def find_mic(v, cell, pbc=True):
"""Finds the minimum-image representation of vector(s) v using either one
of two find mic algorithms depending on the given cell, v and pbc."""
cell = Cell(cell)
pbc = cell.any(1) & pbc2pbc(pbc)
dim = np.sum(pbc)
v = np.asarray(v)
single = v.ndim == 1
v = np.atleast_2d(v)
if dim > 0:
naive_find_mic_is_safe = False
if dim == 3:
vmin, vlen = naive_find_mic(v, cell)
# naive find mic is safe only for the following condition
if (vlen < 0.5 * min(cell.lengths())).all():
naive_find_mic_is_safe = True # hence skip Minkowski reduction
if not naive_find_mic_is_safe:
vmin, vlen = general_find_mic(v, cell, pbc=pbc)
else:
vmin = v.copy()
vlen = np.linalg.norm(vmin, axis=1)
if single:
return vmin[0], vlen[0]
else:
return vmin, vlen
def update(self, cell, pbc):
cell = Cell(cell)
mags = cell.lengths()
angles = cell.angles()
for i in range(3):
for j in range(3):
if np.isnan(cell[i][j]):
cell[i][j] = 0
self.cell_grid[i][j].value = cell[i][j]
if np.isnan(mags[i]):
mags[i] = 0
self.cell_grid[i][3].value = mags[i]
if np.isnan(angles[i]):
angles[i] = 0
self.angles[i].value = angles[i]
self.pbc[i].var.set(bool(pbc[i]))
