merge heaven: working
This commit is contained in:
parent
8d56cf2939
commit
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@ -16,4 +16,5 @@ def config_sim(d):
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for M in process_variables(d["M"])
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for M in process_variables(d["M"])
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]
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]
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else:
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else:
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d["M"] = [{}]
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return d
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return d
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@ -55,7 +55,6 @@ class Executor:
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Ts.append(x.sim_config['T'])
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Ts.append(x.sim_config['T'])
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Ns.append(x.sim_config['N'])
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Ns.append(x.sim_config['N'])
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var_dict_list.append(x.sim_config['M'])
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var_dict_list.append(x.sim_config['M'])
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states_lists.append([x.state_dict])
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states_lists.append([x.state_dict])
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eps.append(list(x.exogenous_states.values()))
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eps.append(list(x.exogenous_states.values()))
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configs_structs.append(config_proc.generate_config(x.state_dict, x.mechanisms, eps[config_idx]))
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configs_structs.append(config_proc.generate_config(x.state_dict, x.mechanisms, eps[config_idx]))
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@ -2,7 +2,7 @@ import pandas as pd
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from tabulate import tabulate
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from tabulate import tabulate
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# The following imports NEED to be in the exact order
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# The following imports NEED to be in the exact order
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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 simulations.validation import sweep_config
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from simulations.validation import config1, config2 # sweep_config
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from SimCAD import configs
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from SimCAD import configs
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exec_mode = ExecutionMode()
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exec_mode = ExecutionMode()
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@ -2,10 +2,9 @@ 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 datetime import timedelta
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from SimCAD import configs
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from SimCAD.configuration import append_configs
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from SimCAD.configuration import Configuration
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from SimCAD.configuration.utils import proc_trigger, bound_norm_random, ep_time_step
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from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
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from SimCAD.configuration.utils.parameterSweep import config_sim
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ep_time_step
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seed = {
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seed = {
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@ -17,46 +16,46 @@ seed = {
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# Behaviors per Mechanism
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# Behaviors per Mechanism
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def b1m1(step, sL, s):
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def b1m1(_g, step, sL, s):
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return {'param1': 1}
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return {'param1': 1}
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def b2m1(step, sL, s):
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def b2m1(_g, step, sL, s):
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return {'param2': 4}
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return {'param2': 4}
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def b1m2(step, sL, s):
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def b1m2(_g, step, sL, s):
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return {'param1': 'a', 'param2': 2}
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return {'param1': 'a', 'param2': 2}
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def b2m2(step, sL, s):
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def b2m2(_g, step, sL, s):
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return {'param1': 'b', 'param2': 4}
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return {'param1': 'b', 'param2': 4}
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def b1m3(step, sL, s):
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def b1m3(_g, step, sL, s):
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return {'param1': ['c'], 'param2': np.array([10, 100])}
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return {'param1': ['c'], 'param2': np.array([10, 100])}
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def b2m3(step, sL, s):
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def b2m3(_g, step, sL, s):
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return {'param1': ['d'], 'param2': np.array([20, 200])}
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return {'param1': ['d'], 'param2': np.array([20, 200])}
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# Internal States per Mechanism
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# Internal States per Mechanism
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def s1m1(step, sL, s, _input):
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def s1m1(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m1(step, sL, s, _input):
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def s2m1(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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def s1m2(step, sL, s, _input):
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def s1m2(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m2(step, sL, s, _input):
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def s2m2(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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def s1m3(step, sL, s, _input):
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def s1m3(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m3(step, sL, s, _input):
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def s2m3(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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@ -66,19 +65,19 @@ def s2m3(step, sL, s, _input):
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proc_one_coef_A = 0.7
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proc_one_coef_A = 0.7
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proc_one_coef_B = 1.3
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proc_one_coef_B = 1.3
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def es3p1(step, sL, s, _input):
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def es3p1(_g, step, sL, s, _input):
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y = 's3'
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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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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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return (y, x)
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def es4p2(step, sL, s, _input):
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def es4p2(_g, step, sL, s, _input):
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y = 's4'
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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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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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ts_format = '%Y-%m-%d %H:%M:%S'
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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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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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def es5p2(_g, 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, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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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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@ -103,14 +102,11 @@ genesis_states = {
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}
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}
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# remove `exo_update_per_ts` to update every ts
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raw_exogenous_states = {
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exogenous_states = exo_update_per_ts(
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{
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"s3": es3p1,
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"s3": es3p1,
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"s4": es4p2,
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"s4": es4p2,
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"timestamp": es5p2
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"timestamp": es5p2
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}
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}
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)
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env_processes = {
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env_processes = {
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@ -153,19 +149,19 @@ mechanisms = {
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}
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}
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sim_config = {
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sim_config = config_sim(
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"N": 2,
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{
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"T": range(5)
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"N": 2,
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}
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"T": range(5),
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}
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)
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configs.append(
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Configuration(
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sim_config=sim_config,
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append_configs(
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state_dict=genesis_states,
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sim_configs=sim_config,
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seed=seed,
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state_dict=genesis_states,
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exogenous_states=exogenous_states,
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seed=seed,
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env_processes=env_processes,
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raw_exogenous_states=raw_exogenous_states,
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mechanisms=mechanisms
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env_processes=env_processes,
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)
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mechanisms=mechanisms
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)
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)
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@ -2,11 +2,9 @@ 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 datetime import timedelta
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from SimCAD import configs
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from SimCAD.configuration import append_configs
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from SimCAD.configuration import Configuration
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from SimCAD.configuration.utils import proc_trigger, bound_norm_random, ep_time_step
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from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
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from SimCAD.configuration.utils.parameterSweep import config_sim
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ep_time_step
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seed = {
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seed = {
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'z': np.random.RandomState(1),
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'z': np.random.RandomState(1),
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@ -17,46 +15,46 @@ seed = {
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# Behaviors per Mechanism
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# Behaviors per Mechanism
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def b1m1(step, sL, s):
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def b1m1(_g, step, sL, s):
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return {'param1': 1}
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return {'param1': 1}
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def b2m1(step, sL, s):
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def b2m1(_g, step, sL, s):
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return {'param2': 4}
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return {'param2': 4}
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def b1m2(step, sL, s):
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def b1m2(_g, step, sL, s):
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return {'param1': 'a', 'param2': 2}
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return {'param1': 'a', 'param2': 2}
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def b2m2(step, sL, s):
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def b2m2(_g, step, sL, s):
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return {'param1': 'b', 'param2': 4}
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return {'param1': 'b', 'param2': 4}
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def b1m3(step, sL, s):
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def b1m3(_g, step, sL, s):
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return {'param1': ['c'], 'param2': np.array([10, 100])}
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return {'param1': ['c'], 'param2': np.array([10, 100])}
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def b2m3(step, sL, s):
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def b2m3(_g, step, sL, s):
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return {'param1': ['d'], 'param2': np.array([20, 200])}
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return {'param1': ['d'], 'param2': np.array([20, 200])}
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# Internal States per Mechanism
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# Internal States per Mechanism
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def s1m1(step, sL, s, _input):
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def s1m1(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m1(step, sL, s, _input):
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def s2m1(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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def s1m2(step, sL, s, _input):
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def s1m2(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m2(step, sL, s, _input):
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def s2m2(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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def s1m3(step, sL, s, _input):
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def s1m3(_g, step, sL, s, _input):
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y = 's1'
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y = 's1'
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x = _input['param1']
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x = _input['param1']
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return (y, x)
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return (y, x)
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def s2m3(step, sL, s, _input):
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def s2m3(_g, step, sL, s, _input):
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y = 's2'
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y = 's2'
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x = _input['param2']
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x = _input['param2']
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return (y, x)
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return (y, x)
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@ -66,19 +64,19 @@ def s2m3(step, sL, s, _input):
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proc_one_coef_A = 0.7
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proc_one_coef_A = 0.7
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proc_one_coef_B = 1.3
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proc_one_coef_B = 1.3
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def es3p1(step, sL, s, _input):
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def es3p1(_g, step, sL, s, _input):
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y = 's3'
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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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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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return (y, x)
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def es4p2(step, sL, s, _input):
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def es4p2(_g, step, sL, s, _input):
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y = 's4'
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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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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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ts_format = '%Y-%m-%d %H:%M:%S'
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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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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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def es5p2(_g, 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, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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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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@ -103,14 +101,11 @@ genesis_states = {
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}
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}
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# remove `exo_update_per_ts` to update every ts
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raw_exogenous_states = {
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exogenous_states = exo_update_per_ts(
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{
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"s3": es3p1,
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"s3": es3p1,
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"s4": es4p2,
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"s4": es4p2,
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"timestamp": es5p2
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"timestamp": es5p2
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}
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}
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)
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env_processes = {
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env_processes = {
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@ -153,19 +148,19 @@ mechanisms = {
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}
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}
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sim_config = {
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sim_config = config_sim(
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"N": 2,
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{
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"T": range(5)
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"N": 2,
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}
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"T": range(5),
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}
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)
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configs.append(
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Configuration(
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sim_config=sim_config,
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append_configs(
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state_dict=genesis_states,
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sim_configs=sim_config,
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seed=seed,
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state_dict=genesis_states,
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exogenous_states=exogenous_states,
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seed=seed,
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env_processes=env_processes,
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raw_exogenous_states=raw_exogenous_states,
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mechanisms=mechanisms
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env_processes=env_processes,
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)
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mechanisms=mechanisms
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)
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)
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