conviction/abc_sim-Copy2.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 = .5 #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)\n",
    "\n",
    "dust = 10**-8"
   ]
  },
  {
   "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 = np.random.normal(2, .5, size=n)\n",
    "phat0 = g*F0/S0 #derivative, integral and proportion\n",
    "#agents as controllers, co-steering\n",
    "\n",
    "#wakeup rates\n",
    "gamma =  sts.expon.rvs(loc=1,scale=5, size=n)\n",
    "\n",
    "#copy_cat_sensitivity\n",
    "nu = .5*np.random.rand(n)\n",
    "\n",
    "#holdings fiat\n",
    "h = sts.expon.rvs( loc=100,scale=1000, 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 = 200\n",
    "\n",
    "#phi for exiting funds\n",
    "phi = .05\n",
    "\n",
    "#beta is param for armijo rule\n",
    "beta = .9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([14., 11.,  9., 10.,  8., 10., 10., 12.,  8.,  8.]),\n",
       " array([0.00138631, 0.05054949, 0.09971266, 0.14887584, 0.19803902,\n",
       "        0.2472022 , 0.29636538, 0.34552856, 0.39469173, 0.44385491,\n",
       "        0.49301809]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a174b7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {\n",
    "    'kappa': [kappa],\n",
    "    'lambda': [lam],\n",
    "    'gains': [g],\n",
    "    'copy_wt':[nu],\n",
    "    'rates':[1/gamma],\n",
    "    'population':[n],\n",
    "    'beta':[beta],\n",
    "    'phi': [phi],\n",
    "    'invariant': [V0],\n",
    "    'dust' : [dust]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "                      'actions': {}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'actions': {},\n",
       " 'funds': 50000.0,\n",
       " 'holdings': array([ 736.24804104,  282.54517218, 3040.80699337,  238.29200059,\n",
       "         193.68918034,  144.91631681,  315.22143539,  527.65477299,\n",
       "         316.55580683,  449.8114287 , 2039.87432819,  938.7898018 ,\n",
       "         137.62212507,  193.02007489, 1029.59194525,  198.58194666,\n",
       "         516.84172832, 1376.82557549,  925.49901934,  618.55295627,\n",
       "        1401.63799281, 1597.3941186 ,  104.23834524, 1649.64224589,\n",
       "        1373.72334367,  257.11580653,  828.10992001,  768.24772969,\n",
       "         224.87830295,  969.00844016,  918.71837889,  910.39391321,\n",
       "        1512.35978146, 1959.3132925 ,  900.59257776,  141.89285306,\n",
       "         796.95217752, 4149.94119104,  830.0988348 , 1216.45030126,\n",
       "        1584.74084265,  110.06147541,  198.9885803 ,  685.58595916,\n",
       "        1338.45215261,  159.01372186, 2363.13478701, 1727.18748489,\n",
       "         518.39622804,  936.02133175,  115.64967442, 3552.75356032,\n",
       "        1282.15502716,  742.54742804,  617.2132287 , 1448.51166242,\n",
       "         181.92932515, 1310.5235614 , 2094.09528252, 2268.05922354,\n",
       "        2248.01745435, 1954.10587588, 1067.22225822, 1090.03048838,\n",
       "         913.58395247, 2843.23206177,  930.09918195,  673.29012595,\n",
       "         469.14962564, 4363.79409071,  890.33843689,  214.98161417,\n",
       "        1351.48451173,  785.29877986,  185.52558493,  155.6681959 ,\n",
       "         587.04547915, 1923.24465041,  861.21384892,  327.72478232,\n",
       "        1371.07880081, 5459.90307664,  866.03130163,  624.20546516,\n",
       "         811.0392485 ,  827.96608351, 1131.45858024, 1532.40360714,\n",
       "        1114.0365756 , 1252.89941747, 4332.21346216,  159.62885642,\n",
       "         207.44326707, 1639.36020496,  232.5829481 ,  120.85284259,\n",
       "        1032.62111616,  603.09959692,  580.19533226, 1201.03940305]),\n",
       " 'prices': array([0.12916082, 0.13220259, 0.09469516, 0.08983435, 0.09491088,\n",
       "        0.10125977, 0.11412706, 0.13912151, 0.06332464, 0.11733218,\n",
       "        0.10595573, 0.13543322, 0.10304663, 0.11349136, 0.1105685 ,\n",
       "        0.13216231, 0.11852944, 0.07448447, 0.08946345, 0.11723991,\n",
       "        0.07641012, 0.07560446, 0.08765526, 0.09424239, 0.11990811,\n",
       "        0.0845326 , 0.16172935, 0.0664441 , 0.12136223, 0.07271466,\n",
       "        0.04395202, 0.13420317, 0.0888691 , 0.09551432, 0.09027454,\n",
       "        0.09439385, 0.15362726, 0.0810152 , 0.11353742, 0.12518945,\n",
       "        0.12821461, 0.08078122, 0.10567455, 0.15577485, 0.11834015,\n",
       "        0.09264049, 0.08503681, 0.12520127, 0.08711241, 0.09758359,\n",
       "        0.10177263, 0.13048235, 0.10303206, 0.10947657, 0.12130558,\n",
       "        0.11525752, 0.09486688, 0.06743382, 0.07587707, 0.10991492,\n",
       "        0.11181029, 0.07902358, 0.11166424, 0.0840894 , 0.08744321,\n",
       "        0.09657562, 0.13316527, 0.11192112, 0.08637722, 0.12427133,\n",
       "        0.0644407 , 0.07796823, 0.09760486, 0.13107395, 0.11161457,\n",
       "        0.06224311, 0.10419632, 0.06085929, 0.12168676, 0.08327188,\n",
       "        0.09561579, 0.13452787, 0.1178075 , 0.09229771, 0.08658972,\n",
       "        0.09452543, 0.11287405, 0.10552784, 0.11473215, 0.10928019,\n",
       "        0.09634387, 0.09671645, 0.12646539, 0.0927046 , 0.11447527,\n",
       "        0.12561493, 0.0911274 , 0.07387136, 0.07676283, 0.07236304]),\n",
       " 'reserve': 50000.0,\n",
       " 'spot_price': 0.09999999999999999,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([ 6421.59171035,  6954.24876844, 10488.00526885,  4877.545312  ,\n",
       "        21944.2151465 , 15503.09842566, 15812.04068917,  7892.04320405,\n",
       "         5447.42173146, 11936.68233156, 13367.40284872,  8516.78772008,\n",
       "         7420.4453078 ,  9295.17098251,  8342.61134682, 14328.50075514,\n",
       "         5667.69559642, 10194.45627051,  7899.2052204 ,  8798.58240947,\n",
       "         7544.4058351 , 17013.35197274, 11344.73372693,  7991.64919209,\n",
       "         4789.8097187 , 13807.0802963 ,  7673.79731555,  7499.50627611,\n",
       "         9683.3167896 , 16565.73943939,  6320.84640326,  4957.87838072,\n",
       "         9135.44044638, 13543.10059729,  5637.40742898, 10496.93623183,\n",
       "         7539.6887413 , 10529.68318762,  6280.13189098, 16945.83754722,\n",
       "         4767.49816804, 10192.66995999, 11736.28790471, 16990.95378575,\n",
       "         8948.33707822,  6178.16462646,  9599.41901189,  8571.92115493,\n",
       "         9290.60574028,  5169.265629  ,  9331.76386262,  6407.21935429,\n",
       "         6078.39322623, 19519.44892951,  7279.18996627, 10110.06202905,\n",
       "        11184.95717983,  5454.4749908 ,  8789.79314282,  7644.69664309,\n",
       "         6359.44019414,  8144.89785817,  7660.35038285, 11619.03199845,\n",
       "         6917.86081387,  5258.23454003,  8332.7751082 ,  5557.1645654 ,\n",
       "         9998.76548568,  7819.79815762, 13208.74621388, 18390.51486003,\n",
       "         7205.75013863, 15005.75253067,  5648.90516382,  7810.14283927,\n",
       "         6235.74730281, 11786.7372383 , 13509.45033446, 15798.42741572,\n",
       "         5983.24967973, 10457.54824262,  8394.82495838,  5308.74402866,\n",
       "         8496.01470334,  5671.96176441, 18404.22237846,  8072.07510627,\n",
       "        28866.22554103,  5593.51818846,  7891.13350383, 21042.22816266,\n",
       "         4846.70255413, 19265.81496978,  6019.32265533, 10723.02026309,\n",
       "        12964.04854049, 18768.65002374, 16312.27138045,  4996.71536538])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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(loc = 0, scale=1/lam)\n",
    "    delF= 1-1/lam+rv\n",
    "    \n",
    "    #avoid the crash (temporary hacks, tune martingale process better)\n",
    "    #if delF <1:\n",
    "    #    if s['funds'] <1000:\n",
    "    #        delF =100\n",
    "    \n",
    "    return({'delF':delF})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_prices(params, step, sL, s, _input):\n",
    "    \n",
    "    #can also add a term for extrapolating the trend\n",
    "    g = params['gains']\n",
    "    nu = params['copy_wt']\n",
    "    phat = g*s['funds']/s['supply']*(1-nu)+ nu*s['spot_price']\n",
    "    \n",
    "    key = 'prices'\n",
    "    value = phat\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change in F (revenue and spending accounted for)\n",
    "def choose_agents(params, step, sL, s):\n",
    "    n = params['population']\n",
    "    rates = params['rates']\n",
    "    \n",
    "    agents = []\n",
    "    for a in range(n):\n",
    "        sq_gap = (s['spot_price']-s['prices'][a])**2\n",
    "        pr = (rates[a]+sq_gap)/(1+sq_gap) #rates when sq_gap =0, 1 when sq_gap -> infty\n",
    "        rv = np.random.rand()\n",
    "        if rv < pr:\n",
    "            agents.append(a)\n",
    "    \n",
    "    #shuffle\n",
    "    shuffled_agents =np.random.choice(agents,len(agents), False)     \n",
    "        \n",
    "    return({'agents':shuffled_agents})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agent_actions(params, step, sL, s, _input):\n",
    "    \n",
    "    R = s['reserve']\n",
    "    S = s['supply']\n",
    "    F = s['funds']\n",
    "    V0 = params['invariant']\n",
    "    P=s['spot_price']\n",
    "    \n",
    "    actions = []\n",
    "    for a in _input['agents']:\n",
    "        h_a = s['holdings'][a]\n",
    "        phat_a = s['prices'][a]\n",
    "        s_a =  s['tokens'][a]\n",
    "        beta = params['beta']\n",
    "\n",
    "        if P>phat_a: #equiv: pbar(0)>phat_a\n",
    "            mech = 'burn'\n",
    "                        \n",
    "            #approx for burn s.t. p=phat\n",
    "            #armijo style\n",
    "            amt = s_a\n",
    "            \n",
    "            def pbar(amt):\n",
    "                output = withdraw_with_tax(amt, R,S, V0, params['phi'], params['kappa'])\n",
    "\n",
    "                if not(output[2])>0:\n",
    "                    return np.Infinity\n",
    "                else:\n",
    "                    return output[2]\n",
    "\n",
    "            if amt > params['dust']:\n",
    "                while pbar(amt)< phat_a:\n",
    "                    amt = amt*beta\n",
    "\n",
    "        else: # P<phat_a; #equiv pbar(0)<phat_a\n",
    "            mech = 'bond'\n",
    "            #approx for buy s.t. p=phat\n",
    "            #armijo style\n",
    "            amt = h_a\n",
    "  \n",
    "            def pbar(amt):\n",
    "                output = mint(amt, R,S, V0, params['kappa'])\n",
    "\n",
    "                if not(output[1])>0:\n",
    "                    return 0\n",
    "                else:\n",
    "                    return output[1]\n",
    "                \n",
    "            if amt > params['dust']:\n",
    "                while pbar(amt)> phat_a:\n",
    "                    amt = amt*beta\n",
    "        \n",
    "        action = {'agent':a, 'mech':mech, 'amt':amt, 'pbar':pbar(amt),'posterior':{}}\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",
    "        action['posterior'] = {'F':F, 'S':S, 'R':R,'P':P, 'a':a,'s_a':s_a, 'h_a':h_a}\n",
    "        actions.append(action)\n",
    "        \n",
    "    key = 'actions'\n",
    "    value = actions\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resolve_actions(params, step, sL, s):\n",
    "    \n",
    "    H_a = s['holdings']\n",
    "    S_a =  s['tokens']\n",
    "    \n",
    "    actions = s['actions']\n",
    "    \n",
    "    for action in actions:\n",
    "        a= action['agent']\n",
    "        H_a[a] = action['posterior']['h_a']\n",
    "        S_a[a] = action['posterior']['s_a']\n",
    "    \n",
    "    #last action only\n",
    "    F = action['posterior']['F']\n",
    "    R = action['posterior']['R']\n",
    "    P = action['posterior']['P']\n",
    "    S = action['posterior']['S']\n",
    "    \n",
    "    return({'F':F, 'S':S, 'R':R,'P':P, 'S_a':S_a, 'H_a':H_a})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
    "    \n",
    "    key = 'holdings'\n",
    "    value = H_a\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_tokens(params, step, sL, s, _input):\n",
    "    \n",
    "    S_a = _input['S_a']\n",
    "    \n",
    "    sumS = np.sum(S_a)\n",
    "    S = _input['S']\n",
    "    \n",
    "    tokens = S_a*S/sumS\n",
    "    \n",
    "    key = 'tokens'\n",
    "    value = tokens\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_agents\n",
    "        },\n",
    "        'variables': { \n",
    "            'actions': agent_actions, \n",
    "        }\n",
    "    },\n",
    "    {\n",
    "      'policies': {\n",
    "          'act': resolve_actions,\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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_periods_per_run = 1000\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'N': 1, 'T': range(0, 1000), 'M': {'kappa': 2, 'lambda': 200, 'gains': array([2.58321646, 2.6440519 , 1.89390324, 1.79668704, 1.89821767,\n",
      "       2.0251954 , 2.28254118, 2.78243019, 1.26649273, 2.34664364,\n",
      "       2.11911451, 2.70866446, 2.06093269, 2.26982727, 2.21137004,\n",
      "       2.64324613, 2.37058879, 1.48968937, 1.78926908, 2.34479821,\n",
      "       1.52820249, 1.51208917, 1.7531052 , 1.88484784, 2.39816217,\n",
      "       1.69065206, 3.23458695, 1.328882  , 2.4272447 , 1.45429321,\n",
      "       0.87904041, 2.68406337, 1.77738203, 1.91028646, 1.80549082,\n",
      "       1.88787696, 3.07254518, 1.62030406, 2.27074834, 2.50378902,\n",
      "       2.5642922 , 1.6156245 , 2.11349103, 3.11549701, 2.36680293,\n",
      "       1.8528099 , 1.70073615, 2.50402548, 1.74224826, 1.95167172,\n",
      "       2.03545253, 2.60964693, 2.06064123, 2.1895313 , 2.4261115 ,\n",
      "       2.30515032, 1.8973376 , 1.34867643, 1.51754133, 2.1982984 ,\n",
      "       2.23620583, 1.58047151, 2.23328472, 1.68178803, 1.74886417,\n",
      "       1.9315124 , 2.66330537, 2.23842232, 1.72754435, 2.48542662,\n",
      "       1.28881395, 1.55936467, 1.95209729, 2.62147908, 2.23229131,\n",
      "       1.24486225, 2.08392634, 1.21718582, 2.43373521, 1.66543753,\n",
      "       1.91231571, 2.69055739, 2.35615004, 1.84595425, 1.73179441,\n",
      "       1.89050859, 2.25748103, 2.11055677, 2.29464307, 2.18560382,\n",
      "       1.92687749, 1.93432904, 2.52930779, 1.85409206, 2.28950531,\n",
      "       2.51229859, 1.82254804, 1.47742713, 1.53525652, 1.44726073]), 'copy_wt': array([0.06025362, 0.36505211, 0.05267044, 0.06850175, 0.30578861,\n",
      "       0.25735357, 0.04033182, 0.09250809, 0.19917317, 0.3498856 ,\n",
      "       0.29877174, 0.49301809, 0.16780534, 0.39025647, 0.02677478,\n",
      "       0.00138631, 0.26996666, 0.08078287, 0.40945662, 0.1594742 ,\n",
      "       0.3270803 , 0.17414379, 0.3505244 , 0.18119566, 0.30678627,\n",
      "       0.01489682, 0.30236662, 0.1360623 , 0.24862539, 0.18972881,\n",
      "       0.02566768, 0.07204347, 0.34016837, 0.05066982, 0.34551344,\n",
      "       0.26173636, 0.00663229, 0.47158648, 0.02899259, 0.19023925,\n",
      "       0.20550784, 0.27063377, 0.02174919, 0.37204693, 0.46790907,\n",
      "       0.05485806, 0.20370069, 0.15233815, 0.09285284, 0.2372323 ,\n",
      "       0.03652267, 0.34911242, 0.13925909, 0.20411058, 0.29369587,\n",
      "       0.49160174, 0.43357307, 0.1555917 , 0.40969031, 0.14069629,\n",
      "       0.24221793, 0.38935914, 0.10871842, 0.34761342, 0.39139849,\n",
      "       0.18343394, 0.11983906, 0.11822255, 0.21093323, 0.47892304,\n",
      "       0.31056213, 0.37726806, 0.06823984, 0.42213789, 0.40201593,\n",
      "       0.28549598, 0.39199196, 0.33972144, 0.28045614, 0.40147855,\n",
      "       0.28914297, 0.42172926, 0.01575988, 0.31221524, 0.2440519 ,\n",
      "       0.01733812, 0.42044535, 0.13071428, 0.1369768 , 0.04592151,\n",
      "       0.08104894, 0.4903476 , 0.18374842, 0.02346249, 0.27031077,\n",
      "       0.37585018, 0.14247653, 0.44708136, 0.03083187, 0.46926744]), 'rates': array([0.07759092, 0.39356354, 0.22820128, 0.0861253 , 0.08591832,\n",
      "       0.52307186, 0.1234122 , 0.11875009, 0.08646772, 0.35399962,\n",
      "       0.271065  , 0.11443164, 0.38456972, 0.3645478 , 0.23720792,\n",
      "       0.09364075, 0.41017169, 0.29142904, 0.46846934, 0.1496817 ,\n",
      "       0.0865705 , 0.16473581, 0.22724141, 0.15463766, 0.18157483,\n",
      "       0.24195451, 0.31020796, 0.12093914, 0.32674524, 0.14852449,\n",
      "       0.43167415, 0.25718141, 0.69740914, 0.12383361, 0.32000464,\n",
      "       0.23437092, 0.17803106, 0.50218627, 0.37349758, 0.21750902,\n",
      "       0.10389826, 0.17508539, 0.09885692, 0.06645807, 0.15999522,\n",
      "       0.06062773, 0.18470383, 0.11247222, 0.16255895, 0.31805185,\n",
      "       0.19608006, 0.18590217, 0.1486692 , 0.24961481, 0.23034023,\n",
      "       0.09727982, 0.33240212, 0.60353414, 0.12451738, 0.0737853 ,\n",
      "       0.79989863, 0.07381551, 0.18146056, 0.40942662, 0.44496655,\n",
      "       0.30416134, 0.1242727 , 0.06201643, 0.42608417, 0.11239868,\n",
      "       0.07845347, 0.28948248, 0.11592431, 0.63781215, 0.21456035,\n",
      "       0.09059607, 0.30355107, 0.6786074 , 0.0535327 , 0.14728062,\n",
      "       0.11720245, 0.4000356 , 0.17479473, 0.06841876, 0.15132069,\n",
      "       0.08808861, 0.57236691, 0.20610594, 0.26746407, 0.58988855,\n",
      "       0.07025979, 0.25362264, 0.33260061, 0.08509552, 0.2599373 ,\n",
      "       0.38497785, 0.07431973, 0.25894044, 0.15417676, 0.28842638]), 'population': 100, 'beta': 0.9, 'phi': 0.05, 'invariant': 20000000.0, 'dust': 1e-08}}]\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": 18,
   "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": 19,
   "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 0x1a19609278>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a19609278>]\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",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:56: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = quantity_recieved/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:47: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:56: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  realized_price = quantity_recieved/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:47: RuntimeWarning: divide by zero 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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_index = 0\n",
    "df = results[experiment_index]['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a197ee080>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19609908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.funds.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a237872b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ee40f28>"
      ]
     },
     "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].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['token_wts'] = (rdf.tokens/rdf.supply)\n",
    "rdf['wt_mean_price'] = (rdf.token_wts*rdf.prices).apply(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['holding_wts'] = (rdf.holdings/rdf.holdings.apply(sum))\n",
    "rdf['h_wt_mean_price'] = (rdf.holding_wts*rdf.prices).apply(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['wealth'] = rdf.holdings + rdf.spot_price*rdf.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['wealth_wts'] = rdf.wealth/rdf.wealth.apply(sum)\n",
    "rdf['w_wt_mean_price'] = (rdf.wealth_wts*rdf.prices).apply(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a2430b3c8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r1vuEFvxzjT/7t3GtoMqvs6n6aa9/JxExRS9vYe0Sga17FGpdgphjRCKOU5KJvqAH4Wd0pnZjMa6dba85ajVehFVFsZuoXV+Et6iWss92NutQ1l4IprRydXU1V199NcOGDWPQoEF8/PHHANTW1nLJJZeQnp7OxRdfTG1tQ6+I1NRUior0Xd4555zDkCFD6Nu3L/PmzfOPCQ0N5d5772XAgAGMHDnypC7e1n40/Sa0lJTVGjiGJPhfAol3D8e7rwZPQQ3ln+6kelU+juGJAYKnJaSUoEBYRvDlni0dQok8txtlH20HwNo18vC/xAmAMCkk/KWhIGtIz4ZdWMSkzpT9tyFpTavx4q7RTTKOEUlETE5DejWK/72ZqhV7sXQMQ420EpYRGNEUf9NApNMbUBAuLKMDFcuyqd1UgrVL2z3j6l/3Uf1zHsJmwtYzmpo1BdT8Xgh1ZqnIKV3a7N58MQvy1x983KGQ2B/+cPCOVwcrrdynTx9OPfVU3njjDcrKyhg+fDinnXYar7zyCna7nXXr1rFu3ToGD25eKhzgjTfeIDo6mtraWoYNG8b5559PTEwM1dXVjBw5kkceeYS77rqLV199lfvuu691n8EJQrsV+kcDU4QVU4QVW48obL2i2Td3NTXrCg8o9KVHo3ZTEWighBza4w8dkYR9UDxateeQ4vDbG/b0OL/QN3cI9ZvKMCnYBzSU5o6c2pWSd7fgLarFnNK8lIXqMEMTR7ow6/H6VStyCZvQMajM48Oh3v+gWFXCxqXoeQv1XclW5uEtdhJ1dtcTzkdzMOpLKwMtllbOyclh8eLFzJ07FwCn00l2djbff/89t956KwDp6emkp7dczuu5557jo48+AmDPnj1kZmYSExODxWLhrLPOAmDIkCEsWbKkrb/qcYsh9FsJc2wItt7RVK/MJ/y0zijWlqN6qlfn+6NEFPuhP37Foh52xFB7QbGbSZw1DOeWEuzpcQibifJPd2IfHB8wLiQ9FtvaaJybSw4pGsfSORzXznLyHllJyqNjWnv5AH5Hc+xVfTEnOrB2i8S1vYyIyWmUf74L56Zi8jYV+5MKW5UgNPK24mCllVVVZdGiRfTs2bPZtQfLMP/2229ZunQpP/30E3a7nfHjx+N06iZAs9nsv15V1RbLOJ8stBub/vFA6OhkpNtHxZdZ+03kqs/8tXaPJKRv7NFcXrvCFGkjdGQyit2MUASR07o2r/opBBFnpgJwKKXY/Q7VRgl/rY1W6cbWOxpzoh4lFvPH3iTOGo59YOCLy1PYpi2ljzvOPPNMnn/+eX/t/DVr1gAwduxYFixYAMCGDRtYt25ds2vLy8uJiorCbrezZcsWfv7556O38BMIQ+i3IvXRO1U/7qVwnv5H2bjxg6ewxt/MJO5P/Q/ZvGNw6JgS7ISfmUr0RT0OPrgOS8cw4m4YAEDthqKDhlNqLh/OzFKK392Ca3fFfjOIK77OZu8jK/GVu/DkV/sFPoBiNWGKtKKGW3AMSyS0rhZU4bx1OHeUITWJ9GpIr4Z7T6W/UuyJgLfMiTuvGuk9eBXc+++/H4/HQ3p6Ov369eP+++8H4IYbbqCqqor09HSeeOIJhg9vXshv0qRJeL1e0tPTuf/++xk5cmSrf5f2gDjeutEMHTpU1sffHioVS3cjLCphY49dCYKc+5aDV3+mYad2pHLZHpJnn4I7uxKt1kvJu1uM/rEnAFKT7H3oJ6TThwgxEXtlnxZDcqWU7PvHr3gLawOOt5Qg1jjcFyBx5tADdhUrnLcO187y/Z5XQs0k3jEExX5wv8PmzZvp3bv3Qce1NlJKv89FCbNgauSjkJoEKRGqoXseKi39PoUQv0ophx7s2nb1tMNP63xMBT5Awm1DCB2rR+VULtMTf/b+7WeK3thAybt6IlF9opfB8YtQhN9cJGu9FL68rsUqrlU/5DYT+ADu3Oa13xtr9qYE+0HbSMZcceBcAa3KQ+2mkgOOOdZIZ8MuSav1Bux8vYU1ePKqjTaIR5l2JfSPB8yxIX47sh9f4B91MJqZwbEn+uKeRJzVEDpZu7EI6dP0ek2axJNf7e8X3BT37gpq1hUGHGvsuE+4edBB76/YTP4+xOaOYZhbaKZT+UNOqxYCbG2kT39RquEW8Gp499WguXz+mldQ10+5sAbNyEo+KhhG5Tag8XZVjbH5k4j85y3Gu/ZEQA2zEDamA/b0OApe/p3SRZmUvLfVf94vkJMcxN86COny4S12okZYyHtkJeVf7CKkT4xuv08ORXP5sHQKI+aPfQJqRR2I+FsG4c6uwN5fD0X1lbtw51Ri6xNDzdpCSt/finNrCSG9j9Ns3roXkuIw65q+R8NbWONPjIOG9qW+YidKoqPFaQxaD0P6tBGR53QDIOEvg7EPjidsfApx16cTMSXN6BF7gqGGW4g6p1sz845WpWfOxl7dDyEEis2EpUMoaqiF6Om98JW62DvnJwr+uZbce5bjya1CWFTUsOCzpk0RVr/AB722UkjfWIQQ2NP16K/itzYdtXpBh4rUACEQqoIpNgRRF8rsK9EVIdEomEF6tZPS1CM1qfs+CmvQnG0fSmpo+m1E6MgkQkcmAYEtCY9W2V6D1sXarSE7N+KsLrh3V1C7vghTgr3FSqkhfWJAEc1eFN7i5vb/w0WoCrY+MTg3FVP8zmYSZw5r0+qg3nIX+CRqlPWgiovUJJ686oBYWaHqFWq9xbVIl27KUcMsaCYF6fYhXfo/0QYd3I4H6l9ojZ+d9OoFHOtrRnk9TizJh9YT+1Bpn0/XwKCVEYpAjbDiK3dhHxBH2JgDl9AQJoW4P6dT/Us+jiHxlC3egSe/htir+7XquqIv6Un1T3sp/yKL2o1FKCEmbN0O3NxeahJfpRs11Ow3RWoeH0IR+42k0Wq9aHVhokqoGWFRkVK2KPw1pxdvibPF5Ij6UtiefD2kVZgVTBarP8rHW1SLOcmB9EqERWlXu2JfiROt1osabUO1m/0CHxpavbZ1lzgwhL6BQdDE3TgA56bioM0z1s7hWDvrjWDibhiIt8SJOa51+x0oFhXHiCTKv9pNyQI9OixiShqOEUn7zdz2a9peDVNMCFJKvSGPqmBJatmmrrkaReFUeZA2DV+JE1O8vdl9vMUNAl8JszQrZSEU4a8uWy/UhRAodjNajQdvUS3So6GGW/dbQNBX4wF54CZEx4L9haFKTfqrqvpKnAhVQatuIdfiKLzkDJu+gUGQmCKshJ6SfFjXKlZ1vwL1SFFsJmyNzE/ln+2i9P2tLY711Xj8phXN6cNb6mzorezT/NE2jZGaRDSSFFqNx2+T9xbU4MmvRnM1skXXmZjq4/KbdjED3dTTVDCaom0Iq+rXen0Vrv1G9PhKnPhKnQFJalLKZmGhbYnUpP48G0VP+cpcehhqo+copcSzVw/hrTddeQtr0GrrTFxRVqjbSZrj274JUlBCXwgxSQixVQixXQgxq4XzY4UQvwkhvEKIC1o4Hy6EyBVC/LM1Fm1gYBCIY0RSwOfajcUUvLCWovkbdVNLHX4HqkUFKdGqPfhKG857S11+oSmlxFtciye/GumVIERA1E090qvhLaxtcEJqEiU0MBELgiut7FQ8XHfnjYyaMo7hk8bw0YIP/NdmZGQwePBgBqUP5KfVKwFY9uXXjB8/nvPPO5/ePXtx2aWXHrXS1L5KN74SZ8DzrdfmPXnV+m4E/C9ZADXU3ND7Q0rUSCuqw4IlOfSQHPxHwkHNO0IIFXgBOB3IAVYJIRZLKTc1GpYNzAD+up9p/gZ8d2RLNTAw2B8hfWKI/VM/1HALWl0yWX1ET/6WEnznR/iF+dxtz7CtertugmmsFCsCNKmHk9Y7oRvnAIi6l4Ummzmoe4R156/chhplqzNvtGymCKq08qTTeOPf8ynZU8App2Vw5rmTiY+PZ8mSJZhqYOvGzVxx05/46X/fg5SsWbOGNUtXkpyQxPhzT+eHpd8x/uzT2t4fUJd/I51eNKcXxWZCqEJ/QaJr/UqICc2tC/16U5g50eHPUj4W2cjB2PSHA9ullDsBhBDvAWcDfqEvpcyqO9dsbyiEGAIkAP8DDpoibGBgcHjYujc4cMNP60TF0mz/Z+nx6dmxdaYXYRIgFL1VY51QEua6KBqf1KOA6gW+oNnLoT7XRHplwIvBv2vYj9APtrTyP559GiS4XC52rttGp35dueXWW1iz6jdUVSVz53YUm4pQBUMHDCElSXesp/dJZ3d2Flq1BzW0bTVn6fbp39Mn8RY7McXY/PWFhFXVo5HcPrRqLwjh930IITDF29Eq3f4Q1qNJMEK/A9C4kWgOMCKYyYUQCvAU8Edg4gHGXQdcB9Cpk9Eg2sDgSAkdlYwnvxrHKckUvao3TKkPF71r8F2YGvVm9lV70Gq9mGND/DZ+U3wI3n01KA4zwqzoWqvD3GIfh3qbtq/U6Tdv7C909FBLK7vr+kE//PjjxMfHs/qrH5EWhbCUKECghFuwWuqEuyIwhZjxen3+9baVti89evE7YTOhRpnxFtX6/QtqtA3FquLJr26xRAfUlUg/SBmOtiKYvUVLTy1YT8mNwOdSyubdpxtPJuU8KeVQKeXQuLi4Aw01MDAIAsVuJubyPti6RpI855QAM0LTXg+qw4y5rp+0YjPptv464a3YTCh2M2qUbb8NXYQi/KGYaqRVT8YKMuO4KU1LK/++YyMAZUWlJEbGoSgK7/z3fXw+H2qEBcWkIswqaqQVS3KoLkxD9O/XUq2k1qLeca2GW3Rt3aT4bfeKVUWoSoCN/ngqvRLMbyYHaFwhLAXYG+T8pwA3CyGygLnAFUKIY9fBwcDgJESx6jV86oX3gUp6C6sKArT6PgJKXY6CwxxU4pcaasGc7DhsW3XT0spznnwYNcrGn6+8hrff+TcZ004lc0cmDkfDPYQqAkw59SYT6fTqu5gmEUC+KjfuvCq0w3wpSE3iK9NLRwiznksQ8Ezro5caCXo16vjpgHbQ0spCCBOwDd08kwusAi6VUm5sYex84FMp5QctnJsBDJVS3nyg+x1JaWUDA4OWOZTSyp7CGr/Wakqwo5iPbac2KaVe16hIN5WYEx0thoE2xlNQ4/dVAJji7EiX11//B/SXgynKhubyodhNQZuCNKcXb1EtwqT4K6dqbh/eAr3hTeNmPr5qD8KstHq3uzYtrSyl9AI3A18Cm4H/SCk3CiEeEkJMq7vZMCFEDnAh8IoQotkLwcDA4MSgXkNVwizHXOAD/rpGppgQXaMOImu1aStSb2ENvgp3gMlHunx48qt1X0SVp8UchZaod9aa4hps8opFxRRvDyifDbrp7HhrbxpURq6U8nPg8ybHHmj08yp0s8+B5pgPzD/kFRoYGBxVFLsJYbIfd9VglRBT0N3mFIdZr+NjVlpseamEWtCqGo77yl34qtz6LqKJxu+rciPMqt8X4k/GamLuOt6E+/44vn6rBgYGxxwhhO6MPIHr3ghR51gOt2JKsDc+oUfchFtQ7GaUEFODxu6TASYh0E1LvjIX3sa9in1SD1s9QZ+PUXvHwMCgXaOYVb2Im0dDmBS/P8DUKLvYnByKJ68ab4kLc4KKUATSq/mjmAA8+2r0WMa6aKUTFUPoGxgYtHtaqvUTcF4RKDYVrdaLZ28VSqjZ3y+hHtkoCuhYJFW1FoZ5x8DAwAA9qUrUOa6bCnxTbJNEqhO414sh9A0MDNqcsrIyXnzxxYOO+/bbbznrrLOO6F5ZWVm88847h3ydXh4hBFO8HWFVUULNmBMdmJMcKDYT5uRQTPVVME/gDl+G0DcwMGhzghX6rcHhCn2oc2JbVMxxdkyRNt0HUJ8Epujn1GgbpuhjU0KhNTCEvoGBQZsza9YsduzYwcCBA5k5cyZSSmbOnEm/fv3o378/77//frNrVq1axaBBg9i5c2fA8cmTJ7Nu3ToABg0axEMPPQTo2byvvfYas2bN4ocffmDgwIE8/fTTAdd+++23jBs3josuuogePXowa9YsFixYwPDhw+nfvz87duwAoLCwkPPPP59hw4YxbNgwVqxYAcAvv/xCxmnjGDx8CKNGjWLrVr1vwfz58znvvPOYNGkS3bt356677mrdB9iKGI5cA4OTjPxHH8W1eUurzmnt3YvEe+6vyB5dAAAgAElEQVTZ7/nHHnuMDRs2sHbtWgAWLVrE2rVr+f333ykqKmLYsGGMHTvWP/7HH3/klltu4eOPP25WhHHs2LH88MMPpKamYjKZ/AJ5+fLlXH755XTr1o25c+fy6aeftriW33//nc2bNxMdHU2XLl245ppr+OWXX3j22Wd5/vnneeaZZ/jLX/7C7bffzpgxY8jOzubMM89k8+bN9OrVi++//x6TycTSpUu55557WLRoEQBr165lzZo1WK1WevbsyS233ELHjh1bXMOxxBD6BgYGR53ly5czffp0VFUlISGBcePGsWrVKsLDw9m8eTPXXXcdX331FcnJzTuVZWRk8Nxzz5GWlsaUKVNYsmQJNTU1ZGVl0bNnT/Ly8g5472HDhpGUpDed6dq1K2eccQYA/fv355tvvgFg6dKlbNrU0DKkoqKCyspKysvLufLKK8nMzEQIgcfT4PCdOHEiERERAPTp04fdu3cbQt/AwODYcyCN/GhxoJpfSUlJOJ1O1qxZ06LQHzZsGKtXr6ZLly6cfvrpFBUV8eqrrzJkyJCg7n2w8s4Amqbx008/ERISaLu/5ZZbmDBhAh999BFZWVmMHz++xXlVVfXPdbxh2PQNDAzanLCwMCorK/2fx44dy/vv6yWSCwsL+f777xk+fDgAkZGRfPbZZ9xzzz18++23zeayWCx07NiR//znP4wcOZKMjAzmzp1LRkZGi/c6HM444wz++c+G7q71Zqny8nI6dNAbtsyfP/+I7nGsMIS+gYFBmxMTE8Po0aPp168fM2fO5NxzzyU9PZ0BAwZw6qmn8sQTT5CYmOgfn5CQwCeffMJNN93EypUrm82XkZFBQkICdrudjIwMcnJy/EI/PT0dk8nEgAEDmjlyg+W5555j9erVpKen06dPH15++WUA7rrrLu6++25Gjx6Nz9dy0/bjnYOWVj7aGKWVDQxan0MprWxw/NOmpZUNDAwMDNoPhtA3MDAwOIkwhL6BgYHBSYQh9A0MDAxOIgyhb2BgYHASYQh9AwMDg5MIQ+gbGBi0OVlZWfTr1++QrpkxYwYffPBBs+OHW375SKpvticMoW9gYHBSYAh9HUPoGxgYHBV8Ph/XXnstffv25YwzzqC2thbQSxyMHDmS9PR0zj33XEpLS5td+7///Y9evXoxZswYPvzwwxbnP9Yll0eNGsWgQYOO+5LLRsE1A4OTjB/+s42iPVWtOmdsx1AyLupxwDGZmZm8++67vPrqq1x00UUsWrSIyy+/nCuuuILnn3+ecePG8cADDzBnzhyeeeYZ/3VOp5Nrr72WZcuW0a1bNy6++OIW5zdKLgdHUJq+EGKSEGKrEGK7EGJWC+fHCiF+E0J4hRAXNDo+UAjxkxBioxBinRCi5d+WgYFBuyctLY2BAwcCMGTIELKysigvL6esrIxx48YBcOWVV/L9998HXLdlyxbS0tLo3r07Qgguv/zyFufPyMjg+++/Z/ny5UyZMoWqqqqAkssHo77kstVqbVZyOSsrC9BLLt98880MHDiQadOmBZRcvvDCC+nXrx+33347Gzdu9M9bX3LZZrP5Sy4fSw6q6QshVOAF4HQgB1glhFgspdzUaFg2MAP4a5PLa4ArpJSZQohk4FchxJdSyrJWWb1Bm+MrL0cJD0errkFx2BFCBJx37dyFJS212XGD45eDaeRtRdPSw/XmnWAI5u/LKLkcHMFo+sOB7VLKnVJKN/AecHbjAVLKLCnlOkBrcnyblDKz7ue9QAEQ1yorNzhkPHl57L5yBp69e/c7pnEBPl95OdtGjNT/DR1K/gMPBJyr/Pprdk6eTP4DD7bpug3aLxEREURFRfHDDz8A8K9//cuv9dfTq1cvdu3a5berv/vuuy3OZZRcDo5ghH4HYE+jzzl1xw4JIcRwwALsONRrDQ6dopdeomDuXKSUyLruPntuvImalSvZfuYk8v/2MJ78/IBrKpd9w9aBg8i67HJq16+noM6uqlVUAFC28ANcu3YBkPfgbHJuurnu+EIKX3yR8k8+RXq9VH79NdohaHEGJzdvvfUWM2fOJD09nbVr1/JAI+UCwGazMW/ePKZMmcKYMWPo3LnzfucySi4fnIOWVhZCXAicKaW8pu7zH4HhUspbWhg7H/hUSvlBk+NJwLfAlVLKn1u47jrgOoBOnToNOdY2r/bA5l562VU1JgZfcTEdnn+O3FtubTYu7s47iL32WgD2/Pl6qlasQLHb/YIeIPTUU4m86EL23vlXtOpq7CNHUrNqFfh8xFz/Z0rffQ+tvBwAU2Ii3vx8QidMoONLLx6Fb2oQDEZp5fZFW5dWzgEau5pTgP3bB5oghAgHPgPua0ngA0gp50kph0oph8bFta71p3b9eryFhQB4i4rY99jj+I5wi9fa1GvjrdnbQJjNAPiKiwH8Aj/sjDNIXbiQ0DqbY+FT/8C1cye+igpcO3YQOn4cHZ56yj9Pl08W0/HFFwgbP57O7yzA2rs3NT//DD4f0VdfTfxtt9HhH08Rd9ttRF54Ad663UPVN99QunAhRS+/jK+qdSNFDAwMDp9gQjZXAd2FEGlALnAJcGkwkwshLMBHwNtSyoWHvcrDxLVzF1kXXoQlLY3U994lc4y+zbP17UvE1LPwVVaiWK0Ii+VoLy2A0gXvsO/hh/2fk5+aS8SUKYc9n3vPHqTHg33kSELS0wkZkO43xdiHDiWkfz86vvwS3uJiMjPGsnNyw73C/zCJ0IwxpP7nfXylpVi7d/efs/XsSdqHi8i79z7KP/yQkEF6JEbo6NGEjh6N1DSklFi7dKXkrbfIv1/fptf+vg5b/34odjuhY8di7dLlsL+bgYHBkXFQTV9K6QVuBr4ENgP/kVJuFEI8JISYBiCEGCaEyAEuBF4RQtTHK10EjAVmCCHW1v0b2CbfpAVc27YB4N61i+LXXvcfL3zmGYrfeJNtw4az5/rr8RYWIjWNXeedz9bBQ/BVVR+tJVKxZEmAwAfYe+df2dyrN5t79ab8s88Oab7adevYO+tuACIvvID4O24nbOJEOr72GgC2RqnwppgYQgYN8n92jB5N9FVXARCSnk5oE4ca6FEUiQ/cT9Lf/07YqacGnlMUkh9+mJirr6LrV18S9xd9d1H1zTcUPfc8BY89zs7JU8j+0zW49+zBnZOL5nZTu2Ej0uul/NPPjF2BgUEb067bJRa//joFT84NOBZ7880UNfK+1xM6cSJVX3/t/9z9xxWYoqPxVVSQNf1S4u+8o5mQO1JKFixg3990gd916RLMycl4C4vYOXkyWrX+4lEjI+n2/XcoQexGatevJ+vCiwDdtt7tm2UBoW7S60WYAjd3nrw8dl/+RxSHg7RFH/jNQq2F9HjY0j896PHW7t2IOPtsoq+6CqGqrbqW/SGlBCkRSvtNUDds+u0Lo11iCxTPn0/Bk3Mxp6SgRkfrB00mYv98HYmzH0SNjMQUF4cpPh4gQOAD5P7lNqq++46CJ+fi3rGDnBtvQnO5Wm19UkoK//E01h496PLZp1hSUhCKgjkhnm5fL6Xzgn/Tcd4r+MrKmr24AKSmkf/QQ+z4w2R2TPoD3tJSnBs2+M/HXHNNs9jmpgIfwJyURLevl9Jl8cetLvBB9y10XbqUrv/7gt5bNtNr4wZirvnTfse7MrdTMPcpCp99Ds3pJPPUU9ncqzeunTuRrRjfXLN6tT+qKfuPV5A1fXqrzW1gcDzTLsswePYVUPDY4wCETzqT2BtvxFtSirlDMkIIoi65hKhLLgFA+nxIt5vMjLFoVVV0mv8m7qzd5M+erUeoNGLroMGEDB6EpVNnTAnxRJ57LpZOnQ5rjb7iYrTqaiIvuABr164B59TISOxDhuj28Z49Kf3XvzAnJRF91QyEEHjy88mafinevDz/NZmnjNJ/MJno8vF/m815LLGkNET4ClUl5s9/xpOXj33oEApfeFH3CUw8FSXETuGzz+LcsIHiefNAEXj36t9x5+QpYDYTMWUKSY8+glAUfFVVCCFQHA4Aqn/6iaIXXyL2hutxjBoVsAZPXh5ZF12MGh2Nt6QYX2EREeecQ/Jjf6embme5Y/IUQsePJ+GumUfluTi3biP31luJvOhCYv60/xehgUFr0i7NO7l33EnF558D0O377zDXafMHonb9eqq++57YG64HRSF7xlXUrFzpPx8+dSrVK1bgKylpdq0aHU2XTxZjiokJeo27Z1xFzc8/0/HVeYTWxRG3hLekhD3XXodz40YiL7mYsFNPpea33yh++RV9gNmMY9hQqn/8CQDHqFPo9MYbQa/jWCN9vmZmHOl2s2PqVDy7swFwjBtL9XcNqflqZCRdly4h6+JLcGdn0+v3tSAE2ydO9L8kEh+aQ8TUqSghITi3bWPXtIB8Qj/WHj38vp96Or31Fo4Rw1vza7ZIyTvvsO+hvwHQ8/e1KI0yN1ub9mjemTFjBmeddRYXXHABzzzzDNdddx12ux2A0NBQqo7QP7R27Vr27t3L5MmTW2O5rYph3mmClHpisGK3ByXwAUL69yfu5psQqooQgpQX/kmXLz6n59o1pH38Xzo8+QQ9flxB9NVXAxB7003+a30lJWSOHkPW9EvR3O6g7lfzsx69ah88+IDjTNHRpC78D+HTplL23vvsue7PFL/8Crb+/en09lv0Xr+OTm+8QcqLLxJ7880kzpkT1P2PF1qy2wuLhY4vvYQwm7GkppLyzDPY+vTBPnIk4VOn4isrI/OUUbh37ACPhx1/+AP5c+bg3ZuHqS7kN/+BB9k6aDAVS5ZQWpfBaenalW7ffkPS3/9O8uOPAQ3O/qS//52Ya69FjYwk56abcG3f3ubf3ZvXkBxX9OJLeAsLKVu0CK2mps3v3d545plnqGnl57Z27Vo+r1Me2xVSyuPq35AhQ+SRsOeWW+Wmnr3kpp695NZTRh3RXC2h+XzSW1EhpZTSvXevrFiyRJa8+67/ngXPPnvg6zVNegoK5KaevWTOX2cGf1+vV+bc+Vf/fSq///6IvseJgGtPjvRWVjU7XvLe+/7n0PRf5bffyuK3/yXz5syR2zLGyi1Dh8nMUyfKTT17SXd+fsA81atWycJX5klXVpb/mDs3V24ZPETuvupq6XM62+y7Nf4O2dffEPAdcu+9V1b/+pvUNK3V7rdp06ZWm+twePzxx+Wzdf83brvtNjlhwgQppZRLly6Vl112mZRSyi+//FKOHDlSDho0SF5wwQWysrJSSinlnDlz5NChQ2Xfvn3ltdde638uV155pVy4cKF89tlnpdlslv369ZPjx4+XUkrpcDjkPffcI9PT0+WIESNkfpPfvZRS9uvXT5aWlkpN02R0dLR86623pJRSXn755fLLL7+UHTt2lLGxsXLAgAHyvffeC7j2zTfflGeffbY866yzZGpqqnz++eflU089JQcOHChHjBghi4uLpZRSbt++XZ555ply8ODBcsyYMXLz5s1SSikXL14shw8fLgcOHCgnTpzoX9+DDz4or7rqKjlu3DiZlpbmf2ZNaen3CayWQcjYdmfeqc9EBYi6dDqJTVK625KcW/9C5ZIldHr9tWY25Xoqly0j50Z9l5A4+0G/byFYpKah1dSihjqOeL0nKrLOCe7ato3Ym2+i8Jlnqf7xRxLuvpvoK/7oH+fOzib72mvx7M7GkpZG1y+C09ryH3qI0nfexdy5E13/9782KSa35/obqPr2W2KuvZaoyy5l+/gJAFhSU3HXVXRUo6Lo/uOKVrl/Y3PAN/PnUbB75xHP2Zj4zl2YMOO6/Z7/+eefeeqpp1i4cCEZGRm4XC5WrFjBo48+SmJiIueffz7nnXceX3zxBQ6Hg8cffxyXy8UDDzxASUkJ0XXBGH/84x+56KKLmDp1aoB5JzU1ldWrVxMbGwvoocWLFy9m6tSp3HXXXYSHh3PfffcFrOn6669n6tSpdO7cmauuuoqBAwfy6quv0r17d9asWcMHH3zA6tWrA2rt1DN//nwefvhh1qxZg9PppFu3bjz++ONcf/313H777XTu3JnbbruNiRMn8vLLL9O9e3dWrlzJ3XffzbJlyygtLSUyMhIhBK+99hqbN2/mqaeeYvbs2Xz11Vd88803VFZW0rNnT/Lz8zE3CbI4EvNOu3LkSi2g3hsJ99xzVO+fOGc2lV99RfbVf8IxejQpL/wTX0kJ5uRkALylpX6BH3H22YRPmnTI9xCKclILfND/Q8ffeYf/c6fXX9M1mCbC0dKpE53feovt4ycQce65Qc8fc801lL7zLp7d2RTPe5Ww00/H2iVtv+O9paV4cnKoWf0r4ZMnU/j8cyTMnIkaEREwrua333Bu2kzkBefj3LwZR0YGcbf9BaGqdPt6Ke49e7B26+ZPIvSVlpJ39z1EXnA+IYMG+c0+nj17MCUmothsKHU27OMZqWkMHjSIX3/9lcrKSqxWK4MHD2b16tX88MMPPPfcc/z8889s2rSJ0aNHA+B2uznllFMA+Oabb3jiiSeoqamhpKSEvn37MnXq1APe02Kx+FsqDhkyhCVLljQbU1+KuXPnztxwww3MmzeP3NxcoqOjCQ0NPej3mjBhAmFhYYSFhREREeFfU//+/Vm3bh1VVVX8+OOPXHjhhf5rXHURgDk5OVx88cXk5eXhdrtJS2v4+5oyZQpWqxWr1Up8fDz79u0jJSXloOsJlnYl9LVGjpuoS6e3GKLYlpiiokh88AHy5zxE9YoVbB2oJz4l3Hsvhc8+i3247hx0jBrltykbtA7704bNiYn0WPWLP8InGMzJyfRav45tp4yi8OmnKXz6aSzdutJl8eJmsfze4mIyR4/xfy54XI8aE6qJpDmzA8bm3nEn3vx8fzJe8mN/9/s0zB06YK6r0pj6/nsoYWHk/uU2yv/7X8r/+9/9rrXrl//DcoACZC1xII28tZFS4t69G626ms4pKbz55puMGjWK9PR0li1bxo4dO+jduzc7duzg9NNPb1ZB0+l0cuONN7J69Wo6duzI7NmzcTqdB72v2Wz2/03sr5zx2LFjeeGFF8jOzuaRRx7ho48+4oMPPvAXaDsYByvFrGkakZGR/kqcjbnlllu44447mDZtGt9++y2zZ89ucd62KMXcrhy5vrqiX4mzHyTh3nuPyRqipk+n17rfA47te+QRtKoqqpYtA6DDM4dX4c/g8FDDwg458UqYzXR4+h9Y6kJf3dt3UPTPf1Iwdy4Fc+dSu3Yt7t27AwR+Y8ref5/yTwOzqU2N6kpF/+lqHHWabFNCBgzA2qULaR8uOug6c++4Ey0IIXis0Kqq/ImGowcMYO6TT5IxZgwZGRm88uKL9O/WDaRk+MCBrFixgu11DvSamhq2bdvmF/CxsbFUVVW12CgdDq9scseOHSkqKiIzM5MuXbowZsyYVi3FHB4eTlpaGgsX6hVopJT8/rsuGxqXYn7rrbcO+x6HQzsT+nplSFNs7FHL5mwJYbGQ8oJuB4y98UZ9TXFxKOHhCKsVNTz8mK3NIHhCMzLo+tmndPvuO8JOP52iF1+i+LXXKX7tdbIumc6OM3XznLlzJ7p8sliP8nr2WVLffw9zSgp7//pX9txwI1uHDCXn9ttxrl+PGhFBp7ffImHmwXMBhNlM1/99QeLsB+mx6hd6/LKSLp9+QsL999Hz97V0eO5ZnBs3snXgIFw7js+K5dKpmzPMKSmMGjCAvPx8BkVGEhsaitVsZvTAgTg3bSK8vJxX585l+vTppKenM3LkSLZs2UJkZCTXXnst/fv355xzzmHYsGEt3ue6667jD3/4AxMmTDik9Y0YMYIePfSmMhkZGeTm5jJmjP4inzBhAps2bWLgwIG8//77h/X9FyxYwOuvv86AAQPo27cvH3/8MQCzZ8/mwgsvJCMjw++HOFq0K0du9c8ryZ4xg07z5+MYOaKVV3b4ePLzEWYzalgYvooKTEf5l2xw5DQuJ+HIyKD6p5/A60VYLPT8dXWzbGZvURE7p53dLK8jdNw4Or7ycqutK+/++ylb+AFqbCxdP/8MxeFoUeFpjTh9X2UlaFozX0VLSCnxFhT4K9za+vZFq67Gk7sX6WkIa1ZstoCdirVHj6BKjrQXpJRoVVUodjvewkLUyEgUm+2g1xmO3Dq0at2mrwThhDmamBMT/T8bAv/ERJjNdHrzDUre/hcpzz93UH+RKTaWtI8+pOrb7wgZNBD39u0Uz3+LpIf/1qrrSpw9m/CpU8m+4kq21ZmaEmbOJGLaVNTIyBav8VVW4t23D3Nyst8R7C0sRNhCUMNa/r/jq6zEXdfnwmo2IywWtOpqfffaxJ/iLS3Fk5sbcEwIgRoaitKjO1plJe7sbFAULGlpSLcb6fHgzs7GtW0blrQ0tOpqTNHR+33O0ufT6yUdZb/dkeCrqkKrqUGx21FDQ5FeL+6cnABfpFZVhbVbtzZdx4nzxIKg/uHt7w/XwOBIcJxyyn7t8C1hTkgg6mK9AJ6tRw/C2yCzU6gqjuHDCZ82lYrFnwCw79FHKX79dTo88zSWzp0DhH9jgezevRslPBwlJATPvn0AWLt1C9A0pdeL9HoDEsZcOwPDPdXwcJTwcNSICIQQ/p4KAKb4+ABzphACNTxcL9ktpZ4MGRICISGYk5Lw5OXhruvO5isqwpKa2ixCSfp8eHJz8VVUYO7QAVNUlP+4r6JCX8dRKp7XNGpMczrRnE7/s/Afd7v9obigJ44KVQ0Q+ECb1L9qSrsS+r7K41PTNzBoaxLvvRdLSgpqZCRlH36Ea8sWdk/X215Yu3fHd8/dSE1r0MCF0IVkaSm+0lL/PK4dO7B26YISEoK3rAxPTg4Aii0EoSiYkpP9x+rxVVTgq6jAm78PS6eOSE3DFBOLKS52v5p4SyUnTDExCLNZ3wWgh3q6d2dj7dHdb7Jquovw5OYiLBZ8xcVIjwetthatqgpLx47N5m9tvCUlePbuxRQTgykxUa+LtXcvWk0N3r15mDt2RA0LRXM6/Rne9S+2+peosFixdOqIr7RUf3EeQpTZ4dKuHLn1b01D6BucbKgREcTdeivRV1xBl/9+RKf5b/rPuTIz0Wpq/OYZAEunzphTUgJMj9auXRGKgic/X9emGwl3zVkLJhOmyEhsPXvqL4DowFpT0uvRdwFSImzWwzK9qOHh2Hr2xNanD5ZOnZA+L87Nm9FcLt1P0GgXUa8tu3ftwldR4e/L7Csvx7M36OZ+h019tKC3uBhPTk7AjkhqPjx7stHcbrxFRfp6w8JQo6Kw9e7tfzaWlA4oNhvmpKSjIvChnWn6WnWVvj0ym8nLyyMpKelYL+mEZ3vpdhIcCXz80wJG9BhH96QG55Hb6+blhXO4YNL1JEe1vWZlEDyOkSPpvWUzWm0tRS++SD74QyfNCYkBJlDF7kCrqUYJCcEUF4cnPx9PnXA1xcaihIbiyc72h5wKsxlbnz76+cQEqOuYppWV+c1EwTgj90e9iUMND0dYLEi3G1dmJmpkJNLnQ42MRKus1HcGNhuePXt0k0pkpL7WnBy8JSWoMTFtVsROc7l0n0ZoKEJV8ZWX+0t/q5GRqOHhuPfsCSjmZ22UT2Ht2RPpch3Rczpc2pXQ91VVoYSGsnz5cpYtW8a1117rj4Wtp6KigvCTIGTSp/nQ0DArDTbCbXkbef6F2/nz9Y/TL2VQs2uklGwq2URKaAoR1gj2FmaxYObNhNbqfyZvh3/E/S9/gkW1sHDpPNa+9wHRlRZeWnoNM//5H+xWByalXf1JnfAoISHE33knRRs2oEZFISwW1NhADV2xh6DYQwC9Yqy3oMBv8lHCw1HtdpTevVtMgBOKAoqCAJS4ONSYGLTaWpSQkFZZvyUtDV9REd7iYnxlZYBehFBplKFqqWu/WW/HVx0OnNsy68xAAjUsDDUqUs/Y93rxlZfjLS7W/QUOxyE30JGahiszE9CDNITVqmv59S/VpCSEquqmnLodR+MdFei+DXEMBD60I6Hv8XjYUVOLIzaWXVl6zPLOnEzmvnIN6SPGccWkmSz497/ZuWsXp5xyCmeeeeYxXnHbcv+TF1JYU8acv/6btft+4/QuZ/LqnL+QWmrj47vv4eWOHs688DrOSD8bq6prQ68svIeCz9ZQFeIlacJg8petIb62QVOKrDAz++Hp9B13BtsWfkh0pR5aF1ll5tUZl1ES6SFt8qns+HIZ/SZP5rKz/nJMvrtBc4SqYmmiALU4rpHdXlisfk002Po/QlFa1UyhmM0oSUmY4uPx5OSguVzNhGVTgS3MZkwxMXiL9HBRraYaz758muLOyvLvJMyJSSihjqA0b60uYUuxN4w3JyTg2rkTYTb7/Q9qVBTS5UaE2DDtJ5LqWNBu4vSrq6t58sknSdu+icwO8ZhCYvE6dxK1S4+THnHbnSz98jv/+N69e9O9UdNvgJiYGDofYkr78cpTF58V8Dmrn4/UDc3jt3O7enn8b59gVs3MvuUMQopMoGqYPA36wLjexfQ/fQYvLvg3WnFDjLZP9THjtBj+9etufKVhSMDk0/8Duk0akx5+kGpnFSN7jjN2AMeYQ43Tb6m15omElBJfWRnCZMKzJwep+QLOm+Li/DkEfoTA2r17szwBd24uit3ujxLyFBTgLSjA1qdPwAtH+nz6rqcNCvQ1xYjTB5S62uS5ERHY3AJvCIQW6+ec8Sl+ga9WlaPZQ9i8eTObN28OmMNkMvF/s/4Ps6ntw6Ya83v2r3zyzdvk+vZhrtH462VP0Ckq9bDn82k+fEKiyoY/vnqBb0mo4uIzJrPyi/fYUppAhx0m/vavGzApdsIKLLiT3Ew/fSKL3v4BgFtuPAPLOL3B+e0d+vLLv+7gm82xmHwKSYPCiL/639w27L9oWSvIz89iwXeVCEVicZlZNkuPSf9uxDvEd+6GLTyMicPPJTni4BqnwbGlLQR+VlYWkyZNYsyYMfz8888MGDCAq666igcffJCCggIWLFgAwG233UZtbS0hIYbpFcgAACAASURBVCG8+eab9OzZk3/84x9s2LCBN954g/Xr1zN9+nR++eUXf9OUZusXwi+kld69dHOVpqE4HGguF2pEhG7a0TQUux1PQQG+khI8u3ejxsaiRkSg1dbiKy3DV6ZHOPnKy3UtXtN0jb7pDuMYVgE4FNqNpg/w8OwHESUFSEXFG6knQdl3bqSmS18AzMX5VFjWE5cTjWwk2L2hKtGDRpCXX4Utdyf9Ljmbnp0amnnvK8khIbp5lTtFUUhMTMTU5D+IxWIJ+m2/bvevvP/3e4gubVhPYZwHb3oCzupq/nTe/zGoc/BdnKrcVTz/2p1YvsvFllzOtC7JhHbqzevv/YLQFJLGxHPpLW+AlHh2rOC5ewMLv6UNDeO8me/i/e1d3JUl2MfdFHgDn4fcBX9i0apMZlx5C+FDL204p/lw/ecazPE9Wf/9Ar4uDEcWRTVbY1n3EC760yxKKgro33UYP/6+hFOHTuPfHz/NlIlX0CHm8FpQGuyfxpph2Sc7cO+tbtX5LckOIqceuEVnVlYW3bp1Y82aNfTt25dhw4YxYMAAXn/9dRYvXsybb77J22+/jd1ux2QysXTpUl566SUWLVqEpmmMHz+e22+/nUceeYRnn33WX5GztWgpqWx/KCEhx7QlqaHp16FaFHyqCRq9gesFfpzvWxLC9/IHcwEPJ/UnNq/BTGEpg/KffoG0Pjg7dGH1D+tZzfrDXkdaWhqnnHIKCQkJRBwgZf2rtR/z3UsvEl1mxhFeARI8XjNxhSHwtW6W+mDnA/R/9tOgzCOb9/w/e+cdHkdx/vHPXNfpTr1LVnOTe+9gDAYbU4wJBkNCDzWBQEhCSUhCCAkBEtIg/CD0EiCYEoOpBoxtcO9dlixZVrO6Trp+t/P7485nyZJt2chYtubzPHp0uzu7N7O7952Z931nZhMvP/gzkhwhO/ycWZeQOeNOAH6U+jLvvPYPzjvthlBiITD2O43+A1rYVWgH4PyhTQy8/h8AGEZfgQEoKytjT5tQPwCyr2WUeQsbXZmwdGn7Y5mhlcWYNo5xZSvwFy1mRV17k1lUA7z/2FMAfE1oUrFNr4UWpv/31w+gS7YzcdJ52K0xWGwWLAYLuk4cbVarlT7dEI+9v+HzXXTLezt5eXkMGzYMgCFDhjB9+nSEEAwbNozS0lKam5u55ppr2LVrVyjuPbx4vU6n48UXX2T48OHcfPPN3S74EJolV2c2E2hqQmtqjpiEdFFWTLk5BJub0ZzOUKjmSdKq74xTSvTBT9BqRxrb2+R0OsENyRJz9iwYcQV/eH4mdfF69mHCRej/rm1DsO7egjfWh9kRhd+g4UswEV1zYFpTw+hs0vv0IyM9j1h7AmZ9NI1tBrYArF27lpKSEkpKSsjOzub68PKKByOlZNmT/yLeYSA3o4xL7vkPxGSAq4Glj5zHuvpMZFBPQo2FR5+9k1/e1H4hh6AW5KFnb6OybBd6g5EH7nqB5YteiQi+Jb2GzLNvj6S3TryaK4fOguj200DMvuNZmv86mXJdGgX3rSEoDDTV10eOv/feezR0si4wANu/PMRzaHvzp0DXVqyMoAXgm6XLu5T2jjvuID6+Y2+iM5x+J2a9Gb3Q8/HG95g6eAbRpmh+9egPcOPlL3e/hU6cUkNXOuVILfLjyZGmI/71r3/NmWeeybvvvktpaSnTpk2LpN+1axc2m43K4xiDr7NaMVmtEF4Doy2GhARISCAYH484jusZH2+6JPpCiHOBvwN64Fkp5Z8OOj4V+BswHLhcSjm/zbFrgP1L1jwkpTwu84jWt9TS0urAZErocKxPn2zM1x1w4uqufIeUre+QojNC0Aeb32L+wJ1sqs3iuowK3HFOPigaiaXV3e468pvtVLKd/a9ca4qB6Nx0EtKzuPby+9DpdIwbN46amhqWL1/Ozp07+fzzz4mJiWHEiBGY2jiI9jVWEK0l4RuYiCv7fF75bD2wPnRw8N0keFuQWoCSPeXoCnU88Id7yeszCJ3Q0ep2ULW7EGPAQhahVtPTDz4SLmx/okxebKkTeeW1/3Tt5hU8DsC61/9LZWUlbnf7ck+fPj2yoMUx0VITUvLYDJCSwNLHeevD1+lncrCkOZM+0c2cbdxHUAjeasmjtTUakzOKVrseq699BR7QaSROH8vkEefz9ttv8/J/XiIhJhSCWFVXhqOulpikZNKT2puInJ4WKosLkUKi00IO5+X2rxnQdyRRtalEAb/95Z2IKDP984cj6LzVHx8fz8yZMzuY9I6FssZSnnzzAQYPHMc1Z/74yCf0AtpOOfziiy+223/HHXewZMkSbrvtNubPn8/cuXNPSB71J/ngzyO+uUIIPfAkcA5QDqwWQiyQUm5rk6wMuBb4+UHnJgC/BcYCElgbPrd987gb0BqqsbolgTYa8T3jEpbFz41MlRqh3/TQ337m/Iu5/57OXN0amPc/8HvY/dLt7HAnMDCqkR2uBGbH7GGDP56i8mwsvtBts9UEoGYvjezlocWziRs+gNt+9GcyMzMZNmwYO3bsYGnY/LFw4UKi2sQuO50OdH1C0UMeaYSD50TX20APybZK6lwCnSNA0c5NSKMOAkFM0ogM9zCFkMiw01bqNWKzBuIzWjteswskJyfTt29f4sIhZjqdjoEDB347kYtv32oynP4Trgo6IWsck5b8GfrPg8GzQej4yeKHYfv7/LWxAHt1MhBgSP/tbN0Vsl/qgdb3l7HE3wpOB83uVsz5ZiTgqW7EhAFPdSOFTU1ExcSSHJNKU0s97n11GDCE3kJA6sHggt2bN0S66kZND05JaXkRaYkdfTher5eioiK2bdvG6NGjI+M92pqFDvXZZrORk5PTrqX7+QfPkLDCw55t6/k48BFmw7HFbZtMJvLz8zGbzcTHx5/UZqq7776ba665hscff5yzzjorsv+nP/0pP/rRjxgwYADPPfccZ555JlOnTiUl5Si7kYojO3KFEJOAB6SUM8Pb9wFIKR/uJO2LwAf7W/pCiCuAaVLKm8PbTwOLpZSvH3zufo7VkSs9Lfzx7rvxJ6RG9j1w88WQPqJrF2itgT1fQ8GFoDfA/26DLW+DLQVaquGs+6FoEXuLl7PeH8ds0z7e86axqS4L4ddjdYdj1i8Yzw+vOrAur5SSbdu2UdpmsiWADZ8sBCSXXTyV/mdeceh8uRqQr/+AFzbW0rjvwACPQFwrl8UXkRdvhNwpbFm7jNpAFMMGDSLpxne6VuaeiqYReGEWT2xqIs3s5PIZc/n8q5fxBwwM1Tfz4r6hRDk6H/zjs7sxtRw45hgVh9HlI2qni2CMk5tTN1PsiUUIyRdloVGlQb3GpD6F1Pus7GxKQe8yk3b1DK48v+M4g3Xr1vHFF1/QetBEWV1hv+MfQJMa1VXFQPcOvb/ssssYHB4t25bumFpZ0XM43o7cTGBvm+1yoKuT1Xd27nGJ1xMWOyLYPha3y4IPIXEf0mYd1Qv/ARf8FfRG8HvAaIHJt9Nn01v0WfEkXPgWc+b/kDnmUAXVJPX8vXwM2sKVLCn4gKnjQnHyQgiGDBnCkCEhh/I+RxVPvfob7PuqMCY10H/KXw6fL2sC4voPuf7j+1j85dusKeuPkDrmDrWQN/dDSA9FGQ2duBz2bYGC87te5p6KTodh3qvc2TAZMkbBWb9hursJjFFgT+Mnnz7I0y0T0cmOLdpL+rcSaNyFX+r4rDmHmLDFzGsKcPcYHYardjK6fDXoDGS+9AMaA2byzS6Ml78IjSX4Vj7PYxuT2ffyZzzlbOLWy37b7vqjR49m9OjReL1efL7QvPAHN5zabkspkVJSXFxMYWEhWngd59LKnehb/SCbMHuL0LcJGa9K9eHLsTFr5jXMGnrREW9XRUUFDQ0NvP/++3zwwQd8+eWXxMbGkpOT0+Wl/xS9h66Ifmd9xa7GeXbpXCHETcBNANnZxx6uZzI68BEyJcybN++YrwOEI4DCTj1jm2738EtDfwC3fg0eB7jqiXv3Zq6XG/nvzvGs/vP/sTjvZe7/4xsECUamQvAH/Tx271Wk14euN2BgLpi6sLC1EHDuw0yLimPa4nAH63tfRAQfgJxJob9TBVsy/GwHCF2o/Bf+LXIops9EfvH8DIJSoEPS5LewyJPGGEst+Ve8C63V4Kxj2KpneWFXDU1VKRDtxXDNuxAVBwNDK16l3P4FKXWFkHcGWEO+INPIHzD0D+dQVJSO6+3VLC74kJEDJhNnaT+icv/C1fsJaAHq3fUkW5PZULWOUelj2plZxo4dy4hRI3AH3cSYYvjtHecRU60jLbWKi8YN5KkvatF5zJhiHaTvi4F9Prat+jeW30VxZsGMdt99cLRRZmYmmZmZuN1uSktLCQQCFBYWUlRUxKhRo7q0yLei93DKmHcAdj52Lq87J5Kamsqtt956TNf4Vnz1KK999BLVJaGV7RttPqSAKTfdyJJv/kd6Wi6Bd0OLJM/I3srQK36PGH3ld5/PU4HytSFz3MDzYOeHsH0BTLkTBrUZiexqQL5xFfO3ljE+PZqcn6/o2rW/+APrP3mWL0qHRHY1DrXx+/tf72Avl1LywL9uxLWlFKtHT3O0nz61VpKuPYdrZrU3D/3hmR9Tu3k71gkDiV24B0O8g7suuxAx7gYCfxuF2+XG0ncCf13qQe8KNQyCQlIX58WbH4Ozth67x0DQIHAZfVjj43nozpeJieoYFlxUVMSrr75KRkYGUVFRDBo0iL59+2K32zsNfz0YIUS3+QY0Gerd9IbIqO+Kb2Pe6YroG4BCYDpQAawGvi+l3NpJ2hdpL/oJwFpgdDjJOmCMlPIQMYDHLvouh49XH/qKSuvXzJs378TYL4MB+H0ibk3wr52dL5gNcHbeFkZccCdMuKV9L0LR/Xhb4MUL4PSfhZzFXcHTDM/N5NUSN9V7syKRPMGpuejNJqLSkjhzzIXERSXwn5tv7vQSPoPGlF/eydQhB1rpf7zqPMy+kPAF9RpXnx1P2vWhUai4m0K+o5gMtIf7IIBPWzPZUp5/2Kw2jYjh1/e8iEnfPsrJ6/Xyzjvv4Ha70TSN4cOHk5XV0Tl9KIQQ2Gy2bhH+Rmc9WjCI1WonynDA3yKlRCKPW2UghCAqKqpLldzJxnEV/fDFziMUkqkHnpdS/kEI8SCwRkq5QAgxDngXiAc8QLWUckj43OuBX4Yv9Qcp5Qsdv+EAxyr6fm+QZ+74igHjUzn7usEnLoKhZgd8ej/vb9lCYUk/9AlNBBvamwZ+evUgdOc/dmLyp+gaUsK/JiFrtrPVF8sHtf0wthzaFBed0IAW1ONqtWGOduJzxOAyB6gfG4uxxIG9bx8C6/YQ6zSCycclGdvI/elHkDas48UclSH/xQd38eGqFWytyCU/uQoJtHijqHPbiIty4XBbwWOmJcrPJQ8+zLDs0R2vFWb79u3k5+dHBjsdjkAggKvNSlknM3q9PmKGE0J0uadzInA6nUgpu2SOO+6i/11yzNE7UvKvW0ODhSZd3JfRM0/wxGlv/AB2fADATq8ds04jXvioCkZR8LudXbPlK04sjaWw+S346lGCAR9/2xFyigaNAfRtJqS7ZeDXROu0dqdu88aysGRYB2ezJ76VX6Wth4uehFFHMO1JCU9NhpptYE0EV32Hw49Vj0HfZMVhD3LTY8+QGpve6ejto43e2e9w/jZIJLW1ZeAMoBMamtShCZBCok+wQV37qSA80ZCd3H0DxxobG9tVcpqmYbfb2/liDsZoNB63BuN+p35naJpGTU1Nuwivw6GmYaB9TPTWpRUnXvQvfQl2fQLxeQx8/yeQNABsqcTWbFeCf7IQnwtTfwFTf4F+2wIu+s8tLHWncK2tBCHAr+kISB1RF/4Zxl4PQT+4G8ESw+B3b8YiPmelK4W+hhYKvXHsq0njohw/3LYT7Ef+YSME3LAIGkogbWhon7clVBmlDUPUFXHnP8axJDqdjRX5PHn3D2my+8k6azJXT72VrJjOzTlBLUijt5E4c1ykgvAEPBh0hsj2wa1hf9DP7rpdGIWBnKS+6HV6NKl1aprxB/04fA6aGmuIDq/FkJSWQkttFe6AERBQ23HuH0srlJlKyY7LJagFMegMHQT4vffeY8CAAZ2GpR5MYuKBdQOklFRXV9PS0kJLeGrkzjCZTNjtdgwGA/punmrB4XDgdB5+ziNj9PGX5FNG9NviqPMQ9GvojSewG6c3HAifvGHRicuHonsYPJt+l3not+oZuOBV2PwWxi1vY8yfFhJ8IcBgAnt4nMglz5Gv/xH5W98BLcB4Wy0k7oKxd3ZN8Pdjij4g+ABm+wGTUFI/THOf4ex3bmC7P4bEmiQSHSZ4ZSP/XHgd039yJ6f1O7OdHd3pd1JdW0a0x0ClrRmbPR6LwUxtVTlBgyQ7rV+7nsJ+u3ujo47Y1lAU2u5gITG2OJyOZiwxMViNVqxGa8SvUF1XhrFVI7qNvOis8cSmm7E3V9Dq8uEKj6JMjtHhcbvRpMAZMGFpCFLk3YkpIDDYrKTFZLTLz3vvvccFF1xAUp9EPH43yTGpkfJJKfFpvsj6EG0RQpCUlETw4LDuNrhcLjweD/XhaUjMZnO3tvo9Xg+a0NCZdNhMNgJagCZ3Iya/DikkhqDA2dhCoj3pyBf7Fpwy5h2A1367gqZ9IVvkvPvHk5SlQtUUJxgpQxWCqyHUQs8YFdruTnxO/C9cwCc7amnxRVFRlxJxPjfYfZQMldx+2gNMGDOJveW7Is7k/QT1En0wlN5r1khNy8FisIQqiIZydEEwoUPv7vDNEbxGDRFrIc2WTv2eUkTYrGU3ejEZBMaMITidTi677DLK9+4l6HPzyztu5pePPMG8Sy/ly8Vfovm9/PMvfyUvN4e9FRXcde991DU1kpGeyQsvvEB5eTkXXHAB0TYrMTY7zz3xBBkDskhJy6HJ3UgwGCDQ4sISG0NqzLEtlRoIBPD5fHg8HgKBwGHTSiSa1NCLAz2CoAyiSS3US2kTsR7QAshgEJ3fD1oQZ1QAIYn0hPYjhCQ1f8AR86nMO2Euv3889ZWtvPXwGlYu2M15tw47qYekK04B9r9/1oTIWIBuxxSN8doFXLDoAVj7EiQX8pUjnTUV/UhoMZGwHHSjA+yu2onNZ+Dr1Wuob2oAKdoNmhGEBtFIncBssuD1uDsMtDEKjYDUdRhskxQfz5RxY6ltLEUvBXqTnyS9F5HYPxKh9vHHH5ORkcHChQtBC9Lc3MQvH3mCmLg4Vq1ew8vPPc1DDz/I8888y/2/+x2XXjyHy773PZ5f8CY/vv3HLHhvAdOnT2PmGWdywazQWAu8UFO5B5Nfhx7Qo0Ora6WaStJiOk6adiQMBgMGg+GQ8/S3pbq5Ek+Lg6BFR1ZcNhaDhdKKnRj9Aoc5iDXaToY9k2ZvM/7qJvRBgV4fJCj12FwHpDfa4MMdNBBlCGA7zKy83UXPdGMfI3qjjsSMUOu+dFMdtWWHtt0pFKcUZjuc/xf4TR3cX8MZs6/jytx1fD9vHaaoUO93/1rHCDDqBEadhkkXRCckAjDqNISQCE3i8R8Q/P1VgxAgTFEYdRoGoWEUGnoh0SEjwzD39xgslmhExigw2yBsnhk2bBiLFi3innvuYenX3xAbH7K5X3FFaBqSK66+nhXrt5JqcbJ2w0Zuuiw0P9ZVs77H10uXUVpfTFt/ucHox2QIYPIfkDEhJEKCrGvF5XMd0nH6bQlqQTwOB1FePbZmQXltCfXueiweHfqgwO4yIBtcVLZWUt9QFbkv8YlJJFh8kevE2/XYswaSkpqAPb0vIrbrYbXHyinV0gfa2fGbalxYY0zY4lUsvKIXYTDDtHtILTgftrzN7aueYYshJDR6fZA5pw1Cn1oAiJD5qbEkNAtq6lACdcXUOQ4IZbTZQ7TJiMcTwGIxoksZGDqntSYUVuppCo0xkEGC0olP0+PWDERHJ3fI1oABA1i7di0ffvgh9913HzNmhMYwdJikLn0kCB361EHE1u6mzq9HIIhqDuXLYvQRZ/JgjI5Br/kiPgGzPohA4gyacPpN7KspQ9OD3mwixZ6GQWcgoAVo8TpocNfTJzaHaGM0QRk8quU8pZQUN+wixqcPVTKAzWWgNVCHGV24Ag1CUI9W5yRaC107NcWOsCWCLZGUxj0EPa0YE/qH/H+d3K/jxSkn+gCz7xjJgr9v4LPnQhOBjr8wj3Hn553gXCkU3zFpQ0N/03+Dfusm0mz+0FxSsQXQdkbP5AKQGgiBISEHq2cXLl/IaWtNyEZntmJtqT4gTEIccFhbYiAuNHWK3uckyt1IlN8VauEfRGVlJQkJCVx55ZXYbLbI1Mlvvvkm9957L2+++WZoCm8hmDx5Mm+89Q5XfW8Wi+Y/yZTxowCwRUfjlRYscUlgiQeDGYuzNhQ5ZbaDwYy9dR/eZidR3rC8uYLUt+7FZ9DQBwXGoI74oIlabzmVBg1DUIclNga7yY7dZD/ibW31thDTHLLjx9kMmKx2amsbIr6SpGQ7emsCLZXFOMOhvVaDH9HGga9LyEW339/zHXNKir49oX3LftX7JYyZlYtO17vt+wF/kG3LKhk8JQOD6cjhaMGgxpLXCzFHGagobGTQlAyGTg3Nl+d1B2iodPLVf3YyakY2AyccRUSK4rtFCNCbIPUQcfpCwH5npN6IPaUPUXWlGAw6hMUGOj3EdWGFMlN06O8QbN68mV/84hfodDqMRiNPPfUUc+fOxev1MmHCBDRN4/XXQzO0/OMf/+D666/nscceIzk5mRee+hupsXDtpbO4+Z6HeOLpZ5k/fz59+/YNTZbYlrhsEijH0ewAwBMwYvTrMPrbW7OjvHqivOFye500GlrQpWURfVAZyh3lRBmjSIwKmaNcraHrmvQBzEn5oNMT73FS3+xHr9PQ21JAp8OWlg1VpRh0GlEpnTQ6T5C/8ZSK3tmPy+HjhbuXtds39fIBDD0jk6Bf65Lgneys+3QPPneA0TNzcLf4iUmy8Paja9lX4sBk0ZM/OoVRZ2cTn26NdK9LN9ex+LWdxCRZGDMrl/Wf7KGisKnddc++dhA5Q5NY/J+dFK+riewfekYmQicYcVYWW5ZUMmhSOgkZ3TttsOLY6alTK+fm5rJmzRqSkro5TFHK0JiGoA/pqKLVp8Mn9SBDdv9YoxeH34xGyDm9P4TUYw6ii7ESb4mnydOEw+cgwWEiqJM4ov0YMWDx6TB6ITXBgthfGQb9yIbdCGNUpOcTuqAjFMp7jGslHAoVvXMQ5vAAh1Ezshk9I4fnfr6UJW8UsuSNQgB++OfTsdiMh7vESc/yd4oBWPtRaH3bzIHx7CsJtVACPo0d31Sx45sq0vJjmXPXKPQGHRs/34vPHaC51s0H/9wYudbQqZkMmJDGV//ZyaIXt7f7ntPnDWDbsgq2fBVaUHrzl+UAFK6q5ns/H03Ap5GQHo3o5b0sxXeMECHTEyCik7D73SETUNAbmiPLlkK8szZUORijsDvraHT6wWuAWi9NhkqMAR2JhCoDQ1CQ4Ah91oRE6iSibXSQ3ohIHtgxH+E89CROyZb+wWxZUsFX/9kZ2Z44J5/RM3Mo3VxPTKKFxMyQ/dHZ7MViNX7ng7p2b6hlw6IymmvcaJpk1s1DyejftXVfO8PZ5OXFe7/u9NjY83MZOjWTmj0tbF5czt5tDVhjTUTHmqkta6FgcjrjL8jji5e301Lv4YrfTIjcD02TFK+tYdn8XbiafZx5ZQGDT8vA4/Tjcfrxe4IsfHIjEnA7fOx/tQaMTyU61ozfFyR3WBLZQxJUKO13TE9t6fckpKcZZ00FroAB7aDpM2KMHpxBE0Et9FvQmSQpfY4cT3+8UHPvdAGP04+z2cuXr+yItHj3k94vliGnZURascPPyuK0S/sjhEBKyY7l1eSNSMIS3b29Ay2osf6zMla8t/uQaSZf0o9R53RtjYFgUGPjor0Ur6+lptTB9GsHUTAxHSkl6z7Zw4r3dnPRnSPJKjgQLz7/kTWR+5GYaWP6tYNI7nN4Z5a71Ufhyn0MmZqBwdjeVBbwBdEbdDRUOSlaW8OaD0s7nG+ONjD+gjzqK50kZ9moLWuh37hUNi7ay6gZ2ST1sWOOOiU7oSeM7du3U1BQoCrbIxEMIL0Ogq5mdAEXQgYRUbEQkwXuRqTPSavDidkeiynhuKwHdUSklOzYsUOJflfxeQJ89vw2SjfVHTbdoCnpDJ2aSfmORpa/W4wpysD1fz4Nvb77egH/+9t6ync0EpscxUU/HYXRrCcY0Fj82s5I/oSAi346iswBHVv+ZdvqaW30otMJBkxIY8+Wej781yYgJODzfjWunVnF6w50EFN3i4+l/92FEHD2tYO73QwjpWTpm7tobfRgS7Cw45sqdHqB13X40Y5nXzeY/uNS+Xr+Lppr3Uz7fgFRdiN6Q/fc/4AvGPHt7FhRhcGop9+YU3e91ZKSEux2O4mJiUr4vy2aFnZ+f/f3UUpJfX09LS0t5OW1dw4r0T8CwaCG3xMk6Nfwe4N8/MwW6itamXXLMHat3kfR2ppOz7PFmzFbDdgTo+g3OpmkbDtCCBLSj85p6XX5efaupQw/K4spc/t3iCwKBkL5ev3Blbhb/Iy/MI/RM7LR6XW0Nnp4/58baajsfPKmnKGJTJnbj/i0nulIba51sfBfm7HHmynb1oAl2khSHxvRcWaK19UQ8IVG4AyckMbOldXtzs0qiGfCRfmk5cXiqHMjdCISrVW2tZ4tSyoYMjWTnCGJ7c5zNnt5/58bMVn0eF2hyKOx5+cy4cJ8nrzlCyBUUeYMTWTinPzvRBhbGjx8Pb+IfmNSjnuF4/f7KS8vx+PxHNfvURx/LBYLWVlZGI3tLQ9K9I8SZ7OX+vJWsockhlqnbxRSuHofsSlWgn6NfmNTqCl1ULKx8x5C/qhksgcnMOT0rnX5Xrh7GS6Hj9l3jKTPoEMPz2+ocvL5S9upKXWg0wksdiOu5gMj+hAQxvbqHAAAIABJREFUHWPCGd6XPzKZWbd0Mkd7D8Xj9GO06CM9qGBQAwkfPLGR8h2NQMghv/7TsnbnTZnbj+XvFqMFJVf/cTJCCF6674AfIz7NSr+xqYyY3oeqoiYWPhnqAekMAi1w+Hd+1DnZTJyTj64be3WdsXlxeSS44JqHJ6tBhIpvhRL940Tt3hbKtzcy/KwsNiwqY/1nZXidB0wVU+b2Y+TZh7fB+31BnvnJVwDc+q8zuzR+YOfKaha9sC2yPfysLMael0uULRRR0NLgobXBQ0Km7ZSwhwcDGsve2kV8WjTDz8yiclcjHmcAW7yZJW8UdvDL7KdgcjolG2sjzyQxMxqDSc++EgfDz8piwux8mmvc6AyCb+YXUbatgfR+sUy9fAAt9R52b6hlx/Jqhk3LYvIlfTv4LLqLmj0O3no49J4bzXoMJh0puTFUFzdz+rwBZBXEEx176HnfFYqDUaL/HeNx+vn4mS1U7GwkNS+Gs64a1Gmc+tqPSyOO26mXD2DYtK7PteFx+tm7vYGaUgfjZ+dj7AXjDTojGND48KnN1Je3MGRqJqveLwFCprcrfjsBnV4gJexeV8PS/+7C6wqQkBHNFb+Z0O46Uko0TXbw03z67BZ2rakhPs3KZb8ad9TCL6U8onnok39voWhtDbnDkxg0OZ2P/m8zAAajjoA/ZN5KyIhm3q/GHfceh+LUQIn+CSDgD/LG71fRXOPGYNIx7IwsHPUeJl/Sl+J1tcQmR0V+3MOmZTFxTj4my8nfKu8JtHXMtsXZ5OXDpzYxemYOfUd3zW7ucvhY+ORGavaEJuxLybEz/Kw+hxx1vP6zMorW7MPrCmCNMVFV3MzFPxvVIex205flFK4KRYKter+EpCwbc342GqNJT31FKw1VTjL6x/HKr5YTDISE3xJtJCU3hpRcOy11HgK+IDqDDiklsUlRjLswr1uDC44HDVVOHHVusgcnqArsOKJE/wThdfkp29bAp892WDc+wswbh57SkSKnCkveKGTz4vLI9oTZeQidwN3qp/+YVIwWPa//buUhz5/9k5H0GZyA1CRCF/I5tDZ6AYiONXHJPWM7TBkC4HMH0Bt1bF5cztfziw6bx/xRyZxz/eDjZob6tjibvbzx4Co8Tj/DpmUx9fIDse2lm+tobfAw9IysLvWOFIdHif4JZu+OBhb8bQMFk9MpWr2PgF/DZNFjsZu48sGJ6gU/CZBS4nMHqCpuZuWC3dTtbe00ndGsZ+z5uUhNUlfeSmJGNNuWVdHS6MFk1uPzBLElmGltCAn+sGlZjLvggD/mcOzd0cDGRXvpPzbUSHA6fNSWtZCQHo2jzs2O5aHoprOvG9wj5z/a9OVelr65C1u8ORReHHakz71nLPMfaf87n/S9voyecYKXOT2JUaLfg/C5A+j0Ar1RRzCg9dhWmeLQSCmZ/6c11OxpYdLFfakpdbBnWwM5QxKZeeOQDpW4x+nns+e3Uba1/WLmE2bnM/a83G7L14ZFZXw9vwghYNLF/Qj4gww+LQNrjOmQDQspJVVFzaTmxUTGPbhbfJgshkOORve6A7z1x9Wk5of8Vfqwiamz7/A4/dSVt/LJv7fgaQ0tTH7LE9PY+MVednxTRWO1K5I2ym7E3XJg8fLxF+YxZlYuAV+wV5g+A74g6z4tIyXHztallYw5N4e0/GNbSEWJvkLRzQQDGk37XJFpO46ElBIpQacTeF1+Whu9XT73aPB5Arzz2FrqKw6M28joH8eEi/KJT7ViiTZGBt1VFjXx7p/XAZAzLJHcYUkkZ9tZ8PcNRMeZufD2Ee1MTgFfEI/TT/G6Wpa9tSuyf8jpGWz/uorhZ2WRmGkjJScmErjwwRMb2bPlQGWXPyqZWTcfCCOuK29l9QcleJx+Zt8xEpfDh6fVz8f/3oKj9sCajAMnpjFhdn4HE9j+kecVhU2MOz83IpLBoMa+3Q7S+8aesLme/L4gAV+wQy9OapL3n9hIVXEzMYkWJlyYj6ZJPvn3lnbpbPHmSAjy0aJEX6HoRbQ2eln/2R4Cfo2yLfUR38F+sgrimXnjUF64exla8NC/eb1Bx+w7RpDRP56VC3az5sNSdHqBLd6Mo87D4NMz2La0stNz41KtDBifGommOn3eAIacntHlUdQuh49v3i5qNyAvOtbEZb8ajzUmJKL7ezb7ETrB1Hn9KVpXQ1O1C2ezj9zhSZx749DjPodW6eY6Vi8spf/YFEZM74MQgi9e3s72b6rIKohn3AV5ZPSLw1Hn5pX7lwMQmxxFc237xYZzhiZSvbsZs9XAtB8UkFUQr0RfoVAcHU37XHz09Gaaql1oWsff+CV3j6Gl3kNLg4cdy6tobfIy56ej+PS5rfjcAc67dThvP7oWALPVgNcVICXHztx7x9La6OW9x9cxcEIa5TsaqSpu7nD9GTcMof/Y1GPKe31lK5ZoI1VFzZGW8NAzMhk8JYO3H10biWzKGZZIc42bpn2uDteIS7Vy+f3jj5vwa5rktd+uiPRMkvrYOPPKgsjYCwiF3559/WAWPb+NgF8jym7kB7+bSGO1i3f/sg4tKLn0vrGk5MR0iyO7W0VfCHEu8HdADzwrpfzTQcfNwMvAGKAemCelLBVCGIFngdGEpnF+WUr58OG+S4m+QtG91JW3sq+kmcWv7cSWYObcG4eRmndgyl+/N4i7xUdMUhQNlU7e+P3KyAyp5906jNS8WNZ/uochp2cSl9pxwXCphdbIbaxysXVZBfUVTs69aWi3TFC4bVklX766o92+WbcMo2mfi/xRydjizWxdUonL4SW9XxwJGdFs+LSMzV9VkD8qGbPVQHrfWHKHh+br97kD7CtxsHVpJWdcMZD4dOtR+9mklJG1KYZMzcSeYGbVByWRkd4T5+STOTCej5/egrMp1OOKSbJw1UOT213D5wl260DKbhN9IYQeKATOAcqB1cAVUsptbdL8CBgupbxFCHE5cLGUcp4Q4vvAbCnl5UIIK7ANmCalLD3U9ynRVyhOLIWrq/nsuW1YbEau+v0kTCd4hLemSXavr2Xz4nJcDh/zfjXuiAshLXl9J5vDazwcDoNJR8CnMezMLNL7xnbaOwkGNHR60W6xoYVPbsKWYObqhyYjdIKtSytY/NpOjBY9Nz4+FaETeFr9zH90DdGxZmbeODRiojpedKfoTwIekFLODG/fB9C2xS6E+CScZrkQwgBUA8nA5cD3gYuBWGA5MFFK2XCo71Oir1CceIJBrUcO+uqqGURqkorCRsxWIyv+t7tDFNXEOfms/WgPfl8wNIV62AQ29fIBDJmaiU4nCAY1tIDkjYdWYYszc8b3B+J1+inZVMf6T8v44V9Oj/RmpJRU7moiOs5MXErH3tB3QXeunJUJ7G2zXQ5MOFQaKWVACNEMJALzgYuAKsAK/PRwgq9QKHoGPVHwgS7bvYVORNaNuPD2Efg8obmYDCY9PncAS7SRoWdkEfRrRNmM1Fc6WfZWYWSFvfg0a7vQUketOzIQz2jWk5xtb2e+EkJ0Ov15T6QrT7azu3xw9+BQacYDQSADyAN+JoTI7/AFQtwkhFgjhFhTW1vbhSwpFApF1zFZDJgshtBMtWGxNkcZQuMZdIKkLBuzfzKSyd/rR+bA+HaCH5Nk4eKfjWLAhJDpx+8NkpbX85ZB7CpdaemXA33abGcBB8ds7U9THjbvxAINhEw7H0sp/UCNEOJrYCzQbqkoKeUzwDMQMu8cQzkUCoXiW6HT6xg1I5tRM7IjU2e0JaN/POdcN4TKXU1HvX5GT6IrLf3VQH8hRJ4QwkTITr/goDQLgGvCn+cCX8iQs6AMOEuEiAYmAjtQKBSKHszhBndl9I/DYuvepVO/S44o+lLKAHAb8AmwHfivlHKrEOJBIcTscLLngEQhRBFwF3BveP+TgA3YQqjyeEFKuamby6BQKBSKLqIGZykUCsUpQFejd3qmi16hUCgUxwUl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvQgl+gqFQtGLUKKvUCgUvYguib4Q4lwhxE4hRJEQ4t5OjpuFEG+Gj68UQuS2OTZcCLFcCLFVCLFZCGHpvuwrFAqF4mg4ougLIfTAk8AsYDBwhRBi8EHJfgg0Sin7AX8FHgmfawBeBW6RUg4BpgH+bsu9QqFQKI6KrrT0xwNFUsrdUkof8AZw0UFpLgJeCn+eD0wXQghgBrBJSrkRQEpZL6UMdk/WFQqFQnG0dEX0M4G9bbbLw/s6TSOlDADNQCIwAJBCiE+EEOuEEHd/+ywrFAqF4lgxdCGN6GSf7GIaA3AaMA5wAZ8LIdZKKT9vd7IQNwE3AWRnZ3chSwqFQqE4FrrS0i8H+rTZzgIqD5UmbMePBRrC+7+SUtZJKV3Ah8Dog79ASvmMlHKslHJscnLy0ZdCoVAoFF2iK6K/GugvhMgTQpiAy4EFB6VZAFwT/jwX+EJKKYFPgOFCCGu4MjgD2NY9WVcoFArF0XJE846UMiCEuI2QgOuB56WUW4UQDwJrpJQLgOeAV4QQRYRa+JeHz20UQjxOqOKQwIdSyoXHqSwKhUKhOAIi1CDvOYwdO1auWbPmRGdDoVAoTirC/tKxR0qnRuQqFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQilOgrFApFL0KJvkKhUPQiurJc4klFi8ePyaCjtM7FwDQ7jU4fcVYjoXXaew9NLh86nSDGYozsq2p288QXRVw9KZfM+Chs5vaP3x/UWLG7noRoEwVpMfgCGte8sAqLUY/HF+SsQSncPDUfIQTFta28vrKMFSX1zBmZyXVT8pBSYtCrdoRC0ZM5pebTl1Jy5p8XU1rvAuDeWQX86aMdjOgTxzu3TqbVE6CmxUP/VHt3ZrlHMu/p5awsaaB/ig2zUcfsERn88cMd7dIMyYjhzIEp3HXOAHQ6wf3vbebVFWWHve7QzBj6Jdv4qrCWRpc/sj/apAdgdE48K0sauGpiDvecW4A/qBFtPuXaFgpFj6Or8+mfUqK/p97JGY8tPmK6v18+kumDUinc10KK3UxWvBWAjXubMBt1FKTFHNP3HwsOj5/HPy3ks2378AY0AprG1ZNyGZIRg8Pt56yCFBJt5i5fLxDUWFnSwA+eXdnp8YRoEzecnseSwlqKa53UtnjbHR+XG8+cUZk8+vFOmt1+7ptVwKjseIZkxPDWmr3MX1fOlgoHAKOz43j8spEs2VXLhrImHJ4Ai7bv6/CdMwanYjXpKW90MzQzlh9MyCY/2YZeF+p9aZpEpxM0u/zERBl6Xa9MoegOeqXof7ZtHze+3P7cfik23L4gFU3uQ56XFmNhUt9E3l1fAUCy3cw/rxjFkIwY6lp9/Pq9LcwekcFl4/oc8hrHwqMf7+C5ZSV4AxqJ0SYkoBOCutYDQpyXFM2/rx5Dv5SOvZOd1S1UNrkJapKzClLYVdPK7CeW4Q1oALx2wwQK0uzYLAY+3FzFK8v38NilI+ibbANCPaM/LNzOs8tKALhoZAZ3nTOAnMRoNE3iDWhEhVvwbdnb4OKtteXcNDW/g4loa2UzqTEW1u1pZFVJQ+Tah8Jk0OEPahh1OnxBjaGZMQxOj+HmM/rSN9yjaHB6mTU0HYuxY16OlRaPH3vY9LW8uB6TQceYnPhuu75C8V3T60S/utnDxIc/B+DsQSks2l4DQMnD5+HyBVlX1ojFqKdPvJV/fLGLRdv2oROCOKsRtz/InrBJSAiQMvRfJwRB7cD96ZMQRbTJQFCT9Eux0T/FRrTZQGqMhQuGpx+VPXvxzhqufWE1wzJjuefcAk7rnxQ5tq6skeXF9Rj1gn9+XkSrL8DMwWlcf1oe6bEWalq83PDS6nbmlbbYzAYev2wEM4akdSkv9a1emtz+SGXQ3azYXY83oJFsM7OpvIm6Vi9PflmM2x+MpDEZdPjClRWAQSeYNSyd9zdWtrvWlH6JXDqmDxeNzOCLHTV4AxrnDUsH4JklxSzYWMm0ASlMH5TC0MxYjHodHn+QN1aV8eTiYkx6HW5/kAanj/OHpTNzaBo/eX09AEk2E8MyY/nrvJHEWU3H5V60Ze2eRq55fhVnD0rhz5eOUP4Qxbei14l+IKjR71cfAbDzoXMZeP/H/OycAdw+vX+Xzl9SWMt76yv4zYWDMRl0/P6DbXywqYoUuxmrycDY3HiKalrZuDdkxjgYS9gs9MeLhzE449DmISklFU1uTnvkSwAW3XUG/VIOLbYldU7+9WURn27bR7O7o8hPyk9Ep4OVuxsIaJLbz+rHz2YM7FKZTySt3gAGnWBblYOs+ChS7BYAdlQ70AnB3z/fxcJNVQCcNyyNDzdXH/Z6drOBFm/H53LpmCyCUvLOulAvbnxeAqtKGjqkMxt0kR5Sks3E3TMLuGBEOlbT8fFHbC5v5sInlkW2C9LsDM2MpbTOyU1T88mIi6Jfiq1bezeKU5teJ/oAufcuBKD0T+ezv1zdbR9ucvkoqXMyKjueNaUNrNnTiC+gsa3SwdfFdbR4AuQkWrnn3AKmD0qhxuGlT4IVKSX+oOR7T30dsYlfNTGH388Z2qXvbfH4eX1VGc8sKUFKyX9vmXTcWuY9ASklK3Y3kGw30y/FRrPLjzcQJD7axHvrK7jn7U1oEuKtxnY9nsU/n8aq0gZaPQF2Vrcwf115pLe25Bdnkp1ojaRdV9bIpr1N5CXbmNI3EZ0IVUL3vL2JrZUOMuOi+NMlw8iIi6JPvBWTofOWeHmjiwanj8omD/1TbazYXc/FozI7VBhl9S4K97UwJieeH760mnVlTZw/PJ1ZQ9P49XtbaHT525UnyqjnkbnDyU6wkp1gxRmu1LyBIAnRZpzeAH0SrB3y09PYta+F8iY3U/omHfIeKr49vVL0f/nuZqJNen51/uBuzlXXqGv1cuPLa1hf1tRuv81soPWgVuhvLxzMDybkqB/Bt8DjD0ZawnWtXuKijB1MJBVNbp7+qpi5Y7IYnhXXpes2u/38bVEhr68qw+MPtf6tJj1/uXQEfk2yp85JdqKVGIuRB97fGjENtiUn0cpDc4Zyev9kKpvcpMVYuPbF1SwprI2kueucAfxoWl8Meh3VzR4qmlwMSo/h1+9t5avCGjx+rcN7czDXTcnlnnMLemyPYFulg0ue+ga3P8j4vAQuGJ5Oit3C9EEpPPLRDnbua+GS0VlUNru5aGQmmXFRJzrLJy29UvR7AoGgRpPbzz3zN/H5jpp2x6KMehKiTSy5+8xI5Iqi51Jc28qSwlo2VzRHzEOdEW81clr/ZErrnAQ1icWoo7zRTc1BkVH7uXBEBtdMymFsbsJhvz+oSf69dDd//ayQS8dmUdfio8HpY3edkxFZseyobokEKJw7JI37ziugpsXL2Jz4w/ZwPf4gK0samJCXEKksKprcxFgMEed2Z+fc/MpaBqXH8NNz+mPS62j1BjpNX+PwsGFvEy8tL+XronogVDm9sWpvxI+TZDNR1+prd57NbODeWQVcNrYP5Y0uchOj0Z1Cv5Pi2lb21DspSIshIy6KQFBjxe4GFm6uIis+ioWbqrjrnAGcPTj1mK6vRP8EI6Wk0eUnIdpETYsHs15PtFmPL6gdNzux4vgRCGo8vWQ3b63Zy0/PGYA/KNlS0czIPnHMGZXZIb3HH+TVFXvYVN6MxaijxRPgm+J6/u/KMUzqm9ht+fpyZw3z15SzaPu+iE8CYEJeArmJ0WQnWjmtX1IkFPk/K8v45bubAeibHE1BWgzJdjOvrNhDss3M7dP78b1RWUSZ9FQ3e1i4uSpiVnr8s8JO85AZF8XANDtXjM/m9P5JfP/fK1jXprc7c0gqT181Fl9Ao3BfC++ur+CLHTWYDTqeunIMb6wuw+kN8MX2GiqbPZHz8pOjuW5yLldOzIlUYjUOD//bUMkHm6uobHJz6Zgsbjw9n6LaVjRN8t815Vwxvs8RK9TuYGd1C2+sLmNSfiLnDE5FCMFrK/fw/sZKJuYncsHwdPql2Gl2+fnNgi38b8OBoISBqXZK6p3tghcAEqNNrLn/7GMyS3er6AshzgX+DuiBZ6WUfzrouBl4GRgD1APzpJSlbY5nA9uAB6SUfz7cd50qoq9QfJesK2vkb4t2UdXkJjvBysbypg4t6b7J0RTXOoHQYLook6FdePB+M+TIPnHcN6uAec+s6PA9j182gueWlbC10nHY/IzKjuNn5wxkXF48ZkPXTE/NLj8fb63iuWUltHgCGPU6yhpCprP7ZhUwOCOGu+dvoipcMcRbjTS5/XQmYXNGZvCnS4Z3avaSUn5rX19Vs5vz/7GMBmfoHqfGmPnZOQO5++1NkTQmvY7ZIzOYv7YcAKNe8PuLhrK0qC4SpDAmJ55fzBzIqpIG0mMtTOmXRMYxmri6TfSFEHqgEDgHKAdWA1dIKbe1SfMjYLiU8hYhxOXAxVLKeW2Ovw1owEol+grF8cfjD/Le+grykqL5fEcN2yodLCuqI8Zi4CfT+3P9lDwCmkQn4Jvieqqa3Vw2tg+fbK3mx/9ZH3F+33BaHrOGpfPU4mKunZwbCS12+QJICdUOD25fkCaXnx3VDr4prqeyyc3L148nJcbyrcqgaZLHPt3JU4uL2+2/8fQ8WjwBrpqUQ1CTvL5qLyt21zNtYDIT8hL5dFs176yrICs+Co8/SJLNzNDMWKwmPTUOL59t30dQk5w/LJ1EmwmnN8gZA5MZnB7TaSSd2xfEZNBFTLI7q1uY+bclAPz6gsFYjDqe/mp3pIJ69JLhZCVE8fyykkjo+JUTs3lozrDINZtdforrWhnVJ67bgk26U/QnEWqhzwxv3wcgpXy4TZpPwmmWCyEMQDWQLKWUQog5wBTACbQq0VcoTgxaWMiPZCdfu6eBP320g9E58dw9s+CE+580TbKhvInVJQ00uf38YsbAI5Zh4aYqHvl4B0a9iPRu2hJt0uP0Bdvt0wm4bGwfhmTGMjDVzvLienbXtfK/DZVkxUcxpW/ITNbk8rNgYyX/+sHoyBiR+lYvzyzdTWZcFFdPyo1cc0lhLYk2E0MyYr/9jTgC3Sn6c4FzpZQ3hLevAiZIKW9rk2ZLOE15eLsYmAC4gUWEegk/5xCiL4S4CbgJIDs7e8yePXu6VEiFQqE4ElJKFhfW0uzy0y/FhhAwJCOWndUtaFKSnWDl66I63ttQwcdbqtG64OY8c2AyL1w3/vhn/ijoquh3xaPYWZV68G05VJrfAX+VUrYergsjpXwGeAZCLf0u5EmhUCi6hBCCMwemdNg/MO3A1CYzhqQxY0gada1e9tQ7KappxWzQExNlwGLUMyk/keLaVjx+jfc3VTJraPp3WYRupSuiXw60nXQmC6g8RJrysHknFmgg1NqfK4R4FIgDNCGER0r5xLfOuUKhUHQzSTYzSTYzY3I6Rv/sn/9qaObxN9UcT7oi+quB/kKIPKACuBz4/kFpFgDXAMuBucAXMmQ3On1/AiHEA4TMO0rwFQqF4gRxRNGXUgaEELcBnxAK2XxeSrlVCPEgsEZKuQB4DnhFCFFEqIV/+fHMtEKhUCiODTU4S6FQKE4BuurIVRO/KBQKRS9Cib5CoVD0IpToKxQKRS9Cib5CoVD0IpToKxQKRS+ix0XvCCFqgWOdhyEJqOvG7JxIVFl6JqdKWU6VcoAqy35ypJTJR0rU40T/2yCEWNOVkKWTAVWWnsmpUpZTpRygynK0KPOOQqFQ9CKU6CsUCkUv4lQT/WdOdAa6EVWWnsmpUpZTpRygynJUnFI2fYVCoVAcnlOtpa9QKBSKw3DKiL4Q4lwhxE4hRJEQ4t4TnZ+uIIQoFUJsFkJsEEKsCe9LEEJ8JoTYFf4fH94vhBD/CJdvkxBi9AnM9/NCiJrwimn79x11voUQ14TT7xJCXNODyvKAEKIi/Fw2CCHOa3PsvnBZdgohZrbZf8LfPyFEHyHEl0KI7UKIrUKIO8L7T6pnc5hynHTPRQhhEUKsEkJsDJfld+H9eUKIleH7+6YQwhTebw5vF4WP5x6pjEeNlPKk/yM05XMxkA+YgI3A4BOdry7kuxRIOmjfo8C94c/3Ao+EP58HfERolbKJhBaZP1H5ngqMBrYca76BBGB3+H98+HN8DynLA8DPO0k7OPxumYG88Dun7ynvH5AOjA5/tgOF4TyfVM/mMOU46Z5L+N7awp+NwMrwvf4vcHl4//8Bt4Y//wj4v/Dny4E3D1fGY8nTqdLSHw8USSl3Syl9wBvARSc4T8fKRcBL4c8vAXPa7H9ZhlgBxAkhTsiabVLKJYTWTWjL0eZ7JvCZlLJBStkIfAace/xz355DlOVQXAS8IaX0SilLgCJC716PeP+klFVSynXhzy3AdiCTk+zZHKYch6LHPpfwvW0NbxrDfxI4C5gf3n/wM9n/rOYD04UQgkOX8ag5VUQ/E9jbZrucw78kPQUJfCqEWCtCi8MDpEopqyD08gP7F/fs6WU82nz39PLcFjZ5PL/fHMJJVJawWWAUoZblSftsDioHnITPRQihF0JsAGoIVaDFQJOUMtBJviJ5Dh9vBhLpxrKcKqLflcXbeyJTpJSjgVnAj4UQUw+T9mQt46Hy3ZPL8xTQFxgJVAF/Ce8/KcoihLABbwN3Sikdh0vayb4eU55OynFSPhcpZVBKOZLQ+uLjgUGdJQv/P+5lOVVEvyuLt/c4pJSV4f81wLuEXoh9+8024f814eQ9vYxHm+8eWx4p5b7wD1UD/s2BbnSPL4sQwkhIKF+TUr4T3n1VlKDVAAABrklEQVTSPZvOynEyPxcAKWUTsJiQTT9OCLF/udq2+YrkOXw8lpD5sdvKcqqIfmTx9rAX/HL+v737V2kYiuI4/j2TiggqODhacHVycHCUgm5CBydBfYxCH8HN0cnBwUk3B3V3UWtF1L6Es0MczgkthWojaBPv7wMhaZLhHG566P1T4i9rLy0zmzazmfwYqAMdei+ZJ/bncXwB7MaKizXgPe+yl0TRuC+BupnNRTe9HufGbmCuZBtvF/BcdmKFxRKwDNxSkucvxn6Pgecsyw77LlWqbYblUcV2MbMFM5uN4ylgA5+juAEacdtgm+Rt1QCuM5/JHZZjcX85k/2bG74S4RUfL2uOO54R4q3hs/EPwFMeMz5+dwW8xX4+660COIr8HoHVMcZ+inevP/BfIAc/iRvYxyekusBeiXI5iVjb8WVb7Lu/Gbm8AJtlev6AdbzL3wbuY9uqWtt8kUfl2gVYAe4i5g7QivM1vGh3gTNgIs5PxuduXK99l2PRTf/IFRFJyH8Z3hERkRGo6IuIJERFX0QkISr6IiIJUdEXEUmIir6ISEJU9EVEEqKiLyKSkE8otyqp+ftWjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2430ba90>"
      ]
     },
     "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.wt_mean_price.plot()\n",
    "rdf.h_wt_mean_price.plot()\n",
    "rdf.w_wt_mean_price.plot()\n",
    "rdf.prices.apply(np.max).plot()\n",
    "rdf.spot_price.plot()\n",
    "plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1e5896a0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1da85358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.prices.apply(np.median).plot()\n",
    "rdf.prices.apply(np.mean).plot()\n",
    "rdf.wt_mean_price.plot()\n",
    "rdf.h_wt_mean_price.plot()\n",
    "rdf.w_wt_mean_price.plot()\n",
    "rdf.spot_price.plot()\n",
    "plt.legend(['median','mean','tok wt mean','hold wt mean','wealth wt mean', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1da99898>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T/FEJoeWN6NLNZF01FMGsp+6JhUjeKHn3j0c0tX9P3bakUNoZfWn+sATL8Ewsg11qvtKWYI/mj0qSkmFtB+e0czMHVzQQLfOpibFbXI2erzbhn1OVaGMsSiFzytAOhSS0shH/bzU4jylOioRUJHmvzq/IkbgaDbpFtOPuMMHlDTgm5iW9LEotkURVawBDrg3r8Mx2AgKQ/4+JXVo+QSt70gZNRDQ0dg3/bzV4Pt6QtM1YnAIKpBxThDHPjmjRt5vcB7XKgRJV1+ExFNjJuGhwQiT9v1Tj/60Gy5AMfLPKsQxxdVgSCNR8l5Sji2iYvpxoaYv6+aiiTkcSKoqC/+dqvF9sSmwT7QZybxubqFmXKPmzuA7iCoJRJPuGkUiBGLI/hvudtTgOLyTl8B6du3G7QXiDKqzGwhQypxyEoBPxfLEJ/0+qEKae1Av7xHykQIyaB+YBYBuTQ3BlY1KBVddfBhLe4EHnMGI7OLfLLxmaO0tDQ6PL2MbmqG/DW+ZgPG2y8bcuFJZx2ZBEcIch14Yu3Ux4VVNCQMyDXITXNOH9ajPpf+5P/YvLEn346tRSP2ln9sM60kvT66vbjcH3fUUimsw6OhvnMcW7dA2CIGCfmIcggGDSE6sN4P+pCs8XG0k9sTeCQWxXM06RFBr/u5p4fesqnC3flqJPN2PdhbDurhBe41bL8ZS10DB9Oa4LByUEBNSqCYZcO5Fy9R4acm04JxfjPLEXdc8sRmoK4zy+J5bBGUkuy72BJiIaGhpJCIKAbWxrvS/zYBc1D87D2CMlUSusrTvKdcFAdOlmtfzL/8rwz64k7ZTe+DIs+OdW4jiiR+I45/E98X61GdGqRzDpsAxykf/ABKRAjHhTmGh5C7HqgFo8c6tF08VlE1QhyQdUyyS0uonAb7VEq/xkXDQ4URkCQJ9tJeWIHrjfXpe0LV4XxP32Wky9nJ1yO3YFJSbh/7kaXboZ28gsWr4rx/2uOg5DjpXUk/vQ9PbaxMqioM4lbXW15dw0ivBaN6a+3VdaZ0do7iwNDY2dosgKgiioVanXufF8pLq70s7qh21U8uStHJUQjTokX5SaRxdAXBWDlOOKcUwqILioDlPv1B0GBShxmWi5j5Yfykk9uTeGzN1ffydS6qXhxeVJ21KOLsJYlILOacSQaSVWGyC01o19XC6IApENHppeX419UgGWgemIFrVQqqIoICnE6oJ4v9ykruGTZ+/SuGoeW4DkDpN2Rl9sY3KS5oEyrxqKqdhJZJOHhpdUK9DUy0nmlKG7dzN2gubO0tDQ2KNsnYvQOU3Yx+ZiHZaJElc69LlvjS7TOYzYx+Um3DL6dHNiFcydnk8vqg/LXgftsWswFTvJf3AiLf8rw7clxNqQb1eXY9iCIceWVAHZMsiFdXgm/jmVicRf68gsjIUOPJ+0Row1/GsZaaf3JbCwFufknhgLOlewNe4OI7nVlUitW8TYcXiPhIhsLSpq6pWaKOzpnNyzq7egW9AsEQ0NjW5DDsdp/PcqrMMzsR2cu9+U2Im7wwQW1ZFyeI+dRi/Fm0LUPbsksTZNZzDk2MicclC7HK7Q6iaQlURodWhNE03/Xd2uoGd8i7DsycXTdgXNEtHQ0NgvEM16sq4etq+H0Q59uhnn0Z2rXK13Wci9fSyKpKBEJeqfX5rI1TEU2HGdO4DAgjoim73YxmTj+WIzsdoA7vfX4/rLoC2LvynIgVgiiCD9nP5I/hjKFlef3pUsFvtKPLqCZoloaGho7AKKrAoCorDdEFr3u+vUKgOocxhtJ/G3RTDqyLt//H5VvkirnaWhoaHRTQiigM5h3GEORtrpfbFsqVW2rYCkn9sfy/DWkGElKu1XArKraO4sDQ0NjT2MYBBx/WUQiqQQWtmIIceKLtWUyOC3DstC+XN/mj8sabci54GGJiIaGhoa3YSgE7abqCgIAuln9tvLI9rzaO4sDQ0NDY0uo4mIhoaGhkaX0UREQ0NDQ6PLaCKioaGhodFlNBHR0NDQ0OgymohoaGhoaHQZTUQ0NDQ0NLqMJiIaGhoaGl1GExENDQ0NjS6jiYiGhoaGRpfRRERDQ0NDo8toIqLRZSSvl11ZSiC8fj3xxkbCa9cSLStrt9/344/Eamv35BA1NDS6Ga0Ao8ZOiVVVES0rwzZhQmJbZMMGNp14EqlnnUX6JZdgLC5CEFvfSeRQCPd//4vocJBy/PGIFgubTz4lqd++P81Fn5FBcNEiKq66GtnnA6D3t98gmC0YsrOQQyEEs/mALpWtofF7RluUSmOnlF92OYGff8Y26U9EN5eScfXVNDz1FPH6+kQb0WbDdflluK68EkEUaXz5ZRqemKbuFARo+z3T6UCS0Llc2A+ZSLS8gtCSJa37RRFkGcfRR+H7/gfshx1GwXPPakKiobGX0Bal6iY8H36E58OPAAjMm0f5pZcSWrFyH48qGTkaxfvFlwQXL8b95gzizc273afkVy2EwJy5xCoqqLnzzoSA5D3+OKLNhhwI0PD0MzS/9Tah5ctxv/ZvDEWF9Jj+YkJA7IcfTr+FCxiwcgVFb7yOIScH76efEVqyBGOvXvRftpQeL7+EfdIknKecgm/mdyBJ+GfNouKqq9hw5FEEfpuPEo/vkhtNQ0Oj+9AskU4Sb2qiZOIhABS/8zal55wLgHX0aLLvvIPQ8uXIoTDpF1+EIAj4fvyR0LJlZF59NYLRuNvn7wyS30/lddcR/HVeYps+O5uUE08gVl1N+gUXYB01qtP9yeEw/rlzqbrueizDh+M843RMffrQMO1JggsWkHrOn8mdOhUARZYpPedcwsuXJ45PPfcccu+7j1h9PbGyMqxjxrQ7R3DBAsouvoT8fz5GyvHHJ+3zfv4FSjRCaOVKPG+/0+EYs++5G+cppyLarAiCgBwKIVosxGpr0aen77V7r6Hxe2JXLBFNRDpJ4NdfKb/k0k61tR9+OP4ffkh87vnJx5gHDACg6qabsR1yCKmnn7bbY0oa37zfqL79duK1tdgmTMBQVEi8voHAzz+jhMMACAYDfb6fhT6z40Vy2iKHw6wfNz5xbPH772M5aAgAiiTRPGMG9kMPxVhUlDgmvHYtm09Vryt/2hM4jjkGQb/zaTc5GkXcycNeDoXwz55N1f/d2H6nIIBOh6DXJ8YLYBowAOepp5B6xhnoHA4URel2l5gcjaJEIugcjm49j4ZGd6KJSBv2hIg0PPMsjS+8gGAw4Jh8HC2ffQ5Anx++xz97DrVTpyI6HKqbJRRqd7yYkoLrssvwzZqVeFPP+fv9mPr0wTxwIIoko7PbdmuMm884k2h5OTl334XzlNYJ7Fh9Pd5PP0XncFD30MMYe/Wix0vTMWRlJdpEKyqove8+Ar/8CkDuww+DLFFz190ApP75z+TeP7VT41AUhVhlJcYePXbrerZHtLycSEkJ9sMOI7x2Lb6ZM2l6cfoOjzEWFVH4+utUTJlCZN06Mm+6CUN+HpYhQ5JEsC2KohCvrUWXloZoNrfbH1y0CDkYwjJsKI3Pv0DK8ZOxDBtG1d9upeXzz+n15ReIZjOG/Pw9ct2dIbh4CYacbAx5eXvtnBq/TzQRacPuikikpIRNJ50MQOpZZ5Ez9T6k5mZ0Lle7t1pFUSAWo/Gll2l87jkKnn8OU+/elJ53PpLbvcPzWA8+GL0rHV26i8z/uwGd3d7pMW61ANIvuYTs227dbjvPBx9Qc/c9CGYzeQ89iG3SocTratl0wonbPabnp59i7r9/L+HZ/P776FKchJYsQTAaSZl8HIg6fN/NxDdrFtHNpaqrKxhsd2zKiSeS+48H8M+eQ3D+fLLvulMNDJj+Eg1PPompf3/yHnsUU58+CDodiiThfv0N6h99tF1feY89SvWttyVt6/nRh5gHDeq2a99KZOPGxO+x/+JFiFZrt59T4/eLJiJt2B0RURSFlq++ovrmWwAYsHxZp3zsiqIQq6jAWFgIQGjFCprfnIH98MMJ/DaP9PPOI7R8OZ4PPkyOStqCYLFgGTKE/KefQp+evtPzrf/Tn5AaGunx0nTskybtsG1o2TLqHnqY0LJlSdut48fhPPFE7IceStWNNxFcsADXlClk3dSB++gAw/fDD1Reex1IEsXvvUvVzbcQq6hAn5dLvLoG0elE9noBMBQUkHbuudT/85/Jnej15NxzD/qsTCqvvgYA1xVX4P38c+Lb5LZYx40jsn49ktuN6HBQ8Pxz2MaO7bbrUxSFls8+o/q22wFIOfkkcu65h5avvsY+6U8YcnO77dwav080EWnD7ohIzT334Hn/g8TngWvX7KlhAaBEo4RWrsQyYgSx8nK8n36G5PfR8vkXSM3NWEaOpPA//97ufIGiKLR8/jnVt96GYDTSf9nSTvn85WiU+kcfo3nGDACybr0V16WX7NFr298ILl4MkoR1zBjkcJhYZSWmPn3wfvZZO+thK66rr0JndxBcuJDIpo3EysoT+/KeeBznCSckPoeWL6fplVexTZhAyoknoLPbiVZUUH755cTKyil4/jkcRx7ZLdfmmzWLyr9eC0DqWWcmfWcNPXpgnzQJ2/hxOI46qlvOr/H7Y1dEBEVRftf/Ro0apXSV1f0HJP7VPvRQl/vpCt6vvkqc2/3Ou4osy+3aVN15Z6JNwwsv7PI5ZFlWpECgw77/SPh/nafU3P93JVJWpjRMf0kpv+pqpWnGjKQ2Uiik1Pz9gcT97iy+OXMTxwQWLerUMbIsK+GNG9Wx/fabIkUi7dpI4bASWrtOURRFqb7vPmV1/wFK/bPPKXIkolTe8jdlzdBhSs2DDyZ9h93vvafI8XjyuSSp3TYNDWCh0slnrGaJ7ICSI44gXl0DwIA1q/d6slvtPx6k+c03ATD27k100ybyn5yGf/YcDLk5NL7wLwDyn34ax+GHaeGsewH3mzMwFhdjP2Rip4/xff891bfehuz3A2pYeI9XXm43Ya9Eo1Rcey2BOXOTttuPPJIezz+XtG1rMqchL49YdTUpx08mf9q0xH45HEY0m2mc/hL+OXMILVqU2KdLS0PaJn9IdDgoev2/mAcO7PR17Qvcr79O89vvkHnDDaQcd2zSPjkSQTSZ9tHIfl9o7qw2dFVEFEVh3YiRWEeNIvuuuzD16tkNo9v5GOK1tZRfcQXRDRs7bNOdbhKNPYf/55+pufMu4nV1ANgm/QlTn74Efv0V+6RJWMeOoeKyy7d7fOrZZ5Nzz93E3W70GRnU/v0BPO++C6gvGMXvvrPDYIy4283mM84kXlOz3Taiw0HRjDcx99s/AymilVVs3OKSE61WMv/vBqzjxmHu14/6J5+i+Y03yH/mGeRQEPPAgRgLCvbxiA9cNBFpQ1dFRPIHWD96NFm33Izr8u3/ce8NIps3U3bOuaSecw7+OXOIbthAyvGTCS5ZSs8PP9ByEg4g4s3NNL/xRsKK7IieH30IgkBk4yaMRYU0z3gL7yefdNg2/9lnSDn66E6dW/J48H7+BfY/HaKGpEci+GbOxH7kkUhuNxVXXoXU3KwK1r33dCrHp6sokoQSj3facpDDYTwffkjdA/8gf9oT1D74EFJTEwDOU07G++ln7Y7pv2ghom33QucPVCSvF8Fk6jA8vTNoItKGropItKyMjcceR+4jD5N66qndMLKuocgyyHK3/oFrdD+1Dz1E8+tvkHP//ShSHM/bb2MoLCL/icc7/MNvfHE6TS+/jBwIoHM6kbxeUk48kfzH/9lB710jtGwZFddei9TQiPOM0xF0etLOPQdj797bDe6Qg0Ga/vMfUo47DlOvXoAabqzEpe2Ghm99QQMo/vADLIMHEy0vT0QztiVWW4v3889b67AB/eb9Cno9LV9/Te099ya2u6ZMwfvJJ4mSPILRSK/PP0PyeDD27rPbuVj7E5LXS3jtOvSZGYn7Hly8hNr77kV0OgktXIT90EPVskNdQBORNnRVRIKLFlF2/gX0ePll7H86pBtGpvFHRpFlwitWYB46dJfn2pR4XE3oLC7ulrFV3XQzLV99lfhsKCwk9+/3YyjogT7DlRC50PLllJ79Z0B9YKdfdCHmgQOpuulmgHZ/O3IoRHj1auRgiIorrkhsNx90EOEVK9QcnxNOwDpmDCnHT0Y0m6m+/Y4kK8x55hnkPvBMo02tAAAgAElEQVRA4p4pioJ/1iyCCxaQefPNCbGrf2IaTS+/nDhOdDjIf/yf2A89NOla42433o8/puHZ58i8/vpElKIiSbR88w22CRPQp6Xt/k3tBJLPh2izJaphxxsa8M+Zi/PkkxAMhtYxtynBBGAZNgzH5OOof6R97lJXc4Y0EWlDV0WkZeZMqq67PvGmpKHxR0FqaaH+8SdQ4nFCS5YQ3bw5ab95yBDyH/8n1XffTWjhlgn7LZWXtyXnvntJO/dcmt9/P2E16LOzidfVkX3vPdT9/YHtjiPt/PPxfvYZgl5P/rQnsI0f3+lrUBSF4IIFlF94UetGQVDL9wxR/57bhkZvxTVlCuGVKwivL0FqbMSQn6+6jFNTO33uruD/6WcqLr8c08CBFL40HX1mJnWP/RP3a6+p47r8MjJvvpl4fT0bDj0MUBNlw2vXJM2X5j36CIEFCzBkZZN23rnoMzK6NB5NRNrQVRHxfPwJNXfcQe+Z/+u2Eh5/VCRZQhAERCG5iHREimDSadE1+xvRyioq//pXIuvWdbg//6mnMOTnEV61Gs/77xNetYrid96m/qmnCS1ZQs+PP6b0rLOQA4GkxM4Bq1aCJFF9+x3oUp0okpwIFmhLxjVXk3n99V0au+T3o8RiKNEYG7ZYIWnnn0/GNVdTMeVKwqtWAaqwmQcMwD97dof99PttHjqns0tj6AybzzgzMRbBaKTg2WeouPKqpDbpF12If/YcoqWl2CZOJP/JaYgWC/VPTKP5nXcoeO65XYoa3BGaiLShqyLifuNN6h58kPzvZ/HKO+9w7rnnkqtl/iaQFZkXv3iIEw+5gMK04u22ERASrof7n7wAb0MTlkYJIcPEfQ98hEFnoKa5ki9/fZfSD79FKbRy753vYDJ0bUJQo3sJrVpFaNFi6h56CICcB/5O2llnJfbL0SjRzZsx9+9PrL6ezSedjLRFNDL++ldcl19G/RPTcBx1JLZx4zo8h6IoREtL8bz7Hv65c8l77NE94g3wff8Dlddck7Qt49pr0TmdmPr2xTp2DN5PPyPwyy+kHHcshrw8Aj//TP3jT5B69tlYx45Fn+HCuqX6gOz3E29qovG558m46kpMfft2aVz1Tz5F0/Tp2CZMIPPGG6m87rpEFYT0iy4k8/rrqbrtNvzfzUocs23KgbKHi4tqItKGrorI8qefJjjjLazT/8VHn35G3359KG35npzcXlx2yt0sXryYb7/5hjFjx3DkkUf9rhdMevvzR1i64lfuuvkNFtcsYlzBeJ76zw0YZlbhs8Tw9zMz6aizOXrk6Vj0FgDmr57Juy8+AjqB3uPG0NTSjPW7yqR+WwZZGH7USaz74QsMK1rrWrlzZRAFxpx6Nou//4Lew0Zz0WkdZ5Vr7BsUWVYn+XcSGej98stE2aCit2ZgHTlybwxvuyiShOe996i9/+8AFL/7DpZhw3Z4TN0jj+L+z38SnwWrFduE8UkP9a0124JLlpJx5RQsw4dj6t17p+OJ1dUl3FP9Fi5AZ7cTmD8/4Ybr/d13GAvyUWSZsgv+guT10mP6i90evqyJSBu6KiIP33UXWZWVLMr3kWUcihJ0k1K2CYC+F57J4gWlibYFBQX03eYtxGq1Mnr06N+FuEy94hgcLa3RObXZUXLq2kfr1GVGuPa+FynM7M2jU09FvyZOXC+hj+sSbawZTUwckMms9R7k+pSk4wt7NVDhN6HUpyCJMjq51d2Vcd5hCCYjR489jdx0zb14oKAoCoGffsIyYsQuFRXtbhRZJlJSgrl//522lSMRmqa/hBzw4/3qK6SGxqT9hoICYpXJL0gIAoWvvdpuHqfh+eeRW3xk3a6+FPlmzqTq+hvoMf3FpEn/wLx5KHFpj7mndhVNRNrQFRGR/AGevvtuwsTR+dyEevRFiEawb1xBOLsHsfRsAIRYFDHYguR0Ae3FYvSkQZxw+Fl7VUjmrpnJZ/+ZhhyRsLUIFJ15FJdOvgVZkREFcZfH4vY38uy1F2APtQ8pjlkiXHrR2Sz46CXWBhwQsFKbHiboEulVYiTkiHL9ddfz8rNPgM/GqZMs9L76XXUStnEDK966ka/XeNC5nQhFYW567DvkysV4fnoFwZrC9C/moUQNENdhjKnn91njSIUpxOo92AYWc/mF95GbuvfKrWtotHzzDdGycizDhhJvaCTluGMJr11HZP06HMceR/Nbb9Hw1FMIOh22QyeRMnkygTlz8X76abu+rGPHElywQI2islj2wdV0jCYibeiqJfLys89QW1GGsamWUA/VytAFfUgWCygCpvoqHBnziZQUs+0dDKdaiecOQgwHQYqR068/JqP6BamoLMHmcJLuzGJbXC4Xxm3i8YuKiujfibclSZb49zfTaJjxA8Z4+1WPJVEhMCqVqTe/2SkhURSF6Z8+TMXSBaSviSHmB7ly8mT0eYP59vVHWV/qwDlI5PL7tiR5STGeuulYpNrWcMI+ww2ccsfH4K1Crl+D0OdIIpFI0nnk0nnMfO06Djn5Jmyjz00exNovIR7Bt2kBcxbMoqS2R5JVsxX/QCdKNIY+xUa03kP6oD40LV5N5tCBjDvkJA7qOQq9Tt/u3mpodBfRyioqr76aSElJu33G3r2JbmyNqDIWF9P7m6/35vB2iiYibeiqiLzyyrNUbapCMRhhmyii05XPyRJKyRk4mVmr5/KtpwiDLCALCvZmG6aojnBeL5Qtsd2SDqwZLkK+FsRgDABnYQ8MeiNGgwlQiMXiNDU1Ja0dHo/HAbDZbOTn53Peeedtd7yPvHAVhtmqST22aD09M3IRPWW801iE4m31W6dfeDiXnHBzu+N/XfcjmxtKCMXDnDPhUqpqNvD5rXcl9l9568XYR52pfoiFqH37/3BNvAhD7wmJNr7FH/Hq888SjZi48MRe5Jz6CJhbXVazZs1i7tzkulB7FUFGiUsYLRb0Oj2i2CpIer2eCy64gMxOrPq4LcFYEIvegiAIfLvgIzZVrObq0+/ekyPXOABRZBnJ61WrKssStnHjUGQF85DBKKEQ8fp66h55FPPAAWTdcsu+Hm4Sv1sREQShF3AX4FQU5czOHNNVEbnnkRvQhTtOMrrjlhswRZshrSf8+jz8766k/f+OFeDe0JOcXptJJcrK0r7o5fbWwVbCZhnDgHzy+w3EYDBy2vFTMOiNNDc388svv1BXV0d5eTmTJ0/GZrPRr1+/dm/VU6ecjOjI5qB8B+YBR6lLxgKKp5Lo5nnoiLGkwYxO0iEXpDBs0EQMOgNxOc7Cpd9jrGtdVjamk0EAwxaLxpWpUDT6FDpFxAe+WnD1weP1Ul7eWj49EomQlZXF0KFDO9dXRzRvBvdmKBgDdSuJ1W9gTpkPHTJS1IggC4iWMHpRJhwxYYiqQh5ON2L0J1tgUYNCSmE+fYuHsnjxYnLysykqKAagsaWOkvVLSc/MpX9R8nglWeK3+d+gSDKY9FgaYoSzjEwYdRy/zfwEQ1zE71QwW+2MGnYYOqG99QRgNpsZN24clj3gxojLcV74+hHSHZlcMOnK3e5P44/NfikigiC8BpwI1CuKMqTN9uOApwEd8IqiKI90oq8PultEHrrmCqJZrb724fJilulGMXbsWCZPntz+AEUBRYZIC8y8j6bFb5J25D2IGX34+I2/sbwxH4fdhz9oY3h6FRvCqQSb0rCEDe26CjjBVJTNX2+cht3qpKqqipfbZN8KgoCjTVSML+BBkdQHvslk2q67So4EiMbV37ciqP8AxPY5YgnMBhEM5nbWWGcpKCggvc3CWsOGDSN/Ty4ZK8Xgk2vAnkV842xEOYbY7xiwZ8O6r6HsJ57x9idWrboPzdkNhOuSrY3wsAwioTREbBgsFnSijnDAn9ivCCDodZgMZmRFJtbBEsjbRSdgMnWcMRyJRDCZTBx00EGkpaWh1+vV/BlRnbva9ue2n3NycpLu68+LP+PnJ18n7nTR68hDyXC0d5d2arg6HQUFBZjNZnJycjAY2n8/NX7/7K8iMgnwA69vFRFBEHTAeuBooBJYAJyLKigPb9PFpYqi1G85rttF5OErLyaSW5z4fNvpI7AM7eTbeDQAKz+EASeCNR1+eQ5m3Q/pvaCxBA6/AyoX0bhhLt+EUjnLWMmP8QzmNRQhRvVYQ6qVEerl4I5/vIFOpycYDBKPx6moqGDjxo1Jbq9lP/4PnQKjh7o49tK/b39cUgzlvYv4aMEKSqta1xaP6yWOy1/LQRY3Yq9DWb1mKWtC6fTPNjLkjt8SVs1+jxRXxU5MFjzlsxt4cfZPSLLIVUcfxdpf3mBRLI1jjHX8u3EgJnfnClh602Vs/fPQ/1qLLChc0mc+C0KZGBWFhZ489AHVouhZWEo0rqfKlwY+G8GRGdx162vtkis3bdrEF198gc/nIxaL7fLlWreUs1AUhWDYh6Dot3yWEUQRRVb/3/a8O0Juk3U+btw4DjnkEEwmkyYmfzD2SxEBEAShGPiijYiMB6YqinLsls93ACiKsq2AbNtPt4vII5ddQLhHn8Tnu+++G/3uFD2UZfXhJsVBt6WfxhL4biocNRWWvQ1znwAgpsCz/n4oldkEUuD4//sbowcf2q7LqqYynn70r2SXQSzNx+2PvgXOTrzlr/uaiv9eypfNPfG70zhmUClDr/tcdc8JguqOatoIqT0gtX1RvAMOWYaPrgApAqe/AlULYc3nMORMlI+m8NQmE3JtBlJKAF1La5G+I/uXkKP4icYVZoezqa3JQ9xivl130FKMt5VA3WowmJG+up3akoVkWwT0Z74EioRSMpMnZy5GaUin2Rnn5mkzSLO72g1PURQikQiSJCUW+pFlebs/BwIBSkpKEsKzZuMiohvVEvN5hnW4K1vdYwoKXlucSJ6Ff9z/HkbdjoMLWlpaqK+v54cffqCqqgoAURRJT0/nkksuwfYHrYr7R+NAEpEzgeMURbl8y+e/AAcrinLtdo53AQ+iWi6vbE9sBEGYAkwBKCwsHFVWVrbLY/3ujpH8ZDoZgCuvvHLvZavLMqz/BvnzG3lgUx52t/pHO+ymyygpXc5Fp92CTqdHliWeuvjMRCTWkHFmjr3xgx31nIy3En58GJa9C5fPhLwR3XE1+z+xMDwzHHw14MhFaamhNGbDKUZJv/JrVURjQVj5ISXfPsVnG4Yjpbdw6/M/QJuJeWQZfNWQkp9kuTXOuIRXvq7FENPRmBYje+wwDj38DEb0PLjD4ZS5N7O8fBErF83lqGPO45P3n+PMU69lRK/k9hvr1vHFL29z1cl38I87TiO1TKR3XjmnXP4AX71yF6tbspmYXs6v5a2RfQ19DAw5+ljGDjoMQSdS2rARoyQiCjpS0zIYnDM0YbU0NTUlLN41a9ZQWlrKEUccQWFhIYIgYDKZyM7O/l3kQWm050ASkbOAY7cRkbGKoly3p87ZVUvk1zd/5NsNP2K327llX0RO+OpoemIY7/nzaKnKSUzMt1hj2MJ6ZEHBIIlETTFu7TkP3ZmvwkGdMs40OiLsBYNNFYDKBWDLBNc2GcelP+F75RRMafkYb17euX6bS+HpYfyz8SDEhtYifj0uO4lDxp6AzeLAaVJrMj336T+IvDWvXRdRvcwFz/2LHmmtLsjHnpiCbn514rMls5FrbngA+h4N67+FX56Fw25n3r8uojlmZpU3G8G/42qu0rH9+NslT7QThlgsxmOPPdbO5ZaVldWpaDabzcbBBx+MTtdxgMGu8FP5XOas/45TR/yZQa5Bu91fZxFFEYfD8YcRzQNJRLrkztoVuioi/7ntJ8xFQSaffzDObiy8tkPWfgnvnMdr/p40V3Rc5uCIvssYMXUD6LXChXuFqsVgdUGbB/pOiQYIvnIc3252UxuxEaxLnvRuscaIGGQyvdv/HbY4JMZeeznLF83GVVBI5Q+zcG5WH8o6Zwt/PWEUhlOebH/gyg/BUw6LX+fF9SYCDWpVVwUFoYME2dK+cW698UV6uIqTtrvdbnw+H7IsI8sy69atY+PGjlfbTLr0aBSfz7fTdgcCffr0YfCWGl6iKDJgwABM++lyvE1NTTQ2NnYqx6wjDiQR0aNOrB8JVKFOrJ+nKMqqPXXOrorIW/f/RnNNgNHHF3Pwyb321HC6hPL+JaxaPIv+Bh9fhvLwxI2MNjexOJzBBTc8h1g8YeedaOxbapbDlzdD5XxaZB3PbzoYY6z9m3leVg3npG9AAYKSAZsuxneBXJZU9UYnt3/on9NzMfk9+8Cl/1Oj6LaHLKNMP5TG8hIyTWp0maJAQ9RKpjFITBGZVj0Sg89Ciz3O+X9/gn75e2YJhM2bN9O8zZruXWH52jnU/rgk8bnZHiOuV8gbPRz3L8txBPU022OY4iJNuXDxMTdsN7x6V5k9ezbeLYUktzJx4kRGj97+c9bpdCKKXYtq3F2efPJJvF4vU6dO7dLx+6WICILwNnAYkAHUAfcpivKqIAjHA0+hRmS9pijKg3vyvLtjiQS8UQCueeFwBHEfmrHRIMy8F/KGw28vgqcCBp2iTg5fvwQs3bvWgcYeJBaG5e+w4ZM7+NDdm8vS12JVFDZFnEQFGGFpQrhuy/e1cT2k94afnmTFgq/5vqUQsywT14uE61PJyq3iL1c+AANP7Ny5w17VMul/AhitEAvBms9gyBnQUkPLyyfwfnU2brcLjz2GL1Uh8+ChnDbxQobkdVykMCbFeG/tuxxaeBgFDtVarvRVIisyhSkdB2VEpSjHvfgnMgIWHp/yXwpTiwjFQ4ninW3xRrzMr53PE+/dydEL1XJDJ501nGXffUd5s2pVSaLSocD+dqzMv859g6gUJdOaiV7semBMPB5Psqjefvtt6resoLg9cnNzOfLII8nIyCB1D69HsnLlSj755BO2fX7Lihpdp8gKlnwjt11xZ5f63y9FZF/RVRF5/qrvEz//+e4xZBTsJ+uYyzIoEugMrRFfGgceG39QLZPDboe6VfDzU5A9BC75OinLH1BNhv/dDfNeUHORtmwSCg+Gy/63B8f0PbxxGjN8xdRWtha59JvjpJ/9J44cdxqFjkLsRrWQ4qq6Fdzz0hTGr0xnQ+8wo047g76Z/Xn7xX+gjyhc8rfHGJM3NtGPJEvUBGrYsGk5ix56AYC1hT6KjzuMjf/7AePIYg4qHs3BRRMYkTUCnajjkTduRvp2NeY2Vtv1zz2LIeZBmvMks2cvZ0lzHigip44K4K2spTzgpCSchhjXsbivB3tIR6x3Kjf/+SEGu5KtK0mW+GTJO3z76/uccsxlnNDvpC33V2Fx/WIGuwZj1re38BoaGhLRax1RVlbG0qVLEw/5nJycPZJUupXKukpC4SAhl5+RBWMx6838b+2XuGoFJFHB6dcj+au59bUPu7RGjyYibejyxPrHG1n8rRrVdeTFAxkwTltLRGMfs/WlIRZS3WMFo5MjxPYEioLyxY3MnfMVjWEb5YE0pEDrhPy6Hj5It3LTZU/wn5fvJXNFOOnwsFHCHFXH1OiMMOzS8zln3MV8X/o90965g+JaK2KunaLFO37uzB7ewJ9PuZ4V//ovmfVqWPK4jDJ62d3kPlzeet2BRqKf3Yxn7XyybpoJZif46yl7+VI+XmlCirWGNFdmh7lu6nR6pasBE/Orf+Opf91E7yo7qQEDXluMPledxeaadVR7KnH+2oB/kJNp18zoUEh2ht/vp6ysjIqKCqqrq3fYNi7HqQ/Wk2JKwW5QRdoT9lDjqybLnk2KKQWDqObqVPgqiDR4cDR4MHoaqMoIUZYTZMLK9uHjV7/2FlZbSrvtO0MTkTZ0VUQAZEnmX3/9EYCLH5mILXX/nETT0NijyBIsfw9WfQx1K2lsbOS/G8cmNYnpZAxbqiSkpLuxIlHb7AJF3ZaR6qbel4rHLnHFIy/y0p1X4WpOTli8sN8K5tVlsd6bvcPhCJlubuofhbNfVyPmOgoiUZTkpFhPBZEZ5/PRYgmTIUpQ1lPnddFsj9Lj4hO4ZMLVTL33z7g2tEacKSiEjTKWaLIwr+0b4un7Psdq2PW1yjvLK7OeYP37X1OXHmH46adzzYhrmPLQsQxdaac+NcLmQTLPXD6DNXUrmfXgY6T5jdhtPqS4gVCkVeAOz95IeSiFLLOf8SefiXDYHTueK9sOmoi0YXdEBFrdWiOPK2L8qTtfZEZD43dH3Sqkf58AIQ8zmwsp8WYSDauuGZPDx5RxDoyOVFjzOaUhB/NbcjltfDFLV61kTmlvyrOCFNarD+Aj8tcyq6o/DoePK6e9C/OeJzr3BYKSgaBkoCFsozZqozpixx1Q36D7j7Jy4q3v7fq441H45CpI743ir+fFz5YQ9Kl9luT76VvVur6JK6OBCbZ6vqjtjxJR505cTjdNfidIOpQzhzL58POxG+0UpbSp9iDHaQw1kmHJ6PKcSzAW5G9/O4FeNWpOWGNKhMjxfch/pyKp3exRbjKb9AwqVa/hwitOJs0cY+abr7LGk8NJh+XR94p/qYnCZiekdN17oolIG3ZXRFbNreLHGevQm3SMOq6InkMzcOXvP4vraGjsNYJu+Plp5JUfsaosQrNkYFxvO8bLPgNHtmoNrPsaFv0HznwVNs9h+tMP4nerNb769NjMKefdRsuHt2DP74d4zU9qv9EA+OvUSgnVS9QqABXziDVsosTnot/Zt6IfP2W3hx/+95nM+W0TK3xZEFcf+AMKN3KUuQ7doBPQjziH2EfX4PVFyTAFocc4orXreHp9X4gY8VnjeOwxGFPI5PFn8+2KT9GFFUzflxI8NJ+Lj7mBWWWzuGjIRWRYMjo1prgc5/gnx3Ps/GwM5jBj07z8VJtJVK9giulIsfo5vCDIt2UphNtYHDc+8zhi9gD1Q8UClAWvIhxx5x6rMKGJSBt2V0TaurS2ctVzh6HTaxPau7qus785jNGix1sfwmDWkZqV7B6oWt9MWo4Na4q27sd+jaJAxW9Qv0ZNcDVtP+gk+OW9/OeDOWTqw5w85XpMY85T53NcvcG4gxIqigJhD6z6BIaeveO2nSXohpn3oGyaw9qKIPP92Zx95Q1Y+hwCJrv69h7yQP1qKByvusdaatj07Cl8tMqFIO14/qkpJYrTr2dtkQ+5OI3HL3yVTGtyMmZ5SzkKSsKamV/6C3NvU9erv/jEAlxn/5NFD5/Kj2tUS++ciw4n//ibCX79AC+/+RPxuIHj89Yy8MkNu38/doAmIm3YXRGB5EgtgNNuHkle3z9OWG3AG8HfHCG7uHWCzucO8+bdvzL25J4MnJDX7sEvxWXW/FKDwaSjaIgLg0nHi9f+2NpAgAsfnIAj3Uzluma+emE5sYgEwNl3jkHUCaTn2Qi2RDHbDeh0mmgfkMgSLHkDLOlqQdL9JZqwYT1sng2jLlYjHXeEpwLfu39FLpvH5kAas2qTl8LukdJERUs6265uuulQC5edcQeDMwYjIrK+eT33vXwlSlziL+feRigeomXVRvzvzWNoVjlHT5sPBjNK9TIWPnwusiIw9h/fIqQWgKIQeOMvEItgu/itnY95N9FEpA0diUgsFqOyspJwOLydo5LxNantdHoBaUspdYNZpy4wYzf87kshhHxR4lEZvVFElhSMFj3RUBxZav3uCCIYzXqMZj0IEA3HiQTiHXcoAjIggE4vIssKitT+eyjqBeS4gk4vYEkxdnifzWYzBQUFWpVZjb1Dcxnx7x8hVLUW6lbil6zknPB/+Oa9RXNTMz0yDGysjvBlXV9icT01GWFW9/bh8Ok5aGMKti3zLR5bDFFRrZeetTb+esv5mMe0WdmzchHIMSgct08uUxORNnQkIps3b8bhcOByuTolALGIhCgK6Awifk+Y4JYkRAB7qgmr04QiKyiAuC+TEgFZVogEYgiiQDwqYXEYd9v15q4JEI9KHe5zpJvxN0cS8fC2VBN6o46WxhCCKOBIM+FtUDOkTRY9KRkWBFEgFpUIeCJEQ6rQGEw6UrOtxKMSIX8MURQItrTeZ1EnIksyDpcZg0mnrq8htpbj6Nmz525do4ZGl4hHQW9Uw6+jPtUlFnTj//4Z3vnoB7yBneeXRc1R7pj+vnrsfsKuiMhu1DY/cAmHwxQXF3fagjCYWn2h9lQzRrOekC9KJBjH74kQ8seQJQVBAHu6GZ1BJBKIEQ7EcGZak46XJRlBFLrFepHiMt6GUNIDP+SPoTeqVpPVacRs7fwbezwmE4vEiUclDCYdZrsBvUEk4I0SDcWxOIyJf4qi4KkLEvC0rqFuthkwWQ1kFOiQ4krSfTAY1TmReEyipTGMLVVdTMtg0mMwqV9Lo0X9Px6VCfmjILVahVsxWqwE/HXUbPTiyrdRs8FLwcA0Ni1uILPI0W7eRUNjj6Lf4sYVxVYRsKZjP3Eql/YchX/Bu6xYtIbmsJ4csw9RUBg8+WxCZUtxlyzj+6ZiMtIN+5WA7Cq/W0tEEISTgJP69OlzRUlJSdK+NWvWMHDgwN0+Rzwm46kLJLl1OsKWasKaYkSWFNzVAXQGkbQc6x4VknAghs8dVssd2I2IegFFVggH4shS60JD6bk29Mb2E4RbLSlQrSlZkmmsbF3dLz3Pht6gHqcoCpFAHINZl2TlxGMS7uoAAKnZ1oTF8P/t3Xd8VFXe+PHPmZLeKyWUhIROqIFASARFQAXLCro8FpQVdi1Y9lkUO+tPXeVBVNTVFVHURUVEBDugIEUQQgsQIAUCaaSR3qad3x8zGdJJQiCF83698mLmzr13zpkb5pvTvre11AiSAqoKnJKazN7/1p+bya+HGzG396VrqBc5qcWUFxvoGuqFvp7PoDWU2v6o8AtSM/iUasxG0Ojq3uDt5Faksw+i60XcMvoSUN1Z1dTXndVaQQSs3Ucmgxmz0YJGKzBWmqksN2E2WnB01lFZXv+4gM7B+gXs4KTFYpE4uujQ6bX2GU8bNmwgPj6ehQsXNqkceRnWL3x3H2tLqXr5jBUmNFoNhdllSAnufunR/bQAACAASURBVE44OltvxSqltAagan/hV7UAqrqaXL0ccfVs+kJLaZGXLNeYlBKLWaLVaZBSYjZa2L8nrsEgUuXaOQPZ9GE8AJ7+zrh6OdK9rxfDp/SyB5Tq5TaUm0jYm0XXPp51pnRLKTmw8QyV5Sb8e7izddVxomaEMWBcV9a8Ekt2ShHRt4fh5Kanb0SXS/Ap1CWlJGFPFt5dXAjo1fwVyopSnQoi1VzqIFIfk9FMeZHB2kWjERTlllNZdj6Y6By0mI2WOsnTqrh6OdqDSlOUFxsoPleBq6djo6vqjZUmCrLKkVLi6KxD56gFaZ19VR8hBH493Nr9xIGjR+MJ7hFKQVYZWp2GwGDrl2heegmlhZXs/uYkOWcaTkd+3d+GkHOmmPidGcx6bgyOLjrW/CvWfsygmO70GeZP0ABvKkqNbHjzILmpJXXO4+btSEl+zc9y/Mwwwq8OuuSfYc6ZYr58eS8A97wa1aygryi1qSBSTVsEkdqklJiMFnR6DWaThbT0VKZOncq4yHHs2r2boeFDmXXbHbz0yovk5uXw7zc+ICHxOPEJcbz3/rvcc889eHh4EBsby9mzZ1m8eDEzZpy/AVVOajHSIhvsqqrOYpGUFxnqBA4PXyccnHUIjaCy1IShwoSjqx5H5/Y/bHah62koN/HzB0epKDVy48NDSTmcx+kjefQc6MOONYk1AnxjuoV5ETzUj51fWefo3/5MBMn7czAZzCTty7YHkMibQygrMnDyQA4l+ZVETAtm9LRLN/BvMprZ/FE8yftzAGtX4thb+nBi91kGRHWl95CmLXxTlCpqYL0Z/vntUeIzilr1nAO7efD89PPZQoUQ9i6TqtZFUlISa9as4YNBg4iIiGD9D1+ze8/vbFi/gXeWv86Ua67HWGmmtNA68ykzM5MdO3Zw/PhxbrzxRmbMmIHFbB0jkBaJo4v+ggEErOMdrl6OODjrrLOjKkx4d3GxD2YDOLnpcXLrPFNmHZx1TJ9/PpV5vzFd6DfG2s0UOjKArxbvIy+tBL8ebjVaGDc/Nhwndz0VJUbOxJ/jwMYzZCQWAPCXJdE4uent2Z1H3xhCXnoJXgEuOLlaP7vxM8LY9FE8e787RUpcLn9aMKLJrcvmiN+RaQ8g19wzgF9WHuPH9w4DcPKgdXtQf2+mzx+KRq23UVrZFR9E2kpwcDBDhgwBYNCgQVxzzTVoNBrCh4aTmnYGF0/rbKXSgkoqy0xMu+FGKkqMDOg/gKysLCwWWWPg27mZX/pVU2qvdDoHLX9+5nxyQWmR5GWU4uSqw837fJqJ7n29CRsVwOoX99JnuH+dIKt30NIluOYMG6ERjJ8ZRvqJfHLOFLP6xb0MjOpG8FC/Bj/7gqwyMpMLOHuqiD7D/Dmw6Qxjb+lTZ5zjXEYpR7enM2JqLxL2nAWsQa97P2/cvJ2I35HB8Gt72ru40o7n8/WS/QyO6Y5/T3frJIXschxdra1PvYOWgN4ebT5F/UIsFonZZLlkEyOU5rvig0j1FsPlVP22mhqNxv5co9FgMpnQaASOLrbFexJM5ZKSfOvKcYtFkptq7a+vCgbtfdyioxAa0eDMKr8gd+5bGt2sdTcuHg7cu3g8Wz49RvzOTH7/Oonfv05i8l8G4d/T2opxt00L37LqOPHbz6cMr3q85l+x/PnZ0TUG+I9sS+fw1jTitqQBMPGu/nTv5w1AUD9vgmyPZz0/BmOFmaR9WRzcnErWqYZb3QOjujLhjv5tewO2Rkgp+fXjY5z44ywjr+tF5E3nE6JWlpvIzyylS0jHnSrbUV3xQaQ9ExphnSrr1PBfXe6+TiqAXEaOzVhnU13Mn/vRa7AfBdll7FqXzMYVDd8B2ivQBWOFidJCg22qNny9ZD/9xnThXGYJnn7OJB+wdlM5ezgQeVMIA6O61Xsun67WnFOBwR4E9PJg44qjdA31xNndgVOHcq1Twt31lBcbid+ZSfzOTG59YmSdVlV7kJtawok/rK2ufT+eJi+tBCc3PWNvCWXjB0dITyigd7gfpQWVDLu2x2WbGXelU0GkA9DqNHj4ORPQywNjpRkhwC/IzZqK5BL0sSutT6vXEDLcmoxv6NU9WP3SHvLPliE0AhcPB/sizdotDoCi3HK+fesQh7daWx3pJ6zjMh5+Ttz14rgmlyEsIpCwiIbv3bFjTSKHfkll7eJ93PyodTzIt9uF17s0NxEnQPbpIs5llNJvTJcmtXxOHsyxj/Pc9lQE+zeeJinWenvaxL3ZmE3WtVApcbkAbFoRj3egq721d6UoyC7DxcOB/MwyXDwdcPdp/r1EmkvNzlI6vI54PctLDBzeksaIKb2sGQWkBEmDX6jGSjNnjubh4eeMyWDm8G/pRNzQG+8urZDd1kZKSWFOOd+/E0dBVhlgXRs3enowI6+rm+HBbLaQFJvNts9PEH51D0ZM6YVWr+GP9SepKDNy1ax+9Y6xFGSVser53QCEDPMnakYo+35MYVBMdzz8nO0TEwAS92bx63+PY6o8n4Xhr29dhU6vxWyysOvrZI7uSMdksHDr4yPJPl1E+okCMhILqCg1MjimO+UlBvqMCCBsVP0BtDCnjPSEAvoM929xS/Nyy0jMJzE2GzdvR4Zf2xOT0cK2LxI4sfusfR9HFx1/eS26RT0VaopvNSqIdH7qerau3LRivn8nrs6aFzcfRwbHdMfN24nQUQHEfp9C7A8pNfbx8HOiKNe6cDWgtweT/zIQT38XDBUmjvyWzpn4cwT18+aPDScbLUPEDb0ZeV1vvn/nEKnH8nFw0hIxLZju/bzx71GzdWEyWFPn+HQ7H1DPnipk84fx9rxtAF37eHLjo8PsrXezycL2LxNJ3pdNRakRVy9Hbvnf4RTnVWAyWNi5NolB0d0YNql17tHRGKPBTFJsNl1DPe2petIT8jmw8QwjpvTEr4e7fRHx/o2n2fV1co3jXTwcauSaq6rvtPlDayw+bioVRKpRQaTzU9fz0jEbLRzZns6OL2umDnL1crR3wfUdHYhGIzixJwtpsX6fhAzz58yxc3j6OTNj4Ui+fDmW/MzSGue455UoDv2SyoFNZxotw8Dx3Zh4Z/9ml91kMPPb5ydw83aistTI4d/SAZj55CgCennww7txnDqUa99fq9eAxN41VqXvmEAm3TPwko49Hvktjd8+TwBg7C19GDqpB589v9sekD38nZnx+EjOZZbyzdIDAEy6ZwClhQZ2rbMGlD4jAphwRz/y0kpw8XS4qFaqCiLVqCDS+anreemZjGakhD0bThK/M9OeEieovzfX3x9uT6559mQhCXuyGHdrH9KO5/P9O3H2gXsHJy03PBjOt28dImxUIFffbb1mFovEUG6iosSafqcwt5y89BKO2L70J9070L6u52Js+vAoCXuyAAjo5U726WKcXPVodYL+Y7sSMtyfbV8kkHWqCHdfJ/qMCOD04Vzyz5ZxzewB9A73Q6vT1EgkKqV1yrFWp2lxkDEazKz4+3bMJgu+3V3JSy8ldGQASfusYz5B/b1JP5GP1kFr79a768WxePhZb1yVl1FC4t4swif2aLUbuqkgUo0KIp2fup6Xl9ls4eT+HAwVJvpHdrX+Bd+AqsF6sOYv6zu6C2azpUk3GZMWSVZKEYG9PVpl2rHZZCF5fzZHtqWTmVQIwHV/HULwUD/7+aVFWrM12MZGpEWybul+MpMK0WgFFrNkxJReBA/14+j2dIRGcGxnJmGjAhgxtTenj+Qy+KqgJmd6MJst1pu1SZhwRz8Gju/Glv8e59jOTABuenQYQf19SDmcy/fvxAHWoHLTo8Mv+vNojAoi1agg0vmp69l+mY0WYn9KwdPPmbCIwHZzW+mc1GIyEgoYPKH7BQNaUW45G1ccJS+9BJPB0ui+Vfx6uDHtwaF1ctnlnCnGYpH2u4RmJOaz7rUDBAZ7MOMJ63e2ocLEj+8dRlok0x4aas9EcSouF2mW9ll+l5JKe6IoSrug1WsYMz2krYtRh38P9zoD9A3x8HO2f8FXlpv4fW0SRbnWAXudg5bo28I4uj2D3NRiuvX1IjE2m9zUEjYsO8iwST3pHe6LyWAhaV82v6+15l0bOqkHFrO0r7y/7q9D7O/n4KSrt6URHN4+c6B12iBS7X4ibV2US2rChAksWbKEUaNGcf311/PZZ5/h5XXl3P9dUS4nR2ddvYP8Y285v3p+5NTepMTl8vMHR/j1k2P1nufQ5lT7Y2d3PS6erTOW0RY6bRCRUn4LfDtq1Ki5bV2Wy+WHH35o6yIoigL0Dvfj9mdGkxKXS8rhPM5llqLRCHy7u3HN7AFkny4i7Xg+J/44S7cwrw6ddaLTBpH2LCUlhalTpzJ+/Hh2797N0KFDuffee3n++efJzs5m1apVDBo0iPnz53P48GFMJhOLFi3ipptuory8nHvvvZf4+HgGDBhAefn5efC9e/cmNjYWPz8/br75ZlJTU6moqOCRRx5h3rx5ALi5ufHII4/w3Xff4ezszPr16wkMbHgVs6IoLeMV4MKwST3rXWfSe4gfvYf4MXr6pbtFwOWigsiPC+Hs4dY9Z5chcN0rje5SlQr+/fffJyIigs8++4wdO3awYcMGXn75ZQYOHMjVV1/Nhx9+SEFBAaNHj2bSpEn85z//wcXFhbi4OOLi4hgxYkS95//www/x8fGhvLyciIgIbr31Vnx9fSktLSUyMpKXXnqJxx9/nOXLl/PMM8+0bv0VRWmSliwEbG86fg06qPpSwQshGDJkCCkpKaSlpbFhwwaWLFkCQEVFBWfOnGHbtm08/PDDAISHhxMeXv+9mZctW8a6desASE1NJTExEV9fXxwcHJg2bRoAI0eOZNOmTZe6qoqidGIqiFygxXCpXCgVvFarZe3atfTr16/OsRfqP926dSubN29m165duLi4MGHCBCoqrCtf9Xq9/XitVovJ1LS7+imKotSnfUzaVuqYMmUKb731lv0+7AcOWFMdxMTEsGrVKgCOHDlCXFxcnWMLCwvx9vbGxcWF48ePs3v37stXcEVRrigqiLRTzz77LEajkfDwcAYPHsyzzz4LwP33309JSQnh4eEsXryY0aNH1zl26tSpmEwmwsPDefbZZ4mMjLzcxVcU5QqhVqwrHZ66norSupqzYl21RBRFUZQWU0FEURRFaTEVRBRFUZQWU0FEURRFaTEVRBRFUZQWU0FEURRFaTEVRBRFUZQWU0FEURRFaTEVRNpASkoK/fv357777mPw4MHccccdbN68maioKMLCwtizZw+lpaXMmTOHiIgIhg8fzvr16+3HRkdHM2LECEaMGMHvv/8OWPNlTZgwgRkzZtC/f3/uuOMOOvtCUkVR2t4Vn4Dx1T2vcvzc8VY9Z3+f/jwx+olG92lpKviAgAA2bdqEk5MTiYmJzJo1i6oV+QcOHODo0aN069aNqKgodu7cyfjx41u1boqiKNV12iDS3m+P29JU8N26deOhhx7i4MGDaLVaEhIS7OccPXo0QUFBAAwbNoyUlBQVRBRFuaQ6bRBp6u1xL9RiuFRamgp+0aJFBAYGcujQISwWC05OTvWeU6V5VxTlclBjIu1UQ6ngCwsL6dq1KxqNhk8//RSz2dyWxVQU5Qqngkg71VAq+AceeICPP/6YyMhIEhIScHV1beOSKopyJVOp4JUOT11PRWldKhW8oiiKclmoIKIoiqK0mAoiiqIoSoupIKIoiqK0mAoiiqIoSoupIKIoiqK0mAoibaCgoIB///vfF9xv69atTJs27aLeKyUlhc8+++yizqEoitIQFUTaQFODSGtQQURRlEtJBZE2sHDhQpKTkxk2bBgLFixASsmCBQsYPHgwQ4YMYfXq1XWO2bt3L8OHD+fkyZM1tl9//fXExcUBMHz4cF544QXAuuL9gw8+YOHChWzfvp1hw4bx+uuv1zh269atXHXVVdx222307duXhQsXsmrVKkaPHs2QIUNITk4GICcnh1tvvZWIiAgiIiLYuXMnAHv27GHcuHEMHz6ccePGceLECQBWrlzJn/70J6ZOnUpYWBiPP/54636AiqK0G502AWNTnX35ZSqPtW4qeMcB/eny1FMNvv7KK69w5MgRDh48CMDatWs5ePAghw4dIjc3l4iICGJiYuz7//7778yfP5/169fTs2fPGueKiYlh+/bt9O7dG51OZ/+C37FjB3feeSehoaEsWbKE7777rt6yHDp0iGPHjuHj40NISAj33Xcfe/bs4c033+Stt97ijTfe4JFHHuGxxx5j/PjxnDlzhilTpnDs2DH69+/Ptm3b0Ol0bN68maeeeoq1a9cCcPDgQQ4cOICjoyP9+vVj/vz59OjR46I+V0VR2p8rPoi0Bzt27GDWrFlotVoCAwO56qqr2Lt3Lx4eHhw7dox58+axceNGunXrVufY6Oholi1bRnBwMDfccAObNm2irKyMlJQU+vXrR2ZmZqPvHRERQdeuXQHo06cPkydPBmDIkCFs2bIFgM2bNxMfH28/pqioiOLiYgoLC5k9ezaJiYkIITAajfZ9rrnmGjw9PQEYOHAgp0+fVkFEUTqhKz6INNZiuFway1/WtWtXKioqOHDgQL1BJCIigtjYWEJCQrj22mvJzc1l+fLljBw5sknvfaGU9AAWi4Vdu3bh7Oxc49j58+czceJE1q1bR0pKChMmTKj3vCotvaJ0XmpMpA24u7tTXFxsfx4TE8Pq1asxm83k5OSwbds2Ro8eDYCXlxfff/89Tz31FFu3bq1zLgcHB3r06MGXX35JZGQk0dHRLFmyhOjo6HrfqyUmT57M22+/bX9e1Q1XWFhI9+7dAes4iKIoV54OGUSEEDcLIZYLIdYLISa3dXmay9fXl6ioKAYPHsyCBQu45ZZbCA8PZ+jQoVx99dUsXryYLl262PcPDAzk22+/5cEHH+SPP/6oc77o6GgCAwNxcXEhOjqatLQ0exAJDw9Hp9MxdOjQOgPrTbVs2TJiY2MJDw9n4MCBvPfeewA8/vjjPPnkk0RFRan7mijKFapJqeCFEF7AB8BgQAJzpJS7mv1mQnwITAOypZSDa702FXgT0AIfSClfacL5vIElUsq/NLSPSgXf+anrqSitqzmp4Js6JvIm8JOUcoYQwgFwqfWGAUC5lLK42rZQKWVSrfOsBN4GPql1vBZ4B7gWSAP2CiE2YA0o/6p1jjlSymzb42dsxymKoiht4IJBRAjhAcQA9wBIKQ2AodZuVwH3CyGul1JWCCHmArcA11ffSUq5TQjRu563GQ0kSSlP2t7zC+AmKeW/sLZcapdJAK8AP0op91+oDoqiKMql0ZQxkRAgB/hICHFACPGBEKLGPVmllGuAn4AvhBB3AHOA25pRju5AarXnabZtDZkPTAJmCCH+Vt8OQojpQoj3CwsLm1EMRVEUpTmaEkR0wAjgXSnlcKAUWFh7JynlYqACeBe4UUpZ0oxyiHq2NThYI6VcJqUcKaX8m5TyvQb2+VZKOa9qrYKiKIrS+poSRNKANCll1bSgr7AGlRqEENFYB97XAc83sxxpQPWVaEFARjPPoSiKolxmFwwiUsqzQKoQop9t0zVAfPV9hBDDgeXATcC9gI8Q4sVmlGMvECaECLYN3P8Z2NCM4xVFUZQ20NR1IvOBVUKIOGAY8HKt112AmVLKZCmlBZgNnK59EiHE58AuoJ8QIk0I8RcAKaUJeAj4GTgGfCmlPNqSCnUEKSkpDB48+MI7VnPPPffw1Vdf1dne0nTxKruvoiitoUlTfKWUB4EG5wxLKXfWem7E2jKpvd+sRs7xA/BDU8qjXLyqIPI///M/bV0URVE6sA65Yr0zMJvNzJ07l0GDBjF58mTKy8sBa0qRyMhIwsPDueWWW8jPz69z7E8//UT//v0ZP348X3/9db3nVyniFUW5HK74BIzbv0wgN7U5E8kuzK+HG9G39W10n8TERD7//HOWL1/Obbfdxtq1a7nzzju5++67eeutt7jqqqt47rnn+Oc//8kbb7xhP66iooK5c+fy66+/Ehoayu23317v+VWKeEVRLocrPoi0leDgYIYNGwbAyJEjSUlJobCwkIKCAq666ioAZs+ezcyZM2scd/z4cYKDgwkLCwPgzjvv5P33369zfpUiXlGUy+GKDyIXajFcKrVTpVd1ZzWFdcF+41SKeEVRLgc1JtKOeHp64u3tzfbt2wH49NNP7a2SKv379+fUqVP2cYnPP/+83nOpFPGKolwOKoi0Mx9//DELFiwgPDycgwcP8txzz9V43cnJiffff58bbriB8ePH06tXrwbPpVLEK4pyqTUpFXxHplLBd37qeipK62pOKnjVElEURVFaTAURRVEUpcVUEFEURVFaTAURRVEUpcVUEFEURVFaTAURRVEUpcVUEOkkqqeKf+ONNygrK7O/5ubmdtHnP3jwID/8oJIsK4pSkwoinVDtINIaVBBRFKU+Koi0gcWLF7Ns2TIAHnvsMa6++moAfvnlF+68804ANm7cyNixYxkxYgQzZ86kpMSaafiFF14gIiKCwYMHM2/ePGovFl22bBkZGRlMnDiRiRMn2rc//fTTDB06lMjISLKysuqUaciQIRQUFCClxNfXl08++QSAu+66i40bN/Lcc8+xevVqhg0bxurVq2scu3LlSm6++WamT59OcHAwb7/9NkuXLmX48OFERkZy7tw5AJKTk5k6dSojR44kOjqa48ePA/Dtt98yZswYhg8fzqRJk+zlW7RoEXPmzGHChAmEhITYPzNFUdqPKz4B45aV75N9+mSrnjOgVwgT75nX4OsxMTG89tprPPzww8TGxlJZWYnRaGTHjh1ER0eTm5vLiy++yObNm3F1deXVV19l6dKlPPfcczz00EP2VCh33XUX3333HdOnT7ef++GHH2bp0qVs2bIFPz8/AEpLS4mMjOSll17i8ccfZ/ny5TzzzDM1yhQVFcXOnTvp1asXISEhbN++nbvvvpvdu3fz7rvv8sILLxAbG1sjV1Z1R44c4cCBA1RUVBAaGsqrr77KgQMHeOyxx/jkk0949NFHmTdvHu+99x5hYWH88ccfPPDAA/z666+MHz+e3bt3I4Tggw8+YPHixbz22muANWvxli1bKC4upl+/ftx///3o9fqLuj6KorSeKz6ItIWRI0eyb98+iouLcXR0ZMSIEcTGxrJ9+3aWLVvG7t27iY+PJyoqCgCDwcDYsWMB2LJlC4sXL6asrIxz584xaNCgGkGkPg4ODvZb6I4cOZJNmzbV2Sc6Oppt27bRq1cv7r//ft5//33S09Px8fFp0pjKxIkTcXd3x93dHU9PT3uZhgwZQlxcHCUlJfz+++81UttXVlYCkJaWxu23305mZiYGg4Hg4GD7PjfccAOOjo44OjoSEBBAVlYWQUFBFyyPoiiXxxUfRBprMVwqer2e3r1789FHHzFu3DjCw8PZsmULycnJDBgwgOTkZK699to6GXorKip44IEHiI2NpUePHixatIiKioomvV9V+viG0q/HxMTwzjvvcObMGV566SXWrVvHV199ZU/YeCEXSh1vsVjw8vKyZ/qtbv78+fz973/nxhtvZOvWrSxatKje86rU8YrS/qgxkTYSExPDkiVLiImJITo6mvfee49hw4YhhCAyMpKdO3eSlJQEQFlZGQkJCfaA4efnR0lJiX02Vm0tSfPeo0cPcnNzSUxMJCQkhPHjx7dq6ngPDw+Cg4NZs2YNAFJKDh06BNRMHf/xxx+3+D0URbn8Om0QEUJMF0K8X1hY2NZFqVd0dDSZmZmMHTuWwMBAnJyc7F/Y/v7+rFy5klmzZhEeHk5kZCTHjx/Hy8uLuXPnMmTIEG6++WYiIiLqPfe8efO47rrragysN8WYMWPo27evvXzp6emMHz8esHZXxcfH1zuw3lSrVq1ixYoVDB06lEGDBrF+/XrAOoA+c+ZMoqOj7eM4iqJ0DCoVvNLhqeupKK1LpYJXFEVRLgsVRBRFUZQWU0FEURRFabErfoqv0jJSSvLLjHg46dBpm/a3yNnCCjQCDGYLznotvm7np+9KKTlzrgxPZz1eLg6XqtiKorQyFUQaUW4wYbJICsuNBHo4UWYw4ajT4qTXtnXRLqvSShNlBhP+7k72bUXlRtLyy9BqBF09nXF30qGvFkyMZgtp+eXoNAI/d0e0QpBdXHNNi6ujDie9lvwyA6nnrLm+CsuNCCHQawXOei1Gs0SnFWhs61wURWlfVBBpRGJ2if2xySwpqjACMKibB1rNldMTmJ5fToXJTEGZkQqThW6eTqQXlANgtkjS8q0BIMDdkUAPJ4QQ5JUaKK4wohGC/DJDjfN5OOkpNZhIzinB1UGHyWKdIajXapASTueVAuDmqKOk0oSro44QP1f7gklFUdoPFUSaqCqAAJRUmik3VFJYbqKnjzPODu3rY6w0mtFrNZilRKcRF/3lq9VYjy83mgHsAQSgj78bGYXllBvMZBdX4qTX4uqoo6jciKNOQ08fF3sw9nFxoLu3M0IIygwm0vPL7Z9rVaAoM5jJLalEKwTnbMGntNLE2aIKBODt6oCj7spqCSpKe9a+vv3aEUsj62eq/lIGKCg34uygw2S2IMHepWOREiRoNJf3r+ec4goyC893G3k46enh44xFWoNBc7uFKoxmSg0mnPRavJz1ODloSc8vx2i24OXigKujjrAAd6SUJGSVcObc+RT0ns56nB109O/iTqnBjHe1sQ4XBx2hAW7kl1m7xVwddQghcHXU4epo/bV00GuwWKDUYCKn2JpnK6fEgJeznkqTBQ9nHb6ujiiK0naunD6ZZjJb6g8itQeRc4orScktJT6ziGOZRZRVmjCYLJzMKeVIRiGF5cYa+xtNFk6eOkX//v257777GDx4MHfccQebN28mKiqKsLAw9uzZw549exg3bhzDhw9n3LhxnDhxAoClS5cyZ84cAA4fPszgwYMpKyvDYpGcLTwfQKpCRVGFrfDm6gAAGgRJREFUkaMZ1rKl5JbWSR3fECkl2UUVpOVbWx2+bg4EeDjh4aQnLNANN0cdXi7ns+kKIfB3Px8k3J30dPNyBsBBp60RQKof4+2iJ8TPlQC3usEgwN2JLp5OhPi50sffDWcHrW1A30CZwcTZwgqOZhRSZjCxcucp9p/JZ+mmBLKLK3h+/RG2nshuUl0VRWm5K37FesG3yRgySuscZ5GScoO5xjZnB2s3SqXJjEYIdBpBhdFS51izvxNl0d3sz7t7OePr5oiUksPpheRmpjF57DAOHDjAoEGDiIiIYOjQoaxYsYINGzbw0Ucf8cknn+Di4oJOp2Pz5s28++67rF27FovFwoQJE3jsscd46aWXePPNN4mKiiLtXBnnygw46DSE+ruh02qQUpJbUlmjZRLs54q7U91U6iaLBSmtwUen1WAwWTh+tggAJ72WvoHuF/yspZQUlBspN5jp4uF0SVphmQXl5JcZ8XLRk1tibZ1knTnJ3A2Z9e7v7+7IitmjCA/yIj6jCJ1WEOrvdslaiCfOFpNRUM7E/gGX5PyKcjk0Z8W66s5qgMFkDQ4OOo39cVVXkLP+/Mfm7CAwmi32v/xN1VowVQPDGQUVlFaaKSi39vFXmiz07NWb3mH9sUgYMHAg11xzDUIIhgwZQkpKCoWFhcyePZvExESEEBiN1haNRqNh5cqVhIeH89e//tWeLr7UYMLdSU9vXxf7GIi1deCEn5sjEkjIKuZUbimhAW64VBvHKak0cTLn/CQCT2e9fRwEINDj/KysxlhbFg54uzRp9xbp4mltnQgh6OblTIXRTNaZhvfPKa7k5nd28tOjMVy/bDsAUwYFEh7kRWSILyN7edd7nMlsYWdyHoO7edSYilzly9hUyg1mokJ9mffJPh6ZFMZNw7pz54o/yCmu5P27RuLr5sDIXj6tUu8LsVgkH+48RRdPJ6aFd7vwAYrSSq74IOI1vU+92+PSCgAIDXAjyTYwHBzk1aRzZhVVUFZUgaeznl6+LiRmldgDSBWNTs+pXGsLqLjCTGGltdVQWG7EZDLx7LPPMnHiRNatW0dKSgoTJkywH5uYmIibmxsZGRkAlBlMVJoseDrr6x1EF0IggB7eLiTnlJCcXUpPH2c8nPVUGC01AghQowtucHfPdjW9tnb9nPRaAj0c+eV/r+LgmQIc9Rr7l+i2hByOZRbx7m/J3PLOTvsxPx/N4uej1rsn3jysG4tnDOWno2fZlZzLy7cMQQjB65sTeGdLMr19XVjzt3H4u1sDicUi+XT3aZ7fcLRGOR754iDuTjryS63Xed6n+wDY8FAU4U38vbkYRzIKefH7YwBEh/nj6axu3KVcHmpM5AK0GoFA4FFPF1BD/N0dCfF3w9vVAa1GQw8fFzyd9fT0ccHDSU+wryt6rabGepNyo5mMgnLOFlViMFnIzjtHt27WL8OVK1fa9yssLOSRRx5h27Zt5OXl8dVXX5GcYw1GVQPSDXF11NGvizvODlpOnyvjcHohidnW9O7uTtbyDe7miZvtPAEeTu0qgDREr9XQx9+NW0cG1fgrPKavP3+9qg+vzRxKqa1rcu39Y3FxOP+5f3Mwg4c+28/ybSf5fE8qb/+aROq5Mt7ZkgxASl4ZES9tZuy/fmFvyjm2JebYA8jz0wcSFnD+hl1zVsZiskiiw/wI9LAGnfv/u9/+x8KllHru/Iy5FTtOIaXkSHohlSZzI0cpysW74lsiF6LVCIYEeTbrGI0Q9i9ioMaMIy8XB1JKtGg1ghA/V86VGvBydcDPzdHeYrBIyaz7HuKJ/32Q119/3X4PdrDek/2BBx6gb9++rFixgokTJ/LxN8Nx9/Kt8Z4NcdRpCfZz5WROiX3KrrXF5GrfJ8T/wncy7EiuGRDI4hnhIGFkLx92PHE1RzMKGR/qx8s/HGP59lP2fV/blMBrmxIAGBviS28/FzbFZ5FVVMG9H+1l0gDrWMeCKf24NyqYe6Osd2H86chZnl53mJmjenBfdDB+bo4cSS/kjg/+4Ln1R/hkzuhLts7lcFohD362H4CJ/fxZ9ksiK7afpNRgZuqgLjw3feAlG6NSlCt+YL0hVd1ZQ7p7XvZFbiazdXZXpclCaIBrg+tQDCYzx89aWxI+rg4ENWMwQkqJ0Swps42laDvwF8zFpIKXUjL3k1g2H8tm1uiefL7HOsDi5aJn++MT7ZMQUnJL+Z/lu8mwTVJIeeWGJp3/nS1J/N/PJ3Bz1BH7zKRLku1g6aYElv2SyJDunqyYPYqJS7ZSajDj4qClrNrkkGMvTLVPDlGUxqhU8K2oLVZJ67QaQvxdkUgSs0tIyS3FImWNrgkppT2A6LQafF2bl29KCIGDToOXi0OHDiAXSwjBB7MjSHnlBv71pyGkvHIDhxdNJvbpSTVmsfX2c2XDfOsNusYEN32w/M7IXoB18sIbmxOJzyhqdP+iCiOJWcW8vimBc6UGnvgqjuRaY1YAyTklvPR9PAaThd+Tcunu5cyXfx1LgIcTv/5jAi/ePJjYZybVOGbGe7+z59Q5iiuMlBlMZBVVkJxTwsmcEgpqZRVor9Lyy/hqXxrFFcYL76xcFqol0oDSSutgtU8zv5xbU1ZRBVlFNfNN+bs7kl9mXQ1eWmlCCMHgbh5XdEqQy3lTquyiCpwdtPVOk27Mbe/tYk/KOfvzQ89PrjP4nXqujOjFW+oc6+GkI27RlBrbnvw6js/3pNqfv3jzYHvAqi4hq5iSShOf7jrNugPpjZbx3TtGcN2Qrk2qT1uwWCQ3vrODI+lFTB3UhffuGml/Lae4koOpBVw7MLANS9h5qCm+rcA6jtG2ZQhwd8TH1YGk7BKMZus046qV2ybbc5VT6vIKaOJ059peuHkQT319mP1nrN2kC9YcYnyYH1tP5BAT5kd0X3+uee23eo8tqjDxv18eYvGMcKSU6LSaGqlfbhnenTvG9Kz32Kr1PSN6epNTXMmOpNwGy/jYlwcJC3QnNKB9jokdTi/kSLq1JffT0bPc93EskwcFMmNEEPeu3MOR9CIemNCHMoOZyQMDGReqbrV8OaiWSAeQX2ogNb8MbxcHiitMSClxd9JTWG5kQFf3Jqdi76w60vUsrTTx/raTvPlLYr2vuzvq2LJgAgCnckvp7evKP789yndx5xdT+ro6kGebSvztQ+ObPPGjzGBi49Esrh/SFa1GUGowsTk+ixvCu5JdVMmUN7ZRZjA32KppSxaL5MvYVBZ+fZivHxjH4p+Os/uktWUXHebH7pN5GM01v8sOL5rc7BZjR2exSDQawe9Jufi7OxLWhEXC9WlOS0QFkQ7CaLZYkypaJBYp0Ws1WKTsEFNwL7WOeD2nvbWdI+lFTOjnj6ujju/jMvF01rNtwUQ8XWp+8UkpefiLg3x7KKPG9iBvZ3Y8cTWt5cfDmdy/yjrLa8XsUTjptYzr43vBlu6xzCJ6+brYF7BWGK0ZHRx09f9xYzBZGPT8TwD88vcJ9PRteEJIucHM8bNF3PLv3+3bDjx7LV4uehKzS5j8+jb79v930yCWbz9lz9/W1dOJjY/F1JlO3xnkFFcSm3KOiGAf/GyLYc/klXHHit14uzgQl1aIViNIfvn6Fp1fBZFqOksQURrWEa9nVlEFSzcm8My0AU36a9likZzKK8XP1ZHCciNfxqYy76qQZq1faopKk5lJS3+rse7k9duHcsvwoDr7lhvMvPrTcVb+nsKwHl58dE8Ezg5a7lrxB2n55Wz5x4R6v7x3Jecxa/lu+/MVs0ex4Ks47orsxdg+vkSG+NpfW/zTcf69Ndn+fFQvb9b8baw9sEkpeevXJPadzufdO0fYA9kjXxxg/cEMNAIsEv40ojuvzRxab0DcmZTLNwfS+duEPvSpNr1dStmmXcXlBjPJOSUM7l6zpSmlJHrxFnteuwVT+jGwmwf3frS3zjmSXrquRT0VKohU01mDyDfffEPfvn0ZOHBgWxelzXWG69me/HTkLH/7774a27p4OOHv7sg/pvQjLMCNbl7OPLXuMJ/9UTPnjI+rA+dsXW0je3nz7p0jCLDdzGz13jOs3pvK1f0DWLIxgfAgT+LSCustwzM3DOAv44OZ+d4uYk/nM2lAAG//z4gmtygqjGYWbTjKF3vPTz4I9nPl50dj7C0kk9nCyz8cZ+3+NArLrXfp/PHRGM6VGCgoN/DY6oM8MCGUOeODm/bBXYTErGL+d80hJg0I5OFrwoDz08MjenuzYEp/Rgf7UGkyM+KFTfVO4QaYGx3M7pPn8HDWMf/qMCJ6+7Ro9qUKItV01iByzz33MG3aNGbMmNHWRWlzneF6tjf7TucDkkqThRe+jbdPJ6/ywd2jeOvXRA6lFRIe5EmghxOb4rPsr0/s589vCTncEN6Nt2YN5/b/7OKPU+dqnOPgc9fyw+GzPLXucKNlmTkyiP+bObTZdbBYJK/+dJwunk4kZJXw+Z4z3D22F0/fMABHnbZOENRqBMF+rvY0R1X+b0Y4M0f1aPb7N8e7W5N59afjANw+qgf/vGkQ/Z/9qcY+2x+fSFy1haWbHotBCMGkpdYJGY9N6ssjk8JapTwqiFTTHoNIaWkpt912G2lpaZjNZp599lmeeOIJbr/9drZssU7x/OyzzwgNDeX06dPMmTOHnJwc/P39+eijj0hLS2PatGl4enri6enJ2rVr6dOn/hxgV4K2vp6dXYXRzNxPYunh48Lqvak1bpOg1wq2/GMC3b2cMVskH+w4xeubEtj+xET+u+s0y35N4vuHxzPtrR1IaW1hvPj9MQI9HNn9pDXpaLnBTE5xJekF5ew/k098ZhG/J+WSX2ZdC/L09QOYGxNyUXWQUnLNa79xMreU7l7OvH/3SB5YtZ/Tedbxk/AgT+bFhPDQZwfsxwzo6sGxTOtssJ0Lr6a77dYGrc1othD29I/4uTkwvKc3m+KzuHVEEGv3p6HVCB6+Ooz3tyXjqNfaW3nH/99Ue6ssp7iSNftSmRXRE+9WWpKggkg1FwoiP/74I2fPnm3V9+zSpQvXXXddg6+vXbuWn376ieXLlwPWfFhDhw5l7ty5PP3003zyySd8+eWXfPfdd0yfPp0ZM2Ywe/ZsPvzwQzZs2MA333yjWiLVqCBy+ZQZTOw5dY57bP3vK2aP4poBNddmmC0SrUZQWG5k/Ku/UlxhAqx99w9M6MN3cZmMCfZpdLq0yWzh+Nli3vstmSem9qeHz8Wnhj6cVsj0t3fU2PbE1P6MCfHB382RHj4unMotZUdiDjNH9UAI2H+6gFnLdzNrdE+iw/zwdXVgTIgvFoukxHZ3zme/OcJz0we2ONHmze/s5GBqAQum9OPBiaE8uGo/3x+2zsZbdd8YokL9+D4u094C8Xd3ZO/Tkxo75UXr9EFECHEzcAMQALwjpdzY0L7tMYgkJCQwZcoUbrvtNqZNm0Z0dDS9e/fm119/JSQkBKPRSJcuXcjLy8PPz4/MzEz0ej1Go5GuXbuSm5urgkg1KohcflJKzBZ5wUHbD7aftGcX/uy+MW2+dkNKydr96fxjzSEAvpgXWWMgvz7Prz/Cx7tO259HhfoSE+bPv348bt+m0wgWXtefdQfS+ceUfsSE+TdpLCItv4zxr1p7HxJfug69VsOp3FImLtkKwJ6nrrEH2+fXH6HCaOGVW4dc8gH/S7LYUAihBWKBdCnltBYW7ENgGpAtpRxc67WpwJuAFvhASvlKQ+eRUn4DfCOE8AaWAA0GkQtp7Mv+Uunbty/79u3jhx9+4Mknn2Ty5MlAzRQrDf2SqIWFSnsghECnvfDv4uxxvcksrOCW4d3rzDJqC0IIZowMYvrQrmw9kdOkFDZ/v7Yfx88Wk5ZfTnpBOTuT8tiZlGd/veqeQ1XB8t6P9uLloufHR6Lp6lmzC2zDoQyMJgu3jrTOdjueaR1rWvO3sfZba1dNALBIWaO19s+banxlthvNWbH+CHAM8Kj9ghAiACiXUhZX2xYqpUyqtetK4G3gk1rHa4F3gGuBNGCvEGID1oDyr1rnmCOlrLrv6TO24zqUjIwMfHx8uPPOO3Fzc7Onel+9ejULFy5k9erVjB07FoBx48bxxRdfcNddd7Fq1SrGj7fmb3J3d6e4uLiht1CUdkGv1fDstPY3g9BRp2XKoC5N2tfTRc8X8yIBa1fdg5/tZ1N8FuFBXnT3duafNw7i56NniU3J5/aIHvzz23iOZRbx8OcHWHrbMLp5OVNmMHEwtYCHP7eOuaQXlGMyW6jqBwqrlSWgX5eWLRJsC03qzhJCBAEfAy8Bf6/dEhFCzATuB66XUlYIIeYCt0gp66x0EUL0Br6r3hIRQowFFkkpp9iePwkgpawdQKr2F8ArwCYp5ebGyt4eB9Z//vlnFixYgEajQa/X8+677zJjxgzuvfdefvjhBywWC59//jmhoaGkpKQwZ84ccnNz7QPrPXv2ZOfOncydOxdHR0e++uorNbCuurOUduQ/vyXX6O5qjF4rSHypZYsCL5VL0Z31BvA4UG94lFKuEUIEA18IIdYAc7C2KpqqO5Ba7XkaMKaR/ecDkwBPW4vnvdo7CCGmA9NDQ0ObUYzLY8qUKUyZMqXO9gcffJDnn3++xraqsZLaoqKiiI+Pv2RlVBSl5f56VR/cnHS8tjHBPqOqyld/G8vuk3n857eTFFea6qRr6WguGESEEFVjGPuEEBMa2k9KuVgI8QXwLtBHSlk3f3Ujb1PfKRt5r2XAssZOKKX8Fvh21KhRc5tRDkVRlFZxx5he3DGmF6nnyujq6VRjEsKo3j48dHUYX8am4tXBb2XclJZIFHCjEOJ6wAnwEEL8V0p5Z/WdhBDRwGBgHfA88FAzypEGVF/NEwRkNLBvp5SSktLWRVAU5RJobHrybZd4EePlcMGkKlLKJ6WUQVLK3sCfgV/rCSDDgeXATcC9gI8Q4sVmlGMvECaECBZCONjeZ0MzjlcURVHaQGvlEHcBZkopk6WUFmA2cLr2TkKIz4FdQD8hRJoQ4i8AUkoT1pbLz1hngH0ppTzaSmWrV0dcH6PUpa6jorStZt2USkq5Fdhaz/adtZ4bsbZMau83q5Fz/wD80JzytJSTkxN5eXn4+l44zbXSfkkpycvLw8mpZTeKUhTl4l2RdzYMCgoiLS2NnJycti6KcpGcnJwICqqbplxRlMvjigwier2e4OBLn95ZURSls7uy76uqKIqiXBQVRBRFUZQWU0FEURRFabEOmQq+OYQQOdQz3biJ/IDcVixOW+ks9QBVl/aqs9Sls9QDLq4uvaSU/k3ZsdMHkYshhIhtahKy9qyz1ANUXdqrzlKXzlIPuHx1Ud1ZiqIoSoupIKIoiqK0mAoijXu/rQvQSjpLPUDVpb3qLHXpLPWAy1QXNSaiKIqitJhqiSiKoigtpoJIPYQQU4UQJ4QQSUKIhW1dnqYQQqQIIQ4LIQ4KIWJt23yEEJuEEIm2f71t24UQYpmtfnFCiBFtXPYPhRDZQogj1bY1u+xCiNm2/ROFELPbST0WCSHSbdfloO2+PFWvPWmrxwkhxJRq29v8908I0UMIsUUIcUwIcVQI8Yhte0e8Lg3VpUNdGyGEkxBijxDikK0e/7RtDxZC/GH7fFfbbqeBEMLR9jzJ9nrvC9WvRaSU6qfaD6AFkoEQwAE4BAxs63I1odwpgF+tbYuBhbbHC4FXbY+vB37EekfJSOCPNi57DDACONLSsgM+wEnbv962x97toB6LgH/Us+9A2++WIxBs+53TtpffP6ArMML22B1IsJW5I16XhurSoa6N7bN1sz3WA3/YPusvgT/btr8H3G97/ADwnu3xn4HVjdWvpeVSLZG6RgNJUsqTUkoD8AXWm211RDcBH9sefwzcXG37J9JqN+AlhOjaFgUEkFJuA87V2tzcsk8BNkkpz0kp84FNwNRLX/rzGqhHQ24CvpBSVkopTwFJWH/32sXvn5QyU0q53/a4GOt9frrTMa9LQ3VpSLu8NrbPtuq243rbjwSuBr6yba99Taqu1VfANUIIQcP1axEVROrqDqRWe55G479w7YUENgoh9gkh5tm2BUopM8H6HwkIsG3vCHVsbtnbc50esnXxfFjV/UMHqoetG2Q41r98O/R1qVUX6GDXRgihFUIcBLKxBuRkoEBab+xXu0z28tpeLwR8aeV6qCBSV313qeoIU9iipJQjgOuAB4UQMY3s21HrCA2Xvb3W6V2gDzAMyARes23vEPUQQrgBa4FHpZRFje1az7Z2VZ966tLhro2U0iylHAYEYW09DGikTJelHiqI1JUG9Kj2PAjIaKOyNJmUMsP2bzawDusvWFZVN5Xt32zb7h2hjs0te7usk5Qyy/Yf34L1bp9V3Qbtvh5CCD3WL91VUsqvbZs75HWpry4d+dpIKQuw3mU2EmvXYdW9oaqXyV5e2+ueWLtbW7UeKojUtRcIs814cMA6ILWhjcvUKCGEqxDCveoxMBk4grXcVbNhZgPrbY83AHfbZtREAoVVXRTtSHPL/jMwWQjhbeuWmGzb1qZqjTXdgvW6gLUef7bNoAkGwoA9tJPfP1vf+QrgmJRyabWXOtx1aaguHe3aCCH8hRBetsfOwCSs4ztbgBm23Wpfk6prNQP4VVpH1huqX8tcrpkFHekH60yTBKz9jU+3dXmaUN4QrLMtDgFHq8qMtf/zFyDR9q+PbbsA3rHV7zAwqo3L/znW7gQj1r+S/tKSsgNzsA4SJgH3tpN6fGorZ5ztP2/Xavs/bavHCeC69vT7B4zH2sURBxy0/VzfQa9LQ3XpUNcGCAcO2Mp7BHjOtj0EaxBIAtYAjrbtTrbnSbbXQy5Uv5b8qBXriqIoSoup7ixFURSlxVQQURRFUVpMBRFFURSlxVQQURRFUVpMBRFFURSlxVQQURRFUVpMBRFFURSlxVQQURRFUVrs/wPAUeykbFz/QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26613710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.prices.apply(np.min).plot(logy=True)\n",
    "rdf.prices.apply(np.median).plot(logy=True)\n",
    "rdf.prices.apply(np.mean).plot(logy=True)\n",
    "rdf.wt_mean_price.plot(logy=True)\n",
    "rdf.h_wt_mean_price.plot(logy=True)\n",
    "rdf.w_wt_mean_price.plot(logy=True)\n",
    "rdf.prices.apply(np.max).plot(logy=True)\n",
    "rdf.spot_price.plot(logy=True)\n",
    "plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['median_price']=rdf.prices.apply(np.median)\n",
    "rdf['mean_price']=rdf.prices.apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a20841fd0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ed31240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(np.sign(rdf['mean_price']-rdf['spot_price'])*(rdf['mean_price']-rdf['spot_price'])**2).apply(np.log10).plot(alpha=1)\n",
    "(-np.sign(rdf['mean_price']-rdf['spot_price'])*(rdf['mean_price']-rdf['spot_price'])**2).apply(np.log10).plot(alpha=.5)\n",
    "plt.legend(['over','under'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['est_err'] = rdf.spot_price - rdf.wt_mean_price\n",
    "rdf['sq_est_err'] = rdf['est_err']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20346c88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f5d37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.est_err.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21875550>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2173c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.est_err.apply(np.abs).plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a23df9e10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21f0d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#head T\n",
    "T = 50\n",
    "rdf.head(T).prices.apply(np.min).plot()\n",
    "rdf.head(T).prices.apply(np.median).plot()\n",
    "rdf.head(T).prices.apply(np.mean).plot()\n",
    "rdf.head(T).wt_mean_price.plot()\n",
    "rdf.head(T).h_wt_mean_price.plot()\n",
    "rdf.head(T).w_wt_mean_price.plot()\n",
    "rdf.head(T).prices.apply(np.max).plot()\n",
    "rdf.head(T).spot_price.plot()\n",
    "plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a24c955f8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23e1c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 50\n",
    "rdf.tail(T).prices.apply(np.min).plot()\n",
    "rdf.tail(T).prices.apply(np.median).plot()\n",
    "rdf.tail(T).prices.apply(np.mean).plot()\n",
    "rdf.tail(T).wt_mean_price.plot()\n",
    "rdf.tail(T).h_wt_mean_price.plot()\n",
    "rdf.tail(T).w_wt_mean_price.plot()\n",
    "rdf.tail(T).prices.apply(np.max).plot()\n",
    "rdf.tail(T).spot_price.plot()\n",
    "plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tx_data = rdf.actions.values\n",
    "transactions = []\n",
    "states = []\n",
    "for t in range(time_periods_per_run):\n",
    "    for tx in range(len(tx_data[t])):\n",
    "        states.append(tx_data[t][tx]['posterior'])\n",
    "        transactions.append(tx_data[t][tx])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf = pd.DataFrame(states)\n",
    "tdf = pd.DataFrame(transactions).drop('posterior', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind=tdf[tdf.amt==0].index\n",
    "tdf.drop(ind, inplace=True)\n",
    "sdf.drop(ind, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tx_summary=tdf[['agent','mech','pbar','amt']].groupby(['agent','mech']).agg(['median','count']).T.iloc[:-1].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pbar</th>\n",
       "      <th>amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <th>mech</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.362480e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103447</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.825452e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.244003e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.099083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.877545e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098867</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.097211e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101603</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.599257e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101869</td>\n",
       "      <td>265.0</td>\n",
       "      <td>1.131081e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.152214e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.276548e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093114</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.447422e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.498114e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098958</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.110472e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100149</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.141129e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103981</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.387898e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101096</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.386958e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100513</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.880837e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.930201e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.029592e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103371</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.985819e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.168417e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.019446e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095690</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.899205e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102631</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.185530e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.544406e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.701335e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094255</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.134473e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097992</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.991649e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.373723e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.380708e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098829</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.281099e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.855256e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096480</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.810143e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">76</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098958</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.175067e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101507</td>\n",
       "      <td>147.0</td>\n",
       "      <td>9.928291e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097474</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.178674e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100599</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.612138e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097963</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.579843e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.099392</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.983250e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.459903e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105843</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660313e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.308744e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098437</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.496015e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.671962e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099603</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.131459e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">87</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099318</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.631147e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.098886</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.009676e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100026</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.114037e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">89</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.097015</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.831722e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.099259</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.862120e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100537</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.891134e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">91</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.596289e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101343</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.253326e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.074433e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096081</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.926581e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106403</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.325829e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102380</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.208528e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.296405e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100323</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.876865e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100486</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.631227e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.996715e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pbar                  amt\n",
       "              median  count        median\n",
       "agent mech                               \n",
       "0     bond  0.102966    1.0  7.362480e+02\n",
       "1     bond  0.103447    1.0  2.825452e+02\n",
       "2     burn  0.098400    2.0  5.244003e+03\n",
       "3     burn  0.099083    1.0  4.877545e+03\n",
       "4     burn  0.098867    2.0  1.097211e+04\n",
       "5     bond  0.101603    5.0  5.599257e+00\n",
       "      burn  0.101869  265.0  1.131081e-09\n",
       "6     bond  0.104428    1.0  3.152214e+02\n",
       "7     bond  0.103046    1.0  5.276548e+02\n",
       "8     burn  0.093114    1.0  5.447422e+03\n",
       "9     bond  0.102133    1.0  4.498114e+02\n",
       "10    bond  0.098958   13.0  3.110472e-10\n",
       "      burn  0.100149  106.0  4.141129e-10\n",
       "11    bond  0.103981    1.0  9.387898e+02\n",
       "12    bond  0.101096   15.0  3.386958e-10\n",
       "      burn  0.100513  212.0  8.880837e-10\n",
       "13    bond  0.106203    1.0  1.930201e+02\n",
       "14    bond  0.102854    1.0  1.029592e+03\n",
       "15    bond  0.103371    1.0  1.985819e+02\n",
       "16    bond  0.103741    1.0  5.168417e+02\n",
       "17    burn  0.093973    1.0  1.019446e+04\n",
       "18    burn  0.095690    1.0  7.899205e+03\n",
       "19    bond  0.102631    1.0  6.185530e+02\n",
       "20    burn  0.096075    1.0  7.544406e+03\n",
       "21    burn  0.095299    1.0  1.701335e+04\n",
       "22    burn  0.094255    1.0  1.134473e+04\n",
       "23    burn  0.097992    1.0  7.991649e+03\n",
       "24    bond  0.102103    1.0  1.373723e+03\n",
       "25    burn  0.095450    1.0  1.380708e+04\n",
       "26    bond  0.098829    1.0  8.281099e+02\n",
       "...              ...    ...           ...\n",
       "74    bond  0.102840    1.0  1.855256e+02\n",
       "75    burn  0.096480    1.0  7.810143e+03\n",
       "76    bond  0.098958   17.0  1.175067e-10\n",
       "      burn  0.101507  147.0  9.928291e-10\n",
       "77    burn  0.097474    1.0  1.178674e+04\n",
       "78    bond  0.100599    1.0  8.612138e+02\n",
       "79    burn  0.097963    1.0  1.579843e+04\n",
       "80    burn  0.099392    1.0  5.983250e+03\n",
       "81    bond  0.104624    1.0  5.459903e+03\n",
       "82    bond  0.105843    1.0  8.660313e+02\n",
       "83    burn  0.100517    1.0  5.308744e+03\n",
       "84    burn  0.098437    1.0  8.496015e+03\n",
       "85    burn  0.100363    1.0  5.671962e+03\n",
       "86    bond  0.099603    1.0  1.131459e+03\n",
       "87    bond  0.099318   13.0  4.631147e-10\n",
       "      burn  0.098886   73.0  1.009676e-09\n",
       "88    bond  0.100026    1.0  1.114037e+03\n",
       "89    bond  0.097015    8.0  1.831722e-10\n",
       "      burn  0.099259   13.0  7.862120e-10\n",
       "90    burn  0.100537    1.0  7.891134e+03\n",
       "91    bond  0.099319    1.0  1.596289e+02\n",
       "      burn  0.101343  150.0  1.253326e-09\n",
       "92    bond  0.102540    1.0  2.074433e+02\n",
       "93    burn  0.096081    1.0  1.926581e+04\n",
       "94    bond  0.106403    1.0  2.325829e+02\n",
       "95    bond  0.102380    1.0  1.208528e+02\n",
       "96    burn  0.094699    1.0  1.296405e+04\n",
       "97    burn  0.100323    1.0  1.876865e+04\n",
       "98    burn  0.100486    1.0  1.631227e+04\n",
       "99    burn  0.100702    1.0  4.996715e+03\n",
       "\n",
       "[112 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25692be0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25692940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tx_summary.pbar['median'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25cdada0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a257c7f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot(logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2697db00>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26993ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d48bda0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a262f7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf.F.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "bond_amts = [tdf.iloc[k].amt for k in range(time_periods_per_run) if tdf.iloc[k].mech=='bond']\n",
    "burn_amts = [tdf.iloc[k].amt for k in range(time_periods_per_run) if tdf.iloc[k].mech=='burn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d7df390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bond_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e0b6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(burn_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['invariant'] = rdf.supply.apply(lambda x: x**kappa)/rdf.reserve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1ec8f3c8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHTsA97I/9913H8OGDWPq1KnExMRUjFDy8/Np164dJSUlbNq0iZEjR9K/f3/OOussNmzYAMCWLVs444wzGDBgAA8++GCl+tayZUtmzJjBCy+8gKqSmprKWWedRb9+/ejXrx8///wzAJMmTWLJkiX06dOH6dOnH7WeMXVVfrGLd5al0dg/jwFNnmBQ4+vILdhFp64f0LPnlwQHd/B2iKdEbZmY4Af0B84FgoFfRGQpcKTpHXqMcqq4z8GFqjOAGeBeMeGYkVeCe+HANFylZRUfEjs9MqzK7a1bt47+/fsfVBYWFkZ0dDQpKSkALF++nISEBEJCQhgwYACjR48mLS2NNm3aMHfuXAD2799PSUkJt99+O59//jktWrRg9uzZ3H///cycOROA7OxsFi92X6JbtWoVixcv5uyzz+aLL77gggsuwN/fn/Hjx/PKK6/QuXNnli1bxm233cbChQu54447uPXWWxk7diwvvvhipfvXsWNHysrK2L17Ny1btmTBggUEBQWRnJzM1VdfTXx8PNOmTePpp5/myy+/BNxJ8Uj1jKmr9ueX0MJ3Lj1C/4UfWewp+xO/Zt9Cn5B+dWbmW2XUdBLaJSKRqrpDRCKB8tNu6UCmquYBeSLyA9DbKW/nsX8UkAFkAhEi4ueMdsrL8dgnXUT8gHDcpwWP1la1Kv9vJsTfl9BGgeQVuXh7adpJLZehqkf8JfQsHzFiBM2auS9SXnrppfz444+MGjWKu+++m4kTJ3LRRRdx1llnkZCQQEJCAiNGjADcd1SNjIysaNPz1g5XXnkls2fP5uyzz2bWrFncdttt5Obm8vPPP3P55ZdX1CsqKgLgp59+4uOPPwbc9yCaOHHiCfURoKSkhAkTJrB69Wp8fX1JSko6Yv3K1jOmLsjPT2HHllvpG/YtB0pj2V42g8yirohPKWHBtWXscGrUdG/mAOOAac73z53yz4EXnKQRAAzCPalgA9BZRDoA23FPLLhGVVVEFgGX4b5O5NlW+TF+cbYvdOrPAd4TkWeANkBnYHk195ecAheu0jJCG7lvdxAa6EdOYQk5Ba4qJ6HY2NiKN/eK4+TksG3bNk477TRWrlx5WJISEbp06cLKlSuZN28ekydP5vzzz+cPf/gDsbGx/PLLL0c8luetHS6++GImT55MVlYWK1eu5JxzziEvL4+IiAhWrz7yjf2q8h/b5s2b8fX1pWXLljz66KO0atWKNWvWUFZWRlBQ0BH3mT59eqXqGVMb5Re7yClw0SiwlMyd00lL+wc+PgE0bvkU32w8D1ep4OdbWmuW2jmVqnOK9vu4E0FXEUkXkZtwJ58RIpIMjHCeo6qJwFfAWtyJ4T+qmuCMciYAXwOJwAeqWn43tYnAnSKSgvuaz+tO+etAM6f8TpwZeM5+HwDrnWP9n6qWVlf/y4UF++Hn60NekftaUF6RCz9fn5P6b+bcc88lPz+ft99+G3CPXu666y6uv/56QkJCAFiwYAFZWVkUFBTw2WefMWTIEDIyMggJCeG6667j7rvvZtWqVXTt2pU9e/ZUJKGSkpKDbljnqVGjRgwcOJA77riDiy66CF9fX8LCwujQoQMffvgh4B7BrFmzBoAhQ4Ywa9YsAN59991K9W3Pnj385S9/YcKECYgI+/fvJzIyEh8fH9555x1KS90/skNvFXG0esbUZpm5hXy+ejsPfPobby56l+9/6klq6oM0b34xAwduoH/3u3lgdA/uHNGVB0Z3O6nT+LWWqtrXMb769++vh1q/fv1hZceSmLFfJ3+yVu/5cLVO/mStJmbsP6H9j2Tr1q160UUXaadOnbRjx446YcIELSwsVFXVN954Qy+//HIdNWqUdunSRR955BFVVf3qq6+0Z8+e2rt3b42Li9MVK1aoquqvv/6qZ511lvbq1Uu7d++uM2bMUFXVYcOGVdQp9+GHHyqg33//fUXZ5s2b9YILLtBevXppt27d9NFHH60oHzx4sMbFxek///lPDQ0NPWJffHx8tHfv3tq9e3ft1auXPvXUU1paWqqqqklJSdqzZ08dNGiQTpo0qaKN4uJiPeecc7RXr176zDPPHLXeoU70Z2dMdflyzXY9+6lFOuix2Trt/RG6aBE6/7sofXrOS5pXVOLt8E4aEK+VeI+1Wzkcx6m6lUP5cLs2LRzYENmtHExtkJlbyFWv/kLf5l8zvM3LBPnmsSj9MkKb3cO+Aj/uHNGV1uF1+5Sy3cqhlqmNCwcaY2qO5z+i23av4brOfyMmbC2b9/fgs813sDWnHcMDfAgJPLnT9XVNw+mpMcZ4SfnHNMpK8+kS+jodg9+kVUgQX6ffS0LWKNIPFFGqio9QLycfHEvD6ekppkeZJm1qLzv1bLyh/GMabYKW0TnoMQJlKxlFFxHabAor1uzHVVZGk5AAbj4rhkv6RjWoBASWhKokKCiIvXv30qxZM0tEdYSqsnfvXpu6bWpEZm4hGdmFtIkIorBwF7HBE2kbNJ9CjSHZ9SbJB/pw5+CuDDmdinrNGzXM301LQlUQFRVFeno6p2pJH1MzgoKCiIqK8nYYpp6buzaD6QuSKC0rJa7lPC5s/x8iAwvYWnwbWfIXcot88fMtrZik1FCTTzlLQlXg7+9Phw71Y90mY8ypk5lbyPQFSUQ1SuOC6H8RGfwbqQf60KXzS2xIbOQs3dXwrvsci70KxhhzkspPv+3L3cewyNc4M3I2xaWNWLjjAX5IP5vn+nbjgdGN7GMaR2CvhDHGnITy02/tGy1ndPt/c1abHSRkjWRF1l/Zm98IP59S2kQE2cc0jsJeEWOMqaLM3EJmLFrKHzu+TLcm37K3MJpXfnuSXUVxqIKfTyl3nd+lwV/3ORZLQsYYUwWqZWxOe4W/xD5IgE8RKzJvZFXWWHYWljFlTCwRIQENetZbZVkSMsaYSnj4szV8s34353dvyT3n+ZKUdAv5OT+zq6AP32XcTTEd3QsU+yjd24RZ8qkkS0LGGHMcMZPcN4L09ykid+9rLF3+CUEBEXTt+gYFe0awIykZV1khfj5ip99OkCUhY4w5hoc/c9+aJLbZKsbFvkTLkJ0sST8PaXw/Q4cOJzISBnRs1uA/dFpVloSMMeYQnise/JS8kVt6vcQZbRazI68t05Y/zoasXkSGlfDAGHf95o0s+VSVJSFjjPHgueJB/xZfcU+/V/GRAj5LuZovN12BS/0BOL97Sy9HWj9YEjLGGEf5igdtGm1jZLt/0SZkDWkHevHK6lvZkdfuoLqPXtLbS1HWL5aEjDENXsWKB3k5nNV6JkPbvE9JWTCLdtzH4vRzefWGfnwcn1YxO84S0KljScgY06CVn36LCl3F72OmM6xtOuv2nc+KvX8lMz+sYsWDRy/pzaOXeDva+seSkDGmwcrMLeSVhSu4pMOr9Gg6n31FbfnPummk5w8AxFY8qAGWhIwxDU5mbiHb9xWQvfdd/hL7AEF+uazcO5aVe28gI1+ZMqY7ESGBNuW6BlgSMsY0KHPXZvDmD4u4sN0zdAxfRWZBdxbtuJcCujgrHpTSvU24JZ8aYknIGNNg7M7JYflvDzG++zuUqT8L0u/i29TzCAkKRNVWPPAGS0LGmAZh//6f2bjuJs5uu4FNB87mx11/I7+0BcGBhTx6sS046i0+1dWwiMwUkd0ikuBR1lREFohIsvO9ySH7DBCRUhG5zKNsnFM/WUTGeZT3F5HfRCRFRJ4TETnWMcTtOaf+WhHpV119N8bUHi7XfpKSbuPXX4fiQy6zUh7n0y2Pkl/awjn9JnRvE0avqAhLQF5QbUkIeBMYeUjZJOA7Ve0MfOc8B0BEfIEngK89ypoCDwODgIHAwx6J62VgPNDZ+So/1tGOcaFH3fHO/saYekpV2bPnY5Yv70ZGxqtERd3BoIGJjBk0jsKSUnblFFJYYrPfvK3aTsep6g8iEnNI8RhguPP4LeB7YKLz/HbgY2CAR/0LgAWqmgUgIguAkSLyPRCmqr845W8DlwDzj3GMMcDbqqrAUhGJEJFIVd1x0p01xtQK+cUucgpcBPrsID31Dvbu/YJGjfrSs+cXNG7cH4ALezZiQIemtuBoLVHT14Ralb/pq+oOEWkJICJtgT8A53BwEmoLbPN4nu6UtXUeH1p+1GMco63DkpCIjMc9WiI6OvrEe2mMqXGJGTm8s2wzUf7v0SX0Bfx9hI4dnyIq6m/4+Bz8VmcLjtYe1Xk67kT8G5ioqqWHlMsR6uoxyo+l0vuo6gxVjVPVuBYtWhynWWOMt+UXu/gs/mvOCLuO7o2eIqe0P4uzP6Z568MTkKldavqns6v8FJiIRAK7nfI4YJYzt6A5MEpEXLhHK8M99o/CfXot3XnsWZ5xnGOkA+2Oso8xpo4qLc0nJeUBBjV+jlIiSHX9i306mtySQnIKXIQEWBKqzWp6JDQHKJ/hNg74HEBVO6hqjKrGAB8Bt6nqZ7gnKZwvIk2cCQnnA187p9sOiMhgZ1bc2PK2jnYMp3ysM0tuMLDfrgcZU7dlZX3DihU9yNo9nYzii1mZN4d9ehF5RaX4+foQFmwJqLartp+QiLyPexTTXETScc9ymwZ8ICI3AVuBy4/VhqpmichjwAqnaEr5JAXgVtwz8IJxT0iY75Qf7RjzgFFACpAP3HCSXTTGeElx8W5SUu5k9+53CQ7uQu/ei2hd2I/1S9NwFRbg5+vD2MHtbRRUB4h7spg5mri4OI2Pj/d2GMYY3NOud+16m5SUOyktPUB09CSio+/D19c9yaB8dlxYsJ8lIC8TkZWqGne8evZTMsbUamdMnceOA0qPFjv4x7nvk529kLCwM+nadQahobEH1Q0JsORT19hPyxhTa8VMmouvuBjd4VPGdHqfjD1+9O7+Mm3ajEektkzuNSfDkpAxplZ5+LM1fLN+N3tziukYvpEbejxPu8apxO88k/8m3kLw8ub8cr8loPrCkpAxplbIL3bR/SH3ql1Bvvlcdvo7nNf+S7KLmvLsqgf4dfdgALKL7Dp2fWJJyBjjdYkZOUx41z0JtneL5Yzt/jJNgjJZuHUUHyWNo7A0pKJuZOMjfe7c1FWWhIwxXpVf7OKdZWkcKNjBrb1nMChyCekHonlp2ZNsyu52WP1f7h/lhShNdbEkZIzxqv35xbT2+4gpZz6DrxTxcdJ1zNvyR0rVH4AAoBj3CMgSUP1jScgY4zX5+RvJ2PxnejVewv7SATz+083sym97UJ2kaaO9FJ2pCZaEjDE1JmbSXAB8pYTvbkkgLe0f+PoGE9bqOb7ZMIxh3ZWVWzLJKXAxqldrHr2kt5cjNtXNkpAxpkaUJ6DTwjdwfY/nSU1No0WLK+jU6VkCA1tz+mnlqx3E2gdOGxD7SRtjqk1+sYtLnltMUmYhQb75/LHLO5wb/SX7Cpvx75UPsnrPIFKntQZstYOGyn7ixphqkZiRw4XPLQHKp12/RJOgvSzcOpqPksYeNO3aNFyWhIwxp9y2rDwufW4JYQH7uLab57TriUecdm0aLktCxphTau7aDJ76KpG4tt9wZdeZBPoVHjbtulyqzXxr8CwJGWNOWvktFEpKS3nzh0Xc0O0Z2jdexcasWN5cN4Edee0O28cSkAFLQsaYk5SYkcM7y9IoLS0i0u8txnebQRn+fJN+F+8nDEMPuYGzJR/jyZKQMabKypfcaRG4ga7BDxMiiazeM4Sfd92J+EfSrXUJ63ceACC2ZQhz7zzbyxGb2saSkDGmyrLzcugU+DQdgv+Li2Zsdr3Asn392J1fAFKIn4/w8rV9ubBnG2+HamopS0LGmCrJyvqWtI3j6RiyhR0lV7CHe8gpCqFDi1KevKwX+/JLaBMRRPNGQd4O1dRiloSMMZVSPvkg2G8/GVsnsmvXWwQHd6Fpu3l8szYKV2kZfr6ljB3cnnZNQ2nX1NsRm7rAkpAx5rjckw9SaeE7n9hGTxDgc4Do6Ptp3/4BfH2D6NSufMkdW/XAnBj7bTHGHFN+sYsPli8jrvFUmvot5kBpD+JzHmFg1Bh8fd1vIbbkjqkq+60xxhzm1e+TmfvbDn7XqRnnd/iaIWEPIqKkl05mT9mfyC4uJqfAZYnHnDT7DTLGHKTHA3PJdUFk6DbCC58ja2cie4oHk146Bb+A9uQVufDz9SEs2N5j70PvAAAgAElEQVQ+zMnzOX6VqhGRmSKyW0QSPMqaisgCEUl2vjdxyq8VkbXO188i0ttjn5EislFEUkRkkkd5BxFZ5rQ1W0QCnPJA53mKsz3GY5/JTvlGEbmguvpuTF316vfJFJSWcPFp7zNlyO1EhqYzY+2dzEyYyp6CluzYX0B+iXvygY2CzKlQnb9FbwIvAG97lE0CvlPVaU5CmQRMBLYAw1R1n4hcCMwABomIL/AiMAJIB1aIyBxVXQ88AUxX1Vki8gpwE/Cy832fqnYSkauceleKSHfgKiAWaAN8KyJdVLW0Gl8DY2q9zNxCMrILaRMRxOpN3/Homf8kqvFWlu74He8mjudAcQQRQcXcOvw0gv39bPKBOaWq7TdJVX/wHIU4xgDDncdvAd8DE1X1Z486S4Eo5/FAIEVVNwOIyCxgjIgkAucA13i09QjuJDTGeQzwEfCCiIhTPktVi4AtIpLitP/LyfXUmLqr/EZzgb4F3NhrFld0/ISswmZMX/kQa/YMrKjXrmkIrcKCLPmYU66mf6NaqeoOAFXdISItj1DnJmC+87gtsM1jWzowCGgGZKuqy6O87aH7qKpLRPY79dviTnAcYR9jGpzyBNSj+Uquj32R5sG7id89hvfWXUtW0cH3+nnq8j6WgEy1qFW/VSJyNu4kNLS86AjV9BjlVd3n0DjGA+MBoqOjjxGxMXXPNwkZjP/vr4T653DN6a8xpO0iMnKj+MfSJ0nJ7s6cCUP4JWWPMzuuBbed29kSkKk2lfrNEpEJwLuquu8kj7dLRCKdUVAksNvjGL2A/wAXqupepzgd8FwDPgrIADKBCBHxc0ZD5eWe+6SLiB8QDmQdo63DqOoM3NeliIuLO2KiMqYuGj19Eet25TGo9Q9c220GIf65fJ5yFV9uvoKSsgAA2kQEccvwztwyvLOXozUNQWVnx7XGPSngA2e22pFGFZUxBxjnPB4HfA4gItHAJ8CfVDXJo/4KoLMzEy4A98SCOaqqwCLgskPbOuQYlwELnfpzgKuc2XMdgM7A8ir2w5g6JTO3kBmLk8nI3sod/R7j1j5PkVnQkkd+fpZPU66rSECArfVmapS4358rUdGdeM4HbgDigA+A11V101Hqv497EkJzYBfwMPCZs180sBW4XFWzROQ/wB+BNGd3l6rGOe2MAv4N+AIzVXWqU94RmAU0BX4FrlPVIhEJAt4B+uIeAV3lMbHhfuBGwAX8TVXLrz0dVVxcnMbHx1fqNTKmtskvdvHpqnRe/3ETXRt/ysWnzcRHyvgk+Tq+Sb0Yxfeg+navH3OqiMjK8vfxY9arbBJyGu2NOwmNxD0SGQwsUNV7qxpobWdJyNRViRk5vPbjJlZvXsW13f7NaeEJJGT24a11E9hT0Pqw+paAzKlU2SRU2WtCf8V9iisT93Wbe1S1RER8gGSg3iYhY+qi/GIX/12WwmnBb3DhwFdxlQUyO/kuvk8bToHrf2fTY1uFMPfvdqM54z2VnfLSHLhUVdM8C1W1TEQuOvVhGWNO1L0frGTB+t30ax/BX4eVMLDxeML9NrJ6z1nM2fx/5JQ0JbpZADkFJQyOacKFvSI5v4fdbM541zGTkIiU3xHk34c8B0BVs1Q1sZpiM8ZUUvlnfvx9imiqM8lM/QR/mpBY8CzJxUPYW5hJaVkpJaVlPPT77nanU1NrHG8ktJKjf/5GgY6nPCJjzAm594OVAHRpksCNPZ6jdWgGi7edzxdbbuJ3XTsTGujDud1aMrxLC87s1Nxmv5la5ZhJSFU71FQgxpiq+TE5jbHd/8M50fPZnd+KJ5b/g8SsPvgAVw2MJrppqK33ZmqtSv9WisiluFcyUGCJqn5WbVEZY44qaWcOCdv306NtOE39fuDBwbcR6LOHr7Zcwicp11Fc6h7phAb50rFFqI18TK1W2dlxLwGdgPedor+IyAhV/b9qi8wYc5jyaz+N/fdzXfcZDIpcTLPGsUz67l427+96UN0n/9jLEpCp9So7EhoG9HBWHkBE3gJ+q7aojDEHGfHUtyTvLQKUQZE/cG23Vwnxy2fOpmu5ZdS/WTi5+UGz4564vK8lIFMnVDYJbcS9ykH5FO12wNpqicgYc5Dy0U+TwEzGxb5In5Yr2JTdhZkJf2V7bgxDMwroGglPXtHfy5Eac+Iqm4SaAYkiUr7W2gDgFxGZA6CqF1dHcMY0ZIs37uLPb8QjlDGs3ddc2dW95M57iTezIO33FUvu9Ggb7uVIjam6yiahh6o1CmPMQS5/aQkrtubQKmQ7N/R4ntObJrB+by/eSPjrYUvudGkd5qUojTl5lUpCqrpYRFrjvhOpAitUdWe1RmZMA7V44y5WbtvHyJjPubTzf3GV+TEz4XZ+SD+fQz+uZ+u9mbqusrPjbsY9GlqI+6/geRGZoqozqzM4YxqilSk/88DgiXQMT2bVrkG8vf5WsouaH1THB9hsCcjUA5U9HXcP0Lf8ZnMi0gz4GbAkZMxJuu/jX/k2cQ8jukVwc7+59At9nJyiUF5afS/Ld56F5+inkR98P+lcm/lm6o3KJqF04IDH8wPAtlMfjjENS/nMtw7hG+kW8CxpaVtp2fJaZi65kuU7/3fPyc7Ng5h542DaNQ31VqjGVIvKJqHtwDIR+Rz3NaExwHIRuRNAVZ+ppviMqbfu+/hXAnwK+UPnd7kg5nOyi5owfeXDxMaM4e1b+rJ44y6WJGVyVpfmDOvaytvhGlMtKpuENjlf5cpvpd341IZjTMOxOeNbHhsynVahO1i0bSQfbLyBAlcoOwr28DgwrGsrSz6m3qvs7LhHqzsQYxoKlyuHTZvu5S89X2V3fmumLX+cDVm9Kraf162FF6MzpmZVdnbcIv53S4cKqnrOKY/ImHps7955JCXdQlFRBlFRf2f8G0MqFhwt9/gf+3opOmNqXmVPx93t8TgI+CPgOvXhGFM/lZTsJSXl7+za9Q4hId3p1+8jwsIGkTT1f7PjzuvWwhKQaXDEWZP0xHcUWayqw05xPLVOXFycxsfHezsMU4ft2fMxSUm34XJlER09ifbtH8DHJ9DbYRlTrURkparGHa9eZU/Hed7W2weIA1ofpboxBigu3kVy8gT27PmIRo360bv3NzRq1NvbYRlTq1T2dFz5bb4FKAFSgZuqKSZj6qTM3EIysguJDA+kNO9jUlLuoLQ0jw4d/km7dnfj42N3NjXmUJX9q5gIfKWqOSLyINAPyK++sIypW6Z+uY73l2+lZWgmV3d9ic4RSwkLO5OuXV8nNPR0b4dnTK3lc/wqADzgJKChwAjgTeDlaovKmDoiv9jF2U9+y2s/bqF/y3nc3e8vRDf6la+3TqBd528sARlzHJVNQqXO99HAK6r6ORBwrB1EZKaI7BaRBI+ypiKyQESSne9NnHIRkedEJEVE1opIP499xjn1k0VknEd5fxH5zdnnORGRqh7DmKr4am0GY15YQm5BGvcOuJ8berxAak4nHvjpRb5JG8WO/SXeDtGYWq+ySWi7iLwKXAHME5HASuz7JjDykLJJwHeq2hn4znkOcCHQ2fkajzPKciZEPAwMwn0biYfLk4pTZ7zHfiOrcgxjquLhz9dy2/vxxAS/zz+G/B8dwpN5I2ECT66Yyp6C1hSVQJsIW2TUmOOpbBK6AvgaGKmq2UBT3CtrH5Wq/gBkHVI8BnjLefwWcIlH+dvqthSIEJFI4AJggapmqeo+YAEw0tkWpqq/qHuO+duHtHUixzDmhCTtzOHb35YyeeAkru32Ghv29eD+H19icfpIyle8vnZQtK10bUwlVHbZnnzgE4/nO4AdVTheK2dfVHWHiLR0ytty8Krc6U7ZscrTj1BelWMc1g8RGY97tER0dPQJdtHUZ2VlLlI2T+ORM56muCyQ13+7iyXbh+N5u4UuLYKZPDrWazEaU5fUljmjcoQyrUJ5VY5xeKHqDGAGuD+sepx2TQORm5vAxo03ElK8glWZZ/DehtvILmpS8Ys1pGNTxp7ZnvN7tPFqnMbUJTWdhHaJSKQzQokEdjvl6UA7j3pRQIZTPvyQ8u+d8qgj1K/KMYw5prKyErZunUZa2mP4+YXTvftsfsrtxv7ibYDi6yNcNyiKR8b0Om5bxpiD1XQSmgOMA6Y53z/3KJ8gIrNwT0LY7ySRr4HHPSYjnA9MVtUsETkgIoOBZcBY4PmqHKMa+2rqgQMHfmXDhhvIy1tDy5ZX06nTswQEtOD+i+DyuPYkbN9Pj7bhdGkd5u1QjamTqi0Jicj7uEcxzUUkHfcst2nAByJyE7AVuNypPg8YBaTg/hDsDQBOsnkMWOHUm6Kq5ZMdbsU9Ay8YmO98caLHMOZIysqKSE19jK1bpxEQ0IIePT6jefMxB9Xp0jrMko8xJ6nKC5g2FLaAacOTk7OCDRtuID9/Ha1ajaNTp+n4+zc5/o7GmAqndAFTY+qz/GIXOQUuGgWUsCvjMbZt+xeBgW3o2XMezZpd6O3wjKnXLAmZBi0xI4d3lqXRSFbRu/EjhPqmEhn5Z0477Sn8/MK9HZ4x9V5lP6xqTL2TX+zi3WUbiA19ijPCb8BXiok/MIN2HV6yBGRMDbGRkGmwdu75njPCbiTUdxt7Sq8lo+xOdhf5klPgIiTA/jSMqQn2l2YaHJcrl82bJ5GR8SJCFL8VvIHL70zyilz4+ZYSFmx/FsbUFPtrMw3Kvn0L2bjxJgoL02jb9g5KQiby4/JMXKUF+Pn6MHZwexsFGVOD7K/NNAguVw6bNt3Ljh2vEhzcmT59fiAiYigAD4xuQU6Bi7BgP0tAxtQw+4sz9V5W1jds3HgzRUXbiYq6iw4dHsPXN7hie0iAJR9jvMX+8ky95XLtJyXlLnbufJ2QkNPp2/cnwsMHezssY4wHS0KmXtq7dz5JSeMpKsqgXbuJxMQ8gq+v3d/HmNrGkpCpF95duoWvE3YxMjaYfk2eZefONwgJ6U6/fh8TFjbQ2+EZY47CkpCp8/o/Mpe9hdC7xQqC9z/P9sJsOrS/j5iYh/DxCfR2eMaYY7AkZOqs/GIXkz5aTYErl5t7zmBo24VsO9Cef696iPHBF9OxoyUgY2o7S0KmzskvdrEybR8fxW8jfeccpg59kbCAbD5PuYovNl2JS/35OmEX1w7u4O1QjTHHYUnI1CmJGTm88fMWEralcUG75/lDv2/ZdiCGf696iLScThX1LujRyotRGmMqy5KQqTPyi128syyN1gGL+V2PRwn228+Xm6/i0+QrKVX/inrNgrBRkDF1hCUhU2fsO7Cb2OBJtA2ax+6CTnyx7WnW7Y4mJLCEYlcZPsAlfSL552V9vR2qMaaSLAmZWuuCp79jY2YhXZsH8e71uaRuvIXIwL1sLb6NpIIbScvJI8CvjHO6NuHcbi05s1NzmjeyzwIZU5dYEjK1UsykuQCE+h9gWJunSUj4ntDQ3oS1+YhvV4fjKiujV1Q4F/duQ7/2TWzZHWPqKPvLNbXOBU9/B0Dflku5PvYFQv0P8FnK1WzcP5b5dw3ltLYuW3DUmHrC/oJNrZO+fw/je73KmW2+Z2tOB/4VP4WtBzoCpYAtOGpMfWJ/yaZWycz8nCd+dxtBvjl8lnI1X2y6omLmW9fmdr3HmPrGkpCpFUpKskhO/iu7d79Lq4je3PXNTc7o53++vvtcL0VnjKkuPt4OwJjMzDmsWBHLnj2zad/+Yfr3X84P999eMfLp2jyI1GmjvRylMaY6eGUkJCJ3AH8GBHhNVf8tIn2AV4AgwAXcpqrLRUSAZ4FRQD5wvaquctoZBzzgNPsPVX3LKe8PvAkEA/OAO1RVRaQpMBuIAVKBK1R1X/X32BwqZtJcQvxyubbbqwxpu4jQ0F707DmPxo3/9xkfG/kYU//V+EhIRHrgTkADgd7ARSLSGXgSeFRV+wAPOc8BLgQ6O1/jgZeddpoCDwODnLYeFpEmzj4vO3XL9xvplE8CvlPVzsB3znNTw2ImzaV3i+VMHXobgyMX83nKVfTvv+KgBGSMaRi8cTquG7BUVfNV1QUsBv4AKBDm1AkHMpzHY4C31W0pECEikcAFwAJVzXJGMwuAkc62MFX9RVUVeBu4xKOtt5zHb3mUmxrS/YHZ3NxzOn/vP4XckjCmLH2GT1Ouo+N9C7wdmjHGC7xxOi4BmCoizYAC3KfZ4oG/AV+LyNO4k+OZTv22wDaP/dOdsmOVpx+hHKCVqu4AUNUdItLySAGKyHjcIymio6Or1ktzmL175zF16G2EBWQzZ9OVzEm5CpfHmm/GmIanxpOQqiaKyBO4Ry65wBrc14BuBf6uqh+LyBXA68B5uK8bHdZMFcpPJMYZwAyAuLi4E9rXHM7l2k9Kyp3s3DmTvJJonl31IKk5nb0dljGmFvDK7DhVfV1V+6nq74AsIBkYB3ziVPkQ93UecI9k2nnsHoX7VN2xyqOOUA6wyzldh/N996nqkzmyrKyvWbGiBzt3vkl09GSuGZ10xARks9+MaZi8koTKT4OJSDRwKfA+7kQxzKlyDu7EBDAHGCtug4H9zim1r4HzRaSJMyHhfOBrZ9sBERnszKwbC3zu0dY45/E4j3JzirlcOWzcOJ61a0fi69uYfv1+oWPHx/HxCTws4VgCMqbh8taHVT92rgmVAP+nqvtE5M/AsyLiBxTiXJPBPcV6FJCCe4r2DQCqmiUijwErnHpTVDXLeXwr/5uiPd/5ApgGfCAiNwFbgcurr4sN175937Fhw40UFaXTrt09xMRMwdf34NUOLPEYYwDEPYHMHE1cXJzGx8d7O4xa7dXvk/kqYScX9mjM8DYzyMh4meDgLpx++puEh5/h7fCMMV4gIitVNe549WzZHnNSut83l/wyOL3pWiLynyV9+26i2/2dDh2m4usb7O3wjDG1nC3bY6rs7+/H45JCru32KpMG3oeqD/9cNo3v0m+1BGSMqRQbCZkqycwtZGP6IqacOZ3WoTtYkPZ7PkwaR3FpEF8l7OSW4TYF2xhzfJaEzAkrLS0gJeUe7o57ib0FLZm2/HE2ZPWq2D6yR2svRmeMqUssCZkTkpOzjMTEcRQWbOTXzIuZsfpPFJX+79RbiA82CjLGVJolIVMpZWVFpKY+wtatTxIY2JZevb6hYHss7bYmsTevmPxiF6NiWzL96uNOhjHGmAqWhMxxHTiwksTEceTnr6N16xvp1OkZ/PzCubApDOjQlIzsQtpEBNG8kd351BhzYiwJmaMqKyshLW0qW7dOxd+/BT17fkmzZgd/yLR5I0s+xpiqsyRkjig39zc2bBhHbu6vtGp1HZ06PYe/f5Pj72iMMSfAkpA5SFmZi23bniI19WH8/JoQG/spLVrYbZeMMdXDkpCpkJe3gQ0bxnHgwHJatLiczp1fJCCghbfDMsbUY5aEDKplpKc/y5Yt9+HjE0K3bu/TqtVV3g7LGNMAWBJq4AoKNrFhww3s37+EZs1+T5cuMwgMtA+bGmNqhiWhBua6GT+xPDWbgTHhPDH6NzZtugcRX7p2fYPWrcfhvgWTMcbUDEtCDUjMpLkANA3aw+CmD5Kc/CtNmoyga9fXCQpqd5y9jTHm1LMk1ADcOPMXFiZlAcrQtt9xzekz8JEy3lp3GyX+V/Pf3paAjDHeYUmonisf/YQH7OP6Hs/Tt+VyNmT14PXf/saegtYE+Oz3coTGmIbMklA9duPMXwAY0HoJ47q/RIBvEe8l3syCtItR51ZSA2MivBmiMaaBsyRUj63ZlsqtvV9mUOQSNmV34T+//Z0deQefevvv+CFeis4YYywJ1VuZmV/y2JDbCfDZz8dJf2LulssoU9+K7UM7RlgCMsZ4nSWheiIzt5CM7EJaNS5m347J7Nw5kxbhvbjzm4fZdqDjQXVTp40+SivGGFOzLAnVA5+s2sZz3ybTvvGvXNrxKcIC99A+ejIxMQ+zZEAgN878hZ83Z3Fmx6bMvPEMb4drjDEVLAnVcb9s2sPUL1cxpuMb/C7qc/YWRvFG4vM8E3cjPj6BAJZ4jDG1liWhOiy/2MWX8V8wMe4hWoZs56eMS5ibdiN+vqFkZBfafX6MMbWejzcOKiJ3iEiCiKwTkb95lN8uIhud8ic9yieLSIqz7QKP8pFOWYqITPIo7yAiy0QkWURmi0iAUx7oPE9xtsfUTI9PjTXb9jHzx82s2baPsrIiNm26j/Na/hk/nxJe+e1J5m2dQH5xID5AmwhLQMaY2q/GR0Ii0gP4MzAQKAa+EpG5QBQwBuilqkUi0tKp3x24CogF2gDfikgXp7kXgRFAOrBCROao6nrgCWC6qs4SkVeAm4CXne/7VLWTiFzl1LuyRjp+kv7+/ko+W7sTFNqFbeauAc8T7p/M9uI/8NOuCWzc58JVVoIg/G1EZxsFGWPqBG+cjusGLFXVfAARWQz8AYgDpqlqEYCq7nbqjwFmOeVbRCQFdwIDSFHVzU47s4AxIpIInANc49R5C3gEdxIa4zwG+Ah4QUREVbWa+npKrNm2j0/X7MRXShl92sdcfNp75JU0IrT1e8SFj2Z9fhpndS6h2FXGuDPbc8Zpdg8gY0zd4I0klABMFZFmQAEwCogHugBnichUoBC4W1VXAG2BpR77pztlANsOKR8ENAOyVdV1hPpty/dRVZeI7HfqZ57SHp5i837bQauQ7fy51zN0itjIip1DeXvdrfw9dBA3nh7GA6O7kVPgIizYj5AAu8xnjKk7avwdS1UTReQJYAGQC6wBXE4sTYDBwADgAxHpCBzp3gLKka9n6THqc5xtFURkPDAeIDo6+ljdqXZ5RcX4F73JlCHPUVLqzytr7mHpjt8BQv/2TQAICbDkY4ypm7zyzqWqrwOvA4jI47hHK92AT5xTY8tFpAxo7mzzXGsmCshwHh+pPBOIEBE/ZzTkWb+8rXQR8QPCgawjxDcDmAEQFxdXo6fqyhccBdjwSC/WJYwjrskiNuUM5PmV/0d2UTMALuzegt7tmtRkaMYYc8p5JQmJSEtV3S0i0cClwBlAGe5rOd87Ew8CcCeUOcB7IvIM7okJnYHluEc1nUWkA7Ad9+SFa1RVRWQRcBkwCxgHfO4ceo7z/Bdn+8LadD3ofwlIObPNQhYuuYJGgcK6vIfY53MFI3u5SM3MJzzEn39d2cersRpjzKngrXM4HzvXhEqA/1PVfSIyE5gpIgm4Z82NcxLEOhH5AFiP+7Td/6lqKYCITAC+BnyBmaq6zml/IjBLRP4B/Ioz6nK+v+NMbsjCnbhqhfIE1Dggm+tjX6B/q6VszOrOf36+k3fGX87bS9MoU+jYshFjB7e302/GmHpBatFAoFaKi4vT+Pj4aj9OzKS59G25lBtinyfYP4+Pk8bydeoYFF9Sp40mv9hlkw+MMXWGiKxU1bjj1bN3s1rA5drPzT2nM7Ttd6TuP43XVkxle27MQXVs8oExpj6ydzUv27dvIRs2XM/QttuZk3Iln2+6ilL1r9huK14bY+ozryzbY6C0tIDk5L+xZs25+PgE06/fzzxz8yxLQMaYBsVGQl6Qk7OCDRvGkp+/gbZtb6djx2n4+oYAlniMMQ2LJaEaVFZWQlraVNLS/kFgYCS9ei2gadPzvB2WMcZ4jSWhGpKXl0hi4p/IzV1Jq1bX0anT8/j7R3g7LGOM8SpLQtVMtYzt259n8+ZJ+PiEEhv7ES1a/NHbYRljTK1gSagaFRZuZcOGG8jOXkjTpqPp2vU/BAa29nZYxhhTa1gSqiZ7985n/fqrgDK6dHmNyMibEDnS+qnGGNNwWRKqJsHBnQkLO4MuXV4iOLijt8MxxphayZJQNQkJ6UTv3l95OwxjjKnV7MOqxhhjvMaSkDHGGK+xJGSMMcZrLAkZY4zxGktCxhhjvMaSkDHGGK+xJGSMMcZrLAkZY4zxGlFVb8dQq4nIHiDN23EAzYFMbwdxilhfaifrS+1Tl/vRXlVbHK+SJaE6QkTiVTXO23GcCtaX2sn6UvvUl34ci52OM8YY4zWWhIwxxniNJaG6Y4a3AziFrC+1k/Wl9qkv/TgquyZkjDHGa2wkZIwxxmssCRljjPEaS0JeICK+IvKriHzpPBcRmSoiSSKSKCJ/9Sh/TkRSRGStiPTzaGOciCQ7X+M8yvuLyG/OPs9JNd9T/Ah9OVdEVonIahH5UUQ6OeWBIjLbiWuZiMR4tDHZKd8oIhd4lI90ylJEZFI19yPVed1Wi0i8U9ZU/r+984+xorri+OerK5haYEFAm65GtkoQEosJEi01tWgUUfFXIjbGRGnTFltNNf5K+ENTE4Noqn+oIcYYJagIokmDElhNwLQFFn+tKEVYkMYVU7SIJsQfMZ7+cc9jZ59vX/s2780M4XySm3fmzJ0399zz5p2Ze+/cK3V5HXdJGu36UvtlEFvul7TNy/uipPZM/obqX9IE9+EO9+mwPG3J7LtVkkka69ul9ctgdki60ev4PUmLMvrS+qTpmFmknBNwC/AMsMq3rweWAEf49nj/nA2sBgScCWxy/Rhgl3+Odnm07+sGzvJjVgMX5mzLduBUl28AnszIi12+GnjO5clADzAcmADsBI70tBPoBIZ5nskttGM3MLZKtwi40+U7gfsOBb8MYsv5QJvL92Vsabj+geXA1S4vBubnaYvrTwDWkF4kH1t2vwzik18CrwDDfbty3ZfaJ81O8SSUM5I6gIuAxzPq+cCfzew7ADPb6/pLgSWW2Ai0S/oRcAHQZWb7zOwzoAuY5ftGmtkGS7/GJcBlOdtiwEiXRwF7MrY85fLzwLl+13kpsMzMvjazD4BeYLqnXjPbZWbfAMs8b55ky/wU/XVZar/UwszWmtm3vrkR6MjY8n/Xv/tsJsmHMLBe8uRB4HbS763CoeaX+cBCM/savnfdH4o+GRIRhPLnIdLF811G9xNgrqTXJa2WdIrrfwx8mMnX57p6+r4a+lZRy5bfAC9L6gOuBRa6/mCZ/c/wc+BYGrexVRiwVtIbkn7ruuPM7GMv88fAeKX0Ry8AAAS3SURBVNeX3S+1bMkyj3TXD43bciywPxPQcrdF0hzgIzPrqcpbZr/U8slE4GxvRlsv6QzXl90nTaWt6AIcTki6GNhrZm9IOiezazjwlZlNk3QF8ARwNqmJoBobgr7p1LHlZmC2mW2SdBvwF1JgarTMtW6QWvk+wQwz2yNpPNAlaVudvKX1i/M9W8zsNQBJC4Bvgac9b6P1X7gtwAJS82I1ZfZLLTvaSM2DZwJnAMslddYpV1l80lTiSShfZgBzJO0mPUrPlLSUdOey0vO8CJzmch+p7btCB6l5q56+o4a+FdSy5SXgp2a2yfM8B/zM5YNlltRGaqrbR+M2tgQz2+Ofe0k+mA7825ts8M9Kc0mZ/TKYLXiH/MXANd78xP8ocy39p6RmrrYqfUuoYcsvSP0kPf7b6wDelHT8EGzJzS+D+KQPeMGbD7tJLQpjh2BHrj5pOkV3Sh2uCTiH/s78hcC8jH6zyxcxsKO12/VjgA9Id1GjXR7j+zZ73kpH6+y8bCHd2X0KTHT9r4GVLv+BgQMTlrs8hYGdsLtIHbBtLk+gvxN2SovKfwwwIiP/A5gF3M/AgQmLyu6XOrbMArYC46ryN1z/wAoGdoLfkKctVXl20z8woZR+qeOT35P6giE1zX3o5SitT1ri56ILcLgmBgahduAlYAuwgfQ0gf8gHyGNiNkCTMscP4/UYdkLXJ/RTwPe9WMexmfFyNGWy72sPcA6oNP1R/uF0ksakdSZOX6Bl/d9MqOTSKOdtvu+BS0sf6eXtwd4r3IuUlv7q8AO/6z8cZXWL3Vs6fU/ubc9LR5q/fs5uv07V+Cju/KypSrPbvqDUCn9Uscnw4Clfv43gZll90krUkzbEwRBEBRG9AkFQRAEhRFBKAiCICiMCEJBEARBYUQQCoIgCAojglAQBEFQGBGEgiAIgsKIIBQEBeNLEDT1Wsy8PR8EpSaCUBAUgKSTlNaOepT0ouK1kjYorcW0QtIPPd9CSVt9fZwHXDdO0kpJmz3NcP3dkh6TtBZY4hNjTsmcc52vn3OMpCf82Lck5T07eRAcJF5WDYICUFrUbxdpbr1e4AXSm/EHJN1BmrLlYdIMGpPMzCS1m9l+Sc8Aj5rZ3ySdCKwxs1Ml3Q1cAvzczL6UdDPQbmZ3+dx3681soqR7ga1mtlRpcbtu4HQzO5BrJQQBMYt2EBTJv8xso89IPhn4uy/sOYwUfL4AvgIe98lhV/lx5wGT1b8I6EhJI1z+q5l96fJy0to5dwFXkaZzgTQD9RxJt/r20cCJwD+bb2IQ1CeCUBAUR+XJQ6RF135VnUHSdOBc0qSvfyQtXnYEcFYm2FTyZr8TM/tI0n8knQbMBX6XOd+VZvZ+c80JgsaJPqEgKJ6NwAxJJwNI+oGkid4vNMrMXgb+BEz1/GtJAQnPP7X6CzMsIy08OMrMtrhuDXCjr8iJpNObak0QNEAEoSAoGDP7BLgOeFbSO6SgNAkYAaxy3XrSgoEANwHTfLDCVtKSAIPxPL50RkZ3D3AU8I6kd307CAohBiYEQRAEhRFPQkEQBEFhRBAKgiAICiOCUBAEQVAYEYSCIAiCwoggFARBEBRGBKEgCIKgMCIIBUEQBIXxX1506Dhv4eC2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e2baeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.plot(x='reserve', y='supply', kind='scatter', alpha=.5)\n",
    "axis = plt.axis()\n",
    "xrange = np.arange(axis[0], axis[1], (axis[1]-axis[0])/100)\n",
    "yrange = np.array([supply(x, V0, kappa) for x in xrange ])\n",
    "plt.plot(xrange, yrange, 'y')\n",
    "plt.title('Bonding Curve Invariant')\n",
    "plt.legend(['Invariant', 'Observed Data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini(x):\n",
    "\n",
    "    # Mean absolute difference\n",
    "    mad = np.abs(np.subtract.outer(x, x)).mean()\n",
    "    # Relative mean absolute difference\n",
    "    rmad = mad/np.mean(x)\n",
    "    # Gini coefficient\n",
    "    g = 0.5 * rmad\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([48.,  6., 12., 11., 12.,  3.,  5.,  1.,  0.,  2.]),\n",
       " array([   0.        ,  519.46310197, 1038.92620394, 1558.38930591,\n",
       "        2077.85240788, 2597.31550985, 3116.77861182, 3636.24171379,\n",
       "        4155.70481576, 4675.16791773, 5194.6310197 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1eb9b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].holdings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_h'] = rdf.holdings.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e131d68>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2056aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_h.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([51., 12., 18.,  9.,  5.,  1.,  2.,  1.,  0.,  1.]),\n",
       " array([    0.        ,  6264.35577549, 12528.71155098, 18793.06732647,\n",
       "        25057.42310196, 31321.77887745, 37586.13465294, 43850.49042843,\n",
       "        50114.84620392, 56379.20197941, 62643.5577549 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fcdIFmzHU/3MM/tn3ZeDh9rhuxsbw08BDbQyPAr/f+i9i8KmCIwzC8QWt/5y2fqQ9f9Gy93pXq/MJln0C4XQdc6wM9Jmpv9X6SHs8dmIfs3QctX1cCiy2Y+nvGATyTI1h+cNvikpSJ2ZhDl2SNAIDXZI6YaBLUicMdEnqhIEuSZ0w0CWpEwa6JHXCQJekTvwfDf4MMavZhsIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f053c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_s'] = rdf.tokens.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f31e3c8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d9ead30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f49d7f0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f603390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.tokens.apply(np.count_nonzero).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['asset_value'] = rdf.holdings + rdf.spot_price*rdf.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([20., 30., 21., 12.,  9.,  2.,  2.,  1.,  2.,  1.]),\n",
       " array([ 669.66474558, 1213.34914015, 1757.03353473, 2300.7179293 ,\n",
       "        2844.40232387, 3388.08671844, 3931.77111301, 4475.45550759,\n",
       "        5019.13990216, 5562.82429673, 6106.5086913 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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An7Tx1zIIrMeBLwIntfGT2/rj7fHXLnqtD7S+HwPeMe3eluj1Mv7/LJcu+mt9PNRujwIfaONdvD9bXVuB+fYe/TsGZ6msu/68UlSSOtHzIRdJelkx0CWpEwa6JHXCQJekThjoktQJA12SOmGgS1InDHRJ6sT/AeENZndvKmC6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f7ed550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].asset_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini'] = rdf.asset_value.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a205e0f60>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fa6edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['pref_gap'] = (rdf.prices - rdf.spot_price)/rdf.spot_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 1.,  5., 21., 30., 34.,  7.,  2.]),\n",
       "  array([ 1.,  5., 21., 30., 35.,  6.,  2.]),\n",
       "  array([ 1.,  5., 21., 30., 35.,  6.,  2.]),\n",
       "  array([ 1.,  5., 20., 31., 34.,  7.,  2.]),\n",
       "  array([ 1.,  5., 20., 31., 34.,  7.,  2.]),\n",
       "  array([ 1.,  5., 21., 30., 34.,  7.,  2.]),\n",
       "  array([ 1.,  7., 19., 30., 35.,  6.,  2.])],\n",
       " array([-0.56435314, -0.4139288 , -0.26350445, -0.1130801 ,  0.03734424,\n",
       "         0.18776859,  0.33819293,  0.48861728]),\n",
       " <a list of 7 Lists of Patches objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1dfc4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-7:].pref_gap, bins=7)"
   ]
  },
  {
   "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
}