def test_wavelet_denoising_dwt(self):
dwt_config = DwtWaveletConfiguration(wavelet='db8')
assert dwt_config.m_wavelet == 'db8'
wavelet_dwt = WaveletTimeDenoisingModule(
wavelet_configuration=dwt_config,
name_in='wavelet_dwt',
image_in_tag='images',
image_out_tag='wavelet_dwt',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(wavelet_dwt)
self.pipeline.run_module('wavelet_dwt')
data = self.pipeline.get_data('wavelet_dwt')
assert np.allclose(data[0, 10, 10],
0.09650639476873678,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0024998798596330475,
rtol=limit,
atol=0.)
assert data.shape == (40, 20, 20)
def test_wavelet_denoising_cwt_morlet(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet='morlet',
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'morlet'
assert np.allclose(cwt_config.m_wavelet_order, 5, 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_cwt_morlet',
image_in_tag='images',
image_out_tag='wavelet_cwt_morlet',
padding='mirror',
median_filter=False,
threshold_function='hard')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_morlet')
data = self.pipeline.get_data('wavelet_cwt_morlet')
assert np.allclose(data[0, 10, 10], 0.09805577173716859, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0025019409784314286, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
data = self.pipeline.get_attribute('wavelet_cwt_morlet', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
def test_wavelet_denoising_cwt_dog(self):
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.)
wavelet_cwt = WaveletTimeDenoisingModule(
wavelet_configuration=cwt_config,
name_in='wavelet_cwt_dog',
image_in_tag='images',
image_out_tag='wavelet_cwt_dog',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(wavelet_cwt)
self.pipeline.run_module('wavelet_cwt_dog')
data = self.pipeline.get_data('wavelet_cwt_dog')
assert np.allclose(data[0, 10, 10],
0.09805577173716859,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.002502083112599873,
rtol=limit,
atol=0.)
assert data.shape == (40, 20, 20)
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('wavelet_cwt_dog')
data_multi = self.pipeline.get_data('wavelet_cwt_dog')
assert np.allclose(data, data_multi, rtol=limit, atol=0.)
assert data.shape == data_multi.shape
def test_wavelet_denoising_dwt(self) -> None:
dwt_config = DwtWaveletConfiguration(wavelet='db8')
assert dwt_config.m_wavelet == 'db8'
module = WaveletTimeDenoisingModule(wavelet_configuration=dwt_config,
name_in='wavelet_dwt',
image_in_tag='images',
image_out_tag='wavelet_dwt',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_dwt')
data = self.pipeline.get_data('wavelet_dwt')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_wavelet_denoising_cwt_dog(self) -> None:
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_cwt_dog',
image_in_tag='images',
image_out_tag='wavelet_cwt_dog',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_dog')
data = self.pipeline.get_data('wavelet_cwt_dog')
assert np.sum(data) == pytest.approx(105.1035789572968,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('wavelet_cwt_dog')
data_multi = self.pipeline.get_data('wavelet_cwt_dog')
assert data == pytest.approx(data_multi, rel=self.limit, abs=0.)
assert data.shape == data_multi.shape
def test_wavelet_denoising_cwt_morlet(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet='morlet',
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'morlet'
assert cwt_config.m_wavelet_order == 5
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_morlet',
image_in_tag='images',
image_out_tag='wavelet_cwt_morlet',
padding='mirror',
median_filter=False,
threshold_function='hard')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_morlet')
data = self.pipeline.get_data('wavelet_cwt_morlet')
assert np.sum(data) == pytest.approx(104.86262840716438,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_attribute('wavelet_cwt_morlet',
'NFRAMES',
static=False)
assert data[0] == data[1] == 5
def test_wavelet_denoising_cwt_morlet(self):
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet="morlet",
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == "morlet"
assert np.allclose(cwt_config.m_wavelet_order, 5, rtol=limit, atol=0.)
assert not cwt_config.m_keep_mean
assert np.allclose(cwt_config.m_resolution, 0.5, rtol=limit, atol=0.)
wavelet_cwt = WaveletTimeDenoisingModule(
wavelet_configuration=cwt_config,
name_in="wavelet_cwt_morlet",
image_in_tag="images",
image_out_tag="wavelet_cwt_morlet",
padding="mirror",
median_filter=False,
threshold_function="hard")
self.pipeline.add_module(wavelet_cwt)
self.pipeline.run_module("wavelet_cwt_morlet")
data = self.pipeline.get_data("wavelet_cwt_morlet")
assert np.allclose(data[0, 10, 10],
0.09805577173716859,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0025019409784314286,
rtol=limit,
atol=0.)
assert data.shape == (40, 20, 20)
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.)