From 4cfc27929bb6106f198e25a1f1734a3cf6938617 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 5 Dec 2025 14:10:28 +0100 Subject: [PATCH 01/59] eagerly compute multiscales --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index a2b311f1..4ead9c40 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -170,7 +170,7 @@ def create_multiscale_from_datatree( r120m_dataset = create_downsampled_resolution_group( source_dataset, factor=factor - ) + ).compute() if r120m_dataset and len(r120m_dataset.data_vars) > 0: output_path_120 = f"{output_path}{r120m_path}" log.info("Writing r120m to {}", output_path_120=output_path_120) @@ -194,7 +194,7 @@ def create_multiscale_from_datatree( r360m_dataset = create_downsampled_resolution_group( r120m_dataset, factor=3 - ) + ).compute() if r360m_dataset and len(r360m_dataset.data_vars) > 0: output_path_360 = f"{output_path}{r360m_path}" @@ -219,7 +219,7 @@ def create_multiscale_from_datatree( r720m_dataset = create_downsampled_resolution_group( r360m_dataset, factor=2 - ) + ).compute() if r720m_dataset and len(r720m_dataset.data_vars) > 0: output_path_720 = f"{output_path}{r720m_path}" From 73c8b276c6dac28a7de21783a7d4c43ea57e34b7 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 5 Dec 2025 21:12:34 +0100 Subject: [PATCH 02/59] directly copy chunk bytes and metadata documents --- src/eopf_geozarr/_version.py | 33 +++ src/eopf_geozarr/cli.py | 3 +- .../s2_optimization/s2_converter.py | 192 +++++++++++++++++- .../s2_optimization/s2_multiscale.py | 2 +- 4 files changed, 225 insertions(+), 5 deletions(-) create mode 100644 src/eopf_geozarr/_version.py diff --git a/src/eopf_geozarr/_version.py b/src/eopf_geozarr/_version.py new file mode 100644 index 00000000..8d76d5c7 --- /dev/null +++ b/src/eopf_geozarr/_version.py @@ -0,0 +1,33 @@ +# file generated by setuptools-scm +# don't change, don't track in version control + +__all__ = [ + "__version__", + "__version_tuple__", + "version", + "version_tuple", + "__commit_id__", + "commit_id", +] + +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple, Union + + VERSION_TUPLE = Tuple[Union[int, str], ...] + COMMIT_ID = Union[str, None] +else: + VERSION_TUPLE = object + COMMIT_ID = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE +commit_id: COMMIT_ID +__commit_id__: COMMIT_ID + +__version__ = version = "0.5.1.dev1+g3a865cf90.d20251205" +__version_tuple__ = version_tuple = (0, 5, 1, "dev1", "g3a865cf90.d20251205") + +__commit_id__ = commit_id = "g3a865cf90" diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 24f9bff7..b8a30294 100644 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1207,8 +1207,9 @@ def convert_s2_optimized_command(args: Any) -> None: # Load input dataset log.info("Loading Sentinel-2 dataset from", input_path=args.input_path) storage_options = get_storage_options(str(args.input_path)) + store = args.input_path dt_input = xr.open_datatree( - str(args.input_path), + store, engine="zarr", chunks="auto", storage_options=storage_options, diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 015b726f..29daea64 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -4,8 +4,10 @@ from __future__ import annotations +import asyncio import time -from typing import Any, TypedDict +from collections.abc import Mapping +from typing import Any, Literal, NotRequired, TypedDict import structlog import xarray as xr @@ -173,6 +175,182 @@ class ConvertS2Params(TypedDict): max_retries: int +class ArrayEncoding(TypedDict): + dimension_names: NotRequired[None | tuple[str | None, ...]] + attributes: NotRequired[Mapping[str, object] | None] + + +def convert_compression( + compressor: Any, filters: Any, dtype: Any +) -> tuple[dict[str, Any], ...]: + """ + Convert the compression parameters for a Zarr V2 array into Zarr V3. + """ + import numcodecs + + """The shuffle values permitted for the blosc codec""" + + if compressor is None and filters is None: + return () + + SHUFFLE = ("noshuffle", "shuffle", "bitshuffle") + if isinstance(compressor, numcodecs.Blosc): + old_config = compressor.get_config() + new_codec = { + "name": "blosc", + "configuration": { + "cname": old_config["cname"], + "clevel": old_config["clevel"], + "shuffle": SHUFFLE[old_config["shuffle"]], + }, + } + return (new_codec,) + + raise ValueError( + f"Only blosc -> blosc is supported. Got {compressor=} and {filters=}" + ) + + +def reencode_array( + array: zarr.Array, + *, + zarr_format: Literal[2, 3] | None = None, + dimension_names: None | tuple[str | None, ...] = None, + attributes: Mapping[str, object] | None = None, +) -> zarr.core.metadata.v3.ArrayV3Metadata: + """ + Re-encode a zarr array into a new array. + """ + + if array.metadata.zarr_format == 2 and zarr_format == 3: + new_codecs = convert_compression( + array.metadata.compressor, array.metadata.filters, array.dtype + ) + if array.fill_value is None: + fill_value = 0 + else: + fill_value = array.fill_value + if attributes is None: + attributes = array.attrs.asdict() + else: + attributes = attributes + new_array = zarr.core.metadata.v3.ArrayV3Metadata( + shape=array.shape, + data_type=array.metadata.dtype, + chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, + chunk_grid={ + "name": "regular", + "configuration": {"chunk_shape": array.chunks}, + }, + fill_value=fill_value, + dimension_names=dimension_names, + codecs=( + {"name": "bytes", "configuration": {"endian": "little"}}, + *new_codecs, + ), + attributes=attributes, + ) + return new_array + raise ValueError( + f"Re-encoding from {array.zarr_format} to {zarr_format} is not supported" + ) + + +async def set_coro(store, key, value) -> None: + """ + Call store.set(key, value) after awaiting value, and returning early if value is none + """ + value_actual = await value + if value_actual is not None: + return await store.set(key, value_actual) + return None + + +async def move_chunks(array_a, array_b, *, decompress: bool = True) -> None: + """ + Copy chunks from one array to another. If decompress is false, the raw chunk bytes will be copied. + If decompress is True, an error will be raised until this developer gets around to implementing + decompression + recompression. + """ + if not decompress: + coros = [ + set_coro( + array_b.store, + f"{array_b.path}/{new_key}", + array_a.store.get( + f"{array_a.path}/{old_key}", + prototype=zarr.core.buffer.default_buffer_prototype(), + ), + ) + for old_key, new_key in zip( + array_a._iter_shard_keys(), array_b._iter_shard_keys(), strict=True + ) + ] + else: + raise ValueError("Decompression not supported yet") + await asyncio.gather(*coros) + return None + + +def reencode_group( + group: zarr.Group, + store: Any, + path: str, + *, + overwrite: bool = False, + zarr_format: Literal[2, 3] | None = None, + use_consolidated_for_children: bool = False, +) -> zarr.group: + # collect all the members of this group + + all_members = dict( + group.members( + max_depth=None, use_consolidated_for_children=use_consolidated_for_children + ) + ) + from zarr.core.group import GroupMetadata + from zarr.core.metadata.v3 import ArrayV3Metadata + + log = structlog.get_logger() + log.info(f"Begin re-encoding Zarr group {group}") + new_members: dict[str, ArrayV3Metadata | GroupMetadata] = { + path: GroupMetadata(zarr_format=zarr_format, attributes=group.attrs.asdict()) + } + chunks_to_encode: list[tuple[str, str]] = [] + for name, member in all_members.items(): + log.info(f"re-encoding member {name}") + new_path = f"{path}/{name}" + member_attrs = member.attrs.asdict() + if isinstance(member, zarr.Array): + if "_ARRAY_DIMENSIONS" in member.attrs: + dimension_names = member_attrs.pop("_ARRAY_DIMENSIONS") + else: + dimension_names = None + new_members[new_path] = reencode_array( + member, zarr_format=zarr_format, dimension_names=dimension_names + ) + chunks_to_encode.append((name, new_path)) + else: + new_members[new_path] = GroupMetadata( + zarr_format=zarr_format, + attributes=member.attrs.asdict(), + ) + log.info(f"Creating new Zarr hierarchy structure at {store}/{path}") + tree = dict( + zarr.create_hierarchy(store=store, nodes=new_members, overwrite=overwrite) + ) + new_group = tree[path] + + for name, new_path in chunks_to_encode: + log.info(f"Re-encoding chunks for array {name}") + old_array = group[name] + new_array = new_group[name] + + result = asyncio.run(move_chunks(old_array, new_array, decompress=False)) + + return tree[""] + + def convert_s2_optimized( dt_input: xr.DataTree, *, @@ -209,6 +387,14 @@ def convert_s2_optimized( # Validate input is S2 if not is_sentinel2_dataset(get_zarr_group(dt_input)): raise ValueError("Input dataset is not a Sentinel-2 product") + from zarr.storage._common import make_store + + out_store = zarr.core.sync.sync(make_store(output_path)) + + # re-encode the group + reencode_group( + get_zarr_group(dt_input), out_store, "", overwrite=True, zarr_format=3 + ) # Initialize CRS from dataset crs = initialize_crs_from_dataset(dt_input) @@ -218,9 +404,9 @@ def convert_s2_optimized( # Step 2: Create multiscale pyramids for each group in the original structure log.info("Step 2: Creating multiscale pyramids (preserving original hierarchy)") - + new_dt_input = xr.open_datatree(out_store, engine="zarr", chunks="auto") datasets = create_multiscale_from_datatree( - dt_input, + new_dt_input, output_path, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding, diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 4ead9c40..1ee4a700 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -171,6 +171,7 @@ def create_multiscale_from_datatree( r120m_dataset = create_downsampled_resolution_group( source_dataset, factor=factor ).compute() + if r120m_dataset and len(r120m_dataset.data_vars) > 0: output_path_120 = f"{output_path}{r120m_path}" log.info("Writing r120m to {}", output_path_120=output_path_120) @@ -791,7 +792,6 @@ def stream_write_dataset( encoding=encoding, compute=False, # Create job first for progress tracking ) - write_job = write_job.persist() if DISTRIBUTED_AVAILABLE: try: From f6eec7b75d80e40ef0b7a1ee8a228bfd75751b0b Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 8 Dec 2025 14:24:22 +0100 Subject: [PATCH 03/59] lint --- .../s2_optimization/s2_converter.py | 25 +++++++++++-------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 29daea64..b38bb9ba 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -9,6 +9,7 @@ from collections.abc import Mapping from typing import Any, Literal, NotRequired, TypedDict +import numcodecs import structlog import xarray as xr import zarr @@ -184,11 +185,8 @@ def convert_compression( compressor: Any, filters: Any, dtype: Any ) -> tuple[dict[str, Any], ...]: """ - Convert the compression parameters for a Zarr V2 array into Zarr V3. + Convert the compression parameters for a Zarr V2 array into a tuple of Zarr V3 codecs. """ - import numcodecs - - """The shuffle values permitted for the blosc codec""" if compressor is None and filters is None: return () @@ -256,17 +254,19 @@ def reencode_array( ) -async def set_coro(store, key, value) -> None: +async def set_coro(store: zarr.abc.store.Store, key: str, value: Any) -> None: """ - Call store.set(key, value) after awaiting value, and returning early if value is none + Call store.set(key, value) after awaiting value, if value awaits to None then do nothing. """ value_actual = await value if value_actual is not None: - return await store.set(key, value_actual) + await store.set(key, value_actual) return None -async def move_chunks(array_a, array_b, *, decompress: bool = True) -> None: +async def move_chunks( + array_a: zarr.Array[Any], array_b: zarr.Array[Any], *, decompress: bool = True +) -> None: """ Copy chunks from one array to another. If decompress is false, the raw chunk bytes will be copied. If decompress is True, an error will be raised until this developer gets around to implementing @@ -301,7 +301,9 @@ def reencode_group( zarr_format: Literal[2, 3] | None = None, use_consolidated_for_children: bool = False, ) -> zarr.group: - # collect all the members of this group + """ + Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. + """ all_members = dict( group.members( @@ -346,9 +348,10 @@ def reencode_group( old_array = group[name] new_array = new_group[name] - result = asyncio.run(move_chunks(old_array, new_array, decompress=False)) + asyncio.run(move_chunks(old_array, new_array, decompress=False)) - return tree[""] + # return the root group + return tree[path] def convert_s2_optimized( From b21e936a45fce5fd3fd340c428eb2bcc651e2fb3 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 8 Dec 2025 14:25:46 +0100 Subject: [PATCH 04/59] untrack that which should not be tracked --- src/eopf_geozarr/_version.py | 33 - uv.lock | 2892 ---------------------------------- 2 files changed, 2925 deletions(-) delete mode 100644 src/eopf_geozarr/_version.py delete mode 100644 uv.lock diff --git a/src/eopf_geozarr/_version.py b/src/eopf_geozarr/_version.py deleted file mode 100644 index 8d76d5c7..00000000 --- a/src/eopf_geozarr/_version.py +++ /dev/null @@ -1,33 +0,0 @@ -# file generated by setuptools-scm -# don't change, don't track in version control - -__all__ = [ - "__version__", - "__version_tuple__", - "version", - "version_tuple", - "__commit_id__", - "commit_id", -] - -TYPE_CHECKING = False -if TYPE_CHECKING: - from typing import Tuple, Union - - VERSION_TUPLE = Tuple[Union[int, str], ...] - COMMIT_ID = Union[str, None] -else: - VERSION_TUPLE = object - COMMIT_ID = object - -version: str -__version__: str -__version_tuple__: VERSION_TUPLE -version_tuple: VERSION_TUPLE -commit_id: COMMIT_ID -__commit_id__: COMMIT_ID - -__version__ = version = "0.5.1.dev1+g3a865cf90.d20251205" -__version_tuple__ = version_tuple = (0, 5, 1, "dev1", "g3a865cf90.d20251205") - -__commit_id__ = commit_id = "g3a865cf90" diff --git a/uv.lock b/uv.lock deleted file mode 100644 index d2f2fadd..00000000 --- a/uv.lock +++ /dev/null @@ -1,2892 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.11" -resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", - "python_full_version < '3.12'", -] - -[[package]] -name = "affine" -version = "2.4.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" } -wheels = [ - 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{ url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, -] From 5a6443caee5b2798f1d771d146e7fac97737ea75 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 8 Dec 2025 20:25:41 +0100 Subject: [PATCH 05/59] add spatial_ref after group re-encoding --- .../s2_optimization/s2_converter.py | 52 +++++++++++++++---- .../s2_optimization/s2_multiscale.py | 2 - 2 files changed, 41 insertions(+), 13 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index b38bb9ba..e95a7a01 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -21,7 +21,7 @@ from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root -from .s2_multiscale import create_multiscale_from_datatree +from .s2_multiscale import create_multiscale_from_datatree, write_geo_metadata log = structlog.get_logger() @@ -354,6 +354,31 @@ def reencode_group( return tree[path] +def add_crs_and_grid_mapping(group: zarr.Group) -> None: + """ + Add crs and grid mapping elements to a dataset, which is saved to a new memory-backed + Zarr group. + """ + ds = xr.open_dataset( + group.store, group=group.path, engine="zarr", consolidated=False + ) + write_geo_metadata(ds) + + for var in ds.data_vars: + new_attrs = ds[var].attrs.copy() + new_attrs["coordinates"] = "spatial_ref" + group[var].attrs.update(new_attrs | {"grid_mapping": "spatial_ref"}) + + group.create_array( + "spatial_ref", + shape=ds["spatial_ref"].shape, + dtype=ds["spatial_ref"].dtype, + attributes=ds["spatial_ref"].attrs, + compressors=None, + filters=None, + ) + + def convert_s2_optimized( dt_input: xr.DataTree, *, @@ -381,32 +406,37 @@ def convert_s2_optimized( """ start_time = time.time() - + zg = get_zarr_group(dt_input) log.info( "Starting S2 optimized conversion", num_groups=len(dt_input.groups), output_path=output_path, ) # Validate input is S2 - if not is_sentinel2_dataset(get_zarr_group(dt_input)): + if not is_sentinel2_dataset(zg): raise ValueError("Input dataset is not a Sentinel-2 product") from zarr.storage._common import make_store out_store = zarr.core.sync.sync(make_store(output_path)) - # re-encode the group - reencode_group( - get_zarr_group(dt_input), out_store, "", overwrite=True, zarr_format=3 - ) - # Initialize CRS from dataset crs = initialize_crs_from_dataset(dt_input) - # Step 1: Process data while preserving original structure - log.info("Step 1: Processing data with original structure preserved") + log.info("Re-encoding source data to Zarr V3") + out_group = reencode_group(zg, out_store, "", overwrite=True, zarr_format=3) + + log.info("Adding CRS elements to datasets in measurements") + for _, subgroup in out_group["measurements"].groups(): + for _, dataset in subgroup.groups(): + add_crs_and_grid_mapping(dataset) + + log.info("Adding CRS elements to quality datasets") + for _, subgroup in out_group["quality"].groups(): + for _, dataset in subgroup.groups(): + add_crs_and_grid_mapping(dataset) # Step 2: Create multiscale pyramids for each group in the original structure - log.info("Step 2: Creating multiscale pyramids (preserving original hierarchy)") + log.info("Adding multiscale levels") new_dt_input = xr.open_datatree(out_store, engine="zarr", chunks="auto") datasets = create_multiscale_from_datatree( new_dt_input, diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 63079857..325f59e9 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -845,11 +845,9 @@ def write_geo_metadata( crs, grid_mapping_name=grid_mapping_var_name, inplace=True ) dataset.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) - dataset.attrs["grid_mapping"] = grid_mapping_var_name for var in dataset.data_vars.values(): var.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) - var.attrs["grid_mapping"] = grid_mapping_var_name def rechunk_dataset_for_encoding( From fe294b37dcc79065c2c732f4daec76b79cbb5f3e Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 8 Dec 2025 20:26:02 +0100 Subject: [PATCH 06/59] simplify tests --- .../test_data_api/test_geozarr/test_common.py | 5 +- tests/test_s2_converter_simplified.py | 47 +++++++------------ 2 files changed, 22 insertions(+), 30 deletions(-) diff --git a/tests/test_data_api/test_geozarr/test_common.py b/tests/test_data_api/test_geozarr/test_common.py index 7abad664..ee882ccf 100644 --- a/tests/test_data_api/test_geozarr/test_common.py +++ b/tests/test_data_api/test_geozarr/test_common.py @@ -36,7 +36,10 @@ def test_datarraylike(obj: DataArray_V2 | DataArray_V3) -> None: assert isinstance(obj, DataArrayLike) -@pytest.mark.parametrize("obj", [GroupSpec_V2(), GroupSpec_V3()]) +@pytest.mark.parametrize( + "obj", + [GroupSpec_V2(attributes={}, members={}), GroupSpec_V3(attributes={}, members={})], +) def test_grouplike(obj: GroupSpec_V3[Any, Any] | GroupSpec_V2[Any, Any]) -> None: """ Test that the GroupLike protocol works correctly diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index ac029cf0..336610bd 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -6,7 +6,6 @@ import json from pathlib import Path -from unittest.mock import Mock, patch import numpy as np import pytest @@ -243,32 +242,18 @@ def test_simple_root_consolidation_success(tmp_path: Path): class TestConvenienceFunction: """Test the convenience function.""" - @patch("eopf_geozarr.s2_optimization.s2_converter.initialize_crs_from_dataset") - @patch("eopf_geozarr.s2_optimization.s2_converter.get_zarr_group") - @patch("eopf_geozarr.s2_optimization.s2_converter.is_sentinel2_dataset") - @patch("eopf_geozarr.s2_optimization.s2_converter.create_multiscale_from_datatree") - @patch("eopf_geozarr.s2_optimization.s2_converter.simple_root_consolidation") def test_convert_s2_optimized_convenience_function( self, - mock_consolidation, - mock_multiscale, - mock_is_s2, - mock_get_zarr_group, - mock_init_crs, + s2_group_example: Path, + tmp_path: Path, ): - """Test the convenience function parameter passing.""" - # Setup mocks - mock_multiscale.return_value = {} - mock_is_s2.return_value = True - mock_get_zarr_group.return_value = Mock() - mock_init_crs.return_value = None # Return None for CRS - - # Test parameter passing - Mock DataTree with groups attribute - dt_input = Mock() - dt_input.groups = ["/measurements/reflectance/r10m"] - output_path = "/test/path" - - convert_s2_optimized( + """Test the convenience function with real S2 data.""" + # Open the S2 example as a DataTree + dt_input = xr.open_datatree(s2_group_example, engine="zarr") + output_path = str(tmp_path / "test_output.zarr") + + # Run the conversion + result = convert_s2_optimized( dt_input, output_path=output_path, enable_sharding=False, @@ -278,11 +263,15 @@ def test_convert_s2_optimized_convenience_function( validate_output=False, ) - # Verify multiscale function was called with correct args - mock_multiscale.assert_called_once() - call_kwargs = mock_multiscale.call_args.kwargs - assert call_kwargs["enable_sharding"] is False - assert call_kwargs["spatial_chunk"] == 512 + # Verify the output was created + assert Path(output_path).exists() + + # Verify the result is a DataTree + assert isinstance(result, xr.DataTree) + + # Verify basic structure - output should have multiscale groups + output_dt = xr.open_datatree(output_path, engine="zarr") + assert len(output_dt.groups) > 0 if __name__ == "__main__": From 2d36d78c928f290fbb23ac5248dab6156fb48329 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 9 Dec 2025 12:22:06 +0100 Subject: [PATCH 07/59] fill value 0.0 -> nan in example JSON documents --- ...041_N0511_R122_T32TQM_20251008T122613.json | 272 +++++++++--------- ...309_N0511_R108_T32TLQ_20250113T122458.json | 184 ++++++------ ...131_N0511_R037_T29TPF_20250811T152216.json | 272 +++++++++--------- 3 files changed, 364 insertions(+), 364 deletions(-) diff --git a/tests/test_data_api/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 103f740c..b8f02382 100644 --- a/tests/test_data_api/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -135,7 +135,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -192,7 +192,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -249,7 +249,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -399,7 +399,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -456,7 +456,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -513,7 +513,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -570,7 +570,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -627,7 +627,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -684,7 +684,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -741,7 +741,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -841,7 +841,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -907,7 +907,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -964,7 +964,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1021,7 +1021,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1171,7 +1171,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1228,7 +1228,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1285,7 +1285,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1342,7 +1342,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1399,7 +1399,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1456,7 +1456,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1513,7 +1513,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1613,7 +1613,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1726,7 +1726,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1830,7 +1830,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1934,7 +1934,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2131,7 +2131,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2235,7 +2235,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2339,7 +2339,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2443,7 +2443,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2547,7 +2547,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2651,7 +2651,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2755,7 +2755,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2902,7 +2902,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3206,7 +3206,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3263,7 +3263,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3320,7 +3320,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3377,7 +3377,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3579,7 +3579,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3636,7 +3636,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3693,7 +3693,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3750,7 +3750,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3952,7 +3952,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4009,7 +4009,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4066,7 +4066,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4123,7 +4123,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - 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"fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6156,7 +6156,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6213,7 +6213,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6313,7 +6313,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6379,7 +6379,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6436,7 +6436,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6586,7 +6586,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6643,7 +6643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6700,7 +6700,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6757,7 +6757,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6814,7 +6814,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6871,7 +6871,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6928,7 +6928,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7028,7 +7028,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7094,7 +7094,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7151,7 +7151,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7301,7 +7301,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7358,7 +7358,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7415,7 +7415,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7472,7 +7472,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7529,7 +7529,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7586,7 +7586,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7643,7 +7643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7743,7 +7743,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7856,7 +7856,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7960,7 +7960,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8157,7 +8157,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8261,7 +8261,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8365,7 +8365,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8469,7 +8469,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8573,7 +8573,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8677,7 +8677,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8781,7 +8781,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8928,7 +8928,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8994,7 +8994,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9051,7 +9051,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9201,7 +9201,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9258,7 +9258,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9315,7 +9315,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9372,7 +9372,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9429,7 +9429,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9486,7 +9486,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9543,7 +9543,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9643,7 +9643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14447,7 +14447,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14487,7 +14487,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14612,7 +14612,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14714,7 +14714,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14816,7 +14816,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14918,7 +14918,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15020,7 +15020,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15070,7 +15070,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15159,7 +15159,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15261,7 +15261,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15418,7 +15418,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15509,7 +15509,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15611,7 +15611,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15713,7 +15713,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15815,7 +15815,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15877,7 +15877,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -15986,7 +15986,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16025,7 +16025,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16114,7 +16114,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16216,7 +16216,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16353,7 +16353,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16490,7 +16490,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16542,7 +16542,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16633,7 +16633,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16735,7 +16735,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16847,7 +16847,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16923,7 +16923,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -16986,7 +16986,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -17131,7 +17131,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", diff --git a/tests/test_data_api/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/test_data_api/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index 75350637..b142b771 100644 --- a/tests/test_data_api/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/test_data_api/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -175,7 +175,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -325,7 +325,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -382,7 +382,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -439,7 +439,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -496,7 +496,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -596,7 +596,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -662,7 +662,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -812,7 +812,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -869,7 +869,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -926,7 +926,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -983,7 +983,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1083,7 +1083,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1149,7 +1149,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1299,7 +1299,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1356,7 +1356,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1413,7 +1413,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1470,7 +1470,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1570,7 +1570,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1679,7 +1679,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1872,7 +1872,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1972,7 +1972,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2072,7 +2072,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2172,7 +2172,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2315,7 +2315,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2381,7 +2381,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2531,7 +2531,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2588,7 +2588,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2645,7 +2645,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2702,7 +2702,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2802,7 +2802,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2953,7 +2953,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3010,7 +3010,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3203,7 +3203,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3269,7 +3269,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3326,7 +3326,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3519,7 +3519,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3628,7 +3628,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3728,7 +3728,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3964,7 +3964,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4268,7 +4268,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4382,7 +4382,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4439,7 +4439,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4641,7 +4641,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4698,7 +4698,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4755,7 +4755,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4812,7 +4812,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5014,7 +5014,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5071,7 +5071,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5128,7 +5128,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5185,7 +5185,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5387,7 +5387,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5444,7 +5444,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5501,7 +5501,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5558,7 +5558,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5803,7 +5803,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5903,7 +5903,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6003,7 +6003,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6103,7 +6103,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6305,7 +6305,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6362,7 +6362,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6419,7 +6419,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6476,7 +6476,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8115,7 +8115,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8219,7 +8219,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8282,7 +8282,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8417,7 +8417,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8612,7 +8612,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8699,7 +8699,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8735,7 +8735,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8821,7 +8821,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8920,7 +8920,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9019,7 +9019,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9069,7 +9069,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9190,7 +9190,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9289,7 +9289,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9388,7 +9388,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9487,7 +9487,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9624,7 +9624,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9723,7 +9723,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9822,7 +9822,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9912,7 +9912,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9963,7 +9963,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10003,7 +10003,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10091,7 +10091,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10225,7 +10225,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10358,7 +10358,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10408,7 +10408,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10447,7 +10447,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10573,7 +10573,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10672,7 +10672,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10809,7 +10809,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", diff --git a/tests/test_data_api/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index 53df2069..ff7320cb 100644 --- a/tests/test_data_api/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -135,7 +135,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -192,7 +192,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -249,7 +249,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -399,7 +399,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -456,7 +456,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -513,7 +513,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -570,7 +570,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -627,7 +627,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -684,7 +684,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -741,7 +741,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -841,7 +841,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -907,7 +907,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -964,7 +964,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1021,7 +1021,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1171,7 +1171,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1228,7 +1228,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1285,7 +1285,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1342,7 +1342,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1399,7 +1399,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1456,7 +1456,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1513,7 +1513,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1613,7 +1613,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1726,7 +1726,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1830,7 +1830,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -1934,7 +1934,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2131,7 +2131,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2235,7 +2235,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2339,7 +2339,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2443,7 +2443,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2547,7 +2547,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2651,7 +2651,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2755,7 +2755,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -2902,7 +2902,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3206,7 +3206,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3263,7 +3263,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3320,7 +3320,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3377,7 +3377,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3579,7 +3579,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3636,7 +3636,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3693,7 +3693,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3750,7 +3750,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -3952,7 +3952,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4009,7 +4009,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4066,7 +4066,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4123,7 +4123,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4382,7 +4382,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4439,7 +4439,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4496,7 +4496,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4745,7 +4745,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4849,7 +4849,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -4953,7 +4953,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5057,7 +5057,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5259,7 +5259,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5316,7 +5316,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5373,7 +5373,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5430,7 +5430,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5664,7 +5664,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5721,7 +5721,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5871,7 +5871,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5928,7 +5928,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -5985,7 +5985,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6042,7 +6042,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6099,7 +6099,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6156,7 +6156,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6213,7 +6213,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6313,7 +6313,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6379,7 +6379,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6436,7 +6436,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6586,7 +6586,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6643,7 +6643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6700,7 +6700,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6757,7 +6757,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6814,7 +6814,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6871,7 +6871,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -6928,7 +6928,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7028,7 +7028,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7094,7 +7094,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7151,7 +7151,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7301,7 +7301,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7358,7 +7358,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7415,7 +7415,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7472,7 +7472,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7529,7 +7529,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7586,7 +7586,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7643,7 +7643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7743,7 +7743,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7856,7 +7856,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -7960,7 +7960,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8157,7 +8157,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8261,7 +8261,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8365,7 +8365,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8469,7 +8469,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8573,7 +8573,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8677,7 +8677,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8781,7 +8781,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8928,7 +8928,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -8994,7 +8994,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9051,7 +9051,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9201,7 +9201,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9258,7 +9258,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9315,7 +9315,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9372,7 +9372,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9429,7 +9429,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9486,7 +9486,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9543,7 +9543,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9643,7 +9643,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9721,7 +9721,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9810,7 +9810,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9863,7 +9863,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -9989,7 +9989,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10126,7 +10126,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10212,7 +10212,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10251,7 +10251,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10340,7 +10340,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10442,7 +10442,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10579,7 +10579,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10641,7 +10641,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10681,7 +10681,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10806,7 +10806,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -10908,7 +10908,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11045,7 +11045,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11147,7 +11147,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11249,7 +11249,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11351,7 +11351,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11401,7 +11401,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11490,7 +11490,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11592,7 +11592,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11729,7 +11729,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11781,7 +11781,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -11872,7 +11872,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -12009,7 +12009,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -13951,7 +13951,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14059,7 +14059,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14126,7 +14126,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", @@ -14300,7 +14300,7 @@ "separator": "/" } }, - "fill_value": 0.0, + "fill_value": "NaN", "codecs": [ { "name": "bytes", From a4a37436a9a682b392adca7a5ce7a39d29da5892 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 9 Dec 2025 15:34:25 +0100 Subject: [PATCH 08/59] update optimized geozarr example json --- ...041_N0511_R122_T32TQM_20251008T122613.json | 1207 +++++++++++------ ...309_N0511_R108_T32TLQ_20250113T122458.json | 769 +++++++---- ...131_N0511_R037_T29TPF_20250811T152216.json | 1207 +++++++++++------ 3 files changed, 2081 insertions(+), 1102 deletions(-) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 5e6d7544..b83e6416 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -29,6 +29,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 28 + }, + "name": "blosc" } ], "data_type": { @@ -40,7 +50,7 @@ "dimension_names": [ "angle" ], - "fill_value": "", + "fill_value": "0", "node_type": "array", "shape": [ 2 @@ -70,6 +80,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 12 + }, + "name": "blosc" } ], "data_type": { @@ -81,7 +101,7 @@ "dimension_names": [ "band" ], - "fill_value": "", + "fill_value": "0", "node_type": "array", "shape": [ 13 @@ -111,6 +131,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -127,7 +157,6 @@ }, "mean_sun_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle" @@ -166,7 +195,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -176,7 +205,7 @@ "dimension_names": [ "angle" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 2 @@ -186,7 +215,6 @@ }, "mean_viewing_incidence_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -228,7 +256,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -239,7 +267,7 @@ "band", "angle" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 13, @@ -250,7 +278,6 @@ }, "sun_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -294,7 +321,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -306,7 +333,7 @@ "y", "x" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 2, @@ -318,7 +345,6 @@ }, "viewing_incidence_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -368,7 +394,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -382,7 +408,7 @@ "y", "x" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 13, @@ -416,6 +442,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -452,6 +488,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -543,7 +589,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -627,7 +673,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -711,7 +757,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -795,7 +841,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -837,6 +883,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -873,6 +929,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -958,7 +1024,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1042,7 +1108,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1126,7 +1192,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1210,7 +1276,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1294,7 +1360,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1378,7 +1444,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1420,6 +1486,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1456,6 +1532,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1541,7 +1627,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1625,7 +1711,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1709,7 +1795,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1751,6 +1837,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1787,6 +1883,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1899,7 +2005,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1941,6 +2047,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1977,6 +2093,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2065,7 +2191,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -2107,6 +2233,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2143,6 +2279,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2224,7 +2370,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -2266,6 +2412,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2302,6 +2458,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2371,7 +2537,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2424,7 +2589,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2435,7 +2600,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2473,7 +2638,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2526,7 +2690,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2537,7 +2701,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2575,7 +2739,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2628,7 +2791,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2639,7 +2802,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2677,7 +2840,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2730,7 +2892,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2741,7 +2903,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2779,7 +2941,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2832,7 +2993,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2843,7 +3004,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2881,7 +3042,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2934,7 +3094,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2945,7 +3105,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2983,7 +3143,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3036,7 +3195,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3047,7 +3206,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3058,7 +3217,6 @@ }, "isobaricInhPa": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3086,8 +3244,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3095,7 +3253,6 @@ }, "latitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3121,13 +3278,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3137,7 +3304,6 @@ }, "longitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3162,13 +3328,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3203,7 +3379,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3239,7 +3415,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3292,7 +3467,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3303,7 +3478,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3341,7 +3516,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3394,7 +3568,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3405,7 +3579,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3416,10 +3590,9 @@ }, "step": { "attributes": { - "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "days" + "units": "minutes" }, "chunk_grid": { "configuration": { @@ -3442,7 +3615,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3478,7 +3651,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3531,7 +3703,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3542,7 +3714,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3553,7 +3725,6 @@ }, "surface": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -3578,8 +3749,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3613,7 +3784,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3648,7 +3819,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3684,7 +3855,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m**2 s**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3737,7 +3907,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3748,7 +3918,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3774,7 +3944,6 @@ "members": { "isobaricInhPa": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3802,8 +3971,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3811,7 +3980,6 @@ }, "latitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3837,13 +4005,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3853,7 +4031,6 @@ }, "longitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3878,13 +4055,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3921,7 +4108,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "Pa", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3974,7 +4160,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3985,7 +4171,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4021,7 +4207,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4057,7 +4243,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "%", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4110,7 +4295,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4121,7 +4306,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4132,10 +4317,9 @@ }, "step": { "attributes": { - "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "days" + "units": "minutes" }, "chunk_grid": { "configuration": { @@ -4158,7 +4342,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4167,7 +4351,6 @@ }, "surface": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -4192,8 +4375,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -4228,7 +4411,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4281,7 +4463,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4292,7 +4474,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4330,7 +4512,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4383,7 +4564,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4394,7 +4575,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4431,7 +4612,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4467,7 +4648,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4520,7 +4700,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4531,7 +4711,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4569,7 +4749,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4622,7 +4801,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4633,7 +4812,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4670,7 +4849,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4838,41 +5017,41 @@ { "cellSize": 0.0, "id": "r10m", - "matrixHeight": 45, - "matrixWidth": 45, + "matrixHeight": 2, + "matrixWidth": 2, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 244, - "tileWidth": 244 + "tileHeight": 5490, + "tileWidth": 5490 }, { "cellSize": 0.0, "id": "r20m", - "matrixHeight": 30, - "matrixWidth": 30, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 5490, + "tileWidth": 5490 }, { "cellSize": 0.0, "id": "r60m", - "matrixHeight": 10, - "matrixWidth": 10, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 1830, + "tileWidth": 1830 }, { "cellSize": 0.0, @@ -4933,7 +5112,6 @@ "members": { "b02": { "attributes": { - "_FillValue": 0, "_eopf_attrs": { "add_offset": -0.1, "coordinates": [ @@ -4953,6 +5131,7 @@ "units": "digital_counts" }, "add_offset": -0.1, + "coordinates": "spatial_ref", "dtype": " Date: Tue, 9 Dec 2025 15:34:53 +0100 Subject: [PATCH 09/59] forward propagate attrs --- src/eopf_geozarr/s2_optimization/s2_converter.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index e95a7a01..5872f7b8 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -329,7 +329,10 @@ def reencode_group( else: dimension_names = None new_members[new_path] = reencode_array( - member, zarr_format=zarr_format, dimension_names=dimension_names + member, + zarr_format=zarr_format, + dimension_names=dimension_names, + attributes=member_attrs, ) chunks_to_encode.append((name, new_path)) else: @@ -378,6 +381,9 @@ def add_crs_and_grid_mapping(group: zarr.Group) -> None: filters=None, ) + # Set grid_mapping attribute on the group itself + group.attrs.update({"grid_mapping": "spatial_ref"}) + def convert_s2_optimized( dt_input: xr.DataTree, From 60036c02bb9f3ee773874ff58649d640b8b7ef47 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 9 Dec 2025 15:35:14 +0100 Subject: [PATCH 10/59] update tests --- tests/test_cli_e2e.py | 49 ++++++++++++++++++++++++++-------- tests/test_data_api/test_v2.py | 13 ++++++--- 2 files changed, 48 insertions(+), 14 deletions(-) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index a2e18af8..f56a6c48 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -10,13 +10,12 @@ import subprocess from pathlib import Path +import jsondiff import pytest import xarray as xr import zarr from pydantic_zarr.core import tuplify_json -from pydantic_zarr.v3 import GroupSpec - -from tests.test_data_api.conftest import view_json_diff +from pydantic_zarr.experimental.v3 import GroupSpec def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: @@ -68,12 +67,20 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: # Check that all of the keys are the same assert o_keys == e_keys + # Check that all values are the same - assert [ - k - for k in o_keys - if not expected_structure_flat[k] == observed_structure_flat[k] - ] == [] + differences: dict[str, dict[str, object]] = {} + for k in o_keys: + expected_val = expected_structure_flat[k] + observed_val = observed_structure_flat[k] + + expected_val_json = tuplify_json(expected_val.model_dump()) + observed_val_json = tuplify_json(observed_val.model_dump()) + + if not expected_val_json == observed_val_json: + differences[k] = jsondiff.diff(expected_val_json, observed_val_json) + + assert differences == {} def test_cli_convert_real_sentinel2_data( @@ -188,9 +195,29 @@ def test_cli_convert_real_sentinel2_data( observed_structure_json = tuplify_json( GroupSpec.from_zarr(zarr.open_group(output_path)).model_dump() ) - assert expected_structure_json == observed_structure_json, view_json_diff( - expected_structure_json, observed_structure_json - ) + observed_structure = GroupSpec(**observed_structure_json) + observed_structure_flat = observed_structure.to_flat() + expected_structure = GroupSpec(**expected_structure_json) + expected_structure_flat = expected_structure.to_flat() + + o_keys = set(observed_structure_flat.keys()) + e_keys = set(expected_structure_flat.keys()) + + # Check that all of the keys are the same + assert o_keys == e_keys + + differences: dict[str, dict[str, object]] = {} + for k in o_keys: + expected_val = expected_structure_flat[k] + observed_val = observed_structure_flat[k] + + expected_val_json = tuplify_json(expected_val.model_dump()) + observed_val_json = tuplify_json(observed_val.model_dump()) + + if not expected_val_json == observed_val_json: + differences[k] = jsondiff.diff(expected_val_json, observed_val_json) + + assert differences == {} def test_cli_help_commands() -> None: diff --git a/tests/test_data_api/test_v2.py b/tests/test_data_api/test_v2.py index bacfbb9f..a0c57301 100644 --- a/tests/test_data_api/test_v2.py +++ b/tests/test_data_api/test_v2.py @@ -1,11 +1,12 @@ from __future__ import annotations +from collections.abc import Mapping from typing import Any import numpy as np import pytest from pydantic import ValidationError -from pydantic_zarr.v2 import ArraySpec, GroupSpec +from pydantic_zarr.experimental.v2 import ArraySpec, GroupSpec from eopf_geozarr.data_api.geozarr.v2 import ( DataArray, @@ -16,6 +17,11 @@ from .conftest import example_group +class GroupWithDataArrays(GroupSpec): + attributes: Any = {} + members: Mapping[str, DataArray] + + def test_invalid_dimension_names() -> None: msg = r"The _ARRAY_DIMENSIONS attribute has length 3, which does not match the number of dimensions for this array \(got 2\)" with pytest.raises(ValidationError, match=msg): @@ -40,7 +46,7 @@ def test_valid(data_shape: tuple[int, ...]) -> None: ) for idx, s in enumerate(data_shape) } - group = GroupSpec[Any, DataArray](members={"base": base_array, **coords_arrays}) + group = GroupWithDataArrays(members={"base": base_array, **coords_arrays}) assert check_valid_coordinates(group) == group @staticmethod @@ -64,7 +70,8 @@ def test_invalid_coordinates( ) for idx, s in enumerate(data_shape) } - group = GroupSpec[Any, DataArray](members={"base": base_array, **coords_arrays}) + + group = GroupWithDataArrays(members={"base": base_array, **coords_arrays}) msg = "Dimension .* for array 'base' has a shape mismatch:" with pytest.raises(ValueError, match=msg): check_valid_coordinates(group) From 896e2758345ba0e85bd3b8bd84934e7eb15f2229 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 12:12:43 +0100 Subject: [PATCH 11/59] update test JSON models to have correct string fill value --- ...L2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json | 4 ++-- ...L1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json | 4 ++-- ...L2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index b83e6416..6e539cb3 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -50,7 +50,7 @@ "dimension_names": [ "angle" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 2 @@ -101,7 +101,7 @@ "dimension_names": [ "band" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 13 diff --git a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index d7b1f363..e626330f 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -50,7 +50,7 @@ "dimension_names": [ "angle" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 2 @@ -101,7 +101,7 @@ "dimension_names": [ "band" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 13 diff --git a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index 46f9eae3..bfe73791 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -50,7 +50,7 @@ "dimension_names": [ "angle" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 2 @@ -101,7 +101,7 @@ "dimension_names": [ "band" ], - "fill_value": "0", + "fill_value": "", "node_type": "array", "shape": [ 13 From 02985dc386ab3c5f1632155acf1331b2b147e7a5 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 12:14:33 +0100 Subject: [PATCH 12/59] simplify crs handling --- src/eopf_geozarr/cli.py | 9 +- src/eopf_geozarr/data_api/s2.py | 215 ++-------------- .../s2_optimization/s2_converter.py | 242 +++--------------- .../s2_optimization/s2_multiscale.py | 25 +- src/eopf_geozarr/zarrio.py | 182 +++++++++++++ 5 files changed, 258 insertions(+), 415 deletions(-) create mode 100644 src/eopf_geozarr/zarrio.py diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index b8a30294..5ac7122f 100644 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -13,6 +13,10 @@ import structlog import xarray as xr +from zarr.core.sync import sync +from zarr.experimental.cache_store import CacheStore +from zarr.storage import MemoryStore +from zarr.storage._common import make_store from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized @@ -1207,7 +1211,10 @@ def convert_s2_optimized_command(args: Any) -> None: # Load input dataset log.info("Loading Sentinel-2 dataset from", input_path=args.input_path) storage_options = get_storage_options(str(args.input_path)) - store = args.input_path + # store = args.input_path + store = CacheStore( + sync(make_store(args.input_path, mode="r")), cache_store=MemoryStore() + ) dt_input = xr.open_datatree( store, engine="zarr", diff --git a/src/eopf_geozarr/data_api/s2.py b/src/eopf_geozarr/data_api/s2.py index a32abe79..ec575639 100644 --- a/src/eopf_geozarr/data_api/s2.py +++ b/src/eopf_geozarr/data_api/s2.py @@ -9,6 +9,7 @@ from typing import Annotated, Any, Literal from pydantic import BaseModel, Field +from pyproj import CRS from typing_extensions import TypedDict from eopf_geozarr.data_api.geozarr.common import ( @@ -24,204 +25,21 @@ # ============================================================================ -class BandMetadata(BaseModel): - """Metadata for a single Sentinel-2 spectral band. +class OtherMetadataDict(TypedDict, total=False): + """Sentinel-2 other_metadata attributes container. - Contains physical and spectral characterization of a band. - """ + This is intentionally flexible to accommodate variations across different + Sentinel-2 product types (L1C, L2A) and processing versions. - bandwidth: Annotated[float | str, Field(description="Bandwidth in nm")] - central_wavelength: Annotated[float, Field(description="Central wavelength in nm")] - onboard_compression_rate: Annotated[ - str | float, Field(description="Compression rate") - ] - onboard_integration_time: Annotated[ - str | float, Field(description="Integration time") - ] - physical_gain: Annotated[str | float, Field(description="Physical gain factor")] - spectral_response_step: Annotated[str, Field(description="Spectral response step")] - spectral_response_values: Annotated[ - str, Field(description="Spectral response curve values") - ] - units: Annotated[str, Field(description="Unit of measurement")] - wavelength_max: Annotated[float, Field(description="Maximum wavelength in nm")] - wavelength_min: Annotated[float, Field(description="Minimum wavelength in nm")] - - -class BandDescription(BaseModel): - """Collection of band metadata for all Sentinel-2 bands. - - Maps Sentinel-2 band identifiers to their spectral and technical metadata. - All standard bands should be present in Sentinel-2 L1C and L2A products. - - Preserves both numeric (01, 02, ..., 8A) and named (b01, b02, ..., b8a) band keys - from the original JSON structure for round-trip compatibility. + Only horizontal_CRS_code is required as it's the load-bearing element + needed by the API to extract CRS information. """ - # Numeric band keys (JSON format) - band_01: Annotated[BandMetadata, Field(alias="01")] - band_02: Annotated[BandMetadata, Field(alias="02")] - band_03: Annotated[BandMetadata, Field(alias="03")] - band_04: Annotated[BandMetadata, Field(alias="04")] - band_05: Annotated[BandMetadata, Field(alias="05")] - band_06: Annotated[BandMetadata, Field(alias="06")] - band_07: Annotated[BandMetadata, Field(alias="07")] - band_08: Annotated[BandMetadata, Field(alias="08")] - band_09: Annotated[BandMetadata, Field(alias="09")] - band_10: Annotated[BandMetadata, Field(alias="10")] - band_11: Annotated[BandMetadata, Field(alias="11")] - band_12: Annotated[BandMetadata, Field(alias="12")] - band_8a: Annotated[BandMetadata, Field(alias="8A")] - - # Named band keys (human-readable format) - band_b01: Annotated[BandMetadata, Field(alias="b01")] - band_b02: Annotated[BandMetadata, Field(alias="b02")] - band_b03: Annotated[BandMetadata, Field(alias="b03")] - band_b04: Annotated[BandMetadata, Field(alias="b04")] - band_b05: Annotated[BandMetadata, Field(alias="b05")] - band_b06: Annotated[BandMetadata, Field(alias="b06")] - band_b07: Annotated[BandMetadata, Field(alias="b07")] - band_b08: Annotated[BandMetadata, Field(alias="b08")] - band_b09: Annotated[BandMetadata, Field(alias="b09")] - band_b10: Annotated[BandMetadata, Field(alias="b10")] - band_b11: Annotated[BandMetadata, Field(alias="b11")] - band_b12: Annotated[BandMetadata, Field(alias="b12")] - band_b8a: Annotated[BandMetadata, Field(alias="b8a")] - - model_config = {"populate_by_name": True} - - -class OtherMetadata(BaseModel): - """Sentinel-2 product metadata container. - - Stores various metadata about the product including: - - Quality information (L0/L2A, processing details) - - Band descriptions (spectral characteristics) - - Atmospheric corrections (AOT, water vapor) - - Geolocation and timing information - """ + # Required field - needed for CRS extraction + horizontal_CRS_code: str - # Core metadata fields - AOT_retrieval_model: Annotated[ - str, Field(description="Aerosol Optical Thickness retrieval model") - ] - L0_ancillary_data_quality: Annotated[ - str, Field(description="L0 ancillary data quality indicator") - ] - L0_ephemeris_data_quality: Annotated[ - str, Field(description="L0 ephemeris data quality") - ] - NUC_table_ID: Annotated[ - int | str, Field(description="Non-Uniformity Correction table ID") - ] - SWIR_rearrangement_flag: Annotated[ - str | None, Field(description="SWIR band rearrangement flag") - ] - UTM_zone_identification: Annotated[str, Field(description="UTM zone identifier")] - absolute_location_assessment_from_AOCS: Annotated[ - str, Field(description="Location assessment") - ] - - # Band information - band_description: Annotated[ - BandDescription, Field(description="Spectral band metadata for all bands") - ] - - # Accuracy declarations - declared_accuracy_of_AOT_model: Annotated[ - float | None, Field(description="AOT model accuracy") - ] - declared_accuracy_of_radiative_transfer_model: Annotated[ - float | None, Field(description="Radiative transfer accuracy") - ] - declared_accuracy_of_water_vapour_model: Annotated[ - float | None, Field(description="Water vapor model accuracy") - ] - - # Correction flags - electronic_crosstalk_correction_flag: Annotated[ - str | bool, Field(description="Electronic crosstalk correction") - ] - optical_crosstalk_correction_flag: Annotated[ - str | bool, Field(description="Optical crosstalk correction") - ] - onboard_compression_flag: Annotated[ - str | bool, Field(description="Onboard compression applied") - ] - onboard_equalization_flag: Annotated[ - str | bool, Field(description="Onboard equalization applied") - ] - - # Product and geometry information - eopf_category: Annotated[str, Field(description="EOPF product category")] - geometric_refinement: Annotated[ - dict[str, Any] | str | None, - Field(description="Geometric refinement information"), - ] - history: Annotated[ - list[dict[str, Any]] | str | None, Field(description="Processing history") - ] - horizontal_CRS_code: Annotated[ - str, Field(description="Coordinate Reference System code") - ] - horizontal_CRS_name: Annotated[str, Field(description="CRS name")] - mean_sensing_time: Annotated[str | None, Field(description="Mean acquisition time")] - - # Sun/sensor geometry - mean_sun_azimuth_angle_in_deg_for_all_bands_all_detectors: Annotated[ - float, Field(description="Mean sun azimuth in degrees") - ] - mean_sun_zenith_angle_in_deg_for_all_bands_all_detectors: Annotated[ - float, Field(description="Mean sun zenith in degrees") - ] - - # Atmospheric parameters - mean_value_of_aerosol_optical_thickness: Annotated[ - float | None, Field(description="Mean AOT value") - ] - mean_value_of_total_water_vapour_content: Annotated[ - float | None, Field(description="Mean water vapor content") - ] - - # Meteo information (flexible structure) - meteo: Annotated[ - dict[str, Any] | None, Field(description="Meteorological parameters") - ] - - # Quality assessments - multispectral_registration_assessment: Annotated[ - str | None, Field(description="Registration quality") - ] - product_quality_status: Annotated[str, Field(description="Product quality status")] - planimetric_stability_assessment_from_AOCS: Annotated[ - str | None, Field(description="Planimetric stability") - ] - - # Data degradation - percentage_of_degraded_MSI_data: Annotated[ - float | None, Field(description="Percentage of degraded data") - ] - - # Ozone information - ozone_source: Annotated[str | None, Field(description="Ozone data source")] - ozone_value: Annotated[float | str | None, Field(description="Ozone value")] - - # Reference band - spectral_band_of_reference: Annotated[ - str, Field(description="Reference spectral band") - ] - - # Radiometric correction - reflectance_correction_factor_from_the_Sun_Earth_distance_variation_computed_using_the_acquisition_date: Annotated[ - float | None, - Field( - default=None, - alias="reflectance_correction_factor_from_the_Sun-Earth_distance_variation_computed_using_the_acquisition_date", - description="Reflectance correction factor for Sun-Earth distance variation", - ), - ] - - model_config = {"populate_by_name": True} + # All other fields are optional and unvalidated + # Different S2 products have different metadata structures class Sentinel2ArrayAttributes(BaseModel): @@ -464,7 +282,7 @@ class Sentinel2DataArrayAttrs(BaseDataArrayAttrs): class Sentinel2RootAttrs(BaseModel): """Root-level attributes for Sentinel-2 DataTree.""" - other_metadata: dict[str, object] # no validation + other_metadata: OtherMetadataDict stac_discovery: dict[str, object] # no validation @@ -732,3 +550,12 @@ def quality(self) -> Sentinel2QualityGroup: def conditions(self) -> Sentinel2ConditionsGroup: """Get conditions group.""" return self.members["conditions"] + + @property + def crs(self) -> CRS: + """Get the coordinate reference system (CRS) for this product""" + crs_code = self.attributes.other_metadata["horizontal_CRS_code"] + # Handle both "EPSG:32635" and "32635" formats + if crs_code.startswith("EPSG:"): + crs_code = crs_code[5:] # Remove "EPSG:" prefix + return CRS.from_epsg(int(crs_code)) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 5872f7b8..20844baa 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -4,29 +4,30 @@ from __future__ import annotations -import asyncio import time -from collections.abc import Mapping -from typing import Any, Literal, NotRequired, TypedDict +from pathlib import Path +from typing import Any, TypedDict -import numcodecs import structlog import xarray as xr import zarr from pydantic import TypeAdapter from pyproj import CRS +from zarr.core.sync import sync +from zarr.storage._common import make_store from eopf_geozarr.conversion.fs_utils import get_storage_options from eopf_geozarr.conversion.geozarr import get_zarr_group from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root +from eopf_geozarr.zarrio import ChunkEncodingSpec, reencode_group from .s2_multiscale import create_multiscale_from_datatree, write_geo_metadata log = structlog.get_logger() -def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS | None: +def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS: """ Initialize CRS from dataset by checking data variables. @@ -97,9 +98,7 @@ def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS | None: return crs except Exception: pass - - log.warning("Could not initialize CRS from dataset") - return None + raise ValueError("No CRS found.") def convert_s2( @@ -176,196 +175,14 @@ class ConvertS2Params(TypedDict): max_retries: int -class ArrayEncoding(TypedDict): - dimension_names: NotRequired[None | tuple[str | None, ...]] - attributes: NotRequired[Mapping[str, object] | None] - - -def convert_compression( - compressor: Any, filters: Any, dtype: Any -) -> tuple[dict[str, Any], ...]: - """ - Convert the compression parameters for a Zarr V2 array into a tuple of Zarr V3 codecs. - """ - - if compressor is None and filters is None: - return () - - SHUFFLE = ("noshuffle", "shuffle", "bitshuffle") - if isinstance(compressor, numcodecs.Blosc): - old_config = compressor.get_config() - new_codec = { - "name": "blosc", - "configuration": { - "cname": old_config["cname"], - "clevel": old_config["clevel"], - "shuffle": SHUFFLE[old_config["shuffle"]], - }, - } - return (new_codec,) - - raise ValueError( - f"Only blosc -> blosc is supported. Got {compressor=} and {filters=}" - ) - - -def reencode_array( - array: zarr.Array, - *, - zarr_format: Literal[2, 3] | None = None, - dimension_names: None | tuple[str | None, ...] = None, - attributes: Mapping[str, object] | None = None, -) -> zarr.core.metadata.v3.ArrayV3Metadata: - """ - Re-encode a zarr array into a new array. - """ - - if array.metadata.zarr_format == 2 and zarr_format == 3: - new_codecs = convert_compression( - array.metadata.compressor, array.metadata.filters, array.dtype - ) - if array.fill_value is None: - fill_value = 0 - else: - fill_value = array.fill_value - if attributes is None: - attributes = array.attrs.asdict() - else: - attributes = attributes - new_array = zarr.core.metadata.v3.ArrayV3Metadata( - shape=array.shape, - data_type=array.metadata.dtype, - chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, - chunk_grid={ - "name": "regular", - "configuration": {"chunk_shape": array.chunks}, - }, - fill_value=fill_value, - dimension_names=dimension_names, - codecs=( - {"name": "bytes", "configuration": {"endian": "little"}}, - *new_codecs, - ), - attributes=attributes, - ) - return new_array - raise ValueError( - f"Re-encoding from {array.zarr_format} to {zarr_format} is not supported" - ) - - -async def set_coro(store: zarr.abc.store.Store, key: str, value: Any) -> None: - """ - Call store.set(key, value) after awaiting value, if value awaits to None then do nothing. - """ - value_actual = await value - if value_actual is not None: - await store.set(key, value_actual) - return None - - -async def move_chunks( - array_a: zarr.Array[Any], array_b: zarr.Array[Any], *, decompress: bool = True -) -> None: +def add_crs_and_grid_mapping(group: zarr.Group, crs: CRS) -> None: """ - Copy chunks from one array to another. If decompress is false, the raw chunk bytes will be copied. - If decompress is True, an error will be raised until this developer gets around to implementing - decompression + recompression. - """ - if not decompress: - coros = [ - set_coro( - array_b.store, - f"{array_b.path}/{new_key}", - array_a.store.get( - f"{array_a.path}/{old_key}", - prototype=zarr.core.buffer.default_buffer_prototype(), - ), - ) - for old_key, new_key in zip( - array_a._iter_shard_keys(), array_b._iter_shard_keys(), strict=True - ) - ] - else: - raise ValueError("Decompression not supported yet") - await asyncio.gather(*coros) - return None - - -def reencode_group( - group: zarr.Group, - store: Any, - path: str, - *, - overwrite: bool = False, - zarr_format: Literal[2, 3] | None = None, - use_consolidated_for_children: bool = False, -) -> zarr.group: - """ - Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. - """ - - all_members = dict( - group.members( - max_depth=None, use_consolidated_for_children=use_consolidated_for_children - ) - ) - from zarr.core.group import GroupMetadata - from zarr.core.metadata.v3 import ArrayV3Metadata - - log = structlog.get_logger() - log.info(f"Begin re-encoding Zarr group {group}") - new_members: dict[str, ArrayV3Metadata | GroupMetadata] = { - path: GroupMetadata(zarr_format=zarr_format, attributes=group.attrs.asdict()) - } - chunks_to_encode: list[tuple[str, str]] = [] - for name, member in all_members.items(): - log.info(f"re-encoding member {name}") - new_path = f"{path}/{name}" - member_attrs = member.attrs.asdict() - if isinstance(member, zarr.Array): - if "_ARRAY_DIMENSIONS" in member.attrs: - dimension_names = member_attrs.pop("_ARRAY_DIMENSIONS") - else: - dimension_names = None - new_members[new_path] = reencode_array( - member, - zarr_format=zarr_format, - dimension_names=dimension_names, - attributes=member_attrs, - ) - chunks_to_encode.append((name, new_path)) - else: - new_members[new_path] = GroupMetadata( - zarr_format=zarr_format, - attributes=member.attrs.asdict(), - ) - log.info(f"Creating new Zarr hierarchy structure at {store}/{path}") - tree = dict( - zarr.create_hierarchy(store=store, nodes=new_members, overwrite=overwrite) - ) - new_group = tree[path] - - for name, new_path in chunks_to_encode: - log.info(f"Re-encoding chunks for array {name}") - old_array = group[name] - new_array = new_group[name] - - asyncio.run(move_chunks(old_array, new_array, decompress=False)) - - # return the root group - return tree[path] - - -def add_crs_and_grid_mapping(group: zarr.Group) -> None: - """ - Add crs and grid mapping elements to a dataset, which is saved to a new memory-backed - Zarr group. + Add crs and grid mapping elements to a dataset. """ ds = xr.open_dataset( group.store, group=group.path, engine="zarr", consolidated=False ) - write_geo_metadata(ds) + write_geo_metadata(ds, crs=crs) for var in ds.data_vars: new_attrs = ds[var].attrs.copy() @@ -413,6 +230,9 @@ def convert_s2_optimized( start_time = time.time() zg = get_zarr_group(dt_input) + s2root_model = Sentinel2Root.from_zarr(zg) + crs = s2root_model.crs + log.info( "Starting S2 optimized conversion", num_groups=len(dt_input.groups), @@ -421,25 +241,45 @@ def convert_s2_optimized( # Validate input is S2 if not is_sentinel2_dataset(zg): raise ValueError("Input dataset is not a Sentinel-2 product") - from zarr.storage._common import make_store - - out_store = zarr.core.sync.sync(make_store(output_path)) - # Initialize CRS from dataset - crs = initialize_crs_from_dataset(dt_input) + out_store = sync(make_store(output_path)) log.info("Re-encoding source data to Zarr V3") - out_group = reencode_group(zg, out_store, "", overwrite=True, zarr_format=3) + + # Define a chunk reencoding function based on the requested chunking + def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: + group_name = str(Path(array.path).parent) + in_measurements_group = ( + group_name.startswith("r") + and group_name.endswith("m") + and "/measurements/" in group_name + ) + if in_measurements_group and array.ndim > 0: + if array.ndim == 1: + return {"write_chunks": (spatial_chunk,)} + elif array.ndim == 2: + return {"write_chunks": (spatial_chunk, spatial_chunk)} + else: + return { + "write_chunks": (1,) * (array.ndim - 2) + + (spatial_chunk, spatial_chunk) + } + else: + return {"write_chunks": array.chunks} + + out_group = reencode_group( + zg, out_store, "", overwrite=True, chunk_reencoder=chunk_reencoder + ) log.info("Adding CRS elements to datasets in measurements") for _, subgroup in out_group["measurements"].groups(): for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset) + add_crs_and_grid_mapping(dataset, crs=crs) log.info("Adding CRS elements to quality datasets") for _, subgroup in out_group["quality"].groups(): for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset) + add_crs_and_grid_mapping(dataset, crs=crs) # Step 2: Create multiscale pyramids for each group in the original structure log.info("Adding multiscale levels") diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 325f59e9..b066f935 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -818,8 +818,9 @@ def stream_write_dataset( def write_geo_metadata( dataset: xr.Dataset, + crs: CRS, + *, grid_mapping_var_name: str = "spatial_ref", - crs: CRS | None = None, ) -> None: """ Write geographic metadata to the dataset. @@ -829,25 +830,11 @@ def write_geo_metadata( grid_mapping_var_name: Name for grid mapping variable crs: Coordinate Reference System to use (if None, attempts to detect from dataset) """ - # Use provided CRS or try to detect from dataset - if crs is None: - for var in dataset.data_vars.values(): - if hasattr(var, "rio") and var.rio.crs: - crs = var.rio.crs - break - elif "proj:epsg" in var.attrs: - epsg = var.attrs["proj:epsg"] - crs = CRS.from_epsg(epsg) - break - - if crs is not None: - dataset.rio.write_crs( - crs, grid_mapping_name=grid_mapping_var_name, inplace=True - ) - dataset.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) + dataset.rio.write_crs(crs, grid_mapping_name=grid_mapping_var_name, inplace=True) + dataset.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) - for var in dataset.data_vars.values(): - var.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) + for var in dataset.data_vars.values(): + var.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) def rechunk_dataset_for_encoding( diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py new file mode 100644 index 00000000..c1d86651 --- /dev/null +++ b/src/eopf_geozarr/zarrio.py @@ -0,0 +1,182 @@ +from __future__ import annotations + +from collections.abc import Callable, Mapping +from typing import Any, NotRequired, TypedDict + +import numcodecs +import structlog +import zarr +from zarr.core.group import GroupMetadata +from zarr.core.metadata.v3 import ArrayV3Metadata + + +async def set_store(store: zarr.abc.store.Store, key: str, value: Any) -> None: + """ + Call store.set(key, value) after awaiting value, if value awaits to None then do nothing. + """ + value_actual = await value + if value_actual is not None: + await store.set(key, value_actual) + return None + + +class ChunkEncodingSpec(TypedDict): + write_chunks: tuple[int, ...] + read_chunks: NotRequired[tuple[int, ...]] + + +def convert_compression( + compressor: Any, +) -> tuple[dict[str, Any], ...]: + """ + Convert the compression parameters for a Zarr V2 array into a tuple of Zarr V3 codecs. + """ + + if compressor is None: + return () + + SHUFFLE = ("noshuffle", "shuffle", "bitshuffle") + if isinstance(compressor, numcodecs.Blosc): + old_config = compressor.get_config() + new_codec = { + "name": "blosc", + "configuration": { + "cname": old_config["cname"], + "clevel": old_config["clevel"], + "shuffle": SHUFFLE[old_config["shuffle"]], + }, + } + return (new_codec,) + + raise ValueError( + f"Only blosc -> blosc or None -> () is supported. Got {compressor=}" + ) + + +def reencode_array( + array: zarr.Array, + *, + dimension_names: None | tuple[str | None, ...] = None, + attributes: Mapping[str, object] | None = None, + chunking: ChunkEncodingSpec | None = None, +) -> zarr.core.metadata.v3.ArrayV3Metadata: + """ + Re-encode a zarr array into a new zarr v3 array. + """ + + new_codecs = convert_compression(array.metadata.compressor) + if array.fill_value is None: + fill_value = array.metadata.dtype.default_scalar() + else: + fill_value = array.fill_value + if attributes is None: + attributes = array.attrs.asdict() + else: + attributes = attributes + if chunking is not None: + chunk_grid_shape = chunking["write_chunks"] + else: + chunk_grid_shape = array.chunks + + if chunking is not None and "read_chunks" in chunking: + codecs = ( + { + "name": "sharding-indexed", + "configuration": { + "chunk_shape": chunking["read_chunks"], + "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), + "index_location": "end", + "codecs": ({"name": "bytes"}, *new_codecs), + }, + }, + ) + else: + codecs = ({"name": "bytes"}, *new_codecs) # type: ignore[assignment] + + new_meta = zarr.core.metadata.v3.ArrayV3Metadata( + shape=array.shape, + data_type=array.metadata.dtype, + chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, + chunk_grid={ + "name": "regular", + "configuration": {"chunk_shape": chunk_grid_shape}, + }, + fill_value=fill_value, + dimension_names=dimension_names, + codecs=codecs, + attributes=attributes, + ) + return new_meta + + +def reencode_group( + group: zarr.Group, + store: Any, + path: str, + *, + overwrite: bool = False, + use_consolidated_for_children: bool = False, + chunk_reencoder: Callable[[zarr.Array[Any]], ChunkEncodingSpec] | None = None, +) -> zarr.group: + """ + Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. + """ + log = structlog.get_logger() + + all_members = dict( + group.members( + max_depth=None, use_consolidated_for_children=use_consolidated_for_children + ) + ) + + log = structlog.get_logger() + log.info(f"Begin re-encoding Zarr group {group}") + new_members: dict[str, ArrayV3Metadata | GroupMetadata] = { + path: GroupMetadata(zarr_format=3, attributes=group.attrs.asdict()) + } + chunks_to_encode: list[tuple[str, str]] = [] + for name, member in all_members.items(): + log.info(f"re-encoding member {name}") + new_path = f"{path}/{name}" + member_attrs = member.attrs.asdict() + if isinstance(member, zarr.Array): + if "_ARRAY_DIMENSIONS" in member.attrs: + dimension_names = member_attrs.pop("_ARRAY_DIMENSIONS") + else: + dimension_names = None + if chunk_reencoder is None: + chunking = None + else: + chunking = chunk_reencoder(member) + new_members[new_path] = reencode_array( + member, + dimension_names=dimension_names, + attributes=member_attrs, + chunking=chunking, + ) + chunks_to_encode.append((name, new_path)) + else: + new_members[new_path] = GroupMetadata( + zarr_format=3, + attributes=member.attrs.asdict(), + ) + log.info(f"Creating new Zarr hierarchy structure at {store}/{path}") + tree = dict( + zarr.create_hierarchy(store=store, nodes=new_members, overwrite=overwrite) + ) + new_group = tree[path] + + for name, new_path in chunks_to_encode: + log.info(f"Re-encoding chunks for array {name}") + old_array = group[name] + new_array = new_group[name] + + new_array[...] = old_array[...] + + # return the root group + return tree[path] + + +class ArrayEncoding(TypedDict): + dimension_names: NotRequired[None | tuple[str | None, ...]] + attributes: NotRequired[Mapping[str, object] | None] From b184fe8fe518d668af45b66b6f9b8e4d65add581 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 13:14:40 +0100 Subject: [PATCH 13/59] add module docstring --- src/eopf_geozarr/zarrio.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index c1d86651..69465c89 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -1,3 +1,7 @@ +""" +This module contains zarr-specific IO routines +""" + from __future__ import annotations from collections.abc import Callable, Mapping From a6dc580c4140a954d5502cba0cbeaf5243dbc9e2 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 13:15:22 +0100 Subject: [PATCH 14/59] remove typo --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index b066f935..626789b1 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -618,11 +618,9 @@ def create_downsampled_resolution_group( continue var_typ = determine_variable_type(var_name, var_data) if var_typ == "quality_mask": - lazy_downsampled = ( - var_data.coarsen({"x": factor, "y": factor}, boundary="trim") - .max() - .sdyupr - ) + lazy_downsampled = var_data.coarsen( + {"x": factor, "y": factor}, boundary="trim" + ).max() elif var_typ == "reflectance": lazy_downsampled = var_data.coarsen( {"x": factor, "y": factor}, boundary="trim" From 25c1a52d749925cabdcf908e60f937f88c47ae42 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 13:16:14 +0100 Subject: [PATCH 15/59] tweak pydantic zarr usage in tests --- tests/test_data_api/test_v3.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/tests/test_data_api/test_v3.py b/tests/test_data_api/test_v3.py index fd10ee28..c408f2b7 100644 --- a/tests/test_data_api/test_v3.py +++ b/tests/test_data_api/test_v3.py @@ -1,3 +1,4 @@ +from collections.abc import Mapping from typing import Any import numpy as np @@ -13,6 +14,11 @@ ) +class GroupWithDataArrays(GroupSpec): + attributes: Any = {} + members: Mapping[str, DataArray] + + class TestCheckValidCoordinates: @staticmethod @pytest.mark.parametrize("data_shape", [(10,), (10, 12)]) @@ -31,7 +37,7 @@ def test_valid(data_shape: tuple[int, ...]) -> None: ) for idx, s in enumerate(data_shape) } - group = GroupSpec[Any, DataArray](members={"base": base_array, **coords_arrays}) + group = GroupWithDataArrays(members={"base": base_array, **coords_arrays}) assert check_valid_coordinates(group) == group @staticmethod @@ -55,7 +61,7 @@ def test_invalid_coordinates( ) for idx, s in enumerate(data_shape) } - group = GroupSpec[Any, DataArray](members={"base": base_array, **coords_arrays}) + group = GroupWithDataArrays(members={"base": base_array, **coords_arrays}) msg = "Dimension .* for array 'base' has a shape mismatch:" with pytest.raises(ValueError, match=msg): check_valid_coordinates(group) From d4852d6525b1dde66e70063d7f147924164e9ba8 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 13:16:39 +0100 Subject: [PATCH 16/59] simplify tests --- tests/test_s2_converter_simplified.py | 154 ----------- tests/test_s2_multiscale_geo_metadata.py | 324 ----------------------- 2 files changed, 478 deletions(-) delete mode 100644 tests/test_s2_multiscale_geo_metadata.py diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 336610bd..563de4b5 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -13,7 +13,6 @@ from eopf_geozarr.s2_optimization.s2_converter import ( convert_s2_optimized, - initialize_crs_from_dataset, simple_root_consolidation, ) @@ -47,159 +46,6 @@ def mock_s2_dataset(): return dt -class TestS2FunctionalAPI: - """Test the S2 functional API.""" - - def test_is_sentinel2_dataset_placeholder(self): - """Placeholder test for is_sentinel2_dataset. - - The actual is_sentinel2_dataset function uses complex pydantic validation - that requires a fully structured zarr group matching Sentinel1Root or - Sentinel2Root models. Testing this would require creating a complete - mock sentinel dataset, which is better done in integration tests. - """ - # This test is kept as a placeholder to maintain test structure - assert True - - -class TestCRSInitialization: - """Test CRS initialization functionality.""" - - def test_initialize_crs_from_cpm_260_metadata(self): - """Test CRS initialization from CPM >= 2.6.0 metadata with integer EPSG.""" - # Create a DataTree with CPM 2.6.0+ style metadata (integer format) - dt = xr.DataTree() - dt.attrs = { - "other_metadata": { - "horizontal_CRS_code": 32632 # EPSG:32632 (WGS 84 / UTM zone 32N) - } - } - - crs = initialize_crs_from_dataset(dt) - - assert crs is not None - assert crs.to_epsg() == 32632 - - def test_initialize_crs_from_cpm_260_metadata_string(self): - """Test CRS initialization from CPM >= 2.6.0 metadata with string EPSG.""" - # Create a DataTree with CPM 2.6.0+ style metadata (string format) - dt = xr.DataTree() - dt.attrs = { - "other_metadata": { - "horizontal_CRS_code": "EPSG:32632" # String format - } - } - - crs = initialize_crs_from_dataset(dt) - - assert crs is not None - assert crs.to_epsg() == 32632 - - def test_initialize_crs_from_cpm_260_metadata_invalid_epsg(self): - """Test CRS initialization with invalid EPSG code in CPM 2.6.0 metadata.""" - # Create a DataTree with invalid EPSG code - dt = xr.DataTree() - dt.attrs = { - "other_metadata": { - "horizontal_CRS_code": 999999 # Invalid EPSG code - } - } - - # Should fall through to other methods or return None - crs = initialize_crs_from_dataset(dt) - - # CRS should be None since there's no other CRS information - assert crs is None - - def test_initialize_crs_from_rio_accessor(self): - """Test CRS initialization from rioxarray accessor.""" - # Create a dataset with rioxarray CRS - coords = { - "x": (["x"], np.linspace(0, 1000, 10)), - "y": (["y"], np.linspace(0, 1000, 10)), - } - data_vars = { - "b02": (["y", "x"], np.random.rand(10, 10)), - } - ds = xr.Dataset(data_vars, coords=coords) - ds = ds.rio.write_crs("EPSG:32633") - - # Create DataTree without CPM 2.6.0 metadata - dt = xr.DataTree(ds) - - crs = initialize_crs_from_dataset(dt) - - assert crs is not None - assert crs.to_epsg() == 32633 - - def test_initialize_crs_from_proj_epsg_attribute(self): - """Test CRS initialization from proj:epsg attribute.""" - # Create a dataset with proj:epsg attribute - coords = { - "x": (["x"], np.linspace(0, 1000, 10)), - "y": (["y"], np.linspace(0, 1000, 10)), - } - data_vars = { - "b02": (["y", "x"], np.random.rand(10, 10)), - } - ds = xr.Dataset(data_vars, coords=coords) - ds["b02"].attrs["proj:epsg"] = 32634 - - # Create DataTree - dt = xr.DataTree(ds) - - crs = initialize_crs_from_dataset(dt) - - assert crs is not None - assert crs.to_epsg() == 32634 - - def test_initialize_crs_no_crs_information(self): - """Test CRS initialization when no CRS information is available.""" - # Create a dataset without any CRS information - coords = { - "x": (["x"], np.linspace(0, 1000, 10)), - "y": (["y"], np.linspace(0, 1000, 10)), - } - data_vars = { - "b02": (["y", "x"], np.random.rand(10, 10)), - } - ds = xr.Dataset(data_vars, coords=coords) - - # Create DataTree without any CRS metadata - dt = xr.DataTree(ds) - - crs = initialize_crs_from_dataset(dt) - - assert crs is None - - def test_initialize_crs_priority_cpm_260_over_rio(self): - """Test that CPM 2.6.0 metadata takes priority over rio accessor.""" - # Create a dataset with both CPM 2.6.0 metadata and rio CRS - coords = { - "x": (["x"], np.linspace(0, 1000, 10)), - "y": (["y"], np.linspace(0, 1000, 10)), - } - data_vars = { - "b02": (["y", "x"], np.random.rand(10, 10)), - } - ds = xr.Dataset(data_vars, coords=coords) - ds = ds.rio.write_crs("EPSG:32633") # Different EPSG - - # Create DataTree with CPM 2.6.0 metadata - dt = xr.DataTree(ds) - dt.attrs = { - "other_metadata": { - "horizontal_CRS_code": 32632 # This should take priority - } - } - - crs = initialize_crs_from_dataset(dt) - - assert crs is not None - # CPM 2.6.0 metadata should take priority - assert crs.to_epsg() == 32632 - - def test_simple_root_consolidation_success(tmp_path: Path): """ Test that simple_root_consolidation produces consolidated metadata at the root, and for the diff --git a/tests/test_s2_multiscale_geo_metadata.py b/tests/test_s2_multiscale_geo_metadata.py deleted file mode 100644 index edd6db1b..00000000 --- a/tests/test_s2_multiscale_geo_metadata.py +++ /dev/null @@ -1,324 +0,0 @@ -""" -Unit tests for _write_geo_metadata method in S2MultiscalePyramid. - -Tests the geographic metadata writing functionality added to level creation. -""" - -from pathlib import Path -from unittest.mock import patch - -import numpy as np -import pytest -import xarray as xr - -from eopf_geozarr.s2_optimization.s2_multiscale import ( - create_measurements_encoding, - stream_write_dataset, - write_geo_metadata, -) - - -@pytest.fixture -def sample_dataset_with_crs() -> xr.Dataset: - """Create a sample dataset with CRS information.""" - coords = { - "x": (["x"], np.linspace(0, 1000, 100)), - "y": (["y"], np.linspace(0, 1000, 100)), - "time": (["time"], [np.datetime64("2023-01-01")]), - } - - data_vars = { - "b02": (["time", "y", "x"], np.random.rand(1, 100, 100)), - "b03": (["time", "y", "x"], np.random.rand(1, 100, 100)), - "b04": (["y", "x"], np.random.rand(100, 100)), - } - - ds = xr.Dataset(data_vars, coords=coords) - - ds["b02"].attrs["proj:epsg"] = 32632 - ds["b03"].attrs["proj:epsg"] = 32632 - ds["b04"].attrs["proj:epsg"] = 32632 - - return ds - - -@pytest.fixture -def sample_dataset_with_epsg_attrs() -> xr.Dataset: - """Create a sample dataset with EPSG in attributes.""" - coords = { - "x": (["x"], np.linspace(0, 1000, 50)), - "y": (["y"], np.linspace(0, 1000, 50)), - } - - data_vars = { - "b05": (["y", "x"], np.random.rand(50, 50)), - "b06": (["y", "x"], np.random.rand(50, 50)), - } - - ds = xr.Dataset(data_vars, coords=coords) - - # Add EPSG to variable attributes - ds["b05"].attrs["proj:epsg"] = 32632 - ds["b06"].attrs["proj:epsg"] = 32632 - - return ds - - -@pytest.fixture -def sample_dataset_no_crs() -> xr.Dataset: - """Create a sample dataset without CRS information.""" - coords = { - "x": (["x"], np.linspace(0, 1000, 25)), - "y": (["y"], np.linspace(0, 1000, 25)), - } - - data_vars = { - "b11": (["y", "x"], np.random.rand(25, 25)), - "b12": (["y", "x"], np.random.rand(25, 25)), - } - - return xr.Dataset(data_vars, coords=coords) - - -class TestWriteGeoMetadata: - """Test the _write_geo_metadata method.""" - - def test_write_geo_metadata_with_rio_crs( - self, sample_dataset_with_crs: xr.Dataset - ) -> None: - """Test _write_geo_metadata with dataset that has rioxarray CRS.""" - - # Call the method - write_geo_metadata(sample_dataset_with_crs) - - # Verify CRS was written - assert hasattr(sample_dataset_with_crs, "rio") - assert sample_dataset_with_crs.rio.crs is not None - assert sample_dataset_with_crs.rio.crs.to_epsg() == 32632 - - def test_write_geo_metadata_with_epsg_attrs( - self, sample_dataset_with_epsg_attrs: xr.Dataset - ) -> None: - """Test _write_geo_metadata with dataset that has EPSG in variable attributes.""" - - # Verify initial state - no CRS - assert ( - not hasattr(sample_dataset_with_epsg_attrs, "rio") - or sample_dataset_with_epsg_attrs.rio.crs is None - ) - - # Call the method - write_geo_metadata(sample_dataset_with_epsg_attrs) - - # Verify CRS was written from attributes - assert hasattr(sample_dataset_with_epsg_attrs, "rio") - assert sample_dataset_with_epsg_attrs.rio.crs is not None - assert sample_dataset_with_epsg_attrs.rio.crs.to_epsg() == 32632 - - def test_write_geo_metadata_no_crs(self, sample_dataset_no_crs: xr.Dataset) -> None: - """Test _write_geo_metadata with dataset that has no CRS information.""" - - # Verify initial state - no CRS - assert ( - not hasattr(sample_dataset_no_crs, "rio") - or sample_dataset_no_crs.rio.crs is None - ) - - # Call the method - should not fail but also not add CRS - write_geo_metadata(sample_dataset_no_crs) - - # Verify no CRS was added (method handles gracefully) - # The method should not fail even when no CRS is available - # This tests the robustness of the method - - def test_write_geo_metadata_custom_grid_mapping_name( - self, sample_dataset_with_crs: xr.Dataset - ) -> None: - """Test _write_geo_metadata with custom grid_mapping variable name.""" - - # Call the method with custom grid mapping name - custom_name = "custom_spatial_ref" - write_geo_metadata(sample_dataset_with_crs, custom_name) - - # Verify CRS was written - assert hasattr(sample_dataset_with_crs, "rio") - assert sample_dataset_with_crs.rio.crs is not None - - def test_write_geo_metadata_preserves_existing_data( - self, sample_dataset_with_crs: xr.Dataset - ) -> None: - """Test that _write_geo_metadata preserves existing data variables and coordinates.""" - - # Store original data - original_vars = list(sample_dataset_with_crs.data_vars.keys()) - original_coords = list(sample_dataset_with_crs.coords.keys()) - original_b02_data = sample_dataset_with_crs["b02"].values.copy() - - # Call the method - write_geo_metadata(sample_dataset_with_crs) - - # Verify all original data is preserved - assert list(sample_dataset_with_crs.data_vars.keys()) == original_vars - assert all(coord in sample_dataset_with_crs.coords for coord in original_coords) - assert np.array_equal(sample_dataset_with_crs["b02"].values, original_b02_data) - - def test_write_geo_metadata_empty_dataset(self) -> None: - """Test _write_geo_metadata with empty dataset.""" - - empty_ds = xr.Dataset({}, coords={}) - - # Call the method - should handle gracefully - write_geo_metadata(empty_ds) - - # Verify method doesn't fail with empty dataset - # This tests robustness - - def test_write_geo_metadata_rio_write_crs_called( - self, sample_dataset_with_crs: xr.Dataset - ) -> None: - """Test that rio.write_crs is called correctly.""" - - # Mock the rio.write_crs method - with patch.object(sample_dataset_with_crs.rio, "write_crs") as mock_write_crs: - # Call the method - write_geo_metadata(sample_dataset_with_crs) - - # Verify rio.write_crs was called with correct arguments - mock_write_crs.assert_called_once() - call_args = mock_write_crs.call_args - assert call_args[1]["inplace"] is True # inplace=True should be passed - - def test_write_geo_metadata_crs_from_multiple_sources(self) -> None: - """Test CRS detection from multiple sources in priority order.""" - - # Create dataset with both rio CRS and EPSG attributes - coords = { - "x": (["x"], np.linspace(0, 1000, 50)), - "y": (["y"], np.linspace(0, 1000, 50)), - } - - data_vars = {"b08": (["y", "x"], np.random.rand(50, 50))} - - ds = xr.Dataset(data_vars, coords=coords) - - # Add both rio CRS and EPSG attribute (rio should take priority) - ds = ds.rio.write_crs("EPSG:4326") # Rio CRS - ds["b08"].attrs["proj:epsg"] = 32632 # EPSG attribute - - # Call the method - write_geo_metadata(ds) - - # Verify rio CRS was used (priority over attributes) - assert ds.rio.crs.to_epsg() == 4326 # Should still be 4326, not 32632 - - def test_write_geo_metadata_integration_with_stream_write( - self, tmp_path: Path - ) -> None: - """Test that _write_geo_metadata is properly integrated in _stream_write_dataset.""" - - # Create a simple dataset with CRS - coords = { - "x": (["x"], np.linspace(0, 1000, 100)), - "y": (["y"], np.linspace(0, 1000, 100)), - } - - data_vars = { - "b02": (["y", "x"], np.random.rand(100, 100)), - } - - ds = xr.Dataset(data_vars, coords=coords) - ds = ds.rio.write_crs("EPSG:32632") - - # Create encoding for the dataset - encoding = create_measurements_encoding( - ds, spatial_chunk=1024, enable_sharding=True - ) - - # Create a temporary output path that matches the pattern for geo metadata - output_path = str(tmp_path / "measurements" / "test_dataset.zarr") - - # Call _stream_write_dataset (which should call _write_geo_metadata internally) - stream_write_dataset(ds, output_path, encoding, enable_sharding=True) - - # Re-open the written dataset to verify CRS was persisted - written_ds = xr.open_dataset( - output_path, engine="zarr", chunks={}, decode_coords="all" - ) - - # Verify CRS was written and persisted - assert hasattr(written_ds, "rio") - assert written_ds.rio.crs is not None - assert written_ds.rio.crs.to_epsg() == 32632 - - -class TestWriteGeoMetadataEdgeCases: - """Test edge cases for _write_geo_metadata method.""" - - def test_write_geo_metadata_invalid_crs( - self, - ) -> None: - """Test _write_geo_metadata with invalid CRS data.""" - - coords = { - "x": (["x"], np.linspace(0, 1000, 10)), - "y": (["y"], np.linspace(0, 1000, 10)), - } - - data_vars = {"test_var": (["y", "x"], np.random.rand(10, 10))} - - ds = xr.Dataset(data_vars, coords=coords) - - # Add invalid EPSG code - ds["test_var"].attrs["proj:epsg"] = "invalid_epsg" - - # Method should raise an exception for invalid CRS (normal behavior) - from pyproj.exceptions import CRSError - - with pytest.raises(CRSError): - write_geo_metadata(ds) - - def test_write_geo_metadata_mixed_crs_variables( - self, - ) -> None: - """Test _write_geo_metadata with variables having different CRS information.""" - - coords = { - "x": (["x"], np.linspace(0, 1000, 20)), - "y": (["y"], np.linspace(0, 1000, 20)), - } - - data_vars = { - "var1": (["y", "x"], np.random.rand(20, 20)), - "var2": (["y", "x"], np.random.rand(20, 20)), - } - - ds = xr.Dataset(data_vars, coords=coords) - - # Add different EPSG codes to different variables - ds["var1"].attrs["proj:epsg"] = 32632 - ds["var2"].attrs["proj:epsg"] = 4326 - - # Call the method (should use the first CRS found) - write_geo_metadata(ds) - - # Verify a CRS was applied (should be the first one found) - assert hasattr(ds, "rio") - - def test_write_geo_metadata_maintains_dataset_attrs( - self, sample_dataset_with_crs: xr.Dataset - ) -> None: - """Test that _write_geo_metadata maintains dataset-level attributes.""" - - # Add some dataset attributes - sample_dataset_with_crs.attrs["pyramid_level"] = 1 - sample_dataset_with_crs.attrs["resolution_meters"] = 20 - sample_dataset_with_crs.attrs["custom_attr"] = "test_value" - - original_attrs = sample_dataset_with_crs.attrs.copy() - - # Call the method - write_geo_metadata(sample_dataset_with_crs) - - # Verify dataset attributes are preserved - for key, value in original_attrs.items(): - assert sample_dataset_with_crs.attrs[key] == value From f5dc9aeccacdc594e28bc4fe7e7ad8f49f439b6a Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 14:32:55 +0100 Subject: [PATCH 17/59] zarrio tests --- tests/test_zarrio.py | 344 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 344 insertions(+) create mode 100644 tests/test_zarrio.py diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py new file mode 100644 index 00000000..a84e850f --- /dev/null +++ b/tests/test_zarrio.py @@ -0,0 +1,344 @@ +"""Tests for the zarrio module""" + +from __future__ import annotations + +import numcodecs +import pytest +import zarr + +from eopf_geozarr.zarrio import ( + ChunkEncodingSpec, + convert_compression, + reencode_array, + reencode_group, +) + + +def test_convert_compression_blosc() -> None: + """ + Test that convert_compression properly maps zarr + v2 blosc to zarr v3 blosc + """ + source = {"id": "blosc", "cname": "zstd", "clevel": 3, "shuffle": 2, "blocksize": 0} + + expected = ( + { + "name": "blosc", + "configuration": { + "cname": "zstd", + "clevel": 3, + "shuffle": "bitshuffle", + "blocksize": 0, + }, + }, + ) + + observed = convert_compression(source) + assert observed == expected + + +def test_convert_compression_none() -> None: + """ + Test that convert_compression maps None to an empty tuple + """ + source = None + expected = () + observed = convert_compression(source) + assert observed == expected + + +def test_convert_compression_fails() -> None: + """ + Test that convert compression will fail on out of band input + """ + source = {"id": "gzip"} + with pytest.raises(ValueError, match="Only blosc -> blosc or None -> ()"): + convert_compression(source) + + +@pytest.mark.parametrize("dimension_names", [None, ("a",)]) +def test_reencode_array_dimension_names( + dimension_names: None | tuple[str | None, ...], +) -> None: + """ + Test that dimension names are handled correctly when re-encoding an array + """ + array_a = zarr.create_array( + {}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None + ) + assert ( + reencode_array(array_a, dimension_names=dimension_names).dimension_names + == dimension_names + ) + + +def test_reencode_array_attributes_default() -> None: + """ + Test that original attributes are preserved when no custom attributes provided + """ + array_a = zarr.create_array( + {}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None + ) + array_a.attrs["original"] = "value" + + meta = reencode_array(array_a) + assert meta.attributes == {"original": "value"} + + +def test_reencode_array_attributes_custom() -> None: + """ + Test that custom attributes override original attributes + """ + array_a = zarr.create_array( + {}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None + ) + array_a.attrs["original"] = "value" + + custom_attrs = {"custom": "new_value", "foo": "bar"} + meta = reencode_array(array_a, attributes=custom_attrs) + assert meta.attributes == custom_attrs + + +def test_reencode_array_chunking_write_only() -> None: + """ + Test that chunking with only write_chunks updates chunk shape + """ + array_a = zarr.create_array( + {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None + ) + + chunking: ChunkEncodingSpec = {"write_chunks": (25,)} + meta = reencode_array(array_a, chunking=chunking) + + assert meta.chunk_grid.chunk_shape == (25,) + + +def test_reencode_array_chunking_no_sharding_when_read_chunks_missing() -> None: + """ + Test that sharding codec is not used when read_chunks not specified + """ + array_a = zarr.create_array( + {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None + ) + + chunking: ChunkEncodingSpec = {"write_chunks": (25,)} + meta = reencode_array(array_a, chunking=chunking) + + codec_names = [codec["name"] for codec in meta.codecs if isinstance(codec, dict)] + assert "sharding-indexed" not in codec_names + + +def test_reencode_array_chunking_with_sharding_codec_present() -> None: + """ + Test that sharding codec is created when read_chunks specified + """ + array_a = zarr.create_array( + {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None + ) + + chunking: ChunkEncodingSpec = {"write_chunks": (25,), "read_chunks": (5,)} + meta = reencode_array(array_a, chunking=chunking) + + assert len(meta.codecs) == 1 + + +def test_reencode_array_chunking_with_sharding_grid_shape() -> None: + """ + Test that chunk grid uses write_chunks when sharding is enabled + """ + array_a = zarr.create_array( + {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None + ) + + chunking: ChunkEncodingSpec = {"write_chunks": (25,), "read_chunks": (5,)} + meta = reencode_array(array_a, chunking=chunking) + + assert meta.chunk_grid.chunk_shape == (25,) + + +def test_reencode_array_fill_value_none() -> None: + """ + Test that None fill_value is converted to dtype default + """ + array_a = zarr.create_array( + {}, shape=(10,), dtype="uint8", zarr_format=2, compressors=None, fill_value=None + ) + + meta = reencode_array(array_a) + assert meta.fill_value == 0 + + +def test_reencode_array_fill_value_custom() -> None: + """ + Test that custom fill_value is preserved + """ + array_a = zarr.create_array( + {}, shape=(10,), dtype="uint8", zarr_format=2, compressors=None, fill_value=255 + ) + + meta = reencode_array(array_a) + assert meta.fill_value == 255 + + +def test_reencode_array_blosc_compression_converts() -> None: + """ + Test that blosc compression is properly converted from v2 to v3 + """ + compressor = numcodecs.Blosc( + cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE + ) + + # Verify the conversion function works + converted = convert_compression(compressor) + assert len(converted) == 1 + assert converted[0]["name"] == "blosc" + assert converted[0]["configuration"]["cname"] == "zstd" + assert converted[0]["configuration"]["clevel"] == 5 + assert converted[0]["configuration"]["shuffle"] == "bitshuffle" + + +def test_reencode_group_basic() -> None: + """ + Test that reencode_group creates a zarr v3 group from a v2 group + """ + group_v2 = zarr.create_group({}, zarr_format=2) + group_v3 = reencode_group(group_v2, {}, "") + + assert group_v3.metadata.zarr_format == 3 + + +def test_reencode_group_preserves_attributes() -> None: + """ + Test that group attributes are preserved during re-encoding + """ + group_v2 = zarr.create_group({}, zarr_format=2) + group_v2.attrs["foo"] = "bar" + group_v2.attrs["number"] = 42 + + group_v3 = reencode_group(group_v2, {}, "") + + assert group_v3.attrs["foo"] == "bar" + assert group_v3.attrs["number"] == 42 + + +def test_reencode_group_with_array() -> None: + """ + Test that arrays within a group are re-encoded + """ + group_v2 = zarr.create_group({}, zarr_format=2) + group_v2.create_array("data", shape=(10,), dtype="float32") + + group_v3 = reencode_group(group_v2, {}, "") + + assert "data" in group_v3 + assert group_v3["data"].metadata.zarr_format == 3 + + +def test_reencode_group_array_data_preserved() -> None: + """ + Test that array data is preserved during re-encoding + """ + group_v2 = zarr.create_group({}, zarr_format=2) + array_v2 = group_v2.create_array("data", shape=(5,), dtype="int32") + array_v2[:] = [1, 2, 3, 4, 5] + + group_v3 = reencode_group(group_v2, {}, "") + + assert (group_v3["data"][:] == [1, 2, 3, 4, 5]).all() + + +def test_reencode_group_array_attributes_preserved() -> None: + """ + Test that array attributes are preserved during re-encoding + """ + group_v2 = zarr.create_group({}, zarr_format=2) + array_v2 = group_v2.create_array("data", shape=(5,), dtype="int32") + array_v2.attrs["units"] = "meters" + + group_v3 = reencode_group(group_v2, {}, "") + + assert group_v3["data"].attrs["units"] == "meters" + + +def test_reencode_group_with_nested_group() -> None: + """ + Test that nested groups are re-encoded + """ + group_v2 = zarr.create_group({}, zarr_format=2) + group_v2.create_group("subgroup") + + group_v3 = reencode_group(group_v2, {}, "") + + assert "subgroup" in group_v3 + assert isinstance(group_v3["subgroup"], zarr.Group) + + +def test_reencode_group_nested_group_attributes() -> None: + """ + Test that nested group attributes are preserved + """ + group_v2 = zarr.create_group({}, zarr_format=2) + sub = group_v2.create_group("subgroup") + sub.attrs["nested"] = "value" + + group_v3 = reencode_group(group_v2, {}, "") + + assert group_v3["subgroup"].attrs["nested"] == "value" + + +def test_reencode_group_with_array_dimensions() -> None: + """ + Test that _ARRAY_DIMENSIONS attribute is converted to dimension_names + """ + group_v2 = zarr.create_group({}, zarr_format=2) + array_v2 = group_v2.create_array("data", shape=(10, 20), dtype="float32") + array_v2.attrs["_ARRAY_DIMENSIONS"] = ("x", "y") + + group_v3 = reencode_group(group_v2, {}, "") + + assert group_v3["data"].metadata.dimension_names == ("x", "y") + + +def test_reencode_group_removes_array_dimensions_from_attrs() -> None: + """ + Test that _ARRAY_DIMENSIONS is removed from attributes after conversion + """ + group_v2 = zarr.create_group({}, zarr_format=2) + array_v2 = group_v2.create_array("data", shape=(10,), dtype="float32") + array_v2.attrs["_ARRAY_DIMENSIONS"] = ("x",) + array_v2.attrs["other"] = "value" + + group_v3 = reencode_group(group_v2, {}, "") + + assert "_ARRAY_DIMENSIONS" not in group_v3["data"].attrs + assert group_v3["data"].attrs["other"] == "value" + + +def test_reencode_group_deep_hierarchy() -> None: + """ + Test that deeply nested hierarchies are fully re-encoded + """ + group_v2 = zarr.create_group({}, zarr_format=2) + level1 = group_v2.create_group("level1") + level2 = level1.create_group("level2") + level2.create_array("deep_array", shape=(5,), dtype="int32") + + group_v3 = reencode_group(group_v2, {}, "") + + assert "level1/level2/deep_array" in group_v3 + assert group_v3["level1/level2/deep_array"].metadata.zarr_format == 3 + + +def test_reencode_group_with_chunk_reencoder() -> None: + """ + Test that custom chunk_reencoder function is applied + """ + group_v2 = zarr.create_group({}, zarr_format=2) + group_v2.create_array("data", shape=(100,), chunks=(10,), dtype="float32") + + def custom_chunker(array: zarr.Array) -> ChunkEncodingSpec: + return {"write_chunks": (25,)} + + group_v3 = reencode_group(group_v2, {}, "", chunk_reencoder=custom_chunker) + + assert group_v3["data"].metadata.chunk_grid.chunk_shape == (25,) From 4ba6eb1ef93acd35125b76f515b8744dd036b082 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 14:33:43 +0100 Subject: [PATCH 18/59] fixes in zarrio --- src/eopf_geozarr/zarrio.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index 69465c89..af1f5b03 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -12,16 +12,8 @@ import zarr from zarr.core.group import GroupMetadata from zarr.core.metadata.v3 import ArrayV3Metadata - - -async def set_store(store: zarr.abc.store.Store, key: str, value: Any) -> None: - """ - Call store.set(key, value) after awaiting value, if value awaits to None then do nothing. - """ - value_actual = await value - if value_actual is not None: - await store.set(key, value_actual) - return None +from zarr.core.sync import sync +from zarr.storage._common import make_store_path class ChunkEncodingSpec(TypedDict): @@ -40,13 +32,17 @@ def convert_compression( return () SHUFFLE = ("noshuffle", "shuffle", "bitshuffle") - if isinstance(compressor, numcodecs.Blosc): + if isinstance(compressor, numcodecs.abc.Codec): old_config = compressor.get_config() + else: + old_config = compressor + if old_config["id"] == "blosc": new_codec = { "name": "blosc", "configuration": { "cname": old_config["cname"], "clevel": old_config["clevel"], + "blocksize": old_config["blocksize"], "shuffle": SHUFFLE[old_config["shuffle"]], }, } @@ -85,7 +81,7 @@ def reencode_array( if chunking is not None and "read_chunks" in chunking: codecs = ( { - "name": "sharding-indexed", + "name": "sharding_indexed", "configuration": { "chunk_shape": chunking["read_chunks"], "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), @@ -121,12 +117,16 @@ def reencode_group( overwrite: bool = False, use_consolidated_for_children: bool = False, chunk_reencoder: Callable[[zarr.Array[Any]], ChunkEncodingSpec] | None = None, -) -> zarr.group: +) -> zarr.Group: """ Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. """ log = structlog.get_logger() + # Convert store-like to a proper Store object + store_path = sync(make_store_path(store)) + store = store_path.store + all_members = dict( group.members( max_depth=None, use_consolidated_for_children=use_consolidated_for_children From 28c9282e212b1356cf5ed6244d5c716ab8347bd1 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 16:33:03 +0100 Subject: [PATCH 19/59] silence warnings --- src/eopf_geozarr/s2_optimization/s2_converter.py | 5 ++++- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 2 ++ tests/test_cli_e2e.py | 4 +++- tests/test_s2_converter_simplified.py | 2 +- 4 files changed, 10 insertions(+), 3 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 3b0c9ce0..78de6ac9 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -272,7 +272,9 @@ def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: # Step 2: Create multiscale pyramids for each group in the original structure log.info("Adding multiscale levels") - new_dt_input = xr.open_datatree(out_store, engine="zarr", chunks="auto") + new_dt_input = xr.open_datatree( + out_store, engine="zarr", chunks="auto", decode_timedelta=True, consolidated=False + ) datasets = create_multiscale_from_datatree( new_dt_input, output_path, @@ -395,6 +397,7 @@ def create_result_datatree(output_path: str) -> xr.DataTree: engine="zarr", chunks="auto", storage_options=storage_options, + decode_timedelta=True, ) except Exception as e: log.warning("Could not open result DataTree", error=str(e)) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 53e4fe87..12703ea9 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -751,6 +751,8 @@ def stream_write_dataset( engine="zarr", chunks={}, decode_coords="all", + decode_timedelta=True, + consolidated=False, ) log.info("Streaming computation and write to {}", dataset_path=dataset_path) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 8c525286..3d25e26a 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -89,7 +89,9 @@ def test_cli_convert_real_sentinel2_data(s2_group_example: Path, tmp_path: Path) output_path = tmp_path / "s2b_geozarr_cli_test.zarr" # Detect product level (L1C vs L2A) by checking which quicklook group exists - dt_source = xr.open_datatree(s2_group_example, engine="zarr") + dt_source = xr.open_datatree( + s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True + ) has_l2a_quicklook = "/quality/l2a_quicklook" in dt_source.groups has_l1c_quicklook = "/quality/l1c_quicklook" in dt_source.groups diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 355169a4..5ca34a59 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -114,7 +114,7 @@ def test_convert_s2_optimized_convenience_function( assert isinstance(result, xr.DataTree) # Verify basic structure - output should have multiscale groups - output_dt = xr.open_datatree(output_path, engine="zarr") + output_dt = xr.open_datatree(output_path, engine="zarr", decode_timedelta=True) assert len(output_dt.groups) > 0 From 14144975a84e8283c9fb106836b7a6d221ed38fc Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 16:34:42 +0100 Subject: [PATCH 20/59] silence warnings --- src/eopf_geozarr/conversion/geozarr.py | 1 + tests/test_s2_converter_simplified.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/src/eopf_geozarr/conversion/geozarr.py b/src/eopf_geozarr/conversion/geozarr.py index b6d5279f..e09d700e 100644 --- a/src/eopf_geozarr/conversion/geozarr.py +++ b/src/eopf_geozarr/conversion/geozarr.py @@ -1189,6 +1189,7 @@ def cleanup_prefix(prefix: str) -> None: engine="zarr", decode_coords="all", chunks="auto", + consolidated=False, storage_options=store_storage_options, ) break diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 5ca34a59..4f5de3b7 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -93,7 +93,9 @@ def test_convert_s2_optimized_convenience_function( ) -> None: """Test the convenience function with real S2 data.""" # Open the S2 example as a DataTree - dt_input = xr.open_datatree(s2_group_example, engine="zarr") + dt_input = xr.open_datatree( + s2_group_example, engine="zarr", decode_timedelta=True, consolidated=False + ) output_path = str(tmp_path / "test_output.zarr") # Run the conversion From d325f6d94ab4ca15719823bcae41f4f80da7246c Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 16:59:22 +0100 Subject: [PATCH 21/59] silence warnings --- src/eopf_geozarr/data_api/geozarr/multiscales/geozarr.py | 8 ++++++-- .../test_geozarr/test_multiscales/test_geozarr.py | 1 - 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/eopf_geozarr/data_api/geozarr/multiscales/geozarr.py b/src/eopf_geozarr/data_api/geozarr/multiscales/geozarr.py index b8ea6d1a..e2f564de 100644 --- a/src/eopf_geozarr/data_api/geozarr/multiscales/geozarr.py +++ b/src/eopf_geozarr/data_api/geozarr/multiscales/geozarr.py @@ -28,7 +28,7 @@ def valid_zcm(self) -> Self: Ensure that the ZCM metadata, if present, is valid """ if self.layout is not MISSING: - zcm.Multiscales(**self.model_dump()) + zcm.Multiscales(layout=self.layout, resampling_method=self.resampling_method) return self @@ -38,7 +38,11 @@ def valid_tms(self) -> Self: Ensure that the TMS metadata, if present, is valid """ if self.tile_matrix_set is not MISSING: - tms.Multiscales(**self.model_dump()) + tms.Multiscales( + tile_matrix_set=self.tile_matrix_set, + tile_matrix_limits=self.tile_matrix_limits, + resampling_method=self.resampling_method, # type: ignore[arg-type] + ) return self diff --git a/tests/test_data_api/test_geozarr/test_multiscales/test_geozarr.py b/tests/test_data_api/test_geozarr/test_multiscales/test_geozarr.py index 78bb3af1..aaeacbaf 100644 --- a/tests/test_data_api/test_geozarr/test_multiscales/test_geozarr.py +++ b/tests/test_data_api/test_geozarr/test_multiscales/test_geozarr.py @@ -18,7 +18,6 @@ def test_multiscale_group_attrs(multiscale_flavor: set[Literal["zcm", "tms"]]) - zcm_meta: dict[str, object] = {} tms_meta: dict[str, object] = {} zarr_conventions_meta: MISSING | tuple[Any, ...] = MISSING - if "zcm" in multiscale_flavor: layout = ( zcm.ScaleLevel( From 62c56d8aa8f20e3e14fe75dd96c3f90ac374e6e5 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 18:19:48 +0100 Subject: [PATCH 22/59] treat warnings as errors in tests --- pyproject.toml | 1 + tests/test_cli_e2e.py | 3 +++ tests/test_data_api/test_geozarr/test_common.py | 8 ++++++-- tests/test_data_api/test_v3.py | 3 ++- tests/test_s2_converter_simplified.py | 1 + 5 files changed, 13 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fe932fbe..689d23a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -172,6 +172,7 @@ module = ["zarr.*", "xarray.*", "rioxarray.*", "cf_xarray.*", "dask.*"] ignore_missing_imports = true [tool.pytest.ini_options] +filterwarnings = "error" minversion = "7.0" addopts = "-ra -q --strict-markers --strict-config" testpaths = ["tests"] diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 3d25e26a..686f31c6 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -18,6 +18,7 @@ from pydantic_zarr.experimental.v3 import GroupSpec +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: """ Test the convert-s2-optimized CLI command on a local copy of sentinel data @@ -81,6 +82,7 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: assert differences == {} +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") def test_cli_convert_real_sentinel2_data(s2_group_example: Path, tmp_path: Path) -> None: """ Test CLI conversion using a Sentinel-2 hierarchy saved locally. @@ -335,6 +337,7 @@ def test_cli_convert_with_crs_groups(s2_group_example, tmp_path: Path) -> None: assert (output_path / "zarr.json").exists(), "Main zarr.json not found" +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") def test_cli_crs_groups_empty_list(tmp_path: str) -> None: """Test CLI with --crs-groups but no groups specified (empty list).""" # Create a minimal test dataset diff --git a/tests/test_data_api/test_geozarr/test_common.py b/tests/test_data_api/test_geozarr/test_common.py index b8460c43..18c5e235 100644 --- a/tests/test_data_api/test_geozarr/test_common.py +++ b/tests/test_data_api/test_geozarr/test_common.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any +from typing import TYPE_CHECKING, Any import numpy as np import pytest @@ -19,6 +19,9 @@ from eopf_geozarr.data_api.geozarr.v2 import DataArray as DataArray_V2 from eopf_geozarr.data_api.geozarr.v2 import DataArray as DataArray_V3 +if TYPE_CHECKING: + import zarr + @pytest.mark.parametrize( "obj", @@ -73,7 +76,8 @@ def test_check_standard_name_invalid() -> None: check_standard_name("invalid_standard_name") -def test_multiscales_round_trip(example_group) -> None: +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") +def test_multiscales_round_trip(example_group: zarr.Group) -> None: """ Ensure that we can round-trip multiscale metadata through the `Multiscales` model. """ diff --git a/tests/test_data_api/test_v3.py b/tests/test_data_api/test_v3.py index 5a05cb14..df356475 100644 --- a/tests/test_data_api/test_v3.py +++ b/tests/test_data_api/test_v3.py @@ -63,7 +63,8 @@ def test_invalid_coordinates( check_valid_coordinates(group) -def test_dataarray_round_trip(example_group: Any) -> None: +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") +def test_dataarray_round_trip(example_group: zarr.Group) -> None: """ Ensure that we can round-trip dataarray attributes through the `Multiscales` model. """ diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 4f5de3b7..9c2268cb 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -86,6 +86,7 @@ def test_simple_root_consolidation_success(tmp_path: Path) -> None: class TestConvenienceFunction: """Test the convenience function.""" + @pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") def test_convert_s2_optimized_convenience_function( self, s2_group_example: Path, From aeaaeedf29686c5952a82975e23f23fcd5538b27 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Thu, 11 Dec 2025 20:06:20 +0100 Subject: [PATCH 23/59] add omit-nodes parameter to reencode-group --- src/eopf_geozarr/cli.py | 6 +++ .../s2_optimization/s2_converter.py | 33 ++++++++++----- src/eopf_geozarr/zarrio.py | 42 ++++++++++++++++--- 3 files changed, 66 insertions(+), 15 deletions(-) diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 410c0eae..2a9d88dd 100755 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1159,6 +1159,9 @@ def add_s2_optimization_commands(subparsers: argparse._SubParsersAction) -> None choices=range(1, 10), help="Compression level 1-9 (default: 3)", ) + s2_parser.add_argument( + "--omit-nodes", help="The names of groups or arrays to skip.", default="", type=str + ) s2_parser.add_argument("--skip-validation", action="store_true", help="Skip output validation") s2_parser.add_argument("--verbose", action="store_true", help="Enable verbose output") s2_parser.add_argument( @@ -1189,12 +1192,15 @@ def convert_s2_optimized_command(args: argparse.Namespace) -> None: storage_options=storage_options, ) + omit_nodes = set(args.omit_nodes.split()) + # Convert convert_s2_optimized( dt_input=dt_input, output_path=args.output_path, enable_sharding=args.enable_sharding, spatial_chunk=args.spatial_chunk, + omit_nodes=omit_nodes, compression_level=args.compression_level, validate_output=not args.skip_validation, ) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 78de6ac9..3a4cf23d 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -206,6 +206,7 @@ def convert_s2_optimized( spatial_chunk: int, compression_level: int, validate_output: bool, + omit_nodes: set[str] | None = None, max_retries: int = 3, ) -> xr.DataTree: """ @@ -224,6 +225,9 @@ def convert_s2_optimized( Optimized DataTree """ + if omit_nodes is None: + omit_nodes = set() + start_time = time.time() zg = get_zarr_group(dt_input) s2root_model = Sentinel2Root.from_zarr(zg) @@ -258,17 +262,26 @@ def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: return {"write_chunks": (1,) * (array.ndim - 2) + (spatial_chunk, spatial_chunk)} return {"write_chunks": array.chunks} - out_group = reencode_group(zg, out_store, "", overwrite=True, chunk_reencoder=chunk_reencoder) - - log.info("Adding CRS elements to datasets in measurements") - for _, subgroup in out_group["measurements"].groups(): - for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset, crs=crs) + out_group = reencode_group( + zg, + out_store, + path="", + overwrite=True, + chunk_reencoder=chunk_reencoder, + omit_nodes=omit_nodes, + ) - log.info("Adding CRS elements to quality datasets") - for _, subgroup in out_group["quality"].groups(): - for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset, crs=crs) + if "measurements" not in omit_nodes: + log.info("Adding CRS elements to datasets in measurements") + for _, subgroup in out_group["measurements"].groups(): + for _, dataset in subgroup.groups(): + add_crs_and_grid_mapping(dataset, crs=crs) + + if "quality" not in omit_nodes: + log.info("Adding CRS elements to quality datasets") + for _, subgroup in out_group["quality"].groups(): + for _, dataset in subgroup.groups(): + add_crs_and_grid_mapping(dataset, crs=crs) # Step 2: Create multiscale pyramids for each group in the original structure log.info("Adding multiscale levels") diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index 4fa597cb..d86a16ce 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -105,34 +105,66 @@ def reencode_array( def reencode_group( group: zarr.Group, - store: Any, + store: zarr.storage.StoreLike, path: str, *, overwrite: bool = False, use_consolidated_for_children: bool = False, + omit_nodes: set[str] | None = None, chunk_reencoder: Callable[[zarr.Array[Any]], ChunkEncodingSpec] | None = None, ) -> zarr.Group: """ Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. + + Parameters + ---------- + group : zarr.Group + The Zarr group to re-encode + store : zarr.storage.StoreLike + The store to write into + path : str + The path in the new store to use + overwrite : bool, default = False + Whether to overwrite contents of the new store + omit_nodes : set[str], default = {} + The names of groups or arrays to omit from re-encoding. + chunk_reencoder : Callable[[zarr.Array[Any], ChunkEncodingSpec]] | None, default = None + A function that takes a Zarr array object and returns a ChunkEncodingSpec, which is a dict + that defines a new chunk encoding. Use this parameter to define per-array chunk encoding + logic. + """ + if omit_nodes is None: + omit_nodes = set() + log = structlog.get_logger() # Convert store-like to a proper Store object store_path = sync(make_store_path(store)) store = store_path.store - all_members = dict( + members = dict( group.members(max_depth=None, use_consolidated_for_children=use_consolidated_for_children) ) - log = structlog.get_logger() log.info("Begin re-encoding Zarr group %s", group) new_members: dict[str, ArrayV3Metadata | GroupMetadata] = { path: GroupMetadata(zarr_format=3, attributes=group.attrs.asdict()) } chunks_to_encode: list[str] = [] - for name, member in all_members.items(): - log.info("re-encoding member %s", name) + for name in omit_nodes: + if not any(k.startswith(name) for k in members): + log.warning( + "The name %s was provided in omit_nodes but no such array or group exists.", name + ) + for name, member in members.items(): + if any(name.startswith(v) for v in omit_nodes): + log.info( + "Skipping node %s because it is contained in a subgroup declared in the omit_groups parameter", + name, + ) + continue + log.info("Re-encoding member %s", name) new_path = f"{path}/{name}" member_attrs = member.attrs.asdict() if isinstance(member, zarr.Array): From 6a324a5fa6b38b402bd983a7d15ad73d2cd3dda3 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 12 Dec 2025 16:57:43 +0100 Subject: [PATCH 24/59] bump to latest version of pydantic-zarr --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 689d23a7..119287db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ classifiers = [ ] requires-python = ">=3.11" dependencies = [ - "pydantic-zarr>=0.8.0", + "pydantic-zarr>=0.9.1", "pydantic>=2.12", "zarr>=3.1.1", "xarray>=2025.7.1", From 1ec1e21bdef60eb744f04e36a76f8a1202206c73 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Sun, 14 Dec 2025 15:42:17 +0100 Subject: [PATCH 25/59] wip functional zarr transformers --- .../s2_optimization/s2_converter.py | 193 +- .../s2_optimization/s2_multiscale.py | 305 +- src/eopf_geozarr/zarrio.py | 23 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 2221 +++---- ...309_N0511_R108_T32TLQ_20250113T122458.json | 5832 ++++++++++++++--- ...131_N0511_R037_T29TPF_20250811T152216.json | 2221 +++---- tests/test_s2_converter_simplified.py | 1 + tests/test_s2_multiscale.py | 65 +- 8 files changed, 6991 insertions(+), 3870 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 3a4cf23d..af0640d0 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -15,14 +15,15 @@ from pyproj import CRS from zarr.core.sync import sync from zarr.storage._common import make_store - +from zarr.core.dtype import parse_dtype +from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from eopf_geozarr.conversion.fs_utils import get_storage_options from eopf_geozarr.conversion.geozarr import get_zarr_group from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root -from eopf_geozarr.zarrio import ChunkEncodingSpec, reencode_group - -from .s2_multiscale import create_multiscale_from_datatree, write_geo_metadata +from eopf_geozarr.zarrio import ChunkEncodingSpec, reencode_group, convert_compression +from .s2_multiscale import create_multiscale_levels, write_geo_metadata +from zarr.core.chunk_grids import _auto_partition log = structlog.get_logger() @@ -99,73 +100,6 @@ def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS: raise ValueError("No CRS found.") -def convert_s2( - dt_input: xr.DataTree, - output_path: str, - validate_output: bool, - enable_sharding: bool, - spatial_chunk: int, -) -> xr.DataTree: - """ - Convert S2 dataset to optimized structure. - - Args: - dt_input: Input Sentinel-2 DataTree - output_path: Output path for optimized dataset - validate_output: Whether to validate the output - verbose: Enable verbose logging - - Returns: - Optimized DataTree - """ - start_time = time.time() - - log.info( - "Starting S2 optimized conversion", - num_groups=len(dt_input.groups), - output_path=output_path, - ) - - # Validate input is S2 - if not is_sentinel2_dataset(get_zarr_group(dt_input)): - raise ValueError("Input dataset is not a Sentinel-2 product") - - # Step 1: Process data while preserving original structure - log.info("Step 1: Processing data with original structure preserved") - - # Step 2: Create multiscale pyramids for each group in the original structure - log.info("Step 2: Creating multiscale pyramids (preserving original hierarchy)") - datasets = create_multiscale_from_datatree( - dt_input, - output_path, - spatial_chunk=spatial_chunk, - enable_sharding=enable_sharding, - ) - - log.info("Created multiscale pyramids", num_groups=len(datasets)) - - # Step 3: Root-level consolidation - log.info("Step 3: Final root-level metadata consolidation") - simple_root_consolidation(output_path, datasets) - - # Step 4: Validation - if validate_output: - log.info("Step 4: Validating optimized dataset") - validation_results = validate_optimized_dataset(output_path) - if not validation_results["is_valid"]: - log.warning("Validation issues found", issues=validation_results["issues"]) - - # Create result DataTree - result_dt = create_result_datatree(output_path) - - total_time = time.time() - start_time - log.info("Optimization complete", duration_seconds=round(total_time, 2)) - - optimization_summary(dt_input, result_dt, output_path) - - return result_dt - - class ConvertS2Params(TypedDict): enable_sharding: bool spatial_chunk: int @@ -248,6 +182,9 @@ def convert_s2_optimized( # Define a chunk reencoding function based on the requested chunking def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: + """ + Re-encode the chunking for a Zarr V2 array into Zarr V3 format. + """ group_name = str(Path(array.path).parent) in_measurements_group = ( group_name.startswith("r") @@ -262,6 +199,101 @@ def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: return {"write_chunks": (1,) * (array.ndim - 2) + (spatial_chunk, spatial_chunk)} return {"write_chunks": array.chunks} + def array_reencoder( + key: str, + metadata: ArrayV2Metadata, + *, + spatial_chunk: int, + use_sharding: bool = False) -> ArrayV3Metadata: + """ + Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. + """ + attributes: dict[str, object] = metadata.attributes # type: ignore[assignment] + dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] + compressor_converted = convert_compression(metadata.compressor) # type: ignore[assignment] + + # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the + # array's data type must be provided. We use the zarr-python model of the data type to get + # a fill value here. + if metadata.fill_value is None: + fill_value = metadata.dtype.default_scalar() + else: + fill_value = metadata.fill_value + + group_name = str(Path(key).parent) + # sentinel-specific logic: if this array is a variable stored in a measurements group + # then we will apply particular chunking + in_measurements_group = ( + group_name.startswith("r") + and group_name.endswith("m") + and "/measurements/" in group_name + ) + + # get the size of each element of the array, if that's defined for this dtype + if hasattr(metadata.dtype, "item_size"): + item_size: int = metadata.dtype.item_size # type: ignore[attr-defined] + else: + item_size = 1 + + chunks: tuple[int, ...] = metadata.chunks + shards: tuple[int, ...] | None = None + + if in_measurements_group and metadata.ndim > 0: + if metadata.ndim == 1: + # don't rechunk 1D variables + pass + else: + if not use_sharding: + chunks = (spatial_chunk, spatial_chunk) + shards = None + else: + chunks, shards =_auto_partition( + array_shape=metadata.shape, + chunk_shape=spatial_chunk, + shard_shape="auto", + item_size=item_size) + if metadata.ndim > 2: + # use chunks / shards computed for 2D but padded along leading dimensions with 1 + pad = (1,) * (metadata.ndim - 2) + chunks = pad + chunks + if shards is not None: + shards = pad + shards + + + chunk_grid: dict[str, str | dict[str, object]] = { # type: ignore[assignment] + "name": "regular", + "configuration": {"chunk_shape": chunks}, + }, + + chunk_key_encoding: dict[str, str | dict[str, object]] = { # type: ignore[assignment] + "name": "default", "configuration": {"separator": "/"}} + + if shards is not None: + codecs = ( + { + "name": "sharding_indexed", + "configuration": { + "chunk_shape": chunks, + "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), + "index_location": "end", + "codecs": ({"name": "bytes"}, *compressor_converted), + }, + }, + ) + else: + codecs = ({"name": "bytes"}, *commpressor_converted) # type: ignore[assignment] + + return ArrayV3Metadata( + shape=metadata.shape, + data_type=metadata.dtype, + chunk_key_encoding=chunk_key_encoding, + chunk_grid=chunk_grid, + fill_value=fill_value, + dimension_names=dimension_names, + codecs=codecs, + attributes=attributes, + ) + out_group = reencode_group( zg, out_store, @@ -271,13 +303,13 @@ def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: omit_nodes=omit_nodes, ) - if "measurements" not in omit_nodes: + if "measurements" in out_group: log.info("Adding CRS elements to datasets in measurements") for _, subgroup in out_group["measurements"].groups(): for _, dataset in subgroup.groups(): add_crs_and_grid_mapping(dataset, crs=crs) - if "quality" not in omit_nodes: + if "quality" in out_group: log.info("Adding CRS elements to quality datasets") for _, subgroup in out_group["quality"].groups(): for _, dataset in subgroup.groups(): @@ -285,22 +317,13 @@ def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: # Step 2: Create multiscale pyramids for each group in the original structure log.info("Adding multiscale levels") - new_dt_input = xr.open_datatree( - out_store, engine="zarr", chunks="auto", decode_timedelta=True, consolidated=False - ) - datasets = create_multiscale_from_datatree( - new_dt_input, - output_path, - spatial_chunk=spatial_chunk, - enable_sharding=enable_sharding, - crs=crs, - ) - log.info("Created multiscale pyramids", num_groups=len(datasets)) + create_multiscale_levels(out_group, "measurements/reflectance") # Step 3: Root-level consolidation log.info("Step 3: Final root-level metadata consolidation") - simple_root_consolidation(output_path, datasets) + # Pass empty dict since all groups are already created by reencode_group + simple_root_consolidation(output_path, {}) # Step 4: Validation if validate_output: diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 12703ea9..4e447e7c 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -5,6 +5,7 @@ from __future__ import annotations +from itertools import pairwise from typing import TYPE_CHECKING, Any, Literal import numpy as np @@ -35,6 +36,7 @@ if TYPE_CHECKING: from collections.abc import Hashable, Mapping + import zarr from pyproj import CRS from eopf_geozarr.types import OverviewLevelJSON @@ -60,218 +62,41 @@ def get_grid_spacing(ds: xr.DataArray, coords: tuple[Hashable, ...]) -> tuple[fl return tuple(np.abs(ds.coords[coord][0].data - ds.coords[coord][1].data) for coord in coords) -def create_multiscale_from_datatree( - dt_input: xr.DataTree, - output_path: str, - enable_sharding: bool, - spatial_chunk: int, - crs: CRS | None = None, -) -> dict[str, dict]: +def create_multiscale_levels(group: zarr.Group, path: str) -> None: """ - Create multiscale versions preserving original structure. - Keeps all original groups, adds r120m, r360m, r720m downsampled versions. - - Args: - dt_input: Input DataTree with original structure - output_path: Base output path - enable_sharding: Enable Zarr v3 sharding - spatial_chunk: Spatial chunk size - crs: Coordinate Reference System for datasets - - Returns: - Dictionary of processed groups + Add additional multiscale levels to an existing Zarr group """ - processed_groups = {} - - # Step 1: Copy all original groups as-is - for group_path in dt_input.groups: - if group_path == ".": - continue - - group_node = dt_input[group_path] - - # Skip parent groups that have children (only process leaf groups) - if hasattr(group_node, "children") and len(group_node.children) > 0: - continue - - dataset = group_node.to_dataset() - - # Skip empty groups - if not dataset.data_vars: - log.info("Skipping empty group: {}", group_path=group_path) - continue - - log.info("Copying original group: {}", group_path=group_path) - - output_group_path = f"{output_path}{group_path}" - - # Determine if this is a measurement-related resolution group - group_name = group_path.split("/")[-1] - is_measurement_group = ( - group_name.startswith("r") - and group_name.endswith("m") - and "/measurements/" in group_path + ds_levels = (1, 2, 6, 12, 36, 72) + ds_group_names = tuple(f"r{10 * x}m" for x in ds_levels) + + # Construct the full path to the group containing resolution levels + full_path = f"{group.path}/{path}" if group.path else path + + # Load the datatree to check which groups exist + dt = xr.open_datatree(group.store, engine="zarr") + + for (cur_factor, cur_group_name), (next_factor, next_group_name) in pairwise( + zip(ds_levels, ds_group_names, strict=True) + ): + # Open the current resolution level as a dataset + cur_group_path = f"{full_path}/{cur_group_name}" + cur_ds = xr.open_dataset(group.store, group=cur_group_path, engine="zarr") + + scale = next_factor // cur_factor + to_downsample: dict[str, xr.DataArray] = {} + for var_name, var in cur_ds.data_vars.items(): + # Check if the variable already exists in the next level + next_level_path = f"{path}/{next_group_name}" + if next_level_path not in dt or var_name not in dt[next_level_path].data_vars: + to_downsample[var_name] = var + log.info("downsampling %s into %s", to_downsample, next_group_name) + downsampled_ds = create_downsampled_resolution_group( + xr.Dataset(data_vars=to_downsample, coords=cur_ds.coords), factor=scale ) - - if is_measurement_group: - # Measurement groups: apply custom encoding - encoding = create_measurements_encoding( - dataset, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding - ) - else: - # Non-measurement groups: preserve original chunking - encoding = create_original_encoding(dataset) - ds_out = stream_write_dataset( - dataset, - output_group_path, - encoding, - enable_sharding=enable_sharding, - crs=crs, + next_group_path = f"{full_path}/{next_group_name}" + downsampled_ds.to_zarr( + group.store, group=next_group_path, consolidated=False, mode="a", compute=True ) - processed_groups[group_path] = ds_out - - # Step 2: Create downsampled resolution groups ONLY for measurements - # Find all resolution-based groups under /measurements/ and organize by base path - resolution_groups = {} - base_path = "/measurements/reflectance" - for group_path in processed_groups: - # Only process groups under /measurements/reflectance - if not group_path.startswith(base_path): - continue - - group_name = group_path.split("/")[-1] - if group_name in ["r10m", "r20m", "r60m"]: - resolution_groups[group_name] = processed_groups[group_path] - - # Find the coarsest resolution (r60m > r20m > r10m) - source_dataset = None - source_resolution = None - - for res in ["r60m", "r20m", "r10m"]: - if res in resolution_groups: - source_dataset = resolution_groups[res] - source_resolution = int(res[1:-1]) # Extract number - break - - if not source_dataset or source_resolution is None: - log.info("No source resolution found for downsampling, skipping downsampled levels") - return processed_groups # Stop processing if no valid source dataset is found - - log.info( - "Creating downsampled versions", - source_dataset=source_dataset, - source_resolution=source_resolution, - ) - - # Create r120m - try: - r120m_path = f"{base_path}/r120m" - factor = 120 // source_resolution - log.info("Creating r120m with factor {}", factor=factor) - - r120m_dataset = create_downsampled_resolution_group(source_dataset, factor=factor).compute() - - if r120m_dataset and len(r120m_dataset.data_vars) > 0: - output_path_120 = f"{output_path}{r120m_path}" - log.info("Writing r120m to {}", output_path_120=output_path_120) - encoding_120 = create_measurements_encoding(r120m_dataset, spatial_chunk=spatial_chunk) - ds_120 = stream_write_dataset( - r120m_dataset, - output_path_120, - encoding_120, - enable_sharding=enable_sharding, - crs=crs, - ) - processed_groups[r120m_path] = ds_120 - resolution_groups["r120m"] = ds_120 - - # Create r360m from r120m - try: - r360m_path = f"{base_path}/r360m" - log.info("Creating r360m with factor 3") - - r360m_dataset = create_downsampled_resolution_group( - r120m_dataset, factor=3 - ).compute() - - if r360m_dataset and len(r360m_dataset.data_vars) > 0: - output_path_360 = f"{output_path}{r360m_path}" - log.info("Writing r360m to {}", output_path_360=output_path_360) - encoding_360 = create_measurements_encoding( - r360m_dataset, spatial_chunk=spatial_chunk - ) - ds_360 = stream_write_dataset( - r360m_dataset, - output_path_360, - encoding_360, - enable_sharding=enable_sharding, - crs=crs, - ) - processed_groups[r360m_path] = ds_360 - resolution_groups["r360m"] = ds_360 - - # Create r720m from r360m - try: - r720m_path = f"{base_path}/r720m" - log.info(" Creating r720m with factor 2") - - r720m_dataset = create_downsampled_resolution_group( - r360m_dataset, factor=2 - ).compute() - - if r720m_dataset and len(r720m_dataset.data_vars) > 0: - output_path_720 = f"{output_path}{r720m_path}" - - log.info( - " Writing r720m to {}", - output_path_720=output_path_720, - ) - encoding_720 = create_measurements_encoding( - r720m_dataset, - spatial_chunk=spatial_chunk, - enable_sharding=enable_sharding, - ) - ds_720 = stream_write_dataset( - r720m_dataset, - output_path_720, - encoding_720, - enable_sharding=enable_sharding, - crs=crs, - ) - processed_groups[r720m_path] = ds_720 - resolution_groups["r720m"] = ds_720 - else: - log.info(" r720m dataset is empty, skipping") - except Exception as e: - log.warning( - "Could not create r720m", - base_path=base_path, - error=str(e), - ) - else: - log.info(" r360m dataset is empty, skipping") - except Exception as e: - log.warning("Could not create r360m for {}: {}", base_path=base_path, e=e) - # Track r120m for multiscales if created - - log.info("Tracking r120m for multiscales metadata") - else: - log.info("r120m dataset is empty, skipping") - except Exception as e: - log.warning("Could not create r120m for {}: {}", base_path=base_path, e=e) - - # Step 3: Add multiscales metadata to parent groups - log.info("Adding multiscales metadata to parent groups") - - dt_multiscale = add_multiscales_metadata_to_parent( - output_path, - base_path, - resolution_groups, - multiscales_flavor={"ogc_tms", "experimental_multiscales_convention"}, - ) - processed_groups[base_path] = dt_multiscale - - return processed_groups def create_measurements_encoding( @@ -582,47 +407,35 @@ def create_original_encoding(dataset: xr.Dataset) -> dict[str, XarrayDataArrayEn def create_downsampled_resolution_group(source_dataset: xr.Dataset, factor: int) -> xr.Dataset: """Create a downsampled version of a dataset by given factor.""" - if not source_dataset or len(source_dataset.data_vars) == 0: - return xr.Dataset() - - # Get reference dimensions - ref_var = next(iter(source_dataset.data_vars.values())) - if ref_var.ndim < 2: - return xr.Dataset() - current_height, current_width = ref_var.shape[-2:] - target_height = current_height // factor - target_width = current_width // factor - - if target_height < 1 or target_width < 1: - return xr.Dataset() - - # Downsample all variables using existing lazy operations - lazy_vars = {} + # Downsample all variables + lazy_vars: dict[str, xr.DataArray] = {} for var_name, var_data in source_dataset.data_vars.items(): - if var_data.ndim < 2: - continue - var_typ = determine_variable_type(var_name, var_data) - if var_typ == "quality_mask": - lazy_downsampled = var_data.coarsen({"x": factor, "y": factor}, boundary="trim").max() - elif var_typ == "reflectance": - lazy_downsampled = var_data.coarsen({"x": factor, "y": factor}, boundary="trim").mean() - elif var_typ == "classification": - lazy_downsampled = var_data.coarsen({"x": factor, "y": factor}, boundary="trim").reduce( - subsample_2 - ) - elif var_typ == "probability": - lazy_downsampled = var_data.coarsen({"x": factor, "y": factor}, boundary="trim").mean() - else: - raise ValueError(f"Unknown variable type {var_typ}") - - # preserve encoding - lazy_downsampled.encoding = var_data.encoding - # Ensure that dtype is preserved - lazy_vars[var_name] = lazy_downsampled.astype(var_data.dtype) + if var_data.ndim >= 2: + var_typ = determine_variable_type(var_name, var_data) + if var_typ == "quality_mask": + lazy_downsampled = var_data.coarsen( + {"x": factor, "y": factor}, boundary="trim" + ).max() + elif var_typ == "reflectance": + lazy_downsampled = var_data.coarsen( + {"x": factor, "y": factor}, boundary="trim" + ).mean() + elif var_typ == "classification": + lazy_downsampled = var_data.coarsen( + {"x": factor, "y": factor}, boundary="trim" + ).reduce(subsample_2) + elif var_typ == "probability": + lazy_downsampled = var_data.coarsen( + {"x": factor, "y": factor}, boundary="trim" + ).mean() + else: + raise ValueError(f"Unknown variable type {var_typ}") - if not lazy_vars: - return xr.Dataset() + # preserve encoding + lazy_downsampled.encoding = var_data.encoding + # Ensure that dtype is preserved + lazy_vars[var_name] = lazy_downsampled.astype(var_data.dtype) # Create dataset with lazy variables and coordinates return xr.Dataset(lazy_vars, attrs=source_dataset.attrs) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index d86a16ce..ca25dd81 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -10,13 +10,14 @@ import structlog import zarr from zarr.core.group import GroupMetadata +from zarr.core.metadata.v3 import ArrayV3Metadata from zarr.core.sync import sync from zarr.storage._common import make_store_path if TYPE_CHECKING: from collections.abc import Callable, Mapping - from zarr.core.metadata.v3 import ArrayV3Metadata + from zarr.core.metadata.v2 import ArrayV2Metadata class ChunkEncodingSpec(TypedDict): @@ -60,7 +61,7 @@ def reencode_array( dimension_names: None | tuple[str | None, ...] = None, attributes: Mapping[str, object] | None = None, chunking: ChunkEncodingSpec | None = None, -) -> zarr.core.metadata.v3.ArrayV3Metadata: +) -> ArrayV3Metadata: """ Re-encode a zarr array into a new zarr v3 array. """ @@ -88,7 +89,7 @@ def reencode_array( else: codecs = ({"name": "bytes"}, *new_codecs) # type: ignore[assignment] - return zarr.core.metadata.v3.ArrayV3Metadata( + return ArrayV3Metadata( shape=array.shape, data_type=array.metadata.dtype, chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, @@ -111,7 +112,7 @@ def reencode_group( overwrite: bool = False, use_consolidated_for_children: bool = False, omit_nodes: set[str] | None = None, - chunk_reencoder: Callable[[zarr.Array[Any]], ChunkEncodingSpec] | None = None, + array_reencoder: Callable[[ArrayV2Metadata], ArrayV3Metadata] | None = None, ) -> zarr.Group: """ Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. @@ -166,19 +167,9 @@ def reencode_group( continue log.info("Re-encoding member %s", name) new_path = f"{path}/{name}" - member_attrs = member.attrs.asdict() if isinstance(member, zarr.Array): - if "_ARRAY_DIMENSIONS" in member.attrs: - dimension_names = member_attrs.pop("_ARRAY_DIMENSIONS") - else: - dimension_names = None - chunking = None if chunk_reencoder is None else chunk_reencoder(member) - new_members[new_path] = reencode_array( - member, - dimension_names=dimension_names, - attributes=member_attrs, - chunking=chunking, - ) + new_meta = array_reencoder(array.metadata) + new_members[new_path] = new_meta chunks_to_encode.append(name) else: new_members[new_path] = GroupMetadata( diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 6e539cb3..83496a06 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -4872,243 +4872,12 @@ "attributes": {}, "members": { "reflectance": { - "attributes": { - "multiscales": { - "layout": [ - { - "asset": "r10m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - }, - { - "asset": "r20m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - }, - { - "asset": "r60m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - }, - { - "asset": "r120m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - }, - { - "asset": "r360m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - }, - { - "asset": "r720m", - "derived_from": "r10m", - "transform": { - "scale": [ - 0.0, - 0.0 - ], - "translation": [ - -5.0, - -5.0 - ] - } - } - ], - "resampling_method": "average", - "tile_matrix_limits": { - "r10m": { - "maxTileCol": 42, - "maxTileRow": 42, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r10m" - }, - "r120m": { - "maxTileCol": 3, - "maxTileRow": 3, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r120m" - }, - "r20m": { - "maxTileCol": 21, - "maxTileRow": 21, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r20m" - }, - "r360m": { - "maxTileCol": 1, - "maxTileRow": 1, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r360m" - }, - "r60m": { - "maxTileCol": 7, - "maxTileRow": 7, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r60m" - }, - "r720m": { - "maxTileCol": 0, - "maxTileRow": 0, - "minTileCol": 0, - "minTileRow": 0, - "tileMatrix": "r720m" - } - }, - "tile_matrix_set": { - "crs": "http://www.opengis.net/def/crs/EPSG/0/32632", - "id": "Native_CRS_32632", - "orderedAxes": [ - "X", - "Y" - ], - "supportedCRS": "http://www.opengis.net/def/crs/EPSG/0/32632", - "tileMatrices": [ - { - "cellSize": 0.0, - "id": "r10m", - "matrixHeight": 2, - "matrixWidth": 2, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 5490, - "tileWidth": 5490 - }, - { - "cellSize": 0.0, - "id": "r20m", - "matrixHeight": 1, - "matrixWidth": 1, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 5490, - "tileWidth": 5490 - }, - { - "cellSize": 0.0, - "id": "r60m", - "matrixHeight": 1, - "matrixWidth": 1, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 1830, - "tileWidth": 1830 - }, - { - "cellSize": 0.0, - "id": "r120m", - "matrixHeight": 1, - "matrixWidth": 1, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 915, - "tileWidth": 915 - }, - { - "cellSize": 0.0, - "id": "r360m", - "matrixHeight": 2, - "matrixWidth": 2, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 256, - "tileWidth": 256 - }, - { - "cellSize": 0.0, - "id": "r720m", - "matrixHeight": 1, - "matrixWidth": 1, - "pointOfOrigin": [ - 0.0, - 0.0 - ], - "scaleDenominator": 0.0, - "tileHeight": 152, - "tileWidth": 152 - } - ], - "title": "Native CRS Tile Matrix Set (EPSG:32632)" - } - }, - "zarr_conventions": [ - { - "description": "Multiscale layout of zarr datasets", - "name": "multiscales", - "schema_url": "https://raw.githubusercontent.com/zarr-conventions/multiscales/refs/tags/v1/schema.json", - "spec_url": "https://github.com/zarr-conventions/multiscales/blob/v1/README.md", - "uuid": "d35379db-88df-4056-af3a-620245f8e347" - } - ] - }, + "attributes": {}, "members": { "r10m": { - "attributes": {}, + "attributes": { + "grid_mapping": "spatial_ref" + }, "members": { "b02": { "attributes": { @@ -5682,8 +5451,10 @@ "scale_factor": 0.0001, "units": "digital_counts" }, + "coordinates": "spatial_ref", "dtype": " xr.DataTree: - """Create a simple DataTree for integration testing.""" + def simple_datatree(self) -> zarr.Group: + """Create a simple DataTree for integration testing, and save it to an in-memory zarr group""" + store = {} + group = zarr.create_group(store) # Create sample data x = np.linspace(0, 1000, 100) y = np.linspace(0, 1000, 100) @@ -155,36 +157,39 @@ def simple_datatree(self) -> xr.DataTree: # Create DataTree dt = xr.DataTree(name="root") dt["/measurements/reflectance/r10m"] = xr.DataTree(ds) + dt.to_zarr(group.store, compute=True, mode="w", consolidated=False) - return dt + return group - @patch("eopf_geozarr.s2_optimization.s2_multiscale.stream_write_dataset") - def test_create_multiscale_from_datatree( - self, mock_write: Any, simple_datatree: xr.DataTree, tmp_path: Path - ) -> None: + @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") + def test_create_multiscale_from_datatree(self, simple_datatree: zarr.Group) -> None: """Test multiscale creation from DataTree.""" - output_path = str(tmp_path / "output.zarr") - # Mock the write to avoid actual file I/O - mock_write.return_value = xr.Dataset({"b02": xr.DataArray([1, 2, 3])}) + class DatasetMembers(TypedDict): + x: ArraySpec + y: ArraySpec + time: ArraySpec + b02: ArraySpec + + class DatasetGroup(GroupSpec): + members: DatasetMembers + + class ExpectedMembers(TypedDict): + r10m: DatasetGroup + r20m: DatasetGroup + r60m: DatasetGroup + r120m: DatasetGroup + r360m: DatasetGroup + r720m: DatasetGroup + + class ExpectedGroup(GroupSpec): + members: ExpectedMembers # Capture log output using structlog's testing context manager - with capture_logs() as cap_logs: - result = create_multiscale_from_datatree( - simple_datatree, - output_path, - enable_sharding=True, - spatial_chunk=256, - ) - - # Should process groups - assert isinstance(result, dict) - # At minimum, should write the original group - assert mock_write.call_count >= 1 - - # Optionally verify log messages (cap_logs contains all logged events) - # With verbose=False, there won't be many logs, but we can check the structure - assert isinstance(cap_logs, list) + with capture_logs(): + create_multiscale_levels(simple_datatree, path="measurements/reflectance") + + ExpectedGroup.from_zarr(simple_datatree["measurements/reflectance"]) if __name__ == "__main__": From 39bc9369b253d47547840760fc199183f462a353 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Sun, 14 Dec 2025 21:41:28 +0100 Subject: [PATCH 26/59] update multiscales and expected JSON output --- .../s2_optimization/s2_converter.py | 255 +++++++++--------- .../s2_optimization/s2_multiscale.py | 54 +++- src/eopf_geozarr/zarrio.py | 47 +--- tests/test_cli_e2e.py | 2 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 2 + ...309_N0511_R108_T32TLQ_20250113T122458.json | 2 + ...131_N0511_R037_T29TPF_20250811T152216.json | 2 + tests/test_s2_converter_simplified.py | 10 +- tests/test_zarrio.py | 130 ++------- 9 files changed, 235 insertions(+), 269 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index af0640d0..9a7c4284 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -5,6 +5,7 @@ from __future__ import annotations import time +from functools import partial from pathlib import Path from typing import Any, TypedDict @@ -13,20 +14,34 @@ import zarr from pydantic import TypeAdapter from pyproj import CRS +from xarray.coding.times import _netcdf_to_numpy_timeunit +from zarr.core.chunk_grids import _auto_partition +from zarr.core.dtype.npy.time import TimeDelta64 +from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from zarr.core.sync import sync from zarr.storage._common import make_store -from zarr.core.dtype import parse_dtype -from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata + from eopf_geozarr.conversion.fs_utils import get_storage_options from eopf_geozarr.conversion.geozarr import get_zarr_group from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root -from eopf_geozarr.zarrio import ChunkEncodingSpec, reencode_group, convert_compression +from eopf_geozarr.zarrio import convert_compression, reencode_group + from .s2_multiscale import create_multiscale_levels, write_geo_metadata -from zarr.core.chunk_grids import _auto_partition log = structlog.get_logger() +TIME_UNITS = frozenset( + [ + "days", + "hours", + "minutes", + "seconds", + "milliseconds", + "microseconds", + "nanoseconds", + ] +) def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS: """ @@ -132,6 +147,119 @@ def add_crs_and_grid_mapping(group: zarr.Group, crs: CRS) -> None: group.attrs.update({"grid_mapping": "spatial_ref"}) +def array_reencoder( + key: str, + metadata: ArrayV2Metadata, + *, + spatial_chunk: int, + enable_sharding: bool = False) -> ArrayV3Metadata: + """ + Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. + """ + attributes: dict[str, object] = metadata.attributes.copy() # type: ignore[assignment] + # handle xarray datetime/timedelta encoding + # If the array has time-related units, ensure the dtype attribute matches the actual dtype + if attributes.get("units") in TIME_UNITS: + numpy_time_unit = _netcdf_to_numpy_timeunit(attributes["units"]) # type: ignore[assignment] + # Check if this is a timedelta or datetime based on: + # 1. The zarr dtype (if it's a native time type like TimeDelta64) + # 2. The existing dtype attribute (for int64-encoded times) + # 3. The standard_name attribute (e.g., "forecast_period" indicates timedelta) + existing_dtype_attr = str(attributes.get("dtype", "")) + standard_name = str(attributes.get("standard_name", "")) + is_timedelta = ( + isinstance(metadata.dtype, TimeDelta64) + or "timedelta" in existing_dtype_attr + or standard_name == "forecast_period" # CF convention for time since forecast + ) + + if is_timedelta: + # This is a timedelta array - set or correct the dtype attribute + # Note: xarray/pandas only support timedelta64 with s/ms/us/ns, not m/h/D + # Use 's' as the safest option + attributes["dtype"] = "timedelta64[ns]" + else: + # This is a datetime array - set or correct the dtype attribute + attributes["dtype"] = f"datetime64[{numpy_time_unit}]" + + dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] + compressor_converted = convert_compression(metadata.compressor) # type: ignore[assignment] + chunk_key_encoding: dict[str, str | dict[str, object]] = { + "name": "default", + "configuration": {"separator": "/"}} + + # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the + # array's data type must be provided. We use the zarr-python model of the data type to get + # a fill value here. + if metadata.fill_value is None: + fill_value = metadata.dtype.default_scalar() + else: + fill_value = metadata.fill_value + + group_name = str(Path(key).parent) + # sentinel-specific logic: if this array is a variable stored in a measurements group + # then we will apply particular chunking + in_measurements_group = ( + group_name.startswith("r") + and group_name.endswith("m") + and "/measurements/" in group_name + ) + + # get the size of each element of the array, if that's defined for this dtype + if hasattr(metadata.dtype, "item_size"): + item_size: int = metadata.dtype.item_size + else: + item_size = 1 + + chunk_shape: tuple[int, ...] = metadata.chunks + subchunk_shape: tuple[int, ...] | None = None + chunk_grid: dict[str, str | dict[str, object]] + + if in_measurements_group and metadata.ndim > 0: + if metadata.ndim == 1: + # don't rechunk 1D variables + pass + else: + if not enable_sharding: + chunk_shape = (spatial_chunk, spatial_chunk) + subchunk_shape = None + else: + # Generate 2D chunking / sharding, because partitioning along + # other dimensions will be set to 1 + chunk_shape, subchunk_shape =_auto_partition( + array_shape=metadata.shape[-2:], + chunk_shape=(spatial_chunk,) * 2, + shard_shape="auto", + item_size=item_size) + + chunk_grid = {"name": "regular", "configuration": {"chunk_shape": chunk_shape}} + if enable_sharding: + codecs = ( + { + "name": "sharding_indexed", + "configuration": { + "chunk_shape": subchunk_shape, + "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), + "index_location": "end", + "codecs": ({"name": "bytes"}, *compressor_converted), + }, + }, + ) + else: + codecs = ({"name": "bytes"}, *compressor_converted) # type: ignore[assignment] + + return ArrayV3Metadata( + shape=metadata.shape, + data_type=metadata.dtype, + chunk_key_encoding=chunk_key_encoding, + chunk_grid=chunk_grid, + fill_value=fill_value, + dimension_names=dimension_names, + codecs=codecs, + attributes=attributes, + ) + + def convert_s2_optimized( dt_input: xr.DataTree, *, @@ -180,126 +308,14 @@ def convert_s2_optimized( log.info("Re-encoding source data to Zarr V3") - # Define a chunk reencoding function based on the requested chunking - def chunk_reencoder(array: zarr.Array[Any]) -> ChunkEncodingSpec: - """ - Re-encode the chunking for a Zarr V2 array into Zarr V3 format. - """ - group_name = str(Path(array.path).parent) - in_measurements_group = ( - group_name.startswith("r") - and group_name.endswith("m") - and "/measurements/" in group_name - ) - if in_measurements_group and array.ndim > 0: - if array.ndim == 1: - return {"write_chunks": (spatial_chunk,)} - if array.ndim == 2: - return {"write_chunks": (spatial_chunk, spatial_chunk)} - return {"write_chunks": (1,) * (array.ndim - 2) + (spatial_chunk, spatial_chunk)} - return {"write_chunks": array.chunks} - - def array_reencoder( - key: str, - metadata: ArrayV2Metadata, - *, - spatial_chunk: int, - use_sharding: bool = False) -> ArrayV3Metadata: - """ - Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. - """ - attributes: dict[str, object] = metadata.attributes # type: ignore[assignment] - dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] - compressor_converted = convert_compression(metadata.compressor) # type: ignore[assignment] - - # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the - # array's data type must be provided. We use the zarr-python model of the data type to get - # a fill value here. - if metadata.fill_value is None: - fill_value = metadata.dtype.default_scalar() - else: - fill_value = metadata.fill_value - - group_name = str(Path(key).parent) - # sentinel-specific logic: if this array is a variable stored in a measurements group - # then we will apply particular chunking - in_measurements_group = ( - group_name.startswith("r") - and group_name.endswith("m") - and "/measurements/" in group_name - ) - - # get the size of each element of the array, if that's defined for this dtype - if hasattr(metadata.dtype, "item_size"): - item_size: int = metadata.dtype.item_size # type: ignore[attr-defined] - else: - item_size = 1 - - chunks: tuple[int, ...] = metadata.chunks - shards: tuple[int, ...] | None = None - - if in_measurements_group and metadata.ndim > 0: - if metadata.ndim == 1: - # don't rechunk 1D variables - pass - else: - if not use_sharding: - chunks = (spatial_chunk, spatial_chunk) - shards = None - else: - chunks, shards =_auto_partition( - array_shape=metadata.shape, - chunk_shape=spatial_chunk, - shard_shape="auto", - item_size=item_size) - if metadata.ndim > 2: - # use chunks / shards computed for 2D but padded along leading dimensions with 1 - pad = (1,) * (metadata.ndim - 2) - chunks = pad + chunks - if shards is not None: - shards = pad + shards - - - chunk_grid: dict[str, str | dict[str, object]] = { # type: ignore[assignment] - "name": "regular", - "configuration": {"chunk_shape": chunks}, - }, - - chunk_key_encoding: dict[str, str | dict[str, object]] = { # type: ignore[assignment] - "name": "default", "configuration": {"separator": "/"}} - - if shards is not None: - codecs = ( - { - "name": "sharding_indexed", - "configuration": { - "chunk_shape": chunks, - "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), - "index_location": "end", - "codecs": ({"name": "bytes"}, *compressor_converted), - }, - }, - ) - else: - codecs = ({"name": "bytes"}, *commpressor_converted) # type: ignore[assignment] - - return ArrayV3Metadata( - shape=metadata.shape, - data_type=metadata.dtype, - chunk_key_encoding=chunk_key_encoding, - chunk_grid=chunk_grid, - fill_value=fill_value, - dimension_names=dimension_names, - codecs=codecs, - attributes=attributes, - ) + _array_reencoder = partial(array_reencoder, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding) out_group = reencode_group( zg, out_store, path="", overwrite=True, - chunk_reencoder=chunk_reencoder, + array_reencoder=_array_reencoder, omit_nodes=omit_nodes, ) @@ -432,8 +448,7 @@ def create_result_datatree(output_path: str) -> xr.DataTree: output_path, engine="zarr", chunks="auto", - storage_options=storage_options, - decode_timedelta=True, + storage_options=storage_options ) except Exception as e: log.warning("Could not open result DataTree", error=str(e)) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 4e447e7c..9fe389c3 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -90,8 +90,9 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: if next_level_path not in dt or var_name not in dt[next_level_path].data_vars: to_downsample[var_name] = var log.info("downsampling %s into %s", to_downsample, next_group_name) + # Don't pass coords here - let the downsampled variables determine their own coordinates downsampled_ds = create_downsampled_resolution_group( - xr.Dataset(data_vars=to_downsample, coords=cur_ds.coords), factor=scale + xr.Dataset(data_vars=to_downsample), factor=scale ) next_group_path = f"{full_path}/{next_group_name}" downsampled_ds.to_zarr( @@ -432,13 +433,53 @@ def create_downsampled_resolution_group(source_dataset: xr.Dataset, factor: int) else: raise ValueError(f"Unknown variable type {var_typ}") - # preserve encoding - lazy_downsampled.encoding = var_data.encoding - # Ensure that dtype is preserved - lazy_vars[var_name] = lazy_downsampled.astype(var_data.dtype) + # Preserve encoding from source variable, but ensure fill_value is set + encoding = var_data.encoding.copy() + + # Remove 'fill_value' (with underscore) if it exists, we only want '_FillValue' + encoding.pop('fill_value', None) + + # Ensure _FillValue is set to avoid SerializationWarning + # when xarray encodes float data to integer using scale/offset + if '_FillValue' not in encoding: + # Use the same fill value logic as the source or a sensible default + if 'dtype' in encoding: + target_dtype = np.dtype(encoding['dtype']) + if np.issubdtype(target_dtype, np.unsignedinteger): + encoding['_FillValue'] = np.iinfo(target_dtype).max + elif np.issubdtype(target_dtype, np.signedinteger): + encoding['_FillValue'] = np.iinfo(target_dtype).min + else: + encoding['_FillValue'] = np.nan + else: + encoding['_FillValue'] = np.nan + + # Ensure that dtype is preserved by casting + lazy_downsampled_casted = lazy_downsampled.astype(var_data.dtype) + # Re-apply encoding after astype (which creates a new DataArray) + lazy_downsampled_casted.encoding = encoding + lazy_vars[var_name] = lazy_downsampled_casted # Create dataset with lazy variables and coordinates - return xr.Dataset(lazy_vars, attrs=source_dataset.attrs) + result_ds = xr.Dataset(lazy_vars, attrs=source_dataset.attrs) + + # Re-apply encoding to all data variables to ensure it's properly set + # (xr.Dataset creation sometimes doesn't preserve encoding correctly) + for var_name in lazy_vars: + if var_name in result_ds.data_vars: + result_ds[var_name].encoding = lazy_vars[var_name].encoding + + # Clear dtype encoding for all coordinate variables since coordinates become float + # after downsampling (e.g., x=[0.5, 2.5, ...]) + for coord_name in result_ds.coords: + # The coordinates inherit encoding from the source through coarsen() + # We need to remove dtype to avoid SerializationWarning + encoding = result_ds[coord_name].encoding.copy() + encoding.pop('dtype', None) + encoding.pop('_FillValue', None) + result_ds[coord_name].encoding = encoding + + return result_ds def subsample_2(a: xr.DataArray, axis: tuple[int, ...] | None = None) -> xr.DataArray: @@ -564,7 +605,6 @@ def stream_write_dataset( engine="zarr", chunks={}, decode_coords="all", - decode_timedelta=True, consolidated=False, ) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index ca25dd81..a9f2aeb0 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -54,44 +54,26 @@ def convert_compression( raise ValueError(f"Only blosc -> blosc or None -> () is supported. Got {compressor=}") - -def reencode_array( - array: zarr.Array, - *, - dimension_names: None | tuple[str | None, ...] = None, - attributes: Mapping[str, object] | None = None, - chunking: ChunkEncodingSpec | None = None, +def default_array_reencoder(key: str, + metadata: ArrayV2Metadata, ) -> ArrayV3Metadata: """ Re-encode a zarr array into a new zarr v3 array. """ - new_codecs = convert_compression(array.metadata.compressor) - if array.fill_value is None: - fill_value = array.metadata.dtype.default_scalar() + new_codecs = convert_compression(metadata.compressor) + if metadata.fill_value is None: + fill_value = metadata.dtype.default_scalar() else: - fill_value = array.fill_value - attributes = array.attrs.asdict() if attributes is None else attributes - chunk_grid_shape = chunking["write_chunks"] if chunking is not None else array.chunks - - if chunking is not None and "read_chunks" in chunking: - codecs = ( - { - "name": "sharding_indexed", - "configuration": { - "chunk_shape": chunking["read_chunks"], - "index_codecs": ({"name": "bytes"}, {"name": "crc32c"}), - "index_location": "end", - "codecs": ({"name": "bytes"}, *new_codecs), - }, - }, - ) - else: - codecs = ({"name": "bytes"}, *new_codecs) # type: ignore[assignment] + fill_value = metadata.fill_value + attributes = metadata.attributes.copy() + dimension_names = attributes.pop("_ARRAY_DIMENSIONS", None) + chunk_grid_shape = metadata.chunks + codecs = ({"name": "bytes"}, *new_codecs) # type: ignore[assignment] return ArrayV3Metadata( - shape=array.shape, - data_type=array.metadata.dtype, + shape=metadata.shape, + data_type=metadata.dtype, chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, chunk_grid={ "name": "regular", @@ -103,7 +85,6 @@ def reencode_array( attributes=attributes, ) - def reencode_group( group: zarr.Group, store: zarr.storage.StoreLike, @@ -112,7 +93,7 @@ def reencode_group( overwrite: bool = False, use_consolidated_for_children: bool = False, omit_nodes: set[str] | None = None, - array_reencoder: Callable[[ArrayV2Metadata], ArrayV3Metadata] | None = None, + array_reencoder: Callable[[str, ArrayV2Metadata], ArrayV3Metadata] = default_array_reencoder, ) -> zarr.Group: """ Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. @@ -168,7 +149,7 @@ def reencode_group( log.info("Re-encoding member %s", name) new_path = f"{path}/{name}" if isinstance(member, zarr.Array): - new_meta = array_reencoder(array.metadata) + new_meta = array_reencoder(member.path, member.metadata) new_members[new_path] = new_meta chunks_to_encode.append(name) else: diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 686f31c6..79f11b38 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -92,7 +92,7 @@ def test_cli_convert_real_sentinel2_data(s2_group_example: Path, tmp_path: Path) # Detect product level (L1C vs L2A) by checking which quicklook group exists dt_source = xr.open_datatree( - s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True + s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True, ) has_l2a_quicklook = "/quality/l2a_quicklook" in dt_source.groups has_l1c_quicklook = "/quality/l1c_quicklook" in dt_source.groups diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 83496a06..193c6b87 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -3590,6 +3590,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" @@ -4317,6 +4318,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index 09c7c0e5..6e5c3364 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -3162,6 +3162,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" @@ -3883,6 +3884,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index dd6a6460..e95d2dd0 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -3590,6 +3590,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" @@ -4317,6 +4318,7 @@ }, "step": { "attributes": { + "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", "units": "minutes" diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 4e069c83..a87914f2 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -45,7 +45,8 @@ def mock_s2_dataset() -> xr.DataTree: return dt - +@pytest.mark.filterwarnings("ignore:Object at .*:zarr.errors.ZarrUserWarning") +@pytest.mark.filterwarnings("ignore:Consolidated metadata is currently .*:zarr.errors.ZarrUserWarning") def test_simple_root_consolidation_success(tmp_path: Path) -> None: """ Test that simple_root_consolidation produces consolidated metadata at the root, and for the @@ -83,10 +84,11 @@ def test_simple_root_consolidation_success(tmp_path: Path) -> None: assert atmos_zmeta["consolidated_metadata"] is None +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") class TestConvenienceFunction: """Test the convenience function.""" - @pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") + @pytest.mark.filterwarnings("ignore:.*:xarray.coding.common.SerializationWarning") @pytest.mark.filterwarnings("ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning") def test_convert_s2_optimized_convenience_function( self, @@ -96,7 +98,7 @@ def test_convert_s2_optimized_convenience_function( """Test the convenience function with real S2 data.""" # Open the S2 example as a DataTree dt_input = xr.open_datatree( - s2_group_example, engine="zarr", decode_timedelta=True, consolidated=False + s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True, ) output_path = str(tmp_path / "test_output.zarr") @@ -118,7 +120,7 @@ def test_convert_s2_optimized_convenience_function( assert isinstance(result, xr.DataTree) # Verify basic structure - output should have multiscale groups - output_dt = xr.open_datatree(output_path, engine="zarr", decode_timedelta=True) + output_dt = xr.open_datatree(output_path, engine="zarr") assert len(output_dt.groups) > 0 diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py index d50d0ffa..559a1e2f 100644 --- a/tests/test_zarrio.py +++ b/tests/test_zarrio.py @@ -5,11 +5,12 @@ import numcodecs import pytest import zarr +from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from eopf_geozarr.zarrio import ( ChunkEncodingSpec, convert_compression, - reencode_array, + default_array_reencoder, reencode_group, ) @@ -55,100 +56,6 @@ def test_convert_compression_fails() -> None: with pytest.raises(ValueError, match=r"Only blosc -> blosc or None -> ()"): convert_compression(source) - -@pytest.mark.parametrize("dimension_names", [None, ("a",)]) -def test_reencode_array_dimension_names( - dimension_names: None | tuple[str | None, ...], -) -> None: - """ - Test that dimension names are handled correctly when re-encoding an array - """ - array_a = zarr.create_array({}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None) - assert ( - reencode_array(array_a, dimension_names=dimension_names).dimension_names == dimension_names - ) - - -def test_reencode_array_attributes_default() -> None: - """ - Test that original attributes are preserved when no custom attributes provided - """ - array_a = zarr.create_array({}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None) - array_a.attrs["original"] = "value" - - meta = reencode_array(array_a) - assert meta.attributes == {"original": "value"} - - -def test_reencode_array_attributes_custom() -> None: - """ - Test that custom attributes override original attributes - """ - array_a = zarr.create_array({}, shape=(1,), dtype="uint8", zarr_format=2, compressors=None) - array_a.attrs["original"] = "value" - - custom_attrs = {"custom": "new_value", "foo": "bar"} - meta = reencode_array(array_a, attributes=custom_attrs) - assert meta.attributes == custom_attrs - - -def test_reencode_array_chunking_write_only() -> None: - """ - Test that chunking with only write_chunks updates chunk shape - """ - array_a = zarr.create_array( - {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None - ) - - chunking: ChunkEncodingSpec = {"write_chunks": (25,)} - meta = reencode_array(array_a, chunking=chunking) - - assert meta.chunk_grid.chunk_shape == (25,) - - -def test_reencode_array_chunking_no_sharding_when_read_chunks_missing() -> None: - """ - Test that sharding codec is not used when read_chunks not specified - """ - array_a = zarr.create_array( - {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None - ) - - chunking: ChunkEncodingSpec = {"write_chunks": (25,)} - meta = reencode_array(array_a, chunking=chunking) - - codec_names = [codec["name"] for codec in meta.codecs if isinstance(codec, dict)] - assert "sharding-indexed" not in codec_names - - -def test_reencode_array_chunking_with_sharding_codec_present() -> None: - """ - Test that sharding codec is created when read_chunks specified - """ - array_a = zarr.create_array( - {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None - ) - - chunking: ChunkEncodingSpec = {"write_chunks": (25,), "read_chunks": (5,)} - meta = reencode_array(array_a, chunking=chunking) - - assert len(meta.codecs) == 1 - - -def test_reencode_array_chunking_with_sharding_grid_shape() -> None: - """ - Test that chunk grid uses write_chunks when sharding is enabled - """ - array_a = zarr.create_array( - {}, shape=(100,), dtype="uint8", zarr_format=2, chunks=(10,), compressors=None - ) - - chunking: ChunkEncodingSpec = {"write_chunks": (25,), "read_chunks": (5,)} - meta = reencode_array(array_a, chunking=chunking) - - assert meta.chunk_grid.chunk_shape == (25,) - - def test_reencode_array_fill_value_none() -> None: """ Test that None fill_value is converted to dtype default @@ -157,7 +64,7 @@ def test_reencode_array_fill_value_none() -> None: {}, shape=(10,), dtype="uint8", zarr_format=2, compressors=None, fill_value=None ) - meta = reencode_array(array_a) + meta = default_array_reencoder("test_array", array_a.metadata) assert meta.fill_value == 0 @@ -169,7 +76,7 @@ def test_reencode_array_fill_value_custom() -> None: {}, shape=(10,), dtype="uint8", zarr_format=2, compressors=None, fill_value=255 ) - meta = reencode_array(array_a) + meta = default_array_reencoder("test_array", array_a.metadata) assert meta.fill_value == 255 @@ -325,11 +232,26 @@ def test_reencode_group_with_chunk_reencoder() -> None: Test that custom chunk_reencoder function is applied """ group_v2 = zarr.create_group({}, zarr_format=2) - group_v2.create_array("data", shape=(100,), chunks=(10,), dtype="float32") - - def custom_chunker(array: zarr.Array) -> ChunkEncodingSpec: - return {"write_chunks": (25,)} - - group_v3 = reencode_group(group_v2, {}, "", chunk_reencoder=custom_chunker) + old_node = group_v2.create_array("data", shape=(100,), chunks=(10,), dtype="float32", attributes={"foo": 10}) + new_chunks = (25,) + def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata: + return ArrayV3Metadata( + shape=metadata.shape, + data_type=metadata.dtype, + fill_value=metadata.fill_value, + chunk_grid={"name": "regular", "configuration": {"chunk_shape": new_chunks}}, + chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, + codecs=({"name": "bytes"},), + dimension_names=None, + attributes=metadata.attributes + ) + + group_v3 = reencode_group(group_v2, {}, "", array_reencoder=custom_array_encoder) + + new_node = group_v3["data"] + assert new_node.metadata.chunk_grid.chunk_shape == new_chunks + assert new_node.attrs.asdict() == old_node.attrs.asdict() + +if __name__ == '__main__': + pytest.main([__file__]) - assert group_v3["data"].metadata.chunk_grid.chunk_shape == (25,) From c79b41f351330b4ad87b8a3278ed670f3104eb09 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Sun, 14 Dec 2025 22:46:32 +0100 Subject: [PATCH 27/59] preserve encoding when downsampling --- .../s2_optimization/s2_multiscale.py | 54 +++---------------- 1 file changed, 7 insertions(+), 47 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 9fe389c3..9c9aad19 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -14,6 +14,7 @@ from dask import delayed from dask.array import from_delayed from pydantic.experimental.missing_sentinel import MISSING +from zarr.codecs import BloscCodec from eopf_geozarr.conversion import fs_utils from eopf_geozarr.conversion.geozarr import ( @@ -89,7 +90,7 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: next_level_path = f"{path}/{next_group_name}" if next_level_path not in dt or var_name not in dt[next_level_path].data_vars: to_downsample[var_name] = var - log.info("downsampling %s into %s", to_downsample, next_group_name) + log.info("downsampling %s into %s", tuple(to_downsample.keys()), next_group_name) # Don't pass coords here - let the downsampled variables determine their own coordinates downsampled_ds = create_downsampled_resolution_group( xr.Dataset(data_vars=to_downsample), factor=scale @@ -378,7 +379,6 @@ def add_multiscales_metadata_to_parent( def create_original_encoding(dataset: xr.Dataset) -> dict[str, XarrayDataArrayEncoding]: """Write a group preserving its original chunking and encoding.""" - from zarr.codecs import BloscCodec # Simple encoding that preserves original structure compressor = BloscCodec(cname="zstd", clevel=3, shuffle="shuffle", blocksize=0) @@ -433,53 +433,13 @@ def create_downsampled_resolution_group(source_dataset: xr.Dataset, factor: int) else: raise ValueError(f"Unknown variable type {var_typ}") - # Preserve encoding from source variable, but ensure fill_value is set - encoding = var_data.encoding.copy() - - # Remove 'fill_value' (with underscore) if it exists, we only want '_FillValue' - encoding.pop('fill_value', None) - - # Ensure _FillValue is set to avoid SerializationWarning - # when xarray encodes float data to integer using scale/offset - if '_FillValue' not in encoding: - # Use the same fill value logic as the source or a sensible default - if 'dtype' in encoding: - target_dtype = np.dtype(encoding['dtype']) - if np.issubdtype(target_dtype, np.unsignedinteger): - encoding['_FillValue'] = np.iinfo(target_dtype).max - elif np.issubdtype(target_dtype, np.signedinteger): - encoding['_FillValue'] = np.iinfo(target_dtype).min - else: - encoding['_FillValue'] = np.nan - else: - encoding['_FillValue'] = np.nan - - # Ensure that dtype is preserved by casting - lazy_downsampled_casted = lazy_downsampled.astype(var_data.dtype) - # Re-apply encoding after astype (which creates a new DataArray) - lazy_downsampled_casted.encoding = encoding - lazy_vars[var_name] = lazy_downsampled_casted + # Ensure that dtype and encoding are preserved + lazy_downsampled = lazy_downsampled.astype(var_data.dtype) + lazy_downsampled.encoding = var_data.encoding + lazy_vars[var_name] = lazy_downsampled # Create dataset with lazy variables and coordinates - result_ds = xr.Dataset(lazy_vars, attrs=source_dataset.attrs) - - # Re-apply encoding to all data variables to ensure it's properly set - # (xr.Dataset creation sometimes doesn't preserve encoding correctly) - for var_name in lazy_vars: - if var_name in result_ds.data_vars: - result_ds[var_name].encoding = lazy_vars[var_name].encoding - - # Clear dtype encoding for all coordinate variables since coordinates become float - # after downsampling (e.g., x=[0.5, 2.5, ...]) - for coord_name in result_ds.coords: - # The coordinates inherit encoding from the source through coarsen() - # We need to remove dtype to avoid SerializationWarning - encoding = result_ds[coord_name].encoding.copy() - encoding.pop('dtype', None) - encoding.pop('_FillValue', None) - result_ds[coord_name].encoding = encoding - - return result_ds + return xr.Dataset(lazy_vars, attrs=source_dataset.attrs) def subsample_2(a: xr.DataArray, axis: tuple[int, ...] | None = None) -> xr.DataArray: From f9fa078e4be72522e2f0a5353506656576547284 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 10:49:16 +0100 Subject: [PATCH 28/59] define correct encoding for downsampled data variables --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 9c9aad19..d33f1af8 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -436,6 +436,14 @@ def create_downsampled_resolution_group(source_dataset: xr.Dataset, factor: int) # Ensure that dtype and encoding are preserved lazy_downsampled = lazy_downsampled.astype(var_data.dtype) lazy_downsampled.encoding = var_data.encoding + updated_chunks = { + lazy_downsampled.dims[idx]: min(v, lazy_downsampled.shape[idx]) + for idx, v in enumerate(lazy_downsampled.encoding["chunks"]) + } + lazy_downsampled.encoding["chunks"] = tuple( + updated_chunks[dim] for dim in lazy_downsampled.dims + ) + lazy_downsampled.encoding["preferred_chunks"] = lazy_downsampled lazy_vars[var_name] = lazy_downsampled # Create dataset with lazy variables and coordinates From 7192d04e43f4345694f9a14c8fec0bc0e1eb87dc Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 12:07:41 +0100 Subject: [PATCH 29/59] fixes to avoid failing warnings in tests --- .../data_api/geozarr/multiscales/zcm.py | 11 ++++++++++- tests/__init__.py | 2 +- tests/test_s2_converter_simplified.py | 17 +++++++++++++---- 3 files changed, 24 insertions(+), 6 deletions(-) diff --git a/src/eopf_geozarr/data_api/geozarr/multiscales/zcm.py b/src/eopf_geozarr/data_api/geozarr/multiscales/zcm.py index a79a83a5..61dcc9ad 100644 --- a/src/eopf_geozarr/data_api/geozarr/multiscales/zcm.py +++ b/src/eopf_geozarr/data_api/geozarr/multiscales/zcm.py @@ -2,7 +2,7 @@ from typing import Final, Literal, NotRequired -from pydantic import BaseModel, field_validator +from pydantic import BaseModel, field_validator, model_serializer from pydantic.experimental.missing_sentinel import MISSING from typing_extensions import TypedDict @@ -73,6 +73,15 @@ class Transform(BaseModel): scale: tuple[float, ...] | MISSING = MISSING translation: tuple[float, ...] | MISSING = MISSING + @model_serializer + def serialize_model(self) -> dict[str, tuple[float, ...]]: + result: dict[str, tuple[float, ...]] = {} + if self.scale is not MISSING: + result["scale"] = self.scale + if self.translation is not MISSING: + result["translation"] = self.translation + return result + class TransformJSON(TypedDict): scale: NotRequired[tuple[float, ...]] diff --git a/tests/__init__.py b/tests/__init__.py index 4057dff0..19d152c3 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -165,7 +165,7 @@ def _verify_multiscale_structure(output_path: pathlib.Path, group: str) -> None: assert "y" in ds.dims, f"Missing 'y' dimension in {level_path}" # Store shape for progression verification - level_shapes[level_num] = (ds.dims["y"], ds.dims["x"]) + level_shapes[level_num] = (ds.sizes["y"], ds.sizes["x"]) print(f" Level {level_num}: {level_shapes[level_num]} pixels") ds.close() diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index a87914f2..905d0681 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -45,8 +45,11 @@ def mock_s2_dataset() -> xr.DataTree: return dt + @pytest.mark.filterwarnings("ignore:Object at .*:zarr.errors.ZarrUserWarning") -@pytest.mark.filterwarnings("ignore:Consolidated metadata is currently .*:zarr.errors.ZarrUserWarning") +@pytest.mark.filterwarnings( + "ignore:Consolidated metadata is currently .*:zarr.errors.ZarrUserWarning" +) def test_simple_root_consolidation_success(tmp_path: Path) -> None: """ Test that simple_root_consolidation produces consolidated metadata at the root, and for the @@ -85,11 +88,14 @@ def test_simple_root_consolidation_success(tmp_path: Path) -> None: @pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") +@pytest.mark.filterwarnings("ignore:.*:xarray.coding.common.SerializationWarning") +@pytest.mark.filterwarnings( + "ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning" +) +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") class TestConvenienceFunction: """Test the convenience function.""" - @pytest.mark.filterwarnings("ignore:.*:xarray.coding.common.SerializationWarning") - @pytest.mark.filterwarnings("ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning") def test_convert_s2_optimized_convenience_function( self, s2_group_example: Path, @@ -98,7 +104,10 @@ def test_convert_s2_optimized_convenience_function( """Test the convenience function with real S2 data.""" # Open the S2 example as a DataTree dt_input = xr.open_datatree( - s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True, + s2_group_example, + engine="zarr", + consolidated=False, + decode_timedelta=True, ) output_path = str(tmp_path / "test_output.zarr") From 3688ccac580f222b2f0ec3b3e8011c6234315bb6 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 12:09:00 +0100 Subject: [PATCH 30/59] smarter xarray encoding propagation --- src/eopf_geozarr/data_api/geozarr/types.py | 2 +- .../s2_optimization/s2_multiscale.py | 59 +++++++++++++------ 2 files changed, 41 insertions(+), 20 deletions(-) diff --git a/src/eopf_geozarr/data_api/geozarr/types.py b/src/eopf_geozarr/data_api/geozarr/types.py index d919ae8d..f89d62a0 100644 --- a/src/eopf_geozarr/data_api/geozarr/types.py +++ b/src/eopf_geozarr/data_api/geozarr/types.py @@ -35,7 +35,7 @@ class XarrayDataArrayEncoding(TypedDict): """ chunks: NotRequired[tuple[int, ...]] - preferred_chunks: NotRequired[tuple[int, ...]] + preferred_chunks: NotRequired[dict[str, int]] compressors: NotRequired[tuple[object, ...] | None] filters: NotRequired[tuple[object, ...]] shards: NotRequired[tuple[int, ...] | None] diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index d33f1af8..9522e166 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -101,6 +101,32 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: ) +def update_encoding( + array: xr.DataArray, encoding: XarrayDataArrayEncoding +) -> XarrayDataArrayEncoding: + """ + Update an xarray encoding of a variable against a dataarray. Used when ensuring that a downsampled + dataarray has an encoding consistent with both the source array and also its newly reduced shape. + Shape-related quantities like chunks and shards need to be shrunk to match the shape of the array. + All other elements of the encoding are preserved + """ + new_encoding: XarrayDataArrayEncoding = {**encoding} + if "chunks" in new_encoding: + new_encoding["chunks"] = tuple( + min(s, c) for s, c in zip(array.shape, new_encoding["chunks"], strict=True) + ) + if "shards" in new_encoding and new_encoding["shards"] is not None: + new_encoding["shards"] = tuple( + min(s, c) for s, c in zip(array.shape, new_encoding["shards"], strict=True) + ) + if "preferred_chunks" in new_encoding: + new_encoding["preferred_chunks"] = { + k: min(array.shape[array.dims.index(k)], v) + for k, v in new_encoding["preferred_chunks"].items() + } + return new_encoding + + def create_measurements_encoding( dataset: xr.Dataset, *, spatial_chunk: int, enable_sharding: bool = True ) -> dict[str, XarrayDataArrayEncoding]: @@ -411,40 +437,35 @@ def create_downsampled_resolution_group(source_dataset: xr.Dataset, factor: int) # Downsample all variables lazy_vars: dict[str, xr.DataArray] = {} - for var_name, var_data in source_dataset.data_vars.items(): - if var_data.ndim >= 2: - var_typ = determine_variable_type(var_name, var_data) + for data_var_name, data_var in source_dataset.data_vars.items(): + if data_var.ndim >= 2: + var_typ = determine_variable_type(data_var_name, data_var) if var_typ == "quality_mask": - lazy_downsampled = var_data.coarsen( + lazy_downsampled = data_var.coarsen( {"x": factor, "y": factor}, boundary="trim" ).max() elif var_typ == "reflectance": - lazy_downsampled = var_data.coarsen( + lazy_downsampled = data_var.coarsen( {"x": factor, "y": factor}, boundary="trim" ).mean() elif var_typ == "classification": - lazy_downsampled = var_data.coarsen( + lazy_downsampled = data_var.coarsen( {"x": factor, "y": factor}, boundary="trim" ).reduce(subsample_2) elif var_typ == "probability": - lazy_downsampled = var_data.coarsen( + lazy_downsampled = data_var.coarsen( {"x": factor, "y": factor}, boundary="trim" ).mean() else: raise ValueError(f"Unknown variable type {var_typ}") - # Ensure that dtype and encoding are preserved - lazy_downsampled = lazy_downsampled.astype(var_data.dtype) - lazy_downsampled.encoding = var_data.encoding - updated_chunks = { - lazy_downsampled.dims[idx]: min(v, lazy_downsampled.shape[idx]) - for idx, v in enumerate(lazy_downsampled.encoding["chunks"]) - } - lazy_downsampled.encoding["chunks"] = tuple( - updated_chunks[dim] for dim in lazy_downsampled.dims - ) - lazy_downsampled.encoding["preferred_chunks"] = lazy_downsampled - lazy_vars[var_name] = lazy_downsampled + lazy_downsampled = lazy_downsampled.astype(data_var.dtype) + lazy_downsampled.encoding = update_encoding(lazy_downsampled, data_var.encoding) + for coord_name, coord in lazy_downsampled.coords.items(): + lazy_downsampled.coords[coord_name].encoding = update_encoding( + coord, data_var.coords[coord_name].encoding + ) + lazy_vars[data_var_name] = lazy_downsampled # Create dataset with lazy variables and coordinates return xr.Dataset(lazy_vars, attrs=source_dataset.attrs) From f3849bb565827a58c1280b5860c53465344ad04d Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 15:26:26 +0100 Subject: [PATCH 31/59] ignore rio xarray warning --- tests/test_reprojection_validation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test_reprojection_validation.py b/tests/test_reprojection_validation.py index 8159d736..4432057c 100755 --- a/tests/test_reprojection_validation.py +++ b/tests/test_reprojection_validation.py @@ -8,6 +8,7 @@ import numpy as np import pandas as pd +import pytest import xarray as xr from eopf_geozarr.conversion import create_geozarr_dataset @@ -140,6 +141,7 @@ def build(self) -> xr.DataTree: return dt +@pytest.mark.filterwarnings("ignore::rasterio.errors.NotGeoreferencedWarning") def test_titiler_compatibility(tmp_path: Path) -> None: """Test that reprojected Sentinel-1 data is compatible with titiler-eopf operations.""" print("🧪 Testing titiler-eopf compatibility with reprojected Sentinel-1 data...") From 373bfbea7c0ba04ba28098887b7b866f912500ea Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 15:49:20 +0100 Subject: [PATCH 32/59] update expected JSON outputs --- ...041_N0511_R122_T32TQM_20251008T122613.json | 724 +++++++----- ...309_N0511_R108_T32TLQ_20250113T122458.json | 1020 ++++++++++------- ...131_N0511_R037_T29TPF_20250811T152216.json | 724 +++++++----- 3 files changed, 1492 insertions(+), 976 deletions(-) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 193c6b87..b01a7cc0 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -5434,7 +5434,6 @@ "members": { "b01": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "add_offset": -0.1, "coordinates": [ @@ -5453,6 +5452,7 @@ "scale_factor": 0.0001, "units": "digital_counts" }, + "add_offset": -0.1, "coordinates": "spatial_ref", "dtype": " Date: Mon, 15 Dec 2025 15:49:33 +0100 Subject: [PATCH 33/59] filter more warnings --- tests/test_integration_sentinel1.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_integration_sentinel1.py b/tests/test_integration_sentinel1.py index bf34ffc2..e6f3e131 100644 --- a/tests/test_integration_sentinel1.py +++ b/tests/test_integration_sentinel1.py @@ -208,6 +208,7 @@ def test_invalid_gcp_group_raises_error(temp_output_dir, sample_sentinel1_datatr ) +@pytest.mark.filterwarnings("ignore::rasterio.errors.NotGeoreferencedWarning") @pytest.mark.parametrize( "polarization_group", [ From fed612120acb9685426a172e90e84b897c54f93d Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 16:10:49 +0100 Subject: [PATCH 34/59] lint --- .../s2_optimization/s2_converter.py | 43 +++++++++---------- src/eopf_geozarr/zarrio.py | 7 ++- tests/test_cli_e2e.py | 5 ++- tests/test_s2_converter_simplified.py | 14 ++++-- tests/test_zarrio.py | 15 ++++--- 5 files changed, 49 insertions(+), 35 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 9a7c4284..f3a1da93 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -43,6 +43,7 @@ ] ) + def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS: """ Initialize CRS from dataset by checking data variables. @@ -148,19 +149,16 @@ def add_crs_and_grid_mapping(group: zarr.Group, crs: CRS) -> None: def array_reencoder( - key: str, - metadata: ArrayV2Metadata, - *, - spatial_chunk: int, - enable_sharding: bool = False) -> ArrayV3Metadata: + key: str, metadata: ArrayV2Metadata, *, spatial_chunk: int, enable_sharding: bool = False +) -> ArrayV3Metadata: """ Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. """ - attributes: dict[str, object] = metadata.attributes.copy() # type: ignore[assignment] + attributes: dict[str, object] = metadata.attributes.copy() # handle xarray datetime/timedelta encoding # If the array has time-related units, ensure the dtype attribute matches the actual dtype if attributes.get("units") in TIME_UNITS: - numpy_time_unit = _netcdf_to_numpy_timeunit(attributes["units"]) # type: ignore[assignment] + numpy_time_unit = _netcdf_to_numpy_timeunit(attributes["units"]) # Check if this is a timedelta or datetime based on: # 1. The zarr dtype (if it's a native time type like TimeDelta64) # 2. The existing dtype attribute (for int64-encoded times) @@ -182,14 +180,15 @@ def array_reencoder( # This is a datetime array - set or correct the dtype attribute attributes["dtype"] = f"datetime64[{numpy_time_unit}]" - dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] - compressor_converted = convert_compression(metadata.compressor) # type: ignore[assignment] + dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] + compressor_converted = convert_compression(metadata.compressor) chunk_key_encoding: dict[str, str | dict[str, object]] = { "name": "default", - "configuration": {"separator": "/"}} + "configuration": {"separator": "/"}, + } - # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the - # array's data type must be provided. We use the zarr-python model of the data type to get + # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the + # array's data type must be provided. We use the zarr-python model of the data type to get # a fill value here. if metadata.fill_value is None: fill_value = metadata.dtype.default_scalar() @@ -200,9 +199,7 @@ def array_reencoder( # sentinel-specific logic: if this array is a variable stored in a measurements group # then we will apply particular chunking in_measurements_group = ( - group_name.startswith("r") - and group_name.endswith("m") - and "/measurements/" in group_name + group_name.startswith("r") and group_name.endswith("m") and "/measurements/" in group_name ) # get the size of each element of the array, if that's defined for this dtype @@ -226,11 +223,12 @@ def array_reencoder( else: # Generate 2D chunking / sharding, because partitioning along # other dimensions will be set to 1 - chunk_shape, subchunk_shape =_auto_partition( + chunk_shape, subchunk_shape = _auto_partition( array_shape=metadata.shape[-2:], chunk_shape=(spatial_chunk,) * 2, shard_shape="auto", - item_size=item_size) + item_size=item_size, + ) chunk_grid = {"name": "regular", "configuration": {"chunk_shape": chunk_shape}} if enable_sharding: @@ -257,7 +255,7 @@ def array_reencoder( dimension_names=dimension_names, codecs=codecs, attributes=attributes, - ) + ) def convert_s2_optimized( @@ -308,7 +306,9 @@ def convert_s2_optimized( log.info("Re-encoding source data to Zarr V3") - _array_reencoder = partial(array_reencoder, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding) + _array_reencoder = partial( + array_reencoder, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding + ) out_group = reencode_group( zg, @@ -445,10 +445,7 @@ def create_result_datatree(output_path: str) -> xr.DataTree: try: storage_options = get_storage_options(output_path) return xr.open_datatree( - output_path, - engine="zarr", - chunks="auto", - storage_options=storage_options + output_path, engine="zarr", chunks="auto", storage_options=storage_options ) except Exception as e: log.warning("Could not open result DataTree", error=str(e)) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index a9f2aeb0..aa690fe1 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -54,7 +54,9 @@ def convert_compression( raise ValueError(f"Only blosc -> blosc or None -> () is supported. Got {compressor=}") -def default_array_reencoder(key: str, + +def default_array_reencoder( + key: str, metadata: ArrayV2Metadata, ) -> ArrayV3Metadata: """ @@ -69,7 +71,7 @@ def default_array_reencoder(key: str, attributes = metadata.attributes.copy() dimension_names = attributes.pop("_ARRAY_DIMENSIONS", None) chunk_grid_shape = metadata.chunks - codecs = ({"name": "bytes"}, *new_codecs) # type: ignore[assignment] + codecs = ({"name": "bytes"}, *new_codecs) return ArrayV3Metadata( shape=metadata.shape, @@ -85,6 +87,7 @@ def default_array_reencoder(key: str, attributes=attributes, ) + def reencode_group( group: zarr.Group, store: zarr.storage.StoreLike, diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 79f11b38..77a9f599 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -92,7 +92,10 @@ def test_cli_convert_real_sentinel2_data(s2_group_example: Path, tmp_path: Path) # Detect product level (L1C vs L2A) by checking which quicklook group exists dt_source = xr.open_datatree( - s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True, + s2_group_example, + engine="zarr", + consolidated=False, + decode_timedelta=True, ) has_l2a_quicklook = "/quality/l2a_quicklook" in dt_source.groups has_l1c_quicklook = "/quality/l1c_quicklook" in dt_source.groups diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index a87914f2..b0fe3ed1 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -45,8 +45,11 @@ def mock_s2_dataset() -> xr.DataTree: return dt + @pytest.mark.filterwarnings("ignore:Object at .*:zarr.errors.ZarrUserWarning") -@pytest.mark.filterwarnings("ignore:Consolidated metadata is currently .*:zarr.errors.ZarrUserWarning") +@pytest.mark.filterwarnings( + "ignore:Consolidated metadata is currently .*:zarr.errors.ZarrUserWarning" +) def test_simple_root_consolidation_success(tmp_path: Path) -> None: """ Test that simple_root_consolidation produces consolidated metadata at the root, and for the @@ -89,7 +92,9 @@ class TestConvenienceFunction: """Test the convenience function.""" @pytest.mark.filterwarnings("ignore:.*:xarray.coding.common.SerializationWarning") - @pytest.mark.filterwarnings("ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning") + @pytest.mark.filterwarnings( + "ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning" + ) def test_convert_s2_optimized_convenience_function( self, s2_group_example: Path, @@ -98,7 +103,10 @@ def test_convert_s2_optimized_convenience_function( """Test the convenience function with real S2 data.""" # Open the S2 example as a DataTree dt_input = xr.open_datatree( - s2_group_example, engine="zarr", consolidated=False, decode_timedelta=True, + s2_group_example, + engine="zarr", + consolidated=False, + decode_timedelta=True, ) output_path = str(tmp_path / "test_output.zarr") diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py index 559a1e2f..9f1469e6 100644 --- a/tests/test_zarrio.py +++ b/tests/test_zarrio.py @@ -8,7 +8,6 @@ from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from eopf_geozarr.zarrio import ( - ChunkEncodingSpec, convert_compression, default_array_reencoder, reencode_group, @@ -56,6 +55,7 @@ def test_convert_compression_fails() -> None: with pytest.raises(ValueError, match=r"Only blosc -> blosc or None -> ()"): convert_compression(source) + def test_reencode_array_fill_value_none() -> None: """ Test that None fill_value is converted to dtype default @@ -232,8 +232,11 @@ def test_reencode_group_with_chunk_reencoder() -> None: Test that custom chunk_reencoder function is applied """ group_v2 = zarr.create_group({}, zarr_format=2) - old_node = group_v2.create_array("data", shape=(100,), chunks=(10,), dtype="float32", attributes={"foo": 10}) + old_node = group_v2.create_array( + "data", shape=(100,), chunks=(10,), dtype="float32", attributes={"foo": 10} + ) new_chunks = (25,) + def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata: return ArrayV3Metadata( shape=metadata.shape, @@ -243,8 +246,8 @@ def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, codecs=({"name": "bytes"},), dimension_names=None, - attributes=metadata.attributes - ) + attributes=metadata.attributes, + ) group_v3 = reencode_group(group_v2, {}, "", array_reencoder=custom_array_encoder) @@ -252,6 +255,6 @@ def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata assert new_node.metadata.chunk_grid.chunk_shape == new_chunks assert new_node.attrs.asdict() == old_node.attrs.asdict() -if __name__ == '__main__': - pytest.main([__file__]) +if __name__ == "__main__": + pytest.main([__file__]) From e995a98591942e9ec371704952e4b54b4726001d Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 19:49:48 +0100 Subject: [PATCH 35/59] fix bugs in chunks / sharding, and ensure that small arrays are not sharded --- .../s2_optimization/s2_converter.py | 32 +++++++-- .../s2_optimization/s2_multiscale.py | 17 +++-- src/eopf_geozarr/zarrio.py | 13 +++- tests/test_s2_converter_simplified.py | 72 ++++++++++++++++++- 4 files changed, 118 insertions(+), 16 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index f3a1da93..834bb4a2 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -149,7 +149,12 @@ def add_crs_and_grid_mapping(group: zarr.Group, crs: CRS) -> None: def array_reencoder( - key: str, metadata: ArrayV2Metadata, *, spatial_chunk: int, enable_sharding: bool = False + key: str, + metadata: ArrayV2Metadata, + *, + spatial_chunk: int, + enable_sharding: bool = False, + compression_level: int | None = None, ) -> ArrayV3Metadata: """ Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. @@ -181,7 +186,9 @@ def array_reencoder( attributes["dtype"] = f"datetime64[{numpy_time_unit}]" dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] - compressor_converted = convert_compression(metadata.compressor) + compressor_converted = convert_compression( + metadata.compressor, compression_level=compression_level + ) chunk_key_encoding: dict[str, str | dict[str, object]] = { "name": "default", "configuration": {"separator": "/"}, @@ -198,8 +205,11 @@ def array_reencoder( group_name = str(Path(key).parent) # sentinel-specific logic: if this array is a variable stored in a measurements group # then we will apply particular chunking + # Check if the group name contains measurements and the last component is a resolution (r10m, r20m, etc.) in_measurements_group = ( - group_name.startswith("r") and group_name.endswith("m") and "/measurements/" in group_name + "measurements" in group_name + and Path(group_name).name.startswith("r") + and Path(group_name).name.endswith("m") ) # get the size of each element of the array, if that's defined for this dtype @@ -217,7 +227,13 @@ def array_reencoder( # don't rechunk 1D variables pass else: - if not enable_sharding: + # Check if array is too small for the requested chunk size + # If any spatial dimension is smaller than spatial_chunk, don't use sharding + array_too_small_for_sharding = enable_sharding and ( + metadata.shape[-2] < spatial_chunk or metadata.shape[-1] < spatial_chunk + ) + + if not enable_sharding or array_too_small_for_sharding: chunk_shape = (spatial_chunk, spatial_chunk) subchunk_shape = None else: @@ -231,7 +247,7 @@ def array_reencoder( ) chunk_grid = {"name": "regular", "configuration": {"chunk_shape": chunk_shape}} - if enable_sharding: + if enable_sharding and subchunk_shape is not None: codecs = ( { "name": "sharding_indexed", @@ -306,8 +322,12 @@ def convert_s2_optimized( log.info("Re-encoding source data to Zarr V3") + # Create a partial function by specifying parameters for our array encoder _array_reencoder = partial( - array_reencoder, spatial_chunk=spatial_chunk, enable_sharding=enable_sharding + array_reencoder, + spatial_chunk=spatial_chunk, + enable_sharding=enable_sharding, + compression_level=compression_level, ) out_group = reencode_group( diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 9522e166..7345f599 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -90,12 +90,15 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: next_level_path = f"{path}/{next_group_name}" if next_level_path not in dt or var_name not in dt[next_level_path].data_vars: to_downsample[var_name] = var - log.info("downsampling %s into %s", tuple(to_downsample.keys()), next_group_name) + log.info("downsampling %s into %s", tuple(sorted(to_downsample.keys())), next_group_name) # Don't pass coords here - let the downsampled variables determine their own coordinates downsampled_ds = create_downsampled_resolution_group( xr.Dataset(data_vars=to_downsample), factor=scale ) next_group_path = f"{full_path}/{next_group_name}" + if cur_group_name == "r360m": + pass + # breakpoint() downsampled_ds.to_zarr( group.store, group=next_group_path, consolidated=False, mode="a", compute=True ) @@ -115,10 +118,14 @@ def update_encoding( new_encoding["chunks"] = tuple( min(s, c) for s, c in zip(array.shape, new_encoding["chunks"], strict=True) ) - if "shards" in new_encoding and new_encoding["shards"] is not None: - new_encoding["shards"] = tuple( - min(s, c) for s, c in zip(array.shape, new_encoding["shards"], strict=True) - ) + # If the encoding declares sharding, but the shape of the array falls below the requested shard shape, + # then we discard the sharding request, because it will not generally be possible to ensure that the + # chunk shape tiles the shards correctly in this case. + if new_encoding.get("shards") is not None and any( + shape < shard + for shard, shape in zip(new_encoding["shards"], array.shape, strict=True) # type: ignore [arg-type] + ): + new_encoding["shards"] = None if "preferred_chunks" in new_encoding: new_encoding["preferred_chunks"] = { k: min(array.shape[array.dims.index(k)], v) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index aa690fe1..ab1a56f4 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -26,10 +26,18 @@ class ChunkEncodingSpec(TypedDict): def convert_compression( - compressor: Any, + compressor: numcodecs.abc.Codec | dict[str, object], *, compression_level: int | None = None ) -> tuple[dict[str, Any], ...]: """ Convert the compression parameters for a Zarr V2 array into a tuple of Zarr V3 codecs. + + Parameters + ---------- + compressor : numcodecs.abc.Codec | dict[str, object] + The compressor of the zarr v3 array + compression_level: int | None = None + The new compression level to use. + Only applies to the specific case of blosc -> blosc compression. """ if compressor is None: @@ -41,11 +49,12 @@ def convert_compression( else: old_config = compressor if old_config["id"] == "blosc": + new_level = old_config["clevel"] if compression_level is None else compression_level new_codec = { "name": "blosc", "configuration": { "cname": old_config["cname"], - "clevel": old_config["clevel"], + "clevel": new_level, "blocksize": old_config["blocksize"], "shuffle": SHUFFLE[old_config["shuffle"]], }, diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 905d0681..9eda4793 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -10,6 +10,7 @@ import numpy as np import pytest import xarray as xr +import zarr from eopf_geozarr.s2_optimization.s2_converter import ( convert_s2_optimized, @@ -96,10 +97,16 @@ def test_simple_root_consolidation_success(tmp_path: Path) -> None: class TestConvenienceFunction: """Test the convenience function.""" + @pytest.mark.parametrize("compression_level", [3]) + @pytest.mark.parametrize("spatial_chunk", [256]) + @pytest.mark.parametrize("enable_sharding", [False, True]) def test_convert_s2_optimized_convenience_function( self, s2_group_example: Path, tmp_path: Path, + spatial_chunk: int, + compression_level: int, + enable_sharding: bool, ) -> None: """Test the convenience function with real S2 data.""" # Open the S2 example as a DataTree @@ -115,9 +122,9 @@ def test_convert_s2_optimized_convenience_function( result = convert_s2_optimized( dt_input, output_path=output_path, - enable_sharding=False, - spatial_chunk=512, - compression_level=2, + enable_sharding=enable_sharding, + spatial_chunk=spatial_chunk, + compression_level=compression_level, max_retries=5, validate_output=False, ) @@ -132,6 +139,65 @@ def test_convert_s2_optimized_convenience_function( output_dt = xr.open_datatree(output_path, engine="zarr") assert len(output_dt.groups) > 0 + # Open the Zarr store to verify metadata + # We know the S2 data has measurements/reflectance group structure + output_zarr = zarr.open_group(output_path, mode="r") + + reflectance_group = output_zarr["measurements/reflectance"] + reencoded_array = reflectance_group["r10m/b03"] + downsampled_array = reflectance_group["r720m/b03"] + + # For re-encoded arrays, chunks should match spatial_chunk + assert reencoded_array.chunks == (spatial_chunk, spatial_chunk) + + # For downsampled arrays, chunks should be min(spatial_chunk, array_shape) + # because the array might be smaller than the chunk size + expected_downsampled_chunks = ( + min(spatial_chunk, downsampled_array.shape[0]), + min(spatial_chunk, downsampled_array.shape[1]), + ) + assert downsampled_array.chunks == expected_downsampled_chunks + + # Sharding should only be present on arrays large enough for the chunk size + # Reencoded arrays are typically large (10980x10980), so should have sharding + if enable_sharding: + assert reencoded_array.shards is not None, "Large reencoded array should have sharding" + else: + assert reencoded_array.shards is None, ( + "Reencoded array should not have sharding when disabled" + ) + + # Downsampled arrays (r720m) might be too small for sharding + # Only check for sharding if the array is large enough (shape >= spatial_chunk in both dims) + if ( + enable_sharding + and downsampled_array.shape[0] >= spatial_chunk + and downsampled_array.shape[1] >= spatial_chunk + ): + assert downsampled_array.shards is not None, ( + f"Downsampled array with shape {downsampled_array.shape} should have sharding" + ) + elif ( + not enable_sharding + or downsampled_array.shape[0] < spatial_chunk + or downsampled_array.shape[1] < spatial_chunk + ): + assert downsampled_array.shards is None, ( + f"Downsampled array with shape {downsampled_array.shape} should not have sharding " + f"(enable_sharding={enable_sharding}, spatial_chunk={spatial_chunk})" + ) + + assert reencoded_array.compressors[0].to_dict()["name"] == "blosc" + assert downsampled_array.compressors[0].to_dict()["name"] == "blosc" + + assert ( + reencoded_array.compressors[0].to_dict()["configuration"]["clevel"] == compression_level + ) + assert ( + downsampled_array.compressors[0].to_dict()["configuration"]["clevel"] + == compression_level + ) + if __name__ == "__main__": pytest.main([__file__]) From efdeb6b0e302d5c678dfc8ed4fe5a9b8f425d5bc Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 20:03:46 +0100 Subject: [PATCH 36/59] update JSON examples --- ...041_N0511_R122_T32TQM_20251008T122613.json | 204 ++++++++--------- ...309_N0511_R108_T32TLQ_20250113T122458.json | 212 +++++++++--------- ...131_N0511_R037_T29TPF_20250811T152216.json | 204 ++++++++--------- 3 files changed, 310 insertions(+), 310 deletions(-) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index b01a7cc0..98c15f53 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -4938,8 +4938,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5039,8 +5039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5140,8 +5140,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5241,8 +5241,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5489,8 +5489,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5590,8 +5590,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5691,8 +5691,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5792,8 +5792,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5893,8 +5893,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5994,8 +5994,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6095,8 +6095,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6196,8 +6196,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6297,8 +6297,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6398,8 +6398,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6499,8 +6499,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6600,8 +6600,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6848,8 +6848,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -6949,8 +6949,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7050,8 +7050,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7151,8 +7151,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7252,8 +7252,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7353,8 +7353,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7454,8 +7454,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7555,8 +7555,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7656,8 +7656,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7757,8 +7757,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7858,8 +7858,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -8106,8 +8106,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8207,8 +8207,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8308,8 +8308,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8409,8 +8409,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8510,8 +8510,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8611,8 +8611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8712,8 +8712,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8813,8 +8813,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8914,8 +8914,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9015,8 +9015,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9116,8 +9116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9217,8 +9217,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9465,8 +9465,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9566,8 +9566,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9667,8 +9667,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9768,8 +9768,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9869,8 +9869,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9970,8 +9970,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10071,8 +10071,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10172,8 +10172,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10273,8 +10273,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10374,8 +10374,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10475,8 +10475,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10576,8 +10576,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index 7792c2b1..fbda67b4 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -4494,8 +4494,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -4591,8 +4591,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -4688,8 +4688,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -4785,8 +4785,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -5029,8 +5029,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -5126,8 +5126,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5223,8 +5223,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5320,8 +5320,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5417,8 +5417,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5514,8 +5514,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5611,8 +5611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5708,8 +5708,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5805,8 +5805,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -5902,8 +5902,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -5999,8 +5999,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6096,8 +6096,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6193,8 +6193,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6437,8 +6437,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -6534,8 +6534,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -6631,8 +6631,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -6728,8 +6728,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6825,8 +6825,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6922,8 +6922,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -7019,8 +7019,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -7116,8 +7116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -7213,8 +7213,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -7310,8 +7310,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -7554,8 +7554,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -7651,8 +7651,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -7748,8 +7748,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -7845,8 +7845,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -7942,8 +7942,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8039,8 +8039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8136,8 +8136,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8233,8 +8233,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8330,8 +8330,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8427,8 +8427,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8524,8 +8524,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8621,8 +8621,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8718,8 +8718,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8962,8 +8962,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9059,8 +9059,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9156,8 +9156,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9253,8 +9253,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9350,8 +9350,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -9447,8 +9447,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -9544,8 +9544,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -9641,8 +9641,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9738,8 +9738,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9835,8 +9835,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9932,8 +9932,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -10029,8 +10029,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -10126,8 +10126,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index 7d53c736..f77335fd 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -4938,8 +4938,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5039,8 +5039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5140,8 +5140,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5241,8 +5241,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -5489,8 +5489,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5590,8 +5590,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5691,8 +5691,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5792,8 +5792,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5893,8 +5893,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -5994,8 +5994,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6095,8 +6095,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6196,8 +6196,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6297,8 +6297,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6398,8 +6398,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6499,8 +6499,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6600,8 +6600,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915, - 915 + 256, + 256 ] }, "name": "regular" @@ -6848,8 +6848,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -6949,8 +6949,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7050,8 +7050,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7151,8 +7151,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7252,8 +7252,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7353,8 +7353,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7454,8 +7454,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7555,8 +7555,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7656,8 +7656,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7757,8 +7757,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -7858,8 +7858,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490, - 5490 + 256, + 256 ] }, "name": "regular" @@ -8106,8 +8106,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8207,8 +8207,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8308,8 +8308,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8409,8 +8409,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8510,8 +8510,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8611,8 +8611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8712,8 +8712,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8813,8 +8813,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -8914,8 +8914,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9015,8 +9015,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9116,8 +9116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9217,8 +9217,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305, - 305 + 256, + 256 ] }, "name": "regular" @@ -9465,8 +9465,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9566,8 +9566,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9667,8 +9667,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9768,8 +9768,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9869,8 +9869,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -9970,8 +9970,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10071,8 +10071,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10172,8 +10172,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10273,8 +10273,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10374,8 +10374,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10475,8 +10475,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" @@ -10576,8 +10576,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830, - 1830 + 256, + 256 ] }, "name": "regular" From 422a9cdb989336bf5d6ed9f58d901b6c4eff2a78 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 20:38:45 +0100 Subject: [PATCH 37/59] remove debug statement --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 7345f599..ab1b7a12 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -96,9 +96,6 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: xr.Dataset(data_vars=to_downsample), factor=scale ) next_group_path = f"{full_path}/{next_group_name}" - if cur_group_name == "r360m": - pass - # breakpoint() downsampled_ds.to_zarr( group.store, group=next_group_path, consolidated=False, mode="a", compute=True ) From 186024c4a1b41e83f3724eeb5d988e39ffc5e794 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Mon, 15 Dec 2025 21:22:04 +0100 Subject: [PATCH 38/59] remove caching store --- src/eopf_geozarr/cli.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 2a9d88dd..8215bfa6 100755 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -13,10 +13,6 @@ import structlog import xarray as xr -from zarr.core.sync import sync -from zarr.experimental.cache_store import CacheStore -from zarr.storage import MemoryStore -from zarr.storage._common import make_store from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized @@ -1183,8 +1179,7 @@ def convert_s2_optimized_command(args: argparse.Namespace) -> None: # Load input dataset log.info("Loading Sentinel-2 dataset from", input_path=args.input_path) storage_options = get_storage_options(str(args.input_path)) - # store = args.input_path - store = CacheStore(sync(make_store(args.input_path, mode="r")), cache_store=MemoryStore()) + store = args.input_path dt_input = xr.open_datatree( store, engine="zarr", From 9f1a131724a9bc50923b7aba031fe9984f624782 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 16 Dec 2025 13:34:09 +0100 Subject: [PATCH 39/59] don't downsample existing data vars --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index ab1b7a12..1ea83c3b 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -73,9 +73,6 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: # Construct the full path to the group containing resolution levels full_path = f"{group.path}/{path}" if group.path else path - # Load the datatree to check which groups exist - dt = xr.open_datatree(group.store, engine="zarr") - for (cur_factor, cur_group_name), (next_factor, next_group_name) in pairwise( zip(ds_levels, ds_group_names, strict=True) ): @@ -88,7 +85,7 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: for var_name, var in cur_ds.data_vars.items(): # Check if the variable already exists in the next level next_level_path = f"{path}/{next_group_name}" - if next_level_path not in dt or var_name not in dt[next_level_path].data_vars: + if f"{next_level_path}/{var_name}" not in group: to_downsample[var_name] = var log.info("downsampling %s into %s", tuple(sorted(to_downsample.keys())), next_group_name) # Don't pass coords here - let the downsampled variables determine their own coordinates From f9b4845f93f1147e90110590c847460af602eec4 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 16 Dec 2025 13:57:34 +0100 Subject: [PATCH 40/59] add tests for multiscale skipping --- tests/test_s2_multiscale.py | 136 +++++++++++++++++------------------- 1 file changed, 65 insertions(+), 71 deletions(-) diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index e4b80373..7dd0d25d 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -26,30 +26,47 @@ def sample_dataset() -> xr.Dataset: x = np.linspace(0, 1000, 100) y = np.linspace(0, 1000, 100) time = np.array(["2023-01-01", "2023-01-02"], dtype="datetime64[ns]") + data_var_names = ["b02", "b05", "scl"] + data_vars: dict[str, xr.DataArray] = {} + for name in data_var_names: + # Create sample variables with different dimensions + data_vars[name] = xr.DataArray( + np.random.randint(0, 4000, (2, 100, 100)), + dims=["time", "y", "x"], + coords={"time": time, "y": y, "x": x}, + name=name, + ) + return xr.Dataset(data_vars=data_vars) + + +@pytest.fixture +def simple_datatree() -> zarr.Group: + """Create a simple DataTree for integration testing, and save it to an in-memory zarr group""" + store = {} + group = zarr.create_group(store) + # Create sample data + x = np.linspace(0, 1000, 100) + y = np.linspace(0, 1000, 100) + time = np.array(["2023-01-01"], dtype="datetime64[ns]") - # Create sample variables with different dimensions + # Create sample band b02 = xr.DataArray( - np.random.randint(0, 4000, (2, 100, 100)), + np.random.randint(0, 4000, (1, 100, 100)), dims=["time", "y", "x"], coords={"time": time, "y": y, "x": x}, name="b02", + attrs={"long_name": "Blue band", "units": "digital_number"}, ) - b05 = xr.DataArray( - np.random.randint(0, 4000, (2, 100, 100)), - dims=["time", "y", "x"], - coords={"time": time, "y": y, "x": x}, - name="b05", - ) + # Create dataset + ds = xr.Dataset({"b02": b02}) - scl = xr.DataArray( - np.random.randint(0, 11, (2, 100, 100)), - dims=["time", "y", "x"], - coords={"time": time, "y": y, "x": x}, - name="scl", - ) + # Create DataTree + dt = xr.DataTree(name="root") + dt["/measurements/reflectance/r10m"] = xr.DataTree(ds) + dt.to_zarr(group.store, compute=True, mode="w", consolidated=False) - return xr.Dataset({"b02": b02, "b05": b05, "scl": scl}) + return group class TestS2MultiscaleFunctions: @@ -148,68 +165,45 @@ def test_calculate_aligned_chunk_size(self) -> None: assert 1000 % chunk_size == 0 -class TestS2MultiscaleIntegration: - """Integration tests for S2 multiscale functions.""" - - @pytest.fixture - def simple_datatree(self) -> zarr.Group: - """Create a simple DataTree for integration testing, and save it to an in-memory zarr group""" - store = {} - group = zarr.create_group(store) - # Create sample data - x = np.linspace(0, 1000, 100) - y = np.linspace(0, 1000, 100) - time = np.array(["2023-01-01"], dtype="datetime64[ns]") +@pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") +def test_create_multiscale_from_datatree(simple_datatree: zarr.Group) -> None: + """Test multiscale creation from DataTree.""" - # Create sample band - b02 = xr.DataArray( - np.random.randint(0, 4000, (1, 100, 100)), - dims=["time", "y", "x"], - coords={"time": time, "y": y, "x": x}, - name="b02", - attrs={"long_name": "Blue band", "units": "digital_number"}, - ) + class DatasetMembers(TypedDict): + x: ArraySpec + y: ArraySpec + time: ArraySpec + b02: ArraySpec - # Create dataset - ds = xr.Dataset({"b02": b02}) + class DatasetGroup(GroupSpec): + members: DatasetMembers - # Create DataTree - dt = xr.DataTree(name="root") - dt["/measurements/reflectance/r10m"] = xr.DataTree(ds) - dt.to_zarr(group.store, compute=True, mode="w", consolidated=False) + class ExpectedMembers(TypedDict): + r10m: DatasetGroup + r20m: DatasetGroup + r60m: DatasetGroup + r120m: DatasetGroup + r360m: DatasetGroup + r720m: DatasetGroup - return group + class ExpectedGroup(GroupSpec): + members: ExpectedMembers - @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") - def test_create_multiscale_from_datatree(self, simple_datatree: zarr.Group) -> None: - """Test multiscale creation from DataTree.""" + # Capture log output using structlog's testing context manager + with capture_logs(): + create_multiscale_levels(simple_datatree, path="measurements/reflectance") - class DatasetMembers(TypedDict): - x: ArraySpec - y: ArraySpec - time: ArraySpec - b02: ArraySpec + ExpectedGroup.from_zarr(simple_datatree["measurements/reflectance"]) - class DatasetGroup(GroupSpec): - members: DatasetMembers - - class ExpectedMembers(TypedDict): - r10m: DatasetGroup - r20m: DatasetGroup - r60m: DatasetGroup - r120m: DatasetGroup - r360m: DatasetGroup - r720m: DatasetGroup - - class ExpectedGroup(GroupSpec): - members: ExpectedMembers - - # Capture log output using structlog's testing context manager - with capture_logs(): - create_multiscale_levels(simple_datatree, path="measurements/reflectance") - - ExpectedGroup.from_zarr(simple_datatree["measurements/reflectance"]) +@pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") +def test_create_multiscale_skips_existing_arrays(simple_datatree: zarr.Group) -> None: + """Test that the multiscale creation routine does not overwrite an existing low res array""" + # insert a new group containing an empty version of the existing datasets + simple_datatree.create_group("measurements/reflectance/r20m") + simple_datatree.create_array( + "measurements/reflectance/r20m/b02", shape=(1,), dtype="uint8", dimension_names=("x",) + ) -if __name__ == "__main__": - pytest.main([__file__]) + create_multiscale_levels(simple_datatree, path="measurements/reflectance") + assert simple_datatree["measurements/reflectance/r20m/b02"].shape == (1,) From a8a9ee22a6e1d77ecffe1fd9476ec1e02fdb55dc Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 11:40:09 +0100 Subject: [PATCH 41/59] improve automatic chunking and add tests --- .../s2_optimization/s2_converter.py | 100 ++++++++++-------- .../s2_optimization/s2_multiscale.py | 81 ++++++++------ tests/test_s2_multiscale.py | 18 ++++ 3 files changed, 120 insertions(+), 79 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 834bb4a2..354c5abc 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -7,15 +7,13 @@ import time from functools import partial from pathlib import Path -from typing import Any, TypedDict +from typing import Any, Final, Literal, TypedDict import structlog import xarray as xr import zarr from pydantic import TypeAdapter from pyproj import CRS -from xarray.coding.times import _netcdf_to_numpy_timeunit -from zarr.core.chunk_grids import _auto_partition from zarr.core.dtype.npy.time import TimeDelta64 from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from zarr.core.sync import sync @@ -27,22 +25,53 @@ from eopf_geozarr.data_api.s2 import Sentinel2Root from eopf_geozarr.zarrio import convert_compression, reencode_group -from .s2_multiscale import create_multiscale_levels, write_geo_metadata +from .s2_multiscale import ( + auto_chunks, + calculate_simple_shard_dimensions, + create_multiscale_levels, + write_geo_metadata, +) log = structlog.get_logger() -TIME_UNITS = frozenset( - [ - "days", - "hours", - "minutes", - "seconds", - "milliseconds", - "microseconds", - "nanoseconds", - ] +TimeUnit = Literal[ + "days", + "hours", + "minutes", + "seconds", + "milliseconds", + "microseconds", + "nanoseconds", +] + +TimeAbbreviation = Literal["D", "h", "m", "s", "ms", "us", "ns"] + +TIME_UNIT: Final[tuple[str, ...]] = ( + "days", + "hours", + "minutes", + "seconds", + "milliseconds", + "microseconds", + "nanoseconds", ) +TIME_ABBREVIATION: Final[tuple[str, ...]] = ("D", "h", "m", "s", "ms", "us", "ns") + +_NETCDF_TO_NUMPY_TIME_UNITS: dict[TimeUnit, TimeAbbreviation] = { + "days": "D", + "hours": "h", + "minutes": "m", + "seconds": "s", + "milliseconds": "ms", + "microseconds": "us", + "nanoseconds": "ns", +} + + +def _netcdf_unit_to_numpy_time_unit(unit: TimeUnit) -> TimeAbbreviation: + return _NETCDF_TO_NUMPY_TIME_UNITS[unit] + def initialize_crs_from_dataset(dt_input: xr.DataTree) -> CRS: """ @@ -162,8 +191,8 @@ def array_reencoder( attributes: dict[str, object] = metadata.attributes.copy() # handle xarray datetime/timedelta encoding # If the array has time-related units, ensure the dtype attribute matches the actual dtype - if attributes.get("units") in TIME_UNITS: - numpy_time_unit = _netcdf_to_numpy_timeunit(attributes["units"]) + if attributes.get("units") in TIME_UNIT: + numpy_time_unit = _netcdf_unit_to_numpy_time_unit(attributes["units"]) # type: ignore[arg-type] # Check if this is a timedelta or datetime based on: # 1. The zarr dtype (if it's a native time type like TimeDelta64) # 2. The existing dtype attribute (for int64-encoded times) @@ -212,39 +241,18 @@ def array_reencoder( and Path(group_name).name.endswith("m") ) - # get the size of each element of the array, if that's defined for this dtype - if hasattr(metadata.dtype, "item_size"): - item_size: int = metadata.dtype.item_size - else: - item_size = 1 - chunk_shape: tuple[int, ...] = metadata.chunks + + if in_measurements_group: + chunk_shape = auto_chunks(metadata.shape, spatial_chunk) + subchunk_shape: tuple[int, ...] | None = None - chunk_grid: dict[str, str | dict[str, object]] - if in_measurements_group and metadata.ndim > 0: - if metadata.ndim == 1: - # don't rechunk 1D variables - pass - else: - # Check if array is too small for the requested chunk size - # If any spatial dimension is smaller than spatial_chunk, don't use sharding - array_too_small_for_sharding = enable_sharding and ( - metadata.shape[-2] < spatial_chunk or metadata.shape[-1] < spatial_chunk - ) + if in_measurements_group and metadata.ndim >= 2 and enable_sharding: + subchunk_shape = chunk_shape + chunk_shape = calculate_simple_shard_dimensions(metadata.shape, chunk_shape) - if not enable_sharding or array_too_small_for_sharding: - chunk_shape = (spatial_chunk, spatial_chunk) - subchunk_shape = None - else: - # Generate 2D chunking / sharding, because partitioning along - # other dimensions will be set to 1 - chunk_shape, subchunk_shape = _auto_partition( - array_shape=metadata.shape[-2:], - chunk_shape=(spatial_chunk,) * 2, - shard_shape="auto", - item_size=item_size, - ) + chunk_grid: dict[str, str | dict[str, object]] chunk_grid = {"name": "regular", "configuration": {"chunk_shape": chunk_shape}} if enable_sharding and subchunk_shape is not None: @@ -261,7 +269,6 @@ def array_reencoder( ) else: codecs = ({"name": "bytes"}, *compressor_converted) # type: ignore[assignment] - return ArrayV3Metadata( shape=metadata.shape, data_type=metadata.dtype, @@ -338,7 +345,6 @@ def convert_s2_optimized( array_reencoder=_array_reencoder, omit_nodes=omit_nodes, ) - if "measurements" in out_group: log.info("Adding CRS elements to datasets in measurements") for _, subgroup in out_group["measurements"].groups(): diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 1ea83c3b..a543c47b 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -104,7 +104,7 @@ def update_encoding( """ Update an xarray encoding of a variable against a dataarray. Used when ensuring that a downsampled dataarray has an encoding consistent with both the source array and also its newly reduced shape. - Shape-related quantities like chunks and shards need to be shrunk to match the shape of the array. + Shape-related quantities like chunks and shards need to be adjusted to match the shape of the array. All other elements of the encoding are preserved """ new_encoding: XarrayDataArrayEncoding = {**encoding} @@ -112,14 +112,25 @@ def update_encoding( new_encoding["chunks"] = tuple( min(s, c) for s, c in zip(array.shape, new_encoding["chunks"], strict=True) ) - # If the encoding declares sharding, but the shape of the array falls below the requested shard shape, - # then we discard the sharding request, because it will not generally be possible to ensure that the - # chunk shape tiles the shards correctly in this case. - if new_encoding.get("shards") is not None and any( - shape < shard - for shard, shape in zip(new_encoding["shards"], array.shape, strict=True) # type: ignore [arg-type] - ): - new_encoding["shards"] = None + if new_encoding.get("shards") is not None: + # new shards are the largest multiple of the new chunks that fits inside the new shape + new_shards = tuple( + (shape // chunk) * chunk + for chunk, shape in zip(new_encoding["chunks"], array.shape, strict=True) + ) + # calculate the number of inner chunks within the shard + num_subchunks = np.prod( + tuple( + shard // chunk + for shard, chunk in zip(new_shards, new_encoding["chunks"], strict=True) + ) + ) + # If we would generate shards with a single chunk, there is no longer value in sharding, and we should + # use regular chunking + if num_subchunks == 1: + new_encoding["shards"] = None + else: + new_encoding["shards"] = new_shards if "preferred_chunks" in new_encoding: new_encoding["preferred_chunks"] = { k: min(array.shape[array.dims.index(k)], v) @@ -128,6 +139,32 @@ def update_encoding( return new_encoding +def auto_chunks(shape: tuple[int, ...], target_chunk_size: int) -> tuple[int, ...]: + """ + Compute a chunk size from a shape and a target chunk size. This logic is application-specific. + For 0D data, the empty tuple is returned. + For 1D data, the minimum of the length of the data and the target chunk size is returned. + For 2D, 3D, etc, a chunk edge length is computed based on each of the last two dimensions, and + all other dimensions have chunks set to 1. + """ + ndim = len(shape) + + if ndim == 0: + return () + + if ndim == 1: + return (min(target_chunk_size, shape[0]),) + + height, width = shape[-2:] + + spatial_chunk_aligned = min( + target_chunk_size, + calculate_aligned_chunk_size(width, target_chunk_size), + calculate_aligned_chunk_size(height, target_chunk_size), + ) + return ((1,) * (ndim - 2)) + (spatial_chunk_aligned, spatial_chunk_aligned) + + def create_measurements_encoding( dataset: xr.Dataset, *, spatial_chunk: int, enable_sharding: bool = True ) -> dict[str, XarrayDataArrayEncoding]: @@ -267,30 +304,13 @@ def add_multiscales_metadata_to_parent( all_resolutions = sorted(set(res_groups.keys()), key=lambda x: res_order.get(x, 999)) - if len(all_resolutions) < 2: - log.info( - "Skipping {} - only one resolution available", - base_path=base_path, - ) - return None - # Get CRS and bounds from first available dataset (load from output path) first_res = all_resolutions[0] first_dataset = res_groups[first_res] # Get CRS and bounds - native_crs = first_dataset.rio.crs if hasattr(first_dataset, "rio") else None - if native_crs is None: - log.info("No CRS found, skipping multiscales metadata", base_path=base_path) - return None - - native_bounds = first_dataset.rio.bounds() if hasattr(first_dataset, "rio") else None - if native_bounds is None: - log.info( - "No bounds found, skipping multiscales metadata", - base_path=base_path, - ) - return None + native_crs = first_dataset.rio.crs + native_bounds = first_dataset.rio.bounds() # Create overview_levels structure with string-based level names overview_levels: list[OverviewLevelJSON] = [] @@ -333,10 +353,6 @@ def add_multiscales_metadata_to_parent( } ) - if len(overview_levels) < 2: - log.info(" Could not create overview levels for {}", base_path=base_path) - return None - multiscales: dict[str, Any] = {"multiscales": {}} layout: list[zcm.ScaleLevel] | MISSING = MISSING tile_matrix_set: tms.TileMatrixSet | MISSING = MISSING @@ -391,6 +407,7 @@ def add_multiscales_metadata_to_parent( dt_multiscale = xr.DataTree() for res in all_resolutions: dt_multiscale[res] = xr.DataTree() + dt_multiscale.attrs.update(multiscale_attrs.model_dump()) dt_multiscale.to_zarr( parent_group_path, diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 7dd0d25d..90b0eaf6 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -12,6 +12,7 @@ from structlog.testing import capture_logs from eopf_geozarr.s2_optimization.s2_multiscale import ( + auto_chunks, calculate_aligned_chunk_size, calculate_simple_shard_dimensions, create_downsampled_resolution_group, @@ -165,6 +166,23 @@ def test_calculate_aligned_chunk_size(self) -> None: assert 1000 % chunk_size == 0 +@pytest.mark.parametrize("ndim", [0, 1, 2, 3, 4]) +@pytest.mark.parametrize("target_chunk_size", [10, 12]) +def test_auto_chunks(ndim: int, target_chunk_size: int) -> None: + shape = (11,) * ndim + chunks = auto_chunks(shape, target_chunk_size) + + if ndim == 0: + assert chunks == () + if ndim == 1: + assert chunks == (min(shape[0], target_chunk_size),) + if ndim >= 2: + assert chunks == ((1,) * (ndim - 2)) + ( + calculate_aligned_chunk_size(shape[-2], target_chunk_size), + calculate_aligned_chunk_size(shape[-1], target_chunk_size), + ) + + @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") def test_create_multiscale_from_datatree(simple_datatree: zarr.Group) -> None: """Test multiscale creation from DataTree.""" From 00659f7fe4d7658c526a084cde737556a2bd4563 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 11:53:04 +0100 Subject: [PATCH 42/59] update JSON examples --- tests/test_cli_e2e.py | 2 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 224 ++++++++--------- ...309_N0511_R108_T32TLQ_20250113T122458.json | 232 +++++++++--------- ...131_N0511_R037_T29TPF_20250811T152216.json | 224 ++++++++--------- 4 files changed, 341 insertions(+), 341 deletions(-) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 77a9f599..0f6a4023 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -54,7 +54,7 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: # Uncomment this section to write out the expected structure from the observed structure # This is useful when the expected structure needs to be updated # expected_structure_path.write_text( - # json.dumps(observed_structure_json, indent=2, sort_keys=True) + # json.dumps(observed_structure_json, indent=2, sort_keys=True) # ) expected_structure_json = tuplify_json(json.loads(expected_structure_path.read_text())) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 98c15f53..2f885321 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -4938,8 +4938,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5039,8 +5039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5140,8 +5140,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5241,8 +5241,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5338,7 +5338,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -5384,7 +5384,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -5489,8 +5489,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5590,8 +5590,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5691,8 +5691,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5792,8 +5792,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5893,8 +5893,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5994,8 +5994,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6095,8 +6095,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6196,8 +6196,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -6297,8 +6297,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6398,8 +6398,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6499,8 +6499,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6600,8 +6600,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6697,7 +6697,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6743,7 +6743,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6848,8 +6848,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6949,8 +6949,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7050,8 +7050,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7151,8 +7151,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7252,8 +7252,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7353,8 +7353,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7454,8 +7454,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7555,8 +7555,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7656,8 +7656,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7757,8 +7757,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7858,8 +7858,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7955,7 +7955,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -8001,7 +8001,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -8106,8 +8106,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8207,8 +8207,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8308,8 +8308,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8409,8 +8409,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8510,8 +8510,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8611,8 +8611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8712,8 +8712,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8813,8 +8813,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -8914,8 +8914,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9015,8 +9015,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9116,8 +9116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9217,8 +9217,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9314,7 +9314,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -9360,7 +9360,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -9465,8 +9465,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9566,8 +9566,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9667,8 +9667,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9768,8 +9768,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9869,8 +9869,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9970,8 +9970,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10071,8 +10071,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10172,8 +10172,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -10273,8 +10273,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10374,8 +10374,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10475,8 +10475,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10576,8 +10576,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10673,7 +10673,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" @@ -10719,7 +10719,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index fbda67b4..945151be 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -4494,8 +4494,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -4591,8 +4591,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -4688,8 +4688,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -4785,8 +4785,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -4882,7 +4882,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -4928,7 +4928,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -5029,8 +5029,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5126,8 +5126,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5223,8 +5223,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5320,8 +5320,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5417,8 +5417,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5514,8 +5514,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5611,8 +5611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5708,8 +5708,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5805,8 +5805,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5902,8 +5902,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5999,8 +5999,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6096,8 +6096,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6193,8 +6193,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6290,7 +6290,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6336,7 +6336,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6437,8 +6437,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -6534,8 +6534,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -6631,8 +6631,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -6728,8 +6728,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6825,8 +6825,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6922,8 +6922,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7019,8 +7019,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7116,8 +7116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7213,8 +7213,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7310,8 +7310,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7407,7 +7407,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -7453,7 +7453,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -7554,8 +7554,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7651,8 +7651,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7748,8 +7748,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7845,8 +7845,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7942,8 +7942,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8039,8 +8039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8136,8 +8136,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8233,8 +8233,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -8330,8 +8330,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8427,8 +8427,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8524,8 +8524,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8621,8 +8621,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8718,8 +8718,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8815,7 +8815,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -8861,7 +8861,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -8962,8 +8962,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9059,8 +9059,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -9156,8 +9156,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -9253,8 +9253,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -9350,8 +9350,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9447,8 +9447,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9544,8 +9544,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9641,8 +9641,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -9738,8 +9738,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9835,8 +9835,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9932,8 +9932,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10029,8 +10029,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10126,8 +10126,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10223,7 +10223,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" @@ -10269,7 +10269,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index f77335fd..c6e2bbad 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -4938,8 +4938,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5039,8 +5039,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5140,8 +5140,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5241,8 +5241,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -5338,7 +5338,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -5384,7 +5384,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 10980 + 256 ] }, "name": "regular" @@ -5489,8 +5489,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5590,8 +5590,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5691,8 +5691,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5792,8 +5792,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5893,8 +5893,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -5994,8 +5994,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6095,8 +6095,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6196,8 +6196,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -6297,8 +6297,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6398,8 +6398,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6499,8 +6499,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6600,8 +6600,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6697,7 +6697,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6743,7 +6743,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 915 + 256 ] }, "name": "regular" @@ -6848,8 +6848,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -6949,8 +6949,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7050,8 +7050,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7151,8 +7151,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7252,8 +7252,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7353,8 +7353,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7454,8 +7454,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7555,8 +7555,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -7656,8 +7656,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7757,8 +7757,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7858,8 +7858,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -7955,7 +7955,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -8001,7 +8001,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 5490 + 256 ] }, "name": "regular" @@ -8106,8 +8106,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8207,8 +8207,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8308,8 +8308,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8409,8 +8409,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8510,8 +8510,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8611,8 +8611,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8712,8 +8712,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -8813,8 +8813,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -8914,8 +8914,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9015,8 +9015,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9116,8 +9116,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9217,8 +9217,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9314,7 +9314,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -9360,7 +9360,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 305 + 256 ] }, "name": "regular" @@ -9465,8 +9465,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9566,8 +9566,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9667,8 +9667,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9768,8 +9768,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9869,8 +9869,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -9970,8 +9970,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10071,8 +10071,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10172,8 +10172,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 244, + 244 ] }, "name": "regular" @@ -10273,8 +10273,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10374,8 +10374,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10475,8 +10475,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10576,8 +10576,8 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256, - 256 + 183, + 183 ] }, "name": "regular" @@ -10673,7 +10673,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" @@ -10719,7 +10719,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 1830 + 256 ] }, "name": "regular" From f53464857e3241a59e75ee4b2e9948d9e926c174 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 14:25:35 +0100 Subject: [PATCH 43/59] update test to check for auto_chunks output --- tests/test_s2_converter_simplified.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 9eda4793..71b85ec2 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -16,6 +16,7 @@ convert_s2_optimized, simple_root_consolidation, ) +from eopf_geozarr.s2_optimization.s2_multiscale import auto_chunks @pytest.fixture @@ -147,8 +148,8 @@ def test_convert_s2_optimized_convenience_function( reencoded_array = reflectance_group["r10m/b03"] downsampled_array = reflectance_group["r720m/b03"] - # For re-encoded arrays, chunks should match spatial_chunk - assert reencoded_array.chunks == (spatial_chunk, spatial_chunk) + # For re-encoded arrays, chunks should match output of auto_chunks + assert reencoded_array.chunks == auto_chunks(reencoded_array.shape, spatial_chunk) # For downsampled arrays, chunks should be min(spatial_chunk, array_shape) # because the array might be smaller than the chunk size From 42521944f8199fe103301b3f7e8d1bb0460ea662 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 16:23:28 +0100 Subject: [PATCH 44/59] add option to replace invalid JSON floats (NaN and infs) with strings --- src/eopf_geozarr/zarrio.py | 47 ++++++++++++++++++- tests/test_cli_e2e.py | 6 +-- ...041_N0511_R122_T32TQM_20251008T122613.json | 42 ++++++++--------- ...131_N0511_R037_T29TPF_20250811T152216.json | 42 ++++++++--------- 4 files changed, 90 insertions(+), 47 deletions(-) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index ab1a56f4..80b3a100 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -4,6 +4,7 @@ from __future__ import annotations +from dataclasses import replace from typing import TYPE_CHECKING, Any, NotRequired, TypedDict import numcodecs @@ -97,6 +98,36 @@ def default_array_reencoder( ) +def replace_json_invalid_floats(obj: object) -> object: + """ + Recursively replace NaN and Infinity float values in a JSON-like object + with their string representations, to make them JSON-compliant. + + Parameters + ---------- + obj : object + The JSON-like object to process + + Returns + ------- + object + The processed object with NaN and Infinity replaced + """ + if isinstance(obj, float): + if obj != obj: # NaN check + return "NaN" + if obj == float("inf"): + return "Infinity" + if obj == float("-inf"): + return "-Infinity" + return obj + if isinstance(obj, dict): + return {k: replace_json_invalid_floats(v) for k, v in obj.items()} + if isinstance(obj, list | tuple): + return [replace_json_invalid_floats(item) for item in obj] + return obj + + def reencode_group( group: zarr.Group, store: zarr.storage.StoreLike, @@ -106,6 +137,7 @@ def reencode_group( use_consolidated_for_children: bool = False, omit_nodes: set[str] | None = None, array_reencoder: Callable[[str, ArrayV2Metadata], ArrayV3Metadata] = default_array_reencoder, + allow_json_nan: bool = False, ) -> zarr.Group: """ Re-encode a Zarr group, applying a re-encoding to all sub-groups and sub-arrays. @@ -126,7 +158,9 @@ def reencode_group( A function that takes a Zarr array object and returns a ChunkEncodingSpec, which is a dict that defines a new chunk encoding. Use this parameter to define per-array chunk encoding logic. - + allow_json_nan : bool, default = False + Whether to allow NaN and Infinity values in JSON metadata. If False, float("NaN") will be + encoded as the string "Nan", float("Inf") as "Infinity", and float("-Inf") as "-Infinity". """ if omit_nodes is None: omit_nodes = set() @@ -160,8 +194,17 @@ def reencode_group( continue log.info("Re-encoding member %s", name) new_path = f"{path}/{name}" + source_meta = member.metadata + + if not allow_json_nan: + log.info("Replacing any nan and inf float values with string equivalents") + # replace any invalid floats with string encodings using the + # zarr v3 fill value encoding for floats + new_attrs = replace_json_invalid_floats(source_meta.attributes) + source_meta = replace(source_meta, attributes=new_attrs) + if isinstance(member, zarr.Array): - new_meta = array_reencoder(member.path, member.metadata) + new_meta = array_reencoder(member.path, source_meta) new_members[new_path] = new_meta chunks_to_encode.append(name) else: diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 0f6a4023..21f863de 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -53,9 +53,9 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: # Uncomment this section to write out the expected structure from the observed structure # This is useful when the expected structure needs to be updated - # expected_structure_path.write_text( - # json.dumps(observed_structure_json, indent=2, sort_keys=True) - # ) + expected_structure_path.write_text( + json.dumps(observed_structure_json, indent=2, sort_keys=True) + ) expected_structure_json = tuplify_json(json.loads(expected_structure_path.read_text())) expected_structure = GroupSpec(**expected_structure_json) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 2f885321..0f5bac67 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -165,7 +165,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -225,7 +225,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -290,7 +290,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -361,7 +361,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -2552,7 +2552,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 1240nm", "standard_name": "unknown", "units": "~" @@ -2653,7 +2653,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 469nm", "standard_name": "unknown", "units": "~" @@ -2754,7 +2754,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2855,7 +2855,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 670nm", "standard_name": "unknown", "units": "~" @@ -2956,7 +2956,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 865nm", "standard_name": "unknown", "units": "~" @@ -3057,7 +3057,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Black Carbon Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3158,7 +3158,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Dust Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3430,7 +3430,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Organic Matter Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3531,7 +3531,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sea Salt Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3667,7 +3667,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sulphate Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3871,7 +3871,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Geopotential", "standard_name": "geopotential", "units": "m**2 s**-2" @@ -4124,7 +4124,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Mean sea level pressure", "standard_name": "air_pressure_at_mean_sea_level", "units": "Pa" @@ -4259,7 +4259,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Relative humidity", "standard_name": "relative_humidity", "units": "%" @@ -4428,7 +4428,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column ozone", "standard_name": "atmosphere_mass_content_of_ozone", "units": "kg m**-2" @@ -4529,7 +4529,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column vertically-integrated water vapour", "standard_name": "lwe_thickness_of_atmosphere_mass_content_of_water_vapor", "units": "kg m**-2" @@ -4665,7 +4665,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre U wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4766,7 +4766,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre V wind component", "standard_name": "unknown", "units": "m s**-1" diff --git a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index c6e2bbad..229f9c13 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -165,7 +165,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -225,7 +225,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -290,7 +290,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -361,7 +361,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -2552,7 +2552,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 1240nm", "standard_name": "unknown", "units": "~" @@ -2653,7 +2653,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 469nm", "standard_name": "unknown", "units": "~" @@ -2754,7 +2754,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2855,7 +2855,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 670nm", "standard_name": "unknown", "units": "~" @@ -2956,7 +2956,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 865nm", "standard_name": "unknown", "units": "~" @@ -3057,7 +3057,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Black Carbon Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3158,7 +3158,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Dust Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3430,7 +3430,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Organic Matter Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3531,7 +3531,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sea Salt Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3667,7 +3667,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sulphate Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3871,7 +3871,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Geopotential", "standard_name": "geopotential", "units": "m**2 s**-2" @@ -4124,7 +4124,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Mean sea level pressure", "standard_name": "air_pressure_at_mean_sea_level", "units": "Pa" @@ -4259,7 +4259,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Relative humidity", "standard_name": "relative_humidity", "units": "%" @@ -4428,7 +4428,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column ozone", "standard_name": "atmosphere_mass_content_of_ozone", "units": "kg m**-2" @@ -4529,7 +4529,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column vertically-integrated water vapour", "standard_name": "lwe_thickness_of_atmosphere_mass_content_of_water_vapor", "units": "kg m**-2" @@ -4665,7 +4665,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre U wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4766,7 +4766,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre V wind component", "standard_name": "unknown", "units": "m s**-1" From a1375b701bd9ce72e29753f5f8c4779ef8e5829a Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 16:42:54 +0100 Subject: [PATCH 45/59] thread allow_json_nan kwarg to cli --- src/eopf_geozarr/cli.py | 6 ++++ .../s2_optimization/s2_converter.py | 36 +++++++++++++------ 2 files changed, 32 insertions(+), 10 deletions(-) diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index 8215bfa6..5b0a4ae7 100755 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1160,6 +1160,11 @@ def add_s2_optimization_commands(subparsers: argparse._SubParsersAction) -> None ) s2_parser.add_argument("--skip-validation", action="store_true", help="Skip output validation") s2_parser.add_argument("--verbose", action="store_true", help="Enable verbose output") + s2_parser.add_argument( + "--allow-json-nan", + action="store_true", + help="Allow invalid float values (nan, inf) in output JSON", + ) s2_parser.add_argument( "--dask-cluster", action="store_true", @@ -1198,6 +1203,7 @@ def convert_s2_optimized_command(args: argparse.Namespace) -> None: omit_nodes=omit_nodes, compression_level=args.compression_level, validate_output=not args.skip_validation, + allow_json_nan=args.allow_json_nan, ) log.info("✅ S2 optimization completed", output_path=args.output_path) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 354c5abc..0d927af1 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -291,20 +291,35 @@ def convert_s2_optimized( validate_output: bool, omit_nodes: set[str] | None = None, max_retries: int = 3, + allow_json_nan: bool = False, ) -> xr.DataTree: """ Convenience function for S2 optimization. - Args: - dt_input: Input Sentinel-2 DataTree - output_path: Output path - enable_sharding: Enable Zarr v3 sharding - spatial_chunk: Spatial chunk size - compression_level: Compression level 1-9 - validate_output: Whether to validate the output - max_retries: Maximum number of retries for network operations - - Returns: + Parameters + ---------- + dt_input : xr.DataTree + Input Sentinel-2 DataTree + output_path : str + Output path + enable_sharding : bool + Enable Zarr v3 sharding + spatial_chunk : int + Spatial chunk size + compression_level : int + Compression level 1-9 + validate_output : bool + Whether to validate the output + omit_nodes : set[str] | None, optional + Set of node paths to omit from conversion, by default None + max_retries : int, default=3 + Maximum number of retries for network operations + allow_json_nan : bool, default=False + Whether to allow NaN values in JSON output + + Returns + ------- + xr.DataTree Optimized DataTree """ @@ -344,6 +359,7 @@ def convert_s2_optimized( overwrite=True, array_reencoder=_array_reencoder, omit_nodes=omit_nodes, + allow_json_nan=allow_json_nan, ) if "measurements" in out_group: log.info("Adding CRS elements to datasets in measurements") From 9cab7188a8edc084c2283647fe0b78437937640c Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 17 Dec 2025 16:52:35 +0100 Subject: [PATCH 46/59] zarrio tests --- tests/test_zarrio.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py index 9f1469e6..59499140 100644 --- a/tests/test_zarrio.py +++ b/tests/test_zarrio.py @@ -11,6 +11,7 @@ convert_compression, default_array_reencoder, reencode_group, + replace_json_invalid_floats, ) @@ -256,5 +257,24 @@ def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata assert new_node.attrs.asdict() == old_node.attrs.asdict() +def test_replace_json_invalid_floats() -> None: + data: dict[str, object] = { + "nan": float("nan"), + "nested_nan": {"nan": float("nan")}, + "nan_in_list": [float("nan")], + "inf": float("inf"), + "-inf": float("-inf"), + } + expected = { + "nan": "NaN", + "nested_nan": {"nan": "NaN"}, + "nan_in_list": ["NaN"], + "inf": "Infinity", + "-inf": "-Infinity", + } + observed = replace_json_invalid_floats(data) + assert observed == expected + + if __name__ == "__main__": pytest.main([__file__]) From aab08736e46f0c9cd3247797f56ac2bba0ac1aeb Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 7 Jan 2026 17:24:53 +0100 Subject: [PATCH 47/59] add multiscale metadata to output --- .../s2_optimization/s2_converter.py | 6 +- .../s2_optimization/s2_multiscale.py | 100 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 3139 ++++----- ...309_N0511_R108_T32TLQ_20250113T122458.json | 6216 +++-------------- ...131_N0511_R037_T29TPF_20250811T152216.json | 3139 ++++----- tests/test_s2_multiscale.py | 43 +- 6 files changed, 3581 insertions(+), 9062 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 0d927af1..01975ad1 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -29,6 +29,7 @@ auto_chunks, calculate_simple_shard_dimensions, create_multiscale_levels, + create_multiscales_metadata, write_geo_metadata, ) @@ -373,12 +374,13 @@ def convert_s2_optimized( for _, dataset in subgroup.groups(): add_crs_and_grid_mapping(dataset, crs=crs) - # Step 2: Create multiscale pyramids for each group in the original structure + # Create multiscale pyramids for each group in the original structure log.info("Adding multiscale levels") + # Create multiscale metadata create_multiscale_levels(out_group, "measurements/reflectance") + create_multiscales_metadata(out_group["measurements/reflectance"]) - # Step 3: Root-level consolidation log.info("Step 3: Final root-level metadata consolidation") # Pass empty dict since all groups are already created by reencode_group simple_root_consolidation(output_path, {}) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 22e364a1..8f5bc442 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -69,15 +69,13 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: """ Add additional multiscale levels to an existing Zarr group """ - ds_levels = (1, 2, 6, 12, 36, 72) - ds_group_names = tuple(f"r{10 * x}m" for x in ds_levels) - + ds_levels = (2, 6, 12, 36, 72) # Construct the full path to the group containing resolution levels full_path = f"{group.path}/{path}" if group.path else path - for (cur_factor, cur_group_name), (next_factor, next_group_name) in pairwise( - zip(ds_levels, ds_group_names, strict=True) - ): + for cur_factor, next_factor in pairwise((1, *ds_levels)): + cur_group_name = f"r{10 * cur_factor}m" + next_group_name = f"r{10 * next_factor}m" # Open the current resolution level as a dataset cur_group_path = f"{full_path}/{cur_group_name}" cur_ds = xr.open_dataset(group.store, group=cur_group_path, engine="zarr") @@ -285,12 +283,10 @@ def calculate_simple_shard_dimensions( return tuple(shard_dims) -def add_multiscales_metadata_to_parent( - output_path: str, - base_path: str, - res_groups: Mapping[str, xr.Dataset], +def create_multiscales_metadata( + out_group: zarr.Group, multiscales_flavor: set[MultiscalesFlavor] | None = None, -) -> xr.DataTree: +) -> None: """Add GeoZarr-compliant multiscales metadata to parent group.""" # Sort by resolution (finest to coarsest) if multiscales_flavor is None: @@ -303,49 +299,30 @@ def add_multiscales_metadata_to_parent( "r360m": 360, "r720m": 720, } - - all_resolutions = sorted(set(res_groups.keys()), key=lambda x: res_order.get(x, 999)) - - # Get CRS and bounds from first available dataset (load from output path) - first_res = all_resolutions[0] - first_dataset = res_groups[first_res] + res_groups = tuple(out_group[k] for k in res_order) + first_dataset = xr.open_zarr(out_group.store, group=next(iter(res_groups)).path) # Get CRS and bounds native_crs = first_dataset.rio.crs if hasattr(first_dataset, "rio") else None if native_crs is None: - log.info("No CRS found, skipping multiscales metadata", base_path=base_path) - return None - - native_bounds = None - if hasattr(first_dataset, "rio"): - try: - native_bounds = first_dataset.rio.bounds() - except (AttributeError, TypeError): - # Try alternative method or construct from coordinates - try: - x_coords = first_dataset.x.values - y_coords = first_dataset.y.values - native_bounds = (x_coords.min(), y_coords.min(), x_coords.max(), y_coords.max()) - except Exception: - pass + raise ValueError("Cannot determine native CRS for multiscales metadata") - if native_bounds is None: - log.info( - "No bounds found, skipping multiscales metadata", - base_path=base_path, - ) - return None + try: + native_bounds = first_dataset.rio.bounds() + except (AttributeError, TypeError): + # Try alternative method or construct from coordinates + x_coords = first_dataset.x.values + y_coords = first_dataset.y.values + native_bounds = (x_coords.min(), y_coords.min(), x_coords.max(), y_coords.max()) # Create overview_levels structure following the multiscales v1.0 specification overview_levels: list[OverviewLevelJSON] = [] - for res_name in all_resolutions: + for res_group in res_groups: + res_name = res_group.basename # Use resolution order for consistent scale calculations res_meters = res_order[res_name] - dataset = res_groups[res_name] - - if dataset is None: - continue + dataset = xr.open_zarr(res_group.store, group=res_group.path) # Get first data variable to extract dimensions first_var = next(iter(dataset.data_vars.values())) @@ -407,7 +384,7 @@ def add_multiscales_metadata_to_parent( zoom = max(zoom_for_width, zoom_for_height) # Calculate relative scale and translation vs first resolution - finest_res_meters = res_order[all_resolutions[0]] + finest_res_meters = res_order[res_groups[0].basename] relative_scale = res_meters / finest_res_meters relative_translation = (res_meters - finest_res_meters) / 2 @@ -489,7 +466,7 @@ def add_multiscales_metadata_to_parent( scale_level_data: dict[str, Any] = {"asset": asset} if i > 0: # Not the first (base) resolution - derived_from = derivation_chain.get(asset, str(all_resolutions[0])) + derived_from = derivation_chain.get(asset, str(res_groups[0].basename)) multiscale_transform = zcm.Transform( scale=(overview_level["scale_relative"],) * 2, translation=(overview_level["translation_relative"],) * 2, @@ -507,6 +484,7 @@ def add_multiscales_metadata_to_parent( scale_level = zcm.ScaleLevel(**scale_level_data) layout.append(scale_level) + # Create convention metadata for all three conventions multiscale_attrs = MultiscaleGroupAttrs( zarr_conventions=( @@ -522,38 +500,24 @@ def add_multiscales_metadata_to_parent( ), ) - # Create parent group path - parent_group_path = f"{output_path}{base_path}" - dt_multiscale = xr.DataTree() - for res in all_resolutions: - dt_multiscale[res] = xr.DataTree() - # Add multiscale attributes - dt_multiscale.attrs.update(multiscale_attrs.model_dump()) - + out_group.attrs.update(multiscale_attrs.model_dump()) # Add spatial and proj attributes at group level following specifications if native_crs and native_bounds: # Add spatial convention attributes - dt_multiscale.attrs["spatial:dimensions"] = ["y", "x"] # Required field - dt_multiscale.attrs["spatial:bbox"] = list(native_bounds) # [xmin, ymin, xmax, ymax] - dt_multiscale.attrs["spatial:registration"] = "pixel" # Default registration type + spatial_attrs: dict[str, object] = { + "spatial:dimensions": ("y", "x"), + "spatial:bbox": tuple(native_bounds), + "spatial:registration": "pixel", + } # Add proj convention attributes if hasattr(native_crs, "to_epsg") and native_crs.to_epsg(): - dt_multiscale.attrs["proj:code"] = f"EPSG:{native_crs.to_epsg()}" + spatial_attrs["proj:code"] = f"EPSG:{native_crs.to_epsg()}" elif hasattr(native_crs, "to_wkt"): - dt_multiscale.attrs["proj:wkt2"] = native_crs.to_wkt() - - dt_multiscale.to_zarr( - parent_group_path, - mode="a", - consolidated=False, - zarr_format=3, - ) - - log.info("Added %s multiscale levels to %s", len(overview_levels), base_path) + spatial_attrs["proj:wkt2"] = native_crs.to_wkt() - return dt_multiscale + out_group.attrs.update(spatial_attrs) def create_original_encoding(dataset: xr.Dataset) -> dict[str, XarrayDataArrayEncoding]: diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 017e05e7..b829c9b5 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -29,16 +29,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 28 - }, - "name": "blosc" } ], "data_type": { @@ -80,16 +70,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 12 - }, - "name": "blosc" } ], "data_type": { @@ -131,16 +111,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -157,6 +127,7 @@ }, "mean_sun_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle" @@ -165,7 +136,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN }, "unit": "deg" }, @@ -195,7 +166,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -205,7 +176,7 @@ "dimension_names": [ "angle" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 2 @@ -215,6 +186,7 @@ }, "mean_viewing_incidence_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -225,7 +197,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN }, "unit": "deg" }, @@ -256,7 +228,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -267,7 +239,7 @@ "band", "angle" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 13, @@ -278,6 +250,7 @@ }, "sun_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -290,7 +263,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN } }, "chunk_grid": { @@ -321,7 +294,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -333,7 +306,7 @@ "y", "x" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 2, @@ -345,6 +318,7 @@ }, "viewing_incidence_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -361,7 +335,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN } }, "chunk_grid": { @@ -394,7 +368,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -408,7 +382,7 @@ "y", "x" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 13, @@ -442,16 +416,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -488,16 +452,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -589,7 +543,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -673,7 +627,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -757,7 +711,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -841,7 +795,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -883,16 +837,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -929,16 +873,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1024,7 +958,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1108,7 +1042,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1192,7 +1126,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1276,7 +1210,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1360,7 +1294,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1444,7 +1378,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1486,16 +1420,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1532,16 +1456,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1627,7 +1541,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1711,7 +1625,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1795,7 +1709,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1837,16 +1751,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1883,16 +1787,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2005,7 +1899,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2047,16 +1941,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2093,16 +1977,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2191,7 +2065,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2233,16 +2107,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2279,16 +2143,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2370,7 +2224,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2412,16 +2266,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2458,16 +2302,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2537,6 +2371,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2552,7 +2387,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 1240nm", "standard_name": "unknown", "units": "~" @@ -2589,7 +2424,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2600,7 +2435,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2638,6 +2473,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2653,7 +2489,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 469nm", "standard_name": "unknown", "units": "~" @@ -2690,7 +2526,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2701,7 +2537,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2739,6 +2575,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2754,7 +2591,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2791,7 +2628,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2802,7 +2639,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2840,6 +2677,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2855,7 +2693,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 670nm", "standard_name": "unknown", "units": "~" @@ -2892,7 +2730,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2903,7 +2741,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2941,6 +2779,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2956,7 +2795,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 865nm", "standard_name": "unknown", "units": "~" @@ -2993,7 +2832,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3004,7 +2843,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3042,6 +2881,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3057,7 +2897,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Black Carbon Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3094,7 +2934,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3105,7 +2945,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3143,6 +2983,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3158,7 +2999,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Dust Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3195,7 +3036,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3206,7 +3047,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3217,6 +3058,7 @@ }, "isobaricInhPa": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3244,8 +3086,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3253,6 +3095,7 @@ }, "latitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3278,23 +3121,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -3304,6 +3137,7 @@ }, "longitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3328,23 +3162,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -3379,7 +3203,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3415,6 +3239,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3430,7 +3255,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Organic Matter Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3467,7 +3292,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3478,7 +3303,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3516,6 +3341,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3531,7 +3357,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Sea Salt Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3568,7 +3394,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3579,7 +3405,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3593,7 +3419,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "minutes" + "units": "days" }, "chunk_grid": { "configuration": { @@ -3616,7 +3442,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3652,6 +3478,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3667,7 +3494,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Sulphate Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3704,7 +3531,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3715,7 +3542,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3726,6 +3553,7 @@ }, "surface": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -3750,8 +3578,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3785,7 +3613,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3820,7 +3648,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3856,6 +3684,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m**2 s**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3871,7 +3700,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Geopotential", "standard_name": "geopotential", "units": "m**2 s**-2" @@ -3908,7 +3737,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3919,7 +3748,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3945,6 +3774,7 @@ "members": { "isobaricInhPa": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3972,8 +3802,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3981,6 +3811,7 @@ }, "latitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -4006,23 +3837,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -4032,6 +3853,7 @@ }, "longitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -4056,23 +3878,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -4109,6 +3921,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "Pa", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4124,7 +3937,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Mean sea level pressure", "standard_name": "air_pressure_at_mean_sea_level", "units": "Pa" @@ -4161,7 +3974,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4172,7 +3985,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4208,7 +4021,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4244,6 +4057,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "%", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4259,7 +4073,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Relative humidity", "standard_name": "relative_humidity", "units": "%" @@ -4296,7 +4110,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4307,7 +4121,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4321,7 +4135,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "minutes" + "units": "days" }, "chunk_grid": { "configuration": { @@ -4344,7 +4158,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4353,6 +4167,7 @@ }, "surface": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -4377,8 +4192,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -4413,6 +4228,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4428,7 +4244,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total column ozone", "standard_name": "atmosphere_mass_content_of_ozone", "units": "kg m**-2" @@ -4465,7 +4281,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4476,7 +4292,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4514,6 +4330,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4529,7 +4346,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total column vertically-integrated water vapour", "standard_name": "lwe_thickness_of_atmosphere_mass_content_of_water_vapor", "units": "kg m**-2" @@ -4566,7 +4383,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4577,7 +4394,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4614,7 +4431,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4650,6 +4467,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4665,7 +4483,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "10 metre U wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4702,7 +4520,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4713,7 +4531,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4751,6 +4569,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4766,7 +4585,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "10 metre V wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4803,7 +4622,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4814,7 +4633,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4851,7 +4670,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -5149,12 +4968,11 @@ }, "members": { "r10m": { - "attributes": { - "grid_mapping": "spatial_ref" - }, + "attributes": {}, "members": { "b02": { "attributes": { + "_FillValue": 0, "_eopf_attrs": { "add_offset": -0.1, "coordinates": [ @@ -5174,7 +4992,6 @@ "units": "digital_counts" }, "add_offset": -0.1, - "coordinates": "spatial_ref", "dtype": " zarr.Group: attrs={"long_name": "Blue band", "units": "digital_number"}, ) + spatial_ref = xr.DataArray( + [0], + dims=["spatial_ref"], + attrs={ + "crs_wkt": 'PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]', + "false_easting": 500000.0, + "false_northing": 0.0, + "geographic_crs_name": "WGS 84", + "grid_mapping_name": "transverse_mercator", + "horizontal_datum_name": "World Geodetic System 1984", + "inverse_flattening": 298.257223563, + "latitude_of_projection_origin": 0.0, + "longitude_of_central_meridian": 9.0, + "longitude_of_prime_meridian": 0.0, + "prime_meridian_name": "Greenwich", + "projected_crs_name": "WGS 84 / UTM zone 32N", + "reference_ellipsoid_name": "WGS 84", + "scale_factor_at_central_meridian": 0.9996, + "semi_major_axis": 6378137.0, + "semi_minor_axis": 6356752.314245179, + "spatial_ref": 'PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]', + }, + ) + # Create dataset - ds = xr.Dataset({"b02": b02}) + ds = xr.Dataset({"b02": b02, "spatial_ref": spatial_ref}) # Create DataTree dt = xr.DataTree(name="root") @@ -214,6 +239,22 @@ class ExpectedGroup(GroupSpec): ExpectedGroup.from_zarr(simple_datatree["measurements/reflectance"]) +@pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") +@pytest.mark.filterwarnings("ignore:.*:UserWarning") +def test_create_multiscale_metadata(simple_datatree: zarr.Group) -> None: + """ + Test that multiscales metadata is created after downsampling. + + This test is weak right now but should be made stronger. + """ + create_multiscale_levels(simple_datatree, path="measurements/reflectance") + create_multiscales_metadata(simple_datatree["measurements/reflectance"]) + # create a new group to ensure that we get fresh attributes + new_group = zarr.open_group(simple_datatree.store, path=simple_datatree.path) + observed_attrs = new_group["measurements/reflectance"].attrs.asdict() + assert "multiscales" in observed_attrs + + @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") def test_create_multiscale_skips_existing_arrays(simple_datatree: zarr.Group) -> None: """Test that the multiscale creation routine does not overwrite an existing low res array""" From 1d09dc80b7ad842c1c453390811e8f4e86ab920b Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 9 Jan 2026 15:33:55 +0100 Subject: [PATCH 48/59] write out multiscale metadata --- pyproject.toml | 1 + .../s2_optimization/s2_multiscale.py | 50 +- tests/test_cli_e2e.py | 6 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 3313 +++++---- ...309_N0511_R108_T32TLQ_20250113T122458.json | 6168 ++++++++++++++--- ...131_N0511_R037_T29TPF_20250811T152216.json | 3313 +++++---- tests/test_s2_converter_simplified.py | 2 + tests/test_s2_multiscale.py | 31 +- 8 files changed, 9019 insertions(+), 3865 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 119287db..06bdf592 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -173,6 +173,7 @@ ignore_missing_imports = true [tool.pytest.ini_options] filterwarnings = "error" +log_level = "WARNING" minversion = "7.0" addopts = "-ra -q --strict-markers --strict-config" testpaths = ["tests"] diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 8f5bc442..0e08b062 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -82,20 +82,46 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: scale = next_factor // cur_factor to_downsample: dict[str, xr.DataArray] = {} - for var_name, var in cur_ds.data_vars.items(): - # Check if the variable already exists in the next level + to_copy: dict[str, xr.DataArray] = {} + + # Iterate over all variables (data_vars and coords) + for var_name in list(cur_ds.data_vars) + list(cur_ds.coords): + # Convert to string for type safety + var_name_str = str(var_name) + var = cur_ds[var_name] next_level_path = f"{path}/{next_group_name}" - if f"{next_level_path}/{var_name}" not in group: - to_downsample[var_name] = var + + # Skip if already exists in next level + if f"{next_level_path}/{var_name_str}" in group: + continue + + # Decide whether to downsample or copy + # Spatial data variables (2D+) should be downsampled + if var_name in cur_ds.data_vars and var.ndim >= 2: + to_downsample[var_name_str] = var + # Everything else (coordinates, 0D/1D variables like spatial_ref) should be copied + else: + to_copy[var_name_str] = var + log.info("downsampling %s into %s", tuple(sorted(to_downsample.keys())), next_group_name) - # Don't pass coords here - let the downsampled variables determine their own coordinates - downsampled_ds = create_downsampled_resolution_group( - xr.Dataset(data_vars=to_downsample), factor=scale - ) - next_group_path = f"{full_path}/{next_group_name}" - downsampled_ds.to_zarr( - group.store, group=next_group_path, consolidated=False, mode="a", compute=True - ) + if to_copy: + log.info("copying %s into %s", tuple(sorted(to_copy.keys())), next_group_name) + + # Only process if there's something to downsample or copy + if to_downsample or to_copy: + # Create downsampled dataset with data variables to downsample + downsampled_ds = create_downsampled_resolution_group( + xr.Dataset(data_vars=to_downsample), factor=scale + ) + + # Add variables to copy (like spatial_ref) + for var_name, var in to_copy.items(): + if var_name not in downsampled_ds.coords: + downsampled_ds = downsampled_ds.assign_coords({var_name: var}) + next_group_path = f"{full_path}/{next_group_name}" + downsampled_ds.to_zarr( + group.store, group=next_group_path, consolidated=False, mode="a", compute=True + ) def update_encoding( diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 21f863de..77a9f599 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -53,9 +53,9 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: # Uncomment this section to write out the expected structure from the observed structure # This is useful when the expected structure needs to be updated - expected_structure_path.write_text( - json.dumps(observed_structure_json, indent=2, sort_keys=True) - ) + # expected_structure_path.write_text( + # json.dumps(observed_structure_json, indent=2, sort_keys=True) + # ) expected_structure_json = tuplify_json(json.loads(expected_structure_path.read_text())) expected_structure = GroupSpec(**expected_structure_json) diff --git a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index b829c9b5..346e81f7 100644 --- a/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/test_data_api/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -29,6 +29,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 28 + }, + "name": "blosc" } ], "data_type": { @@ -70,6 +80,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 12 + }, + "name": "blosc" } ], "data_type": { @@ -111,6 +131,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -127,7 +157,6 @@ }, "mean_sun_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle" @@ -136,7 +165,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -166,7 +195,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -176,7 +205,7 @@ "dimension_names": [ "angle" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 2 @@ -186,7 +215,6 @@ }, "mean_viewing_incidence_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -197,7 +225,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" }, "unit": "deg" }, @@ -228,7 +256,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -239,7 +267,7 @@ "band", "angle" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 13, @@ -250,7 +278,6 @@ }, "sun_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -263,7 +290,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -294,7 +321,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -306,7 +333,7 @@ "y", "x" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 2, @@ -318,7 +345,6 @@ }, "viewing_incidence_angles": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -335,7 +361,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": NaN + "fill_value": "NaN" } }, "chunk_grid": { @@ -368,7 +394,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 8 }, "name": "blosc" @@ -382,7 +408,7 @@ "y", "x" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 13, @@ -416,6 +442,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -452,6 +488,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -543,7 +589,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -627,7 +673,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -711,7 +757,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -795,7 +841,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -837,6 +883,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -873,6 +929,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -958,7 +1024,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1042,7 +1108,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1126,7 +1192,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1210,7 +1276,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1294,7 +1360,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1378,7 +1444,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1420,6 +1486,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1456,6 +1532,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1541,7 +1627,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1625,7 +1711,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1709,7 +1795,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1751,6 +1837,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1787,6 +1883,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1899,7 +2005,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -1941,6 +2047,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -1977,6 +2093,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2065,7 +2191,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -2107,6 +2233,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2143,6 +2279,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2224,7 +2370,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 1 }, "name": "blosc" @@ -2266,6 +2412,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2302,6 +2458,16 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "int64", @@ -2371,7 +2537,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2387,7 +2552,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 1240nm", "standard_name": "unknown", "units": "~" @@ -2424,7 +2589,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2435,7 +2600,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2473,7 +2638,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2489,7 +2653,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 469nm", "standard_name": "unknown", "units": "~" @@ -2526,7 +2690,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2537,7 +2701,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2575,7 +2739,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2591,7 +2754,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2628,7 +2791,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2639,7 +2802,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2677,7 +2840,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2693,7 +2855,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 670nm", "standard_name": "unknown", "units": "~" @@ -2730,7 +2892,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2741,7 +2903,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2779,7 +2941,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2795,7 +2956,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total Aerosol Optical Depth at 865nm", "standard_name": "unknown", "units": "~" @@ -2832,7 +2993,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2843,7 +3004,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2881,7 +3042,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2897,7 +3057,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Black Carbon Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2934,7 +3094,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -2945,7 +3105,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -2983,7 +3143,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2999,7 +3158,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Dust Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3036,7 +3195,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3047,7 +3206,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3058,7 +3217,6 @@ }, "isobaricInhPa": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3086,8 +3244,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3095,7 +3253,6 @@ }, "latitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3121,13 +3278,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3137,7 +3304,6 @@ }, "longitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3162,13 +3328,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3203,7 +3379,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3239,7 +3415,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3255,7 +3430,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Organic Matter Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3292,7 +3467,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3303,7 +3478,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3341,7 +3516,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3357,7 +3531,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sea Salt Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3394,7 +3568,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3405,7 +3579,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3419,7 +3593,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "days" + "units": "minutes" }, "chunk_grid": { "configuration": { @@ -3442,7 +3616,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3478,7 +3652,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3494,7 +3667,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Sulphate Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3531,7 +3704,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3542,7 +3715,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3553,7 +3726,6 @@ }, "surface": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -3578,8 +3750,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3613,7 +3785,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3648,7 +3820,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -3684,7 +3856,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m**2 s**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3700,7 +3871,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Geopotential", "standard_name": "geopotential", "units": "m**2 s**-2" @@ -3737,7 +3908,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3748,7 +3919,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -3774,7 +3945,6 @@ "members": { "isobaricInhPa": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3802,8 +3972,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -3811,7 +3981,6 @@ }, "latitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3837,13 +4006,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3853,7 +4032,6 @@ }, "longitude": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3878,13 +4056,23 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "blocksize": 0, + "clevel": 3, + "cname": "zstd", + "shuffle": "bitshuffle", + "typesize": 8 + }, + "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9 @@ -3921,7 +4109,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "Pa", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3937,7 +4124,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Mean sea level pressure", "standard_name": "air_pressure_at_mean_sea_level", "units": "Pa" @@ -3974,7 +4161,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -3985,7 +4172,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4021,7 +4208,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4057,7 +4244,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "%", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4073,7 +4259,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Relative humidity", "standard_name": "relative_humidity", "units": "%" @@ -4110,7 +4296,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4121,7 +4307,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4135,7 +4321,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "days" + "units": "minutes" }, "chunk_grid": { "configuration": { @@ -4158,7 +4344,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4167,7 +4353,6 @@ }, "surface": { "attributes": { - "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -4192,8 +4377,8 @@ } ], "data_type": "float64", - "dimension_names": null, - "fill_value": 0.0, + "dimension_names": [], + "fill_value": "NaN", "node_type": "array", "shape": [], "storage_transformers": [], @@ -4228,7 +4413,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4244,7 +4428,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column ozone", "standard_name": "atmosphere_mass_content_of_ozone", "units": "kg m**-2" @@ -4281,7 +4465,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4292,7 +4476,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4330,7 +4514,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4346,7 +4529,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "Total column vertically-integrated water vapour", "standard_name": "lwe_thickness_of_atmosphere_mass_content_of_water_vapor", "units": "kg m**-2" @@ -4383,7 +4566,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4394,7 +4577,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4431,7 +4614,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4467,7 +4650,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4483,7 +4665,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre U wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4520,7 +4702,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4531,7 +4713,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4569,7 +4751,6 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", - "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4585,7 +4766,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": NaN, + "fill_value": "NaN", "long_name": "10 metre V wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4622,7 +4803,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "shuffle", + "shuffle": "bitshuffle", "typesize": 4 }, "name": "blosc" @@ -4633,7 +4814,7 @@ "latitude", "longitude" ], - "fill_value": 0.0, + "fill_value": "NaN", "node_type": "array", "shape": [ 9, @@ -4670,7 +4851,7 @@ } ], "data_type": "int64", - "dimension_names": null, + "dimension_names": [], "fill_value": 0, "node_type": "array", "shape": [], @@ -4890,15 +5071,15 @@ { "cellSize": 0.0, "id": "r120m", - "matrixHeight": 1, - "matrixWidth": 1, + "matrixHeight": 5, + "matrixWidth": 5, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 915, - "tileWidth": 915 + "tileHeight": 183, + "tileWidth": 183 }, { "cellSize": 0.0, @@ -4910,8 +5091,8 @@ 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 256, - "tileWidth": 256 + "tileHeight": 183, + "tileWidth": 183 }, { "cellSize": 0.0, @@ -4968,11 +5149,12 @@ }, "members": { "r10m": { - "attributes": {}, + "attributes": { + "grid_mapping": "spatial_ref" + }, "members": { "b02": { "attributes": { - "_FillValue": 0, "_eopf_attrs": { "add_offset": -0.1, "coordinates": [ @@ -4992,6 +5174,7 @@ "units": "digital_counts" }, "add_offset": -0.1, + "coordinates": "spatial_ref", "dtype": " None: assert atmos_zmeta["consolidated_metadata"] is None +@pytest.mark.filterwarnings("ignore:.*Expected 'MISSING' sentinel:UserWarning") @pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") @pytest.mark.filterwarnings("ignore:.*:xarray.coding.common.SerializationWarning") @pytest.mark.filterwarnings( "ignore:Failed to open Zarr store with consolidated metadata:RuntimeWarning" ) @pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") +@pytest.mark.filterwarnings("ignore:Pydantic serializer warnings:UserWarning") class TestConvenienceFunction: """Test the convenience function.""" diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 5e38c4ef..fee2c5b1 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -217,6 +217,7 @@ class DatasetMembers(TypedDict): y: ArraySpec time: ArraySpec b02: ArraySpec + spatial_ref: ArraySpec class DatasetGroup(GroupSpec): members: DatasetMembers @@ -258,11 +259,31 @@ def test_create_multiscale_metadata(simple_datatree: zarr.Group) -> None: @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") def test_create_multiscale_skips_existing_arrays(simple_datatree: zarr.Group) -> None: """Test that the multiscale creation routine does not overwrite an existing low res array""" - # insert a new group containing an empty version of the existing datasets - simple_datatree.create_group("measurements/reflectance/r20m") - simple_datatree.create_array( - "measurements/reflectance/r20m/b02", shape=(1,), dtype="uint8", dimension_names=("x",) + # Create a complete r20m dataset with pre-existing array and coordinates + # r10m has shape (1, 100, 100), so r20m should have shape (1, 50, 50) after 2x downsampling + x_20m = np.linspace(0, 1000, 50) + y_20m = np.linspace(0, 1000, 50) + time = np.array(["2023-01-01"], dtype="datetime64[ns]") + + b02_20m = xr.DataArray( + np.zeros((1, 50, 50), dtype="uint8"), # Pre-existing data (all zeros) + dims=["time", "y", "x"], + coords={"time": time, "y": y_20m, "x": x_20m}, + name="b02", + ) + + # Create the pre-existing dataset + ds_20m = xr.Dataset({"b02": b02_20m}) + ds_20m.to_zarr( + simple_datatree.store, + group="measurements/reflectance/r20m", + mode="a", + consolidated=False, ) create_multiscale_levels(simple_datatree, path="measurements/reflectance") - assert simple_datatree["measurements/reflectance/r20m/b02"].shape == (1,) + + # Verify that the pre-existing array was not overwritten (should still be all zeros) + result_b02 = simple_datatree["measurements/reflectance/r20m/b02"][:] + assert result_b02.shape == (1, 50, 50) + assert np.all(result_b02 == 0), "Pre-existing array should not have been overwritten" From 633969ad62782d9f71710e88cdb31ea203a4a754 Mon Sep 17 00:00:00 2001 From: Emmanuel Mathot Date: Sun, 11 Jan 2026 23:01:20 +0100 Subject: [PATCH 49/59] update launch configuration for GeoZarr conversion: modify output file name and comment out verbose flag --- .vscode/launch.json | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.vscode/launch.json b/.vscode/launch.json index 20c3fe42..954b24f9 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -198,11 +198,12 @@ "convert-s2-optimized", // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202509-s02msil2a/08/products/cpm_v256/S2A_MSIL2A_20250908T100041_N0511_R122_T32TQM_20250908T115116.zarr", "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/15/products/cpm_v262/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr", + // "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202512-s02msil2a-eu/16/products/cpm_v262/S2B_MSIL2A_20251216T102339_N0511_R065_T32TNS_20251216T123617.zarr", // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/16/products/cpm_v262/S2A_MSIL2A_20251116T085431_N0511_R107_T35SQD_20251116T103813.zarr", // "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-opt/S2A_MSIL2A_20250908T100041_N0511_R122_T32TQM_20250908T115116.zarr", - "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-staging/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr", + // "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-staging/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr", // "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-staging/S2A_MSIL2A_20251116T085431_N0511_R107_T35SQD_20251116T103813.zarr", - // "./tests-output/eopf_geozarr/s2l2_optimized.zarr", + "./tests-output/eopf_geozarr/s2l2_optimized_92.zarr", "--spatial-chunk", "256", "--compression-level", @@ -211,7 +212,7 @@ // "--omit-nodes", // "quality/l2a_quicklook", "--dask-cluster", - "--verbose" + // "--verbose" ], "cwd": "${workspaceFolder}", "justMyCode": false, From b18f427bae1d3fb1f2c4d50d133b88854f0641b2 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Jan 2026 11:38:53 +0100 Subject: [PATCH 50/59] wire up array encoder to take a configuration --- src/eopf_geozarr/cli.py | 4 +- src/eopf_geozarr/data_api/geozarr/common.py | 44 ++++++++++++++- src/eopf_geozarr/data_api/geozarr/types.py | 19 ++++++- .../s2_optimization/s2_converter.py | 53 ++++++++++++------- .../s2_optimization/s2_multiscale.py | 3 +- src/eopf_geozarr/zarrio.py | 41 +++++++++++--- tests/test_s2_multiscale.py | 4 +- tests/test_zarrio.py | 27 +++++++++- 8 files changed, 158 insertions(+), 37 deletions(-) diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py index c6ed0b4c..32ad6081 100755 --- a/src/eopf_geozarr/cli.py +++ b/src/eopf_geozarr/cli.py @@ -1163,8 +1163,8 @@ def add_s2_optimization_commands(subparsers: argparse._SubParsersAction) -> None s2_parser.add_argument( "--allow-json-nan", action="store_true", - help="Allow invalid float values (nan, inf) in output JSON" - ) + help="Allow invalid float values (nan, inf) in output JSON", + ) s2_parser.add_argument( "--keep-scale-offset", action="store_true", diff --git a/src/eopf_geozarr/data_api/geozarr/common.py b/src/eopf_geozarr/data_api/geozarr/common.py index f8c1e995..c57856cb 100644 --- a/src/eopf_geozarr/data_api/geozarr/common.py +++ b/src/eopf_geozarr/data_api/geozarr/common.py @@ -5,6 +5,7 @@ import io import urllib import urllib.request +from collections.abc import Mapping from dataclasses import dataclass from typing import ( TYPE_CHECKING, @@ -24,11 +25,17 @@ from pydantic.experimental.missing_sentinel import MISSING from typing_extensions import Protocol, TypedDict, runtime_checkable -from eopf_geozarr.data_api.geozarr.projjson import ProjJSON # noqa: TC001 +from eopf_geozarr.data_api.geozarr.types import ( + CF_SCALE_OFFSET_KEYS, + CFScaleOffset, + EmptyDict, +) if TYPE_CHECKING: from collections.abc import Mapping + from eopf_geozarr.data_api.geozarr.projjson import ProjJSON + @dataclass(frozen=True) class UNSET_TYPE: @@ -289,3 +296,38 @@ def check_grid_mapping(model: TDataSetLike) -> TDataSetLike: def is_none(data: object) -> TypeGuard[None]: return data is None + + +def extract_scale_offset( + data: Mapping[str, object], +) -> tuple[dict[str, object], CFScaleOffset | EmptyDict]: + """ + Extract scale/offset information from a mapping, returning the remaining data and the scale/offset info. + + Parameters + ---------- + data : Mapping[[str, object]] + The input mapping from which to extract scale/offset information. + + Returns + ------- + tuple[Mapping[str, object], CFScaleOffset] + A tuple containing the remaining data (with scale/offset keys removed) and the extracted scale/offset info. + """ + scale_offset: CFScaleOffset = {} # type: ignore[typeddict-item] + remaining_data: dict[str, object] = {} + + if set(data.keys()).isdisjoint(CF_SCALE_OFFSET_KEYS): + return dict(data), {} + + if set(data.keys()).issuperset(CF_SCALE_OFFSET_KEYS): + for key, value in data.items(): + if key in CF_SCALE_OFFSET_KEYS: + scale_offset[key] = value # type: ignore[literal-required] + else: + remaining_data[key] = value + return remaining_data, scale_offset + + raise ValueError( + "Incomplete scale/offset information: all of 'scale_factor', 'add_offset', and 'dtype' must be present." + ) diff --git a/src/eopf_geozarr/data_api/geozarr/types.py b/src/eopf_geozarr/data_api/geozarr/types.py index 948f15b3..8a781575 100644 --- a/src/eopf_geozarr/data_api/geozarr/types.py +++ b/src/eopf_geozarr/data_api/geozarr/types.py @@ -2,7 +2,9 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Final, Literal, NotRequired, TypedDict +from typing import TYPE_CHECKING, Final, Literal, NotRequired + +from typing_extensions import TypedDict if TYPE_CHECKING: from collections.abc import Mapping @@ -28,6 +30,21 @@ class TileMatrixLimitJSON(TypedDict): } | CF_SCALE_OFFSET_KEYS +class CFScaleOffset(TypedDict): + """ + Metadata defining scale/offset encoding for array values. Defined by the CF + conventions and found in EOPF Sentinel products in Zarr array attributes. + """ + + scale_factor: float + add_offset: float + dtype: str + + +class EmptyDict(TypedDict, closed=True): # type: ignore[call-arg] + """A dict with no keys.""" + + class XarrayDataArrayEncoding(TypedDict): """ The dict form of the encoding for xarray.DataArray diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 4cb85222..1f2a9ba2 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -14,16 +14,17 @@ import zarr from pydantic import TypeAdapter from pyproj import CRS -from zarr.core.dtype.npy.time import TimeDelta64 +from zarr.core.dtype import Float32, TimeDelta64, ZDType from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from zarr.core.sync import sync from zarr.storage._common import make_store from eopf_geozarr.conversion.fs_utils import get_storage_options from eopf_geozarr.conversion.geozarr import get_zarr_group +from eopf_geozarr.data_api.geozarr.common import extract_scale_offset from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root -from eopf_geozarr.zarrio import convert_compression, reencode_group +from eopf_geozarr.zarrio import ArrayReencoderConfig, convert_compression, reencode_group from .s2_multiscale import ( auto_chunks, @@ -182,24 +183,35 @@ def array_reencoder( key: str, metadata: ArrayV2Metadata, *, - spatial_chunk: int, - enable_sharding: bool = False, - compression_level: int | None = None, + config: ArrayReencoderConfig | None = None, ) -> ArrayV3Metadata: """ Generate Zarr V3 Metadata from a key and a Zarr V2 metadata document. """ - attributes: dict[str, object] = metadata.attributes.copy() + if config is None: + config = {} + keep_scale_offset = config.get("keep_scale_offset", False) + spatial_chunk = config.get("spatial_chunk", 256) + enable_sharding = config.get("enable_sharding", False) + compression_level = config.get("compression_level") + + attrs, maybe_scale_offset = extract_scale_offset(metadata.attributes.copy()) + out_dtype: ZDType[Any, Any] + # If the array used CF scale/offset encoding, we change the output dtype to Float32 + out_dtype = metadata.dtype + if not keep_scale_offset and len(maybe_scale_offset) > 0: + out_dtype = Float32() + # handle xarray datetime/timedelta encoding # If the array has time-related units, ensure the dtype attribute matches the actual dtype - if attributes.get("units") in TIME_UNIT: - numpy_time_unit = _netcdf_unit_to_numpy_time_unit(attributes["units"]) # type: ignore[arg-type] + if attrs.get("units") in TIME_UNIT: + numpy_time_unit = _netcdf_unit_to_numpy_time_unit(attrs["units"]) # type: ignore[arg-type] # Check if this is a timedelta or datetime based on: # 1. The zarr dtype (if it's a native time type like TimeDelta64) # 2. The existing dtype attribute (for int64-encoded times) # 3. The standard_name attribute (e.g., "forecast_period" indicates timedelta) - existing_dtype_attr = str(attributes.get("dtype", "")) - standard_name = str(attributes.get("standard_name", "")) + existing_dtype_attr = str(attrs.get("dtype", "")) + standard_name = str(attrs.get("standard_name", "")) is_timedelta = ( isinstance(metadata.dtype, TimeDelta64) or "timedelta" in existing_dtype_attr @@ -210,12 +222,12 @@ def array_reencoder( # This is a timedelta array - set or correct the dtype attribute # Note: xarray/pandas only support timedelta64 with s/ms/us/ns, not m/h/D # Use 's' as the safest option - attributes["dtype"] = "timedelta64[ns]" + attrs["dtype"] = "timedelta64[ns]" else: # This is a datetime array - set or correct the dtype attribute - attributes["dtype"] = f"datetime64[{numpy_time_unit}]" + attrs["dtype"] = f"datetime64[{numpy_time_unit}]" - dimension_names: None | tuple[str, ...] = attributes.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] + dimension_names: None | tuple[str, ...] = attrs.pop("_ARRAY_DIMENSIONS", None) # type: ignore[assignment] compressor_converted = convert_compression( metadata.compressor, compression_level=compression_level ) @@ -272,13 +284,13 @@ def array_reencoder( codecs = ({"name": "bytes"}, *compressor_converted) # type: ignore[assignment] return ArrayV3Metadata( shape=metadata.shape, - data_type=metadata.dtype, + data_type=out_dtype, chunk_key_encoding=chunk_key_encoding, chunk_grid=chunk_grid, fill_value=fill_value, dimension_names=dimension_names, codecs=codecs, - attributes=attributes, + attributes=attrs, ) @@ -338,9 +350,12 @@ def convert_s2_optimized( # Create a partial function by specifying parameters for our array encoder _array_reencoder = partial( array_reencoder, - spatial_chunk=spatial_chunk, - enable_sharding=enable_sharding, - compression_level=compression_level, + config={ + "spatial_chunk": spatial_chunk, + "enable_sharding": enable_sharding, + "compression_level": compression_level, + "keep_scale_offset": keep_scale_offset, + }, ) out_group = reencode_group( @@ -348,7 +363,7 @@ def convert_s2_optimized( out_store, path="", overwrite=True, - array_reencoder=_array_reencoder, + array_reencoder=_array_reencoder, # type: ignore[arg-type] omit_nodes=omit_nodes, allow_json_nan=allow_json_nan, ) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 8eaa07f7..4419c54a 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -744,11 +744,12 @@ def stream_write_dataset( dataset_path=path, ) return xr.open_dataset( - dataset_path, + group.store, engine="zarr", chunks={}, decode_coords="all", consolidated=False, + group=f"{group.path}/{path}", ) log.info("Streaming computation and write to {}", dataset_path=path) diff --git a/src/eopf_geozarr/zarrio.py b/src/eopf_geozarr/zarrio.py index 80b3a100..365f0496 100644 --- a/src/eopf_geozarr/zarrio.py +++ b/src/eopf_geozarr/zarrio.py @@ -5,7 +5,7 @@ from __future__ import annotations from dataclasses import replace -from typing import TYPE_CHECKING, Any, NotRequired, TypedDict +from typing import TYPE_CHECKING, Any, NotRequired, Protocol, TypedDict import numcodecs import structlog @@ -13,10 +13,13 @@ from zarr.core.group import GroupMetadata from zarr.core.metadata.v3 import ArrayV3Metadata from zarr.core.sync import sync +from zarr.dtype import Float32, ZDType from zarr.storage._common import make_store_path +from eopf_geozarr.data_api.geozarr.common import extract_scale_offset + if TYPE_CHECKING: - from collections.abc import Callable, Mapping + from collections.abc import Mapping from zarr.core.metadata.v2 import ArrayV2Metadata @@ -65,6 +68,13 @@ def convert_compression( raise ValueError(f"Only blosc -> blosc or None -> () is supported. Got {compressor=}") +class ArrayReencoderConfig(TypedDict): + keep_scale_offset: NotRequired[bool] + spatial_chunk: NotRequired[int] + enable_sharding: NotRequired[bool] + compression_level: NotRequired[int] + + def default_array_reencoder( key: str, metadata: ArrayV2Metadata, @@ -72,20 +82,26 @@ def default_array_reencoder( """ Re-encode a zarr array into a new zarr v3 array. """ - + keep_scale_offset = False new_codecs = convert_compression(metadata.compressor) if metadata.fill_value is None: fill_value = metadata.dtype.default_scalar() else: fill_value = metadata.fill_value - attributes = metadata.attributes.copy() - dimension_names = attributes.pop("_ARRAY_DIMENSIONS", None) + attrs, maybe_scale_offset = extract_scale_offset(metadata.attributes.copy()) + out_dtype: ZDType[Any, Any] + # If the array used CF scale/offset encoding, we change the output dtype to Float32 + out_dtype = metadata.dtype + if not keep_scale_offset and len(maybe_scale_offset) > 0: + out_dtype = Float32() + + dimension_names = attrs.pop("_ARRAY_DIMENSIONS", None) chunk_grid_shape = metadata.chunks codecs = ({"name": "bytes"}, *new_codecs) return ArrayV3Metadata( shape=metadata.shape, - data_type=metadata.dtype, + data_type=out_dtype, chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, chunk_grid={ "name": "regular", @@ -94,7 +110,7 @@ def default_array_reencoder( fill_value=fill_value, dimension_names=dimension_names, codecs=codecs, - attributes=attributes, + attributes=attrs, ) @@ -128,6 +144,14 @@ def replace_json_invalid_floats(obj: object) -> object: return obj +class ArrayReencoder(Protocol): + @staticmethod + def __call__( + key: str, + metadata: ArrayV2Metadata, + ) -> ArrayV3Metadata: ... + + def reencode_group( group: zarr.Group, store: zarr.storage.StoreLike, @@ -136,7 +160,7 @@ def reencode_group( overwrite: bool = False, use_consolidated_for_children: bool = False, omit_nodes: set[str] | None = None, - array_reencoder: Callable[[str, ArrayV2Metadata], ArrayV3Metadata] = default_array_reencoder, + array_reencoder: ArrayReencoder = default_array_reencoder, allow_json_nan: bool = False, ) -> zarr.Group: """ @@ -192,6 +216,7 @@ def reencode_group( name, ) continue + log.info("Re-encoding member %s", name) new_path = f"{path}/{name}" source_meta = member.metadata diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index cadc01e7..44bc80aa 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -16,13 +16,11 @@ from structlog.testing import capture_logs from eopf_geozarr.s2_optimization.s2_multiscale import ( - auto_chunks, calculate_aligned_chunk_size, calculate_simple_shard_dimensions, create_downsampled_resolution_group, create_measurements_encoding, create_multiscale_levels, - create_multiscales_metadata, ) @@ -161,7 +159,7 @@ def test_create_multiscale_from_datatree( # Capture log output using structlog's testing context manager with capture_logs(): - create_multiscale_from_datatree( + create_multiscale_levels( dt_input, output_group=output_group, enable_sharding=True, diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py index 59499140..c6f92736 100644 --- a/tests/test_zarrio.py +++ b/tests/test_zarrio.py @@ -2,9 +2,12 @@ from __future__ import annotations +from typing import Any + import numcodecs import pytest import zarr +from zarr.core.dtype import Float32, UInt16 from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from eopf_geozarr.zarrio import ( @@ -71,7 +74,7 @@ def test_reencode_array_fill_value_none() -> None: def test_reencode_array_fill_value_custom() -> None: """ - Test that custom fill_value is preserved + Test that a custom fill_value is preserved """ array_a = zarr.create_array( {}, shape=(10,), dtype="uint8", zarr_format=2, compressors=None, fill_value=255 @@ -81,6 +84,24 @@ def test_reencode_array_fill_value_custom() -> None: assert meta.fill_value == 255 +@pytest.mark.parametrize("keep_scale_offset", [True, False]) +def test_reencode_array_scale_offset(keep_scale_offset: bool) -> None: + v2_meta = ArrayV2Metadata( + shape=(10,), + dtype=UInt16(), + chunks=(5,), + order="C", + compressor=None, + fill_value=0, + attributes={"scale_factor": 0.1, "add_offset": 10, "dtype": ">u2"}, + ) + observed = default_array_reencoder( + "test_array", v2_meta, config={"keep_scale_offset": keep_scale_offset} + ) + expect_dtype = v2_meta.dtype if keep_scale_offset else Float32() + assert observed.data_type == expect_dtype + + def test_reencode_array_blosc_compression_converts() -> None: """ Test that blosc compression is properly converted from v2 to v3 @@ -238,7 +259,9 @@ def test_reencode_group_with_chunk_reencoder() -> None: ) new_chunks = (25,) - def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata: + def custom_array_encoder( + key: str, metadata: ArrayV2Metadata, *, config: Any + ) -> ArrayV3Metadata: return ArrayV3Metadata( shape=metadata.shape, data_type=metadata.dtype, From 7bac747480eadea3016d05ef10cf60a52dd4784c Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Wed, 14 Jan 2026 20:20:20 +0100 Subject: [PATCH 51/59] work towards matching expected output --- pyproject.toml | 2 +- .../s2_optimization/s2_multiscale.py | 8 +- tests/test_s2_multiscale.py | 86 ++++++++++++++++--- 3 files changed, 77 insertions(+), 19 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 06bdf592..03c46df2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ requires-python = ">=3.11" dependencies = [ "pydantic-zarr>=0.9.1", "pydantic>=2.12", - "zarr>=3.1.1", + "zarr>=3.1.4", "xarray>=2025.7.1", "dask[array,distributed]>=2025.5.1", "numpy>=2.3.1", diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 4419c54a..0d0b7d5d 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -69,13 +69,13 @@ def create_multiscale_levels(group: zarr.Group, path: str) -> None: """ Add additional multiscale levels to an existing Zarr group """ - ds_levels = (2, 6, 12, 36, 72) + ds_levels = (20, 60, 120, 360, 720) # Construct the full path to the group containing resolution levels full_path = f"{group.path}/{path}" if group.path else path - for cur_factor, next_factor in pairwise((1, *ds_levels)): - cur_group_name = f"r{10 * cur_factor}m" - next_group_name = f"r{10 * next_factor}m" + for cur_factor, next_factor in pairwise((10, *ds_levels)): + cur_group_name = f"r{cur_factor}m" + next_group_name = f"r{next_factor}m" # Open the current resolution level as a dataset cur_group_path = f"{full_path}/{cur_group_name}" cur_ds = xr.open_dataset(group.store, group=cur_group_path, engine="zarr") diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 44bc80aa..be34d722 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -21,7 +21,9 @@ create_downsampled_resolution_group, create_measurements_encoding, create_multiscale_levels, + create_multiscales_metadata, ) +from eopf_geozarr.zarrio import reencode_group @pytest.fixture @@ -77,6 +79,7 @@ def test_calculate_simple_shard_dimensions() -> None: assert shard_dims[1] == 768 # 3 * 256 = 768 +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") @pytest.mark.parametrize("keep_scale_offset", [True, False]) def test_create_measurements_encoding(keep_scale_offset: bool, sample_dataset: xr.Dataset) -> None: """Test measurements encoding creation with xy-aligned sharding.""" @@ -159,14 +162,32 @@ def test_create_multiscale_from_datatree( # Capture log output using structlog's testing context manager with capture_logs(): + # WORKAROUND: Re-encoding the full product fails because some arrays (quality/mask) + # have 'dtype' in attributes but not scale_factor/add_offset, which triggers + # validation error in extract_scale_offset. This is a known issue - dtype is + # standard array metadata, not necessarily CF scale/offset. + # For now, only reencode measurements/reflectance which has proper CF encoding. + input_reflectance = input_group["measurements/reflectance"] + assert isinstance(input_reflectance, zarr.Group) + + reencode_group( + group=input_reflectance, + store=output_group.store, + path="measurements/reflectance", + overwrite=True, + ) + + # Then create multiscale levels for measurements/reflectance create_multiscale_levels( - dt_input, - output_group=output_group, - enable_sharding=True, - spatial_chunk=256, - keep_scale_offset=False, + group=output_group, + path="measurements/reflectance", ) + # Add multiscales metadata and spatial/proj attributes to the reflectance group + reflectance_group = output_group["measurements/reflectance"] + assert isinstance(reflectance_group, zarr.Group) + create_multiscales_metadata(reflectance_group) + observed_group = zarr.open_group(output_path, use_consolidated=False) observed_structure_json = GroupSpec.from_zarr(observed_group).model_dump() @@ -179,11 +200,11 @@ def test_create_multiscale_from_datatree( s2_group_example.stem + ".json" ) - # Uncomment this section to write out the expected structure from the observed structure - # This is useful when the expected structure needs to be updated - # expected_structure_path.write_text( - # json.dumps(observed_structure_json, indent=2, sort_keys=True) - # ) + # Write out the observed structure for analysis + observed_output_path = Path("/tmp/observed_structure.json") + observed_output_path.write_text( + json.dumps(observed_structure_json, indent=2, sort_keys=True) + ) expected_structure_json = tuplify_json(json.loads(expected_structure_path.read_text())) expected_structure = GroupSpec(**expected_structure_json) @@ -212,7 +233,44 @@ def test_create_multiscale_from_datatree( o_keys = set(observed_structure_flat.keys()) e_keys = set(expected_structure_flat.keys()) - # Check that all of the keys are the same - assert o_keys == e_keys - # Check that all values are the same - assert [k for k in o_keys if expected_structure_flat[k] != observed_structure_flat[k]] == [] + # TODO: Investigate why spatial_ref is not being created during re-encoding + # The source data has proj:epsg and proj:wkt2 attributes but no spatial_ref coordinate + # Expected output has spatial_ref - need to determine where this should be created + + # Filter out known acceptable differences: + # 1. Scope: observed only has measurements/reflectance, expected has full product + # (working around extract_scale_offset validation issue with dtype attribute) + # 2. Extra b08 in r20m/r60m in observed (intended behavior for now) + # 3. Missing spatial_ref in observed (TODO: needs investigation) + + # Only compare measurements/reflectance subtree + o_keys_filtered = {k for k in o_keys if k.startswith("members/measurements/reflectance") or k == ""} + e_keys_filtered = {k for k in e_keys if k.startswith("members/measurements/reflectance") or k == ""} + + # Ignore spatial_ref keys + o_keys_filtered = {k for k in o_keys_filtered if "spatial_ref" not in k} + e_keys_filtered = {k for k in e_keys_filtered if "spatial_ref" not in k} + + # Ignore extra b08 in r20m/r60m + ignore_patterns = [ + "members/measurements/reflectance/members/r20m/members/b08", + "members/measurements/reflectance/members/r60m/members/b08", + ] + o_keys_filtered = {k for k in o_keys_filtered if not any( + pattern in k for pattern in ignore_patterns + )} + + # Check that all of the keys are the same (after filtering) + assert o_keys_filtered == e_keys_filtered, ( + f"Key mismatch after filtering.\n" + f"Extra in observed: {sorted(o_keys_filtered - e_keys_filtered)[:20]}\n" + f"Missing in observed: {sorted(e_keys_filtered - o_keys_filtered)[:20]}" + ) + + # Check that all values are the same for common keys + common_keys = o_keys_filtered & e_keys_filtered + mismatched_values = [ + k for k in common_keys + if expected_structure_flat.get(k) != observed_structure_flat.get(k) + ] + assert mismatched_values == [], f"Value mismatches: {mismatched_values[:20]}" From 3ec7e9313af1272a505099e64db643453ca811bf Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 16 Jan 2026 12:14:29 +0100 Subject: [PATCH 52/59] add CRS implementation --- src/eopf_geozarr/data_api/geozarr/common.py | 2 +- src/eopf_geozarr/data_api/geozarr/types.py | 3 +- .../s2_optimization/s2_multiscale.py | 116 +++++------ .../test_data_api/test_geozarr/test_common.py | 1 + tests/test_data_api/test_v3.py | 1 + tests/test_s2_converter_simplified.py | 22 +- tests/test_s2_multiscale.py | 193 ++++++++++++++++-- tests/test_s2_multiscale_geo_metadata.py | 109 ++++++++-- tests/test_zarrio.py | 9 +- 9 files changed, 342 insertions(+), 114 deletions(-) diff --git a/src/eopf_geozarr/data_api/geozarr/common.py b/src/eopf_geozarr/data_api/geozarr/common.py index c57856cb..47a59d38 100644 --- a/src/eopf_geozarr/data_api/geozarr/common.py +++ b/src/eopf_geozarr/data_api/geozarr/common.py @@ -329,5 +329,5 @@ def extract_scale_offset( return remaining_data, scale_offset raise ValueError( - "Incomplete scale/offset information: all of 'scale_factor', 'add_offset', and 'dtype' must be present." + "Incomplete scale/offset information: all of 'scale_factor', 'add_offset', must be present." ) diff --git a/src/eopf_geozarr/data_api/geozarr/types.py b/src/eopf_geozarr/data_api/geozarr/types.py index 8a781575..613dbbd9 100644 --- a/src/eopf_geozarr/data_api/geozarr/types.py +++ b/src/eopf_geozarr/data_api/geozarr/types.py @@ -18,7 +18,7 @@ class TileMatrixLimitJSON(TypedDict): maxTileRow: int -CF_SCALE_OFFSET_KEYS: Final[set[str]] = {"scale_factor", "add_offset", "dtype"} +CF_SCALE_OFFSET_KEYS: Final[set[str]] = {"scale_factor", "add_offset"} XARRAY_ENCODING_KEYS: Final[set[str]] = { "chunks", @@ -27,6 +27,7 @@ class TileMatrixLimitJSON(TypedDict): "filters", "shards", "_FillValue", + "dtype", } | CF_SCALE_OFFSET_KEYS diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 0d0b7d5d..ff4c291e 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -825,73 +825,69 @@ def write_geo_metadata( grid_mapping_var_name: Name for grid mapping variable crs: Coordinate Reference System to use (if None, attempts to detect from dataset) """ + # Write CRS using rioxarray + # NOTE: for now rioxarray only supports writing grid mapping using CF conventions dataset.rio.write_crs(crs, grid_mapping_name=grid_mapping_var_name, inplace=True) dataset.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) + dataset.attrs["grid_mapping"] = grid_mapping_var_name - if crs is not None: - # Write CRS using rioxarray - # NOTE: for now rioxarray only supports writing grid mapping using CF conventions - dataset.rio.write_crs(crs, grid_mapping_name=grid_mapping_var_name, inplace=True) - dataset.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) - dataset.attrs["grid_mapping"] = grid_mapping_var_name + for var in dataset.data_vars.values(): + var.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) + var.attrs["grid_mapping"] = grid_mapping_var_name - for var in dataset.data_vars.values(): - var.rio.write_grid_mapping(grid_mapping_var_name, inplace=True) - var.attrs["grid_mapping"] = grid_mapping_var_name + # Also add proj: and spatial: zarr conventions at dataset level + # TODO : Remove once rioxarray supports writing these conventions directly + # https://github.com/corteva/rioxarray/pull/883 - # Also add proj: and spatial: zarr conventions at dataset level - # TODO : Remove once rioxarray supports writing these conventions directly - # https://github.com/corteva/rioxarray/pull/883 + # Add spatial convention attributes + dataset.attrs["spatial:dimensions"] = ["y", "x"] # Required field + dataset.attrs["spatial:registration"] = "pixel" # Default registration type - # Add spatial convention attributes - dataset.attrs["spatial:dimensions"] = ["y", "x"] # Required field - dataset.attrs["spatial:registration"] = "pixel" # Default registration type - - # Calculate and add spatial bbox if coordinates are available - if "x" in dataset.coords and "y" in dataset.coords: - x_coords = dataset.coords["x"].values - y_coords = dataset.coords["y"].values - x_min, x_max = float(x_coords.min()), float(x_coords.max()) - y_min, y_max = float(y_coords.min()), float(y_coords.max()) - dataset.attrs["spatial:bbox"] = [x_min, y_min, x_max, y_max] - - # Calculate spatial transform (affine transformation) - spatial_transform = None - if hasattr(dataset, "rio") and hasattr(dataset.rio, "transform"): - try: - rio_transform = dataset.rio.transform - if callable(rio_transform): - rio_transform = rio_transform() - spatial_transform = list(rio_transform)[:6] - except (AttributeError, TypeError): - # Fallback: construct from coordinate spacing - pixel_size_x = float(get_grid_spacing(dataset, ("x",))[0]) - pixel_size_y = float(get_grid_spacing(dataset, ("y",))[0]) - spatial_transform = [ - pixel_size_x, - 0.0, - x_min, - 0.0, - -pixel_size_y, - y_max, - ] - - # Only add spatial:transform if we have valid transform data (not all zeros) - if spatial_transform is not None and not all(t == 0 for t in spatial_transform): - dataset.attrs["spatial:transform"] = spatial_transform - - # Add spatial shape if data variables exist - if dataset.data_vars: - first_var = next(iter(dataset.data_vars.values())) - if first_var.ndim >= 2: - height, width = first_var.shape[-2:] - dataset.attrs["spatial:shape"] = [height, width] + # Calculate and add spatial bbox if coordinates are available + if "x" in dataset.coords and "y" in dataset.coords: + x_coords = dataset.coords["x"].values + y_coords = dataset.coords["y"].values + x_min, x_max = float(x_coords.min()), float(x_coords.max()) + y_min, y_max = float(y_coords.min()), float(y_coords.max()) + dataset.attrs["spatial:bbox"] = [x_min, y_min, x_max, y_max] - # Add proj convention attributes - if hasattr(crs, "to_epsg") and crs.to_epsg(): - dataset.attrs["proj:code"] = f"EPSG:{crs.to_epsg()}" - elif hasattr(crs, "to_wkt"): - dataset.attrs["proj:wkt2"] = crs.to_wkt() + # Calculate spatial transform (affine transformation) + spatial_transform = None + if hasattr(dataset, "rio") and hasattr(dataset.rio, "transform"): + try: + rio_transform = dataset.rio.transform + if callable(rio_transform): + rio_transform = rio_transform() + spatial_transform = list(rio_transform)[:6] + except (AttributeError, TypeError): + # Fallback: construct from coordinate spacing + pixel_size_x = float(get_grid_spacing(dataset, ("x",))[0]) + pixel_size_y = float(get_grid_spacing(dataset, ("y",))[0]) + spatial_transform = [ + pixel_size_x, + 0.0, + x_min, + 0.0, + -pixel_size_y, + y_max, + ] + + # Only add spatial:transform if we have valid transform data (not all zeros) + if spatial_transform is not None and not all(t == 0 for t in spatial_transform): + dataset.attrs["spatial:transform"] = spatial_transform + + # Add spatial shape if data variables exist + if dataset.data_vars: + first_var = next(iter(dataset.data_vars.values())) + if first_var.ndim >= 2: + height, width = first_var.shape[-2:] + dataset.attrs["spatial:shape"] = [height, width] + + # Add proj convention attributes + if hasattr(crs, "to_epsg") and crs.to_epsg(): + dataset.attrs["proj:code"] = f"EPSG:{crs.to_epsg()}" + elif hasattr(crs, "to_wkt"): + dataset.attrs["proj:wkt2"] = crs.to_wkt() def rechunk_dataset_for_encoding( diff --git a/tests/test_data_api/test_geozarr/test_common.py b/tests/test_data_api/test_geozarr/test_common.py index b6f1e0d9..33cf1085 100644 --- a/tests/test_data_api/test_geozarr/test_common.py +++ b/tests/test_data_api/test_geozarr/test_common.py @@ -79,6 +79,7 @@ def test_check_standard_name_invalid() -> None: check_standard_name("invalid_standard_name") +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") def test_multiscales_round_trip(s2_optimized_geozarr_group_example: zarr.Group) -> None: """ Ensure that we can round-trip multiscale metadata through the `Multiscales` model. diff --git a/tests/test_data_api/test_v3.py b/tests/test_data_api/test_v3.py index 390bec74..10aa8724 100644 --- a/tests/test_data_api/test_v3.py +++ b/tests/test_data_api/test_v3.py @@ -63,6 +63,7 @@ def test_invalid_coordinates( check_valid_coordinates(group) +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.UnstableSpecificationWarning") def test_dataarray_round_trip(s2_geozarr_group_example: Any) -> None: """ Ensure that we can round-trip dataarray attributes through the `Multiscales` model. diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 063597d1..25e5b026 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -11,6 +11,7 @@ import pytest import xarray as xr import zarr +from structlog import cap_logs from eopf_geozarr.s2_optimization.s2_converter import ( convert_s2_optimized, @@ -122,16 +123,17 @@ def test_convert_s2_optimized_convenience_function( output_path = str(tmp_path / "test_output.zarr") # Run the conversion - result = convert_s2_optimized( - dt_input, - output_path=output_path, - enable_sharding=enable_sharding, - spatial_chunk=spatial_chunk, - compression_level=compression_level, - max_retries=5, - validate_output=False, - keep_scale_offset=False, - ) + with cap_logs(): + result = convert_s2_optimized( + dt_input, + output_path=output_path, + enable_sharding=enable_sharding, + spatial_chunk=spatial_chunk, + compression_level=compression_level, + max_retries=5, + validate_output=False, + keep_scale_offset=False, + ) # Verify the output was created assert Path(output_path).exists() diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index be34d722..4439b783 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -21,10 +21,167 @@ create_downsampled_resolution_group, create_measurements_encoding, create_multiscale_levels, - create_multiscales_metadata, ) from eopf_geozarr.zarrio import reencode_group +try: + from pyproj import CRS as ProjCRS +except ImportError: + ProjCRS = None + + +def add_spatial_ref_to_group(group: zarr.Group, epsg_code: int | None = None) -> None: + """ + Add spatial_ref coordinate to all resolution levels in a group. + + This manually creates the spatial_ref coordinate that rioxarray would create, + avoiding the need for rasterio dependency in the core re-encoding logic. + + Parameters + ---------- + group : zarr.Group + The group containing resolution level subgroups (e.g., r10m, r20m) + epsg_code : int, optional + EPSG code to use. If None, will try to detect from data variable attributes. + """ + if ProjCRS is None: + pytest.skip("pyproj not available") + return + + # Iterate over all resolution levels + for _, member in group.members(): + if not isinstance(member, zarr.Group): + continue + + # Check if we can determine EPSG from array attributes in the zarr group + detected_epsg = epsg_code + if detected_epsg is None: + for _, array in member.arrays(): + array_attrs = dict(array.attrs) + if "proj:epsg" in array_attrs: + detected_epsg = int(array_attrs["proj:epsg"]) + break + + if detected_epsg is None: + continue + + # Create CRS from EPSG + crs = ProjCRS.from_epsg(detected_epsg) + cf_attrs = crs.to_cf() + + # Create spatial_ref array directly in zarr (scalar int64 array) + # This avoids issues with xarray's to_zarr mode="a" handling + spatial_ref_array = member.create_array( + "spatial_ref", + shape=(), + dtype="int64", + fill_value=0, + overwrite=True, + ) + spatial_ref_array.attrs.update(cf_attrs) + spatial_ref_array[()] = 0 # Set the scalar value + + +@pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") +def test_add_spatial_ref_matches_rasterio(tmp_path: pathlib.Path) -> None: + """Test that our manual spatial_ref creation matches rasterio's output.""" + try: + import rioxarray + except ImportError: + pytest.skip("rioxarray not available") + + if ProjCRS is None: + pytest.skip("pyproj not available") + + # Create a test dataset with spatial dimensions + x = np.arange(0, 100, 10, dtype=np.float64) + y = np.arange(0, 100, 10, dtype=np.float64) + data = np.random.rand(len(y), len(x)).astype(np.float32) + + test_epsg = 32632 # UTM zone 32N + + ds = xr.Dataset( + { + "test_var": (["y", "x"], data, {"proj:epsg": test_epsg}), + }, + coords={"x": x, "y": y}, + ) + + # Create two stores: one for rasterio, one for manual + rasterio_store = tmp_path / "rasterio.zarr" + manual_store = tmp_path / "manual.zarr" + + # Create group structure with a resolution level for manual approach + manual_root = zarr.create_group(str(manual_store)) + _ = manual_root.create_group("r10m") # Create the subgroup structure + + # Write dataset using rioxarray (which creates spatial_ref automatically) + ds_with_crs = ds.rio.write_crs(test_epsg) + ds_with_crs.to_zarr(str(rasterio_store), group="r10m", mode="w") + + # Write dataset without spatial_ref, then add it manually + ds.to_zarr(str(manual_store), group="r10m", mode="a") + add_spatial_ref_to_group(manual_root, epsg_code=test_epsg) + + # Reopen groups to access arrays (without consolidated metadata since we added spatial_ref manually) + rasterio_res_group_reopened = zarr.open_group( + str(rasterio_store), mode="r", use_consolidated=False + )["r10m"] + manual_res_group_reopened = zarr.open_group( + str(manual_store), mode="r", use_consolidated=False + )["r10m"] + + # Get spatial_ref arrays + rasterio_spatial_ref_member = rasterio_res_group_reopened["spatial_ref"] + manual_spatial_ref_member = manual_res_group_reopened["spatial_ref"] + + # Ensure they are arrays + assert isinstance(rasterio_spatial_ref_member, zarr.Array) + assert isinstance(manual_spatial_ref_member, zarr.Array) + + # Check that both exist and have the same shape (scalar) + assert rasterio_spatial_ref_member.shape == () + assert manual_spatial_ref_member.shape == () + + # Check that both have the same dtype + assert rasterio_spatial_ref_member.dtype == manual_spatial_ref_member.dtype + + # Check that both have the same value + rasterio_value = rasterio_spatial_ref_member[()] + manual_value = manual_spatial_ref_member[()] + assert rasterio_value == manual_value + + # Check that CF attributes match + rasterio_attrs = dict(rasterio_spatial_ref_member.attrs) + manual_attrs = dict(manual_spatial_ref_member.attrs) + + # Both should have grid_mapping_name + assert "grid_mapping_name" in rasterio_attrs + assert "grid_mapping_name" in manual_attrs + assert rasterio_attrs["grid_mapping_name"] == manual_attrs["grid_mapping_name"] + + # Both should have crs_wkt, but the format may differ (WKT1 vs WKT2) + # Verify both are valid WKT and represent the same CRS + assert "crs_wkt" in rasterio_attrs + assert "crs_wkt" in manual_attrs + + # Parse both WKT strings and verify they represent the same EPSG code + rasterio_crs = ProjCRS.from_wkt(str(rasterio_attrs["crs_wkt"])) + manual_crs = ProjCRS.from_wkt(str(manual_attrs["crs_wkt"])) + + # Both should have the same EPSG code + assert rasterio_crs.to_epsg() == test_epsg + assert manual_crs.to_epsg() == test_epsg + + # Verify the manual implementation has the expected CF attribute keys + # (pyproj's to_cf() generates these) + expected_cf_keys = {"grid_mapping_name", "crs_wkt"} + assert expected_cf_keys.issubset(set(manual_attrs.keys())) + + # The manual implementation should have all standard CF-compliant attributes + # that pyproj generates (there may be additional ones) + assert len(manual_attrs) >= len(expected_cf_keys) + @pytest.fixture def sample_dataset(s2_group_example: pathlib.Path) -> xr.Dataset: @@ -183,10 +340,17 @@ def test_create_multiscale_from_datatree( path="measurements/reflectance", ) + # TODO: Add spatial_ref coordinate to all resolution levels + # This should be done during re-encoding, but for now we skip it + # Uncomment when spatial_ref creation is implemented: + # add_spatial_ref_to_group(output_group["measurements/reflectance"]) + # Add multiscales metadata and spatial/proj attributes to the reflectance group - reflectance_group = output_group["measurements/reflectance"] - assert isinstance(reflectance_group, zarr.Group) - create_multiscales_metadata(reflectance_group) + # NOTE: This will fail with "Cannot determine native CRS" because spatial_ref + # is missing. For now, we skip this step in the test. + # reflectance_group = output_group["measurements/reflectance"] + # assert isinstance(reflectance_group, zarr.Group) + # create_multiscales_metadata(reflectance_group) observed_group = zarr.open_group(output_path, use_consolidated=False) @@ -202,9 +366,7 @@ def test_create_multiscale_from_datatree( # Write out the observed structure for analysis observed_output_path = Path("/tmp/observed_structure.json") - observed_output_path.write_text( - json.dumps(observed_structure_json, indent=2, sort_keys=True) - ) + observed_output_path.write_text(json.dumps(observed_structure_json, indent=2, sort_keys=True)) expected_structure_json = tuplify_json(json.loads(expected_structure_path.read_text())) expected_structure = GroupSpec(**expected_structure_json) @@ -244,8 +406,12 @@ def test_create_multiscale_from_datatree( # 3. Missing spatial_ref in observed (TODO: needs investigation) # Only compare measurements/reflectance subtree - o_keys_filtered = {k for k in o_keys if k.startswith("members/measurements/reflectance") or k == ""} - e_keys_filtered = {k for k in e_keys if k.startswith("members/measurements/reflectance") or k == ""} + o_keys_filtered = { + k for k in o_keys if k.startswith("members/measurements/reflectance") or k == "" + } + e_keys_filtered = { + k for k in e_keys if k.startswith("members/measurements/reflectance") or k == "" + } # Ignore spatial_ref keys o_keys_filtered = {k for k in o_keys_filtered if "spatial_ref" not in k} @@ -256,9 +422,9 @@ def test_create_multiscale_from_datatree( "members/measurements/reflectance/members/r20m/members/b08", "members/measurements/reflectance/members/r60m/members/b08", ] - o_keys_filtered = {k for k in o_keys_filtered if not any( - pattern in k for pattern in ignore_patterns - )} + o_keys_filtered = { + k for k in o_keys_filtered if not any(pattern in k for pattern in ignore_patterns) + } # Check that all of the keys are the same (after filtering) assert o_keys_filtered == e_keys_filtered, ( @@ -270,7 +436,6 @@ def test_create_multiscale_from_datatree( # Check that all values are the same for common keys common_keys = o_keys_filtered & e_keys_filtered mismatched_values = [ - k for k in common_keys - if expected_structure_flat.get(k) != observed_structure_flat.get(k) + k for k in common_keys if expected_structure_flat.get(k) != observed_structure_flat.get(k) ] assert mismatched_values == [], f"Value mismatches: {mismatched_values[:20]}" diff --git a/tests/test_s2_multiscale_geo_metadata.py b/tests/test_s2_multiscale_geo_metadata.py index 4a6cc0ad..98dd6edd 100644 --- a/tests/test_s2_multiscale_geo_metadata.py +++ b/tests/test_s2_multiscale_geo_metadata.py @@ -11,6 +11,7 @@ import pytest import xarray as xr import zarr +from pyproj import CRS from eopf_geozarr.s2_optimization.s2_multiscale import ( create_measurements_encoding, @@ -19,6 +20,29 @@ ) +def get_crs_from_dataset(ds: xr.Dataset) -> CRS | None: + """ + Extract CRS from a dataset. + + Tries multiple sources in priority order: + 1. Existing rio.crs + 2. proj:epsg in variable attributes + + Returns None if no CRS can be determined. + """ + # First try existing rio CRS + if hasattr(ds, "rio") and ds.rio.crs is not None: + return ds.rio.crs + + # Try to find EPSG in variable attributes + for var in ds.data_vars.values(): + if "proj:epsg" in var.attrs: + epsg_code = var.attrs["proj:epsg"] + return CRS.from_epsg(epsg_code) + + return None + + @pytest.fixture def sample_dataset_with_crs() -> xr.Dataset: """Create a sample dataset with CRS information.""" @@ -87,8 +111,12 @@ class TestWriteGeoMetadata: def test_write_geo_metadata_with_rio_crs(self, sample_dataset_with_crs: xr.Dataset) -> None: """Test _write_geo_metadata with dataset that has rioxarray CRS.""" + # Get CRS from dataset + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify CRS was written assert hasattr(sample_dataset_with_crs, "rio") @@ -106,8 +134,12 @@ def test_write_geo_metadata_with_epsg_attrs( or sample_dataset_with_epsg_attrs.rio.crs is None ) + # Get CRS from dataset attributes + crs = get_crs_from_dataset(sample_dataset_with_epsg_attrs) + assert crs is not None + # Call the method - write_geo_metadata(sample_dataset_with_epsg_attrs) + write_geo_metadata(sample_dataset_with_epsg_attrs, crs=crs) # Verify CRS was written from attributes assert hasattr(sample_dataset_with_epsg_attrs, "rio") @@ -120,21 +152,25 @@ def test_write_geo_metadata_no_crs(self, sample_dataset_no_crs: xr.Dataset) -> N # Verify initial state - no CRS assert not hasattr(sample_dataset_no_crs, "rio") or sample_dataset_no_crs.rio.crs is None - # Call the method - should not fail but also not add CRS - write_geo_metadata(sample_dataset_no_crs) + # Try to get CRS - should return None + crs = get_crs_from_dataset(sample_dataset_no_crs) - # Verify no CRS was added (method handles gracefully) - # The method should not fail even when no CRS is available - # This tests the robustness of the method + # Skip the test if no CRS is available (can't call write_geo_metadata without CRS) + if crs is None: + pytest.skip("No CRS available - write_geo_metadata requires a CRS parameter") def test_write_geo_metadata_custom_grid_mapping_name( self, sample_dataset_with_crs: xr.Dataset ) -> None: """Test _write_geo_metadata with custom grid_mapping variable name.""" + # Get CRS from dataset + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Call the method with custom grid mapping name custom_name = "custom_spatial_ref" - write_geo_metadata(sample_dataset_with_crs, custom_name) + write_geo_metadata(sample_dataset_with_crs, crs=crs, grid_mapping_var_name=custom_name) # Verify CRS was written assert hasattr(sample_dataset_with_crs, "rio") @@ -145,13 +181,17 @@ def test_write_geo_metadata_preserves_existing_data( ) -> None: """Test that _write_geo_metadata preserves existing data variables and coordinates.""" + # Get CRS from dataset + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Store original data original_vars = list(sample_dataset_with_crs.data_vars.keys()) original_coords = list(sample_dataset_with_crs.coords.keys()) original_b02_data = sample_dataset_with_crs["b02"].values.copy() # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify all original data is preserved assert list(sample_dataset_with_crs.data_vars.keys()) == original_vars @@ -163,24 +203,29 @@ def test_write_geo_metadata_empty_dataset(self) -> None: empty_ds = xr.Dataset({}, coords={}) - # Call the method - should handle gracefully - write_geo_metadata(empty_ds) + # Try to get CRS - should return None + crs = get_crs_from_dataset(empty_ds) - # Verify method doesn't fail with empty dataset - # This tests robustness + # Skip the test if no CRS is available + if crs is None: + pytest.skip("No CRS available - write_geo_metadata requires a CRS parameter") def test_write_geo_metadata_rio_write_crs_called( self, sample_dataset_with_crs: xr.Dataset ) -> None: """Test that rio.write_crs is called correctly.""" + # Get CRS from dataset + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Mock the rio.write_crs method with patch.object(sample_dataset_with_crs.rio, "write_crs") as mock_write_crs: # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify rio.write_crs was called with correct arguments - mock_write_crs.assert_called_once() + mock_write_crs.assert_called() call_args = mock_write_crs.call_args assert call_args[1]["inplace"] is True # inplace=True should be passed @@ -201,12 +246,18 @@ def test_write_geo_metadata_crs_from_multiple_sources(self) -> None: ds = ds.rio.write_crs("EPSG:4326") # Rio CRS ds["b08"].attrs["proj:epsg"] = 32632 # EPSG attribute + # Get CRS - should prioritize rio.crs + crs = get_crs_from_dataset(ds) + assert crs is not None + assert crs.to_epsg() == 4326 # Should be 4326 (rio CRS), not 32632 + # Call the method - write_geo_metadata(ds) + write_geo_metadata(ds, crs=crs) # Verify rio CRS was used (priority over attributes) assert ds.rio.crs.to_epsg() == 4326 # Should still be 4326, not 32632 + @pytest.mark.filterwarnings("ignore::RuntimeWarning") def test_write_geo_metadata_integration_with_stream_write(self, tmp_path: Path) -> None: """Test that _write_geo_metadata is properly integrated in _stream_write_dataset.""" @@ -223,6 +274,10 @@ def test_write_geo_metadata_integration_with_stream_write(self, tmp_path: Path) ds = xr.Dataset(data_vars, coords=coords) ds = ds.rio.write_crs("EPSG:32632") + # Get CRS for passing to stream_write_dataset + crs = get_crs_from_dataset(ds) + assert crs is not None + # Create encoding for the dataset encoding = create_measurements_encoding(ds, spatial_chunk=1024, enable_sharding=True) @@ -235,6 +290,7 @@ def test_write_geo_metadata_integration_with_stream_write(self, tmp_path: Path) group=zarr.create_group(tmp_path), encoding=encoding, enable_sharding=True, + crs=crs, ) # Re-open the written dataset to verify CRS was persisted @@ -271,8 +327,9 @@ def test_write_geo_metadata_invalid_crs( # Method should raise an exception for invalid CRS (normal behavior) from pyproj.exceptions import CRSError - with pytest.raises(CRSError): - write_geo_metadata(ds) + # Try to get CRS - should raise error with invalid EPSG + with pytest.raises((CRSError, ValueError, TypeError)): + get_crs_from_dataset(ds) def test_write_geo_metadata_mixed_crs_variables( self, @@ -295,10 +352,14 @@ def test_write_geo_metadata_mixed_crs_variables( ds["var1"].attrs["proj:epsg"] = 32632 ds["var2"].attrs["proj:epsg"] = 4326 - # Call the method (should use the first CRS found) - write_geo_metadata(ds) + # Get CRS (should use the first CRS found) + crs = get_crs_from_dataset(ds) + assert crs is not None + + # Call the method + write_geo_metadata(ds, crs=crs) - # Verify a CRS was applied (should be the first one found) + # Verify a CRS was applied assert hasattr(ds, "rio") def test_write_geo_metadata_maintains_dataset_attrs( @@ -313,8 +374,12 @@ def test_write_geo_metadata_maintains_dataset_attrs( original_attrs = sample_dataset_with_crs.attrs.copy() + # Get CRS from dataset + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify dataset attributes are preserved for key, value in original_attrs.items(): diff --git a/tests/test_zarrio.py b/tests/test_zarrio.py index c6f92736..49bd8d6d 100644 --- a/tests/test_zarrio.py +++ b/tests/test_zarrio.py @@ -2,14 +2,13 @@ from __future__ import annotations -from typing import Any - import numcodecs import pytest import zarr from zarr.core.dtype import Float32, UInt16 from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata +from eopf_geozarr.s2_optimization.s2_converter import array_reencoder from eopf_geozarr.zarrio import ( convert_compression, default_array_reencoder, @@ -95,7 +94,7 @@ def test_reencode_array_scale_offset(keep_scale_offset: bool) -> None: fill_value=0, attributes={"scale_factor": 0.1, "add_offset": 10, "dtype": ">u2"}, ) - observed = default_array_reencoder( + observed = array_reencoder( "test_array", v2_meta, config={"keep_scale_offset": keep_scale_offset} ) expect_dtype = v2_meta.dtype if keep_scale_offset else Float32() @@ -259,9 +258,7 @@ def test_reencode_group_with_chunk_reencoder() -> None: ) new_chunks = (25,) - def custom_array_encoder( - key: str, metadata: ArrayV2Metadata, *, config: Any - ) -> ArrayV3Metadata: + def custom_array_encoder(key: str, metadata: ArrayV2Metadata) -> ArrayV3Metadata: return ArrayV3Metadata( shape=metadata.shape, data_type=metadata.dtype, From a276bd6fe013eca4c195482f1b66f5ec1d0bd4f5 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 20 Jan 2026 10:10:34 +0100 Subject: [PATCH 53/59] exclude dtype from encoding when stripping scale - offset --- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 3 ++- tests/test_s2_converter_simplified.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index ff4c291e..4d8899a5 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -245,7 +245,8 @@ def create_measurements_encoding( keep_keys = XARRAY_ENCODING_KEYS - {"compressors", "shards", "chunks"} if not keep_scale_offset: - keep_keys = keep_keys - CF_SCALE_OFFSET_KEYS + # Remove scale/offset keys AND dtype to prevent transformation + keep_keys = keep_keys - CF_SCALE_OFFSET_KEYS - {"dtype"} for key in keep_keys: if key in var_data.encoding: diff --git a/tests/test_s2_converter_simplified.py b/tests/test_s2_converter_simplified.py index 25e5b026..50b27727 100644 --- a/tests/test_s2_converter_simplified.py +++ b/tests/test_s2_converter_simplified.py @@ -11,7 +11,7 @@ import pytest import xarray as xr import zarr -from structlog import cap_logs +from structlog.testing import capture_logs as cap_logs from eopf_geozarr.s2_optimization.s2_converter import ( convert_s2_optimized, From e881a35e0aba3f9f860213b0fd375b28b57563b1 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 20 Jan 2026 11:49:14 +0100 Subject: [PATCH 54/59] simplify test_s2_multiscale.py --- tests/test_s2_multiscale.py | 102 ++++++++---------------------------- 1 file changed, 23 insertions(+), 79 deletions(-) diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 4439b783..4536fdda 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -15,14 +15,13 @@ from pydantic_zarr.v3 import GroupSpec from structlog.testing import capture_logs +from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized from eopf_geozarr.s2_optimization.s2_multiscale import ( calculate_aligned_chunk_size, calculate_simple_shard_dimensions, create_downsampled_resolution_group, create_measurements_encoding, - create_multiscale_levels, ) -from eopf_geozarr.zarrio import reencode_group try: from pyproj import CRS as ProjCRS @@ -307,57 +306,33 @@ def test_calculate_aligned_chunk_size() -> None: @pytest.mark.filterwarnings("ignore:.*:RuntimeWarning") @pytest.mark.filterwarnings("ignore:.*:FutureWarning") @pytest.mark.filterwarnings("ignore:.*:UserWarning") -def test_create_multiscale_from_datatree( - s2_group_example: zarr.Group, +def test_convert_s2_optimized( + s2_group_example: Path, tmp_path: pathlib.Path, ) -> None: """Test multiscale creation from DataTree.""" - output_path = str(tmp_path / "output.zarr") input_group = zarr.open_group(s2_group_example) - output_group = zarr.create_group(output_path) - dt_input = xr.open_datatree(input_group.store, engine="zarr", chunks="auto") - # Capture log output using structlog's testing context manager - with capture_logs(): - # WORKAROUND: Re-encoding the full product fails because some arrays (quality/mask) - # have 'dtype' in attributes but not scale_factor/add_offset, which triggers - # validation error in extract_scale_offset. This is a known issue - dtype is - # standard array metadata, not necessarily CF scale/offset. - # For now, only reencode measurements/reflectance which has proper CF encoding. - input_reflectance = input_group["measurements/reflectance"] - assert isinstance(input_reflectance, zarr.Group) - - reencode_group( - group=input_reflectance, - store=output_group.store, - path="measurements/reflectance", - overwrite=True, - ) + # Use a separate output directory to avoid overwriting the input + output_path = tmp_path / "output.zarr" - # Then create multiscale levels for measurements/reflectance - create_multiscale_levels( - group=output_group, - path="measurements/reflectance", + dt_input = xr.open_datatree(input_group.store, engine="zarr", chunks="auto") + with capture_logs(): + convert_s2_optimized( + dt_input, + output_path=str(output_path), + enable_sharding=True, + spatial_chunk=256, + compression_level=1, + validate_output=False, + keep_scale_offset=False, ) - - # TODO: Add spatial_ref coordinate to all resolution levels - # This should be done during re-encoding, but for now we skip it - # Uncomment when spatial_ref creation is implemented: - # add_spatial_ref_to_group(output_group["measurements/reflectance"]) - - # Add multiscales metadata and spatial/proj attributes to the reflectance group - # NOTE: This will fail with "Cannot determine native CRS" because spatial_ref - # is missing. For now, we skip this step in the test. - # reflectance_group = output_group["measurements/reflectance"] - # assert isinstance(reflectance_group, zarr.Group) - # create_multiscales_metadata(reflectance_group) - - observed_group = zarr.open_group(output_path, use_consolidated=False) - - observed_structure_json = GroupSpec.from_zarr(observed_group).model_dump() + observed_group = zarr.open_group(output_path) + observed_structure = GroupSpec.from_zarr(observed_group) # Comparing JSON objects is sensitive to the difference between tuples and lists, but we # don't care about that here, so we convert all lists to tuples before creating the GroupSpec + observed_structure_json = observed_structure.model_dump() observed_structure = GroupSpec(**tuplify_json(observed_structure_json)) observed_structure_flat = observed_structure.to_flat() expected_structure_path = Path("tests/_test_data/optimized_geozarr_examples/") / ( @@ -395,46 +370,15 @@ def test_create_multiscale_from_datatree( o_keys = set(observed_structure_flat.keys()) e_keys = set(expected_structure_flat.keys()) - # TODO: Investigate why spatial_ref is not being created during re-encoding - # The source data has proj:epsg and proj:wkt2 attributes but no spatial_ref coordinate - # Expected output has spatial_ref - need to determine where this should be created - - # Filter out known acceptable differences: - # 1. Scope: observed only has measurements/reflectance, expected has full product - # (working around extract_scale_offset validation issue with dtype attribute) - # 2. Extra b08 in r20m/r60m in observed (intended behavior for now) - # 3. Missing spatial_ref in observed (TODO: needs investigation) - - # Only compare measurements/reflectance subtree - o_keys_filtered = { - k for k in o_keys if k.startswith("members/measurements/reflectance") or k == "" - } - e_keys_filtered = { - k for k in e_keys if k.startswith("members/measurements/reflectance") or k == "" - } - - # Ignore spatial_ref keys - o_keys_filtered = {k for k in o_keys_filtered if "spatial_ref" not in k} - e_keys_filtered = {k for k in e_keys_filtered if "spatial_ref" not in k} - - # Ignore extra b08 in r20m/r60m - ignore_patterns = [ - "members/measurements/reflectance/members/r20m/members/b08", - "members/measurements/reflectance/members/r60m/members/b08", - ] - o_keys_filtered = { - k for k in o_keys_filtered if not any(pattern in k for pattern in ignore_patterns) - } - - # Check that all of the keys are the same (after filtering) - assert o_keys_filtered == e_keys_filtered, ( + # Check that all of the keys are the same + assert o_keys == e_keys, ( f"Key mismatch after filtering.\n" - f"Extra in observed: {sorted(o_keys_filtered - e_keys_filtered)[:20]}\n" - f"Missing in observed: {sorted(e_keys_filtered - o_keys_filtered)[:20]}" + f"Extra in observed: {sorted(o_keys - e_keys)[:20]}\n" + f"Missing in observed: {sorted(e_keys - o_keys)[:20]}" ) # Check that all values are the same for common keys - common_keys = o_keys_filtered & e_keys_filtered + common_keys = o_keys & e_keys mismatched_values = [ k for k in common_keys if expected_structure_flat.get(k) != observed_structure_flat.get(k) ] From 3ea6b6844c776560ba2fde4e7dc609df727e71a8 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Tue, 20 Jan 2026 11:51:49 +0100 Subject: [PATCH 55/59] get latest example JSON documents --- ...041_N0511_R122_T32TQM_20251008T122613.json | 272 +- ...309_N0511_R108_T32TLQ_20250113T122458.json | 184 +- ...131_N0511_R037_T29TPF_20250811T152216.json | 272 +- ...041_N0511_R122_T32TQM_20251008T122613.json | 2169 +++------- ...309_N0511_R108_T32TLQ_20250113T122458.json | 3574 ++++------------- ...131_N0511_R037_T29TPF_20250811T152216.json | 2169 +++------- 6 files changed, 2348 insertions(+), 6292 deletions(-) diff --git a/tests/_test_data/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/_test_data/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index b8f02382..103f740c 100644 --- a/tests/_test_data/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/_test_data/geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -135,7 +135,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -192,7 +192,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -249,7 +249,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -399,7 +399,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -456,7 +456,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -513,7 +513,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -570,7 +570,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -627,7 +627,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -684,7 +684,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -741,7 +741,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -841,7 +841,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -907,7 +907,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -964,7 +964,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1021,7 +1021,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1171,7 +1171,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1228,7 +1228,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1285,7 +1285,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1342,7 +1342,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1399,7 +1399,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1456,7 +1456,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1513,7 +1513,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1613,7 +1613,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1726,7 +1726,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1830,7 +1830,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1934,7 +1934,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2131,7 +2131,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2235,7 +2235,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2339,7 +2339,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2443,7 +2443,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2547,7 +2547,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2651,7 +2651,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2755,7 +2755,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2902,7 +2902,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3206,7 +3206,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3263,7 +3263,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3320,7 +3320,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3377,7 +3377,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3579,7 +3579,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3636,7 +3636,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3693,7 +3693,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3750,7 +3750,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3952,7 +3952,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4009,7 +4009,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4066,7 +4066,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4123,7 +4123,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4382,7 +4382,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4439,7 +4439,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4496,7 +4496,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4745,7 +4745,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4849,7 +4849,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4953,7 +4953,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5057,7 +5057,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5259,7 +5259,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5316,7 +5316,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5373,7 +5373,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5430,7 +5430,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5664,7 +5664,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5721,7 +5721,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5871,7 +5871,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5928,7 +5928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5985,7 +5985,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6042,7 +6042,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6099,7 +6099,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6156,7 +6156,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6213,7 +6213,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6313,7 +6313,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6379,7 +6379,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6436,7 +6436,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6586,7 +6586,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6643,7 +6643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6700,7 +6700,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6757,7 +6757,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6814,7 +6814,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6871,7 +6871,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6928,7 +6928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7028,7 +7028,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7094,7 +7094,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7151,7 +7151,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7301,7 +7301,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7358,7 +7358,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7415,7 +7415,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7472,7 +7472,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7529,7 +7529,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7586,7 +7586,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7643,7 +7643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7743,7 +7743,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7856,7 +7856,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7960,7 +7960,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8157,7 +8157,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8261,7 +8261,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8365,7 +8365,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8469,7 +8469,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8573,7 +8573,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8677,7 +8677,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8781,7 +8781,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8928,7 +8928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8994,7 +8994,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9051,7 +9051,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9201,7 +9201,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9258,7 +9258,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9315,7 +9315,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9372,7 +9372,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9429,7 +9429,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9486,7 +9486,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9543,7 +9543,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9643,7 +9643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14447,7 +14447,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14487,7 +14487,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14612,7 +14612,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14714,7 +14714,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14816,7 +14816,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14918,7 +14918,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15020,7 +15020,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15070,7 +15070,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15159,7 +15159,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15261,7 +15261,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15418,7 +15418,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15509,7 +15509,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15611,7 +15611,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15713,7 +15713,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15815,7 +15815,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15877,7 +15877,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -15986,7 +15986,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16025,7 +16025,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16114,7 +16114,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16216,7 +16216,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16353,7 +16353,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16490,7 +16490,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16542,7 +16542,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16633,7 +16633,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16735,7 +16735,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16847,7 +16847,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16923,7 +16923,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -16986,7 +16986,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -17131,7 +17131,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", diff --git a/tests/_test_data/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json b/tests/_test_data/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json index b142b771..75350637 100644 --- a/tests/_test_data/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json +++ b/tests/_test_data/geozarr_examples/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.json @@ -175,7 +175,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -325,7 +325,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -382,7 +382,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -439,7 +439,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -496,7 +496,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -596,7 +596,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -662,7 +662,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -812,7 +812,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -869,7 +869,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -926,7 +926,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -983,7 +983,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1083,7 +1083,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1149,7 +1149,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1299,7 +1299,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1356,7 +1356,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1413,7 +1413,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1470,7 +1470,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1570,7 +1570,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1679,7 +1679,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1872,7 +1872,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1972,7 +1972,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2072,7 +2072,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2172,7 +2172,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2315,7 +2315,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2381,7 +2381,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2531,7 +2531,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2588,7 +2588,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2645,7 +2645,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2702,7 +2702,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2802,7 +2802,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2953,7 +2953,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3010,7 +3010,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3203,7 +3203,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3269,7 +3269,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3326,7 +3326,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3519,7 +3519,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3628,7 +3628,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3728,7 +3728,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3964,7 +3964,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4268,7 +4268,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4382,7 +4382,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4439,7 +4439,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4641,7 +4641,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4698,7 +4698,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4755,7 +4755,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4812,7 +4812,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5014,7 +5014,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5071,7 +5071,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5128,7 +5128,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5185,7 +5185,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5387,7 +5387,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5444,7 +5444,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5501,7 +5501,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5558,7 +5558,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5803,7 +5803,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5903,7 +5903,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6003,7 +6003,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6103,7 +6103,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6305,7 +6305,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6362,7 +6362,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6419,7 +6419,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6476,7 +6476,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8115,7 +8115,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8219,7 +8219,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8282,7 +8282,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8417,7 +8417,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8612,7 +8612,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8699,7 +8699,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8735,7 +8735,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8821,7 +8821,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8920,7 +8920,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9019,7 +9019,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9069,7 +9069,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9190,7 +9190,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9289,7 +9289,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9388,7 +9388,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9487,7 +9487,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9624,7 +9624,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9723,7 +9723,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9822,7 +9822,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9912,7 +9912,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9963,7 +9963,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10003,7 +10003,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10091,7 +10091,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10225,7 +10225,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10358,7 +10358,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10408,7 +10408,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10447,7 +10447,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10573,7 +10573,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10672,7 +10672,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10809,7 +10809,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", diff --git a/tests/_test_data/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json b/tests/_test_data/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json index ff7320cb..53df2069 100644 --- a/tests/_test_data/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json +++ b/tests/_test_data/geozarr_examples/S2C_MSIL2A_20250811T112131_N0511_R037_T29TPF_20250811T152216.json @@ -135,7 +135,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -192,7 +192,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -249,7 +249,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -399,7 +399,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -456,7 +456,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -513,7 +513,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -570,7 +570,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -627,7 +627,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -684,7 +684,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -741,7 +741,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -841,7 +841,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -907,7 +907,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -964,7 +964,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1021,7 +1021,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1171,7 +1171,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1228,7 +1228,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1285,7 +1285,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1342,7 +1342,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1399,7 +1399,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1456,7 +1456,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1513,7 +1513,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1613,7 +1613,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1726,7 +1726,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1830,7 +1830,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -1934,7 +1934,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2131,7 +2131,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2235,7 +2235,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2339,7 +2339,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2443,7 +2443,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2547,7 +2547,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2651,7 +2651,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2755,7 +2755,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -2902,7 +2902,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3206,7 +3206,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3263,7 +3263,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3320,7 +3320,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3377,7 +3377,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3579,7 +3579,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3636,7 +3636,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3693,7 +3693,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3750,7 +3750,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -3952,7 +3952,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4009,7 +4009,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4066,7 +4066,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4123,7 +4123,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4325,7 +4325,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4382,7 +4382,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4439,7 +4439,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4496,7 +4496,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4745,7 +4745,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4849,7 +4849,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -4953,7 +4953,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5057,7 +5057,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5259,7 +5259,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5316,7 +5316,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5373,7 +5373,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5430,7 +5430,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5664,7 +5664,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5721,7 +5721,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5871,7 +5871,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5928,7 +5928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -5985,7 +5985,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6042,7 +6042,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6099,7 +6099,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6156,7 +6156,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6213,7 +6213,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6313,7 +6313,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6379,7 +6379,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6436,7 +6436,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6586,7 +6586,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6643,7 +6643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6700,7 +6700,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6757,7 +6757,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6814,7 +6814,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6871,7 +6871,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -6928,7 +6928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7028,7 +7028,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7094,7 +7094,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7151,7 +7151,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7301,7 +7301,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7358,7 +7358,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7415,7 +7415,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7472,7 +7472,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7529,7 +7529,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7586,7 +7586,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7643,7 +7643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7743,7 +7743,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7856,7 +7856,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -7960,7 +7960,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8157,7 +8157,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8261,7 +8261,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8365,7 +8365,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8469,7 +8469,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8573,7 +8573,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8677,7 +8677,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8781,7 +8781,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8928,7 +8928,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -8994,7 +8994,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9051,7 +9051,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9201,7 +9201,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9258,7 +9258,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9315,7 +9315,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9372,7 +9372,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9429,7 +9429,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9486,7 +9486,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9543,7 +9543,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9643,7 +9643,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9721,7 +9721,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9810,7 +9810,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9863,7 +9863,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -9989,7 +9989,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10126,7 +10126,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10212,7 +10212,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10251,7 +10251,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10340,7 +10340,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10442,7 +10442,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10579,7 +10579,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10641,7 +10641,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10681,7 +10681,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10806,7 +10806,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -10908,7 +10908,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11045,7 +11045,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11147,7 +11147,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11249,7 +11249,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11351,7 +11351,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11401,7 +11401,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11490,7 +11490,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11592,7 +11592,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11729,7 +11729,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11781,7 +11781,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -11872,7 +11872,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -12009,7 +12009,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -13951,7 +13951,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14059,7 +14059,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14126,7 +14126,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", @@ -14300,7 +14300,7 @@ "separator": "/" } }, - "fill_value": "NaN", + "fill_value": 0.0, "codecs": [ { "name": "bytes", diff --git a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 9e78fea6..f85b3438 100644 --- a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -29,16 +29,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 28 - }, - "name": "blosc" } ], "data_type": { @@ -80,16 +70,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 12 - }, - "name": "blosc" } ], "data_type": { @@ -131,16 +111,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -157,6 +127,7 @@ }, "mean_sun_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle" @@ -165,7 +136,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN }, "unit": "deg" }, @@ -195,7 +166,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -205,7 +176,7 @@ "dimension_names": [ "angle" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 2 @@ -215,6 +186,7 @@ }, "mean_viewing_incidence_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -225,7 +197,7 @@ "angle" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN }, "unit": "deg" }, @@ -256,7 +228,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -267,7 +239,7 @@ "band", "angle" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 13, @@ -278,6 +250,7 @@ }, "sun_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -290,7 +263,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN } }, "chunk_grid": { @@ -321,7 +294,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -333,7 +306,7 @@ "y", "x" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 2, @@ -345,6 +318,7 @@ }, "viewing_incidence_angles": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "angle", @@ -361,7 +335,7 @@ "x" ], "eopf_is_masked": true, - "fill_value": "NaN" + "fill_value": NaN } }, "chunk_grid": { @@ -394,7 +368,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 8 }, "name": "blosc" @@ -408,7 +382,7 @@ "y", "x" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 13, @@ -442,16 +416,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -488,16 +452,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -589,7 +543,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -673,7 +627,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -757,7 +711,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -841,7 +795,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -883,16 +837,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -929,16 +873,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1024,7 +958,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1108,7 +1042,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1192,7 +1126,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1276,7 +1210,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1360,7 +1294,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1444,7 +1378,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1486,16 +1420,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1532,16 +1456,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1627,7 +1541,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1711,7 +1625,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1795,7 +1709,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -1837,16 +1751,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -1883,16 +1787,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2005,7 +1899,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2047,16 +1941,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2093,16 +1977,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2191,7 +2065,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2233,16 +2107,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2279,16 +2143,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2370,7 +2224,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 1 }, "name": "blosc" @@ -2412,16 +2266,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2458,16 +2302,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -2537,6 +2371,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2552,7 +2387,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 1240nm", "standard_name": "unknown", "units": "~" @@ -2589,7 +2424,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2600,7 +2435,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2638,6 +2473,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2653,7 +2489,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 469nm", "standard_name": "unknown", "units": "~" @@ -2690,7 +2526,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2701,7 +2537,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2739,6 +2575,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2754,7 +2591,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -2791,7 +2628,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2802,7 +2639,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2840,6 +2677,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2855,7 +2693,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 670nm", "standard_name": "unknown", "units": "~" @@ -2892,7 +2730,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -2903,7 +2741,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -2941,6 +2779,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -2956,7 +2795,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total Aerosol Optical Depth at 865nm", "standard_name": "unknown", "units": "~" @@ -2993,7 +2832,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3004,7 +2843,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3042,6 +2881,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3057,7 +2897,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Black Carbon Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3094,7 +2934,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3105,7 +2945,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3143,6 +2983,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3158,7 +2999,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Dust Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3195,7 +3036,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3206,7 +3047,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3217,6 +3058,7 @@ }, "isobaricInhPa": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3244,8 +3086,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3253,6 +3095,7 @@ }, "latitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -3278,23 +3121,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -3304,6 +3137,7 @@ }, "longitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -3328,23 +3162,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -3379,7 +3203,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3415,6 +3239,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3430,7 +3255,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Organic Matter Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3467,7 +3292,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3478,7 +3303,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3516,6 +3341,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3531,7 +3357,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Sea Salt Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3568,7 +3394,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3579,7 +3405,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3593,7 +3419,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "minutes" + "units": "days" }, "chunk_grid": { "configuration": { @@ -3616,7 +3442,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3652,6 +3478,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "~", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3667,7 +3494,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Sulphate Aerosol Optical Depth at 550nm", "standard_name": "unknown", "units": "~" @@ -3704,7 +3531,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3715,7 +3542,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3726,6 +3553,7 @@ }, "surface": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -3750,8 +3578,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3785,7 +3613,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3820,7 +3648,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -3856,6 +3684,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m**2 s**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -3871,7 +3700,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Geopotential", "standard_name": "geopotential", "units": "m**2 s**-2" @@ -3908,7 +3737,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -3919,7 +3748,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -3945,6 +3774,7 @@ "members": { "isobaricInhPa": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "pressure", "positive": "down", "standard_name": "air_pressure", @@ -3972,8 +3802,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -3981,6 +3811,7 @@ }, "latitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "latitude", "standard_name": "latitude", "stored_direction": "decreasing", @@ -4006,23 +3837,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "latitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -4032,6 +3853,7 @@ }, "longitude": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "longitude", "standard_name": "longitude", "units": "degrees_east" @@ -4056,23 +3878,13 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "float64", "dimension_names": [ "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9 @@ -4109,6 +3921,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "Pa", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4124,7 +3937,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Mean sea level pressure", "standard_name": "air_pressure_at_mean_sea_level", "units": "Pa" @@ -4161,7 +3974,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4172,7 +3985,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4208,7 +4021,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4244,6 +4057,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "isobaricInhPa", "GRIB_units": "%", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4259,7 +4073,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Relative humidity", "standard_name": "relative_humidity", "units": "%" @@ -4296,7 +4110,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4307,7 +4121,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4321,7 +4135,7 @@ "dtype": "timedelta64[ns]", "long_name": "time since forecast_reference_time", "standard_name": "forecast_period", - "units": "minutes" + "units": "days" }, "chunk_grid": { "configuration": { @@ -4344,7 +4158,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4353,6 +4167,7 @@ }, "surface": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "long_name": "original GRIB coordinate for key: level(surface)", "units": "1" }, @@ -4377,8 +4192,8 @@ } ], "data_type": "float64", - "dimension_names": [], - "fill_value": "NaN", + "dimension_names": null, + "fill_value": 0.0, "node_type": "array", "shape": [], "storage_transformers": [], @@ -4413,6 +4228,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4428,7 +4244,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total column ozone", "standard_name": "atmosphere_mass_content_of_ozone", "units": "kg m**-2" @@ -4465,7 +4281,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4476,7 +4292,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4514,6 +4330,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "kg m**-2", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4529,7 +4346,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "Total column vertically-integrated water vapour", "standard_name": "lwe_thickness_of_atmosphere_mass_content_of_water_vapor", "units": "kg m**-2" @@ -4566,7 +4383,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4577,7 +4394,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4614,7 +4431,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -4650,6 +4467,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4665,7 +4483,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "10 metre U wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4702,7 +4520,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4713,7 +4531,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4751,6 +4569,7 @@ "GRIB_totalNumber": 0, "GRIB_typeOfLevel": "surface", "GRIB_units": "m s**-1", + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "coordinates": [ "latitude", @@ -4766,7 +4585,7 @@ "longitude" ], "eopf_is_masked": true, - "fill_value": "NaN", + "fill_value": NaN, "long_name": "10 metre V wind component", "standard_name": "unknown", "units": "m s**-1" @@ -4803,7 +4622,7 @@ "blocksize": 0, "clevel": 3, "cname": "zstd", - "shuffle": "bitshuffle", + "shuffle": "shuffle", "typesize": 4 }, "name": "blosc" @@ -4814,7 +4633,7 @@ "latitude", "longitude" ], - "fill_value": "NaN", + "fill_value": 0.0, "node_type": "array", "shape": [ 9, @@ -4851,7 +4670,7 @@ } ], "data_type": "int64", - "dimension_names": [], + "dimension_names": null, "fill_value": 0, "node_type": "array", "shape": [], @@ -5071,15 +4890,15 @@ { "cellSize": 0.0, "id": "r120m", - "matrixHeight": 5, - "matrixWidth": 5, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 915, + "tileWidth": 915 }, { "cellSize": 0.0, @@ -5091,8 +4910,8 @@ 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 256, + "tileWidth": 256 }, { "cellSize": 0.0, @@ -5695,6 +5514,13 @@ "endian": "little" }, "name": "bytes" + }, + { + "configuration": { + "checksum": false, + "level": 0 + }, + "name": "zstd" } ], "data_type": "int64", @@ -5710,7 +5536,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256 + 10980 ] }, "name": "regular" @@ -5727,16 +5553,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -5756,7 +5572,7 @@ "chunk_grid": { "configuration": { "chunk_shape": [ - 256 + 10980 ] }, "name": "regular" @@ -5773,16 +5589,6 @@ "endian": "little" }, "name": "bytes" - }, - { - "configuration": { - "blocksize": 0, - "clevel": 3, - "cname": "zstd", - "shuffle": "bitshuffle", - "typesize": 8 - }, - "name": "blosc" } ], "data_type": "int64", @@ -5824,6 +5630,7 @@ "members": { "b01": { "attributes": { + "_FillValue": "AAAAAAAA+H8=", "_eopf_attrs": { "add_offset": -0.1, "coordinates": [ @@ -5842,8 +5649,6 @@ "scale_factor": 0.0001, "units": "digital_counts" }, - "add_offset": -0.1, - "coordinates": "spatial_ref", "dtype": " Date: Tue, 20 Jan 2026 13:58:38 +0100 Subject: [PATCH 56/59] update to include b08 at lower scale levels --- ...041_N0511_R122_T32TQM_20251008T122613.json | 639 +- ...309_N0511_R108_T32TLQ_20250113T122458.json | 6058 +++++++++++++++-- ...131_N0511_R037_T29TPF_20250811T152216.json | 639 +- 3 files changed, 6625 insertions(+), 711 deletions(-) diff --git a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index f85b3438..e6f07975 100644 --- a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -6482,6 +6482,129 @@ "storage_transformers": [], "zarr_format": 3 }, + "b08": { + "attributes": { + "_FillValue": "AAAAAAAA+H8=", + "_eopf_attrs": { + "add_offset": -0.1, + "coordinates": [ + "x", + "y" + ], + "dimensions": [ + "y", + "x" + ], + "dtype": " zarr.Group: """ source_path: pathlib.Path = request.param store = {} - return GroupSpecV3(**read_json(source_path)).to_zarr(store, path="") + # Need to ignore warnings about fixed-length string dtypes + with pytest.warns(zarr.errors.UnstableSpecificationWarning): + return GroupSpecV3(**read_json(source_path)).to_zarr(store, path="") @pytest.fixture(params=optimized_geozarr_example_paths, ids=get_stem) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 6a417691..210c9d21 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -23,7 +23,7 @@ def test_convert_s2_optimized(s2_group_example: Path, tmp_path: Path) -> None: """ Test the convert-s2-optimized CLI command on a local copy of sentinel data """ - output_path = tmp_path + output_path = tmp_path / (s2_group_example.stem + '_geozarr.zarr') cmd = [ "python", diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 4536fdda..0a8c262d 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -313,7 +313,6 @@ def test_convert_s2_optimized( """Test multiscale creation from DataTree.""" input_group = zarr.open_group(s2_group_example) - # Use a separate output directory to avoid overwriting the input output_path = tmp_path / "output.zarr" dt_input = xr.open_datatree(input_group.store, engine="zarr", chunks="auto") @@ -339,6 +338,8 @@ def test_convert_s2_optimized( s2_group_example.stem + ".json" ) + + # Write out the observed structure for analysis observed_output_path = Path("/tmp/observed_structure.json") observed_output_path.write_text(json.dumps(observed_structure_json, indent=2, sort_keys=True)) @@ -347,6 +348,12 @@ def test_convert_s2_optimized( expected_structure = GroupSpec(**expected_structure_json) expected_structure_flat = expected_structure.to_flat() + # Uncomment this section to write out the expected structure from the observed structure + # This is useful when the expected structure needs to be updated + # expected_structure_path.write_text( + # json.dumps(observed_structure_json, indent=2, sort_keys=True) + #) + # check that all multiscale levels have the same data type # this check is redundant with the later check, but it's expedient to check this here. # eventually this check should be spun out into its own test @@ -370,16 +377,44 @@ def test_convert_s2_optimized( o_keys = set(observed_structure_flat.keys()) e_keys = set(expected_structure_flat.keys()) + # Write out the expected structure to the temporary test directory + # This is useful when the expected structure needs to be updated + + observed_tree_path = tmp_path/"observed_tree.json" + observed_flat_path = tmp_path/"observed_flat.json" + expected_flat_path = tmp_path/"expected_flat.json" + + observed_tree_path.write_text( + json.dumps(observed_structure_json, indent=2, sort_keys=True) + ) + + observed_flat_path.write_text(json.dumps({k: v.model_dump() for k,v in observed_structure_flat.items()})) + expected_flat_path.write_text(json.dumps({k: v.model_dump() for k,v in expected_structure_flat.items()})) + # Check that all of the keys are the same - assert o_keys == e_keys, ( - f"Key mismatch after filtering.\n" + if o_keys != e_keys: + (tmp_path / "extra_observed_keys.json").write_text(json.dumps({k: v.model_dump() for k, v in observed_structure_flat if k in o_keys - e_keys}, indent=2)) + (tmp_path / "extra_expected_keys.json").write_text(json.dumps({k: v.model_dump() for k, v in expected_structure_flat if k in e_keys - o_keys}, indent=2)) + + raise AssertionError( + f"Key mismatch.\n" f"Extra in observed: {sorted(o_keys - e_keys)[:20]}\n" - f"Missing in observed: {sorted(e_keys - o_keys)[:20]}" - ) + f"Missing in observed: {sorted(e_keys - o_keys)[:20]}\n" + f"Check {observed_tree_path!s} to see the observed JSON structure." + ) # Check that all values are the same for common keys common_keys = o_keys & e_keys - mismatched_values = [ + mismatch_values = [ k for k in common_keys if expected_structure_flat.get(k) != observed_structure_flat.get(k) ] - assert mismatched_values == [], f"Value mismatches: {mismatched_values[:20]}" + + if mismatch_values != []: + mismatch_values_path = (tmp_path / "mismatch_values.json") + mismatch_values_path.write_text( + json.dumps( + {k: {"expected": expected_structure_flat[k].model_dump(), "observed": observed_structure_flat[k].model_dump()} for k in mismatch_values}, indent=2)) + raise AssertionError( + f"Value mismatches: {mismatch_values[:20]}\n" + f"Check {mismatch_values_path!s} to see the observed JSON structure." + ) From 6b94699af109c625367445a50fcc3acbe15d2003 Mon Sep 17 00:00:00 2001 From: Davis Vann Bennett Date: Fri, 20 Mar 2026 10:59:17 +0100 Subject: [PATCH 58/59] make workflow far more declarative --- .vscode/launch.json | 2 +- src/eopf_geozarr/data_api/s2.py | 22 +- .../s2_optimization/s2_converter.py | 163 +- .../s2_optimization/s2_multiscale.py | 385 +-- src/eopf_geozarr/s2_optimization/s2_plan.py | 726 +++++ ...041_N0511_R122_T32TQM_20251008T122613.json | 166 +- ...309_N0511_R108_T32TLQ_20250113T122458.json | 145 +- ...131_N0511_R037_T29TPF_20250811T152216.json | 166 +- tests/test_s2_multiscale.py | 62 +- tests/test_s2_multiscale_geo_metadata.py | 24 +- uv.lock | 2790 ++++++++--------- 11 files changed, 2465 insertions(+), 2186 deletions(-) create mode 100644 src/eopf_geozarr/s2_optimization/s2_plan.py diff --git a/.vscode/launch.json b/.vscode/launch.json index 5cf43fa1..9d4e71e3 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -197,7 +197,7 @@ "args": [ "convert-s2-optimized", // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202509-s02msil2a/08/products/cpm_v256/S2A_MSIL2A_20250908T100041_N0511_R122_T32TQM_20250908T115116.zarr", - "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/15/products/cpm_v262/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr", + // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/15/products/cpm_v262/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr", // "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202512-s02msil2a-eu/16/products/cpm_v262/S2B_MSIL2A_20251216T102339_N0511_R065_T32TNS_20251216T123617.zarr", // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/16/products/cpm_v262/S2A_MSIL2A_20251116T085431_N0511_R107_T35SQD_20251116T103813.zarr", // "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202512-s02msil2a-eu/27/products/cpm_v262/S2A_MSIL2A_20251227T100441_N0511_R122_T33TVF_20251227T121715.zarr", diff --git a/src/eopf_geozarr/data_api/s2.py b/src/eopf_geozarr/data_api/s2.py index 1f3ed8ea..c9933ecb 100644 --- a/src/eopf_geozarr/data_api/s2.py +++ b/src/eopf_geozarr/data_api/s2.py @@ -282,7 +282,7 @@ class Sentinel2DataArrayAttrs(BaseDataArrayAttrs): class Sentinel2RootAttrs(BaseModel): """Root-level attributes for Sentinel-2 DataTree.""" - other_metadata: OtherMetadataDict + other_metadata: dict[str, object] # unvalidated — keys vary across CPM versions stac_discovery: dict[str, object] # no validation @@ -533,8 +533,18 @@ def conditions(self) -> Sentinel2ConditionsGroup: @property def crs(self) -> CRS: - """Get the coordinate reference system (CRS) for this product""" - crs_code = self.attributes.other_metadata["horizontal_CRS_code"] - # Handle both "EPSG:32635" and "32635" formats - crs_code = crs_code.removeprefix("EPSG:") # Remove "EPSG:" prefix - return CRS.from_epsg(int(crs_code)) + """Get the coordinate reference system (CRS) for this product. + + Handles both CPM >= 2.6.0 (horizontal_CRS_code) and older formats + (horizontal_crs_code), plus both "EPSG:32635" and "32635" string formats. + """ + om = self.attributes.other_metadata + crs_code = om.get("horizontal_CRS_code") or om.get("horizontal_crs_code") + if crs_code is None: + raise ValueError( + f"No CRS code found in other_metadata. Available keys: {sorted(om.keys())}" + ) + # Handle both "EPSG:32635" and "32635" formats, and integer values + if isinstance(crs_code, str): + return CRS.from_epsg(int(crs_code.split(":")[-1])) + return CRS.from_epsg(int(str(crs_code))) diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 0cd0301e..8e765e79 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -5,6 +5,7 @@ from __future__ import annotations import time +from collections import defaultdict from functools import partial from pathlib import Path from typing import Any, Final, Literal, TypedDict @@ -24,15 +25,14 @@ from eopf_geozarr.data_api.geozarr.common import extract_scale_offset from eopf_geozarr.data_api.s1 import Sentinel1Root from eopf_geozarr.data_api.s2 import Sentinel2Root -from eopf_geozarr.zarrio import ArrayReencoderConfig, convert_compression, reencode_group +from eopf_geozarr.zarrio import ArrayReencoderConfig, convert_compression from .s2_multiscale import ( auto_chunks, calculate_simple_shard_dimensions, - create_multiscale_levels, - create_multiscales_metadata, - write_geo_metadata, + create_downsampled_resolution_group, ) +from .s2_plan import FillSpec, plan_s2_hierarchy log = structlog.get_logger() @@ -157,31 +157,6 @@ class ConvertS2Params(TypedDict): max_retries: int -def add_crs_and_grid_mapping(group: zarr.Group, crs: CRS) -> None: - """ - Add crs and grid mapping elements to a dataset. - """ - ds = xr.open_dataset(group.store, group=group.path, engine="zarr", consolidated=False) - write_geo_metadata(ds, crs=crs) - - for var in ds.data_vars: - new_attrs = ds[var].attrs.copy() - new_attrs["coordinates"] = "spatial_ref" - group[var].attrs.update(new_attrs | {"grid_mapping": "spatial_ref"}) - - group.create_array( - "spatial_ref", - shape=ds["spatial_ref"].shape, - dtype=ds["spatial_ref"].dtype, - attributes=ds["spatial_ref"].attrs, - compressors=None, - filters=None, - ) - - # Set grid_mapping attribute on the group itself - group.attrs.update({"grid_mapping": "spatial_ref"}) - - def array_reencoder( key: str, metadata: ArrayV2Metadata, @@ -303,6 +278,94 @@ def array_reencoder( ) +def fill_s2_hierarchy( + tree: dict[str, zarr.Array | zarr.Group], + source_group: zarr.Group, + fill_specs: dict[str, FillSpec], +) -> None: + """ + Fill array data into a pre-created zarr hierarchy. + + Processes fill specs in dependency order: + 1. Copy source arrays (native data) + 2. Write spatial_ref scalars + 3. Write pre-computed coordinate values + 4. Downsample derived level arrays (finest-to-coarsest) + """ + # Phase 1: Copy source arrays and simple fills + for array_path, spec in fill_specs.items(): + if spec.source_path is not None: + log.info("Copying array %s from source", array_path) + source_array = source_group[spec.source_path] + target_array = tree[array_path] + target_array[...] = source_array[...] + elif spec.is_spatial_ref: + log.info("Writing spatial_ref at %s", array_path) + tree[array_path][...] = 0 + elif spec.computed_values is not None: + log.info("Writing pre-computed values at %s", array_path) + tree[array_path][...] = spec.computed_values + + # Phase 2: Downsample derived level arrays (ordered by dependency) + # Group downsample specs by target level upfront + level_groups: dict[str, list[tuple[str, FillSpec]]] = defaultdict(list) + for path, spec in fill_specs.items(): + if spec.downsample_from is not None: + target_group_path = path.rsplit("/", 1)[0] + level_groups[target_group_path].append((path, spec)) + + # Sort levels by resolution priority (finest to coarsest) + res_priority = {"r20m": -2, "r60m": -1, "r120m": 0, "r360m": 1, "r720m": 2} + + def level_sort_key(group_path: str) -> int: + for res_name, priority in res_priority.items(): + if f"/{res_name}" in group_path: + return priority + return 0 + + sorted_levels = sorted(level_groups.keys(), key=level_sort_key) + + for target_group_path in sorted_levels: + specs_for_level = level_groups[target_group_path] + # All specs in a level share the same parent group and scale factor + first_spec = specs_for_level[0][1] + assert first_spec.downsample_from is not None + parent_group_path = first_spec.downsample_from.rsplit("/", 1)[0] + + # Open parent as xarray Dataset for downsampling + parent_group = tree[parent_group_path] + assert isinstance(parent_group, zarr.Group) + parent_ds = xr.open_dataset(parent_group.store, group=parent_group.path, engine="zarr") + + # Collect all arrays for this level + level_vars: dict[str, xr.DataArray] = {} + for array_path, spec in specs_for_level: + assert spec.downsample_from is not None + var_name = array_path.rsplit("/", 1)[1] + parent_var_name = spec.downsample_from.rsplit("/", 1)[1] + if parent_var_name in parent_ds.data_vars: + level_vars[var_name] = parent_ds[parent_var_name] + + if level_vars: + log.info( + "Downsampling %d arrays into %s (factor=%d)", + len(level_vars), + target_group_path, + first_spec.scale_factor, + ) + source_ds = xr.Dataset(data_vars=level_vars) + downsampled_ds = create_downsampled_resolution_group( + source_ds, factor=first_spec.scale_factor + ) + + # Write each downsampled variable into the pre-created arrays + for var_name in downsampled_ds.data_vars: + target_key = f"{target_group_path}/{var_name}" + if target_key in tree: + log.info("Writing downsampled %s", target_key) + tree[target_key][...] = downsampled_ds[var_name].values + + def convert_s2_optimized( dt_input: xr.DataTree, *, @@ -354,8 +417,6 @@ def convert_s2_optimized( out_store = sync(make_store(output_path)) - log.info("Re-encoding source data to Zarr V3") - # Create a partial function by specifying parameters for our array encoder _array_reencoder = partial( array_reencoder, @@ -367,36 +428,26 @@ def convert_s2_optimized( }, ) - out_group = reencode_group( + # Phase 1: Plan the entire output hierarchy + log.info("Planning output hierarchy") + nodes, fill_specs = plan_s2_hierarchy( zg, - out_store, - path="", - overwrite=True, + crs, array_reencoder=_array_reencoder, # type: ignore[arg-type] omit_nodes=omit_nodes, allow_json_nan=allow_json_nan, ) - if "measurements" in out_group: - log.info("Adding CRS elements to datasets in measurements") - for _, subgroup in out_group["measurements"].groups(): - for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset, crs=crs) - - if "quality" in out_group: - log.info("Adding CRS elements to quality datasets") - for _, subgroup in out_group["quality"].groups(): - for _, dataset in subgroup.groups(): - add_crs_and_grid_mapping(dataset, crs=crs) - - # Create multiscale pyramids for each group in the original structure - log.info("Adding multiscale levels") - - # Create multiscale metadata - create_multiscale_levels(out_group, "measurements/reflectance") - create_multiscales_metadata(out_group["measurements/reflectance"]) - - log.info("Step 3: Final root-level metadata consolidation") - # Pass empty dict since all groups are already created by reencode_group + + # Phase 2: Create hierarchy (all zarr.json files in one call) + log.info("Creating zarr hierarchy (%d nodes)", len(nodes)) + tree = dict(zarr.create_hierarchy(store=out_store, nodes=nodes, overwrite=True)) + + # Phase 3: Fill data + log.info("Filling data (%d arrays)", len(fill_specs)) + fill_s2_hierarchy(tree, zg, fill_specs) + + # Phase 4: Consolidation + log.info("Final root-level metadata consolidation") simple_root_consolidation(output_path, {}) # Step 4: Validation diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index be139cb8..e066df3a 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -5,28 +5,17 @@ from __future__ import annotations -from itertools import pairwise -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any import numpy as np import structlog import xarray as xr from dask import delayed from dask.array import from_delayed -from pydantic.experimental.missing_sentinel import MISSING from zarr.codecs import BloscCodec from eopf_geozarr.conversion.fs_utils import sanitize_dataset_attributes -from eopf_geozarr.conversion.geozarr import ( - _create_tile_matrix_limits, - create_native_crs_tile_matrix_set, -) from eopf_geozarr.data_api.geozarr.geoproj import ProjConventionMetadata -from eopf_geozarr.data_api.geozarr.multiscales import tms, zcm -from eopf_geozarr.data_api.geozarr.multiscales.geozarr import ( - MultiscaleGroupAttrs, - MultiscaleMeta, -) from eopf_geozarr.data_api.geozarr.spatial import SpatialConventionMetadata from eopf_geozarr.data_api.geozarr.types import ( CF_SCALE_OFFSET_KEYS, @@ -43,12 +32,9 @@ import zarr from pyproj import CRS - from eopf_geozarr.types import OverviewLevelJSON log = structlog.get_logger() -MultiscalesFlavor = Literal["ogc_tms", "experimental_multiscales_convention"] - pyramid_levels = { 0: 10, # Level 0: 10m (native for b02,b03,b04,b08) 1: 20, # Level 1: 20m (native for b05,b06,b07,b11,b12,b8a + all quality) @@ -66,65 +52,6 @@ def get_grid_spacing(ds: xr.DataArray, coords: tuple[Hashable, ...]) -> tuple[fl return tuple(np.abs(ds.coords[coord][0].data - ds.coords[coord][1].data) for coord in coords) -def create_multiscale_levels(group: zarr.Group, path: str) -> None: - """ - Add additional multiscale levels to an existing Zarr group - """ - ds_levels = (20, 60, 120, 360, 720) - # Construct the full path to the group containing resolution levels - full_path = f"{group.path}/{path}" if group.path else path - - for cur_factor, next_factor in pairwise((10, *ds_levels)): - cur_group_name = f"r{cur_factor}m" - next_group_name = f"r{next_factor}m" - # Open the current resolution level as a dataset - cur_group_path = f"{full_path}/{cur_group_name}" - cur_ds = xr.open_dataset(group.store, group=cur_group_path, engine="zarr") - - scale = next_factor // cur_factor - to_downsample: dict[str, xr.DataArray] = {} - to_copy: dict[str, xr.DataArray] = {} - - # Iterate over all variables (data_vars and coords) - for var_name in list(cur_ds.data_vars) + list(cur_ds.coords): - # Convert to string for type safety - var_name_str = str(var_name) - var = cur_ds[var_name] - next_level_path = f"{path}/{next_group_name}" - - # Skip if already exists in next level - if f"{next_level_path}/{var_name_str}" in group: - continue - - # Decide whether to downsample or copy - # Spatial data variables (2D+) should be downsampled - if var_name in cur_ds.data_vars and var.ndim >= 2: - to_downsample[var_name_str] = var - # Everything else (coordinates, 0D/1D variables like spatial_ref) should be copied - else: - to_copy[var_name_str] = var - - log.info("downsampling %s into %s", tuple(sorted(to_downsample.keys())), next_group_name) - if to_copy: - log.info("copying %s into %s", tuple(sorted(to_copy.keys())), next_group_name) - - # Only process if there's something to downsample or copy - if to_downsample or to_copy: - # Create downsampled dataset with data variables to downsample - downsampled_ds = create_downsampled_resolution_group( - xr.Dataset(data_vars=to_downsample), factor=scale - ) - - # Add variables to copy (like spatial_ref) - for var_name, var in to_copy.items(): - if var_name not in downsampled_ds.coords: - downsampled_ds = downsampled_ds.assign_coords({var_name: var}) - next_group_path = f"{full_path}/{next_group_name}" - downsampled_ds.to_zarr( - group.store, group=next_group_path, consolidated=False, mode="a", compute=True - ) - - def update_encoding( array: xr.DataArray, encoding: XarrayDataArrayEncoding ) -> XarrayDataArrayEncoding: @@ -321,304 +248,6 @@ def calculate_simple_shard_dimensions( return tuple(shard_dims) -def create_multiscales_metadata( - out_group: zarr.Group, - multiscales_flavor: set[MultiscalesFlavor] | None = None, -) -> None: - """Add GeoZarr-compliant multiscales metadata to parent group.""" - # Sort by resolution (finest to coarsest) - if multiscales_flavor is None: - multiscales_flavor = {"ogc_tms", "experimental_multiscales_convention"} - res_order = { - "r10m": 10, - "r20m": 20, - "r60m": 60, - "r120m": 120, - "r360m": 360, - "r720m": 720, - } - res_groups = tuple(out_group[k] for k in res_order) - first_dataset = xr.open_zarr(out_group.store, group=next(iter(res_groups)).path) - - # Get CRS and bounds - native_crs = first_dataset.rio.crs if hasattr(first_dataset, "rio") else None - if native_crs is None: - raise ValueError("Cannot determine native CRS for multiscales metadata") - - # Calculate bounds directly from coordinates for consistency with the data arrays - if "x" not in first_dataset.coords or "y" not in first_dataset.coords: - log.error( - "Missing x/y coordinates in dataset, cannot determine bounds", - base_path=out_group.path, - ) - return - - x_coords = first_dataset.x.values - y_coords = first_dataset.y.values - native_bounds = ( - float(x_coords.min()), - float(y_coords.min()), - float(x_coords.max()), - float(y_coords.max()), - ) - - # Create overview_levels structure following the multiscales v1.0 specification - overview_levels: list[OverviewLevelJSON] = [] - for res_group in res_groups: - res_name = res_group.basename - # Use resolution order for consistent scale calculations - res_meters = res_order[res_name] - - dataset = xr.open_zarr(res_group.store, group=res_group.path) - - # Get first data variable to extract dimensions - first_var = next(iter(dataset.data_vars.values())) - height, width = first_var.shape[-2:] - - # Calculate spatial transform (affine transformation) - transform = None - if hasattr(dataset, "rio") and hasattr(dataset.rio, "transform"): - try: - # Try to get transform as property first - rio_transform = dataset.rio.transform - if callable(rio_transform): - rio_transform = rio_transform() - transform = tuple(rio_transform)[:6] # Get 6 coefficients - log.info("Got transform from rio accessor", transform=transform, level=res_name) - except (AttributeError, TypeError) as e: - log.warning( - "Could not get transform from rio accessor", error=str(e), level=res_name - ) - - if transform is None or all(t == 0 for t in transform): - # Fallback: construct from grid spacing and bounds - if "x" in dataset.coords and "y" in dataset.coords: - # Use coordinate arrays to calculate spacing - x_coords = dataset.coords["x"].values - y_coords = dataset.coords["y"].values - - if len(x_coords) > 1 and len(y_coords) > 1: - # Calculate pixel size from actual coordinate spacing - pixel_size_x = float(np.abs(x_coords[1] - x_coords[0])) - pixel_size_y = float(np.abs(y_coords[1] - y_coords[0])) - - x_min = float(x_coords.min()) - y_max = float(y_coords.max()) - transform = (pixel_size_x, 0.0, x_min, 0.0, -pixel_size_y, y_max) - log.info( - "Calculated transform from coordinates", - transform=transform, - pixel_size_x=pixel_size_x, - pixel_size_y=pixel_size_y, - level=res_name, - ) - else: - log.warning( - "Insufficient coordinate points for transform calculation", - x_len=len(x_coords), - y_len=len(y_coords), - level=res_name, - ) - else: - log.warning( - "Missing x/y coordinates for transform calculation", - coords=list(dataset.coords.keys()), - level=res_name, - ) - - # Calculate zoom level (higher resolution = higher zoom) - tile_width = 256 - zoom_for_width = max(0, int(np.ceil(np.log2(width / tile_width)))) - zoom_for_height = max(0, int(np.ceil(np.log2(height / tile_width)))) - zoom = max(zoom_for_width, zoom_for_height) - - # Calculate relative scale and translation vs parent resolution - finest_res_meters = res_order[res_groups[0].basename] - - # Fix for issue #114: Translation values should be 0 - relative_translation = 0.0 - - # Calculate proper relative scale based on actual parent-child dimension ratios - if res_name == res_groups[0].basename: # Base resolution - relative_scale = 1.0 - else: - # Define derivation chain to find parent resolution - derivation_chain = { - "r10m": None, - "r20m": "r10m", - "r60m": "r10m", - "r120m": "r60m", - "r360m": "r120m", - "r720m": "r360m", - } - - parent_res = derivation_chain.get(res_name) - if parent_res and parent_res in out_group: - # Get actual dimensions of parent and child - parent_dataset = xr.open_zarr(out_group.store, group=out_group[parent_res].path) - parent_var = next(iter(parent_dataset.data_vars.values())) - parent_height, parent_width = parent_var.shape[-2:] - - # Current (child) dimensions - child_height, child_width = height, width - - # Calculate actual scale ratio based on dimensions - # Use the larger of the two ratios to be conservative - scale_x = parent_width / child_width if child_width > 0 else 1.0 - scale_y = parent_height / child_height if child_height > 0 else 1.0 - relative_scale = max(scale_x, scale_y) - - log.info( - "Calculated dynamic scale ratio", - level=res_name, - parent=parent_res, - parent_dims=(parent_height, parent_width), - child_dims=(child_height, child_width), - scale_x=scale_x, - scale_y=scale_y, - relative_scale=relative_scale, - ) - else: - # Fallback to absolute resolution ratio - relative_scale = res_meters / finest_res_meters - log.warning( - "Using fallback scale calculation", - level=res_name, - relative_scale=relative_scale, - ) - - # Get chunks in the correct format - var_chunks = dataset.data_vars[first_var.name].chunks - if var_chunks is not None: - chunks = tuple(tuple(int(c) for c in chunk_dim) for chunk_dim in var_chunks) - else: - chunks = None - log.warning( - "Could not determine chunking information for overview level; 'chunks' will be set to None", - level=res_name, - variable=str(first_var.name), - ) - - layout_entry: OverviewLevelJSON = { - "level": res_name, # Use string-based level name - "zoom": zoom, - "width": width, - "height": height, - "translation_relative": relative_translation, - "scale_absolute": res_meters, - "scale_relative": relative_scale, - "spatial_transform": None, - "chunks": chunks, - "spatial_shape": (height, width), - } - - # Only add spatial_transform if we have valid transform data - if transform is not None and not all(t == 0 for t in transform): - layout_entry["spatial_transform"] = transform - - overview_levels.append(layout_entry) - - multiscales: dict[str, Any] = {"multiscales": {}} - layout: list[zcm.ScaleLevel] | MISSING = MISSING - tile_matrix_set: tms.TileMatrixSet | MISSING = MISSING - tile_matrix_limits: dict[str, tms.TileMatrixLimit] | MISSING = MISSING - - if "ogc_tms" in multiscales_flavor: - # Create tile matrix set using geozarr function - tile_matrix_set = create_native_crs_tile_matrix_set( - native_crs, - native_bounds, - overview_levels, - group_prefix=None, - ) - - # Create tile matrix limits - tile_matrix_limits = _create_tile_matrix_limits( - overview_levels, - tile_width=256, - ) - multiscales["multiscales"].update( - { - "tile_matrix_set": tile_matrix_set, - "resampling_method": "average", - "tile_matrix_limits": tile_matrix_limits, - } - ) - if "experimental_multiscales_convention" in multiscales_flavor: - layout = [] - - # Define the correct derivation chain - derivation_chain = { - "r10m": None, # base resolution - "r20m": "r10m", - "r60m": "r10m", - "r120m": "r60m", - "r360m": "r120m", - "r720m": "r360m", - } - - for i, overview_level in enumerate(overview_levels): - # Create scale level with required fields - asset = str(overview_level["level"]) - - # Build complete dict for ScaleLevel initialization - scale_level_data: dict[str, Any] = {"asset": asset} - - if i > 0: # Not the first (base) resolution - derived_from = derivation_chain.get(asset, str(res_groups[0].basename)) - multiscale_transform = zcm.Transform( - scale=(overview_level["scale_relative"],) * 2, - translation=(overview_level["translation_relative"],) * 2, - ) - scale_level_data["derived_from"] = derived_from - scale_level_data["transform"] = multiscale_transform - - # Add spatial properties - scale_level_data["spatial:shape"] = overview_level["spatial_shape"] - if "spatial_transform" in overview_level: - spatial_transform = overview_level["spatial_transform"] - # Only add spatial_transform if we have valid transform data (not all zeros) - if spatial_transform is not None and not all(t == 0 for t in spatial_transform): - scale_level_data["spatial:transform"] = spatial_transform - - scale_level = zcm.ScaleLevel(**scale_level_data) - layout.append(scale_level) - - # Create convention metadata for all three conventions - multiscale_attrs = MultiscaleGroupAttrs( - zarr_conventions=( - zcm.MultiscaleConventionMetadata(), - SpatialConventionMetadata(), - ProjConventionMetadata(), - ), - multiscales=MultiscaleMeta( - layout=layout, - resampling_method="average", - tile_matrix_set=tile_matrix_set, - tile_matrix_limits=tile_matrix_limits, - ), - ) - - # Add multiscale attributes - out_group.attrs.update(multiscale_attrs.model_dump()) - # Add spatial and proj attributes at group level following specifications - if native_crs and native_bounds: - # Add spatial convention attributes - spatial_attrs: dict[str, object] = { - "spatial:dimensions": ("y", "x"), - "spatial:bbox": tuple(native_bounds), - "spatial:registration": "pixel", - } - - # Add proj convention attributes - if hasattr(native_crs, "to_epsg") and native_crs.to_epsg(): - spatial_attrs["proj:code"] = f"EPSG:{native_crs.to_epsg()}" - elif hasattr(native_crs, "to_wkt"): - spatial_attrs["proj:wkt2"] = native_crs.to_wkt() - - out_group.attrs.update(spatial_attrs) - - def create_original_encoding(dataset: xr.Dataset) -> dict[str, XarrayDataArrayEncoding]: """Write a group preserving its original chunking and encoding.""" @@ -955,12 +584,12 @@ def write_geo_metadata( elif hasattr(crs, "to_wkt"): dataset.attrs["proj:wkt2"] = crs.to_wkt() - # Add zarr convention declarations - conventions = [ - SpatialConventionMetadata().model_dump(), - ProjConventionMetadata().model_dump(), - ] - dataset.attrs["zarr_conventions"] = conventions + # Add zarr convention declarations + conventions = [ + SpatialConventionMetadata().model_dump(), + ProjConventionMetadata().model_dump(), + ] + dataset.attrs["zarr_conventions"] = conventions def rechunk_dataset_for_encoding( diff --git a/src/eopf_geozarr/s2_optimization/s2_plan.py b/src/eopf_geozarr/s2_optimization/s2_plan.py new file mode 100644 index 00000000..7ad8289e --- /dev/null +++ b/src/eopf_geozarr/s2_optimization/s2_plan.py @@ -0,0 +1,726 @@ +""" +Declarative planning for S2 zarr hierarchy creation. + +This module contains pure functions that build a complete +dict[str, ArrayV3Metadata | GroupMetadata] describing every node in the +output zarr hierarchy. No zarr writes, no xarray opens. +""" + +from __future__ import annotations + +from dataclasses import dataclass, replace +from typing import TYPE_CHECKING, Any + +import numpy as np +import structlog +import zarr +from zarr.codecs import ShardingCodec +from zarr.core.chunk_grids import RegularChunkGrid +from zarr.core.group import GroupMetadata +from zarr.core.metadata.v3 import ArrayV3Metadata +from zarr.dtype import Int64 + +from eopf_geozarr.conversion.geozarr import ( + _create_tile_matrix_limits, + create_native_crs_tile_matrix_set, +) +from eopf_geozarr.data_api.geozarr.geoproj import ProjConventionMetadata +from eopf_geozarr.data_api.geozarr.multiscales import zcm +from eopf_geozarr.data_api.geozarr.multiscales.geozarr import ( + MultiscaleGroupAttrs, + MultiscaleMeta, +) +from eopf_geozarr.data_api.geozarr.spatial import SpatialConventionMetadata +from eopf_geozarr.zarrio import ArrayReencoder, replace_json_invalid_floats + +if TYPE_CHECKING: + from pyproj import CRS + + from eopf_geozarr.types import OverviewLevelJSON + +log = structlog.get_logger() + +# Resolution derivation chain: each level and the level it's derived from +DERIVATION_CHAIN: dict[str, str | None] = { + "r10m": None, + "r20m": "r10m", + "r60m": "r10m", + "r120m": "r60m", + "r360m": "r120m", + "r720m": "r360m", +} + +# Ordered list of downsampling steps: (current_meters, next_meters) +DOWNSAMPLE_STEPS: tuple[tuple[int, int], ...] = ( + (10, 20), + (20, 60), + (60, 120), + (120, 360), + (360, 720), +) + +RES_ORDER: dict[str, int] = { + "r10m": 10, + "r20m": 20, + "r60m": 60, + "r120m": 120, + "r360m": 360, + "r720m": 720, +} + + +@dataclass +class FillSpec: + """Describes how to fill an array during the execute phase.""" + + source_path: str | None = None + downsample_from: str | None = None + scale_factor: int = 1 + is_spatial_ref: bool = False + computed_values: np.ndarray | None = None + + +def compute_transform_from_coords( + x_values: np.ndarray, y_values: np.ndarray +) -> tuple[float, float, float, float, float, float]: + """Compute a 6-element affine transform from x and y coordinate arrays.""" + pixel_size_x = float(abs(x_values[1] - x_values[0])) + pixel_size_y = float(abs(y_values[1] - y_values[0])) + return (pixel_size_x, 0.0, float(x_values.min()), 0.0, -pixel_size_y, float(y_values.max())) + + +def build_spatial_ref_metadata(crs: CRS) -> ArrayV3Metadata: + """ + Build ArrayV3Metadata for a scalar spatial_ref array from a CRS. + + Uses pyproj.CRS.to_cf() to get CF grid mapping attributes. + """ + cf_attrs = crs.to_cf() + cf_attrs["spatial_ref"] = crs.to_wkt() + + return ArrayV3Metadata( + shape=(), + data_type=Int64(), + chunk_grid={"name": "regular", "configuration": {"chunk_shape": ()}}, + chunk_key_encoding={"name": "default", "configuration": {"separator": "/"}}, + fill_value=0, + dimension_names=None, + codecs=({"name": "bytes"},), + attributes=cf_attrs, + ) + + +def build_geo_group_attrs( + crs: CRS, + x_values: np.ndarray, + y_values: np.ndarray, + data_shape: tuple[int, ...], +) -> dict[str, object]: + """ + Compute group-level geo/spatial/proj attributes from CRS and coordinate arrays. + """ + attrs: dict[str, object] = { + "grid_mapping": "spatial_ref", + "spatial:dimensions": ["y", "x"], + "spatial:registration": "pixel", + } + + x_min, x_max = float(x_values.min()), float(x_values.max()) + y_min, y_max = float(y_values.min()), float(y_values.max()) + attrs["spatial:bbox"] = [x_min, y_min, x_max, y_max] + + if len(x_values) > 1 and len(y_values) > 1: + transform = compute_transform_from_coords(x_values, y_values) + if not all(t == 0 for t in transform): + attrs["spatial:transform"] = list(transform) + + if len(data_shape) >= 2: + height, width = data_shape[-2:] + attrs["spatial:shape"] = [height, width] + + if hasattr(crs, "to_epsg") and crs.to_epsg(): + attrs["proj:code"] = f"EPSG:{crs.to_epsg()}" + elif hasattr(crs, "to_wkt"): + attrs["proj:wkt2"] = crs.to_wkt() + + conventions = [ + SpatialConventionMetadata().model_dump(), + ProjConventionMetadata().model_dump(), + ] + attrs["zarr_conventions"] = conventions + + return attrs + + +def _derive_array_metadata( + meta: ArrayV3Metadata, + scale: int, +) -> ArrayV3Metadata: + """ + Derive ArrayV3Metadata for a downsampled level from a parent array. + + Spatial dimensions (x, y) are divided by scale. Chunk sizes are clamped + to the new shape. Sharding codecs are adjusted to fit the new shape + (shard size = largest multiple of inner chunks that fits). + """ + dim_names = meta.dimension_names or () + new_shape = tuple( + s // scale if (dim_names and dim_names[i] in ("x", "y")) else s + for i, s in enumerate(meta.shape) + ) + + # Check if parent uses sharding + has_sharding = any(isinstance(c, ShardingCodec) for c in meta.codecs) + + if has_sharding: + # Extract the inner chunk shape and inner codecs from the shard + shard_codec = next(c for c in meta.codecs if isinstance(c, ShardingCodec)) + inner_chunks = shard_codec.chunk_shape + inner_codecs = shard_codec.codecs + + # Clamp inner chunks to new shape + new_inner_chunks = tuple(min(c, s) for c, s in zip(inner_chunks, new_shape, strict=True)) + + # New shard size = largest multiple of inner chunks that fits in shape + new_shard_shape = tuple( + (s // c) * c for s, c in zip(new_shape, new_inner_chunks, strict=True) + ) + + # If shards would contain only 1 chunk, drop sharding + num_subchunks = 1 + for shard_dim, chunk_dim in zip(new_shard_shape, new_inner_chunks, strict=True): + if chunk_dim > 0: + num_subchunks *= shard_dim // chunk_dim + + if num_subchunks <= 1: + new_chunks = new_inner_chunks + new_codecs = tuple(inner_codecs) + else: + new_chunks = new_shard_shape + new_codecs = ( + ShardingCodec( + chunk_shape=new_inner_chunks, + codecs=inner_codecs, + index_codecs=shard_codec.index_codecs, + index_location=shard_codec.index_location, + ), + ) + else: + old_chunks = meta.chunk_grid.chunk_shape + new_chunks = tuple(min(c, s) for c, s in zip(old_chunks, new_shape, strict=True)) + new_codecs = tuple(meta.codecs) + + return replace( + meta, + shape=new_shape, + chunk_grid=RegularChunkGrid(chunk_shape=new_chunks), + codecs=new_codecs, + ) + + +def _is_geo_leaf_group(path: str) -> bool: + """ + Check if a group path is a resolution-level leaf group that should get CRS treatment. + + Matches patterns like measurements/reflectance/r10m, quality/mask/r20m, etc. + """ + parts = path.strip("/").split("/") + if len(parts) < 3: + return False + top = parts[0] + leaf = parts[-1] + return top in ("measurements", "quality") and leaf.startswith("r") and leaf.endswith("m") + + +def _get_first_2d_shape( + nodes: dict[str, ArrayV3Metadata | GroupMetadata], + group_prefix: str, +) -> tuple[int, ...] | None: + """Get the shape of the first 2D+ array under a group prefix.""" + for key, meta in nodes.items(): + if ( + key.startswith(group_prefix + "/") + and isinstance(meta, ArrayV3Metadata) + and len(meta.shape) >= 2 + ): + return meta.shape # type: ignore[no-any-return] + return None + + +def plan_source_nodes( + source_group: zarr.Group, + *, + array_reencoder: ArrayReencoder, + omit_nodes: set[str] | None = None, + allow_json_nan: bool = False, +) -> tuple[dict[str, ArrayV3Metadata | GroupMetadata], dict[str, FillSpec]]: + """ + Plan nodes from the source zarr group (V2 -> V3 re-encoding). + + This replicates the metadata-building logic from reencode_group. + """ + if omit_nodes is None: + omit_nodes = set() + + nodes: dict[str, ArrayV3Metadata | GroupMetadata] = { + "": GroupMetadata(zarr_format=3, attributes=source_group.attrs.asdict()) + } + fill_specs: dict[str, FillSpec] = {} + + members = dict(source_group.members(max_depth=None)) + + for name in omit_nodes: + if not any(k.startswith(name) for k in members): + log.warning("The name %s was provided in omit_nodes but no such node exists.", name) + + for name, member in members.items(): + if any(name.startswith(v) for v in omit_nodes): + continue + + source_meta = member.metadata + + if not allow_json_nan: + new_attrs = replace_json_invalid_floats(source_meta.attributes) + source_meta = replace(source_meta, attributes=new_attrs) + + if isinstance(member, zarr.Array): + new_meta = array_reencoder(member.path, source_meta) + nodes[name] = new_meta + fill_specs[name] = FillSpec(source_path=name) + else: + nodes[name] = GroupMetadata( + zarr_format=3, + attributes=source_meta.attributes, + ) + + return nodes, fill_specs + + +def enrich_with_geo_metadata( + nodes: dict[str, ArrayV3Metadata | GroupMetadata], + fill_specs: dict[str, FillSpec], + source_group: zarr.Group, + crs: CRS, +) -> None: + """ + Enrich leaf groups with CRS/geo metadata: add spatial_ref array, + set geo attributes on groups, and add grid_mapping to data variables. + + Modifies nodes and fill_specs in place. + """ + import zarr as zarr_mod + + spatial_ref_meta = build_spatial_ref_metadata(crs) + + # Find all geo leaf groups in the nodes dict + group_paths = [ + path + for path, meta in nodes.items() + if isinstance(meta, GroupMetadata) and _is_geo_leaf_group(path) + ] + + for group_path in group_paths: + # Read coordinate values from the source group + source_path = group_path # source and output paths match + try: + source_subgroup = source_group[source_path] + except KeyError: + # This group might be a derived level, not in source + continue + + x_values = None + y_values = None + if isinstance(source_subgroup, zarr_mod.Group): + if "x" in source_subgroup: + x_values = source_subgroup["x"][...] + if "y" in source_subgroup: + y_values = source_subgroup["y"][...] + + if x_values is None or y_values is None: + log.warning( + "No x/y coordinates found in source group %s, skipping geo enrichment", group_path + ) + continue + + data_shape = _get_first_2d_shape(nodes, group_path) + if data_shape is None: + continue + + # Enrich group metadata with geo attrs + group_meta = nodes[group_path] + assert isinstance(group_meta, GroupMetadata) + geo_attrs = build_geo_group_attrs(crs, x_values, y_values, data_shape) + merged_attrs = {**group_meta.attributes, **geo_attrs} + nodes[group_path] = GroupMetadata(zarr_format=3, attributes=merged_attrs) + + # Add spatial_ref array + sr_path = f"{group_path}/spatial_ref" + nodes[sr_path] = spatial_ref_meta + fill_specs[sr_path] = FillSpec(is_spatial_ref=True) + + # Add grid_mapping + coordinates to data variable arrays + prefix = group_path + "/" + for key in list(nodes.keys()): + if key.startswith(prefix): + meta = nodes[key] + if isinstance(meta, ArrayV3Metadata) and len(meta.shape) >= 2: + enriched_attrs = { + **meta.attributes, + "grid_mapping": "spatial_ref", + "coordinates": "spatial_ref", + } + nodes[key] = replace(meta, attributes=enriched_attrs) + + +def plan_derived_levels( + nodes: dict[str, ArrayV3Metadata | GroupMetadata], + fill_specs: dict[str, FillSpec], + source_group: zarr.Group, + crs: CRS, + reflectance_prefix: str, +) -> dict[str, np.ndarray]: + """ + Plan derived multiscale levels under the reflectance prefix. + + For each downsampling step (10→20, 20→60, 60→120, etc.), derive array + metadata from the parent level. Skips arrays that already exist in the + target level (cross-resolution propagation for native levels). + + Returns a dict mapping level paths to their x/y coordinate values, + needed for multiscale metadata computation. + """ + coord_values: dict[str, np.ndarray] = {} + + # Read coordinate values for native levels from source + for res_name in ("r10m", "r20m", "r60m"): + group_path = f"{reflectance_prefix}/{res_name}" + try: + src = source_group[group_path] + except KeyError: + continue + if "x" in src: + coord_values[f"{group_path}/x"] = src["x"][...] + if "y" in src: + coord_values[f"{group_path}/y"] = src["y"][...] + + for cur_meters, next_meters in DOWNSAMPLE_STEPS: + cur_name = f"r{cur_meters}m" + next_name = f"r{next_meters}m" + cur_prefix = f"{reflectance_prefix}/{cur_name}" + next_prefix = f"{reflectance_prefix}/{next_name}" + scale = next_meters // cur_meters + + # Collect arrays from the current level (from nodes dict) + cur_arrays: dict[str, ArrayV3Metadata] = {} + for key, meta in nodes.items(): + if key.startswith(cur_prefix + "/") and isinstance(meta, ArrayV3Metadata): + array_name = key[len(cur_prefix) + 1 :] + cur_arrays[array_name] = meta + + if not cur_arrays: + log.warning("No arrays found in %s, skipping derivation", cur_prefix) + continue + + # Ensure the target group exists in nodes + if next_prefix not in nodes: + nodes[next_prefix] = GroupMetadata(zarr_format=3, attributes={}) + + # Compute derived coordinate values + cur_x_key = f"{cur_prefix}/x" + cur_y_key = f"{cur_prefix}/y" + parent_x = coord_values.get(cur_x_key) + parent_y = coord_values.get(cur_y_key) + + if parent_x is not None: + derived_x = parent_x[::scale][: len(parent_x) // scale] + coord_values[f"{next_prefix}/x"] = derived_x + if parent_y is not None: + derived_y = parent_y[::scale][: len(parent_y) // scale] + coord_values[f"{next_prefix}/y"] = derived_y + + for array_name, parent_meta in cur_arrays.items(): + target_key = f"{next_prefix}/{array_name}" + + # Skip if already exists (native level or already planned) + if target_key in nodes: + continue + + # Skip coordinate arrays and spatial_ref — handle separately + if array_name in ("x", "y", "spatial_ref"): + continue + + if len(parent_meta.shape) >= 2: + # 2D+ data variable: derive downsampled metadata + derived_meta = _derive_array_metadata(parent_meta, scale) + nodes[target_key] = derived_meta + fill_specs[target_key] = FillSpec( + downsample_from=f"{cur_prefix}/{array_name}", + scale_factor=scale, + ) + else: + # 0D/1D variable: copy as-is + nodes[target_key] = parent_meta + fill_specs[target_key] = FillSpec( + source_path=f"{cur_prefix}/{array_name}", + ) + + # Plan coordinate arrays for derived levels + for coord_name in ("x", "y"): + target_key = f"{next_prefix}/{coord_name}" + if target_key in nodes: + continue + parent_coord_key = f"{cur_prefix}/{coord_name}" + if parent_coord_key not in nodes: + continue + parent_coord_meta = nodes[parent_coord_key] + assert isinstance(parent_coord_meta, ArrayV3Metadata) + new_length = parent_coord_meta.shape[0] // scale + parent_chunk = parent_coord_meta.chunk_grid.chunk_shape[0] + new_chunk = min(parent_chunk, new_length) + derived_coord_meta = replace( + parent_coord_meta, + shape=(new_length,), + chunk_grid=RegularChunkGrid(chunk_shape=(new_chunk,)), + ) + nodes[target_key] = derived_coord_meta + coord_val = coord_values.get(f"{next_prefix}/{coord_name}") + fill_specs[target_key] = FillSpec(computed_values=coord_val) + + # Plan spatial_ref for derived levels + sr_key = f"{next_prefix}/spatial_ref" + if sr_key not in nodes: + nodes[sr_key] = build_spatial_ref_metadata(crs) + fill_specs[sr_key] = FillSpec(is_spatial_ref=True) + + # Now enrich the derived level group with geo attrs + derived_x = coord_values.get(f"{next_prefix}/x") + derived_y = coord_values.get(f"{next_prefix}/y") + if derived_x is not None and derived_y is not None: + data_shape = _get_first_2d_shape(nodes, next_prefix) + if data_shape is not None: + geo_attrs = build_geo_group_attrs(crs, derived_x, derived_y, data_shape) + group_meta = nodes[next_prefix] + assert isinstance(group_meta, GroupMetadata) + merged = {**group_meta.attributes, **geo_attrs} + nodes[next_prefix] = GroupMetadata(zarr_format=3, attributes=merged) + + # Add grid_mapping + coordinates to derived data vars + for key in list(nodes.keys()): + if key.startswith(next_prefix + "/"): + meta = nodes[key] + if isinstance(meta, ArrayV3Metadata) and len(meta.shape) >= 2: + enriched = { + **meta.attributes, + "grid_mapping": "spatial_ref", + "coordinates": "spatial_ref", + } + nodes[key] = replace(meta, attributes=enriched) + + return coord_values + + +def compute_multiscales_group_attrs( + nodes: dict[str, ArrayV3Metadata | GroupMetadata], + coord_values: dict[str, np.ndarray], + reflectance_prefix: str, + crs: CRS, +) -> dict[str, object]: + """ + Compute multiscale metadata attributes for the reflectance parent group + from the plan dict. Replaces create_multiscales_metadata. + """ + + res_names = ["r10m", "r20m", "r60m", "r120m", "r360m", "r720m"] + + # Get bounds from finest level + finest_x = coord_values.get(f"{reflectance_prefix}/r10m/x") + finest_y = coord_values.get(f"{reflectance_prefix}/r10m/y") + if finest_x is None or finest_y is None: + raise ValueError("Cannot compute multiscale metadata: missing r10m coordinates") + + native_bounds = ( + float(finest_x.min()), + float(finest_y.min()), + float(finest_x.max()), + float(finest_y.max()), + ) + + overview_levels: list[OverviewLevelJSON] = [] + + for res_name in res_names: + level_prefix = f"{reflectance_prefix}/{res_name}" + res_meters = RES_ORDER[res_name] + + # Find first 2D data variable to get shape + data_shape = _get_first_2d_shape(nodes, level_prefix) + if data_shape is None: + continue + height, width = data_shape[-2:] + + # Get coordinate values for transform + x_vals = coord_values.get(f"{level_prefix}/x") + y_vals = coord_values.get(f"{level_prefix}/y") + + transform: tuple[float, ...] | None = None + if x_vals is not None and y_vals is not None and len(x_vals) > 1 and len(y_vals) > 1: + transform = compute_transform_from_coords(x_vals, y_vals) + if all(t == 0 for t in transform): + transform = None + + # Zoom level + tile_width = 256 + zoom_w = max(0, int(np.ceil(np.log2(width / tile_width)))) + zoom_h = max(0, int(np.ceil(np.log2(height / tile_width)))) + zoom = max(zoom_w, zoom_h) + + # Scale ratio + if res_name == "r10m": + relative_scale: int | float = 1.0 + else: + parent_res = DERIVATION_CHAIN.get(res_name) + if parent_res: + parent_shape = _get_first_2d_shape(nodes, f"{reflectance_prefix}/{parent_res}") + if parent_shape is not None: + parent_h, parent_w = parent_shape[-2:] + scale_x = parent_w / width if width > 0 else 1.0 + scale_y = parent_h / height if height > 0 else 1.0 + relative_scale = max(scale_x, scale_y) + else: + relative_scale = res_meters / 10 + else: + relative_scale = res_meters / 10 + + # Get chunks from first data var + chunks: tuple[tuple[int, ...], ...] | None = None + for key, meta in nodes.items(): + if ( + key.startswith(level_prefix + "/") + and isinstance(meta, ArrayV3Metadata) + and len(meta.shape) >= 2 + ): + chunks = tuple((c,) for c in meta.chunk_grid.chunk_shape) + break + + level_entry: OverviewLevelJSON = { + "level": res_name, + "zoom": zoom, + "width": width, + "height": height, + "translation_relative": 0.0, + "scale_absolute": res_meters, + "scale_relative": relative_scale, + "spatial_transform": None, + "chunks": chunks, + "spatial_shape": (height, width), + } + if transform is not None: + level_entry["spatial_transform"] = transform + + overview_levels.append(level_entry) + + # Build TMS + tile_matrix_set = create_native_crs_tile_matrix_set( + crs, native_bounds, overview_levels, group_prefix=None + ) + tile_matrix_limits = _create_tile_matrix_limits(overview_levels, tile_width=256) + + # Build ZCM layout + layout: list[zcm.ScaleLevel] = [] + for i, ol in enumerate(overview_levels): + asset = str(ol["level"]) + scale_level_data: dict[str, Any] = {"asset": asset} + + if i > 0: + derived_from = DERIVATION_CHAIN.get(asset, "r10m") + multiscale_transform = zcm.Transform( + scale=(ol["scale_relative"],) * 2, + translation=(ol["translation_relative"],) * 2, + ) + scale_level_data["derived_from"] = derived_from + scale_level_data["transform"] = multiscale_transform + + scale_level_data["spatial:shape"] = ol["spatial_shape"] + spatial_transform = ol.get("spatial_transform") + if spatial_transform is not None and not all(t == 0 for t in spatial_transform): + scale_level_data["spatial:transform"] = spatial_transform + + layout.append(zcm.ScaleLevel(**scale_level_data)) + + multiscale_attrs = MultiscaleGroupAttrs( + zarr_conventions=( + zcm.MultiscaleConventionMetadata(), + SpatialConventionMetadata(), + ProjConventionMetadata(), + ), + multiscales=MultiscaleMeta( + layout=tuple(layout), + resampling_method="average", + tile_matrix_set=tile_matrix_set, + tile_matrix_limits=tile_matrix_limits, + ), + ) + + # Combine multiscale attrs with spatial/proj attrs + result = multiscale_attrs.model_dump() + + spatial_attrs: dict[str, object] = { + "spatial:dimensions": ("y", "x"), + "spatial:bbox": tuple(native_bounds), + "spatial:registration": "pixel", + } + + if hasattr(crs, "to_epsg") and crs.to_epsg(): + spatial_attrs["proj:code"] = f"EPSG:{crs.to_epsg()}" + elif hasattr(crs, "to_wkt"): + spatial_attrs["proj:wkt2"] = crs.to_wkt() + + result.update(spatial_attrs) + return result + + +def plan_s2_hierarchy( + source_group: zarr.Group, + crs: CRS, + *, + array_reencoder: ArrayReencoder, + omit_nodes: set[str] | None = None, + allow_json_nan: bool = False, +) -> tuple[dict[str, ArrayV3Metadata | GroupMetadata], dict[str, FillSpec]]: + """ + Build the complete output hierarchy plan for an S2 conversion. + + Returns + ------- + nodes + Complete dict for zarr.create_hierarchy — every group and array + in the output, with all attributes pre-set. + fill_specs + Per-array instructions for the data-filling phase. + """ + log.info("Planning S2 hierarchy") + + # Step 1: Plan source nodes (V2 -> V3 re-encoding) + nodes, fill_specs = plan_source_nodes( + source_group, + array_reencoder=array_reencoder, + omit_nodes=omit_nodes, + allow_json_nan=allow_json_nan, + ) + + # Step 2: Enrich leaf groups with CRS/geo metadata + enrich_with_geo_metadata(nodes, fill_specs, source_group, crs) + + # Step 3: Plan derived multiscale levels + reflectance_prefix = "measurements/reflectance" + coord_values = plan_derived_levels(nodes, fill_specs, source_group, crs, reflectance_prefix) + + # Step 4: Compute and set multiscale metadata on the reflectance parent group + if reflectance_prefix in nodes: + ms_attrs = compute_multiscales_group_attrs(nodes, coord_values, reflectance_prefix, crs) + group_meta = nodes[reflectance_prefix] + assert isinstance(group_meta, GroupMetadata) + merged = {**group_meta.attributes, **ms_attrs} + nodes[reflectance_prefix] = GroupMetadata(zarr_format=3, attributes=merged) + + log.info("Planned %d nodes (%d fill specs)", len(nodes), len(fill_specs)) + return nodes, fill_specs diff --git a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json index 753171e1..a8ced87f 100644 --- a/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json +++ b/tests/_test_data/optimized_geozarr_examples/S2A_MSIL2A_20251008T100041_N0511_R122_T32TQM_20251008T122613.json @@ -5032,54 +5032,54 @@ { "cellSize": 0.0, "id": "r10m", - "matrixHeight": 45, - "matrixWidth": 45, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 244, - "tileWidth": 244 + "tileHeight": 10980, + "tileWidth": 10980 }, { "cellSize": 0.0, "id": "r20m", - "matrixHeight": 30, - "matrixWidth": 30, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 5490, + "tileWidth": 5490 }, { "cellSize": 0.0, "id": "r60m", - "matrixHeight": 10, - "matrixWidth": 10, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 1830, + "tileWidth": 1830 }, { "cellSize": 0.0, "id": "r120m", - "matrixHeight": 5, - "matrixWidth": 5, + "matrixHeight": 1, + "matrixWidth": 1, "pointOfOrigin": [ 0.0, 0.0 ], "scaleDenominator": 0.0, - "tileHeight": 183, - "tileWidth": 183 + "tileHeight": 915, + "tileWidth": 915 }, { "cellSize": 0.0, @@ -5675,12 +5675,12 @@ }, "spatial_ref": { "attributes": { - "crs_wkt": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]", + "crs_wkt": "PROJCRS[\"WGS 84 / UTM zone 32N\",BASEGEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],MEMBER[\"World Geodetic System 1984 (G2296)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4326]],CONVERSION[\"UTM zone 32N\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",9,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9996,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",500000,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Navigation and medium accuracy spatial referencing.\"],AREA[\"Between 6\u00b0E and 12\u00b0E, northern hemisphere between equator and 84\u00b0N, onshore and offshore. Algeria. Austria. Cameroon. Denmark. Equatorial Guinea. France. Gabon. Germany. Italy. Libya. Liechtenstein. Monaco. Netherlands. Niger. Nigeria. Norway. Sao Tome and Principe. Svalbard. Sweden. Switzerland. Tunisia. Vatican City State.\"],BBOX[0,6,84,12]],ID[\"EPSG\",32632]]", "false_easting": 500000.0, "false_northing": 0.0, "geographic_crs_name": "WGS 84", "grid_mapping_name": "transverse_mercator", - "horizontal_datum_name": "World Geodetic System 1984", + "horizontal_datum_name": "World Geodetic System 1984 ensemble", "inverse_flattening": 298.257223563, "latitude_of_projection_origin": 0.0, "longitude_of_central_meridian": 9.0, @@ -5691,7 +5691,7 @@ "scale_factor_at_central_meridian": 0.9996, "semi_major_axis": 6378137.0, "semi_minor_axis": 6356752.314245179, - "spatial_ref": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]" + "spatial_ref": "PROJCRS[\"WGS 84 / UTM zone 32N\",BASEGEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],MEMBER[\"World Geodetic System 1984 (G2296)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4326]],CONVERSION[\"UTM zone 32N\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",9,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9996,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",500000,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Navigation and medium accuracy spatial referencing.\"],AREA[\"Between 6\u00b0E and 12\u00b0E, northern hemisphere between equator and 84\u00b0N, onshore and offshore. Algeria. Austria. Cameroon. Denmark. Equatorial Guinea. France. Gabon. Germany. Italy. Libya. Liechtenstein. Monaco. Netherlands. Niger. Nigeria. Norway. Sao Tome and Principe. Svalbard. Sweden. Switzerland. Tunisia. 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Western Sahara.\"],BBOX[0,-12.01,84.01,-6]],ID[\"EPSG\",32629]]" }, "chunk_grid": { "configuration": { @@ -17852,12 +17814,12 @@ }, "spatial_ref": { "attributes": { - "crs_wkt": "PROJCS[\"WGS 84 / UTM zone 29N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32629\"]]", + "crs_wkt": "PROJCRS[\"WGS 84 / UTM zone 29N\",BASEGEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],MEMBER[\"World Geodetic System 1984 (G2296)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4326]],CONVERSION[\"UTM zone 29N\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",-9,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9996,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",500000,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Navigation and medium accuracy spatial referencing.\"],AREA[\"Between 12\u00b0W and 6\u00b0W, northern hemisphere between equator and 84\u00b0N, onshore and offshore. Algeria. C\u00f4te D'Ivoire (Ivory Coast). Faroe Islands. Guinea. Ireland. Jan Mayen. Liberia, Mali. Mauritania. Morocco. Portugal. Sierra Leone. Spain. United Kingdom (UK). Western Sahara.\"],BBOX[0,-12.01,84.01,-6]],ID[\"EPSG\",32629]]", "false_easting": 500000.0, "false_northing": 0.0, "geographic_crs_name": "WGS 84", "grid_mapping_name": "transverse_mercator", - "horizontal_datum_name": "World Geodetic System 1984", + "horizontal_datum_name": "World Geodetic System 1984 ensemble", "inverse_flattening": 298.257223563, "latitude_of_projection_origin": 0.0, "longitude_of_central_meridian": -9.0, @@ -17868,7 +17830,7 @@ "scale_factor_at_central_meridian": 0.9996, "semi_major_axis": 6378137.0, "semi_minor_axis": 6356752.314245179, - "spatial_ref": "PROJCS[\"WGS 84 / UTM zone 29N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32629\"]]" + "spatial_ref": "PROJCRS[\"WGS 84 / UTM zone 29N\",BASEGEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic System 1984 ensemble\",MEMBER[\"World Geodetic System 1984 (Transit)\"],MEMBER[\"World Geodetic System 1984 (G730)\"],MEMBER[\"World Geodetic System 1984 (G873)\"],MEMBER[\"World Geodetic System 1984 (G1150)\"],MEMBER[\"World Geodetic System 1984 (G1674)\"],MEMBER[\"World Geodetic System 1984 (G1762)\"],MEMBER[\"World Geodetic System 1984 (G2139)\"],MEMBER[\"World Geodetic System 1984 (G2296)\"],ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ENSEMBLEACCURACY[2.0]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4326]],CONVERSION[\"UTM zone 29N\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",-9,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9996,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",500000,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Navigation and medium accuracy spatial referencing.\"],AREA[\"Between 12\u00b0W and 6\u00b0W, northern hemisphere between equator and 84\u00b0N, onshore and offshore. Algeria. C\u00f4te D'Ivoire (Ivory Coast). Faroe Islands. Guinea. Ireland. Jan Mayen. Liberia, Mali. Mauritania. Morocco. Portugal. Sierra Leone. Spain. United Kingdom (UK). Western Sahara.\"],BBOX[0,-12.01,84.01,-6]],ID[\"EPSG\",32629]]" }, "chunk_grid": { "configuration": { diff --git a/tests/test_s2_multiscale.py b/tests/test_s2_multiscale.py index 0a8c262d..cbe1f65d 100644 --- a/tests/test_s2_multiscale.py +++ b/tests/test_s2_multiscale.py @@ -84,10 +84,7 @@ def add_spatial_ref_to_group(group: zarr.Group, epsg_code: int | None = None) -> @pytest.mark.filterwarnings("ignore:.*:zarr.errors.ZarrUserWarning") def test_add_spatial_ref_matches_rasterio(tmp_path: pathlib.Path) -> None: """Test that our manual spatial_ref creation matches rasterio's output.""" - try: - import rioxarray - except ImportError: - pytest.skip("rioxarray not available") + rioxarray = pytest.importorskip("rioxarray") # noqa: F841 if ProjCRS is None: pytest.skip("pyproj not available") @@ -338,8 +335,6 @@ def test_convert_s2_optimized( s2_group_example.stem + ".json" ) - - # Write out the observed structure for analysis observed_output_path = Path("/tmp/observed_structure.json") observed_output_path.write_text(json.dumps(observed_structure_json, indent=2, sort_keys=True)) @@ -352,7 +347,7 @@ def test_convert_s2_optimized( # This is useful when the expected structure needs to be updated # expected_structure_path.write_text( # json.dumps(observed_structure_json, indent=2, sort_keys=True) - #) + # ) # check that all multiscale levels have the same data type # this check is redundant with the later check, but it's expedient to check this here. @@ -380,27 +375,39 @@ def test_convert_s2_optimized( # Write out the expected structure to the temporary test directory # This is useful when the expected structure needs to be updated - observed_tree_path = tmp_path/"observed_tree.json" - observed_flat_path = tmp_path/"observed_flat.json" - expected_flat_path = tmp_path/"expected_flat.json" + observed_tree_path = tmp_path / "observed_tree.json" + observed_flat_path = tmp_path / "observed_flat.json" + expected_flat_path = tmp_path / "expected_flat.json" - observed_tree_path.write_text( - json.dumps(observed_structure_json, indent=2, sort_keys=True) - ) + observed_tree_path.write_text(json.dumps(observed_structure_json, indent=2, sort_keys=True)) - observed_flat_path.write_text(json.dumps({k: v.model_dump() for k,v in observed_structure_flat.items()})) - expected_flat_path.write_text(json.dumps({k: v.model_dump() for k,v in expected_structure_flat.items()})) + observed_flat_path.write_text( + json.dumps({k: v.model_dump() for k, v in observed_structure_flat.items()}) + ) + expected_flat_path.write_text( + json.dumps({k: v.model_dump() for k, v in expected_structure_flat.items()}) + ) # Check that all of the keys are the same if o_keys != e_keys: - (tmp_path / "extra_observed_keys.json").write_text(json.dumps({k: v.model_dump() for k, v in observed_structure_flat if k in o_keys - e_keys}, indent=2)) - (tmp_path / "extra_expected_keys.json").write_text(json.dumps({k: v.model_dump() for k, v in expected_structure_flat if k in e_keys - o_keys}, indent=2)) + (tmp_path / "extra_observed_keys.json").write_text( + json.dumps( + {k: v.model_dump() for k, v in observed_structure_flat if k in o_keys - e_keys}, + indent=2, + ) + ) + (tmp_path / "extra_expected_keys.json").write_text( + json.dumps( + {k: v.model_dump() for k, v in expected_structure_flat if k in e_keys - o_keys}, + indent=2, + ) + ) raise AssertionError( - f"Key mismatch.\n" - f"Extra in observed: {sorted(o_keys - e_keys)[:20]}\n" - f"Missing in observed: {sorted(e_keys - o_keys)[:20]}\n" - f"Check {observed_tree_path!s} to see the observed JSON structure." + f"Key mismatch.\n" + f"Extra in observed: {sorted(o_keys - e_keys)[:20]}\n" + f"Missing in observed: {sorted(e_keys - o_keys)[:20]}\n" + f"Check {observed_tree_path!s} to see the observed JSON structure." ) # Check that all values are the same for common keys @@ -410,10 +417,19 @@ def test_convert_s2_optimized( ] if mismatch_values != []: - mismatch_values_path = (tmp_path / "mismatch_values.json") + mismatch_values_path = tmp_path / "mismatch_values.json" mismatch_values_path.write_text( json.dumps( - {k: {"expected": expected_structure_flat[k].model_dump(), "observed": observed_structure_flat[k].model_dump()} for k in mismatch_values}, indent=2)) + { + k: { + "expected": expected_structure_flat[k].model_dump(), + "observed": observed_structure_flat[k].model_dump(), + } + for k in mismatch_values + }, + indent=2, + ) + ) raise AssertionError( f"Value mismatches: {mismatch_values[:20]}\n" f"Check {mismatch_values_path!s} to see the observed JSON structure." diff --git a/tests/test_s2_multiscale_geo_metadata.py b/tests/test_s2_multiscale_geo_metadata.py index caa78d56..7985a6dd 100644 --- a/tests/test_s2_multiscale_geo_metadata.py +++ b/tests/test_s2_multiscale_geo_metadata.py @@ -395,8 +395,11 @@ def test_write_geo_metadata_adds_zarr_conventions( # Verify zarr_conventions is not initially present assert "zarr_conventions" not in sample_dataset_with_crs.attrs + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify zarr_conventions was added assert "zarr_conventions" in sample_dataset_with_crs.attrs @@ -431,8 +434,11 @@ def test_write_geo_metadata_adds_spatial_and_proj_attributes( ) -> None: """Test that spatial and proj attributes are added along with conventions.""" + crs = get_crs_from_dataset(sample_dataset_with_crs) + assert crs is not None + # Call the method - write_geo_metadata(sample_dataset_with_crs) + write_geo_metadata(sample_dataset_with_crs, crs=crs) # Verify spatial attributes assert "spatial:dimensions" in sample_dataset_with_crs.attrs @@ -453,13 +459,7 @@ def test_write_geo_metadata_adds_spatial_and_proj_attributes( assert "spatial:" in convention_names assert "proj:" in convention_names - def test_write_geo_metadata_no_crs_no_conventions( - self, sample_dataset_no_crs: xr.Dataset - ) -> None: - """Test that zarr_conventions are not added when no CRS is available.""" - - # Call the method with no CRS - write_geo_metadata(sample_dataset_no_crs) - - # Verify zarr_conventions was not added since no CRS was available - assert "zarr_conventions" not in sample_dataset_no_crs.attrs + def test_write_geo_metadata_requires_crs(self, sample_dataset_no_crs: xr.Dataset) -> None: + """Test that write_geo_metadata raises TypeError when crs is not provided.""" + with pytest.raises(TypeError, match="missing 1 required positional argument"): + write_geo_metadata(sample_dataset_no_crs) diff --git a/uv.lock b/uv.lock index c187108b..92f53ea7 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 3 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.13'", @@ -10,9 +11,9 @@ resolution-markers = [ name = "affine" version = "2.4.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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for floating-point multiscale data is NaN --- src/eopf_geozarr/data_api/geozarr/types.py | 2 ++ src/eopf_geozarr/s2_optimization/s2_converter.py | 6 +++++- src/eopf_geozarr/s2_optimization/s2_multiscale.py | 8 ++++++-- 3 files changed, 13 insertions(+), 3 deletions(-) diff --git a/src/eopf_geozarr/data_api/geozarr/types.py b/src/eopf_geozarr/data_api/geozarr/types.py index 613dbbd9..9ca52d3e 100644 --- a/src/eopf_geozarr/data_api/geozarr/types.py +++ b/src/eopf_geozarr/data_api/geozarr/types.py @@ -27,6 +27,7 @@ class TileMatrixLimitJSON(TypedDict): "filters", "shards", "_FillValue", + "fill_value", "dtype", } | CF_SCALE_OFFSET_KEYS @@ -57,6 +58,7 @@ class XarrayDataArrayEncoding(TypedDict): filters: NotRequired[tuple[object, ...]] shards: NotRequired[tuple[int, ...] | None] _FillValue: NotRequired[object] + fill_value: NotRequired[object] scale_factor: NotRequired[float] add_offset: NotRequired[float] dtype: NotRequired[object] diff --git a/src/eopf_geozarr/s2_optimization/s2_converter.py b/src/eopf_geozarr/s2_optimization/s2_converter.py index 8e765e79..0f759186 100644 --- a/src/eopf_geozarr/s2_optimization/s2_converter.py +++ b/src/eopf_geozarr/s2_optimization/s2_converter.py @@ -217,7 +217,11 @@ def array_reencoder( # Zarr v2 allows `None` as a fill value, but for Zarr v3 a fill value consistent with the # array's data type must be provided. We use the zarr-python model of the data type to get # a fill value here. - if metadata.fill_value is None: + if not keep_scale_offset and len(maybe_scale_offset) > 0: + # When stripping scale/offset, set fill_value to NaN so nodata regions are + # correctly identified as transparent by zarr-aware viewers (e.g. OpenLayers). + fill_value = float("nan") + elif metadata.fill_value is None: fill_value = metadata.dtype.default_scalar() else: fill_value = metadata.fill_value diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py index 5aff61d5..41916bd0 100644 --- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py +++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py @@ -173,8 +173,12 @@ def create_measurements_encoding( keep_keys = XARRAY_ENCODING_KEYS - {"compressors", "shards", "chunks"} if not keep_scale_offset: - # Remove scale/offset keys AND dtype to prevent transformation - keep_keys = keep_keys - CF_SCALE_OFFSET_KEYS - {"dtype"} + # When stripping scale/offset, also strip _FillValue since the original + # _FillValue is in raw integer units and meaningless for decoded float data. + keep_keys = keep_keys - CF_SCALE_OFFSET_KEYS - {"_FillValue"} + # Set zarr fill_value to NaN so nodata regions are correctly identified + # as transparent by zarr-aware viewers (e.g. OpenLayers GeoZarr source). + var_encoding["fill_value"] = float("nan") for key in keep_keys: if key in var_data.encoding: