def test_run_module(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
read = FitsReadingModule(name_in="read", image_tag="im_arr")
assert pipeline.add_module(read) is None
assert pipeline.run_module("read") is None
os.remove(self.test_dir + "PynPoint_database.hdf5")
def test_add_module(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
read = FitsReadingModule(name_in='read1',
input_dir=None,
image_tag='im_arr1')
assert pipeline.add_module(read) is None
read = FitsReadingModule(name_in='read2',
input_dir=self.test_dir,
image_tag='im_arr2')
assert pipeline.add_module(read) is None
with pytest.warns(UserWarning) as warning:
pipeline.add_module(read)
assert len(warning) == 1
assert warning[0].message.args[0] == 'Pipeline module names need to be unique. ' \
'Overwriting module \'read2\'.'
process = BadPixelSigmaFilterModule(name_in='badpixel',
image_in_tag='im_arr1',
image_out_tag='im_out')
assert pipeline.add_module(process) is None
write = FitsWritingModule(name_in='write1',
file_name='result.fits',
data_tag='im_arr1')
assert pipeline.add_module(write) is None
write = FitsWritingModule(name_in='write2',
file_name='result.fits',
data_tag='im_arr1',
output_dir=self.test_dir)
assert pipeline.add_module(write) is None
assert pipeline.run() is None
assert pipeline.get_module_names() == [
'read1', 'read2', 'badpixel', 'write1', 'write2'
]
os.remove(self.test_dir + 'result.fits')
os.remove(self.test_dir + 'PynPoint_database.hdf5')
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_read_data(self):
module = FitsReadingModule(name_in='read1',
image_tag='data1',
input_dir=self.test_dir+'data1',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read2',
image_tag='data2',
input_dir=self.test_dir+'data2',
overwrite=True,
check=True)
self.pipeline.add_module(module)
module = FitsReadingModule(name_in='read3',
image_tag='data3',
input_dir=self.test_dir+'data3',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
self.pipeline.run_module('read2')
self.pipeline.run_module('read3')
data = self.pipeline.get_data('data1')
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data('data2')
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data('data3')
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_read_data(self):
read = FitsReadingModule(name_in="read1",
image_tag="data1",
input_dir=self.test_dir+"data1",
overwrite=True,
check=True)
self.pipeline.add_module(read)
read = FitsReadingModule(name_in="read2",
image_tag="data2",
input_dir=self.test_dir+"data2",
overwrite=True,
check=True)
self.pipeline.add_module(read)
read = FitsReadingModule(name_in="read3",
image_tag="data3",
input_dir=self.test_dir+"data3",
overwrite=True,
check=True)
self.pipeline.add_module(read)
self.pipeline.run_module("read1")
self.pipeline.run_module("read2")
self.pipeline.run_module("read3")
data = self.pipeline.get_data("data1")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data("data2")
assert np.allclose(data[0, 50, 50], 0.09798413502193704, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
data = self.pipeline.get_data("data3")
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_read_data(self):
read_science = FitsReadingModule(name_in="read_science",
input_dir=self.test_dir + "adi",
image_tag="im_arr")
self.pipeline.add_module(read_science)
read_dark = FitsReadingModule(name_in="read_dark",
input_dir=self.test_dir + "dark",
image_tag="dark_arr")
self.pipeline.add_module(read_dark)
read_flat = FitsReadingModule(name_in="read_flat",
input_dir=self.test_dir + "flat",
image_tag="flat_arr")
self.pipeline.add_module(read_flat)
self.pipeline.run_module("read_science")
self.pipeline.run_module("read_dark")
self.pipeline.run_module("read_flat")
data = self.pipeline.get_data("im_arr")
assert np.allclose(data[0, 61, 39],
-0.00022889163546536875,
rtol=limit,
atol=0.)
assert data.shape == (82, 102, 100)
data = self.pipeline.get_data("dark_arr")
assert np.allclose(data[0, 61, 39],
2.368170995592123e-05,
rtol=limit,
atol=0.)
assert data.shape == (16, 100, 100)
data = self.pipeline.get_data("flat_arr")
assert np.allclose(data[0, 61, 39],
0.98703416941301647,
rtol=limit,
atol=0.)
assert data.shape == (16, 100, 100)
def test_run_module(self) -> None:
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
module = FitsReadingModule(name_in='read', image_tag='im_arr')
assert pipeline.add_module(module) is None
assert pipeline.run_module('read') is None
os.remove(self.test_dir + 'PynPoint_database.hdf5')
def test_get_shape(self) -> None:
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
module = FitsReadingModule(name_in='read', image_tag='images')
pipeline.add_module(module)
pipeline.run_module('read')
assert pipeline.get_shape('images') == (5, 11, 11)
def test_add_module(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
read = FitsReadingModule(name_in="read1",
input_dir=None,
image_tag="im_arr1")
assert pipeline.add_module(read) is None
read = FitsReadingModule(name_in="read2",
input_dir=self.test_dir,
image_tag="im_arr2")
assert pipeline.add_module(read) is None
with pytest.warns(UserWarning) as warning:
pipeline.add_module(read)
assert len(warning) == 1
assert warning[0].message.args[0] == "Processing module names need to be unique. " \
"Overwriting module 'read2'."
process = BadPixelSigmaFilterModule(name_in="badpixel",
image_in_tag="im_arr1")
assert pipeline.add_module(process) is None
write = FitsWritingModule(name_in="write1",
file_name="result.fits",
data_tag="im_arr1")
assert pipeline.add_module(write) is None
write = FitsWritingModule(name_in="write2",
file_name="result.fits",
data_tag="im_arr1",
output_dir=self.test_dir)
assert pipeline.add_module(write) is None
assert pipeline.run() is None
assert pipeline.get_module_names() == [
'read1', 'read2', 'badpixel', 'write1', 'write2'
]
os.remove(self.test_dir + "result.fits")
os.remove(self.test_dir + "PynPoint_database.hdf5")
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_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_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir+'self.limits')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.sum(data) == pytest.approx(108.43655133957289, rel=self.limit, abs=0.)
assert data.shape == (10, 21, 21)
def test_read_data(self) -> None:
module = FitsReadingModule(name_in='read1',
image_tag='dither',
input_dir=self.test_dir + 'dither')
self.pipeline.add_module(module)
self.pipeline.run_module('read1')
data = self.pipeline.get_data('dither')
assert np.sum(data) == pytest.approx(211.20534661914408,
rel=self.limit,
abs=0.)
assert data.shape == (20, 21, 21)
module = FitsReadingModule(name_in='read2',
image_tag='science',
input_dir=self.test_dir + 'science')
self.pipeline.add_module(module)
self.pipeline.run_module('read2')
data = self.pipeline.get_data('science')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
module = FitsReadingModule(name_in='read3',
image_tag='sky',
input_dir=self.test_dir + 'sky')
self.pipeline.add_module(module)
self.pipeline.run_module('read3')
data = self.pipeline.get_data('sky')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_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_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_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='read',
input_dir=self.test_dir+'limits')
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('read')
assert np.allclose(data[0, 10, 10], 0.00012958496246258364, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.00010029494781738066, rtol=limit, atol=0.)
assert data.shape == (40, 100, 100)
def test_read_data(self):
read = FitsReadingModule(name_in="read1",
image_tag="science",
input_dir=self.test_dir + "science")
self.pipeline.add_module(read)
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)
read = FitsReadingModule(name_in="read2",
image_tag="reference",
input_dir=self.test_dir + "reference")
self.pipeline.add_module(read)
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_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_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_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_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_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_read_data(self):
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir+'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('images')
assert np.allclose(data[0, 10, 10], 0.09799496683489618, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0025020285041348557, rtol=limit, atol=0.)
assert data.shape == (40, 20, 20)
def test_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_run_module_wrong_tag(self) -> None:
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
module = FitsReadingModule(name_in='read')
pipeline.add_module(module)
module = FitsWritingModule(name_in='write',
file_name='result.fits',
data_tag='im_list')
pipeline.add_module(module)
module = BadPixelSigmaFilterModule(name_in='badpixel',
image_in_tag='im_list',
image_out_tag='im_out')
pipeline.add_module(module)
with pytest.raises(AttributeError) as error:
pipeline.run_module('badpixel')
assert str(error.value) == 'Pipeline module \'badpixel\' is looking for data under a ' \
'tag which does not exist in the database.'
with pytest.raises(AttributeError) as error:
pipeline.run_module('write')
assert str(error.value) == 'Pipeline module \'write\' is looking for data under a tag ' \
'which does not exist in the database.'
with pytest.raises(AttributeError) as error:
pipeline.run()
assert str(error.value) == 'Pipeline module \'write\' is looking for data under a tag ' \
'which is not created by a previous module or the data does ' \
'not exist in the database.'
assert pipeline.validate_pipeline_module('test') == (False, 'test')
with pytest.raises(TypeError) as error:
pipeline._validate('module', 'tag')
assert str(error.value) == 'type of argument "module" must be one of (ReadingModule, ' \
'WritingModule, ProcessingModule); got str instead'
os.remove(self.test_dir + 'PynPoint_database.hdf5')
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_read_data(self) -> None:
module = FitsReadingModule(name_in='read',
image_tag='images',
input_dir=self.test_dir + 'images',
overwrite=True,
check=True)
self.pipeline.add_module(module)
self.pipeline.run_module('read')
data = self.pipeline.get_data('images')
assert np.sum(data) == pytest.approx(105.54278879805277,
rel=self.limit,
abs=0.)
assert data.shape == (10, 11, 11)
def test_fits_read_files_exists(self):
module = FitsReadingModule(name_in='read9',
input_dir=None,
image_tag='files',
overwrite=True,
check=True,
filenames=['fits/image00.fits',
'fits/image03.fits'])
self.pipeline.add_module(module)
with pytest.raises(ValueError) as error:
self.pipeline.run_module('read9')
assert str(error.value) == 'The file fits/image00.fits does not exist. ' \
'Please check that the path is correct.'
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_read_data(self):
read = FitsReadingModule(name_in="read",
image_tag="read",
input_dir=self.test_dir + "limits")
self.pipeline.add_module(read)
self.pipeline.run_module("read")
data = self.pipeline.get_data("read")
assert np.allclose(data[0, 10, 10],
0.00012958496246258364,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010029494781738066,
rtol=limit,
atol=0.)
assert data.shape == (40, 100, 100)
