skdh.activity.FragmentationEndpoints

class skdh.activity.FragmentationEndpoints(level, cutpoints=None, state='wake')

Compute fragmentation metrics for the desired intensity level. Fragmentation endpoints are computed on 1 minute windows of data.

Parameters:

level{“sed”, “light”, “mod”, “vig”, “MVPA”, “SLPA”}

Level of intensity to compute the total time for.

cutpoints{str, None}

Cutpoints to use for the thresholding. If None, will use migueles_wrist_adult.

state{‘wake’, ‘sleep’}

State during which the endpoint is being computed.

Methods

predict(results, i, accel_metric, ...)	Compute and save the lengths of runs of the specified intensity level.
reset_cached()	Compute the fragmentation metrics based on the previously saved lengths of intensity level runs.

predict(results, i, accel_metric, accel_metric_60, epoch_s, epochs_per_min, **kwargs)

Compute and save the lengths of runs of the specified intensity level.

Parameters:

resultsdict

Dictionary containing the initialized results arrays. Keys in results are taken from the names of endpoints.

iint

Index of the day, used to index into individual result arrays, e.g. results[self.name][i] = 5.0

accel_metricnumpy.ndarray

Computed acceleration metric (e.g. ENMO).

accel_metric_60numpy.ndarray

Computed acceleration metric for a 60 second window.

epoch_sint

Duration in seconds of each sample of accel_metric.

epochs_per_minint

Number of epochs per minute.

reset_cached()

Compute the fragmentation metrics based on the previously saved lengths of intensity level runs.