c6M38BoNHcCG/snWDs+N3cuVOLrMnC1BAQkU7WQuPa4r342vud5zrVuYJ+8+HuvfihG0DD0z1z3Qannz7Jppe0AJCsnP4nigyaMIgeuEsq7jPF36t1nlvdNlfQx36tvbjPDOGXN2movs8++vB9zj51irHXHMb9SrwAkNUCQLIiagiIbCTN2gJD9PH7qXej5W1b5ZxoKH50DG48CPs/2/lrcyM7oaAik6ZL585w+amfs/d8tADQm9s+YOLQdvZqASDpgRoCIuuBtdEDd4v14hsfdZ5bGp3rue+7vcuvze2KevoqJJnUdQGgm65xw9GD3HHz0bTDkw1ADQGRtPlNmLq0yFz1F6PV6doYGL4xKuhb9sUT4MzvxY9BSb8rvh41PZfXnn2W6osT7J7ezEShyqu/MsHe44c4uGVb2uHJBqKGgCzN/FUq2z7b5ONWtG+JX2u9xFH/cIHfj38nmst+vkJ5rqAnTX4zfCPknc5zZV2bmrgaLwBk2hYAGr//GNtL5bTDkw1IDYFeXT0L3/h4y4YNVPikPypb5wr6jrs7J78ZGYPKFj1wN2BmFgC65a0hbguHObvlAyY+tZVb7z6sBYCkr9QQ6FVxBA5+sX1b2w9ws7R9HT/0V7JviV9LcaxtHLOT4sQP3GV4MRpZe2d/+RJTP36TfZe3sN+McmanFgCStaWGQK+GtsFvPZp2FCKSEX7g05ieolGr4dZrNOs1vIaL33AJGk1C1yN0PWgGDF0MuGniOqbzQ5wen2DPMS0AJGtPDQERGWhhGOI2atRr03OFu9bAiwt34DaxrodtBuAGGC8k50Heg4JnKPg5in6eUpCnFDiUrEMBGI5f7XJACSgREPJBeYrTv9pg/Mh9fEILAElK1BAQkXUlDEM8r0mjNoVbm8at12nWG/iNRkuv28e6PjQDaIbkPEves+Q9g+PncII8JT8u3KFDDkMVqHb9isX4BfVcEzfv0cwHNAsBfiGkXvWZdgJCx2CLTSjmMKUCuZJDvlQkXy7ilMs4lTKlapVSpUq5OkSlWOamXA5NACxpU0NARPrO930atcmW4fI6Xj0q3L4bDZdb1wc3gGZAzrPkmpa8byh4hmKQp+jnKQYFyoFDgXzcr+7GiV/QND6NvEcz79MsBHiFELcUUnNCwqKHdaLCTVy4cyWHQrlIoVzCKVcoVSqUqlXKlaHoTz20JxuQGgIi0iEIA9xajUZ9GrdWw63X8Rr1qHC33ud2fWiG0XB5Mx4u96Ned2vhLtrCAsPl+fgFPkFb4W7mA3zH4lY8wqJP6DShmIdSfrbXXSjFhbtSplipxD3uKqXKEKNOkdE1/ZsTWX/UEBDZAMIwpNls0Ijvc7u1Gl69gddozN7nnu11N0NMPFye82zbfe5ifJ+7EhbJQcJwuWFmuDzE4uY83LyHWwjw8lGvu1b1mXQCQseDogvFmcJdIB/3uAvlMsVKOe51D1GuDlN1igxrhkORNaWGgEhKovvc07PF26vXo6fL6y6B68a9bh/bDKLC3YwKd943OJ7BmS3cBcpBkTw5ykD3KWfmhstdExfulvvcbjkeLnfi4fJSA1PMY9ruc5dwqmWKleg+d6U6RKlcoarhcpF1TQ2BHk1eu8qbT56MPiTNx9M6ac9ic/a0HTr3wSRee9GLLP61l3ONxc5r2WaSdizj76DVsv4Okq6ReIztPCppf9v32HLFjombov0miJ4ud/zcbOEuxcPlM+V5pOPMAjP/PX0T0Mh5uHmfZsHHK4R4jqVR9Qkdn7DYnO11z9znzs/e5y7NFu5ytUq5Oky5oP/2IjJHPxF6VJua4LZfLn4XMkyoyHb2z+4V1HY5dr7ZY1Z6Xsu5SzovoSJ3O7PtyIT4ul5jOccuEFP3OJbyPXYekfgVFok1yNtouLwUD5cX5w+Xx4W7VKRQKVMsVyhVK5Qr0XD5cKnU5b66iMjqUEOgR9fv2A0P7Z79rKVARURkPVFDoEcq/CIisp6piomIiAwwNQREREQGmBoCIiIiA8zYLr/ytNEZY94Hzi1wyCbg2jK2A2wDrvQYWj8tFHtWrr+SayznnKUcu9gxC+1P2pf13ID+5odyQ7nR7+v3Mz82Um5csdb+ZsdWa61e817A48vZHu97Ie24V/I9Zen6K7nGcs5ZyrGLHbNIDiTlTaZzo9/5odxQbqzn/BiE3NCtge5+sMzt60G/Y1+N66/kGss5ZynHLnbMQvuVH/27tnIjPYP+s2PD58ZA3hroB2PMC9baT6Ydh2SPckOSKDckyVrmhkYEVs/jaQcgmaXckCTKDUmyZrmhEQEREZEBphEBERGRAaaGQB+ZyL8YY34Sv46mHZNkizFmvzHmbNpxSHbEPzceNcacMsb81BjzcNoxSTYYYwrGmG8ZY54zxjxvjPn0alxXaw301wPAHmvtPcaYXcCTxpg7rLV+2oFJ+owxXwa+CFyfciiSLZ8j+rlxrzEmBzxrjPkva+3zaQcmqfsdwLHWHjLG3AL8CLi114tqRKC/jgD/AWCtfYdocojbUo1IsuQScDjtICRzngT+KH5vgSLQTC8cyQpr7XeBP44/7gGursZ11RDor/kzRk3E20Sw1j5hra2nHYdki7W2bq29aozJA48B/2ut/VnacUk2WGt9Y8xjRPMPfHM1rqmGQH9NAKMtn0dZpRaciGxcxpgtwH8CDeZGB0QAsNY+COwA/tQY0/NcA2oI9NczwGfjh392Ef3DnU45JhHJMGPMCPA08D1r7V9aa8O0Y5JsMMZ8yRjzUPyxDnhAz/mhhkB//TvwNnAK+D7woLU2SDckEcm4rwC7gS8YY07EryNpByWZ8B3gLmPMSeAk8G+rcdtIEwotgzHmG0DBWvsnLdvywD8QPf09Avw38GfW2supBCmpUG5IEuWGJMlKbmhEYAniof1HgC912f13wB8CXwDuB3YBT6xddJIm5YYkUW5IkqzlhuYRWIQxZi/wLeAOomH+1n1F4C+AP7fW/ije9nvAWWPMIWvtc2sdr6wd5YYkUW5IkizmhkYEFncIOA98DJg/A9xBoqGbEzMbrLVvAW+h3w8fBMoNSaLckCSZyw2NCCzCWvtt4NsAxpj5u3fFf16Yt/0i0cM+soEpNySJckOSZDE3NCLQmyoQWmu9edtdoJxCPJIdyg1JotyQJKnkhhoCvakDOWPM/JGVEjCdQjySHcoNSaLckCSp5IYaAr05H/+5Y972nXQO7chgUW5IEuWGJEklN9QQ6M1LwCTR4kIAGGP2EC0G8Uw6IUlGKDckiXJDkqSSG3pYsAfWWtcY83Xgn4wxV4D3gK8DT2vJ0MGm3JAkyg1JklZuqCHQu4cAh+gpUId4FqhUI5KsUG5IEuWGJFnz3NAUwyIiIgNMzwiIiIgMMDUEREREBpgaAiIiIgNMDQEREZEBpoaAiIjIAFNDQEREZICpISAiIjLA1BAQEREZYGoIiIiIDDA1BERERAbY/wMcXzB6M65qMgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 1, figsize=(8, 10), sharex=True, sharey='row')\n",
+    "plt.sca(ax[0])\n",
+    "for ii in range(num_methods):\n",
+    "    plt.errorbar(df.num_trials.unique(), c2sts[:, ii], vc2sts[:, ii], c=colors[ii+1])\n",
+    "plt.xscale(\"log\");\n",
+    "plt.ylabel(\"C2ST\")\n",
+    "\n",
+    "plt.sca(ax[1])\n",
+    "for ii in range(num_methods):\n",
+    "    plt.errorbar(df.num_trials.unique(), mers[:, ii], vmers[:, ii], c=colors[ii+1])\n",
+    "plt.ylabel(\"bias / std\")\n",
+    "\n",
+    "plt.sca(ax[2])\n",
+    "for ii in range(num_methods):\n",
+    "    plt.errorbar(df.num_trials.unique(), vrs[:, ii], vvrs[:, ii], c=colors[ii+1])\n",
+    "plt.ylabel(\"var / true var\")\n",
+    "\n",
+    "plt.sca(ax[3])\n",
+    "for ii in range(num_methods):\n",
+    "    plt.errorbar(df.num_trials.unique(), rts[:, ii], vrts[:, ii], c=colors[ii+1])\n",
+    "plt.ylabel(\"RT\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0df6c26a",
+   "metadata": {},
+   "source": [
+    "## Save results\n",
+    "\n",
+    "- save a dataframe with benchmark results from all trials and LAN, NLE, and NLE-ensemble as algorithms\n",
+    "    - includes mean errors, variance errors and c2sts\n",
+    "- save SBC results for all 4 methods\n",
+    "- save samples as np arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "e837c8df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 99, 4, 4)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "00f12771",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"data/df_LAN-NLE-NLE*-all-trials-posthoc-metrics-N99.csv\")\n",
+    "\n",
+    "with open(\"data/posterior-samples-ANA-LAN-NLE-NLE*-all-trials-posthoc-metrics-N99.p\", \"wb\") as fh:\n",
+    "    pickle.dump(dict(true_params=true_params, \n",
+    "                     true_samples=true_samples, \n",
+    "                     lan_samples=lan_samples, \n",
+    "                     nle_samples=nle_samples,\n",
+    "                     nlee_samples=nlee_samples,\n",
+    "                     ranks=ranks, \n",
+    "                     meanerr=meanerr,\n",
+    "                     meanerrabs=meanerrabs,\n",
+    "                     varerr=varerr,\n",
+    "                     varerrabs=varerrabs,\n",
+    "                     dispersion=dispersion,\n",
+    "                    ), fh)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37a8b49f",
+   "metadata": {},
+   "source": [
+    "## posterior plots for different #trials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "id": "51ad27d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-0.1677,  1.2243,  0.4250,  1.1840]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "obss = [11, 111, 211, 311]\n",
+    "labels = [\"LAN\", \"NLE\", \"NLE*\"]\n",
+    "\n",
+    "sss = []\n",
+    "for obs in obss:\n",
+    "    paths = []\n",
+    "    for alg in labels:\n",
+    "        idx = df[df.num_observation==obs].algorithm == alg\n",
+    "        p = df[df.num_observation==obs].loc[idx, \"path\"].values\n",
+    "        [paths.append(pi) for pi in p]\n",
+    "\n",
+    "    sss.append([\n",
+    "        sbibm.get_task(\"ddm\").get_reference_posterior_samples(obs)\n",
+    "         ] + [get_ndarray_from_csv(path+\"/posterior_samples.csv.bz2\") for path in paths])\n",
+    "\n",
+    "print(sbibm.get_task(\"ddm\").get_true_parameters(obs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "id": "1d30f5d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 99, 10000, 4)"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_samples.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "id": "2945b1a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': 1, 'b': 3}"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw = dict(a=1, b=2)\n",
+    "dict(kw, **dict(b=3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "id": "3ac8330e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x864 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx = 10\n",
+    "for ii in [2]:\n",
+    "    pairplot([\n",
+    "        true_samples[ii, idx, :, :], \n",
+    "        lan_samples[ii, idx, :, :], \n",
+    "        nle_samples[ii, idx, :, :],\n",
+    "        nlee_samples[ii, idx, :, :]\n",
+    "    ],\n",
+    "             points=sbibm.get_task(\"ddm\").get_true_parameters(idx+1), \n",
+    "#              limits=limits, #[[-2, 2], [0.5, 2.0], [.3, .7], [.2, 1.8]], \n",
+    "#              ticks = [[-2, 2], [0.5, 2.0], [.3, .7], [.2, 1.8]], \n",
+    "             samples_colors=colors[:4], \n",
+    "             diag=\"kde\",\n",
+    "             upper=\"scatter\",\n",
+    "             kde_diag=dict(bw_method=\"scott\", bins=100),\n",
+    "             kde_offdiag=dict(bw_method=\"scott\", bins=100),\n",
+    "             scatter_offdiag=dict(alpha=0.5, s=2),\n",
+    "             contour_offdiag=dict(levels=[.1], percentile=False),\n",
+    "             points_offdiag=dict(marker=\"+\", markersize=10), \n",
+    "             points_colors=[\"k\"], \n",
+    "             labels=[r\"$v$\", r\"$a$\", r\"$w$\", r\"$\\tau$\"],\n",
+    "             figsize=(18, 12),\n",
+    "            );"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "id": "c97b3aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dx = 0.2\n",
+    "limits = torch.stack((sbibm.get_task(\"ddm\").get_true_parameters(idx+1) - dx, \n",
+    "             sbibm.get_task(\"ddm\").get_true_parameters(idx+1) + dx)).squeeze().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6189efc",
+   "metadata": {},
+   "source": [
+    "## Visualize Likelihood for 10 Benchmark observations\n",
+    "We can also visualize the single trial likelihoods for the 10 random observations used in the benchmark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88a11d59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ll_true = []\n",
+    "ll_lan = []\n",
+    "ll_nle = []\n",
+    "\n",
+    "for ii in range(1, 11):\n",
+    "    \n",
+    "    xo = task.get_observation(ii).reshape(-1, 1)\n",
+    "    test_thetas = prior.sample((1000,))\n",
+    "\n",
+    "    # Extract positive RTs and choices for mixed model.\n",
+    "    rs = abs(xo)\n",
+    "    cs = torch.ones_like(rs)\n",
+    "    cs[xo < 0] = 0\n",
+    "  \n",
+    "    ll_true.append(task.get_log_likelihood(test_thetas, data=xo.reshape(1, -1), l_lower_bound=l_lower_bound))\n",
+    "\n",
+    "    ll_lan.append(lan_likelihood(test_thetas, \n",
+    "                                xo, \n",
+    "                                net=lan_kde, \n",
+    "                                ll_lower_bound=np.log(l_lower_bound)))\n",
+    "    \n",
+    "    ll_nle.append(nle.log_prob(rs, cs, test_thetas))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e63d3fcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(3, 3, figsize=(18, 12), sharey=True, sharex=True)\n",
+    "mpl.rcParams[\"axes.spines.right\"] = False\n",
+    "mpl.rcParams[\"axes.spines.top\"] = False\n",
+    "s = 7\n",
+    "idx = 0\n",
+    "alpha = 1.0\n",
+    "for ii in range(0, 9):\n",
+    "    if ii%3 == 0:\n",
+    "        plt.sca(ax[idx, 0])\n",
+    "    elif ii%3 == 1:\n",
+    "        plt.sca(ax[idx, 1])\n",
+    "    elif ii%3 == 2:\n",
+    "        plt.sca(ax[idx, 2])\n",
+    "        idx += 1\n",
+    "\n",
+    "    plt.title(f\"Observation {ii+1}\")\n",
+    "    \n",
+    "    plt.scatter(ll_true[ii], ll_lan[ii], alpha=alpha, color=\"C2\", s=s)\n",
+    "    plt.scatter(ll_true[ii], ll_nle[ii], alpha=alpha, color=\"C3\", s=s)\n",
+    "    plt.plot(ll_true[ii], ll_true[ii], \"k\")\n",
+    "    if not ii:\n",
+    "        plt.legend([\"Identity\", \"LAN\", \"NLE\", \n",
+    "                   ], frameon=False, fontsize=12)\n",
+    "    if ii in [0, 3, 6]:\n",
+    "        plt.ylabel(\"synthetic log L\")\n",
+    "    if ii > 5: \n",
+    "        plt.xlabel(\"analytic log L\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d650e863",
+   "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": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lan_nle_comparison/LAN-NLE-Likelihood-Comparison.ipynb b/lan_nle_comparison/LAN-NLE-Likelihood-Comparison.ipynb
new file mode 100644
index 00000000..0c09d9f1
--- /dev/null
+++ b/lan_nle_comparison/LAN-NLE-Likelihood-Comparison.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "004c8e82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed 57637\n"
+     ]
+    }
+   ],
+   "source": [
+    "import keras\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "import sbibm\n",
+    "import torch\n",
+    "\n",
+    "# Plotting settings\n",
+    "mpl.rcParams[\"axes.spines.top\"] = False\n",
+    "mpl.rcParams[\"axes.spines.right\"] = False\n",
+    "mpl.rcParams[\"axes.labelsize\"] = \"medium\"\n",
+    "mpl.rcParams[\"legend.frameon\"] = False\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 14\n",
+    "mpl.rcParams[\"font.size\"] = 20\n",
+    "\n",
+    "# Fix seed \n",
+    "seed = 57637 #torch.randint(1000000, (1,))\n",
+    "torch.manual_seed(seed)\n",
+    "print(f\"seed {seed}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce1d2a31",
+   "metadata": {},
+   "source": [
+    "## Load DDM task from `sbibm`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5279e900",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task = sbibm.get_task(\"ddm\")\n",
+    "prior = task.get_prior_dist()\n",
+    "simulator = task.get_simulator(seed=seed) # Passing the seed to Julia."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4229e528",
+   "metadata": {},
+   "source": [
+    "### Load pretrained LANs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "868009d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# network trained on KDE likelihood for 4-param ddm\n",
+    "lan_kde_path = \"../sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5\"\n",
+    "lan_ana_path = \"../sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5\"\n",
+    "lan_kde = keras.models.load_model(lan_kde_path, compile=False)\n",
+    "lan_ana = keras.models.load_model(lan_ana_path, compile=False)\n",
+    "\n",
+    "def lan_likelihood(theta, data, net, ll_lower_bound):\n",
+    "    \"\"\"Return log likelihood summed over all trials in data, \n",
+    "        given a batch of parameters theta.\n",
+    "    \n",
+    "    Args\n",
+    "        theta: batch of parameters\n",
+    "        data: batch of iid reaction times and choices encoded\n",
+    "            as negative and positive reaction times. \n",
+    "        net: lan keras model\n",
+    "        ll_lower_bound: lower bound of single trial log likelihood.\n",
+    "        \n",
+    "    Returns\n",
+    "        llsum: batch of log likelihoods over trials. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Convert to positive rts.\n",
+    "    rts = abs(data)\n",
+    "    num_trials = rts.numel()\n",
+    "    num_parameters = theta.shape[0]\n",
+    "    assert rts.shape == torch.Size([num_trials, 1])\n",
+    "    theta = torch.tensor(theta, dtype=torch.float32)\n",
+    "    # Convert DDM boundary seperation to symmetric boundary size.\n",
+    "    theta[:, 1] *= 0.5\n",
+    "\n",
+    "    # Code down -1 up +1.\n",
+    "    cs = torch.ones_like(rts)\n",
+    "    cs[data < 0] *= -1\n",
+    "\n",
+    "    # Repeat theta trial times\n",
+    "    theta_repeated = theta.repeat(num_trials, 1)\n",
+    "    # repeat trial data theta times.\n",
+    "    rts_repeated = torch.repeat_interleave(rts, num_parameters, dim=0)\n",
+    "    cs_repeated = torch.repeat_interleave(cs, num_parameters, dim=0)\n",
+    "\n",
+    "    # stack everything for the LAN net.\n",
+    "    theta_x_stack = torch.cat((theta_repeated, rts_repeated, cs_repeated), dim=1)\n",
+    "    ll_each_trial = torch.tensor(\n",
+    "        net.predict_on_batch(theta_x_stack.numpy()),\n",
+    "        dtype=torch.float32,\n",
+    "    ).reshape(num_trials, num_parameters)\n",
+    "\n",
+    "    # Lower bound on each trial log likelihood.\n",
+    "    # Sum across trials.\n",
+    "    llsum = torch.where(\n",
+    "        torch.logical_and(\n",
+    "            rts.repeat(1, num_parameters) > theta[:, -1], \n",
+    "            ll_each_trial > ll_lower_bound,\n",
+    "        ),\n",
+    "        ll_each_trial,\n",
+    "        ll_lower_bound * torch.ones_like(ll_each_trial),\n",
+    "    ).sum(0)\n",
+    "    \n",
+    "    return llsum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b76dd7ea",
+   "metadata": {},
+   "source": [
+    "## Likelihood comparison for single example\n",
+    "\n",
+    "##### Sample example parameter from prior\n",
+    "\n",
+    "For creating a figure showing the likelihood over the entire data space given a fixed parameter combination we sample a single parameter combination from the prior and evaluate the synthetic likelihoods for a large range of reaction times and both choices, while holding the parameters fixed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "de62e5e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1.3266, 1.0394, 0.6262, 0.4935]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample one parameter from the prior for a likelihood example.\n",
+    "theta_o = prior.sample((1,))\n",
+    "l_lower_bound = 1e-7\n",
+    "theta_o"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "70e92c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load pretrained NLE model\n",
+    "with open(\"../sbibm/algorithms/lan/nle_pretrained/mm93.p\", \"rb\") as fh: \n",
+    "    nle = pickle.load(fh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62a28671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/.local/lib/python3.7/site-packages/julia/core.py:691: FutureWarning: Accessing `Julia().<name>` to obtain Julia objects is deprecated.  Use `from julia import Main; Main.<name>` or `jl = Julia(); jl.eval('<name>')`.\n",
+      "  FutureWarning,\n",
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# construct rts and choices for nle in [-test_tmax, test_tmax]\n",
+    "\n",
+    "# RT range\n",
+    "test_tmax = 5\n",
+    "# Number of test points\n",
+    "ntest = 1000\n",
+    "rs = torch.cat((torch.linspace(test_tmax, 1e-7, ntest//2), \n",
+    "           torch.linspace(1e-7, test_tmax, ntest//2))).reshape(-1, 1)\n",
+    "cs = torch.cat((torch.zeros(ntest//2), \n",
+    "           torch.ones(ntest//2))).reshape(-1, 1)\n",
+    "\n",
+    "test_rts = torch.linspace(-test_tmax, test_tmax, ntest)\n",
+    "\n",
+    "# get NLE synthetic likelihood for each data point with fixed theta.\n",
+    "lps_nle = torch.tensor([nle.log_prob(r.reshape(-1, 1), c.reshape(-1, 1), theta_o) for r, c in zip(rs, cs)])\n",
+    "\n",
+    "# from analytical likelihood\n",
+    "lps_true = torch.tensor([task.get_log_likelihood(theta_o, d.reshape(-1, 1), l_lower_bound=l_lower_bound) \n",
+    "                     for d in test_rts])\n",
+    "\n",
+    "# and from both LANs.\n",
+    "lps_lanana = torch.tensor([lan_likelihood(theta_o, \n",
+    "                                   d.reshape(-1, 1), \n",
+    "                                   net=lan_ana, \n",
+    "                                   ll_lower_bound=np.log(l_lower_bound)) \n",
+    "                    for d in test_rts])\n",
+    "lps_lankde = torch.tensor([lan_likelihood(theta_o, \n",
+    "                                   d.reshape(-1, 1), \n",
+    "                                   net=lan_kde, \n",
+    "                                   ll_lower_bound=np.log(l_lower_bound)) \n",
+    "                    for d in test_rts])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "af86a6df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, sharex=True, figsize=(12, 7))\n",
+    "\n",
+    "labels = [\"Analytical\", \n",
+    "          \"LAN-ANA\", \n",
+    "          \"LAN-KDE\", \n",
+    "          \"NLE\", \n",
+    "         ]\n",
+    "\n",
+    "plt.sca(ax[0])\n",
+    "plt.plot(test_rts, lps_true.exp(), label=\"Analytical L\");\n",
+    "plt.plot(test_rts, lps_lanana.exp(), label=\"LAN-ANA\");\n",
+    "plt.plot(test_rts, lps_lankde.exp(), label=\"LAN-KDE\", ls=\"-.\", c=\"C2\");\n",
+    "plt.plot(test_rts, lps_nle.exp(), label=\"NLE\", ls=\"--\", c=\"C3\");\n",
+    "plt.ylabel(\"likelihood\")\n",
+    "plt.legend(labels)\n",
+    "# plt.suptitle(fr\"v={theta_o[0, 0]:.2f}, a={theta_o[0, 1]:.2f}, w={theta_o[0, 2]:.2f}, $\\tau$={theta_o[0, 3]:.2f}\");\n",
+    "\n",
+    "plt.sca(ax[1])\n",
+    "plt.plot(test_rts, lps_true)\n",
+    "plt.plot(test_rts, lps_lanana)\n",
+    "plt.plot(test_rts, lps_lankde, c=\"C2\")\n",
+    "plt.plot(test_rts, lps_nle, ls=\"--\", c=\"C3\")\n",
+    "plt.legend(labels)\n",
+    "plt.xlabel(\"reaction time [s]\")\n",
+    "plt.ylabel(\"log likelihood\");\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"likelihood_example.png\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d96868e",
+   "metadata": {},
+   "source": [
+    "## Systematic Likelihood comparison\n",
+    "\n",
+    "Next we do a systematic comparison based on the Huber loss (the LAN training loss) and the mean squared error (MSE) between analytical and synthetic log likelihoods of LAN and NLE.\n",
+    "\n",
+    "To mimick the inference setup we sample an observation from the simulator via parameters from the prior and obtain the likelihood of this observation given a large batch of parameters, e.g., `1000`, sampled from the prior. The Huber loss and MSE is then calculated across this batch, giving a single number. This procedure we repeat for many observation, e.g., `100` and then show boxplots over the resulting 100 numbers. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7b0567e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define losses.\n",
+    "def huberloss(y, yhat):\n",
+    "    diff = abs(y-yhat)\n",
+    "    \n",
+    "    err = np.zeros(y.numel())\n",
+    "    err[diff <= 1.0] = 0.5 * diff[diff <= 1.0]**2\n",
+    "    err[diff > 1.0] = 0.5 + diff[diff > 1.0]\n",
+    "    return err.mean()\n",
+    "\n",
+    "def mse(y, yhat):\n",
+    "    return torch.mean((y - yhat)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3c62f219",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# mimick the MCMC setting: xo is fixed, thetas are scored with loss\n",
+    "num_observations = 100\n",
+    "num_thetas_per_observation = 1000\n",
+    "\n",
+    "# first sample observations xo\n",
+    "xos = simulator(prior.sample((num_observations,)))\n",
+    "\n",
+    "labels = [\n",
+    "    \"LAN-ANA\", \n",
+    "    \"LAN-KDE\", \n",
+    "    \"NLE\",\n",
+    "]\n",
+    "errors = []\n",
+    "for xo in xos:\n",
+    "    \n",
+    "    # Sample test thetas from prior.\n",
+    "    test_thetas = prior.sample((num_thetas_per_observation,))\n",
+    "    xo = xo.reshape(-1, 1)\n",
+    "    # Extract positive RTs and choices for mixed model.\n",
+    "    rs = abs(xo)\n",
+    "    cs = torch.ones_like(rs)\n",
+    "    cs[xo < 0] = 0\n",
+    "\n",
+    "    # Evaluate\n",
+    "    lps_nle = nle.log_prob(rs, cs, test_thetas).squeeze()    \n",
+    "    lps_true = task.get_log_likelihood(test_thetas, data=xo.reshape(1, -1), l_lower_bound=l_lower_bound)\n",
+    "    lps_lanana = lan_likelihood(test_thetas, xo, lan_ana, np.log(l_lower_bound))\n",
+    "    lps_lankde = lan_likelihood(test_thetas, xo, lan_kde, np.log(l_lower_bound))\n",
+    "    \n",
+    "\n",
+    "    # Score\n",
+    "    errors.append([\n",
+    "            [\n",
+    "                huberloss(lps_lanana, lps_true),\n",
+    "                huberloss(lps_lankde, lps_true),\n",
+    "                huberloss(lps_nle, lps_true),\n",
+    "            ],\n",
+    "            [\n",
+    "                huberloss(lps_lanana.exp(), lps_true.exp()),\n",
+    "                huberloss(lps_lankde.exp(), lps_true.exp()),\n",
+    "                huberloss(lps_nle.exp(), lps_true.exp()),\n",
+    "            ],            \n",
+    "            [\n",
+    "                mse(lps_lanana, lps_true),\n",
+    "                mse(lps_lankde, lps_true),\n",
+    "                mse(lps_nle, lps_true),\n",
+    "            ], \n",
+    "            [\n",
+    "                mse(lps_lanana.exp(), lps_true.exp()),\n",
+    "                mse(lps_lankde.exp(), lps_true.exp()),\n",
+    "                mse(lps_nle.exp(), lps_true.exp()),\n",
+    "            ],         \n",
+    "        ])\n",
+    "errors = np.array(errors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "80b6621e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract separate dataframes for huber and mse.\n",
+    "dfhuber_log = pd.DataFrame(data=np.array(errors)[:, 0, :], columns=labels)\n",
+    "dfhuber = pd.DataFrame(data=np.array(errors)[:, 1, :], columns=labels)\n",
+    "dfmse_log = pd.DataFrame(data=np.array(errors)[:, 2, :], columns=labels)\n",
+    "dfmse = pd.DataFrame(data=np.array(errors)[:, 3, :], columns=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "15d2bb2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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nAucBt5Yz/OzawLolSYvQQq4jmJk3UcxUtxJ464TDpwBLgfMy876xnRGxb0TsO6Gceynax6U8fp2mt5Xlf3PCMLvLyu0xEfH08RdExCspErYHgctn+7kkSY/XyOF5XwACeDbwbuA4ivUrJElqmMy8nmJI3PkAEREUidTz5lDcWygSkzMi4gBgFHgBxZpKNwB9E84fm+s/Jux/P7AaeGdE7AdcVca0BriDxydl/5diHab/BYxGxL9QTATRQTF0L4ATMnPDHD6TJGmChiVNmXnU2J8jYifgJY2qW5KkqWRmAteWr9lee1NErAI+QLE47quA24HTgVMy864ZlrMhIl4EnAQcQtFGbgA+B5yYmbdNOH9LRLyKIpn6K4rnl54MbAT+FTgjMy+d7eeRJE2ukokgymeavl5F3ZIk1VNm/gI4eobnTuxhGn9sI8UojONmWNYjwCfKlyRpATXymSZJkiRJajkNTZrKCSAubmSdkiRJkjQfM0qaIuIZEfG4tSDmYBeKMd+SJEmS1BKmTZoi4o3AfwK3Lnw4kiRJktRcak4EERFvBz4EHJeZn2lIRJIkNZCLr0uSpjNdT1OUr82THozoigjXWpIktbJzcfF1SVINNXuaMvPjEXE/8ImIeHFm/vWEU74LbI6I64EfAj8oX1dn5gMLErEkSfXl4uuSpJqmXacpM8+KiH+jWGBvovXAc4Fnla/Xlfs3R8R1FAnUWDJ1dR3ilSSprlx8XZI0nRktbpuZN0fE6kn2vwwgIvYCngesKl/PBTrL15Hl6ZuBB+cdsaQpRUy5buasZGZdypFajYuvS5ImM6OkCSBr/BaVmTcCNwJfGtsXEfvwWBL1PIpEagfA38akBTJdsrPyhIu55dSDGxSNJElSe5hx0jRbmXk9cD1wPkAUX4F3UCRQkiTNW0Q8Azg3M7vnWc6Hgedkpt8qSJIeZ0aL29ZDFq7NzPMaVackqX3VeR1BF1+XJE1pzklTRGw/38rrUYYkafEp1xE8HTh+/EQOkiQthPn0NP0sIo6LiCfN9sKI+OOIGALeNY/6JUmLV811BCVJqqf5JE3fBE4Dbo+If4qI7lo9RxHxzIj43xFxBfAj4I+B4XnUL0lapDLz48DbKdYR/OzE4y6+LkmqpzlPBJGZR0bEJ4F+4JjytTkiRoHbgbuA7YDlwD4U48UDuAPoAz6emQ/NL3xJ0mI1zTqCLr4uSaqbec2el5n/ARxYrtPUCxwA7Eexqvp4/w18BbgQuDAzH5lPvZLULuqxttZiXldrqnUEcfF1SVId1WXK8XKdphMAyuEQT6foYXoAuCMzb69HPZLUblxba/4mW0fQxdclSfVU93WaMvN+ioVub6x32ZIkzYaLr0uS6mHBFreVJKkZufi6JGm2TJokSYtaObzv2vIlSdLjzGfKcUmSmo6Lr0uS6s2kSZLUblx8XZJUVyZNkqR24+LrkqS68pkmSVJbcfF1SVK91S1pioiXAr/LzKvrVaYkSXPh4uuSpHqqZ0/TMHAW8JY6lilJ0py5+LokqR7qmTTdSdEISZLUdFx8XZI0V/WcCGI98OI6lidJkiRJlatn0vQ3wD4R8XcR8YQ6litJkiRJlann8Lz3AdcA7wd6I+L/Ab8GcsJ5mZm9daxXkiRJkhZMPZOmo8b9+anlazJJMZORJEmSJDW9eiZNe9SxLEmSJElqCnVLmjLz1nqVJUmSJEnNop49TY+KiKXA3sAOmXnZQtQhSZIkSY1Qz9nziIjdIuJC4C7gBxQL3o4d64qIayNidT3rlCRpooh4aUTsPovznxMRr1/ImCRJratuSVNEPA34PrAG+DpwBRDjTvk+sCtwaL3qlCRpCsNsPUEREfHeiNgwxfmvBj630EFJklpTPXuaTqJIil6emWuBfxt/MDMfAS4D9q9jnZIkTSYm2bcdsHOD45AktYF6Jk2vAr6amcM1zvk58Ad1rFOSJEmSFlQ9k6YVwI3TnPMIsLSOdUqSJElqkMHBQTo7O1m7di2dnZ0MDg5WHVJD1HP2vI3AH05zzt7Ar+tYpyRJkqQGGBwcpK+vj4GBATZu3MiyZcvo7e0FoKenp+LoFlY9e5q+B/x5RDx1soMRsRdwEONm1JMkSZLUGvr7+xkYGKC7u5slS5bQ3d3NwMAA/f39VYe24OqZNH2U4iHb70TEK4EnQ7FmU/n+a8AW4GN1rFOSpKlk1QFIUjsZHR2lq6trq31dXV2Mjo5WFFHj1G14XmZ+PyLeBPwTxZTjY35XbjcBb8jM/6pXnZIk1XByRJw8cWdEbK4gFklqeR0dHYyMjNDd3f3ovpGRETo6OiqMqjHqurhtZp4DdAJnAFcBNwE/Aj4FPCczz69nfZIk1RCzfEmSaujr66O3t5fh4WE2bdrE8PAwvb299PX1VR3agqvnRBAAZOaNwDvqXW5E7AZ8gOK5qOXA7cBFwCmZedcsylkGnAgcAjwN2AB8AzgxM2+b5Py/AP4E2A/4Y2BH4PzMfN3cP40kaSFlZl2/FJQkPTbZw7p16xgdHaWjo4P+/v62nwQCFiBpWggRsSdwOcXiuUPAdcDzgeOAgyJi/8ycapX38eUsL8vZG/g28CVgX+Bo4OCIeFFm3jzhsr+hSJbuBW4rz5ckSZIWnZ6eHnp6ehgaGmLNmjVVh9MwdU+aImIH4NXAc4GdgLuBHwP/kpn3zrHYT1EkTMdm5pnj6jqNolerH3jzDMr5EEXCdFpmHj+unGOB08t6DppwzTsokqWfUvQ4OfufJLW4iPhz4GUUw/K+m5kXVhySJKmJ1XX4QkS8FrgVOBd4O0UPztvL97eWQ91mW+aewIHALcA/Tjh8EnAfcERE1Fw0t0zmjijPP3nC4U+Wcb8iIp45/kBmDmfmjZnpLEyS1CIi4s8i4rsR8SeTHPsc8C/AscA64MsRYdIkSZpS3ZKmiHg5MEjxzM8XKBKmV5bb88r9gxHxv2ZZ9Nj0HJdm5pbxBzLzHor1oZ4MvHCacl4IbA98r7xufDlbgG9OqE+S1Lr+HPifwPfH74yIPwWOBO4HPgi8F7gZOCQi2n9QviRpTuo5PO9E4CHgJZn5ownHPh8RnwS+W57377Mod59ye8MUx2+k6InaG/jWPMuhLEeS1NqeD1yWmQ9O2P8GivWbjs7M/wsQEedRzPZ6OMWXf5IkbaWew/OeC1wwScIEQGb+APgyxTd/s7FTub17iuNj+3duUDmSpOb3VGCydQFfCvwWeHQ4Xmb+GriYoh2TJOlx6tnT9BDFNOC1/Ko8b1GIiGOAYwBWrFjB+vXrqw1ogbTr52pX3q/W0473bPXq1QtdxVOAh8fviIjdgWXA1yZ5TvVnFEP6JEl6nHomTZcB+09zzv4UQ/RmY6wHaKcpjo/t/22DypmxzDwbOBtg1apV2YBfEhpuaGioEb/8qF6+cbH3q9V4z+bqHmC3CfueV25/PMU1E4fySZIE1Hd43nuB50TEqRNnsouIpRHxEaATOGGW5V5fbqd61mivcjvVs0r1LkeS1Pz+k2L9vR3G7Xs1xfNMI5OcvwfTj5aQJC1Sc+5piohzJtn9E+DdwDER8SPgN8AKiueYdqLoZXoP0DuLqsbWRTowIrYZP4NeROxI0Xt1P3DlNOVcCTwA7B8RO46fQS8itqGYTGJ8fZKk1nU+cBbwnYj4PMUXZocDv2bCz/mICKALuKLRQUqSWsN8hucdVePYzhSLBk70JxQP4c44acrMmyLiUoqk5q3AmeMOnwIsBc7KzPvGdkbEvuW1140r595yhqRjKNZpOn5cOW8DVgLfzMybZxqbJKlpDQBrgVcA+1EsYvsIcFxmbp5w7gEUE0fMZmZXSdIiMp+kaY+6RTG9twCXA2dExAHAKPACijWVbgD6Jpw/Wm5jwv73A6uBd0bEfsBVQAewBriDIinbSkQcAhxSvn1quX1RRJxb/vnOzHzX7D+SJGmhZOaWiDgY6AFeDGwAvpKZV09y+i7A6cBXGxehJKmVzDlpysxb6xnINHXdFBGrgA8ABwGvohh7fjpwSmbeNcNyNkTEi4CTKBKhl1A0pJ8DTszM2ya5bD+KhRDHe2b5ArgVMGmSpCZTDuc+v3zVOu9LwJcaEpQktbjBwUH6+/sZHR2lo6ODvr4+enraf23wes6et6Ay8xfA0TM8d2IP0/hjG4HjytdMyjqZYjifJEmStGgNDg7S19fHwMAAGzduZNmyZfT2Fk/dtHviVLekKSJm+ixQZuae9apXkqSJIuL1c7kuM78wh7p247GREMspRkJcxCxGQpTlLANOpBgJ8TSKkRDfYOqREBOvfx1wXvn2jZn52Zl/CkmaXn9/PwMDA3R3dzM0NER3dzcDAwOsW7fOpGkWtqGYynWinXlsDaRfUTyIK0nSQjqXydukqUR5/qySpojYk+KZ212BIeA64PkUoxkOioj9M3PDDMpZXpazN/BtiuGC+1KMsDg4Il5Ua6KiiPhD4JPAvcAOU50nSfMxOjpKV1fXVvu6uroYHR2d4or2UbekKTNXTnUsIv4IOINiprtX1KtOSZJq2AR8jccmB1oIn6JImI7NzEdnd42I04B3AP3Am2dQzocoEqbTMvPR2V0j4liK53c/RdGT9TjllOmfo5zsAp+zlbRAOjo6GBkZobu7+9F9IyMjdHR0VBhVYzTkmabM/GlErAWuoZiE4X2NqFeStGh9h2KZi1dTrBf4GeDLmflgvSooe5kOBG4B/nHC4ZMolrg4IiKOH78sxiTl7AAcAdzH45+h/STwTuAVEfHMKXqbjqVY5mM1ky/3IUl10dfXR29vLwMDA2zatInh4WF6e3vp7++vOrQF17CJIDLzwYj4N4rpX02apDlYfcFqNjw47UifKe3YAc/+/AnzimH5dstZf+j6eZUhLbTM7C5HObyRYgbUzwGnR8Q/A5/JzJ/UoZqxr1ovHb/weln/PRHxPYqk6oXAt2qU80Jg+7Kce8YfKKdO/yZFAtYNbJU0RUQHcCpwemZ+NyJMmiQtmLHnltatW/fo7Hn9/f1t/zwTFM8hNdImHlvrSNIszSdhaqcYpJnIzJ9m5nuB3YC/BL4P/G/gxxFxVUT0RsTSeVSxT7m9YYrjN5bbvReinIhYQjHxw88p1iGUWs7g4CCdnZ2sXbuWzs5OBgcHqw5J0+jp6eGaa67hK1/5Ctdcc82iSJiggT1NEbELxTCJXzSqTkmSMnMTcCFwYUQ8A/hr4CjgbOC0iDgoM6+YQ9FjkxzdPcXxsf07L1A5JwLPBboy84Fp6thKRBxD0XvFihUrWL9+/Wwubxnt+rnaxbe+9S0GBgZ497vfze67787Pf/5zjj/+eK699loOOOCAqsPTDLTj/7HVq1dPur+eU46fOMWhJcAfAmsoGgaH5klaNG7oegmb77xzztdfAoxeNPfn+rfdZRf2Hrlszte3m3Jh9r+NiCuATwNPB36/2qhmLyJeQNG79LG5JHyZeTZF0siqVatyql8SWtnQ0NCUv/yoObztbW/j/PPPf3T66ne84x3st99+rFu3jr/7u7+rOjxNY7H9H6tnT9PJ0xz/HfDBzPxIHeuUpKY2n4SpHepvJhHxB8AbytczgAeBfwZ+NMcix3qAdpri+Nj+39aznHJY3hcohvP97XRBSs1qMU9frdZTz6Spe4r9W4C7gOvKIRKSJDVERGwD/CnFkLyDKNq9/6RYR+m8zJxqSNxMXF9up3pmaa9yO9WzSnMtZ4dx5z5YzDj+OJ+JiM9QTBDx9mnqlyqxmKevVuup5zpN36lXWZIkzUdE7AH0UiwO+zSK6bw/TzFz3lV1qma43B4YEduMn0EvInYE9gfuB66cppwrgQeA/SNix/Ez6JVJ34ET6nsIGJiirP9J8ZzTCEUyNpdntaSGWMzTV6v1NGwiCEmSGuin5fYHFGsmDdZaK2kuMvOmiLiUIql5K3DmuMOnUCzoftb4eiNi3/La68aVc29EnEcxMcPJwPHjynkbsBL45tgaTeWkD389WUwRcTJF0vT5zPzs/D6htLAW8/TVaj3zSpoiYve5XJeZP59PvZIkTSOARyh6mU4ETpxiGNt4mZnPmGU9bwEuB86IiAOAUeAFFEPWbwD6Jpw/9rDGxGDeT7E47TsjYj/gKqCDYhKlOyiSMqnt9PT00NPTw9DQEGvWrKk6HM3A4OAg/f39jya6fX19iyLRnW9P0y1AzvKarEO9kiRN5wkUazQtmLK3aRXwAYpnpl4F3A6cDpySmXfNsJwNEfEiil6xQ4CXABsoFuU9MTNvW4DwJWlWBgcH6evrY2BggI0bN7Js2TJ6e3sB2j5xmm/y8nMenzTtTDHTz63zLFuSpDnJzIYt3p6Zv6B4dmom507Z3ZWZGykmqDhuHrGczPSz2UrSnPT39zMwMPDoNPHd3d0MDAywbt06k6ZaMnPlxH3leOq/zcw95lO2JEmSpOaxmKeJX4hv4mY7XE+SJElSkxubJn68xTJNvM8WSZIkqRKLdVKBVtXX18ehhx7K0qVLufXWW3nGM57Bfffdx+mnn151aAvOpEmSJEkNt5gnFWgHM5iRtK007EFZSZIkacz4SQWWLFny6KQCLm7bvPr7+7ngggv42c9+xle+8hV+9rOfccEFFyyKe2bSJEmSpIZbzJMKtKrFfM9MmiRJktRwi3lSgVa1mO/ZvJKmiNg88UWx8vqkx8rXprpELkmSpJbV19dHb28vw8PDbNq0ieHhYXp7e+nr66s6NE1hMd+z+U4EMZcnwBbXU2OSJEl6nJ6eHi6//HJe+cpX8tBDD/GkJz2JN77xjU4C0cTG7s26desenfGwv79/Udyz+S5u6/A+SZIkzdrg4CAXX3wxl1xyyVaz5734xS9eFL+Et6qenh56enoYGhpizZo1VYfTMCY9kiRJajhnz1MrMWmSJElSwy3mmdjUelzcVpIkSQtqqoVQn/jEJ87q/MysW0zSbNjTJEmSpAWVmY97ffGLX2SPPfbg29/+Nru/6yK+/e1vs8cee/DFL35x0vNNmFQle5okSZLUcONnYvv5taOsu2TxzMSm1mPSJEmSpEqMzcS28oSLuebUg6sOR5qSw/MkSZIkqQZ7miRJkiRtZarJOGajnZ5DM2mSWsg1R11TdQiFI6sOoHU86/rrqg6B9mmyJEmNMl3Cs/KEi7llEQ2pNGmSWkjnuZ1Vh6BZunaffasOQZIkzZPPNEmSJElSDSZNkiRJklSDSZMkSZIk1WDSJEmSJEk1mDRJkiRJUg0mTZqTwcFBOjs7Wbt2LZ2dnQwODlYdkiRJkrQgnHJcszY4OEhfXx8DAwNs3LiRZcuW0dvbC0BPT0/F0UmSJEn1ZU+TZq2/v5+BgQG6u7tZsmQJ3d3dDAwM0N/fX3VokiRJUt2ZNGnWRkdH6erq2mpfV1cXo6OjFUUkSZIkLRyTJs1aR0cHIyMjW+0bGRmho6OjoogkSZKkhWPSpFnr6+ujt7eX4eFhNm3axPDwML29vfT19VUdmiRJklR3TgShWevp6eHyyy/nla98JQ899BBPetKTeOMb3+gkEJIkSWpLJk2atcHBQS6++GIuueSSrWbPe/GLX2ziJEmSpLZj0qRZ6+/v57DDDmPdunWMjo7S0dHBYYcdRn9/v0nTAlu+3XI2PLih8hgkSZIWE5Mmzdq1117Lb37zG3bYYQcyk/vuu4+zzjqLDRuq/WV+MVh/6Pp5Xb/yhIu55dSD6xOMJEnSImHSpFnbdttt2bx5M+ecc86jw/Ne85rXsO2221YdmiRJklR3zp6nWdu0aRNPetKTttr3pCc9iU2bNlUUkSRJkrRwTJo0J0cffTTr1q3jL//yL1m3bh1HH3101SFJkiRJC8LheZq13XbbjXPPPZfzzz//0eF5hx9+OLvttlvVoUmSpAqsvmD1vCYq2rEDnv35E+YVw/Ltls/72V9pKiZNmrWPfOQjHHfccbzhDW/g5z//ObvvvjubNm3iYx/7WNWhSZKkClQ9s2uzxKD25fA8zVpPTw+nn346S5cuBWDp0qWcfvrpTjcuSZKktmRPk+akp6eHnp4ehoaGWLNmTdXhSJIEFAuw9/f3P7qOYF9fn1/qSRPc0PUSNt9557zKuAQYvehdc75+2112Ye+Ry+YVQyOZNGlObJQkSc1mcHCQvr4+BgYGHn3mtre3F8A2ShpnvglTu8QwGw7P06wNDg5y3HHHcd999z26uO1xxx3H4OBg1aFJTWfbXXZZ1PVLjdTf38/AwADd3d0sWbKE7u5uBgYG6O/vrzo0SS3OnibN2nve8x623XbbrRa3Peyww3jPe97jN3nSBPMderDyhIu55dSD6xSN1N5GR0fp6uraal9XVxejo6MVRSSpXdjTpFm77bbbJl2n6bbbbqs6NEnSItbR0cHIyMhW+0ZGRujo6KgoIkntwp4mzcnHPvYxHn74YQD+67/+ixtvvLHiiCRJi11fXx+9vb0MDAywadMmhoeH6e3tdXiepHkzadKcjCVMU72XJKnRxoaIr1u37tGJivr7+x06LmneTJo0Z9tssw1btmx5dCtJUtVcEkPSQjBp0pxEBNtuuy1btmxh2223JTPJzKrDkiRJFbjmqGuqDqFwZNUBqF2ZNGnOTj31VJ7+9Kfzy1/+kne9a+6Lm0mSVC+uI1iNznM7qw5Bs/Cs66+rOgQAWunrdpMmzUlm8sEPfpC77rqLpzzlKfYySZIq5+K20sxcu8++VYfQckyaNGd33XXXVltJkqrU39/PYYcdttVEEIcddpiTQUiaN5Mmzdpuu+3Gb37zGx555JFH9z3hCU9gxYoVFUYlSVrsrr32Wu6///7H9TTdcsstVYcmqcW5uK1m7SMf+Qg777wzK1euJCJYuXIlO++8Mx/5yEeqDk2StIg98YlP5G1vexvd3d0sWbKE7u5u3va2t/HEJz6x6tAktTiTJs1aT08Phx56KLfffjuZye23386hhx7q0AdJUqUefvhhzjzzTIaHhx9d3PbMM890LUFJ8+bwPM3a4OAgF198MZdccslWwx9e/OIXmzhJkirzrGc9i0MOOWSrZ5oOP/xwLrrooqpDa3vLt1vOhgc3VB6DtFBMmjRr/f39DAwM0N3dzdDQEN3d3QwMDLBu3TqTJklSZfr6+njTm97Egw8+yJYtW7jhhhs444wzOOuss6oOre2tP3T9vK5fecLF3HLqwfUJRloADs/TrI2OjtLV1bXVvq6uLkZHRyuKSJIkuPzyy7n33nvZsmULAFu2bOHee+/l8ssvrzgyqblsu8suVYfQFDHMRsv0NEXEbsAHgIOA5cDtwEXAKZk54zmvI2IZcCJwCPA0YAPwDeDEzLxtIetuFx0dHYyMjNDd3f3ovpGRETo6OiqMSpIaz7apuXz6058mIti8eTMAmzdvZptttuHTn/40Z555ZsXRSc1j75HL5l3GYusdbImepojYE/ghcDRwFfBx4GbgOOCKiJjRINbyvCvK624qy7mqLPeHEfHMhaq7nfT19dHb27vVg7a9vb309fVVHZokNYxtU/PZtGkTW7ZsYZttil9vttlmG7Zs2cKmTZsqjkxSq2uVnqZPAbsCx2bmo18VRcRpwDuAfuDNMyjnQ8DewGmZefy4co4FTi/rOWiB6m5JETHlsZe97GVbvT/ssMM47LDDHndeZtY9LklqArZNTeqjH/0oT3/60/nlL3/J8ccfP/0FkjSNpu9pKr9NOxC4BfjHCYdPAu4DjoiIpdOUswNwRHn+yRMOfxK4FXjF+G/06lV3K8vMmq9nvPfr054jSe3Gtql5Tfyyr9aXf5I0U63Q0zT24Mylmbll/IHMvCcivkfReLwQ+FaNcl4IbF+Wc8+EcrZExDeBY8r6bq5z3c3ro3vBfXfM+fJbtuPxzfxsLN0V3n3jPAqQpErYNlVsqmQoMyftXZrsfL/YkzRTrZA07VNub5ji+I0UjcPe1G4cZlIOZTn1rrt5zSNhaov6JWlubJsW2jRf6uVJvzf/Ok7eaepjfqknaZxWSJrGfqLdPcXxsf07L0A59aq7acUpv6s6BPLkqiOQpFmzbVpg8Z6fVhzB78h3VxyCpKbRCklTy4qIYyiGVbBixQrWr19fbUCTGB4ernl8/LTiC1VHM/69tKqZ3K/4++nLme6eqX7qcc9a8X6tXr266hAWtVZonx764O488ZHfVlb/w0/YuSn/XlqV7VPrsX3aWiskTWPfmE3Vhz62/7cLUM686s7Ms4GzAVatWpWt+EvCdOO9h4aGWLNmTYOi0XS8X63He9ayWrZtghZpn1bfOq/L5/t/54nA6nlFoPH8Wdd6vGdba/rZ84Dry+3eUxzfq9xONbZ7PuXUq25JUnuxbZKkRaQVkqaxfr0DI2KreCNiR2B/4H7gymnKuRJ4ANi/vG58OdtQPDQ7vr561i1Jai+2TZK0iDR90pSZNwGXAiuBt044fAqwFDgvM+8b2xkR+0bEvhPKuRc4rzz/5AnlvK0s/5uZefO4a2ZdtySp/dk2SdLi0grPNAG8BbgcOCMiDgBGgRdQrFVxA9A34fzRcjtxUYb3UwxRfmdE7AdcBXQAa4A7eHzjM5e6JUmLg22TJC0STd/TBI9+q7YKOJeiUTge2BM4HXhhZm6YYTkbgBcBZwB/VJbzAuBzwPPKehakbklSe7FtkqTFo1V6msjMXwBHz/DcyZcJL45tBI4rX3WvW5K0eNg2SdLi0BI9TZIkSZJUFZMmSZIkSarBpEmSJEmSaojpVvtVfUTEfwPzW968OT0b+M+qg9CMeb9aT7veszsz86Cqg5Dtk5qG96v1tOs9m7R9MmnSvERE1nq4Wc3F+9V6vGfS3Ph/p7V4v1rPYrtnDs+TJEmSpBpMmiRJkiSpBpMmzdfPqg5As+L9aj3eM2lu/L/TWrxfrWdR3TOfaZIkSZKkGuxpkiRJkqQaTJokSZIkqQaTJkmSJEmqwaRJkiRJkmowaZIkSZKkGkyaJEmSJKkGkyZJkiRJqsGkSZIkSZJqMGmSJEmSpBpMmiRJkiSpBpMmSZIkSarBpEmSJEmSajBpkiRJkqQaTJokSZIkqQaTpgY56KCDEmi719DQUOUx+PJ+tfOrje+ZmoTtk69meHm/Wu/VxvdsUiZNDXLnnXdWHYIkSY9j+yRJ0zNpkiRJkqQaTJokSZIkqQaTJkmSJEmqwaRJkiRJkmowaZIkSZKkGkyaJEmSJKkGkyZJakKDg4N0dnaydu1aOjs7GRwcrDokSZIWrSVVByBJ2trg4CB9fX0MDAywceNGli1bRm9vLwA9PT0VRydJ0uJjT5MkNZn+/n4GBgbo7u5myZIldHd3MzAwQH9/f9WhSZK0KNnTJElNZnR0lK6urq32dXV1MTo6WlFEkjQ/EVGXcjKzLuVIs2VPkyQ1mY6ODkZGRrbaNzIyQkdHR0URSdL8ZGbN1zPe+/VpzzFhUpVMmiSpyfT19dHb28vw8DCbNm1ieHiY3t5e+vr6qg5NkqRFyeF5ktRkxiZ7WLduHaOjo3R0dNDf3+8kEJIkVcSkSZKaUE9PDz09PQwNDbFmzZqqw5EkaVFzeJ4kSZIk1WDSJEmSJEk1mDRJkiRJUg0mTZIkSZJUg0mTJEmSJNVg0iRJkiRJNZg0SZIkSVINJk2SJEmSVINJkyRJkiTVYNIkSZIkSTWYNEmSJElSDSZNkiRJklSDSZMkSZIk1WDSJEmSJEk1mDRJkiRJUg0mTZIkSZJUg0mTJEmSJNVg0iRJkiRJNZg0SZIkSVINS6qoNCJeDrwceCmwO7AL8ABwB3A18G3gq5n5yyrikyRJkqQxDUuaIuLJwLHAmygSpSgPPUiRLG0PPBPYE3gNcHpEfA34h8y8olFxSpIkSdJ4DRmeFxFvAG4EPkTRo3QKRU/Tzpn55MzcLTOXUyRxzwLeAFwIvBIYiYgLImL3RsQqSZIkSeM1qqfps8BFwIcz8z+mOikzE7iufJ0bEb8HHAmcABwFfGDBI5UkSZKkcRqVNK3KzB/N9qLM/B1wZkR8BlhZ96gkSZIkaRoNSZrmkjBNuP5Bit4nSZIkSWoopxyXJKkFRcTyiPjriPiXiPhpRDwQEXdHxEhE9EaEbbwk1UklU45LkqR5ey3wT8DtwDDwc2AFsJbiWeJXRsRry+eFJUnz0LRJU0R8GHhOZh5cdSySJDWhG4A/By7OzC1jOyPi/cBVFMt3rKWYjVaSNA/N3HW/C3BQ1UFIktSMMvPbmfm18QlTuf/XwKfLt6sbHpgktaGm7WmSJKmVRcTLKdYkfCnFou67UKxVeAdwNfBt4KuZ+csFqP6RcrtpAcqWpEXHpEmSpDqJiCcDxwJvokiUojz0IEWytD3wTGBPiuFzp0fE14B/yMwr6hTDEuD15dtv1KNMSVrsGjY8LyK6ysZEkqS2ExFvAG4EPkTRo3QKRU/Tzpn55MzcLTOXU3xh+SzgDRTPG70SGImICyJi9zqEcirQCfxrZn6zDuVJ0qLXyJ6m7wKbI+J64IfAD8rX1Zn5QAPjkCRpIXwWuAj4cGb+x1QnlbPZXVe+zo2I3wOOBE4AjgI+MNcAIuJY4Piy7CNqnHcMcAzAihUrWL9+/VyrbGrt+rnalfer9bTjPVu9evWk+xuZNK0Hnkvx7dqzgNeV+zdHxHUUCdRYMnV1A+OSJKkeVs1lMffM/B1wZkR8Blg518oj4m3A6cC1wAGZubFGnWcDZwOsWrUqp/oloZUNDQ1N+cuPmtA3LvZ+tZjF9n+sYUlTZr4MICL2Ap4HrCpfz6UYRtBJ8U0bwGaK8d+SJLWEuSRME65/kKKHaNYi4u3Ax4FrKBKmO+YTiyRpaw2fCCIzb6QY8/2lsX0RsQ+PJVHPo0ikdgBckE+SpBoi4r0UzzFdDbw8M++sNiJJaj9NMXteZl4PXA+cDxARAXRQJFCSJLWl+S7kHhF/S/EM1A+BA2sNyZMkzV1TJE0TlQ/JXlu+JElqV3NeyD0ijqRImDYDlwHHFt85buWWzDx3PgFKkpo0aZIkSdPao9xuC7x9inO+A5zbiGAkqZ01bJ0mSZJUP5l5cmbGNK/VVccpSe3ApEmSpDpxIXdJak+VDM+LiJtncNoW4HfAKPCVzLxwYaOSJGneXMhdktpQVc80bVPW/Qfl+03ABmD5uJh+BewK7Af8VUT8K3BIZm5ubKiSJM3YelzIXZLaTlXD854D/JJitp8uYLvMfBqwHfCScv9twNOBfYBvAK8CjqskWkmSZiAzX5aZT6Fouw6jWHD2MuABikXcjwLOAC4H7gEOrSZSSdJsVNXT1A/sBOyfmZvGdmbmFuB7EfFy4CdAf2YeGxGvpVgl/XDgtCoCliRpplzIXZLaS1VJ06uBL45PmMbLzIcj4mtAD3BsZt4fEd8C/qKRQUqSVC8u5C5JrauqpGk58MRpznlCed6YX+O6UpKkNuFC7pLUOqp6pulm4DURseNkByPi94DXAD8bt/tpwMYGxCZJkiRJj6oqaTqbYpKH70fE4RGxMiK2L7evA75PMbPeWfDoEIbVONOQJEmSpAarZLhbZp5ePhD7ZuALk5wSwNmZeXr5fldgEPi3BoUoSZIkSUCFzwhl5lsi4osU06/uRzGb3u+AHwNfyMzvjjv3N8D7KghTkiRJ0iJX6cQKmTkCjFQZgyRJCyEibp7BaVsovjAcBb6SmRcubFSSpLlwNjpJkhbGNhTt7B+U7zcBGyhmhh1rf39FMQR9P+CvIuJfgUMyc3NjQ5Uk1VLVRBBbiYgdI+IPy1nzJElqB88BfglcBnQB22Xm04DtgJeU+2+jmBhpH+AbwKuA4yqJVpI0pcqSpohYEhEnRMRPgd8CtwB3RcRPy/32gkmSWlk/xfO6B2Tm5Zm5BSAzt2Tm94CXAzsD/Zl5I/BaiiTr8IrilSRNoZKkKSKeCFxK0aCsBH4BXFVuV5b7/708T5KkVvRq4KuZuWmyg5n5MPA1YG35/n7gW8DeDYtQkjQjVfU0vZNi3aWLgY7MXJmZL8rMlRRDFL5GMXThnRXFJ0nSfC0Hpvvy7wnleWN+jc8bS1LTqSppOgy4huJh1xvHH8jMmyi+dfsvHKIgSWpdNwOviYgdJztYPsf7GuBn43Y/DdjYgNgkSbNQVdL0R8AlY+O7Jyr3XwLs2dCoJEmqn7MpJnn4fkQcHhErI2L7cvs64PsUM+udBRARQTEK4+qK4pUkTaGqIQAPAztMc85S4JEGxCJJUt1l5ukRsQ/wZuALk5wSwNmZeXr5fldgEPi3BoUoSZqhqpKmnwB/EREnZ+Z/TzwYEbsAfwH8v4ZHJklSnWTmWyLii8BRFGsx7USxmO2PgS9k5nfHnfsb4H0VhClJmkZVSdMngS8BV0XEB4Fh4HbgqRRDE/4G+H3g2IrikySpLjJzBBipOg5J0txVkjRl5pcjYj/gBIox3xMF8JHM/HJDA5MkaYGUE0LsDNydmb+rOBxJ0ixUtrhtZr4feDFwDsUwhZvL7TnA/pl5QlWxSZJUDy7kLkntodIf1pl5JXBllTFIkrQQygXavwH8CZAUC7jfTjGt+EqKhdwPiogDy4VuJUlNqrKeJkmS2pwLuUtSm2hIT1NEnDPHSzMze+sajCRJjTF+Ifet1iXMzJsiYi3FmkyHA6c2PjxJ0kw1anjeUXO8LgGTJklSK/oj4MxaC7lHxCXAusaGJUmarUYlTXs0qB5JkpqFC7lLUptoSNKUmbc2oh5JkpqIC7lLUptwIghJkhbGJykWar8qInoj4pkRsX1E7BERRwPfL49/stIoJUnTcn0ISZIWgAu5S1L7MGmSJGmBZOb7I+KrFJMaPRfYCbibcjH3zLyiyvgkSTNj0iRJ0gJyIXdJan0+0yRJkiRJNZg0SZIkSVINTTk8LyJeD2wCLs7Mu6uOR5Kk6UTEOXO8NDPThdwlqYk1ZdIEnAskcE9E/BPw8cy8o9qQJEmq6ag5XpcUE0VIkppUsyZNX6CYivXZwLuB44AnVxqRJEm17VF1AJKkhdGUSVNmHjX254jYCXhJddFIkjS9zLy16hgkSQuj6SeCyMy7M/PrVcchSZIkLXaDg4N0dnaydu1aOjs7GRwcrDqkhmjKniZJkiRJzWVwcJC+vj4GBgbYuHEjy5Yto7e3eCSzp6en4ugWVtP2NEXEhyPi4qrjkCRJkgT9/f0MDAzQ3d3NkiVL6O7uZmBggP7+/qpDW3BNmzQBuwAHVR2EJEmSJBgdHaWrq2urfV1dXYyOjlYUUeM0c9IkSZIkqUl0dHQwMjKy1b6RkRE6OjoqiqhxfKZJkqQKuJC72snqC1az4cENc75+xw549udPmFcMy7dbzvpD18+rDNXW19dHb28vAwMDbNq0ieHhYXp7exfF8LyGJU0R0QX8KDPvb1SdkiQ1sXNxIXe1ifkkTO0UQ7sbm+xh3bp1jI6O0tHRQX9/f9tPAgGN7Wn6LrA5Iq4Hfgj8oHxdnZkPNDAOSZKagQu5S2o5PT099PT0MDQ0xJo1a6oOp2EamTStB54LPKt8va7cvzkirqNIoMaSqasbGJckSQ3nQu6S1DoaljRl5ssAImIv4HnAqvL1XKCzfB1Znr4ZeLBRsUmSVKXymSYXcpekJtXwiSAy80bgRuBLY/siYh8eS6KeR5FI7UAx1luSJEmSKtMUs+dl5vXA9cD5ABERQAdFAiVJUluKiA8Dz8nMg6uORZI0taZImibKzASuLV+SJLUrF3KXpBbg4raSJEmSVENdk6aIeEZEDNezTEmSJEmqUt2Spoh4I/CfwK31KlOSpFYSEV0R4VpLktRm6vJMU0S8HfgQcFxmfqYeZUqS1IJcyF2S2lC9JoKI8rV5RidH3DyD07YAvwNGga9k5oVzD0+SpIZYjwu5S1LbqUvSlJkfj4j7gU9ExIsz86+nuWSbsu4/KN9vAjYAy8fF9CtgV2A/4K8i4l+BQzJzRomZJEmN5kLuktSe6vZMU2aeRZHg7DWD058D/BK4DOgCtsvMpwHbAS8p998GPB3YB/gG8CrguHrFK0nSQsnMGzPzS5n5rsxcnZk7Uaw/eARwBvA94CGKhdwlSU2urus0ZebNEbF6Bqf2AzsB+2fmpnHXbwG+FxEvB34C9GfmsRHxWuA64HDgtHrGLElSI7iQuyS1rrqv01QuTDudVwNfHZ8wTSjjYeBrwNry/f3At4C96xWnJDWzwcFBOjs7Wbt2LZ2dnQwODlYdkuosC9dm5nlVxyJJqq2uPU2zsBx44jTnPKE8b8yvqS5eSWqYwcFB+vr6GBgYYOPGjSxbtoze3l4Aenp6Ko5OkqTFp+49TTN0M/CaiNhxsoMR8XvAa4Cfjdv9NGBjA2KTpEr19/czMDBAd3c3S5Ysobu7m4GBAfr7+6sOTbiQuyQtRlUlTWdTTPLw/Yg4PCJWRsT25fZ1wPcpZtY7Cx4d970ap2eVtAiMjo7S1dW11b6uri5GR0crikhjXMhdkhanSoa7ZebpEbEP8GbgC5OcEsDZmXl6+X5XYBD4twaFKEmV6ejoYGRkhO7u7kf3jYyM0NHRUWFUciF3SVq8KntGKDPfEhFfBI6imKp8J4rFbH8MfCEzvzvu3N8A76sgTElquL6+Pnp7exkYGGDTpk0MDw/T29vr8LzquZC7JC1SlU6skJkjwEiVMUhSsxmb7GHdunWMjo7S0dFBf3+/k0BUzIXcJWnxquqZpq1ExI4R8YflBBCStOj19PRwzTXX8JWvfIVrrrnGhKlJuJC7pMVusS6JUVlPU0QsAd4F/DWwx7j9PwM+C/zDVOs4SZJUFRdyl7RYLeYlMSrpaYqIJwKXUjQoK4FfAFeV25Xl/n8vz5Mkqam4kLukxWgxL4lR1fC8d1JMIX4x0JGZKzPzRZm5kmKIwtcohi68s6L4JEmaLxdyl9RWFvOSGFUlTYcB11A87Hrj+AOZeRPFt27/RTFEQZKkVuRC7pLaytiSGOMtliUxqkqa/gi4pBzX/Tjl/kuAPedTSUTsFhHnRMSvIuKhiLglIj4REU+ZR5kvjYjNEZER8cH5xCdJEVHzdcghh0x7jpqWC7lLaitjS2IMDw9vtSRGX19f1aEtuKqGADwM7DDNOUuBR+ZaQUTsCVxOMZXrEMXDtc+nmJXooIjYPzM3zLLMHYHPA/czffySNK3pHo1ZecLF3HLqwQ2KRvXkQu6S2s1iXhKjqp6mnwB/ERG/P9nBiNgF+Avg/82jjk9RNEDHZuYhmXlCZr4M+DjFc1NzeWLtdIqZkD48j7gkSYtEZr4FeCnwOYrF22+m6En6HLA6M9887tzfZOb7MvPbVcQqSTOxWJfEqKqn6ZPAl4CryiFuw8DtwFMphib8DfD7wLFzKbzsZToQuAX4xwmHTwKOAY6IiOMz874ZlrkGOBo4Ah/SlSTNkAu5S1Lrq6SnKTO/DJwKPINizPeNwL3ATynWaNoD+Gh53lx0l9tLJz43lZn3AN8Dngy8cCaFRcSuwGeAizLzn+cYkyRpEXMhd0lqXVUNzyMz3w+8GDiHx4Ys/Lh8v39mnjCP4vcptzdMcXxsxr6ZroXxGYq/qzdPd6IkSWMiYklEnBARPwV+SzEC4q6I+Gm535ELktQCKv1hnZlXAlcuQNE7ldu7pzg+tn/n6QqKiDcAfw4cmpm/mU0QEXEMxVBAVqxYwfr162dzecto18/Vrrxfracd79nq1aurDmHBlQu0fwP4EyApFnC/nWJa8ZUUz9YeFBEHlgvdSpKalN9w1RARK4FPAP9nLkMFM/NsiuGHrFq1Ktvxl4ShoaFF8ctPu/B+taBvXOw9a11jC7l/HTh+/LqE5bO3HwP+rDzv1NkWHhF/QZGQ7Qf8MbAjcH5mvm6+gUuzdc1R11QdQuHIqgNQu2pI0hQR58zx0szM3jlcN9aTtNMUx8f2/3aacs4BHgDeMocYJEmL2/iF3Cc+X3tTRKylmEnvcOaQNFFMmvTHFM8E3wbsO69opXnoPLez6hCkBdWonqaj5nhdAnNJmq4vt1M9s7RXuZ3qmacx/5MiwfrvKRaQ7IuIPmAoMw+ZbZCSpLb2R8CZtRZyj4hLgHVzLP8dFMnSTyl6nIbnWI4kaRqNSpr2aFA9Y8YajgMjYpvxDVa5QO3+FAvUTvc81RcoZtmbaC+KdTeuBn5IMYGFJEnjLehC7pn5aJI0xRd7kqQ6aUjSlJm3NqKecfXdFBGXUqzV9FbgzHGHT6FopM4av0ZTROxbXnvduHImXScqIo6iSJouzsy/qfsHkCS1g7GF3E/OzP+eeLBOC7lLkhqgnSeCeAtwOXBGRBwAjAIvoFjD6Qagb8L5o+XWr+skSfWwoAu5S5Iap1ETQWyfmQ80soyyt2kV8AHgIOBVFI3V6cApmXnXfOKRJKmWzPxyROwHnEA5k+oEAXxkHgu5S5IapFE9TT+LiA8Dn87Mh2ZzYUT8MUXi8wPg72ZzbWb+Ajh6hufOuIcpM88Fzp1NLJKkxScz3x8RX6WY1Oi5FJML3U25mHtmXlFFXK4jqHblPW+sdvz7nmqZj0YlTd8ETgNOiogLgC8DV07VcxQRzwReAbweeD7FgoAfbVCskiTVzQIu5D5nriOouvt81QEUvOf1U48JZjKzDpE0h0ZNBHFkRHySYvXzsW+3NkfEKMWQubuA7YDlwD7ALhTDFu6gePbo47PtoZIkSZI0N9MlPCtPuJhbTj24QdFUr2ETQWTmf1BMAb4XxTCFAyhWMX/2hFP/G/gKcCFwYWbOaSpWSZIkSaqHhs+el5k3UjwUS0Q8GXg6RQ/TA8AdmXl7o2OSJGm+IuKcOV6amTmXhdwlSQ1S6ZTjmXk/cGP5kiSplR01x+uSYgTGrETEIcAh5dunltsXRcS55Z/vzMx3zTEmSdI47bxOkyRJjbRHg+vbDzhywr5nli+AWwGTJkmqA5MmSZLqIDNvbXB9JwMnN7JOSVqstqk6AEmSJElqZiZNkiTVQURs3wxlSJLqz6RJkqT6+FlEHBcRT5rthRHxxxExhM8gSVJT8pkmqY3UY/VuaK8VvKUG+iZwGnBSRFwAfBm4MjMfmOzkiHgm8Arg9cDzgV8AH21QrJKkWagkaYqIlwK/y8yrq6hfaleu3i1VJzOPjIhPAv3AMeVrc0SMArcDdwHbUaxNuA+wCxDAHUAf8PHMfKiK2CVJtVXV0zQMnAW8paL6JUmqu8z8D+DAiNiLYu2lAyimBn/2hFP/G/gKcCFwYWY+0sg4JUmzU1XSdCcw6XAFSZJaXWbeCJwAEBFPBp5O0cP0AHBHZt5eYXiSpFmqKmlaD7y4orolSWqYzLwfuLF8SZJaUFWz5/0NsE9E/F1EPKGiGCRJkiRpWlX1NL0PuAZ4P9AbEf8P+DUw8Sn2zMzeRgcnSZIkSWOqSpqOGvfnp5avySTFg7SSJEmSVImqkqY9KqpXkiRJkmalkqQpM2+tol5JkiRJmq2qepoeFRFLgb2BHTLzsqrjkSRJkqTxqpo9j4jYLSIupFgh/QcUC96OHeuKiGsjYnVF4UmSJEkSUFHSFBFPA74PrAG+DlwBxLhTvg/sChza+OgkSZqbiHhpROw+i/OfExGvX8iYJEnzV1VP00kUSdHLM3Mt8G/jD2bmI8BlwP4VxCZJ0lwNs/UMsUTEeyNiwxTnvxr43EIHJUman6qSplcBX83M4Rrn/Bz4gwbFI0lSPcQk+7YDdm5wHFJDLd9uedUhNEUMal9VTQSxArhxmnMeAZY2IBZJkiTNw/pD18/r+pUnXMwtpx5cn2CkBVBVT9NG4A+nOWdv4NcNiEWSJEmSplRV0vQ94M8j4qmTHYyIvYCDGDejniRJkiRVoaqk6aMUY7y/ExGvBJ4MxZpN5fuvAVuAj1UUnyRJkiQBFT3TlJnfj4g3Af9EMeX4mN+V203AGzLzvxoenCRJ85NVByBJqq+qJoIgM8+JiMuAtwAvBJYDdwNXAp/MzOurik2SpHk4OSJOnrgzIjZXEIskqQ4qS5oAMvNG4B1VxiBJUp1NNu14LfZMSVKTqzRpkqR2d0PXS9h8551zvv4SYPSid835+m132YW9Ry6b8/Wancys6llhSdICqjRpiogdKFZDfy6wE8XwvB8D/5KZ91YZmyTVw3wSpnaoX5LUfOb7hR4svi/1KkuaIuK1wKcpVkkfP5QhgU9ExJsy8/9WEZskSQshIv4ceBlFu/fdzLyw4pAkLULN8IVaM8QwG5UkTRHxcmCQYlrxLwDrKRayfSrQDRwGDEbEbzPz36uIUZKk2YqIPwPeDfxtZn5nwrHPAa/nsS8K3xYRF2XmaxocpiRplqoae30i8BDwwsw8OjM/n5nfLLdHAS8GHinPm7OI2C0izomIX0XEQxFxS0R8IiKeMsPrl0bE4RHxxYi4LiLui4h7IuIHEXF8RDxxPvFJktrOnwP/E/j++J0R8afAkcD9wAeB9wI3A4dERE+jg5QkzU5VSdNzgQsy80eTHczMHwBfpmh45iQi9gR+CBwNXAV8nKKBOg64IiKWz6CYlwD/DLwCuAY4E/gi8HTgH4DhiNhurjFKktrO84HLMvPBCfvfQDH8/OjMPDEzP0rRxjwIHN7gGCVJs1RV0vQQcPs05/yqPG+uPgXsChybmYdk5gmZ+TKK5GkfoH8GZfwaeB3wtMz8i7KMNwF7Az+i6BF76zxilCS1l6cCky3M/lLgt8CjzzBl5q+Biym+SJQkNbGqkqbLgP2nOWd/4LtzKbzsZToQuAX4xwmHTwLuA46IiKW1ysnMqzPz/Mx8eML+e4CPlW9XzyVGSVJbegqwVZsREbsDy4CRzJy4JtPPKBZ3lyQ1sapmz3svxRC5U4G/y8z7xg6UicxJQCdFT85cdJfbSzNzy/gDmXlPRHyPIql6IfCtOdbxSLndNMfrpVlbfcFqNjy4Yc7X79gBz/78CfOKYfl2y1l/6Pp5lSG1sXuA3Sbse165/fEU10wcyidJajINSZoi4pxJdv+EYoahYyLiR8BvgBUUzzHtRNHL9B6gdw5V7lNub5ji+I0USdPezD1pekO5/cYcr5dmbT4JUzvFIDWx/wQOjogdxq03+GqK55lGJjl/D6Yfri5JqlijepqOqnFsZ4o1Kyb6E4ox4HNJmnYqt3dPcXxs/85zKJuIeBtwEHA1MFlCOHbeMcAxACtWrGD9+vVzqa7ptevn0tS85zO3ouoAaM77tXr16qpDWCjnA2cB34mIz1N8OXc4xTOyw+NPjIgAuoArGh2kJGl2GpU07dGgehZcRKwFPkHRAL4mMx+Z6tzMPBs4G2DVqlXZjr8kDA0NtfMvP83n81UHUPCez9xo1QHg/WqwAWAtxayr+1GsyfQIcFxmbp5w7gEUE0e4HqEkNbmGJE2ZeWsj6hlnrCdppymOj+3/7WwKjYhDgC8BdwDdmXnzXIKTJLWnzNwSEQcDPRTP5W4AvpKZV09y+i7A6cBXGxehJMGzrr+u6hCAYtxyq6hqIoiFdn253XuK43uV26meeXqciHgtxRpNvwZelpk3zj08SVK7KicgOr981TrvSxRfxElSQ127z75Vh9ByKkmaImKmPTSZmXvOoYqxceMHRsQ242fQi4gdKaYzvx+4ciaFRcThFAOjfok9TJIkSdKiUlVP0zZM3iO3M48NnfsVj03rPSuZeVNEXEoxQ95bgTPHHT4FWAqcNWGq833La7fqr4yIIykme7iVImFq9FBDSVKLiIjXz+W6zPxCvWORJNVPJUlTZq6c6lhE/BFwBkVi84p5VPMW4HLgjIg4gOJ57BdQrOF0A9A34fyx57VjXCzdFAnTNhS9V0cXkx1t5beZ+Yl5xClJah/nMrth+lGeb9IkSU2s6Z5pysyfljPUXUOxyO375ljOTRGxCvgAxfTgr6JYC+N04JTMvGsGxTyDImGCx9ZlmuhWitn0JEmCYtHzr9EckydKkuqg6ZImgMx8MCL+jWL2oTklTWU5vwCOnuG5j+tCysxzKb41lCRpJr5Dsc7gqymW6foM8OXMfLDSqCRJ89KUSVNpE8X6FZJK1xx1TdUhFI6sOgCpOWVmdznM/I0U/1M+B5weEf8MfCYzf1JpgJKkOWnKpCkidqH4lu4XVcciNZPOczurDkGz1AxrYbTSOhjtIDN/Crw3IvqANRQJ1P8G3hIRPwTOAr40fjIiSVJzq2rK8ROnOLQE+EOKRmYn5jE0T5KagWthLF6ZuQm4ELgwIp4B/DVwFHA2cFpEHJSZV1QYoiRphqrqaTp5muO/Az6YmR9pQCySJC2ocrmKv42IK4BPA08Hfr/aqCRJM1VV0tQ9xf4twF3AdeU3dJIktbSI+AOKGVjfQDEr64PAPwM/qjIuSdLMVbVO03eqqFeSpEaIiG2AP6UYkncQRXv7n8BxwHmZeXeF4UmSZqkpJ4KQJKkVRcQeQC/FchdPA+4DPk8xc95VVcYmSWO23WUXNt95Z+UxtJKGJU0RsftcrsvMn9c7FkmSFshPy+0PKBZoH3SWPEnNZu+Ry+ZdxsoTLuaWUw+uQzStoZE9Tbcw+5lvE3vDJEmtI4BHKHqZTgROjHjc2ukTZWY+Y6EDkyTNXSMTkp/z+KRpZ4qpxW9tYBySJC2kJwC7VR2EJKl+GpY0ZebKifsi4mTgbzNzj0bFIbWy5dstZ8ODGyqPQdLkMnObqmOQJNVf1UPfXKhemoX1h66f1/WLbfyxJElSPfiNmCRJkiTVYNIkSZIkSTWYNEmSJElSDSZNkrSAql68r+r6JUlqB1VPBCFJbW2+Cwg6eYckSdVrWNIUEZvncCwz08ROkiRJUmUamZBMuyR6na6RJEmSpLpp5OK2Pj8lSZIkqeWYyEiSJElSDSZNkiRJklSDkyxIbSRi+scA4++nLycz6xCNJElSe7CnSWojmVnzddFFF017jgmTJEnS1kyaJEmSJKkGkyZJkiRJqsFnmiRJkiRtpR7PSbfTkH+TJtU0k/8w02mn/zCSJEmLwXS/vw0NDbFmzZoGRVM9h+eppukmDHjGe7/upAKSJElqa/Y0SVKFHP4gaTFwSQy1OnuaJKlC9ZgmXpKanUtiqNWZNEmSJElSDSZNkiRJklSDSZMkSZIk1eBEEIvdR/eC++6Y8+W3bAecPI/6l+4K775xHgVIkhYjl8SQ1Ej2NC1280iY2qJ+SVJLckkMSY3UtklTROwWEedExK8i4qGIuCUiPhERT5llOcvK624py/lVWe5uCxW7JEkzUa+2TpJUW1sOz4uIPYHLgV2BIeA64PnAccBBEbF/Zm6YQTnLy3L2Br4NfAnYFzgaODgiXpSZNy/Mp2iMOOV3VYdAnlx1BJLUeurV1kmSpteWSRPwKYpG5NjMPHNsZ0ScBrwD6AfePINyPkSRMJ2WmcePK+dY4PSynoPqGHfD5Um/V3UIkqS5qVdb15x85lZSE2m74XnlN28HArcA/zjh8EnAfcAREbF0mnJ2AI4ozz95wuFPArcCr4iIZ84/akmSZq5ebV1Tq/qZ16rrl9RU2rGnqbvcXpqZW8YfyMx7IuJ7FA3NC4Fv1SjnhcD2ZTn3TChnS0R8EzimrK+lh+hJklpOvdq6puXwcUnNpB2Tpn3K7Q1THL+RoiHZm9oNyUzKoSyndS3dtdpv05buWl3dktS66tXWNS2Hj0tqJu2YNO1Ubu+e4vjY/p0bVE5zm2a8tutgSFJTav82yi/1JDWRdkyamkZEHEMxhI8VK1awfv36agOag+Hh4ZrH7777bnbaaaea57Ti525n3o/W0473bPXq1VWHsKi1RPv0/31mXpfPpH2aVjP+vbSxpvx3qJra8Z5N1T61Y9I09u3aVD8px/b/dqHLycyzgbMBVq1ale34S8LQ0JC//LQQ71fr8Z5pCvNqo2yf1Gy8X61nsd2ztps9D7i+3E71rNFe5XaqceD1LkeSpHqzjZKkBmrHpGlsPNmBEbHV54uIHYH9gfuBK6cp50rgAWD/8rrx5WxD8YDt+PokSWqUerV1kqQZaLukKTNvAi4FVgJvnXD4FGApcF5m3je2MyL2jYh9J5RzL3Beef7JE8p5W1n+NzPT6cYlSQ01l7ZOkjR37fhME8BbgMuBMyLiAGAUeAHFuhY3AH0Tzh8ttxOnins/sBp4Z0TsB1wFdABrgDt4fEMlSVKjzLatkyTNUdv1NMGj38CtAs6laECOB/YETgdemJkbZljOBuBFwBnAH5XlvAD4HPC8sh5JkhquXm2dJGl67drTRGb+Ajh6hudOuRhRZm4EjitfkiQ1jdm0dZKkuWvLniZJkiRJqheTJkmSJEmqwaRJkiRJkmqIzKw6hkUhIv4buLXqOBbAs4H/rDoIzZj3q/W06z27MzMPqjoI2T6paXi/Wk+73rNJ2yeTJs1LRGStiTTUXLxfrcd7Js2N/3dai/er9Sy2e+bwPEmSJEmqwaRJkiRJkmowadJ8/azqADQr3q/W4z2T5sb/O63F+9V6FtU985kmSZIkSarBniZJkiRJqsGkSZIkSZJqMGlqUhGRETGnsZNj1053/fjzIuLvpzjn4fL41+cYy53j6uircd514867d4pz/qU8/sg0dc7o89dTK9+viPhtec1vpzmeEXFCue+6CfGMf20pr3nPDD/LVK9/melnaEYTPssfTnPOjpPtn0M9U72uq8dnklr5Z92E6xdF2zS+3vlca/tk+zRx/xzqafn2yaSpzUTERRPef3iGl757AWIJYPm4Xe+b4aVLI+KYOdZ50YT3M/38lWim+zVRRGwfEQ8BO5W7/jIzT53k1LvL173AI0CU1/x9RNw/8QfuFNdO9rq0Lh+kOfyoAXU8wNR/l5c0oH5pSs30s862aWaa6Z5NZPtUV7ZPM2TS1H7+tNzeU26Pm+F1ERH/WudYvlFux759WxoRvz/Da/9xjnXO9fNXpZnu1/jC/5CikXkikMALMvP/THLqI5m5c/naMTOfWC50N1Qe3x7476nqGXftZK9/qvfnqtAuEXHgAtdxfI2/y3cscN3SdJrpZ51t08w00z0bX7jtU33ZPs2QSVMbiYgDgG3Lt08vt9tP1fU6zg/L7Ssj4gl1DOmAcnsBcF/55+m+nRn74bwkIj49m8rm8fkr0YT3ayyuFwM/p/j5sBl4emZeNZsyMvMQ4MTy7ZMi4oy6Btlabi+3QzXPktpUE/6ss22aRhPes7G4bJ/qy/ZpFkya2ss/l9sNmXkPjzUG35ji/DGDwKbyz9fUI5CIWEP5AzczjwDOLA/98TSXJvDV8s9vmmW1c/38VWma+zUmIg4Hvle+fQjYPjNvr3HJlDLz73gszjfUIbxW9T/K7Xa1xtFLbaxpftbZNs1Y09yzMbZPC8L2aRZMmtrLU8vtWFfnWGPwrBlc+8Zyu3dE7FGHWD5bbu8EyMyxMeMREcfXujAz1wBbypMvm0Wd8/n8VWim+wWwA481lHdl5naZWfPB5hm4pdwunexg+UDuZK8ph0y0msy8C/hB+XYhn2P4WI2/z6b9RluLQjP9rLNtmplmumdg+7QgbJ9mx6SpTUTEBeUfMzPPK//wvnHHT6p1fWaey2PDD35Q49SZxBLALuXbt487dFu5PXkGxfSX265pHtQcq3Nen7/Rmul+jTM2FOPhzFxWpzJvmeb4TlO8dql1UavJzP+v/OM2ETG4QNVsz9R/ny3TKKm9NNPPOtummWmmezaO7dMCsX2aOZOm9rG23P5kwv6xxmAm3a6ry+2ycgjDpKKYznPiNwXjp98cG8KQmXn+uP2vK7c7RMRTagWSmScCD5dvZ9LFX4/P30jNdL8memJE1GtYRdQ6mJkx1atO9TeTL5Tbv1qg8t9S4+/z8gWqU5pOM/2ss22amWa6ZxPZPi0M26cZMGlqAxGxP7CkfPtnEw6PNQZPjoin1SonM38E/KJ8e0GNU/fh8d8UdI87/spyu9UP3Mz8DsWDmzCz6Tr/stzuHhFTjjev1+dvlCa8X2PuBsbWz/gfEfHTWvXP0DPqUEZbyMwjKf/9R8SPKw5HWnBN+LPOtmkaTXjPxtg+LSDbp5kxaWoP438g/Ty2Xoxu/bhjM3nodOyhwCdFxN9OdsIU3xTsDBARr+SxbvQ/jgmLmI079rzpAsnMIeCu8m2t8eP1/PyN0DT3a5Jz/wz4Uvl2z4j4xWTnzcLKcjvpopCL0HvL7X4x8ymOpVbVND/rbJtmrGnu2STn2j4tLNunaZg0tYex6UA3UQwbmPga+wbt2dMVVM6Sc0X59uQ5xPK5cX+eLJaxYQ0REW+dQXnPL7c7AntNcU7dPn+DNNP9mqzMHuAz5dvdIuI3cymnbCTHvrE8ux6xtbrM/BjFIn8A/1llLFIDNNPPOtummWmmezZZmbZPC8T2aXomTS0uIs4b+3NmPiEznzTxRfEAXnn69FNKZuaLyz9uA8x2nYUV5fazk8VSxnN3ec60M7Vk5k+BG8u3/2Pi8YX4/AupCe/XVGUeA5xWvt01IjbO5vqI+L/AB8q3D2VmzVmpFpmxIUIrap4ltbAm/Fln2zSNJrxnU5Vp+7RwbJ9qMGlqchHxcI3XHwGHlqf+eqoyspiWc2wowYlTnTfBZ6c/5XGxPvrwZma+scapY7Pw7BgzmH2ISRqkcRbq889JK92v6ZQNycnl26dExD2TnPaEcQ/v/q78nAm8pjx+PzDlg9WTPAA8/vXxun6gJlE+P3HHTM6dwb+n8WpN6VrrwWpp1lrpZ51tU6GV7tl0bJ8Whu1TbSZNze8JNV4v47Fvbo6cppx3ltulMxmrWjYsm6c7b4I/Lbe/nKbsf6JYKBBm8NBt+UP6cedFxPNZoM8/D610v6aVmacA7yrf7hAR90fExJmDxh7e3ZHi8yXFN7bvysylmfkAU5tqCtKdeOwbr3a03wzPq/XvadcJ59aa0nWyB6ul+Wiln3W2TYVWumfTsn1aMPvN8LxF1z5FZk5/liRJkiQtUvY0SZIkSVINJk2SJEmSVINJkyRJkiTVYNIkSZIkSTWYNEmSJElSDSZNkiRJklSDSZMkSZIk1WDSJEmSJEk1mDRJkiRJUg0mTZIkSZJUw/8PQlFIxsJ95SMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(12, 8), sharex=True)\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 12\n",
+    "mpl.rcParams[\"font.size\"] = 15\n",
+    "colors = [\"C1\", \"C2\", \"C3\"]\n",
+    "grid = True\n",
+    "\n",
+    "# Plot Huber loss boxplot\n",
+    "plt.sca(ax[0, 0])\n",
+    "bdict = dfhuber.boxplot(ax=ax[0, 0], patch_artist=True, return_type=\"dict\", \n",
+    "                        medianprops={\"color\": \"k\"}, grid=grid)\n",
+    "plt.ylabel(\"Huber $(\\hat{L}, L)$\");\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i])\n",
+    "plt.yticks(np.linspace(0, 0.02, 3));\n",
+    "\n",
+    "plt.sca(ax[1, 0])\n",
+    "bdict = dfhuber_log.boxplot(ax=ax[1, 0], patch_artist=True, return_type=\"dict\", \n",
+    "                        medianprops={\"color\": \"k\"}, grid=grid)\n",
+    "plt.ylabel(\"Huber $(\\hat{\\log L}, \\log L)$\");\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i])\n",
+    "plt.yticks(np.linspace(0, 0.4, 3));\n",
+    "\n",
+    "# Plot MSE boxplot\n",
+    "plt.sca(ax[0, 1])\n",
+    "bdict = dfmse.boxplot(ax=ax[0, 1], patch_artist=True, return_type=\"dict\", \n",
+    "                      medianprops={\"color\": \"k\"}, grid=grid);\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i])\n",
+    "plt.ylabel(r\"MSE $(\\hat{L}, L)$\")\n",
+    "plt.yticks(np.linspace(0, 0.08, 3));\n",
+    "\n",
+    "plt.sca(ax[1, 1])\n",
+    "bdict = dfmse_log.boxplot(ax=ax[1, 1], patch_artist=True, return_type=\"dict\", \n",
+    "                      medianprops={\"color\": \"k\"}, grid=grid);\n",
+    "for i,box in enumerate(bdict['boxes']):\n",
+    "    box.set_color(colors[i])\n",
+    "plt.ylabel(r\"MSE $(\\hat{\\log L}, \\log L)$\");\n",
+    "plt.yticks(np.linspace(0, 2., 3));\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"likelihood_comparison.png\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6189efc",
+   "metadata": {},
+   "source": [
+    "## Visualize Likelihood for 10 Benchmark observations\n",
+    "We can also visualize the single trial likelihoods for the 10 random observations used in the benchmark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "88a11d59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/janfb/anaconda3/envs/ddm/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
+     ]
+    }
+   ],
+   "source": [
+    "ll_true = []\n",
+    "ll_lan = []\n",
+    "ll_nle = []\n",
+    "\n",
+    "for ii in range(1, 11):\n",
+    "    \n",
+    "    xo = task.get_observation(ii).reshape(-1, 1)\n",
+    "    test_thetas = prior.sample((1000,))\n",
+    "\n",
+    "    # Extract positive RTs and choices for mixed model.\n",
+    "    rs = abs(xo)\n",
+    "    cs = torch.ones_like(rs)\n",
+    "    cs[xo < 0] = 0\n",
+    "  \n",
+    "    ll_true.append(task.get_log_likelihood(test_thetas, data=xo.reshape(1, -1), l_lower_bound=l_lower_bound))\n",
+    "\n",
+    "    ll_lan.append(lan_likelihood(test_thetas, \n",
+    "                                xo, \n",
+    "                                net=lan_kde, \n",
+    "                                ll_lower_bound=np.log(l_lower_bound)))\n",
+    "    \n",
+    "    ll_nle.append(nle.log_prob(rs, cs, test_thetas))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e63d3fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(3, 3, figsize=(18, 12), sharey=True, sharex=True)\n",
+    "mpl.rcParams[\"axes.spines.right\"] = False\n",
+    "mpl.rcParams[\"axes.spines.top\"] = False\n",
+    "s = 7\n",
+    "idx = 0\n",
+    "alpha = 1.0\n",
+    "for ii in range(0, 9):\n",
+    "    if ii%3 == 0:\n",
+    "        plt.sca(ax[idx, 0])\n",
+    "    elif ii%3 == 1:\n",
+    "        plt.sca(ax[idx, 1])\n",
+    "    elif ii%3 == 2:\n",
+    "        plt.sca(ax[idx, 2])\n",
+    "        idx += 1\n",
+    "\n",
+    "    plt.title(f\"Observation {ii+1}\")\n",
+    "    \n",
+    "    plt.scatter(ll_true[ii], ll_lan[ii], alpha=alpha, color=\"C2\", s=s)\n",
+    "    plt.scatter(ll_true[ii], ll_nle[ii], alpha=alpha, color=\"C3\", s=s)\n",
+    "    plt.plot(ll_true[ii], ll_true[ii], \"k\")\n",
+    "    if not ii:\n",
+    "        plt.legend([\"Identity\", \"LAN\", \"NLE\", \n",
+    "                   ], frameon=False, fontsize=12)\n",
+    "    if ii in [0, 3, 6]:\n",
+    "        plt.ylabel(\"synthetic log L\")\n",
+    "    if ii > 5: \n",
+    "        plt.xlabel(\"analytic log L\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d650e863",
+   "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": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lan_nle_comparison/README.md b/lan_nle_comparison/README.md
new file mode 100644
index 00000000..2fe3a368
--- /dev/null
+++ b/lan_nle_comparison/README.md
@@ -0,0 +1,72 @@
+# Comparison between LANs and NLE on the simlpe drift-diffusion model
+
+This is a short demonstration for how to reproduce the comparison between LANs and NLE.
+
+We perform the comparison in (log) likelihood space, comparing the synthetic likelihoods
+of LAN and NLE against the analytic likelihoods obtained from https://github.com/DrugowitschLab/DiffModels.jl. 
+
+Additionally, we use MCMC via slice sampling to compare two approaches in posterior space. 
+For this comparison we have added the DDM as a task in a framework developed for benchmarking 
+simulation-based inference algorithms, `sbibm`. 
+
+In general, the code relies on three repositories, [`sbi`](https://github.com/mackelab/sbi) for using NLE, 
+[`sbibm`](https://github.com/sbi-benchmark/sbibm) for simulating the data and loading the LAN keras weights, 
+and [`benchmarking-results`](https://github.com/sbi-benchmark/results/tree/main/benchmarking_sbi) for running the benchmark. 
+
+## Comparison in likelihood space
+For a demo of the likelihood comparison you find a jupyter notebook in this folder. For
+executing the notebook locally perform the steps outlined below. 
+
+```bash
+# clone repo
+git clone https://github.com/mackelab/sbibm.git
+# switch to branch
+cd sbibm
+git checkout ddm-task
+# install locally (e.g., in a new conda env)
+pip install -e .
+# install missing nflow dependency
+pip install UMNN
+# open the notebook at /lan_nle_comparison
+```
+
+## Comparison in posterior space using `sbibm`
+
+For a general overview over the benchmarking suite see https://sbi-benchmark.github.io. 
+
+To run the benchmark on your local machine, please follow the steps below.
+
+- **optional**: create and activate a new conda environment
+```bash
+conda create -n ddmtest python=3.8
+conda activate ddmtest
+```
+
+- clone and install `benchmarking-results` repo from https://github.com/sbi-benchmark/results/tree/main/benchmarking_sbi
+
+```bash
+git clone https://github.com/mackelab/results.git
+cd results/benchmarking_sbi
+git checkout ddm
+pip install -r requirements.txt
+cd ../..
+```
+
+- clone and install sbibm repo on the `ddm-task` branch
+
+```bash
+git clone https://github.com/mackelab/sbibm.git
+cd sbibm
+git checkout ddm-task
+pip install -e .
+cd ..
+```
+
+- run the benchmark
+
+```bash
+cd results/benchmarking_sbi
+python run.py task=ddm task.num_observation=1 algorithm=lan
+```
+
+More details about how to run the benchmark can be found at https://github.com/sbi-benchmark/results/tree/main/benchmarking_sbi. 
diff --git a/lan_nle_comparison/ddm_transforms.p b/lan_nle_comparison/ddm_transforms.p
new file mode 100644
index 00000000..6a1e74b8
Binary files /dev/null and b/lan_nle_comparison/ddm_transforms.p differ
diff --git a/lan_nle_comparison/reproduce_figure_5.py b/lan_nle_comparison/reproduce_figure_5.py
new file mode 100644
index 00000000..621359f9
--- /dev/null
+++ b/lan_nle_comparison/reproduce_figure_5.py
@@ -0,0 +1,106 @@
+import keras
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import numpy as np
+import pandas as pd
+import pickle
+import sbibm
+import torch
+import time
+from joblib import Parallel, delayed
+
+from sbibm.tasks.ddm.utils import run_mcmc, LANPotentialFunctionProvider
+from sbibm.algorithms.sbi.utils import wrap_prior_dist
+
+
+# network trained on KDE likelihood for 4-param ddm
+lan_kde_path = "../sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5"
+lan_ana_path = "../sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5"
+lan_kde = keras.models.load_model(lan_kde_path, compile=False)
+lan_ana = keras.models.load_model(lan_ana_path, compile=False)
+
+# Load pretrained NLE model
+with open("../sbibm/algorithms/lan/nle_pretrained/mm_688_4.p", "rb") as fh:
+    nle = pickle.load(fh)
+
+num_workers = 80
+m = num_workers
+n = 1024
+l_lower_bound = 1e-7
+num_samples = 10000
+
+
+task = sbibm.get_task("ddm")
+prior = task.get_prior_dist()
+simulator = task.get_simulator(num_trials=n)  # Passing the seed to Julia.
+
+thos = prior.sample((m,))
+xos = task.get_simulator()(thos)
+
+mcmc_parameters = {
+    "num_chains": 100,
+    "thin": 10,
+    "warmup_steps": 100,
+    "init_strategy": "sir",
+    "sir_batch_size": 100,
+    "sir_num_batches": 1000,
+}
+
+with open("ddm_transforms.p", "rb") as fh:
+    transforms = pickle.load(fh)["transforms"]
+prior_transformed = wrap_prior_dist(prior, transforms)
+
+
+def local_run(xi):
+
+    tic = time.time()
+    # Get potential function for mixed model.
+    potential_fn_mm = nle.get_potential_fn(
+        xi.reshape(-1, 1),
+        transforms,
+        # Pass untransformed prior and correct internally with ladj.
+        prior=prior,
+        ll_lower_bound=np.log(l_lower_bound),
+    )
+
+    # Run MCMC in transformed space.
+    transformed_samples = run_mcmc(
+        prior=prior_transformed,
+        potential_fn=potential_fn_mm,
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    nle_samples = transforms.inv(transformed_samples)
+    nle_time = time.time() - tic
+
+    tic = time.time()
+    # Use potential function provided refactored from SBI toolbox for LAN.
+    potential_fn_lan = LANPotentialFunctionProvider(transforms, lan_kde, l_lower_bound)
+
+    lan_transformed_samples = run_mcmc(
+        prior=prior_transformed,
+        # Pass original prior to pf and correct potential with ladj.
+        potential_fn=potential_fn_lan(
+            prior=prior,
+            sbi_net=None,
+            x=xi.reshape(-1, 1),
+            mcmc_method="slice_np_vectorized",
+        ),
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    lan_samples = transforms.inv(lan_transformed_samples)
+    lan_time = time.time() - tic
+
+    return nle_samples, lan_samples, nle_time, lan_time
+
+
+# run in parallel
+results = Parallel(n_jobs=num_workers)(delayed(local_run)(_) for _ in xos)
+
+with open("figure_5_results.p", "wb") as fh:
+    pickle.dump(dict(thos=thos, xos=xos, results=results), fh)
+
+print("Done")
diff --git a/sbibm/algorithms/lan/julia.py b/sbibm/algorithms/lan/julia.py
new file mode 100644
index 00000000..0e8efc5e
--- /dev/null
+++ b/sbibm/algorithms/lan/julia.py
@@ -0,0 +1,89 @@
+import logging
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+
+from sbibm.tasks.task import Task
+
+from sbibm.algorithms.sbi.utils import wrap_prior_dist
+from sbibm.tasks.ddm.utils import run_mcmc
+
+
+def run(
+    task: Task,
+    num_samples: int,
+    num_simulations: int,
+    num_observation: Optional[int] = None,
+    observation: Optional[torch.Tensor] = None,
+    automatic_transforms_enabled: bool = True,
+    mcmc_method: str = "slice_np_vectorized",
+    mcmc_parameters: Dict[str, Any] = {
+        "num_chains": 100,
+        "thin": 10,
+        "warmup_steps": 100,
+        "init_strategy": "sir",
+        "sir_batch_size": 1000,
+        "sir_num_batches": 100,
+    },
+    l_lower_bound: float = 1e-7,
+) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
+    """Runs MCMC with analytical DDM likelihood.
+
+    Args:
+        task: Task instance, here DDM.
+        num_observation: Observation number to load, alternative to `observation`
+        observation: Observation, alternative to `num_observation`
+        num_samples: Number of samples to generate from posterior
+        num_simulations: Simulation budget
+        num_rounds: Number of rounds
+        automatic_transforms_enabled: Whether to enable automatic transforms
+        mcmc_method: MCMC method
+        mcmc_parameters: MCMC parameters
+        l_lower_bound: lower bound for single trial likelihood evaluations.
+
+    Returns:
+        Samples from posterior, number of simulator calls, log probability of true params if computable
+    """
+    assert not (num_observation is None and observation is None)
+    assert not (num_observation is not None and observation is not None)
+    assert (
+        task.name == "ddm"
+    ), "This algorithm works only for the DDM task as it uses its analytical likeklihood."
+
+    log = logging.getLogger(__name__)
+    log.info(f"Running MCMC with analytical likelihoods from Julia package.")
+
+    prior = task.get_prior_dist()
+    if observation is None:
+        observation = task.get_observation(num_observation)
+
+    transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
+    if automatic_transforms_enabled:
+        prior_transformed = wrap_prior_dist(prior, transforms)
+
+    # sbi needs the trials in first dimension.
+
+    llj = task._get_log_prob_fn(
+        None,
+        observation,
+        "experimental",
+        posterior=True,
+        automatic_transforms_enabled=automatic_transforms_enabled,
+        l_lower_bound=l_lower_bound,
+    )
+
+    def potential_fn_julia(theta):
+        theta = torch.as_tensor(theta, dtype=torch.float32)
+
+        return llj(theta)
+
+    # Run MCMC in transformed space.
+    samples = run_mcmc(
+        prior=prior_transformed,
+        potential_fn=potential_fn_julia,
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    # Return untransformed samples.
+    return transforms.inv(samples), num_simulations, None
diff --git a/sbibm/algorithms/lan/lan.py b/sbibm/algorithms/lan/lan.py
new file mode 100644
index 00000000..820506ab
--- /dev/null
+++ b/sbibm/algorithms/lan/lan.py
@@ -0,0 +1,98 @@
+import logging
+import pathlib
+from typing import Any, Dict, Optional, Tuple
+
+import keras
+import torch
+from sbibm.tasks.task import Task
+from sbibm.tasks.ddm.utils import LANPotentialFunctionProvider, run_mcmc
+
+from sbibm.algorithms.sbi.utils import wrap_prior_dist
+
+
+def run(
+    task: Task,
+    num_samples: int,
+    num_simulations: int,
+    num_observation: Optional[int] = None,
+    observation: Optional[torch.Tensor] = None,
+    automatic_transforms_enabled: bool = True,
+    mcmc_method: str = "slice_np_vectorized",
+    mcmc_parameters: Dict[str, Any] = {
+        "num_chains": 10,
+        "thin": 10,
+        "warmup_steps": 100,
+        "init_strategy": "sir",
+        "sir_batch_size": 1000,
+        "sir_num_batches": 100,
+    },
+    l_lower_bound: float = 1e-7,
+) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
+    """Runs pretrained LAN based on analytical likelihood targets.
+
+    Args:
+        task: Task instance
+        num_observation: Observation number to load, alternative to `observation`
+        observation: Observation, alternative to `num_observation`
+        num_samples: Number of samples to generate from posterior
+        num_simulations: Simulation budget
+        num_rounds: Number of rounds
+        automatic_transforms_enabled: Whether to enable automatic transforms
+        mcmc_method: MCMC method
+        mcmc_parameters: MCMC parameters
+        l_lower_bound: lower bound for single trial likelihood evaluations.
+
+    Returns:
+        Samples from posterior, number of simulator calls, log probability of true params if computable
+    """
+    assert not (num_observation is None and observation is None)
+    assert not (num_observation is not None and observation is not None)
+    assert (
+        task.name == "ddm"
+    ), "This algorithm works only for the DDM task as it uses its analytical likeklihood."
+
+    log = logging.getLogger(__name__)
+    log.info(f"Running LAN pretrained with KDE targets.")
+    # Set LAN budget from paper.
+    lan_budget = int(1e5 * 1.5e6)
+
+    prior = task.get_prior_dist()
+    if observation is None:
+        observation = task.get_observation(num_observation)
+
+    # Maybe transform to unconstrained parameter space for MCMC.
+    transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
+    if automatic_transforms_enabled:
+        prior_transformed = wrap_prior_dist(prior, transforms)
+    else:
+        prior_transformed = prior
+
+    num_trials = observation.shape[1]
+    # sbi needs the trials in first dimension.
+    observation_sbi = observation.reshape(num_trials, 1)
+
+    # network trained on KDE likelihood for 4-param ddm
+    lan_kde_path = (
+        f"{pathlib.Path(__file__).parent.resolve()}/lan_pretrained/model_final_ddm.h5"
+    )
+    # load weights as keras model
+    lan_kde = keras.models.load_model(lan_kde_path, compile=False)
+
+    # Use potential function provided refactored from SBI toolbox for LAN.
+    potential_fn_lan = LANPotentialFunctionProvider(transforms, lan_kde, l_lower_bound)
+
+    samples = run_mcmc(
+        prior=prior_transformed,
+        # Pass original prior to pf and correct potential with ladj.
+        potential_fn=potential_fn_lan(
+            prior=prior,
+            sbi_net=None,
+            x=observation_sbi,
+            mcmc_method=mcmc_method,
+        ),
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    # Return untransformed samples.
+    return transforms.inv(samples), lan_budget, None
diff --git a/sbibm/algorithms/lan/lan_ana.py b/sbibm/algorithms/lan/lan_ana.py
new file mode 100644
index 00000000..fb2f7071
--- /dev/null
+++ b/sbibm/algorithms/lan/lan_ana.py
@@ -0,0 +1,97 @@
+import logging
+import pathlib
+from typing import Any, Dict, Optional, Tuple
+
+import keras
+import torch
+from sbibm.tasks.task import Task
+from sbibm.tasks.ddm.utils import LANPotentialFunctionProvider, run_mcmc
+
+from sbibm.algorithms.sbi.utils import wrap_prior_dist
+
+
+def run(
+    task: Task,
+    num_samples: int,
+    num_simulations: int,
+    num_observation: Optional[int] = None,
+    observation: Optional[torch.Tensor] = None,
+    automatic_transforms_enabled: bool = True,
+    mcmc_method: str = "slice_np_vectorized",
+    mcmc_parameters: Dict[str, Any] = {
+        "num_chains": 100,
+        "thin": 10,
+        "warmup_steps": 100,
+        "init_strategy": "sir",
+        "sir_batch_size": 1000,
+        "sir_num_batches": 100,
+    },
+    l_lower_bound: float = 1e-7,
+) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
+    """Runs pretrained LAN based on analytical likelihood targets.
+
+    Args:
+        task: Task instance
+        num_observation: Observation number to load, alternative to `observation`
+        observation: Observation, alternative to `num_observation`
+        num_samples: Number of samples to generate from posterior
+        num_simulations: Simulation budget
+        num_rounds: Number of rounds
+        automatic_transforms_enabled: Whether to enable automatic transforms
+        mcmc_method: MCMC method
+        mcmc_parameters: MCMC parameters
+        l_lower_bound: lower bound for single trial likelihood evaluations.
+
+    Returns:
+        Samples from posterior, number of simulator calls, log probability of true params if computable
+    """
+    assert not (num_observation is None and observation is None)
+    assert not (num_observation is not None and observation is not None)
+    assert (
+        task.name == "ddm"
+    ), "This algorithm works only for the DDM task as it uses its analytical likeklihood."
+
+    log = logging.getLogger(__name__)
+    log.info(f"Running LAN pretrained with analytical targets.")
+    # Set LAN budget from paper.
+    lan_budget = int(1e5 * 1.5e6)
+
+    prior = task.get_prior_dist()
+    if observation is None:
+        observation = task.get_observation(num_observation)
+
+    # Maybe transform to unconstrained parameter space for MCMC.
+    transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
+    if automatic_transforms_enabled:
+        prior_transformed = wrap_prior_dist(prior, transforms)
+    else:
+        prior_transformed = prior
+
+    num_trials = observation.shape[1]
+    # sbi needs the trials in first dimension.
+    observation_sbi = observation.reshape(num_trials, 1)
+
+    # network trained on analytical likelihood for 4-param ddm
+    lan_ana_path = f"{pathlib.Path(__file__).parent.resolve()}/lan_pretrained/model_final_ddm_analytic.h5"
+    # load weights as keras model
+    lan_ana = keras.models.load_model(lan_ana_path, compile=False)
+
+    # Use potential function provided refactored from SBI toolbox for LAN.
+    potential_fn_lan = LANPotentialFunctionProvider(transforms, lan_ana, l_lower_bound)
+
+    # Run MCMC in transformed space.
+    samples = run_mcmc(
+        prior=prior_transformed,
+        # Pass original prior to pf and correct potential with ladj.
+        potential_fn=potential_fn_lan(
+            prior=prior,
+            sbi_net=None,
+            x=observation_sbi,
+            mcmc_method=mcmc_method,
+        ),
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    # Return untransformed samples.
+    return transforms.inv(samples), lan_budget, None
diff --git a/sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5 b/sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5
new file mode 100644
index 00000000..abdea7d3
Binary files /dev/null and b/sbibm/algorithms/lan/lan_pretrained/model_final_ddm.h5 differ
diff --git a/sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5 b/sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5
new file mode 100644
index 00000000..7fb87165
Binary files /dev/null and b/sbibm/algorithms/lan/lan_pretrained/model_final_ddm_analytic.h5 differ
diff --git a/sbibm/algorithms/lan/nle-ddm-pretrained.py b/sbibm/algorithms/lan/nle-ddm-pretrained.py
new file mode 100644
index 00000000..9817f8d1
--- /dev/null
+++ b/sbibm/algorithms/lan/nle-ddm-pretrained.py
@@ -0,0 +1,108 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
+
+import numpy as np
+import pickle
+import torch
+from sbi import inference as inference
+
+from sbibm.algorithms.sbi.utils import wrap_prior_dist
+from sbibm.tasks.task import Task
+from sbibm.tasks.ddm.utils import run_mcmc
+
+
+def run(
+    task: Task,
+    num_samples: int,
+    num_simulations: int,
+    num_observation: Optional[int] = None,
+    observation: Optional[torch.Tensor] = None,
+    base_distribution: str = "lognormal",
+    num_transforms: int = 5,
+    num_bins: int = 5,
+    tails: str = "rectified",
+    tail_bound: float = 5.0,
+    tail_bound_eps: float = 1e-5,
+    automatic_transforms_enabled: bool = True,
+    mcmc_parameters: Dict[str, Any] = {
+        "num_chains": 10,
+        "thin": 10,
+        "warmup_steps": 100,
+        "init_strategy": "sir",
+        "sir_batch_size": 100,
+        "sir_num_batches": 1000,
+    },
+    l_lower_bound: float = 1e-7,
+    use_log_rts: bool = True,
+) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
+    """Runs NLE for DDM using a mixed model for discrete choices and continuous rts.
+
+    Args:
+        task: Task instance
+        num_samples: Number of samples to generate from posterior
+        num_simulations: Simulation budget
+        num_observation: Observation number to load, alternative to `observation`
+        observation: Observation, alternative to `num_observation`
+        base_distribution: base distribution of neural spline flow, "normal" or "lognormal".
+        num_transforms: number of transforms in neural spline flow.
+        num_bins: number of bins for splines.
+        tails: kind of spline tails, "linear" or "rectified"
+        tail_bound: symmetric extend of the splines.
+        tail_bound_eps: epsilon to add to tail bounds when rectified.
+        automatic_transforms_enabled: Whether to enable automatic transforms
+        mcmc_method: MCMC method
+        mcmc_parameters: MCMC parameters
+        l_lower_bound: lower bound on single trial likelihood evaluations.
+        use_log_rts: whether to transform rts to log space.
+    Returns:
+        Samples from posterior, number of simulator calls, log probability of true params if computable
+    """
+    assert not (num_observation is None and observation is None)
+    assert not (num_observation is not None and observation is not None)
+    assert (
+        task.name == "ddm"
+    ), "This algorithm works only for the DDM task as it uses its analytical likeklihood."
+
+    log = logging.getLogger(__name__)
+
+    prior = task.get_prior_dist()
+    if observation is None:
+        observation = task.get_observation(num_observation)
+
+    transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
+    if automatic_transforms_enabled:
+        prior_transformed = wrap_prior_dist(prior, transforms)
+    else:
+        prior_transformed = prior
+
+    # Train choice net
+    log.info(f"Running Mixed Model NLE")
+
+    pretrained_model_filename = "mm_315_1"
+    path_to_model = f"/home/janfb/qode/sbibm/sbibm/algorithms/lan/nle_pretrained/{pretrained_model_filename}.p"
+
+    with open(path_to_model, "rb") as fh:
+        mixed_model = pickle.load(fh)
+    log.info(f"Loaded pretrained model {pretrained_model_filename}.")
+
+    # Get potential function for mixed model.
+    potential_fn_mm = mixed_model.get_potential_fn(
+        observation.reshape(-1, 1),
+        transforms,
+        # Pass untransformed prior and correct internally with ladj.
+        prior=prior,
+        ll_lower_bound=np.log(l_lower_bound),
+    )
+
+    # Run MCMC in transformed space.
+    log.info(f"Starting MCMC")
+    samples = run_mcmc(
+        prior=prior_transformed,
+        potential_fn=potential_fn_mm,
+        mcmc_parameters=mcmc_parameters,
+        num_samples=num_samples,
+    )
+
+    # Return untransformed samples.
+    return transforms.inv(samples), num_simulations, None
diff --git a/sbibm/algorithms/lan/nle_pretrained/arglist10000.p b/sbibm/algorithms/lan/nle_pretrained/arglist10000.p
new file mode 100644
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new file mode 100644
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diff --git a/sbibm/algorithms/lan/nle_pretrained/mm10k7.p b/sbibm/algorithms/lan/nle_pretrained/mm10k7.p
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diff --git a/sbibm/algorithms/lan/nle_pretrained/mm115.p b/sbibm/algorithms/lan/nle_pretrained/mm115.p
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diff --git a/sbibm/algorithms/lan/nle_pretrained/mm403.p b/sbibm/algorithms/lan/nle_pretrained/mm403.p
new file mode 100644
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diff --git a/sbibm/algorithms/lan/nle_pretrained/mm93.p b/sbibm/algorithms/lan/nle_pretrained/mm93.p
new file mode 100644
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diff --git a/sbibm/algorithms/pyro/mcmc.py b/sbibm/algorithms/pyro/mcmc.py
index a77d8730..dd3bc8df 100644
--- a/sbibm/algorithms/pyro/mcmc.py
+++ b/sbibm/algorithms/pyro/mcmc.py
@@ -1,6 +1,6 @@
 import time
 import warnings
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional
 
 import torch
 from pyro.infer.mcmc import HMC, NUTS
@@ -27,6 +27,7 @@ def run(
     observation: Optional[torch.Tensor] = None,
     num_chains: int = 10,
     num_warmup: int = 10000,
+    potential_fn: Optional[Callable] = None,
     kernel: str = "slice",
     kernel_parameters: Optional[Dict[str, Any]] = None,
     thinning: int = 1,
@@ -43,7 +44,7 @@ def run(
     Produces `num_samples` while accounting for warmup (burn-in) and thinning.
 
     Note that the actual number of simulations is not controlled for with MCMC since
-    algorithms are only used as a reference method in the benchmark. 
+    algorithms are only used as a reference method in the benchmark.
 
     MCMC is run on the potential function, which returns the unnormalized
     negative log posterior probability. Note that this requires a tractable likelihood.
@@ -57,6 +58,8 @@ def run(
         num_chains: Number of chains
         num_warmup: Warmup steps, during which parameters of the sampler are adapted.
             Warmup samples are not returned by the algorithm.
+        potential_fn: Callable potential function taking a dict as argument and
+            returning the potential given fixed observed data.
         kernel: HMC, NUTS, or Slice
         kernel_parameters: Parameters passed to kernel
         thinning: Amount of thinning to apply, in order to avoid drawing
@@ -110,14 +113,21 @@ def run(
             parameters=",".join([f"{k}={v}" for k, v in kernel_parameters.items()]),
         )
     )
+    # Pass either model or potential_fn, not both.
+    if potential_fn is None:
+        kernel_parameters["model"] = conditioned_model
+    else:
+        kernel_parameters["potential_fn"] = potential_fn
+        log.info("Using explicit potential function instead implicit one through Pyro.")
+
     if kernel.lower() == "nuts":
-        mcmc_kernel = NUTS(model=conditioned_model, **kernel_parameters)
+        mcmc_kernel = NUTS(**kernel_parameters)
 
     elif kernel.lower() == "hmc":
-        mcmc_kernel = HMC(model=conditioned_model, **kernel_parameters)
+        mcmc_kernel = HMC(**kernel_parameters)
 
     elif kernel.lower() == "slice":
-        mcmc_kernel = Slice(model=conditioned_model, **kernel_parameters)
+        mcmc_kernel = Slice(**kernel_parameters)
 
     else:
         raise NotImplementedError
diff --git a/sbibm/algorithms/pytorch/baseline_rejection.py b/sbibm/algorithms/pytorch/baseline_rejection.py
index 62fb75bf..6e681ffb 100644
--- a/sbibm/algorithms/pytorch/baseline_rejection.py
+++ b/sbibm/algorithms/pytorch/baseline_rejection.py
@@ -62,6 +62,7 @@ def run(
         observation=observation,
         implementation="experimental",
         posterior=True,
+        **kwargs,
     )
 
     log.info("Finding M")
diff --git a/sbibm/algorithms/sbi/__init__.py b/sbibm/algorithms/sbi/__init__.py
index 0a426fcd..a384ed73 100644
--- a/sbibm/algorithms/sbi/__init__.py
+++ b/sbibm/algorithms/sbi/__init__.py
@@ -1,4 +1,5 @@
 from sbibm.algorithms.sbi.mcabc import run as mcabc
+from sbibm.algorithms.sbi.sl import run as sl
 from sbibm.algorithms.sbi.smcabc import run as smcabc
 from sbibm.algorithms.sbi.snle import run as snle
 from sbibm.algorithms.sbi.snpe import run as snpe
diff --git a/sbibm/algorithms/sbi/mcabc.py b/sbibm/algorithms/sbi/mcabc.py
index d9aa6282..7d62dd12 100644
--- a/sbibm/algorithms/sbi/mcabc.py
+++ b/sbibm/algorithms/sbi/mcabc.py
@@ -84,26 +84,26 @@ def run(
         distance=distance,
         show_progress_bars=True,
     )
-    posterior, distances = inference_method(
+    samples, summary = inference_method(
         x_o=observation,
         num_simulations=num_simulations,
         eps=eps,
         quantile=quantile,
-        return_distances=True,
         lra=lra,
         sass=sass,
         sass_expansion_degree=sass_feature_expansion_degree,
         sass_fraction=sass_fraction,
+        return_summary=True,
     )
 
+    distances = summary["distances"]
+
     assert simulator.num_simulations == num_simulations
 
     if save_distances:
         save_tensor_to_csv("distances.csv", distances)
 
     if kde_bandwidth is not None:
-        samples = posterior._samples
-
         log.info(
             f"""KDE on {samples.shape[0]} samples with bandwidth option {kde_bandwidth}.
             Beware that KDE can give unreliable results when used with too few samples
@@ -112,12 +112,10 @@ def run(
         kde = get_kde(samples, bandwidth=kde_bandwidth)
 
         samples = kde.sample(num_samples)
-    else:
-        samples = posterior.sample((num_samples,)).detach()
 
-    if num_observation is not None:
+    if num_observation is not None and kde_bandwidth is not None:
         true_parameters = task.get_true_parameters(num_observation=num_observation)
-        log_prob_true_parameters = posterior.log_prob(true_parameters)
+        log_prob_true_parameters = kde.log_prob(true_parameters)
         return samples, simulator.num_simulations, log_prob_true_parameters
     else:
         return samples, simulator.num_simulations, None
diff --git a/sbibm/algorithms/sbi/mnle.py b/sbibm/algorithms/sbi/mnle.py
new file mode 100644
index 00000000..7775c52a
--- /dev/null
+++ b/sbibm/algorithms/sbi/mnle.py
@@ -0,0 +1,160 @@
+import logging
+import math
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+from sbi import inference as inference
+from sbi.utils.get_nn_models import likelihood_nn
+
+from sbibm.algorithms.sbi.utils import (
+    wrap_posterior,
+    wrap_prior_dist,
+    wrap_simulator_fn,
+)
+from sbibm.tasks.task import Task
+
+from sbibm.tasks.ddm.task import DDM
+from sbibm.tasks.ddm.utils import map_x_to_two_D
+
+
+def run(
+    task: Task,
+    num_samples: int,
+    num_simulations: int,
+    num_observation: Optional[int] = None,
+    observation: Optional[torch.Tensor] = None,
+    num_rounds: int = 1,
+    neural_net: str = "mnle",
+    hidden_features: int = 30,
+    simulation_batch_size: int = 1000,
+    training_batch_size: int = 10000,
+    automatic_transforms_enabled: bool = True,
+    mcmc_method: str = "slice_np_vectorized",
+    mcmc_parameters: Dict[str, Any] = {
+        "num_chains": 100,
+        "thin": 10,
+        "warmup_steps": 25,
+        "init_strategy": "sir",
+        # NOTE: sir kwargs changed: num_candidate_samples = num_batches * batch_size
+        "init_strategy_parameters": {
+            "num_candidate_samples": 10000,
+        },
+    },
+    z_score_x: str = "independent",
+    z_score_theta: str = "independent",
+    mnle_kwargs: Optional[Dict[str, Any]] = None,
+    max_num_epochs: Optional[int] = 2**31 - 1,
+) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
+    """Runs MNLE from `sbi`
+    Args:
+        task: Task instance
+        num_observation: Observation number to load, alternative to `observation`
+        observation: Observation, alternative to `num_observation`
+        num_samples: Number of samples to generate from posterior
+        num_simulations: Simulation budget
+        num_rounds: Number of rounds
+        neural_net: Neural network to use, one of maf / mdn / made / nsf
+        hidden_features: Number of hidden features in network
+        simulation_batch_size: Batch size for simulator
+        training_batch_size: Batch size for training network
+        automatic_transforms_enabled: Whether to enable automatic transforms
+        mcmc_method: MCMC method
+        mcmc_parameters: MCMC parameters
+        z_score_x: Whether to z-score x
+        z_score_theta: Whether to z-score theta
+        max_num_epochs: Maximum number of epochs
+    Returns:
+        Samples from posterior, number of simulator calls, log probability of true params if computable
+    """
+    assert not (num_observation is None and observation is None)
+    assert not (num_observation is not None and observation is not None)
+
+    log = logging.getLogger(__name__)
+
+    if num_rounds == 1:
+        log.info(f"Running MNLE")
+        num_simulations_per_round = num_simulations
+    else:
+        log.info(f"Running SMNLE")
+        num_simulations_per_round = math.floor(num_simulations / num_rounds)
+
+    if simulation_batch_size > num_simulations_per_round:
+        simulation_batch_size = num_simulations_per_round
+        log.warn("Reduced simulation_batch_size to num_simulation_per_round")
+
+    if training_batch_size > num_simulations_per_round:
+        training_batch_size = num_simulations_per_round
+        log.warn("Reduced training_batch_size to num_simulation_per_round")
+
+    prior = task.get_prior_dist()
+    if observation is None:
+        observation = task.get_observation(num_observation)
+
+    simulator = task.get_simulator(max_calls=num_simulations)
+
+    transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
+    if automatic_transforms_enabled:
+        prior = wrap_prior_dist(prior, transforms)
+        simulator = wrap_simulator_fn(simulator, transforms)
+
+    # DDM specific.
+    if isinstance(task, DDM):
+        observation = map_x_to_two_D(observation)
+
+    density_estimator_fun = likelihood_nn(
+        model=neural_net.lower(),
+        hidden_features=hidden_features,
+        z_score_x=z_score_x,
+        z_score_theta=z_score_theta,
+        **(mnle_kwargs or {}),
+    )
+    inference_method = inference.MNLE(
+        density_estimator=density_estimator_fun,
+        prior=prior,
+    )
+
+    posteriors = []
+    proposal = prior
+
+    for r in range(num_rounds):
+        theta, x = inference.simulate_for_sbi(
+            simulator,
+            proposal,
+            num_simulations=num_simulations_per_round,
+            simulation_batch_size=simulation_batch_size,
+        )
+        # Map to (rts, choices) for DDM.
+        if isinstance(task, DDM):
+            x = map_x_to_two_D(x)
+
+        density_estimator = inference_method.append_simulations(
+            theta, x, from_round=r
+        ).train(
+            training_batch_size=training_batch_size,
+            retrain_from_scratch=False,
+            discard_prior_samples=False,
+            show_train_summary=True,
+            max_num_epochs=max_num_epochs,
+        )
+        if r > 1:
+            mcmc_parameters["init_strategy"] = "latest_sample"
+
+        posterior = inference_method.build_posterior(
+            density_estimator=density_estimator,
+            sample_with="mcmc",
+            mcmc_method=mcmc_method,
+            mcmc_parameters=mcmc_parameters,
+        )
+        # Copy hyperparameters, e.g., mcmc_init_samples for "latest_sample" strategy.
+        if r > 0:
+            posterior.copy_hyperparameters_from(posteriors[-1])
+        proposal = posterior.set_default_x(observation)
+        posteriors.append(posterior)
+
+    posterior = wrap_posterior(posteriors[-1], transforms)
+
+    assert simulator.num_simulations == num_simulations
+
+    samples = posterior.sample((num_samples,)).detach()
+
+    return samples, simulator.num_simulations, None
diff --git a/sbibm/algorithms/sbi/sl.py b/sbibm/algorithms/sbi/sl.py
index 3d89bf98..7e281f7e 100644
--- a/sbibm/algorithms/sbi/sl.py
+++ b/sbibm/algorithms/sbi/sl.py
@@ -1,11 +1,11 @@
 import logging
-import math
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
 from sbi import inference as inference
-from sbi.inference.posteriors.likelihood_based_posterior import LikelihoodBasedPosterior
+from sbi.inference.posteriors.mcmc_posterior import MCMCPosterior
+from sbi.inference.potentials import likelihood_estimator_based_potential
 from torch import Tensor
 
 from sbibm.algorithms.sbi.utils import (
@@ -68,7 +68,7 @@ def run(
     mcmc_method: str = "slice_np",
     mcmc_parameters: Dict[str, Any] = {},
     diag_eps: float = 0.0,
-) -> (torch.Tensor, int, Optional[torch.Tensor]):
+) -> Tuple[Tensor, int, Optional[Tensor]]:
     """Runs (S)NLE from `sbi`
 
     Args:
@@ -100,8 +100,9 @@ def run(
     simulator = task.get_simulator()
 
     transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
-    prior = wrap_prior_dist(prior, transforms)
-    simulator = wrap_simulator_fn(simulator, transforms)
+    if automatic_transforms_enabled:
+        prior = wrap_prior_dist(prior, transforms)
+        simulator = wrap_simulator_fn(simulator, transforms)
 
     likelihood_estimator = SynthLikNet(
         simulator=simulator,
@@ -109,12 +110,19 @@ def run(
         diag_eps=diag_eps,
     )
 
-    posterior = LikelihoodBasedPosterior(
-        method_family="snle",
-        neural_net=likelihood_estimator,
+    potential_fn, theta_transform = likelihood_estimator_based_potential(
+        likelihood_estimator=likelihood_estimator,
         prior=prior,
+        x_o=None,
+        enable_transform=not automatic_transforms_enabled,
+    )
+    posterior = MCMCPosterior(
+        potential_fn=potential_fn,
+        proposal=prior,
+        theta_transform=theta_transform,
+        method=mcmc_method,
         x_shape=observation.shape,
-        mcmc_parameters=mcmc_parameters,
+        **mcmc_parameters,
     )
 
     posterior.set_default_x(observation)
diff --git a/sbibm/algorithms/sbi/snle.py b/sbibm/algorithms/sbi/snle.py
index 9d75edfb..1ab4fde7 100644
--- a/sbibm/algorithms/sbi/snle.py
+++ b/sbibm/algorithms/sbi/snle.py
@@ -30,16 +30,18 @@ def run(
     mcmc_parameters: Dict[str, Any] = {
         "num_chains": 100,
         "thin": 10,
-        "warmup_steps": 100,
+        "warmup_steps": 25,
         "init_strategy": "sir",
-        "sir_batch_size": 1000,
-        "sir_num_batches": 100,
+        # NOTE: sir kwargs changed: num_candidate_samples = num_batches * batch_size
+        "init_strategy_parameters": {
+            "num_candidate_samples": 10000,
+        },
     },
-    z_score_x: bool = True,
-    z_score_theta: bool = True,
+    z_score_x: str = "independent",
+    z_score_theta: str = "independent",
+    max_num_epochs: Optional[int] = 2**31 - 1,
 ) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
     """Runs (S)NLE from `sbi`
-
     Args:
         task: Task instance
         num_observation: Observation number to load, alternative to `observation`
@@ -56,7 +58,7 @@ def run(
         mcmc_parameters: MCMC parameters
         z_score_x: Whether to z-score x
         z_score_theta: Whether to z-score theta
-
+        max_num_epochs: Maximum number of epochs
     Returns:
         Samples from posterior, number of simulator calls, log probability of true params if computable
     """
@@ -104,7 +106,6 @@ def run(
 
     posteriors = []
     proposal = prior
-    mcmc_parameters["warmup_steps"] = 25
 
     for r in range(num_rounds):
         theta, x = inference.simulate_for_sbi(
@@ -118,14 +119,19 @@ def run(
             theta, x, from_round=r
         ).train(
             training_batch_size=training_batch_size,
-            retrain_from_scratch_each_round=False,
+            retrain_from_scratch=False,
             discard_prior_samples=False,
             show_train_summary=True,
+            max_num_epochs=max_num_epochs,
         )
         if r > 1:
             mcmc_parameters["init_strategy"] = "latest_sample"
+
         posterior = inference_method.build_posterior(
-            density_estimator, mcmc_method=mcmc_method, mcmc_parameters=mcmc_parameters
+            density_estimator=density_estimator,
+            sample_with="mcmc",
+            mcmc_method=mcmc_method,
+            mcmc_parameters=mcmc_parameters,
         )
         # Copy hyperparameters, e.g., mcmc_init_samples for "latest_sample" strategy.
         if r > 0:
diff --git a/sbibm/algorithms/sbi/snpe.py b/sbibm/algorithms/sbi/snpe.py
index 04af4fa2..860ea187 100644
--- a/sbibm/algorithms/sbi/snpe.py
+++ b/sbibm/algorithms/sbi/snpe.py
@@ -4,13 +4,17 @@
 
 import torch
 from sbi import inference as inference
+from sbi.neural_nets.embedding_nets import FCEmbedding, PermutationInvariantEmbedding
 from sbi.utils.get_nn_models import posterior_nn
+from torch import nn
 
 from sbibm.algorithms.sbi.utils import (
     wrap_posterior,
     wrap_prior_dist,
     wrap_simulator_fn,
 )
+from sbibm.tasks.ddm.task import DDM
+from sbibm.tasks.ddm.utils import map_x_to_two_D
 from sbibm.tasks.task import Task
 
 
@@ -27,11 +31,23 @@ def run(
     training_batch_size: int = 10000,
     num_atoms: int = 10,
     automatic_transforms_enabled: bool = False,
-    z_score_x: bool = True,
-    z_score_theta: bool = True,
+    z_score_x: str = "independent",
+    z_score_theta: str = "independent",
+    max_num_epochs: Optional[int] = 2**31 - 1,
+    trial_net_kwargs: Optional[dict] = dict(
+        input_dim=2,
+        output_dim=12,
+        num_hiddens=20,
+        num_layers=2,
+    ),
+    perm_net_kwargs: Optional[dict] = dict(
+        combining_operation="mean",
+        num_layers=2,
+        num_hiddens=20,
+        output_dim=12,
+    ),
 ) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
-    """Runs (S)NPE from `sbi`
-
+    """Runs (S)NPE for iid data from `sbi`
     Args:
         task: Task instance
         num_samples: Number of samples to generate from posterior
@@ -47,7 +63,7 @@ def run(
         automatic_transforms_enabled: Whether to enable automatic transforms
         z_score_x: Whether to z-score x
         z_score_theta: Whether to z-score theta
-
+        max_num_epochs: Maximum number of epochs
     Returns:
         Samples from posterior, number of simulator calls, log probability of true params if computable
     """
@@ -75,7 +91,7 @@ def run(
     if observation is None:
         observation = task.get_observation(num_observation)
 
-    simulator = task.get_simulator(max_calls=num_simulations)
+    simulator = task.get_simulator()
 
     transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
 
@@ -83,11 +99,27 @@ def run(
         prior = wrap_prior_dist(prior, transforms)
         simulator = wrap_simulator_fn(simulator, transforms)
 
+    # DDM specific.
+    if isinstance(task, DDM):
+        observation = map_x_to_two_D(observation)
+    num_trials = observation.shape[0]
+
+    # embedding net needed?
+    if num_trials > 1:
+        embedding_net = PermutationInvariantEmbedding(
+            trial_net=FCEmbedding(**trial_net_kwargs),
+            trial_net_output_dim=trial_net_kwargs["output_dim"],
+            **perm_net_kwargs,
+        )
+    else:
+        embedding_net = nn.Identity()
+
     density_estimator_fun = posterior_nn(
         model=neural_net.lower(),
         hidden_features=hidden_features,
         z_score_x=z_score_x,
         z_score_theta=z_score_theta,
+        embedding_net=embedding_net,
     )
 
     inference_method = inference.SNPE_C(prior, density_estimator=density_estimator_fun)
@@ -95,32 +127,43 @@ def run(
     proposal = prior
 
     for _ in range(num_rounds):
-        theta, x = inference.simulate_for_sbi(
-            simulator,
-            proposal,
-            num_simulations=num_simulations_per_round,
-            simulation_batch_size=simulation_batch_size,
-        )
+        theta = proposal.sample((num_simulations_per_round,))
+
+        if num_trials > 1:
+            # copy theta for iid trials
+            theta_per_trial = theta.tile(num_trials).reshape(
+                theta.shape[0] * num_trials, -1
+            )
+
+            x = simulator(theta_per_trial)
+            if isinstance(task, DDM):
+                x = map_x_to_two_D(x)
+
+            # rearrange to have trials as separate dim
+            x = x.reshape(num_simulations_per_round, num_trials, -1)
+        else:
+            x = simulator(theta)
+            if isinstance(task, DDM):
+                x = map_x_to_two_D(x)
 
         density_estimator = inference_method.append_simulations(
             theta, x, proposal=proposal
         ).train(
             num_atoms=num_atoms,
             training_batch_size=training_batch_size,
-            retrain_from_scratch_each_round=False,
+            retrain_from_scratch=False,
             discard_prior_samples=False,
             use_combined_loss=False,
             show_train_summary=True,
+            max_num_epochs=max_num_epochs,
         )
-        posterior = inference_method.build_posterior(
-            density_estimator, sample_with_mcmc=False
-        )
+        posterior = inference_method.build_posterior(density_estimator)
         proposal = posterior.set_default_x(observation)
         posteriors.append(posterior)
 
     posterior = wrap_posterior(posteriors[-1], transforms)
 
-    assert simulator.num_simulations == num_simulations
+    # assert simulator.num_simulations == num_simulations
 
     samples = posterior.sample((num_samples,)).detach()
 
diff --git a/sbibm/algorithms/sbi/snre.py b/sbibm/algorithms/sbi/snre.py
index 6beb01e3..b3ca1c7e 100644
--- a/sbibm/algorithms/sbi/snre.py
+++ b/sbibm/algorithms/sbi/snre.py
@@ -11,6 +11,8 @@
     wrap_prior_dist,
     wrap_simulator_fn,
 )
+from sbibm.tasks.ddm.task import DDM
+from sbibm.tasks.ddm.utils import map_x_to_two_D
 from sbibm.tasks.task import Task
 
 
@@ -31,17 +33,19 @@ def run(
     mcmc_parameters: Dict[str, Any] = {
         "num_chains": 100,
         "thin": 10,
-        "warmup_steps": 100,
+        "warmup_steps": 25,
         "init_strategy": "sir",
-        "sir_batch_size": 1000,
-        "sir_num_batches": 100,
+        # NOTE: sir kwargs changed: num_candidate_samples = num_batches * batch_size
+        "init_strategy_parameters": {
+            "num_candidate_samples": 10000,
+        },
     },
-    z_score_x: bool = True,
-    z_score_theta: bool = True,
+    z_score_x: str = "independent",
+    z_score_theta: str = "independent",
     variant: str = "B",
+    max_num_epochs: Optional[int] = 2**31 - 1,
 ) -> Tuple[torch.Tensor, int, Optional[torch.Tensor]]:
     """Runs (S)NRE from `sbi`
-
     Args:
         task: Task instance
         num_samples: Number of samples to generate from posterior
@@ -60,7 +64,7 @@ def run(
         z_score_x: Whether to z-score x
         z_score_theta: Whether to z-score theta
         variant: Can be used to switch between SNRE-A (AALR) and -B (SRE)
-
+        max_num_epochs: Maximum number of epochs
     Returns:
         Samples from posterior, number of simulator calls, log probability of true params if computable
     """
@@ -88,6 +92,10 @@ def run(
     if observation is None:
         observation = task.get_observation(num_observation)
 
+    # DDM specific.
+    if isinstance(task, DDM):
+        observation = map_x_to_two_D(observation)
+
     simulator = task.get_simulator(max_calls=num_simulations)
 
     transforms = task._get_transforms(automatic_transforms_enabled)["parameters"]
@@ -114,7 +122,6 @@ def run(
 
     posteriors = []
     proposal = prior
-    mcmc_parameters["warmup_steps"] = 25
 
     for r in range(num_rounds):
         theta, x = inference.simulate_for_sbi(
@@ -123,20 +130,26 @@ def run(
             num_simulations=num_simulations_per_round,
             simulation_batch_size=simulation_batch_size,
         )
+        # Map to (rts, choices) for DDM.
+        if isinstance(task, DDM):
+            x = map_x_to_two_D(x)
 
         density_estimator = inference_method.append_simulations(
             theta, x, from_round=r
         ).train(
             training_batch_size=training_batch_size,
-            retrain_from_scratch_each_round=False,
+            retrain_from_scratch=False,
             discard_prior_samples=False,
             show_train_summary=True,
+            max_num_epochs=max_num_epochs,
             **inference_method_kwargs,
         )
         if r > 1:
             mcmc_parameters["init_strategy"] = "latest_sample"
         posterior = inference_method.build_posterior(
-            density_estimator, mcmc_method=mcmc_method, mcmc_parameters=mcmc_parameters
+            density_estimator,
+            mcmc_method=mcmc_method,
+            mcmc_parameters=mcmc_parameters,
         )
         # Copy hyperparameters, e.g., mcmc_init_samples for "latest_sample" strategy.
         if r > 0:
diff --git a/sbibm/metrics/__init__.py b/sbibm/metrics/__init__.py
index 4038915a..b63bfda8 100644
--- a/sbibm/metrics/__init__.py
+++ b/sbibm/metrics/__init__.py
@@ -2,3 +2,5 @@
 from sbibm.metrics.ksd import ksd  # noqa: F401
 from sbibm.metrics.mmd import mmd  # noqa: F401
 from sbibm.metrics.ppc import median_distance  # noqa: F401
+from sbibm.metrics.moments import mean_error  # noqa: F401
+from sbibm.metrics.moments import variance_error  # noqa: F401
\ No newline at end of file
diff --git a/sbibm/metrics/moments.py b/sbibm/metrics/moments.py
new file mode 100644
index 00000000..e15e491a
--- /dev/null
+++ b/sbibm/metrics/moments.py
@@ -0,0 +1,45 @@
+import torch
+
+
+def mean_error(
+    samples: torch.Tensor,
+    reference_posterior_samples: torch.Tensor,
+) -> torch.Tensor:
+    """Return normalized MSE between posterior means.
+    Args:
+        samples: Approximate samples
+        reference_posterior_samples: Reference posterior samples
+        prior_variance: variance of the prior for each dimension
+    Returns:
+        mean squared error in ratio of the mean difference and prior variance.
+    """
+    assert samples.ndim == 2
+    assert reference_posterior_samples.ndim == 2
+
+    squared_error = (
+        samples.mean(0) - reference_posterior_samples.mean(0)
+    ) ** 2 / reference_posterior_samples.std(0)
+
+    return torch.mean(squared_error)
+
+
+def variance_error(
+    samples: torch.Tensor,
+    reference_posterior_samples: torch.Tensor,
+) -> torch.Tensor:
+    """Return normalized MSE between posterior variances.
+    Args:
+        samples: Approximate samples
+        reference_posterior_samples: Reference posterior samples
+        prior_variance: variance of the prior for each dimension
+    Returns:
+        mean squared error in ratio of variance difference and prior variance.
+    """
+    assert samples.ndim == 2
+    assert reference_posterior_samples.ndim == 2
+
+    squared_error = (
+        samples.var(0) - reference_posterior_samples.var(0)
+    ) ** 2 / reference_posterior_samples.var(0)
+
+    return torch.mean(squared_error)
diff --git a/sbibm/tasks/__init__.py b/sbibm/tasks/__init__.py
index babbb6b7..4f42ce52 100644
--- a/sbibm/tasks/__init__.py
+++ b/sbibm/tasks/__init__.py
@@ -13,6 +13,11 @@ def get_task(task_name: str, *args: Any, **kwargs: Any) -> Task:
     Returns:
         Task instance
     """
+    if task_name == "ddm":
+        from sbibm.tasks.ddm.task import DDM
+
+        return DDM(*args, **kwargs)
+
     if task_name == "lotka_volterra":
         from sbibm.tasks.lotka_volterra.task import LotkaVolterra
 
diff --git a/sbibm/tasks/ddm/__init__.py b/sbibm/tasks/ddm/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sbibm/tasks/ddm/files/num_observation_1/observation.csv b/sbibm/tasks/ddm/files/num_observation_1/observation.csv
new file mode 100644
index 00000000..37c9734a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_1/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-0.51835656
diff --git a/sbibm/tasks/ddm/files/num_observation_1/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_1/observation_seed.csv
new file mode 100644
index 00000000..eb1c2470
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_1/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+0,1
diff --git a/sbibm/tasks/ddm/files/num_observation_1/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_1/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..b477d9e3
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_1/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_1/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_1/true_parameters.csv
new file mode 100644
index 00000000..39094a45
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_1/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.01497364,1.6523327,0.33539099,0.41124877
diff --git a/sbibm/tasks/ddm/files/num_observation_10/observation.csv b/sbibm/tasks/ddm/files/num_observation_10/observation.csv
new file mode 100644
index 00000000..d213584e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_10/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.7392728
diff --git a/sbibm/tasks/ddm/files/num_observation_10/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_10/observation_seed.csv
new file mode 100644
index 00000000..16072424
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_10/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+9,10
diff --git a/sbibm/tasks/ddm/files/num_observation_10/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_10/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..efc2c65f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_10/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_10/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_10/true_parameters.csv
new file mode 100644
index 00000000..26971e17
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_10/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.62313795,0.95303404,0.49195856,1.4437977
diff --git a/sbibm/tasks/ddm/files/num_observation_100/observation.csv b/sbibm/tasks/ddm/files/num_observation_100/observation.csv
new file mode 100644
index 00000000..3e10883e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_100/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.1693182
diff --git a/sbibm/tasks/ddm/files/num_observation_100/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_100/observation_seed.csv
new file mode 100644
index 00000000..8679aeb3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_100/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+102,100
diff --git a/sbibm/tasks/ddm/files/num_observation_100/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_100/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..65525b4a
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_100/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_100/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_100/true_parameters.csv
new file mode 100644
index 00000000..70090274
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_100/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.97406,1.6620548,0.321059,0.88146836
diff --git a/sbibm/tasks/ddm/files/num_observation_101/observation.csv b/sbibm/tasks/ddm/files/num_observation_101/observation.csv
new file mode 100644
index 00000000..b7559717
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_101/observation.csv
@@ -0,0 +1,2 @@
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+-0.6079387,0.7688221,-1.0182762,-0.59613293,-0.6892885,0.85524315,-0.82406604,0.95134324,0.97016865,-0.8112
diff --git a/sbibm/tasks/ddm/files/num_observation_101/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_101/observation_seed.csv
new file mode 100644
index 00000000..cc57ab4f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_101/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+0,101
diff --git a/sbibm/tasks/ddm/files/num_observation_101/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_101/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ec63b8fa
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_101/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_101/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_101/true_parameters.csv
new file mode 100644
index 00000000..39094a45
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_101/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.01497364,1.6523327,0.33539099,0.41124877
diff --git a/sbibm/tasks/ddm/files/num_observation_102/observation.csv b/sbibm/tasks/ddm/files/num_observation_102/observation.csv
new file mode 100644
index 00000000..62a35966
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_102/observation.csv
@@ -0,0 +1,2 @@
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+1.4464449,1.8390905,1.4868184,1.4609164,1.6211028,1.9195508,-1.5177845,1.5563009,1.4551548,1.4728844
diff --git a/sbibm/tasks/ddm/files/num_observation_102/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_102/observation_seed.csv
new file mode 100644
index 00000000..136a77f5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_102/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1,102
diff --git a/sbibm/tasks/ddm/files/num_observation_102/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_102/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..277d4d36
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_102/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_102/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_102/true_parameters.csv
new file mode 100644
index 00000000..ac8d73e5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_102/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+1.0305264,0.9189663,0.46122772,1.3754951
diff --git a/sbibm/tasks/ddm/files/num_observation_103/observation.csv b/sbibm/tasks/ddm/files/num_observation_103/observation.csv
new file mode 100644
index 00000000..3bb7aeae
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_103/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_103/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_103/observation_seed.csv
new file mode 100644
index 00000000..0efec5d0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_103/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+2,103
diff --git a/sbibm/tasks/ddm/files/num_observation_103/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_103/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3d6bd900
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_103/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_103/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_103/true_parameters.csv
new file mode 100644
index 00000000..50fff476
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_103/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.4587803,1.0715193,0.5548457,0.9591377
diff --git a/sbibm/tasks/ddm/files/num_observation_104/observation.csv b/sbibm/tasks/ddm/files/num_observation_104/observation.csv
new file mode 100644
index 00000000..33cf4e6f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_104/observation.csv
@@ -0,0 +1,2 @@
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+-0.34743774,-0.4466441,-0.2580195,-0.30133525,-0.27628508,-0.38642567,-0.33872578,-0.28086945,-0.66810393,-0.27915817
diff --git a/sbibm/tasks/ddm/files/num_observation_104/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_104/observation_seed.csv
new file mode 100644
index 00000000..45fff511
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_104/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+3,104
diff --git a/sbibm/tasks/ddm/files/num_observation_104/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_104/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c399defc
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_104/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_104/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_104/true_parameters.csv
new file mode 100644
index 00000000..5e1174f8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_104/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.9829459,0.65835404,0.41433698,0.24312773
diff --git a/sbibm/tasks/ddm/files/num_observation_105/observation.csv b/sbibm/tasks/ddm/files/num_observation_105/observation.csv
new file mode 100644
index 00000000..db402f99
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_105/observation.csv
@@ -0,0 +1,2 @@
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+-0.76226383,-1.0096009,0.8670077,0.8233824,0.96921915,-0.6477523,-0.7235447,-0.9018093,-0.5671424,0.740656
diff --git a/sbibm/tasks/ddm/files/num_observation_105/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_105/observation_seed.csv
new file mode 100644
index 00000000..4fbd6bd8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_105/observation_seed.csv
@@ -0,0 +1,2 @@
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+4,105
diff --git a/sbibm/tasks/ddm/files/num_observation_105/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_105/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..eec07c86
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_105/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_105/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_105/true_parameters.csv
new file mode 100644
index 00000000..f0e29862
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_105/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.23855448,1.3386385,0.33658275,0.5360051
diff --git a/sbibm/tasks/ddm/files/num_observation_106/observation.csv b/sbibm/tasks/ddm/files/num_observation_106/observation.csv
new file mode 100644
index 00000000..e355f3fd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_106/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_106/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_106/observation_seed.csv
new file mode 100644
index 00000000..cfca40e0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_106/observation_seed.csv
@@ -0,0 +1,2 @@
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+5,106
diff --git a/sbibm/tasks/ddm/files/num_observation_106/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_106/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..57a9586c
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diff --git a/sbibm/tasks/ddm/files/num_observation_106/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_106/true_parameters.csv
new file mode 100644
index 00000000..3928609f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_106/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.3210075,0.68916637,0.6629878,1.5118833
diff --git a/sbibm/tasks/ddm/files/num_observation_107/observation.csv b/sbibm/tasks/ddm/files/num_observation_107/observation.csv
new file mode 100644
index 00000000..be1d8a29
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_107/observation.csv
@@ -0,0 +1,2 @@
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+-2.5582683,-1.5476047,-1.5142143,1.2064462,-1.6278894,-2.344375,1.2543702,3.361743,1.1938022,1.2578015
diff --git a/sbibm/tasks/ddm/files/num_observation_107/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_107/observation_seed.csv
new file mode 100644
index 00000000..64069eb7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_107/observation_seed.csv
@@ -0,0 +1,2 @@
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+6,107
diff --git a/sbibm/tasks/ddm/files/num_observation_107/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_107/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5d25118d
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diff --git a/sbibm/tasks/ddm/files/num_observation_107/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_107/true_parameters.csv
new file mode 100644
index 00000000..7bcb99b8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_107/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.28879023,1.3308133,0.69473195,1.1728473
diff --git a/sbibm/tasks/ddm/files/num_observation_108/observation.csv b/sbibm/tasks/ddm/files/num_observation_108/observation.csv
new file mode 100644
index 00000000..bc74115a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_108/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_108/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_108/observation_seed.csv
new file mode 100644
index 00000000..13984b92
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_108/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+7,108
diff --git a/sbibm/tasks/ddm/files/num_observation_108/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_108/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..aaaf56bf
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diff --git a/sbibm/tasks/ddm/files/num_observation_108/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_108/true_parameters.csv
new file mode 100644
index 00000000..017ec574
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_108/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.13969016,0.7982048,0.5636847,1.2510244
diff --git a/sbibm/tasks/ddm/files/num_observation_109/observation.csv b/sbibm/tasks/ddm/files/num_observation_109/observation.csv
new file mode 100644
index 00000000..76a5cd0c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_109/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_109/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_109/observation_seed.csv
new file mode 100644
index 00000000..9915bfb7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_109/observation_seed.csv
@@ -0,0 +1,2 @@
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+8,109
diff --git a/sbibm/tasks/ddm/files/num_observation_109/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_109/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..aa6a7ff4
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diff --git a/sbibm/tasks/ddm/files/num_observation_109/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_109/true_parameters.csv
new file mode 100644
index 00000000..ed10c2a0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_109/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.3917091,1.7679442,0.6785641,0.6744485
diff --git a/sbibm/tasks/ddm/files/num_observation_11/observation.csv b/sbibm/tasks/ddm/files/num_observation_11/observation.csv
new file mode 100644
index 00000000..ea7d758a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_11/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.265265
diff --git a/sbibm/tasks/ddm/files/num_observation_11/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_11/observation_seed.csv
new file mode 100644
index 00000000..33a7316c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_11/observation_seed.csv
@@ -0,0 +1,2 @@
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+10,11
diff --git a/sbibm/tasks/ddm/files/num_observation_11/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_11/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..8fbc8686
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diff --git a/sbibm/tasks/ddm/files/num_observation_11/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_11/true_parameters.csv
new file mode 100644
index 00000000..e1fa9cb6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_11/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.16766143,1.2242849,0.42499933,1.1840346
diff --git a/sbibm/tasks/ddm/files/num_observation_110/observation.csv b/sbibm/tasks/ddm/files/num_observation_110/observation.csv
new file mode 100644
index 00000000..2302e6fe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_110/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_110/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_110/observation_seed.csv
new file mode 100644
index 00000000..46ad300e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_110/observation_seed.csv
@@ -0,0 +1,2 @@
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+9,110
diff --git a/sbibm/tasks/ddm/files/num_observation_110/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_110/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_110/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_110/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_110/true_parameters.csv
new file mode 100644
index 00000000..26971e17
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_110/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.62313795,0.95303404,0.49195856,1.4437977
diff --git a/sbibm/tasks/ddm/files/num_observation_111/observation.csv b/sbibm/tasks/ddm/files/num_observation_111/observation.csv
new file mode 100644
index 00000000..ba8247fa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_111/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_111/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_111/observation_seed.csv
new file mode 100644
index 00000000..b3149ac4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_111/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_111/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_111/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_111/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_111/true_parameters.csv
new file mode 100644
index 00000000..e1fa9cb6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_111/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.16766143,1.2242849,0.42499933,1.1840346
diff --git a/sbibm/tasks/ddm/files/num_observation_112/observation.csv b/sbibm/tasks/ddm/files/num_observation_112/observation.csv
new file mode 100644
index 00000000..7cb5e088
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_112/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_112/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_112/observation_seed.csv
new file mode 100644
index 00000000..dd0614c0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_112/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_112/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_112/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_112/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_112/true_parameters.csv
new file mode 100644
index 00000000..032aa7d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_112/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.4038332,1.2298297,0.6942646,0.46944225
diff --git a/sbibm/tasks/ddm/files/num_observation_113/observation.csv b/sbibm/tasks/ddm/files/num_observation_113/observation.csv
new file mode 100644
index 00000000..ec481095
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_113/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_113/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_113/observation_seed.csv
new file mode 100644
index 00000000..ec0f1a96
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_113/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_113/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_113/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_113/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_113/true_parameters.csv
new file mode 100644
index 00000000..6e69700e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_113/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.13725019,0.8491502,0.4810884,1.1393795
diff --git a/sbibm/tasks/ddm/files/num_observation_114/observation.csv b/sbibm/tasks/ddm/files/num_observation_114/observation.csv
new file mode 100644
index 00000000..dd1dc723
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_114/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_114/observation_seed.csv
new file mode 100644
index 00000000..da1e8e18
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_114/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_114/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_114/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_114/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_114/true_parameters.csv
new file mode 100644
index 00000000..49173c40
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_114/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.6327319,1.2190709,0.6242205,0.22417144
diff --git a/sbibm/tasks/ddm/files/num_observation_115/observation.csv b/sbibm/tasks/ddm/files/num_observation_115/observation.csv
new file mode 100644
index 00000000..8fac933b
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_115/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_115/observation_seed.csv
new file mode 100644
index 00000000..46474652
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_115/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_115/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_115/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_115/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_115/true_parameters.csv
new file mode 100644
index 00000000..c9b31c10
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_116/observation.csv b/sbibm/tasks/ddm/files/num_observation_116/observation.csv
new file mode 100644
index 00000000..d5a3dd47
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_116/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_116/observation_seed.csv
new file mode 100644
index 00000000..ec7cdd9a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_116/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_116/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_116/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_116/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_116/true_parameters.csv
new file mode 100644
index 00000000..989ca1de
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_116/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.8106823,0.91489315,0.6189425,0.8190068
diff --git a/sbibm/tasks/ddm/files/num_observation_117/observation.csv b/sbibm/tasks/ddm/files/num_observation_117/observation.csv
new file mode 100644
index 00000000..3d6ab11b
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_117/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_117/observation_seed.csv
new file mode 100644
index 00000000..cf4837ea
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_117/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_117/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_117/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_117/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_117/true_parameters.csv
new file mode 100644
index 00000000..99ee3631
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_118/observation.csv b/sbibm/tasks/ddm/files/num_observation_118/observation.csv
new file mode 100644
index 00000000..c6a84efe
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_118/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_118/observation_seed.csv
new file mode 100644
index 00000000..bc0269e6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_118/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_118/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_118/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_118/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_118/true_parameters.csv
new file mode 100644
index 00000000..ab5a5650
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_118/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_119/observation.csv b/sbibm/tasks/ddm/files/num_observation_119/observation.csv
new file mode 100644
index 00000000..9c4d09de
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_119/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_119/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_119/observation_seed.csv
new file mode 100644
index 00000000..bf3106b2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_119/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_119/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_119/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_119/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_119/true_parameters.csv
new file mode 100644
index 00000000..4be4d199
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_119/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.016774654,1.1881349,0.4584257,1.4034001
diff --git a/sbibm/tasks/ddm/files/num_observation_12/observation.csv b/sbibm/tasks/ddm/files/num_observation_12/observation.csv
new file mode 100644
index 00000000..7d34238e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_12/observation.csv
@@ -0,0 +1,2 @@
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+-0.7517734
diff --git a/sbibm/tasks/ddm/files/num_observation_12/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_12/observation_seed.csv
new file mode 100644
index 00000000..af8ace51
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_12/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_12/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_12/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_12/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_12/true_parameters.csv
new file mode 100644
index 00000000..032aa7d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_12/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.4038332,1.2298297,0.6942646,0.46944225
diff --git a/sbibm/tasks/ddm/files/num_observation_120/observation.csv b/sbibm/tasks/ddm/files/num_observation_120/observation.csv
new file mode 100644
index 00000000..5c402724
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_120/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_120/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_120/observation_seed.csv
new file mode 100644
index 00000000..5db498a8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_120/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_120/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_120/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_120/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_120/true_parameters.csv
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index 00000000..942e0ea0
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diff --git a/sbibm/tasks/ddm/files/num_observation_121/observation.csv b/sbibm/tasks/ddm/files/num_observation_121/observation.csv
new file mode 100644
index 00000000..e90fbba1
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diff --git a/sbibm/tasks/ddm/files/num_observation_121/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_121/observation_seed.csv
new file mode 100644
index 00000000..3edc1e7d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_121/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_121/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_121/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_121/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_121/true_parameters.csv
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index 00000000..ca1380b0
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_122/observation.csv b/sbibm/tasks/ddm/files/num_observation_122/observation.csv
new file mode 100644
index 00000000..f1670f83
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_122/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_122/observation_seed.csv
new file mode 100644
index 00000000..2466ece1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_122/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+21,122
diff --git a/sbibm/tasks/ddm/files/num_observation_122/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_122/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..1b388e94
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_122/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_122/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_122/true_parameters.csv
new file mode 100644
index 00000000..b1768282
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_122/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.105726,1.4203981,0.30482924,0.26335487
diff --git a/sbibm/tasks/ddm/files/num_observation_123/observation.csv b/sbibm/tasks/ddm/files/num_observation_123/observation.csv
new file mode 100644
index 00000000..80564e36
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_123/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_123/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_123/observation_seed.csv
new file mode 100644
index 00000000..f4410613
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_123/observation_seed.csv
@@ -0,0 +1,2 @@
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+22,123
diff --git a/sbibm/tasks/ddm/files/num_observation_123/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_123/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a25d367f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_123/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_123/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_123/true_parameters.csv
new file mode 100644
index 00000000..97af6f31
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_123/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.5364101,1.5537838,0.42414045,0.21552125
diff --git a/sbibm/tasks/ddm/files/num_observation_124/observation.csv b/sbibm/tasks/ddm/files/num_observation_124/observation.csv
new file mode 100644
index 00000000..04e1a62d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_124/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_124/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_124/observation_seed.csv
new file mode 100644
index 00000000..2f72e09f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_124/observation_seed.csv
@@ -0,0 +1,2 @@
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+23,124
diff --git a/sbibm/tasks/ddm/files/num_observation_124/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_124/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_124/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_124/true_parameters.csv
new file mode 100644
index 00000000..62f91f5e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_124/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.28696752,0.9332939,0.4689707,0.77128375
diff --git a/sbibm/tasks/ddm/files/num_observation_125/observation.csv b/sbibm/tasks/ddm/files/num_observation_125/observation.csv
new file mode 100644
index 00000000..d7977d9d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_125/observation.csv
@@ -0,0 +1,2 @@
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+-1.2742074,-1.1356664,-1.3179768,-1.1362773,-1.2765353,-1.0806696,1.2808473,1.2579699,1.1190835,1.2626064
diff --git a/sbibm/tasks/ddm/files/num_observation_125/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_125/observation_seed.csv
new file mode 100644
index 00000000..6b753854
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_125/observation_seed.csv
@@ -0,0 +1,2 @@
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+24,125
diff --git a/sbibm/tasks/ddm/files/num_observation_125/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_125/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..28e7142d
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diff --git a/sbibm/tasks/ddm/files/num_observation_125/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_125/true_parameters.csv
new file mode 100644
index 00000000..82ff1a68
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_125/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.0577168,1.0625803,0.3300339,1.0492514
diff --git a/sbibm/tasks/ddm/files/num_observation_126/observation.csv b/sbibm/tasks/ddm/files/num_observation_126/observation.csv
new file mode 100644
index 00000000..7016cc27
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_126/observation.csv
@@ -0,0 +1,2 @@
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+-1.7485031,-1.6785694,-1.7122595,-1.7054293,1.7551544,1.8070078,2.080465,1.7398032,-1.7165163,1.8036712
diff --git a/sbibm/tasks/ddm/files/num_observation_126/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_126/observation_seed.csv
new file mode 100644
index 00000000..0f94d816
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_126/observation_seed.csv
@@ -0,0 +1,2 @@
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+25,126
diff --git a/sbibm/tasks/ddm/files/num_observation_126/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_126/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5461618f
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diff --git a/sbibm/tasks/ddm/files/num_observation_126/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_126/true_parameters.csv
new file mode 100644
index 00000000..e9d6be78
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_126/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.0071115,0.7893351,0.3251578,1.6588026
diff --git a/sbibm/tasks/ddm/files/num_observation_127/observation.csv b/sbibm/tasks/ddm/files/num_observation_127/observation.csv
new file mode 100644
index 00000000..93c091c3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_127/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_127/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_127/observation_seed.csv
new file mode 100644
index 00000000..4fc94f7c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_127/observation_seed.csv
@@ -0,0 +1,2 @@
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+26,127
diff --git a/sbibm/tasks/ddm/files/num_observation_127/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_127/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_127/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_127/true_parameters.csv
new file mode 100644
index 00000000..127ae726
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_127/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_128/observation.csv b/sbibm/tasks/ddm/files/num_observation_128/observation.csv
new file mode 100644
index 00000000..ef2eda94
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_128/observation.csv
@@ -0,0 +1,2 @@
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+-0.76659,0.6446581,0.6906117,0.6214994,-0.6231002,0.66443926,0.5951259,0.5861081,0.59766996,-0.7492317
diff --git a/sbibm/tasks/ddm/files/num_observation_128/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_128/observation_seed.csv
new file mode 100644
index 00000000..8d26e957
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_128/observation_seed.csv
@@ -0,0 +1,2 @@
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+27,128
diff --git a/sbibm/tasks/ddm/files/num_observation_128/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_128/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7c8c66ab
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diff --git a/sbibm/tasks/ddm/files/num_observation_128/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_128/true_parameters.csv
new file mode 100644
index 00000000..b003a1f1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_128/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_129/observation.csv b/sbibm/tasks/ddm/files/num_observation_129/observation.csv
new file mode 100644
index 00000000..0debbac3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_129/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_129/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_129/observation_seed.csv
new file mode 100644
index 00000000..c466f05a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_129/observation_seed.csv
@@ -0,0 +1,2 @@
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+28,129
diff --git a/sbibm/tasks/ddm/files/num_observation_129/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_129/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a6022074
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diff --git a/sbibm/tasks/ddm/files/num_observation_129/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_129/true_parameters.csv
new file mode 100644
index 00000000..f37ee4e6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_129/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_13/observation.csv b/sbibm/tasks/ddm/files/num_observation_13/observation.csv
new file mode 100644
index 00000000..e8435a66
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_13/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.2118715
diff --git a/sbibm/tasks/ddm/files/num_observation_13/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_13/observation_seed.csv
new file mode 100644
index 00000000..5ed7e76e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_13/observation_seed.csv
@@ -0,0 +1,2 @@
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+12,13
diff --git a/sbibm/tasks/ddm/files/num_observation_13/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_13/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_13/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_13/true_parameters.csv
new file mode 100644
index 00000000..6e69700e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_13/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.13725019,0.8491502,0.4810884,1.1393795
diff --git a/sbibm/tasks/ddm/files/num_observation_130/observation.csv b/sbibm/tasks/ddm/files/num_observation_130/observation.csv
new file mode 100644
index 00000000..3feb0227
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_130/observation.csv
@@ -0,0 +1,2 @@
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+-3.130661,-1.6932613,1.4402677,-1.5982488,-1.6782936,-1.5809062,-1.720875,1.6773477,-2.1382124,-1.8638107
diff --git a/sbibm/tasks/ddm/files/num_observation_130/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_130/observation_seed.csv
new file mode 100644
index 00000000..1b264685
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_130/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_130/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_130/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_130/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_130/true_parameters.csv
new file mode 100644
index 00000000..efa0c15a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_130/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.5097759,1.9032619,0.6743709,1.3260251
diff --git a/sbibm/tasks/ddm/files/num_observation_131/observation.csv b/sbibm/tasks/ddm/files/num_observation_131/observation.csv
new file mode 100644
index 00000000..59a2566c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_131/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_131/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_131/observation_seed.csv
new file mode 100644
index 00000000..ff8816e6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_131/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_131/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_131/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_131/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_131/true_parameters.csv
new file mode 100644
index 00000000..001a5871
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_131/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_132/observation.csv b/sbibm/tasks/ddm/files/num_observation_132/observation.csv
new file mode 100644
index 00000000..bb82ac14
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_132/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_132/observation_seed.csv
new file mode 100644
index 00000000..95429a5e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_132/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_132/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_132/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_132/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_132/true_parameters.csv
new file mode 100644
index 00000000..b8131afb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_132/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_133/observation.csv b/sbibm/tasks/ddm/files/num_observation_133/observation.csv
new file mode 100644
index 00000000..9ab8a50f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_133/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_133/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_133/observation_seed.csv
new file mode 100644
index 00000000..1edd85ba
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_133/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_133/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_133/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_133/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_133/true_parameters.csv
new file mode 100644
index 00000000..95570ea4
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_134/observation.csv b/sbibm/tasks/ddm/files/num_observation_134/observation.csv
new file mode 100644
index 00000000..44b9dfea
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_134/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_134/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_134/observation_seed.csv
new file mode 100644
index 00000000..4b1645d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_134/observation_seed.csv
@@ -0,0 +1,2 @@
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+33,134
diff --git a/sbibm/tasks/ddm/files/num_observation_134/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_134/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_134/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_134/true_parameters.csv
new file mode 100644
index 00000000..09e55664
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_134/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.47437,1.3381087,0.3774832,0.7375551
diff --git a/sbibm/tasks/ddm/files/num_observation_135/observation.csv b/sbibm/tasks/ddm/files/num_observation_135/observation.csv
new file mode 100644
index 00000000..4bc6e34f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_135/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_135/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_135/observation_seed.csv
new file mode 100644
index 00000000..72a97fd0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_135/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_135/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_135/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_135/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_135/true_parameters.csv
new file mode 100644
index 00000000..ea87e8f5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_135/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_136/observation.csv b/sbibm/tasks/ddm/files/num_observation_136/observation.csv
new file mode 100644
index 00000000..32c708d6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_136/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_136/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_136/observation_seed.csv
new file mode 100644
index 00000000..5d77929d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_136/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_136/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_136/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_136/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_136/true_parameters.csv
new file mode 100644
index 00000000..7a5cd0c4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_136/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.9517343,0.72130716,0.6749995,0.5039969
diff --git a/sbibm/tasks/ddm/files/num_observation_137/observation.csv b/sbibm/tasks/ddm/files/num_observation_137/observation.csv
new file mode 100644
index 00000000..ab7c011b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_137/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_137/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_137/observation_seed.csv
new file mode 100644
index 00000000..7bdabc9c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_137/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_137/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_137/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_137/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_137/true_parameters.csv
new file mode 100644
index 00000000..d78633a5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_137/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.0086352825,1.1533749,0.30529553,1.017472
diff --git a/sbibm/tasks/ddm/files/num_observation_138/observation.csv b/sbibm/tasks/ddm/files/num_observation_138/observation.csv
new file mode 100644
index 00000000..986b6781
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_138/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_138/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_138/observation_seed.csv
new file mode 100644
index 00000000..e1ec9017
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_138/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_138/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_138/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_138/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_138/true_parameters.csv
new file mode 100644
index 00000000..c6adc53a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_138/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_139/observation.csv b/sbibm/tasks/ddm/files/num_observation_139/observation.csv
new file mode 100644
index 00000000..7d223ea0
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_139/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_139/observation_seed.csv
new file mode 100644
index 00000000..951d36cc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_139/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_139/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_139/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_139/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_139/true_parameters.csv
new file mode 100644
index 00000000..69f8ec58
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_139/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_14/observation.csv b/sbibm/tasks/ddm/files/num_observation_14/observation.csv
new file mode 100644
index 00000000..407367f6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_14/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_14/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_14/observation_seed.csv
new file mode 100644
index 00000000..691d0ca7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_14/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_14/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_14/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_14/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_14/true_parameters.csv
new file mode 100644
index 00000000..49173c40
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_14/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.6327319,1.2190709,0.6242205,0.22417144
diff --git a/sbibm/tasks/ddm/files/num_observation_140/observation.csv b/sbibm/tasks/ddm/files/num_observation_140/observation.csv
new file mode 100644
index 00000000..89460fbd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_140/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_140/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_140/observation_seed.csv
new file mode 100644
index 00000000..791e8c9e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_140/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_140/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_140/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_140/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_140/true_parameters.csv
new file mode 100644
index 00000000..edbe0f1b
--- /dev/null
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@@ -0,0 +1,2 @@
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+-1.985502,1.1760348,0.40485936,0.7657287
diff --git a/sbibm/tasks/ddm/files/num_observation_141/observation.csv b/sbibm/tasks/ddm/files/num_observation_141/observation.csv
new file mode 100644
index 00000000..5e6935cf
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_141/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_141/observation_seed.csv
new file mode 100644
index 00000000..050612cf
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_141/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_141/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_141/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_141/true_parameters.csv
new file mode 100644
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--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_142/observation.csv b/sbibm/tasks/ddm/files/num_observation_142/observation.csv
new file mode 100644
index 00000000..07e2ccd5
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diff --git a/sbibm/tasks/ddm/files/num_observation_142/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_142/observation_seed.csv
new file mode 100644
index 00000000..8c84fcfb
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_142/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_142/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_142/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_142/true_parameters.csv
new file mode 100644
index 00000000..d8534bfe
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_143/observation.csv b/sbibm/tasks/ddm/files/num_observation_143/observation.csv
new file mode 100644
index 00000000..cde1080f
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_143/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_143/observation_seed.csv
new file mode 100644
index 00000000..313fc0d1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_143/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_143/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_143/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_143/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_143/true_parameters.csv
new file mode 100644
index 00000000..87b76408
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_144/observation.csv b/sbibm/tasks/ddm/files/num_observation_144/observation.csv
new file mode 100644
index 00000000..4520287b
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_144/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_144/observation_seed.csv
new file mode 100644
index 00000000..ad030ba3
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_144/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_144/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_145/observation.csv b/sbibm/tasks/ddm/files/num_observation_145/observation.csv
new file mode 100644
index 00000000..6a15e0f1
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diff --git a/sbibm/tasks/ddm/files/num_observation_145/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_145/observation_seed.csv
new file mode 100644
index 00000000..4dc287db
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_145/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_145/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_146/observation.csv b/sbibm/tasks/ddm/files/num_observation_146/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_146/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_146/observation_seed.csv
new file mode 100644
index 00000000..1858f9fd
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diff --git a/sbibm/tasks/ddm/files/num_observation_146/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_146/reference_posterior_samples.csv.bz2
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_147/observation.csv b/sbibm/tasks/ddm/files/num_observation_147/observation.csv
new file mode 100644
index 00000000..c5ade7c6
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diff --git a/sbibm/tasks/ddm/files/num_observation_147/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_147/observation_seed.csv
new file mode 100644
index 00000000..cc340d94
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diff --git a/sbibm/tasks/ddm/files/num_observation_147/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_147/reference_posterior_samples.csv.bz2
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_148/observation.csv b/sbibm/tasks/ddm/files/num_observation_148/observation.csv
new file mode 100644
index 00000000..2be4a1dd
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diff --git a/sbibm/tasks/ddm/files/num_observation_148/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_148/observation_seed.csv
new file mode 100644
index 00000000..7506dbd3
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diff --git a/sbibm/tasks/ddm/files/num_observation_148/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_148/reference_posterior_samples.csv.bz2
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_149/observation.csv b/sbibm/tasks/ddm/files/num_observation_149/observation.csv
new file mode 100644
index 00000000..8dc402ef
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diff --git a/sbibm/tasks/ddm/files/num_observation_149/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_149/observation_seed.csv
new file mode 100644
index 00000000..b027f79b
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diff --git a/sbibm/tasks/ddm/files/num_observation_149/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_149/reference_posterior_samples.csv.bz2
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new file mode 100644
index 00000000..ddd372c4
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diff --git a/sbibm/tasks/ddm/files/num_observation_15/observation.csv b/sbibm/tasks/ddm/files/num_observation_15/observation.csv
new file mode 100644
index 00000000..060f2d29
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_15/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_15/observation_seed.csv
new file mode 100644
index 00000000..f93804fd
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_15/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_15/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_15/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_15/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_150/observation.csv b/sbibm/tasks/ddm/files/num_observation_150/observation.csv
new file mode 100644
index 00000000..4e114a66
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diff --git a/sbibm/tasks/ddm/files/num_observation_150/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_150/observation_seed.csv
new file mode 100644
index 00000000..64b43960
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diff --git a/sbibm/tasks/ddm/files/num_observation_150/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_150/reference_posterior_samples.csv.bz2
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_151/observation.csv b/sbibm/tasks/ddm/files/num_observation_151/observation.csv
new file mode 100644
index 00000000..c1bc6ea5
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diff --git a/sbibm/tasks/ddm/files/num_observation_151/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_151/observation_seed.csv
new file mode 100644
index 00000000..b5e9f8d7
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diff --git a/sbibm/tasks/ddm/files/num_observation_151/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_151/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_152/observation.csv b/sbibm/tasks/ddm/files/num_observation_152/observation.csv
new file mode 100644
index 00000000..1444898b
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diff --git a/sbibm/tasks/ddm/files/num_observation_152/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_152/observation_seed.csv
new file mode 100644
index 00000000..822a8170
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diff --git a/sbibm/tasks/ddm/files/num_observation_152/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_152/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_153/observation.csv b/sbibm/tasks/ddm/files/num_observation_153/observation.csv
new file mode 100644
index 00000000..0af26a77
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_153/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_153/observation_seed.csv
new file mode 100644
index 00000000..730ee949
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_153/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_153/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_154/observation.csv b/sbibm/tasks/ddm/files/num_observation_154/observation.csv
new file mode 100644
index 00000000..bc816eb0
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diff --git a/sbibm/tasks/ddm/files/num_observation_154/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_154/observation_seed.csv
new file mode 100644
index 00000000..8eda887a
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_154/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_154/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_155/observation.csv b/sbibm/tasks/ddm/files/num_observation_155/observation.csv
new file mode 100644
index 00000000..7a6fd4a5
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new file mode 100644
index 00000000..a34c15e9
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diff --git a/sbibm/tasks/ddm/files/num_observation_155/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_155/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_156/observation.csv b/sbibm/tasks/ddm/files/num_observation_156/observation.csv
new file mode 100644
index 00000000..09df898b
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diff --git a/sbibm/tasks/ddm/files/num_observation_156/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_156/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_156/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_156/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_157/observation.csv b/sbibm/tasks/ddm/files/num_observation_157/observation.csv
new file mode 100644
index 00000000..5fbfbfc2
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diff --git a/sbibm/tasks/ddm/files/num_observation_157/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_157/observation_seed.csv
new file mode 100644
index 00000000..9a7f8cf7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_157/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_157/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_157/reference_posterior_samples.csv.bz2
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_157/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_157/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_157/true_parameters.csv
new file mode 100644
index 00000000..d81a368b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_157/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.55396676,1.4854469,0.30085462,0.8605856
diff --git a/sbibm/tasks/ddm/files/num_observation_158/observation.csv b/sbibm/tasks/ddm/files/num_observation_158/observation.csv
new file mode 100644
index 00000000..6777834b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_158/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_158/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_158/observation_seed.csv
new file mode 100644
index 00000000..5e5b5041
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_158/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_158/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_158/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_158/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_158/true_parameters.csv
new file mode 100644
index 00000000..1a197fcb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_158/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.13188863,1.0338011,0.5034857,0.50224847
diff --git a/sbibm/tasks/ddm/files/num_observation_159/observation.csv b/sbibm/tasks/ddm/files/num_observation_159/observation.csv
new file mode 100644
index 00000000..af1218b6
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_159/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_159/observation_seed.csv
new file mode 100644
index 00000000..c7c22f41
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_159/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_159/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_159/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_159/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_159/true_parameters.csv
new file mode 100644
index 00000000..48fe0e82
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_159/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_16/observation.csv b/sbibm/tasks/ddm/files/num_observation_16/observation.csv
new file mode 100644
index 00000000..96557311
--- /dev/null
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@@ -0,0 +1,2 @@
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+0.90114164
diff --git a/sbibm/tasks/ddm/files/num_observation_16/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_16/observation_seed.csv
new file mode 100644
index 00000000..584edbe9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_16/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_16/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_16/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_16/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_16/true_parameters.csv
new file mode 100644
index 00000000..989ca1de
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_16/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.8106823,0.91489315,0.6189425,0.8190068
diff --git a/sbibm/tasks/ddm/files/num_observation_160/observation.csv b/sbibm/tasks/ddm/files/num_observation_160/observation.csv
new file mode 100644
index 00000000..0650cef2
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_160/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_160/observation_seed.csv
new file mode 100644
index 00000000..464c57dc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_160/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_160/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_160/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_160/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_160/true_parameters.csv
new file mode 100644
index 00000000..5fffe8a7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_160/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.9057128,0.9661008,0.6432512,0.99857265
diff --git a/sbibm/tasks/ddm/files/num_observation_161/observation.csv b/sbibm/tasks/ddm/files/num_observation_161/observation.csv
new file mode 100644
index 00000000..1ee80302
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_161/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_161/observation_seed.csv
new file mode 100644
index 00000000..497c3a05
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_161/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_161/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_161/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_161/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_161/true_parameters.csv
new file mode 100644
index 00000000..9c690fe9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_161/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.7432625,1.673662,0.4702126,1.3369156
diff --git a/sbibm/tasks/ddm/files/num_observation_162/observation.csv b/sbibm/tasks/ddm/files/num_observation_162/observation.csv
new file mode 100644
index 00000000..0e25a14b
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_162/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_162/observation_seed.csv
new file mode 100644
index 00000000..88698951
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_162/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_162/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_162/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_162/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_162/true_parameters.csv
new file mode 100644
index 00000000..7684bd6c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_162/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_163/observation.csv b/sbibm/tasks/ddm/files/num_observation_163/observation.csv
new file mode 100644
index 00000000..93ac7cbb
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_163/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_163/observation_seed.csv
new file mode 100644
index 00000000..50a6b4b0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_163/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_163/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_163/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_163/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_163/true_parameters.csv
new file mode 100644
index 00000000..fd4cd8eb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_163/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.8031149,1.3984046,0.37085247,0.6221838
diff --git a/sbibm/tasks/ddm/files/num_observation_164/observation.csv b/sbibm/tasks/ddm/files/num_observation_164/observation.csv
new file mode 100644
index 00000000..e89e7d88
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_164/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_164/observation_seed.csv
new file mode 100644
index 00000000..2d831094
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_164/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_164/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_164/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_164/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_164/true_parameters.csv
new file mode 100644
index 00000000..0582142b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_164/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_165/observation.csv b/sbibm/tasks/ddm/files/num_observation_165/observation.csv
new file mode 100644
index 00000000..dc6a5658
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_165/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_165/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_165/observation_seed.csv
new file mode 100644
index 00000000..8412ebd3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_165/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_165/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_165/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_165/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_165/true_parameters.csv
new file mode 100644
index 00000000..c17b68d5
--- /dev/null
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+-0.009516716,0.53449786,0.39083534,1.113286
diff --git a/sbibm/tasks/ddm/files/num_observation_166/observation.csv b/sbibm/tasks/ddm/files/num_observation_166/observation.csv
new file mode 100644
index 00000000..21091e35
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_166/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_166/observation_seed.csv
new file mode 100644
index 00000000..1383e451
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_166/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_166/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_166/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_166/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_166/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_167/observation.csv b/sbibm/tasks/ddm/files/num_observation_167/observation.csv
new file mode 100644
index 00000000..6b9f1ffc
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diff --git a/sbibm/tasks/ddm/files/num_observation_167/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_167/observation_seed.csv
new file mode 100644
index 00000000..27df86ca
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_167/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_167/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_168/observation.csv b/sbibm/tasks/ddm/files/num_observation_168/observation.csv
new file mode 100644
index 00000000..966b0217
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_168/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_168/observation_seed.csv
new file mode 100644
index 00000000..ef4a5b61
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_168/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_168/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_168/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_168/true_parameters.csv
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--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_169/observation.csv b/sbibm/tasks/ddm/files/num_observation_169/observation.csv
new file mode 100644
index 00000000..f53beff8
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_169/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_169/observation_seed.csv
new file mode 100644
index 00000000..e8c5bb7e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_169/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+70,169
diff --git a/sbibm/tasks/ddm/files/num_observation_169/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_169/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..37363d3e
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_169/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_169/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_169/true_parameters.csv
new file mode 100644
index 00000000..a8af80ed
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_169/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.25993013,1.0387154,0.43645293,0.6230247
diff --git a/sbibm/tasks/ddm/files/num_observation_17/observation.csv b/sbibm/tasks/ddm/files/num_observation_17/observation.csv
new file mode 100644
index 00000000..0029cb0f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_17/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-0.85878193
diff --git a/sbibm/tasks/ddm/files/num_observation_17/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_17/observation_seed.csv
new file mode 100644
index 00000000..56d510a3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_17/observation_seed.csv
@@ -0,0 +1,2 @@
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+16,17
diff --git a/sbibm/tasks/ddm/files/num_observation_17/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_17/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4b096d5e
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_17/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_17/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_17/true_parameters.csv
new file mode 100644
index 00000000..99ee3631
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_17/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.3499353,1.4359648,0.67192674,0.6094304
diff --git a/sbibm/tasks/ddm/files/num_observation_170/observation.csv b/sbibm/tasks/ddm/files/num_observation_170/observation.csv
new file mode 100644
index 00000000..6615756a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_170/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_170/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_170/observation_seed.csv
new file mode 100644
index 00000000..73b4d5a9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_170/observation_seed.csv
@@ -0,0 +1,2 @@
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+71,170
diff --git a/sbibm/tasks/ddm/files/num_observation_170/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_170/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c0246cc6
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_170/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_170/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_170/true_parameters.csv
new file mode 100644
index 00000000..f03ad156
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_170/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.029078245,1.0020678,0.6877688,1.442681
diff --git a/sbibm/tasks/ddm/files/num_observation_171/observation.csv b/sbibm/tasks/ddm/files/num_observation_171/observation.csv
new file mode 100644
index 00000000..0de23350
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_171/observation.csv
@@ -0,0 +1,2 @@
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+-1.0733548,1.1996373,-1.1519564,1.1579268,-1.4900513,-1.1792411,-1.14944,-1.2804879,-1.4322078,-1.2681092
diff --git a/sbibm/tasks/ddm/files/num_observation_171/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_171/observation_seed.csv
new file mode 100644
index 00000000..576c8ee7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_171/observation_seed.csv
@@ -0,0 +1,2 @@
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+72,171
diff --git a/sbibm/tasks/ddm/files/num_observation_171/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_171/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..427e9708
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_171/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_171/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_171/true_parameters.csv
new file mode 100644
index 00000000..f2d879e3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_171/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.7000828,1.0447176,0.37668568,1.0381906
diff --git a/sbibm/tasks/ddm/files/num_observation_172/observation.csv b/sbibm/tasks/ddm/files/num_observation_172/observation.csv
new file mode 100644
index 00000000..4ef19522
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_172/observation.csv
@@ -0,0 +1,2 @@
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+-1.3121351,1.2430831,-1.2920644,1.4108051,1.223007,-1.2885307,1.3396044,1.2044104,1.3592474,1.2277316
diff --git a/sbibm/tasks/ddm/files/num_observation_172/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_172/observation_seed.csv
new file mode 100644
index 00000000..62acb1e0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_172/observation_seed.csv
@@ -0,0 +1,2 @@
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+73,172
diff --git a/sbibm/tasks/ddm/files/num_observation_172/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_172/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..49bd7b85
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_172/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_172/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_172/true_parameters.csv
new file mode 100644
index 00000000..a00c924b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_172/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.11447668,0.7424447,0.6547832,1.1945688
diff --git a/sbibm/tasks/ddm/files/num_observation_173/observation.csv b/sbibm/tasks/ddm/files/num_observation_173/observation.csv
new file mode 100644
index 00000000..3df55407
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_173/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_173/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_173/observation_seed.csv
new file mode 100644
index 00000000..b351ecab
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_173/observation_seed.csv
@@ -0,0 +1,2 @@
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+74,173
diff --git a/sbibm/tasks/ddm/files/num_observation_173/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_173/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..bca82603
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_173/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_173/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_173/true_parameters.csv
new file mode 100644
index 00000000..14be6fe1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_173/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.0551524,1.5089879,0.63767415,0.96818316
diff --git a/sbibm/tasks/ddm/files/num_observation_174/observation.csv b/sbibm/tasks/ddm/files/num_observation_174/observation.csv
new file mode 100644
index 00000000..c6626097
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_174/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_174/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_174/observation_seed.csv
new file mode 100644
index 00000000..b7c4e2fd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_174/observation_seed.csv
@@ -0,0 +1,2 @@
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+75,174
diff --git a/sbibm/tasks/ddm/files/num_observation_174/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_174/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c07da3e9
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diff --git a/sbibm/tasks/ddm/files/num_observation_174/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_174/true_parameters.csv
new file mode 100644
index 00000000..a3ef77d7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_174/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.5947857,0.99441767,0.65473676,1.19272
diff --git a/sbibm/tasks/ddm/files/num_observation_175/observation.csv b/sbibm/tasks/ddm/files/num_observation_175/observation.csv
new file mode 100644
index 00000000..7ac9365c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_175/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_175/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_175/observation_seed.csv
new file mode 100644
index 00000000..b817d1fb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_175/observation_seed.csv
@@ -0,0 +1,2 @@
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+76,175
diff --git a/sbibm/tasks/ddm/files/num_observation_175/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_175/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..72619e1c
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_175/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_175/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_175/true_parameters.csv
new file mode 100644
index 00000000..a31b094d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_175/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_176/observation.csv b/sbibm/tasks/ddm/files/num_observation_176/observation.csv
new file mode 100644
index 00000000..8145c534
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_176/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_176/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_176/observation_seed.csv
new file mode 100644
index 00000000..913c7fda
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_176/observation_seed.csv
@@ -0,0 +1,2 @@
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+77,176
diff --git a/sbibm/tasks/ddm/files/num_observation_176/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_176/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..f0e837d0
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diff --git a/sbibm/tasks/ddm/files/num_observation_176/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_176/true_parameters.csv
new file mode 100644
index 00000000..28df9338
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_176/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.8323462,0.92852104,0.46082965,0.9431598
diff --git a/sbibm/tasks/ddm/files/num_observation_177/observation.csv b/sbibm/tasks/ddm/files/num_observation_177/observation.csv
new file mode 100644
index 00000000..626f1074
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_177/observation.csv
@@ -0,0 +1,2 @@
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+-2.0409703,-1.5257552,1.5283573,-1.5335786,-1.5123584,-1.5241572,1.5982581,-1.6589116,-1.5943594,-1.4585451
diff --git a/sbibm/tasks/ddm/files/num_observation_177/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_177/observation_seed.csv
new file mode 100644
index 00000000..0b18b9fa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_177/observation_seed.csv
@@ -0,0 +1,2 @@
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+78,177
diff --git a/sbibm/tasks/ddm/files/num_observation_177/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_177/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..164ce61e
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diff --git a/sbibm/tasks/ddm/files/num_observation_177/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_177/true_parameters.csv
new file mode 100644
index 00000000..c3d741bf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_177/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.6624286,0.73588187,0.43061274,1.4472694
diff --git a/sbibm/tasks/ddm/files/num_observation_178/observation.csv b/sbibm/tasks/ddm/files/num_observation_178/observation.csv
new file mode 100644
index 00000000..30cec893
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_178/observation.csv
@@ -0,0 +1,2 @@
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+-0.5825405,-0.6422969,0.48411113,-0.7330161,0.48868018,-0.53179216,0.48390692,0.5494539,-0.68283266,-0.71181893
diff --git a/sbibm/tasks/ddm/files/num_observation_178/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_178/observation_seed.csv
new file mode 100644
index 00000000..4d75b9d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_178/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_178/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_178/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4f7131c5
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diff --git a/sbibm/tasks/ddm/files/num_observation_178/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_178/true_parameters.csv
new file mode 100644
index 00000000..7ca02b0b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_178/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.3158362,0.8784044,0.6301043,0.45672023
diff --git a/sbibm/tasks/ddm/files/num_observation_179/observation.csv b/sbibm/tasks/ddm/files/num_observation_179/observation.csv
new file mode 100644
index 00000000..2f5d54f0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_179/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_179/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_179/observation_seed.csv
new file mode 100644
index 00000000..846465e7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_179/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_179/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_179/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_179/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_179/true_parameters.csv
new file mode 100644
index 00000000..07371896
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_179/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_18/observation.csv b/sbibm/tasks/ddm/files/num_observation_18/observation.csv
new file mode 100644
index 00000000..7bde8917
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_18/observation.csv
@@ -0,0 +1,2 @@
+data_1
+0.452791
diff --git a/sbibm/tasks/ddm/files/num_observation_18/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_18/observation_seed.csv
new file mode 100644
index 00000000..5b610095
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_18/observation_seed.csv
@@ -0,0 +1,2 @@
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+17,18
diff --git a/sbibm/tasks/ddm/files/num_observation_18/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_18/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_18/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_18/true_parameters.csv
new file mode 100644
index 00000000..ab5a5650
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_18/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.26303506,1.3026644,0.6320835,0.39818174
diff --git a/sbibm/tasks/ddm/files/num_observation_180/observation.csv b/sbibm/tasks/ddm/files/num_observation_180/observation.csv
new file mode 100644
index 00000000..daa13a91
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_180/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_180/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_180/observation_seed.csv
new file mode 100644
index 00000000..1413472a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_180/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_180/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_180/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_180/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_180/true_parameters.csv
new file mode 100644
index 00000000..b8b06fb9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_180/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_181/observation.csv b/sbibm/tasks/ddm/files/num_observation_181/observation.csv
new file mode 100644
index 00000000..6d4f58b3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_181/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_181/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_181/observation_seed.csv
new file mode 100644
index 00000000..bf627fc9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_181/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_181/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_181/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_181/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_181/true_parameters.csv
new file mode 100644
index 00000000..b1fc450e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_181/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.16891408,1.2972815,0.55722964,0.47171885
diff --git a/sbibm/tasks/ddm/files/num_observation_182/observation.csv b/sbibm/tasks/ddm/files/num_observation_182/observation.csv
new file mode 100644
index 00000000..cf17dc1d
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_182/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_182/observation_seed.csv
new file mode 100644
index 00000000..fbe927b5
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+++ b/sbibm/tasks/ddm/files/num_observation_182/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_182/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_182/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_182/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_182/true_parameters.csv
new file mode 100644
index 00000000..262e2554
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_182/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_183/observation.csv b/sbibm/tasks/ddm/files/num_observation_183/observation.csv
new file mode 100644
index 00000000..3a3aadeb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_183/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_183/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_183/observation_seed.csv
new file mode 100644
index 00000000..8869c11e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_183/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_183/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_183/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_183/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_183/true_parameters.csv
new file mode 100644
index 00000000..65ea7c65
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_183/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_184/observation.csv b/sbibm/tasks/ddm/files/num_observation_184/observation.csv
new file mode 100644
index 00000000..0daf6197
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_184/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_184/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_184/observation_seed.csv
new file mode 100644
index 00000000..7d0682f3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_184/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_184/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_184/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_184/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_184/true_parameters.csv
new file mode 100644
index 00000000..b9ed672e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_184/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_185/observation.csv b/sbibm/tasks/ddm/files/num_observation_185/observation.csv
new file mode 100644
index 00000000..86730e39
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_185/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_185/observation_seed.csv
new file mode 100644
index 00000000..3008e7fb
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_185/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_185/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_185/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_185/true_parameters.csv
new file mode 100644
index 00000000..d447c949
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_186/observation.csv b/sbibm/tasks/ddm/files/num_observation_186/observation.csv
new file mode 100644
index 00000000..3d029db3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_186/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_186/observation_seed.csv
new file mode 100644
index 00000000..28e0f2fd
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_186/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_186/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_186/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_186/true_parameters.csv
new file mode 100644
index 00000000..460dd4d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_186/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.9854798,1.8396294,0.48190144,1.7703261
diff --git a/sbibm/tasks/ddm/files/num_observation_187/observation.csv b/sbibm/tasks/ddm/files/num_observation_187/observation.csv
new file mode 100644
index 00000000..b58923d3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_187/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_187/observation_seed.csv
new file mode 100644
index 00000000..42a7979e
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_187/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_187/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_187/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_187/true_parameters.csv
new file mode 100644
index 00000000..0dadd7ac
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_188/observation.csv b/sbibm/tasks/ddm/files/num_observation_188/observation.csv
new file mode 100644
index 00000000..b37de6ea
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_188/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_188/observation_seed.csv
new file mode 100644
index 00000000..b1a753b5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_188/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_188/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_188/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_188/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_188/true_parameters.csv
new file mode 100644
index 00000000..b701adbf
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_189/observation.csv b/sbibm/tasks/ddm/files/num_observation_189/observation.csv
new file mode 100644
index 00000000..a8fbae86
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_189/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_189/observation_seed.csv
new file mode 100644
index 00000000..848e8beb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_189/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_189/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_189/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_189/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_189/true_parameters.csv
new file mode 100644
index 00000000..7fc95ee9
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_19/observation.csv b/sbibm/tasks/ddm/files/num_observation_19/observation.csv
new file mode 100644
index 00000000..5c79a09f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_19/observation.csv
@@ -0,0 +1,2 @@
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+-1.9944705
diff --git a/sbibm/tasks/ddm/files/num_observation_19/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_19/observation_seed.csv
new file mode 100644
index 00000000..263e448c
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_19/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_19/reference_posterior_samples.csv.bz2
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index 00000000..4be4d199
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diff --git a/sbibm/tasks/ddm/files/num_observation_190/observation.csv b/sbibm/tasks/ddm/files/num_observation_190/observation.csv
new file mode 100644
index 00000000..fde68179
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diff --git a/sbibm/tasks/ddm/files/num_observation_190/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_190/observation_seed.csv
new file mode 100644
index 00000000..96a051f8
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diff --git a/sbibm/tasks/ddm/files/num_observation_190/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_190/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_191/observation.csv b/sbibm/tasks/ddm/files/num_observation_191/observation.csv
new file mode 100644
index 00000000..ead099ec
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new file mode 100644
index 00000000..240e5b40
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diff --git a/sbibm/tasks/ddm/files/num_observation_191/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_191/reference_posterior_samples.csv.bz2
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new file mode 100644
index 00000000..3359c809
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_191/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.0931613,0.95239305,0.42918408,1.226284
diff --git a/sbibm/tasks/ddm/files/num_observation_192/observation.csv b/sbibm/tasks/ddm/files/num_observation_192/observation.csv
new file mode 100644
index 00000000..0f2f7181
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_192/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_192/observation_seed.csv
new file mode 100644
index 00000000..3de1c18f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_192/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_192/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_192/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_192/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_192/true_parameters.csv
new file mode 100644
index 00000000..92e7acc1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_192/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.3489544,0.50057954,0.47944793,1.6095066
diff --git a/sbibm/tasks/ddm/files/num_observation_193/observation.csv b/sbibm/tasks/ddm/files/num_observation_193/observation.csv
new file mode 100644
index 00000000..68b193b6
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_193/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_193/observation_seed.csv
new file mode 100644
index 00000000..2d77c281
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_193/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_193/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_193/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_193/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_193/true_parameters.csv
new file mode 100644
index 00000000..9755287e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_193/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_194/observation.csv b/sbibm/tasks/ddm/files/num_observation_194/observation.csv
new file mode 100644
index 00000000..5a48bb1a
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diff --git a/sbibm/tasks/ddm/files/num_observation_194/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_194/observation_seed.csv
new file mode 100644
index 00000000..9dd5d005
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_194/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_194/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_194/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_194/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_194/true_parameters.csv
new file mode 100644
index 00000000..fa733389
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_195/observation.csv b/sbibm/tasks/ddm/files/num_observation_195/observation.csv
new file mode 100644
index 00000000..fa8a05f2
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_195/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_195/observation_seed.csv
new file mode 100644
index 00000000..02a7128b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_195/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_195/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_195/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_195/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_195/true_parameters.csv
new file mode 100644
index 00000000..6e927078
--- /dev/null
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@@ -0,0 +1,2 @@
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+-0.9692168,1.0458748,0.45215094,1.6773119
diff --git a/sbibm/tasks/ddm/files/num_observation_196/observation.csv b/sbibm/tasks/ddm/files/num_observation_196/observation.csv
new file mode 100644
index 00000000..5a32c2f5
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_196/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_196/observation_seed.csv
new file mode 100644
index 00000000..1321e5cb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_196/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_196/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_196/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_196/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_196/true_parameters.csv
new file mode 100644
index 00000000..a2cde553
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_196/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.15833426,0.65166086,0.38015568,0.3534606
diff --git a/sbibm/tasks/ddm/files/num_observation_197/observation.csv b/sbibm/tasks/ddm/files/num_observation_197/observation.csv
new file mode 100644
index 00000000..4f929e1d
--- /dev/null
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@@ -0,0 +1,2 @@
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+-2.4473517,-2.4309378,-2.6273885,-2.0606098,1.9478,-2.293651,-2.0320897,-2.450511,-2.6567492,-2.8070574
diff --git a/sbibm/tasks/ddm/files/num_observation_197/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_197/observation_seed.csv
new file mode 100644
index 00000000..0112fff8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_197/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_197/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_197/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_197/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_197/true_parameters.csv
new file mode 100644
index 00000000..20c523c8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_197/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.5867612,1.9552338,0.67922807,1.765934
diff --git a/sbibm/tasks/ddm/files/num_observation_198/observation.csv b/sbibm/tasks/ddm/files/num_observation_198/observation.csv
new file mode 100644
index 00000000..e06c8ebf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_198/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_198/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_198/observation_seed.csv
new file mode 100644
index 00000000..7a9f62af
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_198/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_198/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_198/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_198/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_198/true_parameters.csv
new file mode 100644
index 00000000..0082c431
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_198/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.5533428,1.7237647,0.4050248,0.9742041
diff --git a/sbibm/tasks/ddm/files/num_observation_199/observation.csv b/sbibm/tasks/ddm/files/num_observation_199/observation.csv
new file mode 100644
index 00000000..f2d929ac
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_199/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_199/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_199/observation_seed.csv
new file mode 100644
index 00000000..6a061ba0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_199/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_199/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_199/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_199/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_199/true_parameters.csv
new file mode 100644
index 00000000..443ddc23
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_199/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.207802,1.1754313,0.33636057,1.6195135
diff --git a/sbibm/tasks/ddm/files/num_observation_2/observation.csv b/sbibm/tasks/ddm/files/num_observation_2/observation.csv
new file mode 100644
index 00000000..3fc42ea8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_2/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_2/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_2/observation_seed.csv
new file mode 100644
index 00000000..90f3925d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_2/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_2/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_2/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_2/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_2/true_parameters.csv
new file mode 100644
index 00000000..ac8d73e5
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_20/observation.csv b/sbibm/tasks/ddm/files/num_observation_20/observation.csv
new file mode 100644
index 00000000..04b30b6b
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_20/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_20/observation_seed.csv
new file mode 100644
index 00000000..f9bdd923
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_20/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_20/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_20/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_20/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_20/true_parameters.csv
new file mode 100644
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--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_200/observation.csv b/sbibm/tasks/ddm/files/num_observation_200/observation.csv
new file mode 100644
index 00000000..793b4629
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_200/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_200/observation_seed.csv
new file mode 100644
index 00000000..bf189089
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_200/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_200/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_200/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_200/true_parameters.csv
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index 00000000..70090274
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_201/observation.csv b/sbibm/tasks/ddm/files/num_observation_201/observation.csv
new file mode 100644
index 00000000..7ee833ed
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diff --git a/sbibm/tasks/ddm/files/num_observation_201/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_201/observation_seed.csv
new file mode 100644
index 00000000..be5ef4ef
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_201/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+0,201
diff --git a/sbibm/tasks/ddm/files/num_observation_201/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_201/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..55b547bc
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_201/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_201/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_201/true_parameters.csv
new file mode 100644
index 00000000..39094a45
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_201/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.01497364,1.6523327,0.33539099,0.41124877
diff --git a/sbibm/tasks/ddm/files/num_observation_202/observation.csv b/sbibm/tasks/ddm/files/num_observation_202/observation.csv
new file mode 100644
index 00000000..81fbe68e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_202/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_202/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_202/observation_seed.csv
new file mode 100644
index 00000000..23a1028c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_202/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1,202
diff --git a/sbibm/tasks/ddm/files/num_observation_202/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_202/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_202/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_202/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_202/true_parameters.csv
new file mode 100644
index 00000000..ac8d73e5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_202/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+1.0305264,0.9189663,0.46122772,1.3754951
diff --git a/sbibm/tasks/ddm/files/num_observation_203/observation.csv b/sbibm/tasks/ddm/files/num_observation_203/observation.csv
new file mode 100644
index 00000000..30968d6e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_203/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_203/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_203/observation_seed.csv
new file mode 100644
index 00000000..bc23c9e2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_203/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+2,203
diff --git a/sbibm/tasks/ddm/files/num_observation_203/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_203/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..625048a5
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_203/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_203/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_203/true_parameters.csv
new file mode 100644
index 00000000..50fff476
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_203/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.4587803,1.0715193,0.5548457,0.9591377
diff --git a/sbibm/tasks/ddm/files/num_observation_204/observation.csv b/sbibm/tasks/ddm/files/num_observation_204/observation.csv
new file mode 100644
index 00000000..65268433
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_204/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_204/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_204/observation_seed.csv
new file mode 100644
index 00000000..f88c433f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_204/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+3,204
diff --git a/sbibm/tasks/ddm/files/num_observation_204/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_204/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..94c1162a
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_204/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_204/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_204/true_parameters.csv
new file mode 100644
index 00000000..5e1174f8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_204/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.9829459,0.65835404,0.41433698,0.24312773
diff --git a/sbibm/tasks/ddm/files/num_observation_205/observation.csv b/sbibm/tasks/ddm/files/num_observation_205/observation.csv
new file mode 100644
index 00000000..87b2c0c6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_205/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_205/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_205/observation_seed.csv
new file mode 100644
index 00000000..fea3bcdd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_205/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+4,205
diff --git a/sbibm/tasks/ddm/files/num_observation_205/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_205/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c52c2a13
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diff --git a/sbibm/tasks/ddm/files/num_observation_205/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_205/true_parameters.csv
new file mode 100644
index 00000000..f0e29862
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_205/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_206/observation.csv b/sbibm/tasks/ddm/files/num_observation_206/observation.csv
new file mode 100644
index 00000000..281da82c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_206/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_206/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_206/observation_seed.csv
new file mode 100644
index 00000000..31845490
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_206/observation_seed.csv
@@ -0,0 +1,2 @@
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+5,206
diff --git a/sbibm/tasks/ddm/files/num_observation_206/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_206/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2c69a2d5
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diff --git a/sbibm/tasks/ddm/files/num_observation_206/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_206/true_parameters.csv
new file mode 100644
index 00000000..3928609f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_206/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_207/observation.csv b/sbibm/tasks/ddm/files/num_observation_207/observation.csv
new file mode 100644
index 00000000..60740162
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_207/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_207/observation_seed.csv
new file mode 100644
index 00000000..b8ed5c16
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_207/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+6,207
diff --git a/sbibm/tasks/ddm/files/num_observation_207/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_207/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_207/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_207/true_parameters.csv
new file mode 100644
index 00000000..7bcb99b8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_207/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_208/observation.csv b/sbibm/tasks/ddm/files/num_observation_208/observation.csv
new file mode 100644
index 00000000..2b1181aa
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_208/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_208/observation_seed.csv
new file mode 100644
index 00000000..4a44992b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_208/observation_seed.csv
@@ -0,0 +1,2 @@
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+7,208
diff --git a/sbibm/tasks/ddm/files/num_observation_208/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_208/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_208/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_208/true_parameters.csv
new file mode 100644
index 00000000..017ec574
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_208/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_209/observation.csv b/sbibm/tasks/ddm/files/num_observation_209/observation.csv
new file mode 100644
index 00000000..b1363167
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_209/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_209/observation_seed.csv
new file mode 100644
index 00000000..57c656d6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_209/observation_seed.csv
@@ -0,0 +1,2 @@
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+8,209
diff --git a/sbibm/tasks/ddm/files/num_observation_209/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_209/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_209/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_209/true_parameters.csv
new file mode 100644
index 00000000..ed10c2a0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_209/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_21/observation.csv b/sbibm/tasks/ddm/files/num_observation_21/observation.csv
new file mode 100644
index 00000000..5373903c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_21/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_21/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_21/observation_seed.csv
new file mode 100644
index 00000000..c9b0bb10
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_21/observation_seed.csv
@@ -0,0 +1,2 @@
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+20,21
diff --git a/sbibm/tasks/ddm/files/num_observation_21/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_21/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_21/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_21/true_parameters.csv
new file mode 100644
index 00000000..ca1380b0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_21/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_210/observation.csv b/sbibm/tasks/ddm/files/num_observation_210/observation.csv
new file mode 100644
index 00000000..4186339b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_210/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_210/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_210/observation_seed.csv
new file mode 100644
index 00000000..3e412764
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_210/observation_seed.csv
@@ -0,0 +1,2 @@
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+9,210
diff --git a/sbibm/tasks/ddm/files/num_observation_210/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_210/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_210/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_210/true_parameters.csv
new file mode 100644
index 00000000..26971e17
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_210/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_211/observation.csv b/sbibm/tasks/ddm/files/num_observation_211/observation.csv
new file mode 100644
index 00000000..19d5887c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_211/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_211/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_211/observation_seed.csv
new file mode 100644
index 00000000..1f791d44
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_211/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_211/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_211/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_211/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_211/true_parameters.csv
new file mode 100644
index 00000000..e1fa9cb6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_211/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.16766143,1.2242849,0.42499933,1.1840346
diff --git a/sbibm/tasks/ddm/files/num_observation_212/observation.csv b/sbibm/tasks/ddm/files/num_observation_212/observation.csv
new file mode 100644
index 00000000..8a3d4f8b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_212/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_212/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_212/observation_seed.csv
new file mode 100644
index 00000000..e89c7396
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_212/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+11,212
diff --git a/sbibm/tasks/ddm/files/num_observation_212/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_212/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e5b4c549
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_212/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_212/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_212/true_parameters.csv
new file mode 100644
index 00000000..032aa7d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_212/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.4038332,1.2298297,0.6942646,0.46944225
diff --git a/sbibm/tasks/ddm/files/num_observation_213/observation.csv b/sbibm/tasks/ddm/files/num_observation_213/observation.csv
new file mode 100644
index 00000000..bc86917c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_213/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_213/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_213/observation_seed.csv
new file mode 100644
index 00000000..3ef6eebe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_213/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+12,213
diff --git a/sbibm/tasks/ddm/files/num_observation_213/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_213/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5be925bd
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_213/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_213/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_213/true_parameters.csv
new file mode 100644
index 00000000..6e69700e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_213/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.13725019,0.8491502,0.4810884,1.1393795
diff --git a/sbibm/tasks/ddm/files/num_observation_214/observation.csv b/sbibm/tasks/ddm/files/num_observation_214/observation.csv
new file mode 100644
index 00000000..a9dc6395
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_214/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_214/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_214/observation_seed.csv
new file mode 100644
index 00000000..39b34199
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_214/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+13,214
diff --git a/sbibm/tasks/ddm/files/num_observation_214/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_214/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..433f1a93
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_214/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_214/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_214/true_parameters.csv
new file mode 100644
index 00000000..49173c40
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_214/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.6327319,1.2190709,0.6242205,0.22417144
diff --git a/sbibm/tasks/ddm/files/num_observation_215/observation.csv b/sbibm/tasks/ddm/files/num_observation_215/observation.csv
new file mode 100644
index 00000000..96eebd39
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_215/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_215/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_215/observation_seed.csv
new file mode 100644
index 00000000..bb3b3704
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_215/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+14,215
diff --git a/sbibm/tasks/ddm/files/num_observation_215/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_215/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a0b54d05
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_215/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_215/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_215/true_parameters.csv
new file mode 100644
index 00000000..c9b31c10
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_215/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.2779796,0.5070803,0.67210114,1.3611715
diff --git a/sbibm/tasks/ddm/files/num_observation_216/observation.csv b/sbibm/tasks/ddm/files/num_observation_216/observation.csv
new file mode 100644
index 00000000..f30e4e4a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_216/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_216/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_216/observation_seed.csv
new file mode 100644
index 00000000..f3602dca
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_216/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+15,216
diff --git a/sbibm/tasks/ddm/files/num_observation_216/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_216/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3b25d6b0
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diff --git a/sbibm/tasks/ddm/files/num_observation_216/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_216/true_parameters.csv
new file mode 100644
index 00000000..989ca1de
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_216/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.8106823,0.91489315,0.6189425,0.8190068
diff --git a/sbibm/tasks/ddm/files/num_observation_217/observation.csv b/sbibm/tasks/ddm/files/num_observation_217/observation.csv
new file mode 100644
index 00000000..e02df3db
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_217/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_217/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_217/observation_seed.csv
new file mode 100644
index 00000000..6cba1e60
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_217/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+16,217
diff --git a/sbibm/tasks/ddm/files/num_observation_217/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_217/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..50110b34
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_217/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_217/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_217/true_parameters.csv
new file mode 100644
index 00000000..99ee3631
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_217/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.3499353,1.4359648,0.67192674,0.6094304
diff --git a/sbibm/tasks/ddm/files/num_observation_218/observation.csv b/sbibm/tasks/ddm/files/num_observation_218/observation.csv
new file mode 100644
index 00000000..dc4d97e4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_218/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_218/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_218/observation_seed.csv
new file mode 100644
index 00000000..dec3fb24
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_218/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+17,218
diff --git a/sbibm/tasks/ddm/files/num_observation_218/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_218/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ca3245f0
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diff --git a/sbibm/tasks/ddm/files/num_observation_218/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_218/true_parameters.csv
new file mode 100644
index 00000000..ab5a5650
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_218/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.26303506,1.3026644,0.6320835,0.39818174
diff --git a/sbibm/tasks/ddm/files/num_observation_219/observation.csv b/sbibm/tasks/ddm/files/num_observation_219/observation.csv
new file mode 100644
index 00000000..9d192856
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_219/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_219/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_219/observation_seed.csv
new file mode 100644
index 00000000..33021785
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_219/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+18,219
diff --git a/sbibm/tasks/ddm/files/num_observation_219/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_219/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ef696a23
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diff --git a/sbibm/tasks/ddm/files/num_observation_219/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_219/true_parameters.csv
new file mode 100644
index 00000000..4be4d199
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_219/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.016774654,1.1881349,0.4584257,1.4034001
diff --git a/sbibm/tasks/ddm/files/num_observation_22/observation.csv b/sbibm/tasks/ddm/files/num_observation_22/observation.csv
new file mode 100644
index 00000000..b6fa1d99
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_22/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-0.51168317
diff --git a/sbibm/tasks/ddm/files/num_observation_22/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_22/observation_seed.csv
new file mode 100644
index 00000000..66e6d3b3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_22/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+21,22
diff --git a/sbibm/tasks/ddm/files/num_observation_22/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_22/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..114486d3
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diff --git a/sbibm/tasks/ddm/files/num_observation_22/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_22/true_parameters.csv
new file mode 100644
index 00000000..b1768282
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_22/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.105726,1.4203981,0.30482924,0.26335487
diff --git a/sbibm/tasks/ddm/files/num_observation_220/observation.csv b/sbibm/tasks/ddm/files/num_observation_220/observation.csv
new file mode 100644
index 00000000..8a935279
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_220/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_220/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_220/observation_seed.csv
new file mode 100644
index 00000000..9e2864c9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_220/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+19,220
diff --git a/sbibm/tasks/ddm/files/num_observation_220/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_220/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..f69ed375
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diff --git a/sbibm/tasks/ddm/files/num_observation_220/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_220/true_parameters.csv
new file mode 100644
index 00000000..942e0ea0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_220/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_221/observation.csv b/sbibm/tasks/ddm/files/num_observation_221/observation.csv
new file mode 100644
index 00000000..81146c77
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_221/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_221/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_221/observation_seed.csv
new file mode 100644
index 00000000..16af7771
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_221/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+20,221
diff --git a/sbibm/tasks/ddm/files/num_observation_221/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_221/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_221/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_221/true_parameters.csv
new file mode 100644
index 00000000..ca1380b0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_221/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_222/observation.csv b/sbibm/tasks/ddm/files/num_observation_222/observation.csv
new file mode 100644
index 00000000..ab0dcfe4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_222/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_222/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_222/observation_seed.csv
new file mode 100644
index 00000000..6616c5f3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_222/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+21,222
diff --git a/sbibm/tasks/ddm/files/num_observation_222/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_222/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3cde7c91
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_222/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_222/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_222/true_parameters.csv
new file mode 100644
index 00000000..b1768282
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_222/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.105726,1.4203981,0.30482924,0.26335487
diff --git a/sbibm/tasks/ddm/files/num_observation_223/observation.csv b/sbibm/tasks/ddm/files/num_observation_223/observation.csv
new file mode 100644
index 00000000..6a913fba
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_223/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_223/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_223/observation_seed.csv
new file mode 100644
index 00000000..4e103cbe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_223/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+22,223
diff --git a/sbibm/tasks/ddm/files/num_observation_223/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_223/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..19e4ba51
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_223/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_223/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_223/true_parameters.csv
new file mode 100644
index 00000000..97af6f31
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_223/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.5364101,1.5537838,0.42414045,0.21552125
diff --git a/sbibm/tasks/ddm/files/num_observation_224/observation.csv b/sbibm/tasks/ddm/files/num_observation_224/observation.csv
new file mode 100644
index 00000000..dff25fe4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_224/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_224/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_224/observation_seed.csv
new file mode 100644
index 00000000..e359e939
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_224/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+23,224
diff --git a/sbibm/tasks/ddm/files/num_observation_224/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_224/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_224/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_224/true_parameters.csv
new file mode 100644
index 00000000..62f91f5e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_224/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_225/observation.csv b/sbibm/tasks/ddm/files/num_observation_225/observation.csv
new file mode 100644
index 00000000..b51d8fd5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_225/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_225/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_225/observation_seed.csv
new file mode 100644
index 00000000..c9fe51f1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_225/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+24,225
diff --git a/sbibm/tasks/ddm/files/num_observation_225/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_225/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5c1d3208
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diff --git a/sbibm/tasks/ddm/files/num_observation_225/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_225/true_parameters.csv
new file mode 100644
index 00000000..82ff1a68
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_225/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_226/observation.csv b/sbibm/tasks/ddm/files/num_observation_226/observation.csv
new file mode 100644
index 00000000..6e3f0170
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_226/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_226/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_226/observation_seed.csv
new file mode 100644
index 00000000..5a70eddb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_226/observation_seed.csv
@@ -0,0 +1,2 @@
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+25,226
diff --git a/sbibm/tasks/ddm/files/num_observation_226/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_226/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..dba8ad88
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diff --git a/sbibm/tasks/ddm/files/num_observation_226/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_226/true_parameters.csv
new file mode 100644
index 00000000..e9d6be78
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_226/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_227/observation.csv b/sbibm/tasks/ddm/files/num_observation_227/observation.csv
new file mode 100644
index 00000000..5ee6f6ef
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_227/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_227/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_227/observation_seed.csv
new file mode 100644
index 00000000..afe76216
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_227/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+26,227
diff --git a/sbibm/tasks/ddm/files/num_observation_227/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_227/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_227/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_227/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_227/true_parameters.csv
new file mode 100644
index 00000000..127ae726
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_227/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
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diff --git a/sbibm/tasks/ddm/files/num_observation_228/observation.csv b/sbibm/tasks/ddm/files/num_observation_228/observation.csv
new file mode 100644
index 00000000..90ea1cbd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_228/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_228/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_228/observation_seed.csv
new file mode 100644
index 00000000..042ced9d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_228/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+27,228
diff --git a/sbibm/tasks/ddm/files/num_observation_228/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_228/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c5eec2f3
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diff --git a/sbibm/tasks/ddm/files/num_observation_228/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_228/true_parameters.csv
new file mode 100644
index 00000000..b003a1f1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_228/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
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diff --git a/sbibm/tasks/ddm/files/num_observation_229/observation.csv b/sbibm/tasks/ddm/files/num_observation_229/observation.csv
new file mode 100644
index 00000000..d1338c43
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_229/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_229/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_229/observation_seed.csv
new file mode 100644
index 00000000..93abdb0c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_229/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+28,229
diff --git a/sbibm/tasks/ddm/files/num_observation_229/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_229/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_229/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_229/true_parameters.csv
new file mode 100644
index 00000000..f37ee4e6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_229/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_23/observation.csv b/sbibm/tasks/ddm/files/num_observation_23/observation.csv
new file mode 100644
index 00000000..75871958
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_23/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-0.8601763
diff --git a/sbibm/tasks/ddm/files/num_observation_23/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_23/observation_seed.csv
new file mode 100644
index 00000000..2db9af8b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_23/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+22,23
diff --git a/sbibm/tasks/ddm/files/num_observation_23/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_23/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_23/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_23/true_parameters.csv
new file mode 100644
index 00000000..97af6f31
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_23/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_230/observation.csv b/sbibm/tasks/ddm/files/num_observation_230/observation.csv
new file mode 100644
index 00000000..6c785349
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_230/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_230/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_230/observation_seed.csv
new file mode 100644
index 00000000..ebe8d4d7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_230/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+29,230
diff --git a/sbibm/tasks/ddm/files/num_observation_230/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_230/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_230/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_230/true_parameters.csv
new file mode 100644
index 00000000..efa0c15a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_230/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.5097759,1.9032619,0.6743709,1.3260251
diff --git a/sbibm/tasks/ddm/files/num_observation_231/observation.csv b/sbibm/tasks/ddm/files/num_observation_231/observation.csv
new file mode 100644
index 00000000..af3459a1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_231/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_231/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_231/observation_seed.csv
new file mode 100644
index 00000000..1d5edcbf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_231/observation_seed.csv
@@ -0,0 +1,2 @@
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+30,231
diff --git a/sbibm/tasks/ddm/files/num_observation_231/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_231/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_231/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_231/true_parameters.csv
new file mode 100644
index 00000000..001a5871
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_231/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_232/observation.csv b/sbibm/tasks/ddm/files/num_observation_232/observation.csv
new file mode 100644
index 00000000..4f2d4a41
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_232/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_232/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_232/observation_seed.csv
new file mode 100644
index 00000000..2906a298
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_232/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+31,232
diff --git a/sbibm/tasks/ddm/files/num_observation_232/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_232/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_232/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_232/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_232/true_parameters.csv
new file mode 100644
index 00000000..b8131afb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_232/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.0808825,1.1327745,0.41001016,1.0276242
diff --git a/sbibm/tasks/ddm/files/num_observation_233/observation.csv b/sbibm/tasks/ddm/files/num_observation_233/observation.csv
new file mode 100644
index 00000000..55929df1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_233/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_233/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_233/observation_seed.csv
new file mode 100644
index 00000000..4af7005d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_233/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+32,233
diff --git a/sbibm/tasks/ddm/files/num_observation_233/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_233/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7d7c3a07
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_233/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_233/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_233/true_parameters.csv
new file mode 100644
index 00000000..95570ea4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_233/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.5026147,0.9081001,0.46562207,1.4571584
diff --git a/sbibm/tasks/ddm/files/num_observation_234/observation.csv b/sbibm/tasks/ddm/files/num_observation_234/observation.csv
new file mode 100644
index 00000000..32964842
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_234/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_234/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_234/observation_seed.csv
new file mode 100644
index 00000000..4cb350b4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_234/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+33,234
diff --git a/sbibm/tasks/ddm/files/num_observation_234/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_234/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_234/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_234/true_parameters.csv
new file mode 100644
index 00000000..09e55664
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_234/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_235/observation.csv b/sbibm/tasks/ddm/files/num_observation_235/observation.csv
new file mode 100644
index 00000000..efcf143d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_235/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_235/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_235/observation_seed.csv
new file mode 100644
index 00000000..019fe7bf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_235/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+34,235
diff --git a/sbibm/tasks/ddm/files/num_observation_235/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_235/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..84347da3
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diff --git a/sbibm/tasks/ddm/files/num_observation_235/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_235/true_parameters.csv
new file mode 100644
index 00000000..ea87e8f5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_235/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_236/observation.csv b/sbibm/tasks/ddm/files/num_observation_236/observation.csv
new file mode 100644
index 00000000..a5453955
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_236/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_236/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_236/observation_seed.csv
new file mode 100644
index 00000000..12c0199c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_236/observation_seed.csv
@@ -0,0 +1,2 @@
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+35,236
diff --git a/sbibm/tasks/ddm/files/num_observation_236/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_236/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_236/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_236/true_parameters.csv
new file mode 100644
index 00000000..7a5cd0c4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_236/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_237/observation.csv b/sbibm/tasks/ddm/files/num_observation_237/observation.csv
new file mode 100644
index 00000000..d4c35500
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_237/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_237/observation_seed.csv
new file mode 100644
index 00000000..f9830169
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_237/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+36,237
diff --git a/sbibm/tasks/ddm/files/num_observation_237/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_237/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..70708584
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_237/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_237/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_237/true_parameters.csv
new file mode 100644
index 00000000..d78633a5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_237/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_238/observation.csv b/sbibm/tasks/ddm/files/num_observation_238/observation.csv
new file mode 100644
index 00000000..38b514a6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_238/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_238/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_238/observation_seed.csv
new file mode 100644
index 00000000..e67acefe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_238/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+37,238
diff --git a/sbibm/tasks/ddm/files/num_observation_238/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_238/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..b944b861
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_238/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_238/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_238/true_parameters.csv
new file mode 100644
index 00000000..c6adc53a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_238/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+1.1645238,1.462801,0.62365294,0.46545362
diff --git a/sbibm/tasks/ddm/files/num_observation_239/observation.csv b/sbibm/tasks/ddm/files/num_observation_239/observation.csv
new file mode 100644
index 00000000..b9deae26
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_239/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_239/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_239/observation_seed.csv
new file mode 100644
index 00000000..27b1bc42
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_239/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+38,239
diff --git a/sbibm/tasks/ddm/files/num_observation_239/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_239/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2f88f115
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diff --git a/sbibm/tasks/ddm/files/num_observation_239/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_239/true_parameters.csv
new file mode 100644
index 00000000..69f8ec58
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_239/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.00766778,1.451766,0.33408332,1.432564
diff --git a/sbibm/tasks/ddm/files/num_observation_24/observation.csv b/sbibm/tasks/ddm/files/num_observation_24/observation.csv
new file mode 100644
index 00000000..514c3409
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_24/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.2398248
diff --git a/sbibm/tasks/ddm/files/num_observation_24/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_24/observation_seed.csv
new file mode 100644
index 00000000..0a837cad
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_24/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+23,24
diff --git a/sbibm/tasks/ddm/files/num_observation_24/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_24/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..fb11e05c
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diff --git a/sbibm/tasks/ddm/files/num_observation_24/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_24/true_parameters.csv
new file mode 100644
index 00000000..62f91f5e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_24/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.28696752,0.9332939,0.4689707,0.77128375
diff --git a/sbibm/tasks/ddm/files/num_observation_240/observation.csv b/sbibm/tasks/ddm/files/num_observation_240/observation.csv
new file mode 100644
index 00000000..543d39ee
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_240/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_240/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_240/observation_seed.csv
new file mode 100644
index 00000000..1db97bf8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_240/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+39,240
diff --git a/sbibm/tasks/ddm/files/num_observation_240/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_240/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c1742685
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diff --git a/sbibm/tasks/ddm/files/num_observation_240/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_240/true_parameters.csv
new file mode 100644
index 00000000..edbe0f1b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_240/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.985502,1.1760348,0.40485936,0.7657287
diff --git a/sbibm/tasks/ddm/files/num_observation_241/observation.csv b/sbibm/tasks/ddm/files/num_observation_241/observation.csv
new file mode 100644
index 00000000..6aae8135
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_241/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_241/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_241/observation_seed.csv
new file mode 100644
index 00000000..06ec6bfc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_241/observation_seed.csv
@@ -0,0 +1,2 @@
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+40,241
diff --git a/sbibm/tasks/ddm/files/num_observation_241/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_241/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_241/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_241/true_parameters.csv
new file mode 100644
index 00000000..d35e51fa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_241/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.5284867,1.7991529,0.3694826,1.345133
diff --git a/sbibm/tasks/ddm/files/num_observation_242/observation.csv b/sbibm/tasks/ddm/files/num_observation_242/observation.csv
new file mode 100644
index 00000000..048fa6d6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_242/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_242/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_242/observation_seed.csv
new file mode 100644
index 00000000..07d35380
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_242/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+41,242
diff --git a/sbibm/tasks/ddm/files/num_observation_242/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_242/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7a217ec8
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_242/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_242/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_242/true_parameters.csv
new file mode 100644
index 00000000..d8534bfe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_242/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.0542145,0.83992696,0.6202121,0.47070026
diff --git a/sbibm/tasks/ddm/files/num_observation_243/observation.csv b/sbibm/tasks/ddm/files/num_observation_243/observation.csv
new file mode 100644
index 00000000..7114a887
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_243/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_243/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_243/observation_seed.csv
new file mode 100644
index 00000000..bd85000a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_243/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+42,243
diff --git a/sbibm/tasks/ddm/files/num_observation_243/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_243/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..803e4897
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_243/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_243/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_243/true_parameters.csv
new file mode 100644
index 00000000..87b76408
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_243/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+1.529077,1.8725059,0.4531455,1.734889
diff --git a/sbibm/tasks/ddm/files/num_observation_244/observation.csv b/sbibm/tasks/ddm/files/num_observation_244/observation.csv
new file mode 100644
index 00000000..476bf1bf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_244/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_244/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_244/observation_seed.csv
new file mode 100644
index 00000000..f281d15a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_244/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+43,244
diff --git a/sbibm/tasks/ddm/files/num_observation_244/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_244/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3e271eca
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_244/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_244/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_244/true_parameters.csv
new file mode 100644
index 00000000..d5e4c16f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_244/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.18412685,0.79473686,0.66841424,0.75388646
diff --git a/sbibm/tasks/ddm/files/num_observation_245/observation.csv b/sbibm/tasks/ddm/files/num_observation_245/observation.csv
new file mode 100644
index 00000000..701fd1a2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_245/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_245/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_245/observation_seed.csv
new file mode 100644
index 00000000..232e5e28
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_245/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+44,245
diff --git a/sbibm/tasks/ddm/files/num_observation_245/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_245/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..543ef099
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_245/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_245/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_245/true_parameters.csv
new file mode 100644
index 00000000..a2c92409
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_245/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.8783617,1.5960152,0.63112146,0.41495788
diff --git a/sbibm/tasks/ddm/files/num_observation_246/observation.csv b/sbibm/tasks/ddm/files/num_observation_246/observation.csv
new file mode 100644
index 00000000..4cd54f8a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_246/observation.csv
@@ -0,0 +1,2 @@
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+-2.0986457,-2.158829,-1.9875445,-1.8918056,-1.9971622,-1.9599811,-1.985488,2.0593894,-2.3469205,-2.3790667,-2.9711094,-2.554622,-2.1674726,1.9214427,-2.728972,-2.2543495,-2.2613747,-2.9525871,-2.2804039,-3.2892995,-1.8838999,-2.3298697,-2.6416907,-2.2612953,1.7737701,-1.868898,-2.225857,2.191551,-2.8247638,-2.2470205,-2.211913,-2.6048877,-1.8626729,-1.7733672,-1.8960371,-2.2872207,-1.8195248,2.6588864,-2.6300278,-1.9679215,1.94208,-2.5515406,-2.2602916,-2.693562,-1.9125006,-3.0379763,2.9661999,-2.0188115,-2.423155,3.199097,-2.5424335,-2.4940305,-2.1505256,-1.8967471,-2.1153626,-3.2858112,-2.9848354,-2.0351806,-2.1359506,-2.4773123,1.7584398,-2.0360186,-3.3302376,-3.6308682,-2.160261,3.6624255,-3.0274997,-2.5493426,-3.3134134,-2.4915075,-2.6141136,-1.7906004,1.8898708,-2.2670534,-2.2314737,-2.272057,-1.9567059,-1.9584343,-1.8837134,-1.8115971,-1.8903637,-2.106723,-1.8125141,-1.8441323,-2.2561224,-1.9959679,-2.4319813,-2.450445,-1.8868157,-1.9408277,-2.1305413,-2.140396,-4.0743084,-1.8671741,-2.1127093,-2.0050743,1.844674,2.604442,-2.4660957,-1.9024351
diff --git a/sbibm/tasks/ddm/files/num_observation_246/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_246/observation_seed.csv
new file mode 100644
index 00000000..2fa0e856
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_246/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+45,246
diff --git a/sbibm/tasks/ddm/files/num_observation_246/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_246/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..1221e71d
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_246/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_246/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_246/true_parameters.csv
new file mode 100644
index 00000000..72b74610
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_246/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.2522099,1.9419925,0.5733796,1.6380733
diff --git a/sbibm/tasks/ddm/files/num_observation_247/observation.csv b/sbibm/tasks/ddm/files/num_observation_247/observation.csv
new file mode 100644
index 00000000..a06f6216
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_247/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_247/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_247/observation_seed.csv
new file mode 100644
index 00000000..2f3789c3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_247/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+46,247
diff --git a/sbibm/tasks/ddm/files/num_observation_247/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_247/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..bc227320
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_247/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_247/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_247/true_parameters.csv
new file mode 100644
index 00000000..05587a59
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_247/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
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diff --git a/sbibm/tasks/ddm/files/num_observation_248/observation.csv b/sbibm/tasks/ddm/files/num_observation_248/observation.csv
new file mode 100644
index 00000000..f44d9709
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_248/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_248/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_248/observation_seed.csv
new file mode 100644
index 00000000..007d407e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_248/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+47,248
diff --git a/sbibm/tasks/ddm/files/num_observation_248/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_248/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2d4d8bf9
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diff --git a/sbibm/tasks/ddm/files/num_observation_248/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_248/true_parameters.csv
new file mode 100644
index 00000000..d6b7ec28
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_248/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.7878096,0.5748792,0.48706836,1.6010994
diff --git a/sbibm/tasks/ddm/files/num_observation_249/observation.csv b/sbibm/tasks/ddm/files/num_observation_249/observation.csv
new file mode 100644
index 00000000..35a52908
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_249/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_249/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_249/observation_seed.csv
new file mode 100644
index 00000000..f07b8975
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_249/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+48,249
diff --git a/sbibm/tasks/ddm/files/num_observation_249/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_249/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..f983ce9b
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diff --git a/sbibm/tasks/ddm/files/num_observation_249/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_249/true_parameters.csv
new file mode 100644
index 00000000..ddd372c4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_249/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.08996582,0.76976466,0.39710277,0.54662514
diff --git a/sbibm/tasks/ddm/files/num_observation_25/observation.csv b/sbibm/tasks/ddm/files/num_observation_25/observation.csv
new file mode 100644
index 00000000..c8f5eaa7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_25/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.2742074
diff --git a/sbibm/tasks/ddm/files/num_observation_25/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_25/observation_seed.csv
new file mode 100644
index 00000000..6ce26614
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_25/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+24,25
diff --git a/sbibm/tasks/ddm/files/num_observation_25/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_25/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_25/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_25/true_parameters.csv
new file mode 100644
index 00000000..82ff1a68
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_25/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_250/observation.csv b/sbibm/tasks/ddm/files/num_observation_250/observation.csv
new file mode 100644
index 00000000..cb462fef
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_250/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_250/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_250/observation_seed.csv
new file mode 100644
index 00000000..49521baa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_250/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+50,250
diff --git a/sbibm/tasks/ddm/files/num_observation_250/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_250/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..812c9fef
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_250/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_250/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_250/true_parameters.csv
new file mode 100644
index 00000000..a2d343a4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_250/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_251/observation.csv b/sbibm/tasks/ddm/files/num_observation_251/observation.csv
new file mode 100644
index 00000000..fe229db4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_251/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_251/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_251/observation_seed.csv
new file mode 100644
index 00000000..185fdec3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_251/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+51,251
diff --git a/sbibm/tasks/ddm/files/num_observation_251/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_251/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_251/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_251/true_parameters.csv
new file mode 100644
index 00000000..54c6db84
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_251/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_252/observation.csv b/sbibm/tasks/ddm/files/num_observation_252/observation.csv
new file mode 100644
index 00000000..09987416
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_252/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_252/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_252/observation_seed.csv
new file mode 100644
index 00000000..6abec1ee
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_252/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+52,252
diff --git a/sbibm/tasks/ddm/files/num_observation_252/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_252/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_252/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_252/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_252/true_parameters.csv
new file mode 100644
index 00000000..5e23ffd2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_252/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_253/observation.csv b/sbibm/tasks/ddm/files/num_observation_253/observation.csv
new file mode 100644
index 00000000..84e6943a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_253/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_253/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_253/observation_seed.csv
new file mode 100644
index 00000000..9502a5ab
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_253/observation_seed.csv
@@ -0,0 +1,2 @@
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+53,253
diff --git a/sbibm/tasks/ddm/files/num_observation_253/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_253/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_253/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_253/true_parameters.csv
new file mode 100644
index 00000000..d480360b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_253/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_254/observation.csv b/sbibm/tasks/ddm/files/num_observation_254/observation.csv
new file mode 100644
index 00000000..0256777a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_254/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_254/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_254/observation_seed.csv
new file mode 100644
index 00000000..735ff647
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_254/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+54,254
diff --git a/sbibm/tasks/ddm/files/num_observation_254/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_254/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_254/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_254/true_parameters.csv
new file mode 100644
index 00000000..cd776a25
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_254/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_255/observation.csv b/sbibm/tasks/ddm/files/num_observation_255/observation.csv
new file mode 100644
index 00000000..09a390ce
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_255/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_255/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_255/observation_seed.csv
new file mode 100644
index 00000000..309e12af
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_255/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+55,255
diff --git a/sbibm/tasks/ddm/files/num_observation_255/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_255/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_255/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_255/true_parameters.csv
new file mode 100644
index 00000000..f98ddc7b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_255/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_256/observation.csv b/sbibm/tasks/ddm/files/num_observation_256/observation.csv
new file mode 100644
index 00000000..1d8cc520
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_256/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_256/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_256/observation_seed.csv
new file mode 100644
index 00000000..5445d7a3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_256/observation_seed.csv
@@ -0,0 +1,2 @@
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+56,256
diff --git a/sbibm/tasks/ddm/files/num_observation_256/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_256/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_256/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_256/true_parameters.csv
new file mode 100644
index 00000000..af8385f9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_256/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_257/observation.csv b/sbibm/tasks/ddm/files/num_observation_257/observation.csv
new file mode 100644
index 00000000..c201511c
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_257/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_257/observation_seed.csv
new file mode 100644
index 00000000..2e958a00
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_257/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+57,257
diff --git a/sbibm/tasks/ddm/files/num_observation_257/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_257/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_257/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_257/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_257/true_parameters.csv
new file mode 100644
index 00000000..d81a368b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_257/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_258/observation.csv b/sbibm/tasks/ddm/files/num_observation_258/observation.csv
new file mode 100644
index 00000000..a0833f21
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_258/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_258/observation_seed.csv
new file mode 100644
index 00000000..d4b9cf98
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_258/observation_seed.csv
@@ -0,0 +1,2 @@
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+58,258
diff --git a/sbibm/tasks/ddm/files/num_observation_258/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_258/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ac8ec3e4
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diff --git a/sbibm/tasks/ddm/files/num_observation_258/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_258/true_parameters.csv
new file mode 100644
index 00000000..1a197fcb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_258/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_259/observation.csv b/sbibm/tasks/ddm/files/num_observation_259/observation.csv
new file mode 100644
index 00000000..3634f197
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_259/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_259/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_259/observation_seed.csv
new file mode 100644
index 00000000..6227cce8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_259/observation_seed.csv
@@ -0,0 +1,2 @@
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+59,259
diff --git a/sbibm/tasks/ddm/files/num_observation_259/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_259/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_259/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_259/true_parameters.csv
new file mode 100644
index 00000000..48fe0e82
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_259/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_26/observation.csv b/sbibm/tasks/ddm/files/num_observation_26/observation.csv
new file mode 100644
index 00000000..a4ff1a65
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_26/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_26/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_26/observation_seed.csv
new file mode 100644
index 00000000..dd8a9e7b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_26/observation_seed.csv
@@ -0,0 +1,2 @@
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+25,26
diff --git a/sbibm/tasks/ddm/files/num_observation_26/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_26/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_26/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_26/true_parameters.csv
new file mode 100644
index 00000000..e9d6be78
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_26/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_260/observation.csv b/sbibm/tasks/ddm/files/num_observation_260/observation.csv
new file mode 100644
index 00000000..2c2573ce
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_260/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_260/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_260/observation_seed.csv
new file mode 100644
index 00000000..3c4f0c34
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_260/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+60,260
diff --git a/sbibm/tasks/ddm/files/num_observation_260/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_260/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c0f622a5
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diff --git a/sbibm/tasks/ddm/files/num_observation_260/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_260/true_parameters.csv
new file mode 100644
index 00000000..5fffe8a7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_260/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.9057128,0.9661008,0.6432512,0.99857265
diff --git a/sbibm/tasks/ddm/files/num_observation_261/observation.csv b/sbibm/tasks/ddm/files/num_observation_261/observation.csv
new file mode 100644
index 00000000..c2a4a636
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_261/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_261/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_261/observation_seed.csv
new file mode 100644
index 00000000..b188a8b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_261/observation_seed.csv
@@ -0,0 +1,2 @@
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+61,261
diff --git a/sbibm/tasks/ddm/files/num_observation_261/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_261/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_261/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_261/true_parameters.csv
new file mode 100644
index 00000000..9c690fe9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_261/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.7432625,1.673662,0.4702126,1.3369156
diff --git a/sbibm/tasks/ddm/files/num_observation_262/observation.csv b/sbibm/tasks/ddm/files/num_observation_262/observation.csv
new file mode 100644
index 00000000..d1b59ca2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_262/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_262/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_262/observation_seed.csv
new file mode 100644
index 00000000..ccdf26bc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_262/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+62,262
diff --git a/sbibm/tasks/ddm/files/num_observation_262/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_262/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5348a7be
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_262/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_262/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_262/true_parameters.csv
new file mode 100644
index 00000000..7684bd6c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_262/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.5648236,1.7435629,0.38460886,1.0351449
diff --git a/sbibm/tasks/ddm/files/num_observation_263/observation.csv b/sbibm/tasks/ddm/files/num_observation_263/observation.csv
new file mode 100644
index 00000000..c4c05d14
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_263/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_263/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_263/observation_seed.csv
new file mode 100644
index 00000000..11fe8e7e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_263/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+64,263
diff --git a/sbibm/tasks/ddm/files/num_observation_263/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_263/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..6662fe2d
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diff --git a/sbibm/tasks/ddm/files/num_observation_263/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_263/true_parameters.csv
new file mode 100644
index 00000000..fd4cd8eb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_263/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.8031149,1.3984046,0.37085247,0.6221838
diff --git a/sbibm/tasks/ddm/files/num_observation_264/observation.csv b/sbibm/tasks/ddm/files/num_observation_264/observation.csv
new file mode 100644
index 00000000..0f6285b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_264/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_264/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_264/observation_seed.csv
new file mode 100644
index 00000000..90c92b43
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_264/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+65,264
diff --git a/sbibm/tasks/ddm/files/num_observation_264/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_264/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c819b80a
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diff --git a/sbibm/tasks/ddm/files/num_observation_264/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_264/true_parameters.csv
new file mode 100644
index 00000000..0582142b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_264/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_265/observation.csv b/sbibm/tasks/ddm/files/num_observation_265/observation.csv
new file mode 100644
index 00000000..5e213bba
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_265/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_265/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_265/observation_seed.csv
new file mode 100644
index 00000000..1decca2e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_265/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+66,265
diff --git a/sbibm/tasks/ddm/files/num_observation_265/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_265/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7ca67f3f
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diff --git a/sbibm/tasks/ddm/files/num_observation_265/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_265/true_parameters.csv
new file mode 100644
index 00000000..c17b68d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_265/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.009516716,0.53449786,0.39083534,1.113286
diff --git a/sbibm/tasks/ddm/files/num_observation_266/observation.csv b/sbibm/tasks/ddm/files/num_observation_266/observation.csv
new file mode 100644
index 00000000..59261ccc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_266/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_266/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_266/observation_seed.csv
new file mode 100644
index 00000000..52485670
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_266/observation_seed.csv
@@ -0,0 +1,2 @@
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+67,266
diff --git a/sbibm/tasks/ddm/files/num_observation_266/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_266/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ae539cc7
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diff --git a/sbibm/tasks/ddm/files/num_observation_266/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_266/true_parameters.csv
new file mode 100644
index 00000000..faff7636
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_266/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_267/observation.csv b/sbibm/tasks/ddm/files/num_observation_267/observation.csv
new file mode 100644
index 00000000..9acbbcec
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_267/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_267/observation_seed.csv
new file mode 100644
index 00000000..974e45fb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_267/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_267/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_267/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_267/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_267/true_parameters.csv
new file mode 100644
index 00000000..48d1d698
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_267/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_268/observation.csv b/sbibm/tasks/ddm/files/num_observation_268/observation.csv
new file mode 100644
index 00000000..d15989ac
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_268/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_268/observation_seed.csv
new file mode 100644
index 00000000..6c2049d6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_268/observation_seed.csv
@@ -0,0 +1,2 @@
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+69,268
diff --git a/sbibm/tasks/ddm/files/num_observation_268/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_268/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_268/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_268/true_parameters.csv
new file mode 100644
index 00000000..cba71259
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_268/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_269/observation.csv b/sbibm/tasks/ddm/files/num_observation_269/observation.csv
new file mode 100644
index 00000000..d285c2de
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_269/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_269/observation_seed.csv
new file mode 100644
index 00000000..145499e0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_269/observation_seed.csv
@@ -0,0 +1,2 @@
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+70,269
diff --git a/sbibm/tasks/ddm/files/num_observation_269/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_269/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_269/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_269/true_parameters.csv
new file mode 100644
index 00000000..a8af80ed
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_269/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_27/observation.csv b/sbibm/tasks/ddm/files/num_observation_27/observation.csv
new file mode 100644
index 00000000..11a574a8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_27/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_27/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_27/observation_seed.csv
new file mode 100644
index 00000000..437446d2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_27/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_27/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_27/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_27/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_27/true_parameters.csv
new file mode 100644
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--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_270/observation.csv b/sbibm/tasks/ddm/files/num_observation_270/observation.csv
new file mode 100644
index 00000000..ce6a056c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_270/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_270/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_270/observation_seed.csv
new file mode 100644
index 00000000..8fd43a48
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_270/observation_seed.csv
@@ -0,0 +1,2 @@
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+71,270
diff --git a/sbibm/tasks/ddm/files/num_observation_270/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_270/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_270/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_270/true_parameters.csv
new file mode 100644
index 00000000..f03ad156
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_270/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_271/observation.csv b/sbibm/tasks/ddm/files/num_observation_271/observation.csv
new file mode 100644
index 00000000..c535ecae
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_271/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_271/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_271/observation_seed.csv
new file mode 100644
index 00000000..b34b1a7e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_271/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_271/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_271/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_271/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_271/true_parameters.csv
new file mode 100644
index 00000000..f2d879e3
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_272/observation.csv b/sbibm/tasks/ddm/files/num_observation_272/observation.csv
new file mode 100644
index 00000000..f09c09e0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_272/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_272/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_272/observation_seed.csv
new file mode 100644
index 00000000..4a2b6530
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_272/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+73,272
diff --git a/sbibm/tasks/ddm/files/num_observation_272/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_272/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..b5f8b92d
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_272/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_272/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_272/true_parameters.csv
new file mode 100644
index 00000000..a00c924b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_272/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_273/observation.csv b/sbibm/tasks/ddm/files/num_observation_273/observation.csv
new file mode 100644
index 00000000..8b1d10a4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_273/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_273/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_273/observation_seed.csv
new file mode 100644
index 00000000..2844c519
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_273/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+74,273
diff --git a/sbibm/tasks/ddm/files/num_observation_273/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_273/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_273/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_273/true_parameters.csv
new file mode 100644
index 00000000..14be6fe1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_273/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_274/observation.csv b/sbibm/tasks/ddm/files/num_observation_274/observation.csv
new file mode 100644
index 00000000..555601d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_274/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_274/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_274/observation_seed.csv
new file mode 100644
index 00000000..1d7ddc1a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_274/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+75,274
diff --git a/sbibm/tasks/ddm/files/num_observation_274/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_274/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a7105f0d
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diff --git a/sbibm/tasks/ddm/files/num_observation_274/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_274/true_parameters.csv
new file mode 100644
index 00000000..a3ef77d7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_274/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_275/observation.csv b/sbibm/tasks/ddm/files/num_observation_275/observation.csv
new file mode 100644
index 00000000..c77a66bd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_275/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_275/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_275/observation_seed.csv
new file mode 100644
index 00000000..99ba6363
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_275/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+76,275
diff --git a/sbibm/tasks/ddm/files/num_observation_275/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_275/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_275/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_275/true_parameters.csv
new file mode 100644
index 00000000..a31b094d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_275/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_276/observation.csv b/sbibm/tasks/ddm/files/num_observation_276/observation.csv
new file mode 100644
index 00000000..ef26ea26
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_276/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_276/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_276/observation_seed.csv
new file mode 100644
index 00000000..0565d427
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_276/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_276/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_276/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_276/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_276/true_parameters.csv
new file mode 100644
index 00000000..28df9338
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_276/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_277/observation.csv b/sbibm/tasks/ddm/files/num_observation_277/observation.csv
new file mode 100644
index 00000000..cf984e33
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diff --git a/sbibm/tasks/ddm/files/num_observation_277/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_277/observation_seed.csv
new file mode 100644
index 00000000..734f016a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_277/observation_seed.csv
@@ -0,0 +1,2 @@
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+78,277
diff --git a/sbibm/tasks/ddm/files/num_observation_277/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_277/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_277/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_277/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_277/true_parameters.csv
new file mode 100644
index 00000000..c3d741bf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_277/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_278/observation.csv b/sbibm/tasks/ddm/files/num_observation_278/observation.csv
new file mode 100644
index 00000000..6cf43d33
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_278/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_278/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_278/observation_seed.csv
new file mode 100644
index 00000000..60764dc2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_278/observation_seed.csv
@@ -0,0 +1,2 @@
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+79,278
diff --git a/sbibm/tasks/ddm/files/num_observation_278/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_278/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_278/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_278/true_parameters.csv
new file mode 100644
index 00000000..7ca02b0b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_278/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_279/observation.csv b/sbibm/tasks/ddm/files/num_observation_279/observation.csv
new file mode 100644
index 00000000..74902d08
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_279/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_279/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_279/observation_seed.csv
new file mode 100644
index 00000000..3b035d54
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_279/observation_seed.csv
@@ -0,0 +1,2 @@
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+80,279
diff --git a/sbibm/tasks/ddm/files/num_observation_279/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_279/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_279/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_279/true_parameters.csv
new file mode 100644
index 00000000..07371896
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_279/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_28/observation.csv b/sbibm/tasks/ddm/files/num_observation_28/observation.csv
new file mode 100644
index 00000000..8f29e268
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_28/observation.csv
@@ -0,0 +1,2 @@
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+-0.76659
diff --git a/sbibm/tasks/ddm/files/num_observation_28/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_28/observation_seed.csv
new file mode 100644
index 00000000..2b0de90c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_28/observation_seed.csv
@@ -0,0 +1,2 @@
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+27,28
diff --git a/sbibm/tasks/ddm/files/num_observation_28/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_28/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_28/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_28/true_parameters.csv
new file mode 100644
index 00000000..b003a1f1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_28/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_280/observation.csv b/sbibm/tasks/ddm/files/num_observation_280/observation.csv
new file mode 100644
index 00000000..4c8ace09
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_280/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_280/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_280/observation_seed.csv
new file mode 100644
index 00000000..a10bdc0d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_280/observation_seed.csv
@@ -0,0 +1,2 @@
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+81,280
diff --git a/sbibm/tasks/ddm/files/num_observation_280/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_280/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_280/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_280/true_parameters.csv
new file mode 100644
index 00000000..b8b06fb9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_280/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_281/observation.csv b/sbibm/tasks/ddm/files/num_observation_281/observation.csv
new file mode 100644
index 00000000..f09b637e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_281/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_281/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_281/observation_seed.csv
new file mode 100644
index 00000000..daaa2f35
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_281/observation_seed.csv
@@ -0,0 +1,2 @@
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+82,281
diff --git a/sbibm/tasks/ddm/files/num_observation_281/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_281/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_281/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_281/true_parameters.csv
new file mode 100644
index 00000000..b1fc450e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_281/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_282/observation.csv b/sbibm/tasks/ddm/files/num_observation_282/observation.csv
new file mode 100644
index 00000000..a1108b1a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_282/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_282/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_282/observation_seed.csv
new file mode 100644
index 00000000..88fb721f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_282/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+83,282
diff --git a/sbibm/tasks/ddm/files/num_observation_282/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_282/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_282/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_282/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_282/true_parameters.csv
new file mode 100644
index 00000000..262e2554
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_282/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_283/observation.csv b/sbibm/tasks/ddm/files/num_observation_283/observation.csv
new file mode 100644
index 00000000..140153dc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_283/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_283/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_283/observation_seed.csv
new file mode 100644
index 00000000..3a3bdc90
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_283/observation_seed.csv
@@ -0,0 +1,2 @@
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+84,283
diff --git a/sbibm/tasks/ddm/files/num_observation_283/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_283/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_283/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_283/true_parameters.csv
new file mode 100644
index 00000000..65ea7c65
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_283/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_284/observation.csv b/sbibm/tasks/ddm/files/num_observation_284/observation.csv
new file mode 100644
index 00000000..7adfe7e0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_284/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_284/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_284/observation_seed.csv
new file mode 100644
index 00000000..0075330f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_284/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+85,284
diff --git a/sbibm/tasks/ddm/files/num_observation_284/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_284/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_284/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_284/true_parameters.csv
new file mode 100644
index 00000000..b9ed672e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_284/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_285/observation.csv b/sbibm/tasks/ddm/files/num_observation_285/observation.csv
new file mode 100644
index 00000000..2faf4827
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_285/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_285/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_285/observation_seed.csv
new file mode 100644
index 00000000..c1e61d8a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_285/observation_seed.csv
@@ -0,0 +1,2 @@
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+86,285
diff --git a/sbibm/tasks/ddm/files/num_observation_285/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_285/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_285/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_285/true_parameters.csv
new file mode 100644
index 00000000..d447c949
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_285/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_286/observation.csv b/sbibm/tasks/ddm/files/num_observation_286/observation.csv
new file mode 100644
index 00000000..b4f16664
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_286/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_286/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_286/observation_seed.csv
new file mode 100644
index 00000000..fbfb338f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_286/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_286/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_286/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_286/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_286/true_parameters.csv
new file mode 100644
index 00000000..460dd4d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_286/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_287/observation.csv b/sbibm/tasks/ddm/files/num_observation_287/observation.csv
new file mode 100644
index 00000000..d5672a90
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diff --git a/sbibm/tasks/ddm/files/num_observation_287/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_287/observation_seed.csv
new file mode 100644
index 00000000..13379319
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_287/observation_seed.csv
@@ -0,0 +1,2 @@
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+88,287
diff --git a/sbibm/tasks/ddm/files/num_observation_287/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_287/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_287/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_287/true_parameters.csv
new file mode 100644
index 00000000..0dadd7ac
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_287/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_288/observation.csv b/sbibm/tasks/ddm/files/num_observation_288/observation.csv
new file mode 100644
index 00000000..d9de0ccc
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diff --git a/sbibm/tasks/ddm/files/num_observation_288/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_288/observation_seed.csv
new file mode 100644
index 00000000..e7e2bf02
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_288/observation_seed.csv
@@ -0,0 +1,2 @@
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+89,288
diff --git a/sbibm/tasks/ddm/files/num_observation_288/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_288/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_288/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_288/true_parameters.csv
new file mode 100644
index 00000000..b701adbf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_288/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_289/observation.csv b/sbibm/tasks/ddm/files/num_observation_289/observation.csv
new file mode 100644
index 00000000..3709e2e9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_289/observation.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_289/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_289/observation_seed.csv
new file mode 100644
index 00000000..91873e3d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_289/observation_seed.csv
@@ -0,0 +1,2 @@
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+91,289
diff --git a/sbibm/tasks/ddm/files/num_observation_289/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_289/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_289/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_289/true_parameters.csv
new file mode 100644
index 00000000..7fc95ee9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_289/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_29/observation.csv b/sbibm/tasks/ddm/files/num_observation_29/observation.csv
new file mode 100644
index 00000000..731d9d75
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_29/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_29/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_29/observation_seed.csv
new file mode 100644
index 00000000..ad27b985
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_29/observation_seed.csv
@@ -0,0 +1,2 @@
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+28,29
diff --git a/sbibm/tasks/ddm/files/num_observation_29/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_29/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_29/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_29/true_parameters.csv
new file mode 100644
index 00000000..f37ee4e6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_29/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_290/observation.csv b/sbibm/tasks/ddm/files/num_observation_290/observation.csv
new file mode 100644
index 00000000..b82cf33e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_290/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_290/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_290/observation_seed.csv
new file mode 100644
index 00000000..425a8bc7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_290/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_290/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_290/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_290/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_290/true_parameters.csv
new file mode 100644
index 00000000..72539a88
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_290/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_291/observation.csv b/sbibm/tasks/ddm/files/num_observation_291/observation.csv
new file mode 100644
index 00000000..37e773ee
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_291/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_291/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_291/observation_seed.csv
new file mode 100644
index 00000000..c5a6bd23
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_291/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_291/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_291/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_291/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_291/true_parameters.csv
new file mode 100644
index 00000000..3359c809
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_291/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_292/observation.csv b/sbibm/tasks/ddm/files/num_observation_292/observation.csv
new file mode 100644
index 00000000..40423ff7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_292/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_292/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_292/observation_seed.csv
new file mode 100644
index 00000000..e804312d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_292/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+94,292
diff --git a/sbibm/tasks/ddm/files/num_observation_292/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_292/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..6c9f5bfa
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_292/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_292/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_292/true_parameters.csv
new file mode 100644
index 00000000..92e7acc1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_292/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.3489544,0.50057954,0.47944793,1.6095066
diff --git a/sbibm/tasks/ddm/files/num_observation_293/observation.csv b/sbibm/tasks/ddm/files/num_observation_293/observation.csv
new file mode 100644
index 00000000..4d1f3122
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_293/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_293/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_293/observation_seed.csv
new file mode 100644
index 00000000..8fe7a6fc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_293/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+95,293
diff --git a/sbibm/tasks/ddm/files/num_observation_293/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_293/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_293/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_293/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_293/true_parameters.csv
new file mode 100644
index 00000000..9755287e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_293/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
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diff --git a/sbibm/tasks/ddm/files/num_observation_294/observation.csv b/sbibm/tasks/ddm/files/num_observation_294/observation.csv
new file mode 100644
index 00000000..d693af71
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_294/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_294/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_294/observation_seed.csv
new file mode 100644
index 00000000..b4e92be1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_294/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+96,294
diff --git a/sbibm/tasks/ddm/files/num_observation_294/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_294/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4fa65833
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_294/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_294/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_294/true_parameters.csv
new file mode 100644
index 00000000..fa733389
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_294/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_295/observation.csv b/sbibm/tasks/ddm/files/num_observation_295/observation.csv
new file mode 100644
index 00000000..9e167c9c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_295/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_295/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_295/observation_seed.csv
new file mode 100644
index 00000000..d0c09370
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_295/observation_seed.csv
@@ -0,0 +1,2 @@
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+97,295
diff --git a/sbibm/tasks/ddm/files/num_observation_295/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_295/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ae445a6c
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_295/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_295/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_295/true_parameters.csv
new file mode 100644
index 00000000..6e927078
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_295/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_296/observation.csv b/sbibm/tasks/ddm/files/num_observation_296/observation.csv
new file mode 100644
index 00000000..936c943e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_296/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_296/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_296/observation_seed.csv
new file mode 100644
index 00000000..bf81317a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_296/observation_seed.csv
@@ -0,0 +1,2 @@
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+98,296
diff --git a/sbibm/tasks/ddm/files/num_observation_296/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_296/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_296/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_296/true_parameters.csv
new file mode 100644
index 00000000..a2cde553
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_296/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_297/observation.csv b/sbibm/tasks/ddm/files/num_observation_297/observation.csv
new file mode 100644
index 00000000..827f1251
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_297/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_297/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_297/observation_seed.csv
new file mode 100644
index 00000000..4d757365
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_297/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+99,297
diff --git a/sbibm/tasks/ddm/files/num_observation_297/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_297/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..084a3090
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_297/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_297/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_297/true_parameters.csv
new file mode 100644
index 00000000..20c523c8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_297/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.5867612,1.9552338,0.67922807,1.765934
diff --git a/sbibm/tasks/ddm/files/num_observation_298/observation.csv b/sbibm/tasks/ddm/files/num_observation_298/observation.csv
new file mode 100644
index 00000000..c1af98cc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_298/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_298/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_298/observation_seed.csv
new file mode 100644
index 00000000..c2731395
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_298/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+100,298
diff --git a/sbibm/tasks/ddm/files/num_observation_298/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_298/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ee4b7191
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_298/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_298/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_298/true_parameters.csv
new file mode 100644
index 00000000..0082c431
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_298/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.5533428,1.7237647,0.4050248,0.9742041
diff --git a/sbibm/tasks/ddm/files/num_observation_299/observation.csv b/sbibm/tasks/ddm/files/num_observation_299/observation.csv
new file mode 100644
index 00000000..55a48e3d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_299/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_299/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_299/observation_seed.csv
new file mode 100644
index 00000000..0df996e2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_299/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+101,299
diff --git a/sbibm/tasks/ddm/files/num_observation_299/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_299/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e0bf8965
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_299/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_299/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_299/true_parameters.csv
new file mode 100644
index 00000000..443ddc23
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_299/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.207802,1.1754313,0.33636057,1.6195135
diff --git a/sbibm/tasks/ddm/files/num_observation_3/observation.csv b/sbibm/tasks/ddm/files/num_observation_3/observation.csv
new file mode 100644
index 00000000..84bd8074
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_3/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.0860429
diff --git a/sbibm/tasks/ddm/files/num_observation_3/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_3/observation_seed.csv
new file mode 100644
index 00000000..a8cc9516
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_3/observation_seed.csv
@@ -0,0 +1,2 @@
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+2,3
diff --git a/sbibm/tasks/ddm/files/num_observation_3/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_3/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..d0b761b4
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_3/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_3/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_3/true_parameters.csv
new file mode 100644
index 00000000..50fff476
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_3/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+0.4587803,1.0715193,0.5548457,0.9591377
diff --git a/sbibm/tasks/ddm/files/num_observation_30/observation.csv b/sbibm/tasks/ddm/files/num_observation_30/observation.csv
new file mode 100644
index 00000000..8c926483
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_30/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-3.130661
diff --git a/sbibm/tasks/ddm/files/num_observation_30/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_30/observation_seed.csv
new file mode 100644
index 00000000..9e8abc33
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_30/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+29,30
diff --git a/sbibm/tasks/ddm/files/num_observation_30/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_30/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..47f6c490
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_30/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_30/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_30/true_parameters.csv
new file mode 100644
index 00000000..efa0c15a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_30/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.5097759,1.9032619,0.6743709,1.3260251
diff --git a/sbibm/tasks/ddm/files/num_observation_300/observation.csv b/sbibm/tasks/ddm/files/num_observation_300/observation.csv
new file mode 100644
index 00000000..160e372a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_300/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_300/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_300/observation_seed.csv
new file mode 100644
index 00000000..16c0410e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_300/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+102,300
diff --git a/sbibm/tasks/ddm/files/num_observation_300/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_300/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4e964dc2
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_300/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_300/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_300/true_parameters.csv
new file mode 100644
index 00000000..70090274
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_300/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.97406,1.6620548,0.321059,0.88146836
diff --git a/sbibm/tasks/ddm/files/num_observation_301/observation.csv b/sbibm/tasks/ddm/files/num_observation_301/observation.csv
new file mode 100644
index 00000000..0d1c23b8
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_301/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_301/observation_seed.csv
new file mode 100644
index 00000000..d929c159
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_301/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_302/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_302/observation_seed.csv
new file mode 100644
index 00000000..b9f61f47
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_302/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_302/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_302/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_302/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_302/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_303/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_303/observation_seed.csv
new file mode 100644
index 00000000..c42559d0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_303/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_303/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_303/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..396ab9a3
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_303/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_303/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_303/true_parameters.csv
new file mode 100644
index 00000000..50fff476
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_303/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_304/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_304/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_305/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_305/observation_seed.csv
new file mode 100644
index 00000000..c37409a8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_305/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_305/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_305/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_305/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_305/true_parameters.csv
new file mode 100644
index 00000000..f0e29862
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diff --git a/sbibm/tasks/ddm/files/num_observation_306/observation.csv b/sbibm/tasks/ddm/files/num_observation_306/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_306/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_306/observation_seed.csv
new file mode 100644
index 00000000..604dfe82
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_306/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_306/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_306/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7aaa9d79
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_306/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_306/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_306/true_parameters.csv
new file mode 100644
index 00000000..3928609f
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diff --git a/sbibm/tasks/ddm/files/num_observation_307/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_307/observation_seed.csv
new file mode 100644
index 00000000..9e0fdb88
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_307/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+6,307
diff --git a/sbibm/tasks/ddm/files/num_observation_307/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_307/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..bb6c4d8d
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_307/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_307/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_307/true_parameters.csv
new file mode 100644
index 00000000..7bcb99b8
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_308/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_308/observation_seed.csv
new file mode 100644
index 00000000..7afca2eb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_308/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_308/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_308/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..dc7a16af
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_308/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_308/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_308/true_parameters.csv
new file mode 100644
index 00000000..017ec574
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_309/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_309/observation_seed.csv
new file mode 100644
index 00000000..0525f221
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_309/observation_seed.csv
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new file mode 100644
index 00000000..205236f7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_310/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_310/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_310/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_310/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_310/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_311/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_311/observation_seed.csv
new file mode 100644
index 00000000..429dd512
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_311/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_311/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_311/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..65a94051
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_311/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_311/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_311/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_312/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_312/observation_seed.csv
new file mode 100644
index 00000000..2697a1c5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_312/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_313/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_313/observation_seed.csv
new file mode 100644
index 00000000..20c171b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_313/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_313/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_313/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4bd396c7
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_313/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_313/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_313/true_parameters.csv
new file mode 100644
index 00000000..6e69700e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_313/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_314/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_314/observation_seed.csv
new file mode 100644
index 00000000..19bf9065
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_314/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_315/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_315/observation_seed.csv
new file mode 100644
index 00000000..ad4fc71a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_315/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+14,315
diff --git a/sbibm/tasks/ddm/files/num_observation_315/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_315/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3299502e
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_315/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_315/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_315/true_parameters.csv
new file mode 100644
index 00000000..c9b31c10
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_316/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_316/observation_seed.csv
new file mode 100644
index 00000000..899528b2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_316/observation_seed.csv
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index 00000000..e4a90b1c
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_318/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_318/observation_seed.csv
new file mode 100644
index 00000000..8de0ecef
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_318/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_319/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_319/observation_seed.csv
new file mode 100644
index 00000000..59f33b06
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_319/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_319/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_319/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..9b0854c7
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diff --git a/sbibm/tasks/ddm/files/num_observation_319/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_319/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_320/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_320/observation_seed.csv
new file mode 100644
index 00000000..54ed4e0b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_320/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_320/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_320/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..556635fd
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_320/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_320/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_320/true_parameters.csv
new file mode 100644
index 00000000..942e0ea0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_320/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_321/observation.csv b/sbibm/tasks/ddm/files/num_observation_321/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_321/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_321/observation_seed.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_322/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_322/observation_seed.csv
new file mode 100644
index 00000000..11ac81db
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_322/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_323/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_323/observation_seed.csv
new file mode 100644
index 00000000..4387d057
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_323/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_324/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_324/observation_seed.csv
new file mode 100644
index 00000000..82564f63
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_324/observation_seed.csv
@@ -0,0 +1,2 @@
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index 00000000..62f91f5e
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diff --git a/sbibm/tasks/ddm/files/num_observation_325/observation.csv b/sbibm/tasks/ddm/files/num_observation_325/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_325/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_325/observation_seed.csv
new file mode 100644
index 00000000..90fb9d80
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_325/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+24,325
diff --git a/sbibm/tasks/ddm/files/num_observation_325/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_325/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..72f7273f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_325/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_325/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_325/true_parameters.csv
new file mode 100644
index 00000000..82ff1a68
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_325/true_parameters.csv
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index 00000000..3645783d
--- /dev/null
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@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_327/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_327/observation_seed.csv
new file mode 100644
index 00000000..894c87dd
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_327/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_327/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_327/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a391358b
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_327/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_327/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_327/true_parameters.csv
new file mode 100644
index 00000000..127ae726
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_327/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_328/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_328/observation_seed.csv
new file mode 100644
index 00000000..ac8c1794
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_328/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_328/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_328/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_328/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_328/true_parameters.csv
new file mode 100644
index 00000000..b003a1f1
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diff --git a/sbibm/tasks/ddm/files/num_observation_329/observation.csv b/sbibm/tasks/ddm/files/num_observation_329/observation.csv
new file mode 100644
index 00000000..5be96006
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diff --git a/sbibm/tasks/ddm/files/num_observation_329/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_329/observation_seed.csv
new file mode 100644
index 00000000..95c21078
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_329/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_329/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_329/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..dc9a44b6
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_329/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_329/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_329/true_parameters.csv
new file mode 100644
index 00000000..f37ee4e6
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diff --git a/sbibm/tasks/ddm/files/num_observation_330/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_330/observation_seed.csv
new file mode 100644
index 00000000..187f5520
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_330/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_330/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_330/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..75ddc25e
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diff --git a/sbibm/tasks/ddm/files/num_observation_330/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_330/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_331/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_331/observation_seed.csv
new file mode 100644
index 00000000..29afbaa6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_331/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_331/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_331/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..690f1171
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_331/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_331/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_331/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_332/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_332/observation_seed.csv
new file mode 100644
index 00000000..a8453a1b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_332/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+31,332
diff --git a/sbibm/tasks/ddm/files/num_observation_332/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_332/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..0ab5dc29
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_332/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_332/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_332/true_parameters.csv
new file mode 100644
index 00000000..b8131afb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_332/true_parameters.csv
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new file mode 100644
index 00000000..67220d14
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_333/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_333/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_333/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..549690b6
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diff --git a/sbibm/tasks/ddm/files/num_observation_333/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_333/true_parameters.csv
new file mode 100644
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new file mode 100644
index 00000000..9b90a0d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_334/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_335/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_335/observation_seed.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_336/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_336/observation_seed.csv
new file mode 100644
index 00000000..ba326449
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_336/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_337/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_337/observation_seed.csv
new file mode 100644
index 00000000..9194aeb6
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_337/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_337/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_337/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2db1ed28
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_337/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_337/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_337/true_parameters.csv
new file mode 100644
index 00000000..d78633a5
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_338/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_338/observation_seed.csv
new file mode 100644
index 00000000..a7a8761e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_338/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_339/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_339/observation_seed.csv
new file mode 100644
index 00000000..4125046c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_339/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_339/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_339/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2899835f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_339/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_339/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_339/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_340/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_340/observation_seed.csv
new file mode 100644
index 00000000..28f069ba
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_340/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
index 00000000..edbe0f1b
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diff --git a/sbibm/tasks/ddm/files/num_observation_341/observation.csv b/sbibm/tasks/ddm/files/num_observation_341/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_341/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_341/observation_seed.csv
new file mode 100644
index 00000000..f93a201c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_341/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_341/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_341/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c447aff8
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_341/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_341/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_341/true_parameters.csv
new file mode 100644
index 00000000..d35e51fa
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_342/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_342/observation_seed.csv
new file mode 100644
index 00000000..06b31543
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_342/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_342/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_342/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_342/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_342/true_parameters.csv
new file mode 100644
index 00000000..d8534bfe
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diff --git a/sbibm/tasks/ddm/files/num_observation_343/observation.csv b/sbibm/tasks/ddm/files/num_observation_343/observation.csv
new file mode 100644
index 00000000..9cf92013
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diff --git a/sbibm/tasks/ddm/files/num_observation_343/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_343/observation_seed.csv
new file mode 100644
index 00000000..b9ba89fa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_343/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_343/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_343/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..b0532777
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_343/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_343/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_343/true_parameters.csv
new file mode 100644
index 00000000..87b76408
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_343/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_344/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_344/observation_seed.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_345/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_345/observation_seed.csv
new file mode 100644
index 00000000..7fa3a540
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_345/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_345/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_345/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_346/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_346/observation_seed.csv
new file mode 100644
index 00000000..de23f52b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_346/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_346/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_346/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..0e8af2d0
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_346/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_346/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_346/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_347/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_347/observation_seed.csv
new file mode 100644
index 00000000..beed551b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_347/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
index 00000000..88bc5dd7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_348/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_348/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_348/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_348/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_348/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_349/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_349/observation_seed.csv
new file mode 100644
index 00000000..fc347273
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_349/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_350/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_350/observation_seed.csv
new file mode 100644
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new file mode 100644
index 00000000..6b5d147b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_351/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_351/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_351/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_352/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_352/observation_seed.csv
new file mode 100644
index 00000000..22b74e7a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_352/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_352/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_352/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..1dffd0a5
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_352/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_352/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_352/true_parameters.csv
new file mode 100644
index 00000000..5e23ffd2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_352/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_353/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_353/observation_seed.csv
new file mode 100644
index 00000000..5acc0802
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_353/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_353/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_353/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..50ce63d8
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_353/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_353/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_353/true_parameters.csv
new file mode 100644
index 00000000..d480360b
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diff --git a/sbibm/tasks/ddm/files/num_observation_354/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_354/observation_seed.csv
new file mode 100644
index 00000000..005f1902
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_354/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_354/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_354/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e9050034
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_354/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_354/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_354/true_parameters.csv
new file mode 100644
index 00000000..cd776a25
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_355/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_355/observation_seed.csv
new file mode 100644
index 00000000..ecaa36ba
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_355/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_355/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_355/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..36ebdc05
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_355/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_355/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_355/true_parameters.csv
new file mode 100644
index 00000000..f98ddc7b
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_356/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_356/observation_seed.csv
new file mode 100644
index 00000000..e1717f82
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_356/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_356/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_356/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4d25399a
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_356/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_356/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_356/true_parameters.csv
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index 00000000..678f40e5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_357/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_358/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_358/observation_seed.csv
new file mode 100644
index 00000000..56b0bca7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_358/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
index 00000000..1a197fcb
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diff --git a/sbibm/tasks/ddm/files/num_observation_359/observation.csv b/sbibm/tasks/ddm/files/num_observation_359/observation.csv
new file mode 100644
index 00000000..79e2332d
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index 00000000..2f9b18b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_359/observation_seed.csv
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_360/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_360/observation_seed.csv
new file mode 100644
index 00000000..25375c7b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_360/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_360/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_360/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5c5fee45
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_360/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_360/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_360/true_parameters.csv
new file mode 100644
index 00000000..5fffe8a7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_360/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_361/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_361/observation_seed.csv
new file mode 100644
index 00000000..9b23fd3a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_361/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_361/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_361/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..9860a20c
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_361/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_361/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_361/true_parameters.csv
new file mode 100644
index 00000000..9c690fe9
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diff --git a/sbibm/tasks/ddm/files/num_observation_362/observation.csv b/sbibm/tasks/ddm/files/num_observation_362/observation.csv
new file mode 100644
index 00000000..c3c14f7b
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diff --git a/sbibm/tasks/ddm/files/num_observation_362/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_362/observation_seed.csv
new file mode 100644
index 00000000..3d3e87c3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_362/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_362/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_362/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e53b56ee
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_362/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_362/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_362/true_parameters.csv
new file mode 100644
index 00000000..7684bd6c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_362/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_363/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_363/observation_seed.csv
new file mode 100644
index 00000000..015ae460
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_363/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_364/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_364/observation_seed.csv
new file mode 100644
index 00000000..b76a6ea1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_364/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_365/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_365/observation_seed.csv
new file mode 100644
index 00000000..91b7e3b8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_365/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+66,365
diff --git a/sbibm/tasks/ddm/files/num_observation_365/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_365/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..74e38ef6
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_365/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_365/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_365/true_parameters.csv
new file mode 100644
index 00000000..c17b68d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_365/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_366/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_366/observation_seed.csv
new file mode 100644
index 00000000..c55c4246
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_366/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_366/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_366/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4f244c3d
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_366/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_366/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_366/true_parameters.csv
new file mode 100644
index 00000000..faff7636
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_366/true_parameters.csv
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_367/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_367/observation_seed.csv
new file mode 100644
index 00000000..c703cbdb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_367/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_367/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_367/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_367/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_367/true_parameters.csv
new file mode 100644
index 00000000..48d1d698
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diff --git a/sbibm/tasks/ddm/files/num_observation_368/observation.csv b/sbibm/tasks/ddm/files/num_observation_368/observation.csv
new file mode 100644
index 00000000..20543b6e
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diff --git a/sbibm/tasks/ddm/files/num_observation_368/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_368/observation_seed.csv
new file mode 100644
index 00000000..b92ffe84
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_368/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_368/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_368/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..967f70de
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_368/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_368/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_368/true_parameters.csv
new file mode 100644
index 00000000..cba71259
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_369/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_369/observation_seed.csv
new file mode 100644
index 00000000..56829e5f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_369/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_370/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_370/observation_seed.csv
new file mode 100644
index 00000000..86fabed3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_370/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_370/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_370/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4ea8eeca
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_370/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_370/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_370/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_371/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_371/observation_seed.csv
new file mode 100644
index 00000000..c5b09e01
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_371/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_371/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_371/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..0058c107
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diff --git a/sbibm/tasks/ddm/files/num_observation_371/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_371/true_parameters.csv
new file mode 100644
index 00000000..f2d879e3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_371/true_parameters.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_372/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_372/observation_seed.csv
new file mode 100644
index 00000000..371d018c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_372/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_372/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_372/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..5cac1568
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_372/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_372/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_372/true_parameters.csv
new file mode 100644
index 00000000..a00c924b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_372/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_373/observation.csv b/sbibm/tasks/ddm/files/num_observation_373/observation.csv
new file mode 100644
index 00000000..c34fb9fa
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diff --git a/sbibm/tasks/ddm/files/num_observation_373/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_373/observation_seed.csv
new file mode 100644
index 00000000..72976e03
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_373/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_373/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_373/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..fa707958
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_373/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_373/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_373/true_parameters.csv
new file mode 100644
index 00000000..14be6fe1
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_374/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_374/observation_seed.csv
new file mode 100644
index 00000000..41cca5ea
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_374/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_374/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_374/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..0c0db61a
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_374/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_374/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_374/true_parameters.csv
new file mode 100644
index 00000000..a3ef77d7
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diff --git a/sbibm/tasks/ddm/files/num_observation_375/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_375/observation_seed.csv
new file mode 100644
index 00000000..74615856
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_375/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_375/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_375/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..ccc59af5
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_375/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_375/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_375/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_376/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_376/observation_seed.csv
new file mode 100644
index 00000000..42b9ae74
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_376/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_376/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_376/true_parameters.csv
new file mode 100644
index 00000000..28df9338
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_376/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_377/observation.csv b/sbibm/tasks/ddm/files/num_observation_377/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_377/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_377/observation_seed.csv
new file mode 100644
index 00000000..99a69d22
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_377/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_377/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_377/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..22ca23d8
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_377/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_377/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_377/true_parameters.csv
new file mode 100644
index 00000000..c3d741bf
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_378/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_378/observation_seed.csv
new file mode 100644
index 00000000..33b7c3d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_378/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_378/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_378/true_parameters.csv
new file mode 100644
index 00000000..7ca02b0b
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diff --git a/sbibm/tasks/ddm/files/num_observation_379/observation.csv b/sbibm/tasks/ddm/files/num_observation_379/observation.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_379/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_379/observation_seed.csv
new file mode 100644
index 00000000..b5eaa644
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_379/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_379/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_379/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..097dfa47
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_379/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_379/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_379/true_parameters.csv
new file mode 100644
index 00000000..07371896
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_379/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_380/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_380/observation_seed.csv
new file mode 100644
index 00000000..164950a4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_380/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_381/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_381/observation_seed.csv
new file mode 100644
index 00000000..3b7518c2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_381/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_381/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_381/true_parameters.csv
new file mode 100644
index 00000000..b1fc450e
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diff --git a/sbibm/tasks/ddm/files/num_observation_382/observation.csv b/sbibm/tasks/ddm/files/num_observation_382/observation.csv
new file mode 100644
index 00000000..763238c0
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diff --git a/sbibm/tasks/ddm/files/num_observation_382/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_382/observation_seed.csv
new file mode 100644
index 00000000..3ccd8c9a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_382/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_382/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_382/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..990fdec5
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_382/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_382/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_382/true_parameters.csv
new file mode 100644
index 00000000..262e2554
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diff --git a/sbibm/tasks/ddm/files/num_observation_383/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_383/observation_seed.csv
new file mode 100644
index 00000000..1ae90720
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_383/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_383/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_383/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..c7105622
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_383/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_383/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_383/true_parameters.csv
new file mode 100644
index 00000000..65ea7c65
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_383/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_384/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_384/observation_seed.csv
new file mode 100644
index 00000000..ae5de022
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_384/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_385/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_385/observation_seed.csv
new file mode 100644
index 00000000..4e121098
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_385/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_385/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_385/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_385/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_385/true_parameters.csv
new file mode 100644
index 00000000..d447c949
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diff --git a/sbibm/tasks/ddm/files/num_observation_386/observation.csv b/sbibm/tasks/ddm/files/num_observation_386/observation.csv
new file mode 100644
index 00000000..860f3a0e
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diff --git a/sbibm/tasks/ddm/files/num_observation_386/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_386/observation_seed.csv
new file mode 100644
index 00000000..f4be170a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_386/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+87,386
diff --git a/sbibm/tasks/ddm/files/num_observation_386/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_386/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..135f9b3d
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_386/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_386/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_386/true_parameters.csv
new file mode 100644
index 00000000..460dd4d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_386/true_parameters.csv
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new file mode 100644
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new file mode 100644
index 00000000..a95373ee
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_387/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_387/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_387/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_387/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_387/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_387/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_388/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_388/observation_seed.csv
new file mode 100644
index 00000000..f407af42
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_388/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_388/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_388/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..13452e7b
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_388/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_388/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_388/true_parameters.csv
new file mode 100644
index 00000000..b701adbf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_388/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_389/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_389/observation_seed.csv
new file mode 100644
index 00000000..257da494
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_389/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_389/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_389/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..6accb070
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diff --git a/sbibm/tasks/ddm/files/num_observation_389/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_389/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_390/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_390/observation_seed.csv
new file mode 100644
index 00000000..946ba723
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_390/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_391/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_391/observation_seed.csv
new file mode 100644
index 00000000..bc8d5e16
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_391/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+93,391
diff --git a/sbibm/tasks/ddm/files/num_observation_391/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_391/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..cd696ab9
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_391/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_391/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_391/true_parameters.csv
new file mode 100644
index 00000000..3359c809
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_391/true_parameters.csv
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new file mode 100644
index 00000000..012aba3f
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_392/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_392/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_392/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_392/true_parameters.csv
new file mode 100644
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new file mode 100644
index 00000000..28d1d296
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_393/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_393/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_393/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_393/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_393/true_parameters.csv
new file mode 100644
index 00000000..9755287e
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diff --git a/sbibm/tasks/ddm/files/num_observation_394/observation.csv b/sbibm/tasks/ddm/files/num_observation_394/observation.csv
new file mode 100644
index 00000000..9e625316
--- /dev/null
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new file mode 100644
index 00000000..9b50dbf8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_394/observation_seed.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_394/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_394/reference_posterior_samples.csv.bz2
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new file mode 100644
index 00000000..356137b9
--- /dev/null
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_396/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_396/observation_seed.csv
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_397/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_397/observation_seed.csv
new file mode 100644
index 00000000..0ac7489d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_397/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+99,397
diff --git a/sbibm/tasks/ddm/files/num_observation_397/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_397/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2b60f83b
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_397/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_397/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_397/true_parameters.csv
new file mode 100644
index 00000000..20c523c8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_397/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_398/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_398/observation_seed.csv
new file mode 100644
index 00000000..9411ce9a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_398/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_398/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_398/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a2da3c9f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_398/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_398/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_398/true_parameters.csv
new file mode 100644
index 00000000..0082c431
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_398/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_399/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_399/observation_seed.csv
new file mode 100644
index 00000000..63fe75bc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_399/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
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diff --git a/sbibm/tasks/ddm/files/num_observation_399/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_399/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..79b672ae
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_399/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_399/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_399/true_parameters.csv
new file mode 100644
index 00000000..443ddc23
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_399/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_4/observation.csv b/sbibm/tasks/ddm/files/num_observation_4/observation.csv
new file mode 100644
index 00000000..ce4ddce9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_4/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_4/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_4/observation_seed.csv
new file mode 100644
index 00000000..126961b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_4/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_4/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_4/reference_posterior_samples.csv.bz2
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_4/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_4/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_4/true_parameters.csv
new file mode 100644
index 00000000..5e1174f8
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+++ b/sbibm/tasks/ddm/files/num_observation_4/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_40/observation.csv b/sbibm/tasks/ddm/files/num_observation_40/observation.csv
new file mode 100644
index 00000000..0775e44a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_40/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_40/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_40/observation_seed.csv
new file mode 100644
index 00000000..c81fe100
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_40/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_40/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_40/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_40/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_40/true_parameters.csv
new file mode 100644
index 00000000..edbe0f1b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_40/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_400/observation.csv b/sbibm/tasks/ddm/files/num_observation_400/observation.csv
new file mode 100644
index 00000000..7e8eb330
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_400/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_400/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_400/observation_seed.csv
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index 00000000..4998e4c7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_400/observation_seed.csv
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_400/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_400/true_parameters.csv
new file mode 100644
index 00000000..70090274
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_400/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.97406,1.6620548,0.321059,0.88146836
diff --git a/sbibm/tasks/ddm/files/num_observation_41/observation.csv b/sbibm/tasks/ddm/files/num_observation_41/observation.csv
new file mode 100644
index 00000000..6ca8572f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_41/observation.csv
@@ -0,0 +1,2 @@
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+-1.5566516
diff --git a/sbibm/tasks/ddm/files/num_observation_41/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_41/observation_seed.csv
new file mode 100644
index 00000000..af58c84d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_41/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_41/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_41/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_41/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_41/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_41/true_parameters.csv
new file mode 100644
index 00000000..d35e51fa
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_41/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.5284867,1.7991529,0.3694826,1.345133
diff --git a/sbibm/tasks/ddm/files/num_observation_42/observation.csv b/sbibm/tasks/ddm/files/num_observation_42/observation.csv
new file mode 100644
index 00000000..cd247b7e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_42/observation.csv
@@ -0,0 +1,2 @@
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+-0.5833155
diff --git a/sbibm/tasks/ddm/files/num_observation_42/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_42/observation_seed.csv
new file mode 100644
index 00000000..28384758
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_42/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_42/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_42/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_42/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_42/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_42/true_parameters.csv
new file mode 100644
index 00000000..d8534bfe
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_42/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.0542145,0.83992696,0.6202121,0.47070026
diff --git a/sbibm/tasks/ddm/files/num_observation_43/observation.csv b/sbibm/tasks/ddm/files/num_observation_43/observation.csv
new file mode 100644
index 00000000..c8f51ac4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_43/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_43/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_43/observation_seed.csv
new file mode 100644
index 00000000..3a8940ce
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_43/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_43/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_43/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_43/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_43/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_43/true_parameters.csv
new file mode 100644
index 00000000..87b76408
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_43/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_44/observation.csv b/sbibm/tasks/ddm/files/num_observation_44/observation.csv
new file mode 100644
index 00000000..88b01b9e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_44/observation.csv
@@ -0,0 +1,2 @@
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+-0.9423116
diff --git a/sbibm/tasks/ddm/files/num_observation_44/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_44/observation_seed.csv
new file mode 100644
index 00000000..810ceafb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_44/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_44/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_44/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_44/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_44/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_44/true_parameters.csv
new file mode 100644
index 00000000..d5e4c16f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_44/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.18412685,0.79473686,0.66841424,0.75388646
diff --git a/sbibm/tasks/ddm/files/num_observation_45/observation.csv b/sbibm/tasks/ddm/files/num_observation_45/observation.csv
new file mode 100644
index 00000000..f577f4c9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_45/observation.csv
@@ -0,0 +1,2 @@
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+0.79570556
diff --git a/sbibm/tasks/ddm/files/num_observation_45/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_45/observation_seed.csv
new file mode 100644
index 00000000..9f54b287
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_45/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_45/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_45/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_45/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_45/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_45/true_parameters.csv
new file mode 100644
index 00000000..a2c92409
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_45/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_46/observation.csv b/sbibm/tasks/ddm/files/num_observation_46/observation.csv
new file mode 100644
index 00000000..6d5fac7b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_46/observation.csv
@@ -0,0 +1,2 @@
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+-2.0986457
diff --git a/sbibm/tasks/ddm/files/num_observation_46/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_46/observation_seed.csv
new file mode 100644
index 00000000..ae5c8b20
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_46/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_46/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_46/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..44e7322e
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diff --git a/sbibm/tasks/ddm/files/num_observation_46/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_46/true_parameters.csv
new file mode 100644
index 00000000..72b74610
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_46/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.2522099,1.9419925,0.5733796,1.6380733
diff --git a/sbibm/tasks/ddm/files/num_observation_47/observation.csv b/sbibm/tasks/ddm/files/num_observation_47/observation.csv
new file mode 100644
index 00000000..bdd117ff
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_47/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_47/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_47/observation_seed.csv
new file mode 100644
index 00000000..612b096e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_47/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_47/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_47/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_47/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_47/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_47/true_parameters.csv
new file mode 100644
index 00000000..05587a59
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_47/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_48/observation.csv b/sbibm/tasks/ddm/files/num_observation_48/observation.csv
new file mode 100644
index 00000000..a7f61d68
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_48/observation.csv
@@ -0,0 +1,2 @@
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+-1.6612982
diff --git a/sbibm/tasks/ddm/files/num_observation_48/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_48/observation_seed.csv
new file mode 100644
index 00000000..506986c7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_48/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_48/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_48/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..a1dbc273
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_48/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_48/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_48/true_parameters.csv
new file mode 100644
index 00000000..d6b7ec28
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_48/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.7878096,0.5748792,0.48706836,1.6010994
diff --git a/sbibm/tasks/ddm/files/num_observation_49/observation.csv b/sbibm/tasks/ddm/files/num_observation_49/observation.csv
new file mode 100644
index 00000000..cfee65f9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_49/observation.csv
@@ -0,0 +1,2 @@
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+-0.94817656
diff --git a/sbibm/tasks/ddm/files/num_observation_49/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_49/observation_seed.csv
new file mode 100644
index 00000000..ab0becc7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_49/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_49/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_49/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_49/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_49/true_parameters.csv
new file mode 100644
index 00000000..ddd372c4
--- /dev/null
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@@ -0,0 +1,2 @@
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+-0.08996582,0.76976466,0.39710277,0.54662514
diff --git a/sbibm/tasks/ddm/files/num_observation_5/observation.csv b/sbibm/tasks/ddm/files/num_observation_5/observation.csv
new file mode 100644
index 00000000..30a8bd96
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_5/observation.csv
@@ -0,0 +1,2 @@
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+-0.76226383
diff --git a/sbibm/tasks/ddm/files/num_observation_5/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_5/observation_seed.csv
new file mode 100644
index 00000000..81843a67
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_5/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_5/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_5/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_5/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_5/true_parameters.csv
new file mode 100644
index 00000000..f0e29862
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_5/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_50/observation.csv b/sbibm/tasks/ddm/files/num_observation_50/observation.csv
new file mode 100644
index 00000000..1c45cc66
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_50/observation.csv
@@ -0,0 +1,2 @@
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+-0.5238464
diff --git a/sbibm/tasks/ddm/files/num_observation_50/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_50/observation_seed.csv
new file mode 100644
index 00000000..95baea8c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_50/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_50/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_50/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_50/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_50/true_parameters.csv
new file mode 100644
index 00000000..a2d343a4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_50/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_51/observation.csv b/sbibm/tasks/ddm/files/num_observation_51/observation.csv
new file mode 100644
index 00000000..69550069
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_51/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_51/observation_seed.csv
new file mode 100644
index 00000000..d84abc9e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_51/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_51/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_51/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_51/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_51/true_parameters.csv
new file mode 100644
index 00000000..54c6db84
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_52/observation.csv b/sbibm/tasks/ddm/files/num_observation_52/observation.csv
new file mode 100644
index 00000000..1aa41c30
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_52/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_52/observation_seed.csv
new file mode 100644
index 00000000..fc89f49e
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_52/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_52/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_52/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_52/true_parameters.csv
new file mode 100644
index 00000000..5e23ffd2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_52/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_53/observation.csv b/sbibm/tasks/ddm/files/num_observation_53/observation.csv
new file mode 100644
index 00000000..89ff3c5c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_53/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_53/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_53/observation_seed.csv
new file mode 100644
index 00000000..a1072b1d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_53/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_53/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_53/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_53/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_53/true_parameters.csv
new file mode 100644
index 00000000..d480360b
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_54/observation.csv b/sbibm/tasks/ddm/files/num_observation_54/observation.csv
new file mode 100644
index 00000000..83f70c07
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_54/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_54/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_54/observation_seed.csv
new file mode 100644
index 00000000..1a8d0621
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_54/observation_seed.csv
@@ -0,0 +1,2 @@
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+54,54
diff --git a/sbibm/tasks/ddm/files/num_observation_54/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_54/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..61e6d3e8
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_54/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_54/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_54/true_parameters.csv
new file mode 100644
index 00000000..cd776a25
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_54/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.26736045,1.4703965,0.6957245,1.2308364
diff --git a/sbibm/tasks/ddm/files/num_observation_55/observation.csv b/sbibm/tasks/ddm/files/num_observation_55/observation.csv
new file mode 100644
index 00000000..93294b29
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_55/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.521314
diff --git a/sbibm/tasks/ddm/files/num_observation_55/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_55/observation_seed.csv
new file mode 100644
index 00000000..d429e918
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_55/observation_seed.csv
@@ -0,0 +1,2 @@
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+55,55
diff --git a/sbibm/tasks/ddm/files/num_observation_55/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_55/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..25bc0398
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_55/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_55/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_55/true_parameters.csv
new file mode 100644
index 00000000..f98ddc7b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_55/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.3428833,1.9066758,0.59756565,1.1079048
diff --git a/sbibm/tasks/ddm/files/num_observation_56/observation.csv b/sbibm/tasks/ddm/files/num_observation_56/observation.csv
new file mode 100644
index 00000000..8cc86e19
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_56/observation.csv
@@ -0,0 +1,2 @@
+data_1
+2.3612196
diff --git a/sbibm/tasks/ddm/files/num_observation_56/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_56/observation_seed.csv
new file mode 100644
index 00000000..60148a6c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_56/observation_seed.csv
@@ -0,0 +1,2 @@
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+56,56
diff --git a/sbibm/tasks/ddm/files/num_observation_56/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_56/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..33ca97cd
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_56/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_56/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_56/true_parameters.csv
new file mode 100644
index 00000000..af8385f9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_56/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.8124592,1.3410467,0.4414158,1.7128112
diff --git a/sbibm/tasks/ddm/files/num_observation_57/observation.csv b/sbibm/tasks/ddm/files/num_observation_57/observation.csv
new file mode 100644
index 00000000..5ef85668
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_57/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.0291167
diff --git a/sbibm/tasks/ddm/files/num_observation_57/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_57/observation_seed.csv
new file mode 100644
index 00000000..986f79ec
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_57/observation_seed.csv
@@ -0,0 +1,2 @@
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+57,57
diff --git a/sbibm/tasks/ddm/files/num_observation_57/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_57/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..69ff4d95
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_57/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_57/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_57/true_parameters.csv
new file mode 100644
index 00000000..d81a368b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_57/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.55396676,1.4854469,0.30085462,0.8605856
diff --git a/sbibm/tasks/ddm/files/num_observation_58/observation.csv b/sbibm/tasks/ddm/files/num_observation_58/observation.csv
new file mode 100644
index 00000000..7f109a17
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_58/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-0.6760785
diff --git a/sbibm/tasks/ddm/files/num_observation_58/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_58/observation_seed.csv
new file mode 100644
index 00000000..02aedbcb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_58/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+58,58
diff --git a/sbibm/tasks/ddm/files/num_observation_58/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_58/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..7a506178
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_58/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_58/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_58/true_parameters.csv
new file mode 100644
index 00000000..1a197fcb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_58/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.13188863,1.0338011,0.5034857,0.50224847
diff --git a/sbibm/tasks/ddm/files/num_observation_59/observation.csv b/sbibm/tasks/ddm/files/num_observation_59/observation.csv
new file mode 100644
index 00000000..e248d501
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_59/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.005083
diff --git a/sbibm/tasks/ddm/files/num_observation_59/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_59/observation_seed.csv
new file mode 100644
index 00000000..1f0b13eb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_59/observation_seed.csv
@@ -0,0 +1,2 @@
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+59,59
diff --git a/sbibm/tasks/ddm/files/num_observation_59/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_59/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..52268b0f
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_59/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_59/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_59/true_parameters.csv
new file mode 100644
index 00000000..48fe0e82
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_59/true_parameters.csv
@@ -0,0 +1,2 @@
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+0.21195626,1.9728471,0.46594727,0.4875632
diff --git a/sbibm/tasks/ddm/files/num_observation_6/observation.csv b/sbibm/tasks/ddm/files/num_observation_6/observation.csv
new file mode 100644
index 00000000..81388577
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_6/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.5541819
diff --git a/sbibm/tasks/ddm/files/num_observation_6/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_6/observation_seed.csv
new file mode 100644
index 00000000..be1104c3
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_6/observation_seed.csv
@@ -0,0 +1,2 @@
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+5,6
diff --git a/sbibm/tasks/ddm/files/num_observation_6/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_6/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e7c95471
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_6/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_6/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_6/true_parameters.csv
new file mode 100644
index 00000000..3928609f
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_6/true_parameters.csv
@@ -0,0 +1,2 @@
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+1.3210075,0.68916637,0.6629878,1.5118833
diff --git a/sbibm/tasks/ddm/files/num_observation_60/observation.csv b/sbibm/tasks/ddm/files/num_observation_60/observation.csv
new file mode 100644
index 00000000..e6ae3b6b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_60/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.037611
diff --git a/sbibm/tasks/ddm/files/num_observation_60/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_60/observation_seed.csv
new file mode 100644
index 00000000..4a04a3b1
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_60/observation_seed.csv
@@ -0,0 +1,2 @@
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+60,60
diff --git a/sbibm/tasks/ddm/files/num_observation_60/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_60/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..3fa93933
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_60/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_60/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_60/true_parameters.csv
new file mode 100644
index 00000000..5fffe8a7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_60/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.9057128,0.9661008,0.6432512,0.99857265
diff --git a/sbibm/tasks/ddm/files/num_observation_61/observation.csv b/sbibm/tasks/ddm/files/num_observation_61/observation.csv
new file mode 100644
index 00000000..f29b8c28
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_61/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.4885246
diff --git a/sbibm/tasks/ddm/files/num_observation_61/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_61/observation_seed.csv
new file mode 100644
index 00000000..951f4312
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_61/observation_seed.csv
@@ -0,0 +1,2 @@
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+61,61
diff --git a/sbibm/tasks/ddm/files/num_observation_61/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_61/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..575cb9cf
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_61/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_61/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_61/true_parameters.csv
new file mode 100644
index 00000000..9c690fe9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_61/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.7432625,1.673662,0.4702126,1.3369156
diff --git a/sbibm/tasks/ddm/files/num_observation_62/observation.csv b/sbibm/tasks/ddm/files/num_observation_62/observation.csv
new file mode 100644
index 00000000..26ab37f4
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_62/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_62/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_62/observation_seed.csv
new file mode 100644
index 00000000..becba45d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_62/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_62/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_62/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4cbf9bde
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_62/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_62/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_62/true_parameters.csv
new file mode 100644
index 00000000..7684bd6c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_62/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_63/observation.csv b/sbibm/tasks/ddm/files/num_observation_63/observation.csv
new file mode 100644
index 00000000..ff71e17e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_63/observation.csv
@@ -0,0 +1,2 @@
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+-1.2017726
diff --git a/sbibm/tasks/ddm/files/num_observation_63/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_63/observation_seed.csv
new file mode 100644
index 00000000..7e7b702e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_63/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_63/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_63/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..e3d17324
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_63/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_63/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_63/true_parameters.csv
new file mode 100644
index 00000000..fd4cd8eb
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_63/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.8031149,1.3984046,0.37085247,0.6221838
diff --git a/sbibm/tasks/ddm/files/num_observation_64/observation.csv b/sbibm/tasks/ddm/files/num_observation_64/observation.csv
new file mode 100644
index 00000000..47bf391e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_64/observation.csv
@@ -0,0 +1,2 @@
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+-0.53593826
diff --git a/sbibm/tasks/ddm/files/num_observation_64/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_64/observation_seed.csv
new file mode 100644
index 00000000..741368ca
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_64/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_64/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_64/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..eb44b481
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_64/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_64/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_64/true_parameters.csv
new file mode 100644
index 00000000..0582142b
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_64/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_65/observation.csv b/sbibm/tasks/ddm/files/num_observation_65/observation.csv
new file mode 100644
index 00000000..c3f61f01
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_65/observation.csv
@@ -0,0 +1,2 @@
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+-1.1572595
diff --git a/sbibm/tasks/ddm/files/num_observation_65/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_65/observation_seed.csv
new file mode 100644
index 00000000..d282a149
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_65/observation_seed.csv
@@ -0,0 +1,2 @@
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+66,65
diff --git a/sbibm/tasks/ddm/files/num_observation_65/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_65/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..dc3e1cb6
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diff --git a/sbibm/tasks/ddm/files/num_observation_65/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_65/true_parameters.csv
new file mode 100644
index 00000000..c17b68d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_65/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.009516716,0.53449786,0.39083534,1.113286
diff --git a/sbibm/tasks/ddm/files/num_observation_66/observation.csv b/sbibm/tasks/ddm/files/num_observation_66/observation.csv
new file mode 100644
index 00000000..1c93a539
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_66/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.4180833
diff --git a/sbibm/tasks/ddm/files/num_observation_66/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_66/observation_seed.csv
new file mode 100644
index 00000000..86cf1ac7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_66/observation_seed.csv
@@ -0,0 +1,2 @@
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+67,66
diff --git a/sbibm/tasks/ddm/files/num_observation_66/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_66/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..be24d7cc
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diff --git a/sbibm/tasks/ddm/files/num_observation_66/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_66/true_parameters.csv
new file mode 100644
index 00000000..faff7636
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_66/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_67/observation.csv b/sbibm/tasks/ddm/files/num_observation_67/observation.csv
new file mode 100644
index 00000000..62bc9b0d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_67/observation.csv
@@ -0,0 +1,2 @@
+data_1
+1.7071023
diff --git a/sbibm/tasks/ddm/files/num_observation_67/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_67/observation_seed.csv
new file mode 100644
index 00000000..05e268d5
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_67/observation_seed.csv
@@ -0,0 +1,2 @@
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+68,67
diff --git a/sbibm/tasks/ddm/files/num_observation_67/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_67/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..98a6a01b
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diff --git a/sbibm/tasks/ddm/files/num_observation_67/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_67/true_parameters.csv
new file mode 100644
index 00000000..48d1d698
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_67/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.40361047,1.3281391,0.34017122,0.65497166
diff --git a/sbibm/tasks/ddm/files/num_observation_68/observation.csv b/sbibm/tasks/ddm/files/num_observation_68/observation.csv
new file mode 100644
index 00000000..ae3e0840
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_68/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_68/observation_seed.csv
new file mode 100644
index 00000000..2cb8bb6a
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_68/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_68/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_68/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_68/true_parameters.csv
new file mode 100644
index 00000000..cba71259
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_69/observation.csv b/sbibm/tasks/ddm/files/num_observation_69/observation.csv
new file mode 100644
index 00000000..c310b4b4
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_69/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_69/observation_seed.csv
new file mode 100644
index 00000000..bef81ec7
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_69/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_69/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_69/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_69/true_parameters.csv
new file mode 100644
index 00000000..a8af80ed
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_69/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_7/observation.csv b/sbibm/tasks/ddm/files/num_observation_7/observation.csv
new file mode 100644
index 00000000..7511bb66
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_7/observation.csv
@@ -0,0 +1,2 @@
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+-2.5582683
diff --git a/sbibm/tasks/ddm/files/num_observation_7/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_7/observation_seed.csv
new file mode 100644
index 00000000..1bffaefc
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_7/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_7/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_7/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_7/true_parameters.csv
new file mode 100644
index 00000000..7bcb99b8
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diff --git a/sbibm/tasks/ddm/files/num_observation_70/observation.csv b/sbibm/tasks/ddm/files/num_observation_70/observation.csv
new file mode 100644
index 00000000..b1664d8d
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_70/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_70/observation_seed.csv
new file mode 100644
index 00000000..9bcca298
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_70/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_70/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_70/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_70/true_parameters.csv
new file mode 100644
index 00000000..f03ad156
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_71/observation.csv b/sbibm/tasks/ddm/files/num_observation_71/observation.csv
new file mode 100644
index 00000000..47afb0bc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_71/observation.csv
@@ -0,0 +1,2 @@
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+-1.0733548
diff --git a/sbibm/tasks/ddm/files/num_observation_71/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_71/observation_seed.csv
new file mode 100644
index 00000000..f5438230
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_71/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_71/reference_posterior_samples.csv.bz2
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_71/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_71/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_71/true_parameters.csv
new file mode 100644
index 00000000..f2d879e3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_72/observation.csv b/sbibm/tasks/ddm/files/num_observation_72/observation.csv
new file mode 100644
index 00000000..914b66cf
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_72/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_72/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_72/observation_seed.csv
new file mode 100644
index 00000000..80b549e3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_72/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_72/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_72/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_72/true_parameters.csv
new file mode 100644
index 00000000..a00c924b
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_73/observation.csv b/sbibm/tasks/ddm/files/num_observation_73/observation.csv
new file mode 100644
index 00000000..a1d64d53
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_73/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_73/observation_seed.csv
new file mode 100644
index 00000000..df4ac594
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_73/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_73/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_73/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_73/true_parameters.csv
new file mode 100644
index 00000000..14be6fe1
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_74/observation.csv b/sbibm/tasks/ddm/files/num_observation_74/observation.csv
new file mode 100644
index 00000000..4268408f
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_74/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_74/observation_seed.csv
new file mode 100644
index 00000000..673af778
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_74/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_74/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_74/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_74/true_parameters.csv
new file mode 100644
index 00000000..a3ef77d7
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_75/observation.csv b/sbibm/tasks/ddm/files/num_observation_75/observation.csv
new file mode 100644
index 00000000..25f064ad
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diff --git a/sbibm/tasks/ddm/files/num_observation_75/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_75/observation_seed.csv
new file mode 100644
index 00000000..199e7636
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diff --git a/sbibm/tasks/ddm/files/num_observation_75/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_75/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_75/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_75/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_76/observation.csv b/sbibm/tasks/ddm/files/num_observation_76/observation.csv
new file mode 100644
index 00000000..7e8e1bbf
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diff --git a/sbibm/tasks/ddm/files/num_observation_76/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_76/observation_seed.csv
new file mode 100644
index 00000000..366c3142
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diff --git a/sbibm/tasks/ddm/files/num_observation_76/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_76/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_76/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_76/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_77/observation.csv b/sbibm/tasks/ddm/files/num_observation_77/observation.csv
new file mode 100644
index 00000000..1faf3e63
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diff --git a/sbibm/tasks/ddm/files/num_observation_77/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_77/observation_seed.csv
new file mode 100644
index 00000000..e6ffbffd
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_77/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_77/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_77/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_77/true_parameters.csv
new file mode 100644
index 00000000..c3d741bf
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_78/observation.csv b/sbibm/tasks/ddm/files/num_observation_78/observation.csv
new file mode 100644
index 00000000..f720ec21
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_78/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_78/observation_seed.csv
new file mode 100644
index 00000000..cb5c6d43
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_78/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_78/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_78/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_78/true_parameters.csv
new file mode 100644
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--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_79/observation.csv b/sbibm/tasks/ddm/files/num_observation_79/observation.csv
new file mode 100644
index 00000000..5fbd208d
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diff --git a/sbibm/tasks/ddm/files/num_observation_79/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_79/observation_seed.csv
new file mode 100644
index 00000000..c89fd541
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_79/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_79/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_79/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_79/true_parameters.csv
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diff --git a/sbibm/tasks/ddm/files/num_observation_8/observation.csv b/sbibm/tasks/ddm/files/num_observation_8/observation.csv
new file mode 100644
index 00000000..6db42b20
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diff --git a/sbibm/tasks/ddm/files/num_observation_8/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_8/observation_seed.csv
new file mode 100644
index 00000000..f2e2ea61
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diff --git a/sbibm/tasks/ddm/files/num_observation_8/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_8/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_8/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_8/true_parameters.csv
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_80/observation.csv b/sbibm/tasks/ddm/files/num_observation_80/observation.csv
new file mode 100644
index 00000000..2344e6b8
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diff --git a/sbibm/tasks/ddm/files/num_observation_80/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_80/observation_seed.csv
new file mode 100644
index 00000000..9e77e264
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diff --git a/sbibm/tasks/ddm/files/num_observation_80/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_80/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_80/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_80/true_parameters.csv
new file mode 100644
index 00000000..b8b06fb9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_80/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_81/observation.csv b/sbibm/tasks/ddm/files/num_observation_81/observation.csv
new file mode 100644
index 00000000..aded15f0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_81/observation.csv
@@ -0,0 +1,2 @@
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+-1.4137552
diff --git a/sbibm/tasks/ddm/files/num_observation_81/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_81/observation_seed.csv
new file mode 100644
index 00000000..40eca9df
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_81/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_81/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_81/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_81/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_81/true_parameters.csv
new file mode 100644
index 00000000..b1fc450e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_81/true_parameters.csv
@@ -0,0 +1,2 @@
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+-0.16891408,1.2972815,0.55722964,0.47171885
diff --git a/sbibm/tasks/ddm/files/num_observation_82/observation.csv b/sbibm/tasks/ddm/files/num_observation_82/observation.csv
new file mode 100644
index 00000000..b0cccf25
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_82/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_82/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_82/observation_seed.csv
new file mode 100644
index 00000000..13025c60
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_82/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_82/reference_posterior_samples.csv.bz2
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_82/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_82/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_82/true_parameters.csv
new file mode 100644
index 00000000..262e2554
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_82/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_83/observation.csv b/sbibm/tasks/ddm/files/num_observation_83/observation.csv
new file mode 100644
index 00000000..ed468fb0
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_83/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_83/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_83/observation_seed.csv
new file mode 100644
index 00000000..fc64c14b
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_83/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_83/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_83/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_83/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_83/true_parameters.csv
new file mode 100644
index 00000000..65ea7c65
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_83/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_84/observation.csv b/sbibm/tasks/ddm/files/num_observation_84/observation.csv
new file mode 100644
index 00000000..8c5e48bc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_84/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_84/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_84/observation_seed.csv
new file mode 100644
index 00000000..95b3c36e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_84/observation_seed.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_84/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_84/reference_posterior_samples.csv.bz2
new file mode 100644
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Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_84/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_84/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_84/true_parameters.csv
new file mode 100644
index 00000000..b9ed672e
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_85/observation.csv b/sbibm/tasks/ddm/files/num_observation_85/observation.csv
new file mode 100644
index 00000000..234f0e90
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_85/observation.csv
@@ -0,0 +1,2 @@
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+-0.8360848
diff --git a/sbibm/tasks/ddm/files/num_observation_85/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_85/observation_seed.csv
new file mode 100644
index 00000000..60a658d8
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_85/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_85/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_85/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_85/true_parameters.csv
new file mode 100644
index 00000000..d447c949
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_85/true_parameters.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_86/observation.csv b/sbibm/tasks/ddm/files/num_observation_86/observation.csv
new file mode 100644
index 00000000..f18bee00
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_86/observation.csv
@@ -0,0 +1,2 @@
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+-1.993002
diff --git a/sbibm/tasks/ddm/files/num_observation_86/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_86/observation_seed.csv
new file mode 100644
index 00000000..ad378ed3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_86/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_86/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_86/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_86/true_parameters.csv
new file mode 100644
index 00000000..460dd4d9
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_86/true_parameters.csv
@@ -0,0 +1,2 @@
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+-1.9854798,1.8396294,0.48190144,1.7703261
diff --git a/sbibm/tasks/ddm/files/num_observation_87/observation.csv b/sbibm/tasks/ddm/files/num_observation_87/observation.csv
new file mode 100644
index 00000000..2208df6a
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_87/observation.csv
@@ -0,0 +1,2 @@
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+-1.2568742
diff --git a/sbibm/tasks/ddm/files/num_observation_87/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_87/observation_seed.csv
new file mode 100644
index 00000000..79d23667
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_87/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_87/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_87/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_87/true_parameters.csv
new file mode 100644
index 00000000..0dadd7ac
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_88/observation.csv b/sbibm/tasks/ddm/files/num_observation_88/observation.csv
new file mode 100644
index 00000000..40baca2d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_88/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_88/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_88/observation_seed.csv
new file mode 100644
index 00000000..816f6ceb
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_88/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_88/reference_posterior_samples.csv.bz2
new file mode 100644
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diff --git a/sbibm/tasks/ddm/files/num_observation_88/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_88/true_parameters.csv
new file mode 100644
index 00000000..b701adbf
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_89/observation.csv b/sbibm/tasks/ddm/files/num_observation_89/observation.csv
new file mode 100644
index 00000000..5c6bf75c
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_89/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_89/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_89/observation_seed.csv
new file mode 100644
index 00000000..2eb4ed3f
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_89/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_89/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_89/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_89/true_parameters.csv
new file mode 100644
index 00000000..7fc95ee9
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_9/observation.csv b/sbibm/tasks/ddm/files/num_observation_9/observation.csv
new file mode 100644
index 00000000..ffc289d4
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_9/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_9/observation_seed.csv
new file mode 100644
index 00000000..eb66fab2
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_9/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_9/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_9/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_9/true_parameters.csv
new file mode 100644
index 00000000..ed10c2a0
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_90/observation.csv b/sbibm/tasks/ddm/files/num_observation_90/observation.csv
new file mode 100644
index 00000000..028ae66c
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diff --git a/sbibm/tasks/ddm/files/num_observation_90/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_90/observation_seed.csv
new file mode 100644
index 00000000..121496ff
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_90/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_90/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_90/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_90/true_parameters.csv
new file mode 100644
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--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_91/observation.csv b/sbibm/tasks/ddm/files/num_observation_91/observation.csv
new file mode 100644
index 00000000..e5f05fe4
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diff --git a/sbibm/tasks/ddm/files/num_observation_91/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_91/observation_seed.csv
new file mode 100644
index 00000000..d39ee694
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_91/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_91/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_91/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_91/true_parameters.csv
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index 00000000..3359c809
--- /dev/null
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diff --git a/sbibm/tasks/ddm/files/num_observation_92/observation.csv b/sbibm/tasks/ddm/files/num_observation_92/observation.csv
new file mode 100644
index 00000000..7c093d4d
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diff --git a/sbibm/tasks/ddm/files/num_observation_92/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_92/observation_seed.csv
new file mode 100644
index 00000000..1d072e18
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_92/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_92/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_92/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_92/true_parameters.csv
new file mode 100644
index 00000000..92e7acc1
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diff --git a/sbibm/tasks/ddm/files/num_observation_93/observation.csv b/sbibm/tasks/ddm/files/num_observation_93/observation.csv
new file mode 100644
index 00000000..d2a7751e
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diff --git a/sbibm/tasks/ddm/files/num_observation_93/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_93/observation_seed.csv
new file mode 100644
index 00000000..b5f49664
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diff --git a/sbibm/tasks/ddm/files/num_observation_93/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_93/reference_posterior_samples.csv.bz2
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diff --git a/sbibm/tasks/ddm/files/num_observation_93/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_93/true_parameters.csv
new file mode 100644
index 00000000..9755287e
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diff --git a/sbibm/tasks/ddm/files/num_observation_94/observation.csv b/sbibm/tasks/ddm/files/num_observation_94/observation.csv
new file mode 100644
index 00000000..5c6aba54
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/ddm/files/num_observation_94/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_94/observation_seed.csv
new file mode 100644
index 00000000..6abe8932
--- /dev/null
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@@ -0,0 +1,2 @@
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+96,94
diff --git a/sbibm/tasks/ddm/files/num_observation_94/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_94/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..f10392dd
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_94/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_94/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_94/true_parameters.csv
new file mode 100644
index 00000000..fa733389
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_94/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+1.8393755,1.4870267,0.6330423,0.80022997
diff --git a/sbibm/tasks/ddm/files/num_observation_95/observation.csv b/sbibm/tasks/ddm/files/num_observation_95/observation.csv
new file mode 100644
index 00000000..08dddc95
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_95/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-2.4259582
diff --git a/sbibm/tasks/ddm/files/num_observation_95/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_95/observation_seed.csv
new file mode 100644
index 00000000..2beb9583
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_95/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+97,95
diff --git a/sbibm/tasks/ddm/files/num_observation_95/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_95/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4f6bf937
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_95/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_95/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_95/true_parameters.csv
new file mode 100644
index 00000000..6e927078
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_95/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.9692168,1.0458748,0.45215094,1.6773119
diff --git a/sbibm/tasks/ddm/files/num_observation_96/observation.csv b/sbibm/tasks/ddm/files/num_observation_96/observation.csv
new file mode 100644
index 00000000..c72e3ca2
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_96/observation.csv
@@ -0,0 +1,2 @@
+data_1
+0.38608247
diff --git a/sbibm/tasks/ddm/files/num_observation_96/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_96/observation_seed.csv
new file mode 100644
index 00000000..4b16251d
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_96/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+98,96
diff --git a/sbibm/tasks/ddm/files/num_observation_96/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_96/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..2310fa37
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_96/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_96/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_96/true_parameters.csv
new file mode 100644
index 00000000..a2cde553
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_96/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-0.15833426,0.65166086,0.38015568,0.3534606
diff --git a/sbibm/tasks/ddm/files/num_observation_97/observation.csv b/sbibm/tasks/ddm/files/num_observation_97/observation.csv
new file mode 100644
index 00000000..075858a7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_97/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-2.4473517
diff --git a/sbibm/tasks/ddm/files/num_observation_97/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_97/observation_seed.csv
new file mode 100644
index 00000000..f16838ab
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_97/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+99,97
diff --git a/sbibm/tasks/ddm/files/num_observation_97/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_97/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..758b77fe
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_97/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_97/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_97/true_parameters.csv
new file mode 100644
index 00000000..20c523c8
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_97/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.5867612,1.9552338,0.67922807,1.765934
diff --git a/sbibm/tasks/ddm/files/num_observation_98/observation.csv b/sbibm/tasks/ddm/files/num_observation_98/observation.csv
new file mode 100644
index 00000000..884aa488
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_98/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.875977
diff --git a/sbibm/tasks/ddm/files/num_observation_98/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_98/observation_seed.csv
new file mode 100644
index 00000000..bb6f116e
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_98/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+100,98
diff --git a/sbibm/tasks/ddm/files/num_observation_98/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_98/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..0c69f317
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_98/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_98/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_98/true_parameters.csv
new file mode 100644
index 00000000..0082c431
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_98/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.5533428,1.7237647,0.4050248,0.9742041
diff --git a/sbibm/tasks/ddm/files/num_observation_99/observation.csv b/sbibm/tasks/ddm/files/num_observation_99/observation.csv
new file mode 100644
index 00000000..841e7be7
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_99/observation.csv
@@ -0,0 +1,2 @@
+data_1
+-1.6575036
diff --git a/sbibm/tasks/ddm/files/num_observation_99/observation_seed.csv b/sbibm/tasks/ddm/files/num_observation_99/observation_seed.csv
new file mode 100644
index 00000000..9ad9eabc
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_99/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+101,99
diff --git a/sbibm/tasks/ddm/files/num_observation_99/reference_posterior_samples.csv.bz2 b/sbibm/tasks/ddm/files/num_observation_99/reference_posterior_samples.csv.bz2
new file mode 100644
index 00000000..4edede73
Binary files /dev/null and b/sbibm/tasks/ddm/files/num_observation_99/reference_posterior_samples.csv.bz2 differ
diff --git a/sbibm/tasks/ddm/files/num_observation_99/true_parameters.csv b/sbibm/tasks/ddm/files/num_observation_99/true_parameters.csv
new file mode 100644
index 00000000..443ddc23
--- /dev/null
+++ b/sbibm/tasks/ddm/files/num_observation_99/true_parameters.csv
@@ -0,0 +1,2 @@
+v,a,w,ndt
+-1.207802,1.1754313,0.33636057,1.6195135
diff --git a/sbibm/tasks/ddm/task.py b/sbibm/tasks/ddm/task.py
new file mode 100644
index 00000000..cac27ac8
--- /dev/null
+++ b/sbibm/tasks/ddm/task.py
@@ -0,0 +1,405 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional
+
+import numpy as np
+import pandas as pd
+import pyro
+import torch
+from pyro import distributions as pdist
+from sbibm.tasks.ddm.utils import DDMJulia
+
+import sbibm  # noqa -- needed for setting sysimage path
+from sbibm.tasks.simulator import Simulator
+from sbibm.tasks.task import Task
+from sbibm.utils.decorators import lazy_property
+
+
+class DDM(Task):
+    def __init__(
+        self,
+        dt: float = 0.001,
+        num_trials: int = 1,
+        dim_parameters: int = 4,
+    ):
+        """Drift-diffusion model.
+
+        Args:
+            dt: integration step size in s.
+            num_trials: number of trials to run for each parameter.
+            dim_parameters: if 2, use only drift v and boundary separation a
+                if 4 use v, a, w and tau as in the LAN paper.
+        """
+        self.dt = dt
+        self.num_trials = num_trials
+        assert dim_parameters in [2, 3, 4], "dim_parameters must be 2 or 3."
+
+        num_trials_list = [1, 10, 100, 1000]
+        observation_seeds = torch.arange(0, 103, 1)
+        invalid_seeds = [49, 63, 90]
+        for iseed in invalid_seeds:
+            mask = observation_seeds == iseed
+            observation_seeds = observation_seeds[~mask]
+        observation_seeds = observation_seeds.repeat(len(num_trials_list)).tolist()
+        num_observations = len(observation_seeds)
+
+        super().__init__(
+            dim_parameters=dim_parameters,
+            dim_data=num_trials,
+            name=Path(__file__).parent.name,
+            name_display="DDM",
+            num_observations=num_observations,
+            num_posterior_samples=10000,
+            num_reference_posterior_samples=10000,
+            num_simulations=[100, 1000, 10000, 100000, 1000000],
+            path=Path(__file__).parent.absolute(),
+            observation_seeds=observation_seeds,
+        )
+
+        # Prior
+        self.prior_params = {
+            "low": torch.tensor([-2.0, 0.5, 0.3, 0.2][:dim_parameters]),
+            "high": torch.tensor([2.0, 2.0, 0.7, 1.8][:dim_parameters]),
+        }
+        self.prior_labels = ["v", "a", "w", "ndt"][:dim_parameters]
+        self.prior_dist = pdist.Uniform(**self.prior_params).to_event(1)
+        self.num_trials_per_observation = (
+            torch.tensor(num_trials_list).repeat_interleave(100).tolist()
+        )
+
+    @lazy_property
+    def ddm(self):
+        return DDMJulia(
+            dt=self.dt, num_trials=self.num_trials, dim_parameters=self.dim_parameters
+        )
+
+    def get_labels_parameters(self) -> List[str]:
+        """Get list containing parameter labels"""
+        return self.prior_labels
+
+    def get_prior(self) -> Callable:
+        def prior(num_samples=1):
+            return pyro.sample("parameters", self.prior_dist.expand_by([num_samples]))
+
+        return prior
+
+    def get_simulator(
+        self,
+        max_calls: Optional[int] = None,
+        seed: int = -1,
+        num_trials: int = -1,
+    ) -> Simulator:
+        """Get function returning samples from simulator given parameters
+
+        Args:
+            max_calls: Maximum number of function calls. Additional calls will
+                result in SimulationBudgetExceeded exceptions. Defaults to None
+                for infinite budget
+
+        Return:
+            Simulator callable
+        """
+        if not num_trials == -1:
+            self.num_trials = num_trials
+            self.dim_data = num_trials
+        # Two-parameter case.
+        if self.dim_parameters == 2:
+
+            def simulator(parameters):
+                v, a = parameters.numpy().T
+                rts, choices = self.ddm.simulate(
+                    v,
+                    a,
+                    seed=seed,
+                    num_trials=self.num_trials,
+                )
+                # encode zero choices as negative RTs.
+                rts[choices == 0] *= -1
+                return torch.tensor(rts, dtype=torch.float32)
+
+        elif self.dim_parameters == 3:
+
+            def simulator(parameters):
+                v, a, w = parameters.numpy().T
+                # using boundary separation a and offset w
+                # pass negative lower bound as required by DiffModels.
+                bl = -w * a
+                bu = (1 - w) * a
+
+                rts, choices = self.ddm.simulate_simpleDDM(
+                    v,
+                    bl,
+                    bu,
+                    seed=seed,
+                    num_trials=self.num_trials,
+                )
+                # encode zero choices as negative RTs.
+                rts[choices == 0] *= -1
+                return torch.tensor(rts, dtype=torch.float32)
+
+        elif self.dim_parameters == 4:
+
+            def simulator(parameters):
+                v, a, w, ndt = parameters.numpy().T
+                # using boundary separation a and offset w
+                # pass negative lower bound as required by DiffModels.
+                bl = -w * a
+                bu = (1 - w) * a
+                rts, choices = self.ddm.simulate_simpleDDM(
+                    v,
+                    bl,
+                    bu,
+                    seed=seed,
+                    num_trials=self.num_trials,
+                )
+                rts += ndt.reshape(-1, 1)
+                # encode zero choices as negative RTs.
+                rts[choices == 0] *= -1
+                return torch.tensor(rts, dtype=torch.float32)
+
+        else:
+            raise NotImplementedError()
+
+        return Simulator(task=self, simulator=simulator, max_calls=max_calls)
+
+    def get_log_likelihood(self, parameters, data, l_lower_bound=1e-7):
+        """Return likelihood given parameters and data.
+
+        Takes product of likelihoods across iid trials.
+
+        Batch dimension is only across parameters, the data is fixed.
+        """
+        assert data.ndim == 2, "Data must have shape (1, num_trials)."
+        data_batch_size, num_trials = data.shape
+        assert data_batch_size == 1, "Data must be a single batch of trials."
+        parameters = parameters.numpy()
+        # Decode choices from RT sign.
+        choices = torch.where(
+            data[0, :] > 0, torch.ones(num_trials), torch.zeros(num_trials)
+        ).numpy()
+        # Convert negative RTs.
+        rts = abs(data.numpy()[0, :])
+
+        if self.dim_parameters == 2:
+            log_likelihoods = self.ddm.log_likelihood(
+                parameters[:, 0],  # v
+                parameters[:, 1],  # boundary separation a
+                # Pass rts and choices separately.
+                rts,
+                choices,
+                l_lower_bound=l_lower_bound,
+            )
+        elif self.dim_parameters == 3:
+            # using boundary separation a and offset w
+            # pass negative lower bound as required by DiffModels.
+            v, a, w = parameters.T
+            bl = -w * a
+            bu = (1 - w) * a
+            log_likelihoods = self.ddm.log_likelihood_simpleDDM(
+                v,
+                bl,
+                bu,
+                # Pass rts and choices separately.
+                rts,
+                choices,
+                l_lower_bound=l_lower_bound,
+            )
+        elif self.dim_parameters == 4:
+            # using boundary separation a and offset w
+            # pass negative lower bound as required by DiffModels.
+            v, a, w, ndt = parameters.T
+            bl = -w * a
+            bu = (1 - w) * a
+            log_likelihoods = self.ddm.log_likelihood_simpleDDM(
+                v,
+                bl,
+                bu,
+                # Pass rts and choices separately.
+                rts,
+                choices,
+                # Pass ndt to be subtracted in Julia.
+                ndt=ndt,
+                l_lower_bound=l_lower_bound,
+            )
+        else:
+            raise NotImplementedError()
+
+        return torch.tensor(log_likelihoods)
+
+    def get_potential_fn(
+        self,
+        num_observation: int,
+        observation: torch.Tensor,
+        automatic_transforms_enabled: bool,
+    ) -> Callable:
+        """Return potential function for fixed data.
+
+        Potential: $-[\log r(x_o, \theta) + \log p(\theta)]$
+
+        The data can consists of multiple iid trials.
+        Then the overall likelihood is defined as the product over iid likelihood.
+        """
+        log_prob_fun = self._get_log_prob_fn(
+            num_observation,
+            observation,
+            posterior=True,
+            implementation="experimental",
+            automatic_transforms_enabled=automatic_transforms_enabled,
+        )
+
+        def potential_fn(parameters: Dict) -> torch.Tensor:
+            return -log_prob_fun(parameters["parameters"])
+
+        return potential_fn
+
+    def _get_log_prob_fn(
+        self,
+        num_observation: Optional[int],
+        observation: Optional[torch.Tensor],
+        implementation: str,
+        posterior: bool,
+        automatic_transforms_enabled: bool = True,
+        l_lower_bound: float = 1e-7,
+    ) -> Callable:
+        transforms = self._get_transforms(
+            num_observation=num_observation,
+            observation=observation,
+            automatic_transforms_enabled=automatic_transforms_enabled,
+        )["parameters"]
+        if observation is None:
+            observation = self.get_observation(num_observation)
+
+        def log_prob_fn(parameters: torch.Tensor) -> torch.Tensor:
+            # We need to calculate likelihoods in constrained space.
+            parameters_constrained = transforms.inv(parameters)
+
+            # Get likelihoods from DiffModels.jl in constrained space.
+            log_likelihood_constrained = self.get_log_likelihood(
+                parameters_constrained, observation, l_lower_bound
+            )
+            # But we need log probs in unconstrained space. Get log abs det jac
+            log_abs_det = transforms.log_abs_det_jacobian(
+                parameters_constrained, parameters
+            )
+            # Without transforms, logabsdet returns second dimension.
+            if log_abs_det.ndim > 1:
+                log_abs_det = log_abs_det.sum(-1)
+
+            if posterior:
+                posterior_potential_constrained = (
+                    log_likelihood_constrained
+                    + self.get_prior_dist().log_prob(parameters_constrained)
+                )
+                # Return posterior potential moved to unconstrained space.
+                return posterior_potential_constrained - log_abs_det
+            else:
+                # Likelihood in unconstrained space is:
+                # prob_constrained * 1/abs_det_jacobian
+                # log_prob_constrained - log_abs_det
+                return log_likelihood_constrained - log_abs_det
+
+        return log_prob_fn
+
+    def _sample_reference_posterior(
+        self,
+        num_samples: int,
+        num_observation: Optional[int] = None,
+        observation: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Sample reference posterior for given observation
+
+        Args:
+            num_observation: Observation number
+            num_samples: Number of samples to generate
+            observation: Observed data, if None, will be loaded using `num_observation`
+            kwargs: Passed to run_mcmc
+
+        Returns:
+            Samples from reference posterior
+        """
+        from sbibm.algorithms.pyro.mcmc import run as run_mcmc
+
+        if num_observation is not None:
+            initial_params = self.get_true_parameters(num_observation=num_observation)
+        else:
+            initial_params = None
+        num_chains = 5
+        num_warmup = 10_000
+        automatic_transforms_enabled = True
+
+        samples = run_mcmc(
+            task=self,
+            potential_fn=self.get_potential_fn(
+                num_observation,
+                observation,
+                automatic_transforms_enabled=automatic_transforms_enabled,
+            ),
+            kernel="Slice",
+            jit_compile=False,
+            num_warmup=num_warmup,
+            num_chains=num_chains,
+            num_observation=num_observation,
+            observation=observation,
+            num_samples=num_samples,
+            initial_params=initial_params.repeat(num_chains, 1),
+            automatic_transforms_enabled=automatic_transforms_enabled,
+        )
+        return samples
+
+    def _setup(self, n_jobs: int = -1, create_reference: bool = True, **kwargs: Any):
+        """Setup the task: generate observations and reference posterior samples
+
+        In most cases, you don't need to execute this method, since its results are stored to disk.
+
+        Re-executing will overwrite existing files.
+
+        Args:
+            n_jobs: Number of to use for Joblib
+            create_reference: If False, skips reference creation
+        """
+        from joblib import Parallel, delayed
+
+        def run(num_observation, observation_seed, **kwargs):
+            np.random.seed(observation_seed)
+            torch.manual_seed(observation_seed)
+            self._save_observation_seed(num_observation, observation_seed)
+
+            prior = self.get_prior()
+            true_parameters = prior(num_samples=1)
+            self._save_true_parameters(num_observation, true_parameters)
+
+            num_trials = int(self.num_trials_per_observation[num_observation - 1])
+            self.dim_data = num_trials
+            self.num_trials = num_trials
+            simulator = self.get_simulator(
+                seed=int(observation_seed),
+                num_trials=num_trials,
+            )
+            observation = simulator(true_parameters)
+            self._save_observation(num_observation, observation)
+
+            if create_reference:
+                reference_posterior_samples = self._sample_reference_posterior(
+                    num_observation=num_observation,
+                    num_samples=self.num_reference_posterior_samples,
+                    **kwargs,
+                )
+                num_unique = torch.unique(reference_posterior_samples, dim=0).shape[0]
+                assert num_unique == self.num_reference_posterior_samples
+                self._save_reference_posterior_samples(
+                    num_observation,
+                    reference_posterior_samples,
+                )
+
+        Parallel(n_jobs=n_jobs, verbose=50, backend="loky")(
+            delayed(run)(num_observation, observation_seed, **kwargs)
+            for num_observation, observation_seed in enumerate(
+                self.observation_seeds, start=1
+            )
+        )
+
+
+if __name__ == "__main__":
+    task = DDM(num_trials=1, dim_parameters=4)
+    task._setup(n_jobs=-1)
diff --git a/sbibm/tasks/ddm/utils.py b/sbibm/tasks/ddm/utils.py
new file mode 100644
index 00000000..faf68577
--- /dev/null
+++ b/sbibm/tasks/ddm/utils.py
@@ -0,0 +1,989 @@
+import os
+from pathlib import Path
+from typing import Callable
+
+import numpy as np
+import torch
+
+import logging
+from torch import Tensor, nn
+from sbi.utils.torchutils import atleast_2d, ensure_theta_batched
+from sbi.samplers.mcmc import SliceSamplerVectorized, sir_init
+from sbi.utils import tensor2numpy
+from torch.distributions import Bernoulli, Distribution, TransformedDistribution
+from torch import Tensor, optim
+
+from torch.utils import data
+
+from julia import Julia
+from warnings import warn
+
+JULIA_PROJECT = str(Path(__file__).parent / "julia")
+os.environ["JULIA_PROJECT"] = JULIA_PROJECT
+
+
+def find_sysimage():
+    """Find sysimage for DiffModels.jl"""
+
+    if "JULIA_SYSIMAGE_DIFFMODELS" in os.environ:
+        environ_path = Path(os.environ["JULIA_SYSIMAGE_DIFFMODELS"])
+        if environ_path.exists():
+            return str(environ_path)
+        else:
+            warn("JULIA_SYSIMAGE_DIFFMODELS is set but image does not exist")
+            return None
+    else:
+        warn("JULIA_SYSIMAGE_DIFFMODELS not set")
+        default_path = Path("~/.julia_sysimage_diffmodels.so").expanduser()
+        if default_path.exists():
+            warn(f"Defaulting to {default_path}")
+            return str(default_path)
+        else:
+            return None
+
+
+# initialize Julia
+jl = Julia(
+    compiled_modules=False,
+    sysimage=find_sysimage(),
+    runtime="julia",
+)
+from julia import Main
+
+
+class DDMJulia:
+    def __init__(
+        self,
+        dt: float = 0.001,
+        num_trials: int = 1,
+        dim_parameters: int = 2,
+        seed: int = -1,
+    ) -> None:
+        """Wrapping DDM simulation and likelihood computation from Julia.
+
+        Based on Julia package DiffModels.jl
+
+        https://github.com/DrugowitschLab/DiffModels.jl
+
+        Calculates likelihoods via Navarro and Fuss 2009.
+        """
+
+        self.dt = dt
+        self.num_trials = num_trials
+        self.seed = seed
+
+        Main.eval("using DiffModels")
+        Main.eval("using Random")
+
+        # forward model and likelihood for two-param case, symmetric bounds.
+        if dim_parameters == 2:
+            self.simulate = Main.eval(
+                f"""
+                    function simulate(vs, as; dt={self.dt}, num_trials={self.num_trials}, seed={self.seed})
+                        num_parameters = size(vs)[1]
+                        rt = fill(NaN, (num_parameters, num_trials))
+                        c = fill(NaN, (num_parameters, num_trials))
+
+                        # seeding
+                        if seed > 0
+                            Random.seed!(seed)
+                        end
+                        for i=1:num_parameters
+                            drift = ConstDrift(vs[i], dt)
+                            # Pass 0.5a to get bound from boundary separation.
+                            bound = ConstSymBounds(0.5 * as[i], dt)
+                            s = sampler(drift, bound)
+                        
+                            for j=1:num_trials
+                                rt[i, j], cj = rand(s)
+                                c[i, j] = cj ? 1.0 : 0.0
+                            end
+                        end
+                        return rt, c
+                    end
+                """
+            )
+            self.log_likelihood = Main.eval(
+                f"""
+                    function log_likelihood(vs, as, rts, cs; dt={self.dt}, l_lower_bound=1e-29)
+                        batch_size = size(vs)[1]
+                        num_trials = size(rts)[1]
+
+                        logl = zeros(batch_size)
+
+                        for i=1:batch_size
+                            drift = ConstDrift(vs[i], dt)
+                            # Pass 0.5a to get bound from boundary separation.
+                            bound = ConstSymBounds(0.5 * as[i], dt)
+
+                            for j=1:num_trials
+                                if cs[j] == 1.0
+                                    logl[i] += log(max(l_lower_bound, pdfu(drift, bound, rts[j])))
+                                else
+                                    logl[i] += log(max(l_lower_bound, pdfl(drift, bound, rts[j])))
+                                end
+                            end
+                        end
+                        return logl
+                    end
+                """
+            )
+        # forward model and likelihood for four-param case via asymmetric bounds
+        # as in LAN paper, "simpleDDM".
+        else:
+            self.simulate_simpleDDM = Main.eval(
+                f"""
+                    function simulate_simpleDDM(v, bl, bu; dt={self.dt}, num_trials={self.num_trials}, seed={self.seed})
+                        num_parameters = size(v)[1]
+                        rt = fill(NaN, (num_parameters, num_trials))
+                        c = fill(NaN, (num_parameters, num_trials))
+
+                        # seeding
+                        if seed > 0
+                            Random.seed!(seed)
+                        end
+
+                        for i=1:num_parameters
+                            drift = ConstDrift(v[i], dt)
+                            bound = ConstAsymBounds(bu[i], bl[i], dt)
+                            s = sampler(drift, bound)
+
+                            for j=1:num_trials
+                                # Simulate DDM.
+                                rt[i, j], cj = rand(s)
+                                c[i, j] = cj ? 1.0 : 0.0
+                            end
+
+                        end
+                        return rt, c
+                    end
+                """
+            )
+            self.log_likelihood_simpleDDM = Main.eval(
+                f"""
+                    function log_likelihood_simpleDDM(v, bl, bu, rts, cs; ndt=0, dt={self.dt}, l_lower_bound=1e-29)
+                        # eps is the numerical lower bound for the likelihood used in HDDM.
+                        parameter_batch_size = size(v)[1]
+                        num_trials = size(rts)[1]
+                        # If no ndt is passed, use zeros without effect.
+                        if ndt == 0
+                            ndt = zeros(parameter_batch_size)
+                        end
+
+                        logl = zeros(parameter_batch_size)
+
+                        for i=1:parameter_batch_size
+                            drift = ConstDrift(v[i], dt)
+                            bound = ConstAsymBounds(bu[i], bl[i], dt)
+
+                            for j=1:num_trials
+                                # Subtract the current ndt from rt to get correct likelihood.
+                                rt = rts[j] - ndt[i]
+                                # If rt negative (too high ndt) likelihood is 0.
+                                if rt < 0
+                                    # 1e-29 is the lower bound for negative rts used in HDDM.
+                                    logl[i] += log(l_lower_bound)
+                                else
+                                    if cs[j] == 1.0
+                                        logl[i] += log(max(l_lower_bound, pdfu(drift, bound, rt)))
+                                    else
+                                        logl[i] += log(max(l_lower_bound, pdfl(drift, bound, rt)))
+                                    end
+                                end
+                            end
+                        end
+                        return logl
+                    end
+                """
+            )
+
+
+class WeibullDDM:
+    """Implementation of the DDM with collapsing decision boundaries as used
+    in Fengler et al. 2021.
+    """
+
+    def __init__(
+        self,
+        dt: float = 0.001,
+        num_trials: int = 1,
+        dim_parameters: int = 5,
+        seed: int = -1,
+        tmax: int = 20,
+    ) -> None:
+        """Wrapping DDM simulation and likelihood computation from Julia.
+
+        Based on Julia package DiffModels.jl
+
+        https://github.com/DrugowitschLab/DiffModels.jl
+
+        Calculates likelihoods via Navarro and Fuss 2009.
+        """
+
+        self.dt = dt
+        self.num_trials = num_trials
+        self.seed = seed
+        self.tmax = tmax
+
+        self.jl = Julia(
+            compiled_modules=False,
+            sysimage=find_sysimage(),
+            runtime="julia",
+        )
+        self.jl.eval("using DiffModels")
+        self.jl.eval("using Random")
+
+        self.simulate = self.jl.eval(
+            f"""
+                function simulate_fullDDM(v, bu, bl, ndt, alpha, beta; dt={self.dt}, num_trials={self.num_trials}, seed={self.seed})
+                    num_parameters = size(v)[1]
+                    rt = fill(NaN, (num_parameters, num_trials))
+                    c = fill(NaN, (num_parameters, num_trials))
+                    t = range(0, {self.tmax}, step=dt)
+
+                    # seeding
+                    if seed > 0
+                        Random.seed!(seed)
+                    end
+
+                    for i=1:num_parameters
+                        for j=1:num_trials
+                            # Weibull fun for collapsing bound.
+                            b = bu[i] * exp.(- t.^alpha[i] / beta[i])
+                            # time derivative of bound.
+                            bg = -bu[i] * exp.(-t.^alpha[i] / beta[i]) .* alpha[i] .* t.^(alpha[i]-1) ./ beta[i]
+                            upper = VarBound(b, bg, dt)
+                            # lower bound
+                            b = bl[i] * exp.(- t.^alpha[i] / beta[i])
+                            # time derivative of bound.
+                            bg = -bl[i] * exp.(-t.^alpha[i] / beta[i]) .* alpha[i] .* t.^(alpha[i]-1) ./ beta[i]
+                            lower = VarBound(b, bg, dt)
+                            bound = VarAsymBounds(upper, lower)
+                            drift = ConstDrift(v[i], dt)
+                            s = sampler(drift, bound)
+
+                            # Simulate DDM.
+                            rt[i, j], cj = rand(s)
+                            c[i, j] = cj ? 1.0 : 0.0
+                        end
+                    end
+                    return rt, c
+                end
+            """
+        )
+
+
+class LinearCollapseDDM:
+    """Implementation of the DDM with linearly collapsing decision boundaries as used
+    in Fengler et al. 2021.
+    """
+
+    def __init__(
+        self,
+        dt: float = 0.001,
+        num_trials: int = 1,
+        dim_parameters: int = 4,
+        seed: int = -1,
+        tmax: int = 20,
+    ) -> None:
+        """Wrapping DDM simulation and likelihood computation from Julia.
+
+        Based on Julia package DiffModels.jl
+
+        https://github.com/DrugowitschLab/DiffModels.jl
+
+        Calculates likelihoods via Navarro and Fuss 2009.
+        """
+
+        self.dt = dt
+        self.num_trials = num_trials
+        self.seed = seed
+        self.tmax = tmax
+
+        self.jl = Julia(
+            compiled_modules=False,
+            sysimage=find_sysimage(),
+            runtime="julia",
+        )
+        self.jl.eval("using DiffModels")
+        self.jl.eval("using Random")
+
+        self.simulate = self.jl.eval(
+            f"""
+                function simulate(v, bu, bl, gamma; dt={self.dt}, num_trials={self.num_trials}, seed={self.seed})
+                    num_parameters = size(v)[1]
+                    rt = fill(NaN, (num_parameters, num_trials))
+                    c = fill(NaN, (num_parameters, num_trials))
+                    t = range(0, {self.tmax}, step=dt)
+
+                    # seeding
+                    if seed > 0
+                        Random.seed!(seed)
+                    end
+
+                    for i=1:num_parameters
+                        for j=1:num_trials
+                            # Linear collapsing bound.
+                            b = bu[i] .+ t .* gamma[i]
+                            # b = bu[i] .- (t .* sin(gamma[i]) / cos(gamma[i]))
+                            # time derivative of bound.
+                            bg = fill(gamma[i], length(t))
+                            # bg = fill(-sin(gamma[i]) / cos(gamma[i]), length(t))
+                            upper = VarBound(b, bg, dt)
+
+                            # lower bound
+                            b = bl[i] .+ t .* gamma[i]
+                            # b = bl[i] .- (t .* sin(gamma[i]) / cos(gamma[i]))
+                            lower = VarBound(b, bg, dt)
+                            bound = VarAsymBounds(upper, lower)
+                            drift = ConstDrift(v[i], dt)
+                            s = sampler(drift, bound)
+
+                            # Simulate DDM.
+                            rt[i, j], cj = rand(s)
+                            c[i, j] = cj ? 1.0 : 0.0
+                        end
+                    end
+                    return rt, c
+                end
+            """
+        )
+
+
+class FullDDMJulia:
+    """Implementation of the full DDM model as used in Fengler et al. 2021.
+
+    Difference to simple DDM: drift v, offset w and non-decision time change over trials.
+
+    This results in three additional parameters for that change.
+
+    """
+
+    def __init__(
+        self,
+        dt: float = 0.001,
+        num_trials: int = 1,
+        dim_parameters: int = 7,
+        seed: int = -1,
+    ) -> None:
+        """Wrapping DDM simulation and likelihood computation from Julia.
+
+        Based on Julia package DiffModels.jl
+
+        https://github.com/DrugowitschLab/DiffModels.jl
+
+        Calculates likelihoods via Navarro and Fuss 2009.
+        """
+
+        self.dt = dt
+        self.num_trials = num_trials
+        self.seed = seed
+
+        self.jl = Julia(
+            compiled_modules=False,
+            sysimage=find_sysimage(),
+            runtime="julia",
+        )
+        self.jl.eval("using DiffModels")
+        self.jl.eval("using Random")
+
+        self.simulate = self.jl.eval(
+            f"""
+                function simulate_fullDDM(v, a, w, tau, sv, eps_w, eps_tau; dt={self.dt}, num_trials={self.num_trials}, seed={self.seed})
+                    num_parameters = size(v)[1]
+                    rt = fill(NaN, (num_parameters, num_trials))
+                    c = fill(NaN, (num_parameters, num_trials))
+
+                    # seeding
+                    if seed > 0
+                        Random.seed!(seed)
+                    end
+
+                    for i=1:num_parameters
+                        for j=1:num_trials
+                            # Perturb for current trial.
+                            v_j = v[i] + randn() * sv[i]
+                            w_j = w[i] - eps_w[i] + rand() * 2 * eps_w[i]
+                            tau_j = tau[i] - eps_tau[i] + rand() * 2 * eps_tau[i]
+                            bl = -w_j * a[i]
+                            bu = (1 - w_j) * a[i]
+
+                            drift = ConstDrift(v_j, dt)
+                            bound = ConstAsymBounds(bu, bl, dt)
+                            s = sampler(drift, bound)
+
+                            # Simulate DDM.
+                            rt[i, j], cj = rand(s)
+                            c[i, j] = cj ? 1.0 : 0.0
+                        end
+                    end
+                    return rt, c
+                end
+            """
+        )
+
+
+# Refactored from
+# https://github.com/mackelab/sbi/blob/main/sbi/inference/posteriors/likelihood_based_posterior.py
+class LANPotentialFunctionProvider:
+    """
+    This class is initialized without arguments during the initialization of the
+     Posterior class. When called, it specializes to the potential function appropriate
+     to the requested mcmc_method.
+
+    Returns:
+        Potential function for use by either numpy or pyro sampler.
+    """
+
+    def __init__(self, transforms, lan_net, l_lower_bound: float = 1e-7) -> None:
+        self.transforms = transforms
+        self.lan_net = lan_net
+        self.l_lower_bound = l_lower_bound
+
+    def __call__(self, prior, sbi_net: nn.Module, x: Tensor, mcmc_method: str):
+        r"""Return potential function for posterior $p(\theta|x)$.
+
+        Switch on numpy or pyro potential function based on mcmc_method.
+
+        Args:
+            prior: Prior distribution that can be evaluated, untransformed.
+            likelihood_nn: Neural likelihood estimator that can be evaluated.
+            x: Conditioning variable for posterior $p(\theta|x)$. Can be a batch of iid
+                x.
+            mcmc_method: One of `slice_np`, `slice`, `hmc` or `nuts`.
+
+        Returns:
+            Potential function for sampler.
+        """
+        self.likelihood_nn = self.lan_net
+        assert not isinstance(prior, TransformedDistribution)
+        self.prior = prior
+        self.device = "cpu"
+        self.x = atleast_2d(x).to(self.device)
+        return self.posterior_potential
+
+    def posterior_potential(self, theta: np.array):
+        r"""Return posterior log prob. of theta $p(\theta|x)$"
+
+        Args:
+            theta: Parameters $\theta$, batch dimension 1, possibly in transformed space.
+
+        Returns:
+            Posterior log probability of the theta, $-\infty$ if impossible under prior.
+        """
+        theta = ensure_theta_batched(torch.as_tensor(theta, dtype=torch.float32))
+        theta_untransformed = self.transforms.inv(theta)
+
+        ll_transformed = self._log_likelihoods_over_trials(
+            self.x,
+            theta,
+            ll_lower_bound=np.log(self.l_lower_bound),
+        )
+        # Because ladj is subtracted from ll already we can just add the untransformed
+        # prior log prob.
+        potential_transformed = ll_transformed + self.prior.log_prob(
+            theta_untransformed
+        )
+
+        return potential_transformed
+
+    def _log_likelihoods_over_trials(
+        self, observation, theta_unconstrained, ll_lower_bound: float = -16.11809
+    ):
+        # lower bound for likelihood set to 1e-7 as in
+        # https://github.com/lnccbrown/lans/blob/f2636958bbdb6cb891393a137d1d353be5aa69cd/al-mlp/method_comparison_sim.py#L374
+
+        # move to parameters to constrained space.
+        parameters_constrained = self.transforms.inv(theta_unconstrained)
+        # turn boundary separation into symmetric boundary for LAN.
+        parameters_constrained[:, 1] *= 0.5
+
+        # convert RTs on real line to positive RTs.
+        rts = abs(observation)
+        num_trials = rts.numel()
+        num_parameters = parameters_constrained.shape[0]
+        assert rts.shape == torch.Size([num_trials, 1])
+
+        # Code down choices as -1 and up choices as +1.
+        cs = torch.ones_like(rts)
+        cs[observation < 0] *= -1
+
+        # Repeat theta trial times
+        theta_repeated = parameters_constrained.repeat(num_trials, 1)
+        # repeat trial data theta times.
+        rts_repeated = torch.repeat_interleave(rts, num_parameters, dim=0)
+        cs_repeated = torch.repeat_interleave(cs, num_parameters, dim=0)
+
+        # stack everything for the LAN net.
+        theta_x_stack = torch.cat((theta_repeated, rts_repeated, cs_repeated), dim=1)
+        ll_each_trial = torch.tensor(
+            self.lan_net.predict_on_batch(theta_x_stack.numpy()),
+            dtype=torch.float32,
+        ).reshape(num_trials, num_parameters)
+
+        # Lower bound on each trial ll.
+        # Sum across trials.
+        llsum = torch.where(
+            torch.logical_and(
+                # Apply lower bound
+                ll_each_trial >= ll_lower_bound,
+                # Set to lower bound value when rt<=tau.
+                # rts need shape of ll_each_trial.
+                rts.repeat(1, num_parameters) > parameters_constrained[:, 3],
+            ),
+            ll_each_trial,
+            ll_lower_bound * torch.ones_like(ll_each_trial),
+        ).sum(0)
+
+        # But we need log probs in unconstrained space. Get log abs det jacobian
+        log_abs_det = self.transforms.log_abs_det_jacobian(
+            self.transforms.inv(theta_unconstrained), theta_unconstrained
+        )
+        # With identity transform, logabsdet returns second dimension, so sum over it.
+        if log_abs_det.ndim > 1:
+            log_abs_det = log_abs_det.sum(-1)
+        # Double check.
+        assert llsum.numel() == num_parameters
+
+        return llsum - log_abs_det
+
+
+def run_mcmc(
+    prior: Distribution,
+    potential_fn: Callable,
+    mcmc_parameters: dict,
+    num_samples: int,
+) -> Tensor:
+    """Run slice sampling MCMC given prior and potential function, return samples.
+
+    Args:
+        prior: prior distribution, usually a TransformedDistribution in unconstrained space.
+        potential_fn: Callable returning the negative log posterior potential.
+        mcmc_parameters: MCMC hyperparameters.
+        num_samples: number of samples to obtain.
+
+    Returns:
+        Tensor: [description]
+    """
+
+    num_chains = mcmc_parameters["num_chains"]
+    num_warmup = mcmc_parameters["warmup_steps"]
+    thin = mcmc_parameters["thin"]
+    init_strategy = mcmc_parameters["init_strategy"]
+
+    # Obtain initial parameters for each chain using sequential importantce reweighting.
+    if init_strategy == "sir":
+        initial_params = torch.cat(
+            [
+                sir_init(prior, potential_fn, **mcmc_parameters)
+                for _ in range(num_chains)
+            ]
+        )
+    else:
+        initial_params = prior.sample((num_chains,))
+    dim_samples = initial_params.shape[1]
+
+    # Use vectorized slice sampling.
+    posterior_sampler = SliceSamplerVectorized(
+        init_params=tensor2numpy(initial_params),
+        log_prob_fn=potential_fn,
+        num_chains=num_chains,
+        verbose=False,
+    )
+    # Extract relevant samples.
+    warmup_ = num_warmup * thin
+    num_samples_ = np.ceil((num_samples * thin) / num_chains)
+    samples = posterior_sampler.run(warmup_ + num_samples_)
+    samples = samples[:, warmup_:, :]  # discard warmup steps
+    samples = samples[:, ::thin, :]  # thin chains
+    samples = torch.from_numpy(samples)  # chains x samples x dim
+
+    samples = samples.reshape(-1, dim_samples)[:num_samples, :]
+    return samples
+
+
+# Mixed model utils
+class BernoulliMN(nn.Module):
+    """Net for learning a conditional Bernoulli mass function over choices given parameters.
+
+    Takes as input parameters theta and learns the parameter p of a Bernoulli.
+
+    Defines log prob and sample functions.
+    """
+
+    def __init__(self, n_input=4, n_output=1, n_hidden_units=20, n_hidden_layers=2):
+        """Initialize Bernoulli mass network.
+
+        Args:
+            n_input: number of input features
+            n_output: number of output features, default 1 for a single Bernoulli variable.
+            n_hidden_units: number of hidden units per hidden layer.
+            n_hidden_layers: number of hidden layers.
+        """
+        super(BernoulliMN, self).__init__()
+
+        self.n_hidden_layers = n_hidden_layers
+
+        self.activation_fun = nn.Sigmoid()
+
+        self.input_layer = nn.Linear(n_input, n_hidden_units)
+
+        # Repeat hidden units hidden layers times.
+        self.hidden_layers = nn.ModuleList()
+        for _ in range(self.n_hidden_layers):
+            self.hidden_layers.append(nn.Linear(n_hidden_units, n_hidden_units))
+
+        self.output_layer = nn.Linear(n_hidden_units, n_output)
+
+    def forward(self, theta):
+        """Return Bernoulli probability predicted from a batch of parameters.
+
+        Args:
+            theta: batch of input parameters for the net.
+
+        Returns:
+            Tensor: batch of predicted Bernoulli probabilities.
+        """
+        assert theta.dim() == 2, "theta needs to have a batch dimension."
+
+        # forward path
+        theta = self.activation_fun(self.input_layer(theta))
+
+        # iterate n hidden layers, input x and calculate tanh activation
+        for layer in self.hidden_layers:
+            theta = self.activation_fun(layer(theta))
+
+        p_hat = self.activation_fun(self.output_layer(theta))
+
+        return p_hat
+
+    def log_prob(self, theta, x):
+        """Return Bernoulli log probability of choices x, given parameters theta.
+
+        Args:
+            theta: parameters for input to the BernoulliMN.
+            x: choices to evaluate.
+
+        Returns:
+            Tensor: log probs with shape (x.shape[0],)
+        """
+        # Predict Bernoulli p and evaluate.
+        p = self.forward(theta=theta)
+        return Bernoulli(probs=p).log_prob(x)
+
+    def sample(self, theta, num_samples):
+        """Returns samples from Bernoulli RV with p predicted via net.
+
+        Args:
+            theta: batch of parameters for prediction.
+            num_samples: number of samples to obtain.
+
+        Returns:
+            Tensor: Bernoulli samples with shape (batch, num_samples, 1)
+        """
+
+        # Predict Bernoulli p and sample.
+        p = self.forward(theta)
+        return Bernoulli(probs=p).sample((num_samples,))
+
+
+def get_data_loaders(theta, choices, batch_size, validation_fraction):
+    """Return train and test data loaders given data.
+
+    Args:
+        theta: DDM parameters.
+        choices: Corresponding DDM choices.
+        batch_size: training batch size.
+        validation_fraction: fraction of test data set.
+
+    Returns:
+        Dataloader, Dataloader: train and test dataloaders.
+    """
+    num_examples = theta.shape[0]
+    num_training_examples = int((1 - validation_fraction) * num_examples)
+
+    dataset = data.TensorDataset(theta, choices)
+    permuted_indices = torch.randperm(num_examples)
+    train_indices, val_indices = (
+        permuted_indices[:num_training_examples],
+        permuted_indices[num_training_examples:],
+    )
+
+    train_loader = data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        drop_last=True,
+        sampler=data.sampler.SubsetRandomSampler(train_indices),
+    )
+
+    val_loader = data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        drop_last=True,
+        shuffle=False,
+        sampler=data.sampler.SubsetRandomSampler(val_indices),
+    )
+
+    return train_loader, val_loader
+
+
+def train_choice_net(
+    theta,
+    choices,
+    net: BernoulliMN,
+    batch_size: int = 1000,
+    max_num_epochs: int = 1000,
+    learning_rate=5e-4,
+    validation_fraction=0.1,
+    stop_after_epochs=20,
+):
+    """Return trained BernoulliMN given data and training hyperparameters.
+
+    Args:
+        theta: DDM parameters
+        choices: corresponding DDM choices.
+        net: initialized BernoulliMN
+        batch_size: training batch size
+        max_num_epochs: maximum number of epochs to train.
+        learning_rate: learning rate.
+        validation_fraction: fraction of validation data.
+        stop_after_epochs: number of epochs to wait without validation loss reduction
+            before stopping training.
+
+    Returns:
+        nn.Module, Tensor: Trained net, validation log probs.
+    """
+    optimizer = optim.Adam(
+        list(net.parameters()),
+        lr=learning_rate,
+    )
+
+    train_loader, val_loader = get_data_loaders(
+        theta, choices, batch_size, validation_fraction
+    )
+
+    vallp = []
+    converged = False
+    num_epochs_trained = 0
+    largest_vallp = -float("inf")
+    last_vallp_change = 0
+
+    log = logging.getLogger(__name__)
+    while num_epochs_trained < max_num_epochs and not converged:
+        net.train()
+        for batch in train_loader:
+            optimizer.zero_grad()
+            theta_batch, x_batch = (
+                batch[0],
+                batch[1],
+            )
+            # Evaluate on x with theta as context.
+            log_prob = net.log_prob(x=x_batch, theta=theta_batch)
+            loss = -torch.mean(log_prob)
+            loss.backward()
+            optimizer.step()
+
+        # Calculate validation performance.
+        net.eval()
+        log_prob_sum = 0
+        with torch.no_grad():
+            for batch in val_loader:
+                theta_batch, x_batch = (
+                    batch[0],
+                    batch[1],
+                )
+                # Evaluate on x with theta as context.
+                log_prob = net.log_prob(x=x_batch, theta=theta_batch)
+                log_prob_sum += log_prob.sum().item()
+        # Take mean over all validation samples.
+        _val_log_prob = log_prob_sum / (len(val_loader) * val_loader.batch_size)
+        vallp.append(_val_log_prob)
+
+        if largest_vallp < _val_log_prob:
+            last_vallp_change = 0
+            largest_vallp = _val_log_prob
+        else:
+            last_vallp_change += 1
+
+        converged = last_vallp_change > stop_after_epochs
+        num_epochs_trained += 1
+
+    return net, vallp
+
+
+class MixedModelSyntheticDDM(nn.Module):
+    """Class for combining a Bernoulli choice net and a flow over reaction times for a
+    joint DDM synthetic likelihood."""
+
+    def __init__(
+        self, choice_net: nn.Module, rt_net: nn.Module, use_log_rts: bool = False
+    ):
+        """Initializa synthetic likelihood class from a choice net and reaction time
+        flow.
+
+        Args:
+            choice_net: BernoulliMN net trained to predict choices from DDM parameters.
+            rt_net: generative model of reaction time given DDM parameters and choices.
+            use_log_rts: whether the rt_net was trained with reaction times transformed
+                to log space.
+        """
+        super(MixedModelSyntheticDDM, self).__init__()
+
+        self.choice_net = choice_net
+        self.rt_net = rt_net
+        self.use_log_rts = use_log_rts
+
+    def sample(self, theta, num_samples: int = 1, track_gradients=False):
+        """Return choices and reaction times given DDM parameters.
+
+        Args:
+            theta: DDM parameters, shape (batch, 4)
+            num_samples: number of samples to generate.
+
+        Returns:
+            Tensor: samples (rt, choice) with shape (num_samples, 2)
+        """
+        assert theta.shape[0] == 1, "for samples, no batching in theta is possible yet."
+
+        with torch.set_grad_enabled(track_gradients):
+            # Sample choices given parameters, from BernoulliMN.
+            choices = self.choice_net.sample(theta, num_samples).reshape(num_samples, 1)
+            # Pass num_samples=1 because the choices in the context contains num_samples elements already.
+            samples = self.rt_net.sample(
+                num_samples=1,
+                # repeat the single theta to match number of sampled choices.
+                context=torch.cat((theta.repeat(num_samples, 1), choices), dim=1),
+            ).reshape(num_samples, 1)
+        return samples.exp() if self.use_log_rts else samples, choices
+
+    def log_prob(
+        self, rts, choices, theta, ll_lower_bound=np.log(1e-7), track_gradients=False
+    ):
+        """Return joint log likelihood of a batch rts and choices,
+        for each entry in a batch of parameters theta.
+
+        Note that we calculate the joint log likelihood over the batch of iid trials.
+        Therefore, only theta can be batched and the data is fixed (or a batch of data
+        is interpreted as iid trials)
+        """
+        num_parameters = theta.shape[0]
+        num_trials = rts.shape[0]
+        assert rts.ndim > 1
+        assert rts.shape == choices.shape
+
+        # Repeat parameters for each trial.
+        theta_repeated = theta.repeat(num_trials, 1)
+        # Repeat choices and rts for each parameter in batch.
+        choices_repeated = torch.repeat_interleave(choices, num_parameters, dim=0)
+        rts_repeated = torch.repeat_interleave(
+            torch.log(rts) if self.use_log_rts else rts, num_parameters, dim=0
+        )
+
+        with torch.set_grad_enabled(track_gradients):
+            # Get choice log probs from choice net.
+            # There are only two choices, thus we only have to get the log probs of those.
+            # (We could even just calculate one and then use the complement.)
+            zero_choice = torch.zeros(1, 1)
+            zero_choice_lp = self.choice_net.log_prob(
+                theta,
+                torch.repeat_interleave(zero_choice, num_parameters, dim=0),
+            ).reshape(1, -1)
+
+            # Calculate complement one-choice log prob.
+            one_choice_lp = torch.log(1 - zero_choice_lp.exp())
+            zero_one_lps = torch.cat((zero_choice_lp, one_choice_lp), dim=0)
+
+            lp_choices = zero_one_lps[
+                choices.type_as(torch.zeros(1, dtype=np.int)).squeeze()
+            ].reshape(-1)
+
+            # Get rt log probs from rt net.
+            lp_rts = self.rt_net.log_prob(
+                rts_repeated,
+                context=torch.cat((theta_repeated, choices_repeated), dim=1),
+            )
+
+        # Combine into joint lp with first dim over trials.
+        lp_combined = (lp_choices + lp_rts).reshape(num_trials, num_parameters)
+
+        # Maybe add log abs det jacobian of RTs: log(1/rt) = - log(rt)
+        if self.use_log_rts:
+            lp_combined -= torch.log(rts)
+
+        # Set to lower bound where reaction happend before non-decision time tau.
+        lp = torch.where(
+            torch.logical_and(
+                # If rt < tau the likelihood should be zero (or at lower bound).
+                rts.repeat(1, num_parameters) > theta[:, -1],
+                # Apply lower bound.
+                lp_combined > ll_lower_bound,
+            ),
+            lp_combined,
+            ll_lower_bound * torch.ones_like(lp_combined),
+        )
+
+        # Return sum over iid trial log likelihoods.
+        return lp.sum(0)
+
+    def get_potential_fn(
+        self,
+        data: Tensor,
+        transforms,
+        prior: Distribution,
+        ll_lower_bound: float,
+    ):
+        """Return potential function for DDM synthetic likelihood.
+
+        Args:
+            data: data to condition on, batch of iid trials of (rt, choice)s.
+            transforms: applied transforms
+            prior: prior in untransformed space.
+            ll_lower_bound: lower bound on the log likelihood.
+        """
+
+        # Encode rts and choices.
+        rts = abs(data)
+        choices = torch.ones_like(data)
+        choices[data < 0] = 0
+
+        def pf(theta_transformed):
+            """Return log posterior potential for parameters theta.
+
+            Args:
+                theta_transformed: parameters in unconstrained space.
+
+            Returns:
+                Tensor: potential.
+            """
+            theta_transformed = ensure_theta_batched(
+                torch.as_tensor(theta_transformed, dtype=torch.float32)
+            )
+            # Go to constrained space to get the likelihood.
+            theta = transforms.inv(theta_transformed)
+            # Get the log abs det jacobian of the transforms.
+            ladj = transforms.log_abs_det_jacobian(theta, theta_transformed)
+
+            # Without transforms, logabsdet returns second dimension.
+            if ladj.ndim > 1:
+                ladj = ladj.sum(-1)
+
+            # Get synthetic log likelihood in constrained space.
+            ll = self.log_prob(rts, choices, theta, ll_lower_bound)
+
+            # Get potential in untransformed space.
+            potential = ll + prior.log_prob(theta)
+
+            # Return potential in transformed space.
+            return potential - ladj
+
+        return pf
+
+
+def map_x_to_two_D(x: Tensor) -> Tensor:
+    """Return DDM data encoded as (rts, choices)."""
+    x = x.reshape(-1)
+    x_2d = torch.ones(x.shape[0], 2)
+    x_2d[:, 0] = x.abs()
+    x_2d[x < 0, 1] = -1.0
+
+    return x_2d
diff --git a/sbibm/tasks/task.py b/sbibm/tasks/task.py
index aa123b6c..adbfbb83 100644
--- a/sbibm/tasks/task.py
+++ b/sbibm/tasks/task.py
@@ -67,33 +67,27 @@ def __init__(
 
     @abstractmethod
     def get_prior(self) -> Callable:
-        """Get function returning parameters from prior
-        """
+        """Get function returning parameters from prior"""
         raise NotImplementedError
 
     def get_prior_dist(self) -> torch.distributions.Distribution:
-        """Get prior distribution
-        """
+        """Get prior distribution"""
         return self.prior_dist
 
     def get_prior_params(self) -> Dict[str, torch.Tensor]:
-        """Get parameters of prior distribution
-        """
+        """Get parameters of prior distribution"""
         return self.prior_params
 
     def get_labels_data(self) -> List[str]:
-        """Get list containing parameter labels
-        """
+        """Get list containing parameter labels"""
         return [f"data_{i+1}" for i in range(self.dim_data)]
 
     def get_labels_parameters(self) -> List[str]:
-        """Get list containing parameter labels
-        """
+        """Get list containing parameter labels"""
         return [f"parameter_{i+1}" for i in range(self.dim_parameters)]
 
     def get_observation(self, num_observation: int) -> torch.Tensor:
-        """Get observed data for a given observation number
-        """
+        """Get observed data for a given observation number"""
         path = (
             self.path
             / "files"
@@ -103,8 +97,7 @@ def get_observation(self, num_observation: int) -> torch.Tensor:
         return get_tensor_from_csv(path)
 
     def get_reference_posterior_samples(self, num_observation: int) -> torch.Tensor:
-        """Get reference posterior samples for a given observation number
-        """
+        """Get reference posterior samples for a given observation number"""
         path = (
             self.path
             / "files"
@@ -115,13 +108,11 @@ def get_reference_posterior_samples(self, num_observation: int) -> torch.Tensor:
 
     @abstractmethod
     def get_simulator(self) -> Callable:
-        """Get function returning parameters from prior
-        """
+        """Get function returning parameters from prior"""
         raise NotImplementedError
 
     def get_true_parameters(self, num_observation: int) -> torch.Tensor:
-        """Get true parameters (parameters that generated the data) for a given observation number
-        """
+        """Get true parameters (parameters that generated the data) for a given observation number"""
         path = (
             self.path
             / "files"
@@ -131,13 +122,11 @@ def get_true_parameters(self, num_observation: int) -> torch.Tensor:
         return get_tensor_from_csv(path)
 
     def save_data(self, path: Union[str, Path], data: torch.Tensor):
-        """Save data to a given path
-        """
+        """Save data to a given path"""
         save_tensor_to_csv(path, data, self.get_labels_data())
 
     def save_parameters(self, path: Union[str, Path], parameters: torch.Tensor):
-        """Save parameters to a given path
-        """
+        """Save parameters to a given path"""
         save_tensor_to_csv(path, parameters, self.get_labels_parameters())
 
     def flatten_data(self, data: torch.Tensor) -> torch.Tensor:
@@ -188,7 +177,9 @@ def _get_log_prob_fn(
         )
 
         log_prob_fn, _ = get_log_prob_fn(
-            conditioned_model, implementation=implementation, **kwargs,
+            conditioned_model,
+            implementation=implementation,
+            **kwargs,
         )
 
         def log_prob_pyro(parameters):
@@ -234,7 +225,7 @@ def _get_log_prob_grad_fn(
             kwargs: Passed to `sbibm.utils.pyro.get_log_prob_grad_fn`
 
         Returns:
-            `log_prob_grad_fn` that returns gradients as `batch_size` x 
+            `log_prob_grad_fn` that returns gradients as `batch_size` x
             `dim_parameter`
         """
         assert not (num_observation is None and observation is None)
@@ -248,7 +239,9 @@ def _get_log_prob_grad_fn(
             posterior=posterior,
         )
         log_prob_grad_fn, _ = get_log_prob_grad_fn(
-            conditioned_model, implementation=implementation, **kwargs,
+            conditioned_model,
+            implementation=implementation,
+            **kwargs,
         )
 
         def log_prob_grad_pyro(parameters):
@@ -289,7 +282,7 @@ def _get_transforms(
             num_observation: Observation number
             observation: Instead of passing an observation number, an observation may be
                 passed directly
-            automatic_transforms_enabled: If True, will automatically construct 
+            automatic_transforms_enabled: If True, will automatically construct
                 transforms to unconstrained space
 
         Returns:
@@ -300,14 +293,14 @@ def _get_transforms(
         )
 
         _, transforms = get_log_prob_fn(
-            conditioned_model, automatic_transform_enabled=automatic_transforms_enabled,
+            conditioned_model,
+            automatic_transform_enabled=automatic_transforms_enabled,
         )
 
         return transforms
 
     def _get_observation_seed(self, num_observation: int) -> int:
-        """Get observation seed for a given observation number
-        """
+        """Get observation seed for a given observation number"""
         path = (
             self.path
             / "files"
@@ -372,8 +365,7 @@ def _sample_reference_posterior(
         raise NotImplementedError
 
     def _save_observation_seed(self, num_observation: int, observation_seed: int):
-        """Save observation seed for a given observation number
-        """
+        """Save observation seed for a given observation number"""
         path = (
             self.path
             / "files"
@@ -387,8 +379,7 @@ def _save_observation_seed(self, num_observation: int, observation_seed: int):
         ).to_csv(path, index=False)
 
     def _save_observation(self, num_observation: int, observation: torch.Tensor):
-        """Save observed data for a given observation number
-        """
+        """Save observed data for a given observation number"""
         path = (
             self.path
             / "files"
@@ -401,8 +392,7 @@ def _save_observation(self, num_observation: int, observation: torch.Tensor):
     def _save_reference_posterior_samples(
         self, num_observation: int, reference_posterior_samples: torch.Tensor
     ):
-        """Save reference posterior samples for a given observation number
-        """
+        """Save reference posterior samples for a given observation number"""
         path = (
             self.path
             / "files"
@@ -415,8 +405,7 @@ def _save_reference_posterior_samples(
     def _save_true_parameters(
         self, num_observation: int, true_parameters: torch.Tensor
     ):
-        """Save true parameters (parameters that generated the data) for a given observation number
-        """
+        """Save true parameters (parameters that generated the data) for a given observation number"""
         path = (
             self.path
             / "files"
@@ -461,7 +450,8 @@ def run(num_observation, observation_seed, **kwargs):
                 num_unique = torch.unique(reference_posterior_samples, dim=0).shape[0]
                 assert num_unique == self.num_reference_posterior_samples
                 self._save_reference_posterior_samples(
-                    num_observation, reference_posterior_samples,
+                    num_observation,
+                    reference_posterior_samples,
                 )
 
         Parallel(n_jobs=n_jobs, verbose=50, backend="loky")(
diff --git a/sbibm/utils/tensorboard.py b/sbibm/utils/tensorboard.py
index b137fc68..e1de355d 100644
--- a/sbibm/utils/tensorboard.py
+++ b/sbibm/utils/tensorboard.py
@@ -4,7 +4,7 @@
 from typing import Any, Callable
 
 import torch
-from sbi.utils.plot import pairplot
+from sbi.analysis import pairplot
 from torch.utils.tensorboard import SummaryWriter
 
 
@@ -18,10 +18,10 @@ def tb_plot_posterior(
 
 
 def tb_make_writer(
-    logger: logging.Logger = None, basepath: str = "tensorboard",
+    logger: logging.Logger = None,
+    basepath: str = "tensorboard",
 ) -> (SummaryWriter, Callable):
-    """Builds tensorboard summary writers
-    """
+    """Builds tensorboard summary writers"""
     log_dir = Path(f"{basepath}/summary")
     if log_dir.exists() and log_dir.is_dir():
         shutil.rmtree(log_dir)
@@ -41,7 +41,10 @@ def close_fn():
 
 class TensorboardHandler(logging.Handler):
     def __init__(
-        self, writer: SummaryWriter, *args: Any, **kwargs: Any,
+        self,
+        writer: SummaryWriter,
+        *args: Any,
+        **kwargs: Any,
     ):
         super().__init__(*args, **kwargs)
         self.writer = writer
diff --git a/sbibm/utils/torch.py b/sbibm/utils/torch.py
index 0de94628..e978d651 100644
--- a/sbibm/utils/torch.py
+++ b/sbibm/utils/torch.py
@@ -103,7 +103,7 @@ def get_log_abs_det_jacobian(
         vals = vals.sum(-1)
 
     assert (
-        vals.numel == batch_size
+        vals.numel() == batch_size
     ), "Mismatch in batch size, took sum over whole batch?"
 
     return vals
diff --git a/setup.py b/setup.py
index f9918cdd..4b6e9962 100644
--- a/setup.py
+++ b/setup.py
@@ -29,11 +29,13 @@
     "pandas>=1.0.0",
     "pyabc>=0.10.8",
     "pyabcranger>=0.0.48",
-    "sbi>=0.14.2",
+    "sbi>=0.20.0,<0.22.0",
     "pyro-ppl",
     "scikit-learn",
-    "torch>=1.5.1",
+    "torch>=1.8.0",
     "tqdm",
+    "tensorflow",
+    "keras",
 ]
 
 EXTRAS = {
diff --git a/tests/algorithms/sbi/test_sbi_run_methods.py b/tests/algorithms/sbi/test_sbi_run_methods.py
new file mode 100644
index 00000000..e6fb8a88
--- /dev/null
+++ b/tests/algorithms/sbi/test_sbi_run_methods.py
@@ -0,0 +1,45 @@
+import pytest
+
+import sbibm
+from sbibm.algorithms.sbi import mcabc, smcabc, snle, snpe, snre, sl
+
+
+# a fast test
+@pytest.mark.parametrize(
+    "run_method", (mcabc, smcabc, snle, snpe, snre, sl)
+)
+@pytest.mark.parametrize("task_name", ("gaussian_linear",))
+@pytest.mark.parametrize("num_observation", (1, 3))
+def test_sbi_api(
+    run_method, task_name, num_observation, num_simulations=2_000, num_samples=100
+):
+    task = sbibm.get_task(task_name)
+
+    if run_method in (mcabc, smcabc):  # abc algorithms
+        kwargs = dict()
+    else:  # neural algorithms
+        kwargs = dict(
+            num_rounds=1,
+            max_num_epochs=2,
+            neural_net="mlp" if run_method == snre else "mdn",
+        )
+    if run_method in (snle, snre):
+        kwargs["mcmc_parameters"] = dict(
+            num_chains=100, warmup_steps=10, thin=1, init_strategy="proposal"
+        )
+
+    predicted, _, _ = run_method(
+        task=task,
+        num_observation=num_observation,
+        num_simulations=num_simulations,
+        num_samples=num_samples,
+        **kwargs,
+    )
+
+    reference_samples = task.get_reference_posterior_samples(
+        num_observation=num_observation
+    )
+
+    expected = reference_samples[:num_samples, :]
+
+    assert expected.shape == predicted.shape
diff --git a/tests/algorithms/test_baseline_rejection.py b/tests/algorithms/test_baseline_rejection.py
index ae7c6c7b..7cfbb48a 100644
--- a/tests/algorithms/test_baseline_rejection.py
+++ b/tests/algorithms/test_baseline_rejection.py
@@ -1,6 +1,6 @@
 import pytest  # noqa
 import torch
-from sbi.utils.plot import pairplot
+from sbi.analysis import pairplot
 
 import sbibm
 from sbibm.algorithms.pytorch.baseline_rejection import run
diff --git a/tests/algorithms/test_baseline_sir.py b/tests/algorithms/test_baseline_sir.py
index e289ca0e..d722ca53 100644
--- a/tests/algorithms/test_baseline_sir.py
+++ b/tests/algorithms/test_baseline_sir.py
@@ -1,6 +1,6 @@
 import pytest
 import torch
-from sbi.utils.plot import pairplot
+from sbi.analysis import pairplot
 
 import sbibm
 from sbibm.algorithms.pytorch.baseline_sir import run
diff --git a/tests/metrics/utils.py b/tests/metrics/utils.py
index 99224745..b6d367c8 100644
--- a/tests/metrics/utils.py
+++ b/tests/metrics/utils.py
@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-from sbi.utils.plot import pairplot
+from sbi.analysis import pairplot
 from sklearn.utils import check_random_state
 
 
diff --git a/tests/tasks/ddm/test_ddm_task.py b/tests/tasks/ddm/test_ddm_task.py
new file mode 100644
index 00000000..56c4e8ce
--- /dev/null
+++ b/tests/tasks/ddm/test_ddm_task.py
@@ -0,0 +1,146 @@
+import sbibm
+import torch
+
+from sbibm.metrics.c2st import c2st
+
+from sbi.inference import SNRE, SNPE, MNLE
+
+from sbi.neural_nets.embedding_nets import FCEmbedding, PermutationInvariantEmbedding
+from sbi.utils import posterior_nn
+
+
+mcmc_parameters = dict(
+    num_chains=50,
+    thin=10,
+    warmup_steps=50,
+    init_strategy="proposal",
+)
+
+
+def test_loading_ddm_task():
+    sbibm.get_task("ddm")
+
+
+def test_simulation_ddm_task():
+    task = sbibm.get_task("ddm")
+    prior = task.get_prior()
+    simulator = task.get_simulator()
+    simulator(prior(1))
+
+
+def map_x_to_two_D(x):
+    x = x.squeeze()
+    x_2d = torch.zeros(x.shape[0], 2)
+    x_2d[:, 0] = x.abs()
+    x_2d[x >= 0, 1] = 1
+
+    return x_2d
+
+
+def test_inference_with_nre():
+    task = sbibm.get_task("ddm")
+    num_observation = 101
+    num_simulations = 10000
+    num_samples = 1000
+    x_o = map_x_to_two_D(task.get_observation(num_observation))
+
+    prior = task.get_prior_dist()
+    simulator = task.get_simulator()
+
+    theta = prior.sample((num_simulations,))
+    x = map_x_to_two_D(simulator(theta))
+
+    trainer = SNRE(prior)
+    trainer.append_simulations(theta, x).train()
+    posterior = trainer.build_posterior(
+        mcmc_method="slice_np_vectorized", mcmc_parameters=mcmc_parameters
+    )
+    samples = posterior.sample((num_samples,), x=x_o)
+
+    reference_samples = task.get_reference_posterior_samples(num_observation)[
+        :num_samples
+    ]
+    score = c2st(reference_samples, samples)
+    print(score)
+    assert score <= 0.6, f"score={score} must be below 0.6"
+
+
+def test_inference_with_mnle():
+    task = sbibm.get_task("ddm")
+    num_observation = 101
+    num_simulations = 10000
+    num_samples = 1000
+    x_o = map_x_to_two_D(task.get_observation(num_observation))
+
+    prior = task.get_prior_dist()
+    simulator = task.get_simulator()
+
+    theta = prior.sample((num_simulations,))
+    x = map_x_to_two_D(simulator(theta))
+
+    trainer = MNLE(prior)
+    trainer.append_simulations(theta, x).train()
+    posterior = trainer.build_posterior(
+        mcmc_method="slice_np_vectorized", mcmc_parameters=mcmc_parameters
+    )
+    samples = posterior.sample((num_samples,), x=x_o)
+
+    reference_samples = task.get_reference_posterior_samples(num_observation)[
+        :num_samples
+    ]
+    score = c2st(reference_samples, samples)
+    print(score)
+    assert score <= 0.6, f"score={score} must be below 0.6"
+
+
+def test_inference_with_npe():
+    task = sbibm.get_task("ddm")
+    num_observation = 101
+    num_simulations = 10000
+    num_samples = 1000
+    x_o = map_x_to_two_D(task.get_observation(num_observation))
+    num_trials = x_o.shape[0]
+
+    prior = task.get_prior_dist()
+    simulator = task.get_simulator()
+
+    theta = prior.sample((num_simulations,))
+
+    # copy theta for iid trials
+    theta_per_trial = theta.tile(num_trials).reshape(num_simulations * num_trials, -1)
+    x = map_x_to_two_D(simulator(theta_per_trial))
+
+    # rearrange to have trials as separate dim
+    x = x.reshape(num_simulations, num_trials, 2)
+
+    single_trial_net = FCEmbedding(
+        input_dim=2,
+        output_dim=4,
+        num_hiddens=10,
+        num_layers=2,
+    )
+
+    embedding_net = PermutationInvariantEmbedding(
+        trial_net=single_trial_net,
+        trial_net_output_dim=4,
+        combining_operation="mean",
+        num_layers=2,
+        num_hiddens=20,
+        output_dim=10,
+    )
+
+    de_provider = posterior_nn(
+        model="mdn", num_components=4, embedding_net=embedding_net
+    )
+
+    trainer = SNPE(prior, density_estimator=de_provider).append_simulations(theta, x)
+    trainer.train()
+    posterior = trainer.build_posterior()
+    samples = posterior.sample((num_samples,), x=x_o)
+
+    reference_samples = task.get_reference_posterior_samples(num_observation)[
+        :num_samples
+    ]
+    score = c2st(reference_samples, samples)
+    print(score)
+    assert score <= 0.6, f"score={score} must be below 0.6"