679 lines
394 KiB
Plaintext
679 lines
394 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Three Sided Market Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/jpeg": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from IPython.display import Image\n",
|
|
"Image(filename='threesidedmarket.jpeg')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from decimal import Decimal\n",
|
|
"from datetime import timedelta\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"import pandas as pd\n",
|
|
"from tabulate import tabulate\n",
|
|
"\n",
|
|
"from __future__ import print_function\n",
|
|
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
|
|
"import ipywidgets as widgets\n",
|
|
"from IPython.display import clear_output\n",
|
|
"\n",
|
|
"from SimCAD.configuration import Configuration\n",
|
|
"from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
|
|
" ep_time_step\n",
|
|
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sim_config = {\n",
|
|
" 'N': 1,\n",
|
|
" 'T': range(50)\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Environmental Processes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"eta = 1\n",
|
|
"def tx_volume_generator(step, sL, s, _input):\n",
|
|
" y = 'tx_volume'\n",
|
|
" x = eta*s['tx_volume']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"alpha = .999\n",
|
|
"beta = 1.0\n",
|
|
"def cost_of_production_generator(step, sL, s, _input):\n",
|
|
" y = 'cost_of_production'\n",
|
|
" x = alpha*s['cost_of_production']+beta\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#log quadratic overhead model; parameters\n",
|
|
"a = 1.0\n",
|
|
"b = 1.0\n",
|
|
"c = 1.0\n",
|
|
"d = 0\n",
|
|
"def overhead_cost_generator(step, sL, s, _input):\n",
|
|
" #unit fiat\n",
|
|
" y = 'overhead_costs'\n",
|
|
" q = a+b*s['tx_volume']+c*s['volume_production']+d*s['tx_volume']*s['volume_production']\n",
|
|
" x = np.log(q)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#State Variables"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"seed = {}\n",
|
|
"env_processes = {}\n",
|
|
"initial_condition = {\n",
|
|
" 'fiat_reserve': float(2000),#unit: fiat\n",
|
|
" 'token_reserve': float(2000),#unit: tok\n",
|
|
" 'token_supply': float(5000),#unit: tok\n",
|
|
" 'tx_volume': float(2000), #unit: fiat\n",
|
|
" 'txo_fiat': float(1000), #unit: fiat\n",
|
|
" 'txo_token': float(1000), #unit: tok\n",
|
|
" 'txi_fiat': float(1000), #unit: fiat\n",
|
|
" 'txi_token': float(1000), #unit: tok\n",
|
|
" 'conversion_rate': float(1), #unit: tok/fiat\n",
|
|
" 'cost_of_production': float(25), #unit: fiat/labor \n",
|
|
" 'volume_of_production': float(100), #unit: labor\n",
|
|
" 'timestamp': '2019-01-01 00:00:00'\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#exogenous states\n",
|
|
"ts_format = '%Y-%m-%d %H:%M:%S'\n",
|
|
"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
|
|
"def time_model(step, sL, s, _input):\n",
|
|
" y = 'timestamp'\n",
|
|
" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"exogenous_states = exo_update_per_ts(\n",
|
|
" {\n",
|
|
" 'timestamp': time_model\n",
|
|
" }\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Behavoirs (two types: controlled and uncontrolled)\n",
|
|
"#user behavoir is uncontrolled (estimated or modeled),\n",
|
|
"#governance policies are controlled (enforced)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ system policy for token/fiat unit of value conversion\n",
|
|
"def conversion_policy(step, sL, s):\n",
|
|
" ncr = 1 #placeholder logic: fixed value\n",
|
|
" return {'new_conversion_rate': ncr}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ determines the conditions or schedule of new tokens minted\n",
|
|
"\n",
|
|
"final_supply = 10000.0 #unit: tokens\n",
|
|
"release_rate = .01 #percent of remaining\n",
|
|
"\n",
|
|
"def mint_policy(step, sL, s):\n",
|
|
" mint = s['token_supply']\n",
|
|
" return {'new_conversion_rate': ncr}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the provider consumer\n",
|
|
"\n",
|
|
"def consumer_choice(step, sL, s):\n",
|
|
" #fiat paid by consumers\n",
|
|
" #note: balance of consumption vol covered in tokens (computed later)\n",
|
|
" txi_fiat= \n",
|
|
" return {'txi_fiat': txi_fiat}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the provider role\n",
|
|
"\n",
|
|
"def provider_choice(step, sL, s):\n",
|
|
" #fiat claimed by providers\n",
|
|
" #note: balance of provided vol covered in tokens (computed later)\n",
|
|
" txo_fiat = \n",
|
|
" return {'txo_fiat': txo_fiat}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the producer role\n",
|
|
"\n",
|
|
"roi_threshold = 1.1 #estimatable parameter\n",
|
|
"\n",
|
|
"def producer_choice(step, sL, s):\n",
|
|
" #ROI heuristic\n",
|
|
" # add or remove resources until roi_threshold hit\n",
|
|
" return {'delta_labor': delta_labor}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ system policy for compensating producers\n",
|
|
"#consider transaction volume, labor committed and token reserve and supply to determine payout\n",
|
|
"\n",
|
|
"def producer_compensation_policy(step, sL, s):\n",
|
|
" tokens_paid = \n",
|
|
" return {'tokens_paid': tokens_paid}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#used to trigger mechanisms with no explicit input\n",
|
|
"def dummy_behavior(step, sL, s):\n",
|
|
" return {'value': 0}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Mechanisms"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_conversion_rate(step, sL, s):\n",
|
|
" y = 'conversion_rate'\n",
|
|
" x = _input['new_conversion_rate']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#minting process mints into the reserve\n",
|
|
"def mint1(step, sL, s):\n",
|
|
" y = 'token_supply'\n",
|
|
" x = s['token_supply'] + _input['mint']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def mint2(step, sL, s):\n",
|
|
" y = 'token_reserve'\n",
|
|
" x = s['token_reserve'] + _input['mint']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def commit_delta_production(step, sL, s, _input):\n",
|
|
" y = 'volume_of_production'\n",
|
|
" x = s['volume_of_production']+_input['delta_production']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def compensate_production(step, sL, s, _input):\n",
|
|
" y = 'token_reserve'\n",
|
|
" x = s['token_reserve']-_input['tokens_paid']\n",
|
|
" return (y, x)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"conversion_fee = .01\n",
|
|
"\n",
|
|
"def capture_consumer_payments1(step, sL, s, _input):\n",
|
|
" #fiat inbound\n",
|
|
" y = 'fiat_reserve'\n",
|
|
" x = s['fiat_reserve']+_input['txi_fiat']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def capture_consumer_payments2(step, sL, s, _input):\n",
|
|
" #tokens inbound\n",
|
|
" y = 'token_reserve'\n",
|
|
" fiat_eq = s['tx_volume']-_input['txi_fiat']\n",
|
|
" x = s['token_reserve']+s['conversion_rate']*fiat_eq*(1.0+conversion_fee)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"platform_fee = 0.03\n",
|
|
"\n",
|
|
"def compensate_providers1(step, sL, s, _input):\n",
|
|
" #fiat outbound\n",
|
|
" y = 'fiat_reserve'\n",
|
|
" x = s['fiat_reserve']-_input['txo_fiat']*(1.0-platform_fee)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def compensate_providers2(step, sL, s, _input):\n",
|
|
" #tokens outbound\n",
|
|
" y = 'token_reserve'\n",
|
|
" fiat_eq = s['tx_volume']-_input['txo_fiat']\n",
|
|
" x = s['token_reserve']-s['conversion_rate']*fiat_eq*(1.0-platform_fee-conversion_fee)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#~~~~ break point (copy pasted started code below)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Behaviors choose actions depending on states\n",
|
|
"# 1) increase contact rate, beta (create incentive to spread)\n",
|
|
"# 2) decrease recover rate, gamma (improve stickiness)\n",
|
|
"\n",
|
|
"def add_incentive(step, sL, s):\n",
|
|
" incentive_allocation_as_share_of_budget = 0.02\n",
|
|
" target_beta = .5\n",
|
|
" potential_spend = s['Budget']*incentive_allocation_as_share_of_budget\n",
|
|
" \n",
|
|
" potential_delta = target_beta-s['beta']\n",
|
|
" \n",
|
|
" cost_of_potential_delta = potential_delta * incentive_cost * s['Ps']\n",
|
|
" if cost_of_potential_delta <= potential_spend:\n",
|
|
" delta = potential_delta\n",
|
|
" else:\n",
|
|
" delta = potential_spend/(incentive_cost * s['Ps'])\n",
|
|
"\n",
|
|
" return {'delta': delta}\n",
|
|
"\n",
|
|
"def add_stickiness(step, sL, s):\n",
|
|
" stickiness_allocation_as_share_of_budget = 0.08\n",
|
|
" target_gamma = .1\n",
|
|
" potential_spend = s['Budget']*stickiness_allocation_as_share_of_budget\n",
|
|
" \n",
|
|
" potential_delta = s['gamma']-target_gamma\n",
|
|
" \n",
|
|
" cost_of_potential_delta = potential_delta * stickiness_cost * s['Pi']\n",
|
|
" if cost_of_potential_delta <= potential_spend:\n",
|
|
" delta = potential_delta\n",
|
|
" else:\n",
|
|
" delta = potential_spend/(stickiness_cost * s['Pi'])\n",
|
|
" \n",
|
|
" \n",
|
|
" return {'delta': delta}\n",
|
|
"\n",
|
|
"# def add_stickiness(step, sL, s):\n",
|
|
"# delta = 0.0\n",
|
|
"# potential_delta = s['gamma'] * delta_gamma\n",
|
|
"# if (s['Pr'] > 2 * s['Pi']) and s['Budget'] > \\\n",
|
|
"# abs(potential_delta * stickiness_cost * s['Pi']):\n",
|
|
"# delta = potential_delta\n",
|
|
"# return {'delta': delta}\n",
|
|
"\n",
|
|
"# def add_incentive(step, sL, s):\n",
|
|
"# delta = 0.0\n",
|
|
"# potential_delta = s['beta'] * delta_beta\n",
|
|
"# if (s['Ps'] > 3 * s['Pi']) and s['Budget'] > \\\n",
|
|
"# abs(potential_delta * incentive_cost * s['Ps']):\n",
|
|
"# delta = potential_delta\n",
|
|
"# return {'delta': delta}\n",
|
|
"\n",
|
|
"def dummy_behavior(step, sL, s):\n",
|
|
" return {'delta': 0.0}\n",
|
|
"\n",
|
|
"# Mechanisms incur cost to modify beta or gamma\n",
|
|
"# 1) incur cost to create incentive to spread\n",
|
|
"# 2) incur cost to improve stickiness\n",
|
|
"\n",
|
|
"def incur_incentive_cost(step, sL, s, _input):\n",
|
|
" y = 'Budget'\n",
|
|
" x = s['Budget'] - abs(_input['delta'] * s['Ps'] * incentive_cost)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def incur_stickiness_cost(step, sL, s, _input):\n",
|
|
" y = 'Budget'\n",
|
|
" x = s['Budget'] - abs(_input['delta'] * s['Pi'] * stickiness_cost)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def update_beta(step, sL, s, _input):\n",
|
|
" y = 'beta'\n",
|
|
" x = s['beta'] + _input['delta']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def update_gamma(step, sL, s, _input):\n",
|
|
" y = 'gamma'\n",
|
|
" x = s['gamma'] - _input['delta']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def S_model(step, sL, s, _input):\n",
|
|
" y = 'Ps'\n",
|
|
" x = s['Ps'] - s['beta'] * s['Ps']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def I_model(step, sL, s, _input):\n",
|
|
" y = 'Pi'\n",
|
|
" x = s['Pi'] + s['beta'] * s['Ps'] - s['gamma'] * s['Pi']\n",
|
|
" return (y, x)\n",
|
|
" \n",
|
|
"def R_model(step, sL, s, _input):\n",
|
|
" y = 'Pr'\n",
|
|
" x = s['Pr'] + s['gamma'] * s['Pi']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def collect_subscription(step, sL, s, _input):\n",
|
|
" y = 'Budget'\n",
|
|
" x = s['Budget'] + s['Pi'] * epsilon * subscription_fee\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def incentive_degrade(step, sL, s, _input):\n",
|
|
" y = 'beta'\n",
|
|
" x = s['beta']*(1-incentive_degredation_rate)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def stickiness_degrade(step, sL, s, _input):\n",
|
|
" y = 'gamma'\n",
|
|
" x = (s['gamma']+stickiness_degredation_rate)/(1+stickiness_degredation_rate)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"mechanisms = {\n",
|
|
" 'spread': {\n",
|
|
" 'behaviors': {\n",
|
|
" 'dummy': dummy_behavior\n",
|
|
" },\n",
|
|
" 'states': {\n",
|
|
" 'Ps': S_model,\n",
|
|
" 'Pi': I_model,\n",
|
|
" 'Pr': R_model,\n",
|
|
" 'Budget': collect_subscription,\n",
|
|
" 'beta': incentive_degrade,\n",
|
|
" 'gamma': stickiness_degrade \n",
|
|
" } \n",
|
|
" },\n",
|
|
" 'create_incentive': {\n",
|
|
" 'behaviors': {\n",
|
|
" 'action': add_incentive,\n",
|
|
" },\n",
|
|
" 'states': {\n",
|
|
" 'beta': update_beta,\n",
|
|
" 'Budget': incur_incentive_cost,\n",
|
|
" }\n",
|
|
" },\n",
|
|
" 'improve_stickiness': {\n",
|
|
" 'behaviors': {\n",
|
|
" 'action': add_stickiness\n",
|
|
" },\n",
|
|
" 'states': {\n",
|
|
" 'gamma': update_gamma,\n",
|
|
" 'Budget': incur_stickiness_cost,\n",
|
|
" }\n",
|
|
" }\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def widget_handler(beta=float(0.05), gamma=float(0.20),\n",
|
|
" subscription_fee=float(1.0), \n",
|
|
" incentive_cost=float(10.0), \n",
|
|
" stickiness_cost=float(5.0)):\n",
|
|
" initial_condition['beta'] = beta\n",
|
|
" initial_condition['gamma'] = gamma\n",
|
|
" subscription_fee = subscription_fee\n",
|
|
" incentive_cost = incentive_cost\n",
|
|
" stickiness_cost = stickiness_cost\n",
|
|
" \n",
|
|
" config = Configuration(\n",
|
|
" sim_config=sim_config,\n",
|
|
" state_dict=initial_condition,\n",
|
|
" seed=seed,\n",
|
|
" exogenous_states=exogenous_states,\n",
|
|
" env_processes=env_processes,\n",
|
|
" mechanisms=mechanisms)\n",
|
|
"\n",
|
|
" exec_mode = ExecutionMode()\n",
|
|
" exec_context = ExecutionContext(exec_mode.single_proc)\n",
|
|
" executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
|
|
" raw_result, tensor = executor.main()\n",
|
|
" df = pd.DataFrame(raw_result)\n",
|
|
" df['timestamp'] = pd.to_datetime(df['timestamp'], format=ts_format)\n",
|
|
" \n",
|
|
" fig = plt.figure(figsize=(7, 14))\n",
|
|
" \n",
|
|
" sir = fig.add_subplot(3, 1, 1)\n",
|
|
" sir.plot('timestamp', 'Ps', data=df, marker='', color='C0', linewidth=2)\n",
|
|
" sir.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
|
|
" sir.plot('timestamp', 'Pr', data=df, marker='', color='green', linewidth=2)\n",
|
|
" sir.legend()\n",
|
|
" \n",
|
|
" beta_gamma = fig.add_subplot(3, 1, 2)\n",
|
|
" beta_gamma.plot('timestamp', 'beta', data=df, marker='', color='C0', linewidth=2)\n",
|
|
" beta_gamma.plot('timestamp', 'gamma', data=df, marker='', color='orange', linewidth=2)\n",
|
|
" beta_gamma.legend()\n",
|
|
" \n",
|
|
" budget_pi = fig.add_subplot(3, 1, 3)\n",
|
|
" budget_pi.plot('timestamp', 'Budget', data=df, marker='', color='C0', linewidth=2)\n",
|
|
" budget_pi.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
|
|
" budget_pi.legend()\n",
|
|
" \n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
"sliders = interact_manual(widget_handler, \n",
|
|
" beta=(0, 1, 0.01),\n",
|
|
" gamma=(0, 1, 0.01),\n",
|
|
" subscription_fee=(0, 10, 0.1),\n",
|
|
" incentive_cost=(0, 20, 0.5),\n",
|
|
" stickiness_cost=(0, 20, 0.5)\n",
|
|
" )\n",
|
|
"sliders"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"config = Configuration(\n",
|
|
" sim_config=sim_config,\n",
|
|
" state_dict=initial_condition,\n",
|
|
" seed=seed,\n",
|
|
" exogenous_states=exogenous_states,\n",
|
|
" env_processes=env_processes,\n",
|
|
" mechanisms=mechanisms)\n",
|
|
"\n",
|
|
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
|
"exec_mode = ExecutionMode()\n",
|
|
"exec_context = ExecutionContext(exec_mode.single_proc)\n",
|
|
"executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
|
|
"raw_result, tensor = executor.main()\n",
|
|
"df = pd.DataFrame(raw_result)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.plot('timestamp', ['Ps','Pi', 'Pr'])\n",
|
|
"df.plot('timestamp', ['beta', 'gamma'])\n",
|
|
"df.plot('timestamp', ['Budget', 'Pi'])\n",
|
|
"df[['Ps','Pi', 'Pr']].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.beta.diff().describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.beta.diff().plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|