pre refactor upload
This commit is contained in:
Joshua E. Jodesty
parent
c55e433920
commit
715e6f9a74
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@ -1,3 +1,4 @@
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from pprint import pprint
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from typing import Any, Callable, Dict, List, Tuple
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from pathos.pools import ThreadPool as TPool
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from copy import deepcopy
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@ -113,7 +114,9 @@ class Executor:
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) -> List[Dict[str, Any]]:
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last_in_obj: Dict[str, Any] = deepcopy(sL[-1])
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_input: Dict[str, Any] = self.policy_update_exception(self.get_policy_input(sweep_dict, sub_step, sH, last_in_obj, policy_funcs))
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_input: Dict[str, Any] = self.policy_update_exception(
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self.get_policy_input(sweep_dict, sub_step, sH, last_in_obj, policy_funcs)
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)
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# ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
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@ -211,6 +214,9 @@ class Executor:
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time_step += 1
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pprint(states_list)
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print()
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return states_list
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# state_update_pipeline
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@ -260,7 +266,9 @@ class Executor:
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states_list_copy: List[Dict[str, Any]] = list(generate_init_sys_metrics(deepcopy(states_list)))
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first_timestep_per_run: List[Dict[str, Any]] = self.run_pipeline(sweep_dict, states_list_copy, configs, env_processes, time_seq, run)
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first_timestep_per_run: List[Dict[str, Any]] = self.run_pipeline(
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sweep_dict, states_list_copy, configs, env_processes, time_seq, run
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)
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del states_list_copy
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return first_timestep_per_run
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@ -271,5 +279,4 @@ class Executor:
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list(range(runs))
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)
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)
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return pipe_run
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@ -145,7 +145,8 @@ partial_state_update_block = [
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sim_config = config_sim(
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{
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"N": 2,
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"N": 1,
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# "N": 5,
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"T": range(5),
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}
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)
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@ -0,0 +1,763 @@
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{
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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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"### Exogenous Example\n",
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"## Authored by BlockScience, MV Barlin\n",
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"### Updated July-10-2019 \n"
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]
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},
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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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"Key assumptions and space:\n",
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"1. Implementation of System Model in cell 2\n",
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"2. Timestep = day\n",
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"3. Launch simulation, without intervention from changing governance policies"
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]
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},
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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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"#### Library Imports"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Image\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import math\n",
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"#from tabulate import tabulate\n",
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"from scipy import stats\n",
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"sns.set_style('whitegrid')\n",
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"from decimal import Decimal\n",
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"from datetime import timedelta\n",
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"\n",
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"%matplotlib inline"
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]
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},
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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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"## cadCAD Setup\n",
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"#### ----------------cadCAD LIBRARY IMPORTS------------------------"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
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"#from simulations.validation import sweep_config\n",
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"from cadCAD import configs\n",
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"from cadCAD.configuration import append_configs\n",
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"from cadCAD.configuration.utils import proc_trigger, ep_time_step, config_sim"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#from cadCAD.configuration.utils.parameterSweep import config_sim"
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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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"source": [
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"from typing import Dict, List"
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]
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},
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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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"#### ----------------Random State Seed-----------------------------"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"seed = {\n",
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"# 'z': np.random.RandomState(1)\n",
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"}"
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]
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},
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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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"#### Timestamp"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"ts_format = '%Y-%m-%d %H:%M:%S'\n",
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"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
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"def set_time(_g, step, sL, s, _input):\n",
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" y = 'timestamp'\n",
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" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
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" return (y, x)"
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]
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},
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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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"# ASSUMED PARAMETERS"
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]
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},
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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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"### PRICE LIST"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# dai_xns_conversion = 1.0 # Assumed for static conversion 'PUBLISHED PRICE LIST' DEPRECATED"
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]
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},
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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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"#### Initial Condition State Variables"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"del_stake_pct = 2\n",
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"\n",
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"starting_xns = float(10**10) # initial supply of xns tokens\n",
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"starting_broker_xns = float(1 * 10**8) # inital holding of xns token by broker app\n",
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"starting_broker_fiat = float(1 * 10**5) # inital holding of xns token by broker app\n",
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"starting_broker_stable = float(1 * 10**6) # inital holding of stable token by broker app\n",
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"starting_deposit_acct = float(100) # inital deposit locked for first month of resources TBD: make function of resource*price\n",
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"starting_entrance = float(1 * 10**4) # TBD: make function of entrance fee % * cost * # of initial apps\n",
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"starting_app_usage = float(10) # initial fees from app usage \n",
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"starting_platform = float(100) # initial platform fees \n",
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"starting_resource_fees = float(10) # initial resource fees usage paid by apps \n",
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"starting_app_subsidy = float(0.25* 10**9) # initial application subsidy pool\n",
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"starting_stake = float(4 * 10**7)\n",
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"starting_stake_pool = starting_stake + ((3*10**7)*(del_stake_pct)) # initial staked pool + ((3*10**7)*(del_stake_pct))\n",
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"\n",
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"#starting_block_reward = float(0) # initial block reward MOVED ABOVE TO POLICY\n",
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"starting_capacity_subsidy = float(7.5 * 10**7) # initial capacity subsidy pool\n",
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"starting_delegate_holdings = 0.15 * starting_xns\n"
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]
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},
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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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"#### Initial Condition Composite State Variables"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# subsidy limit is 30% of the 10B supply\n",
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"starting_treasury = float(5.5 * 10**9) \n",
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"starting_app_income = float(0) # initial income to application\n",
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"starting_resource_income = float(0) # initial income to application\n",
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"starting_delegate_income = float(0) # initial income to delegate"
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]
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},
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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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"#### Initial Condition Exogoneous State Variables "
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"starting_xns_fiat = float(0.01) # initial xns per fiat signal\n",
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"starting_fiat_ext = float(1) # initial xns per fiat signal\n",
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"starting_stable_ext = float(1) # initial stable signal"
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]
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},
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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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"### Exogenous Price Updates"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def delta_price(mean,sd):\n",
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" '''Returns normal random variable generated by first two central moments of price change of input ticker'''\n",
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" rv = np.random.normal(mean, sd)\n",
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" return rv"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def xns_ext_update(_g, step, sL, s, _input):\n",
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" key = 'XNS_fiat_external'\n",
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" \n",
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" value = s['XNS_fiat_external'] * (1 + delta_price(0.000000, 0.005))\n",
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" \n",
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" return key, value"
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]
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},
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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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"From Currency Analysis of DAI-USD pair \n",
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"May-09-2018 through June-10-2019 \n",
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"Datasource: BitFinex \n",
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"Analysis of daily return percentage performed by BlockScience"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"DAI_mean = 0.0000719\n",
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"DAI_sd = 0.006716"
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]
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},
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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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"The daily return is computed as: \n",
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"$$ r = \\frac{Price_n - Price_{n-1}}{Price_{n-1}} $$ \n",
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"Thus, the modelled current price can be as: \n",
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"$$ Price_n = Price_{n-1} * r + Price_{n-1} $$"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def stable_update(_g, step, sL, s, _input):\n",
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" key = 'stable_external'\n",
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" \n",
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" value = s['stable_external'] * (1 + delta_price(DAI_mean, DAI_sd))\n",
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" return key, value\n"
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]
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},
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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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"#### Assumed Parameters"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"apps_deployed = 1 # Make part of test- application deployment model\n",
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"\n",
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"starting_deposit_acct = float(100) # inital deposit locked for first month of resources TBD: make function of resource*price\n",
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"\n",
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"app_resource_fee_constant = 10**1 # in STABLE, assumed per day per total nodes \n",
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"platform_fee_constant = 10 # in XNS\n",
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"# ^^^^^^^^^^^^ MAKE A PERCENTAGE OR FLAT FEE as PART of TESTING"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1000"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"alpha = 100 # Fee Rate\n",
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"beta = 0.10 # FIXED Too high because multiplied by constant and resource fees\n",
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"app_platform = alpha * platform_fee_constant\n",
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"app_platform"
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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": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10.0"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"beta_out =beta*100\n",
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"beta_out"
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.15"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"starting_capacity_subsidy / (5 * 10**7) / 10"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"weight = 0.95 # 0.95 internal weight 5% friction from external markets\n",
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"\n",
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"def xns_int_update(_g, step, sL, s, _input):\n",
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" key = 'XNS_fiat_internal'\n",
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"\n",
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" internal = s['XNS_fiat_internal'] * weight\n",
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" external = s['XNS_fiat_external'] * (1 - weight)\n",
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" value = internal + external\n",
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" \n",
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" return key, value"
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]
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},
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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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"#### CONFIGURATION DICTIONARY"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"time_step_count = 3652 # days = 10 years\n",
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"run_count = 1"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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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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"#### Genesis States"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"#----------STATE VARIABLE Genesis DICTIONARY---------------------------\n",
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"genesis_states = {\n",
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" 'XNS_fiat_external' : starting_xns_fiat,\n",
|
||||
" 'XNS_fiat_internal' : starting_xns_fiat,\n",
|
||||
" # 'fiat_external' : starting_fiat_ext,\n",
|
||||
" 'stable_external' : starting_stable_ext,\n",
|
||||
" 'timestamp': '2018-10-01 15:16:24', #es5\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#--------------EXOGENOUS STATE MECHANISM DICTIONARY--------------------\n",
|
||||
"exogenous_states = {\n",
|
||||
" 'XNS_fiat_external' : xns_ext_update,\n",
|
||||
"# 'fiat_external' : starting_fiat_ext,\n",
|
||||
" 'stable_external' : stable_update,\n",
|
||||
" \"timestamp\": set_time,\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"#--------------ENVIRONMENTAL PROCESS DICTIONARY------------------------\n",
|
||||
"env_processes = {\n",
|
||||
"# \"Poisson\": env_proc_id\n",
|
||||
"}\n",
|
||||
"#----------------------SIMULATION RUN SETUP----------------------------\n",
|
||||
"sim_config = config_sim(\n",
|
||||
" {\n",
|
||||
" \"N\": run_count,\n",
|
||||
" \"T\": range(time_step_count)\n",
|
||||
"# \"M\": g # for parameter sweep\n",
|
||||
"}\n",
|
||||
")\n",
|
||||
"#----------------------MECHANISM AND BEHAVIOR DICTIONARY---------------\n",
|
||||
"partial_state_update_block = {\n",
|
||||
" \"price\": { \n",
|
||||
" \"policies\": { \n",
|
||||
" },\n",
|
||||
" \"variables\": {\n",
|
||||
" 'XNS_fiat_internal' : xns_int_update\n",
|
||||
"# 'app_income' : app_earn,\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"append_configs(\n",
|
||||
" sim_configs=sim_config,\n",
|
||||
" initial_state=genesis_states,\n",
|
||||
" seeds=seed,\n",
|
||||
" raw_exogenous_states= exogenous_states,\n",
|
||||
" env_processes=env_processes,\n",
|
||||
" partial_state_update_blocks=partial_state_update_block\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Running cadCAD"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Simulation Execution: Single Configuration\n",
|
||||
"\n",
|
||||
"single_proc: [<cadCAD.configuration.Configuration object at 0x0000024B3B37AF60>]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\mbarl\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\cadCAD\\utils\\__init__.py:89: FutureWarning: The use of a dictionary to describe Partial State Update Blocks will be deprecated. Use a list instead.\n",
|
||||
" FutureWarning)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exec_mode = ExecutionMode()\n",
|
||||
"\n",
|
||||
"print(\"Simulation Execution: Single Configuration\")\n",
|
||||
"print()\n",
|
||||
"first_config = configs # only contains config1\n",
|
||||
"single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n",
|
||||
"run1 = Executor(exec_context=single_proc_ctx, configs=first_config)\n",
|
||||
"run1_raw_result, tensor_field = run1.main()\n",
|
||||
"result = pd.DataFrame(run1_raw_result)\n",
|
||||
"# print()\n",
|
||||
"# print(\"Tensor Field: config1\")\n",
|
||||
"# print(tabulate(tensor_field, headers='keys', tablefmt='psql'))\n",
|
||||
"# print(\"Output:\")\n",
|
||||
"# print(tabulate(result, headers='keys', tablefmt='psql'))\n",
|
||||
"# print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"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>XNS_fiat_external</th>\n",
|
||||
" <th>XNS_fiat_internal</th>\n",
|
||||
" <th>run</th>\n",
|
||||
" <th>stable_external</th>\n",
|
||||
" <th>substep</th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th>timestep</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.010000</td>\n",
|
||||
" <td>0.010000</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2018-10-01 15:16:24</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.009944</td>\n",
|
||||
" <td>0.010000</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.000172</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:25</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0.009889</td>\n",
|
||||
" <td>0.009997</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.003516</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:26</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.009848</td>\n",
|
||||
" <td>0.009992</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.990655</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:27</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.009814</td>\n",
|
||||
" <td>0.009985</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.001346</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:28</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>0.009798</td>\n",
|
||||
" <td>0.009976</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.002495</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:29</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>0.009706</td>\n",
|
||||
" <td>0.009967</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.994911</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:30</td>\n",
|
||||
" <td>6</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>0.009625</td>\n",
|
||||
" <td>0.009954</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.998919</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:31</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>0.009632</td>\n",
|
||||
" <td>0.009938</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.995047</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:32</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.009648</td>\n",
|
||||
" <td>0.009922</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.980786</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2018-10-01 15:16:33</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" XNS_fiat_external XNS_fiat_internal run stable_external substep \\\n",
|
||||
"0 0.010000 0.010000 1 1.000000 0 \n",
|
||||
"1 0.009944 0.010000 1 1.000172 1 \n",
|
||||
"2 0.009889 0.009997 1 1.003516 1 \n",
|
||||
"3 0.009848 0.009992 1 0.990655 1 \n",
|
||||
"4 0.009814 0.009985 1 1.001346 1 \n",
|
||||
"5 0.009798 0.009976 1 1.002495 1 \n",
|
||||
"6 0.009706 0.009967 1 0.994911 1 \n",
|
||||
"7 0.009625 0.009954 1 0.998919 1 \n",
|
||||
"8 0.009632 0.009938 1 0.995047 1 \n",
|
||||
"9 0.009648 0.009922 1 0.980786 1 \n",
|
||||
"\n",
|
||||
" timestamp timestep \n",
|
||||
"0 2018-10-01 15:16:24 0 \n",
|
||||
"1 2018-10-01 15:16:25 1 \n",
|
||||
"2 2018-10-01 15:16:26 2 \n",
|
||||
"3 2018-10-01 15:16:27 3 \n",
|
||||
"4 2018-10-01 15:16:28 4 \n",
|
||||
"5 2018-10-01 15:16:29 5 \n",
|
||||
"6 2018-10-01 15:16:30 6 \n",
|
||||
"7 2018-10-01 15:16:31 7 \n",
|
||||
"8 2018-10-01 15:16:32 8 \n",
|
||||
"9 2018-10-01 15:16:33 9 "
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue