NumPy Docstrings

pyproject.toml

@@ -75,7 +75,34 @@ packages = ["viscy"]
 write_to = "viscy/_version.py"
 
 [tool.ruff]
-src = ["viscy", "tests"]
 line-length = 88
-lint.extend-select = ["I001"]
-lint.isort.known-first-party = ["viscy"]
+src = ["viscy", "tests"]
+extend-include = ["*.ipynb"]
+target-version = "py311"
+# Exclude the following for now. Later on we should check every Python file.
+extend-exclude = ["viscy/scripts/*", "applications/*", "examples/*"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+docstring-code-format = true
+docstring-code-line-length = "dynamic"
+
+[tool.ruff.lint]
+select = [
+  "D",      # pydocstyle
+  "I",      # isort
+]
+ignore = [
+  "D100", # Missing docstring in public module
+  "D104", # Missing docstring in public package
+  "D105", # __magic__ methods are often self-explanatory, allow missing docstrings
+  "D107", # Missing docstring in __init__
+  # Disable one in each pair of mutually incompatible rules
+  "D203", # We don’t want a blank line before a class docstring
+  "D213", # <> We want docstrings to start immediately after the opening triple quote
+  "D400", # first line should end with a period [Bug: doesn’t work with single-line docstrings]
+  "D401", # First line should be in imperative mood; try rephrasing
+]
+per-file-ignores."tests/*" = ["D"]
+pydocstyle.convention = "numpy"

tests/data/test_hcs.py

@@ -3,7 +3,6 @@
 from iohub import open_ome_zarr
 from monai.transforms import RandSpatialCropSamplesd
 from pytest import mark
-
 from viscy.data.hcs import HCSDataModule
 from viscy.trainer import VisCyTrainer
 

tests/data/test_select.py

@@ -1,6 +1,5 @@
 import pytest
 from iohub.ngff import open_ome_zarr
-
 from viscy.data.select import SelectWell
 
 

tests/data/test_triplet.py

@@ -1,7 +1,6 @@
 import pandas as pd
 from iohub import open_ome_zarr
 from pytest import mark
-
 from viscy.data.triplet import TripletDataModule
 
 

tests/evaluation/test_cell_feature_metrics.py

@@ -2,7 +2,6 @@
 import pandas as pd
 import pytest
 from skimage import measure
-
 from viscy.representation.evaluation.feature import CellFeatures, DynamicFeatures
 
 

tests/evaluation/test_evaluation_metrics.py

@@ -3,7 +3,6 @@
 import torch
 from skimage import data, measure
 from skimage.util import img_as_float
-
 from viscy.translation.evaluation_metrics import (
     POD_metric,
     VOI_metric,

tests/preprocessing/generate_masks_tests.py

@@ -8,7 +8,6 @@
 import pandas as pd
 import skimage.io as sk_im_io
 from testfixtures import TempDirectory
-
 from viscy.preprocessing.generate_masks import MaskProcessor
 from viscy.utils import aux_utils as aux_utils
 
@@ -129,7 +128,7 @@ def test_generate_masks_uni(self):
         nose.tools.assert_equal(len(frames_meta), exp_len)
         for idx in range(exp_len):
             nose.tools.assert_equal(
-                "im_c003_z00{}_t000_p001.npy".format(idx),
+                f"im_c003_z00{idx}_t000_p001.npy",
                 frames_meta.iloc[idx]["file_name"],
             )
 

tests/preprocessing/resize_images_tests.py

@@ -4,10 +4,9 @@
 import cv2
 import numpy as np
 import pandas as pd
-from testfixtures import TempDirectory
-
 import viscy.preprocessing.resize_images as resize_images
 import viscy.utils.aux_utils as aux_utils
+from testfixtures import TempDirectory
 
 
 class TestResizeImages(unittest.TestCase):
@@ -133,7 +132,7 @@ def test_resize_volumes(self):
                     ),
                     ignore_index=True,
                 )
-            op_fname = "im_c00{}_z000_t005_p007_3.3-0.8-1.0.npy".format(c)
+            op_fname = "im_c00{c}_z000_t005_p007_3.3-0.8-1.0.npy".format(c)
             exp_meta_dict.append(
                 {
                     "time_idx": self.time_idx,
@@ -169,7 +168,7 @@ def test_resize_volumes(self):
         exp_meta_dict = []
         for c in channel_ids:
             for s in [0, 2]:
-                op_fname = "im_c00{}_z00{}_t005_p007_3.3-0.8-1.0.npy".format(c, s)
+                op_fname = "im_c00{c}_z00{s}_t005_p007_3.3-0.8-1.0.npy".format(c, s)
                 exp_meta_dict.append(
                     {
                         "time_idx": self.time_idx,

tests/preprocessing/test_pixel_ratio.py

@@ -1,5 +1,4 @@
 from numpy.testing import assert_allclose
-
 from viscy.preprocessing.pixel_ratio import sematic_class_weights
 
 

tests/representation/test_feature.py

@@ -4,7 +4,6 @@
 import pandas as pd
 import pytest
 from iohub import open_ome_zarr
-
 from viscy.representation.evaluation.feature import (
     CellFeatures,
     DynamicFeatures,

tests/representation/test_lca.py

@@ -1,7 +1,6 @@
 import numpy as np
 import torch
 from sklearn.linear_model import LogisticRegression
-
 from viscy.representation.evaluation.lca import linear_from_binary_logistic_regression
 
 

tests/transforms/test_adjust_contrast.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 from monai.transforms import AdjustContrast, Compose
-
 from viscy.transforms import BatchedRandAdjustContrast, BatchedRandAdjustContrastd
 
 

tests/transforms/test_crop.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 from monai.transforms import Compose
-
 from viscy.transforms._crop import (
     BatchedCenterSpatialCrop,
     BatchedCenterSpatialCropd,

tests/transforms/test_flip.py

@@ -1,6 +1,5 @@
 import pytest
 import torch
-
 from viscy.transforms import BatchedRandFlip, BatchedRandFlipd
 
 

tests/transforms/test_gaussian_smooth.py

@@ -7,7 +7,6 @@
     get_gaussian_kernel3d,
 )
 from monai.transforms.intensity.array import GaussianSmooth
-
 from viscy.transforms import BatchedRandGaussianSmooth, BatchedRandGaussianSmoothd
 from viscy.transforms._gaussian_smooth import filter3d_separable
 

tests/transforms/test_noise.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 from monai.transforms import Compose
-
 from viscy.transforms import BatchedRandGaussianNoise, BatchedRandGaussianNoised
 
 

tests/transforms/test_scale_intensity.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 from monai.transforms import RandScaleIntensity
-
 from viscy.transforms import BatchedRandScaleIntensity, BatchedRandScaleIntensityd
 
 

tests/transforms/test_transforms.py

@@ -1,6 +1,5 @@
 import pytest
 import torch
-
 from viscy.transforms._decollate import Decollate
 from viscy.transforms._transforms import (
     BatchedScaleIntensityRangePercentiles,

tests/translation/test_evaluation.py

@@ -6,7 +6,6 @@
 import pytest
 from lightning.pytorch.loggers import CSVLogger
 from numpy.testing import assert_array_equal
-
 from viscy.data.segmentation import SegmentationDataModule
 from viscy.trainer import Trainer
 from viscy.translation.evaluation import SegmentationMetrics2D

tests/unet/networks/Unet25D_tests.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import torch
-
 import viscy.utils.cli_utils as io_utils
 from viscy.unet.networks.Unet25D import Unet25d
 

tests/unet/networks/Unet2D_tests.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import torch
-
 import viscy.utils.cli_utils as io_utils
 from viscy.unet.networks.Unet2D import Unet2d
 

tests/unet/networks/layers/ConvBlock2D_tests.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import torch
-
 import viscy.utils.cli_utils as io_utils
 from viscy.unet.networks.layers.ConvBlock2D import ConvBlock2D
 

tests/unet/networks/layers/ConvBlock3D_tests.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import torch
-
 import viscy.utils.cli_utils as io_utils
 from viscy.unet.networks.layers.ConvBlock3D import ConvBlock3D
 

tests/unet/test_fcmae.py

@@ -1,5 +1,4 @@
 import torch
-
 from viscy.unet.networks.fcmae import (
     FullyConvolutionalMAE,
     MaskedAdaptiveProjection,

tests/utils/image_utils_tests.py

@@ -1,5 +1,4 @@
 import numpy as np
-
 from viscy.utils.image_utils import grid_sample_pixel_values, preprocess_image
 
 

tests/utils/masks_utils_tests.py

@@ -2,7 +2,6 @@
 import numpy as np
 from skimage import draw
 from skimage.filters import gaussian
-
 from viscy.utils.masks import (
     create_unimodal_mask,
     get_unet_border_weight_map,

tests/utils/mp_utils_tests.py

@@ -5,11 +5,10 @@
 import numpy as np
 import numpy.testing
 import skimage.io as sk_im_io
-from testfixtures import TempDirectory
-
 import viscy.utils.aux_utils as aux_utils
 import viscy.utils.image_utils as image_utils
 import viscy.utils.mp_utils as mp_utils
+from testfixtures import TempDirectory
 from viscy.utils.masks import create_otsu_mask
 
 

viscy/__init__.py

@@ -1 +0,0 @@
-"""Learning vision for cells"""

viscy/cli.py

@@ -6,25 +6,39 @@
 import torch
 from jsonargparse import lazy_instance
 from lightning.pytorch import LightningDataModule, LightningModule
-from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
 from lightning.pytorch.loggers import TensorBoardLogger
 
 from viscy.trainer import VisCyTrainer
 
 
 class VisCyCLI(LightningCLI):
-    """Extending lightning CLI arguments and defualts."""
+    """Extending Lightning CLI arguments and defaults for VisCy."""
 
     @staticmethod
     def subcommands() -> dict[str, set[str]]:
+        """Define subcommands and their required arguments.
+
+        Returns
+        -------
+        dict[str, set[str]]
+            Dictionary mapping subcommand names to sets of required argument names.
+        """
         subcommands = LightningCLI.subcommands()
         subcommand_base_args = {"model"}
         subcommands["preprocess"] = subcommand_base_args
         subcommands["export"] = subcommand_base_args
         subcommands["precompute"] = subcommand_base_args
         return subcommands
 
-    def add_arguments_to_parser(self, parser) -> None:
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
+        """Add default arguments to the Lightning CLI parser.
+
+        Parameters
+        ----------
+        parser : LightningArgumentParser
+            Lightning CLI parser instance to configure.
+        """
         parser.set_defaults(
             {
                 "trainer.logger": lazy_instance(
@@ -45,8 +59,8 @@ def _setup_environment() -> None:
 
 
 def main() -> None:
-    """
-    Main Lightning CLI entry point.
+    """Run the Lightning CLI entry point.
+
     Parse log level and set TF32 precision.
     Set default random seed to 42.
     """