From a0a5c7ec0051d8d7496c3601f6627d309259234a Mon Sep 17 00:00:00 2001 From: Talley Lambert Date: Wed, 24 Jun 2026 15:34:45 -0400 Subject: [PATCH 1/3] Refactor code structure for improved readability and maintainability --- pyproject.toml | 41 +- src/microsim/_data_array.py | 6 +- src/microsim/cosem/_xarray.py | 196 --- src/microsim/cosem/models.py | 11 - src/microsim/schema/simulation.py | 4 +- src/microsim/util.py | 145 +- uv.lock | 2618 +++++++++++++++-------------- 7 files changed, 1401 insertions(+), 1620 deletions(-) delete mode 100644 src/microsim/cosem/_xarray.py diff --git a/pyproject.toml b/pyproject.toml index b986617..be5c8dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,16 +31,15 @@ classifiers = [ ] dependencies = [ "annotated-types >=0.7.0", - # min pins per-python reflect the first release shipping a wheel for that - # interpreter (newer pythons need newer compiled deps). + # min pins per-python reflect the first release shipping a wheel "numpy >=2.3.2; python_version >= '3.14'", "numpy >=2.1.0; python_version >= '3.13'", "numpy >=2.0", - "pint >=0.23", + "pint >=0.24.4", "platformdirs >=4.3.6", "pydantic >=2.12; python_version >= '3.14'", "pydantic >=2.8", - "fpbase", + "fpbase >=0.1.2", "pydantic-settings >2.5.0", "scipy >=1.16.1; python_version >= '3.14'", "scipy >=1.15.2", @@ -52,8 +51,9 @@ dependencies = [ "tensorstore >=0.1.79; python_version >= '3.14'", "tensorstore >=0.1.76", "tqdm >=4.67.1", - "xarray >=2024.1.0", - "zarr >=2.17,<3.0", + "xarray >=2025.7.0; python_version >= '3.14'", + "xarray >=2025.1.1", + "zarr >=3", ] [project.urls] @@ -64,22 +64,30 @@ repository = "https://github.com/tlambert03/microsim" [project.optional-dependencies] view = [ "ndv[pyqt,vispy]", - "matplotlib>=3.10.5; python_version >= '3.14'", - "matplotlib>=3.10.1", + # matplotlib <3.10.7 calls pyparsing's deprecated `oneOf` (warns with + # pyparsing >=3.3). + "matplotlib>=3.10.7", # optional OpenGL speedup; no cp314 wheel and fails to build from source "pyopengl-accelerate>=3.1.9; python_version < '3.14'", ] io = ["tifffile>=2025.2.18", "imageio[ffmpeg]>=2.22"] cosem = [ - "dask>=2024.1.0", "imageio>=2.22", "boto3>=1.32", - "supabase>=2.13.0", + # supabase <2.17 bundles the deprecated `gotrue` (warns at import; renamed + # to supabase_auth in 2.17). + "supabase>=2.17.0", "s3fs>=0.4.2", - "xarray-datatree>=0.0.14", ] -torch = ["torch>=2.6.0", "torchaudio>=2.7.0"] -jax = ["jax[cpu]>=0.5.2"] +torch = [ + # torch/torchaudio only ship cp314 wheels from 2.9.0. + "torch>=2.9.0; python_version >= '3.14'", + "torchaudio>=2.9.0; python_version >= '3.14'", + "torch>=2.6.0", + "torchaudio>=2.7.0", +] +# jaxlib only ships cp314 wheels from 0.7.1 (jax/jaxlib versions are coupled). +jax = ["jax[cpu]>=0.7.1; python_version >= '3.14'", "jax[cpu]>=0.5.2"] all = ["microsim[view,io,cosem]", "rich>=13"] [dependency-groups] @@ -194,6 +202,13 @@ filterwarnings = [ "ignore::pytest.PytestUnraisableExceptionWarning", # supabase>=2.31 passes now-deprecated params (timeout/verify/proxy) to postgrest "ignore:The '.*' parameter is deprecated:DeprecationWarning", + # zarr v3 (esp. 3.0.x) warns that the string-coord dtype / vlen-utf8 codec + # used for channel names isn't in the v3 spec yet; intentional and benign. + "ignore:The data type .* does not have a Zarr V3 specification:FutureWarning", + "ignore:The \\w+ .* is currently not part in the Zarr format 3 specification:UserWarning", + # numcodecs >=0.16.4 (pulled in with older zarr 3.x) deprecates its bundled + # crc32c at import time; harmless and outside our control. + "ignore:crc32c usage is deprecated:DeprecationWarning", ] # https://coverage.readthedocs.io/en/6.4/config.html diff --git a/src/microsim/_data_array.py b/src/microsim/_data_array.py index 93a805c..34bfe8b 100644 --- a/src/microsim/_data_array.py +++ b/src/microsim/_data_array.py @@ -62,7 +62,7 @@ def __mul__(self, other: Any) -> ArrayProtocol: ... def from_cache(path: Path, xp: NumpyAPI | None = None) -> xrDataArray: - data_set = xarray.open_zarr(path) + data_set = xarray.open_zarr(path, consolidated=False) # get the first data variable first_da = next(iter(data_set.data_vars)) # xarray.open_zarr uses dask by default. we may want that eventually @@ -85,7 +85,9 @@ def to_cache(da: xrDataArray, path: Path, dtype: npt.DTypeLike | None = None) -> # coerce to a contiguous numpy array: numcodecs calls np.array(..., copy=False), # which raises on numpy>=2 for non-contiguous or non-numpy (e.g. jax) buffers. da.data = np.ascontiguousarray(da.data) - da.to_zarr(path, mode="w") + # consolidated metadata is not part of the zarr v3 spec (zarr emits a warning); + # skip it so reads/writes stay spec-compliant across zarr 2 and 3. + da.to_zarr(path, mode="w", consolidated=False) def _serializable_attrs(attrs: Any) -> Any: diff --git a/src/microsim/cosem/_xarray.py b/src/microsim/cosem/_xarray.py deleted file mode 100644 index fdf5cc7..0000000 --- a/src/microsim/cosem/_xarray.py +++ /dev/null @@ -1,196 +0,0 @@ -"""Code mostly borrowed/adapted from fibsem_tools. - -...to minimize dependencies and avoid pydantic hard pinning. - -https://github.com/janelia-cellmap/fibsem-tools - -Copyright 2019 Howard Hughes Medical Institute -MIT License - -""" - -from __future__ import annotations - -from collections.abc import Sequence # noqa: TC003 -from pathlib import Path -from typing import TYPE_CHECKING, Any, Literal - -import dask.array as da -import numpy as np -import xarray as xr -import zarr -from datatree import DataTree -from pydantic import BaseModel - -if TYPE_CHECKING: - PathLike = Path | str - - -def access_zarr( - store: PathLike, path: PathLike = "", **kwargs: Any -) -> zarr.Array | zarr.Group: - if isinstance(store, Path | str): - store = str(store) - if ".n5" in store: - store, path = str(store).split("n5") - store += "n5" - store = zarr.N5FSStore(store, anon=True) - else: - raise NotImplementedError(f"Can only read n5 sources, not {store!r}") - - # set default dimension separator to / - if "shape" in kwargs and "dimension_separator" not in kwargs: - kwargs["dimension_separator"] = "/" - - attrs = kwargs.pop("attrs", {}) - access_mode = kwargs.pop("mode", "a") - - array_or_group = zarr.open(store, path=str(path), **kwargs, mode=access_mode) - - if access_mode != "r" and len(attrs) > 0: - array_or_group.attrs.update(attrs) - return array_or_group - - -def stt_coord( - length: int, dim: str, scale: float, translate: float, units: str -) -> xr.DataArray: - return xr.DataArray( - (np.arange(length) * scale) + translate, dims=(dim,), attrs={"units": units} - ) - - -class STTransform(BaseModel): - order: Literal["C", "F"] | None = "C" - axes: Sequence[str] - units: Sequence[str] - translate: Sequence[float] - scale: Sequence[float] - - def to_coords(self, shape: tuple[int, ...]) -> list[xr.DataArray]: - axes = self.axes if self.order == "C" else reversed(self.axes) - return [ - stt_coord( - shape[idx], - dim=k, - scale=self.scale[idx], - translate=self.translate[idx], - units=self.units[idx], - ) - for idx, k in enumerate(axes) - ] - - -# def access_parent(node: zarr.Array | zarr.Group, **kwargs: Any) -> zarr.Group: -# """Get the parent (zarr.Group) of a Zarr array or group.""" -# parent_path = "/".join(node.path.split("/")[:-1]) -# return access_zarr(store=node.store, path=parent_path, **kwargs) - - -def infer_coords(array: zarr.Array) -> list[xr.DataArray]: - # group = access_parent(array, mode="r") - - if (transform := array.attrs.get("transform", None)) is not None: - return STTransform.model_validate(transform).to_coords(array.shape) - - raise NotImplementedError('No "transform" attribute found in array.') - - -def create_datatree( - element: zarr.Group, - chunks: Literal["auto"] | tuple[int, ...] = "auto", - use_dask: bool = True, - attrs: dict[str, Any] | None = None, - name: str | None = None, -) -> DataTree: - if name is None: - name = element.basename - - nodes: dict[str, xr.Dataset | xr.DataArray | DataTree | None] = { - name: create_dataarray( - array, - chunks=chunks, - use_dask=use_dask, - attrs=None, - name="data", - ) - for name, array in element.arrays() - } - if attrs is None: - attrs = dict(element.attrs) - - # insert root element - nodes["/"] = xr.Dataset(attrs=attrs) - dtree = DataTree.from_dict(nodes, name=name) - return dtree - - -def create_dataarray( - element: zarr.Array | zarr.Group | da.Array, - chunks: Literal["auto"] | tuple[int, ...] = "auto", - use_dask: bool = True, - attrs: dict[str, Any] | None = None, - name: str | None = None, -) -> xr.DataArray: - name = name or element.basename - coords = infer_coords(element) - if attrs is None: - attrs = dict(getattr(element, "attrs", {})) - if use_dask: - element = da.from_array(element, chunks=chunks, inline_array=True) - result = xr.DataArray(element, coords=coords, attrs=attrs, name=name) - return result - - -def zarr_to_xarray( - element: zarr.Array | zarr.Group, - chunks: Literal["auto"] | tuple[int, ...] = "auto", - use_dask: bool = True, - attrs: dict[str, Any] | None = None, - name: str | None = None, -) -> xr.DataArray | DataTree: - if isinstance(element, zarr.Group): - return create_datatree( - element, - chunks=chunks, - attrs=attrs, - use_dask=use_dask, - name=name, - ) - if isinstance(element, zarr.Array): - return create_dataarray( - element, - chunks=chunks, - attrs=attrs, - use_dask=use_dask, - name=name, - ) - raise ValueError( - "This function only accepts instances of zarr.Group and zarr.Array. ", - f"Got {type(element)} instead.", - ) - - -def read_xarray( - path: PathLike, - chunks: Literal["auto"] | tuple[int, ...] = "auto", - use_dask: bool = True, - attrs: dict[str, Any] | None = None, - name: str | None = None, - **kwargs: Any, -) -> xr.DataArray | DataTree: - element = access_zarr(path) - return zarr_to_xarray( - element, - chunks=chunks, - use_dask=use_dask, - attrs=attrs, - name=name, - ) - - -if __name__ == "__main__": - from rich import print - - g = "s3://janelia-cosem-datasets/jrc_hela-2/jrc_hela-2.n5/labels/chrom_pred" - print(read_xarray(g)) diff --git a/src/microsim/cosem/models.py b/src/microsim/cosem/models.py index 0563e46..5319f11 100644 --- a/src/microsim/cosem/models.py +++ b/src/microsim/cosem/models.py @@ -24,8 +24,6 @@ if TYPE_CHECKING: import numpy as np - import xarray as xr - from datatree import DataTree from tensorstore import TensorStore # ------------------------ MODELS ------------------------ @@ -172,15 +170,6 @@ def read( bin_mode=_bin_mode, ) - def read_xarray(self) -> "xr.DataArray | DataTree": - """Read image as xarray or DataTree. - - This is less tested and used than `read`. Let me know if you need it. - """ - from microsim.cosem._xarray import read_xarray - - return read_xarray(self.url) - @property def attrs(self) -> dict[str, Any]: if not getattr(self, "_attrs", None): diff --git a/src/microsim/schema/simulation.py b/src/microsim/schema/simulation.py index ed4f402..cadc4e6 100644 --- a/src/microsim/schema/simulation.py +++ b/src/microsim/schema/simulation.py @@ -347,7 +347,9 @@ def _write(self, result: xr.DataArray) -> None: result.attrs = {"microsim.Simulation": sim_data} result.coords[Axis.C] = [c.name for c in result.coords[Axis.C].values] if self.output_path.suffix == ".zarr": - result.to_zarr(self.output_path, mode="w") + # consolidated metadata is not part of the zarr v3 spec (zarr warns); + # skip it so reads/writes stay spec-compliant across zarr 2 and 3. + result.to_zarr(self.output_path, mode="w", consolidated=False) elif self.output_path.suffix in (".nc",): result.to_netcdf(self.output_path) elif self.output_path.suffix in (".tif", ".tiff"): diff --git a/src/microsim/util.py b/src/microsim/util.py index bfc642a..71701d3 100644 --- a/src/microsim/util.py +++ b/src/microsim/util.py @@ -1,28 +1,24 @@ from __future__ import annotations -import itertools import shutil import warnings -from typing import TYPE_CHECKING, Any, Protocol, TypeVar, cast +from typing import TYPE_CHECKING, Any, TypeVar, cast from urllib import parse, request from urllib.error import HTTPError import numpy as np import numpy.typing as npt import platformdirs -import tqdm -from scipy import signal from ._data_array import ArrayProtocol, DataArray, xrDataArray from .schema.dimensions import Axis if TYPE_CHECKING: - from collections.abc import Callable, Iterator, Mapping, Sequence + from collections.abc import Callable, Mapping, Sequence from pathlib import Path from typing import Literal from cmap._colormap import ColorStopsLike - from numpy.typing import DTypeLike, NDArray ShapeLike = Sequence[int] @@ -112,143 +108,6 @@ def uniformly_spaced_xarray( return DataArray(array_creator(shape), dims=tuple(axes), coords=coords, attrs=attrs) -def get_fftconvolve_shape( - in1: npt.NDArray, - in2: npt.NDArray, - mode: Literal["full", "valid", "same"] = "full", - axes: int | Sequence[int] | None = None, -) -> tuple[int, ...]: - """Get output shape of an fftconvolve operation (without performing it). - - Parameters - ---------- - in1 : array_like - First input. - in2 : array_like - Second input. Should have the same number of dimensions as `in1`. - mode : str {'full', 'valid', 'same'}, optional - A string indicating the size of the output: - axes : int or array_like of ints or None, optional - Axes over which to compute the convolution. The default is over all axes. - - Returns - ------- - tuple - Tuple of ints, with output shape - - Raises - ------ - ValueError - If in1.shape and in2.shape are invalid for the provided mode. - """ - if mode == "same": - return in1.shape - - s1 = in1.shape - s2 = in2.shape - ndim = in1.ndim - if axes is None: - _axes = set(range(ndim)) - else: - _axes = set(range(axes) if isinstance(axes, int) else axes) - - full_shape = tuple( - max((s1[i], s2[i])) if i not in _axes else s1[i] + s2[i] - 1 for i in _axes - ) - - if mode == "valid": - final_shape = tuple( - full_shape[a] if a not in _axes else s1[a] - s2[a] + 1 - for a in range(len(full_shape)) - ) - if any(i <= 0 for i in final_shape): - raise ValueError( - "For 'valid' mode, one must be at least " - "as large as the other in every dimension" - ) - elif mode == "full": - final_shape = full_shape - - else: - raise ValueError("Acceptable mode flags are 'valid', 'same', or 'full'") - return final_shape - - -def _centered(arr: NDArray, newshape: ShapeLike) -> NDArray: - """Return the center `newshape` portion of `arr`.""" - _newshape = np.asarray(newshape) - currshape = np.array(arr.shape) - startind: NDArray = (currshape - _newshape) // 2 - endind: NDArray = startind + _newshape - myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] - return arr[tuple(myslice)] - - -def _iter_block_locations( - chunks: tuple[tuple[int, ...]], -) -> Iterator[tuple[list[tuple[int, int]], tuple[int, ...], ShapeLike]]: - """Iterate block indices. - - Examples - -------- - >>> chunks = ((2, 2), (3, 3), (4, 4, 4)) - >>> list(_iter_block_locations(chunks)) - [ - ([(0, 2), (0, 3), (0, 4)], (0, 0, 0), (2, 3, 4)), - ([(0, 2), (0, 3), (4, 8)], (0, 0, 1), (2, 3, 4)), - ([(0, 2), (0, 3), (8, 12)], (0, 0, 2), (2, 3, 4)), - ... - ] - """ - starts = [(0, *tuple(np.cumsum(i))) for i in chunks] - for block_id in itertools.product(*(range(len(c)) for c in chunks)): - arr_slc = [(starts[ij][j], starts[ij][j + 1]) for ij, j in enumerate(block_id)] - chunk_shape = tuple(chunks[ij][j] for ij, j in enumerate(block_id)) - yield arr_slc, block_id, chunk_shape - - -class Convolver(Protocol): - def __call__( - self, in1: NDArray, in2: NDArray, mode: Literal["full", "valid", "same"] - ) -> NDArray: ... - - -def tiled_convolve( - in1: npt.NDArray, - in2: npt.NDArray, - mode: Literal["full", "valid", "same"] = "full", - chunks: tuple | None = None, - func: Convolver = signal.convolve, - dtype: DTypeLike | None = None, -) -> npt.NDArray: - from dask.array.core import normalize_chunks - - if chunks is None: - chunks = getattr(in1, "chunks", None) or (100,) * in1.ndim # TODO: change 100 - - _chunks: tuple[tuple[int, ...]] = normalize_chunks(chunks, in1.shape) - - final_shape = get_fftconvolve_shape(in1, in2, mode="full") - - out = np.zeros(final_shape, dtype=dtype) - for loc, *_ in tqdm.tqdm(list(_iter_block_locations(_chunks))): - block = np.asarray(in1[tuple(slice(*i) for i in loc)]) - result = func(block, in2, mode="full") - if hasattr(result, "get"): - result = result.get() - out_idx = tuple( - slice(i, i + s) for (i, _), s in zip(loc, result.shape, strict=False) - ) - out[out_idx] += result - del result - - if mode == "same": - return _centered(out, in1.shape) - elif mode == "valid": - return _centered(out, get_fftconvolve_shape(in1, in2, mode="valid")) - return out - - # convenience function we'll use a couple times def ortho_plot( img: ArrayProtocol, diff --git a/uv.lock b/uv.lock index 020b867..f3f8d46 100644 --- a/uv.lock +++ b/uv.lock @@ -9,13 +9,14 @@ resolution-markers = [ "python_full_version == '3.14.*' and sys_platform == 'emscripten'", "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version 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