conviction/abc_sim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#import conviction files\n",
    "#from conviction_helpers import *\n",
    "#from conviction_system_logic3 import *\n",
    "from bonding_curve_eq import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hatch_raise = 100000 # fiat units\n",
    "hatch_price = .1 #fiat per tokens\n",
    "theta = .35 #share of funds going to funding pool at launch\n",
    "\n",
    "R0 = hatch_raise*(1-theta)\n",
    "F0 = hatch_raise*theta\n",
    "S0 = hatch_raise/hatch_price\n",
    "\n",
    "kappa = 2\n",
    "V0 = invariant(R0,S0,kappa)\n",
    "P0 = spot_price(R0, V0, kappa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "agent initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of agents\n",
    "n= 100\n",
    "\n",
    "#gain factors\n",
    "g = sts.expon.rvs(loc=.1, scale=1, size=n)\n",
    "phat0 = g*F0/S0\n",
    "\n",
    "#holdings fiat\n",
    "h = sts.expon.rvs( loc=10,scale=10, size=n)\n",
    "\n",
    "#holdings tokens\n",
    "s_dist = sts.expon.rvs(loc=10, scale=10, size=n)\n",
    "s0 = s_dist/sum(s_dist)*S0\n",
    "\n",
    "#lambda for revenue process\n",
    "lam = 20\n",
    "\n",
    "#phi for exiting funds\n",
    "phi = .05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {\n",
    "    'kappa': [kappa],\n",
    "    'lambda': [lam],\n",
    "    'gains': [g],\n",
    "    'population':[n],\n",
    "    'phi': [phi],\n",
    "    'invariant': [V0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_conditions = {'holdings': h,\n",
    "                      'tokens': s0,\n",
    "                      'supply': S0,\n",
    "                      'prices': phat0,\n",
    "                      'funds':F0,\n",
    "                      'reserve': R0,\n",
    "                      'spot_price': P0,\n",
    "                      'action': {}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'action': {},\n",
       " 'funds': 35000.0,\n",
       " 'holdings': array([31.58200339, 29.0449534 , 13.38309347, 39.86546713, 14.46790442,\n",
       "        11.2871474 , 24.12455596, 12.06277393, 12.62677834, 12.72431123,\n",
       "        12.0581096 , 23.56626593, 18.71295693, 17.15550518, 12.09218682,\n",
       "        18.71051653, 42.47594894, 14.9008994 , 19.74554576, 13.16518859,\n",
       "        23.55333755, 28.66635179, 10.63352331, 64.25158855, 21.33348353,\n",
       "        13.71217834, 23.71843029, 15.76629049, 35.7052015 , 11.02843696,\n",
       "        10.317351  , 21.17203975, 25.84668755, 20.01629419, 17.60016477,\n",
       "        10.31846458, 21.49851613, 13.60206495, 38.73222353, 14.71441894,\n",
       "        22.65470923, 10.23002233, 12.62138797, 14.37124452, 11.73630219,\n",
       "        15.02644971, 10.92532854, 46.4010839 , 27.32493113, 32.29756272,\n",
       "        11.81808269, 27.33989042, 13.99222474, 11.74013444, 12.77155953,\n",
       "        16.01732288, 34.45910585, 10.73274779, 10.58319649, 21.62325174,\n",
       "        22.06088202, 46.44276033, 29.903635  , 12.4121705 , 25.91732883,\n",
       "        10.16000427, 11.77920764, 11.94735412, 38.51189421, 31.86998079,\n",
       "        10.64190129, 30.05592175, 23.62857443, 16.57122234, 18.54347254,\n",
       "        15.00950717, 27.37536279, 30.80260508, 13.42683293, 10.45062174,\n",
       "        15.50091856, 12.46509344, 51.80670911, 15.6468271 , 38.60054239,\n",
       "        13.09513746, 15.87655028, 42.49206989, 18.38555965, 10.39305713,\n",
       "        22.79628517, 29.17734091, 11.10175388, 15.36119359, 37.99383596,\n",
       "        22.70064061, 17.18026394, 12.42264013, 13.8902754 , 45.50172664]),\n",
       " 'prices': array([0.15059378, 0.08107743, 0.01402543, 0.01048169, 0.02766831,\n",
       "        0.02999851, 0.02490189, 0.05478936, 0.03093358, 0.03278822,\n",
       "        0.10903366, 0.09215265, 0.02583883, 0.07208382, 0.0561782 ,\n",
       "        0.01303988, 0.00518078, 0.00985617, 0.01276663, 0.02807175,\n",
       "        0.03455491, 0.04795666, 0.01374762, 0.0398707 , 0.01716832,\n",
       "        0.04888493, 0.046086  , 0.02436093, 0.01589939, 0.05515488,\n",
       "        0.0278789 , 0.04212962, 0.04304507, 0.00637168, 0.19100402,\n",
       "        0.03461325, 0.0221437 , 0.13092546, 0.01195011, 0.01403811,\n",
       "        0.06533656, 0.01102863, 0.01620356, 0.02667744, 0.02234697,\n",
       "        0.07584373, 0.03481409, 0.0225988 , 0.01352033, 0.0218228 ,\n",
       "        0.06797646, 0.01795691, 0.07415803, 0.0279671 , 0.03053828,\n",
       "        0.01217995, 0.00939741, 0.06557077, 0.03194412, 0.0342237 ,\n",
       "        0.00578721, 0.03014732, 0.00808006, 0.02970001, 0.04228996,\n",
       "        0.007174  , 0.08516905, 0.04816447, 0.02550767, 0.07252689,\n",
       "        0.0037419 , 0.02337823, 0.00922323, 0.02280749, 0.01044978,\n",
       "        0.02961406, 0.01504145, 0.04223732, 0.07249289, 0.0148142 ,\n",
       "        0.00665742, 0.03278855, 0.09271294, 0.07950636, 0.02524091,\n",
       "        0.0187724 , 0.01036979, 0.01392327, 0.01831863, 0.0342545 ,\n",
       "        0.01612215, 0.0151121 , 0.00639963, 0.04583544, 0.05557914,\n",
       "        0.00582766, 0.04165019, 0.08253628, 0.04806662, 0.01563301]),\n",
       " 'reserve': 65000.0,\n",
       " 'spot_price': 0.13,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([11792.3035626 ,  6691.24642581,  5914.40049019,  5605.21257999,\n",
       "        12782.04975636,  6666.5150573 , 15216.84762105,  8394.09060558,\n",
       "        14584.01576425,  6094.45252195,  9357.95010546, 11068.73776234,\n",
       "         6576.78728318, 10977.36069695, 27265.37389499,  6639.77331058,\n",
       "        13609.97652219, 13435.15453682, 13262.07690678, 10432.89062086,\n",
       "        12426.65284676, 12587.77436043,  6630.77609937,  9460.57678804,\n",
       "         5381.98161693, 16601.16921956,  8311.30336511,  9615.29534836,\n",
       "         7747.71641845,  5264.67868403, 15275.72505327, 11140.6933726 ,\n",
       "         5850.15405587,  9816.53689769,  5716.51776032, 20903.01539875,\n",
       "         7560.81637688,  8179.89430606,  5767.26708927, 11256.66112127,\n",
       "         5789.46104487,  8483.28698518,  8557.20867717,  9030.11388641,\n",
       "        11928.18447926,  8201.87321987, 13899.07777221,  7607.5420815 ,\n",
       "        10121.26719286,  5867.79363524,  7114.24971408,  7878.26338996,\n",
       "        11868.1182996 ,  9749.50755393,  6384.44180487,  8031.35792073,\n",
       "        10140.58620752,  5888.73504073, 15917.48226775, 20581.7811761 ,\n",
       "         5354.95152993,  5602.5569788 ,  5333.68233155,  6834.50932277,\n",
       "         9489.86474889,  7795.99395954, 13198.32878409,  6388.73615404,\n",
       "         7749.24805554,  5870.62088506, 17776.74926527,  8584.38560165,\n",
       "         8176.75120903, 14343.77895045,  5936.51199282,  5264.89028067,\n",
       "        11700.65751467,  7100.50720998, 10428.45715111, 13524.44077852,\n",
       "         7086.30022932, 15017.32044677, 15345.49775692, 12369.30624932,\n",
       "         9394.40322101,  8876.33801359, 12558.62755447,  5334.46627751,\n",
       "        22183.47783199,  5631.10453879,  9912.59369291, 10041.13787703,\n",
       "        18306.78076844, 20067.12243301,  9221.63181115,  6056.06124884,\n",
       "         7337.42994172,  6795.97267291, 11136.31334776,  5969.7368281 ])}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change in F (revenue and spending accounted for)\n",
    "def revenue_process(params, step, sL, s):\n",
    "    lam = params['lambda']\n",
    "    rv = sts.expon.rvs(scale=1/lam)\n",
    "    delF= 1-1/lam+rv\n",
    "    return({'delF':delF})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']*_input['delF']\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_prices(params, step, sL, s, _input):\n",
    "    \n",
    "    g = params['gains']\n",
    "    phat = g*s['funds']/s['supply']\n",
    "    \n",
    "    key = 'prices'\n",
    "    value = phat\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change in F (revenue and spending accounted for)\n",
    "def choose_agent(params, step, sL, s):\n",
    "    n = params['population']\n",
    "    rv = np.random.randint(0,n)\n",
    "    return({'agent':rv})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agent_action(params, step, sL, s, _input):\n",
    "    \n",
    "    a = _input['agent']\n",
    "    h_a = s['holdings'][a]\n",
    "    phat_a = s['prices'][a]\n",
    "    s_a =  s['tokens'][a]\n",
    "    p = s['spot_price']\n",
    "    \n",
    "    if p>phat_a:\n",
    "        mech = 'burn'\n",
    "        #solve for burn s.t. p=phat\n",
    "        #if ha is enough\n",
    "        amt = 10000 #fill in logic here\n",
    "        if amt> s_a:\n",
    "            amt = s_a\n",
    "        \n",
    "    else: # p<phat_a:\n",
    "        mech = 'bond'\n",
    "        #solve for buy s.t. p=phat\n",
    "        #if sa is enough\n",
    "        amt = 10000 #fill in logic here\n",
    "        if amt> h_a:\n",
    "            amt = h_a\n",
    "        \n",
    "    key = 'action'\n",
    "    value = {'agent':a, 'mech':mech, 'amt':amt}\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resolve_action(params, step, sL, s):\n",
    "    action = s['action']\n",
    "    a = action['agent']\n",
    "    amt = action['amt']\n",
    "    h_a = s['holdings'][a]\n",
    "    s_a =  s['tokens'][a]\n",
    "    R = s['reserve']\n",
    "    S = s['supply']\n",
    "    F = s['funds']\n",
    "    V0 = params['invariant']\n",
    "    \n",
    "    if action['mech'] == 'bond':\n",
    "        h_a = h_a-amt\n",
    "        dS, pbar = mint(amt, R,S, V0, params['kappa'])\n",
    "        R = R+amt\n",
    "        S = S+dS\n",
    "        s_a = s_a+dS\n",
    "        P = spot_price(R, V0, kappa)\n",
    "    \n",
    "    elif action['mech'] == 'burn':\n",
    "        s_a = s_a-amt\n",
    "        dR, pbar = withdraw(amt, R,S, V0, params['kappa'])\n",
    "        R = R-dR\n",
    "        F = F + params['phi']*dR\n",
    "        S = S-amt\n",
    "        h_a = h_a + (1-params['phi'])*dR\n",
    "        P = spot_price(R, V0, kappa)\n",
    "    \n",
    "    return({'F':F, 'S':S, 'R':R,'P':P, 'a':a,'s_a':s_a, 'h_a':h_a})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_F(params, step, sL, s, _input):\n",
    "    \n",
    "    F = _input['F']\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = F\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_S(params, step, sL, s, _input):\n",
    "    \n",
    "    S = _input['S']\n",
    "    \n",
    "    key = 'supply'\n",
    "    value = S\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_R(params, step, sL, s, _input):\n",
    "    \n",
    "    R = _input['R']\n",
    "    \n",
    "    key = 'reserve'\n",
    "    value = R\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_P(params, step, sL, s, _input):\n",
    "    \n",
    "    P = _input['P']\n",
    "    \n",
    "    key = 'spot_price'\n",
    "    value = P\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_holdings(params, step, sL, s, _input):\n",
    "    \n",
    "    h_a = _input['h_a']\n",
    "    a = _input['a']\n",
    "    \n",
    "    h = s['holdings']\n",
    "    h[a] = h_a\n",
    "    \n",
    "    key = 'holdings'\n",
    "    value = h\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_tokens(params, step, sL, s, _input):\n",
    "    \n",
    "    s_a = _input['s_a']\n",
    "    a = _input['a']\n",
    "    \n",
    "    tokens = s['holdings']\n",
    "    tokens[a] = s_a\n",
    "    \n",
    "    key = 'holdings'\n",
    "    value = tokens\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The Partial State Update Blocks\n",
    "partial_state_update_blocks = [\n",
    "    { \n",
    "        'policies': { \n",
    "            #new proposals or new participants\n",
    "            'random': revenue_process\n",
    "        },\n",
    "        'variables': {\n",
    "            'funds': update_funds,\n",
    "            'prices': update_prices\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "      'policies': {\n",
    "          'random': choose_agent\n",
    "        },\n",
    "        'variables': { \n",
    "            'action': agent_action, \n",
    "        }\n",
    "    },\n",
    "    {\n",
    "      'policies': {\n",
    "          'act': resolve_action,\n",
    "        },\n",
    "        'variables': {\n",
    "            'funds': update_F, #\n",
    "            'supply': update_S, \n",
    "            'reserve': update_R,\n",
    "            'spot_price': update_P,\n",
    "            'holdings': update_holdings,\n",
    "            'tokens': update_tokens\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_periods_per_run = 10000\n",
    "monte_carlo_runs = 1\n",
    "\n",
    "from cadCAD.configuration.utils import config_sim\n",
    "simulation_parameters = config_sim({\n",
    "    'T': range(time_periods_per_run),\n",
    "    'N': monte_carlo_runs,\n",
    "    'M': params\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'N': 1, 'T': range(0, 10000), 'M': {'kappa': 2, 'lambda': 20, 'gains': array([4.30267931, 2.31649808, 0.40072669, 0.29947672, 0.79052321,\n",
      "       0.85710034, 0.71148247, 1.56541014, 0.88381662, 0.93680619,\n",
      "       3.11524746, 2.63293283, 0.7382524 , 2.05953768, 1.60509151,\n",
      "       0.37256795, 0.14802233, 0.28160496, 0.36476087, 0.80204989,\n",
      "       0.98728308, 1.37019036, 0.39278921, 1.13916299, 0.49052357,\n",
      "       1.39671233, 1.31674289, 0.69602648, 0.45426833, 1.57585358,\n",
      "       0.79653988, 1.20370353, 1.22985914, 0.1820479 , 5.45725759,\n",
      "       0.98894992, 0.6326771 , 3.74072733, 0.34143169, 0.40108877,\n",
      "       1.86675886, 0.31510373, 0.46295893, 0.7622127 , 0.63848493,\n",
      "       2.16696366, 0.99468836, 0.64567998, 0.38629526, 0.62350857,\n",
      "       1.94218455, 0.51305449, 2.11880099, 0.79905988, 0.87252215,\n",
      "       0.34799867, 0.26849755, 1.87345064, 0.9126891 , 0.97781999,\n",
      "       0.16534882, 0.86135212, 0.23085874, 0.84857163, 1.20828451,\n",
      "       0.20497155, 2.43340139, 1.37612762, 0.7287905 , 2.07219692,\n",
      "       0.10691132, 0.66794938, 0.26352085, 0.65164269, 0.29856511,\n",
      "       0.84611596, 0.42975566, 1.20678056, 2.07122533, 0.42326272,\n",
      "       0.19021207, 0.93681583, 2.64894105, 2.27161015, 0.72116873,\n",
      "       0.53635419, 0.29627963, 0.39780774, 0.52338934, 0.97869986,\n",
      "       0.46063284, 0.43177441, 0.18284669, 1.3095841 , 1.5879754 ,\n",
      "       0.16650463, 1.19000535, 2.35817933, 1.37333193, 0.44665741]), 'population': 100, 'phi': 0.05, 'invariant': 15384615.384615384}}]\n"
     ]
    }
   ],
   "source": [
    "from cadCAD.configuration import append_configs\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# The configurations above are then packaged into a `Configuration` object\n",
    "append_configs(\n",
    "    initial_state=initial_conditions, #dict containing variable names and initial values\n",
    "    partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
    "    sim_configs=simulation_parameters #dict containing simulation parameters\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabulate import tabulate\n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "from cadCAD import configs\n",
    "import pandas as pd\n",
    "\n",
    "exec_mode = ExecutionMode()\n",
    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
    "run = Executor(exec_context=multi_proc_ctx, configs=configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                            __________   ____ \n",
      "          ________ __ _____/ ____/   |  / __ \\\n",
      "         / ___/ __` / __  / /   / /| | / / / /\n",
      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
      "        by BlockScience\n",
      "        \n",
      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x1a0e47d390>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a0e47d390>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:37: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "verbose = False\n",
    "results = {}\n",
    "for raw_result, tensor_field in run.execute():\n",
    "    result = pd.DataFrame(raw_result)\n",
    "    if verbose:\n",
    "        print()\n",
    "        print(f\"Tensor Field: {type(tensor_field)}\")\n",
    "        print(tabulate(tensor_field, headers='keys', tablefmt='psql'))\n",
    "        print(f\"Output: {type(result)}\")\n",
    "        print(tabulate(result, headers='keys', tablefmt='psql'))\n",
    "        print()\n",
    "    results[i] = {}\n",
    "    results[i]['result'] = result\n",
    "    results[i]['simulation_parameters'] = simulation_parameters[i]\n",
    "    i += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_index = 0\n",
    "df = results[experiment_index]['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0e61ee10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e4e7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.funds.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0e68b898>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXl8FPX5xz/f3AmQhCTcEMIl9yEEBDkERUHwtp6tt9J6tNqqFa1VW7Faba219afiUa+KinfFCxDlkDPIfYUjQMKRQCAQINfu9/fHzux+d3au3Z3Zndk879eLF7Ozszvfyc4++3yf7/N8HsY5B0EQBOF+kuI9AIIgCMIayKATBEEkCGTQCYIgEgQy6ARBEAkCGXSCIIgEgQw6QRBEgmBo0BljrzPGKhljG4R9jzPG1jHG1jDGvmWMdbR3mARBEIQRzCgPnTE2DkAtgLc45wOkfdmc82PS9m8A9OOc/8ruwRIEQRDaGHronPOFAKoV+44JD1sAoOokgiCIOJMS6QsZY08AuB5ADYAJZl5TUFDAi4qKIj0lQRBEs6SkpOQQ57yN0XGGIRcAYIwVAfhCDrkonnsQQAbn/FGN104DMA0ACgsLh+3evdvwfARBEEQAxlgJ57zY6DgrslzeBXC51pOc85mc82LOeXGbNoY/MARBEESERGTQGWO9hIcXAdhizXAIgiCISDGMoTPGZgEYD6CAMVYO4FEAUxhjvQF4AewGQBkuBEEQccbQoHPOr1HZ/ZoNYyEIgiCigCpFCYIgEgQy6ARBEAkCGXSCIIgEgQw6QQh4vBzvLNuN2vqmeA+FIMKGDDpBCHy3pRIPf7oBM3/YEe+hEETYkEEnCIHDtfUAgB1VJ+I8EoIIHzLoBCHwyU8VAIA56/ejyeON82gIIjzIoBOEwJSBHfzbh2ob4jgSgggfMugEIdAiPVBrl5zE4jgSgggfMugEISCGWY7VNcZxJAQRPmTQCUJg+sfr/ds/bj+E1xfvghmJaYJwAhE3uCCIROePn20EAPRu3wqjexbEeTQEYQx56ARhwM9fXY6GJsp4IZwPGXSCMMFXG/bHewgEYQgZdIIwQUZqcryHQBCGkEEnCBNkZ6TGewgEYQgZdIIwAQdluhDOhww6QZjg2ClSXyScDxl0ghAY26sAg7vkhuz/1TslJKlLOB4y6AQhwDmQolHyf6CmLsajIYjwIINOEAJezqEl4eLxUhydcDZk0AlCwMs5GFO36GTQCadDBp0gBLwcmh56I+mjEw6HtFwIQmDFrmpN2dwGMuiEwyEPnSAUaIVWSB2dcDpk0AnCJCnJ9HUhnA3doQShoKBlOm4aXRSynxZFCadDBp0gAMxasQdF0+cAAA7V1uOq4V1CjqGm0YTTIYNOEACen18a9PhEvce//YuRhQCAJvLQCYdDBp0gAGSlBeRxR/fMR3pK4KsxuX8HAEDpweMxHxdBhAMZdIJAcEri6V1ao0eblv7Ha/YeAQA8PmdzzMdFEOFABp1wBZe8sAR3/LfEtvdv2yrDv80YkCZ46BP6tAUATOrfzrbzE4QVkEEnXMGavUfx5foDtr2/GGJhjAUVF7XP9hn7EUV5Ia/7sKQcO6tqbRsXQYQDGXSCAFDXGFgEVRaKyvnnHpU10ftmr8X5/1xk59AIwjSGBp0x9jpjrJIxtkHY9wxjbAtjbB1j7BPGWKiANEG4iNV7jvq3maImVJbT9XjV0xbrm7zYffgEyo+ctG+ABGECMx76GwAmK/bNBTCAcz4IwDYAD1o8LlvwejnqmzzGBxKOoup4fUzPp/TQ5fCLMm1R1Ec/65nvMeavC2wfG0HoYWjQOecLAVQr9n3LOZfbtywD0NmGsVnOPe+vQe+Hv473MIgwufXNlbafQ0xbTEpS99CVbeiozyjhNKyIod8M4CsL3sd2Pl+7DwBw9GRDnEdChMPa8hrbz9G2Vbrq/sGdc/we+ks/7Ah6rkktqE4QcSQq+VzG2B8ANAH4r84x0wBMA4DCwsJoTmcZh080IDcrLd7DICLA6+UhHrQVlB0OxL/l3qFrHzkP6alJIQ0vVpZVo67RgxW7giauBBF3IjbojLEbAFwA4BzOuaarwjmfCWAmABQXF8fNpak8Foh3avWMJJxJ1/ws7JYMbvXJBhS0VPemreLF73fggcl9kJOVGvLcyYYmXPHSUlvPTxCRElHIhTE2GcADAC7inDt+af9376/BiL/M9z9WZjEQzma34D1f9K/Flr9/ye4jpo+dYVAtumbvUTz51Wbo+DgJxYcl5Xh72e54D4OQMJO2OAvAUgC9GWPljLFbAPwbQCsAcxljaxhjL9k8zqj4+KeKoMeNGulnhPPZJ2SWWMXlL/5o+tgvpHUYLS55YQle/mEn6ptC77GPSspRNH0O5m46GPYYncp9s9fij59uMD6QiAmGIRfO+TUqu1+zYSwxg3StiUg5VtdkfBB8/UczUpOD9t07ey0A4La3VqHsqamWj40gEr5S9HBtaA4zZScQekwZ2D7q92ike4yIAwnfJFrNGycP3T3UnGqM2bnG9irA3ef0wrCuraN+L2qGQcSDhDfoan0gmyiG7hqueMl8fDtadlTWolhFgCsSGsigE3Eg4UMuany0ujzeQyBMsu1g7JQM9RZcJ/ZtG9Z73fPeGhRNn4MjJwJFbHanW8YTmvU6g4Q36GrpY+8s2xOHkRBuRtZEN8sqKRVyw75AlWsiVyhvo25OjiChDXptfRM+WEXeOKGN6FnmZIYWEslcMSy0abQZZP2XY3WNQeJeN7y+IqL3cxKis0QSws4goQ36w5+sx1+/3hLvYRAWY2XRzrvLA0UxeguwaSlJQdkvZhdOZR2YQY99G7T/h21V4QzTkcRaBZMwJqEMekOTFz9uP+R/LGpcE4mDUsY2GvYLcfNrz9DXGhLzyqcO7GDyDIkbWz5eH5yTv2BLJZ78kvquxpOEMuhPf70F1766HP+cVwoA6JKXGecREdFQduhE0OOxvQoA+Ip2rCJJEt5iDHjikgG6x4oSu40eL9Y+eh62zThf9zVW/vg4jTRFBtlNb6zEywt3NhvZAyeSUAZ9u9Tb8R/ztuHoyQakJCXU5TU7FpYGwhLn9muH8b19C5ONTdYZDFm5sUN2RoiqopKstECW7z/mbUNOZirSUpKC+o8qyVRUiyYSKcnq101FVfEjYS3ewWP1IZ1nCHchGsozuuVBth9W5ng/P983m/vTxfreORAcM75rQk//dsnDE7HioXNUX3O4Vjuzxe3FR+KC8sS+7fzbF9ogoEaYI6EMuniDJTFgXQwaIxD2Icoc1zV6sKf6FADgRL05PZVw0GpwIbKoNLA+0yUvy7+dm5WGttkZqq/ZV3NK8/2+3nggjBE6DzGykimEo7ZSCmPcSCiDfkSR5ytmLYztVUCCSC4jVRGjHdg5GwDgjTJGW3boBFaWhd+cIjszEHIxO4Y2rdI1Y8onG9zb35Zzjs37j/kfK2cbFEePDwll0AsFr+lUoweXDe3kf3zwWHAVYF2je79MzQVxEbLJy/0GfrGQyRQJ4//2fUiTCo8JA5SREhjPo59tNHWuJg/XrEBd7+IZ5DvL92Da2yX+x19tCJ5tlFbGrsKXCJBQBr1aKLOuOHIqqKgor4Wv5dyFgzsCgKpeNeEsMoVFyA45Gf5F7kdMGtNw8JrIRjm9MNe/HY6M7qeCHr8oIeDmxhBGGuhJBgvMhD0klEFftjMwjf7LV8H5sM9ddToAYESRryCkwYRB33f0FEY/9R32Vju+KVNCkp4SuD2vLO6CquMBT/fVRTstPZeZ9EKxkvQ35/Qy9b4z5mzGsbpA6O/hqf2CnnezUddDL/OHsI+EMugie6uDF6Pa5/gWreRpu5lc5vdW7kXF0VN4byVpv8QDMQrCGMPeI4HP1KgVnBlEr9yMuNRwSYnx52cU4nfnnqZ5XLeCFrhiWGf/42TBWy1QLL4marcfMw4TYT0JZdAn9W9neEya5PUZ3XCjnpzvT2mj7u7xQbmwZnVz7+ekzxcwZ9DH926DT+8cjRkGBUidW2fimSsG+x+P6Vng32YAfnlW9/AH6zJW7abvTDxIKIPeq20rw2P8Bt3AQxdLwg8eI82KeKA0sWra9tHwvGDQxbQ7LRhjGNIlV7MA6cpin1c+snu+5ntkpibjgUl9whyp+3ht8a54D6FZklAG3cM5UpMZWqZr9+2olRazxAVUJUrP8IJBZnU7CCtRJp70aNPClvPkZKZiWGH0XYpapvti7HLsX55QyAvwH/xyFJKSmL86NZG5bmTXeA+hWZJQBt3LORhj6NG2peYxuVm+bJcHPlqneczRk8Gqe5mpydhQ4d4UM6dTfuQkvli3L2S/vJj4zM8GAQDO6xd5r89jdY2aYZWaU42WGNkrJA9dHuf5A3yOQJXU11ZtgpGohq+FjlNF2EdiGXQvRzJjSNPQmAB8hR4AsPuwduaK8mv/97nbcMG/FlPuuk1c+n8/4q53fwqZGd3x39UAgM6tffUFaSmR3a6nGjwY9Ni3eGLOZlsLXvp2yEbZU1NRmO8b75Idvnz5H7b6NGnEVL6ShyeiZXoKuAvVGM006qAORvEhoQy6x+tLl1pZdkTzmFQdYy+jVQVIXro9yBop4sxIzEBpke6Lb0eaCne83ve+7yzbHRLGsZNfjusBAJizfj+A4PHnt0xHi/RkS4XGYsUmoUJUix93HI7BSAglCWXQF5VWoVah83H18C7Y8KdJ/sfiF1rLW9My6KRRYR3Pzt2Goulzgjy5//xY5t8WKzcZAoZQzHTRWwcRkV/f4PFGLRsQDi0zgsMOymIbBob3V+3FkigrX2ONUsW0Y06ojs3/1oaG0Aj7ca1B55zjhQXbg77UauXGD57fN2iRtHf7QCbMCwu2a7y3+jnzpPg7ER0eL/dnmHwpea9AcNaJaHg3Cn05e7QJrI8MfXyuKcVC0bFX+2gHdc4xM+yw6dI6WI9fOcOQ79273l1ty/ntQimbe8npnTSOJGKNaw36il3VeOabrbqLmwCQkxXcJ1LsOrOz6oTycADaHnrb7MTt2h5LHv50vX/7cK16SqhXsNNHBZG1UT2CUwJ3HVL/DEXET1Pto71trD154UpxMaVBl1Nnj5zUbn3nRFIVHvrEfoH6j/enjYz1cAgB1xp0eaZ+TKcPpBG7NUr6taaLTSTcbwmzVuz1b2tporwhhF/yWwRmRsofVfE4LUS5XbVFSLvSUpWFUFr6Jnpptk5k56HgmfBQIeVzRLc8dMzJwCVDOsZ6WARcbNBlbyeamGjJbvXF0798qd5YmlburaegpfqsR2zuLTZPUHqH2xVhNs453l+5J2hR9aUfArovatlNRp2KIkVZCKU8jawOqlz3cTp3v7dG8znGGDLSktFI35W44GKD7vtfNrLfxKBZwLWvLrf9HImOMuVN7AJ0ZXFnlKiUjItVnMr4bXvFgtwDH63DAx+tx/n/XOTft3RHYNHxhtdXRDbwCFBmVClneFb2RnUSqUlJru/G5FZca9Dl6evqPUcBALMFqdxuBeYqCocKcqhEbBC9ZQD4bstB//YHq8px+Ys+nXJZ76Rvh+ygdQ+l16sMV/ywzZfzLWYkiXrksqTDjWcWRXoJplFmgygN+H4NnXS3k5LMKDwZJ1xr0JULTmJD4Scu9YknvX5jseprP7r9TACBH4NERvSAncBLP+wIerxWo8lDUb7vR/k/Nw4P2n9WrzZBj4d1DS7ZV9PdURNi23LAOJc6WpQeen5L7Syp6QaL+24iJYmZkiMmrMe1Bv2QkB0xe9XeoC/tmT187ebO7qOuvih65oncKuv1xbsw/Il5MTFeViMbBGVmSGF+Fn47MSBdG+m6xrKd1RFXnkZKh5xMzefeW7kXqyJoi+cUtj9xPjb/ebL/MVVVxwd3La8LvCdkStz/YXjejbgItnTHYZwpyJsa4fVy14gryX0zd1adQJ/22XEejY9OuZmoOKrdOBnwzSo8UnhCTTJ39Z7AYnY0C9UL75+AA8fsC3uEu9a689AJFEua627gd+eehjG9fN+dlOQkyB36tGZdhP241kPPSLVm6Go3X6dcbU/qpAM8j+fnl+LcZ38wPE72bp00/f2Z0PhBi6rj9TghNVBW+/EUY9EflpRjwZbKiMbSPicDQ7rYt45i1IZNnGkA7muL2DU/KyhlUQl1+oo9hlaRMfY6Y6ySMbZB2HcFY2wjY8zLGFMPVNvMxUOsqU4T0+NkurdpgVYZ6pOXhiYvak42mupBaRfPzt1m2IR3Q0UNVkmaNvEcqxLZ4+7TXlu7PjMtGc98szXoeJHugozuqt1HcNMbKzXfS2sN4cNfjTI13mgQQzrXjOgS8vxvzukZ9LiuIf7OghFF0+f4t43CKgkczXQsZtzcNwBMVuzbAOAyAAutHpBZtPLPrYiLLio9hOMaBS8nG5ow+M/f4upXlkV9Hju54F+L/eEEJ3no2VJfzn9fO1TzGNEDVxPkenhqP9OFK/9Zot5oIRahjU65mbhrQk8suG88/nLpwJDnlfnvJ11g0EX+t3a/7vN1Te66nkTA0PpxzhcCqFbs28w532rbqEygZaPG9TIXDx/ZPbIv9B6pMGXFrmrNsnWrOXKiAQ9+vD7ihaZdh/S9+Vgix7yzNWZAQHBWippBz0hNxj+uGmLqfHLWS2aqcUciq2GM4b5JvdGtoIWp4qWTje4qMFLKMBDxx/YYOmNsGmNsFWNsVVVVlfELTKLloat5Qmro9YXskpeJ9JQk1fjq9I8DOiTDZszDPoMFPit4+putmLViDz79qSKi15cdck4sU/7c0lO0DWzF0VP+ClJleqqM2epO+eW5Ck0fp/D2LSP82yfr3eXRnq5Rx/HAZF+LvUQtnHIytht0zvlMznkx57y4TZs2xi8wiVZcuG12qJSnGnLTBDUK87IwoFMOXr9xOIryg48bd1rwDMAoY8MK1u715cuv2RucN2825fI8E82zY4Vs0JUVnyIpSQx9O7TSNBjh0FXKZx8TRiZTLGktKHiaLYhzCqM0eqf26eBbHzFqxE5Yj2uzXKINC2copuDvLt/jN85Lth9Gye4jyGuRhv4dg6VV31m2J+hxLPRdZEGqE4oYa6PJarx3l+8xPihGyH8uvQyQ57/bDi/nhlkiRmyvPI7T2vmMy/WjiqJ6r1jgkmxYP1qzpDRpWmT2/iSsw7UG3WPhEvqxukY89Ml6jH7qu5Dn5qzXX/iJRQbJoE6+H5UebVrg4n8v9u/XEhdTsnyXcwpW5B/ApCSgQKNycu3eo+AcCMe+eb0cNQrlzZLdR/wzgiSH3umiTdxTbf9sLxrMzkblUMv+GmdfTyJiJm1xFoClAHozxsoZY7cwxi5ljJUDGAVgDmPsG7sHqkQt3DAiwsyF5+eVGh+kQSwySJ7/zteI47l5pUF58z/tNWfQnYT8uSUxhkO12h2HftxxOCwPvcHjxUPC+gYAVB6rDzqfExHHlZNpf5y/aPocPPONupqoEZ+tMbeGI3volLYYewwrRTnn12g89YnFYwkLtVDHBxHmFr+6WD21zQwH4iiwVKmiW+J05HWyZDMGNgwbfLLBEzKbavR4/edLYgytMlJwvK4pJobTLG1bBeSDm7z2xpzfkFI4X1iwA/dP6hP261uZ1G2Xm8qIKplEbHDoRFSbq15ein6PfB0SQ4+kjdjATqGv0UpFTNPIttAqQIoF4XZQ+uOnG/BaFD9eVlB+xJdxI1aAqn0OQHg6O2qNTjyc+0Mu4sf3v7vGmH5fu8lvmY7FD0yIiaDVY//bFNXrxRqP/956huZxcmYSKS7GHlcZ9O2Vx7F8VzVONnj8sWu54jCSCbVaHu2wGfNUj73n3F6q++2+ZfWMWq+22tWWary9bDce/yK6L3W05Ks0tLiyuLNqXcDKMvMhJbU1lZbpqX6DzhhDd6kfqZM8dMCXcZWanOT4BipdhMywLB3vW64dcFL9Q3PBNQbd4+VBGSa/l+RG06OoDFUrK5eZOtDXluyru8ciPSUJl2o0wrW7wW/5Ee2FpXeX70bR9Dn4sKRc8xiZY3XO6Fspyue2k2YYjDE887PBUb1vk4eH6Puc1q6lP46bxBievnwQ7prQE9mZztOkS0liOHaqEYP/9C2+3xqZNo3diL83ep3C5DDk377dhi/WqbdzJOzBNQb9799uVe0f6Z8GRrDopVW0Ir5d3w7Z2DrjfLTRaJXm5T5Vw3mbDqo+Hy1ix3slC7b6CrXum73Wv+/DkvIgvQ2ZeMb6tZCrOJMYQ3ZGdF5zo8frD93I/zd5eSCrhgG927fCfZN629ZyLhpSkhlKK2tRc6oRT8zZrHrMgZq6uBbriLOg3CxtbXfRUbrr3Z9sHRMRjGsM+v99v0N1fzTaLXqvVWZFqJWgy1zx0lLc+taqiMehR7iZAqJxF3FykQdj+oVGWrQQpv0eL8dIqdDlkQv7AQA+/akC90p/D6dmucgkJzFs2ufTrReF19bsPYplOw/jVIMHI5+cj+kfrdd6C9t5dZGv29SD5/dBDyl8pcbQrtoKjIS9uMKg6zXRzc30eQqHIujMoxezVE4p4+XVmRU4MlpAjHUKWaPHixMGzY/bS1W9TV6uO1vS4qdHzsPM64YBAE7UN4Fzn2GUZQO+2hDoM+t0DftDtQ04paLVc8kLS3D1zGWYL7Xqm7sp8t650a4dLCo9ZOp9UpOTXFcklSi4wqDrGSs5VS2SEvxn527TfM4pOhRmFz6NlPoaPLHVCbn+tRXo/6h+ecIkSZKgocmLtJQkbPrzpLDOkZaS5G89+Ny8Ung4RxIL9txl3G5g5NBFNL/LPz+jEABwTp+2FoxIH4ev7yYs7jDoKvs65mRgrEllxUhwcohCDa2Ut8FSOmcsmyc89Ml6LN152PC4EqnzUK0kVZyVFrxYmddCO04rM0VavJ4ysL2vmxRjqsJfTg+5mEVL1tkM8t9Ab0HTDOH+KRO5zaPTcIdBV7FFDR5vRNN0Eb0mC2rT3wsHm9PgtoqTDU2q+dVqqP0AFXdtjVvGdtd83i7MaseM7uH7QdYyEFoL0SKt0n3T/22VtfByjuQkhnSVblaJYc6jQ17UDFdjxevlQfcPC/Ov6fR0zETCFQZdLcf4UG2DbtqhGfQ8fDVP6MYzi6I6X7ic9cz3uPbV5QCAe889TffYf84PDR+t2n0EqdLfSPTQj5zQLrm3GjXvTP4hlSsJm4TwVqqwOGrGk8yT9GDeXb4HHi8kDz30to72xz8ebD1w3NL3k/+ei7cfMv2af84rRfeHvvQvLgPAkDBVMI+adEqI6HHFXa71xU5NTsKVxcY9KrW4bVx3zec2ShkHIoMjqEaNFI+XB7VPa2lQkfrOsj2q6YopkiETPay7ZtmbOy+yZPthjH36O5ySYvzZGSn+bBTZyDYKHpwYdjEjwJYhGO/Xl+xCbX2T6gJ2tGGGeGBWO8UskfwJ/jHP5yj8b20gn7woPzyZX7fpvLsZdxh0jSkbB0dyFDJ6LQVtCr0OOjIpMfTyejz0ZdDjFpKh69O+Fc42uag1oFO2X/FOTPtcst04vm0Vv3htOfZWn8J2KRWP80CIRZ5haXnoWnILIukmOxGZPc4pcM41F/r7P/J1RHFp8Xu0sixyBc5wJ8YPSEWAhP24w6Br3LtLth+OKuwiGgytStBwaLIxMyY9NQkPT+2L124cjtdvHG7qNb3atkKn3EwAwOb9oTOOWMKlpW1R51z+gRQXdFOkH+gLB3fES78YZvi+GSbrEFqaFJZyCk1ejs/WqFdZnmjwoOxw+F2oxO/RFS8tjXRoujUZMreN7ebfNrNATliDSwy6ukWvOdWIi6RmwUYxZjVEj3tsr+i7KZ2MsOenGb7deBC3ju3uN9BmYCyiAlpb+Pu327B271GcaPD4PbyAhx74fM/p65t9zLhkAIpMdPDRmjWJ6x1DLeh8FGuMhK0icR6sCjuZqcn4w9R++Oj2My05n9v4bE0FiqbPwY9hrFVYhSsMut4q+fCiPOz4yxT8+hx18SyzRFKpqKSuwYMp/1yEN1UkCqIlkkyBJg8POyPBLlaWVePiF5YAgOChSwZduLbHLuqPH6efHXURjChFu3rPUZ0jnUmjgZRuJIkjsV5HUGrrNBfufm8NAPgTGmKJK/7iRvehmSmgHsO6tvZP9WX6d8wO+31ONXqwaf8xPPr5RizcFn5D7EaPV1Nz5VqpKETmmhGFqseJtM/JUFWU1OoFGQ1XvbwUD3yoHSsN+owEnRwgWD43NTkJHcOYhWiR3yI8aWGnYeShz1y4M+z3jMSgR1PrYWYNhLAWV/zFr311mep+KzyAzX+ejPenjQzx0D+9c7Tq8R1yMjQVHj9Ytde//W+py1A4PPr5Rox8cj6OqygjtlM0v/7NOT013+e6kV0xoXcb3D+pNzJSk0MWfAvztBtkR8ryXdV4X7h+JfWNAY9T9tCHFrbGgvvG45oRXSwbhxySEnV63NZ8GTBudnH0ZPipp6JXb3btaVUYEsZKonW0iPBxhUFXSsjKGiB1jdEvQmamJSMlOSkouwLQzlte+uA5+MdVQ1Sfe2FBIJNkRQRZBHJBzqkGj1+PREYZtuyQo+3FdsnLxH9uGuG/BuW1vL9qL74y6JUaDblZoeGSBo9o0AP7uxW0sFQnp3Nr399FNFhujOUaeejzt4QvsStmxhh53h4vx4KtlaoFdmYRZwR1Nq4vEQFcYdCVPDgl/PZZRoRTGj65f3sAxl+KSPRlAODhTzfgUG09hhcFVOvUnB35R+hMRVhFGT46rFJI9B8b4vwyammmor6KnXF9+cyyd3jjmUWmJATizcNT+wY9tqPbz6wVgRmUUQruzIU7cdN/VobsD6f/gBjhiXenrOaCKw26FTFWJWI1m1F8OimJoeypqRhnkBmzLcJKv28lbfXgmUmoEXzz5hG4bmRXTOzbLmi/crahhpX6GtXCD4bXy1XXPE4I4mF2Zt5USH8z2aC7paDo1rHBRW5Gi6IAsLdaP3WRc47XFu/CIZW2ipkGefkbNHT45/3uLMNxybQR+qXGO202lgzuEr+sKlcY9GKFvnJmajKuLO6M2RE2hVajuxBnLco3F2NetVs/rOLxcpQfOYmXftgRkQEVv4hqHvqZPQrw+CUDsL68rzvCAAAgAElEQVQi+MunLLZShm+A8Nq7GSFOpz2cGyoCWq3t8c+rAyGwSdLs6fJhnTGxbzvcNUF7rcFpXCbUQhw8ZtyQZOzTC3Sf37z/OB7/YhN++/6akOc+X6vfSWjOOvWQXJcw1l/EJhhGPyCJxNq9vqwqO8UDtXCFQe+tENHyeDme/tlgDC8K7UMZKV2FcmazWhdGXXY8nOO2t0rw1FdbdFvJAepfYFFESc8IKr2fA4r3UvPQrESMV3u83NArtloo7Jigu3OFJAWRnZGKV28oRlvFYrKT2VEVaGyhJXD2j6vMt+qTF1aPnmwM6Skges+xYHTP2Bu3eCDq78RDlMwVBv2RC/vh3dsCXcbXVWi3ZbMCWcjfiDEGv8BNHo56yXttMCgEMQpD6MnfKoXE5M4ySm4Y1dW/bVVKWfmRkxjxl/n+x/WNXsP4r9VSvh0Eo+3mTvM9Be37H3cEqis/EzKuLj09WLuo8rixJ7++oiakNqLKoCGMVWGxf197OgDgnvfXYHkzqBgVs4/i0VPBFQY9PSUZZ/YIGM/RKrnV8eAiAzndiqMn/eEHow83Gr3uvh2CZzDKZhfyomnF0cCX36pGyb98uyTocb3HY5hyZ3Vcu59QMxDrRh5W8sgF/fzb8rrEL8/qrhuTvf61FZrPibrwz3yzNayxdGltTWrrgI6BGoNvNtrTd9dJiKmaVoY1zeIKg64kmlQqM8iddIwwSrdLTU7yL1xV1+rnDWvlFS+8fwJuG9sN/TpoFzopx6HULdkvFSvN2xz4Qh0yGI9ZlKqUX67bb1jFWBlBu0A9OuZm+lU381xcUJSTlRqyLtRapxkzoL8wWm+yfaHIRyXl+N/afdhjsOBqFlGb3mNiodftxLsBuSsNut3l7PdP6m362KmDOmge7/FyDJG8K7U0Mc455qzbj++3VmLiswtDnr/s9E4ozM/CH6b20+2Jmay4iR67qH/Q412HThheh1V8Ji22idWfSuZust5T+/PFA/DpnaNdWUQkopSmNfpb6RkQrVnfZUM7aWoC3Tt7LX496yeDUZpHnCU0NoNGF2YyzOzEXRJ0EsZ5FNHR02QfTwB44dqhANSntKnJSf6KxeoToV7p7JJy/F6nXH7SgPamxjDj0gH4euMB/PyMQgzunIvLh0avHBkpcmiplVCdesuYbrbnIWekJvt/PN1MviJn3qjJhZ75UIts3T6+B6prGwzDYlYhVuzaqUbqFOKdJesqD11e4e9sUXxPyT+vHoJLhkTfZu61G4oB+Co25YyOX70T2lSi2qBzkJyCZ0RBy3SUPTUVT1w6EFcO7xLTad/EvsHa7BsqfCEY8cbuajINlIDmTOzzu0bjh/vHh+zXy1dXW6sY3DkXx+sbcfBYfUjmixGPXtjP+CAFYiFSuK3v3Ei86x5cZdAvPb0zyp6aGrUSnxYXD+mE564+Par36Nm2pV93ZW/1Kazarb0wopf2eFq7llGNQ+SByb7KWju6vWtN68/oHkgpVUoPDFPUFRDayJ70oM65Qam1MnoZQ2qmpdHjxZfrDwAARgrZSWaIJE1YTGk1yvRKBJRRpUjWMaLBVQbdycgCU5mpyX6hr0c/36j7Gj2BpNPamQ/7GJEpLUzZUWGrZdBFI75fUJC8fXwPzLptpOXjaK7oOYRq3uLpgja87KEPfPQb3PxGaJm/kkgyscTZ4mGb6yGcgFxAKBcV1TXE9keMDLpFbJFinesrakwr2elpsFu5cNhHRabWKrQU9eQv/y1jugUVzAwrbB0UVyXsQ606WS1ceby+Cd9tqUSlQXVqFN0eAQDLdkbe9s6pbKioCaqUlj102QaUHY5dQgJABt0yfhKaKJjtc6onkGRFByWZkd3z8d29Z+GK4s5Y+8h5lr0voJ9fX/bUVPzxgn5BZeQkqRoeo3tEXmEZbjh3hEoIRuz8FE2tBACM7umM+hGrOHisDhf8azH+8MkGvLpoJ9buPeqfFS3Y6uuHcPd71mUMmYEMug1oeejPzduG+2ev9T9O1TFuVopnAUD3Ni3BGEOOirRtNGwXvG8RMY9ZFDvTS78kQnlRpa+qUttIpvpEQ7BQmsYt1MpEQ3QAGFGUF5QCG61BTzRqJEG/teVHMWPOZlz8whLnL4oyxl5njFUyxjYI+/IYY3MZY6XS/81+leube8b5t7U+0+fmlWJ2STkA31Rt60HtlLQMG8WMlFknh2vrI9Z72Vnlm1IqjcQXgviT6Jkpc+YJfdTCU2KoTpRYHvr4XAx9fK7/sZZTIOag682wQo1TdMYq0X4Q5D+PeFXXvuJrOyd3GDvrNOtm2mYw46G/AWCyYt90APM5570AzJceN2uKCgJG0kxZ/QX/Wozn5pWG7D+3Xzt0K2iBP1/cX+VV1jBGIZQ0bMY8FM+YF9V7fnfv+KDHx4WUuPG9A9k1x1S6MRHh8aeLBmBEUR4GdsrRTVHV8tDF1+gJSN04uijocaRJKrIWvt3ptF+t34/7hBmw3cgpyR6VH045VJZjUOlrNYYGnXO+EIByNeNiAG9K228CuMTicbmOVCFubtQ8QI+Z1w3DgvvGI19F8tYqPl5dASCyNmZaKNX7xIYWohRBc9LFjhQjPe3e7Vvhg1+NQm5Wqq6HvURDNfTWMd382006Bv2CQb6aDHndI1L1wIIYKDseOdGA2/+7Gh9KM2A7Kdldjae+2oKrZy4FEJilisizqOfnhzptdhKp5WnHOd8PANL/1ic4uwwxNmw2y0WNWBQF/eacXgCizwveX6MtCTy8m3rOcl8dTRrCxyvXDcOk/u0wsrtx3nfNKfUZz6qyavx7gXpf28uHBRQb95noqtVHkq+O1KDLd/SPJmWpI+HaV5f7t62WZ1Zy+YtL8dIPO4KatihRahzFCtsXRRlj0xhjqxhjq6qqquw+XVw5r187PP2zQYYG3eoFz3BpLS2MnvvsQtV2cWYZ9eR3ms9pXSIVFRnTNjsDL19XjPem6TdwWVR6SNU7BIDNJrtlmTGyo7r71kBamlxMVVJ22LdA3uTlKJo+B19vOBDR++ghzvzkH565mw7iypeXxuX7Jje5AHyzh1gRqUE/yBjrAADS/5odaznnMznnxZzz4jZtYrtAEGtmXl+MK4u7GKbmWdHcOhrkkFDNqUactEm5Umu1P7GWxeLLCI1ZEAD88VN/DgMemNxHU62zkwkZjfsm9cZnUQifZaUFL/D/6p0SjSOtQZZDuO2tVVixq1q1p264PP7FJrz8ww7jAyWGFgYcl8e/2BT1+c0SqUH/HMAN0vYNAD6zZjiJgTJs0jEnuGuO1jT512fHpl1aUIchC/U1yp6aGnhfzRU5y07X7CnKz0KHHOOOTL3atsSXd4/FrienhDxnRtI2IzU5qj6Zk02KzFmF8p6eYYFBfW3xLjz51RbTx989sZd/++uN1s9ItDCTtjgLwFIAvRlj5YyxWwA8BeBcxlgpgHOlx4QGtyv6WqotZBXmZeHe88zL9kaDmPZmphlxOPxRatKgZdDtlj5uTqQkJ2F/TR2Kps/RPU7OwhAdjVeu9wnIqYnGWc1fLh1o+zlETn98blCYRdnwJdbE8vyGQTHO+TUaT51j8VgSirwWaf4ijwxFLrFac99fjCwM2WcXYi641W2yZAlbpSCXTIKlIscVrcI0r5fjsqGd/NlMauskeS1il05nZ02FFu+t3OvfXiPEsxMdV+qhuwExJdDMDR1Lz1X01KzOCBjSJRfTxnXHTYocZv+5LT1b80brR7PB40XPtgG1TrXJUqIX7O6oDFQwW9khy+4Mmmih0n+b+Oj2M/3bZgy63W31RLYJFarLhMa90WS8yCQnMTw0pS865AQrO8rFTK10JIOJ8FhYqp419vHqCsxeFcjHVit8SdHQG5p+fh9M7NsOXfKsV+aMJXopheFysiFQJPfIZxv8P4YFLdVnObeP7xGy73cfrLFsPHqQh24TBUJhkJm0pWfnbvPnh9vNwM4B1cUHPlrv3/ZwjiSbfOhXbyjGgZo6Ulq0kG0HA15o8YxAyf9Dn6wPOk5Nu0Wrmplz32dlNbeO6YZXha5V68trgu5Dq6mz0EH6fE1AxkIM5ZyoDz7HrienaNaRfLy6As9eOcSyMWlB3y6bEA3XJhPVkfeee5qdwwlCK08+0sIRM2SkJqPI5f0+nYxe0+/xKnoiWqm123T0haJhVI9gpcXtVfacR0b0qqOlQqP4SjmrNioKtPJHRgsy6DYhxjcvGNTB8PiuMTR2WgJZ17+2ImZjIGKHmqHREsqyS5pBlJcGgsvl6xo9qDyur8UeLt9stK6fwL++U6+4BYBsnWIr5Yz7oIHevBWQQbcJsft3BxOdgmKpcSLKFIgdbFaUVeO7LdZ9EQh7aR2FFLKWQU+3KSSmPJ2o/XP2377HiCfmW27UrcBoxnKsTnsm8DvFrHt7pbrUtJWQQbeJ9JTAQmin3EwsvH+Cbk/PWCYdiCGXTQrNifXl4f+wyO22iNgy8/rIY92pim5ZcuFQoUrfUiu45PROQY/FBfh9UovCVxbutOXcQOQqn2LSQLRsMSnHEA1k0G1CufhXmJ+FX4zsqnl86xjKbIoeurLJ8PqK8HN2X7theNRjIsKnc+vIM1GUap7ye+Xa1IBdqcaptlzzyqJdWLClEtM/WhfROZQSAyKRykM/8pl+X2CZ568xbi6fa3FzGTXIoMeQCQoPvVNuJn4jlfvHstCjME9bv2PeZk1ZniDWlQcMP2WuxAet1EORh6f21XxOlGo4LoUO7GoRmKbImV9Zpt5f9KY3VgZlkpglOyMFVxZ30Xzezvzxsqem4qLBHQ2Pq7IwH14LSluMI+f1b4c7z+6JdjkZuFQxJbWTAh2t9Vbp5m4JsU8oER/MyDTfOra7qfeSNV3s+nFWGvSvDBQXT9Q3oYXJexHwFVOlpSShY04G9tXUgbHwe6razR3j7ddqItcqjuw+fBLpKcn4+RldHdNrU5lepkUsC6EIdVKSrbtnrhtZhGnjuttWC6G8v4082iNhNl+pa/SivtGDefeeBSB2xvz+Seb0l8b2KojJTJYMuo1ce0ZhiGciEk8TPmWgugLet5vMZbnodbohYoNRyCUc3ZyR3fPw0JS+Qd2l7ORzoeesGk1hqIDK2TFvLt2NrDT7xv/4JQNC9o3sbs4B+pnQVMROyKDbyF8uHYhtT5yv+fzEfu1iOJpgHpjcJ6rXv7t8j0UjISLFyEMvnaF97ynJ1FlQjAfhNJSW2z+qGc3rR2knIoRL2aHQZiJGE2t5bWx0z9hkgpFBjzGr/3iuf/sqnUUcu1FO/2TPTK9QQo0bzyyyakhEmChj6F/fM9a/XfbU1LB622oVm1lJq4wUdDJRkwEADR7zIT25mcogFSkBZUP0cBEbgxxT6WNgVB36hyl9kZ6ShOwYaRiRQY8xYjZLPOPmWanBhlvWx9YrlFDjOgs9ICI8lMakT/tsvHXzCHx+1+iw3yscjzhS1jxyHhb+fkLIfrWsq4Ym8yEXpd77GUInp2j75oqjUBPZM5LLuHxYZ2ydcX7MMsEoy6WZkp4afIOZXQwNeR9KWXQU41R0W8wQC516MSWyuyB10TU/C3uqTwYdG45Ov7wAKs8yZONZmJeFHZXqPVfNv3fAYKu14LNT/ygS6NvYTFFbrD2zRz6Kw2ziHAvPjtDmrAgNuBKj0IHV7BTi0Wr9Z4+HMVOUXy//Xiwq9TW+3lN9Eqkp0V1X+ZFT6FbQAqv/eC46qRRyOc2gk4ceB1pnpaJHm5bGB9qIGO55/OL+AHyCYifqg79I5UdOoqBluqamu7Nu5+bHmzePwOLSQ6rGxqkM7pyD43VNfgldNaOo9Nj1kF+vFsK8Znghnv56a8Rjra1vQm19E/JapOE8lSQGrWbo8YIMehz46ZHz4j2EIOQy8NTkJDQq0sXG/HUB2mWnY/lDE1VfW0/56HFnjMu0dDJSk7G2vAYX/nux5jGfrqnAtWfot2W8+Y2V6JSbiWnjfMVTarPF1lFUYCsbvjDG8OGvRiElOQmXvLAEgPPSdynkQvinqqnJLCh2WSOt6h885itZ/nL9fjw/vzTotbGeqhPWcm4cUme1WueJ6AnZyXy3pRJvL9uNL9f7qpYrjvh0y/sKmSkAcNPoItMV0CKvL9kVsq+4KA9DuuT6Wyzmx1Cywwxk0Al/V/KvNhxAaWUt1kpNdc/+2/dBx93x39V4du62oH1F+dq6MITzefHnQ7H5z5Njek4zVcay0W/0ePFhSblue8QXFvj0yhdv97Xku3xosIxGWnISGr3hZ7uU7D6i+dz08/vg3dvOwIBO9nVdigQy6ESIaNBbS3cDAA5rtM4TY57kobublOSkmBcVbaioMTxmywGfjPOri3bhvtlr8fFPFZrHyp2wJvb1zTbkHwPZU09NTkJdoxd//XpLWOPU05tJT0nGmT2cF+oig06ExB65wULPzir7hfqJxEUp2azGB1KTa1nT5VCttlLhunLfD8T43r4wTa20sC979XJF7Yvf74hwxO6BDHozRs4Llu15u2zf4qiaOd8n9FXU8twJwmpkZ8NMeqC8FrRgi08CeqvUbchMzD5RaD5XSoRw9XCf9ID8ZZFvfDUPXWy6Ky9CEYSdfLByr1/eQC+GLiM7JnInpzdu8jVeUSvZD4ccm5p+2AEZ9GaMUhddrrBrVPny/GNuILvFrs7wRPPlsQv7heybXbLXP4v8dI12DF2mtt632JrXIg1lT031h2Cibf02X5LkdQOUh96MuX18D6SnJuHmMd0ABDqxH6ypC+lQPkfwypftVO82QxCRwgG0bZWOSmGBvqHJ6zfoO6qMS/i1aiKmDuyAH7ZVRTw2u9ry2QF56M2YjNRk3DG+Z0iM8ae9R021y3r68kF2DY1oZsxeVY5vfzsuaF99k1czxXGXipTtoM65qseWVkbnodvVls8OyKATfq4s9ulJm9VudlO5OeFsak41IjcrLajP6cFjdZqdh15fHFz0k5uVqpl+qddD1wxuSs0lg074eewin6bLQpPTUxfd54TDqRCyqGSKi/LwyqKd/sfiwujby3abfu/sCEMm2RkpGFqo7vU7FTLohJ+MlICHY6b4I4LiO4LAvN+ZW2Scu+lgULqinm7K0ZPhZbIc1slrl2mZnoLucRbRCxcy6IQfUa3u74oSfzVWlNHiKBE+Pdu2xCJFo4tebY0NpxhPz82KfKFyfXkNhs2Yhw9LynWP83Aek05OVkIGnVDFRetAhAvprFh/uevsnv7t60aqd8F6TQi/iLNJI8qPBMI5/1myC++u8PXD/WKdfqNqjze+XcUigQw6oYqssHjXhJ6ax5zexV3xRcI5MMYwoFNAFXHygPb+7bsn9lJ9jZxnDgD1TeZlm1fsCswk//S/TZglGfQfdxzWfZ2Xc7ityDSq4TLG7maMbWCMbWSM3WPVoAjncPUI7UbWak15CcIsn985xr+dLnjcWl2wODiW7TyMz9fuwwSpaGjqwA6G5+nfMVt1v16bu/IjJ1F9ogHvLNtj+P5OImKDzhgbAOA2ACMADAZwAWNM/aeVcC16LebC6SpPEEqSkhgW3j8Bn94Z3NQ6NVn9nqs+0YCrZy7Db2b95FdSfPLygYbn0WqUoadBF04WjZOI5hvZF8AyzvlJznkTgB8AXGrNsAinwBgwZaBvOty9oAXOF6bGKS6LLxLOozA/C0MUobtWGeoLnmWHA23pnvhyMwCYWrTs3DoLd4zvEda42mdnhHW8U4jGoG8AMI4xls8YywIwBYD2/JxwBaLBBnweejvp5r72jEIcrg0oLbqpgo5wPy1UCofMJqEs2X4orHP1ae+bAag1U3cyEY+Wc74ZwF8BzAXwNYC1AEJadTPGpjHGVjHGVlVVRa6nQMQGpai/+IXxco4k4Y4hD52IJWqLmHohQZEpKrH2Pu1bGb7uzZtHmHp/pxDVzw/n/DXO+VDO+TgA1QBKVY6ZyTkv5pwXt2nTJprTETGgd7vgm5yBoVOuL8WsfU4mUgSLTh46EW+SGMNHt4/CF78eo3ucmt3fcuC4vxmGkmpJ8z9FI57vVKLNcmkr/V8I4DIAs6wYFBE//qf4YiQx4JYx3fDetJG4cFCHoHQxN2lcEIlJWkoShnXNM+ztuahUPeRy1ctLVfff+e5qAO5zWqKVz/2IMZYPoBHAnZxz7a6qhCuQNdFlkhgDYwwju+cDANZIDaQJwk3IreyUbNx3TPd1bgsrRhtyGcs578c5H8w5n2/VoAjnoHTCryj2rXvLaWME4QbevCmyWLjbPHR3LeESMUeZq5sq3eBXSVK7BOEG8hXdufR49tut/m1xzcgNuGu0RMzxKCx6snSD6ynfEUS0LJl+dtx6eT7/3Xb/NnnoRELROist6PFlQzsBACb0aRuP4RDNhE65mbh/Um/L3zNc3LbuTwadCOG0dj4p0/sn9Q7xUAZ0ykHZU1PRw2U60YT7uHyotWG993850vAYj2LmyfX0ARwINYkmQvjmnnE41ehBVhrdHkT80GopFymdWxu3olN269q0/zh6tjUuQHIK5KETITDGyJgTjuLFnw/1b8+4ZEDE76Mm/CV64UoFRjNqjk6CDDpBEI6na34L//bI7nno1yE7orL8nw0LDeN8sW4/AODL9fsx7e2SoOdoUZQgCMJiyo8ElBbTkpPx5d1jcdZp4UuJyGJbXfICC6S/nvUTAGDe5oNBx2amWhvyiQU0ryYIwrG8cdNwpKUkBdVDRJN58qUkPqfWwm7htmB5gAadBhhOhQw6QRCOZbzUmahJMK7R9PmsOu5rrZiVHmr6JvRug9lC42gxbu8WKORCEITjEbtjRRPV/tNF/X0binTEoulzsLIs0Hu0e0ELnNc/uDeAGyCDThCEqzCrga5GuiQ+t66iJuQ5sSNSbxNa6U6EDDpBEK4imsSTo6caAfgc9Nm/GqV53B3je0Z+kjhCBp0gCFcRjQ6/mLlS3LW15nFKGWm34M5REwTRbIkmy6WoIJDPrvfDwOGukn8ZMugEQbiKaGLoB2vqLByJ8yCDThCEq4gmhl5VW2/qOLd1KpIhg04QhKuIJoZuppT/l+O6u1ZNlAqLCIJwFdHE0PuZaJ344JS+kZ8gzpBBJwii2TDutDa4a0JPXD2ii+rzamqMboIMOkEQriI9ypTC+4ROSKVPnI+q4/W47P9+xIFjdWj0uDO7RYZi6ARBuIp0FWGtSElNTkLH3EwcOJYY2S/koRME4Qq+vmcsSg/WxnsYjoYMOkEQrqBP+2z0aW+8qNmcoZALQRBEgkAGnSAIQuLMHvnxHkJUkEEnCIKQcFsPUSVk0AmCICSiqUJ1AmTQCYIgJNyq4SJDBp0gCELiimGd4z2EqCCDThAEIdGrnTtFuWQoD50giGbPot9PwMerK1yrsihDBp0giGZPl7ws3D2xV7yHETUUciEIgkgQojLojLHfMsY2MsY2MMZmMcYyrBoYQRAEER4RG3TGWCcAvwFQzDkfACAZwNVWDYwgCIIIj2hDLikAMhljKQCyAOyLfkgEQRBEJERs0DnnFQD+BmAPgP0Aajjn31o1MIIgCCI8ogm5tAZwMYBuADoCaMEY+4XKcdMYY6sYY6uqqqoiHylBEAShSzQhl4kAdnHOqzjnjQA+BnCm8iDO+UzOeTHnvLhNmzZRnI4gCILQIxqDvgfASMZYFvMp2pwDYLM1wyIIgiDChXEeeVNUxtifAFwFoAnATwBu5ZzX6xxfBWB3hKcrAHAowtc6DboW55Eo1wHQtTiVaK6lK+fcMMQRlUGPJYyxVZzz4niPwwroWpxHolwHQNfiVGJxLVQpShAEkSCQQScIgkgQ3GTQZ8Z7ABZC1+I8EuU6ALoWp2L7tbgmhk4QBEHo4yYPnSAIgtDBFQadMTaZMbaVMbadMTY93uNRgzFWxhhbzxhbwxhbJe3LY4zNZYyVSv+3lvYzxtjz0vWsY4wNFd7nBun4UsbYDTEa++uMsUrG2AZhn2VjZ4wNk/4226XX2ta4UeNaHmOMVUifzRrG2BThuQelcW1ljE0S9qvec4yxboyx5dI1vs8YS7PpOrowxhYwxjZLiqZ3S/td97noXIsbP5cMxtgKxtha6Vr+pHd+xli69Hi79HxRpNdoCs65o//Bp+K4A0B3AGkA1gLoF+9xqYyzDECBYt/TAKZL29MB/FXangLgKwAMwEgAy6X9eQB2Sv+3lrZbx2Ds4wAMBbDBjrEDWAFglPSarwCcH+NreQzAfSrH9pPup3T4JCx2SPeb5j0H4AMAV0vbLwG43abr6ABgqLTdCsA2abyu+1x0rsWNnwsD0FLaTgWwXPp7q54fwB0AXpK2rwbwfqTXaOafGzz0EQC2c853cs4bALwHn4aMG7gYwJvS9psALhH2v8V9LAOQyxjrAGASgLmc82rO+REAcwFMtnuQnPOFAKrtGLv0XDbnfCn33clvCe8Vq2vR4mIA73HO6znnuwBsh+9+U73nJA/2bAAfSq8X/y6WwjnfzzlfLW0fh68KuxNc+LnoXIsWTv5cOOe8VnqYKv3jOucXP68PAZwjjTesazQ7PjcY9E4A9gqPy6F/M8QLDuBbxlgJY2yatK8d53w/4LupAbSV9mtdk5Ou1aqxd5K2lftjzV1SKOJ1OUyB8K8lH8BRznmTYr+tSNP00+HzBl39uSiuBXDh58IYS2aMrQFQCd8P5A6d8/vHLD1fI43XFhvgBoOuFtdzYmrOaM75UADnA7iTMTZO51ita3LDtYY7didc04sAegAYAp/U89+l/Y6/FsZYSwAfAbiHc35M71CVfU6/Fld+LpxzD+d8CIDO8HnUfXXOH9NrcYNBLwfQRXjcGQ5spME53yf9XwngE/g+6IPS1BbS/5XS4VrX5KRrtWrs5dK2cn/M4JwflL6EXgCvwPfZAOFfyyH4Qhkpiv22wBhLhc8A/pdz/rG025Wfi9q1uPVzkeGcHwXwPXwxdK3z+8csPZ8DX0jQHhtgxyhBrsQAAAF+SURBVMKBlf/g64q0E76FA3mRoH+8x6UYYwsArYTtH+GLfT+D4AWsp6XtqQhewFoh7c8DsAu+xavW0nZejK6hCMELiZaNHcBK6Vh58W1KjK+lg7D9W/hilwDQH8ELUzvhW5TSvOcAzEbw4tcdNl0Dgy+u/Zxiv+s+F51rcePn0gZArrSdCWARgAu0zg/gTgQvin4Q6TWaGp+dXywL/4hT4FsZ3wHgD/Eej8r4ukt/+LUANspjhC9WNh9AqfS//EViAF6Qrmc9fH1Z5fe6Gb4Fku0AborR+GfBN+VthM9DuMXKsQMoBrBBes2/IRW0xfBa3pbGug7A5wpD8gdpXFshZHlo3XPSZ71CusbZANJtuo4x8E211wFYI/2b4sbPReda3Pi5DIJPWXad9Ld7RO/8ADKkx9ul57tHeo1m/lGlKEEQRILghhg6QRAEYQIy6ARBEAkCGXSCIIgEgQw6QRBEgkAGnSAIIkEgg04QBJEgkEEnCIJIEMigEwRBJAj/D84kwXCZ81TYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e62c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.funds.apply(np.log).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a10468908>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0f1ea5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.funds.diff()/df.funds).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf = df[df.substep == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a10475438>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4FFX3x793Nz0kEBJaCBB6aAGUjgqCioIivq8FVOw/RUTEir2g+No7RRAEFQQEqaII0pt0QksgCSGN9F422XJ/f8yWqbuzm91sNrmf5+FhZ+bOzN3NzLnnnnsKoZSCwWAwGI0Ljbc7wGAwGAz3w4Q7g8FgNEKYcGcwGIxGCBPuDAaD0Qhhwp3BYDAaIUy4MxgMRiOECXcGg8FohDDhzmAwGI0QJtwZDAajEeLnrRtHRUXR2NhYb92ewWAwfJLjx48XUEpbOWrnNeEeGxuLY8eOeev2DAaD4ZMQQq6oacfMMgwGg9EIYcKdwWAwGiFMuDMYDEYjxGs2dwaD0bTR6/XIzMyETqfzdlcaJEFBQYiJiYG/v79L5zPhzmAwvEJmZibCwsIQGxsLQoi3u9OgoJSisLAQmZmZ6Ny5s0vXYGYZBoPhFXQ6HSIjI5lgl4EQgsjIyDrNaphwZzAYXoMJdmXq+tsw4c5g2KHy4EHUpqd7uxsMhtMw4c5g2CH9sceRcss4b3eD4SU2bdqEjz76yNvdcAm2oMpgMBgKTJw4ERMnTvR2N1yCae4MBqNJkpaWhri4ODzxxBPo27cvHnjgAezYsQMjR45E9+7dceTIESxbtgwzZswAADzyyCOYOXMmRowYgS5dumDt2rVe/gb2cai5E0KWArgdQB6ltK/M8QcAzDZvVgB4mlJ62q29ZDAYjZr3Np/D+ewyt16zd3Q43rmjj902ycnJ+O2337Bo0SIMHjwYK1euxP79+7Fp0yZ8+OGHmDRpkqD91atXsX//fiQmJmLixIm4++673dpnd6JGc18G4FY7xy8DGEUpjQfwPoBFbugXg8FgeJzOnTujX79+0Gg06NOnD8aOHQtCCPr164e0tDRJ+0mTJkGj0aB3797Izc2t/w47gUPNnVK6lxASa+f4Qd7mYQAxde8Wg8FoSjjSsD1FYGCg9bNGo7FuazQaGAwGu+0ppZ7vYB1wt839cQB/uvmaDAaDwXASt3nLEEJuBCfcr7PT5kkATwJAx44d3XVrBoPBYIggaqYWZrPMFrkFVfPxeADrAdxGKb2o5saDBg2irFgHoyGjz8tD8g2jAAC9Ei94uTeNjwsXLqBXr17e7kaDRu43IoQcp5QOcnRunc0yhJCOAH4HMFWtYGcwfAFDfr63u8BguIwaV8hfAYwGEEUIyQTwDgB/AKCULgTwNoBIAPPNuRAMakYVBqPBYzR6uwcMhsuo8ZaZ4uD4EwCecFuPGIwGAmXCneHDsAhVBkMJJtwZPgwT7gyGAlTGz5nB8BWYcGcwFKB6vbe7wGC4DBPuDIYCTLgz1DJ69GhYXLvHjx+PkpISL/eIpfxlMBShemaWYTjP1q1bvd0FAExzZzAUYZp740ZNyt/Kyko89thjGDx4MAYOHIiNGzcCAKqrqzF58mTEx8fjvvvuQ3V1tfW6sbGxKCgoAMAlGrv22mvRp08fLFpky6nYrFkzvPHGG+jfvz+GDRvmkSRkTHNnMBRgwr0e+fNVIOeMe6/Zth9wm/0qSo5S/vbu3RtjxozB0qVLUVJSgiFDhuCmm27C999/j5CQECQkJCAhIQHXXHON7PWXLl2Kli1borq6GoMHD8Z///tfREZGorKyEsOGDcPcuXPxyiuvYPHixXjzzTfd+vWZcGcwFKAGJtwbO5aUvwBkU/5mZmZi06ZN+OyzzwAAOp0O6enp2Lt3L2bOnAkAiI+PR3x8vOz1v/nmG6xfvx4AkJGRgUuXLiEyMhIBAQG4/fbbAQDXXnsttm/f7vbvxoQ7g6EA09zrEQcatqdwlPJXq9Vi3bp16Nmzp+Rcc0S+Irt378aOHTtw6NAhhISEYPTo0dDpdAAAf39/6/larVY2vXBdYTZ3BkMJJtybPOPGjcO3335rzd1+8uRJAMANN9yAFStWAADOnj2LhIQEybmlpaWIiIhASEgIEhMTcfjw4frrOJhwZzAUYZo746233oJer0d8fDz69u2Lt956CwDw9NNPo6KiAvHx8fjkk08wZMgQybm33norDAYD4uPj8dZbb2HYsGH12ndVKX89AUv5y2joFCxciPyvvgbAUv56Apby1zFeTfnLYDRWmJ87w5dhwp3BUICZZRi+DBPuDIYCfOHe0IshMxhimHBnMBTgZ4Us27LFiz1hMJyHCXcGQwGqr7V+rj55yos9YTCchwl3BkMBvlnGxMsdwmD4Aky4MxgKCGzuRuY5w/AtmHBnMJTgh4QbWMk9hm/BhDuDoYDALFNT48WeMDxBXVL+pqWl4frrr8c111yDa665BgcPHgTA5ZMZPXo07r77bsTFxeGBBx7wmqcVSxzGYCjAD2Kq+OcfL/ak8fPxkY+RWJTo1mvGtYzD7CGz7bZxNeVv69atsX37dgQFBeHSpUuYMmWKtRLTyZMnce7cOURHR2PkyJE4cOAArrvuOrd+NzUw4c5gKMCCmBo/rqb8jY6OxowZM3Dq1ClotVpcvHjRes0hQ4YgJiYGADBgwACkpaU1TOFOCFkK4HYAeZTSvjLHCYCvAYwHUAXgEUrpCXd3lMGob5hwrz8cadiewtWUv++++y7atGmD06dPw2QyISgoSPaankrnqwY1NvdlAG61c/w2AN3N/54EsKDu3WIwvA8T7gyllL+lpaVo164dNBoNfv75ZxiNDW/B3aFwp5TuBVBkp8mdAH6iHIcBtCCEtHNXBxkMb8GPUPXv1BHFq1axNARNDKWUv9OnT8fy5csxbNgwXLx4EaGhoV7uqRRVKX8JIbEAtiiYZbYA+IhSut+8/Q+A2ZRSu/l8WcpfRkMnddJdqEkULvK1//YbhN98s5d61LhgKX8d4+2Uv3K1pmRHDELIk4SQY4SQY/n5+W64NYPhOeRqqJrKyr3QEwbDedwh3DMBdOBtxwDIlmtIKV1EKR1EKR3UqlUrN9yawfAccjZ36qXFMQbDWdwh3DcBeIhwDANQSim96obrMhjeRa5Yh6nhLZwxGHKocYX8FcBoAFGEkEwA7wDwBwBK6UIAW8G5QSaDc4V81FOdZTDqE2NZmWQfW1Bl+AoOhTuldIqD4xTAM27rEYPRQDBVVEh3MuHO8BFYbhkGwxmYbGf4CEy4MxjOwDR3ho/AhDuD4QxMuDN8BCbcGQxGk0RNyt8jR45gxIgRGDhwIEaMGIGkpCQAwBdffIHHHnsMAHDmzBn07dsXVVVV3vw6ElhWSAbDCXI//BAtH5rq7W40OnI+/BA1F9yb8jewVxzavv663TaOUv7+9NNP2Lt3L/z8/LBjxw68/vrrWLduHWbNmoXRo0dj/fr1mDt3Lr7//nuEhIS4tf91hQl3BoPRZHGU8re0tBQPP/wwLl26BEII9ObANo1Gg2XLliE+Ph5PPfUURo4c6c2vIQsT7gwGw+s40rA9haOUv2+99RZuvPFGrF+/HmlpaRg9erS1/aVLl9CsWTNkZ8sG5HsdZnNnMBSImjHD211geJnS0lK0b98eALBs2TLB/ueeew579+5FYWEh1q5d66UeKsOEO4OhBPOMafK88soreO211zBy5EhBzvbnn38e06dPR48ePbBkyRK8+uqryMvL82JPpahK+esJWMpfRkMn/9vvUDBvnmR/r8QLXuhN44Ol/HWMt1P+MhhNikpzpXsGoyHDhDuD4STpjz3u7S4wGA5hwp3BYDAaIUy4MxhKsAVVhg/DhDuDwWA0QphwZzCchPj7e7sLDIZDmHBnMJwksEcPb3eB0UDYsGEDzp8/7+1uyMKEO4OhCLO5M+zDhDuD4UPos7NxIa4Xqo4dBwjxdncYHqKyshITJkxA//790bdvX6xevRqxsbGYPXs2hgwZgiFDhiA5ORkAcOXKFYwdOxbx8fEYO3Ys0tPTcfDgQWzatAkvv/wyBgwYgJSUFC9/IyEscRiDIaLqxEnu/yNHZIW7voEmivJl9q25iIIMmZq1dSCqQzNcf6+yCe2vv/5CdHQ0/vjjDwBcvpjZs2cjPDwcR44cwU8//YRZs2Zhy5YtmDFjBh566CE8/PDDWLp0KWbOnIkNGzZg4sSJuP3223H33Xe7te/ugGnuDIYI4mdf5zEWF9dTTxiepF+/ftixYwdmz56Nffv2oXnz5gCAKVOmWP8/dOgQAODQoUO4//77AQBTp07F/v37vdNpJ2CaO4PhABIUBKrTebsbjRp7Gran6NGjB44fP46tW7fitddewy233AIAILzZGlEwyyntb0gwzZ3BECN6cYPj473UEYYnyc7ORkhICB588EG89NJLOHHiBABg9erV1v+HDx8OABgxYgRWrVoFAFixYgWuu+46AEBYWBjKy8u90HvHqBLuhJBbCSFJhJBkQsirMsc7EkJ2EUJOEkISCCHj3d9VBsMLUApomA7UGDlz5gyGDBmCAQMGYO7cuXjzzTcBADU1NRg6dCi+/vprfPnllwCAb775Bj/++CPi4+Px888/4+uvvwYATJ48GZ9++ikGDhzY4BZUHab8JYRoAVwEcDOATABHAUyhlJ7ntVkE4CSldAEhpDeArZTSWHvXZSl/GQ2Vsr//RtbM5+y26bZnD/zbtK6nHjVOGmLK39jYWBw7dgxRUVHe7goAz6f8HQIgmVKaSimtBbAKwJ2iNhRAuPlzcwDMnYDhMUw6HfSeLIygwp5Ka2s8d38Gww2oEe7tAWTwtjPN+/i8C+BBQkgmgK0AnnVL7xgMGTKefArJN4zy2PVVLZaxpGKNkrS0tAajtdcVNcJd7kkXP9lTACyjlMYAGA/gZ0KI5NqEkCcJIccIIcfy8/Od7y2DAbP/ubdhwt0teKsSnC9Q199GjXDPBNCBtx0DqdnlcQBrzB06BCAIgGT4o5QuopQOopQOatWqlWs9ZjAYjYKgoCAUFhYyAS8DpRSFhYUICgpy+Rpq/NyPAuhOCOkMIAvAZAD3i9qkAxgLYBkhpBc44c5Uc4bbqbl0yfM3YWaZeiEmJgaZmZlgs3h5goKCEBMT4/L5DoU7pdRACJkBYBsALYCllNJzhJA5AI5RSjcBeBHAYkLI8+BMNo9QNhwzPICxtNTbXQAAUBN7vMXo8/JQuf8AWvznLlXt/f390blzZw/3qumiKkKVUroV3EIpf9/bvM/nAYx0b9cYDCGUUlCjSbDtC5GCTYWMadNQc/4Cmo26AX6Rkd7uTpOHRWcwfIaS335D+sMP23Z4anKoYsCoPHDAM/f2YYz5BQAAajB6uScMgAl3hg9R9sdW4Q6TSb5hPVBsDkVn2GCW2IYFE+4Mn6Hq338F29Rjwp2XOCokRLaFPiNDdn9TxlhQYP7EhHxDgAl3hs9SeznN4/fwj24nu5/W1nr83j4LWwdpEDDhzvBZSICnClXbNE+2YKsebUQE94GZZxoETLgzfBZteLjjRi5gLCribTkv3Es3bcLl/za8yjwexzIQGtmCakOgyQp3Y3k58r76ClSv93ZXGK7iIQ3x6ptv2TZc0NyzX5kN3blzbuyRj2BOjey5tRCGMzRZ4V7w3XcoXPg9Sjdv8XZXGC5Sc/Gi268psaU7IdxrMzJQk5pqu1ZTM09Yfiom3BsETbbMnqmqGgBbGPNlqk+fRuiIEW69Ztm2v4U7nCjUkXLzLcIdRiPgoB5rY4KYpTtlZpkGQZPV3K2wBTPfRZp4tM5UHRG6W6o1uctp6U3OPGF5l5rajKWBwoQ7w3fxwMBc8tta4S3USnf+2o1F229qGmxT/d4NFCbcGb6Lph5mXSoHEIGuatbY+XlwmgTm34olVWsYNGHhzh5AX0cTEFAPN1H5isiZIkxNTIO1Lqg2se/dQGnCwp3h6wTG1UNxZUJA1BRMkLGvO1pYLN+5CyXr1rnaswaHpfhak5uxNFCanHDPeuEFJI8Z6+1uMNwBrQchQoDoTz5W0RcZzd3BwmLm9Om4+sabLnasAWJdUGXCvSHQ5IR72dY/oc8WVwlk+CT14Y1CAaLVOm4mY2em1dXI//Y7GCsqPdGzhgdbUG1QNDnhbqHq5Elvd4HhJGG33irYro+FO11CgjqXSxlttXTLHyiYNw+FS37wQM8aIFbFna1nNQSarHCntSztgM9TTwt3/jHtHTeSmUUYCs3FK3Q17u5Sg0R/JZ37wBZUGwRNVrhbYTFMPgMRuT7WV5BQUI8ekn1+7USpgOXs6wYD9399uGw2IJpc8FYDpekKd8v7xmaQPoRISHph+h8yeDAAoMVdkwT7ZSNUzeXmmlzaYCbcGwRNVrirjjxkNBjKtorK7HnBK6Pjsh/NtxbdW9YV0qy5ywj3xqzdstwyDYMmK9wZvo83hAjRagF/f+msQdYsY+mfVLhXHjrk/s41FNiCaoOgUQt3U1UVjCUl9hsxBd5nMVhrdroHtSl6CSGSWYOcJk4tbeQ095rGu8hatu0vb3eBAZXCnRByKyEkiRCSTAh5VaHNvYSQ84SQc4SQle7tpmukjJ+Ai8OGyx6rvXKF+8CUDJ8ld877Xrkv1eulqaLlBgY7kZr67Ku8UxvXQ1i6tvFE3foyDoU7IUQLYB6A2wD0BjCFENJb1KY7gNcAjKSU9gEwywN9dRpDTo5gu+T39dJGjezFYtQBtc8CpSha/pNwn5zmbscl0FjKm1EyGzXDA6jR3IcASKaUplJKawGsAnCnqM3/AZhHKS0GAEppnnu7WXeqz53D1ddft26T4GDu/6BAb3WJ0dCoi5C1Z3OX85bhafWNeXGV4T3UCPf2ADJ425nmfXx6AOhBCDlACDlMCLkVMhBCniSEHCOEHMvPz3etxyrRX70q2BbbOLXNmnEfWJIjhpnCJUucPqfq+HFUHT8un37AMljIyfYSprkzPIuaGmByS47iJ9kPQHcAowHEANhHCOlLKRWsZlJKFwFYBACDBg3yqD3EkCeePAi/hsEyuLAkRwwz+izncw5deeBBAEDX7X9LDxqVNffilbZlqcaQRbHyyBFvd4EhQo3mngmgA287BoD4LcgEsJFSqqeUXgaQBE7Yew/RNFkc3WhtxqbEPkFDW3QMio8XbFNLNCp/n9pny8fD9U1VVUh/6GFvd4MhQo1wPwqgOyGkMyEkAMBkAJtEbTYAuBEACCFR4Mw0qfAiEmGgFCXIfHJ9gwY0CAfGxcEvKkqwz5AnNTNW7tvHfZB5xkKGDrV+9vWgH1NVlbe7wJDBoXCnlBoAzACwDcAFAGsopecIIXMIIRPNzbYBKCSEnAewC8DLlNJCT3XaJZSEOzPL+AYeEu6UUhgKnXxUNUTaHzszC6qrluxrcc89to0GNHC5wqXrrvd2FxgyqLG5g1K6FcBW0b63eZ8pgBfM/xoEknweCsKdmWV8A0+ZZYqWL0feRx+jqxOBN0Sjlbg5Fi1frtze31+6z8/26lGDb2vujIZJ441Q9bO9UIbiYiiGojKzjE8gCRoyU5uZWafrVh44yF0nLQ2maqmGLYtWI3luKnbtkjTzj4nhjh08KHMR2/nJo0apuy+D4QSNVrgTP1v1nNING+3Y3JnW5Atk/N+Tsvsr9u6t03UtXlU1Kako27JF1TmEaFQ9N5rQUO7a5y+43kEGw0UarXDnV8+pvXxZsRmrGuMbVCtVzqrj368mKQmATMZJe2i16p4bnskv5bbxqDp2zHasgXn/uBtTdTWM5eXe7kaTpvEKd960t2TNGuUEYSps7vqcHOizstzUL4Y7qUlJrtP5ze/kfAKa/+cu1ecQQlQFHlHeYn3t5cvI/UhFoe1GQsqECbg4eIi3u9GkUbWg6pOoXShV4S2TPPpGAECXP7YgsGvXuvSK4Waovm7lEkmQOQ2Fxr6e0+7DD6E7f57b0Gpl/doliIOTeKZBpTWExoLBnBjNWF4ObViYl3vTNGm0mrtaLxhnzDIF8+a52h2Gp6ijcLeUwBM/L51+FiYGa/Gfu9D2zTds56h4viQDAC+QLnu2bHLVRkfBd+yd8RaNRribamoEL1P1CZuNttnYscqpfZ1whWxoUZKNFV1iouq2pjpqwMSyNsMb5P1atULwoEHK58i4QsphrcRkOc9O8QC9KINpY8FUo/N2F5osjUa4J/UfgCsPP2Ldzp071/qZ+PsrLmCpeUktyEUhMtxL4ZKluDzpLmQ8M0NVe7+IiLrd0GKO4T0Hptpau3VPDfn50J1OQO4nn9q/tl4o3KtPn1Zsmjz6RrPLbiOD6UNeo1HZ3KuPH5fdX7F7Nyr27JE9ps9Q7ydN1fpBM1ym7O9tAICKf/5R1T70+jpGR8qZZRzM0GouXgQAFC1dKr1caChMlZXcZdTY5XkYi4vrPlgxGGYajeZuD6rTKQrm0vUyBTz45/JedGdfVobzNL/9DgCApnlzVe2JnzT60xnkzDKOFlft4R8dbf1sVNDEi9eskd1fdfSY7H6fhpkyvUaTEO6RT09z+dzSDRutnxu7h0NDQBMcBAAwlZaqO8GFILTqc+egS+K0b2jNwW48rylty5bWz8EDBjh3ca3WYZOct9+R3V+xc6dz9/IFWO4mr9GozDJKaAKDXD636rhNm2Kau/swFBWB6nTwj45GTXIy/Nu3hyY4WDmSWAFXcqGn/fduAECvxAvWVNACs4zZh71XovORpX6tWqFGYUFYnCZYjKm28RXNZk4I3qNJaO61l13PPswv9uvrqVkbEpdGjETymLEw1dQg9fY7kPW8JeecY+HeYvJ96LTiF26jjukjjJYZAs8sE9Cli8vXi/7kY0Q9K78Y7Mgnv/Zymsv3bSjwUxkDAK1i61TeokkI99KN4vTzLsI0d/dj/k0r//2X23Zg725201i0fecdaMxlEp3V3MWRxiW/reU+8MwH0Z9+4tQ1+fhFRCDi3nvlDzoYtwK7e7e+jSsYiooE21WWv6MZp9I6MNyKTwv36tOnUWMnb4zbcdJkwHCMJROjdcHbwU+sDW8OQoht0dNJzT157E2y+/O/+tp2D0t9XVdRsLsH9e5t9zRfTHGR8478+gEfNuP1Dj4t3NPum4zU28bX2/0MeXnQi2qzXojrhcznn6+3PjQ2xB4l9vzLuQbm/80C1N31RwPckF5C0dtGb3/mV5vq1eJlLmFx+7QQNm6ctBET7l7Bp4W7WgK6uS8fzJX7H5DsK/9TfaEHT1K6cSPSZPrXkNGEhwMASEAAt0Pl7MhVzd0RQX3sa9dKdNu9C3HnznIbCsK9rnlwGiRE+F1NMpkgmebuHRqdcKeUwqQThTyL8se0m/uBvIahAn0di0N4kuzZr6L6xAlvd8M5zC++1c1URrjLely4oLmr8dyQq5rkiLCbb4J/27YgFnOMRt4s0yiFu2ggozKujzU+OCNpDDQ64Q6jEYbcXOE+Uf6YgI4dEfP1Vy7fIu+LLyXT0YaEL5UOrD53TrRHKtzl8vG7pLmrEO6VBw+pv57lsiqTzzVK4S6uZikzOFYfa4TBWT5AoxPulf/+i4oDBwT7JNNCkbahu+CcP3PhokXInz/fpf7VB74kRLKenSncIaO5m8rKpCdaNXf3CnfD1avqr2dBNJhqggLlby/6u9TF5bIhoDt/3uZKakY2poQ5IniFRifcS9etQ+6c9wX79BkZwkYi4V4pGgzUoDt/HqYa7wed6JKSkDxunPAl81GXTUqpNaiIj2xtU/PfkOp03p9FiYQ7v/g1n8oDBwTJwfzbtfNot+xBKUXRihXq68bKcPk//4XudIJgX/htt0ra+Xfs6PI9GK7T6IS7SedY4Iq9GVyJPDUWFDYIL4CC+Qugv5KOSl4RZp9dwDIYBHbx2vR0AJAt12axb+d++D8kXaucnleAh6Il5ezMSlQdPWr9rFWZP8cTVOzcidz3P0De51+49bpyz5423HvfsynT6IQ7VaNNi15yWmvHjKEwpay5dKlhCFFL//jfyUdDvqnRKFj8TrllHEzV1cj96CPrPv+2Zm3XleRenvpdXKzjSvz90f6brx039ACmas7pwFhU6N7rykSk0kaYVsEXUPWGEEJuJYQkEUKSCSGKJWQIIXcTQighRKUq5X7UJPeyLIBZFn/s2aijZjyjfKEGIdy5/2gDEu6G/HxceehhSfSiI7gZlLDvpooKGHJsC+RR054CYNPc7ZH31VdIvvkW3g3c87tEzXxWuMOJBWyj6DcJv+UWdFy6BH7R7RB2q9Sk4SmI1mzWcnOcgFyUrZzAZ3geh8KdEKIFMA/AbQB6A5hCCJE4AxNCwgDMBPCv+Jin4Qs2VTZEs4eFNiqqbvdtAMLdGvQjkO1CIZZ2/wO4ENdLsvjlKYp++glVR46ghJeXRw1Ur5cIyoq9ewX7rN4YCu6GfAoXfi9Yb3GXF1Gr6dPR/mubxu2MWSbn3fdsG+a/XeiIEdCGhdfvQrjl93MhTsBQXKz4nmlCQyX7qI4Jd2+gRnMfAiCZUppKKa0FsArAnTLt3gfwCYD6r6vFE2a6s2cdNzdrK9FzPwAABPe3k61PYcodFB8PavC+cOep7rZdIuFu8X3Xu+IJ4gKWhWZNYIBT59HqapRt3y7YpxSSb9E8nbq+G1M2kwCey5+rzwHP5Ef8/EANnHAvWLQYtWInADdD/FyP8L00fAQuT7pL4aj0fanLoi3DddS8Ie0B8J+0TPM+K4SQgQA6UEq32LsQIeRJQsgxQsix/Hw3lqzjCTNVi1QWzT0ykjvd3gOuoJXpEhJktZ7c/32EysOHHffBXVgFhGOzjCsBOq6gO3ceAFB55KiDlkJyP/kUFTvUVWAS52+xN4tKHHiNuZH7zFXE3zZw8RdJnbuIULhDb4ChqAj5X3yB9Ecfq2sX7WPxNjK4NluovXJFdr+cc0LRihUu3YNRN9QId7kVRetbQrhSNl8CeNHRhSiliyilgyilg1q1aqW+lw6oSU6xfvZr21ZwrOVj0pfEIsytdls7U1N7UY1yAqVo+XKkP/Ko3f7WBVNNDS7E9ULhEnOJN8vCIr+fCuYHTZDree2dwVLusHL/fqfOq02XFxhyiD2edGfOKLa1JCUTm2WZy9x0AAAgAElEQVSMFRWqimvI3t8dAyXP7ZP4+4Pq9dZnUmybdzeaQM4X3+2mIJMJgT16CHbVnHc+Lz6j7qgR7pkAOvC2YwBk87bDAPQFsJsQkgZgGIBN9bmoyvdzrklKEhyT8zm2avfmFynr+ReQNHSYwsXtaHtesLlbAnoKf/yR2yEz9CoNSPUduSpnf7WHU0JAJJTTJk9BdYLN57omVUW2UKMRAbGxwsu2UrkO48QsIHb1KnT8ablkP+GvG/j7CbRek4wJKWnQYPdpweZZQ9Uh984yqcHg9N+d4RnUCPejALoTQjoTQgIATAZgTZBOKS2llEZRSmMppbEADgOYSCmtt5hje9oH3wYfl3AaHX9ciuB+fbkdPGGnWNbNZJLV0sJuu1Wgued9/rnT3iGuYPX0EbtoCmzuQNlff6Hs778BAJqQEG5/PQ9G7njJg/r0kb2OXPZIA8/UZ8jNkV5M7AJrMqE2JUWwjxB1tvzq06ftHg/s3cv6Obh/f2jNCdL4NBtzo+2+fv6gBgPKLWYpGfOGqaICue9/oKp/DnFz1GhQv37cB6PR+l0jpk516z0YzuHwSaaUGgDMALANwAUAayil5wghcwghEz3dQTVUJyi/aPzgHhIQgNDhw63bfm3aqLg6lX0RAjp2EmhahYt/wKURI9V1uC5Y1gA0Guhzc1FrNkkJ85tQZM16HlkznxOdWr8ukhahTE0mVJ9xvNCtRPDAgQAcl6njC2/ZKFHRzKV82zZpG5VCL+yWm62fo56RusvGrlwJ+Puj7btcvvMa0SAS0KULwkaPtt3W3x+6M2dQtGyZ7P3cX67OvcLdauYxmqzeQ6EjR7j1HgznUFVDlVK6FcBW0b63FdqOrnu3nKPm0iXZ/UH9+tm1xaqxQVOTST5gxmhwyr/ZbZhswj151Gjbfns2dw+lx3WEZcZQtHQp8j77HJ1++Rkhg5yz1lGj0erlIvBQkaFw2TKE3WQuxqGVebRFArL2SrpMp9Vp7n68ItpRTz0pvUxQEHqdsZmJxOafoLg4wbZlMFJ6lt2OmzV3bUQL7rIaAojXtBheoVFEqIYohJ93/m2N3fOUcoBY0F+9iqIlS2VfhMIflkhyV/tFez5XiMVubt8sI9LyiEwR6HrAYs6qSeFSvip5WNjFaLSW1AvsYj8vf/Wx47Z7+0kFS8n6DYLtuhQ855vqrLno7SAO7hEvxss9i5RSm8nR3Zq7m3N5tZ0zB61eeAHBgwbZZpcqTVwMz9Aofn17/sskONjl62bP5oJxqYKfrthvPDC2s8v3Uo1FQEu0IhURqvUs3C0CTRPMzZAsIe/OQA1GhFzDmWVav+TQIcuKuMITNZmQ/4Uwj0rFnj3SE1UKPWe9ZSTtRbMo4i8V7tkvvojEfg5MUS7isOKVk/hFRCDqyf8DIcS2LuRCLALDfTSKX9+eBqZY8kzuOkYjsl54EQWLF5t32NeWqKhsmjORii5jFtCSTJe8voq7bZlhVPE0W4DzyKiL9qpEyODBAGBbuLaYSIzy99KEhSleixoNNtfVQPlUunJkPDVNsG0sKZG00adLzTLBffqou4GDWZ8YsTCVxFbIDBZlW//k2lLqds3do+svllkJ09y9is/++nyhpDuToNjOv317xWNikgYNRtnWrcj//AsYiosdTrepXjRj4L2wBncGafEQmxasfRGYZeQHmfIdO6yfa1JTkRTfH1cefsSd3QMAEPNahqGQ8x6yDLBKwWLNRo1SvpjRaNVy1QzURculLoeAdCBWInSkukVxi7B2NZ0tFQ10dk2EIhOOW9JeuKCIUKMRF+J6OWynbcHZ3zXBQdDy1iaaCnlffoXMZ5913NDD+KRw1+flIbFvP+u2RcPhY/GsiP74I8kxJfjml0vDRwAOppWSDJR810oPhVwXfv+9/AG+4FTS8ngDYur4CQBsAUfuourESWsOm7xPPuF2OggWsyfYqMFoGxRULNDl/k/+760mWKfNW2+ixX33OWxnodPPPyF2pYt+56KBrnTDRsWm1GgU/E0N+fnIfG4WsmfPdu3ecG2A0F+VcS+Vod0H76PtO28jKD4eLf5zl1MzrsZA4fffo3z7DscNPYxPCnfd+fMO28SYU6nWxdeaOEhORYKE9nyB9uxKSloe1WfO4kJcL8F3rTxyRLG9wLzCG2T4wTxVx46hZJ1zybycgVKKK/ffz6Vm4GGxvRav+Y1LBCYiZMgQ5WsaDU5p7koYrmY7bBM6YoRTtuiQwYPh52ryObGZRlT3V5BGQyyIKUX5tm0o3bgJLuNBs4y2eXNETJnC/ZZaP9CamnpLWsew4ZPCPXPa0w7biLVB/w4dFFrau4j9F12S7Y73EtZ1warcnEDLIgzL/tqG9IceVmyf+4EtuIUf3Zg6fryg3dU33vSInR2AcpCUeZDUp6cj48mnJIe14co2dxhNnOYuo7U7o2UX/fyLbUP0bLT/4nO0fPRRibuiJ/EXpclo/apQC+f/jajRKNS03WF/d8kt1vn7VpkVkpz33nPQkuFufFK4q8IsXC2aVeRj8vle7CYacyCgjaLybm51NbS8fJYUCbNmqT7VUR1Qo1xNUjegNGg49JrQaBDcv7/8Nc2au5zPdLv33pU9p+xPqZlO4Bop+rv6x8SgzexX3O5BYo82rwnLImiCQwTbAjOS0SicrbpBuLv0rLpgyqk+eRIAULHPuTxDjLrT6IW7JiQEvRIvIGLKFNlmIcMUcsrwrgEA4RMmSA6LXST5Nvi6CnqLOYVotLJ5Ruyem5ws2NaIQt/VLi46i5xwz5z1PArmL7B/okajbJc1GrnUyk4ExMitwTQbO9b62R1lFuuKNSWEZVtUVFusuRd+v8i27RbN3fZ8qn1W65JkTBwTwvA8jV64O8Legivf46XdB+9LrmkJzrFu85OW1dGjoWLnTu4DNSnmNFc8V5yNUSwMFFwS64rcy1/+11+OTzRRkCB54Z736Wco+vFHxVgD2cvJFMzOfvU162dx1sL69v/vfuigZJ/YMyugsy1mghqMgjqymTPq7okhEOgqn9V6LSbCqDONSrg3nzTJ+lntFFsTFIQ2b78le4y/MKgJDkYbkV3UKoBlcJuJhhB1ApKPuHiEOGGWpzRVF69befCgNTeJhWZjxqg6t8fRI+i6/W+BS6KspxJPMEVNF67ZWHLX1Bd+ERGSfbUZmYJtvrAv27JZ8PvUJCbWvRO8BVW1njNKz41/TEzd+8NwO41KuIeOsCUFcyZ3hpLJRox/p07qO2M0IuOpabZCES5StGy50wOF+GUVbxsKCurUJ8X7uijcw8bcKMjJDwAx875Tda42LAwBHToIBjBHbqihI4QJrRpCDhRJniNeEY2alFRBAry6YKqsRNp9k1GTZBsg1GrkRT//LLtfadYFAKHXXw+AS81R9PMvSL5lHExVVU70mOEqjUq4C+yyTgh31Vq+ihwiFqjJhIo9e0Crq2XNBGox5OejeOWvTp0jDpARmzSu3P+A9bM78+GIhTt/JmWPgK7dUHvZ5rLZ8vHHnF7c5EfsOtJsNYGBaH6nXKVIz9Jp5QrErlsre0wj8hgSrIu4aRZY8P0iZL/2OqpPnxasg1SfsmVVrT5zFsVrhDmZDMXFuBDXC2WbNstf2E4ls+hPPuaukX0VuXPnQp+ejpK18r8Bw700KuFO+JkAPeD54FQwBk9bdsY7perYMUkUoLGwUP19Aec81lyooal4W/FCrUq/dL43TcjwYWjz8stu65MSwdfWbUblCiHXXKOY3kCsudempVk/a5tLc8G7Qv6XX6LcnOOfj1/r1tbPaffcg5y33xEcr7lgv4iKfztlBcEvIkLikWYvYIvhPhqVcBdElDqZ14L/gCtBAtQL91r+IqgTA42zWrocYTzPEEcYcnNRdeJkne8JQFKPUxyYowQJDLSFqbsYXCPOumih7Tuymam5lAd+foqadH1j8bH3j46WKBFBfft69N6OZpb28tAEdOuK9l98bvd8cQCTmiBERt1pVMK9lue94qzi3u7DDx22IYHqzTI1SRdtG7xpdcW+/Yq26Yynp6Ns61bZY85gL0umHFfuv7/O9wQAQ54wn45JnJ7BjHgw0TRrhqhp5kRfLpoglPKgN//vf2X3+7dpg15nz6hPFOZhguLi0HX73+j6zw40nyisgZP98isevbfDv78d18uwsTdZc8moJeKhxluhqfrcOcF28pixXM1jhSIsnqRRCXcdf/ropHSnNY61TGds7vwFK0vq4MrDh5Hxf/+HgvnzZc+p2LVL9fXtUTBvnluu4ywZTzwh2FbS3MXChAtTtyQXc29BEWf+Zt4moEMHEEJk0/8qUR8++uIZGR9X0voGxAijxXVJF5HzwVwPVJvyDOU7d6HqGFdFVJ+bi+Sbb0Gtec1Hny1Mc2HZzvvo4/rtJHxUuCtNwS3VYAA4n9tFRXvLdFlNpjt+3pmqo0dBKbXa3h0G9TRAKKXW76H6HCcEdeXefQCA6hMnrPsi7lfnxdTocJDTiE/Jb795sCMcWc8pR0e3uPdeF64ofIYu33knin/5RTLzqyu1V67gQlwvVP6rnJPJWSr27UPm9Om48iA3+yhesRL6jAykP/wIjKWlKFjQcN5tnxTuStpKBF8jdFJzD7nG8QKbteCCCgEndvcq/mWFIMRcn5sHfW6eddvoZARf5JNcabeep9xjL3dEyerVuDL1IWvOG1U4oVXKFc4QB4k5S/O7pSaZlo89Vqdr1gtOPLs5781x6Cprqq6GPi/Pbht72DPziXPkqMFYJv+su7u4R9XRowCA0k3uW8DViTyxLJ5p+uxs5H3xJWrO26wHuosX4U18U7graIRaczk2AE4Ld7s5ZiyXNCecUqO5m6qEi1Ql69YBGlufkkeNQjIvj7kz2nzrl19C6xeeR6/EC6rqwLoDi6B1lLfGgjYiwin//I5Ll0j2+ceoz8Uvi8wg3Px2aRoJe9y96W7MP7EAOamlqK024PQ/Gfhr0RlUlzu3ruEMROPcs5vYuw8uxPVSVBCuPPIIkm+wkzPfRfzseMnYo+C771B99pxkv7PVrRxikQFGE/S5ue65Js+7jFKKwG6cFSF84h0Ss2rFTveYWV3FN4W7yAZIQkLQYfEiQVFkdwamdP2Ly1WibdECbd99Bx0XL0LoddfZPacmMUmwTfV6ZDz+hEJr5xZBdaJr1wuWAVWu8LQMQb16AUYjgnr3VtU+oEsXyb6wG29U3T05jOZiIYJ+qeyPhdSCNNBFPbHuk+NY/Pxe7P/tElJO5GPXL26IElXENTdepeA03WnlYjYWjBUVTt/Pr3UrVe3avPmmZF/a3XdLG3oocVvphg1IHjUatTKVt5yGV+Qk6dpB1ih1QjQIEBduERVEqe81BZ8U7hD5U8edOI5m118vyPznTuHOTwUbMXky/KOj0fGHxXbPkdTwdCC8nVkYK9ssH0wSdtutqq/hLBYvHtVTZ60W1GQCCQlGyNChDpvLVb1q5oRLpxwVu3fX6fyMsgxEVMmbHS6f9kyULwCXhZzlmS9YvBgX+vZD5rMzVZ8rLl5em5aGKgeFXKL/9z9V124hYx4DgIIFCzzqRSKJzK6Dacp2TZ7mXlVlM1MSIkljIZ651nduHp8U7vw/Wtv359gOmBdFA7p1rdP1O6u00TlTwk9S81SEOKrUGSy/QbCKYsqu/jbWGqQq4gfavPEGl3nRaOT81lWYGeRiCPhRqh2X/aiqn63dGACVXp4OYkeLrq7wnGlGjsDe9kvcZZm/e/7nXwAGg1PrI3pRbpuUW2/DlQceVDStNb/zTgTKzLbkUDId5n/9jdCLxIFmaywtxYW4Xkg3rzc5opifwx9waqFaEYU8+NUJCShculS4U/R1aG0DFO6EkFsJIUmEkGRCyKsyx18ghJwnhCQQQv4hhDiRhMV5+FpuxD33WD/7RUSgzeuvo+Ni+1q1I/havz2f3M4b1qPbzn/Q+pVXEPPdty7dy2RxF3QiDW/U9OmC7Yh77kGXLZvR/I7bHZ6rZgCwiwrNPXTkCECrhe78eVSfOAGDivJsmgD79la1hTRaPvyQ9XPzu+4CAMQskHc9dUROZQ5aVLdRPL51/hmXrusIuWLeABA1bZrdoCs584va1BdlW7eiNjNTsj/3I/msqS0mqy+UohaluAgL1WfOArB5Vjm8nkyOobyvvqqT+ygVJ+UzEzJ4sDS7plhzV+Fu7U4cvqmEEC2AeQBuA9AbwBRCiNhweRLAIEppPIC1AD5xd0ctUEphshPO3/KhqXbDoVXdg1982040nTYsDP7R0Yh87FE0c9E+XG3OPOmM22DLR6QVmQK7dYNWRck3cR5xC2oXP5VKD+Z/Z/OtJ35+AvNNbVoa2r77jtxpNhwspjnyyrAIG34FrnbmGY2rtvurhXm4MUXok3/nrAF44D2uBkBOainKCqsxb9pOLJvtvmIUJgUhQPz9nQ66ynz+eVXtyv/+W9aMU/yTNFlY9KefIsTJTJrt5s5F65dfQvCgaxXb2MuyCgDVp06pvh81maAXDVYZTz2FwoXf1y1lstoKVhoNqs8KB/8SUc4eT6NGcx8CIJlSmkoprQWwCoAg6xKldBel1OL7dxiAx3KA8gV7pxW/2GnpOn48bxh9un1zigVXbfzEzx/63DxhLnhHKExfxcm2oj/5GB2XLUPoyJGI/vwzRD07Q3CuhuddZEkuVrJhA3LmzIESSnbYgu9sWRyJVispENziP/9RvKZc3y1027sHnX93XPe17TvvIO7cWeE1ReX05Oz6SlBKQZb3tG4X3HMIk14YiJi4lmjRxjZA/vzGIQBAZWmtdcHMZKI4tD4ZVWWumW1ojfx5rths1Wq5gOMcMhbUzBDFtPjvfxD5+OPwbxet2MbRupQzmTETe0sHQUvBkJpLl5xOuWEoLET5zl0Cmzuf0o0iU65GI/nty/7a5tQ964oa4d4eAF/CZZr3KfE4AGkpHACEkCcJIccIIcfy810LWCgzJz4KGT4MIdcqawFqKMmrQq1OOkXza2XzAmjBM/t4AuKnRfKoUYrh8xY6b1hv/SyurKRE84kTETpsKDou+QHNJ0xAq2eeQfHKldbjLR+ymTAsU9irr76G4pW/KlZ/Kv39d4er/nLHSUAAeiVeQLddQu3MUTI2/9atVXm4EEKsA2yzMWPQdo6wZmeHRd+jixOpHeY/LXRjW5u5Cn/WrIPBpDylz77ImVNSTuThxLZ0/PiKi9q8wu8rjn50By3ukfFasYOcy6oz2FWCeOs5+qwsyXMUco262UJ1gn3vIH1WltMpN9L/7/+QOX06TApeRZJobBnTj1MKnBtQI9zlVCrZp48Q8iCAQQA+lTtOKV1EKR1EKR3UqpU6NyoxOW9xiaCqDh126Xxqopg3bSd+efsQVrx9GItn7bXbPsjJabBWVIih1azn7J9gZ5Gn+4H96LxpI7ru2IGguDjErl2L1i+/bDcdrsVtUw1Rz86wfhbbJ6kd+6de5Fkhxp6GKTYLRX9k87ho+87biF2z2u61+ZzZnYnDG1Mk+zvMn4cIUeRksxtuQICLfvPr+34JAPj6xNf48zL3+z76idQVdsOXnDZoqK1bCgVLfnRNeDjCxo2z7lcT0+Csu11zBzMqK+ZZkDgXvrPYTa1gNuXpkpKQPPYmFP/0k/BwpGOzIwDnAu1UYllwrktEcH0XhVEj3DMB8JNBxACQqBCEkJsAvAFgIqXU/spIHYj9jbNbubqgU5DJjbyleTZhVpjF7Yu4/34EDxgAAAi7+SYAzk2FeyVeQI9DBwVRo9aEWEoo2PBChg6FX2Qkgnr0sAql4L59EPm4/QjLgNhYhI8fL1l0tdD8TltSKkII2n/9NdeNKpFwt/O9HZk3Aux4EWmCbWkZArp0Qfhtt1m3I6ZMQXC8ugXf6opa7F11Ecf/vAKTi5kkleBnQVw4/DnkhqVZt/dlclPtkPAAPP759ZJzLx7JgYa33pCfUY5503Yi97LNnJh8PA9ndksXLy2EmwV67K8r4R9tM2NYTFv2YixMlc4VwlBK5SGm89rfJGYvNWQnlwizStrR3C21bWvTOOXBkr/Fgr0cN3y0kZFO9tI+1GRySw1YpSLwnkKNcD8KoDshpDMhJADAZACb+A0IIQMBfA9OsNfdmdQOwf36IXbVr2j7+usunS/nybfqfS73RNu330LsKi7lbpBZyGhCQ52+hzNRo0qr736RjqNglWj/xedoNVN+0SjiQaH3jyaEE7ZFy5eLOqYsMC0vq2URVqwtkoAAiVmEf8xC2zffULyHPfIzyrH0JZvJoyxffX1VNZw/wOkulf6lkmN/pv2JKj0nQINCuUXggGA/dBvEpYzevvQ8aqpsU/I1c7kQ+LUfH8PZvVwa6G2Lz2LvqosCkyClFJlJxaCUIvzWWxGXcBqBXbta/w6tX37Z+tu1/+xTtHn9dVlBaapwTgiJyxsqQbRap9eVclJLsf6zEzj2Z5p1ny5B2cPIv0MHUJMJWc+ZZ7vil1Wll4vBRZOvEu7KF1O0bBlKfl+P9HpKgeFQuFNKDQBmANgG4AKANZTSc4SQOYQQixr4KYBmAH4jhJwihGxSuJxbCB4wQPXiWK3OIAgVN+jVeYW0fPhhxCxcwLn1eZCrCoNUy0cf9cj9gvoKzUwWTVpiS7c3vTcZkT9/PpLHmIOM5F46Fd4/rk7xLQLTwop3DsNoMKGi2D0TxqR/OdfNkE7yg8bQlbagrDue7Y/bZ/RHVIxtcXrfavmcIntWJglmGYtn7cW8aTsxb9pOzH96FzZ+eRIntnFaq/X5tvwdeKY4bYsWaPnQVPT4V2qavPqGNBrUHqpD/l1wGCg1D7pXztqKzdjzPiP+/sJnSZTMz9EsujYzCxfieqFoyVK77Zyl2kEwlzNcff11VB48JAka8wSq/NwppVsppT0opV0ppXPN+96mlG4yf76JUtqGUjrA/G+i/SvWH4tn7cXSlzktLye1FOs+lv9DiQsSaAICEDZ6tNPl3pyFX3GHT3C/fh65HyEE7T76H7ps5sZfwjOTFPNqZJb98YfyRUwmFHzzLQw5OaCUyvoNO3oRXQ2mklsAB4CFM3Zj+WsHoK9x3t59NbkE+9fYFrRrdFzfr3RybLvt2CcS7bo2R8voZg7bAsDh9dI1Aj4XjwhzoPh35CyifPOMBUEuJTOVBw6o6gfAKTAA0GqWctZHC8TJLKvlRTrs+JET5GUF6mZWuvPnBS7BagrD16SkWDX1KpnBzh3YK1aiBiLjfpwyznPR5BZ8MkJVLXxN9Ogfl7HuE6lgj4njFkBLHTyAWReLcTmhALU6AypLarDth7OorVaeJnZc9iM6LPoeABBbD2lZHXFmdybmTdsJSilaTJpktbXyFzhzP7Qtbub+7yNrLU2xoOb7xBtLSmSFux/P5z584h2CY93370Pn1baF07wrZZg3bSeKsh0H3OxbY9+rqDDbcY4USimuppRan4/fPzuB0zszUFVWC5PRhKJMzuzyV+UxxWsYRWslMT0jFFoCzSJspo8ze7MU2wFAy3ZCM2DElCnouHQJwsbdIts+SOUahRyh140EAERNe8pxYxWau65Sjz8XnkFlSQ02fmlbd6outz0/kU8p3yvvo4/tZgIN6CyNiE2dcDsujRoNACCBdUuiV3X0KHLmvC89UMecMG1mz67T+a7SqIW7ZYoLAEc2XxYcm/btaIyfHo9hkzgNcsXbh+2O0Bu+OImt8xOweNZeLHv1AJKP5eHcfmXXtNBhw9DshhsAAIF1TIfgDvau4kwFZ/cIhYtSUBMA5H3GlU8zifyuay7aBGzy2JsEwt9ScDvsttvQ/ssv0OnXlWj/iTCmzS8qSrCWkX6Om7ZfOCj8PZOP56FWZ4DRwA0mRqMJiQe5rJSXIo/jmYVjMPmtIYJzCjNtwp2aKHSV0hlEyol8/P7pcez6JRHFObYBJfHwVavHCwC7+buyK4R99Q/U4t7XB1t94O97cwge/eQ6RLYPxX9evhbT5o0GABgczCyKrgoHOKLRIHTECMUZZJtXnRccoaO459KpSE0VaScST2ci9VQ+Nnx50mqSsWBRhMLH3yZ3qhVaLVwQFvSRN6Aa+LmbTCYYCguRLRKibZxcl7sy9SGBqzD/+q7Sbc8eRNwnzXmvCQuTae1eGrVwP7xBXgvoObQttP4adI6PQmS0TcjMn75LMCBYMCoELlSWqLTx8rSeTr/KPDwexGSiVuEIAFdThIuEfO8VybmWgDFR3pss3jSeVlUJ3CYtgp4QgvDbblMVyRgeZV7UvVqF1XOPIPl4Hkpyq7Bt8VksnrUXC2fs5lxYDy60nrOvy28wmAxo0VY4OBVk2IR7wq5MLHlxH8qLhD7I2xZzXh8XDlzFynf/te4/9HsKriZzv09qy9N2+7wicYVkX6uOYXjgvWGYNn8UQtpoEBIegMlvDUVYyyBoRWkb4sfEoM8NUq+i4pwqVJfXQlehzjPEsnhvb5Dm0/7bb2yF5J0QWmrSEC/Y8wMAoCTXJqBvfoyLUSg0z8qCevZE7FrlFAriNYDMZ2danym+88Gl4cL1moynpknWeVo+NFVVem4AdlMCU+qacPePjoZ/G/nazJ1+kUb+uptGK9xz02yuZ+262XK1B4b64aZHeyO1NBWTNkxCXq3wj3pIxiaq18lrW6f/cT56lWi1COjWVeCS6EkWTN+FhTN2W7cDQ4R+xvaEO8Clgi37077tM/djm2Ye1DPOqf7lXSmzLoSmnytEQUYFti0+KxlQywqrsfmULcKv1q8al0svC4SmRksEZpmcVE5Qn+fNsMQapRJ/97C/KLfiglS4xy+Px4u7X8Scw3MwdOVQiRfRTY/agrGuGdcJo+/viWcWjrHuI4SbbSx9eT+WvKQusjSgSxcEdO6MGIXSjWICu3VDUB+uH34q0lVYUJOSOv6KNItnu25cdTSLuzHAufQqIS5yU7FrF7Je4WrI2kuupzsr76bZ4XubQoF490IAACAASURBVODXRjlPULLZtANIvb+qjwnNuRFTpyKov31zWM/Tp9D1b+WIVHH0tCfwOeFeU6VHRmKRQBsVY9SbkHLC5pHZb7QtG8K4dzrjUPYhrLywEimlKdiftR9TPxguOP/oH5cFJprEQ8oFKixaYWFWhaK2xRfuhrw8dN2yBdEfS2sqRjzwAAD7hUNy08pwbl+WYOGQmihMJoqaKj22LkiwCkv+QxraPED2u8h5SwTG2QR09suvIOfddxX7A/AWvrRatP/qS7tt+VSW1uC3/x2THVCNIq+mgowKTDonDAjbk8lVb3rk45Hof1MHxA1ri8KsSuv39vPnHu9jW9OQfp4z/Wz+VmV+ErOi2kshFYAYvUkPCoq/r/yN3y/9DgDQGXUor7W5JvYc2haR7blF0IAgP5zOP439WfvRvicnAAeOE+bby0+379ZYVlgNXa0GXf/citBhjtMqBw+6FoGdO3MJyFavcsrvWhulHHSYm8atmYiZMD0ezSICERDsh/2/XcLFo7YEcrFr16LTypUI7NlTcE7J+g2S61hSgLiS8IsfORrUSz6rZs4Hc0UnUeguXkRNivzit+78ecGaEZ+ep04i7uwZaAID7Qpw/w4dFI+5C58T7lfOFWLTV6cEUz8L2cklmDdtJxY+uxs5ZvND81bBiO5ufnlu7ojx68fjye1P4nIpZ4MnhCA8KhgDb7Yl2j+y+TLmT99lNbscWJsMAAiPCsK070YL7Lw/vX4QZ3ZnYtX7R7Byjm2Kr4RilJpGg9azX0HrV15B9wPKYetrPzqG3SuSsOi5Paip0oOaKOZP34UF03ch5WQ+Lp8uwPLXOI8J/gBQWcoJKUOtCWkJdnKRa7XoYk51ENCtq8Oc3nxivvla1oNDCXs50XevEIZq7//NZudf3Z/LVBjXkhuEQpsH4rq7u6NVp3DUVhusA27iYZsw2fwNZ2apqXQsIDb1tuXJyfLT4qFS5UR1FmoMUhPd0ZyjGPHrCGvgEwBMfmsInlk4Bv6BWjy49UE8veNp3PncQDz4/jAMmyhcMFzzobRm7YWD2djw5QmUF+nw8xuH7KY4sCSz67B4EVo++ig6mP21iVarKNiJwkxO20wa72EyR3uv/Ui48Hztcy1w7xuDERsfBUIIItuHwqg3YfsSmxtkcN8+CLlmILQtWgjOlatVYKrRoTohAbmixU410bi1WbY1JrkAwMpDh1D8iyhHFaW4PPFOpE64XTarZuQTjyveTxMUJCvUxa7N9VG43eeEe6TZ5UzsFZF9qRjrP7MVV7bYlk3j0xDaPBDT598IDLdp80dyuMAlywPSd7TU/rnmw6PYscz2QE54pj+0fhpEtm+Ge98YbN1vWaysLqtVdMVrcd99CLv5JvgpRc+ZTNAEBCDysUdVT9l+eGEfjmyxLRSLtV25ARDgcp+knSlA3hUZocWzW9Ymp9jNwCkmZOgwu8ezk0twcF2y9TdP2KUcpSnWWvk+7CbCCejFCcLUzhZfc4sdvdcIYXbQytIahEVKPSo694/Cg+8Px82P9cb27suQ3dw2kJRptXixyJaCd7K5/mfvSGG+mxqjVLj/ncblQTqae1Ry7FIxz+uHAM1bhYBoCCY8I5zu8z1NAGDnT4nISirBT6/bkmht/va0ZNbY/cB+xMyfh86bNqLZ9dejzexXoBUt4hkNJhzemIKaagPizp5Bz+PHEHfyBDr9zIX9t33vPVQFtwbpLp/bRy547ELrQ/i/Iw+jVQfbvZpH8YrFi5KpdVgwH1EzZsAetckpyPv0M8n+xF62fvl36ig5DgDNJ0xAi3vvRY8j/3JpeUWkPyoTUMQbNPLMEdx8/EXmHUu+nWZjxkjant2bhWWvHoC/OWV1s1Gj0G13/ZTf8znh3qJNCDQagu1Lzls10MRDV7H+c/ksb/POclpYalkqntoudcPSEs5kEtYyCHHDhWllq8pqkcTT/vhuaq06hAls+RZ+eHEvLp/OR00V97LV6gwozK5Au/feRcy3yjnfxTlp5Eg+Lg3+PbY1zfqZ70pYVlAtOMYn8XAO/piXgN/+p+zq5wpyVZoqS2pwYhuXImD9Zydwcns6jv/FLVoX8zxDAoK0mD7/Rtw+w76p4GpYKkqDOL/mE3knBMcsJg+j3oTTOzNAIXRDXP7qAev2pBcGokPvlhjzcC8Mf7QDmrcKRkqrk0iJkj5H/G/1RiHnpXG+kBv0b1h1A/ot7ycr3FNLuQV9yzPG58N/P7R+5p/bqa9w8Ffj2pl+rhBLXtqHsrBOSOp+HwzaQPhFRoIQgqAePSTti3MqMW/aThzakILjf17BnpVJIH5+Vg+mkMGD0SvxAsLvvhuHh76DfV2FqYCpieLfzalY+Z5wplrlX44Dsb9L7qfxt/2CK94+JNC4NSEhaDXjGYn3iLgKl6XYtQVxThz9FfkSesTfH+3mvAetOdlel622+A2l5Hh84S4X7RrYrZtgO2T4cLR58020m/uBpO2+1Rc5C8ANE9DqmwWIWbjApaLiruBzwl3rp0F4K04T+GN+As7szsQ/y5VTleq1NUgtSbWGjIupNXBTeEIIxj6snH0wZoA0E2ObzlLhbjJQbF1wBj+8sA/p5wuxeNZerJpzBBmJRZIAnOqglqgOikSzMWPQ/nOpZmKhsrQGmYlFVi8PJfia+M9vHpKYPeJvlGZiLsquROrM1agKFq7qE5kUCkr5aqznyISyL3v1AA6tT8GBtTZN9d+NqchKKrbaxAFg5D3dQTQEHXoJB7l+N8ZY1wsAYHfXlQIXRb5Lon+gTYjuX3MJiQevoqK4Brc/yw0YlNpMQe17RGDizAE40OwP3LD6BpzKO4X3D8v4OCvQ3sgJgOIaTtiPWzdO0uZMARdq/8OZH/DB4Q9QWmPzVBrQeoD18/CVtjUfQgjuenEgbpvGBbHxZzDZl+SLeFg4du0ryGp/AzJipBokH8vM5vQOzpZ96WiuwB5u0BtRkluFpS9y5qRanRF7f02y9qUwuwLH/kiTuA7/NOhNGLScwMyuyMbpfM4U1qm3bcCq1Rkx/+ldUrdjkYklsIf9nDeOUiFEfyxfZCSwSxdrwrykeAVFgtcXU7lwcI07f84aPRz55JOIWbgAhBC0fPAB+MkoaBbX2F/f+xerfzdJZmKexOeEOyA0N1hMInzihtlGRr22BnduvBPJJcmy18rPEOaI7ivjngYAS6u+xuYUoT2Q70Yph8XOCwCbvjqFzd+cEkyfDw17H4eGzUGH+fMUQ/FL86uwbPYBbPzK/kKg1k+DvCv2F+CG3ikNAvl1zr9ISyjA4aHvIK3jOGj7XQOAs5+LCezeTbKPjziKkR9qn7BTaILZ8OVJayqIZxaOQe+RXARmlbEKC4fbFk5vuK8HJs6yrVPU+AkH6aVnhV4tA26SLlS179FCss/CoWwuH/vB7IOCxU8x/IXVtgYDsrQEf6XZ9yJqGWRzw1udtBoPbn0QAFBrrMUPZ36wHjNQA+7dfK9Vo43uHoHO/TlPlkO/p2DetJ04fyAb6z8XzlSUKI5ULsdn0MubDbcvOW/1UFo8ay9WvHMYtTwvsTN7srDmw6Oo1RmgkYlWzW2WJtget24cHtz6IHal70KXga1wy+NCD5myQqF7qri4dPgt8kFbAJeS21FxG3vBXY6ibfnDDD/Fb1Dv3oJzW7/wPMJGj5acfzmhAPOm7UTamQJJupONXzmXR74u+KRwb91JOQBgyttDMfaR3lg4/DmBkHj74Nuy7RfnCFP+jjK7p4ltnxUBRRJBYjEDWBh4i7zdz0JOahmWvLQPtTqDwN983rSdWP/5CRRkViDtjFDb/uUtaUh1ZPtQtI61zSSmzh2OLgOU3dqe+mYUpi+4EQFBfnj4f8r5XFK7TMT2yMex5MV9MGgCQUVRPGF2Xjhxrp9VHxzBgunqbItv7H8DSUXcAurP5zn/33NtDlhNFC3bhWLh8OewaOjz0PkLF7j6txJqXyPvlmp8fv5agVYPAB8c5qbQJrMPMz9PO6EUu67wBqOeE/BLdg7+TeM03RzzmsjLe6T1WiN4QqdIVyQ4llaWBgCY+qe0dOOFoguYf9rmzigOWtr1c6LkHCVaXj8YJblVOLAu2TrA6ir1WP7aAfy7UTkCdPlrB3HxaA5MRuWFysWz9kriBlYOnIM/ei2UbT9zF2fS6T64DfqPsQ28/GAzAGj73rvQ+4Ugt9U1uNpmCIJ69UKvRPkZubG8HKW/S80/fAXp/PFS7FsjVPwsv4WjvFR8jd5QYHsfLecV51Ti1A55MxAAbJ3P5ZM/sDZZsi5RlF3pUooMV/BJ4T7iP/Ia5MLhz6FFu2DoDMKHr5m/eg8OC7H9OGFZ7VeO9X2/RF5YOpJLkjHv1DxsTtkMEzUhoh035TIE6fDMwjHo2EddqtHFs/bi90+FXijZl0qw+oMj+GNeAg6tT8a6T47JupcBwI1Te+GeVwfhmYVj8MzCMQiPDEZzXnWgIXd0tn4ODg+AX4DWKiyaRTgO0dZV6rHil0rsGv0dSsNirfuJVouArly0bXD//oKpLwkOwb+bUq0LZuKX1x6bUjbh7s1c0Yj2YdzMaV+XNVb7u7XCkYYTxEuv5uImc2pbf43UldNitgOA6+7lhD1/4E2LOIPVSZwr27Fcbt1h8Rnb4uzyq7mIMpkwLycPWzKygckrEAAghFJgkv0MgauzcvDfMvnvfm0brriMxV4vZuFpoYDUKAQO9RzWFlM/GG6Ngu0/VjhbKS2owV+Lz+LU9nTkppZCX2PEkhf3oaK4Bqd2KMdmVJfVYs9K+aRnfLZ8Z5uRUj8jyoIKUeunHD9gMYlGdbS9hwWZ5dBV6q0DRVk5sO+6T3Guz+O40OthGPRGFGRWYOfoeTjdbxp0gTaTh6QwhpnwCRO44wAO7ihCws5M67Nzbl8WFkzfhaqyWtRcvix7vhz8Un3lgW2we2US/lx4BgfWJuPCwavWCOgT265g3rSdWPeJbR1LyaFh0XN7nM677wo+Kdzb94wQuCPePqM/NvT5CgDw49kfcccGYS6TCr3wZXupsBjjK2wa4K50eQ3z8c+vx8qB7wvyeS88vRCv738dM3fOBNWY8P2w5/HDAC7sOaq984OIHCe2pSMnVd5LpXWnMLThae17M/ei3/J+0AfbHqTILjbhNvWD4SirLcP2K7YkWPe/OxSjHxD6FyuR01YY3t957W/oumM7YlevQkBXbpDNbjcCeVHxOLY1DT++sh9/LbZfOFrrZ3vsMpvbXB6NJqNAWPdb3g+UUmxLswWDTCqvwGBdDV41L2zOPzUPz/zzjOD6456wmQAqenIa1qDxsdZ9Z9rZL9Ay0GyCuaFah04GgyAjIwbIV/CZXViMVVk5aGc04o3CItk2x3OP49+rjt1lLTykMMu68cE4hEcFIyqmGULCA3DdPd3Rk2eKLMyqtA6uv392Aoue2yO5RlQXThkIbi0cQOzlS5JjZT/hIuJ9ZVLT1q4M7v1q38MmoC8dy8OSF/dZvX4qs4QLl+s/O2FdYyqM7IeDw6WLlWIqD3Oz3F2jbfV8d/6ciJzUUqtr7dXkEkQ88hiSuk8WDBhqSAgagXN7s1Ccw71rO3+6gCUv7sOlo7nWWA2l9/aZhWMQ1cEmH6pKXSvB6Aw+KdwBCOpYtokLRU44Nxp/deIr5FTmKJ0GAOhdW4uP8wvR2xw2P3PXTPxz5R9Ju6BQf4zueoPsNfZk7kFpbSkoMQEEOJV3Cn4hBOVRudBffwXdzfm9B02IdeXrSXjgvWGY9PxA3PHcAHx85GNcLOY0rC2pWwAABZG2aWJIG9uf1T9Ai5G/jsQLu19AbiUXjRvRNhR9rm+PfqM4LXn808oZKKsHcEVLLFrR+aNF2LomD/vWXIS+bWdkx92OxJ4PIKGDrXhKynHhizr2EZsNeNT9PXHjVFuQ1B+9bJrwysSVeGXvK4Jz115ai5f32swfzxZz5qwwc+j85bI07M3ciwWnFlhNK607hWPi8/3xR9wCTNsxDf2W9wMhBOOejcPhjpuR1Zz77UzOhJVH2GZDc4qkL/CDZeXoY/a+sJdE94m/n7B+nlhegak8H/q2IW1QY6xBv+X9sOzsMoQ2l8+1zh8cLTRvZT/SWEzAgAr8OuADLOwxG32uV65r2qmf8mz0976fozxIOJDdUSH1C7esL4S1DMLkt4YgKNRfoNVu/vY0QuL7Cs7Ju1Iu0XwLW/ZGy0cegd4vGBWhtj7X+jdDUUQckjrcgdCnhBkuEw9eFSQM/GvRWSz9LA1Z7a/Hmb7/p/jdACC77TBc7nSr1QYfVCvN7w8Afy85Z/c6FocAvsOGmiR5dcXzMbAeQuunwaXI4ygOuYrwi/Yz7VloYzCgTKNBD/NLOC83Hzd25DxIZu2ehRMPnkBGeQbCA8MRFRyFtNI0gcYrhm9TnfrnVLQIbIGS7iWAATjzxBncYn6PB42PRcI/mTj4u/yiriPa92yBFm1C0KJNCLIqsvDLhV+wIXkDxnYcay37RsL0yA7LRHR5N1CtESVBeSgLLARg85yoNNgeKEopekxsjv9v78zDo6iyxv3eXtNJZ99JAsFA2HcEAQFBUUBHXEEdB9wXdNxGHcRvHHTGdWbUwY1x1E9FBwVxAX+g4oIiYtgh7GQj+752kt7v90dVku5sBiaA4Vfv8/TT1dXVVff0vXXq3nPOPXf83GTMejN3vHIeKx5P81uhCkAXYCb++x3NeaO+/4/SAyo4Uq04SeM6TwQV0SuI+r6FvD/qcZ5OfomhUxIoy2vp3UnRMjx9bttzbX5vEAYiAiKa/+sYjwfCk7FU5fgd9+qeV9lTvoeXpr+EUWdkj3ELeeH+duoNrs/YndCycPdtX93WQaFTYMFaeMEneuqOH8Gl/DeX11TT297IDb3amc7ebwZkbGBafQPfBQWS5HIxraGRap2ONcH+I7uLbQ1MtNt5uLKau2Kj+YESxr43FoB/7PgHAyMHEjSvjAmBU5vT527t9xl30TYaZvSFfdi6NpsR05PY8+0vp8UwOgKosZSBB865KoX9m/wToV23ZDw56RWMmJ7IT6szCY4KYMT0JPbtzOH71xW7fWVg206UToLZ68Xh43j0DWaITLAy4vwk0ta02P5z91cQGvXL5sI9w+9i4sPn8VGOEq9utlcR2FBMVYTaeTjg4iBdW1kKoC64D7XWJGrC+hExewZx2d/5LdF3aKDiG7HYK4gp3UWx+cQSAJ47VwlHDffJg1RZVE/S4BNfkKcr9Ejlvrt0t+KUUkN4d25rXwF/ml/IE1ER7FTD+r7OUxuw0AMeolrlLxn93ujmbb3Qc1Fy2/A2v3KU+Hu+rUYr1Q4lXK3KXkV4gDLs0+t1jLqwd7Pdt8mWbjDpuOKhMWTtKsPt8rJ7g7+TpnTAQWIOD+KskTEcrjzM9euux+5R7I02l43PMltWXD9Uvo+1Q1ZidgdyqXyfD0YpU6qz17YMyed8OoeZyTP529S/MfzdFodx+oJ09HodU+alsvYl/4RZ5Xk23v/ziefJnvPIMJ5Me5K6gEqW1NzLopxFnN/rguM6x7zE83ktYxUbcgtAb4KbvkIU7YYtD/odt7lgM6OXj2bH9Tt4ZNMjft8drTrq57AESCtuayK5p7Iabt8DgRHwaAno1FvEbFVeKqN8kqXtzVbrzWCBa96HnE38870rKdfrCPV4MaHYgX2V++SGRib62I5t7djXb/1K6VnuvP4Kzq7ow18PPUZ25F7qXfU8/MPDmPVmnj/veQD0Rh13LZuO2+npULlf+9h4AkNMbFp5BMuAclAChQgIMjLpqn7NM7FBGd2FxynRYE1+izpnHTfuvJYbUNpWU9gjwE85eawJDmKoOYrf1dbxRlgon+YXclmi0sMushURb1UmlbU3ykj/vmsdtA1vtfgrHAHhOAKOz7TSmu1jFykbRyBVDCD3nMnYAyKJrGgxLZZEj+HAoBuO67xePEyY05+0z7IIUR9ceoOOS+8bybrX0jtNn9Jd9Ejl3l60QWs+zi8ixeXmleIyJiQncaHvcHH+p5AwBp7qeDjqkR7WZa/r9Bp/SfO3A+bbfJwvzjq2FG7h27xv+ftU/xj2O185j8NpJSQMCCMk0kJ0UjBSSgaMjyNnbxlpa7L5NuU9jkRsI3ZaLL0S7+Kxte1H+zTx3uEPQIDdaOOjQy15Lw5W+kccfJHzBfMGtL/+bO8hkSx8dRoOdQp/weEqvxu+K/zU5xOCHREMK55KeWA+Uz6c0jxJp7yxnAe/f5D0Bf42+SfKKngsuv3hv2+UU5zHA7d8BcGxYJrIE2va/93/bG67GtEVazpeCPrW6hpsQsdVdTZSXS5FsQMYO+hNjr8TXdprrMsrJNLjUWKKlvgM2ftdgACifToPrVX33VXVEJUK5YqJqK/Lzc4OLneo8hA3l10Hqqjn/KfjmcAGU0tU0GUPjOLT51s6IJm1GUQFhZAx5geiiltyDLm8LkZe0Jvo3sGkb8kldmJbtfB93vccqz2G3ajY8nf1+trv+2Ap+W2tDea/z8J353BhfQMprhb7/YWrLyTGEkNpYynfz/IPP27N8OmJbUJnm8jY3rVVPI0BeuJTQsnd377/oz2OyEGg1kFFZIupsiKqawvnXHTr0GY/gQ49Y2clM2J6kl+kVtLACG7/59Qul+m/oUcq9464v7KKFyKUJ3m8mmTIKiXpTT2rPpOUnljCWDAFwsPZ7HyuLxkmI3MT4js6LaD0zt4IDWFpRMcx075sLtjMU1uVWYgmnYknJj2B2+tm6odTeXry05w7/lyEzy0vhOCB9IUgYNeElhuyxF7SYRhnR7x1uPO0wjd+6Z/nYvGmxTw1WSmr0AkCgowEBBmxn8CEi729NqL3GHEYGimM/RqHp+05fvPJbxhnuQaTR7mTRtv9Z3deWmdjhMPJX6JaDVvjhkOiYrbAZOVyWz39nS7yjQYeimkJBW0yVXXE5mN5TOrTEmHy+6qalprQdyHnx6xnYPR8kl5TJx8Z2tHKU/8I3z8LAy+BmMFgr+Hmox8ggRtr6gjzeuHKNyBeiQi68M0prKaq7XmA69a178QFOFJ1hH5h/ZBSotf5h3tG9w7mmj+Na14j+L6N91FraZvP59wV55L22zQSUsOZuWUK/ATp/VsewGsz17L4x5bc6L4hxgCTGhqh12go3AkhiRiBQU6l3t8pLGGBar4qbVQU8+K9D/HEI8+Ru7+Shlqn32Lht744BSFEs3KP6RPMrDuGN+dL6iqz7xhGREIQ77/wA85C5X+59J6RbPzPIWrL24+2+W+Y8VAKyX0jCL26mppVLTri3k2/Z1PBJiYnTObVC7qWubO7OKOUe7JPT8EkJSzKg6yNsFLt6d/YqiceGNHcEFfnF3FlYscKXgBX1tm6rNybFDvA2qy1mPQmVh9dDcC937XcHE292C+yv2BX6amb4ODL2qy1zcrdl4hfmKTly6w7hnHlTsWM5dG72J60nn5OJ9BWWebU5pAz4lmapov0sSZwZW0dq0MUs8WT5Upvq41yv8XH6a1GsAx1OhnqdNI24lzhu9z8Zr9KEyFe5YG/22xiiMPp36u+c0tXxIXYwdDnXBhxDYxuZyQ5bbHy8uG+Jf9q+XDe4mbFDjAxKIl/5xwiye3mn+FhrG8nUVd7XLnmyubt12e8zoReExi22MDGAz/xUXYxV6deTcbUb7Ds6k1tQPuJ2hrcDRytOkpySHLzvvSydHJqcxgTO8ZPsbdmWXEp4xvt8NhhKD0AUf3gto3w+f2wYC2jn247K3pzwWbOL5jEoIhBjO49hmBacr6YAhSVtODpSdRVNDanDB5wTpxfKpDWJA4MpyijptnckTgwghWHVrC0z1PcUahMyDNEeBlzfxjfPdp5wEVX2J64nrH5s9iWuI7DMVv54WA85TvKKW0sZWrMPGrNlQzKhU0FyizfTQWb8Eovui4setJdiFMRb9keY8eOldu3H39uk23F27jpy5ZkPzdV11BoMDC3zsaI4fMZU6aEzaXP+xEC1PQAx7ZA3DA/m2kzpYfgVSVd6rC+ik382po6VoS2TJRaWVDEoFs2wavn8HOAmcFOJ0UGA1clxGP1erEd5/qSvqy5bA1pRWk8mfbkLx+scnN1DfdU1eBBicx4IzSEf0aE8YeKKv4ReWI2yLTr0gg0BnL7htv5qfAn9szfg0Dw6p1tw0SHTOmFTojmFLWOBhfTvmkJ2wvyeqk/jv8k/fy3sb95PmcnK73p9OxciEqlseIo49R9C2pqefCeVgup2Gth1QLI/JapvROo1OuZV1vHhyFK3V1bU8fiyipeCgvl9XClLezNzkXc/gP8yycK6tFicNjA2nFa227h76lgK4EBs2Hee+Db0z74OXyopHx+ICaKDUFdW3yjNekL0hn2TosZITkkuXnyVHdi9nr5pKCYpKY0vEvajyRh81KyNj7OnMSOTaB3bGmZDX3XsunUOmtZdXgVcwfMJdik1GVZbh0rn2qbgM33d2V5dax8chtDZ8Vyd/U1zd8NL5zGxGOX8dbZi3AaGrn+4P8Q1BhO75QY8g50brbRGQRed4uO/GzqM0Qe68eB2M3N8y4A9EKH5xeir6YkTmHptKW4pRuzvv1IqK4ghNghpRz7i8f1NOX+yq6XWbb3X6Q4nSyqqOIc3yH9jV8g/3em0hP7Uznou7iyO0DZYWZ+dhkFRgN7s3MZrir6F0vKOL+hUWm8jdXwbJ8OT9H0cIh3uynqhmT8D1dU8V5IMAK41FbPWS4XD8VEtZiZ2uGdkGD+HhnOaLudSQ12JjU2stli4Te2ejJNRn60WHhffXCtyS/kUp+b7vUZr3PbhpYIkrWXraWXOZGSrFrWLFXSH+xI+JIHFy7gm9xveH3v66y/Yj1xQXGMWt6SIiAtJ4/xyf4Ta3Zn5/KfkGBCvV4e9bGT/7m8gqv+UAgbn+H5Pa8x1m5nykNFoNPBklAqdDqyTEbOtjs6ViBAw+NhlOv1A/3wEAAAE81JREFU9Ha7eSAmipm2ei5saIQ7NsOySawPCmSA08lZXh38qUzJH7J3JaRMA2v7q+WcUprKEzOIb75+iPu8ivN/kMPJQbMy+lmfV0Ci29PcztrjhiE38Pb+t7t82Vuqa3gjrOP1A1qzpKyC8xsaFbMSwNzl0P/Cjv0TAEtCqRMCh05wfXwcBUb/e6NJuf884w3euuwdv8CGvfP3Nk/Ae+nfy9HtUMJ3r/7zKLK2VrJj/TGMwYLrnjybfRX7ePCzxdQGVCghyr5IAWpklpBKx2Pr1dt5U82fE907uN38+fOfmtgci//x0OcpDW67UtuJ8PL0l5madGK29zNWubPmHhy73sEkVSfVwjSw10B0KgSEwYfXQ/xImNrRQL0TlqiN/Ler8b5/JQdMJoY6nYp5J0CdOFRToCgDey38+DwEx0FeGljjYFvLLMe1QYE06HTMq7OxMtja1sTwC+zMzm0bL60zgtelhOXF+Th5CnfD8svg9k3w4tDWv1LoOwWylck7f40MZ16tjf7mCHLtFVyc1HGvakL8BLYUbaFP5RAm5lzOilFPNt8kHZE+4C6KNyxmRm/lRrynsppbfeK53woN5rDJxDNlFS3OSCnh8TDoOxUWrFEObNoHikkmsZP2vO1N+H8PwM0b4M0Zyr5bv1Uc51kb4d05So/52hWdlr01S9bsZ/aweMb1Pblha36s/yOkKbNVa3Q6loaH8kBlNUF9z4PoAci0ZdQLQaNO8FGwlVfDu2YqBBjbaGe7RVHET5eWc0l9Q/OopzO+yi0g3jefyyUvwIjrOlfqTTgb4O/9wWnju0AL98T6j5BG51/I0OLJvDvmMWYkX9Am/HhSr0k8O+VZrn7/Uian38aG1HeoDizm7Zlvc88nD+IwNDY7eo+H9AXpHCo9zC0f30mNpaz5ITN38VhWPqXopptfnsjCp//MmIKLeHvs4jbpL7rC9PoGvm01EhsYksyqy9vmru8K3archRAzgX8CeuANKeUzrb43A+8CY4AKYJ6UMqezc56wcm+shheGwn17W6IauouCnSC9ihLJ3wFbXobL/wWGLibWz/5BcchZY2HpSIgdBvOWw9KRnJeUQIVBz5acPAoMBgKlZLaPUk10uXilpIw5ib3436ISxk5/Ekb9DmzFYIkAc7ASZ11XBJGdxNvaayFnkxKJEdVOzG91Lrw4DKYugvMWITO+ZvhPD/gdMtpubw4fPR7m1Nm4v7KayMeq4Odl8IW6YHH8CLjdZ1Zo00P0hnXKf21Qh6g1BYoprT3zmQ8NTjcujyTU0sHIzNWoPAj1Pj1ErwcQyoigC2zPqeRIiY3Fnyg+kf4xVo6WKgpk3T2TGdyrbZbQbsNlh32rlTqP7AevTVAU6WWvKr6G4nTY+jqknA8f38pOg2CQ09lswgJIdroY7nCwJthKiMfDWLuDUXYHC2rrcAr4NjCQmaPvQkxbDE+EszA2mk2BSoji3No6VoYE08fl4pi6UtfeyS8jQhIgrHfX74f22LwUueFPbA8w80GwlWiPt3kk2d2kZ+dSptcxXfW5LCsu5Y64jkdp0zKuY0DZeJZNuJfbtjxPSXAOBdN+YHvpbgLc1uYHyPLCYg6ZTDzZTodtUUUlqU4X0W4PMR6PkrICWB4SzHM+JtO9dWbE3SeWcrvblLsQQg8cAWYA+cA24Fop5QGfYxYCw6WUdwghrgEul1K2H2+ncsLK3YdGpwe9TmAy6MgorSPAqCcxXHlCVtgcjPmrEq518ImZHCqu5ZNdBby7xX9Y9e5N45iS2rGtVUrJqh355FY0sHBaCoEmAy6PF6Oau9zu8mDQCQzt5DL34z/z4MgXcO9eqC2ExipWf34LK0OsPFRZzcDgZKwzn1V64L9dDf2PLxa8qaxOjxezofNeWGtsj4cxK6kX1Xo9QV4vPx/LJy3AzC3x/pN0Ps8r5JIOevn9nU4+NvaHy5dBqOpEczsUJSuE/xR+lLpzeryEBBg4VtFAn8jA5uH3jmNVDIgLxmpuUc4HCmuZvbTtuqJZT83uMAdLe9Q0uHB7vQSaDFhMemwONyvScpl7dhJ78qqZ/9bWLp/rq/unkJ5fwx9W7WHG4Fg2HPBfj3fptaO4Z8UubpiYTJnNwXNXDifIbKCszsHPWRX8foXiQH9ghjJh4+5p/Y5Llmb2fwrJ5/KHt8fxlTVIGSnp1A5BsRr1kjhOeejd8jV43bD3A+SYm1qyHK64Dg6ruc5jh0LJPogZjCw9oIyu/lzdpg5BaXOvfZ/Jc1+0pJHoF2PljfljWbUjjwUTkokJadVRqDoGZYdhw58gPBmOfMEOs9lvUtiO7FwWxsWQZjn+TgbApmP5hD1SBJVZysMR4NKXYM3v+SrQwh9i27nnpRK/JoVE59XjFV6/Ueq5DY38o7ScwAWfw76PydzzDpEeL2FeL27U6JSLn4eQBOX/S72oZYSd9i/k+odbnPePVXW5o9Ga7lTuE4AlUsqL1M+PAEgpn/Y55kv1mC1CCANQDETLTk5+osq9wubg718dZsOBUsptbRdIOBHCA41UNbgYHB/CyN5hrEsvYtbQeJxuL6t3drxa0MSUSH7KrGj+PKlfJJszlM+LZg1k9Y58jpbaSAy3cP8FqWSW2ciramRiSiRldQ7i3QVMseaRGTKeo9WCvUUNjEgK5f2fczlcotj/UmOtHCmxkRprJa+ykcYOUrYOSwglvUCxSY/rG0Gd3c3BolpSY61EBpnZkqWUa2pqNBcMjuUvaw8gBCSEW7hubAIXy41s3LGE4UPvpWHALURRQ9DyMYR7PKyxBnFuo50XBm7gQecyoo6uBKAqdS65xz6lyGAgedgf+dJwMct/zqHcpkxuuXh4PJU2Z/O1ARLDLSw8r19zj9iXsX3C2X6s/XDArhAeaGRscgSZZTayyvyHz7OHxVFYbWd3XktO9CiruUtt6NHZg3hyXcdrBnQnJoMOp9vL0IQQEsMC0esEg3uFkFFq45NdykSf0b3DSIm2EmQ20OB0kxAWSJ/IQAY2bCNlwwLy5nxKdsBgLEY9DU4PdQ4XWzIrMOh1NDjcBJoNlNba+fqgEpr4lzlDiA0JIDjAyJf7i5FSctGQOIpq7IRYjNgcLlZtzyezzEZJ7fHfc4nhFq4ak0iFzUlVg5ORSWH0CrOwv7CGyf2j0QnB19vSOT/rajJMRqY02Gm4OZuQnC+I2vki3zRk81BMFNfX1DLfEcJ2Uc7iaCX01dcnsSNgDM5DSnssuWo/NQ4vmzPLuWRYPD9mlDMgLhi9TpAqclm58RrWBwUS5fHiEIJFFVVsDgzgg5C2o4g4t5sbamoZPPBhipLmsvFwKb3CLPQLdqI3BXOkwsmQXiFkl9ezO7+a8EAjDU4P/WOC+eZgCUU1dkb2DmP+OX0IMhuoaXTRNyqIXmHHlzKiie5U7lcBM6WUt6iffweMl1Le7XPMPvWYfPVzpnpMh4tknqhyX7unsLnH0xUuGR7P53tbJmwkhFl46ophZJTa+HhnPvsLu76MnMbpZUBsMItmDWRc3wjSC2r4w8o9FFR3nI3wRJg2IJoXrxlFgFHHtwdLGZYYSmJ4IG6PF7dXEmDUN48iQi1GahpdRAebmTU0jrOTI5g1NI4DRbWsSy9m2feZjOkTjgCKauycFR3EpqMtt8Td0/pRUe9gxdZfThfQE+gTGcgr143mfz7d5/cQ/W/R48GABwdGQBCInQHiGGdbv2J13XziRBWNxlCynF2PFIuhCq8hgN6ePA7K3jRiJgg7r1n+ittt5ahMZIZlA3YhuLfuCTJlEt5uTMV153kp/HHmwF8+sB26U7lfDVzUSrmPk1L+3ueY/eoxvsp9nJSyotW5bgNuA+jdu/eYY8eO3/NcVe8kq7yeIb1CqLO7ibKacHkkRr1ACEGD043ZoEd/AsPbeoebOrubiCATlfVOimvt9I0MIjjA0Dxc9nolOp1ASonDrZhnPF6JEFDV4ESnDl335FUTYjESEWRiV241AjhWUc+ElCj0OsGh4lqcbi9Ws4F9hTUEGPSkxFiJtprV/wompkSRW9lAgFFHeJAJi1FPdnk9qbHKjNYmM4aUktpGNyEWA1UNLo6U1HFWdBC1jS7CA01UN7o4KyoIu8uLRHKgsJbYkAASwizsya+m3uGhoLqBBqeHCPU6dXY3xbV2SmrtNDo9nBVtJavMRqTVjN3lwagX5FU2UlpnZ2JKFGGBRuodHib2i2RkUhger+SZ9YfoHRHI7GHxhAUaKaqxY1aTXtU73HilMnJoMr80Oj0EGHV+ctU53NhdHiKDzJ3WqccrqbO7CAkwklFmw2LUkxhuQQiB1ysprrUTaTUdt8nqdOD1Sg4U1SIE9I0K4mBRLUFmA8mRQZj0OgprGhFCYDHqqax3siu3Cr1OUO/0UFprJyzQRJjFSJDZQFFNI0EmA/sLa0iJsTIsIRSH28vBolpmDo3DajZQ3eDiUHEdxyrq2Z1XzRWjE6izu5ESjlU0kBprpV+MFYfbS+/IQOxOD6GBxi7/l033ipRQ3ejkUHEddqeHvKoGYkMCsDnc7DhWRVG1nS1ZFcwZ2Yt+0Va+PFDMxcN6kV5QTa9QC/VON5nqiCw11sqRYhsBJj1J4RaMeh1RVhN5lY3EhJgZmqD4dXYeqyIl2sqe/Gr6RgURZDZQ1eBkd241fSIDaXR5KK5xEGIx4HR7OVJSx6ikcDLLbFhMeqKtZgLNerbnVJEQprSnyf2jKKqxExNsJtRipNzmQK8TSh043Iw/K5KfMyuwmPQEmQ2kxgbjlZKt2ZU0Oj3EhpiZkBJJv5gT8zWcsWYZDQ0Njf+f6apy78o4YxvQXwjRVwhhAq4B1rQ6Zg2wQN2+Cvi2M8WuoaGhoXFy+cWZNlJKtxDibuBLlFDIt6SU+4UQTwDbpZRrgDeB5UKIDKAS5QGgoaGhoXGa6NI0SinlOmBdq32P+Wzbgau7t2gaGhoaGidKj12JSUNDQ0OjYzTlrqGhoXEGoil3DQ0NjTMQTblraGhonIFoyl1DQ0PjDOS0pfwVQpQBJ5ocOQroMLVBD0OT5dfJmSLLmSIHaLI00UdK+Ysry5w25f7fIITY3pUZWj0BTZZfJ2eKLGeKHKDJcrxoZhkNDQ2NMxBNuWtoaGicgfRU5f766S5AN6LJ8uvkTJHlTJEDNFmOix5pc9fQ0NDQ6Jye2nPX0NDQ0OiEHqfchRAzhRCHhRAZQohFp7s87SGEyBFCpAshdgshtqv7IoQQG4QQR9X3cHW/EEIsVeXZK4QY7XOeBerxR4UQCzq6XjeX/S0hRKm6ulbTvm4ruxBijPrfZKi/PYFFQ/8rWZYIIQrUutkthJjt890jarkOCyEu8tnfbptT02CnqTJ+qKbEPhlyJAkhvhNCHBRC7BdC3Kvu73H10oksPbFeAoQQW4UQe1RZHu/s+kIIs/o5Q/0++URl7BJSyh7zQkk5nAmcBZiAPcDg012udsqZA0S12vccsEjdXgQ8q27PBtYDAjgHSFP3RwBZ6nu4uh1+Cso+BRgN7DsZZQe2AhPU36wHZp1iWZYAD7Zz7GC1PZmBvmo703fW5oCVwDXq9jLgzpMkRzwwWt0ORlmwfnBPrJdOZOmJ9SIAq7ptBNLU/7vd6wMLgWXq9jXAhycqY1dePa3nPg7IkFJmSSmdwAfAnNNcpq4yB3hH3X4HuMxn/7tS4WcgTAgRD1wEbJBSVkopq4ANwMyTXUgp5Q8oOfm7vezqdyFSyi1SadXv+pzrVMnSEXOAD6SUDillNpCB0t7abXNqz3Y68JH6e9//pVuRUhZJKXeq23XAQSCBHlgvncjSEb/mepFSSpv60ai+ZCfX962vj4Dz1fIel4xdLV9PU+4JgO9qwvl03jBOFxL4SgixQyjrxgLESimLQGngQIy6vyOZfk2ydlfZE9Tt1vtPNXer5oq3mkwZHL8skUC1lNLdav9JRR3Kj0LpJfboemklC/TAehFC6IUQu4FSlIdlZifXby6z+n2NWt6TogN6mnJvzw74awz3mSSlHA3MAu4SQkzp5NiOZOoJsh5v2X8NMr0GpAAjgSLgH+r+X70sQggrsBq4T0pZ29mh7ez7tcvSI+tFSumRUo4EElF62oM6uf4plaWnKfd8IMnncyJQeJrK0iFSykL1vRT4BKXSS9ThL+p7qXp4RzL9mmTtrrLnq9ut958ypJQl6g3pBf6NUjdw/LKUo5g7DK32nxSEEEYUZfi+lPJjdXePrJf2ZOmp9dKElLIa2Ihic+/o+s1lVr8PRTEbnhwdcDIcDSfrhbIsYBaK06HJwTDkdJerVRmDgGCf7Z9QbOV/w9/59Zy6fTH+zq+t6v4IIBvF8RWubkecIhmS8XdCdlvZURZcP4cWx93sUyxLvM/2/Si2ToAh+Du1slAcWh22OWAV/o6zhSdJBoFiB3+x1f4eVy+dyNIT6yUaCFO3LcAm4JKOrg/chb9DdeWJytil8p3MG+sk/aGzUTzsmcCjp7s87ZTvLLUS9gD7m8qIYlv7BjiqvjfdVAJ4RZUnHRjrc66bUJwrGcCNp6j8K1CGxS6UnsPN3Vl2YCywT/3Ny6gT6U6hLMvVsu4F1rRSKo+q5TqMT7RIR21OreutqoyrAPNJkuNclOH4XmC3+prdE+ulE1l6Yr0MB3apZd4HPNbZ9YEA9XOG+v1ZJypjV17aDFUNDQ2NM5CeZnPX0NDQ0OgCmnLX0NDQOAPRlLuGhobGGYim3DU0NDTOQDTlrqGhoXEGoil3DQ0NjTMQTblraGhonIFoyl1DQ0PjDOT/AAkiQ1fq4003AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a104756a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.prices.apply(np.min).plot()\n",
    "rdf.prices.apply(np.median).plot()\n",
    "rdf.prices.apply(np.mean).plot()\n",
    "rdf.prices.apply(np.max).plot()\n",
    "rdf.spot_price.plot()\n",
    "plt.legend(['min', 'median','mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a130ed780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d38ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.prices.apply(np.min).apply(np.log).plot()\n",
    "rdf.prices.apply(np.median).apply(np.log).plot()\n",
    "rdf.prices.apply(np.mean).apply(np.log).plot()\n",
    "rdf.prices.apply(np.max).apply(np.log).plot()\n",
    "rdf.spot_price.apply(np.log).plot()\n",
    "plt.legend(['min', 'median','mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3        {'agent': 26, 'mech': 'burn', 'amt': 8311.3033...\n",
       "6              {'agent': 14, 'mech': 'burn', 'amt': 10000}\n",
       "9        {'agent': 29, 'mech': 'burn', 'amt': 5264.6786...\n",
       "12              {'agent': 4, 'mech': 'burn', 'amt': 10000}\n",
       "15             {'agent': 16, 'mech': 'burn', 'amt': 10000}\n",
       "18             {'agent': 52, 'mech': 'burn', 'amt': 10000}\n",
       "21             {'agent': 11, 'mech': 'burn', 'amt': 10000}\n",
       "24             {'agent': 46, 'mech': 'burn', 'amt': 10000}\n",
       "27       {'agent': 77, 'mech': 'burn', 'amt': 7100.5072...\n",
       "30       {'agent': 90, 'mech': 'burn', 'amt': 9912.5936...\n",
       "33       {'agent': 33, 'mech': 'burn', 'amt': 9816.5368...\n",
       "36             {'agent': 81, 'mech': 'burn', 'amt': 10000}\n",
       "39             {'agent': 93, 'mech': 'burn', 'amt': 10000}\n",
       "42       {'agent': 10, 'mech': 'bond', 'amt': 12.058109...\n",
       "45       {'agent': 69, 'mech': 'burn', 'amt': 5870.6208...\n",
       "48             {'agent': 17, 'mech': 'burn', 'amt': 10000}\n",
       "51             {'agent': 16, 'mech': 'burn', 'amt': 10000}\n",
       "54       {'agent': 71, 'mech': 'burn', 'amt': 8584.3856...\n",
       "57       {'agent': 62, 'mech': 'burn', 'amt': 5333.6823...\n",
       "60       {'agent': 61, 'mech': 'burn', 'amt': 5602.5569...\n",
       "63       {'agent': 5, 'mech': 'burn', 'amt': 6666.51505...\n",
       "66             {'agent': 93, 'mech': 'burn', 'amt': 10000}\n",
       "69             {'agent': 35, 'mech': 'burn', 'amt': 10000}\n",
       "72       {'agent': 90, 'mech': 'burn', 'amt': 9912.5936...\n",
       "75       {'agent': 50, 'mech': 'bond', 'amt': 11.818082...\n",
       "78       {'agent': 55, 'mech': 'burn', 'amt': 8031.3579...\n",
       "81       {'agent': 84, 'mech': 'burn', 'amt': 9394.4032...\n",
       "84       {'agent': 23, 'mech': 'burn', 'amt': 9460.5767...\n",
       "87             {'agent': 17, 'mech': 'burn', 'amt': 10000}\n",
       "90             {'agent': 79, 'mech': 'burn', 'amt': 10000}\n",
       "                               ...                        \n",
       "29913          {'agent': 91, 'mech': 'burn', 'amt': 10000}\n",
       "29916          {'agent': 18, 'mech': 'burn', 'amt': 10000}\n",
       "29919          {'agent': 82, 'mech': 'burn', 'amt': 10000}\n",
       "29922          {'agent': 58, 'mech': 'burn', 'amt': 10000}\n",
       "29925    {'agent': 28, 'mech': 'burn', 'amt': 7747.7164...\n",
       "29928    {'agent': 89, 'mech': 'burn', 'amt': 5631.1045...\n",
       "29931          {'agent': 44, 'mech': 'burn', 'amt': 10000}\n",
       "29934    {'agent': 87, 'mech': 'burn', 'amt': 5334.4662...\n",
       "29937          {'agent': 19, 'mech': 'burn', 'amt': 10000}\n",
       "29940          {'agent': 70, 'mech': 'burn', 'amt': 10000}\n",
       "29943          {'agent': 34, 'mech': 'bond', 'amt': 10000}\n",
       "29946          {'agent': 70, 'mech': 'burn', 'amt': 10000}\n",
       "29949          {'agent': 11, 'mech': 'burn', 'amt': 10000}\n",
       "29952          {'agent': 11, 'mech': 'burn', 'amt': 10000}\n",
       "29955          {'agent': 35, 'mech': 'burn', 'amt': 10000}\n",
       "29958    {'agent': 65, 'mech': 'burn', 'amt': 7795.9939...\n",
       "29961    {'agent': 97, 'mech': 'burn', 'amt': 6795.9726...\n",
       "29964    {'agent': 27, 'mech': 'burn', 'amt': 9615.2953...\n",
       "29967          {'agent': 44, 'mech': 'burn', 'amt': 10000}\n",
       "29970    {'agent': 90, 'mech': 'burn', 'amt': 9912.5936...\n",
       "29973          {'agent': 98, 'mech': 'burn', 'amt': 10000}\n",
       "29976    {'agent': 75, 'mech': 'burn', 'amt': 5264.8902...\n",
       "29979          {'agent': 88, 'mech': 'burn', 'amt': 10000}\n",
       "29982          {'agent': 59, 'mech': 'burn', 'amt': 10000}\n",
       "29985          {'agent': 81, 'mech': 'burn', 'amt': 10000}\n",
       "29988    {'agent': 29, 'mech': 'burn', 'amt': 5264.6786...\n",
       "29991    {'agent': 55, 'mech': 'burn', 'amt': 8031.3579...\n",
       "29994          {'agent': 35, 'mech': 'burn', 'amt': 10000}\n",
       "29997    {'agent': 72, 'mech': 'burn', 'amt': 8176.7512...\n",
       "30000    {'agent': 94, 'mech': 'burn', 'amt': 9221.6318...\n",
       "Name: action, Length: 10000, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf.action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a10501400>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD9CAYAAABQvqc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXecFPX5xz/P9ePu6L3IgSBNmiIWFBEUUIxojC3RGDUhscTElmBMiMGuMSb+YqImGrvYFYOFErAh5ZDe5MATjo6UOziu7vf3x87sfWf2OztlZ/vzfr3udbOzszvf2Zl55vk+lYQQYBiGYTKDrEQPgGEYhokfLPQZhmEyCBb6DMMwGQQLfYZhmAyChT7DMEwGwUKfYRgmg3Ak9IloIhFtJKJyIpqqeP9WIlpHRKuIaB4R9ZTeayKiFdrfTD8HzzAMw7iD7OL0iSgbwNcAzgFQCWApgCuEEOukbc4CsFgIUUNE1wMYI4S4THvvsBCiOFYHwDAMwzjHiaY/EkC5EGKLEKIewAwAk+UNhBDzhRA12stFALr7O0yGYRjGD5wI/W4AtkmvK7V1VlwH4EPpdQERlRHRIiK60MMYGYZhGJ/IcbANKdYpbUJEdCWAEQDOlFYfI4TYQUS9AfyPiFYLITabPjcFwBQAKCoqOrF///6OBs8wDMMEWbZs2T4hRAe77ZwI/UoAPaTX3QHsMG9ERGcDuAvAmUKIOn29EGKH9n8LES0AMByAQegLIZ4G8DQAjBgxQpSVlTkYFsMwDKNDRN862c6JeWcpgL5E1IuI8gBcDsAQhUNEwwE8BeACIcQeaX0bIsrXltsDGAVgHRiGYZiEYKvpCyEaiegmAB8DyAbwrBBiLRFNB1AmhJgJ4BEAxQDeICIA2CqEuADAAABPEVEAwQfMg3LUD8MwDBNfbEM24w2bdxiGYdxDRMuEECPstuOMXIZhmAyChT7DMEwGwUKfYRgmg2ChzzAMk0Gw0GcYn5i9dhd2V9UmehgMExEW+gzjA4GAwJQXl+GSJ79M9FAYJiIs9BkmSo7UNeLsv3wCANi6v8Zma4ZJLE7KMDAMo+BgTT2GTZ+Dy0/qgS37jiR6OAzjCNb0GcYj2/YfBQDMWLrNZkuGSR5Y6DOMRz75eo/9RgyTZLDQZxiP/Hn214keAsO4hoU+wzBMBsFCn2EYJoNgoc8wPrOHE7SYJIaFPsP4zNodVYkeAsNYwkKfYXymvimQ6CEwjCUs9BnGZ+obWegzyQsLfYbxmcYAC30meWGhzzA+09CYXC1IGUaGhT7D+Ewd2/SZJIaFPsP4TAPb9JkkhoU+w/hMA2v6TBLDQp9hfOZoQ1Oih8AwlrDQZxgPCGHtrD1c2xjHkTCMO1joM4wHAgqZ3744DwDQIi87zqNhGOew0GcYDwQ0TT8ni0LrbhvfDwDwefm+hIyJYZzAQp9hPKAL/UZJ5Z8wqDMA4KutBxMyJoZxAgt9hvGAyqTftigv/gNhGJew0GcYD+w7XJfoITCMJ1joM4wHth84mughMIwnWOgzjAc6tyowvL7xrGMTNBKGcQcLfYbxQJPmwD2uU7H2OpGjYRjnsNBnGA/oUTvnDe4CADh/SPD/sB6toUdxlu85jM83cfgmk1zkJHoADJOKjH/sUwDAcZ1KUPHgpND6QV1bYtv+GgDA2X/5BAAM7zNMomFNn2GiIIvI8Do/Jxt1jQEcqmkIrTtcx2UZmOSBhT7DRIGckQsAOdmEw3WNmLF0a2jdym2crMUkDyz0GcYlR+ubq2hmm4S+rtU/8OGG0DoutcwkE46EPhFNJKKNRFRORFMV799KROuIaBURzSOintJ7VxPRJu3vaj8HzzCJYMC0j0LLWSahr6q+yY3SmWTCVugTUTaAJwCcC2AggCuIaKBps+UARgghhgB4E8DD2mfbAvgjgJMBjATwRyJq49/wGSaxmM077y7fEbZNTT3X12eSByea/kgA5UKILUKIegAzAEyWNxBCzBdC1GgvFwHori1PADBHCLFfCHEAwBwAE/0ZOsMkHnM5hpKC8IA42ZH7zb4jOHCkPubjYhgrnAj9bgC2Sa8rtXVWXAfgQ4+fZZiUotJUjqFYIfRlk89Zf16Acx77JObjYhgrnAh9UqxTtg0ioisBjADwiJvPEtEUIiojorK9e/c6GBLDJCcdS/LD17U0lmzYd5g1fSZxOBH6lQB6SK+7AwgzXBLR2QDuAnCBEKLOzWeFEE8LIUYIIUZ06NDB6dgZJu6YHbUBUwutfp1Kwj5Tx45cJolwIvSXAuhLRL2IKA/A5QBmyhsQ0XAATyEo8PdIb30MYDwRtdEcuOO1dQyTkryzfLvh9U/P6G14rTLv1HGjdCaJsBX6QohGADchKKzXA3hdCLGWiKYT0QXaZo8AKAbwBhGtIKKZ2mf3A7gHwQfHUgDTtXUMk5Isl7pizbr5dBSa+uHeeFafsM+wps8kE45q7wghPgDwgWndNGn57AiffRbAs14HyDDJRF5Os57UpOiO3iIvB1NG98bTn24JrZu5YgeuPKVnmCmIYRIBZ+QyjAue+fyb0HL74nCnLQCU5Bt1qSUVwcltQNVjkWHiDAt9BasqD2LhZi6Jy0Sma+tC5XqVXR8AmiShr8rcZZh4wEJfwQV//wI//NfiRA+DSVHO6tdRuT4gmfbruR4PkyC4nr7E+p1V2LTncKKHwaQ4pe2LlOtlTb+uMYD8nGzldgwTS1jTlzj3b5/h5leXJ3oYTJRU1TZgxpKtMTGh3Dw2GJ2z9C7L2AVLZMdvLYdxMgmCNX0m7bjrnTV4f+UOHNe5BCcc4299v/W7qgEA7YvzXH9Wjt6pa2DzDpMYWNNn0o7vtCJoR2LQsWrOut0AACJVhZHImM07DJMIWOgzacfuqloA6jj6eHNG3/ah0M4Am3eYJICFPpN2bN57BEByCP2urQpR1xgU8EZNn4U+kxhY6Efgxle+SvQQmChoTAKh3yI/O2S/b2KbPpMEsNCPwKxVOxM9BCYKkkHTb5GXjfqmAN5bsd0Qp1/Lmj6TIFjoM2lLLDT9jiX56N1BHYevokVeMEDuVzNWGMw763ZUoXTqLMNsck91LR6ft4mzdZmYwkKfSVuaAv6bUDq1LEBpO+dCvzC3OQFLnnk8t7ACgHE2edMry/GXOV9j2bcHoh9okvPtd0dQXduQ6GFkJCz0mbSlscl/jbkxIJDlIlyzQBL6csSOyvS05JtgYbZMaKR+5iMLcPE/FyZ6GBkJC30mrXhneWVo2e/6NoGAwPqdVfjuSJ39xhr5Uinmo5LQP1DTrOXOW7/b8JmGDKnL8/VuLnmSCFjoM2nFLa+tDC37HSGzbGvQ7CI3UrHirH7Btp/5uc23mJUGf93zZYbX9Zy4xcQQLsPApA1mG7Hfmn6WiyTcxy4bhsoDR7HrUG1o3dF6ZxnCmWDeYRIHa/pM2mC24fut6edkOb9dWrfIw/HdWjnS9M3c/uZK+40YxiMs9G3g8LnUYdX2Q4bXfme95mS7r7cjO3KdCn3zJffE/HKs3XFIvTHDuISFvg0NMYgAYWLDbsmUAvhvG3ej6esUSDXzy7S2iVb0sqjD/8jHGzHp8c9d75thVLDQt4FrpKQObyzbZnj97f6aBI2kmcK85lvs3RU7Im7bsaS55+6+w3WoPFDDM03Gd9iRa0NdYwAliR4E44ilFcakJr0Msl94aWzupjtWbnbzA2LEvXMBALeec5zrfSY7/CBLLKzp28Dhc6mNn/V3dKH/0MWDHX+mMM+Z0G9oCiBbER70lzlfO95XqpAEJZEyGhb6NnCzi9Tm/ZWRTSpu0BXUtkX5kTeUkB25kdh/pB65HhzFqYiXGRPjHyz0JVTZ9azppzbVdY2hTlrRogsrN/H6BTnObrEf/XtxxjRK/3jtrkQPIaPJWKH/r0+3oHTqLOypbo74UCkg7MhNbT79ei9OvHcu5vpg39fNEm5q7+RkO7vFyvcc9hQSmoq8u9y/2RfjnowV+vd9sB4AsEaL7d5mEenB5p3UZvGW7wAAi7T/0aBr+h7a4xpo0yLX4vuj+95UYe56fx3sjDsyVujr6FmbRy16lrJ5J7XRSzF4KcnQFBB4d/n2UG9bETLvRCf1rez8mWjr5kie+MNCvzGAldsOYvxjn1q8z+adVKZWe6h7aUT+4pcV+PVrK/BaWTD+f+W24KwwWqHfqWVBaPm9G0eFljNBAAZM05lkaGmZaWS80K9vDOD5Lyss3+depunBhl3Vrj9z8GiwgNtOLdN3+n/XAXDnyFVR2q5FaLltUV5oWQjr725fnKd+I8WYvc7oxGXzafzJeKFfU99oqHluxu9KjUzs6NOxGBMGdcLQHq1xep/2hve2Hzjq+vv0ZCmziY+i1PRLCppt+nnStRcQAr07FCs/U5SfHnmU5rImdR5mYEx0pMeVFAXVtY3IixBh4cUswPhHQ1MAOVlkK2jX7ahC+Z7DqKlrxMI7x+G7w3U4UctqBYKhm27Rdxku9F1/lYGekqafI6n2AWF8LZMuM85c073Gmn78yXhNv6q2AfmSY60kPwdv33AaZt8yGkD6O3JrG5rw+aZ9iR6GktqGJvS960P8efZG222/KA8eww7NFJNnmr156Ub18EfB/W47YIzscpvl+/cfDje8vmZUr9CyrMEvrdiP/Ufqld+RLr4l86yahX78yXih/6/PvjFobkTACce0QZdWQWebm4syEBC45bUVWFVp31kpWXjgg/W48pnFodDVZEKfZb345be225qFvDnRafLQrp7HUWkyDbk1+Z0/xLhvudxCQW42hnZvhW6tC3GwpgF7qtWJZKkuHC998ku8t2K7QtNPj4dZKpGxQr9lQbOG9dQnW0LLU88dAKBZaLi52fZU1+Gd5dvxsxfK7DdOEiq+C2qxey2ETSLRhaMTIWvWIM0lDaIJEqk6auzI1eBBAP9mYr+wdVeMPAYA0KdjiW3kTqoL/SUV+/GrGSvCzku6mK1SiYy16XdsWYCq2vDGzLrwyM0mENnb9P+5YDM27KrCPRceHxJS5g5OyYyudL68eCvO6t8xsYMxof+KTkxscocqINzZesShTb/yQA3aFeWjMC8beTlZqG8ML4Tmxbl/w5g+6Na6EMdqjtqKByeF3ivIzbIV6k0BgcamgOMM32TFPP5Uf5ilIql9BUWBeZqpU9o+6GQjIuTn2N+MD320Ae+t2IHRD88PrUul2GN9rMmYJakrv05+TlXdGtkp6tSRe/pD83Hd80sBALdpZY3Hmh6GXvwDADB5WDcc361V2PqC3Gx8Z2HLl0kHAWl2grN5J/44EvpENJGINhJRORFNVbw/moi+IqJGIvqB6b0mIlqh/c30a+DRYlXRcGj31qHl/JxsxyFlB2sa8Nu3VgEAGlMozLPQYRXIhODi2akKu5UFjNygxI6Fm4MlG3SttK4xgNWVzT6Pbq1bKD/nFfOVKMfuy6Sq0JdNV2YrFpt34o+teYeIsgE8AeAcAJUAlhLRTCHEOmmzrQB+AuB2xVccFUIM82GsvnGwph6rKtWOS9ks4GTaLfO/DXsAAA0ppOnnOqwCmeyoZldyTLiXKCxdWNU1NmHFtuYGLSN7tfUwQms+M0VPHdO2hTKKJ1W1YqOgN8Xpp+iDLJVxcsePBFAuhNgihKgHMAPAZHkDIUSFEGIVgJQ4g/fOWm/5nqx17a6qw4yl27DPZWlePxt3xJrcaNNLY4hwoer//MVlAIA3fnGq8v1aB8LFypn69lfbDWG9ftOy0Kh7FWj+iQKTnyJVtWL5V31sziYAzaazVH2QpTJOhH43AHLz0UptnVMKiKiMiBYR0YWuRhcjzNEYMqrEm5k2vU3NpJTQT2LHoFVAy56qWlTXqs9hC4tOVU7MdObTJu/faTMUL7QsMFbd1P0TbVsYzTy1KSog5Yfp51o+RVNoFpWaD7JUxskdr1IF3Ui1Y4QQIwD8EMBfiejYsB0QTdEeDGV79+518dXesLqB37r+VGXmp9PIj1QkmaNBrC6ykffPw7hHP1G+Z1UMzYlwkR20TQFhqHoZqVRHtLQsbBb6vTsU4YcnB0M5h2j+Jd3vkg6avo6e+8BlGOKPkyu5EkAP6XV3AI5VXyHEDu3/FgALAAxXbPO0EGKEEGJEhw4dnH61Z6y020FdwyMrAODRNOxTqpPMLfoixa7vqa7DN/uOhK1X9ZkFjKG3gYDAPxaU45Bpxve3eZtCy8f+7gM88OEGAED/ziWxFfpSzsiUM3pjwqDOqHhwEkrbFwFo7rObqiVBVKcxW3s4s6Yff5xcyUsB9CWiXkSUB+ByAI6icIioDRHla8vtAYwCsC7yp2JPXo5aMFgJjHTGbFpIJuymk2f9eUFYCQmrUyg7cj8v34eHP9qIae+tMWyzYWeV8rMNTYHQWO6ZPMhmVO6RC7DJQQB6lrHecjFVBaSqT0BWFgv9RGEr9IUQjQBuAvAxgPUAXhdCrCWi6UR0AQAQ0UlEVAngEgBPEdFa7eMDAJQR0UoA8wE8aIr6SQhWmn52hEpaqZRl64ZOLYOhjMd2KErwSMKRZcXJ98/FpMc/C9tmvUlQWxVmk7Vk/WvNETJWD/3Ne4/gHq2s8hAppNcvCiU/hPxw0mcXlOJasSoSqVf7FsjOInbkJgBHGblCiA8AfGBaN01aXoqg2cf8uYUABkc5Rt+xEvpZETT9OT70WE1GdAGYjA5dOXpnd1UddleFR1E1BIyC0MqmL2fR6sLUbCOPNNPbsveI7TZekX1Msl9hx8Gg3Xu79j9VzTtrd4TPoC4c1g1/nbspZf0UqUzy3elxIBkFXKLQtemk7BvgIFygodG4kZVMrlNo0GYtMyfL/rqItqyyih9qNXgAo6ZvLr6Wqpq+6pwQEQpys1P2mFKZjJR+Xp2X6djOTg8v9VpaIJY4+bXN45Y1/fsvap5kGoW+7hh1rum72cYtsnlHPp4SU+OUmvrGpCyMZ4dqBt0YCGhlTlJz9pLKZKTQf/ur7QCAG8Y0R4+W/f5s2885ranTo22ht4HFkX2H6/DSom9x/wfBRLVDNda5C4nCyTPWbN6RNfGurZt70dY3BkIPbX0bc9y7VQMTt2OKBnnGVVxgFPrT3luLk+6bi4M19nV6kolWhepgASe1rRj/yUihr9tITzu2uaVe++Lw2iwLp441vHaayp8KjVeufW4pfv/umtCDLBkjl5xk5JormmYZymgY8zF0gaoL7qP1JqHvYAYY6xmRfO0UW7RIdFKcLZlQJSt2aVWo1bZK/nsl3chIoa9jTnM307W1UWN3qpWkgvai1x46Z2AnAMmZpOVI049g3jFn5+rnRX+YmB2jbYvsi7LFWug3RND0dVJBqZC55Mkvlevzc9m8kwiS706PMYel7Fq3qfWqm03l2DtY05CUNnIVuuBLxsqgzmz6wvS6+TiO79oKU0b3xpTRvQE0R+voDxNzPZ42LZzkLMRmRvSnC4Lx//I1VqAoFw2khlIRidemnAKAzTuJIuOEvoydpm9GJfStYvuf/nSLcn2yoQvCZGv8Ul3bYFlfR6ahKWBwsMtCPyuL8LvzBqCP1rjErFXK53POut2hDNxInHCM/3H6AFCiafXymKwqoKaapm9GL16Xn8PRO4kgYztnAe5DN1VT0ewsUjp4d2kNupMd/ZiSLWRz8N2zHW3X2BRA1dHm2VuXVuFOdL2rli4sZbPR/iP1aFuUh8VbvnO0P6vkr2g5e2AnDOvRGjeP6xtaZ1X6IdWFfpPmfM/PycLBo6nln0gHMk7Tl1PC3RbDVGklVg5QJ1pqMqAfU71JY05WAgGzOUfgsqeDNuPzh3QxhD/q6MLTHKIJACu3BZvYmxurx5uWBbl498ZR6K3NSgDgvMFd0K11If77y9MN26aSHVxlNtRPYX5uFjtyE0DGafqyXFPVBImEWRuubwygRosAeev60wAAf3h3DdbtrEJ1bWpU5qyTtN/GgEjqAmxA+DloaApgw65qAMDAri2Vn2luch88V3JUUPmewzirf8ekTNgrzs/BF6YIMiC1bPp97vowtHzvhcdj56GjOPGYNgDYvJMoku9KjzGyNnuspFVZcf6QLqFls1Yya3VzsdETe7bBiT3bhBpiJJvQH3nfXDz7+Tdh6+Wy0algNjCfg6MOShPkmwqWyc/6+7Q8hURr+m6oqU8NTd88I2lZmIs7JvQPJWvl52Rh6/4afLx2VyKGl7GkzpXuE24tGHdNGhBavuJfiwwXqKrOS3F+MALEaSNuv9h/pB7vrdhu+f6e6jpM/294rbsqyQyVClpXXZNRkMhjbrJwRuuOQ31bldB0Ujr58pN62G4TD1LBDAeE32vmEFn9N9e7njHxIeOEvm7SufKUY2y2DGIOm3ttaXMTsTyFSUCPwtA16MoDNfh6d7Wnsbrhple+wq9mrAglnjlFLuubLM1iIgk1s6Yv16K36k3cXGAtKHT+saA8bBsnmv607w203SYefLgmsmZc29CEA0mQwLV2h7EPdViHMClkOlUeZOlA2gj9QzUN+NkLZZivNSe3Qr+0+nUqARAsxfDwD4ZYbp9vCuuUQwI3KoS5nkW5dX8NAOD0h+Zj/GOf2o4/WvSaLJf8c6Grz8na12/eXOXrmGKB2aYva/pWuRF6aK4el68yYzmx6VtV8Iw3/9uwBys0B7SKHz+zBMPvmRPHEal5f+VOw+sJgzoZXsuzq2SLHktn0kboB4TAnHW7UfFdeDclAFi+9QBKp87CjS9/FVyh3cC/mdgfl46wnrabtfnPpKYdf527yby569h/v9ATzXZIoaKPzt6IkffNVW7fTcs2loX+lw7DFmNNJKXPXHdG1vwbLMxTIUeudqwjStuEbaOatZlJZKmK7w3tani9/4h14bUlFftjPRxb1mw/hOcWVhjWmcNdDUI/BUyL6ULaCH1dI7eyS1/0j6AGvPib4A3h9P51W57gsgTZfVXmif/7Xzn2VNcZZj/6NFqP0jFntCYDkUZ0xb8WG15/Jwk/K03f7Mgd3sMo9Lftr4GDqsoJ1fTNdXjMZq491bW4e+bapMkEP///PrfdJj9Hri6afNdhupI2Ql/X1FRxvyp7IUWRTh+pmUWfjiWh5UiOVb+RQy0Pm2zzn25qbjavC75kDFHUiWTf1WsF6ew73Kz511s5cnOMjlxzqO72g0eVs4sPbj4DFQ9Owul9goX5ElmT7jcT+hlem80hf3p/HZ5bWIF565ub/ZhzGpIN2XSaSrkHqU7y3vkuycnOQk4Wob4p/OL5ojzcbBGN0ubURv+rGSu878QlshB/edG3hvfkqbP+UEwW+7Rbln5jNF3IDwir+kHNs8DgtWGWhQ1NAWWinh73//SPT8S8286MWTauE9oU5Rlem80h+sjkmW6y28nZvJMY0kboA0ETh0rTr1Jkx0ajtelOWp3Jw7pabNlMrKMTZF+D2cQlhyjqIY9uE9PiiXlkRVKWrbmbVJWUD2Fr3gldG8Y91DUEIv4eLfJyHOV0xBPzOdbNe4YHfJILUtm8k+xjTSfSSuhbVe1TNceIxrxjZvzAzrbbxFPr+sucrw1ar1wSQredJq/ID3fkfvbbsWhn0nRVWNmFiQh5OVmhpilmrT5VSlDImB9wunnzDikCK9m1Z1nT53IM8SPNhH620jaoqlbo50x9VJ92hteqJJ54azJ7DzdrxLI2rEewJLOm/8nXew2v2xblYfYto20/1xiw/o3zpVmg+ditzDvJjDnBTOXI/+eCzfEajidkm77KLJvOBALCcZE/v0kvoZ+bpY7BVoRmRGuflbVn86xBld1Zud9d0lS0yJqTXBJCn3Fs2asObU0GZGekTr6D3gfd27SwfE+u82J+3tU1RjbvJCNmk+UMKWlQ59kvwstuJIqHLw7PhTGYdzJM03958be47OlF+Mgm0S4WpJfQtzLvKIqIRRuJIZf+PVJvjJZRCahp762Jboc2mOu8L5K0iKqjUqmFhgD2VCVX2WchBGqk31AVWeQkjv43E/tZvpdFwKtLtiIQEGECPij0g8tvXX+qw1Enltp6694AMps8ZoP/b8NubDP5rqJB1frSYN5Jcqez3+h+wW8t8opiSVoJ/TwLoa9KqnGj6K+6e3zE9zu1LDC8Vgkocxil33QoMbb6m/r26tCyLPTrmwKWRcraFuXhhpeXoXTqrNgM0oKnPt2CgdM+DmUVqx7SdtU/f35mb4PmaEZ3AM9cuSNM01/yzf6QTb+0XVFKNLbv0dZ6ViNzjsds8GufK8M5j33i6bMq+ncOr4Aqn69r/rPUt32lAirHe7xIq9LKKpv+sOmzQ/VwZNyELJprhoR/l3kc8Rf6eREEnlz8ra4hoLT/nnZsO9Q3BvDB6vhPNz9cHUzX37irGh1K8tWOd5vzFcm0I7OrqhadWhofkO+v3IETtZlSFhHm3nomIrgHkoJ4+IhU/Qd0PlqzC3ura3HVqaW237Pyj+PRqjD8HjKXOMkk8rKD92sikunS6lfPzwm36R+sacC2GNvTw9LLFRdz5YHYjiGSJjy4W6vQcn1Tk/KBl8h+pSu1Ju1XPhPMtvXSpN3pI3znwaMhgS4X3dPNO1lEyM/JVjZjSSYSHZnzi5eW4Q/vrXW0rUrgA84qm6YruTnBKzYRZq20+tXdCK5YOu4imRlihUo71hnYpXlqXdcQUGafHm1owurth8LfSABesoWdTtye//Lb0Lm/YqQs9LUfJUVy1szX+fdP6Ga57dE41993Gv6aiPskWdBNwIl4eKeV0M/LycKqykN46hP7ULV3l++w3cYJPzw5vERzvBtybNtfg9fLKi3fr5VMXvVN6kiVRVuMma5W2a2x4KLhRoGVG+N6B/rhFygc7okstWDHgtvH4N0bR6GkICfMjNmxpMDiU8ANLy9DdW2Do7IMD364AR+t2Wm7XSTe/spZ+ZFM1vTzE2jTT6tfXdccHvhwg+22qixdL6hkRLwv5qlvRy6JLNcKqmsIOErMimcymbkyqZV5Z1iP5gilU3u3w9kDOoZeN7kItNcjSeTzpD8IE1lqwY7S9kUY1qO10owZaXI0f+NeDL57Nu6ZFd5Ex8yTn2zGL176yvMYn5hfjtveWOlo24y26bPQ9wezsI00zfRryquSEVbT1n9/tgVvLbPWyL2SbVMiUnZ5AZabAAAgAElEQVTI7a+pd6Txxfpi3H+kHr99cxWO1jeFRTsV5at/vz9dMCi0/ODFg3GJVBLbTQtB/fBzpN8tlaw7qt6yTtpzvhlhNgjAl5jxRz7e6HhbJ2G46UpI6LNNPzrMmkOvOz+w3NYcW++VRkXqv5Wmf++s9SEtaMOuKpROnYX1O6uiHoOdOUTW9B90MAsCYh8d8te5X+O1sm0YMO2jMHNY6xbBkgu/nzQAM28aFVovh6USyCA03Al9XatvXqefxSRW9EOoQpNf+PJbi62bsXMaVh7wLy7fCV4c9umC7rfauKsae6rjmzeTVr+6HgblhJN6tvVln6pMSCfT1sfnBRuwzFoVnf0UsHd8mktBm236l5zYPewzsc6QlCOIzOPXZ2hj+nXEkO7NJp2urZvj54mM8fxHXTzEQ1o9KdalgK4fNO+4n6nahQc6yXoGjDNoN2Y1O/792Za4+pISiX6dbdhVjZH3zcPrCjkSK9JK6LuxEV4+MtwB6xd209bvDteF4uH9mN6pagvJmJOxpr9vtO0GRPjsJNb1zWXtXjaHrZTaANo5VXM9avqb9x7Wvr95B7qdP1U1fSfYBdWoZqhflO8LWzd7XXOZjGjNgNed3iu0fO+s9XitLH7CL5GYM5Q/U/zOsSK9hL6N8JOF8che/mj6ynHYaEzLtzYLNj9s53bZquYkm3mmPsL1TYGwBLa6xgBmLNmKzzfF5mKUz4X8AJj8xBeOnKpZWWQQ+nY+mjP6tg8t/+eLiuD3S+8/81ny1Kmxw+zIXeiTwFDdPz/69+KwdXIZjxe+rMD7K71Hwv3hfGOz+YM1/gRYJDvmCVI8ZzhpJvSbha3KiXv/9wd7/m4rwTre1MkpOI7IP+tLi5vtr37YzlUF5WSsyi6E3q9vCss6rmsMYOrbq0MJU34jC3rzb+vEqUoAOko2fjtN/99XjwhbJ2v63x2pD1uXrJgduXJf5GjIcdIzEgiVywCCkXK/fHW5L/sHEp90Fi/M8snNTDVa0kroy4Jk7Y5wB2nrwlyDoHDDR78ejR+f2jNsfd9O4c017IS+LFY+XrsLCzcbNbW91XWYv9GojUdCz+5T0btDUVhxLp32xUGHadui3DBN389iWypkLd2s0ats7jq6yYcoWH/mrvMGAABqbB5sqoiqLCIMNxWqSwGZr5l3mo/XbqbnFN3PZLvd/8p92Z/O7ycNCC0ne7evWFHtUwi5E9JK6MvCVtWYeWz/jvj416Mx99YzXX/3sR2Kcff3BoWtVzXusEvOkrXJ/Ufq8UNTs++rnlmMa/6z1Nbx9vi8TSidOgsvLdpquc2pvdtZavo/O6M3bh9/HO6aNBDFJqH/69di2+pR/o3MWs+9Wjy5yqmqf05/7/KRPdCtdSF+Na6P+0EQMOa4juZVSY/ZvONX6ONGm4qc3+w7EpNifLI5NHM0feNrJyG3fuHoaiGiiUS0kYjKiWiq4v3RRPQVETUS0Q9M711NRJu0v6v9GriKSA3LgaAduE1RHvp09Nb6LkvhWVRdpHbp5Y02EQ8VWrlVO6H/lzlfR3xfH4vV/hoDAjeN7YtWhbkozo9v7b1ID8YDml1XpXXrAk5/r6QgF19MHYsTPURjZVH4PpI5OUtnx6FafL37MOZrvpl4NblfWrHf8j2rnJhCBxFBBXKJ5TRvkP5G2Tas31kV5shNKqFPRNkAngBwLoCBAK4gooGmzbYC+AmAV0yfbQvgjwBOBjASwB+JqE30w1bjtNJiNLxw7UjD67Jvw28EuygiuwtbF2wNjdGHw0Uai/zAuufC46Pelxvys2VNX72NUuhrD1QvpZN+OdY4GyAitDAVVkt+kQ9s0HI7rnluKTbtrsbzX1bEZb+RzJavK6Ju3r1xFNbfM9H2ewsySNO/481VOPdvn4Vdv7vi2OPCiYowEkC5EGKLEKIewAwAk+UNhBAVQohVAMxnbAKAOUKI/UKIAwDmALC/CjySF8G27Rejj+tgeL1me7jvwM6mb65zY0bXguuibCH3yR1jUBBh1iHPJCLVbokFsqZ/3wfrlduotG79t/VSklbuKwAEY8zN5yoFFH2USE73e2etxzqF/yoWRDIjmVtcAsayGZHIFKEvR+gkslGbE6HfDYD8GK/U1jkhms+6JlJy1k9OK43VbhXjMP6sP1IUZXPy+WhugIoHJ6Fnu6KImr5ZcFoJvNKps3yN0ADUpjIzkeoaeYl6Mj9ESvJzwsxMqWDeKZbKVHzy9V7DmHUzXSwKxy2K0NM1mmQ++cGbzo7c5xZWhJa3xjhQIhJOhL7q8nH6nHL0WSKaQkRlRFS2d2+4xuCUSFEMU8/t7/l73SLfhAtuH2OoEeOEPAeCzSp1+4qRxn1FmnWYndDFedZ2/WhisVXIYX9WqMInH710KEYf1wE927k35ZkjlLKyKO4VUf2gpak+vfzw1o/HafilG56PUOohmtBjWdNP5165VZLd/m+KSKl4xeo7uTIqAciSpDsApxLA0WeFEE8LIUYIIUZ06NDB/LZjrDJTTyptoyyj6wfdWkdurVfavshR1c0bXl6GQ5oDU3cER9L0R943T7n+vgsHY9N954Zey8dtlqEjSo3ulaI4OXMP1TTgnv/aV3xUKd3Dj2mDF64d6cl5qboG4uUE9RNzU5JDktkqFkXM5CxpK6JxwMpVVtNZ05flwM3j+oa9H69jd3KFLAXQl4h6EVEegMsBzHT4/R8DGE9EbTQH7nhtXUywuuDvviA81NIP8nOyMO17Zp+2ejs7Pli9Cy98WQHAmaZvhTlTVd73pMFdDNueP6Sr4bU5bNOMH5rI7qpaDJ0+235D+O9UVV0fqVjpUdX+U2dI92CXtPsu8s8xP/mJL2y3WVpxADsOeusOJ0e7Jap7WzyQLRFtWoR3E4vXLMf2ihdCNAK4CUFhvR7A60KItUQ0nYguAAAiOomIKgFcAuApIlqrfXY/gHsQfHAsBTBdWxcTrIRrrLIsN9wzERMGdbbdzmkhK/1Jrwv9C5/4Aqc9oNbonSLfUOZIFTPH2DTb7nPXh/jvqujMPDe87KJWu8+nTWXKSUXzTmGutdB/7LJhmDHlFFwyogfumNDP0fftPHQU89bvjriNE8f5zR79PgZNP8ZCf+Ouatz59mpH5cX9RlYwVCGa8XrgObrihRAfCCGOE0IcK4S4T1s3TQgxU1teKoToLoQoEkK0E0IMkj77rBCij/b3n9gcRhCrqXqsWiM6dfo5bapS3xTAoaMNWPbtgdC6HYdqsUUrEOYFed92MdNOYqqjrQrqpoys3xUv00Xom5vOyBTl5+CU3u0AqGcxe6prUTp1luEau/gfC3Hd82UR97lgo9HXpvrdvJYScBK9M2PJVgya9lHUwvqnLyzFq0u2YlsMykg3NgXw0qJvLWfEeZICpsqxiVeOQupd8RGwuoH9LP/qBcdCvzGA7YoG6t/ur3Hk+Jwyunf4viUBUWCj6TtJ5/cSKmnYhwsHo98RKLIQ7N2+CEBqtuxz2ltWdT8s1sKFn/08WGAuEBCOavds2mPM1i1QfLdXQSqfAyvBd/f7a3GkvilqbTiWvWlfXboNv393DZ79Ql28z07BSCpNP1XoWJKP7m3CHauqUgnxxOlN2tAUwBvLwpNcrvnPUpx031wc0IqCWfG78waErZO1KDtN3onWWx/lb+nGcep3+GSeIjTQTQ+GZCGSpi9zjqIYoG7q1IWr01mwebMqhXnC64TaoOlbacnZup8rOm1YvxdjIWBr6oK/iZWCplKqFk4dG1pOGpt+KpGTnYX3bhwVtj5a7dTMpSPCm46Y+WLqWMy9dTQA5wWxXlq0NVT2V8X1Ly9z9D0ybsw7ToT+kbpGLN7ynecCUZGKw5nx3ZErHZ9+TbRWONSSHatINHPiYNfWhfjzJUOV2+rltv+xYLNv4zpc562UgCFOXyGMr3pmcegho7/f2BTwNIM3B0kIIfDx2l2+yAi7vreqWWXborzQ8jXPLfX8G7ohrYQ+oBZcfteVeejiIdhy/3kRt+nWuhB9OpYAcKexRtq0Yl9w+rzkG+e+cHmWUSiZd55RlBp2ooUv33oAlz29CD9/0f0DyOk+dPz2v6sat/SwcV4nI1aa/l8vGxa2zupBrmvUZtuybvaKJ/L9oRKYn0k9HXRhfdzvP8TZf/nE9b7yTYJ54ebv8PMXl+HR2fZ1rOyw63uryp2Qg0z2Ha7DO1/530M7bJ8x30OcMZtSXv7pyTi+Wytf90FEjjJKvZAdQdJV1zYgEBC49KkvHX+flaavaiIzstS+aJmuXJVJjkA3uLHp++3Ilev9PHnliaHl7Bidy1hh1fVNdRhm7VIv9GWljXZTmEcB64JqfmNndtEFakAEq366pVnTD5qJdM16cxTBEkIIvLmsMnS91ks1s77ZdwSnPjAPj835Gu+s2B72WfM5i9Z86oS0E/qyKWXSkC4Y1ad9hK2Tj0g21iP1TZZOIitkR26kGvYAcO7gLvjyzrFh61W4cYT9/t3VeFvTYHJcZE37HXUla73d2zYLt9aFqWXiMTe80VGdU7Omr/+kVufPygTo1vrx+W/PcvcBDTuhX9cQwJrthzx9NxAM2QSaj183ldlV6I3E7HW7cfsbK/HE/GCfAVnT/+eCcuw8VIu/zdukjHzLIjLMrvw2RatIO6EvX/i3nXNcAkfiDTsz5b2z1MXJrJBnPrJMsBK9XVpFzjD2wkuLtuLW11cCiGze6WkytfidXyEXAJO/2ZzhmgqofhrVOrOmrwuV+qaAMrSw0CLCq9alA9VrxVs7O31dY5OhwNxd76x29f17NCerLpgLTDZ+L+itOvceDn73+yt34K1lQSXHLoiDyNjVLR4F59JO6Mv07uCtbn46Id/0yVBMLJLQl2clN4/ri1Y+O1lzLGY6uqBLpfBNVQy+6iFpPqZNe4JmjPrGgLKAllVVVrsexDLm+k9uWbP9EH7wz4VK7fu9FTsMwQAvL97qaR+6cNUTJ+ui0PRD17T0g972RlDJeXGRdb0iIHgdysmbrOkzUWMQ+tL6RMn/SOWvZa3ohGOcleV1i26/l22p+m/09I/DndvJispBq7bpG4X4P7VonYM19fjNm6vCttcFqjmb102o5MAuLR1vqzNY8rvdPXMtyr49gFWV4Wac5xZWeK6XJAdA6EI/J0sPYfUubPVz0eTRHGkXveQ38W2XxBjIM7W988qlI7qjc0t1PXxZu5U1Qb+dpE4QQkSs/hiPWUl+ThZq6psMZjTdrhvJiZ5s5OdkwdzcMFJ7STNVtY14Z3m4Y1GIYFluAHjk442h9ZWKpEE/6de5BKs1W70u1K20Xq/1kuQACLOQj8amHxL6HpNAjclprOmnNS1tCpw5ITeb8PAPhuLW8fZ1Vgw2/QTIt8ueXhTSrFQY/A8xGsNjlw1D/84lBodlV61SaiqVZFAJPic2fTtkQfvW9aeFluWwSTu8RKDce+HxGKDNEHJt4t39OE+fbdqHheXhoaBz1+0OOXudEm3RPvm6j0elTdb048Tm+89DQAj0vevD0Lri/BzsOxw5y9aO5dPGO97WaWRibjb5ksW8bX+NoSLkkm/2Y7kUvXP5ST1ARHh1SdAuK9v0Y/VQmjCoc1iRvOmTB2FkaVucVBqzTp6+ozbvqGz67jKO5fN+XCdvPjEvWbMFudk4f0gXrN9ZFXrgW2m9fgj9uet3Y+763fjvL08H0Kzp//SFYA0ifbbjBCuT5ey1uxx9Xo44jEdWbuqoNilOtlby+LlrTsKwHq3xrx+P8KXGv5vEM6ezz49/PRo3nnUsFtw+xtugNM54eD5GPzzfsE4WKt8/oXtIE/3D+QNN/of4TUVa5OXgUu0BlCo4t+m7u8VlTdPtAyP0HR5NFPpY9daLVuadsNyDKEJ77565FkB0ZhWr62aKwwRG+fNHOCM3/RjTryPevXEUzhnYCdeMKo3rvmUNLJJ8692hGHdM6I/S9kV46OLBUe1TVaNFHoOu3dc1NhkEWQrJ34SgEsjKnsIO6/ToyALbafmQjfdONNT5OerRPm4uQW718DAfZzQmET3J0GuFUMDffrfVdd7Km7iBhX4Cuewkd71zvaLXCvIydRw3ILxol1+s2HqwuQBWQyAuNv10wWv0jh2ydh1p5pNnaNSTbYj2eeqTLa72qWOu3Pl5udqPcOdbxth8v5yf3pO+vEn98waH9+I4HEFJ8gsW+hlAqP2ifEM7FKvti/PxbwehjNsPHsXdM9e6imDYfvBoKDEq29yvlqV+RFTds1RC2m2JiQM1kTXN68ccCwD4fKox49aPHAezpq+KLgKAjbuNjtZfz1jh6PvtZi7n/9/njr7HjFdNXzWTUTVX8RsW+glm9d3OHbFe0Qt0eZ3COnGc/ebNlXhuYQV+/mIZdh5yFt537aheuOqUnvjVuL6YMrq3QXtMREhpKvHID4biBk0A+4ldP9xJg7ug4sFJ6FhiDBH2wz/l9cHxvw17Ir7/yuKt+GrrgZiVWPf6raoZSjXb9NOfEos6Kn6iX+wHaxpwz+RBIELE0EkzToS+Hjkyd/0eXPdcGbbtj9xQ45Wfnoxj2rVAXk4WbjnnOBTkZhs0Ma+lmzOFDiX5+M3E5lpFs28ZbbntY5cNVTbi9oI8mRgv2fFlge21UmesMqJ/985qfP8fC337vikvlGHon2aHTGFeNX2V0D+2Q+yrnKal0H/lZyfjjw4alicLi383LqZO3Y4t8wEEKyxedWopvnlgkqsqoU6Evqylr9tZhTNMUTtmTlMUwpPNE7FOBko3jutUYvneRcO749YIdajkngKdtGvFCnkG9o8fnYB10ycAMPoOZkw5xXa8KlT+BzeROYeONuCe/64LBSys3XEIpVNneRpLJGav241DRxsw7b01rscoozLvdLJIsvSTtBT6px3bHteM6pXoYTimU8sCTDs/dg+p4T3aaP+9lTZQJZ+YtTK/E5u8RoAw7vnl2L6Yr4Xn2gXCyAnVOdlZaJEX9C3I57+jR8Gl6hPQ6MJH9Pi8TXjm82/wRlmw2NmHq53FyXtl9tpgM3mv5h2V0OcyDBmE2Qk3tHsrrFTUHvHCqce2w5K7xoXZYZ2imnbXNQYghAiN22+hH69+oUzQZ6471O0Sq6x8LX70JFBp+m6uA30EH6/dheL8HPxdK3UcK+o9mnd6dyjClr1HlL81l2HIYJ67ZiSAYE8AP/Aq8AHrsD9jIo+/l9Jpx7bz9fsYa4ia6+jbCZ1Y9ptR5RS4qX6pf/6zTfvw69ecRfRY8dGaXQgERMSaPF4rYt79vUEAjPfP01cFm/pwaeUMpk1RHioenISfndE77L1fju0T17FYafGygPBb0+/f2dpGzTRT4kMrUELzQ9tc3dTcgjGWSXNWM0rnn/evyf0vXlqGu95djf5/+MjSZq8L6I8dllvQ0Y9TzpsZP6gzTu3dLurG705goZ9EmBtbA+obYYSDtoZ+YiXQa+ubQpqQnzccwCGbTpl/x5iIkTsyViYYvf3nBzefgX+ZcjIuHN7N8NqNjd0tqrDPN5c57xm7q6rWz+Hg1SXbAFgfs776uYUVrr5Xz0cwZxK3yMuOy3XPQj+J+MWZ4Vq9SujHYwoo08Kim9Itr69A/z98BABYsDFyrDQTG9oX50eM3JExZ7zq6Nr7wK4tbUOIY1kbRnWtmxu3W/H17mq84rGhihWhRudR3m/msue6Ka1NizzD+md+chJe/8WpUe3LCSz0kwhVlExLRSu/usYmLLh9DBbdOS4ew0JBbjYW3TkuFJetN7z4ovw7AEAgILB5r/sm1RFhRd93zBmv3ojdibGbLUZqazn+sU/9Hk5I2EfrXP3lOKM5tkVeNh69ZChe+unJUX2vV1joJxFDe7RG55YFmDCoOemlfXE+nr92JL4vTbMbmgIobV+Ezq1iH9Or07lVAf54wSBMHNQZ155eanhvZWXkLE4mOdA1aXNwgBsxHquOZoB9mYREFeGrbwzgozU7UTp1Fs7/v88M78n9eq1QJUJefGJ3dGvtfz9qJ7DQTyJys7Ow6Hfj8NRVRrvqmcd1wO7qZnvlxEH+RPS4pVvrQjx51YloXWicll7kINtxye/GhWqXO4GrbPqPLvT7m81BNj/2OzechrMHdMLyP5wT0/LTdt/tVzXLt284zX4jiYv+8QV+8dJXAIA1241C/rzHP1N9xIC5z0Gir20W+inCKCmDtdDCxh4vPIVnkj/1WRjv6OaTYzsam6PYyaDhx7TBv68egTZFeTZbxha/hKWbHhQAsPNQdA5i88Ms0X0bWOinCN8b0hVAc5XDROI1PFOVccnED/n379G22bSQaM3TKe2LI5eIcEphnJUPs3Un0T83Z+SmCD3atsAnd4xJmB1Qxkt4JoFc3WyJvjHSEdmRG4uKpjNvGoV9h+t8+S4V//7xCIz584Kov8dtY5loSbaHKqteKUTPdkXIibIJsx94vWl0s1Sy3QSZwpJv9gMAvjtSbyiz7df5GNK9Ncb2j13Tne5tjArPdad7q68Vb01/bD/jb5Lo6z/xEoRJObyWXCjQZghOHHKJtnumM6u2HcTUc5vLMifTL/3+TdbOfrPC8+NTe3rah9m39PDFQzx9j1NatcjFhnsmhl4nOvGQhT7jGi82/erahlA550h+CTd1/hlvHKlvxORh3fD9E4JhwMn0fB3cvRVO7NkmlAsSCa/x87mmh4cfxeKs0B9M8oMm0b83C33GNV5s+qXtgs0hKh6chN9KzT/MmG9Ixn+qjgazavUZWxR9xWPCW9efhreuN4ZVPn7F8LDtahuaMH3yIF/2ecXI2PSrnj75+LB1iX7G8h3GuMaLecdp0xbdXxDvUhOZhH4u9H7G+4/EzvnqFXOiVlctEfGMvs2hy+2K811Xj9WjltZNn4Ax/YK1roiAB74/OJrhuiMVNH0imkhEG4monIimKt7PJ6LXtPcXE1Gptr6UiI4S0Qrt70l/h88kgli1tQOAp648EeP6d4yYcs94Q9eKO2vdsd5dvgMA8OfZzurbxBMrn44edXT/RYPRrXUh8nLcSdCnrgwmPrbIy7GsKZXu2IZsElE2gCcAnAOgEsBSIpophFgnbXYdgANCiD5EdDmAhwBcpr23WQhhrM/KpDRuIogKc7NdZUCe3LsdTu7NtfRjwQ9O7I7FW/bjtvH9AAQf3uZKj8lEXk5WaManPwN0f5Le4jEv253gbgxI5cC169ivTF8AaFmQg6rayEXpGmPUoN0pTu7ekQDKhRBbhBD1AGYAmGzaZjKA57XlNwGMIw6/YAAU5WdjQJeWiR4Gg6B2+8SPTgj1YT1Va1STrL0LVDPKP35vECYP64qx/TsCcB9UoOoB4bUZior2Jc0JZFZ9iRNtunTyi3UDsE16XamtU24jhGgEcAiArq71IqLlRPQJEZ0R5XiZFENuFMEkF49eOhTXjuqF913URIonKqHfuVUB/nb58FA0jGuh3xAu9P2c7cgPkJvH9TW8d4MWtSY3ok8ETn4xlcZunp9YbbMTwDFCiOEAbgXwChGFqX1ENIWIyoiobO/evQ6GxKQKyWw+yHRKCnIx7XsDkzZiSlVq3O02o/oYTYVy+0M9QU1OVIuWSFr8HRP6Ye2fJqB1i8TWMHJytisB9JBedweww2obIsoB0ArAfiFEnRDiOwAQQiwDsBlA2JxHCPG0EGKEEGJEhw7h3aOY1MXPqTOTWchlI6zs4Haa/gMXDcGXd44Nva6V2hG+/dV2AMCsVTsB+BOv39AkMKpPO9ymMO0QEYp8aG8ZLU6E/lIAfYmoFxHlAbgcwEzTNjMBXK0t/wDA/4QQgog6aI5gEFFvAH0BbPFn6EwqcNHw7okeApOiyOYdq0Qsu0iyJiEMeSVnD2guiTB5WLCIYd9OwaqjdvX8ndDQGMDLPz0FvzSZdpIJW6Gv2ehvAvAxgPUAXhdCrCWi6UR0gbbZMwDaEVE5gmYcPaxzNIBVRLQSQQfvL4QQ+/0+CCa5WDltPObcMhp3TOiHhy6OY/wzk1bIs0Qrs4mdpn+krtFQXVTOjD1J6zWtPxRys4Lb/X7SAG8DBlCXAjNbR3MNIcQHAD4wrZsmLdcCuETxubcAvBXlGJkUQ0Cgb6cS9HXYu5VhVMgtOK00fTubfm1Dk2UGuTl6JzcnC6jz1lZyXP+OmLdhD35yWqnrz8abxBuYmJRkZK+2oaqNZhIdksakH/VNamdrro2mP7BrS0tbvf7A0K9X/XW+B8d2r/ZF2HL/eQmvq+OE5HTbM0nPWf06Wr5XXMC6BOMvtRahv3Y2/RZ51tdiKGSzUdf0NYntQXC3yM9BVhalRHVYFvqMJ1Q3249P7YmKBydFvNEYximyhm5VMiE3OwvjB3qr4d+rfbAI4DCt2fvxXYOVPb3U249laRK/4buT8YSqkcqROv/inRmmb8dibNhVjbycrFC7UBU/Oa0Us9fttv0+c1LUgC4tMf/2MejZtgWAYLLaFSOPQVsPvYAnDOrs+jOJInUeT0xSMbxHm7B1qeDEYlKHllrRvSevPCFilVZZAbEq2/3pHWdh/m1jwtb3al8U+u4WeTkYfVwHR1q7XvUTAO676Hj0MTWbT2ZY6DOeGNi1JdZPb+4G1K4oD4O72ze+YBinODWZyNE5l47ojotPCM8NOaZdC7RxqME7SaB68qoTQ8uBxNZPcw0LfcYzhZKdlcstMH6jC/MGm6qU8nWYn5uNRy8dGtV+u7YuRPviYOE0q5DQ3h2aNfu6htQya7LQZ3yBwzQZv9HNNnZtEWXHa4NP1+ElI4KzhV+d3Rcqy1Jxfk6oMbvXto2JgoU+4wupduEzyY9u3rHTpGWh3yLfn8YoehG6hqaAZXJXy4Kgz6GWNX0mkyhJggJSTHqiC9taO01fNu946N+sYqRWomFYj9aWpR708g4s9JmMoiW3NWRihK7B22n6KofvJ3eMwes/P9Xzvk/v2x5f/eEcjOnXMSTcX/3ZKYZt9Do+VoljyfrTK50AAAaaSURBVAoLfSYqTtO6L/39h8MTPBIm3XCqSauyYHu2K8LIXm2j2r8er68Ld3NuSkHI55Bamj7PzZmomD75eJxxXAecHyF5hmG8oGv6RxNsPimwMBmxps9kJIV52bhgKAt8xn8mDekCAI4UiheuHYkFt4+JyThU2edAs1kp1Wz6rOkzDJOU9O5QjIoHJznadvRxseu4p2v65nDQ7m2C5Rv6JWljeStY6DMMw0RA1/TNUUTHd2uF9286HQO6sNBnGIZJG/QwUFUUUSqWHmGbPsMwTASsNP1UhYU+wzBMBHSbfqo5bK1goc8wDBOBTi2Dxddys5O/K5YT2KbPMAwTgZvH9UX74nxcMLQb+nYsQVmFujd0qsBCn2EYJgIFudm4VquoeXy3Vji+W+o5b2XYvMMwDJNBsNBnGIbJIFjoMwzDZBAs9BmGYTIIFvoMwzAZBAt9hmGYDIKFPsMwTAbBQp9hGCaDICFEosdggIj2Avg2iq9oD2CfT8NJJOlyHAAfS7KSLseSLscBRHcsPYUQto0Fkk7oRwsRlQkhRiR6HNGSLscB8LEkK+lyLOlyHEB8joXNOwzDMBkEC32GYZgMIh2F/tOJHoBPpMtxAHwsyUq6HEu6HAcQh2NJO5s+wzAMY006avoMwzCMBWkj9IloIhFtJKJyIpqa6PFYQUQVRLSaiFYQUZm2ri0RzSGiTdr/Ntp6IqLHtWNaRUQnSN9ztbb9JiK6Ok5jf5aI9hDRGmmdb2MnohO136Zc+2xMWhVZHMfdRLRdOy8riOg86b07tTFtJKIJ0nrlNUdEvYhosXZ8rxFRXiyOQ9tXDyKaT0TriWgtEf1KW59S5yXCcaTceSGiAiJaQkQrtWP5U6T9E1G+9rpce7/U6zE6QgiR8n8AsgFsBtAbQB6AlQAGJnpcFmOtANDetO5hAFO15akAHtKWzwPwIQACcAqAxdr6tgC2aP/baMtt4jD20QBOALAmFmMHsATAqdpnPgRwbhyP424Atyu2HahdT/kAemnXWXakaw7A6wAu15afBHB9DM9JFwAnaMslAL7WxpxS5yXCcaTcedF+p2JtORfAYu23Vu4fwA0AntSWLwfwmtdjdPKXLpr+SADlQogtQoh6ADMATE7wmNwwGcDz2vLzAC6U1r8ggiwC0JqIugCYAGCOEGK/EOIAgDkAJsZ6kEKITwGYe8X5MnbtvZZCiC9F8Ip/QfqueByHFZMBzBBC1AkhvgFQjuD1przmNC14LIA3tc/Lv4nvCCF2CiG+0parAawH0A0pdl4iHIcVSXtetN/2sPYyV/sTEfYvn6s3AYzTxuvqGJ2OL12EfjcA26TXlYh8wSQSAWA2ES0joinauk5CiJ1A8OIH0FFbb3VcyXS8fo29m7ZsXh9PbtJMHs/q5hC4P452AA4KIRpN62OOZhYYjqBmmbLnxXQcQAqeFyLKJqIVAPYg+ADdHGH/oTFr7x/SxhuT+z9dhL7KxpisYUmjhBAnADgXwI1ENDrCtlbHlQrH63bsiT6mfwI4FsAwADsBPKqtT4njIKJiAG8B+LUQoirSpop1SXM8iuNIyfMihGgSQgwD0B1BzXxAhP3H9VjSRehXAughve4OYEeCxhIRIcQO7f8eAO8geEHs1qbR0P7v0Ta3Oq5kOl6/xl6pLZvXxwUhxG7tRg0A+BeC5wVwfxz7EDSZ5JjWxwwiykVQUL4shHhbW51y50V1HKl8XgBACHEQwAIEbfpW+w+NWXu/FYLmx9jc/7FwZMT7D0AOgo6nXmh2bAxK9LgU4ywCUCItL0TQFv8IjE63h7XlSTA63ZZo69sC+AZBh1sbbbltnI6hFEYHqG9jB7BU21Z3GJ4Xx+PoIi3fgqAtFQAGwehM24KgI83ymgPwBowOuxtieByEoJ39r6b1KXVeIhxHyp0XAB0AtNaWCwF8BuB8q/0DuBFGR+7rXo/R0fhidTHG+w/BqISvEbSd3ZXo8ViMsbd2glYCWKuPE0H73TwAm7T/+s1GAJ7Qjmk1gBHSd12LoGOnHMA1cRr/qwhOsRsQ1Dau83PsAEYAWKN95u/QkgfjdBwvauNcBWCmSdjcpY1pI6TIFatrTjvPS7TjewNAfgzPyekITu1XAVih/Z2XauclwnGk3HkBMATAcm3MawBMi7R/AAXa63Lt/d5ej9HJH2fkMgzDZBDpYtNnGIZhHMBCn2EYJoNgoc8wDJNBsNBnGIbJIFjoMwzDZBAs9BmGYTIIFvoMwzAZBAt9hmGYDOL/AfnKjNfsqrNyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0ea71b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.spot_price.plot()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}