plesty.lib.experiment.schedule

Experiment plans: ordered schedules of atomic measurement steps.

A Plan is the frozen schedule of an experiment run — an ordered list of Step items, each the smallest unit of work that either completes (its result is persisted and journaled) or is re-run on resume. The plan is written to disk at run start and never mutated; its content hash lets a resumed run verify it is continuing the same schedule.

Classes

`Step`	One atomic measurement step in an experiment plan.
`Plan`	A frozen, hashable schedule of atomic steps plus the run configuration.

Module Contents

class plesty.lib.experiment.schedule.Step

One atomic measurement step in an experiment plan.

Variables:

id – Stable, deterministic identifier unique within the plan, e.g. "scan[x=3,y=5]". Resume matches completed steps by this id, so it must not depend on run time or randomness.
op – Name of the experiment method to call for this step.
params – Keyword arguments passed to the method.

id: str

op: str

params: dict[str, Any]

to_dict() → dict[str, Any]

Return a JSON-serializable representation of the step.

Return type:: dict[str, Any]

classmethod from_dict(data: dict[str, Any]) → Step

Reconstruct a step from to_dict() output.

Parameters:: data (dict[str, Any])
Return type:: Step

class plesty.lib.experiment.schedule.Plan(steps: list[Step], config: dict[str, Any] | None = None)

A frozen, hashable schedule of atomic steps plus the run configuration.

Usage:

plan = Plan(
    steps=[Step(id=f"scan[{i}]", op="scan_point", params={"x": i}) for i in range(10)],
    config={"exposure_s": 0.1},
)
plan.save("runs/run_001/plan.json")

Initialize the plan with its steps and optional configuration.

Parameters:

steps (list[Step]) – Ordered atomic steps; step ids must be unique.
config (dict[str, Any] | None) – Experiment configuration recorded alongside the schedule.

Raises:

ValueError – If two steps share the same id.

steps: list[Step]

config: dict[str, Any]

to_dict() → dict[str, Any]

Return a JSON-serializable representation of the plan.

Return type:: dict[str, Any]

classmethod from_dict(data: dict[str, Any]) → Plan

Reconstruct a plan from to_dict() output.

Parameters:: data (dict[str, Any])
Return type:: Plan

content_hash() → str

Return a stable SHA-256 hash of the schedule and configuration.

Used on resume to verify that the persisted plan matches the plan the experiment would generate now — resuming under changed parameters is refused rather than silently mixing two schedules.

Return type:: str

save(path: str | pathlib.Path) → pathlib.Path

Write the plan as JSON to path, creating parent directories.

Parameters:: path (str | pathlib.Path)
Return type:: pathlib.Path

classmethod load(path: str | pathlib.Path) → Plan

Load a plan previously written by save().

Parameters:: path (str | pathlib.Path)
Return type:: Plan

__len__() → int

Return the number of steps in the plan.

Return type:: int