Manual Lifecycle Control

Manual Lifecycle Control#

While the context manager is the preferred interface, Logbook can also be used manually. This is useful in scripts or programs where setup and teardown are more complex, or you want to control exactly when a run is marked complete or failed.

Basic Pattern#

from notata import Logbook
import numpy as np

log = Logbook("manual_heat_run", params={"Nx": 64, "Ny": 64, "kappa": 0.01})

try:
    Nx = Ny = 64
    T = np.ones((Nx, Ny))
    for step in range(500):
        # update T ...
        if (step + 1) % 100 == 0:
            log.info(f"step={step+1}")
    log.array("T_final", T)
    log.mark_complete()

except Exception as e:
    log.mark_failed(str(e))

mark_complete#

log.mark_complete()

Explicitly finalizes the run. Updates metadata.json with:

  • status: complete

  • end_time

  • runtime_sec

mark_failed#

log.mark_failed("NaN detected in simulation")

Sets the run status to failed and records the reason. Use this when:

  • You catch an exception but still want to finalize the run cleanly

  • You detect invalid or unstable results manually

  • You want to explicitly tag a run as invalid post-hoc

Checking status#

log.status

Returns one of:

  • initialized

  • complete

  • failed

This can be used in long workflows to checkpoint progress or resume logic.

Typical Use Case#

This mode is common when:

  • Using notata in larger scripts or frameworks

  • Integrating with Jupyter notebooks where context managers are awkward

  • Wanting to defer marking complete until validations are finished

Next Steps#

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