def test_dithering_center(self):
pca_dither = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=((25., 75.), (75., 75.),
(75., 25.), (25., 25.)),
cubes=1,
size=0.8,
gaussian=0.1,
subframe=None,
pca_number=5,
mask_star=0.1,
bad_pixel=None,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(pca_dither)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.allclose(data[0, 14, 14],
0.05302488794328089,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010406279782666378,
rtol=1e-3,
atol=0.)
assert data.shape == (80, 31, 31)
def test_dithering_center(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=((5, 5), (5, 15), (15, 15),
(15, 5)),
cubes=1,
size=0.2,
gaussian=0.05,
subframe=None,
pca_number=1,
mask_star=0.05,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.sum(data) == pytest.approx(209.8271898501695,
rel=1e-6,
abs=0.)
assert data.shape == (20, 9, 9)
def test_dithering_attributes(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.2,
gaussian=0.05,
subframe=0.1,
pca_number=1,
mask_star=0.05)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.sum(data) == pytest.approx(54.62410860562912, rel=self.limit, abs=0.)
assert data.shape == (20, 9, 9)
data = self.pipeline.get_data('dither_dither_star1')
assert np.sum(data) == pytest.approx(54.873885838788595, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.sum(data) == pytest.approx(54.204960755115245, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_background1')
assert np.sum(data) == pytest.approx(-0.24977723315947564, rel=self.limit, abs=0.)
assert data.shape == (15, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.sum(data) == pytest.approx(-0.6816458444287745, rel=1e-5, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.sum(data) == pytest.approx(55.63879076093719, rel=1e-6, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.sum(data) == pytest.approx(360.0, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('pca_dither1')
assert np.sum(data) == pytest.approx(208.24417329569593, rel=1e-6, abs=0.)
assert data.shape == (20, 9, 9)
attr = self.pipeline.get_attribute('dither_dither_pca_res1', 'STAR_POSITION', static=False)
assert np.sum(attr) == pytest.approx(40., rel=self.limit, abs=0.)
assert attr.shape == (5, 2)
def test_dithering_center(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=[(5, 5), (5, 15), (15, 15), (15, 5)],
cubes=1,
size=0.2,
gaussian=0.05,
subframe=None,
pca_number=1,
mask_star=0.05)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.sum(data) == pytest.approx(208.24417332523367, rel=1e-6, abs=0.)
assert data.shape == (20, 9, 9)
def test_dithering_attributes(self):
pca_dither = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.8,
gaussian=0.1,
subframe=0.5,
pca_number=5,
mask_star=0.1,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(pca_dither)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.allclose(data[0, 14, 14],
0.05304008435511765,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0002606205855710527,
rtol=1e-6,
atol=0.)
assert data.shape == (80, 31, 31)
data = self.pipeline.get_data('dither_dither_star1')
assert np.allclose(data[0, 14, 14],
0.05304008435511765,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010414302265833978,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.allclose(data[0, 14, 14],
0.0530465391626132,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010426228104479674,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_background1')
assert np.allclose(data[0, 14, 14],
-0.00010629310882411674,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
3.5070523360436835e-07,
rtol=1e-6,
atol=0.)
assert data.shape == (60, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.allclose(data[0, 14, 14],
1.5196412298279846e-05,
rtol=1e-5,
atol=0.)
assert np.allclose(np.mean(data),
1.9779802529804516e-07,
rtol=1e-4,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.allclose(data[0, 14, 14],
0.05302488794281937,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010412324285580998,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.allclose(data[0, 14, 14], 0., rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data),
0.9531737773152965,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('pca_dither1')
assert np.allclose(data[0, 14, 14],
0.05302488794281937,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.001040627977720779,
rtol=1e-6,
atol=0.)
assert data.shape == (80, 31, 31)
data = self.pipeline.get_attribute('dither_dither_pca_res1',
'STAR_POSITION',
static=False)
assert np.allclose(data[0, 0], [15., 15.], rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 15., rtol=1e-6, atol=0.)
assert data.shape == (20, 2)
def test_dithering_attributes(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.2,
gaussian=0.05,
subframe=0.1,
pca_number=1,
mask_star=0.05,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.sum(data) == pytest.approx(54.62410860562912,
rel=self.limit,
abs=0.)
assert data.shape == (20, 9, 9)
data = self.pipeline.get_data('dither_dither_star1')
assert np.sum(data) == pytest.approx(54.873885838788595,
rel=self.limit,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.sum(data) == pytest.approx(54.204960755115245,
rel=self.limit,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_background1')
assert np.sum(data) == pytest.approx(-0.24977723315947564,
rel=self.limit,
abs=0.)
assert data.shape == (15, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.sum(data) == pytest.approx(-0.01019999314121019,
rel=1e-5,
abs=0.)
print(np.sum(data))
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.sum(data) == pytest.approx(54.884085831929795,
rel=1e-6,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.sum(data) == pytest.approx(360.0, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('pca_dither1')
assert np.sum(data) == pytest.approx(208.774670964812,
rel=1e-6,
abs=0.)
assert data.shape == (20, 9, 9)
attr = self.pipeline.get_attribute('dither_dither_pca_res1',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(51., rel=self.limit, abs=0.)
assert attr.shape == (5, 2)