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4. Order can change meaning
Concept
“How much of the region is at or below freezing?” differs from “Is the regional mean at or below freezing?” Run the shared setup first.
Tiny example
cold_cells = pipe(cube) | v.threshold_state(threshold=0, direction="below")
regional_mean = pipe(cube) | v.mean(dim=("y", "x"), keep_dim=False)
Explanation
threshold_state makes the comparison explicit. Thresholding cells before averaging produces a fraction; thresholding the spatial mean produces one Boolean per day. These operations answer different questions even when both scripts are perfectly rerunnable. Authored order is therefore scientific syntax, not just formatting.
Try it / worked example
fraction_cold = cold_cells | v.mean(dim=("y", "x"), keep_dim=False)
mean_is_cold = regional_mean | v.threshold_state(threshold=0, direction="below")
print(fraction_cold.explain()) # current result: summary
print(mean_is_cold.explain()) # current result: condition
fraction = fraction_cold.unwrap()["state"]
mean_condition = mean_is_cold.unwrap()["state"].astype(float)
fraction.plot(label="Fraction of cells at or below freezing")
mean_condition.plot(label="Regional mean at or below freezing")
plt.legend()
plt.show()
These are unweighted cell summaries, not area-weighted estimates. Look for
days when the curves differ before choosing a summary for your question.
CubeDynamics preserves the written order and records an
ORDER_CHANGES_MEANING note for either path; it does not rewrite one into the
other. Reducing a condition labels the result and its state variable as a
summary proportion rather than leaving stale Boolean-condition metadata.