skdh.activity.MaxAcceleration

class skdh.activity.MaxAcceleration(window_lengths, required_points=1.0, state='wake')

Compute the maximum acceleration over windows of the specified length.

Parameters:

window_lengths{list, int}

List of window lengths, or a single window length.

required_pointsint, float, optional

Number of points required in the window to compute the endpoint. If float, should be between 0 and 1 and will be interpreted as a fraction of the total number of points in the window. Default is 1.0, meaning all points in the window must be present.

state{‘wake’, ‘sleep}

State during which the endpoint is being computed.

Methods

predict(results, i, accel_metric, ...)	Compute the maximum acceleration during this set of data, and compare it to the previous largest detected value.
reset_cached()	Called after all the blocks during the desired state have been run.

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

Compute the maximum acceleration during this set of data, and compare it to the previous largest detected value.

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.