Bundle Analysis

PyFibreBundle provides tools to analyse the spatial arrangement and packing geometry of fibre cores within a bundle. This is useful for understanding bundle quality, measuring local disorder, and detecting structural defects.

Cores can be localised using the pybundle.find_cores() function, which returns their (x, y) coordinates. The pybundle.core_pattern_statistics() function then takes these coordinates as input to perform a detailed analysis of the core pattern.

For example, assuming you have a calibration image of your bundle, you can run:

import pybundle as pyb core_spacing = pyb.find_core_spacing(calib_img) core_x, core_y = pyb.find_cores(calib_img, core_spacing) stats = pyb.core_pattern_statistics(core_x, core_y)

This analyses the core pattern for:

Local packing order: Uses the complex bond-order parameter ψ₆ to measure how close each core neighbourhood is to ideal hexagonal packing.
Spacing uniformity: Reports nearest-neighbour distance statistics (mean, standard deviation, and coefficient of variation).
Topological defects: Identifies cores with non-6 coordination numbers
Global disorder: Computes the magnitude of the globally-averaged bond-order parameter as a large-scale disorder indicator.

The function returns a dictionary with:

Scalar summaries: num_cores, mean_nearest_neighbour_dist, cv_nearest_neighbour_dist, mean_local_psi6_mag, global_psi6_mag, defect_fraction
Per-core arrays: nearest_neighbour_distances, local_psi6, local_psi6_mag, coord_num
Masks: analysis_mask and boundary_mask indicating which cores were included or excluded

Statistics are biased by cores on the bundle boundary, which have incomplete neighbourhoods. The function uses an alpha-shape concave-hull algorithm to identify and exclude boundary cores, optionally removing multiple layers of Delaunay neighbours. This improves the quality of interior-core statistics and makes results more representative of true bundle packing quality.

Example

See the Analysis Example for a complete worked example that: