img_slices

tit.plotting.nilearn.img_slices

TI-Toolbox Publication-Ready Visualizations Creates PDF visualizations with multiple views and atlas contours for publication.

Usage

python img_slices.py --subject 001 --simulation montage1 python img_slices.py --subject 001 --simulation montage1 --cutoff 0.5 --atlas aal

create_pdf_entry_point

create_pdf_entry_point(subject_id: str, simulation_name: str, min_cutoff: float = 0.3, max_cutoff: float = None, atlas_name: str = 'harvard_oxford_sub', selected_regions: list = None, output_callback=None)

Entry point for PDF 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
atlas_name	str	Atlas name for contours	'harvard_oxford_sub'
selected_regions	list	List of region indices to include (0-indexed), if None includes all	None
output_callback		Optional callback function for output (for GUI integration)	None

Source code in tit/plotting/nilearn/img_slices.py

def create_pdf_entry_point(
    subject_id: str,
    simulation_name: str,
    min_cutoff: float = 0.3,
    max_cutoff: float = None,
    atlas_name: str = "harvard_oxford_sub",
    selected_regions: list = None,
    output_callback=None,
):
    """
    Entry point for PDF 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
        atlas_name: Atlas name for contours
        selected_regions: List of region indices to include (0-indexed), if None includes all
        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_pdf_visualization(
                subject_id,
                simulation_name,
                min_cutoff,
                max_cutoff,
                atlas_name,
                selected_regions,
            )

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

create_pdf_entry_point_group

create_pdf_entry_point_group(averaged_img, base_filename: str, output_dir: str, min_cutoff: float = 0.3, max_cutoff: float = None, atlas_name: str = 'harvard_oxford_sub', selected_regions: list = None, output_callback=None)

Entry point for PDF 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
atlas_name	str	Atlas name for contours	'harvard_oxford_sub'
selected_regions	list	List of region indices to include (0-indexed), if None includes all	None
output_callback		Optional callback function for output (for GUI integration)	None

Source code in tit/plotting/nilearn/img_slices.py

def create_pdf_entry_point_group(
    averaged_img,
    base_filename: str,
    output_dir: str,
    min_cutoff: float = 0.3,
    max_cutoff: float = None,
    atlas_name: str = "harvard_oxford_sub",
    selected_regions: list = None,
    output_callback=None,
):
    """
    Entry point for PDF 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
        atlas_name: Atlas name for contours
        selected_regions: List of region indices to include (0-indexed), if None includes all
        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_pdf_visualization_group(
                averaged_img,
                base_filename,
                output_dir,
                min_cutoff,
                max_cutoff,
                atlas_name,
                selected_regions,
            )

        # 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"✓ PDF visualization completed: {result}")
            return result
        else:
            output_callback("✗ PDF visualization failed")
            return None
    else:
        # Normal operation - print to stdout
        visualizer = NilearnVisualizer()
        result = visualizer.create_pdf_visualization_group(
            averaged_img,
            base_filename,
            output_dir,
            min_cutoff,
            max_cutoff,
            atlas_name,
            selected_regions,
        )
        if result:
            print(f"\n✓ PDF visualization completed: {result}")
            return result
        else:
            print("\n✗ PDF visualization failed")
            return None

main

main()

Main CLI entry point for publication-ready visualizations.

Source code in tit/plotting/nilearn/img_slices.py

def main():
    """Main CLI entry point for publication-ready visualizations."""
    parser = argparse.ArgumentParser(
        description="TI-Toolbox Publication-Ready Visualizations",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Create PDF visualization with atlas contours
  python img_slices.py --subject 001 --simulation montage1

  # Use custom cutoffs and atlas
  python img_slices.py --subject 001 --simulation montage1 --min-cutoff 0.5 --max-cutoff 10.0 --atlas aal

  # Use specific regions from atlas
  python img_slices.py --subject 001 --simulation montage1 --atlas harvard_oxford_sub --regions 1 2 3
        """,
    )

    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(
        "--atlas",
        "-a",
        default="harvard_oxford_sub",
        choices=["harvard_oxford", "harvard_oxford_sub", "aal", "schaefer_2018"],
        help="Atlas for contour overlay (default: harvard_oxford_sub)",
    )
    parser.add_argument(
        "--regions",
        "-r",
        nargs="*",
        type=int,
        help="Region indices to include (0-indexed, space-separated). If not specified, all regions are included.",
    )

    args = parser.parse_args()

    # Run the PDF visualization
    return create_pdf_entry_point(
        args.subject,
        args.simulation,
        args.min_cutoff,
        args.max_cutoff,
        args.atlas,
        args.regions,
    )