Fixing Unstable Y-axis Range in Matplotlib Multi-scale Data Visualization

Project Structure

32shot_fewshot/
├── 32shot_fewshot_origin_circle_scale_3.0/
│   ├── output/
│   │   ├── detection_results_2024_11_06_08_05_57.csv
│   │   ├── detection_results_2024_11_07_06_49_11.csv
│   │   └── ...
│   ├── train/
│   ├── val/
│   └── model_checkpoints/
├── 32shot_fewshot_origin_circle_scale_3.1/
└── ...

The Challenge

When dealing with multiple scale experiments:

1. Each scale folder contains multiple CSV files with detection results
2. Need to compare performance across different scales
3. Visualization issues with y-axis stability
4. Data type inconsistencies affecting plot reliability

Solution Implementation

1. Data Loading and Processing

def load_scale_results(base_path):
    """
    Load first detection results from each scale folder
    """
    scale_results = []
    scale_folders = glob.glob(os.path.join(base_path, "*scale*"))
    
    for folder in scale_folders:
        scale = float(re.search(r'scale_(\d+\.\d+)', folder).group(1))
        csv_files = glob.glob(os.path.join(folder, "output", "*.csv"))
        
        if csv_files:
            df = pd.read_csv(csv_files[0])
            scale_results.append({
                'scale': scale,
                'data': df
            })
    
    return sorted(scale_results, key=lambda x: x['scale'])

2. Visualization with Stable Y-axis

def plot_scale_comparison(scale_results, methods):
    plt.figure(figsize=(15, 8))
    
    # Fix y-axis range first
    plt.ylim(0, 100)
    
    colors = plt.cm.rainbow(np.linspace(0, 1, len(scale_results)))
    x = np.arange(len(methods))
    width = 0.8 / len(scale_results)
    
    for idx, data in enumerate(scale_results):
        scale = data['scale']
        # Ensure numeric conversion
        accuracies = pd.to_numeric(data['data']['acc'][1:9])
        
        x_pos = x - (0.4 - width/2) + (idx * width)
        plt.bar(x_pos, accuracies, width, 
               label=f'Scale {scale}', 
               color=colors[idx], 
               alpha=0.7)
    
    plt.xlabel('Methods', fontsize=12)
    plt.ylabel('Accuracy (%)', fontsize=12)
    plt.title('Model Performance Across Different Scales')
    plt.xticks(x, methods, rotation=45)
    plt.legend(bbox_to_anchor=(1.05, 1))
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.tight_layout()

Key Learnings

1. Data Type Consistency
   • Always convert string data to numeric using pd.to_numeric()
   • Handle missing values appropriately
2. Plot Stability
   • Set y-axis limits before plotting
   • Use consistent color schemes
   • Proper layout management
3. Best Practices
   • Fix axis ranges early in the plotting process
   • Use plt.ylim() before creating plots
   • Ensure data types are numeric before visualization
   • Apply tight_layout() for better spacing