def test_simulation_consistency(self):
"""
Copied from NB1-PoolExploration-0x8b6
"""
# Spot price reference must be a symbol of a token in the pool in ALL_CAPS, you can ignore the spot price parameter for the simulations in this notebook.
parameters = {
'spot_price_reference': ['DAI'],
'decoding_type': ['SIMPLIFIED']
}
initial_values = generate_initial_state(
initial_values_json=
'../data/0x8b6e6e7b5b3801fed2cafd4b22b8a16c2f2db21a-initial_pool_states-prices.json',
spot_price_base_currency=parameters['spot_price_reference'][0])
result = generate_partial_state_update_blocks(
'../data/0x8b6e6e7b5b3801fed2cafd4b22b8a16c2f2db21a-actions-prices.json'
)
partial_state_update_blocks = result['partial_state_update_blocks']
steps_number = result['steps_number']
sim_config = config_sim({
'N': 1, # number of monte carlo runs
'T': range(steps_number - 1), # number of timesteps
'M': parameters, # simulation parameters
})
df = run(initial_values, partial_state_update_blocks, sim_config)
p_df = post_processing(df, include_spot_prices=False)
p_df_ref = pd.read_pickle(
"0x8b6-reference-p_df-commit-886b55321957449d6cbf3afafdf57b9e64a8cadb.pickle"
)
if not p_df_ref.equals(p_df):
for i in range(len(p_df)):
if not p_df_ref.loc[i].equals(p_df.loc[i]):
print("Discrepancy starts at timestep", i)
refrow = p_df_ref.loc[i]
row = p_df.loc[i]
print(refrow.compare(row))
break
final_row = len(p_df) - 1
print("Discrepancy at end of simulation", final_row)
print(p_df_ref.iloc[final_row].compare(p_df.iloc[final_row]))
def test_simulation_dataframe_structure():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = 1000
RUNS = 1
model = Model(initial_state=states,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
experiment = Experiment([simulation, simulation, simulation])
data_radcad = experiment.run()
df_radcad = pd.DataFrame(data_radcad).drop(['run'], axis=1)
c = config_sim({"N": RUNS, "T": range(TIMESTEPS), "M": params})
exp = cadCADExperiment()
exp.append_configs(model_id='a',
initial_state=states,
partial_state_update_blocks=state_update_blocks,
sim_configs=c)
exp.append_configs(model_id='b',
initial_state=states,
partial_state_update_blocks=state_update_blocks,
sim_configs=c)
exp.append_configs(model_id='c',
initial_state=states,
partial_state_update_blocks=state_update_blocks,
sim_configs=c)
exec_mode = ExecutionMode()
local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
simulation = Executor(exec_context=local_mode_ctx, configs=exp.configs)
data_cadcad, tensor_field, sessions = simulation.execute()
df_cadcad = pd.DataFrame(data_cadcad).drop(['run'], axis=1)
assert_frame_equal(df_radcad, df_cadcad)
assert df_radcad.equals(df_cadcad)
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import genesis_states
from .partial_state_update_block import partial_state_update_block
from .sys_params import sys_params
sim_config = config_sim({
'N': 1, # number of monte carlo runs
'T': range(
1060000 - 2
), # number of timesteps - 147439 is the length of uniswap_events 3438 // 899703
'M': sys_params, # simulation parameters
})
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block)
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import genesis_states
from .partial_state_update_block import partial_state_update_block
from .sys_params import sys_params
from .parts.utils import AdoptionPool
from copy import deepcopy
from cadCAD import configs
sim_config = config_sim({
'N': 1, # number of monte carlo runs
'T': range(1000), # number of timesteps
'M': sys_params, # system parameters
})
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # GENESIS SWEEP LOGIC # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
for c in configs: # for each configuration object
c.initial_state = deepcopy(
c.initial_state
) # make a deepcopy of the initial state dict (it's shared across configs by default)
# import libraries
from decimal import Decimal
import numpy as np
from datetime import timedelta
from cadCAD.configuration import append_configs
from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim
seeds = {
# 'z': np.random.RandomState(1),
# 'a': np.random.RandomState(2)
}
sim_config = config_sim({
'T': range(10), #number of discrete iterations in each experiement
'N': 1, #number of times the simulation will be run (Monte Carlo runs)
#'M': g #parameter sweep dictionary
})
# define the time deltas for the discrete increments in the model
# ts_format = '%Y-%m-%d %H:%M:%S'
# t_delta = timedelta(days=0, minutes=1, seconds=0)
# def time_model(_g, step, sL, s, _input):
# y = 'time'
# x = ep_time_step(s, dt_str=s['time'], fromat_str=ts_format, _timedelta=t_delta)
# return (y, x)
# Behaviors
# Mechanisms
def update_A(_g, step, sL, s, _input):
's4': 1.0,
'timestamp': '2018-10-01 15:16:24'
}
# Environment Process
# ToDo: Validate - make env proc trigger field agnostic
env_process["s3"] = [lambda _g, x: _g['beta'], lambda _g, x: x + 1]
env_process["s4"] = env_timestep_trigger(trigger_field='timestep', trigger_vals=[5], funct_list=[lambda _g, x: _g['beta']])
# config_sim Necessary
sim_config = config_sim(
{
"N": 1,
"T": range(5),
"M": g, # Optional
}
)
# New Convention
partial_state_update_blocks = psub_list(psu_block, psu_steps)
append_configs(
# user_id='user_a',
sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
env_processes=env_process,
partial_state_update_blocks=partial_state_update_blocks
)
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import genesis_states
from .partial_state_update_block import PSUBs
from .sys_params import sys_params as sys_params
from .sim_params import *
sim_config = config_sim(
{
'N': MONTE_CARLO_RUNS,
'T': range(SIMULATION_TIME_STEPS),
'M': sys_params,
}
)
exp = Experiment()
exp.append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=PSUBs
)
},
"m2": {
"policies": {
"p1": p1m2,
"p2": p2m2
},
"variables": variables
},
"m3": {
"policies": {
"p1": p1m3,
"p2": p2m3
},
"variables": variables
}
}
sim_config = config_sim({
"N": 1,
"T": range(3),
})
exp = Experiment()
exp.append_model(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b,
lambda y: y * 2] # Default: lambda a, b: a + b
)
# import libraries
from decimal import Decimal
import numpy as np
from datetime import timedelta
from cadCAD.configuration import append_configs
from cadCAD.configuration.utils import proc_trigger, bound_norm_random, ep_time_step, config_sim
seeds = {
# 'z': np.random.RandomState(1),
# 'a': np.random.RandomState(2)
}
sim_config = config_sim({
'T': range(360), #number of discrete iterations in each experiement
'N': 1, #number of times the simulation will be run (Monte Carlo runs)
})
# parameters (later add tuning)
eta = .04 # for tx_volume_generator
tampw = 10000 # transactions limit
alpha = .5 # for data acquisition cost
beta = .2 # for data acquisition cost
cost = 2.5
periodInvestment = 1000
# external states
#cell defines the model function for the tx_volume generator stochastic process
def tx_volume_generator(_g, step, sL, s, _input):
y = 'tx_volume'
x = s['tx_volume'] * (1 + 2 * eta * np.random.rand() *
import unittest
from cadCAD.configuration.utils import config_sim
val_error_indicator = False
try:
sim_config = config_sim(
{
"N": 0,
"T": range(5)
}
)
except ValueError:
val_error_indicator = True
class RunExceptionTest(unittest.TestCase):
def test_multi_model(self):
self.assertEqual(val_error_indicator, True, "ValueError raised when runs (N) < 1")
if __name__ == '__main__':
unittest.main()
},
{
"policies": {
"b1": p1m3,
"b2": p2m3
},
"variables": {
"s1": s1m3,
"s2": s2m3,
# "s3": es3p1,
# "s4": es4p2,
}
}
]
sim_config = config_sim(
{
"N": 1,
# "N": 5,
"T": range(5),
}
)
append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b]
)
def easy_run(state_variables,
params,
psubs,
N_timesteps,
N_samples,
use_label=False,
assign_params=True,
drop_substeps=True) -> pd.DataFrame:
"""
Run cadCAD simulations without headaches.
"""
simulation_parameters = {
'N': N_samples,
'T': range(N_timesteps),
'M': params
}
sim_config = config_sim(simulation_parameters)
from cadCAD import configs
del configs[:]
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=state_variables,
partial_state_update_blocks=psubs)
from cadCAD import configs
exec_mode = ExecutionMode()
exec_context = ExecutionContext(exec_mode.local_mode)
executor = Executor(exec_context=exec_context, configs=configs)
(records, tensor_field, _) = executor.execute()
df = pd.DataFrame(records)
if drop_substeps == True:
# Drop all intermediate substeps
first_ind = (df.substep == 0) & (df.timestep == 0)
last_ind = df.substep == max(df.substep)
inds_to_drop = (first_ind | last_ind)
df = df.loc[inds_to_drop].drop(columns=['substep'])
else:
pass
if assign_params == False:
pass
else:
M_dict = configs[0].sim_config['M']
params_set = set(M_dict.keys())
# Logic for getting the assign params criteria
if type(assign_params) is list:
selected_params = set(assign_params) & params_set
elif type(assign_params) is set:
selected_params = assign_params & params_set
else:
selected_params = params_set
# Attribute parameters to each row
df = df.assign(**select_config_M_dict(configs, 0, selected_params))
for i, (_, n_df) in enumerate(df.groupby(['simulation', 'subset', 'run'])):
df.loc[n_df.index] = n_df.assign(**select_config_M_dict(configs,
i,
selected_params))
# Based on Vitor Marthendal (@marthendalnunes) snippet
if use_label == True:
psub_map = {order + 1: psub.get('label', '')
for (order, psub)
in enumerate(psubs)}
psub_map[0] = 'Initial State'
df['substep_label'] = df.substep.map(psub_map)
return df
def simulate(
network: Network,
parameters: SimulationParameters,
observers: Dict[str, Callable[[BeaconState],
Any]] = {}) -> pd.DataFrame:
"""
Args:
network (Network): Network of :py:class:`beaconrunner.validatorlib.BRValidator`
parameters (BRSimulationParameters): Simulation parameters
Returns:
pandas.DataFrame: Results of the simulation contained in a pandas data frame
"""
initial_conditions = {'network': network}
psubs = [
{
'policies': {
'action': attest_policy # step 1
},
'variables': {
'network': update_attestations # step 2
}
},
{
'policies': {
'action': chunk_response_policy # step 3
},
'variables': {
'network': update_chunk_responses # step 4
}
},
{
'policies': {
'action': bit_challenge_policy # step 3
},
'variables': {
'network': update_bit_challenges # step 4
}
},
{
'policies': {
'action': propose_policy # step 3
},
'variables': {
'network': update_blocks # step 4
}
},
{
'policies': {},
'variables': {
'network': tick # step 5
}
},
]
# Determine how many steps the simulation is running for
num_slots = parameters.num_epochs * SLOTS_PER_EPOCH
steps = num_slots * SECONDS_PER_SLOT * parameters.frequency
simulation_parameters = {
'T': range(steps),
'N': 1,
'M': {
"frequency": [parameters.frequency],
"network_update_rate": [parameters.network_update_rate],
}
}
print("will simulate", parameters.num_epochs, "epochs (", num_slots,
"slots ) at frequency", parameters.frequency, "moves/second")
print("total", steps, "simulation steps")
# Add our observers to the simulation
observed_ic = add_loop_ic(
get_observed_initial_conditions(initial_conditions, observers))
observed_psubs = add_loop_psubs(get_observed_psubs(psubs, observers))
# Final simulation parameters and execution
configs = []
for sim_param in config_sim(simulation_parameters):
config = Configuration(sim_param,
initial_state=observed_ic,
partial_state_update_blocks=observed_psubs)
configs.append(config)
exec_mode = ExecutionMode()
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run = Executor(exec_context=single_proc_ctx, configs=configs)
raw_result, tensor_field = run.execute()
return pd.DataFrame(raw_result).assign(run=parameters.num_run)
# import libraries
from decimal import Decimal
import numpy as np
from datetime import timedelta
from cadCAD.configuration import append_configs
from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim
seeds = {}
sim_config = config_sim({'T': range(10), 'N': 1})
# Behaviors
def robot_arm(_g, step, sL, s):
add_to_A = 0
if (s['box_A'] > s['box_B']):
add_to_A = -1
elif (s['box_A'] < s['box_B']):
add_to_A = 1
return ({'add_to_A': add_to_A, 'add_to_B': -add_to_A})
# Mechanisms
def increment_A(_g, step, sL, s, _input):
y = 'box_A'
x = s['box_A'] + _input['add_to_A']
return (y, x)
def increment_B(_g, step, sL, s, _input):
y = 'box_B'
from datetime import timedelta
import numpy as np
from typing import Dict, List
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim, access_block
from copy import deepcopy
from cadCAD import configs
from .state_variables import state_variables
from .partial_state_update_block import partial_state_update_blocks
from .parts.sys_params import *
from .parts.utils import *
sim_config = config_sim({
'T': range(100), #day
'N': 1,
'M': params,
})
seeds = {
'p': np.random.RandomState(1),
}
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=state_variables,
seeds=seeds,
partial_state_update_blocks=partial_state_update_blocks)
# Initialize network x
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import initial_state
from .partial_state_update_block import partial_state_update_block
from .sys_params import params
sim_config = config_sim({
'N': 1, # number of monte carlo runs
'T': range(
1000), # number of timesteps - 147439 is the length of uniswap_events
'M': params, # simulation parameters
})
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=initial_state,
partial_state_update_blocks=partial_state_update_block)
genesis_states = {
's1': 0.0,
's2': 0.0,
's3': 1.0,
's4': 1.0,
'timestamp': '2018-10-01 15:16:24'
}
# Environment Process
env_process["s3"] = [lambda _g, x: _g['beta'], lambda _g, x: x + 1]
env_process["s4"] = env_timestep_trigger(trigger_field='timestep',
trigger_vals=[5],
funct_list=[lambda _g, x: _g['beta']])
# config_sim Necessary
sim_config = config_sim({
"N": 5,
"T": range(2),
"M": g, # Optional
})
# New Convention
partial_state_update_blocks = psub_list(psu_block, psu_steps)
sweep_exp.append_configs(
# user_id='user_a',
sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
env_processes=env_process,
partial_state_update_blocks=partial_state_update_blocks)
from cadCAD.configuration.utils import config_sim
from cadCAD.configuration import Experiment
from model.state_variables import initial_states
from model.sys_params import sys_params
from model.sys_params import CONTRIBUTIONS_SEQUENCE
from model.partial_state_update_block import partial_state_update_blocks
sim_params = {'N': 1, 'T': range(len(CONTRIBUTIONS_SEQUENCE)), 'M': sys_params}
sim_config = config_sim(sim_params)
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=initial_states,
partial_state_update_blocks=partial_state_update_blocks)
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import genesis_states
from .partial_state_update_block import partial_state_update_block
from .sys_params import sys_params
sim_config = config_sim (
{
'N': 5, # number of monte carlo runs
'T': range(300), # number of timesteps
'M': sys_params, # simulation parameters
}
)
exp = Experiment()
exp.append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block
)
def cadcad():
try:
n = getInteger('participants') #initial participants
m = getInteger('proposals') #initial proposals
alpha = getFloat('alpha')
beta = getFloat('beta')
exit_tribute = getFloat('exit_tribute')
theta = getFloat('theta')
initial_sentiment = 0.1 # getFloat('initial_sentiment')
hatch_price = 0.1 # getFloat('hatch_price')
kappa = 6 # getFloat('kappa')
rho = 0.05 # getFloat('rho')
except Exception as err:
return str(err), 422
#initializer
network, initial_supply, total_requested = initialize_network(n, m)
initial_funds = total_funds_given_total_supply(initial_supply, theta,
hatch_price)
initial_reserve, invariant, starting_price = initialize_bonding_curve(
initial_supply, initial_price=hatch_price, kappa=kappa, theta=theta)
initial_conditions = {
'supply': initial_supply,
'funds': initial_funds,
'reserve': initial_reserve,
'spot_price': starting_price,
'sentiment': initial_sentiment,
'network': network
}
def trigger_threshold(requested, funds, supply, beta=beta, rho=rho):
share = requested / funds
if share < beta:
return rho * supply / (beta - share)**2
else:
return np.inf
params = {
'sensitivity': [.75],
'tmin':
[7], #unit days; minimum periods passed before a proposal can pass
'min_supp': [
50
], #number of tokens that must be stake for a proposal to be a candidate
'sentiment_decay': [.01], #termed mu in the state update function
'alpha': [alpha],
'base_completion_rate': [100],
'base_failure_rate': [200],
'trigger_func': [trigger_threshold],
'kappa': [kappa], #bonding curve curvature
'invariant': [invariant], #set by bonding curve choices
'tax_rate': [exit_tribute]
}
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Settings of general simulation parameters, unrelated to the system itself
# `T` is a range with the number of discrete units of time the simulation will run for;
# `N` is the number of times the simulation will be run (Monte Carlo runs)
time_periods_per_run = 100
monte_carlo_runs = 1
from cadCAD.configuration.utils import config_sim
simulation_parameters = config_sim({
'T': range(time_periods_per_run),
'N': monte_carlo_runs,
'M': params
})
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# The Partial State Update Blocks
partial_state_update_blocks = [
{
'policies': {
#new proposals or new participants
'random': driving_process
},
'variables': {
'network': update_network,
'funds': increment_funds,
'supply': increment_supply,
'reserve': increment_reserve
}
},
{
'policies': {
'completion':
check_progress #see if any of the funded proposals completes
},
'variables':
{ # The following state variables will be updated simultaneously
'sentiment':
update_sentiment_on_completion, #note completing decays sentiment, completing bumps it
'network': complete_proposal #book-keeping
}
},
{
'policies': {
'release':
trigger_function #check each proposal to see if it passes
},
'variables':
{ # The following state variables will be updated simultaneously
'funds': decrement_funds, #funds expended
'sentiment':
update_sentiment_on_release, #releasing funds can bump sentiment
'network':
update_proposals #reset convictions, and participants sentiments
#update based on affinities
}
},
{
'policies': {
#currently naive decisions; future: strategic
'participants_act':
participants_decisions, #high sentiment, high affinity =>buy
#low sentiment, low affinities => burn
#assign tokens to top affinities
},
'variables': {
'supply':
update_supply, #book-keeping from participants decisions
'reserve': update_reserve, #funds under the bonding curve
'spot_price': update_price, #new bonding curve spot price
'funds': update_funds, #capture taxes
'network': update_tokens #update everyones holdings
#and their conviction for each proposal
}
}
]
from cadCAD import configs
configs.clear()
from cadCAD.configuration import append_configs
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# The configurations above are then packaged into a `Configuration` object
append_configs(
initial_state=
initial_conditions, #dict containing variable names and initial values
partial_state_update_blocks=
partial_state_update_blocks, #dict containing state update functions
sim_configs=simulation_parameters #dict containing simulation parameters
)
from tabulate import tabulate
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from cadCAD import configs
import pandas as pd
exec_mode = ExecutionMode()
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run = Executor(exec_context=multi_proc_ctx, configs=configs)
i = 0
verbose = False
results = {}
for raw_result, tensor_field in run.execute():
result = pd.DataFrame(raw_result)
if verbose:
print()
print(f"Tensor Field: {type(tensor_field)}")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print(f"Output: {type(result)}")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
results[i] = {}
results[i]['result'] = result
results[i]['simulation_parameters'] = simulation_parameters[i]
i += 1
for ind in range(len(results)):
r = results[ind]['result']
#print(results[ind]['simulation_parameters'])
r.plot(x='timestep', y='funds')
plt.savefig('static/plot8-' + str(ind) + '.png')
plt.clf()
fig, ax1 = plt.subplots()
ax2 = ax1.twinx(
) # instantiate a second axes that shares the same x-axis
df = r
rdf = df[df.substep == 4].copy()
rdf.plot(x='timestep', y=['funds', 'reserve', 'supply'], ax=ax1)
rdf.plot(x='timestep',
y='spot_price',
style='--',
color='red',
ax=ax2,
legend=False)
ax2.set_ylabel('Price in xDAI per Token', color='red')
ax1.set_ylabel('Quantity of Assets')
ax2.tick_params(axis='y', labelcolor='red')
plt.title('Summary of Local Economy')
plt.savefig('static/plot9-' + str(ind) + '.png')
plt.clf()
return jsonify({'results': ['plot8-0.png', 'plot9-0.png']})
from cadCAD.configuration.utils import config_sim
from cadCAD.configuration import Experiment
from cadCAD.engine import ExecutionMode, ExecutionContext
from cadCAD.engine import Executor
from cadCAD import configs
del configs[:] # Clear any prior configs
config = {
"N": RUNS,
"T": range(TIMESTEPS),
"M": params
}
c = config_sim(config)
exp = Experiment()
exp.append_configs(
initial_state = states,
partial_state_update_blocks = psubs,
sim_configs = c
)
exp.append_configs(
initial_state = states,
partial_state_update_blocks = psubs,
sim_configs = c
)
exec_mode = ExecutionMode()
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .psub import psubs
from .state import genesis_state
from .sys_params import params
from .sim_setup import SIMULATION_TIME_STEPS, MONTE_CARLO_RUNS
# Parameters
# Values are lists because they support sweeping.
simulation_config = config_sim({
'N': MONTE_CARLO_RUNS,
'T': range(SIMULATION_TIME_STEPS), # number of timesteps
'M': params,
})
exp = Experiment()
exp.append_configs(sim_configs=simulation_config,
initial_state=genesis_state,
partial_state_update_blocks=psubs)
# from cadCAD import configuration
# from .psub import psubs
# from .state import genesis_state
# from .sys_params import params
# # Parameters
# # Values are lists because they support sweeping.
"m2": {
"policies": {
"b1": p1m2,
# "b2": p2m2
},
"states": {
"s1": s1m2,
# "s2": s2m2
}
},
"m3": {
"policies": {
"b1": p1m3,
"b2": p2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config = config_sim({
"N": 2,
"T": range(5),
})
append_configs(sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block)
import pandas as pd
from cadCAD.configuration.utils import config_sim
from cadCAD.configuration import Experiment as cadCADExperiment
from cadCAD.engine import ExecutionMode, ExecutionContext
from cadCAD.engine import Executor
from cadCAD import configs
from tests.test_cases import benchmark_model
states = benchmark_model.states
state_update_blocks = benchmark_model.state_update_blocks
params = benchmark_model.params
TIMESTEPS = 100_000
RUNS = 5
c = config_sim({"N": RUNS, "T": range(TIMESTEPS), "M": params})
exp = cadCADExperiment()
exp.append_configs(initial_state=states,
partial_state_update_blocks=state_update_blocks,
sim_configs=c)
exec_mode = ExecutionMode()
local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
simulation = Executor(exec_context=local_mode_ctx, configs=configs)
if __name__ == "__main__":
results, _, _ = simulation.execute()
assert len(results) > 0
"s1": s1m2,
"s2": s2m2,
# "s3": es3p1,
# "s4": es4p2,
}
},
{
"policies": {
"b1": p1m3,
"b2": p2m3
},
"variables": {
"s1": s1m3,
"s2": s2m3,
# "s3": es3p1,
# "s4": es4p2,
}
}
]
sim_config_dict = {"N": 1, "T": range(5)}
sim_config = config_sim(sim_config_dict)
append_configs(config_list=configs,
user_id='user_a',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b])
import math
from decimal import Decimal
from datetime import timedelta
import numpy as np
from typing import Dict, List
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim, access_block
from .genesis_states import genesis_states
from .partial_state_update_block import partial_state_update_blocks
sim_config = config_sim({
'N': 1,
'T': range(60), #day
})
seeds = {
'p': np.random.RandomState(1),
}
exp = Experiment()
exp.append_configs(sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
partial_state_update_blocks=partial_state_update_blocks)
def get_configs():
'''
return 'policies', _input
external_data = {'ds1': None, 'ds2': None, 'ds3': None}
state_dict = {
'increment': 0,
'external_data': external_data,
'policies': external_data
}
policies = {"p1": p1, "p2": p2}
states = {
'increment': increment,
'external_data': integrate_ext_dataset,
'policies': view_policies
}
PSUB = {'policies': policies, 'states': states}
# needs M1&2 need behaviors
partial_state_update_blocks = {'PSUB1': PSUB, 'PSUB2': PSUB, 'PSUB3': PSUB}
sim_config = config_sim({"N": 2, "T": range(4)})
append_configs(sim_configs=sim_config,
initial_state=state_dict,
partial_state_update_blocks=partial_state_update_blocks,
policy_ops=[lambda a, b: {
**a,
**b
}])
MONTE_CARLO_RUNS = 5 # N monte carlo runs
from cadCAD.configuration import append_configs
from cadCAD.configuration.utils import config_sim
from .model.state_variables import genesis_states
from .model.partial_state_update_block import partial_state_update_block
from .model.sys_params import sys_params as sys_params
from .sim_params import SIMULATION_TIME_STEPS
sim_config = config_sim({
'N': 1,
'T': range(SIMULATION_TIME_STEPS), # number of timesteps
'M': sys_params,
})
append_configs(sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block)
'sentiment_decay': [.001], #termed mu in the state update function
'alpha': [0.5],
'base_completion_rate': [10],
'trigger_func': [trigger_threshold]
}
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Settings of general simulation parameters, unrelated to the system itself
# `T` is a range with the number of discrete units of time the simulation will run for;
# `N` is the number of times the simulation will be run (Monte Carlo runs)
time_periods_per_run = 250
monte_carlo_runs = 1
simulation_parameters = config_sim({
'T': range(time_periods_per_run),
'N': monte_carlo_runs,
'M': params
})
from cadCAD.configuration import append_configs
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# The configurations above are then packaged into a `Configuration` object
append_configs(
initial_state=
initial_conditions, #dict containing variable names and initial values
partial_state_update_blocks=
partial_state_update_blocks, #dict containing state update functions
sim_configs=simulation_parameters #dict containing simulation parameters
)
'food_growth_rate': 1,
'maximum_food_per_site': 15,
'reproduction_probability': 1.0,
'maximum_age': 100,
'minimum_food': 7,
'hungry_threshold': 5,
'reproduction_food': 2
}
params = {
'reproduction_probability': [0.75, 1.0]
}
simulation_parameters = config_sim({
'T': range(50),
'N': 1,
'M': params
})
#policies
@np.vectorize
def augment_food(x, x_m, increment):
if x < x_m:
return x + increment
else:
return x
def regenerate_food(params, step, sL, s):
"""