def _get_colors_for_allowed_bands(
phase: Phase,
highest_hkl: Union[List[int], np.ndarray, None] = None,
color_cycle: Optional[List[str]] = None,
):
"""Return an array of Miller indices of allowed Kikuchi bands for a
point group and a corresponding color.
The idea with this function is to always get the same color for the
same band in the same point group.
Parameters
----------
phase
A phase container with a space and point group describing the
allowed symmetry operations.
highest_hkl
Highest Miller indices to consider. If None (default), [9, 9, 9]
is used.
color_cycle
A list of color names recognized by Matplotlib. If None
(default), a color palette based on EDAX TSL's coloring of bands
is cycled through.
Returns
-------
hkl_color
Array with Miller indices and corresponding colors of shape
(nhkl, 2, 3), with hkl and color in index 0 and 1 along axis=1,
respectively.
"""
if highest_hkl is None:
highest_hkl = [9, 9, 9]
rlp = ReciprocalLatticePoint.from_highest_hkl(
phase=phase,
highest_hkl=highest_hkl,
)
rlp2 = rlp[rlp.allowed]
# TODO: Replace this ordering with future ordering method in
# diffsims
g_order = np.argsort(rlp2.gspacing)
new_hkl = np.atleast_2d(rlp2.hkl.data)[g_order]
rlp3 = ReciprocalLatticePoint(phase=rlp.phase, hkl=new_hkl)
hkl = np.atleast_2d(rlp3.hkl.data)
families, families_idx = _get_hkl_family(hkl=hkl, reduce=True)
if color_cycle is None:
color_cycle = TSL_COLORS
n_families = len(families)
n_times = int(np.ceil(n_families / len(color_cycle)))
colors = (color_cycle * n_times)[:n_families]
colors = [mcolors.to_rgb(i) for i in colors]
hkl_colors = np.zeros(shape=(rlp3.size, 2, 3))
for hkl_idx, color in zip(families_idx.values(), colors):
hkl_colors[hkl_idx, 0] = hkl[hkl_idx]
hkl_colors[hkl_idx, 1] = color
return hkl_colors
def test_init_from_highest_hkl(
self,
silicon_carbide_phase,
highest_hkl,
desired_highest_hkl,
desired_lowest_hkl,
desired_size,
):
rlp = ReciprocalLatticePoint.from_highest_hkl(
phase=silicon_carbide_phase, highest_hkl=highest_hkl
)
assert np.allclose(rlp[0].hkl.data, desired_highest_hkl)
assert np.allclose(rlp[-1].hkl.data, desired_lowest_hkl)
assert rlp.size == desired_size
