def test_psf_preparation(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='science',
image_out_tag='science_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('science_prep')
assert np.allclose(data[0, 0, 0], 0.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 99, 99], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
4.534001223501053e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
module = PSFpreparationModule(name_in='prep2',
image_in_tag='reference',
image_out_tag='reference_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep2')
data = self.pipeline.get_data('reference_prep')
assert np.allclose(data[0, 0, 0], 0.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 99, 99], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
1.227592050148539e-07,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_psf_preparation(self):
prep = PSFpreparationModule(name_in="prep1",
image_in_tag="science",
image_out_tag="science_prep",
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep1")
data = self.pipeline.get_data("science_prep")
assert np.allclose(data[0, 0, 0], 0.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 99, 99], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
4.534001223501053e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
prep = PSFpreparationModule(name_in="prep2",
image_in_tag="reference",
image_out_tag="reference_prep",
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep2")
data = self.pipeline.get_data("reference_prep")
assert np.allclose(data[0, 0, 0], 0.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 99, 99], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
1.227592050148539e-07,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_psf_preparation_no_mask_out(self):
prep = PSFpreparationModule(name_in="prep3",
image_in_tag="read",
image_out_tag="prep3",
mask_out_tag=None,
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep3")
data = self.pipeline.get_data("prep3")
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 99, 99],
-0.000287573978535779,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_psf_preparation_norm_mask(self):
prep = PSFpreparationModule(name_in="prep1",
image_in_tag="read",
image_out_tag="prep1",
mask_out_tag="mask1",
norm=True,
cent_size=0.1,
edge_size=1.0)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep1")
data = self.pipeline.get_data("prep1")
assert np.allclose(data[0, 0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[0, 99, 99], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
0.0001690382058762809,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data("mask1")
assert np.allclose(data[0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[99, 99], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.4268, rtol=limit, atol=0.)
assert data.shape == (100, 100)
def test_psf_preparation_norm_mask(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='read',
image_out_tag='prep1',
mask_out_tag='mask1',
norm=True,
cent_size=0.1,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('prep1')
assert np.allclose(data[0, 0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[0, 99, 99], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
0.0001690382058762809,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data('mask1')
assert np.allclose(data[0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[99, 99], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.4268, rtol=limit, atol=0.)
assert data.shape == (100, 100)
def test_psf_preparation_norm_resize_mask(self):
prep = PSFpreparationModule(name_in="prep1",
image_in_tag="read",
image_out_tag="prep1",
mask_out_tag="mask1",
norm=True,
resize=2.,
cent_size=0.1,
edge_size=1.0)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep1")
data = self.pipeline.get_data("prep1")
assert np.allclose(data[0, 0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[0, 25, 25], 0., rtol=limit, atol=0.)
assert np.allclose(data[0, 99, 99], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
0.0001818623671899089,
rtol=limit,
atol=0.)
assert data.shape == (40, 200, 200)
data = self.pipeline.get_data("mask1")
assert np.allclose(data[0, 0], 0., rtol=limit, atol=0.)
assert np.allclose(data[120, 120], 1., rtol=limit, atol=0.)
assert np.allclose(data[100, 100], 0., rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.1067, rtol=limit, atol=0.)
assert data.shape == (200, 200)
def test_psf_preparation(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='science',
image_out_tag='science_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.05,
edge_size=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('science_prep')
assert np.sum(data) == pytest.approx(5.029285028467547,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
module = PSFpreparationModule(name_in='prep2',
image_in_tag='reference',
image_out_tag='reference_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.05,
edge_size=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('prep2')
data = self.pipeline.get_data('reference_prep')
assert np.sum(data) == pytest.approx(5.029285028467547,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
def test_psf_preparation_sdi(self) -> None:
module = PSFpreparationModule(name_in='prep4',
image_in_tag='read_ifs',
image_out_tag='prep4',
mask_out_tag=None,
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('prep4')
data = self.pipeline.get_data('prep4')
assert np.sum(data) == pytest.approx(749.8396528807369, rel=self.limit, abs=0.)
assert data.shape == (3, 10, 21, 21)
def test_psf_preparation_no_mask_out(self) -> None:
module = PSFpreparationModule(name_in='prep3',
image_in_tag='read',
image_out_tag='prep3',
mask_out_tag=None,
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('prep3')
data = self.pipeline.get_data('prep3')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_psf_preparation_norm_mask(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='read',
image_out_tag='prep1',
mask_out_tag='mask1',
norm=True,
cent_size=0.1,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('prep1')
assert np.sum(data) == pytest.approx(-1.5844830188044685, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_data('mask1')
assert np.sum(data) == pytest.approx(52, rel=self.limit, abs=0.)
assert data.shape == (11, 11)
