Fix some pre-commit errors

pyproject.toml

@@ -73,7 +73,7 @@ repository = "https://github.com/CAREamics/careamics"
 line-length = 88
 target-version = "py38"
 src = ["src"]
-select = [
+lint.select = [
     "E",  # style errors
     "W",  # style warnings
     "F",  # flakes
@@ -86,7 +86,7 @@ select = [
     "A001", # flake8-builtins
     "RUF",  # ruff-specific rules
 ]
-ignore = [
+lint.ignore = [
     "D100", # Missing docstring in public module
     "D107", # Missing docstring in __init__
     "D203", # 1 blank line required before class docstring
@@ -103,13 +103,13 @@ ignore = [
     "UP006", # Replace typing.List by list, mandatory for py3.8
     "UP007", # Replace Union by |, mandatory for py3.9
 ]
-ignore-init-module-imports = true
+lint.ignore-init-module-imports = true
 show-fixes = true
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "tests/*.py" = ["D", "S"]
 "setup.py" = ["D"]
 

src/careamics/callbacks/hyperparameters_callback.py

@@ -1,3 +1,5 @@
+"""Callback saving CAREamics configuration as hyperparameters in the model."""
+
 from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.callbacks import Callback
 
@@ -11,13 +13,18 @@ class HyperParametersCallback(Callback):
     This allows saving the configuration as dictionnary in the checkpoints, and
     loading it subsequently in a CAREamist instance.
 
+    Parameters
+    ----------
+    config : Configuration
+        CAREamics configuration to be saved as hyperparameter in the model.
+
     Attributes
     ----------
     config : Configuration
         CAREamics configuration to be saved as hyperparameter in the model.
     """
 
-    def __init__(self, config: Configuration):
+    def __init__(self, config: Configuration) -> None:
         """
         Constructor.
 
@@ -28,14 +35,14 @@ def __init__(self, config: Configuration):
         """
         self.config = config
 
-    def on_train_start(self, trainer: Trainer, pl_module: LightningModule):
+    def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
         """
         Update the hyperparameters of the model with the configuration on train start.
 
         Parameters
         ----------
         trainer : Trainer
-            PyTorch Lightning trainer.
+            PyTorch Lightning trainer, unused.
         pl_module : LightningModule
             PyTorch Lightning module.
         """

src/careamics/callbacks/progress_bar_callback.py

@@ -1,3 +1,5 @@
+"""Progressbar callback."""
+
 import sys
 from typing import Dict, Union
 
@@ -10,7 +12,13 @@ class ProgressBarCallback(TQDMProgressBar):
     """Progress bar for training and validation steps."""
 
     def init_train_tqdm(self) -> tqdm:
-        """Override this to customize the tqdm bar for training."""
+        """Override this to customize the tqdm bar for training.
+
+        Returns
+        -------
+        tqdm
+            A tqdm bar.
+        """
         bar = tqdm(
             desc="Training",
             position=(2 * self.process_position),
@@ -23,7 +31,13 @@ def init_train_tqdm(self) -> tqdm:
         return bar
 
     def init_validation_tqdm(self) -> tqdm:
-        """Override this to customize the tqdm bar for validation."""
+        """Override this to customize the tqdm bar for validation.
+
+        Returns
+        -------
+        tqdm
+            A tqdm bar.
+        """
         # The main progress bar doesn't exist in `trainer.validate()`
         has_main_bar = self.train_progress_bar is not None
         bar = tqdm(
@@ -37,7 +51,13 @@ def init_validation_tqdm(self) -> tqdm:
         return bar
 
     def init_test_tqdm(self) -> tqdm:
-        """Override this to customize the tqdm bar for testing."""
+        """Override this to customize the tqdm bar for testing.
+
+        Returns
+        -------
+        tqdm
+            A tqdm bar.
+        """
         bar = tqdm(
             desc="Testing",
             position=(2 * self.process_position),
@@ -52,6 +72,19 @@ def init_test_tqdm(self) -> tqdm:
     def get_metrics(
         self, trainer: Trainer, pl_module: LightningModule
     ) -> Dict[str, Union[int, str, float, Dict[str, float]]]:
-        """Override this to customize the metrics displayed in the progress bar."""
+        """Override this to customize the metrics displayed in the progress bar.
+
+        Parameters
+        ----------
+        trainer : Trainer
+            The trainer object.
+        pl_module : LightningModule
+            The LightningModule object, unused.
+
+        Returns
+        -------
+        dict
+            A dictionary with the metrics to display in the progress bar.
+        """
         pbar_metrics = trainer.progress_bar_metrics
         return {**pbar_metrics}

src/careamics/config/algorithm_model.py

@@ -1,3 +1,5 @@
+"""Algorithm configuration."""
+
 from __future__ import annotations
 
 from pprint import pformat
@@ -17,9 +19,9 @@ class AlgorithmConfig(BaseModel):
     training algorithm: which algorithm, loss function, model architecture, optimizer,
     and learning rate scheduler to use.
 
-    Currently, we only support N2V and custom algorithms. The `n2v` algorithm is only
-    compatible with `n2v` loss and `UNet` architecture. The `custom` algorithm allows
-    you to register your own architecture and select it using its name as
+    Currently, we only support N2V, CARE, N2N and custom models. The `n2v` algorithm is
+    only compatible with `n2v` loss and `UNet` architecture. The `custom` algorithm
+    allows you to register your own architecture and select it using its name as
     `name` in the custom pydantic model.
 
     Attributes

src/careamics/config/architectures/architecture_model.py

@@ -1,3 +1,5 @@
+"""Base model for the various CAREamics architectures."""
+
 from typing import Any, Dict
 
 from pydantic import BaseModel
@@ -16,6 +18,11 @@ def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
         """
         Dump the model as a dictionary, ignoring the architecture keyword.
 
+        Parameters
+        ----------
+        **kwargs : Any
+            Additional keyword arguments from Pydantic BaseModel model_dump method.
+
         Returns
         -------
         dict[str, Any]

src/careamics/config/architectures/custom_model.py

@@ -1,3 +1,5 @@
+"""Custom architecture Pydantic model."""
+
 from __future__ import annotations
 
 from pprint import pformat
@@ -84,6 +86,11 @@ def custom_model_is_known(cls, value: str) -> str:
         value : str
             Name of the custom model as registered using the `@register_model`
             decorator.
+
+        Returns
+        -------
+        str
+            The custom model name.
         """
         # delegate error to get_custom_model
         model = get_custom_model(value)
@@ -134,7 +141,7 @@ def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
 
         Parameters
         ----------
-        kwargs : Any
+        **kwargs : Any
             Additional keyword arguments from Pydantic BaseModel model_dump method.
 
         Returns

src/careamics/config/architectures/register_model.py

@@ -1,3 +1,5 @@
+"""Custom model registration utilities."""
+
 from typing import Callable
 
 from torch.nn import Module
@@ -53,7 +55,7 @@ def add_custom_model(model: Module) -> Module:
         Parameters
         ----------
         model : Module
-            Module class to register
+            Module class to register.
 
         Returns
         -------

src/careamics/config/architectures/unet_model.py

@@ -1,3 +1,5 @@
+"""UNet Pydantic model."""
+
 from __future__ import annotations
 
 from typing import Literal

src/careamics/config/architectures/vae_model.py

@@ -1,3 +1,5 @@
+"""VAE Pydantic model."""
+
 from typing import Literal
 
 from pydantic import (

src/careamics/config/callback_model.py

@@ -1,4 +1,4 @@
-"""Checkpoint saving configuration."""
+"""Callback Pydantic models."""
 
 from __future__ import annotations
 
@@ -13,13 +13,7 @@
 
 
 class CheckpointModel(BaseModel):
-    """_summary_.
-
-    Parameters
-    ----------
-    BaseModel : _type_
-        _description_
-    """
+    """Checkpoint saving callback Pydantic model."""
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -46,13 +40,7 @@ class CheckpointModel(BaseModel):
 
 
 class EarlyStoppingModel(BaseModel):
-    """_summary_.
-
-    Parameters
-    ----------
-    BaseModel : _type_
-        _description_
-    """
+    """Early stopping callback Pydantic model."""
 
     model_config = ConfigDict(
         validate_assignment=True,

src/careamics/config/configuration_example.py

@@ -1,3 +1,5 @@
+"""Example of configurations."""
+
 from .algorithm_model import AlgorithmConfig
 from .architectures import UNetModel
 from .configuration_model import Configuration
@@ -19,7 +21,7 @@
 
 
 def full_configuration_example() -> Configuration:
-    """Returns a dictionnary representing a full configuration example.
+    """Return a dictionnary representing a full configuration example.
 
     Returns
     -------
@@ -56,7 +58,7 @@ def full_configuration_example() -> Configuration:
                 "name": SupportedTransform.NORMALIZE.value,
             },
             {
-                "name": SupportedTransform.NDFLIP.value,
+                "name": SupportedTransform.XY_FLIP.value,
             },
             {
                 "name": SupportedTransform.XY_RANDOM_ROTATE90.value,

src/careamics/config/configuration_factory.py

@@ -111,7 +111,7 @@ def _create_supervised_configuration(
                 "name": SupportedTransform.NORMALIZE.value,
             },
             {
-                "name": SupportedTransform.NDFLIP.value,
+                "name": SupportedTransform.XY_FLIP.value,
             },
             {
                 "name": SupportedTransform.XY_RANDOM_ROTATE90.value,
@@ -526,7 +526,7 @@ def create_n2v_configuration(
                 "name": SupportedTransform.NORMALIZE.value,
             },
             {
-                "name": SupportedTransform.NDFLIP.value,
+                "name": SupportedTransform.XY_FLIP.value,
             },
             {
                 "name": SupportedTransform.XY_RANDOM_ROTATE90.value,

src/careamics/config/data_model.py

@@ -17,14 +17,14 @@
 
 from .support import SupportedTransform
 from .transformations.n2v_manipulate_model import N2VManipulateModel
-from .transformations.nd_flip_model import NDFlipModel
 from .transformations.normalize_model import NormalizeModel
+from .transformations.xy_flip_model import XYFlipModel
 from .transformations.xy_random_rotate90_model import XYRandomRotate90Model
 from .validators import check_axes_validity, patch_size_ge_than_8_power_of_2
 
 TRANSFORMS_UNION = Annotated[
     Union[
-        NDFlipModel,
+        XYFlipModel,
         XYRandomRotate90Model,
         NormalizeModel,
         N2VManipulateModel,
@@ -70,7 +70,7 @@ class DataConfig(BaseModel):
     ...             "std": 47.2,
     ...         },
     ...         {
-    ...             "name": "NDFlip",
+    ...             "name": "XYFlip",
     ...         }
     ...     ]
     ... )
@@ -97,7 +97,7 @@ class DataConfig(BaseModel):
                 "name": SupportedTransform.NORMALIZE.value,
             },
             {
-                "name": SupportedTransform.NDFLIP.value,
+                "name": SupportedTransform.XY_FLIP.value,
             },
             {
                 "name": SupportedTransform.XY_RANDOM_ROTATE90.value,
@@ -202,7 +202,7 @@ def validate_prediction_transforms(
 
         if SupportedTransform.N2V_MANIPULATE in transform_list:
             # multiple N2V_MANIPULATE
-            if transform_list.count(SupportedTransform.N2V_MANIPULATE) > 1:
+            if transform_list.count(SupportedTransform.N2V_MANIPULATE.value) > 1:
                 raise ValueError(
                     f"Multiple instances of "
                     f"{SupportedTransform.N2V_MANIPULATE} transforms "
@@ -211,7 +211,7 @@ def validate_prediction_transforms(
 
             # N2V_MANIPULATE not the last transform
             elif transform_list[-1] != SupportedTransform.N2V_MANIPULATE:
-                index = transform_list.index(SupportedTransform.N2V_MANIPULATE)
+                index = transform_list.index(SupportedTransform.N2V_MANIPULATE.value)
                 transform = transforms.pop(index)
                 transforms.append(transform)
 
@@ -250,7 +250,7 @@ def add_std_and_mean_to_normalize(self: Self) -> Self:
         Self
             Data model with mean and std added to the Normalize transform.
         """
-        if self.mean is not None or self.std is not None:
+        if self.mean is not None and self.std is not None:
             # search in the transforms for Normalize and update parameters
             for transform in self.transforms:
                 if transform.name == SupportedTransform.NORMALIZE.value: