pept.AdaptiveWindow#

class pept.AdaptiveWindow(window: float, max_elems: int = 9223372036854775807)[source]#

Bases: object

Define a sample_size as a time window with a maximum limit of elements. All samples with more than max_elems elements will be shortened.

You can use this as a direct replacement of the sample_size and overlap.

points = pept.PointData(sample_size = pept.AdaptiveWindow(5.5, 200))
points.overlap = AdaptiveWindow(2.)

The adaptive time window approach combines the advantages of fixed sample sizes and time windowing:

  • Time windows are robust to tracers moving in and out of the field of view, as they simply ignore the time slices where almost no LoRs are recorded.

  • Fixed sample sizes effectively adapt their spatio-temporal resolution, allowing for higher accuracy when tracers are passing through more active scanner regions.

All samples with more than ideal_elems are shortened, such that time windows are shrinked when the tracer activity permits. There exists an ideal time window such that most samples will have roughly ideal_elems, with a few higher activity ones that are shortened; OptimizeWindow finds this ideal time window for pept.AdaptiveWindow.

New in pept-0.5.1

__init__(window: float, max_elems: int = 9223372036854775807)[source]#

Methods

__init__(window[, max_elems])