def visualize_bins_to_text()

in scripts/metadata_to_text.py [0:0]

• 22 lines of code
• 10 McCabe index (conditional complexity)

def visualize_bins_to_text(values_1, values_2, name_1, name_2, text_bins, save_dir, output_column_name, default_bins=100, lower_range=None):
    # Save both histograms into a single figure
    fig, axs = plt.subplots(2, figsize=(8,6), sharex=True)
    
    # Plot histogram and vertical lines for subplot 1
    axs[0].hist(values_1, bins=default_bins, color='blue', alpha=0.7)
    _, bin_edges1 = np.histogram(values_1, bins=len(text_bins), range=(lower_range, values_1.max()) if lower_range else None)
    for edge in bin_edges1:
        axs[0].axvline(x=edge, color='red', linestyle='--', linewidth=1)


    # Plot histogram and vertical lines for subplot 2
    axs[1].hist(values_2, bins=default_bins, color='green', alpha=0.7)
    _, bin_edges2 = np.histogram(values_2, bins=len(text_bins), range=(lower_range, values_2.max()) if lower_range else None)
    for edge in bin_edges2:
        axs[1].axvline(x=edge, color='red', linestyle='--', linewidth=1)

    # Add labels and title
    axs[0].set_title(name_1)
    axs[1].set_title(name_2)
    axs[0].set_yscale('log')
    axs[1].set_yscale('log')
    axs[0].set_ylabel('Frequency')
    axs[1].set_ylabel('Frequency')
    axs[1].set_xlabel(f'{output_column_name}')

    # Adjust layout
    plt.tight_layout()

    filename = f"{output_column_name}.png"
    filepath = os.path.join(save_dir, filename)
    plt.savefig(filepath)
    print(f"Plots saved at '{filename}'!")