conviction/abc_sim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#import conviction files\n",
    "#from conviction_helpers import *\n",
    "#from conviction_system_logic3 import *\n",
    "from bonding_curve_eq import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hatch_raise = 100000 # fiat units\n",
    "hatch_price = .1 #fiat per tokens\n",
    "theta = .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",
    "#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([34., 26.,  9., 14.,  6.,  2.,  5.,  1.,  1.,  2.]),\n",
       " array([ 1.08275068,  3.04308878,  5.00342687,  6.96376497,  8.92410307,\n",
       "        10.88444117, 12.84477927, 14.80511736, 16.76545546, 18.72579356,\n",
       "        20.68613166]),\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 0x1a12f13a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {\n",
    "    'kappa': [kappa],\n",
    "    'lambda': [lam],\n",
    "    'gains': [g],\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([1381.81065484,  503.6421824 , 1733.23237835,  458.02194035,\n",
       "        1706.46225568,  853.70697172,  182.49819741, 2995.59667896,\n",
       "         822.16382629,  817.14190299,  881.79605743,  730.53715879,\n",
       "         116.19339824, 1309.95958537,  320.56264777, 5724.38540714,\n",
       "         503.91742415, 2943.20729819,  513.27959567, 2445.70269028,\n",
       "         924.58820267,  208.09101348,  857.4889706 , 1471.15499747,\n",
       "        1763.76064489, 1115.67060044,  389.318447  , 4425.49943663,\n",
       "         139.81127818,  706.08557629,  778.50055115,  173.56589587,\n",
       "         334.36011272, 1296.45045381,  335.12823218,  240.75442822,\n",
       "        1101.81670675,  558.43391288,  496.12354716, 2516.78800005,\n",
       "        2189.24226166, 1063.22712387,  962.43232271,  459.12366463,\n",
       "        1797.46902454, 2334.30370876,  372.86359938,  333.87271775,\n",
       "         522.88075376,  872.79251341,  355.51011185,  404.73901018,\n",
       "         572.72101236, 2189.89651087, 1103.17268324,  502.77697018,\n",
       "         140.09530567, 2114.77034261,  148.13609902,  249.75589557,\n",
       "        2259.49100235,  108.61401168,  434.65021234,  257.5009031 ,\n",
       "         943.08108967,  953.97831407,  914.92848415,  575.0304384 ,\n",
       "         778.46924857,  701.71425254, 1586.65346159,  663.06600571,\n",
       "         364.42184462,  866.14874865,  554.40235946,  539.7785914 ,\n",
       "        1086.09885709,  654.15598407,  150.87128974,  108.97229986,\n",
       "         560.87920307,  670.5429347 ,  319.66648034,  864.87964818,\n",
       "         964.76662356,  456.32422256, 1826.76884412, 1128.59470578,\n",
       "         750.86896487, 1008.76326731,  103.7525841 , 2265.29321387,\n",
       "        1488.36252151,  691.62220431, 1146.06745811,  236.2546681 ,\n",
       "         996.52332282, 3394.53603698, 2447.20658619,  585.81184803]),\n",
       " 'prices': array([0.11460833, 0.0994093 , 0.09999875, 0.13085012, 0.07375945,\n",
       "        0.12567568, 0.07580268, 0.08339116, 0.12346773, 0.04867935,\n",
       "        0.1095207 , 0.14681311, 0.11271305, 0.09095258, 0.10154267,\n",
       "        0.09806326, 0.11968544, 0.11246763, 0.1194562 , 0.10774645,\n",
       "        0.08023104, 0.0614897 , 0.11834785, 0.10258067, 0.07798741,\n",
       "        0.10878303, 0.07718892, 0.16774693, 0.07479344, 0.11657278,\n",
       "        0.1161499 , 0.11989753, 0.1174245 , 0.08714268, 0.10535101,\n",
       "        0.12791093, 0.08248714, 0.06955112, 0.09596518, 0.1305738 ,\n",
       "        0.14242214, 0.11469237, 0.07967625, 0.13552737, 0.09372385,\n",
       "        0.09663526, 0.11099613, 0.06975833, 0.12875316, 0.10169638,\n",
       "        0.12165051, 0.06718596, 0.09513467, 0.07911955, 0.09431241,\n",
       "        0.05574346, 0.13860538, 0.08329906, 0.04425275, 0.07922365,\n",
       "        0.07523034, 0.09550485, 0.09645117, 0.13055522, 0.11717516,\n",
       "        0.07320233, 0.10860504, 0.13718911, 0.10276974, 0.10803494,\n",
       "        0.10249197, 0.1120276 , 0.12455413, 0.0826834 , 0.08452409,\n",
       "        0.12034737, 0.12737126, 0.10327325, 0.09030484, 0.08940042,\n",
       "        0.1200259 , 0.10557954, 0.09575522, 0.09634416, 0.0915879 ,\n",
       "        0.10636172, 0.09739669, 0.0834077 , 0.08351887, 0.09581914,\n",
       "        0.0628077 , 0.07872786, 0.05560359, 0.08488553, 0.09310047,\n",
       "        0.07097648, 0.08696429, 0.14748732, 0.09760117, 0.08573467]),\n",
       " 'reserve': 50000.0,\n",
       " 'spot_price': 0.09999999999999999,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([ 8719.69794221,  7243.16074014,  8538.05189244,  8167.01852833,\n",
       "         9969.56685893,  7491.20707993,  5270.24810987,  8283.75622144,\n",
       "         9084.32975476,  5490.23161267, 22846.1592021 , 12726.50072724,\n",
       "        11037.82671199,  6881.77881623,  5144.33802248, 17586.26439125,\n",
       "         6833.00068794,  4998.42756026,  7360.15154346,  9063.68666479,\n",
       "        14473.95969497,  6644.18723371,  8335.56509502,  8292.36014693,\n",
       "         7208.114868  , 14574.19274757,  6745.07033749, 11549.24881837,\n",
       "        18902.04616166,  8339.73307086, 11103.94788854, 13449.12120715,\n",
       "        24199.07005087,  8917.89811522,  6725.94111676, 11288.09501095,\n",
       "         6700.95265896,  5288.44028069, 13020.96493535,  9487.44322897,\n",
       "         9416.32824505,  6170.40268363,  9984.48314903,  5794.56021817,\n",
       "         5066.50697699,  5028.73925994, 16638.66669037,  9969.68792078,\n",
       "        17567.36917508, 21124.29203108,  8785.79974355,  6642.52823785,\n",
       "         5385.28941157, 10655.0846598 ,  8391.89925663,  6602.79958386,\n",
       "        12885.04148572,  7894.25233403,  5527.5165085 ,  9612.96753504,\n",
       "        16623.3221804 ,  8399.90369824,  7820.0021796 ,  6719.73292821,\n",
       "         5226.82945311, 19905.13242755, 10845.95501694, 25746.49404924,\n",
       "         5139.09058409, 12024.16777538,  9561.44051153,  4890.05927312,\n",
       "         7725.7756836 ,  5622.59243654, 16908.93875319,  5178.66103412,\n",
       "         5182.15263859, 19013.70871225, 13444.99138899, 15341.29434478,\n",
       "         4897.53421605,  9572.87718583, 16729.98518941,  5366.1118055 ,\n",
       "         9305.74392792,  7346.51248442, 14184.2133215 ,  7150.80090179,\n",
       "         7520.99668748, 10820.07529842,  7409.78248953,  9975.65357449,\n",
       "         5162.85865025, 13046.87462676,  7345.16320689, 10494.81025325,\n",
       "         6004.21465655, 11203.32828955, 14999.76111841, 13016.48620336])}"
      ]
     },
     "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",
    "    g = params['gains']\n",
    "    phat = g*s['funds']/s['supply']\n",
    "    \n",
    "    key = 'prices'\n",
    "    value = phat\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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 > 10**-8:\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([2.2921666 , 1.98818595, 1.99997495, 2.61700232, 1.47518894,\n",
      "       2.51351361, 1.51605356, 1.66782316, 2.46935463, 0.97358694,\n",
      "       2.19041391, 2.9362623 , 2.25426095, 1.81905155, 2.03085349,\n",
      "       1.96126515, 2.39370876, 2.24935254, 2.38912398, 2.15492905,\n",
      "       1.60462071, 1.22979399, 2.36695694, 2.05161337, 1.55974821,\n",
      "       2.17566063, 1.54377836, 3.35493869, 1.49586875, 2.33145561,\n",
      "       2.32299809, 2.39795069, 2.34849001, 1.74285357, 2.10702026,\n",
      "       2.55821851, 1.6497427 , 1.39102242, 1.91930368, 2.61147596,\n",
      "       2.84844282, 2.29384739, 1.59352497, 2.7105475 , 1.87447698,\n",
      "       1.93270526, 2.21992251, 1.39516659, 2.57506325, 2.0339276 ,\n",
      "       2.43301018, 1.34371915, 1.90269332, 1.58239104, 1.88624816,\n",
      "       1.11486924, 2.7721075 , 1.66598127, 0.88505501, 1.58447293,\n",
      "       1.50460689, 1.91009698, 1.92902333, 2.61110437, 2.34350311,\n",
      "       1.46404661, 2.17210079, 2.74378223, 2.0553947 , 2.16069873,\n",
      "       2.04983946, 2.2405521 , 2.49108269, 1.65366794, 1.69048173,\n",
      "       2.40694747, 2.5474253 , 2.06546507, 1.80609674, 1.7880083 ,\n",
      "       2.40051795, 2.11159074, 1.9151044 , 1.92688312, 1.83175792,\n",
      "       2.12723437, 1.94793373, 1.66815402, 1.67037743, 1.91638279,\n",
      "       1.25615397, 1.57455723, 1.11207182, 1.69771067, 1.86200944,\n",
      "       1.41952969, 1.73928578, 2.94974646, 1.95202331, 1.71469343]), 'rates': array([0.30816667, 0.13303194, 0.14012475, 0.21861855, 0.87763869,\n",
      "       0.0760448 , 0.06810303, 0.19493994, 0.13033145, 0.16819258,\n",
      "       0.46493704, 0.43690449, 0.30479068, 0.17960878, 0.06321659,\n",
      "       0.10573791, 0.07558613, 0.25174711, 0.42410376, 0.08278858,\n",
      "       0.56782003, 0.13970737, 0.4992912 , 0.23011957, 0.15516219,\n",
      "       0.31356584, 0.10385885, 0.09968212, 0.92357365, 0.29954494,\n",
      "       0.24226334, 0.26221117, 0.4822624 , 0.38097634, 0.45806025,\n",
      "       0.6288877 , 0.24030832, 0.22689182, 0.73670584, 0.42291755,\n",
      "       0.36916739, 0.51835718, 0.05945343, 0.28354578, 0.20189928,\n",
      "       0.23197259, 0.40133512, 0.15138996, 0.64489727, 0.52331692,\n",
      "       0.74523024, 0.88322337, 0.3671692 , 0.11438319, 0.40615975,\n",
      "       0.11236885, 0.15976133, 0.09926391, 0.1273019 , 0.26085877,\n",
      "       0.70213741, 0.13097763, 0.10386274, 0.12038372, 0.28262886,\n",
      "       0.20042198, 0.35824772, 0.11851071, 0.17095808, 0.06878177,\n",
      "       0.20555845, 0.31930625, 0.08689322, 0.30531369, 0.33513039,\n",
      "       0.22565395, 0.04834157, 0.6441107 , 0.13564606, 0.05203031,\n",
      "       0.43278593, 0.25755617, 0.17160871, 0.0704277 , 0.62933098,\n",
      "       0.1419389 , 0.45187155, 0.36661232, 0.31435238, 0.21349777,\n",
      "       0.26275337, 0.1125094 , 0.45105008, 0.85873302, 0.51456162,\n",
      "       0.09378675, 0.12184586, 0.47680567, 0.17688786, 0.24625096]), '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 0x1a1506e5f8>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a1506e5f8>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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: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: 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 0x1a1506ed68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15244d68>"
      ]
     },
     "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 0x1a23ca59e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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31kbp/4PARzNzcexVjcAAOHsrfp3F4JyI2AD8NHBileuud6P0P+1G6j0iLgW+ANyWmd9Z82rHbyyvfWY+CbxI/1zINBml/6uAP46Iw8B7gPe1D8VOlAFw9lbzdRb7gO1t+beBf8r+GaB9wM3tSoHLgc3A1zuqe1xG6X/aDd17RFwI7AfuzMx/7qzi8Rql/8vbL0Qi4rXAzwOHuyl7bIbuPzN/NTNnM3MW+FPgjzJz8lfCTfos9DTegBuBf6N/RcD729jdwG+25fPon+lfoP8L/nUD676/rfcUcMOke5lA/4fp/0W0SP+vpS1d1z+J3oE/pP9X72MDt0sm3U+H/b+N/snPx4BvADdNupcu+z9tGx9gnVwF5CeBJakoDwFJUlEGgCQVZQBIUlEGgCQVZQBIUlEGgCQVZQBIUlEGgCQV9X9wR+kb0QDYfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e6655f8>"
      ]
     },
     "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 0x1a1651da58>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a263fccf8>"
      ]
     },
     "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 0x1a1651d710>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1651d048>"
      ]
     },
     "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 0x1a1e6807b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e676550>"
      ]
     },
     "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 0x1a1d150278>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b9DftC44/VnWx8vO/wh2rxyf5rW0MfsEiekmHENiEwI3TnSllTsG9zwOeq7CNVIZtzS3AMz9h2qtbAe7/CssOHSeMlHPSskNOWypdv/SSTojhS07buH3sGxpDFRae8k5mRtKy/VnB65YGydt7eYiqzMaahudKBiVqWAbSMnydwbdLwMAcdj7L3wkse7v6/cmX8sPGn0/JUZ2QdS2kM1Sj3K80sGJXNCle9md99VfP44l6jHQMBBPfuDcF4z3xAvaaIvFKRNfJtuB9Nzzmv9c9MxYhuJrXvVl6wiT/856iHZtdfvvTO2Kj78aqnj9AP3ztpXjy06/GlN4i+koOth4IDP7hkQr2DZVx7c9W1zyvLNA2Bt+xLfzsiW34we+3KJ9XPSb5eB6Fp5N0QIG961it9q2P+OVRgZTOySEe0+y5wI4n2eCx/o7IZt2hiIFGIx0uOmWa8vdwlgZ/7W3AlkfY+5DRotKDlrrt5aPMyPu6tcTwYwz+i3uPYQRduNdbwQykpPu//JRp2PRFdR2dpp23RHoE5NBTjWHZfWQUv163p/Ygq8vedorSMYl6XACYslAcOPrbyihwZAdKjvqburTlYg1/lCU7OKNtKIeu8yMbU4YsD+2NJUCiH8VmjMthvYMnxrYNlCqFs4Q+/5/3bcRHb3oysrnueATAZafNxOYvvQEnS6UVugrxBv8jN7J9b9wbjbwZq3jo4gN0f1cBk3uLIIRgSm9RSb4SFX3Ha93btjH4BVsNn3rXeWzaXHYpZyc0SKEmCCQdSgM2e3QnpvcFNfNTSToiZLA6yqbN8mcSpvQWlb8bvcHCwM2bwozocLPlFaqSQ3j32iAzVhj8068EAHiKwU+p4Y8eBroG2HwZUOLw00cqqJE9hBC8YVnA9Jtm+P0zg/dySKTG4O/j8djrdtUIbRQGf/KJwWe2ev+V/TvFYADQSRbP/AR4/L/gEPU7meGPVlz88pmd8TXcizXqw8gGX7OPwyNVXLwoIBsjaaXEx77NiIRmn0LWiyUtStKkuD7SdTv4EvC7bwJP/RC47/8GP5O0nNueipbyGNX0vR2H9c7/nqJdc4CPlGEHe07DEhzAauvIfrdUNiZDtI3B/+LVL8MfnjPP/3vRDNbBh8ZcOBYBAfXrXkcYvjDQ5WPo43rb7Eld6ZiscCKFY61DCO+rXh30vIVT8OrTZ+I9FywAEMwYUj94OhzZATzwVX0J4uoYYBf8RWMo79Tff/+5/rWtibGjmqUk2X7G0s4SCIkYi3MXTPHfN1xPyOYGrtATfCYP1BqDf+3rlgAAHn5xP16UWd3e9cFAOXIQWP1j9v6UVwO93EjaocV35MV4bMmg6RgsL6xmhw2+ZIiu+/UG/PkPn8ADG3gkzq5nWRnmMpcPhPHsVWeIPmSD/8I9wDM3KwXdntt5RJlh7K03GWl9dME7QRxGKq4+t8PPoSmoBE1g22OMVBzaqvysVrDFqMaAxxGQ7gSGL1ANtf22p7Zj8/7hyIwMAAZ7VIZ/7/NBUt54rHvbNgb/1afPxNI5gfYm2MPnfr4WFiEgYPIOANVpS6kylRej9ZlzB2Mdi1rUqPdS9Sjeff58/PUVLLY8TXjfSdODafj0/hK+9Z6VmMM9/10F4WBqguEf5qWHn7k5+p07xqJkuCESkk5/l5M+P6E6JsXgg+3LrQAvPYxKmc0gJvfEh86xByCQdATkULemGb78kIl+YNlaRnrZkhkAWCjfZV+ViqId3Azs5Bqs7MCXjboTYviT5gbvC9I1StCop/eqceXyYCfKCPizj22P8o14PoFbBvqmA0vfwl7l4wMhSQfAvg0YK6tGveTY+N2nLsVAl1N/TZidUY1alolGddKc8CnYRT3Dj1mXOjx2hMnW3/w0vV7eVbRrPmNhhv/Rm1gyYdjnArDaOrJsfMNDm/33P3psa2T7rNE2Bh8IzIJFgB2H2BTtkc0HYXOG/4Qf1hdm+KKCoeWP1j1FO+goo4cZW9ovFfQKo8bqTJWqh4JtYeWJbOGvJM+/YAY3vO8c/zMRG0z4QEXAsvjqctxFkLB4QHVMrT/EHzSLpDT2+zawkgKyL4AQVkNm433o3/V7AFGpS2mCR7UMX/aHjDXC8Kk0q5OvgWxgNIZ3sCemrdXRYGBTpBrJkIclHYD5KACg0KtnsCEULPU72eALhurLBaIdxGL9tzLM2tAzBTjnT4LBSBxXU69oB52q/F1yLMye1I2F0/sy8R3J0qB2f7ufYa+xZVH0CBvgmx9vvPR0T8FBuerB9Si27B/G7U/viMwq5QgmmaXrGH5YIpJj/u95rvl1dGuhvQw+v9a2RfAy7mlfMX8yPEpBQHHHs/yCRhh+ULlQLHDRVbRRFR1SGPqk8rHhdVtD9c0rHjP4It17LGYK+cNHXsJY1cPlp83ECYNBko7oGKITTeopYmpvCfubqSkjG5eNv1G/8w0+Z/henQZ/B2M5GJGde8FvLf759P74xeTZ9dcwfNngN5K1KIcn6hi+XdQa3nCp26ChY4EBlQ2+XZA6peY8xbUsdEMhIHHNrqqDu3zuwuD7SUziuMRiOvfh7SynIdhZ8D3ADP45fxwwfcvG1kNq3xKGradgq6GI6+4Adj6tb7S4Ll2TIl/JzFurk7/0O/ZqF/QMXwOdNPSpnz2T+JskdBfZcUcqLn63cR8+cuOTfiKkuH29JX1/1Gn4XQVbGTDk/AZ7HDL728vg84eFEIKrebW7JbMnoeJ6IbMRurC+pEN9I99TsFH1KOtAG+5mX/dMQSzKIYYvr2gExmYKNvENdlx0yXW/Znr6vqExOLbldwLRMU6fPYBPv/F0fO3tZ2JqX7Ghwk4BpIdj62PqV+4YN1LsuEIXTV3G3WeM0g+kH2+fciEA4OJF8aucVTyPPehhhi9JOnXJbgJK8o5s8Pk9sQtaw1J0LKU2ig+F4Uv3lZCgX2ijZITB70l22nIs3/YDpYqoPLsZixh80X5pf7JsI65B12DQvr4ZwIwl/rbhwALZwCmMfOdqYN0vsH7XUXzu52tUhi2uS1gygqqb11wYRDcD0pTsdjPWwbt5LP1I2fUHvHLVw7U/Xe03RZYVZWOuk3RKjqU4jWUjPx55nu1l8AWZIgSEF0Oqeh7KVQ8EFEcpd9AJhi9C/qSOIySdy0+fiX+48gzQykiwY02n9SEY/oKXs3IA0qINVZdNCYu2LRl8yaCIlbkQsCjx/ZffsoydkyTp/PFFCzGtr4RpfaXYxCNKKf7xf9cmR5Qk9TC3whybvobPtk3N8IXBl8MPpfejlL2fNaBmI8oggBKWKdAVCnGte9lBJfwyhcEfO8rkKUqV2iiUUpZ/MXJIz/ABYOErmX7fPyvaDnE9bCcinWmbTYEL7PV+vLhsaHyGLwynOJfHvhPswJYNPv/+lMuA094ETBZhoaLeVFFZH1pGT9HRSjCfv2Mtbnhos1plVkTA8fOquh7uXc8clRXX8wsUJibQlQaCa7X2VmD4ALtvu0LMnVJtxEwShF8mDmI2OVpx/WS/ctXDTZLeLj/L8j3RSTqlgo1y1fNnIjLDlxML80J7GXz+6rNim6Dishh9AopfeuGCaUR12kqSztknTsZ7LlgAG1LHDqd1y8aiOsoW+Fj4CuCs9wKLX+N/JUb0nqIdSDqys3HtLX6Kv0jvFtE3bzl7Lqb2FrXTvd6S/sEDWAjhdx7chHd/mznvbn58G/793rAPIuHh8Fw2wHFDRLmBsNL2GMKNcteA/KH/bsxlOxJRUWHc8L5zuHGNllTuD/3mWOgaPLrpABZce4e+wNhLD7OoGgF534KdWyGDv3M1M5zUU449WvGAh65jRl9o9GGDPXclcPFf6adGvn7uIJWkQykocfAlTgLGqi7W7DiMF/Yc9WeMvtSiGzh0DL/QA8xaKmWhi0GoGMsHeku21nc0exIbvDfu4+SHUinsl+3s+gc24n03PIbfrNuNiksxh8uWe3UGf3AeGyh7psC/Pp4LPHe7fkEeqkm8rIHvSH4yHXqKQXCEz/AlgjFroEuZrasMXyfpqIROBED86ztW+Pc1T7SVwYfPQtmfjkVQ9YCz5rNp6+VLhHwglVgAVbIrq+GRVzby4dT0sBYsHqjQwy30ya6irZd0hHGFNIWUOk7Vo7A1BqNgk9hwMjFAiON84idP4//euS7Y4ODm5MqMXlUxRDLD33NkFMs+exee2ynNHigFnv1ZYEzFVP6Mq4JtpHMY8dh16C/pDb4fCaRx2oa19LDxuWM1i71+cIOmWNjG+1jJ3aDhwVtxr+0CuzZP/DcrJ+yOsWth2X4yDRAqa2HzNtWzBJ/Y1pIZfvzmHgUosVCwWe2o0YqHN1z3IC7/2v2+AfGNkdbgyxq+NJuRIc064prSXZCIhuTs7y0F8gf7rhq0g79uO8iiibYfGkXF9TBbGPyjGoPvVYOwWbn/UwptETXq1c3wa8EPfw4xfIETp/Yo5E2ONtKVZxb9RwwMws7M6C8ptfPzQlsZ/CjDZ1E3jmVh8czeIAws3Hlkp63nsagesY0cmx1m+NVQoamYlZnEA9BdiJF03Ir/4Inv5X7reRS2hlqXHCt4wD0XePKHwKYH2C75DrS5BNUx4Kkbo6VnZQiDH3HaAr9ZtwdHRqv4nhRSBrfMjP3IoeDvUl9Iuw5YpLDRvXEG379FUUkn/GCEz1Hcu1SPPqXAlt8zp2bYCB7eBux4Qrk/RYm1KbMrce/rMfjieNJ1Tmo1pRTEskEIiTj/RD/wjZGuHbaG4cca/KI/yL+RJ7qJR6KrYAWEhc+Oq56H/3mEZbn7NX7kCC2+r6JYna7qoex6GOBLC2olHa8aLw3q6gJRLxJs9onXLI5uF8KVy+fgqhUnaL/rlurrlPyV9YJ7NK2/5F+LJ7ccxD/d9bz/nZ7hc4PPfyOCFsZDzgFyNviEkA8TQtYTQtYQQr6c57EANUoHYPV1yrwDlGyCMb+sq1xiQWYLFM9uP6KyBNnI8+0OHCuzao+VUHYeiTH4/MHsKdq+I0fV8Ks+wxfrav73H5/rf131qLZDFGwrMHa71wCHtgCbHwQgG3yNAQlHFOkgHjZhPLkBIYT4Or4yfRZFx0RMuVdRGSX7MXu1bJQ9CsciSiTD//xJILn5MxoNww87ThtekINY7Dxf/C2b8fgGWGq3VVCicGSDryTkiNldPQTTl5BsBNVbkzV8OVpL7kOiHk1g8DUGUSfphP1S/Lr/+vkDuJ1nqQqpRqDoWGztCY/6+3E96rdnaIzLLX7ilOOfl8+S+WLsRdvCtL5iDMN3o+0D2L7iGH6or1xwckyimYR/uWYFvv6Hy7XfyRp+QTM7n95X8hn+qs0HldBKndNW9F2h9X/xqmX4+zeejrPmT67ZziyQm8EnhLwKwJUAllFKzwDwT3kdSyDsWHRsyzd8JccKHCq+MUGE4cs1xwFoJZ333fAoPvGTp6MMP1wfhUMYhu6CjYJNQAiPsBgbYjXKvYrfsV2P4sy5k7BkVqB9ux7VOksLtoWKeMBD8owwguEpruvRSK33CMQ14cxzrOriPp4RaBHiXz5l1+HKmBIr9uGzxwLKVQ9Fx/J10cGeAi6U6gQRRVNWz4GErkVldIQtGl4ZxWjF9c89krkY/tsuBMsPWk5gROR220VmuLhGLy+OMSxLSeHBLQ20kk6yhi/6ATO60cHBHwSqmugtuY1zuIELh4vy/T+48Qh+vW4P/0i93rLRFlq67GPyo3v80ghdGKlUsHbHkWCxoqqHj1++GFefNTc++EAiQsqzRd3gmZ19pvR5VNKRWXYj61t0Sxq+mJ3IIaQ9Rdtn6ydMVtc6SGL4YtCY1FPA+y9aGLnGeSFPhv/nAL5EKR0DAEpp7ituB4lXQShj2RUGP4nhJzBEOaKDG3/GrGmU4cfA1/ALfDru2MyRu+MJ4NFvMdmId2yP0kgmq0up4s0XKApJx3OD0g48+zBOyxwaqyYussEa4bJrQhjzPDZWxe9fZHo4gSSZUIrDIxWWNRxm+DqDz6/76p3H8J0HN6HoWP5DJNorDEdgQKIMP3Iddj/BSglsexRLPn0nfvjIFv2G4fssSw6Wo0osArajGHy5JPHI6LD6e3aQxLYq8CSDn8JpS/li2wDrg7qQ1HLViybRTT051Eaw6KFX/o145aSlAAAgAElEQVQq8wDKoByHouy8FM58yWDd9NhWPLPtsDJjWr31EF5/3QP+DKTserj6rLm4aNE09JQcjOgyphWDH9bw+fZTFgYBEjRaHl2ekekMcC10F6JO25FQ6GXFZdFBct4MANiaWbl4jqvjuWqYhDwN/mIAFxNCHiGE3EcISXaHZ4CwpOPYBGL29ReXnIxb/+Ll7A9FLoAi6UQga4X8fcEmnN2EWAnf7y1PblOSPYTWKqZzpYLFGL5b4dmnB32t0g05aCkPNdOVMxADD33u50zSAfxBKS7MbWisqo9wUM5ZlBhgzNO2LN8cWYTgm/ey8gEepTjzc3dj2WfvDoynU1vSWb+HDZRF2wp8FtJMDJAWryCoqYtXY6qaRsaJpHrwls2uHSEhg68yfLkkRnlMGvB9SadRp21CHD4vy+DRwLAWbUvrsC9XPaAS44yXjTshep+T8NkkGPyS7LzkfSXMUB96cZ80Y3LQw/u+Lhqn5Fj6jOk4hu9JDJ/YSlJWxOCHV0GpEyIq69hY1R88frwqCMks+VE3biR6TJcEVpBnRy1AU1eDEHIPIeRZzb8rATgAJgM4H8BfA/gx0cxbCCEfIISsIoSs2rs3uQBZLfgMn5+VY1mo8Ive5VjocmQrIl4DSUdbvMiLOm197Tyy+g43aLuG8NPHt/n7E9Nsoen5+qtXDR5wXoO86lFleiz6jI7h+8Zyl1R+1qvit+v34K3/8bvouYCt+rP1QA1JR0zFucG3LMCC0PADvVjpzzTEjhMYvsUNWNGx/IdIREeJKW/QVaJOW0CdnsdVSA4XtUpcLUlIOsRWDaFg/vyz/++ql/l+lkpFGjjtRgy+7LRN0PB54IA8+1P8NxxnzBlgRem2PxFzjilkJw3Dv+KMWThpWi/+5OKTAECNVuHkgYRmCrMGuhQ/QS83jDt5yRN5BlpyLBwdreKzt69RlzuUrruSwEddvf9jw92Rma3M8MV3rz59JtJCBBUMjVb95+2hF1jdrL99/WlBEEbFU0oshM/Rb4+0HGsr0JTBp5ReTildqvl3G4BtAH5GGR4F4AGIeFAopddTSldSSldOnx6fdZmyPQACh59jEwTVRyl8wxF2CPLfeTqmKDrtincBi18LgN20ijSdDWN6fwll1/OXghMPZtERDjebGfww0969FtMqOxWDL9irLg5f6MmKo8qrYrW0FCCgLie49+gYvvdgTE0gkTwk2sUfJpsXnwMAi1bwcusZzMQBlU35jMvy2xHV8LlcUwgiXsQDKfblM/zwLCyE//3wRfjGO1ew5or7FhqwI5U0ZYPfPah+Z9lMCrEsRDKxqee3/fyTpuLnH74IAFCpSDp5UwxfMlrhk/U8fzuF4TsWntup5hnM6C+hXPVAX3oodCA53j8lpMFhWl8Rv/nEJX6d+KLvvJRITyhg4RfP7MTuw8P+vroLrA07DjPpTzaGRcfC9kMj+N7Dm/F1vtSl4kcCEhi+5OPZtwG0rPrVFINPKdb942vxH+8+O/VlKPBZ6FC5GjHolkUkTT5q8HW6vO/DOB4Zfg3cCuBSACCELAZQBFBjFeXG8dTWQ/j8HazWjWBBDl8FK2zYAwhWxTqPMBwfvWxRsIl4KEsDQJHFBDNnKY0yRn6Dp/UxBrt3iLEZ3+DbMsN3Vf/AjCXApvtwYvkFxbiHtW0ZhZD+zdpL0VdStw3XEhka0cg9hS6WNAYEDlguNVgWAeEM364ewznWekwlR5TLOSoqKwpG5pZjJR1bMHw70PAn88Jk4gGyajD83pLjrz3sn/9LD6MP8vJxoYdKjuwIRypZTiQUlf2GSv4MBmFEKmWZ4fPBMkk2CsPTMXzpXPe/CDz0df/PgS4Hc6eIPkj8pTgFxLWrTl2iP14ag8/bZEuMnYQGQNGPy7LBD+377rW78Ykf8ZmGHRj8PTwaR44ek6NZ/JyK8H4VDV8+rq1c864NP1fbGmL4XQW77po1/V1smcNiyAdgkYCgjFZcxaF/4clT8apTowRWSDrtaPC/C+AkQsizAG4C8F6aY8Fn2egFDF9Me4XRENtI7FGSdMQ9GOjWJKhI03zHJqzOS8Tgs22m9rKH/8AxZhDEzS0Ihl+wWCiXHON/8mVAdQxjcLQGP85pC0haITdUfQV123A9b63BJ0EIJp66kb0WugFC+HoCDDaXe0ZRVHTI4bEx5RowSSdcIZLLEQX2uUUIHNvCl65+GW7+M1ZbRzxAgb3nMddP3RgZsH22JN37OSQoU61o3JQGNevZB2rTLDuQdMLRVtRTPhNtVBi+KNUrCMLZ70VNhCWdcBmJ6mgQbWM5uHjRdHzyCmbMw2wSkAx+XPJRmkgQ4YSVZmfhn6kavmR4Qzh8LCAOovuKeyIvOSo7U/06MxGDH4rS8auB2upMeVQtJSJr+I1an96So2j4ArZFlDDrgvT937x2CRzNPSpY0Vj+8URuBp9SWqaUvptLPGdRSn9T+1eN44w5QRhjEMlAmMEMM3yZPUpZe1WfiYciAgClwxVlDZ9YwNKr2Re80wtHThCRQHl7uNNWSDoyw7dYNMgYLShOW2Hw48IyAWCnxeuBnPRKAEBvQWUP4d8Oj+lC9iRmKx42hxVPY+sJ8Ha47CEepUWlxPPIqKT7i32EjYAYiLnBF+d2zbnzMX8qY66lMAMSbT+4OSKBiQfQjXl4FBZ1aAtbt1iAUtWIyFp9SVrgpTLMksmkcxFtrFal9ogEM+qxRUZE6eMkyE5bAAiXkZBX4BKRNpNYgpAuL0MYN0+K+lKPl8LI8HtPbH1OCSCHZbqS/Bc9nl/O2S6ANTc4fjWk4Qv4ztvIQCKdr+epREwiXuEzlA3+t9+7MvackmATgluf2qGG4YI9V12S07YgJUfGDa2C9NVd/ykjtE2mrVxQS2hntu+0rcHwfY2UM3F5ZJbrinMoko7c4XinF7//199swDd+s8E3/IHB55KOHDtPCOC5GKXqAiM+w9c84CfzBVK2HxgGBuf7Gny3Zuopw9MswajqyBx8ARSLEFjC4HO5ZxRFJTpmtCzp/r7+GjIafM0Ar4e5cnRT65Kfei72Hc9K/RA3yXgQ6ZFXGH6kjITcH8CYopB0+iSn3vN3sddjgRop2lgVDH/yAtXgx+RjROCHZYps0lAIqnyfpp4CvOIT/sIlusxrYUw8z42GW6YFjTL8MITB//StazBaFjO7aHscEgxotkQagKjTVmAsNcOXonQkgx+e3Ihn6eqzTsDimfoFU2ph4z7Wd77z4Cblc5nhj1Y8RdKJKzLoWK2N0mmwV0xsCHtdsAgbSQlCDF/aWEq88vhNUKZicsfiKDiEM3waaL+A76QUN/6RTQdQcT285gxWKbEoGfzy6JBiRMQ+xqijyDdJGv5ps/mshlYSa4ZH1s/V6cyaBCc4Xb4xElE6Fk82YwY/2H5UZFfK5YzDmcdH2YLv7uBCANuVB0RADnNj+4g3+GIAjZMwyq6Hq775EJbMGsAXzw3JWGG265QCY92tKYMt6f8iec5n+IterUbZpDX4OoavLMgiMXxiKU5wXSa+uB7UlZydtsNmHPtf8H1QieD9UNHwQ8cSBn/tziP48SO78J7p0DN83+AXYFmMNIirqGj4ElnzAwHCORGRcijSDEBaaMbTcNh1//harQRWLyyLoGATv+02IUp/lZ/RuG57XEfpTFTIUTprdhzBE1sOQ8vwQ4lXgrEqhiiG4e8/Vsa2A0dUg887pzyNLLuenw0r9ltybBARTVDqh7/0H4AxaisMXxgzXfE0v4O5ogwCH1CGd2IQQQTHqpcOqj+UBwTBLou90QgTu+gbbWHw7bFDKFMHwygpLG20XIZfisEfJEPda8pCAMDgNMagdWuFfvSyRRjocrB8noii0dwLDvEQdx3b7n8mXyUWKSUSwxKSzZwS0DM1YPiWFbt8HsBmkEXbQrUiRzNJbYypqRQL2ajRBIMvQcvwfSe+G2zvdDNn/PJ3RJc11IH3ZVXDDzttg2MfOCoicTTnLO6XzRi+pTD84F5G/FPlYz450DJ8ILg2xALmBWVI7OG9foCBQCOOWh3kIANADACBBJkmW1bM1NvRadsyBFE67PQ27R9WyWs4LNN32rKNijUkHfH9P97+DOvo004FuicDc1lumcwmylWPdwapDkrBQrUqRbVIkQYVams1fF2HFdtRT3U2ztx2F8611kW2968PNAa/a1KU9UqzBjE9t8YO4TD6ABCFpY2Vy9EZRtgInHQpcNHHMWsym5nIWasCKxdMwerPXhEsJ6g8RGr7CjbBLOxH91CQCBOWdGyLMI0/KXrm3A/4kprf5uXvDG2kXv+SYwUMX57JyMY2LeTcha2PSvuSZmahfepmR+Iz6roB6519JjunyQvStUU4baXImfCRZAnGLx+uO2cp09ay1Hsjz8ouWhSK1t75NCuVAahyl8DJrwr6qnh+pP06yMeYymHEAFMSxDMYjuiOk3T8ASLjqp5p0Z6SjsTwAUhavYbhS1N1f1EC2eB7Lvs9Hzwopf5Ns8HD9Up9wPl/5v9E9tZXXIqyy34jGIBjWZIBIjzph/WYiofUko7/mS8j8AeeAlXEs0wbHiC+F8aq0KMyaELYPvmrn3jlVVGGqPsTbD9SrgC9dnDNgKgRsCzA6sK8yR4uWzIDf37JybFt9DEsLaYRGpBsi6CblPUJcxAGn69TrCu25Z+rGKjcIJQ0/MBG6snYgeONhAhCoww/jASGr7MnguB4ngsUB4CLPq4upJ4GQtJxigDUpfwElGgVce0194BKsgwBQcGCX8xQnh2ePnsg9ENpX/K1WfZ2tipXqT+o9BqSDe9aswteHevfpkGRl7GwLaLYBrmuVLhoWxzZF4NyRZMlPR5oS4YvdDXHCndMTeKVxPzcOEmHP2yf/9+1OPvz9+DQCHsQLFDtnZV/Lxi+PGtwLAJ4YkpKuKNThKsR1WlL4w0+4R2OijaKtHgAbk2Dz6E4DOXPpdhwy/J/Q+CBIuosLZfLUvJRAusDG1C/875zsHJBwpKRPmjMezaTqypCQZThOxZhunBM+QW2IzFQxUS3aOBYxC8ZHYkRj6maGotQH9p+aAQLrr0Dj2+SfDwpZg3CEPszvkJXulBMGSdfCgzMwUiXZoWu0HEAOdokyeCz6yFHv8mzQ1kKYdq2tK+CVJ9m6smB1KYJlwZY6QuascH/5ccuBsCyaeXZt20R/7kMk454gy/8TsbgZ4Yb3sc0vcDwhhk+gs8lg7+fJ0rNkYsgSVmW335wEw4cK+MAXzicxBh82biPcYMvDwKOTQBXMorEChg+z2wVCOLw9bfKJoR1NsHIwTpfNeHWKpJOmJ36G8l1V+zA4FMKj4rQMlnSqUQlnXqljVoIM3xC4FJbyfiV70aFszLG8BMeMD8c1UWwpFf4vqp/2xbxM7PX7x7CX/zPE3hhzxCP5U953ie/SusreG4HiyV/ZKNUaiTFUmOij3liVtoI+mcBZ78XxJE0/EjiVdCWVS/tZ74YyoqHzZ8SOIYpgCC/AJCWIlZmhwDwngtOBMCd9TTG4MsQ9YJCg+sp0/tSMfy/e8Np+PJb060wdfL0PhQdCyMV1TFrkaBUeFiSj43S8TV8I+lkBnGtoxUXNWGZkk56dLSCeVO6sWiGFIetYWyiwzODH+88A4By1UU5lHZdsC0QPyVdNfhVjyhV9oRRjQsysCwSYfhAHQyfSAYuzuBbdhClAw8uhHNQNvjVYF9Hd/u/axqKoY5KOi6s0DggMXw+s3I9LyrpDMwBjrB67yAWWwDl2D6gl2dHRh7Y6LFFGO+hURd3rN6Jd507H6dIBKEm5p/P/sWBenh08wHMHezGnLCkozFqvqTjNiArhZAUcSIzfAKWHdsNioeuvRQAsODaO1h7CJR+WZJOIRxZ9Q9XLsWOQyPYeXgUyrV2NAa/PAxseYS9D51nybEwe6ALP/rghSw5MgaiLlBadBdsjJSryrhrEeI/lxFJJ2Y/4h71HtsKHO5K50jPEG3J8MXF9o1sWMNXEq8CQ3DxKdPw27+6RPW2a8LsROytlcLgD5ddvOeCBfihvLiHRaSKlLbK8D01IsfzJZ0YecQiTK4Qejs7U1RpSoYvd005M1aOw7acYJCgFBTRaelolRv8oT3Asz/lu86ge4WXkZQgEsKSNHzLIlgw8hxbl1bgrPcAZ14T/E0s4Mkf8PfyrFCCJstXMPxueRUjr0ljK4dDUg8Pv7gPWw8Op7qWvu+IVuuXlcLNkA1+6LuS4tClIIQZ/XDkyYp5A/zXbA/yLFdXWKzk8FW8/LK3BX0+wdCu4H1oNHIpUHBszJ/a49f+yQIFm6ASqmQrr4z3kRufxB/8W1DDKC5iR1zWuQd+z1ZaG2e0p8H3naPs9aXSqaEoBQ3DtxwANJoOrTH4p/OsXqYXJ2v4VY/i9dc9gEVS0odjS8f1GT7P9qVEedj8sMyYO8UknZDBp3UwfAFCgBOkolKy0SIqwxdTZvmhZU4oEtTF579rGkp0TZRlE6jJNrKi7/F1BLzDO/DcziNBMbX+WaozU3k4ieYz/bGFhl/ixeBGK159cfhhnPMnwHl/HrhOIKq4IrpPjT0pWELSaaINHEqoZOhYSoIRKDbuPYYv3LHWX+P44WsvxYKpPazhguUDKEq2+9ThJ4HfflHxrfhVZP1wzpjkr4Ty3q5HtRFMzYJw6dRSDL5Kzp7aekjaPn4/vl9p3wZg3wuZtzUJ7Wnw+auQRjb0LAdmnKZh+JAMvqWNNBChen60DFysmDSEOZO6cNqsPu2DpRvdZRbqWARETuiS4q/Lrp4JxTF8SzB8hDV8G/Om6PVPm8RMdS0bmHkGf69KOo7vtKW+wZen5b5unJCh2RBkKSakw1uEtUe+a/K5ubzU9MHhMu5au4utBQAE96xrUvp2DKhrntoW8XXormKwDF5TBr9vulLWQXFHp2H4SuJVswxflm3U/iz3bwKKyT0FEFC8uJcVpJsz2I1J3YVg4BEavjSILC6zcuDyPS0VeMkRP7gi5hxcTWkQDs+jyCLJKgxLPKJE/oxogykA7Xgc/E6SA/HMTzJrYxq0p8HnV1vUtvD7p1NisfI9U4MNlVWHNAafP8BiunqJ9RSmPv9jnNJfCZylKSAbR8diGj4VkhCXdCgoRqpUKRORVDwN4EzT142lgQJWbBlYVcMPSRjCsMgPG5GidKiLt5/DHGxyaFnYCQcgORQyLfiAvHrbIdy7Tl00jRACmwSSDgWFJRkQStl1E7OTICqXn+tZ72Flr7UIXe9TLlf+dOyA4XcV2ODIGL7b9MzG90GJdgOpDL5wCNI6oo1i95UiaxS8dZN6irBAsWlvUL7CsS0ueRGWozJrKajUpoAASRm3jqVKOmnCVcNfURopcpYFLMJYuXwpmKQTv30cWISXybTNDL6kIx4AcW1LfcCiy9mUXkCu561j+CGDP5OwaVuJVBPZ3NzQ+pZyREvBJigQlxPzwOC7HouAkQ2+0MnjOpAlZgdyWCaPw5/SW8T0/mgcdqykA0gGX6YygaRDKMUlS2biklOnK+WHBZvbPzQWZNAmLTiSFoPzAQCPbzmIu9fujHztSBMzpiCoko68WhcND+ilPn//kcE+fL1DjNm2LJ+ldXFJZ6RZhh8CEYMXpZFBRNcbirbFskyro0q5gUaQNjOVgLHf7qKNg8OB1OLPgIjFHJOnvQljVrBojSffNI5SwVIZftwsJUHSyY/hk0idHjuJ4SdcPkbSjMHPHNp68Qokp62GTTz0wj7c9tQ2UCmrVDgJmaYXH4K38kR1FXo5YsDmce0esQHhB6Aeqh6TS+RMRvEzXfE0gLMFTRz+KIqs/LCmQ1pJmYhy1I7/mS05bdmxCralzFpcl80y/urHT+LuNdyploXBP5EtNmIRoq2ZYxHA4wbCoxQWKE6a3ouTpvXCo0xnTTzfMOKctiHIcfilrCSdcFO4eJbE8E+aHhjRgm2hhAqzoXHhjCkh97ekK0FA4XlAT8FSVqtyLKKENAOqEQwYrszwbbaAC61l8JMZfj4aPpRF5AEmzSQSsRj4Gn4L0NYGX4y+sQZfqmHDOpe63Zd+uQ53r9mNDXuG/AQToRkXbHBvYdwIr34eZvgOqnBFhq1g+C6FC6suhm8TMMMjxeGPOX3YQmeCQM/UbG2UTgLD1yQWhR8qKqbvlAabF1IU66oFLstZhPjF7WQ4RJ2YEVC87ex5WD5/EK5HmXwm2pjmGdP5eTSwCWGZ2YQN0ITw0r5KLH9jEJo5i4AhQTy7BpeeOsN/79gEXSgzY5Ihw0+qEUNA4VGK7oKlrPfLJB31+ZD7sI7hi1LDlark39LBrbDonUuujX6VI8MPK7gnTe9NNOxxYLNDqTMe2JRBC9OhrQ2+rnyuArG4BgC/rK+ERTP7QEBxyox+v5wpY/iADcqm9DHMK9wN5BBGxyJw4MGDrRh8wfC7NNUD4zV8GjBAPyyTG4wYhq+VdPyG6xKPCCyiMvxfPLNL+ZnLBx1xlTB3JTA9ZuWlenHGH8AivChYCLYk6YjbbJEgIc2WVuvKklP5iVckKJnxw0e2BLOtZsBPyKIJUTocIqsc4IvCg/uWmjT4ioafsJ2YgXQVLByRGH7JsWATNWxZTXzXM3xA8g3FafgiN0ZjbD1Km164XAeLM3wxGH/ytUswo79LKT3eg1F0gyVvJks68GeHAICDxuBngpqSDrFCa3Kq23kexbS+IiwrJOmAomh5iAz5yr7VP2WGb9sWHLhwiWTwPRdVzwOF5TMd+XdxWmGB8IcnJOkA3PCllXTkchPya/i9Ni+BszleGpkArH57o9mekbaxmvy6krKK05Yf2+ZTbY87bQmAfXRSrG767/e+iAdfCK++WUPSsbmkJ9Uv2n+sjK37jzVn8CsjWPDsv2Ip2ejHuLPmhBKv+BddUky8Y3OpkKLpWYYSpZNwKSx4EsMPJLxvvWclrn3tqcqPVYbP38gavr/4jb5sgo+E58719KuBNQvRn0SClViLQn6+3mj/Dq+zHvW3j4NjWao/IOuM9AS0tcGvaW/kC20XIhSw4lIIOT2QdHj0hyirHHOQcCibIulYhBn8MMN3OcN3olE6cQbfsoSyFBh8UfogLmysr6jbV0jSCTF83xlKo2n7JYdns8qSTlbGnh+fRZ/qJR3x8DA7wHTWLjqM17u/xqzKS3yQJrEM//GXDuClfaE1bmtJOlYg6QSgqFSbTHo6xCp/LiLb/YgjF1asUeiW6hUUbALLHwCbu/4qw0+SdNh1f/WSGfjWe8IrSqntkKUhT6Phi5mtn8AVG2kUf36eR2P9Xc1AaPii3aKGv3xOFqhfj7+201aOlMsgXyUl2tvg89dIdIa/gWzww+uvstR8lohFVEkHrFxw0vQ9fMNlp60jGD5kDZ+i4noRScetIen4DF96AISkYxGCOz5yceQ3b36ZtKJTuKExGv7cQS4R8NnE284OUsJLBdsPDQ06coYPHWEF5apujKQDCjgl/y7bFkHV7sYUeggzKjv4gxjfnpIT5FmkdtoSYH51kxIx4sfNN8PYykcBAhxGLwg8PEdPwkMnvF9ddlFqndxXGLOlAQFoAunrxzMNf3JPAQun9Ya+Up8PeZeJDL8Sv06ubr8yPJqnhk/951G0VamtI/Wzupy27cDwCSE/IoQ8xf9tJoQ8ldexEhpR4/uwwVcHhnLVQ4GzVVnSAaVsNZ86mJTM8EVsuAcrmCVQD8NlFx4sTO0LBh9/TduYB9CxpAecimiV4PT0nT9FWKb6JT7wipNw319f4j9s7zhvvv9tybFYlI7IPSYkB4ZPVN2TwxZO2xXv5horZQlZloX9dAB93lEQwphXnNO25FhRP4/c/r7p0eNaBCV3RPnMl8oyjNLxiCWx4SjCBl84UZu9/kmZtmobRbfTtDHUDq3TFhqD75edrl/S8WiysW0UFiHwvOA5Fm2VL5MlVZJNaoEVjsPPouZUSuRWPI1S+ofiPSHkqwAO53WsONS87RFJR+20Fdfzp4difdSX6ExQOsoWBGnUaWuzSbILBDH01MPQWBUUBLMGAodbrcQrx+IhboSxOwA1O13idfErXsoMhKDLJjhxSo/fXrk9JccCLbNrkUt8MSGxYZkOEQbOCmY2Fqs46oIlZons4Li2lQpWQugugKVviR7XVqt0AswZTikye4At/7ziId+Hgs1Wlcqa4SfZT3+ASchhEVAYvi8Rqpm2gKThn3hhzFFp7PlRXk4ja/iSjs/weT0t6eJM7ing4DExQ47fV4ThN5kkVw9yn0sQJnK9HcCNeR8rtg1xV1/uyXYBOobPOg/xk4lWeaeysExCpQzX5F0DKsN/5eLp+ORrF2Nqn7SOKvVwbKwKx7Ex2BOUJ5jR34VXLp6uaLUyHEvEacN/6AKnbcx5aw1fmOHT0Hc0eDiJpZRrLgpJhBAQPywz24eOafhRSUeEywHBQ2QTVtTKo2wGIDT8OJQcW1OfnMS858e1SOQyElC2n6ajdNjLHQNvx8M4Uxuzrbu1LAQ1Iw2/jjh8djxdn1I/I4Sg6Fi4/LSZQQkdOSyTs+Zq1WVlL3pDK2H5u/ViW+VRKNVms4LvtPU1/CBcWIBQWvvZAx9MZQ1/9vLM2xuH8RhaLgawm1K6YRyOpaAup60Vw/AttiNReEuU463N8ENOW8mg9HcV0N9fYr+tiH1QnDZ7AF87b7niCLpo0bToEnASWDoAZzxc593fy1aSiut08e5LxDJ8/++ZZwB9M+C4IYbPI1Yo5SpmltNqzvB15RtYWCYbbNh69Sy1nj2gzIlpgaJK48+6pqSjORdd8owFygb2Zs6dUt/n5IHwWVO6n9o2ycaPgHCUTvz5+DMKLcMPSzrA8nmDWDSzD/QFjaQjM/yka5go6VDt+s/NwuL9zDf4Gg2fUC9w2ibsy7GIvxwG5p+nrwiaE5rqFYSQewghz2r+XSlt9g4ksHtCyAcIIasIIav27t0bt1lj7RMhc7WctrajNUqnrHwAACAASURBVHRll/o3VBj8URSxc+5rcaA0N5BjdLsOO23DIYWChUlO22l9Jbz2ZXPSnyCYhu/HaRd7gYv/H+wYWK5tQyIiGn7I4IMyqeL0NwPTFikdnYVl8jh8Kjp7xhq+RaTVlQLY4u4S4q8T2lN0WNw+JUEJZUQZudx+16OhfpLM8Fk9/HBbPM7wm5B0KA2NtenT8P1icsyR0ngbUE8cfgLDDz0fwvGpDJbasMwasyQd0eKZxRS0DodzeohaOoHTVkg6wTbyanBJDN9fw6IFaMrgU0ovp5Qu1fy7DQAIIQ6AqwH8KGEf11NKV1JKV06fHnWONYPUDN8uajcuV11ecpZgpCwYvo0j/aegXOhPnDqHdxfViGnAnJXOW19ndXyGy/fhlGp2uroZPkiEwSkafsEO4vDhJTLChkAsVq0whuF7XNKpuB4IKHqKNl8chWn8BEiO0uHMUrlHNc4hzPA/fvliELDkuabYNfWUmkAWSU4YkwcpMbh5CRp3WqR1fBIgnuGHwieFLGJb7OzCA5lwQH/r/o1Ifg40z935HwJ4W/LR8FVJRxRoC/d10c+SLp9aPC37tiYhbw3/cgDrKKXbcj6OFqmdtsv+MPhM6oR+HL4k6QCiKBfRM42Yo4cXh/AZfvdkvsxdY87OIA5dioDwAmNRu2WhT/2emmz8ZBZVZKmD7CGOhDdmAOG01SZeBZJO2WVn0Vuy/QfUIiJ6It6hLJhlfIKe/vzl/X3wlSfBgjTbahjqTIMQfd0V/y7Jvj9CJFdOc9d/7uTuYGW3xF1RjBYn+wZX/Up9PoTjUxjk8EAh7kPc0qHBfjXfO0W+z7wYPtv3OXwdZrnelQBRonSSNXy/7+QgPyUhb4N/DVrprE3D8Et9vHpm1NCJNVEBYKQiVYakLLlneKwae5Dwx4M94Th/3mlXvAuYfwGwe03KRqsYsQdwc8/bWb1/v33sVbA0tZBbDaMWE5YZZnByuKdwYDENXewqY0mHSDXEJfhhmcTyZTMh6XiUwJb08FgNnzPLaizDj9PwQ20RsSfNROlwhi9gEUSOEwch6QBo2pDMm9KDj796MdtVYuIVRZU4vsFVoAnL9Cj8RYa8kBTkhzrWIj8JwRKU1pNDkB4iOOAb71yBX3zkYiUcVm6Xz/ATLKuj1NIZX4Ofq7eAUvq+PPdfC76GH9d/eHQMe88vvLTxg5+8FPSpXQA8heFTChwZrWISPDy74yiWLtIdW8XyeYPqB7Ic5FWA4QNqO1LCsi2MoaAYGdGZxK4KtoVzF0zBo5sPJMs5gF7S4XkCMsIaPuGOUb9meNYM3wLmutvYeqbFoCibTQQjJihXA0nHIgQuBXfaetzg68/9nAWTMbp4RnzCjpbhW0FoIZhBsHiORnOSTsiTQOJ9D2GINtBm2yCaArUf6eBLOjF7UCUdNWwyHM7pZ6/WYvj+kaOQZxBZQjD8nqLjr3gX2UZKvEp02tokOQw4R7R1pm3NwVM2+DGOOYdT1rIkyVBQvO3suVgxbwCLZulvfm17J3fqxjuoTRBJzPG5A5EeLEHgYxsaZvhhB2aChu/YIKBwKUARHzLXOBjDP83bABxTHfu2YMDcacsMvsTwiWh9dJYisGR6D5bPG0wouqVh+DaJMHF/SGlaw2c7FrdNJ0Xp/CS+hp+RLUkz0LBaOjH3OxKHT3zJ5ZfuuZGErS5f0gn+T7PfoL1cysxNw0++IIRLh0CyD6S7YMMVYTptJum0FKk0/DDDj2GBn3ztEtz7iUsAsBr1r1g8Ha/+o0+htOD8mGPXOHqYQTcIttBEyOBTquj3YRdX4tFqhWVKxxUoORYLSfSoNI5lr+HrokGCapkEcyZ14+0r52Gg22HLyIGEGH4MqiNx38TCChkAQkiQeNW0hh9cPasGw1cGHSuj8g4h6LrnuVzLvuCkqZjWH1OZMyTpEMKyVT3KFujZdWRUOQHHtvDeC07ERy+rUXgvJixTULL8GH6NjShN5bTtKsh5H20k6Ux4aKSK6NMVPH62PBUF4hND+K41dlLdbxYMX2Pwwws1gAasJ9bs+U1Jy/BVDb/MmWU+ziiC806agpULJkcHHhJIGDMndeGNs2YDDpN0RDFb/8zj7oUdXRVMPXxtDV84h9n2zUfp+CuMEWiZpe7qWjx8tp6lN9NAR15u/MD5cD2K4jOHEOsXoh5AgiRCIYvMntQFCoJjY9XI8/e5K5cCq9cC5WPhvck7hu4KiL4Xt/5zM4jLA5EhJ/glEb6ugihFMv5oa4Mvpr2xfV+qP4NSP1uKLbwxpcEiHFYNn4C8a/BOErexrOE3y/Bp2OBH65Y4RDL4yuFiJJ16GL5toQKg6nHxJAeGH1QQVdvxpxctRP/2rfwaBgOTCGdkZp+x5tjbVnPhdb3ct8pdBDrjNO6oJiCEZuC05Suq8T/ZwJWMWz50IZ7eesjX8LMorcCaEn9k2xKVWPXGl+9B+U7MVl63dBYu/NBFGHxhH9LE70e/10s6rl3CEO1GTw66hW6JwzAIvPQM3w0rC+ODjpB0YvutZeNHj72EL9+5jmWQnvVHiQ+/sHFplicjhMSGRUobhVpaP1hRpyjDDxH8IAgnvINu7kwu8mqMDWj4PSWHO22F7py9hu8jdO1Pnt6LGf1dCJ8Z8Rk+E3NYobq43ddobwzDf8BbBnfJm4PP4NU2VrVA5aqsrMFJ/Y0CWDF/Mt738oWwLMmPkME9SDVZSzpfr6oMfqyUN1vFy49ai4vfb0DSGT33w/iO+7pcGH7cTEvZhkoMv4bBD8qEGIOfGWoOnnYRu4eBw8Pxa2TKLEWw5rROsUTDlyPDDz8PVJJ4Inxx3vnAsrcD0xapbanB8GXH2GB3IXDaUo+T7SwZvtRNww+db5UspZ0suYfAAiSGn11khKjXIody2iQDpy1n6ELSsYKaeDWRWaSQ3xKG5DuZYJzHhgIigTjHZwzDTzyq/vxcSkBh5Zp4pcNX33YmVs4fBAWFR2s7bbsclqjYinVtO9vgzz8f17tvRlcxQdmSrKfYXzqGX+v4GWn4JM5pK0s6UrvCDxixgKknS1OAdAxfxoAw+EzETxGYVifsAuAInT3UjhMvYGubWrbSTosA/RhG0T2GXjKaWB65NvQMH1CTtYIyD01KOggkHQJ9dMgfX7wQ/V0OXrE48CMpUToZDLgBw08iLjFRWW4FqIwAXUEUm+KA1hELeZ8NHNNf/zknp21cB3rL2XOxbG4/QJEqLLO7yMOYIwvo5I/2NvgpjM5IxVWWFEyCMKJpa5skJoDUZDHp4K+8JCGs4bPnP/hblfBDbdBq+FZsZwfYqksiDt/fKsuO3D0ILH9XtF3ysULHswiBTYKCCjXv2JSF8d/FxOEDYYafQZQOpb7TltL40gpnzJmEZz57BZezGJTQ0CwknTR7inMQV9narvLausJpC2WvMZJOrdmxLkqHy+L5ROkka/hhD1PSINlVsHmhvTxCmJPR5k5b9hpnqyquB9ejypKCUTQm6ZCaw43UaZuUdMKVHsMaPkJhmuGWqn9qjBVJZvgFS4RlgpdHzqET+zczaQH2QNIhQt7gP3P9nNsYzDgdOLApbseRT0QFXk8x+GLzbDR8IFlKCIP5jbKTdIL9Jn0bY5xlqU3sB9Kzo63ZJP02SYePcdoKhp9naYXY73kanufH4cfv64ozZsHdPZcVYDMMf/wgsme1adICEpuoz2lbY4OMNHxLU6aXhhh+UMFSJ+mkYPgJSUsAW3iDgMkbfnnkzJGGqwdHFnXwCQhudV+OB7xlqWdm0d1qDL4dZfh+lmtTTkMRpUPxmTedwcew9O32ZwQZSjqJWPaHwJI36H7NXqR2KLkLiXkvKTR8zff++s85GNFaA6/FZ3eB0za+DTMHujBnUncuA1MttDnDT76go7w+TlpJh9TF8GvJCNnodzoN/w9WnICzpPo5FAHrTl1aQd6OhP4OYd6UHkx6xQfRf+p80HtuBrJ22kLaX00rxDV8izF8Ar4IeC0MzmOvs16Wqjk6Db9AsonDZ05bilkDXUiQjvXtIhmGZfLXxOeo2Kv/PCLdIFQmIknDr/FsxHzvrw6XywIoyQMvL1uXWJU17pfjifY2+DW+Fwy/lFrS4Z+kZPiJm2Wo4YcN/vJ5g0rtHqEFA8IWy8eNY/ieuo1GSrnzYxfjyEgVcwa78WdXnMV+hhyctsr+Ei5qSNIhPCdARE4k3o/uycCrPlXj2AEEO5MXtmFJYGhawweYwbcsEYdfB8OH0N4zuP5NRZHoGL707CQx/HnnJju+Y5y63UUbr3/ZLMwZ7G6wzfEIZ1aH4dkF3ORdjv20RhJfGOMs6bS1wReIjRqrcoOfxPAVSUcw/DQGv9aDmhHD11RtDEMun9yQpEP0nsMlmjpCLMszuT0NIRXDV42LOFfB8Bs2XzFx+EAoSodkEaXDaulsorNZ1A0JnJFpYBMqvPSNt0E0pZndaBi+6vhMuJ9S5dd4RBs2e1I3vvmus+tpZWrUTLwiDvbQySijNRm0adHWGn4tGxFIOuke0PqctuPH8KPrsYYPFThxG5J0UghU/paUT29zk3RqPVCcIXOGD0gGP0sN32f4wT4Pk37s7j4JzS1KTTFp6kyces5lcGxSN8O3iVgaMRsNv+m9yAzfQlTDb+SeJK5DkQ9qJV4xTtRI/zIMPzPUipOZO7kb33jnCpw5dzBhq+Am1hOHDz0pVvebwUPJMm3TbJdyhykTr+KQrkBsI6hP0rF5lA4hxI+cyBKilpDM8LdhFtYOrtTXhk8L6uGUGQP44nnL/I/qqX5pWcmre9WLhiOuYjT8CMNvxEjW0vhzQK0idlb6R0SFkXSyQ61rOdhTxBuX1VhDVpN4lc7e17D4mTF8xNfr4WA16gMzrB41RtJpkOEDOfXhOiUdQlionM2rZtb8aZ3wGb60CpcVWgWrIXjqAt61DE0YGzEPz01ajFc11woAjTJW8eNorRitht/I9TrxwnhncU6oFZYZlyBXG+M8cI3r0cYZWV/KehKvak/x8ovSiRxJOlRNSUfUPmmU4dMcFkABYgYibQsAAAun9WL5vEmYN7kbd378Val+WQ9sjYZPUB8b12LsKF/yku+zRnRIGAetydhZOqnJRjA0J+nEafjhzxu4YLOXsezwcUQtp22Mm0uPFpRUEOgIht98DZWw0zbdL8ZDwxdp5J5HY1PKKeS4/FpPse5BlNhYLUPub5OTpJMy8WrlgilYeeYcYPQIhizhtM3uQbt0yQy88IXXKbHU9ertWowcBAaCWWeaapkybE1eRqPIxGkbicMXfzhsedFmHNzjCFHLP/Z7NCrpNNqixtDWBj+zqyklXr3j3Hk4I2aJs/BPamv4zTfNkZyHxRiDn7jiVRhxGr74LMECCCaaD8P3R+/0EFmrjfy2BpihD4ljpEmG77nA9FOBwfmhfdYjp6XPzK0FKhzwjf1atMj/RDmX3qnAhR9uqn3jiZpx+PX093BS4ziirQ1+JjZHujmEEHzx6mUJG0vHRg09N2uGn3is4Ei1+aKmTSnlFJbRm9d0NY0EEPI1CINvNRmWmRL1rD+rhWVHslbr3Wctw1QPaDOkJIbht1DNaAq1wjLFac6f0oMtB4Zr7K0NJR1CyHIA/wGgC0AVwIcopY/mdTxtGzLZS2OGOR3Dz0bDB5Co46uSTgiROHwxFZDdO+kcbEK/Z7vIScNPakPY1zDvPODF34LwAl55Gxs2rc/2IMwvkH6fVgqfTmpkruHXdy4TCe86fz5ec8bM2O/FTOjWv3g59h4dS96ZbvY8TsiT4X8ZwOcopb8khLye/31JjseLRdMLcjRwU2pr+GKr5jBrUhfOlsoo6CAXUyOgyUd1uoAFF6nJLz1Tgnr5icfh+yckR0mnjsSW+ecD88+HdUysd5CvscmDwdZbbiXNykz1IHsN//g0+MsSQ7eD+zTQ5WBKbz1hue1j8CkAIXZPArAjx2NpIXS15sLLGvxtzSclG4Z/5fITcOXyE5KPpEg6NUAIsPBi9bPpp7J/NeDlqk02IOlwBNFVGTcpfPQcGKx+0ZB4WFaWkk62v87SvzDREOTopNm6PRn+xwDcRQj5J7Dwzwt1GxFCPgDgAwAwf/583SYNo5WSTu3d5rRf3aGQsOJVxqg5g2h4xylkpbjw0ex9tlrUG1GTbp/1DVSJ6yjXCSrlbzTwY/YqGbS5k7uxYl4yUz5eURe5PF7DMgkh9wCYpfnqbwFcBuDjlNKfEkLeDuA7AC4Pb0gpvR7A9QCwcuXKTK9EZoNng5JOMrJh+GngSess5HlEwURJIORnh9Rx+FGMVxXaXBh+nQk9dtZROg1fu6iG/+7zT8S7zz+x2WZNaNR/+48jhk8pjRhwAULI9wF8lP/5EwDfbuZYjSDrKJ3Mjk3puDJ8IPBj+Bp7DqBUZvgtiMNnG0Q+qXelskZRR35abvvMctBpqodqGH47I2kN2yhaJ+nkmWm7A8Ar+ftLAWzI8VhaCCPXXP9vMEqH/2ZaXxEXL5oWs9E4STpUXvEqP6MXMPw8nbYpYuPifpptiyJgTtusNfz6JZ3wkpeNgjH87OLw2xmBht/Gkk4N/CmAfyGEOABGwXX6cUUrJR3+k99/6jI4dmhc1RSWyhPatZIXvgIYPczqwGd5nLzMqpwpnATNwzRuTltkUFohBIsQuHWU3P3LS0/BQHchs+Mbhp8Ofu9sZ0knCZTSBwHkU5w6JTK5lI1KOuLn+p3yjcbPaRspntY3E1jw8qwPJBVny+HcaiymHnfMcWX4Ge+z3uzdWhFb9aC5MhGdxfB9UpFq6/aUdFqObLTqxhOvgBj7NM4Mn9JgmdU8o3RYVU5h9fMw+KR2LR3dxykTx5pGDk7bVsauN+Vm6jSGf5xIOm1t8DNDQ5JOQpgWsYAzrgKmLW62ZamgXfEqhwcxGMYCnp8t9HH2aiOi3xdsgns/cUnN5JlmYdVVMjEdmi7X0OzxJ8Aejid0rKQzEdBKSSfx55YFzFjS1H7rbIWUaZsfPKW0Qg4gSCHp6BN+FkzrBVKubNYomIafdVhm/tFFcWArpWUXh9/OqK9AX3smXrUc2VzL5iSdiQBK5UXMaT5x8hAlHHJ80AfmAl2T4r/P+phn/RErV5wSeZRW+Ojli5RFVsYTFNnG4bczLN/e1yvpGIPfMP7r/efi0HDZ/7vpGjr+jhoPy5wIpUPeed58nDZ7AD/4/Rbp0xw6GgU2ebOwY8bpODMPg7/8HSnakOEFnzSX/UuJPBKvzpqfXRRVIzBROukgzrLu0grjjLYy+K9cPF35eyIkXmW56Eaj+KvXBHVw8tTwPQrsxhQcHHxZ5vtOhxQaf55HzyFKp5VoPn8F6BSG7/vs6r1oJkonO6SM3K6BRhOv+K8nmAXIs38VHQuvWDwdswe78jvIBEYr9fY8QJvJyu4whh9IOilgJJ2cMAESrybe458f85rSW8T3339u5vtNjTxqG9QBq1aawHEGucpqA7/mr51h8MUDn07SM2GZuSATDb/hxKsGp3g5o0ptuN1TAbuemt3HC1or6RzP9d51aMpp22EMv67KJYUe4ORLAafI/o0j2prhmyidKHZhKg6e8QeYPVB7XV6D+pBHaYVWgkr5Gw38mr9O0AchY9Q1o7cLwPzz2L9xRlsz/MzIVhN9to2e/4mPFks67ea0lfM36v9pZzH88arX1CzamuFngjwSr3RY8S6o68jmh/Z9BlsdpTPxJLxm0byG3xkIwjIn9nm3tcFvraRTp9d2MNvVvgzGH3kkXrUSTZ1LhzH8iRukoaKtDX5maGLFq4kQhx9G2z6DhPDlvVp0eEx8hlcPsl7xqp3x+pfNxvknTcWM/lKrm5IIY/BrocHnN816HQZZo7XGpe0YPrJd07ad0d9VQH9XdusQ5IXOMPjNPISnvQnomVL3z7JJ+soHmZWcmJBobWlJw/D9X/PXdu5rxx/a2uBn0tUarGrZcKq1QeNodeIVabOwTJhaOu2Gtjb4rcREduK07TO45A2tDcsEAa25yPrxhYZLK8w4jQUiFHqybZBBUzAGPydM1Fo6bY1Sf0sPb1kAdVvahEzRVN8tdLN/BhMKuRl8QsiZAP4DQB+AzQDeRSk9ktfxdGiprU1a8Wo8sesZYN/zwNK3+B+1K8FvNf7slSfDbSNNp+V91yBz5Mnwvw3gE5TS+wgh7wfw1wA+nePxJhTqqq2RJ8aGgL3PA5XRFjdkAmDJ6wEnv0qeFy+aXnuj4wlNOW0NJiLyTO08FcD9/P2vALwlYdtc0Mq+OmE0/N5p7HV4v/9Rxz7Es88Epp9aezsDAM2ueGUwEZGnwX8WwJv5+7cBmKfbiBDyAULIKkLIqr179+bYnPHFhFnxqnc6MPUU8+Qa1I2pvUWcMGh0+HZCU5IOIeQeALM0X/0tgPcDuI4Q8vcAbgdQ1mwHSun1AK4HgJUrV7baPGaGCbPiVfcgsOxt/I8n+asx/ga18XdvPL3VTTDIGE0ZfErp5TU2eQ0AEEIWA3hDM8dqBq0wuiZKx8DAYKIhN0mHEDKDv1oA/g4sYmdc0XAMcSbHbtmha2Iit83AwCA/5Knhv4MQ8jyAdQB2ALghx2NNWBiCb2BgMFGQW1gmpfRfAPxLXvuf6JioSxwCRsE3MOhUtPWKVy3FBK6W2Uqpy8DAoHUwBj8nGJNqYGAw0WAMfk4IqmW2uCEamMHIwKAzYQx+TpjIK14ZGBh0JozBzwkTecUrI+EbGHQmOsLgt8LoTphaOhKMoTcw6Gy0tcFvpYGb2GGZxvIbGHQi2trgtxITkeEbGBh0NozBzxkTkOAbacfAoENhDH5OmN5XwnkLp6CrYC6xgYHBxIBZ0zYnXHjKNFx4yrRWN8PAwMDAh6GfHQSj5BgYdDaMwe9AGA3fwKAzYQy+gYGBQYegIwz+BAyUaSlMtUwDg85EWxt8Y9ZUGENvYNDZaGuDb6CHMfsGBp0JY/ANDAwMOgRtbfAti3FZk/ykwig7BgadiaYsISHkbYSQNYQQjxCyMvTdpwghLxBC1hNCrmiumY1hxbxBfPSyRfj625e34vAGBgYGEwrNZto+C+BqAP8pf0gIOR3ANQDOADAHwD2EkMWUUrfJ49UFQgg+/urF43nI4wKmWqaBQWeiKYZPKX2OUrpe89WVAG6ilI5RSjcBeAHAuc0cy6B5GDNvYNDZyEvcPgHAVunvbfyzCAghHyCErCKErNq7d29OzTGQYTR8A4PORE1JhxByD4BZmq/+llJ6W9zPNJ9p858opdcDuB4AVq5caXKkDAwMDHJCTYNPKb28gf1uAzBP+nsugB0N7McgBxiCb2DQmchL0rkdwDWEkBIhZCGARQAezelYBgYGBgYp0GxY5lWEkG0ALgBwByHkLgCglK4B8GMAawHcCeAvxjtCxyABhuIbGHQkmgrLpJTeAuCWmO++AOALzezfIFsYZ62BQWfDpKB2EMT6uiYO38CgM2EMfgfCMH0Dg86EMfgdBGPoDQw6G8bgdyCM3Tcw6EwYg29gYGDQITAGvwNhVr4yMOhMNFst08BgXFCpVLBt2zaMjo62uiktQVdXF+bOnYtCodDqphgcxzAGvwNxPPL7bdu2ob+/HwsWLOi4GQqlFPv378e2bduwcOHCVjfH4DiGkXQ6CMdz/P3o6CimTp3accYeYBLc1KlTO3Z2Y5AdjMHvQByvNrMTjb1AJ5+7QXYwBt/AwMCgQ2AMfgfieJZ22gmUUnie1+pmGHQQjME3MKgDX/va17B06VIsXboU//zP/4xPfvKT+OY3v+l//9nPfhZf/epXAQBf+cpXcM4552DZsmX4zGc+AwDYvHkzTjvtNHzoQx/CWWedha1bt2qPY2CQB0yUTifiOCf4n/v5GqzdcSTTfZ4+ZwCfedMZids8/vjjuOGGG/DII4+AUorzzjsPP/jBD/Cxj30MH/rQhwAAP/7xj3HnnXfi7rvvxoYNG/Doo4+CUoo3v/nNuP/++zF//nysX78eN9xwgzJQGBiMB4zB7yQc54a+1XjwwQdx1VVXobe3FwBw9dVX44EHHsCePXuwY8cO7N27F5MnT8b8+fNx3XXX4e6778aKFSsAAENDQ9iwYQPmz5+PE088Eeeff34rT8WgQ2EMfgfieA/4qMXE8wKl+iWX3/rWt+Lmm2/Grl27cM011/jbfupTn8IHP/hBZdvNmzf7A4aBwXjDaPgGBinxile8ArfeeiuGh4dx7Ngx3HLLLbj44otxzTXX4KabbsLNN9+Mt771rQCAK664At/97ncxNDQEANi+fTv27NnTyuYbGBiG34k4zgn+/9/e/cdWdZYBHP8+KaVXKJEfw9rQaYvSACE3eGlKGwhpdJRt/+DISMsfCiiZQQ36xxIwaFNIY4JB/jAaFowLUwjbmC5bkGXQifIjAewULoWG0s0a65BC5zaQUiw8/nHedne1vf1xb3vuuef5JDf33Pec3j7Pfc99eu77np7rm1gsxoYNGygvLwdg06ZN/UM2t2/fZs6cORQWFgJQXV1NS0sLlZWVAOTn53PgwAFycnL8Cd4YQIb6mOqHsrIybWpq8juMrFX6wze43/uQS/XVTIsE65osLS0tLFiwwO8wfGWvgRmKiLytqmXDbZfql5ivFZHLIvJQRMoS2meJyAkRuSMiP0/ld5j0s//aNCacUh3DbwbWACcHtN8DfgQ8m+LzmzSyMm9MuKU0hq+qLfD/R4yq+h/gtIh8MZXnN+PDCr8x4eT7WToi8oyINIlI082bN/0OxxhjstawR/gi0gh8dpBV21X1tVQDUNV9wD7wJm1TfT4zPBvCNyachi34qvrYRARixp8VemPCzfchHTPx7GqZ/qiqqsJOOzZ+SvW0zKdEpAOoBH4vIm8mrGsH9gAbRKRDRBamFKkxIfPgwQO/QzBZJqWCr6qvqmqRquapaoGqrkpYV6yqM1U1321zJfVwTTooNlUyFu3t7SxatKj/8e7du6mvr6eq1xMsWAAABtZJREFUqoqtW7dSXl5OaWkpp06dAqC7u5va2lqi0Sg1NTV0d3f3/+yxY8eorKwkFouxdu3a/kswFBcXs3PnTpYvX87hw4cnNkGT9ezSCiZ4rjXCnRvpfc78Apg39umq3t5ezp8/z9GjR9mxYweNjY3s3buXKVOmEI/HicfjxGIxAG7dukVDQwONjY1MnTqVXbt2sWfPHurq6gCIRCKcPn06LWkZk8gKvjFpsGbNGgCWLFlCe3s7ACdPnmTLli0ARKNRotEoAGfPnuXKlSssW7YMgPv37/dfcwegpqZmAiM3YWIF3wRPCkfiqZg0adInvpLw3r17/ct5eXkA5OTk0Nvb298+2GUsVJWVK1dy6NChQX+PXT7ZjBc7S8eYESooKKCzs5Ouri56eno4cuRI0u1XrFjBwYMHAWhubiYejwNQUVHBmTNnaGtrA+Du3bu0traOb/DGYEf4xoxYbm4udXV1LF26lJKSEubPn590+82bN7Nx40ai0SiLFy/uv6zy7Nmz2b9/P+vWraOnpweAhoYGSktLxz0HE252eeQQqfjxW/zro3tc2bmKKZOD9bfeLg1sr4EZ2kgvjxysd71JycvfquRPrZ2BK/bGmPSwMfwQ+dysKXytstjvMIwxPrGCb4wxIWEF3wRGJs03TbQw527Sxwq+CYRIJEJXV1coC5+q0tXVRSQS8TsUE3A2e2cCoaioiI6ODsL6JTmRSISioiK/wzABZwXfBEJubi4lJSV+h2FMoNmQjjHGhIQVfGOMCQkr+MYYExIZdWkFEbkJ/H2MP/4IcCuN4fjJcslM2ZJLtuQBlkufz6vq7OE2yqiCnwoRaRrJtSSCwHLJTNmSS7bkAZbLaNmQjjHGhIQVfGOMCYlsKvj7/A4gjSyXzJQtuWRLHmC5jErWjOEbY4xJLpuO8I0xxiSRFQVfRB4Xkasi0iYi2/yOZzgi0i4il0Tkgog0ubaZInJcRK65+xmuXUTkZy63uIjEfI79eRHpFJHmhLZRxy4i693210RkfQblUi8i/3R9c0FEnkxY9wOXy1URWZXQ7vv+JyKPisgJEWkRkcsi8j3XHqi+SZJH4PpFRCIicl5ELrpcdrj2EhE5517fl0RksmvPc4/b3Pri4XIcNVUN9A3IAd4B5gKTgYvAQr/jGibmduCRAW0/Aba55W3ALrf8JPAGIEAFcM7n2FcAMaB5rLEDM4F33f0MtzwjQ3KpB54dZNuFbt/KA0rcPpeTKfsfUAjE3PI0oNXFHKi+SZJH4PrFvbb5bjkXOOde65eBWtf+HLDZLX8beM4t1wIvJctxLDFlwxF+OdCmqu+q6n3gRWC1zzGNxWrgBbf8AvDVhPZfq+csMF1ECv0IEEBVTwLvD2gebeyrgOOq+r6q/hs4Djw+/tF/0hC5DGU18KKq9qjq34A2vH0vI/Y/Vb2uqn9xy7eBFmAOAeubJHkMJWP7xb22d9zDXHdT4MvAK659YJ/09dUrwFdERBg6x1HLhoI/B/hHwuMOku8gmUCBYyLytog849oKVPU6eDs98BnXHoT8Rht7puf0XTfM8XzfEAgBysUNBXwJ74gysH0zIA8IYL+ISI6IXAA68f54vgN8oKq9g8TVH7Nb/yEwizTmkg0FXwZpy/RTj5apagx4AviOiKxIsm0Q8+szVOyZnNNe4AvAYuA68FPXHohcRCQf+C3wfVX9KNmmg7RlTD6D5BHIflHVB6q6GCjCOypfMNhm7n7cc8mGgt8BPJrwuAh4z6dYRkRV33P3ncCreDvCjb6hGnff6TYPQn6jjT1jc1LVG+5N+hD4JR9/dM74XEQkF69IHlTV37nmwPXNYHkEuV8AVPUD4I94Y/jTRaTvu0gS4+qP2a3/NN6QY9pyyYaC/2dgnpv5now32fG6zzENSUSmisi0vmWgGmjGi7nvjIj1wGtu+XXg6+6sigrgw76P6BlktLG/CVSLyAz30bzatfluwPzIU3h9A14ute5MihJgHnCeDNn/3Fjvr4AWVd2TsCpQfTNUHkHsFxGZLSLT3fKngMfw5iROAE+7zQb2SV9fPQ38Qb1Z26FyHL2JnLUerxveGQeteONj2/2OZ5hY5+LNuF8ELvfFizdW9xZwzd3P1I9n+n/hcrsElPkc/yG8j9T/xTvy+OZYYge+gTf51AZszKBcfuNijbs3WmHC9ttdLleBJzJp/wOW433MjwMX3O3JoPVNkjwC1y9AFPiri7kZqHPtc/EKdhtwGMhz7RH3uM2tnztcjqO92X/aGmNMSGTDkI4xxpgRsIJvjDEhYQXfGGNCwgq+McaEhBV8Y4wJCSv4xhgTElbwjTEmJKzgG2NMSPwPkxW+WVkUbQoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fed0710>"
      ]
     },
     "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 0x1a1ffacda0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a202a0a20>"
      ]
     },
     "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 0x1a260887b8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fe16048>"
      ]
     },
     "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 0x1a21bb4550>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21276be0>"
      ]
     },
     "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 0x1a21c6e208>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21c6e160>"
      ]
     },
     "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": [
    "txdf = sdf.merge(tdf, right_index=True, left_index=True)"
   ]
  },
  {
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>a</th>\n",
       "      <th>h_a</th>\n",
       "      <th>s_a</th>\n",
       "      <th>agent</th>\n",
       "      <th>amt</th>\n",
       "      <th>mech</th>\n",
       "      <th>pbar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49868.738051</td>\n",
       "      <td>0.098338</td>\n",
       "      <td>48351.484524</td>\n",
       "      <td>983376.677820</td>\n",
       "      <td>60</td>\n",
       "      <td>3825.580705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60</td>\n",
       "      <td>1.662332e+04</td>\n",
       "      <td>burn</td>\n",
       "      <td>0.094210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49868.738051</td>\n",
       "      <td>0.098678</td>\n",
       "      <td>48686.612756</td>\n",
       "      <td>986778.726525</td>\n",
       "      <td>34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10127.989823</td>\n",
       "      <td>34</td>\n",
       "      <td>3.351282e+02</td>\n",
       "      <td>bond</td>\n",
       "      <td>0.098508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49868.738051</td>\n",
       "      <td>0.098678</td>\n",
       "      <td>48686.612756</td>\n",
       "      <td>986778.726525</td>\n",
       "      <td>89</td>\n",
       "      <td>1008.763267</td>\n",
       "      <td>10820.075298</td>\n",
       "      <td>89</td>\n",
       "      <td>1.500982e-09</td>\n",
       "      <td>burn</td>\n",
       "      <td>0.096707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49915.936387</td>\n",
       "      <td>0.097717</td>\n",
       "      <td>47742.646027</td>\n",
       "      <td>977165.758990</td>\n",
       "      <td>59</td>\n",
       "      <td>1146.524288</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59</td>\n",
       "      <td>9.612968e+03</td>\n",
       "      <td>burn</td>\n",
       "      <td>0.093287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49986.129937</td>\n",
       "      <td>0.096269</td>\n",
       "      <td>46338.775022</td>\n",
       "      <td>962691.799295</td>\n",
       "      <td>20</td>\n",
       "      <td>2258.265658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>1.447396e+04</td>\n",
       "      <td>burn</td>\n",
       "      <td>0.092143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              F         P             R              S   a          h_a  \\\n",
       "0  49868.738051  0.098338  48351.484524  983376.677820  60  3825.580705   \n",
       "1  49868.738051  0.098678  48686.612756  986778.726525  34     0.000000   \n",
       "2  49868.738051  0.098678  48686.612756  986778.726525  89  1008.763267   \n",
       "3  49915.936387  0.097717  47742.646027  977165.758990  59  1146.524288   \n",
       "4  49986.129937  0.096269  46338.775022  962691.799295  20  2258.265658   \n",
       "\n",
       "            s_a  agent           amt  mech      pbar  \n",
       "0      0.000000     60  1.662332e+04  burn  0.094210  \n",
       "1  10127.989823     34  3.351282e+02  bond  0.098508  \n",
       "2  10820.075298     89  1.500982e-09  burn  0.096707  \n",
       "3      0.000000     59  9.612968e+03  burn  0.093287  \n",
       "4      0.000000     20  1.447396e+04  burn  0.092143  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "emas = ['P', 'pbar']\n",
    "for com in [2,4,8,16]:\n",
    "    k = 'pbar_ewma'+str(com)\n",
    "    emas.append(k)\n",
    "    txdf[k] = txdf.pbar.ewm(com).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20621278>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2139bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "txdf[emas].plot(alpha=.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3000, 0.095, 0.115]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22b6cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "txdf[emas].plot(alpha=.8)\n",
    "plt.axis([0, 3000, .095, .115])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "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>0.101549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.381811e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106027</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.737093e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.109251</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.518447e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.107529</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.075911e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.109361</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.216649e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101784</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.580219e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.092839</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.969567e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.537070e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.270248e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.283756e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.221638e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.490232e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.097491</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.817961e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.097601</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.305372e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.161934e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096354</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.881779e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106875</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.935405e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.109564</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.766455e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105688</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.775428e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.107188</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.347808e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105579</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.039174e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.943207e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.132796e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.445703e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.092143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.447396e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095243</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.644187e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.574890e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.108854</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.382432e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.106981</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.098385e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.092926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.208115e+03</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>burn</th>\n",
       "      <td>0.092635</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.690894e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.097785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.397786e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104822</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.086099e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">77</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104391</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.393748e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.108150</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.533906e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096461</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.344499e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095844</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.534129e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100801</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.608792e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">81</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100332</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.352715e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.109134</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.333665e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.672999e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.104053</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.446912e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093643</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.305744e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.563242e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">86</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.108909</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.393736e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.106566</td>\n",
       "      <td>418.0</td>\n",
       "      <td>2.084695e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096304</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.150801e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.520997e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098072</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.350884e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.409782e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.975654e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.092879</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.162859e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.090638</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.304687e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095392</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.102470e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.049481e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.004215e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100298</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.394536e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">98</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105450</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.448575e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.106684</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1.542172e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.301649e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pbar                  amt\n",
       "              median  count        median\n",
       "agent mech                               \n",
       "0     bond  0.101549    1.0  1.381811e+03\n",
       "1     bond  0.106027   19.0  5.737093e-10\n",
       "      burn  0.109251   74.0  1.518447e-09\n",
       "2     bond  0.107529   18.0  3.075911e-10\n",
       "      burn  0.109361   57.0  8.216649e-10\n",
       "3     bond  0.101784    1.0  4.580219e+02\n",
       "4     burn  0.092839    1.0  9.969567e+03\n",
       "5     bond  0.102752    1.0  8.537070e+02\n",
       "6     burn  0.097383    1.0  5.270248e+03\n",
       "7     burn  0.093518    1.0  8.283756e+03\n",
       "8     bond  0.104104    1.0  8.221638e+02\n",
       "9     burn  0.093529    1.0  5.490232e+03\n",
       "10    bond  0.097491    1.0  8.817961e+02\n",
       "11    bond  0.097601    1.0  7.305372e+02\n",
       "12    bond  0.098165    1.0  1.161934e+02\n",
       "13    burn  0.096354    1.0  6.881779e+03\n",
       "14    bond  0.106875    5.0  1.935405e-10\n",
       "      burn  0.109564    6.0  1.766455e-09\n",
       "15    bond  0.105688   12.0  6.775428e+01\n",
       "      burn  0.107188  104.0  1.347808e-09\n",
       "16    bond  0.105579    1.0  5.039174e+02\n",
       "17    bond  0.099902    1.0  2.943207e+03\n",
       "18    bond  0.099571    1.0  5.132796e+02\n",
       "19    bond  0.104804    1.0  2.445703e+03\n",
       "20    burn  0.092143    1.0  1.447396e+04\n",
       "21    burn  0.095243    1.0  6.644187e+03\n",
       "22    bond  0.101748    1.0  8.574890e+02\n",
       "23    bond  0.108854    7.0  1.382432e-10\n",
       "      burn  0.106981   10.0  1.098385e-09\n",
       "24    burn  0.092926    1.0  7.208115e+03\n",
       "...              ...    ...           ...\n",
       "74    burn  0.092635    1.0  1.690894e+04\n",
       "75    bond  0.097785    1.0  5.397786e+02\n",
       "76    bond  0.104822    1.0  1.086099e+03\n",
       "77    bond  0.104391    4.0  6.393748e-10\n",
       "      burn  0.108150    5.0  1.533906e-09\n",
       "78    burn  0.096461    1.0  1.344499e+04\n",
       "79    burn  0.095844    1.0  1.534129e+04\n",
       "80    bond  0.100801    1.0  5.608792e+02\n",
       "81    bond  0.100332    2.0  3.352715e+02\n",
       "      burn  0.109134    1.0  6.333665e-10\n",
       "82    burn  0.096266    1.0  1.672999e+04\n",
       "83    burn  0.104053   20.0  2.446912e-09\n",
       "84    burn  0.093643    1.0  9.305744e+03\n",
       "85    bond  0.101101    1.0  4.563242e+02\n",
       "86    bond  0.108909   16.0  2.393736e+02\n",
       "      burn  0.106566  418.0  2.084695e-09\n",
       "87    burn  0.096304    1.0  7.150801e+03\n",
       "88    burn  0.096551    1.0  7.520997e+03\n",
       "89    burn  0.098072    5.0  1.350884e-09\n",
       "90    burn  0.094263    1.0  7.409782e+03\n",
       "91    burn  0.095553    1.0  9.975654e+03\n",
       "92    burn  0.092879    1.0  5.162859e+03\n",
       "93    burn  0.090638    1.0  1.304687e+04\n",
       "94    burn  0.095392    3.0  1.102470e+03\n",
       "95    burn  0.094583    1.0  1.049481e+04\n",
       "96    burn  0.094995    1.0  6.004215e+03\n",
       "97    bond  0.100298    1.0  3.394536e+03\n",
       "98    bond  0.105450   10.0  6.448575e+02\n",
       "      burn  0.106684  158.0  1.542172e-09\n",
       "99    burn  0.096315    1.0  1.301649e+04\n",
       "\n",
       "[117 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21281160>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a233cd358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tx_summary.pbar['median'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23613be0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a233be240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot(logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d43a828>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a234a3d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a234a9f98>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a264be518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf.F.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23b22d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bond_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15ce25c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(burn_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['invariant'] = rdf.supply.apply(lambda x: x**kappa)/rdf.reserve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a16f8d860>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15f90e48>"
      ]
     },
     "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": 57,
   "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": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([52.,  2., 10.,  8., 10., 10.,  2.,  2.,  2.,  2.]),\n",
       " array([   0.        ,  382.55807046,  765.11614092, 1147.67421137,\n",
       "        1530.23228183, 1912.79035229, 2295.34842275, 2677.9064932 ,\n",
       "        3060.46456366, 3443.02263412, 3825.58070458]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l/4/Rb/lrl5IH+B1GF3d2Ab89cL6/aft/hNHnKM0vr4+3fI8D7xryuQd+hdGfvI8AD7Wvy6Zl/o6Qb1rm75eAB1uOR4E/mvdz8kCbiy8DJ7Tx17T1Xe3+Ny6Ue6B832jz9yjwt7z8ip1l//lo3/vtvFz2g8+d76CVpA4cC+fsJUljsuwlqQOWvSR1wLKXpA5Y9pLUActekjpg2UtSByx7SerA/wMuvRKVjqEj1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a163a0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].holdings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_h'] = rdf.holdings.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b5f80b8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16f876d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_h.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([48., 18., 14.,  9.,  8.,  0.,  1.,  1.,  0.,  1.]),\n",
       " array([    0.        ,  7192.42112159, 14384.84224317, 21577.26336476,\n",
       "        28769.68448635, 35962.10560793, 43154.52672952, 50346.94785111,\n",
       "        57539.36897269, 64731.79009428, 71924.21121587]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4nG1b9wC/Nan/Lx0y+zA3NqobnavJ36dzzvVzh2mfdwN7gf+l85t1PZ1zo1uAHe1+/5MUdP7YxyvA88BM13Z+G9jZbt0vkhnghbbOn3PQBaMBcv4ynX+2PQc8025XTlpW4BeAp1vOF4A/auNn0Lnyv5NOaR7Vxo9u8zvb42d0betzLct2ut4lMOzXCQcW+sTlbJmebbcX929r0p77tp1zgdn2/H+TTtFNYs4PAj8CPtw1NnE5F7r5SVFJKmI5nEOXJPXBQpekIix0SSrCQpekIix0SSrCQpekIix0SSrCQpekIv4Pzk52Nt/NrmMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b54db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_s'] = rdf.tokens.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b50ea90>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tjubpi4jkEGToJ1Iphb6ISA5Bhn4qpZ6+iEguQYZ+IpUiplMri4gMElzou7vG9EVE8ggu9JOp9IeFFfoiIoOFF/qu0BcRySe80FdPX0Qkr+BCPxGFvr4uUURksOBCPxWFvr4YXURksOBCv7+nrxPqi4gMElzopzSmLyKSV3Chn+np68NZIiKDBRf6mr0jIpKfQl9EpIqEF/r6cJaISF7hhb56+iIieQUb+vpwlojIYMGGvj6cJSIyWHChrw9niYjkF1zoq6cvIpJfsKEfrwmuaSIiIxZcMvb39INrmYjIyAUXjcd6EgBMqIuXuRIRkcoTXOgf6O4FYNqEujJXIiJSecIL/WMKfRGRfIIL/YPHeqmL1TC+LlbuUkREKk5wod/dm2R8fQzTlE0RkUGCC/3eRIq6WHDNEhEpieDSsS+Zoi4eXLNEREqiqHQ0sxVmtt3M2s3sjhy332xmXWa2Obr8edZtN5nZjuhyUymLz6VHoS8ikteQk9nNLAbcB1wPdAIbzKzV3bcO2PQhd791wL7TgK8CLYADG6N9D5ak+hw0vCMikl8x6Xg50O7uHe7eC6wBVhZ5/8uB9e5+IAr69cCKUyu1OL2JFPXq6YuI5FRMOs4BdmUtd0brBvqomT1nZg+b2bzh7Gtmt5hZm5m1dXV1FVl6br2JFLXq6YuI5FRMOuaa++gDln8ONLv7RcCjwIPD2Bd3f8DdW9y9pampqYiS8tOBXBGR/IpJx05gXtbyXGB39gbuvt/de6LFHwCXFbtvqfUq9EVE8iomHTcAi81sgZnVAauA1uwNzGx21uINwLbo+jpgmZlNNbOpwLJo3ajRgVwRkfyGnL3j7gkzu5V0WMeA1e6+xczuBtrcvRW4zcxuABLAAeDmaN8DZvYN0n84AO529wOj0I5+vQn19EVE8inq/MPuvhZYO2DdXVnX7wTuzLPvamD1CGoclt6kevoiIvkEl47JlOv7cUVE8ggu9PuSTkxfmyUiklNw6ZhMpahVT19EJKfgQj+RdGI1Cn0RkVzCC/2U6xO5IiJ5BJeOiVRKPX0RkTwCDH2nVqEvIpJTUKGfTDnuaPaOiEgeQaVjIpUC0Dx9EZE8wgr9ZPoEnnEN74iI5BRW6Kei0NfsHRGRnIJKx0QyGt5RT19EJKegQj/Z39NX6IuI5BJU6PelNKYvIlJIUKGf7D+QG1SzRERKJqh07NOUTRGRgoIK/f4xffX0RURyCiod+6LZOzr3johIbkGFfqanr/Ppi4jkFlTo90UHctXTFxHJLajQP9nTD6pZIiIlE1Q6JjSmLyJSUFihrzF9EZGCAgv9TE8/qGaJiJRMUOmoUyuLiBQWVujrhGsiIgWFGfoa3hERySmodNT59EVECgsr9DW8IyJSUFihr1Mri4gUVFQ6mtkKM9tuZu1mdkeB7W40Mzezlmi52cyOm9nm6HJ/qQrPJalTK4uIFBQfagMziwH3AdcDncAGM2t1960DtpsI3AY8PeAuXnb3S0pUb0F9mrIpIlJQMT39y4F2d+9w915gDbAyx3bfAO4BTpSwvmE5+R25Gt4REcmlmHScA+zKWu6M1vUzs0uBee7+ixz7LzCzTWb2uJldnesBzOwWM2szs7aurq5iax+k/5uz1NMXEcmpmNDPlaDef6NZDfA3wJdybLcHOMvdLwW+CPzIzCYNujP3B9y9xd1bmpqaiqs8h1RKp1YWESmkmNDvBOZlLc8FdmctTwQuAB4zs1eBK4BWM2tx9x533w/g7huBl4ElpSg8lyjzqTGFvohILsWE/gZgsZktMLM6YBXQmrnR3Q+7+wx3b3b3ZuAp4AZ3bzOzpuhAMGa2EFgMdJS8FZGUp1NfHX0RkdyGnL3j7gkzuxVYB8SA1e6+xczuBtrcvbXA7tcAd5tZAkgCf+HuB0pReC6Znr6ppy8iktOQoQ/g7muBtQPW3ZVn22uzrv8U+OkI6hsWd1cvX0SkgKDmNqbcNZ4vIlJAYKGvg7giIoUEFvqOMl9EJL+gQt/V0xcRKSio0E+ldCBXRKSQsEJfPX0RkYICC32N6YuIFBJc6Ou8OyIi+QUX+hreERHJL7DQ1ykYREQKCSr0dRoGEZHCggr9VEqzd0RECgkr9NXTFxEpKLDQ15i+iEghQYW+u1MTVItEREorqIjUlE0RkcICC30dyBURKSSw0NdpGERECgkq9HVqZRGRwoIKfU3ZFBEpLKjQT6Z0IFdEpJCgQl/z9EVECgsq9N2dWFAtEhEpraAiUvP0RUQKCyz0NbwjIlJIYKGv2TsiIoUEFfqapy8iUlhQoa+evohIYcGFvsb0RUTyCyz0UU9fRKSAokLfzFaY2XYzazezOwpsd6OZuZm1ZK27M9pvu5ktL0XR+bimbIqIFBQfagMziwH3AdcDncAGM2t1960DtpsI3AY8nbXufGAV8A7gTOBRM1vi7snSNeEknVpZRKSwYnr6lwPt7t7h7r3AGmBlju2+AdwDnMhatxJY4+497v4K0B7d36hIpnRqZRGRQooJ/TnArqzlzmhdPzO7FJjn7r8Y7r7R/reYWZuZtXV1dRVVeC6JVIpanYdBRCSvYhIyV9/Z+280qwH+BvjScPftX+H+gLu3uHtLU1NTESXllkg6tTF19UVE8hlyTJ9073xe1vJcYHfW8kTgAuCxaLrkLKDVzG4oYt+S6k2miKunLyKSVzEJuQFYbGYLzKyO9IHZ1syN7n7Y3We4e7O7NwNPATe4e1u03SozqzezBcBi4JmStyKSSDq1mrMpIpLXkD19d0+Y2a3AOiAGrHb3LWZ2N9Dm7q0F9t1iZj8BtgIJ4HOjNXMHoC+pMX0RkUKKGd7B3dcCawesuyvPttcOWP4m8M1TrG9Y+pKu4R0RkQKCSshEKkWdDuSKiOQVVOj3JXQgV0SkkKASsi/lGtMXESkgqIRMH8jV8I6ISD7BhH4y5bhDvCaYJomIlFwwCdmXTAFQG1dPX0Qkn/BCXz19EZG8gknIvmT6lD4a0xcRyS+Y0I/VGP/hotksaGosdykiIhWrqE/kjgWTx9Vy35+8s9xliIhUtGB6+iIiMjSFvohIFVHoi4hUEYW+iEgVUeiLiFQRhb6ISBVR6IuIVBGFvohIFTF3L3cNb2NmXcDOU9x9BrCvhOWUk9pSmUJpSyjtALUlY767Nw21UcWF/kiYWZu7t5S7jlJQWypTKG0JpR2gtgyXhndERKqIQl9EpIqEFvoPlLuAElJbKlMobQmlHaC2DEtQY/oiIlJYaD19EREpIJjQN7MVZrbdzNrN7I5y11MMM3vVzJ43s81m1hatm2Zm681sR/RzarTezOx/Re17zszK9uUBZrbazPaa2QtZ64Zdt5ndFG2/w8xuqqC2fM3MXo+el81m9sGs2+6M2rLdzJZnrS/768/M5pnZb81sm5ltMbMvROvH1HNToB1j7nkxswYze8bMno3a8vVo/QIzezr6/33IzOqi9fXRcnt0e/NQbRw2dx/zFyAGvAwsBOqAZ4Hzy11XEXW/CswYsO4e4I7o+h3At6PrHwQeAQy4Ani6jHVfA7wTeOFU6wamAR3Rz6nR9akV0pavAV/Ose350WurHlgQveZilfL6A2YD74yuTwReimoeU89NgXaMuecl+r9tjK7XAk9H/9c/AVZF6+8HPhNd/yxwf3R9FfBQoTaeSk2h9PQvB9rdvcPde4E1wMoy13SqVgIPRtcfBD6Stf7/eNpTwBQzm12OAt39CeDAgNXDrXs5sN7dD7j7QWA9sGL0q3+7PG3JZyWwxt173P0VoJ30a68iXn/uvsfd/z26fgTYBsxhjD03BdqRT8U+L9H/7dFosTa6OLAUeDhaP/A5yTxXDwPvMzMjfxuHLZTQnwPsylrupPCLpFI48Csz22hmt0TrznD3PZB+8QMzo/WV3sbh1l3p7bk1GvJYnRkOYQy1JRoWuJR0z3LMPjcD2gFj8Hkxs5iZbQb2kv4D+jJwyN0TOerqrzm6/TAwnRK2JZTQtxzrxsK0pCvd/Z3AB4DPmdk1BbYdq23MV3clt+d7wCLgEmAPcG+0fky0xcwagZ8Ct7v7W4U2zbGuYtqTox1j8nlx96S7XwLMJd07Py/XZtHPUW9LKKHfCczLWp4L7C5TLUVz993Rz73AP5N+QbyZGbaJfu6NNq/0Ng637optj7u/Gf2ipoAfcPJtdMW3xcxqSQfl/3P3f4pWj7nnJlc7xvLzAuDuh4DHSI/pTzGzeI66+muObp9MevixZG0JJfQ3AIujI+J1pA+AtJa5poLMbIKZTcxcB5YBL5CuOzNb4ibgZ9H1VuA/RzMurgAOZ96yV4jh1r0OWGZmU6O36cuidWU34FjJH5N+XiDdllXRDIsFwGLgGSrk9ReN/f5vYJu7fyfrpjH13ORrx1h8XsysycymRNfHAe8nfYzit8CN0WYDn5PMc3Uj8BtPH8nN18bhO51HskfzQnomwkukx8v+qtz1FFHvQtJH458FtmRqJj1+92tgR/Rzmp+cBXBf1L7ngZYy1v5j0m+v+0j3QP7sVOoG/pT0Aal24FMV1JYfRrU+F/2yzc7a/q+itmwHPlBJrz/gKtJv+Z8DNkeXD46156ZAO8bc8wJcBGyKan4BuCtav5B0aLcD/wjUR+sbouX26PaFQ7VxuBd9IldEpIqEMrwjIiJFUOiLiFQRhb6ISBVR6IuIVBGFvohIFVHoi4hUEYW+iEgVUeiLiFSR/w9IIPOvAbmSPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b99b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1be463c8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc150f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.tokens.apply(np.count_nonzero).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['asset_value'] = rdf.holdings + rdf.spot_price*rdf.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([32., 34., 16., 10.,  5.,  1.,  0.,  1.,  0.,  1.]),\n",
       " array([ 683.78753094, 1421.73923066, 2159.69093039, 2897.64263011,\n",
       "        3635.59432983, 4373.54602955, 5111.49772928, 5849.449429  ,\n",
       "        6587.40112872, 7325.35282844, 8063.30452816]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b83e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].asset_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini'] = rdf.asset_value.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cbcb828>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b9f04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['pref_gap'] = (rdf.prices - rdf.spot_price)/rdf.spot_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 5., 15., 28., 25., 20.,  6.,  1.]),\n",
       "  array([ 5., 16., 29., 23., 20.,  6.,  1.]),\n",
       "  array([ 5., 15., 28., 25., 20.,  6.,  1.]),\n",
       "  array([ 5., 15., 28., 25., 20.,  6.,  1.]),\n",
       "  array([ 5., 16., 28., 24., 20.,  6.,  1.]),\n",
       "  array([ 5., 15., 28., 25., 20.,  6.,  1.]),\n",
       "  array([ 5., 16., 28., 24., 20.,  6.,  1.])],\n",
       " array([-0.55218258, -0.37203459, -0.1918866 , -0.01173861,  0.16840938,\n",
       "         0.34855737,  0.52870537,  0.70885336]),\n",
       " <a list of 7 Lists of Patches objects>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f4D+SfJ3ZgP/C4rQ4HpdcRrsB2NxtbwauP3JAVf1OVa2pqmngT4B/WMww72HkHLrTN3yM2d4/tIi9jdLnFBOHz++lwO7q/pK1TIycQ7dU8XfAi5fxeu0x51FVD1XVyqqa7v4v3M7sfJZTmEO/f1P/xOwfqkmyktklmHsXtcsxGOijbQMuSHIPcEF3myQzSf5+STvrr88cXg78OvCqJHd2l/VL0+7/69bEHz3FxF5gV1XdneTPk7y4G3Yt8JQk3wDexOyRPMtGzzn8NbO/3X2o+9ovu/Mi9ZzHstdzHp8Evpvkq8CtwFuq6rtL03F/vlNUkhrhHrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEf8LcwSnRQCT8FwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cc43780>"
      ]
     },
     "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
}