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_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_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_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_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) == 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.sum(data) == pytest.approx(104.93318507061295, rel=self.limit, abs=0.)
assert data.shape == (10, 9, 9)
attr = self.pipeline.get_attribute('extract1', 'STAR_POSITION', static=False)
assert np.sum(attr) == pytest.approx(100, rel=self.limit, abs=0.)
assert attr.shape == (10, 2)
def test_star_extract_cpu(self) -> None:
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=0.2,
fwhm_star=0.1,
position=(2, 2, 0.05))
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract4')
assert len(warning) == 2
assert warning[0].message.args[0] == 'The \'index_out_port\' can only be used if ' \
'CPU = 1. No data will be stored to this output port.'
assert warning[1].message.args[0] == 'Chosen image size is too large to crop the image ' \
'around the brightest pixel (image index = 0, ' \
'pixel [x, y] = [2, 2]). Using the center of the ' \
'image instead.'
def test_extract_too_large(self) -> None:
module = StarExtractionModule(name_in='extract3',
image_in_tag='star',
image_out_tag='extract3',
index_out_tag=None,
image_size=0.2,
fwhm_star=0.1,
position=(2, 2, 0.05))
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract3')
assert len(warning) == 10
assert warning[0].message.args[0] == f'Chosen image size is too large to crop the image ' \
f'around the brightest pixel (image index = 0, ' \
f'pixel [x, y] = [2, 2]). Using the center of ' \
f'the image instead.'
data = self.pipeline.get_data('extract3')
assert np.sum(data) == pytest.approx(104.93318507061295, rel=self.limit, abs=0.)
assert data.shape == (10, 9, 9)
def test_extract_position(self) -> None:
module = StarExtractionModule(name_in='extract7',
image_in_tag='star',
image_out_tag='extract7',
index_out_tag=None,
image_size=0.2,
fwhm_star=0.1,
position=(5, 5, 0.2))
self.pipeline.add_module(module)
self.pipeline.run_module('extract7')
data = self.pipeline.get_data('extract7')
assert np.sum(data) == pytest.approx(104.93318507061295, rel=self.limit, abs=0.)
assert data.shape == (10, 9, 9)
def test_apply_function_to_images_memory_none(self) -> None:
module = StarExtractionModule(name_in='extract',
image_in_tag='im_subtract',
image_out_tag='extract',
index_out_tag=None,
image_size=0.5,
fwhm_star=0.1,
position=(None, None, 0.1))
self.pipeline.add_module(module)
self.pipeline.run_module('extract')
data = self.pipeline.get_data('extract')
assert np.mean(data) == pytest.approx(1.8259937251367536e-05,
rel=self.limit,
abs=0.)
assert data.shape == (5, 5, 5)
def test_apply_function_to_images_memory_none(self) -> None:
module = StarExtractionModule(name_in='extract',
image_in_tag='im_subtract',
image_out_tag='extract',
index_out_tag=None,
image_size=0.5,
fwhm_star=0.1,
position=(None, None, 0.1))
self.pipeline.add_module(module)
self.pipeline.run_module('extract')
data = self.pipeline.get_data('extract')
assert np.allclose(np.mean(data),
1.5591859111937413e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 5, 5)
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.'