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.)
class TestFitsReading:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir + 'fits')
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=['fits'],
files=['filenames.dat'])
def test_fits_reading(self) -> None:
module = FitsReadingModule(name_in='read1',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=False,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('input')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_fits_reading_overwrite(self) -> None:
module = FitsReadingModule(name_in='read2',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('input')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_static_not_found(self) -> None:
self.pipeline.set_attribute('config', 'DIT', 'Test', static=True)
module = FitsReadingModule(name_in='read3',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=True,
check=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read3')
assert len(warning) == 2
for item in warning:
assert item.message.args[0] == 'Static attribute DIT (=Test) not found in the FITS ' \
'header.'
self.pipeline.set_attribute('config',
'DIT',
'ESO DET DIT',
static=True)
def test_static_changing(self) -> None:
with fits.open(self.test_dir + 'fits/images_0.fits') as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + 'fits/images_0.fits', overwrite=True)
with fits.open(self.test_dir + 'fits/images_1.fits') as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.2
hdu.writeto(self.test_dir + 'fits/images_1.fits', overwrite=True)
module = FitsReadingModule(name_in='read4',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=True,
check=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read4')
assert len(warning) == 1
assert warning[0].message.args[0] == f'Static attribute ESO DET DIT has changed. ' \
f'Possibly the current file {self.test_dir}fits/' \
f'images_1.fits does not belong to the data set ' \
f'\'input\'. Attribute value is updated.'
def test_header_attribute(self) -> None:
with fits.open(self.test_dir + 'fits/images_0.fits') as hdu:
header = hdu[0].header
header['PARANG'] = 1.0
hdu.writeto(self.test_dir + 'fits/images_0.fits', overwrite=True)
with fits.open(self.test_dir + 'fits/images_1.fits') as hdu:
header = hdu[0].header
header['PARANG'] = 2.0
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + 'fits/images_1.fits', overwrite=True)
module = FitsReadingModule(name_in='read5',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read5')
def test_non_static_not_found(self) -> None:
self.pipeline.set_attribute('config', 'DIT', 'None', static=True)
for i in range(2):
with fits.open(f'{self.test_dir}/fits/images_{i}.fits') as hdu:
header = hdu[0].header
del header['HIERARCH ESO DET DIT']
del header['HIERARCH ESO DET EXP NO']
hdu.writeto(f'{self.test_dir}/fits/images_{i}.fits',
overwrite=True)
module = FitsReadingModule(name_in='read6',
input_dir=self.test_dir + 'fits',
image_tag='input',
overwrite=True,
check=True)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read6')
assert len(warning) == 2
for item in warning:
assert item.message.args[0] == 'Non-static attribute EXP_NO (=ESO DET EXP NO) not ' \
'found in the FITS header.'
def test_fits_read_files(self) -> None:
module = FitsReadingModule(name_in='read7',
input_dir=None,
image_tag='files',
overwrite=False,
check=True,
filenames=[
self.test_dir + 'fits/images_0.fits',
self.test_dir + 'fits/images_1.fits'
])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read7')
assert len(warning) == 2
for item in warning:
assert item.message.args[0] == 'Non-static attribute EXP_NO (=ESO DET EXP NO) not ' \
'found in the FITS header.'
data = self.pipeline.get_data('files')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_fits_read_textfile(self) -> None:
with open(self.test_dir + 'filenames.dat', 'w') as file_obj:
file_obj.write(self.test_dir + 'fits/images_0.fits\n')
file_obj.write(self.test_dir + 'fits/images_1.fits\n')
module = FitsReadingModule(name_in='read8',
input_dir=None,
image_tag='files',
overwrite=True,
check=True,
filenames=self.test_dir + 'filenames.dat')
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read8')
assert len(warning) == 2
for item in warning:
assert item.message.args[0] == 'Non-static attribute EXP_NO (=ESO DET EXP NO) not ' \
'found in the FITS header.'
data = self.pipeline.get_data('files')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_fits_read_files_exists(self) -> None:
module = FitsReadingModule(name_in='read9',
input_dir=None,
image_tag='files',
overwrite=True,
check=True,
filenames=[
f'{self.test_dir}fits/images_0.fits',
f'{self.test_dir}fits/images_2.fits'
])
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('read9')
assert str(error.value) == f'The file {self.test_dir}fits/images_2.fits does not exist. ' \
f'Please check that the path is correct.'
def test_fits_read_textfile_exists(self) -> None:
with open(self.test_dir + 'filenames.dat', 'w') as file_obj:
file_obj.write(self.test_dir + 'fits/images_0.fits\n')
file_obj.write(self.test_dir + 'fits/images_2.fits\n')
module = FitsReadingModule(name_in='read10',
input_dir=None,
image_tag='files',
overwrite=True,
check=True,
filenames=self.test_dir + 'filenames.dat')
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('read10')
assert str(error.value) == f'The file {self.test_dir}fits/images_2.fits does not exist. ' \
f'Please check that the path is correct.'
class TestNearInitModule(object):
def setup_class(self) -> None:
self.test_dir = os.path.dirname(__file__) + '/'
self.fitsfile = self.test_dir + 'near/images_1.fits'
create_near_data(path=self.test_dir + 'near')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
self.pipeline.set_attribute('config',
'NFRAMES',
'ESO DET CHOP NCYCLES',
static=True)
self.pipeline.set_attribute('config',
'EXP_NO',
'ESO TPL EXPNO',
static=True)
self.pipeline.set_attribute('config', 'NDIT', 'None', static=True)
self.pipeline.set_attribute('config',
'PARANG_START',
'None',
static=True)
self.pipeline.set_attribute('config',
'PARANG_END',
'None',
static=True)
self.pipeline.set_attribute('config', 'DITHER_X', 'None', static=True)
self.pipeline.set_attribute('config', 'DITHER_Y', 'None', static=True)
self.pipeline.set_attribute('config', 'PIXSCALE', 0.045, static=True)
self.pipeline.set_attribute('config', 'MEMORY', 100, static=True)
self.positions = ('chopa', 'chopb')
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['near'])
def test_near_read(self) -> None:
module = NearReadingModule(name_in='read1a',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
self.pipeline.run_module('read1a')
for item in self.positions:
data = self.pipeline.get_data(item)
assert np.allclose(np.mean(data), 0.060582854, rtol=limit, atol=0.)
assert data.shape == (20, 10, 10)
def test_near_subtract_crop_mean(self) -> None:
module = NearReadingModule(name_in='read1b',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1],
subtract=True,
crop=(None, None, 0.3),
combine='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('read1b')
data = self.pipeline.get_data(self.positions[0])
assert np.allclose(np.mean(data), 0.0, rtol=limit, atol=0.)
assert data.shape == (4, 7, 7)
data = self.pipeline.get_data(self.positions[1])
assert np.allclose(np.mean(data), 0.0, rtol=limit, atol=0.)
assert data.shape == (4, 7, 7)
def test_near_median(self) -> None:
module = NearReadingModule(name_in='read1c',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1],
combine='median')
self.pipeline.add_module(module)
self.pipeline.run_module('read1c')
data = self.pipeline.get_data(self.positions[0])
assert np.allclose(np.mean(data), 0.060582854, rtol=limit, atol=0.)
assert data.shape == (4, 10, 10)
data = self.pipeline.get_data(self.positions[1])
assert np.allclose(np.mean(data), 0.060582854, rtol=limit, atol=0.)
assert data.shape == (4, 10, 10)
def test_static_not_found(self) -> None:
self.pipeline.set_attribute('config', 'DIT', 'Test', static=True)
module = NearReadingModule(name_in='read2',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read2')
assert len(warning) == 8
for item in warning:
assert item.message.args[0] == 'Static attribute DIT (=Test) not found in the FITS ' \
'header.'
self.pipeline.set_attribute('config',
'DIT',
'ESO DET SEQ1 DIT',
static=True)
def test_nonstatic_not_found(self) -> None:
self.pipeline.set_attribute('config', 'NDIT', 'Test', static=True)
module = NearReadingModule(name_in='read3',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read3')
assert len(warning) == 8
for item in warning:
assert item.message.args[0] == 'Non-static attribute NDIT (=Test) not found in the ' \
'FITS header.'
self.pipeline.set_attribute('config', 'NDIT', 'None', static=True)
def test_check_header(self) -> None:
with fits.open(self.fitsfile) as hdulist:
hdulist[0].header['ESO DET CHOP ST'] = 'F'
hdulist[0].header['ESO DET CHOP CYCSKIP'] = 1
hdulist[0].header['ESO DET CHOP CYCSUM'] = 'T'
hdulist.writeto(self.fitsfile, overwrite=True)
module = NearReadingModule(name_in='read4',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read4')
assert len(warning) == 3
assert warning[0].message.args[
0] == 'Dataset was obtained without chopping.'
assert warning[1].message.args[
0] == 'Chop cycles (1) have been skipped.'
assert warning[2].message.args[
0] == 'FITS file contains averaged images.'
with fits.open(self.fitsfile) as hdulist:
hdulist[0].header['ESO DET CHOP ST'] = 'T'
hdulist[0].header['ESO DET CHOP CYCSKIP'] = 0
hdulist[0].header['ESO DET CHOP CYCSUM'] = 'F'
hdulist.writeto(self.fitsfile, overwrite=True)
def test_frame_type_invalid(self) -> None:
with fits.open(self.fitsfile) as hdulist:
hdulist[10].header['ESO DET FRAM TYPE'] = 'Test'
hdulist.writeto(self.fitsfile, overwrite=True)
module = NearReadingModule(name_in='read5',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('read5')
assert str(error.value) == 'Frame type (Test) not a valid value. Expecting HCYCLE1 or ' \
'HCYCLE2 as value for ESO DET FRAM TYPE.'
def test_frame_type_missing(self) -> None:
with fits.open(self.fitsfile) as hdulist:
hdulist[10].header.remove('ESO DET FRAM TYPE')
hdulist.writeto(self.fitsfile, overwrite=True)
module = NearReadingModule(name_in='read6',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('read6')
assert str(
error.value
) == 'Frame type not found in the FITS header. Image number: 9.'
def test_same_cycle(self) -> None:
with fits.open(self.fitsfile) as hdulist:
with pytest.warns(UserWarning) as warning:
hdulist[10].header['ESO DET FRAM TYPE'] = 'HCYCLE1'
assert len(warning) == 1
assert warning[0].message.args[0] == 'Keyword name \'ESO DET FRAM TYPE\' is greater ' \
'than 8 characters or contains characters not ' \
'allowed by the FITS standard; a HIERARCH card ' \
'will be created.'
hdulist.writeto(self.fitsfile, overwrite=True)
module = NearReadingModule(name_in='read7',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read7')
assert len(warning) == 2
assert warning[0].message.args[0] == 'Previous and current chop position (HCYCLE1) are ' \
'the same. Skipping the current image.'
assert warning[1].message.args[0] == 'The number of images is not equal for chop A and ' \
'chop B.'
def test_odd_number_images(self) -> None:
with fits.open(self.fitsfile) as hdulist:
del hdulist[11]
hdulist.writeto(self.fitsfile, overwrite=True)
module = NearReadingModule(name_in='read8',
input_dir=self.test_dir + 'near',
chopa_out_tag=self.positions[0],
chopb_out_tag=self.positions[1])
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('read8')
assert len(warning) == 2
assert warning[0].message.args[0] == f'FITS file contains odd number of images: ' \
f'{self.fitsfile}'
assert warning[1].message.args[0] == 'The number of chop cycles (5) is not equal to ' \
'half the number of available HDU images (4).'
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 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 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, )
class TestFitsReadingModule(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir + "fits")
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=["fits"])
def test_fits_reading(self):
read = FitsReadingModule(name_in="read1",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=False,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read1")
data = self.pipeline.get_data("input")
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)
def test_fits_reading_overwrite(self):
read = FitsReadingModule(name_in="read2",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read2")
data = self.pipeline.get_data("input")
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)
def test_static_not_found(self):
self.pipeline.set_attribute("config",
"DIT",
"ESO DET DIT",
static=True)
read = FitsReadingModule(name_in="read3",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=True,
check=True)
self.pipeline.add_module(read)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("read3")
assert len(warning) == 4
for item in warning:
assert item.message.args[0] == "Static attribute DIT (=ESO DET DIT) not found in " \
"the FITS header."
def test_static_changing(self):
with fits.open(self.test_dir + "fits/image01.fits") as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + "fits/image01.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image02.fits") as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + "fits/image02.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image03.fits") as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.2
hdu.writeto(self.test_dir + "fits/image03.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image04.fits") as hdu:
header = hdu[0].header
header['HIERARCH ESO DET DIT'] = 0.2
hdu.writeto(self.test_dir + "fits/image04.fits", overwrite=True)
read = FitsReadingModule(name_in="read4",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=True,
check=True)
self.pipeline.add_module(read)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("read4")
assert len(warning) == 2
assert warning[0].message.args[0] == "Static attribute ESO DET DIT has changed. " \
"Possibly the current file image03.fits does " \
"not belong to the data set 'input'. Attribute " \
"value is updated."
assert warning[1].message.args[0] == "Static attribute ESO DET DIT has changed. " \
"Possibly the current file image04.fits does " \
"not belong to the data set 'input'. Attribute " \
"value is updated."
def test_header_attribute(self):
with fits.open(self.test_dir + "fits/image01.fits") as hdu:
header = hdu[0].header
header['PARANG'] = 1.0
hdu.writeto(self.test_dir + "fits/image01.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image02.fits") as hdu:
header = hdu[0].header
header['PARANG'] = 2.0
hdu.writeto(self.test_dir + "fits/image02.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image03.fits") as hdu:
header = hdu[0].header
header['PARANG'] = 3.0
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + "fits/image03.fits", overwrite=True)
with fits.open(self.test_dir + "fits/image04.fits") as hdu:
header = hdu[0].header
header['PARANG'] = 4.0
header['HIERARCH ESO DET DIT'] = 0.1
hdu.writeto(self.test_dir + "fits/image04.fits", overwrite=True)
read = FitsReadingModule(name_in="read5",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read5")
def test_non_static_not_found(self):
self.pipeline.set_attribute("config", "DIT", "None", static=True)
for i in range(1, 5):
with fits.open(self.test_dir + "fits/image0" + str(i) +
".fits") as hdu:
header = hdu[0].header
del header['HIERARCH ESO DET DIT']
del header['HIERARCH ESO DET EXP NO']
hdu.writeto(self.test_dir + "fits/image0" + str(i) + ".fits",
overwrite=True)
read = FitsReadingModule(name_in="read6",
input_dir=self.test_dir + "fits",
image_tag="input",
overwrite=True,
check=True)
self.pipeline.add_module(read)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("read6")
assert len(warning) == 4
for item in warning:
assert item.message.args[0] == "Non-static attribute EXP_NO (=ESO DET EXP NO) not " \
"found in the FITS header."
def test_fits_read_files(self):
folder = os.path.dirname(os.path.abspath(__file__))
read = FitsReadingModule(name_in="read7",
input_dir=None,
image_tag="files",
overwrite=False,
check=True,
filenames=[
folder + "/fits/image01.fits",
folder + "/fits/image03.fits"
])
self.pipeline.add_module(read)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("read7")
assert len(warning) == 2
for item in warning:
assert item.message.args[0] == "Non-static attribute EXP_NO (=ESO DET EXP NO) not " \
"found in the FITS header."
data = self.pipeline.get_data("files")
assert np.allclose(data[0, 50, 50],
0.09798413502193704,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010032245393723324,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
class TestPsfSubtractionSdi:
def setup_class(self) -> None:
self.limit = 1e-5
self.test_dir = os.path.dirname(__file__) + '/'
create_ifs_data(self.test_dir+'science')
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=['science'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='science',
input_dir=self.test_dir+'science',
ifs_data=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(749.8396528807368, rel=self.limit, abs=0.)
assert data.shape == (3, 10, 21, 21)
self.pipeline.set_attribute('science', 'WAVELENGTH', [1., 1.1, 1.2], static=False)
self.pipeline.set_attribute('science', 'PARANG', np.linspace(0., 180., 10), static=False)
def test_psf_subtraction_sdi(self) -> None:
processing_types = ['ADI', 'SDI+ADI', 'ADI+SDI']
expected = [[-0.176152493909826, -0.7938155399702668, 19.552033067005578, -0.21617058715490922],
[-0.004568154679096975, -0.08621264803633322, 2.2901225325010888, -0.010269745733878437],
[0.008630501634061892, -0.05776205365084376, -0.4285370289350482, 0.0058856438951644455]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21), (1, 1, 3, 21, 21)]
pca_numbers = [range(1, 3), (range(1, 3), range(1, 3)), ([1], [1])]
res_arr_tags = [None, None, 'res_arr_single_sdi_ADI+SDI']
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(pca_numbers=pca_numbers[i],
name_in='pca_single_sdi_'+p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single_sdi_'+p_type,
res_median_tag='res_median_single_sdi_'+p_type,
res_weighted_tag='res_weighted_single_sdi_'+p_type,
res_rot_mean_clip_tag='res_clip_single_sdi_'+p_type,
res_arr_out_tag=res_arr_tags[i],
basis_out_tag='basis_single_sdi_'+p_type,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single_sdi_'+p_type)
data = self.pipeline.get_data('res_mean_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][0], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][1], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_single_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][2], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_single_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
# data = self.pipeline.get_data('basis_single_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(-1.3886119555248766, rel=self.limit, abs=0.)
# assert data.shape == (5, 30, 30)
def test_psf_subtraction_sdi_multi(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
processing_types = ['SDI', 'ADI+SDI']
pca_numbers = [range(1, 3), (range(1, 3), range(1, 3))]
expected = [[-0.0044942456603888695, 0.02613693149969979, -0.15045543311096457, -0.008432530081399985],
[-0.0094093643053501, -0.08171546066331437, 0.560810054788774, -0.014527353460544753]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21)]
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(pca_numbers=pca_numbers[i],
name_in='pca_multi_sdi_'+p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi_sdi_'+p_type,
res_median_tag='res_median_multi_sdi_'+p_type,
res_weighted_tag='res_weighted_multi_sdi_'+p_type,
res_rot_mean_clip_tag='res_clip_multi_sdi_'+p_type,
res_arr_out_tag=None,
basis_out_tag=None,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_sdi_'+p_type)
data = self.pipeline.get_data('res_mean_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][0], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][1], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_multi_sdi_'+p_type)
assert np.sum(data) == pytest.approx(expected[i][2], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_multi_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
class TestFluxAndPosition(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir + "flux", npix_x=101, npix_y=101)
create_star_data(path=self.test_dir + "psf",
npix_x=15,
npix_y=15,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
ndit=1,
nframes=1,
noise=False)
create_fake(path=self.test_dir + "adi",
ndit=[5, 5, 5, 5],
nframes=[5, 5, 5, 5],
exp_no=[1, 2, 3, 4],
npix=(15, 15),
fwhm=3.,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
angles=[[0., 50.], [50., 100.], [100., 150.], [150.,
200.]],
sep=5.5,
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(self.test_dir, folders=["flux", "adi", "psf"])
def test_read_data(self):
read = FitsReadingModule(name_in="read1",
image_tag="read",
input_dir=self.test_dir + "flux")
self.pipeline.add_module(read)
self.pipeline.run_module("read1")
data = self.pipeline.get_data("read")
assert np.allclose(data[0, 50, 50],
0.0986064357966972,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.827812356946396e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 101, 101)
read = FitsReadingModule(name_in="read2",
image_tag="adi",
input_dir=self.test_dir + "adi")
self.pipeline.add_module(read)
self.pipeline.run_module("read2")
data = self.pipeline.get_data("adi")
assert np.allclose(data[0, 7, 7],
0.09823888178122618,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.008761678820997612,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
read = FitsReadingModule(name_in="read3",
image_tag="psf",
input_dir=self.test_dir + "psf")
self.pipeline.add_module(read)
self.pipeline.run_module("read3")
data = self.pipeline.get_data("psf")
assert np.allclose(data[0, 7, 7],
0.09806026673451182,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.004444444429123135,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
def test_aperture_photometry(self):
photometry = AperturePhotometryModule(radius=0.1,
position=None,
name_in="photometry",
image_in_tag="read",
phot_out_tag="photometry")
self.pipeline.add_module(photometry)
self.pipeline.run_module("photometry")
data = self.pipeline.get_data("photometry")
assert np.allclose(data[0][0], 0.9702137183213615, rtol=limit, atol=0.)
assert np.allclose(data[39][0],
0.9691512171281103,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.9691752104364761,
rtol=limit,
atol=0.)
assert data.shape == (40, 1)
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_data("header_read/PARANG")
assert data[5] == 2.7777777777777777
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_fake_planet(self):
fake = FakePlanetModule(position=(0.5, 90.),
magnitude=6.,
psf_scaling=1.,
interpolation="spline",
name_in="fake",
image_in_tag="read",
psf_in_tag="read",
image_out_tag="fake")
self.pipeline.add_module(fake)
self.pipeline.run_module("fake")
data = self.pipeline.get_data("fake")
assert np.allclose(data[0, 50, 50],
0.09860622347589054,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.867026482551375e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 101, 101)
def test_psf_subtraction(self):
pca = PcaPsfSubtractionModule(pca_numbers=(2, ),
name_in="pca",
images_in_tag="fake",
reference_in_tag="fake",
res_mean_tag="res_mean",
res_median_tag=None,
res_arr_out_tag=None,
res_rot_mean_clip_tag=None,
extra_rot=0.)
self.pipeline.add_module(pca)
self.pipeline.run_module("pca")
data = self.pipeline.get_data("res_mean")
assert np.allclose(data[0, 49, 31],
4.8963214463463886e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.8409659677297164e-08,
rtol=limit,
atol=0.)
assert data.shape == (1, 101, 101)
def test_false_positive(self):
false = FalsePositiveModule(position=(31., 49.),
aperture=0.1,
ignore=True,
name_in="false",
image_in_tag="res_mean",
snr_out_tag="snr_fpf")
self.pipeline.add_module(false)
self.pipeline.run_module("false")
data = self.pipeline.get_data("snr_fpf")
assert np.allclose(data[0, 2], 0.5280553948214145, rtol=limit, atol=0.)
assert np.allclose(data[0, 3], 94.39870535499551, rtol=limit, atol=0.)
assert np.allclose(data[0, 4], 8.542166952478182, rtol=limit, atol=0.)
assert np.allclose(data[0, 5],
9.54772666372783e-07,
rtol=limit,
atol=0.)
def test_simplex_minimization(self):
simplex = SimplexMinimizationModule(position=(31., 49.),
magnitude=6.,
psf_scaling=-1.,
name_in="simplex",
image_in_tag="fake",
psf_in_tag="read",
res_out_tag="simplex_res",
flux_position_tag="flux_position",
merit="hessian",
aperture=0.1,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.1,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(simplex)
self.pipeline.run_module("simplex")
data = self.pipeline.get_data("simplex_res")
assert np.allclose(data[0, 50, 31],
0.00020085220731657478,
rtol=limit,
atol=0.)
assert np.allclose(data[65, 50, 31],
2.5035345163849688e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.416893585673146e-09,
rtol=limit,
atol=0.)
assert data.shape == (66, 101, 101)
data = self.pipeline.get_data("flux_position")
assert np.allclose(data[65, 0], 32.14539423594633, rtol=limit, atol=0.)
assert np.allclose(data[65, 1], 50.40994810153265, rtol=limit, atol=0.)
assert np.allclose(data[65, 2],
0.4955803200991986,
rtol=limit,
atol=0.)
assert np.allclose(data[65, 3], 90.28110395762462, rtol=limit, atol=0.)
assert np.allclose(data[65, 4], 5.744096115502183, rtol=limit, atol=0.)
assert data.shape == (66, 6)
def test_mcmc_sampling_poisson(self):
self.pipeline.set_attribute("adi",
"PARANG",
np.arange(0., 200., 10.),
static=False)
scale = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in="scale1",
image_in_tag="adi",
image_out_tag="adi_scale")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale1")
data = self.pipeline.get_data("adi_scale")
assert np.allclose(data[0, 7, 7],
9.82388817812263,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
scale = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in="scale2",
image_in_tag="psf",
image_out_tag="psf_scale")
self.pipeline.add_module(scale)
self.pipeline.run_module("scale2")
data = self.pipeline.get_data("psf_scale")
assert np.allclose(data[0, 7, 7],
9.806026673451198,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
avg_psf = DerotateAndStackModule(name_in="take_psf_avg",
image_in_tag="psf_scale",
image_out_tag="psf_avg",
derotate=False,
stack="mean")
self.pipeline.add_module(avg_psf)
self.pipeline.run_module("take_psf_avg")
data = self.pipeline.get_data("psf_avg")
assert data.shape == (15, 15)
mcmc = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5, 0.5)),
name_in="mcmc",
image_in_tag="adi_scale",
psf_in_tag="psf_avg",
chain_out_tag="mcmc",
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=0.1,
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior="flat",
variance="poisson")
self.pipeline.add_module(mcmc)
self.pipeline.run_module("mcmc")
single = self.pipeline.get_data("mcmc")
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)
def test_mcmc_sampling_gaussian(self):
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
mcmc = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5, 0.5)),
name_in="mcmc_gaussian",
image_in_tag="adi_scale",
psf_in_tag="psf_avg",
chain_out_tag="mcmc_gaussian",
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=0.1,
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior="flat",
variance="gaussian")
self.pipeline.add_module(mcmc)
self.pipeline.run_module("mcmc_gaussian")
single = self.pipeline.get_data("mcmc_gaussian")
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)
class TestFluxPosition:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir+'flux', npix_x=101, npix_y=101)
create_star_data(path=self.test_dir+'ref', npix_x=101, npix_y=101)
create_star_data(path=self.test_dir+'psf',
npix_x=15,
npix_y=15,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
ndit=1,
nframes=1,
noise=False)
create_fake(path=self.test_dir+'adi',
ndit=[5, 5, 5, 5],
nframes=[5, 5, 5, 5],
exp_no=[1, 2, 3, 4],
npix=(15, 15),
fwhm=3.,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
angles=[[0., 50.], [50., 100.], [100., 150.], [150., 200.]],
sep=5.5,
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(self.test_dir, folders=['flux', 'adi', 'psf', 'ref'])
def test_read_data(self):
module = FitsReadingModule(name_in='read1',
image_tag='read',
input_dir=self.test_dir+'flux')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 50, 50], 0.0986064357966972, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.827812356946396e-05, rtol=limit, atol=0.)
assert data.shape == (40, 101, 101)
module = FitsReadingModule(name_in='read2',
image_tag='adi',
input_dir=self.test_dir+'adi')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('adi')
assert np.allclose(data[0, 7, 7], 0.09823888178122618, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.008761678820997612, rtol=limit, atol=0.)
assert data.shape == (20, 15, 15)
module = FitsReadingModule(name_in='read3',
image_tag='psf',
input_dir=self.test_dir+'psf')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('psf')
assert np.allclose(data[0, 7, 7], 0.09806026673451182, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.004444444429123135, rtol=limit, atol=0.)
assert data.shape == (4, 15, 15)
module = FitsReadingModule(name_in='read4',
image_tag='ref',
input_dir=self.test_dir+'ref')
self.pipeline.add_module(module)
self.pipeline.run_module('read4')
data = self.pipeline.get_data('ref')
assert np.allclose(data[0, 50, 50], 0.0986064357966972, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.827812356946396e-05, rtol=limit, atol=0.)
assert data.shape == (40, 101, 101)
def test_aperture_photometry(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = AperturePhotometryModule(radius=0.1,
position=None,
name_in='photometry',
image_in_tag='read',
phot_out_tag='photometry')
self.pipeline.add_module(module)
self.pipeline.run_module('photometry')
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = AperturePhotometryModule(radius=0.1,
position=None,
name_in='photometry_multi',
image_in_tag='read',
phot_out_tag='photometry_multi')
self.pipeline.add_module(module)
self.pipeline.run_module('photometry_multi')
data = self.pipeline.get_data('photometry')
assert np.allclose(data[0][0], 0.9853286992326858, rtol=limit, atol=0.)
assert np.allclose(data[39][0], 0.9835251375574492, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.9836439188900222, rtol=limit, atol=0.)
assert data.shape == (40, 1)
data_multi = self.pipeline.get_data('photometry_multi')
assert data.shape == data_multi.shape
# Does not pass on Travis CI
# assert np.allclose(data, data_multi, rtol=limit, atol=0.)
def test_angle_interpolation(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = AngleInterpolationModule(name_in='angle',
data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
data = self.pipeline.get_data('header_read/PARANG')
assert data[5] == 2.7777777777777777
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_fake_planet(self):
module = FakePlanetModule(position=(0.5, 90.),
magnitude=6.,
psf_scaling=1.,
interpolation='spline',
name_in='fake',
image_in_tag='read',
psf_in_tag='read',
image_out_tag='fake')
self.pipeline.add_module(module)
self.pipeline.run_module('fake')
data = self.pipeline.get_data('fake')
assert np.allclose(data[0, 50, 50], 0.09860622347589054, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.867026482551375e-05, rtol=limit, atol=0.)
assert data.shape == (40, 101, 101)
def test_psf_subtraction(self):
module = PcaPsfSubtractionModule(pca_numbers=[2, ],
name_in='pca',
images_in_tag='fake',
reference_in_tag='fake',
res_mean_tag='res_mean',
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('pca')
data = self.pipeline.get_data('res_mean')
assert np.allclose(data[0, 49, 31], 4.8963214463463886e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 1.8409659677297164e-08, rtol=limit, atol=0.)
assert data.shape == (1, 101, 101)
def test_false_positive(self):
module = FalsePositiveModule(position=(31., 49.),
aperture=0.1,
ignore=True,
name_in='false',
image_in_tag='res_mean',
snr_out_tag='snr_fpf')
self.pipeline.add_module(module)
self.pipeline.run_module('false')
data = self.pipeline.get_data('snr_fpf')
assert np.allclose(data[0, 0], 31.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 1], 49.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 2], 0.513710034941892, rtol=limit, atol=0.)
assert np.allclose(data[0, 3], 93.01278750418334, rtol=limit, atol=0.)
assert np.allclose(data[0, 4], 7.333740467578795, rtol=limit, atol=0.)
assert np.allclose(data[0, 5], 4.5257622875993775e-06, rtol=limit, atol=0.)
def test_simplex_minimization_hessian(self):
module = SimplexMinimizationModule(name_in='simplex1',
image_in_tag='fake',
psf_in_tag='read',
res_out_tag='simplex_res',
flux_position_tag='flux_position',
position=(31, 49),
magnitude=6.,
psf_scaling=-1.,
merit='hessian',
aperture=0.1,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.1,
edge_size=None,
extra_rot=0.,
reference_in_tag=None,
residuals='median')
self.pipeline.add_module(module)
self.pipeline.run_module('simplex1')
data = self.pipeline.get_data('simplex_res')
assert np.allclose(data[0, 50, 31], 0.00013866444247059368, rtol=limit, atol=0.)
assert np.allclose(data[35, 50, 31], 3.482226033356121e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 3.125137239656066e-07, rtol=limit, atol=0.)
assert data.shape == (36, 101, 101)
data = self.pipeline.get_data('flux_position')
assert np.allclose(data[35, 0], 31.420497036174158, rtol=limit, atol=0.)
assert np.allclose(data[35, 1], 50.03219573166646, rtol=limit, atol=0.)
assert np.allclose(data[35, 2], 0.5016473331983896, rtol=limit, atol=0.)
assert np.allclose(data[35, 3], 89.90071436787039, rtol=limit, atol=0.)
assert np.allclose(data[35, 4], 6.012537296489204, rtol=limit, atol=0.)
assert data.shape == (36, 6)
def test_simplex_minimization_reference(self):
module = SimplexMinimizationModule(name_in='simplex2',
image_in_tag='fake',
psf_in_tag='read',
res_out_tag='simplex_res_ref',
flux_position_tag='flux_position_ref',
position=(31, 49),
magnitude=6.,
psf_scaling=-1.,
merit='poisson',
aperture=0.1,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.1,
edge_size=None,
extra_rot=0.,
reference_in_tag='ref',
residuals='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('simplex2')
data = self.pipeline.get_data('simplex_res_ref')
assert np.allclose(data[0, 50, 31], 0.00014188043631450017, rtol=limit, atol=0.)
assert np.allclose(data[43, 50, 31], 7.217091961625108e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 1.3228426986034557e-06, rtol=limit, atol=0.)
assert data.shape == (44, 101, 101)
data = self.pipeline.get_data('flux_position_ref')
assert np.allclose(data[43, 0], 31.519027018276986, rtol=limit, atol=0.)
assert np.allclose(data[43, 1], 49.85541939005469, rtol=limit, atol=0.)
assert np.allclose(data[43, 2], 0.4990015399214498, rtol=limit, atol=0.)
assert np.allclose(data[43, 3], 90.44822801080721, rtol=limit, atol=0.)
assert np.allclose(data[43, 4], 5.9953709695084605, rtol=limit, atol=0.)
assert data.shape == (44, 6)
def test_mcmc_sampling(self):
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
self.pipeline.set_attribute('adi', 'PARANG', np.arange(0., 200., 10.), static=False)
module = DerotateAndStackModule(name_in='stack',
image_in_tag='psf',
image_out_tag='psf_stack',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('psf_stack')
assert data.shape == (1, 15, 15)
module = MCMCsamplingModule(name_in='mcmc',
image_in_tag='adi',
psf_in_tag='psf_stack',
chain_out_tag='mcmc',
param=(0.15, 0., 1.),
bounds=((0.1, 0.2), (-2., 2.), (-1., 2.)),
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture=(7, 13, 0.1),
mask=None,
extra_rot=0.,
merit='gaussian',
residuals='median',
scale=2.,
sigma=(1e-3, 1e-1, 1e-2))
self.pipeline.add_module(module)
self.pipeline.run_module('mcmc')
data = self.pipeline.get_data('mcmc')
data = data[50:, :, :].reshape((-1, 3))
assert np.allclose(np.median(data[:, 0]), 0.15, rtol=0., atol=0.1)
assert np.allclose(np.median(data[:, 1]), 0., rtol=0., atol=1.0)
assert np.allclose(np.median(data[:, 2]), 0.0, rtol=0., atol=1.)
attr = self.pipeline.get_attribute('mcmc', 'ACCEPTANCE', static=True)
assert np.allclose(attr, 0.3, rtol=0., atol=0.2)
class TestExtract:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir+'star')
create_fake_data(self.test_dir+'binary')
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', 'binary'])
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)
self.pipeline.run_module('read1')
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)
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.sum(data) == pytest.approx(11.012854046962481, rel=self.limit, abs=0.)
assert data.shape == (10, 21, 21)
self.pipeline.set_attribute('binary', 'PARANG', -1.*np.linspace(0., 180., 10), static=False)
def test_extract_position_none(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.'
assert warning[1].message.args[0] == 'Can not store the attribute \'PIXSCALE\' because ' \
'the dataset \'index\' does not exist.'
assert warning[2].message.args[0] == 'Can not store the attribute \'History: ' \
'StarExtractionModule\' 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) -> None:
module = StarExtractionModule(name_in='extract2',
image_in_tag='star',
image_out_tag='extract2',
index_out_tag='index',
image_size=0.2,
fwhm_star=0.1,
position=(None, None, 0.2))
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('extract2')
assert len(warning) == 3
assert warning[0].message.args[0] == 'Can not store the attribute \'INSTRUMENT\' because ' \
'the dataset \'index\' does not exist.'
assert warning[1].message.args[0] == 'Can not store the attribute \'PIXSCALE\' because ' \
'the dataset \'index\' does not exist.'
assert warning[2].message.args[0] == 'Can not store the attribute \'History: ' \
'StarExtractionModule\' because the dataset ' \
'\'index\' does not exist.'
data = self.pipeline.get_data('extract2')
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_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_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_binary(self) -> None:
with h5py.File(self.test_dir+'PynPoint_database.hdf5', 'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = ExtractBinaryModule(pos_center=(10., 10.),
pos_binary=(10., 16.),
name_in='extract5',
image_in_tag='binary',
image_out_tag='extract5',
image_size=0.15,
search_size=0.07,
filter_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('extract5')
data = self.pipeline.get_data('extract5')
assert np.sum(data) == pytest.approx(1.3419098759577548, rel=self.limit, abs=0.)
assert data.shape == (10, 7, 7)
def test_extract_binary_filter(self) -> None:
module = ExtractBinaryModule(pos_center=(10., 10.),
pos_binary=(10., 16.),
name_in='extract6',
image_in_tag='binary',
image_out_tag='extract6',
image_size=0.15,
search_size=0.07,
filter_size=0.05)
self.pipeline.add_module(module)
self.pipeline.run_module('extract6')
data = self.pipeline.get_data('extract6')
assert np.sum(data) == pytest.approx(1.3789593661036972, rel=self.limit, abs=0.)
assert data.shape == (10, 7, 7)
class TestStackSubset:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_ifs_data(self.test_dir+'data_ifs')
create_star_data(self.test_dir+'data')
create_star_data(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) -> None:
remove_test_data(self.test_dir, folders=['data_ifs', 'extra', 'data'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='images',
input_dir=self.test_dir+'data',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('images')
assert np.mean(data) == pytest.approx(0.08722544528764692, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
module = FitsReadingModule(name_in='read2',
image_tag='extra',
input_dir=self.test_dir+'extra',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
extra = self.pipeline.get_data('extra')
assert data == pytest.approx(extra, rel=self.limit, abs=0.)
module = FitsReadingModule(name_in='read_ifs',
image_tag='images_ifs',
input_dir=self.test_dir+'data_ifs',
overwrite=True,
check=True,
ifs_data=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read_ifs')
self.pipeline.set_attribute('images_ifs', 'PARANG', np.linspace(0., 180., 10), static=False)
data = self.pipeline.get_data('images_ifs')
assert np.sum(data) == pytest.approx(749.8396528807369, rel=self.limit, abs=0.)
assert data.shape == (3, 10, 21, 21)
def test_stack_and_subset(self) -> None:
self.pipeline.set_attribute('images', 'PARANG', np.arange(10.), static=False)
module = StackAndSubsetModule(name_in='stack1',
image_in_tag='images',
image_out_tag='stack1',
random=4,
stacking=2,
combine='mean',
max_rotation=None)
self.pipeline.add_module(module)
self.pipeline.run_module('stack1')
data = self.pipeline.get_data('stack1')
assert np.mean(data) == pytest.approx(0.08758276283743936, rel=self.limit, abs=0.)
assert data.shape == (4, 11, 11)
data = self.pipeline.get_data('header_stack1/INDEX')
assert data == pytest.approx(np.arange(4), rel=self.limit, abs=0.)
assert data.shape == (4, )
data = self.pipeline.get_data('header_stack1/PARANG')
assert data == pytest.approx([0.5, 2.5, 6.5, 8.5], rel=self.limit, abs=0.)
assert data.shape == (4, )
def test_stack_max_rotation(self) -> None:
angles = np.arange(10.)
angles[1:6] = 3.
angles[9] = 50.
self.pipeline.set_attribute('images', 'PARANG', angles, static=False)
module = StackAndSubsetModule(name_in='stack2',
image_in_tag='images',
image_out_tag='stack2',
random=None,
stacking=2,
combine='median',
max_rotation=1.)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('stack2')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Testing of util.module.stack_angles has been ' \
'limited, please use carefully.'
data = self.pipeline.get_data('stack2')
assert np.mean(data) == pytest.approx(0.08580759396987508, rel=self.limit, abs=0.)
assert data.shape == (7, 11, 11)
data = self.pipeline.get_data('header_stack2/INDEX')
assert data == pytest.approx(np.arange(7), rel=self.limit, abs=0.)
assert data.shape == (7, )
data = self.pipeline.get_data('header_stack2/PARANG')
assert data.shape == (7, )
self.pipeline.set_attribute('images', 'PARANG', np.arange(10.), static=False)
def test_stack_cube(self) -> None:
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.mean(data) == pytest.approx(0.08722544528764689, rel=self.limit, abs=0.)
assert data.shape == (2, 11, 11)
attribute = self.pipeline.get_attribute('mean', 'INDEX', static=False)
assert np.mean(attribute) == pytest.approx(0.5, rel=self.limit, abs=0.)
assert attribute.shape == (2, )
attribute = self.pipeline.get_attribute('mean', 'NFRAMES', static=False)
assert np.mean(attribute) == pytest.approx(1, rel=self.limit, abs=0.)
assert attribute.shape == (2, )
def test_derotate_and_stack(self) -> None:
module = DerotateAndStackModule(name_in='derotate1',
image_in_tag='images',
image_out_tag='derotate1',
derotate=True,
stack='mean',
extra_rot=10.)
self.pipeline.add_module(module)
self.pipeline.run_module('derotate1')
data = self.pipeline.get_data('derotate1')
assert np.mean(data) == pytest.approx(0.08709860116308817, rel=self.limit, abs=0.)
assert data.shape == (1, 11, 11)
module = DerotateAndStackModule(name_in='derotate2',
image_in_tag='images',
image_out_tag='derotate2',
derotate=False,
stack='median',
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('derotate2')
data = self.pipeline.get_data('derotate2')
assert np.mean(data) == pytest.approx(0.0861160094566323, rel=self.limit, abs=0.)
assert data.shape == (1, 11, 11)
data = self.pipeline.get_data('derotate2')
assert np.mean(data) == pytest.approx(0.0861160094566323, rel=self.limit, abs=0.)
assert data.shape == (1, 11, 11)
module = DerotateAndStackModule(name_in='derotate_ifs1',
image_in_tag='images_ifs',
image_out_tag='derotate_ifs1',
derotate=True,
stack='mean',
extra_rot=0.,
dimension='time')
self.pipeline.add_module(module)
self.pipeline.run_module('derotate_ifs1')
data = self.pipeline.get_data('derotate_ifs1')
assert np.mean(data) == pytest.approx(0.1884438996655355, rel=self.limit, abs=0.)
assert data.shape == (3, 1, 21, 21)
module = DerotateAndStackModule(name_in='derotate_ifs2',
image_in_tag='images_ifs',
image_out_tag='derotate_ifs2',
derotate=False,
stack='median',
extra_rot=0.,
dimension='wavelength')
self.pipeline.add_module(module)
self.pipeline.run_module('derotate_ifs2')
data = self.pipeline.get_data('derotate_ifs2')
assert np.mean(data) == pytest.approx(0.055939644983170146, rel=self.limit, abs=0.)
assert data.shape == (1, 10, 21, 21)
module = DerotateAndStackModule(name_in='derotate_ifs3',
image_in_tag='images_ifs',
image_out_tag='derotate_ifs3',
derotate=True,
stack=None,
extra_rot=0.,
dimension='wavelength')
self.pipeline.add_module(module)
self.pipeline.run_module('derotate_ifs3')
data = self.pipeline.get_data('derotate_ifs3')
assert np.mean(data) == pytest.approx(0.05653316989966066, rel=self.limit, abs=0.)
assert data.shape == (3, 10, 21, 21)
def test_combine_tags(self) -> None:
module = CombineTagsModule(image_in_tags=['images', 'extra'],
check_attr=True,
index_init=False,
name_in='combine1',
image_out_tag='combine1')
self.pipeline.add_module(module)
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.mean(data) == pytest.approx(0.0872254452876469, rel=self.limit, abs=0.)
assert data.shape == (20, 11, 11)
data = self.pipeline.get_data('header_combine1/INDEX')
assert data[19] == 9
assert data.shape == (20, )
module = CombineTagsModule(image_in_tags=['images', 'extra'],
check_attr=False,
index_init=True,
name_in='combine2',
image_out_tag='combine2')
self.pipeline.add_module(module)
self.pipeline.run_module('combine2')
data = self.pipeline.get_data('combine1')
extra = self.pipeline.get_data('combine2')
assert data == pytest.approx(extra, rel=self.limit, abs=0.)
data = self.pipeline.get_data('header_combine2/INDEX')
assert data[19] == 19
assert data.shape == (20, )
class TestPsfPreparation:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(self.test_dir+'prep')
create_ifs_data(self.test_dir+'prep_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=['prep', 'prep_ifs'])
def test_read_data(self) -> None:
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.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+'prep_ifs',
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_angle_interpolation(self) -> None:
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.sum(data) == pytest.approx(900., rel=self.limit, abs=0.)
assert data.shape == (10, )
def test_angle_calculation(self) -> None:
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.sum(data) == pytest.approx(-550.2338300130718, rel=self.limit, abs=0.)
assert data.shape == (10, )
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')
warning_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.'
warning_1 = '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.'
if len(warning) == 2:
assert warning[0].message.args[0] == warning_0
assert warning[1].message.args[0] == warning_1
data = self.pipeline.get_data('header_read/PARANG')
assert np.sum(data) == pytest.approx(1704.220236104952, rel=self.limit, abs=0.)
assert data.shape == (10, )
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')
warning_0 = 'AngleCalculationModule has not been tested for SPHERE/IFS data.'
warning_1 = '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.'
warning_2 = '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.'
if len(warning) == 3:
assert warning[0].message.args[0] == warning_0
assert warning[1].message.args[0] == warning_1
assert warning[2].message.args[0] == warning_2
data = self.pipeline.get_data('header_read/PARANG')
assert np.sum(data) == pytest.approx(-890.8506520762833, rel=self.limit, abs=0.)
assert data.shape == (10, )
def test_angle_sort(self) -> None:
index = self.pipeline.get_data('header_read/INDEX')
self.pipeline.set_attribute('read', 'INDEX', index[::-1], static=False)
module = SortParangModule(name_in='sort1',
image_in_tag='read',
image_out_tag='read_sorted')
self.pipeline.add_module(module)
self.pipeline.run_module('sort1')
self.pipeline.set_attribute('read', 'INDEX', index, static=False)
parang = self.pipeline.get_data('header_read/PARANG')[::-1]
parang_sort = self.pipeline.get_data('header_read_sorted/PARANG')
assert np.sum(parang) == pytest.approx(np.sum(parang_sort), rel=self.limit, abs=0.)
parang_set = [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]
self.pipeline.set_attribute('read_ifs', 'PARANG', parang_set, static=False)
data = self.pipeline.get_data('read_sorted')
assert np.sum(data[0]) == pytest.approx(9.71156815235485, rel=self.limit, abs=0.)
def test_angle_sort_ifs(self) -> None:
index = self.pipeline.get_data('header_read_ifs/INDEX')
self.pipeline.set_attribute('read_ifs', 'INDEX', index[::-1], static=False)
module = SortParangModule(name_in='sort2',
image_in_tag='read_ifs',
image_out_tag='read_ifs_sorted')
self.pipeline.add_module(module)
self.pipeline.run_module('sort2')
self.pipeline.set_attribute('read_ifs', 'INDEX', index, static=False)
parang = self.pipeline.get_data('header_read_ifs/PARANG')[::-1]
parang_sort = self.pipeline.get_data('header_read_ifs_sorted/PARANG')
assert np.sum(parang) == pytest.approx(np.sum(parang_sort), rel=self.limit, abs=0.)
data = self.pipeline.get_data('read_ifs_sorted')
assert np.sum(data[0, 0]) == pytest.approx(21.185139976163477, rel=self.limit, abs=0.)
def test_angle_interpolation_mismatch(self) -> None:
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')
warning_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.'
if len(warning) == 1:
assert warning[0].message.args[0] == warning_0
data = self.pipeline.get_data('header_read/PARANG')
assert np.sum(data) == pytest.approx(900., rel=self.limit, abs=0.)
assert data.shape == (10, )
def test_psf_preparation_norm_mask(self) -> None:
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.sum(data) == pytest.approx(-1.5844830188044685, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
data = self.pipeline.get_data('mask1')
assert np.sum(data) == pytest.approx(52, rel=self.limit, abs=0.)
assert data.shape == (11, 11)
def test_psf_preparation_none(self) -> None:
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.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_psf_preparation_no_mask_out(self) -> None:
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.sum(data) == pytest.approx(105.54278879805277, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
def test_psf_preparation_sdi(self) -> None:
module = PSFpreparationModule(name_in='prep4',
image_in_tag='read_ifs',
image_out_tag='prep4',
mask_out_tag=None,
norm=False,
cent_size=None,
edge_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('prep4')
data = self.pipeline.get_data('prep4')
assert np.sum(data) == pytest.approx(749.8396528807369, rel=self.limit, abs=0.)
assert data.shape == (3, 10, 21, 21)
def test_sdi_preparation(self) -> None:
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.sum(data) == pytest.approx(21.084666133914183, rel=self.limit, abs=0.)
assert data.shape == (10, 11, 11)
attribute = self.pipeline.get_attribute('sdi', 'History: SDIpreparationModule')
assert attribute == '(line, continuum) = (0.65, 0.6)'
class TestPsfSubtractionSdi:
def setup_class(self) -> None:
self.limit = 1e-5
self.test_dir = os.path.dirname(__file__) + '/'
create_ifs_data(self.test_dir + 'science')
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=['science'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='science',
input_dir=self.test_dir + 'science',
ifs_data=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(749.8396528807368,
rel=self.limit,
abs=0.)
assert data.shape == (3, 10, 21, 21)
self.pipeline.set_attribute('science',
'WAVELENGTH', [1., 1.1, 1.2],
static=False)
self.pipeline.set_attribute('science',
'PARANG',
np.linspace(0., 180., 10),
static=False)
def test_psf_subtraction_sdi(self) -> None:
processing_types = ['ADI', 'SDI+ADI', 'ADI+SDI', 'CODI']
expected = [[
-0.16718942968552664, -0.790697125718532, 19.507979777136892,
-0.21617058715490922
],
[
-0.001347198747121658, -0.08621264803633322,
2.3073192270025333, -0.010269745733878437
],
[
0.009450917836998779, -0.05776205365084376,
-0.43506678222476264, 0.0058856438951644455
],
[
-0.2428739554898396, -0.5069023645693083,
9.326414176548905, 0.00
]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21), (1, 1, 3, 21, 21),
(2, 3, 21, 21)]
pca_numbers = [
range(1, 3), (range(1, 3), range(1, 3)), ([1], [1]),
range(1, 3)
]
res_arr_tags = [None, None, 'res_arr_single_sdi_ADI+SDI', None]
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(
pca_numbers=pca_numbers[i],
name_in='pca_single_sdi_' + p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single_sdi_' + p_type,
res_median_tag='res_median_single_sdi_' + p_type,
res_weighted_tag='res_weighted_single_sdi_' + p_type,
res_rot_mean_clip_tag='res_clip_single_sdi_' + p_type,
res_arr_out_tag=res_arr_tags[i],
basis_out_tag='basis_single_sdi_' + p_type,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single_sdi_' + p_type)
data = self.pipeline.get_data('res_mean_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][0],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][1],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_single_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][2],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_single_sdi_' + p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
# data = self.pipeline.get_data('basis_single_sdi_'+p_type)
# assert np.sum(data) == pytest.approx(-1.3886119555248766, rel=self.limit, abs=0.)
# assert data.shape == (5, 30, 30)
def test_psf_subtraction_sdi_multi(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
processing_types = ['SDI', 'ADI+SDI']
pca_numbers = [range(1, 3), (range(1, 3), range(1, 3))]
expected = [[
-0.004159475403024583, 0.02613693149969979, -0.12940723035023394,
-0.008432530081399985
],
[
-0.006580571531064533, -0.08171546066331437,
0.5700432018961117, -0.014527353460544753
]]
shape_expc = [(2, 3, 21, 21), (2, 2, 3, 21, 21)]
for i, p_type in enumerate(processing_types):
module = PcaPsfSubtractionModule(
pca_numbers=pca_numbers[i],
name_in='pca_multi_sdi_' + p_type,
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi_sdi_' + p_type,
res_median_tag='res_median_multi_sdi_' + p_type,
res_weighted_tag='res_weighted_multi_sdi_' + p_type,
res_rot_mean_clip_tag='res_clip_multi_sdi_' + p_type,
res_arr_out_tag=None,
basis_out_tag=None,
extra_rot=0.,
subtract_mean=True,
processing_type=p_type)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_sdi_' + p_type)
data = self.pipeline.get_data('res_mean_multi_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][0],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_median_multi_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][1],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_weighted_multi_sdi_' + p_type)
assert np.sum(data) == pytest.approx(expected[i][2],
rel=self.limit,
abs=0.)
assert data.shape == shape_expc[i]
data = self.pipeline.get_data('res_clip_multi_sdi_' + p_type)
# assert np.sum(data) == pytest.approx(expected[i][3], rel=self.limit, abs=0.)
assert data.shape == shape_expc[i]
class TestPsfSubtractionAdi:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_fake_data(self.test_dir + 'science')
create_fake_data(self.test_dir + 'reference')
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=['science', 'reference'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='science',
input_dir=self.test_dir + 'science')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(11.012854046962481,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
self.pipeline.set_attribute('science',
'PARANG',
np.linspace(0., 180., 10),
static=False)
module = FitsReadingModule(name_in='read2',
image_tag='reference',
input_dir=self.test_dir + 'reference')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('reference')
assert np.sum(data) == pytest.approx(11.012854046962481,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
def test_psf_preparation(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='science',
image_out_tag='science_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.05,
edge_size=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('science_prep')
assert np.sum(data) == pytest.approx(5.029285028467547,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
module = PSFpreparationModule(name_in='prep2',
image_in_tag='reference',
image_out_tag='reference_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.05,
edge_size=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('prep2')
data = self.pipeline.get_data('reference_prep')
assert np.sum(data) == pytest.approx(5.029285028467547,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
def test_classical_adi(self) -> None:
module = ClassicalADIModule(threshold=None,
nreference=None,
residuals='mean',
extra_rot=0.,
name_in='cadi1',
image_in_tag='science_prep',
res_out_tag='cadi_res',
stack_out_tag='cadi_stack')
self.pipeline.add_module(module)
self.pipeline.run_module('cadi1')
data = self.pipeline.get_data('cadi_res')
assert np.sum(data) == pytest.approx(0.8381625719865213,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('cadi_stack')
assert np.sum(data) == pytest.approx(0.08395606034388256,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
def test_classical_adi_threshold(self) -> None:
module = ClassicalADIModule(threshold=(0.1, 0.03, 1.),
nreference=5,
residuals='median',
extra_rot=0.,
name_in='cadi2',
image_in_tag='science_prep',
res_out_tag='cadi_res',
stack_out_tag='cadi_stack')
self.pipeline.add_module(module)
self.pipeline.run_module('cadi2')
data = self.pipeline.get_data('cadi_res')
assert np.sum(data) == pytest.approx(0.7158207863548083,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('cadi_stack')
assert np.sum(data) == pytest.approx(0.07448334552227256,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
def test_psf_subtraction_pca_single(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_single',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single',
res_median_tag='res_median_single',
res_weighted_tag='res_weighted_single',
res_rot_mean_clip_tag='res_clip_single',
res_arr_out_tag='res_arr_single',
basis_out_tag='basis_single',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single')
data = self.pipeline.get_data('res_mean_single')
assert np.sum(data) == pytest.approx(-0.00011857022709778602,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_median_single')
assert np.sum(data) == pytest.approx(-0.002184868916566093,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_weighted_single')
assert np.sum(data) == pytest.approx(0.08102176735226937,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
# data = self.pipeline.get_data('res_clip_single')
# assert np.sum(data) == pytest.approx(7.09495495339349e-05, rel=self.limit, abs=0.)
# assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_arr_single1')
assert np.sum(data) == pytest.approx(-0.0002751385418691618,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('basis_single')
assert np.sum(data) == pytest.approx(0.09438697731322143,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_no_mean',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_no_mean')
data = self.pipeline.get_data('res_mean_no_mean')
assert np.sum(data) == pytest.approx(0.0006081272007585688,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_no_mean')
assert np.sum(data) == pytest.approx(5.118005177367776,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref')
data = self.pipeline.get_data('res_mean_ref')
assert np.sum(data) == pytest.approx(0.0006330226118859073,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref')
assert np.sum(data) == pytest.approx(0.0943869773132221,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref_no_mean',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean')
data = self.pipeline.get_data('res_mean_ref_no_mean')
assert np.sum(data) == pytest.approx(0.0006081272007585764,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_no_mean')
assert np.sum(data) == pytest.approx(5.118005177367774,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_pca_single_mask(self) -> None:
pca = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_single_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_single_mask',
res_median_tag='res_median_single_mask',
res_weighted_tag='res_weighted_single_mask',
res_rot_mean_clip_tag='res_clip_single_mask',
res_arr_out_tag='res_arr_single_mask',
basis_out_tag='basis_single_mask',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(pca)
self.pipeline.run_module('pca_single_mask')
data = self.pipeline.get_data('res_mean_single_mask')
assert np.sum(data) == pytest.approx(0.00010696166038626307,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_median_single_mask')
assert np.sum(data) == pytest.approx(-0.0021005307611346156,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_weighted_single_mask')
assert np.sum(data) == pytest.approx(0.06014309988789256,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_clip_single_mask')
# assert np.sum(data) == pytest.approx(9.35120662148806e-05, rel=self.limit, abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('res_arr_single_mask1')
assert np.sum(data) == pytest.approx(0.0006170872862547557,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
data = self.pipeline.get_data('basis_single_mask')
assert np.sum(data) == pytest.approx(0.08411251293842359,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_no_mean_mask')
data = self.pipeline.get_data('res_mean_no_mean_mask')
assert np.sum(data) == pytest.approx(2.3542359949502915e-05,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_no_mean_mask')
assert np.sum(data) == pytest.approx(5.655460951633232,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 3),
name_in='pca_ref_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_mask',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_mask')
data = self.pipeline.get_data('res_mean_ref_mask')
assert np.sum(data) == pytest.approx(9.400558926815758e-06,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_mask')
assert np.sum(data) == pytest.approx(0.08411251293842326,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_ref_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_ref_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean_mask')
data = self.pipeline.get_data('res_mean_ref_no_mean_mask')
assert np.sum(data) == pytest.approx(2.354235994950671e-05,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
data = self.pipeline.get_data('basis_ref_no_mean_mask')
assert np.sum(data) == pytest.approx(5.655460951633233,
rel=self.limit,
abs=0.)
assert data.shape == (2, 21, 21)
def test_psf_subtraction_pca_multi(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_multi',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi',
res_median_tag='res_median_multi',
res_weighted_tag='res_weighted_multi',
res_rot_mean_clip_tag='res_clip_multi',
res_arr_out_tag=None,
basis_out_tag='basis_multi',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi')
data_single = self.pipeline.get_data('res_mean_single')
data_multi = self.pipeline.get_data('res_mean_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_median_single')
data_multi = self.pipeline.get_data('res_median_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_weighted_single')
data_multi = self.pipeline.get_data('res_weighted_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('basis_single')
data_multi = self.pipeline.get_data('basis_multi')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
def test_psf_subtraction_pca_multi_mask(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 3),
name_in='pca_multi_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_multi_mask',
res_median_tag='res_median_multi_mask',
res_weighted_tag='res_weighted_multi_mask',
res_rot_mean_clip_tag='res_clip_multi_mask',
res_arr_out_tag=None,
basis_out_tag='basis_multi_mask',
extra_rot=45.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_mask')
data_single = self.pipeline.get_data('res_mean_single_mask')
data_multi = self.pipeline.get_data('res_mean_multi_mask')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_median_single_mask')
data_multi = self.pipeline.get_data('res_median_multi_mask')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('res_weighted_single_mask')
data_multi = self.pipeline.get_data('res_weighted_multi_mask')
assert data_single.shape == data_multi.shape
assert data_single[data_single > 1e-12] == \
pytest.approx(data_multi[data_multi > 1e-12], rel=self.limit, abs=0.)
data_single = self.pipeline.get_data('basis_single_mask')
data_multi = self.pipeline.get_data('basis_multi_mask')
assert data_single.shape == data_multi.shape
assert data_single == pytest.approx(data_multi, rel=self.limit, abs=0.)
def test_psf_subtraction_len_parang(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 1
parang = self.pipeline.get_data('header_science/PARANG')
self.pipeline.set_attribute('science_prep',
'PARANG',
np.append(parang, 0.),
static=False)
module = PcaPsfSubtractionModule(pca_numbers=[
1,
],
name_in='pca_len_parang',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_len_parang',
extra_rot=0.)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('pca_len_parang')
assert str(error.value) == 'The number of images (10) is not equal to the number of ' \
'parallactic angles (11).'
class TestFitsWritingModule:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'fits')
create_config(self.test_dir + 'PynPoint_config.ini')
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def teardown_class(self):
files = [
'test.fits', 'test000.fits', 'test001.fits', 'test002.fits',
'test003.fits'
]
remove_test_data(self.test_dir, folders=['fits'], files=files)
def test_fits_reading(self):
module = FitsReadingModule(name_in='read',
input_dir=self.test_dir + 'fits',
image_tag='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, 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)
def test_fits_writing(self):
module = FitsWritingModule(file_name='test.fits',
name_in='write1',
output_dir=None,
data_tag='images',
data_range=None,
overwrite=True)
self.pipeline.add_module(module)
self.pipeline.run_module('write1')
def test_filename_extension(self):
with pytest.raises(ValueError) as error:
FitsWritingModule(file_name='test.dat',
name_in='write3',
output_dir=None,
data_tag='images',
data_range=None,
overwrite=True,
subset_size=None)
assert str(
error.value) == 'Output \'file_name\' requires the FITS extension.'
def test_data_range(self):
module = FitsWritingModule(file_name='test.fits',
name_in='write4',
output_dir=None,
data_tag='images',
data_range=(0, 10),
overwrite=True,
subset_size=None)
self.pipeline.add_module(module)
self.pipeline.run_module('write4')
def test_not_overwritten(self):
module = FitsWritingModule(file_name='test.fits',
name_in='write5',
output_dir=None,
data_tag='images',
data_range=None,
overwrite=False,
subset_size=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('write5')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Filename already present. Use overwrite=True ' \
'to overwrite an existing FITS file.'
def test_subset_size(self):
module = FitsWritingModule(file_name='test.fits',
name_in='write6',
output_dir=None,
data_tag='images',
data_range=None,
overwrite=True,
subset_size=10)
self.pipeline.add_module(module)
self.pipeline.run_module('write6')
def test_subset_size_data_range(self):
module = FitsWritingModule(file_name='test.fits',
name_in='write7',
output_dir=None,
data_tag='images',
data_range=(8, 18),
overwrite=True,
subset_size=10)
self.pipeline.add_module(module)
self.pipeline.run_module('write7')
def test_attribute_length(self):
text = 'long_text_long_text_long_text_long_text_long_text_long_text_long_text_long_text'
self.pipeline.set_attribute('images', 'short', 'value', static=True)
self.pipeline.set_attribute('images',
'longer_than_eight1',
'value',
static=True)
self.pipeline.set_attribute('images',
'longer_than_eight2',
text,
static=True)
module = FitsWritingModule(file_name='test.fits',
name_in='write8',
output_dir=None,
data_tag='images',
data_range=None,
overwrite=True,
subset_size=None)
self.pipeline.add_module(module)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module('write8')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Key \'hierarch longer_than_eight2\' with value ' \
'\'long_text_long_text_long_text_long_text_long_' \
'text_long_text_long_text_long_text\' is too ' \
'long for the FITS format. To avoid an error, ' \
'the value was truncated to \'long_text_long_text' \
'_long_text_long_text_long_tex\'.'
class TestFluxPosition:
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + '/'
create_star_data(path=self.test_dir + 'flux', npix_x=101, npix_y=101)
create_star_data(path=self.test_dir + 'psf',
npix_x=15,
npix_y=15,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
ndit=1,
nframes=1,
noise=False)
create_fake(path=self.test_dir + 'adi',
ndit=[5, 5, 5, 5],
nframes=[5, 5, 5, 5],
exp_no=[1, 2, 3, 4],
npix=(15, 15),
fwhm=3.,
x0=[7., 7., 7., 7.],
y0=[7., 7., 7., 7.],
angles=[[0., 50.], [50., 100.], [100., 150.], [150.,
200.]],
sep=5.5,
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(self.test_dir, folders=['flux', 'adi', 'psf'])
def test_read_data(self):
module = FitsReadingModule(name_in='read1',
image_tag='read',
input_dir=self.test_dir + 'flux')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 50, 50],
0.0986064357966972,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.827812356946396e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 101, 101)
module = FitsReadingModule(name_in='read2',
image_tag='adi',
input_dir=self.test_dir + 'adi')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('adi')
assert np.allclose(data[0, 7, 7],
0.09823888178122618,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.008761678820997612,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
module = FitsReadingModule(name_in='read3',
image_tag='psf',
input_dir=self.test_dir + 'psf')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('psf')
assert np.allclose(data[0, 7, 7],
0.09806026673451182,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.004444444429123135,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
def test_aperture_photometry(self):
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = AperturePhotometryModule(radius=0.1,
position=None,
name_in='photometry',
image_in_tag='read',
phot_out_tag='photometry')
self.pipeline.add_module(module)
self.pipeline.run_module('photometry')
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = AperturePhotometryModule(radius=0.1,
position=None,
name_in='photometry_multi',
image_in_tag='read',
phot_out_tag='photometry_multi')
self.pipeline.add_module(module)
self.pipeline.run_module('photometry_multi')
data = self.pipeline.get_data('photometry')
assert np.allclose(data[0][0], 0.9853286992326858, rtol=limit, atol=0.)
assert np.allclose(data[39][0],
0.9835251375574492,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.9836439188900222,
rtol=limit,
atol=0.)
assert data.shape == (40, 1)
data_multi = self.pipeline.get_data('photometry_multi')
assert data.shape == data_multi.shape
# Outputs zeros sometimes for data_multi on Travis CI
# for i, item in enumerate(data_multi):
# assert np.allclose(data[i], item, rtol=1e-6, atol=0.)
def test_angle_interpolation(self):
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = AngleInterpolationModule(name_in='angle', data_tag='read')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
data = self.pipeline.get_data('header_read/PARANG')
assert data[5] == 2.7777777777777777
assert np.allclose(np.mean(data), 10.0, rtol=limit, atol=0.)
assert data.shape == (40, )
def test_fake_planet(self):
module = FakePlanetModule(position=(0.5, 90.),
magnitude=6.,
psf_scaling=1.,
interpolation='spline',
name_in='fake',
image_in_tag='read',
psf_in_tag='read',
image_out_tag='fake')
self.pipeline.add_module(module)
self.pipeline.run_module('fake')
data = self.pipeline.get_data('fake')
assert np.allclose(data[0, 50, 50],
0.09860622347589054,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.867026482551375e-05,
rtol=limit,
atol=0.)
assert data.shape == (40, 101, 101)
def test_psf_subtraction(self):
module = PcaPsfSubtractionModule(pca_numbers=(2, ),
name_in='pca',
images_in_tag='fake',
reference_in_tag='fake',
res_mean_tag='res_mean',
res_median_tag=None,
res_arr_out_tag=None,
res_rot_mean_clip_tag=None,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('pca')
data = self.pipeline.get_data('res_mean')
assert np.allclose(data[0, 49, 31],
4.8963214463463886e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
1.8409659677297164e-08,
rtol=limit,
atol=0.)
assert data.shape == (1, 101, 101)
def test_false_positive(self):
module = FalsePositiveModule(position=(31., 49.),
aperture=0.1,
ignore=True,
name_in='false',
image_in_tag='res_mean',
snr_out_tag='snr_fpf')
self.pipeline.add_module(module)
self.pipeline.run_module('false')
data = self.pipeline.get_data('snr_fpf')
assert np.allclose(data[0, 0], 31.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 1], 49.0, rtol=limit, atol=0.)
assert np.allclose(data[0, 2], 0.513710034941892, rtol=limit, atol=0.)
assert np.allclose(data[0, 3], 93.01278750418334, rtol=limit, atol=0.)
assert np.allclose(data[0, 4], 7.333740467578795, rtol=limit, atol=0.)
assert np.allclose(data[0, 5],
4.5257622875993775e-06,
rtol=limit,
atol=0.)
def test_simplex_minimization(self):
module = SimplexMinimizationModule(position=(31., 49.),
magnitude=6.,
psf_scaling=-1.,
name_in='simplex',
image_in_tag='fake',
psf_in_tag='read',
res_out_tag='simplex_res',
flux_position_tag='flux_position',
merit='hessian',
aperture=0.1,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.1,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('simplex')
data = self.pipeline.get_data('simplex_res')
assert np.allclose(data[0, 50, 31],
0.00012976212788352575,
rtol=limit,
atol=0.)
assert np.allclose(data[42, 50, 31],
1.2141761821389107e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.461337432531517e-09,
rtol=limit,
atol=0.)
assert data.shape == (43, 101, 101)
data = self.pipeline.get_data('flux_position')
assert np.allclose(data[42, 0], 31.6456737445356, rtol=limit, atol=0.)
assert np.allclose(data[42, 1], 49.9199601480223, rtol=limit, atol=0.)
assert np.allclose(data[42, 2],
0.49557152090327206,
rtol=limit,
atol=0.)
assert np.allclose(data[42, 3], 90.24985480686087, rtol=limit, atol=0.)
assert np.allclose(data[42, 4], 5.683191873535635, rtol=limit, atol=0.)
assert data.shape == (43, 6)
def test_mcmc_sampling_gaussian(self):
self.pipeline.set_attribute('adi',
'PARANG',
np.arange(0., 200., 10.),
static=False)
module = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in='scale1',
image_in_tag='adi',
image_out_tag='adi_scale')
self.pipeline.add_module(module)
self.pipeline.run_module('scale1')
data = self.pipeline.get_data('adi_scale')
assert np.allclose(data[0, 7, 7],
9.82388817812263,
rtol=limit,
atol=0.)
assert data.shape == (20, 15, 15)
module = ScaleImagesModule(scaling=(None, None, 100.),
pixscale=False,
name_in='scale2',
image_in_tag='psf',
image_out_tag='psf_scale')
self.pipeline.add_module(module)
self.pipeline.run_module('scale2')
data = self.pipeline.get_data('psf_scale')
assert np.allclose(data[0, 7, 7],
9.806026673451198,
rtol=limit,
atol=0.)
assert data.shape == (4, 15, 15)
module = DerotateAndStackModule(name_in='take_psf_avg',
image_in_tag='psf_scale',
image_out_tag='psf_avg',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('take_psf_avg')
data = self.pipeline.get_data('psf_avg')
assert data.shape == (1, 15, 15)
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc',
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture={
'type': 'circular',
'pos_x': 7.0,
'pos_y': 12.5,
'radius': 0.1
},
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior='flat',
variance='gaussian')
self.pipeline.add_module(module)
with pytest.warns(FutureWarning) as warning:
self.pipeline.run_module('mcmc')
assert warning[0].message.args[0] == 'Using a non-tuple sequence for multidimensional ' \
'indexing is deprecated; use `arr[tuple(seq)]` ' \
'instead of `arr[seq]`. In the future this will be ' \
'interpreted as an array index, ' \
'`arr[np.array(seq)]`, which will result either ' \
'in an error or a different result.'
single = self.pipeline.get_data('mcmc')
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)
def test_mcmc_sampling_poisson(self):
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc_prior',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc_prior',
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture={
'type': 'elliptical',
'pos_x': 7.0,
'pos_y': 12.5,
'semimajor': 0.1,
'semiminor': 0.1,
'angle': 0.0
},
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior='aperture',
variance='poisson')
self.pipeline.add_module(module)
with pytest.warns(FutureWarning) as warning:
self.pipeline.run_module('mcmc_prior')
assert warning[0].message.args[0] == 'Using a non-tuple sequence for multidimensional ' \
'indexing is deprecated; use `arr[tuple(seq)]` ' \
'instead of `arr[seq]`. In the future this will be ' \
'interpreted as an array index, ' \
'`arr[np.array(seq)]`, which will result either ' \
'in an error or a different result.'
single = self.pipeline.get_data('mcmc_prior')
single = single[:, 20:, :].reshape((-1, 3))
assert np.allclose(np.median(single[:, 0]), 0.148, rtol=0., atol=0.01)
assert np.allclose(np.median(single[:, 1]), 0., rtol=0., atol=0.2)
assert np.allclose(np.median(single[:, 2]), 0., rtol=0., atol=0.1)
def test_mcmc_sampling_wrong_prior(self):
module = MCMCsamplingModule(param=(0.1485, 0., 0.),
bounds=((0.1, 0.25), (-5., 5.), (-0.5,
0.5)),
name_in='mcmc_wrong_prior',
image_in_tag='adi_scale',
psf_in_tag='psf_avg',
chain_out_tag='mcmc_prior',
nwalkers=50,
nsteps=150,
psf_scaling=-1.,
pca_number=1,
aperture={
'type': 'circular',
'pos_x': 7.0,
'pos_y': 12.5,
'radius': 0.1
},
mask=None,
extra_rot=0.,
scale=2.,
sigma=(1e-3, 1e-1, 1e-2),
prior='test',
variance='gaussian')
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('mcmc_wrong_prior')
assert str(error.value) == 'Prior type not recognized.'
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 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 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 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 TestPsfSubtraction:
def setup_class(self) -> None:
self.test_dir = os.path.dirname(__file__) + '/'
create_fake(path=self.test_dir + 'science',
ndit=[20, 20, 20, 20],
nframes=[20, 20, 20, 20],
exp_no=[1, 2, 3, 4],
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=10.,
contrast=3e-3)
create_fake(path=self.test_dir + 'reference',
ndit=[10, 10, 10, 10],
nframes=[10, 10, 10, 10],
exp_no=[1, 2, 3, 4],
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_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=['science', 'reference'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='science',
input_dir=self.test_dir + 'science')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('science')
assert np.allclose(data[0, 50, 50],
0.09798413502193708,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010063896953157961,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
module = FitsReadingModule(name_in='read2',
image_tag='reference',
input_dir=self.test_dir + 'reference')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('reference')
assert np.allclose(data[0, 50, 50],
0.09798413502193708,
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) -> None:
module = AngleInterpolationModule(name_in='angle', data_tag='science')
self.pipeline.add_module(module)
self.pipeline.run_module('angle')
data = self.pipeline.get_data('header_science/PARANG')
assert np.allclose(data[0], 0., rtol=limit, atol=0.)
assert np.allclose(data[15], 19.736842105263158, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 50.0, rtol=limit, atol=0.)
assert data.shape == (80, )
def test_psf_preparation(self) -> None:
module = PSFpreparationModule(name_in='prep1',
image_in_tag='science',
image_out_tag='science_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep1')
data = self.pipeline.get_data('science_prep')
assert np.allclose(data[0, 0, 0], 0.0, 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], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
4.534001223501053e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
module = PSFpreparationModule(name_in='prep2',
image_in_tag='reference',
image_out_tag='reference_prep',
mask_out_tag=None,
norm=False,
resize=None,
cent_size=0.2,
edge_size=1.0)
self.pipeline.add_module(module)
self.pipeline.run_module('prep2')
data = self.pipeline.get_data('reference_prep')
assert np.allclose(data[0, 0, 0], 0.0, 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], 0.0, rtol=limit, atol=0.)
assert np.allclose(np.mean(data),
1.227592050148539e-07,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
def test_classical_adi(self) -> None:
module = ClassicalADIModule(threshold=None,
nreference=None,
residuals='mean',
extra_rot=0.,
name_in='cadi1',
image_in_tag='science',
res_out_tag='cadi_res',
stack_out_tag='cadi_stack')
self.pipeline.add_module(module)
self.pipeline.run_module('cadi1')
data = self.pipeline.get_data('cadi_res')
assert np.allclose(np.mean(data),
-6.359018260066029e-08,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('cadi_stack')
assert np.allclose(np.mean(data),
-8.318786331552922e-08,
rtol=limit,
atol=0.)
assert data.shape == (1, 100, 100)
def test_classical_adi_threshold(self) -> None:
module = ClassicalADIModule(threshold=(0.1, 0.03, 1.),
nreference=5,
residuals='median',
extra_rot=0.,
name_in='cadi2',
image_in_tag='science',
res_out_tag='cadi_res',
stack_out_tag='cadi_stack')
self.pipeline.add_module(module)
self.pipeline.run_module('cadi2')
data = self.pipeline.get_data('cadi_res')
assert np.allclose(np.mean(data),
1.6523183877608216e-07,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('cadi_stack')
assert np.allclose(np.mean(data),
1.413437242880268e-07,
rtol=limit,
atol=0.)
assert data.shape == (1, 100, 100)
def test_psf_subtraction_pca_single(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_single',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_single',
res_median_tag='res_median_single',
res_weighted_tag='res_weighted_single',
res_rot_mean_clip_tag='res_clip_single',
res_arr_out_tag='res_arr_single',
basis_out_tag='basis_single',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_single')
data = self.pipeline.get_data('res_mean_single')
assert np.allclose(np.mean(data),
2.6959819771522928e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_median_single')
assert np.allclose(np.mean(data),
-2.4142571236920345e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_weighted_single')
assert np.allclose(np.mean(data),
-5.293559651636843e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_clip_single')
assert np.allclose(np.mean(data),
2.6199554737979536e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_arr_single5')
assert np.allclose(np.mean(data),
3.184676024912723e-08,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('basis_single')
assert np.allclose(np.mean(data),
-1.593245396350998e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_no_mean',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_no_mean')
data = self.pipeline.get_data('res_mean_no_mean')
assert np.allclose(np.mean(data),
2.413203757426239e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_no_mean')
assert np.allclose(np.mean(data),
7.4728664805632875e-06,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref')
data = self.pipeline.get_data('res_mean_ref')
assert np.allclose(np.mean(data),
1.1662201512335965e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref')
assert np.allclose(np.mean(data),
-1.6780507257603104e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref_no_mean(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref_no_mean',
images_in_tag='science',
reference_in_tag='reference',
res_mean_tag='res_mean_ref_no_mean',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean')
data = self.pipeline.get_data('res_mean_ref_no_mean')
assert np.allclose(np.mean(data),
3.7029738044199534e-07,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_no_mean')
assert np.allclose(np.mean(data),
2.3755682312090375e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_pca_single_mask(self) -> None:
pca = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_single_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_single_mask',
res_median_tag='res_median_single_mask',
res_weighted_tag='res_weighted_single_mask',
res_rot_mean_clip_tag='res_clip_single_mask',
res_arr_out_tag='res_arr_single_mask',
basis_out_tag='basis_single_mask',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(pca)
self.pipeline.run_module('pca_single_mask')
data = self.pipeline.get_data('res_mean_single_mask')
assert np.allclose(np.mean(data),
-1.6536519510012155e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_median_single_mask')
assert np.allclose(np.mean(data),
5.6094356668078245e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_weighted_single_mask')
assert np.allclose(np.mean(data),
4.7079857263662695e-08,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_clip_single_mask')
assert np.allclose(np.mean(data),
-4.875856901892831e-10,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('res_arr_single_mask5')
assert np.allclose(np.mean(data),
-1.700674890172441e-09,
rtol=limit,
atol=0.)
assert data.shape == (80, 100, 100)
data = self.pipeline.get_data('basis_single_mask')
assert np.allclose(np.mean(data),
5.584100479595007e-06,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_no_mean_mask')
data = self.pipeline.get_data('res_mean_no_mean_mask')
assert np.allclose(np.mean(data),
-1.0905008724474168e-09,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_no_mean_mask')
assert np.allclose(np.sum(np.abs(data)),
1025.2018448288406,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref_mask(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=range(1, 21),
name_in='pca_ref_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_mask',
extra_rot=0.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_mask')
data = self.pipeline.get_data('res_mean_ref_mask')
assert np.allclose(np.mean(data),
-9.962692629500833e-10,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_mask')
assert np.allclose(np.mean(data),
-2.3165670099810983e-05,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_ref_no_mean_mask(self) -> None:
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_ref_no_mean_mask',
images_in_tag='science_prep',
reference_in_tag='reference_prep',
res_mean_tag='res_mean_ref_no_mean_mask',
res_median_tag=None,
res_weighted_tag=None,
res_rot_mean_clip_tag=None,
res_arr_out_tag=None,
basis_out_tag='basis_ref_no_mean_mask',
extra_rot=0.,
subtract_mean=False)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_ref_no_mean_mask')
data = self.pipeline.get_data('res_mean_ref_no_mean_mask')
assert np.allclose(np.mean(data),
3.848255803450399e-07,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
data = self.pipeline.get_data('basis_ref_no_mean_mask')
assert np.allclose(np.sum(np.abs(data)),
1026.3329224435665,
rtol=limit,
atol=0.)
assert data.shape == (20, 100, 100)
def test_psf_subtraction_pca_multi(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_multi',
images_in_tag='science',
reference_in_tag='science',
res_mean_tag='res_mean_multi',
res_median_tag='res_median_multi',
res_weighted_tag='res_weighted_multi',
res_rot_mean_clip_tag='res_clip_multi',
res_arr_out_tag=None,
basis_out_tag='basis_multi',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi')
data_single = self.pipeline.get_data('res_mean_single')
data_multi = self.pipeline.get_data('res_mean_multi')
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_median_single')
data_multi = self.pipeline.get_data('res_median_multi')
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_weighted_single')
data_multi = self.pipeline.get_data('res_weighted_multi')
assert np.allclose(data_single, data_multi, rtol=1e-6, atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('basis_single')
data_multi = self.pipeline.get_data('basis_multi')
assert np.allclose(data_single, data_multi, rtol=1e-5, atol=0.)
assert data_single.shape == data_multi.shape
def test_psf_subtraction_pca_multi_mask(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 4
module = PcaPsfSubtractionModule(
pca_numbers=range(1, 21),
name_in='pca_multi_mask',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_multi_mask',
res_median_tag='res_median_multi_mask',
res_weighted_tag='res_weighted_multi_mask',
res_rot_mean_clip_tag='res_clip_multi_mask',
res_arr_out_tag=None,
basis_out_tag='basis_multi_mask',
extra_rot=-15.,
subtract_mean=True)
self.pipeline.add_module(module)
self.pipeline.run_module('pca_multi_mask')
data_single = self.pipeline.get_data('res_mean_single_mask')
data_multi = self.pipeline.get_data('res_mean_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_median_single_mask')
data_multi = self.pipeline.get_data('res_median_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('res_weighted_single_mask')
data_multi = self.pipeline.get_data('res_weighted_multi_mask')
assert np.allclose(data_single[data_single > 1e-12],
data_multi[data_multi > 1e-12],
rtol=1e-6,
atol=0.)
assert data_single.shape == data_multi.shape
data_single = self.pipeline.get_data('basis_single_mask')
data_multi = self.pipeline.get_data('basis_multi_mask')
assert np.allclose(data_single, data_multi, rtol=1e-5, atol=0.)
assert data_single.shape == data_multi.shape
def test_psf_subtraction_len_parang(self) -> None:
database = h5py.File(self.test_dir + 'PynPoint_database.hdf5', 'a')
database['config'].attrs['CPU'] = 1
parang = self.pipeline.get_data('header_science/PARANG')
self.pipeline.set_attribute('science_prep',
'PARANG',
np.append(parang, 0.),
static=False)
module = PcaPsfSubtractionModule(pca_numbers=[
5,
],
name_in='pca_len_parang',
images_in_tag='science_prep',
reference_in_tag='science_prep',
res_mean_tag='res_mean_len_parang',
extra_rot=0.)
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('pca_len_parang')
assert str(error.value) == 'The number of images (80) is not equal to the number of ' \
'parallactic angles (81).'
class TestProcessing:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(5, 11, 11))
large_data = np.random.normal(loc=0, scale=2e-4, size=(10000, 5, 5))
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'w') as hdf_file:
hdf_file.create_dataset('images', data=images)
hdf_file.create_dataset('large_data', data=large_data)
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)
self.pipeline.set_attribute('images', 'PIXSCALE', 0.1, static=True)
self.pipeline.set_attribute('large_data', 'PIXSCALE', 0.1, static=True)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['images'])
def test_output_port_name(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir + 'images')
module.add_output_port('test')
with pytest.warns(UserWarning) as warning:
module.add_output_port('test')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Tag \'test\' of ReadingModule \'read\' is already ' \
'used.'
module = BadPixelSigmaFilterModule(name_in='badpixel',
image_in_tag='images',
image_out_tag='im_out')
module.add_output_port('test')
with pytest.warns(UserWarning) as warning:
module.add_output_port('test')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Tag \'test\' of ProcessingModule \'badpixel\' is ' \
'already used.'
def test_output_port_set_connection(self) -> None:
self.pipeline.m_data_storage.open_connection()
module = BadPixelSigmaFilterModule(name_in='badpixel2',
image_in_tag='images',
image_out_tag='im_out')
self.pipeline.add_module(module)
port = module.add_output_port('test1')
self.pipeline.m_data_storage.close_connection()
def test_apply_function(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 20, static=True)
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract',
image_in_tag='images',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract')
data = self.pipeline.get_data('images')
assert np.mean(data) == pytest.approx(1.1824138000882435e-05,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
data = self.pipeline.get_data('im_subtract')
assert np.mean(data) == pytest.approx(-1.2544487946113274e-21,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_args_none(self) -> None:
module = TimeNormalizationModule(name_in='norm',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm')
data = self.pipeline.get_data('im_norm')
assert np.mean(data) == pytest.approx(2.4012571778516812e-06,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_args_none_memory_none(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
module = TimeNormalizationModule(name_in='norm_none',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none')
data = self.pipeline.get_data('im_norm')
assert np.mean(data) == pytest.approx(2.4012571778516812e-06,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_same_port(self) -> None:
module = LineSubtractionModule(name_in='subtract_same',
image_in_tag='im_subtract',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_same')
data = self.pipeline.get_data('im_subtract')
assert np.mean(data) == pytest.approx(-1.4336557652700885e-21,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
def test_apply_function_args_none_memory_none_same_port(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
data = self.pipeline.get_data('images')
assert np.mean(data) == pytest.approx(1.1824138000882435e-05,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
module = TimeNormalizationModule(name_in='norm_none_same',
image_in_tag='images',
image_out_tag='images')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none_same')
data = self.pipeline.get_data('images')
assert np.mean(data) == pytest.approx(2.4012571778516812e-06,
rel=self.limit,
abs=0.)
assert data.shape == (5, 11, 11)
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)
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 TestProcessing:
def setup_class(self) -> None:
self.test_dir = os.path.dirname(__file__) + '/'
np.random.seed(1)
images = np.random.normal(loc=0, scale=2e-4, size=(100, 10, 10))
large_data = np.random.normal(loc=0,
scale=2e-4,
size=(10000, 100, 100))
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'w') as hdf_file:
hdf_file.create_dataset('images', data=images)
hdf_file.create_dataset('large_data', data=large_data)
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)
self.pipeline.set_attribute('images', 'PIXSCALE', 0.1, static=True)
self.pipeline.set_attribute('large_data', 'PIXSCALE', 0.1, static=True)
def teardown_class(self) -> None:
remove_test_data(self.test_dir, folders=['images'])
def test_output_port_name(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir + 'images')
module.add_output_port('test')
with pytest.warns(UserWarning) as warning:
module.add_output_port('test')
assert len(warning) == 1
assert warning[0].message.args[0] == 'Tag \'test\' of ReadingModule \'read\' is already ' \
'used.'
module = BadPixelSigmaFilterModule(name_in='badpixel',
image_in_tag='images',
image_out_tag='im_out')
module.add_output_port('test')
with pytest.warns(UserWarning) as warning:
module.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()
def test_apply_function(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 20, static=True)
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract',
image_in_tag='images',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract')
data = self.pipeline.get_data('images')
assert np.allclose(np.mean(data),
1.9545313398209947e-06,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
data = self.pipeline.get_data('im_subtract')
assert np.allclose(np.mean(data),
5.529431079676073e-22,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function_args_none(self) -> None:
module = TimeNormalizationModule(name_in='norm',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm')
data = self.pipeline.get_data('im_norm')
assert np.allclose(np.mean(data),
-3.3117684144801347e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function_args_none_memory_none(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
module = TimeNormalizationModule(name_in='norm_none',
image_in_tag='images',
image_out_tag='im_norm')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none')
data = self.pipeline.get_data('im_norm')
assert np.allclose(np.mean(data),
-3.3117684144801347e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function_same_port(self) -> None:
module = LineSubtractionModule(name_in='subtract_same',
image_in_tag='im_subtract',
image_out_tag='im_subtract',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_same')
data = self.pipeline.get_data('im_subtract')
assert np.allclose(np.mean(data),
7.318364664277155e-22,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
def test_apply_function_args_none_memory_none_same_port(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 0, static=True)
data = self.pipeline.get_data('images')
assert np.allclose(np.mean(data),
1.9545313398209947e-06,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
module = TimeNormalizationModule(name_in='norm_none_same',
image_in_tag='images',
image_out_tag='images')
self.pipeline.add_module(module)
self.pipeline.run_module('norm_none_same')
data = self.pipeline.get_data('images')
assert np.allclose(np.mean(data),
-3.3117684144801347e-07,
rtol=limit,
atol=0.)
assert data.shape == (100, 10, 10)
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_multiproc_large_data(self) -> None:
self.pipeline.set_attribute('config', 'MEMORY', 1000, static=True)
self.pipeline.set_attribute('config', 'CPU', 1, static=True)
module = LineSubtractionModule(name_in='subtract_single',
image_in_tag='large_data',
image_out_tag='im_sub_single',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_single')
self.pipeline.set_attribute('config', 'CPU', 4, static=True)
module = LineSubtractionModule(name_in='subtract_multi',
image_in_tag='large_data',
image_out_tag='im_sub_multi',
combine='mean',
mask=None)
self.pipeline.add_module(module)
self.pipeline.run_module('subtract_multi')
data_single = self.pipeline.get_data('im_sub_single')
data_multi = self.pipeline.get_data('im_sub_multi')
assert np.allclose(data_single, data_multi, rtol=limit, atol=0.)
assert data_single.shape == data_multi.shape
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 TestFluxPosition:
def setup_class(self) -> None:
self.limit = 1e-10
self.test_dir = os.path.dirname(__file__) + '/'
create_fake_data(self.test_dir + 'adi')
create_star_data(self.test_dir + 'psf', npix=21, pos_star=10.)
create_star_data(self.test_dir + 'ref', 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(self.test_dir, folders=['adi', 'psf', 'ref'])
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='adi',
input_dir=self.test_dir + 'adi')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('adi')
assert np.sum(data) == pytest.approx(11.012854046962481,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
self.pipeline.set_attribute('adi',
'PARANG',
np.linspace(0., 180., 10),
static=False)
module = FitsReadingModule(name_in='read2',
image_tag='psf',
input_dir=self.test_dir + 'psf')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('psf')
assert np.sum(data) == pytest.approx(108.43655133957289,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
module = FitsReadingModule(name_in='read3',
image_tag='ref',
input_dir=self.test_dir + 'psf')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('ref')
assert np.sum(data) == pytest.approx(108.43655133957289,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
def test_aperture_photometry(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 1
module = AperturePhotometryModule(name_in='photometry1',
image_in_tag='psf',
phot_out_tag='photometry1',
radius=0.1,
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('photometry1')
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = AperturePhotometryModule(name_in='photometry2',
image_in_tag='psf',
phot_out_tag='photometry2',
radius=0.1,
position=None)
self.pipeline.add_module(module)
self.pipeline.run_module('photometry2')
data = self.pipeline.get_data('photometry1')
assert np.sum(data) == pytest.approx(100.80648929590365,
rel=self.limit,
abs=0.)
assert data.shape == (10, 1)
data_multi = self.pipeline.get_data('photometry2')
assert data.shape == data_multi.shape
assert data == pytest.approx(data_multi, rel=self.limit, abs=0.)
def test_aperture_photometry_position(self) -> None:
module = AperturePhotometryModule(name_in='photometry3',
image_in_tag='psf',
phot_out_tag='photometry3',
radius=0.1,
position=(10., 10.))
self.pipeline.add_module(module)
self.pipeline.run_module('photometry3')
data = self.pipeline.get_data('photometry3')
assert np.sum(data) == pytest.approx(100.80648929590365,
rel=self.limit,
abs=0.)
assert data.shape == (10, 1)
def test_fake_planet(self) -> None:
module = FakePlanetModule(position=(0.2, 180.),
magnitude=2.5,
psf_scaling=1.,
interpolation='spline',
name_in='fake',
image_in_tag='adi',
psf_in_tag='psf',
image_out_tag='fake')
self.pipeline.add_module(module)
self.pipeline.run_module('fake')
data = self.pipeline.get_data('fake')
assert np.sum(data) == pytest.approx(21.51956021269913,
rel=self.limit,
abs=0.)
assert data.shape == (10, 21, 21)
def test_psf_subtraction(self) -> None:
module = PcaPsfSubtractionModule(pca_numbers=[
1,
],
name_in='pca',
images_in_tag='fake',
reference_in_tag='fake',
res_mean_tag='res_mean',
extra_rot=0.)
self.pipeline.add_module(module)
self.pipeline.run_module('pca')
data = self.pipeline.get_data('res_mean')
assert np.sum(data) == pytest.approx(0.014757351752469366,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
def test_false_positive(self) -> None:
module = FalsePositiveModule(position=(10., 2.),
aperture=0.06,
ignore=True,
name_in='false1',
image_in_tag='res_mean',
snr_out_tag='snr_fpf1',
optimize=False)
self.pipeline.add_module(module)
self.pipeline.run_module('false1')
data = self.pipeline.get_data('snr_fpf1')
assert data[0, 1] == pytest.approx(2., rel=self.limit, abs=0.)
assert data[0, 2] == pytest.approx(0.216, rel=self.limit, abs=0.)
assert data[0, 3] == pytest.approx(180., rel=self.limit, abs=0.)
assert data[0, 4] == pytest.approx(23.555448981008507,
rel=self.limit,
abs=0.)
assert data[0, 5] == pytest.approx(3.1561982060476726e-08,
rel=self.limit,
abs=0.)
assert data.shape == (1, 6)
def test_false_positive_optimize(self) -> None:
module = FalsePositiveModule(position=(10., 2.),
aperture=0.06,
ignore=True,
name_in='false2',
image_in_tag='res_mean',
snr_out_tag='snr_fpf2',
optimize=True,
offset=0.1,
tolerance=0.01)
self.pipeline.add_module(module)
self.pipeline.run_module('false2')
data = self.pipeline.get_data('snr_fpf2')
assert data[0, 1] == pytest.approx(2.0681640624999993,
rel=self.limit,
abs=0.)
assert data[0, 2] == pytest.approx(0.21416845852767494,
rel=self.limit,
abs=0.)
assert data[0, 3] == pytest.approx(179.47800221910444,
rel=self.limit,
abs=0.)
assert data[0, 4] == pytest.approx(24.254455766076823,
rel=self.limit,
abs=0.)
assert data[0, 5] == pytest.approx(2.5776271254831863e-08,
rel=self.limit,
abs=0.)
assert data.shape == (1, 6)
def test_simplex_minimization_hessian(self) -> None:
module = SimplexMinimizationModule(name_in='simplex1',
image_in_tag='fake',
psf_in_tag='psf',
res_out_tag='simplex_res',
flux_position_tag='flux_position',
position=(10., 3.),
magnitude=2.5,
psf_scaling=-1.,
merit='hessian',
aperture=0.06,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.06,
edge_size=None,
extra_rot=0.,
reference_in_tag=None,
residuals='median',
offset=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('simplex1')
data = self.pipeline.get_data('simplex_res')
assert np.sum(data) == pytest.approx(0.07079158286664607,
rel=self.limit,
abs=0.)
assert data.shape == (25, 21, 21)
data = self.pipeline.get_data('flux_position')
assert data[24, 0] == pytest.approx(9.933213305898484,
rel=self.limit,
abs=0.)
assert data[24, 1] == pytest.approx(2.637268518518516,
rel=self.limit,
abs=0.)
assert data[24, 2] == pytest.approx(0.198801928351391,
rel=self.limit,
abs=0.)
assert data[24, 3] == pytest.approx(179.48028924294857,
rel=self.limit,
abs=0.)
assert data[24, 4] == pytest.approx(2.4782450274348378,
rel=self.limit,
abs=0.)
assert data.shape == (25, 6)
def test_simplex_minimization_reference(self) -> None:
module = SimplexMinimizationModule(
name_in='simplex2',
image_in_tag='fake',
psf_in_tag='psf',
res_out_tag='simplex_res_ref',
flux_position_tag='flux_position_ref',
position=(10., 3.),
magnitude=2.5,
psf_scaling=-1.,
merit='poisson',
aperture=0.06,
sigma=0.,
tolerance=0.1,
pca_number=1,
cent_size=0.06,
edge_size=None,
extra_rot=0.,
reference_in_tag='ref',
residuals='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('simplex2')
data = self.pipeline.get_data('simplex_res_ref')
assert np.sum(data) == pytest.approx(9.914746160040783,
rel=self.limit,
abs=0.)
assert data.shape == (28, 21, 21)
data = self.pipeline.get_data('flux_position_ref')
assert data[27, 0] == pytest.approx(10.049019964116436,
rel=self.limit,
abs=0.)
assert data[27, 1] == pytest.approx(2.6444836362361936,
rel=self.limit,
abs=0.)
assert data[27, 2] == pytest.approx(0.19860335205689572,
rel=self.limit,
abs=0.)
assert data[27, 3] == pytest.approx(180.38183525629643,
rel=self.limit,
abs=0.)
assert data[27, 4] == pytest.approx(2.5496922175196,
rel=self.limit,
abs=0.)
assert data.shape == (28, 6)
def test_mcmc_sampling(self) -> None:
with h5py.File(self.test_dir + 'PynPoint_database.hdf5',
'a') as hdf_file:
hdf_file['config'].attrs['CPU'] = 4
module = DerotateAndStackModule(name_in='stack',
image_in_tag='psf',
image_out_tag='psf_stack',
derotate=False,
stack='mean')
self.pipeline.add_module(module)
self.pipeline.run_module('stack')
data = self.pipeline.get_data('psf_stack')
assert np.sum(data) == pytest.approx(10.843655133957288,
rel=self.limit,
abs=0.)
assert data.shape == (1, 21, 21)
module = MCMCsamplingModule(name_in='mcmc',
image_in_tag='adi',
psf_in_tag='psf_stack',
chain_out_tag='mcmc',
param=(0.15, 0., 1.),
bounds=((0.1, 0.2), (-2., 2.), (-1., 2.)),
nwalkers=6,
nsteps=5,
psf_scaling=-1.,
pca_number=1,
aperture=(10, 16, 0.06),
mask=None,
extra_rot=0.,
merit='gaussian',
residuals='median',
sigma=(1e-3, 1e-1, 1e-2))
self.pipeline.add_module(module)
self.pipeline.run_module('mcmc')
def test_systematic_error(self) -> None:
module = SystematicErrorModule(name_in='error',
image_in_tag='adi',
psf_in_tag='psf',
offset_out_tag='offset',
position=(0.162, 0.),
magnitude=5.,
angles=(0., 180., 2),
psf_scaling=1.,
merit='gaussian',
aperture=0.06,
tolerance=0.1,
pca_number=1,
mask=(None, None),
extra_rot=0.,
residuals='median',
offset=1.)
self.pipeline.add_module(module)
self.pipeline.run_module('error')
data = self.pipeline.get_data('offset')
assert data[0, 0] == pytest.approx(-0.0028749671933526733,
rel=self.limit,
abs=0.)
assert data[0, 1] == pytest.approx(0.2786088210998514,
rel=self.limit,
abs=0.)
assert data[0, 2] == pytest.approx(-0.02916297162565762,
rel=self.limit,
abs=0.)
assert data[0, 3] == pytest.approx(-0.02969350583704866,
rel=self.limit,
abs=0.)
assert data[0, 4] == pytest.approx(-0.10640807184499579,
rel=self.limit,
abs=0.)
assert data.shape == (2, 5)
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 TestFitsWritingModule(object):
def setup_class(self):
self.test_dir = os.path.dirname(__file__) + "/"
create_star_data(path=self.test_dir + "fits")
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=["fits"], files=["test.fits"])
def test_fits_reading(self):
read = FitsReadingModule(name_in="read",
input_dir=self.test_dir + "fits",
image_tag="images",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read")
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)
def test_fits_writing(self):
write = FitsWritingModule(file_name="test.fits",
name_in="write1",
output_dir=None,
data_tag="images",
data_range=None,
overwrite=True)
self.pipeline.add_module(write)
self.pipeline.run_module("write1")
def test_filename_string(self):
with pytest.raises(ValueError) as error:
FitsWritingModule(file_name=0.,
name_in="write2",
output_dir=None,
data_tag="images",
data_range=None,
overwrite=True)
assert str(error.value) == "Output 'file_name' needs to be a string."
def test_filename_extension(self):
with pytest.raises(ValueError) as error:
FitsWritingModule(file_name="test.dat",
name_in="write3",
output_dir=None,
data_tag="images",
data_range=None,
overwrite=True)
assert str(
error.value) == "Output 'file_name' requires the FITS extension."
def test_data_range(self):
write = FitsWritingModule(file_name="test.fits",
name_in="write4",
output_dir=None,
data_tag="images",
data_range=(0, 10),
overwrite=True)
self.pipeline.add_module(write)
self.pipeline.run_module("write4")
def test_not_overwritten(self):
write = FitsWritingModule(file_name="test.fits",
name_in="write5",
output_dir=None,
data_tag="images",
data_range=None,
overwrite=False)
self.pipeline.add_module(write)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("write5")
assert len(warning) == 1
assert warning[0].message.args[0] == "Filename already present. Use overwrite=True " \
"to overwrite an existing FITS file."
def test_attribute_length(self):
text = "long_text_long_text_long_text_long_text_long_text_long_text_long_text_long_text"
self.pipeline.set_attribute("images", "short", "value", static=True)
self.pipeline.set_attribute("images",
"longer_than_eight1",
"value",
static=True)
self.pipeline.set_attribute("images",
"longer_than_eight2",
text,
static=True)
write = FitsWritingModule(file_name="test.fits",
name_in="write6",
output_dir=None,
data_tag="images",
data_range=None,
overwrite=True)
self.pipeline.add_module(write)
with pytest.warns(UserWarning) as warning:
self.pipeline.run_module("write6")
assert len(warning) == 1
assert warning[0].message.args[0] == "Key 'hierarch longer_than_eight2' with value " \
"'long_text_long_text_long_text_long_text_long_" \
"text_long_text_long_text_long_text' is too " \
"long for the FITS format. To avoid an error, " \
"the value was truncated to 'long_text_long_text" \
"_long_text_long_text_long_tex'."
