ci: replace Pylint, Black and Unimport with Ruff (#216)

## Summary by Sourcery

Replace Pylint, Black, and Unimport with Ruff for code linting, formatting, and import sorting

Enhancements:
- Consolidate multiple code quality tools into a single, performant tool
- Simplify project configuration by reducing the number of linting and formatting tools

CI:
- Replace multiple linting and formatting tools with Ruff, a fast and comprehensive Python linter and formatter

Author: ruancomelli

.pre-commit-config.yaml

@@ -1,13 +1,32 @@
 repos:
 
-- repo: https://github.com/astral-sh/uv-pre-commit
-  rev: 0.6.9
+- repo: https://gitlab.com/vojko.pribudic.foss/pre-commit-update
+  rev: v0.6.1
   hooks:
-      # Update the uv lockfile
-  - id: uv-lock
-        # Install all packages
-  - id: uv-sync
-    args: [--locked, --all-packages]
+  - id: pre-commit-update
+
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v5.0.0
+  hooks:
+  - id: check-added-large-files
+  - id: check-json
+  - id: check-toml
+  - id: check-xml
+  - id: check-yaml
+  - id: end-of-file-fixer
+  - id: no-commit-to-branch
+    args: [--branch, main]
+  - id: pretty-format-json
+    args: [--autofix, --indent, '2']
+  - id: trailing-whitespace
+
+- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
+  rev: v2.14.0
+  hooks:
+  - id: pretty-format-yaml
+    args: [--autofix, --indent, '2']
+  - id: pretty-format-toml
+    args: [--autofix, --indent, '4', --no-sort]
 
 - repo: https://github.com/compilerla/conventional-pre-commit
   rev: v2.1.1
@@ -40,19 +59,4 @@ repos:
   - id: isort
     name: isort (pyi)
     types: [pyi]
-    args: [--check]
-
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
-  hooks:
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-  - id: check-ast
-
-- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
-  rev: v2.5.0
-  hooks:
-  - id: pretty-format-yaml
-    args: [--autofix, --indent, '2']
+    args: [--check]

Makefile

@@ -1,14 +1,6 @@
 .PROJECT = boiling_learning
 .TESTS_FOLDER = tests
 
-.UNIMPORT = $(shell uv run unimport --remove --gitignore --ignore-init --include-star-import $(.PROJECT) $(.TESTS_FOLDER))
-.BLACK = $(shell uv run black $(.PROJECT) $(.TESTS_FOLDER))
-.ISORT = $(shell uv run isort $(.PROJECT) $(.TESTS_FOLDER))
-.FORMAT = $(foreach command,.UNIMPORT .BLACK .ISORT,$(call $(command)))
-
-.READD = $(shell git update-index --again)
-.CHECK = $(shell pre-commit run)
-
 .PHONY: coverage
 coverage:
 	@uv run coverage run --source=$(.PROJECT)/ -m pytest $(.TESTS_FOLDER)
@@ -18,29 +10,22 @@ coverage:
 test:
 	@uv run pytest --doctest-modules $(.PROJECT) $(.TESTS_FOLDER) -vv
 
-.PHONY: check
-check:
-	@$(call $(.CHECK))
+.PHONY: format
+format:
+	@uv run --group format ruff format $(.PROJECT) $(.TESTS_FOLDER)
+
+.PHONY: lint
+lint:
+	@uv run --group lint ruff check --fix $(.PROJECT) $(.TESTS_FOLDER)
 
 .PHONY: typecheck
 typecheck:
 	@uv run mypy $(.PROJECT)
 
-.PHONY: format
-format:
-	@$(call $(.FORMAT))
-
 .PHONY: vulture
 vulture:
 	@uv run vulture --ignore-decorators @*.dispatch*,@*.instance* --ignore-names __*[!_][!_] $(.PROJECT) main.py
 
-.PHONY: autofix
-autofix:
-	@-$(call $(.FORMAT))
-	@$(call $(.READD))
-	@-$(call $(.CHECK))
-	@$(call $(.READD))
-
 .PHONY: release
 release:
 # for v1.0.0 and after, the following line should be used to bump the project version:
@@ -53,7 +38,3 @@ release:
 	uv run cz bump --increment $(shell uv run cz bump --dry-run | grep -q "MAJOR" && echo "MINOR" || echo "PATCH")
 	git push
 	git push --tags
-
-.PHONY: run
-run:
-	uv run python main.py

README.md

@@ -3,11 +3,10 @@ Boiling Learning
 </h1>
 
 [![uv-managed](https://img.shields.io/badge/managed-261230?label=uv&logo=uv&labelColor=gray)](https://github.com/astral-sh/uv)
+[![Code style: Ruff](https://img.shields.io/badge/Ruff-checked-261230.svg?logo=ruff)](https://docs.astral.sh/ruff/)
+[![Sourcery](https://img.shields.io/badge/Sourcery-enabled-brightgreen)](https://sourcery.ai)
 
 [![Github Actions](https://github.com/commitizen-tools/commitizen/workflows/Python%20package/badge.svg?style=flat-square)](https://github.com/ruancomelli/boiling-learning/actions)
-[![Sourcery](https://img.shields.io/badge/Sourcery-enabled-brightgreen)](https://sourcery.ai)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
-[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
 [![SemVer](https://img.shields.io/badge/semver-2.0.0-green)](https://semver.org/spec/v2.0.0.html)
 [![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg)](https://conventionalcommits.org)
 [![Author: ruancomelli](https://img.shields.io/badge/ruancomelli-blue?style=flat&label=author)](https://github.com/ruancomelli)

boiling_learning/__main__.py

@@ -37,39 +37,41 @@
 )
 
 app = typer.Typer()
-app.add_typer(animate.app, name='animate')
-app.add_typer(architectures.app, name='architectures')
-app.add_typer(automl.app, name='automl')
-app.add_typer(automl_cross_surface.app, name='automl-cross-surface')
-app.add_typer(automl_learning_curve.app, name='automl-learning-curve')
-app.add_typer(automl_strategies.app, name='automl-strategies')
-app.add_typer(automl_transfer_learning_curve.app, name='automl-transfer-learning-curve')
-app.add_typer(boiling_curve.app, name='boiling-curve')
-app.add_typer(consecutive_frames.app, name='consecutive-frames')
-app.add_typer(cross_surface.app, name='cross-surface')
-app.add_typer(data_augmentation.app, name='data-augmentation')
-app.add_typer(data_augmentation_automl.app, name='data-augmentation-automl')
-app.add_typer(data_split.app, name='data-split')
-app.add_typer(dataset_sizes.app, name='dataset-sizes')
-app.add_typer(diameter_effects.app, name='diameter-effects')
-app.add_typer(downscaling_preprocessing.app, name='downscaling-preprocessing')
-app.add_typer(downscaling_training.app, name='downscaling-training')
-app.add_typer(example_frames.app, name='example-frames')
-app.add_typer(example_frames_matrix.app, name='example-frames-matrix')
-app.add_typer(heat_flux_levels.app, name='heat-flux-levels')
-app.add_typer(image_brightness.app, name='image-brightness')
-app.add_typer(image_normalization.app, name='image-normalization')
-app.add_typer(image_standardization.app, name='image-standardization')
-app.add_typer(inclination_effects.app, name='inclination-effects')
-app.add_typer(learning_curve.app, name='learning-curve')
-app.add_typer(preprocessing.app, name='preprocessing')
-app.add_typer(single_surface.app, name='single-surface')
-app.add_typer(transfer_learning_curve.app, name='transfer-learning-curve')
-app.add_typer(uncertainties.app, name='uncertainties')
-app.add_typer(validate.app, name='validate')
-app.add_typer(visualization_window.app, name='visualization-window')
-app.add_typer(visualization_window_multi_surface.app, name='visualization-window-multi-surface')
+app.add_typer(animate.app, name="animate")
+app.add_typer(architectures.app, name="architectures")
+app.add_typer(automl.app, name="automl")
+app.add_typer(automl_cross_surface.app, name="automl-cross-surface")
+app.add_typer(automl_learning_curve.app, name="automl-learning-curve")
+app.add_typer(automl_strategies.app, name="automl-strategies")
+app.add_typer(automl_transfer_learning_curve.app, name="automl-transfer-learning-curve")
+app.add_typer(boiling_curve.app, name="boiling-curve")
+app.add_typer(consecutive_frames.app, name="consecutive-frames")
+app.add_typer(cross_surface.app, name="cross-surface")
+app.add_typer(data_augmentation.app, name="data-augmentation")
+app.add_typer(data_augmentation_automl.app, name="data-augmentation-automl")
+app.add_typer(data_split.app, name="data-split")
+app.add_typer(dataset_sizes.app, name="dataset-sizes")
+app.add_typer(diameter_effects.app, name="diameter-effects")
+app.add_typer(downscaling_preprocessing.app, name="downscaling-preprocessing")
+app.add_typer(downscaling_training.app, name="downscaling-training")
+app.add_typer(example_frames.app, name="example-frames")
+app.add_typer(example_frames_matrix.app, name="example-frames-matrix")
+app.add_typer(heat_flux_levels.app, name="heat-flux-levels")
+app.add_typer(image_brightness.app, name="image-brightness")
+app.add_typer(image_normalization.app, name="image-normalization")
+app.add_typer(image_standardization.app, name="image-standardization")
+app.add_typer(inclination_effects.app, name="inclination-effects")
+app.add_typer(learning_curve.app, name="learning-curve")
+app.add_typer(preprocessing.app, name="preprocessing")
+app.add_typer(single_surface.app, name="single-surface")
+app.add_typer(transfer_learning_curve.app, name="transfer-learning-curve")
+app.add_typer(uncertainties.app, name="uncertainties")
+app.add_typer(validate.app, name="validate")
+app.add_typer(visualization_window.app, name="visualization-window")
+app.add_typer(
+    visualization_window_multi_surface.app, name="visualization-window-multi-surface"
+)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     app()

boiling_learning/app/automl/autofit_dataset.py

@@ -5,7 +5,9 @@
 
 from boiling_learning.app.automl.evaluation import cached_best_model_evaluator
 from boiling_learning.app.automl.tuning import autofit
-from boiling_learning.app.datasets.preprocessed.boiling1d import baseline_boiling_dataset
+from boiling_learning.app.datasets.preprocessed.boiling1d import (
+    baseline_boiling_dataset,
+)
 from boiling_learning.app.paths import analyses_path
 from boiling_learning.app.training.common import get_baseline_compile_params
 from boiling_learning.automl.hypermodels import ConvImageRegressor, HyperModel
@@ -22,7 +24,7 @@ def autofit_dataset(
     *,
     strategy: LazyDescribed[tf.distribute.Strategy],
     target: str,
-    experiment: Literal['boiling1d', 'condensation'],
+    experiment: Literal["boiling1d", "condensation"],
     normalize_images: bool = True,
     max_model_size: int | None = None,
     goal: float | None = None,
@@ -31,15 +33,15 @@ def autofit_dataset(
     compile_params = get_baseline_compile_params(strategy=strategy)
 
     hypermodel = ConvImageRegressor(
-        loss=compile_params['loss'],
-        metrics=compile_params['metrics'],
+        loss=compile_params["loss"],
+        metrics=compile_params["metrics"],
         tuner=tuner_class,
         directory=_get_autofit_to_dataset_allocator(experiment).allocate(
             ConvImageRegressor,
             datasets,
             tuner=tuner_class,
-            loss=compile_params['loss'],
-            metrics=compile_params['metrics'],
+            loss=compile_params["loss"],
+            metrics=compile_params["metrics"],
             normalize_images=normalize_images,
             max_model_size=max_model_size,
             goal=goal,
@@ -57,11 +59,11 @@ def autofit_dataset(
                 LazyDescribed.from_constructor(tf.keras.callbacks.TerminateOnNaN),
                 LazyDescribed.from_constructor(
                     tf.keras.callbacks.EarlyStopping,
-                    monitor='val_loss',
+                    monitor="val_loss",
                     min_delta=0,
                     patience=10,
                     baseline=None,
-                    mode='auto',
+                    mode="auto",
                     restore_best_weights=True,
                     verbose=1,
                 ),
@@ -86,7 +88,7 @@ def best_model_for_dataset(
     *,
     strategy: LazyDescribed[tf.distribute.Strategy],
     target: str,
-    experiment: Literal['boiling1d', 'condensation'],
+    experiment: Literal["boiling1d", "condensation"],
     normalize_images: bool = True,
     max_model_size: int | None = None,
     goal: float | None = None,
@@ -103,7 +105,7 @@ def best_model_for_dataset(
         tuner_class=tuner_class,
     )
     best_model_evaluator = cached_best_model_evaluator(
-        experiment='boiling1d',
+        experiment="boiling1d",
         strategy=strategy,
     )
     return best_model_evaluator(
@@ -128,7 +130,7 @@ def best_baseline_boiling1d_model(
         datasets,
         strategy=strategy,
         target=target,
-        experiment='boiling1d',
+        experiment="boiling1d",
         normalize_images=normalize_images,
         max_model_size=max_model_size,
         goal=goal,
@@ -138,7 +140,9 @@ def best_baseline_boiling1d_model(
 
 @cache
 def _get_autofit_to_dataset_allocator(
-    experiment: Literal['boiling1d', 'condensation'],
+    experiment: Literal["boiling1d", "condensation"],
     /,
 ) -> JSONAllocator:
-    return JSONAllocator(analyses_path() / 'autofit' / 'autofit-to-dataset' / experiment)
+    return JSONAllocator(
+        analyses_path() / "autofit" / "autofit-to-dataset" / experiment
+    )

boiling_learning/app/automl/evaluation.py

@@ -17,13 +17,13 @@
 @functools.cache
 def cached_best_model_evaluator(
     *,
-    experiment: Literal['boiling1d', 'condensation'],
+    experiment: Literal["boiling1d", "condensation"],
     strategy: LazyDescribed[tf.distribute.Strategy],
 ):
     model_evaluator = cached_model_evaluator(experiment)
 
     @cache(
-        JSONAllocator(shared_cache_path() / 'best-model-evaluations' / experiment),
+        JSONAllocator(shared_cache_path() / "best-model-evaluations" / experiment),
         loader=load_with_strategy(strategy),
     )
     def hypermodel_evaluator(
@@ -32,7 +32,7 @@ def hypermodel_evaluator(
         *,
         measure_uncertainty: bool = True,
         gt10: bool = True,
-        metric_name: str = 'loss',
+        metric_name: str = "loss",
     ) -> LazyDescribed[ModelArchitecture]:
         return min(
             map(LazyDescribed.from_describable, hypermodel.iter_best_models()),

boiling_learning/app/automl/tuning.py

@@ -5,7 +5,11 @@
 
 from boiling_learning.app.datasets.bridging import to_tensorflow_triplet
 from boiling_learning.automl.hypermodels import HyperModel
-from boiling_learning.automl.tuning import TuneModelParams, TuneModelReturn, fit_hypermodel
+from boiling_learning.automl.tuning import (
+    TuneModelParams,
+    TuneModelReturn,
+    fit_hypermodel,
+)
 from boiling_learning.image_datasets import ImageDatasetTriplet
 from boiling_learning.lazy import LazyDescribed
 from boiling_learning.model.callbacks import MemoryCleanUp
@@ -17,7 +21,7 @@ def autofit(
     datasets: LazyDescribed[ImageDatasetTriplet],
     params: TuneModelParams,
     target: str,
-    experiment: Literal['boiling1d', 'condensation'],
+    experiment: Literal["boiling1d", "condensation"],
     strategy: LazyDescribed[tf.distribute.Strategy],
 ) -> TuneModelReturn:
     datasets_tf = to_tensorflow_triplet(

boiling_learning/app/constants.py

@@ -8,26 +8,26 @@
 
 @cache
 def masters_path() -> Path:
-    return resolve(os.environ['MASTERS_PATH'])
+    return resolve(os.environ["MASTERS_PATH"])
 
 
 @cache
 def figures_path() -> Path:
-    return resolve(os.environ['FIGURES_PATH'])
+    return resolve(os.environ["FIGURES_PATH"])
 
 
 @cache
 def high_speed_cache_path() -> Path:
-    '''A path to a high speed cache.
+    """Get the path to a high speed cache.
 
     Store in this path data that has to be written/read frequently, such as machine learning
     training data.
-    '''
-    return resolve(os.environ['HIGH_SPEED_CACHE_PATH'])
+    """
+    return resolve(os.environ["HIGH_SPEED_CACHE_PATH"])
 
 
 BOILING_BASELINE_BATCH_SIZE = round_to_multiple(200, base=8)
 BASELINE_BOILING_MSE_DIRECT = 13  # W / cm**2
 BASELINE_BOILING_MSE_INDIRECT = 33  # W / cm**2
 
-DEFAULT_CONDENSATION_MASS_RATE_TARGET = 'mass_rate'
+DEFAULT_CONDENSATION_MASS_RATE_TARGET = "mass_rate"