exogenous_states = {
#'time': time_model
}
env_processes = {
}
#build mechanism dictionary to "wire up the circuit"
mechanisms = [
{
'policies': {
},
'variables': { # The following state variables will be updated simultaneously
'box_A': update_A,
'box_B': update_B
}
}
]
append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
raw_exogenous_states=exogenous_states,
env_processes=env_processes,
partial_state_update_blocks=mechanisms
)
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
)
print()
print("Policie State Update Block:")
pp.pprint(partial_state_update_blocks)
print()
print()
"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])
update_timestamp = time_model('timestamp',
substeps=3,
time_delta=timedelta(days=0,
minutes=0,
seconds=1),
ts_format='%Y-%m-%d %H:%M:%S')
states['timestamp'] = update_timestamp
PSUB = {'policies': policies, 'states': states}
# needs M1&2 need behaviors
partial_state_update_blocks = [PSUB] * substeps
# pp.pprint(partial_state_update_blocks)
sim_config = config_sim({"N": 2, "T": range(4)})
# ToDo: Bug without specifying parameters
append_configs(
sim_configs=sim_config,
initial_state=state_dict,
seeds={},
raw_exogenous_states={},
env_processes={},
partial_state_update_blocks=partial_state_update_blocks,
# policy_ops=[lambda a, b: {**a, **b}]
)
print()
print("State Updates:")
pp.pprint(partial_state_update_blocks)
print()
"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)
psubs = {
"PSUB1": PSUB,
"PSUB2": PSUB,
"PSUB3": PSUB
}
sim_config = config_sim(
{
"N": 1,
"T": range(3),
}
)
append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=psubs
)
exec_mode = ExecutionMode()
local_proc_ctx = ExecutionContext(context=exec_mode.local_mode)
run = Executor(exec_context=local_proc_ctx, configs=configs)
raw_result, tensor_field, sessions = run.execute()
result = pd.DataFrame(raw_result)
cols = ['run','substep','timestep','x','nonexsistant','last_x','2nd_to_last_x','3rd_to_last_x','4th_to_last_x']
result = result[cols]
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
"policies": {
"p1": p1m2,
"p2": p2m2
},
"variables": variables
},
"m3": {
"policies": {
"p1": p1m3,
"p2": p2m3
},
"variables": variables
}
}
sim_config = config_sim({
"N": 2,
"T": range(3),
})
# Aggregation == Reduce Map / Reduce Map Aggregation
# using env functions (include in reg test using / for env proc)
append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block,
# ToDo: subsequent functions should include policy dict for access to each policy (i.e shouldnt be a map)
policy_ops=[
lambda a, b: a + b, lambda y: y * 2
] # Default: lambda a, b: a + b ToDO: reduction function requires high lvl explanation
)
},
"m3": {
"policies": {
"b1": p1m3,
"b2": p2m3
},
"variables": {
"s1": s1m3,
"s2": s2m3
}
}
}
# config_sim Necessary
sim_config = config_sim(
{
"N": 2,
"T": range(5),
"M": g # Optional
}
)
# New Convention
append_configs(
sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
raw_exogenous_states={}, #raw_exogenous_states,
env_processes={}, #env_processes,
partial_state_update_blocks=partial_state_update_block
)
for m in psu_steps:
psu_block[m]["variables"]['udo_policy_tracker'] = udo_policy_tracker
def update_timestamp(_g, step, sL, s, _input, **kwargs):
y = 'timestamp'
return y, time_step(dt_str=s[y],
dt_format='%Y-%m-%d %H:%M:%S',
_timedelta=timedelta(days=0, minutes=0, seconds=1))
for m in psu_steps:
psu_block[m]["variables"]['timestamp'] = var_timestep_trigger(
y='timestamp', f=update_timestamp)
# psu_block[m]["variables"]['timestamp'] = var_trigger(
# y='timestamp', f=update_timestamp,
# pre_conditions={'substep': [0, system_substeps]}, cond_op=lambda a, b: a and b
# )
# psu_block[m]["variables"]['timestamp'] = update_timestamp
# ToDo: Bug without specifying parameters
# New Convention
partial_state_update_blocks = psub_list(psu_block, psu_steps)
append_configs(sim_configs=sim_config,
initial_state=state_dict,
partial_state_update_blocks=partial_state_update_blocks)
print()
print("State Updates:")
pp.pprint(partial_state_update_blocks)
print()
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']})
"p2": p2
},
"variables": {
"ds1": s1,
"ds2": s2
}
},
"m3": {
"policies": {
"p1": p1,
"p2": p2
},
"variables": {
"ds1": s1,
"ds2": s2
}
}
}
sim_config = config_sim({
"N": 2,
"T": range(4),
})
append_configs(sim_configs=sim_config,
initial_state=genesis_states,
seeds=seeds,
raw_exogenous_states=raw_exogenous_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b])
"states": {
"s1": s1m2,
# "s2": s2m2
}
},
"m3": {
"policies": {
"b1": p1m3,
"b2": p2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config_dict = {
"N": 3,
"T": range(5),
}
sim_config = config_sim(sim_config_dict)
append_configs(config_list=configs,
user_id='user_b',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block)
"variables": {
"s1": s1m2,
"s2": s2m2,
# "s3": es3p1,
# "s4": es4p2,
}
},
{
"policies": {
"b1": p1m3,
"b2": p2m3
},
"variables": {
"s1": s1m3,
"s2": s2m3,
# "s3": es3p1,
# "s4": es4p2,
}
}
]
sim_config = config_sim({
"N": 2,
"T": range(1),
})
append_configs(sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=psubs,
policy_ops=[lambda a, b: a + b])
"variables": variables
},
"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),
})
append_configs(
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
)
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
}])
}
},
{
'policies': {
# The following policy functions will be evaluated and their returns will be passed to the state update functions
'p1': agent_arrival
},
'variables': { # The following state variables will be updated simultaneously
'network': update_network,
'balls': update_network_balls,
'robot': get_robot
}
}
]
simulation_parameters = {
'T': range(T),
'N': 1,
'M': {}
}
append_configs(
sim_configs=simulation_parameters, #dict containing state update functions
initial_state=initial_conditions, #dict containing variable names and initial values
partial_state_update_blocks= partial_state_update_blocks #, #dict containing state update functions
# policy_ops=[lambda a, b: {**a, **b}]
)
# config = Configuration(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_config=simulation_parameters #dict containing simulation parameters
# )
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
)
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
for raw_result, tensor_field in run.execute():
result = pd.DataFrame(raw_result)
},
'variables': {
'agents': update_agents
}
},
{
'policies': {
'natural_death': kill_agents
},
'variables': {
'agents': update_agents
}
}
]
#config and execution
from cadCAD import configs
append_configs(
sim_configs=simulation_parameters,
initial_state=initial_conditions,
partial_state_update_blocks=partial_state_update_blocks
)
exec_mode = ExecutionMode()
exec_context = ExecutionContext(exec_mode.multi_proc)
executor = Executor(exec_context, configs)
results = []
for raw_result, _ in executor.execute():
results.append(raw_result)
