def run_simulation(c: CommonsSimulationConfiguration):
initial_conditions, simulation_parameters = bootstrap_simulation(c)
exp = Experiment()
exp.append_configs(
initial_state=initial_conditions,
partial_state_update_blocks=partial_state_update_blocks,
sim_configs=simulation_parameters
)
# Do not use multi_proc, breaks ipdb.set_trace()
exec_mode = ExecutionMode()
single_proc_context = ExecutionContext(exec_mode.local_mode)
executor = Executor(single_proc_context, configs)
raw_system_events, tensor_field, sessions = executor.execute()
df = pd.DataFrame(raw_system_events)
df_final = df[df.substep.eq(2)]
result = {
"timestep": list(df_final["timestep"]),
"funding_pool": list(df_final["funding_pool"]),
"token_supply": list(df_final["token_supply"]),
"collateral": list(df_final["collateral_pool"]),
"sentiment": list(df_final["sentiment"])
}
return result, df_final
def run_simulation(c: CommonsSimulationConfiguration):
initial_conditions, simulation_parameters = bootstrap_simulation(c)
exp = Experiment()
exp.append_configs(initial_state=initial_conditions,
partial_state_update_blocks=partial_state_update_blocks,
sim_configs=simulation_parameters)
# Do not use multi_proc, breaks ipdb.set_trace()
exec_mode = ExecutionMode()
single_proc_context = ExecutionContext(exec_mode.local_mode)
executor = Executor(single_proc_context, configs)
raw_system_events, tensor_field, sessions = executor.execute()
df = pd.DataFrame(raw_system_events)
return df