1375 lines
330 KiB
Plaintext
1375 lines
330 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#import networkx as nx\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"import scipy.stats as sts\n",
|
|
"import seaborn as sns\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"\n",
|
|
"#import conviction files\n",
|
|
"#from conviction_helpers import *\n",
|
|
"#from conviction_system_logic3 import *\n",
|
|
"from bonding_curve_eq import *"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"System initialization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hatch_raise = 100000 # fiat units\n",
|
|
"hatch_price = .1 #fiat per tokens\n",
|
|
"theta = .35 #share of funds going to funding pool at launch\n",
|
|
"\n",
|
|
"R0 = hatch_raise*(1-theta)\n",
|
|
"F0 = hatch_raise*theta\n",
|
|
"S0 = hatch_raise/hatch_price\n",
|
|
"\n",
|
|
"kappa = 2\n",
|
|
"V0 = invariant(R0,S0,kappa)\n",
|
|
"P0 = spot_price(R0, V0, kappa)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"agent initialization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#number of agents\n",
|
|
"n= 100\n",
|
|
"\n",
|
|
"#gain factors\n",
|
|
"g = np.random.normal(1, .05, size=n)\n",
|
|
"phat0 = g*F0/S0\n",
|
|
"\n",
|
|
"#holdings fiat\n",
|
|
"h = sts.expon.rvs( loc=10,scale=10, size=n)\n",
|
|
"\n",
|
|
"#holdings tokens\n",
|
|
"s_dist = sts.expon.rvs(loc=10, scale=10, size=n)\n",
|
|
"s0 = s_dist/sum(s_dist)*S0\n",
|
|
"\n",
|
|
"#lambda for revenue process\n",
|
|
"lam = 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": [],
|
|
"source": [
|
|
"params= {\n",
|
|
" 'kappa': [kappa],\n",
|
|
" 'lambda': [lam],\n",
|
|
" 'gains': [g],\n",
|
|
" 'population':[n],\n",
|
|
" 'beta':[beta],\n",
|
|
" 'phi': [phi],\n",
|
|
" 'invariant': [V0]}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"initial_conditions = {'holdings': h,\n",
|
|
" 'tokens': s0,\n",
|
|
" 'supply': S0,\n",
|
|
" 'prices': phat0,\n",
|
|
" 'funds':F0,\n",
|
|
" 'reserve': R0,\n",
|
|
" 'spot_price': P0,\n",
|
|
" 'action': {}}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'action': {},\n",
|
|
" 'funds': 35000.0,\n",
|
|
" 'holdings': array([23.8758429 , 19.69422134, 24.04963987, 22.02095349, 10.30212993,\n",
|
|
" 18.17136723, 11.0043178 , 19.91731541, 11.33560817, 10.09139318,\n",
|
|
" 17.7053175 , 23.82566017, 10.73375734, 13.65966005, 25.25668118,\n",
|
|
" 13.64483165, 21.24892853, 15.09909976, 22.12760802, 10.1742936 ,\n",
|
|
" 32.93410966, 15.60721614, 19.62665191, 14.81617445, 30.7923421 ,\n",
|
|
" 10.49769916, 11.57350107, 50.82820249, 14.53672601, 20.63413621,\n",
|
|
" 14.46596895, 27.23749848, 26.18647322, 21.99187696, 26.46082563,\n",
|
|
" 19.81674324, 10.72164709, 34.54482858, 19.15387341, 11.98270767,\n",
|
|
" 10.28310962, 31.44593751, 33.26254118, 21.9739018 , 18.14631281,\n",
|
|
" 22.71897435, 13.78070507, 20.24983316, 41.44776176, 52.27322915,\n",
|
|
" 11.49594833, 28.15857322, 32.95311975, 14.32106799, 34.07549769,\n",
|
|
" 11.31316484, 13.10084631, 12.2771365 , 11.65251749, 27.54629303,\n",
|
|
" 29.87989537, 18.75105406, 13.5188925 , 26.11613234, 21.28990219,\n",
|
|
" 13.73389379, 24.8243575 , 17.48803045, 16.53398343, 24.51172924,\n",
|
|
" 11.21343435, 16.05665033, 23.68896036, 10.69592126, 82.73836673,\n",
|
|
" 10.75284293, 28.32992138, 23.66916012, 24.68709331, 12.18293859,\n",
|
|
" 14.1596323 , 10.32690517, 15.95187301, 16.39609523, 28.29099073,\n",
|
|
" 13.40494752, 11.27886685, 15.56199492, 16.46844093, 15.79546914,\n",
|
|
" 14.56434796, 25.1496766 , 20.66393744, 15.02630471, 13.33624718,\n",
|
|
" 19.60155423, 13.78843037, 13.20060671, 11.64378238, 16.00211942]),\n",
|
|
" 'prices': array([0.03470159, 0.03500804, 0.03726687, 0.03344652, 0.03213049,\n",
|
|
" 0.03525703, 0.03539745, 0.03711468, 0.03692828, 0.03305984,\n",
|
|
" 0.03482426, 0.0337479 , 0.03384781, 0.03497247, 0.03566482,\n",
|
|
" 0.03609999, 0.03437414, 0.0342663 , 0.03705737, 0.03275593,\n",
|
|
" 0.03497879, 0.03457201, 0.03393455, 0.03442325, 0.03158911,\n",
|
|
" 0.03508276, 0.03741888, 0.03416743, 0.03523503, 0.03262323,\n",
|
|
" 0.03507102, 0.03616242, 0.03585136, 0.03573338, 0.03400736,\n",
|
|
" 0.03444992, 0.03462262, 0.03452512, 0.03460134, 0.03496697,\n",
|
|
" 0.03400877, 0.03515976, 0.03466613, 0.03366685, 0.03654846,\n",
|
|
" 0.03255082, 0.03511811, 0.03586033, 0.0351966 , 0.03334444,\n",
|
|
" 0.0374944 , 0.03293575, 0.03781311, 0.03581515, 0.03509336,\n",
|
|
" 0.03426052, 0.03397062, 0.03392005, 0.03337805, 0.03417704,\n",
|
|
" 0.0352511 , 0.03598789, 0.03577451, 0.03514706, 0.03549599,\n",
|
|
" 0.0333581 , 0.03328864, 0.0324255 , 0.03401465, 0.03815283,\n",
|
|
" 0.03269629, 0.0350796 , 0.03689409, 0.03358905, 0.03502877,\n",
|
|
" 0.03482317, 0.03492169, 0.03654598, 0.03630682, 0.03736066,\n",
|
|
" 0.0357415 , 0.03691795, 0.03356862, 0.03457017, 0.03056036,\n",
|
|
" 0.03230114, 0.03705038, 0.03412075, 0.03532472, 0.03372417,\n",
|
|
" 0.03529863, 0.03556363, 0.03435224, 0.03488725, 0.03609893,\n",
|
|
" 0.03700703, 0.03116559, 0.03587418, 0.03397282, 0.03579 ]),\n",
|
|
" 'reserve': 65000.0,\n",
|
|
" 'spot_price': 0.13,\n",
|
|
" 'supply': 1000000.0,\n",
|
|
" 'tokens': array([ 9925.84165174, 11979.66554792, 5898.43943069, 13664.73847701,\n",
|
|
" 13725.31889431, 13546.78963751, 6267.07890741, 7789.26203413,\n",
|
|
" 5838.76061467, 8196.77420537, 7001.32381358, 5624.32665692,\n",
|
|
" 6909.71875448, 8624.52745608, 12027.7694072 , 13293.46558105,\n",
|
|
" 17446.19252401, 23273.80344711, 12985.8504656 , 7887.01470363,\n",
|
|
" 10234.29912381, 15294.20367012, 12876.46172764, 7623.07330876,\n",
|
|
" 5332.64282205, 5413.47974442, 20258.92296578, 5606.9453675 ,\n",
|
|
" 6106.89723381, 6809.24773321, 6414.35549592, 6850.15462329,\n",
|
|
" 5747.79991777, 15771.95639109, 7094.59899961, 5652.91097672,\n",
|
|
" 15318.81342931, 5656.54799369, 9234.04071511, 7924.01841117,\n",
|
|
" 7769.97404631, 5843.28761882, 9884.87155334, 5708.67582438,\n",
|
|
" 7600.76722975, 12708.19533046, 7582.94981851, 11295.60887875,\n",
|
|
" 9116.12774247, 9156.69653133, 12412.90496277, 26035.47956537,\n",
|
|
" 6149.58343288, 21487.50439719, 5463.08395137, 16980.19989006,\n",
|
|
" 10558.67078993, 11910.71272695, 11984.31144319, 5876.7426404 ,\n",
|
|
" 11648.49786715, 11517.35456974, 5379.85626641, 5523.27720696,\n",
|
|
" 7782.54649481, 15213.37854179, 28594.42655746, 11505.52601904,\n",
|
|
" 9031.36270184, 6024.16803197, 7636.67031153, 12587.55714032,\n",
|
|
" 6118.24323775, 5446.42986602, 14959.9256471 , 7568.09763833,\n",
|
|
" 5395.85819135, 6860.21887197, 11601.59973909, 10928.6311004 ,\n",
|
|
" 7696.80597608, 8028.24780907, 14476.50014015, 17626.77183137,\n",
|
|
" 7440.99301769, 6945.85925264, 19067.78726727, 6681.5111842 ,\n",
|
|
" 6594.89635261, 13884.39040369, 8036.43838779, 10645.67055988,\n",
|
|
" 12892.44490757, 7878.44151939, 6595.059729 , 11317.34863869,\n",
|
|
" 5334.45582409, 7137.52524358, 5729.10901873, 6011.73570009])}"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"initial_conditions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#change in F (revenue and spending accounted for)\n",
|
|
"def revenue_process(params, step, sL, s):\n",
|
|
" lam = params['lambda']\n",
|
|
" rv = sts.expon.rvs(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": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_funds(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" funds = s['funds']*_input['delF']\n",
|
|
" \n",
|
|
" key = 'funds'\n",
|
|
" value = funds\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_prices(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" g = params['gains']\n",
|
|
" phat = g*s['funds']/s['supply']\n",
|
|
" \n",
|
|
" key = 'prices'\n",
|
|
" value = phat\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#change in F (revenue and spending accounted for)\n",
|
|
"def choose_agent(params, step, sL, s):\n",
|
|
" n = params['population']\n",
|
|
" rv = np.random.randint(0,n)\n",
|
|
" return({'agent':rv})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def agent_action(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" a = _input['agent']\n",
|
|
" h_a = s['holdings'][a]\n",
|
|
" phat_a = s['prices'][a]\n",
|
|
" s_a = s['tokens'][a]\n",
|
|
" p = s['spot_price']\n",
|
|
" \n",
|
|
" beta = params['beta']\n",
|
|
" \n",
|
|
" if p>phat_a: #equiv: pbar(0)>phat_a\n",
|
|
" mech = 'burn'\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, s['reserve'],s['supply'], params['invariant'], params['phi'], params['kappa'])\n",
|
|
" \n",
|
|
" if not(output[2])>0:\n",
|
|
" return np.Infinity\n",
|
|
" else:\n",
|
|
" return output[2]\n",
|
|
" \n",
|
|
" \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, s['reserve'],s['supply'], params['invariant'], params['kappa'])\n",
|
|
" \n",
|
|
" if not(output[1])>0:\n",
|
|
" return 0\n",
|
|
" else:\n",
|
|
" return output[1]\n",
|
|
" \n",
|
|
" while pbar(amt)> phat_a:\n",
|
|
" amt = amt*beta\n",
|
|
" \n",
|
|
" #print(mech)\n",
|
|
" #print(amt)\n",
|
|
" #print(pbar(amt))\n",
|
|
" key = 'action'\n",
|
|
" value = {'agent':a, 'mech':mech, 'amt':amt, 'pbar':pbar(amt)}\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def resolve_action(params, step, sL, s):\n",
|
|
" action = s['action']\n",
|
|
" a = action['agent']\n",
|
|
" amt = action['amt']\n",
|
|
" h_a = s['holdings'][a]\n",
|
|
" s_a = s['tokens'][a]\n",
|
|
" R = s['reserve']\n",
|
|
" S = s['supply']\n",
|
|
" F = s['funds']\n",
|
|
" V0 = params['invariant']\n",
|
|
" \n",
|
|
" if action['mech'] == 'bond':\n",
|
|
" h_a = h_a-amt\n",
|
|
" dS, pbar = mint(amt, R,S, V0, params['kappa'])\n",
|
|
" R = R+amt\n",
|
|
" S = S+dS\n",
|
|
" s_a = s_a+dS\n",
|
|
" P = spot_price(R, V0, kappa)\n",
|
|
" \n",
|
|
" elif action['mech'] == 'burn':\n",
|
|
" s_a = s_a-amt\n",
|
|
" dR, pbar = withdraw(amt, R,S, V0, params['kappa'])\n",
|
|
" R = R-dR\n",
|
|
" F = F + params['phi']*dR\n",
|
|
" S = S-amt\n",
|
|
" h_a = h_a + (1-params['phi'])*dR\n",
|
|
" P = spot_price(R, V0, kappa)\n",
|
|
" \n",
|
|
" return({'F':F, 'S':S, 'R':R,'P':P, 'a':a,'s_a':s_a, 'h_a':h_a})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_F(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" F = _input['F']\n",
|
|
" \n",
|
|
" key = 'funds'\n",
|
|
" value = F\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_S(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" S = _input['S']\n",
|
|
" \n",
|
|
" key = 'supply'\n",
|
|
" value = S\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_R(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" R = _input['R']\n",
|
|
" \n",
|
|
" key = 'reserve'\n",
|
|
" value = R\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_P(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" P = _input['P']\n",
|
|
" \n",
|
|
" key = 'spot_price'\n",
|
|
" value = P\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_holdings(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" h_a = _input['h_a']\n",
|
|
" a = _input['a']\n",
|
|
" \n",
|
|
" h = s['holdings']\n",
|
|
" h[a] = h_a\n",
|
|
" \n",
|
|
" key = 'holdings'\n",
|
|
" value = h\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_tokens(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" s_a = _input['s_a']\n",
|
|
" a = _input['a']\n",
|
|
" \n",
|
|
" tokens = s['tokens']\n",
|
|
" tokens[a] = s_a\n",
|
|
" \n",
|
|
" key = 'tokens'\n",
|
|
" value = tokens\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
|
|
"# The Partial State Update Blocks\n",
|
|
"partial_state_update_blocks = [\n",
|
|
" { \n",
|
|
" 'policies': { \n",
|
|
" #new proposals or new participants\n",
|
|
" 'random': revenue_process\n",
|
|
" },\n",
|
|
" 'variables': {\n",
|
|
" 'funds': update_funds,\n",
|
|
" 'prices': update_prices\n",
|
|
" }\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'policies': {\n",
|
|
" 'random': choose_agent\n",
|
|
" },\n",
|
|
" 'variables': { \n",
|
|
" 'action': agent_action, \n",
|
|
" }\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'policies': {\n",
|
|
" 'act': resolve_action,\n",
|
|
" },\n",
|
|
" 'variables': {\n",
|
|
" 'funds': update_F, #\n",
|
|
" 'supply': update_S, \n",
|
|
" 'reserve': update_R,\n",
|
|
" 'spot_price': update_P,\n",
|
|
" 'holdings': update_holdings,\n",
|
|
" 'tokens': update_tokens\n",
|
|
" }\n",
|
|
" }\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_periods_per_run = 300\n",
|
|
"monte_carlo_runs = 1\n",
|
|
"\n",
|
|
"from cadCAD.configuration.utils import config_sim\n",
|
|
"simulation_parameters = config_sim({\n",
|
|
" 'T': range(time_periods_per_run),\n",
|
|
" 'N': monte_carlo_runs,\n",
|
|
" 'M': params\n",
|
|
"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[{'N': 1, 'T': range(0, 300), 'M': {'kappa': 2, 'lambda': 200, 'gains': array([0.99147412, 1.00022973, 1.06476773, 0.95561498, 0.91801408,\n",
|
|
" 1.00734382, 1.01135583, 1.06041933, 1.05509357, 0.94456693,\n",
|
|
" 0.99497888, 0.9642258 , 0.96708019, 0.99921331, 1.01899485,\n",
|
|
" 1.03142817, 0.98211835, 0.97903723, 1.05878192, 0.93588367,\n",
|
|
" 0.99939403, 0.9877718 , 0.96955855, 0.98352141, 0.90254589,\n",
|
|
" 1.00236469, 1.06911097, 0.97621233, 1.00671504, 0.93209234,\n",
|
|
" 1.00202922, 1.03321211, 1.02432448, 1.02095369, 0.97163898,\n",
|
|
" 0.98428338, 0.98921774, 0.98643192, 0.98860959, 0.99905631,\n",
|
|
" 0.97167925, 1.00456465, 0.99046072, 0.96190988, 1.04424163,\n",
|
|
" 0.93002334, 1.00337459, 1.02458094, 1.00561722, 0.95269826,\n",
|
|
" 1.07126847, 0.94102154, 1.08037467, 1.02329009, 1.00266749,\n",
|
|
" 0.97887202, 0.97058923, 0.96914434, 0.95365847, 0.97648692,\n",
|
|
" 1.00717438, 1.02822531, 1.02212882, 1.00420173, 1.01417108,\n",
|
|
" 0.95308863, 0.95110406, 0.92644275, 0.97184703, 1.09008079,\n",
|
|
" 0.93417976, 1.00227415, 1.05411677, 0.95968721, 1.00082199,\n",
|
|
" 0.9949477 , 0.9977625 , 1.04417088, 1.03733777, 1.06744737,\n",
|
|
" 1.02118574, 1.0547987 , 0.95910353, 0.98771918, 0.87315306,\n",
|
|
" 0.92288967, 1.05858229, 0.97487866, 1.00927773, 0.96354765,\n",
|
|
" 1.00853229, 1.01610359, 0.98149246, 0.99677845, 1.03139812,\n",
|
|
" 1.05734373, 0.89044556, 1.02497663, 0.97065212, 1.02257147]), 'population': 100, 'beta': 0.9, 'phi': 0.05, 'invariant': 15384615.384615384}}]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from cadCAD.configuration import append_configs\n",
|
|
"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
|
|
"# The configurations above are then packaged into a `Configuration` object\n",
|
|
"append_configs(\n",
|
|
" initial_state=initial_conditions, #dict containing variable names and initial values\n",
|
|
" partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
|
|
" sim_configs=simulation_parameters #dict containing simulation parameters\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tabulate import tabulate\n",
|
|
"from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
|
"from cadCAD import configs\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"exec_mode = ExecutionMode()\n",
|
|
"multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
|
|
"run = Executor(exec_context=multi_proc_ctx, configs=configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" __________ ____ \n",
|
|
" ________ __ _____/ ____/ | / __ \\\n",
|
|
" / ___/ __` / __ / / / /| | / / / /\n",
|
|
" / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
|
|
" \\___/\\__,_/\\__,_/\\____/_/ |_/_____/ \n",
|
|
" by BlockScience\n",
|
|
" \n",
|
|
"Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x1a0ea792e8>]\n",
|
|
"Configurations: [<cadCAD.configuration.Configuration object at 0x1a0ea792e8>]\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: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",
|
|
"/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:37: RuntimeWarning: invalid value encountered in double_scalars\n",
|
|
" realized_price = deltaR/deltaS\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"i = 0\n",
|
|
"verbose = False\n",
|
|
"results = {}\n",
|
|
"for raw_result, tensor_field in run.execute():\n",
|
|
" result = pd.DataFrame(raw_result)\n",
|
|
" if verbose:\n",
|
|
" print()\n",
|
|
" print(f\"Tensor Field: {type(tensor_field)}\")\n",
|
|
" print(tabulate(tensor_field, headers='keys', tablefmt='psql'))\n",
|
|
" print(f\"Output: {type(result)}\")\n",
|
|
" print(tabulate(result, headers='keys', tablefmt='psql'))\n",
|
|
" print()\n",
|
|
" results[i] = {}\n",
|
|
" results[i]['result'] = result\n",
|
|
" results[i]['simulation_parameters'] = simulation_parameters[i]\n",
|
|
" i += 1\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"experiment_index = 0\n",
|
|
"df = results[experiment_index]['result']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a0ebfce80>"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a0ebfc780>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.funds.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a0ef414a8>"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a0ebfc3c8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"(df.funds.diff()/df.funds).hist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf = df[df.substep == 3].copy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"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": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1a0f253908>"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a0f260128>"
|
|
]
|
|
},
|
|
"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.prices.apply(np.max).plot()\n",
|
|
"rdf.spot_price.plot()\n",
|
|
"plt.legend(['min', 'median','mean','wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1a0f22e9b0>"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a0f22ecc0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.prices.apply(np.min).apply(np.log).plot()\n",
|
|
"rdf.prices.apply(np.median).apply(np.log).plot()\n",
|
|
"rdf.prices.apply(np.mean).apply(np.log).plot()\n",
|
|
"rdf.wt_mean_price.apply(np.log).plot()\n",
|
|
"rdf.prices.apply(np.max).apply(np.log).plot()\n",
|
|
"rdf.spot_price.apply(np.log).plot()\n",
|
|
"plt.legend(['min', 'median','mean','wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"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": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1a0f234630>"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a173610f0>"
|
|
]
|
|
},
|
|
"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": 28,
|
|
"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": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a0ebfc358>"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a17257cc0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.est_err.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a173e0dd8>"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a173b8240>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.est_err.plot(logy=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1a1765e6d8>"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a0efb64e0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#tail T\n",
|
|
"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).prices.apply(np.max).plot()\n",
|
|
"rdf.tail(T).spot_price.plot()\n",
|
|
"plt.legend(['min', 'median','mean','wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bond_amts = [rdf.iloc[k].action['amt'] for k in range(time_periods_per_run) if rdf.iloc[k].action['mech']=='bond']\n",
|
|
"burn_amts = [rdf.iloc[k].action['amt'] for k in range(time_periods_per_run) if rdf.iloc[k].action['mech']=='burn']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(array([66., 4., 2., 4., 2., 1., 1., 0., 1., 1.]),\n",
|
|
" array([ 0. , 150.0450635 , 300.09012699, 450.13519049,\n",
|
|
" 600.18025398, 750.22531748, 900.27038098, 1050.31544447,\n",
|
|
" 1200.36050797, 1350.40557147, 1500.45063496]),\n",
|
|
" <a list of 10 Patch objects>)"
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a177cb9e8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(bond_amts)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(array([155., 10., 22., 6., 16., 5., 2., 0., 0., 2.]),\n",
|
|
" array([ 0. , 2859.44265575, 5718.88531149, 8578.32796724,\n",
|
|
" 11437.77062299, 14297.21327873, 17156.65593448, 20016.09859022,\n",
|
|
" 22875.54124597, 25734.98390172, 28594.42655746]),\n",
|
|
" <a list of 10 Patch objects>)"
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a17856e80>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(burn_amts)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['invariant'] = rdf.supply.apply(lambda x: x**kappa)/rdf.reserve"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['resid'] = rdf.invariant-V0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1794aa90>"
|
|
]
|
|
},
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADj5JREFUeJzt3W2sZWV5xvH/BUck1Q5oQXlzOhjRSn0ZdZcAajFAW6WtfvAlEmM1ilMjTYG0NlgaGuKXltIqRmNLoba2TbWKtkCMCFRrTdR6piXyMiJYK+BQwahYmxCrvfthr4HhuGfOmVnb2Zy5/79kZ9bLffbz7GeeOdestc5aJ1WFJKmfAxbdAUnSYhgAktSUASBJTRkAktSUASBJTRkAktSUASBJTRkAktSUASBJTS0tugO7c9hhh9WmTZsW3Q1JWje2bt36zao6fC21j+gA2LRpE8vLy4vuhiStG0m+ttZaTwFJUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlOjAyDJ5iSfS3JjkuUkJ+ymdkOSryd599h2JUnjzOMI4GLgoqraDFw4rO/K24F/nkObkqSR5hEABWwYlg8Bts8qSvI84InAJ+bQpiRppKU5vMe5wLVJLmEaKCevLEhyAPDHwGuB03b3Zkm2AFsANm7cOIfuSZJmWVMAJLkeOGLGrguYfkM/r6quTPIq4Arg9BV1bwE+VlV3JdltW1V1GXAZwGQyqbX0T5K059YUAFW18hv6g5K8HzhnWP0QcPmMspOAFyZ5C/BY4KAk36uq8/ewv5KkOZnHKaDtwCnAp4BTgdtXFlTVa3YsJ3k9MPGbvyQt1jwC4E3ApUmWgAcYzt8nmQBvrqqz5tCGJGnOUvXIPc0+mUxqeXl50d2QpHUjydaqmqyl1juBJakpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmjIAJKkpA0CSmhoVAEk2J/lckhuTLCc5YRd1G5N8Ism2JLcm2TSmXUnSeGOPAC4GLqqqzcCFw/os7wf+qKqeDpwA3DuyXUnSSEsjv76ADcPyIcD2lQVJjgeWquo6gKr63sg2JUlzMDYAzgWuTXIJ06OJk2fUPBX4TpKPAMcC1wPnV9UPR7YtSRph1QBIcj1wxIxdFwCnAedV1ZVJXgVcAZw+o40XAs8B7gQ+CLx+qJ3V3hZgC8DGjRvX9CEkSXsuVbX3X5zcDxxaVZUkwP1VtWFFzYnAH1TVi4b11wInVtXZq73/ZDKp5eXlve6fJHWTZGtVTdZSO/Yi8HbglGH5VOD2GTVfAB6X5PCd6m4d2a4kaaSx1wDeBFyaZAl4gOHUTZIJ8OaqOquqfpjkt4EbhqOErcCfj2xXkjTSqACoqs8Az5uxfRk4a6f164BnjWlLkjRf3gksSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0ZAJLUlAEgSU0tLboD+7uLrr6FW7d/F4Djj9rA7//qzy68vZ1rVppHH3e8/zw/76w+7+n77+l77Ku/u12N19hxHDsXVhozBns7lvOcS2uZ96uNx2rjOI85ubt25s0jAElqKlW16D7s0mQyqeXl5UV3Q5LWjSRbq2qyllqPACSpKQNAkpoyACSpKQNAkpoyACSpKQNAkpoyACSpKQNAkpoyACSpKQNAkpoaHQBJNif5XJIbkywnOWEXdRcnuSXJtiTvSpKxbUuS9t48jgAuBi6qqs3AhcP6wyQ5GXg+8CzgGcDPAafMoW1J0l6ax+OgC9gwLB8CbN9FzcHAQUCARwHfmEPbkqS9NI8AOBe4NsklTI8oTl5ZUFWfTfJJ4B6mAfDuqto2h7YlSXtpTQGQ5HrgiBm7LgBOA86rqiuTvAq4Ajh9xdc/BXg6cMyw6bokP19Vn57R1hZgC8DGjRvX+jkkSXto9O8DSHI/cGhV1XBh9/6q2rCi5q3AwVX19mH9QuCBqvqR6wU78/cBSNKe2de/D2A7D13QPRW4fUbNncApSZaSPGqo9xSQJC3QPK4BvAm4NMkS8ADD6ZskE+DNVXUW8GGm4XAT0wvCH6+qq+fQtiRpL40OgKr6DPC8GduXgbOG5R8Cvz62LUnS/HgnsCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1ZQBIUlMGgCQ1NSoAkjw7yWeT3JTk6iQbdlH34iS3Jbkjyflj2pQkzcfYI4DLgfOr6pnAR4G3rixIciDwHuAlwPHAmUmOH9muJGmksQHwNODTw/J1wMtn1JwA3FFV/1FV3wc+ALxsZLuSpJHGBsDNwEuH5VcCT5pRczRw107rdw/bZkqyJclykuX77rtvZPckSbuyagAkuT7JzTNeLwPeAJydZCvwk8D3Z73FjG21q/aq6rKqmlTV5PDDD1/r55Ak7aGl1Qqq6vRVSn4RIMlTgV+esf9uHn5kcAywfa0dlCT9eIz9KaAnDH8eAPwe8Kczyr4AHJfk2CQHAa8GrhrTriRpvLHXAM5M8mXgS0z/V/8+gCRHJfkYQFX9APgN4FpgG/D3VXXLyHYlSSOlapen4xduMpnU8vLyorshSetGkq1VNVlLrXcCS1JTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNWUASFJTBoAkNTUqAJI8O8lnk9yU5OokG2bUPCnJJ5NsS3JLknPGtClJmo+xRwCXA+dX1TOBjwJvnVHzA+C3qurpwInA2UmOH9muJGmksQHwNODTw/J1wMtXFlTVPVX1b8PyfwPbgKNHtitJGmlsANwMvHRYfiXwpN0VJ9kEPAf4/Mh2JUkjLa1WkOR64IgZuy4A3gC8K8mFwFXA93fzPo8FrgTOrarv7qZuC7AFYOPGjat1T5K0l1JV83mj5KnA31TVCTP2PQq4Bri2qv5kre85mUxqeXl5Lv2TpA6SbK2qyZpqxwRAkidU1b1JDgD+EvhUVf3FipoAfwV8q6rO3cP3vw/42k6bDgO+udcd7sEx2j3HZ3WO0eoeyWP001V1+FoKxwbAOcDZw+pHgLdVVSU5Cri8qs5I8gLgX4CbgP8ban+3qj62F+0trzXZunKMds/xWZ1jtLr9ZYxWvQawO1V1KXDpjO3bgTOG5c8AGdOOJGn+vBNYkppabwFw2aI7sA44Rrvn+KzOMVrdfjFGc/spIEnS+rLejgAkSXOyLgIgyYuT3JbkjiTnL7o/i7KrB+sleXyS65LcPvz5uGF7krxrGLcvJnnuYj/BvpHkwCT/nuSaYf3YJJ8fxueDSQ4atj96WL9j2L9pkf3eV5IcmuTDSb40zKWTnEMPl+S84d/YzUn+LsnB++M8esQHQJIDgfcALwGOB85s/DC5XT1Y73zghqo6DrhhWIfpmB03vLYA7933XV6Ic5g+c2qHPwTeMYzPt4E3DtvfCHy7qp4CvGOo6+BS4ONV9TPAs5mOlXNokORo4DeBSVU9AzgQeDX74zyqqkf0CziJ6R3EO9bfxvR+g4X3bdEv4B+BXwBuA44cth0J3DYs/xlw5k71D9btry/gGKbfwE5levd5mN6ws7RyPgHXAicNy0tDXRb9GX7M47MB+OrKz+kcethYHA3cBTx+mBfXAL+0P86jR/wRAA/9ZexwNz5NdOWD9Z5YVffA9OmrwBOGso5j907gd3jopsOfAr5TVT8Y1ncegwfHZ9h//1C/P3sycB/wvuE02eVJHoNz6EFV9XXgEuBO4B6m82Ir++E8Wg8BMOsmstY/urTWB+vRbOyS/Apwb1Vt3XnzjNJaw7791RLwXOC9VfUc4H946HTPLO3GaLj+8TLgWOAo4DFMT4WttO7n0XoIgLt5+GOmjwG2L6gvCzc8WO9K4G+r6iPD5m8kOXLYfyRw77C929g9H3hpkv8EPsD0NNA7gUOT7LjrfecxeHB8hv2HAN/alx1egLuBu6tqxyPZP8w0EJxDDzkd+GpV3VdV/8v0MTcnsx/Oo/UQAF8AjhuuwB/E9GLMVQvu00IMD9a7AthWD3+q6lXA64bl1zG9NrBj+68NP8lxInD/jsP8/VFVva2qjqmqTUznyT9V1WuATwKvGMpWjs+OcXvFUL8u/ue2t6rqv4C7kjxt2HQacCvOoZ3dCZyY5CeGf3M7xmj/m0eLvgixxosyZwBfBr4CXLDo/ixwHF7A9NDyi8CNw+sMpucbbwBuH/58/FAfpj9B9RWmD+ObLPoz7MOxehFwzbD8ZOBfgTuADwGPHrYfPKzfMex/8qL7vY/GZjOwPMyjfwAe5xz6kTG6CPgS01969dfAo/fHeeSdwJLU1Ho4BSRJ+jEwACSpKQNAkpoyACSpKQNAkpoyACSpKQNAkpoyACSpqf8HQGtts2Usr3cAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a1753ee10>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.resid.apply(np.log10).plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x1a17b874e0>"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a17afd390>"
|
|
]
|
|
},
|
|
"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": 39,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a179b7588>"
|
|
]
|
|
},
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a179a6128>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"(rdf.tokens.apply(sum)-rdf.supply).plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"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": 41,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini_h'] = rdf.holdings.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a17cfb6d8>"
|
|
]
|
|
},
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a171cd6d8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.gini_h.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini_s'] = rdf.tokens.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a176dda20>"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a176020b8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.gini_s.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a17808b00>"
|
|
]
|
|
},
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a17614668>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.tokens.apply(np.count_nonzero).plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['asset_value'] = rdf.holdings + rdf.spot_price*rdf.tokens"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini'] = rdf.asset_value.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1758f0f0>"
|
|
]
|
|
},
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xl4XNWd5vHvT/tmWatXeV/wAgaDwhpCAk5iMt2G7qE7kJCEp8nQ6YSZTDNJhzQZJkNPz3Qg0ySTMAmks3QSMiTQCRDaxNDGIRt2sGNjvGAQMrZkyZZk7ftSv/mjrkRJlq2SLalUVe/nefSo7rmnrs4tl+utc+6955q7IyIikhLrBoiIyPSgQBAREUCBICIiAQWCiIgACgQREQkoEEREBFAgiIhIQIEgIiKAAkFERAJpsW7AeJSUlPjixYtj3QwRkbiya9euBncvHateXAXC4sWL2blzZ6ybISISV8zsSDT1NGQkIiKAAkFERAIKBBERARQIIiISUCCIiAigQBARkYACQUREAAWCiEyC3x9u5EBNa6ybIeOkQBCRCXf3v+zlf24+GOtmyDjF1ZXKIjL9hUJOdVMXHb39sW6KjJN6CCIyoerbe+gdCHGitYfW7r5YN0fGIaoegpltBL4KpAL/5O7/MGL9XcDHgX6gHvgLdz9iZu8BHoyougq42d2fNLPvAdcALcG629x9z7nsjIhMrhdfr+foyY5hZbPysyjITmft/JnkZaZR3dQ1tO7NunbWLyyc6mbKWRozEMwsFXgIeC9QDbxsZk+7+4GIaruBcnfvNLO/Au4HPuju24CLgu0UARXAcxHP+6y7PzExuyIyXCjkpKRYrJuRMDp6+vmL773MQMhHXf/n5WXcf9OFVDd1DpW9Wd9x1oFwsr2HN+rauXxp8Vk9X8Yvmh7CpUCFu1cCmNljwA3AUCAEH/yDtgO3jrKdm4Bn3b1zlHUiE+qx3x/lvz61j5WzZ7B+YQGXLinmsiVFzM7PinXT4tZrx9sYCDkPfvBCrl4Rnkk55E5tczdffu4Qv3q9AXcf6iGkpRh7q5u5ctmZP9CLcjPISk89pfy+Zw7w1J4anv301ayemz/xOzSN1bf18NSeY/RHhO8HyxdQmJsxqX83mkCYD1RFLFcDl52h/u3As6OU3wz844iyvzeze4GtwN3u3hNFe0TGtONwI1npqRTmZPDk7hp+uP0oAIuKc1hXVkBGagqFOel87vpVpKfqUFo0DtaGTyN9x+IiSvIyh8pnzcji/Wvn8Os39vHWyU6ONXdRmJPO7Pwsvv/SEb7/0plnXl47L59//U9Xn1Le2NELwBee3Mf71sxmzswsNl04D7Op7/X95OUq9h5rnrK/t+21eo41dw0r27B69rQIhNFe/VH7jGZ2K1BO+NhAZPlc4AJgS0Tx54HjQAbwCPA54L5RtnkHcAfAwoULo2iuCFTWt3NhWQE//Phl9A+EOFjbxo7DJ9lxuJE9VU1094Wob+vhutWzuWKMb7DTmbtzvLWb3Mw08rPSJ/VvHaxtJT8rjfkF2aesG+wF/PjlKg7WtlJWmMMDf7aOV6rO/CH6m4qT/PyVGpo7eynIGf5h194TPktp15Emdh1pAuCpPTUsn5V3ynZmZqfziWuWkToJQ4Sdvf3c8+SrpKemkD1KT2YyFOdl8OSnruK82TOGyjLTJv+LSzSBUA0siFguA2pGVjKzDcA9wDWjfNP/c+Bn7j50yoG71wYPe8zsu8BnRvvj7v4I4cCgvLx89MFLkQjuTmV9B39y8XwA0lJTuKBsJheUzeTjVy8FoLmzl4vue56dbzXGdSA8uuMoX3hyHzMy0/j9PRvIzoj+A8vdef7ACbr6BriwrICZ2aMHiln4A/dgbSur5uaP+g19SUkuZYXZfPPFNwG48aJ5rJqTz6o5Zx7qWVCYw89fqeGV6hauWTn8hl41zV382SVl3HfD+TjOd3/7Ft/85Zu89ObJYfUG3OntD3HV8hIuWlAQ9f5Ha+dbTfQNOP/0sUtOaWOiiSYQXgZWmNkS4BjhoZ8PRVYws/XAw8BGd68bZRu3EO4RRD5nrrvXWvjddSOw7yzaL3KK+rYe2nr6WVZ66jfJQQU5GaycncfO4JtnvHrteHgYp62nn0Mn2sb1gfhKdQt3/GBXVHXzs9Lo6B3gI5cvGnW9mfH4J67gyMnwIcI186Ib87+gbCZm8EpV87AP297+EHVtPcwvzB4KuU+9Zzmfes/yU7Zx6Hgb7//Kr6hq7JyUQPjtmw2kpxrvWJz4Z0uNGQju3m9mdxIe7kkFvuPu+83sPmCnuz8NPADkAY8H3x6OuvsmADNbTLiH8eKITT9qZqWEh6T2AJ+YkD2SpPdmffi0yKWluWesV764iJ/vqeGlN08OfQuOt4OXtc3d5GWm0d7Tz2u1reP6QKxqDH94f/PWi2nq7KOnb2DUev0h53BDB739IW659PTDtnNnZjN35qnDSWcyIyud5aV5vPBaHdetnsXaeTMBON7SjTvMG2V4aqSywnCdqqazP18lFHL+5l/2Ut3USVpKCqkpRlqKkZpi/OFoM+sXFJKTkfjX8Ua1h+6+Gdg8ouzeiMcbzvDctwgfmB5Zfm3UrRSJ0onWbh7fGT4HYukZeggAVywt5kc7jnLLt7YPlX3xj9dw21VLJrWNE6mmpZtLlxSxo/Lk0EHfaNW2hA9aXrm8ZNKPP5zJ5UuL+cH2I/z5N19i+99ex4ysdKqbwx/uZVEEQm5mGkW5GVQ1do1Z93SONnbyxK5qls/KY2Z2Ov0hZyAUon/AKc7N4MOXJ8fxy8SPPEkq3/pVJT/dfYz5BdnMHeMU0w9cMJd5BVn09IcA+PavD/PFnx/gvmcOkJpizM7PIiMthVQz1szLpyA7neK8THIz07jpkrLTjrlPpdqWLi5ZVMCqufkcPN42rufWBL2LWIYBwL1/vIZ3rSzlP3x/Jz/bfYyPXrGYY8Gpq9H0EAAWFGYPu/5hvF49Fr4+9isfvIjz58886+3EOwWCJJSali6WlOSy5T+/a8yL0lJTjEsWFQ0trysr4NHtR2jv6Q9PvdDSTX/I6e4LsaOykc7eflq7w2e+uPvQAepY6eodoLmzj7kzs3GHJ3cf4/svvcVHLl8U1amZtS1dzJkZ++sy0lNTeO+a2awrm8n/+NeDfO2FCrp6w8NXcwuia19ZUc45za66r6aFjNQUVkac1ZOMFAiSUI63dDN3Zvib/XjlZabxl9csO2Odnv4B3nX/NvYdazljvalQ0zL4LTqLlbNn8MSuau59aj/vOW8WC4pyxnz+4Gs1Xfz3TWt5fFc1HpxLuHJ2Hplp0Z01VVaYzfP7T5z11en7j7Vy3pwZZ/W+SSQKBIlL2w7VUdXYyUevWDys/ERrD5ctKRr9SRMgMy2VtfNmcmCc4/WToba5GwgfzL18aTGPfvwybvrmS1TUtUcVCDUt3WOeFjqV1i8sPOtpLhYU5tA7EOJvf/bqWZ2vv6eqmT++cO5Z/e1EokCQuPSd3xxmz9HmYcMjoZBzorWb2ZP8rXftvHxefL2e7r6BUadcmCpDPYTgzJ7B02wr6tp5z6pZZ3xub3+IhvaeaTFkNBHKFxcyJz+LX+w/flbPz0oPD1slOwVCkqtt6eJHO44ScifFwgdSP3zZwphMDzAehxs6aOvpp6ale+jK2ZMdvfSHnDmTPF/R2nn5DIScA7WtXBzDmTwHewizZ4ankSjMzaA4N4OKuvZT6oZCzo7DjfT0h8fmGzt6g9M6EyMQVs3JZ/vfXhfrZsQ9BUKS+8nL1XzthQrSUmxoIq1rV82K+uyOsQyEnFse2c6RxrenTC7Jy2RGVhp3vGsp164a/7ey7r6BoXleXj/eNhQIx1uCD8hJDoTBs1D+9P/+Lnw20oxMcjLD/5WKcjPIHeVq4ZK8zLM+K2n9wkL+3bpThzNqW7ooycscNs6+bFYeb9SderbRT3ZWcfdPXz2lfKxTcyW5KBCSXFVTJ7PzM9nxtxvY/Gotn3z0D7R09U1YINQ0d/H7txq5Ymkxi4pzCLlT19bDK1XNfP2FirMKhKONnUMHHg+daBsaHjneGg6EyR4GKSvM4Tu3lbP/WCtdfQMcb+mmpz9EyJ2G9h5OBpOyDXKH/TWtdPSM/w5iPf0hnnqlZtRAqGnpPuUb/vJZeTzzSs3QFcyDf/+hX1awrmwmX9y0dqg8JyN12Fw5IgqEJFfV2MmCwvAByBlZ4bdDW/fE3frwzfrw8MVfv3cll0Yc7H1oWwUPbDlEbUvXuK9urQy2aRbuIQwaCoQpmOL62lWzzyrMxutLv3iNb/2qEnc/ZRivtrnrlKuxV8+ZwY929LPxK78+ZVv/7Y/WxnSIS6Y/BUKSq27qGpqjZfACpbYRtz2sae6ipz9EeqoxvyB7XMcXKk8zjcT158/hgS2HuPNHu1m/oICSGZmjPX2YOflZXL2ihMqG8DYvXljIcwdOcMPXfwOEzzBKMSjJm9wpgqdSUU4G/SGnraf/lAvIalu6uWp5ybCyPytfwJyZ2fQPhIaVz8hK56rl8TuJn0wNBUIS6x8Icby1e+gUxcEeQuR9cH9X0cCH/mnH0HJJXgaXLCokN5jXJTXFWD03n+K8DN6/ds4pZ91UNrSTn5VG8Yh53JeW5vGn6+ezp6qZH2w/MnS18FjMICc9ldIZmXzy3cv4wfa359ovzM3gpkvKSEug+xsMzn/f3NE3LBBau/to7+k/ZcgoKz1VZ8vIWVMgJLHalm4GQh4xZDTYQ3h7yGh3MJ/9Azeto3cgxK63mthT3Uxf8A20qzfE47uqAfj0dSv46/euHPY3Kus7WFKaN2qv4h8/eBEQPvDcO0YgOE5FXTu/PFTPkZOdXLGsmOtWz+a61Yn94VeUG/43aezsZWHx29cWRF6DIDJRFAhJ5Bf7jvPEriq+dsvFZGekDs0OOThb5FAPoevtHsKh4CyePysP3xLjw5edOv1xXWs3n//pq3z3t4e5ekXJsDn5K+raeeeIYY2RUlMsqnn815UVsK5s4qc3ns4Kg5vGNI04UP32VcoKBJk4CoQk8uiOI/z6jQY++eguLlxQwKHggOzgkFFWeioZaSnDegivn2jjvDlnPhNlVn4Wf/3elfzJ//0tN33zpVPWrxzj+XJ6RcGQUeOIQBjsISTKdQQyPSgQksjg+P6Lr9ez7VA9AAuKsoedppmflTY0gVvfQIg369t593lnvuoVwufmv/Bf3s0bdW30Dbx9Y7u0FOPKZWfuIcjpDR5DaOocHgjhefuN0ryxD8aLREuBkEQa2nu4ankxP7z9smHlkeP7+VnptHb3EQo5Ww+eoG/AOW9OdBcvLSjKiWoOHYnejMw00lLslB7C/ppWVsyekVAH0CX29G5KIPuOtfDVf3uD31Y0jLr+ZHsvJXmZmNmwn0gzstJo6+5ny/7jfOKHfwDggiSeHz7WzIzC3IxhPQR3Z9+xFs6P8jaVItFSDyGBPPj862x9rY6lJbm88Jl3n7L+ZHsPxblnHmKYkZVOW3cfB2tbSTF48lNXsXyWjgHEUlFOxrAeQm1LNyc7ermgTEEtEyuqHoKZbTSzQ2ZWYWZ3j7L+LjM7YGZ7zWyrmS0Kyt9jZnsifrrN7MZg3RIz22Fmb5jZj80sca4mipHG4Ftk44jxZgjfTKWjd4CSGWd+mfOz02jt6qOyoYOywpykO6tnOirMTaepo4+e/gG6+wbYfTR8KnAy39lLJseYPQQzSwUeAt4LVAMvm9nT7n4gotpuoNzdO83sr4D7gQ+6+zbgomA7RUAF8FzwnC8BD7r7Y2b2TeB24BsTtF9JqaUzfLpoS1ffKTcKaWjvAaBkrB5CZjpt3f1U1neMeZN6mRq5GWlsfa2O877wi6Gy1BRj9TS6l4EkhmiGjC4FKty9EsDMHgNuAIYCIfjgH7QduHWU7dwEPBuEhgHXAh8K1v0z8EUUCOekObh+wD18JWtBztu9gcEJ16LpIbR09dHW3c/lSzXVwXTwjiVFvPxWIx+7cjHZGakYxrLS3Kiu3RAZj2gCYT5QFbFcDVx2mroQ/qb/7CjlNwP/GDwuBprdffCE9+rg78hZCoWc5s5e5hdkc6y5i6bO4YHQ0BbuIURzDGFwGgn1EKaHT1yzjE+McWtPkYkQzTGE0WYy81HKMLNbgXLggRHlc4ELgC1nsc07zGynme2sr6+PornJqa2nn5DDkpLwh3jziOMIJzuCQBhj4rfBq5UBlpYoEESSSTSBUA0siFguA2pGVjKzDcA9wCZ37xmx+s+Bn7n74JwIDUCBmQ1++oy6TQB3f8Tdy929vLS0NIrmJqfB4weLS8LXATR39tHVO8CuI03sOtLEq8FN4UvGuJBp9dx8stJTKMxJZ41OaxRJKtEMGb0MrDCzJcAxwkM/H4qsYGbrgYeBje5eN8o2bgE+P7jg7m5m2wgfV3gM+Bjw1FntgQDQ3BXuESwuzh1a/vvNB/jh9qNDdYpzM8a8B/DlS4t57e+un7yGisi0NWYguHu/md1JeLgnFfiOu+83s/uAne7+NOEhojzg8eBCp6PuvgnAzBYT7mG8OGLTnwMeM7P/QfgspW9PyB4lqebBHkIQCE0dfRyoaWXtvHz+ZuMqABYUaiI0ETm9qC5Mc/fNwOYRZfdGPN5whue+xSgHjIOzli6NtqFyZoNXsi4szsEsfAyhsqGDD1wwl2tWaqhNRMamqSsSREtwymlhTgYzs9OpbOigubNPB4ZFJGoKhAQxOGQ0MzudwpyMoatZdeqoiERLgZAgmjv7yM0I38+gICedY83hG6gsLYluplIREU1uF+e6+wY41txFdVPn0IVoBdnh2y6mp9rQ3dBERMaiQIhzn35sN1v2nwDgwgXhieguWlDItkP1XL60WPPli0jUFAhx7vUT7ZQvKuQjVywamv3y0xtW8OkNK2LcMhGJNwqEOObu1DR3sWH1LG64SFNBici50XhCHGvq7KOnP8TcmTpOICLnToEQx2qCM4nmFWTFuCUikggUCHFsMBDUQxCRiaBAiGO1Ld0AzFUPQUQmgAIhjtW0dJGRmjLmbTFFRKKhQIhjtc3dzJmZNezeySIiZ0uBEMdqmruYO1PDRSIyMRQIcexIYycLi3Ji3QwRSRAKhDjV1TtAfVsPi4oVCCIyMRQIcepoYycAC4s1vbWITAwFQpw6crIDgEUaMhKRCRJVIJjZRjM7ZGYVZnb3KOvvMrMDZrbXzLaa2aKIdQvN7DkzOxjUWRyUf8/MDpvZnuDnoonaqWQw1ENQIIjIBBkzEMwsFXgIuB5YA9xiZmtGVNsNlLv7OuAJ4P6Idd8HHnD31YTvoVwXse6z7n5R8LPnHPYj6Rxt7GRGVhoFOemxboqIJIhoegiXAhXuXunuvcBjwA2RFdx9m7t3BovbgTKAIDjS3P35oF57RD05B0dOdrKoOAczXYMgIhMjmkCYD1RFLFcHZadzO/Bs8Hgl0GxmPzWz3Wb2QNDjGPT3wTDTg2amy23H4XBDB4t0QFlEJlA0gTDaV1AftaLZrUA58EBQlAZcDXwGeAewFLgtWPd5YFVQXgR87jTbvMPMdprZzvr6+iiam/h6+geobupkWYkCQUQmTjSBUA0siFguA2pGVjKzDcA9wCZ374l47u5guKkfeBK4GMDdaz2sB/gu4aGpU7j7I+5e7u7lpaWl0e5XQjtyspOQw9LSvFg3RUQSSDSB8DKwwsyWmFkGcDPwdGQFM1sPPEw4DOpGPLfQzAY/ya8FDgTPmRv8NuBGYN+57EgyqaxvB2BpqXoIIjJxxryFprv3m9mdwBYgFfiOu+83s/uAne7+NOEhojzg8eAg51F33+TuA2b2GWBr8MG/C/hWsOlHg6AwYA/wiYneuUT1Zn34GoQlGjISkQkU1T2V3X0zsHlE2b0Rjzec4bnPA+tGKb82+mYKwEDI6ejt540TbcyakcmMLJ1yKiITJ6pAkOnhtu/+nl+/0QDAlcuKY9waEUk0CoQ44e7sPtrM5UuL2LB6Nlev0AF2EZlYCoQ40djRS3tPP+9bM4e/eOeSWDdHRBKQJreLE5q7SEQmmwIhTgwGgu5/ICKTRYEQJ46cDAfCAvUQRGSSKBDixNHGTmbnZ5KVnjp2ZRGRs6BAiBNHT+r+ySIyuRQIcaKmpYt5BdmxboaIJDAFQpxo6uilKDcj1s0QkQSmQIgDvf0hOnoHKMpRIIjI5FEgxIHmzl4ACtRDEJFJpECIA41BIKiHICKTSYEQB5o6+gAozNHspiIyeRQIcaAp6CEUashIRCaRAiEODAWChoxEZBIpEOJAc2d4yKhAQ0YiMokUCHGgsaOXnIxUTVshIpMqqkAws41mdsjMKszs7lHW32VmB8xsr5ltNbNFEesWmtlzZnYwqLM4KF9iZjvM7A0z+7GZaTzkNJo6ezVcJCKTbsxAMLNU4CHgemANcIuZrRlRbTdQ7u7rgCeA+yPWfR94wN1XA5cCdUH5l4AH3X0F0ATcfi47ksiaOnopzNVwkYhMrmh6CJcCFe5e6e69wGPADZEV3H2bu3cGi9uBMoAgONLc/fmgXru7d5qZAdcSDg+AfwZuPOe9SVBNnX3qIYjIpIsmEOYDVRHL1UHZ6dwOPBs8Xgk0m9lPzWy3mT0Q9DiKgWZ3749ym0mtsUNDRiIy+aIJBBulzEetaHYrUA48EBSlAVcDnwHeASwFbhvnNu8ws51mtrO+vj6K5iaW/oEQNc1dzC/UTKciMrmiCYRqYEHEchlQM7KSmW0A7gE2uXtPxHN3B8NN/cCTwMVAA1BgZmln2iaAuz/i7uXuXl5aWhrNPiWU2pZu+kPOIt0LQUQmWTSB8DKwIjgrKAO4GXg6soKZrQceJhwGdSOeW2hmg5/k1wIH3N2BbcBNQfnHgKfOfjcS1+CtMxcV58a4JSKS6MYMhOCb/Z3AFuAg8BN3329m95nZpqDaA0Ae8LiZ7TGzp4PnDhAeLtpqZq8SHir6VvCczwF3mVkF4WMK357A/UoYRxo7AFhUrB6CiEyutLGrgLtvBjaPKLs34vGGMzz3eWDdKOWVhM9gkjM4erKTjLQU5uRnxbopIpLgdKXyNPfWyQ4WFGaTkjLacXgRkYmjQJjmjpzs1PEDEZkSCoRpLBRyjpzsZLECQUSmgAJhGjve2k1X3wBLSxUIIjL5FAjTWGV9+AwjBYKITAUFwjRW2dAOwNKSvBi3RESSgQJhGqus7yA3I5XZ+ZmxboqIJAEFwjTl7rxZ386S0lzCk8OKiEwuBcI09e+/8Tt+/UYDy0o1XCQiU0OBMA0NhJw9Vc1cuayY/3jtilg3R0SShAJhGjrZ3kPI4frz57B8lnoIIjI1FAjTUF1bePbw0hmav0hEpo4CYRqqa+sGYJbOLhKRKaRAmIbqWsM9hFkzFAgiMnUUCNPQidbBISMFgohMHQXCNFTX1k1hTjqZaamxboqIJBEFwjRU19bDLB1QFpEppkCYhuraenRAWUSmXFSBYGYbzeyQmVWY2d2jrL/LzA6Y2V4z22pmiyLWDQT3WR6613JQ/j0zOxyx7qKJ2aX49q1fVfJmXbuOH4jIlBvznspmlgo8BLwXqAZeNrOn3f1ARLXdQLm7d5rZXwH3Ax8M1nW5++k+7D/r7k+cffMTS3ffAP/z2YMU5mTw7vNmxbo5IpJkoukhXApUuHulu/cCjwE3RFZw923u3hksbgfKJraZyeFoYyfu8MVNa9l04bxYN0dEkkw0gTAfqIpYrg7KTud24NmI5Swz22lm283sxhF1/z4YZnrQzJJ+jKSyfvD+B7ohjohMvWgCYbS5l33Uima3AuXAAxHFC929HPgQ8BUzWxaUfx5YBbwDKAI+d5pt3hEEys76+voomhu/KhvCd0hbrEAQkRiIJhCqgQURy2VAzchKZrYBuAfY5O49g+XuXhP8rgR+CawPlms9rAf4LuGhqVO4+yPuXu7u5aWlpVHtVLw6XN/BrBmZ5GWOeWhHRGTCRRMILwMrzGyJmWUANwNPR1Yws/XAw4TDoC6ivHBwKMjMSoCrgAPB8tzgtwE3AvvOfXfi2+GGDpaodyAiMTLmV1F37zezO4EtQCrwHXffb2b3ATvd/WnCQ0R5wOPB3b2OuvsmYDXwsJmFCIfPP0ScnfSomZUSHpLaA3xigvct7hxu6OB9a2fHuhkikqSiGptw983A5hFl90Y83nCa5/0OuOA0666NvpmJr6Wzj5MdveohiEjM6ErlaeLwyfAB5SUluiGOiMSGAmGaONwQPuVUPQQRiRUFwjRRWd9BisHCopxYN0VEkpQCYZqobOhgQVEOGWn6JxGR2NCnzzRxuF6nnIpIbCkQpoFQyHUNgojEnAJhGqiob6erb4C182bGuikiksQUCNPA7qNNAKxfWBDjlohIMlMgTAO7jzYzMztds5yKSEwpEKaBPxxtYv3CAoJpP0REYkKBEGOt3X28UdfO+gWFsW6KiCQ5BUKM7a1qwR0uXqTjByISWwqEGNt9tAkzuHCBAkFEYkuBEGN/ONrE8tI88rPSY90UEUlyCoQYcnd2VzVz8UIdPxCR2FMgxFBVYxfNnX2sW6AL0kQk9hQIMbSvpgWAC+YrEEQk9hQIMbTvWAtpKcZ5c2bEuikiItEFgpltNLNDZlZhZnePsv4uMztgZnvNbKuZLYpYN2Bme4KfpyPKl5jZDjN7w8x+bGYZE7NL8ePVYy2snD2DzLTUWDdFRGTsQDCzVOAh4HpgDXCLma0ZUW03UO7u64AngPsj1nW5+0XBz6aI8i8BD7r7CqAJuP0c9iPuuDv7a1o5f35+rJsiIgJE10O4FKhw90p37wUeA26IrODu29y9M1jcDpSdaYMWnqPhWsLhAfDPwI3jaXi8q23pprGjl/N1/EBEpoloAmE+UBWxXB2Unc7twLMRy1lmttPMtpvZ4Id+MdDs7v1RbjPh7DsWPqCsKa9FZLpIi6LOaDOu+agVzW4FyoFrIooXunuNmS0FXjCzV4HWcWzzDuAOgIULF0bR3Piw71gLKQZr5mrISESmh2h6CNXAgojlMqBmZCUz2wDcA2xy957BcnevCX5XAr8E1gMNQIGZDQbSqNsMnveIu5e7e3lpaWkUzY0P+2paWT4rj+wMHVBmqiraAAAMsElEQVQWkekhmkB4GVgRnBWUAdwMPB1ZwczWAw8TDoO6iPJCM8sMHpcAVwEH3N2BbcBNQdWPAU+d687Ek33HWjhfw0UiMo2MGQjBOP+dwBbgIPATd99vZveZ2eBZQw8AecDjI04vXQ3sNLNXCAfAP7j7gWDd54C7zKyC8DGFb0/YXk1zDe091LX1sGaehotEZPqI5hgC7r4Z2Dyi7N6IxxtO87zfARecZl0l4TOYks6bde0ALJ+VF+OWiIi8TVcqx0BlQwcAy0oVCCIyfSgQYqCyvp3MtBTmFWTHuikiIkMUCDFQWd/BkpJcUlN0D2URmT4UCDHwZn07S0tzY90MEZFhFAhTrLc/RFVTl44fiMi0o0CYYq+faGMg5KycrSmvRWR6USBMsd1VzQBctKAgxi0RERlOgTDFXqlqpjg3g7JCnWEkItOLAmGKvVLVzIULCgjPAC4iMn0oEKZQS2cfFfXtXFim4SIRmX4UCFNoy4HjuMO7VpbEuikiIqdQIEyhn79Sw8KiHB1QFpFpSYEwRerbevhtRQObLpyn4wciMi0pEKbI5ldrCTlsumherJsiIjKqqKa/lvHr7O2npy80tPzUnmOsmjNDF6SJyLSlQJgEb5xo4/qv/pr+0PDbRH/2/efFqEUiImNTIEyCZ/bWEnLnC/9uNWnBjKZpqSn8yfr5MW6ZiMjpKRAmwZb9xylfXMTHr14a66aIiEQtqoPKZrbRzA6ZWYWZ3T3K+rvM7ICZ7TWzrWa2aMT6fDM7ZmZfjyj7ZbDNPcHPrHPfndg7crKD14638b41s2PdFBGRcRkzEMwsFXgIuB5YA9xiZmtGVNsNlLv7OuAJ4P4R6/8OeHGUzX/Y3S8KfurG3fpp6Kd/OIYZXH/B3Fg3RURkXKLpIVwKVLh7pbv3Ao8BN0RWcPdt7t4ZLG4HygbXmdklwGzguYlp8vQVCjlP7KrmnctLmK/bY4pInIkmEOYDVRHL1UHZ6dwOPAtgZinA/wY+e5q63w2Gi/6rJcDVWtsrT3KsuYubLikbu7KIyDQTTSCM9kHto5RhZrcC5cADQdEngc3uXjVK9Q+7+wXA1cHPR06zzTvMbKeZ7ayvr4+iubHzk51VzMhK4/1r58S6KSIi4xZNIFQDCyKWy4CakZXMbANwD7DJ3XuC4iuAO83sLeDLwEfN7B8A3P1Y8LsN+BHhoalTuPsj7l7u7uWlpaVR7VQstHb38ey+42y6cB5Z6amxbo6IyLhFc9rpy8AKM1sCHANuBj4UWcHM1gMPAxsjDw67+4cj6txG+MDz3WaWBhS4e4OZpQN/BPzbue5MLG3Zd5ye/hD/XsNFIhKnxgwEd+83szuBLUAq8B13329m9wE73f1pwkNEecDjwaGAo+6+6QybzQS2BGGQSjgMvnVuuxJbm1+tZX5BNus1k6mIxKmoLkxz983A5hFl90Y83hDFNr4HfC943AFcMo52TmstXX38pqKB265crJlMRSRuabbTCfD8gRP0DTgf0LUHIhLHFAgTYHC4SDe+EZF4pkA4B/0DIX6x7zi/fqOe68+fo+EiEYlrmtzuHPxs9zE++8ReQDe+EZH4p0A4B8/uO878gmx+/JeXU1aYE+vmiIicEw0ZnQV350BNK795o4GN589RGIhIQlAP4Sw8s7eW//j/dgPozCIRSRjqIZyFX+w7TkleJj/6D5dxyaLCWDdHRGRCKBDGqbc/xIuv17Nh9SyuXFYS6+aIiEwYBcI4/f5wI+09/Vy3WndEE5HEokAYp62vnSAzLYV3LlfvQEQSiwJhHNydrQfruHJZMdkZmuJaRBKLAmEcKuraOdrYqeEiEUlICoRx+LeD4Vs9XLd6VoxbIiIy8RQI4/DCaydYOy+fuTOzY90UEZEJp0CIUlNHL7uONGm4SEQSlgIhSj/fW0PI4bpVGi4SkcSkQIhCXVs3X95yiEuXFLGubGasmyMiMimiCgQz22hmh8yswszuHmX9XWZ2wMz2mtlWM1s0Yn2+mR0zs69HlF1iZq8G2/w/No1vJnDfzw/Q3Rfif/3pBbrngYgkrDEDwcxSgYeA64E1wC1mtmZEtd1AubuvA54A7h+x/u+AF0eUfQO4A1gR/Gwcd+vHoat34Kye98JrJ3hmby13XrucZaV5E9wqEZHpI5oewqVAhbtXunsv8BhwQ2QFd9/m7p3B4nagbHCdmV0CzAaeiyibC+S7+0vu7sD3gRvPaU/O4FOP/oG//OGucT+vtbuPL/xsHytn5/GJa5ZNQstERKaPaKa/ng9URSxXA5edof7twLMAZpYC/G/gI8B1I7ZZPWKb86Noy1lZPXcGX37udfZWN7OoOJd/3VvL/poW/u6G80lJeXsIqKKujW/96jC1rd3UtXZzuKGD3oEQT3zoSjLSdLhFRBJbNIEw2qC5j1rR7FagHLgmKPoksNndq0aMvY9nm3cQHlpi4cKFUTT3VLdcupCvvVDBpq//luLcDNq6++kdCJFiRk//AB29A9y9cRWfenQ3VU2dLJ+Vx4KiHK5aXsIHLpijKa5FJClEEwjVwIKI5TKgZmQlM9sA3ANc4+49QfEVwNVm9kkgD8gws3bgq0QMK51umwDu/gjwCEB5efmooTGW4rxMPvnu5ew80sih421kpafwrpUl/GD7EbLTUzGDza/W4g4Pf+QS3r92ztn8GRGRuBZNILwMrDCzJcAx4GbgQ5EVzGw98DCw0d3rBsvd/cMRdW4jfOD57mC5zcwuB3YAHwW+dm67cmaf3rACgIb2Hjp6+llYlEN9Ww8zstI53trN4zuryEhL4X1rdOGZiCSnMQPB3fvN7E5gC5AKfMfd95vZfcBOd38aeIBwD+DxYGjoqLtvGmPTfwV8D8gmfMzh2bPei3EoycukJC8TgFn5WQAsKcnlbzaumoo/LyIybVn4JJ/4UF5e7jt37ox1M0RE4oqZ7XL38rHq6dQZEREBFAgiIhJQIIiICKBAEBGRgAJBREQABYKIiAQUCCIiAsTZdQhmVg8cOYunlgANE9yceKbXYzi9HsPp9XhborwWi9y9dKxKcRUIZ8vMdkZzUUay0OsxnF6P4fR6vC3ZXgsNGYmICKBAEBGRQLIEwiOxbsA0o9djOL0ew+n1eFtSvRZJcQxBRETGliw9BBERGUPCB4KZbTSzQ2ZWYWZ3x7o9k83MFpjZNjM7aGb7zezTQXmRmT1vZm8EvwuDcjOz/xO8PnvN7OLY7sHkMLNUM9ttZs8Ey0vMbEfwevzYzDKC8sxguSJYvziW7Z4MZlZgZk+Y2WvB++SKZH5/mNlfB/9X9pnZ/zOzrGR9fyR0IJhZKvAQcD2wBrjFzNbEtlWTrh/4L+6+Grgc+FSwz3cDW919BbA1WIbwa7Mi+LkD+MbUN3lKfBo4GLH8JeDB4PVoAm4Pym8Hmtx9OfBgUC/RfBX4hbuvAi4k/Lok5fvDzOYD/4nw3RzPJ3wTsJtJ1veHuyfsD+F7Om+JWP488PlYt2uKX4OngPcCh4C5Qdlc4FDw+GHgloj6Q/US5YfwPbu3AtcCzwBG+GKjtJHvE8J3BrwieJwW1LNY78MEvhb5wOGR+5Ss7w9gPlAFFAX/3s8A70/W90dC9xB4+x97UHVQlhSC7ux6wvetnu3utQDB71lBtWR4jb4C/A0QCpaLgWZ37w+WI/d56PUI1rcE9RPFUqAe+G4whPZPZpZLkr4/3P0Y8GXgKFBL+N97F0n6/kj0QLBRypLitCozywP+BfjP7t56pqqjlCXMa2RmfwTUufuuyOJRqnoU6xJBGnAx8A13Xw908Pbw0GgS+vUIjpXcACwB5gG5hIfJRkqK90eiB0I1sCBiuQyoiVFbpoyZpRMOg0fd/adB8QkzmxusnwvUBeWJ/hpdBWwys7eAxwgPG30FKDCztKBO5D4PvR7B+plA41Q2eJJVA9XuviNYfoJwQCTr+2MDcNjd6929D/gpcCVJ+v5I9EB4GVgRnDGQQfhg0dMxbtOkMjMDvg0cdPd/jFj1NPCx4PHHCB9bGCz/aHA2yeVAy+DQQSJw98+7e5m7Lyb87/+Cu38Y2AbcFFQb+XoMvk43BfUT5hugux8HqszsvKDoOuAASfr+IDxUdLmZ5QT/dwZfj6R8f8T8IMZk/wAfAF4H3gTuiXV7pmB/30m4C7sX2BP8fIDwOOdW4I3gd1FQ3wififUm8Crhsy1ivh+T9Nq8G3gmeLwU+D1QATwOZAblWcFyRbB+aazbPQmvw0XAzuA98iRQmMzvD+C/A68B+4AfAJnJ+v7QlcoiIgIk/pCRiIhESYEgIiKAAkFERAIKBBERARQIIiISUCCIiAigQBARkYACQUREAPj/ct2h8V7awHMAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a17dbfdd8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.gini.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([3040526.00591377, 292346.41324499, 1834911.75066471,\n",
|
|
" 191306.33867813, 2676437.18439034, 606503.89825269,\n",
|
|
" 1913536.35613044, 202520.81288736, 1757170.10210485,\n",
|
|
" 942629.03361786, 2146360.00963785, 1164235.6179812 ,\n",
|
|
" 525138.6253403 , 405352.79043558, 3688078.31557373,\n",
|
|
" 358923.57068847, 837417.24115256, 6982141.0341338 ,\n",
|
|
" 2815214.29691066, 268158.49992348, 3070289.7371441 ,\n",
|
|
" 4588261.10103541, 38629.38518291, 1989622.13358692,\n",
|
|
" 133316.07055125, 1624043.9233252 , 6104295.96660485,\n",
|
|
" 123352.79808506, 1863959.91612605, 476647.34132436,\n",
|
|
" 1954128.81816917, 1493642.7972997 , 1764743.65166815,\n",
|
|
" 4801389.36574534, 329201.81578263, 220463.52809205,\n",
|
|
" 873172.3654704 , 294140.49567193, 2819621.45423573,\n",
|
|
" 2377205.52335175, 2330992.21389353, 1833572.86993064,\n",
|
|
" 269770.50349571, 956149.43352349, 2280230.16892458,\n",
|
|
" 2287475.15948303, 2315509.59142972, 3441584.33213069,\n",
|
|
" 1621235.29017322, 137350.44797002, 4529274.79296003,\n",
|
|
" 911241.78478785, 1933913.95716358, 6446251.31915593,\n",
|
|
" 1563748.01882698, 747128.79516262, 3167601.23697947,\n",
|
|
" 774196.32725153, 2600595.58315978, 58767.42640397,\n",
|
|
" 3235698.6480154 , 3481873.20465884, 1646080.76455036,\n",
|
|
" 99418.98972525, 155650.92989622, 547681.62750429,\n",
|
|
" 486105.25147687, 184088.41630462, 63219.5389129 ,\n",
|
|
" 1883174.12646815, 717847.00928362, 113288.01426288,\n",
|
|
" 896291.58400347, 332232.22182739, 1246728.95326845,\n",
|
|
" 2270429.2914982 , 21583.43276542, 2058065.66158954,\n",
|
|
" 3538017.73082683, 3304775.72676737, 1662774.34593578,\n",
|
|
" 3365680.51471341, 2026626.47389789, 3155192.1578157 ,\n",
|
|
" 275316.74165456, 854340.6880745 , 5720336.18018235,\n",
|
|
" 220489.8690786 , 1111687.17879573, 3248947.35446268,\n",
|
|
" 2449464.12575332, 447118.16351515, 528590.24121039,\n",
|
|
" 0. , 323157.92672094, 3451359.98493326,\n",
|
|
" 213378.23296341, 2170611.87686151, 446870.50346133,\n",
|
|
" 1824823.61414407])"
|
|
]
|
|
},
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.tokens.sum()"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|