def test_get_correction_affine_and_rotation(self, calgen):
affine = np.array(
[
[0.97579077, 0.01549655, 0.0],
[0.01549655, 0.99008051, 0.0],
[0.0, 0.0, 1.0],
]
)
calgen.affine_matrix = affine
real_line = Line2DROI(
x1=2.69824, y1=81.4867, x2=229.155, y2=61.6898, linewidth=3
)
recip_line = Line2DROI(
x1=-0.30367, y1=-1.21457, x2=0.344978, y2=1.24927, linewidth=0.115582
)
value = calgen.get_rotation_calibration(
real_line=real_line, reciprocal_line=recip_line
)
corr = calgen.get_correction_matrix()
np.testing.assert_almost_equal(
corr,
np.array(
[
[0.1805777, 0.9783954, 0.0],
[-0.9590618, 0.1524534, 0.0],
[0.0, 0.0, 1.0],
]
),
decimal=3
)
def test_get_rotation_calibration(self, calgen):
real_line = Line2DROI(
x1=2.69824, y1=81.4867, x2=229.155, y2=61.6898, linewidth=3
)
recip_line = Line2DROI(
x1=-0.30367, y1=-1.21457, x2=0.344978, y2=1.24927, linewidth=0.115582
)
value = calgen.get_rotation_calibration(
real_line=real_line, reciprocal_line=recip_line
)
np.testing.assert_almost_equal(value, -80.24669411537899,decimal=3)
def test_line2droi_angle(self):
# 1. Testing quantitative measurement for different quadrants:
r = self.r
r_angles = np.array([rr.angle() for rr in r])
angles_h = np.array([
-135., -150., 150., 135., -120., -135., 135., 120., -60., -45.,
45., 60., -45., -30, 30., 45.
])
angles_v = np.array([
-135., -120., -60., -45., -150., -135., -45., -30., 150., 135.,
45., 30., 135., 120., 60., 45.
])
assert np.allclose(r_angles, angles_h)
r_angles = np.array([rr.angle(axis='vertical') for rr in r])
assert np.allclose(r_angles, angles_v)
# 2. Testing unit conversation
r = Line2DROI(np.random.rand(), np.random.rand(), np.random.rand(),
np.random.rand())
assert r.angle(units='degrees') == (r.angle(units='radians') / np.pi *
180.)
# 3. Testing raises:
with pytest.raises(ValueError):
r.angle(units='meters')
with pytest.raises(ValueError):
r.angle(axis='z')
def test_get_navigation_calibration_no_data(self, empty_calgen):
line = Line2DROI(x1=2.5, y1=13.0, x2=193.0, y2=12.5, linewidth=3.5)
empty_calgen.get_navigation_calibration(line_roi=line,
x1=12.0,
x2=172.0,
n=1,
xspace=500.0)
def test_get_navigation_calibration_no_data(self, empty_calgen):
line = Line2DROI(x1=2.5, y1=13.0, x2=193.0, y2=12.5, linewidth=3.5)
with pytest.raises(ValueError, match="requires an Au X-grating image"):
empty_calgen.get_navigation_calibration(
line_roi=line, x1=12.0, x2=172.0, n=1, xspace=500.0
)
def test_get_navigation_calibration(self, calgen):
line = Line2DROI(x1=2.5, y1=13.0, x2=193.0, y2=12.5, linewidth=3.5)
value = calgen.get_navigation_calibration(line_roi=line,
x1=12.0,
x2=172.0,
n=1,
xspace=500.0)
np.testing.assert_almost_equal(calgen.navigation_calibration, value)
def test_plot_calibrated_data_im(self, calgen):
line = Line2DROI(x1=2.5, y1=13.0, x2=193.0, y2=12.5, linewidth=3.5)
calgen.get_navigation_calibration(line_roi=line,
x1=12.0,
x2=172.0,
n=1,
xspace=500.0)
calgen.plot_calibrated_data(data_to_plot="au_x_grating_im")
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 accept(self):
if self.is_on():
if self.ndim == 1:
roi = SpanROI(0, 1)
elif self.ndim > 1:
roi = Line2DROI(0, 0, 1, 1, 1)
roi._on_widget_change(self.widget) # ROI gets coords from widget
self.accepted[BaseInteractiveROI].emit(roi)
self.accepted[BaseInteractiveROI, SignalFigureTool].emit(roi, self)
def get_diffraction_calibration(self, mask_length, linewidth):
"""Determine the diffraction pattern pixel size calibration in units of
reciprocal Angsstroms per pixel.
Parameters
----------
mask_length : float
Halfwidth of the region excluded from peak finding around the
diffraction pattern center.
linewidth : float
Width of Line2DROI used to obtain line trace from distortion
corrected diffraction pattern.
Returns
-------
diff_cal : float
Diffraction calibration in reciprocal Angstroms per pixel.
"""
# Check that necessary calibration data is provided
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.")
dpeg = self.calibration_data.au_x_grating_dp
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))
# Define line roi along which to take trace for calibration
line = Line2DROI(x1=5, y1=5, x2=250, y2=250, linewidth=linewidth)
# Obtain line trace
trace = line(dpegm)
trace = trace.as_signal1D(0)
# Find peaks in line trace either side of direct beam
db = (np.sqrt(2) * 128) - (5 * np.sqrt(2))
pka = trace.isig[db + mask_length:].find_peaks1D_ohaver()[0]['position']
pkb = trace.isig[:db - mask_length].find_peaks1D_ohaver()[0]['position']
# Determine predicted position of 022 peak of Au pattern d022=1.437
au_pre = db - (self.ring_params[0] / 1.437)
au_post = db + (self.ring_params[0] / 1.437)
# Calculate differences between predicted and measured positions
prediff = np.abs(pkb - au_pre)
postdiff = np.abs(pka - au_post)
# Calculate new calibration value based on most accurate peak positions
dc = (2 / 1.437) / (pka[postdiff == min(postdiff)] - pkb[prediff == min(prediff)])
# Store diffraction calibration value as attribute
self.diffraction_calibration = dc[0]
return dc[0]
def setup_method(self, method):
np.random.seed(0) # Same random every time, Line2DROi test requires it
self.s_s = Signal1D(np.random.rand(50, 60, 4))
self.s_s.axes_manager[0].scale = 5
self.s_s.axes_manager[0].units = 'nm'
self.s_s.axes_manager[1].scale = 5
self.s_s.axes_manager[1].units = 'nm'
# 4D dataset
self.s_i = Signal2D(np.random.rand(100, 100, 4, 4))
# Generate ROI for test of angle measurements
self.r = []
t = np.tan(30. / 180. * np.pi)
for x in [-1., -t, t, 1]:
for y in [-1., -t, t, 1]:
self.r.append(Line2DROI(x1=0., x2=x, y1=0., y2=y))
def test_interactive_snap(self, snap):
kwargs = {}
if snap != 'default':
kwargs['snap'] = snap
else:
# default is True
snap = True
s = self.s
r = RectangularROI(left=3, right=7, top=2, bottom=5)
s.plot()
_ = r.interactive(s, **kwargs)
for w in r.widgets:
old_position = w.position
new_position = (3.25, 2.2)
w.position = new_position
assert w.position == old_position if snap else new_position
assert w.snap_all == snap
assert w.snap_position == snap
assert w.snap_size == snap
p1 = Point1DROI(4)
_ = p1.interactive(s, **kwargs)
for w in p1.widgets:
old_position = w.position
new_position = (4.2, )
w.position = new_position
assert w.position == old_position if snap else new_position
assert w.snap_position == snap
p2 = Point2DROI(4, 5)
_ = p2.interactive(s, **kwargs)
for w in p2.widgets:
old_position = w.position
new_position = (4.3, 5.3)
w.position = new_position
assert w.position == old_position if snap else new_position
assert w.snap_position == snap
span = SpanROI(4.01, 5)
_ = span.interactive(s, **kwargs)
for w in span.widgets:
old_position = w.position
new_position = (4.2, )
w.position = new_position
assert w.position == old_position if snap else new_position
assert w.snap_all == snap
assert w.snap_position == snap
assert w.snap_size == snap
# check that changing snap is working fine
new_snap = not snap
w.snap_all = new_snap
old_position = w.position
new_position = (4.2, )
w.position = new_position
assert w.position == old_position if new_snap else new_position
line2d = Line2DROI(4, 5, 6, 6, 1)
_ = line2d.interactive(s, **kwargs)
for w in line2d.widgets:
old_position = w.position
new_position = ([4.3, 5.3], [6.0, 6.0])
w.position = new_position
assert w.position == old_position if snap else new_position
assert w.snap_all == snap
assert w.snap_position == snap
assert w.snap_size == snap
def test_line2droi_length(self):
line = Line2DROI(x1=0., x2=2, y1=0., y2=2)
np.testing.assert_allclose(line.length, np.sqrt(8))
def test_2d_line_getitem(self):
r = Line2DROI(10, 10, 150, 50, 5)
assert tuple(r) == (10, 10, 150, 50, 5)
def test_2d_line_spec_plot(self):
r = Line2DROI(10, 10, 150, 50, 5)
s = self.s_s
s2 = r(s)
np.testing.assert_allclose(
s2.data,
np.array([[0.96779467, 0.5468849, 0.27482357, 0.59223042],
[0.89676116, 0.40673335, 0.55207828, 0.27165277],
[0.27734027, 0.52437981, 0.11738029, 0.15984529],
[0.04680635, 0.97073144, 0.00386035, 0.17857997],
[0.61286675, 0.0813696, 0.8818965, 0.71962016],
[0.96638997, 0.50763555, 0.30040368, 0.54950057],
[0.22956744, 0.50686296, 0.73685316, 0.09767637],
[0.5149222, 0.93841202, 0.22864655, 0.67714114],
[0.5149222, 0.93841202, 0.22864655, 0.67714114],
[0.59288027, 0.0100637, 0.4758262, 0.70877039],
[0.80546244, 0.58610794, 0.56928692, 0.51208072],
[0.97176308, 0.36384478, 0.78791575, 0.55529411],
[0.39563367, 0.95546593, 0.59831597, 0.11891694],
[0.4175392, 0.78158173, 0.69374702, 0.91634033],
[0.44679332, 0.83699037, 0.22182403, 0.49394526],
[0.92961874, 0.66721471, 0.79807902, 0.55099397],
[0.98046646, 0.58866215, 0.04551071, 0.1979828],
[0.70340703, 0.35307496, 0.15442542, 0.31268984],
[0.88432423, 0.95853234, 0.20751273, 0.78846839],
[0.27334874, 0.88713154, 0.16554561, 0.66595992],
[0.08421126, 0.97389332, 0.70063334, 0.84181574],
[0.15946909, 0.41702974, 0.42681952, 0.26810926],
[0.13159685, 0.03921054, 0.02523183, 0.27155029],
[0.13159685, 0.03921054, 0.02523183, 0.27155029],
[0.46185344, 0.72624328, 0.4748717, 0.90405082],
[0.52917427, 0.54280647, 0.71405379, 0.51655594],
[0.13307599, 0.77345467, 0.4062725, 0.96309389],
[0.28351378, 0.26307878, 0.3335074, 0.57231702],
[0.89486974, 0.17628164, 0.2796788, 0.58167984],
[0.64937273, 0.5006921, 0.28355772, 0.2861476],
[0.31342052, 0.19085, 0.90192363, 0.85839813]]),
rtol=0.05)
r.linewidth = 50
s3 = r(s)
np.testing.assert_allclose(
s3.data,
np.array([[0.40999384, 0.27111487, 0.3345655, 0.47553854],
[0.44475117, 0.40330205, 0.48113292, 0.26780132],
[0.57911599, 0.38999298, 0.38509116, 0.37418655],
[0.29175157, 0.37856367, 0.34420691, 0.48316543],
[0.55975912, 0.57155145, 0.57640677, 0.39718605],
[0.41300845, 0.45929259, 0.27489573, 0.40120352],
[0.46271229, 0.60908378, 0.25796662, 0.46526239],
[0.37843991, 0.54919334, 0.40469436, 0.48612034],
[0.44717148, 0.44934708, 0.29064827, 0.51334849],
[0.3966089, 0.59853786, 0.50392157, 0.39123649],
[0.50281456, 0.62863149, 0.43051921, 0.32015553],
[0.40527468, 0.44258442, 0.55694228, 0.41142292],
[0.47856163, 0.49720026, 0.62012372, 0.47537808],
[0.46695064, 0.5159018, 0.53532036, 0.4691573],
[0.44267241, 0.46886762, 0.37363574, 0.54369291],
[0.76138395, 0.54406653, 0.47305104, 0.45083095],
[0.74812744, 0.53414434, 0.38487816, 0.44611049],
[0.59011489, 0.5456799, 0.41782293, 0.5948403],
[0.47546595, 0.52536805, 0.39267032, 0.58787463],
[0.39387115, 0.4784124, 0.36765754, 0.46951847],
[0.54076839, 0.69257203, 0.44540576, 0.39236971],
[0.41195904, 0.5148879, 0.51199686, 0.63694563],
[0.44885787, 0.46886977, 0.42150512, 0.52556669],
[0.60826081, 0.3987657, 0.55875628, 0.5293137],
[0.44151911, 0.4188617, 0.37734811, 0.51166705],
[0.52878209, 0.41050467, 0.57149806, 0.52577575],
[0.50474464, 0.3294767, 0.63519013, 0.56126315],
[0.37607782, 0.58086952, 0.45089019, 0.62929377],
[0.59956085, 0.5173887, 0.64790597, 0.49865165],
[0.57646846, 0.46468029, 0.45267259, 0.44889072],
[0.4382186, 0.49576157, 0.6192481, 0.45031413]]))
def test_plot_au_x_grating_dp_with_roi(self, library):
line = Line2DROI(x1=1, y1=1, x2=3, y2=3, linewidth=1.0)
library.plot_calibration_data(data_to_plot="au_x_grating_dp", roi=line)
def test_2d_line_img_plot(self):
s = self.s_i
r = Line2DROI(0, 0, 4, 4, 1)
s2 = r(s)
nt.assert_true(
np.allclose(
s2.data,
np.array([[[0.5646904, 0.83974605, 0.37688365, 0.499676],
[0.08130241, 0.3241552, 0.91565131, 0.85345237],
[0.5941565, 0.90536555, 0.42692772, 0.93761072],
[0.9458708, 0.56996783, 0.05020319, 0.88466194]],
[[0.55342858, 0.71776076, 0.9698018, 0.84684608],
[0.77676046, 0.32998726, 0.49284904, 0.63849364],
[0.94969472, 0.99393561, 0.79184028, 0.60493951],
[0.99584095, 0.83632682, 0.51592399, 0.53049253]],
[[0.55342858, 0.71776076, 0.9698018, 0.84684608],
[0.77676046, 0.32998726, 0.49284904, 0.63849364],
[0.94969472, 0.99393561, 0.79184028, 0.60493951],
[0.99584095, 0.83632682, 0.51592399, 0.53049253]],
[[0.32270396, 0.28878038, 0.64165074, 0.92820531],
[0.24836647, 0.37477366, 0.18406007, 0.11019336],
[0.38678734, 0.9174347, 0.47658793, 0.45095935],
[0.95232706, 0.96468026, 0.5158903, 0.69112322]],
[[0.72414297, 0.64417135, 0.17938658, 0.12279276],
[0.90632348, 0.90345183, 0.21473533, 0.34087282],
[0.2579504, 0.65663038, 0.27606922, 0.33695786],
[0.46466925, 0.34991125, 0.73593611, 0.32203574]],
[[0.72414297, 0.64417135, 0.17938658, 0.12279276],
[0.90632348, 0.90345183, 0.21473533, 0.34087282],
[0.2579504, 0.65663038, 0.27606922, 0.33695786],
[0.46466925, 0.34991125, 0.73593611, 0.32203574]],
[[0.97259866, 0.13527587, 0.48531393, 0.31607768],
[0.13656701, 0.40578067, 0.64221493, 0.46036815],
[0.30466093, 0.88706533, 0.30914269, 0.01833664],
[0.56143007, 0.09026307, 0.81898535, 0.4518825]]])))
r.linewidth = 10
s3 = r(s)
nt.assert_true(
np.allclose(
s3.data,
np.array([[[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.12385935, 0.17534623, 0.08266437, 0.08533342],
[0.06072978, 0.18213069, 0.13162582, 0.14526251],
[0.11950599, 0.09530544, 0.05814531, 0.10613925],
[0.13243216, 0.13388253, 0.15641767, 0.07678893]],
[[0.10387718, 0.18591981, 0.21704829, 0.16594489],
[0.26554947, 0.27280648, 0.23534874, 0.15751378],
[0.11329239, 0.16440693, 0.19378236, 0.23418843],
[0.20414672, 0.24669051, 0.08809065, 0.21252996]],
[[0.32737802, 0.24354627, 0.25713232, 0.42447693],
[0.22132115, 0.34440789, 0.1769873, 0.18348862],
[0.32205928, 0.29038094, 0.22570116, 0.20305065],
[0.45399669, 0.29687212, 0.313637, 0.27469796]],
[[0.38104394, 0.2654458, 0.51666151, 0.47973295],
[0.34333797, 0.36907303, 0.34349318, 0.25681538],
[0.32849871, 0.27963978, 0.47319042, 0.37358476],
[0.48767599, 0.23022751, 0.32004745, 0.37714935]],
[[0.59093609, 0.54976286, 0.54934114, 0.54753303],
[0.48284716, 0.35797562, 0.49739056, 0.46934957],
[0.29954848, 0.45448276, 0.50639968, 0.56140708],
[0.55790493, 0.55105139, 0.40859302, 0.47408336]],
[[0.63293155, 0.38872956, 0.55044015, 0.37731745],
[0.49091568, 0.54173188, 0.51292652, 0.53813843],
[0.56463766, 0.73848284, 0.41183566, 0.37515417],
[0.48426503, 0.23582684, 0.45947953,
0.49322732]]])))
def test_2d_line_spec_plot(self):
r = Line2DROI(10, 10, 150, 50, 5)
s = self.s_s
s2 = r(s)
np.testing.assert_almost_equal(
s2.data,
np.array([[0.96779467, 0.5468849, 0.27482357, 0.59223042],
[0.75928245, 0.36454463, 0.50106317, 0.37638916],
[0.93916091, 0.50631222, 0.99980858, 0.19725947],
[0.8075638, 0.05100731, 0.62716071, 0.50245307],
[0.88118825, 0.91641901, 0.2715511, 0.60754536],
[0.8996517, 0.11646325, 0.16318171, 0.6962192],
[0.01497626, 0.4573887, 0.64439714, 0.06037948],
[0.72362309, 0.28890677, 0.97364152, 0.85953663],
[0.72362309, 0.28890677, 0.97364152, 0.85953663],
[0.24783865, 0.1928807, 0.21518258, 0.33911841],
[0.50438632, 0.55765081, 0.31787285, 0.614175],
[0.04143613, 0.40084015, 0.79034035, 0.64846836],
[0.08067386, 0.79973013, 0.16217087, 0.19457452],
[0.54601389, 0.8121854, 0.81069635, 0.71423997],
[0.83030369, 0.1979076, 0.10475427, 0.05690645],
[0.30969465, 0.60847027, 0.76278635, 0.54082552],
[0.61242728, 0.11588176, 0.60404942, 0.83680764],
[0.57727334, 0.36670814, 0.04252509, 0.43254571],
[0.89750871, 0.44035511, 0.6426097, 0.38146028],
[0.32126274, 0.6520685, 0.47213129, 0.05101303],
[0.30668186, 0.51515877, 0.69551083, 0.28720246],
[0.95568239, 0.12785748, 0.77676417, 0.31861386],
[0.80813283, 0.93128198, 0.28999035, 0.39246662],
[0.80813283, 0.93128198, 0.28999035, 0.39246662],
[0.3329224, 0.95850972, 0.8414333, 0.28555722],
[0.84350086, 0.93051062, 0.82974137, 0.52569971],
[0.97002278, 0.82518232, 0.92972355, 0.52315373],
[0.23035238, 0.43235565, 0.04142909, 0.04025669],
[0.54124516, 0.89630549, 0.5923778, 0.25663082],
[0.60156, 0.51353417, 0.78213733, 0.8870782],
[0.08783705, 0.59293514, 0.39825928, 0.55032904]]))
r.linewidth = 50
s3 = r(s)
np.testing.assert_almost_equal(
s3.data,
np.array([[0.44265776, 0.35745439, 0.27446296, 0.41595865],
[0.26916342, 0.37715306, 0.32971154, 0.33530845],
[0.35426084, 0.47282874, 0.44090125, 0.35714432],
[0.4460463, 0.44976548, 0.40195731, 0.53014602],
[0.5426024, 0.53311424, 0.54084761, 0.3422711],
[0.45614115, 0.43188745, 0.25545383, 0.44532886],
[0.46822001, 0.62948798, 0.3153703, 0.43942564],
[0.47173014, 0.67098345, 0.25334107, 0.37146341],
[0.51756393, 0.37361861, 0.43797954, 0.4143497],
[0.42342347, 0.54374975, 0.63824925, 0.46078933],
[0.59427019, 0.49465564, 0.50841493, 0.64876998],
[0.55886454, 0.48120671, 0.53824089, 0.59540268],
[0.57945739, 0.64865535, 0.44827414, 0.50337253],
[0.53121262, 0.47216267, 0.55630321, 0.4016824],
[0.43845159, 0.47476225, 0.40063072, 0.35417257],
[0.45185363, 0.42844148, 0.5125228, 0.47386537],
[0.45610424, 0.56858719, 0.60593161, 0.47611862],
[0.47861857, 0.57419121, 0.51392978, 0.59486733],
[0.47641588, 0.54820991, 0.52233701, 0.57604036],
[0.34994652, 0.4514264, 0.57328452, 0.57024109],
[0.34159438, 0.59016962, 0.56304279, 0.4982093],
[0.55053388, 0.65012605, 0.55998794, 0.47428968],
[0.51392427, 0.56827423, 0.74809453, 0.60156568],
[0.64152041, 0.56110823, 0.57792954, 0.49621454],
[0.53466093, 0.55640288, 0.42431698, 0.50720362],
[0.59924828, 0.39606699, 0.56290688, 0.59880921],
[0.43478479, 0.46415729, 0.41994764, 0.5724431],
[0.26896758, 0.41500659, 0.47386699, 0.56557835],
[0.62045775, 0.49978237, 0.62594581, 0.48231989],
[0.3378933, 0.62744914, 0.61643326, 0.57795806],
[0.4226275, 0.53682311, 0.58369682, 0.43471438]]))