uvisbox.Modules.SquidGlyphs.squid_glyphs

Top-level API for squid glyph visualization.

This module provides high-level functions that orchestrate the three-stage pipeline:

  1. Compute statistics (vector depths, spreads, variations)

  2. Build mesh geometry (vertices, triangles)

  3. Render visualization (matplotlib or pyvista)

For fine-grained control, use the individual stage functions directly:

  • squid_glyphs_2d_summary_statistics() / squid_glyphs_3d_summary_statistics()

  • squid_glyphs_2d_mesh() / squid_glyphs_3d_mesh()

  • visualize_squid_glyphs_2d() / visualize_squid_glyphs_3d()

Module Contents

uvisbox.Modules.SquidGlyphs.squid_glyphs.squid_glyph_3D(positions, ensemble_vectors, point_values=None, percentile=95, scale=0.5, show_edges=True, glyph_color='lightblue', ax=None)[source]

Visualize 3D uncertainty squid glyphs.

This function orchestrates the three-stage pipeline:

  1. Convert Cartesian → Spherical, compute vector depths, spreads, and PCA

  2. Build superelliptical glyph mesh (vertices, triangles)

  3. Render with pyvista

Based on: T. A. J. Ouermi et al., “Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields,” IEEE UncertaintyVis 2024.

Parameters:

  • positions (numpy.ndarray) – Array of shape (n, 3) The positions of the squid glyphs.

  • ensemble_vectors (numpy.ndarray) – Array of shape (n, m, 3) The ensemble vectors for each position in Cartesian coordinates.

  • point_values (numpy.ndarray, optional) – Array of shape (n,) The values associated with each position for coloring.

  • percentile (float (0-100)) – Percentile of ensemble members to include based on depth ranking. Higher values include more vectors (larger glyphs showing more variation). - percentile=50: Include top 50% deepest vectors - percentile=95: Include top 95% deepest vectors (typical, default) - percentile=100: Include ALL vectors (maximum variation)

  • scale (float, optional) – The scale factor for the glyphs. Default is 0.5.

  • show_edges (bool, optional) – Whether to show edges of the glyphs. Default is True.

  • glyph_color (str, optional) – The color of the glyphs. Default is ‘lightblue’.

  • ax (pyvista.Plotter, optional) – The pyvista plotter to use. If None, a new plotter will be created.

Returns:

  • plotter (pyvista.Plotter) – The pyvista plotter with the drawn squid glyphs.

  • points (numpy.ndarray) – The points of the squid glyphs.

  • polygons (numpy.ndarray) – The polygon connectivity for the glyphs.

uvisbox.Modules.SquidGlyphs.squid_glyphs.squid_glyph_2D(positions, ensemble_vectors, percentile=95, scale=0.2, ax=None, workers=None)[source]

Visualize 2D uncertainty squid glyphs.

This function orchestrates the three-stage pipeline:

  1. Convert Cartesian → Polar, compute vector depths and spreads

  2. Build glyph mesh (vertices, triangles)

  3. Render with matplotlib

Based on: T. A. J. Ouermi et al., “Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields,” IEEE UncertaintyVis 2024.

Parameters:

  • positions (numpy.ndarray) – Array of shape (n, 2) representing the positions of the squid glyphs.

  • ensemble_vectors (numpy.ndarray) – Array of shape (n, m, 2) representing the ensemble vectors for each position.

  • percentile (float (0-100)) – Percentile of ensemble members to include based on depth ranking. Higher values include more vectors (larger glyphs showing more variation). - percentile=50: Include top 50% deepest vectors - percentile=95: Include top 95% deepest vectors (typical, default) - percentile=100: Include ALL vectors (maximum variation)

  • scale (float) – The scale factor for the glyphs.

  • ax (matplotlib axis) – The axis to draw on. If None, a new figure and axis will be created.

  • workers (int, optional) – Number of parallel workers for computation. Default is None (sequential).

Returns:

ax – The axis with the drawn squid glyphs.

Return type:

matplotlib axis