def process_stack(data,
xat,
yat,
upsample_factor=100,
use_sobel=False,
ref_frame_num=0):
hypercube, lsx, lsy = get_hypercube(data, xat, yat)
if bn.anynan(hypercube):
raise NanInsideHypercube(True)
calculate_shift = RegisterTranslation(upsample_factor=upsample_factor)
filterfn = sobel if use_sobel else lambda x: x
shifts, aligned_stack = alignstack(hypercube.T,
shiftfn=calculate_shift,
ref_frame_num=ref_frame_num,
filterfn=filterfn)
xmin, ymin = shifts[:, 0].min(), shifts[:, 1].min()
xmax, ymax = shifts[:, 0].max(), shifts[:, 1].max()
xmin, xmax = int(round(xmin)), int(round(xmax))
ymin, ymax = int(round(ymin)), int(round(ymax))
shape = hypercube.shape
slicex = slice(max(xmax, 0), min(shape[1], shape[1] + xmin))
slicey = slice(max(ymax, 0), min(shape[0], shape[0] + ymin))
cropped = np.array(aligned_stack).T[slicey, slicex]
# transform numpy array back to Orange.data.Table
return shifts, build_spec_table(
*_spectra_from_image(cropped, getx(data),
np.linspace(*lsx)[slicex],
np.linspace(*lsy)[slicey]))
def process_stack(data, xat, yat, upsample_factor=100, use_sobel=False, ref_frame_num=0):
hypercube, lsx, lsy = get_hypercube(data, xat, yat)
calculate_shift = RegisterTranslation(upsample_factor=upsample_factor)
filterfn = sobel if use_sobel else lambda x: x
shifts, aligned_stack = alignstack(hypercube.T,
shiftfn=calculate_shift,
ref_frame_num=ref_frame_num,
filterfn=filterfn)
xmin, ymin = shifts[:, 0].min(), shifts[:, 1].min()
xmax, ymax = shifts[:, 0].max(), shifts[:, 1].max()
xmin, xmax = int(round(xmin)), int(round(xmax))
ymin, ymax = int(round(ymin)), int(round(ymax))
shape = hypercube.shape
slicex = slice(max(xmax, 0), min(shape[1], shape[1]+xmin))
slicey = slice(max(ymax, 0), min(shape[0], shape[0]+ymin))
cropped = np.array(aligned_stack).T[slicey, slicex]
# transform numpy array back to Orange.data.Table
return shifts, build_spec_table(*_spectra_from_image(cropped,
getx(data),
np.linspace(*lsx)[slicex],
np.linspace(*lsy)[slicey]))
def test_hypercube_roundtrip(self):
d = self.mosaic
xat = [v for v in d.domain.metas if v.name == "map_x"][0]
yat = [v for v in d.domain.metas if v.name == "map_y"][0]
hypercube, lsx, lsy = get_hypercube(d, xat, yat)
features = getx(d)
ndom = Orange.data.Domain([xat, yat])
datam = Orange.data.Table(ndom, d)
coorx = datam.X[:, 0]
coory = datam.X[:, 1]
coords = np.ones((lsx[2], lsy[2], 2))
coords[index_values(coorx, lsx), index_values(coory, lsy)] = datam.X
x_locs = coords[:, 0, 0]
y_locs = coords[0, :, 1]
features, spectra, data = _spectra_from_image(hypercube, features,
x_locs, y_locs)
nd = build_spec_table(features, spectra, data)
np.testing.assert_equal(d.X, nd.X)
np.testing.assert_equal(d.Y, nd.Y)
np.testing.assert_equal(d.metas, nd.metas)
self.assertEqual(d.domain, nd.domain)
def test_none_attr(self):
with self.assertRaises(InvalidAxisException):
get_hypercube(self.mosaic, None, None)