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img_glass

tit.plotting.nilearn.img_glass

TI-Toolbox Glass Brain Visualizations Creates glass brain visualizations using nilearn's plot_glass_brain.

Usage

python img_glass.py --subject 001 --simulation montage1 python img_glass.py --subject 001 --simulation montage1 --cutoff 0.5 --cmap plasma

create_glass_brain_entry_point 

create_glass_brain_entry_point(subject_id: str, simulation_name: str, min_cutoff: float = 0.3, max_cutoff: float = None, cmap: str = 'hot', output_callback=None)

Entry point for glass brain visualization creation.

Parameters:

Name Type Description Default
subject_id str

Subject ID

 required
simulation_name str

Simulation name

 required
min_cutoff float

Minimum cutoff for visualization (V/m)

 0.3
max_cutoff float

Maximum cutoff for visualization (V/m), if None uses 99.9th percentile

 None
cmap str

Colormap name for visualization

 'hot'
output_callback

Optional callback function for output (for GUI integration)

 None
Source code in tit/plotting/nilearn/img_glass.py 
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def create_glass_brain_entry_point(
    subject_id: str,
    simulation_name: str,
    min_cutoff: float = 0.3,
    max_cutoff: float = None,
    cmap: str = "hot",
    output_callback=None,
):
    """
    Entry point for glass brain visualization creation.

    Args:
        subject_id: Subject ID
        simulation_name: Simulation name
        min_cutoff: Minimum cutoff for visualization (V/m)
        max_cutoff: Maximum cutoff for visualization (V/m), if None uses 99.9th percentile
        cmap: Colormap name for visualization
        output_callback: Optional callback function for output (for GUI integration)
    """
    # Redirect stdout if callback provided (for GUI integration)
    import sys
    from contextlib import redirect_stdout
    import io

    if output_callback:
        # Capture stdout and send to callback
        captured_output = io.StringIO()
        with redirect_stdout(captured_output):
            visualizer = NilearnVisualizer()
            result = visualizer.create_glass_brain_visualization(
                subject_id, simulation_name, min_cutoff, max_cutoff, cmap
            )

        # Send captured output to callback line by line
        output_text = captured_output.getvalue()
        for line in output_text.split("\n"):
            if line.strip():
                output_callback(line)

        if result:
            output_callback(f"✓ Glass brain visualization completed: {result}")
            return result
        else:
            output_callback("✗ Glass brain visualization failed")
            return None
    else:
        # Normal operation - print to stdout
        visualizer = NilearnVisualizer()
        result = visualizer.create_glass_brain_visualization(
            subject_id, simulation_name, min_cutoff, max_cutoff, cmap
        )
        if result:
            print(f"\n✓ Glass brain visualization completed: {result}")
            return result
        else:
            print("\n✗ Glass brain visualization failed")
            return None

create_glass_brain_entry_point_group 

create_glass_brain_entry_point_group(averaged_img, base_filename: str, output_dir: str, min_cutoff: float = 0.3, max_cutoff: float = None, cmap: str = 'hot', output_callback=None)

Entry point for glass brain visualization creation with pre-averaged nifti data.

Parameters:

Name Type Description Default
averaged_img

Pre-averaged nibabel Nifti1Image

 required
base_filename str

Base filename for output (without extension)

 required
output_dir str

Output directory path

 required
min_cutoff float

Minimum cutoff for visualization (V/m)

 0.3
max_cutoff float

Maximum cutoff for visualization (V/m), if None uses 99.9th percentile

 None
cmap str

Colormap name for visualization

 'hot'
output_callback

Optional callback function for output (for GUI integration)

 None
Source code in tit/plotting/nilearn/img_glass.py 
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def create_glass_brain_entry_point_group(
    averaged_img,
    base_filename: str,
    output_dir: str,
    min_cutoff: float = 0.3,
    max_cutoff: float = None,
    cmap: str = "hot",
    output_callback=None,
):
    """
    Entry point for glass brain visualization creation with pre-averaged nifti data.

    Args:
        averaged_img: Pre-averaged nibabel Nifti1Image
        base_filename: Base filename for output (without extension)
        output_dir: Output directory path
        min_cutoff: Minimum cutoff for visualization (V/m)
        max_cutoff: Maximum cutoff for visualization (V/m), if None uses 99.9th percentile
        cmap: Colormap name for visualization
        output_callback: Optional callback function for output (for GUI integration)
    """
    # Redirect stdout if callback provided (for GUI integration)
    import sys
    from contextlib import redirect_stdout
    import io
    import os
    import numpy as np
    import matplotlib.pyplot as plt
    from nilearn import plotting

    if output_callback:
        # Capture stdout and send to callback
        captured_output = io.StringIO()
        with redirect_stdout(captured_output):
            # Load and analyze data
            data = averaged_img.get_fdata()
            data_nonzero = data[data > 0]
            if len(data_nonzero) == 0:
                print("Warning: No non-zero field values found")
                return None

            max_value = np.max(data)
            percentile_999 = np.percentile(data_nonzero, 99.9)
            min_value = np.min(data_nonzero)

            # Use provided max_cutoff or default to 99.9th percentile
            if max_cutoff is None:
                max_cutoff = percentile_999

            print(f"Electric field statistics (averaged data):")
            print(f"  Absolute maximum: {max_value:.2f} V/m")
            print(f"  99.9th percentile: {percentile_999:.2f} V/m")
            print(f"  Minimum (non-zero): {min_value:.2f} V/m")
            print(f"  Visualization range: {min_cutoff:.2f} - {max_cutoff:.2f} V/m")

            # Create output filename
            pdf_filename = f"{base_filename}_glass_brain.pdf"
            pdf_filepath = os.path.join(output_dir, pdf_filename)

            # Create glass brain visualization
            plotting.plot_glass_brain(
                stat_map_img=averaged_img,
                threshold=min_cutoff,
                vmax=max_cutoff,
                cmap=cmap,
                colorbar=True,
                plot_abs=False,
                symmetric_cbar=False,
                display_mode="lyrz",
                title=f"Electric Field - Group Average\n{min_cutoff:.2f}-{max_cutoff:.2f} V/m",
                output_file=pdf_filepath,
            )

            print(f"✓ Saved glass brain visualization: {pdf_filepath}")
            result = pdf_filepath

        # Send captured output to callback line by line
        output_text = captured_output.getvalue()
        for line in output_text.split("\n"):
            if line.strip():
                output_callback(line)

        if result:
            output_callback(f"✓ Glass brain visualization completed: {result}")
            return result
        else:
            output_callback("✗ Glass brain visualization failed")
            return None
    else:
        # Normal operation - print to stdout
        # Load and analyze data
        data = averaged_img.get_fdata()
        data_nonzero = data[data > 0]
        if len(data_nonzero) == 0:
            print("Warning: No non-zero field values found")
            return None

        max_value = np.max(data)
        percentile_999 = np.percentile(data_nonzero, 99.9)
        min_value = np.min(data_nonzero)

        # Use provided max_cutoff or default to 99.9th percentile
        if max_cutoff is None:
            max_cutoff = percentile_999

        print(f"Electric field statistics (averaged data):")
        print(f"  Absolute maximum: {max_value:.2f} V/m")
        print(f"  99.9th percentile: {percentile_999:.2f} V/m")
        print(f"  Minimum (non-zero): {min_value:.2f} V/m")
        print(f"  Visualization range: {min_cutoff:.2f} - {max_cutoff:.2f} V/m")

        # Create output filename
        pdf_filename = f"{base_filename}_glass_brain.pdf"
        pdf_filepath = os.path.join(output_dir, pdf_filename)

        # Create glass brain visualization
        plotting.plot_glass_brain(
            stat_map_img=averaged_img,
            threshold=min_cutoff,
            vmax=max_cutoff,
            cmap=cmap,
            colorbar=True,
            plot_abs=False,
            symmetric_cbar=False,
            display_mode="lyrz",
            title=f"Electric Field - Group Average\n{min_cutoff:.2f}-{max_cutoff:.2f} V/m",
            output_file=pdf_filepath,
        )

        print(f"✓ Saved glass brain visualization: {pdf_filepath}")
        result = pdf_filepath

        if result:
            print(f"\n✓ Glass brain visualization completed: {result}")
            return result
        else:
            print("\n✗ Glass brain visualization failed")
            return None

main 

main()

Main CLI entry point for glass brain visualizations.

Source code in tit/plotting/nilearn/img_glass.py 
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def main():
    """Main CLI entry point for glass brain visualizations."""
    parser = argparse.ArgumentParser(
        description="TI-Toolbox Glass Brain Visualizations",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Create glass brain visualization
  python img_glass.py --subject 001 --simulation montage1

  # Use custom cutoffs and colormap
  python img_glass.py --subject 001 --simulation montage1 --min-cutoff 0.5 --max-cutoff 10.0 --cmap plasma
        """,
    )

    parser.add_argument(
        "--subject", "-s", required=True, help="Subject ID (e.g., 001, 101)"
    )
    parser.add_argument("--simulation", "-sim", required=True, help="Simulation name")
    parser.add_argument(
        "--min-cutoff",
        "-c",
        type=float,
        default=0.3,
        help="Minimum cutoff for visualization (V/m, default: 0.3)",
    )
    parser.add_argument(
        "--max-cutoff",
        "-mc",
        type=float,
        default=None,
        help="Maximum cutoff for visualization (V/m, default: 99.9th percentile)",
    )
    parser.add_argument(
        "--cmap",
        default="hot",
        choices=["hot", "plasma", "inferno", "viridis", "cividis", "coolwarm"],
        help="Colormap for visualization (default: hot)",
    )

    args = parser.parse_args()

    # Run the glass brain visualization
    return create_glass_brain_entry_point(
        args.subject, args.simulation, args.min_cutoff, args.max_cutoff, args.cmap
    )