Documents · Verb reference
timing_synchrony
Measure whether one-to-one matched events happen at similar label times.
Callable type: Grammar verb / pipe stage · Browse: Synchrony and comparison
Usage
from cubedynamics import verbs as v
v.timing_synchrony(*, event_anchor='start', match_tolerance='7D', score='exponential', timescale='3D', spatial_mode='neighbors', radius_km=None, k_neighbors=None, reference=None)
Arguments
| Argument | Meaning | Default |
|---|---|---|
| event_anchor | See implementation docstring below; no parameter-specific description supplied. | 'start' |
| match_tolerance | See implementation docstring below; no parameter-specific description supplied. | '7D' |
| score | See implementation docstring below; no parameter-specific description supplied. | 'exponential' |
| timescale | See implementation docstring below; no parameter-specific description supplied. | '3D' |
| spatial_mode | See implementation docstring below; no parameter-specific description supplied. | 'neighbors' |
| radius_km | See implementation docstring below; no parameter-specific description supplied. | None |
| k_neighbors | See implementation docstring below; no parameter-specific description supplied. | None |
| reference | See implementation docstring below; no parameter-specific description supplied. | None |
Accepts
EventResult -> synchrony Dataset with lag and unmatched-event diagnostics.
event_anchor selects start, peak, or end event labels. This is event-time
alignment and does not establish equality of source observation windows.
Returns
EventResult -> synchrony Dataset with lag and unmatched-event diagnostics.
event_anchor selects start, peak, or end event labels. This is event-time
alignment and does not establish equality of source observation windows.
Order / grammar behavior
EventResult -> synchrony Dataset with lag and unmatched-event diagnostics.
event_anchor selects start, peak, or end event labels. This is event-time
alignment and does not establish equality of source observation windows.
Minimal example
Run from the repository root after python -m pip install -e '.[vignettes]'. Uses the checked observational PRISM fixture; no network is required.
from pathlib import Path
import xarray as xr
import matplotlib.pyplot as plt
from cubedynamics import pipe, verbs as v
# Frozen, reviewed PRISM observations; run from the repository root.
path = Path("tests/fixtures/real_data/prism_boulder_january_2024.nc")
with xr.open_dataset(path, engine="scipy") as observed:
cube = observed["tmax"].load()
assert cube.attrs["units"] == "degC"
events = (pipe(cube) | v.threshold_state(threshold=0, direction="below") | v.detect_events()).unwrap()
result = (pipe(events) | v.timing_synchrony(spatial_mode="reference", reference="center")).unwrap()
result["timing_synchrony"].squeeze().plot()
plt.show()
Works with
EventResult -> synchrony Dataset with lag and unmatched-event diagnostics.
event_anchor selects start, peak, or end event labels. This is event-time
alignment and does not establish equality of source observation windows.
See also
Implementation notes
No additional implementation notes in the current docstring.
Implementation source. Signatures and descriptions on this page are generated from this checkout, not hand-maintained copies.