timedelta input
This commit is contained in:
parent
21f1155ae7
commit
d60411b7b4
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@ -35,7 +35,7 @@ import pandas as pd
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from tabulate import tabulate
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from tabulate import tabulate
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from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
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from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
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# from sandboxUX import config1, config2
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sandbox
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from SimCAD import configs
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from SimCAD import configs
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# ToDo: pass ExecutionContext with execution method as ExecutionContext input
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# ToDo: pass ExecutionContext with execution method as ExecutionContext input
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@ -19,6 +19,12 @@ class ExecutionContext:
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self.name = context
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self.name = context
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self.method = None
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self.method = None
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def single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns):
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l = [simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns]
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simulation, states_list, config, env_processes, T, N = list(map(lambda x: x.pop(), l))
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result = simulation(states_list, config, env_processes, T, N)
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return flatten(result)
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def parallelize_simulations(fs, states_list, configs, env_processes, Ts, Ns):
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def parallelize_simulations(fs, states_list, configs, env_processes, Ts, Ns):
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l = list(zip(fs, states_list, configs, env_processes, Ts, Ns))
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l = list(zip(fs, states_list, configs, env_processes, Ts, Ns))
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with Pool(len(configs)) as p:
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with Pool(len(configs)) as p:
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@ -27,7 +33,7 @@ class ExecutionContext:
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return results
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return results
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if context == 'single_proc':
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if context == 'single_proc':
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self.method = None
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self.method = single_proc_exec
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elif context == 'multi_proc':
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elif context == 'multi_proc':
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self.method = parallelize_simulations
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self.method = parallelize_simulations
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@ -62,16 +68,7 @@ class Executor:
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# Dimensions: N x r x mechs
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# Dimensions: N x r x mechs
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def single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns):
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if self.exec_context == ExecutionMode.multi_proc:
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l = [simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns]
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simulation, states_list, config, env_processes, T, N = list(map(lambda x: x.pop(), l))
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# print(states_list)
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result = simulation(states_list, config, env_processes, T, N)
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return flatten(result)
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if self.exec_context == ExecutionMode.single_proc:
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return single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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elif self.exec_context == ExecutionMode.multi_proc:
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if len(self.configs) > 1:
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if len(self.configs) > 1:
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simulations = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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simulations = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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results = []
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results = []
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@ -79,5 +76,5 @@ class Executor:
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print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
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print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
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results.append(flatten(result))
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results.append(flatten(result))
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return results
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return results
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else:
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else:
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return single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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return self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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@ -59,7 +59,7 @@ class Executor:
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return sL
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return sL
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def block_gen(self, states_list, configs, env_processes, t_step, run):
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def mech_pipeline(self, states_list, configs, env_processes, t_step, run):
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m_step = 0
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m_step = 0
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states_list_copy = deepcopy(states_list)
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states_list_copy = deepcopy(states_list)
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# print(states_list_copy)
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# print(states_list_copy)
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@ -80,12 +80,14 @@ class Executor:
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# rename pipe
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# rename pipe
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def pipe(self, states_list, configs, env_processes, time_seq, run):
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def block_pipeline(self, states_list, configs, env_processes, time_seq, run):
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time_seq = [x + 1 for x in time_seq]
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time_seq = [x + 1 for x in time_seq]
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simulation_list = [states_list]
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simulation_list = [states_list]
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for time_step in time_seq:
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for time_step in time_seq:
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# print(run)
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# print(run)
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pipe_run = self.block_gen(simulation_list[-1], configs, env_processes, time_step, run)
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pipe_run = self.mech_pipeline(simulation_list[-1], configs, env_processes, time_step, run)
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# pp.pprint(pipe_run)
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# exit()
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_, *pipe_run = pipe_run
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_, *pipe_run = pipe_run
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simulation_list.append(pipe_run)
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simulation_list.append(pipe_run)
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@ -99,11 +101,11 @@ class Executor:
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run += 1
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run += 1
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# print("Run: "+str(run))
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# print("Run: "+str(run))
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states_list_copy = deepcopy(states_list) # WHY ???
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states_list_copy = deepcopy(states_list) # WHY ???
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head, *tail = self.pipe(states_list_copy, configs, env_processes, time_seq, run)
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head, *tail = self.block_pipeline(states_list_copy, configs, env_processes, time_seq, run)
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genesis = head.pop()
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genesis = head.pop()
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genesis['mech_step'], genesis['time_step'], genesis['run'] = 0, 0, run
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genesis['mech_step'], genesis['time_step'], genesis['run'] = 0, 0, run
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first_timestep = [genesis] + tail.pop(0)
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first_timestep_per_run = [genesis] + tail.pop(0)
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pipe_run += [first_timestep] + tail
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pipe_run += [first_timestep_per_run] + tail
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del states_list_copy
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del states_list_copy
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return pipe_run
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return pipe_run
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@ -10,7 +10,7 @@ def state_identity(k):
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return lambda step, sL, s, _input: (k, s[k])
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return lambda step, sL, s, _input: (k, s[k])
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# fix
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# Make returntype chosen by user. Must Classify Configs
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def b_identity(step, sL, s):
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def b_identity(step, sL, s):
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return {'indentity': 0}
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return {'indentity': 0}
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@ -21,16 +21,18 @@ def proc_trigger(trigger_step, update_f, step):
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# accept timedelta instead of timedelta params
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# accept timedelta instead of timedelta params
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def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=30):
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t_delta = timedelta(days=0, minutes=0, seconds=30)
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def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
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dt = datetime.strptime(dt_str, dt_format)
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dt = datetime.strptime(dt_str, dt_format)
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t = dt + timedelta(days=days, minutes=minutes, seconds=seconds)
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t = dt + _timedelta
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return t.strftime(dt_format)
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return t.strftime(dt_format)
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# accept timedelta instead of timedelta params
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# accept timedelta instead of timedelta params
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def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', days=0, minutes=0, seconds=1):
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
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if s['mech_step'] == 0:
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if s['mech_step'] == 0:
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return time_step(dt_str, fromat_str, days, minutes, seconds)
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return time_step(dt_str, fromat_str, _timedelta)
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else:
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else:
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return dt_str
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return dt_str
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@ -2,8 +2,9 @@ import pandas as pd
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from tabulate import tabulate
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from tabulate import tabulate
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from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
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from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
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from sandboxUX import config1, config2
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from sandbox.validation import config1, config2
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# from sandboxUX import config4
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# from sandbox import config4
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# from sandbox import config_zx
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from SimCAD import configs
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from SimCAD import configs
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# ToDo: pass ExecutionContext with execution method as ExecutionContext input
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# ToDo: pass ExecutionContext with execution method as ExecutionContext input
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@ -25,6 +26,7 @@ print()
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print("Simulation Run 2: Pairwise Execution")
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print("Simulation Run 2: Pairwise Execution")
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print()
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print()
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multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
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multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
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# configs = [config1, config1]
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run2 = Executor(multi_proc_ctx, configs)
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run2 = Executor(multi_proc_ctx, configs)
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run2_raw_results = run2.main()
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run2_raw_results = run2.main()
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for raw_result in run2_raw_results:
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for raw_result in run2_raw_results:
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@ -1,5 +1,6 @@
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from decimal import Decimal
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from decimal import Decimal
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import numpy as np
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import numpy as np
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from datetime import timedelta
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from SimCAD import Configuration, configs
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from SimCAD import Configuration, configs
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from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
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from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
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@ -72,9 +73,11 @@ def es4p2(step, sL, s, _input):
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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return (y, x)
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return (y, x)
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def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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ts_format = '%Y-%m-%d %H:%M:%S'
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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y = 'timestamp'
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y = 'timestamp'
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x = ep_time_step(s, s['timestamp'], seconds=1)
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x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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return (y, x)
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return (y, x)
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@ -1,5 +1,6 @@
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from decimal import Decimal
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from decimal import Decimal
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import numpy as np
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import numpy as np
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from datetime import timedelta
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from SimCAD import Configuration, configs
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from SimCAD import Configuration, configs
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from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
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from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
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@ -74,9 +75,11 @@ def es4p2(step, sL, s, _input):
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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return (y, x)
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return (y, x)
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def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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ts_format = '%Y-%m-%d %H:%M:%S'
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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y = 'timestamp'
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y = 'timestamp'
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x = ep_time_step(s, s['timestamp'], seconds=1)
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x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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return (y, x)
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return (y, x)
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@ -0,0 +1,157 @@
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from fn.op import foldr
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from fn import _
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from decimal import Decimal
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import numpy as np
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from SimCAD import Configuration, configs
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from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
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ep_time_step
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seed = {
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'z': np.random.RandomState(1),
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'a': np.random.RandomState(2),
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'b': np.random.RandomState(3),
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'c': np.random.RandomState(3)
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}
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# Behaviors per Mechanism
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def b1m1(step, sL, s):
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return s['s1'] + 1
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def b2m1(step, sL, s):
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return s['s1'] + 1
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def b1m2(step, sL, s):
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return s['s1'] + 1
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def b2m2(step, sL, s):
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return s['s1'] + 1
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def b1m3(step, sL, s):
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return s['s1'] + 1
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def b2m3(step, sL, s):
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return s['s2'] + 1
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# Internal States per Mechanism
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def s1m1(step, sL, s, _input):
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y = 's1'
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x = s['s1'] + _input
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return (y, x)
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def s2m1(step, sL, s, _input):
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y = 's2'
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x = s['s2'] + _input
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return (y, x)
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def s1m2(step, sL, s, _input):
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y = 's1'
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x = s['s1'] + _input
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return (y, x)
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def s2m2(step, sL, s, _input):
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y = 's2'
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x = s['s2'] + _input
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return (y, x)
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def s1m3(step, sL, s, _input):
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y = 's1'
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x = s['s1'] + _input
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return (y, x)
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def s2m3(step, sL, s, _input):
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y = 's2'
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x = s['s2'] + s['s3'] + _input
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return (y, x)
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# Exogenous States
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proc_one_coef_A = 0.7
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proc_one_coef_B = 1.3
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def es3p1(step, sL, s, _input):
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y = 's3'
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x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
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return (y, x)
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def es4p2(step, sL, s, _input):
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y = 's4'
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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return (y, x)
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def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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y = 'timestamp'
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x = ep_time_step(s, s['timestamp'], seconds=1)
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return (y, x)
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# Environment States
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def env_a(x):
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return 10
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def env_b(x):
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return 10
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# def what_ever(x):
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# return x + 1
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# Genesis States
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state_dict = {
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's1': Decimal(0.0),
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's2': Decimal(0.0),
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's3': Decimal(1.0),
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's4': Decimal(1.0),
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'timestamp': '2018-10-01 15:16:24'
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}
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exogenous_states = exo_update_per_ts(
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{
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"s3": es3p1,
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"s4": es4p2,
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"timestamp": es5p2
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}
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)
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env_processes = {
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"s3": proc_trigger('2018-10-01 15:16:25', env_a),
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"s4": proc_trigger('2018-10-01 15:16:25', env_b)
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}
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# lambdas
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# genesis Sites should always be there
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# [1, 2]
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# User Defined Aggregate Function
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behavior_udaf = [ foldr(_ + _), lambda x: x + 0 ]
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# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
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mechanisms = {
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"m1": {
|
||||||
|
"behaviors": {
|
||||||
|
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||||
|
"b2": b2m1
|
||||||
|
},
|
||||||
|
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||||
|
"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
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
sim_config = {
|
||||||
|
"N": 2,
|
||||||
|
"T": range(5)
|
||||||
|
}
|
||||||
|
|
||||||
|
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_udaf))
|
||||||
Loading…
Reference in New Issue