def test_correlation_methods(lt_ctx, cls, dtype, kwargs):
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 8
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
template = m.radial_gradient(centerX=radius + 1,
centerY=radius + 1,
imageSizeX=2 * radius + 2,
imageSizeY=2 * radius + 2,
radius=radius)
match_patterns = [
blobfinder.RadialGradient(radius=radius),
blobfinder.Circular(radius=radius),
blobfinder.BackgroundSubtraction(radius=radius),
blobfinder.RadialGradientBackgroundSubtraction(radius=radius),
blobfinder.UserTemplate(template=template)
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
udf = cls(match_pattern=match_pattern,
peaks=peaks.astype(dtype),
**kwargs)
res = lt_ctx.run_udf(dataset=dataset, udf=udf)
print(peaks)
print(res['refineds'].data[0])
print(peaks - res['refineds'].data[0])
print(res['peak_values'].data[0])
print(res['peak_elevations'].data[0])
# import matplotlib.pyplot as plt
# fig, ax = plt.subplots()
# plt.imshow(data[0])
# for p in np.flip(res['refineds'].data[0], axis=-1):
# ax.add_artist(plt.Circle(p, radius, fill=False, color='y'))
# plt.show()
assert np.allclose(res['refineds'].data[0], peaks, atol=0.5)
def test_run_refine_fastmatch(lt_ctx):
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
drop = np.random.choice([True, False], size=len(indices), p=[0.9, 0.1])
indices = indices[drop]
radius = 10
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
template = m.radial_gradient(centerX=radius + 1,
centerY=radius + 1,
imageSizeX=2 * radius + 2,
imageSizeY=2 * radius + 2,
radius=radius)
match_patterns = [
blobfinder.RadialGradient(radius=radius),
blobfinder.Circular(radius=radius),
blobfinder.BackgroundSubtraction(radius=radius),
blobfinder.RadialGradientBackgroundSubtraction(radius=radius),
blobfinder.UserTemplate(template=template)
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
(res, real_indices) = blobfinder.run_refine(
ctx=lt_ctx,
dataset=dataset,
zero=zero + np.random.uniform(-1, 1, size=2),
a=a + np.random.uniform(-1, 1, size=2),
b=b + np.random.uniform(-1, 1, size=2),
matcher=matcher,
match_pattern=match_pattern)
print(peaks - grm.calc_coords(res['zero'].data[0], res['a'].data[0],
res['b'].data[0], indices))
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['a'].data[0], a, atol=0.2)
assert np.allclose(res['b'].data[0], b, atol=0.2)