def test_set_and_get_attribute(self) -> None:
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
pipeline.set_attribute('images', 'PIXSCALE', 0.1, static=True)
pipeline.set_attribute('images',
'PARANG',
np.arange(1., 11., 1.),
static=False)
attribute = pipeline.get_attribute('images', 'PIXSCALE', static=True)
assert attribute == pytest.approx(0.1, rel=self.limit, abs=0.)
attribute = pipeline.get_attribute('images', 'PARANG', static=False)
assert attribute == pytest.approx(np.arange(1., 11., 1.),
rel=self.limit,
abs=0.)
pipeline.set_attribute('images',
'PARANG',
np.arange(10., 21., 1.),
static=False)
attribute = pipeline.get_attribute('images', 'PARANG', static=False)
assert attribute == pytest.approx(np.arange(10., 21., 1.),
rel=self.limit,
abs=0.)
def test_set_and_get_attribute(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
pipeline.set_attribute('images', 'PIXSCALE', 0.1, static=True)
pipeline.set_attribute('images',
'PARANG',
np.arange(1., 11., 1.),
static=False)
attribute = pipeline.get_attribute('images', 'PIXSCALE', static=True)
assert np.allclose(attribute, 0.1, rtol=limit, atol=0.)
attribute = pipeline.get_attribute('images', 'PARANG', static=False)
assert np.allclose(attribute,
np.arange(1., 11., 1.),
rtol=limit,
atol=0.)
pipeline.set_attribute('images',
'PARANG',
np.arange(10., 21., 1.),
static=False)
attribute = pipeline.get_attribute('images', 'PARANG', static=False)
assert np.allclose(attribute,
np.arange(10., 21., 1.),
rtol=limit,
atol=0.)
def test_create_none_config(self) -> None:
file_obj = open(self.test_dir + 'PynPoint_config.ini', 'w')
file_obj.write('[header]\n\n')
file_obj.write('INSTRUMENT: None\n')
file_obj.write('NFRAMES: None\n')
file_obj.write('EXP_NO: None\n')
file_obj.write('NDIT: None\n')
file_obj.write('PARANG_START: ESO ADA POSANG\n')
file_obj.write('PARANG_END: None\n')
file_obj.write('DITHER_X: None\n')
file_obj.write('DITHER_Y: None\n')
file_obj.write('DIT: None\n')
file_obj.write('LATITUDE: None\n')
file_obj.write('LONGITUDE: None\n')
file_obj.write('PUPIL: None\n')
file_obj.write('DATE: None\n')
file_obj.write('RA: None\n')
file_obj.write('DEC: None\n\n')
file_obj.write('[settings]\n\n')
file_obj.write('PIXSCALE: None\n')
file_obj.write('MEMORY: None\n')
file_obj.write('CPU: None\n')
file_obj.close()
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
attribute = pipeline.get_attribute('config', 'MEMORY', static=True)
assert attribute == 0
attribute = pipeline.get_attribute('config', 'CPU', static=True)
assert attribute == 0
attribute = pipeline.get_attribute('config', 'PIXSCALE', static=True)
assert attribute == pytest.approx(0., rel=self.limit, abs=0.)
attribute = pipeline.get_attribute('config', 'INSTRUMENT', static=True)
assert attribute == 'None'
create_config(self.test_dir + 'PynPoint_config.ini')
def test_create_none_config(self):
file_obj = open(self.test_dir + "PynPoint_config.ini", 'w')
file_obj.write('[header]\n\n')
file_obj.write('INSTRUMENT: None\n')
file_obj.write('NFRAMES: None\n')
file_obj.write('EXP_NO: None\n')
file_obj.write('NDIT: None\n')
file_obj.write('PARANG_START: ESO ADA POSANG\n')
file_obj.write('PARANG_END: None\n')
file_obj.write('DITHER_X: None\n')
file_obj.write('DITHER_Y: None\n')
file_obj.write('DIT: None\n')
file_obj.write('LATITUDE: None\n')
file_obj.write('LONGITUDE: None\n')
file_obj.write('PUPIL: None\n')
file_obj.write('DATE: None\n')
file_obj.write('RA: None\n')
file_obj.write('DEC: None\n\n')
file_obj.write('[settings]\n\n')
file_obj.write('PIXSCALE: None\n')
file_obj.write('MEMORY: None\n')
file_obj.write('CPU: None\n')
file_obj.close()
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
attribute = pipeline.get_attribute("config", "MEMORY", static=True)
assert attribute == 0
attribute = pipeline.get_attribute("config", "CPU", static=True)
assert attribute == 0
attribute = pipeline.get_attribute("config", "PIXSCALE", static=True)
assert np.allclose(attribute, 0.0, rtol=limit, atol=0.)
attribute = pipeline.get_attribute("config", "INSTRUMENT", static=True)
assert attribute == "None"
create_config(self.test_dir + "PynPoint_config.ini")
def test_create_pipeline_existing_database(self):
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(10, 100, 100))
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'w') as hdf_file:
dset = hdf_file.create_dataset('images', data=images)
dset.attrs['PIXSCALE'] = 0.01
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
data = pipeline.get_data('images')
assert np.allclose(data[0, 0, 0], 0.00032486907273264834, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 1.0506056979365338e-06, rtol=limit, atol=0.)
assert pipeline.get_attribute('images', 'PIXSCALE') == 0.01
os.remove(self.test_dir+'PynPoint_database.hdf5')
def test_create_pipeline_existing_database(self) -> None:
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(5, 11, 11))
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'w') as hdf_file:
dset = hdf_file.create_dataset('images', data=images)
dset.attrs['PIXSCALE'] = 0.01
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
data = pipeline.get_data('images')
assert np.mean(data) == pytest.approx(1.1824138000882435e-05, rel=self.limit, abs=0.)
assert data.shape == (5, 11, 11)
assert pipeline.get_attribute('images', 'PIXSCALE') == 0.01
os.remove(self.test_dir+'PynPoint_database.hdf5')
def test_create_pipeline_existing_database(self):
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(10, 100, 100))
h5f = h5py.File(self.test_dir + "PynPoint_database.hdf5", "w")
dset = h5f.create_dataset("images", data=images)
dset.attrs['PIXSCALE'] = 0.01
h5f.close()
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
data = pipeline.get_data("images")
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.0506056979365338e-06,
rtol=limit,
atol=0.)
assert pipeline.get_attribute("images", "PIXSCALE") == 0.01
os.remove(self.test_dir + "PynPoint_database.hdf5")
class TestFrameSelection:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir + 'images')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['images'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir + 'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
attr = self.pipeline.get_attribute('read', 'NDIT', static=False)
assert np.sum(attr) == pytest.approx(10, rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('read', 'NFRAMES', static=False)
assert np.sum(attr) == pytest.approx(10, rel=self.limit, abs=0.)
assert attr.shape == (2, )
self.pipeline.set_attribute('read', 'NDIT', [4, 4], static=False)
def test_remove_last_frame(self) -> None:
module = RemoveLastFrameModule(name_in='last',
image_in_tag='read',
image_out_tag='last')
self.pipeline.add_module(module)
self.pipeline.run_module('last')
data = self.pipeline.get_data('last')
assert np.sum(data) == pytest.approx(84.68885503527224,
rel=self.limit,
abs=0.)
assert data.shape == (8, 11, 11)
self.pipeline.set_attribute('last',
'PARANG',
np.arange(8.),
static=False)
self.pipeline.set_attribute('last',
'STAR_POSITION',
np.full((8, 2), 5.),
static=False)
attr = self.pipeline.get_attribute('last', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(28., rel=self.limit, abs=0.)
assert attr.shape == (8, )
attr = self.pipeline.get_attribute('last',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(80., rel=self.limit, abs=0.)
assert attr.shape == (8, 2)
def test_remove_start_frame(self) -> None:
module = RemoveStartFramesModule(frames=1,
name_in='start',
image_in_tag='last',
image_out_tag='start')
self.pipeline.add_module(module)
self.pipeline.run_module('start')
data = self.pipeline.get_data('start')
assert np.sum(data) == pytest.approx(64.44307047549808,
rel=self.limit,
abs=0.)
assert data.shape == (6, 11, 11)
attr = self.pipeline.get_attribute('start', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(24., rel=self.limit, abs=0.)
assert attr.shape == (6, )
attr = self.pipeline.get_attribute('start',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(60., rel=self.limit, abs=0.)
assert attr.shape == (6, 2)
def test_remove_frames(self) -> None:
module = RemoveFramesModule(name_in='remove',
image_in_tag='start',
selected_out_tag='selected',
removed_out_tag='removed',
frames=[2, 5])
self.pipeline.add_module(module)
self.pipeline.run_module('remove')
data = self.pipeline.get_data('selected')
assert np.sum(data) == pytest.approx(43.68337741822863,
rel=self.limit,
abs=0.)
assert data.shape == (4, 11, 11)
data = self.pipeline.get_data('removed')
assert np.sum(data) == pytest.approx(20.759693057269445,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
attr = self.pipeline.get_attribute('selected', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(14., rel=self.limit, abs=0.)
assert attr.shape == (4, )
attr = self.pipeline.get_attribute('selected',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(40., rel=self.limit, abs=0.)
assert attr.shape == (4, 2)
attr = self.pipeline.get_attribute('removed', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(10., rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('removed',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(20., rel=self.limit, abs=0.)
assert attr.shape == (2, 2)
def test_frame_selection(self) -> None:
module = FrameSelectionModule(name_in='select1',
image_in_tag='start',
selected_out_tag='selected1',
removed_out_tag='removed1',
index_out_tag='index1',
method='median',
threshold=2.,
fwhm=0.1,
aperture=('circular', 0.1),
position=(None, None, 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('select1')
data = self.pipeline.get_data('selected1')
assert np.sum(data) == pytest.approx(54.58514780071149,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
data = self.pipeline.get_data('removed1')
assert np.sum(data) == pytest.approx(9.857922674786586,
rel=self.limit,
abs=0.)
assert data.shape == (1, 11, 11)
data = self.pipeline.get_data('index1')
assert np.sum(data) == pytest.approx(5, rel=self.limit, abs=0.)
assert data.shape == (1, )
attr = self.pipeline.get_attribute('selected1', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(17., rel=self.limit, abs=0.)
assert attr.shape == (5, )
attr = self.pipeline.get_attribute('selected1',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(50, rel=self.limit, abs=0.)
assert attr.shape == (5, 2)
attr = self.pipeline.get_attribute('removed1', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(7., rel=self.limit, abs=0.)
assert attr.shape == (1, )
attr = self.pipeline.get_attribute('removed1',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(10, rel=self.limit, abs=0.)
assert attr.shape == (1, 2)
module = FrameSelectionModule(name_in='select2',
image_in_tag='start',
selected_out_tag='selected2',
removed_out_tag='removed2',
index_out_tag='index2',
method='max',
threshold=1.,
fwhm=0.1,
aperture=('annulus', 0.05, 0.1),
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('select2')
data = self.pipeline.get_data('selected2')
assert np.sum(data) == pytest.approx(21.42652724866543,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
data = self.pipeline.get_data('removed2')
assert np.sum(data) == pytest.approx(43.016543226832646,
rel=self.limit,
abs=0.)
assert data.shape == (4, 11, 11)
data = self.pipeline.get_data('index2')
assert np.sum(data) == pytest.approx(10, rel=self.limit, abs=0.)
assert data.shape == (4, )
attr = self.pipeline.get_attribute('selected2', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(8., rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('selected2',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(20, rel=self.limit, abs=0.)
assert attr.shape == (2, 2)
attr = self.pipeline.get_attribute('removed2', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(16., rel=self.limit, abs=0.)
assert attr.shape == (4, )
attr = self.pipeline.get_attribute('removed2',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(40, rel=self.limit, abs=0.)
assert attr.shape == (4, 2)
module = FrameSelectionModule(name_in='select3',
image_in_tag='start',
selected_out_tag='selected3',
removed_out_tag='removed3',
index_out_tag='index3',
method='range',
threshold=(10., 10.7),
fwhm=0.1,
aperture=('circular', 0.1),
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('select3')
data = self.pipeline.get_data('selected3')
assert np.sum(data) == pytest.approx(22.2568501695632,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
data = self.pipeline.get_data('removed3')
assert np.sum(data) == pytest.approx(42.18622030593487,
rel=self.limit,
abs=0.)
assert data.shape == (4, 11, 11)
data = self.pipeline.get_data('index3')
assert np.sum(data) == pytest.approx(12, rel=self.limit, abs=0.)
assert data.shape == (4, )
def test_image_statistics_full(self) -> None:
module = ImageStatisticsModule(name_in='stat1',
image_in_tag='read',
stat_out_tag='stat1',
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('stat1')
data = self.pipeline.get_data('stat1')
assert np.sum(data) == pytest.approx(115.68591492205017,
rel=self.limit,
abs=0.)
assert data.shape == (10, 6)
def test_image_statistics_posiiton(self) -> None:
module = ImageStatisticsModule(name_in='stat2',
image_in_tag='read',
stat_out_tag='stat2',
position=(5, 5, 0.1))
self.pipeline.add_module(module)
self.pipeline.run_module('stat2')
data = self.pipeline.get_data('stat2')
assert np.sum(data) == pytest.approx(118.7138708968444,
rel=self.limit,
abs=0.)
assert data.shape == (10, 6)
def test_frame_similarity_mse(self) -> None:
module = FrameSimilarityModule(name_in='simi1',
image_tag='read',
method='MSE',
mask_radius=(0., 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('simi1')
attr = self.pipeline.get_attribute('read', 'MSE', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(0.11739141370277852,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_frame_similarity_pcc(self) -> None:
module = FrameSimilarityModule(name_in='simi2',
image_tag='read',
method='PCC',
mask_radius=(0., 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('simi2')
attr = self.pipeline.get_attribute('read', 'PCC', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(9.134820985662829,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_frame_similarity_ssim(self) -> None:
module = FrameSimilarityModule(name_in='simi3',
image_tag='read',
method='SSIM',
mask_radius=(0., 0.2),
temporal_median='constant')
self.pipeline.add_module(module)
self.pipeline.run_module('simi3')
attr = self.pipeline.get_attribute('read', 'SSIM', static=False)
assert np.min(attr) > 0.
assert np.sum(attr) == pytest.approx(9.096830542868524,
rel=self.limit,
abs=0.)
assert attr.shape == (10, )
def test_select_by_attribute(self) -> None:
self.pipeline.set_attribute('read',
'INDEX',
np.arange(44),
static=False)
module = SelectByAttributeModule(name_in='frame_removal_1',
image_in_tag='read',
attribute_tag='SSIM',
number_frames=6,
order='descending',
selected_out_tag='select_sim',
removed_out_tag='remove_sim')
self.pipeline.add_module(module)
self.pipeline.run_module('frame_removal_1')
attr = self.pipeline.get_attribute('select_sim', 'INDEX', static=False)
assert np.sum(attr) == pytest.approx(946, rel=self.limit, abs=0.)
assert attr.shape == (44, )
attr = self.pipeline.get_attribute('select_sim', 'SSIM', static=False)
assert np.sum(attr) == pytest.approx(5.556889532446573,
rel=self.limit,
abs=0.)
assert attr.shape == (6, )
attr = self.pipeline.get_attribute('remove_sim', 'SSIM', static=False)
assert np.sum(attr) == pytest.approx(3.539941010421951,
rel=self.limit,
abs=0.)
assert attr.shape == (4, )
def test_residual_selection(self) -> None:
module = ResidualSelectionModule(name_in='residual_select',
image_in_tag='start',
selected_out_tag='res_selected',
removed_out_tag='res_removed',
percentage=80.,
annulus_radii=(0.1, 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('residual_select')
data = self.pipeline.get_data('res_selected')
assert np.sum(data) == pytest.approx(41.77295229983322,
rel=self.limit,
abs=0.)
assert data.shape == (4, 11, 11)
data = self.pipeline.get_data('res_removed')
assert np.sum(data) == pytest.approx(22.670118175664847,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
class TestBackground:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_fake(path=self.test_dir + 'dither',
ndit=[20, 20, 20, 20],
nframes=[20, 20, 20, 20],
exp_no=[1, 2, 3, 4],
npix=(100, 100),
fwhm=3.,
x0=[25, 75, 75, 25],
y0=[75, 75, 25, 25],
angles=[[0., 25.], [25., 50.], [50., 75.], [75., 100.]],
sep=None,
contrast=None)
create_fake(path=self.test_dir + 'star',
ndit=[10, 10, 10, 10],
nframes=[10, 10, 10, 10],
exp_no=[1, 3, 5, 7],
npix=(100, 100),
fwhm=3.,
x0=[50, 50, 50, 50],
y0=[50, 50, 50, 50],
angles=[[0., 25.], [25., 50.], [50., 75.], [75., 100.]],
sep=None,
contrast=None)
create_fake(path=self.test_dir + 'sky',
ndit=[5, 5, 5, 5],
nframes=[5, 5, 5, 5],
exp_no=[2, 4, 6, 8],
npix=(100, 100),
fwhm=None,
x0=[50, 50, 50, 50],
y0=[50, 50, 50, 50],
angles=[[0., 25.], [25., 50.], [50., 75.], [75., 100.]],
sep=None,
contrast=None)
create_fake(path=self.test_dir + 'line',
ndit=[4, 4, 4, 4],
nframes=[4, 4, 4, 4],
exp_no=[1, 3, 5, 7],
npix=(20, 20),
fwhm=3.,
x0=[10, 10, 10, 10],
y0=[10, 10, 10, 10],
angles=[[0., 25.], [25., 50.], [50., 75.], [75., 100.]],
sep=None,
contrast=None)
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir,
folders=['dither', 'star', 'sky', 'line'])
def test_read_data(self):
read = FitsReadingModule(name_in='read1',
image_tag='dither',
input_dir=self.test_dir + 'dither')
self.pipeline.add_module(read)
read = FitsReadingModule(name_in='read2',
image_tag='star',
input_dir=self.test_dir + 'star')
self.pipeline.add_module(read)
read = FitsReadingModule(name_in='read3',
image_tag='sky',
input_dir=self.test_dir + 'sky')
self.pipeline.add_module(read)
read = FitsReadingModule(name_in='read4',
image_tag='line',
input_dir=self.test_dir + 'line')
self.pipeline.add_module(read)
self.pipeline.run_module('read1')
self.pipeline.run_module('read2')
self.pipeline.run_module('read3')
self.pipeline.run_module('read4')
data = self.pipeline.get_data('dither')
assert np.allclose(data[0, 74, 24],
0.05304008435511765,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010033896953157959,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('star')
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data('sky')
assert np.allclose(data[0, 50, 50],
-7.613171257478652e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
8.937360237872607e-07,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('line')
assert np.allclose(data[0, 10, 10],
0.09799496683489618,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.002502384977510189,
rtol=limit,
atol=0.)
assert data.shape == (16, 20, 20)
def test_simple_background(self):
simple = SimpleBackgroundSubtractionModule(shift=20,
name_in='simple',
image_in_tag='dither',
image_out_tag='simple')
self.pipeline.add_module(simple)
self.pipeline.run_module('simple')
data = self.pipeline.get_data('simple')
assert np.allclose(data[0, 74, 74],
-0.05288064325101517,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.7755575615628916e-22,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
def test_mean_background_shift(self):
mean = MeanBackgroundSubtractionModule(shift=20,
cubes=1,
name_in='mean2',
image_in_tag='dither',
image_out_tag='mean2')
self.pipeline.add_module(mean)
self.pipeline.run_module('mean2')
data = self.pipeline.get_data('mean2')
assert np.allclose(data[0, 74, 24],
0.0530465391626132,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.3970872216676808e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
def test_mean_background_nframes(self):
mean = MeanBackgroundSubtractionModule(shift=None,
cubes=1,
name_in='mean1',
image_in_tag='dither',
image_out_tag='mean1')
self.pipeline.add_module(mean)
self.pipeline.run_module('mean1')
data = self.pipeline.get_data('mean1')
assert np.allclose(data[0, 74, 24],
0.0530465391626132,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.3970872216676808e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
def test_dithering_attributes(self):
pca_dither = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.8,
gaussian=0.1,
subframe=0.5,
pca_number=5,
mask_star=0.1,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(pca_dither)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.allclose(data[0, 14, 14],
0.05304008435511765,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0002606205855710527,
rtol=1e-6,
atol=0.)
assert data.shape == (80, 31, 31)
data = self.pipeline.get_data('dither_dither_star1')
assert np.allclose(data[0, 14, 14],
0.05304008435511765,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010414302265833978,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.allclose(data[0, 14, 14],
0.0530465391626132,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010426228104479674,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_background1')
assert np.allclose(data[0, 14, 14],
-0.00010629310882411674,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
3.5070523360436835e-07,
rtol=1e-6,
atol=0.)
assert data.shape == (60, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.allclose(data[0, 14, 14],
1.5196412298279846e-05,
rtol=1e-5,
atol=0.)
assert np.allclose(np.mean(data),
1.9779802529804516e-07,
rtol=1e-4,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.allclose(data[0, 14, 14],
0.05302488794281937,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010412324285580998,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.allclose(data[0, 14, 14], 0., rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data),
0.9531737773152965,
rtol=1e-6,
atol=0.)
assert data.shape == (20, 31, 31)
data = self.pipeline.get_data('pca_dither1')
assert np.allclose(data[0, 14, 14],
0.05302488794281937,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.001040627977720779,
rtol=1e-6,
atol=0.)
assert data.shape == (80, 31, 31)
data = self.pipeline.get_attribute('dither_dither_pca_res1',
'STAR_POSITION',
static=False)
assert np.allclose(data[0, 0], [15., 15.], rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 15., rtol=1e-6, atol=0.)
assert data.shape == (20, 2)
def test_dithering_center(self):
pca_dither = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=((25., 75.), (75., 75.),
(75., 25.), (25., 25.)),
cubes=1,
size=0.8,
gaussian=0.1,
subframe=None,
pca_number=5,
mask_star=0.1,
bad_pixel=None,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(pca_dither)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.allclose(data[0, 14, 14],
0.05302488794328089,
rtol=1e-6,
atol=0.)
assert np.allclose(np.mean(data),
0.0010406279782666378,
rtol=1e-3,
atol=0.)
assert data.shape == (80, 31, 31)
def test_nodding_background(self):
mean = StackCubesModule(name_in='mean',
image_in_tag='sky',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(mean)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.allclose(data[0, 50, 50],
1.270877476321969e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
8.937360237872607e-07,
rtol=limit,
atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
nodding = NoddingBackgroundModule(name_in='nodding',
sky_in_tag='mean',
science_in_tag='star',
image_out_tag='nodding',
mode='both')
self.pipeline.add_module(nodding)
self.pipeline.run_module('nodding')
data = self.pipeline.get_data('nodding')
assert np.allclose(data[0, 50, 50],
0.09797142624717381,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.945087327935862e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_line_background_mean(self):
module = LineSubtractionModule(name_in='line1',
image_in_tag='line',
image_out_tag='line_mean',
combine='mean',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line1')
data = self.pipeline.get_data('line_mean')
assert np.allclose(data[0, 10, 10],
0.09792388324443534,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0024245904637616735,
rtol=limit,
atol=0.)
assert data.shape == (16, 20, 20)
def test_line_background_median(self):
module = LineSubtractionModule(name_in='line2',
image_in_tag='line',
image_out_tag='line_median',
combine='median',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line2')
data = self.pipeline.get_data('line_median')
assert np.allclose(data[0, 10, 10],
0.09782789699611127,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0024723022374338196,
rtol=limit,
atol=0.)
assert data.shape == (16, 20, 20)
class TestLimits:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir+'self.limits', npix=21, pos_star=10.)
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(path=self.test_dir,
folders=['self.limits'],
files=['model.AMES-Cond-2000.M-0.0.NaCo.Vega'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir+'self.limits')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.sum(data) == pytest.approx(108.43655133957289, rel=self.limit, abs=0.)
assert data.shape == (10, 21, 21)
def test_angle_interpolation(self) -> None:
module = AngleInterpolationModule(name_in='angle',
data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
attr = self.pipeline.get_attribute('read', 'PARANG', static=False)
assert np.sum(attr) == pytest.approx(900., rel=self.limit, abs=0.)
assert attr.shape == (10, )
def test_contrast_curve(self) -> None:
proc = ['single', 'multi']
for item in proc:
if item == 'multi':
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = ContrastCurveModule(name_in='contrast_'+item,
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_'+item,
separation=(0.2, 0.3, 0.2),
angle=(0., 360., 180.),
threshold=('sigma', 5.),
psf_scaling=1.,
aperture=0.05,
pca_number=2,
cent_size=None,
edge_size=1.,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_'+item)
data = self.pipeline.get_data('limits_'+item)
assert data[0, 0] == pytest.approx(0.2, rel=self.limit, abs=0.)
assert data[0, 1] == pytest.approx(2.5223717329932676, rel=self.limit, abs=0.)
assert data[0, 2] == pytest.approx(0.0006250749411563979, rel=self.limit, abs=0.)
assert data[0, 3] == pytest.approx(0.00026866680137822624, rel=self.limit, abs=0.)
assert data.shape == (1, 4)
def test_contrast_curve_fpf(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = ContrastCurveModule(name_in='contrast_fpf',
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_fpf',
separation=(0.2, 0.3, 0.2),
angle=(0., 360., 180.),
threshold=('fpf', 1e-6),
psf_scaling=1.,
aperture=0.05,
pca_number=2,
cent_size=None,
edge_size=1.,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_fpf')
data = self.pipeline.get_data('limits_fpf')
assert data[0, 0] == pytest.approx(0.2, rel=self.limit, abs=0.)
assert data[0, 1] == pytest.approx(1.797063014325614, rel=self.limit, abs=0.)
assert data[0, 2] == pytest.approx(0.0006250749411564145, rel=self.limit, abs=0.)
assert data[0, 3] == pytest.approx(1e-06, rel=self.limit, abs=0.)
assert data.shape == (1, 4)
def test_mass_limits(self) -> None:
separation = np.linspace(0.1, 1.0, 10)
contrast = -2.5*np.log10(1e-4/separation)
variance = 0.1*contrast
limits = np.zeros((10, 4))
limits[:, 0] = separation
limits[:, 1] = contrast
limits[:, 2] = variance
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['contrast_limits'] = limits
url = 'https://home.strw.leidenuniv.nl/~stolker/pynpoint/' \
'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
filename = self.test_dir + 'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
urlretrieve(url, filename)
module = MassLimitsModule(model_file=filename,
star_prop={'magnitude': 10., 'distance': 100., 'age': 20.},
name_in='mass',
contrast_in_tag='contrast_limits',
mass_out_tag='mass_limits',
instr_filter='L\'')
self.pipeline.add_module(module)
self.pipeline.run_module('mass')
data = self.pipeline.get_data('mass_limits')
assert np.mean(data[:, 0]) == pytest.approx(0.55, rel=self.limit, abs=0.)
assert np.mean(data[:, 1]) == pytest.approx(0.001891690765603738, rel=self.limit, abs=0.)
assert np.mean(data[:, 2]) == pytest.approx(0.000964309686441908, rel=self.limit, abs=0.)
assert np.mean(data[:, 3]) == pytest.approx(-0.000696402843279597, rel=self.limit, abs=0.)
assert data.shape == (10, 4)
class TestAttributeReading:
def setup_class(self) -> None:
self.test_dir = os.path.dirname(__file__) + '/'
create_random(self.test_dir, ndit=10, parang=None)
create_config(self.test_dir + 'PynPoint_config.ini')
np.savetxt(self.test_dir + 'parang.dat', np.arange(1., 11., 1.))
np.savetxt(self.test_dir + 'new.dat', np.arange(10., 21., 1.))
np.savetxt(self.test_dir + 'attribute.dat',
np.arange(1, 11, 1),
fmt='%i')
np.savetxt(self.test_dir + 'wavelength.dat', np.arange(1, 11, 1))
data2d = np.random.normal(loc=0, scale=2e-4, size=(10, 10))
np.savetxt(self.test_dir + 'data_2d.dat', data2d)
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir,
files=[
'parang.dat', 'new.dat', 'attribute.dat',
'data_2d.dat', 'wavelength.dat'
])
def test_input_data(self) -> None:
data = self.pipeline.get_data('images')
assert np.allclose(data[0, 75, 25],
6.921353838812206e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.0506056979365338e-06,
rtol=limit,
atol=0.)
assert data.shape == (10, 100, 100)
def test_parang_reading(self) -> None:
module = ParangReadingModule(file_name='parang.dat',
name_in='parang1',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
self.pipeline.run_module('parang1')
data = self.pipeline.get_data('header_images/PARANG')
assert data.dtype == 'float64'
assert np.allclose(data, np.arange(1., 11., 1.), rtol=limit, atol=0.)
assert data.shape == (10, )
def test_parang_reading_same(self) -> None:
module = ParangReadingModule(file_name='parang.dat',
name_in='parang2',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('parang2')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The PARANG attribute is already present and ' \
'contains the same values as are present in ' \
'parang.dat.'
def test_parang_reading_present(self) -> None:
module = ParangReadingModule(file_name='new.dat',
name_in='parang3',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('parang3')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The PARANG attribute is already present. Set the ' \
'\'overwrite\' parameter to True in order to ' \
'overwrite the values with new.dat.'
def test_parang_reading_overwrite(self) -> None:
module = ParangReadingModule(file_name='new.dat',
name_in='parang4',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
self.pipeline.run_module('parang4')
def test_parang_reading_2d(self) -> None:
module = ParangReadingModule(file_name='data_2d.dat',
name_in='parang6',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('parang6')
assert str(error.value) == 'The input file data_2d.dat should contain a 1D data set with ' \
'the parallactic angles.'
def test_attribute_reading(self) -> None:
module = AttributeReadingModule(file_name='attribute.dat',
attribute='EXP_NO',
name_in='attribute1',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
self.pipeline.run_module('attribute1')
data = self.pipeline.get_data('header_images/EXP_NO')
assert data.dtype == 'int64'
assert np.allclose(data, np.arange(1, 11, 1), rtol=limit, atol=0.)
assert data.shape == (10, )
def test_attribute_reading_present(self) -> None:
module = AttributeReadingModule(file_name='parang.dat',
attribute='PARANG',
name_in='attribute3',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('attribute3')
assert warning[0].message.args[0] == 'The attribute \'PARANG\' is already present. Set ' \
'the \'overwrite\' parameter to True in order to ' \
'overwrite the values with parang.dat.'
def test_attribute_reading_invalid(self) -> None:
module = AttributeReadingModule(file_name='attribute.dat',
attribute='test',
name_in='attribute4',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('attribute4')
assert str(error.value) == '\'test\' is not a valid attribute.'
def test_attribute_reading_2d(self) -> None:
module = AttributeReadingModule(file_name='data_2d.dat',
attribute='DITHER_X',
name_in='attribute5',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('attribute5')
assert str(error.value) == 'The input file data_2d.dat should contain a 1D list with ' \
'attributes.'
def test_attribute_reading_same(self) -> None:
module = AttributeReadingModule(file_name='attribute.dat',
attribute='EXP_NO',
name_in='attribute6',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('attribute6')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The \'EXP_NO\' attribute is already present and ' \
'contains the same values as are present in ' \
'attribute.dat.'
def test_attribute_reading_overwrite(self) -> None:
module = AttributeReadingModule(file_name='parang.dat',
attribute='PARANG',
name_in='attribute7',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
self.pipeline.run_module('attribute7')
attribute = self.pipeline.get_attribute('images',
'PARANG',
static=False)
assert np.allclose(attribute,
np.arange(1., 11., 1.),
rtol=limit,
atol=0.)
def test_wavelength_reading(self) -> None:
module = WavelengthReadingModule(file_name='wavelength.dat',
name_in='wavelength1',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
self.pipeline.run_module('wavelength1')
data = self.pipeline.get_data('header_images/WAVELENGTH')
assert data.dtype == 'float64'
assert np.allclose(data, np.arange(1., 11., 1.), rtol=limit, atol=0.)
assert data.shape == (10, )
def test_wavelength_reading_same(self) -> None:
module = WavelengthReadingModule(file_name='wavelength.dat',
name_in='wavelength2',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('wavelength2')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The WAVELENGTH attribute is already present and ' \
'contains the same values as are present in ' \
'wavelength.dat.'
def test_wavelength_reading_present(self) -> None:
module = WavelengthReadingModule(file_name='new.dat',
name_in='wavelength3',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('wavelength3')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The WAVELENGTH attribute is already present. Set ' \
'the \'overwrite\' parameter to True in order to ' \
'overwrite the values with new.dat.'
def test_wavelength_reading_overwrite(self) -> None:
module = WavelengthReadingModule(file_name='new.dat',
name_in='wavelength4',
input_dir=None,
data_tag='images',
overwrite=True)
self.pipeline.add_module(module)
self.pipeline.run_module('wavelength4')
def test_wavelength_reading_2d(self) -> None:
module = WavelengthReadingModule(file_name='data_2d.dat',
name_in='wavelength6',
input_dir=None,
data_tag='images',
overwrite=False)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('wavelength6')
assert str(error.value) == 'The input file data_2d.dat should contain a 1D data set with ' \
'the wavelengths.'
class TestFrameSelection:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'images', ndit=10, nframes=11)
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=['images'])
def test_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir + 'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0001002167910262529,
rtol=limit,
atol=0.)
assert data.shape == (44, 100, 100)
def test_remove_last_frame(self):
module = RemoveLastFrameModule(name_in='last',
image_in_tag='read',
image_out_tag='last')
self.pipeline.add_module(module)
self.pipeline.run_module('last')
data = self.pipeline.get_data('last')
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010020258903646778,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
self.pipeline.set_attribute('last',
'PARANG',
np.arange(0., 40., 1.),
static=False)
star = np.zeros((40, 2))
star[:, 0] = np.arange(40., 80., 1.)
star[:, 1] = np.arange(40., 80., 1.)
self.pipeline.set_attribute('last',
'STAR_POSITION',
star,
static=False)
attribute = self.pipeline.get_attribute('last', 'PARANG', static=False)
assert np.allclose(np.mean(attribute), 19.5, rtol=limit, atol=0.)
assert attribute.shape == (40, )
attribute = self.pipeline.get_attribute('last',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 59.5, rtol=limit, atol=0.)
assert attribute.shape == (40, 2)
def test_remove_start_frame(self):
module = RemoveStartFramesModule(frames=2,
name_in='start',
image_in_tag='last',
image_out_tag='start')
self.pipeline.add_module(module)
self.pipeline.run_module('start')
data = self.pipeline.get_data('start')
assert np.allclose(data[0, 50, 50],
0.09797376304048713,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010011298467340513,
rtol=limit,
atol=0.)
assert data.shape == (32, 100, 100)
attribute = self.pipeline.get_attribute('start',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute), 20.5, rtol=limit, atol=0.)
assert attribute.shape == (32, )
attribute = self.pipeline.get_attribute('start',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 60.5, rtol=limit, atol=0.)
assert attribute.shape == (32, 2)
def test_remove_frames(self):
module = RemoveFramesModule(name_in='remove',
image_in_tag='start',
selected_out_tag='selected',
removed_out_tag='removed',
frames=[5, 8, 13, 25, 31])
self.pipeline.add_module(module)
self.pipeline.run_module('remove')
data = self.pipeline.get_data('selected')
assert np.allclose(data[0, 50, 50],
0.09797376304048713,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.984682304434105e-05,
rtol=limit,
atol=0.)
assert data.shape == (27, 100, 100)
data = self.pipeline.get_data('removed')
assert np.allclose(data[0, 50, 50],
0.09818692015286978,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010155025747035087,
rtol=limit,
atol=0.)
assert data.shape == (5, 100, 100)
attribute = self.pipeline.get_attribute('selected',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute),
20.296296296296298,
rtol=limit,
atol=0.)
assert attribute.shape == (27, )
attribute = self.pipeline.get_attribute('selected',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute),
60.2962962962963,
rtol=limit,
atol=0.)
assert attribute.shape == (27, 2)
attribute = self.pipeline.get_attribute('removed',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute), 21.6, rtol=limit, atol=0.)
assert attribute.shape == (5, )
attribute = self.pipeline.get_attribute('removed',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 61.6, rtol=limit, atol=0.)
assert attribute.shape == (5, 2)
def test_frame_selection(self):
module = FrameSelectionModule(name_in='select1',
image_in_tag='start',
selected_out_tag='selected1',
removed_out_tag='removed1',
index_out_tag='index1',
method='median',
threshold=1.,
fwhm=0.1,
aperture=('circular', 0.2),
position=(None, None, 0.5))
self.pipeline.add_module(module)
self.pipeline.run_module('select1')
data = self.pipeline.get_data('selected1')
assert np.allclose(data[0, 50, 50],
0.09791350617182591,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.980792188317311e-05,
rtol=limit,
atol=0.)
assert data.shape == (22, 100, 100)
data = self.pipeline.get_data('removed1')
assert np.allclose(data[0, 50, 50],
0.09797376304048713,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010078412281191547,
rtol=limit,
atol=0.)
assert data.shape == (10, 100, 100)
data = self.pipeline.get_data('index1')
assert data[-1] == 28
assert np.sum(data) == 115
assert data.shape == (10, )
attribute = self.pipeline.get_attribute('selected1',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute),
22.681818181818183,
rtol=limit,
atol=0.)
assert attribute.shape == (22, )
attribute = self.pipeline.get_attribute('selected1',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (22, 2)
attribute = self.pipeline.get_attribute('removed1',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute), 15.7, rtol=limit, atol=0.)
assert attribute.shape == (10, )
attribute = self.pipeline.get_attribute('removed1',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (10, 2)
module = FrameSelectionModule(name_in='select2',
image_in_tag='start',
selected_out_tag='selected2',
removed_out_tag='removed2',
index_out_tag='index2',
method='max',
threshold=3.,
fwhm=0.1,
aperture=('annulus', 0.1, 0.2),
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('select2')
data = self.pipeline.get_data('selected2')
assert np.allclose(data[0, 50, 50],
0.09797376304048713,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010037996502199598,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('removed2')
assert np.allclose(data[0, 50, 50],
0.097912284606689,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.966801742575358e-05,
rtol=limit,
atol=0.)
assert data.shape == (12, 100, 100)
data = self.pipeline.get_data('index2')
assert data[-1] == 30
assert np.sum(data) == 230
assert data.shape == (12, )
attribute = self.pipeline.get_attribute('selected2',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute), 17.8, rtol=limit, atol=0.)
assert attribute.shape == (20, )
attribute = self.pipeline.get_attribute('selected2',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (20, 2)
attribute = self.pipeline.get_attribute('removed2',
'PARANG',
static=False)
assert np.allclose(np.mean(attribute), 25.0, rtol=limit, atol=0.)
assert attribute.shape == (12, )
attribute = self.pipeline.get_attribute('removed2',
'STAR_POSITION',
static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (12, 2)
def test_image_statistics_full(self):
module = ImageStatisticsModule(name_in='stat1',
image_in_tag='read',
stat_out_tag='stat1',
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('stat1')
data = self.pipeline.get_data('stat1')
assert np.allclose(data[0, 0],
-0.0007312880198509591,
rtol=limit,
atol=0.)
assert np.allclose(np.sum(data),
48.479917666979716,
rtol=limit,
atol=0.)
assert data.shape == (44, 6)
def test_image_statistics_posiiton(self):
module = ImageStatisticsModule(name_in='stat2',
image_in_tag='read',
stat_out_tag='stat2',
position=(70, 20, 0.5))
self.pipeline.add_module(module)
self.pipeline.run_module('stat2')
data = self.pipeline.get_data('stat2')
assert np.allclose(data[0, 0],
-0.0006306714900382097,
rtol=limit,
atol=0.)
assert np.allclose(np.sum(data),
-0.05448258074038106,
rtol=limit,
atol=0.)
assert data.shape == (44, 6)
def test_frame_similarity_mse(self):
module = FrameSimilarityModule(name_in='simi1',
image_tag='read',
method='MSE',
mask_radius=(0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('simi1')
similarity = self.pipeline.get_attribute('read', 'MSE', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
1.7938335695664495e-06,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
4.103008589430469e-08,
rtol=limit,
atol=0.)
def test_frame_similarity_pcc(self):
module = FrameSimilarityModule(name_in='simi2',
image_tag='read',
method='PCC',
mask_radius=(0., 1.))
self.pipeline.add_module(module)
self.pipeline.run_module('simi2')
similarity = self.pipeline.get_attribute('read', 'PCC', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert np.max(similarity) < 1
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
43.974652830856314,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
0.9994193494590345,
rtol=limit,
atol=0.)
def test_frame_similarity_ssim(self):
module = FrameSimilarityModule(name_in='simi3',
image_tag='read',
method='SSIM',
mask_radius=(0., 1.),
temporal_median='constant')
self.pipeline.add_module(module)
self.pipeline.run_module('simi3')
similarity = self.pipeline.get_attribute('read', 'SSIM', static=False)
assert len(similarity) == self.pipeline.get_shape('read')[0]
assert np.min(similarity) > 0
assert np.max(similarity) < 1
assert similarity[4] != similarity[8]
assert np.allclose(np.sum(similarity),
43.999059977871184,
rtol=limit,
atol=0.)
assert np.allclose(similarity[0],
0.9999793908738922,
rtol=limit,
atol=0.)
def test_select_by_attribute(self):
total_length = self.pipeline.get_shape('read')[0]
self.pipeline.set_attribute('read',
'INDEX',
range(total_length),
static=False)
attribute_tag = 'SSIM'
module = SelectByAttributeModule(name_in='frame_removal_1',
image_in_tag='read',
attribute_tag=attribute_tag,
number_frames=6,
order='descending',
selected_out_tag='select_sim',
removed_out_tag='remove_sim')
self.pipeline.add_module(module)
self.pipeline.run_module('frame_removal_1')
index = self.pipeline.get_attribute('select_sim',
'INDEX',
static=False)
similarity = self.pipeline.get_attribute('select_sim',
attribute_tag,
static=False)
sim_removed = self.pipeline.get_attribute('remove_sim',
attribute_tag,
static=False)
# check attribute length
assert self.pipeline.get_shape('select_sim')[0] == 6
assert len(similarity) == 6
assert len(similarity) == len(index)
assert len(similarity) + len(sim_removed) == total_length
# check sorted
assert all(similarity[i] >= similarity[i + 1]
for i in range(len(similarity) - 1))
# check that the selected attributes are in the correct tags
assert np.min(similarity) > np.max(sim_removed)
class TestStackingAndSubsampling(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir+"data")
create_star_data(path=self.test_dir+"extra")
create_config(self.test_dir+"PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=["data", "extra"])
def test_read_data(self):
read = FitsReadingModule(name_in="read1",
image_tag="images",
input_dir=self.test_dir+"data",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read1")
data = self.pipeline.get_data("images")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
read = FitsReadingModule(name_in="read2",
image_tag="extra",
input_dir=self.test_dir+"extra",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read2")
extra = self.pipeline.get_data("extra")
assert np.allclose(data, extra, rtol=limit, atol=0.)
def test_stack_and_subset(self):
self.pipeline.set_attribute("images", "PARANG", np.arange(1., 41., 1.), static=False)
stack = StackAndSubsetModule(name_in="stack",
image_in_tag="images",
image_out_tag="stack",
random=10,
stacking=2)
self.pipeline.add_module(stack)
self.pipeline.run_module("stack")
data = self.pipeline.get_data("stack")
assert np.allclose(data[0, 50, 50], 0.09816320034649725, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.983545774937238e-05, rtol=limit, atol=0.)
assert data.shape == (10, 100, 100)
data = self.pipeline.get_data("header_stack/INDEX")
index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
assert np.allclose(data, index, rtol=limit, atol=0.)
assert data.shape == (10, )
data = self.pipeline.get_data("header_stack/PARANG")
parang = [1.5, 15.5, 19.5, 23.5, 25.5, 29.5, 31.5, 35.5, 37.5, 39.5]
assert np.allclose(data, parang, rtol=limit, atol=0.)
assert data.shape == (10, )
def test_mean_cube(self):
mean = MeanCubeModule(name_in="mean",
image_in_tag="images",
image_out_tag="mean")
self.pipeline.add_module(mean)
self.pipeline.run_module("mean")
data = self.pipeline.get_data("mean")
assert np.allclose(data[0, 50, 50], 0.09805840100024205, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738069, rtol=limit, atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute("mean", "INDEX", static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute("mean", "NFRAMES", static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
def test_derotate_and_stack(self):
derotate = DerotateAndStackModule(name_in="derotate1",
image_in_tag="images",
image_out_tag="derotate1",
derotate=True,
stack="mean",
extra_rot=10.)
self.pipeline.add_module(derotate)
self.pipeline.run_module("derotate1")
data = self.pipeline.get_data("derotate1")
assert np.allclose(data[50, 50], 0.09689679769268554, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010021671152246617, rtol=limit, atol=0.)
assert data.shape == (100, 100)
derotate = DerotateAndStackModule(name_in="derotate2",
image_in_tag="images",
image_out_tag="derotate2",
derotate=False,
stack="median",
extra_rot=0.)
self.pipeline.add_module(derotate)
self.pipeline.run_module("derotate2")
data = self.pipeline.get_data("derotate2")
assert np.allclose(data[50, 50], 0.09809001768003645, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010033064394962, rtol=limit, atol=0.)
assert data.shape == (100, 100)
def test_combine_tags(self):
combine = CombineTagsModule(image_in_tags=("images", "extra"),
check_attr=True,
index_init=False,
name_in="combine1",
image_out_tag="combine1")
self.pipeline.add_module(combine)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("combine1")
assert len(warning) == 1
assert warning[0].message.args[0] == "The non-static keyword FILES is already used but " \
"with different values. It is advisable to only " \
"combine tags that descend from the same data set."
data = self.pipeline.get_data("combine1")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738068, rtol=limit, atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data("header_combine1/INDEX")
assert data[40] == 0
assert data.shape == (80, )
combine = CombineTagsModule(image_in_tags=("images", "extra"),
check_attr=False,
index_init=True,
name_in="combine2",
image_out_tag="combine2")
self.pipeline.add_module(combine)
self.pipeline.run_module("combine2")
data = self.pipeline.get_data("combine1")
extra = self.pipeline.get_data("combine2")
assert np.allclose(data, extra, rtol=limit, atol=0.)
data = self.pipeline.get_data("header_combine2/INDEX")
assert data[40] == 40
assert data.shape == (80, )
class TestCentering:
def setup_class(self) -> None:
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir+'dither',
npix_x=100,
npix_y=100,
x0=[25, 75, 75, 25],
y0=[75, 75, 25, 25],
parang_start=[0., 25., 50., 75.],
parang_end=[25., 50., 75., 100.],
exp_no=[1, 2, 3, 4])
create_star_data(path=self.test_dir+'star_odd',
npix_x=101,
npix_y=101,
x0=[50],
y0=[50],
parang_start=[0.],
parang_end=[25.],
exp_no=[1],
noise=False)
create_star_data(path=self.test_dir+'star_even',
npix_x=100,
npix_y=100,
x0=[49.5],
y0=[49.5],
parang_start=[0.],
parang_end=[25.],
exp_no=[1],
noise=False)
create_waffle_data(path=self.test_dir+'waffle_odd',
npix=101,
x_spot=[20., 20., 80., 80.],
y_spot=[20., 80., 80., 20.])
create_waffle_data(path=self.test_dir+'waffle_even',
npix=100,
x_spot=[20., 20., 79., 79.],
y_spot=[20., 79., 79., 20.])
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(path=self.test_dir,
folders=['dither', 'star_odd', 'star_even', 'waffle_odd', 'waffle_even'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='dither',
input_dir=self.test_dir+'dither',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read2',
image_tag='waffle_odd',
input_dir=self.test_dir+'waffle_odd',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read3',
image_tag='waffle_even',
input_dir=self.test_dir+'waffle_even',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read4',
image_tag='star_odd',
input_dir=self.test_dir+'star_odd',
overwrite=True,
check=True)
self.pipeline.add_module(module)
read = FitsReadingModule(name_in='read5',
image_tag='star_even',
input_dir=self.test_dir+'star_even',
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module('read1')
self.pipeline.run_module('read2')
self.pipeline.run_module('read3')
self.pipeline.run_module('read4')
self.pipeline.run_module('read5')
data = self.pipeline.get_data('dither')
assert np.allclose(data[0, 75, 25], 0.09812948027289994, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data('waffle_odd')
assert np.allclose(data[0, 20, 20], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 20, 80], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 80, 20], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 80, 80], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(np.mean(data), 0.0003921184197627874, rtol=1e-4, atol=0.)
assert data.shape == (1, 101, 101)
data = self.pipeline.get_data('waffle_even')
assert np.allclose(data[0, 20, 20], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 20, 79], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 79, 20], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 79, 79], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(np.mean(data), 0.00040000000000001953, rtol=1e-4, atol=0.)
assert data.shape == (1, 100, 100)
data = self.pipeline.get_data('star_odd')
assert np.allclose(data[0, 50, 50], 0.09806026673451182, rtol=1e-4, atol=0.)
assert np.allclose(np.mean(data), 9.80296049406969e-05, rtol=1e-4, atol=0.)
assert data.shape == (10, 101, 101)
data = self.pipeline.get_data('star_even')
assert np.allclose(data[0, 49, 49], 0.08406157361512759, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 49, 50], 0.08406157361512759, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 50, 49], 0.08406157361512759, rtol=1e-4, atol=0.)
assert np.allclose(data[0, 50, 50], 0.08406157361512759, rtol=1e-4, atol=0.)
assert np.allclose(np.mean(data), 9.99999999999951e-05, rtol=1e-4, atol=0.)
assert data.shape == (10, 100, 100)
def test_star_extract(self) -> None:
module = StarExtractionModule(name_in='extract1',
image_in_tag='dither',
image_out_tag='extract1',
index_out_tag='index',
image_size=1.0,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract1')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The new dataset that is stored under the tag name ' \
'\'index\' is empty.'
data = self.pipeline.get_data('extract1')
assert np.allclose(data[0, 19, 19], 0.09812948027289994, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0006578482216906739, rtol=limit, atol=0.)
assert data.shape == (40, 39, 39)
attr = self.pipeline.get_attribute('extract1', 'STAR_POSITION', static=False)
assert attr[10, 0] == attr[10, 1] == 75
def test_star_align(self) -> None:
module = StarAlignmentModule(name_in='align1',
image_in_tag='extract1',
ref_image_in_tag=None,
image_out_tag='align1',
accuracy=10,
resize=2.,
num_references=10,
subframe=None)
self.pipeline.add_module(module)
self.pipeline.run_module('align1')
data = self.pipeline.get_data('align1')
assert np.allclose(data[0, 39, 39], 0.023556628129942768, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016446205542266847, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_star_align_subframe(self) -> None:
module = StarAlignmentModule(name_in='align2',
image_in_tag='extract1',
ref_image_in_tag=None,
image_out_tag='align2',
accuracy=10,
resize=None,
num_references=10,
subframe=0.5)
self.pipeline.add_module(module)
self.pipeline.run_module('align2')
data = self.pipeline.get_data('align2')
assert np.allclose(data[0, 19, 19], 0.09812948027289994, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0006578482216906739, rtol=limit, atol=0.)
assert data.shape == (40, 39, 39)
def test_star_align_ref(self) -> None:
module = StarAlignmentModule(name_in='align3',
image_in_tag='extract1',
ref_image_in_tag='align2',
image_out_tag='align3',
accuracy=10,
resize=None,
num_references=10,
subframe=None)
self.pipeline.add_module(module)
self.pipeline.run_module('align3')
data = self.pipeline.get_data('align3')
assert np.allclose(data[0, 19, 19], 0.09812948027289994, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0006578482216906739, rtol=limit, atol=0.)
assert data.shape == (40, 39, 39)
def test_star_align_number_ref(self) -> None:
module = StarAlignmentModule(name_in='align4',
image_in_tag='extract1',
ref_image_in_tag='align2',
image_out_tag='align4',
accuracy=10,
resize=None,
num_references=50,
subframe=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('align4')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Number of available images (40) is smaller than ' \
'num_references (50). Using all available images ' \
'instead.'
data = self.pipeline.get_data('align4')
assert np.allclose(data[0, 19, 19], 0.09812948027289994, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0006578482216906739, rtol=limit, atol=0.)
assert data.shape == (40, 39, 39)
def test_shift_images_spline(self) -> None:
module = ShiftImagesModule(shift_xy=(6., 4.),
interpolation='spline',
name_in='shift1',
image_in_tag='align1',
image_out_tag='shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift1')
data = self.pipeline.get_data('shift')
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016430682224782259, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_fft(self) -> None:
module = ShiftImagesModule(shift_xy=(6., 4.),
interpolation='fft',
name_in='shift2',
image_in_tag='align1',
image_out_tag='shift_fft')
self.pipeline.add_module(module)
self.pipeline.run_module('shift2')
data = self.pipeline.get_data('shift_fft')
assert np.allclose(data[0, 43, 45], 0.023556628129942764, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00016446205542266847, rtol=limit, atol=0.)
assert data.shape == (40, 78, 78)
def test_waffle_center_odd(self) -> None:
module = WaffleCenteringModule(size=2.,
center=(50, 50),
name_in='waffle_odd',
image_in_tag='star_odd',
center_in_tag='waffle_odd',
image_out_tag='center_odd',
radius=42.5,
pattern='x',
sigma=0.135)
self.pipeline.add_module(module)
self.pipeline.run_module('waffle_odd')
data = self.pipeline.get_data('star_odd')
assert np.allclose(data[0, 50, 50], 0.09806026673451182, rtol=limit, atol=0.)
data = self.pipeline.get_data('center_odd')
assert np.allclose(data[0, 37, 37], 0.0980602667345118, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00017777777777778643, rtol=limit, atol=0.)
assert data.shape == (10, 75, 75)
attribute = self.pipeline.get_attribute('center_odd', 'History: WaffleCenteringModule')
assert attribute == '[x, y] = [50.0, 50.0]'
def test_waffle_center_even(self) -> None:
module = WaffleCenteringModule(size=2.,
center=(50, 50),
name_in='waffle_even',
image_in_tag='star_even',
center_in_tag='waffle_even',
image_out_tag='center_even',
radius=42.5,
pattern='x',
sigma=0.135)
self.pipeline.add_module(module)
self.pipeline.run_module('waffle_even')
data = self.pipeline.get_data('star_even')
assert np.allclose(data[0, 49, 49], 0.08406157361512759, rtol=limit, atol=0.)
assert np.allclose(data[0, 49, 50], 0.08406157361512759, rtol=limit, atol=0.)
assert np.allclose(data[0, 50, 49], 0.08406157361512759, rtol=limit, atol=0.)
assert np.allclose(data[0, 50, 50], 0.08406157361512759, rtol=limit, atol=0.)
data = self.pipeline.get_data('center_even')
assert np.allclose(data[0, 37, 37], 0.09778822940550569, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00017777777777778643, rtol=limit, atol=0.)
assert data.shape == (10, 75, 75)
attribute = self.pipeline.get_attribute('center_even', 'History: WaffleCenteringModule')
assert attribute == '[x, y] = [49.5, 49.5]'
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_mean(self) -> None:
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_shift_images_tag(self) -> None:
module = ShiftImagesModule(shift_xy='fit_full',
interpolation='spline',
name_in='shift3',
image_in_tag='shift',
image_out_tag='shift_tag_1')
self.pipeline.add_module(module)
self.pipeline.run_module('shift3')
data = self.pipeline.get_data('shift_tag_1')
assert np.allclose(data[0, 39, 39], 0.023563080974545528, rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 0.0001643062943690491, rtol=1e-6, atol=0.)
assert data.shape == (40, 78, 78)
def test_shift_images_tag_mean(self) -> None:
module = ShiftImagesModule(shift_xy='fit_mean',
interpolation='spline',
name_in='shift4',
image_in_tag='shift',
image_out_tag='shift_tag_2')
self.pipeline.add_module(module)
self.pipeline.run_module('shift4')
data = self.pipeline.get_data('shift_tag_2')
assert np.allclose(data[0, 20, 31], 5.348337712000518e-05, rtol=1e-6, atol=0.)
assert np.allclose(np.mean(data), 0.00016430318227546225, rtol=1e-6, atol=0.)
assert data.shape == (40, 78, 78)
class TestDetectionLimits(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir + "limits")
create_config(self.test_dir + "PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=["limits"])
def test_read_data(self):
read = FitsReadingModule(name_in="read",
image_tag="read",
input_dir=self.test_dir + "limits")
self.pipeline.add_module(read)
self.pipeline.run_module("read")
data = self.pipeline.get_data("read")
assert np.allclose(data[0, 10, 10],
0.00012958496246258364,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_angle_interpolation(self):
angle = AngleInterpolationModule(name_in="angle", data_tag="read")
self.pipeline.add_module(angle)
self.pipeline.run_module("angle")
data = self.pipeline.get_attribute("read", "PARANG", static=False)
assert data[5] == 2.7777777777777777
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_contrast_curve(self):
proc = ["single", "multi"]
for item in proc:
if item == "multi":
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a')
database['config'].attrs['CPU'] = 4
contrast = ContrastCurveModule(name_in="contrast_" + item,
image_in_tag="read",
psf_in_tag="read",
contrast_out_tag="limits_" + item,
separation=(0.5, 0.6, 0.1),
angle=(0., 360., 180.),
threshold=("sigma", 5.),
psf_scaling=1.,
aperture=0.1,
pca_number=15,
cent_size=None,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(contrast)
self.pipeline.run_module("contrast_" + item)
data = self.pipeline.get_data("limits_" + item)
assert np.allclose(data[0, 0], 5.00000000e-01, rtol=limit, atol=0.)
assert np.allclose(data[0, 1],
2.3624384190310397,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 2],
0.05234065236317515,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 3],
0.00012147700290954244,
rtol=limit,
atol=0.)
assert data.shape == (1, 4)
class TestPypeline(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
np.random.seed(1)
image_3d = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
image_2d = np.random.normal(loc=0, scale=2e-4, size=(10, 10))
science = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
dark = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
h5f = h5py.File(self.test_dir + "PynPoint_database.hdf5", "w")
h5f.create_dataset("image_3d", data=image_3d)
h5f.create_dataset("image_2d", data=image_2d)
h5f.create_dataset("science", data=science)
h5f.create_dataset("dark", data=dark)
h5f.close()
create_star_data(path=self.test_dir + "images")
create_config(self.test_dir + "PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=["images"])
def test_output_port_name(self):
read = FitsReadingModule(name_in="read",
input_dir=self.test_dir + "images",
image_tag="images")
read.add_output_port("test")
with pytest.warns(UserWarning) as warning:
read.add_output_port("test")
assert len(warning) == 1
assert warning[0].message.args[
0] == "Tag 'test' of ReadingModule 'read' is already used."
process = BadPixelSigmaFilterModule(name_in="badpixel",
image_in_tag="images")
process.add_output_port("test")
with pytest.warns(UserWarning) as warning:
process.add_output_port("test")
assert len(warning) == 1
assert warning[0].message.args[0] == "Tag 'test' of ProcessingModule 'badpixel' is " \
"already used."
self.pipeline.m_data_storage.close_connection()
process._m_data_base = self.test_dir + "database.hdf5"
process.add_output_port("new")
def test_apply_function_to_images_3d(self):
self.pipeline.set_attribute("config", "MEMORY", 1, static=True)
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in="remove1",
image_in_tag="image_3d",
image_out_tag="remove_3d")
self.pipeline.add_module(remove)
self.pipeline.run_module("remove1")
data = self.pipeline.get_data("image_3d")
assert np.allclose(np.mean(data),
1.0141852764605783e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
data = self.pipeline.get_data("remove_3d")
assert np.allclose(np.mean(data),
1.1477029889801025e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 9)
def test_apply_function_to_images_2d(self):
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in="remove2",
image_in_tag="image_2d",
image_out_tag="remove_2d")
self.pipeline.add_module(remove)
self.pipeline.run_module("remove2")
data = self.pipeline.get_data("image_2d")
assert np.allclose(np.mean(data),
1.2869483197883442e-05,
rtol=limit,
atol=0.)
assert data.shape == (10, 10)
data = self.pipeline.get_data("remove_2d")
assert np.allclose(np.mean(data),
1.3957075246029751e-05,
rtol=limit,
atol=0.)
assert data.shape == (10, 9)
def test_apply_function_to_images_same_port(self):
dark = DarkCalibrationModule(name_in="dark1",
image_in_tag="science",
dark_in_tag="dark",
image_out_tag="science")
self.pipeline.add_module(dark)
self.pipeline.run_module("dark1")
data = self.pipeline.get_data("science")
assert np.allclose(np.mean(data),
-3.190113568690675e-06,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
self.pipeline.set_attribute("config", "MEMORY", 0, static=True)
dark = DarkCalibrationModule(name_in="dark2",
image_in_tag="science",
dark_in_tag="dark",
image_out_tag="science")
self.pipeline.add_module(dark)
self.pipeline.run_module("dark2")
data = self.pipeline.get_data("science")
assert np.allclose(np.mean(data),
-1.026073475228737e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in="remove3",
image_in_tag="remove_3d",
image_out_tag="remove_3d")
self.pipeline.add_module(remove)
with pytest.raises(ValueError) as error:
self.pipeline.run_module("remove3")
assert str(error.value) == "Input and output port have the same tag while the input " \
"function is changing the image shape. This is only " \
"possible with MEMORY=None."
def test_apply_function_to_images_memory_none(self):
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in="remove4",
image_in_tag="image_3d",
image_out_tag="remove_3d_none")
self.pipeline.add_module(remove)
self.pipeline.run_module("remove4")
data = self.pipeline.get_data("remove_3d_none")
assert np.allclose(np.mean(data),
1.1477029889801025e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 9)
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_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.)
class TestTimeDenoising:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir + 'images')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['images'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir + 'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('images')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_wavelet_denoising_cwt_dog(self) -> None:
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert cwt_config.m_wavelet_order == 2
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_dog',
image_in_tag='images',
image_out_tag='wavelet_cwt_dog',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_dog')
data = self.pipeline.get_data('wavelet_cwt_dog')
assert np.sum(data) == pytest.approx(105.1035789572968,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('wavelet_cwt_dog')
data_multi = self.pipeline.get_data('wavelet_cwt_dog')
assert data == pytest.approx(data_multi, rel=self.limit, abs=0.)
assert data.shape == data_multi.shape
def test_wavelet_denoising_cwt_morlet(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet='morlet',
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'morlet'
assert cwt_config.m_wavelet_order == 5
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_morlet',
image_in_tag='images',
image_out_tag='wavelet_cwt_morlet',
padding='mirror',
median_filter=False,
threshold_function='hard')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_morlet')
data = self.pipeline.get_data('wavelet_cwt_morlet')
assert np.sum(data) == pytest.approx(104.86262840716438,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_attribute('wavelet_cwt_morlet',
'NFRAMES',
static=False)
assert data[0] == data[1] == 5
def test_wavelet_denoising_dwt(self) -> None:
dwt_config = DwtWaveletConfiguration(wavelet='db8')
assert dwt_config.m_wavelet == 'db8'
module = WaveletTimeDenoisingModule(wavelet_configuration=dwt_config,
name_in='wavelet_dwt',
image_in_tag='images',
image_out_tag='wavelet_dwt',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_dwt')
data = self.pipeline.get_data('wavelet_dwt')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_time_normalization(self) -> None:
module = TimeNormalizationModule(name_in='timenorm',
image_in_tag='images',
image_out_tag='timenorm')
self.pipeline.add_module(module)
self.pipeline.run_module('timenorm')
data = self.pipeline.get_data('timenorm')
assert np.sum(data) == pytest.approx(56.443663773873,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_wavelet_denoising_even_size(self) -> None:
module = AddLinesModule(name_in='add',
image_in_tag='images',
image_out_tag='images_even',
lines=(1, 0, 1, 0))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('images_even')
assert np.sum(data) == pytest.approx(105.54278879805275,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert cwt_config.m_wavelet_order == 2
assert not cwt_config.m_keep_mean
assert cwt_config.m_resolution == 0.5
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_1',
image_in_tag='images_even',
image_out_tag='wavelet_even_1',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_1')
data = self.pipeline.get_data('wavelet_even_1')
assert np.sum(data) == pytest.approx(105.1035789572968,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_even_2',
image_in_tag='images_even',
image_out_tag='wavelet_even_2',
padding='mirror',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_even_2')
data = self.pipeline.get_data('wavelet_even_2')
assert np.sum(data) == pytest.approx(105.06809820408587,
rel=self.limit,
abs=0.)
assert data.shape == (10, 12, 12)
data = self.pipeline.get_attribute('images', 'NFRAMES', static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_1',
'NFRAMES',
static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
data = self.pipeline.get_attribute('wavelet_even_2',
'NFRAMES',
static=False)
assert data == pytest.approx([5, 5], rel=self.limit, abs=0.)
class TestPsfPreparation:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'prep')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=['prep'])
def test_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir + 'prep')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_angle_interpolation(self):
module = AngleInterpolationModule(name_in='angle1', data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle1')
data = self.pipeline.get_data('header_read/PARANG')
assert np.allclose(data[0], 0., rtol=limit, atol=0.)
assert np.allclose(data[15], 7.777777777777778, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_angle_calculation(self):
self.pipeline.set_attribute('read', 'LATITUDE', -25.)
self.pipeline.set_attribute('read', 'LONGITUDE', -70.)
self.pipeline.set_attribute('read', 'DIT', 1.)
self.pipeline.set_attribute('read',
'RA', (90., 90., 90., 90.),
static=False)
self.pipeline.set_attribute('read',
'DEC', (-51., -51., -51., -51.),
static=False)
self.pipeline.set_attribute('read',
'PUPIL', (90., 90., 90., 90.),
static=False)
date = ('2012-12-01T07:09:00.0000', '2012-12-01T07:09:01.0000',
'2012-12-01T07:09:02.0000', '2012-12-01T07:09:03.0000')
self.pipeline.set_attribute('read', 'DATE', date, static=False)
module = AngleCalculationModule(instrument='NACO',
name_in='angle2',
data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle2')
data = self.pipeline.get_data('header_read/PARANG')
assert np.allclose(data[0], -55.04109770947442, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
-54.99858360618869,
rtol=limit,
atol=0.)
assert data.shape == (40, )
self.pipeline.set_attribute('read',
'RA', (60000.0, 60000.0, 60000.0, 60000.0),
static=False)
self.pipeline.set_attribute('read',
'DEC',
(-510000., -510000., -510000., -510000.),
static=False)
module = AngleCalculationModule(instrument='SPHERE/IRDIS',
name_in='angle3',
data_tag='read')
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('angle3')
assert warning[0].message.args[0] == 'For SPHERE data it is recommended to use the ' \
'header keyword \'ESO INS4 DROT2 RA\' to specify ' \
'the object\'s right ascension. The input will be ' \
'parsed accordingly. Using the regular \'RA\' '\
'keyword will lead to wrong parallactic angles.' \
assert warning[1].message.args[0] == 'For SPHERE data it is recommended to use the ' \
'header keyword \'ESO INS4 DROT2 DEC\' to specify ' \
'the object\'s declination. The input will be ' \
'parsed accordingly. Using the regular \'DEC\' '\
'keyword will lead to wrong parallactic angles.' \
data = self.pipeline.get_data('header_read/PARANG')
assert np.allclose(data[0], 170.39102715170227, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
170.46341123194824,
rtol=limit,
atol=0.)
assert data.shape == (40, )
module = AngleCalculationModule(instrument='SPHERE/IFS',
name_in='angle4',
data_tag='read')
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('angle4')
assert warning[0].message.args[0] == 'AngleCalculationModule has not been tested for ' \
'SPHERE/IFS data.'
assert warning[1].message.args[0] == 'For SPHERE data it is recommended to use the ' \
'header keyword \'ESO INS4 DROT2 RA\' to specify ' \
'the object\'s right ascension. The input will be ' \
'parsed accordingly. Using the regular \'RA\' '\
'keyword will lead to wrong parallactic angles.' \
assert warning[2].message.args[0] == 'For SPHERE data it is recommended to use the ' \
'header keyword \'ESO INS4 DROT2 DEC\' to specify ' \
'the object\'s declination. The input will be ' \
'parsed accordingly. Using the regular \'DEC\' '\
'keyword will lead to wrong parallactic angles.' \
data = self.pipeline.get_data('header_read/PARANG')
assert np.allclose(data[0], -89.12897284829768, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
-89.02755918786514,
rtol=limit,
atol=0.)
assert data.shape == (40, )
def test_angle_interpolation_mismatch(self):
self.pipeline.set_attribute('read', 'NDIT', [9, 9, 9, 9], static=False)
module = AngleInterpolationModule(name_in='angle5', data_tag='read')
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('angle5')
assert len(warning) == 1
assert warning[0].message.args[0] == 'There is a mismatch between the NDIT and NFRAMES ' \
'values. The parallactic angles are calculated ' \
'with a linear interpolation by using NFRAMES ' \
'steps. A frame selection should be applied ' \
'after the parallactic angles are calculated.'
data = self.pipeline.get_data('header_read/PARANG')
assert np.allclose(data[0], 0., rtol=limit, atol=0.)
assert np.allclose(data[15], 7.777777777777778, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_psf_preparation_norm_mask(self):
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_none(self):
module = PSFpreparationModule(name_in='prep2',
image_in_tag='read',
image_out_tag='prep2',
mask_out_tag='mask2',
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('prep2')
data = self.pipeline.get_data('prep2')
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_no_mask_out(self):
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.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_sdi_preparation(self):
module = SDIpreparationModule(name_in='sdi',
wavelength=(0.65, 0.6),
width=(0.1, 0.5),
image_in_tag='read',
image_out_tag='sdi')
self.pipeline.add_module(module)
self.pipeline.run_module('sdi')
data = self.pipeline.get_data('sdi')
assert np.allclose(data[0, 25, 25],
-2.6648118007008814e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.0042892634995876e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
attribute = self.pipeline.get_attribute(
'sdi', 'History: SDIpreparationModule')
assert attribute == '(line, continuum) = (0.65, 0.6)'
class TestCentering:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir + 'star')
create_waffle_data(self.test_dir + 'waffle')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(path=self.test_dir, folders=['star', 'waffle'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='star',
input_dir=self.test_dir + 'star',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read2',
image_tag='waffle',
input_dir=self.test_dir + 'waffle',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
self.pipeline.run_module('read2')
data = self.pipeline.get_data('star')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_data('waffle')
assert np.sum(data) == pytest.approx(4.000000000000196,
rel=self.limit,
abs=0.)
assert data.shape == (1, 101, 101)
def test_star_extract(self) -> None:
module = StarExtractionModule(name_in='extract1',
image_in_tag='star',
image_out_tag='extract1',
index_out_tag='index',
image_size=0.2,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract1')
assert len(warning) == 3
assert warning[0].message.args[0] == 'Can not store the attribute \'INSTRUMENT\' ' \
'because the dataset \'index\' does not exist.'
data = self.pipeline.get_data('extract1')
assert np.sum(data) == pytest.approx(104.93318507061295,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
def test_star_align(self) -> None:
module = StarAlignmentModule(name_in='align1',
image_in_tag='extract1',
ref_image_in_tag=None,
image_out_tag='align1',
accuracy=10,
resize=2.,
num_references=10,
subframe=None)
self.pipeline.add_module(module)
self.pipeline.run_module('align1')
data = self.pipeline.get_data('align1')
assert np.sum(data) == pytest.approx(104.70747423205349,
rel=self.limit,
abs=0.)
assert data.shape == (10, 18, 18)
def test_star_align_subframe(self) -> None:
module = StarAlignmentModule(name_in='align2',
image_in_tag='extract1',
ref_image_in_tag=None,
image_out_tag='align2',
accuracy=10,
resize=None,
num_references=10,
subframe=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('align2')
data = self.pipeline.get_data('align2')
assert np.sum(data) == pytest.approx(104.39031104541652,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
def test_star_align_ref(self) -> None:
module = StarAlignmentModule(name_in='align3',
image_in_tag='extract1',
ref_image_in_tag='align2',
image_out_tag='align3',
accuracy=10,
resize=None,
num_references=10,
subframe=None)
self.pipeline.add_module(module)
self.pipeline.run_module('align3')
data = self.pipeline.get_data('align3')
assert np.sum(data) == pytest.approx(104.46997194330757,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
def test_star_align_number_ref(self) -> None:
module = StarAlignmentModule(name_in='align4',
image_in_tag='extract1',
ref_image_in_tag='align2',
image_out_tag='align4',
accuracy=10,
resize=None,
num_references=20,
subframe=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('align4')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Number of available images (10) is smaller than ' \
'num_references (20). Using all available images ' \
'instead.'
data = self.pipeline.get_data('align4')
assert np.sum(data) == pytest.approx(104.46997194330757,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
def test_shift_images_spline(self) -> None:
module = ShiftImagesModule(shift_xy=(1., 2.),
interpolation='spline',
name_in='shift1',
image_in_tag='align1',
image_out_tag='shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift1')
data = self.pipeline.get_data('shift')
assert np.sum(data) == pytest.approx(104.20425101355242,
rel=self.limit,
abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_fft(self) -> None:
module = ShiftImagesModule(shift_xy=(1., 2.),
interpolation='fft',
name_in='shift2',
image_in_tag='align1',
image_out_tag='shift_fft')
self.pipeline.add_module(module)
self.pipeline.run_module('shift2')
data = self.pipeline.get_data('shift_fft')
assert np.sum(data) == pytest.approx(104.70747423205349,
rel=self.limit,
abs=0.)
assert data.shape == (10, 18, 18)
def test_waffle_center_odd(self) -> None:
module = AddLinesModule(name_in='add',
image_in_tag='star',
image_out_tag='star_add',
lines=(45, 45, 45, 45))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('star_add')
assert np.sum(data) == pytest.approx(105.54278879805278,
rel=self.limit,
abs=0.)
assert data.shape == (10, 101, 101)
module = WaffleCenteringModule(size=0.2,
center=(50, 50),
name_in='waffle',
image_in_tag='star_add',
center_in_tag='waffle',
image_out_tag='center',
radius=35.,
pattern='x',
sigma=0.05)
self.pipeline.add_module(module)
with pytest.warns(DeprecationWarning) as warning:
self.pipeline.run_module('waffle')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The \'pattern\' parameter will be deprecated in a ' \
'future release. Please Use the \'angle\' ' \
'parameter instead and set it to 45.0 degrees.'
data = self.pipeline.get_data('center')
assert np.sum(data) == pytest.approx(104.93318507061295,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
attr = self.pipeline.get_attribute('center',
'History: WaffleCenteringModule')
assert attr == '[x, y] = [50.0, 50.0]'
def test_waffle_center_even(self) -> None:
module = AddLinesModule(name_in='add1',
image_in_tag='star_add',
image_out_tag='star_even',
lines=(0, 1, 0, 1))
self.pipeline.add_module(module)
self.pipeline.run_module('add1')
data = self.pipeline.get_data('star_even')
assert np.sum(data) == pytest.approx(105.54278879805275,
rel=self.limit,
abs=0.)
assert data.shape == (10, 102, 102)
module = AddLinesModule(name_in='add2',
image_in_tag='waffle',
image_out_tag='waffle_even',
lines=(0, 1, 0, 1))
self.pipeline.add_module(module)
self.pipeline.run_module('add2')
data = self.pipeline.get_data('waffle_even')
assert np.sum(data) == pytest.approx(4.000000000000195,
rel=self.limit,
abs=0.)
assert data.shape == (1, 102, 102)
module = ShiftImagesModule(shift_xy=(0.5, 0.5),
interpolation='spline',
name_in='shift3',
image_in_tag='star_even',
image_out_tag='star_shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift3')
data = self.pipeline.get_data('star_shift')
assert np.sum(data) == pytest.approx(105.54278879805274,
rel=self.limit,
abs=0.)
assert data.shape == (10, 102, 102)
module = ShiftImagesModule(shift_xy=(0.5, 0.5),
interpolation='spline',
name_in='shift4',
image_in_tag='waffle_even',
image_out_tag='waffle_shift')
self.pipeline.add_module(module)
self.pipeline.run_module('shift4')
data = self.pipeline.get_data('waffle_shift')
assert np.sum(data) == pytest.approx(4.000000000000194,
rel=self.limit,
abs=0.)
assert data.shape == (1, 102, 102)
module = WaffleCenteringModule(size=0.2,
center=(50, 50),
name_in='waffle_even',
image_in_tag='star_shift',
center_in_tag='waffle_shift',
image_out_tag='center_even',
radius=35.,
pattern='x',
sigma=0.05)
self.pipeline.add_module(module)
with pytest.warns(DeprecationWarning) as warning:
self.pipeline.run_module('waffle_even')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The \'pattern\' parameter will be deprecated in a ' \
'future release. Please Use the \'angle\' ' \
'parameter instead and set it to 45.0 degrees.'
data = self.pipeline.get_data('center_even')
assert np.sum(data) == pytest.approx(105.22695036281449,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
attr = self.pipeline.get_attribute('center_even',
'History: WaffleCenteringModule')
assert attr == '[x, y] = [50.5, 50.5]'
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_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_shift_images_tag(self) -> None:
module = ShiftImagesModule(shift_xy='fit_full',
interpolation='spline',
name_in='shift5',
image_in_tag='shift',
image_out_tag='shift_tag_1')
self.pipeline.add_module(module)
self.pipeline.run_module('shift5')
data = self.pipeline.get_data('shift_tag_1')
assert np.sum(data) == pytest.approx(103.76504482668594,
rel=1e-6,
abs=0.)
assert data.shape == (10, 18, 18)
def test_shift_images_tag_mean(self) -> None:
module = ShiftImagesModule(shift_xy='fit_mean',
interpolation='spline',
name_in='shift6',
image_in_tag='shift',
image_out_tag='shift_tag_2')
self.pipeline.add_module(module)
self.pipeline.run_module('shift6')
data = self.pipeline.get_data('shift_tag_2')
assert np.sum(data) == pytest.approx(103.42285579230325,
rel=1e-6,
abs=0.)
assert data.shape == (10, 18, 18)
class TestFrameSelection(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir+"images", ndit=10, nframes=11)
create_config(self.test_dir+"PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=["images"])
def test_read_data(self):
read = FitsReadingModule(name_in="read",
image_tag="read",
input_dir=self.test_dir+"images",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read")
data = self.pipeline.get_data("read")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0001002167910262529, rtol=limit, atol=0.)
assert data.shape == (44, 100, 100)
def test_remove_last_frame(self):
last = RemoveLastFrameModule(name_in="last",
image_in_tag="read",
image_out_tag="last")
self.pipeline.add_module(last)
self.pipeline.run_module("last")
data = self.pipeline.get_data("last")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010020258903646778, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
self.pipeline.set_attribute("last", "PARANG", np.arange(0., 40., 1.), static=False)
star = np.zeros((40, 2))
star[:, 0] = np.arange(40., 80., 1.)
star[:, 1] = np.arange(40., 80., 1.)
self.pipeline.set_attribute("last", "STAR_POSITION", star, static=False)
attribute = self.pipeline.get_attribute("last", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 19.5, rtol=limit, atol=0.)
assert attribute.shape == (40, )
attribute = self.pipeline.get_attribute("last", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 59.5, rtol=limit, atol=0.)
assert attribute.shape == (40, 2)
def test_remove_start_frame(self):
start = RemoveStartFramesModule(frames=2,
name_in="start",
image_in_tag="last",
image_out_tag="start")
self.pipeline.add_module(start)
self.pipeline.run_module("start")
data = self.pipeline.get_data("start")
assert np.allclose(data[0, 50, 50], 0.09797376304048713, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010011298467340513, rtol=limit, atol=0.)
assert data.shape == (32, 100, 100)
attribute = self.pipeline.get_attribute("start", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 20.5, rtol=limit, atol=0.)
assert attribute.shape == (32, )
attribute = self.pipeline.get_attribute("start", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 60.5, rtol=limit, atol=0.)
assert attribute.shape == (32, 2)
def test_remove_frames(self):
remove = RemoveFramesModule(frames=(5, 8, 13, 25, 31),
name_in="remove",
image_in_tag="start",
selected_out_tag="selected",
removed_out_tag="removed")
self.pipeline.add_module(remove)
self.pipeline.run_module("remove")
data = self.pipeline.get_data("selected")
assert np.allclose(data[0, 50, 50], 0.09797376304048713, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.984682304434105e-05, rtol=limit, atol=0.)
assert data.shape == (27, 100, 100)
data = self.pipeline.get_data("removed")
assert np.allclose(data[0, 50, 50], 0.09818692015286978, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010155025747035087, rtol=limit, atol=0.)
assert data.shape == (5, 100, 100)
attribute = self.pipeline.get_attribute("selected", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 20.296296296296298, rtol=limit, atol=0.)
assert attribute.shape == (27, )
attribute = self.pipeline.get_attribute("selected", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 60.2962962962963, rtol=limit, atol=0.)
assert attribute.shape == (27, 2)
attribute = self.pipeline.get_attribute("removed", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 21.6, rtol=limit, atol=0.)
assert attribute.shape == (5, )
attribute = self.pipeline.get_attribute("removed", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 61.6, rtol=limit, atol=0.)
assert attribute.shape == (5, 2)
def test_frame_selection(self):
select = FrameSelectionModule(name_in="select1",
image_in_tag="start",
selected_out_tag="selected1",
removed_out_tag="removed1",
index_out_tag="index1",
method="median",
threshold=1.,
fwhm=0.1,
aperture=("circular", 0.2),
position=(None, None, 0.5))
self.pipeline.add_module(select)
self.pipeline.run_module("select1")
data = self.pipeline.get_data("selected1")
assert np.allclose(data[0, 50, 50], 0.09791350617182591, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.980792188317311e-05, rtol=limit, atol=0.)
assert data.shape == (22, 100, 100)
data = self.pipeline.get_data("removed1")
assert np.allclose(data[0, 50, 50], 0.09797376304048713, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010078412281191547, rtol=limit, atol=0.)
assert data.shape == (10, 100, 100)
data = self.pipeline.get_data("index1")
assert data[-1] == 28
assert np.sum(data) == 115
assert data.shape == (10, )
attribute = self.pipeline.get_attribute("selected1", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 22.681818181818183, rtol=limit, atol=0.)
assert attribute.shape == (22, )
attribute = self.pipeline.get_attribute("selected1", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (22, 2)
attribute = self.pipeline.get_attribute("removed1", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 15.7, rtol=limit, atol=0.)
assert attribute.shape == (10, )
attribute = self.pipeline.get_attribute("removed1", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (10, 2)
select = FrameSelectionModule(name_in="select2",
image_in_tag="start",
selected_out_tag="selected2",
removed_out_tag="removed2",
index_out_tag="index2",
method="max",
threshold=3.,
fwhm=0.1,
aperture=("annulus", 0.1, 0.2),
position=None)
self.pipeline.add_module(select)
self.pipeline.run_module("select2")
data = self.pipeline.get_data("selected2")
assert np.allclose(data[0, 50, 50], 0.09797376304048713, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010037996502199598, rtol=limit, atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data("removed2")
assert np.allclose(data[0, 50, 50], 0.097912284606689, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.966801742575358e-05, rtol=limit, atol=0.)
assert data.shape == (12, 100, 100)
data = self.pipeline.get_data("index2")
assert data[-1] == 30
assert np.sum(data) == 230
assert data.shape == (12, )
attribute = self.pipeline.get_attribute("selected2", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 17.8, rtol=limit, atol=0.)
assert attribute.shape == (20, )
attribute = self.pipeline.get_attribute("selected2", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (20, 2)
attribute = self.pipeline.get_attribute("removed2", "PARANG", static=False)
assert np.allclose(np.mean(attribute), 25.0, rtol=limit, atol=0.)
assert attribute.shape == (12, )
attribute = self.pipeline.get_attribute("removed2", "STAR_POSITION", static=False)
assert np.allclose(np.mean(attribute), 50.0, rtol=limit, atol=0.)
assert attribute.shape == (12, 2)
class TestResizing:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir + 'resize')
create_ifs_data(self.test_dir + 'resize_ifs')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['resize', 'resize_ifs'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir + 'resize',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
module = FitsReadingModule(name_in='read_ifs',
image_tag='read_ifs',
input_dir=self.test_dir + 'resize_ifs',
overwrite=True,
check=True,
ifs_data=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read_ifs')
data = self.pipeline.get_data('read_ifs')
assert np.sum(data) == pytest.approx(749.8396528807369,
rel=self.limit,
abs=0.)
assert data.shape == (3, 10, 21, 21)
def test_crop_images(self) -> None:
module = CropImagesModule(size=0.2,
center=None,
name_in='crop1',
image_in_tag='read',
image_out_tag='crop1')
self.pipeline.add_module(module)
self.pipeline.run_module('crop1')
module = CropImagesModule(size=0.2,
center=(4, 4),
name_in='crop2',
image_in_tag='read',
image_out_tag='crop2')
self.pipeline.add_module(module)
self.pipeline.run_module('crop2')
module = CropImagesModule(size=0.2,
center=(4, 4),
name_in='crop_ifs',
image_in_tag='read_ifs',
image_out_tag='crop_ifs')
self.pipeline.add_module(module)
self.pipeline.run_module('crop_ifs')
data = self.pipeline.get_data('crop1')
assert np.sum(data) == pytest.approx(104.93318507061295,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
data = self.pipeline.get_data('crop2')
assert np.sum(data) == pytest.approx(105.64863165433025,
rel=self.limit,
abs=0.)
assert data.shape == (10, 9, 9)
data = self.pipeline.get_data('crop_ifs')
assert np.sum(data) == pytest.approx(15.870936600122521,
rel=self.limit,
abs=0.)
assert data.shape == (3, 10, 9, 9)
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_add_lines(self) -> None:
module = AddLinesModule(lines=(2, 5, 0, 3),
name_in='add',
image_in_tag='read',
image_out_tag='add')
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('add')
assert np.sum(data) == pytest.approx(105.54278879805275,
rel=self.limit,
abs=0.)
assert data.shape == (10, 14, 18)
def test_remove_lines(self) -> None:
module = RemoveLinesModule(lines=(2, 5, 0, 3),
name_in='remove',
image_in_tag='read',
image_out_tag='remove')
self.pipeline.add_module(module)
self.pipeline.run_module('remove')
data = self.pipeline.get_data('remove')
assert np.sum(data) == pytest.approx(67.49726677462391,
rel=self.limit,
abs=0.)
assert data.shape == (10, 8, 4)
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('remove')
data_multi = self.pipeline.get_data('remove')
assert data == pytest.approx(data_multi, rel=self.limit, abs=0.)
assert data.shape == data_multi.shape
class TestHdf5WritingModule:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_random(self.test_dir)
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, files=['test.hdf5'])
def test_hdf5_writing(self):
write = Hdf5WritingModule(file_name='test.hdf5',
name_in='write1',
output_dir=None,
tag_dictionary={'images':'data1'},
keep_attributes=True,
overwrite=True)
self.pipeline.add_module(write)
self.pipeline.run_module('write1')
def test_no_data_tag(self):
write = Hdf5WritingModule(file_name='test.hdf5',
name_in='write2',
output_dir=None,
tag_dictionary={'empty':'empty'},
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('write2')
assert len(warning) == 1
assert warning[0].message.args[0] == 'No data under the tag which is linked by the ' \
'InputPort.'
def test_overwrite_false(self):
write = Hdf5WritingModule(file_name='test.hdf5',
name_in='write3',
output_dir=None,
tag_dictionary={'images':'data2'},
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
self.pipeline.run_module('write3')
def test_dictionary_none(self):
write = Hdf5WritingModule(file_name='test.hdf5',
name_in='write4',
output_dir=None,
tag_dictionary=None,
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
self.pipeline.run_module('write4')
def test_hdf5_reading(self):
read = Hdf5ReadingModule(name_in='read',
input_filename='test.hdf5',
input_dir=self.test_dir,
tag_dictionary={'data1':'data1', 'data2':'data2'})
self.pipeline.add_module(read)
self.pipeline.run_module('read')
data1 = self.pipeline.get_data('data1')
data2 = self.pipeline.get_data('data2')
data3 = self.pipeline.get_data('images')
assert np.allclose(data1, data2, rtol=limit, atol=0.)
assert np.allclose(data2, data3, rtol=limit, atol=0.)
attribute1 = self.pipeline.get_attribute('images', 'PARANG', static=False)
attribute2 = self.pipeline.get_attribute('data1', 'PARANG', static=False)
attribute3 = self.pipeline.get_attribute('data2', 'PARANG', static=False)
assert np.allclose(attribute1, attribute2, rtol=limit, atol=0.)
assert np.allclose(attribute2, attribute3, rtol=limit, atol=0.)
class TestLimits:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir+'limits')
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(path=self.test_dir,
folders=['limits'],
files=['model.AMES-Cond-2000.M-0.0.NaCo.Vega'])
def test_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir+'limits')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 10, 10], 0.00012958496246258364, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
def test_angle_interpolation(self):
module = AngleInterpolationModule(name_in='angle',
data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
data = self.pipeline.get_attribute('read', 'PARANG', static=False)
assert data[5] == 2.7777777777777777
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_contrast_curve(self):
proc = ['single', 'multi']
for item in proc:
if item == 'multi':
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = ContrastCurveModule(name_in='contrast_'+item,
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_'+item,
separation=(0.5, 0.6, 0.1),
angle=(0., 360., 180.),
threshold=('sigma', 5.),
psf_scaling=1.,
aperture=0.1,
pca_number=15,
cent_size=None,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_'+item)
data = self.pipeline.get_data('limits_'+item)
assert np.allclose(data[0, 0], 5.00000000e-01, rtol=limit, atol=0.)
assert np.allclose(data[0, 1], 1.2034060712266164, rtol=limit, atol=0.)
assert np.allclose(data[0, 2], 0.2594381985736874, rtol=limit, atol=0.)
assert np.allclose(data[0, 3], 0.00012147700290954244, rtol=limit, atol=0.)
assert data.shape == (1, 4)
def test_contrast_curve_fpf(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = ContrastCurveModule(name_in='contrast_fpf',
image_in_tag='read',
psf_in_tag='read',
contrast_out_tag='limits_fpf',
separation=(0.5, 0.6, 0.1),
angle=(0., 360., 180.),
threshold=('fpf', 1e-6),
psf_scaling=1.,
aperture=0.1,
pca_number=15,
cent_size=None,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('contrast_fpf')
data = self.pipeline.get_data('limits_fpf')
assert np.allclose(data[0, 0], 5.00000000e-01, rtol=limit, atol=0.)
assert np.allclose(data[0, 1], 0.6820548720545829, rtol=limit, atol=0.)
assert np.allclose(data[0, 2], 0.2594381985736874, rtol=limit, atol=0.)
assert np.allclose(data[0, 3], 1e-6, rtol=limit, atol=0.)
assert data.shape == (1, 4)
def test_mass_limits(self):
separation = np.linspace(0.1, 1.0, 10)
contrast = -2.5*np.log10(1e-4/separation)
variance = 0.1*contrast
limits = np.zeros((10, 4))
limits[:, 0] = separation
limits[:, 1] = contrast
limits[:, 2] = variance
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['contrast_limits'] = limits
url = 'https://phoenix.ens-lyon.fr/Grids/AMES-Cond/ISOCHRONES/' \
'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
filename = self.test_dir + 'model.AMES-Cond-2000.M-0.0.NaCo.Vega'
urlretrieve(url, filename)
module = MassLimitsModule(model_file=filename,
star_prop={'magnitude': 10., 'distance': 100., 'age': 20.},
name_in='mass',
contrast_in_tag='contrast_limits',
mass_out_tag='mass_limits',
instr_filter='L\'')
self.pipeline.add_module(module)
self.pipeline.run_module('mass')
data = self.pipeline.get_data('mass_limits')
assert np.allclose(np.mean(data[:, 0]), 0.55, rtol=limit, atol=0.)
assert np.allclose(np.mean(data[:, 1]), 0.001891690765603738, rtol=limit, atol=0.)
assert np.allclose(np.mean(data[:, 2]), 0.000964309686441908, rtol=limit, atol=0.)
assert np.allclose(np.mean(data[:, 3]), -0.000696402843279597, rtol=limit, atol=0.)
assert data.shape == (10, 4)
class TestPypeline:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
np.random.seed(1)
image_3d = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
image_2d = np.random.normal(loc=0, scale=2e-4, size=(1, 10, 10))
science = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
dark = np.random.normal(loc=0, scale=2e-4, size=(4, 10, 10))
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'w') as hdf_file:
hdf_file.create_dataset('image_3d', data=image_3d)
hdf_file.create_dataset('image_2d', data=image_2d)
hdf_file.create_dataset('science', data=science)
hdf_file.create_dataset('dark', data=dark)
create_star_data(path=self.test_dir + 'images')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=['images'])
def test_output_port_name(self):
read = FitsReadingModule(name_in='read',
input_dir=self.test_dir + 'images',
image_tag='images')
read.add_output_port('test')
with pytest.warns(UserWarning) as warning:
read.add_output_port('test')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Tag \'test\' of ReadingModule \'read\' is already ' \
'used.'
process = BadPixelSigmaFilterModule(name_in='badpixel',
image_in_tag='images',
image_out_tag='im_out')
process.add_output_port('test')
with pytest.warns(UserWarning) as warning:
process.add_output_port('test')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Tag \'test\' of ProcessingModule \'badpixel\' is ' \
'already used.'
self.pipeline.m_data_storage.close_connection()
process._m_data_base = self.test_dir + 'database.hdf5'
process.add_output_port('new')
def test_apply_function_to_images_3d(self):
self.pipeline.set_attribute('config', 'MEMORY', 1, static=True)
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in='remove1',
image_in_tag='image_3d',
image_out_tag='remove_3d')
self.pipeline.add_module(remove)
self.pipeline.run_module('remove1')
data = self.pipeline.get_data('image_3d')
assert np.allclose(np.mean(data),
1.0141852764605783e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
data = self.pipeline.get_data('remove_3d')
assert np.allclose(np.mean(data),
1.1477029889801025e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 9)
def test_apply_function_to_images_2d(self):
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in='remove2',
image_in_tag='image_2d',
image_out_tag='remove_2d')
self.pipeline.add_module(remove)
self.pipeline.run_module('remove2')
data = self.pipeline.get_data('image_2d')
assert np.allclose(np.mean(data),
1.2869483197883442e-05,
rtol=limit,
atol=0.)
assert data.shape == (1, 10, 10)
data = self.pipeline.get_data('remove_2d')
assert np.allclose(np.mean(data),
1.3957075246029751e-05,
rtol=limit,
atol=0.)
assert data.shape == (1, 10, 9)
def test_apply_function_to_images_same_port(self):
dark = DarkCalibrationModule(name_in='dark1',
image_in_tag='science',
dark_in_tag='dark',
image_out_tag='science')
self.pipeline.add_module(dark)
self.pipeline.run_module('dark1')
data = self.pipeline.get_data('science')
assert np.allclose(np.mean(data),
-3.190113568690675e-06,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
dark = DarkCalibrationModule(name_in='dark2',
image_in_tag='science',
dark_in_tag='dark',
image_out_tag='science')
self.pipeline.add_module(dark)
self.pipeline.run_module('dark2')
data = self.pipeline.get_data('science')
assert np.allclose(np.mean(data),
-1.026073475228737e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 10)
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in='remove3',
image_in_tag='remove_3d',
image_out_tag='remove_3d')
self.pipeline.add_module(remove)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('remove3')
assert str(error.value) == 'Input and output port have the same tag while the input ' \
'function is changing the image shape. This is only ' \
'possible with MEMORY=None.'
def test_apply_function_to_images_memory_none(self):
remove = RemoveLinesModule(lines=(1, 0, 0, 0),
name_in='remove4',
image_in_tag='image_3d',
image_out_tag='remove_3d_none')
self.pipeline.add_module(remove)
self.pipeline.run_module('remove4')
data = self.pipeline.get_data('remove_3d_none')
assert np.allclose(np.mean(data),
1.1477029889801025e-05,
rtol=limit,
atol=0.)
assert data.shape == (4, 10, 9)
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_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 == (1, 12, 12)
attribute = self.pipeline.get_attribute('scale_2d',
'PIXSCALE',
static=True)
assert np.allclose(attribute, 0.08333333333333334, rtol=limit, atol=0.)
class TestTimeDenoising:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir+'images',
npix_x=20,
npix_y=20,
x0=[10, 10, 10, 10],
y0=[10, 10, 10, 10],
parang_start=[0., 25., 50., 75.],
parang_end=[25., 50., 75., 100.],
exp_no=[1, 2, 3, 4])
create_config(self.test_dir+'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=['images'])
def test_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir+'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('images')
assert np.allclose(data[0, 10, 10], 0.09799496683489618, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0025020285041348557, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
def test_wavelet_denoising_cwt_dog(self):
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert np.allclose(cwt_config.m_wavelet_order, 2, rtol=limit, atol=0.)
assert not cwt_config.m_keep_mean
assert np.allclose(cwt_config.m_resolution, 0.5, rtol=limit, atol=0.)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_dog',
image_in_tag='images',
image_out_tag='wavelet_cwt_dog',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_dog')
data = self.pipeline.get_data('wavelet_cwt_dog')
assert np.allclose(data[0, 10, 10], 0.09805577173716859, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.002502083112599873, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.run_module('wavelet_cwt_dog')
data_multi = self.pipeline.get_data('wavelet_cwt_dog')
assert np.allclose(data, data_multi, rtol=limit, atol=0.)
assert data.shape == data_multi.shape
def test_wavelet_denoising_cwt_morlet(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
cwt_config = CwtWaveletConfiguration(wavelet='morlet',
wavelet_order=5,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'morlet'
assert np.allclose(cwt_config.m_wavelet_order, 5, rtol=limit, atol=0.)
assert not cwt_config.m_keep_mean
assert np.allclose(cwt_config.m_resolution, 0.5, rtol=limit, atol=0.)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_cwt_morlet',
image_in_tag='images',
image_out_tag='wavelet_cwt_morlet',
padding='mirror',
median_filter=False,
threshold_function='hard')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_cwt_morlet')
data = self.pipeline.get_data('wavelet_cwt_morlet')
assert np.allclose(data[0, 10, 10], 0.09805577173716859, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0025019409784314286, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
data = self.pipeline.get_attribute('wavelet_cwt_morlet', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
def test_wavelet_denoising_dwt(self):
dwt_config = DwtWaveletConfiguration(wavelet='db8')
assert dwt_config.m_wavelet == 'db8'
module = WaveletTimeDenoisingModule(wavelet_configuration=dwt_config,
name_in='wavelet_dwt',
image_in_tag='images',
image_out_tag='wavelet_dwt',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_dwt')
data = self.pipeline.get_data('wavelet_dwt')
assert np.allclose(data[0, 10, 10], 0.09650639476873678, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0024998798596330475, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
def test_time_normalization(self):
module = TimeNormalizationModule(name_in='timenorm',
image_in_tag='images',
image_out_tag='timenorm')
self.pipeline.add_module(module)
self.pipeline.run_module('timenorm')
data = self.pipeline.get_data('timenorm')
assert np.allclose(data[0, 10, 10], 0.09793500165714215, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0024483409033199985, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
def test_wavelet_denoising_odd_size(self):
module = AddLinesModule(name_in='add',
image_in_tag='images',
image_out_tag='images_odd',
lines=(1, 0, 1, 0))
self.pipeline.add_module(module)
self.pipeline.run_module('add')
data = self.pipeline.get_data('images_odd')
assert np.allclose(data[0, 10, 10], 0.05294085050174391, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.002269413609192613, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
cwt_config = CwtWaveletConfiguration(wavelet='dog',
wavelet_order=2,
keep_mean=False,
resolution=0.5)
assert cwt_config.m_wavelet == 'dog'
assert np.allclose(cwt_config.m_wavelet_order, 2, rtol=limit, atol=0.)
assert not cwt_config.m_keep_mean
assert np.allclose(cwt_config.m_resolution, 0.5, rtol=limit, atol=0.)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_odd_1',
image_in_tag='images_odd',
image_out_tag='wavelet_odd_1',
padding='zero',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_odd_1')
data = self.pipeline.get_data('wavelet_odd_1')
assert np.allclose(data[0, 10, 10], 0.0529782051386938, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0022694631406801565, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
module = WaveletTimeDenoisingModule(wavelet_configuration=cwt_config,
name_in='wavelet_odd_2',
image_in_tag='images_odd',
image_out_tag='wavelet_odd_2',
padding='mirror',
median_filter=True,
threshold_function='soft')
self.pipeline.add_module(module)
self.pipeline.run_module('wavelet_odd_2')
data = self.pipeline.get_data('wavelet_odd_2')
assert np.allclose(data[0, 10, 10], 0.05297146283932275, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0022694809842930034, rtol=limit, atol=0.)
assert data.shape == (40, 21, 21)
data = self.pipeline.get_attribute('images', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
data = self.pipeline.get_attribute('wavelet_odd_1', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
data = self.pipeline.get_attribute('wavelet_odd_2', 'NFRAMES', static=False)
assert np.allclose(data, [10, 10, 10, 10], rtol=limit, atol=0.)
class TestBackground:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_dither_data(self.test_dir + 'dither')
create_star_data(self.test_dir + 'science')
create_star_data(self.test_dir + 'sky')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['dither', 'science', 'sky'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='dither',
input_dir=self.test_dir + 'dither')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('dither')
assert np.sum(data) == pytest.approx(211.20534661914408,
rel=self.limit,
abs=0.)
assert data.shape == (20, 21, 21)
module = FitsReadingModule(name_in='read2',
image_tag='science',
input_dir=self.test_dir + 'science')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
module = FitsReadingModule(name_in='read3',
image_tag='sky',
input_dir=self.test_dir + 'sky')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('sky')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_simple_background(self) -> None:
module = SimpleBackgroundSubtractionModule(shift=5,
name_in='simple',
image_in_tag='dither',
image_out_tag='simple')
self.pipeline.add_module(module)
self.pipeline.run_module('simple')
data = self.pipeline.get_data('simple')
assert np.sum(data) == pytest.approx(3.552713678800501e-15,
rel=self.limit,
abs=0.)
assert data.shape == (20, 21, 21)
def test_mean_background_shift(self) -> None:
module = MeanBackgroundSubtractionModule(shift=5,
cubes=1,
name_in='mean2',
image_in_tag='dither',
image_out_tag='mean2')
self.pipeline.add_module(module)
self.pipeline.run_module('mean2')
data = self.pipeline.get_data('mean2')
assert np.sum(data) == pytest.approx(2.473864361018551,
rel=self.limit,
abs=0.)
assert data.shape == (20, 21, 21)
def test_mean_background_nframes(self) -> None:
module = MeanBackgroundSubtractionModule(shift=None,
cubes=1,
name_in='mean1',
image_in_tag='dither',
image_out_tag='mean1')
self.pipeline.add_module(module)
self.pipeline.run_module('mean1')
data = self.pipeline.get_data('mean1')
assert np.sum(data) == pytest.approx(2.473864361018551,
rel=self.limit,
abs=0.)
assert data.shape == (20, 21, 21)
def test_dithering_attributes(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither1',
image_in_tag='dither',
image_out_tag='pca_dither1',
center=None,
cubes=None,
size=0.2,
gaussian=0.05,
subframe=0.1,
pca_number=1,
mask_star=0.05,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither1')
data = self.pipeline.get_data('dither_dither_crop1')
assert np.sum(data) == pytest.approx(54.62410860562912,
rel=self.limit,
abs=0.)
assert data.shape == (20, 9, 9)
data = self.pipeline.get_data('dither_dither_star1')
assert np.sum(data) == pytest.approx(54.873885838788595,
rel=self.limit,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_mean1')
assert np.sum(data) == pytest.approx(54.204960755115245,
rel=self.limit,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_background1')
assert np.sum(data) == pytest.approx(-0.24977723315947564,
rel=self.limit,
abs=0.)
assert data.shape == (15, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_fit1')
assert np.sum(data) == pytest.approx(-0.01019999314121019,
rel=1e-5,
abs=0.)
print(np.sum(data))
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_res1')
assert np.sum(data) == pytest.approx(54.884085831929795,
rel=1e-6,
abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('dither_dither_pca_mask1')
assert np.sum(data) == pytest.approx(360.0, rel=self.limit, abs=0.)
assert data.shape == (5, 9, 9)
data = self.pipeline.get_data('pca_dither1')
assert np.sum(data) == pytest.approx(208.774670964812,
rel=1e-6,
abs=0.)
assert data.shape == (20, 9, 9)
attr = self.pipeline.get_attribute('dither_dither_pca_res1',
'STAR_POSITION',
static=False)
assert np.sum(attr) == pytest.approx(51., rel=self.limit, abs=0.)
assert attr.shape == (5, 2)
def test_dithering_center(self) -> None:
module = DitheringBackgroundModule(name_in='pca_dither2',
image_in_tag='dither',
image_out_tag='pca_dither2',
center=((5, 5), (5, 15), (15, 15),
(15, 5)),
cubes=1,
size=0.2,
gaussian=0.05,
subframe=None,
pca_number=1,
mask_star=0.05,
crop=True,
prepare=True,
pca_background=True,
combine='pca')
self.pipeline.add_module(module)
self.pipeline.run_module('pca_dither2')
data = self.pipeline.get_data('pca_dither2')
assert np.sum(data) == pytest.approx(209.8271898501695,
rel=1e-6,
abs=0.)
assert data.shape == (20, 9, 9)
def test_nodding_background(self) -> None:
module = StackCubesModule(name_in='mean',
image_in_tag='sky',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.sum(data) == pytest.approx(21.108557759610548,
rel=self.limit,
abs=0.)
assert data.shape == (2, 11, 11)
attr = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.sum(attr) == pytest.approx(1, rel=self.limit, abs=0.)
assert attr.shape == (2, )
attr = self.pipeline.get_attribute('mean', 'NFRAMES', static=False)
assert np.sum(attr) == pytest.approx(2, rel=self.limit, abs=0.)
assert attr.shape == (2, )
module = NoddingBackgroundModule(name_in='nodding',
sky_in_tag='mean',
science_in_tag='science',
image_out_tag='nodding',
mode='both')
self.pipeline.add_module(module)
self.pipeline.run_module('nodding')
data = self.pipeline.get_data('nodding')
assert np.sum(data) == pytest.approx(1.793466459074705,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_line_background_mean(self) -> None:
module = LineSubtractionModule(name_in='line1',
image_in_tag='science',
image_out_tag='science_line1',
combine='mean',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line1')
data = self.pipeline.get_data('science_line1')
assert np.sum(data) == pytest.approx(104.55443019231085,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_line_background_median(self) -> None:
module = LineSubtractionModule(name_in='line2',
image_in_tag='science',
image_out_tag='science_line2',
combine='median',
mask=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('line2')
data = self.pipeline.get_data('science_line2')
assert np.sum(data) == pytest.approx(106.09825573198366,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
class TestHdf5WritingModule(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_random(self.test_dir)
create_config(self.test_dir+"PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, files=["test.hdf5"])
def test_hdf5_writing(self):
write = Hdf5WritingModule(file_name="test.hdf5",
name_in="write1",
output_dir=None,
tag_dictionary={"images":"data1"},
keep_attributes=True,
overwrite=True)
self.pipeline.add_module(write)
self.pipeline.run_module("write1")
def test_no_data_tag(self):
write = Hdf5WritingModule(file_name="test.hdf5",
name_in="write2",
output_dir=None,
tag_dictionary={"empty":"empty"},
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("write2")
assert len(warning) == 1
assert warning[0].message.args[0] == "No data under the tag which is linked by the " \
"InputPort."
def test_overwrite_false(self):
write = Hdf5WritingModule(file_name="test.hdf5",
name_in="write3",
output_dir=None,
tag_dictionary={"images":"data2"},
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
self.pipeline.run_module("write3")
def test_dictionary_none(self):
write = Hdf5WritingModule(file_name="test.hdf5",
name_in="write4",
output_dir=None,
tag_dictionary=None,
keep_attributes=True,
overwrite=False)
self.pipeline.add_module(write)
self.pipeline.run_module("write4")
def test_hdf5_reading(self):
read = Hdf5ReadingModule(name_in="read",
input_filename="test.hdf5",
input_dir=self.test_dir,
tag_dictionary={"data1":"data1", "data2":"data2"})
self.pipeline.add_module(read)
self.pipeline.run_module("read")
data1 = self.pipeline.get_data("data1")
data2 = self.pipeline.get_data("data2")
data3 = self.pipeline.get_data("images")
assert np.allclose(data1, data2, rtol=limit, atol=0.)
assert np.allclose(data2, data3, rtol=limit, atol=0.)
attribute1 = self.pipeline.get_attribute("images", "PARANG", static=False)
attribute2 = self.pipeline.get_attribute("data1", "PARANG", static=False)
attribute3 = self.pipeline.get_attribute("data2", "PARANG", static=False)
assert np.allclose(attribute1, attribute2, rtol=limit, atol=0.)
assert np.allclose(attribute2, attribute3, rtol=limit, atol=0.)
class TestPSFpreparation(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir + "prep")
create_config(self.test_dir + "PynPoint_config.ini")
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=["prep"])
def test_read_data(self):
read = FitsReadingModule(name_in="read",
image_tag="read",
input_dir=self.test_dir + "prep")
self.pipeline.add_module(read)
self.pipeline.run_module("read")
data = self.pipeline.get_data("read")
assert np.allclose(data[0, 25, 25],
2.0926464668090656e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_angle_interpolation(self):
angle = AngleInterpolationModule(name_in="angle1", data_tag="read")
self.pipeline.add_module(angle)
self.pipeline.run_module("angle1")
data = self.pipeline.get_data("header_read/PARANG")
assert np.allclose(data[0], 0., rtol=limit, atol=0.)
assert np.allclose(data[15], 7.777777777777778, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_angle_calculation(self):
self.pipeline.set_attribute("read", "LATITUDE", -25.)
self.pipeline.set_attribute("read", "LONGITUDE", -70.)
self.pipeline.set_attribute("read", "DIT", 1.)
self.pipeline.set_attribute("read",
"RA", (90., 90., 90., 90.),
static=False)
self.pipeline.set_attribute("read",
"DEC", (-51., -51., -51., -51.),
static=False)
self.pipeline.set_attribute("read",
"PUPIL", (90., 90., 90., 90.),
static=False)
date = ("2012-12-01T07:09:00.0000", "2012-12-01T07:09:01.0000",
"2012-12-01T07:09:02.0000", "2012-12-01T07:09:03.0000")
self.pipeline.set_attribute("read", "DATE", date, static=False)
angle = AngleCalculationModule(instrument="NACO",
name_in="angle2",
data_tag="read")
self.pipeline.add_module(angle)
self.pipeline.run_module("angle2")
data = self.pipeline.get_data("header_read/PARANG")
assert np.allclose(data[0], -55.041097524594186, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
-54.99858342139904,
rtol=limit,
atol=0.)
assert data.shape == (40, )
self.pipeline.set_attribute("read",
"RA", (60000.0, 60000.0, 60000.0, 60000.0),
static=False)
self.pipeline.set_attribute("read",
"DEC",
(-510000., -510000., -510000., -510000.),
static=False)
angle = AngleCalculationModule(instrument="SPHERE/IRDIS",
name_in="angle3",
data_tag="read")
self.pipeline.add_module(angle)
self.pipeline.run_module("angle3")
data = self.pipeline.get_data("header_read/PARANG")
assert np.allclose(data[0], 170.39102733657813, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
170.46341141667205,
rtol=limit,
atol=0.)
assert data.shape == (40, )
angle = AngleCalculationModule(instrument="SPHERE/IFS",
name_in="angle4",
data_tag="read")
self.pipeline.add_module(angle)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("angle4")
assert len(warning) == 2
assert warning[0].message.args[0] == "AngleCalculationModule has not been tested for " \
"SPHERE/IFS data."
assert warning[1].message.args[0] == "For SPHERE data it is recommended to use the " \
"header keywords \"ESO INS4 DROT2 RA/DEC\" to " \
"specify the object's position. The input will be " \
"parsed accordingly. Using the regular RA/DEC "\
"parameters will lead to wrong parallactic angles." \
data = self.pipeline.get_data("header_read/PARANG")
assert np.allclose(data[0], -89.12897266342185, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
-89.02755900320116,
rtol=limit,
atol=0.)
assert data.shape == (40, )
def test_angle_interpolation_mismatch(self):
self.pipeline.set_attribute("read", "NDIT", [9, 9, 9, 9], static=False)
angle = AngleInterpolationModule(name_in="angle5", data_tag="read")
self.pipeline.add_module(angle)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("angle5")
assert len(warning) == 1
assert warning[0].message.args[0] == "There is a mismatch between the NDIT and NFRAMES " \
"values. The derotation angles are calculated with " \
"a linear interpolation by using NFRAMES steps. A " \
"frame selection should be applied after the " \
"derotation angles are calculated."
data = self.pipeline.get_data("header_read/PARANG")
assert np.allclose(data[0], 0., rtol=limit, atol=0.)
assert np.allclose(data[15], 7.777777777777778, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
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_none(self):
prep = PSFpreparationModule(name_in="prep2",
image_in_tag="read",
image_out_tag="prep2",
mask_out_tag="mask2",
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(prep)
self.pipeline.run_module("prep2")
data = self.pipeline.get_data("prep2")
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_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_sdi_preparation(self):
sdi = SDIpreparationModule(name_in="sdi",
wavelength=(0.65, 0.6),
width=(0.1, 0.5),
image_in_tag="read",
image_out_tag="sdi")
self.pipeline.add_module(sdi)
self.pipeline.run_module("sdi")
data = self.pipeline.get_data("sdi")
assert np.allclose(data[0, 25, 25],
-2.6648118007008814e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.0042892634995876e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
attribute = self.pipeline.get_attribute(
"sdi", "History: SDIpreparationModule")
assert attribute == "(line, continuum) = (0.65, 0.6)"
class TestExtract:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'star',
npix_x=51,
npix_y=51,
x0=[10., 10., 10., 10.],
y0=[10., 10., 10., 10.])
create_fake(path=self.test_dir + 'binary',
ndit=[20, 20, 20, 20],
nframes=[20, 20, 20, 20],
exp_no=[1, 2, 3, 4],
npix=(101, 101),
fwhm=3.,
x0=[50, 50, 50, 50],
y0=[50, 50, 50, 50],
angles=[[0., 25.], [25., 50.], [50., 75.], [75., 100.]],
sep=20.,
contrast=1.)
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(path=self.test_dir, folders=['star', 'binary'])
def test_read_data(self):
module = FitsReadingModule(name_in='read1',
image_tag='star',
input_dir=self.test_dir + 'star',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('star')
assert np.allclose(data[0, 10, 10],
0.09834884212021108,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00038538535294683216,
rtol=limit,
atol=0.)
assert data.shape == (40, 51, 51)
module = FitsReadingModule(name_in='read2',
image_tag='binary',
input_dir=self.test_dir + 'binary',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('binary')
assert np.allclose(data[0, 50, 50],
0.0986064357966972,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00019636787665654158,
rtol=limit,
atol=0.)
assert data.shape == (80, 101, 101)
def test_angle_interpolation(self):
module = AngleInterpolationModule(name_in='angle', data_tag='binary')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
data = self.pipeline.get_attribute('binary', 'PARANG', static=False)
assert data[5] == 6.578947368421053
assert np.allclose(np.mean(data), 50.0, rtol=limit, atol=0.)
assert data.shape == (80, )
parang = self.pipeline.get_attribute('binary', 'PARANG', static=False)
self.pipeline.set_attribute('binary',
'PARANG',
-1. * parang,
static=False)
data = self.pipeline.get_attribute('binary', 'PARANG', static=False)
assert data[5] == -6.578947368421053
assert np.allclose(np.mean(data), -50.0, rtol=limit, atol=0.)
assert data.shape == (80, )
def test_extract_position_none(self):
module = StarExtractionModule(name_in='extract1',
image_in_tag='star',
image_out_tag='extract1',
index_out_tag='index',
image_size=0.4,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract1')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The new dataset that is stored under the tag name ' \
'\'index\' is empty.'
data = self.pipeline.get_data('extract1')
assert np.allclose(data[0, 7, 7],
0.09834884212021108,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.004444871536643222,
rtol=limit,
atol=0.)
assert data.shape == (40, 15, 15)
attr = self.pipeline.get_attribute('extract1',
'STAR_POSITION',
static=False)
assert attr[10, 0] == attr[10, 1] == 10
def test_extract_center_none(self):
module = StarExtractionModule(name_in='extract2',
image_in_tag='star',
image_out_tag='extract2',
index_out_tag='index',
image_size=0.4,
fwhm_star=0.1,
position=(None, None, 1.))
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract2')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The new dataset that is stored under the tag name ' \
'\'index\' is empty.'
data = self.pipeline.get_data('extract2')
assert np.allclose(data[0, 7, 7],
0.09834884212021108,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.004444871536643222,
rtol=limit,
atol=0.)
assert data.shape == (40, 15, 15)
attr = self.pipeline.get_attribute('extract2',
'STAR_POSITION',
static=False)
assert attr[10, 0] == attr[10, 1] == 10
def test_extract_position(self):
module = StarExtractionModule(name_in='extract7',
image_in_tag='star',
image_out_tag='extract7',
index_out_tag=None,
image_size=0.4,
fwhm_star=0.1,
position=(10, 10, 0.1))
self.pipeline.add_module(module)
self.pipeline.run_module('extract7')
data = self.pipeline.get_data('extract7')
assert np.allclose(data[0, 7, 7],
0.09834884212021108,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.004444871536643222,
rtol=limit,
atol=0.)
assert data.shape == (40, 15, 15)
attr = self.pipeline.get_attribute('extract7',
'STAR_POSITION',
static=False)
assert attr[10, 0] == attr[10, 1] == 10
def test_extract_too_large(self):
module = StarExtractionModule(name_in='extract3',
image_in_tag='star',
image_out_tag='extract3',
index_out_tag=None,
image_size=0.8,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract3')
assert len(warning) == 40
for i, item in enumerate(warning):
assert item.message.args[0] == f'Chosen image size is too large to crop the image ' \
f'around the brightest pixel (image index = {i}, ' \
f'pixel [x, y] = [10, 10]). Using the center of ' \
f'the image instead.'
data = self.pipeline.get_data('extract3')
assert np.allclose(data[0, 0, 0],
0.09834884212021108,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0004499242959139202,
rtol=limit,
atol=0.)
assert data.shape == (40, 31, 31)
attr = self.pipeline.get_attribute('extract3',
'STAR_POSITION',
static=False)
assert attr[10, 0] == attr[10, 1] == 25
def test_star_extract_cpu(self):
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = StarExtractionModule(name_in='extract4',
image_in_tag='star',
image_out_tag='extract4',
index_out_tag='index',
image_size=0.8,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract4')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Chosen image size is too large to crop the image ' \
'around the brightest pixel. Using the center of ' \
'the image instead.'
def test_extract_binary(self):
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = ExtractBinaryModule(pos_center=(50., 50.),
pos_binary=(50., 70.),
name_in='extract5',
image_in_tag='binary',
image_out_tag='extract5',
image_size=0.5,
search_size=0.2,
filter_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('extract5')
data = self.pipeline.get_data('extract5')
assert np.allclose(data[0, 9, 9],
0.09774483733119443,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.0027700881940171283,
rtol=limit,
atol=0.)
assert data.shape == (80, 19, 19)
def test_extract_binary_filter(self):
module = ExtractBinaryModule(pos_center=(50., 50.),
pos_binary=(50., 70.),
name_in='extract6',
image_in_tag='binary',
image_out_tag='extract6',
image_size=0.5,
search_size=0.2,
filter_size=0.1)
self.pipeline.add_module(module)
self.pipeline.run_module('extract6')
data = self.pipeline.get_data('extract6')
assert np.allclose(data[0, 9, 9],
0.09774483733119443,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.002770040591615301,
rtol=limit,
atol=0.)
assert data.shape == (80, 19, 19)
class TestStackSubset:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'data')
create_star_data(path=self.test_dir + 'extra')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
remove_test_data(self.test_dir, folders=['data', 'extra'])
def test_read_data(self):
read = FitsReadingModule(name_in='read1',
image_tag='images',
input_dir=self.test_dir + 'data',
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('images')
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
read = FitsReadingModule(name_in='read2',
image_tag='extra',
input_dir=self.test_dir + 'extra',
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module('read2')
extra = self.pipeline.get_data('extra')
assert np.allclose(data, extra, rtol=limit, atol=0.)
def test_stack_and_subset(self):
self.pipeline.set_attribute('images',
'PARANG',
np.arange(1., 41., 1.),
static=False)
stack = StackAndSubsetModule(name_in='stack',
image_in_tag='images',
image_out_tag='stack',
random=10,
stacking=2)
self.pipeline.add_module(stack)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('stack')
assert np.allclose(data[0, 50, 50],
0.09816320034649725,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.983545774937238e-05,
rtol=limit,
atol=0.)
assert data.shape == (10, 100, 100)
data = self.pipeline.get_data('header_stack/INDEX')
index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
assert np.allclose(data, index, rtol=limit, atol=0.)
assert data.shape == (10, )
data = self.pipeline.get_data('header_stack/PARANG')
parang = [1.5, 15.5, 19.5, 23.5, 25.5, 29.5, 31.5, 35.5, 37.5, 39.5]
assert np.allclose(data, parang, rtol=limit, atol=0.)
assert data.shape == (10, )
def test_mean_cube(self):
with pytest.warns(DeprecationWarning) as warning:
mean = MeanCubeModule(name_in='mean',
image_in_tag='images',
image_out_tag='mean')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The MeanCubeModule will be be deprecated in a ' \
'future release. Please use the StackCubesModule ' \
'instead.'
self.pipeline.add_module(mean)
self.pipeline.run_module('mean')
data = self.pipeline.get_data('mean')
assert np.allclose(data[0, 50, 50],
0.09805840100024205,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738069,
rtol=limit,
atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
def test_stack_cube(self):
module = StackCubesModule(name_in='stackcube',
image_in_tag='images',
image_out_tag='mean',
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stackcube')
data = self.pipeline.get_data('mean')
assert np.allclose(data[0, 50, 50],
0.09805840100024205,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738069,
rtol=limit,
atol=0.)
assert data.shape == (4, 100, 100)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.allclose(np.mean(attribute), 1.5, rtol=limit, atol=0.)
assert attribute.shape == (4, )
attribute = self.pipeline.get_attribute('mean',
'NFRAMES',
static=False)
assert np.allclose(np.mean(attribute), 1, rtol=limit, atol=0.)
assert attribute.shape == (4, )
def test_derotate_and_stack(self):
derotate = DerotateAndStackModule(name_in='derotate1',
image_in_tag='images',
image_out_tag='derotate1',
derotate=True,
stack='mean',
extra_rot=10.)
self.pipeline.add_module(derotate)
self.pipeline.run_module('derotate1')
data = self.pipeline.get_data('derotate1')
assert np.allclose(data[0, 50, 50],
0.09689679769268554,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010021671152246617,
rtol=limit,
atol=0.)
assert data.shape == (1, 100, 100)
derotate = DerotateAndStackModule(name_in='derotate2',
image_in_tag='images',
image_out_tag='derotate2',
derotate=False,
stack='median',
extra_rot=0.)
self.pipeline.add_module(derotate)
self.pipeline.run_module('derotate2')
data = self.pipeline.get_data('derotate2')
assert np.allclose(data[0, 50, 50],
0.09809001768003645,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010033064394962,
rtol=limit,
atol=0.)
assert data.shape == (1, 100, 100)
def test_combine_tags(self):
combine = CombineTagsModule(image_in_tags=('images', 'extra'),
check_attr=True,
index_init=False,
name_in='combine1',
image_out_tag='combine1')
self.pipeline.add_module(combine)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('combine1')
assert len(warning) == 1
assert warning[0].message.args[0] == 'The non-static keyword FILES is already used but ' \
'with different values. It is advisable to only ' \
'combine tags that descend from the same data set.'
data = self.pipeline.get_data('combine1')
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738068,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('header_combine1/INDEX')
assert data[40] == 0
assert data.shape == (80, )
combine = CombineTagsModule(image_in_tags=('images', 'extra'),
check_attr=False,
index_init=True,
name_in='combine2',
image_out_tag='combine2')
self.pipeline.add_module(combine)
self.pipeline.run_module('combine2')
data = self.pipeline.get_data('combine1')
extra = self.pipeline.get_data('combine2')
assert np.allclose(data, extra, rtol=limit, atol=0.)
data = self.pipeline.get_data('header_combine2/INDEX')
assert data[40] == 40
assert data.shape == (80, )
