def test_add_lines(self) -> None:
module = AddLinesModule(lines=(2, 5, 0, 3),
name_in='add',
image_in_tag='read',
image_out_tag='add')
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('add')
assert np.sum(data) == pytest.approx(105.54278879805275, rel=self.limit, abs=0.)
assert data.shape == (10, 14, 18)
def test_add_lines(self):
module = AddLinesModule(lines=(2, 5, 0, 9),
name_in='add',
image_in_tag='read',
image_out_tag='add')
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('add')
assert np.allclose(data[0, 50, 50], 0.02851872141873229, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 8.599412485413757e-05, rtol=limit, atol=0.)
assert data.shape == (40, 109, 107)
def test_waffle_center_odd(self) -> None:
module = AddLinesModule(name_in='add',
image_in_tag='star',
image_out_tag='star_add',
lines=(45, 45, 45, 45))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('star_add')
assert np.sum(data) == pytest.approx(105.54278879805278,
rel=self.limit,
abs=0.)
assert data.shape == (10, 101, 101)
module = WaffleCenteringModule(size=0.2,
center=(50, 50),
name_in='waffle',
image_in_tag='star_add',
center_in_tag='waffle',
image_out_tag='center',
radius=35.,
pattern='x',
sigma=0.05)
self.pipeline.add_module(module)
with pytest.warns(DeprecationWarning) as warning:
self.pipeline.run_module('waffle')
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')
assert np.sum(data) == pytest.approx(104.93318507061295,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
attr = self.pipeline.get_attribute('center',
'History: WaffleCenteringModule')
assert attr == '[x, y] = [50.0, 50.0]'
def test_wavelet_denoising_odd_size(self):
module = AddLinesModule(name_in='add',
image_in_tag='images',
image_out_tag='images_odd',
lines=(1, 0, 1, 0))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('images_odd')
assert np.allclose(data[0, 10, 10], 0.05294085050174391, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.002269413609192613, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert np.allclose(cwt_config.m_wavelet_order, 2, rtol=limit, atol=0.)
assert not cwt_config.m_keep_mean
assert np.allclose(cwt_config.m_resolution, 0.5, rtol=limit, atol=0.)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_odd_1',
image_in_tag='images_odd',
image_out_tag='wavelet_odd_1',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_odd_1')
data = self.pipeline.get_data('wavelet_odd_1')
assert np.allclose(data[0, 10, 10], 0.0529782051386938, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0022694631406801565, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_odd_2',
image_in_tag='images_odd',
image_out_tag='wavelet_odd_2',
padding='mirror',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_odd_2')
data = self.pipeline.get_data('wavelet_odd_2')
assert np.allclose(data[0, 10, 10], 0.05297146283932275, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0022694809842930034, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
data = self.pipeline.get_attribute('images', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
data = self.pipeline.get_attribute('wavelet_odd_1', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
data = self.pipeline.get_attribute('wavelet_odd_2', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
def test_wavelet_denoising_even_size(self) -> None:
module = AddLinesModule(name_in='add',
image_in_tag='images',
image_out_tag='images_even',
lines=(1, 0, 1, 0))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('images_even')
assert np.sum(data) == pytest.approx(105.54278879805275,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert cwt_config.m_wavelet_order == 2
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_1',
image_in_tag='images_even',
image_out_tag='wavelet_even_1',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_1')
data = self.pipeline.get_data('wavelet_even_1')
assert np.sum(data) == pytest.approx(105.1035789572968,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_2',
image_in_tag='images_even',
image_out_tag='wavelet_even_2',
padding='mirror',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_2')
data = self.pipeline.get_data('wavelet_even_2')
assert np.sum(data) == pytest.approx(105.06809820408587,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
data = self.pipeline.get_attribute('images', 'NFRAMES', static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_1',
'NFRAMES',
static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_2',
'NFRAMES',
static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
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]'