feat: save opened anemoi_dataset

src/anemoi/datasets/create/filters/transform.py

@@ -40,8 +40,6 @@ def execute(self, input: ekd.FieldList) -> ekd.FieldList:
 
         Parameters
         ----------
-        context : Any
-            The context in which the execution occurs.
         input : ekd.FieldList
             The input data to be transformed.
 

src/anemoi/datasets/data/__init__.py

@@ -16,6 +16,7 @@
 # from .dataset import Shape
 # from .dataset import TupleIndex
 from .misc import _open_dataset
+from .misc import _save_dataset
 from .misc import add_dataset_path
 from .misc import add_named_dataset
 
@@ -92,6 +93,21 @@ def open_dataset(*args: Any, **kwargs: Any) -> "Dataset":
     return ds
 
 
+def save_dataset(recipe: dict, zarr_path: str, n_workers: int = 1) -> None:
+    """Open a dataset and save it to disk.
+
+    Parameters
+    ----------
+    recipe : dict
+        Recipe used with open_dataset (not a dataset creation recipe).
+    zarr_path : str
+        Path to store the obtained anemoi dataset to disk.
+    n_workers : int
+        Number of workers to use for parallel processing. If none, sequential processing will be performed.
+    """
+    _save_dataset(recipe, zarr_path, n_workers)
+
+
 def list_dataset_names(*args: Any, **kwargs: Any) -> list[str]:
     """List the names of datasets.
 

src/anemoi/datasets/data/misc.py

@@ -539,3 +539,162 @@ def _open_dataset(*args: Any, **kwargs: Any) -> "Dataset":
         return dataset._subset(**kwargs)
 
     return sets[0]._subset(**kwargs)
+
+
+def append_to_zarr(new_data: np.ndarray, new_dates: np.ndarray, zarr_path: str) -> None:
+    """Append data from a subset (for one date) to the Zarr store.
+
+    Parameters
+    ----------
+    new_data : np.ndarray
+        The new data to append.
+    new_dates : np.ndarray
+        The new dates to append.
+    zarr_path : str
+        The path to the Zarr store.
+
+    Notes
+    -----
+    - "dates" dataset is created with chunks equal to len(big_dataset.dates).
+    - "data" dataset is created with chunk size 1 along the first (time) dimension.
+    """
+    print("Appending data for", new_dates, flush=True)
+    # Re-open the zarr store to avoid root object accumulating memory.
+    root = zarr.open(zarr_path, mode="a")
+    # Convert new dates to strings (using str) regardless of input dtype.
+    new_dates = np.array(new_dates, dtype="datetime64[ns]")
+    dates_ds = root["dates"]
+    old_len = dates_ds.shape[0]
+    dates_ds.resize((old_len + len(new_dates),))
+    dates_ds[old_len:] = new_dates
+    # Append to "data" dataset.
+    data_ds = root["data"]
+    old_shape = data_ds.shape  # (time, n_vars, ensembles, gridpoints)
+    new_shape = (old_shape[0] + len(new_dates),) + old_shape[1:]
+    data_ds.resize(new_shape)
+    data_ds[old_shape[0] :] = new_data
+
+
+def process_date(date: Any, big_dataset: Any) -> Tuple[np.ndarray, np.ndarray]:
+    """Open the subset corresponding to the given date and return (date, subset).
+
+    Parameters
+    ----------
+    date : Any
+        The date to process.
+    big_dataset : Any
+        The dataset to process.
+
+    Returns
+    -------
+    Tuple[np.ndarray, np.ndarray]
+        The subset and the date.
+    """
+    print("Processing:", date, flush=True)
+    subset = _open_dataset(big_dataset, start=date, end=date).mutate()
+    s = subset[:]
+    date = subset.dates
+    return s, date
+
+
+def initialize_zarr_store(root: Any, big_dataset: Any, recipe: Dict[str, Any]) -> None:
+    """Initialize the Zarr store with the given dataset and recipe.
+
+    Parameters
+    ----------
+    root : Any
+        The root of the Zarr store.
+    big_dataset : Any
+        The dataset to initialize the store with.
+    recipe : Dict[str, Any]
+        The recipe for initializing the store.
+    """
+    ensembles = big_dataset.shape[1]
+    # Create or append to "dates" dataset.
+    if "dates" not in root:
+        full_length = len(big_dataset.dates)
+        root.create_dataset("dates", data=np.array([], dtype="datetime64[s]"), chunks=(full_length,))
+
+    if "data" not in root:
+        dims = (1, len(big_dataset.variables), ensembles, big_dataset.grids[0])
+        root.create_dataset(
+            "data",
+            shape=dims,
+            dtype=np.float64,
+            chunks=dims,
+        )
+
+    for k, v in big_dataset.statistics.items():
+        if k not in root:
+            root.create_dataset(
+                k,
+                data=v,
+                compressor=None,
+            )
+
+    # Create spatial coordinate datasets if missing.
+    if "latitudes" not in root or "longitudes" not in root:
+        root.create_dataset("latitudes", data=big_dataset.latitudes, compressor=None)
+        root.create_dataset("longitudes", data=big_dataset.longitudes, compressor=None)
+
+    # Set store-wide attributes if not already set.
+    if "frequency" not in root.attrs:
+        root.attrs["frequency"] = "10m"
+        root.attrs["resolution"] = "1km"
+        root.attrs["name_to_index"] = {k: i for i, k in enumerate(big_dataset.variables)}
+        root.attrs["ensemble_dimension"] = 1
+        root.attrs["field_shape"] = big_dataset.field_shape
+        root.attrs["flatten_grid"] = True
+        root.attrs["recipe"] = recipe
+
+
+def _save_dataset(recipe: Dict[str, Any], zarr_path: str, n_workers: int = 1) -> None:
+    """Incrementally create (or update) a Zarr store from an Anemoi dataset.
+
+    Parameters
+    ----------
+    recipe : Dict[str, Any]
+        The recipe for creating the dataset.
+    zarr_path : str
+        The path to the Zarr store.
+    n_workers : int, optional
+        The number of worker processes to use, by default 1.
+
+    Notes
+    -----
+    Worker processes extract data for each date in parallel, but all writes
+    to the store happen sequentially in the main process (i.e. single-writer).
+
+    The "dates" dataset is created with chunking equal to the full length of
+    big_dataset.dates, while "data" is chunked with 1 in the time dimension.
+    """
+    from concurrent.futures import ProcessPoolExecutor
+
+    full_ds = _open_dataset(recipe).mutate()
+    print("Opened full dataset.", flush=True)
+
+    # Use ProcessPoolExecutor for parallel data extraction.
+    # Workers return (date, subset) tuples.
+    root = zarr.open(zarr_path, mode="a")
+    initialize_zarr_store(root, full_ds, recipe)
+    print("Zarr store initialized.", flush=True)
+
+    existing_dates = np.array(sorted(root["dates"]), dtype="datetime64[s]")
+    all_dates = full_ds.dates
+    # To resume creation of the Zarr store in case the job is interrupted.
+    dates_to_process = np.array(sorted(set(all_dates).difference(existing_dates)), dtype="datetime64[s]")
+
+    use_pool = False
+
+    if use_pool:
+        with ProcessPoolExecutor(n_workers) as pool:
+            futures = [pool.submit(process_date, date, full_ds) for date in dates_to_process]
+            for future in futures:
+                subset, date = future.result()
+                # All appends happen sequentially here to
+                #  avoid dates being added in a random order.
+                append_to_zarr(subset, date, zarr_path)
+    else:
+        for date in dates_to_process:
+            subset, date = process_date(date, full_ds)
+            append_to_zarr(subset, date, zarr_path)