def test_shift_images_tag_mean(self) -> None:
module = ShiftImagesModule(shift_xy='fit_mean',
interpolation='spline',
name_in='shift6',
image_in_tag='shift',
image_out_tag='shift_tag_2')
self.pipeline.add_module(module)
self.pipeline.run_module('shift6')
data = self.pipeline.get_data('shift_tag_2')
assert np.sum(data) == pytest.approx(103.42285579230325, rel=1e-6, abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_tag_mean(self) -> None:
module = ShiftImagesModule(shift_xy='fit_mean',
interpolation='spline',
name_in='shift4',
image_in_tag='shift',
image_out_tag='shift_tag_2')
self.pipeline.add_module(module)
self.pipeline.run_module('shift4')
data = self.pipeline.get_data('shift_tag_2')
assert np.allclose(data[0, 20, 31], 5.348337712000518e-05, rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 0.00016430318227546225, rtol=1e-6, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_tag(self) -> None:
module = ShiftImagesModule(shift_xy='fit_full',
interpolation='spline',
name_in='shift5',
image_in_tag='shift',
image_out_tag='shift_tag_1')
self.pipeline.add_module(module)
self.pipeline.run_module('shift5')
data = self.pipeline.get_data('shift_tag_1')
assert np.sum(data) == pytest.approx(103.76504482668594, rel=1e-6, abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_fft(self) -> None:
module = ShiftImagesModule(shift_xy=(1., 2.),
interpolation='fft',
name_in='shift2',
image_in_tag='align1',
image_out_tag='shift_fft')
self.pipeline.add_module(module)
self.pipeline.run_module('shift2')
data = self.pipeline.get_data('shift_fft')
assert np.sum(data) == pytest.approx(104.70747423205349, rel=self.limit, abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_spline(self) -> None:
module = ShiftImagesModule(shift_xy=(1., 2.),
interpolation='spline',
name_in='shift1',
image_in_tag='align1',
image_out_tag='shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift1')
data = self.pipeline.get_data('shift')
assert np.sum(data) == pytest.approx(104.20425101355242, rel=self.limit, abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_tag(self) -> None:
module = ShiftImagesModule(shift_xy='fit_full',
interpolation='spline',
name_in='shift3',
image_in_tag='shift',
image_out_tag='shift_tag_1')
self.pipeline.add_module(module)
self.pipeline.run_module('shift3')
data = self.pipeline.get_data('shift_tag_1')
assert np.allclose(data[0, 39, 39], 0.023563080974545528, rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 0.0001643062943690491, rtol=1e-6, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_fft(self) -> None:
module = ShiftImagesModule(shift_xy=(6., 4.),
interpolation='fft',
name_in='shift2',
image_in_tag='align1',
image_out_tag='shift_fft')
self.pipeline.add_module(module)
self.pipeline.run_module('shift2')
data = self.pipeline.get_data('shift_fft')
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016446205542266847, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_fft(self):
shift = ShiftImagesModule(shift_xy=(6., 4.),
interpolation="spline",
name_in="shift2",
image_in_tag="align",
image_out_tag="fft")
self.pipeline.add_module(shift)
self.pipeline.run_module("shift2")
data = self.pipeline.get_data("fft")
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016430682224782259, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_spline(self):
module = ShiftImagesModule(shift_xy=(6., 4.),
interpolation='spline',
name_in='shift1',
image_in_tag='align',
image_out_tag='shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift1')
data = self.pipeline.get_data('shift')
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016430682224782259, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_waffle_center_even(self) -> None:
module = AddLinesModule(name_in='add1',
image_in_tag='star_add',
image_out_tag='star_even',
lines=(0, 1, 0, 1))
self.pipeline.add_module(module)
self.pipeline.run_module('add1')
data = self.pipeline.get_data('star_even')
assert np.sum(data) == pytest.approx(105.54278879805275,
rel=self.limit,
abs=0.)
assert data.shape == (10, 102, 102)
module = AddLinesModule(name_in='add2',
image_in_tag='waffle',
image_out_tag='waffle_even',
lines=(0, 1, 0, 1))
self.pipeline.add_module(module)
self.pipeline.run_module('add2')
data = self.pipeline.get_data('waffle_even')
assert np.sum(data) == pytest.approx(4.000000000000195,
rel=self.limit,
abs=0.)
assert data.shape == (1, 102, 102)
module = ShiftImagesModule(shift_xy=(0.5, 0.5),
interpolation='spline',
name_in='shift3',
image_in_tag='star_even',
image_out_tag='star_shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift3')
data = self.pipeline.get_data('star_shift')
assert np.sum(data) == pytest.approx(105.54278879805274,
rel=self.limit,
abs=0.)
assert data.shape == (10, 102, 102)
module = ShiftImagesModule(shift_xy=(0.5, 0.5),
interpolation='spline',
name_in='shift4',
image_in_tag='waffle_even',
image_out_tag='waffle_shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift4')
data = self.pipeline.get_data('waffle_shift')
assert np.sum(data) == pytest.approx(4.000000000000194,
rel=self.limit,
abs=0.)
assert data.shape == (1, 102, 102)
module = WaffleCenteringModule(size=0.2,
center=(50, 50),
name_in='waffle_even',
image_in_tag='star_shift',
center_in_tag='waffle_shift',
image_out_tag='center_even',
radius=35.,
pattern='x',
sigma=0.05)
self.pipeline.add_module(module)
with pytest.warns(DeprecationWarning) as warning:
self.pipeline.run_module('waffle_even')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The \'pattern\' parameter will be deprecated in a ' \
'future release. Please Use the \'angle\' ' \
'parameter instead and set it to 45.0 degrees.'
data = self.pipeline.get_data('center_even')
assert np.sum(data) == pytest.approx(105.22695036281449,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
attr = self.pipeline.get_attribute('center_even',
'History: WaffleCenteringModule')
assert attr == '[x, y] = [50.5, 50.5]'