choose_grid

anri.index.choose_grid(ops, B, rings, geom, lit, max_chance=0.5, steps=(3.0, 2.5, 2.0, 1.5, 1.0), n_sample=16384, seed=0, log=<built-in function print>)

Choose the coarsest grid step whose chance completeness is at most max_chance.

Chance completeness is the median completeness over a random sample of the grid: most grid orientations are wrong. A coarser grid has larger tolerances, so more chance matches. If no step qualifies, the finest is used.

Parameters:

• ops (Array | ndarray | bool | number | bool | int | float | complex) – [n, 3, 3] Laue-group rotations

• B (Array | ndarray | bool | number | bool | int | float | complex) – See completeness_of()

• rings (dict) – See completeness_of()

• geom (dict) – See completeness_of()

• lit (dict) – See completeness_of()

• max_chance (float, default: 0.5) – Largest acceptable chance completeness

• steps (tuple, default: (3.0, 2.5, 2.0, 1.5, 1.0)) – Grid steps to try (degrees), coarsest first

• n_sample (int, default: 16384) – Orientations sampled per step

• seed (int, default: 0) – Random seed for the sample

• log (Callable, default: <built-in function print>) – Progress messages

Returns:

float – Grid step in degrees