Fig A3 Eigenstates comparison TFIM J=1 (Fig 1 setup).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simple_exact_diagonalization_routines.local_matrix_class import *\n",
    "from scipy.linalg import expm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# System size\n",
    "L = 3\n",
    "\n",
    "# Create objects of Pauli operators\n",
    "X = X_class(L)\n",
    "Y = Y_class(L)\n",
    "Z = Z_class(L)\n",
    "S_plus = S_plus_class(L)\n",
    "S_minus = S_minus_class(L)\n",
    "\n",
    "# Trivial initialization of the right size matrix\n",
    "H_TFIM = 0*np.eye( 2**L ) \n",
    "\n",
    "for x in range(L-1):\n",
    "    H_TFIM = H_TFIM + X.at(x+1).dot(X.at(x+2)) + Z.at(x+1)\n",
    "H_TFIM = H_TFIM + Z.at(L)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow = double_bracket_flow( H_TFIM )\n",
    "\n",
    "flow.flow_step_max =1\n",
    "flow.flow_step_min = 0\n",
    "flow.nmb_search_points_minimizing_s_search = 100\n",
    "flow.please_use_binary_search  =False\n",
    "\n",
    "flow.nmb_flow_steps = 15\n",
    "flow.flow_forwards( states = np.eye(2**L).tolist() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axin1.set_xticklabels([1]+['']*(2**L-2)+[2**L])\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axin2.set_xticklabels([1]+['']*(2**L-2)+[2**L])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flow step  0\n",
      "Found minimum norm =  4.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  4.0\n",
      "Found minimum norm =  4.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  4.0\n",
      "Found minimum norm =  4.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  4.0\n",
      "Found minimum norm =  3.325045249589565  for s =  0.9246231155778895\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.9246231155778895   minimal norm  3.325045249589565\n",
      "Found minimum norm =  4.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  4.0\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.11055276381909548\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.11055276381909548   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  3.3250452495895653  for s =  0.9246231155778895\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.9246231155778895   minimal norm  3.3250452495895653\n",
      "Found minimum norm =  4.0  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  4.0\n",
      "Found minimum norm =  2.8643124521700267  for s =  0.05527638190954774\n",
      "Found that canonical is worse because  2.8643124521700267 2.5874102682250926\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.11055276381909548\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  1\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  2.455388987485165  for s =  0.09547738693467336\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.09547738693467336   minimal norm  2.455388987485165\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.19095477386934673\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.19095477386934673   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  2.455388987485165  for s =  0.09547738693467336\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.09547738693467336   minimal norm  2.455388987485165\n",
      "Found minimum norm =  2.5874102682250926  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  2.5874102682250926\n",
      "Found minimum norm =  2.1715049522534065  for s =  0.06030150753768844\n",
      "Found that canonical is worse because  2.1715049522534065 1.9224835119222163\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.19095477386934673\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  2\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.22613065326633167\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.22613065326633167   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  1.9046961556531559  for s =  0.08542713567839195\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.08542713567839195   minimal norm  1.9046961556531559\n",
      "Found minimum norm =  1.35350697253272  for s =  0.22613065326633167\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.22613065326633167   minimal norm  1.35350697253272\n",
      "Found minimum norm =  1.9224835119222163  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  1.9224835119222163\n",
      "Found minimum norm =  1.6477711446531873  for s =  0.07537688442211056\n",
      "Found that canonical is worse because  1.6477711446531873 1.3535069725327193\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.22613065326633167\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  3\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.23115577889447236\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.23115577889447236   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.3136411712037872  for s =  0.12060301507537688\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.12060301507537688   minimal norm  1.3136411712037872\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.3535069725327193  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  1.3535069725327193\n",
      "Found minimum norm =  1.1274463245664144  for s =  0.08542713567839195\n",
      "Found that canonical is worse because  1.1274463245664144 0.7177017806195328\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.23115577889447236\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  4\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.12060301507537688\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.12060301507537688   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.5747578633026214  for s =  0.10050251256281408\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.10050251256281408   minimal norm  0.5747578633026214\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.7177017806195328  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.7177017806195328\n",
      "Found minimum norm =  0.533628341644623  for s =  0.04020100502512563\n",
      "Found that canonical is worse because  0.533628341644623 0.5069588552189711\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.12060301507537688\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  5\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.4700577732110795  for s =  0.10050251256281408\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.10050251256281408   minimal norm  0.4700577732110795\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.1457286432160804\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.1457286432160804   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.5069588552189711  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.5069588552189711\n",
      "Found minimum norm =  0.3804833066952034  for s =  0.06030150753768844\n",
      "Found that canonical is worse because  0.3804833066952034 0.3382863653879185\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.1457286432160804\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  6\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.15577889447236182\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.15577889447236182   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.31068725325376223  for s =  0.11557788944723618\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.11557788944723618   minimal norm  0.31068725325376223\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.3382863653879185  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.3382863653879185\n",
      "Found minimum norm =  0.2899936222626213  for s =  0.05025125628140704\n",
      "Found that canonical is worse because  0.2899936222626213 0.24594534171209037\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.15577889447236182\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  7\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.2352064828666014  for s =  0.06532663316582915\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.06532663316582915   minimal norm  0.2352064828666014\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.192898695937051  for s =  0.1306532663316583\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.1306532663316583   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.24594534171209037  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.24594534171209037\n",
      "Found minimum norm =  0.21256399186583796  for s =  0.04020100502512563\n",
      "Found that canonical is worse because  0.21256399186583796 0.192898695937051\n",
      "Found minimum norm =  0.192898695937051  for s =  0.1306532663316583\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  8\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.17388695908729002  for s =  0.10552763819095477\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.10552763819095477   minimal norm  0.17388695908729002\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.1457286432160804\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.1457286432160804   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.192898695937051  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.192898695937051\n",
      "Found minimum norm =  0.1723161391940537  for s =  0.05025125628140704\n",
      "Found that canonical is worse because  0.1723161391940537 0.16962204861996638\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.1457286432160804\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  9\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.16962204861996638\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  0.132057807667527  for s =  0.16080402010050251\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.16080402010050251   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.1577382001777305  for s =  0.1306532663316583\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.1306532663316583   minimal norm  0.1577382001777305\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.16962204861996638  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.16962204861996638\n",
      "Found minimum norm =  0.1499951092202984  for s =  0.05527638190954774\n",
      "Found that canonical is worse because  0.1499951092202984 0.132057807667527\n",
      "Found minimum norm =  0.132057807667527  for s =  0.16080402010050251\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  10\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.13153048062849063  for s =  0.020100502512562814\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.020100502512562814   minimal norm  0.13153048062849063\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.1658291457286432\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.1658291457286432   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.132057807667527  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.132057807667527\n",
      "Found minimum norm =  0.12014453120379677  for s =  0.04020100502512563\n",
      "Found that canonical is worse because  0.12014453120379677 0.10378036798200431\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.1658291457286432\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  11\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.09469074274158497  for s =  0.08040201005025126\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.08040201005025126   minimal norm  0.09469074274158497\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.12060301507537688\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.12060301507537688   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.10378036798200431  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.10378036798200431\n",
      "Found minimum norm =  0.09145838880725454  for s =  0.035175879396984924\n",
      "Found that canonical is worse because  0.09145838880725454 0.08776156156717264\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.12060301507537688\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  12\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.135678391959799\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.135678391959799   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.07920965894405  for s =  0.16080402010050251\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.16080402010050251   minimal norm  0.07920965894405\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.08776156156717264  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.08776156156717264\n",
      "Found minimum norm =  0.07795364522499004  for s =  0.06030150753768844\n",
      "Found that canonical is worse because  0.07795364522499004 0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.135678391959799\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  13\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.2763819095477387\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.2763819095477387   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.07601809250797667  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.07601809250797667\n",
      "Found minimum norm =  0.06909285308954821  for s =  0.07537688442211056\n",
      "Found that canonical is worse because  0.06909285308954821 0.053769995124971035\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  0.053769995124971035  for s =  0.2763819095477387\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  14\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.0409724323847762  for s =  0.10050251256281408\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.10050251256281408   minimal norm  0.0409724323847762\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.044690193607194895  for s =  0.09045226130653267\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.09045226130653267   minimal norm  0.044690193607194895\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.053769995124971035  for s =  0.0\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  0.0   minimal norm  0.053769995124971035\n",
      "Found minimum norm =  0.04400412649100088  for s =  0.03015075376884422\n",
      "Found that canonical is worse because  0.04400412649100088 0.0409724323847762\n",
      "Found minimum norm =  0.0409724323847762  for s =  0.10050251256281408\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "flow2 = double_bracket_flow( H_TFIM )\n",
    "flow2.please_be_exhaustively_verbose = False\n",
    "flow2.please_be_verbose = True\n",
    "ini_state = np.kron( np.kron([0,1],[1,0]), [0,1])\n",
    "flow2.compute_observables_during_flow( initial_state=ini_state )\n",
    "flow2.flow_step_max =1\n",
    "flow2.flow_step_min = 0\n",
    "flow2.nmb_search_points_minimizing_s_search = 200\n",
    "flow2.please_use_binary_search  = False\n",
    "flow2.nmb_flow_steps = 15\n",
    "flow2.flow_via_best_Z_search( states = np.eye(2**L).tolist(), please_also_check_canonical_bracket=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axin1.set_xticklabels([1]+['']*(2**L-2)+[2**L])\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axin2.set_xticklabels([1]+['']*(2**L-2)+[2**L])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow2.show_flow_forwards_results()\n",
    "flow2.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1Z1',\n",
       " '1Z1',\n",
       " 'Z11',\n",
       " 'Z11',\n",
       " 'Z11',\n",
       " '1Z1',\n",
       " 'Z11',\n",
       " '1Z1',\n",
       " '1Z1',\n",
       " 'Z11',\n",
       " '1Z1',\n",
       " '1Z1',\n",
       " 'Z11',\n",
       " '1Z1',\n",
       " 'Z11']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow2.flow_outputs['strs_of_minimizer_Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TmuMREZGeqd4WEckg73jHO2a89a1vnbdjx46ckpKSM773ve9NAFi1alXR9ddfX59seRkzZsHMioCLgA8DOOeCQDCdMYmISPdUb4uI9MEAj1X4wx/+sKPrsrq6On8wGPSVlZWFki0vk1oWZgB1wN1m9qqZ3WVm+ekOSkREuqV6W0RkCCguLg6vXbu2T0lKxrQs4MVyNvAJ59zLZvYD4DPAF2M3MrNbgFsAysrKBj1IERHp0Gu9HVtnl5SUUFFRkfRBGhoa+rTfQMq0mDItHsi8mDItHsi8mDItHsjMmDLRTTfddHD8+PHhgSg7k5KFaqDaOfdydP5BvB+dTpxzdwJ3AixevNgNXngiItJFr/V21zp76dKlSR+koqKCvuw3kDItpkyLBzIvpkyLBzIvpkyLBzIzpkyU7IPWkpEx3ZCcczXAHjNrf5rdpcDGNIYkIiI9UL0tIjL8ZVLLAsAngHvMLBvYDtyc5nhERKRnqrdFRIaxjEoWnHOvAYvTHYeIiCRG9baIyPCWMd2QREREREQksyhZEBERERGRuJQsiIiIiIgMkhseu6H8hsduKO99y8ygZEFEREREROJSsiAiIiIiMoQtWbKkvLKyMnvr1q1Z55577txZs2adOnv27FO/+tWvTgSoq6vzX3755bP6UraSBRERERGRYSArK4vvfOc71du2bduwZs2aTT//+c8nrlu3Lre4uDhcX18fqKmp8SdbZkbdOlVEREREZCj64l++OHXrka2jettu+9HteeCNXeht29ljZzd99S1f3ZNoDNOmTWubNm1aG8DYsWMjs2bNat69e3f2okWLWpYtW1a/cuXKMck+7VktCyIiIiIiw0xlZWX2xo0bR73tbW9rAFixYkX9o48+OibZctSyICIiIiLST4m2ALS3KNy7/N7KgYrl6NGjvuuuu27WN7/5zT3jxo2LACxcuLB1+/btucmWpZYFEREREZFhorW11a6++upZK1asOPyhD32ovn15VVVV9tSpU1uTLU/JgoiIiIjIMBCJRHjf+943be7cuS3/8R//URu7buXKlWOWL19en2yZShZERERERIaB1atXFzz88MPjX3jhhcJ58+YtmDdv3oL777+/CGDVqlVF119/fX2yZWrMgoiIiIjIIBnIsQrLli1rcM6t67q8rq7OHwwGfWVlZaFky1TLgoiIiIjIMFZcXBxeu3Ztn5IUJQsiIiIiIkPYTTfddHD8+PHhgShb3ZBERERERIawZB+0lgy1LIiIiIiISFxKFkREREREJC4lCyIiIiIiEpeSBRERERERiUvJgoiIiIiIxKVkQURERERkkOxY8d7yHSveW57uOBKlZEFEREREROJSsiAiIiIiMoQtWbKkvLKyMrupqclOP/30+eXl5Qtmz5596m233TYJoK6uzn/55ZfP6kvZeiibiIiIiMgwkJub61544YXKoqKiSGtrq51zzjnlTz/99NFLL720sb6+PlBTU+MvLS1N6knPShZERERERPpp3+c+P7V1y5ZRvW3Xum1bHnhjF3rbNmfOnKZJ3/j6nkRj8Pl8FBUVRQCCwaCFQiEzMwCWLVtWv3LlyjHJPu1Z3ZBERERERIaJUCjEvHnzFpSUlCx829veduySSy5pBFixYkX9o48+OibZ8tSyICIiIiLST4m2ALS3KMx4YGXlQMQRCATYvHnzxoMHD/qvvvrqWWvWrMk955xzWhYuXNi6ffv23GTLy7iWBTPzm9mrZvZYumMREZGeqc4WEclMEyZMCF944YXH//CHPxQBVFVVZU+dOrU12XIyLlkAPglsSncQIiKSENXZIiIZYt++fYGDBw/6ARoaGuyZZ54ZPX/+/BaAlStXjlm+fHl9smVmVLJgZlOAq4G70h2LiIj0THW2iEhm2bNnT9aFF15YPnfu3AVnnXXWgosvvvjYDTfccBRg1apVRddff319smVm2piF7wOfBgq728DMbgFuASgrKxucqEREJJ7vk0SdXVJSQkVFRVIH+M+XmwmHw0By+w20hoaGpD/LQMq0eCDzYsq0eCDzYsq0eCAzY+qvgRqrAHDuuec2b9q0aWPX5XV1df5gMOgrKysLJVtmxiQLZrYcOOCcW2dmS7vbzjl3J3AnwOLFi93gRCciIrH6WmcvXdrtpnH9b+VL1NfXk+x+A62ioiKjYsq0eCDzYsq0eCDzYsq0eCAzYxqKiouLw2vXru1TkpJJ3ZDeArzTzHYC9wGXmNlv0xuSiIh0Y0jW2df/9CWu/+lL6Q5DRCSlbrrppoPjx49P6mFricqYZME591nn3BTn3HTgfcCfnXM3pfo4A/FDcf+XP8P9X/5MSssUEclkg1Vni4hI72699dZDEyZMGN7JgoiIiIiIZJaMGbMQyzlXQaaNZhMRkbhGap3d3kp9/9+fn+ZIREQGjloWhjl1kRIRGRo0nkJEMpGShQykE3wRERERyQQZ2Q1ppHKRCOFQiEjYG5/S0tjQsc7Mumzdef7k1d6CSMQrK9jchHOu40XX9+jxHQ4XcYDz/u8iOEd0u+h0Cq360X8D8PZ/+teYpScfxMU7sG6cKxkgb/RoRo0uSncYMkw554g4iDhHxDlcdDoc8Za3rz/W6jhwvAXnIBxxfOy363DAj248O1pOTJldKs/O6zofO97ynuM9Mb2vIcLWAw3dbZlgianjxXN80I/bk0yLKdPigdTF5DNjZnFBCiIaeUZUshAKRzja3EaWv+uZdf+0tbTQWH+Ep+66g3AoRLitzXuFT0yHQm1E2teF2jpvFwoRDrV1JAnt7vi796Usxh9++L0pK2sg3H3b36c7BJE+Of89N3LBihvTHYb0UzjiaAyGaGgJ0dga4nirN90QfT8x30ZDa4jtu1uoPBok7BwrfvJix8l7+4m8N+9OWh6JOMLOEY7EnvR7y5vbwjhgzudXEYme9CflmadPWnTht55JzRfUVy88m97jd/XCc+mO4GSZFlOmxQMpiWlCQQ5rv3BZCoLpvwf+c005wIrPnjNgD2dLpZGVLEQcVbXHmVCQk9Jyjx2so/nYUapefhF/Vhb+QAB/IKvTdFZOLv78Lsuzsk7azp+VxZt//hMAZy17R/QIXa8CdfkB6TIfO/f6k6sAx5lXXA1mmPm8Rgczr7XCDMMwn0Hse+z6mOmXf38/AOddl5pE5q8P3Re/vJOaSrq2pXS/Xco4N7Dly7AwYYqeJD+QDjcGOdriuOv57YQiJ064QzEn4x2vLifqofDJy6pqjxOOOK654y80tLR1JAONwcTuOJiX5acgN4A/HKEt7F2tDPh85AQMM/D7DL8Zvui739c+7W3ri13vo2PaZ8aqN/cDcO1Zk71tjY51PvNamDuWd+znTW/duoXyuXM71t/53HYAPnbx7I7YY2uzrlVb7LzFbNm+/H+e3gLArZfO6fU7am8J37hxIwsWLOh+u15LSq3e4kmHTIsp0+KB1MWUE1DP+74aUclCbpaforwsjjQFiUQcPl//qyrnHK1NjeSNLuJjP7snBVHCztfXAbDo6mv6Xdb2V/4GwOJ3XNfvsgAWXHhxSsppN/+tS1NanogML7XHWjjW4vja45tOWmcGgejJtN8X84qZ95kR8J84QW9pi+D3GUV5WUwZk0dBToCC3AAFOQEKo++x8/k50emcLPJz/AT83glHRUUF/1vpXXi695bzUvJZ1+89CsCnr5yX9L4VwZ0sPW9ax/z7lqQ2ib3mzMlJ7zP6SBVLF05KaRz9UZhh8UDmxZRp8UBmxpSJlixZUv6b3/xmR3l5eRAgFApx+umnLygtLQ0+88wzW+vq6vw33njj9NWrV29LtuwRlSwAjB2VzZGmNt7Ye5Qzp47pd3lHa2uIhELk5Of3P7io67/0zZSVJSIylM0pKeBo/VF+f9vlJ07+o4nAyWO5etd+t6Ff/92SVIcqIpIxvva1r5XMnj27uaGhwQ9QXFwcrq+vD9TU1PhLS0uTenjbCEwWsgBYvbEmJclC9ab1AOSMGtXvsgaCEg8RGcoCPh9+n1GYm5XuUEREevT0rzdNPby3odcTwiM1TXlwYuxCT8ZNLmi69IPz9yQTx7Zt27L+9Kc/FX32s5/d/73vfa+kffmyZcvqV65cOebWW289lEx5I64DV8DvozA3wOqNtSkpr3rTBnx+P4Hs1I6DEBGRkeX+vz9fD3gTkX77+Mc/PvVb3/pWtc/X+TR/xYoV9Y8++uiYZMsbcS0LAONGZVNV28DOg41Mn9C/7kPVm9eTMyq/T83hIiIydOnEXkRiJdoCMJB3Q7r33nuLJkyYELrwwgubHnvsscLYdQsXLmzdvn17brJljriWBYjtitS/1oXjhw5ytLaG7AztgiQiIiIiI8cLL7xQsHr16jGTJ08+/cMf/vDMv/71r4XXXHPNDICqqqrsqVOntiZb5ohMFnKy/MwrLex3slC9eQMA77jtsxobICIiIiJpdccdd+ytra19Y+/evW/+8pe/3H7eeecdf+SRR3YArFy5cszy5cvrky1zxCUL7X1Cr1hQwtpdhzncGOxzWXs3rSc7bxTF06anLkARERERkRRbtWpV0fXXX1+f7H4jLllod/mCUiIOnt7U99aF6k0bmDxvAT6fP4WRiYiIiMhwteKz51QOxtObly9ffvyZZ57ZClBXV+cPBoO+srKyULLljNhk4bTJozmlKLfPXZGajh3lUPVupsw/LcWRiYjIkHH31d5LRCSDFRcXh9euXdunBGVE3g0JvMfRX76ghJVr99AcDJOXnVzrwN5N3niFKfNPHYjwRERkAGT8HYzaE4+bH09vHCIypNx0000Hx48fn9TD1hKVUMuCmY01sw+a2e/NbIOZPWZmHzWziQMR1GC5fEEJLW0RXth6MOl9qzetJ5CdQ8nM2QMQmYiISD+p1UNkxLj11lsPTZgwIT3Jgpk9BDwETAT+zTl3KvAxIBf4rZlVDERgg+HcGeMpzAmwemNN0vtWb9rApLnl+AN6qqiIyEC5/+/P57Pn5qU7DAElHyIjVCLdkP7OOVcfu8A5txv4IfBDMxszAHENiuyAj6XzJvL0pgOEIw6/L7EHq7U2NXJg13bOf/cNAxyhiIjIMJTq7lapLK8vZTnX+Z2u8/FZJAzhZMeb9lxmckV1LssibRCKd5fIFB4zUdHYfOEgtLWkpkx/FuimNEnrNVnomigkuz7TXbGghD+8vo9Xdx9h8fRxCe2zt3IjOKfBzSIikjqhVgg2QCQCO56HcBAiIQi3xUwHvflO021M37EFQs923ufQFu+E66G/BxeJeYWj7+7Eski4yzbt62OWH9jkxfmTC09e16kMb935Lc2wNqtL2dH92pq8sr5W0vN30svJdodw9AT3q8XdbnJRJALPJ9D7OtzmvX9lAuB6SAD6fwL9NoDn+l1MymRaPAAXATyfosImngofezFFhY0cfRrgbGb3AdF/Tex3zn06dSENrqXlxWT5jSc31iacLFRv2oDPH+CUOXPh7qv5/cb3QunpXHv72f0LJpVlpbq86JWW3x/+KsCw/6yKLQPKG0mxyeCJuWr8+++8AqSgPusL5+DQNti7Fvaug+q1UPMmRKI/rb9anlRx0wH2ZEWvnEbfW48BBrtfBPOD+Tq/fH4w67K8y3a+QHS76LQZjJ4cXW8n1sXZ/1BtLZMmTY5f7saHvcBPf08Cny6BVv83H/Tez1jR7SZ7du9mWllZ72W98UC0rPdGD28nYrD2WCzBdTHzcezYsYMZM2b0HlNXiXWESLqw7Tt2MLO7eCylB02QsX37dmbOnJma4k67LjXljDB9vRvSS865HwCY2fgUxjPoCnOzOG/meFZvrOWzV83DEvjHUL1pPaWz5pCVkzsIEYqIyJDXePBEUrB3nfdqqffWZeXD5LPh/I9B5Z+8E/Cr/ss74Y89+fdneyfsnaazwZ9FxXMvsPTiizsfc6C6+tx4X0KbV1VUMGnp0vgrr/xGamJqd/mXe91kR0UF07qLJ9Zl/9HvcBK1y1Uw421LB+14vdkdqWDmRUvTHUYnu8MVzLxwabrDGNH6mixcY2YNwPPOuapUBpQOVywo4YuPbGBbXQOzJxb2uG1bawu127awePm1gxSdiMjIduarn4cdY9LfGpCothaoeSOaGESTgyM7vXXmg4kLYME1MGUxTF4ExfNO9KOuXue9z7gwuWOm5aqviPTFPZ+7rRzg/d/43oA/mC0Vek0WzKwcqHKuU8fBm4CFwHVmNss599GBCnAg3PzEzQDcfeXdAFwWTRae3Fjba7Kwf0slkXBY4xVERMTrThRqhtfu9RKD6rVQu94bNwAwegpMWQSL/w4mL4ZJZ0J2flpD7jM9+0FkREqkZeFBYJqZVQFvAG9G39c55/44kMENllOK8jhjShGrN9bysaU9PzehetN6zHxMKp8/SNGJiEimsUgIXv0t7FsHoRZ4+B8guxAmnwUX3Hqi1aCwNH1B6uReZMRYsmRJ+W9+85sd5eXlwcmTJ5+en58f9vl8BAIBt379+k11dXX+G2+8cfrq1au3JVt2IndDOt3McoAzgFVAI7AcONXMcM6lsSZMncvnl/Cd1VUcONbCxNHdj0Wo3rSB4ukzyBk1RK8MiYhI34WC8No9LPnbN6DlgNdKMG42vO8emDAXfAk961REZEA9++yzVaecckrHfXmLi4vD9fX1gZqaGn9paWlSD29LaMyCc64VWGNmDc65T7QvN7OxyRwsk11+qpcsPLXpADeeG/9uCeFQG/urNnPG5VcNcnQiIpJWbS3w6m/ghe/DsWraCueQd92P4IUfeOMFJs5LzXHUGiAyZP3pf78/9eCeXaN62+7Q3j15cGLsQk8mTJ3WtOwf/3lPKuJbtmxZ/cqVK8fceuuth5LZL9lLIJ1uKuycO5Lk/t0ys6lm9oyZbTSzDWb2yVSV3cndV1P+9AWUP395p8XlJYVMHZfX49Oca7ZtJdQWZMr8UwckNBGRoWTQ6u10CjbBSz+GHyyEVf8KRVPgpod45exvw9xlGlgsIhnp0ksvnXPqqafO/+///u8J7ctWrFhR/+ijj45JtqxEBjjfAbwSfQ1krRgCbnfOvWJmhcA6M1vtnNs4gMfsYGZcPr+U3768i8bWEPk5J3811ZvWAzB5npIFERHSXG8PqNYGWPsLePF/oLEOpl8I7/6Z924G1RXpjlBEMkyiLQADfTekF154YfOMGTPa9u7dG7jkkkvmnnrqqS1XXXVVw8KFC1u3b9+e9H3/E2lZeB04E/g+UBi9gvSAmX3ZzK5P9oDdcc7td869Ep0+DmwCJqeq/ERccWoJwVCE56rq4q7fu2k946eUMWp00WCGJSKSkTKh3k65lmPw/Hfg+6fD6i9CyWlw8x/hw4/BjIvUkiAiGW/GjBltAJMnTw5dffXV9S+99FI+QFVVVfbUqVNbky0vkQHOd8bOm9kU4HS8Ac/LgfuTPWhvzGw6cBbwcqrL7sniaWMZMyqLJzfWctXpp3RaF4mE2Vu5kflvXTqYIYmIDAnpqrdTpvkIvPxT+Ov/eg9Lm3MFXPRpmHpOuiMTEUnYsWPHfOFwmLFjx0aOHTvme+aZZ0Z//vOf3wewcuXKMcuXL69PtsykHspmZqcCn3POvR8YkNummlkB8Dvgn51zx+KsvwW4BaAskce2JyHg93HJvIk8vekAbeEIWf4TDS91O3cQbG5msp6vICLSSU/1dmydXVJSQkVFRdLlnx4OU19fz2sVFdTXRwD6VA7AmfX1AB1l5XCMXb/4MZP3Pk4g3MTB8eeya8EKjo+eA9saYVv84zQ0NFBRUdGpvHRqjyeTZFpMmRYPZF5MmRYPZGZMmay6ujpw7bXXzgYIh8P27ne/+9B73vOeYwCrVq0quu+++3YkW2ayT3B+Cjg/2YMkysyy8H5w7nHOPRRvm2hLx50AixcvdvG26Y8rFpTw0Ct7WbPzMBfM6hgTQvWmDQBM0XgFEZEOvdXbXevspUuXJn2M+lf9jBkzhqVLl3JknfcE56VL+/gE5x1jvP0Xn8qkF7/AjOAjZO1u9p6ofNGnmFB6OhN6LgHwkpWlS5eeKK8PnyuVOuLJIJkWU6bFA5kXU6bFA5kZU38N5JObFyxYEKysrDxp3FhdXZ0/GAz6ysrKQvH260myycIVwNeB9yd7oN6YmQE/BzY5576b6vITdeGcYrIDPlZvrO2SLKynqKSUwvGJ/IykSSSCa2sm3NxMqLmJUGOAHHcE17yLQ2/6vfXtL+dwEYeLRLzlzkXXuY71HcvDEVzNBBxGXvNr4BzbHtnnbYPDRQDncA6ci4DDW9exrH3am8c52DWN3FANHGzmjburOj6C95zwaA7YkQq66DqLXXhiWwfsme2VV9fIKz/d1Gm/+MW5Lrf2innfP4+cUC0caGTNHRt6/s6do9dx//vnd5T3tx+t73nb3uxfQE6oFlfbyN9+2M+yAGoWkN1WC5lY3hCI7dDBmbQ0tpGbn9X/+IagTKm3E9ZQB81HiDTVc/Tby3Atk3k1cCOLP/ERfKUL+l3877/jJTLX3t7HREZEZIAUFxeH165d26ckJZG7IfmccxEA59ybdEkUYtf301uADwBvmtlr0WWfc86tSkHZCcvPCXDh7Ams3ljLvy9fQPTBc1Rv3sCss5ckXpBzuLYWIq1NhJqiJ+8trYSaWwi3thJqaSXcGiTUGiTU2kYoGCK8q4y80F44cJS/fvdVQqEI4TYIhRzhEITCRijkIxT2EY74CYX9hCIBQi5AOJJFyGURJicmiOhdDBtg5x1J3VI3qr0blh/4UKc12/rdCe093lszbE9JD+drTpT3an/LmtxR1o43+1tWl/KO9resKR1l7ewlj0mqvJZMLG8oxNZGa1NoxCYLZEi9fRLn4Gg17H+d8L43ObJtN3XVLdQ1jOVg2/upC00n5PI6Nt/6k2Occ3UtsxZNxOfTAGYRGXpuuummg+PHj0/qYWuJSqRlYbWZHQQeBh53zh0zs1HAlcC1eIOdz+xvIM65FxjYW7Mm7PIFJTy9+QCb9h9nwaTRHN67h5bjxzo/XyESxm1exZ6dDl/rERp3rOe3n6jsOGn3Xtl0f8MpA3Kir3YrvLdm4GiEgLV5L18Ivy9EwB8m4I8QCETI8kcIBEL4AyECAQhkGYEsH/4sH4EsP4GcAIGdf2bX0enYqPHMOacEM8N8Bj5fx7T37vOWd0z7Oi/3GfbC9zFzrGlYARjnvnPWiX2sfX+fd6OQ9v0tuiz2mObzynvkYzy19UooLufym0+NlhP79Vj0b0P7cjuxHKJlt+9gcP9NrKp6J0ycz9X/eIa3zLztjS77xy7rKCLmeL+5lkc2vRtXchrvui2RK4TW81/cX72D3296L5Sc1v8rjncv98oqPZ3r/nVR/8oCuPtqfr/RKy8lV0NTWd4Qia2oOK/37YepjKi3IxE4vB32v0Zo73oObdtH3b5W6ppKOdg2k0OhswhzLgCBQITinN0sGLuB4qs+wBvPVBMKhjGf8eTPNzD28R0sfvt0Zi8uUdIgIkNKsg9aS0Yid0O61MwW4F26fTzaP9UBfwK+137bvOHk0vklmL3J6o21LJg0uuP5ClPmnwbBRsJr76Fq9Rpeq7uAw6F/JceOMzanjsKxDn8g6J24B3wEsn34s/wEsttfAfw52QRysgjk5njTubneKy8P/x9v5U9bl0PJGVz7r4u8E+L+uPsXVG1cAmNPZ/Y1/Tw52uCNWQxGZgAwYeHC/pWXGyLsGwVZY8grKelfWQBZEZxlg38UWYWj+1eWDzA/5gvgCyTbUy9eedHEKZqIpaQs3b5RRqJwG6PDWxkT2Urw0Qc4uOMAdTURDjZPpi40k8Oht+DwA5CTHaZ4SjZnzJpI8fRxTJhawJiJo7BfLffKOv/TbHpxP1k5ft5121lse7WOtat2sPoXG1nz+E4Wv306c85JIGnQE5dFZJhL6Ewo+oCdjcB/mlmec655YMNKr+LCHM6aOobVm2r45GVzqN60gYIxY8hZ+3PWvlDDG8cupjlyA+PHh7lk9B/ZVHcadsrZXNHfq5U5rZj5O67Ci4gI5Dbvh+adNH11PuH6D7C5bSZ/3XkK7S23eXlhJs7IZfrMUoqnF1E8tZDC8bkJ16PmM2Yvmsiss4rZ/lodax7fyVN3b2TtqmjSsHgiPn8/E30RkSEq6cumwz1RaHf5glL+64nN1FatYfe6v5Bro/j1Y4sIkUPZTD9nLj+dKfPHYr/8PpsP6naqIiIDxRcJQsBP9rk3svvxJQRyfSy5eCbF00ZTXFZIflFO74UkwHzGrLMnMvPMYra/fiJpWBPtnjT3nBIlDSIy4qSgj8UwFIlwTf4GZrj7ePEHc2lqCdGWv4jTFo1n4dULGD+pIN0RioiMGE3508geM4bAVV9h/Eav5+s5y2cO2PHMZ8w6ayIzFxaz442DrHl8B0//chNro92T5i5JQddJEZEhQslCrLYWwq/dz7Y/vcBrNedQF/oI4eDrAFz3qWuYMn92mgMUEZHBYj5j5pnFzFg4gR2vR5OGX21izaqdFMxwRC6MqKVBRIY9JQsAjQdp/cvdbKzYzhv1S2mI3MCYohAtZflUvrKXMwoKmVw+cFexREQkc5mdSBp2vnGQvz22g31/c9yz/a8sumo65eeV4lfSICIJqv3Rq+UAJf901oA9nC2VEk4WzOwTwG+dc0cGMJ7BVVfFsT//gjfWhNjYeDFtbhGTp8JFy09n+ukTeGXPEepf3Edg5qz+38VGRESGNDNjxsJipp8xgcf+r4LmXVk885vNrPvjThZdNR3nnG5OISLDTjJnwCXAGjNbaWZX2lCtEZ1jmm8Ll0Ue4Ymv389vn7mMNxuvZMbp41jx2cW86/OXMGNhsXd3jPwIY0LH2J1dmu6oRURGtJutlpufuDndYQBe0lA42Vjx2cVc/bEzyM33koYDO49RX9vE5pf2U1/bFH3KvYjIwFuyZEl5ZWVlNsDBgwf9V1555cwZM2acOnPmzFOfeuqp/Lq6Ov/ll18+qy9lJ9yy4Jz7gpl9EbgCuBn4kZmtBH7unNvWl4OnxaGtzAhN5+XGvyc7K8SZSydy+hVzKRyXe9Km+yu9x7y+2FBEMBQhO6DWBRER8ZgZ08+YwLTTx7Nr/SGeunsjzQ1Bnv7VJgBy87MomTma0hlFlM4czcTpo8nOTexn9/ff8QZyp+SBhCIyotxyyy1Tr7jiimNPPPHE9paWFmtoaPBNmDAhXF9fH6ipqfGXlpYm9aTnpMYsOOecmdUANUAIGAs8aGarnXOfTqastMmfwH5fIcFRR/joN67pseKu3rQBX3YuuxjDX7cf4qK5xYMYqIiIDAVmxvTTJzB+cgHOOd52Qzk1249Ss+MYtduPsuvNQ9HtYNzkAkpneslD6YwiiibmDXjXJSUeIoPj8INVU9tqGkf1tl3oQFMenBi70JOs0vymce+ZuyfRGA4dOuR/+eWXCx988MGdALm5uS43NzcMsGzZsvqVK1eOSfZpz8mMWfgk8EHgIHAX8CnnXJuZ+YAtwNBIFvLGUslsyGvt9QpP9ab1TJm3gNyWLJ7cWKNkQUREemRmjJ9cwPjJBZx64WQAWhrbqN1xzEsgth+l6m81bHhuLwC5BVmUzhhNycwiSmcWMXFaYcKtD+mi5EMkc1VWVmaPGzcutGLFiukbN24cdcYZZzT+7Gc/2zN69OjIihUr6m+//fYpA5YsAOOA65xzu2IXOuciZrY8mYOmW9hCGJEet2k6dpRD1buZ/9alXNQwgac2HuCr12jwmoiIJCc3P4tpp41n2mnjAYhEHEf2N3YkDzXbj7GzvfXBZ4yfnE/TsSCBLB+vP70Hf8DwBXz4/Ibf7737Aj78fuuYbjrkqNt93Nsmuq3P78MfMCLhCJgRbAmBAwe4SHQ8hQPnHO3DK5xzJy+Lbtu+LBT0ejAcO9iM+Qwzw3zg85k37zPMIBJyhEORjvl4v5+pTjxSUV77WJPYsvTbL4lItAVgIO+GFAqFbNOmTaN+8IMf7L7kkksab7755qlf/OIXS3/wgx/sW7hwYev27dtP7nffi2SShWPAu7v8gzkKrHPOvZbsgdPlb7TwwPyfcln1+3vcbm/lRgCmzD+NyxtH86cNtby59yhnTBkzCFGKiMhw5fPFb32o2X60owXiUHUDzsELD2xJuNwdq9f0uP5n//xcv+Lu6jdfeKnXbTY9WNExbcaJZMJn+AzaghEMuOv257wsJso57z8di6JJzokMpn2+fdZbEAl7C/73Y8/QaXh5tKwN9/85Zllin/PH//jMiRkDi34Yi1nmLbeY9dEPHJ0MtXkJVla2v9Ohw6EIVb9/9uRwYgfHx5kMh7wLnr3dstcl8CHbvzOf33ARx6YHT3zemE9JzMKeZk/EFjPOM24UcRZ2jTcS9v5cY2Pqj0jYMWbiKN7/5fNSUl4mmj59erCkpCR4ySWXNAJcf/31R775zW+WAlRVVWVPnTq1Ndkyk0kWFgGLgT9E55cDbwD/YGYPOOe+lezB02EmWYR9YQ7k9Zz87d20nkBWNiWz5nBp0OEzWL2xdmglCzc/zrUpLAtIaXkpKyvV5Sm2zChvJMUmgydal0FmdaPJzc9i+ukTmH76BAAe+u91OAdXf+wMwqEIkbAjEo4QDrmO6UjYdax77dXXOXXBaZ2WtW/z2lO7cQ5Of9sU72Q25iq/+QDar/zjnQRHz/7MrPP20RPhtX/0OhicvWya19oQ8V6RiNcK4ZwjEnFs27qdGdNndMx723nbRKL7bVt3AAfMOrM4eqDosaP/MU4siz1R77RNzFl71d9qACg/tzSmIM+uXbuYPn165y/e4k6CGZtf3A/AvPNLOycmzsUkKSf+4yU4nZOa9vVb1x0AHLPPjnn6t0F19R6mTJnU+XPHxBA/NtiyphYgwaeJ99wq0v6dzV1Syu7duygrKyPm03Vy0g2+4p7wd3PEeHlHAht2jqn/Zi+amLKyMlFZWVmotLQ0+Prrr+csXLiw9cknnxxdXl7eArBy5coxy5cvr0+2zGSShSnA2c65BgAz+xLwOHARsA4YEsnCBPyMbh3ba7JQvWkDp8wpJ5CVxdgsOGf6OFZvrOX2K3odi9J3OgEREenVvk2/4P4vr+T6L30z3aEMGIuetOfmZyW0/bZa78Fx8XgnqnDWFak54dr4F+8kev4Fp/S43bHsHSxeOr3HbQ7uaQDgohtS89t6/rXd3xmypWIP5y5N/AGrS5bPSEVIALx1xZy4yysq9vLWpfHX9eSC62b3N6QOsd9Za8Uezl/ap7trDphMjCnT/fCHP9z9/ve/f2YwGLSysrLWe++9dyfAqlWriu67774dyZaXTLIwEYhtumgDSpxzzWaWdJNGOk1snMyuoi2EI2H8Pv9J61ubmjiwYzvnXnd9x7LLF5Twtcc3sftQE2Xjex3oLiIiMiylskUmk1p3RAbLQD+5+YILLmhev379pthldXV1/mAw6CsrKwslW14yycI9wMtm9kh0/h3A/5lZPrAx2QOn08TGSWwdt56t9VspH3fy1Yx9VZtwLsKU+ad2LLtiQSlfe3wTT26s4SMXxlyZUGuAiIhkMJ2Qi0hxcXF47dq1fUpSEkoWok9r/iXwR+At0cX/4JxbG53uebRwhpnY5A0oW1u7Nm6yUL1pPT6/n0lz5nUsKxs/ivKSQlZvrO2cLIiIiKSYTvBFJBk33XTTwfHjxyf1sLVEJfRIYufdR2yVc26tc+4H0dfaXnfMUPltheS3FbGudl3c9dWbNlAyaw5ZuZ3vLnXFqSWs2XmYI43Bk/a5++67ufvuuwckXhERGRj3f/kz3P/lz6Q7DBGRfrn11lsPTZgwIX3JQtQrZnbOQASRDhObprKudl3H/ZTbtQVbqdlaxZT5p520z+ULSog4+PNmb7DY9T99iet/2vut40REpO9eO+vrUHo64F1xnzC1IM0RxXft7WerRUBEhp1kkoVzgb+a2TYze8PM3jSzNwYqsIE2sbmMwy2H2XGs86Dwmi2VRMKhTuMV2p0+uYjS0bk8ubFmsMIUEREREUmbZAY4LxuwKNJgYvNUANbVrmNm0YkxCNWbNoAZk8sXnLSPmXHZgon8bt1eWtpS39LT3kpx/9+f39Gl6eabb075cUREREREEpFMy8Ju4ELgQ865XXjP3UjkaSAZqbBtLBPyJpw0bqF603omTptJzqj8uPtdvqCU5rYwf9l6cDDCFBERERFJm2SShR8D5wM3ROePA3ekPKJBEqh+mokHs1hbs7Zj3EI41Ma+qs1xuyC1O3/meApzAqzeWDtYoYqIyBCgwdIiMhwlNWbBOfdxoAXAOXcEyB6QqAZJWcs4aptq2de4D4Da7dsIBVvjDm5ulx3w8bbyYp7aVHvS4OhMowHYIiIiIpnlzjvvLL/zzjtT89jyQZBMstBmZn687keYWTEQGZCoBklZyziAjq5I1ZvWAzC5h5YF8O6KdLAhSENr0g/BG7KUeIjIcHHzEzdz8xOZPx5MLRUikgmSSRb+B/g9MNHMvg68AHxjQKIaJMVthYzOHt2RLOzdvIFxk6cyanRRj/stLZ9IwGccaWobjDCHJSUfIpKMqiNV7Dq2i9frXseRulbdcFsbweZmjtTso+nYUcKh4XkRSImHyPC2ZMmS8srKyuzXX389Z968eQvaXwUFBWd95StfmVhXV+e//PLLZ/Wl7ITvhuScu8fM1gGXAga8yzm3qS8H7Y6ZXQn8APADdznnvpnK8gG4+XEqn7iZGX+sx5jI2SVns652HZFImL2bN1J+wYW9FlGUl8V5M8ezdtdhysaNSnmIqTbc76wUexcpERlcg1Fvh1yILF8Wh5oPcdOqmxg/OZ8zGqawtLGWkvzk77NhIcfo3a08+PUvsn9LJQC/+OQtHesD2TnkjBpF9qh8ckaNImdUPjl5o8jJz+9YdmDvfjZYmOxRo8jJ85aFgkHM56OlsQG/P4Av4MfnD2BmKfsuhqP2JOb6L6X+J19kpFm4cGHr5s2bNwKEQiFKS0sXvu9976svLi4O19fXB2pqavylpaVJ3dIzmVun4pzbDGxOZp9ERbs43QFcDlQDa8zsUefcxoE4XrvFJYup2FPBlqrXaG1q7HG8QqwrTi3hha0HaQ4OyMPy+sQ5R0NriPqmNo42ey/nHFuC2TjgyQ01RBxEnCMccUSci05DJDofdi46Tcc24YhjX30zDvifp7ew7pVXiQCnn35Gp/Jit484RyRCR3lh13l9Ve1xHPCRX63BOa9vW/sYEG+ajmuHHcujCxzOW++86Y37jwHw3p++BO7E+hNluc5lOhdTdtfyYNehRoCkEsHuhq/sPtwEwNRxeQnv0x53PNVHvPKmjE1Nktpe3uQxJ8cnPbtwTjH/8c6euywOd4NVbwcswIyiGZQVlnHtnGv52Z+/zzNjK3n2d1dw/innc83sa7h46sXkBnK7LcM5R83WKtZXrGbec4fwBx2HJ1RTOKGY7Nw8znnnu2ltaqK1qYHWpiaCTY3R+UaCTU0cP1hHa3MTrY2NhIKtAOz5y5/jHuuOv3tfp3nz+TolDz6/H18ggN/v96bbl0W38Ufn63btxMz4/be+gs/nw8yHmWE+n/dqnzYfNbU1PFn1prfMout9J6bra73nA1X8+i4ikTAuEsFFIkTCYSLR6fZ5F4kQiURO2i52+aE9uwAYP6Us+iENw7z3aG509Ogxaiue8C4vwknric7X7fKed/TA174QrRijdXjHb4JXObvoOtpXORddd+IH42D1bi+uyVO61LGO48ePs+eJ37fPxqxp/8GIXehNt39vY0pKY/5AuyR/XeaNruu9tyP79wIw9pTJHcc/3nCc3X986KTjxk53/hjRuA54cRUVp+amlEfrvJu2BEYVsPMPK1NSVm+xxR33GWdZU1MTOx65r18xxTpl7jyu/Md/Tll5sR5++OGpBw4c6PUH+uDBg3ngjV3obduJEyc2vetd79rTl3geffTR0WVlZa1z584NAixbtqx+5cqVY2699dZDyZSTcLJgZjnAu4Hpsfs5576SzAF7sATY6pzbHj3efcA1wIAmC4tKFgGwdu1TAEyel9iP/2XzS/j3RzZwpCkIwPHjx2lpaeGRRx4hOzub7OxssrKyOr33tCwrK6uj7EjE0Rw2msM+Xt9TT31zG/VNQY42t1HfFH01B6PTQeqb2zgaTRBCkZP/oVUyBoB7f7PupHXJ+u7qKiAfcLz03HZ8Zvh84DfD5zP8PuuY9ln85X4zWkPecJd99S2Y93uBceJHxCD64xOdN29Z+xU648Q+7QwwHxi+jjK95THlRstsL6/rMjBqjjYD3ScL3V0kPOkHAjhwvAWAWcXxnzjb0wXHeOWVlxR2v0MflJcUEucwkoApY5VgMcj1tt/n59o51xL8v5c5HGgk/PZyHt32KJ9+7tMUZhVy5YwruWb2NZwx4YyOuqLhyGE2PvdnNjz7NIf37iGQncPxqdkcmZ3LDz/8c1Z+9XMAnPq2SxOOIxwK8czqJ1l81lm0NjXy1We/hC/oOHNzIS4SYeHlb/dOwsNhIuEQkXCYcCh0Yj4UJhxdHgmFiETCHdNvHngDi8Cc0bOJhMOA4/ihgxCJ4JzzTuydAxeJnuQ7nIvQ0txMS+2+jpN757zlTcEmcI5A2HDAG0//yUs8fD58fn9H4uHz+fD5Yuaj605a7vPhz8rCfH4AsvO8etK1X3GJPZmPrWgdRFyEeCf7LuL9HrS1tnRJKOxEq4yBz3xxk42O3wUzfH4vrrzC0dFNTlRwLeEIBWPHnfiDjFl3ovXHOq0+fsQ7pyqKJgsnn8t2XnDSCXDM/LG6AwAnYjCj1UHh+PFdWp86x3DyMqOx/ggA4yZP6RpQXPF+T2KNn+w9e+pAXR3ji4u73S6RLoBNR+u7je3kRCpOXF2W1R04wISJE3s9bqKKJg7Zu/4n7d577x33nve8pyMxWLFiRf3tt98+ZcCSBeAR4CiwDmhN5iAJmgzEZk7VeE+NHlDzxs0jL5DH3o0bGDexhNETuv9HEmvSmDxGZfs50hRk/5EGDh8+AmZsqqwi1NZGKNTW86XjLhww2/kI4eNTX66gzfkIEmD9nb+k1QVoJUDQBWjFjwVyyMrOITcvj8JReZwyMZ+i/FzGjMpmzKgsxuRlUzQqi++vrsIMzs/a5fUbu+adJ53c+6z9JB780XmfWXSajpP7D/3ib5jBvR89j1//6leY9a9bU3vXoSuzvYaqVJSlbkgigy4t9TbAuFA+15/1T3zszI+xtmYtj2x7hMe2P8YDVQ8wI38aV0XOYcyWFva/uQHnIkyaO5/Lb/kE5ee/lX947p8A74p/X/gDAQJ5oxhTegoAjVXejQHza8YCsHj5tX3+XH+IDrz+9yu/nVT3nIqKCpYuXXrS8vaB3Fe+XJJwWanSXUxdDVY3pETjGUyZFlOmxQOZGVN3Em0BaG9RuOWWWyoHKpaWlhZ76qmnir773e9Wty9buHBh6/bt27tvgu1GMsnCFOfclckeINXM7BbgFoCysrJ+lxfwBTir+Ewie/Yx5bzErywBjB2Vzd76Zt777Ye5NNvxp9Y57G9uHxzt8OEIECGLMAGLkG0R8rMgPwCjAo5cP+T5HTm+CNnmOHC0kSyLMGtcDsfrDzHahRhdkEWkrZlga0v0KlNUW/Tl9cDxrhTl5RHMy+N4Xh6hvDzKWxsI+7IoiNTj8/lorN4cbaK2aJN25+l4y9qnC9sOA1C9Zzetrd7V8urq6l7373ZdJARmHVdhIpETN9Zq3y5RsUnCcB+fITLUxNbZJSUlVFRUJF1GQ0MD9Q31gHfiUF9/YrrdZe4yFgfK2VL5PJHt+2lqW0Ndboj6OblMWrCY8SXncdiXzUt/W9Np/3hlJRpT+z7tZdTX5/SprFjtZT3zzDMcrj+CM8fqZ1YTcRFc+/+cI8KJ+YiL0NDYwENPPdRpuwgRag/XYhg7miP4HPzuqd/ha/+fee9mhh8/hnUs89nJidQPan4AwCdLP5n0dxRPR3lv88rrz/eWinjSIdNiyrR4IDNjGgoefPDBogULFjRNnTq1464NVVVV2VOnTk36gn8yycKLZna6c+7NZA+SoL3A1Jj5KdFlnTjn7gTuBFi8eHFKbolxZqCcptZ9jJs9M6n9Skfn4PcZ7xxzhGP7fLxlouPtVy4mLyvAqGw/+Tl+8rIDjMryk5ftJyfg6/Ek+PqfvkQQ+Pzfnx/3pLetrY3m5uZeX01NTRw9epT81qP4XRtHnZdk/PGPf0z+y4maFn3/5S/Xdiy76667+lzevOj77uj7V77Sc2+22O+tp+lwNKH6+te/3ml9ou+x083NXnek/Pz4T/NONN52DQ0NABQUdNclKbn+QO3lFRb2v2vS8ePHU1bWSLRixQomTZqU7jDSodd6u2ud3ZcrhBUVFYwJjAFg6dKl1D77RMd009F6Nr1QwfqKpzi4eyfZWVnMPuciSs5ZyMu5W/jL9j+wuvEBCmueYNmMZVwz6xqKmosws5PK6qot0kZDsIGGYAPH2453en9146uUji2lIdhAva+eiIvw5zmNRHCsD/+ONtdGKBIiHAkTioS8lwt1TLdF2gi7mHXRV2u4FYfj1t23wpnRQHYneOe4+u5X3TM/WtvufSWxsgC/+fGZj4AvgM98tIS8bkJfqPlCzBgz1zFWrH3eRbsXhcNh/Ef9HfOx+zgc4YhXX9+25zavu2i025HR+YJR7PL2eazz8oagVx+Ozhl90udo36+trY3spuxOyzq6xVjn7Q+3eBfIxuWOO/E5Y50067qdP9p6FICinBN3Wmw/bjAYJKcpJ24MHdNdvotDzV4Pkgl5E076rF319rtS11QHQPEor1dFc3Mzea3xu1gm8mypg80H48aW6O9bvO5SLS0t5LYmfTE8rkvKLuFT53wqJWVluvvuu2/ce9/73sOxy1auXDlm+fLl9cmWlUyy8FbgZjPbjtcNyQDnnDsj2YN2Yw0wx8xm4P3YvA+4MUVl92hKfT5VwKEJyQ1WDvh9nDI6h0j9PgpG5TKjKMgl8wauL1z72IbRo0+uDONp756zLGsTkUiEG264IdqP9US/10SmI5EI//7Iesw5vrh8Pk884f24XnbZZSdtF2/fntatW7cO5xxnnXVWR9yxFVK86Z7Wr1/vPSvj1FNPjbtPb++x01VVVTjnmDXr5DuN9eWBfFu2bAFg+vTpSe2X6Q//i2coxtwf2dlD+vmU/ZGWets5R8vx4zz87a+x49U1RMJhTpldzmUf+Rjl519EbjQhP4dlfOysE92UHt/+OA9WPUiOP4exOWP59ppv88b4N2j1tfH0k7fQ0NbA8eBxGtq8BKEl3NJzIEcg159L2IUxM2qyW/E7g6YaAhbA7/MT8AXICeSQ78sny7II+AIdL7/5O6azfN66J3Y8gZlx7exrvSv+5vdaZM3nTUev/reva5/eWrmVefPmeS0DMa+fvP4TAG454xbCLkw4EibiIt60i05Hwp3nu2wXcRFCkRBP7nwSh+PSsks7n7zHTMfOV1dXM3XK1BPrrfNJ+uPbHwfg6plXdyQQnRKP9iQjJtGAzglJ+/Kndz8NeCeDXbdtf9+/bz+nTDrlpPop9sS+fd1z1c9hGBdMuqDT54rV2ziA9u0r9lQAsHTq0k7HANi3fx+TTpkUN4buYkvlrYPby22PtaamhtLYwdwDJN5vRLzP5XAcqD1ASYrGGUwbPa33jYaBY8eO+V544YXRv/rVr3bFLl+1alXRfffdtyPZ8pJJFq5KtvBkOOdCZvZPwJ/wbsH3C+fchoE8Zoc9R2nKCbMhtC3pD5nXVk9TUxMTJvSe4aeLmeH3+/t0hbzdLz9xostXXp531WHu3Ln9jm3r1q1A/Kt6fbF3r3dRc9myZSkpT0S6l456+8DO7eyv2kwkHKal4TiLrn4Xp77t0hN35enCZz6WnLKEJacs4XPnfo4ndz7Jt9Z8i5qmGh6oegB/XoScSICstkYKsws5Jf8UCrMLKcgqoCC7oPN0VmHH++trXmfZxcvI8mV1jAu4+8q7+/351h/0Lnj845n/mNR+FfsqWDpn6UnLV1Z6d7a5eubV/Y7t35b8W3IxNVaw9JyTY2r3z4v+uX8Bxfj8eZ/vPZ6KCpae3308A+VL53+p23UVFRUsvWDp4AXTi4qKCpa+dWm6w+ikoqKCpRcuTXcYKTWQYxUARo8eHamvr38tdlldXZ0/GAz6ysrKkn6YTDLJwm7g/cBM59xXzKwMKAV29bxb4pxzq4BVqSqvO+/74QZKZn+CrNZCnHPs27yR4KQ8Nh5IvHm2XWHLAXw+X8cJdCZp78t/990DcrfblEj12AKNVRAZXINVb7cbN2kKuQUF5I0ewwf/63867n6TiPysfK6dcy2PbnsU5xy/vOqXJwbXfjS5wbXb/NvI8mX1vqGISAYoLi4Or127tk9JSjLJwo+BCHAJ8BXgOPA74Jy+HDhTHKurpeHwIcYvns8fDq+msa2R/KwEr8A7R0HLAWbMmEFomD71U0QkkwSysxkXvc1jMolCVyPlQWmpaO0Qkcx30003HRw/fvyAPPwrmWThXOfc2Wb2KoBz7oiZDfmOutWbvBbz0858C+H1T/D6gde5YPIFvezlyQ41khNuYt68eR195UcCXb0XkeEi054arJN7EemLZJ+dkIxkkoW26NM6HYCZFeO1NAw588bN45h5V6SqN60nt6CQ80+7DP+G/2Bt7dqEk4XCVu8BK+Xl5RmdLOjkXkRk4OgEX0SGs2SeSPM/wO+BiWb2deAF4BsDEtUgqt60nsnzTiU/p4AF4xewrjbxpxwXthygOWt0wncnEhEREZFhJRKJRIZ0v8Zo/N02ACScLDjn7gE+DfwnsB94l3PugX5HmEYtoUbqa/YzZb53m81FJYt48+CbtIZ7f17F8ePHyWs7yvGc1D2CXERERESGlPV1dXVFQzVhiEQiVldXVwR020UmmW5IOOc2A5l7a50kHWmpAWDK/NMAL1n45YZf8mbdmywuXdzjvpWV3oDyhtzUJQuxTyJW1yERERGRzBYKhT5SU1NzV01NzWkk12MnU0SA9aFQ6CPdbZBUsjDcHGmuISs3j4nTvSc3nzXxLAxjXe26hJKFoD+P1oD38B+d3IuIiIiMLIsWLToAvDPdcQykoZgBpczhlhoml8/vuP1eUU4Rc8bO6XXcQmtrK9u3b+d47kQYIbffExFJt/f9cAO7PvBBAE5rPY8DP30jzRGdsOsDH+yITURkOEm4ZcG8m1Kf9FA259zfBiy6ARSMtNIQPMKZ89/RafmikkU8vPVh2iJt3T5wZ+vWrYTDYW5fcTHTp08fhGhFRCRVdPciEZHEJdOy8GPgfOCG6Pxx4I6URzRI6oN1AEyODm5ut6hkEc2hZjYf6n5oRmVlJXl5eUydOnVAYxQRkZFHrRQikklG7EPZjgQP4DM/pbPmdlq+qGQRAOtq13F68ekn7RcOh6mqqqK8vBy/399Roee99V8BmPj3Zwxw5CIiIolr/52a9ptfZ1RZIjI0JNOyMGweygZeslCUU0wgq3NXowl5E5g+enq34xZ2795NS0sL8+bNG5C4dn3gg+y57f6M6osrIpJud195N/PGDUy9K+mTylYUtciIDIxkWhbaH8pWEn0o23uALwxIVAOszTmOtdUzq+DMuOsXlSziyV1PEnERfNY5n9q8eTOBQIBZs2YNQqT9t+sDH8Q34Wpy5s1Xq4eIyBAQaW3FtbbS8Je/4NraIBTCtbXhQiFcsO3EdFsbLuTN52/dxoF163Btoc7r29po3boVgL3/crt3Uw6fDwzMDMwXXWZg1nmZgfl8QPs+RnD3bgBqvvo1XCQM4UjHO5EwLua9qLaWPSsfgHAYF4nEfW/dsgWAnde/z/vwZideHfNgdF0WO+99lpboLc13/7+PnNiunRljDh9i9z3/B7H3JYluY3TetunVVwEYdeaZOBw4561zeNPtLxzOuehl1C7rnIvuC63R2HLKyztiBhhbf5Rdd/38pHg6TXdZ1rJhAwC5p3XuRt0d6+VGLM3rvfLyTjuVMYePsPvXv/Y+U1dxFtFlu5aNG73YFiyIF0icZb3H2h5Tqkz9+c97/U6ks4STBefcPWa2Drg0uuhdzrlNAxPWwGolwpisCYzLOyXu+kUli/jdlt+x5cgWyseVdyx3zrF582ZmzpxJdvaQ7YHVZ6lOPFJZXibHluryFFvfy4LUdRlUIj60xXanaW/JTcefY6S1lZYNG2l+/XWaX3uN5tdeI1RbC8Ce/9ftbc9PUgAczsrCsrIg+m6BAJaVRaS5GfBO5JyLeCd9kciJE9r2k9voMoeDSMxJbyTinSdGIkSamgA4+thjXiLh93f77m9upq2lJe56CwSwnGyI3o3QV1DQ+eQb4pyYE403ctLJuXMOwmHvO21o8D5Du+ikr7GJsM8fs9x1fo+Zdm1tAITq6npPYOIlL7QnX75O6ywrq/PxfD7vFS+O9j+LLp+jI8bWIL1yLu45fifhMJgRaW7BgkEizS0nPmdXvZ3wt8cWDnWJI35svcbqXOeYJC2SuRvSv3RZdJWZXQCsc869ltKoBliB+Vky/nJ8eaPiro8dtxCbLNTW1nL06FEuuuiiQYlTRESGl7b9+zuSgqbXXqN146aOE9OsyZMZtXgxzW++iS8vj9J//2Knk37LyoJANBHICnRa9+zzz7P04ovjHjNdYxYqKio4Y+nShMor+/ldAx5bIvEkWlaq7KioYGGCMQ2GigyLBzIzppEmmW5Ii6OvP0TnlwNvAP9gZg84576V6uDSZVLBJE7JP4V1teu4cf6NHcs3b/bukFReXt7driIiIkBMq8Frr3W0HLS3GlhuLrmnncq4D32QvDPPJG/hQgLFxcCJE9VRixYlfrAh2q0ilSfjmVqWyFCXTLIwBTjbOdcAYGZfAh4HLgLWAUMmWQju3Ele9jHILiJ8LIh/9MldihaVLOLFfS/inOvo21ZZWcmUKVMoKCgY7JBFRCTDRVpbOfbHP8ZvNZgyhVHnnNORGOTOK/daCgaBTqJFpD+SSRYmAq0x821AiXOu2cxau9kn47i2NiLBINktxwlYAfu/8RK55YWMvrKc7FPyO7ZbVLKIx7Y/xs5jO5lRNIP6+nr279/PZZddlsboRUQkk0SCQeoffJDmN97Atbay97Z/6bHVQERkqEkmWbgHeNnMHonOvwP4PzPLBzamPLIBYllZ5M6dy7HsAIGN95OTfwoudD4tla8QKPFR9Pb55M4d22ncwoyiGVRG72QwULdMFRGRIaStjcO/vYdDP/sZodpafAUFBEpKmPzd75JbPrdfrQa6ei8imSSZuyF91cz+CLwluugfnHNro9PvT3lkA62whEjJDMpuewuHfns/jS/uJdJ8Pofu3oBvVJjiK+YwMbuYdbXreM/c91BZWcn48eOZMGFCuiMXERnRrv/SN9P2LJpISwv1Kx9gwh13UHv0KHmLFzHpm/9J3R0/xszIS/B2liIiQ0UyLQsA2/Ae5JYLjDKzi5xzz6U+rMETKC6m5LZ/IvIPzdQ/9DBH//AcvrFnU/+wnzv5NH/a/zcaFxxn586dnH/++ekOV0RE0sBLElZy6Gd3EaqrIzxnDjN+8ANGnbsEM+Pgj/833SGKiAyIZG6d+hHgk3gDnV8DzgNeAi4ZkMgGmS8vj3Hvv4GxN1zP8WcqOPJ/j+EPT+OdXMya76wikh1hdsn0dIcpIiKDKNLczJH77+fQz39OuO4go5YsYdJ//zdrmpvIP+/cdIcnIjLgkmlZ+CRwDvBX59zFZjYP+MbAhJU+5vMx+tJLGH3pJWz+y+O8+sP/oHXS28gLZOH/v/3UPnuIMVfPI3tGkZ4AKCIyiFwohAUCuHAEF3GYb+Dq4EhTE0fuu59Dv/gF4YMHGXXeeRR/97uMOuccb4OKigE7tohIJkkmWWhxzrWY9zj4HOfcZjMb1g8cmHvBVfy/nd/g8m1h5pmf0OYnofUC6u58E19hhKKryvEeXdjr8xFFRKQfXDhM8+uvg9/Pnk98Ed/oK3EuSN3PXyFnVjHZkwvImlSAP79/tyONNDVx5N77vCTh0CHyLzifCT/4fnLPPBARGUaSSRaqzWwM8DCw2syOALsGIqhM4TMf5+acCxHjrBvew7TbJlP/wEMce+IZfBPO4chKH77i9xJp3IALO8yvlgYRkYHgQiGypkwh0thIqHY3rvphfGPKaG4qo3VLY8d2lhMh65RR5MyeSPbUQrInFeAvPPlZOieVH2zh0F13cegXdxM+fJj8t7yFCR//OKPOPmsgP5aISMZL5m5I10Yn/8PMngGKgCcGJKoMMrlpMkfsCEWnFOEvKGD8zR9k3AdCHHtyNUfu/wMEZhMoPp/gjkO4tkb2/MsOcGGwCJjDfA58YH7ADxbwYQHDsnxYlh/L9mPZASwngC8ni0h4PDQepa2mmsa1ufjyc/GNysGXn+e9cgID2vQuIpKJfDk5ZJWUAN6tRWvvWEv44G7yyoM0b6gguPMw4WOGr3Ay4YNlBHe2dOxrWWECpXnkziome2oRWZNPPFgz3NBI898epvmVx3DNx8l/61uZ8PGPMeosJQkiIpBgsmBe5/wpzrk9AM65Zwc0qgzhnCNUG6I2r5bXD79O6ehSACwQoOjtVzH6qivZce11tOx9mqxpl4IFsFHZgOHCBs6Ha88ULOCt9wXAl4X54zeVZ5Vd5x27GY48WBs/rkgIIiFw7a9ockIEfA4z5yUmPnD+hYSPHaH5jVfY9+XNXq8pM6z93ee9e6/otA9vPEZ02tvOiISn4I7W0bK5jbqf7Om0n8Xu3z7vMyCmjNhlBpHQONzxQwR3bObI746e+IBGxzZ0THaex2JnIBIq8sravZX6x5s6jyfp2DSmjI7yTt7OzIiECnCNRwju3cGxp9vi/jmc2K/35C0Sysc11NO2byfHK8K9bp9YWbs4XhHpV1kd5TXW07Z/F8ef7V+XulSWNRCxAbTt9xpEU1JecwORYP/+PKVvLJBNoHQ2Y993BmOjy1xbG63bt9OyYSPNG/5C6/Y6wkccvlGlhA6VEdzdgtleAHwTbyTStJ/dH/1vQrVbCExawORv3EbewoX9iqv9lq4T//6MfpUjIpIpEkoWnHPOzFYBpw9EEGb2bbyHvAXxbs96s3OufiCOlYx9+/bR3NjMwZKDrKtdx7LpyzqtNzP8hYX4CyHv7POAxH4gXDhMpKmZcGMLrjH63txKpKmVgz+5C8tfgH/8ePIWjMW1tuFaw0TawrhgGEIRIiEHoQguDITBRYCI4ZwPwtEExWvGwDd2HubLAvMRbmg/UfdFT959SX0fWWVXdUy37kxq1/jlTXt3x3Tjmq5ru57I9XxilzXtvd5EGBqe7+XkPqHYbvAmgnBsdVMKyrsRANcKR59oSGFZx1MXWwsc/eOxjClrIGOD1JUXqu3/34+hKBPrbcvKIre8nNzycsZ4111w4TDBXbtp2biR5g1rad1WS6iuDcspxj9mGoGyt5E17RIwCO4vInt2W7/HPYiIDCfJjFl4xczOcc6ddFqXAquBzzrnQmb2X8BngX8bgOMkZfPmzZgZE8smsq52XcrKNb8ff2EB/sKCk9Yd/sUP8BVNJGf2fCbc3P8rU7s+8EFswtXkzJvfKZFxzoFzuHAYwhFcJOK9x0x3Wh6JsO+zn8dX9Faypk1n3Ltm4SLOy1Qizisv4qLLHEQi4Ogyf2I7nOPgT+/EChYSOKWU0RdN9dZDTF7g2oM98e5OrHKcmD/y299i+QsITCyh4PxTTnwBHfvGLjp5Wcd20eX1Dz6AjSonUFxM/uLSHr5hRyLj2+sfeggbNZfAhGJGLZrY+w49OPr7h7FRcwhMmMCos0v6VRbA0Yej5Y3vf3lHH3kEGzU7JWWluryjj3gPn8+a6iX2qSjPN3oBOfNH7MDXjKy3uzK/n5yZM8iZOYOi5VcDXh2w8/r3ETn0GmU/uo/aO14jcizI8Wf30PDiXgrOn0TBhZPxF/Q+1kFEZLhLJlk4F7jJzHYCjURvA+Sc6/cZrXPuyZjZvwLv6W+ZqVBZWUlZWRmlU0q547U7ONp6lKKconSHlRLt3YzM54MEL6L5/CF8ufkExk4gd960fsdw+OeN+ArGkj15JqMv699fo6P3/Rhf4Xiyp81hzDv6n2Qde/hn+EZPIHvGPMa+JwXlPfpzr7yZ8xj33v6Vd/wPv4iWNZ9x1/c/tuOPRcubNZ9x7+tnbI//At/o81JSVqrLO/74LwDInjUfIDWfddRofHnJPttyeMjUejsRZoYvJ8ebDvjw5Qbw5QYY+65ZHPvzHo4/V03Di/vIP38ShRclljRM+82vAdL2ZGkRkYGSzK/cst43SYm/A+4fpGN16/Dhwxw4cIBly5aRVZKFw/HqgVdZOnVpukMTEck0GVFv91dWST7jb5hH26VlHPvzbhqer6bxpX3kn3sKhW+bktBdlUREhptkkoXdwPuBmc65r5hZGVBKgrdPNbOnott39Xnn3CPRbT4PhIB7eijnFuAWgLKysiTCT05lZSUA5eXl5Bflk+XLYl3tOiULIjJipKLejq2zS0pKqOjDw8waGhqoqKhgbH09ADsqKphc74252tjHh6P1WlYpZBXA2O0RIi9Uc+zFao5NdRyZ4Qjnnoipq/7G1VfdxZNOmRZTpsUDmRdTpsUDmRnTSJNMsvBjIAJcAnwFOA78Du+pzr1yzl3W03oz+zCwHLjUOddtL3Dn3J3AnQCLFy8esKehbd68mYkTJzJu3DgATp9wekrHLYiIZLpU1Ntd6+ylS5cmHUdFRQVLly5l18+9rmQLly7lQKXX3WfO0r51J0umrLaDzRz/8258rx1gzF4fBUtKeTN7DxfG+Sz9jauv2r+jTJJpMWVaPJB5MWVaPJCZMY00ydwO51zn3MeBFgDn3BEgJW2yZnYl8Gngnc65tN9apLGxkd27dzNv3ryOZYtKFrHx0Eaa2tIenohI2mVavT2QsibkMe695ZTevphRC4tp+Os+pj3n48gjWwkdbU13eCIiAyqZZKHNzPxE7/1iZsV4LQ2p8COgEO/J0K+Z2U9SVG6fbNmyBecc5eXlHcsWlywm7MK8Vvda+gITEckcGVVvD4bA+DzGrZhL6e2LOT7J0fhyDTXfWsORh7cSqm/pvQARkSEomW5I/wP8HphoZl/Hu/PFF1IRhHNudirKSdS03/ya+7/8GQBO5V0nrd+8eTOFhYVMmjSpY9nCiQvxm591teu4YNIFgxWqiEhGGux6O5MExudRd5qj/MbFHK/YQ+OaGhrX1JC/uATXFsGyknuGjYhIJks4WXDO3WNm64BL8W6b+i7n3KYBiyxN2tra2LZtGwsXLuz0FOD8rHzmj5uvcQsiIgJAYFwuY6+bQ+ElUzleUU3jmhoIO3z5WTT8dT/ZZYVkleRj/t6f8t4dPRFaRNIt4WTBzP4FuN85d8cAxpN227dvp62trdN4hXaLShZx7+Z7aQ23kuPPSUN0IiKSaQJjchn7rtkULp3KgTteI9LYRv3DWwGwLB9ZkwvILiske+posssKCRTp90NEho5kuiEVAk+a2WG8+2k/4JyrHZiw0mfz5s3k5OQwffr0k9YtKlnErzb+ivUH17OoZMQ+tVVEROIIjMkhMCEPNz6X8SvmEtxz3HvtPk7DX/ZBeC8AvtHZZE8tJHtqITllhWRNLsSX4x/w+NRKISJ9kUw3pC8DXzazM4DrgWfNrLq3W+tlquu/9M2TlkUiEaqqqpg9ezaBwMlfzdklZwOwrnadkgUREYnLzAiMzyMwPo9RZ04EwIUitO1vJLj7GK3RJKJlw6HoDt4D4bzWB+8VmDgK8/W9+9JAU+IhMnIk07LQ7gBQAxwCJqY2nPSqrq6msbExbhckgKKcIuaMnaNxCyIiaTDtN7/umB5qJ6kW8HUkAgXRZeHGtmjLwzGCe47T9MZBGv9W422f4yd7SgGhw82Yzzj+bDX4DfMb5vd1TBOdH1UHLVuOdFnni27vTbtQBAwiTW1g5o0+NMN8dJrH6DRmbzAo+RDJXMmMWfgY8F6gGHgA+KhzbuNABZYOlZWV+Hw+Zs/u/iYfiyYu4pFtjxCKhAj4+pJriYiIgD8/i7x548ib5z3800UcoUPNBHcf7+jCFDkaBODoH3f0WNYk/Bxctz6h4+77yl8TCzAmibBOyYThgmGvrK/91Us42tdF9zGDshYfNa+sjc5H9/fFKc8HbfsbAai78434sXSXu8RJaoLVxwEje0pBp+WTjvio2/pmYp+9o6wGAK+sroeKOXanMGJnuuzTuusYADnTiwAoPejj4O6+nUq17jzaqaz+aC+rtKCbeHrIHbtdlaJ8s7TOx6Hq1J5ujr1uDr5RWSktczizHh6W3HlDs//EG+D8WnT+rcAN0Qe1pcXixYvd2rVrU1beD3/4Q4qKivjgBz/Y7TZP7HyCTz37Ke69+l5Om3Bayo4tIiOLma1zzi1OdxyDqa91diY+wbW7mFJ9hbz2J68DUHzzaRCO4MIOF3Yd00S8+XVr1nL2wrNw4QhEt/HWR3Ahb7tjFXsAKLhgkvfEJOcgAs65E/PRdxdpn47Od2zvLW9afxAc5C0YB46Y7V3H/IHaWiYWT/Ri7Fq+I1qWtyy41zspzzolP/Evp5vTl7YaL/HIKu1c1tGjRykqSu7EuqOsklEnHbvj8LHnUbExtS+PWRY61AxAYFweAA2NDRTkd05qEhU63Lms/mgvqyUnHCee7s8TEzyF7JdgXSN+vx//uNyUlVn80dPxFyT/XOGRWG9DcmMWPmtmZ5nZt/BaGHYADw1YZIOsrq6OQ4cOce655/a43aKJ3liFdbXrlCyIiMiAau8O5A2A7n4QdOsWyJnR84lw4ysHACh8y+R+xxWMtgSMvXZOt9u8WVHD/KXzEypvMLohbayoYM7ShQNWfl9sqqhg6dKz0x1Gh4oMiwfaY7oo3WGMaL0mC2Y2F7gh+jqIdyckc85dPMCxDarKykqATk9tjqd4VDHTRk9jbe1aPnTqhwYjNBERkWFNYxVEMlciLQubgeeB5c65rQBmdtuARpUGmzdv5pRTTkmoiXJRySKe2vUUERfBZ3pSp4jIYLv77rsBuPnmm9Mcycikk3uRkSORM93rgP3AM2b2MzNrf4LzsNHQ0EB1dXWvrQrtFpUs4ljwGFvrtw5wZCIiIqkx8e/P0Em+iCSt15YF59zDwMNmlg9cA/wzMNHM/hf4vXPuyQGNcBC0d0Hq7papXbU/Y2Fd7Trmjp07YHGJiMjgyNSWCp3ci0i6JdyHxjnX6Jz7P+fcO4ApwKvAvw1YZINo8+bNjBkzhpKSkoS2n5Q/idL8Uj1vQUREOtHVexEZbvrU4d45d8Q5d6dz7tJUBzTYWltb2b59O+Xl5Qk/hMbMWFSyiLU1a0n01rMiIiIiIkPNiH2qWHuT87nnnks4HE64C1K7RSWLeHz749z4+I3kBnK5+8q7ByJMEREREZG0GfG38qmsrCQ3N5eysrKk9msft3A8eHwgwhIRERERSbsRnSw456iqqmLu3Ln4/d0/7CaeGaNnMC53HA1tDQMUnYiIiIhIeo3oZKG1tZXm5uakuyDBiXELalkQEZFYd999d0dXVxGRoW5EJwtNTU34/X5mzZrVp/0XlSwiGAnSGm5NcWQiIiJKPEQk/UbsAGfnHM3NzcycOZOcnJw+ldE+bqEhmLquSDc/4d3jOxUDplNZlohIJgmHwwSDQbZt24bP58PM8Pl8Ha/Y+UTW6c52IiLxjdhkoa2tjVAolPBTm+OZM2YOfvNzNHiUmsYaAr4A2f5ssn3ZZPmy8PuSGwchIiKJaWlp4eDBg/zmN79Jabn/9V//RU5OTq+v/fv3s3HjxpOW5+bm4pxL+Fbcgy1THz4nIplrxCYLLS0tAP1KFvw+PwVZBRxuOczlD15+8nrzk+3P9pIIXzbZfi+JaH/P8md1LM/2ZZPlz2L70e0AfOb5zwCcdLXL0eXq10mzJxZsq98GwKef/TQY+MyHtf/PvHef+TqmO967Wb9612oArph+RZ+/s3ZP7nyy27KMk39k4y4bwB/jP+74IwBXzbgqZWXG+wwytF067VIWFi9MdxgjUm5uLqWlpVx11VVEIhGcc0QikZOmE123du1aIpEI5eXltLa2drwaGho4dOhQx3woFOqIobKystv4zIxvfOMbnVov4r33tM7n81FbWwvAr3/9647t470OHjzIwYMHe9zGzDh8+DAAq1at6jbuRJdv2LABgNNOOy3uPnv27KG1NbFuuuvXr++xrL7oGvOePXsIBoMpKz8VMi2mTIsHBiamOXPmMGPGjJSWOZyN2GShsLCQ3NxcCgsL+1XOtNHTOB48zodP+zDBcJBgOEhbpI1gJEhbuM2bDgcJRk6sawufWB+MBGkINnRs19jWCMAbdW90HKPrSWbXCrC7k9DmUDMOx8bDG3HO4XBEXATwkpAI3g9l+7re1jeFmgB4aMtD/frO2mOLV1a8rgAnJUhxtIa8H6ScQN+6lHVX3v2V96ekvFR2cQiGvUoz25+dsjKlb6YXTVeykCZ+vx+/38+0adNSUt6WLVsAuPrqq3vcLhQKEQwGefbZZznrrLM6JRbtr7/97W8451iwYEGnhCTee0/rIpFIx3Hb2tpO2j721djYyP79+09a3nXb9gtlb775Zo/1UnfrYpe3tbUBsG7durjbhsNhDhw40OP32bWstWvXJrR9b9qTukDgxGlOOBympqamz2Ule9fEeMLhcKeyIpEI+/fvT0lZqdCfeAbKQMRUUFCgZCEJIzZZMDOys/t/spXtz2Z83nium3NdCqIaOWMWMjk2EZHuBAIBAoEAeXl5lJaWxt2mqqoKgCuvvLLfx0u021BFRQVLly5NWXmpkGhMgyXT4oHMiynT4oHMjGmkGbHJgqSXkgQRkcGnsQoikiwlCxlGJ9EiIiIikimULAxjSjxERIY2tQSISLopWRAREUkhneCLyHCSUU9wNrPbzcyZ2YR0xyIiIr1TvS0iMrxlTMuCmU0FrgB2D8bxUnXlR119RGSkGux6W0REBl/GJAvA94BPA4+kOxAREUnIsKm31XVIRCQ+S+XDovochNk1wCXOuU+a2U5gsXPuYDfb3gLcAlBWVrZo165dgxeoiEiKmNk659zidMfRV4nW27F1dklJyaL77rsv6WM1NDRQUFDQadmrr74KwFlnnZV0eakQL6Z0yrR4IPNiyrR4IPNiyrR4ILNiuvjii4d0vd1Xg5YsmNlTQLwn2Hwe+BxwhXPuaG/JQqzFixe7VD3tUURkMA2FZCHV9XZf6+xMfChTpsWUafFA5sWUafFA5sWUafFAZsU0FOrtgTBo3ZCcc5fFW25mpwMzgNfNDGAK8IqZLXHOJf9cdhERSQnV2yIikvYxC865N4GJ7fPJtCyIiMjgU70tIjJyZNStU0VEREREJHOkvWWhK+fc9HTHICIiiVO9LSIyfKllQURERERE4lKyICIiIiIicSlZEBERERGRuJQsiIiIiIhIXEoWREREREQkLiULIiIiIiISl5IFERERERGJS8mCiIiIiIjEpWRBRERERETiUrIgIiIiIiJxKVkQEREREZG4lCyIiIiIiEhcShZERERERCQuJQsiIiIiIhKXkgUREREREYlLyYKIiIiIiMSlZEFEREREROJSsiAiIiIiInEpWRARERERkbiULIiIiIiISFxKFkREREREJC4lCyIiIiIiEpeSBRERERERiUvJgoiIiIiIxKVkQURERERE4lKyICIiIiIicSlZEBERERGRuDImWTCzT5jZZjPbYGbfSnc8IiLSM9XbIiLDXyDdAQCY2cXANcBC51yrmU1Md0wiItI91dsiIiNDprQs/CPwTedcK4Bz7kCa4xERkZ6p3hYRGQEyJVmYC1xoZi+b2bNmdk66AxIRkR6p3hYRGQHMOTc4BzJ7CiiNs+rzwNeBZ4BbgXOA+4GZLk5wZnYLcAtAWVnZol27dg1YzCIiA8XM1jnnFqc7jp6kot6OrbNLSkoW3XfffUnH0dDQQEFBQdL7DaRMiynT4oHMiynT4oHMiynT4oHMiuniiy/O+Hp7IAxastBjEGZPAP/lnHsmOr8NOM85V9fTfosXL3Zr164djBBFRFJqKCQLPelLvd3XOruiooKlS5f2NdQBkWkxZVo8kHkxZVo8kHkxZVo8kFkxDfV6u68ypRvSw8DFAGY2F8gGDqYzIBER6dHDqN4WERn2MuJuSMAvgF+Y2XogCHwoXhckERHJGKq3RURGgIxIFpxzQeCmdMchIiKJUb0tIjIyZEo3JBERERERyTBKFkREREREJC4lCyIiIiIiEpeSBRERERERiUvJgoiIiIiIxKVkQURERERE4lKyICIiIiIicSlZEBERERGRuJQsiIiIiIhIXEoWREREREQkLiULIiIiIiISl5IFERERERGJS8mCiIiIiIjEpWRBRERERETiUrIgIiIiIiJxKVkQEREREZG4lCyIiIiIiEhcShZERERERCQuJQsiIiIiIhKXkgUREREREYlLyYKIiIiIiMSlZEFEREREROIy51y6Y+gzM6sDdvVh1wnAwRSH0x+ZFg9kXkyZFg8opkRkWjyQOTFNc84VpzuIwTSM6mzIvJgyLR7IvJgyLR7IvJgyLR7IrJhGXL0NQzxZ6CszW+ucW5zuONplWjyQeTFlWjygmBKRafFAZsYkPcvEP7NMiynT4oHMiynT4oHMiynT4oHMjGmkUTckERERERGJS8mCiIiIiIjENVKThTvTHUAXmRYPZF5MmRYPKKZEZFo8kJkxSc8y8c8s02LKtHgg82LKtHgg82LKtHggM2MaUUbkmAUREREREendSG1ZEBERERGRXoyoZMHMrjSzSjPbamafyYB4pprZM2a20cw2mNkn0x0TgJn5zexVM3ss3bEAmNkYM3vQzDab2SYzOz/N8dwW/fNab2b3mlluGmL4hZkdMLP1McvGmdlqM9sSfR+bATF9O/rn9oaZ/d7MxqQznph1t5uZM7MJgxWP9E0m1duqsxOTaXV2NCbV24nFk7Y6u7uYYtap3k6TEZMsmJkfuAO4ClgA3GBmC9IbFSHgdufcAuA84OMZEBPAJ4FN6Q4ixg+AJ5xz84CFpDE2M5sM3Aosds6dBviB96UhlF8CV3ZZ9hngaefcHODp6Hy6Y1oNnOacOwOoAj6b5ngws6nAFcDuQYxF+iAD623V2YnJmDobVG8nGU866+zuYlK9nWYjJlkAlgBbnXPbnXNB4D7gmnQG5Jzb75x7JTp9HK9CnZzOmMxsCnA1cFc642hnZkXARcDPAZxzQedcfVqDggCQZ2YBYBSwb7ADcM49Bxzusvga4FfR6V8B70p3TM65J51zoejsX4Ep6Ywn6nvApwEN2Mp8GVVvq87uXYbW2aB6O6F40llndxdTlOrtNBpJycJkYE/MfDVpruRjmdl04Czg5TSH8n28f5CRNMfRbgZQB9wdbWa/y8zy0xWMc24v8N94Vzf2A0edc0+mK54uSpxz+6PTNUBJOoOJ4++AP6YzADO7BtjrnHs9nXFIwjK23lad3a2MqrNB9XY/pL3OBtXbmWAkJQsZy8wKgN8B/+ycO5bGOJYDB5xz69IVQxwB4Gzgf51zZwGNDH73mg7R/qTX4P0gTgLyzeymdMXTHefd5ixjrsCY2efxunDck8YYRgGfA/49XTHI8KA6u0cZVWeD6u2+yIQ6OxqH6u0MMJKShb3A1Jj5KdFlaWVmWXg/Ovc45x5KczhvAd5pZjvxmvsvMbPfpjckqoFq51z71bsH8X6I0uUyYIdzrs451wY8BFyQxnhi1ZrZKQDR9wNpjgcAM/swsBx4v0vvvZpn4Z0svB79Oz4FeMXMStMYk/Qs4+pt1dm9yrQ6G1RvJyWD6mxQvZ0RRlKysAaYY2YzzCwbb3DTo+kMyMwMr1/nJufcd9MZC4Bz7rPOuSnOuel438+fnXNpvfrinKsB9phZeXTRpcDGNIa0GzjPzEZF//wuJXMGFj4KfCg6/SHgkTTGAnh3ssHrIvFO51xTOmNxzr3pnJvonJse/TteDZwd/TsmmSmj6m3V2QnFlGl1NqjeTlgm1dmgejtTjJhkITpg55+AP+FVEiudcxvSGxVvAT6AdzXotejr7WmOKRN9ArjHzN4AzgS+ka5AolfLHgReAd7E+zc06E+XNLN7gZeAcjOrNrP/B3wTuNzMtuBdSftmBsT0I6AQWB39+/2TNMcjQ0gG1tuqsxOTMXU2qN5OMp601dk9xCRppic4i4iIiIhIXCOmZUFERERERJKjZEFEREREROJSsiAiIiIiInEpWRARERERkbiULIiIiIiISFxKFkREREREJC4lCyIiIiIiEpeSBRlSzCwc8zCk18xsupk1DMJxx5jZxwb6OCIiw4nqbJGhTw9lkyHFzBqccwW9LRuA404HHnPOnTaQxxERGU5UZ4sMfWpZkGHFzP7FzNZHX/8cXfYpM7s1Ov09M/tzdPoSM7snThn5Zva4mb0eLed64JvArOiVsW9Ht7vJzP4WXfZTM/NHr5ptNrN7zGyTmT1oZqPiHOMhM/uamT1nZrvN7LIB/FpERDKS6myRzKdkQYaavJjm7N/HrjCzRcDNwLnAecBHzews4Hngwuhmi4ECM8uKLnsuzjGuBPY55xZGr0o9AXwG2OacO9M59ykzmw9cD7zFOXcmEAbeH92/HPixc24+cAyI1xR+OlDvnLsI+GTMviIiw4nqbJEhTsmCDDXN0cr/TOfctV3WvRX4vXOu0TnXADyE9+OyDlhkZqOBVuAlvB+gC/F+lLp6E7jczP7LzC50zh2Ns82lwCJgjZm9Fp2fGV23xzn3l+j0b6NxdYhetSoCvhddlAXUJ/TpRUSGFtXZIkNcIN0BiAw051ybme0APgy8CLwBXAzMBjbF2b7KzM4G3g58zcyeBn7dZTMDfuWc+2ynhV4/2a4DgbrOLwDWOefC0fkzgPVJfiwRkWFJdbZIZlHLggwnzwPvMrNRZpYPXMuJq1DPA/+K14T9PPAPwKsuzgh/M5sENDnnfgt8GzgbOA4Uxmz2NPAeM5sY3WecmU2Lriszs/Oj0zcCL3Q5xOnAazHzZ+D9GIqIjCSqs0WGACULMmw4514Bfgn8DXgZuMs592p09fPAKcBLzrlaoIX4zdng/TD8LdpU/SXga865Q8BfooPnvu2c2wh8AXjSzN4AVkfLB6gEPm5mm4CxwP/GKf+1mPnT0FUqERlhVGeLDA26dapICul2fSIiQ4fqbJHeqWVBRERERETiUsuCiIiIiIjEpZYFERERERGJS8mCiIiIiIjEpWRBRERERETiUrIgIiIiIiJxKVkQEREREZG4lCyIiIiIiEhcShZERERERCQuJQsiIiIiIhLX/wcSkRRfgao8ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f = plt.figure(figsize = (12,5))\n",
    "\n",
    "f.add_subplot(121)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k, flow.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    plt.errorbar( np.linspace( i * 0.02, flow.nmb_flow_steps + i * 0.02, \n",
    "                               flow.nmb_flow_steps), e, yerr = ef ) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1))\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.ylabel(r'Average energy $\\langle \\hat H_n \\rangle$')\n",
    "plt.title(r'Wegner flow')\n",
    "    \n",
    "\n",
    " \n",
    "f.add_subplot(122)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    plt.errorbar( np.linspace( i * 0.05, flow.nmb_flow_steps + i * 0.02, \n",
    "                              flow.nmb_flow_steps), e, yerr = ef , \n",
    "                 label= label) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1), labels=[])\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.title(r'Wegner and $Z$-product flow')\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1, 1.05))\n",
    "plt.savefig( 'figs/eigenstates_L_%d.pdf'%L)\n",
    "plt.show()\n"
   ]
  },
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     "output_type": "display_data"
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1Z1', '1Z1', 'Z11', 'Z11', 'Z11', '1Z1', 'Z11', '1Z1', '1Z1', 'Z11', '1Z1', '1Z1', 'Z11', '1Z1', 'Z11']\n"
     ]
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import plotly.express as px\n",
    "color_discrete_sequence=px.colors.qualitative.G10\n",
    "\n",
    "fig = make_subplots(rows=1, cols=3, subplot_titles=(\"Plot 1\", \"Plot 2\", \"plot 3\"))\n",
    "\n",
    "fig.layout.annotations[0].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.16, y=1.01, text=r'$\\text{Norm off-diagonal } \\|\\sigma(\\hat H_k)\\|_{\\text{HS}}$')\n",
    "fig.layout.annotations[1].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.50, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "fig.layout.annotations[2].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.855, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "\n",
    "norms = flow.flow_outputs['minimal_norm_sigma_H_s']\n",
    "norms2 = flow2.flow_outputs['minimal_norm_sigma_H_s']\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=x,\n",
    "    y=norms,\n",
    "    mode = 'lines+markers',\n",
    "        name = \"GWW\",\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    " \n",
    "fig.add_trace( go.Scatter(\n",
    "        name = \"Variational\",\n",
    "    x=x,\n",
    "    y=norms2\n",
    ",mode = 'lines+markers',\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    "x_selection = [1,2,5]\n",
    "y_selection = [0,1,-1]\n",
    "y_selection2 = [1,2,5,-1]\n",
    "fig.update_layout(\n",
    "    xaxis1=dict(\n",
    "    title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [x[s] for s in x_selection],\n",
    "        ticktext = [round(x[s],2) for s in x_selection]\n",
    "    ),\n",
    "    yaxis1=dict(\n",
    "        title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [norms[s] for s in y_selection]+[norms2[s] for s in y_selection2],\n",
    "        ticktext = [round(norms[s],1) for s in y_selection]+[round(norms2[s],1) for s in y_selection2]\n",
    "    ))\n",
    "\n",
    "#fig_norm.update_yaxes(range=[min(norms)-5,max(norms)+6])\n",
    "\n",
    "y_selection = [0,1,2,3,7,13,-1]\n",
    "y_labels_rounded = [ round(y, 1 ) for y in norms ]\n",
    "y_vals = [norms[i] for i in y_selection]\n",
    "y_labels = [ y_labels_rounded[i] for i in y_selection]\n",
    "x_selection = [0,1,3,7,13,-2]\n",
    "x_vals = [x[i] for i in x_selection]\n",
    "x_labels = [ round(x[i],2)for i in x_selection]\n",
    "\n",
    "\n",
    "\n",
    "x = [x+1 for x in range((flow.nmb_flow_steps))]\n",
    "\n",
    "#Canonical flow\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k,\n",
    "        flow.flow_outputs['expected_energy_fluctuation'] )\n",
    "    )\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "    label = r'$|'+str(flow.binary_label_from_state_number(k)) +r'\\rangle$'\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e, line=dict(color=color_discrete_sequence[i])\n",
    "                  , name = label),\n",
    "        row=1, col=2\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=2\n",
    "    )   \n",
    "\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k,\n",
    "            flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e,line=dict(color=color_discrete_sequence[i]), name = label,showlegend=False),\n",
    "        row=1, col=3\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=3\n",
    "    )   \n",
    "fig.add_annotation(x=-.045, y=1.1,\n",
    "            text='a)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ")  \n",
    "fig.add_annotation(x=.315, y=1.1,\n",
    "            text='b)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "fig.add_annotation(x=.70, y=1.1,\n",
    "            text='c)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "\n",
    "# fig.add_annotation(x=.8, y=8,\n",
    "#             text=\"GWW flow\",\n",
    "#             showarrow=True,\n",
    "#             arrowhead=1)\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "fig.add_annotation(dict(\n",
    "                x= x[1],\n",
    "                y= norms[1]+.3,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\"GWW flow\",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 4,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "fig.add_annotation(dict(\n",
    "                x= x[2],\n",
    "                y= norms[2]+.4,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 3.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "        yshift=0.2\n",
    "                )\n",
    "            )\n",
    "x2 = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "fig.add_annotation(dict(\n",
    "                x= x[5]-.3,\n",
    "                y= norms[5]+.5,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 3.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "for i, Z_name in enumerate(flow2.flow_outputs['strs_of_minimizer_Z']):\n",
    "     if i<6:\n",
    "            fig.add_annotation(dict(     x=x[i+1], y=norms2[i+1],\n",
    "                    text=Z_name,\n",
    "                             font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=12\n",
    "        ),\n",
    "                    xref=\"x\", yref=\"y\",\n",
    "                    showarrow=True,\n",
    "                    axref = \"x\", ayref='y',\n",
    "                    ax= x[i+1]-(.9 if Z_name=='Canonical' else .6),\n",
    "                    ay= norms2[i+1],\n",
    "                    arrowhead = 3,\n",
    "                    arrowwidth=1.5,\n",
    "                    )\n",
    "                )\n",
    "i=7\n",
    "fig.add_annotation(dict(     x=x[i+1]-.5, y=norms2[i+1],\n",
    "text='...',\n",
    "     font=dict(\n",
    "family='Courier New, monospace',\n",
    "size=12\n",
    "),\n",
    "xref=\"x\", yref=\"y\",\n",
    "showarrow=False\n",
    ")\n",
    ")\n",
    "fig.update_xaxes(title_text=r'$\\text{Total flow duration } \\ell$', row =1, col = 1)\n",
    "fig.update_xaxes(title_text=r'$\\text{Flow step } k$', row =1, col = 2)\n",
    "fig.update_xaxes(title_text=r'$\\text{Flow step } k$', row =1, col = 3)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_layout(margin=dict(l=56, r=0, b=0, t=26))\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=300)\n",
    "\n",
    "fig.show()\n",
    "print(flow2.flow_outputs['strs_of_minimizer_Z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image( 'figs/simple_eigenstates_comparison_TFIM_J=1.pdf' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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By Side Subplots but colors additive so wrong color\"},\"height\":600,\"width\":800},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('8535dfd5-6407-4d8e-acac-4c6fef431093');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2)\n",
    "\n",
    "x = [x for x in range((flow.nmb_flow_steps))]\n",
    "\n",
    "#Canonical flow\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k,\n",
    "        flow.flow_outputs['expected_energy_fluctuation'] )\n",
    "    )\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e),\n",
    "        row=1, col=1\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy_fluctuation'] )\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e),\n",
    "        row=1, col=2\n",
    "    )\n",
    "\n",
    "fig.update_layout(height=600, width=800, title_text=\"Side By Side Subplots but colors additive so wrong color\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checks \n",
    "The landscape for 3 qubits is a bit unusual and exploring large flow steps can speed things up.  Above the brute force check found fairly large steps while binary search may get stuck in local minima, cf this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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+K8cRQojcINclIQqGX3YF88mfR7G2tGD1W82pVvphH4WEEObg9u3bjBkzhp9//pnu3bunrK9bty6LFi1KWb548SJvvfUWW7dupXDhwrzzzjsMHz4cSGrt8+abb/Lzzz9z/vx52rVrx4IFC7Czs8vUvq+//jqLFi3i5MmTREZGMmHCBH766SeuXr2Ki4sLX375JV27duXll18mJCSEF154AUtLS8aMGcP777/P8ePHef311wkICKB8+fJ8/fXXdOrUKSV7euewsrJK9fyj8j/q+Okdu0qVKrzxxhv88ssvnDlzht69e/PVV18xcOBAtm/fTqNGjVi2bBnFihV7rH+z+wtD165d488//2TQoEHcvn0bgFu3brFo0SL27dv3WMcXIjtF3LnB8QUjaHD9T85ZuBIX+x42tnbZeo5sKxYppToCV7XW+5VSrZ7kWFrrWcAsAB8fH92qVeYPV373v+kWjIoXsiErxxFCiJy2atUquS4Jkc+ZTJpv/j7JzC1neNqjJIcv3Gb44oOsfKMZdtaWRscTQhhk165dxMbG0rlz54duYzKZeOGFF+jcuTOLFy8mLCyMZ599Fg8PD5577jkAfvvtN9avX4+dnR3NmjVj/vz5vPbaa5nad/Hixfz11184OztjZWVF5cqV2bZtG2XKlGHZsmX069eP06dP88svv7Bt2zZmz57Ns88+C0B8fDwvvPACgwcP5p9//mH79u107twZf39/PDw8Ul7Dg+d40MPyZ+b46R17xYoVbNiwgYSEBOrWrcvBgweZM2cONWrUoEOHDkyZMoVPPvnksf7N7i8W/fjjj/Tu3RtXV1cuXboEwNy5c2nWrBnVq1d/rOMLkV2ObP0D539HUk9fZ2eZvtTt/022F4oge7uhNQM6KaWCgSXAM0qphQ9scwFwAVBKWQFOQLa21x71nAf2D3w4U8CNyDjeXnKQm5G51+RTCCGEEAVXXIKJd38LYOaWM/Rt5MpP/X34tkcdTly+y7h1J4yOJ4QwUHh4eJoCiouLC5aWllhYWNCgQQPWrl3LtWvXGDNmDDY2NlSqVIkhQ4awZMkS7ty5Q1hYGE5OTpQrV47ixYtTtWpV/u///g9PT08GDBiQal9/f38iIiLo1asXL730EgDDhw/HxcUFe3t7AF588UXKlSuHhYUFvXr1omrVquzduzfd/Lt37yYiIoIPPvgAGxsbnnnmGTp27MjixYtTbffgOR40fPjwlPwvvPACAQEBmT5+esd+6623KF26NOXLl6dFixY0atSIunXrYmdnR9euXTl48GDW/7GS3SsWxcfHM3PmTIYPH06RIkW4ffs2JpOJadOmMWLEiJTt33nnnSc6nxBZdffWdfZN7ovXvwOJUXacemEFTV+fjn2hwjlyvmxrWaS1/hD4ECC5ZdFIrXW/Bzb7ExgA7AJ6AP/qbB7drUvd8kDS2EUXbkVRvqgD7zxbldCb0UzbfJrtp6/zZVcvnvMsk52nFSLPcS5SCNeSmWiG61Qq2899JzYx442AIrbZ/627KQtXFItHjJgWcPoqJMSkWd+2pQ/r169/jGRCiILkTkw8r/2yn51nrjPqOQ+GtaqMUoqnq5diUDM35u0IpmU1Z56pXtroqEIIA1y4cIGrV6+SkJCQUjA6evQoRYoUoUKFCjRu3JjJkydz8eLFVOPfJCYm0qJFC/73v/9hZ2eHg4MDANevX2fLli20adOG5cuX89RTT3HhwgWKFi2KyWQiMjISBwcHWrRowaRJk2jYsCEuLi6pMv38889MnDiR4OBgACIiIggPTzUaSIqLFy/i4uKChcV/bQsqVqzIhQupJw168BwPKlPmv7+5HBwcuHjxYqaPn96xS5f+75pqb2+fZjkiIuKReR7FycmJ8PBwli9fTp06dahWrRqHDh3i9u3b/PXXX1hZWdGuXbuU7Y8fP06NGjUe+3xCZMXhzcsoveV96umb7CzXn3r9x2FnXyhHz5ndYxaloZT6DPDXWv8JzAF+UUqdBm4AvXPinF3qlqdL3fKsWbOGjh2fSVnf1rM0o5YFMvSX/bxQpxxjO3lSvJBNTkQQIlsNHjyYNWvWUKpUKY4cOZKpfVxLFmPbuLcy3M6i4/AnjZfG+jO3MrVdu8pFs/3ckfGmTG9byPrhjSsLObti49kjzfrwcP/HyiWEKDgu3Y5m0Lx9nL4awcSedehWr0Kq50e3q86uM9cZtSyQdW+3oJRj9jcNF0LkbZ6enlhaWrJq1aqUMYuKFCmS8nxMTAyFCxfG3d2doKCgVPvu37+fb7/9NmVsH4CzZ89SvHjxlHVPPfUUhw4d4tatW7z//vtUq1aNV199NdVx7p9H6Pz58wwZMoRNmzbRpEkTLC0t8fb2TpmV7cE5h8qVK0doaCgmkymloBMSEkK1atUeeo6syMzxs3kepAw5OTlx9uxZJk+ezGeffQYk/ZvduXOHH374gbfeeitVpujoaOzs7Bg3bhyRkZEp+wiRne7cusap+W/ic2s95yxcufn8PJrWb5Ur587W2dDu0Vr7aa07Jv88JrlQhNY6Rmv9ota6ita6odb6bE6c/54//vgjZUAyAM9yTqx6sxnvtanG+iOXaDNxC2sPX8rJCEJki4EDB0prllymLCyxti+c5iGEMG8nL9+l2/SdhN2MZv6ghmkKRQB21pb80KcuEbEJvPfbIUxZafIohCgQChcuTJ06dRg2bBjLly/n7t27mEwmXn31VS5evMjGjRuZMWMGjo6OjB8/nujoaBITEwkMDGTIkCFMmDAh1fGqVKnC9evXiYiIICEhgSNHjqC1Zvz48Rw/fpwTJ07g7e2Nl5dXup8ZIyMjUUpRsmRJAObNm5fqC8jSpUtz9ux/f5o1atQIBwcHvvnmG+Lj4/Hz82P16tX07p093/Xn9PEfh5OTE1u3buXu3bu0bdsWSCoWBQQEsG/fPgYOHJiy7dWrV3F0dGTQoEG4urry+eef53pxSxR8gZsWEzupAd43/2Fn+UGUHbUHj1wqFEEOFYvygiNHjjB37tyUGQHusba04K3WVVn9VnPKFrVj2KIDvLHoAOERsQYlFSJjLVu2pHjx4kbHMCtKKSxt7NI8hBDma9eZ6/SYuROT1vw2tAnNqzo/dNuqpR35X8eabAsKZ+6Oc7mYUghhpEaNGuHt7c2rr77K2bNnsbOzo3///pQsWZLSpUtz+PBhZs6cyeDBg5kxYwZr1qwhICAAd3d3nJ2d6dy5M/Xr16dChdSF6GLFivH888+zfft2WrRogbu7O40bNyYgIIANGzYwZcoUbG1tGT16NEOGDMFkSt3SumbNmrz33ns0adIkJUezZs1Snv/www/54osvKFq0KBMmTMDGxobVq1ezbt06nJ2dGTZsGD///HO2De6c08d/HE5OTly8eDHV349FihTh0qVLDBw4kMKF//vS8PDhwxw4cIBKlSqljBElRHa5c/0q/t+/SO1tr3HXoghnu/xJ0yGTsLN3yNUcOd4NzShz5szBwsKC5cuXM2jQoDQzDlUvU4Q/hjVj1tazTN4YxK6z1xnbyZOOtctKVVgIARaWWKXXkuhWricRQuQBfx66yMjfDlGxhAPzBzekfNH0B3O9X99Grmw5dY3x60/QuFIJvMo75UJSIfI3pZQl4A9cuNdT4b7nXIEFQFHAEvhAa70210M+wp49ewDw8/Nj/vz5zJ8/P93tQkJC6NChA2PHjk01qHPfvn35+++/cXNzA2Dz5s188MEHjBs3LtV2s2bNwtLSkm+++YbXXnuNRo0aMWjQIADmz5/PuHHjaNCgQapzfvnll3z55Zfp5uncuXOamds8PT3ZsmXLQ1/rvbGPMvv8p59+munjp3fsB9ctXJh6LqVXX301TVe8rOjdu3ealk2enp6kN8Tu4cOHmTJlCvPmzSMwMJDatWs/9nmFuN+hDQupsOP/qKPvstPlVeq//AW2thl/5sgJBbJlUWJiIgsWLMBkMhEVFUWfPn2IiopKs521pQVvPF2FNcOb41LMnrcWH+T1hQe4dldaGYn8adasWfj4+ODj40P4nUij4+RrysICa7vCaR5CCPOitWbW1jMMX3wQb9eiLH+taaYKRZDUQnF899oUL2TDiCUHiYpLyOG0QhQII4DjD3nuY+A3rXVdksY+nZ5rqbLB/WMTrVq1Kt1WNIsWLSIkJITg4GAmTJhA//79GTduHJDU9Qng5s2bTJ8+PaUw0qVLF/z8/ICkWdhOnTpFpUqVcvjVmLfDhw9Tr1495s+fz4gRI7hx44bRkUQ+dzv8EvsndqPOjje4ZVGM893X0PTV7wwrFEEBbVnk5+dHfHx8yvLt27d5//33mTp1arrbVyvtyIrXmzJ7+zkmbjjF7u+3MLaTJ53qlJNWRiJf8fX1xdfXF4B6ldOOoyEyTylLrKQ4JIRZSzRpPl9zjPk7g3m+dlkm9qyDrVXWZnEsXsiGiT296TdnD5+vOc7X3WrlUFoh8j+lVAXgeeBL4N10NtHAvVGinYCLuRQtW3zwwQecPHkSCwsLKlasyMyZMwHw9/dn5syZzJ49+5H7jxgxgkOHDgEwZsyYlMGgn3vuOf755x9q1qyJpaUl3377LSVKlMjZF5MPPdjT5MHlrJgzZ07Kz5s3b86RcwjzEfD3Alx2/Y9aOoKdrr749PsCG1tbo2MVzGLRjz/+mGraxOjoaObOncvLL79Mo0aN0t3HytKC156qzLM1SjNq+SFGLAlgTeAlvuziRakiMk6JEOZGWVik3w1NCGEWYuITeXtJAOuPXmZIC3c+bF8DC4vH+wKpWRVnhraszMwtZ3iqmjPtvMpmc1ohCoxJwPuA40Oe/xT4Ryn1FlAIePZhB1JK+QK+AK6urtkaMjNatWqVplCwYsWKdLf18fFJt1A0cODAVIMq398N7X5KKSZOnMjEiRMfO685yI1CjhSLRFbcvHaRcz8Po97dzQRZVuZml99oWqux0bFSFLhiUVRUFGvWrEmzPjo6ml69enHy5ElsH1Glq1KqMMtfa8rc7eeY8M9J2ny/lU9eqEnXuuWllZEwTJ8+ffDz8yM8PJwKFSowduxYXnnllUfv5FQKi47DH70N8I5DjUzneO7wrkxt165y0UwfM7sVss5879rIeNNDn1NKYWWdtRYEAKGhofTv358rV66glMLX15cRI0Zk+ThCCOPcjIzj1Z/9ORBykzEdazK4ufsTH/PdNtXYeSac0SsOU8elKGWdjGtWLkRepJTqCFzVWu9XSrV6yGZ9gPla6++UUk2AX5RSXlrrNDd0rfUsYBaAj4+PTEkohMg7tObg+nm47fkELx3FTrdhNOj7KdY2xrcmul+BKxb98ccfWFqm/wfetWvX+OSTT1L6/T6MpYViSMtKtK5RiveXB/Lub4dYE3iJr7rWooyTtDISue9h3ySJnKOUwsY265dIKysrvvvuO+rVq8fdu3epX78+bdq0oWbNmjmQUgiR3UJvRDFg3l7CbkYz7aV6dKiVPa2AbKwsmNy7Ls9P2cbbSwL4dUhjLB+zpZIQBVQzoJNSqgNgBxRRSi3UWve7b5tXgHYAWutdSik7wBm4mutp0+Hs7JwyMHVeFhkZSaFChYyOkSUFKXNwcDDh4eEGJBJ5wfUroYT8Moy6EVs5ZVmVm91n0LRmg4x3NECBKxZNnz49VRe0+0VFRTFlyhT69OlDnTp1MjxWpZKFWTq0CfN3BvPt3ydo8/0W/texJi/WryCtjIQo4JQFWNtlvWVR2bJlKVs26Y9LR0dHatSowYULF6RYJEQ+cDjsNoPm7yM+0cSiVxvRwK14th7f3bkQYzt5Mmp5IDO3nOGNp6tk6/GFyM+01h8CHwIktywa+UChCCAEaA3MV0rVIKmodC0XYz6Sm5sb/v7+RsfIkJ+fX77rHlWQMvv4+OR+GGE4bTJxcN0c3PeNpaaOYWelt2jw0hisrW2MjvZQBWo2tEuXLrF///5HbnOvO1pCQuZmJLG0ULzS3J31I1pSo0wR3l8eyMB5+7h4Kzo7Igsh8iilFNa2VmkeWREcHMzBgwcfOlaaECLv2HzyKr1m7cLWyoIVrzfJ9kLRPT3qV6Bj7bJM3HCKgyE3c+QcQhQkSqnPlFKdkhffA4YopQ4Bi4GBOr15zYUQIg+5fimEQxM6Um/fSK5aleNSn39oOuCLPF0oggJWLFq4cGGmWvyEhoZm2BXtQW7OhVji25ixnTzZe+4Gz32/laX7QpD7kxAFk7JI6ob24OPatWv4+PikPGbNmpXu/hEREXTv3p1JkyZRpEiRdLcRQuQNS/eF8OoCf9ydC/HHsKZUKfWwsXWfnFKKL7vWokwRO0YsCeBuTHzGOwlhZrTWflrrjsk/j9Fa/5n88zGtdTOtdR2ttbfW+h9jkwqRvr3nbpBgkr8TzZ02mdj/5wysfmxM9ci97Kr8NpVH78Ctej2jo2VKgeqG9uOPPxITE5PhdlFRUYwdO5ZevXpRtWrVTB/fwkIxoKkbT3uU4v0Vhxi94jBrAi8xrnttyheVgSqF8WbNmpVSvJC+0E9GKbBKZ7DskiVLZtjEPD4+nu7du9O3b1+6deuWUxGFEE9Ia82kjUFM3hREy2olmd63HoUfY6yyrHKyt2ZSb296/biLT1YdZWIv7xw/pxBCiJwXE5/I+PUnmLcjmN4eNg+frk8UeOEXg7mwcCj1o3Zz3LoGDj1m0sTD2+hYWVJgWhYdOXKEsLCwR25jYWGBk5MTHh4etG7dmtjY2Mc6l2sJB359tTGfd/Fi//mbtJ24hUV7zksrI2E4X19f/P398ff3x9nZ2eg4+drjdkPTWvPKK69Qo0YN3n333VxIKoR4HPGJJkavCGTypiB61K/AnAE+uVIouqeBW3HeeqYqvx+8wMqDF3LtvEIIIXLGict36Dx1B/N2BDOwqRvPuBaodhkik7TJhP/KH7CZ1ZRqkQfYVXUk1UZvp2I+KxRBAWpZdODAASwtLalYsSIuLi5ERkZy6NAhTKb/ZtL09fVlxowZ2XI+CwvFy40r0qpaST74PZD/++MIaw9fYly32rgUd8iWcwghjGNhoShsl/YSeSuD/Xbs2MEvv/xCrVq18Pb2BuCrr76iQ4cO2Z5RCPF4ImMTGLboAFtOXWN466q882xVQyaueOuZKmw/Hc7HK49Qz7UYriXk84MQQuQ3JpNm/s5gxq0/QRE7a+YNasDTHqXw88szY6+LXHI17CyXFw3FJ3ovx6y9KNxzBk2q1jY61mMrMMWi/v37079//5TlhQsXMmTIkFTd0oKCgrL9vC7FHVj4SiMW7w3lq7XHeW7SVj5sX52+jSpiIVPiCpFvWVooHNMpFmWkefPm0spQiDzs6t0YBs/fx/FLdxnXrRa9G7oalsXK0oJJvbzpMHkbI5YeZNnQJlhZFphG30IIUeBdvRPDyOWBbD11jWdrlGJc99o4F7Y1OpbIZUmtiaZQPXAcVbSJXR6jadhrNJaWWZ9ZOS8pMMWiB7m6umJhkfoDV2hoaI6cSynFS41cecqjJB+sCOR/q47y1+FLjO9em4olCuXIOYXILs8d3pXpbWuVyt7fZ6sjGzK9bYJXm2w9N0DhdMYkusdSKQrbWWf7OYUQxjlzLYIBc/dyPSKO2f19eLp6KaMj4VLcgS+71WL44oNM2RTEu209jI4khBAiE/45epkPfj9MVFwCX3Txom8jV0NaqQpjXQkJ4uqvQ2kQs58jNrUp2msmTSp7Gh0rWxTYr69cXFxITExMte7y5cs5es7yRe35eXBDvulem6MX7tBu0jbm7ziHSUbCFyLfUQrsrS3TPIQQ+dP+8zfoPmMnMfGJLB3aOE8Uiu7pVKcc3etVYOrm0+w5e93oOEIIIR4hKi6Bj/44jO8v+ynrZMeat1rQr3FFKRSZGW0ysW/FdxSa04JK0UfYXf0jao72o0IBKRRBAW5ZVL58eeLjU09HGxkZSWxsLLa2Odc0UClFzwYutKjmzIe/H+bT1cdYe/gy3/SojZuztDISIr943G5oQoi8Z/2Ry4xYcpByRe1ZMKhhnhwbaGxnT/zP3+CdpQGsG9ESJwdp2SiEEHnN4bDbjFh6kHPhkQx9qhLvtfHAxqrAtr8QD3H5/EnCfx1Kg9iDHLH1pljvH2lcqbrRsbJdgf3NtrGxwc7OLtU6Ozu7DGdMyy5lneyZN7AB3/aozfHLd2g3eSuzt50lUVoZCZEvWKikAa4ffAgh8pcFO4N5fdF+apYrworXm+bJQhFAYVsrJveuy9W7sXz4R6CMfSaEEHlIokkzw+8MXafvICo2kUWvNOLD9jWkUGRmtCmRvb99Q5G5LXCPOc5uzzHUHL2Z8gWwUAQFuGURQLFixYiKikpZtrKyIjQ0lMqVK+fK+ZVSvOjjQouqJfm/Pw7zxV/HWXckqZVR5ZKFcyWDMC+zZs1i1qxZAISHhxucJn+ztFC5Oo22ECJ7mUya8etP8OPWs7StWZrJvetib5O3u5J6uxTl3bbV+Gb9SZb5h9GzgYvRkYQQwuxdvBXNO0sD2HPuBs/XKsuXXb0o6mBjdCyRyy6eO86tJb40jA3ksF09SvSZSWO3gj3OYIEuhZYqlXo8gsTEREJCQnI9RxknO2YP8OH7XnU4fTWCDpO3MWvrGWllJLKdr68v/v7++Pv74+zsbHScfM1CgYO1ZZqHECLvi01I5O2lAfy49SwvN67IjH7183yh6J6hLSvTpFIJPvnzKGeuRRgdRwghzNqawIu0m7SVIxdu822P2kx9qa4UisyMKTGRvUu+ouj8p3CJCWK311i8Rm+iXAEvFEEBLxaVK1cu1XJUVJQhxSJIamXUtW4FNrzTkpbVSvLV2hP0mLmT01fvGpJHCPFoSilsrSzSPIQQedvt6HgGzN3Ln4cuMrpddT7r7ImlRf4ZdNTSQjGxVx1srS0YseQgcQkmoyMJIYTZuRsTz3u/HeLNXw9SqWRh1o5owYs+LjKItZm5eOYoJ8Y/RcMT4wmyr0Xkq9tp3ONtlIV5/E1QoF9l2bJlsbH5r/JrMpk4deqUgYmgVBE7Zr1cn8m9vQkOj6TDlO3M8DtDQqJ8GBQiL5GWRULkPxdvRdNz5i72n7/JpF7evN6qcr78YF/WyZ5x3Wpz5MIdvvvnpNFxhBDCrOw/f5MOU7bxx8EwhreuyrLXmlCxhExUZE5MCQns/fVziv3cigqxZ9hb+wtqv7+BMi5VjI6Wqwr0gBzOzs7Y2dkRFxeXsu706dMGJkqilKKzd3maVnbmfyuPMH79CdYfucS3L9ahWmlHo+OJAuJObCLrz9zKcLt2lYtm/7mnfZCp7Yq8MS7Tx7Q6siFT2yV6tcn0MR/VEdRCKSkOCZGPnLh8h4Fz9xEZm8CCQQ1pWiV/d8Vt51WGlxq58uPWs7SoWpLmVfP36xFCiLwuIdHE1M2n+eHf05R1suO3oU3wcStudCyRy8KCAon4bSgN448R4NCIsv1m0rB8JaNjGaJAtywqWbJkmnUXLlwwIEn6SjraMqNfPaa+VJfQm9F0nLKdqf8GES+tjIQwXFLLIos0DyFE3rPzdDgvztgFwG+vNcn3haJ7/vd8TSqXLMS7vwVwPSLW6DhCCFFghVyPouePu5i0MYgXapdl7YgWUigyM4kJCexZ+CnOC5+hbPx59nh/RZ1R6yltpoUiMINi0f2tigCuXr1qUJr0KaXoWLscG95pSRvP0kz45xRdp+/g+KU7RkcTwqzda1kk3dCEyNtWBVxgwLy9lC1qx+/DmlKjbBGjI2UbextLpvSpy62oeEavCERrmRhDCCGyk9aaFfvD6DBlG0FXI5jc25tJvetSxM7a6GgiF4WcCuD0+GY0Ov09xx18iB2yi0Zd3jCbsYkepkC/eicnJxISElKtS0xM5Pbt2wYlergShW2Z9lI9ZvStx+XbMXSaup0pm6SVkRBGUYC1hUWahxAib9BaM8PvDCOWBFC/YjGWvdaUckXtjY6V7TzLOTG6fXU2Hr/Kwt3njY4jhBAFxu2oeN5afJD3lh2iZtkirBvRgs7e5Y2OJXJRYkI8e375H6UXPUvp+DD21R+P96i1lCpf0ehoeUKBHrPIwsKC4sWLp2pNZGdnR2hoKE5OTgYme7j2tcrSqFIJPv3zKBM3nGL9kct8+2JtPMvlzbxCFFQWSmFjmf8GxhXCHCSaNJ/+eZRfdp/nhTrlmPBibWytCm7Lv8HN3NgWdI0v/jpOQ/cSeJSR8Q1FwaWUsgT8gQta647pPN8T+JSkoQcPaa1fyt2EoiDYffY67y4N4OrdWEY958FrT1XOVzNniicXcsKf2OWv0yjhFPsLtcD15ek0KOtqdKw8pcB/TV6uXLlUy0opQkNDDUqTOcUL2TClT11+fLk+V+/G0nnqDiZuOCXT54oMzZo1Cx8fH3x8fLhz87rRcfI1BdhYqjQPIYSxouMSeW3hfn7ZfZ6hT1Vici/vAl0ogqTPLt/2qIOjnRXDFx8kJj7R6EhC5KQRwPH0nlBKVQU+BJpprT2Bt3MxlygA4hJMjF9/gj4/7cbW2pIVrzfljaerSKHIjCTEx7FnwUeUWfwczgmX2NfgO+qN/JOSUihKo8AXi9zd3VMtx8fHExISYlCarHnOswwb323JC3XKMWVTEJ2mbufIhbzXhU7kHb6+vvj7++Pv70+RYiWMjpOvKfX4xaL169fj4eFBlSpVGDcu8zO+CSEe7UZkHC/N3s3G41cY28mTD9vXwMJMPuCXdLRlwot1OHnlLuPWnTA6jhA5QilVAXgemP2QTYYA07TWNwG01nlrMFKRp525FkH3GTuZ4XeGXj4urHmrOXVcihodS+Si4GN7CR7fhEbnphFYuDmJr++hwfOvmv3YRA9T4N+VatWqpVqOjo4mODjYmDCPoaiDDd/38mZ2fx9uRMbRedoOJvx9ktgE+VZRiJykACsLleaRkcTERN544w3WrVvHsWPHWLx4MceOHcv5wEIUcOevR9J9xk6OXbzDjL71GdDUzehIua6VRykGN3Nn/s5g/j1xxeg4QuSEScD7wMOa01cDqimldiildiul2uVaMpFvaa1ZvDeEjlO2E3ozipn96jOue20K2RboEVnEfeLjYtk9bzTllrajeMJV9jeaRP2RK3EuLWNUPUqBLxa5u7tjb596wMuTJ08alObxPVuzNBveeYqudcszdfNpXvhhO4dCbxkdS4gC63FbFu3du5cqVapQqVIlbGxs6N27N6tWrcqFxEIUXIdCb9F9xk5uRsXx65BGtPMqY3Qkw4xu70GNskUYuSyQq3dijI4jRLZRSnUErmqt9z9iMyugKtAK6AP8pJQq+pDj+Sql/JVS/teuXcvuuCKfuBEZh+8v+/nw98PUr1iM9SNamvU9xBydPbyLkPGNaXx+JoFFnoJhe6jffhBKmUfL5CdR4MupLi4uWFtbEx0dnbLu3LlzBiZ6fE4O1kx4sQ7P1y7LhysO03X6DoY+VZkRratiJ1N6iwcUsbWkXeWi2XpMqyMbMnfuNzLX9WrMxjOZPvdnz7bJ9LaZtf7MrYc+px4ywPW1a9fw8fFJWfb19cXX1zdl+cKFC7i4uKQsV6hQgT179mRPYCHM0L8nrvDGooOUKGzDgsENqVyysNGRDGVrZcmU3t68MHU77y07xIJBDc2mK54o8JoBnZRSHQA7oIhSaqHWut9924QBe7TW8cA5pdQpkopH+x48mNZ6FjALwMfHR+d4epHnbD11jfeWHeJ2VDwfP1+Dwc3c5XppRuLjYtj/y8fUD5nLbeXIgcY/4NOuv9Gx8pUCXyxydXVF69T3h4sXLxqUJns87VGKf95tyZdrjjPD7wwbjl3h2x61qetazOhoQhQYSpuwSIhNs75kyZL4+/sbkEgI87N4bwgfrzxCzbJFmDPQh1KOdkZHyhOqlnbkfx1r8n9/HGHujnO82qKS0ZGEeGJa6w9JGrwapVQrYOQDhSKAlSS1KJqnlHImqVva2dxLKfKDmPhEvll/krk7zlG1VGEWDGpIzXJFjI4lctGZwO2oVW/QODGYfU5tqNJ/KvWcpUVZVhX4bmguLi7ExKRupn3jxg1Mpvw9s1gRO2vG96jNgsENiYpNoPuMnXy99rjMkCJEdtEalRCb5pGR8uXLp5pxMSwsjPLlpT+0EFmhtWbiPyf58PfDNK/izBLfxlIoesBLDV1pW7M049efkMkvRIGmlPpMKdUpefFv4LpS6hiwGRiltZbpX0WKk5fv0mXaDubuOMeAJhVZ/VZzKRSZkbiYaHbPfoeKK17AMfE2B5rNoMG7yykmhaLHUuCLRU5OTlhapu6iZWVlxdWrBWPyhKeqleTvd1rSq4ErP249S4fJ29h//obRsYTI/7QJEuLSPjLQoEEDgoKCOHfuHHFxcSxZsoROnTpluJ8QIkl8oolRywOZ8u9pevpUYPYAHxmENB1KKcZ3r03xQjYMX3KQqLgEoyMJkW201n5a647JP4/RWv+Z/LPWWr+rta6pta6ltV5ibFKRV2itmbfjHC9M3U54RCzzBjZgbGcvGarDjJwO2MrFbxrSOGwuB4q2xWb4Xuq1ecnoWPlagS8WQVK3kfvZ2Nik+uY/v3O0s+brbrVY+EojYhNM9Ji5i8/XHCM6TloZmZtZs2bh4+ODj48P4eHhRsfJ3x6zZZGVlRVTp07lueeeo0aNGvTs2RNPT89cCCxE/hcRm8Dg+ftYvj+Mt5+tyvjutbG2NIuPKo+lWCEbvu/pzbnwSD5fI7MuCiHM09W7MQyct4+xq4/Roooz699uydPVSxkdS+SS2JhI9swajtsfnXEwRRDQYhYN31mKU3H5HXhSZvFVnYuLS5riUEhICA0aNDAoUc5oXtWZv99pyfh1J5iz/Rybjl/hmx51aOhe3OhoIpfcP9hyvXr1DE6Tz2kTKuHxZhrq0KEDHTp0yOZAQhRsV+/EMGj+Pk5cvss33WvTs4FLxjsJmlZx5rWnKjPD7wwtq5akfa2yRkcSQohcs+HYFUavCCQyNoHPO3vSr3FFmeXKjATt34zNX2/RyBTKnmIdqN7/B7yLOxsdq8Awi6/rKlVKPfBjTExMgWpZdL/CtlZ83sWLX4c0IlFres3axad/HpXm6UJkkdIaFR+b5iGEyH6nr96l6/SdnAuPZM4AHykUZdG7bapRp4ITH/x+mIu3ojPeQQgh8rnouET+74/DDPnZnzJF7PhreHNebuImhSIzERMVwZ4f36DSn12xM0VxqNVcGr29GCcpFGUrsygWeXh4YGHx30uNi4vj9OnTBibKeU0rO7N+REsGNHFj/s5g2k3axu6zMv6fEJmmTaj46DQPIUT22hd8g+4zdhGbYGKpbxNaeUiz8ayytrRgcu+6xCeaeGdpAIkmmSVcCFFwHblwm+d/2Mave0MY2rISf7zRlCqlHI2OJXLJyX0buTqhIY0uLcS/+AvYv+NPnVbdjY5VIJlFNzRXV1ccHByIiIhIWRcUFGRgotxRyNaKTzt50s6rDKNXBNJ71m76N6nI6HbVZbBQ8VgSvNpkajurIxsytd1nz2bueADW4ZmbGXf17cx3u2xXuehDn/tSa3Tc43VDE0JkzrrDlxixNIAKxexZMKghLsUdjI6Ub7k5F+Kzzl6MXHaImVvO8MbTVYyOJLJDXBRcPAhuzYxOIoThTCbNrG1n+e6fk5QoZMuiVxrRtIq0JDEXMVF3OfTzKBpcWsJVVYLAp+fT6KmuRscq0MyiYuDi4pJmRrTz588blCb3Na5UgnUjWjDh71PM23mOf09c5ZvuteXiKsQjaFMipuhIo2MIUWDN3X6Oz/86Rj3XYszu70OxQjZGR8r3utcrz5ZT15i44RRNK5egrmsxoyOJxxUfDf7zYPv3EBcB7x4De/n3FObrerSJvrP3sOvsddp7leHrbrUo6iD3DXNxcs96Cq1/m0b6Erudu+DZ/3tqO8m4vDnNLLqhubq6kpCQesyey5cvG5TGGA42Vox5oSbLhjbB2tKCl2bv4f/+OExErIxlJES6tAkdE5XmIYR4MiaT5os1x/hszTHa1izNolcbSaEomyil+KKLF2WK2DFiSQB3Y+KNjiSyKiEW9syCyd7w94dQ0gP6rTC7QpFSqp1S6qRS6rRS6oN0nrdVSi1Nfn6PUsrtvuc+TF5/Uin13L3169evx8PDgypVqjBu3LiUY/Xt2xcPDw+8vLwYPHgw8fFJ/99orRk+fDhVqlShdu3aHDhwIEdfs3i4vwIvMWZnNIfCbvFNj9pM71tPCkVmIjriDnumD6Hq2t4oTBxu/QuN31qAoxSKcoVZFIsqVKhAdHTqsUbu3r1LXFycQYmM4+NWnLXDWzCkhTu/7g3hue+3si3omtGxhMh7TCZM0ZFpHkKIxxcTn8hbSw4ye/s5BjZ1Y3rf+thZW2a8o8g0J3trJvf2JuxmFJ+sOmp0HJFZCXGwbw5MqQvrRkHxSjBgNQxcAxWbGp0uVymlLIFpQHugJtBHKVXzgc1eAW5qrasA3wPjk/etCfQGPIF2wHSllKXWmjfeeIN169Zx7NgxFi9ezLFjx4CkYtGJEyc4fPgw0dHRzJ49G4B169YRFBREUFAQs2bN4vXXX8/5Fy9SiYhNYOSyQ7zx6wFKO1iwdngLevq4yCDWZuL4rrXc+M6HRld/Y2/Jbji9u5daLToZHcusmEWxyNbWlsKFC6daZ29vz4ULFwxKZCx7G0v+7/maLH+tKbbWFrw8Zy8f/h7IHfkGUogU2mRCx0SmeQghHs/tqHj6z93LX4GX+KhDdT55oSaWFvKBPyf4uBVneOuq/H7wAisPmudnnXwjMR72L4Af6sFf70KR8vDyShi0FtxbGp3OKA2B01rrs1rrOGAJ0PmBbToDC5J/Xg60VkkVhM7AEq11rNb6HHAaaBgZGUmVKlWoVKkSNjY29O7dm1WrVgHQoUMHlFIopWjYsCFhYWEArFq1iv79+6OUonHjxty6dYtLly7l+IsXSQ6E3KTD5G38fiCM4c9U4aNGdrg5FzI6lsgFURG32DttMDX+7oNGcaTtrzR+cy6FHYsaHc3smMWYRQClS5fmzp07KctWVlaEhobi7u5uYCpj1a9YjLXDW/D9xlP8tPUsfiev8XW3WjITTT42a9YsZs2aBUB4eLjBafI5bULHygDXQmSHC7eiGTh3L+evRzGlT1061SlndKQC782nq7A9KJyPVx6hnmsxXEvI4OF5SmICBC6FLePh1nkoXx86ToIqrUFaTZQHQu9bDgMaPWwbrXWCUuo2UCJ5/e4H9i0fHx+Pi4tLysoKFSqwZ8+eVAeMj4/nl19+YfLkyQBcuHAhzT4XLlygbNmyqfa7/7NXWFgYfn5+WXy5uS8iIiLP5kw0adacjWfVmXiK2So+aGhHNZtLRERE5tnMD5OX3+e86uj21RTf9C4+pmvsLtWTWv0nUMHRyehYZstsikUVK1ZMNQNaYmIiISEhBibKG+ysLfmwfQ3ae5Vl1LJDDJy3jxfrV+DjjjVxsrc2Op7IIl9fX3x9fQGoV6+ewWnyOZOJxGgZo0iIJ3X04m0GzdtHdHwiCwY3pEnlEkZHMgtWlhZM6u1N+8nbGLH0IMuGNsHK0iwalOdtpkQ4vCypSHTjLJStAx2+haptpUhksGHDhtGyZUtatGiRpf3u/+zl4+NDq1atciBd9vLz88uTOUOuR/HObwHsPx9FF+9yfNbFiyJ2SX+P5NXMj5IfMxsl8s5Nji54m4bXVxKqynG83RIaN2lndCyzZzafGqpWrZpqOTo6OlPFoge7r6Xn1VdfTen3/NVXX6V6rmnTjPuZZ+YcOc3bpSir32rOsFaV+f3gBdp+v4V/T1wxOpYQhtEmTUJkTJqHECLztgVdo9ePu7G0UCx/rakUinJZhWIOfNm1FgdDbjFlU1DGO4icY0qEw8thWiP4YyhYF4Lev4LvFqj2nBSKUrsAuNy3XCF5XbrbKKWsACfg+sP2tba2JjT0v8ZKYWFhlC9fPmV57NixXLt2jYkTJ6asK1++/CP3EdlHa83vB8LoMGUbpy7fZXJvbyb1rptSKBIF25Ftq7j7fQN8wlexq3QfnEfuxVMKRXmC2RSLqlSpgo3Nf6PmJyYmcurUqWw59uzZs6lZM2ncvQeLRTt37syWc2TVg7O/ZYadtSXvt6vOH8OaUtTehsHz/Xn3twBuR8lYRsL8aJOJ+MjoNA8hROb8fiCMQfP2UaGYPX8Ma4ZHGUejI5mlTnXK0aN+BaZuPs2es9eNjmN+TCY4+gfMaAorXgELK+j5MwzdCtWflyJR+vYBVZVS7kopG5IGrP7zgW3+BAYk/9wD+FdrrZPX906eLc0dqArsLVSoEEFBQZw7d464uDiWLFlCp05JA+XOnj2bv//+m8WLF2Nh8d+fRp06deLnn39Ga83u3btxcnJK0wVNPLnb0fEMXxLAu78dokZZR9aOaEFnbynKmYO7t66zd0o/vDb1Jw4bTj6/jCavz8S+kHxeyCvMphuai4sLdnZ2qWZAO3PmTKb39/Pz49NPP8XZ2ZkjR45Qv359Fi5ciFKKVq1aMWHCBJYvX050dDTe3t54enqyaNEiChcuTEREBBEREXTu3JmbN28SHx/PF198QefOD47V95/g4GDat29P8+bN2blzJ+XLl2fVqlXY29sTEBDAa6+9RlRUFJUrV2bu3LkUK1aMVq1a4e3tzfbt2+nTpw+rV6+mbt26bNu2jcjISH7++We+/vprDh8+TK9evfjiiy/SPXftCkX5861mTPv3NNP8zrA9KJwvu9aiTc3SmX/DheFMGiLjTRluV8g6+2vGiV5tsv2Yq29nborMXi//X6aPeXvntIc+pxNNxEtLIiGyTGvNdL8zfPv3SZpWLsHMl+vLt8MG+7STJ/7BN3hnaQDrRrTEyUH+PXKcyQQn1oDfOLh6FJw9oMc8qNkFLMzmu9rHkjwG0ZvA34AlMFdrfVQp9Rngr7X+E5gD/KKUOg3cIKmgRPJ2vwHHgATgDa11oo+PD1OnTuW5554jMTGRwYMH4+npCcBrr71GxYoVadKkCQDdunVjzJgxdOjQgbVr11KlShUcHByYN29eLr8TBd/us9d5d2kAV+7GMrJtNV5vVUUmPjATR7Ysp+Tm96mvb7CrXD/qvjweOwfje9uI1MymWOTq6ppmmsX7m5ZmxsGDBzl69CjlypWjWbNm7Nixg+bNm6c8P27cOKZOnUpAQECafe3s7Pjjjz8oUqQI4eHhNG7cmE6dOj1y6segoCAWL17MTz/9RM+ePVmxYgX9+vWjf//+/PDDDzz11FOMGTOGsWPHMmnSJADi4uLw9/cHYPXq1djY2ODv78/kyZPp3Lkz+/fvp3jx4lSuXJl33nmHEiXS7xJga2XJu209aOtZhlHLAxnysz9dvMvxyQueFCtkk+4+QhQkWmsSY+Iy3lAIkSIh0cQnfx5l0Z4QuniX45sedbCxkj+MjVbY1orJvevSfcZOPvwjkGkv1ZOpp3OK1nByHfh9BZcPQ4kq0G02eHUDC0uj0+UbWuu1wNoH1o257+cY4MWH7Psl8OWD6zt06ECHDh3SbP+w1vhKKaZNe/iXSuLxxSWYmLTxFDO2nKFicQdWvN4Ub5eiRscSueDOrXBOLXgLn5trOW9RgaAOv9PE5xmjY4mHMJtPcC4uLqlaFQFcvXo1S8do2LAhFSpUwMLCAm9vb4KDgzO9r9aajz76iNq1a/Pss89y4cIFrlx59JhA7u7ueHt7A1C/fn2Cg4O5ffs2t27d4qmnngJgwIABbN26NWWfXr16pTrGvSa2tWrVwtPTk7Jly2Jra0ulSpUyVSzzKu/Eqjea8fazVVkTeIk2329l/ZHLmX7dQuRX2mQiPio6zeNJjBo1iurVq1O7dm26du1KRERENqUVwnhRcQm8tnA/i/aE8Hqrykzs6S2FojykjktR3mvrwdrDl1nmH2Z0nIJHazj1D/z0NCzpA7ER0GUmDNsDtV+UQpEQyc5ci6D7jJ1M9ztDz/ou/DW8hRSKzETgv0uJndQA7xvr2VluAKVH7aW6FIryNLP5FFe6dOk0xaL4+Hju3r2b6WPY2tqm/GxpaZmlcYEWLVrEtWvX2L9/PwEBAZQuXZqYmEd3cXmc8xUqVCjdY1hYWKQ6noWFRabz21hZ8Paz1fjzzeaULmLLawv38+avB7geEZup/YXIj5LGLIpJ83gSbdq04ciRIwQGBlKtWjVWrFiRTWmFMFZ4RCx9ftrDvyeu8nlnT0a3q46FdCXIc4a2rETTyiX45M+jnLkmxepsoTXFbhyE2c/Cry9C1HXoNBXe3AfefcDSbBrxC/FIWmsW7w2h45TthN6MYma/eozvUZtCtvL/SEF3++Y1/Cf1ovZWXyIsHDnTeRVNfadgZ18o452FocymWGRpaUmxYsVSrbO3t89yV7SMWFtbEx+fdkDo27dvU6pUKaytrdm8eTPnz59/rOM7OTlRrFgxtm3bBsAvv/yS0soop9UsV4SVbzTjvTbV+PvoZdp+v5W1hy/lyrlF5syaNQsfHx98fHy4fj3c6Dj5mk7UxEfGpXk8ibZt22JllfShqHHjxly/LoPNivwvODyS7jN2cvLyHWb2q8/LTdyMjiQewsJCMbGnN7bWFoxYcpC4hIzHtRMPoTWc9YO57agT+CncvQwdJ8Gb+6Hey2Ap40I9LqWUpVLqoFJqzSO26a6U0kopn9zMJh7Pjcg4hv6ynw9/P0z9isVYP6Il7bxksHBzELjpV+ImN8D75j/sLD+Icu/vxqNeS6NjiUwym2IRQLly5VItW1hYEBISkq3n8PX1pXbt2vTt2zfV+r59++Lv70+tWrX4+eefqV69+mOfY8GCBYwaNYratWsTEBDAmDFjMt4pm1hbWvBW66qsfqs55YraM2zRAYYt2k+4tDLKE3x9ffH398ff358SJZyNjpOvaZMmLiI+zSO7zJ07l3r16mXb8YQwwsGQm3SbsZM70fH8OqQxbT3LGB1JZKCMkx3ju9fmyIU7fPfPSaPj5E/B22H+8/BzZ7gVwqmqr8HwA+AzCKxkXMdsMAI4/rAnlVKOydvsybVE4rFtPXWNdpO24nfyGh8/X4OfBzekjJOd0bFEDrt9/TL7J/ag9rbXuWvhxLmuq2k6ZBK2dg5GRxNZYFbt/tzd3QkMDExZjo+Pz7Bl0b0xRVq1akWrVq1S1k+dOjXlZz8/v5Sfx48fz/jx49Ps7+zszK5dux55jvu5ublx5MiRlOWRI0em/Ozt7c3u3bvT7HN/jgeXH8z/4LZZVb1MEf4Y1pRZ284yaUMQu85s4bPOXnSsXVYGzRQFgjZpEmLSdtW8du0aPj7/fZHp6+uLr69vyvKzzz7L5ctpx/X68ssvU2ZA/PLLL7Gyssq1VoFC5ISNx67w5uIDlHK0Y/6gBlQqKbOY5BfPeZahbyNXftx6lhZVS9K8qny5kCkhu2Hzl3BuKxQuDe2/gXoDuLhjN9WsbDPeX2RIKVUBeJ6kAarffchmnwPjgVG5lUtkXUx8It/+fZI5289RpVRh5g1qgGc5J6NjiVwQ8M8vuOz8mNr6Ljtdh+DT7wtsbKVAmB+ZVbGoatWqqZajoqI4d+6cQWnyPytLC4a1qkKbGqUZuTyQtxYfZE3gRT7v4kUpR7kgiPwtqWVR2m5nJUuWTJlxMD0bN2585HHnz5/PmjVr2LRpExs2bHjinEIYYdGe8/xv5RG8yjsxZ0ADSjrKH8r5zcfP12TPuRu8+1sA60a0oERh+Td8qNB9SbObnfkXCpWE574Cn8FgbW90soJoEvA+4Jjek0qpeoCL1vovpdQji0VKKV/AF5JmRRa559SVuwxffJATl+/Sv0lFPupQAztrGeS9oLt17RJnfh5G/bv/csayEjc7L6Fp7SZGxxJPwKyKRZUqVcLe3p7o6P9mNDp16pSBiQqGqqUdWfFaE2ZvP8fEDado+/1WPn3Bk87e5aSVkYEsFBSyzrinaWR85sesKJyJ4wHoTB5v/ZlbmT53u8pFM7Xd7Z2Zn+b258CHz0ioTZr4yHS6nT1BD4P169fzzTffsGXLFhwcpBmuyH+01kz45yTTNp/hmeqlmPpSXRxszOqjRIFhb2PJlN516TJtB6NXBPJTfx+5Zz/own7Y/DWc3gAOJaDN59DgFbCRQVlzglKqI3BVa71fKdUqnectgInAwMwcT2s9C5gF4OPjk9mPJuIJaK1ZsDOYr9adoIidFfMGNuDp6qWMjiVywcH186i4ewy1dCQ7K76GT9/PsLGVLyHyO7P6hOfi4oKNjU2qYtHZs2cNTFRwWFla8NpTlXm2RmlGLT/E20sDWBN4ia+6elGqiLQyyg7ff/89s2fPRilFrVq1mDdvHnZ28t7mFJ2oiUu3WPT434y9+eabxMbG0qZNGwDKlCmT0jVNiLwuLsHEB78H8vuBC/Rp6MLnnb2wsjSroQ8LnJrlivBB++p8tuYYC3efl8HJ77l0KKlIdGod2BeD1p9AQ1+wla6WOawZ0Ekp1QGwA4oopRZqrfslP+8IeAF+yYXNMsCfSqlOWuuHN/kVueLq3RhGLQtky6lrPFO9FN/0qI2ztFgs8G5cCSP4l2HUi9hCkGUVbnadTlOvRkbHEtnErIpFrq6umEypW1FcvHjRoDQFU5VShVn+WlPm7TjHt3+f5NmJW/jkBU+61Ssv31g+gQsXLjBlyhSOHTuGvb09PXv2ZMmSJQwcONDoaAWWyaSJi0qnWFTs8YtFp0+fTrW8atWqxz6WELnpbkw8ry88wPbT4bzXphpvPlNFrukFxKBmbmwNusYXfx2noXsJPMqk2/vHPFw+An5fw4k1YOcET38MjYaCXRGjk5kFrfWHwIcAyS2LRt5XKEJrfRtIGWBLKeWXvI0Uigy26fgV3l8eSERsAp939qRf44pyjyjotObAurm47/0ULx3FLvc38On7KdbWMsh/QWJWxSIXFxdiYmJSrbt+/Tpaa7mgZSNLC8WrLSrxTPVSvL88kPeWHeKvw5f4qmstmf3gCSQkJBAdHY21tTVRUVFpZvcT2UsD0YkyrbQQV+7EMHDePoKu3OXbHrV50cfF6EgiGyml+LZHHdpP3srwxQdZ9WYz8xtb5Opx8BsHx1aCbRF46gNo/DrYFzU6mQCUUp8B/lrrP43OIlKLjkvky7XHWLg7hJpli7CktzdVS5txwdlMXL8SQujPr1Mvcjsnraph020GTWr6ZLyjyHfMqv140aJFsbBI/ZKtrKy4du2aQYkKtkolC7N0aBPGdKzJzjPhtPl+C7/5h6K1dBvPqvLlyzNy5EhcXV0pW7YsTk5OtG3b1uhYBVqi1kQkmNI8hDAnQVfu0nXaDkKuRzJ3YAMpFBVQJR1tmfBiHU5eucu4dSeMjpN7rp2C5a/A9CZweiO0HAUjDsHTH0qhyGBaaz+tdcfkn8ekVyjSWreSVkXGOXLhNh1/2MbC3SH4tqzEH280lUJRAadNJvavmYXljCbUiNjDzkrDqTx6B+5SKCqwzKpYpJTC2Tn19LA2NjaEhIQYlKjgs7RQDG7uzvoRLalRtgjvLw9kwLx9XLwVnfHOIsXNmzdZtWoV586d4+LFi0RGRrJw4cI0282aNQsfHx98fHwIDw83IGnBYdIQkaDTPIQwF3vOXqf7jJ3EmzRLhzahZbWSRkcSOaiVRykGN3Nn/s5g/j3x8MH/C4TrZ+B3X5jeCE6ug+Zvw4hAeOZjcChudDoh8jSTSfPjljN0nb6DiNgEFr7SiI861MDWysxaJJqZ8EvnOTTheer7j+KyVQUu9dlA0/6fYyXdzgo0syoWQVJXtAeFhoYakMS8uDkXYsmQxozt5Mm+czdo+/1WFu8NkVZGmbRx40bc3d0pWbIk1tbWdOvWjZ07d6bZztfXF39/f/z9/dMURkXWJGqkZZEwW2sCL/LynL2UdLTlj2FN8SrvZHQkkQtGt/egRtkijFwWyNU7MRnvkN/cOAcrh8HUBnDsT2jyRlJLomc/hUIljE4nRJ536XY0/ebs4et1J3i2RmnWj2hJ86ryebMg0yYT+/+cgc2PTageuY9dVd6l6gc7cKte1+hoIheYXbGoUqVKqZZjY2OlWJRLLCwUA5q68ffbLalV3okPfz9M/7l7CbsZZXS0PM/V1ZXdu3cTFRWF1ppNmzZRo0YNo2MVaCY0kYmmNA8hCrrZ287y5q8HqePixIrXm1KhmIPRkUQusbWyZEpvb6LiEnhv2SFMpgLyhc7N8/DnWzDVB46sSBq0esQhaPsFFJYWc0Jkxl+Bl2g3aRsBobf4pnttpvetR7FC0qqkILt24RyB37aj/oEPuGBdkSt9N9Gk3ydYWpnVsMdmzez+pT08PLCwsEiZFS02NjbNDEUiZ7mWcGDRq434dW8IX689znPfb+Wj52vwUkNXGWj8IRo1akSPHj2oV68eVlZW1K1bF19fX6NjFWgmLQNcC/OSaNJ88dcx5u0IpkOtMkzs6W1+Ax0LqpZ25H8da/J/fxxh7o5zvNqiUsY75VW3w2Dbd3DgF1AKfF6B5u9AkbJGJxMi34iITeDTP4+yfH8YdVyKMqmXN+7OhYyOJXKQNpnwXzUNj0NfUU0nsKvaKBr2+kCKRGbI7P7FXV1dsbe3JzIyMmVdUFCQgYnMk4WFol/jijxVrSQf/n6Y//vjCH8FXmJ899q4FJdvsdMzduxYxo4dm+ntQ0JCaNGiRap1165do2TJjL9Fzex2OXHMh233ZS6fO6aIHbMSrhIbG4u3t3fKNtK9TxREMfGJvLM0gHVHLjO4mTsfP18DCwsp3purlxq6suXkNcavP0HjSiXyXzfEOxdh20Q4sAC0hnr9ocV74FTe6GRC5CsHQm7y9pIAwm5G8dYzVRjeuirWlmbXMcWsXA07w+WFQ2kQs49j1l449vqRJlW8jI4lDGKWxSKrB6qi58+fNyiNcCnuwC+vNGTJvlC+/Os4z03aygftq9OvUUX5Q+UJpTfAtY+PD/7+GU8cktntcuKY+eXcQhQUt6LiGPKzP/uCb/Lx8zXyd0sSkS2UUozvXpt2k7cyfMlB1rzVHAebfPCR8e4V2P49+M8FnQjefaHlSCjqanQyIfKVhEQT0zafYcq/QZQpYsfSoU1o4CaDvxdk2mTC/4/J1Dg8niraxO7qH9Cw5/tYWEoLY3OWD+782cvFxYXExMRU6y5dumRQGgFJH0r7NHSlZXIrozGrjvJX4CW+6VGbiiWkmasQQuSU0BtRDJy3l9Ab0Ux9qS4da5czOpLII4oVsuH7nt70nbOHz9cc4+tutY2O9HAR12DHJNg3BxLjwLsPtBwFxdyMTiZEvhN6I4q3lwaw//xNuniX47MuXhSxszY6lshBV0JOcfXXoTSIOcBRm9oU7f0jjSvVNDqWyAPMrlhUoUIFoqNTT9t+584d4uPjsbaWC6GRyhe1Z8GgBizzD+PzNcdoN2kb77fzYEATN2llJIQQ2ezIhdsMmr+P2PhEfnmlIY0qyWxQIrWmVZx57anKzPA7Q8uqJWlfK4+N9RN5HXZOgb2zICEGavdKKhKVqGx0MiHyHa01KwMu8L+VR1HApF7edKkrXTcLMm0yse/3iXge/pbCwJ6a/0eDHu9JayKRwuyKRfb29tjb2xMREZGyzs7OjosXL1KxYkUDkwlIamXUs4ELLao589Hvhxm7+hhrD1/imx51ZDC9bJDZQbGzMnh2dh8zv5xbiPxsy6lrDFu4n6IONvz6aiOqlnY0OpLIo95tU42dp8P54PfD1HEpSrmi9kZHgqgbsGsq7PkR4iKhVg94ajQ4VzU6mRD50u3oeD5eeYTVhy7SwK0YE3t6yxiiBdyl4BPcWDyUhrEBHLb1psRLs2jk5mF0LJHHmOUIZWXKlEm1bG1tTUhIiEFpRHrKOtkzd2ADvnuxDicv36XdpK3M3naWxIIyja9BClrBRopFQmTdMv9QXpm/D9cShfh9WFMpFIlHsra0YHLvusQnmnhnaYCx9+HoW7D5K5hUO2mWs6ptYNhu6D5bCkVCPKY9Z6/TYfI21h6+xMi21Vji20QKRQWYKTGRPUvH4zSvJRVjTrLbcwyeozdTTgpFIh1m17IIksYtOn36dMqyyWQiNDTUwEQiPUoputevQPOqSa2MvvjrOGsPX+LbF+tQuWRho+MJIUS+orXmh39PM3HDKZpXcWZGv3o4yjgUIhPcnAvxWWcvRi47xMwtZ3jj6Sq5GyDmDuyZCTunQuxtqNEJWn0ApT1zN4cQBUh8oolJG08x3e8MFYs7sOL1pni7FDU6lshBF88e49aSoTSKCyTQrj4lX5pJ44rVjI4l8jCzLBZVq1aNzZs3pyxHRUXJjGh5WOkidswe4MPKgAt8+ucxOkzexrttqvFqi0pYylhGQgiRoYREE/9bdYTFe0PpVq8847rVxsbKLBsXi8fUvV55tpy6xsQNp2hauQR1XYvl/Elj7yaNR7RjCsTcAo/nk4pEZfPwYNtC5AOXI010n7GTwLDb9PJxYcwLNSlka5Z/FpoFU2Ii+34bR60Tk3HEgj21P6Nh17dQFvI5QDyaWf6GVK5cOdVg1omJiQQFBRmYSGREKUXXuhXY8G5LnqpWkq/XnaD7jJ2cvnrX6Gj5wuDBgylVqhReXl5GR8mzvv/+ezw9PfHy8qJPnz7ExMQYHUmIbBEVl4DvL/tZvDeUN5+uwncv1pFCkcgypRRfdPGiTBE7RiwJ4G5MfM6dLC4Stk+CyXVg02fg0gh8/aDPr1IoEuIJaK1ZsjeEMTujOX89ihl96zG+R20pFBVgF84c5sT4ljQ6+Q2n7OsQ+eoOGnUfIYUikSlm+VtSsWJF7OzsUq27v1uayLtKOdrx48v1mdKnLuevR9Jhynam+50mIdFkdLQ8beDAgaxfv97oGHnWhQsXmDJlCv7+/hw5coTExESWLFlidCwhnti1u7H0nrUbv5NX+bKrFyOf80ApaZEpHo+TvTWTe3sTdjOKT1Ydzf4TxEUldTWbXAc2fgJlveHVTdD3NyhXN/vPJ4QZuRkZx2sL9/PB74epUtSCv99umfdmOBTZxpSQwJ5Fn1H852eoEHeWPXW+pM77f1PGRWaLFJlnlmVkFxcXLB6opsqYRfmHUopOdcrRpFIJxqw6wjfrT7L+yGW+7VEHjzIyUGt6WrZsSXBwsNEx8rSEhASio6OxtrYmKiqKcuXKGR1JiCdy9loEA+ft4+rdGGa97MOzNUsbHUkUAD5uxRneuiqTNgbRslrJ7JlaOz4G9s+H7RMh4gpUagWtPgLXRk9+bCEE24Ku8d5vh7gZFcf/dahB5cTzlHGyy3hHkS+FBR0i8rehNIo/ToBDY8r1+5FG5d2MjiXyIbNsWeTq6kpcXFyqdVevXjUojXhcJR1tmdGvPtNeqkfYzWg6/rCNqf8GES+tjEQWlS9fnpEjR+Lq6krZsmVxcnKibdu2RscS4rEdCLlJ9xk7iYhNYIlvEykUiWz15tNV8KlYjI9XHiHketTjHyghFvb+BFO8Yf1oKFEVBq6F/qukUCRENoiJT+TzNcd4ec5eithbs/KNZgxpWQkLaWFaICUmJLBn4Sc4L2xNmfgQ9tYdR51R6yglhSLxmMyyWFSmTJk0xaK4uDgiIyMNSiSexPO1y7LhnZY851mGCf+cosu0HRy/dMfoWCIfuXnzJqtWreLcuXNcvHiRyMhIFi5caHQsIR7LP0cv02fWborYW/O7zG4jcoCVpQWTenujFIxYejDrXcET4sB/LkypB2tHQjE3GLAaBv0Fbs1yJLPIP5RSlkqpg0qpNek8965S6phSKlAptUkpVdGIjPnBqSt36TJtB3O2n6N/k4qsfrM5nuWcjI4lcsj5Ewc5M64pjU5P4lihhsT77qJh59dlbCLxRMzyt8fS0pKiRYumWmdnZydd0fKxEoVtmfpSPWb0rceVOzF0mrqdyRullZHInI0bN+Lu7k7JkiWxtramW7du7Ny50+hYQmTZL7uCeW3hfqqXLcLvrzfFzbmQ0ZFEAVWhmANfdq3FwZBbTNmUyUlCEuPhwM/wQ31Y8w4UKQsv/wGD1oF7y5wNLPKTEcDxhzx3EPDRWtcGlgPf5FqqfEJrzfwd53jhh+2ER8Qyd6APn3X2wt7G0uhoIgckJsSz5+ePKbO4DSUTLuLvM4G6I9fgXE7qqOLJmWWxCKBs2dQDullaWhISEmJQGpFd2tcqyz/vPEWHWmX5fuMpOk3dwdGLt42OJfI4V1dXdu/eTVRUFFprNm3aRI0aNYyOJUSmmUyacetO8L9VR3mmeikWD2lEicK2RscSBVynOuXoUb8CUzefZs/Z6w/fMDEBAn6FqT7w51tQyBn6LodXNkDlZ0C6xIhkSqkKwPPAIaCVUuq0UuqDe89rrTdrraOUUrZAF6CTUmqPUsrtvmN8mLzfSaXUc/fWr1+/Hg8PD6pUqcK4ceNSzjl16lSqVKmCUorw8PCU9X5+fjg5OeHt7Y23tzefffZZDr7y7HH1bgyD5u/j09XHaFq5BOtGtOSZ6tINuaA6f2wfZ8c1odHZHzhSuDEJr+3Ep+MQaU0kso3Z/ia5ubmlWo6Pj5eWRQVE8UI2TO5dl1kv1yc8IpbOU3cwccMp4hLMt5VRnz59aNKkCSdPnqRChQrMmTPH6Eh5SqNGjejRowf16tWjVq1amEwmfH19jY4lRKbEJZh497cAZm45Q99GrszsVx8HG7Ocv0IY4NNOnrgWd+CdpQHcjopP/aQpEQJ/g2kNYeXrYFsE+iyFIf9C1TZSJBLpmQR8AAwH9gI1gT5KqZoPbPcKUB74EvgeGA+QvF1vwBNoB0xXSllqrXnjjTdYt24dx44dY/HixRw7dgyAZs2asXHjRipWTNsSo0WLFgQEBBAQEMCYMWNy4OVmn03Hr9B+0jZ2nbnO5509mTuwASUd5UuDgighLpbd8z+k7NJ2OCdcwb/h99QbuZqSZVyNjiYKGLP9NOnh4cGaNf91hY6KipLZogqYtp5laOhenM9WH2PKpiD+OZo0Y1qtCubXX3vx4sVGR8jzxo4dy9ixY42OIUSW3ImJ57Vf9rPzzHVGPefBsFaVUfIHuMhFhW2tmNy7Lt1n7OTDPwKZ9lI9lNZw9HfYMh7CT0FpL+i1CKo/LwUi8VBKqY7AVZL+PrkARGmt45RSS4DOwLH7Nn8t+b/fAonAVJV08esMLNFaxyql2gDOwJELFy5Qp04dKlWqBEDv3r1ZtWoVNWvWpG7durny+nJKdFwiX649xsLdIdQoW4Qlvb2pWlpmBy6ogo/uIeH312mceIb9jq1w6z8dn1LZMCulEOkw25ZF7u7u2NmlnjLy5MmTBqUROaWogw0Te3kzZ4APN6Pi6DJ9B9/+fYLYhESjowkhxBO5dDuanjN3sffcDSb2rMMbT1eRQpEwRB2XorzX1oN1hy+ya/VcmNEUVrwCyhJeXABDt0GNjlIoEhlpBnQCVgFewDNKqYVAGEmtiABQSj0LVAP6aq1jtdYJwG2gRPJ2oQBa61kkjWv0v0KFCuHi4pJyogoVKnDhwoUMA+3atYs6derQvn17jh49mj2vMhsduXCbjj9sY+HuEHxbVmLlG02lUFRAxcfFsnvu+5T7rT3FEsM50Hgy9UeuooQUikQOMtuWRa6urtjY2BATE5Oy7uzZswYmEjmpdY3S/FOxOJ//dYxpm8+w4dgVvu1RhzoyS5AQIh86efkuA+ft5W5MAvMHNaR5VWejIwlzpjVDSx2jQ+H/UfHAOeKKVcGmx1yo2RVk7AyRSVrrD4EPlVI9gEFJq3Q/pdTL97ZRStUFfgRCgEcMlPXk6tWrx/nz5ylcuDBr166lS5cuBAWlHcx91qxZzJo1C4CwsDD8/PxyMhYAJq1ZHxzPilPxONooRvnY4elwhV3br2Rq/4iIiFzJmZ3MOfOZwF2oVa/TOPEc+4o8S5X+U6lXsmzGOwrxhMy2WHT/twv3XLx40YAkIrc4OVgz4cU6PF+7LB+uOEzX6TvwbVmZt5+tip21zBAhhMgfdp4JZ+gv+3GwseS3oU2oWa6I0ZGEudIaTq2HzV9hcTmQ8kUr8dHt4RxRrVlevSU2UigSj+cCUJKkLmkAA5PXQVK3s8KAPfCvUuoE0A1wIql4dAG4/0N+BeCCtbV1qrFJw8LCKF/+0S0yihT579raoUMHhg0bRnh4OM7OqYvzvr6+KeMc+vj40KpVq8y/0sdw6XY07/12iJ1nomjnWYavu9WiWCGbLB3Dz88vx3NmN3PMHBcbw4GF/0f9kHncVo4caDqNBm37ZV9AITJgtndxV1fXVK2KAMLDw9FaG5RI5JanPUrxz7st6enjwswtZ3h+yjYOhNw0OpYQQmRoVcAFBs7dR5kidvw+rJkUioQxtIagDfDT07C4N8TegS4zsHprH0/1eIPAi5F894907RePbR9JYw29pZSySf55PIDW+lmtdWngQ+BfrXUnoEfyzxr4E+itlLJVSrkDVYG9hQoVIigoiHPnzhEXF8eSJUvo1KnTI0Ncvnw55e+CvXv3YjKZKFGiRM684kxae/gS7SZtIyD0Ft90r82MfvWyXCgS+cPpgG1cGN+IxqGzCSjaGqs391JPCkUil5ltsahYsWJpCkMWFhZcv56jLVpFHlHEzppx3WuzYHBDouMS6TFjJ1+tPU5MvIxlJITIe7TW/LjlDCOWBODtWpTlrzWlfFF7o2MJc6M1nPkX5rSBRT0g8jp0+gHe9Afvl8DSiuc8y9C3kSs/bj3L9qDwjI8pxAOSxyB6E/gbOA78prU+qpT6TCl1r8IzByihlDoNvEvSDGporY8Cv5E0GPZ64A2tdaJSiqlTp/Lcc89Ro0YNevbsiaenJwBTpkyhQoUKhIWFUbt2bV599VUAli9fjpeXF3Xq1GH48OEsWbLEsHHhImITGLXsEMMWHcCthAN/DW9BzwYuMk5dARQbE8Xun0bg9kcnCpnucLD5jzR4ZxlFnUsbHU2YIbPthqaUomTJkqm6ntna2hISEpKmeakouJ6qVpK/32nJ1+tOMGvrWTYeu8K3L9amfsXiRkcTQggAEk2az9ccY/7OYJ6vXZaJPetgayVdZ0UuO7cVNn8FIbugSAXo+D149wOrtK0aPn6+JnvO3eDd3wJYN6IFJQrL9N0ia7TWa4G1D6wbc9/PMcCLD9n3S+DLB9d36NCBDh06pNl++PDhDB8+PM36N998kzfffDPL2bPbwZCbvL00gJAbUbzxdGXefrYa1pZm+31/gRZ0cAvWq9+ksSmEvUXb4THgB+oWL2V0LGHGzLZYBFC+fPk04xSFhoZSr149gxIJIzjaWfNV11o8X6ss7y8PpMfMXQxu5s7Ith7Y28gfZOZEa83hw4dRSlGrVi2j4whBTHwiI5Yc5O+jVxjSwp0P29fAwkK+SRa5KHgH+H0NwdvAsSx0mAD1+oPVwwtA9jaWTOldly7TdjB6RSA/9feRFhBCZFFCoonpfmeYvCmIMkXsWOrbhIbu8mVmfhMfH09YWFiq4U+++eYbjh8/nrKstYm4iFvYJMYQ2+Z7DtkVxdGuEBevXOfiFen1Ih6fnZ0dFSpUwNra+rH2N+tiUeXKldm3b1/KcmxsLCEhIQYmEkZqVsWZv99pyfh1J5iz/Rybjl/hmx515MZcwGmtWblyJcuWLWP9+vXExMRQpkwZmR1RGO5mZByv/uzPgZCbjOlYk8HN3Y2OJMxJyB7Y/CWc2wKFS0O78VB/IFjbZWr3muWK8EH76ny25hgLd5/n5SZuORpXiIIk9EYU7ywNwP/8TTp7l+Ozzl442T/eH3vCWGFhYTg6OuLm5pZSNNdaU6NGDQBiIu+gbodgW9SBCMuy2JVwxcrKrP9EF9lEa83169cJCwvD3f3xPkOa9W9itWrVUEqljF0UGxsrfyCaucK2VnzexYv2tcowekUgvWbtYkATN95v54GDjVn/71Jg3bhxg27duqVad/HiRcLCwqhQoYJBqYS5C70RxYC5ewm7Fc20l+rRoZZMkStySZh/UnezM5ugUElo+yX4DAYbhywfalAzN7YGXeOLv47T0L0EHmUccyCwEAXLHwfD+N/KoyhgUi9vutR99KxtIm+LiYlJVSi6x5SYSNSNMArF3SBBWRHl6EZhx2IGpRQFkVKKEiVKcO3atcc+hll3eHVzc8PBIfWHn1OnThmURuQlTSs78/fbLRnQxI35O4NpN2kbu85IM9CCqESJEmmq7VZWVqxfv96gRMLcHQ67TdfpO7keGceiVxtJoUjkjosHYVFPmN066ec2n8GIQ9D0zccqFEHSB9Vve9TB0c6K4YsPyiQSQjzC7eh4hi8+yDtLD1GjrCNrR7SQQlEB8WChSJsSSbhynMLxN4i0KopFqRo4SKFI5IAn7QJu1sUiFxeXNM38goODjQkj8hwHGys+7eTJUt/GKAV9ftrN/1YeITI2wehoIpt169YNS8v/xqeKjIxk2bJlBiYS5mrzyav0mrULWysLVrzehAZu0g1W5LBLgbC4D8xqBaF7oPUYeDsQmo0Am0JPfPiSjrZMeLEOJ6/cZdy6E0+eV4gCaO+5G3SYvI2/Dl9iZNtqLPFtgkvxxyvSirxPJcYCmihHdwqXdsdSup2JPMqsi0Wurq4kJqb+luvy5csGpRF5VaNKJVg/oiWvNHdn4Z7zPDdpKztOy3TABUmnTp0oVCj1H0Xbtm0jPj7eoETCHC3dF8KrC/xxdy7EH8OaUqWUdNkROejKUVjaD35skTSI9dP/B28fhhbvgW32/u618ijF4GbuzN8ZzL8nrmTrsYXIz+ITTXz79wl6zdqFlaVixetNefOZqljKRAYFmklZYVm6Bg6ORY2OIsQjmXWxqEKFCkRHR6dad+vWLRISpOWISM3expL/dazJsqFNsLa0oO/sPXz0x2HuxkgxoSBo0qQJcXFxqdZZWVmxa9cugxIJc6K15vsNpxi94jDNqjizdGgTShXJ3CDCQmTZ1ROwbCDMaApn/OCp0UktiZ56H+yK5NhpR7f3oEbZIoxcFsjVOzEZ7yBEAXcuPJIeM3YybfMZetZ3Ye3wFni7FDU6lsgFFlY2WFpKayKR95l1scjBwQF7e/tU6+zs7Lh06ZJBiURe5+NWnHUjWuDbshJL9obQbtI2tp56/EHDRN5gbW1NixYtUq2Liopi9erVBiUS5iI+0cToFYFM3hREj/oVmDPAh8K28gFS5IDwIFjxKkxvDEEboMXIpCLR0x+BfdEcP72tlSVTensTFZfAe8sOYTLpHD+nEHmR1pql+0LoMHkbwdejmNG3HuN71KaQXPtFDipcuPAjn7916xbTp0/PpTQP9+mnnzJhwgQAmjZt+shtszNzdr/+TZs28fLLLz/xcQYPHkypUqXw8vLK0nPZxayLRQClSpVKtWxtbU1ISIhBaUR+YGdtyUcdarD89abYWVvQf+5ePlgRyB1pZZSvvfjii6m6oiUmJvL7778bmEgUdJGxCby6wJ/f/MMY3roq3/aojbWl2d+WRXa7fgZ+HwrTGsKJv5LGIhoRCK3/Bw65OyZW1dKO/K9jTbYFhTN3x7lcPbcQecHNyDheW7if0SsOU69iUf5+uyXtZRIDkQc8TrFEa43JZMqhRLBz585HPp+Xi0WHDh2ibt26T3ycgQMHPnTSnUc9l13M/lOpq6trqmWTyURoaKhBaUR+Us+1GH8Nb8FrT1XmN/9Qnvt+K5tPXjU6lnhM7du3TzOG2YULF6SlocgRV+/G0GvWLrafDufrbrV4t021J56xQohUbpyDlW/A1AZwbCU0HpZUJGozFgqVMCzWSw1daVuzNOPXn+DE5TuG5RAit20PCqfd5K38e+Iq/9ehBr8MbkQZJ+lyLHJPcHAwNWrUYMiQIXh6etK2bduUIVk++OADzpw5g7e3N6NGjQJg4cKFNGzYEG9vb4YOHUpiYiLBwcF4eHjQv39/vLy82LZtG9WrV2fgwIFUq1aNvn37snHjRpo1a0bVqlXZu3dvyvnTOx7Al19+SbVq1WjevDknT55M2f7+1lBdunShfv36eHp6MmvWrCxlvl9kZCTPP/88derUwcvLi6VLl2b59VevXp2+fftSo0YNevToQVRUVJr3+l6xKDY2loEDB/LRRx+hddZb1LZs2ZLixdP/YudRz2UXs2/vWKVKFfz8/FKWo6KiOH/+vHGBRL5iZ23JB+2r086rDKOWHWLQvH28WL8CH3esiZO9tdHxRBZUqFCB0qVLp/r/38rKivXr1zNo0CADk4mC5sy1CAbM3cv1iDh+6l+fZ6qXNjqSKEhuhcDWCRCwCJQlNPSF5m+DYxmjkwFJ0/iO716blt9sZtrmM/zQ58m/eRUiL4tNSOTb9SeZvf0cVUoVZu7ABniWczI6ljDA2NVHOXbxDlFRUThsu50tx6xZrgifvOCZ6e2DgoJYvHgxP/30Ez179mTFihX069ePcePGceTIEQICAgA4fvw4S5cuZceOHVhbWzNs2DAWLVpEy5YtCQoKYsGCBTRu3Jjg4GBOnz7NsmXLmDt3Lg0aNODXX39l+/bt/Pnnn3z11VesXLnyocfz9PRkyZIlBAQEkJCQQL169ahfv36a3HPnzqV48eJER0fToEEDunfvnunM/fv3TznO+vXrKVeuHH/99RcAt28n/Ttk5fWfPHmSOXPm0KxZMwYPHsz06dMZOXJkqryBgYGUKlWK5557jldffZV+/fqler5FixbcvXs3zeucMGECzz77bKb/PXOa2ReL7vXxc3BwoFSpUri4uFCvXj2DU4n8xtulKGuGN2fKpiBmbjnL1qBrfNW1Fq1ryB+B+UmXLl344YcfUprURkZGsmzZMikWiWyz//wNXlngj6VSLPFtTB0ZzFRkl9sXYNt3cOBnUAp8BkPzd6BIOaOTpVGskA29G7owd0cwH7avTrmi9hnvJEQ+NcPvDLO3n+PlxhX5qEMN7G0sjY4kzJi7uzve3t4A1K9fn+Dg4HS327RpE/v376dBgwYAREdHU6pUKVq2bEnFihVp3LhxqmPWqlULAE9PT1q3bo1Silq1aqUc/2HHu3HjBl27dsXBwQFImqE4PVOmTOGPP/4AIDQ0lKCgIMqUSf0lyMPOcb9atWrx3nvvMXr0aDp27JhmzNLMvH4XFxeaNWsGQL9+/ZgyZUqqYlF8fDxnz56lT58+/PjjjzRp0iTN8bdt25buefMasy8WvfXWW7zyyisZDvolREZsrSwZ9Vx12nmWZeSyQ7yywJ9udcsz5oWaFHWwMTqeyITOnTszb9487tz5r2vEli1bSEhIwMrK7C+X4gmtP3KJEUsCKFfUnvmDGlCxRKGMdxIiI3cuwfaJsH8+aA31XoYW74FTBaOTPdKApm7M2X6OBTuD+bBDDaPjCJFjhrSoRF3XYjxVraTRUYTB7rUAOnbsGDVr1jQkg62tbcrPlpaWaWYGv0drzYABA/j6669TrQ8ODk41xueDx7SwsEhZtrCwSJll/GHHmzRpUoaZ/fz82LhxI7t27cLBwYFWrVoRE5N2Vs2HneN+1apV48CBA6xdu5aPP/6Y1q1bM2bMmEwfKzg4OM2wAQ8uHz9+nAYNGnDjxg0sLdMvDkvLonzCwsJCCkUiW9Wq4MTqt5oz9d8gpvudYdvpcL7s4kVbz7zRBUA8XLNmzYiLi0u1ztLSkj179qR8gyDE45i/4xxj1xzD26UocwY0oHghKSCLJ3T3CuyYBP5zITEe6vZNmuGsWEWjk2VKhWIOtK9Vll/3hjC8ddVcmQkqPj6esLCwdP/IeBJOTk4cP348W4+ZXTZs2FDr0KFDwUbnMJAJOGJkgEK2VlIoEnmeo6NjquJF69at6dy5M++8805KC6D0ihuZ9bDjtWzZkoEDB/Lhhx+SkJDA6tWrGTp0aKp9b9++TbFixXBwcODEiRPs3r07S5krVvzvvnjx4kWKFy9Ov379KFq0KLNnz87y6w8JCWHXrl00adKEX3/9lebNm6fKe+jQIZo2bUq/fv3o2rUr//77L6VLp+5tIi2LhDBjNlYWvNvWg7aeZRi1PBDfX/bT2bscn77gSTH5IzHPsrGxoVmzZmzatCllXVRUFH/++acUi8RjMZk049ef4MetZ2lTszRTeteVLgjiyUSGJxWJ9s6GxFio0wdajoLi7kYny7JXmrvzV+AllvmHMrBZzucPCwvD0dERNze3bB1Q/u7duzg6Ombb8bJTYmJigpeXV7jROYxiMpnUtWvXjGnCIUQ+UqJECZo1a4aXlxft27fn22+/5YsvvqBt27aYTCasra2ZNm1amq5fmVWzZs10j9e4cWN69epFnTp1KFWqVEq3r/u1a9eOmTNnUqNGDTw8PFK6wGU28/3FosOHDzNq1CgsLCywtrZmxowZWX79Hh4eTJs2jcGDB1OzZk1ef/31VHkPHTpEw4YNqVatGuPHj6dnz55s3LgRa+usj2fbp08f/Pz8CA8Pp0KFCowdO5ZXXnklw+eyi3qcUblzk4+Pj/b393+sfdesWUPHjh2zOZEQWROXYGKG3xl++DeIog7WfNHFi3ZeMk1qXjVz5kzee++9VDMbVKlShaCgoGw/16pVq+jcufNj76+U2q+19snGSPlSXr1PxCYkMnJZIKsPXeTlxhX5tJMnlhYy45l4TFE3YOcU2DMLEqKhVk946n0oUdnoZE+k6/Qd3IiM49/3WuX4/x/Hjx+nevXq2T7zYF4uFh05ciTKy8srbzZ7eoiEhARq1apVs0yZMnGbN28+ff9z0dHRqkePHu6HDx92KFq0aMKyZcvOenh4xD3sWJBUMGrYsGG9x71PZIWPjw+5cZ4n5efnR6tWrYyOkSX5NXPp0qWpUSN1V1sju6GJJxMcHEzHjh05csTQBotZcvz48TS/g/d71N8T0rJIiBxmY2XBiGer0qZmaUYtP8RrCw/QsXZZxnbypERh24wPIHJVhw4deOedd1KtCw0N5fLly4/9bYowP7ej4xn6iz+7z95gdLvqvPZUpWz/A1WYiagbsGsa7JkJcZHg1R2eGg0lqxmdLFu82rwSb/x6gI3Hr/BcLnTXlv8P874vvviidJUqVaIjIiLSNMOcPHmys5OTU0JISMiRWbNmFXv33Xcr/PXXX2cfdTwLC4tc+2Y8ODgYH5+8/x3OtWvXKFkyf3WNMzJzYkICsddDcEi8TayyBSdXbB0yHsbk2rVrzJs3L82U6Q8OeSBEXiXFIiFySc1yRVj5RjN+3HKGyZuC2HXmOp919uL52tLKKC9xdXWlZMmShIaGpqyztrbmn3/+STX1phAPc/FWNAPn7eVceCSTennTpW55oyOJ/Cj6FuyeAbunQ+wdqNkFWn0ApQrWYNDPeZamfFF75mw/lyvFIpG3nTlzxvrvv/92+vDDDy99//33aaaUXbNmTdFPP/30IsCgQYNujh492tVkMmFhYZH7YdMRHp4/evzllxZQ9zMq86ENiyi/4yOcNPi7jKDey19ia5u5GRx9fHwoW7bsI1t1iPzFzc0tX7UqelJ548oqhJmwtrTgzWeqsuatFpQvZs8bvx5g2KL9hEfEGh1N3KdLly6pPnhGRESwbNkyAxOJ/OL4pTt0m76TS7diWDCooRSKRNbF3IEt38Lk2rBlHFR6Cl7bAT0XFLhCEYCVpQWDmrmx99wNDofdNjqOMNgbb7zh8s0334Q9rPhz5coVG3d39zhI+iKncOHCiVeuXEn3y+8JEyY4e3l51fDy8qpx7dq1HEwtCqLb1y+zf2J36uwYxm2LYpzvtpomr07MdKFIiIJAikVCGMCjjCO/v96U99t5sPHYVdpM3MKfhy6maaYqjNG5c+c0syT++++/JCYmGpRI5Ac7T4fTc+YuAH57rQlNqzgbnEjkK7ERsG1iUpFo8xdQsRkM3Qq9FkIZL6PT5aieDVwoZGPJnO2P7E0kCrjFixc7OTs7J7Ro0SIq460zNnLkyPAjR44cP3LkyPH81uVKGOvg3z+T8ENDat/ezE4XX1xG76ZKHZnoRJgfs+6GpqzsUDYZ9zf1ru6a7ee+FZO5PzqL2mX/rDmZPXdG5w84fRUS0k4/27alD+vXr3+sbObEytKCYa2q0KZGaUYtD2T44oOsOXSRL7p6UcrRzuh4Zq158+bExqZu7WVpacnevXtp0qSJQalEXrby4AVGLT+Eu3Mh5g9qSLmi8s2jyKS4SNg3G3ZMhqjrULVtUnez8vWNTpZrithZ06uBKz/vCuaD9jUo41Sw74GWlpbUqlUrZXnlypW89NJL7Ny5M1P737p1i19//ZVhw4Zl+pzh4eGWs2fPLv7BBx9kSxObVatWOc6bN8955cqV557kOC+++KLbpk2bnEqUKJHQtm3bWxs2bChavnx5p9jYWIvIyEiLzp07u69atSrlHKVLl447d+6cTeXKlePj4+OJiIiwLF26dMKTvyLz4uvra3SELMuNzDevXeTsz8Oof3czpy0rc7PLUprWevzPffnxfRbifgWqWDR48GDWrFlDqVKlMtWXUNkUxsqjU4bb7dgxLTvipbLq5I1MbdfZo7hh587o/IWcXbHx7JFmfXh4/uoDbbSqpR1Z8XpT5mw/y4R/TtFm4lbGdvKks3c5GYjTILa2tjRu3JgtW7akrIuOjmb16tVSLBKpaK2ZseUM36w/SeNKxfnxZR+c7LM+NaowQ/HR4D8Xtn8Pkdeg8jPQ6iNwSTttsDkY1MyN+TvPsWBXMKPbVTc6To6yt7cnICAg1br0CkVaa7TWacbjuXXrFtOnT89Ssej69euWc+bMKZVdxaKDBw86eHt7P3ELoMGDB4ePGDHi6qBBg9ynTZt2Ydq0aRcA1qxZ4/jdd9+Vvr9QBPD888/fmjt3bolnn302ct68ecWaNGlyN6+MV5Sf5MciRk5nPrBuHm57xlBLR7LL7XV8+o7F2ubJJqLx9fXl+PF8NRmhEKkUqKvrwIEDpUVLLlIWlljbF07zEFlnaaHwbVmZtcNbULlkId5eGsCQn/25cidtyy2RO3r27Im9/X+tQxISEvj9998NTCTymkSTZsyqo3yz/iQv1CnHgsENpVAkMhYfA3t+hMne8PdHUKomDP4bXv7DbAtFAC7FHWhbswy/7gkhKs78Gorc6/ocHByMh4cH/fv3x8vLi9DQUJ5//nnq1KmDl5cXS5cu5YMPPuDMmTN4e3szatSoVMeJjIxMtf3atWstAd57770KoaGhttWrV685dOjQCgDTp08vXqtWrRrVq1ev+dJLL1VMSEjg5MmTNu7u7p6dOnVyr1Spkme7du0q3b17N83fC4GBgfb169ePio6OVt27d3d78803y5tMpiy/7vbt20eULFnykf/gb7/9drlFixY5AYwYMSL85s2bVq6url4//PBDmQkTJoRl+aRC3Of6lTAOTOhEvT1vc92yNGG91tNk0LgnLhQJURAUqJZFLVu2JDg42OgYZkMpC6zsCqV9Ilt6mpunKqUKs+y1pszbcY5v/z5Jm4lbGPOCJ93rlZdWRrmsffv2vPfee6nWBQcH58vpZkX2i45LZPiSg2w4doWhT1Vi9HPVsbCQ/0fFIyTEwoGfk8YlunsxaUyiHnPArbnRyfKMV1u4s/7oZVbsD+PlJm45eq6xq49y7OKdbDlWYmIilpaW1CxXhE9e8Mxw++joaLy9vQFwd3fnjz/+SPV8UFAQCxYsoHHjxqxYsYJy5crx119/AXD79m0aNWrEkSNH0rROAli/fn2q7Xft2pUI8N1334V17NjR/sSJE8cADhw4YLd8+fLi/v7+J2xtbXW/fv1cZ86cWaJNmzZ3g4OD7X788cfgtm3bRr744otu3377bcnPPvvsyv3nOX78uEPZsmUvP/XUU9UGDhx4bdiwYamardevX98jMjIyzVgG48aNC+3SpcvdjN6jjh073u3YseNdgEmTJl28t97BwUGvW7dOBrcST0ybTBxYN4dK+8bipaPZVelNGrz0CVbWNkZHEyLPKFDFIpG7lIUFVtKSKNtZWihebVGJ1jVK8/7yQ4xcdoi/Ai/ydbfaBX4ch7zE3d2d4sWLc/FiymdUrK2t+fvvv+nXr5+ByYTRbkTG8cqCfQSE3mJsJ08GNHUzOpLIyxLiIGARbJ0Ad8LApTF0nQnuLUG+BEilfsVi1KngxNwdwfRtVLHAFmDT64Z2v4oVK9K4cWMAatWqxXvvvcfo0aPp2LEjLVq04ObNmw/d98HtixUrlu5269evdzxy5IhDnTp1agDExMRYlCpVKgG4W6ZMmbi2bdtGArz88svXp0yZUgpIKRbFxsaqsLAwm5deeqnS9OnTzz/77LORDx5///79JzPxVggDuLm54ejoiKWlJVZWVoZMR5+R9IYWuXHjBr169SI4OBg3Nzd+++23h/5+ZyT8cghhv7xG/cgdnLKqxu3uM2lS4/HHiUsv76effspPP/3EL7/8gslkokKFCjg5OT32OYQwgtkVi2bNmsWsWbMA0OkMziyywMICKxsZxDWnuDsXYqlvExbsCmb8+hO0+X4L/3u+Ji/6VJBWRrmkc+fO/Pjjj9xrWh8REcHy5culWGTGzl+PZOC8fVy8Fc2MvvVp51XG6Egir0qMh0NLYOs3cCsEyvtApylJYxPJNTxdSileaVGJ4YsPsvnkVVrXKJ1j58pMC6DMunv3Lo6Ojtl2vEKF/mu1Xa1aNQ4cOMDatWv5+OOPad26Nf3793/ovg9uX7NmTesZM2ak2U5rrV588cXr98YIuufkyZM2D37GeHA5ICDArlatWlG3bt2ytLS0THca1ydtWSRy1ubNm3F2zrszdg4cOJA333wz1e/6uHHjaN26NR988AHjxo1j3LhxjB8/PkvH1SYTB/6aRZX9n1FDx7Grygga9P74iVsTpZcX4J133qFs2bLUqFHjiY4vhFEK1JhFmeHr64u/vz/+/v4oK2ml8SSUssTKrnCah8g+FhaKQc3c+fvtltQsW4T3VwQyIPkPVZHzunTpkjKOxD0bN24kMTHzMwqKguNQ6C26z9jJzag4fh3SSApFIn2JCRCwGKY2gD/fBIcS8NIyeHUjVGkthaIMtPcqQ1knO2Zve6JJtgqMixcv4uDgQL9+/Rg1ahQHDhzA0dGRu3fTr7c8uP3x48ctAJycnBIjIyNTPve3a9fuzpo1a4pduHDBCuDKlSuWp06dsgG4dOmSzcaNGwsBLFq0qHjTpk0j7j+Hv7+/fcOGDSOWLFlydsiQIe6hoaFpvnzev3//yRMnThx78CGFIpEZLVu2pHjx1JPsrFq1igEDBgAwYMAAVq5cmaVjhl8M5tCEDtTfP5pLVq5cfmkjTV7+LFu6naWXN6958PNsVtwbVD+n93mUTZs28fLLLz/xcQYPHkypUqXw8vJK85ybmxu1atXC29sbHx+fJz5XQWB2xSKRfe51Q3vwIbJfxRKFWDykMZ919sQ/+AZtv9/K4r0haJ3uF3oim7Rs2ZLY2NhU6ywsLNi/f79BiYRR/j1xhd6zdmNnbcmK15tSv2Le/lAoDGBKhMBlML0RrHwNbAtDnyUwZDNUaytFokyytrRgQFM3dp29ztGLt42OY7jDhw/TsGFDvL29GTt2LB9//DElSpSgWbNmeHl5pRng+sHthw4dGg9QpkyZxPr160dUrVrVc+jQoRXq168f8/HHH19o3bp1tWrVqtV85plnqoWGhloDuLm5xfzwww+lKlWq5Hnr1i2rkSNHpppB7dChQw5eXl7RtWvXjv3888/DunXrVjk2NvaxfsFfeOEF9+bNm1c/d+6cbenSpWt///33ebe5SwGhlKJt27bUr18/pbdFfnDlyhXKli0LQJkyZbhy5UoGeyTRJhP+K6diM6sp1SP92V31Xap+sJ2KHt45mDbJ1KlTuXjxIsHBwSQk5N+B+/NCsejQoUPUrVv3iY+T0YRYmzdvJiAgIE92zzRCgeqG1qdPH/z8/AgPD6dChQp069aNjh07PnR77+qu7NgxLcPjZmWq+cx61JT0OS0r537Ua1dKYWOb9V+h0NBQ+vfvz5UrV1BK4evry4gRI7J8HHNjYaHo38SNpz1K8f7yQD78/TB/BV5iXPdaVCjmYHS8AsnOzo6GDRuybdu2lHXR0dGsXr2ahg0bGphM5KbFe0P4eOURapYtwpyBPpRylFap4j4mExz7A/zGQ/hJKOUJvRaCx/MgU3o/lj4NXJmyKYg52/+/vfsOj6raGjj82+mVhDTSSKGFkoSQQpMmXUCRLoqKDa/l6tUPL+pVr72B5aKiIgjYC4ooItJ7DS2EHkjoLQklvc3+/phJJUAISSZlvc+Th5lz9pyzUpgzs2bttRN5f3SEucOpcunp6VfcFhQUVNTzBGDAgAEMGDDgsvHfffdduccuOz4+Pr5oibI//vijVLnWQw89dP6hhx4q1QBp//79NlZWVpRdsr6k6dOnF61ANnbs2Itjx46tdFavbEyi+q1duxY/Pz/Onj1Lv379aN26NT169DB3WNdFKVWhlgxnjx/m1LcPE521mb3W7XAc/RmdW4bXQITwyCOP8OKLL7J//36sra05fvw4QUFBNXLuirr99ts5duwY2dnZPPnkk0yYMIGMjAxGjx7N8ePHKSgo4MUXX2TevHlFKzD269ePyZMnlzpORR/zzTffMHXqVHJzc+nUqRPTpk3j2LFjDBw4kKioKLZt20a7du346quvcHAo/d5m586djB8/npycHB5++GF8fX154403rrs1hyyIdX3qVbLo+++/L3V/wYIFZoqkYVAWCutKJIusrKx47733iIyMJC0tjaioKPr160fbtm2rIcr6p6mbA98+2InvNh/lrYV7GfDBap4b1IY7OwbU22ag5jRq1ChiY2PJyjJO/cvPz2fu3Lm89tprZo5MVDetNR8sOcDU5Qn0bOXJtLsicazEc56opwwG2PcHrHwbzu4Bz9Ywaja0GSpJohvk4mDNqCh/vtt8lGcHtsarkSRohagqfn5+AHh5eTFs2DA2b95cJ5JFTZo04dSpU/j4+HDq1Cm8vLyuONZYTfQRrePeopUuYGPIv4kZ8yyWlpe10arWeMGY2PLw8CAhIQH+ehZO76raE3mHwS1vV+qhX375JW5ubmRlZRETE8OIESNYuXLlda3ACJevwljeY/bu3cuPP/7IunXrsLa25tFHH+Xbb7+lR48e7N+/n5kzZ3LTTTdx//33M23aNCZOnFjqHHFxcXh5eTFgwAAefPDBy/qHdu/evdzpuVOmTKFv374V+nkUVt0ppXj44YeZMGFChR5Xn8mrGVFpSoGVjcVlX9fi4+NDZGQkAM7OzrRp04YTJ05c41GiJAsLxbjOgfz9VA86BDTmhd/iuWvGJo6lZpo7tHpn0KBBl207fPgwycnJZohG1JS8AgMTf45j6vIERkf7M+PeaEkUCSOtYd+f8HkP+OkeYyPrETPhkfXQbpgkiqrIfTcFk2/QfLXhiLlDaVBCQkJyDx48uNvccYjqkZGRUfSGOiMjg8WLF5fbu6U2uu2225gzZw4Ac+bMYejQoeWOO3P0IPHv9iMm7iWOWrcg5Z6VdL7zPzWaKAI4depU0e0LFy5gZ1f7kt5Tp06lffv2dO7cmWPHjnHw4EHCwsJYsmQJkyZNYs2aNRVawa0ij1m2bBlbt24lJiaGiIgIli1bxuHDhwFo2rQpN910EwDjxo1j7dq1pR6bl5fH4cOHGTt2LG+99Va5C82sWbOGHTt2XPZV0UQRGKvutm3bxl9//cUnn3zC6tWrK/zY+kpe+YpKU6r8yqLr6aKTlJTE9u3b6dSpU9UF1oD4N3bg6wc68uOWY7z+514GfLiaZ29pzbh6vORwTWvevDmurq5FlUUANjY2LF68mDvvvNOMkYnqkp6TzyPfbGXNwWT+1bclT/ZpKSsQCmOS6OBiWPEGnNoJbs1g2HQIGwkWNfsmpCEI8nCkb5smfLvpCI/3boGdtfyMhbhRZ86cYdiwYYCxUvrOO+9k4MCBZo7qcmVbi7zyyis8++yzjB49mpkzZxIYGMhPP/1U6jHaYCB23oe0iXuXZhjY2OY5Oo56BosaSBKVF+/KlSvZsWMHH374IVZWVgQGBkLzylUAVYeVK1eydOlSNmzYgIODA7169SI7O/u6V2CEiq3aqLXm3nvv5a233iq1PSkp6bLXWGXv7927l5iYGFJTU6+Y9KuKyqK6WnVXnSRZJCpNWZTfs+j4uXOlOshPmDCh3DK+9PR0RowYwYcffkijRo2qNdb6TCnFHR0D6N7Kk+d+3cVL83fzZ9wp3h0ZTqC747UPIK7ptttuY/r06UUNxdPT0/nll18kWVQPnb2UzfhZW9h/Jo13R4QzOqapuUMS5qY1JCyDlW/Cia3gGghDp0H4GLCUl1HV6cFuwSzZc4Zft53gzk4B5g5HiDqvWbNm7Ny509xhXFPZ1iKFli1bVu7200cPkPztBGJythNv257Gd0ync7PW1RliKeXF+8ADDwDGREfLli1rLJaKunjxIo0bN8bBwYF9+/axceNGwLiiopubG+PGjcPV1ZUZM2bw2GOPXXEFxoo+pk+fPgwdOpSnnnoKLy8vUlNTi/YfPXqUDRs20KVLF7777ju6detW6vg7d+6ka9eujBs3jmHDhrF8+fKiaX6FSvYXrYyMjAwMBgPOzs5FVXcvvfTSDR2zPmhwr3KmT59e1PlfppHcGKXA2vby7K6np+c1O8jn5eUxYsQI7rrrLoYPH15dITYofq72zLkvhp+3Hue1BXsY8OFq/j2gNeO7BkmV0Q0aOnQo33//PZcuXSratmTJEgwGAxYy5aTeSDibxr1fbuF8Zi4z7o3m5pAr90MQDYDWcHglrHgTjm8Gl6Zw61SIuBMsrc0dXYPQMdiNUL9GzFx7mDtimsq1TAhRijYUsGXue7Tb/R7OwMZ2L9BxxNM1Uk1U12RmZuLv7190/5///Cf5+fm0adOGkJAQOnfuDBhXVHzmmWewsLDA2tqaTz/9tNQKjLfccstlDa4r+pjXX3+d/v37YzAYsLa25pNPPsHb25uQkBA++eQT7r//ftq2bcsjjzxS6vg7d+6kY8eOtGrVinfeeYfRo0ezdOlSrK2v/1pcXhXYAw88UGeq7mpag0sWlaxyKeybIyrHwkJhW05l0eVrfJSmteaBBx6gTZs2PP3009UTXAOllGJ0dFN6tPTk+Xm7eHXBHv6KP8W7I9sT7CFVRpVVWJpbktaabdu2laqiE3XXlqRUHpwTi7WlBT9O6EKY/7Xn6It6LHGNMUl0dD008oPB70OHu8HKxtyRNShKKR7oFsxTP+5k1cFzksAVQhQ5mbiP8z9MoGPOTnbZdcB97Od0Dgoxd1i1lsFguGzbpEmTyh17PSswFo6vyGPGjBnDmDFjSm1LSkrCysqKb7755orHnzJlStHtIUOGXHW182u5UtVaXam6q2nykbioNKUU9jaWl31dy7p16/j6669Zvnw5ERERREREsHDhwhqIuOHwdrFj5r3RvDeqPftPpzHww9XMWHOYAsP1dJQShezt7S9LCuXk5MiKi5WglPqPUirX9PVXOfudlVJHTfvTlVLdyjtOVVq46xR3zdiEu5MN8x7tKomihuzIepg9BOYMgdTDMGgKPLEdYh6QRJGZDA7zpUkjW2aukRXWhRBgKChg8w9v4Tq7B4HZB9gU+jKhk5bjK4kiIapcg6ssElXH0kLhbHf9f0LdunUr6v0iqo9SihFR/nRr6cF/5u3i9T/3snCXscqohZeTucOrc0aPHs22bduKKozy8vL4+eefefnll80bWB2ilLIGXgb6ArFAslLqVq31HyWGfQGkaa0DlFL/A74Dqq1ZyYoT8Ou6bUQGNGbGPdE0dpSEQIN0bLOxcfXhleDoBQPfhqjxYG1v7sgaPBsrC+7pEsTkv/ez7/QlWntLj0MhGqoTh3dz8YeH6Zi7izj7aLzu/IxOAbWvH5CouKCgIOLj480dhrgCSRbVcvP3p1Z47NAQtyo//9WO+YRSONlJ34barkkjO764J5r5O07y8h+7GTR1Df/XrxUPdm+GpfR/qLBBgwbx3HPPldqWkJBAamoqbm5V/3+vnhoPXNRarwJQSq0CHgNKJov6AoU/6H8D/1RKKV0NGeb3F+/nl0SFdyM7Jg1sLf1QGqLjW42NqxOWgoMH9H8doh8AGwdzRyZKuKtTAB8vT2DmmkQmj2pv7nCEEDXMUFDAlp/eInzf/3DGis3tXyPm9sdR0jdSiGolySJRaRYW4FxOzyJR+yiluL2DH11buPPCvHje+msfC+NPM2VkOC2bOJs7vDqhRYsWODs7k5WVVbTN1taWJUuWXDb/WlxRCFByZYEkoGuZMU7AdgCtdY5SqgBoCRwoOUgp9TUwHMDd3b1SUwJX7AEFnL6UzejPNwDgaKXxtAcvO/C0N3552YOnHdjL01290SgjkZCTv9Dk4nZyrZw45HcHSV79KEi1g8XLzR2eKEe0O8zbdowOlsdoVMkCQF9fXy5cuFClcRWqruMK0dAdOxhHxk8P0ylvDzscOuEz7jM6+jUzd1hCNAjy0ldUmqVSOFViGpowHy9nOz6/O4o/4k7x3/nxDJ66lif7tuThHs2wspRPZ65GKcWtt97Kl19+WTSNMi0tjblz50qyyAy01ncDdwNER0fryjQ7HDIE5v2+gHadepCUnEFSSgaJyZlFtzefK93U3N3RhiAPR4LcHQn2cCi6HeThiJMkzuuG07tg5duwbwHYuULvF7Hp9DBtbJ1pY+7YxFW1PZdO7/dWcdq5JXf2a1WpY+zduxdXV9eqDQzjtcDZueIfvLzxxht89913WFpaYmFhweeff06nTp2qPC6A48ePl7v96aef9nVycip49dVXz1zrGMnJyZYzZsxwe/bZZ89VRUzz5893njVrlsdvv/1W6UZUCQkJ1nfddVdwcnKytVKKe++999yLL754tiriE7VDQX4+sT+8TvuDH+OibNgc8SYxtz1SJ6uJtNYoJdXLoubdaGG+vLoVlVbY4FrULUopbmvvS9fm7rw0P57Jf+/n792nmTyyPSHeUmV0NcOGDePnn3/m0qVLRdsWL16MwWDAog6+eDGD/RinohUKAk6WGZMOdABilVK2gCVwsLoCsraAVk2caVVOhV1WbgFHUjNISs4kKSWDpOQMEpMzWJtwjl+25ZQa6+Fka0wgmZJHwR6OBLob7ztKIsn8zuyBVW/Dnvlg6wK9nofO/wA7aWZeVzTzdKJPay++3XiER3s1x866br7+2LBhAwsWLGDbtm3Y2tqSnJxMbm6uucO6qpSUFMuZM2d6VVWyaPv27Q4RERGZ5e3LzMxUnTp1ap2bm6sKCgrUrbfeev6DDz4odZ04ePCgzejRo5tduHDB0traWj/33HMnX3nlFf9BgwZdioqKyi7vuKI0S0tLwsLCiu7/9ttvhIaGkp5+rTWNb8yFCxf47rvvePTRR6867uiBHWT9/DCd8vax3aEL/nd/RkffoMvGLVu2jNmzZ/P1119XU8Q3zs7OjpSUFNzd3SVhJGqU1pqUlBTs7OwqfYwG9wp2+vTpTJ8+HYDk5ORrjBZXY2mh5NP0OszDyZZpd0XxZ9wpXpofz5CP1vBE75b8o1dzrKXKqFw333xzUYPrQgaDgR07dhAZGWmmqOqUr4BpSqnuwDagJ1C2LGsZ8C+Mja7fBU5UR7+iirC3saS1d6NyG+pm5uZzJMVYhZRoSiQlJWey8sA5zm0t/Um+l7OtMYFUlEhyINDdWJUkCfdqdm6/sZJo9zywcYIe/4Yuj4J9Y3NHJirhgW7B3DljE/N3nGBMTLX1va9Wp06dwsPDA1tbWwA8PDyK9n3zzTdMnTqV3NxcOnXqxLRp07C0tOSrr75iypQpKKUIDw/n66+/5v333+fLL78E4MEHH+Rf//oXYFyG+pZbbqFbt26sX78eZ2dn26VLlyonJyc9adIk7x9//NHD3d09z9fXN7dDhw6lEjaXLl2yuO2225qdOnXKxmAwqH//+98nH3roofP/93//53/s2DHb1q1bt+3Zs+elzz///Pi0adPcPv300yZ5eXkqMjIy46uvvjpy6NAhm4EDB7YMCwvLjI+Pd2jVqlXWzz//nOTs7Fxqze64uDj7++67LyUrK0uNGzcu0MfHJ2/q1KknLCwssLOz02vXrt3v4uJiyMnJUTExMSHLli272KdPn4zCx7/00ks+d9xxR8qkSZPObd261e62225r2bp168yjR4/aSLKoYuzt7dmxY0eNn/fChQtMmzbtismigvx8tnz/Kh0SppGlbNkS9Q7RgydcsZpo586ddOjQoTpDvmH+/v4cP36cc+eqJNcqxHWxs7PD39+/0o9vcO/0J0yYwIQJEwDkzd0NslDgUEc/2RPFBof70LmZGy//sYf3lhxg0e7TTBnVnjY+suJMWQ4ODkRGRrJx48aibbm5ufz555/yfFIBph5Er2FMCClghdb6d1Oj6zVa6xeACUC8UioXyAUGmy/iK3OwsaKNT6Ny/5+k5+RzJKW4Iikx2ZhMWrbvDMnppSsIvBvZEeThQHCJKW1B7saqpLpaOVErJCfAqndg189g7QDdn4Yuj4ODNKOvy7o0d6eNTyNmrk1kdHTTG/uU/q9njdMSq4B9QT5YWoF3GNzy9lXH9u/fn1dffZVWrVrRt29fxowZQ8+ePdm7dy8//vgj69atw9ramkcffZRvv/2WqKgoXn/9ddavX4+Hhwepqals3bqVWbNmsWnTJrTWdOrUiZ49exa9aT548CDff/89X3zxBf369eOrr75qHBYWlj1v3jy3Xbt27cnLyyMiIqJt2WTRr7/+2sjb2ztv5cqVCWCsKAJ47733jg8ZMsR+3759ewC2bdtmN3fuXLfY2Nh9tra2ety4cQGfffaZe79+/dKSkpLsPv/886T+/ftnjBo1Kmjy5MmeZae67d2718HHx+d0z549W40fP/7co48+WrSai4WFBb17926ZkZFhaTAYOHr0qN39998fbG9vb3j77beP3X777WlKKS5dumQJcP78eUsXF5f8PXv2OPTs2bN6y2IakPKSkZMnT8bW1pYnnniCp556ip07d7J8+XKWL1/OzJkz+fbbb0sdIyMjg9GjR3P8+HEKCgp48cUXmTdvHocOHSIiIoJ+/foxefLkoiRpetpFwp1S+faWHBblRvKvucfpeHAN2/79P9q1a8dXX32Fg0PpxQd27tzJ+PHjycnJ4eGHH8bX15c33nijVlXwWFtbExwcbO4whKiUBpcsElXHQins5c1MveDuZMtHYzswOMybF36L59aP1vJ47xY82qsFNlZSZVTSqFGj2LFjR1GFUW5uLj///DMvvviimSOrG7TWrwKvltnWs8Tti0DTmo6rKjnZWtHO14V2vpdPcUrLzuNISmZRAqmwKmnx7jOkZBQnkpQCn0Z2xuSRqSop0N2YVGrqJomkK0o9DKsmQ9wPYGUHNz0BXZ8AR49rP1bUekopHugWzMSfd7LmYDI9WnmaO6Tr5uTkxNatW1mzZg0rVqxgzJgxvP3226Snp7N161ZiYmIAyMrKwsvLi4sXLzJq1KiiCiQ3Nze+/vprhg0bhqOjIwDDhw9nzZo1Rcmi4OBgIiIiAGjbtq0hKSnJNjk52WrQoEEXCqt8+vfvf6FsbJGRkVn/+c9/mj7yyCN+Q4cOvThw4MByky+LFi1yjo+Pd2jfvn0bgOzsbAsvL698IM3b2zu3f//+GQB33313ytSpU72AomRRTk6OOn78uM2dd97ZbNq0aUf69u2bUfb4mzZt2h8aGtr2+PHjtvfdd9+ZTz/99ETJ/W+99dbJfv36tZwxY4ZXZmamhaenZ97bb799zM3NzVD2WKJ8WVlZRX8jwcHBzJs3r2jflZKR3bt357333uOJJ54gNjaWnJwc8vLyWLNmDT169LjsHIsWLcLX15c///wTgIsXL9KpUyfi4+OLqpr27t3LD99/z/sP96bj0S94ZGEOr6eP5p7HXyDhnebMnjOHm266ifvvv59p06YxceLEUueIi4vDy8uLAQMG8OCDDzJu3Ljq+YEJ0UBJskhUmlQW1T8DQ33oFOzOK3/s5sOlB/l79xkmjwwn1E/6ehQaNGjQZYmh/fv3c+HChWppnCrqF2c7a0L9XMr9P3UxK48jRZVIxVVJf+06xfnMvKJxSoGvi72xGqmwT5KpKinAzaFhJnjPH4HVk2HHd2BpDZ0fhZueBCcvc0cmqtit7X14Z9E+ZqxNvLFk0TUqgK5H1nU2uLa0tKRXr1706tWLsLAw5syZQ//+/bn33nt56623So396KOPrjuewilupnPp/Pz8CpVZhIeH52zbtm3PL7/84vLiiy/6LV269NKUKVNOlR2ntVajRo1K+eSTT0olcfbv329TtqKj7P0dO3bYhYWFZV64cMHS0tKy3CnGnTp1CgHw8/PLmTNnjteiRYtcbW1tdWFl0axZs9zGjh2b8vzzz5+NiYkJOXHihO24ceMulD3OlClTPGbPnu0JxibkotjVpqGtXbu23GTkI488wtatW7l06RK2trZERkYSGxvLmjVrmDp16mXHCQsL4//+7/+YNGkSQ4YMoXv37pw/f77UmJ+++ZIta5bw+NZFZFk4UeDQhHGNAlAWFjRt2pSbbroJgHHjxjF16tRSyaK8vDwOHz7M2LFj+fzzz+nSpUsV/XSEEIUadLLoQnYB8/enXnPc0JCqL1tXK7+q0Lihve6p8DEr8r1A1X0/SinsGuKbknqusaMNH97RgUFhPvznt3hu/2Qdj/ZqzuO9WzbMN6FlhISE4OjoSGZmcfW+ra0tS5YsYdSoUWaMTNR1LvbWhPu7Eu7vetm+C5m5JBX2SDKt1paUnMHvO05yKTu/aJyFAr/G9qYV2xwJLFy5zd1YkVTv+pFdOAZrpsD2b0BZQseHoNtT4Oxt7shENbG1suSezoG8t+QAB86klducvjbbv38/FhYWtGzZEoAdO3YQGBhInz59GDp0KE899RReXl6kpqaSlpZG7969GTZsGE8//TTu7u6kpqbSvXt3xo8fz7PPPovWmnnz5l2zwW/v3r3T77///qDXX3/9VF5enlqyZInrvffeW6qJSlJSkrWXl1f+o48+mtq4ceOCmTNnegC4uLgUZGRkFD15DBw48NLw4cNbPP/882f8/Pzyz5w5Y3nx4kVLgFOnTtksXbrUsW/fvhnffvutW9euXUtVJ8XGxtp37Ngx/f77708ZPnx4ixUrVuxv2rRpfskxW7du3V94e+LEiT4ODg6GklPZvvnmG4+//vrrwB133BEYHR2dkZqaan369GkrPz+/UseZOHFi8sSJE5MBoqOjoyry+xFXVjidavbs2XTt2pXw8HBWrFhBQkICbdpcvp5kq1at2LZtGwsXLuSFF16gT58+3HOP8X1NXm4OW799iUbbPmFsuB13P/8/ogbdV/TYpKSkyxKNZe/v3buXmJgYUlNTsbSUD6+FqA4NOlkkboyxsqievfEQRfq386ZjsBuvLtjD1OUJLN5zhskj2xPm37CrjJRSDBkyhFmzZhVtS0tL45dffpFkkag2rg42RDjYENHU9bJ95zNySzTZziDRlFSat+0EaTnF750sLRT+pkRSkLtDqSlu/o3tsapLiaSLJ2Dt+7B1jvF+1H3GvkSNfM0bl6gRd3UO5OMVCXy5NpG3R4SbO5zrkp6ezj//+U8uXLiAlZUVLVq0YPr06Xh4ePD666/Tv39/DAYD1tbWfPLJJ3Tu3Jn//Oc/9OzZE0tLSzp06MDs2bMZP348HTt2BIw9Za7V5Ldbt26Zw4YNSw0NDW3n7u6eFx4eftn0r61bt9o/99xz/hYWFlhZWelp06YdAfD29i6IiopKb9myZbvevXtf/Pzzz4+/8MILJ/r06dPKFKueOnXqUX9//7ygoKDsjz76yGvChAkOLVu2zJ44cWKphNTOnTsdOnbsmBEeHp7z2muvHR8+fHjztWvXHrC1tdUAJ0+etLKxsdEeHh4F6enpasWKFY0mTpx4uuQxfH19c//3v/95/fbbb+4BAQHZp0+ftu7du3erV1999cSYMWMu3thvSFwtGdm9e3emTJnCl19+SVhYGE8//TRRUVHl9gg6efIkbm5ujBs3DldXV2bMmMFjjz3G+dQUjrzThc4Fhzga2oWPfznM89HG9oSFSVKAo0ePsmHDBrp06cJ3331Ht27dSh1/586ddO3alXHjxjFs2DCWL19OkyZNqvmnI0TDosy0yEyFRUdH69jY2Eo9dsGCBQwZMuSK+5u1bc+b3y285nHMWVmka3FlUVTnrny3cMVl2x8c2pfK/s5E7bR83xme+3UXyem5/KNnM57o0xJbq4b7Kc7vv//O3XffzaVLl4q2ubi4cP78+etqqjh//nyGDh1a6TiUUlu11tGVPkA9UZ3XibpMa01qRq5pOlvxym2FzbfTSySSrCwUTd0cCHJ3MFUjFSeS/BrbY2lRS5qFpp2GtR9A7CzQBdDhbuj+f+Bap9tciUp47tdd/LLtOBue7Y27k+21H4CxEqG8CogblXad09BqUnx8fGZoaOjemjjX/v37bYYMGdLy4MGDuyt7jE2bNtmPHz8+uKCgAK21Gjp0aOqUKVNO/etf//KNiYnJuOuuuy5u3brV7qGHHgrKyMiwUErx5ptvHh8+fPilqx03Ojo6Sl6bFnNyciI9Pf2K26602t6yZcsYOHAgFy5cwNHRkVatWvGPf/yDp59++rJz/P333zzzzDNYWFhgbW3NR1M/hL2/8eEH/2PnGU1Mj758NfcvfvzxR956661SSVJvb28GDhxIdHQ0W7dupW3btnz99delGlxPnDiRjh07Mnr0aBYsWMDkyZNZunQp1tbW1fNDE6Keutr7CaksEpVmAdjUpU+iRaX1bt2ExU+58fqCPXyy4hCLd59h8qj25VY5NAS9e/cuanBdKD8/n7i4ONq3b2+mqIQoTSmFu5Mt7k62RAWW/pBAa01yem6p1doKG29vSkwlM7egaKy1ZWEiydE0vc2haNU2X9caSiSln4W1H0LsTCjIg4g7occz0Diw+s8taqUHugXx/eajfLvpKE/0aWnucEQV6dSpU9bevXv3lN3+4Ycfniy8HRUVlb1t27Z9NRtZ/VI2UVR229NPP11uAqhPnz7k5RX30Dtw4MAVzzFgwAAGDBgAwKG4daj5j9KsIImJ991K83s+obGHcbrwmDFjGDNmTKnHJiUlYWVlxTfffHPF40+ZMqXo9pAhQ+rtBz9CmJMki0SlKaWwsawlnzaLaudib83kUe0ZHO7Dc7/uYvi0dTzUoxlP9W3V4FZmcnJyon379mzZsqVoW15eHn/++acki0SdoJTC09kWT2dbYoIuTySdS8sp6o2UmJxZ1Hh7w6EUsvKKE0k2lhYEuBsrkgqbbBdWJfk0ssPiRhNJGcmw7n+w+QsoyIHwO6DnM+DW7MaOK+q8Fl7O9Arx5KsNR3i4Z7MGXe1aW4SEhOTeSFWRqH9ys7PY9s3zRB+bzQXViO03TSO6313mDksIUUENLlk0ffp0pk+fDkDahRQzR1O3KcC6tkxNEDWmV4gXfz/Vgzf/3Mvnqw6zdM8Z3h3ZnqjAxuYOrUaNGjWKuLg4cnJyAMjNzeWnn37i+eefN3NkQtwYpRRejezwamRHp2bupfZprTlzqTiRlFTUKymTNQeTyckvXrnaxsqCQDeH4gSSu3H1tmAPR5o4XyORlJkK6z+CTZ9DXiaEj4Ye/waPFtX1bYs66IFuwdw9czO/7zjJqGiZiihEbZKwYzWWvz9OZ8MRtrgOoNU9H9HBvWI9hYKCgoiPj6/mCIUQ19LgkkUTJkxgwoQJgLFnkag8pah0ZdGiRYt48sknKSgo4MEHH+TZZ5+t4uhEdWpkZ83bI8IZFGasMhr52Xoe7BbM//UPaTBVRoMHD+bll18utW3v3r1cvHgRF5eG3QRc1F9KKbxd7PB2saNL89KJJINBc/pSdoneSJlFU9xWHThHbolEkp21BYFuxuRRYW+kIA9Hmjnl4Rk/A7XxM8hNh9Dh0HMSeIbU9Lcq6oBuLTwIaeLMzLWJjIzyv66ecUKI6pGTncn2r58j+vhXpCpXdnT/nJg+d5g7LCFEJTS4ZJGoOpVNFhUUFPDYY4+xZMkS/P39iYmJ4bbbbqNt27bVEKWoTj1aebLoX915+699fLEmkWV7z/LuyHCig6q+KXxt06ZNG+zt7cnMzCzaZmtry7Jlyxg+fLgZIxPCPCwsFL6u9vi62tO1hUepfQUGzamLWSQlZxZXI6VkkHA2nRX7zmFbkM59lotoa7UQpTJZY30TK5vej41TKMFJDgSlpxLk7oCns60kBEQRpRQPdAvm37/Esf5QCjeV+bsrj9Za/oYaGIPBIL/wGnJg20psFjxOZ8MxNjceRMg9HxHhdu3/l0KI2kmSRaLSjA2ur//6u3nzZlq0aEGzZsaeE3fccQfz58+XZFEd5WxnzRvDwhgU5sOkX+IY9fkG7usazDMDQrC3qb9VRkopBg0aVLScLBhXxPnll18kWSREGZYWCv/GDvg3dqBbyxJvHHLSMGz8DL3+YyxzLnDU62a+d7+PTVm+JCVncPTQYfINxau2OtpYllitzcHUcNuRQHdHPJxsJAnQAN0W4cu7f+9jxprD10wW2dnZkZKSgru7u/ytNBAGg0GdO3dOyn2rWXZWBju+mkTMyW9IVm7s7DmTjjePNHdYQogb1KCTRa52llW2jHyhCi9f3+ueKj0eUOXfyzXPrxRW5fScOHfuHNHRxavvlZz6B3DixAmaNi3uLeDv78+mTZuqJmBhNje18ODvf/XgnUX7+HJdIsv2neHdEeGX9TypT4YPH878+fO5dKl4xd6//vpLPrkW4lpy0mHLF7BuKhZZqdDqFuj1LAG+ETwMPGwall9g4MSFLJJSMo3T20wVSbtPXmTR7tMUlEgkOdtaEVgigVTYcDvI3QE3R0kk1Vd21paM6xzIh0sPknA2nRZeTlcc6+/vz/Hjxzl37lyVxpCdnY2dnV2VHrOqnD592qqgoKAhl3YYgHigubkDqa/2xy7FbuGTdDYcZ7PbEFrfO5X2rvX3tZ8QDUmDThaJG6O0AYv8nMu2e3p6Ehsba4aIhLk52lrx6tBQbgk1VhmNmb6R8V2D+PfAEBxs6t/TTZ8+fcjOzi61LTc3l/j4eMLCwswUlRC1WG4mxM6EtR9CZjK06Ae9ngP/qHKHW1laEOhurBzq2cqz1L68AgPHz2eVaLKdQWJKJnHHL7Jw1ylK5JFwtrMqlUAKNiWVgtwdaexoU43fsKgJ4zoHMm3lIWatS+SNYVd+7rW2tiY4OLjKz79y5Uo6dOhQ5cetCm3btt2ltY6+9sh6T197iLge2Znp7PjqGTqe+p6zyp1dN8+iY0+prBaiPql/795EzdEaVU6y6Fr8/Pw4duxY0f3jx4/j5+dXlZEJM+vS3J1F/+rOu4v2M3t9Esv2neGdEeF0bV6/Ptx0dnYmNDSUbdu2FW3Lz89n4cKFkiwSoqS8LIidBWs/gIyz0OxmuPl5aNqx0oe0trQg2LTSGmX6X+fmGzh+3tgfKTE5s6hH0raj51kQd7JUIsnF3trUZLt45bZAd2PTbRcH60rHJ2qOh5MtwyL8+GXbcSb2D5EEoBDVbN/mv3H861901ifZ5HE7be/5AG+X+t+vUoiGRpJFovK0AZWXfe1xZcTExHDw4EESExPx8/Pjhx9+4LvvvquGAIU5OdhY8fJt7RgU5sO/5+7kzi82Ma5zAM/e0gYn2/rz1DNq1Ch2795NTo4xcZqTk8NPP/3EpEmTzByZELVAXjZs+wrWvAfppyG4B/T6CgK7VOtpbawsaObpRDPPy6ck5eQXcCw1qyiBVDi1bUvSeebvPIkukUhq7GBdarW2QHcHU78kRxrZSSKpNrm/WzA/xh7ju81HeezmFuYOR4h6KSv9EnFf/R8xZ37mtIUn8X2/plO328wdlhCimtSfd2wVNH36dKZPnw5AcnKymaOp4ypZWWRlZcXHH3/MgAEDKCgo4P7776ddu3bVEKCoDToGu/HXkz2Ysng/X65LZMW+c7w7MrxCq9bUBYMHD+b1118vShYBxMfHk5aWhrOzsxkjE8KM8nNh+9fGJNGlExDQFUbMgODu5o4MWytLWng5ldvbJjuvgGOpmUUJpMKqpA2HU/h1+4lSY90dbUw9kYzT2oobbzvWq4R4XRHi7Uz3lh7MWZ/EQ92bYWNlYe6QhKhX9m74i0aL/0UnfZqNnsMJvfcDfJ1dzR2WEKIaNbhXMyWbLUdGRpo5mjpOG6Agt1IPHTRoEIMGDarigERtZW9jyYtD2jIozJtnfo7jrhmbGNsxgOcHtca5jn86Hxoaio2NDRkZGUXbbG1tWbZsGbfffrv5AhPCHAryYMd3sHoyXDwG/h3h9mkQ3BPqQINpO2tLWjZxpmWTyxO92XkFHEkpTiQVNtxel5DML9tKV9l6ONkW90Uq6pVkvO8oiaRq80C3YMbP2sKCuJMMj/Q3dzhC1AuZ6RfYNef/6HRuLidUE3b3+47ONw02d1hCiBogr1hEpSmtUXnXX1kkGq6oQDcWPtmdD5Yc4Is1h1m1/yxvjwinR5nGtXWJUopBgwbx7bffFm1LS0vj119/lWSRaDgK8iHuB1j1Llw4An5RcOuH0LxPnUgSVYSdtSUh3s6EeF+eSMrMzedI4YptRQ23M1l14Bw/bz1eaqyXs23R1LZAD4eiKW5B7o7Y21jW1LdTL/Vs5UkLLydmrElkWAc/WQFPiBu0e90CGi99mk76DBs9RxF+73v4ObmYOywhRA2RZJGoPG1A5WWZOwpRx9hZW/LcoDYMCPXmmZ93cs+XmxkT3ZT/DGlTZ3uADBs2jN9//520tLSibQsXLkRrLW9WRP1mKIBdP8OqdyD1MPhEwKDJ0LJ/vUkSVYSDjRVtfBrRxqfRZfsycvJNlUiZRT2SjqRksGzfWZLTS3/g4t3IrqgCqXiKm7FXkp21JJKuRSnFA92Cee7XXWw8nEqX5rJ8txCVkXHpPPFznqJTyjyOKx/2DPyRzp0HmjssIUQNk2RRFRsaUrGVAObvT63S4wGolV9VaNxvPkMqfMyrnX+yNmDIyrjifiGuJjKgMX8+0Z3/LTvI56sOsfrgOd4cHsbNIV7mDu269e3bt1TPIoCsrCz27t1L27ZtzRSVENXIUAC758HKtyHlIDQJgzu+h5BbGlSSqCIcba1o5+tCO9/LP41Py84rntpmqko6kpLJkj1nSMkoPc3b18XO1GTbsWiKW7CHI03dJJFU0rAOfkz+ez8z1x6WZJEQlRC/Zj7uy/+PGEMyG73voP09U/B3LL8HY15uDvs2LiIzbj4uF/eSb2FDvqU9BZZ2FFjao63sMFg7gLUDytoeZeOAhY0DFraOWNo6YmXrgLWdIzb2TtjYO2Fr54StgxP2js5YWcuqhkKYmySLROUZDOjsTHNHIeowO2tLJg1szcB23jwzdyf3zdrCyCh/Xhzctk4tWe3i4kLbtm3ZsWNH0baCggL+/PNPSRaJ+sVggL3zjUmic/vAqy2M/hpaDwELaSh8vZztrAn1cyHU7/JE0sWsPI4UrtaWnGm8nZLBovhTnM/MKxqnFPi62BdVJAUX9UhyJMDNocE1eraztmRcpwA+WpFAYnIGwR6O5g5JiDoh7WIqe+c8ScfU3zmmfNk/+Gc6d+x32bhLF1I4sO5X2PcXrdI2EEYm2dqawzYhWBpysctPw0ZnY2vIxpYc7HQOdiqvnDNeXa62JFvZkoMtOcqOXGVLnoUdeZZ210xIKRtHLG0dsLI1/luYkLK1d8bG3hFbe0lICVERkiwSlacN6Nzsa48T4hraN3Xlj39246NlCXy66hCrD5zjzWFh9G3bxNyhVdioUaPYs2cPubnGaoCcnBx++uknnnnmGTNHJkQVMBhg3wJjkujsbvAIgZGzoO3tkiSqJi721oT7uxLu73rZvouZeUW9kQqntSWmZLIg7hQXs4rflFko8HW1L5VAKqxKaurmgLVl/fzdjesSyGerDjNrXSKvDg01dzhC1Hq7Vv6C58p/E6VT2OBzFx3ueZemDsUrRp4+lsCR9XNxOPw3Idk7iVYFpNKIfa69sG47mNY3DaXtFaqPAAwFBWRlppGdmU5OVgZ5WenkZKWTl51Bfk4GBTkZFORkYsjJQOdlonMzIS8LlZeJRX4WFgXZWOZnYVWQhZUhu/oSUha25KnSCSmDpT0GU0JKWTtAiYRUYTLKmJAyVUfZO0lCStQbkiwSlWeQaWii6thaWTJxQAgDTFVGD34Vy/AOfrx0a1tcHWr/hXbw4MG8+eabRckigLi4ONLT03FyunyJbiHqBK1h/1+w8k04vQvcW8CImdBuGFjI1CdzcXGwJsLBlYimrpftO5+RW9xk29R0Oyklg992nCAtO79onKWFws/V3tRs28HYI8nUeNu/sT1WdTiR5OVsx20Rvvwce5yn+7WqE9cQIczh0oUU9s/5JzHn/+SIhT8HB/1Kl+jeaIOBQ3HrORs7D88Ty2hRcAhv4JjyZZvPHbh2uJ2WUb3paFWxt5IWlpY4Orvi6Oxabd9LQX4+2VnpV09IZWdgyM00e0LqfOQ/iRr8YDX8FISoWpIsEpWmDQZ0tiSLRNUK83fh98e78fGKBKatSGBNQjKv3x7KgHbe5g7tqsLDw7Eq86LJzs6OFStWcOutt5opKiEqSWs4uMSYJDq5HRoHw7DPIXQkWMpLh9qssaMNjR1tiAxoXGq71prUjFxTk+3M4iluKRlsO3Ke9JziRJKVhcK/sX2pJtuFiSRfV7s6kUi6/6Zg5m49zvebj/FIr+bmDkdUkFLKDlgN2GJ8nzJXa/3fcsaNBl4GNLBTa31nTcZZH+xc8RM+qyYRqc+zwfcewsa+SuKO1Wz6+H4Ck1fTnHMEa8UBmzZsCHwC304jCAyJoKm5A78CSyur2pOQyknD8sQWml/aRGOVhg2ZQCZoSNIBWNnLh4iibmhwr/imT5/O9OnTAUhOTjZzNHWcNqClskhUAxsrC57u14oB7Zow8ec4Hv56K7e19+Xl29rh5lg7PyFWSjFw4EB+/PHHom2XLl1i3rx5kiwSdYfWcGgZrHgLTsSCawAM/QTC75AkUR2nlMLdyRZ3J1uiAksvXqG1Jjk917RqW0bR6m2JyRlsTkwlM7egaKy1paJpY4cSiaTi276u9lha1I4G5219G9G1uTtz1ifxYPfgejvlrh7KAXprrdOVUtbAWqXUX1rrjYUDlFItgeeAm7TW55VSdW9lDDO6eP4cB+c8TvSFRZzGg83ed2CdfgLD+22L+g/tdYzmaPPHaXbTCFp719b0UM27UkLKUFDAkX2xnNm5BNvj62iWuRMXjO+RjilfTrrFYNW8J0FRAwhq4m+GyIWonAb3ym/ChAlMmDABgMjISDNHU7fpAgP5mVnmDkPUY+18Xfj98ZuYtuIQH684yPpDxiqjgaE+5g6tXCNGjGDhwoWkpaUVbVuwYAFaa5SsECVqM60hcRWseBOObQKXpnDr/6D9nWBVOxO0ouoopfB0tsXT2ZaYoMsTSefScky9kTJL9UracCiFrLziRJKNpQVN3cr2SHIk0N0BXxd7LGo4kfRg92Dunx3Lwl2nGBrhV6PnFpWjtdZAuumutelLlxn2EPCJ1vq86TFnay7Cum3n0u9pv/YfRJvue5OM95nvS/UfCrnpNjo4NjJrnLWdNhg4emAHp3cuwfrYWoLTdxDMJYKBE6oJ+xv3wiK4BwFRA2jqF1xrq7GEuJYGlywSVUhrCrJzrz1OiBtgbWnBk31b0r9dE56Zu5N/fLONweE+vHpbO9ydbM0dXin9+vUjJyen1LaMjAz2799P69atzRSVENeQtNaYJDqyDpx9YfB70OFusKpd/7+EeSil8Gpkh1cjOzo1K70UvdaaM5dyiiqSinolJWey5mAyOfmGorE2VhYEujkUJZCMySRjs23vRnbVkkjq1cqLZp6OzFiTyG3tfSVpX0copSyBrUALjEmhTWWGtDKNWwdYAi9rrReVc5wJwASAgICAao25NtMGA7vXLyB06d20L7H9qPLlRJObce1wO62uo/9QQ6QNBk4c3sPJHYuxPLKGwLRtBHKBQOA0HiS4dEEHdadpZH/8AkOQ1LSoL+RZQVSaNhjIy5DKIlEz2vg0Yt6jNzF99WE+XHqADYdSeG1oKIPDa0+VkaurKyEhIezatatom8FgYOHChdWVLIpSSu0ocf92IAiYqLUeUh0nLKSUel5r/WZ1nkNUsyMbjD2JEleDkzfcMhki7wFrO3NHJuoIpRTeLnZ4u9jRuUwiyWDQnL6UXTSlLamwR1JyBqsOnCO3RCLJztqCQDdT8sjUGynQ1CupSSPbSid5LCwU998UzAu/xbMl6Twdg92u/SBhdlrrAiBCKeUKzFNKhWqt40sMsQJaAr0Af2C1UipMa32hzHGmA9MBoqOjy1Yn1Wt5uTns37SIjLjf6XRuLiXXBNwQ+A98u44lMCSChptCu7aTSfs5vv1vVNIaAi5uxZ8U/IFzNCapUTSHA7vh16E/vkFt8JZVQUU91aCTRReyC5i/P/Wa44aGVP2Li+o45m8+FXtvePupBRU+pg6558r7CgzkZWRX+FhC3ChrSwseu7kFfdsYq4we+24bC+K8eXVoKJ7OtaMKYuTIkezfv79oVbTs7Gx+/vlnnn766eo4nUFrHVFyg1IqqDpOVI7nAUkW1UXHthiTRIeWg6MnDHgLou8Da/vyx+dlQ0oCWFgZq42sbMHKrvhfWRVNlMPCQuHrao+vqz1dy/SXNhg0py5lF01nK+yTlHA2nRX7zpFbUJxIsre2JNDdoVST7cL7ns7XTiSNiPRnyuL9zFx7WJJFdYzW+oJSagUwECiZLDoObNJa5wGJSqkDGJNHW8wQZq2RdjGVA2t/Re9bSKu0DYSSWWp/3M2zCO85nC5miq+2O3sikaNbF2E4vBr/i7H46rP4Aqk0IsmpA0kB3fBu35+AluF4SnJINBANOlkkbow2aPIyJVkkal6ItzO/PtKVL9Yk8sGSA2w8vIqXb2tXK6YZDBkyhMmTJxcliwC2bdtGZmYmDg4ONRqLUsoN+BJoBmQCE7TWcUqpXUB34CKQDDyltf5KKfUV8LXWekmJY/gAPwKNMF4zHgEGA/amqqbdWuu7lFLjgCcAG2AT8KjWukAplQ58AfQHTgN3aK3P1cC3L8o6sdXYuDphCTi4Q7/XIOYBsHEsPS7rPBzbDEfWw9GNcHIbFFxlyrGFVenkkaVN6ful/i2ZbCpnzBUfW97jSzxGphfVKRYWCj9Xe/xc7bmphUepfQUGzckLWcVT20xVSftPp7FkzxnyDcUFIo42lkUVSEEeDsW33R3xcLJBKYW9jSV3dQpg2spDHEnJINDdsWw4ohZRSnkCeaZEkT3QD3inzLDfgLHALKWUB8ZpaYdrNNBa4vSxBI6sn4t94mJaZ+0gShVwnkaoEm2eNgQ8TPS41wi3qR0fqtUWyaePkbR1EQWHVuN7Ppam+iRewEUcOezYgaP+99GkfT+CWkfhJskh0UBJskhUmjYYyK+GaWjPPPMMf/zxBzY2NjRv3pxZs2bh6upa5ecRdZuVpQWP9GpOv7ZeTPw5jid/2MGfcad4fVgoXs7mm0YTERGBRZkXFba2tqxYsYLBgwdX9eksSkxDS9RaDyuz/xVgu9b6dqVUb+ArIAJYB9wEHMH4Aru7aV8XjMmgku4E/tZav2HqI+GgtV6jlHq8sKpJKdUGGINxZZo8pdQ04C7TMR2BWK31U0qpl4D/Ao9X2U9AXNupncYk0YG/wL4x9H0ZYh4CW9PSvRePG6ekHd1gTA6d3QNosLAG3w7Q6R/gGwHKAvJzID/7Cv9eZV/2RWPCqbz9hvyrBF9BVnZgWU4i6YqJqnL+tbxaIqu8x5Z4nKwUV2UsLRRN3Rxo6uZA95aepfblFxg4eSG7VJPtpJQMdp+8yKLdpykokUhysrUq6onkYGOJ1vDP77cza3wMbo42Zv9gQVyRDzDHdL2xAH7SWi9QSr2K8VryO/A30F8ptQcoAJ7RWqeYL+SadeLwXo6unIXniaW0KDiEN8b+Q1t97sCuWRfY+SMdMtaQYNkci2Gf0iW0k7lDrhXOnztF4ta/yUtYRZPULQQZjuEBpGl7Djm054T/HXiE9qVZaGc6WErFrBAgySJxA7Q2kF8NDa779evHW2+9hZWVFZMmTeKtt97inXfKfqgkhFELL2d+eaQrM9ce5r3FB+j3/mpevq0tt0f4meXNgIWFBf3792fu3LlF29LT05k3b151JIsum4ZWRjdgBIDWerlSyl0p1QhYA/TAmCz6FJiglPIDzmutM8ocYwvwpWkJ49+01jvKOU8fIArYYvqZ2wOFq9MYMFYmAXwD/Hpd36GovNPxsPIt2LcA7Fyg9wvQcQJcPAFxPxYnhy4eM463cYamHSF0GAR0Ad9IsKmBariCfCjIuXbCqaDk/vLGXCEZVZSsusrxb5SyLE4edZwANz9348cUl7GytCDA3YEAdwd6tiqdSMorMHDifFaJJtsZJKZksuvERY6fN36wFXf8IlGvL8XZzqq4yba7sU9S4RS3xo6y+p85aa3jgA7lbH+pxG0NPG36ajAMBQVs/ultIvZ9gA/5HLBpzYbAJ/DtNJyAlu1JXjSboPXP46gz2Rj8GFF3/hfrBlxNdPF8Moe3LCInYRVeyZtpZkiiMZCpbTloH84G32G4h/alWVgXIqzl/70Q5WlwyaLp06czffp0ANIuNJgPIaqFLtDkZVR9sqh///5Ftzt37lzqTbcQ5bG0UEzo0Zw+bZrw77lxPPXjThbsPMWbw8No0qjmq4xGjBjB33//TVpaGmBcMeiPP/5Aa11bPs1eDTwGBAD/AYYBIzEmkUrRWq9WSvXAOPVstlLqfa31V2WGKWCO1roi75AbVJNRszi7F1a+DXt+MyYwmt1srBA6HgvrP4bsC8ZxTt4Q2AW6/tOYHGrSzjz9hyytjF9lp8PVFK1NVU9XS1ZlQ/pZOL7ZOEXvzG5K/SnrAmP8Pu2NP2tR46wtLYqSPoSU3pebb+Dv3af55/fb8W5kR7+2TUhKyWD7sfMsiDtJiYIkXOytTYmjEtPaTIkkFwfrmv2mhDA5ffQgyd8+SOecHexw6IT3ndNo3bQFAClnjrHjvduIzFjDAatWXBj+KZ3bRps54pqXfuk8h2KXkHVgJR7nNtIs/zAdlCZbW5Ng144NPo/QuG0fmkf0oH0DTqIJcT0aXLJowoQJTJgwAYBmbdtfY7S4Gm3Q5KbnXbb93LlzREcXX6RK/syv15dffsmYMWMqHaNoWJp7OvHTw12YtS6RKYv30+/9Vbx0aztGRNZslVH//v3JyckptS0tLY2EhARatmxZY3FgTP7cBbymlOoFJGutLwGXTH0ebLTWh5VSa4GJlDM9TCkVCBzXWn+hlLIFIjFOL8tTSlmbGowuA+YrpT7QWp819Upy1lofwTiNYCTwA8YpbWur+XtuuM4dgL+fg4Slxdvys+HwCuOXRytoexsEdIWAztA4SHr9gPFnUDjNrFD6WTiVCKd2GKfxndoJF44W73dpakwMlfxy9q7x0EXF2FhZcGt7X77ZeIRjqZn899a2WFkapwvn5BdwLDWrqMl2YnIGR1Iy2ZJ0nvk7T6JLJJIaOxgTScaKJGOfpGAPRzLzJAcuqoc2GIj9/VNab38NZzSbwl+m47AnURYWaIOBbQtn0jz2ZdrqHDY0f4KYsS9i1UCqZDLTL3Jo6zLS96/A7ewmmucdpL0ykKutSLBtwybfh2jUtjctOvQk1K5me0YKUV80uGSRqDraoMktp7LI09OT2NjYqz62b9++nD59+rLtb7zxBkOHDi26bWVlxV133VU1AYsGwdJC8WD3ZqYqo51M/HknC+JO8tbwMHxcrrDaUxVzc3OjZcuW7N69u2ib1pqFCxfy5JNP1kgMJi9jnEIWh7HB9b0l9m0CCstI1gBvUX4ipxfwjFIqD0gHCpdInA7EKaW2mRpcvwAsVkpZAHkYK5eOABlAR9P+sxh7G4mqcvEE7PgWVrxx+T6/aGNSKKCL8V9Hj8vHNHRaG3s2FSaETscZ/007VTzGrTn4RUH0/cakkHd7cHS/8jFFrfVg92Y89FUsf8Wf5tb2vgDYWlnSwsuJFl5Ol43PzivgWGpmUW+kxORMjqRksOlwCvO2nyg11n3jEoI8TCu1uZuqkUxVSU628nJbXL/Usyc4MmcCMRlr2WvTjkZjZ9KpWRsAkk8d4fjX/yAqcz37rUKwG/kZXVpHmjni6pWdlUHCtuWk712By5mNNM/dR5gqIE9bcsgmhC3+9+Lc+maaR/ahrePl/5+FENdPrl6i0ozT0C6vLKqIpUuXXnX/7NmzWbBgAcuWLast03ZEHRPs4ciPE7owZ0MS7y7aT//3V/PCkDaMjm5aI39TI0eO5MCBA+TlGf+PZGdn89NPP1V1smh72Q1a65XAStPtVOD28h6otb67xO31GCuAyhs3B5hTzvZJwKQS93+kuDdR2bENqq9EtTEYIPkAHDWtUhZXzo+74wRoc6sxuWGuaV21lcEA5xOLE0OFX1mpxv3KAjxCILhncbWQd6ix35OoF/q09iLI3YEZaxMZEu5zzWuBnbUlLZs407KJ82X7svMKOJJiTCQt3xyHpWsTEpMzWJ+Qwq/bSieSPJxsi3ojFfVKMjXfdpREkijHjiXf0XTds7TTGWxs8SQxY1/C0srKWE204HNabHuN1jqXjS3/RcwdL2JpVf/+jnJzsjm0YxUXdi+j0ekNtMjZS6jKo0ArDlm3ZKvvWBxCetMiqg+tnV3NHa4Q9VL9e2a5Dq52lgwNcbvmuPn7Uyt8zIoc73pUx7l1yD3XHmTylEObKx/HXpOfVc4qNjdY/bpo0SLeffddVq1aVeNLjYv6xcJCcd9NwfRu7cW/58Yx6ZddLIg7xdsjwvFzrd4qoyFDhvDee+8VJYsAYmNjycrKwt6+ZiqcRB2Xn2ucBlXYiProBuOy9mUF3gQjZkAj3xoPsdYqyIeUg2USQ3GQa+wjhoU1NGkLbYaAdzj4RBh7NtVEQ29hNhYWivu7BfPS/N1sO3qeqMDKv2azs7YkxNuZEG9n7JL30atXeNG+zNx8jqRkmppsZ3AkOZPElAxWHzjH3K3HSx3Hy9m2OHlk6o1UONXN3kZWZGpo0i6msnfWY3S8sJBDlsFcGv4zndsZVzM7dzKJk18/TFTWRvZZt8Vh1Kd0bhVh3oCrUH5eLgk713B+9zKcTm6gefZu2qgcDFpx2KoZ271HYteqF82i+tHKVao7hagJ9SpZ9MEHHzBjxgyUUoSFhTFq1Chzh1Sv6QJNbnmVRTf44ubxxx8nJyeHfv36AcYm15999tkNHVM0bIHujnz/UGe+3XSEt/7ax4APVvP8oDaM7Vh9VUaRkZGXHdvOzo6VK1dyyy23VMs5ayOttdSCX48zuyH+V2Ny6ERs8Upd7i2MCY0j642relnaQNR90O0paORj1pDNLj8Xzu0tnRg6HQ/5xhWwsLI3Vgi1H1NcMeTZBqwaRl8PUdqISH+m/L2fmWsTbyhZdDUONla08WlEG59Gl+3LyMknKSWDpORM07/GKW7L950jOb10IqlJI2MiKdjDkdduD8XastwCUFFP7F73J25LniRKJ7PBfzxR97yDja2dqW/RJ4TseItWOo8NrSbSccxzdb6aqCA/n8PxG0iJX4r98XW0yNpFa2W85iVaBBLndRs2LXrRPLofLdyb0MLM8QrRENXtZ5kSTpw4wdSpU9mzZw/29vaMHj2a1atXM3z4cHOHVm8ZDJrczHKSRY1vLFmUkJBwQ48XojwWFoq7uwTRK8SLSb/E8fy8XSzcdYq3hofR1K3qqwksLCzo27cv8+bNK9qWlpbGb7/91qCSRaKCUg7Bijch/hfjdCifcIh+wNSIOhC2fQ3b5hh77MQ8CN2eBhc/c0dd83IzjQm1ko2nz+4Fg+laZNvIWClU2F/Ip70x0WZZb17uiBvkaGvF2E4BfLH6MMdSM6vl+f9a52/n60I738unN6Zl5xVNbTti6pGUlJLBlqRUSRTVY9lZGeyY/TSdz/zAceXDwcFz6RLTF4Azxw9x5puHicnewh7rUJzHfEaXFmFmjrhyDAUFJO7ZwrldS7A9vp7mmTtpSQYtgSMW/sR7DMSqeS+CovsT7OVHsLkDFkLUn2QRQH5+PllZWVhbW5OZmYmbW/V8YiSMDEB6vsHcYQhxXZq6OfDtg534bvNR3vxzLwM/XM2zg9pwV8cALCyqtspo5MiRLF26lLQ049QXrTV//PEHn3/++Q0fe+3atWBcmazKKKXWm46pgUtAT631vgo8biUwUWt99c72FY/DHZgLxACztdaXrdJWb1w6Cavehe1fG6uFuj1lXMrewQ3SzsDaD+CXB41Ls3cYB93/D1wDzB11zci+CKd3FU8hO7UTkveDNl137N3ANwK6PFacGGocDBbyplpc3fiuQcxck8js9Um8OKStucMp4mxnTaifC6F+0ieroTi4Yw3Wvz9CZ8MxNnkMJ2z8h/g7uRirieZNpXXc27TAwMbWk+g4ehIWlnVnaqI2GDi6fzundy7G+tg6gjN20Jw0mgPHlTf7Gt+MRbMeBEYNJNA3kEBzByyEuEy9SRb5+fkxceJEAgICsLe3p3///kRGXv4+avr06UyfPh2A5OTkmg6zXinQWpJFok5SSnFXp0B6tvLkuV938eJv8fwZd5J3R7QnwL3qPmXu378/OTk5pbZduHCBQ4cO0bx58yo7TxV6F1igtc5XSi0Fvgc6mCGObOBFINT0Vf9kphoTQZung6HAWAnTfSI4N4H0c/D3f2DLTCjIhYix0OMZ41L39VVGCpwu03g69XDxfmcfYzKo7W3FiaFGfsZl74W4Tj4u9gwK8+HHLcf4V9+WONtZmzsk0cDk5+Wy5ZsXiU76gvPKhbheX9Kp1wgAzhw9yNnvHiYmeyu7bcNxveMzOjdrZ+aIr00bDBw/tIuTO5ZgdXQtgWnbCeQCgcBpPDno0g2CuxMQNQD/pi3wN3fAQohrqjfJovPnzzN//nwSExNxdXVl1KhRrFixgiFDhpQaN2HCBCZMmABQbjJJVJzWkFWgzR2GEJXm39iBr+7vyI9bjvH6n3sZ8OFqnr2lNXd3DqySKiMPDw+aNWvGvn2li3MWLlzIP//5T8CYtE5PTycoKKjS51FK9QT+Ah4xrV5WKVrr30rcPQVcVwmLUsoC+BI4rrV+4QbiyADWKqXqX4uCnHTYOA3WfwQ5adD+Duj1rDERlJECS16CzV8YexWFjzEmidxrZWKxcrSGtNOXr0h2qUSvFtdAYzIo4i5jnyafcHDyMlvIon56sHswv+88yY9bjvFg92bmDkc0IEcP7CD7p4fokn+AWJe+tBz/GeFunmiDgS2/vk/bXVNojoFNbZ8nZuTEWl1NdDJxHye2/41KWk3ApW00JZWmwFncSGoUzeHA7vh1GIBvUAjeUvUpRJ1Tb5JFS5cuJTg4GE9PTwCGDx/ODz/8YOao6rcCLdPQRN2nlOKOjgH0MFUZ/ff33fy56xTvjggnyOPGlx4fMWIE77zzDvn5xpUDs7KymDlzJmfOnGH27NmcPHmSXr16sXz58srGPxD4FbhXa/1zmX0+wOFyHwijtdZ/lHO8N4B7ABdg0nWEYgV8C8Rrrd8o57gfADeX87gftNZvX8d56qa8bNg6C1ZPgcxkaD0Eer8AXm2MVUbLXoVNn0NuBoSNgp7/Bo+W5o76xmgNF46UnkZ2aidknDUNUMZ+QgGdi6uFfMLBvrFZwxYNQ7i/KzFBjZm9PonxXYOwkp5AopoZCgrY/NM7tN/3ATnKhq0d3yd60AMAnDqyn5TvHqZjznbi7SJwu+NzOgW3NnPElzt9LIFjW/+GpDX4X4jFl3P4Aim4kOgcSWLTbvh06EfT5mF4SXJIiDqv3iSLAgIC2LhxI5mZmdjb27Ns2TKaNm1q7rDqNQOajAJJFon6wdfVntn3xTB363FeXbCHgf9bzb8HtGZ816AbqjK69dZbmTp1alHfIoADBw6USiBFRERU9vAKmA+MLC/xo7U+BdhfzwG11v9RSmUCdwLe1/HQz4GfyksUmY771PXEUW8U5MPO72Hl28bqmeAe0Oe/4B8NWReMTa03TDMu6d5uOPScBF617w3CNRkMkHrIlBDaUZwYyr5o3K8sjYmxlv2KE0NNQsFWFssT5vNAt2D+8c02Fu85w6CwBr6qoKhWySePcGrOvXTO2c5O+xh875lBlG8QhoICtvwyhbDd79EIxabQl+g44ilULUm0JJ8+SlLsIgyHV+N7fgv++jTewAWcOOzYgaNNH6BJ+34EhkTiXktiFkJUnXqTLOrUqRMjR44kMjISKysrOnTowMCBA80dVr1mkMoiUc8opRgV3ZTuLT15ft4uXl2wh4W7TvHuyHCaeVb8TW1+fj6bN2/mjz/+YO7cuWRnZ5fan5WVVXTb2dmZmJiYyoasgTRgNFBelVBlKovGA0OAYRgrlv5bwVjWAzcrpd7TWmeX3dngKou0hj3zYcUbkHwAfCPh9k+gWS/IvmRsar3+Y8i5CG1uM05Fa1L7e1IAUJAH5/aXnkZ2Jh5y0437LW2N30u7YcWJIa92YG1n3riFKKNfW2+autkzc22iJItEtTm0ayNOv9xJc51eKhl0MnEv57+fQKfcOHbZReJx52d0Cgwxa6ypZ0+QGLuY/EMr8T4fS6DhOB7AJRw45NCeY37j8AzvS7O2HYmsxdPjhBBVo94kiwBeeeUVXnnllaL7CxYsuOr4o0eP0r1791Lbzp07VzSV7WquNG7KdYyt6nHVcu4ojyuOyz54npn5xukEOTk5RdURHh6lHyNEXePtYsfMe6OZt/0EL/++m1v+t4aJ/UO4v1swlhWoMrr77rv55Zdf0FoXVQ9didb6RiqLANoACUqpj8uuGna9lUVKqfuBiUBP4A7gmiuhlTAT6AH8pJQarrUu9Y03mMoireHQcuO0slM7wLM1jPnGOO0sN904DW39R5B9AUIGG5NEPuHmjvrK8rLh7J4yiaHdUGBq3G7tCN5hpv5CpsSQZwhYSsNgUftZWiju6xrMqwv2sP3oeToEyBRIUbV2Lv+BFqueJEM5cGrEb3QK72qcjvbDm4Tt/RBnLNgc/goxw54wSzXRxdRzHI79m5yDK2mSsoVgQxJuQIa2I8E+jFN+I3AP7UuzsK50sKpXbxuFEBXQoP/Xl7caWnR0NLGx1179uaLjquOYdeXcQtRlSimGR/rTrYUHz8+L542Fe1kYf4rJI9vTwuvqVUavvPIKf/31FxcvXrzmeXJzc2nVqlWl49Ran1NKhQN7lFIXbqSxNDAVsAWOArkYp7hdTyzvK6VcgK+VUndprStdeqiUSgIaATZKqduB/lrrPZU9Xo04thmWvgJH1hqXt7/9MwgfbWxWve5/xq+sVGg10Jgk8jXHQnNXkZNurBAqmRg6tw8MpryfnYsxGdRpgrHxtHe4sfm2hXy6LOqu0TFN+WDJAWauTeTjOyVZJKqI1mz84U1i9k0m0aoZLg/8SnPfIE4ciufijw/TKTeeOPsYvO76jI5Na24th7SLqRyKXUz2gZV4JG+mWf5hOihNlrYhwS6UDb6Dady2D83bd6O9jW2NxSWEqJ0adLJICCGuxauRHV/cE8XvO0/y3993M2jqGp7u14oHuwVfsSFqq1atWLZsGT179iQjI+Oqx2/WrBmWlSjl7tatG8A2AK31EeCGu3FrrSvVQEZr3avE7YpOW7vWMYOq4jg1IjUR/n4e9i8ERy+4ZTJE3QuGAtj4Kaz7EDLOQYu+0Ot58I8yd8SQdb646fRp07/JBzHObAQcPY2JoVYDiiuGXANlqXpR7zjZWnFHx6Z8uS6JExey8HO9rjZvQlwmPy+XrZ8/TOfkX9nu2JWQR3/Azs6Rjd+9Rvv9U3FWVmxu/zoxQx+r9mqizPSLJMQuJWP/StzPbaR53kEilCZHW5Ng24ZNfhNwadub5hE9CLNzqNZYhBB1jySLhBDiGpRSDI3wo0tzd178LZ63/9rHX/GnmTIynJZNnMt9TFRUFPPnz+fWW28t1aOorI4dO1ZX2KK6GQpg02ew7DWwsILeL0LnR4zNnGO/hLUfQPoZY5+iXs9DQCfzxJl+1pQY2lFcMXThSPH+Rv7GZFDoyOLEkLO3JIZEg3Fv1yC+XJfEnPVJPD+ojbnDadCUUnbAaoxVrlbA3Ct9CKGUGgHMBWK01rWi7D3tYiqJn46mU/YWNnjfSccHP+Lk4Xgyfn6Eznl72OnQCZ9xn9PRL7hazp+dmU7C1mWk7VtB47ObaJ67n3BVQJ62JMEmhM1N78e59c20iLyZdg6ywIAQ4uokWVTGhAkTqnRcdRyzrpxbiPrGy9mOz8ZFsSDuFP/9fTeDp67lyb4tebhHs3KrjPr06cOcOXO49957y00Y2djY0KVLl5oIXVS1s/vg98fh+BbjtLLB74OjB2z7Cta8B2mnIKg7jJwFQTfVTExaw6UTpaeRndppjKWQWzPwi4To+4zTyHzaG+MWogHzb+zAwFBvvt90lCf6tMTJVl4em1EO0Ftrna6UsgbWKqX+0lpvLDlIKeUMPAlsMkeQ5Tl99ABZs0fStuCYsZH1sCfZ8sPrRBz8mEbKhi0d3iL61n9UaTVRTnYmh7av4uLe5TQ6vYGWOXsJVfnkawsOWbck1vcunFrfTPOoPrRxcqmy8wohGga5GpZR3xI2kiwSomoppbi1vS9dmrvz3/m7mfz3fhbFn2byqHBaeze6bPyoUaM4d+4czzzzDJmZmaX2WVpa0qFDLetbI66uIA/Wfgir3wUbJxg+A9oOhR3fwOr34NJxCOgCw6dDcI/qi8NggPOJpaeRndoJmSnG/coCPFoZYyisFvIOM/YdEkJc5oFuwfwZd4pPVybwVN9WV5xmLKqX1loDpqUVsTZ96XKGvga8AzxTQ6Fd1YFtK3H7/V7cdS57+3yJt1cQCW/fROf8fWx37ErTcZ8R4xt4w+fJy83h0M41nN+9FOdTG2ievYe2KheDVhy2asY271HYh9xMs6h+hLi4VcF3JoRoyCRZJIQQleDhZMsnd0UyeNcpXvwtnls/Wss/e7fkkV7NsS7zJuPRRx/lzJkzTJkypVTCKDc3l7CwsJoOXVTWyR0w/3E4swvaDYcBb0LCEvgoCi4eBf8YGPqxcdpZVU7hMhQY+wmVrBY6HQc5l4z7LazBqw2EDDIlhiKMS9fbSP8JISoqMqAxvVt78cmKQyyIO8U/ejZneKQftlbSwL2mKaUsga1AC+ATrfWmMvsjgaZa6z+VUldMFimlJgATAAICAqot3u2LZtNmw0RSLdy4NPInMrYtoNWyh8hStsRGvUvU4IcqXU1UkJ/Pobh1pMYvxeHkBlpkxdFaGVejPGwRxE6v27Fp2ZPm0QNo4eZJzbXKFkI0BJIsMrn//vtZsGABXl5exMfHmzucWumDDz5gxowZKKUICwtj1qxZ2NnZmTssIcxqUJgPnZu589/fd/P+kgP8vfs0k0e2p61v6Sqjl19+mVOnTvHtt98WJYxcXV1xcJA39LVeXjasehvWTTU2fh79FeRmwKyBcD4JfCNhyAfQos+NJ4nyc+HcXlNSyFQxdCYe8kxJRit78A6FsFHFFUNebcBKVq0R4kbNuCeaxXvO8MmKBJ77dRf/W3qQh3o0Y2zHpjjYyEvmmqK1LgAilFKuwDylVKjWOh5AKWUBvA+Mr8BxpgPTAaKjo8urTrphm3+eTHT8Gxy0DiH/5pew/u1xOucfYJtTd5rePY1o7+tLUhkKCkjcvYlzu5Zid3wdzTN30koZp7EfsWjKLs/BWLfoSVBkf5p5+dKsOr4pIYQwkSufyfjx43n88ce55557zB1KrXTixAmmTp3Knj17sLe3Z/To0fzwww+MHz/e3KEJYXZujjZ8NLYDg8N8eOG3eG77eC2P3dyCx25ugY2V8dNEpRSfffYZZ8+eZfHixWRlZREcXD0NLkUVOrYZfnsUUg5C+zvBNwKWvgKph4w9f8b+aFwxrDJJorwsOLO7dOPpM3vAkGfcb+MMPuEQNb44MeTeEizl0i1EdbCwUAwM9WZAuyasOZjMJysSeG3BHj5ZkcD9NwVxd5cgXOytzR1mg6G1vqCUWgEMBAo/yXUGQoGVyvi86w38rpS6rSabXGuDgU1znqPzkc+Is48m0yOMyMX3kKEc2NrxfSIH3lehaiJtMJC0bytndi7B9vg6gjN20Jx0mgPHlC973Pth2awHQVEDCPQJ4MYnsgkhRMXJK06THj16kJSUZO4warX8/HyysrKwtrYmMzMTX19fc4ckRK0yMNSbTsFuvLpgD/9bdpC/d59myqj2hPoZ+8RYWFjw008/0bt3bzZs2ECbNrLqTq1VkAer3oU1U8DZFyLGwfHNsPM7aBIKY76F1oMrniTKvgSnd5WeSpZ8AHSBcb+9mzEZ1OWx4sRQ42Co5mWVhRCXU0rRo5UnPVp5EpuUyicrEpiy+ACfrzrM3V0Cub9bMB5OUs1XHZRSnkCeKVFkD/TD2JsIAK31RcCjxPiVwMSaTBQZCgrY8tkEOp+byyHLZjjlJRN+fBZbnXsRdM80orz8rvhYbTBwLCGOUzuWYH10DUHp2wnmEsHASeXFAdceqODuNI0cQNOmzWlaU9+UEEKUQ5JFokL8/PyYOHEiAQEB2Nvb079/f/r372/usISodRo72vDBmAgGhfnw/LxdDP1kHY/2as7jvVtga2WJjY0NixYtYujQobRv397c4YryJCfArw/ByW1g7WjctuMb8GxjnILW+tarJ3EyU03VQiUaT6ceKt7v7GOsSmpza3FiyMVflqoXohaKDnJj1n0diT9xkU9XHuLTVYf4cl0id8QEMKFHM3xd7c0dYn3jA8wx9S2yAH7SWi9QSr0KxGqtfzdncLk52ez65E46XVoGQED+EdKUI9s6fUjULfddNl4bDJxM2suJ7YuxOLKWwEtbCeA8AcAZ3Dnk0pmEwO74Rw7ANygE+RhWCFGbSLJIVMj58+eZP38+iYmJuLq6MmrUKL755hvGjRtn7tCEqJX6tW1CTFBjXl2wh4+WJ7B49xkmjwon3N8VJycnli1bxvz5880dpihJa9g6CxY9D/nGHhHkZYCLH4z8EtoOuzxJlHb68qXqLx4r3u8aYEwGRYw1Np72DgfnJjX2LQkhqkaonwuf3BVJwtl0Plt1iG82HuHbTUcY3sGfSHuDucOrN7TWccBly4RqrV+6wvhe1R1ToezMdPZ/NIyorM1F2+Ia9aTZPdOI9PQp2nb66EGObfsbEtfQ9GIsfiTjByTjSpJzJIcDuuHboT/+zdrRRKpHhRC1mCSLRIUsXbqU4OBgPD09ARg+fDjr16+XZJEQV+HqYMP7oyMYEu7Dc7/uYti09TzcoxlP9m0pK+zUNhkp8PvjsH9h8Ta35tDrWQgdYVyK/sLR0quRndoJ6WdMgxW4t4CmnaDjBGOvIe9wcJCli4WoT1p4OTFlVHv+1bcl01cf5octx/gp38DaS9t57ObmtPZudO2DiDon7WIqJz4eRPu8vYAx8XO0y+tEDbibcyeTiP39MwyJq/G7EIufPoM3cB5nDjt24EhAN5q070dgqwg8JDkkhKhDJFkkKiQgIICNGzeSmZmJvb09y5YtIzo62txhCVEn9G7dhMVPufHGn3uYtvIQS/ac4eDZdMCSJzf8WTQu6e3B5guyIUtcDXNuLb7vGgBho8GjlbHP0PZvjImh7AvG/coSPFtD8z7F08i8Q8HW2SzhCyFq1umL2bjYW/Pyre14vHcLXv5uNcv3nuGPnSfp28aLx25uQYeAxuYOU1SRiylncJjahtbK2GMuzi6KrGa3wIGlHN30FgGGE3gCl3DkkEN7jvnfg1d4P4LaRBNlKR8MCSHqLkkWmYwdO5aVK1eSnJyMv78/r7zyCg888IC5w6o1OnXqxMiRI4mMjMTKyooOHTowYcIEc4clRJ3hYm/NuyPbMyjMh/Gztpi2lu5RE/Tsn5IwqkkF+dy09yWIPVR6e/pZY2NrAEsbaNIO2t1eYqn6tmAtfUqEaIh+3Xacp3/aWWqbozU42NmSkVvA0r1nWbr3LDe1cOeTOyNxdbAxU6SiKpw+loD3zKhSl+vw7K2wZyvp2p4Eh3BO+I3GI7QvzUI708FK3loJIeoPeUYz+f77780dQq33yiuv8Morr5g7DCHqtF4hXuYOwSyUUm7Aj0AQkASM1lqfLzPmZuCDEptaA3dorX+r8oCyL8HbTSn12b+1g3HqmE94cWLIszVYylLZQgij/u28ad/0CDuPXSjalpEHGXk5RfdbezuTnJbLhcw8SRbVYcknjxgTRSaZ2pYE+1AyfLrQuF0fmrfvRoS1/H6FEPWXJIuEEELUhGeBZVrrt5VSz5ruTyo5QGu9AoiAouRSArC4WqIxTRnLtnbF7tYpxsSQe3OwkCkDQogrc7K1Yv5jNwGQkZPPpsQUHpgdizbtf7pfK57o09J8AYoq4+TqDhinnVnf/CzNI3oQbmtn5qiEEKLmSLJICCFETRgK9DLdngOspEyyqIyRwF9a68xqiUYpePkiSxcsYEj4kGo5hRCifnO0taJ36ybMGuhIr169zB2OqGJ2Dk7w8kXCzR2IEEKYiSSLhBBC1IQmWutTptungWutH38H8P6VdiqlJgATAJo0acLKlSsrFVRBQUGlHyuEEADp6enyPCKEEKLekWSREELUsKS3BxP07J+ApmTXzLre3FoptRTwLmfXf0re0VprpZQuZ1zhcXyAMODvK43RWk8HpgNER0fryn6qv2DBAqkIEELckJUrV8rziBBCiHqnypJFSqkWwMESm3K01nZlxiQD7iU2faq1frSqYhBCiLoi6e3BzJ8/n6FDh5o7lCqjte57pX1KqTNKKR+t9SlTMujsVQ41Gpintc6r8iCFEEIIIYQQ12RRhcc6BMRorRXgBdgqpRaUGTNMa61MY/YAj1Th+YUQQtRevwP3mm7fC8y/ytixgCxRKYQQQgghhJlUWbJIG8Wa7hb2ojCUGbOmxF2nqjq3EEKIWu9toJ9S6iDQ13QfpVS0UmpG4SClVBDQFFhljiCFEEIIIYQQVdyzSCnlCKSb7uZqrW8rZ0wq0Nh0990rHOcc4AHg5ubG/PlX+wD66m7ksUIIUd0aynOU1joF6FPO9ljgwRL3kwC/motMCCGEEEIIUVaVJou01hmAUkr1B/5WSn2htX6ozBg3jIOOABMpZ+lkrbVn4e3o6Ghd2Z4eCxYsYMgQWRJZCFE71beeRUIIIYQQQoj6oSp7FhXRWi8GsoAxVxnWsrrOL4QQQgghhBBCCCEqR2l9xdWLr+9ASt0BpGqtFyulWgN7gXitdViJMe9orSeZbu8CQk3Nrq923HPAkUqGFQwkVvKxQghR3cKAXTfw+MCSlZgNlVwnhBBm5gEkmzuIK5DrBDd8najNv19RN8nflKhqN/I3dcXrRFUmi2ZTvNINQJbW2kEplQ0c0lq3U0rlUXrq2+da639USQDlx5ShtXasruMLIcSNUErpayXMRfWS64QQ4kYppWK11tHmjkNUD/n9iqomf1OiqlXX31SV9SzSWo8Hxpez3a7EbeuqOp8QQgghhBBCCCGEqHrSM0gIIYQQQgghhBBCFKnvyaJfzR2AEEJchfTKMT+5TgghbtR0cwcgqpX8fkVVk78pUdWq5W+qynoWCSGEEEIIIYQQQoi6r75XFgkhhBBCCCGEEEKI6yDJIiGEEEIIIYQQQghRpM4mi5RS/1FK5Zq+/ipnv7NS6qhpf7pSqluJfYtKPPb5mo1cCNEQKKWWKKW06etcOfsnKKUMJcbcbdr+QYltWim1veajr/vkGiGEqApKqYFKqf1KqQSl1LPl7A9USi1TSsUppVYqpfxN2yOUUhuUUrtN+8bUfPSioirwe7ZVSv1o2r9JKRVkhjBFHVOBv6vxSqlzSqkdpq8HzRGnqBuUUl8qpc4qpeKvsF8ppaaa/t7ilFKRN3rOOpksUkpZAy8D/YDGQC+l1K1lhn0BpGmtbYCZwHemx94K9ATcgT7AK6bjCSFElVBK2QN9gScBL8BDKfVqmWHTgP1aawX8Dnxp2n4GmGTaPhaIUEp1rZnI6we5RgghqoJSyhL4BLgFaAuMVUq1LTNsCvCV1joceBV4y7Q9E7hHa90OGAh8qJRyrZHAxXWp4O/5AeC81roF8AHwTs1GKeqaCv5dAfyotY4wfc2o0SBFXTMb4/XkSm4BWpq+JgCf3ugJ62SyCBgPXNRar9JaZwCrgMfKjOkLfGi6/W/AXymlTONWaa3TtNZrgIum4wkhRFX5DNBa66la63NAMvBImTGWFL/YvB2wwvigt7XW75pu/2Da3w1xPcYj1wghxI3rCCRorQ9rrXOBH4ChZca0BZabbq8o3K+1PqC1Pmi6fRI4C3jWSNTielXk9zwUmGO6PRfoY7pmCHElFfm7EqLCtNargdSrDBmK8cMLrbXeCLgqpXxu5Jx1NVkUgvHNV6EkwLfMGCdgO4DWOgcowJhl8wUOlxiXYjqeEEJUlbZAXon7ZzE+J5WUD7xuur0BQCnVp+QApdQXppsfVEOM9ZlcI4QQVcEPOFbi/nHTtpJ2AsNNt4cBzkop95IDlFIdARvgUDXFKW5MRX7PRWO01vkYP0hwR4grq8jfFcAI05ShuUqppjUTmqinKvo3V2F1NVkkhBB13d2Aj1JKA+GmbVmFO5VSo4AHgdla67xyHi+EEML8JgI9Tf3legInMCafATB9qvs1cJ/W2mCeEIUQtdQfQJBpGusSiqvXhKgV6mqyaD/gUeJ+EHCyzJh0oAMYm9JhnPJx0DSuWYlx7qbjCSFEVdkDlOxz44XxOamI1voHrbWlqTdRjGnbegBTQ7qfgFit9X01E3K9ItcIIURVOAGU/KTf37StiNb6pNZ6uNa6A/Af07YLAEqpRsCfwH9MUwJE7XTN33PJMUopK8AFY+WpEFdSkeePFFN1M8AMIKqGYhP1U0Wey65LXU0WfQW4KKW6K6UcMX6SM63MmGXAv0y33wVOaK21aVxP00o43TE+2c+ukaiFEA3F4xgXJXhMKeWJMXHxeckBSqn+JRonb8BY0o5pfCxwSmsdU4Mx1ydyjRBCVIUtQEulVLBSyga4A+OCBEWUUh5KqcLX089hWqzANH4exv4Rc2swZnH9rvl7Nt2/13R7JLDcdM0Q4koq8vxRsp/MbcDeGoxP1D+/A/eYVkXrjLF/56kbOaBV1cRVs7TWOUqp1zC+2FfACq3170qpVcAarfULGDuAxyulcoFcYLDpsb8rpdZgbA6lgVdliocQoipprdOUUsuBj01fKVrrF5RSF4BdWuvuwBtAtKk/Zi7G6hcwNkhVFE9RA/hQa/1UDX4LdZpcI4QQVUFrna+Uehz4G2P14Zda692m1S1jtda/A72At0zP16spbqY/GugBuCulxpu2jdda76jBb0FUQAV/zzOBr5VSCRivD3eYL2JRF1Tw7+oJpdRtGPtYpiILaoirUEp9j/Ga46GUOg78F9NMBq31Z8BCYBCQgHFFzhuenaAkKS6EEEIIIYQQQgghCtXVaWhCCCGEEEIIIYQQohpIskgIIYQQQgghhBBCFJFkkRBCCCGEEEIIIYQoIskiIYQQQgghhBBCCFFEkkVCCCGEEEIIIYQQoogki4QQQgghhBBCCCFEEUkWCSGEEEIIIYQQQogikiwStZZSqkAptaPEV5BSKr2az/myUmpiFRzHVSn1aJlt62/0uEIIIa6fOa4npvNedi0QQghR+8h1QojLKa21uWMQolxKqXSttdO1tlXxOV8G0rXWUyowVmH8P2QoZ18QsEBrHVrlQQohhLgu5riemM4RhFwLhBCi1pPrhBCXk8oiUWcppZ5WSsWbvv5l2vaMUuoJ0+0PlFLLTbd7K6W+vcJx/qOUOqCUWguEmLYFKaXiS4yZaKo6ClJK7VdKfQXEA02VUr8ppbYqpXYrpSaYHvI20Nz0ycRk0zHSSxyvvNiDlFJ7lVJfmI61WCllf4WY7zWdM84UtxBCiEqqiuuJUspRKfWnUmqn6ThjKP9aME4ptdm07XOllKXp+X+fUupb03VgrlLKoZxzyHO/EEKYgVwnRENkZe4AhLgKe6XUDtPtRK31sMIdSqko4D6gE6CATUqpVcAa4P+AqUA0YKuUsga6A6vLnsB0nDuACIz/H7YBW68RV0vgXq31RtMx7tdap5oSO1uUUr8AzwKhWuuIK5yzvNjPm449Vmv9kFLqJ2AE8E2ZxzsDk4AIrXWuUsr1GvEKIURDV+3XE2AgcFJrPdh0XBdgEyWuBUqpNsAY4CatdZ5Sahpwl+l4IcADWut1SqkvgUeBoipXee4XQohqJdcJIcqQyiJRm2VprSNMX8PK7OsGzNNaZ2it04FfMT4xbwWilFKNgBxgA8Yn7+4Yn9DL6m46TqbW+hLwewXiOlKYKDJ5Qim1E9gINMWY8LmaK8UOxovTDtPtrUBQOY8vAOyB95RS0VrrCxWIWQghGrKauJ7sAvoppd5RSnXXWl8sZ0wfIArjBws7TPebmfYd01qvM93+xhRXSfLcL4QQ1UeuE0KUIckiUa9orfOARGA8sB7jE/XNQAtg73UcKp/S/z/sStzOKLyhlOoF9AW6aK3bA9vLjL1eOSVuF1BO9Z/WOhMIBdYB05U0xRNCiCp3vdcTrfUBIBLjm4HXlVIvlXNYBcwp8YYkRGv9cuEhyh6yzPHluV8IIWoRuU6I+k6SRaKuWgPcrpRyUEo5AsMozuCvASZiLNdcA/wD2K7L7+a+2nQce1Pp5q2m7WcAL6WUu1LKFhhyhThcgPNa60ylVGugs2l7GuBcidivSSnV0vTJxg/AAm4sOSWEEA1dlVxPlFK+QKbW+htgMsY3BGWvBcuAkUopL9Nj3JRSgaZ9AUqpLqbbdwKlek3Ic78QQpiNXCdEgyTJIlEnaa23AbOBzRjn+s7QWm837V4D+AAbtNZngGyukIwxHedHYCfwF7DFtD0PeNV0/CXAviuEsgiwUkrtxdigbqPp8SnAOlPzusnXEXtF/EcZm2xvA4KBadfxWCGEECVU1fUECAM2m6YN/Bd4vey1QGu9B3gBWKyUisN4ffExPX4/8JjpetIY+LTM8eW5XwghzECuE6KhUuUXWwghhBBCiJqgZOlkIYQQVyHXCWEOUlkkhBBCCCGEEEIIIYpIZZEQQgghhBBCCCGEKCKVRUIIIYQQQgghhBCiiCSLhBBCCCGEEEIIIUQRSRYJIYQQQgghhBBCiCKSLBJCCCGEEEIIIYQQRSRZJIQQQgghhBBCCCGKSLJICCGEEEIIIYQQQhSRZJEQQgghhBBCCCGEKPL/Q+eOFCZPMu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow3 = double_bracket_flow( H_TFIM )\n",
    "\n",
    "\n",
    "flow3.flow_step_max =1\n",
    "flow3.flow_step_min = 0\n",
    "flow3.nmb_search_points_minimizing_s_search = 20\n",
    "flow3.please_use_binary_search  =True #by default binary search is used\n",
    "\n",
    "flow3.nmb_flow_steps = 15\n",
    "flow3.flow_forwards( states = np.eye(2**L).tolist() )\n",
    "flow3.show_flow_forwards_results()\n",
    "flow3.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "show_observable_with_errorbars() got multiple values for argument 'observable'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-24da5e6ba681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mflow3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_eigenstates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py\u001b[0m in \u001b[0;36mshow_eigenstates\u001b[0;34m(self, selection, save_path, observable, observable_errorbars, observable_name, observable_errorbars_name)\u001b[0m\n\u001b[1;32m   1119\u001b[0m     observable = None, observable_errorbars = None, observable_name = None, observable_errorbars_name = None ):\n\u001b[1;32m   1120\u001b[0m         self.show_observable_with_errorbars (self, observable = observable, observable_errorbars = observable_errorbars,\n\u001b[0;32m-> 1121\u001b[0;31m         observable_name = 'expected_energy', observable_errorbars_name = 'expected_energy_fluctuation')\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     def show_observable_with_errorbars (self, observable = None, observable_errorbars = None,\n",
      "\u001b[0;31mTypeError\u001b[0m: show_observable_with_errorbars() got multiple values for argument 'observable'"
     ]
    }
   ],
   "source": [
    "flow3.show_eigenstates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}