def test_nodding_background(self) -> None:
module = StackCubesModule(name_in='mean',
image_in_tag='sky',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.sum(data) == pytest.approx(21.108557759610548, rel=self.limit, abs=0.)
assert data.shape == (2, 11, 11)
attr = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.sum(attr) == pytest.approx(1, rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('mean', 'NFRAMES', static=False)
assert np.sum(attr) == pytest.approx(2, rel=self.limit, abs=0.)
assert attr.shape == (2, )
module = NoddingBackgroundModule(name_in='nodding',
sky_in_tag='mean',
science_in_tag='science',
image_out_tag='nodding',
mode='both')
self.pipeline.add_module(module)
self.pipeline.run_module('nodding')
data = self.pipeline.get_data('nodding')
assert np.sum(data) == pytest.approx(1.793466459074705, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_stack_cube(self):
module = StackCubesModule(name_in='stackcube',
image_in_tag='images',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stackcube')
data = self.pipeline.get_data('mean')
assert np.allclose(data[0, 50, 50],
0.09805840100024205,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738069,
rtol=limit,
atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
def test_stack_cube(self) -> None:
module = StackCubesModule(name_in='stackcube',
image_in_tag='images',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stackcube')
data = self.pipeline.get_data('mean')
assert np.mean(data) == pytest.approx(0.08722544528764689,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.mean(attribute) == pytest.approx(0.5, rel=self.limit, abs=0.)
assert attribute.shape == (2, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.mean(attribute) == pytest.approx(1, rel=self.limit, abs=0.)
assert attribute.shape == (2, )
def test_nodding_background(self):
mean = StackCubesModule(name_in='mean',
image_in_tag='sky',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(mean)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.allclose(data[0, 50, 50],
1.270877476321969e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
8.937360237872607e-07,
rtol=limit,
atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
nodding = NoddingBackgroundModule(name_in='nodding',
sky_in_tag='mean',
science_in_tag='star',
image_out_tag='nodding',
mode='both')
self.pipeline.add_module(nodding)
self.pipeline.run_module('nodding')
data = self.pipeline.get_data('nodding')
assert np.allclose(data[0, 50, 50],
0.09797142624717381,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.945087327935862e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)