ruff lint

Author: soodoku

README.md

@@ -85,8 +85,8 @@ import geo_sampling as gs
 
 # Quick sampling for research
 sample = gs.sample_roads_for_region(
-    "Singapore", "Central", 
-    n=100, 
+    "Singapore", "Central",
+    n=100,
     strategy="random"
 )
 

docs/_includes/installation.md

@@ -30,4 +30,4 @@ This will automatically run linting, formatting, and type checking before each c
 
 ```bash
 uv run pre-commit run --all-files
-```
+```

docs/_includes/quickstart.md

@@ -44,8 +44,8 @@ import geo_sampling as gs
 
 # Quick sampling for research
 sample = gs.sample_roads_for_region(
-    "Singapore", "Central", 
-    n=100, 
+    "Singapore", "Central",
+    n=100,
     strategy="random",
     seed=42
 )
@@ -75,5 +75,5 @@ gs.plot_road_segments(sample, title="Delhi Road Sample")
 ## What's Next?
 
 - 📖 Check out [detailed examples](examples/index.md) for more complex use cases
-- 🔧 Learn about [advanced sampling strategies](examples/advanced.md)  
-- 📚 Browse the [API reference](reference/index.md) for complete documentation
+- 🔧 Learn about [advanced sampling strategies](examples/advanced.md)
+- 📚 Browse the [API reference](reference/index.md) for complete documentation

docs/_includes/sampling-strategy.md

@@ -7,7 +7,7 @@ This package implements a systematic approach to sampling street locations for d
 Get all the streets in the region of interest from [OpenStreetMap](https://www.openstreetmap.org/). The package:
 
 1. Downloads administrative boundary data from [GADM](http://www.gadm.org/) in ESRI format
-2. Identifies the geographic bounds of your region of interest  
+2. Identifies the geographic bounds of your region of interest
 3. Extracts road data from [BBBike.org](http://extract.bbbike.org) for the bounded area
 4. Processes the road network into manageable segments
 
@@ -23,7 +23,7 @@ Administrative levels are hierarchical - cities are nested in states, which are
 
 ### Road Type Classification
 The package preserves OpenStreetMap road classifications:
-- **trunk**: National highways and major arterials  
+- **trunk**: National highways and major arterials
 - **primary**: Major roads connecting cities/towns
 - **secondary**: Important roads for regional traffic
 - **tertiary**: Roads connecting smaller settlements
@@ -33,7 +33,7 @@ The package preserves OpenStreetMap road classifications:
 
 ### Sampling Methods
 - **Random sampling**: Equal probability selection across all segments
-- **Stratified sampling**: Maintains proportional representation of road types  
+- **Stratified sampling**: Maintains proportional representation of road types
 - **Filtered sampling**: Restricts sampling to specific road types
 - **Length-based sampling**: Target specific total coverage distances
 
@@ -45,4 +45,4 @@ The output provides GPS coordinates and metadata for each sampled segment:
 - **Road metadata**: Type, name, and OpenStreetMap ID for context
 - **Segment ID**: Unique identifier for tracking and quality control
 
-These coordinates define the geographic areas where field data collection should occur, ensuring systematic coverage of the road network.
+These coordinates define the geographic areas where field data collection should occur, ensuring systematic coverage of the road network.

docs/_static/custom.css

@@ -48,4 +48,4 @@ code.literal {
 img {
     max-width: 100%;
     height: auto;
-}
+}

docs/examples/advanced.md

@@ -73,12 +73,12 @@ for wave in range(n_waves):
     start_idx = wave * wave_size
     end_idx = start_idx + wave_size
     wave_indices = all_indices[start_idx:end_idx]
-    
+
     # Sample from this wave's pool
-    wave_sample = np.random.choice(wave_indices, 
-                                  size=samples_per_wave, 
+    wave_sample = np.random.choice(wave_indices,
+                                  size=samples_per_wave,
                                   replace=False)
-    
+
     wave_roads = roads.iloc[wave_sample]
     wave_roads.to_csv(f'wave_{wave+1}_sample.csv', index=False)
 ```
@@ -159,14 +159,14 @@ neighborhoods = stage2_roads.groupby('district')['neighborhood'].unique()
 sampled_neighborhoods = []
 for district in sampled_districts:
     district_neighborhoods = neighborhoods[district]
-    sampled = np.random.choice(district_neighborhoods, 
-                             size=min(3, len(district_neighborhoods)), 
+    sampled = np.random.choice(district_neighborhoods,
+                             size=min(3, len(district_neighborhoods)),
                              replace=False)
     sampled_neighborhoods.extend(sampled)
 
 # Stage 3: Sample roads within neighborhoods
 final_roads = roads[roads['neighborhood'].isin(sampled_neighborhoods)]
-final_sample = final_roads.sample(n=min(500, len(final_roads)), 
+final_sample = final_roads.sample(n=min(500, len(final_roads)),
                                  random_state=42)
 final_sample.to_csv('multi_stage_sample.csv', index=False)
 ```
@@ -178,39 +178,39 @@ Always validate your samples:
 ```python
 def validate_sample(sample_df, original_df):
     """Validate sample representativeness"""
-    
+
     print("Sample Validation Report")
     print("=" * 40)
-    
+
     # Sample size
     print(f"Sample size: {len(sample_df)}")
     print(f"Original size: {len(original_df)}")
     print(f"Sampling rate: {len(sample_df)/len(original_df):.2%}")
-    
+
     # Road type distribution
     print("\nRoad Type Distribution:")
     sample_dist = sample_df['road_type'].value_counts(normalize=True)
     original_dist = original_df['road_type'].value_counts(normalize=True)
-    
+
     comparison = pd.DataFrame({
         'Sample': sample_dist,
         'Original': original_dist,
         'Difference': sample_dist - original_dist
     })
     print(comparison)
-    
+
     # Geographic spread
     print("\nGeographic Coverage:")
     print(f"Latitude range - Sample: [{sample_df['latitude'].min():.4f}, "
           f"{sample_df['latitude'].max():.4f}]")
     print(f"Latitude range - Original: [{original_df['latitude'].min():.4f}, "
           f"{original_df['latitude'].max():.4f}]")
-    
+
     print(f"Longitude range - Sample: [{sample_df['longitude'].min():.4f}, "
           f"{sample_df['longitude'].max():.4f}]")
     print(f"Longitude range - Original: [{original_df['longitude'].min():.4f}, "
           f"{original_df['longitude'].max():.4f}]")
-    
+
     return comparison
 
 # Run validation
@@ -225,4 +225,4 @@ validation_results = validate_sample(sample, roads)
 4. **Handle edge cases** - Account for missing data or small strata
 5. **Consider field logistics** - Account for accessibility and safety
 6. **Plan for non-response** - Sample extras for expected attrition
-7. **Archive sampling frames** - Save the full dataset used for sampling
+7. **Archive sampling frames** - Save the full dataset used for sampling

docs/examples/cli-usage.md

@@ -221,7 +221,7 @@ geo-sampling extract "Thailand" "Trang" \
 
 geo-sampling extract "Thailand" "Phuket" \
     --road-types primary \
-    --road-types secondary \  
+    --road-types secondary \
     --road-types tertiary \
     --output phuket_roads.csv
 
@@ -264,7 +264,7 @@ Target sample size: 100
 
 Sample summary:
   primary: 25 (25.0%)
-  residential: 35 (35.0%)  
+  residential: 35 (35.0%)
   secondary: 20 (20.0%)
   tertiary: 15 (15.0%)
   unclassified: 5 (5.0%)
@@ -295,7 +295,7 @@ $(OUTPUT_DIR)/$(REGION)_roads.csv:
 	geo-sampling extract "$(COUNTRY)" "$(REGION)" \
 		--output $@
 
-$(OUTPUT_DIR)/$(REGION)_sample.csv: $(OUTPUT_DIR)/$(REGION)_roads.csv  
+$(OUTPUT_DIR)/$(REGION)_sample.csv: $(OUTPUT_DIR)/$(REGION)_roads.csv
 	geo-sampling sample $< \
 		--sample-size $(SAMPLE_SIZE) \
 		--strategy stratified \
@@ -352,7 +352,7 @@ docker run -v $(PWD)/data:/workspace/data geo-sampling \
 # Main help
 geo-sampling --help
 
-# Command-specific help  
+# Command-specific help
 geo-sampling extract --help
 geo-sampling sample --help
 geo-sampling workflow --help
@@ -374,12 +374,12 @@ geo-sampling --version
 ## Next Steps
 
 - 🐍 See [Python API Examples](python-api.md) for programmatic usage
-- 🎯 Explore [Advanced Sampling](advanced.md) strategies  
+- 🎯 Explore [Advanced Sampling](advanced.md) strategies
 - 📚 Check the [API Reference](../reference/index.md)
 - 📁 Download [example outputs](../../examples/outputs/)
 
 ## Source Code
 
 Complete CLI examples are available:
 - [03_cli_usage.py](../../examples/03_cli_usage.py) - Demonstrates all CLI commands
-- [CLI output samples](../../examples/outputs/03_cli_usage/) - Real command outputs
+- [CLI output samples](../../examples/outputs/03_cli_usage/) - Real command outputs

docs/examples/index.md

@@ -11,12 +11,12 @@ This section provides complete, working examples that demonstrate geo-sampling c
 :link: python-api
 :link-type: doc
 
-🐍 **Python API Examples** 
+🐍 **Python API Examples**
 ^^^
 Complete Python workflows with actual Delhi road data. Includes 1,220 road segments and 1,000-sample outputs.
 
-**Features:** Basic extraction, sampling, visualization  
-**Data:** Delhi, India road network  
+**Features:** Basic extraction, sampling, visualization
+**Data:** Delhi, India road network
 **Output:** Sample plots and CSV files
 :::
 
@@ -28,8 +28,8 @@ Complete Python workflows with actual Delhi road data. Includes 1,220 road segme
 ^^^
 Master the geo-sampling CLI with working examples and real command outputs.
 
-**Features:** All CLI commands with examples  
-**Data:** Multiple regions and countries  
+**Features:** All CLI commands with examples
+**Data:** Multiple regions and countries
 **Output:** Command logs and sample files
 :::
 
@@ -41,8 +41,8 @@ Master the geo-sampling CLI with working examples and real command outputs.
 ^^^
 Sophisticated sampling strategies with Singapore road network examples.
 
-**Features:** Random, stratified, filtered sampling  
-**Data:** Singapore Central region (470 segments)  
+**Features:** Random, stratified, filtered sampling
+**Data:** Singapore Central region (470 segments)
 **Output:** Comparison plots and multiple samples
 :::
 
@@ -55,8 +55,8 @@ Sophisticated sampling strategies with Singapore road network examples.
 ^^^
 All example outputs are available for download. Browse CSV files, plots, and sample data.
 
-**Includes:** Sample datasets, visualizations, CLI logs  
-**Format:** CSV, PNG, TXT files  
+**Includes:** Sample datasets, visualizations, CLI logs
+**Format:** CSV, PNG, TXT files
 **Size:** Ready-to-use examples
 :::
 ::::
@@ -70,7 +70,7 @@ All examples use **real OpenStreetMap data** and generate **actual outputs** tha
 - **Output**: 1,220 total road segments → 1,000 random sample
 - **Files**: [`delhi_all_roads.csv`](../examples/outputs/01_basic_workflow/delhi_all_roads.csv), [`delhi_sampled_roads.csv`](../examples/outputs/01_basic_workflow/delhi_sampled_roads.csv)
 
-### Singapore Advanced Sampling  
+### Singapore Advanced Sampling
 - **Input**: Singapore Central region
 - **Output**: Multiple sampling strategies (random, stratified, filtered)
 - **Files**: [`singapore_*.csv`](../examples/outputs/02_advanced_sampling/) with comparison plots
@@ -85,7 +85,7 @@ All examples use **real OpenStreetMap data** and generate **actual outputs** tha
 Each example provides:
 
 1. **🔧 Working code** - Copy-paste ready scripts
-2. **📈 Real outputs** - Actual CSV files and plots you can examine  
+2. **📈 Real outputs** - Actual CSV files and plots you can examine
 3. **⚡ Both interfaces** - Python API and CLI equivalents
 4. **📋 Documentation** - Step-by-step explanations
 
@@ -124,24 +124,24 @@ pip install -e .
 # Run the basic workflow example
 python examples/01_basic_workflow.py
 
-# Try CLI examples 
+# Try CLI examples
 python examples/03_cli_usage.py
 ```
 
 ### Adapting for Your Research
 
 1. **Change the region**: Replace country/region names with your study area
-2. **Adjust sample size**: Modify `sample_size` parameters for your needs  
+2. **Adjust sample size**: Modify `sample_size` parameters for your needs
 3. **Filter road types**: Use `road_types` parameter to focus on specific infrastructure
 4. **Set random seed**: Use `seed` parameter for reproducible research
 
 ### Understanding Output Files
 
 - **`*_all_roads.csv`**: Complete road network for the region
-- **`*_sample.csv`**: Random/stratified sample of road segments  
+- **`*_sample.csv`**: Random/stratified sample of road segments
 - **`*_plot.png`**: Visualization of sampled road segments
 - **Metadata**: Each example includes sampling methodology details
 
 ---
 
-*Ready to dive in? Start with [Python API Examples](python-api.md) or jump to [CLI Usage](cli-usage.md)!*
+*Ready to dive in? Start with [Python API Examples](python-api.md) or jump to [CLI Usage](cli-usage.md)!*