def test_linspace(self):
v = values_to_linspace(np.array([1, 2, 3]))
np.testing.assert_equal(np.linspace(*v), [1, 2, 3])
v = values_to_linspace(np.array([1, 2, 3, float("nan")]))
np.testing.assert_equal(np.linspace(*v), [1, 2, 3])
v = values_to_linspace(np.array([1]))
np.testing.assert_equal(np.linspace(*v), [1])
v = values_to_linspace(np.array([1.001, 2, 3.002]))
np.testing.assert_equal(np.linspace(*v), [1.001, 2.0015, 3.002])
def test_linspace(self):
v = values_to_linspace(np.array([1, 2, 3]))
np.testing.assert_equal(np.linspace(*v), [1, 2, 3])
v = values_to_linspace(np.array([1, 2, 3, float("nan")]))
np.testing.assert_equal(np.linspace(*v), [1, 2, 3])
v = values_to_linspace(np.array([1]))
np.testing.assert_equal(np.linspace(*v), [1])
v = values_to_linspace(np.array([1.001, 2, 3.002]))
np.testing.assert_equal(np.linspace(*v), [1.001, 2.0015, 3.002])
def process_stack(data,
xat,
yat,
upsample_factor=100,
use_sobel=False,
ref_frame_num=0):
ndom = Domain([xat, yat])
datam = Table(ndom, data)
coorx = datam.X[:, 0]
coory = datam.X[:, 1]
lsx = values_to_linspace(coorx)
lsy = values_to_linspace(coory)
lsz = data.X.shape[1]
if lsx is None:
raise InvalidAxisException("x")
if lsy is None:
raise InvalidAxisException("y")
# set data
hypercube = np.ones((lsy[2], lsx[2], lsz)) * np.nan
xindex = index_values(coorx, lsx)
yindex = index_values(coory, lsy)
hypercube[yindex, xindex] = data.X
if np.any(np.isnan(hypercube)):
raise NanInsideHypercube(np.sum(np.isnan(hypercube)))
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_index(self):
a = np.array([1, 2, 3])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2])
a = np.array([1, 2, 3, 4])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 3])
a = np.array([1, 2, 3, 6, 5])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 5, 4])
def test_index(self):
a = np.array([1,2,3])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2])
a = np.array([1, 2, 3, 4])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 3])
a = np.array([1, 2, 3, 6, 5])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 5, 4])
def test_location(self):
lsc = values_to_linspace(np.array([1, 1, 1])) # a constant
lv = location_values([0, 1, 2], lsc)
np.testing.assert_equal(lv, [-1, 0, 1])
def test_location(self):
lsc = values_to_linspace(np.array([1, 1, 1])) # a constant
lv = location_values([0,1,2], lsc)
np.testing.assert_equal(lv, [-1, 0, 1])