def test_multiproc_large_data(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 1000, static=True)
self.pipeline.set_attribute('config', 'CPU', 1, static=True)
module = LineSubtractionModule(name_in='subtract_single',
image_in_tag='large_data',
image_out_tag='im_sub_single',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_single')
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract_multi',
image_in_tag='large_data',
image_out_tag='im_sub_multi',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_multi')
data_single = self.pipeline.get_data('im_sub_single')
data_multi = self.pipeline.get_data('im_sub_multi')
assert np.allclose(data_single, data_multi, rtol=limit, atol=0.)
assert data_single.shape == data_multi.shape
def test_apply_function(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 20, static=True)
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract',
image_in_tag='images',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract')
data = self.pipeline.get_data('images')
assert np.allclose(np.mean(data),
1.9545313398209947e-06,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
data = self.pipeline.get_data('im_subtract')
assert np.allclose(np.mean(data),
5.529431079676073e-22,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 20, static=True)
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract',
image_in_tag='images',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract')
data = self.pipeline.get_data('images')
assert np.mean(data) == pytest.approx(1.1824138000882435e-05,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
data = self.pipeline.get_data('im_subtract')
assert np.mean(data) == pytest.approx(-1.2544487946113274e-21,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_line_background_median(self) -> None:
module = LineSubtractionModule(name_in='line2',
image_in_tag='science',
image_out_tag='science_line2',
combine='median',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line2')
data = self.pipeline.get_data('science_line2')
assert np.sum(data) == pytest.approx(106.09825573198366, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_apply_function_same_port(self) -> None:
module = LineSubtractionModule(name_in='subtract_same',
image_in_tag='im_subtract',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_same')
data = self.pipeline.get_data('im_subtract')
assert np.mean(data) == pytest.approx(-1.4336557652700885e-21,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_same_port(self) -> None:
module = LineSubtractionModule(name_in='subtract_same',
image_in_tag='im_subtract',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_same')
data = self.pipeline.get_data('im_subtract')
assert np.allclose(np.mean(data),
7.318364664277155e-22,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_line_background_median(self):
module = LineSubtractionModule(name_in='line2',
image_in_tag='line',
image_out_tag='line_median',
combine='median',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line2')
data = self.pipeline.get_data('line_median')
assert np.allclose(data[0, 10, 10],
0.09782789699611127,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0024723022374338196,
rtol=limit,
atol=0.)
assert data.shape == (16, 20, 20)
def test_line_background_mean(self):
module = LineSubtractionModule(name_in='line1',
image_in_tag='line',
image_out_tag='line_mean',
combine='mean',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line1')
data = self.pipeline.get_data('line_mean')
assert np.allclose(data[0, 10, 10],
0.09792388324443534,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0024245904637616735,
rtol=limit,
atol=0.)
assert data.shape == (16, 20, 20)