def test_mcmc_sampling_wrong_prior(self):
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc_wrong_prior',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc_prior',
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='test',
variance='gaussian')
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('mcmc_wrong_prior')
assert str(error.value) == 'Prior type not recognized.'
def test_mcmc_sampling_gaussian(self):
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
mcmc = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5, 0.5)),
name_in="mcmc_gaussian",
image_in_tag="adi_scale",
psf_in_tag="psf_avg",
chain_out_tag="mcmc_gaussian",
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="gaussian")
self.pipeline.add_module(mcmc)
self.pipeline.run_module("mcmc_gaussian")
single = self.pipeline.get_data("mcmc_gaussian")
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(self):
self.pipeline.set_attribute('adi',
'PARANG',
np.arange(0., 200., 10.),
static=False)
module = DerotateAndStackModule(name_in='stack',
image_in_tag='psf',
image_out_tag='psf_stack',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('psf_stack')
assert data.shape == (1, 15, 15)
module = MCMCsamplingModule(name_in='mcmc',
image_in_tag='adi',
psf_in_tag='psf_stack',
chain_out_tag='mcmc',
param=(0.15, 0., 1.),
bounds=((0.1, 0.2), (-2., 2.), (-1., 2.)),
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=(7, 13, 0.1),
mask=None,
extra_rot=0.,
merit='gaussian',
residuals='median',
scale=2.,
sigma=(1e-3, 1e-1, 1e-2))
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.'
data = self.pipeline.get_data('mcmc')
data = data[:, 50:, :].reshape((-1, 3))
assert np.allclose(np.median(data[:, 0]), 0.15, rtol=0., atol=0.1)
assert np.allclose(np.median(data[:, 1]), 0., rtol=0., atol=1.0)
assert np.allclose(np.median(data[:, 2]), 0.0, rtol=0., atol=1.)
def test_mcmc_sampling(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.set_attribute('adi',
'PARANG',
np.arange(0., 200., 10.),
static=False)
module = DerotateAndStackModule(name_in='stack',
image_in_tag='psf',
image_out_tag='psf_stack',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('psf_stack')
assert data.shape == (1, 15, 15)
module = MCMCsamplingModule(name_in='mcmc',
image_in_tag='adi',
psf_in_tag='psf_stack',
chain_out_tag='mcmc',
param=(0.15, 0., 1.),
bounds=((0.1, 0.2), (-2., 2.), (-1., 2.)),
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=(7, 13, 0.1),
mask=None,
extra_rot=0.,
merit='gaussian',
residuals='median',
scale=2.,
sigma=(1e-3, 1e-1, 1e-2))
self.pipeline.add_module(module)
self.pipeline.run_module('mcmc')
data = self.pipeline.get_data('mcmc')
data = data[50:, :, :].reshape((-1, 3))
assert np.allclose(np.median(data[:, 0]), 0.15, rtol=0., atol=0.1)
assert np.allclose(np.median(data[:, 1]), 0., rtol=0., atol=1.0)
assert np.allclose(np.median(data[:, 2]), 0.0, rtol=0., atol=1.)
attr = self.pipeline.get_attribute('mcmc', 'ACCEPTANCE', static=True)
assert np.allclose(attr, 0.3, rtol=0., atol=0.3)
def test_mcmc_sampling_poisson(self):
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc_prior',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc_prior',
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture={
'type': 'elliptical',
'pos_x': 7.0,
'pos_y': 12.5,
'semimajor': 0.1,
'semiminor': 0.1,
'angle': 0.0
},
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior='aperture',
variance='poisson')
self.pipeline.add_module(module)
with pytest.warns(FutureWarning) as warning:
self.pipeline.run_module('mcmc_prior')
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_prior')
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(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = DerotateAndStackModule(name_in='stack',
image_in_tag='psf',
image_out_tag='psf_stack',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('psf_stack')
assert np.sum(data) == pytest.approx(10.843655133957288,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
module = MCMCsamplingModule(name_in='mcmc',
image_in_tag='adi',
psf_in_tag='psf_stack',
chain_out_tag='mcmc',
param=(0.15, 0., 1.),
bounds=((0.1, 0.2), (-2., 2.), (-1., 2.)),
nwalkers=6,
nsteps=5,
psf_scaling=-1.,
pca_number=1,
aperture=(10, 16, 0.06),
mask=None,
extra_rot=0.,
merit='gaussian',
residuals='median',
sigma=(1e-3, 1e-1, 1e-2))
self.pipeline.add_module(module)
# with pytest.warns(RuntimeWarning) as warning:
self.pipeline.run_module('mcmc')
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)