from decimal import Decimal import numpy as np from datetime import timedelta import pprint from SimCAD import configs from SimCAD.configuration import Configuration from SimCAD.configuration.utils import proc_trigger, ep_time_step, process_variables, exo_update_per_ts pp = pprint.PrettyPrinter(indent=4) seed = { 'z': np.random.RandomState(1), 'a': np.random.RandomState(2), 'b': np.random.RandomState(3), 'c': np.random.RandomState(3) } g = { 'alpha': [1], 'beta': [2, 5], 'gamma': [3, 4], 'omega': [7] } # beta = 1 # middleware(f1,f2,f3,f4) # Behaviors per Mechanism def b1m1(_g, step, sL, s): return {'param1': 1} def b2m1(_g, step, sL, s): return {'param2': 4} def b1m2(_g, step, sL, s): return {'param1': 'a', 'param2': _g['beta']} def b2m2(_g, step, sL, s): return {'param1': 'b', 'param2': 0} # @curried def b1m3(_g, step, sL, s): return {'param1': np.array([10, 100])} # @curried def b2m3(_g, step, sL, s): return {'param1': np.array([20, 200])} # Internal States per Mechanism # @curried def s1m1(_g, step, sL, s, _input): y = 's1' x = 0 return (y, x) def s2m1(_g, step, sL, s, _input): y = 's2' x = _g['beta'] return (y, x) def s1m2(_g, step, sL, s, _input): y = 's1' x = _input['param2'] return (y, x) def s2m2(_g, step, sL, s, _input): y = 's2' x = _input['param2'] return (y, x) def s1m3(_g, step, sL, s, _input): y = 's1' x = 0 return (y, x) def s2m3(_g, step, sL, s, _input): y = 's2' x = 0 return (y, x) # Exogenous States proc_one_coef_A = 0.7 proc_one_coef_B = 1.3 def es3p1(_g, step, sL, s, _input): y = 's3' x = _g['gamma'] return (y, x) # @curried def es4p2(_g, step, sL, s, _input): y = 's4' x = _g['gamma'] return (y, x) ts_format = '%Y-%m-%d %H:%M:%S' t_delta = timedelta(days=0, minutes=0, seconds=1) def es5p2(_g, step, sL, s, _input): y = 'timestamp' x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta) return (y, x) # Environment States # @curried # def env_a(param, x): # return x + param def env_a(x): return x def env_b(x): return 10 # Genesis States genesis_states = { 's1': Decimal(0.0), 's2': Decimal(0.0), 's3': Decimal(1.0), 's4': Decimal(1.0), 'timestamp': '2018-10-01 15:16:24' } # remove `exo_update_per_ts` to update every ts raw_exogenous_states = { "s3": es3p1, #es3p1, #sweep(beta, es3p1), "s4": es4p2, "timestamp": es5p2 } # ToDo: make env proc trigger field agnostic # ToDo: input json into function renaming __name__ triggered_env_b = proc_trigger('2018-10-01 15:16:25', env_b) env_processes = { "s3": env_a, #sweep(beta, env_a), "s4": triggered_env_b #rename('parameterized', triggered_env_b) #sweep(beta, triggered_env_b) } # parameterized_env_processes = parameterize_states(env_processes) # # pp.pprint(parameterized_env_processes) # exit() # ToDo: The number of values entered in sweep should be the # of config objs created, # not dependent on the # of times the sweep is applied # sweep exo_state func and point to exo-state in every other funtion # param sweep on genesis states mechanisms = { "m1": { "behaviors": { "b1": b1m1, "b2": b2m1 }, "states": { "s1": s1m1, "s2": s2m1 } }, "m2": { "behaviors": { "b1": b1m2, "b2": b2m2, }, "states": { "s1": s1m2, "s2": s2m2 } }, "m3": { "behaviors": { "b1": b1m3, "b2": b2m3 }, "states": { "s1": s1m3, "s2": s2m3 } } } # process_variables(g) def gen_sim_configs(N, T, Ms): return [ { "N": 2, "T": range(5), "M": M } for M in process_variables(Ms) ] sim_configs = gen_sim_configs( N=2, T=range(5), Ms=g ) for sim_config in sim_configs: configs.append( Configuration( sim_config=sim_config, state_dict=genesis_states, seed=seed, exogenous_states=raw_exogenous_states, # exo_update_per_ts env_processes=env_processes, mechanisms=mechanisms ) ) # append_configs( # sim_config=sim_config, # genesis_states=genesis_states, # seed=seed, # raw_exogenous_states=raw_exogenous_states, # env_processes=env_processes, # mechanisms=mechanisms, # _exo_update_per_ts=True #Default # )