def test_psf_subtraction_ref_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_mask',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_mask')
data = self.pipeline.get_data('res_mean_ref_mask')
assert np.sum(data) == pytest.approx(9.400558926815758e-06,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_mask')
assert np.sum(data) == pytest.approx(0.08411251293842326,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_len_parang(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 1
parang = self.pipeline.get_data('header_science/PARANG')
self.pipeline.set_attribute('science_prep',
'PARANG',
np.append(parang, 0.),
static=False)
module = PcaPsfSubtractionModule(pca_numbers=[
5,
],
name_in='pca_len_parang',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_len_parang',
extra_rot=0.)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('pca_len_parang')
assert str(error.value) == 'The number of images (80) is not equal to the number of ' \
'parallactic angles (81).'
def test_psf_subtraction(self):
module = PcaPsfSubtractionModule(pca_numbers=(2, ),
name_in='pca',
images_in_tag='fake',
reference_in_tag='fake',
res_mean_tag='res_mean',
res_median_tag=None,
res_arr_out_tag=None,
res_rot_mean_clip_tag=None,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('pca')
data = self.pipeline.get_data('res_mean')
assert np.allclose(data[0, 49, 31],
4.8963214463463886e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.8409659677297164e-08,
rtol=limit,
atol=0.)
assert data.shape == (1, 101, 101)
def test_psf_subtraction_ref_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_ref_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean_mask')
data = self.pipeline.get_data('res_mean_ref_no_mean_mask')
assert np.sum(data) == pytest.approx(2.354235994950671e-05,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_no_mean_mask')
assert np.sum(data) == pytest.approx(5.655460951633233,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction(self):
pca = PcaPsfSubtractionModule(pca_numbers=(5, ),
name_in="psf_subtraction",
images_in_tag="im_arr_stacked",
reference_in_tag="im_arr_stacked",
res_mean_tag="res_mean",
res_median_tag=None,
res_arr_out_tag=None,
res_rot_mean_clip_tag=None,
extra_rot=0.)
self.pipeline.add_module(pca)
self.pipeline.run_module("psf_subtraction")
data = self.pipeline.get_data("res_mean")
assert np.allclose(data[0, 38, 22],
2.073746596517549e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
-1.5133345435496935e-08,
rtol=limit,
atol=0.)
assert data.shape == (1, 46, 46)
def test_psf_subtraction_ref_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref_no_mean',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean')
data = self.pipeline.get_data('res_mean_ref_no_mean')
assert np.allclose(np.mean(data),
3.7029738044199534e-07,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_no_mean')
assert np.allclose(np.mean(data),
2.3755682312090375e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_pca_single_mask(self) -> None:
pca = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_single_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_single_mask',
res_median_tag='res_median_single_mask',
res_weighted_tag='res_weighted_single_mask',
res_rot_mean_clip_tag='res_clip_single_mask',
res_arr_out_tag='res_arr_single_mask',
basis_out_tag='basis_single_mask',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(pca)
self.pipeline.run_module('pca_single_mask')
data = self.pipeline.get_data('res_mean_single_mask')
assert np.allclose(np.mean(data),
-1.6536519510012155e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_median_single_mask')
assert np.allclose(np.mean(data),
5.6094356668078245e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_weighted_single_mask')
assert np.allclose(np.mean(data),
4.7079857263662695e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_clip_single_mask')
assert np.allclose(np.mean(data),
-4.875856901892831e-10,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_arr_single_mask5')
assert np.allclose(np.mean(data),
-1.700674890172441e-09,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('basis_single_mask')
assert np.allclose(np.mean(data),
5.584100479595007e-06,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_no_mean',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_no_mean')
data = self.pipeline.get_data('res_mean_no_mean')
assert np.allclose(np.mean(data),
2.413203757426239e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_no_mean')
assert np.allclose(np.mean(data),
7.4728664805632875e-06,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref')
data = self.pipeline.get_data('res_mean_ref')
assert np.allclose(np.mean(data),
1.1662201512335965e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref')
assert np.allclose(np.mean(data),
-1.6780507257603104e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_no_mean_mask(self):
pca = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(pca)
self.pipeline.run_module('pca_no_mean_mask')
data = self.pipeline.get_data('res_mean_no_mean_mask')
assert np.allclose(np.mean(data),
-1.0905008724474168e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_no_mean_mask')
assert np.allclose(np.sum(np.abs(data)),
1025.2018448288406,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_pca_single(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_single',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single',
res_median_tag='res_median_single',
res_weighted_tag='res_weighted_single',
res_rot_mean_clip_tag='res_clip_single',
res_arr_out_tag='res_arr_single',
basis_out_tag='basis_single',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single')
data = self.pipeline.get_data('res_mean_single')
assert np.allclose(np.mean(data),
2.6959819771522928e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_median_single')
assert np.allclose(np.mean(data),
-2.4142571236920345e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_weighted_single')
assert np.allclose(np.mean(data),
-5.293559651636843e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_clip_single')
assert np.allclose(np.mean(data),
2.6199554737979536e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_arr_single5')
assert np.allclose(np.mean(data),
3.184676024912723e-08,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('basis_single')
assert np.allclose(np.mean(data),
-1.593245396350998e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_ref_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean_mask')
data = self.pipeline.get_data('res_mean_ref_no_mean_mask')
assert np.allclose(np.mean(data),
3.848255803450399e-07,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_no_mean_mask')
assert np.allclose(np.sum(np.abs(data)),
1026.3329224435665,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref_no_mean',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean')
data = self.pipeline.get_data('res_mean_ref_no_mean')
assert np.sum(data) == pytest.approx(0.0006081272007585764,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_no_mean')
assert np.sum(data) == pytest.approx(5.118005177367774,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref')
data = self.pipeline.get_data('res_mean_ref')
assert np.sum(data) == pytest.approx(0.0006330226118859073,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref')
assert np.sum(data) == pytest.approx(0.0943869773132221,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_mask',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_mask')
data = self.pipeline.get_data('res_mean_ref_mask')
assert np.allclose(np.mean(data),
-9.962692629500833e-10,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_mask')
assert np.allclose(np.mean(data),
-2.3165670099810983e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_pca_single_mask(self) -> None:
pca = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_single_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_single_mask',
res_median_tag='res_median_single_mask',
res_weighted_tag='res_weighted_single_mask',
res_rot_mean_clip_tag='res_clip_single_mask',
res_arr_out_tag='res_arr_single_mask',
basis_out_tag='basis_single_mask',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(pca)
self.pipeline.run_module('pca_single_mask')
data = self.pipeline.get_data('res_mean_single_mask')
assert np.sum(data) == pytest.approx(0.00010696166038626307,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_median_single_mask')
assert np.sum(data) == pytest.approx(-0.0021005307611346156,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_weighted_single_mask')
assert np.sum(data) == pytest.approx(0.06014309988789256,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_clip_single_mask')
# assert np.sum(data) == pytest.approx(9.35120662148806e-05, rel=self.limit, abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_arr_single_mask1')
assert np.sum(data) == pytest.approx(0.0006170872862547557,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('basis_single_mask')
assert np.sum(data) == pytest.approx(0.08411251293842359,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_pca_single(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_single',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single',
res_median_tag='res_median_single',
res_weighted_tag='res_weighted_single',
res_rot_mean_clip_tag='res_clip_single',
res_arr_out_tag='res_arr_single',
basis_out_tag='basis_single',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single')
data = self.pipeline.get_data('res_mean_single')
assert np.sum(data) == pytest.approx(-0.00011857022709778602,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_median_single')
assert np.sum(data) == pytest.approx(-0.002184868916566093,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_weighted_single')
assert np.sum(data) == pytest.approx(0.08102176735226937,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
# data = self.pipeline.get_data('res_clip_single')
# assert np.sum(data) == pytest.approx(7.09495495339349e-05, rel=self.limit, abs=0.)
# assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_arr_single1')
assert np.sum(data) == pytest.approx(-0.0002751385418691618,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('basis_single')
assert np.sum(data) == pytest.approx(0.09438697731322143,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_pca_multi_mask(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_multi_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_multi_mask',
res_median_tag='res_median_multi_mask',
res_weighted_tag='res_weighted_multi_mask',
res_rot_mean_clip_tag='res_clip_multi_mask',
res_arr_out_tag=None,
basis_out_tag='basis_multi_mask',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_mask')
data_single = self.pipeline.get_data('res_mean_single_mask')
data_multi = self.pipeline.get_data('res_mean_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_median_single_mask')
data_multi = self.pipeline.get_data('res_median_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_weighted_single_mask')
data_multi = self.pipeline.get_data('res_weighted_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('basis_single_mask')
data_multi = self.pipeline.get_data('basis_multi_mask')
assert np.allclose(data_single, data_multi, rtol=1e-5, atol=0.)
assert data_single.shape == data_multi.shape
def test_psf_subtraction_pca_multi(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_multi',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi',
res_median_tag='res_median_multi',
res_weighted_tag='res_weighted_multi',
res_rot_mean_clip_tag='res_clip_multi',
res_arr_out_tag=None,
basis_out_tag='basis_multi',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi')
data_single = self.pipeline.get_data('res_mean_single')
data_multi = self.pipeline.get_data('res_mean_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_median_single')
data_multi = self.pipeline.get_data('res_median_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_weighted_single')
data_multi = self.pipeline.get_data('res_weighted_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('basis_single')
data_multi = self.pipeline.get_data('basis_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
def test_psf_subtraction_sdi_multi(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
processing_types = ['SDI', 'ADI+SDI']
pca_numbers = [range(1, 3), (range(1, 3), range(1, 3))]
expected = [[-0.0044942456603888695, 0.02613693149969979, -0.15045543311096457, -0.008432530081399985],
[-0.0094093643053501, -0.08171546066331437, 0.560810054788774, -0.014527353460544753]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21)]
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(pca_numbers=pca_numbers[i],
name_in='pca_multi_sdi_'+p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi_sdi_'+p_type,
res_median_tag='res_median_multi_sdi_'+p_type,
res_weighted_tag='res_weighted_multi_sdi_'+p_type,
res_rot_mean_clip_tag='res_clip_multi_sdi_'+p_type,
res_arr_out_tag=None,
basis_out_tag=None,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_sdi_'+p_type)
data = self.pipeline.get_data('res_mean_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][0], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][1], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][2], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_multi_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
def test_psf_subtraction_sdi(self) -> None:
processing_types = ['ADI', 'SDI+ADI', 'ADI+SDI']
expected = [[-0.176152493909826, -0.7938155399702668, 19.552033067005578, -0.21617058715490922],
[-0.004568154679096975, -0.08621264803633322, 2.2901225325010888, -0.010269745733878437],
[0.008630501634061892, -0.05776205365084376, -0.4285370289350482, 0.0058856438951644455]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21), (1, 1, 3, 21, 21)]
pca_numbers = [range(1, 3), (range(1, 3), range(1, 3)), ([1], [1])]
res_arr_tags = [None, None, 'res_arr_single_sdi_ADI+SDI']
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(pca_numbers=pca_numbers[i],
name_in='pca_single_sdi_'+p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single_sdi_'+p_type,
res_median_tag='res_median_single_sdi_'+p_type,
res_weighted_tag='res_weighted_single_sdi_'+p_type,
res_rot_mean_clip_tag='res_clip_single_sdi_'+p_type,
res_arr_out_tag=res_arr_tags[i],
basis_out_tag='basis_single_sdi_'+p_type,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single_sdi_'+p_type)
data = self.pipeline.get_data('res_mean_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][0], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][1], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][2], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_single_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
def test_psf_subtraction(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=[
1,
],
name_in='pca',
images_in_tag='fake',
reference_in_tag='fake',
res_mean_tag='res_mean',
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('pca')
data = self.pipeline.get_data('res_mean')
assert np.sum(data) == pytest.approx(0.014757351752469366,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
def test_psf_subtraction_pca_multi(self):
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
pca = PcaPsfSubtractionModule(pca_numbers=np.arange(1, 21, 1),
name_in="pca_multi",
images_in_tag="science",
reference_in_tag="science",
res_mean_tag="res_mean_multi",
res_median_tag="res_median_multi",
res_weighted_tag="res_weighted_multi",
res_rot_mean_clip_tag="res_clip_multi",
res_arr_out_tag=None,
basis_out_tag="basis_multi",
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(pca)
self.pipeline.run_module("pca_multi")
data_single = self.pipeline.get_data("res_mean_single")
data_multi = self.pipeline.get_data("res_mean_multi")
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data("res_median_single")
data_multi = self.pipeline.get_data("res_median_multi")
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data("res_weighted_single")
data_multi = self.pipeline.get_data("res_weighted_multi")
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data("basis_single")
data_multi = self.pipeline.get_data("basis_multi")
assert np.allclose(data_single, data_multi, rtol=1e-5, atol=0.)
assert data_single.shape == data_multi.shape
def test_psf_subtraction_sdi(self) -> None:
processing_types = ['ADI', 'SDI+ADI', 'ADI+SDI', 'CODI']
expected = [[
-0.16718942968552664, -0.790697125718532, 19.507979777136892,
-0.21617058715490922
],
[
-0.001347198747121658, -0.08621264803633322,
2.3073192270025333, -0.010269745733878437
],
[
0.009450917836998779, -0.05776205365084376,
-0.43506678222476264, 0.0058856438951644455
],
[
-0.2428739554898396, -0.5069023645693083,
9.326414176548905, 0.00
]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21), (1, 1, 3, 21, 21),
(2, 3, 21, 21)]
pca_numbers = [
range(1, 3), (range(1, 3), range(1, 3)), ([1], [1]),
range(1, 3)
]
res_arr_tags = [None, None, 'res_arr_single_sdi_ADI+SDI', None]
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(
pca_numbers=pca_numbers[i],
name_in='pca_single_sdi_' + p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single_sdi_' + p_type,
res_median_tag='res_median_single_sdi_' + p_type,
res_weighted_tag='res_weighted_single_sdi_' + p_type,
res_rot_mean_clip_tag='res_clip_single_sdi_' + p_type,
res_arr_out_tag=res_arr_tags[i],
basis_out_tag='basis_single_sdi_' + p_type,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single_sdi_' + p_type)
data = self.pipeline.get_data('res_mean_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][0],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][1],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][2],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_single_sdi_' + p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]