data
data
¶
Run data prepared for the interactive interface.
TimelineData(first: int, last: int, steps: tuple[int, ...], bins: tuple[int, ...])
dataclass
¶
A fixed-resolution summary of one run's steps.
RunKey(name: str, nonce: str)
dataclass
¶
Identify one stored run independently of its sequence.
PlotSeries(name: str, run: RunKey, points: tuple[tuple[float, float], ...], sequences: tuple[tuple[float, int], ...], checkpoints: tuple[tuple[float, float], ...] = (), checkpoint_sequences: tuple[int, ...] = ())
dataclass
¶
Name one scalar series rendered by a plot.
RunSnapshot(rows: tuple[tuple[Node, ValueRow], ...], checkpoints: frozenset[int], executions: tuple[str, ...] = ())
dataclass
¶
Values and checkpoints read for one run at a single instant.
DataFrameSource
¶
Bases: Protocol
Provide complete tabular data for external exploration.
to_dataframe() -> DataFrame
¶
Return every source row in a tabular representation.
PlotData
¶
Bases: DataFrameSource, Protocol
Provide the fields and series consumed by saved plots.
plot_series(x_key: str, y_key: str, limit: int) -> tuple[PlotSeries, ...]
¶
Return bounded named series for the selected fields.
RunData(store: ObjectReader, name: str, nonce: str)
¶
Materialize one run once and expose UI-shaped projections of it.
comparison: bool
property
¶
Return whether plots contain multiple runs.
scalar_keys: tuple[str, ...]
property
¶
Return numeric user fields available anywhere in the run.
read(store: ObjectReader) -> RunSnapshot
¶
Read a coherent replacement snapshot without mutating this run.
apply(snapshot: RunSnapshot) -> bool
¶
Apply a snapshot and invalidate cached scalar projections if changed.
timeline(width: int) -> TimelineData
¶
Collapse run events into at most width timeline bins.
nearest(seq: int) -> int
¶
Return the recorded step nearest to seq.
step(seq: int) -> tuple[Node, ValueRow]
¶
Return the identity and folded fields for an exact recorded step.
series(x_key: str, y_key: str, limit: int) -> tuple[tuple[float, float], ...]
cached
¶
Return a bounded scalar series, preserving its final point.
plot_series(x_key: str, y_key: str, limit: int) -> tuple[PlotSeries, ...]
¶
Return this run as one named plot series.
to_dataframe() -> DataFrame
¶
Return every run log row with its node identity and checkpoint state.
WorkspaceData(runs: tuple[RunData, ...])
¶
Expose comparable scalar series across a marked set of runs.
comparison: bool
property
¶
Return whether plots belong to a cross-cutting workspace.
scalar_keys: tuple[str, ...]
property
¶
Return numeric fields available in every currently marked run.
plot_series(x_key: str, y_key: str, limit: int) -> tuple[PlotSeries, ...]
¶
Return one line for each marked run containing both fields.
to_dataframe() -> DataFrame
¶
Return every log row from every run in this workspace.