revalidation_table_styles#

mobgap.utils.tables.revalidation_table_styles(
st: Styler,
thresholds: dict[Hashable, RevalidationInfo],
groupby: Hashable | list[Hashable],
) → Styler[source]#

Apply styles to a DataFrame appropriate for the revalidation.

To use this, set up a dictionary with the column names as keys and the comparison information as values. then you can run df.style.pipe(revalidation_table_styles, thresholds, groupby=<groupby>) to apply the styles.

This applies best_in_group_styler, compare_to_threshold_styler, and border_after_group_styler with the default styles.

Parameters:
st

The Styler object to apply the styles to.

thresholds

A dictionary with the column names as keys and the comparison information as values.

groupby

A valid groupby argument for groupby. This is used by best_in_group_styler and border_after_group_styler.

Examples using mobgap.utils.tables.revalidation_table_styles#

Walking speed estimation

Walking speed estimation

Cadence estimation

Cadence estimation

Stride length estimation

Stride length estimation

Additional Full Pipeline Experiments

Additional Full Pipeline Experiments

Performance of the gait sequences algorithm on the TVS dataset

Performance of the gait sequences algorithm on the TVS dataset

Performance of the initial contact algorithms on the TVS dataset

Performance of the initial contact algorithms on the TVS dataset

Performance of the laterality classification algorithms on the TVS dataset

Performance of the laterality classification algorithms on the TVS dataset

Performance of the cadence algorithms on the TVS dataset

Performance of the cadence algorithms on the TVS dataset

Performance of the stride length algorithms on the TVS dataset

Performance of the stride length algorithms on the TVS dataset