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[lazy memory] Use slice instead of lists for lead time selections. #75

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amitmarkel merged 1 commit into from
Nov 3, 2025
Merged

[lazy memory] Use slice instead of lists for lead time selections. #75

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31 changes: 16 additions & 15 deletions googlehydrology/datasetzoo/multimet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -505,9 +505,9 @@ def _extract_dataset(
self,
data: xr.Dataset,
features: list[str],
indexers: dict[Hashable, int | range],
indexers: dict[Hashable, int | range | slice],
) -> dict[str, np.ndarray | np.float32]:
def extract(feature_name):
def extract(feature_name: str):
key = f"{id(data)}{feature_name}"
feature = self._data_cache.get(key)
if feature is None:
Expand Down Expand Up @@ -572,11 +572,9 @@ def _load_hindcast_features(self) -> list[xr.Dataset]:
if "lead_time" in product_ds:
# The same product may be used both for forecast and hindcast features. For hindcast, we load it with the
# full lead_time similar to forecast, and filter the minimal lead_time values during sampling.
lead_times = [
pd.Timedelta(days=i)
for i in range(MULTIMET_MINIMUM_LEAD_TIME, self.lead_time + 1)
]
product_ds = product_ds.sel(basin=self._basins, lead_time=lead_times)
product_ds = product_ds.sel(
basin=self._basins, lead_time=self._lead_time_slice()
)
else:
product_ds = product_ds.sel(basin=self._basins)

Expand All @@ -602,12 +600,6 @@ def _load_forecast_features(self) -> list[xr.Dataset]:
# Initialize storage for product/band dataframes that will eventually be concatenated.
product_dss = []

# Lead time array.
lead_times = [
pd.Timedelta(days=i)
for i in range(MULTIMET_MINIMUM_LEAD_TIME, self.lead_time + 1)
]

# Load data for the selected products, bands, and basins.
for product, bands in product_bands.items():
product_path = self._dynamics_data_path / product / "timeseries.zarr"
Expand All @@ -619,7 +611,9 @@ def _load_forecast_features(self) -> list[xr.Dataset]:
f"Lead times do not exist for forecast product ({product})."
)

product_ds = product_ds.sel(basin=self._basins, lead_time=lead_times)[bands]
product_ds = product_ds.sel(
basin=self._basins, lead_time=self._lead_time_slice()
)[bands]
product_dss.append(product_ds)

return product_dss
Expand Down Expand Up @@ -650,6 +644,13 @@ def _load_static_features(self) -> xr.Dataset:
features=self._static_features,
)

def _lead_time_slice(self) -> slice:
# https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#endpoints-are-inclusive
return slice(
pd.Timedelta(days=MULTIMET_MINIMUM_LEAD_TIME),
pd.Timedelta(days=self.lead_time),
)

@staticmethod
def collate_fn(
samples: list[
Expand Down Expand Up @@ -681,7 +682,7 @@ def collate_fn(


def _extract_dataarray(
data: xr.DataArray, indexers: dict[Hashable, int | range]
data: xr.DataArray, indexers: dict[Hashable, int | range | slice]
) -> np.ndarray | np.float32:
"""Returns the values in array according to dims given by indexers.

Expand Down