def test_scale_images(self):
module = ScaleImagesModule(scaling=(2., 2., None),
name_in='scale1',
image_in_tag='read',
image_out_tag='scale1')
self.pipeline.add_module(module)
self.pipeline.run_module('scale1')
module = ScaleImagesModule(scaling=(None, None, 2.),
name_in='scale2',
image_in_tag='read',
image_out_tag='scale2')
self.pipeline.add_module(module)
self.pipeline.run_module('scale2')
data = self.pipeline.get_data('scale1')
assert np.allclose(data[0, 100, 100], 0.02356955774929094, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 2.507373695434516e-05, rtol=limit, atol=0.)
assert data.shape == (40, 200, 200)
data = self.pipeline.get_data('scale2')
assert np.allclose(data[0, 50, 50], 0.19596827004387415, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00020058989563476127, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
def test_scale_images(self) -> None:
module = ScaleImagesModule(scaling=(2., 2., None),
name_in='scale1',
image_in_tag='read',
image_out_tag='scale1')
self.pipeline.add_module(module)
self.pipeline.run_module('scale1')
module = ScaleImagesModule(scaling=(None, None, 2.),
name_in='scale2',
image_in_tag='read',
image_out_tag='scale2')
self.pipeline.add_module(module)
self.pipeline.run_module('scale2')
data = self.pipeline.get_data('scale1')
assert np.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 22, 22)
data = self.pipeline.get_data('scale2')
assert np.sum(data) == pytest.approx(211.08557759610554, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_scale_images(self) -> None:
module = ScaleImagesModule(name_in='scale1',
image_in_tag='read',
image_out_tag='scale1',
scaling=(2., 2., None),
pixscale=True)
self.pipeline.add_module(module)
self.pipeline.run_module('scale1')
module = ScaleImagesModule(name_in='scale2',
image_in_tag='read',
image_out_tag='scale2',
scaling=(None, None, 2.),
pixscale=True)
self.pipeline.add_module(module)
self.pipeline.run_module('scale2')
data = self.pipeline.get_data('scale1')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 22, 22)
data = self.pipeline.get_data('scale2')
assert np.sum(data) == pytest.approx(211.08557759610554,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
attr = self.pipeline.get_attribute('read', 'PIXSCALE', static=True)
assert attr == pytest.approx(0.027, rel=self.limit, abs=0.)
attr = self.pipeline.get_attribute('scale1', 'PIXSCALE', static=True)
assert attr == pytest.approx(0.0135, rel=self.limit, abs=0.)
attr = self.pipeline.get_attribute('scale2', 'PIXSCALE', static=True)
assert attr == pytest.approx(0.027, rel=self.limit, abs=0.)
def test_apply_function_to_images_2d_args(self):
self.pipeline.set_attribute("image_2d", "PIXSCALE", 0.1, static=True)
scale = ScaleImagesModule(scaling=(1.2, 1.2, 10.),
pixscale=True,
name_in="scale2",
image_in_tag="image_2d",
image_out_tag="scale_2d")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale2")
data = self.pipeline.get_data("scale_2d")
assert np.allclose(np.mean(data),
8.937141109641279e-05,
rtol=limit,
atol=0.)
assert data.shape == (12, 12)
attribute = self.pipeline.get_attribute("scale_2d",
"PIXSCALE",
static=True)
assert np.allclose(attribute, 0.08333333333333334, rtol=limit, atol=0.)
def test_apply_function_to_images_3d_args(self):
self.pipeline.set_attribute("config", "MEMORY", 1, static=True)
self.pipeline.set_attribute("image_3d", "PIXSCALE", 0.1, static=True)
scale = ScaleImagesModule(scaling=(1.2, 1.2, 10.),
pixscale=True,
name_in="scale1",
image_in_tag="image_3d",
image_out_tag="scale_3d")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale1")
data = self.pipeline.get_data("scale_3d")
assert np.allclose(np.mean(data),
7.042953308754017e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 12, 12)
attribute = self.pipeline.get_attribute("scale_3d",
"PIXSCALE",
static=True)
assert np.allclose(attribute, 0.08333333333333334, rtol=limit, atol=0.)
def test_mcmc_sampling_gaussian(self):
self.pipeline.set_attribute('adi',
'PARANG',
np.arange(0., 200., 10.),
static=False)
module = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in='scale1',
image_in_tag='adi',
image_out_tag='adi_scale')
self.pipeline.add_module(module)
self.pipeline.run_module('scale1')
data = self.pipeline.get_data('adi_scale')
assert np.allclose(data[0, 7, 7],
9.82388817812263,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
module = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in='scale2',
image_in_tag='psf',
image_out_tag='psf_scale')
self.pipeline.add_module(module)
self.pipeline.run_module('scale2')
data = self.pipeline.get_data('psf_scale')
assert np.allclose(data[0, 7, 7],
9.806026673451198,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
module = DerotateAndStackModule(name_in='take_psf_avg',
image_in_tag='psf_scale',
image_out_tag='psf_avg',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('take_psf_avg')
data = self.pipeline.get_data('psf_avg')
assert data.shape == (1, 15, 15)
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc',
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture={
'type': 'circular',
'pos_x': 7.0,
'pos_y': 12.5,
'radius': 0.1
},
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior='flat',
variance='gaussian')
self.pipeline.add_module(module)
with pytest.warns(FutureWarning) as warning:
self.pipeline.run_module('mcmc')
assert warning[0].message.args[0] == 'Using a non-tuple sequence for multidimensional ' \
'indexing is deprecated; use `arr[tuple(seq)]` ' \
'instead of `arr[seq]`. In the future this will be ' \
'interpreted as an array index, ' \
'`arr[np.array(seq)]`, which will result either ' \
'in an error or a different result.'
single = self.pipeline.get_data('mcmc')
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)
def test_mcmc_sampling_poisson(self):
self.pipeline.set_attribute("adi",
"PARANG",
np.arange(0., 200., 10.),
static=False)
scale = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in="scale1",
image_in_tag="adi",
image_out_tag="adi_scale")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale1")
data = self.pipeline.get_data("adi_scale")
assert np.allclose(data[0, 7, 7],
9.82388817812263,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
scale = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in="scale2",
image_in_tag="psf",
image_out_tag="psf_scale")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale2")
data = self.pipeline.get_data("psf_scale")
assert np.allclose(data[0, 7, 7],
9.806026673451198,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
avg_psf = DerotateAndStackModule(name_in="take_psf_avg",
image_in_tag="psf_scale",
image_out_tag="psf_avg",
derotate=False,
stack="mean")
self.pipeline.add_module(avg_psf)
self.pipeline.run_module("take_psf_avg")
data = self.pipeline.get_data("psf_avg")
assert data.shape == (15, 15)
mcmc = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5, 0.5)),
name_in="mcmc",
image_in_tag="adi_scale",
psf_in_tag="psf_avg",
chain_out_tag="mcmc",
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=0.1,
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior="flat",
variance="poisson")
self.pipeline.add_module(mcmc)
self.pipeline.run_module("mcmc")
single = self.pipeline.get_data("mcmc")
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)