Merge pull request #7 from pmclSF/chore/migrate-flake8-to-ruff

Migrate from flake8 to ruff

Author: pmclSF

.github/workflows/ci.yml

@@ -7,8 +7,20 @@ on:
     branches: [main]
 
 jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+    - run: pip install ruff
+    - name: Ruff check
+      run: ruff check src/ tests/
+
   test:
     runs-on: ubuntu-latest
+    needs: lint
     strategy:
       matrix:
         python-version: ['3.8', '3.9', '3.10']
@@ -29,7 +41,6 @@ jobs:
 
     - name: Run tests
       run: |
-        # Run entropy modeling tests and performance tests (legacy tests have pre-existing issues)
         pytest \
           tests/test_entropy_parameters.py \
           tests/test_context_model.py \
@@ -48,36 +59,3 @@ jobs:
       uses: codecov/codecov-action@v4
       with:
         file: ./coverage.xml
-
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
-      with:
-        python-version: '3.10'
-    - run: pip install flake8
-    # Lint only the new entropy modeling and optimization files (legacy files have pre-existing issues)
-    - name: Lint new source files
-      run: |
-        flake8 \
-          src/entropy_parameters.py \
-          src/entropy_model.py \
-          src/context_model.py \
-          src/channel_context.py \
-          src/attention_context.py \
-          src/model_transforms.py \
-          src/constants.py \
-          src/precision_config.py \
-          src/benchmarks.py \
-          --max-line-length=120
-    - name: Lint new test files
-      run: |
-        flake8 \
-          tests/test_entropy_parameters.py \
-          tests/test_context_model.py \
-          tests/test_channel_context.py \
-          tests/test_attention_context.py \
-          tests/test_performance.py \
-          --max-line-length=120 \
-          --ignore=E402,W503  # E402: imports after sys.path, W503: PEP8 updated to prefer breaks before operators

CLAUDE.md

@@ -0,0 +1,27 @@
+[tool.ruff]
+line-length = 120
+target-version = "py38"
+
+[tool.ruff.lint]
+select = ["F", "I", "E", "W"]
+ignore = [
+    "E402",   # module-level import not at top — tests use sys.path before imports
+    "E741",   # ambiguous variable name — common in math-heavy code
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*.py" = ["E402"]
+
+[tool.ruff.lint.isort]
+known-first-party = [
+    "model_transforms", "entropy_model", "entropy_parameters",
+    "context_model", "channel_context", "attention_context",
+    "constants", "precision_config", "data_loader",
+    "training_pipeline", "evaluation_pipeline", "experiment",
+    "compress_octree", "decompress_octree", "octree_coding",
+    "ds_mesh_to_pc", "ds_pc_octree_blocks", "ds_select_largest",
+    "ev_compare", "ev_run_render", "mp_report", "mp_run",
+    "quick_benchmark", "benchmarks", "parallel_process",
+    "point_cloud_metrics", "map_color", "colorbar",
+    "cli_train", "test_utils",
+]

pyproject.toml

@@ -9,3 +9,4 @@ pytest~=7.1.0
 scipy~=1.8.1
 numba~=0.56.0
 tensorflow-probability~=0.19.0
+ruff>=0.4.0

requirements.txt

@@ -11,8 +11,9 @@
 - Global tokens provide long-range context without full attention
 """
 
+from typing import Any, Dict, Optional, Tuple
+
 import tensorflow as tf
-from typing import Tuple, Optional, Dict, Any
 
 from constants import LOG_2_RECIPROCAL
 
@@ -668,8 +669,8 @@ def __init__(self,
         self.num_attention_layers = num_attention_layers
 
         # Import here to avoid circular dependency
-        from entropy_parameters import EntropyParameters
         from entropy_model import ConditionalGaussian
+        from entropy_parameters import EntropyParameters
 
         # Hyperprior-based parameter prediction
         self.entropy_parameters = EntropyParameters(
@@ -803,9 +804,9 @@ def __init__(self,
         self.num_channel_groups = num_channel_groups
         self.num_attention_layers = num_attention_layers
 
-        from entropy_parameters import EntropyParameters
         from channel_context import ChannelContext
         from entropy_model import ConditionalGaussian
+        from entropy_parameters import EntropyParameters
 
         # Hyperprior parameters
         self.entropy_parameters = EntropyParameters(

src/attention_context.py

@@ -20,11 +20,12 @@
     print(f"Peak memory: {mem.peak_mb:.1f} MB")
 """
 
-import tensorflow as tf
 import time
-from typing import Callable, Dict, Any, Optional
-from dataclasses import dataclass, field
 from contextlib import contextmanager
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, Optional
+
+import tensorflow as tf
 
 
 @dataclass

src/benchmarks.py

@@ -7,8 +7,9 @@
 maintaining autoregressive structure across groups.
 """
 
+from typing import Any, Dict, List, Optional, Tuple
+
 import tensorflow as tf
-from typing import Tuple, Optional, Dict, Any, List
 
 from constants import LOG_2_RECIPROCAL
 
@@ -230,8 +231,8 @@ def __init__(self,
         self.channels_per_group = latent_channels // num_groups
 
         # Import here to avoid circular dependency
-        from entropy_parameters import EntropyParameters
         from entropy_model import ConditionalGaussian
+        from entropy_parameters import EntropyParameters
 
         # Hyperprior-based parameter prediction
         self.entropy_parameters = EntropyParameters(

src/channel_context.py

@@ -1,23 +1,25 @@
-import tensorflow as tf
-import os
 import argparse
 import glob
-import numpy as np
+import os
+
 import keras_tuner as kt
+import tensorflow as tf
+
 from ds_mesh_to_pc import read_off
 
+
 def create_model(hp):
     model = tf.keras.Sequential()
     model.add(tf.keras.layers.InputLayer(input_shape=(2048, 3)))
-    
+
     for i in range(hp.Int('num_layers', 1, 5)):
         model.add(tf.keras.layers.Dense(
             hp.Int(f'layer_{i}_units', min_value=64, max_value=1024, step=64),
             activation='relu'
         ))
-    
+
     model.add(tf.keras.layers.Dense(3, activation='sigmoid'))
-    
+
     model.compile(
         optimizer=tf.keras.optimizers.Adam(
             learning_rate=hp.Float('learning_rate', 1e-5, 1e-3, sampling='log')
@@ -28,7 +30,7 @@ def create_model(hp):
 
 def load_and_preprocess_data(input_dir, batch_size):
     file_paths = glob.glob(os.path.join(input_dir, "*.ply"))
-    
+
     def parse_ply_file(file_path):
         mesh_data = read_off(file_path)
         return mesh_data.vertices
@@ -47,7 +49,7 @@ def data_generator():
     dataset = dataset.shuffle(buffer_size=len(file_paths))
     dataset = dataset.batch(batch_size)
     dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
-    
+
     return dataset
 
 def tune_hyperparameters(input_dir, output_dir, num_epochs=10):
@@ -63,10 +65,10 @@ def tune_hyperparameters(input_dir, output_dir, num_epochs=10):
 
     dataset = load_and_preprocess_data(input_dir, batch_size=32)
     tuner.search(dataset, epochs=num_epochs, validation_data=dataset)
-    
+
     best_model = tuner.get_best_models(num_models=1)[0]
     best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
-    
+
     print("Best Hyperparameters:", best_hps.values)
     best_model.save(os.path.join(output_dir, 'best_model'))
 
@@ -95,4 +97,4 @@ def main():
         model.save(os.path.join(args.output_dir, 'trained_model'))
 
 if __name__ == "__main__":
-    main()
+    main()

src/cli_train.py

@@ -1,10 +1,12 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-from typing import Tuple, Callable, Optional, List
-from dataclasses import dataclass
 import json
+from dataclasses import dataclass
 from pathlib import Path
+from typing import Callable, List, Optional, Tuple
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 @dataclass
 class ColorbarConfig:
@@ -67,7 +69,10 @@ def get_colorbar(
     else:
         # Format numeric labels
         formatter = mpl.ticker.FormatStrFormatter(label_format)
-        cbar.ax.xaxis.set_major_formatter(formatter) if orientation == 'horizontal' else cbar.ax.yaxis.set_major_formatter(formatter)
+        if orientation == 'horizontal':
+            cbar.ax.xaxis.set_major_formatter(formatter)
+        else:
+            cbar.ax.yaxis.set_major_formatter(formatter)
 
     # Set font sizes
     cbar.ax.tick_params(labelsize=font_size)
@@ -96,10 +101,10 @@ def save_color_mapping(filename: str,
     # Create mapping
     norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
     cmap = plt.get_cmap(cmap)
-    
+
     values = np.linspace(vmin, vmax, num_samples)
     colors = [cmap(norm(v)) for v in values]
-    
+
     # Save to file
     Path(filename).parent.mkdir(parents=True, exist_ok=True)
     with open(filename, 'w') as f:
@@ -122,4 +127,4 @@ def save_color_mapping(filename: str,
         tick_rotation=45,
         extend='both'
     )
-    plt.show()
+    plt.show()