def _do_match(self, point_selection: grm.PointSelection, zero, polar_vectors):
'''
Return a list with matches of all pairwise combinations of polar_vectors
'''
match_list = []
# we test all pairs of candidate vectors
# and populate match_matrix
for i in range(len(polar_vectors)):
for j in range(i + 1, len(polar_vectors)):
a = polar_vectors[i]
b = polar_vectors[j]
# too parallel, not good lattice vectors
if not angle_check(np.array([a]), np.array([b]), self.min_angle):
continue
if a[0] > b[0]:
bb = a
aa = b
else:
aa = a
bb = b
aa, bb = make_cartesian(np.array([aa, bb]))
try:
match = self._match_all(
point_selection=point_selection, zero=zero, a=aa, b=bb)
match = self._tumble(point_selection, match)
except np.linalg.LinAlgError:
continue
if match is not None:
match_list.append(match)
return match_list
def test_polar_rotate():
y, x, radians = np.random.random(3)
((r, phi),) = ut.make_polar(np.array([(y, x)]))
((r_y, r_x),) = ut.make_cartesian(np.array([(r, phi + radians)]))
r_y2, r_x2 = ut.rotate_rad(y, x, radians)
assert np.allclose(r_y, r_y2)
assert np.allclose(r_x, r_x2)
def cbed_frame(fy=128, fx=128, zero=None, a=None, b=None, indices=None, radius=4, all_equal=False):
if zero is None:
zero = (fy//2, fx//2)
zero = np.array(zero)
if a is None:
a = (fy//8, 0)
a = np.array(a)
if b is None:
b = make_cartesian(make_polar(a) - (0, np.pi/2))
b = np.array(b)
if indices is None:
indices = np.mgrid[-10:11, -10:11]
indices, peaks = frame_peaks(fy=fy, fx=fx, zero=zero, a=a, b=b, r=radius, indices=indices)
data = np.zeros((1, fy, fx), dtype=np.float32)
dists = np.linalg.norm(peaks - zero, axis=-1)
max_dist = dists.max()
for i, p in enumerate(peaks):
data += m.circular(
centerX=p[1],
centerY=p[0],
imageSizeX=fx,
imageSizeY=fy,
radius=radius,
antialiased=True,
) * (1 if all_equal else max_dist - dists[i] + i)
return (data, indices, peaks)
def _do_match(cls, point_selection: PointSelection, zero, polar_vectors, parameters):
'''
Return a matrix with matches of all pairwise combinations of polar_vectors
'''
match_matrix = {}
# we test all pairs of candidate vectors
# and populate match_matrix
for i in range(len(polar_vectors)):
for j in range(i + 1, len(polar_vectors)):
a = polar_vectors[i]
b = polar_vectors[j]
# too parallel, not good lattice vectors
if not angle_check(np.array([a]), np.array([b]), parameters['min_angle']):
continue
a, b = make_cartesian(np.array([a, b]))
match = cls._match_all(
point_selection=point_selection, zero=zero, a=a, b=b, parameters=parameters)
# At least three points matched
if len(match) > 2:
match_matrix[(i, j)] = match
return match_matrix
def _do_match(self, point_selection: grm.PointSelection, zero,
polar_vectors):
'''
Return a matrix with matches of all pairwise combinations of polar_vectors
'''
match_matrix = {}
# we test all pairs of candidate vectors
# and populate match_matrix
for i in range(len(polar_vectors)):
for j in range(i + 1, len(polar_vectors)):
a = polar_vectors[i]
b = polar_vectors[j]
# too parallel, not good lattice vectors
if not angle_check(np.array([a]), np.array([b]),
self.min_angle):
continue
if a[0] > b[0]:
bb = a
aa = b
ii = j
jj = i
else:
aa = a
bb = b
ii = i
jj = j
aa, bb = make_cartesian(np.array([aa, bb]))
match = self._match_all(point_selection=point_selection,
zero=zero,
a=aa,
b=bb)
# At least three points matched
if len(match) > 2:
match_matrix[(ii, jj)] = match
return match_matrix
def test_conversion(points):
assert(np.allclose(points, ut.make_cartesian(ut.make_polar(points))))
