def test_fit_center_full(self) -> None:
module = FitCenterModule(name_in='fit1',
image_in_tag='shift',
fit_out_tag='fit_full',
mask_out_tag='mask',
method='full',
mask_radii=(None, 0.1),
sign='positive',
model='gaussian',
guess=(1., 2., 3., 3., 0.01, 0., 0.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit1')
data = self.pipeline.get_data('fit_full')
assert np.mean(data[:, 0]) == pytest.approx(0.94, rel=0., abs=0.01)
assert np.mean(data[:, 2]) == pytest.approx(2.07, rel=0., abs=0.01)
assert np.mean(data[:, 4]) == pytest.approx(0.08, rel=0., abs=0.01)
assert np.mean(data[:, 6]) == pytest.approx(0.08, rel=0., abs=0.01)
assert np.mean(data[:, 8]) == pytest.approx(0.24, rel=0., abs=0.01)
assert data.shape == (10, 14)
data = self.pipeline.get_data('mask')
assert np.sum(data) == pytest.approx(103.45599730750453,
rel=self.limit,
abs=0.)
assert data.shape == (10, 18, 18)
def test_fit_center_full(self) -> None:
module = FitCenterModule(name_in='fit1',
image_in_tag='shift',
fit_out_tag='fit_full',
mask_out_tag='mask',
method='full',
radius=0.05,
sign='positive',
model='gaussian',
guess=(6., 4., 3., 3., 0.01, 0., 0.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit1')
data = self.pipeline.get_data('fit_full')
assert np.allclose(np.mean(data[:, 0]), 5.999068486622676, rtol=1e-3, atol=0.)
assert np.allclose(np.mean(data[:, 2]), 4.000055166165185, rtol=1e-3, atol=0.)
assert np.allclose(np.mean(data[:, 4]), 0.08106141046470318, rtol=1e-3, atol=0.)
assert np.allclose(np.mean(data[:, 6]), 0.0810026137349896, rtol=1e-3, atol=0.)
assert np.allclose(np.mean(data[:, 8]), 0.024462594420743763, rtol=1e-3, atol=0.)
assert np.allclose(np.mean(data[:, 12]), 3.0281141786814477e-05, rtol=1e-3, atol=0.)
assert data.shape == (40, 14)
data = self.pipeline.get_data('mask')
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(data[0, 43, 55], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010827527282995305, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_fit_center_full(self):
module = FitCenterModule(name_in='fit1',
image_in_tag='shift',
fit_out_tag='fit_full',
mask_out_tag='mask',
method='full',
radius=0.05,
sign='positive',
model='gaussian',
guess=(6., 4., 3., 3., 0.01, 0., 0.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit1')
data = self.pipeline.get_data('fit_full')
assert np.allclose(data[0, 0], 5.997834332959175, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
13.202402625344051,
rtol=1e-4,
atol=0.)
assert data.shape == (40, 14)
data = self.pipeline.get_data('mask')
assert np.allclose(data[0, 43, 45],
0.023556628129942764,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 43, 55], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
0.00010827527282995305,
rtol=limit,
atol=0.)
assert data.shape == (40, 78, 78)
def test_fit_center_mean(self):
module = FitCenterModule(name_in='fit2',
image_in_tag='shift',
fit_out_tag='fit_mean',
mask_out_tag=None,
method='mean',
radius=0.05,
sign='positive',
model='moffat',
guess=(6., 4., 3., 3., 0.01, 0., 0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit2')
data = self.pipeline.get_data('fit_mean')
assert np.allclose(np.mean(data[:, 0]),
5.999072568941366,
rtol=1e-3,
atol=0.)
assert np.allclose(np.mean(data[:, 2]),
4.000051869708742,
rtol=1e-3,
atol=0.)
assert np.allclose(np.mean(data[:, 4]),
0.08384036587023312,
rtol=1e-3,
atol=0.)
assert np.allclose(np.mean(data[:, 6]),
0.08379313488754872,
rtol=1e-3,
atol=0.)
assert np.allclose(np.mean(data[:, 8]),
0.025631328037795074,
rtol=1e-3,
atol=0.)
assert np.allclose(np.mean(data[:, 12]),
-0.0011275279023032867,
rtol=1e-3,
atol=0.)
assert data.shape == (40, 16)
def test_fit_center_mean(self) -> None:
module = FitCenterModule(name_in='fit2',
image_in_tag='shift',
fit_out_tag='fit_mean',
mask_out_tag=None,
method='mean',
mask_radii=(None, 0.1),
sign='positive',
model='moffat',
guess=(1., 2., 3., 3., 0.01, 0., 0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit2')
data = self.pipeline.get_data('fit_mean')
assert np.mean(data[:, 0]) == pytest.approx(0.94, rel=0., abs=0.01)
assert np.mean(data[:, 2]) == pytest.approx(2.06, rel=0., abs=0.01)
assert np.mean(data[:, 4]) == pytest.approx(0.08, rel=0., abs=0.01)
assert np.mean(data[:, 6]) == pytest.approx(0.08, rel=0., abs=0.01)
assert np.mean(data[:, 8]) == pytest.approx(0.24, rel=0., abs=0.01)
assert data.shape == (10, 16)
def test_fit_center_mean(self):
module = FitCenterModule(name_in='fit2',
image_in_tag='shift',
fit_out_tag='fit_mean',
mask_out_tag=None,
method='mean',
radius=0.05,
sign='positive',
model='moffat',
guess=(6., 4., 3., 3., 0.01, 0., 0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('fit2')
data = self.pipeline.get_data('fit_mean')
assert np.allclose(data[0, 0], 5.999072568941366, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
14.525054620661948,
rtol=1e-4,
atol=0.)
assert data.shape == (40, 16)