def test_time_normalization(self):
module = TimeNormalizationModule(name_in='timenorm',
image_in_tag='images',
image_out_tag='timenorm')
self.pipeline.add_module(module)
self.pipeline.run_module('timenorm')
data = self.pipeline.get_data('timenorm')
assert np.allclose(data[0, 10, 10], 0.09793500165714215, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0024483409033199985, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
def test_time_normalization(self) -> None:
module = TimeNormalizationModule(name_in='timenorm',
image_in_tag='images',
image_out_tag='timenorm')
self.pipeline.add_module(module)
self.pipeline.run_module('timenorm')
data = self.pipeline.get_data('timenorm')
assert np.sum(data) == pytest.approx(56.443663773873,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_apply_function_args_none(self) -> None:
module = TimeNormalizationModule(name_in='norm',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm')
data = self.pipeline.get_data('im_norm')
assert np.mean(data) == pytest.approx(2.4012571778516812e-06,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_args_none(self) -> None:
module = TimeNormalizationModule(name_in='norm',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm')
data = self.pipeline.get_data('im_norm')
assert np.allclose(np.mean(data),
-3.3117684144801347e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function_args_none_memory_none(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
module = TimeNormalizationModule(name_in='norm_none',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none')
data = self.pipeline.get_data('im_norm')
assert np.mean(data) == pytest.approx(2.4012571778516812e-06,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_args_none_memory_none(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
module = TimeNormalizationModule(name_in='norm_none',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none')
data = self.pipeline.get_data('im_norm')
assert np.allclose(np.mean(data),
-3.3117684144801347e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
