Merge pull request #165 from fmi-faim/code-style-refactor

Fix various code style issues

.github/workflows/test.yml

@@ -22,7 +22,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.10', '3.11', '3.12']
 
     steps:
     - uses: actions/checkout@v3

.gitignore

@@ -91,3 +91,4 @@ mobie-example-project/
 
 # scratchpad
 scratchpad/
+.pixi

pyproject.toml

@@ -7,33 +7,33 @@ name = "faim-ipa"
 dynamic = ["version"]
 description = 'Tools used at FMI-FAIM for Image Processing and Analysis.'
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = "BSD-3-Clause"
 keywords = []
 authors = [
-  { name = "Tim-Oliver Buchholz", email = "tim-oliver.buchholz@fmi.ch" },
-  { name = "Jan Eglinger", email = "jan.eglinger@fmi.ch" },
+  {name = "Tim-Oliver Buchholz", email = "tim-oliver.buchholz@fmi.ch"},
+  {name = "Jan Eglinger", email = "jan.eglinger@fmi.ch"},
 ]
 classifiers = [
   "Development Status :: 4 - Beta",
   "Programming Language :: Python",
-  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
-  "pydantic",
+  "defusedxml",
   "distributed",
-  "numpy<2",
   "matplotlib",
-  "scikit-image",
-  "pandas",
+  "numpy<2",
   "ome-zarr",
+  "pandas",
+  "pint",
+  "pydantic>=2",
+  "scikit-image",
   "tqdm",
-  "pint"
 ]
 
 [project.urls]
@@ -43,14 +43,46 @@ Homepage = "https://github.com/fmi-faim/faim-ipa.git"
 "Source Code" = "https://github.com/fmi-faim/faim-ipa"
 "User Support" = "https://github.com/fmi-faim/faim-ipa/issues"
 
-[tool.hatch.version]
-source = "vcs"
+[tool.coverage.paths]
+faim_ipa = ["src/faim_ipa", "*/faim-ipa/src/faim_ipa"]
+tests = ["tests", "*/faim-ipa/tests"]
+
+[tool.coverage.report]
+show_missing = true
+exclude_lines = [
+  "no cov",
+  "if __name__ == .__main__.:",
+  "if TYPE_CHECKING:",
+]
+
+[tool.coverage.run]
+source_pkgs = ["faim_ipa", "tests"]
+branch = true
+parallel = true
+omit = [
+  "src/faim_ipa/__about__.py",
+]
+
+[tool.hatch.build.targets.sdist]
+exclude = [
+  "/examples",
+  "/resources",
+  "*/__pycache__",
+  "/tests",
+  "/scratchpad",
+  "/.github",
+  ".pre-commit-config.yaml",
+]
+
+[[tool.hatch.envs.all.matrix]]
+python = ["3.10", "3.11", "3.12"]
 
 [tool.hatch.envs.default]
 dependencies = [
   "coverage[toml]>=6.5",
   "pytest",
 ]
+
 [tool.hatch.envs.default.scripts]
 test = "pytest {args:tests}"
 test-cov = "coverage run -m pytest {args:tests}"
@@ -73,49 +105,31 @@ cov = [
   "cov-report",
 ]
 
-[[tool.hatch.envs.all.matrix]]
-python = ["3.9", "3.10", "3.11", "3.12"]
-
 [tool.hatch.envs.types]
 dependencies = [
   "mypy>=1.0.0",
 ]
+
 [tool.hatch.envs.types.scripts]
 check = "mypy --install-types --non-interactive {args:src/faim_ipa tests}"
 
 [tool.hatch.metadata]
 allow-direct-references = true
 
-[tool.hatch.build.targets.sdist]
-exclude = [
-   "/examples",
-  "/resources",
-  "*/__pycache__",
-  "/tests",
-  "/scratchpad",
-  "/.github",
-  ".pre-commit-config.yaml",
-]
+[tool.hatch.version]
+source = "vcs"
 
-[tool.coverage.run]
-source_pkgs = ["faim_ipa", "tests"]
-branch = true
-parallel = true
-omit = [
-  "src/faim_ipa/__about__.py",
+[tool.pytest.ini_options]
+addopts = [
+    "--import-mode=importlib",
 ]
 
-[tool.coverage.paths]
-faim_ipa = ["src/faim_ipa", "*/faim-ipa/src/faim_ipa"]
-tests = ["tests", "*/faim-ipa/tests"]
+[tool.ruff.lint]
+ignore = ["FA100", "S101"]
 
-[tool.coverage.report]
-show_missing = true
-exclude_lines = [
-  "no cov",
-  "if __name__ == .__main__.:",
-  "if TYPE_CHECKING:",
-]
+[tool.ruff.lint.extend-per-file-ignores]
+"**/tests/*" = ["F811", "INP001", "SLF001"]
+"**/examples/*" = ["INP001"]
 
 [tool.setuptools_scm]
 write_to = "src/faim_ipa/_version.py"

src/faim_ipa/alignment/__init__.py

@@ -1 +1 @@
-from .alignment import GridAlignment, StageAlignment  # noqa: F401
+from faim_ipa.alignment.alignment import GridAlignment, StageAlignment  # noqa: F401

src/faim_ipa/alignment/alignment.py

@@ -1,9 +1,10 @@
-from abc import ABC
+from abc import ABC, abstractmethod
 from copy import copy
 
 import numpy as np
 
-from faim_ipa.stitching import Tile, stitching_utils
+from faim_ipa.stitching import stitching_utils
+from faim_ipa.stitching.tile import Tile
 
 
 class AbstractAlignment(ABC):
@@ -15,6 +16,7 @@ def __init__(self, tiles: list[Tile]) -> None:
         self._unaligned_tiles = stitching_utils.shift_to_origin(tiles)
         self._aligned_tiles = self._align(self._unaligned_tiles)
 
+    @abstractmethod
     def _align(self, tiles: list[Tile]) -> list[Tile]:
         raise NotImplementedError
 
@@ -45,12 +47,12 @@ def _align(self, tiles: list[Tile]) -> list[Tile]:
         grid_positions_x = set()
         tile_map = {}
         for tile in tiles:
-            assert (
-                tile.shape[-2:] == tile_shape[-2:]
-            ), "All tiles must have the same YX shape."
+            if tile.shape[-2:] != tile_shape[-2:]:
+                message = f"All tiles must have the same YX shape. {tile.shape[-2:]} <=> {tile_shape[-2:]}"
+                raise ValueError(message)
             y_pos = int(np.round(tile.position.y / tile_shape[-2]))
             x_pos = int(np.round(tile.position.x / tile_shape[-1]))
-            if (y_pos, x_pos) in tile_map.keys():
+            if (y_pos, x_pos) in tile_map:
                 tile_map[(y_pos, x_pos)].append(tile)
             else:
                 tile_map[(y_pos, x_pos)] = [tile]
@@ -61,7 +63,7 @@ def _align(self, tiles: list[Tile]) -> list[Tile]:
         grid_positions_x = sorted(grid_positions_x)
         for y_pos in grid_positions_y:
             for x_pos in grid_positions_x:
-                if (y_pos, x_pos) in tile_map.keys():
+                if (y_pos, x_pos) in tile_map:
                     for unaligned_tile in tile_map[(y_pos, x_pos)]:
                         new_tile = copy(unaligned_tile)
                         new_tile.position.y = y_pos * tile_shape[-2]

src/faim_ipa/dask_utils.py

@@ -1,100 +1,8 @@
-from multiprocessing import Process, Queue
-from time import sleep
+from __future__ import annotations
 
 import numpy as np
 
 
-class LocalClusterFactory:
-    """Creates a local dask cluster in a sub-process."""
-
-    def __init__(
-        self,
-        n_workers: int = None,
-        threads_per_worker: int = None,
-        processes: bool = None,
-        memory_limit: str = None,
-        local_directory: str = None,
-    ):
-        self.n_workers = n_workers
-        self.threads_per_worker = threads_per_worker
-        self.processes = processes
-        self.memory_limit = memory_limit
-        self.local_directory = local_directory
-        self._queue = Queue(1)
-        self._scheduler_address = None
-
-        self._subprocess = Process(
-            target=self._run_cluster,
-            args=(
-                self._queue,
-                self.n_workers,
-                self.threads_per_worker,
-                self.processes,
-                self.memory_limit,
-                self.local_directory,
-            ),
-        )
-        self._subprocess.start()
-        self._scheduler_address = None
-        self._client = None
-
-    def _get_scheduler_address(self):
-        if self._scheduler_address is None:
-            scheduler_address = self._queue.get()
-            self._scheduler_address = scheduler_address
-            self._queue.close()
-            self._queue = None
-
-        return self._scheduler_address
-
-    def _shutdown(self):
-        if self._client is not None and self._client.scheduler is not None:
-            self._client.shutdown()
-            self._client = None
-
-        if self._subprocess is not None and self._subprocess.is_alive():
-            self._subprocess.join()
-
-    def __del__(self):
-        self._shutdown()
-
-    @staticmethod
-    def _run_cluster(
-        queue: Queue,
-        n_workers: int,
-        threads_per_worker: int,
-        processes: bool,
-        memory_limit: str,
-        local_directory: str,
-    ):
-        import dask
-        import distributed
-
-        dask.config.set({"distributed.workers.memory.spill": 0.90})
-        dask.config.set({"distributed.workers.memory.target": 0.85})
-        dask.config.set({"distributed.workers.memory.terminate": 0.98})
-
-        client = distributed.Client(
-            n_workers=n_workers,
-            threads_per_worker=threads_per_worker,
-            processes=processes,
-            memory_limit=memory_limit,
-            local_directory=local_directory,
-        )
-        queue.put(client.cluster.scheduler.address)
-        while client.cluster.scheduler.status.value != "closed":
-            sleep(5)
-
-    def get_client(self):
-        """Get a dask client for the local cluster."""
-        if self._client is None:
-            import distributed
-
-            self._client = distributed.Client(self._get_scheduler_address())
-
-        return self._client
-
-
 def mean_cast_to(target_dtype):
     """
     Wrap np.mean to cast the result to a given dtype.

src/faim_ipa/detection/blobs.py

@@ -1,11 +1,11 @@
-from typing import Optional
+from __future__ import annotations
 
 import numpy as np
 from scipy.ndimage import gaussian_laplace
 from skimage.feature import peak_local_max
-from skimage.morphology import h_maxima, ball
-from skimage.util import img_as_float32
 from skimage.feature.blob import _prune_blobs
+from skimage.morphology import ball, h_maxima
+from skimage.util import img_as_float32
 
 from faim_ipa.detection.utils import estimate_log_rescale_factor
 
@@ -17,7 +17,7 @@ def detect_blobs(
     h: int,
     scale_factors: list[int],
     overlap: float,
-    background_img: Optional[np.ndarray] = None,
+    background_img: np.ndarray | None = None,
 ) -> np.ndarray:
     """Detect blobs of different sizes.
 
@@ -49,10 +49,11 @@ def detect_blobs(
     -------
     Detected spots.
     """
-    if background_img is not None:
-        image = img_as_float32(img) - img_as_float32(background_img)
-    else:
-        image = img_as_float32(img)
+    image = (
+        img_as_float32(img) - img_as_float32(background_img)
+        if background_img is not None
+        else img_as_float32(img)
+    )
 
     rescale_factor = estimate_log_rescale_factor(
         axial_sigma=axial_sigma, lateral_sigma=lateral_sigma
@@ -62,7 +63,7 @@ def detect_blobs(
         (axial_sigma * f, lateral_sigma * f, lateral_sigma * f) for f in scale_factors
     ]
 
-    scale_cube = np.empty(image.shape + (len(sigmas),), dtype=np.uint8)
+    scale_cube = np.empty((*image.shape, len(sigmas)), dtype=np.uint8)
 
     h_ = img_as_float32(np.array(h, dtype=img.dtype))
     scale_norm = np.mean([axial_sigma, lateral_sigma, lateral_sigma])