loop_proximity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.insert(0,'../sib/')\n",
    "import csv\n",
    "import sib\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.metrics as mm\n",
    "# sir_inference imports\n",
    "from sir_model import FastProximityModel, patient_zeros_states\n",
    "from ranking import csr_to_list\n",
    "import os.path\n",
    "from os import path\n",
    "N=2000\n",
    "#N = 5000\n",
    "#N = 100\n",
    "## new try with 100 spreaders\n",
    "N_patient_zero = 10;\n",
    "#N_patient_zero = 1;\n",
    "lamb_load = 0.05;\n",
    "lamb = 0.05\n",
    "mu = 0.02;\n",
    "scale=1.0; # Easy Case\n",
    "T=120;\n",
    "seed=5;\n",
    "location=\"networks\"\n",
    "# SIR parameters\n",
    "t1 = 10;\n",
    "#seed=int();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "fold_net = Path(location)\n",
    "positions_file = \"pos_proximity_N%dK_s%.1f_T%d_lamb%.2f_s%d.csv\"%(N/1000,scale,T,lamb,seed)\n",
    "pos_nodes = pd.read_csv(fold_net/positions_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed = 5\n",
      "Loading model from networks/interactions_proximity_N2K_s1.0_T120_lamb0.05_s5.csv\n",
      "Load Proximity model\n",
      "Loading transmissions from networks/interactions_proximity_N2K_s1.0_T120_lamb0.05_s5.csv\n",
      "Loading took 1.8s\n",
      "Running SIR simulation\n",
      "t = 0 / 120\n",
      "t = 50 / 120\n",
      "t = 100 / 120\n",
      "Save plain dynamics\n"
     ]
    }
   ],
   "source": [
    "## import sys\n",
    "from time import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os.path\n",
    "from os import path\n",
    "from scenario import Scenario\n",
    "from sir_model import EpidemicModel, patient_zeros_states, symptomatic_individuals\n",
    "from ranking import RANKINGS\n",
    "log_fname=\"interactions_proximity_N%dK_s%.1f_T%d_lamb%.2f_s%d.csv\"%(N/1000,scale,T,lamb_load,seed)\n",
    "csv_file=location+\"/\"+log_fname\n",
    "print(\"seed = %d\"%seed, flush=True)\n",
    "np.random.seed(seed);\n",
    "if path.exists(csv_file): print(\"Loading model from %s\"%csv_file)\n",
    "else:\n",
    "    raise ValueError(\"Could not find the file! Was looking for \\n \"+csv_file+\"\\n Bye-Bye\")\n",
    "    sys.exit()\n",
    "print(\"Load Proximity model\", flush=True)\n",
    "\n",
    "tic = time()\n",
    "model = EpidemicModel(initial_states=np.zeros(N), x_pos=np.zeros(N), y_pos=np.zeros(N))\n",
    "model.load_transmissions(csv_file, new_lambda = lamb)\n",
    "model.recover_probas = mu*np.ones(N)\n",
    "print(f\"Loading took {time()-tic:.1f}s\",flush=True)\n",
    "model.initial_states = patient_zeros_states(N, N_patient_zero)\n",
    "model.time_evolution(model.recover_probas, model.transmissions, print_every=50)\n",
    "t_max = len(model.transmissions)\n",
    "print(\"Save plain dynamics\", flush=True)\n",
    "db = pd.DataFrame()\n",
    "db[\"S\"] = np.sum(model.states==0,axis=1)\n",
    "db[\"I\"] = np.sum(model.states==1,axis=1)\n",
    "db[\"R\"] = np.sum(model.states==2,axis=1)\n",
    "db.to_csv(\"csv/Proximity_N%dK_T%d_s1_pz%d_mu%.2f_l%.2f_seed%d.csv\"%(N/1000,T,N_patient_zero,mu,lamb,seed),\n",
    "          index=False, sep=\"\\t\")\n",
    "\n",
    "all_states = model.states\n",
    "model.initial_states = model.states[t1]\n",
    "model.states = model.states[t1:]\n",
    "model.transmissions = model.transmissions[t1:]\n",
    "model.pruned_transmissions = model.transmissions.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1af2629d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation, PillowWriter\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(7,7), frameon=False)\n",
    "lim=0.6\n",
    "ax.set(xlim=(pos_nodes.x.max()+lim, pos_nodes.x.min()-lim),\n",
    "      ylim=(pos_nodes.y.max()+lim, pos_nodes.y.min()-lim))\n",
    "\n",
    "#fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "plt.tight_layout()\n",
    "time = 0\n",
    "\n",
    "\n",
    "def get_nodes(t):\n",
    "    return {i:np.where(all_states[t]==i)[0] for i in range(3)}\n",
    "def setup_start(ax, pos_nodes, nodes_keys, colors, fn_get_nodes):\n",
    "\n",
    "    nodes_idx = fn_get_nodes(0)\n",
    "    paths = {}\n",
    "    for nk, color in zip(nodes_keys, colors):\n",
    "        g=pos_nodes.iloc[nodes_idx[nk]]\n",
    "        paths[nk] = ax.scatter(g[\"x\"],g[\"y\"], color=color)\n",
    "    return paths\n",
    "\n",
    "paths = setup_start(ax, pos_nodes,  [0,1,2], colors=[None, \"red\",\"green\"],\n",
    "                   fn_get_nodes=get_nodes)\n",
    "def animate(time):\n",
    "    nodes_idx = get_nodes(time)\n",
    "    for k in [0,1,2]:\n",
    "        g=pos_nodes.iloc[nodes_idx[k]]\n",
    "        paths[k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "\n",
    "anim = FuncAnimation(\n",
    "    fig, animate, interval=100, frames=T-1, cache_frame_data=False)\n",
    "\n",
    "plt.draw()\n",
    "plt.show()\n",
    "\n",
    "#anim.save(\"sim.mov\", codec=\"h263p\", bitrate=-1)\n",
    "#anim.save(\"sim.webm\", codec=\"libvpx-vp9\")\n",
    "writer=PillowWriter(fps=15)\n",
    "anim.save(\"sim.gif\",writer=writer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces = {}\n",
    "ranks_status ={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_status(scenario, name):\n",
    "    ranks_status[name]=scenario.status\n",
    "    ranks_status[name][\"t\"]+=t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau = 5\n",
    "# trac parameters\n",
    "trac_tau = tau;\n",
    "trac_lambd = lamb;\n",
    "# MF parameters\n",
    "MF_tau = tau;\n",
    "MF_delta = 15;\n",
    "# observation parameters\n",
    "n_ranking = 40\n",
    "p_untracked=0\n",
    "#seed = int(sys.argv[2]);\n",
    "#seeds for running [32,123,456]\n",
    "#seed=int(sys.argv[1]);\n",
    "################################################\n",
    "\n",
    "intervention_options=dict(quarantine_time=T)\n",
    "observation_options=dict(n_random=0,n_infected=0,n_ranking=n_ranking, p_symptomatic=0.5, tau=5, p_untracked=p_untracked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using step_scenario to rank\n",
      "Using seed=6\n",
      "Selecting 0/2000 untracked individuals\n",
      "\n",
      "SCENARIO BP\n",
      "t = 0 / 110\n",
      "state: S:1921, I:75, R:4 I+R:79\n",
      "obser: S:38, I:2, R:0, I+R:2\n",
      "free birds: 73\n",
      "obs_symp:0(0), obs_rank:2(2)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 1 obs 40 40 0\n",
      "sib.iterate(damp=0):  2/25 8.610e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 6.711e-06/0.0001    \n",
      "winBP: (S,I,R): (1998.3, 1.6, 0.0), seeds: 1.5, ll: -1208.4\n",
      "t = 1 / 110\n",
      "state: S:1905, I:91, R:4 I+R:95\n",
      "obser: S:76, I:4, R:0, I+R:4\n",
      "free birds: 87\n",
      "obs_symp:0(0), obs_rank:2(4)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 2 obs 40 40 0\n",
      "sib.iterate(damp=0):  9/25 5.963e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 5.877e-05/0.0001    \n",
      "winBP: (S,I,R): (1996.0, 3.9, 0.1), seeds: 2.2, ll: -1220.9\n",
      "t = 2 / 110\n",
      "state: S:1892, I:104, R:4 I+R:108\n",
      "obser: S:104, I:16, R:0, I+R:16\n",
      "free birds: 88\n",
      "obs_symp:0(0), obs_rank:12(16)\n",
      "pos_rank:12, inf_cont:1.425\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 3 obs 40 40 0\n",
      "sib.iterate(damp=0):  18/25 7.686e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 1.785e-05/0.0001    \n",
      "winBP: (S,I,R): (1984.1, 15.5, 0.4), seeds: 3.7, ll: -1254.8\n",
      "t = 3 / 110\n",
      "state: S:1872, I:123, R:5 I+R:128\n",
      "obser: S:139, I:21, R:0, I+R:21\n",
      "free birds: 102\n",
      "obs_symp:0(0), obs_rank:5(21)\n",
      "pos_rank:5, inf_cont:2.625\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 4 obs 40 40 0\n",
      "sib.iterate(damp=0):  12/25 8.042e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 3.154e-05/0.0001    \n",
      "winBP: (S,I,R): (1979.4, 19.8, 0.8), seeds: 4.8, ll: -1276.6\n",
      "t = 4 / 110\n",
      "state: S:1851, I:142, R:7 I+R:149\n",
      "obser: S:172, I:28, R:0, I+R:28\n",
      "free birds: 114\n",
      "obs_symp:0(0), obs_rank:7(28)\n",
      "pos_rank:7, inf_cont:0.875\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 5 obs 40 40 0\n",
      "sib.iterate(damp=0):  24/25 6.591e-04/0.0001    \n",
      "sib.iterate(damp=0.5):  5/25 6.630e-05/0.0001    \n",
      "winBP: (S,I,R): (1971.0, 27.6, 1.4), seeds: 6.2, ll: -1303.0\n",
      "t = 5 / 110\n",
      "state: S:1829, I:160, R:11 I+R:171\n",
      "obser: S:207, I:33, R:0, I+R:33\n",
      "free birds: 128\n",
      "obs_symp:0(0), obs_rank:5(33)\n",
      "pos_rank:5, inf_cont:1.05\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 6 obs 40 40 0\n",
      "sib.iterate(damp=0):  20/25 6.618e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 1.663e-05/0.0001    \n",
      "winBP: (S,I,R): (1967.4, 30.7, 1.9), seeds: 6.5, ll: -1318.0\n",
      "t = 6 / 110\n",
      "state: S:1817, I:167, R:16 I+R:183\n",
      "obser: S:244, I:43, R:1, I+R:44\n",
      "free birds: 126\n",
      "obs_symp:8(8), obs_rank:2(35)\n",
      "pos_rank:2, inf_cont:1.15\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 7 obs 48 40 8\n",
      "sib.iterate(damp=0):  24/25 5.629e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 4.085e-02/0.0001    \n",
      "winBP: (S,I,R): (1938.3, 56.8, 4.9), seeds: 8.1, ll: -1371.5\n",
      "t = 7 / 110\n",
      "state: S:1797, I:186, R:17 I+R:203\n",
      "obser: S:264, I:65, R:2, I+R:67\n",
      "free birds: 123\n",
      "obs_symp:6(14), obs_rank:19(54)\n",
      "pos_rank:19, inf_cont:3.05\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 8 obs 46 40 6\n",
      "sib.iterate(damp=0):  24/25 3.831e-02/0.0001           \n",
      "sib.iterate(damp=0.5):  24/25 5.476e-04/0.0001            \n",
      "winBP: (S,I,R): (1912.4, 80.4, 7.2), seeds: 9.7, ll: -1413.4\n",
      "t = 8 / 110\n",
      "state: S:1781, I:200, R:19 I+R:219\n",
      "obser: S:278, I:95, R:5, I+R:100\n",
      "free birds: 108\n",
      "obs_symp:8(22), obs_rank:23(77)\n",
      "pos_rank:23, inf_cont:4.65\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 9 obs 48 40 8\n",
      "sib.iterate(damp=0):  24/25 1.060e-01/0.0001        \n",
      "sib.iterate(damp=0.5):  24/25 6.351e-03/0.0001    \n",
      "winBP: (S,I,R): (1869.5, 117.7, 12.8), seeds: 10.6, ll: -1482.2\n",
      "t = 9 / 110\n",
      "state: S:1761, I:216, R:23 I+R:239\n",
      "obser: S:291, I:123, R:7, I+R:130\n",
      "free birds: 98\n",
      "obs_symp:10(32), obs_rank:25(102)\n",
      "pos_rank:25, inf_cont:4.8\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 10 obs 50 40 10\n",
      "sib.iterate(damp=0):  24/25 2.990e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 1.496e-01/0.0001    \n",
      "winBP: (S,I,R): (1826.3, 155.7, 18.0), seeds: 11.1, ll: -1554.2\n",
      "t = 10 / 110\n",
      "state: S:1735, I:239, R:26 I+R:265\n",
      "obser: S:306, I:151, R:8, I+R:159\n",
      "free birds: 95\n",
      "obs_symp:9(41), obs_rank:24(126)\n",
      "pos_rank:24, inf_cont:4.55\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 11 obs 49 40 9\n",
      "sib.iterate(damp=0):  24/25 3.816e-01/0.0001        \n",
      "sib.iterate(damp=0.5):  24/25 2.973e-02/0.0001    \n",
      "winBP: (S,I,R): (1781.4, 195.3, 23.2), seeds: 11.9, ll: -1591.5\n",
      "t = 11 / 110\n",
      "state: S:1718, I:253, R:29 I+R:282\n",
      "obser: S:324, I:171, R:11, I+R:182\n",
      "free birds: 89\n",
      "obs_symp:6(47), obs_rank:19(145)\n",
      "pos_rank:19, inf_cont:5.05\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 12 obs 46 40 6\n",
      "sib.iterate(damp=0):  24/25 6.440e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 8.062e-02/0.0001    \n",
      "winBP: (S,I,R): (1762.6, 208.3, 29.2), seeds: 12.3, ll: -1611.6\n",
      "t = 12 / 110\n",
      "state: S:1700, I:267, R:33 I+R:300\n",
      "obser: S:341, I:193, R:15, I+R:208\n",
      "free birds: 85\n",
      "obs_symp:9(56), obs_rank:19(164)\n",
      "pos_rank:19, inf_cont:5.2\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 13 obs 49 40 9\n",
      "sib.iterate(damp=0):  24/25 9.091e-01/0.0001       \n",
      "sib.iterate(damp=0.5):  24/25 5.312e-02/0.0001            \n",
      "winBP: (S,I,R): (1741.2, 222.6, 36.2), seeds: 12.5, ll: -1644.8\n",
      "t = 13 / 110\n",
      "state: S:1679, I:285, R:36 I+R:321\n",
      "obser: S:363, I:212, R:17, I+R:229\n",
      "free birds: 86\n",
      "obs_symp:7(63), obs_rank:16(180)\n",
      "pos_rank:16, inf_cont:3.125\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 14 obs 47 40 7\n",
      "sib.iterate(damp=0):  24/25 6.568e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 2.145e-01/0.0001               \n",
      "winBP: (S,I,R): (1709.6, 247.1, 43.3), seeds: 13.4, ll: -1688.5\n",
      "t = 14 / 110\n",
      "state: S:1665, I:293, R:42 I+R:335\n",
      "obser: S:384, I:233, R:18, I+R:251\n",
      "free birds: 77\n",
      "obs_symp:9(72), obs_rank:18(198)\n",
      "pos_rank:18, inf_cont:2.725\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 15 obs 49 40 9\n",
      "sib.iterate(damp=0):  24/25 7.192e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 7.159e-02/0.0001    \n",
      "winBP: (S,I,R): (1682.4, 268.9, 48.7), seeds: 13.1, ll: -1768.8\n",
      "t = 15 / 110\n",
      "state: S:1653, I:302, R:45 I+R:347\n",
      "obser: S:396, I:262, R:19, I+R:281\n",
      "free birds: 58\n",
      "obs_symp:12(84), obs_rank:27(225)\n",
      "pos_rank:27, inf_cont:3.675\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 16 obs 52 40 12\n",
      "sib.iterate(damp=0):  24/25 5.729e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 8.384e-02/0.0001    \n",
      "winBP: (S,I,R): (1645.6, 299.4, 55.0), seeds: 13.4, ll: -1819.6\n",
      "t = 16 / 110\n",
      "state: S:1642, I:307, R:51 I+R:358\n",
      "obser: S:423, I:278, R:21, I+R:299\n",
      "free birds: 50\n",
      "obs_symp:8(92), obs_rank:11(236)\n",
      "pos_rank:11, inf_cont:4.025\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 17 obs 48 40 8\n",
      "sib.iterate(damp=0):  24/25 7.271e-01/0.0001                \n",
      "sib.iterate(damp=0.5):  24/25 2.214e-02/0.0001            \n",
      "winBP: (S,I,R): (1638.2, 301.0, 60.8), seeds: 13.3, ll: -1826.4\n",
      "t = 17 / 110\n",
      "state: S:1633, I:313, R:54 I+R:367\n",
      "obser: S:451, I:292, R:23, I+R:315\n",
      "free birds: 44\n",
      "obs_symp:8(100), obs_rank:10(246)\n",
      "pos_rank:10, inf_cont:3.3\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 18 obs 48 40 8\n",
      "sib.iterate(damp=0):  24/25 1.399e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 3.241e-02/0.0001    \n",
      "winBP: (S,I,R): (1624.2, 308.2, 67.6), seeds: 13.7, ll: -1916.6\n",
      "t = 18 / 110\n",
      "state: S:1622, I:322, R:56 I+R:378\n",
      "obser: S:476, I:310, R:23, I+R:333\n",
      "free birds: 37\n",
      "obs_symp:9(109), obs_rank:15(261)\n",
      "pos_rank:15, inf_cont:1.925\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 19 obs 49 40 9\n",
      "sib.iterate(damp=0):  24/25 8.509e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 3.926e-02/0.0001        \n",
      "winBP: (S,I,R): (1610.6, 316.9, 72.4), seeds: 13.3, ll: -1927.2\n",
      "t = 19 / 110\n",
      "state: S:1616, I:321, R:63 I+R:384\n",
      "obser: S:507, I:320, R:23, I+R:343\n",
      "free birds: 33\n",
      "obs_symp:6(115), obs_rank:9(270)\n",
      "pos_rank:9, inf_cont:2.975\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 20 obs 46 40 6\n",
      "sib.iterate(damp=0):  24/25 6.909e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 6.732e-02/0.0001        \n",
      "winBP: (S,I,R): (1611.7, 311.7, 76.6), seeds: 13.6, ll: -1947.4\n",
      "t = 20 / 110\n",
      "state: S:1611, I:319, R:70 I+R:389\n",
      "obser: S:543, I:325, R:23, I+R:348\n",
      "free birds: 32\n",
      "obs_symp:5(120), obs_rank:4(274)\n",
      "pos_rank:4, inf_cont:1.025\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 21 obs 45 40 5\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 0.0\n",
      "sib.iterate(damp=0):  24/25 2.531e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 3.177e-01/0.0001    \n",
      "winBP: (S,I,R): (1614.1, 305.2, 80.7), seeds: 14.8, ll: -2835.4\n",
      "t = 21 / 110\n",
      "state: S:1603, I:319, R:78 I+R:397\n",
      "obser: S:571, I:337, R:25, I+R:362\n",
      "free birds: 27\n",
      "obs_symp:5(125), obs_rank:10(284)\n",
      "pos_rank:10, inf_cont:1.4\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 22 obs 45 40 5\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 1.496272602794966\n",
      "sib.iterate(damp=0):  24/25 7.260e-01/0.0001            \n",
      "sib.iterate(damp=0.5):  24/25 1.047e-01/0.0001    \n",
      "winBP: (S,I,R): (1612.8, 302.6, 84.6), seeds: 21.3, ll: -2895.3\n",
      "t = 22 / 110\n",
      "state: S:1600, I:318, R:82 I+R:400\n",
      "obser: S:607, I:341, R:26, I+R:367\n",
      "free birds: 25\n",
      "obs_symp:4(129), obs_rank:3(287)\n",
      "pos_rank:3, inf_cont:2.45\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 23 obs 44 40 4\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 3.5506116934886496\n",
      "sib.iterate(damp=0):  24/25 5.218e-01/0.0001        \n",
      "sib.iterate(damp=0.5):  24/25 2.412e-02/0.0001                   \n",
      "winBP: (S,I,R): (1620.8, 290.8, 88.4), seeds: 21.9, ll: -2881.6\n",
      "t = 23 / 110\n",
      "state: S:1594, I:317, R:89 I+R:406\n",
      "obser: S:641, I:346, R:28, I+R:374\n",
      "free birds: 26\n",
      "obs_symp:5(134), obs_rank:4(291)\n",
      "pos_rank:4, inf_cont:0.775\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 24 obs 45 40 5\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 13.974719400400229\n",
      "sib.iterate(damp=0):  24/25 3.653e-01/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 7.282e-03/0.0001    \n",
      "winBP: (S,I,R): (1615.5, 289.8, 94.7), seeds: 29.6, ll: -2915.4\n",
      "t = 24 / 110\n",
      "state: S:1585, I:321, R:94 I+R:415\n",
      "obser: S:679, I:348, R:28, I+R:376\n",
      "free birds: 33\n",
      "obs_symp:2(136), obs_rank:2(293)\n",
      "pos_rank:2, inf_cont:1.025\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 25 obs 42 40 2\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 17.833335029702198\n",
      "sib.iterate(damp=0):  24/25 2.227e-01/0.0001        \n",
      "sib.iterate(damp=0.5):  24/25 1.891e-03/0.0001               \n",
      "winBP: (S,I,R): (1625.0, 276.8, 98.2), seeds: 36.5, ll: -2951.2\n",
      "t = 25 / 110\n",
      "state: S:1581, I:320, R:99 I+R:419\n",
      "obser: S:714, I:353, R:28, I+R:381\n",
      "free birds: 32\n",
      "obs_symp:2(138), obs_rank:5(298)\n",
      "pos_rank:5, inf_cont:0.575\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 26 obs 42 40 2\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 24.891911790676048\n",
      "sib.iterate(damp=0):  24/25 2.950e-01/0.0001                \n",
      "sib.iterate(damp=0.5):  24/25 5.023e-03/0.0001        \n",
      "winBP: (S,I,R): (1624.5, 273.8, 101.6), seeds: 48.8, ll: -3036.9\n",
      "t = 26 / 110\n",
      "state: S:1572, I:324, R:104 I+R:428\n",
      "obser: S:748, I:361, R:28, I+R:389\n",
      "free birds: 33\n",
      "obs_symp:4(142), obs_rank:6(304)\n",
      "pos_rank:6, inf_cont:1.15\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 27 obs 44 40 4\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 27.660232332536776\n",
      "sib.iterate(damp=0):  24/25 3.901e-02/0.0001        \n",
      "sib.iterate(damp=0.5):  24/25 1.766e-03/0.0001    \n",
      "winBP: (S,I,R): (1623.7, 274.4, 101.9), seeds: 61.3, ll: -3128.4\n",
      "t = 27 / 110\n",
      "state: S:1563, I:328, R:109 I+R:437\n",
      "obser: S:763, I:386, R:28, I+R:414\n",
      "free birds: 16\n",
      "obs_symp:1(143), obs_rank:25(329)\n",
      "pos_rank:25, inf_cont:1.8\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 28 obs 41 40 1\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 51.20448213884565\n",
      "sib.iterate(damp=0):  24/25 1.274e-03/0.0001    \n",
      "sib.iterate(damp=0.5):  9/25 8.199e-05/0.0001        \n",
      "winBP: (S,I,R): (1606.5, 288.8, 104.7), seeds: 108.1, ll: -2946.6\n",
      "t = 28 / 110\n",
      "state: S:1559, I:328, R:113 I+R:441\n",
      "obser: S:796, I:392, R:29, I+R:421\n",
      "free birds: 14\n",
      "obs_symp:2(145), obs_rank:6(335)\n",
      "pos_rank:6, inf_cont:2.7\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 29 obs 42 40 2\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 72.40307746024212\n",
      "sib.iterate(damp=0):  24/25 1.558e-03/0.0001        \n",
      "sib.iterate(damp=0.5):  10/25 9.237e-05/0.0001    \n",
      "winBP: (S,I,R): (1620.6, 276.7, 102.7), seeds: 121.2, ll: -2864.6\n",
      "t = 29 / 110\n",
      "state: S:1556, I:329, R:115 I+R:444\n",
      "obser: S:830, I:398, R:29, I+R:427\n",
      "free birds: 10\n",
      "obs_symp:4(149), obs_rank:6(341)\n",
      "pos_rank:6, inf_cont:1.625\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 30 obs 44 40 4\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 105.99287644248628\n",
      "sib.iterate(damp=0):  24/25 7.097e-04/0.0001    \n",
      "sib.iterate(damp=0.5):  7/25 9.399e-05/0.0001    \n",
      "winBP: (S,I,R): (1633.3, 266.8, 100.0), seeds: 134.6, ll: -2756.0\n",
      "t = 30 / 110\n",
      "state: S:1555, I:323, R:122 I+R:445\n",
      "obser: S:864, I:404, R:29, I+R:433\n",
      "free birds: 5\n",
      "obs_symp:2(151), obs_rank:6(347)\n",
      "pos_rank:6, inf_cont:2.025\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 31 obs 42 40 2\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 140.2053513214067\n",
      "sib.iterate(damp=0):  24/25 4.965e-03/0.0001    \n",
      "sib.iterate(damp=0.5):  22/25 8.586e-05/0.0001               \n",
      "winBP: (S,I,R): (1645.9, 255.8, 98.4), seeds: 151.2, ll: -2650.6\n",
      "t = 31 / 110\n",
      "state: S:1554, I:314, R:132 I+R:446\n",
      "obser: S:902, I:405, R:30, I+R:435\n",
      "free birds: 5\n",
      "obs_symp:3(154), obs_rank:1(348)\n",
      "pos_rank:1, inf_cont:1.55\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 32 obs 43 40 3\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 175.85500306711722\n",
      "sib.iterate(damp=0):  24/25 1.759e-03/0.0001        \n",
      "sib.iterate(damp=0.5):  10/25 9.769e-05/0.0001    \n",
      "winBP: (S,I,R): (1655.1, 247.2, 97.7), seeds: 162.5, ll: -2577.1\n",
      "t = 32 / 110\n",
      "state: S:1554, I:307, R:139 I+R:446\n",
      "obser: S:942, I:406, R:30, I+R:436\n",
      "free birds: 4\n",
      "obs_symp:4(158), obs_rank:0(348)\n",
      "pos_rank:0, inf_cont:0.05\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 33 obs 44 40 4\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 187.47876051145195\n",
      "sib.iterate(damp=0):  24/25 1.037e-02/0.0001    \n",
      "sib.iterate(damp=0.5):  24/25 2.093e-04/0.0001    \n",
      "winBP: (S,I,R): (1677.1, 231.8, 91.1), seeds: 164.7, ll: -2504.8\n",
      "t = 33 / 110\n",
      "state: S:1553, I:302, R:145 I+R:447\n",
      "obser: S:982, I:407, R:30, I+R:437\n",
      "free birds: 4\n",
      "obs_symp:1(159), obs_rank:0(348)\n",
      "pos_rank:0, inf_cont:0.55\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 34 obs 41 40 1\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 200.38114122862407\n",
      "sib.iterate(damp=0):  24/25 4.481e-02/0.0001            \n",
      "sib.iterate(damp=0.5):  24/25 8.523e-04/0.0001            \n",
      "winBP: (S,I,R): (1688.5, 222.4, 89.1), seeds: 173.5, ll: -2455.9\n",
      "t = 34 / 110\n",
      "state: S:1553, I:298, R:149 I+R:447\n",
      "obser: S:1018, I:410, R:31, I+R:441\n",
      "free birds: 1\n",
      "obs_symp:1(160), obs_rank:3(351)\n",
      "pos_rank:3, inf_cont:0.375\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 35 obs 41 40 1\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 222.47255188363002\n",
      "sib.iterate(damp=0):  14/25 8.074e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 7.722e-05/0.0001    \n",
      "winBP: (S,I,R): (1700.7, 211.8, 87.4), seeds: 182.0, ll: -2405.8\n",
      "t = 35 / 110\n",
      "state: S:1551, I:298, R:151 I+R:449\n",
      "obser: S:1058, I:410, R:31, I+R:441\n",
      "free birds: 3\n",
      "obs_symp:0(160), obs_rank:0(351)\n",
      "pos_rank:0, inf_cont:0.25\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 36 obs 40 40 0\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 242.02503879250443\n",
      "sib.iterate(damp=0):  12/25 7.130e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 6.020e-05/0.0001    \n",
      "winBP: (S,I,R): (1722.9, 194.6, 82.4), seeds: 179.1, ll: -2310.3\n",
      "t = 36 / 110\n",
      "state: S:1551, I:291, R:158 I+R:449\n",
      "obser: S:1098, I:410, R:31, I+R:441\n",
      "free birds: 3\n",
      "obs_symp:0(160), obs_rank:0(351)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 37 obs 40 40 0\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 269.4915031028969\n",
      "sib.iterate(damp=0):  14/25 4.163e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 4.967e-05/0.0001    \n",
      "winBP: (S,I,R): (1734.9, 185.5, 79.6), seeds: 182.9, ll: -2229.9\n",
      "t = 37 / 110\n",
      "state: S:1551, I:288, R:161 I+R:449\n",
      "obser: S:1136, I:411, R:32, I+R:443\n",
      "free birds: 2\n",
      "obs_symp:0(160), obs_rank:1(352)\n",
      "pos_rank:1, inf_cont:0.0\n",
      "\n",
      "SCENARIO BP\n",
      "Observations: 38 obs 40 40 0\n",
      "...adjust prob_i0 and prob_r0\n",
      "...drop first time and reset observations\n",
      "pI at intial time: 270.98794272611553\n",
      "sib.iterate(damp=0):  22/25 8.627e-05/0.0001    \n",
      "sib.iterate(damp=0.5):  0/25 5.905e-05/0.0001    \n",
      "winBP: (S,I,R): (1739.9, 182.1, 77.9), seeds: 178.0, ll: -2190.8\n",
      "t = 38 / 110\n",
      "state: S:1551, I:285, R:164 I+R:449\n",
      "obser: S:1173, I:413, R:33, I+R:446\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:2(354)\n",
      "pos_rank:2, inf_cont:0.375\n",
      "t = 39 / 110\n",
      "state: S:1551, I:278, R:171 I+R:449\n",
      "obser: S:1213, I:413, R:33, I+R:446\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 40 / 110\n",
      "state: S:1551, I:272, R:177 I+R:449\n",
      "obser: S:1251, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:1(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 41 / 110\n",
      "state: S:1551, I:264, R:185 I+R:449\n",
      "obser: S:1291, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.1\n",
      "t = 42 / 110\n",
      "state: S:1551, I:263, R:186 I+R:449\n",
      "obser: S:1331, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 43 / 110\n",
      "state: S:1551, I:256, R:193 I+R:449\n",
      "obser: S:1371, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 44 / 110\n",
      "state: S:1551, I:251, R:198 I+R:449\n",
      "obser: S:1411, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 45 / 110\n",
      "state: S:1551, I:248, R:201 I+R:449\n",
      "obser: S:1451, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 46 / 110\n",
      "state: S:1551, I:243, R:206 I+R:449\n",
      "obser: S:1491, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 47 / 110\n",
      "state: S:1551, I:239, R:210 I+R:449\n",
      "obser: S:1531, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 48 / 110\n",
      "state: S:1551, I:232, R:217 I+R:449\n",
      "obser: S:1571, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 49 / 110\n",
      "state: S:1551, I:225, R:224 I+R:449\n",
      "obser: S:1611, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 50 / 110\n",
      "state: S:1551, I:220, R:229 I+R:449\n",
      "obser: S:1651, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 51 / 110\n",
      "state: S:1551, I:214, R:235 I+R:449\n",
      "obser: S:1691, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 52 / 110\n",
      "state: S:1551, I:211, R:238 I+R:449\n",
      "obser: S:1731, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 53 / 110\n",
      "state: S:1551, I:204, R:245 I+R:449\n",
      "obser: S:1771, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 54 / 110\n",
      "state: S:1551, I:197, R:252 I+R:449\n",
      "obser: S:1811, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 55 / 110\n",
      "state: S:1551, I:190, R:259 I+R:449\n",
      "obser: S:1851, I:413, R:35, I+R:448\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 56 / 110\n",
      "state: S:1551, I:187, R:262 I+R:449\n",
      "obser: S:1890, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 57 / 110\n",
      "state: S:1551, I:184, R:265 I+R:449\n",
      "obser: S:1930, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 58 / 110\n",
      "state: S:1551, I:180, R:269 I+R:449\n",
      "obser: S:1970, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 59 / 110\n",
      "state: S:1551, I:179, R:270 I+R:449\n",
      "obser: S:2010, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 60 / 110\n",
      "state: S:1551, I:175, R:274 I+R:449\n",
      "obser: S:2050, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 61 / 110\n",
      "state: S:1551, I:173, R:276 I+R:449\n",
      "obser: S:2090, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 62 / 110\n",
      "state: S:1551, I:168, R:281 I+R:449\n",
      "obser: S:2130, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 63 / 110\n",
      "state: S:1551, I:166, R:283 I+R:449\n",
      "obser: S:2170, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 64 / 110\n",
      "state: S:1551, I:162, R:287 I+R:449\n",
      "obser: S:2210, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 65 / 110\n",
      "state: S:1551, I:155, R:294 I+R:449\n",
      "obser: S:2250, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 66 / 110\n",
      "state: S:1551, I:152, R:297 I+R:449\n",
      "obser: S:2290, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 67 / 110\n",
      "state: S:1551, I:149, R:300 I+R:449\n",
      "obser: S:2330, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 68 / 110\n",
      "state: S:1551, I:143, R:306 I+R:449\n",
      "obser: S:2370, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 69 / 110\n",
      "state: S:1551, I:140, R:309 I+R:449\n",
      "obser: S:2410, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 70 / 110\n",
      "state: S:1551, I:136, R:313 I+R:449\n",
      "obser: S:2450, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 71 / 110\n",
      "state: S:1551, I:132, R:317 I+R:449\n",
      "obser: S:2490, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 72 / 110\n",
      "state: S:1551, I:130, R:319 I+R:449\n",
      "obser: S:2530, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 73 / 110\n",
      "state: S:1551, I:127, R:322 I+R:449\n",
      "obser: S:2570, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 74 / 110\n",
      "state: S:1551, I:126, R:323 I+R:449\n",
      "obser: S:2610, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 75 / 110\n",
      "state: S:1551, I:124, R:325 I+R:449\n",
      "obser: S:2650, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 76 / 110\n",
      "state: S:1551, I:120, R:329 I+R:449\n",
      "obser: S:2690, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 77 / 110\n",
      "state: S:1551, I:118, R:331 I+R:449\n",
      "obser: S:2730, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 78 / 110\n",
      "state: S:1551, I:114, R:335 I+R:449\n",
      "obser: S:2770, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 79 / 110\n",
      "state: S:1551, I:112, R:337 I+R:449\n",
      "obser: S:2810, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 80 / 110\n",
      "state: S:1551, I:110, R:339 I+R:449\n",
      "obser: S:2850, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 81 / 110\n",
      "state: S:1551, I:108, R:341 I+R:449\n",
      "obser: S:2890, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 82 / 110\n",
      "state: S:1551, I:105, R:344 I+R:449\n",
      "obser: S:2930, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 83 / 110\n",
      "state: S:1551, I:102, R:347 I+R:449\n",
      "obser: S:2970, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 84 / 110\n",
      "state: S:1551, I:100, R:349 I+R:449\n",
      "obser: S:3010, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 85 / 110\n",
      "state: S:1551, I:99, R:350 I+R:449\n",
      "obser: S:3050, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 86 / 110\n",
      "state: S:1551, I:98, R:351 I+R:449\n",
      "obser: S:3090, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 87 / 110\n",
      "state: S:1551, I:98, R:351 I+R:449\n",
      "obser: S:3130, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 88 / 110\n",
      "state: S:1551, I:96, R:353 I+R:449\n",
      "obser: S:3170, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 89 / 110\n",
      "state: S:1551, I:96, R:353 I+R:449\n",
      "obser: S:3210, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 90 / 110\n",
      "state: S:1551, I:94, R:355 I+R:449\n",
      "obser: S:3250, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 91 / 110\n",
      "state: S:1551, I:92, R:357 I+R:449\n",
      "obser: S:3290, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 92 / 110\n",
      "state: S:1551, I:91, R:358 I+R:449\n",
      "obser: S:3330, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 93 / 110\n",
      "state: S:1551, I:88, R:361 I+R:449\n",
      "obser: S:3370, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 94 / 110\n",
      "state: S:1551, I:85, R:364 I+R:449\n",
      "obser: S:3410, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 95 / 110\n",
      "state: S:1551, I:84, R:365 I+R:449\n",
      "obser: S:3450, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 96 / 110\n",
      "state: S:1551, I:84, R:365 I+R:449\n",
      "obser: S:3490, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 97 / 110\n",
      "state: S:1551, I:84, R:365 I+R:449\n",
      "obser: S:3530, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 98 / 110\n",
      "state: S:1551, I:83, R:366 I+R:449\n",
      "obser: S:3570, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 99 / 110\n",
      "state: S:1551, I:82, R:367 I+R:449\n",
      "obser: S:3610, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 100 / 110\n",
      "state: S:1551, I:81, R:368 I+R:449\n",
      "obser: S:3650, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 101 / 110\n",
      "state: S:1551, I:77, R:372 I+R:449\n",
      "obser: S:3690, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 102 / 110\n",
      "state: S:1551, I:77, R:372 I+R:449\n",
      "obser: S:3730, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 103 / 110\n",
      "state: S:1551, I:77, R:372 I+R:449\n",
      "obser: S:3770, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 104 / 110\n",
      "state: S:1551, I:75, R:374 I+R:449\n",
      "obser: S:3810, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 105 / 110\n",
      "state: S:1551, I:72, R:377 I+R:449\n",
      "obser: S:3850, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 106 / 110\n",
      "state: S:1551, I:68, R:381 I+R:449\n",
      "obser: S:3890, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 107 / 110\n",
      "state: S:1551, I:66, R:383 I+R:449\n",
      "obser: S:3930, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 108 / 110\n",
      "state: S:1551, I:63, R:386 I+R:449\n",
      "obser: S:3970, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 109 / 110\n",
      "state: S:1551, I:61, R:388 I+R:449\n",
      "obser: S:4010, I:413, R:36, I+R:449\n",
      "free birds: 0\n",
      "obs_symp:0(161), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Save bp strategy\n",
      "Saving to csv/Proximity_N2K_T120_s1_ti10_pz10_mu0.02_l0.05_seed5_obs40_bp_info\n"
     ]
    }
   ],
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import sib\n",
    "import bp_ranking\n",
    "imp.reload(bp_ranking)\n",
    "from bp_ranking import bp_ranker_class\n",
    "\n",
    "mu_r = np.log(1 + mu)\n",
    "win = 21\n",
    "#bp_tau = 10\n",
    "name_csv = \"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp_no_tau_win%d_2iter.csv\"%(N/1000, T,t1,N_patient_zero,mu,lamb,seed,n_ranking,win)\n",
    "prob_seed=1/N\n",
    "prob_sus = 0.55\n",
    "pseed = prob_seed / (2 - prob_seed)\n",
    "psus = prob_sus * (1 - pseed)\n",
    "pautoinf = 1e-10\n",
    "\n",
    "bp_ranker = bp_ranker_class(params = sib.Params(\n",
    "                                 prob_r = sib.Exponential(mu=mu_r),\n",
    "                                 prob_i = sib.Uniform(p=lamb),\n",
    "                                 pseed = pseed,\n",
    "                                 psus = psus,\n",
    "                                 pautoinf = pautoinf,\n",
    "                                 fn_rate = 0.001,\n",
    "                                 fp_rate = 0.001),\n",
    "                                 \n",
    "                 maxit0 = 25,\n",
    "                 maxit1 = 25,\n",
    "                 tol = 1e-4,\n",
    "                 memory_decay = 0.1,\n",
    "                 window_length = win\n",
    "                            #tau=bp_tau\n",
    "                           )\n",
    "bp_ranker.init(N, T)\n",
    "bp_ranker.__name__ = \"bp\"\n",
    "scenario_bp = Scenario( model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=bp_ranker.step_scenario, lamb = 1.0),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    "                         save_csv = name_csv\n",
    ")\n",
    "scenario_bp.run(t_max-t1, print_every = 1)\n",
    "print(\"Save bp strategy\", flush=True)\n",
    "#scenario_bp.counts.to_csv(name_csv,\n",
    "#          index=False, sep=\"\\t\")\n",
    "scenario_bp.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking))\n",
    "cnts = scenario_bp.counts\n",
    "cnts[\"t\" ]=cnts.index\n",
    "traces[\"BP\"] = cnts\n",
    "\n",
    "ranks_status[\"BP\"]=scenario_bp.status\n",
    "\n",
    "ranks_status[\"BP\"][\"t\"]+=t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/pandas/core/indexes/range.py:720: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  return super().__getitem__(key)\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1aef54950>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(db.index, db.I, label=\"No intervention\")\n",
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"], label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ranking_backtrack to rank\n",
      "Using seed=6\n",
      "Selecting 0/2000 untracked individuals\n",
      "t = 0 / 110\n",
      "state: S:1921, I:75, R:4 I+R:79\n",
      "obser: S:38, I:2, R:0, I+R:2\n",
      "free birds: 73\n",
      "obs_symp:0(0), obs_rank:2(2)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "t = 1 / 110\n",
      "state: S:1905, I:91, R:4 I+R:95\n",
      "obser: S:74, I:6, R:0, I+R:6\n",
      "free birds: 85\n",
      "obs_symp:0(0), obs_rank:4(6)\n",
      "pos_rank:4, inf_cont:0.0\n",
      "t = 2 / 110\n",
      "state: S:1893, I:103, R:4 I+R:107\n",
      "obser: S:106, I:14, R:0, I+R:14\n",
      "free birds: 89\n",
      "obs_symp:0(0), obs_rank:8(14)\n",
      "pos_rank:8, inf_cont:1.275\n",
      "t = 3 / 110\n",
      "state: S:1874, I:121, R:5 I+R:126\n",
      "obser: S:138, I:22, R:0, I+R:22\n",
      "free birds: 99\n",
      "obs_symp:0(0), obs_rank:8(22)\n",
      "pos_rank:8, inf_cont:2.55\n",
      "t = 4 / 110\n",
      "state: S:1854, I:138, R:8 I+R:146\n",
      "obser: S:170, I:29, R:1, I+R:30\n",
      "free birds: 109\n",
      "obs_symp:0(0), obs_rank:7(29)\n",
      "pos_rank:7, inf_cont:3.3\n",
      "t = 5 / 110\n",
      "state: S:1836, I:153, R:11 I+R:164\n",
      "obser: S:198, I:38, R:4, I+R:42\n",
      "free birds: 116\n",
      "obs_symp:0(0), obs_rank:9(38)\n",
      "pos_rank:9, inf_cont:1.95\n",
      "t = 6 / 110\n",
      "state: S:1825, I:160, R:15 I+R:175\n",
      "obser: S:224, I:56, R:5, I+R:61\n",
      "free birds: 107\n",
      "obs_symp:8(8), obs_rank:13(51)\n",
      "pos_rank:13, inf_cont:2.1\n",
      "t = 7 / 110\n",
      "state: S:1802, I:182, R:16 I+R:198\n",
      "obser: S:243, I:78, R:6, I+R:84\n",
      "free birds: 107\n",
      "obs_symp:5(13), obs_rank:20(71)\n",
      "pos_rank:20, inf_cont:3.225\n",
      "t = 8 / 110\n",
      "state: S:1786, I:196, R:18 I+R:214\n",
      "obser: S:259, I:105, R:6, I+R:111\n",
      "free birds: 96\n",
      "obs_symp:8(21), obs_rank:24(95)\n",
      "pos_rank:24, inf_cont:4.1\n",
      "t = 9 / 110\n",
      "state: S:1768, I:211, R:21 I+R:232\n",
      "obser: S:277, I:129, R:6, I+R:135\n",
      "free birds: 88\n",
      "obs_symp:10(31), obs_rank:22(117)\n",
      "pos_rank:22, inf_cont:4.7\n",
      "t = 10 / 110\n",
      "state: S:1758, I:217, R:25 I+R:242\n",
      "obser: S:296, I:153, R:7, I+R:160\n",
      "free birds: 73\n",
      "obs_symp:8(39), obs_rank:20(137)\n",
      "pos_rank:20, inf_cont:5.325\n",
      "t = 11 / 110\n",
      "state: S:1742, I:229, R:29 I+R:258\n",
      "obser: S:325, I:163, R:10, I+R:173\n",
      "free birds: 77\n",
      "obs_symp:5(44), obs_rank:8(145)\n",
      "pos_rank:8, inf_cont:3.15\n",
      "t = 12 / 110\n",
      "state: S:1731, I:236, R:33 I+R:269\n",
      "obser: S:347, I:184, R:11, I+R:195\n",
      "free birds: 65\n",
      "obs_symp:11(55), obs_rank:17(162)\n",
      "pos_rank:17, inf_cont:2.4\n",
      "t = 13 / 110\n",
      "state: S:1719, I:243, R:38 I+R:281\n",
      "obser: S:368, I:206, R:11, I+R:217\n",
      "free birds: 55\n",
      "obs_symp:7(62), obs_rank:19(181)\n",
      "pos_rank:19, inf_cont:3.075\n",
      "t = 14 / 110\n",
      "state: S:1709, I:251, R:40 I+R:291\n",
      "obser: S:401, I:213, R:12, I+R:225\n",
      "free birds: 58\n",
      "obs_symp:8(70), obs_rank:6(187)\n",
      "pos_rank:6, inf_cont:4.625\n",
      "t = 15 / 110\n",
      "state: S:1696, I:260, R:44 I+R:304\n",
      "obser: S:435, I:221, R:13, I+R:234\n",
      "free birds: 62\n",
      "obs_symp:5(75), obs_rank:5(192)\n",
      "pos_rank:5, inf_cont:1.85\n",
      "t = 16 / 110\n",
      "state: S:1674, I:280, R:46 I+R:326\n",
      "obser: S:469, I:231, R:13, I+R:244\n",
      "free birds: 74\n",
      "obs_symp:8(83), obs_rank:6(198)\n",
      "pos_rank:6, inf_cont:1.675\n",
      "t = 17 / 110\n",
      "state: S:1664, I:283, R:53 I+R:336\n",
      "obser: S:503, I:240, R:13, I+R:253\n",
      "free birds: 74\n",
      "obs_symp:5(88), obs_rank:6(204)\n",
      "pos_rank:6, inf_cont:2.075\n",
      "t = 18 / 110\n",
      "state: S:1649, I:292, R:59 I+R:351\n",
      "obser: S:535, I:251, R:14, I+R:265\n",
      "free birds: 76\n",
      "obs_symp:6(94), obs_rank:7(211)\n",
      "pos_rank:7, inf_cont:2.025\n",
      "t = 19 / 110\n",
      "state: S:1633, I:300, R:67 I+R:367\n",
      "obser: S:558, I:271, R:14, I+R:285\n",
      "free birds: 71\n",
      "obs_symp:5(99), obs_rank:17(228)\n",
      "pos_rank:17, inf_cont:2.325\n",
      "t = 20 / 110\n",
      "state: S:1614, I:316, R:70 I+R:386\n",
      "obser: S:587, I:284, R:15, I+R:299\n",
      "free birds: 76\n",
      "obs_symp:6(105), obs_rank:10(238)\n",
      "pos_rank:10, inf_cont:4.475\n",
      "t = 21 / 110\n",
      "state: S:1597, I:326, R:77 I+R:403\n",
      "obser: S:614, I:302, R:16, I+R:318\n",
      "free birds: 74\n",
      "obs_symp:10(115), obs_rank:12(250)\n",
      "pos_rank:12, inf_cont:2.1\n",
      "t = 22 / 110\n",
      "state: S:1586, I:333, R:81 I+R:414\n",
      "obser: S:639, I:317, R:19, I+R:336\n",
      "free birds: 67\n",
      "obs_symp:5(120), obs_rank:12(262)\n",
      "pos_rank:12, inf_cont:3.275\n",
      "t = 23 / 110\n",
      "state: S:1578, I:332, R:90 I+R:422\n",
      "obser: S:667, I:328, R:21, I+R:349\n",
      "free birds: 62\n",
      "obs_symp:7(127), obs_rank:10(272)\n",
      "pos_rank:10, inf_cont:3.275\n",
      "t = 24 / 110\n",
      "state: S:1566, I:337, R:97 I+R:434\n",
      "obser: S:699, I:337, R:23, I+R:360\n",
      "free birds: 64\n",
      "obs_symp:8(135), obs_rank:6(278)\n",
      "pos_rank:6, inf_cont:2.15\n",
      "t = 25 / 110\n",
      "state: S:1559, I:337, R:104 I+R:441\n",
      "obser: S:729, I:348, R:25, I+R:373\n",
      "free birds: 57\n",
      "obs_symp:8(143), obs_rank:8(286)\n",
      "pos_rank:8, inf_cont:1.3\n",
      "t = 26 / 110\n",
      "state: S:1545, I:345, R:110 I+R:455\n",
      "obser: S:754, I:364, R:28, I+R:392\n",
      "free birds: 54\n",
      "obs_symp:7(150), obs_rank:12(298)\n",
      "pos_rank:12, inf_cont:2.225\n",
      "t = 27 / 110\n",
      "state: S:1533, I:354, R:113 I+R:467\n",
      "obser: S:781, I:377, R:29, I+R:406\n",
      "free birds: 52\n",
      "obs_symp:5(155), obs_rank:12(310)\n",
      "pos_rank:12, inf_cont:2.975\n",
      "t = 28 / 110\n",
      "state: S:1518, I:362, R:120 I+R:482\n",
      "obser: S:814, I:384, R:29, I+R:413\n",
      "free birds: 60\n",
      "obs_symp:3(158), obs_rank:7(317)\n",
      "pos_rank:7, inf_cont:2.075\n",
      "t = 29 / 110\n",
      "state: S:1507, I:370, R:123 I+R:493\n",
      "obser: S:843, I:397, R:29, I+R:426\n",
      "free birds: 58\n",
      "obs_symp:5(163), obs_rank:11(328)\n",
      "pos_rank:11, inf_cont:2.35\n",
      "t = 30 / 110\n",
      "state: S:1497, I:372, R:131 I+R:503\n",
      "obser: S:875, I:407, R:29, I+R:436\n",
      "free birds: 58\n",
      "obs_symp:3(166), obs_rank:8(336)\n",
      "pos_rank:8, inf_cont:2.275\n",
      "t = 31 / 110\n",
      "state: S:1487, I:374, R:139 I+R:513\n",
      "obser: S:905, I:418, R:30, I+R:448\n",
      "free birds: 57\n",
      "obs_symp:6(172), obs_rank:9(345)\n",
      "pos_rank:9, inf_cont:1.3\n",
      "t = 32 / 110\n",
      "state: S:1475, I:383, R:142 I+R:525\n",
      "obser: S:935, I:429, R:30, I+R:459\n",
      "free birds: 58\n",
      "obs_symp:5(177), obs_rank:10(355)\n",
      "pos_rank:10, inf_cont:2.125\n",
      "t = 33 / 110\n",
      "state: S:1466, I:385, R:149 I+R:534\n",
      "obser: S:970, I:437, R:30, I+R:467\n",
      "free birds: 58\n",
      "obs_symp:7(184), obs_rank:5(360)\n",
      "pos_rank:5, inf_cont:1.3\n",
      "t = 34 / 110\n",
      "state: S:1454, I:390, R:156 I+R:546\n",
      "obser: S:996, I:452, R:31, I+R:483\n",
      "free birds: 53\n",
      "obs_symp:5(189), obs_rank:13(373)\n",
      "pos_rank:13, inf_cont:1.775\n",
      "t = 35 / 110\n",
      "state: S:1439, I:401, R:160 I+R:561\n",
      "obser: S:1020, I:470, R:32, I+R:502\n",
      "free birds: 49\n",
      "obs_symp:5(194), obs_rank:15(388)\n",
      "pos_rank:15, inf_cont:2.925\n",
      "t = 36 / 110\n",
      "state: S:1433, I:398, R:169 I+R:567\n",
      "obser: S:1050, I:479, R:33, I+R:512\n",
      "free birds: 46\n",
      "obs_symp:5(199), obs_rank:9(397)\n",
      "pos_rank:9, inf_cont:2.225\n",
      "t = 37 / 110\n",
      "state: S:1427, I:400, R:173 I+R:573\n",
      "obser: S:1084, I:488, R:33, I+R:521\n",
      "free birds: 43\n",
      "obs_symp:4(203), obs_rank:6(403)\n",
      "pos_rank:6, inf_cont:1.575\n",
      "t = 38 / 110\n",
      "state: S:1424, I:395, R:181 I+R:576\n",
      "obser: S:1114, I:500, R:33, I+R:533\n",
      "free birds: 33\n",
      "obs_symp:4(207), obs_rank:10(413)\n",
      "pos_rank:10, inf_cont:1.925\n",
      "t = 39 / 110\n",
      "state: S:1419, I:391, R:190 I+R:581\n",
      "obser: S:1146, I:510, R:33, I+R:543\n",
      "free birds: 28\n",
      "obs_symp:5(212), obs_rank:8(421)\n",
      "pos_rank:8, inf_cont:3.6\n",
      "t = 40 / 110\n",
      "state: S:1415, I:384, R:201 I+R:585\n",
      "obser: S:1182, I:515, R:33, I+R:548\n",
      "free birds: 26\n",
      "obs_symp:6(218), obs_rank:4(425)\n",
      "pos_rank:4, inf_cont:1.975\n",
      "t = 41 / 110\n",
      "state: S:1407, I:387, R:206 I+R:593\n",
      "obser: S:1214, I:523, R:34, I+R:557\n",
      "free birds: 26\n",
      "obs_symp:3(221), obs_rank:7(432)\n",
      "pos_rank:7, inf_cont:1.05\n",
      "t = 42 / 110\n",
      "state: S:1405, I:380, R:215 I+R:595\n",
      "obser: S:1248, I:527, R:36, I+R:563\n",
      "free birds: 24\n",
      "obs_symp:3(224), obs_rank:4(436)\n",
      "pos_rank:4, inf_cont:1.375\n",
      "t = 43 / 110\n",
      "state: S:1403, I:373, R:224 I+R:597\n",
      "obser: S:1285, I:531, R:36, I+R:567\n",
      "free birds: 21\n",
      "obs_symp:1(225), obs_rank:3(439)\n",
      "pos_rank:3, inf_cont:1.2\n",
      "t = 44 / 110\n",
      "state: S:1398, I:371, R:231 I+R:602\n",
      "obser: S:1321, I:537, R:36, I+R:573\n",
      "free birds: 20\n",
      "obs_symp:2(227), obs_rank:4(443)\n",
      "pos_rank:4, inf_cont:1.4\n",
      "t = 45 / 110\n",
      "state: S:1395, I:368, R:237 I+R:605\n",
      "obser: S:1357, I:541, R:36, I+R:577\n",
      "free birds: 18\n",
      "obs_symp:2(229), obs_rank:4(447)\n",
      "pos_rank:4, inf_cont:1.55\n",
      "t = 46 / 110\n",
      "state: S:1389, I:365, R:246 I+R:611\n",
      "obser: S:1393, I:545, R:39, I+R:584\n",
      "free birds: 18\n",
      "obs_symp:4(233), obs_rank:1(448)\n",
      "pos_rank:1, inf_cont:0.625\n",
      "t = 47 / 110\n",
      "state: S:1386, I:359, R:255 I+R:614\n",
      "obser: S:1429, I:548, R:40, I+R:588\n",
      "free birds: 18\n",
      "obs_symp:1(234), obs_rank:3(451)\n",
      "pos_rank:3, inf_cont:1.125\n",
      "t = 48 / 110\n",
      "state: S:1383, I:354, R:263 I+R:617\n",
      "obser: S:1468, I:549, R:40, I+R:589\n",
      "free birds: 20\n",
      "obs_symp:1(235), obs_rank:1(452)\n",
      "pos_rank:1, inf_cont:0.8\n",
      "t = 49 / 110\n",
      "state: S:1375, I:358, R:267 I+R:625\n",
      "obser: S:1504, I:554, R:40, I+R:594\n",
      "free birds: 23\n",
      "obs_symp:2(237), obs_rank:4(456)\n",
      "pos_rank:4, inf_cont:0.175\n",
      "t = 50 / 110\n",
      "state: S:1372, I:353, R:275 I+R:628\n",
      "obser: S:1537, I:561, R:40, I+R:601\n",
      "free birds: 19\n",
      "obs_symp:1(238), obs_rank:7(463)\n",
      "pos_rank:7, inf_cont:1.975\n",
      "t = 51 / 110\n",
      "state: S:1369, I:348, R:283 I+R:631\n",
      "obser: S:1573, I:567, R:40, I+R:607\n",
      "free birds: 16\n",
      "obs_symp:3(241), obs_rank:4(467)\n",
      "pos_rank:4, inf_cont:1.075\n",
      "t = 52 / 110\n",
      "state: S:1367, I:342, R:291 I+R:633\n",
      "obser: S:1604, I:575, R:41, I+R:616\n",
      "free birds: 9\n",
      "obs_symp:1(242), obs_rank:8(475)\n",
      "pos_rank:8, inf_cont:1.575\n",
      "t = 53 / 110\n",
      "state: S:1365, I:339, R:296 I+R:635\n",
      "obser: S:1638, I:581, R:41, I+R:622\n",
      "free birds: 5\n",
      "obs_symp:1(243), obs_rank:6(481)\n",
      "pos_rank:6, inf_cont:1.7\n",
      "t = 54 / 110\n",
      "state: S:1364, I:329, R:307 I+R:636\n",
      "obser: S:1675, I:583, R:42, I+R:625\n",
      "free birds: 3\n",
      "obs_symp:4(247), obs_rank:2(483)\n",
      "pos_rank:2, inf_cont:1.125\n",
      "t = 55 / 110\n",
      "state: S:1363, I:325, R:312 I+R:637\n",
      "obser: S:1711, I:585, R:44, I+R:629\n",
      "free birds: 2\n",
      "obs_symp:1(248), obs_rank:2(485)\n",
      "pos_rank:2, inf_cont:1.225\n",
      "t = 56 / 110\n",
      "state: S:1363, I:319, R:318 I+R:637\n",
      "obser: S:1751, I:585, R:44, I+R:629\n",
      "free birds: 2\n",
      "obs_symp:1(249), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.2\n",
      "t = 57 / 110\n",
      "state: S:1363, I:312, R:325 I+R:637\n",
      "obser: S:1791, I:585, R:44, I+R:629\n",
      "free birds: 2\n",
      "obs_symp:1(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 58 / 110\n",
      "state: S:1363, I:305, R:332 I+R:637\n",
      "obser: S:1831, I:585, R:44, I+R:629\n",
      "free birds: 2\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.05\n",
      "t = 59 / 110\n",
      "state: S:1362, I:301, R:337 I+R:638\n",
      "obser: S:1869, I:585, R:46, I+R:631\n",
      "free birds: 3\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 60 / 110\n",
      "state: S:1362, I:295, R:343 I+R:638\n",
      "obser: S:1908, I:585, R:47, I+R:632\n",
      "free birds: 3\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 61 / 110\n",
      "state: S:1362, I:294, R:344 I+R:638\n",
      "obser: S:1948, I:585, R:47, I+R:632\n",
      "free birds: 3\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 62 / 110\n",
      "state: S:1362, I:285, R:353 I+R:638\n",
      "obser: S:1988, I:585, R:47, I+R:632\n",
      "free birds: 3\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 63 / 110\n",
      "state: S:1361, I:277, R:362 I+R:639\n",
      "obser: S:2028, I:585, R:47, I+R:632\n",
      "free birds: 4\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 64 / 110\n",
      "state: S:1361, I:269, R:370 I+R:639\n",
      "obser: S:2067, I:585, R:48, I+R:633\n",
      "free birds: 4\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 65 / 110\n",
      "state: S:1361, I:266, R:373 I+R:639\n",
      "obser: S:2107, I:585, R:48, I+R:633\n",
      "free birds: 4\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 66 / 110\n",
      "state: S:1360, I:262, R:378 I+R:640\n",
      "obser: S:2147, I:585, R:48, I+R:633\n",
      "free birds: 5\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 67 / 110\n",
      "state: S:1357, I:260, R:383 I+R:643\n",
      "obser: S:2187, I:585, R:48, I+R:633\n",
      "free birds: 8\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 68 / 110\n",
      "state: S:1357, I:256, R:387 I+R:643\n",
      "obser: S:2227, I:585, R:48, I+R:633\n",
      "free birds: 7\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 69 / 110\n",
      "state: S:1355, I:255, R:390 I+R:645\n",
      "obser: S:2267, I:585, R:48, I+R:633\n",
      "free birds: 9\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 70 / 110\n",
      "state: S:1354, I:251, R:395 I+R:646\n",
      "obser: S:2306, I:585, R:49, I+R:634\n",
      "free birds: 10\n",
      "obs_symp:0(250), obs_rank:0(485)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 71 / 110\n",
      "state: S:1351, I:249, R:400 I+R:649\n",
      "obser: S:2345, I:586, R:49, I+R:635\n",
      "free birds: 12\n",
      "obs_symp:0(250), obs_rank:1(486)\n",
      "pos_rank:1, inf_cont:0.0\n",
      "t = 72 / 110\n",
      "state: S:1349, I:246, R:405 I+R:651\n",
      "obser: S:2380, I:592, R:49, I+R:641\n",
      "free birds: 8\n",
      "obs_symp:1(251), obs_rank:5(491)\n",
      "pos_rank:5, inf_cont:0.125\n",
      "t = 73 / 110\n",
      "state: S:1349, I:237, R:414 I+R:651\n",
      "obser: S:2412, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(251), obs_rank:7(498)\n",
      "pos_rank:7, inf_cont:0.9\n",
      "t = 74 / 110\n",
      "state: S:1349, I:232, R:419 I+R:651\n",
      "obser: S:2452, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:1(252), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.05\n",
      "t = 75 / 110\n",
      "state: S:1349, I:230, R:421 I+R:651\n",
      "obser: S:2492, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(252), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 76 / 110\n",
      "state: S:1349, I:225, R:426 I+R:651\n",
      "obser: S:2532, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:1(253), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.075\n",
      "t = 77 / 110\n",
      "state: S:1349, I:218, R:433 I+R:651\n",
      "obser: S:2572, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:1(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 78 / 110\n",
      "state: S:1349, I:214, R:437 I+R:651\n",
      "obser: S:2612, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 79 / 110\n",
      "state: S:1349, I:208, R:443 I+R:651\n",
      "obser: S:2652, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 80 / 110\n",
      "state: S:1349, I:201, R:450 I+R:651\n",
      "obser: S:2692, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 81 / 110\n",
      "state: S:1349, I:198, R:453 I+R:651\n",
      "obser: S:2732, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 82 / 110\n",
      "state: S:1349, I:193, R:458 I+R:651\n",
      "obser: S:2772, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 83 / 110\n",
      "state: S:1349, I:185, R:466 I+R:651\n",
      "obser: S:2812, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 84 / 110\n",
      "state: S:1349, I:182, R:469 I+R:651\n",
      "obser: S:2852, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 85 / 110\n",
      "state: S:1349, I:175, R:476 I+R:651\n",
      "obser: S:2892, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 86 / 110\n",
      "state: S:1349, I:171, R:480 I+R:651\n",
      "obser: S:2932, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 87 / 110\n",
      "state: S:1349, I:161, R:490 I+R:651\n",
      "obser: S:2972, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 88 / 110\n",
      "state: S:1349, I:159, R:492 I+R:651\n",
      "obser: S:3012, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 89 / 110\n",
      "state: S:1349, I:157, R:494 I+R:651\n",
      "obser: S:3052, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 90 / 110\n",
      "state: S:1349, I:154, R:497 I+R:651\n",
      "obser: S:3092, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 91 / 110\n",
      "state: S:1349, I:153, R:498 I+R:651\n",
      "obser: S:3132, I:599, R:50, I+R:649\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 92 / 110\n",
      "state: S:1349, I:151, R:500 I+R:651\n",
      "obser: S:3171, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 93 / 110\n",
      "state: S:1349, I:146, R:505 I+R:651\n",
      "obser: S:3211, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 94 / 110\n",
      "state: S:1349, I:145, R:506 I+R:651\n",
      "obser: S:3251, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 95 / 110\n",
      "state: S:1349, I:139, R:512 I+R:651\n",
      "obser: S:3291, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 96 / 110\n",
      "state: S:1349, I:134, R:517 I+R:651\n",
      "obser: S:3331, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 97 / 110\n",
      "state: S:1349, I:133, R:518 I+R:651\n",
      "obser: S:3371, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 98 / 110\n",
      "state: S:1349, I:131, R:520 I+R:651\n",
      "obser: S:3411, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 99 / 110\n",
      "state: S:1349, I:127, R:524 I+R:651\n",
      "obser: S:3451, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 100 / 110\n",
      "state: S:1349, I:125, R:526 I+R:651\n",
      "obser: S:3491, I:599, R:51, I+R:650\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 101 / 110\n",
      "state: S:1349, I:122, R:529 I+R:651\n",
      "obser: S:3530, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 102 / 110\n",
      "state: S:1349, I:121, R:530 I+R:651\n",
      "obser: S:3570, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 103 / 110\n",
      "state: S:1349, I:118, R:533 I+R:651\n",
      "obser: S:3610, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 104 / 110\n",
      "state: S:1349, I:118, R:533 I+R:651\n",
      "obser: S:3650, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 105 / 110\n",
      "state: S:1349, I:115, R:536 I+R:651\n",
      "obser: S:3690, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 106 / 110\n",
      "state: S:1349, I:113, R:538 I+R:651\n",
      "obser: S:3730, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 107 / 110\n",
      "state: S:1349, I:112, R:539 I+R:651\n",
      "obser: S:3770, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 108 / 110\n",
      "state: S:1349, I:112, R:539 I+R:651\n",
      "obser: S:3810, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 109 / 110\n",
      "state: S:1349, I:109, R:542 I+R:651\n",
      "obser: S:3850, I:599, R:52, I+R:651\n",
      "free birds: 0\n",
      "obs_symp:0(254), obs_rank:0(498)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Save MF strategy\n",
      "Saving to csv/Proximity_N2K_T120_s1_ti10_pz10_mu0.02_l0.05_seed5_obs40_MF_t5_d15_info\n",
      "End seed\n"
     ]
    }
   ],
   "source": [
    "##### MF SCENARIO #######\n",
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import ranking\n",
    "imp.reload(ranking)\n",
    "from ranking import RANKINGS\n",
    "scenario_MF = Scenario(\n",
    "    model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=RANKINGS[\"backtrack\"], \n",
    "                         algo=\"MF\", init=\"all_S\", tau=MF_tau, delta=MF_delta,mu=mu,lamb=lamb), \n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    ")\n",
    "scenario_MF.run(t_max-t1, print_every = 1)\n",
    "print(\"Save MF strategy\", flush=True)\n",
    "#scenario_MF.counts.to_csv(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_MF_t%d_d%d.csv\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,MF_tau,MF_delta),\n",
    "#                  index=False, sep=\"\\t\")\n",
    "scenario_MF.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_MF_t%d_d%d_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,MF_tau,MF_delta))\n",
    "cnts = scenario_MF.counts\n",
    "cnts[\"t\" ]=cnts.index\n",
    "traces[\"MF\"] = cnts\n",
    "save_status(scenario_MF, \"MF\")\n",
    "\n",
    "del scenario_MF\n",
    "# 1h01min per round\n",
    "print(\"End seed\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1a45176d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(db.index), db.I.to_numpy(), label=\"No intervention\")\n",
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"], label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ranking_tracing to rank\n",
      "Using seed=6\n",
      "Selecting 0/2000 untracked individuals\n",
      "t = 0 / 110\n",
      "state: S:1921, I:75, R:4 I+R:79\n",
      "obser: S:38, I:2, R:0, I+R:2\n",
      "free birds: 73\n",
      "obs_symp:0(0), obs_rank:2(2)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "t = 1 / 110\n",
      "state: S:1907, I:89, R:4 I+R:93\n",
      "obser: S:75, I:5, R:0, I+R:5\n",
      "free birds: 84\n",
      "obs_symp:0(0), obs_rank:3(5)\n",
      "pos_rank:3, inf_cont:0.025\n",
      "t = 2 / 110\n",
      "state: S:1894, I:102, R:4 I+R:106\n",
      "obser: S:114, I:6, R:0, I+R:6\n",
      "free birds: 96\n",
      "obs_symp:0(0), obs_rank:1(6)\n",
      "pos_rank:1, inf_cont:0.05\n",
      "t = 3 / 110\n",
      "state: S:1873, I:122, R:5 I+R:127\n",
      "obser: S:148, I:12, R:0, I+R:12\n",
      "free birds: 110\n",
      "obs_symp:0(0), obs_rank:6(12)\n",
      "pos_rank:6, inf_cont:0.175\n",
      "t = 4 / 110\n",
      "state: S:1854, I:138, R:8 I+R:146\n",
      "obser: S:186, I:14, R:0, I+R:14\n",
      "free birds: 124\n",
      "obs_symp:0(0), obs_rank:2(14)\n",
      "pos_rank:2, inf_cont:0.05\n",
      "t = 5 / 110\n",
      "state: S:1836, I:152, R:12 I+R:164\n",
      "obser: S:204, I:34, R:2, I+R:36\n",
      "free birds: 118\n",
      "obs_symp:0(0), obs_rank:20(34)\n",
      "pos_rank:20, inf_cont:2.225\n",
      "t = 6 / 110\n",
      "state: S:1820, I:165, R:15 I+R:180\n",
      "obser: S:225, I:55, R:3, I+R:58\n",
      "free birds: 110\n",
      "obs_symp:7(7), obs_rank:18(52)\n",
      "pos_rank:18, inf_cont:5.75\n",
      "t = 7 / 110\n",
      "state: S:1802, I:181, R:17 I+R:198\n",
      "obser: S:256, I:68, R:3, I+R:71\n",
      "free birds: 113\n",
      "obs_symp:5(12), obs_rank:9(61)\n",
      "pos_rank:9, inf_cont:6.525\n",
      "t = 8 / 110\n",
      "state: S:1785, I:195, R:20 I+R:215\n",
      "obser: S:285, I:83, R:3, I+R:86\n",
      "free birds: 114\n",
      "obs_symp:8(20), obs_rank:11(72)\n",
      "pos_rank:11, inf_cont:5.875\n",
      "t = 9 / 110\n",
      "state: S:1761, I:216, R:23 I+R:239\n",
      "obser: S:313, I:98, R:4, I+R:102\n",
      "free birds: 120\n",
      "obs_symp:9(29), obs_rank:11(83)\n",
      "pos_rank:11, inf_cont:5.525\n",
      "t = 10 / 110\n",
      "state: S:1739, I:235, R:26 I+R:261\n",
      "obser: S:335, I:119, R:7, I+R:126\n",
      "free birds: 119\n",
      "obs_symp:8(37), obs_rank:15(98)\n",
      "pos_rank:15, inf_cont:4.725\n",
      "t = 11 / 110\n",
      "state: S:1713, I:256, R:31 I+R:287\n",
      "obser: S:356, I:140, R:8, I+R:148\n",
      "free birds: 122\n",
      "obs_symp:7(44), obs_rank:18(116)\n",
      "pos_rank:18, inf_cont:5.125\n",
      "t = 12 / 110\n",
      "state: S:1702, I:262, R:36 I+R:298\n",
      "obser: S:382, I:158, R:9, I+R:167\n",
      "free birds: 114\n",
      "obs_symp:9(53), obs_rank:13(129)\n",
      "pos_rank:13, inf_cont:5.8\n",
      "t = 13 / 110\n",
      "state: S:1687, I:272, R:41 I+R:313\n",
      "obser: S:410, I:172, R:11, I+R:183\n",
      "free birds: 112\n",
      "obs_symp:8(61), obs_rank:10(139)\n",
      "pos_rank:10, inf_cont:5.55\n",
      "t = 14 / 110\n",
      "state: S:1664, I:293, R:43 I+R:336\n",
      "obser: S:440, I:186, R:12, I+R:198\n",
      "free birds: 119\n",
      "obs_symp:11(72), obs_rank:9(148)\n",
      "pos_rank:9, inf_cont:4.8\n",
      "t = 15 / 110\n",
      "state: S:1646, I:308, R:46 I+R:354\n",
      "obser: S:467, I:205, R:14, I+R:219\n",
      "free birds: 116\n",
      "obs_symp:10(82), obs_rank:11(159)\n",
      "pos_rank:11, inf_cont:5.1\n",
      "t = 16 / 110\n",
      "state: S:1624, I:325, R:51 I+R:376\n",
      "obser: S:499, I:219, R:15, I+R:234\n",
      "free birds: 123\n",
      "obs_symp:12(94), obs_rank:7(166)\n",
      "pos_rank:7, inf_cont:4.925\n",
      "t = 17 / 110\n",
      "state: S:1614, I:331, R:55 I+R:386\n",
      "obser: S:526, I:233, R:16, I+R:249\n",
      "free birds: 117\n",
      "obs_symp:5(99), obs_rank:12(178)\n",
      "pos_rank:12, inf_cont:4.45\n",
      "t = 18 / 110\n",
      "state: S:1596, I:344, R:60 I+R:404\n",
      "obser: S:552, I:251, R:17, I+R:268\n",
      "free birds: 115\n",
      "obs_symp:7(106), obs_rank:13(191)\n",
      "pos_rank:13, inf_cont:4.45\n",
      "t = 19 / 110\n",
      "state: S:1570, I:365, R:65 I+R:430\n",
      "obser: S:579, I:268, R:20, I+R:288\n",
      "free birds: 121\n",
      "obs_symp:11(117), obs_rank:10(201)\n",
      "pos_rank:10, inf_cont:6.0\n",
      "t = 20 / 110\n",
      "state: S:1545, I:387, R:68 I+R:455\n",
      "obser: S:613, I:277, R:22, I+R:299\n",
      "free birds: 135\n",
      "obs_symp:8(125), obs_rank:4(205)\n",
      "pos_rank:4, inf_cont:5.45\n",
      "t = 21 / 110\n",
      "state: S:1530, I:395, R:75 I+R:470\n",
      "obser: S:644, I:287, R:24, I+R:311\n",
      "free birds: 138\n",
      "obs_symp:11(136), obs_rank:7(212)\n",
      "pos_rank:7, inf_cont:4.75\n",
      "t = 22 / 110\n",
      "state: S:1494, I:424, R:82 I+R:506\n",
      "obser: S:675, I:297, R:27, I+R:324\n",
      "free birds: 163\n",
      "obs_symp:4(140), obs_rank:6(218)\n",
      "pos_rank:6, inf_cont:4.675\n",
      "t = 23 / 110\n",
      "state: S:1472, I:438, R:90 I+R:528\n",
      "obser: S:708, I:309, R:27, I+R:336\n",
      "free birds: 173\n",
      "obs_symp:8(148), obs_rank:7(225)\n",
      "pos_rank:7, inf_cont:4.6\n",
      "t = 24 / 110\n",
      "state: S:1443, I:458, R:99 I+R:557\n",
      "obser: S:741, I:326, R:27, I+R:353\n",
      "free birds: 182\n",
      "obs_symp:12(160), obs_rank:7(232)\n",
      "pos_rank:7, inf_cont:4.15\n",
      "t = 25 / 110\n",
      "state: S:1418, I:472, R:110 I+R:582\n",
      "obser: S:765, I:348, R:29, I+R:377\n",
      "free birds: 180\n",
      "obs_symp:11(171), obs_rank:14(246)\n",
      "pos_rank:14, inf_cont:4.4\n",
      "t = 26 / 110\n",
      "state: S:1394, I:491, R:115 I+R:606\n",
      "obser: S:786, I:367, R:33, I+R:400\n",
      "free birds: 183\n",
      "obs_symp:7(178), obs_rank:15(261)\n",
      "pos_rank:15, inf_cont:5.65\n",
      "t = 27 / 110\n",
      "state: S:1368, I:502, R:130 I+R:632\n",
      "obser: S:816, I:385, R:35, I+R:420\n",
      "free birds: 185\n",
      "obs_symp:15(193), obs_rank:8(269)\n",
      "pos_rank:8, inf_cont:5.45\n",
      "t = 28 / 110\n",
      "state: S:1333, I:531, R:136 I+R:667\n",
      "obser: S:842, I:404, R:38, I+R:442\n",
      "free birds: 199\n",
      "obs_symp:10(203), obs_rank:11(280)\n",
      "pos_rank:11, inf_cont:5.575\n",
      "t = 29 / 110\n",
      "state: S:1295, I:553, R:152 I+R:705\n",
      "obser: S:873, I:422, R:39, I+R:461\n",
      "free birds: 215\n",
      "obs_symp:12(215), obs_rank:8(288)\n",
      "pos_rank:8, inf_cont:5.3\n",
      "t = 30 / 110\n",
      "state: S:1262, I:580, R:158 I+R:738\n",
      "obser: S:901, I:442, R:39, I+R:481\n",
      "free birds: 225\n",
      "obs_symp:11(226), obs_rank:12(300)\n",
      "pos_rank:12, inf_cont:5.525\n",
      "t = 31 / 110\n",
      "state: S:1234, I:597, R:169 I+R:766\n",
      "obser: S:928, I:459, R:41, I+R:500\n",
      "free birds: 233\n",
      "obs_symp:11(237), obs_rank:11(311)\n",
      "pos_rank:11, inf_cont:5.25\n",
      "t = 32 / 110\n",
      "state: S:1202, I:619, R:179 I+R:798\n",
      "obser: S:952, I:486, R:42, I+R:528\n",
      "free birds: 236\n",
      "obs_symp:13(250), obs_rank:15(326)\n",
      "pos_rank:15, inf_cont:5.7\n",
      "t = 33 / 110\n",
      "state: S:1166, I:640, R:194 I+R:834\n",
      "obser: S:978, I:511, R:44, I+R:555\n",
      "free birds: 240\n",
      "obs_symp:15(265), obs_rank:12(338)\n",
      "pos_rank:12, inf_cont:6.65\n",
      "t = 34 / 110\n",
      "state: S:1136, I:655, R:209 I+R:864\n",
      "obser: S:1008, I:534, R:45, I+R:579\n",
      "free birds: 242\n",
      "obs_symp:17(282), obs_rank:9(347)\n",
      "pos_rank:9, inf_cont:6.2\n",
      "t = 35 / 110\n",
      "state: S:1105, I:672, R:223 I+R:895\n",
      "obser: S:1035, I:553, R:47, I+R:600\n",
      "free birds: 247\n",
      "obs_symp:14(296), obs_rank:11(358)\n",
      "pos_rank:11, inf_cont:5.725\n",
      "t = 36 / 110\n",
      "state: S:1085, I:676, R:239 I+R:915\n",
      "obser: S:1054, I:581, R:49, I+R:630\n",
      "free birds: 230\n",
      "obs_symp:11(307), obs_rank:19(377)\n",
      "pos_rank:19, inf_cont:5.175\n",
      "t = 37 / 110\n",
      "state: S:1062, I:694, R:244 I+R:938\n",
      "obser: S:1076, I:606, R:53, I+R:659\n",
      "free birds: 226\n",
      "obs_symp:15(322), obs_rank:14(391)\n",
      "pos_rank:14, inf_cont:5.0\n",
      "t = 38 / 110\n",
      "state: S:1037, I:700, R:263 I+R:963\n",
      "obser: S:1102, I:629, R:57, I+R:686\n",
      "free birds: 221\n",
      "obs_symp:15(337), obs_rank:10(401)\n",
      "pos_rank:10, inf_cont:4.875\n",
      "t = 39 / 110\n",
      "state: S:1008, I:717, R:275 I+R:992\n",
      "obser: S:1127, I:652, R:59, I+R:711\n",
      "free birds: 222\n",
      "obs_symp:13(350), obs_rank:13(414)\n",
      "pos_rank:13, inf_cont:4.7\n",
      "t = 40 / 110\n",
      "state: S:974, I:732, R:294 I+R:1026\n",
      "obser: S:1153, I:674, R:63, I+R:737\n",
      "free birds: 229\n",
      "obs_symp:14(364), obs_rank:10(424)\n",
      "pos_rank:10, inf_cont:6.1\n",
      "t = 41 / 110\n",
      "state: S:948, I:741, R:311 I+R:1052\n",
      "obser: S:1181, I:688, R:67, I+R:755\n",
      "free birds: 236\n",
      "obs_symp:8(372), obs_rank:8(432)\n",
      "pos_rank:8, inf_cont:5.75\n",
      "t = 42 / 110\n",
      "state: S:914, I:756, R:330 I+R:1086\n",
      "obser: S:1211, I:703, R:70, I+R:773\n",
      "free birds: 248\n",
      "obs_symp:8(380), obs_rank:7(439)\n",
      "pos_rank:7, inf_cont:5.225\n",
      "t = 43 / 110\n",
      "state: S:888, I:763, R:349 I+R:1112\n",
      "obser: S:1241, I:721, R:72, I+R:793\n",
      "free birds: 252\n",
      "obs_symp:11(391), obs_rank:8(447)\n",
      "pos_rank:8, inf_cont:4.375\n",
      "t = 44 / 110\n",
      "state: S:849, I:785, R:366 I+R:1151\n",
      "obser: S:1268, I:742, R:74, I+R:816\n",
      "free birds: 264\n",
      "obs_symp:13(404), obs_rank:11(458)\n",
      "pos_rank:11, inf_cont:4.475\n",
      "t = 45 / 110\n",
      "state: S:823, I:788, R:389 I+R:1177\n",
      "obser: S:1295, I:765, R:75, I+R:840\n",
      "free birds: 258\n",
      "obs_symp:14(418), obs_rank:12(470)\n",
      "pos_rank:12, inf_cont:4.8\n",
      "t = 46 / 110\n",
      "state: S:795, I:798, R:407 I+R:1205\n",
      "obser: S:1316, I:792, R:77, I+R:869\n",
      "free birds: 252\n",
      "obs_symp:12(430), obs_rank:17(487)\n",
      "pos_rank:17, inf_cont:5.75\n",
      "t = 47 / 110\n",
      "state: S:769, I:800, R:431 I+R:1231\n",
      "obser: S:1336, I:819, R:80, I+R:899\n",
      "free birds: 240\n",
      "obs_symp:15(445), obs_rank:17(504)\n",
      "pos_rank:17, inf_cont:7.075\n",
      "t = 48 / 110\n",
      "state: S:743, I:805, R:452 I+R:1257\n",
      "obser: S:1360, I:841, R:84, I+R:925\n",
      "free birds: 238\n",
      "obs_symp:11(456), obs_rank:12(516)\n",
      "pos_rank:12, inf_cont:7.0\n",
      "t = 49 / 110\n",
      "state: S:727, I:799, R:474 I+R:1273\n",
      "obser: S:1386, I:863, R:87, I+R:950\n",
      "free birds: 227\n",
      "obs_symp:17(473), obs_rank:11(527)\n",
      "pos_rank:11, inf_cont:6.1\n",
      "t = 50 / 110\n",
      "state: S:708, I:801, R:491 I+R:1292\n",
      "obser: S:1407, I:888, R:93, I+R:981\n",
      "free birds: 217\n",
      "obs_symp:13(486), obs_rank:13(540)\n",
      "pos_rank:13, inf_cont:7.025\n",
      "t = 51 / 110\n",
      "state: S:693, I:800, R:507 I+R:1307\n",
      "obser: S:1432, I:909, R:94, I+R:1003\n",
      "free birds: 206\n",
      "obs_symp:13(499), obs_rank:14(554)\n",
      "pos_rank:14, inf_cont:7.175\n",
      "t = 52 / 110\n",
      "state: S:677, I:796, R:527 I+R:1323\n",
      "obser: S:1457, I:932, R:95, I+R:1027\n",
      "free birds: 196\n",
      "obs_symp:11(510), obs_rank:14(568)\n",
      "pos_rank:14, inf_cont:7.55\n",
      "t = 53 / 110\n",
      "state: S:658, I:795, R:547 I+R:1342\n",
      "obser: S:1485, I:951, R:96, I+R:1047\n",
      "free birds: 194\n",
      "obs_symp:12(522), obs_rank:11(579)\n",
      "pos_rank:11, inf_cont:6.7\n",
      "t = 54 / 110\n",
      "state: S:640, I:799, R:561 I+R:1360\n",
      "obser: S:1512, I:966, R:100, I+R:1066\n",
      "free birds: 197\n",
      "obs_symp:7(529), obs_rank:9(588)\n",
      "pos_rank:9, inf_cont:5.725\n",
      "t = 55 / 110\n",
      "state: S:629, I:793, R:578 I+R:1371\n",
      "obser: S:1542, I:978, R:102, I+R:1080\n",
      "free birds: 191\n",
      "obs_symp:8(537), obs_rank:8(596)\n",
      "pos_rank:8, inf_cont:4.675\n",
      "t = 56 / 110\n",
      "state: S:619, I:781, R:600 I+R:1381\n",
      "obser: S:1568, I:993, R:103, I+R:1096\n",
      "free birds: 182\n",
      "obs_symp:7(544), obs_rank:13(609)\n",
      "pos_rank:13, inf_cont:4.25\n",
      "t = 57 / 110\n",
      "state: S:603, I:779, R:618 I+R:1397\n",
      "obser: S:1598, I:1007, R:103, I+R:1110\n",
      "free birds: 179\n",
      "obs_symp:7(551), obs_rank:10(619)\n",
      "pos_rank:10, inf_cont:3.875\n",
      "t = 58 / 110\n",
      "state: S:588, I:786, R:626 I+R:1412\n",
      "obser: S:1626, I:1021, R:105, I+R:1126\n",
      "free birds: 178\n",
      "obs_symp:8(559), obs_rank:10(629)\n",
      "pos_rank:10, inf_cont:3.8\n",
      "t = 59 / 110\n",
      "state: S:578, I:781, R:641 I+R:1422\n",
      "obser: S:1655, I:1035, R:108, I+R:1143\n",
      "free birds: 171\n",
      "obs_symp:9(568), obs_rank:8(637)\n",
      "pos_rank:8, inf_cont:4.625\n",
      "t = 60 / 110\n",
      "state: S:566, I:776, R:658 I+R:1434\n",
      "obser: S:1687, I:1044, R:109, I+R:1153\n",
      "free birds: 170\n",
      "obs_symp:5(573), obs_rank:7(644)\n",
      "pos_rank:7, inf_cont:4.15\n",
      "t = 61 / 110\n",
      "state: S:555, I:766, R:679 I+R:1445\n",
      "obser: S:1718, I:1054, R:111, I+R:1165\n",
      "free birds: 168\n",
      "obs_symp:4(577), obs_rank:7(651)\n",
      "pos_rank:7, inf_cont:3.975\n",
      "t = 62 / 110\n",
      "state: S:542, I:767, R:691 I+R:1458\n",
      "obser: S:1749, I:1067, R:113, I+R:1180\n",
      "free birds: 165\n",
      "obs_symp:7(584), obs_rank:7(658)\n",
      "pos_rank:7, inf_cont:3.875\n",
      "t = 63 / 110\n",
      "state: S:538, I:761, R:701 I+R:1462\n",
      "obser: S:1777, I:1084, R:115, I+R:1199\n",
      "free birds: 152\n",
      "obs_symp:7(591), obs_rank:10(668)\n",
      "pos_rank:10, inf_cont:3.7\n",
      "t = 64 / 110\n",
      "state: S:530, I:753, R:717 I+R:1470\n",
      "obser: S:1803, I:1100, R:117, I+R:1217\n",
      "free birds: 141\n",
      "obs_symp:5(596), obs_rank:12(680)\n",
      "pos_rank:12, inf_cont:3.875\n",
      "t = 65 / 110\n",
      "state: S:519, I:755, R:726 I+R:1481\n",
      "obser: S:1829, I:1115, R:119, I+R:1234\n",
      "free birds: 136\n",
      "obs_symp:5(601), obs_rank:12(692)\n",
      "pos_rank:12, inf_cont:3.575\n",
      "t = 66 / 110\n",
      "state: S:510, I:748, R:742 I+R:1490\n",
      "obser: S:1860, I:1129, R:119, I+R:1248\n",
      "free birds: 131\n",
      "obs_symp:5(606), obs_rank:9(701)\n",
      "pos_rank:9, inf_cont:4.125\n",
      "t = 67 / 110\n",
      "state: S:503, I:741, R:756 I+R:1497\n",
      "obser: S:1895, I:1136, R:120, I+R:1256\n",
      "free birds: 129\n",
      "obs_symp:5(611), obs_rank:4(705)\n",
      "pos_rank:4, inf_cont:3.85\n",
      "t = 68 / 110\n",
      "state: S:494, I:739, R:767 I+R:1506\n",
      "obser: S:1925, I:1140, R:126, I+R:1266\n",
      "free birds: 133\n",
      "obs_symp:1(612), obs_rank:4(709)\n",
      "pos_rank:4, inf_cont:3.2\n",
      "t = 69 / 110\n",
      "state: S:482, I:736, R:782 I+R:1518\n",
      "obser: S:1954, I:1150, R:128, I+R:1278\n",
      "free birds: 133\n",
      "obs_symp:4(616), obs_rank:9(718)\n",
      "pos_rank:9, inf_cont:2.4\n",
      "t = 70 / 110\n",
      "state: S:476, I:725, R:799 I+R:1524\n",
      "obser: S:1982, I:1161, R:131, I+R:1292\n",
      "free birds: 125\n",
      "obs_symp:5(621), obs_rank:9(727)\n",
      "pos_rank:9, inf_cont:2.575\n",
      "t = 71 / 110\n",
      "state: S:470, I:721, R:809 I+R:1530\n",
      "obser: S:2009, I:1172, R:136, I+R:1308\n",
      "free birds: 120\n",
      "obs_symp:3(624), obs_rank:8(735)\n",
      "pos_rank:8, inf_cont:2.925\n",
      "t = 72 / 110\n",
      "state: S:463, I:719, R:818 I+R:1537\n",
      "obser: S:2035, I:1183, R:141, I+R:1324\n",
      "free birds: 116\n",
      "obs_symp:3(627), obs_rank:9(744)\n",
      "pos_rank:9, inf_cont:3.1\n",
      "t = 73 / 110\n",
      "state: S:456, I:712, R:832 I+R:1544\n",
      "obser: S:2064, I:1193, R:143, I+R:1336\n",
      "free birds: 112\n",
      "obs_symp:4(631), obs_rank:9(753)\n",
      "pos_rank:9, inf_cont:2.925\n",
      "t = 74 / 110\n",
      "state: S:453, I:701, R:846 I+R:1547\n",
      "obser: S:2093, I:1202, R:147, I+R:1349\n",
      "free birds: 105\n",
      "obs_symp:6(637), obs_rank:7(760)\n",
      "pos_rank:7, inf_cont:2.6\n",
      "t = 75 / 110\n",
      "state: S:451, I:693, R:856 I+R:1549\n",
      "obser: S:2119, I:1217, R:148, I+R:1365\n",
      "free birds: 89\n",
      "obs_symp:3(640), obs_rank:13(773)\n",
      "pos_rank:13, inf_cont:2.225\n",
      "t = 76 / 110\n",
      "state: S:447, I:679, R:874 I+R:1553\n",
      "obser: S:2143, I:1225, R:157, I+R:1382\n",
      "free birds: 83\n",
      "obs_symp:3(643), obs_rank:7(780)\n",
      "pos_rank:7, inf_cont:2.825\n",
      "t = 77 / 110\n",
      "state: S:445, I:672, R:883 I+R:1555\n",
      "obser: S:2172, I:1232, R:162, I+R:1394\n",
      "free birds: 78\n",
      "obs_symp:2(645), obs_rank:6(786)\n",
      "pos_rank:6, inf_cont:2.2\n",
      "t = 78 / 110\n",
      "state: S:442, I:668, R:890 I+R:1558\n",
      "obser: S:2202, I:1240, R:165, I+R:1405\n",
      "free birds: 73\n",
      "obs_symp:3(648), obs_rank:7(793)\n",
      "pos_rank:7, inf_cont:1.95\n",
      "t = 79 / 110\n",
      "state: S:441, I:659, R:900 I+R:1559\n",
      "obser: S:2233, I:1246, R:168, I+R:1414\n",
      "free birds: 68\n",
      "obs_symp:1(649), obs_rank:6(799)\n",
      "pos_rank:6, inf_cont:1.55\n",
      "t = 80 / 110\n",
      "state: S:438, I:650, R:912 I+R:1562\n",
      "obser: S:2267, I:1251, R:169, I+R:1420\n",
      "free birds: 62\n",
      "obs_symp:1(650), obs_rank:5(804)\n",
      "pos_rank:5, inf_cont:1.275\n",
      "t = 81 / 110\n",
      "state: S:436, I:634, R:930 I+R:1564\n",
      "obser: S:2291, I:1258, R:178, I+R:1436\n",
      "free birds: 53\n",
      "obs_symp:1(651), obs_rank:7(811)\n",
      "pos_rank:7, inf_cont:1.275\n",
      "t = 82 / 110\n",
      "state: S:434, I:624, R:942 I+R:1566\n",
      "obser: S:2320, I:1266, R:181, I+R:1447\n",
      "free birds: 46\n",
      "obs_symp:1(652), obs_rank:8(819)\n",
      "pos_rank:8, inf_cont:1.275\n",
      "t = 83 / 110\n",
      "state: S:432, I:614, R:954 I+R:1568\n",
      "obser: S:2356, I:1267, R:184, I+R:1451\n",
      "free birds: 47\n",
      "obs_symp:1(653), obs_rank:1(820)\n",
      "pos_rank:1, inf_cont:1.175\n",
      "t = 84 / 110\n",
      "state: S:429, I:607, R:964 I+R:1571\n",
      "obser: S:2392, I:1268, R:187, I+R:1455\n",
      "free birds: 48\n",
      "obs_symp:0(653), obs_rank:1(821)\n",
      "pos_rank:1, inf_cont:1.1\n",
      "t = 85 / 110\n",
      "state: S:427, I:598, R:975 I+R:1573\n",
      "obser: S:2428, I:1270, R:189, I+R:1459\n",
      "free birds: 47\n",
      "obs_symp:0(653), obs_rank:2(823)\n",
      "pos_rank:2, inf_cont:0.75\n",
      "t = 86 / 110\n",
      "state: S:422, I:590, R:988 I+R:1578\n",
      "obser: S:2460, I:1276, R:192, I+R:1468\n",
      "free birds: 45\n",
      "obs_symp:1(654), obs_rank:5(828)\n",
      "pos_rank:5, inf_cont:0.425\n",
      "t = 87 / 110\n",
      "state: S:422, I:575, R:1003 I+R:1578\n",
      "obser: S:2487, I:1285, R:197, I+R:1482\n",
      "free birds: 35\n",
      "obs_symp:1(655), obs_rank:8(836)\n",
      "pos_rank:8, inf_cont:1.1\n",
      "t = 88 / 110\n",
      "state: S:422, I:563, R:1015 I+R:1578\n",
      "obser: S:2516, I:1289, R:204, I+R:1493\n",
      "free birds: 31\n",
      "obs_symp:1(656), obs_rank:4(840)\n",
      "pos_rank:4, inf_cont:1.6\n",
      "t = 89 / 110\n",
      "state: S:419, I:560, R:1021 I+R:1581\n",
      "obser: S:2550, I:1293, R:207, I+R:1500\n",
      "free birds: 30\n",
      "obs_symp:1(657), obs_rank:3(843)\n",
      "pos_rank:3, inf_cont:1.775\n",
      "t = 90 / 110\n",
      "state: S:417, I:549, R:1034 I+R:1583\n",
      "obser: S:2587, I:1294, R:209, I+R:1503\n",
      "free birds: 31\n",
      "obs_symp:1(658), obs_rank:1(844)\n",
      "pos_rank:1, inf_cont:1.35\n",
      "t = 91 / 110\n",
      "state: S:414, I:541, R:1045 I+R:1586\n",
      "obser: S:2623, I:1298, R:210, I+R:1508\n",
      "free birds: 30\n",
      "obs_symp:2(660), obs_rank:3(847)\n",
      "pos_rank:3, inf_cont:0.875\n",
      "t = 92 / 110\n",
      "state: S:413, I:532, R:1055 I+R:1587\n",
      "obser: S:2656, I:1301, R:214, I+R:1515\n",
      "free birds: 28\n",
      "obs_symp:0(660), obs_rank:3(850)\n",
      "pos_rank:3, inf_cont:0.65\n",
      "t = 93 / 110\n",
      "state: S:412, I:525, R:1063 I+R:1588\n",
      "obser: S:2691, I:1303, R:217, I+R:1520\n",
      "free birds: 27\n",
      "obs_symp:0(660), obs_rank:2(852)\n",
      "pos_rank:2, inf_cont:0.375\n",
      "t = 94 / 110\n",
      "state: S:410, I:521, R:1069 I+R:1590\n",
      "obser: S:2720, I:1310, R:221, I+R:1531\n",
      "free birds: 22\n",
      "obs_symp:1(661), obs_rank:7(859)\n",
      "pos_rank:7, inf_cont:0.35\n",
      "t = 95 / 110\n",
      "state: S:409, I:514, R:1077 I+R:1591\n",
      "obser: S:2750, I:1317, R:224, I+R:1541\n",
      "free birds: 16\n",
      "obs_symp:1(662), obs_rank:7(866)\n",
      "pos_rank:7, inf_cont:1.6\n",
      "t = 96 / 110\n",
      "state: S:409, I:506, R:1085 I+R:1591\n",
      "obser: S:2790, I:1317, R:224, I+R:1541\n",
      "free birds: 16\n",
      "obs_symp:1(663), obs_rank:0(866)\n",
      "pos_rank:0, inf_cont:1.775\n",
      "t = 97 / 110\n",
      "state: S:408, I:494, R:1098 I+R:1592\n",
      "obser: S:2825, I:1318, R:228, I+R:1546\n",
      "free birds: 15\n",
      "obs_symp:0(663), obs_rank:1(867)\n",
      "pos_rank:1, inf_cont:1.175\n",
      "t = 98 / 110\n",
      "state: S:407, I:483, R:1110 I+R:1593\n",
      "obser: S:2862, I:1319, R:230, I+R:1549\n",
      "free birds: 15\n",
      "obs_symp:0(663), obs_rank:1(868)\n",
      "pos_rank:1, inf_cont:0.975\n",
      "t = 99 / 110\n",
      "state: S:406, I:472, R:1122 I+R:1594\n",
      "obser: S:2896, I:1324, R:231, I+R:1555\n",
      "free birds: 10\n",
      "obs_symp:0(663), obs_rank:5(873)\n",
      "pos_rank:5, inf_cont:0.875\n",
      "t = 100 / 110\n",
      "state: S:405, I:464, R:1131 I+R:1595\n",
      "obser: S:2932, I:1327, R:232, I+R:1559\n",
      "free birds: 8\n",
      "obs_symp:0(663), obs_rank:3(876)\n",
      "pos_rank:3, inf_cont:1.6\n",
      "t = 101 / 110\n",
      "state: S:405, I:453, R:1142 I+R:1595\n",
      "obser: S:2971, I:1328, R:232, I+R:1560\n",
      "free birds: 7\n",
      "obs_symp:0(663), obs_rank:1(877)\n",
      "pos_rank:1, inf_cont:1.575\n",
      "t = 102 / 110\n",
      "state: S:405, I:441, R:1154 I+R:1595\n",
      "obser: S:3010, I:1328, R:233, I+R:1561\n",
      "free birds: 7\n",
      "obs_symp:0(663), obs_rank:0(877)\n",
      "pos_rank:0, inf_cont:0.975\n",
      "t = 103 / 110\n",
      "state: S:405, I:437, R:1158 I+R:1595\n",
      "obser: S:3048, I:1328, R:235, I+R:1563\n",
      "free birds: 7\n",
      "obs_symp:0(663), obs_rank:0(877)\n",
      "pos_rank:0, inf_cont:0.45\n",
      "t = 104 / 110\n",
      "state: S:405, I:431, R:1164 I+R:1595\n",
      "obser: S:3087, I:1328, R:236, I+R:1564\n",
      "free birds: 6\n",
      "obs_symp:0(663), obs_rank:0(877)\n",
      "pos_rank:0, inf_cont:0.1\n",
      "t = 105 / 110\n",
      "state: S:405, I:423, R:1172 I+R:1595\n",
      "obser: S:3124, I:1328, R:239, I+R:1567\n",
      "free birds: 5\n",
      "obs_symp:0(663), obs_rank:0(877)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 106 / 110\n",
      "state: S:404, I:421, R:1175 I+R:1596\n",
      "obser: S:3159, I:1330, R:242, I+R:1572\n",
      "free birds: 4\n",
      "obs_symp:0(663), obs_rank:2(879)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "t = 107 / 110\n",
      "state: S:404, I:410, R:1186 I+R:1596\n",
      "obser: S:3199, I:1330, R:242, I+R:1572\n",
      "free birds: 4\n",
      "obs_symp:0(663), obs_rank:0(879)\n",
      "pos_rank:0, inf_cont:0.125\n",
      "t = 108 / 110\n",
      "state: S:404, I:403, R:1193 I+R:1596\n",
      "obser: S:3237, I:1330, R:244, I+R:1574\n",
      "free birds: 4\n",
      "obs_symp:0(663), obs_rank:0(879)\n",
      "pos_rank:0, inf_cont:0.1\n",
      "t = 109 / 110\n",
      "state: S:404, I:395, R:1201 I+R:1596\n",
      "obser: S:3277, I:1330, R:244, I+R:1574\n",
      "free birds: 4\n",
      "obs_symp:0(663), obs_rank:0(879)\n",
      "pos_rank:0, inf_cont:0.075\n",
      "Save MF strategy\n",
      "Saving to csv/Proximity_N2K_T120_s1_ti10_pz10_mu0.02_l0.05_seed5_obs40_CT_t5_d15_info\n",
      "End seed\n"
     ]
    }
   ],
   "source": [
    "##### CT SCENARIO #######\n",
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import ranking\n",
    "imp.reload(ranking)\n",
    "from ranking import RANKINGS\n",
    "scenario_CT = Scenario(\n",
    "    model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=RANKINGS[\"tracing\"], \n",
    "                         algo=\"CT\", init=\"all_S\", tau=MF_tau, delta=MF_delta,mu=mu,lamb=lamb), \n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    ")\n",
    "scenario_CT.run(t_max-t1, print_every = 1)\n",
    "print(\"Save MF strategy\", flush=True)\n",
    "#scenario_MF.counts.to_csv(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_MF_t%d_d%d.csv\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,MF_tau,MF_delta),\n",
    "#                  index=False, sep=\"\\t\")\n",
    "scenario_CT.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_CT_t%d_d%d_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,MF_tau,MF_delta))\n",
    "cnts = scenario_CT.counts\n",
    "cnts[\"t\" ]=cnts.index\n",
    "traces[\"CT\"] = cnts\n",
    "save_status(scenario_CT, \"CT\")\n",
    "del scenario_CT\n",
    "# 1h01min per round\n",
    "print(\"End seed\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1a43daa50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(db.index.to_numpy(), db.I.to_numpy(), label=\"None\")\n",
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"], label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$I+R$')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(db.index.to_numpy(), db.I.to_numpy()+db.R.to_numpy(), label=\"None\")\n",
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"]+traces[k][\"R\"], label=k)\n",
    "plt.legend()\n",
    "plt.ylabel(\"$I+R$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1a47a9890>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"], label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.index.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.colors as matcolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATES_CONV={\"S\":0, \"I\":1, \"R\":2, \"Q\":6}\n",
    "\n",
    "def get_nodes_status(time):\n",
    "    if time < t1:\n",
    "        return {k:np.where(all_states[time]==i)[0] \n",
    "                  for k, i in STATES_CONV.items()}\n",
    "\n",
    "    x = status[status.t==time]\n",
    "    out={}\n",
    "    quar = (x.q==1)\n",
    "    out[\"Q\"] = (x[quar].i).values\n",
    "    out[\"S\"] = (x[(x.s==0)].i).values\n",
    "    nq = np.logical_not(quar)\n",
    "    out[\"I\"] = (x[(x.s==1) & nq].i).values\n",
    "    out[\"R\"] = (x[(x.s==2)].i).values\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_start(ax, pos_nodes, nodes_keys, colors, fn_get_nodes):\n",
    "\n",
    "    nodes_idx = fn_get_nodes(0)\n",
    "    paths = {}\n",
    "    for nk, color in zip(nodes_keys, colors):\n",
    "        g=pos_nodes.iloc[nodes_idx[nk]]\n",
    "        paths[nk] = ax.scatter(g[\"x\"],g[\"y\"],color=color)\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation, PillowWriter\n",
    "\n",
    "\n",
    "ALGO=\"CT\"\n",
    "fig,ax = plt.subplots(figsize=(7,7), frameon=False)\n",
    "lim=0.6\n",
    "ax.set(xlim=(pos_nodes.x.max()+lim, pos_nodes.x.min()-lim),\n",
    "      ylim=(pos_nodes.y.max()+lim, pos_nodes.y.min()-lim))\n",
    "\n",
    "#fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "plt.tight_layout()\n",
    "time = 0\n",
    "status=ranks_status[ALGO].convert_dtypes()\n",
    "\n",
    "st_val= [\"S\",\"I\",\"Q\",\"R\"]\n",
    "\n",
    "paths = setup_start(ax, pos_nodes, st_val, colors=[None, \"red\",\"violet\",\"yellowgreen\"],\n",
    "                   fn_get_nodes=get_nodes_status)\n",
    "\n",
    "def animate(time):\n",
    "    if time >= T:\n",
    "        time = T-1\n",
    "    nodes_idx = get_nodes_status(time)\n",
    "    for k in st_val:\n",
    "        g=pos_nodes.iloc[nodes_idx[k]]\n",
    "        paths[k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "    plt.draw()\n",
    "\n",
    "anim = FuncAnimation(\n",
    "    fig, animate, interval=100, frames=T+20, cache_frame_data=False)\n",
    "\n",
    "plt.draw()\n",
    "plt.show()\n",
    "\n",
    "#anim.save(\"sim.mov\", codec=\"h263p\", bitrate=-1)\n",
    "#anim.save(f\"sim_{ALGO}.webm\", codec=\"libvpx-vp9\")\n",
    "writer=PillowWriter(fps=15)\n",
    "anim.save(f\"sim_{ALGO}.gif\",writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATES_CONV={\"S\":0, \"I\":1, \"R\":2, \"Q\":6}\n",
    "\n",
    "def get_nodes_ranker(time,status=None):\n",
    "    if time < t1:\n",
    "        return {k:np.where(all_states[time]==i)[0] \n",
    "                  for k, i in STATES_CONV.items()}\n",
    "\n",
    "    x = status[status.t==time]\n",
    "    out={}\n",
    "    quar = (x.q==1)\n",
    "    out[\"Q\"] = (x[quar].i).values\n",
    "    out[\"S\"] = (x[(x.s==0)].i).values\n",
    "    nq = np.logical_not(quar)\n",
    "    out[\"I\"] = (x[(x.s==1) & nq].i).values\n",
    "    out[\"R\"] = (x[(x.s==2)].i).values\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_start_all(ax, pos_nodes,status, nodes_keys, colors, fn_get_nodes):\n",
    "\n",
    "    nodes_idx = fn_get_nodes(0, status)\n",
    "    paths = {}\n",
    "    for nk, color in zip(nodes_keys, colors):\n",
    "        g=pos_nodes.iloc[nodes_idx[nk]]\n",
    "        paths[nk] = ax.scatter(g[\"x\"],g[\"y\"],color=color)\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "statuses={a:ranks_status[a].convert_dtypes() for a in ranks_status.keys()}\n",
    "\n",
    "st_val= [\"S\",\"I\",\"Q\",\"R\"]\n",
    "def get_nodes(t):\n",
    "    return {i:np.where(all_states[t]==i)[0] for i in range(3)}\n",
    "def setup_start(ax, pos_nodes, nodes_keys, colors, fn_get_nodes):\n",
    "\n",
    "    nodes_idx = fn_get_nodes(0)\n",
    "    paths = {}\n",
    "    for nk, color in zip(nodes_keys, colors):\n",
    "        g=pos_nodes.iloc[nodes_idx[nk]]\n",
    "        paths[nk] = ax.scatter(g[\"x\"],g[\"y\"], color=color)\n",
    "    return paths\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BMi4Mf2gWEGMO1qN8VK0weQL5/iYIm2OZciC7sOW4ZBrqQUEnzGZ6I0y01zLFdOTGNIe7FaN1ICcynovB6cR81EX9QAeKFPc8275nzbu/WCJgWVqncN5lYtFgNTytdtVPNZijbiRjfqyyeKzlrvwxLv7QcHHlj8XhjwYS3TQD4I8NeC6H0fOHxsyYK0d8URD5sbUV82lzPJoxB7KPNUme3YRczOT97ATZmrLg82LO+2eEpk/F5MdnB1BbT+bPhxPyUUdPiqCMm17ZN0eS21+6ARFXb9n39Aj0B+w8bPHgy8w15Z4L68jmd4fiLYRjfhSyGMHgy135YyH4Q9OnKn8MC4vCH6cFt43hD79uZrCQ74U/NBYIamVzDTEV9SJAKHcS8FPz/eQ4TCPdTiTuLy02JPdXuFtsxLTBQN1r1jOYyykhckmq5RAG1pXIgnOJ2Pfg0gDbKjYYkau3EppqjlSuBs7joLU2BTMsRaYwHIrmd7QcUvljYfhDw8WVPwaEyh/BunnIc+W2n+et1V7hMp2NP45kNmGWyo2eOkmwlbOrmBhETf6QOuIWLtMcC4AMqV4Hh0zmcmqyuaZJx9vTZI7qM5pFcG8QTiq4eqPMv1HjEsbIaWE/Kbm3hCjD0lhTny4Ah4ySP1I4ZBH5g6nLkwq35MofPaHyB4uzqZkzc/LHkZSGBa3RkILiqEFvsy6cM8d7BW5bViM4hXdL3gDF3OclggS2HAH+vBdo/bxCgEs1q6MCJkTAN36u02PkCZ/UpDGYjnfRJ3NdxFxw9+TqDb7PsihzJkjSyR7Xb9z4B+4ejTBbSPSiZcCa3zFzyOj4IwOHLCR/EHVJZd6r/DEyDhkpf0jvUZxDcvGHOI0rhhxpRnN2xlCaK6a8LbB8uEk+naHUhQxBbEOgdZE20NbvUIN6mfrdJALki6UZzFVHqX1iCOk1U++ZsS65tLxYauBrXL8xxyuad0Imdw0jn1K0yIMbm31ySE/84aM4f6TKi8oflT8WkUMWhT80HOJY72M9WdTXpk6iqJXTzBSoPV7SBn8SAStXvMDomAAcbqV3D56VLfBGGNSS6q6iDoiRBgTFnt9loCNky8E0aGchpVsnQZuZ6+hJ2D/Exe02PV1kIgEO2YPOQFsSfU8kYDN2EsZpZj4K/qRum70DsrBR/UYbNIq5Lc3Jp9j+M7SxGQFqLE6AP+7CfiFD5w8MXfBHKocsHX+0ZYLJod3JuPJHGn8MjkMWiD+kHHItdoyljE+VC5MiUMnPVLKp9KnEOmMw/7/AnWQdVoxHkcmZ/122FIOF3Q1ifPC1ZuvBmLkRrcQq027nOgw2zVJHjibgKCxqVzrwac4atAxlKOq7rbiWMinbxeXXI0qDY5AZaRfrN9RYZsoSqsPWEhnryjOYsVko2NWO7yeg/9qsP33zhzRJRovKHwPro4Rm20flD11MTC8csgT80Ug4JHGMRV+7klk7Yic8GI4qV6KhCiNXodzKE/4+72mmtyFla18pBqnVKvV9jIZKWr+nCY3DkPK3S7PktO2267V1qTSDyXWEaAJWweSpMb/GaIJy932rOaJggxmTNR+KaymtcsO4o5ykxhSmnQuUhavDGwKSCPWfIY1NNRD5ReGxgfCHz3dt/+jM2ukiMFaoMi0zf0g5pPKHTuvfJYdsLRF/HLxvgENSxlj0tVEWCGq1BC4lTQZhIQlEiRI+7grQeY8rMLG5HbF5XCpuO9ug+9+XsnBw9YtpHMSpSr2+seMEIebyzZa0u2vSc4lCoznUgqwjhZYkh6YuRhjkziTFuU48YOsD3r+41oTSKsOY1wbxU2mid5myrBP+qwf7P8B99whrWoiAS6QRLgpiPOzvgGrdlojLKEvjEPhj2iN/cBM7kcvjkvGHpO0rf+jrLBuHBKwPjbPvwTLwx9UQhyTyRxKHqBcQnIsEUxBRYRxITMVbmdPXrUGDbKcIrIjy2Gdpvs+BUP36A0kkHIg6bTKnB+T62MwZ4F2bzckc9973XD3k0NRRgoRc3AtcN8R9W6D9aa2RnWlNCGFOpYrlNLdUnzrO7ScDdetm0LD3f9LJAENtXCch4FHtCSFwsbtH1AVl3V4o/ogoEzUObBB6CTk3dv5oAulx3WDUyh9/BE45LOIQBX9wWY8aiG+8uAz80RhwHAJ/p/BHEofEWCA2UrRtEiCdsUGyI61r/b29DoxNtOzk5lZspptI//PONYhe/Qbz40snmPA7R9o5JvDt6h/dVM7RZF7p0mzOaCo0RET1hb1Way8Qwisx7oohLaSib0tcFaeJmsktZhIgHTeUrLrLaG61fWdLULfUmLkncbdTjM2hIDQeniWymNjve+UPrx9i/TaJP5z7a8q0bPxRcjPN7QXlj0mbVhj+n50/2ndAyhTDH8dz84dw/kde2yd/WDCxE1jsnJg/UjkkZgExjdW2ZTIVuy5T4sBDZfBtLGK0FpQ254Tj6pEdbf1CHYp2ZbbnB24rWvnDM6MmO9DuEuHeC7Ei/U5DRFRfWAVh+5Bg061S70317cve8yV9YCtRM3ka3EXueMkPxJp3jaxy5A/l87qNaJKkZaHqa0U6LgonacgNVvNnA6WhXR6Dfm+J8FlFAHUR/lAG3zYdcgg2hpL2ohg4f6xB2toUK4/E6r9I/HGwNwjIsKPLxB+K+d9S8kcKh8QsINgOhlSsPS6lmB6Zl9OYsLTBtzFQm9SIjtjA313sCLpun5PJBULie9wGCVmchgxdl5SEIBFyOd8rBWKB7PSFy4iAO+5MqrTuAjPHbzQWVB9e9fppqA+42l9b7vsE2aawsXsMBO+tlMWo+QhZKq2l4wEmS9uuo3HCJqkhi9HoYhgSEXxfZBHxGPQv1SIiM39og29joSpTwNrSxa7SXfNH4/Bk5Q+dZQhLBTpK/vD+f1ygrOYsvapMhy2uV/6IysLE5soGwtxEJsPPS6PjFTmIt0KZXiKCb1MmmFGZCKAj2olQE5H9IAnw7JjcxNK+EcJR6B+aLApsHyzwXikIltUFlI8al0GrGVxvx58bjGrr9rxk/DHg+rDbT7H3nbltAN9psk1RY/dUplzfmKaqLdsas3g46FNwHCoLci+sf1NjZt8Cmfo+A0RwPMC+D084/b1N5fpUj/whDb5NVVCoOaTte3BO9r2MOFT+GB5/QF/Q7BA8Kv7wZKs7z4yxDFp5XfkjEmoLBDRew2jV2hz22OqX9X+P8P+k/NCsmfMpT2NFrWxjc+1j75+ijekt1R26kp5M5t6xmc00/tja7dzFcRKCPnhwHnefLiAtqzQWglhcbCFBZuhEIsFfHuvbXMAXF0yn3fMgqLWODYZNsFTOWn9p5b0OvWeMBTLjuw51PFApW+33F3vijyagHb2RgT+aMXIIKWeVHIL0jVmEkrPyh86a0547Gv6IdPVjOaTyxzdH8cdkNtMsVMOwmh97X4Z0SYHA+FPeoMga/CGxYEr7Yufajpdrq/XQfWI7Ysy7B+73VLQf8WcFP/qOoUWE83zsHjOmb4j7yaIB6TO/YY43Iqe+CnV4lOvDzBicgaY/agxAOTH3KlU/5cqHtbtwzCWP7UDZ1O+reU/p+M/5rkOFeUeSlMw7Tvrgj1x1L7nHEDgEyhAfy1iOQyRYdv6w7kaPEOnh73qK49HxR24Ogb8rf7xLzx+pO1FjSEnlGqNBoQKibOd6wXSc/exQ3MpWKbDZlW+C1vsabIs+ly1EeyPHBSB2N9fkFHZEfV/zgoApDM7/O4eGArsH/ORrTanx8xqh1YyzYES3q9OmqT7PKr9/odUzV8pFqeaOfF9B0JzGxdL/vuv0kn0gNpVrbv64DEGsLlc02BiGyXsyf9j/9M0hjhvyUcQNWRpPUYJDQvFILZadP7AsZu6cb+z8kZVDIiziMeVaSP4osYBYR4RPA1lTQp0kJqAw1GC+Gftqotk7u5kYynDe6zB2tb+JaMAkQijKBSD0LqZA0y9SZFAi6vtFjxBOIi4ERTNQaRHRR8T3IDaRCWG1FbqMawYmpG3AclLfjXTJkpZvXfHcfXM6PJcck6ExQ2jvQovcbep9E7SmUtnXm6tjh4hN5ZqbP1ZDHDIE/pByiII/ot2QQ+9jZqCnv/iz2mOR3CDqm+OP4hmotKj8kZ0/cgfvV/5o4uRPdjMfdITz3mYa9u9HOdJ1NDgzZUCh7TAhPyw0kMwxqVErsmyB0gFgq0JLBG9xAgJfgoWZZNv21cTdXNF3+eTJ/fTm6m3jXXjBpvaGjyIbr7T+370GkTr98gVlH8FArfy5HTc5kM+nAhtT+65TH21qwXMxwcsxgZcOCWP9jwuGJYOYiXueh3G2Lci6JO0rEq23NFiyhPwZFMBK+rRjcbCfT1PW0w74Ax1vA+IPjkMei+APbjIxTeKQB/Y5JDd/tHKkdZGNvn9uRPYRDJU/EjikB/64AeVaaP5ItkAQGryz8JI3QqtLoa88GVAIK9qHENMtK/ic52r3g1CtfIVaH+pZbRrc9u9GaHpK3c117h1fOXJ09u6vfoTK8HE10Qx7C3m/Xt0yCDN+yqq9hIaYTQ+MWH+aWK1NDi1aqHwBcKZXakw2zDVXmXu+Bfk+lOdbY3XgUviGZAUVjDgoq50GiFa5kaZ17pA/Do23rvgjA4esRvBHihsy+Z67R4413/eGRyTPb5R1cYaI/+ibQ54K9KvKH2XS+/fNH2eJe855lYydP9QLiIAJ0fe/k0wyKM1JIxUGVjPluMZIBV8o28oO5teqMcEpJl2ajAkSIZS2m6sNcptMDr3jd33jX52+/3VfI32+1o0n2bWmAC7F+NoyZE+1MbaJjBSa56eaj/v2vSfNzIxvOrkJEzHhaEFZhW4xLh/S9gul8A26dPS8b0wSBC5jaz3zh3SX2uL80RGHTDO7IaMc8okHHpzYxQPCIcn8AfU5KNc+KGtoUTo4/uDKUPljzjX2NFGv+y62SJtMYHFxcVH4Q5WFKcG/l8uCIc16IsquIIi2bwcG98zb3o6NB/fQVG5iZpWkugXtR9JursJ3EafBDdwjOTMDB4X/b3t+aGBg2SuiMj3A35gVj/PLlz4/ejd44TjtJOOJdCwJr5FmAxO9q0CGtX0pW8rVmProG0JrQa/8wZRzFybVZ7vij444hOKPtCxM3fKH9XRoSo6HCP7gFBSD5I/AGH0mcf6wSPwRWrTdYGQEloFvtPyhtUBoVkpSLUBKvuI5MNrIRihgrUC7mcmlRqQVIcp8AimDZjVqB3v0gEdAmb6oQEMMITN7U2LX6NzuNyAgVLmpwczeMCTk3+tQ23kaUk1ubFe78JBHLBphFBOgmhMxQXOUGZkT1vaeu8TY24qUYTvgr50Tg9K4CiG1FvTGHxkyyN3LyB8pHEJZUbJpMwfEH6e9GIisHBLJH9NAH7kwQP6gymDr9HF4/rLzB+Xa5N9zQ/G+o+UP7QIilpy2EjYYUQsDwv/7uvAZFzjXB8nzYwaNX2ZCE3AbyEnkH5wTQoIKkaSU7JM15hncb7aJSeQ2s0JnB2UKKbfXOpqwK26K4sDz2/d1TeraRVTOrBdqxJjPCXPtJLQwhL817xqSYVSq0BT0Tcgx6Io/sEWiqq8i8rgP/ojmkCXkj0kpbW0kf1BlteV7hChXcf6wf7cc4qcoDpRhfxEBn8vMH7sBeX7BuU76vqPlD+0CIsbPUpqe0ffJihYGRKAuJ7xnXl5gcvWoNGViO52K/EgT/Q2LwBNClgAwTDMsFilfczG8dooJYnuSaLu5DApdTeoEmrDQ+IwOgo+I/cnebxMIVGI19V1CGuodCPlygdkkqcSkvldCjgQ3qaLGqDa9b4PcS6WQKMkf1P2ZskXt7bDE/DHJ4baUgT/WGVcg7aIjm/xI5JDKH5+do1LuzTek8YaIDNh0XM5Hwx85YiDumONlR6vh+991tosm4rcpcUuYu3/AB1bkS+jcxw9Gs/V1fsiBjhLEtpdQMCf5RSr8gTVljc0Ik22XYIEfsXSjpbm6zjXp19YBMVlrUsvi3Fei8NDuppptR9OUei+1UCsFYWyBKgtTTg7pgD9UfaSUP/oQUPkDrZPiu8wHYjNuEAtWDsvKHzNkUZ/KIar55RD4Q7WAKBFclSvIRjBpxAJftB1zQ0NQXQY6euWdI2H4f7bJRgGOW4YAACAASURBVA5hFwhSuhVbVkEAm7qsimcXm9QJA0ZvwyLVZkfjBJ7vtiMN0IsOdBROtNoF0WqCEJX6q88UC8OQb/n++2nav4sJw9BQij9SOaQL/gCtpGZ8dBooj/TzQ8HNOWVb5Y9+lAICDrF9+qPm+HrvvMofOHdMQ+2EtCkV39py8Wj4I3YfiOMF0gdSpjO7CY5k58pQgLedTJ+TNg4RR6H1bRV/n2El+RyTCvF5LytIahBxyR2J28nvml9WgOR5VL2LJ42xSPFTzeRCaF2uPgXmUHei9hn4zc8Ms874+F7yJk1JgY7IOKbGrNg3mjDJSzRoGq20e39t3v8hp8btCyX4gxsfE5iM98ofEVzRCX8E+vlpJwGDuyhP4pDKH3S9FB77IQ6x7fsl0NcrfzD1eD2OP7i6n46NP2IWEKUKnZqlIVT5dvLSBg2dazUqQCxSoUtpHyl/zh1JNheBX2IIIfJz/fj9CWJ0m0HZrmYOrMPcb7ST2a0uNXcdapOkMSTtWHDPOwnWiW3fPYRZGGs2n2oR8qF1F4QagTnNmNlH4+spzTy3FTGOtYqHRUBJ0uPGRyp/7GCuEkr+0HJIV/wR6ufHCf/spHYrwB+2vl5rDt/aVPlDxyGVP7rnD3fn69HwR8wCokihicmkRmCFVtZSjQqX6pIyg6/6RALf+24krYZ9XUiql4UkEFv3a5Z8Eie1SZNmp91DQk0zmaWE5Gtj3zdk3ifOjyJ1qg6FY6SBa7A+tW+dQFLCaZMjTBMJqm03aoLUKALJtH2/9fOlspDE3D9kydHKrCFnUkpFMdLzxgdWr7H8cRtii1yr6CZYM91NVCWpkqUccq1D/pDUf07LW1b+MB+te9VzTP3m4I92A8oUn/re+EM4RsbEH5rn5uQPjfJgKryvZOfrwfFHTKAqVbjkQsMk6Yw5VpiyUQ2y7jRCSLPTalSwxnocGsXVltoByb3jzL8GNOaY5v9lpKNNGaHtPr8pUPe2c2aBI/So+suxSm9CdQf12xK8C0vIm4LyhN5rVfB+3GRS86xDz/DGyCNIn28ns1SfmgrHzi5YK1T9Der+AghaLsBKKlhDGh9uTPr3eBjuc17RR6n734Nn3HB8TjV1ztX7kDMppaIYf7jjg+l7Wv7YhtSnvmX0KCLfQ/yh4ZDHO+QPrlwhUNb3LvkjhUM0/KEt0+D4wxsjD4+cP9a75g/Hg+E5YT+l7r/tvOM+hzDpWgfLHzELCKzQVkNzwvo421WZX5GtqxD1ew6iQTreDRggE6VGhUpVRr37jLiG0qyeUgrvoNAgyuUi10QuBNKXPtD+oTLECKOzRNsfjVg0hcz7G34/D2xzr30W2geIPr8J506k9UXc5wKkrBUJKO/d2/0pVuBeVDm4/NY3kQn63DMhIK1ByviM/z5wj9YVTjrGKCFtc7mvwGLuaqAv7mEyj6r3nBbBAWJU/AHaVk0edo4/NBwy6ZA/qHJJ+GNlAPwRkqs5+aMtk4ZDKn8U4A8Ys1zfLMEfjZJD1uF+/v2fbBWBDoeMjj+OZPI79M27B6a2BFPcunedKCc4EvhMmer8TC8cpsy7S10wuIA+7F3nnk+BKNcxxwTO+YTnNHdR5Vyj+odTBi61XNvmZH9AzLbTjIum0Pk2QPinkcwVMfWt0mK7fR4Za41UeDP5qpuQSwExxl8w31tB+z4iNZ0tBzU2b5pnWJlCgnlXN6vPReSaNW39SuogMI5XKZmX6gdu7vmU459uZdqzQ07xOTb+iHTRIPuWwg2xM/5gyrUC8mzo/NGWg+L5C5n5gytrzLmVP+L4IzreMYY/Yjjk+oLzhzqNa0RuejbtYcPAG9RR+cGVOXfJVGV+WSPT/jVUui24H7URlSr4V1A2tAypYPpCw70T00aHykZNdInrqbZsHHIUpZFUvldSfZtnvSTNNx+R41sy+ReVU/FcP89/qN1Ez2aeSdZTqH40YywgkJ8o+QzBs57WkECfGDp/OPfx++kdLwYiN39w9yvKH879rghSfvbOH3BtUJZk5A+/j4bSbXbJH9Gpf8fGHynPj62n0hxyZmT8EZvGVep/6X5Kr5tbXWm1UMQuf9gE5kVB/nhKa8WZJfdNVM55wYA+Z6Wq0pol+IOiAiERIU2YVCuHCmRmtU1l4qFIwDW7r0FcBKfR1LxX6n4DmOZdtHs5M6ZmjODJkRWHG8v2Xmex5yu1M9JnxlqetGOMwtnI58fAao6o70exgBgJf6yDFju0YVUu/miva3mra/5oyzcZA39IZUlG/tBmiOyEPwDXiEmh/X6h+CORQ2L4owsOORv57F74I8cCIta8qzF9ijspQRbnsRUpJlCQRUVMdL37rKvM5i1qIShEqKMVSWdKlJ/aNGUroymOe99t5/mu5cGPi7hMZeVB3ku6S6e2rjeINLbWpUdSNzFZGUSCFJtUOWUKyQCyfRLaPfiuSJkpl0Np/ab2xdzZMajJtcYlpm+MgT+eA5mOjeMS/OFOzi/2wB/B8g2NP9rrzUf2dxW6mh0Ht5uNHvmDm4CeFUwKi/FHgEOi+SOh3WP4Y525bjsTh4yKP3IsIGJW11qtiGa1mLQiVnRGzvfuauzATPVpCzyveJS+X36EkMkyBCap3HlkvnTPzL0Xq01y30thkp5l6ufSIE6137dCkHIa3PWAu0OJtHLsuxJltm4nr5rjNd69jtnzMxEAVZ+qviAcC1Qcl/1+LKj8EZ4wds0f3DO1Mm3R+CM0+e+TP1I068X4I8QhI+KP52CXbux935epXKPij+QMCkgk+D1F2kMpNBk1UgaQBlR0PdbImnNzAHteq40P+QbGZDzR9A+0/Z0By6ZGI847CZPDUP1KBRGaWcmpD6p+Xdh3faZgP4+u64i+yWaGErxj9n4ueFeszEcJgRmTmYsC1TfsTuAiSMeCwbPELajvB4fKH4PkD+qZQZlW+WM+I49fJ+BOVII/kjikIH+wHDIi/rD//7oI16OF5Y/kIGofUMjnveAy68P9aKyGj9FCYBPR6CCiyHKJNoeRakcKlU30PE0954a03Zjz7OLoliBbkN83KdiBcQ6pj9uQYvQUnLPCCH+VH3rX9S/tv4zmzfrGroTaBdl4qDiE2sK5d8kxTuEelxCtJhsQGCPDxpaFKYTKH8Pgj5hnLgl/SL0tXHlCBWjv5eSPrttAOf9hOaTyxzj5I4cLEwZpTmsRlL6dmGlqlrKLpKBcwQC9jKZlcdkinse6fxUmManliHTzCU1UnfZyByclxLeIfM/HnAUI1a8nEp9TabBmwcWD229XW81RpAseZRJukwl0DS6tI7p7tybYNkSe4Au9Rmw0NgncX2xFBWE/2gUDgcofPfNH5DOXhT8kKXi3mMxYk4AHSDR/ZI6DycUfEg6p/NGMjz9KLCCwQNDWRUDciYnBEbQgIAOoEVR4CnJkIRgKyI6nzWQSAamfb0ygV4yPLZXNQgqb23tGpYol6vMKaJ6yByh6zxVlBtP4xnZFXAqsK9I3u/tSBMcyQZ6+X+9UudHYVWkf70Mb3SGy8EdsPVX+SELlj8P88VxCQgPLH3cpzTDFH+b7h+C8qwPijyCHVP4YJ3+UcGEKujt0ZYbrwp0px/sG7t/ZZIGrL/gsVpeSNodznnU2yHNh3Yruj6kjwhJACclYHHLjY+p633WqI6tDI+23Q5m0Em3VMPndNd+LxrIgCJIbL43g/uRYgL97cRPpArnkaQ4OqfyhQ+WPovxxKD//2Phj4BxyDXMLqvzRjwUiSUMcq9UnJhZdBFTneF8UHWj9NVoCqx2PqkuF4Nh1nn1Iay8QXicJTUyUqd5cT71vLI55/ZfTMFjXqasFBLBkf5CtUB215YA66jqex7cobnobevlj5KrCNYMay3b333vw3C1hrnAsfobKKz+X0piy5sDEYVEsniXlqYpDKn8Mlz8Usm+R+cPPzz8q/uiTQ7z33oG2dvnC9TQoxR8zp1+eWhT+KLGAiEkFpjWFYhpILOUWmZ5NUZaY97Xa5hOgTWO1pENyj6I6HvxM+XtuCQbsA45bwtwiiBDuxyOEVws7yB43932R0yQUyk8f7NdQHg5r9hyBy5N2MRkia0l6xGB75oSA+LF9M2LHCJdOcMV535YMKGwRY0m80RhDUl1lmesLOfgjxCGWRCt/jIM/sHFzSOYsGH+4+xb5WF0A/gi2p7AsIiDvTdVtaf5onOcvDH8kLyAKBYJSg28HGQQvECuz41C5rlYie/o7pKHbCdaaU8YfhsGvjcWYMv6RB4uTnIOOiRHAhP+huiSyB2AD1rbHJQgUooLR/EGs7eD7mhhomxjByfnQn3XaugGNgsR03ZLlhiCxgC/AciwmOVJDd5YVCmAqTiCHuVpD/EkCEcayRHPI7VJ7yK/XfERtNMbUXzGLZx8oxB+hvn56ZPyxCQvYI+53S8Ifp73A0RbHnc0/F4Y/oD+0sQ8+7i0Af3DteVCWyh/NKPgjaQFBrWqZXTulHYMafA0xmaAG0ykwBRX1u/NMc9eRSRZWz5LByw3YSY6Vu6A9uAncrtI/0sWaU09UoNk0UaOzFis4A0FdpGkbrtlENOO3HbKUkJk0e4iGGNc1KZaZLCJsOThtF0AzHmM162qBCOUO7ThL7VJLpjDUZrYJ1F8uDX3vKMgfXDa+yQj5A7OyHaVcVRaQP6j2WV00/gA8SyTweHYA/IGNKS7lLJbJcCKw0Ff+GAF/pFogYvxMRSlPvcFnCfo+5FmNxBSElaUgNANymmGX1miXJmF7TAMCdt9fFLQqMZpiDjuOu8EObBaHkWk2dzi/H2rr1em7ribNz8IkITN19inh5EqUIjOgOQyVl5ILLZlotHmhuroDJHYsRSDGvG+uRAwauWqfifSv1qJXKjaqFIrwBzMOJwvGHyF3jMofI+MPuO4i9F0qP38v/AFj6iHPgmA/z7fuXu6N4X4SlyG/LF3yB4bKH0KkZgnSrmo5wjgE6BBWAL7iuf9IEbWqsp3eCh1r4o3ciVmj+WTPRXZLzK2llbRHa2Zl/UUTykDhDgQ7tbsptr6DdjI+E/gRhsq/I9ytMRa3oIw3/BSugp2ssb7L7vjpTK649+FSZKa4DvnlpfrCmnT8C3bEbevW+oQ+Cn+338VkJNK876z1pc/YX7Ry1bq5YIv5LDvId4Ri/OH9HoMx8EezRPwhlfdS/rgpvGdf/GFdVT4BZfwE/L9v/mhAUUi5H/ngxuWMKXNp/rjj9AM7Np6Gz2Xnj/eY8rykkV2pCwhK2G0VJAwJ7sV0AsUg4hAa3G4nChKUfQerdYTUaG16yFykkyugpt1Vk8Krzu/3QHhjuOcM4peRHaOPwcT8HNxTCkxwNhHCKEsfQoj9plc/m5hG1bvGF3QSMte0N9cHbnsC2B9r2v5IPot4b5umcAXGxVV3jLTfKZ/PlgGe2/bN1g2mxKRBKlexhWDTQblyo/JHPH80MAaXgT+svPmM8/+9DPyhUUpq+MNqyZuSHNIjf+TkkGeYspTmj/Pm+wcdvrhYkD9m3jgdMn+8BhYS4rKlujBp/am0ARwxmhFs7wCpD2tyoBERFOemDWvhCkQpcvs/S9ojlHKsxQoScNiAcDvmLFZX4f+3EbcTt90okpgyA0Dsx5yaVpBBTB+63yGzVcokHDCJSwS7ZvxR596jYiYE/XQ3JitaR24kW6F9TsAd4nQHWdFC41zSR4tla8uIwfOHkkNK8EebfeUdCIfsl00x4Rkzf7h7/9yFiVgKfxwvxB9zGZAioO1HXfFHLg7ZBnesi0vAH1sj5Q9R2ZIsEIJVbaMxoyHQrkQPPT/CosBlrRCbmzyN6IPgYuFri066ZZGYviPqOwRJe4RM0G5b+WV72ByfJtxmbgbeg1tFayYG1H0oguE0YRIEBTHSL6Um4Zj3nDj9STP+qHMfCfU3qp+a40nF87t0G2mEddNJClWm/loSkmpOB53adej8EcEhbNYjad9ELGoXCQ5Z03BI5Y+s/MH1rSQrdkjO9MgfTSYOsTwwGP7IxCHrC8gfwbKpd6LWaPSxc+EnaRoqP6UbFzQ5RzzEio/cPZk5n31OCIFsNvvpzzTZcXKCa0/4zS+Xql5id5YN7MZLpUptzYPu+ZtE7uT7SuwgLulzgn6mLgdRXw2xk3H0+E3tj4L+xgWfU/fLsjNz6H218iQnBO3b9FEuLbR9SrprLHHtU0jKSDF/aNtcOK5Lccg15F2L71C+xPyxSWRKCpYthFCf65M/wEKWNAdM6Y+5+SMnh5xZPP4Ilk21gCAKQU3eoxuFedn9DgF/s50S7mFzfIsHl7CSVY0tvGebBhIz9VkLRi9gOvxNsCxQA8UdSFRazGA9UgOSqNO2Ix9KqQnXU644UeUSlJkiriawT0RSOZz6Oj2WCaVXdmyycQd8VimC6kwop8i0DOQYmjTMup48lq6/AhwyA9/rYC51wVia4xAFSZfgkCzPyokF5w+7aDtBuNSU4pB2D4nKH5n4o0sOOTNs/mhdBY9qynakoG9eij8o5aP4eRKfNqehVOY6z//0dIxJB2nEEwLBT2kKNCnQSoB61/tNXflR/NQgCW4gRIFqZ8RPuPF8QPfvD+ddUfjcJptDCR/m9p7SSUBUOdr6YrR2Q3Zp2SA0lW2WqFTf3SQBzLVtRuHPpSidchPZUNkHAi0n5OaQCaTGfJGbGBBEH+QQpH+w+e6ZZ/uyIzaZCPusDrDI/DGF2Ijse7IwcTC+JYRC5Q85f2TjkDMj5o9GUP4cC4gcEfiSRqHOWRVufBPavGZdMAmjVmyHiMOrdD9gOmYr+yEhZtdCqu7vSTfbkiDQTvb5L5jfNqBNqMCr9RKDGSMuKCcn/FsNWI5yZN1tsiNwcoH7Lcv+GIQAft58f8nfKdZ8qttGUIbQZJl9z9hydQwtJ/TFIaEJO8khbjtIOETAH8+Bu2Us+hzzC80fJSeE/niu/FGMP4L9dBn4wyKmbNoFRI4IfG4C3k7quM0/JBoorsNIzUXrIe0C0mlyWwwOBc3FrBATEawDRd2vuCtdEIg53oNr69NgwsQyPkUP5gLlvBFwo9DWU0y79Q1uzG/FvqtUsBMC+Jg3mXsBrgv61Sq1Q5LJ8hjbNHVCmbI4TOEQ9VjN1Dcx/jjOuFuGMPP7R8ccstD8Yf/okEOi+mRkey8Tf7Dvy8juh7xYrBPLyB8rBSLNxedSGS7AXLebuKJsCJ82UcMJM4TEmpYlgSd33GwFVF3B90UgrANV1ooC7xESDpKMT0UReLd9P0tGo6Gup8h26xvrQNTYOAhZDLX5zY/5+a4V5upDmW+EkORYJ7OF2WcR72l9ojdGtHmchj+C5wfGCHatxqolHquZ+ybnbqnBfrwHElvQGYdU/uiPP1Lae5n4Q/C+lOx+3KvXtYHyx34WLfibzMwUyyEqC4TGZAfnPuRtx+5vcEJVzFl4OSrrRPSKMnDdIQi0C1OFJeFWwJ/xNkx0fXNXthzjMYjQsITqPvd7YM/zcUobjJ5ZU7dBZRKx92WuEdcTUd4zzvd2l+KNgfrGu7JFnUUj0EeleyZotLza/ipxxaH6se9yc1WhlRoUIl0+3Dglvz+QYwT6fhPJIdm0dRn6JuZuad1qHvACHkP80QuHLCt/ZOaQGP5Q1dUy80d7vfm4qhijk5hy9sAfjaMku+DGfuXgkCOlBAIU7rwjvO3nBfP9WxwhR1aM0yHUgrykb6LCbHYo7zGiYQ5mAvHQSQ7hVAjqPut7eM+LNWEeQsrAIlzyyHdj7sfllD+0UVHAzHp+LJNMpO/YRXcXY5TbyKrJ0F9F/qfMhBcjnF4UCqlQ8ocv++2k+RIEtrL80T4rhkMGyB9z7paRk9PBc8gi8EcBDonhD3FdLRJ/mA8/zexGYnml/KFFKf6g+nERDkndiZoDVrijzurwNOPK05pd1p28zSpB3pFvIrbqsyazlwOaoJjyjSYwNvBuVFDaTurziElHjOYwamBRgjgQyB0juHwhTpW3tf75318eIgkU0qpLNIxSLa9/fkoZ5volkIB0h/TBTwaHwB+xi4EF5I/RcMgC8EduDqH6eajdSA7xlFALwR+FOAQbo37KbMzTpBf+UGZhTOaQkgsISSEmTGOgZpc+QGl7OtRUdRrY1EOwdhZkbI/YgUUJ4t2IlLHcxNcnIi7jDPX9EDVJJOnGtivSJzDBzml5sc0sVeOO65fIWCMXm8L8+IOaDPbNH77r1xLzh0XlEAaZ2yMnh2D9XCKD7O9XkPExWVD+4DjEWi5z8Aflgr7re5p0wR/Oc3rhEPFGcppJJZxL+Z76mAW0rb1ufsVoJToPKupiYq99X2SQsH6HzOo4KR8xUa6oe8RuLMO9G+QMV5VHuhkiU94QxGOro77H1d8rucZgRJ99yovletace1H7XKIcmAZ69qaP//yx7/zI5eYLXt5uPvnAWvMDX3XulX/6pV+3ymi0epNLElT+6J8/tO2Q+AzNhrNZ+SPHe2a4vgSHbGnLI+Tb4vwxAA7xkTQGNZxwphB/cBzyLR//uclf/8h7X9Pyx/e94ZHZ+1/3NT9jfnu9d25SvRzJbRZyzpUGJG5BsM5QN79SmyKhDjrZX6AAtIFX/u6Plgg2f+TLvuGht/7yB900Z+vNbHZVmHM5yQSZ4R6xmjry3WLaDjQP1EJ8PzuP088oTSwH0djK0SZCUPW3V8Df/ziSGePgfby6bZy6tW1xHmKYmsRxjrrp/OVf+dDNv/PBZ47dd9euJZrmC19+qfk7P/3MffcmK40hgaL58Uug8scw+GNoHMLxx/5YfLelDy+e7LuuSVP7RsurTPKuBIfEKFL3+uaPgXBIk4s/iPje85YTNPxBWKCTOcQon46+8wNPNS5/vOsDT9nnf73hD6yNoznkSIFJtCa1qTugevHPFDSgyhTZ4UApBc37orsH2w78Lb/2YZvmbHLIHW1i/vtd17rIspF0jwRTdgkXASrV8orCN5PCVmJ9tpstSbVhobFG1R8lT2KVC2z/ILQ6/rlWa3pcoVTB3hst/3d89IWTrfA/eODd2421SCALiIP8+ANGDv5oTeWUG0flj/FxCMofBkff/LEPouPrnT/1g5vf803ffj4gY1M5JJmDBsQhQ+CPLBwinGxLY9765o8N6B/BuWLgvefewfIEwR8ThD+SOORIgUmlJrWpa6rsfPMS4WRfu5kRtuLvPDtKwspWQ8RoW0MHnmB1ANam0lk2yHtI6yXBYtB4Jnnbh7vSymuE/wzq4yDYlDmXq3vRBjmSscb4mz5OvFusciHUxyRKEGrTL0zLir431bZfYDRGGKw5eqQxDzn4Y4L0XzcteOWPjEjUjEo5hJQrT774I+j4slZts4AIJVZJ5ZBk/kjkkFD6+7Hxh4RDbJrYhxg3IJFiloh5OzVA/pgS5x3iEMF7z7UvwRPk93CPpuQCQjOplJqQbgUavguTvETToN1NlHLdsu/UCRKtIBoiJiZAZEedCgRrDk0idY+dXNahAJEcR0zy2Jb2ElDtcZ+mvIwm91AdYO8lHNNzbkAxWj0kkPk64xMcq1wI9bGUseqn2eXeG23bV17zObsnXv3DuQmU9WX1UFTBkhEl+MP2Cevjsj/hqPyRDx26gJJtTS2i2xTv5pMrRyqH9MYfwvT3ffKHb6kI8gc10fVg7/e46wYUaxVy+wfwx9oA+WOLOW+qeO+59v2dk2uzP3nzpQnBHzHB+MkLiKBAiPCnm6s8SjAU9AkNNqCAmKQuW/a6IDK9a7QJVknEqHbYdlTrdyetA++dd2BTpGMJnZzqr00OX/qAVhn1a/fST4pJh2oP+L828K1NM3daY1KFHY99twEMXF1KhGXT0UY+IXm2Fahbe+4us/vo886CcSLY6+ZQ25rFQ3N3srJ5ZLZ3EDC9e+RY83ff8PZXzZ9/APfNKQdLQzShhHF1QnHfQ32Dm1gW4pDKH2kcYtv/PeZ4jX+PP/ickzc/7w9vnpRwCNK2VJYcKYdU/qCBbbgZdMmBOt0MJIHws0MtMn+scxxu6uylEH9QY+0jX/Tnrr35Vz50YY4/vurtdl71D81xNpcczJKFifD5wnxW1RH8JbNYxGZKiIj6F5U317tKM1WkgPPze9PHf7559wd+yPfD238PCKSW3Mfuqnp/bCcnNOlYSjtVvcB9qcDmG/A8iXBKyi4m8LP0YYXHo1wdMILvBtTfe80Rqie0LmPHGnNdjkxMMfKs7UuNsv7Z9/bLY3y9r73p1z/ylvuMJeKTDzzYXHrords/+mVvFO2qOkSEJvBMf95j+lyvHFL5Iw3QLuik8lt+9edu/92f/HsTdxKEcQgzVm3GmS+JnShV/kDRWi7U/AGuy3Z/E2xRuHT8cf2PrDUUH4cw995umd78sQ/urH9489jnmkV4Sf4Q7wMBDz40cKFxKL//NncxBo02gNKm59jMJEfcBTVoYiLbcwQQc2USWUGEIC0vbaDO93z4Pfc+/zM7B3XgLx4C97GLh3Ox7ev3VwsI1oquF0cwcG5qIe2De240QAA1TJpXSmhxdRDS8kgWWVuMZekOtv9CxBhNji3C+odWgyqofwqY9faQ5s4Gilpfb/vMLzRffD8cY0WAP7bA8oC18UpBDrFWosof/fBH2y6oRvrHv/Rrj73m3t3t7//AD7Ybc1EcgrXtfsYZc1T+SOOPuU0Nob7V/AEyLrR4aO/TCD1bRs0f1/mNQzlQ1tuD9zSLhTVz2PMets8pxR9RG8kp/P6plZUmsHRaeDMTN8sLm4NauQiJWdnGmumkZVrPaN5ny2QXEeaQRPdzJsbcgeepC8aQu9qWcufjJIAA2mAWsI8g7cjVAUcO9rcQuNigNbCCbGtccJygwie6jC2CcknKpnElmxHvnUtxMAoQLoAaaANLqT6y5sWrxKDyR7x7GDt2zQTo1Pf/1KUHI+9R+SOdP8777Yi4KRXhD40mf6z8AZAqHDn+6I1DYnei1qRqjQm4dEHtqpdUQUTnVL8TuHuJ7gAAIABJREFUdNAmk39tFs2PpkwBf37uHpKOT2mj3XtReaqzDW5EE74b6Uc+DQk/pO53QPPix3Rc8zSwsX1GtYDl+kaAHK4oBRsmI2wdfMqcE5oU+NhPCE/A1i+JgvFTLaQLRs7UnktxMBak8sdc3vVA+3MypvJHP/wh4ZDKH8vLH5x1Sev+u+j80RuHxC4gJIUKBVIHJ/9Cs1dsBWVbsSlWm124VGnLxJn3jzMZJ0Idn9NGuySzUlJLT2jCZYE/upR4u5CKrs1pTfq3I0F+0dk8YhawVN8IkAOlXdomFgQ5Bdq0p2wyQRALxgeUrlrRioMOCK4EcpCalD9Cm5pW/uiHP5oAh1T+WG7+yMkhi84fvXFI7AIipDmwE6q/L8jcEmpYaqMZ8Qoy4tm9af0yWzOaVPM+R9pEx28IzT5FMs8y5UpJs9YItBi50hPegbKSWZZ8gQuao+jFKzHgowOyhRMHanH7ZAd+1Jy8sf2NQidmXaR9tQI5SnHQBcEVgsR6aTnkbiBri4Q/Qpq9yh898If9D8IhbcB85Y/KHzk5ZNH5ozcOiV1AhLTPK2A2uo9I+9XCCgwOksn8yUg/1q4CxZourBlUpxN0Ro0PHtcmt5iOTl3DWZeeyTQJCvUhcUA+scA7gZBlSMhEL15zTBpjBFTE4jbamkZo3GI2kyuiJAjVn3YMJygOxho7ITHbTyCAk3Jftaj8sTj8YfEJ5P7cNZU/5HU7Nv5InRQvDX/0ySFRCwhhBH8rVDjzcSjAViKcjkUSZjZ3ob5dDBih8JDA1Cn1wTs04JSCSEsyM2pHyghIni0OyEe0BXsRQka1ePX6ECYEtdaLKALRCLZYgUaUz+48+4fI5kfFXIOU5ZNspFRi4jc4K2pG/mgCiweLyh8j5Q+lLKr8kSjXoK4vMZahS0Pij1gOWVb+6ItDYi0Q7cteEgh5DqGGsI37w4Jy+ru/DsJdqEMXg0vEKvIxatMXxIwsSUfpuhWRvq/g7+juv7CuTHeZ0wokJThyEh4Y1FSQ/06OxSsR7K8e8M47YAKxiNaaE2hMnWL9CnNjkWRMK6Ek4Pq9Lw+D4z1RwTBIK6oEAb/oRpgbPeR+VPljuPwRStF+ZVH4IzDOi/KH8+zQJm5sRrI++IPjkAXkjw1YsLuWktL8kcwhqZuKWf/nQ7uFKWHzA1+HSqDAWTBcPBe4zxxsRVvfcXPYdKP2M/cA4MxDWQDvTC3iRNmN4L3tZmFBOHXE+b6ehkHQ7mTcDlgMfkBzVisQlPcCvF8oeHrKTOAPvZO2rwXKdINJ+yvNWEO6c3jv0PSttQ7UqbQct4QaL2k9S8H1e87vW1oPL5jvXxL2r3UkJXZ2K2pBYOXXJDiwk57KH+Pkj7n7eM9dCP4owSERcm0jsHhwz5tD5Y9O+GNKuFmV5I9kDom2QHjaB1fzxu0c6mMSWGVtKHbpG6Lvr+0Umu9jwC1GqPiTrUhNy1aEWbltlyeJ1fxmzq3VOS0GDKrnmcD8rQgfwVMRwVkac6O0r3BjTrII6VJrzWVu4VJuqusl1iecgdadgisr1S4Haa6d8+bGR09JF7KB4I9W8yq1bFf+GCd/SMfS2PkjxCGl+UPDIVo5NTT+2Aik/R8yf+xFpLNP4o8cHJK0gHBwPNGqQU3+tRPtdsfDoRBqFy4GXB09i2TC2l9dYvUEWo32uyaw6+M1ZnOWuTIGOmqueIcmIasXteoOLQJLt7F0whoTDNZi1loDOxovVHk07pCx2XNSofX53gfhHsC1iygVZgGC6wNuXbYbDvq7lnfCH/afAXHIIvOHhkPGzB8hDumijVM5ZCz8MdV4xEQm30nFOrEQ9uMzOuGPVA5RLyAQwXGCINN7sJjANkLBMM2g7dsZWFpDqrPY73Nhi8mvfNG8+4uIoG+wegLz3BkhiZ5VlhFNVwZCR0zUieTODTh/H4erIY0BBFDvIJOdnG2M9aEmUzCYv19LV+MlRosf6gtcGuFsUGTikuyKG6qHYCrMsUHIH8fAZeWW07YhDsnBH1sdxq0tO39oOGTM/MHV8QT6f0n+aOBeoRgI7plj4Y89wTyzxcSL1+yTP9bh/6fHxh+qBQQhXCnYuIIVZNDuJZpFbYf9GXO83vu+9eMaTFrDXC4GAaGnzc/floetJ8GqVKrdkwYHSwOGUsidGnDcPg5Y/TZOH16DfPXbJYQP0ofs+LHjqv1MSYe3G5GCNgc5U3WqwSlkg8CmCyLDJjJeWaTjJbYecrpAdgYlf5xyN5gKBHHm4o+WyAfBIQvOH9J+PHb+CI3zovzBuAreBEuf5Jlj4I+gFj8Qt9Ybf1hA+3BcMEj+0LobSQM6Dwl0N1jZ/PcRadAGESx0znz/Dcj3Fxi/wc4J19GOtFu4n4sJ1A4FYHEBVdS1TH3sZ7MSFo0yd24nBAeHAsxTg9KpoE0ycAmpXztxxxbiayV9cJ1g/yPeZ7BPUX0k53jRBApCeTbBSmlxDwgNQ3uOj72ALMqarEBRv+Lx4lyHBYjuMnUy+CxLOfnDHQPmz4dL8Qec30XcWhBLwB9cP763KPwhlA9F+aMtg12Qm2MCxwPw/yCHjIQ/NgP9SYvO+MOr43tj4o/JbCZPegEuG9KgZjJNVikfUxC4lIbACuBONEiERnI3JoKfeacbduBzdclcy23uJypn6jsyfcnuAYEubGOuQe7h19dUc09zfWjARLWzBLnHTahvlboX0XduQzv4Zvab8N0xRNsUkkXifpEL5t2eIvy6n7YuIcrNj96BmOStu8P5nlwykzB0/hgKhywDf6S+Z+UPPRacP9qA+vNCi6MElT8yx0CQ/pKIGeggEtzvpELzZgzWQWMzEfqQlUJOMzipESNMsjaN1yUwQVPXrsCAwwaUqJwZzOsxwWNRAWeRJBm7Si/i7sCZ3wEx7UCZgue0uQJoNLfY+LAT5T1YMLg7zJ6ESfMhEz/cI+Tf3oemnvLrtt9fVLhFXSf8eV/223ZAQb9j54+hcEif/HHF2YOhGH9k4JDKHwosAX+0+5b8rDm+3hm/E1g8+Nm6QnFrFpU/Mi8gOH/JjZz+cDEAgdSafPs0QUeZwYnMFpygZNN4MSnN9mBAPcGYovdCwiSRyGOEj/oagd+r9p7bAsET3deYySCX+jSYaaGkj3UEoXMTk/uR74/Cvg+uT3wT8P/saz+EXC4w1PmH3AYGFvQ7av4YEIf0yR9uQCxlbc3CH4kcUvkDwZLzx6q3eGhh3+2sa8kQxK1V/nhXuB1V5n3OX3IovqPMqnGrtL+qFZqgOdzRloHxN73G+PyG0ni152ID7TyzOU+Ta9M0CoT/3i5Vp/b/gf4X5fcacc8nIeiNw1bg9xg/UC71abRfb8bNFDH/YEoIc/VDubgcen+k3Ww/2s644U8stjL1Cel9Uv26O8NI+CNnG46ZPxq4D7aIqPxR+WMflT+Wmz/UaVwZjcGW1hWkkOk9p1ktCEIDiOUxD5WBasizXn7tg3oyzw65cVht5TlzXEZ8Vo87mRQkwailJmPHEbeFhzxfRl8rovGLDk5MNFowqHc3m4WPlL7GDWZqfDVDmHgptVEx2SPm5EhhV5ZYUJl/2DzpRAyEJAe/aME1FIyAPzrlkIHzBxfzUPkDUPkjHZU/WNljZcGJkFXPk4c7IEeOleSPXBvJqYVuKdN7ZrOaBBuCPOaSMpATXUZAhSZiWwGTfLvA6GsyQpHeE5qFTMC/k52YRE5CuF1CbXDvRmQ/5hY75xSp9LJoSzV1oznXGaMvKIrzG4pzUXQRK4DInyaUJ53ov+exXXbhd8niq4i2vCAGwR89cMiQ+aOBZ3PudJU/Kn/0xR/vVXjRvNbxYhgsh1yflz3tHjhcmmBMHq4RsYNZ+UOVhSlzh8kWwd8ncmQGSqkPqHNMI36QzYK59/79HY0UdZ6WzEpkZeEyI5F1x0xMrFauickCEqhP8X1yZExJeAd2EzbNfeFe/kZFbLYguEazgEjKgpMzs03uMa3MQFKk7w0BlT964w9ssevKSc5SUfmj8kdrMT0rsKDm5I/nFRvHkc9fJg45k5k/juRcIMDf0hUmm0ta2yChshVEaKdiaVmizOZtnQfen9M0udp6ynwWXAFHQuuWsxWjfYN3w0z41yOznVxjAgg19zkAtJ/NDOHjoA+44wtZAOxKNyKCa9lN2OB+0rq5hKRfPQrfU+XQ+uinZsHpY4MwqV+/xv9/GlhkdSn7mq74A7meUjxU/ojgD659QH5W/qj8wVn/XD60/38cGaO5+UOzeOCev0wcMs3JH+IFBNFpRBNJ4lrO9KGaoGrK5glJzW6+FEI7FYvqiZvoSgrBka93b2zCvj8IQMvtlwFLd5Zr0Gh84TkyZN2UmLqhBtOh7xFixSb6wftQYLQpaC58YgGwq9DQhzb0crNySN6JigdZS62bDNektnXKZFzq16/x/98ao+U2hT+I6zlU/ojou5ScrPyRxh/EvGOR+aNRukN3xR8p13HXTgsqsnNzSFb+ONLR6otKF4ft4sjeF2sgadmQgaOa5Asn/ntEwJlob4XQObFwLBWU29AUKwOczw6alDIFFjYusGxSqUGPwUFHTHykkPqhX6K0KUR/TNWEpLSdyrcerDyYMKXq/iakcp1k9uunUhrvtzH0xdx+9dJ+qem/nSaJyIjUPiuZtATvW/kjDpU/UIgmbcy8I4TKH3r+2IMsiUcT67RB2pCar+4UjMvKzSFZ+UOTxnWaMBmJmbAcWtXZjgST2StIqjpp2TgSik59aDuHXb2Bz+pKqQl3T2nCiqY0bOvO/PlwYJGwFtjaXpveVZp6VDNx4e7DQauBSRmL0rbbBvc1F3eId6LSAc+IlLRc3dvNgJ6Ba5scQg6e6W5Oh7lGZU+JKu2Xmv6b0Nf7Rmqf1crPyh9lUPlDn7o6hkPGzh8NIsNn4P5bkj/ezqSoj+IQZ2EQmi9vMHttNEPgkNz8obFAqNPsKa4NZcnhTNfHiTRzWNlCA4T0n1WYplLqqQtoV6DZVqwh97EUS06M9UboNqaZuLTa89J+6Kl9LOQ6Ztv3feZ4h/f9jNkbwzehY9qag/YL1L397UXitxiE/GWnpUzU0n6J1AeZyaukpbIgUvssdT0r+yt/ZEflD73b8XTJ+KPlkI8iu0Kft7Lde7fc/NEI4kI1wBYGWFZGqp3X3HllKQ6RIuf9NAuIlIlk6Frut1DjNTAR3RWUTRK0i23vvgMazGMC09SgXQy0sRapsRkR7mNXS7tOKQfTljYzVSsgzOcVYX3dJDTk9nsMSX0MadN9E6yXhQmbdB/DFnBEHwlOxkNxO9RvEQj1mZY4sUWrxQRM6X5mkWwJBQqZv4eEVLlIXb9J7ZcBf1f+yIjKH/P1YT6uJix+0RjM1utCyLnbIffMwvyBZWFqOSQYSF2CPyS/F+CPLWZ+edmZV2aX833xhyqNa8qKjrs28Jsk1WebrpMtmyIQbzsQSMoGnvSYEWqwEKQPU6UlS61j6fVEnyHTnGnPh2teIgjA9v23J1rEopCaXjLUfqnly9j33HTHIVlD+cAmv5O2vsYoY0qN2cofi4+x8kcXHBJIib1trnmwD/mRwiEj5Y+3BVKTc/s2Jb1XTH3l6APaLEzRD0vQNnKruoOVs2S1iax0JxHR/8GV6UhdDEojtIqfCnf0vZYho8tTXpo58voICwzlB3mgdUHGErUx3QpXrvZ+hUCNO/u9BEOyxFEmdz9LSUjWlNxoUeyXvATWChRUn6/8sRQYJX+U4BAiEQCl3DjkPuOWyd4r9K49csjo+OP6Z9sZ25MrNruhBqq4llwcIlpA9ExYtvGuEIPjnjYABM69qthUg8NWxyt60TMGqp3cCSzMsLq0uzq72PehNMdbIibprrZSk6NaK2zZgUyMJc4MWDrPNIUSZu5e+qGiLJq0wjGLKm5sasg2uEgdGnrkkMof8Vrz3sduX/zh3cedqDda/sjJIdQ4CtRPX3IhmkNGyh9tPEfnHBKxWMvCIVILRG+EBY33EDJouV0N3VUgmgs5ccJwaCDkIkeBm1fwGUPUTkKZHoisS/UqnqsDwi/z0PWJCA1kbUrjXOVSIYcAV5JmUUjK4r0zpVjw20m8qAr0Sw3ZpmZR6QO9cIiWPxwN82MQD2OVVM+a8y4uA3/kLM9Y+YO4j7vBZmk5zXEINY64DIa9yIVUDhkbf3jvfJmIp8Pc45M5RBALVoRDpGlceyUsEN7nQqmnoHI3vZW4/ft5+M2/r5/SKrTByx7xfGpQv2CtHNizmY5BpS3jCNiF9LwuscHkZZbUpRTcJP04DOrTObQACan9uDFjJyulyqWGbZM2PTF8DkKYd/HORFrhXUgxG5sCjxybiCzi7r01pH4ydA6R8oezeHjCIX/7+QR8vwz8oT13EfmDus9xqRWkIIdQ4+UUkw61N7mwrBzSNM0jRBs+mZhGleqXZ5X3zcIhRzryh+5qBUgJGjR7jH9fI2xnggXXw0ijcCToahm51filgIZOSsBD1E5Sz561AT6O9kziUhZaxVPP4zbwmbmrdcZ8zWpTBFoXbiwV9ftMcUsYoEtDNjDubg3xzpg2WoJcY3NI/sHNGDhEoc20lgfq+4tLwB9D5JCu+YN7ZlH+sOBkj/meesfKH+PhD4uri8AhR4ZGWIkTFa4Scwk/qxW6pAy+PO4IeMx9QbLpi5SA2fN6mghKyiR1BWhX8TGTdE74PxMw9/vZFWLN+tcIdwpS6ECZpCn9UKS4JXTl0tBH3xS829UuxoGmjofkH6zAWDiEUjJIdw8eO3+w5y4Jf3DPLM4fgTqu/LG8/MH1yx0NR+fiENECoivCyjBR4QZ9m+ueA5lPmdgVuS2XxBcWu6/ELEzloFdvVd5zIGPKXh+tZk+6itf4JrfC39UuSs3gVOAd5efeQBv6PrT3eRuHlYhpSfFBL+6/3mPf7NI3nxsHZDkY2TvkBcNYOYTamC7knjRU/tgzz9kD/rvjWeipBRzVT68tCX9Qz6TuXfmDR+WP8mOh0fJHDg4Rp3GNeVjEipDqaJdC2REcQbxJuDGdtNcFno/tiEjhYJIhDL5MsYychonnpr9Zi0Y7CQuRvgIZ0TIJ6gAT0HMgdrluN+qhNghrYDJv6/RipLVqqvQfxgjJ9Vl276PekVtRztD3ua6VgvMD3yioTZJkzcoy6Q2MzStMOQYT0JoCLYdE1r1ossLc+1mIgfBxb6T8seosQG7DIudgw0iNdhL+vwz8seModqgFZYvKH2FU/mjyKE2YsdkLf2h2ou5CE0R1tINcxqH7QuW+FwkQJ+MgmMaZBQLNpz45EuZUbgOR1oQcIo79QJnW5zOSqLn0cIdcZOD7rsyBnOUIE9CHgNS5GwDZ7lb7DobY/XrRmK+3MgrL4zCBud+xUqxmmryn+KB34b8u8gOX9EHlxDOLW5H0+czYpMqx18ekrW8kWBKC4y9w74vm8xFkZ/jXjJw/GpB/n8I2FvOB9VNukbtg/OHW8yosvCZMIHfljwXiDyWHbMH9O+EPZmxu9MEf0ixMMYjJ5LAVuB9330sgxK4w7xWcdIFp5wxk7Xg1cPpceaFx56LhQTtFZVbAsi4kTxptR7R1Yk3Y5rgb2GXXzd7xPEy6qYweKjgDibpf6P1D782ZsI9D3u+Joh2l7YG5AOwwz+D6d4uTgp3X2/vlzA5V6lopQu8jygIj6Guad1PLsIjnh8oRLctGjthMQJSrkfs9maUN3H38xcOi8Ieq31T+OLTwsosICpU/FoQ/ImT4+jLzh9oCoViZxWhi15mtwKchKwUcHHak7wT/50zR5ACgtIycGdb7jXK5EU8aGa28D2z/Aey9U1atrOlbkDs59N6hwbCmaUdEk9ggdTS3vwiTr/y284zYvPE+TpvnzdpywHes1iLWB70j//V1Qd1IhJ7YzcIZ962rworCrYhCKBsOioDbSK8Z8HJDyCGxljyKMN3vY7K0jYk/7G8nAhYLFpU/5nBSkn2pbb/KH6Pljxg3yPuWlT9UCwilSYY07QQqgdoKfCtw3yhQ7wSdgsINZJMeUXo2qhO4vzFmbJRwFB0Re48Uf//U66bIAPDfe+aYyLVmRAnQXMlIe0iE3wZh5r5JEH0jtDY0sKicIOfbsfLDIMCOceMyJWiqdNCu0A9cIvREE09ignSQDStGhsE9Q9lwSBCm6YYKaPXdRsYQE6HgEDV/KK7LxiFD5A+iXFoOkSSSqPzxR3VPtkflj1HxR6wb5Ooy8ofWAqEJolqPFGBPBq6TrDQpnCICbm3juzjOBE/dcOMQtH5uEmGSumIPdMRDObShE8X6+0shGkgBzQ1Xr1yfuAP9fIVoS5EGntFeu6v9Kdfv3HsRO6Y3TLt92hy3iHo8gozl0fnJC/zAJZMfqdCWyDKtDFO5ZybIAj8j25gCq6UcEssfkutiOWQU/JFJ6xuasCwLf1BWerQMGCp/jIo/pP1tw7v/UvKHNgZCbFamfDlDBQxdR/xO7cDIBba0PmaYkG/rxvcpO9CGOz5pXEeK9m9rfWkjd3DkOqLfITHfuYaYiGssIFI/wUO+tlD2dSjfRFKvSJ+w5N2691CBjNE+mFQ7cqmC3TZ2rvd3TD8WmLxoLUBRFiPP95ncCVd6nhaxskPS17RWMWU5Qu6ZoT50BatLXxZATE9MfMAQIOKQUvxBnEPtAD9a/sjAIXtLzB/bDndwi4coDqn8MWj+kHLIlLh2DPxxNhd/TGaz0OaZhwpNaRsOaVW6BrHibJAOYBuTcpFCzczOyq1BNv+y97tCCBiroVnpo/5AkHKuMftlJ0zoVPzFtiR7R8RujZS24D5pvTLPpOrbThgeidXWMvdtUyROuDZWau0OrodPzXXqfmXK9hSx0d0hwce0m0ZQFwGnmXF+o+oxeixy/cIfO4I+gNYllJ+KExOPjb4wRA5R8gfXdxaCPwQcsuz8MeeGpsGC88fbkFTGNgbw0bHwh6C/jZY/AmNbzR9aF6ZYs3JRMGaaQ3smwOmSxYPry3aVaKx2xabxc5syAbF7GX2ZtzSCz/PX3Au54WT2gaQ0cJQLgMYMTtX3SmIdTyPqaCrUNDSI1ssNwsb2ObnrxUBEjUsQjv7igXIxkZhwewHV1wQTxVRZRsnHNsi9UfQBqi5VZu4BYnAcIuWPQGDkIvFH+3xK+XJhyflj331LcZ+l4A/AJcSSfszfy2vI/BFwOxs7fzTMIl+yWeYhqFYbGpNMKfNUoGyt6dJW7mPw6QpVjnzvMe/EmaukLhMh4ZWcLtUBVaaHBaZsqoxBwRvZ5uQkn/j+NwT3jH4X4Ttw92XvHXo+wJ/A7/8f2u2857Jn//5WczyaYLJtscFoHf12Cppwu0So3eD/lxnhfw+0lhux409pso4NCBebuYcIaR31wR9O+tUTsJncaXBLdCflHMkuCn80TLlCltul5g/heywqf3AKWv/7yh/98Ic0U12ZNK6MJoBbqRUP8kOe2a6w3B1+OfJFBSPcl0yrqgxWkwTvJa/CEwPoVBpCz9QnDVqTaLqoOv96+8zUdyGCmN8nDC4K1RHmljBx2nWdcV3AcNTrE7eI3WRTxxY3PnyBFZNhIqY/BhGSN87vlL86Ki9iyieRj4B1wa7FWF1uMWbu3jV3OeqoD/5wnuu3iZUPm86zqT6Emv/HyB+JHLLM/NFqikN9d1H5QwN1lrVSHLJk/NGAUiQ6xXN0DMSQ/VwFPmE3mNzYNs3mXP7+gMmq9WFtNJ3aGwSTIfoySweq0HeYbfMIH9bgPUPvAj+FBh/7vICffdDHUBCnMnctaEeL+Y0yY8g++xnPpcPP5ECWpbS/a0jexPoM55BVAV9azJ3ADTp92Sd6iFGx2nEfT5vjRexZqe+wLHESEp93ZuJGyYjKH8vDH+2YXRNm31oo/oByvUTNsSCToJo/SnPIkPkj0Adj+ANTkrTnP4e5bRL3j7NACBE0TxVYTYZcJ6ZMlhxql0nMT691d9hEArKDq0/PX5TqmJ35MjPtIGkLqn7E7UJpuuD/p1PdZAhfxuuKxQP6vEAdpeSit8SALR63OvAbxbQalnh+BrHMnIcxIBE4onInyISQvIlxq5qW1GzBe1LCfxs2rlpDrrP1jeEtI07tOgT+4J7b/ka5+M1tJibgD6t8esiLORodfzBtcWYJ+eOoNI//gvJHA1b8J5AYi6NOebX80UjKvmj8wXEILJ5i+GOD6Lu3Y/ij1AKCHQCFTNShzYEosw0XvMT5kPmr55gBmRRQmEqiGdpBMkhi82Q3jLYvlSC1g1v7PEm7XiM0yR8yx+u9axuwnmn7qQgBq9ir5vgKpDz2/2eFEwXphDC2L4YIl3JnCN0zFRzpkQGa4GKwhlxnYzgoyyQm2wYTlDgC/uCe2/IHyS/Ec6fE6W0bShMWDJI/MrQFVT+Lxh/aZ46KPywQly5Xnr2C7OSt4Q+ujNMF5g+OQ45H8AeVvY/aaT0oi0q5yYQCw6hKuZwQNMflo77DbEUfEyy1F9CcTAsEzRyCNic4AXEO8shBEp2VAOrAus34PnY5MrZw5fafNwNhrS37ppNfft9i5bUrpUn+EugT/t4mayWEFdKPorVqieMrpS+G5E1IziX3MQicfMkcMzheYuqHquvGSQKBQUtiWSYHPaAP/mifi1mj72bmjy1lwoKh8kfquF1E/tiG+Ub0Hhhj4g+vL2HPmDBjRyObQmNs4fjjzGfrVSu/Of6IAXuvIgsIgWDjCDJKmCHPdLMq3WZcVrjGpjrWirSz23cgOsZBue0q3Po2wqdUA8SR6NtyrOoTF23bqf6J5tqL4LeZIzuEZKJwB9x1XIFg++N5TV+Ec887E75V5B5k3cP7WS2CD2zCkUqIEjeCVOLJsjFPgrzhyrkLE43oPgbt+jyyMaDGR7ktS5tJjsKEIKztEad27Z0/nOc+imSpeSUzf3C71o+JP1I5ZBH5432EEqoZCH/4ZXFTvHbNIVqrDMchi8gfm9zmtJH8QV0DYV+oAAAgAElEQVQXxR9HOtpWfAN2yXN3iQwFpajN74Qpk9t4qeEaO9K/8tAOmYKsHrHgSDSX6Y6FUz+Y6TJ2Ujr3jNwuGES52ywamGZQ2xclvqahupcQ8AzRTGkhec4euDJF5QkXZnRJ7ovmg6oHKnvNfpvDtXayEQvKr7QJ7GSL7s8CdRUK8r/hyaRmaPsrjI0/3OeW5A/4nuKQ2Yj4I2ncLjB/YPsgDIU/0BSviZiW5A8hhywifxx16up4Zv5oZU0SfxRbQAQCQDaZGAIX0aYYh3jYzEwhIcn4V4Y6VKhjpPomcyTKkqdTN1NkguOmqQv6xzpkuEaVQXqvLkEJDGajKE1flGhDQn6ukknSBEzZKcJL8pwVeFa7U6q6DQVEXmyDMeECJgWhvnFDkHXtjFdebEMmN/3nnO9w4XfsHH3yh/N8G3eSlT+Y/r6f7Wxk/LGDWPjtuL0GwcaVP4bLH130pSz8ETnJXwT+OAVWNG4uq+UPKmuc+h2LLiCgMGjwJZiM2sKSebIzEQ+FdeS6Oa2CX4mKDsV1jFQ/tVBO8KmiblqzJrUaDQUkkQKPmgRk0KBFQbCYSdJkSO+B9KE9ZyMzac73HP3oGhHMiRHNLVPuBxOf14uQLqGJFBLowWQfJlTSvmWJo2HG1XVuR/kFQS/8YeHIrVUlf7hlWqH6sbC/j4E/1sB1x50c+ik6K3+U54/W1aW11GFpUvvkkMof8fzRWmNz8YfFCTuWU/mj9AIi5Kfnpt/KuXqU+OQd2ngpYC5+iEg3Fusi1P4WDUeAXFaSJ1Y3E3dFCh1Vk+6NE3jS9GvBhVsqhIuZHJoM0T2cNsS0rBeEkySNjyTlazsZQkDuiCfA1KY+fvCkpm9xsmMy8jStQ+cPCYdg/EFtZGo55JIzwd7nD0F/Hwt/HJoc9sQfooXbovGH+XA3Ogul2ab2wdrqiEMqfwyDPxroB8n8UXqzMlF2CkEQixahjrrr7Brpmqopc/HjVLaKwLb1XLBVFtOa+XgE3qcRCiyJeVQbkBQTLDgNBRBpgyAFCGZqyNEXlfcgy+QGSEI7Y33pgVA9MX1UG/zWa0BuYKz1dk8m+PY8ouGR9gsuwDQmY9oY0Rd/hDjkEH8ALjFj6agTVF/5Iz9/uNmkkgLpR8gfXLnOugH2EOCN4VqCjNRwSOWPBeOPLDtRU24hjGbo0Evn9pFnTD2Hgk2cZ0tcRdD7hN6vC+26pv6YuvEtEKqdYJk+kLLL4z1XmwTfBd+TKUtwh08OJWI5NGVidvncdl2LEB/lB5yALHcXdSpPOobcO5b6dXmN21RIKksiyoDeE/7uNZ5gDLvW5wI2tuCnzvkD7knJJSuTHkGezwVaY6j8UZ4//LpeOP6A+4rKxdRVFH8E6sRH5Y93LR5/JC8gQsQeGjSBCbwV1s+a81VBoprJhlAAcRHsool2KeGhhaRuiHPQrdFTn6cQQLfhPFSQSZ7HBCIFt503930K8fOMqhOvrO81x0pIqMP5XF09zCzaKVJthH1/btKUAmEZ/T6pXtQKykESKpQt22IlFo7cIBUise8/FKQs5Ihr3cQQUQqbDjhk2fhjF3GpCb7DAvHH2+D9j3r7i/x+In9gGazQcuXmD8UCrvJHs5j8kaS5clx/SLOe64YBn34FciYwa458AiZvYtOa0tQT65fHbdhx6HvEzFrCvCqCpG6QcywJzygzfOLzpGbNY57wpUxwnJlZsh/BHOA9sSAx0jUhBKdPaMYgV1cbSpPyVGjm3M25eFCU0W/b1D1LNNeuJWxKlA2e3MAw2jStrvwG7X3QjU/BHxOpO2SPHLJM/HHDyZyleodF4A/AJeT5RzLwB7V4OEgHXJA/GgGHVP5YYP44UjBLhVSoSs57zByHrBChgKY2wEhwby7YZBv8Bv1sBm3FUyu7rYi8zp1BUjfuOUDyZJrW2OdBG9rArhRMNYGXtn9EaPKwvSEwHAeikLRpSFBjWRTWGVeJaeRkZhPG1ypoin4WdjPNrukUaEN8TDNnx8Ku1WiOiwYBKvvIIVfMsUGo6ZTU9zQ2VWWHHHKSyIG/FPzhcEjUOywAf4R2gi7BH1hwbFb+cBY77j4FN8HaE2VVETyv8seA+CMlC1OoA29lJHFskZJLqEriGF4kfCIbYVR8Ce0pC6nJW3he9vIHJhBtJg0J/H7GTjQVk4LY91yz7yYYmKF7zo0f6HOUyXpLOSmeOIGf7o6nry9hao2MNdoqnOdbmir3oDxaV5LEGKipJO/3SCHRdEo4RNrfp31xCONTvyz8kf0dGHmi4Y6u+KNZcP5w22C1owyKo+SPRh+zOmj+WCmYpULaMBI3inulBBJiIr3n5OM/MGdjZnSFmVuUTSQXpCZvhWk8qvyUe0BgAmHr8O1In7gNMQehfpZiZm4ytdNG4j258j4ZeD/JeOrDVUeb8elQHWhcSgL9riHuKSnPNY0rSYYMY53KjY4hyZQnGbPS/r7VF4dU/iDrn/s+NJYpefLb5nh4YPzReBl2Qqj8sZj8sa5xRxwDf6QsIKhC3NNoMJGGxfBsycqBMtjO9oo2DZzAR7ekUIpON6c8jyJpuxnJU9hgEwyUUI53PwbDmkZfAxqmFnNlUvouS4G9P5d9QDIBoep0mytv6P2I3zXkJS1/k/GetoxPh9pMMta0/uLtPeG5XPlaVy9svFxiJjncTsIhdC03ugQnp8VjFunvM0Xa7N45ZEn4g5Ohv0FN1gJjOZY/7lGKwoL80Sp+sDTc6DsIzqn8MT7+2GDivS6PkT+isjAx0f/JkeYQMO36ZKNZmAiTVvTzmWwC0gwLrElK6/qQAtsRhWndNClEQxkfVBkrpPUN/YHb5bKT7AZY+zH1IcpsENMnEq7BxspuSvmV5TyR41mh948dx9x19hlKU7UkTW6vqR/7BjGuc/CH2OQ/Jg4ZO38IZbnv+sWNySYTfxx6JnNOMpA2fC3Ex/hYev5A6ovaSLXyh67PZZUbRzL6oqGCWvsCsFgIpm1NDGhqcpmzQ4F4XQl/4WBT+Xsi9b0hWEC0GqhQfQZ9EuGdQsK/k2BCzPeV8GFGt4mX3jOyr1E7pbNjBX7O7RdKlfMOaOCwYNKU+/q7aarHMROM6Qa7asznbZ9M3km4A5/rzkHsXms1WZuJfvaq+hoTh3QBp54peRvNH4CzgmQUrjzn6vNcJv7wn1kMft9k5lJLzR9EWbH5TOWPnvkjxoWJIlO7lf3VLtPPCc2/UsSas1kzbuk6IJ4hHWxaE5cmsxZbn0JTMUdmMeXKCucdtolt4nOnWaT6GrlTOjdWCprrsXIeBTeClGexYw3guri52AuMnTXGlSymf01L70Q/YmDtOIFJzCFUDumFQ5oB8EfDcUhm/tCULRsqf0TNM1sXtMofA8CRCCFzuocsF10gNko/pHVKqgOh9o0bbCsBk/h93HkepBkatiT1KVgdS4W6ymc5p0WIsczMtXGG51L1McmdYjcRVDlP+ZvjZbrvVKAUWUlQiFD93o6b36Oymjiau6I70Y8FTv+vHDIeDkFTPnbAH02IQzLyh/vMICp/lOWPwDzTKsBWFoQ/Ghg7q2PljyMRGorOs1x0CDefsbShQmbc6DpQmLanmsFGmAf3BXPgfaW+4K07yIXECbOEcEqYMbUItnGm52r2LVCPsRzEGNCKpmZ/kLhM7BATevs9Bsn4JNN0wt8pk5ylAOOusRAcEjmhHgOHzKV87II/WheeRBczqbwUc0jlj7IcEjnPlKLyR2ZoVnKchoIagKNIQ0i4MUh9nkNm3JQ6kLhscPfaIyZ00vseAmE2fppy4YFrUlzMQika2YxFBKLe3YetV3O8ZI6Z0Gc4x3M1KVqpCTOKjG4SlNsAtjOqFl2m6D34nnOXINwQpG0k7WfBlIIjQCiOZLQcQriQvqKYPI2NQzrhj3YRkcAhnLycRbrCLBp/kFl0tLImE4fEzDMXgT9ucNnBhsofmgUEp+2gBuBY0hBGD06BL2ZKHUg1T5SgXCUGcLRGCxHoNuD9FnJq8n4CTt1i+4CgcTcCsO8uGXDw3WYgoNxvY+q5p6WDmuhrNq4gB7IQI9eHMpld73lxDR8FYbvfXsQO3hbU96LxSU1kkKw/2WJguvB97xBT5jdfZo6NQ5LGzgg5RHrPIfPHBNxEtIuSReOPZ+BvrH42euAQrg/liM8bIn9sOO+t2kagb/44ksH8c4PLiJE5y0UpRAtE103B6QxXwC9+XVMHiPmPcsc4tOp1nnEZ8afDfGVJ064k+0Pu+uMA73Ylx/1D7jUK8/QGBAVTuIG0MWdOF7sy+S4xVvApBV7WNozts8p7tsJ406t3qwB5o/P/04xGDX0+4We6m8EtJ0es15hiyFL4g0xxOhIOSZZ/yHvaSU078Rgah1T+WBD+sDDX2iQcOfhbPQ4U/ZWcZy4of+SQ9cX540jpIOPcPsA5A5gcUGlP95Tl4oTH1YjrbQaXu147oRlclBPtdSJH/f729LD40dSvNp2fFqL7C/pGyL1GOuA4wUptE8/5/6YM6lx1r74P0WdfJU5/rWRxSo0jkDUc6baw7eu7BUi0tccRC0KIlLH+knMBXWxhPqIkFVk5ZKz8kcAh2HjcTeSQyh+Lwx+9cUjljybEH6myvjh/iF2YBGbW4ihoktFG3mPghEfs9ccQYuJ2/hP5ygbabC2ifku7GQTvL+wb5MCBOpEOOG5iQGoqnKBbyTOkyFX3MffB+qzd7RXDSWFfosZRaP8RHxo5FTt2Q23GErDA3WHw/v9SVP4ozh/S8Ujut6H06aZQ+WNc/NEnh1T+CGNryPyhSuM6gIwipUwyOVbg4tUeYWKjrp8gi4oXXBepSC2fNENFsH5LuxkI7y/pG6F2lvYDbmG5HniPjZzWmlx1H3kfLWlJxmoO7ci+b3MHmhpuDLEELHR3iNbaDxGVP/L1QSWH+HzyGLitYGNc2ucqfxyui1Hyh3PfPjik8kfDzsFIWT8U/lDvRN0zSplkclS0xs0Gc9GgfP8ozHUY5QCWptQTB8eVXFwK7i/pG6F2TiXPbYHQVfe1kNtFrrqPuI8mrWyOSbkEMQIyVoFAjSFJCujggjfR931ocQJDwOj5IxOHrGbgkMofC8IfPXJI5Y9mri1bD5OQDB8Ef4xtAVHE1z7TClw6sKmG3/X2obDgUryhGl3pACbe+URqAGyPE5pg3wi1cyJ52rZ7MndfUwTm9QHNJOIAgVgIzT3tRP19sIPxobpU9r/o+K4EuSGazCb4vg+ljwwJi8AfWg4JIYpDKn9U/siAyh/fPW7+mMxmnEt98KGdaryIqPX9DZ2GQJSSOoGsOVQg7znn+s+Y437i3EPXJezMWKR++2gn4pm3Ic3pqdx9tKv+D6nl1NlruionkcY0BLbsTplPx/R5oi+wmp1QPeWux9R2LXWvLtE1hywCfyg5pBHwRzYOqfyxePxRuqyVP8bNH1ELiBiCzoUuBl7hARNsLHPOU+bjCeEts04Scrx7X5Ne7zprzn/Ay9wzmMmCFNxkIUT6qYSumNBQ7Z00YYntR4LyqPpBbD1y9ZdzMpvSR/oCk8JQ4v41holbbxwCz/ZTVnbCIZU/hoVU2dAFh1T+GC9/xLowYSbUSU7zOVV5UhedxEloSXcAian6MeG9UBO35t2Jc8/05WsMi6fHM/SnzxPuibHIbhfRSQeU40DrQz4DoRXyybQudT4kvuWh8mj7gboeQ/WXK3CxozTKJUClMJSm0B0kfwyEQ7h9BoIpjkfOH7Y8z0Oykbbun6/8ES0buuCQyh8j5Y9Y7ZSUoKOQmq418fqkdHr2GXZFbSdI8HnomcJ0uFhOcVF6Ss27p9YzAzJ9GFc/8Le7eBDVP9zzJfPnC867rC5IDv0udqFNHQdUe+8xcofsb06/XEO00xINi4QcNf0gph6D9UftThqBMezWrKm7MfPHEDgk1LdJDhk6fwjq55KzeGhxDL5HUfmDrhfGkpuTQyp/jJQ/Yi0Qkuj5lIkaWXnCVVdKulduy3iJ1jS44hZowe4RE+B7Au2O5t1LpcWlNGTXAvWzwfjscukMNYG8Q9bKziFW0xAg8R0wG9v77cGE3r8vOWl2rm3TR2LtPRMqKLD+RmmnbwmFpCQ4L7hHA5SjraNV5T062wQuszaqK4Q4pAh/mEMSYJ8qF/vmEK5uQymOh8wf64L6oWKx0O8rf+Bw6oXCHvRlij/E44Bo7ztCF7zKHz3yR+wCIpmgA+DIN8UkJiElTvhOAuboXAL1WSIGwn4fgubdo4kupkMK6mca0Z+oySaGoWtlUUjdLjxwi7E1h1CplI7UOGjHwME1oA29EBG8SS1KkibfSP/zy6Pdo2E1oi9R9dcSb9ZJfmQf6RMhDinCH8IJeurir28OWSdiIG4nuP/1zh/w/fWAclGLyh9x9bIaSgksHQcIf+zAxqNSVP7oiT+iXJgQE+os80SNM2mlmMTQ712TKPhd29UvB8ocnUXraOr3ovl4GiwRFvbzafg+BM27cySdZJImzGshYqcwY/pTqG7v9bVzes+I0eC6/Roza5LXtO0NfUqzeGjhukE0qZNKt/9BZhrXbWMTJhzUDp4UeWr6ElV/q5ndPUYJh0OsW1rTEX9sCd0qovkDJrjXBGOnGIdA3Z736tb+/WiC+98Q+EOiXKSA9TPufi0qf8jh9+l16VzKk9e3EDe0ECp/9IAjOVYsKQFnShPmfYzG465jSrsGAjSYUxvRSK2BpmYb0n9qXGqyBTPCYkGyYEjJJy6xJNnfLpl6Kh2sw2nMn2Gex2k5Rpd1KSNiNuk56NeOFvByICbn4BrkbwmwvU4mkmDPBFkVq4G2kxlU6YLJQE+rhrlBkZrlAjJ1cGjbpUP+sN9fEXDIDuJCIeWP08A9m87+JJ1zSIJFasj8wVr2mHLcYfboqfyhrxcOB30aOESS4tsfBxoOqfzR9McfSSn+Ws2LI5TPJQZzsEFiASHqavZaAc4GHAMwjdQx8LdegWsxYGW5VsAaM4/J5G3muG6OPfh8mzJImzqXwlqmQDkuWIcSGjaVGLeQwupcE3BbDKFgyMKQWhDIQHcY16uSa4i/Q2gtbBQk45cFoh2+RGigX3DaSK2BxoJJ7W+OVo2StdMOg1MHha75w3HNC3HIGly3Hckf9v9nnbYXcUhi5rFl4A+OQ7i5zHmmT1Fa8sofkfzhjW3J/kD+mJRySOWPpl/+iLZAEJoXS8KXcuTxxjQooBGVBMweCPAMWQZEGhm413lP22SF6mZKXfgryXf+1A9ee+th68pnJysT89jZ7Gqi9okK3m5yBMoFfFupjcNIYcLUubVYxFhvooGs+H0rWKe7AyN1LXErwgLdJdesR+4u2loM0XYXJAyIkVEc2on/ptSCqfBb12iWSwWnDgZ98Ieyf1ol0qfMfR7MkKUmyCGl9sVYJP4QxNdRe2OEnuMvfm6X3ItkwfkDC3QPXhM5Rit/NP3yR/QCQprHO6cpxevUSVmgBFkGtpQR6tTeGNaMfTEXyb7ht/41l+aUrFfCxaIRBItmzZKiJPaQ5i1bnaf0U2IyRLWTnSRtdOGS4rnuUJvFtATKBbpj5zdYPRHj5QShidqKbPcSAZIt7Plv8dyPQv1B4reuec/OMjj1iM75IyeHSPkDeaYm85Mm89hS8IdSuSiRIxtE2lfVZKvyRzDQvZFuOoyMFy4LXuWPHvnjSMk83sigTl5FO36zod1mqRz0ksnFIcEj1MhENVpA8MyV8fNvbqvSnDrPmGsHeE8qWHS1y3SoioVazg2HqF2rtf2UWshQ6FSbFNCAH9qF1pSJ8hE/tNDgyu2PF0IjtT/GIttdiliBub/YUVhAuIxLb4MAQc17jnFjuFHwR0YOEfOH+0zmfpU/EpAgR3LUe+UPed3dkLRLIG7NovJH0z9/HCmcx7ukKSVk5grFd3CdPMbfWt1ogoDOuTJ+8oG15gtftnumyZ/DtANXd1hwUtF0qFhgPkxo58gAfg/m5xdqzjDteI687xy6dkmRampECw2N9i1E7tzkKlEDTfUP+/1vB+TXZeh7XJ7zkCxa9XcPNf+/mtpWTJ8eU9B13/yRyiGVP8bPH1ziji2Fe1HljwQOWXD+2IH/n2KeP1r+mMxmXOwTDYGf2w3Gd84GxiYFcDtloLLEkBMeuJbSPu1fp+14MT6sgjLM/f6mj/988+4P/NDsvrt3/Aw1F1ofVuQ5nPsKhjbo73RAE33NyTSSbSLDaBz2F3aBvhc6z5b9M8I806J+yrQjR6Lk/UtlUODu6/1mMde/iD7MtlWG8kbfm+n3+/UO97e7l0tBmt5TZJGmrYg6uQ3veXQsGciGwB8p7Vb5I5o/7Jj8NEyo5iZXA+AP7FxsTw0JlpU/doBfj4Xk0ZLxBzknHCt/JKVxhVUhlqarXe2oA2MjyxDj+6jd7fIKpc0I1If9+3kIDvRXoSHz6VwZ3/+6r9n9yq1f2XzTr3/kLfe9+odrn3zgwebSQ2/d/dEve2NjpRABSiOwjVgi3LqjhGwb2e9udncaBO3MERxBlwNiNRzSPFLuA3tep6dci6Sb1Ej7KdWX2lSO5K6wEVapVGEvXQSwfqoO1FpiBcGlaqC3OPmjSDPYYkK1CdzrSi4LFaNtorLGNWMKuh4CfyRySOUPPX+0E5W2vddS+YOQJyci+QNzsbkUuXhYZv5Yg8xWbSp89/pl5g80zmvM/JGkxQGzis1S8TCR9o1L25kFmrRziuuoiSebEguutZugNEgDrSHXtxoYsoNiZfyeb/r2F1/3HT92/Iu/61rz+m/bbMziYS2QpotqhyepOkCeHUqZ1oCgPUZ0xEaR/jK0sCLz9BPnx0DcT4l2cvPAbwN5Su4v2eiKBVWvfv+w/wft1wtEf9/PhJQzDkVattC9zfkvmWMGx0vE9RL582RkukKsTagJw963/6W/vvc7J9fuzpDUmT7adrEaMPh8W2SfHnTQ9RD4oy2HfSZRhphrKn/g9XMzMBlX8QcjT9Yi+aNBJqPayWGLZeePo20q/ACHVP4Q8Iflgv/qm5586dbR+17C0i/3xR8pMRDB1U5CYFOW5ydcF6o4bnUmaSB7/SUI3PVxOxTADYNWvLIWtMNVgU9pKCidA1UnlLCjgri3ApqB9p5XBedh9X7HsU7sJvrfxmpkRAI1oIUJal7M9U+ZDywji6QsYi1NolaIaz/fyrfpa9kk8gc5h/J7ldQP6stq3A5X3/WBp5rjd2+vUqkzAxrETceCqSlfNk19SfTNH1wZIq+p/IHUTwKHTDNlWtPyhwaVP+ZR+SMDfxisWtf1v/Wh/3HNcEjDcUjX/JFlAVFict8zJBNPagMPaQNRmo2bJTJHaNsBmaDuJGhjtoh7cpYEP0OUq3FZZ3wP/dSZV4hJsuuL6wbGkWZGBTYYjQyZV146IReYqdn+AdeHFg+HnslAm0ZP03exe1N+wUcx8pf0+8DiT1w/1GLkOz9y2S4e/GuP/879py5/5bxbJNV32rGHyRbKhzV7oGrXqPyxMPyxrlTocLJPwiE+YvijAaUPVdf3nAQLlT/mUfkjA3/YL6Qc0jV/ZAlEW0BIdvE91PjOxEOTCxuDndA2kQMzi8aRMGGeJHbsdHGHMrUS92yY9yBdCuBzm7m2cc57Bu6B9f3j7e63YC5OMv1mysMscdsImalD/YPLPkI9M8r9wzenMm57O8J7F3XZYVz3RDvMg1vOod2nv+BlvKv+W5/59CriIiB9h3tOnTwKi9/knbsrsqDyB+6aei3AqyR/2D+UHLKdyh+Oi+PczA1gx+8rUL7KH0S7LTl/bIPrXhPLHxoO6Zo/slogSkX/dw1kJYhlpfEbnwvsvYus7naZzbVCKLl5Chdosw0xHlNtFibC7WqCaJQP8jubT67vPEkE6Z0AU3n77IvQlm7At+9KJglo10Dr1qN121AH33v9I/ROolzdbpmFwXWnoY3uIrLnpD0fS07g3pvJWJJtAU24DcbItv1+QKVett8jC0Cpltb6FvsKoNHJ2kXkkMofpILjrLdBozYL04aCQ0I7SUv5gwv2b9/LnSf4qPwRQOUPEgdcoOAQ6W7wWfgj2wIiV/T/UAgFcWsIlYESFBNY3YX2ItAuAlwXn22BwNSAepdTiAvORad+yCxVzD3dlI3i9kVIegdiStb8/gfERWENyk9N3HZg0qopX9ICT7B4kmSHaJg+S10/A4vM1cILUcwXWrr7q63D5+F8X3s5NJed/X7wfW945DjEQBz8sHvkWGO+98+3bXXOXiNwoRpFfEPfHFL5Y1D8MQ0szjn+4O6r5hANf8C5G4HgbFImR/TByh+fReWP5rPy0HKFkEMw9+9i/BG9D4QPRjMYzGFLDTDCHzlrfvNcBBPz/jHPLlEnRBq8Ncm7SMuT0j8k4O7P5JN3z1nPmR+55MQltQ8Q11tB8Iy12OQoY+T+IxYPc+8AZfe1geQEKEc7pNR3+/w3ffznp9/z4ffs/YnP7Kx86v5Te+/8C39l9f2v+xrJHjR2z5L7pftyjBmxMqJP/uCer7xH5Y9AG9lsa1Je0iJU/wFZZrW+jzCpWM9r+2DljzL84SQQ+TZztBp462L02JD5ozHPf/PHPrjzvT/7D5oTr/7hqd9hOMSJhWgXxn/MMxZYZdv5HDIw5wKC3XQjpnKZPODbgmDUIIhMNFEEw0zKGq7jaTto7ok4Ue47Xj5uTqiLygN1jbkRPe1PWiMXVmT/E7iFuBvDRC2kukaqYOtCM8v0Dc7Mmm3TulwTSOY9ouSQYtEtXugtgutPDIf0zB/YYvbg+RHjMWovFk3bj5g/qA3drJLnUeS+Wl4NbRrGuU5S/MHtZdIrhywjf2g5Yaj8oSkbnOdb7OfGTGx75oyBiPX75gJCp4zbyUvEbn5SQUplogluoGGu/Wnz8Ubnqw+a53wDEzeBmuIjTfY5fS0bJmq/Df5pyXE3sTyUG5H9/mIGNwau/1Hp0dxzNGkHO8uvT53tQyUAACAASURBVPVpgZsTi9TrM2yQ5GvlJONPuzFQ6kZCLTg5NBe3EQLhYmbjiTbOhDNqTDKOmaEhhkOy8YeGQwgC959/VaMB9eIDgvwR2fZj5Y8NYg+JuaxTkeMh1Pe4jH4of0BZsm0OFoPKH0mcMEj+YFzwGtjweMPL6neMcxdO4Y+cWZhiN/3hBAhHHIc2T1NuUBXKRDNVLB4s3mi/h4ndGSg3tTDxy8Bm/rHltytYO5mFlSy7+VzOeAevbNRmddKMUBqiiMmGRGU+OQ0p++5JsyG0dc70D/Sd/bZi+p4IEX16UGAybFyE7yloJzm5vqcQmsA22v7QygrQrK/Dgkq7oaJbhlwZxPpEDIdk4Y+I8RbafwAtl2OJdbnXZrj7YfuHkj+ochycuwT8gWUtjBkPWN+zyR7+lCmzlV2XzfEr0mxsMRxS+aM4f3C/TcfEHxbOfPMc9G9ss2LJO0Tzx0oHjS3JVEJ9H1p8uC+prYRpZIP7iwfse2nHk+Tr9wntAcWulJKOSL3rnoSYoKwSoZp7oRHqfz5Ogi95m5oT7aNenWO422bqcOuy0GR/9BNDd6Lcmuyh31xhdjfXpinO9T0Fax3QThRTJ6P2/7OSY2ZoiOSQXPyhHW+huqXKZS0NlDfARs5JzpLyR9R4QPreTWiTdo5kXWb+LPxN8oeQQw6yPbX12TF/WLe7ZeQP7retBeSPS+bYE7xDNH+slGxsoWmG1DrB9XgC3PmX1FYC1QlmGbK5SDueJF8/Zh6+KSVZQUek6n9FQUxtKj2uPJSF4ISQkEQpUB0NHoa2rAf5u5FyhjSLR4iVfonJ/kJMDFsg/QbzY+UmM1oNdaxV1AeXxYvqazkmoyuhneoDZRhdpqYIDsnFH/7f1DmSuuX6GJdicZp5krOM/BE9HjyrIMUBIf4IccgMrj/EITDZ64o/2syDy8YfWk4YO3+sCesomj9WRqB1ejKw+cxWqBIIDQrWOdoARU6YSiDteKHzuHSqUpJlOyJV/wHNEibs7IDeosrjPGcb6eQhQprT2BBl00ywbfkvI/fUTs5L5AFvQWkPqO9VyG0yF4AiVlaj1wLpq9vQV65QJl7v/NgN1rg2RN0XGO3jaYUWV7rTcC6iGx0y8of/91wqZwF/NNAnuaB+DluZJznLyB8Nw+1ThZxbjeSPkLyYILGnrftJbv6Icp1ZVP7QcsiC8sc95B2i+SNbFqaSgMq6hAyyg6hzZ3UqTqMGf+fIgHQQSB0ZjIeexzxPnMHBDm5JZhOkHNeYQFcKbMYt6Tt5ZWnzc7OpVL1rDraAV2A/MwHcQ7KRlzTTU0qmhajMZiUzkEn7den3YcZ79vSmTJ+da1uiXBhCsosCWlcp7bLokPCHc544lXNm/rCukd/qlUWTEARLY9sZfxBy+yQSwFmcP5CyNBI5512jTR96kNkm0M5apGbqsfF/TW4OGTt/OGXpIs3z9UXljyxZmEqTF9zrKvccqMiGSKOGalBA4OSIpp+5iwe3zKEbBs5L2lAmoBE4WLUiHfA0LB42wWdXOhmXrISDmnq3TmDwrXGZEJDyaxcPDRDdJWKH0hDavoalGUR3WC69ozWH2AxkGbL95HyfXBkyJKDG4ZPCcmE4KCshu6gUwmhdSeXNEDEE/mjPE7ZDbv5o3MWDW2bJTZlzk/gDsEP0w52AbFiDdK520rfSJX94bY5N3ubkhHISxvHHVUHWPwzbiKIsiT+gP2ML5yQOWRD+6JJD1heVP5IXEBk6BXdfTNiHzFMl06gVmdBRoBZFinqlMk35cR7UQDqrEP5SYtLW4ZQz5cEzqUHnktdMoFVaQ+q8EVx3jRHW+8RiflO1IfR/KwSaAq4pURnIAn660nin1AlN5/EhynGoeT66aA7sm8IF5I0OQ+KPDlI5bzHa86sD5A8NqHiLZsD8sRdQNGqx5tU7xgevmuM1yLXvM8dbkPP3U25q29Hp/6cQ7kvlkFj+2BgQf3TGIdcXmD9yWCCyr+Iyk0rOSX/uDhyE3zGUoDrjxM0XzJzXdnbU/GaOW4EcxDnqkHp+4wSg3Uf8PjHlmGjN026dC12j2rz8WErBBkhhDevL2EQHzsM0WOSOy0qoM5BBOZP8dJWCdHCLefORoiWjzqUg2jdlAVD5Y6D8ERjza60CB56hnXQNgT8mAg7BNombSOsdke+UZvgswx9tKvLG5xCAhD9EmxIKEZvBcjog/uiUQ64vKH/kWECIVvhQgQcImJMpUrkcsYjINunvWKOTBEFAkys8KTP1Fmd+g/pQLfYi6hB7vovjEBi0yg0wdwDDJlLY+85lbPGuC2kjpYN/f4EN9eDXnbWYfcb5zsWtTH2NK+d+oCHSJlzQ3VYBQXoAYpHV+WJeiHXFzrzry5KBi0Hlj+Hyh+3HHNyJLKdoOj5g/hBxSA7+EHCIZvLYpuk8LuSPScbdr2P4o2EUcHtd8wfcZ4gcsj4m/siRhYmbPKD5awWp4aiXXNXmRs4YSX9wPzsIlalq+wBnZnTRdlQuFSJXf9Ri7wUrSLC20tSh93yuH2uyCDwJg9LFHcInsVGkO1sPZHxxMSXqbgLBh9Q1OcCVk8o5rcokkQuErHgByHMz17jOBWK8nIcA/Vz7GywSKn8ME1pfbEymtH7eQ+GPWSYOieWP0LjW8EcDi5ix8Ac331zpmj9gEdjknBsuI3+IszAxmR6kwUdulh0qKn3/HEEWA9f8OVgrQCoKZSzAMIPdDNXPEjwnW1YDrt+AUIvxEdXGJbBZGxSm7m0wWWuyfeTSIEldsyRjNjpLiLCcnCwoknVpCNBkCCkdhJwDlT+GU+cl+MNO5iNlamf8kZNDYus2NK61rlJK9MYfknlfjnIh5az8IRhLsf1ZtIDI1OkP0kcxQsMVRJosBsUmEtKKzU3imgkEcX1oEZZlAAufk0VApNZJxkWElGSuB3Yjvcn4GHfZx4Pp8XLXfYayifrWGCbXKWXvq100qPxR+YPpG5yMLDXxzTJeEhcRkjlFqG5uMtaGQfFHX7IqlT/GzCFndHPXqHY5kiPQzfMPpDq9v2EPeY7j53hZmJKzSPpGqX+m9DwlSmQsoHKar4c63zt/6gevvfWXP3j2kB/6bEb5ERYxneaMQYltM8wHkxmoXN0cI7Jx9GFlI8ej92470F9OdShIQ37Bc33LK3PjEIiojaWCtzSE/r5dprONReWPz6LyR4/8kZNDUjgfiYvY35ANKY+kbsbEH1vgdnq2Q9mayh/+PlTBdj4zLv5I4hDpAkITkHGN2GBkXTC53d9t2PEHfAQ5jyxjgYaTVmwJEi+SsQB+Vmk13/Txnz/9Lb/2YTcl2GeDryeT5rohAec5p0v73cUEURHI0mZ+XSFZlhpmMx/K9/MgyFBajkRg43EXxrKf291+f64rbR2U7XlmQ6qtQHs0Hecq7xpjCLbugj/2+6s3EXtWsRlm5Y8l4I+MHDIE/jg5Mv5o95fqzNqTgT8wDwGync+Mjz+SOCR1g5cdbyvzp6CDuMLf+khtupWHBIpswzVrXnagBgko2abKQgXLQKOKYc+37wP3kFRsCRJPDoLxg83ga3IQOu/9gisMvvMjl5v77t6hBtHBc8yfT5sD84k7oW2DDpCrzTgiafs6FwDuo/NALiJw6wJoish3k0KQNCFUNmuqbwT7mUiDP2NzlQ8RYwi2Ls0fN0Czed7rY+1mmLH8ccUpm1h+Vf6o/NEjf9hsUpU/yvIHxyEbI+OPJA45kpha6qSX3x7bnXCC5Z5F3J5Eu30iK7zGyQiANby976ZdAQJifIpDpjkqiCiKxEtsIhZaGXPv/QUvbwcHEVzvk7/bBoNYhTvtRvlFbhVYiEhN0bNSwWQhFN6EkROqkv5A5UfH+pOkbBOQOW4iiHYssxskeeeii3Du9w61f8UyY0WgKH9YQHtifeysF8ip4Q/f9e0hKEsyfxSSRW4ZKn8UAtRvLs7PyR8WK0Nxl1lg/rDYs/1gAfgjiUNEFghC4/MyYhaK2Z2Q+32q0JaSnQT8154VakGxztp4xHfNuxcmSKJI3CEgf0F1M1GzEFoZk+/9yQfwON9PPPDg/kTM6fShXNu9rsI9TXiTqc2kq/ddQd7rfaEU0myGtKLS8zrUbqdaezTlkJatHf9PeWOZLAOce4WSISmWllhQ8rDvhXrH/MGdM83AH638ejwDfzTgalVCFlX+KAyn3VZ75I/tgCWtSeWFTByyyPyxugj8kcoh4o3kEI2PePMPROPir65y7AgYCpbB/AWxVWyoU75MuHa05sMVKMs12CzsinIlSRHQ/YJrUwYh+d7f94ZHmnd94Knm+F0bpvJZ7B45tv+9YCdPSRm6Akfu+2n8tMG1cGx6gel3WiIhNJJ3kY1hXKE0pxmR+lZm9sHMpd1OHd+acmDnzgjhbs95DOqdQ+vPi2nIXRnCbYCpkgMSTRRyjjo2pSsU5o8cfUyyiRfX9hoZd5bhkFYWVf4YHn9wHBJ0HcrIH8fhmncgHPJAqxlHni3ihYwcsij8cRsSnqwsOH+sa/kjZQMPqvH+f/beNtbSLDsPes+9NVXTVa4Odt3+EX6cKlsIhEYiEr+gFIxH8YwjuQJBEFnpLndcrTSMuguKRCi2rgQKEtzMOMZKWT2dRm31hZ7qihyLj0zKEE/AQcZqAYIfRPGP+QHqOgEzUt8yqD9qXDVd9/Du0+u9s88+a6291v54P85Zj3Tq3jr3nL33uz/Ws/f62svg/6uBipyusItJlt2thv4JjCoHBkhzCQsllGKT8gojyJz5cA+e51biSZIqe5apwY+dwsnn/vaXvtz80p++7SwOrep8tvrp/u/e9ya5ZEMwtF/2nHMdEgh/bN5dR+a8///utlAf58Gi5EiHtdRQcSnYZ0v7YBbUblOXTB2Wbgfx2ZvIGHXghP9ZXbDhozRMV2FDfJWpY00OcBo+iSZqKG1VQZTkD+5yq0sj44/uO6Qsal/GH+PkjybC/X3xx8o1j/DtPx/Keqj7XQUvFOGQLeKPVxjZv7/L/CG2QCCnlQdIhovHVJouxkd1FefgTrde+VzqRWpy34CB+pbyYBRufGP+hp0Q407EOT57nCYsRwMTO4UfEpry1RxxhwXvwMDd5En1WxG/bEKL0whP0jmaDGpMMQ3EBbA+Ncw9D5zLhO8vqU1xqDL3xrQQipSDVVMnajKnEP64R8TYP2NI4PtdOxl/3g7SS58uepsCSsMnkR+5fsG9oiZ/dKnEYY7dDdbcgZI/pKnDU/lDwiHbwh9PYV18YUv4I4dDSvMHN5ZzocsVVY6YQ4w/mp3mD9EBgjBpdRkusKCytYA3yaTsSB+I4ipjxiH9ZD0SCdN2PYGTOfbdtcMGQ0ShEOOEaY7PniuDes5FppD0BfSJn+qN2ORdUlx01tXTfb/zxSx2ZwAxD98J7rbgzK05JlVq7DjBzGlsUg+i1Gf9/2NlnvopkonAedZUnWPaLph+12+L5kBCjX2XvQfrZ1+gSlxcpIilBpTIjymkb+2NPxxgTh8RCTk0/IG5wDVK/sCUYVIOuTckfzAHvl3njxwOGYI/JBxCxQFcFSafMP5odpc/ci+SW8twERFAnxBxCI8yF9raQqC0nfB/amO15jPoHWbITUpEo5qs6Yb2XxfkQs8Rkg024TP8lFdtK71JFM5DLL8zepLO1IQvlBqIRWQx5hxEN3xlBRrQ/ZAgE7QQ4s+X8K9UzvF7nJ8oN/bt++8zuda7MdBe6rRkMrU0kbpIsvFkVYm4sb6wk/xBPEeUQxhrWXX+YNaXf4/DTvJHJocMwR9NpAyMP2KyzvjD+KPqRXKUAJIi5aS2tmgIF4aG2VihGgdOoBF1+GSxRPKZr2I6YgKn/dtt2NSkbMCyhaRgLPq85TL1dIx+NoOkNBpsP2gKw0nmQbTDUkBw2Ca2M4NqzePziA+nb+2qeZkONsejt01TYw99RvXHowQN60Nwy6RShj4mvreIaJG7OKj7E0nfutP8QZUldA8cij+kls+d5I8MDhmCP2LrAY0LC2Qd9l3jDzl/fLLN/CE9QGi1XRIBRPmCazR9ywQ/bMzHNdt3mBjsjgR8MhBtpjI2ukWEJICaZNm3XCZqqDWbg6KaWIEGG9NYYm4Ebi7c8cttf1DPHVsLK1/Z8PuBBpTSAh4kBtxT/e8HBj6u7J8fm7cpdVE3lV7gNnycEGa02Y3ge6wbS4m4kh5h/DEd/tBwyGD8kcghu8YfMQ5B+cMvF/iDutuJwi7yB4Uf6SyU28gfORfJcacVzSZ2oYhB2DilKeoRdWwFrShmoqwZ7FhMSNbapGT40lMp1kL3rCqaWE6DHb7PBU1J+0+gCZLM2xSN7CpzjTCGJIQf3FXLP1/yTNq6MPcY7n3R+qDmTIbm8GzN9uDyUQpD8EfXt1TMg/FHvswYhD8yOGSn+CMYg5iLJgXjDxmuEO/7Wv+t4w/RASJBEEgnHbpQmWA4H87El4JavsMpQVI1tPTZQrKm/3pOBoDIiXwUmljou7u5t8siY3ASMVtS0PrukzeHKzZnFMi2KuecJtNNSvlipArhRM3hWF2URscfTEKODt8VlL+L/CFdX0PyRxKHTIg/KOsDe1EcUY7xRz/8sUhR8k2dP2bLJZUePR3Q2dSJV+R6xJjO/M5Q5xRmzEbqS2D8zzNkRQVJsRqw1HZSbZUKSaLejawbOWDG1t3HkHM3yaBg+s6f99HLvohyqEPg2nwQjr00O0psjn5IlPMxzHnR3E2Z68FzNkjA6Nl3JeUzz+L8jV/A2lAbPW3ERocS/CHgENFa3EX+INr7gMiYJam3KH9sK4cI+EMkj4w/BuGPF5nsZ2pr51T4Q3wPhAaRoBJpZ3InumQ3oBSzqtBcqg2SOowM9qVUP0DmdCrpKyp+BdVKJ4IaW/f+lBGN/ckMhD8FIr5CbELQeQqC7pqSqHJcjp6An650jSVpE7u/CQSlpPw7RPrnM3/jvjEhF6Ux8keMQ0jXAkHbRscfXj2x5AmiZybmHpZeV8Mf7xRMpLCNHCKJHb2SWI7xR0X++KBA9rPScG2qzR9VDhCAO5kmFC6fddYGJ6FjpROICpJqvAtjnEbpGNqQknWklB95SvmlYje2xj0jwZ80p5w96LM1zaknAK9KxgyZq9QaO00ksyvKNZaVk1pQV7R8bv1K2mAYHX9IOGQr+IPgkGTXp0xw5btNrDsYllhT28ghkrGRcIjxR8/8USj72eRwrpZZJJeQmRPdENoG6QQKn/kInuGWJ9Ddz1tuonl9oclaVfuZY5afbALa4s0a13dr5BZZR1w5axsPoTVhzglNxh1gryctIFlOITOsqJ19aGx23aWpL/7wyriO3GGwbfyh4ZCh+UN6sdwuckis76QcYvwxAH+MnUNq8IfqACExxZZspHeii90KXQ3wPBQWgv7BDkChVli6Ke/jmQ8j/sdFCGjMCy0DhxL/X8E6igV5zZUbh0eJAnLtkizCL1rkXpGoTXxQ6D6JQ8I9aRBtZYqMzMhcNhpInwHpH3WsQsAhzVCuBT3xh5RD+uIPbdKGJGwhh2B91wWprskJbi0R5fgw/jD+uFqCP7QWCNYU2zbmjUDgZTeyExIDat+4fMyHwlSusUU8mgt3Iv7H2DOvMMT4jE0jq9CKsetIqTmVbBwu+wcBBNzt1R0pNcTmpgG3ir3UMaD6TeL6wc2B4G/YpTq9z8eMg0BS5rKRQTKeNThkSNeCPviD45CstamFt5a/BfVinLYB4w+1VYVcS3AJmfFHJn94f7+L3BM0w+bxrvGHKguTIKsF9bdBotD7zvYQ6R+yT3IyZtQQvNfoS43edGQs/HxSlqzMNlatM7GNmE8zO6eIZ6OyP3CpKrVZW7BLFlffhZ9cPcX7PrGfVu2A32Oa0GJyiVkzTTD21HjFxmfyWWeYZ+j66hHE1cy2hUM04xbpH7Y/UuUhJaOEbWgi5R4HWeOetq9bgk2RqO27yh+xedW+brYv448M/gBZHXMPfrjr/KH9Iue+wgm+eddJrgPcw8BPzrw7FlDPjL1PfXbJmZJBKLwKGzX32YeKw8PbMKFm3mk0q1+J9jg3gtva023FMedO1IMjMjYLAWlcJLSJ4bw4hPkUA2upgDL3mO/GLB01+j629rg5IHHtKhlQSmmPw7FPDRjXyKGxIsYfBwyPzI0/aFekFA6pxR9ee24F7dk4PIyQP5y2eXAIxoZyS3XvG3/k8wf1dx9z5m87wR/nevJvXJTy4a2lMSmU7YFLxcfm0E7056zm1lAwi04tv+3UhdQXuLGhssPMgtzUIfaweeP6M5K+sREeWqjNW/fdmKVj3vPay50DJTffsTovQmwRldd/sQNZZ3L8493GyPgjEnheOjtUDhTtGRN/HDh5OAIrRM7YGH+U4Y/UTXkt/nhXwNG98ofqAIFsViTm1s7XU7wgGNeP3gMJNX6Jkc9KcmhPdRNNBuFWJChqoxxLPdoXyLGBeXKP+Z4qu1GQUYkyy3KxK9ymzv9u9PbOYO2e1oiNEGSrkhx4koUnJp+YtoTYT2nLNmSdSeQP34Jr/FEWu8wf3HodQ1xRbGzIFNrty/ijDH+Isyr2xB9UaubB+CPrJmqB//WZ37zUByvi10zlum/ARDopQs0F0/9Oy/lyX33BjNlztfy2x+4THvNVZG4+Xs3jwrfIcje+fyBdU0G5TdD/1KVX6vZLkRADgWY0KVQn9+xNzeDWAayyRSCM31lxCJPO2/ijfP/vAn+8wSSpGJxDjD+G5Y9IDETyrerXyvFH0X1vKoecy6kAXlKSlp6KOUsFpxk5s0Z45Ug2UJMkX2n2nD6CsCNZdMSa9C2zQJAmQ+jz55HvrFKLev3pZ8OKxjkkpr+cMyR6LWLpkGRNqpYxKKZV4f6WAUo+3fCUHNgBC3NH29ikIP36IHR/9NoxB9ee571g1epW2VQQzxaSpgu0/Si8bb397o2++MPbPETnjvHHZPnDzadmxHFFxh8D80clrf1RIf5AOZrg5SbCKaEcFnOIyAIh0PRJBW1UqxrJLiAx85xAHVHtrbRNY0Yse44g8061569ZPjdPhtYexTYXjObvpP37C953xdqP1L5m2qLOMMGMSdGxGXLTJp13XhtJ6w4h/GNuYk+gfj+7jaj8IRHRvLHxYT3zh9gCaPyxlfzh8NIY+N/4YwO7yB9zRlm6JuOJ9fIUZF+YhnatbqJNUQ45l3lqWuUcbn9GOx85zXVZBO617x8JblF0D3gJOoQjzwPF6ZV8LuSzowBxwiSz58AGkVooVZ+/st82a9Eag2aQCSJ0bcJwJTI23fzGtAOpY1kyuCq2QYtq9mLjhghJVltSYR6ILKnd2DObIKx/qTH3wRGBZI4NBVLzJjno9Mgfc8Vamhx/EGvC+GNdiTPkvVNnMP4Ynj8kZdbkD/e7gkMweRRTNDVc5rvYF6WnuXkJknKdAmRxEx70AElT5cwplFnkAP6GXkYTwbzWc/UFKrUbk9Kt+0z38xjK6O35uzF3p2v4WUoz5TYDIXwXIXV6Qvd3d+ByWoLKaYYlKdXmypSpSWOZmkKYAJdSNnooEY4btWl7Nxy31HkQwSFsQn08pZ5N2b9F5U7F+ZuCbFnTE38sFG2dFH8wa4LCLvLHnRy50ROH7CJ/kDK2Fn8oyqzGH8o+Li13qH2l2gKhiuoXnOAuMTmYL0bcIJwG7nvt6w5xKntMWCGoextq+VjWADX5n8Fzx7SX56GPxXEpfWhihH57VCauDdcesLqoNIMpmokMSLT+MW3+VXjOmOZ1kaHpUgH6/joS8OqE3rGgH6n5/V6nZWaE5D4SCxXVECfO73CDuizUv9JMThLMRqYJT56fPfOHxvd+avxBrYlZ5CLYDsYfzHrqkUNK8cepIAtQ3/wRxv51kGT74Q4H9yLKgQ3+gOdiOeRaD/yh6OOS/CHCXsbJ8Ezby524iRMctsFv4H1JFPoqFSZ2KgNiQNsqfK4laLGKIdZHis9Tk/8y+BL7fUHhQDKuXTtqXTTUgajDPcs7RL2Um8ePZmpTOEFRFEKNguSCOL9f2LHsETeI+y24oEXJ+MSsbZqg2XnG/D5C3IguFJonkjH/gaK8MWnCqfn5YEz8Ae9L19LU+CM2J3aZPw66i+syLCu9cEhB/vC9GB6MgT/gGT5F/iSRsdzhIGZto8aMnAvXpsEfTyFuLgVUqmB9GldGS4xpg7/TToSfUaTq00ISHCw6FUI6t1BjKgk+9evoNjVrmUNSAsG4z2uCMttyyIFtPzeT9BUzdlkBmsIAIQwP4TuUpizWT2S7xxiUDf2EaWOo1LBZ1qLc7+f2o1BWYEkSGmXQbNdfH4xtniBj4GfMCDMureptX5+AEmEKgdThs90aG38QbaWCuyfDH6Dh/YyQt8/av5+bAn8E9WrmRow/Yl4MsX3HqDhEwR9+8oBB+SOnHxWyQmJtW9XFzeHmc4ydP0JvjgbxDkjmD3EaV8yEAp2LEflXnWBtv3NboQXjFq760oywrUqNqdbdxW+zygSGFM99/hBus8UwRzZaWF+eKPqquI8r0nfSw0NXL2em8/tJGxg8OneEIBCXI8s58fkwwLTpwfye04+Sm4qvgA987AC6qHSTddV5QqzH2568Ddf0DDRM+4UC4ashfLax8gfW1i3hD/d5apOyNwX+YA5JJfijgTK7uaRdT6PiEIQPuMPTfCT8kdOPmpvuHwr4gyqzmwv3JsIfDlT2xyz+yD3xcB31tUjHnAjdj57AZ3MDdCiUcndRm8AS36fuOQj7+Q5iunoC7xcL1nKCw01Md7KGnzHztCTTDNeemGk25p5AQeUClPDcyfCCRynXgjBmJcVtoJT5/ZCYd4dcv3mk9RzE9FBY+IGV7f9fpsZNMA8kwYjY8w1l6ueyd5UKZOwTOfzxcfsy/pD3aedyMXX+yOEQCX/krCexbBiCP0BeshwyAv5oiEQIrMs8wh9LhkcWEv5wv0Q4ZEGVvyv8IbZAMB1Fneb3rgNxMgAAIABJREFUvcmA3fj4G6BhWoM7sfacPo16Bm4SSDQo88Tyqc8/goWNHfo2JqAXkJTTl9RFgQ8ytA6SvsNyFz+F4Ml70BdfJPpikRLYpemvwtoWDSSWldTUkiW1haFGZz/Sb9cDV5Z9Yg6Unufs/MZQaF2l4hGhFX5UKpCxZ0j44xD82TG/4Y1+N/6I8sf+xPlDIpMo2fEg2GTuc0oK7XqS9tmA/CHhkEH5A/rmFmIp+T34KeUP6nbntbkeG7OIW9Zh0J6d44/cAwTnUtNpOqjgSff+bWKA+vTbLenuEn6GLZ/wV6M+3xBal2fUaZGbFDF/Re/vWMaO19u//xzRppiwofruGRwIun5oAh/hy97kP5BsMLU+mYpF1GV76TX/u1D4pArylIP0GqC/v4Uc7M5Bn31K9NvXEDI/D5bH78XGjxq3GFETWaPcz1vt++9Tc2Wim/UxIsofMEZ3kQPEeQh6db8bf+wOf8T4t/PlbyLxNlElRYpPv1A2DHZ/iIBDhuYP6lLcr7Svf17BH9jtzugYGn+k41yByfgX2l+/ivz5GUyIOZNG7I3UK7Q14ARB4okQE9KkCQwrHz63sbmBCb8x6Rl/u72EQFd2YyX0MeV8jTlhQxEcRmJ+MN6BZoNZS8sD5R4MlfVGQFCpgjzlIH0Gr78pt8gDpt8oTeCV7mbuREiIWu3DPiCupGbLGCMk/AHznXq+TmYaf+wOf2g4JBZvQ3JIZSvBoPeHRDhkaP6guGCWwB9ncR2SNiAw/qhsgXCT8Wfawf8IieL+AgwApy0Is1cUJW8iC8GGICjg7kJm0aDKJwTa6tnBAhOa0KjgJ/EJX7EwcuIU2DZpD2zw/FcTNpi1tDycT2fKWJRGkiAvYFrNmTOUO0G0PyNaQglRT+kyMJLcU7SlY4CAP+4LEieE/zf+2FL+SHQ3PUrgkJpWAipwl4pP6RPGHzvIH02LFA7JPkAAfoR4fw4ZUyhtRPIV2jFEtCArQZCzYSrgxhCdeAJNTmrwTazunP4vliXLe34KKXEquXMrZl0ZFDkHgdi4JG7WYxD5qjLt4bSEEm1atuldigKbfIrcHwzoU12bP2KuTtz3kmH8MV7+kHKI0BJCrfOaG0NpJqzeYfyxk/xxmGpxK3WAIDvR8zN+XVleLmJakPnApCuZeNwzPOyCwto23yNiB6hJRgbTRNqG4QR826WaIM3kx2INhk7LSpV7QgTL9a4VruGjD+6GvsXQv/E5B11++vcT+iqmJZRo07JM71JIBHRsvjDukIP5VBdC6bXaB3/04j41Uv4g+QHm8BT4I7bOa24MRWUbf4hg/PH1PP6A90mPmPZV/QARI+Ebiks9SpF3TFOwJDrsbtuZfWz6JBuXOdP28PvdDZx+UJiW1A5gImEXPGGXr7j23omVTWyeXI7p61gmLu87nJ+sJC1rjY0hVe6dEWXaKAp4Fs7dsBFczNMQF9VsuBF2gtDf2BB9xmoJJdq0HrNisJt86Xwh3CFT8pGPCbG1ejRC/mh2mD+6mArsxvn34FDwFLnwcEz8sQq2ZuquqViIlm38IYLxR5PPHzkWt3MDR/Y7vOndtJoscK9tnr4oLXsHSbAnuelD6lO1W7hx4SwF2IbEF9jcKZILurwKh4fjcFzg7ynPTGXjeI3JdnMkERyF/WSjz6Uod+paYR+cD3WO69IhMRbSg1dUkyexxvSUVSkmoHPmS29m9BqYKH80u8ofUDd1aG2gzu7+prO4wJHxx7Uh+ENRtvEHD+OPcvyRzCHnBo7sPwENAqpFkILYdDxBtCAp2BgE5SYnZ+MSpi5M3biFn42R4yoFGiFkpbELvnCk2jpjJng01iAmuBP9ZNGxTCD8Yj60uZuNAvVxbe4EjNRlIab90wjCXtyPCoGSgcu2v08zD2hT6gcUxh/T4Q+QD1QgsF/395AAZeOPH8pw7hBTNAajTw4x/uiVP2YF+COZQ/ZqTSD/tkBwiXnMTLZcYJsOJ8A+itxqK8Vcsckp1ndIZpIOnVZHikXQ388LvpMjqMKbLFPqoZ5vFWuQcWNmox1Loq572C2i3fjB5zTPhaLgc+bUx7knHQpvd23gM9+E3+8RN7CKiRMIcCo3MB8iN/t2Mpjr34VAThyBxXAK/TA1/qBu7t1l/uDSbZZQlhh/FMya1SeHGH9UA8exWfwBMrZJ4dJiFgi/YYhGt3OJ+blA831QyC98zghKLguUjxOvTbFBkGZQUp34kb6j0JmEJZ/FbiqWWGVS3R8oc7O2nlisQSkTr2TDyj1TGExcOmtW36ZsTQrGpRfA1TCXAPlBm7F7X1Sm1J7cj0q5nLwV0QpLzfRYQPutKR8axsofToMulMvPYLwu7wB/SOVDqRSxxh/4WIyRQ4w/EBR0WcQuai3BH6s7ZGKufX1YIKjJegOyLTQVNC+LyJX0rwosEa4dv4Gc8rBBoOpzpzn3+rD9/Z2EE7908R0Kn+sE2u9reyVaoRz3By5wbymtR6BdLmXiJeeOosxuDseynqRs8LiLGDsLyG+3r8/al3OH+QwERCo0/RfGGuwxB41rIANimldM0zKoO05BDR6lEQ7RzfdjCCY/s3QJA9qnjFHxh/tHKGv3QTHj3GZ3nT9qpBg3/khXEHAc4sb1DTfngD+W8HuqdcL4oxJ/RDh2EP4oboHgJqvkO4knNdZ/yzu9cRqXi5BqthOa6KVwTH2Np33FrBhhxhXsGSWL7yxdaCSYrcsytabtZeIfnsHkzPWP5DTIh9pgs4S4Gq3WS+L7J0lLyI1dt4FOAVd3J4yuBnPw9XZevNz+/JpkHIP5GPNtjm2MuDGJHvpiAYZ9x4MMoMFbBXhSsTnQ79XuzxkBuDkyG4I/FBxyQShLt5E/ROlYBTD+KMsfXJ828H6YYt+N73vt3LiryJJl/LGD/FHjAKHJAe1/Jzk4OSFdI9e+A+jkm4pMPNJN15x7Rmg317aNdKHMd06JSevKeIyQpUi7ISBokowLu5sUCRzl5k7wrFgaQnEwsSsrkVClrgaYtju6dpD5KJnHDTFfYmMiOvRR8ySydpqKB4tS1q6TSPKCMHUptn65eTCJrEtT5A/kc1wb9wOZtwv8Ed1oOhh/9M4fORwSdRE0/hChZEA8xyG988dsuQy9SvKg8MPc2LxCMMfVlLRryjZS9azVKdWYQ8CaJA9+p6W5ytQX9l03QGj9RH+7Pn2OaNMS4kLUmy2mrrXNZF9a4pr1EM/qL5Yw97xzB2iI/OhZczh4Tu19C2y9zFp4xmxqTpDsKlhbMetBdP4ktPUEEY7RcqXzp5RcgvreCeIg0PWtkCl+OaTSYypg1t1sLPyh4BDpJWnGH8Yf1fgjkHXawzlbt/FH0xt/MBzSyZo7ffNH8QOEcsOzls4x8tBL76Zkzr0oZxDC+r4v2ZQIyaSBLCznmWd8CX6mBM+tfQf+X5RM+yTooN7e3Va4Z3X1U+1h5rAzQ6/5LyYGSkrnGllvUB635pxQ+mPt6wvB+87P+1bG2ksaywShyBGf+DCTe/ApTDrYYcnJqjfbcrLSYY8FSB9xPPJS3/wBdb2BuH7EYPxh/DE1/kDr9sqLrbkG+bvxR5PGH9Jx74s/argwhbfKajadMX9v33TTpdKM3VaLtg/8+7gDxGlmPnoMsQwsSZHwmMsH4a+bG4xaNDe1BKluCQVAPmuJ2IyM59Kao2NmSW7NUabS86n+m5lubFr3lnkJv1Spi0vh539AbFBdoof3S7RlrAj7KMIh9zP44xh4IOVA4YK6tTD+wGH8MV7+2Kgb+Ru35jAYfzTpMlvIIb3wR5UDRIavunZyb6TSJLSHWGdxtzF/xvTNHAgt9JdvFFozCsUCa0pueioELmsw1I2cqc/6AMlygM33pOcixvW77eun21eoJZIcGFP9Y5NJP8MKQcmSZ0SGo0eMhU51GC4cv5OzQb0BmqKtOTAU5BDtXD4fuzU6M2AZg/HHJow/huWPB0iKZK7uQfmjQrIE449EVHFhyhnskv7enOsB1EGdnDmf2/BvuWZplcvJkCjpyqGoM2YiraKBjcwdLuj6bYl5UGOqlq4jhXvMiwiB3FDOVyd0X0504RH1EfP98DBwl8kMc0dJGlXd8aRImR/bjMS5reWPtfHP4A8Oxh/GH6Pkj4zPdPyhWXNJ/JHLIcYfTVH+6OUAkYpcf2+BH/t7yiZRB4uNTUdCMDlZ1phQMh4h05evqX2IIYTNdUJDxG0qsPmhiicpdXhL3BRR8z6lfuq5k4OAOTJVuj25GKVXhIeuqm5DTD+5Z/3DEn78245cf2/jj/IovY4ESpWx8UeDxF+u5M7U+SOSyABDahxZUQ4x/vjZ5HnfhwtTDnL9vVk/dvB/5dIqSjGPmAqllo5BLszSCPVSrhwKH07Kl6+6SxPiix27hEXjFqNNQ6syWTNjSpXjbpHeI+aku5Dma0hWppR+p/poljGGnLuZxlT+MXN4wObqdU/zdlroHpWY7NuLud0Yon3IYTFC/pCmeh3swkUph5R0BRRyyGj4wwEuCQxjId3/u1icyfIHxKM+ggNReEByiTj2CvV7aQ4x/vh6Gn/0Zgp3E9KdHN1pD35Gb+FDbiN2LgknxK2UbpJeCspnbxgF94bw1lvKJPNQUN5G+0Eb8BJxu+6bzE3LvaDgLbtacALNh/PNlKCPS7SOIpewiOcHMrdj4y8+nETGlCpnHz4789ZX16bbjKzQ9jvn79zdjKqde9zt1Rr/6ivKufqa18dd/3XJHcRyDoPwpuBtuoG6b/44Ac7wsexiFMbEH2AReRmp66n3PIPwxwQ4RBrs3tcljNSB82BL+MM9xyyYl68UvvwyxiHGH1/vhz96sUDAgB5D0JqfASOqOaM0FsEJ2Z16nw81c1DnLeqUzgQUkd9JyWwUCWgeOvXiUEHKUoEm1fD1cYkWJ+w6E7V4fgjndleuJqibG1PqJlkfM8QUnh1AD891KfIxtVadW1/wvvRegUWCxot7P8tCELkpWNK+rUAN/vDK9S2Ha+M2Fv5g6hqL+9qYOUS6NsZwCWMR/iA4hJL7Wv6QuIS6dfqpf1dQ256jQvzBWfo7GH/0xB99uTDd9YR/h/Pwfok8uJeQ8i+C9uHV4KDRgIbwiLoduf0bmeYqVYgXdv0pSSJzThtcoHwKlEBbgk+i7zPajMR0TwnPJZONS9V/ML6+j+xV+P+vRzYmUmL9gbAp85I3fytjgtSbD2p9eWPiu5t8Av17HnsWBflKkLuRkhB2J9e2FbX5AxvfUfFHKQ6pFMvDcUjODcokIlrmWcddwvXTp+sXdZPwSalDItxR4rvaXgVr21NK5in4403h/Sfbxh++KyHl6nu4a/xRJIhaEMhEVtJ+TpUpQzmR/IC43jMIlRbeNZ5BEPRUK8DsQ8Gi4rLldIGzvWniiP4veokX0y9dRqGcS8i426WliQFSL4EregFegXFzhPoxuC358QuYBvkJzLXzwkxtxZ5FKO+SL2YaC7i5ZfwR7yNFHxfnwMjarsUf0uDoYyJuDb3MdgBrWtH1C+VTt1lLb0bnks80Qjm+NfwR4f4muKDyEexXLuwCf2RbIIjT7pkJqYIfJGZeo7AY0swaLJhPgo1wintDjWc4jEywWn3E3cPh130CC28ja0XfG6ae3AhIH1mFBhIbU0emzwnbgGqGMjWgJWMltLiLzO8LcIDwb5u/Smw2us/+PxFXleLPgsw5Rzx7pS5mGhogI8Ng5JrB4VPlD7cx+Sc8zha7cfXwDByH1OojiTzprEhvUpnztpBDuDi9K75bUQJ/uPfv7Rp/wDp8F1G+zZCbnTH+3lr+UB0gkBMkdulJKDS4AA3X+bUm0sqtRPC9Kv7DyMnvcgHhWvwZhNmiavSRNL2mO2jcrCVwtVqRkllEarWdIin4/9VUzVRmG0MZ4dftC+CiLgVQPxe4GIJqp1u/Xwssq76rSmfFCMvIfhZ/zoF73yTjIAj+oEjUl41+3/qg+mIb+QObq1o3rirP4Mmb90bIHy5j1m3OrWzLOCQWp5fMH/D+EWPdLpmFLmwnlYWsD/54m7HcHwiL2kr+OJch0ChtXdggrmHOJUMLcV73YBL3fYuyVNOlGbgqz9BNMMZMWKOPYpaPs7prCVxiTqs0n2CB61KcOiH6FuXOJCQa0kc2IbXows+LTQQUOwF1p6KfMjfGq7rh95oauVJYO+wT/u+174voW44VQQJ/NEhwcwjuu7vCH9INTNVniGwuB+UP989YOaQCf7BxepJ2A6h6KOtEFWtOZAPfF39o7/LaGf44l9mRnABfRBq8ChpS1J8kOCLfqxk8VSP7Q+1nUJefOuERLUeXSSv0E3RaSjGU7RGb9LFy29f1IKDMCbnXgTxua4mGcfd7ihy2udSiM6yOATK5cMI39D0eQiNHHdZSyurLQtW3HCsFLX9IOCSF9LaNPzTYNf5oILW7KpC7Bof0yB9YlrsuTu++oN2du+fFkXPIs+DQMgR/PIbXwa7yhziIGkwfUo3P2Y2ATODWcaAp1QTUhL6hYdAKWj78rZeJLwz6Ud+cWPuESpVPCMCmpDYCiadRlacNEmTm9CpoKXjmBmnXF9sXFdz0UmCuZG8OZbT1q+DpsP3K9Vj1dnNmzrD9W6s9Qduofu+C0rH5QgXuc7e89rkmH6TKzqGgnK9r65ZYG+5Q/VHKjdxbxB+dIu6FsfMH8Tcq5W0qf2DxNINxSOB6O2X+OKtH8flSc2ZQDmH6/RnczdIokvpsHX+cq2H6hYDhFYgTayg4RGY/pCP8Q0q0fBAC1TZSSk0XphXQuBt1zxymFMwCY1YLtR9UpoecoLkbkXia0kGCpGZT4H4TExjhfI75H1OaFpdPW9N2rg4UmdkxOFP4GNxtDonNp0N3c+pjfyMKfxNpaUq4wXEgyr811KVhGdDMV7dpOO6ej9A2X/Y2irvIH10yiTtj5w9mHr9ekj88N6qDEXDIoy3iD67+LA4RyM+hOYTS2B8H8sjnkNhdMFvDH5oT3CF0AobQjHHZv4nSv1ETBPANZpHGOsK/HdEJvzdKlN/V4U6c7tQLP5MySHmL6Tk4qYa3oD4E8lKl/vTbB5ufvm7+1LofpJrfc4P8tN/H5nS30HN9H8P5Rgm8RaztxJzE2k6ZExfKdaWZSxzhcv3bC4jbiJewyXDPewDtvQkypBOqfrs/hv/fQ+QC9/wlULv8vqDhD8dLt/x+9mU8BPz7FoNd4A83f78JP89u+NVuAqCNH0LAc583R2vkaY771lg4pJkgfzxmEtvU4pCYfBuUQxD+eAiHh1ve865xSPvz/aDNJ92BAxmHSfOH2ALhaYGodFaN4tTOTXbqJEttYl9rv/N+IEjVQkQYlOrni6dyKIfl7JcIVBVqxGuky+szhVqutkH1fc6fs31fkq7uY8LVBeu3mO8hpxG6GqRuvOvlnE7SfGRo3EKQa20Af9kNIPLkErP5pNxlLnvjHGqIamfn6TX7Ty3sMH90HwmzraBrgeGP7rO3K3FITf5ISRbSTJlD2tcU+UNlfS3EIexaG5pDCNdt8nmv4YlKLsMh5DwiGybNH+cqXY0da6TU7Odv4qnF4UjgLgyGL6T3lUJEEpS6LzAz5W7INO0T9XcBHzuNuTNHO5Ab4KP+PhO0FHvmx5A9IwyEC8s4qyeYo5LsFthlM+c9E/2Bp+Hwb8L2s3qcuYJUEjDULZtVs59wCOZ7EwSVN4muANR6Zp+/AIY24RfDjvJHGJQa45Ba/EGV3Qd/aDgkV7s8Cg5p++xoovzxIrTtIhdDUZBDovKtbw6J8MfbzH1Kc2KNhQorf03Xlu9Vy1cdIBI2kwvlIm2YTTyHA0RrEyImRNzgY6BcdSihTpVDvS8V1NHvw3dq+NhJM5dINqwkPCHpB8JRbg9Uv21oVaj2RPqbEshN8FnX5vclpMMJQoIgpOvswNso3fLm/z64gpxpWJFnTt4AQ1mYBu3pUFmBiEB8CWKuAJS2W/38yg0ZuaEptLHrG7vGH1yGFoxDavGHpIxa/KHhkKi85wBy9LpSiZLEIRPgj3kif/jtuBh75sxN6gPiAKXKvtgjf1yEObWP/E3T5w189k3t84+JP1Ki2DGftCdeYOJaIxV+Za8ytxNLNgOYYPL9R2NBIyknMmyyxHwVUQj8CGPto/o72wcOGS/K134/9F1OxMVAuK18lYX9Rvm0r30n5gdMzNGbnZ+0Xy41n1MC5MGH8iaRjo/DRSBNcqyJOfY8rN8mQWN3RGhXPhpiIwvPl3J4CF0BJHiU8vyCdS6VlU1m7MpQMP7gOaQWf8TKqMYfSg7pNrM58SOhEuU1ij9SOGQi/NEo5WAqf7wNm93UOAXnHq55fwz8scc870JR5UL7/GPjD7UFgvH523iPWwDhaRoWeMrFQBzcYt0rrCHxsShoQo2ZrqUajRDJGi1qvJh0eWGbU6DxVaY+T7YB0aiR39OYTkuZWSPtiwHTivhjjfXVeTBTf0+6dpFyQxy0z3GqLKsEjoQyhLt5WyMHqOe/EmkjOV8ZrdB9JL1gLVeXathB/qBu0aY4pBZ/cG3k3FSK8AfCIf4Fak3BeazlD+o7aBuMP9B+ugGb0hRtNjWPrg7AIVL+6GTWUfi8RAxEw1zsd0/ZL9G5SnDItRr8keLCxG2W7mee/FLQ5QPP8kMm3Gc4oEI9I+iHFdQZ5dbygVP70grBuZJhk3teYAFKvtcXYu1LwSK24dXkkg/K5XzLu0w393oiAsnYsQkNkHVGEcqVxLVFzlelu0ixjV3f2DH+4A4PqJtKDf7IKLs4fyBWgpLzWMsf3Hew940/kH7KUKBJOOS9ds7czU1EU5A/UKWOkj9Sb29n56qCQ4rwR9IBogKOErRHy0B7lX27njeg1AHiWSwLU1dO+6NUkNkis9wat4/GhKhz70gFJ1Aol7ESCzD2PREK+BXG2qe9PVmSqSP1mSXaevRW7MT6cubOUjIeyPhdYjaXKWuLG4OodklYzq5hCvzRcUc0k18t/kgsewj+yJnHWv7gvrPYQv7QoiZ/aCy+B1PgDwRcvETK+oqNgZRDioxlL7fBVpj0H8ODdlHvTQkfwkhb3M2H5zAfxkKoku+4lH+lcrwuZ/hiH0I7NS5jmn7jFkhWf2v9ExPbdwfGUIIwgLDoHEPmVgxq32klqLsxvilZt8T4XabiuxLXFjcGGq3Q4HdsjAhT4I+VO1RFDtkm/sgJotXyh7bvpswfsbseqM9Xm2NKDhk1fxBjiB0ezvosYX3FxkDKIUXGciwWCO7kF+Ip+G1f1d4SKjzdD6LZyzBdi8puf2i0rqlm7Q4XtL50flshG0QYzFTKZSzFD1gKjQa5SW2fws+yC2pf+f7WmGOBX/MHgnVczcWmwPNh43eBiplI0RZybdSYswmXmawMNhOG8cd28UcDfvW3a/OH951G+AxT5I9uY85Z3ESa/xpzTMkhY+aP7rtUQp81yyOybm7m8IfWElmCP3o9QDCCRhO4iPmrRheZwjesqMl27KkWuX4BpI5X8kJv67gNqe2qkGEtooXyNO+r24fcUsv5EWcFhidAMi+qH8Qznk8cI5KT3pJpY4rsudizmX8w7CJ/jJ1DtoE/NP27g/yxtj4q80cjmBtj5o+oBbIyf6TInyz+mC2XEs+DfBDZC7oAtu5WxAuRmxkfMoEpzsWIdMliTrYPQ8tFKYFNPPNjzESV8NkiAozplxNoC9oerw2UtmDVr7XJT9NvgnKS2inti9w2EM/6BNZDd8tlo10bKaDaGrzfIFrAs3EZ28ZIKSPEn1W2QdwntdowRjDr/Bi01a6/HJntbQt/aOSb8Ucatok/Yu3I4I/iHCJoJ5bMZtT8oZETNWW3tF9KtKHPA4TEvYHDavIwCw3bsPoBa0nEMbYNCSEEkk2obXmnygBESXu6sWpKCGcOJRaBlkSCOfYIDr3YfQBsOdo2RA57n5YgoAqbGg2ZFZ0bWkCb3gnG0hHsK8gh/rRveRJiDG3oC8zcXwrll4g/iA3MCSi5eu/r0hsS44+0/h07f0jaIeCPbs+0X5NDtpU/mIvosGcbXHaXaEOfLkw5vmsPI3l23QA9gMtd/FNrtxCuJgRWNT2bJKWfpXzsUt0XNP7DG+2J+HT3kau+RDoyyvf03TAFKSK4DiTzNqMNfl+xLjaMUC0SWBvRlG2Ma8TUWsLftwZCgTpTrptHMO/70IrtUiYmLj1nKf7oxi0s82Ag/tDIN+OPuv07dv6QyNSoi2ZNDtl2/rj2w/TEvvxYEjehbwV/9HmA0G5U/dPQtciG9QFyBXuIGaKtComDM/ekmMuiA+SVSxKh+4xXFyfYUhYQ5TP3WJobnVnofeSqL7GJotqzj/j1Su9pWJu3GW2Yi551NnuxncRH7QR/7ns/cuXZX/upX9j79pe+XEwAwTwt4rcMZWlTLfaBI8SUf55YU4eES+Zlb90UT12LaC+7pBLbnolprPxxKOGGRP6Iyjfjj2xsC39w7ZhL+MO1reWQ+adf+OKjv/qn/s3Hv/knvtrddZPNITvEHxeD92ZEQoBB+AORRahMk5bVp5kbSxslAbphdYvLmVlgkd1ABo6Cny7rGIiDTZuWkV6NTZUVlNtELsDpPh+DagExV53fiaX5cu1xh6/2dQo/XxQK4ZJauxLpyCTtuRj49pcoU/L5RexZ/9p/+2sPunnUTpbZH//k0f6vPfhPvv/BN26U1F4Uyd3uzfmkMgRzLgfiAy+xbj5CXBE6TWR2OxE51GnGTwqm2BwrqBSLQ/KH76ZJckMGf7DyTcsfwjt6jD+myR/cdxZS/mhfs0s/+KODv/73fu1iyx83C6YbNv4YmD8IWdQpRpL4ozcLBKL5ifn+aRayeFEifqMXiYV+P8VEKXXvYcqlrnV37lkSk710Exe2aUPbwbUd0Shgp+UalxA1yj6WQJrFZS7UhKY8Y7SvqGf98//wO6Q5F1Iafg20YS7rxlttOer0iFBfiWfm5jxbhnCwZOJYAAAgAElEQVTO9aaNDC1v7lBDlLvPtVOhnabSzH6vc0HYViRaDqryh4JDkvgj0UWU4w+nFIrB+GOa/EG1Y+3OAeOPneYPzkqSFOPSWxA1BuTBH3gZNaj0Y9jfJAHayYEszOfY8il4z5ITVI4BDfhE6sYyMbjLlcQmS2XQ3qCZEhJcDGacT2okm1hu1i59X81m6Px0K/vHfxG9j+mb2kNEZI095NZuUA63ll6KzN2qWYeItaFZ1zE5RGXskWZfGzzwbmxAXLoabB0W4I/kYPoR8YeTU19g2mL8MWH+kLZ1A8YfD3eBP2pwyKAXyRG+87cTNI+UBqDrrO5wcuRrfBQnRonWALNcpPoCpuJjwUSltJgXlKdykbsHFR/R18FCqrXu2sks4mUQiLnWdvhM995RilYjEjTGAZ2f//fzpFLaWSRuF9JuYdm2uDlErSUnHGPPXiymhpp/mdasmCZyrg0GDNpJZUjZxqDpYmsmkT983+BVhjsoay1eTsghY+GP85Hc/8YfE+YPvx3Krxl/bCl/NC1qcshUtFZcJ1H+ZE6buQ+nqkMs1gEOFZhf7YPE+A3JRibmtuS0QKlwGowSbTzrWwbJ8Q2ZPsFF546mfzpBgPhQN8jz3Kvkp48B9W395Z98mfq85PIgUawMvK/pY8qne5XMINJnRWJquPkXjq2GxL0+ojBDnos8FCHtxMZtW4Omh+YP5/89g1d3Et+YM8AVToPs42kwJmPiD47zjT9k/W38oYTxh6qPivCH+6c2h0zlABHVPEaInxICNyCQ2vfjcp18yx8oZPI7LU7qRoYjCVf+K/CTwklm/dLN1rxE8DIR9KoVyjnQaq1TNqmUX2Htw9HnWC7Rzf23v/Rlap5Q77Pw1xiMs9OUnWoyYhBEIkpmUChgvqk5/+D5OL/QI8V8ozaLz3YgaHpM/MHNmZ9Dxnvt/yPjjwVwSGr9xh/8OBh/EDD+6J0/mtoccm4M/qk9pFrjCOQG4hO2YU72TYOM35lkI7MQXGTUIBdadZqtLhAutX5pwBfbtxJzHWX+bV/P9ZiCTTt3UgK/5yXcEwocInyXF+eq9wlxs/tbOVUp3CikwWOiZAaFAub7SC/MuayEdTgN9uvI5x7AhUQY9hNyxE8OI+IPbm5gqa4vjJg/ujqMP2Qw/tiE8cc0+INrUxEOqXaAQG7k8wWt65x3FH5+7KZO4E/PCQH1ZsLbyPi3lT6ukG0nvA317Kbp1I0UsglzxPx8Si55gb8lpbV7VsOXG5sH8BKTZeImVeLjXD0/NbIxcYeHz9qXsxjsZWZh8iHJHqaxDMyZ7DH+HSg5cSJJm4LEWJ1DOMDNBBcFXSLKuBGZV1VyhI8FI+OPlHsojD8iMP5QzS3jD33fbCN/LJCYBgzd3RNVOaRKFiZ4QKoDfJx4/qWpQSvRKHTuM1CmOrNLTjajkgHEJcqqFdDMRfy3r++nZisg6uLGuKkZsC3UyBfJ9BBpR9UsRV49XFaZpbaPkRvkm1LzgqhPlLkiJ6sGsgHuNMDLSOrqDu5zN5H6e59XfWNs/OF9X3PhpvFHJow/5POpBIw/Rs8fjz1335gCb5VVqfbepNYBQpIWbwUXpFaprrVOySUQRb0+XOe+WUDjWyVtWG1wY9P50Bc8RL1LWDXEiyMzrfB329dX2hc2n9fGZIADW1PS5aU00UQOEGvlavqO+6yknNznROq4FHnOjTq8MihZs3UpXMfIH4yF08H4owK2gD/C7ErhvsP4I7Gvd5Q/DuH/EtmIPXNxDqnlwtTndeJZ6URhMYeXbR0LhJHkGR25vdaW/36lDb0mBeQQdzCQrgOFTIn+IWo/Zy4S8RqvY+Y+QPhZKv/3s4g1rKQrSl8uLw8I7Ygb7xTEsr/MtX0X+6xw/mXFSnR1eOtQeng460uvDIqMtjGF6+j4w4GaM8Yf1TB1/ugStIQp0q8HWmTjD+MPCX9IrLIbXFyTQ5IOEBkxByGobBCak2JWgBzU86onQNzPVwWbfukzzkoF0CJ9IkkBWevWRSkee21Yi+MohJg//qKgX7+fqQfLuoRhT5gRrMQcOSSCJ4vVA/PqVvC8S//QnXBwja0lLqME9Uwl+jk7+FZoQnZuj38Y6S9sM+Vcoi6B5WkIBUEStpA/bgX88RrIWc7ybPyx/fzhxxV2uOgpK30Yfxh/5CQscYrKvYiswzhkCfvFD1L4Q2228B6IS7l4CAufg59RKKUOri6NNvQustDPw/sooB1U8GMVjRr4xN0L+qRRpoCslSqVG0Nf63oxt0w30d2GCX6+GOlbzTyQjtFcOZ6LvjIAweJ3m1EOufVQKWtvZNzxwckLfww1fccF1knv5qDadSn8PjE3pQfTJ87c7MzI8BO1lAapQN1magnrq590wQWwhfxBrYfXqLEw/thp/tDew2P8sQP8wcxPCX84HniZ4w+CQ7rLMpP5I8XvKboxhYYee/mu3c/veA13P28xpx3x5hfpFG1eW8qt4EAh2JrIxq1EdqHQZaSB/y+JBZO0UWU2QloUPcBQmwIvvSPrOiR4RukYLZjPUmMRfp8qtwRi7kC59cTmlXrckTVM5ajW9B33nCJh6bUr1HQf+N+PbFglG4srCXnUP0UsTb0pCDKwbfwxj1ie12D80ew6f3RlYzD+2EH+iMxPjbus6HMQG7Fgri+o6sIU3ZgSZt0/qRDMqs1vKX9IIagT4R9CIFa40c8yEXl1cr5vD5EgLSpohlwYWreniJtAaU07JVgeB2ZuNpCcekZhdgP/QID55h5TQdeR+IEzrUTlOAhUo6Z0OYq5fCSNu3ANkz7Rws+q3Zm8tXTAfJ8jPYmrSsqhrvZdFrWwbfyhydnuYPyxO/yBZV2jMukYf+wuf6SkvB8Ff6QcICQ+oyL/Y0aAlLj4R4oTxtqAbfrJjnc+ry52wnumxtsoqmMQvP5h3ZWIiH7MH/5JxCQr9hsXHDaoMQzz4EsPVXNGe3tTsQGmnvGGl9ZXkoWpKyv8221l/EAHNweP28/cTbwsKyb4UP/hhFiZmBCutnZBGDeSsQ4+q9ngaT7Xvc/9PZaKNTX4/BEhtyiN6liwbfxxyAQ3niJKAeOPPP4I8+BL5eQQ/MFlYfL3CcYfu80f3Gf2kAPuaPgj5QAhOcVJtUyoABHWcYZEgdLhDhN4im2m2AkeiXgXB3EKg2eWkcmDuTyVOpXGSJ4K+rzsTVzNoYrsd6UGkSNwrJzbBbWWMX/G84l9kyQkmTaR81SQtUy1drXQ9HvB7BMxUmPnZjAen0D81J4i4xsGyV0SY8RW8Ye3HjCrolsfxh/l+OMxKHVSkoMMxR8OVEyT8YfxR3R+eulb57DBvwD7qEYQK1aVP8QxEJ3fH2hbvhhoNkNzn8TXLObr9jhSx1m7EoI2zwBlvgJuQBhCvzBp0F2uiSi22ezumOA2hlhw+FGCH+cMiYdgg4zg99C3+KMMv+3cYMdurlDoIx2m1vye5PPr+8pzQVWRNqHvE+4lt7q+LeBTXgO5cyf2ffbvnt/pTeivPazvpIDPdwSSFE/RN7aVPxwg29JNwrfd+ONzS0wYU5fCH6+Cpj8lNsL4Qwjjj+Jz51DwffIzQezbncClaSOeok/+EB0gECHrfw/b5GKdcRYLEAkunCNByu73t9rvfYgIouxgK4/gqVv1ztqq2CDlBstym01X581ImkBJilefNN9rXz8Gbk4YQmKNBhm5X/yNLDM5oxvrQhtTKpYkZsnJRkYwem2fdu08vSsJglUcYKojd+7Evq8ov1RgKPf50d0Lse384QBjTfHprvPHPnI4U/MH9FGOj7zxR3kYf0QgmXs9c0gx/jhXQBu+4e6AmO03YgEYH6xToq7L3qnJN1eLBIrQTC3yv6sQsINhEbuFs32me4nPQo3nZXAzouJCYiZm6rOSNkVRIFieJJkeNrmxQPihNoTieQpr6GCKwbspc0fj2sKVL4hluqq8y4Hr66oH4UQYf4yIP9xm3LMIae/ikPKHUxadIgGgHS+k8EesXSyMP6rA+IOA1jWyAId80Dd/SF2YYpuDeUK6qIYw2Ujb5Gc5Yd1uoPPfCTQm7yBaYUrz9UCb4jRV4xEIdiytW+cD6j/LPbgnIvYssRSvnZvTpzFrTPB8FOYJpryaqJ1ClQPX511e/yd99w03T8P5zt2N0rfmW7MWU8vPdW1ByuGgqYMKdPt4aGsPAeOP8fDHITG3Qw7J4g9wtyAtMon8EWtXbRh/BDD+qMsfSFkceueP2XJJ7RN/CCYIcU2rQXz3lHEb2ch+AP+X3ELqlxHLcvKM8PlaZaYJ2vDd9vWVoM1c6rVi/t1E4HQ3QLH+WfWF3xbqBCwYz2VMgxW0myoP+yx7Ks/9e0L/Fh1Dpu5oH2UmAygKYRA/Nmeqt7mPcdTM6cRyOLB1OFccwhJ00n7vBWVd1bHN/OH6O1i3Lkj+R8bKHxH5v8YhOfzhDhDSNaRdazkcYfzRD4w/fqsIfyRySG/8IXVh4syNsdO/KvsBuCVJNy5hlhOsHq6cVQCK95mrIHRmwuvnxVmVhMBMw7Ngk+nclriLi0K/OmkqTh+LAjmT0c8KzHTheLzXvn/Xu3U2JQPHWd2RDEI1Ee2jAib2kogF8YfwtSxZLmECkldljkpEqTz0Ka5dse9QsUQHCjN2n9ha/kBkFnbQGA1/RObXGodk8gf1OWy8Ve5aCRxyD+T++wX4w/1619uApWbB0cL4Yzf5I+U7vfHHXqKZirrpr9GaHBE3iYa4va8RZDmJm1M2gQlcDPs9+H5zmSkkwctz5XhifXwW+Y+Y0I8h9mLNBF8oQI3btHYHPSqA9z3KJSCYXx9CO9EMQjUh6aPabjlKpM7rpMxRHYTuFaI0nyl96bmAUHJgY/1F6qLW6wlzI23MHSz7ltQ+seX8IT1oj4U/sjlEwh/EuJ/A3+9V4g/uEPVaCn+EcwzKeN69n5oFJwXGHzvLH+h3AG4+DsofIhemXDCmUNYVIfhe57dFXrLFmHpONSlrCVA3AqpNUommqieQbrZhLi4KTcNRlxipSbcntxHKXUGKtfYozKhFxjDVPA7f87VavbpXKc2cH8Ot65ilbs2FIbHeqHtFzOUhda4K5stGGUIZFv79CfRdmGa5VDuLyqWhMWL+6IKFZ1PhD69fohwyNf4owCHS9d3UGMdM96o3kLtIjD8mzh9NC4ZD9hEvorN1HrxfhT9SLpJTgzE5sqakBJcOysx3DBoOn7Cfwr0EB8Rkk/qwlgzeOmQEu7s/4QgmOHZxkR8gJ74JW9HHfZj9KHcFKcL2SLWD2VpAwnQeNY9HFnPp/i0Bdwna1yKCeJFKmPA76V4BLgSuDSH8tai9Ub1rA5Y1Zi37GTKWURnmuch0z3mJkDvPJBsqgdvN6LNibQl/dPFzMZk1Gv7w5iTFIQ9gXU+RP3I5BGtPLxySyh/ed7GLDI0/KvOH1/8xOZDEH+4/Sg75uE/+yNXKo1CYf7L9xALz4hEI6tAUehuEt//+LSAAzDz+JlFG1QuyBGXNg4uLwkt2msB8RwmUoX36NgBzBFvYPjoTOAfJ9fA1Mgil5meOEVRvG8FgLR0wMqMzcSZnRCFMzV1650aR49/Btfc5IIjYHQFcG/YZiwp1nwWbLx82Y13c0s3IfSh7Unniud08nMp9EGPkD78+GKfHIGei/AFjha2BLpXpKPmD4ZDukDNJ/ggOPiX4A/t/U2m95eT351KE96pI8MZh6/kDaUdTkj+68pUccqVP/jhX2symPEln3QtA1HVLKpiJk133PBuX7PQU5MppUM76hQgg/ECgLUkVKFlj1Y3Xn/vfv3P3zvt/6+Cf/OjD5vtf+OLJpR/80Z1rv7jKkhszqT3xAqm5k/NCqY0qpQUU+VUqNO69bgQVpvozYgNrWJNodqcI8zFiAYzdEbMnvGMm7EupdnF1k64yX/4jSg4y30kZ69z7AnrFmPiDqO8A+m8to10ih2x8diz8gbXH+GOzjyL9WXK9JfEHjCHHIb0pEhQcsi38QbUjxCPvcBs+IynDOFnIfS/SlqL8oTpACIW7xvyTS36iuqDdx54J2rX72Gs3l9Gh77Sah0Fb/Q30YaRdksPBIvG5ssbK1fln/9HvvPMf/nf/6YWLn31+3UF7eDj4bLZ33B4qPvrNP/HV2CJcCYRuvBjfwfB6eMx38GPOFzoR7IKm1g4jrPreCGqzLvn3gaT0HzVXr4AFEHOvcFqiGDoSeSyYq9LD9D4j76h10bVFeolW0lhrNrBDY4T8Iaovxh/UGjD+mDx/9MkhSfwR2Uwue1YkaDhkG/iDa4ePP+ZxfCjzuHVxVJtDcvljr4KZTWz+oTILuL8FJuw3CJO2tK67yIb8PHcxFmUiY8zpRQB9civIcHESBMBR7YqdPqmL6KLPJckCEcHRv/c/futM+Hc4tzw97ywSgu+f9+eZpD3EZ1w/vuCCfBmzYgqoSwh/DLTX7xJrpyHM6ic9B1BrLVOLWpcyMS560jqvCOdqyjOsyTtGhl2J9PPjUmPdmaMrzOnSGBt/SOsz/thB/uiZQ1L544j57ps9y4L5jvEH1w4fX6BkXmQecrLpCA5Emox2xflDlYWJu9THVV7iAg2hGWwVpc64sqwCVuDvXLYY13no33Kfoxa4doFwf534qt8nJS7IUlkx3Nz5P7/xZ2Z7SKZEN6l+4nM3phiSs/vEUEJb6DYqRDAbh/BCrGi2mErjQ82rE1iLRbOnMBpAslyFiVx6AdUDJLA1ex5CPe8SMRU1+3PUVoix8QcoZLh5/2n7Mv6A/i/JH9o5O3b+KLEGU/nDPVNQ99g4BEtKsw38cQh/0ly4WoJDivdn6vzdq3CVe+5V8xIzGHfyxrQkowv6ygDXrhvcghCcakVItM4s/uB5XFHkYiGEF/JU8efknkcR0NlA/2vn2+oyKxBYN2FuH3jtcK4SHwrrjz4P8zVqLd2pEfyZopFEvuPmzbpK0ssk4/cZ0Se3kKQLTc489OrBBD/n2sTmoucwlLU0AWPjj4YJgnYXwhl/VOCPxDk7Wv6IPA9n/SrCH+6fCIfcS7nPoCCHvLml/PE2fMcvs6nMIVh8RxZ/5HCINoj6kPO78k4wnWllT5AbWhtU2mFO+W/B/7UnwhAlAx1LgmtXNKI/Uq4UKSn5Dn/lX/r5d45++5trZujWh/WpC6T2yu00KJch9WDReABizlHP0108JE2tpz1chs+EteM84z9ZbHwEt3QX12an+L8ia/5R4I8cWhU6QR/6tDbw/xu+tqktO0YCsXlIyZ5Vitb2Rd0krxlfSZ1jTOHI8kfwXvcZR/J3avCH+0eZInGn+QNAxWxx2W9KzNnR8gfMTep5fItCbf5oiHZI66/FIbexpDRbwh9HwB9dvJTkEJHDIXsV+COZQ86VCrhAzEP7wa3Ga0A+7x7+vfYlvQjm7OSNZCPiSNrHiZLsHC65tvfhHkAQJEfCRxHSYtO9gQnyAWhCODOWWgv1+RxpnQGffbaRhalZLu/DFbL3a7pkEHPubSa4CiNKblFJMnZwB2sJgVBJAvy+Uo8PlHELuaX7/TG5wsSy5sAcxgThRWGfnDCbRyqXt9+2q1yK1vYzsXzhKRv/sVpLVQF7hIvBRcVapu5AUGW0A1erGvyx9ORsdRezCvzB4UDBH+o5O2b+gDlNPc+sR/7g2kHWrzyIT5pDBuSPU5gLEoU6ySHw/dL8kcwh5wqe+rQnGOqUJXGreho5wUkWYncRUIzswhuCDzJOeSU2u6968R/hIa6JaPi4ydCZrV4XnGgX8LcQ7n0Sn5fhXp/Hrl8qrF0IgQjGS8QcpW6JpTBXHjqlvopUv5L1E/OE0oIshtRiFyL1WDtzg8HvIFnQnnp5/1F44xCr57uCMZ5vibZbu641c5DTtI6JP7r57rcvR1M4JH/E8sxL+SNpzvbNH4jMOkV4opufUtldkz8aYTti/KFNZdoLh+wIf1yM1FWDP5I5pGRA0bxH7dhHkYmD+eI9CS73YQfUAf7uAulChJlDaoCc6FTUvOfj52vGHmduKLBnzfVTrg7Cp4+73AZ7HkrDuFD4WIYXSmnnbax+aiO1VI6PKA+5wp+3lp9+rJ3UHMf8pDf6BMaHujSMA+c26d8S/5VIOSnrdPTrUQgNV0yCP8C9YVH4Urah+CNlblLPOfo5i8gsSsk0J55n2TN/SDkkxh/d/x+PhUN2nD+6uh5U4o/k9ai2QBQ8wWhO7Krb9mKmcgqlTHkpQOqm+kbq5rJhMRFoOBpJnan9G0Nhs7MmDqYba0mGBXZR5Wi+CP/MmC8vNx8eKvqSXb+c+V44RqW0UzE5Q7lohH7SZJ8kjiE3Dn7Gn5iWvPdc3iOChkMmwR8lA49jCOqfleYPeJYUDpn3MWcruC1JOWRBPA+W5a0afwgsX1j9c2aN+NkBh+aQXeaPh9AuzgU96xCeuh7PDRAgx31eiugJSzuIqaa8EkKLqJuCM6Omap7Cmx8xk6yov0uZiDsU2KCmkrMfp4P5JcZ8totu2hB/7NCE7uI1jrx+IQWiHxxcYP3mCvBSmyi2nQJBWGtTTY2Dy2BzX/isDye68R+CQybBHzAXyTVaSoYIXSCy+KN93Q/Wl/QAx2nc74+UP6SyaU32IPGY7/fJH2E7MvlDOz41OWSX+eOaMLY3mT9S52KxA4TmBOM19jmY1BpXqjVCKbgIqclN3kpYUGhptOV7OdouRLjESKcvs3KScGH6YSHI7x6dL8zBogZhofUjvsn+5kR7cI/VQ62lXAFexE9fImdKH26FkIwD1Qeru0BS501fc7E2pBwS8Ic2fqlv/jhi5saDguMm5ZAs/vDX1zbwRyKHRDNM7hp/9MAhxh/N+PhDdZFcCRBC5wnc1ren2fARZSVdqsFdckSZ8sAlocSlbFTdnDkLE9yd7+pB5uVakiwaRcH1v/PVJdodBrj7lww2pebG0BcNxuqqpcnStEHwfWytdsKn6jxDtHAi8s+sZ6N8pg/cjbG3U8vvcy4ODUbmPwN3v9Hxh5NfhHtsyUvZpBxi/PF5f/nj0WAXc9XkkF3jD0k7It/F1qoLTv4o98K8GIw/+nFhkmpzME3BBeaGVjQHOFOW+/+7CSf5FFMedXK+CotFOqG5kyXmv0hpmw7gMPY0yAJAahSIZyues5lDJBBqQ0Md0XxR7lqlBQyrTSkslHvxoY6ghKbKt+T5c7uaxhyZK/spdUrGM2b5yPH7jrjIjGF+DMkfF4E/9sfIH4x7y71C/MHV78P44/N+j1lO+uCQXeOPLA5B7pjoDszaO5IG4Q+vrGTL+Rj5o1gWJkWUPBe0o73xliprPyFCPyUKnXO/0GQJ0N7gyA3sBTiVa/pxaFDBQUui/2Pm+jN3LUcCTgMIP0v2AZkcQLEWxlgXCui7pBupvTb61qK+stJwc0VUZ8k+zpiTnIsGl6hiEjD+2IB2jkkyRxl/yN29anPITvFHIQ7x75jYA6+V2hySzR8Opfp5bPxxbgAfxJJBO5zWRRWhn3i6iwXyidoQqft2grbpSvvdF7g6OdQ2aSLlkwciot7YyfhRD88cu5CJXQvK+rLqKgUkfuYItKix9kv9s2toxGJlbvxdcX9I8T5mwGmJbpbyYx4Qxh+bEM8xJX8Zf8ShOnwbf8gQxM+4uXqv/f1IwLdDcYiaP0bKIVX4o+QBQmoGKRa0Q5QlaRMK7QEmENpXc8zRyrpjz52seawd3EWUT6FGCmBpm5xgu075pnOEzbgmdLfQhn63bB+n1oU8Y6rpM/TdbpRzRBNond1mpEzxHFPOzz5dAFjFS2V3vT5g/JHpzqTgEOMPfl1Tlm8Uxh/V+SOJQ4bgjxFzSBX+KHKAgA6jUoKudXBJsvPKeldSd9DeImTrnaipACF/gVwvEaDsPTcVRJyjeayt0ZZqElwcxwNvw+33V4wAryjHGGuT29y/1pbzPvU9hrCp1L9NqsWMqSuanSL1UEh9D8lKFmu/xj87q80IuLmCrRVNRrQ+XYSi6QfbH1M6MJzB+MP4Y2T8sQLxXWmbjD/K8YeaQwbkj7FySBX+yI6B8AZqX9rBEj8uV65bwC67BPxEfcXguy9DXdG6CV+099r3P8z0+4vdAOkG7rVSvobQh85N6aXC8Q61A66k5TwBn8eN/vJ8KV3mlYbYwGv8Dak2zbQ+lVDHZaY8DittY4XYHe5QyIH63oGyH7E2Po3csprU5lBuwNu+3+2zyFqRzs9eXIS652l/vefdjj6V2KYojD/OYPwhg/HH7vFHCocMxR+x5xiEQ4K018X4o4QFgjptPUttoPb0qLRqcBmMkl11hO5MVODo/RHFKiTnW+ba4/1Nmq4W24Sf9ZfX39ipuvss+l2ldkN7cDoKbo7WIkVT4mt0TpDMM6mHQu2zkxdGJVgd1W1mNF6vKlIjFrk/pASQ5zmAsU7O+T1CGH+0MP4w/vDWg/FHPocMxR9j55D94PLcZugDBDUgexkNVLvRKEww3KYoa0MvdGci26M5EFSMVUiKUeHaA0i9NZb1CcaESvvi0iOeIn3bfQc73Jx6Vg8JSlhqVvMwJjCRPu++W+pQyAnCi5o5UjBBwiJVbgjXFzX/uXSgtQ70vQXIDwjjD4DxR2/8ccYBoF02/hg5fyRwyFD8sVMccq4v39Uh3WiCQaHam1UHUk94hwOF08gGnIqbqDIxMmJUYibDEsK/IQ5La22DrA6cFSgkJ86sua88mGmEJoe54IAonQOpiQtIQejVX0ubktJmUm5ID9yp87/UgT6QIbOaAfIjARUvZPxh/FGLP844ANMua/jDW2efEFZz448d4o+hOQThgnlNDjnXp+9qH240QhNOrG41BDftUhuCvchm8DUsWw+UVzNW4ZInLO8KJnGJ9iwFBzzJYUkSJHcRgtAlm3rNwYyr+7h9vS4oo4F+iB0ORH2eKtAE36u2SZW0GRF63GaUPGwR9Whvg80+0BMyhPws9FHVrGk1AW1/noh/MvGl9owAACAASURBVP4w/qjJH9z6lPJHJztEn0XqGSt/XPWt7sYfSfxx3+vnXjiE4IJoqvscDtkbk+9q5qVu2nauNP9UHdIg7kg9M0iTdU1wWQcXyNvb5VHwnMfBRsz9/k6kD7j2SNr0zAXVK+fkHBsjL8i6C3yicKC0CETh1X2C1HULeb8h5uGeoB1kv4Zj1SUugJzPDaSpjc5rScKDGohpRIhkCJchsA5b09xhK+mCH19GwPeo8qWQZu/wg/tTAxzHANfG88j7Hxt/rGD8UZc/UA5R8Me89P0EY+GPUAYaf9Tlj2vr6d25MeOgyf4U+56IQ/ZG5ru6ArKAcyLGuY05msEo8dbA2ODHDkWajf8855DlnsNNWOJwRJH6hciE4toTyzDiz0VNP8yoMfI3vjC2uRC3C+apC5YK0S3S6I2xTH3++4cEwaHZoxLnde8QthMTetwt7FR/LjnhSa0VpI0UtOta+9nalsiaoNp4JadQ4w9Rvxt//BBopj8Bf7CXoCKfHSN/ULy8sYE0/lBbehoFf6Tch5Uj830Zm8wh58bgZoShYF5z9gINoo4UUxLbHwIzIGa6pOIoki//IMxVLo3tXfBN5CYN+bdYe7y/UQulmzeiHN0FTNJOcD4mXF7Cfk+xfnGbo5uxcYP+wubDg6DPRRfJTSwoV9JOsn8hvXEjmANPicPyqnxirXQuhBJtj3beaC5IXPQhhyujWtuNP3rlj3vejfRT4w9JZkTOn55rU/jZofnD4ZJ3QOr6/D1hG4w/NufAcwX5Y5a590jhjyw5nHOAKHmjdE0UDabJqYcjNUKAPgCzJVpmQT+7szS2kUvQ2AkVez73N2RRbTyTgCw4bIwRk62pIdpynHPhX0Sjzx1cwzZfD2Jg3M9bweV20ovkuP4UaS68cooHviUED6uEHjEHLkXmOkeYXJ8tE/tHuvnx5cpU5DCGKbTd+GMd1MangfXIufz0zR+cLOEgjiEjNupPwRJ6JUNOUjz8SMEf2GWza+nq4XMUN6zGy/iD3UO8S8TcnGbwx8NEjk3hjyw5PFsuufU+3IaiJLTtBH80bEE95IIra/RH6TKdKS0iVE8gsPE8YiZ9pcT4Sp+JGQcO7BiltkUDpt1LTQ5/yTwkCPXsPgj4PSZU1voM6xOinMclLqNhnoFsJ7ORELcnshZeYlL7dgcEtYwQtOlFhpRW5QuCyUcphzFMoe3GHyr+oNA7f0ycQz4kDhAnhIW1NIc4Gfdm+3of+RtZ1i7xR2Q9dBvr0fPHIFmYCpuJsxZh7Lsf6NspPpHVJsCEtueauToTqa+5wC4nUwPpq9hmWmuOVo9Rhf6NmuoLlDOXapqCS+aifUaZXYlySrk/SYO/QvetJnPtUWvh44hm7hFYL0Jka88jbmlLjFwqzeFeUKrtuXK4sIzYZf7oNkiPRsAfRTikwv5CgiuF4oOkHIJau9vXz7Uv4w/9engEcSyUZedB0N+D8Uf3vZQ5nGWBKIXU0yB8L9w4ib4rbFPMz1Dc7rFo2gSn9awTcKUx9u/W2Es4WRd3U+KQasFSaBI2yknVtCHabG05nTYltk64jEpSraZ6bnJ1w9+OKWsb/I65JyyR22KLbJIk82cssmQsKCRbGoTMkzlkh/mjCofkaIyF7pGcda9X/ijBIQK3o9BqMCR/sDEdY+UPAYf8OuGCfoy8v7L4tGXfDspRQ2F1ypYnOVmYSkKdRspb2JiZLzuNoetM19mR9JWido8p+w2TJq62/3FSqjB/HCJCgloA3P0a/ni4QPIPC40Jlt1iLQCaQ+IdK9qsOythgvRZSvYesh+Fc79Glolo3fDszlcZzTjmrRU/q9NHyOHB4UelqXEFIDPkjEmWjAg5/NH1IxVMm4Qd4I9uTSx74pDkVJMBhyw57SwyTkPwRwN9GKakfirNsujNFwzYGA3FH58gc9vJ0TfGzh8CDrmBZROF97E4otdr80dpeTKWA0RK0HLM7WE+onaPKlc7CNQXqDS2laotkW6SExTHxAKQ3q/hB5hlLWDow+OArLoA6Bcr3bFC9c2JMt0vVQ52b4qkHyVzX5Lml2sbBUndlEvAHNsIMp/flwpj9zfujhni4NKN+6hkyRbzR+z7u84f3Yb8Zk8cUipdMSVDZmPhDw/hYUfqLsLNbWqMhuKPS8Rm+rWJ8AfLIYQigZuztfmjqDzJioEoiJQ0UjHBcerfpjhwu3vJ1a41S/XsO10iZSPnz3oe3NkwwagxqZby679BaDWxNibfsYK4eIVuGF0gtXReaNPVUYhlm8DiOLrPP4JL4S5USoWYnM2J+bxoDlExJu75Mb9qb3ydVk7UnzuIGvzRwDxsdoU/Rs4hpVL+HjJpS8fEH0eIpfNCJn9s+MCPgD8oJfZsIvxRg0Nq8kfsUtVpWCD8UxScQqkbZCnEBMd+D2beqKkI/NEo15tiudrHZOaudbu4d7KmcKBwJ+JQgpipMg4EYyK5BIhyw1iGWgehOwWrvWDaxGEufZagjS9ADEKyZhP65lRQt3ZeSrVd1PiL3VacSwRsdkpeNrQ18ORrt/kpyR8Ol4fkD+U83nYOyeYPh4gMMf7onz+cVX3K/FGLQ2rxR5eqv4g8GcQCgZyiDrzbFKW5kyXZFapekhXJDx0LNivtKzrqS164vkoo573MerH7NXw88jYmsTSBlMaO0zLExoTN4hIJkJslBzHOZqtyP/CynDTLZRdwrAko7rBQPMta/+VoNgUxJA+C8RUHRCLz6ZSohxLGUe1PRHZgyoix3Z1QHUQfdYeIUvyxioUZgj8E8/hJhTEfLYeU4o9C9U6aPyJJOvrkjy6gOMxGNHb+OETqPU7kkNjFiKX5w713sdBlucNkYcrNMKDMruDMdmpLCzUxpSbeSDaCYhH3knzEKc8/FCT9WyJHNpPB6wkIjnPBRvkW4T6EZgaB399LvWwsMv8kaQlfUpHr58Iff5YfkgCV35vNhKZ8lhIZ1Ki157RdbxGZMZLq1D4DJ/ugP7G+xKDZLG8dSnDImPlDwCGoTMrBrnDILvMHU+8g/AFr4g0ipekY+eNl+P3tEvUOyB9FOGSoA0RxQVXqUBIZ1N9rX1+RpPtjnlHcrkJkUyUta0kEz9nE+hc+/06g+U66qAjp4x8D/0mWXBgNjp9ukyKqJuPyM2maPJ1Am81owbRcatIGirWDNeYstIMi3ioX+CjXKZcW8i8y1pxmimt7KhwyNv4owSHaNTlVDgmek/J/P+vjXeYPBYcYfxCypfQ6uTYcfyS3eegg6lIBUT6STF1KU+5Xkc9SJt5YoAzrZy8Nlsm5jlzZJ1kI6nLEuCc0B2/0b0lzdmjqbMulTtQHyNjsR8b2DjImjcZFABkjaXyG1vVgiODconV640JhUeM5NeZyau7C/7XC3wKny3HI2Pgji0MS+EPNIX3yh1dfeMHpbwT8waV1X7Vtx/mDC6IVl7vD/FG83g+G44/s8dmbckCUNHWVNzGkeZu1nTpPCJSJEZ0q1Rb3/Njn+wyYQ+ryU16+lpJO0T2XNLCrAmIpIBfEmFCYK8ZIgxIpcqMbsoy5lFxnwrh08qV0nWoQczdFkJ9Sqfx2AIclOWSE/EE9o3TOqlM1ajikT/7w6jsODggHQv7Y6OMd5g9neZG6nRh/4LJlUA75oBx/dOmLk/ljEAuEd4rytQmPS5Tb/rivWLAHhFZGm7ptIXxGDdGpT7nM8w8dMMcJTMkNkn1mmDmh/GO937nFymktLxFlbzxfJMhNCiqLBAa1BUswl+62z8Fp+XLqxBAbl6Q6a2pavbJnkXH8DLntdF+hXd4qgHy93v76NegH5598nPP8Y+KPAhySpCVVcEjfAddHyPzXyMPF1PgDwTNpwgaGPzRaauOPH8KloT2C9TgaDrkm4w8q9q/J5Y+hA6MuVryEpREuWEwrE9P8hDhkTorO7/GbXroyKdHVPuX2aXLMKbPvDDN3wB/WxxMvHzY5Bv/q7/+D09//1X/j8QffuHHv/33u+Y/+ld//B/cC7c9lSbpigYm7/Pr+PNBtM/0eBMClpqwV3OIptpoJwK2NlXyB30dhqQvKpuDmy8+D8O/ajKU+3KnL5KDvbnlrZF9xUeMk+MMB5uWdYAN6Ilgn28QfU+KQIvzx6fnnPvzLP/uXPgrScJJZgfw3jD+q8IefBnUUHHJNzh8vQ8Kers0YJneRXJ8aDJUvqaf5CX3PqaC9+wlE936EAEprZ/uIQ9HW1SFMKVY1wwyTTcgPxPuYSSm8MTZ/9h/9zpNf+W/+xuzc8nSlffrRP/r48tf/nksu0TTf/tKXfe1PtxHotFRf9C95gXpiJm4pdGP5+WHhfoLmRKpxReNZwjozgK2Zjfqd2VdRZ005FRtnNwe/5vV3FwRKaQZ3KSZil/gjnNMS2bBN/MHVh/GH2zR9pEgJPzr+uPSDPzr4j77zN5vP9vZ9/ujQxRA6GH/0xx9j45AjpFySPzquBf6YleCPIQ8Q0ny2Jcw+sYmBuiAFwVFU5H0s53PS5CFICEtpmtpHWQRDBLTdIerm+v+xJodyLhBC7vxCZ56J/ADadRNrBzY2//F33rzUCf8OFz970vyV3303JABHKt/3/r8XmhGhTA1OvPzOxTYLRF9hpk6J4K2+yQ3GhdpslPJP33ifW4vEpiPWlv2RbN7GCNG4FOKQreWPjD7KPqC09b4RuKC9BZpSqr5jxI3Jafd/fYf4I7QsG38UAjIusz455NoE+WOQNK7QIWwqLE1+XK7jg8+Q+erb13VOmBHteRIIjo021sytLe0jqn8k/cbUiwlz8iDBZGHqNYe9MDWePsXZbIaO82n71k/84t+V+LGe1Qk/sbXhFusPsPkGv6vGMjb+sTUaKYuK96ieElKSIlEhuEV9wK1FkC1YnvPHgrlItbdIHvKpQjIuJTlkG/kjl0PgT0mHMzg8vC6wuqUqrbaVP6J1wk/jj5445FoBHp0qf1SxQMQ6lNFguA3SHDr8EhWY2b7ua7WknvkGE4TXA2HmJs3rUMZtRqODbZJC7VBNbWFUOyXonxThe0QcHsjAwsKuKkmAvpAK/5QsFBvj/AfPb1S3J6jzJnFAexrRtok3RcJ1I9a+IxpXSkhV9UX26tX6C1P9INW0HjHy6gqyObjokQBnuUH7GpFFW3OZXCH+OJRq7yVrYUv5I4tDYMORqgxwhy0Ml6mAzqE5ZCT8IanT+CN/nN+RcMg1GY9KOGSS/FH8ACHsUOwhGq+TOH/TA1eHV5bKxIsJobY8d9KkhNxtZpPE+iJDXziSCIFupODzmkGVbPCSTOCZgrFmNo4cuDZpoCHpDSHx+NyF5pd/sru4Ul4nrA1M2+liKG5ISFuwDiXzInnzQmSQeayd7wlrgvMLxZIXsP2gELZcIDkFRwxvItolUYYe1z6vf8I4mkmiIH+8DZlTMIRjJZaRW8YfZ3UNwCGcFdb4o4XxxyD80dWJZas6RbT0RzBfyfkr5JBB+MPvo/bnPe1hooYFItqhCAlKb9ft4FshSAGoMHlSwiyWBYfcYCFEKHHx0V78I9ng1ciWIQmaLebrnigEtG16irgSHKoCkGerNbxq56df+OKjw595/cda/9XQ4jBDgv6wOp1wyOnX2DqUzIsSgZhYprXu/+x8T1wTXP84y81t4eevglzq5lpsvmlTd3bfucEI/yVctIgisX/GjlL8wWnlQr9djkM+3GL+YNtQmUNirpzGHzh/OBh/1OMPbtO+p1AcXfX5Q8AhvfNHCQ6pkcaV7VBosPQ7rBUiog19RFw88w7SBiwtIve+5DIjjAgdPiUGhsul/wFxYZTkMiWyf5hyYzhE0pFWMbEXToFGafxOgzSZaelE3SFiubzWvvYuPf3+C3/nS1/mcjP7KTmxOnPTMHLr8EVJ+QXSrHKbQe5vku83Cf2D9Qn3ec1co9ain37Tx1IQCDeLpCZN6Z+xg5u3lKzS8sdeUMaC6f9t5o9YG2L9k3ORoctLz8H4Y7z88QHsr7aRP7RYTJg/svuoxgEipUO571A4inR8Q/jpX0A6560UIRdZIFqtjTqXvnCBYv3jgveep8qNAcq/xUzukr7uJYUANd9nlW4mXTBuSl1d54g6c2/a5dbU26CZiJaf2S/cXJesgxTNp2v/UtEnsZz9orlGrUXI/x6W7/7+JnwnJvu4+mtohocG1x+UrMrhD2oOUHNoa/hD0IbYGtlPVepAbMg3CaWO8ce4+eMq7B+ebCF/NMy+5mTL+CObQ2q4MIlysbev1DSQaw9I+Ze1r3ux7/rCDMqQppTzv0uZprT+49Tnm5h/HVF/kxm8xwLzofPKL+mHXXKTpB2TXCS7ABUIdOLW1EUwfb5acdwk/R0bC/V4Qb9RWSsOa6Z+pdZiZBwlsm8+kvncB8S52NuXpg/JPiXW2nwX+CPSBqp/ThH3I3XcAvSP6zvjj2nxR6ecdRvq7yWWP0r+ANxBkpg8DS4D3Ab+yOaQ4geIlA4VCijyAYnANq7+BSXMInXWXPzVculrg/e0EPqIp6LkJqn25UpFhXhOv3p1u5tMMcwrj5ukv2NjkTResKF7X9rvglisrA05189CebkYw3zuAym52HP5oysjkJFcXN5O8QeiMDL+kI3JNvAHJ5eutJ97IaX8kfOHqt8/mC5/ZHNI1XsgmA5l8yMzAWTinLVQBnXxzCvcd0tBq1npK5d+6rgMgdI57ytru0aHocea6++KWTRy2jrY/Qop9W/7fE6Zv8Yfxh+RuWD8UXkNloLxR/25nsMhtQ8QyYs3eKguYEd1Nb0iC9MoUWtDM/RGSYtt3yTVxNTGetfn2tD1jw2FSNH4Q9F3tcZkKNiaKtJ/kxnvXZ5r13quv/pN1H0/0LahVv/ZuOwObKwNU4bN3/H1nY3JbsHG2zDIAcJgMBgMBoPBYDBsD2qkcTUYDAaDwWAwGAxbCjtAGAwGg8FgMBgMBjtAGAwGg8FgMBgMhvIwC4TBYDAYDAaDwWCwA4TBYDAYDAaDwWAoD7NAGAwGg8FgMBgMBjtAGAwGg8FgMBgMhvIwC4TBYDAYDAaDwWCwA4TBYDAYDAaDwWAoD7NAGAwGg8FgMBgMBjtAGAwGg8FgMBgMhvIwC4TBYDAYDAaDwWCwA4TBYDAYDAaDwWAoD7NAGAwGg8FgMBgMBjtAGAwGg8FgMBgMhvIwC4TBYDAYDAaDwWCwA4TBYDAYDAaDwWAoD7NAGAwGg8FgMBgMBjtAGAwGg8FgMBgMhvIwC4TBYDAYDAaDwWCwA4TB4GM2m/2L7etvt68/aF9P29ej9vX329dfaF//uH0tYy/rUYPBYNgNtDL/Fzz5/08jf/8p7+8/De/9VYZD/qn+n8JgqIdz9Yo2GMaBVnD/u+2PX21fv9O+frF9PWxfP9q+vtq+/mb7+kb7+m3vK2+2r/329W/121KDwWAwjAwft6+fb1//fvD+y/C3y8h3/mT7eha894/LN81gGA52gDBs++HhJ+Hw8MZyufx3gj//nfbv7m+X2r/9Q+87H7U/zrXv/U89NtVgMBgM48N/2b5utrzwH7ScsLJEt78/1/7419vXf9G+fgH5zv/cfvSzHttoMPQOi4EwbDt+qX39Yfv6K9gfWyH/f/iHB4PBYDAYPHyrfV0Fq0KHfw2s1O4AYTDsJOwAYdhatFoiJ+B/qn19pz0k/NHQ7TEYDAbD5OBcXn8X3Jh896X/qn19Qnxnv+Wfc97L9lqGrYNNasM246B9PQcEYDAYDAZDCt5tX3+uPQh8sX398fb3n4b3KDiF1Q+8F/dZg2GSsBgIg8FgMBgMBhq/2b7eaF9/BtyZvte+/vv25WLsMPwLQRD1I+tcw7bBDhCGbYYT2t8HgW8wGAwGgxqtC+zHreXhvwY3pmvt6732vdP2Peor/5sFURu2HXaAMGwtnABvBfz/0P76lfbnhfb/T4Zuk8FgMBgmCeeG9Fvg+v3nB26LwTA4LAbCsO34evu60r7+OvbH9mDx4+3rn+u3SQaDwWCYGP5++/rb7eutVhn1+0M3xmAYGmaBMGw1WkH/u+0B4S+3v/5q+/OfbX/+Z+1rARfJ/an29Rfb14vty1K5GgwGg4HiEhfTYJYHgwFgBwjDLgj+v9EeHv6X9te/1L5+BbIzuRtE/1e4bfrvDtg8g8FgMBgMhklhBhcrGgwGg8FgMBgMBkMUFgNhMBgMBoPBYDAYxLADhMFgMBgMBoPBYLADhMFgMBgMBoPBYCgPs0AYDAaDwWAwGAwGO0AYDAaDwWAwGAyG8jALhMFgMBgMBoPBYBDD7oEwGLYQ137pt9zleEftaw4X5x1+8PWfvT9sq9LbDn+KvjeVZzQYDIYxYwc45EH7ujHF5xsL7B6ILV5Eht2cJ9CWt9vXRe/tx+3r1bHPXaLtT9uXu7Dmgvfek/Y1a1/np/aMht3BmOSCYbwY2zzZEQ4JMYnnGxO28gBRajFOeREZ+sPY5knbng/aH1eRPz1s23NtzGuQabsUqmccG3EbxoES82JscsEwToxxngzNITnrL5NDjD/6dGEaGwEji9FNpLfb95uEdh0Fi7qB/7v3jQAM4nnS8zpxdWjer71xQtcgYWaOtjGC+UCyohjGJlNrY2zPW3BeGH8YisyTAdbIYBySwx/w9xwOMf7o6wAxUgLOFtrexKROsVfbz5yOgezGiiE2BQNuRFhhO8A6WRBz170/io0TVVf7etS+DhR1qZ5R286+MVKZWg0jfd6seSHgjzloSUdxYBojjD8G5Y+hOSSZP1w9TNslMP7oMQsTN9BDIfnkHEzM2ASceZPWfceA92Ev/TREnQKh071/t+d1cggmcB+PvUCy2utZsgapuhqk7c5/9QfBez+A97XPuJorETN3rhVk22RqTYzxeXO1rxL+GEJOTQLGH4Pzx9AcksMfR0TbP2tfMX9944+eDxBZm/WBNnMxYBOTw9BkJ9osOYsJ/HxxSzcFQ25ESGEL/X3Q5zoBTc+r7eshCM2HQn/aUutZsgapMq8gbX8bEf5LeF/1jNIN3oAbOvUYDLTGS2HbOCTGH0s4OEyCQ4w/do8/RsAhOfzRyXW/7Sft6zRYd+7973ifMf74JT1/nBvCzNXDYsQCkg4zzc4c5mM00RJmvnvt+/dq1TngpmA+1Ni4v4HpdOM7oOmmUG2dQHvvD7SeJWuQNTP7gXrQh362pQb+fyMhoE+iIJhh7io9uVWoxmCkLkA7xyFC/nhYSk4ZfxSH8cd4OCSXP96Gw841jz8OEBn/zxh//FYWf5wbYrNeE9xmTpkFIYQ7xX5aguw0pJ9JFNhmaRbUeb1CLuQhNgVF6kzdkDHCltsYDLZOaq5n4Rp0Zd5DtLHY5r3kgVT6nflAG3XXL8fBgekpMwaji+PYNQ6R8ofbrDCuc2I5ZfxRBcYfI1nPCv6g1lwo/4w/mjr8sTeQmasqXP1OWLevPfh5v4BW8jJcPJLiFyipb8OMXcAXNLZZcnW+llF+af/JMdRJjc17ie4hC2ZDMaoNXqn1LDn0RsqcF3ZLTPnOooaLnNAlBHPXmpIL0K5xiIg/YKxLyCnjj/Iw/hjJelbwh6unEcg/4w9ZP0neT7dAMAM7qo1QAiSddQE09a9yk1toMZAOWq52UZKNgPLHvd+nFaimG1HBuZCidaY0MneU7eoFuetZqamXav1Kaqk5zVWHJVJ29kZd2DdHyGVHF5g1OUYXoF3jECl/HIEVgpRTxh/lZLmGQ4w/yiFnPWv4A8bsSCD/jD+aOvwhPkBM3dc2Ikykab/m3OJQ9JF00HI3LZLNUk75fQsR1t2q0EYkNhc2Dljc3CpITFOB5tD7ACxgM+5gULIPkbIaJLjuTficP64uCG+/h7R/ojUftC0MzB3UBWgbOaQUf3ByyvijuiLjuH3/LiRqMP6YOH/AGF9CyliTf0PxR7MuN0JF7Vbwh8YCMVlfW0KYvAfC5DeISZgy2FQfvesHLis0qlmnQ+Fkn41Qe0n1o7/ZzNp8MBsCyaFrriH9LdCwaqARYLeQ+XiMbN678SlyA6o/HtQ8QMYVOzxoN+qSvomuecLnvnNzGvMBdZIcYvwxOf6g5tp5L5jW+GP6/IHx9Eln3Yd4o0H4o5HFRk2ePzQHiCn72lI+qk6YvI68/zEImwvKwab6Yj+4LOvVmCtUyYAkZrI/gE3caAIYI/2ocrdSbA7XyMQ7dFFaxcXUN0UVIT30UgH+N9r+f7+GplrqPgOfe5c4NJx6sWOhL3uJvpGsearvHpYiyUqYKocYf0yLP6Rzyvhju/ijgUQ3zQj44wjq5TB5/tAcICbla5tJUH8IHa41eUlM2W7g7rZlvdD+ZMsr7f5CTPb3S5VfEJqbJNGx5Q4JxAI6I5Oun4iTergYq2yK2rrfaH98DTaxz9rXW227bueU2ROkh16u39jxSWmU1D3E+xx2eAgTTxwoiSmaYUm45qe6EZ8qhxh/TIs/1G5lIYw/BsO28MdFQbEp/IFZDtxBfhD+ODfldHvSUyL81NzvwMY6FIg5OHDtlEya2u4vI3WvoRaKxlxOCZHOBza6gISLsfimCA4PvmXMbWZfB0Ez6kOE4tDL9VuNDbKUVLDPccDK4BDNsCRYk1PdiI+eQ4w/toI/NFxs/DEiGH9E++Z64Mrtft5yB3mFy3Qx/jiXO7CEr9kgAoU6JYLWLzS1ckgiYqSPqMDL7jOazBskcr8/NhBzTWsunzOn/hPids+NcRcsxhqbImd5oN4f9QFCsang+o0y/yatS1gfV4XzpObdEtoMS5PdiE+RQ6bKH7kcsG38QfTlI0jFLnVLNv4YCMYfLG4UyJxZjD9myyWXYpwH4+IxSB5vICFso/AQOudbgrsvigUjQv+8R9XjcozDZ94JBNuT9vWKpO6xjUFNRGIawk0J54N4Av1VpM9KE3BbHrko23KpbA5ZqLGJiJWpiFFJHh+BSXnN75ORIWH8A1kG0Ya7xKH1TBZQ39/mTd+Y5NcU+cP73DspHDKminFDcQAAIABJREFU/q8N4496/MH1b60yJ8gfFKKxB6U5pNRY5R4gSIGbG8yX8oDtdxzBz5jNOvX3s88Ff88WpG2dHxKDvuoj5u8nECdRZQy0/TvWDQsjLI6JAPlunG/2vWFWlPMZoXl81paXe3t8L5uI3DIL9iUn0Lt5ciOwdHHz5vuaZyI2d5QsGOUaq4laHDIQf4Sozh+Rz0Q5xPjD+GMbOWRb+EPDIYlxu1nI3YxQZr6rnhZ1lVYrwSUnNCXf81OhEuXFfLu4WIhnyKZNaxrCcCdiLqJOlNT7IdT+4tKAoNTP10YgHDAz/0VY1KSrUmnf3cJ99BYhhNz7NVA86Cy3zILjw7kX/R6SGvgWZGFz7g4hFglCGnNbakJZkDN/Jn7wiHGIOoHAQPyBoQ/+yOWQneMPr02Uldr4Y+IcskX8IeWQBzlrLJVDcg8QEoF6ABe4aIQFlWYq1jEx3y4sC0pn7g3fK5LZpHQmpUIBMdqFWUM4JAEhI8pHeA5Whr58xYv0ETzfjcAaptpEJQiDGkHLKRfa1PDdptaHO1x+hfAnPYF5tTFvKGJi2hLrw5X2CTRd6vkzxs1ZYQ5JSSBQkz+0F3Maf4yIPxSZcow/dDJ5EA7ZAf5oJBySs8ZyOETld4vgUJjL9jw8iBSxDus6Zg3wsK+COcdtwB765iH4eQsGuIP7/RVmw52d2cTV68zNzgwOP/1BcadVDNT7kjGIbZK1i31MaSOlWXK6cXscjHUt397sPvIW8lVPMLn2v6w8PPhldMLAvU+h6NyHuihXj0eZbdV+l1ofDdPGK5wcUbaF68OHApKIzR+OOKYAKYdQiQX65g//78+8z/mc0ht/RNYK1aZd5g8phxh/6GRynxyy2CH+OHveCIfkrLFkDkmyQASnpUfQoVci/qHzwpaNeYrpijkBNgNlNnlCmLvc+7UsHFT/zkATmpyqFJkbDcyNUpYXyTzCTHqNUnOoBdlHCi1JCU1dShmls/pwrlbPu/6A5895XtF3qfXRvpw7C4VTjaYo0pZDwn/17P4HKJPKuBMj4LFtzqJA+tD3I6Y4hLI0joU/JHfG1ABH8qsbeTnsIH9I1obxh14mF+MQmAMuYQGF7t6DXeCP+zEOyeSPLA5RHyAQQXkAE+Wm4vbeGCSm4kUFE1YYEKMWWAntoe4koN4v4e/H9S9mvhIJB2JucOWmgNscLBWLsjSoPtL4JpbYDKrLkG4iJHMbPoMdiENr5P3M5xV/F1sfbTs5WbWnMfW2r+eotnh962fQOIsL88rEhL+EgCd1JwTRh7c8Vy4ygcAu8EdCm6h1sJTWvWP80cDB5EAQjGr8IZTJEg5R8MfbEe8Yt8Zu7wJ/NC04DoHfc/gji0NSLBBZ2jYJkMnYINmRSgQekkQmbaugzFh7et8ABP1LBZGdbbQVWqqYabjEBr47/aOXynmZSe71qZllNBUaIqLmwqmntY+B0kS495M3EYq5LXGdmWdqJhfMJmBRYh4R36HGEkvAcFZOpG+pNfNMKIumdidEbD1kJxCYKn8w5XJt2jX+uFtJAdRlrdpG/qAsQ8X4IybnlPxxsTR/CJOvkN8dkj8cGIsGFjun4Y8sDkk5QMxTtW2aSvwOo06u2sBDwQQaImvAIBuArn+Z1ITzBC2V6OQP45akpYNxlwj3QYgV0VRoiIjS7O13whbAEfFepXgnam6/G5CAZA4sMjWTV8HF72mQ/EC8bohbPbn6Yxqnx4lrmOqvPcm66CFJQ2mwmj8X6wPP8zWY9+osTLX4IygrunHuiUN2jT8OIG1tjluTxOq/Tfzhb9ivR6xkW8cfBIfkaOy3hj9yOSTlAMEuLKQxnybU0QiFj9gMJZxAXJl9uY+cgknrqJC5NoaSglLie9wFCeWYpSVtHotmVty/3lx4lzjcuoP5xYgWh6rvLHg5EdQc3g/aEJsDvvb3COb6MxCiGs3kBVBOfJpxGHWb1fcRrV+n2en8sJ9nsrSx6fkEZvvs9ZfggjIkos+LjMsN9/9UWViCP5g4B2mZUqjahPBHt85d2tqVV0BlDumbPxpPOWn8obcMhWlHp8YfoTeLv/HmlNWcpZfjnq3njxwOSTlVslkbiIhyJ8zekBTuvu8632k24GdK9phHBbL3pCApEwEM3iFcMrKvzSiQiZQsHJqyYui0EJLxFrcZ+lScCWEs/Qvt22OIM5Yt4RCEbIjLmXOJm8N+G7g5sBoD+N2XEfsw9ykBTpHPFedyQGQ3E8F9xy8DadtBRPifpecL20LIwnBNU/11qYe1PwSi6wGe+zjot2NpdpVK/KHN3tMbh3RzD+IQL8Kc7YtDjD9Gxh8wF6gbgmcT5g93QLgVyFbfjTXlYO/ktfFHItQHCMHGDBOybtK+FpuAQsL1cUhkKzpADizS7D2HGSSVsxkfJB0jOZ7faK2cs9kH7esUfr6oLEsD1aFJejjANnXKdmUj8SCj3YSczW0o9yPkMxfAqqXZYPnA5vZGG4jnfal9f+aNgXauRzdVGc+VqmhoBGMZfU6vv8KUmwfUWij4rM1I18Nd5NB2Ht4fij+k2Xty+GNyHMKOp+MMBYcgZUX97gHGH3kcMhX+eCGyz0zmEOOPNMyWS+qgmgbGJ7LLWXst4dpw8nvgD4kFU7oHu9lNOKZslQmLMGOfXUcuMDlp+81l01Af9FLbscLngh59xma51GQoCcvwL0fjwM6TbQMyVt9tX19FPkr131p/cXMJNP3o3BW2E3Ov2mhDybkuXHPk3yVtErRN/bya55TKvpLPOla0z0iSkttIDMEfkfJXdZTgD+8zxiEylzF0LHacP1zmv5cj2fAmyx+lOQR+N/74up4/cm+ixsD5sMU0OClpuaiAqFkQeHYYmUBHQr9RNsgtwx+5mC8pLBY/G5Yr9x1FrEF2CjsiMOcBZCqRZlkYDbIOZEwZ8KcwQJhaP5jAxLSTZDYnrx71uHpjmhtboprrgiCvkikXJX7YS+HzUpmiHmXIvr7TS04JNfmD45AufWs2f7j/jIRD3hD4y/fNIRJ/codd5w8sixmFKfJHUQ4BxYDxR9OM4gCRkt4qRwByhH8VJkc3URrhAuQEZa2Lmx7EIvoVQugukkr3giIF3pzYNc1/XJFBCSPCIOiITBnnnnUsGlVEe6EO3qPKQLIvaEAFjVEbHTbftASCzbwEh1oSQer1kwyQazK2ZgjtXfSQyz2vVyaVaz5H9tWSP2PCCdF3JwPwx2mw2Wgi82Uo/ohyiJQ/4HNhOdoDAPo8bWde/YmgT7lCQg4JnoHiD03a0uow/ijOH0kcwsD4o0mTP8UPEJr0VoRQCN00YpOCO7BgAj3c0GpPn8XTu8Hz3wqewfXD77l2tH+/h2hfOKKiNi7SDQ36jH9w+YWu3lj9JPwxYEzUYWaGQVA4ZSOlkUs9PDh8ivUPc1g+GjJjQ6x9gk05eoiLZA4hD35Embc8jbL4UsxAhjURN6grGYTYe3rJAXAHuU/oCXWzcmX+8GMpsA3t4PzBcEjnoussI3cV/HHE9Mc8i0Oef4Hq05L8UfriuWQwbjvGHxnQcojxRx35k32AILQaaHorRtuB+co3kpSmzIElulihbq27lerkK9T6UIHnX4Gf1Oa/luvCxjN+/9z55Tf+5ZdzNFJUfxx7Od+zyi4JaOdxxFyuObXX0BCz6YGR+d7kaG1KmOG59mW4RFBrsmG+wwVz34Ay/ftsyP5K8NXmUvjG+hd71rOsTWPQuGpBuGa8MiL+QOVRH/yRySGN5wev4Q9OVkk3GhvP+fjcheaXf/JlSf1a/qAO+0NzSOgGFsL4I0NeKTmkV/744Os/e40Y/63iD/UBIljAj0BAhb72d72LXtYC0RBQm+elt6lktQnBgUUk0L1Bo/AItEtrg6EYKI3pkhIkkmDOeUEXgM/hAqVns7Vn/MU//W/Pv/2lL0vrR8FofPdG6JaBZYLRaqL9uUGd/E8yLBGa+rPMxyXM8JkgzczUc7Wv2CVMnDsJJmQplzFN5qZYCt+oSwc8a3i4ORiDxjUGgcvYKu03/D4Ef8wlG74++KMQh8SAfY+SVdLYnw0O+b+ef2HmDg8Ih5Tgj1cZK9IgHMK4gY2aP7g2GH/I+eMa7VVyvE38ocrClJgVgY3SV2Q9kWY+ITNxeG4cnP9kZy6fIbfcqjKdcG3xn4P5XFKfENrzp10O5ZRKSmSxipSRnZmBA/SJxmUmtjA25kNqpgf4PdxU3Yj45UvrT74NPmUu10JK/cx3ugvoqDWnmo8KGSbK1DOF8UhBYma2XvlDOG964Y+eOITiD2yc3nSHrRHyR5c2vNp6SOCP2HiMjj9qjf0W8ke3l3yX4gn4KapvyvyhtUBoTkpSM6L09kmpNoEyRz0Q3kTt8DGiwU8xh0oD5g4TU56ip9EcTQEDyvQl0vIJbqMMUezW6Arac2ohk2ZSMGlyYxKWtSashS6B1PrstAvXA2LRzImhg3dTguaw7zwBX3AqHigl2Dwmw1ydrxS2DAw9HimgrAVj4g9q3jwFa/tBj/yRyyHNjvDHVeBLbfxLTf7g5toz4jA5NH/E7vFyv+86f/h7yX3kO4+F1u+t4A/tAaKkmTRl8xz1v2TcGaSHn4cFJ7soYIVoM6Y1eALkdCW2gCXmLA2QNmIauFJkn60xL5BSkHQDY07o7LzJGRPhd7n5eTEzLeOgwbspmxrCXPsFxmXOt1JqnjW2efu4glvRFIOpR88fzFy7JExCUZI/cjgkdC/edv7oxruzHJc49OTwB+cG9jLRrqH5g2vD6hABP3eZP9wB4ecIWf/Mu5tIwyGT5Q/tAUKj7Qm/pxEsexW0CRLh/bhkthooC8smgmp92h/3E7UGvcBvI5jdSpC9OLtQhsk5JYhNlQmmLyEZMaXH1mdOELxYg6M190uRQaB+m6nDw7I7GII8Ip8Vez5wJXhPeddADkqmMewLC2ZTpfIVj2ycLyCXaGUlC2A0ijX5Q5Tem1oftdbhyPljVsItpgB/HDKuQNpDR8q8qcEhxh+8EmHP60dWPiNjcMzc3zFa/tAeICjT7kfwkNhFL9FGeSRAZdMQCz/K3AhtO5D4YHIbiAShHC46qb9ucS1QYaiFHUL2JbJT5MTqaNoqGeuqmzpibh97SQsewXqMBX9v9HVsXkv7Q2vuJzZr0Xpi8MqVKjwWkmdl5MuroH3uRctWyc2kNmIXsXXrXqU46uRkiRig2vzh1SFNwiENxNyA8UcatpU/mPnt0vzegzZiB1YOxh9lOOQW4/UwWv5QBVE7wMP7Jp014Zyq9SgR1AF1U4EtWLYCMrCN2dhgWmnUP63PwKG+tU1cwJe03lr9IwwoTApqHHIchM/1xPPXlmh5O7cdaYBecqCjMFiz25TtKwJpky6DSyyfCp5l+1HR9rEfArJQiz9KyJPa/OFtHkQc0nfgaZ9z0fhjmDEQBnf/XpBC3sH4g8djhSyg3CFVMmYM/JF6gMjaOBLlcpHoy1jnCLQGroybOZ3d1vEh4xf/guKZnLvEmitFJnFyudCLaXWJenP680WBRjIkX4nmLmsujRXabA2CTfVq3cLnrhYSaOK+V2aOoTIgpWT2QTXHmdrI1ZqG3N/dvSaujreo7CW1ZOmYUfOZI3Pv4dD8oeWQvvjD+z5rvSm9WTH+GC2HGH/E++jEy8gWsyJKkxusXNymxB8pB4hRao0F38dMzcVSe7bfm6X2Ve5EEDx7sbSCpUFod7H4l2NFKrpRpLisQLjSDXe3mcU089jBTJMKMzX1XYMcXLgbgKObJkV9aP2F1layBWIs87RP1HxmoXY1lT82Ngzwvmp9azikL/6I1KWWv30ikHFc/MvO84dCnhl/9M8fKVbOwfkj5SbqWum4JAFSXNBnrP79wCf7OnKBUXKqSxjM8DvSADgqy8O7lO+48tl9c7lfvnMlGNSFh/AZxoK1NLdVF7+tFznosNprbSwAU5dfhzQIfcH4WWLCiAucqxHo2I0b5VfeKPxAtXLnIazLI8/vl5uj2uQLmmwtQ6c2HAI1nzk291L5I0z9u4o9al/L4BLVrFTJIYf0yB+SDG4a+dsrf7j6I5reEvyxhAvCPsiMyRqSP6hnmyp/aOotyR+HzJ4vtXy3rpup8QeVkSRlILICPaDzu0BEziwyL1B/J1CwwXoNJuXMW7RuMXUnRArhd95QBMBRz7Qf1N8U7vsDQdlieEKH6j8KkhS7mF8y2nfeXHIpC7H7EF7MfK59wfNxm0lNXWd1IGvkBDY32s1siENPgPnlnKQEDwZtbAoIOWzTFGvLEinjJSjnlmKOLhiL5hKesyNVrUCvIktHjmrPHMy9ptBYPAM5EiYmOI8oZmL8oeGQd+BZ+uAPSf+L5e8A/CHhkBT+8MdqltCmUfEHIZ+XE+aPw775w9Psvy2cp1T5J94zrjiEybY0Wv5IOUBgk+UJaHdP3aks7Ej3f3gf/bs/edwAgavCQ2XnUJN4qRQoVKqyBlJ4ugDV1APKDLRTmgGPCg3i2aWIla0tCxM6dyPjLyEhR+SNsu9+JLE/NeR0Vp4/zxnNSOxZWcHtrxHwmX4lEETHkexDHGGGAu0OsabQ1K3esx+BRia2jrn0dB8jG3TNun8zfB4oQ0vMVPkvwxhcC1IgYjglZB5V9phTseaiGn/466Mgf7ysTKPI8YeGQy4gh5Za/JHDIVQ61j75QyJXU/gjKvOnxh/IPuvmVPkD6uUOGTX4oyl0uLrj8XjHIZPjD/UBgtCCzkC7u3Eay9AqqDqHmcScZkkK/yKXW8GzN8oDylVECMaENys0BFoFDiXNXVRZB5Hxj52YXd+8BT8bKjWiTzLgnlUqTWzs825Mf7v9ec97TgqxZ1VpsQMy8LXr4vopkze1ppCYE2yNvwcBow+YcVswF+c8j2zQqXY/h1gEbiMCOql/JX0QWceoJlhZNgpn6WxfnznfevjpLJ+jxdT4I0LsUpzNLYRDNDxUhT8YrXstJWRp/mgiY5TKH5xFQ8Mhxh91+IMb94eV+IMbz/mu8Yc6iFobyJET6BFsbJy/dCO5RVOR6ScMCiNTlYVtjfhcYqkoQ6wFx0RSCKqCYpBUiRyKBdwwY83WS/Tlxh0g1EZXMBYhOh/UBgjzTmJq0qZ08FXmelEHkfYYgEll1UqqO+W78B0qcLvIOgABTF0IVDS4janrm44AS9VTE2PnD2auPQ1iIErzRwy1+UN6h8rg/JGbRSpjLKSZ3Iw/KvAHfDeJQzK/V41Drk2MP1KCqKOnLk9TwmV3ob4nCZDSXk5FbWDeF6a6PFSaJakDChkcA+1piEmdYpKSCMPS5i5pkO/a+HvPzgbPdfMhMYaC0podwGVsXHCa5rlyU8eKb55VrikqjaU28FdbryvrBib4pONOIKXdR1RaTFevoE4JMBcTscxTwrlJUu9P4gAxEf7obhqPXXhYij+ozHm98IdXFnWzenLZNfhDKksK8geVmIXikD7545C4Z2Tr+COTQ1LbXZtDbkyJP84VaAQXCZ/jypE02JrsAZhAQQ4VKdH1q7q8skT+hJkbKq1AZPOip4BoP3Vpytr4M8JdgtyFdR4ylqBZeZDnKpLyNETuzbPMWuTaQPXdPLap8toUy4ZBjk/GuEfbjbQ51o4S4OZi6eA2ysLJWT7Hhinwx9sg07E1VIM/zjK9DMQfXRmT4Y/u+4VliQYXwe3maCj+8BCWL62vGn9EOCSZPzLGPYU/DnvgkPmU+KPEASLldK3VXFCdir2fdSJWTEZusd33y2JMdCWFoHQSVs3dHbYfIeSuDcmndWRhU+lAw9ztc0aYstok/7mY8czVSmBztwuclGgEqHsIDhPm8dncFGhwDyN3OtTICsG2m2gz6a/pPl9oTVDtUs2FyIEt5iap9asfEsYf/GZtCP6IcYjLRmP8gWNI/mhAZmAZwyT7nyr8EeOQCfGH+/8nyD0jjeciuVP8kR0AhQRycEgK9FCmq9IcNnKgCdLrO1qeCxyP+vg5AefcB+BnVppXTaCPpG4i4OoyktlkI9OBQhChmTG8NkkylqxulVTO86y5mxhUJZmb0cxQkabVmOexdlOHMQyzgtnIqLnhiEcEYo5jgaMuMBQD9f7oYPwxSv5g008af0SxlpEn5BAiMLgEf2RxSEX+YDlkQvxxESxiNXGItGu0/JEdRB2COV0nB4AwWuxBbyHWXA4jPBGWbFdvgUUlIK2bGd/o1fJQR+gfSmEJKe7CNj2BFKNXgmDsGSL8VX7ofc5dzfxlfNHPbofmxgXSzRYHt6Yi/vOa21dTL43CkhiwAYEp8wAC4bpLspyC4C3tvBsTjD+G54/MJAXbzh9SbwtfnmAJQmZeEHYR/hj5/oflEOOPafJHjQNElcwgUgHKLNimhuAdUmhySCGc2MSrSWLSSS/ZzCoXp38IWKuX8b+VQJslhgvCLD6XSh/Kx7YOmDZTZtvO71zaJyx5MvWHmXqwTU7WHJ8yjD/GwR8wFip5v0P84a97yl+8kydUZqwa/OHqa/qQw1p5L5wbxh/NtPijRAyENIpc6se9AiFsoidoJGCp8TpTfB28AiWyEBRH6cAigR98LqSmV5HPZYEYjQdMOjUJXBaZJZUqlujPWxINQw68dYX1ITVvqY31YaUAzhKg2oxlR5PcvsqN3Ub8DDOfqYvG7iv9c8fSz6Pkj9R+Mv5Y74v2RxEXzB3lj7czEho4/viM0gwzPvqvYhnDCh7UUvgjyiHGH9PkjxoWiOzTT6nTaB/mvNqnvZ5dn8j+gp/V+lKh5cZMe07Q/n8pOd4ZTY40D7oUzvXpFc/FZiiXn5gJHp23Y9m0atpBfZZ5X7SWmbGTrJdGUD4p/+D30WjqSqOUPC3BIcYfOhh/VOWPNS+OqfHHFDnE+GMYC0TWCb+wVr+PgOoSz4uiB62NRkvgsiQk9aVwwbL3H0SE1znvUOH66F77+etSlzlM09Z+n3reVFwI5i/VbwfOjQNrewEBLEnvu4j1UdcO6KO+/LE5i+J1zGpDaVAZzSq1lmdtHc+gXjYdLGBOxM9QF41tpDSmrDmwcRidxXOE8rRmfnrjjx75Qyj7tp0/wvz8k+KPITkkeO5HkHTlgjfe7h6ou6ECsjB/LD1vBFfPVvBHjQNE1N2hhuAmtMjVNveR53Xa5kugTaMuHap52UkSqIkHf6b8PReCBfu8l1pu4xAkvP9Ac9mPK+e1ttz3OY1zpIxYbuoUrOYvtIfDWdsLHyZjZI2uU+149qz1uhhsHHLa0210MCG9J0kHC1gQa0l80RhDUn1lmRsKJfhD3U/GH6PlD2zdrK3xLeMPKkar6d6fOH9Ex1PYFhGQ58ZiGs8HB8ga/NHhYJv4o/gBopAvG7nxR9xYXEacJsjN2/kDcv7OtZ6322D5E/I/h8Wv3eT0vllgfDwxobbWl4SLEbZg3XjcbT/PBaOF9x9on3kGdTSJgpPzoe+03V09/z977xZj55Wdif2nik2apKjYZgmJncwh82QgnomRPGUUpzGeids2whhGgKRjiVZCwQIUijGBYOJL5WXykHL78hDaao0wMlS2muJMY4DA6FRixx3HhmHodZAZeIB5GvLEsRtQsZOILcqkSJ6cfbR2aZ991nVf/ss5/wJKJZ46//73Ze317b2u4d8fMoHXM6GaZZxW9E7hyyQHamhhKKUApuIEShW0koBf4xeq3csazeGE0Qat+PXGfXDAnmM+b0kp0hkV9IWm5un+ov0PB4Yfh3CBPRVpTbcBPy4hloUG1sQX/9wk/HiNCbx+ugH4wa3nSV9axo+YRvxo6wLBLLTJL1m5+Y5gA4aFUrCiHicl0GsGFBGmubvIIetUIpNSG/ZZaN3IGY9iPbgN+NDoHxnSXjBPlMZlmqnR2UsVnMJB5oaQBSPmUW+V2jeA2bTCYWAfSWO7EpsREoxPm0VkqtF2AVn2Y+phx/wc9JvLrIKBppjC0BqYKsxfKQ19L6gSfnCW4e+O5PEQ8COWJf6zW1uCH1Lxz43BD3juRSKBx9s9wA/MrYZLOXvLUI8ntNCP+DEA/Mi6QFjNYtrvM6bQA0KYUjRNyCSRS5YNKX0XW2yxarKWlOsxFQSsZ8yUm75EM8Vc5GYGIcFP4h3q7zAfoSYtzsKkAbNYoyxqnpWHq4lGmAuaQ6m/FOh6MLFo86S5UvmFSpQy3lKJGBDiii65lIcxf0lFqXpJtfCDwRBrWua+44c0lhE/5LnoHX64wzjwLpWfvxP8CC43oQXB/b4Wu0wF7Vn226wD/JD6oqLLW4ofuVmCuNt51vchiOUyBJM4up3ACGazvmOEzErMlney30UqQ2Ilxrn5lkizHlKJdr+xS5vFlxYnvxbQp8Mgw42GHjBzfF9ZrTGVPoY1uxencGWqTXIaAewZLEiQGw92AT9N8I/lQhj3lwzwQ9qU+Bcb9zyY27eQv6doVCzjncMBIrtSO0EabeG5aF7fX/Tlowy51QVVww9HsOd8LNw0oaZL3/Gj2SL8kPzGLfjh5Uef8cO5Ov459PPP4d9d40cDVjnK/Sgmjq/mTJ9r48djUOq5PhyDZZLqy7bgx+1FX/43q+zKvUBYzWIpgW0hU1vIzASGTcSRtLlDJhL75y9Si58dZr1Ku3lY29sTgOLTxY8T4g2AmPc7julpcDD0/sfhWlyDOXsZhIBEZ8DtDROcTYIwKsJDCLA/iOYnDP6jnrkXpaTUgLllvTkeeBQI4LgfKYcg8l3EuK+6/QD74oYwL9l9gHY9b3qLR41DgzR/nIbOu+PU7FdpGvEjHT8a2IPbgB9O3nwn+LeXlan44ZSSn1TCD3cZampiSIf4URJD3mL6Uhs/ri0+fwEwxKW8fZXpS24f5tE+7St+uP58Cfqk7ltuDIQ1oM/6/VS3mBOXEZgArQ9rdqARERQXpg3zFApELZUOoNS0J6UciwVrPH8PYOwevHbh34+iOVnJ0e5uwIJA07iynab8mHPTCjKUwkPPBRfkXcokLJjENYLdwj9dPnp1AAAgAElEQVTUd59SMRMBUX6WVIC5xhKXFVegoJmiDgmWe71GVjTJT1XDo9WytRWk3uOH+4cBQ2rgh8++8rMIhiz7ZjjsDBk/LkR1f+aZ+HGuEn7sGdekKcBHbeFHKQw5BsXPjS3Aj9lA8UPVt1wLBGsWK/B962HO3R5fhtvlnQSLQhGNSqj1CW64sbboQtgXpenbOn8SadqTTNAhUMQ3fWcp+DbhNvNAuPVza1EizoTSYlGf574PCzL2fKk1CXNECdFJwE8W/qG++4oEjpS2a/Fz0/D+Nt1GGuXctJLVRqEt1B74+p7atdf44T4wYkgN/PBWNgxD9owYMuJHOfzg9mCWFZt557Rj/GgKYYjDgd7gRyEM2d9A/BD7Zq5EjWhjjrAiTobn9xMywHgzEFthlLjxkdWTme8vn6H6KpEwlmW72uw4RouKtm9oe/C3uF8UUXOaVFmWWzv4rY2HOY4Crzy/nE3pl0QanhP4LKkfAahwGU98JWMV/5TmNalN+BsXfE61V6QyszReqzwpTVH/NC6drfSrj/gB3w3TtZrxw7rmzHefarN0ZWAIVUhtZVwjfhTDj0MiU1J1DOkSP6xeHR3wmwk/SmLI5c3DD7FvpxI6EGfBuMYIX1OKPkPu6DCXMsqU0MYl461qn9lESRmPhLH4dr+2+Ik3+xksTV8hc5u2vQPl5QHL583VeNDcgrG1CN+jqdLI+apSBXtygxilfms1DqZ+RK4PGN+fZGGwuFNov6slqk3isrqnyHdfIr852zfj2lYhRPZK1LvUrlb8YDBEBFt4ztVTiMkpZn5LiR/UXqL2cJWseUoMwQ6za/tgxI9i+HEFDqhmlxoFSXKmM/xY/JAVmql2td/tCD+KYcjdzcIPVd+sMRDqiSbAQis8Od/VQ8F/Lny3aXNZNpHw7jh1oLRolKbAmjWkNHGBQTNGCxCuu1hASLEW2HvCvzWI8F9qHxhf1R3E5zZ7M3P9DnhDuv0n9cMLMMbq02eXFuqy6rNE5fruZpPEk7kkaLAomei12/cDV8Iq9QoKkAmoC2AIFif1hcWP8w/X4Mc5C4Yg/IEpT1gMwXggM0V2l3t+k/FjCsHYxQ+EVN8DLfaIH+Xwo1XXosv9xQ+zNTjlAmGZ6JxbHbdwaICQ4t2qDW49hEWLFgdMp+Qf7hOJgUEIaRhVtWm4G32wTpRZ8GPYsNRlMATnopsZ67fiUOJ9CUv0o3SwZBvE7Xnub6XqY1jca4ofzBWHZWoOnJ98bixbW2QF6hoYsqO4hEgHdhJDQv4ADGH7psCPd8DdMpW63PMbjR81D4Rx30f8qIYfIoZsGX7csLz3VMWDiQossMVh3qMFEI5htH5t2oNJuGilLQYrQXNaRi5IKSY3kVHdOEBwlxgH9b5L8I4jcJNYG0OtzZxwKEHjazLWuzNTaQZxe36WOlZCsL67+PxWqK2Hv8XfC91CLkGthVsav9qYFGspHZaHeCmMyToGbUICbF5zMITDD0ssnOZgIuEH524p0Tze8y1jyEbjh/ufFjHEjB8Z671N+MGOlzmYvxhdDmI+2Qr82KkQaa5JxbckKsMFkXvZeqOkAkLuFBxrqmlZE7n+OMxWQM0VfF6FYK6sOfbZda8wDm4DeB/rwwJ5nquZ8502jrg8JM1T4rp1TftIQR+/DySLoTW/+Rk4rIUyx1c5lWgl842GlGvJHWSoDFpubs47Tbc77NSUBYXIgh+5sgR7l8WqRQYUFsQQLX54d0sLub3wVtjXtjFkxI/u8CNnvbcJPxTjpQ7m16N5vd5j/PAX4YdcscUUDDFZIIwmO80tllocn3v5vUoBuKXGOjVYEj5mbqsNMP8Dxoe5WLCohRI0LNLclx4HF/ju277CmMyrk7ApZwX9xdeCTYPPXaXJg576xof7zZxFQ+BRzR6Ns6xovm/hV81achq0d0Aevoa4u+zlBOi2SQkuH8myBHi/ScSQIhpYxXi1+BG7W96HAPHTBvzoBENG/OgMP1JiVrcSP/zzi1/Y96g9OhH+3Sf8CBWpktXEhCGnagkEWFBn5nkdBLgzw/7p4udg8fntIIgKo2nAEGZBXso3UTFWbtFW8h4jGuYPjP1rLVg0hzJAM2kcisD3pLZTTf2ESx4VOL3mYqDVSBOaRczMmiUgOuYdd+nOJc0eTSELT2l4nrsIr2XQAq3SXtsKhQ4OlGGigwdwUPaHGXZeUzGkpG+7MF4tfqy5WybKp95jyKbgR2EMOZ+AH1Y38o3Aj8WvOM3sQWZ/NwE/UEUqYEiWQiG3EjVJsIjXAu3PLpTK9nSJceWZ5QrylnwTsUVzJrOPpGwoCf0bjA90Imgmj0MREDcrHJBkeo4LgmTaYzXSUV8oDYW/vMefv9dHEEidd4Ekwcrle+fIwlMizwey7n0liPT+MJhDRNDohaAisYgfORiygfgxGAwZOn5UwBCp740VQyIl1EbgRyUM2Vem/OU+bxM/LBfhbAypdoFQ+ndOkEl3/57CZm470NWkSSipqVJQa4FNlQPtjoic5e7zvsxRqqn/oGDNCVEjHfSF2vBU4OVuTzVJ5LznKBKiZzH3j7A6apxF48uIpt/KU1KQXvhOMrd8ofoqQ6Fi+NE1hvQEPzYFQ4aAH6UxhCKNK/dtwt1mE/GDm/dbgdUyBz8oF/SHhItQVfxw/9MlhpgrUWsIOklp0ric0Ms+RZPUafAOoQnrpF9tZNCwjhe+r/Y7VFZqTh4nskHMFWBdQBGlYRCqZ5PPLX4+sfKQsI9O+sLMqUTqCpgt8V7R+WPeY+XZNyNXzLcX3zWluyNc2xpCAz3/yT/7ozM//yfvNd//0XHzlxf25v/oh37sm7/57//nPxx9N6bO5WUJGvFjezCkBn4UdB9Kwo9KGJK01zX9aAM/eoAhRWWlBRMuV8IPuNxge+3xT/3Z/zH5u3/ytS84/PiL5/eaX/8PfubR7/71v/1byMUna17UFwhqAAUq3knl2Vsp801RX/vVh/ES1R8dPf7l3/uNd376n/7BSg7kZj6/Iwk0gn9MTJ3bRuqac8+l1pxYtPmE0BI4QfRK0KY1kEsEs7Yv0cz8URacpD2oGU8k76ooNqjx/if/7H9/8D/8wVvPnX3y+OSdD0+daX7xx2803/jBH8nOj98FjfixHfhhkZ8cfix+rt39FQcfqzLz8i8cYRr1+ECcK/9LYFBpDIkTsQwGP3qCIc2m4Qc13oXyqfmV3//NZoEfJ599cup089996fqD/+lv/IfeBbQIhpyq4FdmMcGFppxOfHoVwGbulxYse0qW8aLVHxcMfPqn/vkfXw82zWexAJPFP3/hSPLnK5EpJLeNVFM2+VyGG8UOVxArepfFB9NiqqTm831LZg7FvqDm71xh2cDyh0IJYnKrYsaN9v+/+dP3L4TCf/nCJ48aZ5FALhC9LyQ34sdW4YcFQ1D8WNDp//T//AOfVnnlzLH4/P4//qEvoe4ZBeV/CQwqjSHm2gE9wo8iGKLcF9qYt87xo1kP+EbHJHxnbQwOJ2L8cP92uLK4QMRfz8KQnQITFpMltWl4CxPrRpQmZX5dU7+UbfaZLONF1xoYeELwi5QbvcRFkmzDrYO7tTtLCPxeW5fUPNjBc2EBwHispdbjGbInLcLf4ofJzb0vkPMhx+OafUHM+6EmWNZIEo9plCBTzT4Xxo32//s/+hB9oTNHDzTmoQZ+OJ44HPFj0BhCrvXND/7hHsYzf+8P/0Ej4AfX7rQN/CiAIYegFXb0NOLzIeKHBkNug6tPk3OuQub9uK/4cVkxJsV3ZkqcID/H2igdRG3ZkNq0V8tS8R1XP9RoGsR+KQJTWk+vmKHFsqwDutYMo4bpeam+lciyQbVxX2tJkywGwvyGc+eA8DCufGwABGo9yMxOAs1hHhostzc2LuWe3hMC61RavXjewUSbksKQI4nHNAcNDICxDCXcuNG1/eQL3/Xw/Kd/taZldb6sEdWWj6WoBn44nnA+Lt6XeMSPfuCHZS3ItaYu0Yt94WToVaFvuRiSjR8ShlDzS2SvfG3x+Zd7gh+Oji34AX+T9rXbz9cXz3+Qgx/xvAN+7PUQP2ZcwPfiR4Mfd7D1dTFz//qDDycK/MjGEK0Fwg2W/dwxDiyWmzxNYMW0xI29NrBJ/UJuiLs52g8/jznVZTU3W4qM63CErTXBqCf8AhlIXPXMnSBwejlmyHn9qEDWAkxL1Rg0oanzi23403HlY+26UuvB7EmO5gC+57D+UONSVIbXzGWqVpD6u9VfNyTJCibN7UNGdrr9/y5YZJ5xKfWotV0ckm4ufFZX9pWLgfjVL77iNZJDqQ6rxg9HwH9u/2tJLacr0YgfCBnWwu23T7E2/r/vuuBqfmA04/BDqLqrxZARP2iaW/FDWRk+zA61Dfixz/RtT4Mf1F77k3/z33mLwY/jkjJSa4HQpJHC/Om4hZppb+yZ2hCOVJoKQRutjfkQD3wFcxhn+XBq/PUDTcna+jpGjYN4KAGOjNkJpsfA6CkaFzL3++LHBeBluUdBf7Hqtn5+NW2ZLFLYesD4LMkKJK2HF9xcZfi3Fz9YEFZI05y9ZniuSU0lqLCC7TPyzPMSl2/7DOHbjV6mF79W+/+VefO7P/RjL37xX/6T69/34HjiLuRuT33jB39kqFmWWPxgMKQJMuE0OXK6EoaM+EGQIeYLVTb+93/ntdO//r/+j49PzZ+d5jCEwEys6q56vUf8SLK8cvjhK8P/F1EtsK3Fj7uftcFhCKmFDdo56c/i1+dj+Mp/1PzDH/qxhsCP1Fia9AsEDBbLheurSmNuOz5HN0ZqbQAhIJzP9a0CkyECm4I0B0ZtmyWCt9oKSCcvTj7Q85f++Lefft937u+EWZiU7SxBIyc4dG1TLYjZsDMjL3JWJk5gxd9NpmBPatMlP8y4RPm/Pad4z4pVMqq/4C6G7IFAuUezXQMx/jAAROoFTjoIff6+n/g593drtfpekgI/ZmB5wOZypxKGOPeLF63pFCMa8SOPDiJ5cEK/+9f/9pkvPH1y/Gu//xsrmYcQDKEw88uLtX0htWMjfqDEuT6x+AF78EcVU49ZJb3ciIO8B4sfQd+0+K1Vxn72zp/4OffOt1Iv0cULyYUThghk6kBFWR8eFjioSj7XWgqzvLA5qAmiDowpqbFKHfxNt/VE7RzbJ3eJWPxoovs5M15YLbME5QK+ZG3y2mnNwTLFBWmFBC2Gu9R/O7biKC5R1N8OFGZfziq5B65pJssS9PlFonhUtSxtHEAE/WoIa5RU8ya+jFAVzF/blFSfAn5Yc9BbA0uxfSv5XGtpxI906w67d//xD33p4q/93i3pEjDiRyIJeLCsV7D4WU3FTltei+KHwrNl0PjhvwOKcMnawOKHYImrarFOrURtSdXaZB7+uQVOvkESzHmu4KE0ZeFSzXTaPu0XdJvSaNopbXS4CahqiE3pwHODZiCFF0/StUbvuA9uP2ei7x4FMUM52gEr/0m8Qf2Nslys1LoI3onJCDcH30rQDC4TwhPkA8JRquj+GPKT5sLI5RwvZXkcCuXih5MX1wyHf84PesSPbvBDgyEjfnSPHysWOkLe1cAP7uJvrd/QS/wAulkAPzrDkFQXEc2tTQqk1gav3i/Ql9zUgiQVDt7bzwz+SukTOQ9CQDfWV7TfQnAVx4NFtAPhOBb/vGUM1NRc5J7BeG+DUG+CID93WH4VSU16rUSqXyv/cd8X2qLG7r73Moz3TiU3uiQ+yEkmoCVkzpyF5bFxDyfPVYmkCx1QiX2tktUwH88q9GXED2EeFLzJYciIH9uNHyUxZNPxozMMUVeiNlb7cwL77yvKZktl3alKlSEdp/g6ppaar00t3Hgtpd8fIZoPqtqiv+ituacw/OLdWaj3Z1drZYIzS1UodZt9zs1T6ermbfOIYE5+C/Mjzx2jsi12v5bsg4Ws65PaT2JNeh9orcAPR04uPaF85A34IWn3RvzoCD+IFOhrbr8jfmwffpSU35uOH11iSKoLk+TnvQNmo7PgS0e5qHCaIa5SZUgXEn3lS7kLte47Z2E6BTNyZuQznEnM0FfST5V55pHV8pLoLmHKToW4QJ1HxiG1maUtyM3UlSKgEty/kuNNkP65gNuwsrl2v1ZJJiDNX8IeTp2robo+aeKE3Fp/JMgIDX5IrgEjfnSEH4a9MuLH9uFHslzcQvzoDEOSLhABM3AR5F6ocAGGqQG2sZBKAcwSGTR6QYxQeDGyAmHCQhv0u7YmBkGizUwU0quFtCIaHrqkvYTGmxs0cNb35gS5ZxUqzAEQi2BLjTch+udMvH+FZACR9mtxJYE0fy2Ca1vZ1oqTEj8aRXDhiB8Dxo/g3RLfj/iRKdeCecae6x1+pMrFbcSPLjEk1QKREkHeFM7hm3QALBhQq6KWzIW3iFvk69Jh0wDmK2smgE6cucEHUWmLt9wrOEdaHiKFoLCG94k9cL9SkGJSocJSAGIhDjCYOSXT+kakyZhWQ0lAVg9F5GE1cO2zFVVDijzokwLxcSN+9BQ/GAxxRRhvha6wQ8cPQd5VxY/g3dIlr3f4wcnFDcSPAzg7hZYSk3WoCwzZKRBBHgd8WGgqBG0405OWzMEtcUXLSpeHqkE40BZ1iVMdNmHcLohHQ/vCRriOVDK2UGkrkBTszQZl1lhDY+CaNmMNCTjRGJqutdbCnGr78bHyUF66OjHnTrFnDfbNCWArlXShQ8L6bwnKc6meR/wYJn5wmdpWqhwr2+0lftTAkAS5psIQqj8jfrSCH1PCzaomfmRjSLIFItI+hJo3rnJoTBPhlsWl32oG4Pvbho8yl4mEij/Bbpf7yoDDOwHDY0RtAOqm6zQBK8WCSl7kEEsT1seGGZO0hk5ThtFFqV/wvETUPFtIAyBtaq1JLb6Q1tc8L6kxRQyluFOgfdUUyeSsX21ZUWsRgR/+Iqy1bI/4MUz80O7hoeOHhCG18YPrlzal8VDw44Cx6PQdP55ZvQty8aMEhmRdIAI6l2nVoA7V1sOTr3jYF0CdVjwUatp6G8mEhd4uFYJyWQY9+Ld2o/o+XiXMgUVLq3NCQMjGQAlBaQ1ru5FoD6wc4Ej8NvfWwJb2C9Ufizuk5L5Si6w+30siXCwpYN7T+qBXALguKJwDX3AwrlreCn64//QIQzYZPxrjYW+o+CGtYxtuiFoMmQ4cP6YWj5jE5Du5tE/wMVXhuyp+5GKI+QKBCNfzxCB8JWasEIqWKazavvu5GQYKU5fCwWl7bizG/oExu8adCiA6I266R+D7d9sYZFutEBzWd2ENKYGAtZVCWPuNkae4fTRP9blsWYu/Rl0c9AyZuDTaPY4fVT7oQyMlfpyJNMsaDCmBH7MSWWoK0ogfw8cPaR1r40djiCGZDRw/ninOmZ4mELfWB/zYh39fGhp+mC4QhHClyMUV7CizyFjMoo5hv7n4+eHoc+/H1ae0hm0JB1QzQz2gAUnFrZQ1sWLjRS4oJqAuAO6c4PGF4A4iQYLN76eLn7+2+O4cLsp/uPj5gRqCiHDziEniKYpHHiakoC0Bzlh/rHQR4YXblsNEKiGZuFKDFKUDblLAfF/JiB8Xw/o+QhBnKfzwQN4XDNlk/NBiyNDxg5N3rq33auJHgCFxgG5MHF8NAT9ELb4Qt9YZfjgCjOcwpJf4YXU30gZ0rgj0MFh58c9XtEEbRLDQ1cXnP4Z8/hojkDoB3FJBnFygDPcOKniLybrB+cNqtRXHuRWwmXemPBPSPsJ7j6Bg1Urgnp9jZH4fLH6+EOwdt0m/BM9VETywf9xh6uWgH0+1PEXxSMn9YgkUhP4cwhgcPYV5xch/BwPtmBcmtZIVcBTNb2M84HIBms8MbQ2BkvAjxBDYA1XwA77fhtuQijYcPzg+frop+EHMcUjV8QP6cAPcwMKKx8c51ad7hh+HAj9pqBP8iOb46ZDww1SJ2gkg94zy62SarFquB4yP4hyAozUNUqkxSpUCufcofDYxelnTT6lfGbzEVYY0P6N0odjTVnBcPP9ECPKtVgm49L5pqVr0WlsE7zyCtT0dzeUh4Yd9ViGLqlYMxQjGdhiNw/nlXmNkIWZdegR8FluJybb6Tn3Hjz5hyKbjh6ZvwrMjfhhpw/GDwwrscw2N+FE4BoLLhNBEILhHmQcrBv5Rvn5choHiVNiPlssb3AjvSdGaqfPWZ2aASfHvTfIJFkCSuqVTcydlCKri7sDxFFDKOpR0kbBobg8Ufu/P4JJwBQAgri3CubNIfapNsVZmrgjQ1F5sP4rXtkdBv0PHj15gyDbgRwEMGfHDQFuAH+cAI16LFDKuPy4O9ANj3BrXp9o0F/7dG/w4VdBf8qCkP1wKgUBywr9rZkjyo8UWEv4foynznvdAMHA+m0+ILCf+edEXMBPIU4SP+RkFGFuBiEptWITXmM3MpT49l3LYKHAJDMkyj1wWpq8jmSOuxVpJhc8o9e7adIAE8p2R9j4SV0FdbFfcBnoW9Dto/OgRhnSNHy4VJJcSswh+ZGLIiB8IbTl+uOdfRLK6vQP4cdkYtzbiR8kYCMEnsy++o1wmgbbIPBeMv+l9ZjyclvwdyFLxkFh38lYLz1f1BQx4yWsfm7CvmN9uok+w5PeK+RFylxKX2lCiJF4T/EC5Q3eyX28YnwS/cy6E2nnk5ucNzXgQXpj3pKBaKTmolWO5ft2t0UDwow8Y0jV+NPD8BSIt5ogfI34sacSP7cYPcxpXRmNgNilWMr23kblCGgelueEAiFpIn+2AyhhCuXGEJr33EK35GaU2vbYWcE1boMhffKfUhrRqUVww2uL7rwC4YpTDa9xmtqakbPXgZZzHlCxMa+MJeaFHbjzUOk3AzxftF9L/IyoHf/Rda1GrTmkA+NE6hvQUP0K3wu9Z/Iz4sUojfhSkET9OiPQaAas0KeciOXIf4ujO1MSPUoXkzEK3lum9sFlNJGIcWCEkCYCmjMvCVaKWg5j6SzDJ7yDgYulbLh0g7z4HWmjTRYY5TLAHk8RDyHPM3856/+IEnpsmFFLCUul1Yn7VXu4C3n2/VOE4zbtbumRwlyNUxhEy5BoR+9EUqAvSR+oFfrSNIT3HD/88RSN+jPixNfjREobsE3ty14gheyBDjmEPV8GPYheIBKFLHR7JQ6KlL7ltGEgTEKphNPKgS40nmHPMwuDb5NqeBIfQi0yNjvugPS29aawXk2lCgBi2IcObeMohhLMGTDIOMywPYPsL/p6lLaWEokVYLr77ZlC4xlm23nbWGm0fFKQtEIRSW7EC0Tpha4nJOEoWXkEykNxVCP+u3Lc2Aj98f0q0MxD8oHjVt+toxI/PacQPXfxNUxk/5oasbt8buEH3FkPurstB7EymxRCngPg4qqdTFD9SCsmRDAH/r9UQk4EwKQvdoQsD55t+0dAX8qCrZDjuWepW6/v5ELRUWDuPwV1nr8KmsbrlzKyHiUUfL1MHE2IzaQ4hR4SVxNpOzL8uc4KqkFL0bGPl/Wi/NEg1UcmNLBb+4Xw4gfcGfJcCAfduC13IBICqB86Q/DqByXmikBkWv9epAKi9zcKUgx/I89TBYcQPI364OSeCSsPnR/wY8YPai067/3xgMXNY8S7s0dMV8UN7efDWstxzSysYcnfVNVebJVKLIUXx41Qbty/iWS6I17TQlr5FjP8MGKtG9gCTNjrHbC49q9WKEodtLB1YqU3DXWxi4sBQjHMg+qradMjBBzvoW/rVIO2/i2jZq+TCV2SgOBdogzTr7r6Lkfv8RkG3uBye6yJIV+vXb/H/p77bes7yNrV3yqwpnkb8MMoECUNG/EjHD+LcsUn4sae0GHeNH7nnlk3AkFlJ/DjV0u0Le3bCmKDIdglTmapvCOOzfmUFD8GqeWIOuiJJz2q1onE7hhuwmRQXm5C4iovUpnCfcyQ+Rxx8tCS939MtykWHEf45plRsv8S0a1h36ru7CXP/gAlQz+E56n3L+QwOSiUtmVqrosX6mGSp7AHlau80PCu2O+JHGoaM+JGOO8y5Y5Pxo+kIP7gUw9T7tUQlN7hf0ROmNIYUxY+dlm5fKYs2DRfFuZvAYfY2kqpO2zeO8ZNSHwpl6rm+dEWUUCr1uXn+4Ob7snBJ2GNSylpTsVqeSxWWlk1JFbTZSziMaUjDi84PVbvu1Hefxal4FXP/epTaV3q3lpzbGUa+QFhYRTqUL4cEz6XIBjTtsPZ71u/2jHK1d8nxUiN+FKURP+y4k4IhQ8cPik4O25Xw42fA9daCXyLBu79X8Z13Ivy4DS5aTR8wpDR+WCwQqVpezbNSlpx3hIM/lY50ZmR8Kc826SK0+HVHKMle5MBdgKw30GI3Vsl9LDF4KNn9S/mcRVg+gAxNJTQPNQ9jpDY+WN8/Xfx8SXkQf5uICXkavGdFy8XNvSKmJ4XvHKg0wrzdQjRXp+HzJAuF1qqIzAeZySvHUtkh5eAH9zwr+0f8KE4jfthxZ7pl+NFAJrFd5IyJxbIVxQ/3YUkMCWTIjpCp7CB6XwMXieuLNj5ItXKXlvcl27NcIHIOktKz3N+wRdGkk8P6JjH+M8/cSJDQhcBMyJn8eu1iYD1s58RmhGRwH7MGD5300z9rIcVzFM9Ys6RwRLntPKh0GMN41FvPQrdAjK7EfqlQG6OJsmg8RMa0cgGk5r4UzwUkyZCZRpNXMwtHGxk+OqZcuUg9f0jVy4D/H/GjII34sT4fi193Mi6/uTGYHotQ2UUknqiBH48Ar8KUobeQfp0JMaAGflTAEC1+TAUr951acr4r/FBfIGoG+QrtUosSUnjo4fomxSvsEhlosM1ZVBveJlkP24VurNwmxOYyV8ixZNACUAcXNDjN2HYofLELxHkCALIOYxoeZWqHoPvRgUB4sciNnSmsdeHeqZo3sCyer5iFo7UsUV1QrlwULFYfMO2O+FGYRvwwy3hKXpOuI8b2bzI1EU6sp23ihxVDSuOH72ch2anFjxmjoF5mh6so5zvBjzqkGeEAACAASURBVMl8zrntd0+MS5BqIyamAtRUaHY0X7zXEkeytcQEb6NzSbgeOGZ9CzQWpbMQkXxkeZembaQ9jhfRflUI1tLuO1W2htznSxLTF7fPXwnW5UPGClFVDjD7A2279vpvCo34sRk0ZPwojSFEIoDbzPy83DZ+5GLAQPHjJWEdHkKhWbWcr4UfpXjAmsa1C8DiNsdTawAI4iaDtWvOktDShlS9o6eHC+527v8e999ttpDcWl0D7Qdbo0CYg1uW27pRk8FqAghTI3eLJy1dhj71yeWkC1c+rQbwJpEOUaIT3pWI4Utqf8w2xd2pI7k04kfC/PcQQ1rFD2EOTPhREkMQ336fSOa+EDTdNn7UdnvvHX7c/exy57xXrjPZRbXxuiQRF0g1fpTEENWNh4gupzLiFCUYzFvIIethePsL++o0iYufOfx8yPRTvWgInTBzqfmBvqNZB7Tv6HKtBDpiDsrcXFI1CsgsEtwcwN/2cs2jDFFtTIVAq15l8MrN1pD7fEnS9gX+/Sr8XUtqUBP2piWTGHdJ7SV1JZes+AF9fXPx8wTw4wmTRWXj8KNkf4aKH8IcvFkZP7h2pszeT2mvKuVgwBDxA757IyjKy8XrFsUQ2B+WdotgyKkh+Oe6RRH8XMPJPYwyqbjN/i5xs7IUMnPkLRbx+7PnR3Ej1L6jd77UMLZrhEC/p5hLa40Cag7eY57NPRCEbXCaAE6YZ2snShKsSTLP5D5fkrR98d9jzNY5wfPk3uQqpidcUvtIncklLX44MlbF3UT8KNKfgeMH1c45IltQaTnNYQi1xy8ywdSd4EcuBgwRP/x3Fzx7wKyhJl6XIoovr8AlR9tuEQzRXiA6ByzlAh4QBUTWIv99mxFoS6XRdzB/QniWCpy5GwT75bjVaNeg87VCiBLqJ/6MwdxoCrVJB21qrJJr2n4BE75keuXAoarZtoDfb59cGooRYRI2B88rqNTeNJmre0KdyiXDAUBdFXdD8aOPGNI2fqSOtQR+NAIOSAfTET/6jR/7mRejXmHIqb4BVubG4yZxqoiJ0ESUv7/43q3oIMFG3xOZnU40RECSWVS7Buz3OjoIskxPBI1RpEndKPnLYnTs54HQ5rmCMO9LqfcCP8gwJd1h8D1WsGBaaOjT3Zw1y/F5bMvnvgvepMYG2hyLRkdD5N40znGf/IObDcMQU1XcDcQP9rtbgh8pGFIEP6L++OePPb8gMRAnfR/xY+Pxg+PL+0aMLoIh2qjv1Eq/JirgezkzVkCMiaqAK1VFxubH4nfJ+Z1N4PCo9XEj16pD31ZqXWZKs/M88j28IfgkSuuBpVF1AbSNEKfgDxJSDMq14MCxC4F74fceRm4NZyFAbpmy1WnVFj87Xrvm3lNgzXJ8Hqv73HfIm5xb0cpaFBD+nBxVz3Gf/IMNNBQMIaviDhQ/XDzEM0hB/Fg5/9RaHW0JflgxpDR+vBNdCOO2RvzYTvzg9mZjwehSGKKyQFA3W6Xm0vIMtRgruYyZdveRGAiuAmJMliwsJ+bhaH4uZfhdUnQJDqWH4OtGzie3VnAR6cK3VbrtcnNwknov/DA2A/oAwqjKtdf2YFWtQ3oQzaPWHGiKQSG0RzuERtFckbui6bMNlwZq3pzG9qCiNqk1dw1hb5I50xl51+cLQzaGJGq7Vf77TNtkVdyB4od/bg8OusdhsS8iEJSyhB5sCX54ZeNZZUr3ET94GvGjEDF704wfJTDEVEjO8rJEtweK0fa88Jbahcn9GmJdQeMghMWZC1aaqSH4UvK7lMyly0CZQCudslbqjVzSVK1w6+HMxROs+rGyyvVuYLL+WQbYfSn6FPO1JQZF0pSdgwPMc5HGSvPemi4kbbifcOMxuUwZebe4yx/3DLM3S5mme0sWDMlwmxNlnNC2C7h+BSns+IWB40cD8u9bi3e9kLJW3CV3w/Aj1P7vwsVrQigmHY34sUH4YeTfGbTfCn4we/OgC/yoWQQtxe1hJrTHtXsLhO9tZlziocubnSAV16fC12cGE9PbjAlfay41HRq9VsWZsBc/T5ggv/vB9+5CJpJi5kCFW480fmnc3MHcff5lIcAxXkeL+XqmdJWbKdfvgiIYE3tvTReSNtxPpPGoXKYSTNlFXf4yTOlFTNMbRKluc5SrUfg5maUN3H2wqvCbgB8mDBnxY+Xi5S4RFI34sSH4kSDD97cZP8wXCC7XdAGzlZuERniOtFIwuZ/Zwx0xpgPBFI1uAMa3jPS7RJ55mntoRBiR0mQ7wfh88L1LUAilJNOxh4Fg/KnjlkBxj9Eera0jsh5i/vFgzt1cYnO8n3Do5+hSWOdE2pc5Po8t+dxjAjDl8KM+eAb73LsqxGNLOcQmHXypOUa0m0NI2VoCQ1LdHihMCz/nsrRtAn7cY+IxVDJoxI81oi6Vbr5H/Ngc/FDL8MsjfuhdmIIJ05pDWNMOY6q8JeQyptpNImpMSBVLMvc0YXK6bDHhR9k8sKwS1hs7ltYPG8d5ZL4nhQ8t4mFAyjAhtJ/DE1RBmHg9wpgEKosGlUb4ATO+xphDPl4bt3a/AwfgM9y+zPF5rO1zr/QD1xx+VAdPZI95d7dwTVMOscn+vlbTdG5mri7IgCFm/DA8VwxD+ogfRL+sGKKppzDix+dzT67HiB+Dwo9UN8jdbcQP0wXCGES1nyjAbgrPYe1q6SKxod3ih3SO8Tm9Fwp346VK5ROXGrQevYNK6+dp7sax+K4mu4inVA266jCAjNtr/G4LgbQcTzjeeUjMxz2DBl4z91OO7wQfaa8xpNbt21DA7BKxj091ENxYlAI/8JzDj/bgqZFlpkMs9JvrF/Z9aY9jvO2thnsDjIvQYkgqfmieS8WQQeBHCQyBZzjaFvyQMEQc04gfg8IPLb8dRO1vJX5YXZjUt6NUs5X0nNFcy+Vbl1x7sJLjc4hqD83uHCOtkMW/zfvSJqYAW3u3YNHRUI7PIukn6P7HjR/m9Bn0fR98iM8Bk4tzhbiAed44rDAeym2BnEvfb+ire+8n0f7bE1weLiZYgKYFx5b8PStlmrxZXjPKMm1bng4I692amwMhC27Hc0nMxWPE0jWUuAgVhtTCD+I7lOvkYPGjAIY822L8cOcJ9+P//yHI33mpMY340Vv80Mr9KfHsEPDjQSn8mMznmto3J52mMkSsaFXaJuLG2SAL5wt7aMzXSzMzfN8zxQRp7za16E5wdzF/IEg5X95l30Fjo5k7NBWesU/obZnRFnBWA9VcMWM7KcxjGwXbrs/2dJ2Y+5N+M3wg8WNjfM7MVxBAH4/hhF8Uc9B5PQJOMxP8jZpHTEMsaXmkfWeRBexcQn9cQapG856+UR8xZMQPM4ZsM374A1Oy28eG48dLSCpjp/F+dSj4oeC3weKH9T2lXZhSzcpViTDXHmE1E5hUdA1VcpxYLH9js5ippkxA7LOCvsycj++K/y0yd5hJXkyFl2HGpTRwFCBRc9go23b0ceYcU312c9Qo+i2NYR5tcB+E3RB1Tp5EMRBJ+xKEHQZgpnoX0fdaJ4rXFIcdKqBeO54SskCaS05LVDJIvxb1DkNG/DDx8tPYI2DL8GNS4LK7kfgRxF7GlvQzcS2vPuMHE0+wCfjRMHWxLO6I9guExa/ScvMqQUjfrsTvFExkXIl5zlx11QCI3MF+UtCXmQJpTcDws1TBm7jmFoGurSgutT3NHAPXLrnGrm1oSwrijA/wk4jHw0QDS2sK/H/ufqNMqNiYzXNbk6R1g7+/x7icPA0qgqfuP8vhWBPIOzXOb6eKnJIY0gV+LH6FGsbr8N6r0XvvE5ptr9XbBPwwYciIH6uk4N1NxQ8uhi/+fMSPbvBDm6muigVCdaOyBoaVIOKdrortrcBdhfMxe4W5CFE3tpkxWE0TvJd9C88MoEsJ/MHcvLRrTr3PZzuKSVPlVWrb+zLHgvTrUK8iJ0PMPuHWNgnWlfoORacjnviYqCabu7e4w+msIJ8UPRBK8ib4O3V5aIK/JcuqBFkgVS3G5pKa9+M+mP5LYEgX+MG81/kVvxi431Dr9W2sQNsQ8SMTQ7YZP/xBXOLdTcUPC5mzrNXCkC3Dj6KF/UwxEH32cxV8wqR4Bbd4a58LJqskf71oE0z65sts8U1U+r+ya8687ywzPy9r5l3wNeUqU1szp8R+waKPoSJOZe1ZRltZxG+U2UNLH+bIJfAoumyRfant7yrJm1Sf4RKySvClxdwJPLlA6Y9ioCeeewxr0bSptd+0OAmB/93eayzxJyN+bB1++D3Lxl5sKn5Avz4kxv8AMgma8aM2hvQZPygMcZ+n4IcQ53iIuf0T7adZIJQkmqcq3CanCq0MpQl6avShfwqT7dwdbgu1ARrG3EsxZmu+zARzvqZcG2p+1C4t1K0b/k1tWpWGTWjboomaGjUFObnoKc3ZrAW/UUyr4YDnm4hl5ppB4Kj6nSETJHmT4lY1ranZgnFSwv8YCletpdkDirU+7t8vKq1nQ6Au8IN7r9f+cjQz4sdryIFLTO7QN/wg1iJHLgwZP04zLjzTLcCPBqz4byAxFqeD/lrxo9H0fdPwg8MQOPCn4se16PLg8ONPU/Cj1gWC3QCVTNSSTxin8d9J8CELJzvVfJUVUJgLogxzvqa8PWs2SVKebGD293M3J9G2JpCeHYPghqHNRY9lq/gtRDPTQMG/KlWJBavYp4uff5cK+svkk/hAmCoTJMClFAdSm7nEgR41J3NwMdhj0uxhQYqvE/UIehGUOAD84N4r4UdDyGzJBznWILo1f3co+MGsxTWlZnhb8GNtHJuEH44Ql65Qnn2CVPK24IeIIRuKHylJAjT4gSUG+Dsp+FHLTUbKf0tNynvONOc0K8AQue/UEucrhtEzZgHV+XRz8hVbc4ITpM5BnrhJkrOrwBwcV9qc3POYdteZW619PwwsW0uLFbKumJ/rB8AT8di5woDJ84HwUZJWrcD+yuFFSd5Ici47v7ubR6dNXvzM4edDLusacxidCc9Rf9vtU1D7APHDvzfFp5eKP+H4nrJAnRkQfuTu203Ej2NwHwnpsWUcQ8KPiJewd0yQy0OKbJIwZOPw4/Jn82qV3xJ+XCqJH1UuEArBxoFgkjAL3nmsSGumXWyKsaR5W9GsEoxx0m93C3e+jfBbqwHiQFQ7b7mZELhL23EB/8SbxkIsWtoHbU2DCPpvRgLB8c01Cy/Cd68Fm3IXaQM7QCyD3WDOnBYhJoyHc+dD40aQCzylCvOkyhuunw8hviO16FCY+zwEUF/AykJ+TlIAnXLDbNWdZaj4Ebz3LUKJwOGHD5618P10A/Bjpa/KzzcdP75O8E/TE/yI+xKmeG0bQyyyScKQTcSPQ2PWydbxo1qgrhdsQfDZbU3F3kRNePhOlwnj5aiqJAfk5GJTjKXo/0xiDCu4IcSBqBY8rZYXy6Ut9VCKtZ+8OZl2X436fQxC+weYOgha0mhDSvhdzgnNlIU073mGAKYaiJXrmM2LzEGKOqj4Q8qNxEOYJy6mRnt4COeEO1hhhwD33beRZzqv0TMk/ID33oB3VsMP+Hy2AfiRtW83FD+uEC6GfcEPNMVrJk1r4kfmIX/I+HEafj8siB8YJeNHrRiIJTE+9oeEn14R83sUpHRJiJS/k+hfyWVn2lcwRq5vMuevy/quRb6vsXbNvLEX7R0wPnd3cnxtBV/RZKLaZfxbLbyo0YZIfpdSTE+RAk3K9+zAu46J9H8l1rFagTFF0GIuTRXCXcq6djnq74tIMCLWZpih6YNNyMLUB/wI5JarHVIUP4D2YRz+kIBphIeAH/ehz0lFyEb86BQ/2uClIvhRKG5kiPhxERQa3FmWwg+qqvnJc7n4UfUCIVRcDDP9kHmyCwEPRfvIc2u5neNJRBiKysKkMlMnkpQTfGqYG6/RjNNFapmJFHjUIcDNXxeHG8W4SuRI1rRBrd956KMm53sJPjpSCBoPNB9jue5LUG0hXesiqgDQWZC+0ZI1h6tK69dqpchZ5TF2QZ3gh6NAbu0a8UPChJjfObwZAn7sgevOyuEw4PcRP9rDjzlg7l0iTWqXGDLiRzp+eIWGFT8mgsvSK7n4UfsCQR4sw84Sh9qc26PGJ28l8C0wF59BzMUvEunGUqP7/d+SKQAgqrruzDA3EyFPtXTo5wSeNv2aeHHLJeW4SmgyxDaIA0QD/7/MhKU8JFl8JClfW60Zu2pA7oAPwFRRnzh40sJb0lwPOU1r3/FDgyEYfrxDZOZzGHIrPGCDy4TE70PBj5XDYUf4obq4bSB+UEX4sDSp54lA51lLGDLiR3/wY7cEftQuVqbyS6vgqyhN3krgW2CqpszF16lsFRDkdpfI/sEFWxUxrS1+vQLjaZQCS2MetWY0SAkWnLbg5xuTOK4SvKhtgwl2W/Yp9MuEdcZ46XlpnhgetQa/dRqQK+y1ztrkfKIRDY+Wt6r5+Q+IusIPCUOwwOlbzF46HQTVj/hRHj/CbFJZgfRDww9Q+s2IuL0roW8+BHhjdJQhIy0YMuLHhuFHlUrUnhjN0Mqgc3zkifdylQPvRVX5tO5OaDvS+NrQrlvmj5mb0AIhVsLU9kF6n7BWT0NtEnwmjpPpi3lcmnal54Q21X1yGVgIDdJx6FqE+Cg/H/lai5XZESpdsTSeyyOuqJBWliT0AW0T/r+zeAKDXOqsan1t6go/oE1KLq2Z/uH9VM0Bikb8qI8f8VxvHH5Au6p+MXOVhB/CnMQ04sdXNg8/ilwguI0hbRphoE5Yv734vilI1HLYUAogLgCFPYxHfeo8wFEzNwJ4mszD0vsMAugRfA8VZJr3MYFIYtl5pvx77gH2a4ufHUmow/e5uXo5iF3RCA4Hqo2S99cOTTmk7KOWJ8W1Y/pBAir0rdhlJYUQ14zdkuPvEzGHthT8CBNDJClsWsCQbcSPY7C4qsewQfjxEhI0/zjWMlsIUU6y/SqNH4YL3IgfzWbiR7bmCjEhrpgOQzcM+B1PIGcCcwN+Aw5vatOa0dST6pfHFeyYWuaoTVLODZUGzGweVrxPa9Y8g2QswUxwnJlZU49gjWCcWJBYcvGqgCcse5CbqwOjSXmqTPf2sOTlwdBHa9rCFKKe3csssFiEYM59Xu+dEgWL+kicfEzEj4nWHbJDDNk2/HgMBcVMY9gE/AC6hbzf/ftrmfhBXR7mSL9K40ejwJARPzYYP7KCqIGJ30spgW0Uvq8vflasEFJAE0ye5v1coNox+A3G2Qz8xFMaiXijioFgbZI0N6CJaITbq7r/wvuoACItTS2Bl9G4tJq8A0ZzEwZLulz1LyotZpKgdgGX2Fy9X6B+REiHsL92QVP0h1ALo7imM9CIaLW1lrSFKaRNdYj1pzox2vVmg9K0UvyglS/T1FSVLWLIBSLN6bbgBxXAqxqDAj9SXJDbxI+GOejvVMIPLDi2KH4EF56HQV8egLVnJWGAtk3F+0b86BF+nCrQuaQS2EYQx95RSqhq4hjQ/LggTDRR8TW0pyxpTd7U90KhDabP4v0P3n06yJ7hKPx/iWKwZQ+ahkNByjjdJeO64xfFxpTaXDsYA89RJmtr/u9JEPgZVjz94Rqm1sRYIyltYa4GxXr4mFldSTJjoMSMaUMlJT9o9p2W36ddYQjjnrUV+FELQ4J3nw3caylXjS7xo9lw/Aj5d7elDIqDxI9mQRYM6Tt+7GQ8K92AtZpBjRsFVma7iFBFTKRPQSAdSG5YBjO3KptIKdKavA2m8aT+U+4BxLt3gA9cFfGfgf8P6RGYwRtBAOSYmZsC6zRRmiq5Nrn+3hTGZ6lC2aarjjXj01rawtKZdqI2Nf05sriSFMgw1vrBsUXS8INm32n5fdYVhoz4Qc4/9zmLIQh+uIPrJ5Ct7uWe4UdDVNvGaMSPzcWPfYs74hDw41TFNHeWSpRNcCPDXEVcme2Yirk0ENogVZ51pUaiWpVEgrSaNe33yP4LwY/cfJLvhuCsJsoE0YBZNLROrB0cMs3MpTQN04w2WQ2DND6D+0Bu/63EtXlPysJk0f5ZNDy+TSHA0PfPu3qF5Nbv1uJ57H25lYRruG31hTSptkX5iPB7g6wjlTa7cwzZEvzgxnAEgbhUAhZ0PjPwI7RU+EveyfpUwg+v+NG66o74sZn4ccC4a7pYyttDw4+kLExM7EN2xD0ETIc+2WgWJsKklRzpzmQT0GZYYBnO6vqQQ4a0bpYUolixHi5FKLVZfPYGbR+x7EdN29kNkPXjqm+qTIQpPJHxDLZXHhIXi2wTJ9JP6hJT1B0n0NqcibSPrwquRuT+T7hAatLkiunzSsu4PhGTkngt1baxXbXJf0gYMnT8INbmARLcHLp+cXtyWgg/Vt7JfCebojW8r82etI340VUWuhE/KlsghNiH7BzdcFkQg4gqaAqSzD0KTXsrwl+52Uz+ngoNGyY8vAZKmk/x3TAmSfi3EkyIaS9gPrDUrvupbSbyGlUpnd0r8Ofimk2in4/hII8Fk1rb5vbSLURrcwY+5/xM3QUnpjDY1WI+9zzJ+ROLWqCK2tBOCebbKR5iQi96KRpBTT+GhCFtUDDPlLxNxo+Awn10QZDn3HyWwo/4ndUIiQuhUoNvNX4QfaXOmyN+dIgfKS5MFJg+VeRULioYCwc0pZp7sPk4EUhtgEPGZrOaxrUHKY2A17ybA7P4fa2Tu+y6gLfo4rZmFi9IFK+FIITyF7VXKgkYrJ+nQRv8rdR3KfcSpdXbM2jXVjTXYFq2khvjVcJtQV2JvrCM6wsdICktHT0gLg8jhrSPIV3jRyNgSEn8CN/ZGo34kXzOdBamET96gB8pFwhqo7kA4zuWw3XCu2tSqp+ppHXKmgOl9i1psyXcUC2Ztdj5VL7b8j41lbQIWXyfC7yXmo9JKn9VEjBUPy/GxfGMVEOeUHvn42BtqIOM21//D5XVJOCNqpXoB0gkfyCfjRjSLYaQLmWV8aPhMKQwfoTvFGnEj7r4AfN7iTln7mwIfngM2R0qfpxKWFite8wQs4iE+Yy1CyVp2pPnwKB9m1o3GyIEr2aMNSbvDuJjIUjT6OLXncz3mcyYlTSaomAq9F7t/CftsRLACG1QlBu8pdlLDwjXiAcZbZJpOuH/2Yvy4lcvhH3XlOAiMxgMIVKKavdQnzFkjvmY18YP1750SSiEHyYMGfGjLoYE81sDQ0b8KEw7BWMfqCwXTd+ziARjCzWJWp9nKe1bzhxwB1NNW8+Y9GAplU2xsT5CDmd7Xgg48HGXGPhtPUhJKRqPE4LftHPKkpsrF+QHgYSXFAKrxHstKVp95hEVZfBETNQBEauMaiXNXnL8iNGj1Da5FIDc34h2t5Ii/mo2CUOYlKLaw9OgMKQt/PCXiAwM4eTlPHG/bhp+kFl0rLK/EIZwlrBa6dhH/EiknRqxDy3kVC5NyZtTcYDImQOt5okSlLvEBqbG60xl1rG+uvj5NvL17HoCwfuwOiCxmVBL7JyGgh1+ay5gjUIwZWtSifmnNOtWKgKM3HgKHKqpA8h5v16EG4wj6nPV/uQOMvD/+7De07CGTC5p+HEgJLnIDBlDsvbOADFkE/BjAm4i1kvJpuHHW/D/qfUoSmMIN55cxUxv8ePuZ1Y+FzPXQEXyIrK+Nn6o07haU7Z1kX4ulVLHhrTD1UQQ5yAn9aWQWnfl+8x4Hb1sXZ9S81e7fZgjKrUmla4TyyxGpRdsmGrm1DO5aY9LzU2p/Z2drlXYR28HLkq+aFfoijm3ptaFdpP8TJFni6WG3KQ0rjl82ncMqY0f0t/axpARPzYHP7rGkBE//pdwvzZIRq5kWd8GfpyqnaUIOlpM2FcCEyqu45mxX5yv6Z2E593t+Em0TmgGFyFbzNTgG0oVxeqyYImqfQVvSO412iArSeuDBR1StQS8hi9VQ19q7s3tEDz7KfH17/UuCRmpBl+LsvdMEBk2QS4RGm3tOcSFgl2Ty3zmmhLJIoYUQFyNT0tiyFDxIwNDsP34MBNDRvzYHPzoDEMM/LqN+FErwLsofli0w52bkgv6aWvnYadF8x32/BkEmDiTkdZXlluzvUK+rSV5Q2xfyRuSe43WTMwdDJZBh7HgEMzpOeb6UnOf0g7Gs18gvntByUvUPnqdSP1JkSUmIXXvSmkpxfz/gnl5EAHEShrxo777h2Y/uj1xyOwHDYaM+LE5+NElhoz40YixttM+44f6gNyTYMFSfto1AvXUi0UsvDZFp7tUvE8wjNofD9w0NCTOb23eULav4Q1pnbV8wO0bkmegvzslN3WpuU9sx9pnzV6l2sQ0vBTNjIGXqYJW+rvGesNdeHsfQKylET9EMvGgEUNCcrz2OnPoEDFkxI/NwY+OMWTED5lmfcYPUxrX0u5ICVTrRpVaAyLVzWbNxCaUtsdoLR0fmKAbpYn+ZtQPjqZd84ai/Zx0nPtGPqDWWpNpKNVVKCcdrooS2rGklbUcuq1t5mrOUk34XF9TrDexebmEXOoNjfhRhgcLYMhuAQwZ8WND8KNDDBnxo2Gxzmq9aR0/sgNcW6YqN6pCN3Ct+Y5aeP/9kOZWjS74y4raV2LMx6XmV2FeK01Z6Tg1f1ekw3tLwTMmM29Ft70SZEkre0JC361tfgp8m2P5SjXhU33VpBcWL7wWudTBfhsibQJ+WDGkqYEhI36M+FGARvxo0HNEjvWmVfxQZ2HqA/U9K4nmpu8WiAnkvRo8/53Fz3PEd4tnOio5v12sE/FOn2P8YunsLTnBmJZn3WYmtBSqrEaVgkY1WYg4Yvse9FmyRJDZkqzjlr5P/T11fnPXdUhysS/U93nS8pIBQxoFfhTDkBE/Ng8/cvuqbHvEj2aY+JF8gYiYyhetKn5QE95b5V2VN4y48IvvvLn49YaySTPDtFCNuBhzZ/Lk81HgbW8OC1riDgsS6OcCuuFAo0lLaOp7gdSy8bi9oMupjlrs4Fmyza72Wy5F/PXMWL25xDurvatLDIF3HxqSDhTjkxE/Ngc/oNH1kAAAIABJREFU2sKQET+Gix+mGAim86H2cc2vMuMda4yn9bHL0AxK6VhzSeOX5jLOaAg1cRs1FNh3L3cVq5KTkz+i70ECb4eYAjMnxV5yGjfjPrDGIM0B2KR89lR6zFnCuCcZ+zlpHrl9aIxXkmhw2ZoQ/iL98oeEHz3BkAPm8iCmqBwyfgQKuNeBp1zWorcXfbph7MOIH+1hyIgfA8WPVLOllL4wKzNSrt935vNZmZ4kXzKlXxqXcYb1Z7OMvaJ/PelrzM1PoDkLL6Tu/9+V+oSMZXdohyqC2qhCm7sPqPV+xsgdkt+CtcTWUDN2aXylqqOyKfakvaWNV1LQELM1cRgyZPzoA4Zo6gygGNJ3/PDvZTDEW++97HC/34DPSRrxg5yTSy1gyIgfA8WPJAuEknlyDmok4ylvXTkFNHK15+KNW6EFe0ocnp4qtDuWsdcqNEJpyI6E+aE0Z2e4PgkVVId0qFqjVE2DAOInQMy0S+4DMHmePAM/mMuQRkGB8Rt1uHyqNNFqsnuINRoi9xqrJaTNInBDzNaUmyK3SZ17hYY9d+26xhCO/5cpjgeKH/uK+aGs9+5z1Aox4gc5J26eG04ZKOwjit8vhRZoYr0fK13wRvzoED9SLxAagM45qHECOMckNs0Y2zPJ7aKgQH2biIFwn0tkGXsV1wfq0KuYH+69ZD50RlsdU98PVShp3S4iOmACKN0l4Gl0wI/3ErUPvDbk5BnQhr6WELxJXUooPnCaFs08UFVbUytsp1hCVOBZwqWlsDm7LZIwpAp+KA/ouXKxawzZJ2IgHmVY7zrHD/j8rqBcpHAA/XzEj6wime8H/8b2EbfHT7TqCH7ch8KjWpqO+NENfqReICSAzj2okQJYKVxNfuPRTfo+cfvV+OgWEajOXxMWNMWP0zL2HKAzH3oXbd5mBIBk9p4lCjp/UB7CoaokcTw3IQ744V7SHMJPngGtpgf4lDoO4aVknnOoRARiE43X7e/zDI9zFhAtL6nAs5RffOIls0vi+KsWfsyUB/Qc/JiBpfUa8p5WMCTg/5RYst7ih1K5SBFWwdnRiB/0fHIk4Qd3kSXxw30AGGLJ6udoxI8O8CPpAoEAdOksTJRJ5ayS2dUmGUQjtQeammMYD+a+QGmEcgJeVwguC9bAL6s5igLxogGNBmCntNac5owTdIPLulSQUguyTaM9rnENmyrXw2fa4YI6G/i3GOypFYiIguBCAFAWDbSzgOxkHiJVmuWcIN4hEIIhJbMwcTKQVGIon18hwqJxDQ5OV6Dd1jEk40LZZ/ywKhc11vsRP2zzLNFKHYLF2mtStE4zFK4jfjTd4Udv60BgEwD/pph6RTsIn+WkoPQp8dRp0CBQ63r0/dYPsRlZNChfb3eZ+ljTXmrqMQB26gLxMtN/av0cP7zS5aGry0MgMdcauhfstanSFSlMQUytBybo/SWdymk/K8Rz4RqcZwAtHLs6rR3D1+EhkppHTI70ul7BEIjae4KFLMSQo2DtkvEDvqPCEOgzduBqde37ih/B+yjlIrXHvkpZ70f8MM2zhsJUwppaPifPKNYkphE/mm7xI9WFqfoBiXCBaRimDrUe7y5+XlWmk5NMxiqNEMzFtUiIuUPQYXHBP5msX67mc0uQNkWUpnlP0NiW8G3lDm3ce476cGlLDYasRchcay4CWKC75plQO0kFVGPvd4eKj6n9lZsOklgDjrxP7iFiPeAsIJRbzBXFxWrWcQB2Z9Q2fihcp0IM+VnAkBJJN0QMYQ5sqWmsNw4/FPF1FH5wlnzMzca5OF4b8SMJP7BAd/EZZE00z4/40XSLH6cKg/P7YLIqK/Bw4cEdBlzWHtcPTaYaKcOK1qRL5Z6/kuiKFPbxRFj+8u/9xtFPrx5uPjv0TBavCkBAaifof4qWIZkZjcDOuq2UvLTlHmYITTe2id0eOWjDGhG58VBa0JWDisInmC3KRgA8d8i6WjGDkGYsMbnvfzkK6pP4QXOItPB3leDUPlEX+FEaQ5T4oV17ilc/zpmLTcMPo3JRK0diNwyzW8aIH59fdIlAd2x+UblqUH6N+NF0ix85FghK4O2FGobSWiYvPBQHItbvTsi+cCJ4DBHqxRcNA9kv/st/EmvbRYFMacNhnFbhX4UZEzMBJF/ahKrVJi1Zqqa7LWuEoAVdcfViAt1DtyJ2XWKA57TvlTMIpfLoUnYYLCBcMOlLbizGcRaLpeoxdYIfpTBEix/+fYq1H/EjgzLkyAFcFtVpwx2N+MG6CnNy955mXSLl14gfX+knfuRcIDgmCdOq1XLjSA3yMeeaV5p0kxZNAMi1Pn7fg2PuNk4RZdJKvTxUYUYs8BUOtGubRVvkRqk528vUkqVqutt0SaG0oLGr18xa1l5xyGM1sNz+yjxAUtph9/n/JcgPbc0ZKZj0RN4ZXEPY+WJ4ekhB113jRy6GmGqVKNZ+xI/28eMgEUPiBAkjfuh4mYsh2zj8aD5TUmjeP1j8SK1ErTlATgVfrBoVemOTGkdsrnk3ye7m67RU8Pul0hWDFdUG1/r4F8/vpayHVRPrtATuhyOXBvNN4xypSJqX4O/sXBDt3IaMHJoD/7Syprs1lxSpeq3nd+jTXMvHCh7WVl9PajtRvk0AyF5mnl261GjeHYwPSxVplnfcfBFz8i74cZeuCFyTusaPXAwZ8SMdP+aQuvtD+OkKP8Q6JPDdeG+5Gk0jfiD4AVa985A5sTFkMds4/Lj82Vzclt4/ZPzIsUBIgS6S73MWBSYdLGvFY/DnblI0PsEkh5qv25Q2I+pPmJHiLGhBXiQyekjBLmt9/NUvvtL8yu//5vzsk8cnlohPTp2e/+6/9becb6t1rMeIJSLc6Nz67kXF7jzTUWO1kDQvnMY/7D/l4nShsJWFm18qSJhsH/gvS5vMtIFlksGC3lg/VWvAlkU7aG07VatrSDMoxrBAW5oUoSpitE3YnJwh+trnoOtO8aMAhoz4kY4fHrv2eogf8RhuKSsiY7St+LEH+8dnScqW8QPFjwnymXv/e07ubAJ+JFsgglvOMbMBZzXdX8Cs8wLcBMPbliaDAmcxoA6eGk2ke/4TMD/5779B3O4kgFzr4zd+8EcevvXv/Wff/L8vvDB/tmjuz59/ofmFH/+vJ7/0Ez93jbkxUmO9Sd1UkVssVYgnJDdn1y03Wfc3xIohzQu3oULNRM5BwxLIS85voKlQWacIDYFJm6xtw889aNoxfl9mQhL2kumQZxwf2TZoLufw8yHxvMYqeFPQQmNE9ZmSa89+7j/+u8/+8sLek/lksti2k7uQCceyJ5oEnu5t0HUf8CMTQ0b8sOHHPYVXgBk/sP2SiR+xdtta0MzTtuPHaUgAsCNgyDbix64VPxxv/7c/cfPDj0+f/bARMKRN/ChSB4K6rRKazc5TbCr6LQXXcf58JXIYhznELfUwcvzUWVLOCUXW/PkPCcHtc0xztQauBmZVS1XkR6A1uZCSPhGZ3ziPfLG88oq+aHLTv6TIoLKWYzq3v5bvC+sc8yKaelHD99F3LDwe5y9H5/Qn/+yPmq/8/pvNuScrVv3P6p9EmW+INtzYPmIKW6r611ca8WPz8SMTQ6R+adNGm/AD2tcekEb8QOZzxI98/LBgSNv4kVUHQjKXGCPHWyfGzKMJrss1r1OajXmcwWPxKz5kmE1dhgCcGgGHU2OqU3+JoEzjvqJsDBKTyOxGfa+BjfTtwMTqA+PQbDDSABETK1adVnNxVvGVAOiaNm4p/Hg1ml5r+l3LvrHUlDiNmVw1fB+tneXSOVW4Me7+/J+8Fwt+R+f+8rmL7/3NdTM8Zv08HcgLTPg/gjk5XSEVbnUa8WMr8CMHQ7iAZgxDpEr2WvzwF7U9ZeHBET9WacSPAvjhPtBiSNv4UeQCwVEhwdM2aQqZrG0OEG6WGx5KikNm9TRdhEY9Lq4VE3W4o+JKKLoI9QHQA7LWXxC+53xqsdS3O9CXpcYJDo6liq/k+F6mFJ2Ks9OwbcDzkmleJUAkJQHCR/eJd8+UbU8ru+xoixiRfV788mN3sqD5/o9wz41/9Tvf3o3XzzCG8PAyxCxMKhrxI3nO+oYf+4q9ReJHYrrsezn4EbiovEv4ie8Kbs8jfgg04sf6fMT4YcEQiLttDT+KXiBKmDn7QAhTN4w2o4mEm+bywLnp1Li1m4jRoB8KbjnYJYMTsBQt6wMkpj1c+gsG/HcD1jIM+PZ0LigWVTJgM6et1KJTIVhJbUhZHVS5uj0ZgusuBab+09HB4UjTtmAhyD4AEYDG8XUj8ajLnPZvfPRhI2RU8+un1dLuIO5lg5O1m4ghI36QdYdeiwo0OmVCg1iBObllwZDLBfCDC7QP9+2IH5/TiB9NOfxw/2PAkKfKM2gR/Ch2gVBoRQcFKFjUv9AHLi/427H/O/wt5xIQuviY/PUVRB1QryBC+UYwP9cBFB7G2RcYjU9M2jngcifHWl039xTtQf8pQLkfBOZp+S9Zw6d0+2PBStEGd5F5uTIfOU3eg8XPF4JLufvtkgB8oEx+gGkEH5e6QBNuH+7X68BfT5XVzpc8+qtffOVc7L/68NSZZUa1iKjK3E1NbXFfqAaGjPjRK/w4APygYpBI/DAqczQYosYPkKcHjNV2qsjqaDnDjPjxGY340XzGow4rlBiyoywQXAQ/Slogkk14hiC64hV8NZtbaUbn8oLfwCojp8SHEEEyWq0M16bGTWSq6M8eMPBKMJoiVZ3pgsj5Cxq0Q56WaTmjcTh6BBWq94z8l2UhyrC+zJRtkGtR+HJOzfsFbXo7gh5EFwjyAFQo+PMl0CB5/trVXHg8j37jB39k+f5f+uPffvavfef+zreeu/jsl//Wf7m7+FxTmfs7i5/nJAvohlAShnSJH9z7/d9H/CiCHw3j/mjGECN+cBbqBp6lZP5RAg+O+FEJPwK35v9q8bMT4MnrfcUP9/4FVkzPPPn0/t/7w3/QnP/0ry7+JYMhgceHt/J9d3TWL6ZsyykkF1OSCU9Iy0UBijMnZtPiHW9qCn0oibrRTZBUWksC5vHpCqeQ21h6d9HiSsT8W8ao7Q/qorKgrzutlDOnwW/15nTf9c8yvOw3PkdTIu3gAyQHuDjXRFslM4+ZixYqn5dqp1hpVii9Xcyre0i6Q86FKnd/J8shz6O/8T//+s73PTg+NZnPd/7mG7/zikvHHH11pbIqaGqvwpyEl4e50voxRDJjSMf44dIlfqgtOFgDPwI+aaBWkVSQbZD4Ae9xypyYHvl0p1YMMeAH1feV4rOYzAfrt2m+R/yogx/RYT5cb9Tlp0/4cXfBo7/2e7deOP/4kxcaAUMivr6JpODPT71awQKRanbjBMiUcTv5MD44WG6L8F0suFaj8Xozcmd4WwgQQ7UOiRqy0sWVqJoXcUAb5aeu7Q/lRnQlts4k3vo5/tMUrcL87U+CmKxzHbdVknKzm+U+byBKk0bF/0j7z6qhLhXYyMmhl6zzZph/am+W2jN9oxQMKYYflnkkLMHx+znsCX3qnQb960ySChIXEjBkqPhxQBR0e8CsT0m3IS6jH4of0I+k4mAjflTBDysm9BI/EjAkdvc9E48hFT9KXiBSzW6cAOECClfSbCYI0gMmJ/VUuDyEAbm7wb99gBjWZ4zxRAbNyWKjJGqsMQhQfupa0LekJ01xO6AuCZdAQ+isCQ8Q8ycVEM/xBzrXNQ5xVJu5AFMToCQhB3/mLnTWQ06pzyni5JB0UCQziYXPgTZ7JdZG2/+2XHVaoBQMKYIfCfMoBfSi/YJ3/DbEAIV9eQ3efcWAH1Q/Tr67BfjhYiWaAvsB470ni5+/tnjOF1L9Z4ufvxGMSSroZsKQET+q4wf3t+mQ8CPGEP9duLCG3xXHkIMfxVyYMtw2KMEVMgtFoSnQaprlmIATps53Dv3cm44YE1H8TnZxCROaM+PGCYFZkPWHE6dRR0zc1FifMtaZ+MAzV/SHW+dsEzvCfzFdAF9yDwYoj0ZzjpEDlfPxXBY0d54QPHsYtXmY02bbFJpTYW80sJZcujktr5T+nCLK/U46KKr4gfouxD9U2zN9o0QMKYUf1nmUDhFUv25FlwdPp4MkFVr8kKrtbiN+JO0HhPcegIJ1J1AU/tvw/yR+KDHkUYwhFfEjbvM2KEG3ET+4v802DT8uf7bOzxRjSMaPnZqLrdR+kf7c8DyeAHd9Aay3xZmmEI9hznYSGE/6Hrawp0G4qUBWwYjU/JM+oUibXtvE9Qd7TwOC9KUSt/7gAkf6E8OPA4NPXJ8SNIunQIOn9bfOOcTdQsz2p0v5cLdNCN9gxB1mrLEfubEinrgsXhSvWfiB+u4F4vCxX9ktpTNKwJBS+BH/P/UdzSGC4zGu/sq08CFnG/Fj+Q7q3cTnS4p8xykMkPBDwpA5PL+CIURhz1z8oNzLriNztg34YcWEoePHdSK+Ix5DMn4UvUBU0jrdJARHvACkIHWMh2hQMOZw739LEKYa0jKe9D3SbGsAWZYRqfln5vMZIewmPvc21p/gPceEK4GfW/K90fpxpDk4+awNcZvWQ5fkb51ziKMOG1IRuL4SBaysRs8TwqvHsF/Q4NEMq6hlDa0Vty8hfbXwCObzXUpTNjgqiB/x/6+lclbgRwM8ifKYQm7NCh9ythE/kgLSEdpNxA9pP08Q1/FzQorYpoJ72aCskyXww4ohG4AfE2Ku4jEk40f1StS5/thCIZe4PL06jRpS1Ebjs85tugdIn8X0sML3kvNBByQebJF+HDCVp732xPKu8D0HyPOhf68pP7f/Y2AFoFLyYRS2+S7MAeev2CT4W/tiSb0iIpBTzAWfGecxNRS1YWWF1m+zUKyHKe0t9I3jQW3lcJXPd+3Ckn2nQvjRWFI5V8AP/35TkgPuuyBrW8GPKN7iNsi9R0gAZ238aFISmiCyrUnBj4T9LFEOfnD9mG4jflgxZAPxY4ew7iXhR5ELRCZTiIQIKDQoEROknAYF3F1KRNM3kJVprc9Sg8L3sg4GgqblBESEytM+25SGZrmAlJCfG+u/tr8hnQGBeFNZyCskz2uHiMvRBde/xP1wzOQ9TyaYr7gY2x7EV5CBU9pDO0MlLsSlM2RoiNqHN5k50mgwuUvznNAgrc2V5cDZR+oDfjDzeJ46sBbGj6+mXnyZ75a4WFJB1/cF2bAH+ead/N5pCz+QdVQFpCP9T8UPTgnG0TFcVEvih5g5aovxo00M2d9U/Mi+QBRgCq5dbECSeapIGrUSN8lcKnAwoLJAzIHppI10xSD8tcAkCoFwHZ2JmDPlwTsp02YIXtSGCmkPmfNG8dwRPIdpOZfAApoz9Royl79l3nOhPxJhqd0aAC9OeHJ+utp4p1Ka8tb8/o37UIqh0VyaXfv/YvHzJW1AXiFNWevUJ/xwFH+HkT+l8WOt0GgutXyxpOItmrbxI7o0PiPk9yX4G6dotNKewur1KRFE79L4fhn5/jLlpnUdA/7HKNc6mYofBz3Cj9Yw5O4G40cJC0TxW1xhUCl5a1XfJEtR6sJKGyGax5RUiGH1T6+Ncn6EB4JwswoBzjzn3QmojAyTRT8myIFiop1zpWuUz8uPuZc0AApoNWvsoAPfwzRYKjOxgsxp7qCfWX66RkHatjaq1D60gA96aXYEl2OM1mpADJxG/Ogpfgh7/mLmoasP+DFRYMh80ZcdC36E847Id8yi5fc1hR8+FXkTYwiQBj+cAq0pdIlMSZPK/a0L/GgVQ+5uKH6UuEBwwR4nN3wY4AkJ5uSSoFLs1jokVwHBfWkZkBwIHG4jkZcmmA/TZS9hDrH3h3QOAoN2DVaND7WuQdFzkjZS64u45GWYh3jubkP6Tmy8HxfiNRZUvWUnehfnvz2rIEhPiLhkldZGlSJqbmMLWE59g02iET/6ix9ufzVKOcEpms71GD9UGFICPxQYYvFlPxdYhDX44WTPvSAVatv40TAKOGpOquEHtNNHDJkNCT92Cg24kW744YFWkRqOAxVT+rGCkfQn7blNuPixpKrtgij3pQY2cah9ORJSIXLzR1323keyBZjnMHo/x8eWdGs3wT83pMcKTSDF6/7zfSHjS0g+WB0T9BeYZ0rQPpILPiQs53RKJolsImTF+wCeh6X2dUHCeMD9+62C9Q02iUb86CdpXClCOUHx/c0e4YfXwudiSCp+SPvagh8NXGKGgh/aFPit4AdcApuSZ8NtxI9TlW5xjWA9kCwM1C2sAcZ8EUwxKitAyq21D1QpY0FDxDqQGUWE+ePeU8Sf2b8fNBucpUQ1VxmWJFZjgbTLmbrvJwj0Iptf8NGl9myr8T+Kg8weBPp3LfAtvGUxHfdRO5ZMI370as5L48cyoFyQqZ3jh/v/EhiS6YlA7msjfqRQl/jRutupAj+W2dQKWWW2Ej8m8zl3KWczFDz04K30D1z6EkJ7VGBT6G/IXUowc06Vg0TtDCGpc654nhKU7NpYSfmeIqbT3Dlpmx+EuXkEqX+1NR1q8ji7H7uce6ZvKt7qav+WIG3f+z7GET9G/OgDfmh4scQ7Cu5raW4eMNaGXuFHV3Ofix9DkK+FzipJY9RaIFiLgeGGH/4/+Z3gFhYGDoU0aSN9o8U/swKT1chYoE7tFY/nl3/vN45++p/+wYrVp5nPtRaoIqbT0jEoKWuGWWMStatniGwcWJBhbaFF7sdobPdB4F9sUZBy1kiUt6I+NwHPqzSafQEMjeW0VhajwjTix2c04keH+FEaQ1LlBBIXsSx0i7SjmZsh4ccM3E7V3iMV+6bFj/tRel1Rvl4eEH7kYoj2AmEJyDiCEtpcwAe2MR5BWfowuPeeQYs+7eoQn7MALWcswIrDrZm14vH85J/90aWf+ud//Ebwlc9iJyaT5u4CBIL3UGtVzER5t5A7Wqk1k9oRLsKU1eckyFDbj0zSFmHcg8+vtty3OOc4yVvIejSWS3ilvVyT2qyHkUpt4MeSX6ODGFVHBe3LiB+f06biRykM6Ql+XBgYflzqwO00Fz8w+UHK18vDw48sDMkt8HIfAp18Ofc3gUEmkdb7MJw8opz4BBZLCu6lfK7uU8EysKhqct9344E2NMDHLUAqZQfBuHkOg80WH30QzecxsZlXxvPzf/Jec/ZJHDP2+fj8exb/+1Vifc5b16AFKrVmbDswt1wAeEytB3Ih+3HZB9AUFeFrv6cCWfGSoW8nVd6Feiba4E/uEl5jL9ekIWRrqo0f90CzeS2S/88jwZ4W/Lht5VdHI36M+NEhfrhsUiN+1MUPTr4eDAw/sjDkVMYN8zHcgMP89rHmqIF/r+WeDbUAcFjfUwb3UloQ/0xMJxUSgcK2qPSyktkwNs1NSoI4tO3yRceUHARDjGtljMGYVi5O3/8RWfh4Gj0bg/9KwFIfbuHUGDPWTLP5tKboeVcBXZhWrlQRxgJaGSo/Ova8pm8rqQYtezn67poMkf5egboITLRSVfxwBOsZ7y/nduCE17cS8SN2fROTd2jxo5IsCvsw4kclgvntI3442umLu8wG44ejZ65fG4AfWRiiskAQGp+PELNQ6kGa3ESxFn3xc4PQlpJMAkDytpA6lrtBNhHwHUVtUWQG8WCzxBeqB5maBfZmHLx3jZH+4nncC+DPn39heRALmF6Tiasz4saYsWZaS9FDRd7rpVAqrcXPoFKp4HK1MpZ+aPvm9/+b2r0c8c+aDJH+3nLav95ka2oBP7jvXCyAH55frxfAjwZcrWrIohE/KlOwbl3ih7sUU1o9X5RvjUb8UM29dv12NwQ/sjBEncYV0fioi38gGpf4dlVCiyYFy2D+gpiflwRWHxGuHTGtxXQoD/8UAD2neDZH00EC369+8ZXmK7//ZnPuyefeAA9PnVl+rqjkqelDWySBe8rBax/cJ3yQlb9k7jMaySdweDlDCCUqSL/tgP5SqeBy3Wws/bAkD3DfeR3mnSP/Ll+4iZIh3EXJNP+aNQy+cxZcF3b6miWkMn40BTBEwg9t8g4NT19RYMiIH/3DDwlD2sKPc/DMzyIY8rzXjOdo8QthyKbgxyNIeLKzofgxTU2QklPAgxLMc2zyhNsVdgOaQ2DbiqaVaoeIl9CQmzwL4FxEngmJjOlQ3iSptieZGnxJ00GO6Rs/+CPNL/74DWdxWKjOJ8vf7t/u84DJNQeCrt0qpjmxB4wGJ+b58N+YUDgNFiV36Gs4zXzgT/2+RotfUotBxUYkAEmWJcPSD+K7V+H/MeKEf6ihbpiA3KmiAOYKz3DaQM0aIt9x4/ikj5eHFvCjYYpwne8ZfvhnOFk04kc/8aMR1q0t/PDu3Zhv/2kCE94j2lk7U5TCkA3Cj1eForybgB++KOFVS4HknEJylC8pmqaL8FFdMrDrMMQo+Pa51IvchnIL9TXjxSg++Er+hp5pL1E5hZmYDs1NktOEccIr9xbOauDcZSG4MHCVPEtqZ5rCmhFqjNpc0GsaHBhXrAU6E6w1tWacy8TU4E8dt2/SYkjzWSjrVbYmytIPwh+X8jV/yoDAyYEc9jRFTog7OfKMaWsS+dKHslMr48I1LKataoNq4odPJU4Ut9oz4sd7Co1iDn6IGBL0exPwY2n92xD8kDDkTov4wa3lFHnvrqEdtWwZ8aPZavxQXSAI5r/G5PTFKuaxG8GDPkx0vEHde9+DoBzSTzYAkTht1yO4mWPPrlw2GCCKhRgnTFM2vSfXBjVO1Y2b2tTRJS0WnhbXD4x86t3QJNaUrBnAmGG1lclzDrLUhjsnpIVMOUTMDP7UMU+wWgz4fhg8bApOSwFgBe+1QdTa++w92DyHApXbu17oaw6elNlb867w8xwZ0yq1gR8Bnx0Qh28LfmBysDHiB6YM02LI7S7xg9rnSEKTbcOPHAzpAj+o91LfPXk3gyFx8okRP5rtxY/cQnJXKM0tIoC+Q8Qh3FcOQJrcGXdYgX+rfGSDywx5WBIORMn+uND/FxW50IsKSWLetMy07FshbXUKH4YQrVfUAAAgAElEQVRzRR6CMw+y1oPZDC6haIVO996Miyj2XY0GMtRipForcuIwimWYQvw32cMGt/aLzz9gcq1PDT7ynnw8gtXsLb4r8G8eQvalrcYPYhwihjDWsur4wezzd8Fl5uK24kcmhnSBH9J7KZ7gZN2IHyN+nNBkPqdcgz8nbany4Psa1wtPx4s2XjCWto9JLIfO9Om4VNGVCCwaJJ95oxU4qaZWZv5ibZBmzqi22qxyaSlLb3JLMr6bm4tzhGY7LJ6E7hvhgMHtBXdIfRuyyqTsPa7IFrWvqf6sBPAywFYkP7lijOZ3Ldr8kJiPpXwyyrTl/Anztcu4QL7EaJHD71Sb45I04sdw8MOAwSN+9Bw/hPc6+mqMH0Gbkqwb8UOHHw8Wc7wMbN9E/NBaIKzaLo3rBeULbsl5PE9wo5B8ZJOIWBB/aA8P76ocxhnaGC4I2+rvRplrsy9ciQBn0QKXduUg5wL+H9NYUjRTrjO3F5wAuea054ImkLpwcRV6qX0taXdDv96a/vnalMGWd1GVSs8wmsfzxDzOEtymTrSB8C7WjaUnbmFaGvFjOPihlZ2d4kcihmwbflDv9bSGH4hsoeZrxA8dPeetxpuIHzmF5DiTqGXzzQwxCFkaZsFHNvdwgx1qJsRtsWawYzEhWeuQkuI7meBnW9SVQzEXsQsPtYEbrSuBQpCjfBS5UFgtety+1vAW69dr6AdHmnas78LcY1Y+j8Ga0eKEgpxzm+L2lXjobsHloxR1gR/SBXrED35OL/UVPzIwZKvwI3rve5ZziBCTytG24sdFIXvmMtGD+71J+KG6QCQIAq0AQpmNOeiH5LJ4pFCt4EOr7+20kpY+W0jmmL+VlJQBgOBDKptL8UJamg0Hc0f5rnqXGI0rWrwG1BpKfGSx6DVQU+CAAGJrW7VAWSNfZrV5WiMXKZ5J1Bz2qkCclrrAD8Xh518o2t9G/LDs867wIwlDBoQfBwx+PEjEDyoz5YgfZfBjZs1+tgn4kVxITqB9JihR63okMTZqflMQtdDuc5IyGIjyV5vV0NLnCknive8vPr9VyvQM/bJ8zvKh4jbeCin8HOeBydrSjlsDKlhpJvEpkj2FMp2GgdbvxryG8NacAKYHwPNq4WU8dEgHnJV3KfcS5dNLVXytagUYmItSH/FDkic/GgSkW2ij8YPgPReo/nxU7KxL/EjGkIHjh6NHCe2M+FEfP/ZTsp8NHT/UF4iEzlMuSFrTMXejy3EDMt/MDAxk9leL3hEf8pL8yDOFJOVfXiRWJAeEKeqRK4cY+6OcO8odLknQQWDUZSNQOd//W4J7FBU49gguSirhZT3sEAccb0bG3qXRVt4k0j+LF75a1CO+bpUK4YeEISeuBZuAH8F7/J7AcsqrMZNwt8jFjzWlRB8wpEf7TBM7ejGxnRE/KuLH3QLZz0qT61Ntvq5ygQC6mWlC4W50yWZjWGjrzUzLQGi78LnP3es0Sofh+wiAaUqayg3MxLVfKnbDDMIDIWltZgXaWcvGEoC7Kk6iUKA1B2YXjcIryR3B0L6orUyUCyP1Fz80GLIR+EFgSLLrU0X8QJUSibSJGKJZGw2GjPjRMn5Amzf6Ys1qi6pdIHIBmbnRdaGx1jIQpvl/CTRIYbGQ2P3KkrUqacwGkiw/2YFMG3xY4+YOsxZQ45c0pydaWKU1YW3NQl5dtCHncq6rBST3VyF/alU/29DY5FBLvuW9oBIyIsCQNzYcPywY0jV+SEqJbcYQae60GDLiRwf40XcMqYEfp0p3omQngxudVBW6GsF4KFpjoERXJO2hvI0xS/7HRQCozxutsFZsrdaIwqWBaqdB+EVzcIiLbcVE+f8/E/zES2kBKQF9P9WPG+kn5p7UibYyRUbm+LT3iTRjR75zNRNDmq5cC1rCDy2GtIUfOYkWthlDsLlD639w8oBoJ6QRP0b8uFQCP6jIfJQCpr0UVbVdCsnF7zfBZIz+PYXc4KDQ3MuLn3uwoe61WDDpQFENkpufPcUmnjEHu1bHDO1TgaNrY+6S3Hy7TCuLH3fQvZvDZwXn7rVozV52/KvQFvpDgW/H+T5TNDMeHC4Ic+PcRR4T8iHc3+F8fwgX+7PgVpHDo/twuAnJ/5ucJ4l8f0EmxXUetAUJi5IkQxlieWYIpBl7JQxxxbKubjB+cBiSuzdTZaCLwWisSQnapIHgh7s87ziLsxZDRvwogx+OgCcczo34UcACQTHte4uJpnxNi/jNd6htIA9oiDBOdUWiNLli1opKbg2Y/7ETaG9RbbftXtFXjWzkGrScE9gb4ZywLg2B24JGiyiZvRsQfpr0hmSucPh7ON/hwWbX9ytl7il3hMUPWwyH4zuFa9fp0rVYlHsgKYUxtGn5vI8k4cd9iKtJKXq5zfjBYQh7aaght4P9fBhlbnpMJSUY8ePzuVPgh8btc8SPDPwI/kZhyGl4bqvx41TlXNUrzw3Uh9fi553kipTqz1nrEG3tj2BONY9LSakbqRUS5oQ89Ad75ByhTYwPBFqXATG9IQAV9ax0uMmae8L/+4Dbe8Icaw5jxQ7fRF9uWy+PbWUu64gk/OD840v5M28cfqRiSE0ljKU/PcOPUkHeWaRYm/vEfnGfj/iRiR8wxxKGTE2LuoH4Yb1AaLSd6HOlhFUHIGLx854R8+PMth9zfU7UkFU7RBfKouOE8blKVoLUjdQWcWtDZYfxqSWpMexgfBMBNZa+sQG+k/YUl6xAM6/TlvceN8eavpBzkkBc6sQQDMzrs0FZZyj5qKEi8TCbih+JGFJVCWPoT5/wY0+I+2qLctZmxI98/OAO613gx7sLvuQK1HaCH6daCJDyvp7qDWFwS6jutmLU7CS7Ig38EE0KY+SzUgBFHcQo39u2iVwbhbafOkjMlGZvtaBQuPr4Zyltzkr/or37DOIokg5qir3H8b90WE0Wnph8YvriyQv+3ZS+bEjWmdQAWx9IOuJHWdpm/ODkQx+s2NLakCm0Fz8jfuTjh8QjD11bxN9q4McZJA6jc/yYzOe2DI7IAYFzX/J+8y4TxjMqmMwFCUXto76c8F5qQe/1AVDb1HBBgOglwtXllbbmgukHRStrnvhOFT91RcycLFOwQhAyWigr1Z85hQeFtTvZU8qLBlb0ytz/EnNsyWhifCclnx4aU1Q+zblcIX0axMXCiB8nGMKk8x7xI30tthk/3mTS+3aOISN+dIsfgNEvabIqdoQfRc+9qRhyKuMFLqtFYwBp7a041S0hyW+yNPi2HKxHafR2tZaZQuPfN26KEqa/vlsgSJMhzPnzyDPL1KKBtiBMX+yercGDUwZEL1uqd8LfqAtGjfgUco4rauwp+eT5Xatd39EcUghtVbwOz0NQX2+SCSjHcoRcOF2g7UdxVdjFs1fawo/gwqz13x/xY3j44fipDdeUVBrxo0P8cP9TCUMOovel4scaRhvl7hVGDqsxRGWBEKwCTWKg1Ekb4fc5zTK0L2kqjiO/SfQ9lj71mWAMWPacJiw4xjxbZPzMQafK/PbdAiG44lGaj2NIWZy0NimHGUkLwz1rWJOiaxONc+0iU3PvCvLpatCvRpgLdH6RsV1A6ldMgguDqf2uiOHnwwjMusYPtQVwxI+NxI8GUm93jv8jfqzRiB+CjFcqaTDZOEnBkFOZt6YDeIG42RgNpgsuPFBUUZxAUZ3HAnha/CbJcSHf7QURgpY6lEl+dcXGT2m9K/ptsxatPrh1MJYAal0uKlNermkGMuKDSgZXSQe0bM0eMs496C9abKwCH5B8h8SiUJcJdH6JscVE+cH2NZmAxM9XNBedFvFjapCLg8MPYk+M+LGqxFFboGrSiB/d40fwTClemCXgR2xlRjGEOANQrp8hTVIx5FSbwVZ+goS0bUfMoPdAA0dVzuVo2uMgMhVR88aldAMNM8X41cdfw6UL5sEdBmIKXYTMh+kWQYMUIoo1oNzTkg4zhc20XJCs6lKiWAPuYnW7haQLqgsXAwbc/GJjS6aeZJMpJmtawo+Zoa+Dwg9HxNxRbgjbiB83c+RGSxgy4kcL+BG0VxJD9qP2RPxQjosaW07BVK+kyb5AaBiWJGTw54W0bdygnQbuW0SxM6vfZNa4OiCK+Z8i/nMOKL87mAs3zsOI8cXxtyEQjUG/YmATgJ7pMF3psJkjRKi1aeC59yPNazKYlwJpInZDHXSmyYUtXKw0tSCyNMQwxhcX//s6vNPtvUNubIb5LXnw9CmB+3KBSJa1LePHPrxL09eh4Qd3yIjdGEb8MMqNFjGkBH6sHJh7hh/x/hNlLDP/7sxzK3BxVeMHvK8ohtxNwA//nOJ9rSsutP7I+0gQ54m21x3YnE8hVg4+WNRLIKAuMdqfPaUGbpkKEyn7/hoAA9pX5bjmoMUqRtIcGYhikAvgSxzOxWPkguhMYG4zievq+42s3TsZ/V8j5h1vEnNGaWm/J/j/FGHICYqiRPFuJESwtYkpXA/q0NLFYQabx5zDzQlfaLQi0EboPhTTNHVvwt+vBYDjfl8rtCc0a/Wpob0+acIpWXPUJ/yAz1m5OGD8kHhim/FjD6o+v5RxmG4FQwrhx65SrraKH8T+08pYbP5Pw9r6sWooXDOWFy4PAz9sqVY/JypVsD2NqyHI6SR1a2KKNg1pgoO1Gu03EZO3GKilfUfJYFhhLlfmZPFdcmEX35toxsG8LytAM3onlU0p1oj5oH2sAFv8nQNrv50A6FtQthAgXzT1aylrUw7PMGsgJUlojEGzXJo+aW9W2RPQNtafMCsR5gvrxvkdUCIU71NJUgb4dY4fRvk+GPyAvz0h5MnTxXdODQE/ovdSvJGCH5IXg3Tu6BWGGPDDlHxGeF+2t0Iq3yjxgwsaXlszri9NAuZ2gB9xooomGnsWfqjTuGImFJgMTGP4xuJvH8AzWi2YNheuqmhG3FeGriDMlOLugpUdb6wmMKJt75Lhxv1+SW2jMFfFfVyR8VHCjVoTyTS7dOuJ3qHhm965I8ClsVEU3/LF6ZroYnbW/dt9Lglyju+MIJDDM9zaeroYZTviKjtLfJBinq7m942sIXYg20P2ySOYgxKB8NUoljV9xQ+sr5uAH9AmdZDdGQJ+MAelEvjBpdTU8E2vMMSAH5Rc7QI/cvhGgx/hBUDCj0bAkIPo8z7jxw3i8pKFH7m3Ym7Qt4TNc6x0P/JBb5SpLpdKubusuFoEJilr+xxTcnUO4nl2c4YR9bmmTelzDWHj09JUYZqV3BMo0rotNCmmy1SKxtJw6xGYfz+JTdSK/kl8V4RnhHnTuG0ts1U4zQho9V5BnjmpBSHwQcrer+oqFo4NfmuCVi8m8HsfKAc/Hix+Rvww7GPYa0PHjxwM0eBHzn5SY0hH+DEHn/uGyQLUNX40nDtVAfxYtqPBD/c/AoZMifan3LuNn/caP9QWiIQbn9cEHREVH7/uzdQhcRq4SpSiNeAYJL6FWtvnmPId4tKHCSl3GXsXySHvPteSqMlPMFtKc8eZGJdCDniEMs2Gh+k7pbS/FbUtqr4tflHVoOO1T9GKNInCECNqvx8J1rUG+ng2qNIspj+V1k3gA2pvcnEWKdatUkRmW7Pye09Igx/7iBxr4N9r+3PEDxE/dgeOHxqZpMGPA4b3VlJqlsaQrvCDeHcJq2wx/BCKrB4Z8QOro7PCf5o1Y3hhBn3YWvzIvUBwLjVSxccrYFbBBEibfrsl3V2wjUO2T8SVUG0/I7QuT7HbosIdQhTcRFad7wpyr6dUMKTG5zc9V6H2PPg5uu+8jXwnayMaNpHP9lIsu09BgEoV5NS6uM9VBDzlsktQ+/2KkD0ndGtTFxnj1k3gc+pw+rx7jnpXB4fUTSURP2C+byFrdNq7V7jfi58RP7YDPyT8vUfgx0nAftDnr3aEIamH9GxSyK+u8YNSDD5OwA9/2RX5dPFrxI8EUgdRM4v+ERGA8WCxMM8rKrdWrwQtCTurFkTpg3kSgEJcFBom4AUTamdLBWhpAvOiPjfMGpoqGGqDAqP3U5oE9oBZKqgL6T916Om8CnZGAFpWVVnlnrBSiWB9ic8/TAmY7IL6FqRZgiT8gO9wGOLcLUb82BL8SHw/pZRiMaQGfvR9H4/4sTIXI35UtkA0oHH8HaSt07AAnEakqiYX3h/npF/TchRwd2FdLbD2iQDCcyDMXosOz/5vTc4NX6sBMRwGzRUMtRrcyG0H00os58pwUSllJuZ8OlPWojQlmUgLaNZzYluaVPO3APIaTd/FQq5bbVC2lq+HxOIHrCU1bncAHfFji/DD6nYSyAfMrZLEkMpuRlTgLhWf0iaN+DHiR3sXiMDEHGvxzsDGxRiygQ20Vwu8BSHmg8qSD0yIX2EpP04fBKwRxKnmVslMmXsYZEFJe2ELxr+bwCe1zMTcOzvPepNzEcj0o0/ds9bijxaQ15jjWzuUF9Bomtwhh+BWpcAPH9iJXTIobe2IHxuMH1pZpbzITDtwMyqaCaskjfixnfjRJLZfwgLBavGCynuYBoCiEpMvCbFpKQ1D4sFLw3jcGO4xAVoSE0jvtgAwlm+71EEas1Bp+aRUUHCjnLvjxLUoTjUCahFrXlj5OyeDiNOWNomBZRLIa/ZYK0FtJTSaFLjDn1sLyqxAGiuQJpd9m/jhC3CN+PE53R8YfnC8Ugs/1IfOET9UNOLHV/LwI8BwszwrdYGQNgQVSI1RKQGiydSABuRAgFftQ5/m4EKNYe4LYUVBYdqgNCpTzhTao6L1Y1IHunIHUKxh+D7Xh67qOlDrdrPLTBs1CcYSBxu7tTkMBJKmgA/m6+yteA3iduGDLak9KIG8uMdaDIoWNZrawFT/fYU7ZPWgzEIk7VULf7WFH80W48c+yP2woKGji+DfT7no9Ak/JF6pqVnWZqca8YOnET+afPzIsbiVukBIG4ITxk4QfFxB4EqZkihz4V4geMhDX652QHlwIYUYIWDiiqgUE1AXOp/3+RFkPYgr3jYADjtxkRKiPdUBlJg3LtYAzRwSkVqzbFlLw4Gzs0wbFegAyYJzkgknUSuHxgkZgZMFeWu8TcIYLMRedjIPDDW1pW3QEPGj2Vb8YNzO/LPY5aFP+EFa8Gvjh2HtRvzgacSPcvhx0o7h83IXCMWG4ILgSC2ChZRZF1JoTYCW0g4oDi5kTn3o07nEoDTpYHEmB5iRtTgPbVIHUIt22dErUl+0h8eUtVQeOIsd6to2ZSPv4/o8TThsceBtAU6VhQF5rgsiZSCjtdVeOAcdXD3ix7DwQ6ndj1Nz9wk/Xu4SP3z7RL+rKAXaxJARP6oQJeMnBfAjGUNOKRpuUoOKF79vC4f575R4N7GJr4F59HWj/6xm01bTDkSbj8qx602+WjphAoO/+sXFmr5geAe3FhxNU2MNJKGoPDyq1jJ61/3Ad5tK/UeBselQ17YpO2H9vB++NoWrlB5VDZwtuh+VIKrmhLeEmhIFIPz4iCuY1HfqMX5cSbREbDJ+uLnS0E6BFLEjfhBr0UcMGfGjGnEYO8nEDy7N8X4rFwilMP5ypLm4UDC1JpUS9RXl4cZp3BtlNpgqLgPI3E0KaH2xSsUaf+JU7aU1A8csJdagoFAU1xJ5F5oWGKh01pO2TdnW9fNBWA1TBIgr5qR2u8AabMvCUMjl5G1Ck8rR2rgX7bwZuZvsgcvhMXapHRL1DT98rEDUJ0rb/pCoabFp+KGVD6VSxKa8Y8SPbjBkxA+ECrosfm3xs1MYP2KFSetZmLSH+Y+RA3rN1Jo+C5TmcHOOKcITH/rI2hZgTvI3OtNiGDafVut7HAejQn8kytFeWkDwMfUehXa5lFDMzYYVvtf/f4rPbe4Fp5QW3rJ+J9YgWC+nMW4MFWKxS18rWZEsVPCyih0uOXoUVV/3c4D5qjt3jo9TrIY9o17hh/uPEkN2YQ3i2LFtxI8aKWIxGvGjnP/8iB+V6HJBl/fFM1QR21z8OFGYGNqvcoFI0a7kHoa0wZScwDwH/qLHIPw4TR5X26KBvoS+pysMw4xxmqD1JasiQ/9WmJbJsJTkr2pxPYLfqiwaCu1yKS1eTjYT7XuXWU+M/dIeNtx6/iuLny/A5+6771qEU8SPlgwqN5V9XbotabIFSRfHRPmQS21q8Pw+dOv6fByQC/OeVIBrINQ7/DBgyBmlLN1E/CgVzD7iR1n8kPznPwTFhufdS3BhfHHxTja43dOIH9uLHzUuEOSBhzmYzDJvatZ0jZzpdg+evWrIxKM5dC0ZBgGh0AVGY1aOtb7UeJ4RTOsLd8VzJWU10gI0aToufNArEjjKHViDsVpcvrA++ZiJFJIOGxiYOzD4muYSgew5bbwQxi+p2XSmmosjJx+Aal0sSl1WjxWpiZfzCpctTNvOaYwHETSduK/dHvoegj+r44cRQ3b989h7NxQ/VPJ9xI/W8aMRrE2YPHJ498Zirb4sreuIHyoq6bLIYUjr+FGj8uE+DCQmSkCGgpq7qZEEDP4auEk4zck97IDj/g03efd3jlTvdG1BkJh2HqfCGKm547S+2DMPmT5d1MyVICwuBSlf3wkDs7VrUYCocZvN5uFawu8wnajFT3gfzOoxXQjnyNq3aD6dhkFDO/HaGN0enmrybwt9jdd+lim4qL1zS+JLjNzfnaB1lhz4TX0/t9+eboJZOaQ5MVdWcJkPKWjauK8fg5Z0t0v8KIkhI36M+NEGfnheC/jbQnsKOTrixy+2hh8UhviLRev4MZnPqUQN6WQwaa34hYNLBqXtnXPZbxL6h2VEid93VaPVhNue5qDpJ3tCmCl3orlTjRfT6sC/SXcSRV8bwziT21S+F7V61HRnEdb0mMnC9CGhIVibo5T+C3uksa6N0N5xZN72PPyW1rwdvQsLRrVYv4qN3dKX3H4jbeXIlGNEk5S8Jn0kIo0n5Xb5Stv4EQQiYilS43f6NMgjfoz4MUT8QN+tbA8774z4kYEf2nVvCz+qXCCUDLY8MCcexE/agN9mMGA2KjfRlgMGRU+JC9UKGOZSyUNPylqWohrjqDlW7XOp40rZI0J/pfaeIK6OVYVfRl/VY7ceZtqOvYD3xdV+H0MQ+lBS1xYhw56y8oebz49SLhTKd82jfo/4MeLH0PBj7d0F2hvxo9kM/KgRA5Hjq27JJ99EaaisFT8daHCHpu9d/Oxg7hLgNxprwxuk+BYmCHYRYPGfF8vNLAWjJlIXBau6qsiZOlYqyHBWaFzWPSL1V2oPkxHJ8y8ExktE+ao/JTIc3WcsdCa/1Mx+p1Ks3Zl32JcuSbsXrXvjtFQ1OjNgeVIJPzAa8QOnET/kPYIpibQYYt1zjkb82BD8aOMCoU7LiAjSSQ5TCkGXXMDZLvPuPQZ07ihv5lTbRQ/GFQ4aXaTYpID6EmhramlgybEKLlUu80FMj5A5MgdWBe89G2V6OUJy5J/0l2nH9z+lYJab/5fatEJQl2KIgUBTpFIyAIqQaXL2d0UHiIvlmRYuzn2klCBnK36syd8M/OCoBH6o+p9LI35sDX74tm9pMITBD8ueS8aPVAwZ8aMpjh/VXZhyDgy5Lgucm4Lrw+LHklOXI8o/0Xozr+oOVIJKunJk+vI1tV2aCM11Q5mO4btU9ewXCrjQsCZr5Xy+lND/YvNOvD/Lh58z+xsPeg6oX+2DO9Bl3rf45T70seX5SPH3znLXYJ4f8aPFdcxpT8kDI37o55PFIeOey4kjK4YhjKwd8aMHFogcTUauu4ZUYA67cafQVLjtWkBs1mehXkorhQgByg3tSBGsWMWlCRsrxM5QrkcUv10sYM0RTfHKtZGygMV9egTCNfSlRN+vJOz9rv3ri7n9IPEwwbm4WLJRPKDej+0R+FOYLKJEHRVP3MXnNhTt2/j4h0y5k+JeMeshflg0u51Y0LQYUtKqocSQvuHH3b7iB9VnYzuWPZc676UxZMSPRGpN0+02u9s87rYHv18yph+bQ2DzcZD6EK26F7Qvpc+6iaQMpEwyT4PsCVR7a/0HbTKVPi1+V+sVdwMhbEqBWYC0KRedaVRD1YtowZxQBwYPnBjNFLwtpbs1mayZ/Ua6hC1+bgMPHgd9ejUIvLL0y/p9x3vva2WDIaWv5UB10bBH3gXTvf9st/D+4VI6T1req51TBfw4JlLqTvuGH2AR0aTg7KRie88xpDf4Ibxn8PgBmvwDkIthv75KPMO1l4oh74340R5+tGKBMGib14i6EUcaD6rq3iEcftBbOuETd0Q84zZrkxgDQGkLQt/BrrSJXQWZaQXatEeaNy6fvNdKq/lDydu+XXVQN7ffmCDvBoQKWkhx8SxlSVPPO/RLKpqllg2ahAHwOWbunhjGgu0RLgX0ObgMHaTu6aDvkptlG3u1U6qBH0G7oeZ3JSlHj/CjIWRLchapLcKQPuFHw8i/Z6Xwg8AQbXKPnNgffyi9Fl9mFs9eKYQfUmFXczKBET+a5H3aygWitIAh8oSfRtq/Evh2h3UVbkfgHpsZP4jaD/M0N6WCd+C5G136kEI7lEbhbkVQogTaHDQZ/uCsqdLaluaNAyMqm4o1vTAV9MseZgwF17AgvUaxN7MC6INxaSpdm2UDBabBmoTuJt+BC8BpbCzIHkvVTpovQ0jfNS6QbWlPu6La+IHJot7gR8msehXwQ8KQ5EBZjgSt6cRjlzIGqk3LDeX1sVN4jWMMeQQXTlTmGfZbqktYKfw41xF+xK6qcyRF89bhR5EgakXgzbxUwLCRkcKAuE7qCZQU3jXGoAh6qhVgJtXh8O8+KxSqaU3zxszVWpB0hXf4wE2Rf5igMAthdVp6UcPBSsS+cYD6INbeMgF6OfOZU7xRI+/mscVoaMTxFhfkOOJHt/ih2Nu18EMbHH0oHHhXitnWJk6+p8oIA7Y6F7qPM/DD4+2lbcIPeD+FC80240e2BQIqcl6najIImgLHqFbCbscUzXpgZlXNk7KZGmOQgp5qzRFXhyN8N1V4r2r1a4IoLYrzhS5FbOCmcjkfcTkAACAASURBVB1SU0zGbZQMgLRqOUq6FNwiXJC+BWt3EGiVzxMBejEIcMHlxTQ8ymDayVDdmAAf4mDkWDZSADzfIvyIC0O5OTrsAX5IGFJrjjR7yluRjrXVnVugqqnQhTi9i0pFV4mg4o3BD5jT95BzyCRU7C2+d5vwmNhY/DhVYGLDQzEmNDi/8ZQbonYyH0eb0pw3OZeiG/ekgHAtPgZltqgac6Q95O6AgK0pcNUZqEqYmDN8ZLV9p0DqIeU2FvFnjfnF+J+r+N4GoE4RUz9H94QsTLs1wCwEXkY7ODg3JkE7FspG0u1ji/Bjjoz3NFy+OsMPZbxOl/jh3n21hUN7XzBEitPLwQ9vAW/VJUyInWsDPzjX2yn83b9/d5vw41SB2APqYDBVdGxWUXB8FG3K1MrCSWRwtbIsXJUxeAZjzIQ15kib7s1vtuICNyU4M9aiuDZg3sS+KYFGdViS+o6BFHyvtWB+xR4IrTddAOozY4pPrF8rvNBCocVW5VhJIvCDm/8kUNtQ/KBw1pJGttoYBH/rTvGj5qE9F0P8ngDtdQn84PbMfg5+MEkpqgXzCwf4tvDjXCEM2d80/FBfIAiG42gmdHieODFawXGx4yrKWlO5ZeFqj6G1OUKElM+kdTrikSOL6dPoZ5ll0od3vRtk5Fmm98TAQwM0gqZlpg2UBiD344+zKTXE/CQVccvYA7HvcS03EA5QLRps0czbkoWqi2rw2ZSAHyHPUy4oVFrUbcIPC20bfjSQ2v2lPmJIy/hxHLVJ9puTYS3JOM0+eBrF1nSBHw8hRnNr8UMdRG0MYjkJymCCSr65+PmBlImKNjhn2loJGILPW2F8ZSCrOXilUhYNsX3r54XiRExBeNYgQSk4Mxqbz8ByomVZ/FCFpFy7PxMd3NngOUFbvzYGQ6C0ev5SieGNYsGvNQLfQT5kB+Z1sCePepD+2UQJQZAnfIv4/zt6svj5f1O0nhuEH+YEDl3hB/U3eKwUfmDxNJ1hCLhODQ0/XL8/idqpiiECz3SKIZfpeX+6+HlF6dK1sfhhuUBYMru47CavM4KDzJXNDQSbaPiTpnpu9YOUkvE8JZdeb4PpDBkImtQDf+kMFdbnue8rNJUPBQ3hyjwoLiusoEI0UpYDGTt/ObxE8MZy7NBm1Ywjif0LTe4Y/z7UBl1y46/oBtaqLCtBRvxw3/37oWyMeNQdxi5EtTi2DT98P19NlLWt4UfwPk12mqx57hGGYL75Q8CPpKQlqfwkybfc9cwlpn+HwSG8idaGSvqwcfhhucFxrjbxLeRCWOHOddRNnGN0mMArjNnPUu3S+Q2+iFRkfEDUheD8oU/82d1mhd9JFfrgOeffG9M86KOzPJhrQPj+wdjbqvyJmRHde/2PaZ4TzIVaX2jr81wFY8mFQHIviOeB2j8zoY87YFaOeZKrNKmeP2JfWXiJM+Fz89sKEdWI53BB8Pwb7kv33a/DvzX95sZfgmq33xZZ8MPx/LWQB0MMAcvAmS3Ej0dxlXjrIQD6+CEEPLdZOZrCkKYwb/cFQ5oB4sdD5kxYC0Mk+dYphiD4cQ8uD9eC8cYY8s1twQ9LEDWnkbVmGErZ5JQAci4vH4Q3Ozhgm9pHbmp+E7wY3DR9EREuhzJlSnSC/2amebaLdKspwYzTjgJ8TM8LwWLucJFLU4PvIRfceSlK3XgLtOf3oQ1vFj+vrTZaMLUjuZc78JfVaMbOI4fPSeQKcA3RKD1FCoiVOLBIVLv9tmhb8SN8v4ghtfBDaLs2flj5NYe3e4Ehi58h4geXZakWhrB7uWsMIayWlCy5F6xTjB9/6p6Dc0U4hkHjh/oCoUjVZukkuUkZUxjV1gQCRxuFX2vKJgh98ncVmRYozfXHmUyvCaqbVjJXazOXhN/vIsDH/DwTXKcZ8zERuBe20SgFIdZ3zBR6Orgk7AX94LIs7VcUMFRF8eXYLcGLpSji9yaYQ249/XcpcLhA7P3a2XkGm3UppC3GD1+D5JwSQ2rhB9d2bfywYkgOb/cCQ0DRMCj8CN7RJoaI8q1tDBHw4x0mcHrK4MePxu3AXA8aP0xpXIHBLEEjM+MmPWKsAFye3D1FLt7UTUD57VI3bKod6nOtoBafh2ey08xlZC7B8qerKRBgYXCZ6KaD+EevaFWocQrzrYmB8CnkqMC9lXngBCEBENp95vn/NTCvvg57wGnND6PguXjM7AWAI2jLH6yL8EAuEYH4GpJcAai9f0QE/lfPbNO2H3subSl+cGlWMQyphR+aNmrhhwVDlpn3DO1iPPYiJwNLYcgA8EMjy074P7CWhfEZK5YvQiOfc0g9IiqFJ/NAZfw4x8SJzAznyNDN9zC6RD4eCn6kRLFjPmmPYNAqgUv4lbl/f5nQ4rwhFOjw34vpadi+MDluAq00NbTDtq/wI5T6V83HDlkvLvI+pTpsTOci4XYbNrZm3vbg+avgM30nxQ+Y8J0/jvkJflwWlJeD72r4bY28n7frO+EDzZHXdl4L9spu6EtO8NjzsH+bhAPwAeIOhOXQb4VgfCmXh9gVQENTxt2JPbBYfOUZWdlkxq50RSN+8BhSCz+kNqr6aBswZBLHv1go2JOhDLxO4UcKhgwEPxqjHPT48U50mTkn8Rgc9lPjFJx7uOXzPuCHL26LjXeWsPfnljNUn/DDXIma8flb+4zbAPFtGja4pSiOhlzQ9k4FLbunWUETquRHSJkoG2G+kzVa1HoBk1Ep6M5k+tKSvsqL936gdBkj/S8tfsAW02kpM6si1oWjPWFM2FydBmD7VoImgtS6gh95klYjgw60qS/jFJ0GzaGnZwyvOvBDEyRIGl1GK4RlUcmJXemEthA/fMyDFkNq4QfXRy6+ogh+IBjyZmAlaArysRU/qGfQPoz4gc7TlSDrXikMudQBhmjxw8usg3i8hAsY1w6mhCPPUBqLIIEhl2vgh/kCIRyW7mTe/FLIp2RMcsMQ3Gc4QoV6RtAPK6gz2q3lA2f2pVUSZwLEmNsKcAcV+16CpP6l0FQY20XQglmJ4q0mzHSDBI7VIs3aLV0HqH4g+4wClJ3Ew9UBJbgR8OHcRYod7NqmLcMP7vKAuqnUwI+MtovjB2IlKMnHVvzgnsE+H/EDmacMBZoGQ96HwO+sJAIF8QNV6hjxoxGC7S3nl5ODv8HlsAh+JF0gKtBBgttBE0S9NyWq6wU+utQF4qmUhcm3s/hVKshsltlujeqgkhD1RXOawgJlWgDgzH7AFirgVyj1j6rKqxlT6cOARlu/FjhWEQQ43plr1gNZPymzlXU+OcHNgsMmBldvEX547BAz+dXCj8S2u8CPHD624gf3zGwD8SOFauGHxeK7NwT8QOgpVxmciQmznl/851oMKbKWrVSDrcD0bjFf9v6JlJ9XIqNRfXEFW045kzblW59JVfIdF54b7XpdyPDF3md8AGcF5i3FD1hFSj9kiaT+3YQ11FI4pqI8hvCWRGbfaSNh43P9+qpm3xLrd4GJ70qZT87H3aIV6rzGRo9oCPixdIeqiCGbhB85QbRW/LDO3ZDxw/fRKaE6x48EDOk1fhBruCsE0Jc6v/jPtRhSZC37YoHgbn4NsqDfJHLqWjItULfJTjR7GaZrVduLXxata6pZOzsOIsiiocpskzBvKX7AWrJokJvU/hn8LFcykNTgsburfs2a6rnVXGwKjA9bvzNUzATsGzJjSYJGV62NSs1YtqE04sdm4UfDxRGVxI/gmUY5hiHix0qsJPy/1k//YW0eM2JIn/HDP0sl9FmxPAb75qzWu8X9LbEeyKwGfrR6gWAEjSWA+S3wnzSllTP4hhU12VqEqySoaxA3L0Cp65W80RfvuOEC3oh39xZooT3L50n9i77DCVufhekkeLAyj2n4ovpFPGN86hgRIpjyXM76EhdDSfaca9nM3xltI34E7xbl1Ygfafhhxeih44cjJX6syZQWeEzay33GD9ECyciT3Si+InV9rfInCz8m83mJrJsyEYDrA9h8VcQzRF55T951A2P6ZSVZ5v13tc8V8EMM28EWEzUBl3qvsY/UvBxDv///9r4mRs4jPe/rGYqUyFCIrdElMWYIIzcffCcSBz7sIkAYx4cYC0i0Ai6ygFYiICQIssicEiAZWI4ThM6uokQBGdCkAsOnOINgs0i8QALo7sA67MURO8nCgIZ2QEojDUVOp2v41qim+v2tn+/7urseoDmcnu6q+urnfarev7qItT1oKyWAwpt9qz2TtY+FcrLaaZljqUDauR9tiLqa9TPtiLUqXpBOqHEZYr5zMMqIKmNt6ZM+5ttYwKzzO6Ctdv01E9xyl4o/rPKt8Ufd/l1j/ijaBqm/4G/U3Rij5Q/LGNYca22/lGhDnwcIjXsDh5PJM3/dIwLmZsgJL+zEbc33SiKBdFKIQgzKE9p4bAxAjBcC2W74f7Zw5lBiERQ46O0Hmxh205yz8BWbKDIArOQc1/aXgiSqzg0rjGvwuG95EmMMbegLzDrHbttdCf4wbkgaf1Ts32XgjwyZGx7CqbVUbJ6vKn8Yn21w2V2iDX2STI7vWhi4RZmwHjph4Dpl/nqGXPLSDWASy00PtxA0RATp5FwkZX3+M20XguxUz5SJEunIqHbejS9rQfp/By469L9PgouS1AGHRLnxeFLtvAZk96CnOa4aVzB5XyEC0PqYGyZA+xyZOvnRCbfYauQQe8lPAUgBdasELj1naf54NBL+sMi3xh91+3fU/KHkEJY/YNPYB4esJH8EOIw8ObAxXAn+ODfSQLf4NBRqAjAfryO4UXeLIRW/MOPT/T5oISRzT4q5TAxoUbgCxYLsFmNu9AuoRJq+Q21udMZvsI9c9SWC3qn2bCJxIZoUhJMEU6QmgI7uz8nktY9fePHSS18ddT99eav7zV96o/v9X/jlopl5EuYqV05WGTUA7cJu8sYuoNolXDIvB+umduraGik2x4o++eOygT92NdyQ4W7ByreENcnJr8YfUf8uEX9QZYdjyvKH+9z/mv//p5dfnb37V9+YAH8Ukylrwh8fRGNAjfVK8EefFggsbZQGU4XG+zHcqKtB+L07sGFg06YptcMY2FRZUbkkIu2FdAeAaQFRFgRIM8am+XLtEU7LfWhIS6Qj07THC2Jt/1oFmeawhbbz1/7wRy5+6INLX325tTEfwp979Gn3Gz/8vnuf0n6YoZyrU0M5SWUo5lwO1FotYt08Qm4VLaoVC58fyr1TOMXmWEGlWBySP0I3TZIbMviDlW9W/lDKpcYfy8kf3He2Nfwxf+3MJ+fkLz7+dPLuD//V7Fc+/nExmdL4Yxz8EXIIuHP6tL5JY92bBQKJHn8Ip624wzpN6s75j9ifTIMpEsgiaX0lEyV5y64iYl6rjfA3MWqg3cTFbVrQdnBtR07b2Gm5uoZUm3VCgDaLy7ZBEzqtYElB+/Mf/bd/28Vtv/j0qPtnP/ztz3/vF795dd4/d0Eb5lxy3p/3jTk9omKuaseVK4ctQznncmCymBnk0I5re0ESDp//xgofGqR1LgWAVuUPA4ck8Yck34i6Of5wSqFj4Qboxh/LyR8aDlHzx0tPn0x+e/+fP5hbIfZhnjqXvsYfS8wfBIdsgZy8nlJ+r2lckQ7jgomwDSu1SdQsSoxMtAOuNlFih4j5D8qcbTHJa24fFjdxxCbkNhDMK2HfMq5JKpebQpt7EUI7Vd+P2kn5VU+VZJFySBIPW1R/zi0PbhOygNnX/rXhfH0b5qn1EMFpxB7A2sXuZrGUI22ES+RJr+kOx8mh29RBx+DeUvv5Rw1Cln4UKaW6WI4V4o8ZsaY1HJLMHxRvwuHByh93BK+Dxh/Lyx8ihzT+WHv+KM4hg14kR2z8biZoHqVF6Uw0vxtvcAwbBg3BqAaB8JMrgRkT8Km5LOuCUaurOnxRm/sMn2AzNHX5djLEfLKBYDSh38q5kEV72EL78138MPp/Xz5zdUGIN+cv6wGCWgMPkPXHzSGyHMX4F4upweZEAYsZJ4fc+nqfUCaQfRe1czI2v9+hoVEeFOCPGdwf4S0O20YOGQt/nActMmWBaPyxpPyh5ZDGH+vFH90cNTlkWVL9sb7JiD/Zs8Cn63Uw3S7EOsDCxfxq3fsp8RuaQdC4LaVgAtabEm3U+N0lxzdk+gSbkFAX2T9+QcbZIeZvfRSNqSOCe1Y/fSHrhNlP2gVSE+BcGEx1wPuWjBg5MStFYmqoOQF/pjKKiQjkEIXLyFwg+w5pZ7dGWZeG5I+DyC/4OqxxbM7sQ/BjiCfRfB4Tf2wwsSONP5aYP7CylbKr8ccK8kc3R20OGdQCYYCoeeQ0UYyf6jUw64bX3k+w23ujkz3lRzot5AryJrPJO2DcmTQEpPXBlMpSnbaJk3qfrhjWulLcWLA6Jj1lUpiLqtmH3eSkujP9PPdf9bEPMXyaUjUEX2zUhQo+pyoH0+4qMx+lmPvJOQGEnusOd1+o+0OlbNNsFlc169Jo+EPgkG8hG/Izv4+MP/ydFtayw+83/mj8YULjj0H4o6vNIVUtEAUzpuRqHrlOvoaczNicxPNf36igRT1J2wa+6W9AasEO0Wy9k5mrWasNm6Zkbwo3fcxJvY/0rlKZ1PspGnKp3fXzU7tDxGx25cr39q/PX9385TMsYHCmUDOiNXByEITAr2PLHEI0cJ1Gy6eZc0rUnn8Hhrq9336HvC+1p1iWrTFiRPzRMWOxhSQCuTBi/vBuV5ayQzT+eI7GH0Y0/uidP7DPF+WQahaIubD/fqTZ34FsECnR/KzmUeGjyGmVzZuJ4DR9K8Fv0RIsG5bvBvqd4G9JmlhEE/AQcqCfTylr/iMl6JXyw63himGyKCQGfmu0ctX91BF/SJfl7On85TYtG5lZNLh6NjO1GmorUW7AvHVOJMbquEP+fe6ioKC8CxnzqoYr5CgwMv5IuYei8YeAxh+mudX4g8Y68cc0KlNKZV2VQyazmSaVtg3wgPcUt4T+QEsC1EBorg7nPgNlmq+yJ8o8gpziC1lANM9CPz1Zf2gS3yhUVrGAZu6q9PnrC27MEuvD3KU6aX7kQhnYmHIxkLUdVABf0bqZep6lzENmnji8XlrDrpEZls8pN8BeAzwTDg0eM/C9H3xe9Y2x8UfwfcuFm40/MtH4Qz+fSqDxx+j54zC4u+yi4gLNjdp7k1oHCG2KuWfzhp/rY9LnEoih3oXsHbkaX6YNyRO0D3BjE8RCZB9aoB9Ca82ZvoD/i3VlphX+yfz1DWLTc2ZMBjiwnfZ57YMhuGhYy3vK+Gwn9x33WU05uYSK1HFJmY75tI6gDErWJPX5mDFG/iitoGj8kTc2S8Iffn5Q+47GH2fR+EOeT9r0/6fyqyaH1DpAcBuZM5g3fFKpLnWnwGnPB56pzOOGZzzRJNbY0EvkV2ujOqYDjuKEbdnsafJzh7fPXozGGZsPbj69obGG5faJYlNSqp5PU7SuGWspnM+qvivRz6UOSgoBHmOhnX1pB8eAxh9n0PhjefkDszhSWuTGH40/uLklpWHtFJxYnENqaa60vuzPKtY1NQzOjUADuglZmF4r9IyTigG0ZPwGFcCcEYiYCjeZawZ9SlkGtL6jmmwF3q+Sy7oUYyN6XkvKUyt2ieDJYvXA/HGxFZ2QutICaS1xGSWoZyrRz9nBt9E6pPBYERiOBa+6Pr9UIMh4bGj88TUafywvf5xH3BUvgrKy8cdZNP5AgOzjKIRXF1BrBOOQGewXk/gjyfyr0Gxrr3Yns8EYtOe5qR3NKUWhbc4dQYvsACjEzHoAAdCYJng69K21hEYmK2CHMOlJfZubqSv1c1T91TIAgWn8luBjn1vPHlH+owwXLUleTBP6jvrsDmhiNNY4tWxhnllzMD2SNEBE8oPLwfqvny64ENaEP9w6bPyRiBXmD+s9PI0/1oA/BJclMd4h9HIwcEh2uvkUf2VRsw0NuBNYGNzP/xn9TgbAWbTnBVI7mjZ0Qdu2EC0ihazsQlDn7ahO9/8/j2ic/QTtM1UqhqKadmpOMGnM2M0enLhDza12jKbMZ2eK+otchsbABfBzyK1nm6s3xfIVrGEsfV3Yh5a+455TZY1DZIuTWy8Fl7x1imfeLjBmWCrQzwntZt10wZlYI/7Ywupr/NGtO39wnheNP9aQP4T5qdqvaeWV5xB4PvH6ghoWCFGzTbgF/SXNKUlbR8HUXNSlPlRue+pE+Kfz110kcv7URJQRg0Bpfd34/b/5609cHVGQFuVzzQq6gnETpQ8w1JxwAuIQ+dtp2ltBs+UXqia7QSiMMM3CHSrousJlaClp26zac0v5fl4lWb78GhbaYuk7SSulssYFWpuFORNoa7hn1qRoTDnUDa0gSMWq8Qc3vlh9jT/Whz8sMRCNP9aXP1JS3o+CP85Vqlg1CZjB7pMcKSvMJrHpJ9vmNGLz73xUykSkDL58ZV7eq8QCuB0JryNuo0oJR6rNwmKliDXOg689oHCa7+tMO2JQc/NakNZXk4XJlxX/TZNxKySs40ArcZWqzwBK8JkIkZmnkhDOWrvcZg4xwZJ9FH12p4J/cyjPuGe+LhBR6uGRcl/kNKpjwKrxxy6Ts925O7zW+KMof8R58LVycgj+4LIwhfuExh/rzR9cWRvEAXcU/HGu0iUa4iTgNi7KOkppzTkNEraZYtsWnISxiHd1DIIyK9BpvQRiE5UUwa8+/Ss2nthG80mG3zbZ70YNIncAxMpBDwQpWktiTP0B1j3b28H7ST6JFiGZqq2Fg06YteyOdn3kwtLvwlq0tEmSZ+zcjMbjM4if2kD6zgLNXRJjxErxRxB3RKXnbfxRjj8OQaljUXgMzR9dKatX44+V5Q9fFlVHmLL4Ich+n8gkDorulT9SsjDtK3z1NL5m3MZFU8cJCmQbwiLTsTZxn8falqsF0wTPcJkL9qLbpTv4fY/x42QDh6I+pcbvPiy4DvEtfpTht63tdxLCnCgVg8DhljEQMMnnN/SVh58fFo4B4rKWZY9TBeS2SZJnbPmB3+l16K8NY8a3M2AyYanjKQbEqvGHvzmW4pDGH8/5I84UlsIf3wFNf0psROMPJRp/FJ87U8X7ZB1R7Ns7kUvTllVeleQPUy5zyP/+NuLjH2vRNOmiuI3LDURb7tp6yyCIVBuvKNCFwnZC0J1m0nCQDhrOJeUGszm0pnh1ZnjuUpCYWLn2eb/QLtzIMpNTPFQVCHbsYE5QN2NX3eBCv2kvEevTp906T29x663QOBVFgTaJBwRl+aUCQ7nPa+VLr1hV/ojGn0LjD1tyAZQ/ctzTGn9UQ+OPAgeQnjmkGH+oXJgEd5oJaAVOXT0Qs73/XBdlP8A2VMdEPS/C67QMqEMlUDgztdVUpTSD5QbLThW3cN6DYGnM5E59f8pYN6QDZejaQpWPfVbTJhGZwY4syfSwwU3NjlN7Q2hNVbql3CQNdmDAkOEyEJqOD+EQfEZ+GMrnLHzHWLnGcrqBLT1ryR9Bm/eWgT/cZtw/z/znPWLe1eAPS3IByo0ymUMaf1RB448C/FGIQz7pmz/OFXKnWWiQIhYAy35wCAGlXQFBNPEdCr+HAcXu87cRv0lsMczAJG7ylU3wRe+QOuLbKSkfUHeQuOqCuJUE5EiDAxf5v23I1b7dcwYiCVkHmEykWBKq9w03T5H5fmksmu+C2cK48sO5ugXjYb5VPmjrRKmhlQ601Ob58ZCWHgaNP8bDH7tE/EHMIbn8scHMd01yAf+5bkQc0vgjQuOP+vxh4JDe+WMym3FeK88B2jGu4eRV2Mx3Z1j2A/hdSnsYlyFlOXlG+HydZKaJ2vCT+esbUZu51GvF3DMITZ0fIKl/TvoibAtFWIylJSyL1WBF5e9o50Xuxi/n+0T/Fh1Dpm6qz/36YLM+9Q1DEH88Z6q3uY9xZMaLlHWF+lGsA1yBMAI4IDKyDYpV5g/X35FMckHyf26s/CHI/zMcksMfzu1IWkMp/MG1i2lPye82/kjvKw6NP8r1ZW/8obVAcOZG6fRPntoxkw1oXbSdFWc5werhyjkJQAk+swOCBbtgw2edScqqlKGpm0Sbdkr7M4nbwpjENDc3ipqewMpECdaFeSGZ6SIBfwyaLN+eLjEDhzaDUE1Q/RlvIDRpYPuAJog/VYOei6Rc4UaUSgVq7UdNHa8wl5fl3DdTCyvLH4jsww4ao+EPYX6d4ZBM/qA+F8cOmfhDaNcJoLwwM5ZXFJbgjy4qOzULjhWNP9aTP1I4pDf+OFcjt7ziu6fCAdMIQDAJlxqPEkSSpgsDJnAxSC49JZCT7kvdFkIQLkT+a03oqeb2GAiRbEZ+z4dM5g40FgTxQ3w5KNdnwfmo9oarVB/1iNR5nS2IqQ1A0FfaNJ/mvlaYihdctYS6thmNG3WJpeQOxsmAvg5xFqwyf2jJfSz8kc0hGv6IPseuw5KyEebDnSgD4Ra4MD+28gfDIWHZ1ddb44+15Q+uvRR64w+VC1Mts6FkEkQWbscFoQhuIikpazVxASaXBg5C+3896JN7xAQ90xbNmFnGtQe/c8k0LuGMRt9g+is2hingrC4DujBRZs7HcOs6Zqk748KQWC+2AfCXIH5bcKEIXSPMrgaK+bJQhkKGce4iWP+VaOfgc3pN+OMAypssC3/A51Qcsmz8UYBDtOt7rBzCbZr7bk/jjwL8kTCnT3myD/5QXyQnmQ0Tv8uakhLqpLRVd0ArdT66Q+ERsUmaGXxYSwZv7RKbp40w6BvccN5iAuTI7CXxyVLbx0TgXWnNS642LtZ+a7WDRbSAKQQpWV1GpknuIJD6TWFzrAqoJrTH2P0lHSRA8GMrudepTdTI4Y3SFJ9kP0PGQqqLSswwITaZok+8wu2mj/S/Jqwof/j4OYncR8UfAofsw7peRv7InfeYjOiNQzK13mGSmA72NXcaf9Tlj2jcODmQyh+dkUMe98kfuVr5wf3E3OA5gQfm5z0QoHEu3ZsgvMP3bxAXALnf3yPKqJrfVq4vzQAAIABJREFUHspyh5qOWQAdtOU6cslOF93vgMVypKYTLZF/mAQsQjeGHA4UPqfbCfMoO4NQxoVUHEEV69+EtbTFyAz/XMkX7FD9JYyZv8NkD7K1PYP5/wx+31PcEcC1YZOxqFAX8rF1wXdiuUFhQytPYCN4hSlvlPdBFEYRP2M/70ErfwhyRuQPGCtsDTwhyhgFfzAc4g85S8kfMIaTgvyB/d7VWG8Z/NFBH2I3C59eHNsXgnFYef5A2tGV5o8EDnmlT/5QWyAqnaSz0qIRWo0bWsEs+BXeLKlFM4CbAGyufVi4FytpSooc9n7tD390652P/sPWX3j0affFCy8eXPrqy3eufO8kS+4HzAbOm+Z8IBx3cg7njpRrvKQWUNQkEBp3qf960SQbTPWnzwXuQl2iWwLVX1wK4YeEtSaOlaHS1MVyRatdPGbuaSBlGDLe10tYbkaWGnlp+aNEukVrrNhY+ANrzwrwB9f2rxL4o08OSeIPGEN205zZrhocsir8QbUjxsPAqhc/IyvDeuCQLP44V1q4G02VueSnMjUhftU7kXlvTC4inMCaZgiS0zISTaXZh71f/aM/uP2P/+u/uXDx6dFzX5ivvtx6Otm4MyeFR7/3i9+UFuEkIXMHNr+OwI+f9IWuQZDUumCEVd+a5KRMDxnrZ5vRUD0hYiA6JTlRd8zsJpIr51ZGyTDsvhZ/eVmxTf+yBVeOjD8sGzeSP0bGIY0/cMwS+KNPDkniD1j74piPkENWgT+4dng8gcxsW4Q8I2WYMOZFOCSXPzYqmNnUpkrCNLNgPQhdK+Dna0atxi1kQp2H90kw9dbELky6DlkAu8IYSMIi3NhYTaXJ5kbA3t//H79zenjwODc7Pu80SorvnzHHauYO8RkXYPSqC/JlzIopmAra67vEuugYs3qfmuTSmR5Svz8FK6JzN/Bw//+21jwLn9O4i5BjxpR9UTMP4T4PTg4eRs+X7M7izdEV5nRRjJA/tBzS+GMN+aNnDknlj5PsUfPX2Y55jic9WyK314w/uHaESvoLlDwT5iElC28hbljJLpE5/KHOwuTAmE3CzA3kxT+ugcp6sLRp5+ModfgM2p4gIJPLFuM6D/3bwJeOkRkVuDEA4f42UWzYJ0kXZCHj0mm1MG5e/PG7f2OycXqv0ddwE+bnn5uhJSRn9xmZCxB3IZb7faPvTBrMvDqAZyq6DlLWF9PG1Auo9pHAVke6Tia8kDoPoZ771Hfnry8q9eeorRBj4w9Bnrp5/zmU0fijPH+YLs5s/CFe2hfvGcILSnuRBcxawpLSrAJ/7MKfkng/g0NiFNmXpnDIuR7vKUiNa8C0C+HJW+M+kAJTJH5JCGY9bgycMMaguYhuW9suo5uBx/SnL2/t/NzcdzWG82WFsqTxqmaOpRaPdlExmU0knF6IRcx95ypxy2ouTxAG1FoK/YaLbU4TTadYG2PEmWS4SwhvgGtKuIn5WeIyMNU8DMaQwnFKLnoOietxCIyNPygXgtjtIAWNPxheS5izo+YPYYNZlT/89xluvge8XyR9cgKHxDJ2VfjjA1Bkf4dQANbikK4kf+RwiPUAoRHu1CTaNaRxVKVNoyaRoQy2fOP7fYEbA02bLQFCJclx97f+yq/f3vsvPzhjhn462XjiAuGCcr3W8HJk+qvmzsMsnquRhlpaVNeMwj9+JqxfzzP+k9bnIb+nuKW7+EbU6v8arPdQ0+Z8kY+CA1ZsVZAuIbwWapvm5UsmWWkecrLnEMzOFFI3/oNtVpeZP9w/BIdcyjw8nJZveH9Z+CM7cD1xzo6WPxiZ6zbOs6AdtfmD6lc2FW8PHHITS0qzIvyxB/zhD8Yal54cDuGQozhK4hDrAYIV7oFA975Z5IVYxATVmmwWTt5R2ZSWPcaBUD56KY9re08mQYwguTGgzMth8HV4i6bHDFKcfaI0K5vJ8fninfuFPHu6kEWjm80+dBXP8WFtlwzDpuNiIAi1i0qzOeDWheb7VJKAeANkEgZQxo0hbulOQPhsl6GtYXYK7NkvFtjQUbm8wz7kTOSc22XOxn+sm9UY2sNBSNaoGx+laTVswE43vDGHgBtVLf6YwByt7lZSmj8AlJvsjoE/zHN2zPzB+Ktj99nU5A9NGRr+4DhxFThkKP6YcXMxGIcdQeZwyo1UxVESh5wrZTZCBPqmF0zE5MmxEkjBQZrUa/4iIKu5a7MP9wBKA4CYzMIx6ASC3iOEWqiheFtxok3SQj0vw72ex65fEj9bVnvK9CmlFd40Lipp3km+ipp5e6Z+4pnE7y2xFltqZ24wOCWgDzgfb4XZ+QGs0b+tGOOUZyiVFrYaJLcDwq+Z4ghO0zpm/ujFxawSf3SMm6yFP5Lm7Fj5A/rNsmZr8YemjDP1J3DisnPIkPzxamYMpVNg/WT++qbQphTFURKHmNO4MgvTOnlytGOPEnzdTKnXAoF616iFLoU9jcnM6BeY0ufYs6rdDIaEQTNP5Y6m3qcWFdYvs+A7JXw04/otB/FpjgYiV6tXSCsotXNqDAaP5+w7yG2+0mZR47rkM6h9Qyin91zefULY2Fk4ZFn4g9Io1uaQGvyR0u/Ucy7FnFXcVO+fT6v8qckfVBlc/dQ8sXJfdQ5ZY/7wde1DjIyEFMVR0nosmdHGagLJ0Y6x6bhSU6+5CepMWM6EDaYsro+KugdcWaw76YpxeK7dwKfV366Y0+fbiv6tnp3KAnjmON0gdzsmlmLwfeJ9dFER/XJdmx4N+f4Bkp4vrl87DyVhwKXFy70pNfv72nbCM2Jj9o4hbSN16zAHbhzCtHySljwpl7erY8zrsQKHLAV/wGbdbwJ745BK/JHa79vLOGcRmcVZpDG580Qhv4vxB1HGLJE/KE4chEPWnD8eQN2aGJnk+4SgDtN6LHkTtdUEotW2UnWxsJowGVMeG3Rc4lRM1E3hOOU55u/n9Pm0RxNxKb9Vq2Z+lzDrf2RpU26/IP7YUp9MFWkoczJo7BYyT5cyb7PtVGhRtSRsHUNqHE5cl5Qbxgepc77GehwAFg5ZCv6AuUg+VymZh9RfnD+Cw4W136d9zNnC/GHhkCkld4JyeuGPuIwM/iA5cSAOWWf+uOL+o4jtTeaP1HaXPECkZM94CQSaxRJypsyCQoOaoOSthAphm1M3hQ3huVnTdbQ4sBzpMXoxK6f2JdMPJs08tXi4RVWBsDqq/qCue1G6NjL9qqUtGe5v230G+mrcLAbaTGvkH0USMx/El1p5H3OxB6g4JOIPytViLPxxslatt5UntEHLIcn8MX99iKy/TtCKjpo/CnDImc3n/AdW1zLyB8mJA3FI449ufPxhukiuRCMITckRXNy0YdGkEmUlXarBXWAUXfQVaqfFi5Ey6yZPmdRzM5lI0ItLci73KYl5Oz4lLD3cpS5hOjbtJYNWzXw39EWDUl19EFHuXBfc8rI0J1rU7iepfGIcnXx5b/65m6nl9zkXayOxDw/hIHF5jPzh5C72XPB7Nn8I9S+UDXU3/qAPQRKHSJmQNOO1VvyRyyHMd7PHQovGH3UtEFpgGo4LTJAKp0mltCV3E7Q4pCmPOYlTp2Kf0k47obmTZSzYuBRrd633PBDPVjxns2JhUvEJ20ZzvaT1K3WzM6up68Edq+/sFrlBjz4AbNJ3VhrisGmqUzOeksZOYz1JdJEZw/zIhnLNUM96AJaI0fEHNTcYlwQrf3D1h2j8oXP3kjikxCZ/3fgjl0PcZ25Hd3t0gfWxGoeU4I+gnGTL+Rj5o1gQtSHIZZsJbLMGcVBlbSYE2OwWDBrqjEE+VN3vEf3BPfdlCN7qxpzdIoKbqJY+lsz1J5cMVg7SI02qBQO+hqgLRU5/QltuCBpSL7CKIugf7kZibRnZfez6y2nbnFYafmrnIyfki5j3h0TjjwVY5xjGIUdwsGr8YXf3qs0ha8UfDgX6U7KwFeeQEvwRlZPVz2Pjj5IWCO0pJkXTn6J1MZ2wE093UkCZqg1C3TeNz32hlJtOLSAncW6i7iZM7IdWX/gEjQ+ncRTXgrG+rLpKAQvOAy2q1H6tf3aNDa942IzfKHExXwVwQt6awGKMaPyxCPUcM/JX4w8ZpOUIQ+OPYjEZFPaEWM1aHGLmj5FySBX+KHmA0J5iSuZ/ljbw5gDNjKChpLR5iXVLz/0Kd2nJkL5+hCmNwkECAZZqkxNsVxnfdG4e3+PmQkLAX3JduWNK+G53xvZr1+G0wjzcTqizxMV8pSFlSxl9Tn0BjT9s/ZLDIY0/+HU9s6ydxh/V+SOJQ4bgjxFzSBX+KOLCBB12rOngkmbBoKxnmrrD9jofUxd4Bj+TzXXepATPgWGSWwdWZ8pza9CDSVOrjXZuWL9LjBNmrlfneVe2yT37W9RzC/P4xAKC4CH4Nt9H6iPNoUJdUl7r5DGlvgf+oOr2M22UsuPUzPu9UGdCRrQ+Nfyki2UP7npV0fij8cfI+KNEFsXGH+X4w8whA/LHWDmkCn9kWyCCgdrUdrBGU6I9PQZWAEsK2fh0eH/+/q3MAFtOq+Mn79VSGY6sz21AbZcY7Yn7CPzmUS0Fc0t4Sn51qk0T7rmJwMjXiGwwTyFlLhUszgZPMmtGoz1IHVPqe1aLH9ZGR/CPmJt9k9qMjPt+NI88Doj1bkr/q/xsMqLneQj1LvRZgvvnKND44xSNP3Ro/LF+/JHCIUPxh/QcQ3LIS1TWqlT+KGGBoE5bz1K1YNbTo/EERbV3K0fTHrUBw0XIQhM+0+0Cmv3DaFLnah6TA2qUlh3tifsyp6WAZ3yDOFXvG7UP04Imxj0kU4RfaxofzhRNiTQHUsfU+uxTw/q8Idzsa24zITec8L8T1f061E25x2E46FvDjzzPFqyB68YAujGj8cccjT8afwAaf5ThkKH4Y+wc4hSuX5RySy8RA0ENyEafaccMJyhuU5SlaQ8ChKic3PF7bqN5KyWwlkhFpzWbcUgKqJH8+qNny8EZLQsWOCikZ8SCfn0sATZm7jD0mmEuk+tB+X3fVheg3CXkxMfmQGqQFPU9KuUyqU0pmCCBazM17tccwSgD95LT/1aIHRpL+sWaaPwBaPzRG38cB1zh73to/DFi/kjgkKH4Y604ZKMv31UjiqYlDDXjTHuz6gjrMVwK13mXlgSfPW5i5CAlnS3bHuTZcnHaN0RaMy7N7Zm+DRZsVyglMDXvqXgVCtuK+aCdA6ljSn3vnR587lPanJ2uMNUftFLs0NKnZ1WAihdq/KFD4w87Qn/8Loc/gr99xnyn8cea8Me6cci5Pn1XDSiWlhDR0mLtzaqDqMcKajN4C0678Ym0ysQAbY+L1fhucMDUbH659miCirxv3rFijKRTtCZL00nfwk+pbZYTO+XLfAgWJ40bUwf9IGkOVHPAmOLR8r1qWnBNmxFtDXeJInfAxeox3QTMlW/pp+iZqLXwsLLWqhdAu11cEBb/1PjDhsYfNv7g1qeWP7zskHh/2fhjJ7S6N/5I4o8Pg37ujUOMnJjNIefG5LsaoGRaQqqdx4gFJo7it3Qot0l2J9CfJYJrnTmPW8xbwQQIbw+skvsdnvs7Ud9chngNLjsF1x7NocZpgDbASqTFNlh84jGS0hR6bFnq0nwoELbxzZWXYWOEzbsOmYcvKdpBEl3sdpWaf3uo4Fxp/REuc0cQWHfemO7WmlIQa+Mk90BvUHa87MdXch3U1j0QqNzujxt/nEHjjzr8gXKIgT+0yjH/2aXhj1iGNP6ozh/TaGyoMZPKi9vikrdg2Be+p3qGjZH5rp6gcFpCLsPO61gdiaYkqp4ZaDPfRG6IfgLmvA4mkAb+RJrqlpJK6hcEVx+uPZpnOzb2QxeMzZkxQuaP1X0Ig7pdUL+7yA/rwz/V3BjL1Be+vwvfobJH9WEiLQ5lOzHivgBZOSzpbjlNHZkYwOCWZ5nP2s3I+WB8a7ky9oHtQmmYz6DxB4vGH4uI04x2Sv7gNn3YZ8fIH1Q62wUZ0vijKn/sEOMQjxkHjAsoI8E14XsqDsmxQEyJh7YQZteD5pNsJ1NHiimJ7Q+FGVCr9XDYTjUrEm4Sp2m94D2yXuoPXHuUZt6NhH7oqDEKx5ZwL3OC85CwQsyiTWHKwYzbHF2Xxo3os1moOYC+VV0kt2RBuZp2kv3rMmQYrJqkpo7TzBBtjGGdNxb3w+0ViJOoxiGNP3rjD+9O51NpLht/TDA5A0o/iT98wg5u8xd+dmj+cLgUxA76vr2vbEPjj7r8Mcnce6TwB/e97ZoHiGW5/TSlnSkdKtbDkRohQC9x/mspJMm4SZxMbsZn7rReClR7omejhC120JIEs2qMmGxNHTFmd3Lu6xA0+tzBFYtFeSsQKO7njfn7HwXtETdhAeHvFDCRVvG1T3AHMm0+iTmwD/2LYSoQJtdns8T+oZ6J+mx1RU5lLAOHNP7g+SPkih3CIjoUf3CyhIM6hozYqEt33GhA8fBDA39grlA+Xb2/BNd9jurXkz5t/MHuIW5R45TBHw8SOTaFP7I4JPkAkaPB6BOJ7TR3aIn+iAWDoPlIBac5vQjlx37k2YGNkQ+l6qAFvqnWQwS5gXRlxu8zY3bTWG+IPYK4ZsY+vIaUE2vidzlNE/xf0shNpYMCUs6pNiV3zRsSEMTmd9PaQNbXJ8I4cdYdSkY8SAjA7oza0/A5l2ETvrQc0vhjAZLljdqw984f7v+lOER5uBnVHA4OB1sJHHJi7VbK5rXkD6jbHSBSFNGcZ0yf/JHFIVn3QAwVYGnVjCa0U92hNbWylYSTpG32JtLwZM3duqiG5kbECFZztHmMKs1h1lRfoJxtraYJ+uSits8osytRTin3J63vf+y+lbs2JJcKSjP3EKyDMbI27gqXAq+dOjN/x7qBWRUOafxhtlbOIg36UPyh4RDRZaTC/kKDVwrFB2k5BLV2z1/fmr8af6SN05RRRHvrd6q7UhH+yOWQyWzGWRz7Q8pGHL6DmZAOczNBadrDaEMW6q550LBAoZHJ0aBy9ar7ium3z5BMVn4CW8coy00psY/VfQvtvktk4lkoJ1HTtiBMEsrx7jrSOuEyKlEXL2bPTa5u5lkPXBwFMXeewDPHt40X2SQJ7Tp5/rHIkjEhgz9Cd4JJKQ5ZY/6owiGp/IH03QxJHkNyyBD8UYJDFG5HZ8oZmD/YmI6x8ofEIZAgh5o7NxCLz3vzsnM8H9Rzp5Q8ybpIrhRSovuD72D+Z9lZSMA/ML5gJoYqej3l+SqCy7xQ0/UhKdI/HAfIQBFj4s1+xjF6KxqP+/Px+LTQmGB9fCYAmkPiHSvWoNkHRJ+lBN+S/aic+06AaWBqm6LuXSQzmsNlH2iIZIN7hBweHH4GUuOeZtrIAJnRbGSypFsB/vDfoVwFk7Cm/FGTQ5IzxUQc8r+NHDIEf1Cy6Ymmb6P5oh2jofjjM2RuOzn6/SXgD5ZD4OdCNlE4eGIB1G/X5o/S8mQUB4hE4SC5PfSRhUQbbD2aVIvIpuhZxdsgQ5TIFsNefGP8Dqax8AFmWQsY+tBpGULzng+Afq3SHSuUED0wpvulyjlO7EfN3Jc2JVLbKLB1Qz+6AwGZsjjeCDIma+6W2jNwf8NS+3kQBxc/7qORJSNCDf7og0NWgT8O4hSilTikVLYxazm980eA2D1E6y7CzW1qjIbij0vEZvqtsfOHhkMIRQI3Z2vzR1F5khUDMbBwkATHcXyZVgVwPm4hekm1qDVLDeR3rO2rlDIc7hABWdx3MJTy66cCoG8pylbfsYKY52M3DH/PiNZcaU13SkHKNoHFcYSpIS9Hmv6SqRDD918xflczn8g5RMWYxHPXr1Hk4r9lTtu6TPyxcOP3qvOHlkOWmD+4cibEfmEo/thDLJ0XMvnD30k1Jv6glNiTJeGPGhxSkz92S8qTwQ4Q0cSlrp/nhIM0CJs93MjKBlsHz0j551mFHwntxBoQJbLFcAFx5wnhSmWY4HwmSxAzVcaW4mCrIkvCP5fy51XNASqgCn63+siy2SbiesM25vpowvc1csW6MbHcUotBlUudiO/i0mQWkyXLgkz5qtkYhq5sg8hEwzxedQ4plW1slwkybfzRP388I+b2svBHLQ6pxR9cqv7pUhwgEEFl8fW2DEIpbQAKLnqd2NzV9BVVTayhkBPpj5RBEcCWst59JIgpxEMIRBLbyQgqboMijYmWLLExnyQHMU4mJ8/yydfPcr2bzXzAcWcIKPaYphJ/jpZTEUOyH4zvQyRtMdk+ZD5ZN3ei9keQHbmXDa0ECshXDX9cGIo/MmKhVpJDSvBHUE7jj3Hwhw8ojrMRjZ0/fCzaXgEOYe81qcAfPlV/nFUxSZ4MkoWJiRLXpmazXD7lzHbmWA9qY6g92QrZCIplbFFkIkh6/qGg6d/5Z8hJO/+s6uIg4oTu85VPEGFAZUZBM4PA/++nXjbG9UPwN2p+2cf8ufDHn+VrEqDye7OZ0JTPUiybiSBf3kcOj27MH6dc/MTNAaNcOMlowlxMhMmQnIuqlhqCfF3IDrNs/KF4xh+UyNiyjhyy7vwR/P3+0PwBa+L7RErTMfLHG/D/D0pwyMD80eVyyFAHiOKCiutY62maGVQs/RYlHLjUYtlpZms+/1DQLqb55z4lFslJ+s2EOjW3f8dp78Q0q0w7k+eCQvN6KuhM82syoQXTbHalYhpNtfDUQCJGaFvRdWLcFHKy5e8w1pzqMmSZUJpDxsYfUE7jECM0MmWd+UPJIY0/cJzIltJ7rSvD8EcRDhkqBqJUQFRKPIJmg0OZcr87f20oTbycC4vKLGxss9nUZyw/C1Fdx6GlCT5CadSxvnKBXbejxXIUBHypgfhOHhtMhJzvpm+ntNln5wIyRljWihibTFA5hSGCc6l1Jq4NDMG4UPD3VRR9Tou5XPARtgj/UbiWDIjSHDI2/sjmkAT5buKQPvkjqG/hglP4f8gtm0JfrTN/eFnDldn4A4eXLUU55JNh+KMIhwxllmTz1GYMApq6Kliw2rzNZBYc4n3s81JqMXayEW0m03txz1+i/BwgdYUpL2/D6XlH21fwTN+OnvXb1LMaQRFA+L4kgKfEmJjmAjFGGvNkGFSuBbXxEjdkGXOp9GaeGxcvX5KfsxTcvDCm9uuY9MVoKr81QFEOGSF/ZHFIypq0cEif/BHUdyeSf+7//x7eD7lFzNqzxvxxW5kMo/EHLVsG5ZBPyvGH55Bk/hjEAkGdoqAjssqd/8DKoBasz9sca2qtqdumzDNSZsppae0s8/xFys8AJzAvpPbv/EcNbRdF8uH73GLltJaUeXvh+eC71NzRQnvYyM1ykjqXSmuRuXHxm8Eu4zmLI5gjKt9rBJORZctZWg4ZE38U4JCkNWmQq33yh68vjCvweEH5fTZrzxj5w5CxyMIfFi1144+vER6kS2UE64s/3Lz5M2Y8k/ljsMAoaKjXCLoFtVdRi8YtWOwCDe3FJOHnqWd8A8yjIY4Uk622S0mfLis5Zfa9MDXaBfQzf/PjHx9//C/+1uEn716792cvvfzoVz7+8b1I+3MZubVy4fkUJu7yeB7otnhrJgTApc4l0G5QWvLSlkh2Q+Xqt1rqHNz3hOdIQqQhpODmyw8U8mjdL5OriUH4wwHm5fuIBlpaJ6vEH8vEIUX44/PzL3369/76330E8VycdaXxh9AfBcbuGdyj8MmYOOSKnj/eAHc4TiYt10VyPeecnlpdZFw7Iu0W+X1Fe+PT4WSgOJE+y9fUJUGVTSVx7mHZhPx7UprMBQ3Er/7RHxz91n/+l5Nzs+OTU/7PfPn48m/80CWX6Lrf/4VfDrU/PvuB1wa8GF7yAs8qmbi18HXp8PywgGUKkbS83PjuUOu7ghaZS80Z1//hCOSUNM4uq8eb0E8fKTIG1YxXGRXWhT/gOW9EY+7k0x3hOVeJP7j6KJgyOo6NPy599eXWP/nRv+6ebmyG/OHhYwgdGn/U54/NkXLIHtJWlD+gHf47O0t/kZzGBKrcvGgg5fwWXWSYjAiPhTsDMNPr+fA5DW2mtA0pfZRlhqMC2oi6uf5Xp70rAWIx30baQF2og24Q/umP3rvkhb/HxadH3T/473djAnBp074Ift+IhQqUacEBcvNmUlBgouDTXta34OJQ0pUgGhdtQH62nNKsRWLTIY3zJtZPIG/63LyNEapxKcQhQ/MHlqvf3XR/s4R8T+yjbDeOeb1Ow/ImzPOTFMvzeqln2oVYh5hLvwJZsy78EXuONP6owx/agPxeZFUuf4QyqSR/DJLG1UFKw4csVCmfMisAkQ3vmTLnr6ucMIPvx5kbROElPSfyvvq5tH1ElZNKrvA9TJgfUcFoyixMfWSD0qTGs6dlm0zQcT6ev/Xz3/tP2ps3T+qEn9gCn8GcW5hvKX2omF/qdHVIWdtD5pS3rDtBcKvK4dYiyBYsz/mhYi5SfV00/e2yQTMuJTlkFfkjl0Ny5DYcHt623G9BKa1y2rGE/CHWCT8bf2TCKPv3GNmRJavGzB9VDhBKwSVdBHRJk6vZ2hmEILyqEWYpOZ8tmzArNGXX2GwIY2fPId0ToC+4y3nSN7vEPQr/5+VXu7/83TtnynSf5uqcv64zB7R/B9pHbc5odB1q5kXm5mWwe0ng2X5n/tpQ5mMn+0H7HMzn/IVvE+JvFwUzNNrXBa2zo0Mh/vCpDTVjl3XQWFb+GJhDnjKKlNfHOJdHwh9infNX449MMAdc89q4oltjS8kfxV2YMt0ePDhfxy1XR1CWKRsE5jIxL89lK8DgNEo3U3M+Q184kqgV1KUJZDP1T2a9HZDCWDPCuGe2wGLSW5jPh+cudL/5S/7iSn2dsHHFtJ1Oe3lNs3FQrEPNvMjxc6bcmvaNG0WToAs0vRhxY8kLpH7QumpQa4LTEDlieA/RLqky9Lj2Bf0Tx9EsJQryh/vbS0Q18VipZeSK8cdpXQNwCGeFbfwxR+OP/vlDcXiI195e1EZsbWg4ZBD+CPto/vOe9TBRw5VvsMhWAAAEu0lEQVSA69BTRJHsKXWoBKDrHHe6c8KaiX6nhNlmasaFgAi3kFMjaR1RtFVVf+VsGdMlzQjDPbOYHcmSxejzF148+Id/7eYx4r/qFjpl9gvrdMIhZ9ykdaiZF8lZkmB+34me1T37DZjnYh55zWcQ7DGpCh8j626by48N/9dk3EiJP5iCNYkS/mcIM0Zi/4wdpfjjItOvx4YsYqvMH2wbKnOIs1RTaPxB84dD4496/OEP/hTitbct3a8QySqKQ3rnjxIcUiOImu3Q8HQTaNEoVwlNHaSWFE6S4emM0mZRfumckJNOlhgROnzOuFZhmrerjMuK5mRL9c9DGA+sXAlUQJtmHphRytxGBEX5929k1xFkMXJqw/9Iaxg7ECIL8SBBnblZTrh1+JqmfCzYL2qjBEy4hZsDSXOj0e5Y5h12KJMySH0Awl6y+lBrkfJTncF3nNaHgj9wfUT0eUr/jB3cvD0O52AGf2wo58BnMD6ryh9SG6T+OY7HhHhODO8zWl6Hxh/j5Q+3b3go3Wm0pPzBHvwJxdEO8XFL1qYh+CObQ2pYILgJSp1uzKevoAxKS7pPmHYw7YYTZhio908gnCypBbhtHMi3qNOh8mSL9Y9z5XiZKlcClH9DSBNaJCNMYS0rNd8nxO2OuSC1e0Fd54g6c+9I4Pr/A1gfYvmZ/cKtAc36sK4h6bmxv0k5+1XaUGotEvm33d/fg+/kWPNS+mfs4PqDWv85/MHJyMurzB+KNkhrZDNVJkNsiLvfhELjj/Hyxw7sH45WkD+4g/+zFeOPbA6pYYGQUt5hpxvpOzEmvgzqlAu/q/KlO2EGZWhTyoXfpU6WVg0ANWBsKkzpZEv0Dxa8Z9JcRj7YNW9lLKll7Tt3eXKqwwLaG25NXQTtzncKWXZS+1sai5Tx2kWy3XgXtV2hn3dyhCm1FoVx1Mi+7ZHM5z6g6Q+Nj7GKPwQZeWHV+UNoQ1czvSX0m7vfpPHHcvFHB94HToH4J4nlj5U/OOvY+yvGH9kcUvwAkdKhSgFFlkEEtnGmHSxvtxP2osCvuPg5U1iWhtEavGctO1NQSSipZe31CvrcvlGYPTV1U1lDtnPKL9Tf0liYxyt4bu0dJVXyY2PlZ8jL6Rjmcx9A1oyoBMrlD1+GUkauHX8gQZeNP3Rjsgr8wcmlV8KMmKvAHykKgU+Wlz+yOaTqPRCpKegIrba1DKrukzRnMChVYfHdJ56ZSvmZlcIvdVyGQOm2FoynWAoMPdZcf9fKopHZ1sHuV0ipf9Xnc8r8bfxxisYfjT8GWYOl0PijPn/lcEjtA0QyIUcP1SGXaGg6BduQO9+xkpqiYkAGch9iDYpuaIbeKFmwTG0dI1r/JfXXYBvyoesfGwqRokPjD2Xf1RqTIbBMbR0rWh+a+2pvXfij+k3UJR4otYyhB7MEaj3DMvXNMrV1jGj917DO87fxR+OPnPnT0DikYaADRENDQ0NDQ0NDQ0PD6qBGGteGhoaGhoaGhoaGhhVFO0A0NDQ0NDQ0NDQ0NLQDRENDQ0NDQ0NDQ0NDeTQLRENDQ0NDQ0NDQ0NDO0A0NDQ0NDQ0NDQ0NJRHs0A0NDQ0NDQ0NDQ0NLQDRENDQ0NDQ0NDQ0NDefx/tH63eiYyDfYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x792 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation, PillowWriter\n",
    "\n",
    "TITLES = [\"No intervention\", \"BP\", \"CT\", \"MF\"]\n",
    "ALGO=\"CT\"\n",
    "plt.rc('axes', titlesize=16) \n",
    "\n",
    "fig,axes = plt.subplots(2,2,figsize=(11,11), frameon=False)\n",
    "lim=0.6\n",
    "axes = axes.flatten()\n",
    "for ax,tit in zip(axes, TITLES):\n",
    "    ax.set(xlim=(pos_nodes.x.max()+lim, pos_nodes.x.min()-lim),\n",
    "      ylim=(pos_nodes.y.max()+lim, pos_nodes.y.min()-lim))\n",
    "\n",
    "#fig.patch.set_visible(False)\n",
    "    ax.axis('off')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(tit)\n",
    "plt.tight_layout(h_pad=2., w_pad=3.)\n",
    "axes_rankers = {\"BP\": axes[1], \"CT\": axes[2], \"MF\": axes[3]}\n",
    "paths = {k: setup_start_all(ax, pos_nodes, statuses[k],\n",
    "                    st_val, colors=[None, \"red\",\"violet\",\"limegreen\"],\n",
    "                   fn_get_nodes=get_nodes_ranker) for k, ax in axes_rankers.items()}\n",
    "paths_No = setup_start(axes[0], pos_nodes,  [0,1,2], colors=[None, \"red\",\"limegreen\"],\n",
    "                   fn_get_nodes=get_nodes)\n",
    "def animate(time):\n",
    "    if time >= T:\n",
    "        time = T-1\n",
    "        \n",
    "    nodes_idx = get_nodes(time)\n",
    "    for k in [0,1,2]:\n",
    "        g=pos_nodes.iloc[nodes_idx[k]]\n",
    "        paths_No[k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "\n",
    "        \n",
    "    for rank in axes_rankers.keys():\n",
    "        \n",
    "        nodes_idx = get_nodes_ranker(time, statuses[rank])\n",
    "        for k in st_val:\n",
    "            g=pos_nodes.iloc[nodes_idx[k]]\n",
    "            paths[rank][k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "    plt.draw()\n",
    "\n",
    "anim = FuncAnimation(\n",
    "    fig, animate, interval=100, frames=T+40, cache_frame_data=False)\n",
    "\n",
    "plt.draw()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "anim.save(\"sim_all.mp4\", codec=\"mpeg4\", bitrate=-1, dpi=160)\n",
    "#anim.save(f\"sim_all.webm\", codec=\"libvpx-vp9\", dpi=160)\n",
    "#anim.save(f\"sim_all.mkv\", codec=\"yuv4\", dpi=160)\n",
    "\n",
    "#writer=PillowWriter(fps=15)\n",
    "#anim.save(f\"sim_all.gif\",writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATES_CONV={\"S\":0, \"I\":1, \"R\":2, \"Q\":6}\n",
    "def get_nodes_SI(t):\n",
    "    out = {}\n",
    "    out[\"S\"] = np.where(all_states[t]==0)[0]\n",
    "    out[\"I\"] = np.where(all_states[t]!=0)[0]\n",
    "    return out#{i:np.where(all_states[t]==i)[0] for i in range(3)}\n",
    "\n",
    "def get_nodes_ranker_SI(time,status=None):\n",
    "    if time < t1:\n",
    "        r = get_nodes_SI(time)\n",
    "        r.update({\"Q\":[]})\n",
    "        return r\n",
    "    x = status[status.t==time]\n",
    "    out={}\n",
    "    quar = (x.q==1)\n",
    "    out[\"Q\"] = (x[quar].i).values\n",
    "    out[\"S\"] = (x[(x.s==0)].i).values\n",
    "    nq = np.logical_not(quar)\n",
    "    out[\"I\"] = (x[(x.s==1) | (x.s==2)].i).values\n",
    "    #out[\"R\"] = (x[(x.s==2)].i).values\n",
    "    return out\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_circles(ax, pos_nodes,status, nodes_keys, colors_in, colors_out, fn_get_nodes):\n",
    "\n",
    "    nodes_idx = fn_get_nodes(0, status)\n",
    "    paths = {}\n",
    "    for nk, col_in, col_out in zip(nodes_keys, colors_in, colors_out):\n",
    "        g=pos_nodes.iloc[nodes_idx[nk]]\n",
    "        paths[nk] = ax.scatter(g[\"x\"],g[\"y\"],facecolors=col_in, edgecolors=col_out)\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation, PillowWriter\n",
    "\n",
    "TITLES = [\"No intervention\", \"BP\", \"CT\", \"MF\"]\n",
    "st_val = [\"S\",\"I\"]\n",
    "st_circles = [\"Q\"]\n",
    "plt.rc('axes', titlesize=16)\n",
    "\n",
    "fig,axes = plt.subplots(2,2,figsize=(12,12), frameon=False)\n",
    "lim=0.6\n",
    "axes = axes.flatten()\n",
    "for ax,tit in zip(axes, TITLES):\n",
    "    ax.set(xlim=(pos_nodes.x.max()+lim, pos_nodes.x.min()-lim),\n",
    "      ylim=(pos_nodes.y.max()+lim, pos_nodes.y.min()-lim))\n",
    "\n",
    "#fig.patch.set_visible(False)\n",
    "    ax.axis('off')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(tit)\n",
    "plt.tight_layout(h_pad=2., w_pad=3.)\n",
    "axes_rankers = {\"BP\": axes[1], \"CT\": axes[2], \"MF\": axes[3]}\n",
    "paths = {k: setup_start_all(ax, pos_nodes, statuses[k],\n",
    "                    st_val, colors=[None, \"red\",\"forestgreen\"],\n",
    "                   fn_get_nodes=get_nodes_ranker_SI) for k, ax in axes_rankers.items()}\n",
    "paths_No = setup_start(axes[0], pos_nodes,  [\"S\",\"I\"], colors=[None, \"red\", \"forestgreen\"],\n",
    "                   fn_get_nodes=get_nodes_SI)\n",
    "## circles\n",
    "paths_circles = {k: setup_circles(ax, pos_nodes, statuses[k],\n",
    "                    st_circles, colors_in=[\"red\"], colors_out=[\"black\"],\n",
    "                   fn_get_nodes=get_nodes_ranker_SI) \n",
    "                 for k, ax in axes_rankers.items()}\n",
    "def animate(time):\n",
    "    if time >= T:\n",
    "        time = T-1\n",
    "        \n",
    "    nodes_idx = get_nodes_SI(time)\n",
    "    for k in [\"S\",\"I\"]:\n",
    "        g=pos_nodes.iloc[nodes_idx[k]]\n",
    "        paths_No[k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "\n",
    "        \n",
    "    for rank in axes_rankers.keys():\n",
    "        \n",
    "        nodes_idx = get_nodes_ranker_SI(time, statuses[rank])\n",
    "        for k in st_val:\n",
    "            g=pos_nodes.iloc[nodes_idx[k]]\n",
    "            paths[rank][k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "        for k in st_circles:\n",
    "            g=pos_nodes.iloc[nodes_idx[k]]\n",
    "            paths_circles[rank][k].set_offsets(np.c_[g[\"x\"].values, g[\"y\"].values])\n",
    "        \n",
    "    plt.draw()\n",
    "\n",
    "anim = FuncAnimation(\n",
    "    fig, animate, interval=100, frames=T+40, cache_frame_data=False)\n",
    "\n",
    "plt.draw()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "anim.save(\"sim_circle.mp4\", codec=\"mpeg4\", bitrate=-1, dpi=160)\n",
    "#anim.save(f\"sim_all.webm\", codec=\"libvpx-vp9\", dpi=160)\n",
    "#anim.save(f\"sim_all.mkv\", codec=\"yuv4\", dpi=160)\n",
    "\n",
    "#writer=PillowWriter(fps=15)\n",
    "#anim.save(f\"sim_all.gif\",writer=writer)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "##### RANDOM SCENARIO #######\n",
    "scenario_rnd = Scenario(\n",
    "    model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=RANKINGS[\"random\"]),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options\n",
    ")\n",
    "scenario_rnd.run(t_max-t1,  print_every = 1)\n",
    "print(\"Save random strategy\", flush=True)\n",
    "#scenario_rnd.counts.to_csv(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_rnd.csv\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking),\n",
    "#          index=False, sep=\"\\t\")\n",
    "#scenario_rnd.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_rnd_t%d_d%d_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking))\n",
    "\n",
    "del scenario_rnd"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import sib\n",
    "import bp_ranking\n",
    "imp.reload(bp_ranking)\n",
    "from bp_ranking import bp_ranker_class\n",
    "\n",
    "#mu_r0 = np.log(1 + mu)\n",
    "mu_r = -np.log(1 - mu)\n",
    "#bp_tau = 10\n",
    "name_csv = \"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp_no_tau.csv\"%(N/1000, T,t1,N_patient_zero,mu,lamb,seed,n_ranking)\n",
    "prob_seed=10/N\n",
    "prob_sus = 0.55\n",
    "pseed = prob_seed / (2 - prob_seed)\n",
    "psus = prob_sus * (1 - pseed)\n",
    "\n",
    "pautoinf = 1e-5\n",
    "bp_ranker = bp_ranker_class(params = sib.Params(\n",
    "                                 prob_r = sib.Exponential(mu=mu_r),\n",
    "                                 prob_i = sib.Uniform(p=lamb),\n",
    "                                 pseed = pseed,\n",
    "                                 psus = psus,\n",
    "                                 pautoinf = pautoinf,\n",
    "                                 fn_rate = 0.00,\n",
    "                                 fp_rate = 0.00),\n",
    "                                 \n",
    "                 maxit0 = 30,\n",
    "                 maxit1 = 30,\n",
    "                 tol = 1e-3,\n",
    "                 memory_decay = 1,\n",
    "                 window_length = 21\n",
    "                            #tau=bp_tau\n",
    "                           )\n",
    "bp_ranker.init(N, T)\n",
    "bp_ranker.__name__ = \"bp\"\n",
    "scenario_bp = Scenario( model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=bp_ranker.step_scenario, lamb = 1.0),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    "                         save_csv = name_csv\n",
    ")\n",
    "scenario_bp.run(t_max-t1, print_every = 1)\n",
    "print(\"Save bp strategy\", flush=True)\n",
    "scenario_bp.counts.to_csv(name_csv,\n",
    "          index=False, sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##### TRACING SCENARIO #######\n",
    "scenario_trac = Scenario( model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=RANKINGS[\"tracing\"], tau=trac_tau),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options\n",
    ")\n",
    "scenario_trac.run(t_max-t1, print_every = 1)\n",
    "print(\"Save tracing strategy\", flush=True)\n",
    "#scenario_trac.counts.to_csv(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_trac_t%d.csv\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau),\n",
    "#                  index=False, sep=\"\\t\")\n",
    "scenario_trac.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_trac_t%d_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau))\n",
    "\n",
    "del scenario_trac"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import sib\n",
    "import bp_ranking\n",
    "imp.reload(bp_ranking)\n",
    "from bp_ranking import bp_ranker_class\n",
    "\n",
    "mu_r = np.log(1 + mu)\n",
    "bp_tau = trac_tau\n",
    "name_csv = \"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp%d.csv\"%(N/1000, T,t1,N_patient_zero,mu,lamb,seed,n_ranking,bp_tau)\n",
    "pseed=10/N\n",
    "psus = 0.60\n",
    "pautoinf = 1e-10\n",
    "bp_ranker = bp_ranker_class(params = sib.Params(\n",
    "                                 prob_r = sib.Exponential(mu=mu_r),\n",
    "                                 pseed = pseed,\n",
    "                                 psus = psus,\n",
    "                                 pautoinf = pautoinf),\n",
    "                 maxit0 = 20,\n",
    "                 maxit1 = 20,\n",
    "                 tol = 1e-3,\n",
    "                 memory_decay = 1e-5,\n",
    "                 window_length = 21,\n",
    "                 tau=bp_tau\n",
    "                           )\n",
    "bp_ranker.init(N, T)\n",
    "bp_ranker.__name__ = \"bp\"\n",
    "scenario_bp = Scenario( model, seed=seed, \n",
    "    ranking_options=dict(ranking=bp_ranker.step_scenario),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    "                         save_csv = name_csv\n",
    ")\n",
    "scenario_bp.run(t_max-t1, print_every = 1)\n",
    "print(\"Save bp strategy\", flush=True)\n",
    "scenario_bp.counts.to_csv(name_csv,\n",
    "          index=False, sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "#imp.reload(sib)\n",
    "import sib\n",
    "import bp_ranking\n",
    "imp.reload(bp_ranking)\n",
    "from bp_ranking import bp_ranker_class\n",
    "\n",
    "mu_r = np.log(1 + mu)\n",
    "name_csv = \"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp%d_tau_obs.csv\"%(N, T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau)\n",
    "pseed=10/N\n",
    "psus = 0.60\n",
    "pautoinf = 1e-10\n",
    "bp_ranker = bp_ranker_class(params = sib.Params(\n",
    "                                 prob_r = sib.Exponential(mu=mu_r),\n",
    "                                 pseed = pseed,\n",
    "                                 psus = psus,\n",
    "                                 pautoinf = pautoinf),\n",
    "                 maxit0 = 20,\n",
    "                 maxit1 = 20,\n",
    "                 tol = 1e-3,\n",
    "                 memory_decay = 1e-5,\n",
    "                 window_length = 21,\n",
    "                           )\n",
    "bp_ranker.init(N, T)\n",
    "bp_ranker.set_tau_obs(5)\n",
    "bp_ranker.__name__ = \"bp\"\n",
    "scenario_bp = Scenario( model, seed=seed, \n",
    "    ranking_options=dict(ranking=bp_ranker.step_scenario),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    "                         save_csv = name_csv\n",
    ")\n",
    "scenario_bp.run(t_max-t1, print_every = 1)\n",
    "print(\"Save bp strategy\", flush=True)\n",
    "scenario_bp.counts.to_csv(name_csv,\n",
    "          index=False, sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "import sib\n",
    "import bp_ranking\n",
    "imp.reload(bp_ranking)\n",
    "from bp_ranking import bp_ranker_class\n",
    "\n",
    "mu_r = np.log(1 + mu)\n",
    "name_csv = \"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_bp%d_no_tau.csv\"%(N, T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau)\n",
    "pseed=10/N\n",
    "psus = 0.60\n",
    "pautoinf = 1e-10\n",
    "bp_ranker = bp_ranker_class(params = sib.Params(\n",
    "                                 prob_r = sib.Exponential(mu=mu_r),\n",
    "                                 pseed = pseed,\n",
    "                                 psus = psus,\n",
    "                                 pautoinf = pautoinf),\n",
    "                 maxit0 = 20,\n",
    "                 maxit1 = 20,\n",
    "                 tol = 1e-3,\n",
    "                 memory_decay = 1e-5,\n",
    "                 window_length = 21,\n",
    "                           )\n",
    "bp_ranker.init(N, T)\n",
    "bp_ranker.__name__ = \"bp\"\n",
    "scenario_bp = Scenario( model, seed=seed, \n",
    "    ranking_options=dict(ranking=bp_ranker.step_scenario),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options,\n",
    "                         save_csv = name_csv\n",
    ")\n",
    "scenario_bp.run(t_max-t1, print_every = 1)\n",
    "print(\"Save bp strategy\", flush=True)\n",
    "scenario_bp.counts.to_csv(name_csv,\n",
    "          index=False, sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## CT 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ranking_tracing_secnn to rank\n",
      "Using seed=6\n",
      "Selecting 0/2000 untracked individuals\n",
      "t = 0 / 110\n",
      "state: S:1921, I:75, R:4 I+R:79\n",
      "obser: S:38, I:2, R:0, I+R:2\n",
      "free birds: 73\n",
      "obs_symp:0(0), obs_rank:2(2)\n",
      "pos_rank:2, inf_cont:0.0\n",
      "t = 1 / 110\n",
      "state: S:1907, I:89, R:4 I+R:93\n",
      "obser: S:75, I:5, R:0, I+R:5\n",
      "free birds: 84\n",
      "obs_symp:0(0), obs_rank:3(5)\n",
      "pos_rank:3, inf_cont:0.025\n",
      "t = 2 / 110\n",
      "state: S:1894, I:102, R:4 I+R:106\n",
      "obser: S:114, I:6, R:0, I+R:6\n",
      "free birds: 96\n",
      "obs_symp:0(0), obs_rank:1(6)\n",
      "pos_rank:1, inf_cont:0.05\n",
      "t = 3 / 110\n",
      "state: S:1873, I:122, R:5 I+R:127\n",
      "obser: S:148, I:12, R:0, I+R:12\n",
      "free birds: 110\n",
      "obs_symp:0(0), obs_rank:6(12)\n",
      "pos_rank:6, inf_cont:0.175\n",
      "t = 4 / 110\n",
      "state: S:1854, I:138, R:8 I+R:146\n",
      "obser: S:186, I:14, R:0, I+R:14\n",
      "free birds: 124\n",
      "obs_symp:0(0), obs_rank:2(14)\n",
      "pos_rank:2, inf_cont:0.05\n",
      "Appeding contacts, t=5\n",
      "Appeding contacts, t=4\n",
      "Appeding contacts, t=3\n",
      "Appeding contacts, t=2\n",
      "Appeding contacts, t=1\n",
      "Appeding contacts, t=0\n",
      "first NN c: 230. second NN c: 3241\n",
      "t = 5 / 110\n",
      "state: S:1836, I:152, R:12 I+R:164\n",
      "obser: S:202, I:36, R:2, I+R:38\n",
      "free birds: 116\n",
      "obs_symp:0(0), obs_rank:22(36)\n",
      "pos_rank:22, inf_cont:2.025\n",
      "Appeding contacts, t=6\n",
      "Appeding contacts, t=5\n",
      "Appeding contacts, t=4\n",
      "Appeding contacts, t=3\n",
      "Appeding contacts, t=2\n",
      "Appeding contacts, t=1\n",
      "first NN c: 644. second NN c: 6689\n",
      "t = 6 / 110\n",
      "state: S:1822, I:163, R:15 I+R:178\n",
      "obser: S:220, I:61, R:3, I+R:64\n",
      "free birds: 102\n",
      "obs_symp:7(7), obs_rank:21(57)\n",
      "pos_rank:21, inf_cont:4.425\n",
      "Appeding contacts, t=7\n",
      "Appeding contacts, t=6\n",
      "Appeding contacts, t=5\n",
      "Appeding contacts, t=4\n",
      "Appeding contacts, t=3\n",
      "Appeding contacts, t=2\n",
      "first NN c: 948. second NN c: 8469\n",
      "t = 7 / 110\n",
      "state: S:1809, I:174, R:17 I+R:191\n",
      "obser: S:239, I:85, R:4, I+R:89\n",
      "free birds: 89\n",
      "obs_symp:5(12), obs_rank:20(77)\n",
      "pos_rank:20, inf_cont:5.175\n",
      "Appeding contacts, t=8\n",
      "Appeding contacts, t=7\n",
      "Appeding contacts, t=6\n",
      "Appeding contacts, t=5\n",
      "Appeding contacts, t=4\n",
      "Appeding contacts, t=3\n",
      "first NN c: 1034. second NN c: 8238\n",
      "t = 8 / 110\n",
      "state: S:1794, I:186, R:20 I+R:206\n",
      "obser: S:266, I:100, R:6, I+R:106\n",
      "free birds: 88\n",
      "obs_symp:8(20), obs_rank:11(88)\n",
      "pos_rank:11, inf_cont:3.925\n",
      "Appeding contacts, t=9\n",
      "Appeding contacts, t=8\n",
      "Appeding contacts, t=7\n",
      "Appeding contacts, t=6\n",
      "Appeding contacts, t=5\n",
      "Appeding contacts, t=4\n",
      "first NN c: 1019. second NN c: 6900\n",
      "t = 9 / 110\n",
      "state: S:1776, I:201, R:23 I+R:224\n",
      "obser: S:285, I:123, R:9, I+R:132\n",
      "free birds: 82\n",
      "obs_symp:9(29), obs_rank:18(106)\n",
      "pos_rank:18, inf_cont:3.25\n",
      "Appeding contacts, t=10\n",
      "Appeding contacts, t=9\n",
      "Appeding contacts, t=8\n",
      "Appeding contacts, t=7\n",
      "Appeding contacts, t=6\n",
      "Appeding contacts, t=5\n",
      "first NN c: 1157. second NN c: 7896\n",
      "t = 10 / 110\n",
      "state: S:1762, I:212, R:26 I+R:238\n",
      "obser: S:308, I:143, R:11, I+R:154\n",
      "free birds: 75\n",
      "obs_symp:8(37), obs_rank:15(121)\n",
      "pos_rank:15, inf_cont:4.125\n",
      "Appeding contacts, t=11\n",
      "Appeding contacts, t=10\n",
      "Appeding contacts, t=9\n",
      "Appeding contacts, t=8\n",
      "Appeding contacts, t=7\n",
      "Appeding contacts, t=6\n",
      "first NN c: 1183. second NN c: 6962\n",
      "t = 11 / 110\n",
      "state: S:1742, I:227, R:31 I+R:258\n",
      "obser: S:330, I:163, R:13, I+R:176\n",
      "free birds: 74\n",
      "obs_symp:6(43), obs_rank:16(137)\n",
      "pos_rank:16, inf_cont:4.4\n",
      "Appeding contacts, t=12\n",
      "Appeding contacts, t=11\n",
      "Appeding contacts, t=10\n",
      "Appeding contacts, t=9\n",
      "Appeding contacts, t=8\n",
      "Appeding contacts, t=7\n",
      "first NN c: 1199. second NN c: 6636\n",
      "t = 12 / 110\n",
      "state: S:1732, I:232, R:36 I+R:268\n",
      "obser: S:357, I:177, R:15, I+R:192\n",
      "free birds: 68\n",
      "obs_symp:6(49), obs_rank:11(148)\n",
      "pos_rank:11, inf_cont:3.475\n",
      "Appeding contacts, t=13\n",
      "Appeding contacts, t=12\n",
      "Appeding contacts, t=11\n",
      "Appeding contacts, t=10\n",
      "Appeding contacts, t=9\n",
      "Appeding contacts, t=8\n",
      "first NN c: 1191. second NN c: 6524\n",
      "t = 13 / 110\n",
      "state: S:1723, I:236, R:41 I+R:277\n",
      "obser: S:385, I:192, R:15, I+R:207\n",
      "free birds: 61\n",
      "obs_symp:7(56), obs_rank:12(160)\n",
      "pos_rank:12, inf_cont:3.375\n",
      "Appeding contacts, t=14\n",
      "Appeding contacts, t=13\n",
      "Appeding contacts, t=12\n",
      "Appeding contacts, t=11\n",
      "Appeding contacts, t=10\n",
      "Appeding contacts, t=9\n",
      "first NN c: 1237. second NN c: 5262\n",
      "t = 14 / 110\n",
      "state: S:1710, I:248, R:42 I+R:290\n",
      "obser: S:412, I:210, R:15, I+R:225\n",
      "free birds: 56\n",
      "obs_symp:8(64), obs_rank:13(173)\n",
      "pos_rank:13, inf_cont:3.025\n",
      "Appeding contacts, t=15\n",
      "Appeding contacts, t=14\n",
      "Appeding contacts, t=13\n",
      "Appeding contacts, t=12\n",
      "Appeding contacts, t=11\n",
      "Appeding contacts, t=10\n",
      "first NN c: 1321. second NN c: 5651\n",
      "t = 15 / 110\n",
      "state: S:1699, I:256, R:45 I+R:301\n",
      "obser: S:447, I:216, R:15, I+R:231\n",
      "free birds: 60\n",
      "obs_symp:6(70), obs_rank:5(178)\n",
      "pos_rank:5, inf_cont:3.5\n",
      "Appeding contacts, t=16\n",
      "Appeding contacts, t=15\n",
      "Appeding contacts, t=14\n",
      "Appeding contacts, t=13\n",
      "Appeding contacts, t=12\n",
      "Appeding contacts, t=11\n",
      "first NN c: 1199. second NN c: 3422\n",
      "t = 16 / 110\n",
      "state: S:1690, I:261, R:49 I+R:310\n",
      "obser: S:479, I:228, R:16, I+R:244\n",
      "free birds: 56\n",
      "obs_symp:9(79), obs_rank:7(185)\n",
      "pos_rank:7, inf_cont:2.625\n",
      "Appeding contacts, t=17\n",
      "Appeding contacts, t=16\n",
      "Appeding contacts, t=15\n",
      "Appeding contacts, t=14\n",
      "Appeding contacts, t=13\n",
      "Appeding contacts, t=12\n",
      "first NN c: 1201. second NN c: 3846\n",
      "t = 17 / 110\n",
      "state: S:1681, I:264, R:55 I+R:319\n",
      "obser: S:509, I:238, R:18, I+R:256\n",
      "free birds: 54\n",
      "obs_symp:4(83), obs_rank:8(193)\n",
      "pos_rank:8, inf_cont:2.325\n",
      "Appeding contacts, t=18\n",
      "Appeding contacts, t=17\n",
      "Appeding contacts, t=16\n",
      "Appeding contacts, t=15\n",
      "Appeding contacts, t=14\n",
      "Appeding contacts, t=13\n",
      "first NN c: 1128. second NN c: 3305\n",
      "t = 18 / 110\n",
      "state: S:1674, I:262, R:64 I+R:326\n",
      "obser: S:539, I:248, R:20, I+R:268\n",
      "free birds: 48\n",
      "obs_symp:4(87), obs_rank:8(201)\n",
      "pos_rank:8, inf_cont:2.325\n",
      "Appeding contacts, t=19\n",
      "Appeding contacts, t=18\n",
      "Appeding contacts, t=17\n",
      "Appeding contacts, t=16\n",
      "Appeding contacts, t=15\n",
      "Appeding contacts, t=14\n",
      "first NN c: 1150. second NN c: 2408\n",
      "t = 19 / 110\n",
      "state: S:1666, I:265, R:69 I+R:334\n",
      "obser: S:568, I:258, R:22, I+R:280\n",
      "free birds: 44\n",
      "obs_symp:5(92), obs_rank:9(210)\n",
      "pos_rank:9, inf_cont:2.0\n",
      "Appeding contacts, t=20\n",
      "Appeding contacts, t=19\n",
      "Appeding contacts, t=18\n",
      "Appeding contacts, t=17\n",
      "Appeding contacts, t=16\n",
      "Appeding contacts, t=15\n",
      "first NN c: 1205. second NN c: 2551\n",
      "t = 20 / 110\n",
      "state: S:1662, I:263, R:75 I+R:338\n",
      "obser: S:596, I:271, R:24, I+R:295\n",
      "free birds: 34\n",
      "obs_symp:4(96), obs_rank:10(220)\n",
      "pos_rank:10, inf_cont:2.175\n",
      "Appeding contacts, t=21\n",
      "Appeding contacts, t=20\n",
      "Appeding contacts, t=19\n",
      "Appeding contacts, t=18\n",
      "Appeding contacts, t=17\n",
      "Appeding contacts, t=16\n",
      "first NN c: 1291. second NN c: 3440\n",
      "t = 21 / 110\n",
      "state: S:1653, I:267, R:80 I+R:347\n",
      "obser: S:632, I:276, R:24, I+R:300\n",
      "free birds: 38\n",
      "obs_symp:4(100), obs_rank:4(224)\n",
      "pos_rank:4, inf_cont:2.45\n",
      "Appeding contacts, t=22\n",
      "Appeding contacts, t=21\n",
      "Appeding contacts, t=20\n",
      "Appeding contacts, t=19\n",
      "Appeding contacts, t=18\n",
      "Appeding contacts, t=17\n",
      "first NN c: 1277. second NN c: 1920\n",
      "t = 22 / 110\n",
      "state: S:1642, I:270, R:88 I+R:358\n",
      "obser: S:662, I:288, R:24, I+R:312\n",
      "free birds: 36\n",
      "obs_symp:4(104), obs_rank:10(234)\n",
      "pos_rank:10, inf_cont:1.675\n",
      "Appeding contacts, t=23\n",
      "Appeding contacts, t=22\n",
      "Appeding contacts, t=21\n",
      "Appeding contacts, t=20\n",
      "Appeding contacts, t=19\n",
      "Appeding contacts, t=18\n",
      "first NN c: 1341. second NN c: 3066\n",
      "t = 23 / 110\n",
      "state: S:1633, I:274, R:93 I+R:367\n",
      "obser: S:693, I:297, R:25, I+R:322\n",
      "free birds: 36\n",
      "obs_symp:3(107), obs_rank:8(242)\n",
      "pos_rank:8, inf_cont:2.4\n",
      "Appeding contacts, t=24\n",
      "Appeding contacts, t=23\n",
      "Appeding contacts, t=22\n",
      "Appeding contacts, t=21\n",
      "Appeding contacts, t=20\n",
      "Appeding contacts, t=19\n",
      "first NN c: 1333. second NN c: 3511\n",
      "t = 24 / 110\n",
      "state: S:1621, I:280, R:99 I+R:379\n",
      "obser: S:719, I:312, R:25, I+R:337\n",
      "free birds: 33\n",
      "obs_symp:3(110), obs_rank:14(256)\n",
      "pos_rank:14, inf_cont:2.65\n",
      "Appeding contacts, t=25\n",
      "Appeding contacts, t=24\n",
      "Appeding contacts, t=23\n",
      "Appeding contacts, t=22\n",
      "Appeding contacts, t=21\n",
      "Appeding contacts, t=20\n",
      "first NN c: 1503. second NN c: 3743\n",
      "t = 25 / 110\n",
      "state: S:1616, I:277, R:107 I+R:384\n",
      "obser: S:751, I:320, R:25, I+R:345\n",
      "free birds: 29\n",
      "obs_symp:2(112), obs_rank:8(264)\n",
      "pos_rank:8, inf_cont:3.75\n",
      "Appeding contacts, t=26\n",
      "Appeding contacts, t=25\n",
      "Appeding contacts, t=24\n",
      "Appeding contacts, t=23\n",
      "Appeding contacts, t=22\n",
      "Appeding contacts, t=21\n",
      "first NN c: 1445. second NN c: 1515\n",
      "t = 26 / 110\n",
      "state: S:1613, I:271, R:116 I+R:387\n",
      "obser: S:782, I:327, R:28, I+R:355\n",
      "free birds: 24\n",
      "obs_symp:4(116), obs_rank:6(270)\n",
      "pos_rank:6, inf_cont:1.625\n",
      "Appeding contacts, t=27\n",
      "Appeding contacts, t=26\n",
      "Appeding contacts, t=25\n",
      "Appeding contacts, t=24\n",
      "Appeding contacts, t=23\n",
      "Appeding contacts, t=22\n",
      "first NN c: 1387. second NN c: 1257\n",
      "t = 27 / 110\n",
      "state: S:1611, I:267, R:122 I+R:389\n",
      "obser: S:816, I:332, R:29, I+R:361\n",
      "free birds: 21\n",
      "obs_symp:5(121), obs_rank:5(275)\n",
      "pos_rank:5, inf_cont:1.975\n",
      "Appeding contacts, t=28\n",
      "Appeding contacts, t=27\n",
      "Appeding contacts, t=26\n",
      "Appeding contacts, t=25\n",
      "Appeding contacts, t=24\n",
      "Appeding contacts, t=23\n",
      "first NN c: 1286. second NN c: 871\n",
      "t = 28 / 110\n",
      "state: S:1606, I:267, R:127 I+R:394\n",
      "obser: S:855, I:334, R:29, I+R:363\n",
      "free birds: 24\n",
      "obs_symp:4(125), obs_rank:1(276)\n",
      "pos_rank:1, inf_cont:1.575\n",
      "Appeding contacts, t=29\n",
      "Appeding contacts, t=28\n",
      "Appeding contacts, t=27\n",
      "Appeding contacts, t=26\n",
      "Appeding contacts, t=25\n",
      "Appeding contacts, t=24\n",
      "first NN c: 1176. second NN c: 540\n",
      "t = 29 / 110\n",
      "state: S:1600, I:266, R:134 I+R:400\n",
      "obser: S:888, I:340, R:30, I+R:370\n",
      "free birds: 24\n",
      "obs_symp:5(130), obs_rank:6(282)\n",
      "pos_rank:6, inf_cont:0.775\n",
      "Appeding contacts, t=30\n",
      "Appeding contacts, t=29\n",
      "Appeding contacts, t=28\n",
      "Appeding contacts, t=27\n",
      "Appeding contacts, t=26\n",
      "Appeding contacts, t=25\n",
      "first NN c: 1207. second NN c: 430\n",
      "t = 30 / 110\n",
      "state: S:1595, I:265, R:140 I+R:405\n",
      "obser: S:927, I:342, R:30, I+R:372\n",
      "free birds: 27\n",
      "obs_symp:2(132), obs_rank:1(283)\n",
      "pos_rank:1, inf_cont:0.95\n",
      "Appeding contacts, t=31\n",
      "Appeding contacts, t=30\n",
      "Appeding contacts, t=29\n",
      "Appeding contacts, t=28\n",
      "Appeding contacts, t=27\n",
      "Appeding contacts, t=26\n",
      "first NN c: 1169. second NN c: 387\n",
      "t = 31 / 110\n",
      "state: S:1586, I:270, R:144 I+R:414\n",
      "obser: S:961, I:348, R:30, I+R:378\n",
      "free birds: 30\n",
      "obs_symp:1(133), obs_rank:6(289)\n",
      "pos_rank:6, inf_cont:0.45\n",
      "Appeding contacts, t=32\n",
      "Appeding contacts, t=31\n",
      "Appeding contacts, t=30\n",
      "Appeding contacts, t=29\n",
      "Appeding contacts, t=28\n",
      "Appeding contacts, t=27\n",
      "first NN c: 1273. second NN c: 1132\n",
      "t = 32 / 110\n",
      "state: S:1582, I:269, R:149 I+R:418\n",
      "obser: S:996, I:354, R:30, I+R:384\n",
      "free birds: 27\n",
      "obs_symp:1(134), obs_rank:5(294)\n",
      "pos_rank:5, inf_cont:1.65\n",
      "Appeding contacts, t=33\n",
      "Appeding contacts, t=32\n",
      "Appeding contacts, t=31\n",
      "Appeding contacts, t=30\n",
      "Appeding contacts, t=29\n",
      "Appeding contacts, t=28\n",
      "first NN c: 1377. second NN c: 1084\n",
      "t = 33 / 110\n",
      "state: S:1578, I:265, R:157 I+R:422\n",
      "obser: S:1030, I:360, R:31, I+R:391\n",
      "free birds: 24\n",
      "obs_symp:2(136), obs_rank:5(299)\n",
      "pos_rank:5, inf_cont:1.25\n",
      "Appeding contacts, t=34\n",
      "Appeding contacts, t=33\n",
      "Appeding contacts, t=32\n",
      "Appeding contacts, t=31\n",
      "Appeding contacts, t=30\n",
      "Appeding contacts, t=29\n",
      "first NN c: 1443. second NN c: 1656\n",
      "t = 34 / 110\n",
      "state: S:1574, I:263, R:163 I+R:426\n",
      "obser: S:1049, I:378, R:34, I+R:412\n",
      "free birds: 9\n",
      "obs_symp:2(138), obs_rank:18(317)\n",
      "pos_rank:18, inf_cont:2.075\n",
      "Appeding contacts, t=35\n",
      "Appeding contacts, t=34\n",
      "Appeding contacts, t=33\n",
      "Appeding contacts, t=32\n",
      "Appeding contacts, t=31\n",
      "Appeding contacts, t=30\n",
      "first NN c: 1612. second NN c: 1507\n",
      "t = 35 / 110\n",
      "state: S:1574, I:260, R:166 I+R:426\n",
      "obser: S:1087, I:380, R:34, I+R:414\n",
      "free birds: 7\n",
      "obs_symp:2(140), obs_rank:2(319)\n",
      "pos_rank:2, inf_cont:2.35\n",
      "Appeding contacts, t=36\n",
      "Appeding contacts, t=35\n",
      "Appeding contacts, t=34\n",
      "Appeding contacts, t=33\n",
      "Appeding contacts, t=32\n",
      "Appeding contacts, t=31\n",
      "first NN c: 1511. second NN c: 111\n",
      "t = 36 / 110\n",
      "state: S:1574, I:257, R:169 I+R:426\n",
      "obser: S:1126, I:381, R:35, I+R:416\n",
      "free birds: 6\n",
      "obs_symp:4(144), obs_rank:0(319)\n",
      "pos_rank:0, inf_cont:1.1\n",
      "Appeding contacts, t=37\n",
      "Appeding contacts, t=36\n",
      "Appeding contacts, t=35\n",
      "Appeding contacts, t=34\n",
      "Appeding contacts, t=33\n",
      "Appeding contacts, t=32\n",
      "first NN c: 1323. second NN c: 356\n",
      "t = 37 / 110\n",
      "state: S:1574, I:252, R:174 I+R:426\n",
      "obser: S:1164, I:383, R:36, I+R:419\n",
      "free birds: 4\n",
      "obs_symp:2(146), obs_rank:1(320)\n",
      "pos_rank:1, inf_cont:0.5\n",
      "Appeding contacts, t=38\n",
      "Appeding contacts, t=37\n",
      "Appeding contacts, t=36\n",
      "Appeding contacts, t=35\n",
      "Appeding contacts, t=34\n",
      "Appeding contacts, t=33\n",
      "first NN c: 1291. second NN c: 299\n",
      "t = 38 / 110\n",
      "state: S:1574, I:248, R:178 I+R:426\n",
      "obser: S:1203, I:384, R:36, I+R:420\n",
      "free birds: 3\n",
      "obs_symp:2(148), obs_rank:1(321)\n",
      "pos_rank:1, inf_cont:0.55\n",
      "Appeding contacts, t=39\n",
      "Appeding contacts, t=38\n",
      "Appeding contacts, t=37\n",
      "Appeding contacts, t=36\n",
      "Appeding contacts, t=35\n",
      "Appeding contacts, t=34\n",
      "first NN c: 1225. second NN c: 167\n",
      "t = 39 / 110\n",
      "state: S:1574, I:245, R:181 I+R:426\n",
      "obser: S:1243, I:384, R:36, I+R:420\n",
      "free birds: 3\n",
      "obs_symp:2(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.35\n",
      "Appeding contacts, t=40\n",
      "Appeding contacts, t=39\n",
      "Appeding contacts, t=38\n",
      "Appeding contacts, t=37\n",
      "Appeding contacts, t=36\n",
      "Appeding contacts, t=35\n",
      "first NN c: 1276. second NN c: 0\n",
      "t = 40 / 110\n",
      "state: S:1574, I:240, R:186 I+R:426\n",
      "obser: S:1283, I:384, R:36, I+R:420\n",
      "free birds: 3\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.075\n",
      "Appeding contacts, t=41\n",
      "Appeding contacts, t=40\n",
      "Appeding contacts, t=39\n",
      "Appeding contacts, t=38\n",
      "Appeding contacts, t=37\n",
      "Appeding contacts, t=36\n",
      "first NN c: 1269. second NN c: 0\n",
      "t = 41 / 110\n",
      "state: S:1573, I:234, R:193 I+R:427\n",
      "obser: S:1323, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "Appeding contacts, t=42\n",
      "Appeding contacts, t=41\n",
      "Appeding contacts, t=40\n",
      "Appeding contacts, t=39\n",
      "Appeding contacts, t=38\n",
      "Appeding contacts, t=37\n",
      "first NN c: 1247. second NN c: 0\n",
      "t = 42 / 110\n",
      "state: S:1573, I:230, R:197 I+R:427\n",
      "obser: S:1363, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=43\n",
      "Appeding contacts, t=42\n",
      "Appeding contacts, t=41\n",
      "Appeding contacts, t=40\n",
      "Appeding contacts, t=39\n",
      "Appeding contacts, t=38\n",
      "first NN c: 1240. second NN c: 0\n",
      "t = 43 / 110\n",
      "state: S:1573, I:223, R:204 I+R:427\n",
      "obser: S:1403, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=44\n",
      "Appeding contacts, t=43\n",
      "Appeding contacts, t=42\n",
      "Appeding contacts, t=41\n",
      "Appeding contacts, t=40\n",
      "Appeding contacts, t=39\n",
      "first NN c: 1191. second NN c: 0\n",
      "t = 44 / 110\n",
      "state: S:1572, I:219, R:209 I+R:428\n",
      "obser: S:1443, I:384, R:36, I+R:420\n",
      "free birds: 5\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=45\n",
      "Appeding contacts, t=44\n",
      "Appeding contacts, t=43\n",
      "Appeding contacts, t=42\n",
      "Appeding contacts, t=41\n",
      "Appeding contacts, t=40\n",
      "first NN c: 1187. second NN c: 0\n",
      "t = 45 / 110\n",
      "state: S:1572, I:214, R:214 I+R:428\n",
      "obser: S:1483, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=46\n",
      "Appeding contacts, t=45\n",
      "Appeding contacts, t=44\n",
      "Appeding contacts, t=43\n",
      "Appeding contacts, t=42\n",
      "Appeding contacts, t=41\n",
      "first NN c: 1204. second NN c: 0\n",
      "t = 46 / 110\n",
      "state: S:1572, I:209, R:219 I+R:428\n",
      "obser: S:1523, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=47\n",
      "Appeding contacts, t=46\n",
      "Appeding contacts, t=45\n",
      "Appeding contacts, t=44\n",
      "Appeding contacts, t=43\n",
      "Appeding contacts, t=42\n",
      "first NN c: 1243. second NN c: 0\n",
      "t = 47 / 110\n",
      "state: S:1572, I:203, R:225 I+R:428\n",
      "obser: S:1563, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=48\n",
      "Appeding contacts, t=47\n",
      "Appeding contacts, t=46\n",
      "Appeding contacts, t=45\n",
      "Appeding contacts, t=44\n",
      "Appeding contacts, t=43\n",
      "first NN c: 1191. second NN c: 0\n",
      "t = 48 / 110\n",
      "state: S:1572, I:197, R:231 I+R:428\n",
      "obser: S:1603, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=49\n",
      "Appeding contacts, t=48\n",
      "Appeding contacts, t=47\n",
      "Appeding contacts, t=46\n",
      "Appeding contacts, t=45\n",
      "Appeding contacts, t=44\n",
      "first NN c: 1220. second NN c: 0\n",
      "t = 49 / 110\n",
      "state: S:1572, I:188, R:240 I+R:428\n",
      "obser: S:1643, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=50\n",
      "Appeding contacts, t=49\n",
      "Appeding contacts, t=48\n",
      "Appeding contacts, t=47\n",
      "Appeding contacts, t=46\n",
      "Appeding contacts, t=45\n",
      "first NN c: 1216. second NN c: 0\n",
      "t = 50 / 110\n",
      "state: S:1572, I:185, R:243 I+R:428\n",
      "obser: S:1683, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=51\n",
      "Appeding contacts, t=50\n",
      "Appeding contacts, t=49\n",
      "Appeding contacts, t=48\n",
      "Appeding contacts, t=47\n",
      "Appeding contacts, t=46\n",
      "first NN c: 1178. second NN c: 0\n",
      "t = 51 / 110\n",
      "state: S:1572, I:181, R:247 I+R:428\n",
      "obser: S:1723, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=52\n",
      "Appeding contacts, t=51\n",
      "Appeding contacts, t=50\n",
      "Appeding contacts, t=49\n",
      "Appeding contacts, t=48\n",
      "Appeding contacts, t=47\n",
      "first NN c: 1226. second NN c: 0\n",
      "t = 52 / 110\n",
      "state: S:1572, I:179, R:249 I+R:428\n",
      "obser: S:1763, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=53\n",
      "Appeding contacts, t=52\n",
      "Appeding contacts, t=51\n",
      "Appeding contacts, t=50\n",
      "Appeding contacts, t=49\n",
      "Appeding contacts, t=48\n",
      "first NN c: 1180. second NN c: 0\n",
      "t = 53 / 110\n",
      "state: S:1572, I:176, R:252 I+R:428\n",
      "obser: S:1803, I:384, R:36, I+R:420\n",
      "free birds: 4\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=54\n",
      "Appeding contacts, t=53\n",
      "Appeding contacts, t=52\n",
      "Appeding contacts, t=51\n",
      "Appeding contacts, t=50\n",
      "Appeding contacts, t=49\n",
      "first NN c: 1189. second NN c: 0\n",
      "t = 54 / 110\n",
      "state: S:1571, I:172, R:257 I+R:429\n",
      "obser: S:1843, I:384, R:36, I+R:420\n",
      "free birds: 5\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=55\n",
      "Appeding contacts, t=54\n",
      "Appeding contacts, t=53\n",
      "Appeding contacts, t=52\n",
      "Appeding contacts, t=51\n",
      "Appeding contacts, t=50\n",
      "first NN c: 1189. second NN c: 0\n",
      "t = 55 / 110\n",
      "state: S:1571, I:169, R:260 I+R:429\n",
      "obser: S:1883, I:384, R:36, I+R:420\n",
      "free birds: 5\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=56\n",
      "Appeding contacts, t=55\n",
      "Appeding contacts, t=54\n",
      "Appeding contacts, t=53\n",
      "Appeding contacts, t=52\n",
      "Appeding contacts, t=51\n",
      "first NN c: 1172. second NN c: 0\n",
      "t = 56 / 110\n",
      "state: S:1571, I:167, R:262 I+R:429\n",
      "obser: S:1923, I:384, R:36, I+R:420\n",
      "free birds: 5\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=57\n",
      "Appeding contacts, t=56\n",
      "Appeding contacts, t=55\n",
      "Appeding contacts, t=54\n",
      "Appeding contacts, t=53\n",
      "Appeding contacts, t=52\n",
      "first NN c: 1222. second NN c: 0\n",
      "t = 57 / 110\n",
      "state: S:1570, I:162, R:268 I+R:430\n",
      "obser: S:1963, I:384, R:36, I+R:420\n",
      "free birds: 6\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=58\n",
      "Appeding contacts, t=57\n",
      "Appeding contacts, t=56\n",
      "Appeding contacts, t=55\n",
      "Appeding contacts, t=54\n",
      "Appeding contacts, t=53\n",
      "first NN c: 1239. second NN c: 0\n",
      "t = 58 / 110\n",
      "state: S:1569, I:160, R:271 I+R:431\n",
      "obser: S:2003, I:384, R:36, I+R:420\n",
      "free birds: 7\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=59\n",
      "Appeding contacts, t=58\n",
      "Appeding contacts, t=57\n",
      "Appeding contacts, t=56\n",
      "Appeding contacts, t=55\n",
      "Appeding contacts, t=54\n",
      "first NN c: 1192. second NN c: 0\n",
      "t = 59 / 110\n",
      "state: S:1567, I:157, R:276 I+R:433\n",
      "obser: S:2043, I:384, R:36, I+R:420\n",
      "free birds: 9\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=60\n",
      "Appeding contacts, t=59\n",
      "Appeding contacts, t=58\n",
      "Appeding contacts, t=57\n",
      "Appeding contacts, t=56\n",
      "Appeding contacts, t=55\n",
      "first NN c: 1208. second NN c: 0\n",
      "t = 60 / 110\n",
      "state: S:1566, I:157, R:277 I+R:434\n",
      "obser: S:2083, I:384, R:36, I+R:420\n",
      "free birds: 10\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=61\n",
      "Appeding contacts, t=60\n",
      "Appeding contacts, t=59\n",
      "Appeding contacts, t=58\n",
      "Appeding contacts, t=57\n",
      "Appeding contacts, t=56\n",
      "first NN c: 1213. second NN c: 0\n",
      "t = 61 / 110\n",
      "state: S:1566, I:155, R:279 I+R:434\n",
      "obser: S:2123, I:384, R:36, I+R:420\n",
      "free birds: 10\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=62\n",
      "Appeding contacts, t=61\n",
      "Appeding contacts, t=60\n",
      "Appeding contacts, t=59\n",
      "Appeding contacts, t=58\n",
      "Appeding contacts, t=57\n",
      "first NN c: 1269. second NN c: 0\n",
      "t = 62 / 110\n",
      "state: S:1565, I:154, R:281 I+R:435\n",
      "obser: S:2163, I:384, R:36, I+R:420\n",
      "free birds: 11\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=63\n",
      "Appeding contacts, t=62\n",
      "Appeding contacts, t=61\n",
      "Appeding contacts, t=60\n",
      "Appeding contacts, t=59\n",
      "Appeding contacts, t=58\n",
      "first NN c: 1281. second NN c: 0\n",
      "t = 63 / 110\n",
      "state: S:1564, I:152, R:284 I+R:436\n",
      "obser: S:2203, I:384, R:36, I+R:420\n",
      "free birds: 12\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=64\n",
      "Appeding contacts, t=63\n",
      "Appeding contacts, t=62\n",
      "Appeding contacts, t=61\n",
      "Appeding contacts, t=60\n",
      "Appeding contacts, t=59\n",
      "first NN c: 1240. second NN c: 0\n",
      "t = 64 / 110\n",
      "state: S:1562, I:150, R:288 I+R:438\n",
      "obser: S:2243, I:384, R:36, I+R:420\n",
      "free birds: 13\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=65\n",
      "Appeding contacts, t=64\n",
      "Appeding contacts, t=63\n",
      "Appeding contacts, t=62\n",
      "Appeding contacts, t=61\n",
      "Appeding contacts, t=60\n",
      "first NN c: 1234. second NN c: 0\n",
      "t = 65 / 110\n",
      "state: S:1559, I:151, R:290 I+R:441\n",
      "obser: S:2283, I:384, R:36, I+R:420\n",
      "free birds: 16\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=66\n",
      "Appeding contacts, t=65\n",
      "Appeding contacts, t=64\n",
      "Appeding contacts, t=63\n",
      "Appeding contacts, t=62\n",
      "Appeding contacts, t=61\n",
      "first NN c: 1214. second NN c: 0\n",
      "t = 66 / 110\n",
      "state: S:1557, I:145, R:298 I+R:443\n",
      "obser: S:2323, I:384, R:36, I+R:420\n",
      "free birds: 16\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=67\n",
      "Appeding contacts, t=66\n",
      "Appeding contacts, t=65\n",
      "Appeding contacts, t=64\n",
      "Appeding contacts, t=63\n",
      "Appeding contacts, t=62\n",
      "first NN c: 1255. second NN c: 0\n",
      "t = 67 / 110\n",
      "state: S:1554, I:144, R:302 I+R:446\n",
      "obser: S:2363, I:384, R:36, I+R:420\n",
      "free birds: 19\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=68\n",
      "Appeding contacts, t=67\n",
      "Appeding contacts, t=66\n",
      "Appeding contacts, t=65\n",
      "Appeding contacts, t=64\n",
      "Appeding contacts, t=63\n",
      "first NN c: 1214. second NN c: 0\n",
      "t = 68 / 110\n",
      "state: S:1552, I:145, R:303 I+R:448\n",
      "obser: S:2403, I:384, R:36, I+R:420\n",
      "free birds: 21\n",
      "obs_symp:0(150), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=69\n",
      "Appeding contacts, t=68\n",
      "Appeding contacts, t=67\n",
      "Appeding contacts, t=66\n",
      "Appeding contacts, t=65\n",
      "Appeding contacts, t=64\n",
      "first NN c: 1174. second NN c: 0\n",
      "t = 69 / 110\n",
      "state: S:1550, I:147, R:303 I+R:450\n",
      "obser: S:2443, I:385, R:36, I+R:421\n",
      "free birds: 22\n",
      "obs_symp:1(151), obs_rank:0(321)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Appeding contacts, t=70\n",
      "Appeding contacts, t=69\n",
      "Appeding contacts, t=68\n",
      "Appeding contacts, t=67\n",
      "Appeding contacts, t=66\n",
      "Appeding contacts, t=65\n",
      "first NN c: 1184. second NN c: 87\n",
      "t = 70 / 110\n",
      "state: S:1544, I:152, R:304 I+R:456\n",
      "obser: S:2472, I:396, R:36, I+R:432\n",
      "free birds: 17\n",
      "obs_symp:1(152), obs_rank:11(332)\n",
      "pos_rank:11, inf_cont:0.175\n",
      "Appeding contacts, t=71\n",
      "Appeding contacts, t=70\n",
      "Appeding contacts, t=69\n",
      "Appeding contacts, t=68\n",
      "Appeding contacts, t=67\n",
      "Appeding contacts, t=66\n",
      "first NN c: 1353. second NN c: 622\n",
      "t = 71 / 110\n",
      "state: S:1541, I:153, R:306 I+R:459\n",
      "obser: S:2501, I:407, R:36, I+R:443\n",
      "free birds: 9\n",
      "obs_symp:1(153), obs_rank:11(343)\n",
      "pos_rank:11, inf_cont:2.025\n",
      "Appeding contacts, t=72\n",
      "Appeding contacts, t=71\n",
      "Appeding contacts, t=70\n",
      "Appeding contacts, t=69\n",
      "Appeding contacts, t=68\n",
      "Appeding contacts, t=67\n",
      "first NN c: 1376. second NN c: 735\n",
      "t = 72 / 110\n",
      "state: S:1539, I:152, R:309 I+R:461\n",
      "obser: S:2538, I:410, R:36, I+R:446\n",
      "free birds: 8\n",
      "obs_symp:1(154), obs_rank:3(346)\n",
      "pos_rank:3, inf_cont:1.725\n",
      "Appeding contacts, t=73\n",
      "Appeding contacts, t=72\n",
      "Appeding contacts, t=71\n",
      "Appeding contacts, t=70\n",
      "Appeding contacts, t=69\n",
      "Appeding contacts, t=68\n",
      "first NN c: 1327. second NN c: 315\n",
      "t = 73 / 110\n",
      "state: S:1539, I:150, R:311 I+R:461\n",
      "obser: S:2576, I:411, R:37, I+R:448\n",
      "free birds: 7\n",
      "obs_symp:1(155), obs_rank:1(347)\n",
      "pos_rank:1, inf_cont:0.725\n",
      "Appeding contacts, t=74\n",
      "Appeding contacts, t=73\n",
      "Appeding contacts, t=72\n",
      "Appeding contacts, t=71\n",
      "Appeding contacts, t=70\n",
      "Appeding contacts, t=69\n",
      "first NN c: 1360. second NN c: 336\n",
      "t = 74 / 110\n",
      "state: S:1539, I:150, R:311 I+R:461\n",
      "obser: S:2616, I:411, R:37, I+R:448\n",
      "free birds: 7\n",
      "obs_symp:1(156), obs_rank:0(347)\n",
      "pos_rank:0, inf_cont:0.775\n",
      "Appeding contacts, t=75\n",
      "Appeding contacts, t=74\n",
      "Appeding contacts, t=73\n",
      "Appeding contacts, t=72\n",
      "Appeding contacts, t=71\n",
      "Appeding contacts, t=70\n",
      "first NN c: 1283. second NN c: 0\n",
      "t = 75 / 110\n",
      "state: S:1538, I:147, R:315 I+R:462\n",
      "obser: S:2656, I:412, R:37, I+R:449\n",
      "free birds: 7\n",
      "obs_symp:3(159), obs_rank:0(347)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "Appeding contacts, t=76\n",
      "Appeding contacts, t=75\n",
      "Appeding contacts, t=74\n",
      "Appeding contacts, t=73\n",
      "Appeding contacts, t=72\n",
      "Appeding contacts, t=71\n",
      "first NN c: 1259. second NN c: 72\n",
      "t = 76 / 110\n",
      "state: S:1538, I:144, R:318 I+R:462\n",
      "obser: S:2694, I:414, R:37, I+R:451\n",
      "free birds: 5\n",
      "obs_symp:1(160), obs_rank:2(349)\n",
      "pos_rank:2, inf_cont:0.2\n",
      "Appeding contacts, t=77\n",
      "Appeding contacts, t=76\n",
      "Appeding contacts, t=75\n",
      "Appeding contacts, t=74\n",
      "Appeding contacts, t=73\n",
      "Appeding contacts, t=72\n",
      "first NN c: 1263. second NN c: 28\n",
      "t = 77 / 110\n",
      "state: S:1538, I:143, R:319 I+R:462\n",
      "obser: S:2728, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:5(354)\n",
      "pos_rank:5, inf_cont:0.25\n",
      "t = 78 / 110\n",
      "state: S:1538, I:140, R:322 I+R:462\n",
      "obser: S:2768, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 79 / 110\n",
      "state: S:1538, I:138, R:324 I+R:462\n",
      "obser: S:2808, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.025\n",
      "t = 80 / 110\n",
      "state: S:1538, I:137, R:325 I+R:462\n",
      "obser: S:2848, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 81 / 110\n",
      "state: S:1538, I:133, R:329 I+R:462\n",
      "obser: S:2888, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 82 / 110\n",
      "state: S:1538, I:132, R:330 I+R:462\n",
      "obser: S:2928, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 83 / 110\n",
      "state: S:1538, I:132, R:330 I+R:462\n",
      "obser: S:2968, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 84 / 110\n",
      "state: S:1538, I:128, R:334 I+R:462\n",
      "obser: S:3008, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 85 / 110\n",
      "state: S:1538, I:127, R:335 I+R:462\n",
      "obser: S:3048, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 86 / 110\n",
      "state: S:1538, I:124, R:338 I+R:462\n",
      "obser: S:3088, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 87 / 110\n",
      "state: S:1538, I:122, R:340 I+R:462\n",
      "obser: S:3128, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 88 / 110\n",
      "state: S:1538, I:122, R:340 I+R:462\n",
      "obser: S:3168, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 89 / 110\n",
      "state: S:1538, I:120, R:342 I+R:462\n",
      "obser: S:3208, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 90 / 110\n",
      "state: S:1538, I:116, R:346 I+R:462\n",
      "obser: S:3248, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 91 / 110\n",
      "state: S:1538, I:114, R:348 I+R:462\n",
      "obser: S:3288, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 92 / 110\n",
      "state: S:1538, I:111, R:351 I+R:462\n",
      "obser: S:3328, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 93 / 110\n",
      "state: S:1538, I:106, R:356 I+R:462\n",
      "obser: S:3368, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 94 / 110\n",
      "state: S:1538, I:106, R:356 I+R:462\n",
      "obser: S:3408, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 95 / 110\n",
      "state: S:1538, I:103, R:359 I+R:462\n",
      "obser: S:3448, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 96 / 110\n",
      "state: S:1538, I:101, R:361 I+R:462\n",
      "obser: S:3488, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 97 / 110\n",
      "state: S:1538, I:100, R:362 I+R:462\n",
      "obser: S:3528, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 98 / 110\n",
      "state: S:1538, I:100, R:362 I+R:462\n",
      "obser: S:3568, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 99 / 110\n",
      "state: S:1538, I:97, R:365 I+R:462\n",
      "obser: S:3608, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 100 / 110\n",
      "state: S:1538, I:93, R:369 I+R:462\n",
      "obser: S:3648, I:419, R:38, I+R:457\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 101 / 110\n",
      "state: S:1538, I:91, R:371 I+R:462\n",
      "obser: S:3687, I:419, R:39, I+R:458\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 102 / 110\n",
      "state: S:1538, I:90, R:372 I+R:462\n",
      "obser: S:3726, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 103 / 110\n",
      "state: S:1538, I:88, R:374 I+R:462\n",
      "obser: S:3766, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 104 / 110\n",
      "state: S:1538, I:87, R:375 I+R:462\n",
      "obser: S:3806, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 105 / 110\n",
      "state: S:1538, I:84, R:378 I+R:462\n",
      "obser: S:3846, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 106 / 110\n",
      "state: S:1538, I:82, R:380 I+R:462\n",
      "obser: S:3886, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 107 / 110\n",
      "state: S:1538, I:80, R:382 I+R:462\n",
      "obser: S:3926, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 108 / 110\n",
      "state: S:1538, I:78, R:384 I+R:462\n",
      "obser: S:3966, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "t = 109 / 110\n",
      "state: S:1538, I:77, R:385 I+R:462\n",
      "obser: S:4006, I:419, R:40, I+R:459\n",
      "free birds: 0\n",
      "obs_symp:0(160), obs_rank:0(354)\n",
      "pos_rank:0, inf_cont:0.0\n",
      "Save tracing sec NN strategy\n",
      "Saving to csv/Proximity_N2K_T120_s1_ti10_pz10_mu0.02_l0.05_seed5_obs40_trac2nd_t5_info\n",
      "End seed\n"
     ]
    }
   ],
   "source": [
    "import imp\n",
    "import scenario\n",
    "imp.reload(scenario)\n",
    "from scenario import Scenario\n",
    "##### TRACING UP TO SECOND NN #######\n",
    "scenario_trac_2nd = Scenario( model, seed=seed+1, \n",
    "    ranking_options=dict(ranking=RANKINGS[\"tracing2nd\"], tau=trac_tau, lamb=trac_lambd, save=True),\n",
    "    observation_options=observation_options,\n",
    "    intervention_options=intervention_options\n",
    ")\n",
    "scenario_trac_2nd.run(t_max-t1, print_every = 1)\n",
    "print(\"Save tracing sec NN strategy\", flush=True)\n",
    "#scenario_trac_2nd.counts.to_csv(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_trac2nd_t%d.csv\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau),\n",
    "   #               index=False, sep=\"\\t\")\n",
    "\n",
    "scenario_trac_2nd.save(\"csv/Proximity_N%dK_T%d_s1_ti%d_pz%d_mu%.2f_l%.2f_seed%d_obs%d_trac2nd_t%d_info\"%(N/1000,T,t1,N_patient_zero,mu,lamb,seed,n_ranking,trac_tau))\n",
    "cnts = scenario_trac_2nd.counts\n",
    "cnts[\"t\" ]=cnts.index\n",
    "traces[\"CT2\"] = cnts\n",
    "save_status(scenario_trac_2nd, \"CT2\")\n",
    "del scenario_trac_2nd\n",
    "print(\"End seed\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n",
      "/opt/anaconda3/envs/bigdatalab_cpu_202101/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1aebc4c50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x/8orr2DChAkqvc76hBzpS+qUz3dcFjVl/0dmHWND+fNjJb9wdFvxmbyy5ozINFofadmypSiPyKP8Rx555I7n2bzD2TZZpMKvGXKkL6kzjt9MweqTUXj+oQai1KBEATuzP3g0kGZAF/Dr8QiMU1P4alVG5LXJkCFD8Oabb2L//v1ISUlR67XoMnKoJamzmPw5O6/CzcYUr/VrJD/1/zC6ozceauSEL/+6gqjU+plSmE06H3zwAVq0UKSmltQOUulL6oSD15NFrvypvRoKW7bkzrDFz4Y3B8cxvbPpvOgk6xuenp7CXi+pXaTSl9Q6rMDm7roKD1szjS90rk487cwx/eEmOEQd5Lrg+pOcLTs7+462nj17Ytu2bWJ//PjxMlxThUilL6l19l1JxNnoDEzr3VA6b+8D1wbmalvvbbkgso9KJKpGKn1JrfPd3uvwtjfH4zqaRVOVcGrphaPbCd/HpBXB9da+L1Gj0idb41KSRJLyEja0b0+ym+S6cmtX4bmZJKEkV0kGVGhvR3Je+dw8kvq1DLOecjkuE+dolD+xm5+oHyu5P/YUxskF1ospfHP8shNi5bJEoiqq8le4nGTgf9pmkOwlW20Ab5WPWbEH0mYUSTPlexZSW5nXbhHJZBJ+T0Alx5ToIJtOx8CQRq+PtnJX96VoFf5OlvhhTDuEJefg022X1X05kvqk9EmxH6TNfys/DCVZodzn7bAK7avpPQUkYbQfStKBFL8bba2p7dgtRVjCLxXeI9FRSmikuuVMDHo2dhKjV8mD0dnfAc/38Mea4CjyiyTIj0+iEqo733Yh3R3HO8ptWWFTzpIUVeF10co2luhK2iuFOonJJMEsSUmy4IS2cuxGChIyCzC8jbTlV5dX+wagsYsVZmw4j/TcQhV+O5L6iqqNrJXZ6Xlkf7f2SqGOZDFJEAunVJVoJxtPR8PKxBB9mupOsfO6xsTQAF8/2QqpOYWYufG8TqZpiI+PF7l3/P39ERgYiF69esHc3FykUba3t4efn5/Y79u3r7ovtV6nYUhgkw2P8pWmm8QKI/iKgdg8xItVtntW0i7RUXILi7HzQjwGt3SXi7FqSHMPG7w9sDE+23EFc3dfw5sDGqviK9II2No7fPhwjBs3DqtXrxZtnF8nKysL3bt3FzH6nIRtxIgRar5S3aG6I/2tJOOU+7zdUqF9FHUEJiR+SoftCaUJKIvaOimjdp6p8B6JjqZPzikswfC2d7XiSR6ASd0bYFR7L8z/OxTryMavK/z9998wMjLClClTytt4VM8KX6KmkT7p6FW06UniSPs8Yv+Q5AuStfR4Im259M8T/FpS7hepbS3tXiIpJplKbSXKQ72gjATiunh/KkWio/z+T6SIze/ga6/uS9EJeKz0ybDmiE7LE2YeX0cLtFfxZxv/2WcouKza1MomTZvA9Z137vo8F09p166dSs8pqaHSJ6V9t2oGfe7y+tm0mV1JezBtmt/vfBLtJzQxC/+EpWLGw03EYiOJauB1DgtGt8WQ+Yfx8qrT2PFyd7Xm35doJzK1skTlrDweSQpKD0/IFbi1UmOXC688vuioqLHLZRdVtc7xXiPy2qJZs2ZYv359nZ+3PiOXSEpUSh7Z8TecisbDzd3gYGkiP91agGsRzHi4KfZcTsSSw7wcRnvp3bs3CgoK8NNPP5W3nTx5EgcOHFDjVek2UulLVMof52KRlV+MMZ185Cdbizzb1Rf9A10we8dlbD2rvYFwPEvZtGmTKIbOIZs88p81axbc3eUK7tpCmnckKuU3cuAGOFuSk7E8HZOklpTld6PaYNyyE3htzRmYGOpjQDNXrfysWcGvXcvxH3eyfDnHfkhUiRzpS1TG9YQsnI1Kx6gO3iqzM0vujpmxgUjMxnH8034/jb+vlC2XkUjujlT6EpWx5UwsOFjn0Va8Xk9SF1iaGOKXCR3QyNUSz/8agj2XZI4eyb2RSl+ispWVW87GoGtDRzhbmcpPtQ6xMTfCbxM7oambFaasDMFfF0RaLImkUqTSl6iEU5HpiErNw9DWcgWuuhT/r891RAtPG7zw2yks3B9aL+vsSu6PVPoSlcAplBXORBf5iaoJa1Ma8ZPi53xH//vrKqb+fkrkQJJIKiKjdyQ1pqikFNvPxaEvhRBakeKRqA9zY0PMG9UaLcm5+/mfl1FYfAuLx7aTK6Ml5ciRvqTGHA5NRkpOIYbK6lgaAUdOTXqoAT58tBn2XE7AN3uuqfuSVJZamTNwdu7cWcTzt2zZEmvWrFH35WsdcqQvqTF/nI0l04IhejSWtQ80iWc6+4gaxd/vC0UTV2sMaumm9amVr127hl9++QUBAQGIjY0VydoGDBgAW1tbdd6GViGVvqRGFBSXiDTKvDCIC35INGvE/9HQZriemI1X15xGfGa+WMmrSWso7pZa+W40atTotkVdzs7O4Op6UulXHan0JTXi8PVkkXZhUAvNG0VKFJW3lowLwpvrzuGTbZdwlExxXInL1vzO7JyH1l5DclS2Sj82Ry9LdH/yX0WtytTKJ06cQGFhoTALSaqOtOlLasT283HCtMPx+RLNhBX8T8+0w6xHA3GIOukRPxxDbHqeui+rRsTFxWHs2LFYtmwZOamlGnsQ5EhfUmPTTv9AVxhTuKZEc2GTzviufmjqZo3nVgRjxKKj+GVix9tec68RuSalVs7MzMSgQYPw6aefolOnTrV0ZbqL/EuVVJsjoQrTzmANdBBKKqdjAwesmtwJhRRm+8QPR1FQVFbYTjtSK7M5hx2/zzzzDJ54QhTskzwgUulLqs32c/HStKOFcIK29VO6iHoHydmFSMoqUNvq3QdNrczZOA8ePCiyb7LDl4WjfSQP8Jlr+lLtoKCgW8HBXGlRommmnaBP9wjTDjsGJdpHVn4Rzp6/CGs3X2H397Q1k4u4tJTLly+jadOm/+1QQ0i/B/33tXKkL6kWf19JUph2ZEZNrYVXT9tbmMDV2hTpuYW4kZSNQurMJbqNVPqSarH5dAwcyTzQXUbtaDUcsu9MSt/XwULY+TmmnzsAie4ilb7kgcnILcK+K4kY0sodhgbyJ6TtsInX2swIDZ0sRRRWZGouIlNyUEydgETzeVATfY3+Yslm9BrJRZILJKtITEnsSXaTXFduy+vm0f5MklCSqyQDanJuiXpj83lUOLyNTKOs7ZiamiIlJUUoDhMjA6H4XWjkn0Gmu5vJOSgplYpfk+Hvjb8//h5rPU6flDb/xb9MEkgnzqPHXORyFD8m2UttX1DbDNpnmU77gcrnm5Gwa34PtTWi10kjopax6XQ0GjpbUhSItbovpVa5VVyMPM4D8/ffIsrE+tFHYdq4seI5Uob8vL7xnStbtQlPT09ER0eLVAYVKaZQzgSK7EmI1IeDhbFGpW6Q3A4rfP4e62pxFr/fjH4QRbQ1J4klmUnSU/n8CpL9JNNJhpKsJiVfQNswHvHTtgPJsRpeg6QOiaKp/8nwNLw1oLHOKQJW4unr1yP3xAkUxcSiIDwcpRkZgJERD6mQ8vMSGPv54VZJCYri4oCiIuiZmMDA2homgU1h0b49TCnk0MDGBnpmZii4epWOdRIl6Wmw6j8Alr17aVwnwXlvOItlZaw6EYmZG85jTCdvfDK0uc593/WVait9Ut4x9CP4inYjSXhN9y5q20VtLrQV9dp4S4+dlW/hmcHxCoeIVrbdAb1nMm1Y4O3tXd1LlNSSA5cZ2rryOGptJe/sWcTN+ggFFPpmRKMmIy9PWPfvD4uuXWDRrRtukYLP3L4D2QcOwMDKCtYDBkDfwgKl2VkoTkkVM4LEAwfvOK6euTn0qQPI3PEn9KkzcJw8GfYTxkNPC1IHPNXBG+Fk4vnx4E2Uktn44yHNpA9HB6iJecdOOXrnYUI6yTpqG3Ovt1TSVqkHgjqLxbRZXBanX91rlKiWUvrLXxcSjc4NHOBpxxM77Sbjjz+QuXMnKforNLKPgaGzMzy+/RZWA/pXOqq1HzNayN0oJhNJwY2boiMoyc6GSYMGMOXYaVLwOceOI+3XX5E4Zw5yjh+H+xefw9DBQbyvNC8PGVv/EDMM86B2NCPoDSMXzahANn1gExjo62Hh/huITsvDgqfbyEI5Wk5NzDt9ScJIQQtjIP2RbKRNF5IE2ndTjvJ5fX5ihZG9V4X3eyrNQRIt4XhYiojseL1f3edoUTXZBw8i9q23xajetGUL2D39NGxHjoSBpUW1j2no5CSkMiy7dRWzhvTVq5Hw+Re40a+/MBUZknLPDQkRZiQ2C2Vu3w589DFMmzcX5iCrvn1hWiGdcF2jTwr/bVL8Pg7meHfTBYxZcgKrJ3WCmbFMo13vVuSSQudsTUtJ2ivNO8tJeOks22NSKjhy7Wn/bdpnB+7vSjs+2wb2kgTcz5ErV+RqDq+uPo29FKp58l1SRBTpoa0UJSQgbNhwMbL3XbMa+g8Q+aAK8snWn7ZqFYqiY4RvwCQgAPZjx8CsbVsU3riBrL37kL1vnzA5MdaDBsF5OnVQdL3qZOfFeExZGYKBzVxpxN9Wrt7VcO62IrdGaRjooB/RZiQJV18+TfIciSXJWqXyZ3v/E3SOVOXr36XNs8rXv0rtf97vHFLpa05sfvvP9mBkkBc+GdZc3ZdTbdgJGzl+AvIuXIDfhvXCBKOpsLkobdVqpPz0k3AYW/bsidLcXNwqKKBZQ1fYPDr4rjOL2uLnQzfx6fbLeL5HA8x8+PZl/5J6oPTrAqn0NYNfjoXjgy0XsW1aN5GwS9tgZZmxdStSV65EYegNuJFN3XbYMHVfVpUopCiihC++RMG1a9CnSCFQx1Vw/brwFVj26gXHKc/DrEWLOrkW1hfvb7mAlccjMffJVnisbdVDBSWaofRlPn1JlVhzMgrN3K21UuHnX72GqEmTUJyYCNPAQLh/9RVsBg9S92VVGWNfX3j9sOi2toKbN5GxeQvS1qxB+BN7Yd65E0UJmaMoNlZEC9mOfBLWjzyi8hBRdnDPerQZQhOz8c6m8yI/P4tEe9D8uDGJ2jkdmYaLsZkY2b6iH147yD11GhFjx4p9n19/gS+ZdLRJ4d8NNks5v/4aGu7dC6fXX1f4B6KiRNRPSWYm4mbMRGiv3kiaN498GGWxFKqBU2/Me6oNrE2N8ALZ+DPyeJmORFuQ5h3JfXn+12Acv5mKozN6w8LEUGts9+kbNyJh9mdCEXotWQJjz/qRNoJNMLnHjiH1l1/FugIYGMCSfAA8YzDy8ICxP4WSNmlSHjJaXYLDUzFq8XG09bHDVyNawZsifCSag7TpS6oFT+P7fXMAL/VqiDf6K1IQaDrZR44gkW3gZPc2a9cOnt99C0PH+lnDtzAyEmm//YbsQ4eF6edWfn75c8Y+PnD98ANYdOFI6+qn5HiPQjmLS29hWu+GmNLDXy7g0hCk0pdUi7fXn8WWM7E4QqN8TqWs6aSuWCHi4I28vOD8xht3XWhVH+EZQElKCgpCQ5F/5QrS16xFYVgYbJ8aBaeXX4ahXXluxAciPiMfH2+7iB3n4zG+iy9mDeHobIm6kUpfUq0/5u7/24dR7b01PkyTFRrbr1MW/QCrfv3IWTsH+hTmKLk7pTTqT/puHlKXLxe5hXi9AK8VMPb2EmYgszZtyTRW9bUBn2y7hCWHw/DdqNYY2rp+mNI0GRm9I3lglh0NQwlN2yd119xY9jKFn/D550gjG7bNiMfh9tFH0CM7tuTe8KI0l+lvw2boEGTvP4DckyeR+eefKCVHsMDQENb9+8GOHOHmbdrc9+Oc8XATnItOx4wNiqieRi5W8ivQQKQjV3LXPDtdvtgnQjR/HndHqK9mjfDnfiMWMNk9MxYuM2dKc04NKcnKEr6AzG3bRdbRUnpsM3w4fbYzREbRe5GQmY9B8w7D0sQAG15QFF+XqAdZI1fyQJyOSkM8/QEPbsnpkzSTW4WFSP5+vlD4nDdHKnzVwFlEzZo1E7OAgP1/w2HK82Jh280hQ5G1Z889KzVxAZYfx7ZFHJkGn11+EjkFvPheoknIOH1JpWw7FydK5/Vpqt58L5VRGB2N+E9n43qPnkheuFCYJzgKRTpsVQ+nj3Z+9VX4rl4l9qNfmoawxx9H5u7dd1X+7XzsRW6eC7GZeOG3UygsltW3NO2MIXsAACAASURBVAmp9CWVmnb+pEiMhwKcNC6NLocdRoweg/S1a2HeqSM8f1gEN7Lna0N+em2G0zw02LwJbp99htKcHMRMexlx770n6gxURt9AF3w2vDkOXkvCkPmHERIh0m9JNAD5lyK5q2lnUEtXjfp0itPSEPncJJFHx3fdOnh+8w2sevaUCr+O0DMygu1jw+G/fTscX3wBGRs2InLyZLECuDJGUtTXT88EITOvCI8vOoaZG88jv0hWR1U3UulL7mD7uXgYG7Bpx0Vjsk1yVEnUpMkoItOO18IFMG2s/Tn9tRU9iurhuH6eYeUGh+DGoEFIIjNbcXLyHa/tRyP+3a/3oAgwP1F+ccQPRxGTzpnYJepCRu9I7jDtdP1yn0iu9vM4LpWgPth0EDt9uig1yLBNmbNjWvfrp9brkvxL7unT5FdZhJxDh0QtYfNWrWDeob3I/vnfzJ97LiXgtTVnYES+Irb5d/avWRoIyb2R0TuSKnHsZoqIvHikhZvac+eUKXz7ic+KYieNjh+TCl/D4Ph9758Wo8GOHbCnkFle8JX8w48If+JJRJPdn9NCV7Tzb36pK+zMjTBmyT9YejjsnpFAktpBjvQltzHyx2MIT8nBgbd6qa061q3SUnISvo+MjRvh/OYbcHiOa/NItCnOP/XXX5Hy8xIRVms3ahQcp75YnuYhK78Ib6w9i1008ueQYF7UpQs1lzUNOdKX3JfjNMr/JyxVJM1Sm8Jnk86MGULhO774olT4Whrn70TfXcNdO2FL4Z1cGpJrAnNeJB7Zc0TYD2Pa4c3+jUQJxp5z9mPGhnOITMlV96XXC+RIX1LO0z8dx7WEbByerp5RfmleHmJefU2kA3ai2HCH5yfL2HsdoODGDST873/IOXBQmOqc33yz/HuNJafujwduYNXJKJHyY3gbD0zt1RB+jtUvUC9RIEf6kntyMjwVR2+k0Ci/gVoU/q3iYkRPnYrsgwfhOmuWKAEoF1vpBib+/vD64QfYPf00UpcsReIXX5Tb8t1tzfDR0OY49HYvjOvsiz/OxqLf3AP46eBNEVQgUT0yZFMi+GH/DThaGmN0Rx+1fCKc7THn6DG4ffoJ2YBHym9Fx+AO3OX990R+pNQVvyD6xakoiom5LX3DB48G4hDNMnkV+OwdlzFxxUmkZBeo8ap1E6n0JUjKKsD+a0l4MsgLZsZ1P8rP2ru3PH8O24AlOqz4Z86EM0Vl5Rw/jhuDH0Uyfe8VV/U6W5kKe/8nQ5vhCM08B3x7UNj9JapDKn0JttKUmu2pj7Wt+xzohRERFJo5A6bNm8PlnZny26gHit9hwnj4b98mKnYlfT0XYY89htzg4NteM5ZNPS91EzOA538NEfH9GbmyFq/alT59ObYk60mukFwm6UxiT7Kb5LpyW16Oh/ZnkoSSXCUZUPPLl6gCLnnXwsMGDZ3rNv95KYXzxbz2uqjh6vHtt7LoST3CyN0dXgvmw3PhQpTm5CJizFhETX1JzADK7P2NXa2weWpXvNInQGHr/+YA/r6i2iLv9ZGajvS/I/mLvqQmtG1FcplkBsleagvgrfIxK/xA2owi4VpqA0kWUpusdKFmridk4UJMJoZR1ERdk/jVV8i/dAnun82uN0XLJbdj1bsXGmz7A47kxM87dQqR4ycgbOgwZO3fL5S/kYE+XuvXSCh/O3NjTFh+UsT4S1u/GpQ+KWyupvAQyRJ+TF9QIUk67Q4lWaF8GW+HKfe5fTW9poAkjPZDSTpU9/wS1bDpdAwM9PUwpJV7nX6kWfv2iUpXXJXJqk+fOj23RLPQNzeH07SX0PDvfXCbPRu3CgoQPeUF0QHknTsnXsPFfLZO60rhnP7YciYGvb8+gNUnIuWK3up83tV4TxlcQy+JZBl1AKdJfibh4FoXUupx/ALltiwhOw/loiq8P1rZdgd0nMkkwSxJSXwKSW3AIXFc9LxbQ0c4WdVdhaO88+cR+9bbMA0MhPNbb9bZeSWaX77R9vHHxMjf5b33UHDtGsKfHInISZNFjh8TQwO8NaAJdrzSXZh+Zmw8j4/+uCQV/4N+zg/4+ooYkrQlWUTKnQto5pSZcu6CYjXG7VQaiEvHW0wSxOLk5FSDS5TciwPXk0TGQ14QU1fkX70q0iMb2NvDc9FC6Bsb19m5JdqTwtl+zGj4794NpzdeR/6FC4h46mmkLFsunufau2smd8Jz3fyw/Gg43t9yQcb015HS55F6NCnmf5SP1ys7gQQaoYtsXcptYoXXe1V4vydJbA3OL6kBRSWlmL39MrztzfFwi7rJm891VyMnPAt9MzN4L1sGIxfNSN0s0UwMLC3gOGkSGu7dA6uBA5H45ZdInPuNGNlzhM+7g5qKlCErj0di+oZz4jctqUWlTx88B89G0YffWNnEhtlLJFtJxinbeLtFuc/to+j1JiR+tM+O3hPVPb+kZqw8HoHQxGy8R384PG2ubfgPNX7WLBGT7b1sqXTcSh7I5u/x9VdiHUfK4sWImzFTFNJhxT99YGMR3bMuJBoTlp1EZr4M66yKiaYmTCP5jT58nqPfJJmg7EjWUttE2kaSPKH8o79IbWuVHQNXS55KbbKMjhrgyIdvdl9D9wBHUeSiLsj66y+x4pZttSZ+3OdLJFVHj8J6XWd9CENnJyTPXyD8Qh7fzIVp48YiusfDzgzvkI3/8YVHMf/ptsLmL7nLZ6np+ayDgoJuBVdYuCGpOe9uOo/VJ6PwFznEAsg+WtuUZOfg5iOPwMDRAX7r1ok/YImkuuQcO4aYt99GSVo6TBoFkOJvAqu+fXDepxVe/P0UsvOLMbG7n5gBmBvXdFyrvciEaxLBzaRsofDHdPSuE4XPJHMpvcREuL7/vlT4khpj0bkzGmzeDIcJE2BoZ4/s/fsRPfUleM+fjT3PthIry388cBP95h7ELpnC4Q5kGoZ6xlwy65gY6uOl3uxSqX1yT55E6vLlsH1ihKiyJJGoAkMHBzhTZI/3kp8RcOigSMXNOZxSRz6OGQUXsO7ZtrA0McTkX0Pw3IqTiM/Ilx+8Eqn06xEXYjKw7Vwcnu3qVydx+cVpaYh58y0Ye3nBefq9onklkpoVaudU3H7r18HYxwcJn3wK24kjsdIhAu/088eR0BQ8Mu+QTOGgRCr9esTXu67CxswIkx7idXW1C/uKOMqiJDVVONw4/E4iqU3Yqevz20p408zS2NcXyZ99ht5fvoItLQrhbKlI4fD2+rPC5JOeW1hvv4z66+WoZ4REpOHvq0kU4tZEKP7aJn3NGlEBi6N1eOWtRFIXcBinRaeOMO/YATmHDiFxzhwUfTAd8xs0wLFWffBVcBHWBkfT64AOvvZ4vJ0nHmnhJkxB9QUZvVNPeHnVaey/mojj7/Sp9YiGksxM3Og/gCIrGsF7xXJZAUuiNrgiW+aOHaJwS/7Fi9AzNkZx40CEeTTG7xaNcazEGrbmRvj+qTYUwuxUL6J36k/3Vo9Jzi7AnxfiMKaTT52EsCX/+CNKMjLgMmO6VPgStdv7bYYMgfWjjyLv9Blk7d6N3BMn0HDXOnxQWoqStu2xxLkjxi0pxPSHm2IymT51vUynVPr1gHU0nS0quYXRFKZZ2xRGRYnsmTbDhkmzjkRjYEVu3raNEKaYfE3pa9chbdUqTD51EsNcffFjbA/8dKANmrjbooOfvUjxYEyRbrqG7t2R5I5Mmr+fiEBH+hHXdpGUWyUlSPjySxpKGFII3Svym5BoLIb29iLip+Ge3XD7/HN4mNzC+ydW4MvjP6M4KVGENk/6JRi5hZw8QLeQSl/HORSajKjUPIwm005tUhgeLqofZe/ZS39MU2QyNYnWZPS0HT5MlG90nTULHrGh+Hjbl1jQsACHridhzM//6Fykj1T69SCxmoOFMQY2q71Mmpl/7cTNYcNRcOMG3P/3JRwmT6q1c0kktWX7txs1En7r1tIswA4NvnoXq/IO4XpEEh5fdBSRKbk688FLpa/DXI3Pwp7LCXiqg3et2SZzQ0IQ+9ZbMG3SBA3+2CqcZrruCJPoLiYBAfBdtw72456B9c6tWH1iPhzDLmP4wiM4FZmm7stTCVLp6zDf7b0GC4rWmditdrJaFkZEiJwnXOSaC6LI/PgSXang5TJzJnx+WQETAz3M2vs9xp3dgnGLDmL7OVEUUKuRSl9HuRSbiR3n4/FsV1/YkXlH1RTFxiJq8vNi32vxjzC0s1P5OSQSdWLevj0abNkMWzL79LuwFwv//gar5yzFon3XtLpEo1T6Osq3e67BypRG+d1Vn3Ih7/wFhI0cKcLePBcuEPlOJBJdRN/CAm4ffgjvpUvgZmeOd06uRIO3J2H+x0uQX6Sd5UCk0tfRxGq7LiVgEil8VadcyD50CBFjx0LfyBi+q36nuGeukCmR6DYWXbrAf9sfcPv6a9hZmaLvqq+x8fGJiI1MUPelPTBS6esg8/ZehzWN8ieQaUeV5F+7hphXXoWxnx98166BScOGKj2+RKLJ6BkYwHbQI2i/axvSnxyPFtdPInzoEPy5YqtWFWaXSl8HI3Z4lD++qx+Zd1Q3yi9JTxdOWz0Lc3j9sAiGjo4qO7ZEom2x/Z0/ng7Tn1ag0NQC3p/PwPwxb+L4de0Y9Uulr2Ms2h8Kc2MDTOiiulF+aWEhYl5/HcXx8fD6/nsZpSOREAHdgtBt9x/I6jUQ/U7tQP6Tw7B99BQkrlsvBkmailT6OkRESg62no0VOXZUFbHDhVAin31WFDV3/egjmLVurZLjSiS6gKGlBTotmgunud+gyL8xHM4HI+X993GlW3fcnDqNfGCHNS7SRyZc0yF+OHADhgb6woGrqjh8DsssiouDx9yvYf3IIyo5rkSiazg+QqN9kuOhSZi7fCf8Th9Er8PHUbB3D9C4KTxfngrzoCDoW1urffGiVPo6Qkx6HtaHRGNkey84W5uqJHlaNDltOUWy9/JlMkpHIqkCnRo6odOnY3Apdgg2nghD4obNGHZxF0D+sLIQUDOKeHN9/z0Ye9d+1ttaUfrUaxnQJpgkhqYxg+mxPe2vIWGjcjjJk9Qu1i/TczNpM5GEA1xfpvadNT2/RMH8fdehR/9e6KmaiJqMzZtRcOWKGOHLsEyJ5MEIdLdG4LBWyOgfiGUHn8AvG/6EfWo8AvVz0CnkBHKHDoPzm2/A7qmnoKevr12Vs0iRv04brs5irVT6/6P9VNr/gva5GrYd7U+nfa6Zt4qkA4k7Cc170Iieu+cKh6CgoFvBwdynSO4GJ4Pq/fV+PE22/I+HNq/xB1Wak4PQgQNh7O4Bn9Wr1D4dlUi0nZyCYmw6HYPlR8ORHhGNGRc2oFnMZRj5+MB+zBjYDB8GA0tLza+cRQf1pM0gktkkrPyZoSQ9lfsrSPaTTFe2r6aLKKBtGL03VNkBHKvJNUiA72mUr6+vhxdVNMpPWbIEJUnJcKFIHXUq/MKSQiw5vwSh6aEIsAtAA5sGyCzMRGx2LIwMjPCQ50MItA+UnZJE47EwMRSV657u4C2SIM7b6wX7E4fwZMQR+M+ejaR582A/dizsnxkLA1tbjTbvfEvyNknF6hwupNhFViLektJwVrZ7kByv8LpoZdsd0Hsm04YF3mqye2kLYck52EgjiHGdfeFqUzNb/q2iImRs/QMpS5cJp606I3UupVzCu4ffFQrfzcINuyLILqrEgCyKt+jfwjML4WLugo5uHRHkEoTO7p3halF7KaQlkprCg7P+zVzRL9AFf19tjO/29kTeuXN4Jvwg2i1ciNQVK+D89tuwG/lkTU+leqVPinkwbRJJsYfQfs+qvKWStkptS3TMxbRZXGbeqe411gcW/h0KQ/ohTelZs4idrH37kDD7MxTFxMC0WTP64b2loiu8P2xizCjIQExODILjg7E/aj9OJZ6Cg6kDFvRZIEb0uUW5CM8Mh62JLZzNncWI/1D0IRyIPiC2W29sFT6NHl498FTjp8RM4GrqVeQV52Fwg8Fws3Srs/uRSO4Hz6B7N3FBr8bOOHi9EebsbI6fL13FlKgD8Mg3QpfSWzCgv2uNsunTRX9Om7EkXE+Mh5jWJBtJ2pP0VI7y+S9tP+03Vjpx+Q/8c+X72Yk7ix7f07wjbfp3Jy4jDw/972+RL78mtvz8y5cR/uRIGDdoAKfXXoVljx51YjLJKszC0gtLsebKGmQVZZW3symnt1dvjA0cCxsTm/sep/RWKW6m38Sf4X9i/bX1SM1Pve15nhn09u6N0U1Ho61zW2kOkmgcnMZhy9kYfLXzmojE87I3E7N39tOZGxuq1KZfY0eu8uA80n9T6cidQ/spFRy59rT/Nu03o/3fKzhy95IESEdu9Zm9/RKWHgnH/jd70o/EvFrHKMnOQfiIESjNzYXf5k2idmhtUFxajITcBGGPZ4nIjBAKOq0gDQN8B6CVUyu4W7ijsX1jeFqxq6h6FJQUiJG/maGZOBb7BdZcXSPOxbODJvZN8HjA4/C39YeHpYcwDxnocwCaRKJ+iktKRRqV5fR3fTE2A8ff6VPtdCp1qfQdaH8tCRvjI0meoHYx9KLn3qXNs8rZwavU/uf9ji1H+pWTkVuELl/sRV+yDX43qk21vjf+7mOnT0fmtu0iFt+iA/fHqqOktAQ7w3di2cVluJZ2TYzIK9LRtSNeC3oNzRx4PFC7sJln+83t+O3yb8JPUAZ3Dg1tG6KRXSPRSTS2a4xAh0CYGtZ8rYNEUhMSM/NrtOamVpV+bSKVfuUsIFv+nJ1XsePl7iImuDqkr1+PuPfeh+O0l+A0dWr1v6RKRvV/hv2JxecWCzu8v40/+vj0ESN5tq3zCJsdriYGJio7Z1Xh33tkViRismPEjONG+g3RIV1JvSJmAoyFkQX6+/THo/6PCnNQxZkAd1z6ejJ7iUTzqZWQTYl64OINy46EoUcjp2or/LwLFxH/8SciT7jjlCkqG9lvu7lNKHtWrDx6/rrH1+jr01djFCX7KnysfYT8tzNg89PllMvYF7VPzFA2hW6Cvam9cCRbGlkiJCFEdBBN7Zuip1dPIXyPch2DRJuQI30tZOnhMHy87RLWTO6Ejg3YmvZgcAbAsMdH4FZpKfw2bqhxqUNWmEdij2BuyFxcT7sulOKUVlOEUtQUZV8dc9ABiqT4O+pv4SMoLC0Ufgc2AZ1NOotzSefE63jW0surFwb6DURLx5ayA5BoDHKkr0OjfE6s1qmBffUUfkYGol9+BUWJifD9bWWNFD4r+xPxJ/DD2R8QnBAMT0tPfNXjK2Ea0fbRL9v6WZGzsLmK1wUY6f/rUEvKTRLhohxeuvbqWqy8vBK+1r7o59NP+ASEQ5o+D23/HCS6hzTvaBlrTkYhMaugWs7b/KvXEP3SSyiKj4f7Z7Nh1rLlvV9fnI+U/BRh2mCFxwqeR74cHslkF2ULp6iTmRNmdJiBJxo9AWMD1RdhVzeG+nf+mTiZO2FEoxFCsguzsTtiN7bc2IIlF5aUO6w7uXXCe53eu8OUJJGoE2ne0SIKikvQ43/74U3hmWue71SlUSSPxjlxWsbmLUhbtw4GFhbwmPcdzNvcvdNgB+fqq6ux4dqGcudmGdwB8EiWzTYsbMJ5LOAxhVM2OwmIOk4Xmq14cWkR9Rz0/gIS50Cg0UDASLejYtgsxM5h7iB/OveTCCHl9QE9PHughVMLtTivJfUTPRm9o/38ejwC72++gJUTO6JbwP3LFZZkZSH6xanIPXmSlK0RrPr0gcvMmTDKOg+khQOmNmTHsAVsvITkUh/CqQ3YVMHwgqYu7l3Eatjc4lyyWbdAe2MHen8CvZccyEbmQDwdK/wwEHEESL527wvi8zUfAXR/nc5X/Vh8bYFNQHOC5+CvsL+EechY3xhdPLpgqP9Q4RzWxVmRRHOQSl/LyS0sRo85++HrYI61z3e+7yifF1tFPjdJ5PXgFK42Q4fCMDcM2PU+KWhS0v/hsJkpZjk6IMHQAI9n52FKvh5cjawUipqF49ZjT9NoPv7OkxnT67w7Ab5dAR8SCydFO4c68nu5cwg/BJxdDVzczL9Gsn28CHR7VfG8jsMpJk4lnBKj/7/C/0JyXrJIJ8GLxEY1GSXzBUlqBan0tZx5e69j7u5r2PBCZ7Tzufeq2dKCAkS/8AJyjv8Djy8+hbVntkLhsrI3pxlCzxlAk0Fkdsmi3iSVFNFOzAjfiAaGVvjQtg1aGdkqTDRslslLV2wLc8hE01Sh1B0DFM+TLVvsu7QADKroHkqPBPZ+ApxfS52FJdB6NNBhMh1HNRlCNR12Ch+POy5WCLN/hPMFyRQRktpAKn0tJiW7QOTYYZPOj2PvWGtxR1qF6BenIPdEMNyGuMPWikILSwoBB1KqrZ8G2k9SmGaU8CrVdw6/g9ZOrbGw70KxMKlOiDsLHF8EXNiguD5X6jgaPwI07Ae4t6ZOpHpLz7UJ4Tu5Qr6T6wrfCecc6ubeDUGuQSJrqDnPkCSSaiKVvhYza+tFYc/f9dpD8He6e6GF4qQERD0zEvnhFJ3TIR027dwUijRwGOBJnUUFk1BkZiR+Ov+TyE7ZzqUd5veerx4lk52omIVc3UEXxZm3b5E5iDoej7aKmQBj7wcETdTZ2UBZiog/bvyBc8nnxGyAQ0b7ePfBw34Pi5XMVmRCczBzqDSSSCKpDKn0tZSr8VkY/P0hPBHkhc+G02i4MkqKUPLPSoS/MQdFmaXwGO4GqylfKRTnf2z/8Tnx+P7092LlLIdhcpjly21fFkpG7eQkK2z/EUeBmFOKGQB3AolXFJFA/n2ABj1pVtBcYVKyVPoOdAgOkz2TdEasCN4ZtvO27KOs/Pm74o5AWxe9SeoOqfS1kKKSUgxbcATxGflilO9gWUm4X/gR3Nr8AmK2ZSIrxgzeH78AixHTblP2PJIMTQvF3si9IjKHwzjZgTih+QQ4mt0/CkjtcLRQyHLgNEUVZXAOPyWWrgqzULNhiqggHQsH5XDP04mnkV6Qjoz8DGEGupx6WYTMvtT6JXTz6CYXf0nuilT6Wsg35Lj9jhy4P45thwHNKqkIFXsGWD4YKaH2SDxaBOfpb8NhwoTblMZrf78mUiSULRjigiLT2kyDuyVnt646xUUlCD+XgqvH45CRnA8Hdws4elnCp7kjHD1VW9vznuSkAAkXFMLhotEUjpoSSg5qB6DtMwrHMDuXdRD+DtkMxDO1uJw4kZ2U013wGoD7RXNJ6h96Mk5fuzgfnYFhC49gSCt3fDOykrKFqTdx6+f+yIowRsx+PVj17QePb78p/+Pn0fx7R94TNvvxzcaLvDGsJKpaQaogtwgpMdmIv5mJ2OvpiA1NR1F+CSxsTeBEyj41LgeZpPwZVv4BQS7wbGInOgB9g3ubHkppBsPHLcgrhomZIUwtjWDrYi5KyT0wnCWWTULHfwCu/UmPqXPzCFJEBDV/vOpRRVpEEZnz+HtlnwxnC+UaAZNbTkZX967S+SspRyp9LaKwuBRD5h9GWm4hdr3aAzbm/4lkSbmBwgXDkHCwANnRBqK8ofeK5TCw/HfEveLiCnwV/BVebP0iJjR8DinR2chKzYe5jTGs7E1RmFeC5OgsobjNrRVt+TlFiLmehrjQDGSlKBQ6Y+dqDvcAW/i3dYZHY7ty5ZyXVYjrwYli9J8YobA9G5kawIyUOMMdkDEpdRYTcxLaFheVIvJSCgpyuKTCv/Dzbv42cPK2gpWDGawdTGHvYUHHeoAFTFnx1FuuJzPQr0AS+QHsGyiilazdFesB3NsoFqPpCEXk59hxc0d5VlMO/+SUD00dmoroHxZva2/p/K2n6MmRvvbw7Z5rJNfx8zNBokhKxcLlWWt/RMbyeaTs9aFnYgqnV16F/dgx0DM0LB8FrqIwwN/3bUL34ofhk9YcGYl5dz0XTwwqllQwszKCe0NbOPlY0ajdSihh7hTuR056gegw4qnDKKQZQVkJuEIazfOsoYA6mULa8qm8mtjDr5UjKXdTMdrn93JHwzOK9MTc2yonW1An5UDXwTMInlF4UqdjZnWf6yktVUQDHfiSTECKbJgCjk5q9RTNAiYp1hzoCBztczT2KC4mXxR1Ac4nn0dSXlJ5qUiuXcCdQRvnNqIj4MpkHA1kbmguzUI6jFT6What83BzN8x76t/8OJwGOXrcCGSfvAwD0l02Qx+D/fPTYOTqWm7v3R22B+v+3Amf0Lawz3ODvqGeUJIejRRmF2tHM+RmFohRvKGJgaKNRtUFucXITMmDEbWxmYV+LOq6fZTQLCc7LR8ZSXlkXsoRM5RkkjQyJ3EnwpfmSjMCn+YO4r64czK4mzmJe7PMWAqJSVeEhvIs4Pw6OkkB2aQaUzjrw4Bfd0UkkNW/nau2U1YohlcBR2VFITYnVqS8ZuF0EGVwxBavz+B1AVwXgP08nCq6ztZqSGoVqfS1pD7m44uOIiotD7srRuvQH3HyzLFI2hwCp4fs4PDVZuhZO5cXLtkduRsrD69Ho5CecMhzhwkF5HQb3BQNWjvB2FQ3bNolZBZi5R9+IRlhZ5NFZ8AYGuuLGQnPApx9rEVncM+ZCYeFsvLnmQDnC6JRcnkkUEBf6ggGUVhoD8DYQifTQXAtAM4JxEXpo7OjRWGYiuUjGVb8XDaSi98MajBIhodqKVLpa8HobPqGc1gbHI3vaYT/KDlwBTTCz5k7GpFLTsO6uRPcf98NPQpN5BWcm69vxqrLq2B9wwddIx4j27kBeo0MRKMgV+hVxymqReRmFpY7mJOjskQnIMxKPBPws0HDIGc0au9yb1MQhUGKlcHxFAkUfQII3atIOcELoNzbKmYBrZ7W2UVhFTsDLlTPMwJetMfVwdhUxJ0CK//X2r2Gzu6dpfLXMqTS13Dm7LyCBX/fwMu9G+L1/mR6UFK45ROEf/ArDOzs4LdtL8KLE7Hq9DpcPBUOxzQf+OYEwiSPbN1N7dBvQrMq2d91tdMUM4Fzybh5Jok6gmzhcPZsai9MXO6NbIV/4p4RQsWF77ApqQAAF/hJREFUitF/2AGx/gGxpxQzAU4N0XEK4N+bOoT6sSiKzYW8QOy7U9+JCCGumdDDq4eoGcwzAfYLOJsrZpsSzUQqfQ1m+ZEwzPrjEp7qoFh1W2ZTL7l+DOFjnkFxgQlc16zBvLM7UXjSGi5ZvjSg1YeRhT68GznAp4UDmnRy0/nR/YPA4aZX/4kXHUCZI9va0RQtenqiaRc3ihYyqvqisOAl5BNIUOQv4lDQZo/p5GrgyigsKRTKn5PDHYk5IlJsl9HApoGopzDQd6CIGJJoFlLpayjbzsVi2qrT6NvUBYtGt4Wh0il5KycDUcO6IieaRppzpuPb8MtofqEf2fKL0aKLNwLbeQtHrDqdrtoCRwdFX03DxUMxIkrIwFAfLn7WIgzVi2YC7Bi+7wzg0hbgn0VATAg10Gu9OgBNHwVaPEFO4EoWzukgHBnGJh9OFMeFYg7GHERIfAiKbxWLyKCnmzyN7p7dpSNYV5U+HdCLNr+Q8C+el3supmN9R+2c93cNiS9JOMmT1J6mfM9M2kwk4Zi+l6l95/3OExQUdCs4OLha16jpHAlNxvhlJ9Dayxa/TuwIUyMDxRPknI2Z0BeZNFLdPcIDJywD0Tl8GKz9DDDyla4645xVB0mRWbh2Il74A3iff/68OMyXZkt+LZ3gFWgvopjuStw5hRP4ynZFOCjnwGGzD6ec5pQQ7AuwePDaxdrsD+CFYr9f/l10CBwi2tS+qfABDPYfLGYDEt1R+ry0043ef4r2rWifh0DDSMaTpFL7F9Q+g/btaH867QfS/ioSGiKBvZR7SBrRc4qg7nqm9K8nZGH4wqPwsDUTRVHKF2CVFCP6g2HI2nADf3S0RobXeLgnN4ZnSxsMntQGBkb1w6ZcF/AagshLqRQNlISICykidJVnARwB1LiTq9jy47uSfB04Sz/pCxuBtDBFGxdPbzZc4QPwbFc3N6IBcBTZyYSTOBF3QkQEcdI49gu0cGwhkvpxkjhTLsQj0R3zDp2A5r+Yr5SedNw4Zcewn/YbK0f57HD7XPl6HuXPosfH6pvSzysswdAFh5GaU4g/pnWDm40ywyVNn6MWPInUxZdxoWFLZPo9D70ifXQc4o9Wfb2ql6ZAUiVKKFw2jkb/HA56PSQReRQdZGphhACKAOIOwNnH6t6mNC42w/mALm9TJIajkEiRDoKVf+BQji2tV98Eh4XuCNuBzaGbRUgoVwrj8E9OBdLYvjH8bfxhwJXVJNqp9OngbMo5SNKcJJKOaVvhuTR6bEdb7gyO074owEqPyTuGP+nx+kqOR94ysMDb27tdREREja9Rk5hBoZlrgqPwy7Md0D3AqVxpXFn6BBJ+jUamTWdENhgDJy8r9J0QCHs33YsZ12Q4N1DU5TRcOR6HsDPJYsEYR/50ecwfnk3uXbVMwBXJzvwOnFisSAZn6aKoBxA0gfbrV8QL65fghGCsvLQSh2MOo7CU02UD9qb2IlFcP59+Mluotil9OjAnfKEYN8ymY22kx+l3UfoL6OGx/yj9HfR4Q30a6W85E4NXVp/B1F7+eGtAE0VjWgSOLRiOoq15yLTviwjf4fAm2/LA51vc274sqXU4hQTnFwr5KxzZqQXwbuYgUkiwE92BxMjY4N7pIG7sJQfwj0DoboALoXu2B1yaK2sCNFekgzDSgFoGdZQuIjwjXKSHPhR9CIdiDiG7KFusBn6+5fOibKQsEqPhSp8OyoZoms9iJx1nrrLtKm2keacSLsdl4jGy4zdzt8bqyZ0UkTrZSdj1aU/Y7DLGTf+nkOoQJBYW9R0feG97sqRO4dTS5/6OxuldkcjPLhJtbG7jNBAcBWTnaiGSxFk7mcHSzuROUxDb/zn8M+oEmYEuUihMjqKdHcGs/H27KToELirPyeHsfG8ra6mr0UBcKJ4TxoVnhsPEwAT+tv7CD/BYwGOiboBEsxy5/KteoXTavlqhfQ5tUio4cu1p/23ab0b7v1dw5NIQCAH1xZHL9nvOnMmFUf54qRucrcmpVZSHix/0RPo+G1xqNhFFJk5oP9gP7R72lfZ7DYX/Xjh3ES8ESwjjtNNpSAzPEnmBKmYM5ZmASwMbeFCHwCGht0Vc8QyAHb/sA+BooKh/FHUBiv/NbCo6A9eWFBXURZEt1NZb0TnYeNTh3dadE3h/1H5RMOZK2hWcSzonCv9wGOggv0EiNxBHAcnwZPUrfRqa4BDJeRJFhQ7gHRL6BWMtCf1KwWWOnqBzpCrf8y5tniXhhCevUvuf9zuPLih9VvRjl/yDU5HpIlKHQzRRXID4T5/AyX8CEOk9EOY2JhgwqaUYNUq0Ly9QVlq+SF2dHp+L5JhsJFM4aFJUNm5xZ0DDIxuaAXBHYONsLmYEtrTlzqB8Nke/ByRdVSSHK3MK86rgmODbOwO3VhTzNhBwaqLoCHjBmA6li2Y4xciW0C2iaDwnjmOczZwxre00DPUfKpV/FZGLs9TI3F1XMW9fKL5+ohUeb2wCnPwZYSt34mD+eGRbecMpUB9DJnYV0SIS3aEwvxgJNzMRH5YhZgaipgHNEspmBcamBvCmsFAXX2uR7dTK0RT2ZCa6LSyXZwU5SUB6hCJFxJUdillBebZMTjbE5qHuQMuRNIeupOCOlsIDUs4Syo7gjdc3imRxnAaCiwJxGgjOCiozgt4dqfTVxNmodDy26CiGtnbH3P4OyPpxIHad640Ei0fpV50Lo74ZmDRynLouT1LHsMLnFcKcJC7sXLLIFZSXpfARMJxKg4vWlOVQYr8BVyvj2gPcxukjTAwLYamfDCu9WBgm00Q74rDCV8AzAq9OQJvRNCNorZgNVDFUlGckhQUlKKZwYjZFcfZSTTKncMw/h3/ODZkrFoSVYWNiIwrGc70AdggH2AXAwdRB1AuwM7WDtbG1Rt1HXSKVvhrIJ+ff4O8PI5tGfLumNEfCTyNw4NJYFJg1pPHZBbi/4IuhLR+ttz9KiWI0y1XEhGkoMVfkDOIZQb6yshiHj2anFYisopXBRW+40piltT5KU6NRkJIIk9IUBJgehJ/ZKRg5+ylWCnuSaTdwmMgZxOfkjkZ0PGeTRbpqPkfF4jXc2ZRVWbMgx3TZGhGuZMZRS2yq4lDiul4smEMOcI77j8uOE4nguFYwrwTmqCB+/F94JsAJ4tq7thd5gtq5tIMRL6CrB0ilrwZmb7+Enw6F4ddnWsB84zScuvE0ig1t4BeYgAGvPiOVveSBoodYUfMqYi5ryUo6KyVPmIu4w8hOLyT/gJ4oSZmRkIXsjBIYGRbD2iSdOoE06JfkoPCWBQoMnElxWqG4RKGseUTP+YdYkfN7DUmJc4pqXp2cm1GgPHaBoroa/ZebUShKXjLcEdjSrIT9FaIcJs1CeJbC9Q3s3MzrPF0I1wjgnEDsE2BJyUsRnQJ3CGwiKigpECUlLY0s6XOxFlXEhjYcKjoCfXac1xOlL5O41BJ7LiUIhT+mgwf013+Ik5HP0dS9FEGPlKL9cGnOkTwYhkYGNOo2qLKphnMLhYYkIifDVSjworwcmBbFwyb3Osz1Y2BtkAhbwzi4e9KxfTsqcgex3CdvEJunMmhGUuaj4C1XOROdEYWylnUIDPuo2CzFuY24Q2EzlVtDGxGs8EC1j6sIm3RaO1fu08gtysWxuGOinGR2YbYoJ7kncg+23NgCF3MX9PLqhV7evUSUEJuEuKqYrs7AVZaGobbQxuidiJQcYdbxcTDHzOxNOHezLwyLM/HQlAZo3JEjViUSNcF/75wmmgvHcL2A8DJ/gLKOMkcDcYiojRfgGKAwDTkHkga3vW8tgYrhrGnxOTRLUJTmFDWSqePJpllDWafAiw55dlBx8SFHNnk0shWObS5+Y0wdhRl1GLWVMjyP7nlf5D6ROvpY7DHkl/wbJcUmIDcLN+Es5kIyHDbKIaTsQ9AWpHmnDu34nEgtNj0P88yP49K19qTw09HztSYIaPlvzVuJRGPg1NGxpxUO4RjqCNIjFdFCXFmsHD3FYjFePMahorY+isVkvl0V+1UYFXM6i8SILMSFpgsfRQHNDoq42lmFIjiZNGuoiKjl7GEBew8Kd3U0Ez4GY+osGH06J/sbeDZxz5XRVSCfnOAn408iITdBmInS8tMU/oKsaFxNu4qiUoWznWcB7CNgxzHnEGpo21A4jHmWwTMGTYomkkq/DuCp78urT2PbuTj86BuKsOCGMCCF3+2VhmjahqbQEok2zgiSryrWDnAnkB1PHUIUkHrj307BzJ4Uv5eyIyATsk83ih5qCfLyPvBp2VfBDmbuEHiGkJ6YJ8xIqbE5wpdxN1jxs9nIvaEtbF3MxWM2J6kiSWEB+QJ4wdh5ipTiWgLsML6ZflOUl/wvnpaeaGjXEKYGpuWdBHcOHFnkZeUlcg7VldlIKv06LHn4cfN0FOw1QKm+KTpOtETrLn3r4vQSSd3B6weSLisWkCVeAjKoI0i58W+KaT0DxephG54VeCs6BTYZiX2lPGCWTV73wOaiIgotFZdQcouczPnITM4Xi+G4XnLF8FeOLHJwt4CjlxU8m9jBp5mDMBmpCnYWh2WEIbMgs7zQPPsMuI3zDDHsTM4qyip/D3cGTuZOYmZgZWRVnmnU3NBchJuyKamFUwuVlKKUSr+WWRcchbfWn8OzzUvgsTsSBSaNENg1Bj3HTajtU0skmkNWvGIRGecX4hkBm4q4Q8jkUXEF/yE5SkWyOZF4roVi60A+BAvHKpmKKoNNRFwaM1MZ1ZTGq6PZ2cwzB/Ip6BvowZ46ATH6p3Nw+UwReupO0Tw0MxCmIxV2CmXXxGai62nXy6uOsROZncmcbK5EmYWGOw4uTn+L/nHyuZfavFTjc0ulX4tEpeaiz9wDeMzuFBqeM0eBWSt4O5/Gox+/UZunlUi0hxIagWdEKzqANPIXJNIsIeE8mY9I8kRhvX87A2t3RWoJTjxHoZWKrdW/nQE/5hkEv67MhGRsqZhN8HOVmF3jb2aINQmpZCZibtFMhU1H3DlUhNNisM/AmB3MytNZ2JiIzoFnDO4BNtRZ1E5kDzuWQ9NChY+AVxzXFKn0a5Epv4bA+uL3CIjvj0ITJ/g4Xsbg2S/X5iklEt3xHfAsgHMNpYUrZgb8mP0FLDQCVmyz+cWK15dFGlVGWYfAHQDXLuaoI+5AeBbBswkbaq+gsNlvwDMCXo/AHQD7Df7rYOZ2XjRXXKiIPGJfgZOXpZgZWJJwaCrPEMytFQvlLGyMaVah/rh/GadfSxwNTYbHiYVwynsCpQbF6N6rAC1HSYUvkVRRMylt/w+QPbQoTzFryIzhIbuijTsGEXUUpZxNUAcSeVzRaSjt6wITm39rGVCnYEIRSa40S3A14hxGFc7BtnYxy1DMNEqN3ZCWYYy4sALhO0iljiI2NEOsT6jslrgT4JBU7iAcKfKIF7/xIjZ2MFvamao1bbpcnFUDiktKEfLVp7C/NQpGxfEY8kYnOLWgEYVEIqk9uOgMryFguR88M+BKZklXFKak+POKWQWXtCyraVAFWEXzsjUHclA359mEa0OgYxcUunSg6B4zFJCVKJcOl0V9TDadrqDURLRnpRXhyj/xKDpQIYO8nsJkxH4EninwljsH7iS4s+D0FmxCqi3k4qwasHTKO8hDX5jnXcaIL0bAys1FVd+LRCKpbYryFTMEcqreQWmJ0rTEoaqZin0OWy3bcufBi9sqziIqw8odt5ybIcs6CJnmbZBl1BCZWXrIJpNRpjKFRg6FqVasx8A1NTqQ1BRp3lExPx+6iaPGpuiafRBPL3gTJpZcNVIikWgNRqYKQTUHa4W5inDV/yp+dlpnxSnMTcnXoUczC+uwObAWr6NhvmMjwM4HaOItMqGWOgUizzxAkRuJzEVlGVZrC2neeUC4R/7lWDg+3X4Zj3QchHGj2ijKHkokkvqFsbliMVpV/RDRwYpwVp4lsN+BK6bRTIO1B6/jtWAfAq9f6DCZ7Ei1l59LKv0qkltYjA0h0Vh+NBw3knLQu4kzvh0pFb5EIqmiH8Kvu0Iqi1ziqmncEbAj2og6k1pEKv37UEIj+/UhUZi7+xoSMikyx9MG34xshcEt3WEkR/gSiUQVkUuNBlT3KA+MVPr3SJy29Uwsfj58E9cSstHG2xbfP9UW7X3tdDblqkQi0X2k0v8PnB1z5fEIrDoRibTcIjR2scKCp9vikRauUtlLJBKtRyp95aq74Ig0LD8Sjr8uxovHfZu6YEJXP3RqUHdZ8SQSiaS2qbdKPymrAJfiMnGZ5I+zsbgYmwlrU0M8180PYzr5wMu+dp0pEolEUi+UPo2aB9LmOxLOKfozjaq/qM3z5RWWIDI1Vyh3ljJFn5z9b6HpJq5W+Gx4Cwxr4w5z43rbD0okknqAYR0rfFb0C0j6kUSTnKS2raT4L6n6XBOXn8SZqHSk5Pyr3I0p2ibAxRI9GzujqZs1iRWaulrDzqJ2F0NIJBKJplDXw1ouEBtKSv4mPyCFv5o2Q0lUrvR9HS3gbG0CTztzEjM0ptG8v5OlDLOUSCT1mrpW+pxKL6rCYx7t31FHkDqDybRhgbe3d7VO9P7gwGq9TyKRSHSZus4fUFkYTIVyOsqGW7cWkwSxODk51cFlSSQSSf2grpU+j+y9Kjzm8jB3VheWSCQSiU4o/ZMkAWS+8SNh7+kokq11fA0SiURSb6lTmz6Za4pJ2XPF350kHMmzlNou1uU1SCQSSX2mzoPSScnvoA2LRCKRSOoYmQheIpFI6hFS6UskEkk9Qip9iUQiqUdofGF0cvwm0Saimm93JElW4eWoE125F125D0bei2aiK9+LYw3vw4f0u5PWKf0adhjBvMBL3dehCnTlXnTlPhh5L5qJrnwverV0H9K8I5FIJPUIqfQlEomkHqHrSn+xui9AhejKvejKfTDyXjQTXfleFtfGQXXapi+RSCSS+jXSl0gkEkkFpNKXSCSSeoROKn0KdRpIcpUklGSGuq/nQaDr9SL5m+QyyUWSV5Tt9iS7Sa4rt3bqvtaqQNdpQHKaZJuW34ctyXqSK8rvprMW38tryt/WBZJVJKbaci90XUtJEvnaK7Td9dppf6ZSD7A+GKCeq36ge5mj/I2dI9nEvztV34vOKX1WMso6vA+TcPmsp6hNm8poFZO8Qb6WprTtRDJVef3cee2l9gDeKh9rA9xpXa7wWFvv4zuSv+i6m9C2lfKetO5e6LfE1eteJuEiRc1pa6BMca4t97KcZOB/2iq9duXfDd9bM+V7Fir1gybfy26S5nQvLWl7jWSmqu9F55R+xTq8JFwVvawOr1ZA1xxHckq5n6VULh7Ke1ihfBlvh6nnCqsO/Si5SM4gkp8rNGvjfVjT5iGSJfyYf1ck6dp4LxWy6/6/vfN5qSKK4jh3EZK2UaEoWpggba1VlIvINkm4DhIC+wdaKeGqfUQ7WwQuTGpRURAELlq0qigIF/1Aw0gFMwgSDEIoPwfOwCOcesXUvDPz/cCXcz0P4Xy9d86Mdx4zO/Flsd1fZBTCC3/3x4TPP6Xzarf8LX7nG1pkvOD9oWW9kJu1R9D7j0+QHUOFeqli09/uPbyWCwcHZQ/hEHqK9tgJwfIed5dZW5NcRWPoe0Muoo9eZI8DmfKtquuoI6IX6lwhXEYfkNX8xRpNRC8N5NUevReMoodFe6li02/qPbytTkppF+EOusBCXi+7nr+o/zRhjdpflF1LAdgV8WE0iR87CW+08PbH7+al068aD6B9qIPcSLlV/TPC9oKU0gTBrvhnslRRXqrY9MO/h5cJ3+ENf4Ymc9fTH8nv9c8trpVVX5McQ8PU+t632E4wvhHQR7amlpkL+4/LuO0ngYheTqJFvHxCm4xtfR0N6iUjr/aQvSCldI5gF01nmaMfRXupYtMP/R5eak6+d/ya+b7S8JF5sMVgWLz/v2v7E6j9ItqPenwOHjEeiebDoO5VwhJTc9BTg+hVRC++rXMEL+2+1gb9vlFELxl5tVv+DDbbrB8w7kPPSqivaajTbtKOo2HW3deGj4rzYieSqgmG/M73OzQRrPYBmxY0h166zE+3fzNh3mNXIE/H0QMfh/QB/ei5z8s91BnYyyX0BtlXBadRWxQvcBPZvv2mX/2e/1Xtdvx7H3iLTgXwsuB799mxf61oL3oMgxBC1Igqbu8IIYTIQU1fCCFqhJq+EELUCDV9IYSoEWr6QghRI9T0hRCiRqjpCyFEjdgCSg7oupC1e5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(db.index), db.I, label=\"No intervention\")\n",
    "for k in traces:\n",
    "    plt.plot(traces[k][\"t\"]+t1,traces[k][\"I\"], label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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