def test_circular_average():
image = np.zeros((12, 12))
calib_center = (5, 5)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
assert_array_almost_equal(bin_cen, [0.70710678, 2.12132034,
3.53553391, 4.94974747, 6.36396103,
7.77817459], decimal=6)
assert_array_almost_equal(ring_avg, [8., 2.5, 5.55555556, 0.,
0., 0.], decimal=6)
bin_cen1, ring_avg1 = roi.circular_average(image, calib_center, min_x=0,
max_x=10, nx=None)
assert_array_almost_equal(bin_cen1, [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5,
7.5, 8.5])
mask = np.ones_like(image)
mask[4:6, 2:3] = 0
bin_cen_masked, ring_avg_masked = roi.circular_average(image, calib_center,
min_x=0, max_x=10,
nx=6, mask=mask)
assert_array_almost_equal(bin_cen_masked, [0.83333333, 2.5, 4.16666667,
5.83333333, 7.5, 9.16666667])
assert_array_almost_equal(ring_avg_masked, [8.88888889, 3.84615385, 2.5,
0., 0., 0.])
def test_construct_circ_avg_image():
# need to test with center and dims and without
# and test anisotropic pixels
image = np.zeros((12, 12))
calib_center = (2, 2)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
# check that the beam center and dims yield the circavg in the right place
cimg = nimage.construct_circ_avg_image(bin_cen,
ring_avg,
dims=image.shape,
center=calib_center)
assert_array_almost_equal(
cimg[2],
np.array([
5.0103283, 6.15384615, 6.15384615, 6.15384615, 5.0103283,
3.79296498, 2.19422113, 0.51063356, 0., 0., 0., 0.
]))
# check that the default values center in the same place
cimg2 = nimage.construct_circ_avg_image(bin_cen, ring_avg)
assert_array_almost_equal(
cimg2[12],
np.array([
0., 0., 0., 0., 0., 0., 0., 0.51063356, 2.19422113, 3.79296498,
5.0103283, 6.15384615, 6.15384615, 6.15384615, 5.0103283,
3.79296498, 2.19422113, 0.51063356, 0., 0., 0., 0., 0., 0., 0.
]))
# check that anisotropic pixels are treated properly
cimg3 = nimage.construct_circ_avg_image(bin_cen,
ring_avg,
dims=image.shape,
pixel_size=(2, 1))
assert_array_almost_equal(
cimg3[5],
np.array([
0., 1.16761618, 2.80022015, 4.16720388, 5.250422, 6.08400137,
6.08400137, 5.250422, 4.16720388, 2.80022015, 1.16761618, 0.
]))
with assert_raises(ValueError):
nimage.construct_circ_avg_image(bin_cen,
ring_avg,
center=calib_center,
pixel_size=(2, 1))
def circavg(self):
''' calculate and plot circular average.'''
if hasattr(self.saxsdata,"mask") and self.saxsdata.mask is not None:
mask = self.saxsdata.mask
else:
mask = None
#sqx, sqy = circavg(self.saxsdata.avg_img, x0=self.saxsdata.getxcen(), y0=self.saxsdata.getycen(),\
#noqs=self.saxsdata.getnoqs_circavg(), mask=mask)
x0 = self.saxsdata.getxcen()
y0 = self.saxsdata.getycen()
noqs = self.saxsdata.getnoqs_circavg()
sqx, sqy = circular_average(self.saxsdata.avg_img,(y0,x0),mask=mask,nx=noqs)
p1 = pg.plot(sqx*self.saxsdata.getqperpixel(),sqy)
p1.getPlotItem().setLogMode(True,True)
def test_construct_circ_avg_image():
# need to test with center and dims and without
# and test anisotropic pixels
image = np.zeros((12, 12))
calib_center = (2, 2)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
# check that the beam center and dims yield the circavg in the right place
cimg = nimage.construct_circ_avg_image(bin_cen, ring_avg, dims=image.shape,
center=calib_center)
assert_array_almost_equal(cimg[2], np.array([5.0103283, 6.15384615,
6.15384615, 6.15384615, 5.0103283, 3.79296498,
2.19422113, 0.51063356, 0., 0., 0., 0.]))
# check that the default values center in the same place
cimg2 = nimage.construct_circ_avg_image(bin_cen, ring_avg)
assert_array_almost_equal(cimg2[12], np.array([0., 0., 0., 0., 0.,
0., 0., 0.51063356, 2.19422113, 3.79296498,
5.0103283, 6.15384615, 6.15384615, 6.15384615,
5.0103283, 3.79296498, 2.19422113, 0.51063356,
0., 0., 0., 0., 0., 0., 0.]))
# check that anisotropic pixels are treated properly
cimg3 = nimage.construct_circ_avg_image(bin_cen, ring_avg,
dims=image.shape,
pixel_size=(2, 1))
assert_array_almost_equal(cimg3[5], np.array([0., 1.16761618, 2.80022015,
4.16720388, 5.250422, 6.08400137, 6.08400137,
5.250422, 4.16720388, 2.80022015,
1.16761618, 0.]))
with assert_raises(ValueError):
nimage.construct_circ_avg_image(bin_cen, ring_avg, center=calib_center,
pixel_size=(2, 1))
