suboptimumg.log_analysis.yaw_artifact#

class suboptimumg.log_analysis.yaw_artifact.SegmentationConfig[source]#

Bases: BaseModel

A record of how one log was cut into sub-windows.

Recorded alongside the sub-windows so a saved artifact answers “where did these come from” without re-reading the notebook that made it.

Fields#
Field	Type	Required	Default
`gps_lag_s`	`float`	No	`0.0`
`logfile`	`str`	Yes
`manual_windows`	`list`[`tuple`[`float`, `float`]]	No	factory
`mask_reports`	`list`[`MaskReport`]	No	factory
`max_slip_deg`	`float`	No	`7.0`
`min_speed_mps`	`float`	No	`4.0`
`min_window_s`	`float`	No	`8.0`
`target_hz`	`float`	No	`100.0`
`trim_range_s`	`tuple`[`float`, `float`] \| `None`	No	`None`

Parameters:

logfile (str)
trim_range_s (tuple[float, float] | None)
manual_windows (list[tuple[float, float]])
gps_lag_s (float)
min_speed_mps (float)
max_slip_deg (float)
min_window_s (float)
target_hz (float)
mask_reports (list[MaskReport])

gps_lag_s: float#

logfile: str#

manual_windows: list[tuple[float, float]]#

mask_reports: list[MaskReport]#

max_slip_deg: float#

min_speed_mps: float#

min_window_s: float#

target_hz: float#

trim_range_s: tuple[float, float] | None#

class suboptimumg.log_analysis.yaw_artifact.YawArtifact[source]#

Bases: BaseModel

One log’s yaw-response work: its sub-windows, how they were made, and the fits.

Bundles everything a later session needs to re-evaluate or compare against this log without re-running preparation. save / load round-trip it through pickle, because the PERDA SingleRunData sub-windows have no JSON form. Treat the file as a local cache, not an interchange format: it is tied to the installed PERDA/pydantic versions, and loading one executes arbitrary code, so only load artifacts you produced.

Fields#
Field	Type	Required	Default
`config`	`SegmentationConfig`	Yes
`fits`	`dict`[`str`, `YawFit`]	No	factory
`log_name`	`str`	Yes
`subwindows`	`list`[`SingleRunData`]	Yes

Parameters:

log_name (str)
config (SegmentationConfig)
subwindows (list[SingleRunData])
fits (dict[str, YawFit])

add_fit(fit)[source]#

Record fit under its spec name, replacing any fit of that name.

Parameters:: fit (YawFit)
Return type:: None

config: SegmentationConfig#

fits: dict[str, YawFit]#

static load(path)[source]#

Inverse of save. Only load artifacts you produced – see the class docstring.

Raises:: TypeError – If path does not hold a YawArtifact.
Parameters:: path (str | Path)
Return type:: YawArtifact

log_name: str#

save(path)[source]#

Pickle this artifact to path, creating parent directories.

Returns:: The resolved output path.
Return type:: Path
Parameters:: path (str | Path)

subwindows: list[SingleRunData]#