import psyneulink as pnl
comp = pnl.Composition(name='comp')
A = pnl.TransferMechanism(name='A')
B = pnl.TransferMechanism(name='B')
C = pnl.TransferMechanism(name='C')
comp.add_linear_processing_pathway([A, B, C])
comp.scheduler.add_condition_set({
A: pnl.AtNCalls(A, 0),
B: pnl.Always(),
C: pnl.EveryNCalls(B, 5),
})
comp.run(inputs={A: 1})
# A, B, B, B, B, B, C
print(comp.scheduler.execution_list[comp.default_execution_id])
with open(__file__.replace('.py', '.json'), 'w') as f:
f.write(comp.json_summary + '\n')
comp.add_projection(
projection=pnl.MappingProjection(
name="MappingProjection_from_A_RESULT__to_B_InputPort_0_",
function=pnl.LinearMatrix(matrix=[[1.0]]),
),
sender=A,
receiver=B,
)
comp.add_projection(
projection=pnl.MappingProjection(
name="MappingProjection_from_B_RESULT__to_C_InputPort_0_",
function=pnl.LinearMatrix(matrix=[[1.0]]),
),
sender=B,
receiver=C,
)
comp.scheduler.add_condition(
A,
pnl.AtNCalls(dependency=A,
n=0,
time_scale=pnl.TimeScale.ENVIRONMENT_STATE_UPDATE),
)
comp.scheduler.add_condition(B, pnl.Always())
comp.scheduler.add_condition(C, pnl.EveryNCalls(dependency=B, n=5))
comp.scheduler.termination_conds = {
pnl.TimeScale.ENVIRONMENT_SEQUENCE: pnl.Never(),
pnl.TimeScale.ENVIRONMENT_STATE_UPDATE: pnl.AllHaveRun(),
}