def test_circle_spec(self):
s = self.s_s
s.data = np.ones_like(s.data)
r = CircleROI(20, 25, 20)
r_ann = CircleROI(20, 25, 20, 15)
sr = r(s)
sr_ann = r_ann(s)
scale = s.axes_manager[0].scale
n = int(round(40 / scale))
assert sr.axes_manager.navigation_shape == (n, n)
assert sr_ann.axes_manager.navigation_shape == (n, n)
# Check that mask is same for all images:
for i in range(n):
for j in range(n):
assert (np.all(sr.data.mask[j, i, :] == True) or
np.all(sr.data.mask[j, i, :] == False))
assert (np.all(sr_ann.data.mask[j, i, :] == True) or
np.all(sr_ann.data.mask[j, i, :] == False))
# Check that the correct elements has been masked out:
mask = sr.data.mask[:, :, 0]
print(mask) # To help debugging, this shows the shape of the mask
np.testing.assert_array_equal(
np.where(mask.flatten())[0],
[0, 1, 6, 7, 8, 15, 48, 55, 56, 57, 62, 63])
mask_ann = sr_ann.data.mask[:, :, 0]
print(mask_ann) # To help debugging, this shows the shape of the mask
np.testing.assert_array_equal(
np.where(mask_ann.flatten())[0],
[0, 1, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 25,
26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46,
48, 50, 51, 52, 53, 55, 56, 57, 62, 63])
# Check that mask works for sum
assert np.sum(sr.data) == (n**2 - 3 * 4) * 4
assert np.sum(sr_ann.data) == 4 * 5 * 4
def plot_corrected_diffraction_pattern(self, reference_circle=True):
"""Plot the distortion corrected diffraction pattern with an optional
reference circle.
Parameters
----------
reference_circle : bool
If True a CircleROI widget is added to the plot for reference.
"""
# Check all required parameters are defined as attributes
if self.calibration_data.au_x_grating_dp is None:
raise ValueError("This method requires an Au X-grating diffraction "
"pattern to be provided. Please update the "
"CalibrationDataLibrary.")
if self.affine_matrix is None:
raise ValueError("This method requires a distortion matrix to have "
"been determined. Use get_elliptical_distortion "
"to determine this matrix.")
# Set name for experimental data pattern
dpeg = self.calibration_data.au_x_grating_dp
# Apply distortion corrections to experimental data
size = dpeg.data.shape[0]
dpegs = stack_method([dpeg, dpeg, dpeg, dpeg])
dpegs = ElectronDiffraction2D(dpegs.data.reshape((2, 2, size, size)))
dpegs.apply_affine_transformation(self.affine_matrix,
preserve_range=True,
inplace=True)
dpegm = dpegs.mean((0, 1))
# Plot distortion corrected data
dpegm.plot(cmap='magma', vmax=0.1)
# add reference circle if specified
if reference_circle is True:
circ = CircleROI(cx=128, cy=128, r=53.5, r_inner=0)
circ.add_widget(dpegm)
def test_repr_vals(self):
repr(Point1DROI(1.1))
repr(Point2DROI(1.1, 2.1))
repr(Line2DROI(0, 0, 1, 1, 0.1))
repr(RectangularROI(0, 0, 1, 1))
repr(SpanROI(3., 5.))
repr(CircleROI(5, 5, 3))
repr(CircleROI(5, 5, 3, 1))
def test_interactive_special_case(self):
s = self.s.inav[0]
r = CircleROI(3, 5, 2)
sr = r.interactive(s, None, color="blue")
np.testing.assert_array_equal(
np.where(sr.data.mask.flatten())[0], [0, 3, 12, 15])
r.r_inner = 1
r.cy = 16
sr2 = r(s)
np.testing.assert_array_equal(
np.where(sr.data.mask.flatten())[0], [0, 3, 5, 6, 9, 10, 12, 15])
np.testing.assert_array_equal(sr2.data, sr.data)
def test_interactive_special_case(self):
s = self.s.inav[0]
r = CircleROI(3, 5, 2)
sr = r.interactive(s, None)
np.testing.assert_array_equal(np.where(sr.data.mask.flatten())[0],
[0, 3, 12, 15])
r.r_inner = 1
r.cy = 16
sr2 = r(s)
np.testing.assert_array_equal(np.where(sr.data.mask.flatten())[0],
[0, 3, 5, 6, 9, 10, 12, 15])
np.testing.assert_array_equal(sr2.data, sr.data)
def test_out_special_case(self):
s = self.s.inav[0]
r = CircleROI(3, 5, 2)
sr = r(s)
np.testing.assert_array_equal(
np.where(sr.data.mask.flatten())[0], [0, 3, 12, 15])
r.r_inner = 1
r.cy = 16
sr2 = r(s)
r(s, out=sr)
np.testing.assert_array_equal(
np.where(sr.data.mask.flatten())[0], [0, 3, 5, 6, 9, 10, 12, 15])
np.testing.assert_array_equal(sr2.data, sr.data)
def test_circle_spec(self):
s = self.s_s
s.data = np.ones_like(s.data)
r = CircleROI(20, 25, 20)
r_ann = CircleROI(20, 25, 20, 15)
sr = r(s)
sr_ann = r_ann(s)
scale = s.axes_manager[0].scale
n = int(round(40 / scale))
assert sr.axes_manager.navigation_shape == (n, n)
assert sr_ann.axes_manager.navigation_shape == (n, n)
# Check that mask is same for all images:
for i in range(n):
for j in range(n):
assert (np.all(sr.data.mask[j, i, :] == True) or
np.all(sr.data.mask[j, i, :] == False))
assert (np.all(sr_ann.data.mask[j, i, :] == True) or
np.all(sr_ann.data.mask[j, i, :] == False))
# Check that the correct elements has been masked out:
mask = sr.data.mask[:, :, 0]
print(mask) # To help debugging, this shows the shape of the mask
np.testing.assert_array_equal(
np.where(mask.flatten())[0],
[0, 1, 6, 7, 8, 15, 48, 55, 56, 57, 62, 63])
mask_ann = sr_ann.data.mask[:, :, 0]
print(mask_ann) # To help debugging, this shows the shape of the mask
np.testing.assert_array_equal(
np.where(mask_ann.flatten())[0],
[0, 1, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 25,
26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46,
48, 50, 51, 52, 53, 55, 56, 57, 62, 63])
# Check that mask works for sum
assert np.sum(sr.data) == (n**2 - 3 * 4) * 4
assert np.sum(sr_ann.data) == 4 * 5 * 4
s.plot()
r_signal = r.interactive(signal=s)
r_ann_signal = r_ann.interactive(signal=s)
assert np.sum(r_signal.sum().data) == (n**2 - 3 * 4) * 4
assert np.sum(r_ann_signal.sum().data) == 4 * 5 * 4
def test_annulus_getitem(self):
r_ann = CircleROI(20, 25, 20, 15)
assert tuple(r_ann) == (20, 25, 20, 15)
def test_circle_getitem(self):
r = CircleROI(20, 25, 20)
assert tuple(r) == (20, 25, 20)
def test_get_virtual_image(self, diffraction_pattern):
roi = CircleROI(3, 3, 5)
diffraction_pattern.get_virtual_image(roi)
def test_plot_interactive_virtual_image(self, diffraction_pattern):
roi = CircleROI(3, 3, 5)
diffraction_pattern.plot_interactive_virtual_image(roi)