def test_get_shape(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
read = FitsReadingModule(name_in='read', image_tag='images')
pipeline.add_module(read)
pipeline.run_module('read')
assert pipeline.get_shape('images') == (10, 100, 100)
def test_get_shape(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
read = FitsReadingModule(name_in="read", image_tag="images")
pipeline.add_module(read)
pipeline.run_module("read")
assert pipeline.get_shape("images") == (10, 100, 100)
def test_get_shape(self) -> None:
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
module = FitsReadingModule(name_in='read', image_tag='images')
pipeline.add_module(module)
pipeline.run_module('read')
assert pipeline.get_shape('images') == (5, 11, 11)
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)
