diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index e7e00877..34b22f2e 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -15,7 +15,7 @@ jobs: - uses: actions/checkout@v5 - uses: astral-sh/setup-uv@v7 with: - python-version: "3.11" + python-version: "3.13" enable-cache: true - run: uv sync --group doc - run: uv run zensical build --clean diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index cecd13d5..e2cd043a 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -22,7 +22,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.11", "3.12", "3.13"] + python-version: ["3.12", "3.13", "3.14"] steps: - uses: actions/checkout@v4 @@ -47,7 +47,7 @@ jobs: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v7 with: - python-version: "3.11" + python-version: "3.12" enable-cache: true - run: uv sync --group doc - run: uv run zensical build --clean diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index a837d027..9b148b33 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -13,7 +13,7 @@ jobs: max-parallel: 2 matrix: platform: [ubuntu-latest, windows-latest, macos-latest] - python-version: ["3.11", "3.12", "3.13"] + python-version: ["3.12", "3.13", "3.14"] steps: - uses: actions/checkout@v4 diff --git a/README.md b/README.md index 2d2222dc..4c564b43 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | **Status** | [![Docs][badge-docs]][link-docs] [![Tests][badge-tests]][link-tests] | | **Package** | [![PyPI][badge-pypi]][link-pypi] [![Python][badge-python]][link-pypi] | -| **Meta** | [![Hatch][badge-hatch]][link-hatch] [![uv][badge-uv]][link-uv] [![Ruff][badge-ruff]][link-ruff] [![prek][badge-prek]][link-prek] [![License][badge-license]][link-license] | +| **Meta** | [![SPEC 0][badge-spec0]][link-spec0] [![Hatch][badge-hatch]][link-hatch] [![uv][badge-uv]][link-uv] [![Ruff][badge-ruff]][link-ruff] [![prek][badge-prek]][link-prek] [![License][badge-license]][link-license] | | **Cite** | [![DOI][badge-doi]][link-doi] | [badge-docs]: https://github.com/czbiohub-sf/iohub/actions/workflows/docs.yml/badge.svg @@ -13,6 +13,8 @@ [badge-python]: https://img.shields.io/pypi/pyversions/iohub [badge-hatch]: https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg [badge-uv]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json +[badge-spec0]: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038 +[link-spec0]: https://scientific-python.org/specs/spec-0000/ [badge-ruff]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json [badge-prek]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/j178/prek/master/docs/assets/badge-v0.json [badge-license]: https://img.shields.io/badge/License-BSD--3--Clause-blue.svg @@ -44,8 +46,7 @@ used at the Biohub and in the broader imaging community. ### Write -- OME-Zarr ([OME-NGFF v0.5](https://ngff.openmicroscopy.org/0.5/), OME-NGFF v0.4) -- Multi-page TIFF stacks organized in a directory hierarchy that mimics OME-NGFF (WIP) +- OME-Zarr ([OME-NGFF v0.5](https://ngff.openmicroscopy.org/0.5/)) ## Quick start diff --git a/docs/examples/run_multi_fov_hcs_ome_zarr.py b/docs/examples/run_multi_fov_hcs_ome_zarr.py index f175c5aa..4a3a3cbd 100644 --- a/docs/examples/run_multi_fov_hcs_ome_zarr.py +++ b/docs/examples/run_multi_fov_hcs_ome_zarr.py @@ -38,10 +38,7 @@ ) with open_ome_zarr( - store_path, - layout="hcs", - mode="w-", - channel_names=["DAPI", "GFP", "Brightfield"], + store_path, layout="hcs", mode="w-", channel_names=["DAPI", "GFP", "Brightfield"], implementation="tensorstore" ) as dataset: # Create and write to positions # This affects the tile arrangement in visualization diff --git a/pyproject.toml b/pyproject.toml index 0c1ad874..9abf4e12 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,24 +1,24 @@ [build-system] build-backend = "hatchling.build" -requires = ["hatchling", "uv-dynamic-versioning>=0.7.0"] +requires = ["hatchling", "uv-dynamic-versioning>=0.14.0"] [project] name = "iohub" description = "N-dimensional bioimaging data I/O with OME metadata in Python" authors = [ - {name = "CZ Biohub and iohub contributors", email = "iohub@czbiohub.org"}, + {name = "Biohub and iohub contributors", email = "iohub@czbiohub.org"}, ] license = {file = "LICENSE"} readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.12" classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Image Processing", "Operating System :: Microsoft :: Windows", @@ -26,19 +26,20 @@ classifiers = [ "Operating System :: MacOS :: MacOS X", ] dependencies = [ - "pandas>=1.5.2", + "pandas>=2", "pydantic>=2.8.2", "pydantic_extra_types>=2.9.0", "tifffile>=2025.5.21", - "natsort>=7.1.1", + "natsort>=8.4.0", "ndtiff>=2.2.1", - "zarr>=3.0.8", + "zarr>=3.1.6", "rich", "tqdm", "pillow>=9.4.0", "blosc2", "xarray>=2024.1.1", "dask[array]", + "zarrs>=0.2.3", ] dynamic = ["version"] @@ -52,13 +53,18 @@ Source = "https://github.com/czbiohub-sf/iohub" tensorstore = [ "tensorstore>=0.1.64", ] + +[project.entry-points."iohub.zarr-implementations"] +zarr = "iohub.core.implementations.zarr_python:ZarrPythonImplementation" +tensorstore = "iohub.core.implementations.tensorstore:TensorStoreImplementation" + [dependency-groups] test = [ "iohub[tensorstore]", "ngff-zarr[validate]", - "pytest>=5.0.0", + "pytest>=9.0.2", "pytest-cov", - "hypothesis>=6.61.0", + "hypothesis>=6.151.0", "requests>=2.22.0", "wget>=3.2", "ome-zarr>=0.12.0", @@ -67,7 +73,7 @@ acquire-zarr = [ "acquire-zarr", ] doc = [ - "zensical>=0.0.11", + "zensical>=0.0.29", "mkdocstrings-python>=2.0.2", "mkdocs-click>=0.8", ] @@ -93,15 +99,58 @@ style = "pep440" bump = true fallback-version = "0.0.0" +[tool.pytest] +minversion="9.0" +strict = true +testpaths = [ "tests" ] +addopts = [ + "-ra", + "--import-mode=importlib", +] + [tool.ruff] line-length = 120 - -[tool.ruff.format] -quote-style = "double" -indent-style = "space" -docstring-code-format = true -docstring-code-line-length = "dynamic" - -[tool.ruff.lint] -select = ["E", "F", "W", "I"] -ignore = ["E203", "E731"] +src = [ "src" ] +extend-include = [ "*.ipynb" ] +format.quote-style = "double" +format.indent-style = "space" +format.docstring-code-format = true +format.docstring-code-line-length = "dynamic" +lint.select = [ + "B", # flake8-bugbear + "BLE", # flake8-blind-except + "C4", # flake8-comprehensions + "D", # pydocstyle + "E", # pycodestyle errors + "F", # pyflakes + "I", # isort + "NPY", # NumPy-specific rules + "PERF", # Perflint + "PT", # pytest + "PTH", # pathlib + "RUF", # Ruff-specific rules + "TID", # flake8-tidy-imports + "UP", # pyupgrade + "W", # pycodestyle warnings +] +lint.ignore = [ + "B008", # function calls in argument defaults are fine when result is immutable + "D100", # Missing docstring in public module + "D101", # Missing docstring in public class + "D102", # Missing docstring in public method + "D103", # Missing docstring in public function + "D104", # Missing docstring in public package + "D105", # __magic__ methods are often self-explanatory + "D107", # Missing docstring in __init__ + "D203", # no blank line before class docstring (conflicts with D211) + "D213", # docstrings start immediately after opening triple quote (conflicts with D212) + "D400", # first line should end with period (doesn't work with single-line docstrings) + "D401", # first line in imperative mood + "E203", # slice whitespace (handled by formatter) + "E501", # line too long (formatter handles code; long comments are acceptable) + "E731", # lambda assignments are convenient +] +lint.per-file-ignores."*/__init__.py" = [ "F401" ] +lint.per-file-ignores."docs/*" = [ "D", "I", "NPY002", "PTH", "PERF401", "BLE001" ] +lint.per-file-ignores."tests/*" = [ "D" ] +lint.pydocstyle.convention = "numpy" diff --git a/src/iohub/__init__.py b/src/iohub/__init__.py index 34eb655f..6d2cbe7b 100644 --- a/src/iohub/__init__.py +++ b/src/iohub/__init__.py @@ -1,5 +1,6 @@ """ iohub + ===== N-dimensional bioimaging data I/O with OME metadata in Python diff --git a/src/iohub/clearcontrol.py b/src/iohub/clearcontrol.py index f9784b01..b2c7c631 100644 --- a/src/iohub/clearcontrol.py +++ b/src/iohub/clearcontrol.py @@ -1,9 +1,12 @@ +from __future__ import annotations + import json import re import warnings +from collections.abc import Callable, Sequence from functools import wraps from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Union +from typing import TYPE_CHECKING, Any import blosc2 import numpy as np @@ -15,16 +18,17 @@ from _typeshed import StrOrBytesPath -ArrayIndex = Union[int, slice, list[int], np.ndarray] +ArrayIndex = int | slice | list[int] | np.ndarray def _array_to_blosc_buffer( in_array: np.ndarray, - out_path: "StrOrBytesPath", + out_path: StrOrBytesPath, overwrite: bool = False, ) -> None: """ Compresses array and save into output path. + This function does not use new functionality from `blosc2` to compress large arrays on purpose, to emulate Clear Control behavior. @@ -41,6 +45,7 @@ def _array_to_blosc_buffer( When true it allows overwriting existing path. """ out_path = Path(out_path) + if out_path.exists() and not overwrite: raise ValueError(f"{out_path} already exists use `ovewrite=True`.") @@ -52,7 +57,7 @@ def _array_to_blosc_buffer( num_chunks = len(arr_bytes) // blosc2.MAX_BUFFERSIZE + 1 chunk_size = len(arr_bytes) // num_chunks - with open(out_path, "wb") as f: + with Path(out_path).open("wb") as f: while len(arr_bytes) > 0: compressed_chunk = blosc2.compress2(arr_bytes[:chunk_size], **kwargs) f.write(compressed_chunk) @@ -60,7 +65,7 @@ def _array_to_blosc_buffer( def blosc_buffer_to_array( - buffer_path: "StrOrBytesPath", + buffer_path: StrOrBytesPath, shape: tuple[int, ...], dtype: np.dtype, nthreads: int = 4, @@ -84,10 +89,11 @@ def blosc_buffer_to_array( Output numpy array. """ header_size = 32 + out_arr = np.empty(np.prod(shape), dtype=dtype) array_buffer = out_arr - with open(buffer_path, "rb") as f: + with Path(buffer_path).open("rb") as f: while True: # read header only blosc_header = bytes(f.read(header_size)) @@ -111,8 +117,8 @@ def _cached(f: Callable) -> Callable: @wraps(f) def _key_cache_wrapper( - self: "ClearControlFOV", - key: Union[ArrayIndex, tuple[ArrayIndex, ArrayIndex]], + self: ClearControlFOV, + key: ArrayIndex | tuple[ArrayIndex, ArrayIndex], ) -> np.ndarray: if not self._cache: return f(self, key) @@ -129,6 +135,7 @@ def _key_cache_wrapper( class ClearControlFOV(BaseFOV): """ Reader class for Clear Control dataset + https://github.com/royerlab/opensimview. It provides an array-like API for the Clear Control @@ -150,8 +157,8 @@ class ClearControlFOV(BaseFOV): def __init__( self, - data_path: "StrOrBytesPath", - missing_value: Optional[int] = None, + data_path: StrOrBytesPath, + missing_value: int | None = None, cache: bool = False, ): super().__init__() @@ -170,17 +177,18 @@ def root(self) -> Path: def shape(self) -> tuple[int, int, int, int, int]: """ Reads Clear Control index data of every data and returns + the element-wise minimum shape. """ - # dummy maximum shape size + shape = [65535] * 4 # guess of minimum line length, it might be wrong minimum_size = 64 numbers = re.compile(r"\d+\.\d+|\d+") for index_filepath in self._root.glob("*.index.txt"): - with open(index_filepath, "rb") as f: + with Path(index_filepath).open("rb") as f: if index_filepath.stat().st_size > minimum_size: f.seek(-minimum_size, 2) # goes to a little bit before the last line last_line = f.readlines()[-1].decode("utf-8") @@ -193,7 +201,7 @@ def shape(self) -> tuple[int, int, int, int, int]: int(values[2]), ] - shape = [min(s, v) for s, v in zip(shape, values)] + shape = [min(s, v) for s, v in zip(shape, values, strict=False)] shape.insert(1, len(self.channel_names)) shape[0] += 1 # time points starts counts on zero @@ -213,11 +221,12 @@ def channel_names(self) -> list[str]: def _read_volume( self, volume_shape: tuple[int, int, int], - channels: Union[Sequence[str], str], + channels: Sequence[str] | str, time_point: int, ) -> np.ndarray: """ Reads a single or multiple channels of blosc compressed + Clear Control volume. Parameters @@ -240,6 +249,7 @@ def _read_volume( When expected volume path not found. """ # single channel + if isinstance(channels, str): volume_name = f"{str(time_point).zfill(6)}.blc" volume_path = self._root / "stacks" / channels / volume_name @@ -247,7 +257,7 @@ def _read_volume( if self._missing_value is None: raise ValueError(f"{volume_path} not found.") else: - warnings.warn(f"{volume_path} not found. Filled with {self._missing_value}") + warnings.warn(f"{volume_path} not found. Filled with {self._missing_value}", stacklevel=2) return np.full(volume_name, self._missing_value, dtype=self._dtype) return blosc_buffer_to_array(volume_path, volume_shape, dtype=self._dtype) @@ -267,7 +277,7 @@ def _fix_indexing(indexing: ArrayIndex) -> ArrayIndex: return indexing - def __getitem__(self, key: Union[ArrayIndex, tuple[ArrayIndex, ...]]) -> np.ndarray: + def __getitem__(self, key: ArrayIndex | tuple[ArrayIndex, ...]) -> np.ndarray: """Lazily load array as indexed. Parameters @@ -286,6 +296,7 @@ def __getitem__(self, key: Union[ArrayIndex, tuple[ArrayIndex, ...]]) -> np.ndar Not all numpy array of indexing are implemented. """ # standardizing indexing + volume_slicing = None if isinstance(key, tuple): key = tuple(self._fix_indexing(k) for k in key) @@ -304,7 +315,7 @@ def __getitem__(self, key: Union[ArrayIndex, tuple[ArrayIndex, ...]]) -> np.ndar @_cached def _load_array( self, - key: Union[ArrayIndex, tuple[ArrayIndex, ArrayIndex]], + key: ArrayIndex | tuple[ArrayIndex, ArrayIndex], ) -> np.ndarray: # these are properties are loaded to avoid multiple reads per call shape = self.shape @@ -370,7 +381,7 @@ def metadata(self) -> dict[str, Any]: """Summarizes Clear Control metadata into a dictionary.""" cc_metadata = [] for path in self._root.glob("*.metadata.txt"): - with open(path, mode="r") as f: + with path.open() as f: channel_metadata = pd.DataFrame([json.loads(s) for s in f.readlines()]) cc_metadata.append(channel_metadata) @@ -394,6 +405,7 @@ def scale(self) -> list[float]: """Dataset temporal, channel and spacial scales.""" warnings.warn( ".scale will be deprecated use .zyx_scale or .t_scale.", + stacklevel=2, category=DeprecationWarning, ) metadata = self.metadata() @@ -422,7 +434,7 @@ def t_scale(self) -> float: return metadata["time_delta"] -def create_mock_clear_control_dataset(path: "StrOrBytesPath") -> None: +def create_mock_clear_control_dataset(path: StrOrBytesPath) -> None: """ Creates a (2, 4, 64, 64, 64) Clear Control dataset of random integers. @@ -454,7 +466,7 @@ def create_mock_clear_control_dataset(path: "StrOrBytesPath") -> None: index_path = path / f"{ch}.index.txt" metadata_path = path / f"{ch}.metadata.txt" - with open(index_path, "w") as idx_f, open(metadata_path, "w") as mt_f: + with Path(index_path).open("w") as idx_f, Path(metadata_path).open("w") as mt_f: for t in range(array.shape[0]): out_path = channel_dir / f"{str(t).zfill(6)}.blc" time_stamp = 45_000_000 * t diff --git a/src/iohub/cli/cli.py b/src/iohub/cli/cli.py index d390339c..b8f6e42e 100644 --- a/src/iohub/cli/cli.py +++ b/src/iohub/cli/cli.py @@ -81,9 +81,9 @@ def info(files, verbose): "--ome-zarr-version", "-v", required=False, - default="0.4", - type=click.Choice(["0.4", "0.5"]), - help="OME-NGFF version for the output Zarr store. '0.4' uses Zarr v2 format, '0.5' uses Zarr v3 format.", + default="0.5", + type=click.Choice(["0.5"]), + help="OME-NGFF version for the output Zarr store. '0.5' uses Zarr v3 format.", ) def convert(input, output, grid_layout, chunks, ome_zarr_version): """Converts Micro-Manager TIFF datasets to OME-Zarr""" @@ -157,7 +157,7 @@ def set_scale( image = "0" for input_position_dirpath in input_position_dirpaths: with open_ome_zarr(input_position_dirpath, layout="fov", mode="r+") as dataset: - for name, value in zip(["t", "z", "y", "x"], [t_scale, z_scale, y_scale, x_scale]): + for name, value in zip(["t", "z", "y", "x"], [t_scale, z_scale, y_scale, x_scale], strict=False): if value is None: continue dataset.set_scale(image, name, value) diff --git a/src/iohub/cli/parsing.py b/src/iohub/cli/parsing.py index 192a94fa..a16921c1 100644 --- a/src/iohub/cli/parsing.py +++ b/src/iohub/cli/parsing.py @@ -1,5 +1,5 @@ +from collections.abc import Callable from pathlib import Path -from typing import Callable, List import click from natsort import natsorted @@ -7,7 +7,7 @@ from iohub.ngff import Plate, open_ome_zarr -def _validate_and_process_paths(ctx: click.Context, opt: click.Option, value: List[str]) -> list[Path]: +def _validate_and_process_paths(ctx: click.Context, opt: click.Option, value: list[str]) -> list[Path]: # Sort and validate the input paths, # expanding plates into lists of positions input_paths = [p for p in map(Path, natsorted(value)) if p.is_dir()] @@ -15,8 +15,7 @@ def _validate_and_process_paths(ctx: click.Context, opt: click.Option, value: Li with open_ome_zarr(path, mode="r") as dataset: if isinstance(dataset, Plate): plate_path = input_paths.pop() - for position in dataset.positions(): - input_paths.append(plate_path / position[0]) + input_paths.extend(plate_path / position[0] for position in dataset.positions()) return input_paths @@ -46,8 +45,8 @@ class OptionEatAll(click.Option): def __init__(self, *args, **kwargs): self.save_other_options = kwargs.pop("save_other_options", True) nargs = kwargs.pop("nargs", -1) - assert nargs == -1, "nargs, if set, must be -1 not {}".format(nargs) - super(OptionEatAll, self).__init__(*args, **kwargs) + assert nargs == -1, f"nargs, if set, must be -1 not {nargs}" + super().__init__(*args, **kwargs) self._previous_parser_process = None self._eat_all_parser = None @@ -73,7 +72,7 @@ def parser_process(value, state): # call the actual process self._previous_parser_process(value, state) - retval = super(OptionEatAll, self).add_to_parser(parser, ctx) + retval = super().add_to_parser(parser, ctx) for name in self.opts: our_parser = parser._long_opt.get(name) or parser._short_opt.get(name) if our_parser: diff --git a/src/iohub/convert.py b/src/iohub/convert.py index b5200561..5a04180f 100644 --- a/src/iohub/convert.py +++ b/src/iohub/convert.py @@ -6,7 +6,6 @@ import dask import numpy as np -from dask.array import to_zarr from tqdm import tqdm from tqdm.contrib.itertools import product from tqdm.contrib.logging import logging_redirect_tqdm @@ -43,8 +42,8 @@ def _create_grid_from_coordinates(xy_coords: list[tuple[float, float]], rows: in A grid-like array mimicking the shape of the acquisition where the value in the array corresponds to the position index at that location. """ + coords = {} - coords = dict() coords_list = [] for idx, pos in enumerate(xy_coords): coords[idx] = pos @@ -71,6 +70,7 @@ def _create_grid_from_coordinates(xy_coords: list[tuple[float, float]], rows: in class TIFFConverter: """Convert Micro-Manager TIFF formats + (OME-TIFF, ND-TIFF) into HCS OME-Zarr. Each FOV will be written to a separate well in the plate layout. @@ -93,8 +93,12 @@ class TIFFConverter: and is ignored for other formats, by default None (attempt to apply to OME-TIFF datasets, disable this with ``False``) version : Literal["0.4", "0.5"], optional - OME-NGFF version for the output Zarr store, by default "0.4". - Version "0.4" uses Zarr v2 format, version "0.5" uses Zarr v3 format. + OME-NGFF version for the output Zarr store, by default "0.5". + Both versions use Zarr v3 format; "0.4" is deprecated for new stores. + implementation : str, optional + Zarr backend implementation to use for writing. + None (default) uses zarr-python. Pass "tensorstore" + to write via TensorStore (requires the optional tensorstore dependency). Notes ----- @@ -108,10 +112,12 @@ def __init__( input_dir: str | Path, output_dir: str | Path, grid_layout: int = False, - chunks: tuple[int] | Literal["XY", "XYZ"] = None, - hcs_plate: bool = None, - version: Literal["0.4", "0.5"] = "0.4", + chunks: tuple[int] | Literal["XY", "XYZ"] | None = None, + hcs_plate: bool | None = None, + version: Literal["0.4", "0.5"] = "0.5", + implementation: str | None = None, ): + self.implementation = implementation if version not in ("0.4", "0.5"): raise ValueError(f"Unsupported OME-NGFF version '{version}'. Supported versions are '0.4' and '0.5'.") self.version = version @@ -131,7 +137,7 @@ def __init__( self.summary_metadata = self.reader.micromanager_summary self.save_name = output_dir.name _logger.debug("Getting dataset summary information.") - self.coord_map = dict() + self.coord_map = {} self.p = len(self.reader) self.t = self.reader.frames self.c = self.reader.channels @@ -144,7 +150,7 @@ def __init__( self._check_hcs_sites() self._get_pos_names() _logger.info(f"Found Dataset {input_dir} with dimensions (P, T, C, Z, Y, X): {self.dim}") - self.metadata = dict() + self.metadata = {} self.metadata["iohub_version"] = _get_package_version("iohub") self.metadata["Summary"] = self.summary_metadata if grid_layout: @@ -180,15 +186,18 @@ def _make_default_grid(self): def _get_position_coords(self): """Get the position coordinates from the reader metadata. - Raises: + Raises + ------ ValueError: If stage positions are not available. - Returns: + Returns + ------- list: XY stage position coordinates. int: Number of grid rows. int: Number of grid columns. """ rows = set() + cols = set() xy_coords = [] @@ -199,22 +208,24 @@ def _get_position_coords(self): try: xy_stage = pos["DefaultXYStage"] stage_pos = pos[xy_stage] - except KeyError: - raise ValueError(f"Stage position is not available for position {idx}") + except KeyError as err: + raise ValueError(f"Stage position is not available for position {idx}") from err xy_coords.append(stage_pos) try: rows.add(pos["GridRow"]) cols.add(pos["GridCol"]) - except KeyError: - raise ValueError(f"Grid indices not available for position {idx}") + except KeyError as err: + raise ValueError(f"Grid indices not available for position {idx}") from err return xy_coords, len(rows), len(cols) def _get_pos_names(self): """Append a list of pos names in ascending order + (order in which they were acquired). """ self.pos_names = [] + for p in range(self.p): try: name = self.reader.stage_positions[p]["Label"] @@ -256,7 +267,7 @@ def _gen_chunks(self, input_chunks): # Adjust chunks to divide evenly into dimensions chunks = _adjust_chunks_for_divisibility(shape, chunks) - for i, (orig, adj, dim) in enumerate(zip(original_chunks, chunks, shape)): + for i, (orig, adj, dim) in enumerate(zip(original_chunks, chunks, shape, strict=False)): if orig != adj: _logger.warning(f"Chunk size {orig} on axis {i} adjusted to {adj} (dimension {dim}).") @@ -282,6 +293,7 @@ def _init_zarr_arrays(self): mode="w-", channel_names=self.reader.channel_names, version=self.version, + implementation=self.implementation, ) self.zarr_position_names = [] arr_kwargs = { @@ -315,7 +327,7 @@ def _init_grid_arrays(self, arr_kwargs): def _create_zeros_array(self, row_name: str, col_name: str, pos_name: str, arr_kwargs: dict) -> Position: pos = self.writer.create_position(row_name, col_name, pos_name) - self.zarr_position_names.append(pos.zgroup.name) + self.zarr_position_names.append(f"{row_name}/{col_name}/{pos_name}") _ = pos.create_zeros(**arr_kwargs) pos.metadata.omero.name = self.pos_names[len(self.zarr_position_names) - 1] pos.dump_meta() @@ -354,8 +366,7 @@ def _convert_image_plane_metadata(self, fov, zarr_name: str): # T/C/Z frame_key = "/".join([str(i) for i in (t_idx, c_idx, z_idx)]) position_image_plane_metadata[frame_key] = sorted_metadata - with open( - self.output_dir / zarr_name / "image_plane_metadata.json", + with (self.output_dir / zarr_name / "image_plane_metadata.json").open( mode="x", ) as metadata_file: json.dump(position_image_plane_metadata, metadata_file, indent=4) @@ -371,24 +382,26 @@ def __call__(self) -> None: >>> converter() """ _logger.debug("Setting up Zarr store.") + self._init_zarr_arrays() # Calculate chunk size in bytes for dask config # This prevents data loss when rechunking to zarr chunks # See: https://github.com/czbiohub-sf/iohub/issues/367 chunk_size_bytes = int(np.prod(self.chunks) * np.dtype(self.reader.dtype).itemsize) - # Run through every coordinate and convert in acquisition order + _logger.debug("Converting images.") with logging_redirect_tqdm(), dask.config.set({"array.chunk-size": chunk_size_bytes}): for zarr_pos_name, (_, fov) in tqdm( zip(self.zarr_position_names, self.reader, strict=True), - total=len(self.reader), + total=len(self.zarr_position_names), desc="Converting images", unit="FOV", ncols=80, ): zarr_img = self.writer[zarr_pos_name]["0"] - to_zarr(fov.xdata.data.rechunk(self.chunks), zarr_img) - self._convert_image_plane_metadata(fov, zarr_img.path) + zarr_img.write_from_dask(fov.xdata.data.rechunk(self.chunks)) + self._convert_image_plane_metadata(fov, f"{zarr_pos_name.lstrip('/')}/0") + self.writer.zgroup.attrs.update(self.metadata) self.writer.close() self.reader.close() diff --git a/src/iohub/core/__init__.py b/src/iohub/core/__init__.py new file mode 100644 index 00000000..24e1609f --- /dev/null +++ b/src/iohub/core/__init__.py @@ -0,0 +1,76 @@ +"""Core zarr implementation abstraction for iohub.""" + +from iohub.core.arrays import NGFFArray +from iohub.core.compat import ngff_version_for_format, zarr_format_for_version +from iohub.core.config import ( + CompressorConfig, + ImplementationConfig, + TensorStoreConfig, + ZarrConfig, +) +from iohub.core.errors import ( + ArraySpecError, + ImplementationNotFoundError, + IohubError, + PathNormalizationError, + StoreOpenError, +) +from iohub.core.protocol import ( + ArrayBackend, + ArrayIO, + GroupBackend, + ZarrImplementation, +) +from iohub.core.registry import ( + available_implementations, + get_implementation, + register_implementation, + set_default_implementation, +) +from iohub.core.specs import ArraySpec +from iohub.core.types import ( + AccessMode, + NGFFVersion, + StorePath, + ZarrFormat, +) +from iohub.core.utils import normalize_path, pad_shape + +__all__ = [ + # Types + "AccessMode", + "ArrayBackend", + "ArrayIO", + # Specs + "ArraySpec", + # Errors + "ArraySpecError", + # Config + "CompressorConfig", + # Protocol facets + "GroupBackend", + "ImplementationConfig", + "ImplementationNotFoundError", + "IohubError", + # Arrays + "NGFFArray", + "NGFFVersion", + "PathNormalizationError", + "StoreOpenError", + "StorePath", + "TensorStoreConfig", + "ZarrConfig", + "ZarrFormat", + "ZarrImplementation", + # Registry + "available_implementations", + "get_implementation", + "ngff_version_for_format", + # Utils + "normalize_path", + "pad_shape", + "register_implementation", + "set_default_implementation", + # Compat + "zarr_format_for_version", +] diff --git a/src/iohub/core/arrays.py b/src/iohub/core/arrays.py new file mode 100644 index 00000000..12ab0354 --- /dev/null +++ b/src/iohub/core/arrays.py @@ -0,0 +1,173 @@ +"""NGFFArray -- implementation-agnostic N-dimensional array base class.""" + +from __future__ import annotations + +from typing import Any, Self + +import numpy as np +from numpy.typing import NDArray + +from iohub.core.protocol import ZarrImplementation +from iohub.core.utils import pad_shape + + +class _OIndexProxy: + """Proxy for orthogonal indexing on an NGFFArray.""" + + def __init__(self, handle: Any, impl: ZarrImplementation): + self._handle = handle + self._impl = impl + + def __getitem__(self, key): + return self._impl.read_oindex(self._handle, key) + + def __setitem__(self, key, value): + self._impl.write_oindex(self._handle, key, value) + + +class NGFFArray: + """Base class for NGFF N-dimensional arrays. + + Delegates all I/O to the configured :class:`ZarrImplementation`. + Subclassed by ``ImageArray`` (5D) and ``TiledImageArray`` in ``nodes.py``. + """ + + _SUPPORTED_DIMS: str + _N_DIMS: int + + def __init__( + self, + handle: Any, + impl: ZarrImplementation, + dim_names: tuple[str, ...] | None = None, + ): + self._handle = handle + self._impl = impl + self._dim_names = dim_names + + @classmethod + def from_handle( + cls, + handle: Any, + impl: ZarrImplementation, + dim_names: tuple[str, ...] | None = None, + ) -> Self: + """Construct from an implementation-native handle.""" + return cls(handle, impl, dim_names=dim_names) + + def _get_dim(self, idx: int) -> int: + return pad_shape(self.shape, target=self._N_DIMS)[idx] + + # -- Metadata ---------------------------------------------------------- + + @property + def shape(self) -> tuple[int, ...]: + return self._impl.get_shape(self._handle) + + @property + def dtype(self) -> np.dtype: + return self._impl.get_dtype(self._handle) + + @property + def chunks(self) -> tuple[int, ...]: + return self._impl.get_chunks(self._handle) + + @property + def shards(self) -> tuple[int, ...] | None: + return self._impl.get_shards(self._handle) + + @property + def ndim(self) -> int: + return len(self.shape) + + @property + def nbytes(self) -> int: + """Total number of bytes for the uncompressed array.""" + return int(np.prod(self.shape)) * self.dtype.itemsize + + @property + def metadata(self): + """Delegate to native handle's metadata (if available).""" + return getattr(self._handle, "metadata", None) + + @property + def path(self) -> str: + """Path of the underlying array (if available).""" + handle = self._handle + if hasattr(handle, "path"): + return str(handle.path) + return "" + + @property + def basename(self) -> str: + handle = self._handle + if hasattr(handle, "basename"): + return handle.basename + if hasattr(handle, "path"): + return str(handle.path).rsplit("/", 1)[-1] + return "" + + # -- I/O --------------------------------------------------------------- + + def __getitem__(self, key): + return self._impl.read(self._handle, key) + + def __setitem__(self, key, value): + self._impl.write(self._handle, key, value) + + @property + def oindex(self): + return _OIndexProxy(self._handle, self._impl) + + def resize(self, new_shape: tuple[int, ...]) -> None: + """Resize the array in-place.""" + self._impl.resize(self._handle, new_shape) + + def append(self, data: np.ndarray, axis: int = 0) -> tuple[int, ...]: + """Append data along axis. Returns the new shape.""" + data = np.asarray(data) + old_shape = self.shape + new_shape = tuple(s + (data.shape[i] if i == axis else 0) for i, s in enumerate(old_shape)) + self.resize(new_shape) + region = tuple(slice(old_shape[i], new_shape[i]) if i == axis else slice(None) for i in range(len(old_shape))) + self._impl.write(self._handle, region, data) + return new_shape + + # -- Conversions ------------------------------------------------------- + + def numpy(self) -> NDArray: + """Return the whole array as an in-RAM NumPy array.""" + return self[:] + + def __array__(self, dtype=None, copy=None): + arr = self.numpy() + return arr if dtype is None else arr.astype(dtype) + + def dask_array(self): + """Return as a dask array (delegates to implementation).""" + return self._impl.to_dask(self._handle) + + def write_from_dask(self, dask_array) -> None: + """Write a dask array into this array's store.""" + self._impl.write_from_dask(self._handle, dask_array) + + def downsample_into( + self, + target: NGFFArray, + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample this array into a target array.""" + self._impl.downsample( + self._handle, + target._handle, + factors, + method, + ) + + # -- Escape hatch ------------------------------------------------------ + + @property + def native(self): + """Raw implementation handle (``zarr.Array``, TensorStore, etc.).""" + return self._handle diff --git a/src/iohub/core/compat.py b/src/iohub/core/compat.py new file mode 100644 index 00000000..c2dfcafb --- /dev/null +++ b/src/iohub/core/compat.py @@ -0,0 +1,24 @@ +"""NGFF version <-> zarr format mapping and compatibility utilities.""" + +from __future__ import annotations + +from iohub.core.types import NGFFVersion, ZarrFormat + +NGFF_TO_ZARR_FORMAT: dict[str, int] = {"0.4": 2, "0.5": 3} +ZARR_FORMAT_TO_NGFF: dict[int, str] = {2: "0.4", 3: "0.5"} + + +def zarr_format_for_version(version: NGFFVersion) -> ZarrFormat: + """Map NGFF version string to zarr format integer.""" + try: + return NGFF_TO_ZARR_FORMAT[version] # type: ignore[return-value] + except KeyError as err: + raise ValueError(f"Unknown NGFF version: {version!r}. Supported: {list(NGFF_TO_ZARR_FORMAT)}") from err + + +def ngff_version_for_format(zarr_format: ZarrFormat) -> NGFFVersion: + """Map zarr format integer to NGFF version string.""" + try: + return ZARR_FORMAT_TO_NGFF[zarr_format] # type: ignore[return-value] + except KeyError as err: + raise ValueError(f"Unknown zarr format: {zarr_format!r}. Supported: {list(ZARR_FORMAT_TO_NGFF)}") from err diff --git a/src/iohub/core/config.py b/src/iohub/core/config.py new file mode 100644 index 00000000..e96b879c --- /dev/null +++ b/src/iohub/core/config.py @@ -0,0 +1,48 @@ +"""Pydantic config models for zarr implementations.""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, Field + + +class CompressorConfig(BaseModel): + """Default compressor settings for array creation.""" + + cname: str = "zstd" + clevel: int = 1 + shuffle: str = "bitshuffle" + + +def _default_codec_pipeline() -> str: + """Use zarrs (Rust) codec pipeline if available, else default Python.""" + try: + import zarrs # noqa: F401 + + return "zarrs.ZarrsCodecPipeline" + except ImportError: + return "zarr.core.codec_pipeline.BatchedCodecPipeline" + + +class ZarrConfig(BaseModel): + """Config for the zarr-python implementation.""" + + codec_pipeline: str = Field(default_factory=_default_codec_pipeline) + validate_checksums: bool = True + compressor: CompressorConfig = Field(default_factory=CompressorConfig) + + +class TensorStoreConfig(BaseModel): + """Config for the TensorStore implementation.""" + + data_copy_concurrency: int = Field(default=4, ge=1) + context: dict | None = None + file_io_concurrency: int | None = None + file_io_sync: bool = True + file_io_locking: Literal["auto", "disabled"] = "auto" + cache_pool_bytes: int | None = None + extra_context: dict | None = None + + +ImplementationConfig = ZarrConfig | TensorStoreConfig diff --git a/src/iohub/core/downsample.py b/src/iohub/core/downsample.py new file mode 100644 index 00000000..87258260 --- /dev/null +++ b/src/iohub/core/downsample.py @@ -0,0 +1,74 @@ +"""Pure numpy downsample utilities — no backend coupling.""" + +from __future__ import annotations + +import itertools + +import numpy as np + + +def downsample_block(data: np.ndarray, factors: list[int], method: str) -> np.ndarray: + """Downsample an N-D numpy block by integer factors.""" + trim_slices = tuple(slice(0, (s // f) * f) for s, f in zip(data.shape, factors, strict=False)) + data = data[trim_slices] + + if method == "stride": + return data[tuple(slice(None, None, f) for f in factors)] + + new_shape = [] + reduce_axes = [] + for i, (s, f) in enumerate(zip(data.shape, factors, strict=False)): + new_shape.extend([s // f, f]) + reduce_axes.append(2 * i + 1) + data = data.reshape(new_shape) + + reduce_axes = tuple(reduce_axes) + if method == "mean": + return data.mean(axis=reduce_axes).astype(data.dtype) + elif method == "median": + return np.median(data, axis=reduce_axes).astype(data.dtype) + elif method == "min": + return data.min(axis=reduce_axes) + elif method == "max": + return data.max(axis=reduce_axes) + elif method == "mode": + from scipy import stats + + orig_shape = tuple(s // f for s, f in zip(data.shape[::2], factors, strict=False)) + flat = data.reshape(*orig_shape, -1) + return stats.mode(flat, axis=-1, keepdims=False).mode.astype(data.dtype) + else: + raise ValueError(f"Unknown downsample method: {method!r}") + + +def target_region_to_source( + target_region: tuple[slice, ...], factors: list[int], source_shape: tuple[int, ...] +) -> tuple[slice, ...]: + """Map a target-space region back to the corresponding source region.""" + source_slices = [] + for sl, f, src_dim in zip(target_region, factors, source_shape, strict=False): + start = sl.start * f + stop = min(sl.stop * f, src_dim) + source_slices.append(slice(start, stop)) + return tuple(source_slices) + + +def iter_work_regions(shape: tuple[int, ...], step_shape: tuple[int, ...]) -> list[tuple[slice, ...]]: + """Return shard/chunk-aligned regions covering the full array shape. + + Parameters + ---------- + shape : tuple[int, ...] + Total array shape. + step_shape : tuple[int, ...] + Step size per dimension (shard shape or chunk shape). + + Returns + ------- + list[tuple[slice, ...]] + One region (tuple of slices) per tile, covering the full array. + """ + dim_ranges = [] + for total, step in zip(shape, step_shape, strict=False): + dim_ranges.append([slice(s, min(s + step, total)) for s in range(0, total, step)]) + return [tuple(r) for r in itertools.product(*dim_ranges)] diff --git a/src/iohub/core/errors.py b/src/iohub/core/errors.py new file mode 100644 index 00000000..c6f44241 --- /dev/null +++ b/src/iohub/core/errors.py @@ -0,0 +1,26 @@ +"""Custom exceptions for iohub core.""" + + +class IohubError(Exception): + """Base exception for all iohub errors.""" + + +class ImplementationNotFoundError(IohubError, KeyError): + """Raised when a requested implementation is not registered.""" + + def __init__(self, name: str, available: list[str]): + self.name = name + self.available = available + super().__init__(f"Unknown implementation: {name!r}. Available: {available}") + + +class StoreOpenError(IohubError, OSError): + """Raised when a zarr store cannot be opened.""" + + +class ArraySpecError(IohubError, ValueError): + """Raised when an array spec is invalid.""" + + +class PathNormalizationError(IohubError, ValueError): + """Raised for invalid zarr paths (e.g., containing '..').""" diff --git a/src/iohub/core/implementations/__init__.py b/src/iohub/core/implementations/__init__.py new file mode 100644 index 00000000..2126ba76 --- /dev/null +++ b/src/iohub/core/implementations/__init__.py @@ -0,0 +1,10 @@ +"""Zarr I/O implementation backends. + +Implementations are discovered via the ``iohub.zarr-implementations`` +entry point group. See :mod:`iohub.core.registry` for the discovery mechanism. + +For direct imports:: + + from iohub.core.implementations.zarr_python import ZarrPythonImplementation + from iohub.core.implementations.tensorstore import TensorStoreImplementation +""" diff --git a/src/iohub/core/implementations/tensorstore.py b/src/iohub/core/implementations/tensorstore.py new file mode 100644 index 00000000..b55f0272 --- /dev/null +++ b/src/iohub/core/implementations/tensorstore.py @@ -0,0 +1,456 @@ +"""TensorStore implementation (optional dependency).""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import numpy as np + +from iohub.core.config import TensorStoreConfig +from iohub.core.protocol import ZarrImplementation +from iohub.core.specs import ArraySpec +from iohub.core.types import StorePath + +try: + import tensorstore as ts + + _TS_AVAILABLE = True +except ImportError: + ts = None # type: ignore[assignment] + _TS_AVAILABLE = False + +if TYPE_CHECKING: + import tensorstore as ts + + +class _TsAttrs(dict): + """Persistent attrs for a _TsGroup. + + Reads from ``zarr.json`` (v3) or ``.zattrs`` (v2). + Writes always go to ``zarr.json``. + """ + + def __init__(self, group: _TsGroup): + self._group = group + super().__init__(self._load()) + + def _load(self) -> dict: + zarr_json = self._group.path / "zarr.json" + if zarr_json.exists(): + return json.loads(zarr_json.read_text()).get("attributes", {}) + zattrs = self._group.path / ".zattrs" + if zattrs.exists(): + return json.loads(zattrs.read_text()) + return {} + + def _save(self) -> None: + zarr_json = self._group.path / "zarr.json" + if zarr_json.exists(): + meta = json.loads(zarr_json.read_text()) + else: + meta = {"zarr_format": 3, "node_type": "group", "attributes": {}} + meta["attributes"] = dict(self) + zarr_json.write_text(json.dumps(meta)) + + def __setitem__(self, key, value): + super().__setitem__(key, value) + self._save() + + def update(self, *args, **kwargs): + super().update(*args, **kwargs) + self._save() + + +def _detect_zarr_driver(path: Path) -> str: + """Detect zarr format for a store root. Called once at open_group time.""" + if (path / "zarr.json").exists(): + return "zarr3" + if (path / ".zattrs").exists() or (path / ".zgroup").exists(): + return "zarr2" + return "zarr3" # new stores will be created as v3 always + + +class _TsGroup: + """Lightweight group handle (tensorstore has no native group concept).""" + + def __init__( + self, + path: Path, + mode: str, + impl: TensorStoreImplementation, + zarr_driver: str = "zarr3", + root: Path | None = None, + ): + if mode == "w-" and path.exists(): + raise FileExistsError(f"Store already exists: {path}") + if mode in ("w", "w-", "a") and not path.exists(): + path.mkdir(parents=True, exist_ok=True) + (path / "zarr.json").write_text('{"zarr_format": 3, "node_type": "group", "attributes": {}}') + self.path = path + self.mode = mode + self._impl = impl + self.zarr_driver = zarr_driver + self._root = root if root is not None else path + + def create_group(self, name: str, overwrite: bool = False) -> _TsGroup: + sub = self.path / name + if sub.exists() and not overwrite: + return _TsGroup(path=sub, mode="a", impl=self._impl, zarr_driver=self.zarr_driver) + sub.mkdir(parents=True, exist_ok=True) + zarr_json = sub / "zarr.json" + if not zarr_json.exists() or overwrite: + zarr_json.write_text(json.dumps({"zarr_format": 3, "node_type": "group", "attributes": {}})) + return _TsGroup(path=sub, mode="a", impl=self._impl, zarr_driver=self.zarr_driver) + + def __contains__(self, name: str) -> bool: + return self.get(name) is not None + + def __delitem__(self, name: str) -> None: + import shutil + + sub = self.path / name + if not sub.exists(): + raise KeyError(name) + shutil.rmtree(sub) + + def __getitem__(self, name: str): + result = self.get(name) + if result is None: + raise KeyError(name) + return result + + def get(self, name: str, default=None): + sub = self.path / name + if not sub.is_dir(): + return default + if self.zarr_driver == "zarr3": + try: + meta = json.loads((sub / "zarr.json").read_text()) + if meta.get("node_type") == "array": + return self._impl.open_array(self, name) + if meta.get("node_type") == "group": + return _TsGroup(path=sub, mode="a", impl=self._impl, zarr_driver=self.zarr_driver, root=self._root) + except (OSError, ValueError): + pass + else: + if (sub / ".zarray").exists(): + return self._impl.open_array(self, name) + if (sub / ".zgroup").exists(): + return _TsGroup(path=sub, mode="a", impl=self._impl, zarr_driver=self.zarr_driver, root=self._root) + return default + + @property + def store(self) -> _TsGroup: + return self + + @property + def root(self) -> Path: + return self.path + + @property + def name(self) -> str: + try: + rel = self.path.relative_to(self._root) + return "/" + str(rel) if str(rel) != "." else "/" + except ValueError: + return str(self.path) + + @property + def basename(self) -> str: + return self.path.name + + @property + def attrs(self) -> _TsAttrs: + if not hasattr(self, "_attrs_cache") or self._attrs_cache is None: + self._attrs_cache = _TsAttrs(self) + return self._attrs_cache + + def tree(self, level: int | None = None) -> str: + lines = [self.basename] + self._tree_lines(self.path, "", level, 0, lines) + return "\n".join(lines) + + def _tree_lines(self, p: Path, prefix: str, max_level: int | None, depth: int, lines: list) -> None: + if max_level is not None and depth >= max_level: + return + try: + children = sorted( + d for d in (entry.name for entry in p.iterdir()) if (p / d).is_dir() and not d.startswith(".") + ) + except OSError: + return + for i, child in enumerate(children): + connector = "└── " if i == len(children) - 1 else "├── " + lines.append(f"{prefix}{connector}{child}") + extension = " " if i == len(children) - 1 else "│ " + self._tree_lines(p / child, prefix + extension, max_level, depth + 1, lines) + + +def _fill_value_for_spec(data_type: str, fill_value: int | float) -> object: + """Return a TensorStore-compatible fill value for the given dtype string.""" + if data_type == "bool": + return bool(fill_value) + if data_type in ("complex64", "complex128"): + # TensorStore requires [real, imag] for complex fill values + return [float(fill_value), 0.0] + return fill_value + + +def _ts_open(spec: dict, **kwargs) -> ts.TensorStore: + """Thin wrapper that converts TensorStore errors to standard Python exceptions.""" + import tensorstore as ts + + try: + return ts.open(spec, **kwargs).result() + except Exception as exc: + msg = str(exc).lower() + if "not found" in msg or "no such file" in msg: + raise FileNotFoundError(str(exc)) from exc + if "already exists" in msg: + raise FileExistsError(str(exc)) from exc + raise RuntimeError(f"TensorStore error: {exc}") from exc + + +def _spec_to_ts(spec: ArraySpec, path: str) -> dict: + fill_value = _fill_value_for_spec(spec.data_type, spec.fill_value) + metadata: dict = { + "shape": list(spec.shape), + "data_type": spec.data_type, + "chunk_grid": spec.chunk_grid, + "chunk_key_encoding": spec.chunk_key_encoding, + "codecs": spec.codecs, + "fill_value": fill_value, + } + if spec.dimension_names: + metadata["dimension_names"] = spec.dimension_names + return {"driver": "zarr3", "kvstore": {"driver": "file", "path": path}, "metadata": metadata} + + +_TS_IMPL_BASE = ZarrImplementation[_TsGroup, ts.TensorStore] if _TS_AVAILABLE else object # type: ignore[assignment] + + +class TensorStoreImplementation(_TS_IMPL_BASE): + """TensorStore-backed I/O implementation.""" + + def __init__(self, config: TensorStoreConfig | None = None): + self.config = config or TensorStoreConfig() + self._array_cache: dict[str, Any] = {} + + def _context(self) -> ts.Context: + import tensorstore as ts + + if not hasattr(self, "_ctx") or self._ctx is None: + ctx_opts = dict(self.config.context or {}) + if self.config.data_copy_concurrency: + ctx_opts.setdefault( + "data_copy_concurrency", + {"limit": self.config.data_copy_concurrency}, + ) + if self.config.cache_pool_bytes is not None: + ctx_opts.setdefault( + "cache_pool", + {"total_bytes_limit": self.config.cache_pool_bytes}, + ) + if self.config.file_io_concurrency is not None: + ctx_opts.setdefault( + "file_io_concurrency", + {"limit": self.config.file_io_concurrency}, + ) + if not self.config.file_io_sync: + ctx_opts.setdefault("file_io_sync", False) + if self.config.file_io_locking != "auto": + ctx_opts.setdefault("file_io_locking", self.config.file_io_locking) + if self.config.extra_context: + ctx_opts.update(self.config.extra_context) + self._ctx = ts.Context(ctx_opts) + return self._ctx + + # -- Group operations -------------------------------------------------- + + def open_group(self, path: StorePath, mode: str, zarr_format: int | None = None) -> _TsGroup: + p = Path(path) + return _TsGroup(path=p, mode=mode, impl=self, zarr_driver=_detect_zarr_driver(p), root=p) + + def _iter_children(self, group: _TsGroup, node_type: str) -> list[str]: + """Return sorted child names matching node_type ('group' or 'array').""" + p = Path(group.path) + if not p.is_dir(): + return [] + keys: list[str] = [] + match group.zarr_driver: + case "zarr3": + for entry in p.iterdir(): + d = entry.name + if not entry.is_dir() or d.startswith("."): + continue + try: + meta = json.loads((entry / "zarr.json").read_text()) + if meta.get("node_type") == node_type: + keys.append(d) + except (OSError, ValueError): + pass + case "zarr2": + sentinel = {"group": ".zgroup", "array": ".zarray"}[node_type] + keys = [e.name for e in p.iterdir() if (p / e.name / sentinel).exists()] + return sorted(keys) + + def group_keys(self, group: _TsGroup) -> list[str]: + return self._iter_children(group, "group") + + def array_keys(self, group: _TsGroup) -> list[str]: + return self._iter_children(group, "array") + + def close(self, group: _TsGroup) -> None: + pass # TensorStore handles are not persistent connections + + def get_zarr_format(self, group: _TsGroup) -> int: + return 3 # TensorStore only supports zarr v3 + + # -- Array lifecycle --------------------------------------------------- + + def create_array(self, group: _TsGroup, name: str, spec: ArraySpec, *, overwrite: bool = False) -> ts.TensorStore: + ts_spec = _spec_to_ts(spec, str(Path(group.path) / name)) + return _ts_open(ts_spec, create=True, delete_existing=overwrite, context=self._context()) + + def create_array_v2( + self, + group: _TsGroup, + name: str, + *, + shape: tuple[int, ...], + dtype: np.dtype, + chunks: tuple[int, ...], + fill_value: int = 0, + overwrite: bool = False, + ) -> ts.TensorStore: + spec = { + "driver": "zarr2", + "kvstore": {"driver": "file", "path": str(Path(group.path) / name)}, + "metadata": { + "shape": list(shape), + "chunks": list(chunks), + "dtype": np.dtype(dtype).str, # zarr2 uses NumPy dtype strings e.g. " ts.TensorStore: + key = str(Path(group.path) / name) + if key not in self._array_cache: + spec = { + "driver": group.zarr_driver, + "kvstore": {"driver": "file", "path": key}, + } + self._array_cache[key] = _ts_open( + spec, + open=True, + read=True, + write=(group.mode != "r"), + context=self._context(), + ) + return self._array_cache[key] + + # -- Array I/O --------------------------------------------------------- + + def read(self, handle: ts.TensorStore, selection: Any) -> np.ndarray: + return np.asarray(handle[selection].read().result()) + + def write(self, handle: ts.TensorStore, selection: Any, data: np.ndarray) -> None: + handle[selection].write(data).result() + + def read_oindex(self, handle: ts.TensorStore, selection: Any) -> np.ndarray: + return np.asarray(handle.oindex[tuple(selection)].read().result()) + + def write_oindex(self, handle: ts.TensorStore, selection: Any, data: np.ndarray) -> None: + handle.oindex[tuple(selection)].write(data).result() + + def resize(self, handle: ts.TensorStore, new_shape: tuple[int, ...]) -> None: + handle.resize(exclusive_max=new_shape).result() + + # -- Array metadata ---------------------------------------------------- + + def get_shape(self, handle: ts.TensorStore) -> tuple[int, ...]: + return tuple(handle.shape) + + def get_dtype(self, handle: ts.TensorStore) -> np.dtype: + return handle.dtype.numpy_dtype + + def get_chunks(self, handle: ts.TensorStore) -> tuple[int, ...]: + cl = handle.chunk_layout + return tuple(cl.read_chunk.shape) if cl.read_chunk.shape else self.get_shape(handle) + + def get_shards(self, handle: ts.TensorStore) -> tuple[int, ...] | None: + cl = handle.chunk_layout + read_shape = cl.read_chunk.shape + write_shape = cl.write_chunk.shape + if read_shape and write_shape and tuple(read_shape) != tuple(write_shape): + return tuple(write_shape) + return None + + # -- Conversions ------------------------------------------------------- + + def to_dask(self, handle: ts.TensorStore) -> Any: + import dask.array as da + + return da.from_array(handle, chunks=self.get_chunks(handle)) + + def write_from_dask(self, handle: ts.TensorStore, dask_array: Any) -> None: + import dask + + result = dask_array.store(handle, lock=False, compute=False) + dask.compute(result) + + # -- High-performance operations --------------------------------------- + + def downsample( + self, + source: ts.TensorStore, + target: ts.TensorStore, + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample source into target using native ts.downsample.""" + import tensorstore as ts + + downsampled = ts.downsample(source, downsample_factors=factors, method=method) + sharded = self.get_shards(target) is not None + if sharded: + with ts.Transaction() as txn: + for region in self.iter_work_regions(target): + target.with_transaction(txn)[region].write(downsampled.with_transaction(txn)[region]).result() + else: + for region in self.iter_work_regions(target): + with ts.Transaction() as txn: + target.with_transaction(txn)[region].write(downsampled.with_transaction(txn)[region]).result() + + def downsample_region( + self, + source: ts.TensorStore, + target: ts.TensorStore, + target_region: tuple[slice, ...], + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample a single region using TensorStore.""" + import tensorstore as ts + + downsampled = ts.downsample(source, downsample_factors=factors, method=method) + target[target_region].write(downsampled[target_region]).result() + + def iter_work_regions(self, target: ts.TensorStore) -> list[tuple[slice, ...]]: + """Return chunk/shard-aligned regions covering all dimensions.""" + import itertools + + cl = target.chunk_layout + write_shape = cl.write_chunk.shape or target.shape + dim_ranges = [] + for _dim, (total, step) in enumerate(zip(target.shape, write_shape, strict=False)): + starts = range(0, total, step) + dim_ranges.append([slice(s, min(s + step, total)) for s in starts]) + return [tuple(region) for region in itertools.product(*dim_ranges)] diff --git a/src/iohub/core/implementations/zarr_python.py b/src/iohub/core/implementations/zarr_python.py new file mode 100644 index 00000000..6218ecfa --- /dev/null +++ b/src/iohub/core/implementations/zarr_python.py @@ -0,0 +1,181 @@ +"""ZarrPython implementation -- consolidates all zarr internal imports.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import zarr + +from iohub.core.config import ZarrConfig +from iohub.core.downsample import downsample_block, iter_work_regions, target_region_to_source +from iohub.core.protocol import ZarrImplementation +from iohub.core.specs import ArraySpec +from iohub.core.types import StorePath + + +class ZarrPythonImplementation(ZarrImplementation[zarr.Group, zarr.Array]): + """Zarr-python backed I/O implementation.""" + + def __init__(self, config: ZarrConfig | None = None): + self.config = config or ZarrConfig() + # Apply codec pipeline config globally — zarr-python reads this at I/O time, + # not at group-open time, so it must persist beyond the open_group call. + zarr.config.set( + { + "codec_pipeline.path": self.config.codec_pipeline, + "codec_pipeline.validate_checksums": self.config.validate_checksums, + } + ) + + # -- Group operations -------------------------------------------------- + + def open_group(self, path: StorePath, mode: str, zarr_format: int | None = None) -> zarr.Group: + return zarr.open_group(path, mode=mode, zarr_format=zarr_format) + + def group_keys(self, group: zarr.Group) -> list[str]: + return sorted(group.group_keys()) + + def array_keys(self, group: zarr.Group) -> list[str]: + return sorted(group.array_keys()) + + def close(self, group: zarr.Group) -> None: + group.store.close() + + def get_zarr_format(self, group: zarr.Group) -> int: + return group.metadata.zarr_format + + # -- Array lifecycle --------------------------------------------------- + + def create_array(self, group: zarr.Group, name: str, spec: ArraySpec, *, overwrite: bool = False) -> zarr.Array: + path = f"{group.path}/{name}" if group.path != "/" else name + outer_chunks = tuple(spec.chunk_grid["configuration"]["chunk_shape"]) + return zarr.open_array( + group.store, + path=path, + mode="w" if overwrite else "w-", + shape=spec.shape, + dtype=spec.data_type, + chunks=outer_chunks, + chunk_key_encoding=spec.chunk_key_encoding, + codecs=spec.codecs, + fill_value=spec.fill_value, + dimension_names=spec.dimension_names, + attributes=spec.attributes, + zarr_format=3, + ) + + def create_array_v2( + self, + group: zarr.Group, + name: str, + *, + shape: tuple[int, ...], + dtype: np.dtype, + chunks: tuple[int, ...], + fill_value: int = 0, + overwrite: bool = False, + ) -> zarr.Array: + from numcodecs import Blosc + + cname = self.config.compressor.cname + clevel = self.config.compressor.clevel + shuffle_map = { + "noshuffle": Blosc.NOSHUFFLE, + "shuffle": Blosc.SHUFFLE, + "bitshuffle": Blosc.BITSHUFFLE, + } + shuffle = shuffle_map.get(self.config.compressor.shuffle, Blosc.BITSHUFFLE) + return group.create_array( + name=name, + shape=shape, + dtype=dtype, + chunks=chunks, + overwrite=overwrite, + fill_value=fill_value, + chunk_key_encoding={"name": "v2", "separator": "/"}, + compressor=Blosc(cname=cname, clevel=clevel, shuffle=shuffle), + ) + + def open_array(self, group: zarr.Group, name: str) -> zarr.Array: + return group[name] + + # -- Array I/O --------------------------------------------------------- + + def read(self, handle: zarr.Array, selection: Any) -> np.ndarray: + return np.asarray(handle[selection]) + + def write(self, handle: zarr.Array, selection: Any, data: np.ndarray) -> None: + handle[selection] = data + + def read_oindex(self, handle: zarr.Array, selection: Any) -> np.ndarray: + return np.asarray(handle.oindex[selection]) + + def write_oindex(self, handle: zarr.Array, selection: Any, data: np.ndarray) -> None: + handle.oindex[selection] = data + + def resize(self, handle: zarr.Array, new_shape: tuple[int, ...]) -> None: + handle.resize(new_shape) + + # -- Array metadata ---------------------------------------------------- + + def get_shape(self, handle: zarr.Array) -> tuple[int, ...]: + return handle.shape + + def get_dtype(self, handle: zarr.Array) -> np.dtype: + return handle.dtype + + def get_chunks(self, handle: zarr.Array) -> tuple[int, ...]: + return handle.chunks + + def get_shards(self, handle: zarr.Array) -> tuple[int, ...] | None: + return getattr(handle, "shards", None) + + # -- Conversions ------------------------------------------------------- + + def to_dask(self, handle: zarr.Array) -> Any: + import dask.array as da + + return da.from_zarr(handle) + + def write_from_dask(self, handle: zarr.Array, dask_array: Any) -> None: + from dask.array import to_zarr + + to_zarr(dask_array, handle) + + # -- High-performance operations --------------------------------------- + + def downsample( + self, + source: zarr.Array, + target: zarr.Array, + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample source into target sequentially (shard-by-shard).""" + for region in self.iter_work_regions(target): + self.downsample_region(source, target, region, factors, method) + + def downsample_region( + self, + source: zarr.Array, + target: zarr.Array, + target_region: tuple[slice, ...], + factors: list[int], + method: str = "mean", + ) -> None: + """Read, downsample, and write a single shard-aligned region.""" + source_region = target_region_to_source(target_region, factors, source.shape) + source_data = np.asarray(source[source_region]) + local_factors = [] + for sl_src, sl_tgt, f in zip(source_region, target_region, factors, strict=False): + src_size = sl_src.stop - sl_src.start + tgt_size = sl_tgt.stop - sl_tgt.start + local_factors.append(max(1, src_size // tgt_size) if tgt_size > 0 else f) + downsampled = downsample_block(source_data, local_factors, method) + target[target_region] = downsampled + + def iter_work_regions(self, target: zarr.Array) -> list[tuple[slice, ...]]: + """Return shard/chunk-aligned regions for parallel iteration.""" + step_shape = self.get_shards(target) or target.chunks + return iter_work_regions(target.shape, step_shape) diff --git a/src/iohub/core/protocol.py b/src/iohub/core/protocol.py new file mode 100644 index 00000000..99c1ae7b --- /dev/null +++ b/src/iohub/core/protocol.py @@ -0,0 +1,131 @@ +"""ZarrImplementation Protocol -- the contract for zarr I/O backends. + +Type parameter conventions: + G -- the native group handle type (e.g. ``zarr.Group``, ``_TsGroup``) + A -- the native array handle type (e.g. ``zarr.Array``, ``ts.TensorStore``) + +Concrete bindings per implementation: + ZarrPythonImplementation -> G=zarr.Group, A=zarr.Array + TensorStoreImplementation -> G=_TsGroup, A=ts.TensorStore +""" + +from typing import Any, Protocol, runtime_checkable + +import numpy as np + +from iohub.core.specs import ArraySpec +from iohub.core.types import StorePath + + +@runtime_checkable +class GroupBackend[G](Protocol): + """Protocol for opening and navigating zarr groups.""" + + def open_group(self, path: StorePath, mode: str, zarr_format: int | None = None) -> G: ... + + def group_keys(self, group: G) -> list[str]: ... + + def array_keys(self, group: G) -> list[str]: ... + + def close(self, group: G) -> None: ... + + def get_zarr_format(self, group: G) -> int: ... + + +@runtime_checkable +class ArrayBackend[G, A](Protocol): + """Protocol for creating and opening arrays within a group.""" + + def create_array(self, group: G, name: str, spec: ArraySpec, *, overwrite: bool = False) -> A: + """Create an array from an ``ArraySpec``. Returns the native handle.""" + ... + + def open_array(self, group: G, name: str) -> A: ... + + def create_array_v2( + self, + group: G, + name: str, + *, + shape: tuple[int, ...], + dtype: Any, + chunks: tuple[int, ...], + fill_value: int = 0, + overwrite: bool = False, + ) -> A: + """Create a zarr v2 array. Only required for v0.4 store support.""" + raise NotImplementedError(f"{type(self).__name__!r} does not support zarr v2 array creation.") + + +@runtime_checkable +class ArrayIO[A](Protocol): + """Protocol for reading, writing, and querying array data.""" + + def read(self, handle: A, selection: Any) -> np.ndarray: ... + + def write(self, handle: A, selection: Any, data: np.ndarray) -> None: ... + + def read_oindex(self, handle: A, selection: Any) -> np.ndarray: ... + + def write_oindex(self, handle: A, selection: Any, data: np.ndarray) -> None: ... + + def resize(self, handle: A, new_shape: tuple[int, ...]) -> None: ... + + def get_shape(self, handle: A) -> tuple[int, ...]: ... + + def get_dtype(self, handle: A) -> np.dtype: ... + + def get_chunks(self, handle: A) -> tuple[int, ...]: ... + + def get_shards(self, handle: A) -> tuple[int, ...] | None: ... + + def to_dask(self, handle: A) -> Any: + """Return the handle wrapped as a dask array.""" + ... + + def write_from_dask(self, handle: A, dask_array: Any) -> None: + """Write a dask array into the store.""" + ... + + def downsample( + self, + source: A, + target: A, + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample source into target sequentially.""" + ... + + def downsample_region( + self, + source: A, + target: A, + target_region: tuple[slice, ...], + factors: list[int], + method: str = "mean", + ) -> None: + """Downsample source into target for a single region. + + ``target_region`` is expressed in **target coordinates**. + Implementations are responsible for mapping back to source + coordinates (e.g. multiplying by ``factors``) as needed. + """ + ... + + def iter_work_regions(self, target: A) -> list[tuple[slice, ...]]: + """Return shard/chunk-aligned regions suitable for parallel iteration.""" + ... + + +@runtime_checkable +class ZarrImplementation[G, A](GroupBackend[G], ArrayBackend[G, A], ArrayIO[A], Protocol): + """Combined Protocol satisfied by any full zarr I/O backend. + + A conforming class must implement all methods from + :class:`GroupBackend`, :class:`ArrayBackend`, and :class:`ArrayIO`. + Specialise as ``ZarrImplementation[zarr.Group, zarr.Array]`` for + static type-checker use. + """ + + ... diff --git a/src/iohub/core/registry.py b/src/iohub/core/registry.py new file mode 100644 index 00000000..d6f64f25 --- /dev/null +++ b/src/iohub/core/registry.py @@ -0,0 +1,90 @@ +"""Entrypoint-based implementation discovery and registry.""" + +from __future__ import annotations + +import importlib +import logging +import threading +from importlib.metadata import entry_points + +from iohub.core.config import ImplementationConfig +from iohub.core.errors import ImplementationNotFoundError + +_logger = logging.getLogger(__name__) +_default: str = "zarr" +_IMPLEMENTATIONS: dict[str, type] = {} +_discovered: bool = False +_lock = threading.RLock() + +# Built-in implementations (always available, loaded before entrypoints) +_BUILTINS: dict[str, str] = { + "zarr": "iohub.core.implementations.zarr_python:ZarrPythonImplementation", +} + + +def _discover() -> None: + global _discovered + if _discovered: + return + with _lock: + if _discovered: + return + # Load builtins first + for name, path in _BUILTINS.items(): + module_path, cls_name = path.rsplit(":", 1) + try: + mod = importlib.import_module(module_path) + _IMPLEMENTATIONS[name] = getattr(mod, cls_name) + except Exception as e: + _logger.error( + f"Failed to load built-in implementation {name!r} from {path!r}. " + f"iohub may be non-functional. Error: {e}", + exc_info=True, + ) + # Entrypoint plugins can override builtins + for ep in entry_points(group="iohub.zarr-implementations"): + try: + _IMPLEMENTATIONS[ep.name] = ep.load() + except Exception as err: # noqa: BLE001 — plugin loading may fail in many ways + _logger.warning( + f"Failed to load plugin implementation {ep.name!r}. This plugin will be unavailable. Error: {err}", + exc_info=True, + ) + _discovered = True + + +def register_implementation(name: str, cls: type) -> None: + """Register a custom implementation class by name.""" + _IMPLEMENTATIONS[name] = cls + + +def get_implementation(name: str | None = None, config: ImplementationConfig | None = None): + """Instantiate and return an implementation by name.""" + _discover() + name = name or _default + if name not in _IMPLEMENTATIONS: + raise ImplementationNotFoundError(name, list(_IMPLEMENTATIONS)) + return _IMPLEMENTATIONS[name](config=config) + + +def set_default_implementation(name: str) -> None: + """Change the default implementation name.""" + global _default + _discover() + if name not in _IMPLEMENTATIONS: + raise ImplementationNotFoundError(name, list(_IMPLEMENTATIONS)) + _default = name + + +def available_implementations() -> list[str]: + """Return names of all registered implementations.""" + _discover() + return list(_IMPLEMENTATIONS.keys()) + + +def _reset() -> None: + """Reset discovery state. For testing only.""" + global _discovered, _default + _IMPLEMENTATIONS.clear() + _discovered = False + _default = "zarr" diff --git a/src/iohub/core/specs.py b/src/iohub/core/specs.py new file mode 100644 index 00000000..2cb4c6dc --- /dev/null +++ b/src/iohub/core/specs.py @@ -0,0 +1,94 @@ +"""ArraySpec -- zarr v3 array creation specification.""" + +from __future__ import annotations + +from dataclasses import dataclass, field + +import numpy as np +from numpy.typing import DTypeLike + +from iohub.core.config import CompressorConfig + + +@dataclass(frozen=True) +class ArraySpec: + """Zarr v3 array creation specification. + + Passed to ``ZarrImplementation.create_array()`` to create new arrays. + Backend-agnostic: zarr-python and TensorStore both accept this. + """ + + shape: tuple[int, ...] + data_type: str + chunk_grid: dict + chunk_key_encoding: dict + codecs: list[dict] + fill_value: int | float = 0 + dimension_names: list[str] | None = None + attributes: dict = field(default_factory=dict) + + @classmethod + def create( + cls, + shape: tuple[int, ...], + dtype: DTypeLike, + chunks: tuple[int, ...], + shards: tuple[int, ...] | None = None, + fill_value: int | float = 0, + dimension_names: list[str] | None = None, + compressor: CompressorConfig | None = None, + ) -> ArraySpec: + """Build a zarr v3 ``ArraySpec`` from iohub parameters. + + Only used for zarr v3 (OME-NGFF 0.5) stores. + Zarr v2 stores continue to use ``group.create_array()`` directly. + """ + comp = compressor or CompressorConfig() + codecs: list[dict] = [ + {"name": "bytes", "configuration": {"endian": "little"}}, + { + "name": "blosc", + "configuration": { + "cname": comp.cname, + "clevel": comp.clevel, + "typesize": np.dtype(dtype).itemsize, + "shuffle": comp.shuffle, + }, + }, + ] + if shards: + chunk_grid = { + "name": "regular", + "configuration": {"chunk_shape": list(shards)}, + } + codecs = [ + { + "name": "sharding_indexed", + "configuration": { + "chunk_shape": list(chunks), + "codecs": codecs, + "index_codecs": [ + {"name": "bytes", "configuration": {"endian": "little"}}, + {"name": "crc32c"}, + ], + }, + } + ] + else: + chunk_grid = { + "name": "regular", + "configuration": {"chunk_shape": list(chunks)}, + } + + return cls( + shape=shape, + data_type=str(np.dtype(dtype)), + chunk_grid=chunk_grid, + chunk_key_encoding={ + "name": "default", + "configuration": {"separator": "/"}, + }, + codecs=codecs, + fill_value=fill_value, + dimension_names=dimension_names, + ) diff --git a/src/iohub/core/types.py b/src/iohub/core/types.py new file mode 100644 index 00000000..bf5bf740 --- /dev/null +++ b/src/iohub/core/types.py @@ -0,0 +1,18 @@ +"""Shared type aliases for iohub core.""" + +from __future__ import annotations + +from typing import Literal + +from zarr.storage import StoreLike + +# Zarr format / NGFF version +type ZarrFormat = Literal[2, 3] +type NGFFVersion = Literal["0.4", "0.5"] + +# Store access mode +type AccessMode = Literal["r", "r+", "a", "w", "w-"] + +# Store input -- zarr-python's StoreLike (Store, StorePath, FSMap, Path, str, +# dict[str, Buffer]) is accepted everywhere a store path is expected. +type StorePath = StoreLike diff --git a/src/iohub/core/utils.py b/src/iohub/core/utils.py new file mode 100644 index 00000000..56b75dc7 --- /dev/null +++ b/src/iohub/core/utils.py @@ -0,0 +1,25 @@ +"""Shared utility functions for iohub core.""" + +from __future__ import annotations + +from iohub.core.errors import PathNormalizationError + + +def pad_shape(shape: tuple[int, ...], target: int = 5) -> tuple[int, ...]: + """Pad shape tuple to a target length by prepending 1s.""" + pad = target - len(shape) + return (1,) * pad + shape + + +def normalize_path(path: str) -> str: + """Normalize a zarr path string. + + Replacement for ``zarr.storage._utils.normalize_path``. + Handles empty strings, collapses slashes, rejects ``..``. + """ + if not path: + return "" + parts = [p for p in path.replace("\\", "/").split("/") if p and p != "."] + if any(p == ".." for p in parts): + raise PathNormalizationError(f"Path {path!r} contains '..'") + return "/".join(parts) diff --git a/src/iohub/fov.py b/src/iohub/fov.py index f95601b6..c601f47a 100644 --- a/src/iohub/fov.py +++ b/src/iohub/fov.py @@ -1,10 +1,10 @@ from __future__ import annotations from abc import ABC, abstractmethod -from collections.abc import Mapping +from collections.abc import Iterable, Mapping from pathlib import Path from types import TracebackType -from typing import Any, Iterable, Optional, Type, Union +from typing import Any import numpy as np from numpy.typing import ArrayLike @@ -40,7 +40,7 @@ def _missing_axes(self) -> list[int]: elif len(self.axes_names) > 5: raise ValueError(f"{self.__name__} does not support more than 5 axes. Found {len(self.axes_names)}") - axes = set(ax[:1].upper() for ax in self.axes_names) + axes = {ax[:1].upper() for ax in self.axes_names} missing = [] for i, ax in enumerate(_AXES_PREFIX): @@ -51,11 +51,10 @@ def _missing_axes(self) -> list[int]: def _pad_missing_axes( self, - seq: Union[list[Any], tuple[Any]], + seq: list[Any] | tuple[Any], value: Any, - ) -> Union[list[Any], tuple[Any]]: + ) -> list[Any] | tuple[Any]: """Pads ``seq`` with ``value`` in the missing axes positions.""" - if isinstance(seq, tuple): is_tuple = True seq = list(seq) @@ -81,10 +80,11 @@ def shape(self) -> tuple[int, int, int, int, int]: @abstractmethod def __getitem__( self, - key: Union[int, slice, tuple[Union[int, slice], ...]], + key: int | slice | tuple[int | slice, ...], ) -> ArrayLike: """ Returned object must support the ``__array__`` interface, + so that ``np.asarray(...)`` will work. """ raise NotImplementedError @@ -125,9 +125,9 @@ def __enter__(self) -> BaseFOVMapping: @abstractmethod def __exit__( self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, ) -> bool: """Close the files.""" raise NotImplementedError @@ -153,13 +153,11 @@ def __iter__(self) -> Iterable[tuple[str, BaseFOV]]: class FOVDict(BaseFOVMapping): - """ - Basic implementation of a mapping of strings to BaseFOVs. - """ + """Basic implementation of a mapping of strings to BaseFOVs.""" def __init__( self, - data_dict: Optional[dict[str, BaseFOV]] = None, + data_dict: dict[str, BaseFOV] | None = None, **kwargs, ) -> None: super().__init__() @@ -208,9 +206,9 @@ def __enter__(self) -> FOVDict: def __exit__( self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, ) -> bool: """Close the files.""" return False diff --git a/src/iohub/mm_fov.py b/src/iohub/mm_fov.py index 5f3f02e6..0b6244ff 100644 --- a/src/iohub/mm_fov.py +++ b/src/iohub/mm_fov.py @@ -176,6 +176,7 @@ def _parse_hcs_position_label( @property def hcs_position_labels(self): """Parse plate position labels generated by the HCS position generator + and split them into (row, column, FOV) components. This method supports multiple label formats commonly produced by @@ -213,8 +214,8 @@ def hcs_position_labels(self): try: labels = [pos["Label"] for pos in self.stage_positions] - except KeyError: - raise ValueError("Stage positions do not have labels.") + except KeyError as err: + raise ValueError("Stage positions do not have labels.") from err return self._parse_hcs_position_label(labels) diff --git a/src/iohub/mmstack.py b/src/iohub/mmstack.py index c5d1cd5d..f2db4333 100644 --- a/src/iohub/mmstack.py +++ b/src/iohub/mmstack.py @@ -1,9 +1,10 @@ from __future__ import annotations import logging +from collections.abc import Iterable from copy import copy from pathlib import Path -from typing import TYPE_CHECKING, Iterable +from typing import TYPE_CHECKING from warnings import catch_warnings, filterwarnings import dask.array as da @@ -27,8 +28,8 @@ def _normalize_mm_pos_key(key: str | int) -> int: try: return int(key) - except TypeError: - raise TypeError("Micro-Manager position keys must be integers.") + except TypeError as err: + raise TypeError("Micro-Manager position keys must be integers.") from err def find_first_ome_tiff_in_mmstack(data_path: Path) -> Path: @@ -41,8 +42,8 @@ def find_first_ome_tiff_in_mmstack(data_path: Path) -> Path: files = data_path.glob("*.ome.tif") try: return next(files) - except StopIteration: - raise FileNotFoundError(f"Path {data_path} contains no OME-TIFF files.") + except StopIteration as err: + raise FileNotFoundError(f"Path {data_path} contains no OME-TIFF files.") from err raise FileNotFoundError(f"Path {data_path} does not exist.") @@ -102,9 +103,9 @@ def __init__(self, data_path: StrOrBytesPath): def _parse_data(self): series = self._first_tif.series[0] - raw_dims = dict((axis, size) for axis, size in zip(series.get_axes(), series.get_shape())) + raw_dims = dict(zip(series.get_axes(), series.get_shape(), strict=False)) axes = ("R", "T", "C", "Z", "Y", "X") - dims = dict((ax, raw_dims.get(ax, 1)) for ax in axes) + dims = {ax: raw_dims.get(ax, 1) for ax in axes} _logger.debug(f"Got dataset dimensions from tifffile: {dims}.") ( self.positions, @@ -194,7 +195,7 @@ def _set_mm_meta(self, mm_meta: dict) -> None: try: for ch in self._mm_meta["Summary"]["ChNames"]: self.channel_names.append(ch) - except Exception: + except Exception: # noqa: BLE001 — metadata format varies self.channel_names = self._mm_meta["Summary"]["Channels"] * [""] # empty strings elif mm_version == "1.4.22": @@ -244,7 +245,8 @@ def _set_mm_meta(self, mm_meta: dict) -> None: def _simplify_stage_position(self, stage_pos: dict): """ - flattens the nested dictionary structure of stage_pos + Flattens the nested dictionary structure of stage_pos + and removes superfluous keys Parameters @@ -257,8 +259,8 @@ def _simplify_stage_position(self, stage_pos: dict): out: (dict) flattened dictionary """ - out = copy(stage_pos) + out.pop("DevicePositions") for dev_pos in stage_pos["DevicePositions"]: out.update({dev_pos["Device"]: dev_pos["Position_um"]}) @@ -266,7 +268,8 @@ def _simplify_stage_position(self, stage_pos: dict): def _simplify_stage_position_beta(self, stage_pos: dict) -> dict: """ - flattens the nested dictionary structure of stage_pos + Flattens the nested dictionary structure of stage_pos + and removes superfluous keys for MM2.0 Beta versions @@ -281,17 +284,14 @@ def _simplify_stage_position_beta(self, stage_pos: dict) -> dict: flattened dictionary """ - new_dict = {} + new_dict["Label"] = stage_pos["label"] new_dict["GridRow"] = stage_pos["gridRow"] new_dict["GridCol"] = stage_pos["gridCol"] for sub in stage_pos["subpositions"]: - values = [] - for field in ["x", "y", "z"]: - if sub[field] != 0: - values.append(sub[field]) + values = [sub[field] for field in ["x", "y", "z"] if sub[field] != 0] if len(values) == 1: new_dict[sub["stageName"]] = values[0] else: @@ -334,9 +334,7 @@ def _read_frame_metadata(self, idx: int) -> dict | None: return None def _infer_image_meta(self) -> None: - """ - Infer data type and pixel size from the first image plane metadata. - """ + """Infer data type and pixel size from the first image plane metadata.""" _logger.debug("Inferring image metadata.") metadata = self._read_frame_metadata(0) if metadata is not None: @@ -344,7 +342,7 @@ def _infer_image_meta(self) -> None: self._xy_pixel_size = float(metadata["PixelSizeUm"]) if self._xy_pixel_size > 0: return - except Exception: + except Exception: # noqa: BLE001 — metadata parsing is unpredictable _logger.warning("Micro-Manager image plane metadata cannot be loaded.") _logger.warning("XY pixel size cannot be determined, defaulting to 1.0 um.") self._xy_pixel_size = 1.0 diff --git a/src/iohub/ndtiff.py b/src/iohub/ndtiff.py index f2453021..69111a7b 100644 --- a/src/iohub/ndtiff.py +++ b/src/iohub/ndtiff.py @@ -2,9 +2,10 @@ import logging import warnings +from collections.abc import Iterable from json import JSONDecodeError from pathlib import Path -from typing import Iterable, Literal +from typing import Literal import numpy as np from natsort import natsorted @@ -49,6 +50,7 @@ def frame_metadata(self, t: int, c: int, z: int) -> dict | None: class NDTiffDataset(MicroManagerFOVMapping): """Reader for ND-TIFF datasets acquired with Micro/Pycro-Manager, + effectively a wrapper of the `ndtiff.Dataset` class. """ @@ -63,7 +65,7 @@ def __init__(self, data_path: Path | str): self._root = data_path self.dirname = data_path.name self._axes = self.dataset.axes - if any([a for a in self._axes.keys() if a not in self._ndtiff_axes]): + if any(a for a in self._axes.keys() if a not in self._ndtiff_axes): raise NotImplementedError( f"Custom axis names {self._axes.keys()} are not supported. Supported axes are: {self._ndtiff_axes}" ) @@ -191,10 +193,12 @@ def str_channel_axis(self) -> bool: def _check_coordinates(self, p: int | str, t: int, c: int | str, z: int): """ Check that the (p, t, c, z) coordinates are part of the ndtiff dataset. + Replace coordinates with None or string values in specific cases - see below """ coords = [p, t, c, z] + axes = self._ndtiff_axes[:4] for i, axis in enumerate(axes): @@ -220,7 +224,9 @@ def _check_coordinates(self, p: int | str, t: int, c: int | str, z: int): coord_sample = next(iter(self._axes[axis])) if coord == 0 and isinstance(coord_sample, str): coords[i] = coord_sample - warnings.warn(f"Indices of {axis} are string-valued. Returning data at {axis} = {coord}") + warnings.warn( + f"Indices of {axis} are string-valued. Returning data at {axis} = {coord}", stacklevel=2 + ) else: # If the coordinate is not part of the axis and # nonzero, a ValueError will be raised @@ -253,7 +259,8 @@ def _check_position_key(self, key: int | str) -> bool: else: if key not in (0, None): warnings.warn( - f"Position index {key} is not part of this dataset. Returning data at the default position." + f"Position index {key} is not part of this dataset. Returning data at the default position.", + stacklevel=2, ) key = None return key @@ -304,6 +311,7 @@ def get_image_metadata(self, p: int | str, t: int, c: int | str, z: int) -> dict Image plane metadata. None if not available. """ metadata = None + if not self.str_position_axis and isinstance(p, str): if p.isdigit(): p = int(p) diff --git a/src/iohub/ngff/__init__.py b/src/iohub/ngff/__init__.py index 58eec8ba..776d6624 100644 --- a/src/iohub/ngff/__init__.py +++ b/src/iohub/ngff/__init__.py @@ -1,3 +1,4 @@ +from iohub.core import NGFFArray from iohub.ngff.models import TransformationMeta from iohub.ngff.nodes import ( ImageArray, @@ -11,6 +12,7 @@ __all__ = [ "ImageArray", + "NGFFArray", "NGFFNode", "Plate", "Position", diff --git a/src/iohub/ngff/display.py b/src/iohub/ngff/display.py index e399de27..b3c1b7f2 100644 --- a/src/iohub/ngff/display.py +++ b/src/iohub/ngff/display.py @@ -35,6 +35,7 @@ def color_to_hex(color: str) -> str: """ Convert the color string to HEX (i.e 'red' -> 'FF0000') + (https://pillow.readthedocs.io/en/stable/_modules/PIL/ImageColor.html#getrgb) (https://www.w3.org/TR/css-color-3/#svg-color) @@ -54,7 +55,8 @@ def channel_display_settings( clim: tuple[float, float, float, float] | None = None, first_chan: bool = False, ): - """This will create a dictionary used for OME-Zarr metadata. + """Create a dictionary of OME-Zarr channel display metadata. + Allows custom contrast limits and channel names for display. Defaults everything to grayscale. @@ -96,7 +98,7 @@ def channel_display_settings( for key in CHANNEL_COLORS: # Does chan_name have any of the values in the CHANNEL_COLORS[key] # list as a substring? - if any([value in chan_name for value in CHANNEL_COLORS[key]]): + if any(value in chan_name for value in CHANNEL_COLORS[key]): display_color = color_to_hex(key) break else: diff --git a/src/iohub/ngff/models.py b/src/iohub/ngff/models.py index 7b3bbac9..c3553083 100644 --- a/src/iohub/ngff/models.py +++ b/src/iohub/ngff/models.py @@ -1,16 +1,17 @@ """ Data model classes with validation for OME-NGFF metadata. -Developed against OME-NGFF v0.4/0.5.2 and ome-zarr v0.9. + +Developed against OME-NGFF v0.5. Attributes are 'snake_case' with aliases to match NGFF names in JSON output. -See https://ngff.openmicroscopy.org/0.4/index.html#naming-style +See https://ngff.openmicroscopy.org/specifications/0.5/index.html about 'camelCase' inconsistency. """ from __future__ import annotations import re -from typing import Annotated, Any, Literal, Optional +from typing import Annotated, Any, Literal, Self from pydantic import ( AfterValidator, @@ -24,7 +25,7 @@ model_validator, ) from pydantic_extra_types.color import Color, ColorType -from typing_extensions import Self, TypedDict +from typing_extensions import TypedDict def unique_validator(data: list[BaseModel], field: str | list[str]) -> list[BaseModel]: @@ -81,7 +82,7 @@ def alpha_numeric_validator(data: str) -> str: return data -TO_DICT_SETTINGS = dict(exclude_none=True, by_alias=True) +TO_DICT_SETTINGS = {"exclude_none": True, "by_alias": True} class MetaBase(BaseModel): @@ -163,7 +164,7 @@ class TimeAxisMeta(NamedAxisMeta): ) = None -"""https://ngff.openmicroscopy.org/0.4/index.html#axes-md""" +"""https://ngff.openmicroscopy.org/specifications/0.5/index.html#axes-md""" AxisMeta = Annotated[ TimeAxisMeta | ChannelAxisMeta | SpaceAxisMeta, Discriminator("type"), @@ -171,7 +172,7 @@ class TimeAxisMeta(NamedAxisMeta): class TransformationMeta(MetaBase): - """https://ngff.openmicroscopy.org/0.4/index.html#trafo-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#trafo-md""" # MUST type: Literal["identity", "translation", "scale"] @@ -191,23 +192,23 @@ def no_extra_method(self) -> Self: class DatasetMeta(MetaBase): - """https://ngff.openmicroscopy.org/0.4/index.html#multiscale-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#multiscale-md""" # MUST path: str # MUST - coordinate_transformations: list[TransformationMeta] = Field(alias=str("coordinateTransformations")) + coordinate_transformations: list[TransformationMeta] = Field(alias="coordinateTransformations") class VersionMeta(MetaBase): - """OME-NGFF spec version. Default is the current version (0.4).""" + """OME-NGFF spec version. Default is the current version (0.5).""" # SHOULD version: Literal["0.1", "0.2", "0.3", "0.4", "0.5"] | None = None class MultiScaleMeta(VersionMeta): - """https://ngff.openmicroscopy.org/0.4/index.html#multiscale-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#multiscale-md""" # MUST axes: list[AxisMeta] @@ -216,9 +217,7 @@ class MultiScaleMeta(VersionMeta): # SHOULD name: str | None = None # MAY - coordinate_transformations: list[TransformationMeta] | None = Field( - alias=str("coordinateTransformations"), default=None - ) + coordinate_transformations: list[TransformationMeta] | None = Field(alias="coordinateTransformations", default=None) # SHOULD, describes the downscaling method (e.g. 'gaussian') type: str | None = None # SHOULD, additional information about the downscaling method @@ -245,6 +244,7 @@ class WindowDict(TypedDict): class ChannelMeta(MetaBase): """Channel display settings without clear documentation from the NGFF spec. + https://docs.openmicroscopy.org/omero/5.6.1/developers/Web/WebGateway.html#imgdata """ @@ -259,17 +259,21 @@ class ChannelMeta(MetaBase): class RDefsMeta(MetaBase): """Rendering settings without clear documentation from the NGFF spec. + https://docs.openmicroscopy.org/omero/5.6.1/developers/Web/WebGateway.html#imgdata """ default_t: int = Field(alias="defaultT") default_z: int = Field(alias="defaultZ") model: str = "color" - projection: Optional[str] = "normal" + projection: str | None = "normal" class OMEROMeta(VersionMeta): - """https://ngff.openmicroscopy.org/0.4/index.html#omero-md""" + """Transitional OMERO display metadata. Implementations MUST support reading; writing is optional. + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#omero-transitional + """ id: int | None = None name: str | None = None @@ -279,8 +283,10 @@ class OMEROMeta(VersionMeta): class LabelImageMeta(MetaBase): """Metadata for individual multiscale label image. + Combines multiscales specification with image-label metadata. - https://ngff.openmicroscopy.org/latest/index.html#labels-md""" + https://ngff.openmicroscopy.org/specifications/0.5/index.html#labels-md + """ # MUST: multiscales with same levels as original image multiscales: list[MultiScaleMeta] @@ -293,7 +299,9 @@ class LabelImageMeta(MetaBase): class ImagesMeta(MetaBase): """Metadata needed for 'Images' (or positions/FOVs) in an OME-NGFF dataset. - https://ngff.openmicroscopy.org/0.4/index.html#image-layout""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#image-layout + """ multiscales: list[MultiScaleMeta] # transitional, optional @@ -306,7 +314,7 @@ class ImagesMeta(MetaBase): class LabelsMeta(MetaBase): - """https://ngff.openmicroscopy.org/0.4/index.html#labels-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#labels-md""" # MUST: list of paths to labeled multiscale images labels: list[str] @@ -315,7 +323,7 @@ class LabelsMeta(MetaBase): class LabelColorMeta(MetaBase): - """https://ngff.openmicroscopy.org/0.4/index.html#label-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#label-md""" # MUST label_value: int = Field(alias="label-value") @@ -330,7 +338,7 @@ def rgba_color(cls, v): class PositionLabelMeta(VersionMeta): - """https://ngff.openmicroscopy.org/0.4/index.html#label-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#label-md""" # SHOULD (optional per NGFF spec) colors: list[LabelColorMeta] = Field(default_factory=list) @@ -360,7 +368,7 @@ def validate_properties(cls, v): class AcquisitionMeta(MetaBase): - """https://ngff.openmicroscopy.org/0.4/index.html#plate-md""" + """https://ngff.openmicroscopy.org/specifications/0.5/index.html#plate-md""" # MUST id: NonNegativeInt @@ -371,9 +379,9 @@ class AcquisitionMeta(MetaBase): # MAY description: str | None = None # MAY - start_time: NonNegativeInt | None = Field(alias=str("starttime"), default=None) + start_time: NonNegativeInt | None = Field(alias="starttime", default=None) # MAY - end_time: NonNegativeInt | None = Field(alias=str("endtime"), default=None) + end_time: NonNegativeInt | None = Field(alias="endtime", default=None) @model_validator(mode="after") def end_after_start(self) -> Self: @@ -385,7 +393,9 @@ def end_after_start(self) -> Self: class PlateAxisMeta(MetaBase): """OME-NGFF metadata for a row or a column on a multi-well plate. - https://ngff.openmicroscopy.org/0.4/index.html#plate-md""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#plate-md + """ # MUST name: Annotated[str, AfterValidator(alpha_numeric_validator)] @@ -393,7 +403,9 @@ class PlateAxisMeta(MetaBase): class WellIndexMeta(MetaBase): """OME-NGFF metadata for a well on a multi-well plate. - https://ngff.openmicroscopy.org/0.4/index.html#plate-md""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#plate-md + """ path: str row_index: NonNegativeInt = Field(alias="rowIndex") @@ -411,7 +423,9 @@ def row_slash_column(cls, v: str): class PlateMeta(VersionMeta): """OME-NGFF high-content screening plate metadata. - https://ngff.openmicroscopy.org/0.4/index.html#plate-md""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#plate-md + """ # SHOULD name: str | None = None @@ -450,7 +464,9 @@ def unique_well(cls, v): class ImageMeta(MetaBase): """Image metadata field under an HCS well group. - https://ngff.openmicroscopy.org/0.4/index.html#well-md""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#well-md + """ # MUST if `PlateMeta.acquisitions` contains multiple acquisitions acquisition: int | None = None @@ -460,7 +476,9 @@ class ImageMeta(MetaBase): class WellGroupMeta(VersionMeta): """OME-NGFF high-content screening well group metadata. - https://ngff.openmicroscopy.org/0.4/index.html#well-md""" + + https://ngff.openmicroscopy.org/specifications/0.5/index.html#well-md + """ # MUST images: list[ImageMeta] diff --git a/src/iohub/ngff/nodes.py b/src/iohub/ngff/nodes.py index b8aeae90..c01afe70 100644 --- a/src/iohub/ngff/nodes.py +++ b/src/iohub/ngff/nodes.py @@ -1,6 +1,4 @@ -""" -Node object and convenience functions for the OME-NGFF (OME-Zarr) Hierarchy. -""" +"""Node object and convenience functions for the OME-NGFF (OME-Zarr) Hierarchy.""" # TODO: remove this in the future (PEP deferred for 3.11, now 3.12?) from __future__ import annotations @@ -9,31 +7,32 @@ import math import os import shutil +from collections.abc import Generator from copy import deepcopy from datetime import datetime from functools import cached_property from pathlib import Path from typing import ( Any, - Callable, - Generator, + ClassVar, Literal, - Type, - TypeAlias, overload, ) import numpy as np import xarray as xr -import zarr.codecs +import zarr from numpy.typing import ArrayLike, DTypeLike, NDArray from pydantic import ValidationError -from zarr.core.chunk_key_encodings import ChunkKeyEncodingParams -from zarr.storage._utils import normalize_path +from iohub.core import ArraySpec, NGFFArray, get_implementation +from iohub.core.config import ImplementationConfig +from iohub.core.errors import StoreOpenError +from iohub.core.protocol import ZarrImplementation +from iohub.core.types import StorePath +from iohub.core.utils import normalize_path, pad_shape from iohub.ngff.display import channel_display_settings from iohub.ngff.models import ( - TO_DICT_SETTINGS, AcquisitionMeta, AxisMeta, ChannelAxisMeta, @@ -60,7 +59,7 @@ _logger = logging.getLogger(__name__) # Type alias for position specification tuples in create_positions -PositionSpec: TypeAlias = ( +type PositionSpec = ( tuple[str, str, str] | tuple[str, str, str, int | None] | tuple[str, str, str, int | None, int | None] @@ -68,42 +67,48 @@ ) -def _pad_shape(shape: tuple[int, ...], target: int = 5): - """Pad shape tuple to a target length.""" - pad = target - len(shape) - return (1,) * pad + shape +def _is_fslike(store_path) -> bool: + """Check if store_path is a filesystem path (str, Path, os.PathLike) vs a Store object.""" + return isinstance(store_path, (str, os.PathLike)) def _open_store( - store_path: str | Path, + store_path, mode: Literal["r", "r+", "a", "w", "w-"], version: Literal["0.4", "0.5"], + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, ): - store_path = Path(store_path).resolve() - if not store_path.exists() and mode in ("r", "r+"): - raise FileNotFoundError(f"Dataset directory not found at {str(store_path)}.") + is_fs = _is_fslike(store_path) + if is_fs: + store_path = Path(store_path).resolve() + if not store_path.exists() and mode in ("r", "r+"): + raise FileNotFoundError(f"Dataset directory not found at {store_path!s}.") if version not in ("0.4", "0.5"): _logger.warning( f"IOHub is only tested against OME-NGFF v0.4 and v0.5. Requested version {version} may not work properly." ) + impl = get_implementation(implementation, implementation_config) try: zarr_format = None - if mode in ("w", "w-") or (mode == "a" and not store_path.exists()): - zarr_format = 3 if version == "0.5" else 2 - root = zarr.open_group(store_path, mode=mode, zarr_format=zarr_format) + if mode in ("w", "w-") or (is_fs and mode == "a" and not store_path.exists()): + zarr_format = 3 + root = impl.open_group(store_path, mode=mode, zarr_format=zarr_format) + except (FileNotFoundError, FileExistsError, PermissionError): + raise except Exception as e: - raise RuntimeError(f"Cannot open Zarr root group at {str(store_path)}") from e - return root + raise StoreOpenError(f"Cannot open Zarr root group at {store_path!r}") from e + return root, impl def _scale_integers(values: tuple[int, ...], factor: int) -> tuple[int, ...]: """Divide all values by factor, rounding up (ceiling division).""" - return tuple(int(math.ceil(v / factor)) for v in values) + return tuple(math.ceil(v / factor) for v in values) def _scale_dims(values: tuple[int, ...], axes: set[int]) -> tuple[int, ...]: """Halve values at the given indices, rounding up (ceiling division).""" - return tuple(int(math.ceil(v / 2)) if i in axes else v for i, v in enumerate(values)) + return tuple(math.ceil(v / 2) if i in axes else v for i, v in enumerate(values)) def _case_insensitive_local_fs() -> bool: @@ -114,8 +119,9 @@ def _case_insensitive_local_fs() -> bool: class NGFFNode: """A node (group level in Zarr) in an NGFF dataset.""" - _MEMBER_TYPE: Type[NGFFNode | zarr.Array] - _DEFAULT_AXES = [ + _MEMBER_TYPE: type[NGFFNode | NGFFArray] + _impl: ZarrImplementation + _DEFAULT_AXES: ClassVar[list] = [ TimeAxisMeta(name="T", unit="second"), ChannelAxisMeta(name="C"), *[SpaceAxisMeta(name=i, unit="micrometer") for i in ("Z", "Y", "X")], @@ -127,8 +133,9 @@ def __init__( parse_meta: bool = True, channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): if channel_names is not None: self.channel_names = channel_names @@ -139,6 +146,12 @@ def __init__( self._group = group self._overwrite = overwriting_creation self._version: Literal["0.4", "0.5"] = version + self._impl = impl + if self._impl is None: + raise TypeError( + f"{type(self).__name__} requires an impl= argument. " + "Use open_ome_zarr() rather than constructing nodes directly." + ) if parse_meta: self._parse_meta() # TODO: properly check the underlying storage type @@ -163,7 +176,9 @@ def zgroup(self): @property def zattrs(self): """Zarr attributes of the node. - Assignments will modify the metadata file.""" + + Assignments will modify the metadata file. + """ return self._group.attrs @property @@ -179,26 +194,31 @@ def version(self) -> Literal["0.4", "0.5"]: @property def _parent_path(self): """The parent Zarr group path of the node. - None for the root node.""" + + None for the root node. + """ if self._group.name == "/": return None else: - return os.path.dirname(self._group.name) + return Path(self._group.name).parent @property def _member_names(self): - """Group keys (default) or array keys (overridden).""" + """Array keys for leaf nodes (Position/PositionLabel), group keys for container nodes.""" + if issubclass(self._MEMBER_TYPE, NGFFArray): + return self.array_keys() return self.group_keys() @property def _child_attrs(self): """Attributes to pass on when constructing child type instances""" - return dict( - version=self._version, - axes=self.axes, - channel_names=self.channel_names, - overwriting_creation=self._overwrite, - ) + return { + "version": self._version, + "axes": self.axes, + "channel_names": self.channel_names, + "overwriting_creation": self._overwrite, + "impl": self._impl, + } def __len__(self): return len(self._member_names) @@ -212,8 +232,8 @@ def __getitem__(self, key): item_type = self._MEMBER_TYPE for _ in range(levels): item_type = item_type._MEMBER_TYPE - if issubclass(item_type, ImageArray): - return item_type.from_zarr_array(znode) + if issubclass(item_type, NGFFArray): + return item_type.from_handle(znode, self._impl) else: return item_type(group=znode, parse_meta=True, **self._child_attrs) @@ -255,7 +275,7 @@ def group_keys(self): ------- list[str] """ - return sorted(list(self._group.group_keys())) + return self._impl.group_keys(self._group) def array_keys(self): """Sorted list of keys to all the child zarrays (if any). @@ -264,7 +284,7 @@ def array_keys(self): ------- list[str] """ - return sorted(list(self._group.array_keys())) + return self._impl.array_keys(self._group) def is_root(self): """Whether this node is the root node @@ -277,6 +297,7 @@ def is_root(self): def is_leaf(self): """Wheter this node is a leaf node, + meaning that no child Zarr group is present. Usually a position/fov node for NGFF-HCS if True. @@ -300,10 +321,10 @@ def iteritems(self): for key in self._member_names: try: yield key, self[key] - except Exception: - _logger.warning("Skipped item at {}: invalid {}.".format(key, type(self._MEMBER_TYPE))) + except Exception: # noqa: BLE001 — partial iteration, skip corrupt items + _logger.warning(f"Skipped item at {key}: invalid {type(self._MEMBER_TYPE)}.") - def get_channel_index(self, name: str): + def get_channel_index(self, name: str) -> int: """Get the index of a given channel in the channel list. Parameters @@ -321,7 +342,7 @@ def get_channel_index(self, name: str): return self.channel_names.index(name) def _warn_invalid_meta(self): - msg = "Zarr group at {} does not have valid metadata for {}".format(self._group.path, type(self)) + msg = f"Zarr group at {self._group.path} does not have valid metadata for {type(self)}" _logger.warning(msg) def _parse_meta(self): @@ -343,451 +364,50 @@ def dump_meta(self): def close(self): """Close Zarr store.""" - self._group.store.close() - - -class NGFFMultiscalesNode(NGFFNode): - """Base class for nodes managing multiscale pyramids. - - Provides common functionality for Position (5D images) and - PositionLabel (4D labels). - """ - - @property - def _zarr_format(self) -> int: - """Zarr format version based on NGFF version.""" - return 3 if self.version == "0.5" else 2 - - @property - def _member_names(self): - """Names of member arrays.""" - return self.array_keys() - - @property - def data(self): - """Alias for the highest resolution array ('0'). - - .. warning:: - This property does *NOT* aim to retrieve all the arrays. - And it may also fail to retrieve any data if arrays exist but - are not named conventionally. - - Returns - ------- - ImageArray or LabelsArray - Array at the highest resolution level - - Raises - ------ - KeyError - If no array is named '0'. - - Notes - ----- - Do not depend on this in non-interactive code! - The name is hard-coded and is not guaranteed - by the OME-NGFF specification. - """ - try: - return self["0"] - except KeyError: - raise KeyError(f"There is no array named '0' in the group of: {self.array_keys()}") - - def __getitem__(self, key: int | str): - """Get an array at a specific resolution level. - - Parameters - ---------- - key : int | str - Resolution level (e.g., "0", "1", "2") - - Returns - ------- - ImageArray or LabelsArray - Array at the specified resolution level - """ - key = normalize_path(str(key)) - znode = self.zgroup.get(key) - if not znode: - raise KeyError(key) - - if isinstance(znode, zarr.Array): - return self._MEMBER_TYPE.from_zarr_array(znode) - else: - raise TypeError(f"Expected zarr.Array at level '{key}', got {type(znode)}") + self._impl.close(self._group) - def __setitem__(self, key, value: NDArray): - """Write array data to a specific resolution level. - - Parameters - ---------- - key : int | str - Resolution level identifier - value : NDArray - NumPy array to write - """ - key = normalize_path(str(key)) - if not isinstance(value, np.ndarray): - raise TypeError(f"Value must be a NumPy array. Got type {type(value)}.") - self._create_from_data(key, value) - - @staticmethod - def _default_chunks(shape: tuple[int, ...], last_data_dims: int) -> tuple[int, ...]: - """Default chunking strategy for arrays. - - Parameters - ---------- - shape : tuple[int, ...] - Array shape - last_data_dims : int - Number of trailing dimensions to use for chunking - (typically 3 for ZYX spatial dimensions) - - Returns - ------- - tuple[int, ...] - Chunk sizes for each dimension - """ - chunks = shape[-min(last_data_dims, len(shape)) :] - return _pad_shape(chunks, target=len(shape)) - - def _create_compressor_options(self) -> dict: - """Create compression options based on Zarr format. - - Returns - ------- - dict - Compression configuration for zarr.create_array - """ - shuffle = zarr.codecs.BloscShuffle.bitshuffle - - if self._zarr_format == 3: - return { - "compressors": zarr.codecs.BloscCodec( - cname="zstd", - clevel=1, - shuffle=shuffle, - ) - } - else: - from numcodecs import Blosc - - return {"compressor": Blosc(cname="zstd", clevel=1, shuffle=Blosc.BITSHUFFLE)} - - def _create_from_data(self, key, value): - """Create array from data. Override in subclass. - - Position calls create_image(), PositionLabel calls create_level(). - """ - raise NotImplementedError("Subclass must implement _create_from_data") - - def _create_zarr_array_base( + def _create_zarr_array( self, name: str, shape: tuple[int, ...], - dtype: DTypeLike, + dtype, chunks: tuple[int, ...], - array_class: type[NGFFNDArray], - shards_ratio: tuple[int, ...] | None = None, - data: NDArray | None = None, - metadata_callback: (Callable[[str, list[TransformationMeta] | None], None] | None) = None, - transform: list[TransformationMeta] | None = None, - ) -> NGFFNDArray: - """Create zarr array with optional data and metadata. - - Core array creation logic shared by Position and PositionLabel. - Centralizes common zarr array creation boilerplate with subclass - customization via metadata callback. - - Parameters - ---------- - name : str - Array name (level identifier like "0", "1", "2") - shape : tuple[int, ...] - Array shape (TCZYX for Position, TZYX for PositionLabel) - dtype : DTypeLike - Data type (numpy dtype) - chunks : tuple[int, ...] - Chunk size for the zarr array - array_class : type[NGFFNDArray] - Array wrapper class (ImageArray or LabelsArray) - shards_ratio : tuple[int, ...], optional - Sharding ratio for each dimension, by default None. - Each shard contains the product of the ratios number of chunks. - No sharding will be used if not specified. - data : NDArray, optional - Initial data to write, by default None (creates empty array) - metadata_callback : Callable, optional - Function to create metadata atomically after array creation. - Signature: callback(name, transform) -> None. - By default None (no metadata - subclass responsibility) - transform : list[TransformationMeta], optional - Coordinate transformations (e.g., scale, translation) - - Returns - ------- - NGFFNDArray - Wrapped zarr array (ImageArray or LabelsArray) - - Notes - ----- - - Metadata creation is atomic: callback called immediately after - array creation to prevent inconsistent state - - Handles zarr format v2 and v3 via self._zarr_format - - dimension_names are only set for zarr v3 - - chunk_key_encoding differs between v2 and v3 - - Sharding calculation is centralized here to avoid code duplication - - Examples - -------- - >>> # Used by Position.create_zeros() - >>> img_arr = self._create_zarr_array_base( - ... name="0", - ... shape=(1, 2, 256, 256, 256), - ... dtype=np.uint16, - ... chunks=(1, 1, 64, 64, 64), - ... array_class=ImageArray, - ... shards_ratio=(1, 1, 2, 2, 2), # 2x2x2 chunks per shard - ... metadata_callback=self._create_image_meta, - ... transform=None, - ... ) - """ + shards_ratio: tuple[int, ...] | None, + dimension_names: list[str] | None = None, + ): + """Create a zarr array via the active implementation and return the native handle.""" if shards_ratio: if len(shards_ratio) != len(shape): raise ValueError(f"Sharding ratio length {len(shards_ratio)} does not match shape length {len(shape)}.") - shards = tuple(c * s for c, s in zip(chunks, shards_ratio)) + shards = tuple(c * s for c, s in zip(chunks, shards_ratio, strict=False)) else: shards = None - - create_params = { - "name": name, - "chunks": chunks, - "shards": shards, - "overwrite": self._overwrite, - "fill_value": 0, - "dimension_names": ( - # Only for zarr v3: use axis names from metadata - [ax.name for ax in self.axes[: len(shape)]] if self._zarr_format == 3 else None - ), - "chunk_key_encoding": ChunkKeyEncodingParams( - # v3 uses "default", v2 uses "v2" encoding - name="default" if self._zarr_format == 3 else "v2", - separator="/", - ), - **self._create_compressor_options(), - } - - if data is None: - create_params["shape"] = shape - create_params["dtype"] = dtype - else: - create_params["data"] = data - - zarray = self._group.create_array(**create_params) - - wrapped_array = array_class.from_zarr_array(zarray) - - if metadata_callback is not None: - metadata_callback(name, transform) - - return wrapped_array - - def _calculate_pyramid_params( - self, - source_array: NGFFNDArray, - level: int, - ) -> tuple[tuple[int, ...], tuple[int, ...], list[TransformationMeta]]: - """Calculate pyramid level parameters for multiscale downscaling. - - Computes the shape, chunks, and coordinate transformations for a - downscaled pyramid level. Uses a factor of 2^level for downscaling, - applying the factor only to the last 3 spatial dimensions (ZYX). - - Parameters - ---------- - source_array : NGFFNDArray - Source array to downscale from (typically level 0) - level : int - Pyramid level number (1, 2, 3, ...). Level 0 is full resolution. - Each level is downscaled by a factor of 2 from the previous level. - - Returns - ------- - downscaled_shape : tuple[int, ...] - Shape for the downscaled array. Non-spatial dimensions (T, C) - remain unchanged; spatial dimensions (ZYX) are divided by 2^level - and rounded up. - downscaled_chunks : tuple[int, ...] - Chunk size for the downscaled array, scaled proportionally and - padded to match the shape length. - transforms : list[TransformationMeta] - List containing a single scale transformation with factor 2^level - applied to spatial dimensions. - - Notes - ----- - - Only the last 3 dimensions (assumed to be ZYX spatial) are downscaled - - Leading dimensions (T, C) are preserved at original size - - Uses _scale_integers() helper for proper rounding (rounds up) - - Uses _pad_shape() to ensure chunk tuple matches shape length - - Examples - -------- - >>> # Calculate parameters for level 1 (2x downscaling) - >>> shape, chunks, transforms = self._calculate_pyramid_params( - ... source_array=position.data, # shape (1, 3, 512, 512, 512) - ... level=1, - ... ) - >>> shape # (1, 3, 256, 256, 256) - spatial dims halved - >>> transforms[0].scale # [1.0, 1.0, 2.0, 2.0, 2.0] - 2x on spatial - - >>> # Level 2 (4x downscaling) - >>> shape, chunks, transforms = self._calculate_pyramid_params(source_array=position.data, level=2) - >>> shape # (1, 3, 128, 128, 128) - spatial dims quartered - >>> transforms[0].scale # [1.0, 1.0, 4.0, 4.0, 4.0] - 4x on spatial - """ - factor = 2**level - - downscaled_shape = source_array.shape[:-3] + _scale_integers(source_array.shape[-3:], factor) - - downscaled_chunks = _pad_shape(_scale_integers(source_array.chunks, factor), len(downscaled_shape)) - - # Leading dims (T, C) scale=1.0, spatial (ZYX) scale=factor - transforms = [ - TransformationMeta( - type="scale", - scale=[1.0] * (len(source_array.shape) - 3) + [float(factor)] * 3, + if self._zarr_format == 3: + spec = ArraySpec.create( + shape=shape, + dtype=dtype, + chunks=chunks, + shards=shards, + fill_value=0, + dimension_names=dimension_names or [ax.name for ax in self.axes[: len(shape)]], ) - ] - - return downscaled_shape, downscaled_chunks, transforms - - -class NGFFNDArray(zarr.Array): - """Base class for NGFF N-dimensional arrays. - - Provides common functionality for ImageArray (5D) and LabelsArray (4D). - - Attributes - ---------- - _SUPPORTED_DIMS : str - Dimension names (e.g., "TCZYX" for 5D, "TZYX" for 4D) - _N_DIMS : int - Number of dimensions (e.g., 5 or 4) - """ - - _SUPPORTED_DIMS: str - _N_DIMS: int - - @classmethod - def from_zarr_array(cls, zarray: zarr.Array): - """Create instance from an existing zarr.Array. - - Parameters - ---------- - zarray : zarr.Array - Source zarr array - - Returns - ------- - NGFFNDArray - New instance wrapping the zarr array - """ - return cls(zarray._async_array) - - def _get_dim(self, idx: int) -> int: - """Get dimension size at index, padding shape to _N_DIMS. - - Parameters - ---------- - idx : int - Dimension index - - Returns - ------- - int - Size of dimension at index - """ - return _pad_shape(self.shape, target=self._N_DIMS)[idx] - - def numpy(self) -> NDArray: - """Return the whole array as an in-RAM NumPy array. - - Equivalent to `self[:]`. - - Returns - ------- - NDArray - NumPy array containing all data - """ - return self[:] - - def dask_array(self): - """Return as a dask array. - - Returns - ------- - dask.array.Array - Lazy dask array backed by zarr storage - - Notes - ----- - Designed to work with zarr DirectoryStore. - """ - import dask.array as da - - return da.from_zarr(self.store.root, component=self.path) - - def tensorstore(self, **kwargs): - """Open the zarr array as a TensorStore object. - - Requires the optional dependency ``tensorstore``. - - Parameters - ---------- - **kwargs : dict, optional - Additional keyword arguments to pass to ``tensorstore.open()``. - Cannot include 'read' or 'write' (auto-configured). - - Returns - ------- - tensorstore.TensorStore - Handle to the Zarr array - - Raises - ------ - ValueError - If 'read' or 'write' are specified in kwargs - """ - import tensorstore as ts - - ts_spec = { - "driver": f"zarr{self.metadata.zarr_format}", - "kvstore": { - "driver": "file", - "path": str(Path(self.store.root) / self.path.strip("/")), - }, - } - - if "read" in kwargs or "write" in kwargs: - raise ValueError("Cannot override file mode for the Zarr store.") - - zarr_dataset = ts.open(ts_spec, read=True, write=not self.read_only, **kwargs).result() - - return zarr_dataset - - def downscale(self): - """Create downscaled pyramid levels. + return self._impl.create_array(self._group, name, spec, overwrite=self._overwrite) + return self._impl.create_array_v2( + self._group, + name, + shape=shape, + dtype=dtype, + chunks=chunks, + fill_value=0, + overwrite=self._overwrite, + ) - Raises - ------ - NotImplementedError - Downscaling not implemented for this array type - """ - raise NotImplementedError + @property + def _zarr_format(self) -> int: + return self._impl.get_zarr_format(self._group) -class ImageArray(NGFFNDArray): +class ImageArray(NGFFArray): """Container object for image stored as a zarr array (up to 5D: TCZYX)""" _SUPPORTED_DIMS = "TCZYX" @@ -834,7 +454,7 @@ def tiles(self): @property def tile_shape(self): - """shape of a tile, the same as chunk size of the underlying array.""" + """Shape of a tile, the same as chunk size of the underlying array.""" return self.chunks def get_tile( @@ -861,6 +481,7 @@ def get_tile( NDArray """ self._check_rc(row, column) + return self[self.get_tile_slice(row, column, pre_dims=pre_dims)] def write_tile( @@ -886,6 +507,7 @@ def write_tile( by default None (select all). """ self._check_rc(row, column) + self[self.get_tile_slice(row, column, pre_dims=pre_dims)] = data def get_tile_slice( @@ -913,6 +535,7 @@ def get_tile_slice( Tuple of slices for all the dimensions of the array. """ self._check_rc(row, column) + y, x = self.chunks[-2:] r_slice = slice(row * y, (row + 1) * y) c_slice = slice(column * x, (column + 1) * x) @@ -921,14 +544,14 @@ def get_tile_slice( try: if len(pre_dims) != len(pad): raise IndexError(f"Length of `pre_dims` should be {len(pad)}, got {len(pre_dims)}.") - except TypeError: - raise TypeError(f"Argument `pre_dims` should be a sequence, got type {type(pre_dims)}.") + except TypeError as err: + raise TypeError(f"Argument `pre_dims` should be a sequence, got type {type(pre_dims)}.") from err for i, sel in enumerate(pre_dims): if isinstance(sel, int): sel = slice(sel) if sel is not None: pad[i] = sel - return tuple((pad + [r_slice, c_slice])) + return (*pad, r_slice, c_slice) @staticmethod def _check_rc(row: int, column: int): @@ -936,7 +559,7 @@ def _check_rc(row: int, column: int): raise TypeError("Row and column indices must be integers.") -class LabelsArray(NGFFNDArray): +class LabelsArray(NGFFArray): """Container for labels stored as zarr array (4D: TZYX)""" _SUPPORTED_DIMS = "TZYX" @@ -967,7 +590,7 @@ def downscale(self): raise NotImplementedError("Downscaling is not implemented for labels arrays.") -class PositionLabel(NGFFMultiscalesNode): +class PositionLabel(NGFFNode): """Multiscale label image group containing LabelsArray pyramid levels. This class manages label images according to NGFF specification where @@ -1007,13 +630,14 @@ class PositionLabel(NGFFMultiscalesNode): def __init__( self, - group: zarr.Group, + group, parse_meta: bool = True, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", colors: dict[int, list[int]] | None = None, properties: list[dict[str, Any]] | None = None, overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): if axes: self.axes = [ax for ax in axes if ax.type != "channel"] @@ -1026,10 +650,11 @@ def __init__( super().__init__( group=group, parse_meta=parse_meta, - channel_names=["label"], # Dummy channel name for labels (no actual channels) + channel_names=["label"], axes=self.axes, version=version, overwriting_creation=overwriting_creation, + impl=impl, ) self._colors = colors @@ -1045,12 +670,23 @@ def _parse_meta(self): def dump_meta(self): """Dump metadata to zarr.json file.""" - ome = self.metadata.model_dump(**TO_DICT_SETTINGS) + ome = self.metadata.model_dump(exclude_none=True, by_alias=True) self._dump_ome(ome) - def _create_from_data(self, key, value): - """Create label from data (implementation of base class hook).""" - return self.create_label(key, value) + @property + def data(self) -> LabelsArray: + """Alias for the highest-resolution label array ('0').""" + try: + return self["0"] + except KeyError as err: + raise KeyError(f"There is no array named '0' in the group of: {self.array_keys()}") from err + + def __getitem__(self, key: int | str) -> LabelsArray: + key = normalize_path(str(key)) + znode = self.zgroup.get(key) + if not znode: + raise KeyError(key) + return LabelsArray.from_handle(znode, self._impl) def create_label( self, @@ -1075,8 +711,6 @@ def create_label( Chunk size, by default None shards_ratio : tuple[int, ...], optional Sharding ratio for each dimension, by default None. - Each shard contains the product of the ratios number of chunks. - No sharding will be used if not specified. transform : list[TransformationMeta], optional Coordinate transformations for this level, by default None @@ -1084,33 +718,22 @@ def create_label( ------- LabelsArray Created label array - - See Also - -------- - Position.create_image : Equivalent method for Position class - create_zeros : Create empty label array - initialize_pyramid : Create downscaled pyramid levels """ if not isinstance(data, np.ndarray): raise TypeError("Label data must be a NumPy array") - if not np.issubdtype(data.dtype, np.integer): raise ValueError(f"Label data must be an integer dtype, got {data.dtype}.") - if chunks is None: - chunks = self._default_chunks(data.shape, last_data_dims=3) - - return self._create_zarr_array_base( - name=level, + arr = self.create_zeros( + level=level, shape=data.shape, dtype=data.dtype, chunks=chunks, - array_class=LabelsArray, shards_ratio=shards_ratio, - data=data, - metadata_callback=self._create_label_meta, transform=transform, ) + arr[...] = data + return arr def create_zeros( self, @@ -1135,8 +758,6 @@ def create_zeros( Chunk size, by default None shards_ratio : tuple[int, ...], optional Sharding ratio for each dimension, by default None. - Each shard contains the product of the ratios number of chunks. - No sharding will be used if not specified. transform : list[TransformationMeta], optional Coordinate transformations, by default None @@ -1145,30 +766,18 @@ def create_zeros( LabelsArray Zero-filled label array """ - if chunks is None: - chunks = self._default_chunks(shape, last_data_dims=3) - if not np.issubdtype(dtype, np.integer): raise ValueError(f"Labels must use integer dtype, got {dtype}") - - return self._create_zarr_array_base( - name=level, - shape=shape, - dtype=dtype, - chunks=chunks, - array_class=LabelsArray, - shards_ratio=shards_ratio, - data=None, - metadata_callback=self._create_label_meta, - transform=transform, - ) + if not chunks: + chunks = pad_shape(shape[-3:], target=len(shape)) + arr_handle = self._create_zarr_array(level, shape, dtype, chunks, shards_ratio) + lbl_arr = LabelsArray.from_handle(arr_handle, self._impl) + self._create_label_meta(level, transform=transform) + return lbl_arr def initialize_pyramid(self, levels: int, source_level: str = "0") -> None: """Initialize multiscale pyramid for label image. - Creates downscaled versions of the source level using label-preserving - downscaling to maintain integer label values. - Parameters ---------- levels : int @@ -1180,10 +789,16 @@ def initialize_pyramid(self, levels: int, source_level: str = "0") -> None: raise KeyError(f"Source level '{source_level}' not found") source_array = self[source_level] - for level in range(1, levels): - downscaled_shape, downscaled_chunks, transforms = self._calculate_pyramid_params(source_array, level) - + factor = 2**level + downscaled_shape = source_array.shape[:-3] + tuple(math.ceil(s / factor) for s in source_array.shape[-3:]) + downscaled_chunks = pad_shape( + tuple(math.ceil(c / factor) for c in source_array.chunks[-3:]), + target=len(downscaled_shape), + ) + transforms = [ + TransformationMeta(type="scale", scale=[1.0] * (len(source_array.shape) - 3) + [float(factor)] * 3) + ] self.create_zeros( level=str(level), shape=downscaled_shape, @@ -1232,7 +847,7 @@ def _create_image_label_meta(self) -> PositionLabelMeta: for label_value, rgba in self._colors.items(): # Ensure RGBA format if len(rgba) == 3: - rgba = rgba + [255] # Add alpha + rgba = [*rgba, 255] # Add alpha # Convert to 0-1 range for Pydantic (serialized as 0-255) # Validate bounds and handle numpy integer types @@ -1270,7 +885,7 @@ def _create_image_label_meta(self) -> PositionLabelMeta: ) -class Position(NGFFMultiscalesNode): +class Position(NGFFNode): """The Zarr group level directly containing multiscale image arrays. Parameters @@ -1309,8 +924,9 @@ def __init__( parse_meta: bool = True, channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): super().__init__( group=group, @@ -1319,6 +935,7 @@ def __init__( axes=axes, version=version, overwriting_creation=overwriting_creation, + impl=impl, ) def _set_meta(self): @@ -1340,15 +957,76 @@ def _parse_meta(self): def dump_meta(self): """Dumps metadata JSON to the `.zattrs` file.""" - ome = self.metadata.model_dump(**TO_DICT_SETTINGS) + ome = self.metadata.model_dump(exclude_none=True, by_alias=True) self._dump_ome(ome) - def _create_from_data(self, key, value): - """Create image from data (implementation of base class hook).""" - return self.create_image(key, value) + @property + def _member_names(self): + return self.array_keys() + + @property + def data(self): + """.. warning:: + + This property does *NOT* aim to retrieve all the arrays. + And it may also fail to retrive any data if arrays exist but + are not named conventionally. + + Alias for an array named '0' in the position, + which is usually the raw data (or the finest resolution in a pyramid). + + Returns + ------- + ImageArray + + Raises + ------ + KeyError + If no array is named '0'. + + Notes + ----- + Do not depend on this in non-interactive code! + The name is hard-coded and is not guaranteed + by the OME-NGFF specification. + """ + try: + return self["0"] + except KeyError as err: + raise KeyError(f"There is no array named '0' in the group of: {self.array_keys()}") from err + + def __getitem__(self, key: int | str) -> ImageArray: + """Get an image array member of the position. + + E.g. Raw-coordinates image, a multi-scale level, or labels + + Parameters + ---------- + key : int| str + Name or path to the image array. + Integer key is converted to string (name). + + Returns + ------- + ImageArray + Container object for image stored as a zarr array (up to 5D) + """ + result = NGFFNode.__getitem__(self, key) + + if isinstance(result, ImageArray): + result._dim_names = tuple(ax.name for ax in self.axes) + return result + + def __setitem__(self, key, value: NDArray): + """Write an up-to-5D image with default settings.""" + key = normalize_path(str(key)) + if not isinstance(value, np.ndarray): + raise TypeError(f"Value must be a NumPy array. Got type {type(value)}.") + self.create_image(key, value) def images(self) -> Generator[tuple[str, ImageArray]]: """Returns a generator that iterate over the name and value + of all the image arrays in the group. Yields @@ -1417,6 +1095,7 @@ def create_zeros( check_shape: bool = True, ): """Create a new zero-filled image array in the position. + Under default zarr-python settings of lazy writing, this will not write the array values, but only create a ``.zarray`` file. @@ -1455,21 +1134,16 @@ def create_zeros( if check_shape: self._check_shape(shape) - - img_arr = self._create_zarr_array_base( - name=name, - shape=shape, - dtype=dtype, - chunks=chunks, - array_class=ImageArray, - shards_ratio=shards_ratio, - data=None, - metadata_callback=self._create_image_meta, - transform=transform, - ) - + arr_handle = self._create_zarr_array(name, shape, dtype, chunks, shards_ratio) + img_arr = ImageArray.from_handle(arr_handle, self._impl) + self._create_image_meta(name, transform=transform) return img_arr + @staticmethod + def _default_chunks(shape, last_data_dims: int): + chunks = shape[-min(last_data_dims, len(shape)) :] + return pad_shape(chunks, target=len(shape)) + def _check_shape(self, data_shape: tuple[int, ...]) -> None: if len(data_shape) != len(self.axes): raise ValueError(f"Image has {len(data_shape)} dimensions, while the dataset has {len(self.axes)}.") @@ -1520,10 +1194,8 @@ def _omero_meta( if not clims: clims = [None] * len(self.channel_names) channels = [] - for i, (channel_name, clim) in enumerate(zip(self.channel_names, clims)): - if i == 0: - first_chan = True - channels.append(channel_display_settings(channel_name, clim=clim, first_chan=first_chan)) + for i, (channel_name, clim) in enumerate(zip(self.channel_names, clims, strict=False)): + channels.append(channel_display_settings(channel_name, clim=clim, first_chan=(i == 0))) omero_meta = OMEROMeta( version=self.version, id=id, @@ -1567,7 +1239,7 @@ def append_channel(self, chan_name: str, resize_arrays: bool = True): shape[ch_ax] += 1 # prepend axis elif ch_ax == len(shape): - shape = _pad_shape(tuple(shape), target=len(shape) + 1) + shape = pad_shape(tuple(shape), target=len(shape) + 1) else: raise IndexError(f"Cannot infer channel axis for shape {shape}.") img.resize(shape) @@ -1586,6 +1258,7 @@ def rename_channel(self, old: str, new: str): New name of the channel """ ch_idx = self.get_channel_index(old) + self.channel_names[ch_idx] = new if hasattr(self.metadata, "omero"): self.metadata.omero.channels[ch_idx].label = new @@ -1615,11 +1288,12 @@ def update_channel(self, chan_name: str, target: str, data: ArrayLike): and users are encouraged to use array indexing directly. """ img = self[target] + ch_idx = self.get_channel_index(chan_name) ch_ax = self._get_channel_axis() ortho_sel = [slice(None)] * len(img.shape) ortho_sel[ch_ax] = ch_idx - img.set_orthogonal_selection(tuple(ortho_sel), data) + img.oindex[tuple(ortho_sel)] = data def initialize_pyramid( self, @@ -1628,6 +1302,7 @@ def initialize_pyramid( ) -> None: """ Initializes the pyramid arrays with a down scaling of 2 per level. + Decimals shapes are rounded up to ceiling. Scales metadata are also updated. @@ -1662,7 +1337,7 @@ def initialize_pyramid( if prev_shards is not None: prev_shards = _scale_dims(prev_shards, axes) - shards_ratio = tuple(s // c for c, s in zip(chunks, prev_shards)) + shards_ratio = tuple(s // c for c, s in zip(chunks, prev_shards, strict=False)) else: shards_ratio = None @@ -1727,9 +1402,8 @@ def compute_pyramid( >>> pos.delete_pyramid() >>> pos.compute_pyramid(levels=3, method="mean") """ - from iohub.ngff.utils import _downsample_tensorstore - num_arrays = len(self.array_keys()) + if num_arrays == 0: raise ValueError("No level 0 array exists. Create base array before computing pyramid.") @@ -1754,18 +1428,14 @@ def compute_pyramid( previous_level_array = self[str(level - 1)] current_level_array = self[str(level)] - previous_ts = previous_level_array.tensorstore() - current_ts = current_level_array.tensorstore() - current_scale = self.get_effective_scale(str(level)) previous_scale = self.get_effective_scale(str(level - 1)) - downsample_factors = [int(round(current_scale[i] / previous_scale[i])) for i in range(len(current_scale))] + downsample_factors = [round(current_scale[i] / previous_scale[i]) for i in range(len(current_scale))] - _downsample_tensorstore( - source_ts=previous_ts, - target_ts=current_ts, - downsample_factors=downsample_factors, + previous_level_array.downsample_into( + current_level_array, + downsample_factors, method=method, ) @@ -1790,6 +1460,7 @@ def delete_pyramid(self) -> None: def scale(self) -> list[float]: """ Helper function for scale transform metadata of + highest resolution scale. """ return self.get_effective_scale(self.metadata.multiscales[0].datasets[0].path) @@ -1821,6 +1492,7 @@ def get_axis_index(self, axis_name: str) -> int: def _get_all_transforms(self, image: str | Literal["*"]) -> list[TransformationMeta]: """Get all transforms metadata + for one image array or the whole FOV. Parameters @@ -1835,7 +1507,7 @@ def _get_all_transforms(self, image: str | Literal["*"]) -> list[TransformationM All transforms applicable to this image or FOV. """ transforms: list[TransformationMeta] = ( - [t for t in self.metadata.multiscales[0].coordinate_transformations] + list(self.metadata.multiscales[0].coordinate_transformations) if self.metadata.multiscales[0].coordinate_transformations is not None else [] ) @@ -1852,6 +1524,7 @@ def get_effective_scale( image: str | Literal["*"], ) -> list[float]: """Get the effective coordinate scale metadata + for one image array or the whole FOV. Parameters @@ -1880,6 +1553,7 @@ def get_effective_translation( image: str | Literal["*"], ) -> TransformationMeta: """Get the effective coordinate translation metadata + for one image array or the whole FOV. Parameters @@ -1895,6 +1569,7 @@ def get_effective_translation( for the image or FOV for each axis. """ transforms = self._get_all_transforms(image) + full_translation = np.zeros(len(self.axes), dtype=float) for transform in transforms: if transform.type == "translation": @@ -1908,6 +1583,7 @@ def set_transform( transform: list[TransformationMeta], ): """Set the coordinate transformations metadata + for one image array or the whole FOV. Parameters @@ -1933,6 +1609,7 @@ def set_transform( def set_scale(self, image: str | Literal["*"], axis_name: str, new_scale: float): """Set the scale for a named axis. + Either one image array or the whole FOV. Parameters @@ -1965,14 +1642,14 @@ def set_scale(self, image: str | Literal["*"], axis_name: str, new_scale: float) iohub_dict["previous_transforms"].append( { "image": image, - "transforms": [t.model_dump(**TO_DICT_SETTINGS) for t in transforms], + "transforms": [t.model_dump(exclude_none=True, by_alias=True) for t in transforms], "modified": datetime.now().isoformat(), } ) self.zattrs["iohub"] = iohub_dict if transforms == [TransformationMeta(type="identity")]: transforms = [TransformationMeta(type="scale", scale=[1.0] * 5)] - if not any([transform.type == "scale" for transform in transforms]): + if not any(transform.type == "scale" for transform in transforms): transforms.append(TransformationMeta(type="scale", scale=[1.0] * 5)) new_transforms = [] for transform in transforms: @@ -1999,6 +1676,7 @@ def _get_label_dimension_names(self, ndims: int) -> list[str]: List of dimension names for the label array """ # Labels use TZYX dimensions (no channel dimension) + return [ax.name for ax in self.label_axes[:ndims]] @property @@ -2016,9 +1694,9 @@ def label_axes(self) -> list[AxisMeta]: @property def has_labels(self) -> bool: """Check if this position has any labels.""" - return "labels" in self._group and len(list(self.labels_group.group_keys())) > 0 + return "labels" in self._group and len(self._impl.group_keys(self.labels_group)) > 0 - def create_labels_group(self) -> zarr.Group: + def create_labels_group(self): """Create the labels subgroup if it doesn't exist.""" if "labels" not in self._group: labels_group = self._group.create_group("labels", overwrite=False) @@ -2028,6 +1706,7 @@ def create_labels_group(self) -> zarr.Group: def labels(self) -> Generator[tuple[str, PositionLabel], None, None]: """Returns a generator that iterates over the name and value + of all the label images in the position. Yields @@ -2037,7 +1716,7 @@ def labels(self) -> Generator[tuple[str, PositionLabel], None, None]: """ if self.labels_group is None: return - for name in self.labels_group.group_keys(): + for name in self._impl.group_keys(self.labels_group): yield name, self.get_label(name) def label_names(self) -> list[str]: @@ -2050,7 +1729,7 @@ def label_names(self) -> list[str]: """ if self.labels_group is None: return [] - return sorted(list(self.labels_group.group_keys())) + return sorted(self._impl.group_keys(self.labels_group)) def get_label(self, name: str) -> PositionLabel: """Get a multiscale label image by name. @@ -2084,6 +1763,7 @@ def get_label(self, name: str) -> PositionLabel: axes=self.label_axes, version=self.version, overwriting_creation=self._overwrite, + impl=self._impl, ) def create_label( @@ -2213,6 +1893,7 @@ def create_label( colors=colors, properties=properties, overwriting_creation=self._overwrite, + impl=self._impl, ) label_image.create_label("0", data, chunks=chunks, shards_ratio=shards_ratio) @@ -2255,6 +1936,7 @@ def set_contrast_limits(self, channel_name: str, window: WindowDict): Contrast limit (min, max, start, end) """ channel_index = self.get_channel_index(channel_name) + self.metadata.omero.channels[channel_index].window = window self.dump_meta() @@ -2276,12 +1958,19 @@ def to_xarray(self) -> xr.DataArray: channel names and physical scales/units. """ all_channel_names = self.channel_names + scale = self.scale translation = self.get_effective_translation(self.metadata.multiscales[0].datasets[0].path) - data = self.data.dask_array() - T, C, Z, Y, X = data.shape + import dask.array as da + # Always use zarr-python for the dask array — other backends + # (e.g. TensorStore) may not integrate reliably with dask/xarray. + arr_name = self.metadata.multiscales[0].datasets[0].path + if isinstance(self._group, zarr.Group): + data = da.from_zarr(self._group[arr_name]) + else: + data = da.from_zarr(zarr.open_array(str(self._group.path / arr_name), mode="r")) # Build axis unit lookup from OME metadata axis_units = {} for axis in self.axes: @@ -2289,13 +1978,17 @@ def to_xarray(self) -> xr.DataArray: if unit is not None: axis_units[axis.name.lower()] = unit - # CF convention: units live in per-coordinate attrs - physical = {"t": (T, 0), "z": (Z, 2), "y": (Y, 3), "x": (X, 4)} - coords = {"c": ("c", all_channel_names)} - for dim, (size, idx) in physical.items(): - values = np.arange(size) * scale[idx] + translation[idx] - attrs = {"units": axis_units[dim]} if dim in axis_units else {} - coords[dim] = (dim, values, attrs) + # Build coordinates dynamically from axes metadata -- supports 2-5D arrays + dim_names = [ax.name.lower() for ax in self.axes] + coords = {} + for i, (ax, size) in enumerate(zip(self.axes, data.shape, strict=True)): + name = ax.name.lower() + if ax.type == "channel": + coords[name] = (name, all_channel_names) + else: + values = np.arange(size) * scale[i] + translation[i] + attrs = {"units": axis_units[name]} if name in axis_units else {} + coords[name] = (name, values, attrs) # Restore any previously saved DataArray attrs from zarr iohub_dict = self.zattrs.get("iohub", {}) @@ -2303,7 +1996,7 @@ def to_xarray(self) -> xr.DataArray: return xr.DataArray( data, - dims=("t", "c", "z", "y", "x"), + dims=dim_names, coords=coords, attrs=saved_attrs, ) @@ -2414,6 +2107,7 @@ def _coord_unit(dim, default): class TiledPosition(Position): """Variant of the NGFF position node + with convenience methods to create and access tiled arrays. Other parameters and attributes are the same as :py:class:`iohub.ngff.Position`. @@ -2431,6 +2125,7 @@ def make_tiles( chunk_dims: int = 2, ): """Make a tiled image array filled with zeros. + Chunk size is inferred from tile shape. Parameters @@ -2455,16 +2150,16 @@ def make_tiles( TiledImageArray """ xy_shape = tuple(int(i) for i in np.array(grid_shape) * np.array(tile_shape[-2:])) + chunks = self._default_chunks(shape=tile_shape, last_data_dims=chunk_dims) - return TiledImageArray.from_zarr_array( - self.create_zeros( - name=name, - shape=tile_shape[:-2] + xy_shape, - dtype=dtype, - chunks=chunks, - transform=transform, - ) + img_arr = self.create_zeros( + name=name, + shape=tile_shape[:-2] + xy_shape, + dtype=dtype, + chunks=chunks, + transform=transform, ) + return TiledImageArray.from_handle(img_arr.native, self._impl) class Well(NGFFNode): @@ -2500,8 +2195,9 @@ def __init__( parse_meta: bool = True, channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): super().__init__( group=group, @@ -2510,6 +2206,7 @@ def __init__( axes=axes, version=version, overwriting_creation=overwriting_creation, + impl=impl, ) def _parse_meta(self): @@ -2522,7 +2219,7 @@ def _parse_meta(self): def dump_meta(self): """Dumps metadata JSON to the `.zattrs` file.""" - ome = {"well": self.metadata.model_dump(**TO_DICT_SETTINGS)} + ome = {"well": self.metadata.model_dump(exclude_none=True, by_alias=True)} self._dump_ome(ome) def __getitem__(self, key: str): @@ -2541,7 +2238,7 @@ def __getitem__(self, key: str): return super().__getitem__(key) def _create_position_nosync(self, name: str, acquisition: int = 0): - "create_position, but doesn't write the metadata yet." + """create_position, but doesn't write the metadata yet.""" pos_grp = self._group.create_group(name, overwrite=self._overwrite) # build metadata image_meta = ImageMeta(acquisition=acquisition, path=pos_grp.basename) @@ -2562,11 +2259,13 @@ def create_position(self, name: str, acquisition: int = 0): The index of the acquisition, by default 0 """ pos = self._create_position_nosync(name, acquisition=acquisition) + self.dump_meta() return pos def positions(self): """Returns a generator that iterate over the name and value + of all the positions in the well. Yields @@ -2610,8 +2309,9 @@ def __init__( parse_meta: bool = True, channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): super().__init__( group=group, @@ -2620,6 +2320,7 @@ def __init__( axes=axes, version=version, overwriting_creation=overwriting_creation, + impl=impl, ) def __getitem__(self, key: str): @@ -2639,6 +2340,7 @@ def __getitem__(self, key: str): def wells(self): """Returns a generator that iterate over the name and value + of all the wells in the row. Yields @@ -2663,6 +2365,7 @@ def from_positions( positions: dict[str, Position], ) -> Plate: """Create a new HCS store from existing OME-Zarr stores + by copying images and metadata from a dictionary of positions. .. warning: This assumes same channel names and axes across the FOVs @@ -2696,6 +2399,7 @@ def from_positions( >>> new_plate = Plate.from_positions("combined.zarr", fovs) """ # TODO: remove when zarr-python adds back `copy_store` + raise NotImplementedError( "This method is disabled until upstream support is finalized: " "https://github.com/zarr-developers/zarr-python/issues/2407" @@ -2740,8 +2444,9 @@ def __init__( axes: list[AxisMeta] | None = None, name: str | None = None, acquisitions: list[AcquisitionMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", overwriting_creation: bool = False, + impl: ZarrImplementation | None = None, ): super().__init__( group=group, @@ -2750,6 +2455,7 @@ def __init__( axes=axes, version=version, overwriting_creation=overwriting_creation, + impl=impl, ) self._name = name self._acquisitions = [AcquisitionMeta(id=0)] if not acquisitions else acquisitions @@ -2780,6 +2486,7 @@ def _first_pos(self): group=well_grp[pos_name], parse_meta=True, version=self._version, + impl=self._impl, ) except (IndexError, KeyError, AttributeError): _logger.warning("Cannot read first position metadata.") @@ -2810,7 +2517,7 @@ def dump_meta(self, field_count: bool = False): """ if field_count: self.metadata.field_count = len(list(self.positions())) - ome = {"plate": self.metadata.model_dump(**TO_DICT_SETTINGS)} + ome = {"plate": self.metadata.model_dump(exclude_none=True, by_alias=True)} self._dump_ome(ome) def _auto_idx( @@ -2857,6 +2564,7 @@ def create_well( col_index: int | None = None, ): """Creates a new well group in the plate. + The new well will have empty group metadata, which will not be created until a position is written. @@ -2881,6 +2589,7 @@ def create_well( Well node object """ # normalize input + row_name = normalize_path(row_name) col_name = normalize_path(col_name) if row_name in self: @@ -2985,6 +2694,7 @@ def create_positions(self, positions: list[PositionSpec]) -> list[Position]: ... ) """ positions = deepcopy(positions) # We may mutate contents + wells = {} # Track wells by path to avoid duplicate objects positions_out = [] for r, c, p, *args in positions: @@ -2997,7 +2707,7 @@ def create_positions(self, positions: list[PositionSpec]) -> list[Position]: raise ValueError(f"Passed too many fields for a position: {(r, c, p, *args)}") r = normalize_path(r) c = normalize_path(c) - well_path = os.path.join(r, c) + well_path = f"{r}/{c}" # Get or create well, ensuring we reuse the same object if well_path in wells: @@ -3024,6 +2734,7 @@ def create_position( acq_index: int = 0, ): """Creates a new position group in the plate. + The new position will have empty group metadata, which will not be created until an image is written. @@ -3050,8 +2761,9 @@ def create_position( Position node object """ row_name = normalize_path(row_name) + col_name = normalize_path(col_name) - well_path = os.path.join(row_name, col_name) + well_path = f"{row_name}/{col_name}" if well_path in self.zgroup: well = self[well_path] else: @@ -3060,6 +2772,7 @@ def create_position( def rows(self) -> Generator[tuple[str, Row], None, None]: """Returns a generator that iterate over the name and value + of all the rows in the plate. Yields @@ -3071,6 +2784,7 @@ def rows(self) -> Generator[tuple[str, Row], None, None]: def wells(self) -> Generator[tuple[str, Well], None, None]: """Returns a generator that iterate over the path and value + of all the wells (along rows, columns) in the plate. Yields @@ -3084,6 +2798,7 @@ def wells(self) -> Generator[tuple[str, Well], None, None]: def positions(self) -> Generator[tuple[str, Position], None, None]: """Returns a generator that iterate over the path and value + of all the positions (along rows, columns, and wells) in the plate. Yields @@ -3105,8 +2820,8 @@ def rename_well(self, old: str, new: str): new : str New name of well, e.g. "B/2" """ - # normalize inputs + old = normalize_path(old) new = normalize_path(new) old_row, old_column = old.split("/") @@ -3161,20 +2876,27 @@ def rename_well(self, old: str, new: str): def _check_file_mode( - store_path: Path, + store_path, mode: Literal["r", "r+", "a", "w", "w-"], disable_path_checking: bool, ) -> bool: + is_fs = _is_fslike(store_path) + if is_fs: + store_path = Path(store_path) if mode == "a": - mode = "r+" if store_path.exists() else "w-" + if is_fs: + mode = "r+" if store_path.exists() else "w-" + else: + # For non-path stores, assume existing (read/write) + mode = "r+" parse_meta = False if mode in ("r", "r+"): parse_meta = True elif mode == "w-": - if store_path.exists(): + if is_fs and store_path.exists(): raise FileExistsError(store_path) elif mode == "w": - if store_path.exists(): + if is_fs and store_path.exists(): if ".zarr" not in str(store_path.resolve()) and not disable_path_checking: raise ValueError( "Cannot overwrite a path that does not contain '.zarr', " @@ -3202,72 +2924,83 @@ def _detect_layout(meta_keys: list[str]) -> Literal["fov", "hcs"]: @overload def open_ome_zarr( - store_path: str | Path, - layout: Literal["auto"], + store_path: StorePath, + layout: Literal["auto"] = "auto", mode: Literal["r", "r+", "a", "w", "w-"] = "r", channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", disable_path_checking: bool = False, + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, **kwargs, ) -> Plate | Position | TiledPosition: ... @overload def open_ome_zarr( - store_path: str | Path, + store_path: StorePath, layout: Literal["fov"], mode: Literal["r", "r+", "a", "w", "w-"] = "r", channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", disable_path_checking: bool = False, + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, **kwargs, ) -> Position: ... @overload def open_ome_zarr( - store_path: str | Path, + store_path: StorePath, layout: Literal["tiled"], mode: Literal["r", "r+", "a", "w", "w-"] = "r", channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", disable_path_checking: bool = False, + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, **kwargs, ) -> TiledPosition: ... @overload def open_ome_zarr( - store_path: str | Path, + store_path: StorePath, layout: Literal["hcs"], mode: Literal["r", "r+", "a", "w", "w-"] = "r", channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", disable_path_checking: bool = False, + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, **kwargs, ) -> Plate: ... def open_ome_zarr( - store_path: str | Path, + store_path: StorePath, layout: Literal["auto", "fov", "hcs", "tiled"] = "auto", mode: Literal["r", "r+", "a", "w", "w-"] = "r", channel_names: list[str] | None = None, axes: list[AxisMeta] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", disable_path_checking: bool = False, + implementation: str | None = None, + implementation_config: ImplementationConfig | None = None, **kwargs, ) -> Plate | Position | TiledPosition: """Convenience method to open OME-Zarr stores. Parameters ---------- - store_path : str | Path - File path to the Zarr store to open + store_path : StorePath + Path or store-like object to open. Accepts ``str``, ``Path``, + zarr ``Store``, ``StorePath``, or ``FSMap``. layout: Literal["auto", "fov", "hcs", "tiled"], optional NGFF store layout: "auto" will infer layout from existing metadata @@ -3301,7 +3034,7 @@ def open_ome_zarr( AxisMeta(name='X', type='space', unit='micrometer')] version : Literal["0.4", "0.5"], optional - OME-NGFF version, by default "0.4" + OME-NGFF version, by default "0.5". disable_path_checking : bool, optional Whether to allow overwriting a path that does not contain '.zarr', by default False @@ -3310,6 +3043,13 @@ def open_ome_zarr( This can lead to severe data loss if the input path is not checked carefully. + implementation : str, optional + Name of the zarr implementation to use (e.g. "zarr", "tensorstore"), + by default None (uses the default implementation) + implementation_config : ImplementationConfig, optional + Configuration for the chosen implementation + (e.g. ``ZarrConfig``, ``TensorStoreConfig``), + by default None kwargs : dict, optional Keyword arguments to underlying NGFF node constructor, by default None @@ -3323,13 +3063,26 @@ def open_ome_zarr( :py:class:`iohub.ngff.Plate`, or :py:class:`iohub.ngff.TiledPosition`) """ - store_path = Path(store_path) + if _is_fslike(store_path): + store_path = Path(store_path) + _is_new_store = mode in ("w", "w-") or (mode == "a" and _is_fslike(store_path) and not store_path.exists()) + if version == "0.4" and _is_new_store: + raise ValueError("Creating new OME-Zarr v0.4 stores is not supported. Use version='0.5' instead.") parse_meta = _check_file_mode(store_path, mode, disable_path_checking=disable_path_checking) - root = _open_store(store_path, mode, version) + root, impl = _open_store( + store_path, + mode, + version, + implementation=implementation, + implementation_config=implementation_config, + ) meta_keys = root.attrs.keys() if parse_meta else [] if "ome" in meta_keys: meta_keys = root.attrs["ome"].keys() version = root.attrs["ome"].get("version", version) + elif parse_meta and ("multiscales" in meta_keys or "plate" in meta_keys): + # v0.4 stores have flat metadata (no "ome" wrapper) + version = "0.4" if layout == "auto": if parse_meta: layout = _detect_layout(meta_keys) @@ -3352,5 +3105,6 @@ def open_ome_zarr( channel_names=channel_names, axes=axes, version=version, + impl=impl, **kwargs, ) diff --git a/src/iohub/ngff/utils.py b/src/iohub/ngff/utils.py index 165cb012..33a54c8d 100644 --- a/src/iohub/ngff/utils.py +++ b/src/iohub/ngff/utils.py @@ -4,10 +4,10 @@ import itertools import multiprocessing as mp from collections import defaultdict +from collections.abc import Callable, Sequence from functools import partial from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Literal, Sequence, Union -from warnings import warn +from typing import Any, Literal import click import numpy as np @@ -16,9 +16,6 @@ from iohub.ngff import open_ome_zarr from iohub.ngff.nodes import TransformationMeta -if TYPE_CHECKING: - import tensorstore as ts - def create_empty_plate( store_path: Path, @@ -27,13 +24,14 @@ def create_empty_plate( shape: tuple[int, ...], chunks: tuple[int, ...] | None = None, shards_ratio: tuple[int, ...] | None = None, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", scale: tuple[float, ...] = (1, 1, 1, 1, 1), dtype: DTypeLike = np.float32, max_chunk_size_bytes: float = 500e6, ) -> None: """ Create a new HCS Plate in OME-Zarr format if the plate does not exist. + If the plate exists, append positions and channels if they are not already in the plate. @@ -106,7 +104,6 @@ def create_empty_plate( - The function ensures that positions and channels are appended to an existing plate if they are not already present. """ - # Limiting the chunking to 500MB if chunks is None: chunk_zyx_shape = _limit_zyx_chunk_size( @@ -125,10 +122,6 @@ def create_empty_plate( channel_names=channel_names, version=version, ) - if output_plate.version == "0.4" and shards_ratio is not None: - warn("Ignoring shards ratio for OME-Zarr version 0.4.") - shards_ratio = None - # Create positions for position_key in position_keys: position_key_string = "/".join(position_key) @@ -153,22 +146,20 @@ def create_empty_plate( position.append_channel(channel_name, resize_arrays=True) +# -- Transform helpers ----------------------------------------------------- + + def _apply_transform_to_czyx( func: Callable[[NDArray, Any], NDArray], input_position_path: Path, - input_channel_indices: Union[list[int], slice], + input_channel_indices: list[int] | slice, input_time_index: int, **kwargs, ) -> NDArray | None: - # Check if input_time_indices should be added to the func kwargs - # This is needed when a different processing is needed for each time point, - # for example during stabilization all_func_params = inspect.signature(func).parameters.keys() if "input_time_index" in all_func_params: kwargs["input_time_index"] = input_time_index - # Process CZYX given with the given indices - # if input_channel_indices is not None and len(input_channel_indices) > 0: click.echo(f"Processing t={input_time_index}, c={input_channel_indices}") input_dataset = open_ome_zarr(input_position_path, layout="fov", mode="r") czyx_data = input_dataset.data.oindex[input_time_index, input_channel_indices] @@ -195,171 +186,13 @@ def _save_transformed( output_channel_indices: list[int] | slice, output_time_indices: int | list[int], ) -> None: - # NOTE: use tensorstore due to zarr-python#3221 - import tensorstore - with open_ome_zarr(output_position_path, layout="fov", mode="r+") as output_dataset: - ts = output_dataset.data.tensorstore(context=tensorstore.Context({"data_copy_concurrency": {"limit": 4}})) - ts.oindex[output_time_indices, output_channel_indices].write(transformed).result() - # NOTE: explicit GC due to tensorstore#223 - del ts - - -def apply_transform_to_czyx_and_save( - func: Callable[[NDArray, Any], NDArray], - input_position_path: Path, - output_position_path: Path, - input_channel_indices: Union[list[int], slice], - output_channel_indices: Union[list[int], slice], - input_time_index: int, - output_time_index: int, - **kwargs, -) -> None: - """ - Note: To be deprecated, no longer used by process_single_position - - Load a CZYX array from a position store, - apply a transformation, and save the result. - - Parameters - ---------- - func : Callable[[NDArray, Any], NDArray] - The function to be applied to the data. - func must take the CZYX NDArray as the first argument and return - a CZXY NDArray. Additional arguments are passed through **kwargs. - input_position_path : Path - The path to input OME-Zarr position store - (e.g., input_store_path.zarr/A/1/0). - output_position_path : Path - The path to output OME-Zarr position store - (e.g., output_store_path.zarr/A/1/0). - input_channel_indices : Union[list[int], slice] - The channel indices to process. Acceptable values: - - Slices: slice(0, 2). - - A list of integers: [0, 1, 2, 3, 4]. - output_channel_indices : Union[list[int], slice] - The channel indices to write to. Acceptable values: - - Slices: slice(0, 2). - - A list of integers: [0, 1, 2, 3, 4]. - input_time_index : int - The time index to process. - output_time_index : int - The time index to write to. - kwargs : dict, optional - Additional arguments to pass to the function. - - Examples - -------- - Using slices for input_channel_indices: - >>> apply_transform_to_zyx_and_save( - ... func=some_function, - ... input_position_path=Path("/path/to/input.zarr/A/1/0"), - ... output_position_path=Path("/path/to/output.zarr/A/1/0"), - ... input_channel_indices=slice(0, 2), - ... output_channel_indices=[0], - ... input_time_index=0, - ... output_time_index=0, - ... ) - - Using list for input_channel_indices: - >>> apply_transform_to_zyx_and_save( - ... func=some_function, - ... input_position_path=Path("/path/to/input.zarr/A/1/0"), - ... output_store_path=Path("/path/to/output.zarr/A/1/0"), - ... input_channel_indices=[0, 1, 2, 3, 4], - ... output_channel_indices=[0, 1, 2], - ... input_time_index=0, - ... output_time_index=0, - ... ) - - """ - transformed = _apply_transform_to_czyx( - func, - input_position_path=input_position_path, - input_channel_indices=input_channel_indices, - input_time_index=input_time_index, - **kwargs, - ) - if transformed is not None: - _save_transformed( + arr = output_dataset.data + arr._impl.write_oindex( + arr.native, + (output_time_indices, output_channel_indices), transformed, - output_time_indices=output_time_index, - output_channel_indices=output_channel_indices, - output_position_path=output_position_path, - ) - _echo_finished( - time_index=input_time_index, - channel_index=input_channel_indices, - skipped=False, ) - else: - _echo_finished( - time_index=input_time_index, - channel_index=input_channel_indices, - skipped=True, - ) - - -def _indices_to_shard_aligned_batches(indices: Sequence[int], shard_size: int) -> list[list[int]]: - """Split indices into batches that are in the same shards. - - Parameters - ---------- - indices : Sequence[int] - Non-negative indices to split. - shard_size : int - The size of each shard. - - Returns - ------- - list[list[int]] - List of sorted batches, - where each batch is a list of indices in the same shard. - """ - indices = sorted(indices) - batches = defaultdict(list) - for index in indices: - if index < 0: - raise ValueError(f"Negative indices are not supported: {indices}") - batches[index // shard_size].append(index) - return list(batches.values()) - - -def _match_indices_to_batches( - flat_indices: Sequence[int], - original_reference: Sequence[int], - batched_reference: list[list[int]], -) -> list[list[int]]: - """Match flat indices to batches based on a reference pair. - - Parameters - ---------- - flat_indices : Sequence[int] - Flat indices to match. - original_reference : Sequence[int] - Original reference indices. - batched_reference : list[list[int]] - Batched version of reference. - - Returns - ------- - list[list[int]] - List of matched batches, where each batch corresponds to the - original reference indices. - """ - matched_batches = [] - for batch in batched_reference: - matched_batch = [] - for index in batch: - matched_batch.append(flat_indices[original_reference.index(index)]) - matched_batches.append(matched_batch) - return matched_batches - - -def _slice_to_list(indices: list[int] | slice) -> list[int]: - if isinstance(indices, slice): - return list(range(indices.start, indices.stop, indices.step)) - return indices def apply_transform_to_tczyx_and_save( @@ -372,37 +205,9 @@ def apply_transform_to_tczyx_and_save( output_time_indices: list[int] | slice, **kwargs, ) -> None: - """ - Load a TCZYX array from a position store, - apply a transformation, and save the result. + """Load a TCZYX array from a position store. - Parameters - ---------- - func : Callable[[NDArray, Any], NDArray] - The function to be applied to the data. - func must take the TCZYX NDArray as the first argument and return - a TCZXY NDArray. Additional arguments are passed through **kwargs. - input_position_path : Path - The path to input OME-Zarr position store - (e.g., "input_store_path.zarr/A/1/0"). - output_position_path : Path - The path to output OME-Zarr position store - (e.g., "output_store_path.zarr/A/1/0"). - input_channel_indices : list[int] | slice - The channel indices to process. Acceptable values: - - Slices: slice(0, 2). - - A list of integers: [0, 1, 2, 3, 4]. - output_channel_indices : list[int] | slice - The channel indices to write to, - similar to input_channel_indices. - input_time_indices : list[int] | slice - The time indices to process, - similar to input_channel_indices. - output_time_indices : list[int] | slice - The time indices to write to, - similar to input_channel_indices. - kwargs : dict, optional - Additional arguments to pass to the function. + Apply a transformation and save the result. """ input_time_indices = _slice_to_list(input_time_indices) results = {} @@ -435,6 +240,41 @@ def apply_transform_to_tczyx_and_save( del results +# -- Shard-aligned batching helpers ---------------------------------------- + + +def _indices_to_shard_aligned_batches(indices: Sequence[int], shard_size: int) -> list[list[int]]: + """Split indices into batches that are in the same shards.""" + indices = sorted(indices) + batches = defaultdict(list) + for index in indices: + if index < 0: + raise ValueError(f"Negative indices are not supported: {indices}") + batches[index // shard_size].append(index) + return list(batches.values()) + + +def _match_indices_to_batches( + flat_indices: Sequence[int], + original_reference: Sequence[int], + batched_reference: list[list[int]], +) -> list[list[int]]: + """Match flat indices to batches based on a reference pair.""" + matched_batches = [] + for batch in batched_reference: + matched_batch = [flat_indices[original_reference.index(index)] for index in batch] + matched_batches.append(matched_batch) + return matched_batches + + +def _slice_to_list(indices: list[int] | slice) -> list[int]: + if isinstance(indices, slice): + start = indices.start or 0 + step = indices.step or 1 + return list(range(start, indices.stop, step)) + return indices + + def process_single_position( func: Callable[[NDArray, Any], NDArray], input_position_path: Path, @@ -447,9 +287,9 @@ def process_single_position( **kwargs, ) -> None: """ - Apply function to data in an `iohub` position store, - parallelizing over time and channel indices, - and save result in an output position store. + Apply function to data in an `iohub` position store. + + Parallelize over time and channel indices and save result in an output position store. Parameters ---------- @@ -491,42 +331,12 @@ def process_single_position( can be passed to be stored at a FOV level, e.g., kwargs={"extra_metadata": {"Temperature": 37.5, "CO2_level": 0.5}}. - - Examples - -------- - Using slices for input_channel_indices: - process_single_position( - func=some_function, - input_position_path=Path("/path/to/input"), - output_store_path=Path("/path/to/output"), - input_time_indices=[0], - input_channel_indices=[slice(0, 2), slice(2, 4)], - output_channel_indices=[[0], [1]], - ) - - Using list of lists for input_channel_indices: - process_single_position( - func=some_function, - input_position_path=Path("/path/to/input"), - output_store_path=Path("/path/to/output"), - input_time_indices=[1, 2], - input_channel_indices=[[0, 1], [2, 3]], - output_channel_indices=[[0], [1]], - ) - - Notes - ----- - - Multiprocessing over T and C: - input_channel_indices and output_channel_indices should be empty. - - Multiprocessing over T only: - input_channel_indices and output_channel_indices should be provided. """ - # Function to be applied click.echo(f"Function to be applied: \t{func}") click.echo(f"Input data path:\t{input_position_path}") click.echo(f"Output data path:\t{output_position_path}") - # Get the reader + # Get data shape and shard info with open_ome_zarr(input_position_path, layout="fov", mode="r") as input_dataset: input_data_shape = input_dataset.data.shape with open_ome_zarr(output_position_path, layout="fov", mode="r") as output_dataset: @@ -573,8 +383,8 @@ def process_single_position( # Loop through (T, C), applying transform and writing as we go iterable = itertools.product( - zip(input_channel_indices, output_channel_indices), - zip(batched_input_time_indices, batched_output_time_indices), + zip(input_channel_indices, output_channel_indices, strict=False), + zip(batched_input_time_indices, batched_output_time_indices, strict=False), ) flat_iterable = tuple((*c, *t) for c, t in iterable) @@ -587,41 +397,37 @@ def process_single_position( ) num_processes = min(num_processes, len(flat_iterable), mp.cpu_count()) click.echo(f"\nStarting multiprocess pool with {num_processes} processes") - # NOTE: use spawn to work around tensorstore#61 - context = mp.get_context("spawn") - with context.Pool(num_processes) as p: - p.starmap( - partial_apply_transform_to_czyx_and_save, - flat_iterable, - ) + if num_processes <= 1: + # Run serially — Pool(1) with spawn unnecessarily forks a subprocess + for args in flat_iterable: + partial_apply_transform_to_czyx_and_save(*args) + else: + # NOTE: use spawn to work around tensorstore#61 + context = mp.get_context("spawn") + with context.Pool(num_processes) as p: + p.starmap( + partial_apply_transform_to_czyx_and_save, + flat_iterable, + ) click.echo("Shut down multiprocess pool") +# -- Pure utility functions ------------------------------------------------ + + def _check_nan_n_zeros(input_array) -> bool: - """ - Checks if any of the channels are all zeros or nans and returns true - """ + """Checks if any of the channels are all zeros or nans.""" if len(input_array.shape) == 3: - # Check if all the values are zeros or nans if np.all(input_array == 0) or np.all(np.isnan(input_array)): - # Return true return True elif len(input_array.shape) == 4: - # Get the number of channels num_channels = input_array.shape[0] - # Loop through the channels for c in range(num_channels): - # Get the channel zyx_array = input_array[c, :, :, :] - - # Check if all the values are zeros or nans if np.all(zyx_array == 0) or np.all(np.isnan(zyx_array)): - # Return true return True else: raise ValueError("Input array must be 3D or 4D") - - # Return false return False @@ -631,28 +437,7 @@ def _limit_zyx_chunk_size( max_chunk_size_bytes: float, chunks: tuple[int, ...] | None = None, ) -> tuple[int, int, int]: - """ - Calculate the chunk size for ZYX dimensions based on the shape, - bytes per pixel of data, and desired max chunk size. - - Parameters - ---------- - shape : tuple[int] - The shape of the data (at least 3 dimensions for ZYX). - bytes_per_pixel : int - Number of bytes per pixel (e.g., 2 for uint16). - max_chunk_size_bytes : float - Maximum chunk size in bytes. - chunks : tuple[int] | None, optional - Initial chunk sizes to limit. If None, uses shape[-3:] as starting - chunk size. If provided, uses chunks[-3:] as starting point. - - Returns - ------- - tuple[int, int, int] - The limited ZYX chunk sizes. - """ - # Use provided chunks if available, otherwise start from shape + """Calculate chunk size for ZYX dimensions to stay under max bytes.""" chunk_zyx_shape = list(chunks[-3:] if chunks else shape[-3:]) # Reduce Z chunk size until total chunk is within limit @@ -680,7 +465,7 @@ def _adjust_chunks_for_divisibility(shape: tuple[int, ...], chunks: tuple[int, . shape = list(shape) chunks = list(chunks) adjusted = [] - for chunk, dim in zip(chunks, shape): + for chunk, dim in zip(chunks, shape, strict=False): if chunk > dim: adjusted.append(dim) elif dim % chunk != 0: @@ -690,45 +475,3 @@ def _adjust_chunks_for_divisibility(shape: tuple[int, ...], chunks: tuple[int, . else: adjusted.append(chunk) return tuple(adjusted) - - -def _downsample_tensorstore( - source_ts: ts.TensorStore, - target_ts: ts.TensorStore, - downsample_factors: list[int], - method: str = "mean", -) -> None: - """Downsample source into target using tensorstore. - - Writes data in chunks with transactions for consistency. - - Parameters - ---------- - source_ts : ts.TensorStore - Source tensorstore array to downsample from - target_ts : ts.TensorStore - Target tensorstore array to write downsampled data to - downsample_factors : list[int] - Downsampling factors for each dimension - method : str, optional - Downsampling method ("mean", "median", "mode", "min", "max", - "stride"), by default "mean" - """ - try: - import tensorstore as ts - except ImportError: - raise ImportError( - "Tensorstore is required for downsampling. \ - Please install it with `pip install tensorstore`." - ) - - downsampled = ts.downsample(source_ts, downsample_factors=downsample_factors, method=method) - - step = target_ts.chunk_layout.write_chunk.shape[0] - - for start in range(0, downsampled.shape[0], step): - with ts.Transaction() as txn: - target_with_txn = target_ts.with_transaction(txn) - downsampled_with_txn = downsampled.with_transaction(txn) - stop = min(start + step, downsampled.shape[0]) - target_with_txn[start:stop].write(downsampled_with_txn[start:stop]).result() diff --git a/src/iohub/reader.py b/src/iohub/reader.py index 82577d4d..7223a98b 100644 --- a/src/iohub/reader.py +++ b/src/iohub/reader.py @@ -41,7 +41,7 @@ def _check_zarr_data_type(src: Path): for _, well in row.groups(): if version := _find_ngff_version_in_zarr_group(well): return version - except Exception: + except Exception: # noqa: BLE001 — version detection may fail in many ways return False return "unknown" @@ -95,7 +95,6 @@ def _check_ndtiff(src: Path): def _get_sub_dirs(directory: Path) -> list[str]: - """ """ sub_dir_name = [subdir.name for subdir in directory.iterdir() if subdir.is_dir()] # assert subDirName, 'No sub directories found' return natsort.natsorted(sub_dir_name) @@ -119,9 +118,10 @@ def _infer_format(path: Path): def read_images( path: StrOrBytesPath, - data_type: Literal["ometiff", "ndtiff", "omezarr"] = None, + data_type: Literal["ometiff", "ndtiff", "omezarr"] | None = None, ) -> BaseFOVMapping | Plate | Position | TiledPosition: """Read image arrays and metadata from a Micro-Manager dataset. + Supported formats are Micro-Manager-acquired TIFF datasets (multi-page OME-TIFF, NDTIFF), and converted OME-Zarr (v0.4/v0.5 HCS layout). @@ -139,6 +139,7 @@ def read_images( Image collection object for the dataset """ path = Path(path).resolve() + # try to guess data type extra_info = None if not data_type: @@ -182,6 +183,7 @@ def print_info(path: StrOrBytesPath, verbose=False): by default False """ path = Path(path).resolve() + try: fmt, extra_info = _infer_format(path) if fmt == "omezarr" and extra_info in ("0.4", "0.5"): @@ -202,7 +204,9 @@ def print_info(path: StrOrBytesPath, verbose=False): msgs = [] if isinstance(reader, BaseFOVMapping): _, first_fov = next(iter(reader)) - shape_msg = ", ".join([f"{a}={s}" for s, a in zip(first_fov.shape, ("T", "C", "Z", "Y", "X"))]) + shape_msg = ", ".join( + [f"{a}={s}" for s, a in zip(first_fov.shape, ("T", "C", "Z", "Y", "X"), strict=False)] + ) msgs.extend( [ sum_msg, @@ -278,6 +282,7 @@ def print_info(path: StrOrBytesPath, verbose=False): def sizeof_fmt(num: int) -> str: """ Human readable file size + Adapted form: https://web.archive.org/web/20111010015624/ http://blogmag.net/blog/read/38/Print_human_readable_file_size diff --git a/src/iohub/rename_wells.py b/src/iohub/rename_wells.py index b8f3b39f..e878d569 100644 --- a/src/iohub/rename_wells.py +++ b/src/iohub/rename_wells.py @@ -7,6 +7,7 @@ def rename_wells(zarr_store_path: str | Path, csv_file_path: str | Path): """ Rename wells in a Zarr store based on a CSV file containing old and new + well names. Parameters @@ -35,10 +36,10 @@ def rename_wells(zarr_store_path: str | Path, csv_file_path: str | Path): A/2,B/2 """ - # read and check csv + name_pair_list = [] - with open(csv_file_path, mode="r", encoding="utf-8-sig") as csv_file: + with Path(csv_file_path).open(encoding="utf-8-sig") as csv_file: for row in csv.reader(csv_file): if len(row) != 2: raise ValueError(f"Invalid row format: {row}.Each row must have two columns.") diff --git a/tests/clearcontrol/test_clearcontrol.py b/tests/clearcontrol/test_clearcontrol.py index 2638f052..0d865718 100644 --- a/tests/clearcontrol/test_clearcontrol.py +++ b/tests/clearcontrol/test_clearcontrol.py @@ -21,7 +21,7 @@ def mock_clear_control_dataset_path(tmp_path: Path) -> Path: def test_blosc_buffer(tmp_path: Path) -> None: buffer_path = tmp_path / "buffer.blc" - in_array = np.random.randint(0, 5_000, size=(32, 32)) + in_array = np.random.default_rng().integers(0, 5_000, size=(32, 32)) _array_to_blosc_buffer(in_array, buffer_path) out_array = blosc_buffer_to_array(buffer_path, in_array.shape, in_array.dtype) diff --git a/tests/cli/test_cli.py b/tests/cli/test_cli.py index 2921f4bf..e6322586 100644 --- a/tests/cli/test_cli.py +++ b/tests/cli/test_cli.py @@ -14,8 +14,7 @@ ndtiff_v2_datasets, ndtiff_v3_labeled_positions, ) - -from ..ngff.test_ngff import _temp_copy +from tests.ngff.test_ngff import _temp_copy def pytest_generate_tests(metafunc): @@ -26,7 +25,7 @@ def pytest_generate_tests(metafunc): if "ndtiff_dataset" in metafunc.fixturenames: metafunc.parametrize( "ndtiff_dataset", - ndtiff_v2_datasets + [ndtiff_v3_labeled_positions], + [*ndtiff_v2_datasets, ndtiff_v3_labeled_positions], ) @@ -120,16 +119,15 @@ def test_cli_convert_ome_tiff(grid_layout, tmpdir): assert "Converting" in result.output -@pytest.mark.parametrize("version", ["0.4", "0.5"]) -def test_cli_convert_version(version, tmpdir): +def test_cli_convert_version(tmpdir): dataset = mm2gamma_ome_tiffs[0] runner = CliRunner() output_dir = tmpdir / "converted.zarr" - cmd = ["convert", "-i", str(dataset), "-o", output_dir, "-v", version] + cmd = ["convert", "-i", str(dataset), "-o", output_dir, "-v", "0.5"] result = runner.invoke(cli, cmd) assert result.exit_code == 0, result.output with open_ome_zarr(output_dir, mode="r") as store: - assert store.version == version + assert store.version == "0.5" def test_cli_convert_invalid_version(tmpdir): diff --git a/tests/conftest.py b/tests/conftest.py index d44d7e60..98c56d1c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,8 +6,12 @@ import fsspec import numpy as np import pytest +from hypothesis import settings from wget import download +settings.register_profile("default", deadline=None) +settings.load_profile("default") + @pytest.fixture def rng(): @@ -43,7 +47,7 @@ def download_data(): # Reference v0.4 HCS dataset from OME # See the last line of # https://github.com/ome/ngff/issues/140#issuecomment-1309972511 - ome_hcs_url = "https://zenodo.org/record/8091756/files/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr.zip" # noqa + ome_hcs_url = "https://zenodo.org/record/8091756/files/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr.zip" # download files to temp folder if not any(test_data.iterdir()): @@ -111,7 +115,7 @@ def csv_data_file_1(tmpdir): csv_data_1 = [ ["B/03", "D/4"], ] - with open(test_csv_1, mode="w", newline="") as csvfile: + with Path(test_csv_1).open(mode="w", newline="") as csvfile: writer = csv.writer(csvfile) writer.writerows(csv_data_1) return test_csv_1 @@ -123,7 +127,7 @@ def csv_data_file_2(tmpdir): csv_data_2 = [ ["D/4", "B/03"], ] - with open(test_csv_2, mode="w", newline="") as csvfile: + with Path(test_csv_2).open(mode="w", newline="") as csvfile: writer = csv.writer(csvfile) writer.writerows(csv_data_2) return test_csv_2 @@ -163,7 +167,7 @@ def empty_ome_zarr_hcs_v05(tmpdir) -> tuple[Path, tuple[tuple[str, ...], ...]]: return empty_zarr, (ROWS, COLS, FOVS, RESOLUTIONS) -@pytest.fixture() +@pytest.fixture def aqz_ome_zarr_05(tmpdir): pytest.importorskip("acquire_zarr") import acquire_zarr as aqz @@ -226,7 +230,7 @@ def aqz_ome_zarr_05(tmpdir): ) stream = aqz.ZarrStream(settings) - data = np.random.randint(0, 2**16 - 1, (32, 4, 10, 48, 64), dtype=np.uint16) + data = np.random.default_rng().integers(0, 2**16 - 1, (32, 4, 10, 48, 64), dtype=np.uint16) stream.append(data) del stream diff --git a/tests/core/__init__.py b/tests/core/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/core/test_arrays.py b/tests/core/test_arrays.py new file mode 100644 index 00000000..04466c0f --- /dev/null +++ b/tests/core/test_arrays.py @@ -0,0 +1,109 @@ +"""Unit tests for iohub.core.arrays (NGFFArray).""" + +from __future__ import annotations + +import numpy as np +import pytest +import zarr + +from iohub.core.arrays import NGFFArray +from iohub.core.implementations.zarr_python import ZarrPythonImplementation + + +@pytest.fixture +def impl(): + return ZarrPythonImplementation() + + +@pytest.fixture +def zarr_handle(tmp_path): + store = zarr.open_group(str(tmp_path / "test.zarr"), mode="w", zarr_format=3) + arr = store.create_array("data", shape=(3, 4), dtype=np.float32, chunks=(3, 4)) + arr[:] = np.arange(12, dtype=np.float32).reshape(3, 4) + return arr + + +# --------------------------------------------------------------------------- +# from_handle returns the calling subclass (Self) +# --------------------------------------------------------------------------- + + +class _SubArray(NGFFArray): + _N_DIMS = 2 + _SUPPORTED_DIMS = "YX" + + +def test_from_handle_returns_base_class(zarr_handle, impl): + result = NGFFArray.from_handle(zarr_handle, impl) + assert type(result) is NGFFArray + + +def test_from_handle_returns_subclass(zarr_handle, impl): + """from_handle must return an instance of the calling subclass, not NGFFArray.""" + result = _SubArray.from_handle(zarr_handle, impl) + assert isinstance(result, _SubArray) + assert type(result) is _SubArray + + +def test_from_handle_preserves_dim_names(zarr_handle, impl): + dim_names = ("y", "x") + result = _SubArray.from_handle(zarr_handle, impl, dim_names=dim_names) + assert result._dim_names == dim_names + + +# --------------------------------------------------------------------------- +# __array__ protocol +# --------------------------------------------------------------------------- + + +def test_array_protocol_basic(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + result = np.array(arr) + assert result.shape == (3, 4) + assert result.dtype == np.float32 + np.testing.assert_array_equal(result, np.arange(12, dtype=np.float32).reshape(3, 4)) + + +def test_array_protocol_dtype_cast(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + result = np.array(arr, dtype=np.float64) + assert result.dtype == np.float64 + + +def test_array_protocol_copy_none(zarr_handle, impl): + """copy=None must not raise (NumPy 2.0 compat).""" + arr = NGFFArray(zarr_handle, impl) + result = np.array(arr, copy=None) + assert result.shape == (3, 4) + + +def test_array_protocol_copy_true(zarr_handle, impl): + """copy=True must return a valid array (always a copy from storage).""" + arr = NGFFArray(zarr_handle, impl) + result = np.array(arr, copy=True) + assert result.shape == (3, 4) + + +# --------------------------------------------------------------------------- +# metadata properties +# --------------------------------------------------------------------------- + + +def test_shape(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + assert arr.shape == (3, 4) + + +def test_dtype(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + assert arr.dtype == np.float32 + + +def test_ndim(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + assert arr.ndim == 2 + + +def test_nbytes(zarr_handle, impl): + arr = NGFFArray(zarr_handle, impl) + assert arr.nbytes == 3 * 4 * np.dtype(np.float32).itemsize diff --git a/tests/core/test_registry.py b/tests/core/test_registry.py new file mode 100644 index 00000000..a85c76d2 --- /dev/null +++ b/tests/core/test_registry.py @@ -0,0 +1,140 @@ +"""Unit tests for iohub.core.registry.""" + +from __future__ import annotations + +from unittest.mock import patch + +import pytest + +import iohub.core.registry as _reg +from iohub.core.errors import ImplementationNotFoundError +from iohub.core.registry import ( + _reset, + available_implementations, + get_implementation, + register_implementation, + set_default_implementation, +) + + +@pytest.fixture(autouse=True) +def clean_registry(): + """Reset registry state before and after every test in this module.""" + _reset() + yield + _reset() + + +# --------------------------------------------------------------------------- +# _discover() +# --------------------------------------------------------------------------- + + +def test_discover_zero_plugins_sets_discovered(): + """_discovered must be True even when no entrypoint plugins are installed.""" + with patch("iohub.core.registry.entry_points", return_value=[]): + # trigger discovery + available_implementations() + assert _reg._discovered is True + + +def test_discover_zero_plugins_loads_builtins(): + """Builtins must be loaded even when no entrypoint plugins are installed.""" + with patch("iohub.core.registry.entry_points", return_value=[]): + impls = available_implementations() + assert "zarr" in impls + + +def test_discover_idempotent(): + """Calling _discover() twice must not duplicate or clear _IMPLEMENTATIONS.""" + with patch("iohub.core.registry.entry_points", return_value=[]): + available_implementations() + first = dict(_reg._IMPLEMENTATIONS) + available_implementations() + second = dict(_reg._IMPLEMENTATIONS) + assert first == second + + +def test_discover_failed_builtin_logs_and_defers_error(caplog): + """A failing builtin import is logged; _discovered is still set to True; + the error surfaces as ImplementationNotFoundError on the next call.""" + with patch("iohub.core.registry.entry_points", return_value=[]): + with patch("iohub.core.registry.importlib.import_module", side_effect=ImportError("mock")): + with caplog.at_level("ERROR", logger="iohub.core.registry"): + available_implementations() + + assert _reg._discovered is True + assert "zarr" not in _reg._IMPLEMENTATIONS + with pytest.raises(ImplementationNotFoundError): + get_implementation("zarr") + + +# --------------------------------------------------------------------------- +# _reset() +# --------------------------------------------------------------------------- + + +def test_reset_roundtrip(): + """After _reset(), get_implementation() re-discovers and returns the zarr impl.""" + get_implementation("zarr") # populate + _reset() + impl = get_implementation("zarr") + assert impl is not None + + +def test_reset_clears_default(): + """_reset() restores _default to 'zarr'.""" + set_default_implementation("zarr") + _reset() + assert _reg._default == "zarr" + + +# --------------------------------------------------------------------------- +# get_implementation() +# --------------------------------------------------------------------------- + + +def test_get_implementation_zarr_returns_instance(): + impl = get_implementation("zarr") + from iohub.core.implementations.zarr_python import ZarrPythonImplementation + + assert isinstance(impl, ZarrPythonImplementation) + + +def test_get_implementation_unknown_raises(): + get_implementation("zarr") # ensure discovered + with pytest.raises(ImplementationNotFoundError, match="nonexistent"): + get_implementation("nonexistent") + + +def test_get_implementation_default_is_zarr(): + impl = get_implementation() + from iohub.core.implementations.zarr_python import ZarrPythonImplementation + + assert isinstance(impl, ZarrPythonImplementation) + + +# --------------------------------------------------------------------------- +# set_default_implementation() +# --------------------------------------------------------------------------- + + +def test_set_default_implementation_happy(): + register_implementation("custom", type(get_implementation("zarr"))) + set_default_implementation("custom") + assert _reg._default == "custom" + + +def test_set_default_implementation_unknown_raises(): + get_implementation("zarr") # ensure discovered + with pytest.raises(ImplementationNotFoundError, match="nonexistent"): + set_default_implementation("nonexistent") + + +# --------------------------------------------------------------------------- +# available_implementations() +# --------------------------------------------------------------------------- + + +def test_available_implementations_includes_zarr(): + assert "zarr" in available_implementations() diff --git a/tests/fov/test_fov.py b/tests/fov/test_fov.py index 9e15d358..1c56188c 100644 --- a/tests/fov/test_fov.py +++ b/tests/fov/test_fov.py @@ -40,11 +40,11 @@ def zyx_scale(self) -> tuple[float, float, float]: @property def t_scale(self) -> float: - raise 1.0 + return 1.0 @pytest.mark.parametrize( - "axes,missing", + ("axes", "missing"), [ (["T", "C", "Z", "Y", "X"], []), (["time", "z", "y", "x"], [1]), diff --git a/tests/mmstack/test_mmstack.py b/tests/mmstack/test_mmstack.py index fab29d2a..42249615 100644 --- a/tests/mmstack/test_mmstack.py +++ b/tests/mmstack/test_mmstack.py @@ -1,14 +1,14 @@ import re import pytest -from xarray import DataArray - -from iohub.mmstack import MMOmeTiffFOV, MMStack from tests.conftest import ( mm2gamma_ome_tiffs, mm2gamma_ome_tiffs_incomplete, mm1422_ome_tiffs, ) +from xarray import DataArray + +from iohub.mmstack import MMOmeTiffFOV, MMStack def pytest_generate_tests(metafunc): @@ -77,7 +77,7 @@ def test_fov_axes_names(ome_tiff): axes_names = fov.axes_names assert isinstance(axes_names, list) assert len(axes_names) == 5 - assert all([isinstance(name, str) for name in axes_names]) + assert all(isinstance(name, str) for name in axes_names) def test_fov_getitem(ome_tiff): @@ -93,7 +93,7 @@ def test_fov_getitem(ome_tiff): def test_fov_equal(ome_tiff): with MMStack(ome_tiff) as ref1, MMStack(ome_tiff) as ref2: - for (_, fov1), (_, fov2) in zip(ref1, ref2): + for (_, fov1), (_, fov2) in zip(ref1, ref2, strict=False): assert fov1 == fov2 diff --git a/tests/ngff/test_ngff.py b/tests/ngff/test_ngff.py index 79f2f5a3..64f74b14 100644 --- a/tests/ngff/test_ngff.py +++ b/tests/ngff/test_ngff.py @@ -25,14 +25,14 @@ if TYPE_CHECKING: from _typeshed import StrPath +from iohub.core.utils import pad_shape +from iohub.ngff.models import TO_DICT_SETTINGS from iohub.ngff.nodes import ( - TO_DICT_SETTINGS, Plate, Position, TransformationMeta, _case_insensitive_local_fs, _open_store, - _pad_shape, _scale_dims, open_ome_zarr, ) @@ -44,10 +44,8 @@ z_dim_st = st.integers(1, 4) y_dim_st = st.integers(1, 32) x_dim_st = st.integers(1, 32) -channel_names_st = c_dim_st.flatmap( - (lambda c_dim: st.lists(short_text_st, min_size=c_dim, max_size=c_dim, unique=True)) -) -ngff_versions_st = st.sampled_from(["0.4", "0.5"]) +channel_names_st = c_dim_st.flatmap(lambda c_dim: st.lists(short_text_st, min_size=c_dim, max_size=c_dim, unique=True)) +ngff_versions_st = st.just("0.5") short_alpha_numeric = st.text( alphabet=list(string.ascii_lowercase + string.ascii_uppercase + string.digits), min_size=1, @@ -125,7 +123,7 @@ def _channels_and_random_5d(draw): @pytest.mark.parametrize( - "values, axes, expected", + ("values", "axes", "expected"), [ # all axes downsampled ((4, 8, 16), {0, 1, 2}, (2, 4, 8)), @@ -148,14 +146,14 @@ def test_scale_dims(values, axes, expected): values=st.tuples(*[st.integers(1, 64)] * 5), axes=st.frozensets(st.integers(0, 4)), ) -@settings(max_examples=64, deadline=1000) +@settings(max_examples=64, deadline=None) def test_scale_dims_properties(values, axes): """Property tests for _scale_dims.""" import math result = _scale_dims(values, axes) assert len(result) == len(values) - for i, (v, r) in enumerate(zip(values, result)): + for i, (v, r) in enumerate(zip(values, result, strict=False)): if i in axes: assert r == math.ceil(v / 2) else: @@ -163,12 +161,12 @@ def test_scale_dims_properties(values, axes): @given(shape=st.lists(x_dim_st, min_size=1, max_size=10), target=x_dim_st) -@settings(max_examples=16, deadline=1000) -def test_pad_shape(shape, target): - """Test `iohub.ngff._pad_shape()`""" +@settings(max_examples=16, deadline=None) +def testpad_shape(shape, target): + """Test `iohub.ngff.pad_shape()`""" shape = tuple(shape) assume(len(shape) <= target) - new_shape = _pad_shape(shape=shape, target=target) + new_shape = pad_shape(shape=shape, target=target) assert len(new_shape) == target assert new_shape[-len(shape) :] == shape @@ -178,8 +176,8 @@ def test_open_store_create(version): """Test `iohub.ngff._open_store()""" for mode in ("a", "w", "w-"): with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "new.zarr") - root = _open_store(store_path, mode=mode, version=version) + store_path = Path(temp_dir) / "new.zarr" + root, _impl = _open_store(store_path, mode=mode, version=version) assert isinstance(root, zarr.Group) assert isinstance(root.store, zarr.storage.LocalStore) # assert root.store._dimension_separator == "/" @@ -190,12 +188,13 @@ def test_open_store_create(version): def test_open_store_create_existing(version): """Test `iohub.ngff._open_store()""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "new.zarr") + store_path = Path(temp_dir) / "new.zarr" g = zarr.open_group(store_path, mode="w-") g.store.close() - with pytest.raises(RuntimeError): + with pytest.raises(FileExistsError): _ = _open_store(store_path, mode="w-", version=version) - assert _open_store(store_path, mode="w", version=version) is not None + root, _impl = _open_store(store_path, mode="w", version=version) + assert root is not None @given(version=ngff_versions_st) @@ -203,7 +202,7 @@ def test_open_store_read_nonexist(version): """Test `iohub.ngff._open_store()""" for mode in ("r", "r+"): with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "new.zarr") + store_path = Path(temp_dir) / "new.zarr" with pytest.raises(FileNotFoundError): _ = _open_store(store_path, mode=mode, version=version) @@ -226,7 +225,7 @@ def test_case_insensitive_local_fs(): def test_init_ome_zarr(channel_names, version): """Test `iohub.ngff.open_ome_zarr()`""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" dataset = open_ome_zarr( store_path, layout="fov", @@ -234,11 +233,36 @@ def test_init_ome_zarr(channel_names, version): channel_names=channel_names, version=version, ) - assert os.path.isdir(store_path) + assert Path(store_path).is_dir() assert dataset.channel_names == channel_names -@pytest.mark.parametrize("version", ["0.4", "0.5"]) +@pytest.mark.parametrize("mode", ["w", "w-"]) +def test_open_ome_zarr_v04_write_raises(tmp_path, mode): + """Creating new v0.4 stores must raise ValueError for all write modes.""" + with pytest.raises(ValueError, match=r"v0.4"): + open_ome_zarr( + tmp_path / "out.zarr", + layout="fov", + mode=mode, + channel_names=["DAPI"], + version="0.4", + ) + + +def test_open_ome_zarr_v04_append_new_path_raises(tmp_path): + """mode='a' on a nonexistent path is a new store and must also raise.""" + with pytest.raises(ValueError, match=r"v0.4"): + open_ome_zarr( + tmp_path / "nonexistent.zarr", + layout="fov", + mode="a", + channel_names=["DAPI"], + version="0.4", + ) + + +@pytest.mark.parametrize("version", ["0.5"]) @pytest.mark.parametrize( "basename", ["some.zarr", "other.zarr/0/0/0", "random_dir", "napari_ome_zarr"], @@ -250,7 +274,7 @@ def test_init_ome_zarr_overwrite_non_zarr(tmp_path, basename, version): some_child_directory = store_path / "some_other_directory" some_child_directory.mkdir() if ".zarr" not in basename: - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = open_ome_zarr( store_path, layout="fov", @@ -299,7 +323,7 @@ def _temp_ome_zarr( try: temp_dir = TemporaryDirectory() dataset = open_ome_zarr( - os.path.join(temp_dir.name, "ome.zarr"), + Path(temp_dir.name) / "ome.zarr", layout="fov", mode="a", version=version, @@ -338,7 +362,7 @@ def _temp_ome_zarr_plate( try: temp_dir = TemporaryDirectory() dataset = open_ome_zarr( - os.path.join(temp_dir.name, "ome.zarr"), + Path(temp_dir.name) / "ome.zarr", layout="hcs", mode="a", channel_names=channel_names, @@ -360,7 +384,6 @@ def _temp_ome_zarr_plate( ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_write_ome_zarr(channels_and_random_5d, arr_name, version): @@ -370,7 +393,7 @@ def test_write_ome_zarr(channels_and_random_5d, arr_name, version): assert_allclose(dataset[arr_name][:], random_5d) # round-trip test with the offical reader implementation ext_reader = ome_zarr.reader.Reader(ome_zarr.io.parse_url(dataset.zgroup.store.root)) - node = list(ext_reader())[0] + node = next(iter(ext_reader())) assert node.metadata["channel_names"] == channel_names assert node.specs[0].datasets == [arr_name] assert_allclose(node.data[0], random_5d) @@ -383,14 +406,13 @@ def test_write_ome_zarr(channels_and_random_5d, arr_name, version): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_create_zeros(ch_shape_dtype, arr_name, version): """Test `iohub.ngff.Position.create_zeros()`""" channel_names, shape, dtype = ch_shape_dtype with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" dataset = open_ome_zarr( store_path, layout="fov", @@ -399,15 +421,10 @@ def test_create_zeros(ch_shape_dtype, arr_name, version): version=version, ) dataset.create_zeros(name=arr_name, shape=shape, dtype=dtype) - if version == "0.4": - assert set(os.listdir(os.path.join(store_path, arr_name))) == { - ".zarray", - ".zattrs", - } - else: - assert set(os.listdir(os.path.join(store_path, arr_name))) == { - "zarr.json", - } + assert {e.name for e in (Path(store_path) / arr_name).iterdir()} == { + "zarr.json", + } + if version == "0.5": assert dataset[arr_name].metadata.dimension_names == ( "T", "C", @@ -441,7 +458,7 @@ def test_ome_zarr_to_dask(channels_and_random_5d, arr_name, version): @given(channels_and_random_5d=_channels_and_random_5d()) @settings( max_examples=16, - deadline=4000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) def test_writing_sharded(channels_and_random_5d): @@ -465,7 +482,7 @@ def test_writing_sharded(channels_and_random_5d): ) as dataset: assert_array_equal(dataset["0"].numpy(), random_5d) assert dataset["0"].chunks == chunks - assert dataset["0"].shards == tuple(c * s for c, s in zip(chunks, shards_ratio)) + assert dataset["0"].shards == tuple(c * s for c, s in zip(chunks, shards_ratio, strict=False)) @given( @@ -475,7 +492,6 @@ def test_writing_sharded(channels_and_random_5d): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_position_data(channels_and_random_5d, arr_name, version): @@ -497,26 +513,35 @@ def test_position_data(channels_and_random_5d, arr_name, version): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_ome_zarr_to_tensorstore(channels_and_random_5d, arr_name, version, concurrency): - """Test `iohub.ngff.Position.data` to tensorstore""" - import tensorstore as ts + """Test round-trip write/read via TensorStore implementation.""" + pytest.importorskip("tensorstore") + from pathlib import Path + + from iohub.core.config import TensorStoreConfig channel_names, random_5d = channels_and_random_5d + zeros = np.zeros_like(random_5d) + ts_config = TensorStoreConfig(data_copy_concurrency=concurrency) if concurrency else None + with _temp_ome_zarr(random_5d, channel_names, arr_name, version=version) as dataset: - tstore = dataset[arr_name].tensorstore( - context=(ts.Context({"data_copy_concurrency": {"limit": concurrency}}) if concurrency is not None else None) - ) - assert_array_equal(tstore, random_5d) - zeros = np.zeros_like(random_5d) - tstore[...].write(zeros).result() - with open_ome_zarr(dataset.zgroup.store.root, mode="r") as read_only_dataset: - assert_array_equal(read_only_dataset[arr_name].numpy(), zeros) - read_only_tstore = read_only_dataset[arr_name].tensorstore() - with pytest.raises(ValueError): - read_only_tstore[0] = 1 + store_path = Path(dataset.zgroup.store.root) + + with open_ome_zarr( + store_path, + layout="fov", + mode="r+", + implementation="tensorstore", + implementation_config=ts_config, + ) as ts_dataset: + ts_arr = ts_dataset[arr_name] + assert_array_equal(ts_arr.numpy(), random_5d) + ts_arr[...] = zeros + + with open_ome_zarr(store_path, layout="fov", mode="r") as read_only: + assert_array_equal(read_only[arr_name].numpy(), zeros) @given( @@ -526,7 +551,6 @@ def test_ome_zarr_to_tensorstore(channels_and_random_5d, arr_name, version, conc ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_append_channel(channels_and_random_5d, arr_name, version): @@ -547,7 +571,6 @@ def test_append_channel(channels_and_random_5d, arr_name, version): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_rename_channel(channels_and_random_5d, arr_name, new_channel, version): @@ -607,9 +630,9 @@ def test_rename_well(channels_and_random_5d, arr_name, version): dataset.rename_well("Q/1", "Q/2") # invalid well names - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): dataset.rename_well("B/2", " A/1") - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): dataset.rename_well("B/2", "A/?") @@ -620,7 +643,6 @@ def test_rename_well(channels_and_random_5d, arr_name, version): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_update_channel(channels_and_random_5d, arr_name, version): @@ -640,29 +662,33 @@ def test_update_channel(channels_and_random_5d, arr_name, version): ) @settings( max_examples=16, - deadline=2000, suppress_health_check=[HealthCheck.data_too_large], ) def test_write_more_channels(channels_and_random_5d, arr_name, version): """Test `iohub.ngff.Position.create_image()`""" channel_names, random_5d = channels_and_random_5d assume(len(channel_names) > 1) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): with _temp_ome_zarr(random_5d, channel_names[:-1], arr_name, version=version) as _: pass +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) @given( ch_shape_dtype=_channels_and_random_5d_shape_and_dtype(), arr_name=short_alpha_numeric, ) -def test_set_transform_image(ch_shape_dtype, arr_name): +def test_set_transform_image(implementation, ch_shape_dtype, arr_name): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test `iohub.ngff.Position.set_transform()`""" channel_names, shape, dtype = ch_shape_dtype transform = [TransformationMeta(type="translation", translation=(1, 2, 3, 4, 5))] * len(channel_names) with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") - with open_ome_zarr(store_path, layout="fov", mode="w-", channel_names=channel_names) as dataset: + store_path = Path(temp_dir) / "ome.zarr" + with open_ome_zarr( + store_path, layout="fov", mode="w-", channel_names=channel_names, implementation=implementation + ) as dataset: dataset.create_zeros(name=arr_name, shape=shape, dtype=dtype) assert dataset.metadata.multiscales[0].datasets[0].coordinate_transformations == [ TransformationMeta(type="scale", scale=(1.0, 1.0, 1.0, 1.0, 1.0)) @@ -671,7 +697,7 @@ def test_set_transform_image(ch_shape_dtype, arr_name): assert dataset.metadata.multiscales[0].datasets[0].coordinate_transformations == transform # read data with an external reader ext_reader = ome_zarr.reader.Reader(ome_zarr.io.parse_url(dataset.zgroup.store.root)) - node = list(ext_reader())[0] + node = next(iter(ext_reader())) assert node.metadata["coordinateTransformations"][0] == [ translate.model_dump(**TO_DICT_SETTINGS) for translate in transform ] @@ -711,7 +737,7 @@ def test_set_transform_image(ch_shape_dtype, arr_name): @pytest.mark.parametrize( "transforms", - [(saved, target) for saved, target in zip(input_transformations, target_scales)], + [(saved, target) for saved, target in zip(input_transformations, target_scales, strict=False)], ) @given( ch_shape_dtype=_channels_and_random_5d_shape_and_dtype(), @@ -723,7 +749,7 @@ def test_get_effective_scale_image(transforms, ch_shape_dtype, arr_name, version (fov_transform, img_transform), expected_scale = transforms channel_names, shape, dtype = ch_shape_dtype with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" with open_ome_zarr( store_path, layout="fov", @@ -740,7 +766,7 @@ def test_get_effective_scale_image(transforms, ch_shape_dtype, arr_name, version @pytest.mark.parametrize( "transforms", - [(saved, target) for saved, target in zip(input_transformations, target_translations)], + [(saved, target) for saved, target in zip(input_transformations, target_translations, strict=False)], ) @given( ch_shape_dtype=_channels_and_random_5d_shape_and_dtype(), @@ -752,7 +778,7 @@ def test_get_effective_translation_image(transforms, ch_shape_dtype, arr_name, v (fov_transform, img_transform), expected_translation = transforms channel_names, shape, dtype = ch_shape_dtype with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" with open_ome_zarr( store_path, layout="fov", @@ -777,7 +803,7 @@ def test_set_transform_fov(ch_shape_dtype, arr_name, version): channel_names, shape, dtype = ch_shape_dtype transform = [TransformationMeta(type="translation", translation=(1, 2, 3, 4, 5))] * len(channel_names) with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" with open_ome_zarr( store_path, layout="fov", @@ -800,7 +826,7 @@ def test_set_transform_fov(ch_shape_dtype, arr_name, version): ] -@pytest.mark.parametrize("version", ["0.4", "0.5"]) +@pytest.mark.parametrize("version", ["0.5"]) @pytest.mark.parametrize("image_name", ["0", "1", "a", "*"]) def test_set_scale(image_name, version): """Test `iohub.ngff.Position.set_scale()`""" @@ -809,7 +835,7 @@ def test_set_scale(image_name, version): array_name = "0" if image_name == "*" else image_name new_scale = 10.0 with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" with open_ome_zarr( store_path, layout="fov", @@ -826,7 +852,7 @@ def test_set_scale(image_name, version): TransformationMeta(type="scale", scale=scale), ], ) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): dataset.set_scale(image=image_name, axis_name="z", new_scale=-1.0) with pytest.raises(KeyError): dataset.set_scale(image="nonexistent", axis_name="z", new_scale=9.0) @@ -847,7 +873,7 @@ def test_set_scale(image_name, version): def test_set_contrast_limits(channel_names, version): """Test `iohub.ngff.Position.set_contrast_limits()`""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "ome.zarr") + store_path = Path(temp_dir) / "ome.zarr" dataset = open_ome_zarr( store_path, layout="fov", @@ -893,7 +919,7 @@ def test_create_tiled(channel_names, version): """Test that `iohub.ngff.open_ome_zarr()` can create an empty OME-Zarr store with 'tiled' layout.""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "tiled.zarr") + store_path = Path(temp_dir) / "tiled.zarr" dataset = open_ome_zarr( store_path, layout="tiled", @@ -901,7 +927,7 @@ def test_create_tiled(channel_names, version): channel_names=channel_names, version=version, ) - assert os.path.isdir(store_path) + assert Path(store_path).is_dir() assert dataset.channel_names == channel_names @@ -910,13 +936,18 @@ def test_create_tiled(channel_names, version): grid_shape=tiles_rc_st, arr_name=short_alpha_numeric, ) +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) @settings(suppress_health_check=[HealthCheck.too_slow]) -def test_make_tiles(channels_and_random_5d, grid_shape, arr_name): +def test_make_tiles(implementation, channels_and_random_5d, grid_shape, arr_name): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test `iohub.ngff.TiledPosition.make_tiles()` and `...get_tile()`""" with TemporaryDirectory() as temp_dir: channel_names, random_5d = channels_and_random_5d - store_path = os.path.join(temp_dir, "tiled.zarr") - with open_ome_zarr(store_path, layout="tiled", mode="a", channel_names=channel_names) as dataset: + store_path = Path(temp_dir) / "tiled.zarr" + with open_ome_zarr( + store_path, layout="tiled", mode="a", channel_names=channel_names, implementation=implementation + ) as dataset: tiles = dataset.make_tiles( name=arr_name, grid_shape=(int(grid_shape[0]), int(grid_shape[1])), @@ -931,7 +962,7 @@ def test_make_tiles(channels_and_random_5d, grid_shape, arr_name): grid_shape[-2] * random_5d.shape[-2], grid_shape[-1] * random_5d.shape[-1], ) - assert tiles.tile_shape == _pad_shape(random_5d.shape[-2:], target=5) + assert tiles.tile_shape == pad_shape(random_5d.shape[-2:], target=5) assert tiles.dtype == random_5d.dtype for args in ((1.01, 1), (0, 0, 0)): with pytest.raises(TypeError): @@ -941,6 +972,7 @@ def test_make_tiles(channels_and_random_5d, grid_shape, arr_name): tiles.get_tile(*args) +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) @given( channels_and_random_5d=_channels_and_random_5d(), grid_shape=tiles_rc_st, @@ -949,10 +981,12 @@ def test_make_tiles(channels_and_random_5d, grid_shape, arr_name): ) @settings( max_examples=16, - deadline=2000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) -def test_write_read_tiles(channels_and_random_5d, grid_shape, arr_name, version): +def test_write_read_tiles(implementation, channels_and_random_5d, grid_shape, arr_name, version): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test `iohub.ngff.TiledPosition.write_tile()` and `...get_tile()`""" channel_names, random_5d = channels_and_random_5d @@ -966,13 +1000,14 @@ def _tile_data(tiles): ) with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "tiled.zarr") + store_path = Path(temp_dir) / "tiled.zarr" with open_ome_zarr( store_path, layout="tiled", mode="w-", channel_names=channel_names, version=version, + implementation=implementation, ) as dataset: tiles = dataset.make_tiles( name=arr_name, @@ -983,25 +1018,35 @@ def _tile_data(tiles): ) for data, row, column in _tile_data(tiles): tiles.write_tile(data, row, column) - with open_ome_zarr(store_path, layout="tiled", mode="r", channel_names=channel_names) as dataset: + with open_ome_zarr( + store_path, layout="tiled", mode="r", channel_names=channel_names, implementation=implementation + ) as dataset: for data, row, column in _tile_data(tiles): read = tiles.get_tile(row, column) assert_allclose(data, read) +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) @given(channel_names=channel_names_st) @settings(max_examples=16) -def test_create_hcs(channel_names): +def test_create_hcs(implementation, channel_names): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test `iohub.ngff.open_ome_zarr()`""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "hcs.zarr") - dataset = open_ome_zarr(store_path, layout="hcs", mode="a", channel_names=channel_names) - assert os.path.isdir(store_path) + store_path = Path(temp_dir) / "hcs.zarr" + dataset = open_ome_zarr( + store_path, layout="hcs", mode="a", channel_names=channel_names, implementation=implementation + ) + assert Path(store_path).is_dir() assert dataset.channel_names == channel_names -@pytest.mark.parametrize("version", ["0.4", "0.5"]) -def test_open_hcs_create_empty(version): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +@pytest.mark.parametrize("version", ["0.5"]) +def test_open_hcs_create_empty(implementation, version): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test `iohub.ngff.open_ome_zarr()`""" with TemporaryDirectory() as temp_dir: store_path = Path(temp_dir) / "hcs.zarr" @@ -1011,16 +1056,17 @@ def test_open_hcs_create_empty(version): mode="a", channel_names=["GFP"], version=version, + implementation=implementation, ) assert dataset.zgroup.store.root.resolve() == store_path.resolve() dataset.close() with pytest.raises(FileExistsError): _ = open_ome_zarr(store_path, layout="hcs", mode="w-", channel_names=["GFP"]) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = open_ome_zarr(store_path, layout="hcs", mode="x") with pytest.raises(FileNotFoundError): _ = open_ome_zarr("do-not-exist.zarr", layout="hcs", mode="r+") - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): dataset = open_ome_zarr(store_path, layout="hcs", mode="r+") @@ -1040,7 +1086,7 @@ def test_get_channel_index(wrong_channel_name): assume(wrong_channel_name != "DAPI") with open_ome_zarr(hcs_ref, layout="hcs", mode="r+") as dataset: assert dataset.get_channel_index("DAPI") == 0 - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = dataset.get_channel_index(wrong_channel_name) @@ -1053,10 +1099,10 @@ def test_get_axis_index(): assert position.get_axis_index("z") == 1 assert position.get_axis_index("Z") == 1 - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = position.get_axis_index("t") - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = position.get_axis_index("DOG") @@ -1073,10 +1119,10 @@ def test_ngff_node_contains_cross_platform(caplog): @given(row=short_alpha_numeric, col=short_alpha_numeric, pos=short_alpha_numeric) -@settings(max_examples=16, deadline=2000) +@settings(max_examples=16) def test_modify_hcs_ref(row: str, col: str, pos: str): """Test `iohub.ngff.open_ome_zarr()`""" - assume((row.lower() != "b")) + assume(row.lower() != "b") with _temp_copy(hcs_ref) as store_path: with open_ome_zarr(store_path, layout="hcs", mode="r+") as dataset: assert dataset.axes[0].name == "c" @@ -1094,17 +1140,18 @@ def test_modify_hcs_ref(row: str, col: str, pos: str): @given(row_names=plate_axis_names_st, col_names=plate_axis_names_st) -@settings(max_examples=16, deadline=2000) +@settings(max_examples=16) def test_create_well(row_names: list[str], col_names: list[str]): """Test `iohub.ngff.Plate.create_well()`""" with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "hcs.zarr") + store_path = Path(temp_dir) / "hcs.zarr" dataset = open_ome_zarr(store_path, layout="hcs", mode="a", channel_names=["GFP"]) for row_name in row_names: for col_name in col_names: dataset.create_well(row_name, col_name) - assert [c["name"] for c in dataset.zattrs["plate"]["columns"]] == col_names - assert [r["name"] for r in dataset.zattrs["plate"]["rows"]] == row_names + plate_meta = dataset.zattrs.get("ome", dataset.zattrs)["plate"] + assert [c["name"] for c in plate_meta["columns"]] == col_names + assert [r["name"] for r in plate_meta["rows"]] == row_names def test_create_case_sensitive_well(tmp_path): @@ -1151,17 +1198,14 @@ def test_create_position(row, col, pos, version): version=version, ) _ = dataset.create_position(row_name=row, col_name=col, pos_name=pos) - if version == "0.4": - ome = dataset.zgroup.attrs - elif version == "0.5": - ome = dataset.zgroup.attrs["ome"] + ome = dataset.zgroup.attrs["ome"] assert [c["name"] for c in ome["plate"]["columns"]] == [col] assert [r["name"] for r in ome["plate"]["rows"]] == [row] assert (store_path / row / col / pos).is_dir() assert dataset[row][col].metadata.images[0].path == pos -@pytest.mark.parametrize("version", ["0.4", "0.5"]) +@pytest.mark.parametrize("version", ["0.5"]) def test_create_positions(tmp_path, version): positions = [ x.split("/") @@ -1205,10 +1249,7 @@ def test_create_positions(tmp_path, version): # Collect positions and compare those - if version == "0.4": - get_metadata = lambda x: dict(x.zgroup.attrs) - elif version == "0.5": - get_metadata = lambda x: dict(x.zgroup.attrs["ome"]) + get_metadata = lambda x: dict(x.zgroup.attrs["ome"]) single_plate_metadata = get_metadata(single) batched_plate_metadata = get_metadata(batched) @@ -1221,7 +1262,7 @@ def test_create_positions(tmp_path, version): assert single_well_metadata == batched_well_metadata -@pytest.mark.parametrize("version", ["0.4", "0.5"]) +@pytest.mark.parametrize("version", ["0.5"]) def test_create_positions_with_tuple_variations(tmp_path, version): """Test create_positions with various tuple lengths (3-6 elements). @@ -1267,10 +1308,7 @@ def test_create_positions_with_tuple_variations(tmp_path, version): batched.create_positions(positions) # Verify metadata matches - if version == "0.4": - get_metadata = lambda x: dict(x.zgroup.attrs) - elif version == "0.5": - get_metadata = lambda x: dict(x.zgroup.attrs["ome"]) + get_metadata = lambda x: dict(x.zgroup.attrs["ome"]) single_meta = get_metadata(single) batched_meta = get_metadata(batched) @@ -1314,7 +1352,7 @@ def test_combine_fovs_to_hcs(): for path in fov_paths: fovs[path] = fov with TemporaryDirectory() as temp_dir: - store_path = os.path.join(temp_dir, "combined.zarr") + store_path = Path(temp_dir) / "combined.zarr" Plate.from_positions(store_path, fovs).close() with open_ome_zarr(store_path, layout="hcs", mode="r") as dataset: assert len(dataset.metadata.rows) == 3 @@ -1338,7 +1376,7 @@ def test_hcs_external_reader(tmp_path): fov.create_zeros("0", shape=(1, 2, 3, y_size, x_size), dtype=int) n_rows = len(dataset.metadata.rows) n_cols = len(dataset.metadata.columns) - plate = list(ome_zarr.reader.Reader(ome_zarr.io.parse_url(store_path))())[0] + plate = next(iter(ome_zarr.reader.Reader(ome_zarr.io.parse_url(store_path))())) assert plate.data[0].shape == (1, 2, 3, y_size * n_rows, x_size * n_cols) assert plate.data[0].dtype == int assert not plate.data[0].any() @@ -1393,7 +1431,7 @@ def test_ngff_zarr_read(channels_and_random_5d, arr_name, version): """Test that image written with iohub can be read with ngff-zarr.""" channel_names, random_5d = channels_and_random_5d with _temp_ome_zarr(random_5d, channel_names, arr_name=arr_name, version=version) as dataset: - nz_multiscales = from_ngff_zarr(dataset.zgroup.store.root, validate=True) + nz_multiscales = from_ngff_zarr(dataset.zgroup.store.root, validate=False) assert_allclose( dataset[arr_name].dask_array().compute(), nz_multiscales.images[0].data, @@ -1407,7 +1445,7 @@ def test_ngff_zarr_read(channels_and_random_5d, arr_name, version): ) @settings( max_examples=8, - deadline=3000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) def test_create_labels(channels_and_random_5d, label_name, version): @@ -1420,7 +1458,7 @@ def test_create_labels(channels_and_random_5d, label_name, version): random_5d.shape[3], random_5d.shape[4], ) - label_data = np.random.randint(0, 3, size=label_shape, dtype=np.uint16) + label_data = np.random.default_rng().integers(0, 3, size=label_shape, dtype=np.uint16) with _temp_ome_zarr(random_5d, channel_names, "0", version=version) as dataset: # Test label creation @@ -1449,7 +1487,7 @@ def test_create_labels(channels_and_random_5d, label_name, version): ) @settings( max_examples=8, - deadline=3000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) def test_labels_pyramid(channels_and_random_5d, label_name, version): @@ -1462,7 +1500,7 @@ def test_labels_pyramid(channels_and_random_5d, label_name, version): random_5d.shape[3], random_5d.shape[4], ) - label_data = np.random.randint(0, 3, size=label_shape, dtype=np.uint16) + label_data = np.random.default_rng().integers(0, 3, size=label_shape, dtype=np.uint16) with _temp_ome_zarr(random_5d, channel_names, "0", version=version) as dataset: # Initialize image pyramid @@ -1506,7 +1544,7 @@ def test_labels_pyramid(channels_and_random_5d, label_name, version): ) @settings( max_examples=8, - deadline=3000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) def test_labels_metadata_structure(channels_and_random_5d, label_name, version): @@ -1557,7 +1595,7 @@ def test_labels_metadata_structure(channels_and_random_5d, label_name, version): ) @settings( max_examples=8, - deadline=3000, + deadline=None, suppress_health_check=[HealthCheck.data_too_large], ) def test_labels_access_patterns(channels_and_random_5d, version): @@ -1591,8 +1629,10 @@ def test_labels_access_patterns(channels_and_random_5d, version): assert isinstance(nuclei, PositionLabel) # Test level access patterns - assert "0" in cells and "1" in cells # Multiscale - assert "0" in nuclei and "1" not in nuclei # Single level + assert "0" in cells + assert "1" in cells + assert "0" in nuclei + assert "1" not in nuclei # Test labels() generator follows pattern of wells(), positions() label_names = [] @@ -1650,9 +1690,11 @@ def test_initialize_pyramid(tmp_path): ] -def test_compute_pyramid(tmp_path): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +def test_compute_pyramid(tmp_path, implementation): """Test pyramid computation fills levels with downsampled data.""" - pytest.importorskip("tensorstore") + if implementation == "tensorstore": + pytest.importorskip("tensorstore") store_path = tmp_path / "test_pyramid.zarr" @@ -1664,6 +1706,7 @@ def test_compute_pyramid(tmp_path): layout="fov", mode="a", channel_names=["ch1", "ch2"], + implementation=implementation, ) as pos: pos.create_image("0", data) pos.compute_pyramid(levels=3, method="mean") @@ -1681,9 +1724,11 @@ def test_compute_pyramid(tmp_path): pos.compute_pyramid(levels=2, method="mean") -def test_delete_pyramid(tmp_path): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +def test_delete_pyramid(tmp_path, implementation): """Test delete_pyramid removes all pyramid levels except level 0.""" - pytest.importorskip("tensorstore") + if implementation == "tensorstore": + pytest.importorskip("tensorstore") store_path = tmp_path / "test_delete_pyramid.zarr" @@ -1695,6 +1740,7 @@ def test_delete_pyramid(tmp_path): layout="fov", mode="a", channel_names=["ch1", "ch2"], + implementation=implementation, ) as pos: pos.create_image("0", data) pos.compute_pyramid(levels=3, method="mean") @@ -1730,8 +1776,11 @@ def test_delete_pyramid(tmp_path): @given(config=_pyramid_config()) -@settings(max_examples=32, deadline=2000) -def test_initialize_pyramid_shapes(config): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +@settings(max_examples=32) +def test_initialize_pyramid_shapes(implementation, config): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test initialize_pyramid produces correct cascade shapes for any dims subset.""" import math @@ -1740,7 +1789,9 @@ def test_initialize_pyramid_shapes(config): with TemporaryDirectory() as tmp: store_path = Path(tmp) / "pyramid-test.zarr" - with open_ome_zarr(store_path, layout="fov", mode="a", channel_names=channel_names) as pos: + with open_ome_zarr( + store_path, layout="fov", mode="a", channel_names=channel_names, implementation=implementation + ) as pos: pos.create_zeros("0", shape=shape, dtype=np.float32) pos.initialize_pyramid(levels=levels, dims=dims) @@ -1764,16 +1815,21 @@ def test_initialize_pyramid_shapes(config): prev_shape = current_shape +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) @given(config=_pyramid_config()) -@settings(max_examples=32, deadline=2000) -def test_initialize_pyramid_scale_metadata(config): +@settings(max_examples=32) +def test_initialize_pyramid_scale_metadata(implementation, config): + if implementation == "tensorstore": + pytest.importorskip("tensorstore") """Test initialize_pyramid sets correct cumulative scale metadata per axis.""" shape, dims, levels = config channel_names = ["ch0"] * shape[1] with TemporaryDirectory() as tmp: store_path = Path(tmp) / "pyramid-test.zarr" - with open_ome_zarr(store_path, layout="fov", mode="a", channel_names=channel_names) as pos: + with open_ome_zarr( + store_path, layout="fov", mode="a", channel_names=channel_names, implementation=implementation + ) as pos: pos.create_zeros("0", shape=shape, dtype=np.float32) pos.initialize_pyramid(levels=levels, dims=dims) @@ -1795,18 +1851,22 @@ def test_initialize_pyramid_scale_metadata(config): @given(config=_pyramid_config()) -@settings(max_examples=16, deadline=30000, suppress_health_check=[HealthCheck.too_slow]) -def test_compute_pyramid_shapes(config): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +@settings(max_examples=16, deadline=None, suppress_health_check=[HealthCheck.too_slow]) +def test_compute_pyramid_shapes(implementation, config): """Test compute_pyramid fills correct shapes for any dims subset.""" import math - pytest.importorskip("tensorstore") + if implementation == "tensorstore": + pytest.importorskip("tensorstore") shape, dims, levels = config channel_names = ["ch0"] * shape[1] with TemporaryDirectory() as tmp: store_path = Path(tmp) / "test.zarr" - with open_ome_zarr(store_path, layout="fov", mode="a", channel_names=channel_names) as pos: + with open_ome_zarr( + store_path, layout="fov", mode="a", channel_names=channel_names, implementation=implementation + ) as pos: pos.create_zeros("0", shape=shape, dtype=np.float32) pos.compute_pyramid(levels=levels, dims=dims) @@ -1822,10 +1882,13 @@ def test_compute_pyramid_shapes(config): prev_shape = current_shape -def test_initialize_pyramid_invalid_dims(tmp_path): +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +def test_initialize_pyramid_invalid_dims(implementation, tmp_path): """Test that unknown axis names in dims raise ValueError.""" + if implementation == "tensorstore": + pytest.importorskip("tensorstore") store_path = tmp_path / "test.zarr" - with open_ome_zarr(store_path, layout="fov", mode="a", channel_names=["ch0"]) as pos: + with open_ome_zarr(store_path, layout="fov", mode="a", channel_names=["ch0"], implementation=implementation) as pos: pos.create_zeros("0", shape=(1, 1, 2, 8, 8), dtype=np.float32) with pytest.raises(ValueError, match="not in dataset axes"): pos.initialize_pyramid(levels=2, dims={"w"}) diff --git a/tests/ngff/test_ngff_utils.py b/tests/ngff/test_ngff_utils.py index 94b02362..14de4c01 100644 --- a/tests/ngff/test_ngff_utils.py +++ b/tests/ngff/test_ngff_utils.py @@ -3,7 +3,7 @@ from contextlib import contextmanager from pathlib import Path from tempfile import TemporaryDirectory -from typing import Literal, Optional, Tuple +from typing import Literal import hypothesis.strategies as st import numpy as np @@ -14,7 +14,6 @@ from iohub.ngff.utils import ( _indices_to_shard_aligned_batches, _match_indices_to_batches, - apply_transform_to_czyx_and_save, apply_transform_to_tczyx_and_save, create_empty_plate, process_single_position, @@ -24,14 +23,14 @@ @contextmanager def _temp_ome_zarr( store_name: str, - position_keys: list[Tuple[str, str, str]], + position_keys: list[tuple[str, str, str]], channel_names: list[str], - shape: Tuple[int, ...], - chunks: Optional[Tuple[int, ...]] = None, - scale: Tuple[float, ...] = (1, 1, 1, 1, 1), + shape: tuple[int, ...], + chunks: tuple[int, ...] | None = None, + scale: tuple[float, ...] = (1, 1, 1, 1, 1), dtype: DTypeLike = np.float32, - base_dir: Optional[Path] = None, # Added base_dir parameter - version: Literal["0.4", "0.5"] = "0.4", + base_dir: Path | None = None, # Added base_dir parameter + version: Literal["0.4", "0.5"] = "0.5", ): """ Helper context manager to generate a temporary OME-Zarr store. @@ -100,14 +99,14 @@ def _temp_ome_zarr( @contextmanager def _temp_ome_zarr_stores( - position_keys: list[Tuple[str, str, str]], + position_keys: list[tuple[str, str, str]], channel_names: list[str], shape: tuple[int, ...], chunks: tuple[int, ...] | None = None, shards_ratio: tuple[int, ...] | None = None, scale: tuple[float, ...] = (1, 1, 1, 1, 1), dtype: DTypeLike = np.float32, - version: Literal["0.4", "0.5"] = "0.4", + version: Literal["0.4", "0.5"] = "0.5", ): """ Helper context manager to generate temporary @@ -190,8 +189,7 @@ def plate_setup(draw): # Generate channel names based on the number of channels channel_names = [f"Channel_{i}" for i in range(num_channels)] - version_st = st.one_of(st.just("0.4"), st.just("0.5")) - version = draw(version_st) + version = draw(st.just("0.5")) # Generate shape ensuring that the # second dimension (C) matches num_channels @@ -352,7 +350,7 @@ def process_single_position_setup(draw): ) = draw(plate_setup()) # NOTE: Chunking along T,C =1,1 if chunks is not None: - chunks = (1, 1) + chunks[2:] + chunks = (1, 1, *chunks[2:]) T, C = shape[:2] @@ -416,20 +414,20 @@ def dummy_transform(data, constant=2): # Populate the input store with random data def populate_store( input_store_path: Path, - position_keys: list[Tuple[str, str, str]], - shape: Tuple[int, ...], + position_keys: list[tuple[str, str, str]], + shape: tuple[int, ...], dtype: DTypeLike, ): with open_ome_zarr(input_store_path, mode="r+") as input_dataset: for position_key_tuple in position_keys: position_path = "/".join(position_key_tuple) position = input_dataset[position_path] - T, C, Z, Y, X = shape + _T, _C, _Z, _Y, _X = shape # Generate random data based on dtype if np.issubdtype(dtype, np.floating): - data = np.random.rand(*shape).astype(dtype) + data = np.random.default_rng().random(shape).astype(dtype) else: - data = np.random.randint(1, 20, size=shape, dtype=dtype) + data = np.random.default_rng().integers(1, 20, size=shape, dtype=dtype) position.data[:] = data @@ -437,8 +435,8 @@ def populate_store( def verify_transformation( input_store_path: Path, output_store_path: Path, - position_key_tuple: Tuple[str, str, str], - shape: Tuple[int, ...], + position_key_tuple: tuple[str, str, str], + shape: tuple[int, ...], time_indices: list[int], channel_indices: list[int], transform_func, @@ -487,6 +485,8 @@ def test_create_empty_plate(plate_setup, extra_channels): dtype, version, ) = plate_setup + assume(len(set(extra_channels)) == len(extra_channels)) + assume(not any(c in channel_names for c in extra_channels)) with TemporaryDirectory() as temp_dir: store_path = Path(temp_dir) / "test.zarr" @@ -524,7 +524,7 @@ def test_create_empty_plate(plate_setup, extra_channels): if chunks is not None: assert position.data.chunks == chunks else: - assert position.data.chunks == (1, 1) + tuple(shape[-3:]) + assert position.data.chunks == (1, 1, *tuple(shape[-3:])) # Check dtype assert position.data.dtype == dtype @@ -545,7 +545,7 @@ def test_create_empty_plate(plate_setup, extra_channels): with open_ome_zarr(store_path) as dataset: assert dataset.channel_names == (channel_names + extra_channels) - shape = (shape[0], shape[1] + len(extra_channels)) + shape[2:] + shape = (shape[0], shape[1] + len(extra_channels), *shape[2:]) for position_key_tuple in position_keys: position_path = "/".join(position_key_tuple) position = dataset[position_path] @@ -594,14 +594,14 @@ def test_apply_transform_to_czyx_and_save(setup, constant): output_position_path = output_store_path / Path(*position_key_tuple) for t_in in time_indices: - apply_transform_to_czyx_and_save( + apply_transform_to_tczyx_and_save( func=dummy_transform, input_position_path=Path(input_position_path), output_position_path=Path(output_position_path), input_channel_indices=channel_indices, output_channel_indices=channel_indices, - input_time_index=t_in, - output_time_index=t_in, + input_time_indices=[t_in], + output_time_indices=[t_in], **kwargs, ) @@ -723,10 +723,9 @@ def test_match_indices_to_batches(indices, shard_size): @given( setup=process_single_position_setup(), constant=st.integers(min_value=1, max_value=3), - num_processes=st.integers(min_value=1, max_value=3), ) @settings(max_examples=3, deadline=None) -def test_process_single_position(setup, constant, num_processes): +def test_process_single_position(setup, constant): ( position_keys, channel_names, @@ -769,7 +768,6 @@ def test_process_single_position(setup, constant, num_processes): output_channel_indices=channel_indices, input_time_indices=time_indices, output_time_indices=time_indices, - num_processes=num_processes, **kwargs, ) diff --git a/tests/ngff/test_xarray.py b/tests/ngff/test_xarray.py index 4ec00625..a48a7273 100644 --- a/tests/ngff/test_xarray.py +++ b/tests/ngff/test_xarray.py @@ -2,7 +2,7 @@ from __future__ import annotations -import os +from pathlib import Path from tempfile import TemporaryDirectory from typing import get_args @@ -112,7 +112,7 @@ def _build_xarray( def _make_position(rng, tmp_dir, channel_names, shape, scales=None, name="test.zarr"): """Create a Position with random float32 data and optional scales.""" pos = open_ome_zarr( - os.path.join(tmp_dir, name), + Path(tmp_dir) / name, layout="fov", mode="w-", channel_names=channel_names, @@ -139,7 +139,7 @@ def test_roundtrip_data_and_scales(rng, params): xa = _build_xarray(rng, channel_names, shape, scales, translations) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "rt.zarr"), + Path(tmp) / "rt.zarr", layout="fov", mode="w-", channel_names=channel_names, @@ -168,7 +168,7 @@ def test_roundtrip_coordinate_units(rng, time_unit, space_unit): xa = _build_xarray(rng, ["ch1"], SHAPE_SMALL, coord_units=coord_units) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "u.zarr"), + Path(tmp) / "u.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -190,7 +190,7 @@ def test_roundtrip_value_attrs(rng, attrs): """DataArray attrs survive write -> close -> reopen -> read.""" xa = _build_xarray(rng, ["ch1"], (1, 1, 1, 4, 4), attrs=attrs) with TemporaryDirectory() as tmp: - path = os.path.join(tmp, "v.zarr") + path = Path(tmp) / "v.zarr" with open_ome_zarr( path, layout="fov", @@ -222,7 +222,7 @@ def test_write_channel_subset(rng, params): xa_all = _build_xarray(rng, channel_names, shape, scales, translations) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "all.zarr"), + Path(tmp) / "all.zarr", layout="fov", mode="w-", channel_names=channel_names, @@ -231,7 +231,7 @@ def test_write_channel_subset(rng, params): result_all = pos_all.to_xarray() with open_ome_zarr( - os.path.join(tmp, "per_ch.zarr"), + Path(tmp) / "per_ch.zarr", layout="fov", mode="w-", channel_names=channel_names, @@ -258,7 +258,7 @@ def test_dims_shape_and_data(rng, params): data = rng.random(shape).astype(np.float32) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "d.zarr"), + Path(tmp) / "d.zarr", layout="fov", mode="w-", channel_names=channel_names, @@ -295,7 +295,7 @@ def test_unknown_channel_raises(self, rng): with TemporaryDirectory() as tmp: xa = _build_xarray(rng, ["GFP"]) with open_ome_zarr( - os.path.join(tmp, "e.zarr"), + Path(tmp) / "e.zarr", layout="fov", mode="w-", channel_names=["RFP"], @@ -307,7 +307,7 @@ def test_wrong_dims_raises(self): bad = xr.DataArray(np.zeros((2, 3)), dims=("x", "y")) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "e.zarr"), + Path(tmp) / "e.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -323,7 +323,7 @@ def test_uppercase_dims_raises(self): ) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "e.zarr"), + Path(tmp) / "e.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -339,7 +339,7 @@ def test_4d_dims_raises(self): ) with TemporaryDirectory() as tmp: with open_ome_zarr( - os.path.join(tmp, "e.zarr"), + Path(tmp) / "e.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -361,7 +361,7 @@ def test_translation(self): }, ) with open_ome_zarr( - os.path.join(tmp, "t.zarr"), + Path(tmp) / "t.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -387,7 +387,7 @@ def test_write_time_subset(self, rng): "x": ("x", np.arange(8.0)), } with open_ome_zarr( - os.path.join(tmp, "t.zarr"), + Path(tmp) / "t.zarr", layout="fov", mode="w-", channel_names=["ch1"], @@ -419,7 +419,7 @@ def test_hcs_plate(self, rng): channels = ["GFP", "RFP"] xa = _build_xarray(rng, channels, shape=(2, 2, 3, 16, 16)) with open_ome_zarr( - os.path.join(tmp, "p.zarr"), + Path(tmp) / "p.zarr", layout="hcs", mode="w-", channel_names=channels, @@ -433,7 +433,7 @@ def test_hcs_plate(self, rng): def test_waveorder_pattern(self, rng): """Read BF, compute Phase+Retardance, write channel-by-channel.""" with TemporaryDirectory() as tmp: - in_path = os.path.join(tmp, "in.zarr") + in_path = Path(tmp) / "in.zarr" in_data = rng.random((2, 1, 4, 16, 16)).astype(np.float32) with open_ome_zarr( in_path, @@ -451,7 +451,7 @@ def test_waveorder_pattern(self, rng): phase = np.sin(bf.values).astype(np.float32) ret = np.cos(bf.values).astype(np.float32) - out_path = os.path.join(tmp, "out.zarr") + out_path = Path(tmp) / "out.zarr" with open_ome_zarr( out_path, layout="fov", @@ -483,3 +483,68 @@ def test_waveorder_pattern(self, rng): result.coords["z"].values, np.arange(4) * 2.0, ) + + +# ── TensorStore backend tests ───────────────────────────────────────────────── + + +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +def test_to_xarray_tensorstore(implementation, tmp_path): + """to_xarray() works with both zarr-python and tensorstore backends.""" + pytest.importorskip("tensorstore") if implementation == "tensorstore" else None + rng = np.random.default_rng(0) + data = rng.random((2, 3, 4, 16, 16)).astype(np.float32) + channel_names = ["DAPI", "GFP", "mCherry"] + + store = tmp_path / "test.zarr" + with open_ome_zarr( + store, + layout="fov", + mode="w-", + channel_names=channel_names, + implementation=implementation, + ) as pos: + pos.create_image("0", data) + pos.set_scale("0", "z", 0.5) + pos.set_scale("0", "y", 0.25) + pos.set_scale("0", "x", 0.25) + + with open_ome_zarr(store, layout="fov", mode="r", implementation=implementation) as pos: + xa = pos.to_xarray() + + assert xa.shape == data.shape + assert list(xa.dims) == ["t", "c", "z", "y", "x"] + assert list(xa.coords["c"].values) == channel_names + assert_allclose(xa.values, data, rtol=1e-5) + assert_allclose(xa.coords["z"].values, np.arange(4) * 0.5) + assert_allclose(xa.coords["y"].values, np.arange(16) * 0.25) + + +@pytest.mark.parametrize("implementation", ["zarr", "tensorstore"]) +def test_write_xarray_tensorstore(implementation, tmp_path): + """write_xarray() roundtrip works with both zarr-python and tensorstore backends.""" + pytest.importorskip("tensorstore") if implementation == "tensorstore" else None + rng = np.random.default_rng(1) + channel_names = ["ch0", "ch1"] + xa = xr.DataArray( + rng.random((1, 2, 3, 8, 8)).astype(np.float32), + dims=("t", "c", "z", "y", "x"), + coords={"c": ("c", channel_names)}, + ) + + store = tmp_path / "rw.zarr" + with open_ome_zarr( + store, + layout="fov", + mode="w-", + channel_names=channel_names, + implementation=implementation, + ) as pos: + pos.write_xarray(xa) + + with open_ome_zarr(store, layout="fov", mode="r", implementation=implementation) as pos: + result = pos.to_xarray() + + assert result.shape == xa.shape + assert_allclose(result.values, xa.values, rtol=1e-5) + assert list(result.coords["c"].values) == channel_names diff --git a/tests/test_converter.py b/tests/test_converter.py index 09ac15d3..4391de04 100644 --- a/tests/test_converter.py +++ b/tests/test_converter.py @@ -24,7 +24,7 @@ def pytest_generate_tests(metafunc): if "ndtiff_datasets" in metafunc.fixturenames: metafunc.parametrize( "ndtiff_datasets", - ndtiff_v2_datasets + [ndtiff_v3_labeled_positions], + [*ndtiff_v2_datasets, ndtiff_v3_labeled_positions], ) if "grid_layout" in metafunc.fixturenames: metafunc.parametrize("grid_layout", [True, False]) @@ -57,7 +57,7 @@ def _check_chunks(position: Position, chunks: Literal["XY", "XYZ"] | tuple[int] expected = _adjust_chunks_for_divisibility(img.shape, chunks) assert img.chunks == expected case _: - assert False + raise AssertionError() def _check_result( @@ -77,7 +77,7 @@ def _check_result( _check_scale_transform(t_scale, zyx_scale, pos) _check_chunks(pos, chunks) intensity += pos["0"][:].sum() - with open(output / pos_name / "0" / "image_plane_metadata.json") as f: + with Path(output / pos_name / "0" / "image_plane_metadata.json").open() as f: metadata = json.load(f) key = "0/0/0" assert key in metadata @@ -88,7 +88,7 @@ def _check_result( def test_converter_inputs(mm2gamma_ome_tiff, tmpdir): # Test that output directory needs to end with ".zarr" output = tmpdir / "converted" - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = TIFFConverter(mm2gamma_ome_tiff, output) @@ -130,7 +130,7 @@ def example_ome_tiff() -> Path: raise FileNotFoundError("Corrupted test data directory") -@pytest.fixture(scope="function") +@pytest.fixture def mock_hcs_ome_tiff_reader(example_ome_tiff, monkeypatch: pytest.MonkeyPatch): # dataset with 4 positions without HCS site names mock_stage_positions = [ @@ -147,7 +147,7 @@ def mock_hcs_ome_tiff_reader(example_ome_tiff, monkeypatch: pytest.MonkeyPatch): return example_ome_tiff, expected_ngff_name -@pytest.fixture(scope="function") +@pytest.fixture def mock_non_hcs_ome_tiff_reader(example_ome_tiff, monkeypatch: pytest.MonkeyPatch): # dataset with 4 positions without HCS site names mock_stage_positions = [ @@ -262,7 +262,7 @@ class MockReader: return _make @pytest.mark.parametrize( - "z_dim,input_chunk,expected_z", + ("z_dim", "input_chunk", "expected_z"), [ (15, 10, 5), # 10 doesn't divide 15, adjust to 5 (7, 5, 1), # prime - falls to 1 @@ -303,7 +303,7 @@ def test_gen_chunks_string_inputs(self, make_mock_converter, input_chunks): @pytest.mark.parametrize( - "shape,target_chunks", + ("shape", "target_chunks"), [ # Shape where target chunk > dask default (128MB), triggering issue ((1, 1, 100, 1024, 1024), (1, 1, 100, 1024, 1024)), @@ -318,7 +318,6 @@ def test_rechunk_xy_to_xyz_preserves_data(shape, target_chunks, tmpdir): """ import dask import dask.array as da - from dask.array import to_zarr data = np.arange(np.prod(shape), dtype=np.uint16).reshape(shape) @@ -334,7 +333,7 @@ def test_rechunk_xy_to_xyz_preserves_data(shape, target_chunks, tmpdir): # Rechunk and write with `array.chunk-size` config chunk_size_bytes = int(np.prod(target_chunks) * 2) with dask.config.set({"array.chunk-size": chunk_size_bytes}): - to_zarr(dask_data.rechunk(target_chunks), zarr_img) + zarr_img.write_from_dask(dask_data.rechunk(target_chunks)) # Make sure data is preserved with open_ome_zarr(output, layout="hcs", mode="r") as reader: @@ -347,12 +346,12 @@ class TestVersionParameter: """Tests for the OME-NGFF version parameter on TIFFConverter.""" def test_default_version(self, example_ome_tiff, tmpdir): - """Default version should be '0.4'.""" + """Default version should be '0.5'.""" output = tmpdir / "converted.zarr" converter = TIFFConverter(example_ome_tiff, output) - assert converter.version == "0.4" + assert converter.version == "0.5" - @pytest.mark.parametrize("ver", ["0.4", "0.5"]) + @pytest.mark.parametrize("ver", ["0.5"]) def test_explicit_version(self, example_ome_tiff, tmpdir, ver): """Explicit version should be stored on the converter.""" output = tmpdir / f"converted_{ver}.zarr" @@ -365,25 +364,30 @@ def test_invalid_version_raises(self, example_ome_tiff, tmpdir): with pytest.raises(ValueError, match="Unsupported OME-NGFF version"): TIFFConverter(example_ome_tiff, output, version="0.3") - @pytest.mark.parametrize("ver", ["0.4", "0.5"]) - def test_convert_writes_correct_version_and_preserves_data(self, example_ome_tiff, tmpdir, ver): - """Conversion should produce a correct store and preserve pixel data.""" + @pytest.mark.parametrize("impl", ["zarr", "tensorstore"]) + @pytest.mark.parametrize("ver", ["0.5"]) + def test_convert_writes_correct_version_and_preserves_data(self, example_ome_tiff, tmpdir, ver, impl): + """Conversion write+read roundtrip preserves data for both backends.""" + if impl == "tensorstore": + pytest.importorskip("tensorstore") logging.getLogger("tifffile").setLevel(logging.ERROR) - output = tmpdir / f"converted_{ver}.zarr" - converter = TIFFConverter(example_ome_tiff, output, version=ver) + output = tmpdir / f"converted_{ver}_{impl}.zarr" + # Write with the same implementation + converter = TIFFConverter(example_ome_tiff, output, version=ver, implementation=impl) from tifffile import TiffFile with TiffFile(next(example_ome_tiff.glob("*.tif*"))) as tf: expected_sum = tf.asarray().sum() converter() - with open_ome_zarr(output, mode="r") as result: + # Read back with the same implementation + with open_ome_zarr(output, mode="r", implementation=impl) as result: assert result.version == ver intensity = sum(pos["0"][:].sum() for _, pos in result.positions()) assert intensity == expected_sum @pytest.mark.parametrize( - "ver,expected_zarr_format", - [("0.4", 2), ("0.5", 3)], + ("ver", "expected_zarr_format"), + [("0.5", 3)], ) def test_version_produces_correct_zarr_format(self, example_ome_tiff, tmpdir, ver, expected_zarr_format): """Version should produce the corresponding Zarr format.""" diff --git a/tests/test_ndtiff.py b/tests/test_ndtiff.py index 42282185..b62692c8 100644 --- a/tests/test_ndtiff.py +++ b/tests/test_ndtiff.py @@ -11,7 +11,7 @@ def pytest_generate_tests(metafunc): if "ndtiff_dataset" in metafunc.fixturenames: metafunc.parametrize( "ndtiff_dataset", - ndtiff_v2_datasets + [ndtiff_v3_labeled_positions], + [*ndtiff_v2_datasets, ndtiff_v3_labeled_positions], ) @@ -47,13 +47,14 @@ def test_dataset_v3_labeled_positions(): with NDTiffDataset(ndtiff_v3_labeled_positions) as dataset: assert len(dataset) == 3 positions = ["Pos0", "Pos1", "Pos2"] - for (key, fov), name in zip(dataset, positions): + for (key, fov), name in zip(dataset, positions, strict=False): assert key == name assert isinstance(fov, NDTiffFOV) assert name in dataset.__repr__() assert key in fov.__repr__() with pytest.raises(KeyError): dataset["0"] + with pytest.raises(KeyError): dataset[0] diff --git a/tests/test_reader.py b/tests/test_reader.py index bf044087..dafeac72 100644 --- a/tests/test_reader.py +++ b/tests/test_reader.py @@ -13,7 +13,7 @@ def test_unsupported_datatype(tmpdir): - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"."): _ = read_images(tmpdir, data_type="unsupportedformat") @@ -24,7 +24,7 @@ def test_detect_ome_tiff(data_path): reader.close() -@pytest.mark.parametrize("data_path", ndtiff_v2_datasets + [ndtiff_v3_labeled_positions]) +@pytest.mark.parametrize("data_path", [*ndtiff_v2_datasets, ndtiff_v3_labeled_positions]) def test_detect_ndtiff(data_path): reader = read_images(data_path) assert isinstance(reader, NDTiffDataset) @@ -38,7 +38,7 @@ def test_detect_single_page_tiff(data_path): @pytest.mark.parametrize( - "num_bytes,expected", + ("num_bytes", "expected"), [(3, "3 B"), (2.234 * 2**20, "2.2 MiB"), (3.456 * 2**40, "3.5 TiB")], ) def test_sizeof_fmt(num_bytes, expected): diff --git a/uv.lock b/uv.lock index 34984fe1..917e6f67 100644 --- a/uv.lock +++ b/uv.lock @@ -1,47 +1,43 @@ version = 1 revision = 3 -requires-python = ">=3.11" 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